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Updated some examples

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master
per.andreas.brodtkorb 14 years ago
parent 0b71a79872
commit f5fcfce780
3 changed files with 78 additions and 97 deletions
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4
pywafo/src/wafo/covariance/core.py
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@ -19,7 +19,7 @@ import warnings
#import numpy as np
from numpy import (zeros, sqrt, dot, newaxis, inf, where, pi, nan, #@UnresolvedImport
atleast_1d, hstack, vstack, r_, linspace, flatnonzero, size, #@UnresolvedImport
isnan, finfo, diag, ceil, floor, random) #@UnresolvedImport
isnan, finfo, diag, ceil, floor, random, pi) #@UnresolvedImport
from numpy.fft import fft
from numpy.random import randn
import scipy.interpolate as interpolate
@ -272,7 +272,7 @@ class CovData1D(WafoData):
Rper = (fft(ACF, nfft).real).clip(0) ## periodogram
RperMax = Rper.max()
Rper = where(Rper < trunc * RperMax, 0, Rper)
pi = pi
S = abs(Rper[0:(nf + 1)]) * dT / pi
w = linspace(0, pi / dT, nf + 1)
So = _wafospec.SpecData1D(S, w, type=spectype, freqtype=ftype)

9
pywafo/src/wafo/doc/tutorial_scripts/chapter2.py
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@ -105,7 +105,7 @@ inds, indg = wm.findoutliers(xx[:,1],zcrit,dcrit,ddcrit, verbose=True)
#!
clf()
Lmax = 9500
S = ts.tospecdata(NFFT=Lmax)
S = ts.tospecdata(L=Lmax)
S.plot()
axis([0, 5, 0, 0.7])
show()
@ -123,8 +123,8 @@ mom, text= S.moment(nr=4)
clf()
Lmax0 = 200; Lmax1 = 50
S1 = ts.tospecdata(NFFT=Lmax0)
S2 = ts.tospecdata(NFFT=Lmax1)
S1 = ts.tospecdata(L=Lmax0)
S2 = ts.tospecdata(L=Lmax1)
S1.plot('-.')
S2.plot()
show()
@ -136,11 +136,12 @@ show()
#! unimodal spectral density S1 and compare it with estimated
#! covariance of the signal xx.
clf()
Lmax = 80
Lmax = 85
R1 = S1.tocovdata(nr=1)
Rest = ts.tocovdata(lag=Lmax)
R1.plot('.')
Rest.plot()
axis([0,25,-0.1,0.25])
show()
#! We can see in Figure below that the covariance function corresponding to

154
pywafo/src/wafo/objects.py
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@ -310,7 +310,7 @@ class LevelCrossings(WafoData):
>>> Sj = sm.Jonswap(Hm0=Hs)
>>> S = Sj.tospecdata() #Make spectrum object from numerical values
>>> S.tr = tm.TrOchi(mean=0, skew=0.16, kurt=0, sigma=Hs/4, ysigma=Hs/4)
>>> xs = S.sim(ns=2**16)
>>> xs = S.sim(ns=2**16, iseed=10)
>>> ts = mat2timeseries(xs)
>>> tp = ts.turning_points()
>>> mm = tp.cycle_pairs()
@ -318,16 +318,16 @@ class LevelCrossings(WafoData):
>>> g0, gemp = lc.trdata(monitor=True) # Monitor the development
>>> g1, gemp = lc.trdata(gvar=0.5 ) # Equal weight on all points
>>> g2, gemp = lc.trdata(gvar=[3.5, 0.5, 3.5]) # Less weight on the ends
>>> S.tr.dist2gauss()
5.9322684525265501
>>> np.round(gemp.dist2gauss())
6.0
>>> np.round(g0.dist2gauss())
4.0
>>> np.round(g1.dist2gauss())
4.0
>>> np.round(g2.dist2gauss())
4.0
>>> int(S.tr.dist2gauss()*100)
593
>>> int(gemp.dist2gauss()*100)
431
>>> int(g0.dist2gauss()*100)
391
>>> int(g1.dist2gauss()*100)
311
>>> int(g2.dist2gauss()*100)
357
hold on, trplot(g1,g) # Check the fit
trplot(g2)
@ -734,6 +734,8 @@ class TimeSeries(WafoData):
>>> rf = ts.tocovdata(lag=150)
>>> h = rf.plot()
>>> S = ts.tospecdata()
>>> tp = ts.turning_points()
>>> mm = tp.cycle_pairs()
>>> h1 = mm.plot(marker='x')
@ -861,7 +863,7 @@ class TimeSeries(WafoData):
acf.norm = norm
return acf
def tospecdata(self, L=None, tr=None, method='cov', detrend=detrend_mean, window=parzen, noverlap=0, pad_to=None):
def specdata(self, L=None, tr=None, method='cov', detrend=detrend_mean, window=parzen, noverlap=0, pad_to=None):
"""
Return power spectral density by Welches average periodogram method.
