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pywafo/wafo/integrate.py

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from __future__ import absolute_import, division, print_function
import warnings
import numpy as np
from numpy import pi, sqrt, ones, zeros
from scipy import integrate as intg
import scipy.special.orthogonal as ort
from scipy import special as sp
from scipy.integrate import simps, trapz
from wafo.plotbackend import plotbackend as plt
from wafo.demos import humps
from wafo.misc import dea3
from wafo.dctpack import dct
# from pychebfun import Chebfun
_EPS = np.finfo(float).eps
_NODES_AND_WEIGHTS = {}
__all__ = ['dea3', 'clencurt', 'romberg',
'h_roots', 'j_roots', 'la_roots', 'p_roots', 'qrule',
'gaussq', 'richardson', 'quadgr', 'qdemo']
def _assert(cond, msg):
if not cond:
raise ValueError(msg)
def _assert_warn(cond, msg):
if not cond:
warnings.warn(msg)
def clencurt(fun, a, b, n=5, trace=False):
"""
Numerical evaluation of an integral, Clenshaw-Curtis method.
Parameters
----------
fun : callable
a, b : array-like
Lower and upper integration limit, respectively.
n : integer
defines number of evaluation points (default 5)
Returns
-------
q_val = evaluated integral
tol = Estimate of the approximation error
Notes
-----
CLENCURT approximates the integral of f(x) from a to b
using an 2*n+1 points Clenshaw-Curtis formula.
The error estimate is usually a conservative estimate of the
approximation error.
The integral is exact for polynomials of degree 2*n or less.
Example
-------
>>> import numpy as np
>>> val, err = clencurt(np.exp, 0, 2)
>>> np.allclose(val, np.expm1(2)), err[0] < 1e-10
(True, True)
See also
--------
simpson,
gaussq
References
----------
[1] Goodwin, E.T. (1961),
"Modern Computing Methods",
2nd edition, New yourk: Philosophical Library, pp. 78--79
[2] Clenshaw, C.W. and Curtis, A.R. (1960),
Numerische Matematik, Vol. 2, pp. 197--205
"""
# make sure n_2 is even
n_2 = 2 * int(n)
a, b = np.atleast_1d(a, b)
a_shape = a.shape
a = a.ravel()
b = b.ravel()
a_size = np.prod(a_shape)
s = np.c_[0:n_2 + 1:1]
s_2 = np.c_[0:n_2 + 1:2]
x = np.cos(np.pi * s / n_2) * (b - a) / 2. + (b + a) / 2
if hasattr(fun, '__call__'):
f = fun(x)
else:
x_0 = np.flipud(fun[:, 0])
n_2 = len(x_0) - 1
_assert(abs(x - x_0) <= 1e-8,
'Input vector x must equal cos(pi*s/n_2)*(b-a)/2+(b+a)/2')
f = np.flipud(fun[:, 1::])
if trace:
plt.plot(x, f, '+')
# using a Gauss-Lobatto variant, i.e., first and last
# term f(a) and f(b) is multiplied with 0.5
f[0, :] = f[0, :] / 2
f[n_2, :] = f[n_2, :] / 2
# x = cos(pi*0:n_2/n_2)
# 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
n = n_2 // 2
fft = np.fft.fft
tmp = np.real(fft(f[:n_2, :], axis=0))
c = 2 / n_2 * (tmp[0:n + 1, :] + np.cos(np.pi * s_2) * f[n_2, :])
c[0, :] = c[0, :] / 2
c[n, :] = c[n, :] / 2
c = c[0:n + 1, :] / ((s_2 - 1) * (s_2 + 1))
q_val = (a - b) * np.sum(c, axis=0)
abserr = (b - a) * np.abs(c[n, :])
if a_size > 1:
abserr = np.reshape(abserr, a_shape)
q_val = np.reshape(q_val, a_shape)
return q_val, abserr
def romberg(fun, a, b, releps=1e-3, abseps=1e-3):
"""
Numerical integration with the Romberg method
Parameters
----------
fun : callable
function to integrate
a, b : real scalars
lower and upper integration limits, respectively.
releps, abseps : scalar, optional
requested relative and absolute error, respectively.
Returns
-------
Q : scalar
value of integral
abserr : scalar
estimated absolute error of integral
ROMBERG approximates the integral of F(X) from A to B
using Romberg's method of integration. The function F
must return a vector of output values if a vector of input values is given.
Example
-------
>>> import numpy as np
>>> [q,err] = romberg(np.sqrt,0,10,0,1e-4)
>>> np.allclose(q, 21.08185107)
True
>>> err[0] < 1e-4
True
"""
h = b - a
h_min = 1.0e-9
# Max size of extrapolation table
table_limit = max(min(np.round(np.log2(h / h_min)), 30), 3)
rom = zeros((2, table_limit))
rom[0, 0] = h * (fun(a) + fun(b)) / 2
ipower = 1
f_p = ones(table_limit) * 4
# q_val1 = 0
q_val2 = 0.
q_val4 = rom[0, 0]
abserr = q_val4
# epstab = zeros(1,decdigs+7)
# newflg = 1
# [res,abserr,epstab,newflg] = dea(newflg,q_val4,abserr,epstab)
two = 1
one = 0
converged = False
for i in range(1, table_limit):
h *= 0.5
u_n5 = np.sum(fun(a + np.arange(1, 2 * ipower, 2) * h)) * h
# trapezoidal approximations
# T2n = 0.5 * (Tn + Un) = 0.5*Tn + u_n5
rom[two, 0] = 0.5 * rom[one, 0] + u_n5
f_p[i] = 4 * f_p[i - 1]
# Richardson extrapolation
for k in range(i):
rom[two, k + 1] = (rom[two, k] +
(rom[two, k] - rom[one, k]) / (f_p[k] - 1))
q_val1 = q_val2
q_val2 = q_val4
q_val4 = rom[two, i]
if 2 <= i:
res, abserr = dea3(q_val1, q_val2, q_val4)
# q_val4 = res
converged = abserr <= max(abseps, releps * abs(res))
if converged:
break
# rom(1,1:i) = rom(2,1:i)
two = one
one = (one + 1) % 2
ipower *= 2
_assert(converged, "Integral did not converge to the required accuracy!")
