Small bugfixes

pywafo/.project

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <projectDescription>
-	<name>PYWAFO</name>
+	<name>google_pywafo</name>
 	<comment></comment>
 	<projects>
 	</projects>

pywafo/.pydevproject

@@ -3,7 +3,7 @@
 
 <pydev_project>
 <pydev_pathproperty name="org.python.pydev.PROJECT_SOURCE_PATH">
-<path>/PYWAFO/src</path>
+<path>/google_pywafo/src</path>
 </pydev_pathproperty>
 <pydev_property name="org.python.pydev.PYTHON_PROJECT_VERSION">python 2.6</pydev_property>
 <pydev_property name="org.python.pydev.PYTHON_PROJECT_INTERPRETER">Default</pydev_property>

pywafo/setup.py

@@ -17,6 +17,7 @@ python setup.py sdist bdist_wininst upload --show-response
 import os, sys
 
 sys.argv.append("develop")
+#sys.argv.append("install")
 DISTUTILS_DEBUG = True
 pkg_name = 'wafo'
 root_dir = os.path.join('src',pkg_name)
@@ -44,7 +45,9 @@ testscripts = [os.path.join(subtst, f) for subtst in subtests
                        f.endswith('.old') or f.endswith('.bak'))]
 datadir = 'data'
 datafiles = [os.path.join(datadir, f)   for f in os.listdir(os.path.join(root_dir, datadir))
-				if  not (f.endswith('.py') or f.endswith('test') )]
+				if  not (f.startswith('.') or f.endswith('~') or
+                       f.endswith('.old') or f.endswith('.bak') or 
+                       f.endswith('.py') or f.endswith('test') )]
 libs = [f   for f in os.listdir(os.path.join(root_dir)) if  f.endswith('.pyd') ]
 packagedata = testscripts + datafiles + libs #['c_library.pyd'] #,'disufq1.c','diffsumfunq.pyd','diffsumfunq.pyf','findrfc.c','rfc.pyd','rfc.pyf']
 

pywafo/src/Wafo.egg-info/PKG-INFO

@@ -1,7 +1,12 @@
 Metadata-Version: 1.0
-Name: WAFO
+Name: wafo
 Version: 0.11
-Summary: 
+Summary: UNKNOWN
+Home-page: http://www.maths.lth.se/matstat/wafo/
+Author: WAFO-group
+Author-email: wafo@maths.lth.se
+License: GPL
+Description: 
         WAFO
         =====
         WAFO is a toolbox Python routines for statistical analysis and simulation of random waves and random loads.
@@ -84,9 +89,4 @@ Statistics
         - Reported bugs.
         - List of publications related to WAFO.
         
-Home-page: http://www.maths.lth.se/matstat/wafo/
-Author: WAFO-group
-Author-email: wafo@maths.lth.se
-License: GPL
-Description: UNKNOWN
 Platform: UNKNOWN

pywafo/src/Wafo.egg-info/top_level.txt

@@ -1,5 +1 @@
-wafo\spectrum
-wafo\covariance
 wafo
-wafo\data
-wafo\transform

pywafo/src/wafo/interpolate.py

@@ -19,7 +19,7 @@ from numpy.lib.shape_base import vstack
 from numpy.lib.function_base import linspace
 import polynomial as pl
 
-class PPform1(object):
+class PPform(object):
     """The ppform of the piecewise polynomials is given in terms of coefficients
     and breaks.  The polynomial in the ith interval is
     x_{i} <= x < x_{i+1}

pywafo/src/wafo/kdetools.py

@@ -9,7 +9,7 @@
 # Licence:     LGPL
 #-------------------------------------------------------------------------------
 #!/usr/bin/env python
-#import numpy as np
+import numpy as np
 from scipy.special import gamma
 from numpy import pi, atleast_2d #@UnresolvedImport
 from misc import tranproc, trangood
@@ -112,29 +112,29 @@ class kde(object):
         if d != self.d:
             if d == 1 and m == self.d:
                 # points was passed in as a row vector
-                points = reshape(points, (self.d, 1))
+                points = np.reshape(points, (self.d, 1))
                 m = 1
             else:
                 msg = "points have dimension %s, dataset has dimension %s" % (d,
                     self.d)
                 raise ValueError(msg)
 
