Updated documentation

Added more examples
Added more tests

pywafo/src/wafo/integrate.py

@@ -6,9 +6,8 @@ from numpy import pi, sqrt, ones, zeros #@UnresolvedImport
 from scipy import integrate as intg
 import scipy.special.orthogonal as ort
 from scipy import special as sp
-import matplotlib
 import pylab as plb
-matplotlib.interactive(True)
+
 _POINTS_AND_WEIGHTS = {}
 
 def humps(x=None):
@@ -344,7 +343,7 @@ def h_roots(n, method='newton'):
     >>> import numpy as np
     >>> [x,w] = h_roots(10)
     >>> np.sum(x*w)
-    -5.2516042729766621e-019
+    -5.2516042729766621e-19
 
     See also
     --------
@@ -453,7 +452,7 @@ def j_roots(n, alpha, beta, method='newton'):
     --------
     >>> [x,w]= j_roots(10,0,0)
     >>> sum(x*w)
-    2.7755575615628914e-016
+    2.7755575615628914e-16
 
     See also
     --------
@@ -554,7 +553,7 @@ def la_roots(n, alpha=0, method='newton'):
     >>> import numpy as np
     >>> [x,w] = h_roots(10)
     >>> np.sum(x*w)
-    -5.2516042729766621e-019
+    -5.2516042729766621e-19
 
     See also
     --------
@@ -939,7 +938,7 @@ def gaussq(fun, a, b, reltol=1e-3, abstol=1e-3, alpha=0, beta=0, wfun=1,
     integration of x**2        from 0 to 2 and from 1 to 4
 
     >>> from scitools import numpyutils as npt
-    scitools.easyviz backend is gnuplot
+    scitools.easyviz backend is matplotlib
     >>> A = [0, 1]; B = [2,4]
     >>> fun = npt.wrap2callable('x**2')
     >>> [val1,err1] = gaussq(fun,A,B)
@@ -1165,22 +1164,22 @@ def quadgr(fun,a,b,abseps=1e-5):
     >>> import numpy as np
     >>> Q, err = quadgr(np.log,0,1)
     >>> quadgr(np.exp,0,9999*1j*np.pi)
-    (-2.0000000000122617, 2.1933275196062141e-009)
+    (-2.0000000000122617, 2.1933275196062141e-09)
 
     >>> quadgr(lambda x: np.sqrt(4-x**2),0,2,1e-12)
-    (3.1415926535897811, 1.5809575870662229e-013)
+    (3.1415926535897811, 1.5809575870662229e-13)
 
     >>> quadgr(lambda x: x**-0.75,0,1)
-    (4.0000000000000266, 5.6843418860808015e-014)
+    (4.0000000000000266, 5.6843418860808015e-14)
 
     >>> quadgr(lambda x: 1./np.sqrt(1-x**2),-1,1)
-    (3.141596056985029, 6.2146261559092864e-006)
+    (3.141596056985029, 6.2146261559092864e-06)
 
     >>> quadgr(lambda x: np.exp(-x**2),-np.inf,np.inf,1e-9) #% sqrt(pi)
-    (1.7724538509055152, 1.9722334876348668e-011)
+    (1.7724538509055152, 1.9722334876348668e-11)
 
     >>> quadgr(lambda x: np.cos(x)*np.exp(-x),0,np.inf,1e-9)
-    (0.50000000000000044, 7.3296813063450372e-011)
+    (0.50000000000000044, 7.3296813063450372e-11)
 
     See also
     --------
@@ -1418,7 +1417,7 @@ def qdemo(f,a,b):
         #[x, w]=qrule(n,1)
         #x = (b-a)/2*x + (a+b)/2     % Transform base points X.
         #w = (b-a)/2*w               % Adjust weigths.
-        #q = sum(feval(f,x).*w)
+        #q = sum(feval(f,x)*w)
         qg[k] = q
         eg[k] = abs(q - true_val)
 

pywafo/src/wafo/objects.py

@@ -694,8 +694,9 @@ class TimeSeries(WafoData):
     Examples
     --------
     >>> import wafo.data
+    >>> import wafo.objects as wo
     >>> x = wafo.data.sea()
-    >>> ts = wafo.objects.mat2timeseries(x)
+    >>> ts = wo.mat2timeseries(x)
     >>> rf = ts.tocovdata(lag=150)
     >>> h = rf.plot()
 