return res, abserr
def _h_roots_newton(n, releps=3e-14, max_iter=10):
# pim4=0.7511255444649425
pim4 = np.pi ** (-1. / 4)
# The roots are symmetric about the origin, so we have to
# find only half of them.
m = int(np.fix((n + 1) / 2))
# Initial approximations to the roots go into z.
anu = 2.0 * n + 1
rhs = np.arange(3, 4 * m, 4) * np.pi / anu
theta = _get_theta(rhs)
z = sqrt(anu) * np.cos(theta)
p = zeros((3, len(z)))
k_0 = 0
k_p1 = 1
for _i in range(max_iter):
# Newtons method carried out simultaneously on the roots.
p[k_0, :] = 0
p[k_p1, :] = pim4
for j in range(1, n + 1):
# Loop up the recurrence relation to get the Hermite
# polynomials evaluated at z.
k_m1 = k_0
k_0 = k_p1
k_p1 = np.mod(k_p1 + 1, 3)
p[k_p1, :] = (z * sqrt(2 / j) * p[k_0, :] -
sqrt((j - 1) / j) * p[k_m1, :])
# p now contains the desired Hermite polynomials.
# We next compute p_deriv, the derivatives,
# by the relation (4.5.21) using p2, the polynomials
# of one lower order.
p_deriv = sqrt(2 * n) * p[k_0, :]
d_z = p[k_p1, :] / p_deriv
z = z - d_z # Newtons formula.
converged = not np.any(abs(d_z) > releps)
if converged:
break
_assert_warn(converged, 'Newton iteration did not converge!')
weights = 2. / p_deriv ** 2
return _expand_roots(z, weights, n, m)
def h_roots(n, method='newton'):
"""
Returns the roots (x) of the nth order Hermite polynomial,
H_n(x), and weights (w) to use in Gaussian Quadrature over
[-inf,inf] with weighting function exp(-x**2).
Parameters
----------
n : integer
number of roots
method : 'newton' or 'eigenvalue'
uses Newton Raphson to find zeros of the Hermite polynomial (Fast)
or eigenvalue of the jacobi matrix (Slow) to obtain the nodes and
weights, respectively.
Returns
-------
x : ndarray
roots
w : ndarray
weights
Example
-------
>>> import numpy as np
>>> x, w = h_roots(10)
>>> np.allclose(np.sum(x*w), -5.2516042729766621e-19)
True
See also
--------
qrule, gaussq
References
----------
[1] Golub, G. H. and Welsch, J. H. (1969)
'Calculation of Gaussian Quadrature Rules'
Mathematics of Computation, vol 23,page 221-230,
[2]. Stroud and Secrest (1966), 'gaussian quadrature formulas',
prentice-hall, Englewood cliffs, n.j.
"""
if not method.startswith('n'):
return ort.h_roots(n)
return _h_roots_newton(n)
def _j_roots_newton(n, alpha, beta, releps=3e-14, max_iter=10):
# Initial approximations to the roots go into z.
alfbet = alpha + beta
z = np.cos(np.pi * (np.arange(1, n + 1) - 0.25 + 0.5 * alpha) /
(n + 0.5 * (alfbet + 1)))
p = zeros((3, len(z)))
k_0 = 0
k_p1 = 1
for _i in range(max_iter):
# Newton's method carried out simultaneously on the roots.
tmp = 2 + alfbet
p[k_0, :] = 1
p[k_p1, :] = (alpha - beta + tmp * z) / 2
for j in range(2, n + 1):
# Loop up the recurrence relation to get the Jacobi
# polynomials evaluated at z.
k_m1 = k_0
k_0 = k_p1
k_p1 = np.mod(k_p1 + 1, 3)
a = 2. * j * (j + alfbet) * tmp
tmp = tmp + 2
c = 2 * (j - 1 + alpha) * (j - 1 + beta) * tmp
b = (tmp - 1) * (alpha ** 2 - beta ** 2 + tmp * (tmp - 2) * z)
p[k_p1, :] = (b * p[k_0, :] - c * p[k_m1, :]) / a
# p now contains the desired Jacobi polynomials.
# We next compute p_deriv, the derivatives with a standard
# relation involving the polynomials of one lower order.
p_deriv = ((n * (alpha - beta - tmp * z) * p[k_p1, :] +
2 * (n + alpha) * (n + beta) * p[k_0, :]) /
(tmp * (1 - z ** 2)))
d_z = p[k_p1, :] / p_deriv
z = z - d_z # Newton's formula.
converged = not any(abs(d_z) > releps * abs(z))
if converged:
break
_assert_warn(converged, 'too many iterations in jrule')
x = z # Store the root and the weight.
f = (sp.gammaln(alpha + n) + sp.gammaln(beta + n) -
sp.gammaln(n + 1) - sp.gammaln(alpha + beta + n + 1))
weights = (np.exp(f) * tmp * 2 ** alfbet / (p_deriv * p[k_0, :]))
return x, weights
def j_roots(n, alpha, beta, method='newton'):
"""
Returns the roots of the nth order Jacobi polynomial, P^(alpha,beta)_n(x)
and weights (w) to use in Gaussian Quadrature over [-1,1] with weighting
function (1-x)**alpha (1+x)**beta with alpha,beta > -1.
Parameters
----------
n : integer
number of roots
alpha,beta : scalars
defining shape of Jacobi polynomial
method : 'newton' or 'eigenvalue'
uses Newton Raphson to find zeros of the Hermite polynomial (Fast)
or eigenvalue of the jacobi matrix (Slow) to obtain the nodes and
weights, respectively.
