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""" A collection of functions to find the weights and abscissas for Gaussian Quadrature.
These calculations are done by finding the eigenvalues of a tridiagonal matrix whose entries are dependent on the coefficients in the recursion formula for the orthogonal polynomials with the corresponding weighting function over the interval.
Many recursion relations for orthogonal polynomials are given:
.. math::
a1n f_{n+1} (x) = (a2n + a3n x ) f_n (x) - a4n f_{n-1} (x)
The recursion relation of interest is
.. math::
P_{n+1} (x) = (x - A_n) P_n (x) - B_n P_{n-1} (x)
where :math:`P` has a different normalization than :math:`f`.
The coefficients can be found as:
.. math::
A_n = -a2n / a3n \\qquad B_n = ( a4n / a3n \\sqrt{h_n-1 / h_n})^2
where
.. math::
h_n = \\int_a^b w(x) f_n(x)^2
assume:
.. math::
P_0 (x) = 1 \\qquad P_{-1} (x) == 0
For the mathematical background, see [golub.welsch-1969-mathcomp]_ and [abramowitz.stegun-1965]_.
References ---------- .. [golub.welsch-1969-mathcomp] Golub, Gene H, and John H Welsch. 1969. Calculation of Gauss Quadrature Rules. *Mathematics of Computation* 23, 221-230+s1--s10.
.. [abramowitz.stegun-1965] Abramowitz, Milton, and Irene A Stegun. (1965) *Handbook of Mathematical Functions: with Formulas, Graphs, and Mathematical Tables*. Gaithersburg, MD: National Bureau of Standards. http://www.math.sfu.ca/~cbm/aands/
.. [townsend.trogdon.olver-2014] Townsend, A. and Trogdon, T. and Olver, S. (2014) *Fast computation of Gauss quadrature nodes and weights on the whole real line*. :arXiv:`1410.5286`.
.. [townsend.trogdon.olver-2015] Townsend, A. and Trogdon, T. and Olver, S. (2015) *Fast computation of Gauss quadrature nodes and weights on the whole real line*. IMA Journal of Numerical Analysis :doi:`10.1093/imanum/drv002`. """ # # Author: Travis Oliphant 2000 # Updated Sep. 2003 (fixed bugs --- tested to be accurate)
# Scipy imports. hstack, arccos, arange)
# Local imports.
'jacobi', 'laguerre', 'genlaguerre', 'hermite', 'hermitenorm', 'gegenbauer', 'sh_legendre', 'sh_chebyt', 'sh_chebyu', 'sh_jacobi']
# Correspondence between new and old names of root functions 'roots_chebyt': 't_roots', 'roots_chebyu': 'u_roots', 'roots_chebyc': 'c_roots', 'roots_chebys': 's_roots', 'roots_jacobi': 'j_roots', 'roots_laguerre': 'l_roots', 'roots_genlaguerre': 'la_roots', 'roots_hermite': 'h_roots', 'roots_hermitenorm': 'he_roots', 'roots_gegenbauer': 'cg_roots', 'roots_sh_legendre': 'ps_roots', 'roots_sh_chebyt': 'ts_roots', 'roots_sh_chebyu': 'us_roots', 'roots_sh_jacobi': 'js_roots'}
'eval_chebyc', 'eval_chebys', 'eval_jacobi', 'eval_laguerre', 'eval_genlaguerre', 'eval_hermite', 'eval_hermitenorm', 'eval_gegenbauer', 'eval_sh_legendre', 'eval_sh_chebyt', 'eval_sh_chebyu', 'eval_sh_jacobi']
limits=None, monic=False, eval_func=None): equiv_weights = [weights[k] / wfunc(roots[k]) for k in range(len(roots))] mu = sqrt(hn) if monic: evf = eval_func if evf: knn = kn eval_func = lambda x: evf(x) / knn mu = mu / abs(kn) kn = 1.0
# compute coefficients from roots, then scale poly = np.poly1d(roots, r=True) np.poly1d.__init__(self, poly.coeffs * float(kn))
# TODO: In numpy 1.13, there is no need to use __dict__ to access attributes self.__dict__['weights'] = np.array(list(zip(roots, weights, equiv_weights))) self.__dict__['weight_func'] = wfunc self.__dict__['limits'] = limits self.__dict__['normcoef'] = mu
# Note: eval_func will be discarded on arithmetic self.__dict__['_eval_func'] = eval_func
if self._eval_func and not isinstance(v, np.poly1d): return self._eval_func(v) else: return np.poly1d.__call__(self, v)
if p == 1.0: return try: self._coeffs except AttributeError: self.__dict__['coeffs'] *= p else: # the coeffs attr is be made private in future versions of numpy self._coeffs *= p
evf = self._eval_func if evf: self.__dict__['_eval_func'] = lambda x: evf(x) * p self.__dict__['normcoef'] *= p
"""[x,w] = gen_roots_and_weights(n,an_func,sqrt_bn_func,mu)
Returns the roots (x) of an nth order orthogonal polynomial, and weights (w) to use in appropriate Gaussian quadrature with that orthogonal polynomial.
The polynomials have the recurrence relation P_n+1(x) = (x - A_n) P_n(x) - B_n P_n-1(x)
an_func(n) should return A_n sqrt_bn_func(n) should return sqrt(B_n) mu ( = h_0 ) is the integral of the weight over the orthogonal interval """ k = np.arange(n, dtype='d') c = np.zeros((2, n)) c[0,1:] = bn_func(k[1:]) c[1,:] = an_func(k) x = linalg.eigvals_banded(c, overwrite_a_band=True)
# improve roots by one application of Newton's method y = f(n, x) dy = df(n, x) x -= y/dy
fm = f(n-1, x) fm /= np.abs(fm).max() dy /= np.abs(dy).max() w = 1.0 / (fm * dy)
if symmetrize: w = (w + w[::-1]) / 2 x = (x - x[::-1]) / 2
w *= mu0 / w.sum()
if mu: return x, w, mu0 else: return x, w
# Jacobi Polynomials 1 P^(alpha,beta)_n(x)
r"""Gauss-Jacobi quadrature.
Computes the sample points and weights for Gauss-Jacobi quadrature. The sample points are the roots of the n-th degree Jacobi polynomial, :math:`P^{\alpha, \beta}_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[-1, 1]` with weight function :math:`f(x) = (1 - x)^{\alpha} (1 + x)^{\beta}`.
