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Small refactoring

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master
Per A Brodtkorb 8 years ago
parent ea2edc1ca5
commit 217b607a4c
2 changed files with 182 additions and 175 deletions
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21
wafo/integrate.py
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@ -1159,7 +1159,7 @@ class _Quadgr(object):
return nodes, weights
@staticmethod
def _get_best_estimate(vals0, vals1, vals2, k):
def _get_best_estimate(k, vals0, vals1, vals2):
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]))
@ -1178,6 +1178,16 @@ class _Quadgr(object):
# _val, err1 = dea3(vals0[k - 2], vals0[k - 1], vals0[k])
return q_val, err
def _extrapolate(self, k, val0, val1, val2):
# 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(k, val0, val1, val2)
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))
@ -1204,14 +1214,7 @@ class _Quadgr(object):
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)
q_val, err = self._extrapolate(k, val0, val1, val2)
converged = (err < abseps) | ~np.isfinite(q_val)
if converged:

336
wafo/integrate_oscillating.py
Unescape Escape View File

@ -4,23 +4,33 @@ Created on 20. aug. 2015
@author: pab
"""
from __future__ import division
import numpy as np
from collections import namedtuple
import warnings
import numdifftools as nd # @UnresolvedImport
import numdifftools.nd_algopy as nda # @UnresolvedImport
from numdifftools.limits import Limit # @UnresolvedImport
import numdifftools as nd
import numdifftools.nd_algopy as nda
from numdifftools.extrapolation import dea3
from numdifftools.limits import Limit
import numpy as np
from numpy import linalg
from numpy.polynomial.chebyshev import chebval, Chebyshev
from numpy.polynomial import polynomial
from wafo.misc import piecewise, findcross, ecross
from collections import namedtuple
EPS = np.finfo(float).eps
_FINFO = np.finfo(float)
EPS = _FINFO(float).eps
_EPS = EPS
finfo = np.finfo(float)
_TINY = finfo.tiny
_HUGE = finfo.max
dea3 = nd.dea3
_TINY = _FINFO.tiny
_HUGE = _FINFO.max
def _assert(cond, msg):
if not cond:
raise ValueError(msg)
def _assert_warn(cond, msg):
if not cond:
warnings.warn(msg)
class PolyBasis(object):
@ -40,8 +50,8 @@ class PolyBasis(object):
def derivative(self, t, k, n=1):
c = self._coefficients(k)
dc = self._derivative(c, m=n)
return self.eval(t, dc)
d_c = self._derivative(c, m=n)
return self.eval(t, d_c)
def __call__(self, t, k):
return t**k
@ -66,58 +76,57 @@ class ChebyshevBasis(PolyBasis):
chebyshev_basis = ChebyshevBasis()
def richardson(Q, k):
def richardson(q_val, k):
# license BSD
# Richardson extrapolation with parameter estimation
c = np.real((Q[k - 1] - Q[k - 2]) / (Q[k] - Q[k - 1])) - 1.
