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Fixed some bugs

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master
per.andreas.brodtkorb 14 years ago
parent 3de75c6a43
commit 367774c292
10 changed files with 155 additions and 108 deletions
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76
pywafo/src/wafo/covariance/core.py
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@ -20,7 +20,7 @@ import warnings
from numpy import (zeros, sqrt, dot, newaxis, inf, where, pi, nan, #@UnresolvedImport from numpy import (zeros, sqrt, dot, newaxis, inf, where, pi, nan, #@UnresolvedImport
atleast_1d, hstack, vstack, r_, linspace, flatnonzero, size, #@UnresolvedImport atleast_1d, hstack, vstack, r_, linspace, flatnonzero, size, #@UnresolvedImport
isnan, finfo, diag, ceil, floor, random, pi) #@UnresolvedImport isnan, finfo, diag, ceil, floor, random, pi) #@UnresolvedImport
from numpy.fft import fft from numpy.fft import fft #as fft
from numpy.random import randn from numpy.random import randn
import scipy.interpolate as interpolate import scipy.interpolate as interpolate
from scipy.linalg import toeplitz, sqrtm, svd, cholesky, diagsvd, pinv from scipy.linalg import toeplitz, sqrtm, svd, cholesky, diagsvd, pinv
@ -182,7 +182,7 @@ class CovData1D(WafoData):
## wdata = CovData1D(**kwds) ## wdata = CovData1D(**kwds)
## return wdata ## return wdata
def tospecdata(self, rate=None, method='linear', nugget=0.0, trunc=1e-5, fast=True): def tospecdata(self, rate=None, method='fft', nugget=0.0, trunc=1e-5, fast=True):
''' '''
Computes spectral density from the auto covariance function Computes spectral density from the auto covariance function
@ -192,7 +192,7 @@ class CovData1D(WafoData):
1,2,4,8...2^r, interpolation rate for f (default 1) 1,2,4,8...2^r, interpolation rate for f (default 1)
method: string method: string
interpolation method 'stineman', 'linear', 'cubic' interpolation method 'stineman', 'linear', 'cubic', 'fft'
nugget = scalar, real nugget = scalar, real
nugget effect to ensure that round off errors do not result in nugget effect to ensure that round off errors do not result in
@ -217,7 +217,8 @@ class CovData1D(WafoData):
Example: Example:
>>> import wafo.spectrum.models as sm >>> import wafo.spectrum.models as sm
>>> import numpy as np >>> import numpy as np
>>> import scipy.signal.signaltools as st >>> import scipy.signal as st
>>> import pylab
>>> L = 129 >>> L = 129
>>> t = np.linspace(0,75,L) >>> t = np.linspace(0,75,L)
>>> R = np.zeros(L) >>> R = np.zeros(L)
@ -230,7 +231,13 @@ class CovData1D(WafoData):
>>> S = Sj.tospecdata() >>> S = Sj.tospecdata()
>>> R2 = S.tocovdata() >>> R2 = S.tocovdata()
>>> S1 = R2.tospecdata() >>> S1 = R2.tospecdata()
>>> assert(all(abs(S1.data-S.data)<1e-4) ,'COV2SPEC') >>> abs(S1.data-S.data).max()
>>> S1.plot('r-')
>>> S.plot('b:')
>>> pylab.show()
>>> all(abs(S1.data-S.data)<1e-4)
See also See also
-------- --------
@ -238,10 +245,10 @@ class CovData1D(WafoData):
datastructures datastructures
''' '''
dT = self.sampling_period() dt = self.sampling_period()
# dT = time-step between data points. # dt = time-step between data points.
ACF, unused_ti = atleast_1d(self.data, self.args) acf, unused_ti = atleast_1d(self.data, self.args)
if self.lagtype in 't': if self.lagtype in 't':
spectype = 'freq' spectype = 'freq'
@ -258,30 +265,34 @@ class CovData1D(WafoData):