-        result = zeros((m,), points.dtype)
+        result = np.zeros((m,), points.dtype)
 
         if m >= self.n:
             # there are more points than data, so loop over data
             for i in range(self.n):
-                diff = self.dataset[:,i,newaxis] - points
+                diff = self.dataset[:,i, np.newaxis] - points
-                tdiff = dot(self.inv_cov, diff)
+                tdiff = np.dot(self.inv_cov, diff)
-                energy = sum(diff*tdiff,axis=0)/2.0
+                energy = np.sum(diff*tdiff,axis=0)/2.0
-                result += exp(-energy)
+                result += np.exp(-energy)
         else:
             # loop over points
             for i in range(m):
-                diff = self.dataset - points[:,i,newaxis]
+                diff = self.dataset - points[:,i,np.newaxis]
-                tdiff = dot(self.inv_cov, diff)
+                tdiff = np.dot(self.inv_cov, diff)
                 energy = sum(diff*tdiff,axis=0)/2.0
-                result[i] = sum(exp(-energy),axis=0)
+                result[i] = np.sum(np.exp(-energy),axis=0)
 
         result /= self._norm_factor
 

pywafo/src/wafo/misc.py

@@ -1779,7 +1779,7 @@ def tranproc(x, f, x0, * xi):
                     y.append(y4)
                     if N > 4:
                         warnings.warn('Transformation of derivatives of order>4 not supported.')
-    return y #0,y1,y2,y3,y4
+    return y #y0,y1,y2,y3,y4
 
 
 def test_common_shape():

pywafo/src/wafo/objects.py

@@ -13,10 +13,14 @@
 
 
 from __future__ import division
+from wafo.transform.core import TrData
+from scipy.interpolate.interpolate import interp1d
+from scipy.integrate.quadrature import cumtrapz
+from wafo.interpolate import SmoothSpline
 import warnings
 import numpy as np
 
-from numpy import (inf, pi, zeros, ones, sqrt, where, log, exp, sin, arcsin, mod, #@UnresolvedImport
+from numpy import (inf, pi, zeros, ones, sqrt, where, log, exp, sin, arcsin, mod, finfo, interp, #@UnresolvedImport
                    newaxis, linspace, arange, sort, all, abs, linspace, vstack, hstack, atleast_1d, #@UnresolvedImport
                    polyfit, r_, nonzero, cumsum, ravel, size, isnan, nan, floor, ceil, diff, array) #@UnresolvedImport
 from numpy.fft import fft
@@ -26,10 +30,11 @@ from pylab import stineman_interp
 from matplotlib.mlab import psd
 import scipy.signal
 
-from scipy.special import erf
+
+from scipy.special import erf, ndtri
 
 from wafo.misc import (nextpow2, findtp, findtc, findcross, sub_dict_select,
-                       ecross, JITImport)
+                       ecross, JITImport, DotDict)
 from wafodata import WafoData
 from plotbackend import plotbackend
 import matplotlib
@@ -177,18 +182,23 @@ class LevelCrossings(WafoData):
 
         Z = ((u >= 0) * 2 - 1) * sqrt(-2 * log(G))
 