Returns
-------
x : ndarray
roots
w : ndarray
weights
Example
--------
>>> [x,w]= j_roots(10,0,0)
>>> sum(x*w)
2.7755575615628914e-16
See also
--------
qrule, gaussq
Reference
---------
[1] Golub, G. H. and Welsch, J. H. (1969)
'Calculation of Gaussian Quadrature Rules'
Mathematics of Computation, vol 23,page 221-230,
[2]. Stroud and Secrest (1966), 'gaussian quadrature formulas',
prentice-hall, Englewood cliffs, n.j.
"""
_assert((-1 < alpha) & (-1 < beta),
'alpha and beta must be greater than -1')
if not method.startswith('n'):
return ort.j_roots(n, alpha, beta)
return _j_roots_newton(n, alpha, beta)
def la_roots(n, alpha=0, method='newton'):
"""
Returns the roots (x) of the nth order generalized (associated) Laguerre
polynomial, L^(alpha)_n(x), and weights (w) to use in Gaussian quadrature
over [0,inf] with weighting function exp(-x) x**alpha with alpha > -1.
Parameters
----------
n : integer
number of roots
method : 'newton' or 'eigenvalue'
uses Newton Raphson to find zeros of the Laguerre polynomial (Fast)
or eigenvalue of the jacobi matrix (Slow) to obtain the nodes and
weights, respectively.
Returns
-------
x : ndarray
roots
w : ndarray
weights
Example
-------
>>> import numpy as np
>>> [x,w] = h_roots(10)
>>> np.sum(x*w)
1.3352627380516791e-17
See also
--------
qrule, gaussq
References
----------
[1] Golub, G. H. and Welsch, J. H. (1969)
'Calculation of Gaussian Quadrature Rules'
Mathematics of Computation, vol 23,page 221-230,
[2]. Stroud and Secrest (1966), 'gaussian quadrature formulas',
prentice-hall, Englewood cliffs, n.j.
"""
_assert(-1 < alpha, 'alpha must be greater than -1')
if not method.startswith('n'):
return ort.la_roots(n, alpha)
return _la_roots_newton(n, alpha)
def _get_theta(rhs):
r_3 = rhs ** (1. / 3)
r_2 = r_3 ** 2
c = [9.084064e-01, 5.214976e-02, 2.579930e-03, 3.986126e-03]
theta = r_3 * (c[0] + r_2 * (c[1] + r_2 * (c[2] + r_2 * c[3])))
return theta
def _la_roots_newton(n, alpha, releps=3e-14, max_iter=10):
# Initial approximations to the roots go into z.
anu = 4.0 * n + 2.0 * alpha + 2.0
rhs = np.arange(4 * n - 1, 2, -4) * np.pi / anu
theta = _get_theta(rhs)
z = anu * np.cos(theta) ** 2
d_z = zeros(len(z))
p = zeros((3, len(z)))
p_previous = zeros((1, len(z)))
p_deriv = zeros((1, len(z)))
k_0 = 0
k_p1 = 1
k = slice(len(z))
for _i in range(max_iter):
# Newton's method carried out simultaneously on the roots.
p[k_0, k] = 0.
p[k_p1, k] = 1.
for j in range(1, n + 1):
# Loop up the recurrence relation to get the Laguerre
# polynomials evaluated at z.
km1 = k_0
k_0 = k_p1
k_p1 = np.mod(k_p1 + 1, 3)
p[k_p1, k] = ((2 * j - 1 + alpha - z[k]) * p[k_0, k] -
(j - 1 + alpha) * p[km1, k]) / j
# end
# p now contains the desired Laguerre polynomials.
# We next compute p_deriv, the derivatives with a standard
# relation involving the polynomials of one lower order.
p_previous[k] = p[k_0, k]
p_deriv[k] = (n * p[k_p1, k] - (n + alpha) * p_previous[k]) / z[k]
d_z[k] = p[k_p1, k] / p_deriv[k]
z[k] = z[k] - d_z[k] # Newton?s formula.
# k = find((abs(d_z) > releps.*z))
converged = not np.any(abs(d_z) > releps)
if converged:
break
_assert_warn(converged, 'too many iterations!')
nodes = z
weights = -np.exp(sp.gammaln(alpha + n) -
sp.gammaln(n)) / (p_deriv * n * p_previous)
return nodes, weights
def _p_roots_newton_start(n):
m = int(np.fix((n + 1) / 2))
t = (np.pi / (4 * n + 2)) * np.arange(3, 4 * m, 4)
a = 1 - (1 - 1 / n) / (8 * n * n)
x = a * np.cos(t)
return m, x
def _p_roots_newton(n):
"""
Algorithm given by Davis and Rabinowitz in 'Methods
of Numerical Integration', page 365, Academic Press, 1975.
"""
m, x = _p_roots_newton_start(n)
e_1 = n * (n + 1)
for _j in range(2):
p_km1 = 1
p_k = x
for k in range(2, n + 1):
t_1 = x * p_k
p_kp1 = t_1 - p_km1 - (t_1 - p_km1) / k + t_1
p_km1 = p_k
p_k = p_kp1
den = 1. - x * x
d_1 = n * (p_km1 - x * p_k)
d_pn = d_1 / den
d_2pn = (2. * x * d_pn - e_1 * p_k) / den
d_3pn = (4. * x * d_2pn + (2 - e_1) * d_pn) / den
d_4pn = (6. * x * d_3pn + (6 - e_1) * d_2pn) / den
u = p_k / d_pn
v = d_2pn / d_pn
h = -u * (1 + (.5 * u) * (v + u * (v * v - u * d_3pn / (3 * d_pn))))
p = p_k + h * (d_pn + (.5 * h) * (d_2pn + (h / 3) *
(d_3pn + .25 * h * d_4pn)))
d_p = d_pn + h * (d_2pn + (.5 * h) * (d_3pn + h * d_4pn / 3))
h = h - p / d_p
x = x + h
nodes = -x - h
f_x = d_1 - h * e_1 * (p_k + (h / 2) * (d_pn + (h / 3) *
(d_2pn + (h / 4) *
(d_3pn + (.2 * h) * d_4pn))))
weights = 2 * (1 - nodes ** 2) / (f_x ** 2)
return _expand_roots(nodes, weights, n, m)
def _p_roots_newton1(n, releps=1e-15, max_iter=100):
m, x = _p_roots_newton_start(n)
# Compute the zeros of the N+1 Legendre Polynomial
# using the recursion relation and the Newton-Raphson method
# Legendre-Gauss Polynomials
p = zeros((3, m))
# Derivative of LGP
p_deriv = zeros((m,))
d_x = zeros((m,))
# Compute the zeros of the N+1 Legendre Polynomial
# using the recursion relation and the Newton-Raphson method
# Iterate until new points are uniformly within epsilon of old
# points
k = slice(m)
k_0 = 0
k_p1 = 1
for _ix in range(max_iter):
p[k_0, k] = 1
p[k_p1, k] = x[k]
for j in range(2, n + 1):
k_m1 = k_0
k_0 = k_p1
k_p1 = np.mod(k_0 + 1, 3)
p[k_p1, k] = ((2 * j - 1) * x[k] * p[k_0, k] -
(j - 1) * p[k_m1, k]) / j
p_deriv[k] = n * (p[k_0, k] - x[k] * p[k_p1, k]) / (1 - x[k] ** 2)
d_x[k] = p[k_p1, k] / p_deriv[k]
x[k] = x[k] - d_x[k]
k, = np.nonzero((abs(d_x) > releps * np.abs(x)))
converged = len(k) == 0
if converged:
break
_assert(converged, 'Too many iterations!')