Parameters ---------- n : int quadrature order alpha : float alpha must be > -1 beta : float beta must be > -1 mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad """ m = int(n) if n < 1 or n != m: raise ValueError("n must be a positive integer.") if alpha <= -1 or beta <= -1: raise ValueError("alpha and beta must be greater than -1.")
if alpha == 0.0 and beta == 0.0: return roots_legendre(m, mu) if alpha == beta: return roots_gegenbauer(m, alpha+0.5, mu)
mu0 = 2.0**(alpha+beta+1)*cephes.beta(alpha+1, beta+1) a = alpha b = beta if a + b == 0.0: an_func = lambda k: np.where(k == 0, (b-a)/(2+a+b), 0.0) else: an_func = lambda k: np.where(k == 0, (b-a)/(2+a+b), (b*b - a*a) / ((2.0*k+a+b)*(2.0*k+a+b+2)))
bn_func = lambda k: 2.0 / (2.0*k+a+b)*np.sqrt((k+a)*(k+b) / (2*k+a+b+1)) \ * np.where(k == 1, 1.0, np.sqrt(k*(k+a+b) / (2.0*k+a+b-1)))
f = lambda n, x: cephes.eval_jacobi(n, a, b, x) df = lambda n, x: 0.5 * (n + a + b + 1) \ * cephes.eval_jacobi(n-1, a+1, b+1, x) return _gen_roots_and_weights(m, mu0, an_func, bn_func, f, df, False, mu)
r"""Jacobi polynomial.
Defined to be the solution of
.. math:: (1 - x^2)\frac{d^2}{dx^2}P_n^{(\alpha, \beta)} + (\beta - \alpha - (\alpha + \beta + 2)x) \frac{d}{dx}P_n^{(\alpha, \beta)} + n(n + \alpha + \beta + 1)P_n^{(\alpha, \beta)} = 0
for :math:`\alpha, \beta > -1`; :math:`P_n^{(\alpha, \beta)}` is a polynomial of degree :math:`n`.
Parameters ---------- n : int Degree of the polynomial. alpha : float Parameter, must be greater than -1. beta : float Parameter, must be greater than -1. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- P : orthopoly1d Jacobi polynomial.
Notes ----- For fixed :math:`\alpha, \beta`, the polynomials :math:`P_n^{(\alpha, \beta)}` are orthogonal over :math:`[-1, 1]` with weight function :math:`(1 - x)^\alpha(1 + x)^\beta`.
""" if n < 0: raise ValueError("n must be nonnegative.")
wfunc = lambda x: (1 - x)**alpha * (1 + x)**beta if n == 0: return orthopoly1d([], [], 1.0, 1.0, wfunc, (-1, 1), monic, eval_func=np.ones_like) x, w, mu = roots_jacobi(n, alpha, beta, mu=True) ab1 = alpha + beta + 1.0 hn = 2**ab1 / (2 * n + ab1) * _gam(n + alpha + 1) hn *= _gam(n + beta + 1.0) / _gam(n + 1) / _gam(n + ab1) kn = _gam(2 * n + ab1) / 2.0**n / _gam(n + 1) / _gam(n + ab1) # here kn = coefficient on x^n term p = orthopoly1d(x, w, hn, kn, wfunc, (-1, 1), monic, lambda x: eval_jacobi(n, alpha, beta, x)) return p
# Jacobi Polynomials shifted G_n(p,q,x)
"""Gauss-Jacobi (shifted) quadrature.
Computes the sample points and weights for Gauss-Jacobi (shifted) quadrature. The sample points are the roots of the n-th degree shifted Jacobi polynomial, :math:`G^{p,q}_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[0, 1]` with weight function :math:`f(x) = (1 - x)^{p-q} x^{q-1}`
Parameters ---------- n : int quadrature order p1 : float (p1 - q1) must be > -1 q1 : float q1 must be > 0 mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad """ if (p1-q1) <= -1 or q1 <= 0: raise ValueError("(p - q) must be greater than -1, and q must be greater than 0.") x, w, m = roots_jacobi(n, p1-q1, q1-1, True) x = (x + 1) / 2 scale = 2.0**p1 w /= scale m /= scale if mu: return x, w, m else: return x, w
r"""Shifted Jacobi polynomial.
Defined by
.. math::
G_n^{(p, q)}(x) = \binom{2n + p - 1}{n}^{-1}P_n^{(p - q, q - 1)}(2x - 1),
where :math:`P_n^{(\cdot, \cdot)}` is the nth Jacobi polynomial.
Parameters ---------- n : int Degree of the polynomial. p : float Parameter, must have :math:`p > q - 1`. q : float Parameter, must be greater than 0. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- G : orthopoly1d Shifted Jacobi polynomial.
Notes ----- For fixed :math:`p, q`, the polynomials :math:`G_n^{(p, q)}` are orthogonal over :math:`[0, 1]` with weight function :math:`(1 - x)^{p - q}x^{q - 1}`.
""" if n < 0: raise ValueError("n must be nonnegative.")
wfunc = lambda x: (1.0 - x)**(p - q) * (x)**(q - 1.) if n == 0: return orthopoly1d([], [], 1.0, 1.0, wfunc, (-1, 1), monic, eval_func=np.ones_like) n1 = n x, w, mu0 = roots_sh_jacobi(n1, p, q, mu=True) hn = _gam(n + 1) * _gam(n + q) * _gam(n + p) * _gam(n + p - q + 1) hn /= (2 * n + p) * (_gam(2 * n + p)**2) # kn = 1.0 in standard form so monic is redundant. Kept for compatibility. kn = 1.0 pp = orthopoly1d(x, w, hn, kn, wfunc=wfunc, limits=(0, 1), monic=monic, eval_func=lambda x: eval_sh_jacobi(n, p, q, x)) return pp
# Generalized Laguerre L^(alpha)_n(x)
r"""Gauss-generalized Laguerre quadrature.
Computes the sample points and weights for Gauss-generalized Laguerre quadrature. The sample points are the roots of the n-th degree generalized Laguerre polynomial, :math:`L^{\alpha}_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[0, \infty]` with weight function :math:`f(x) = x^{\alpha} e^{-x}`.