-##        sumcr   = trapz(lc(:,1),lc(:,2))
+        sumcr = trapz(self.data, self.args)
-##        lc(:,2) = lc(:,2)/sumcr
+        lc = self.data / sumcr
-##        mcr     = trapz(lc(:,1),lc(:,1).*lc(:,2))
+        lc1 = self.args
-##        scr     = trapz(lc(:,1),lc(:,1).^2.*lc(:,2))
+        mcr = trapz(lc1 * lc, lc1)
-##        scr     = sqrt(scr-mcr^2)
+        scr = trapz(lc1 ** 2 * lc, lc1)
-##        g       = lc2tr(lc,mcr,scr)
+        scr = sqrt(scr - mcr ** 2)
-##
+        
-##        f = [u u]
+        lc2 = LevelCrossings(lc, lc1, mean=mcr, stdev=scr)
-##        f(:,2) = tranproc(Z,fliplr(g))
+        
-##
+        g = lc2.trdata()
-##        process = tranproc(L,f)
+
-##        process = [(1:length(process)) process]
+        f = [u, u]
+        f = g.dat2gauss(Z)
+        G = TrData(f, u)
+        
+        process = G.dat2gauss(L)
+        return np.vstack((arange(len(process)), process)).T
 ##
 ##
 ##        %Check the result without reference to getrfc:
@@ -224,36 +234,40 @@ class LevelCrossings(WafoData):
 
         Parameters
         ----------
-           options = structure with the fields:
+        mean, sigma : real scalars
-          csm, gsm  - defines the smoothing of the crossing intensity and the
+            mean and standard deviation of the process
-                     transformation g. Valid values must be
+        **options :
-                    0<=csm,gsm<=1. (default csm = 0.9 gsm=0.05)
+        csm, gsm : real scalars
+            defines the smoothing of the crossing intensity and the transformation g. 
+            Valid values must be 0<=csm,gsm<=1. (default csm = 0.9 gsm=0.05)
             Smaller values gives smoother functions.
-             param - vector which defines the region of variation of the data X.
+        param : 
+            vector which defines the region of variation of the data X.
                      (default [-5 5 513]).    
+        monitor : bool
+            if true monitor development of estimation
+        linextrap : bool
+            if true use a smoothing spline with a constraint on the ends to 
+            ensure linear extrapolation outside the range of the data. (default)
+            otherwise use a regular smoothing spline
+        cvar, gvar : real scalars
+            Variances for the crossing intensity and the empirical transformation, g. (default  1) 
+        ne : scalar integer
+            Number of extremes (maxima & minima) to remove from the estimation 
+            of the transformation. This makes the estimation more robust against 
+            outliers. (default 7)
+        ntr :  scalar integer
+            Maximum length of empirical crossing intensity. The empirical 
+            crossing intensity is interpolated linearly  before smoothing if the
+             length exceeds ntr. A reasonable NTR will significantly speed up the
+            estimation for long time series without loosing any accuracy. 
+            NTR should be chosen greater than PARAM(3). (default 1000)
             
-          monitor            monitor development of estimation
-         linextrap - 0 use a regular smoothing spline 
-                     1 use a smoothing spline with a constraint on the ends to 
-                       ensure linear extrapolation outside the range of the data.
-                       (default)
-         cvar      - Variances for the crossing intensity. (default 1)
-         gvar      - Variances for the empirical transformation, g. (default  1) 
-         ne        - Number of extremes (maxima & minima) to remove from the
-                      estimation of the transformation. This makes the
-                      estimation more robust against outliers. (default 7)
-         Ntr        - Maximum length of empirical crossing intensity.
-                      The empirical crossing intensity is interpolated
-                      linearly  before smoothing if the length exceeds Ntr.
-                      A reasonable NTR will significantly speed up the
-                      estimation for long time series without loosing any
-                      accuracy. NTR should be chosen greater than
-                      PARAM(3). (default 1000)
         Returns
         -------
         gs, ge : TrData objects 
             smoothed and empirical estimate of the transformation g.     
-             ma,sa = mean and standard deviation of the process
+             
         
         Notes
         -----
@@ -268,15 +282,21 @@ class LevelCrossings(WafoData):
         
