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Per.Andreas.Brodtkorb 10 years ago
parent 9a2b9f6921
commit f1b07d74b4
5 changed files with 74 additions and 57 deletions
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27
pywafo/src/wafo/covariance/test/test_covariance.py
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@ -1,17 +1,28 @@
from numpy.testing import (run_module_suite, assert_equal, assert_almost_equal, from numpy.testing import (run_module_suite, assert_equal,
assert_array_equal, assert_array_almost_equal) assert_array_almost_equal,
import numpy as np assert_almost_equal, assert_array_equal) # @UnusedImport @IgnorePep8
from numpy import array, cos, exp, linspace, pi, sin, diff, arange, ones
import wafo.spectrum.models as sm import wafo.spectrum.models as sm
from wafo.covariance import CovData1D # from wafo.covariance import CovData1D
def test_covariance(): def test_covariance():
Sj = sm.Jonswap() Sj = sm.Jonswap()
S = Sj.tospecdata() #Make spec S = Sj.tospecdata() # Make spec
R = S.tocovdata() R = S.tocovdata()
dt = R.sampling_period()
assert_equal(dt, 1.0471975511965976)
S1 = R.tospecdata()
assert_array_almost_equal(S.data[:10], S1.data[:10], 11)
x = R.sim(ns=1000, dt=0.2, iseed=0)
assert_array_almost_equal(x[:10, 0], [0.0, 1.04719755, 2.0943951,
3.14159265, 4.1887902, 5.23598776,
6.28318531, 7.33038286, 8.37758041,
9.42477796], decimal=3)
assert_array_almost_equal(x[:10, 1], [0.22155905, 1.21207066, 1.95670282,
2.11634902, 1.57967273, 0.2665005,
-0.79630253, -1.31908028,
-2.20056021, -1.84451748], decimal=3)
x = R.sim(ns=1000,dt=0.2)
if __name__ == '__main__': if __name__ == '__main__':
run_module_suite() run_module_suite()

33
pywafo/src/wafo/polynomial.py
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@ -24,7 +24,7 @@ from numpy.fft import fft, ifft
from numpy import (zeros, ones, zeros_like, array, asarray, newaxis, arange, from numpy import (zeros, ones, zeros_like, array, asarray, newaxis, arange,
logical_or, any, pi, cos, round, diff, all, exp, logical_or, any, pi, cos, round, diff, all, exp,
where, extract, linalg, sign, concatenate, floor, isreal, where, extract, linalg, sign, concatenate, floor, isreal,
conj, remainder, linspace, atleast_1d, hstack, sum) conj, remainder, linspace, sum)
from numpy.lib.polynomial import * # @UnusedWildImport from numpy.lib.polynomial import * # @UnusedWildImport
from scipy.misc.common import pade # @UnresolvedImport from scipy.misc.common import pade # @UnresolvedImport
__all__ = np.lib.polynomial.__all__ __all__ = np.lib.polynomial.__all__
@ -272,8 +272,8 @@ def polydeg(x, y):
p = orthofit(x, y, n) p = orthofit(x, y, n)
ys = orthoval(p, x) ys = orthoval(p, x)
# -- Akaike's Information Criterion # -- Akaike's Information Criterion
aic = 2 * (n + 1) * (1 + (n + 2) / (N - n - 2)) + \ aic = (2 * (n + 1) * (1 + (n + 2) / (N - n - 2)) +
N * (np.log(2 * pi * sum((ys - y) ** 2) / N) + 1) N * (np.log(2 * pi * sum((ys - y) ** 2) / N) + 1))
if aic >= AIC: if aic >= AIC:
nit += 1 nit += 1
@ -634,17 +634,17 @@ def poly2hstr(p, variable='x'):
ix = 1 ix = 1
for expon in range(order, -1, -1): for expon in range(order, -1, -1):
coef = coefs[order - expon] coef = coefs[order - expon]
#% There is no point in adding a zero term (except if it's the only # There is no point in adding a zero term (except if it's the only
#% term, but we'll take care of that later). # term, but we'll take care of that later).