nodes = -x
weights = 2. / ((1 - nodes ** 2) * (p_deriv ** 2))
return _expand_roots(nodes, weights, n, m)
def _expand_roots(x, w, n, m):
if (m + m) > n:
x[m - 1] = 0.0
if not (m + m) == n:
m = m - 1
x = np.hstack((x, -x[m - 1::-1]))
w = np.hstack((w, w[m - 1::-1]))
return x, w
def p_roots(n, method='newton', a=-1, b=1):
"""
Returns the roots (x) of the nth order Legendre polynomial, P_n(x),
and weights to use in Gaussian Quadrature over [-1,1] with weighting
function 1.
Parameters
----------
n : integer
number of roots
method : 'newton' or 'eigenvalue'
uses Newton Raphson to find zeros of the Hermite polynomial (Fast)
or eigenvalue of the jacobi matrix (Slow) to obtain the nodes and
weights, respectively.
Returns
-------
nodes : ndarray
roots
weights : ndarray
weights
Example
-------
Integral of exp(x) from a = 0 to b = 3 is: exp(3)-exp(0)=
>>> import numpy as np
>>> nodes, weights = p_roots(11, a=0, b=3)
>>> np.allclose(np.sum(np.exp(nodes) * weights), 19.085536923187668)
True
>>> nodes, weights = p_roots(11, method='newton1', a=0, b=3)
>>> np.allclose(np.sum(np.exp(nodes) * weights), 19.085536923187668)
True
>>> nodes, weights = p_roots(11, method='eigenvalue', a=0, b=3)
>>> np.allclose(np.sum(np.exp(nodes) * weights), 19.085536923187668)
True
See also
--------
quadg.
References
----------
[1] Davis and Rabinowitz (1975) 'Methods of Numerical Integration',
page 365, Academic Press.
[2] Golub, G. H. and Welsch, J. H. (1969)
'Calculation of Gaussian Quadrature Rules'
Mathematics of Computation, vol 23,page 221-230,
[3] Stroud and Secrest (1966), 'gaussian quadrature formulas',
prentice-hall, Englewood cliffs, n.j.
"""
if not method.startswith('n'):
nodes, weights = ort.p_roots(n)
else:
if method.endswith('1'):
nodes, weights = _p_roots_newton1(n)
else:
nodes, weights = _p_roots_newton(n)
if (a != -1) | (b != 1):
# Linear map from[-1,1] to [a,b]
d_h = (b - a) / 2
nodes = d_h * (nodes + 1) + a
weights = weights * d_h
return nodes, weights
def q5_roots(n):
"""
5 : p(x) = 1/sqrt((x-a)*(b-x)), a =-1, b = 1 Chebyshev 1'st kind
"""
j = np.arange(1, n + 1)
weights = ones(n) * np.pi / n
nodes = np.cos((2 * j - 1) * np.pi / (2 * n))
return nodes, weights
def q6_roots(n):
"""
6 : p(x) = sqrt((x-a)*(b-x)), a =-1, b = 1 Chebyshev 2'nd kind
"""
j = np.arange(1, n + 1)
x_j = j * np.pi / (n + 1)
weights = np.pi / (n + 1) * np.sin(x_j) ** 2
nodes = np.cos(x_j)
return nodes, weights
def q7_roots(n):
"""
7 : p(x) = sqrt((x-a)/(b-x)), a = 0, b = 1
"""
j = np.arange(1, n + 1)
x_j = (j - 0.5) * pi / (2 * n + 1)
nodes = np.cos(x_j) ** 2
weights = 2 * np.pi * nodes / (2 * n + 1)
return nodes, weights
def q8_roots(n):
"""
8 : p(x) = 1/sqrt(b-x), a = 0, b = 1
"""
nodes_1, weights_1 = p_roots(2 * n)
k, = np.where(0 <= nodes_1)
weights = 2 * weights_1[k]
nodes = 1 - nodes_1[k] ** 2
return nodes, weights
def q9_roots(n):
"""
9 : p(x) = sqrt(b-x), a = 0, b = 1
"""
nodes_1, weights_1 = p_roots(2 * n + 1)
k, = np.where(0 < nodes_1)
weights = 2 * nodes_1[k] ** 2 * weights_1[k]
nodes = 1 - nodes_1[k] ** 2
return nodes, weights
def qrule(n, wfun=1, alpha=0, beta=0):
"""
Return nodes and weights for Gaussian quadratures.