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)
R = Q[k] + (Q[k] - Q[k - 1]) / c
return R
return q_val[k] + (q_val[k] - q_val[k - 1]) / c
def evans_webster_weights(omega, gg, dgg, x, basis, *args, **kwds):
def evans_webster_weights(omega, g, d_g, x, basis, *args, **kwds):
def Psi(t, k):
return dgg(t, *args, **kwds) * basis(t, k)
def psi(t, k):
return d_g(t, *args, **kwds) * basis(t, k)
j_w = 1j * omega
nn = len(x)
A = np.zeros((nn, nn), dtype=complex)
F = np.zeros((nn,), dtype=complex)
n = len(x)
a_matrix = np.zeros((n, n), dtype=complex)
rhs = np.zeros((n,), dtype=complex)
dbasis = basis.derivative
lim_gg = Limit(gg)
b1 = np.exp(j_w*lim_gg(1, *args, **kwds))
if np.isnan(b1):
b1 = 0.0
a1 = np.exp(j_w*lim_gg(-1, *args, **kwds))
if np.isnan(a1):
a1 = 0.0
lim_g = Limit(g)
b_1 = np.exp(j_w*lim_g(1, *args, **kwds))
if np.isnan(b_1):
b_1 = 0.0
a_1 = np.exp(j_w*lim_g(-1, *args, **kwds))
if np.isnan(a_1):
a_1 = 0.0
lim_Psi = Limit(Psi)
for k in range(nn):
F[k] = basis(1, k)*b1 - basis(-1, k)*a1
A[k] = (dbasis(x, k, n=1) + j_w * lim_Psi(x, k))
lim_psi = Limit(psi)
for k in range(n):
rhs[k] = basis(1, k) * b_1 - basis(-1, k) * a_1
a_matrix[k] = (dbasis(x, k, n=1) + j_w * lim_psi(x, k))
LS = linalg.lstsq(A, F)
return LS[0]
solution = linalg.lstsq(a_matrix, rhs)
return solution[0]
def osc_weights(omega, g, dg, x, basis, ab, *args, **kwds):
def gg(t):
def osc_weights(omega, g, d_g, x, basis, a_b, *args, **kwds):
def _g(t):
return g(scale * t + offset, *args, **kwds)
def dgg(t):
return scale * dg(scale * t + offset, *args, **kwds)
def _d_g(t):
return scale * d_g(scale * t + offset, *args, **kwds)
w = []
for a, b in zip(ab[::2], ab[1::2]):
for a, b in zip(a_b[::2], a_b[1::2]):
scale = (b - a) / 2
offset = (a + b) / 2
w.append(evans_webster_weights(omega, gg, dgg, x, basis))
w.append(evans_webster_weights(omega, _g, _d_g, x, basis))
return np.asarray(w).ravel()
@ -148,47 +157,47 @@ class QuadOsc(_Integrator):
full_output=full_output)
@staticmethod
def _change_interval_to_0_1(f, g, dg, a, b):
def f1(t, *args, **kwds):
def _change_interval_to_0_1(f, g, d_g, a, _b):
def f_01(t, *args, **kwds):
den = 1-t
return f(a + t / den, *args, **kwds) / den ** 2
def g1(t, *args, **kwds):
def g_01(t, *args, **kwds):
return g(a + t / (1 - t), *args, **kwds)
def dg1(t, *args, **kwds):
def d_g_01(t, *args, **kwds):
den = 1-t
return dg(a + t / den, *args, **kwds) / den ** 2
return f1, g1, dg1, 0., 1.
return d_g(a + t / den, *args, **kwds) / den ** 2
return f_01, g_01, d_g_01, 0., 1.
@staticmethod
def _change_interval_to_m1_0(f, g, dg, a, b):
def f2(t, *args, **kwds):
def _change_interval_to_m1_0(f, g, d_g, _a, b):
def f_m10(t, *args, **kwds):
den = 1 + t
return f(b + t / den, *args, **kwds) / den ** 2
def g2(t, *args, **kwds):
def g_m10(t, *args, **kwds):
return g(b + t / (1 + t), *args, **kwds)
def dg2(t, *args, **kwds):
def d_g_m10(t, *args, **kwds):
den = 1 + t
return dg(b + t / den, *args, **kwds) / den ** 2
return f2, g2, dg2, -1.0, 0.0
return d_g(b + t / den, *args, **kwds) / den ** 2
return f_m10, g_m10, d_g_m10, -1.0, 0.0
@staticmethod
def _change_interval_to_m1_1(f, g, dg, a, b):
def f2(t, *args, **kwds):
def _change_interval_to_m1_1(f, g, d_g, _a, _b):
def f_m11(t, *args, **kwds):
den = (1 - t**2)
return f(t / den, *args, **kwds) * (1+t**2) / den ** 2
def g2(t, *args, **kwds):
def g_m11(t, *args, **kwds):
den = (1 - t**2)
return g(t / den, *args, **kwds)
def dg2(t, *args, **kwds):
def d_g_m11(t, *args, **kwds):
den = (1 - t**2)
return dg(t / den, *args, **kwds) * (1+t**2) / den ** 2
return f2, g2, dg2, -1., 1.
return d_g(t / den, *args, **kwds) * (1+t**2) / den ** 2
return f_m11, g_m11, d_g_m11, -1., 1.