if coef == 0: if coef == 0:
ix += 1 ix += 1
else: else:
#% Append exponent if necessary. # Append exponent if necessary.
if ix > 1: if ix > 1:
exponstr = '%.0f' % ix exponstr = '%.0f' % ix
s = '%s**%s' % (s, exponstr) s = '%s**%s' % (s, exponstr)
ix = 1 ix = 1
#% Is it the first term? # Is it the first term?
isfirst = s == '' isfirst = s == ''
# We need the coefficient only if it is different from 1 or -1 or # We need the coefficient only if it is different from 1 or -1 or
@ -656,7 +656,7 @@ def poly2hstr(p, variable='x'):
# We need the variable except in the constant term. # We need the variable except in the constant term.
needvar = (expon != 0) needvar = (expon != 0)
#% Add sign, but we don't need a leading plus-sign. # Add sign, but we don't need a leading plus-sign.
if isfirst: if isfirst:
if coef < 0: if coef < 0:
s = '-' # % Unary minus. s = '-' # % Unary minus.
@ -666,22 +666,22 @@ def poly2hstr(p, variable='x'):
else: else:
s = '%s + ' % s # % Binary plus (addition). s = '%s + ' % s # % Binary plus (addition).
#% Append the coefficient if it is different from one or when it is # Append the coefficient if it is different from one or when it is
#% the constant term. # the constant term.
if needcoef: if needcoef:
coefstr = '%.20g' % abs(coef) coefstr = '%.20g' % abs(coef)
s = '%s%s' % (s, coefstr) s = '%s%s' % (s, coefstr)
#% Append variable if necessary. # Append variable if necessary.
if needvar: if needvar:
#% Append a multiplication sign if necessary. # Append a multiplication sign if necessary.
if needcoef: if needcoef:
if 1 - isfirst: if 1 - isfirst:
s = '(%s)' % s s = '(%s)' % s
s = '%s*' % s s = '%s*' % s
s = '%s%s' % (s, var) s = '%s%s' % (s, var)
#% Now treat the special case where the polynomial is zero. # Now treat the special case where the polynomial is zero.
if s == '': if s == '':
s = '0' s = '0'
return s return s
@ -1170,7 +1170,6 @@ def chebfit(fun, n=10, a=-1, b=1, trace=False):
else: else:
f = fun f = fun
n = len(f) n = len(f)
#raise ValueError('Function must be callable!')
# N-1 # N-1
# c(k) = (2/N) sum w(n) f(n)*cos(pi*k*(2n+1)/(2N)), 0 <= k < N. # c(k) = (2/N) sum w(n) f(n)*cos(pi*k*(2n+1)/(2N)), 0 <= k < N.
# n=0 # n=0
@ -1618,7 +1617,7 @@ class Cheb1d(object):
return self.coeffs[val] return self.coeffs[val]
def __setitem__(self, key, val): def __setitem__(self, key, val):
#ind = self.order - key # ind = self.order - key
if key < 0: if key < 0:
raise ValueError("Does not support negative powers.") raise ValueError("Does not support negative powers.")