Parameters
----------
n : integer
number of base points
wfun : integer
defining the weight function, p(x). (default wfun = 1)
1 : p(x) = 1 a =-1, b = 1 Gauss-Legendre
2 : p(x) = exp(-x^2) a =-inf, b = inf Hermite
3 : p(x) = x^alpha*exp(-x) a = 0, b = inf Laguerre
4 : p(x) = (x-a)^alpha*(b-x)^beta a =-1, b = 1 Jacobi
5 : p(x) = 1/sqrt((x-a)*(b-x)), a =-1, b = 1 Chebyshev 1'st kind
6 : p(x) = sqrt((x-a)*(b-x)), a =-1, b = 1 Chebyshev 2'nd kind
7 : p(x) = sqrt((x-a)/(b-x)), a = 0, b = 1
8 : p(x) = 1/sqrt(b-x), a = 0, b = 1
9 : p(x) = sqrt(b-x), a = 0, b = 1
Returns
-------
bp = base points (abscissas)
wf = weight factors
The Gaussian Quadrature integrates a (2n-1)th order
polynomial exactly and the integral is of the form
b n
Int ( p(x)* F(x) ) dx = Sum ( wf_j* F( bp_j ) )
a j=1
where p(x) is the weight function.
For Jacobi and Laguerre: alpha, beta >-1 (default alpha=beta=0)
Examples:
---------
>>> import numpy as np
# integral of x^2 from a = -1 to b = 1
>>> bp, wf = qrule(10)
>>> 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)
>>> 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)
>>> np.allclose((bp * wf).sum(), 0.26666666666666755)
True
See also
--------
gaussq
Reference
---------
Abromowitz and Stegun (1954)
(for method 5 to 9)
"""
if wfun == 3: # Generalized Laguerre
return la_roots(n, alpha)
if wfun == 4: # Gauss-Jacobi
return j_roots(n, alpha, beta)
_assert(0 < wfun < 10, 'unknown weight function')
root_fun = [None, p_roots, h_roots, la_roots, j_roots, q5_roots, q6_roots,
q7_roots, q8_roots, q9_roots][wfun]
return root_fun(n)
class _Gaussq(object):
"""
Numerically evaluate integral, Gauss quadrature.
Parameters
----------
fun : callable
a,b : array-like
lower and upper integration limits, respectively.
releps, abseps : real scalars, optional
relative and absolute tolerance, respectively.
(default releps=abseps=1e-3).
wfun : scalar integer, optional
defining the weight function, p(x). (default wfun = 1)
1 : p(x) = 1 a =-1, b = 1 Gauss-Legendre
2 : p(x) = exp(-x^2) a =-inf, b = inf Hermite
3 : p(x) = x^alpha*exp(-x) a = 0, b = inf Laguerre
4 : p(x) = (x-a)^alpha*(b-x)^beta a =-1, b = 1 Jacobi
5 : p(x) = 1/sqrt((x-a)*(b-x)), a =-1, b = 1 Chebyshev 1'st kind
6 : p(x) = sqrt((x-a)*(b-x)), a =-1, b = 1 Chebyshev 2'nd kind
7 : p(x) = sqrt((x-a)/(b-x)), a = 0, b = 1
8 : p(x) = 1/sqrt(b-x), a = 0, b = 1
9 : p(x) = sqrt(b-x), a = 0, b = 1
trace : bool, optional
If non-zero a point plot of the integrand (default False).
gn : scalar integer
number of base points to start the integration with (default 2).
alpha, beta : real scalars, optional
Shape parameters of Laguerre or Jacobi weight function
(alpha,beta>-1) (default alpha=beta=0)
Returns
-------
val : ndarray
evaluated integral
err : ndarray
error estimate, absolute tolerance abs(int-intold)
Notes
-----
GAUSSQ numerically evaluate integral using a Gauss quadrature.
The Quadrature integrates a (2m-1)th order polynomial exactly and the
integral is of the form
b
Int (p(x)* Fun(x)) dx
a
GAUSSQ is vectorized to accept integration limits A, B and
coefficients P1,P2,...Pn, as matrices or scalars and the
result is the common size of A, B and P1,P2,...,Pn.
Examples
---------
integration of x**2 from 0 to 2 and from 1 to 4
>>> 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 = 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, trace=True)
See also
--------
qrule
gaussq2d
"""
@staticmethod
def _get_dx(wfun, jacob, alpha, beta):
def fun1(x):
return x
if wfun == 4:
d_x = jacob ** (alpha + beta + 1)
else:
d_x = [None, fun1, fun1, fun1, None, lambda x: ones(np.shape(x)),
lambda x: x ** 2, fun1, sqrt,
lambda x: sqrt(x) ** 3][wfun](jacob)
return d_x.ravel()
@staticmethod
def _nodes_and_weights(num_nodes, wfun, alpha, beta):
global _NODES_AND_WEIGHTS
name = 'wfun{:d}_{:d}_{:g}_{:g}'.format(wfun, num_nodes, alpha, beta)
nodes_and_weights = _NODES_AND_WEIGHTS.setdefault(name, [])
if len(nodes_and_weights) == 0:
nodes_and_weights.extend(qrule(num_nodes, wfun, alpha, beta))
nodes, weights = nodes_and_weights
return nodes, weights
def _initialize_trace(self, max_iter):
if self.trace:
self.x_trace = [0] * max_iter
self.y_trace = [0] * max_iter
def _plot_trace(self, x, y):
if self.trace:
self.x_trace.append(x.ravel())
self.y_trace.append(y.ravel())
hfig = plt.plot(x, y, 'r.')
plt.setp(hfig, 'color', 'b')
def _plot_final_trace(self):
if self.trace > 0:
plt.clf()
plt.plot(np.hstack(self.x_trace), np.hstack(self.y_trace), '+')
@staticmethod
def _get_jacob(wfun, a, b):
if wfun in [2, 3]:
jacob = ones((np.size(a), 1))
else:
jacob = (b - a) * 0.5
if wfun in [7, 8, 9]:
jacob *= 2
return jacob
@staticmethod
def _warn(k, a_shape):
n = len(k)
if n > 1:
if n == np.prod(a_shape):
tmptxt = 'All integrals did not converge'
else:
tmptxt = '%d integrals did not converge' % (n, )
tmptxt = tmptxt + '--singularities likely!'