def _get_functions(self):
a, b = self.a, self.b
@ -225,131 +234,127 @@ class QuadOsc(_Integrator):
return val
@staticmethod
def _get_best_estimate(k, q0, q1, q2):
def _get_best_estimate(k, q_0, q_1, q_2):
if k >= 5:
qv = np.hstack((q0[k], q1[k], q2[k]))
qw = np.hstack((q0[k - 1], q1[k - 1], q2[k - 1]))
q_v = np.hstack((q_0[k], q_1[k], q_2[k]))
q_w = np.hstack((q_0[k - 1], q_1[k - 1], q_2[k - 1]))
elif k >= 3:
qv = np.hstack((q0[k], q1[k]))
qw = np.hstack((q0[k - 1], q1[k - 1]))
q_v = np.hstack((q_0[k], q_1[k]))
q_w = np.hstack((q_0[k - 1], q_1[k - 1]))
else:
qv = np.atleast_1d(q0[k])
qw = q0[k - 1]
errors = np.atleast_1d(abs(qv - qw))
q_v = np.atleast_1d(q_0[k])
q_w = q_0[k - 1]
errors = np.atleast_1d(abs(q_v - q_w))
j = np.nanargmin(errors)
return qv[j], errors[j]
return q_v[j], errors[j]
def _extrapolate(self, k, q0, q1, q2):
def _extrapolate(self, k, q_0, q_1, q_2):
if k >= 4:
q1[k] = dea3(q0[k - 2], q0[k - 1], q0[k])[0]
q2[k] = dea3(q1[k - 2], q1[k - 1], q1[k])[0]
q_1[k] = dea3(q_0[k - 2], q_0[k - 1], q_0[k])[0]
q_2[k] = dea3(q_1[k - 2], q_1[k - 1], q_1[k])[0]
elif k >= 2:
q1[k] = dea3(q0[k - 2], q0[k - 1], q0[k])[0]
q_1[k] = dea3(q_0[k - 2], q_0[k - 1], q_0[k])[0]
# # Richardson extrapolation
# if k >= 4:
# q1[k] = richardson(q0, k)
# q2[k] = richardson(q1, k)
# q_1[k] = richardson(q_0, k)
# q_2[k] = richardson(q_1, k)
# elif k >= 2:
# q1[k] = richardson(q0, k)
q, err = self._get_best_estimate(k, q0, q1, q2)
# q_1[k] = richardson(q_0, k)
q, err = self._get_best_estimate(k, q_0, q_1, q_2)
return q, err
def _quad_osc(self, f, g, dg, a, b, omega, *args, **kwds):
if a == b:
Q = b - a
err = b - a
return Q, err
q_val = b - a
err = np.abs(b - a)
return q_val, err
abseps = 10**-self.precision
max_iter = self.maxiter
basis = self.basis
if self.endpoints:
xq = chebyshev_extrema(self.s)
x_n = chebyshev_extrema(self.s)
else:
xq = chebyshev_roots(self.s)
# xq = tanh_sinh_open_nodes(self.s)
x_n = chebyshev_roots(self.s)
# x_n = tanh_sinh_open_nodes(self.s)
# One interval
hh = (b - a) / 2
x = (a + b) / 2 + hh * xq # Nodes
x = (a + b) / 2 + hh * x_n # Nodes
dtype = complex
Q0 = np.zeros((max_iter, 1), dtype=dtype) # Quadrature
Q1 = np.zeros((max_iter, 1), dtype=dtype) # First extrapolation
Q2 = np.zeros((max_iter, 1), dtype=dtype) # Second extrapolation
val0 = np.zeros((max_iter, 1), dtype=dtype) # Quadrature
val1 = np.zeros((max_iter, 1), dtype=dtype) # First extrapolation
val2 = np.zeros((max_iter, 1), dtype=dtype) # Second extrapolation
lim_f = Limit(f)
ab = np.hstack([a, b])
wq = osc_weights(omega, g, dg, xq, basis, ab, *args, **kwds)
Q0[0] = hh * np.sum(wq * lim_f(x, *args, **kwds))
a_b = np.hstack([a, b])
wq = osc_weights(omega, g, dg, x_n, basis, a_b, *args, **kwds)
val0[0] = hh * np.sum(wq * lim_f(x, *args, **kwds))
# Successive bisection of intervals
nq = len(xq)
nq = len(x_n)
n = nq
for k in range(1, max_iter):
n += nq*2**k
n += nq * 2**k
hh = hh / 2
x = np.hstack([x + a, x + b]) / 2
ab = np.hstack([ab + a, ab + b]) / 2
wq = osc_weights(omega, g, dg, xq, basis, ab, *args, **kwds)
a_b = np.hstack([a_b + a, a_b + b]) / 2
wq = osc_weights(omega, g, dg, x_n, basis, a_b, *args, **kwds)
Q0[k] = hh * np.sum(wq * lim_f(x, *args, **kwds))
val0[k] = hh * np.sum(wq * lim_f(x, *args, **kwds))
Q, err = self._extrapolate(k, Q0, Q1, Q2)
q_val, err = self._extrapolate(k, val0, val1, val2)
convergence = (err <= abseps) | ~np.isfinite(Q)
if convergence:
converged = (err <= abseps) | ~np.isfinite(q_val)
if converged:
break
else:
warnings.warn('Max number of iterations reached '
'without convergence.')