if key > self.order: if key > self.order:
@ -1921,7 +1920,7 @@ def test_polydeg():
x = np.linspace(0, 10, 300) x = np.linspace(0, 10, 300)
y = np.sin(x ** 3 / 100) ** 2 + 0.05 * np.random.randn(x.size) y = np.sin(x ** 3 / 100) ** 2 + 0.05 * np.random.randn(x.size)
n = polydeg(x, y) n = polydeg(x, y)
#n = 2 # n = 2
p = orthofit(x, y, n) p = orthofit(x, y, n)
xi = linspace(x.min(), x.max()) xi = linspace(x.min(), x.max())
ys0 = orthoval(p, x) ys0 = orthoval(p, x)
@ -1946,4 +1945,4 @@ if __name__ == '__main__':
main() main()
else: else:
test_docstrings() test_docstrings()
#test_polydeg() # test_polydeg()

65
pywafo/src/wafo/spectrum/core.py
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@ -1,6 +1,6 @@
from __future__ import division from __future__ import division
from wafo.misc import meshgrid, gravity, cart2polar, polar2cart from wafo.misc import meshgrid, gravity, cart2polar, polar2cart
from wafo.objects import TimeSeries # mat2timeseries, from wafo.objects import TimeSeries, mat2timeseries
import warnings import warnings
import os import os
import numpy as np import numpy as np
@ -23,6 +23,7 @@ from wafo.containers import PlotData, now
from wafo.misc import sub_dict_select, nextpow2, discretize, JITImport from wafo.misc import sub_dict_select, nextpow2, discretize, JITImport
# from wafo.graphutil import cltext # from wafo.graphutil import cltext
from wafo.kdetools import qlevels from wafo.kdetools import qlevels
from scipy.interpolate.interpolate import interp1d
try: try:
from wafo.gaussian import Rind from wafo.gaussian import Rind
@ -55,7 +56,7 @@ __all__ = ['SpecData1D', 'SpecData2D', 'plotspec']
def _set_seed(iseed): def _set_seed(iseed):
'''Set seed of random generator''' '''Set seed of random generator'''
if iseed != None: if iseed is not None:
try: try:
random.set_state(iseed) random.set_state(iseed)
except: except:
@ -136,7 +137,7 @@ def qtf(w, h=inf, g=9.81):
# tmp1 = 0.5*g*k_w./(w.*sqrt(g*h)) # tmp1 = 0.5*g*k_w./(w.*sqrt(g*h))
# tmp2 = 0.25*w.^2/g # tmp2 = 0.25*w.^2/g
# Wave group velocity # Wave group velocity
c_g = 0.5 * g * (tanh(k_w * h) + k_w * h * (1.0 - tanh(k_w * h) ** 2)) / w c_g = 0.5 * g * (tanh(k_w * h) + k_w * h * (1.0 - tanh(k_w * h) ** 2)) / w
h_dii = (0.5 * (0.5 * g * (k_w / w) ** 2. - 0.5 * w ** 2 / g + h_dii = (0.5 * (0.5 * g * (k_w / w) ** 2. - 0.5 * w ** 2 / g +
g * k_w / (w * c_g)) g * k_w / (w * c_g))
@ -146,7 +147,7 @@ def qtf(w, h=inf, g=9.81):
# k = find(w_1==w_2) # k = find(w_1==w_2)
# h_d(k) = h_dii # h_d(k) = h_dii
#% The NaN's occur due to division by zero. => Set the isnans to zero # The NaN's occur due to division by zero. => Set the isnans to zero
h_dii = where(isnan(h_dii), 0, h_dii) h_dii = where(isnan(h_dii), 0, h_dii)
h_d = where(isnan(h_d), 0, h_d) h_d = where(isnan(h_d), 0, h_d)
@ -197,8 +198,8 @@ def plotspec(specdata, linetype='b-', flag=1):