else:
tmptxt = 'Integral did not converge--singularity likely!'
warnings.warn(tmptxt)
@staticmethod
def _initialize(wfun, a, b, args):
args = np.broadcast_arrays(*np.atleast_1d(a, b, *args))
a_shape = args[0].shape
args = [np.reshape(x, (-1, 1)) for x in args]
a_out, b_out = args[:2]
args = args[2:]
if wfun in [2, 3]:
a_out = zeros((a_out.size, 1))
return a_out, b_out, args, a_shape
def __call__(self, fun, a, b, releps=1e-3, abseps=1e-3, alpha=0, beta=0,
wfun=1, trace=False, args=(), max_iter=11):
self.trace = trace
num_nodes = 2
a_0, b_0, args, a_shape = self._initialize(wfun, a, b, args)
jacob = self._get_jacob(wfun, a_0, b_0)
shift = int(wfun in [1, 4, 5, 6])
d_x = self._get_dx(wfun, jacob, alpha, beta)
self._initialize_trace(max_iter)
# Break out of the iteration loop for three reasons:
# 1) the last update is very small (compared to int and to releps)
# 2) There are more than 11 iterations. This should NEVER happen.
dtype = np.result_type(fun((a_0+b_0)*0.5, *args))
n_k = np.prod(a_shape) # # of integrals we have to compute
k = np.arange(n_k)
opts = (n_k, dtype)
val, val_old, abserr = zeros(*opts), ones(*opts), zeros(*opts)
nodes_and_weights = self._nodes_and_weights
for i in range(max_iter):
x_n, weights = nodes_and_weights(num_nodes, wfun, alpha, beta)
x = (x_n + shift) * jacob[k, :] + a_0[k, :]
params = [xi[k, :] for xi in args]
y = fun(x, *params)
self._plot_trace(x, y)
val[k] = np.sum(weights * y, axis=1) * d_x[k] # do the integration
if any(np.isnan(val)):
val[np.isnan(val)] = val_old[np.isnan(val)]
if 1 < i:
abserr[k] = abs(val_old[k] - val[k]) # absolute tolerance
k, = np.where(abserr > np.maximum(abs(releps * val), abseps))
n_k = len(k) # of integrals we have to compute again
if n_k == 0:
break
val_old[k] = val[k]
num_nodes *= 2 # double the # of basepoints and weights
else:
self._warn(k, a_shape)
# make sure int is the same size as the integration limits
val.shape = a_shape
abserr.shape = a_shape
self._plot_final_trace()
return val, abserr
gaussq = _Gaussq()
def richardson(q_val, k):
# license BSD
# Richardson extrapolation with parameter estimation
c = np.real((q_val[k - 1] - q_val[k - 2]) / (q_val[k] - q_val[k - 1])) - 1.
# The lower bound 0.07 admits the singularity x.^-0.9
c = max(c, 0.07)
return q_val[k] + (q_val[k] - q_val[k - 1]) / c
class _Quadgr(object):
"""
Gauss-Legendre quadrature with Richardson extrapolation.
[q_val,ERR] = QUADGR(FUN,A,B,TOL) approximates the integral of a function
FUN from A to B with an absolute error tolerance TOL. FUN is a function
handle and must accept vector arguments. TOL is 1e-6 by default. q_val is
the integral approximation and ERR is an estimate of the absolute
error.
QUADGR uses a 12-point Gauss-Legendre quadrature. The error estimate is
based on successive interval bisection. Richardson extrapolation
accelerates the convergence for some integrals, especially integrals
with endpoint singularities.
Examples
--------
>>> import numpy as np
>>> q_val, err = quadgr(np.log,0,1)
>>> q, err = quadgr(np.exp,0,9999*1j*np.pi)
>>> np.allclose(q, -2.0000000000122662), err < 1.0e-08
(True, True)
>>> 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)
>>> q, err = quadgr(lambda x: x**-0.75, 0, 1)
>>> np.allclose(q, 4), err < 1.e-13
(True, True)
>>> q, err = quadgr(lambda x: 1./np.sqrt(1-x**2), -1, 1)
>>> np.allclose(q, 3.141596056985029), err < 1.0e-05
(True, True)
>>> 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)
>>> 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)
>>> q, err = quadgr(lambda x: np.cos(x)*np.exp(-x), np.inf, 0, 1e-9)
>>> np.allclose(q, -0.5), err < 1e-9
(True, True)
>>> q, err = quadgr(lambda x: np.cos(x)*np.exp(x), -np.inf, 0, 1e-9)
>>> np.allclose(q, 0.5), err < 1e-9
(True, True)
See also
--------
QUAD,
QUADGK
"""
# Author: jonas.lundgren@saabgroup.com, 2009. license BSD
# Order limits (required if infinite limits)
def _change_variable_and_integrate(self, fun, a, b, abseps, max_iter):
isreal = np.isreal(a) & np.isreal(b) & ~np.isnan(a) & ~np.isnan(b)
_assert(isreal, 'Infinite intervals must be real.')