## add a nugget effect to ensure that round off errors ## add a nugget effect to ensure that round off errors
## do not result in negative spectral estimates ## do not result in negative spectral estimates
ACF[0] = ACF[0] + nugget acf[0] = acf[0] + nugget
n = ACF.size n = acf.size
# embedding a circulant vector and Fourier transform # embedding a circulant vector and Fourier transform
if fast:
nfft = 2 ** nextpow2(2 * n - 2) nfft = 2 ** nextpow2(2 * n - 2) if fast else 2 * n - 2
else:
nfft = 2 * n - 2 if method=='fft':
nfft *= rate
nf = nfft / 2 ## number of frequencies nf = nfft / 2 ## number of frequencies
ACF = r_[ACF, zeros(nfft - 2 * n + 2), ACF[n - 1:0:-1]] acf = r_[acf, zeros(nfft - 2 * n + 2), acf[n - 2:0:-1]]
Rper = (fft(ACF, nfft).real).clip(0) ## periodogram Rper = (fft(acf, nfft).real).clip(0) ## periodogram
# import pylab
# pylab.semilogy(Rper)
# pylab.show()
RperMax = Rper.max() RperMax = Rper.max()
Rper = where(Rper < trunc * RperMax, 0, Rper) Rper = where(Rper < trunc * RperMax, 0, Rper)
S = abs(Rper[0:(nf + 1)]) * dT / pi S = abs(Rper[0:(nf + 1)]) * dt / pi
w = linspace(0, pi / dT, nf + 1) w = linspace(0, pi / dt, nf + 1)
So = _wafospec.SpecData1D(S, w, type=spectype, freqtype=ftype) So = _wafospec.SpecData1D(S, w, type=spectype, freqtype=ftype)
So.tr = self.tr So.tr = self.tr
So.h = self.h So.h = self.h
So.norm = self.norm So.norm = self.norm
if rate > 1: if method != 'fft' and rate > 1:
So.args = linspace(0, pi / dT, nf * rate) So.args = linspace(0, pi / dt, nf * rate)
if method == 'stineman': if method == 'stineman':
So.data = stineman_interp(So.args, w, S) So.data = stineman_interp(So.args, w, S)
else: else:
@ -374,15 +385,15 @@ class CovData1D(WafoData):
_set_seed(iseed) _set_seed(iseed)
ACF = self.data.ravel() acf = self.data.ravel()
n = ACF.size n = acf.size
I = ACF.argmax() I = acf.argmax()
if I != 0: if I != 0:
raise ValueError('ACF does not have a maximum at zero lag') raise ValueError('ACF does not have a maximum at zero lag')
ACF.shape = (n, 1) acf.shape = (n, 1)
dT = self.sampling_period() dT = self.sampling_period()
@ -393,25 +404,24 @@ class CovData1D(WafoData):
## add a nugget effect to ensure that round off errors ## add a nugget effect to ensure that round off errors
## do not result in negative spectral estimates ## do not result in negative spectral estimates
ACF[0] = ACF[0] + nugget acf[0] = acf[0] + nugget
## Fast and exact simulation of simulation of stationary ## Fast and exact simulation of simulation of stationary
## Gaussian process throug circulant embedding of the ## Gaussian process throug circulant embedding of the
## Covariance matrix ## Covariance matrix
floatinfo = finfo(float) floatinfo = finfo(float)
if (abs(ACF[-1]) > floatinfo.eps): ## assuming ACF(n+1)==0 if (abs(acf[-1]) > floatinfo.eps): ## assuming acf(n+1)==0
m2 = 2 * n - 1 m2 = 2 * n - 1
nfft = 2 ** nextpow2(max(m2, 2 * ns)) nfft = 2 ** nextpow2(max(m2, 2 * ns))
ACF = r_[ACF, zeros((nfft - m2, 1)), ACF[-1:0:-1, :]] acf = r_[acf, zeros((nfft - m2, 1)), acf[-1:0:-1, :]]
#disp('Warning: I am now assuming that ACF(k)=0 ') #warnings,warn('I am now assuming that ACF(k)=0 for k>MAXLAG.')
#disp('for k>MAXLAG.')
else: # # ACF(n)==0 else: # # ACF(n)==0
m2 = 2 * n - 2 m2 = 2 * n - 2
nfft = 2 ** nextpow2(max(m2, 2 * ns)) nfft = 2 ** nextpow2(max(m2, 2 * ns))
ACF = r_[ACF, zeros((nfft - m2, 1)), ACF[n - 1:1:-1, :]] acf = r_[acf, zeros((nfft - m2, 1)), acf[n - 1:1:-1, :]]
##m2=2*n-2 ##m2=2*n-2
S = fft(ACF, nfft, axis=0).real ## periodogram S = fft(acf, nfft, axis=0).real ## periodogram
I = S.argmax() I = S.argmax()
k = flatnonzero(S < 0) k = flatnonzero(S < 0)