# #
# S = demospec('dir'); S2 = mkdspec(jonswap,spreading); # S = demospec('dir'); S2 = mkdspec(jonswap,spreading);
# plotspec(S,2), hold on # plotspec(S,2), hold on
# plotspec(S,3,'g') % Same as previous fig. due to frequency independent spreading # plotspec(S,3,'g') # Same as previous fig. due to frequency independent spreading
# plotspec(S2,2,'r') % Not the same as previous figs. due to frequency dependent spreading # plotspec(S2,2,'r') # Not the same as previous figs. due to frequency dependent spreading
# plotspec(S2,3,'m') # plotspec(S2,3,'m')
# % transform from angular frequency and radians to frequency and degrees # % transform from angular frequency and radians to frequency and degrees
# Sf = ttspec(S,'f','d'); clf # Sf = ttspec(S,'f','d'); clf
@ -1004,8 +1005,7 @@ class SpecData1D(PlotData):
max_sim = 30 max_sim = 30
tolerance = 5e-4 tolerance = 5e-4
L = 200 # %maximum lag size of the window function used in L = 200 # maximum lag size of the window function used in estimate
#%spectral estimate
# ftype = self.freqtype #options are 'f' and 'w' and 'k' # ftype = self.freqtype #options are 'f' and 'w' and 'k'
# switch ftype # switch ftype
# case 'f', # case 'f',
@ -1015,8 +1015,8 @@ class SpecData1D(PlotData):
Hm0 = self.characteristic('Hm0') Hm0 = self.characteristic('Hm0')
Tm02 = self.characteristic('Tm02') Tm02 = self.characteristic('Tm02')
if not iseed is None: if iseed is not None:
_set_seed(iseed) # % set the the seed _set_seed(iseed) # set the the seed
n = len(self.data) n = len(self.data)
if ns is None: if ns is None:
@ -1050,25 +1050,27 @@ class SpecData1D(PlotData):
SL.data[indZero] = 0 SL.data[indZero] = 0
maxS = max(S.data) maxS = max(S.data)
# Fs = 2*freq(end)+eps % sampling frequency # Fs = 2*freq(end)+eps # sampling frequency
for ix in xrange(max_sim): for ix in xrange(max_sim):
x2, x1 = self.sim_nl(ns=np, cases=cases, dt=None, iseed=iseed, x2, x1 = self.sim_nl(ns=np, cases=cases, dt=None, iseed=iseed,
method=method, method=method, fnlimit=fn_limit,
fnlimit=fn_limit) output='timeseries')
#%x2(:,2:end) = x2(:,2:end) -x1(:,2:end) x2.data -= x1.data # x2(:,2:end) = x2(:,2:end) -x1(:,2:end)
S2 = x2.tospecdata(L)
S1 = x1.tospecdata(L)
# TODO: Finish spec.to_linspec # TODO: Finish spec.to_linspec
# S2 = dat2spec(x2, L) # S2 = dat2spec(x2, L)
# S1 = dat2spec(x1, L) # S1 = dat2spec(x1, L)
# %[tf21,fi] = tfe(x2(:,2),x1(:,2),1024,Fs,[],512) # %[tf21,fi] = tfe(x2(:,2),x1(:,2),1024,Fs,[],512)
# %Hw11 = interp1q(fi,tf21.*conj(tf21),freq) # %Hw11 = interp1q(fi,tf21.*conj(tf21),freq)
# if True: if True:
# Hw1 = exp(interp1q(S2.args, log(abs(S1.data / S2.data)), freq)) Hw1 = exp(interp1d(log(abs(S1.data / S2.data)), S2.args)(freq))
# else: else:
# Geometric mean # Geometric mean
# Hw1 = exp( Hw1 = exp((interp1d(log(abs(S1.data / S2.data)), S2.args)(freq)
# (interp1q(S2.args, log(abs(S1.data / S2.data)), + log(Hw2)) / 2)
# freq) + log(Hw2)) / 2)
# end # end
# Hw1 = (interp1q( S2.w,abs(S1.S./S2.S),freq)+Hw2)/2 # Hw1 = (interp1q( S2.w,abs(S1.S./S2.S),freq)+Hw2)/2
# plot(freq, abs(Hw11-Hw1),'g') # plot(freq, abs(Hw11-Hw1),'g')
@ -1085,7 +1087,7 @@ class SpecData1D(PlotData):
# end # end
k = nonzero(SL.data < 0)[0] k = nonzero(SL.data < 0)[0]
if len(k): # Make sure that the current guess is larger than zero if len(k): # Make sure that the current guess is larger than zero
#%k # k
# Hw1(k) # Hw1(k)
Hw1[k] = min(S1.data[k] * 0.9, S.data[k]) Hw1[k] = min(S1.data[k] * 0.9, S.data[k])