Parameters ---------- n : int quadrature order alpha : float alpha must be > -1 mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad """ m = int(n) if n < 1 or n != m: raise ValueError("n must be a positive integer.") if alpha < -1: raise ValueError("alpha must be greater than -1.")
mu0 = cephes.gamma(alpha + 1)
if m == 1: x = np.array([alpha+1.0], 'd') w = np.array([mu0], 'd') if mu: return x, w, mu0 else: return x, w
an_func = lambda k: 2 * k + alpha + 1 bn_func = lambda k: -np.sqrt(k * (k + alpha)) f = lambda n, x: cephes.eval_genlaguerre(n, alpha, x) df = lambda n, x: (n*cephes.eval_genlaguerre(n, alpha, x) - (n + alpha)*cephes.eval_genlaguerre(n-1, alpha, x))/x return _gen_roots_and_weights(m, mu0, an_func, bn_func, f, df, False, mu)
r"""Generalized (associated) Laguerre polynomial.
Defined to be the solution of
.. math:: x\frac{d^2}{dx^2}L_n^{(\alpha)} + (\alpha + 1 - x)\frac{d}{dx}L_n^{(\alpha)} + nL_n^{(\alpha)} = 0,
where :math:`\alpha > -1`; :math:`L_n^{(\alpha)}` is a polynomial of degree :math:`n`.
Parameters ---------- n : int Degree of the polynomial. alpha : float Parameter, must be greater than -1. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- L : orthopoly1d Generalized Laguerre polynomial.
Notes ----- For fixed :math:`\alpha`, the polynomials :math:`L_n^{(\alpha)}` are orthogonal over :math:`[0, \infty)` with weight function :math:`e^{-x}x^\alpha`.
The Laguerre polynomials are the special case where :math:`\alpha = 0`.
See Also -------- laguerre : Laguerre polynomial.
""" if alpha <= -1: raise ValueError("alpha must be > -1") if n < 0: raise ValueError("n must be nonnegative.")
if n == 0: n1 = n + 1 else: n1 = n x, w, mu0 = roots_genlaguerre(n1, alpha, mu=True) wfunc = lambda x: exp(-x) * x**alpha if n == 0: x, w = [], [] hn = _gam(n + alpha + 1) / _gam(n + 1) kn = (-1)**n / _gam(n + 1) p = orthopoly1d(x, w, hn, kn, wfunc, (0, inf), monic, lambda x: eval_genlaguerre(n, alpha, x)) return p
# Laguerre L_n(x)
r"""Gauss-Laguerre quadrature.
Computes the sample points and weights for Gauss-Laguerre quadrature. The sample points are the roots of the n-th degree Laguerre polynomial, :math:`L_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[0, \infty]` with weight function :math:`f(x) = e^{-x}`.
Parameters ---------- n : int quadrature order mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad numpy.polynomial.laguerre.laggauss """ return roots_genlaguerre(n, 0.0, mu=mu)
r"""Laguerre polynomial.
Defined to be the solution of
.. math:: x\frac{d^2}{dx^2}L_n + (1 - x)\frac{d}{dx}L_n + nL_n = 0;
:math:`L_n` is a polynomial of degree :math:`n`.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- L : orthopoly1d Laguerre Polynomial.
Notes ----- The polynomials :math:`L_n` are orthogonal over :math:`[0, \infty)` with weight function :math:`e^{-x}`.
""" if n < 0: raise ValueError("n must be nonnegative.")
if n == 0: n1 = n + 1 else: n1 = n x, w, mu0 = roots_laguerre(n1, mu=True) if n == 0: x, w = [], [] hn = 1.0 kn = (-1)**n / _gam(n + 1) p = orthopoly1d(x, w, hn, kn, lambda x: exp(-x), (0, inf), monic, lambda x: eval_laguerre(n, x)) return p
# Hermite 1 H_n(x)
r"""Gauss-Hermite (physicst's) quadrature.
Computes the sample points and weights for Gauss-Hermite quadrature. The sample points are the roots of the n-th degree Hermite polynomial, :math:`H_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[-\infty, \infty]` with weight function :math:`f(x) = e^{-x^2}`.
Parameters ---------- n : int quadrature order mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
Notes ----- For small n up to 150 a modified version of the Golub-Welsch algorithm is used. Nodes are computed from the eigenvalue problem and improved by one step of a Newton iteration. The weights are computed from the well-known analytical formula.
For n larger than 150 an optimal asymptotic algorithm is applied which computes nodes and weights in a numerically stable manner. The algorithm has linear runtime making computation for very large n (several thousand or more) feasible.
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad numpy.polynomial.hermite.hermgauss roots_hermitenorm
References ---------- .. [townsend.trogdon.olver-2014] Townsend, A. and Trogdon, T. and Olver, S. (2014) *Fast computation of Gauss quadrature nodes and weights on the whole real line*. :arXiv:`1410.5286`.
.. [townsend.trogdon.olver-2015] Townsend, A. and Trogdon, T. and Olver, S. (2015) *Fast computation of Gauss quadrature nodes and weights on the whole real line*. IMA Journal of Numerical Analysis :doi:`10.1093/imanum/drv002`. """ m = int(n) if n < 1 or n != m: raise ValueError("n must be a positive integer.")
mu0 = np.sqrt(np.pi) if n <= 150: an_func = lambda k: 0.0*k bn_func = lambda k: np.sqrt(k/2.0) f = cephes.eval_hermite df = lambda n, x: 2.0 * n * cephes.eval_hermite(n-1, x) return _gen_roots_and_weights(m, mu0, an_func, bn_func, f, df, True, mu) else: nodes, weights = _roots_hermite_asy(m) if mu: return nodes, weights, mu0 else: return nodes, weights
"""Helper function for Tricomi initial guesses
For details, see formula 3.1 in lemma 3.1 in the original paper.
Parameters ---------- n : int Quadrature order k : ndarray of type int Index of roots :math:`\tau_k` to compute maxit : int Number of Newton maxit performed, the default value of 5 is sufficient.
Returns ------- tauk : ndarray Roots of equation 3.1
See Also -------- initial_nodes_a roots_hermite_asy """ a = n % 2 - 0.5 c = (4.0*floor(n/2.0) - 4.0*k + 3.0)*pi / (4.0*floor(n/2.0) + 2.0*a + 2.0) f = lambda x: x - sin(x) - c df = lambda x: 1.0 - cos(x) xi = 0.5*pi for i in range(maxit): xi = xi - f(xi)/df(xi) return xi
r"""Tricomi initial guesses
Computes an initial approximation to the square of the `k`-th (positive) root :math:`x_k` of the Hermite polynomial :math:`H_n` of order :math:`n`. The formula is the one from lemma 3.1 in the original paper. The guesses are accurate except in the region near :math:`\sqrt{2n + 1}`.