integrate = self._integrate
# Change of variable
if np.isfinite(a) & np.isinf(b): # a to inf
val, err = integrate(lambda t: fun(a + t / (1 - t)) / (1 - t) ** 2,
0, 1, abseps, max_iter)
elif np.isinf(a) & np.isfinite(b): # -inf to b
val, err = integrate(lambda t: fun(b + t / (1 + t)) / (1 + t) ** 2,
-1, 0, abseps, max_iter)
else: # -inf to inf
val1, err1 = integrate(lambda t: fun(t / (1 - t)) / (1 - t) ** 2,
0, 1, abseps / 2, max_iter)
val2, err2 = integrate(lambda t: fun(t / (1 + t)) / (1 + t) ** 2,
-1, 0, abseps / 2, max_iter)
val = val1 + val2
err = err1 + err2
return val, err
@staticmethod
def _nodes_and_weights():
# Gauss-Legendre quadrature (12-point)
x = np.asarray(
[0.12523340851146894, 0.36783149899818018, 0.58731795428661748,
0.76990267419430469, 0.9041172563704748, 0.98156063424671924])
w = np.asarray(
[0.24914704581340288, 0.23349253653835478, 0.20316742672306584,
0.16007832854334636, 0.10693932599531818, 0.047175336386511842])
nodes = np.hstack((x, -x))
weights = np.hstack((w, w))
return nodes, weights
@staticmethod
def _get_best_estimate(vals0, vals1, vals2, k):
if k >= 6:
q_v = np.hstack((vals0[k], vals1[k], vals2[k]))
q_w = np.hstack((vals0[k - 1], vals1[k - 1], vals2[k - 1]))
elif k >= 4:
q_v = np.hstack((vals0[k], vals1[k]))
q_w = np.hstack((vals0[k - 1], vals1[k - 1]))
else:
q_v = np.atleast_1d(vals0[k])
q_w = vals0[k - 1]
# Estimate absolute error
errors = np.atleast_1d(abs(q_v - q_w))
j = errors.argmin()
err = errors[j]
q_val = q_v[j]
# if k >= 2: # and not iscomplex:
# _val, err1 = dea3(vals0[k - 2], vals0[k - 1], vals0[k])
return q_val, err
def _integrate(self, fun, a, b, abseps, max_iter):
dtype = np.result_type(fun((a+b)/2), fun((a+b)/4))
# Initiate vectors
val0 = zeros(max_iter, dtype=dtype) # Quadrature
val1 = zeros(max_iter, dtype=dtype) # First Richardson extrapolation
val2 = zeros(max_iter, dtype=dtype) # Second Richardson extrapolation
x_n, weights = self._nodes_and_weights()
n = len(x_n)
# One interval
d_x = (b - a) / 2 # Half interval length
x = (a + b) / 2 + d_x * x_n # Nodes
# Quadrature
val0[0] = d_x * np.sum(weights * fun(x), axis=0)
# Successive bisection of intervals
for k in range(1, max_iter):
# Interval bisection
d_x = d_x / 2
x = np.hstack([x + a, x + b]) / 2
# Quadrature
val0[k] = np.sum(np.sum(np.reshape(fun(x), (-1, n)), axis=0) *
weights, axis=0) * d_x
# Richardson extrapolation
if k >= 5:
val1[k] = richardson(val0, k)
val2[k] = richardson(val1, k)
elif k >= 3:
val1[k] = richardson(val0, k)
q_val, err = self._get_best_estimate(val0, val1, val2, k)
converged = (err < abseps) | ~np.isfinite(q_val)
if converged:
break
_assert_warn(converged, 'Max number of iterations reached without '
'convergence.')
_assert_warn(np.isfinite(q_val),
'Integral approximation is Infinite or NaN.')
# The error estimate should not be zero
err = err + 2 * np.finfo(q_val).eps
return q_val, err
@staticmethod
def _order_limits(a, b):
if np.real(a) > np.real(b):
return b, a, True
return a, b, False
def __call__(self, fun, a, b, abseps=1e-5, max_iter=17):
a = np.asarray(a)
b = np.asarray(b)
if a == b:
q_val = b - a
err = b - a
return q_val, err
a, b, reverse = self._order_limits(a, b)
improper_integral = np.isinf(a) | np.isinf(b)
if improper_integral: # Infinite limits
q_val, err = self._change_variable_and_integrate(fun, a, b, abseps,
max_iter)
else:
q_val, err = self._integrate(fun, a, b, abseps, max_iter)
# Reverse direction
if reverse:
q_val = -q_val
return q_val, err
quadgr = _Quadgr()
def boole(y, x):
a, b = x[0], x[-1]
n = len(x)
h = (b - a) / (n - 1)
return (2 * h / 45) * (7 * (y[0] + y[-1]) + 12 * np.sum(y[2:n - 1:4]) +
32 * np.sum(y[1:n - 1:2]) +
14 * np.sum(y[4:n - 3:4]))
def _display(neval, vals_dic, err_dic, plot_error):
# display results
kmax = len(neval)
names = sorted(vals_dic.keys())
num_cols = 2
formats = ['%4.0f, '] + ['%10.10f, '] * num_cols * 2
formats[-1] = formats[-1].split(',')[0]
formats_h = ['%4s, '] + ['%20s, '] * num_cols
formats_h[-1] = formats_h[-1].split(',')[0]
headers = ['evals'] + ['%12s %12s' % ('approx', 'error')] * num_cols
while len(names) > 0:
print(''.join(fi % t for (fi, t) in zip(formats_h,
['ftn'] + names[:num_cols])))
print(' '.join(headers))
data = [neval]
for name in names[:num_cols]:
data.append(vals_dic[name])
data.append(err_dic[name])
data = np.vstack(tuple(data)).T
for k in range(kmax):
tmp = data[k].tolist()
print(''.join(fi % t for (fi, t) in zip(formats, tmp)))
if plot_error:
plt.figure(0)
for name in names[:num_cols]:
plt.loglog(neval, err_dic[name], label=name)
names = names[num_cols:]
if plot_error:
plt.xlabel('number of function evaluations')
plt.ylabel('error')
plt.legend()
def qdemo(f, a, b, kmax=9, plot_error=False):
"""
Compares different quadrature rules.
Parameters
----------
f : callable
function
a,b : scalars
lower and upper integration limits
Details
-------
qdemo(f,a,b) computes and compares various approximations to
the integral of f from a to b. Three approximations are used,
the composite trapezoid, Simpson's, and Boole's rules, all with
equal length subintervals.
In a case like qdemo(exp,0,3) one can see the expected
convergence rates for each of the three methods.
In a case like qdemo(sqrt,0,3), the convergence rate is limited
not by the method, but by the singularity of the integrand.
