From 367774c292937bd8be7fa61c4ccc57329c1d7bd1 Mon Sep 17 00:00:00 2001 From: "per.andreas.brodtkorb" Date: Tue, 25 Jan 2011 14:47:39 +0000 Subject: [PATCH] Fixed some bugs --- pywafo/src/wafo/covariance/core.py | 76 +++++++++++-------- .../src/wafo/doc/tutorial_scripts/chapter1.py | 51 +++++++++---- .../src/wafo/doc/tutorial_scripts/chapter2.py | 3 +- pywafo/src/wafo/misc.py | 2 +- pywafo/src/wafo/objects.py | 62 ++++++++------- pywafo/src/wafo/spectrum/core.py | 35 ++++----- pywafo/src/wafo/spectrum/models.py | 2 + pywafo/src/wafo/transform/core.py | 26 ++++--- pywafo/src/wafo/transform/models.py | 2 +- pywafo/src/wafo/wafodata.py | 4 +- 10 files changed, 155 insertions(+), 108 deletions(-) diff --git a/pywafo/src/wafo/covariance/core.py b/pywafo/src/wafo/covariance/core.py index a19a7a2..9337937 100644 --- a/pywafo/src/wafo/covariance/core.py +++ b/pywafo/src/wafo/covariance/core.py @@ -20,7 +20,7 @@ import warnings 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, pi) #@UnresolvedImport -from numpy.fft import fft +from numpy.fft import fft #as fft from numpy.random import randn import scipy.interpolate as interpolate from scipy.linalg import toeplitz, sqrtm, svd, cholesky, diagsvd, pinv @@ -182,7 +182,7 @@ class CovData1D(WafoData): ## wdata = CovData1D(**kwds) ## 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 @@ -192,7 +192,7 @@ class CovData1D(WafoData): 1,2,4,8...2^r, interpolation rate for f (default 1) method: string - interpolation method 'stineman', 'linear', 'cubic' + interpolation method 'stineman', 'linear', 'cubic', 'fft' nugget = scalar, real nugget effect to ensure that round off errors do not result in @@ -217,7 +217,8 @@ class CovData1D(WafoData): Example: >>> import wafo.spectrum.models as sm >>> import numpy as np - >>> import scipy.signal.signaltools as st + >>> import scipy.signal as st + >>> import pylab >>> L = 129 >>> t = np.linspace(0,75,L) >>> R = np.zeros(L) @@ -230,7 +231,13 @@ class CovData1D(WafoData): >>> S = Sj.tospecdata() >>> R2 = S.tocovdata() >>> 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 -------- @@ -238,10 +245,10 @@ class CovData1D(WafoData): datastructures ''' - dT = self.sampling_period() - # dT = time-step between data points. + dt = self.sampling_period() + # 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': spectype = 'freq' @@ -258,30 +265,34 @@ class CovData1D(WafoData): ## add a nugget effect to ensure that round off errors ## do not result in negative spectral estimates - ACF[0] = ACF[0] + nugget - n = ACF.size + acf[0] = acf[0] + nugget + n = acf.size # embedding a circulant vector and Fourier transform - if fast: - nfft = 2 ** nextpow2(2 * n - 2) - else: - nfft = 2 * n - 2 - + + nfft = 2 ** nextpow2(2 * n - 2) if fast else 2 * n - 2 + + if method=='fft': + nfft *= rate + nf = nfft / 2 ## number of frequencies - ACF = r_[ACF, zeros(nfft - 2 * n + 2), ACF[n - 1:0:-1]] - - Rper = (fft(ACF, nfft).real).clip(0) ## periodogram + acf = r_[acf, zeros(nfft - 2 * n + 2), acf[n - 2:0:-1]] + + Rper = (fft(acf, nfft).real).clip(0) ## periodogram +# import pylab +# pylab.semilogy(Rper) +# pylab.show() RperMax = Rper.max() Rper = where(Rper < trunc * RperMax, 0, Rper) - S = abs(Rper[0:(nf + 1)]) * dT / pi - w = linspace(0, pi / dT, nf + 1) + S = abs(Rper[0:(nf + 1)]) * dt / pi + w = linspace(0, pi / dt, nf + 1) So = _wafospec.SpecData1D(S, w, type=spectype, freqtype=ftype) So.tr = self.tr So.h = self.h So.norm = self.norm - if rate > 1: - So.args = linspace(0, pi / dT, nf * rate) + if method != 'fft' and rate > 1: + So.args = linspace(0, pi / dt, nf * rate) if method == 'stineman': So.data = stineman_interp(So.args, w, S) else: @@ -374,15 +385,15 @@ class CovData1D(WafoData): _set_seed(iseed) - ACF = self.data.ravel() - n = ACF.size + acf = self.data.ravel() + n = acf.size - I = ACF.argmax() + I = acf.argmax() if I != 0: raise ValueError('ACF does not have a maximum at zero lag') - ACF.shape = (n, 1) + acf.shape = (n, 1) dT = self.sampling_period() @@ -393,25 +404,24 @@ class CovData1D(WafoData): ## add a nugget effect to ensure that round off errors ## 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 ## Gaussian process throug circulant embedding of the ## Covariance matrix 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 nfft = 2 ** nextpow2(max(m2, 2 * ns)) - ACF = r_[ACF, zeros((nfft - m2, 1)), ACF[-1:0:-1, :]] - #disp('Warning: I am now assuming that ACF(k)=0 ') - #disp('for k>MAXLAG.') + acf = r_[acf, zeros((nfft - m2, 1)), acf[-1:0:-1, :]] + #warnings,warn('I am now assuming that ACF(k)=0 for k>MAXLAG.') else: # # ACF(n)==0 m2 = 2 * n - 2 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 - S = fft(ACF, nfft, axis=0).real ## periodogram + S = fft(acf, nfft, axis=0).real ## periodogram I = S.argmax() k = flatnonzero(S < 0) diff --git a/pywafo/src/wafo/doc/tutorial_scripts/chapter1.py b/pywafo/src/wafo/doc/tutorial_scripts/chapter1.py index afd2d19..5d09b9a 100644 --- a/pywafo/src/wafo/doc/tutorial_scripts/chapter1.py +++ b/pywafo/src/wafo/doc/tutorial_scripts/chapter1.py @@ -1,6 +1,28 @@ from scipy 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 #! CHAPTER1 demonstrates some applications of WAFO @@ -26,6 +48,7 @@ S1 = S.tospecdata() S1.plot() show() + ## import wafo.objects as wo xs = S1.sim(ns=2000, dt=0.1) @@ -33,6 +56,7 @@ ts = wo.mat2timeseries(xs) ts.plot_wave('-') show() + #! Estimation of spectrum #!~~~~~~~~~~~~~~~~~~~~~~~ #! A common situation is that one wants to estimate the spectrum for wave @@ -42,19 +66,20 @@ clf() Fs = 4; xs = S1.sim(ns=fix(20 * 60 * Fs), dt=1. / Fs) ts = wo.mat2timeseries(xs) -Sest = ts.tospecdata(NFFT=400) +Sest = ts.tospecdata(L=400) S1.plot() Sest.plot('--') axis([0, 3, 0, 5]) # This may depend on the simulation show() -## Section 1.4.2 Probability distributions of wave characteristics. -## Probability distribution of wave trough period -# WAFO gives the possibility of computing the exact probability -# distributions for a number of characteristics given a spectral density. -# In the following example we study the trough period extracted from the -# time series and compared with the theoretical density computed with exact -# spectrum, S1, and the estimated spectrum, Sest. +#! Section 1.4.2 Probability distributions of wave characteristics. +#!