if ~np.isfinite(Q):
warnings.warn('Integral approximation is Infinite or NaN.')
_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 += 2 * np.finfo(Q).eps
return Q, self.info(err, n)
err += 2 * np.finfo(q_val).eps
return q_val, self.info(err, n)
def adaptive_levin_points(M, delta):
m = M - 1
def adaptive_levin_points(m, delta):
m_1 = m - 1
prm = 0.5
while prm * m / delta >= 1:
while prm * m_1 / delta >= 1:
delta = 2 * delta
k = np.arange(M)
x = piecewise([k < prm * m, k == np.ceil(prm * m)],
[-1 + k / delta, 0 * k, 1 - (m - k) / delta])
k = np.arange(m)
x = piecewise([k < prm * m_1, k == np.ceil(prm * m_1)],
[-1 + k / delta, 0 * k, 1 - (m_1 - k) / delta])
return x
def open_levin_points(M, delta):
return adaptive_levin_points(M+2, delta)[1:-1]
def open_levin_points(m, delta):
return adaptive_levin_points(m+2, delta)[1:-1]
def chebyshev_extrema(M, delta=None):
k = np.arange(M)
x = np.cos(k * np.pi / (M-1))
def chebyshev_extrema(m, delta=None):
k = np.arange(m)
x = np.cos(k * np.pi / (m-1))
return x
_EPS = np.finfo(float).eps
def tanh_sinh_nodes(M, delta=None, tol=_EPS):
def tanh_sinh_nodes(m, delta=None, tol=_EPS):
tmax = np.arcsinh(np.arctanh(1-_EPS)*2/np.pi)
# tmax = 3.18
m = int(np.floor(-np.log2(tmax/max(M-1, 1)))) - 1
h = 2.0**-m
t = np.arange((M+1)//2+1)*h
m_1 = int(np.floor(-np.log2(tmax/max(m-1, 1)))) - 1
h = 2.0**-m_1
t = np.arange((m+1)//2+1)*h
x = np.tanh(np.pi/2*np.sinh(t))
k = np.flatnonzero(np.abs(x - 1) <= 10*tol)
y = x[:k[0]+1] if len(k) else x
return np.hstack((-y[:0:-1], y))
def tanh_sinh_open_nodes(M, delta=None, tol=_EPS):
return tanh_sinh_nodes(M+1, delta, tol)[1:-1]
def tanh_sinh_open_nodes(m, delta=None, tol=_EPS):
return tanh_sinh_nodes(m+1, delta, tol)[1:-1]
def chebyshev_roots(m, delta=None):
@ -362,47 +367,47 @@ class AdaptiveLevin(_Integrator):
"""Return integral for the Levin-type and adaptive Levin-type methods"""
@staticmethod
def aLevinTQ(omega, ff, gg, dgg, x, s, basis, *args, **kwds):
def _a_levin(omega, f, g, d_g, x, s, basis, *args, **kwds):
def Psi(t, k):
return dgg(t, *args, **kwds) * basis(t, k)
def psi(t, k):
return d_g(t, *args, **kwds) * basis(t, k)
j_w = 1j * omega
nu = np.ones((len(x),), dtype=int)
nu[0] = nu[-1] = s
S = np.cumsum(np.hstack((nu, 0)))
S[-1] = 0
nn = int(S[-2])
A = np.zeros((nn, nn), dtype=complex)
F = np.zeros((nn,))
dff = Limit(nda.Derivative(ff))
dPsi = Limit(nda.Derivative(Psi))
n = int(S[-2])
a_matrix = np.zeros((n, n), dtype=complex)
rhs = np.zeros((n,))
dff = Limit(nda.Derivative(f))
d_psi = Limit(nda.Derivative(psi))
dbasis = basis.derivative