         Example
         -------
-        Hm0 = 7
+        >>> import wafo.spectrum.models as sm
-        S = jonswap([],Hm0); g=ochitr([],[Hm0/4]); 
+        >>> import wafo.transform.models as tm
-         S.tr = g; S.tr(:,2)=g(:,2)*Hm0/4;
+        >>> Hs = 7.0
-         xs = spec2sdat(S,2^13);
+        >>> Sj = sm.Jonswap(Hm0=Hs)
-         lc = dat2lc(xs)
+        >>> S = Sj.tospecdata()   #Make spectrum object from numerical values
-         g0 = lc2tr2(lc,0,Hm0/4,'plot','iter');         % Monitor the development
+        >>> S.tr = tm.TrOchi(mean=0, skew=sk, kurt=0, sigma=Hs/4, ysigma=Hs/4)
-         g1 = lc2tr2(lc,0,Hm0/4,troptset('gvar', .5 )); % Equal weight on all points
+        >>> xs = S.sim(ns=2**13)
-         g2 = lc2tr2(lc,0,Hm0/4,'gvar', [3.5 .5 3.5]);  % Less weight on the ends
+        >>> ts = mat2timeseries(xs)
-         hold on, trplot(g1,g)                          % Check the fit
+        >>> tp = ts.turning_points()
+        >>> mm = tp.cycle_pairs()
+        >>> lc = mm.level_crossings()  
+        >>> 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
+         hold on, trplot(g1,g)                          # Check the fit
          trplot(g2)
         
          See also  troptset, dat2tr, trplot, findcross, smooth
@@ -298,138 +318,108 @@ class LevelCrossings(WafoData):
         # by pab 29.12.2000
         # based on lc2tr, but the inversion is faster.
         # by IR and PJ
-        pass
+        if mean is None:
+            mean = self.mean
+        if sigma is None:
+            sigma = self.stdev
+        
+        opt = DotDict(chkder=True, plotflag=False, csm=0.9, gsm=.05,
+            param=(-5, 5, 513), delay=2, lin_extrap=True, ntr=1000, ne=7, cvar=1, gvar=1)
+        # If just 'defaults' passed in, return the default options in g
+        
+        opt.update(options)
+        param = opt.param
+        Ne = opt.ne
+        
+        ncr = len(self.data)
+        if ncr > opt.ntr and opt.ntr > 0:
+            x0 = linspace(self.args[Ne], self.args[-1 - Ne], opt.ntr)
+            lc1, lc2 = x0, interp(x0, self.args, self.data)
+            Ne = 0
+            Ner = opt.ne
+            ncr = opt.ntr
+        else:
+            Ner = 0
+            lc1, lc2 = self.args, self.data        
+        ng = len(opt.gvar)
+        if ng == 1:
+            gvar = opt.gvar * ones(ncr)
+        else:
+            gvar = interp1d(linspace(0, 1, ng) , opt.gvar, kind='linear')( linspace(0, 1, ncr))  
+        
+        ng = len(opt.cvar)
+        if ng == 1:
+            cvar = opt.cvar * ones(ncr)
+        else:
+            cvar = interp1d(linspace(0, 1, ng), opt.cvar, kind='linear')( linspace(0, 1, ncr))  
+        
+        
+       
+        uu = linspace(*param)
+        
+        g1 = sigma * uu + mean
+        
+        g22 = lc2.copy() 
+        
+        if Ner > 0: # Compute correction factors
+            cor1 = trapz(lc2[0:Ner + 1], lc1[0:Ner + 1])
+            cor2 = trapz(lc2[-Ner - 1::], lc1[-Ner - 1::])
+        else:
+            cor1 = 0
+            cor2 = 0
+        
+    
+        lc22 = cumtrapz(lc2, lc1) + cor1
+        lc22 = (lc22 + 0.5) / (lc22[-1] + cor2 + 1)
+        lc11 = (lc1 - mean) / sigma
+        
+        # find the mode
+        imin = abs(lc22 - 0.15).argmin()
+        imax = abs(lc22 - 0.85).argmin()
+        
+        inde = slice(imin, imax + 1)
+        lc222 = SmoothSpline(lc11[inde], g22[inde], opt.csm, opt.lin_extrap, cvar[inde])(lc11[inde])
         