Parameters ---------- n : int Quadrature order k : ndarray of type int Index of roots to compute
Returns ------- xksq : ndarray Square of the approximate roots
See Also -------- initial_nodes roots_hermite_asy """ tauk = _compute_tauk(n, k) sigk = cos(0.5*tauk)**2 a = n % 2 - 0.5 nu = 4.0*floor(n/2.0) + 2.0*a + 2.0 # Initial approximation of Hermite roots (square) xksq = nu*sigk - 1.0/(3.0*nu) * (5.0/(4.0*(1.0-sigk)**2) - 1.0/(1.0-sigk) - 0.25) return xksq
r"""Gatteschi initial guesses
Computes an initial approximation to the square of the `k`-th (positive) root :math:`x_k` of the Hermite polynomial :math:`H_n` of order :math:`n`. The formula is the one from lemma 3.2 in the original paper. The guesses are accurate in the region just below :math:`\sqrt{2n + 1}`.
Parameters ---------- n : int Quadrature order k : ndarray of type int Index of roots to compute
Returns ------- xksq : ndarray Square of the approximate root
See Also -------- initial_nodes roots_hermite_asy """ a = n % 2 - 0.5 nu = 4.0*floor(n/2.0) + 2.0*a + 2.0 # Airy roots by approximation ak = specfun.airyzo(k.max(), 1)[0][::-1] # Initial approximation of Hermite roots (square) xksq = (nu + 2.0**(2.0/3.0) * ak * nu**(1.0/3.0) + 1.0/5.0 * 2.0**(4.0/3.0) * ak**2 * nu**(-1.0/3.0) + (9.0/140.0 - 12.0/175.0 * ak**3) * nu**(-1.0) + (16.0/1575.0 * ak + 92.0/7875.0 * ak**4) * 2.0**(2.0/3.0) * nu**(-5.0/3.0) - (15152.0/3031875.0 * ak**5 + 1088.0/121275.0 * ak**2) * 2.0**(1.0/3.0) * nu**(-7.0/3.0)) return xksq
"""Initial guesses for the Hermite roots
Computes an initial approximation to the non-negative roots :math:`x_k` of the Hermite polynomial :math:`H_n` of order :math:`n`. The Tricomi and Gatteschi initial guesses are used in the region where they are accurate.
Parameters ---------- n : int Quadrature order
Returns ------- xk : ndarray Approximate roots
See Also -------- roots_hermite_asy """ # Turnover point # linear polynomial fit to error of 10, 25, 40, ..., 1000 point rules fit = 0.49082003*n - 4.37859653 turnover = around(fit).astype(int) # Compute all approximations ia = arange(1, int(floor(n*0.5)+1)) ib = ia[::-1] xasq = _initial_nodes_a(n, ia[:turnover+1]) xbsq = _initial_nodes_b(n, ib[turnover+1:]) # Combine iv = sqrt(hstack([xasq, xbsq])) # Central node is always zero if n % 2 == 1: iv = hstack([0.0, iv]) return iv
r"""Asymptotic series expansion of parabolic cylinder function
The implementation is based on sections 3.2 and 3.3 from the original paper. Compared to the published version this code adds one more term to the asymptotic series. The detailed formulas can be found at [parabolic-asymptotics]_. The evaluation is done in a transformed variable :math:`\theta := \arccos(t)` where :math:`t := x / \mu` and :math:`\mu := \sqrt{2n + 1}`.
Parameters ---------- n : int Quadrature order theta : ndarray Transformed position variable
Returns ------- U : ndarray Value of the parabolic cylinder function :math:`U(a, \theta)`. Ud : ndarray Value of the derivative :math:`U^{\prime}(a, \theta)` of the parabolic cylinder function.
See Also -------- roots_hermite_asy
References ---------- .. [parabolic-asymptotics] http://dlmf.nist.gov/12.10#vii """ st = sin(theta) ct = cos(theta) # http://dlmf.nist.gov/12.10#vii mu = 2.0*n + 1.0 # http://dlmf.nist.gov/12.10#E23 eta = 0.5*theta - 0.5*st*ct # http://dlmf.nist.gov/12.10#E39 zeta = -(3.0*eta/2.0) ** (2.0/3.0) # http://dlmf.nist.gov/12.10#E40 phi = (-zeta / st**2) ** (0.25) # Coefficients # http://dlmf.nist.gov/12.10#E43 a0 = 1.0 a1 = 0.10416666666666666667 a2 = 0.08355034722222222222 a3 = 0.12822657455632716049 a4 = 0.29184902646414046425 a5 = 0.88162726744375765242 b0 = 1.0 b1 = -0.14583333333333333333 b2 = -0.09874131944444444444 b3 = -0.14331205391589506173 b4 = -0.31722720267841354810 b5 = -0.94242914795712024914 # Polynomials # http://dlmf.nist.gov/12.10#E9 # http://dlmf.nist.gov/12.10#E10 ctp = ct ** arange(16).reshape((-1,1)) u0 = 1.0 u1 = (1.0*ctp[3,:] - 6.0*ct) / 24.0 u2 = (-9.0*ctp[4,:] + 249.0*ctp[2,:] + 145.0) / 1152.0 u3 = (-4042.0*ctp[9,:] + 18189.0*ctp[7,:] - 28287.0*ctp[5,:] - 151995.0*ctp[3,:] - 259290.0*ct) / 414720.0 u4 = (72756.0*ctp[10,:] - 321339.0*ctp[8,:] - 154982.0*ctp[6,:] + 50938215.0*ctp[4,:] + 122602962.0*ctp[2,:] + 12773113.0) / 39813120.0 u5 = (82393456.0*ctp[15,:] - 617950920.0*ctp[13,:] + 1994971575.0*ctp[11,:] - 3630137104.0*ctp[9,:] + 4433574213.0*ctp[7,:] - 37370295816.0*ctp[5,:] - 119582875013.0*ctp[3,:] - 34009066266.0*ct) / 6688604160.0 v0 = 1.0 v1 = (1.0*ctp[3,:] + 6.0*ct) / 24.0 v2 = (15.0*ctp[4,:] - 327.0*ctp[2,:] - 143.0) / 1152.0 v3 = (-4042.0*ctp[9,:] + 18189.0*ctp[7,:] - 36387.0*ctp[5,:] + 238425.0*ctp[3,:] + 259290.0*ct) / 414720.0 v4 = (-121260.0*ctp[10,:] + 551733.0*ctp[8,:] - 151958.0*ctp[6,:] - 57484425.0*ctp[4,:] - 