51
pywafo/src/wafo/doc/tutorial_scripts/chapter1.py
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@ -1,6 +1,28 @@
from scipy import * from scipy import *
from pylab import * from pylab import *
#import wafo.spectrum.models as sm
#Sj = sm.Jonswap()
#S = Sj.tospecdata()
#S.data[0:40] = 0.0
#S.data[100:-1] = 0.0
#Nt = len(S.data)-1
#acf = S.tocovdata(nr=0, nt=Nt)
#S2 = acf.tospecdata()
#S.plot('r')
#S2.plot('b:')
#
#show()
#
#import wafo
#import wafo.objects as wo
#xn = wafo.data.sea()
#ts = wo.mat2timeseries(xn)
#Sest = ts.tospecdata(method='cov')
#Sest.setplotter('semilogy')
#Sest.plot()
#show()
# pyreport -o chapter1.html chapter1.py # pyreport -o chapter1.html chapter1.py
#! CHAPTER1 demonstrates some applications of WAFO #! CHAPTER1 demonstrates some applications of WAFO
@ -26,6 +48,7 @@ S1 = S.tospecdata()
S1.plot() S1.plot()
show() show()
## ##
import wafo.objects as wo import wafo.objects as wo
xs = S1.sim(ns=2000, dt=0.1) xs = S1.sim(ns=2000, dt=0.1)
@ -33,6 +56,7 @@ ts = wo.mat2timeseries(xs)
ts.plot_wave('-') ts.plot_wave('-')
show() show()
#! Estimation of spectrum #! Estimation of spectrum
#!~~~~~~~~~~~~~~~~~~~~~~~ #!~~~~~~~~~~~~~~~~~~~~~~~
#! A common situation is that one wants to estimate the spectrum for wave #! A common situation is that one wants to estimate the spectrum for wave
@ -42,19 +66,20 @@ clf()
Fs = 4; Fs = 4;
xs = S1.sim(ns=fix(20 * 60 * Fs), dt=1. / Fs) xs = S1.sim(ns=fix(20 * 60 * Fs), dt=1. / Fs)
ts = wo.mat2timeseries(xs) ts = wo.mat2timeseries(xs)
Sest = ts.tospecdata(NFFT=400) Sest = ts.tospecdata(L=400)
S1.plot() S1.plot()
Sest.plot('--') Sest.plot('--')
axis([0, 3, 0, 5]) # This may depend on the simulation axis([0, 3, 0, 5]) # This may depend on the simulation
show() show()
## Section 1.4.2 Probability distributions of wave characteristics. #! Section 1.4.2 Probability distributions of wave characteristics.
## Probability distribution of wave trough period #!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# WAFO gives the possibility of computing the exact probability #! Probability distribution of wave trough period:
# distributions for a number of characteristics given a spectral density. #! WAFO gives the possibility of computing the exact probability
# In the following example we study the trough period extracted from the #! distributions for a number of characteristics given a spectral density.
# time series and compared with the theoretical density computed with exact #! In the following example we study the trough period extracted from the
# spectrum, S1, and the estimated spectrum, Sest. #! time series and compared with the theoretical density computed with exact
#! spectrum, S1, and the estimated spectrum, Sest.
clf() clf()
import wafo.misc as wm import wafo.misc as wm
dtyex = S1.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3) dtyex = S1.to_t_pdf(pdef='Tt', paramt=(0, 10, 51), nit=3)
@ -81,11 +106,11 @@ Sp = 15; # spreading parameter
D1 = sm.Spreading(type='cos', theta0=th0, method=None) # frequency independent D1 = sm.Spreading(type='cos', theta0=th0, method=None) # frequency independent
D12 = sm.Spreading(type='cos', theta0=0, method='mitsuyasu') # frequency dependent D12 = sm.Spreading(type='cos', theta0=0, method='mitsuyasu') # frequency dependent
#SD1 = mkdspec(S1, D1) SD1 = D1.tospecdata2d(S1)
#SD12 = mkdspec(S1, D12); SD12 = D12.tospecdata2d(S1)
#plotspec(SD1, plotflag), hold on, plotspec(SD12, plotflag, '-.'); hold off SD1.plot()
#wafostamp('', '(ER)') SD12.plot()#linestyle='dashdot')
#disp('Block = 5'), pause(pstate) show()
#! 3D Simulation of the sea surface #! 3D Simulation of the sea surface
#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