SL.data[k] = max(Hw1[k] * S.data[k], eps) SL.data[k] = max(Hw1[k] * S.data[k], eps)
@ -1120,7 +1122,7 @@ class SpecData1D(PlotData):
#%Hw1(end) #%Hw1(end)
#%maxS*1e-3 #%maxS*1e-3
#%if Hw1(end)*S.>maxS*1e-3, #%if Hw1[-1]*S.data>maxS*1e-3,
#% warning('The Nyquist frequency of the spectrum may be too low') #% warning('The Nyquist frequency of the spectrum may be too low')
#%end #%end
@ -2639,7 +2641,8 @@ class SpecData1D(PlotData):
# function [x2,x,svec,dvec,amp]=spec2nlsdat(spec,np,dt,iseed,method, # function [x2,x,svec,dvec,amp]=spec2nlsdat(spec,np,dt,iseed,method,
# truncationLimit) # truncationLimit)
def sim_nl(self, ns=None, cases=1, dt=None, iseed=None, method='random', def sim_nl(self, ns=None, cases=1, dt=None, iseed=None, method='random',
fnlimit=1.4142, reltol=1e-3, g=9.81, verbose=False): fnlimit=1.4142, reltol=1e-3, g=9.81, verbose=False,
output='timeseries'):
""" """
Simulates a Randomized 2nd order non-linear wave X(t) Simulates a Randomized 2nd order non-linear wave X(t)
@ -2894,7 +2897,7 @@ class SpecData1D(PlotData):
(f_limit_lo, f_limit_up)) (f_limit_lo, f_limit_up))
# if nargout>3, # if nargout>3,
# %compute the sum and frequency effects separately # #compute the sum and frequency effects separately
# [svec, dvec] = disufq((amp.'),w,kw,min(h,10^30),g,nmin,nmax) # [svec, dvec] = disufq((amp.'),w,kw,min(h,10^30),g,nmin,nmax)
# svec = svec.' # svec = svec.'
# dvec = dvec.' # dvec = dvec.'
@ -2902,7 +2905,7 @@ class SpecData1D(PlotData):
# x2s = fft(svec) % 2'nd order sum frequency component # x2s = fft(svec) % 2'nd order sum frequency component
# x2d = fft(dvec) % 2'nd order difference frequency component # x2d = fft(dvec) % 2'nd order difference frequency component
## ##
# % 1'st order + 2'nd order component. # # 1'st order + 2'nd order component.
# x2(:,2:end) =x(:,2:end)+ real(x2s(1:np,:))+real(x2d(1:np,:)) # x2(:,2:end) =x(:,2:end)+ real(x2s(1:np,:))+real(x2d(1:np,:))
# else # else
if False: if False:
@ -2926,7 +2929,10 @@ class SpecData1D(PlotData):
# 1'st order + 2'nd order component. # 1'st order + 2'nd order component.
x2[:, 1::] = x[:, 1::] + x2o[0:ns, :].real x2[:, 1::] = x[:, 1::] + x2o[0:ns, :].real
if output=='timeseries':
xx2 = mat2timeseries(x2[:, 1::], x2[:, 0].ravel())
xx = mat2timeseries(x[:, 1::], x[:, 0].ravel())
return xx2, xx
return x2, x return x2, x
def stats_nl(self, h=None, moments='sk', method='approximate', g=9.81): def stats_nl(self, h=None, moments='sk', method='approximate', g=9.81):
@ -4323,6 +4329,7 @@ def test_mm_pdf():
w = np.linspace(0, 4, 256) w = np.linspace(0, 4, 256)
S1 = Sj.tospecdata(w) # Make spectrum object from numerical values S1 = Sj.tospecdata(w) # Make spectrum object from numerical values
S = sm.SpecData1D(Sj(w), w) # Alternatively do it manually S = sm.SpecData1D(Sj(w), w) # Alternatively do it manually
S0 = S.to_linspec()
mm = S.to_mm_pdf() mm = S.to_mm_pdf()
@ -4332,6 +4339,6 @@ def test_docstrings():
if __name__ == '__main__': if __name__ == '__main__':
test_docstrings() #test_docstrings()
# test_mm_pdf() test_mm_pdf()
# main() # main()

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