132752238.0*ctp[2,:] - 12118727) / 39813120.0 v5 = (82393456.0*ctp[15,:] - 617950920.0*ctp[13,:] + 2025529095.0*ctp[11,:] - 3750839308.0*ctp[9,:] + 3832454253.0*ctp[7,:] + 35213253348.0*ctp[5,:] + 130919230435.0*ctp[3,:] + 34009066266*ct) / 6688604160.0 # Airy Evaluation (Bi and Bip unused) Ai, Aip, Bi, Bip = airy(mu**(4.0/6.0) * zeta) # Prefactor for U P = 2.0*sqrt(pi) * mu**(1.0/6.0) * phi # Terms for U # http://dlmf.nist.gov/12.10#E42 phip = phi ** arange(6, 31, 6).reshape((-1,1)) A0 = b0*u0 A1 = (b2*u0 + phip[0,:]*b1*u1 + phip[1,:]*b0*u2) / zeta**3 A2 = (b4*u0 + phip[0,:]*b3*u1 + phip[1,:]*b2*u2 + phip[2,:]*b1*u3 + phip[3,:]*b0*u4) / zeta**6 B0 = -(a1*u0 + phip[0,:]*a0*u1) / zeta**2 B1 = -(a3*u0 + phip[0,:]*a2*u1 + phip[1,:]*a1*u2 + phip[2,:]*a0*u3) / zeta**5 B2 = -(a5*u0 + phip[0,:]*a4*u1 + phip[1,:]*a3*u2 + phip[2,:]*a2*u3 + phip[3,:]*a1*u4 + phip[4,:]*a0*u5) / zeta**8 # U # http://dlmf.nist.gov/12.10#E35 U = P * (Ai * (A0 + A1/mu**2.0 + A2/mu**4.0) + Aip * (B0 + B1/mu**2.0 + B2/mu**4.0) / mu**(8.0/6.0)) # Prefactor for derivative of U Pd = sqrt(2.0*pi) * mu**(2.0/6.0) / phi # Terms for derivative of U # http://dlmf.nist.gov/12.10#E46 C0 = -(b1*v0 + phip[0,:]*b0*v1) / zeta C1 = -(b3*v0 + phip[0,:]*b2*v1 + phip[1,:]*b1*v2 + phip[2,:]*b0*v3) / zeta**4 C2 = -(b5*v0 + phip[0,:]*b4*v1 + phip[1,:]*b3*v2 + phip[2,:]*b2*v3 + phip[3,:]*b1*v4 + phip[4,:]*b0*v5) / zeta**7 D0 = a0*v0 D1 = (a2*v0 + phip[0,:]*a1*v1 + phip[1,:]*a0*v2) / zeta**3 D2 = (a4*v0 + phip[0,:]*a3*v1 + phip[1,:]*a2*v2 + phip[2,:]*a1*v3 + phip[3,:]*a0*v4) / zeta**6 # Derivative of U # http://dlmf.nist.gov/12.10#E36 Ud = Pd * (Ai * (C0 + C1/mu**2.0 + C2/mu**4.0) / mu**(4.0/6.0) + Aip * (D0 + D1/mu**2.0 + D2/mu**4.0)) return U, Ud
"""Newton iteration for polishing the asymptotic approximation to the zeros of the Hermite polynomials.
Parameters ---------- n : int Quadrature order x_initial : ndarray Initial guesses for the roots maxit : int Maximal number of Newton iterations. The default 5 is sufficient, usually only one or two steps are needed.
Returns ------- nodes : ndarray Quadrature nodes weights : ndarray Quadrature weights
See Also -------- roots_hermite_asy """ # Variable transformation mu = sqrt(2.0*n + 1.0) t = x_initial / mu theta = arccos(t) # Newton iteration for i in range(maxit): u, ud = _pbcf(n, theta) dtheta = u / (sqrt(2.0) * mu * sin(theta) * ud) theta = theta + dtheta if max(abs(dtheta)) < 1e-14: break # Undo variable transformation x = mu * cos(theta) # Central node is always zero if n % 2 == 1: x[0] = 0.0 # Compute weights w = exp(-x**2) / (2.0*ud**2) return x, w
r"""Gauss-Hermite (physicst's) quadrature for large n.
Computes the sample points and weights for Gauss-Hermite quadrature. The sample points are the roots of the n-th degree Hermite polynomial, :math:`H_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[-\infty, \infty]` with weight function :math:`f(x) = e^{-x^2}`.
This method relies on asymptotic expansions which work best for n > 150. The algorithm has linear runtime making computation for very large n feasible.
Parameters ---------- n : int quadrature order
Returns ------- nodes : ndarray Quadrature nodes weights : ndarray Quadrature weights
See Also -------- roots_hermite
References ---------- .. [townsend.trogdon.olver-2014] Townsend, A. and Trogdon, T. and Olver, S. (2014) *Fast computation of Gauss quadrature nodes and weights on the whole real line*. :arXiv:`1410.5286`.
.. [townsend.trogdon.olver-2015] Townsend, A. and Trogdon, T. and Olver, S. (2015) *Fast computation of Gauss quadrature nodes and weights on the whole real line*. IMA Journal of Numerical Analysis :doi:`10.1093/imanum/drv002`. """ iv = _initial_nodes(n) nodes, weights = _newton(n, iv) # Combine with negative parts if n % 2 == 0: nodes = hstack([-nodes[::-1], nodes]) weights = hstack([weights[::-1], weights]) else: nodes = hstack([-nodes[-1:0:-1], nodes]) weights = hstack([weights[-1:0:-1], weights]) # Scale weights weights *= sqrt(pi) / sum(weights) return nodes, weights
r"""Physicist's Hermite polynomial.
Defined by
.. math::
H_n(x) = (-1)^ne^{x^2}\frac{d^n}{dx^n}e^{-x^2};
:math:`H_n` is a polynomial of degree :math:`n`.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- H : orthopoly1d Hermite polynomial.
Notes ----- The polynomials :math:`H_n` are orthogonal over :math:`(-\infty, \infty)` with weight function :math:`e^{-x^2}`.
""" if n < 0: raise ValueError("n must be nonnegative.")
if n == 0: n1 = n + 1 else: n1 = n x, w, mu0 = roots_hermite(n1, mu=True) wfunc = lambda x: exp(-x * x) if n == 0: x, w = [], [] hn = 2**n * _gam(n + 1) * sqrt(pi) kn = 2**n p = orthopoly1d(x, w, hn, kn, wfunc, (-inf, inf), monic, lambda x: eval_hermite(n, x)) return p
# Hermite 2 He_n(x)
r"""Gauss-Hermite (statistician's) quadrature.