3
pywafo/src/wafo/doc/tutorial_scripts/chapter2.py
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@ -180,6 +180,7 @@ glc, gemp = lc.trdata(mean=me, sigma=sa)
g.plot('r') g.plot('r')
glc.plot('b-') #! Transf. estimated from level-crossings glc.plot('b-') #! Transf. estimated from level-crossings
gh.plot('b-.') #! Hermite Transf. estimated from moments gh.plot('b-.') #! Hermite Transf. estimated from moments
grid('on')
show() show()
#! Test Gaussianity of a stochastic process. #! Test Gaussianity of a stochastic process.
@ -205,7 +206,7 @@ show()
#! "light" lower tail. #! "light" lower tail.
clf() clf()
import pylab import pylab
ws.probplot(ts.data, dist='norm', plot=pylab) ws.probplot(ts.data.ravel(), dist='norm', plot=pylab)
show() show()
#! Section 2.2.3 Spectral densities of sea data #! Section 2.2.3 Spectral densities of sea data
#!----------------------------------------------- #!-----------------------------------------------

2
pywafo/src/wafo/misc.py
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@ -2157,4 +2157,4 @@ if __name__ == "__main__":
import doctest import doctest
doctest.testmod() doctest.testmod()
else: else:
test_find_cross() _test_find_cross()