Example
-------
>>> import numpy as np
>>> qdemo(np.exp,0,3, plot_error=True)
true value = 19.08553692
ftn, Boole, Chebychev
evals approx error approx error
3, 19.4008539142, 0.3153169910, 19.5061466023, 0.4206096791
5, 19.0910191534, 0.0054822302, 19.0910191534, 0.0054822302
9, 19.0856414320, 0.0001045088, 19.0855374134, 0.0000004902
17, 19.0855386464, 0.0000017232, 19.0855369232, 0.0000000000
33, 19.0855369505, 0.0000000273, 19.0855369232, 0.0000000000
65, 19.0855369236, 0.0000000004, 19.0855369232, 0.0000000000
129, 19.0855369232, 0.0000000000, 19.0855369232, 0.0000000000
257, 19.0855369232, 0.0000000000, 19.0855369232, 0.0000000000
513, 19.0855369232, 0.0000000000, 19.0855369232, 0.0000000000
ftn, Clenshaw-Curtis, Gauss-Legendre
evals approx error approx error
3, 19.5061466023, 0.4206096791, 19.0803304585, 0.0052064647
5, 19.0834145766, 0.0021223465, 19.0855365951, 0.0000003281
9, 19.0855369150, 0.0000000082, 19.0855369232, 0.0000000000
17, 19.0855369232, 0.0000000000, 19.0855369232, 0.0000000000
33, 19.0855369232, 0.0000000000, 19.0855369232, 0.0000000000
65, 19.0855369232, 0.0000000000, 19.0855369232, 0.0000000000
129, 19.0855369232, 0.0000000000, 19.0855369232, 0.0000000000
257, 19.0855369232, 0.0000000000, 19.0855369232, 0.0000000000
513, 19.0855369232, 0.0000000000, 19.0855369232, 0.0000000000
ftn, Simps, Trapz
evals approx error approx error
3, 19.5061466023, 0.4206096791, 22.5366862979, 3.4511493747
5, 19.1169646189, 0.0314276957, 19.9718950387, 0.8863581155
9, 19.0875991312, 0.0020622080, 19.3086731081, 0.2231361849
17, 19.0856674267, 0.0001305035, 19.1414188470, 0.0558819239
33, 19.0855451052, 0.0000081821, 19.0995135407, 0.0139766175
65, 19.0855374350, 0.0000005118, 19.0890314614, 0.0034945382
129, 19.0855369552, 0.0000000320, 19.0864105817, 0.0008736585
257, 19.0855369252, 0.0000000020, 19.0857553393, 0.0002184161
513, 19.0855369233, 0.0000000001, 19.0855915273, 0.0000546041
"""
true_val, _tol = intg.quad(f, a, b)
print('true value = %12.8f' % (true_val,))
neval = zeros(kmax, dtype=int)
vals_dic = {}
err_dic = {}
# try various approximations
methods = [trapz, simps, boole, ]
for k in range(kmax):
n = 2 ** (k + 1) + 1
neval[k] = n
x = np.linspace(a, b, n)
y = f(x)
for method in methods:
name = method.__name__.title()
q = method(y, x)
vals_dic.setdefault(name, []).append(q)
err_dic.setdefault(name, []).append(abs(q - true_val))
name = 'Clenshaw-Curtis'
q = clencurt(f, a, b, (n - 1) // 2)[0]
vals_dic.setdefault(name, []).append(q[0])
err_dic.setdefault(name, []).append(abs(q[0] - true_val))
name = 'Chebychev'
c_k = np.polynomial.chebyshev.chebfit(x, y, deg=min(n-1, 36))
c_ki = np.polynomial.chebyshev.chebint(c_k)
q = np.polynomial.chebyshev.chebval(x[-1], c_ki)
vals_dic.setdefault(name, []).append(q)
err_dic.setdefault(name, []).append(abs(q - true_val))
# c_k = chebfit(f,n,a,b)
# q = chebval(b,chebint(c_k,a,b),a,b)
# qc2[k] = q; ec2[k] = abs(q - true)
name = 'Gauss-Legendre' # quadrature
q = intg.fixed_quad(f, a, b, n=n)[0]
# [x, w]=qrule(n,1)
# x = (b-a)/2*x + (a+b)/2 % Transform base points X.
# w = (b-a)/2*w % Adjust weigths.
# q = sum(feval(f,x)*w)
vals_dic.setdefault(name, []).append(q)
err_dic.setdefault(name, []).append(abs(q - true_val))
_display(neval, vals_dic, err_dic, plot_error)
def main():
# val, err = clencurt(np.exp, 0, 2)
# valt = np.exp(2) - np.exp(0)
# [Q, err] = quadgr(lambda x: x ** 2, 1, 4, 1e-9)
# [Q, err] = quadgr(humps, 1, 4, 1e-9)
#
# [x, w] = h_roots(11, 'newton')
# sum(w)
# [x2, w2] = la_roots(11, 1, 't')
#
# from scitools import numpyutils as npu #@UnresolvedImport
# fun = npu.wrap2callable('x**2')
# p0 = fun(0)
# A = [0, 1, 1]; B = [2, 4, 3]
# area, err = gaussq(fun, A, B)
#
# fun = npu.wrap2callable('x**2')
# [val1, err1] = gaussq(fun, A, B)
#
#
# Integration of x^2*exp(-x) from zero to infinity:
# fun2 = npu.wrap2callable('1')
# [val2, err2] = gaussq(fun2, 0, np.inf, wfun=3, alpha=2)
# [val2, err2] = gaussq(lambda x: x ** 2, 0, np.inf, wfun=3, alpha=0)
#
# Integrate humps from 0 to 2 and from 1 to 4
# [val3, err3] = gaussq(humps, A, B)
#
# [x, w] = p_roots(11, 'newton', 1, 3)
# y = np.sum(x ** 2 * w)
x = np.linspace(0, np.pi / 2)
_q0 = np.trapz(humps(x), x)
[q, err] = romberg(humps, 0, np.pi / 2, 1e-4)
print(q, err)
if __name__ == '__main__':
from wafo.testing import test_docstrings
test_docstrings(__file__)
# qdemo(np.exp, 0, 3, plot_error=True)
# plt.show('hold')
# main()
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