Computes the sample points and weights for Gauss-Hermite quadrature. The sample points are the roots of the n-th degree Hermite polynomial, :math:`He_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[-\infty, \infty]` with weight function :math:`f(x) = e^{-x^2/2}`.
Parameters ---------- n : int quadrature order mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
Notes ----- For small n up to 150 a modified version of the Golub-Welsch algorithm is used. Nodes are computed from the eigenvalue problem and improved by one step of a Newton iteration. The weights are computed from the well-known analytical formula.
For n larger than 150 an optimal asymptotic algorithm is used which computes nodes and weights in a numerical stable manner. The algorithm has linear runtime making computation for very large n (several thousand or more) feasible.
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad numpy.polynomial.hermite_e.hermegauss """ m = int(n) if n < 1 or n != m: raise ValueError("n must be a positive integer.")
mu0 = np.sqrt(2.0*np.pi) if n <= 150: an_func = lambda k: 0.0*k bn_func = lambda k: np.sqrt(k) f = cephes.eval_hermitenorm df = lambda n, x: n * cephes.eval_hermitenorm(n-1, x) return _gen_roots_and_weights(m, mu0, an_func, bn_func, f, df, True, mu) else: nodes, weights = _roots_hermite_asy(m) # Transform nodes *= sqrt(2) weights *= sqrt(2) if mu: return nodes, weights, mu0 else: return nodes, weights
r"""Normalized (probabilist's) Hermite polynomial.
Defined by
.. math::
He_n(x) = (-1)^ne^{x^2/2}\frac{d^n}{dx^n}e^{-x^2/2};
:math:`He_n` is a polynomial of degree :math:`n`.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- He : orthopoly1d Hermite polynomial.
Notes -----
The polynomials :math:`He_n` are orthogonal over :math:`(-\infty, \infty)` with weight function :math:`e^{-x^2/2}`.
""" if n < 0: raise ValueError("n must be nonnegative.")
if n == 0: n1 = n + 1 else: n1 = n x, w, mu0 = roots_hermitenorm(n1, mu=True) wfunc = lambda x: exp(-x * x / 2.0) if n == 0: x, w = [], [] hn = sqrt(2 * pi) * _gam(n + 1) kn = 1.0 p = orthopoly1d(x, w, hn, kn, wfunc=wfunc, limits=(-inf, inf), monic=monic, eval_func=lambda x: eval_hermitenorm(n, x)) return p
# The remainder of the polynomials can be derived from the ones above.
# Ultraspherical (Gegenbauer) C^(alpha)_n(x)
r"""Gauss-Gegenbauer quadrature.
Computes the sample points and weights for Gauss-Gegenbauer quadrature. The sample points are the roots of the n-th degree Gegenbauer polynomial, :math:`C^{\alpha}_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[-1, 1]` with weight function :math:`f(x) = (1 - x^2)^{\alpha - 1/2}`.
Parameters ---------- n : int quadrature order alpha : float alpha must be > -0.5 mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad """ m = int(n) if n < 1 or n != m: raise ValueError("n must be a positive integer.") if alpha < -0.5: raise ValueError("alpha must be greater than -0.5.") elif alpha == 0.0: # C(n,0,x) == 0 uniformly, however, as alpha->0, C(n,alpha,x)->T(n,x) # strictly, we should just error out here, since the roots are not # really defined, but we used to return something useful, so let's # keep doing so. return roots_chebyt(n, mu)
mu0 = np.sqrt(np.pi) * cephes.gamma(alpha + 0.5) / cephes.gamma(alpha + 1) an_func = lambda k: 0.0 * k bn_func = lambda k: np.sqrt(k * (k + 2 * alpha - 1) / (4 * (k + alpha) * (k + alpha - 1))) f = lambda n, x: cephes.eval_gegenbauer(n, alpha, x) df = lambda n, x: (-n*x*cephes.eval_gegenbauer(n, alpha, x) + (n + 2*alpha - 1)*cephes.eval_gegenbauer(n-1, alpha, x))/(1-x**2) return _gen_roots_and_weights(m, mu0, an_func, bn_func, f, df, True, mu)
r"""Gegenbauer (ultraspherical) polynomial.
Defined to be the solution of
.. math:: (1 - x^2)\frac{d^2}{dx^2}C_n^{(\alpha)} - (2\alpha + 1)x\frac{d}{dx}C_n^{(\alpha)} + n(n + 2\alpha)C_n^{(\alpha)} = 0
for :math:`\alpha > -1/2`; :math:`C_n^{(\alpha)}` is a polynomial of degree :math:`n`.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- C : orthopoly1d Gegenbauer polynomial.
Notes ----- The polynomials :math:`C_n^{(\alpha)}` are orthogonal over :math:`[-1,1]` with weight function :math:`(1 - x^2)^{(\alpha - 1/2)}`.
""" base = jacobi(n, alpha - 0.5, alpha - 0.5, monic=monic) if monic: return base # Abrahmowitz and Stegan 22.5.20 factor = (_gam(2*alpha + n) * _gam(alpha + 0.5) / _gam(2*alpha) / _gam(alpha + 0.5 + n)) base._scale(factor) base.__dict__['_eval_func'] = lambda x: eval_gegenbauer(float(n), alpha, x) return base
# Chebyshev of the first kind: T_n(x) = # n! sqrt(pi) / _gam(n+1./2)* P^(-1/2,-1/2)_n(x) # Computed anew.
r"""Gauss-Chebyshev (first kind) quadrature.
Computes the sample points and weights for Gauss-Chebyshev quadrature. The sample points are the roots of the n-th degree Chebyshev polynomial of the first kind, :math:`T_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[-1, 1]` with weight function :math:`f(x) = 1/\sqrt{1 - x^2}`.
Parameters ---------- n : int quadrature order mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad numpy.polynomial.chebyshev.chebgauss """ m = int(n) if n < 1 or n != m: raise ValueError('n must be a positive integer.') x = _ufuncs._sinpi(np.arange(-m + 1, m, 2) / (2*m)) w = np.full_like(x, pi/m) if mu: return x, w, pi else: return x, w
r"""Chebyshev polynomial of the first kind.
Defined to be the solution of
.. math:: (1 - x^2)\frac{d^2}{dx^2}T_n - x\frac{d}{dx}T_n + n^2T_n = 0;
:math:`T_n` is a polynomial of degree :math:`n`.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- T : orthopoly1d Chebyshev polynomial of the first kind.