for r, t in enumerate(x):
for j in range(S[r - 1], S[r]):
order = ((j - S[r - 1]) % nu[r]) # derivative order
dff.fun.n = order
F[j] = dff(t, *args, **kwds)
dPsi.fun.n = order
for k in range(nn):
A[j, k] = (dbasis(t, k, n=order+1) + j_w * dPsi(t, k))
k1 = np.flatnonzero(1-np.isfinite(F))
rhs[j] = dff(t, *args, **kwds)
d_psi.fun.n = order
for k in range(n):
a_matrix[j, k] = (dbasis(t, k, n=order+1) +
j_w * d_psi(t, k))
k1 = np.flatnonzero(1-np.isfinite(rhs))
if k1.size > 0: # Remove singularities
warnings.warn('Singularities detected! ')
A[k1] = 0
F[k1] = 0
LS = linalg.lstsq(A, F)
v = basis.eval([-1, 1], LS[0])
lim_gg = Limit(gg)
gb = np.exp(j_w * lim_gg(1, *args, **kwds))
if np.isnan(gb):
gb = 0
ga = np.exp(j_w * lim_gg(-1, *args, **kwds))
if np.isnan(ga):
ga = 0
NR = (v[1] * gb - v[0] * ga)
return NR
a_matrix[k1] = 0
rhs[k1] = 0
solution = linalg.lstsq(a_matrix, rhs)
v = basis.eval([-1, 1], solution[0])
lim_g = Limit(g)
g_b = np.exp(j_w * lim_g(1, *args, **kwds))
if np.isnan(g_b):
g_b = 0
g_a = np.exp(j_w * lim_g(-1, *args, **kwds))
if np.isnan(g_a):
g_a = 0
return v[1] * g_b - v[0] * g_a
def _get_integration_limits(self, omega, args, kwds):
a, b = self.a, self.b
@ -486,23 +491,23 @@ class AdaptiveLevin(_Integrator):
num_collocation_point_list = m*2**np.arange(1, 5) + 1
basis = self.basis
Q = 1e+300
q_val = 1e+300
num_function_evaluations = 0
n = 0
ni = 0
for num_collocation_points in num_collocation_point_list:
ni_old = ni
Q_old = Q
n_old = n
q_old = q_val
x = points(num_collocation_points, betam)
ni = len(x)
if ni > ni_old:
Q = self.aLevinTQ(omega, ff, gg, dgg, x, s, basis, *args,
**kwds)
n += ni
err = np.abs(Q-Q_old)
n = len(x)
if n > n_old:
q_val = self._a_levin(omega, ff, gg, dgg, x, s, basis, *args,
**kwds)
num_function_evaluations += n
err = np.abs(q_val-q_old)
if err <= abseps:
break
info = self.info(err, n)
return Q, info
info = self.info(err, num_function_evaluations)
return q_val, info
class EvansWebster(AdaptiveLevin):
@ -515,12 +520,11 @@ class EvansWebster(AdaptiveLevin):
precision=precision, endpoints=endpoints,
full_output=full_output)
def aLevinTQ(self, omega, ff, gg, dgg, x, s, basis, *args, **kwds):
def _a_levin(self, omega, ff, gg, dgg, x, s, basis, *args, **kwds):
w = evans_webster_weights(omega, gg, dgg, x, basis, *args, **kwds)
f = Limit(ff)(x, *args, **kwds)
NR = np.sum(f*w)
return NR
return np.sum(f*w)
def _get_num_points(self, s, prec, betam):
return 8 if s > 1 else int(prec / max(np.log10(betam + 1), 1) + 1)

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