@@ -771,8 +772,9 @@ class TimeSeries(WafoData):
          Example:
          --------
          >>> import wafo.data
+         >>> import wafo.objects as wo
          >>> x = wafo.data.sea()
-         >>> ts = mat2timeseries(x)
+         >>> ts = wo.mat2timeseries(x)
          >>> acf = ts.tocovdata(150)
          >>> h = acf.plot()
         '''
@@ -814,6 +816,7 @@ class TimeSeries(WafoData):
         acf = _wafocov.CovData1D(R[lags], t)
         acf.stdev = sqrt(r_[ 0, 1 , 1 + 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
         return acf
 

pywafo/src/wafo/spectrum/__init__.py

@@ -8,4 +8,3 @@ Spectrum package in WAFO Toolbox.
 from wafo.spectrum.core import SpecData1D
 import wafo.spectrum.models
 import wafo.spectrum.dispersion_relation
-#from wafo.data_structures import SpecData1D

pywafo/src/wafo/spectrum/core.py

@@ -1,9 +1,9 @@
 from __future__ import division
-from scipy.misc.ppimport import ppimport
+from wafo.misc import meshgrid
 from wafo.objects import mat2timeseries, TimeSeries
 import warnings
 import numpy as np
-from numpy import (pi, inf, meshgrid, zeros, ones, where, nonzero, #@UnresolvedImport
+from numpy import (pi, inf, zeros, ones, where, nonzero, #@UnresolvedImport
            flatnonzero, ceil, sqrt, exp, log, arctan2, #@UnresolvedImport
            tanh, cosh, sinh, random, atleast_1d, maximum, #@UnresolvedImport
            minimum, diff, isnan, any, r_, conj, mod, #@UnresolvedImport
@@ -945,7 +945,8 @@ class SpecData1D(WafoData):
         ...     res = fun(x2[:,1::],axis=0)
         ...     m = res.mean()
         ...     sa = res.std()
-        ...     assert(np.abs(m-trueval)<sa)
+        ...     trueval, m, sa
+        ...     np.abs(m-trueval)<sa
 
         waveplot(x1,'r',x2,'g',1,1)
 
@@ -1316,8 +1317,6 @@ class SpecData1D(WafoData):
         if verbose:
             print('2nd order frequency Limits = %g,%g' % (f_limit_lo, f_limit_up))
 
-
-
 ##        if nargout>3,
 ##        %compute the sum and frequency effects separately
 ##        [svec, dvec] = disufq((amp.'),w,kw,min(h,10^30),g,nmin,nmax)
@@ -1402,7 +1401,10 @@ class SpecData1D(WafoData):
 
         See also
         ---------
-        hermitetr, ochitr, lc2tr, dat2tr
+        transform.TrHermite
+        transform.TrOchi
+        objects.LevelCrossings.trdata
+        objects.TimeSeries.trdata
 
         References:
         -----------
@@ -1429,7 +1431,7 @@ class SpecData1D(WafoData):
         w = ravel(self.args)
         S = ravel(self.data)
         if self.freqtype in ['f', 'w']:
-            vari = 't'
+            #vari = 't'
             if self.freqtype == 'f':
                 w = 2. * pi * w
                 S = S / (2. * pi)
@@ -1514,7 +1516,7 @@ class SpecData1D(WafoData):
 ##            skew = sum((6*C2+8*E2).*E)/sa^3   % skewness
 ##            kurt = 3+48*sum((C2+E2).*E2)/sa^4 % kurtosis
         return output
-    def testgaussian(self, ns,test0=None, cases=100, method='nonlinear',**opt):
+    def testgaussian(self, ns,test0=None, cases=100, method='nonlinear',verbose=False,**opt):
         '''
         TESTGAUSSIAN Test if a stochastic process is Gaussian.
         
@@ -1607,6 +1609,7 @@ class SpecData1D(WafoData):
             #xs = cov2sdat(R,[ns Nstep]);
             #[g, tmp] = dat2tr(xs,method, **opt);
             #test1 = [test1; tmp(:)]
+            if verbose:
                 print('finished %d of %d ' % (ix+1,rep) )
         
         if rep>1:
@@ -1704,6 +1707,14 @@ class SpecData1D(WafoData):
     def nyquist_freq(self):
         """
         Return Nyquist frequency
+        
+        Example
+        -------
+        >>> import wafo.spectrum.models as sm
+        >>> Sj = sm.Jonswap(Hm0=5)
+        >>> S = Sj.tospecdata() #Make spectrum ob
+        >>> S.nyquist_freq()
+        3.0
         """
         return self.args[-1]
     
@@ -1722,8 +1733,11 @@ class SpecData1D(WafoData):
 
         Example
         -------
-        S = jonswap
+        >>> import wafo.spectrum.models as sm
-        dt = spec2dt(S)
+        >>> Sj = sm.Jonswap(Hm0=5)
+        >>> S = Sj.tospecdata() #Make spectrum ob
+        >>> S.sampling_period()
+        1.0471975511965976
 
         See also
         '''
@@ -1880,9 +1894,16 @@ class SpecData1D(WafoData):
     