@ -900,12 +902,12 @@ class TimeSeries(WafoData):
yy = self.data.ravel() if tr is None else tr.dat2gauss(self.data.ravel())
yy = detrend(yy) if hasattr(detrend,'__call__') else yy
S, f = psd(yy, Fs=1./dt, NFFT=nfft, detrend=detrend, window=window,
S, f = psd(yy, Fs=1./dt, NFFT=L, detrend=detrend, window=window,
noverlap=noverlap,pad_to=pad_to, scale_by_freq=True)
fact = 2 * 2.0 * pi
fact = 2.0 * pi
w = fact * f
return _wafospec.SpecData1D(S / fact, w)
def specdata(self, L=None, tr=None, method='cov',dflag='mean', ftype='w'):
def tospecdata(self, L=None, tr=None, method='cov',detrend=detrend_mean, window=parzen, noverlap=0, pad_to=None, ftype='w', alpha=None):
'''
Estimate one-sided spectral density from data.
@ -950,7 +952,7 @@ class TimeSeries(WafoData):
References:
-----------
Georg Lindgren and Holger Rootzen (1986)
"Stationära stokastiska processer", pp 173--176.
"Stationara stokastiska processer", pp 173--176.
Gareth Janacek and Louise Swift (1993)
"TIME SERIES forecasting, simulation, applications",
@ -969,21 +971,12 @@ class TimeSeries(WafoData):
wdef = 1; #% 1=parzen window 2=hanning window, 3= bartlett window
dt = self.sampling_period()
yy = self.data if tr is None else tr.dat2gauss(self.data)
#yy = self.data if tr is None else tr.dat2gauss(self.data)
yy = self.data.ravel() if tr is None else tr.dat2gauss(self.data.ravel())
yy = detrend(yy) if hasattr(detrend,'__call__') else yy
n = len(yy)
L = min(L,n);
dflag = dflag.lower()
if dflag=='mean':
yy -= yy.mean()
elif dflag== 'linear':
yy = detrend(yy,1); # % signal toolbox detrend
elif dflag== 'ma':
dL = np.ceil(1200/2/dT); # % approximately 20 min. moving average
yy = detrendma(yy,dL);
dflag = 'mean';
max_L = min(300,n); #% maximum lag if L is undetermined
change_L = L is None
if change_L:
@ -991,81 +984,68 @@ class TimeSeries(WafoData):
if method=='cov' or change_L:
R = self.tocovdata()
tsy = TimeSeries(yy, self.args)
R = tsy.tocovdata()
if change_L:
#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
hasattr(windom, '__call__')
if wdef==1: # % modify L so that hanning and Parzen give appr. the same result
L = min(int(4*L/3),n-2)
print('The default L is set to %d' % L)
try:
win = window(2*L-1)
wname = window.__name__
if wname=='parzen':
v = int(3.71*n/L) # degrees of freedom used in chi^2 distribution
Be = 2*pi*1.33/(L*dt) # % bandwidth (rad/sec)
elif wname=='hanning':
v = int(2.67*n/L); # degrees of freedom used in chi^2 distribution
Be = 2*pi/(L*dt); # % bandwidth (rad/sec)
elif wname=='bartlett':
v = int(3*n/L); # degrees of freedom used in chi^2 distribution
Be = 2*pi*1.33/(L*dt); # bandwidth (rad/sec)
except:
wname = None
win = window
v = None
Be = None
nf = rate*2**nextpow2(2*L-2) # Interpolate the spectrum with rate
nfft = 2*nf
# S = createspec('freq',ftype);
# S.tr = g;
# S.note = ['dat2spec(',inputname(1),'), Method = ' method ];
# S.norm = 0; % not normalized
# S.L = L;
# S.S = zeros(nf+1,m-1);
if method=='psd':
nf = rate*2**nextpow2(2*L-2) # Interpolate the spectrum with rate
nfft = 2*nf
S, f = psd(yy, Fs=1./dt, NFFT=nfft, detrend=detrend, window=window,