Notes ----- The polynomials :math:`T_n` are orthogonal over :math:`[-1, 1]` with weight function :math:`(1 - x^2)^{-1/2}`.
See Also -------- chebyu : Chebyshev polynomial of the second kind.
""" if n < 0: raise ValueError("n must be nonnegative.")
wfunc = lambda x: 1.0 / sqrt(1 - x * x) if n == 0: return orthopoly1d([], [], pi, 1.0, wfunc, (-1, 1), monic, lambda x: eval_chebyt(n, x)) n1 = n x, w, mu = roots_chebyt(n1, mu=True) hn = pi / 2 kn = 2**(n - 1) p = orthopoly1d(x, w, hn, kn, wfunc, (-1, 1), monic, lambda x: eval_chebyt(n, x)) return p
# Chebyshev of the second kind # U_n(x) = (n+1)! sqrt(pi) / (2*_gam(n+3./2)) * P^(1/2,1/2)_n(x)
r"""Gauss-Chebyshev (second kind) quadrature.
Computes the sample points and weights for Gauss-Chebyshev quadrature. The sample points are the roots of the n-th degree Chebyshev polynomial of the second kind, :math:`U_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[-1, 1]` with weight function :math:`f(x) = \sqrt{1 - x^2}`.
Parameters ---------- n : int quadrature order mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad """ m = int(n) if n < 1 or n != m: raise ValueError('n must be a positive integer.') t = np.arange(m, 0, -1) * pi / (m + 1) x = np.cos(t) w = pi * np.sin(t)**2 / (m + 1) if mu: return x, w, pi / 2 else: return x, w
r"""Chebyshev polynomial of the second kind.
Defined to be the solution of
.. math:: (1 - x^2)\frac{d^2}{dx^2}U_n - 3x\frac{d}{dx}U_n + n(n + 2)U_n = 0;
:math:`U_n` is a polynomial of degree :math:`n`.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- U : orthopoly1d Chebyshev polynomial of the second kind.
Notes ----- The polynomials :math:`U_n` are orthogonal over :math:`[-1, 1]` with weight function :math:`(1 - x^2)^{1/2}`.
See Also -------- chebyt : Chebyshev polynomial of the first kind.
""" base = jacobi(n, 0.5, 0.5, monic=monic) if monic: return base factor = sqrt(pi) / 2.0 * _gam(n + 2) / _gam(n + 1.5) base._scale(factor) return base
# Chebyshev of the first kind C_n(x)
r"""Gauss-Chebyshev (first kind) quadrature.
Computes the sample points and weights for Gauss-Chebyshev quadrature. The sample points are the roots of the n-th degree Chebyshev polynomial of the first kind, :math:`C_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[-2, 2]` with weight function :math:`f(x) = 1/\sqrt{1 - (x/2)^2}`.
Parameters ---------- n : int quadrature order mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad """ x, w, m = roots_chebyt(n, True) x *= 2 w *= 2 m *= 2 if mu: return x, w, m else: return x, w
r"""Chebyshev polynomial of the first kind on :math:`[-2, 2]`.
Defined as :math:`C_n(x) = 2T_n(x/2)`, where :math:`T_n` is the nth Chebychev polynomial of the first kind.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- C : orthopoly1d Chebyshev polynomial of the first kind on :math:`[-2, 2]`.
Notes ----- The polynomials :math:`C_n(x)` are orthogonal over :math:`[-2, 2]` with weight function :math:`1/\sqrt{1 - (x/2)^2}`.
See Also -------- chebyt : Chebyshev polynomial of the first kind.
References ---------- .. [1] Abramowitz and Stegun, "Handbook of Mathematical Functions" Section 22. National Bureau of Standards, 1972.
""" if n < 0: raise ValueError("n must be nonnegative.")
if n == 0: n1 = n + 1 else: n1 = n x, w, mu0 = roots_chebyc(n1, mu=True) if n == 0: x, w = [], [] hn = 4 * pi * ((n == 0) + 1) kn = 1.0 p = orthopoly1d(x, w, hn, kn, wfunc=lambda x: 1.0 / sqrt(1 - x * x / 4.0), limits=(-2, 2), monic=monic) if not monic: p._scale(2.0 / p(2)) p.__dict__['_eval_func'] = lambda x: eval_chebyc(n, x) return p
# Chebyshev of the second kind S_n(x)
r"""Gauss-Chebyshev (second kind) quadrature.
Computes the sample points and weights for Gauss-Chebyshev quadrature. The sample points are the roots of the n-th degree Chebyshev polynomial of the second kind, :math:`S_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[-2, 2]` with weight function :math:`f(x) = \sqrt{1 - (x/2)^2}`.
Parameters ---------- n : int quadrature order mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad """ x, w, m = roots_chebyu(n, True) x *= 2 w *= 2 m *= 2 if mu: return x, w, m else: return x, w
r"""Chebyshev polynomial of the second kind on :math:`[-2, 2]`.
Defined as :math:`S_n(x) = U_n(x/2)` where :math:`U_n` is the nth Chebychev polynomial of the second kind.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- S : orthopoly1d Chebyshev polynomial of the second kind on :math:`[-2, 2]`.
Notes ----- The polynomials :math:`S_n(x)` are orthogonal over :math:`[-2, 2]` with weight function :math:`\sqrt{1 - (x/2)}^2`.
See Also -------- chebyu : Chebyshev polynomial of the second kind
References ---------- .. [1] Abramowitz and Stegun, "Handbook of Mathematical Functions" Section 22. National Bureau of Standards, 1972.
""" if n < 0: raise ValueError("n must be nonnegative.")
if n == 0: n1 = n + 1 else: n1 = n x, w, mu0 = roots_chebys(n1, mu=True) if n == 0: x, w = [], [] hn = pi kn = 1.0 p = orthopoly1d(x, w, hn, kn, wfunc=lambda x: sqrt(1 - x * x / 4.0), limits=(-2, 2), monic=monic) if not monic: factor = (n + 1.0) / p(2) p._scale(factor) p.__dict__['_eval_func'] = lambda x: eval_chebys(n, x) return p
# Shifted Chebyshev of the first kind T^*_n(x)
r"""Gauss-Chebyshev (first kind, shifted) quadrature.
Computes the sample points and weights for Gauss-Chebyshev quadrature. The sample points are the roots of the n-th degree shifted Chebyshev polynomial of the first kind, :math:`T_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[0, 1]` with weight function :math:`f(x) = 1/\sqrt{x - x^2}`.