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +#! Probability distribution of wave trough period: +#! WAFO gives the possibility of computing the exact probability +#! distributions for a number of characteristics given a spectral density. +#! In the following example we study the trough period extracted from the +#! time series and compared with the theoretical density computed with exact +#! spectrum, S1, and the estimated spectrum, Sest. clf() import wafo.misc as wm 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 D12 = sm.Spreading(type='cos', theta0=0, method='mitsuyasu') # frequency dependent -#SD1 = mkdspec(S1, D1) -#SD12 = mkdspec(S1, D12); -#plotspec(SD1, plotflag), hold on, plotspec(SD12, plotflag, '-.'); hold off -#wafostamp('', '(ER)') -#disp('Block = 5'), pause(pstate) +SD1 = D1.tospecdata2d(S1) +SD12 = D12.tospecdata2d(S1) +SD1.plot() +SD12.plot()#linestyle='dashdot') +show() #! 3D Simulation of the sea surface #!~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/pywafo/src/wafo/doc/tutorial_scripts/chapter2.py b/pywafo/src/wafo/doc/tutorial_scripts/chapter2.py index ef4291b..e9d4892 100644 --- a/pywafo/src/wafo/doc/tutorial_scripts/chapter2.py +++ b/pywafo/src/wafo/doc/tutorial_scripts/chapter2.py @@ -180,6 +180,7 @@ glc, gemp = lc.trdata(mean=me, sigma=sa) g.plot('r') glc.plot('b-') #! Transf. estimated from level-crossings gh.plot('b-.') #! Hermite Transf. estimated from moments +grid('on') show() #! Test Gaussianity of a stochastic process. @@ -205,7 +206,7 @@ show() #! "light" lower tail. clf() import pylab -ws.probplot(ts.data, dist='norm', plot=pylab) +ws.probplot(ts.data.ravel(), dist='norm', plot=pylab) show() #! Section 2.2.3 Spectral densities of sea data #!----------------------------------------------- diff --git a/pywafo/src/wafo/misc.py b/pywafo/src/wafo/misc.py index c467ebe..48be7a4 100644 --- a/pywafo/src/wafo/misc.py +++ b/pywafo/src/wafo/misc.py @@ -2157,4 +2157,4 @@ if __name__ == "__main__": import doctest doctest.testmod() else: - test_find_cross() + _test_find_cross() diff --git a/pywafo/src/wafo/objects.py b/pywafo/src/wafo/objects.py index e9e9cf1..2ed9622 100644 --- a/pywafo/src/wafo/objects.py +++ b/pywafo/src/wafo/objects.py @@ -197,7 +197,7 @@ class LevelCrossings(WafoData): integral = zeros(u.shape, dtype=float) for i in range(len(integral)): y = factor1 * exp(-u[i] * u[i] * factor2) - integral[i] = trapz(x, y) + integral[i] = trapz(y, x) #end G = G - integral / (2 * pi) G = G / max(G) @@ -215,7 +215,7 @@ class LevelCrossings(WafoData): g = lc2.trdata() - f = [u, u] + #f = [u, u] f = g.dat2gauss(Z) G = TrData(f, u) @@ -320,11 +320,11 @@ class LevelCrossings(WafoData): >>> 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 >>> int(S.tr.dist2gauss()*100) - 593 + 141 >>> int(g0emp.dist2gauss()*100) - 492 + 1544 >>> int(g0.dist2gauss()*100) - 361 + 340 >>> int(g1.dist2gauss()*100) 352 >>> int(g2.dist2gauss()*100) @@ -855,12 +855,13 @@ class TimeSeries(WafoData): c0 = R[0] if norm: R = R / c0 + r0 = R[0] if dt is None: dt = self.sampling_period() t = linspace(0, lag * dt, lag + 1) #cumsum = np.cumsum 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.plot_args_children = ['r:'] acf.norm = norm @@ -910,7 +911,7 @@ class TimeSeries(WafoData): fact = 2.0 * pi w = fact * f 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. @@ -930,9 +931,14 @@ class TimeSeries(WafoData): 'cov' : Frequency smoothing using a parzen window function on the estimated autocovariance function. (default) 'psd' : Welch's averaged periodogram method with no overlapping batches - dflag : string + detrend : function 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 defining frequency type: 'w' or 'f' (default 'w') @@ -968,9 +974,9 @@ class TimeSeries(WafoData): #% Initialize constants #%~~~~~~~~~~~~~~~~~~~~~ - nugget = 0; #%10^-12; + nugget = 1e-12 rate = 2; #% interpolationrate for frequency - tapery = 0; #% taper the data before the analysis + wdef = 1; #% 1=parzen window 2=hanning window, 3= bartlett window dt = self.sampling_period() @@ -981,18 +987,17 @@ class TimeSeries(WafoData): L = min(L, n); max_L = min(300, n); #% maximum lag if L is undetermined - change_L = L is None - if change_L: + estimate_L = L is None + if estimate_L: 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) R = tsy.tocovdata() - if change_L: + if estimate_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 - if wdef == 1: # % modify L so that hanning and Parzen give appr. the same result + 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 L = min(int(4 * L / 3), n - 2) print('The default L is set to %d' % L) try: @@ -1014,9 +1019,9 @@ class TimeSeries(WafoData): Be = None 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, + nfft = 2 ** nextpow2(L) + pad_to = rate*nfft # Interpolate the spectrum with rate + S, f = psd(yy, Fs=1. / dt, NFFT=nfft, detrend=detrend, window=window(nfft), noverlap=noverlap, pad_to=pad_to, scale_by_freq=True) fact = 2.0 * pi w = fact * f @@ -1025,10 +1030,13 @@ class TimeSeries(WafoData): # add a nugget effect to ensure that round off errors # do not result in negative spectral estimates - R.data = R.data[:L] * win[L - 1::] - R.args = R.args[:L] - - spec = R.tospecdata(rate=2, nugget=nugget) + R.data[:L] = R.data[:L] * win[L - 1::] + R.data[L] = 0.0 + R.data = R.data[:L+1] + R.args = R.args[:L+1] + #R.plot() + #R.show() + spec = R.tospecdata(rate=rate, nugget=nugget) spec.Bw = Be if ftype == 'f': @@ -1356,7 +1364,9 @@ class TimeSeries(WafoData): t = self.args[ind] except: 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): """ diff --git a/pywafo/src/wafo/spectrum/core.py b/pywafo/src/wafo/spectrum/core.py index 3483d3f..523fc63 100644 --- a/pywafo/src/wafo/spectrum/core.py +++ b/pywafo/src/wafo/spectrum/core.py @@ -649,20 +649,17 @@ class SpecData1D(WafoData): """ def __init__(self, *args, **kwds): - super(SpecData1D, self).__init__(*args, **kwds) - self.name = 'WAFO Spectrum Object' - self.type = 'freq' - self.freqtype = 'w' + self.name_ = kwds.pop('name', 'WAFO Spectrum Object') + self.type = kwds.pop('type','freq') + self.freqtype = kwds.pop('freqtype','w') self.angletype = '' - self.h = inf - self.tr = None #TrLinear() - self.phi = 0.0 - self.v = 0.0 - self.norm = False - somekeys = ['phi', 'name', 'h', 'tr', 'freqtype', 'v','type', 'norm'] - - self.__dict__.update(sub_dict_select(kwds, somekeys)) - + self.h = kwds.pop('h',inf) + self.tr = kwds.pop('tr',None) #TrLinear() + self.phi = kwds.pop('phi',0.0) + self.v = kwds.pop('v',0.0) + self.norm = kwds.pop('norm',False) + super(SpecData1D, self).__init__(*args, **kwds) + self.setlabels() 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) # 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 r = fft(rper, nfft).real / (2 * n_f - 2) @@ -1194,9 +1191,6 @@ class SpecData1D(WafoData): if self.tr is None: g = TrLinear(var=m[0]) - #y = linspace(-5, 5, 513) - #g = _wafotransform. - #g = TrData(y, sqrt(m[0]) * y) else: g = self.tr @@ -2086,7 +2080,7 @@ class SpecData1D(WafoData): # # opt = troptset(opt,'multip',1) - plotflag = 0 if test0 is None else 1 + plotflag = False if test0 is None else True if cases > 50: print(' ... be patient this may take a while') @@ -2104,9 +2098,6 @@ class SpecData1D(WafoData): ts = TimeSeries(xs[:, iy], xs[:, 0].ravel()) g, tmp = ts.trdata(method, **opt) test1.append(g.dist2gauss()) - #xs = cov2sdat(R,[ns Nstep]); - #[g, tmp] = dat2tr(xs,method, **opt); - #test1 = [test1; tmp(:)] if verbose: print('finished %d of %d ' % (ix + 1, rep)) @@ -2790,6 +2781,8 @@ class SpecData2D(WafoData): """ def __init__(self, *args, **kwds): + + super(SpecData2D, self).