noverlap=noverlap,pad_to=pad_to, scale_by_freq=True)
fact = 2.0 * pi
w = fact * f
spec = _wafospec.SpecData1D(S / fact, w)
else :# cov method
# add a nugget effect to ensure that round off errors
# do not result in negative spectral estimates
R.data[:L] = R.data[:L]*win[L::]
R.data[L:] = 0
R.data = R.data[:L]*win[L-1::]
R.args = R.args[:L]
spec = R.tospecdata(rate=2,nugget=nugget)
if ( ~isempty(p) ),
alpha = (1-p);
#% Confidence interval constants
CI = [v/_invchi2( 1-alpha/2 ,v), v/_invchi2( alpha/2 ,v)];
ind = find(Rper<0);
if any(ind)
Rper(ind) = 0; % set negative values to zero
warning('WAFO:DAT2SPEC','negative spectral estimates')
end
if wname=='parzen':
v = int(3.71*n/L) # degrees of freedom used in chi^2 distribution
Be = 2*pi*1.33/(L*dT) # % bandwidth (rad/sec)
elif wname=='hanning':
v = int(2.67*n/L); # degrees of freedom used in chi^2 distribution
Be = 2*pi/(L*dT); # % bandwidth (rad/sec)
elif wname=='bartlett':
v = int(3*n/L); # degrees of freedom used in chi^2 distribution
Be = 2*pi*1.33/(L*dT); # bandwidth (rad/sec)
if ftype=='w'
S.w = (0:nf)'/nf*pi/dT; % (rad/s)
S.S = real(Rper(1:(nf+1),1))*dT/pi; % (m^2*s/rad)one sided spectrum
S.Bw = Be;
else % ftype == f
S.f = (0:nf)'/nf/2/dT; % frequency Hz if dT is in seconds
S.S = 2*real(Rper(1:(nf+1),1))*dT; % (m^2*s) one sided spectrum
S.Bw = Be/(2*pi); % bandwidth in Hz
spec.Bw = Be
if ftype=='f':
spec.Bw = Be/(2*pi) # bandwidth in Hz
if alpha is not None :
#% Confidence interval constants
CI = [v/_invchi2( 1-alpha/2 ,v), v/_invchi2( alpha/2 ,v)];
spec.tr = tr
spec.L = L
spec.norm = False
spec.note = 'method=%s' % method
# S = createspec('freq',ftype);
# S.tr = g;
# S.note = ['dat2spec(',inputname(1),'), Method = ' method ];
# S.norm = 0; % not normalized
# S.L = L;
# S.S = zeros(nf+1,m-1);
return spec
@ -1148,21 +1128,21 @@ class TimeSeries(WafoData):
>>> Sj = sm.Jonswap(Hm0=Hs)
>>> S = Sj.tospecdata() #Make spectrum object from numerical values
>>> S.tr = tm.TrOchi(mean=0, skew=0.16, kurt=0, sigma=Hs/4, ysigma=Hs/4)
>>> xs = S.sim(ns=2**16)
>>> xs = S.sim(ns=2**16, iseed=10)
>>> ts = mat2timeseries(xs)
>>> g0, gemp = ts.trdata(monitor=True) # Monitor the development
>>> g1, gemp = ts.trdata(method='m', gvar=0.5 ) # Equal weight on all points
>>> g2, gemp = ts.trdata(method='n', gvar=[3.5, 0.5, 3.5]) # Less weight on the ends
>>> S.tr.dist2gauss()
5.9322684525265501
>>> np.round(gemp.dist2gauss())
6.0
>>> np.round(g0.dist2gauss())
4.0
>>> np.round(g1.dist2gauss())
4.0
>>> np.round(g2.dist2gauss())
>>> int(S.tr.dist2gauss()*100)
593
>>> int(gemp.dist2gauss()*100)
431
>>> int(g0.dist2gauss()*100)
342
>>> int(g1.dist2gauss()*100)
4.0
>>> int(g2.dist2gauss()*100)
342
Hm0 = 7;
S = jonswap([],Hm0); g=ochitr([],[Hm0/4]);
@ -1214,7 +1194,7 @@ class TimeSeries(WafoData):
lc = mM.level_crossings(opt.crossdef)
return lc.trdata()
elif method[0] == 'm':
return cdftr()
return self._cdftr()
elif method[0] == 'h':
ga1 = skew(self.data)
ga2 = kurtosis(self.data, fisher=True) #kurt(xx(n+1:end))-3;

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