Parameters ---------- n : int quadrature order mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad """ xw = roots_chebyt(n, mu) return ((xw[0] + 1) / 2,) + xw[1:]
r"""Shifted Chebyshev polynomial of the first kind.
Defined as :math:`T^*_n(x) = T_n(2x - 1)` for :math:`T_n` the nth Chebyshev polynomial of the first kind.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- T : orthopoly1d Shifted Chebyshev polynomial of the first kind.
Notes ----- The polynomials :math:`T^*_n` are orthogonal over :math:`[0, 1]` with weight function :math:`(x - x^2)^{-1/2}`.
""" base = sh_jacobi(n, 0.0, 0.5, monic=monic) if monic: return base if n > 0: factor = 4**n / 2.0 else: factor = 1.0 base._scale(factor) return base
# Shifted Chebyshev of the second kind U^*_n(x) r"""Gauss-Chebyshev (second kind, shifted) quadrature.
Computes the sample points and weights for Gauss-Chebyshev quadrature. The sample points are the roots of the n-th degree shifted Chebyshev polynomial of the second kind, :math:`U_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[0, 1]` with weight function :math:`f(x) = \sqrt{x - x^2}`.
Parameters ---------- n : int quadrature order mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad """ x, w, m = roots_chebyu(n, True) x = (x + 1) / 2 m_us = cephes.beta(1.5, 1.5) w *= m_us / m if mu: return x, w, m_us else: return x, w
r"""Shifted Chebyshev polynomial of the second kind.
Defined as :math:`U^*_n(x) = U_n(2x - 1)` for :math:`U_n` the nth Chebyshev polynomial of the second kind.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- U : orthopoly1d Shifted Chebyshev polynomial of the second kind.
Notes ----- The polynomials :math:`U^*_n` are orthogonal over :math:`[0, 1]` with weight function :math:`(x - x^2)^{1/2}`.
""" base = sh_jacobi(n, 2.0, 1.5, monic=monic) if monic: return base factor = 4**n base._scale(factor) return base
# Legendre
r"""Gauss-Legendre quadrature.
Computes the sample points and weights for Gauss-Legendre quadrature. The sample points are the roots of the n-th degree Legendre polynomial :math:`P_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[-1, 1]` with weight function :math:`f(x) = 1.0`.
Parameters ---------- n : int quadrature order mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad numpy.polynomial.legendre.leggauss """ m = int(n) if n < 1 or n != m: raise ValueError("n must be a positive integer.")
mu0 = 2.0 an_func = lambda k: 0.0 * k bn_func = lambda k: k * np.sqrt(1.0 / (4 * k * k - 1)) f = cephes.eval_legendre df = lambda n, x: (-n*x*cephes.eval_legendre(n, x) + n*cephes.eval_legendre(n-1, x))/(1-x**2) return _gen_roots_and_weights(m, mu0, an_func, bn_func, f, df, True, mu)
r"""Legendre polynomial.
Defined to be the solution of
.. math:: \frac{d}{dx}\left[(1 - x^2)\frac{d}{dx}P_n(x)\right] + n(n + 1)P_n(x) = 0;
:math:`P_n(x)` is a polynomial of degree :math:`n`.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- P : orthopoly1d Legendre polynomial.
Notes ----- The polynomials :math:`P_n` are orthogonal over :math:`[-1, 1]` with weight function 1.
Examples -------- Generate the 3rd-order Legendre polynomial 1/2*(5x^3 + 0x^2 - 3x + 0):
>>> from scipy.special import legendre >>> legendre(3) poly1d([ 2.5, 0. , -1.5, 0. ])
""" if n < 0: raise ValueError("n must be nonnegative.")
if n == 0: n1 = n + 1 else: n1 = n x, w, mu0 = roots_legendre(n1, mu=True) if n == 0: x, w = [], [] hn = 2.0 / (2 * n + 1) kn = _gam(2 * n + 1) / _gam(n + 1)**2 / 2.0**n p = orthopoly1d(x, w, hn, kn, wfunc=lambda x: 1.0, limits=(-1, 1), monic=monic, eval_func=lambda x: eval_legendre(n, x)) return p
# Shifted Legendre P^*_n(x)
r"""Gauss-Legendre (shifted) quadrature.
Computes the sample points and weights for Gauss-Legendre quadrature. The sample points are the roots of the n-th degree shifted Legendre polynomial :math:`P^*_n(x)`. These sample points and weights correctly integrate polynomials of degree :math:`2n - 1` or less over the interval :math:`[0, 1]` with weight function :math:`f(x) = 1.0`.
Parameters ---------- n : int quadrature order mu : bool, optional If True, return the sum of the weights, optional.
Returns ------- x : ndarray Sample points w : ndarray Weights mu : float Sum of the weights
See Also -------- scipy.integrate.quadrature scipy.integrate.fixed_quad """ x, w = roots_legendre(n) x = (x + 1) / 2 w /= 2 if mu: return x, w, 1.0 else: return x, w
r"""Shifted Legendre polynomial.
Defined as :math:`P^*_n(x) = P_n(2x - 1)` for :math:`P_n` the nth Legendre polynomial.
Parameters ---------- n : int Degree of the polynomial. monic : bool, optional If `True`, scale the leading coefficient to be 1. Default is `False`.
Returns ------- P : orthopoly1d Shifted Legendre polynomial.
Notes ----- The polynomials :math:`P^*_n` are orthogonal over :math:`[0, 1]` with weight function 1.
""" if n < 0: raise ValueError("n must be nonnegative.")
wfunc = lambda x: 0.0 * x + 1.0 if n == 0: return orthopoly1d([], [], 1.0, 1.0, wfunc, (0, 1), monic, lambda x: eval_sh_legendre(n, x)) x, w, mu0 = roots_sh_legendre(n, mu=True) hn = 1.0 / (2 * n + 1.0) kn = _gam(2 * n + 1) / _gam(n + 1)**2 p = orthopoly1d(x, w, hn, kn, wfunc, limits=(0, 1), monic=monic, eval_func=lambda x: eval_sh_legendre(n, x)) return p
# ----------------------------------------------------------------------------- # Code for backwards compatibility # -----------------------------------------------------------------------------
# Import functions in case someone is still calling the orthogonal # module directly. (They shouldn't be; it's not in the public API).
eval_chebyt, eval_chebyu, eval_chebys, eval_chebyc, eval_sh_chebyt, eval_sh_chebyu, eval_legendre, eval_sh_legendre, eval_genlaguerre, eval_laguerre, eval_hermite, eval_hermitenorm)
# Make the old root function names an alias for the new ones |