-#        opt = troptset('chkder','on','plotflag','off','csm',.9,'gsm',.05,....
-#            'param',[-5 5 513],'delay',2,'linextrap','on','ntr',1000,'ne',7,'cvar',1,'gvar',1);
-#        # If just 'defaults' passed in, return the default options in g
-#        if nargin==1 && nargout <= 1 && isequal(cross,'defaults')
-#          g = opt; 
-#          return
-#        end
-#        error(nargchk(3,inf,nargin)) 
-#        if nargin>=4 ,  opt  = troptset(opt,varargin{:}); end
-#        csm2 = opt.gsm;
-#        param = opt.param;
-#        ptime = opt.delay;
-#        Ne  = opt.ne;
-#        switch opt.chkder;
-#          case 'off', chkder = 0;
-#          case 'on',  chkder = 1;
-#          otherwise,  chkder = opt.chkder;
-#        end
-#        switch opt.linextrap;
-#          case 'off', def = 0;
-#          case 'on',  def = 1;
-#          otherwise,  def = opt.linextrap;
-#        end
-#        switch opt.plotflag
-#          case {'none','off'},   plotflag = 0;
-#          case 'final', plotflag = 1;
-#          case 'iter',  plotflag = 2;
-#          otherwise,    plotflag = opt.plotflag;
-#        end
-#        ncr = length(cross);
-#        if ncr>opt.ntr && opt.ntr>0,
-#           x0 = linspace(cross(1+Ne,1),cross(end-Ne,1),opt.ntr)';
-#           cros = [ x0,interp1q(cross(:,1),cross(:,2),x0)];
-#           Ne = 0;
-#           Ner = opt.ne;
-#           ncr = opt.ntr;
-#         else
-#           Ner = 0;
-#          cros=cross;
-#        end
-#        
-#        ng = length(opt.gvar);
-#        if ng==1
-#          gvar = opt.gvar(ones(ncr,1));
-#        else
-#          gvar = interp1(linspace(0,1,ng)',opt.gvar(:),linspace(0,1,ncr)','*linear');  
-#        end
-#        ng = length(opt.cvar);
-#        if ng==1
-#          cvar = opt.cvar(ones(ncr,1));
-#        else
-#          cvar = interp1(linspace(0,1,ng)',opt.cvar(:),linspace(0,1,ncr)','*linear');  
-#        end
-#        
-#        g = zeros(param(3),2);
-#        
-#        uu = levels(param);
-#        
-#        g(:,1) = sa*uu' + ma;
-#        
-#        g2   = cros;
-#        
-#        if Ner>0, # Compute correction factors
-#         cor1 = trapz(cross(1:Ner+1,1),cross(1:Ner+1,2));
-#         cor2 = trapz(cross(end-Ner-1:end,1),cross(end-Ner-1:end,2));
-#        else
-#          cor1 = 0;
-#          cor2 = 0;
-#        end
-#        cros(:,2) = cumtrapz(cros(:,1),cros(:,2))+cor1;
-#        cros(:,2) = (cros(:,2)+.5)/(cros(end,2) + cor2 +1);
-#        cros(:,1) = (cros(:,1)-ma)/sa;
-#        
-#        # find the mode
-#        [tmp,imin]= min(abs(cros(:,2)-.15));
-#        [tmp,imax]= min(abs(cros(:,2)-.85));
-#        inde = imin:imax;
         #tmp = smooth(cros(inde,1),g2(inde,2),opt.csm,cros(inde,1),def,cvar(inde));
-#        
-#        [tmp imax] = max(tmp);
-#        u0 = cros(inde(imax),1);
-#        #u0 = interp1q(cros(:,2),cros(:,1),.5)
-#        
-#        
-#        cros(:,2) = invnorm(cros(:,2),-u0,1);
-#        
-#        g2(:,2)   = cros(:,2);
-#        # NB! the smooth function does not always extrapolate well outside the edges
-#        # causing poor estimate of g  
-#        # We may alleviate this problem by: forcing the extrapolation
-#        # to be linear outside the edges or choosing a lower value for csm2.
-#        
-#        inds = 1+Ne:ncr-Ne;# indices to points we are smoothing over
-#        scros2 = smooth(cros(inds,1),cros(inds,2),csm2,uu,def,gvar(inds));
-#        
-#        g(:,2) = scros2';#*sa; #multiply with stdev 
-#        
-#        if chkder~=0
-#           for ix = 1:5
-#            dy = diff(g(:,2));
-#            if any(dy<=0)
-#              warning('WAFO:LCTR2','The empirical crossing spectrum is not sufficiently smoothed.')
-#              disp('        The estimated transfer function, g, is not ')
-#              disp('        a strictly increasing function.')
-#              dy(dy>0)=eps;
-#              gvar = -([dy;0]+[0;dy])/2+eps;
-#              g(:,2) = smooth(g(:,1),g(:,2),1,g(:,1),def,ix*gvar);
-#            else 
-#              break
-#            end
-#          end
-#        end
-#        if 0,  #either
-#          test = sqrt((param(2)-param(1))/(param(3)-1)*sum((uu-scros2).^2));
-#        else # or
-#          #test=sqrt(simpson(uu,(uu-scros2).^2));
-#        # or
-#          test=sqrt(trapz(uu,(uu-scros2).^2));
-#        end 
-#        
-#        
-#        if plotflag>0, 
-#          trplot(g ,g2,ma,sa)
-#          #legend(['Smoothed (T='  num2str(test) ')'],'g(u)=u','Not smoothed',0)
-#          #ylabel('Transfer function g(u)')
-#          #xlabel('Crossing level u')
-#          
-#          if plotflag>1,pause(ptime),end
-#        end
         