62
pywafo/src/wafo/objects.py
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@ -197,7 +197,7 @@ class LevelCrossings(WafoData):
integral = zeros(u.shape, dtype=float) integral = zeros(u.shape, dtype=float)
for i in range(len(integral)): for i in range(len(integral)):
y = factor1 * exp(-u[i] * u[i] * factor2) y = factor1 * exp(-u[i] * u[i] * factor2)
integral[i] = trapz(x, y) integral[i] = trapz(y, x)
#end #end
G = G - integral / (2 * pi) G = G - integral / (2 * pi)
G = G / max(G) G = G / max(G)
@ -215,7 +215,7 @@ class LevelCrossings(WafoData):
g = lc2.trdata() g = lc2.trdata()
f = [u, u] #f = [u, u]
f = g.dat2gauss(Z) f = g.dat2gauss(Z)
G = TrData(f, u) G = TrData(f, u)
@ -320,11 +320,11 @@ class LevelCrossings(WafoData):
>>> g1, g1emp = lc.trdata(gvar=0.5 ) # Equal weight on all points >>> g1, g1emp = lc.trdata(gvar=0.5 ) # Equal weight on all points
>>> g2, g2emp = lc.trdata(gvar=[3.5, 0.5, 3.5]) # Less weight on the ends >>> g2, g2emp = lc.trdata(gvar=[3.5, 0.5, 3.5]) # Less weight on the ends
>>> int(S.tr.dist2gauss()*100) >>> int(S.tr.dist2gauss()*100)
593 141
>>> int(g0emp.dist2gauss()*100) >>> int(g0emp.dist2gauss()*100)
492 1544
>>> int(g0.dist2gauss()*100) >>> int(g0.dist2gauss()*100)
361 340
>>> int(g1.dist2gauss()*100) >>> int(g1.dist2gauss()*100)
352 352
>>> int(g2.dist2gauss()*100) >>> int(g2.dist2gauss()*100)
@ -855,12 +855,13 @@ class TimeSeries(WafoData):
c0 = R[0] c0 = R[0]
if norm: if norm:
R = R / c0 R = R / c0
r0 = R[0]
if dt is None: if dt is None:
dt = self.sampling_period() dt = self.sampling_period()
t = linspace(0, lag * dt, lag + 1) t = linspace(0, lag * dt, lag + 1)
#cumsum = np.cumsum #cumsum = np.cumsum
acf = _wafocov.CovData1D(R[lags], t) acf = _wafocov.CovData1D(R[lags], t)
acf.stdev = sqrt(r_[ 0, 1 , 1 + 2 * cumsum(R[1:] ** 2)] / Ncens) acf.stdev = sqrt(r_[ 0, r0**2 , r0**2 + 2 * cumsum(R[1:] ** 2)] / Ncens)
acf.children = [WafoData(-2. * acf.stdev[lags], t), WafoData(2. * acf.stdev[lags], t)] acf.children = [WafoData(-2. * acf.stdev[lags], t), WafoData(2. * acf.stdev[lags], t)]
acf.plot_args_children = ['r:'] acf.plot_args_children = ['r:']
acf.norm = norm acf.norm = norm
@ -910,7 +911,7 @@ class TimeSeries(WafoData):
fact = 2.0 * pi fact = 2.0 * pi
w = fact * f w = fact * f
return _wafospec.SpecData1D(S / fact, w) return _wafospec.SpecData1D(S / fact, w)
def tospecdata(self, L=None, tr=None, method='cov', detrend=detrend_mean, window=parzen, noverlap=0, pad_to=None, ftype='w', alpha=None): def tospecdata(self, L=None, tr=None, method='cov', detrend=detrend_mean, window=parzen, noverlap=0, ftype='w', alpha=None):
''' '''
Estimate one-sided spectral density from data. Estimate one-sided spectral density from data.
@ -930,9 +931,14 @@ class TimeSeries(WafoData):
'cov' : Frequency smoothing using a parzen window function 'cov' : Frequency smoothing using a parzen window function
on the estimated autocovariance function. (default) on the estimated autocovariance function. (default)
'psd' : Welch's averaged periodogram method with no overlapping batches 'psd' : Welch's averaged periodogram method with no overlapping batches
dflag : string detrend : function
defining detrending performed on the signal before estimation. defining detrending performed on the signal before estimation.
'mean','linear' or 'ma' (= moving average) (default 'mean') (default detrend_mean)
window : vector of length NFFT or function
To create window vectors see numpy.blackman, numpy.hamming,
numpy.bartlett, scipy.signal, scipy.signal.get_window etc.