__init__(*args, **kwds) self.name = 'WAFO Spectrum Object' diff --git a/pywafo/src/wafo/spectrum/models.py b/pywafo/src/wafo/spectrum/models.py index bd88885..2ae535c 100644 --- a/pywafo/src/wafo/spectrum/models.py +++ b/pywafo/src/wafo/spectrum/models.py @@ -1952,6 +1952,8 @@ class Spreading(object): w = specdata.args S = specdata.data D, phi0 = self(theta, w=w, wc=wc) + if D.ndim != 2: # frequency dependent spreading + D = D[:, None] SD = D * S[None,:] diff --git a/pywafo/src/wafo/transform/core.py b/pywafo/src/wafo/transform/core.py index d08b9ef..392856e 100644 --- a/pywafo/src/wafo/transform/core.py +++ b/pywafo/src/wafo/transform/core.py @@ -70,7 +70,7 @@ class TrCommon(object): ------- t0 : real, scalar 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: xn = linspace(xnmin, xnmax, n) @@ -79,7 +79,7 @@ class TrCommon(object): xn = (x-self.mean)/self.sigma yn = (self._dat2gauss(x)-self.ymean)/self.ysigma - t0 = trapz(xn,(xn-yn)**2.) + t0 = trapz((xn-yn)**2., xn) return t0 def gauss2dat(self, y, *yi): @@ -166,14 +166,18 @@ class TrData(WafoData, TrCommon): True """ def __init__(self, *args, **kwds): - super(TrData, self).__init__(*args, **kwds) - self.labels.title = 'Transform' - self.labels.ylab = 'g(x)' - self.labels.xlab = 'x' - self.ymean = kwds.get('ymean', 0e0) - self.ysigma = kwds.get('ysigma', 1e0) - self.mean = kwds.get('mean', None) - self.sigma = kwds.get('sigma', None) + self.ymean = kwds.pop('ymean', 0e0) + self.ysigma = kwds.pop('ysigma', 1e0) + self.mean = kwds.pop('mean', None) + self.sigma = kwds.pop('sigma', None) + + options = dict(title='Transform', + xlab='x', ylab='g(x)', + plot_args=['r'], + plot_args_children=['g--'],) + options.update(**kwds) + super(TrData, self).__init__(*args, **options) + if self.mean is None: #self.mean = np.mean(self.args) # @@ -182,6 +186,8 @@ class TrData(WafoData, TrCommon): yp = self.ymean+self.ysigma ym = self.ymean-self.ysigma 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): return tranproc(self.data, self.args, y, *yi) diff --git a/pywafo/src/wafo/transform/models.py b/pywafo/src/wafo/transform/models.py index 15d0b7d..815f557 100644 --- a/pywafo/src/wafo/transform/models.py +++ b/pywafo/src/wafo/transform/models.py @@ -61,7 +61,7 @@ class TrCommon2(TrCommon): ------- t0 : real, scalar 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: xn = np.linspace(xnmin, xnmax, n) diff --git a/pywafo/src/wafo/wafodata.py b/pywafo/src/wafo/wafodata.py index 467554f..443d2e0 100644 --- a/pywafo/src/wafo/wafodata.py +++ b/pywafo/src/wafo/wafodata.py @@ -81,7 +81,7 @@ class WafoData(object): if self.children != None: plotbackend.hold('on') 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.update(self.plot_kwds_children) child_kwds.update(**kwds) @@ -91,7 +91,7 @@ class WafoData(object): tmp.append(tmp1) if len(tmp) == 0: 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.update(self.plot_kwds) main_kwds.update(kwds)