+        imax = lc222.argmax()
+        u0 = lc22[inde][imax]
+        #u0 = interp1q(cros(:,2),cros(:,1),.5)
+        
+        
+        lc22 = ndtri(lc22)-u0 #invnorm(lc22, -u0, 1);
+        
+        g2 = TrData(lc22.copy(), lc1.copy(), mean, sigma**2)
+        # NB! the smooth function does not always extrapolate well outside the edges
+        # causing poor estimate of g  
+        # We may alleviate this problem by: forcing the extrapolation
+        # to be linear outside the edges or choosing a lower value for csm2.
+        
+        inds = slice(Ne, ncr - Ne) # indices to points we are smoothing over
+        scros2 = SmoothSpline(lc11[inds], lc22[inds], opt.gsm, opt.lin_extrap, gvar[inds])(uu)
+        
+        g = TrData(scros2, g1, mean, sigma**2)  #*sa; #multiply with stdev 
+        
+        if opt.chkder:
+            for ix in range(5):
+                dy = diff(g.data)
+                if any(dy <= 0):
+                    warnings.warn(
+                    ''' The empirical crossing spectrum is not sufficiently smoothed.
+                        The estimated transfer function, g, is not a strictly increasing function.
+                    ''')
+                    eps = finfo(float).eps
+                    dy[dy > 0] = eps
+                    gvar = -(hstack((dy, 0)) + hstack((0, dy))) / 2 + eps
+                    g.data = SmoothSpline(g.args, g.data, 1, opt.lin_extrap, ix * gvar)(g.args)
+                else: 
+                    break
+        
+        if opt.plotflag > 0:
+            g.plot()
+            g2.plot()
+            
+        return g, g2
 
 class CyclePairs(WafoData):
     '''
@@ -707,6 +697,7 @@ class TimeSeries(WafoData):
             n = len(self.data)
             self.args = range(0, n)
     
+   
     def sampling_period(self):
         '''
         Returns sampling interval
@@ -1330,7 +1321,7 @@ def sensortypeid(*sensortypes):
     >>> sensortypeid('W','v')
     [11, 10]
     >>> sensortypeid('rubbish')
-    [1.#QNAN]
+    [nan]
 