noverlap : scalar int
gives the length of the overlap between segments.
ftype : character ftype : character
defining frequency type: 'w' or 'f' (default 'w') defining frequency type: 'w' or 'f' (default 'w')
@ -968,9 +974,9 @@ class TimeSeries(WafoData):
#% Initialize constants #% Initialize constants
#%~~~~~~~~~~~~~~~~~~~~~ #%~~~~~~~~~~~~~~~~~~~~~
nugget = 0; #%10^-12; nugget = 1e-12
rate = 2; #% interpolationrate for frequency rate = 2; #% interpolationrate for frequency
tapery = 0; #% taper the data before the analysis
wdef = 1; #% 1=parzen window 2=hanning window, 3= bartlett window wdef = 1; #% 1=parzen window 2=hanning window, 3= bartlett window
dt = self.sampling_period() dt = self.sampling_period()
@ -981,18 +987,17 @@ class TimeSeries(WafoData):
L = min(L, n); L = min(L, n);
max_L = min(300, n); #% maximum lag if L is undetermined max_L = min(300, n); #% maximum lag if L is undetermined
change_L = L is None estimate_L = L is None
if change_L: if estimate_L:
L = min(n - 2, int(4. / 3 * max_L + 0.5)) L = min(n - 2, int(4. / 3 * max_L + 0.5))
if method == 'cov' or change_L: if method == 'cov' or estimate_L:
tsy = TimeSeries(yy, self.args) tsy = TimeSeries(yy, self.args)
R = tsy.tocovdata() R = tsy.tocovdata()
if change_L: if estimate_L:
#finding where ACF is less than 2 st. deviations. #finding where ACF is less than 2 st. deviations.
L = max_L - (np.abs(R.data[max_L::-1]) > 2 * R.stdev[max_L::-1]).argmax() # a better L value L = max_L + 2 - (np.abs(R.data[max_L::-1]) > 2 * R.stdev[max_L::-1]).argmax() # a better L value
if wdef == 1: # % modify L so that hanning and Parzen give appr. the same result if wdef == 1: # modify L so that hanning and Parzen give appr. the same result
L = min(int(4 * L / 3), n - 2) L = min(int(4 * L / 3), n - 2)
print('The default L is set to %d' % L) print('The default L is set to %d' % L)
try: try:
@ -1014,9 +1019,9 @@ class TimeSeries(WafoData):
Be = None Be = None
if method == 'psd': if method == 'psd':
nf = rate * 2 ** nextpow2(2 * L - 2) # Interpolate the spectrum with rate nfft = 2 ** nextpow2(L)
nfft = 2 * nf pad_to = rate*nfft # Interpolate the spectrum with rate
S, f = psd(yy, Fs=1. / dt, NFFT=nfft, detrend=detrend, window=window, S, f = psd(yy, Fs=1. / dt, NFFT=nfft, detrend=detrend, window=window(nfft),
noverlap=noverlap, pad_to=pad_to, scale_by_freq=True) noverlap=noverlap, pad_to=pad_to, scale_by_freq=True)
fact = 2.0 * pi fact = 2.0 * pi
w = fact * f w = fact * f
@ -1025,10 +1030,13 @@ class TimeSeries(WafoData):
# add a nugget effect to ensure that round off errors # add a nugget effect to ensure that round off errors
# do not result in negative spectral estimates # do not result in negative spectral estimates
R.data = R.data[:L] * win[L - 1::] R.data[:L] = R.data[:L] * win[L - 1::]
R.args = R.args[:L] R.data[L] = 0.0
R.data = R.data[:L+1]
spec = R.tospecdata(rate=2, nugget=nugget) R.args = R.args[:L+1]
#R.plot()
#R.show()
spec = R.tospecdata(rate=rate, nugget=nugget)
spec.Bw = Be spec.Bw = Be
if ftype == 'f': if ftype == 'f':
@ -1356,7 +1364,9 @@ class TimeSeries(WafoData):
t = self.args[ind] t = self.args[ind]
except: except:
t = ind t = ind
return TurningPoints(self.data[ind], t) mean = self.data.mean()
stdev = self.data.std()
return TurningPoints(self.data[ind], t, mean=mean, stdev=stdev)
def wave_periods(self, vh=None, pdef='d2d', wdef=None, index=None, rate=1): def wave_periods(self, vh=None, pdef='d2d', wdef=None, index=None, rate=1):
""" """

35
pywafo/src/wafo/spectrum/core.py
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@ -649,20 +649,17 @@ class SpecData1D(WafoData):
""" """
def __init__(self, *args, **kwds): def __init__(self, *args, **kwds):
super(SpecData1D, self).__init__(*args, **kwds) self.name_ = kwds.pop('name', 'WAFO Spectrum Object')
self.name = 'WAFO Spectrum Object' self.type = kwds.pop('type','freq')
self.type = 'freq' self.freqtype = kwds.pop('freqtype','w')
self.freqtype = 'w'
self.angletype = '' self.angletype = ''
self.h = inf self.h = kwds.pop('h',inf)
self.tr = None #TrLinear() self.tr = kwds.pop('tr',None) #TrLinear()
self.phi = 0.0 self.phi = kwds.pop('phi',0.0)
self.v = 0.0 self.v = kwds.pop('v',0.0)
self.norm = False self.norm = kwds.pop('norm',False)
somekeys = ['phi', 'name', 'h', 'tr', 'freqtype', 'v','type', 'norm'] super(SpecData1D, self).__init__(*args, **kwds)
self.__dict__.update(sub_dict_select(kwds, somekeys))
self.setlabels() self.setlabels()
def tocov_matrix(self, nr=0, nt=None, dt=None): def tocov_matrix(self, nr=0, nt=None, dt=None):
@ -869,7 +866,7 @@ class SpecData1D(WafoData):
nfft = rate * 2 ** nextpow2(2 * n_f - 2) nfft = rate * 2 ** nextpow2(2 * n_f - 2)
# periodogram # periodogram
rper = r_[specn, zeros(nfft - (2 * n_f) + 2), conj(specn[n_f - 1:0:-1])] rper = r_[specn, zeros(nfft - (2 * n_f) + 2), conj(specn[n_f - 2:0:-1])]
time = r_[0:nt + 1] * d_t * (2 * n_f - 2) / nfft time = r_[0:nt + 1] * d_t * (2 * n_f - 2) / nfft
r = fft(rper, nfft).real / (2 * n_f - 2) r = fft(rper, nfft).real / (2 * n_f - 2)
@ -1194,9 +1191,6 @@ class SpecData1D(WafoData):
if self.tr is None: if self.tr is None:
g = TrLinear(var=m[0]) g = TrLinear(var=m[0])
#y = linspace(-5, 5, 513)
#g = _wafotransform.
#g = TrData(y, sqrt(m[0]) * y)
else: else:
g = self.tr g = self.tr
@ -2086,7 +2080,7 @@ class SpecData1D(WafoData):
# #
# opt = troptset(opt,'multip',1) # opt = troptset(opt,'multip',1)
plotflag = 0 if test0 is None else 1 plotflag = False if test0 is None else True
if cases > 50: if cases > 50:
print(' ... be patient this may take a while') print(' ... be patient this may take a while')
@ -2104,9 +2098,6 @@ class SpecData1D(WafoData):
ts = TimeSeries(xs[:, iy], xs[:, 0].ravel()) ts = TimeSeries(xs[:, iy], xs[:, 0].ravel())
g, tmp = ts.trdata(method, **opt) g, tmp = ts.trdata(method, **opt)
test1.append(g.dist2gauss()) test1.append(g.dist2gauss())
#xs = cov2sdat(R,[ns Nstep]);
#[g, tmp] = dat2tr(xs,method, **opt);
#test1 = [test1; tmp(:)]
if verbose: if verbose:
print('finished %d of %d ' % (ix + 1, rep)) print('finished %d of %d ' % (ix + 1, rep))
@ -2790,6 +2781,8 @@ class SpecData2D(WafoData):
""" """
def __init__(self, *args, **kwds): def __init__(self, *args, **kwds):
super(SpecData2D, self).__init__(*args, **kwds) super(SpecData2D, self).__init__(*args, **kwds)
self.name = 'WAFO Spectrum Object' self.name = 'WAFO Spectrum Object'