     See also 
     --------

pywafo/src/wafo/source/c_codes/build_all.py

@@ -5,10 +5,10 @@ import os
 
 def compile_all():
     # Install gfortran and run the following to build the module:
-    #compile_format = 'f2py %s %s -c --fcompiler=gnu95 --compiler=mingw32 -lmsvcr71'
+    compile_format = 'f2py.py %s %s -c --fcompiler=gnu95 --compiler=mingw32 -lmsvcr71'
 
     # Install microsoft visual c++ .NET 2003 and run the following to build the module:
-    compile_format = 'f2py.py %s %s -c'
+    #compile_format = 'f2py.py %s %s -c'
     pyfs = ('c_library.pyf',)
     files =('c_functions.c',)
 

pywafo/src/wafo/spectrum/core.py

@@ -16,16 +16,15 @@ from pylab import stineman_interp
 
 from dispersion_relation import w2k #, k2w
 from wafo.wafodata import WafoData, now
-from wafo.misc import sub_dict_select, nextpow2, discretize, JITImport
+from wafo.misc import sub_dict_select, nextpow2, discretize, JITImport, tranproc
 try:
     from wafo.gaussian import Rind
 except ImportError:
-    
     Rind = None
 try:
     from wafo import c_library
 except ImportError:
-    warnings.warn('Compile the c_libraray.pyd again!')
+    warnings.warn('Compile the c_library.pyd again!')
     c_library = None
     
 from wafo.transform import TrData
@@ -751,8 +750,7 @@ class SpecData1D(WafoData):
         tn = paramt[1]
         Ntime = paramt[2]
         t = linspace(0, tn / A, Ntime) #normalized times
-        Nstart = max(round(t0 / tn * (Ntime - 1)), 1)  #% index to starting point to
+        Nstart = max(round(t0 / tn * (Ntime - 1)), 1)  #% index to starting point to evaluate
-                                             #% evaluate
                                     
         dt = t[1] - t[0]
         nr = 2
@@ -1055,16 +1053,22 @@ class SpecData1D(WafoData):
         if spec.tr is not None:
             print('   Transforming data.')
             g = spec.tr
-            G = fliplr(g) #% the invers of g
             if derivative:
                 for i in range(cases):
-                    tmp = tranproc(hstack((x[:, i + 1], xder[:, i + 1])), G)
+                    x[:, i + 1], xder[:, i + 1] = g.gauss2dat(x[:, i + 1], xder[:, i + 1])
-                    x[:, i + 1] = tmp[:, 0]
-                    xder[:, i + 1] = tmp[:, 1]
-
             else:
                 for i in range(cases):
-                    x[:, i + 1] = tranproc(x[:, i + 1], G)
+                    x[:, i + 1] = g.gauss2dat(x[:, i + 1])
+#            G = fliplr(g) #% the invers of g
+#            if derivative:
+#                for i in range(cases):
+#                    tmp = tranproc(hstack((x[:, i + 1], xder[:, i + 1])), G)
+#                    x[:, i + 1] = tmp[:, 0]
+#                    xder[:, i + 1] = tmp[:, 1]
+#
+#            else:
+#                for i in range(cases):
+#                    x[:, i + 1] = tranproc(x[:, i + 1], G)
 
         if derivative:
             return x, xder

pywafo/src/wafo/transform/core.py

@@ -153,12 +153,19 @@ class TrData(WafoData, TrCommon):
     >>> g.sigma
     array([ 5.])
     
+    >>> g = TrData(y,x,mean=1,sigma=5)
+    >>> g.mean
+    1
+    >>> g.sigma
+    5
+    
+    
     Check that the departure from a Gaussian model is zero
     >>> g.dist2gauss() < 1e-16
     True
     """
     def __init__(self, *args, **kwds):
-        WafoData.__init__(self, *args, **kwds)
+        super(TrData, self).__init__(*args, **kwds)
         self.labels.title = 'Transform'
         self.labels.ylab = 'g(x)'
         self.labels.xlab = 'x'