2
pywafo/src/wafo/spectrum/models.py
Unescape Escape View File

@ -1952,6 +1952,8 @@ class Spreading(object):
w = specdata.args w = specdata.args
S = specdata.data S = specdata.data
D, phi0 = self(theta, w=w, wc=wc) D, phi0 = self(theta, w=w, wc=wc)
if D.ndim != 2: # frequency dependent spreading
D = D[:, None]
SD = D * S[None,:] SD = D * S[None,:]

26
pywafo/src/wafo/transform/core.py
Unescape Escape View File

@ -70,7 +70,7 @@ class TrCommon(object):
------- -------
t0 : real, scalar t0 : real, scalar
a measure of departure from the Gaussian model calculated as a measure of departure from the Gaussian model calculated as
trapz(xn,(xn-g(x))**2.) where int. limits is given by X. trapz((xn-g(x))**2., xn) where int. limits is given by X.
""" """
if x is None: if x is None:
xn = linspace(xnmin, xnmax, n) xn = linspace(xnmin, xnmax, n)
@ -79,7 +79,7 @@ class TrCommon(object):
xn = (x-self.mean)/self.sigma xn = (x-self.mean)/self.sigma
yn = (self._dat2gauss(x)-self.ymean)/self.ysigma yn = (self._dat2gauss(x)-self.ymean)/self.ysigma
t0 = trapz(xn,(xn-yn)**2.) t0 = trapz((xn-yn)**2., xn)
return t0 return t0
def gauss2dat(self, y, *yi): def gauss2dat(self, y, *yi):
@ -166,14 +166,18 @@ class TrData(WafoData, TrCommon):
True True
""" """
def __init__(self, *args, **kwds): def __init__(self, *args, **kwds):
super(TrData, self).__init__(*args, **kwds) self.ymean = kwds.pop('ymean', 0e0)
self.labels.title = 'Transform' self.ysigma = kwds.pop('ysigma', 1e0)
self.labels.ylab = 'g(x)' self.mean = kwds.pop('mean', None)
self.labels.xlab = 'x' self.sigma = kwds.pop('sigma', None)
self.ymean = kwds.get('ymean', 0e0)
self.ysigma = kwds.get('ysigma', 1e0) options = dict(title='Transform',
self.mean = kwds.get('mean', None) xlab='x', ylab='g(x)',
self.sigma = kwds.get('sigma', None) plot_args=['r'],
plot_args_children=['g--'],)
options.update(**kwds)
super(TrData, self).__init__(*args, **options)
if self.mean is None: if self.mean is None:
#self.mean = np.mean(self.args) # #self.mean = np.mean(self.args) #
@ -182,6 +186,8 @@ class TrData(WafoData, TrCommon):
yp = self.ymean+self.ysigma yp = self.ymean+self.ysigma
ym = self.ymean-self.ysigma ym = self.ymean-self.ysigma
self.sigma = (self.gauss2dat(yp)-self.gauss2dat(ym))/2. self.sigma = (self.gauss2dat(yp)-self.gauss2dat(ym))/2.
self.children = [WafoData((self.args-self.mean)/self.sigma, self.args)]
def _gauss2dat(self, y, *yi): def _gauss2dat(self, y, *yi):
return tranproc(self.data, self.args, y, *yi) return tranproc(self.data, self.args, y, *yi)

2
pywafo/src/wafo/transform/models.py
Unescape Escape View File

@ -61,7 +61,7 @@ class TrCommon2(TrCommon):
------- -------
t0 : real, scalar t0 : real, scalar
a measure of departure from the Gaussian model calculated as a measure of departure from the Gaussian model calculated as
trapz(xn,(xn-g(x))**2.) where int. limits is given by X. trapz((xn-g(x))**2., xn) where int. limits is given by X.
""" """
if x is None: if x is None:
xn = np.linspace(xnmin, xnmax, n) xn = np.linspace(xnmin, xnmax, n)

4
pywafo/src/wafo/wafodata.py
Unescape Escape View File

@ -81,7 +81,7 @@ class WafoData(object):
if self.children != None: if self.children != None:
plotbackend.hold('on') plotbackend.hold('on')
tmp = [] tmp = []
child_args = args + tuple(self.plot_args_children) child_args = args if len(args) else tuple(self.plot_args_children)
child_kwds = dict() child_kwds = dict()
child_kwds.update(self.plot_kwds_children) child_kwds.update(self.plot_kwds_children)
child_kwds.update(**kwds) child_kwds.update(**kwds)
@ -91,7 +91,7 @@ class WafoData(object):
tmp.append(tmp1) tmp.append(tmp1)
if len(tmp) == 0: if len(tmp) == 0:
tmp = None tmp = None
main_args = args + tuple(self.plot_args) main_args = args if len(args) else tuple(self.plot_args)
main_kwds = dict() main_kwds = dict()
main_kwds.update(self.plot_kwds) main_kwds.update(self.plot_kwds)
main_kwds.update(kwds) main_kwds.update(kwds)

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