         Example:
         ------- 
-        S = jonswap
+        >>> import wafo.spectrum.models as sm
-        [Sn,mn4] = specnorm(S)
+        >>> Sj = sm.Jonswap(Hm0=5)
-        mts = spec2mom(S,2)     % Should be equal to one!
+        >>> S = Sj.tospecdata() #Make spectrum ob
+        >>> S.moment(2)
+        ([1.5614600345079888, 0.95567089481941048], ['m0', 'm0tt'])
+        >>> Sn = S.copy(); Sn.normalize()
+        
+        Now the moments should be one
+        >>> Sn.moment(2)
+        ([1.0000000000000004, 0.99999999999999967], ['m0', 'm0tt'])
         '''
         mom, unused_mtext = self.moment(nr=4, even=True)
         m0 = mom[0] 
@@ -2018,7 +2039,6 @@ class SpecData1D(WafoData):
 
         Examples:
         ---------
-        >>> import numpy as np
         >>> import wafo.spectrum.models as sm
         >>> Sj = sm.Jonswap(Hm0=5)
         >>> S = Sj.tospecdata() #Make spectrum ob

pywafo/src/wafo/spectrum/models.py

@@ -53,8 +53,6 @@ from numpy import (inf, atleast_1d, newaxis, any, minimum, maximum, array, #@Unr
     isfinite, mod, expm1, tanh, cosh, finfo, ones, ones_like, isnan, #@UnresolvedImport
     zeros_like, flatnonzero, sinc, hstack, vstack, real, flipud, clip) #@UnresolvedImport
 from dispersion_relation import w2k
-#ppimport.enable()
-#_wafospectrum = ppimport.ppimport('wafo.spectrum')
 from wafo.spectrum import SpecData1D
 sech = lambda x: 1.0 / cosh(x)
 
@@ -638,8 +636,6 @@ class Jonswap(ModelSpectrum):
 def phi1(wi, h, g=9.81):
     ''' Factor transforming spectra to finite water depth spectra.
 
-     CALL: tr = phi1(w,h)
-
     Input
     -----
          w : arraylike
@@ -743,7 +739,8 @@ class Tmaspec(Jonswap):
     phi1,
     Torsethaugen
 
-    References:
+    References
+    ----------
     Buows, E., Gunther, H., Rosenthal, W., and Vincent, C.L. (1985)
     'Similarity of the wind wave spectrum in finite depth water: 1 spectral form.'
     J. Geophys. Res., Vol 90, No. C1, pp 975-986
@@ -1361,8 +1358,8 @@ class Spreading(object):
     (Hasselman: spa ~= spb)  (cos-2s) [6.97 9.77  4.06 -2.3  0    1.05 3  ]
     (Banner   : spa ~= spb)  (sech2)  [2.61 2.28  1.3  -1.3  0.56 0.95 1.6]
 
-    Examples :
+    Examples
-
+    --------
     >>> import pylab as plb
     >>> D = Spreading('cos2s',s_a=10.0)
 
@@ -1385,9 +1382,12 @@ class Spreading(object):
     >>> plb.close('all')
 
 
-    See also  mkdspec, plotspec, spec2spec
+    See also
+    --------
+    mkdspec, plotspec, spec2spec
 
     References
+    ---------
     Krogstad, H.E. and Barstow, S.F. (1999)
     "Directional Distributions in Ocean Wave Spectra"
     Proceedings of the 9th ISOPE Conference, Vol III, pp. 79-86

pywafo/src/wafo/transform/core.py

@@ -159,7 +159,7 @@ class TrData(WafoData, TrCommon):
     >>> g.sigma
     5
     >>> g.dat2gauss(1,2,3)
-    
+    [array([ 0.]), array([ 0.4]), array([ 0.6])]
     
     Check that the departure from a Gaussian model is zero
     >>> g.dist2gauss() < 1e-16

pywafo/src/wafo/transform/models.py

@@ -3,6 +3,7 @@ Transform Gaussian models
 -------------------------
 TrHermite
 TrOchi
+TrLinear
 '''
 #-------------------------------------------------------------------------------
 # Name:        transform.models
@@ -84,7 +85,10 @@ class TrHermite(TrCommon):
     
     See also
     --------  
-    spec2skew, ochitr, lc2tr, dat2tr
+    SpecData1d.stats_nl
+    wafo.transform.TrOchi
+    wafo.objects.LevelCrossings.trdata
+    wafo.objects.TimeSeries.trdata
 
     References
     ----------
@@ -306,6 +310,11 @@ class TrLinear(TrCommon):
     
     See also
     --------  
+    TrOchi
+    TrHermite
+    SpecData1D.stats_nl
+    LevelCrossings.trdata
+    TimeSeries.trdata
     spec2skew, ochitr, lc2tr, dat2tr
 
     """

pywafo/src/wafo/transform/test/test_models.py

@@ -0,0 +1,47 @@
+from wafo.transform.models import TrHermite, TrOchi, TrLinear
+
+def test_trhermite():
+    '''
+    >>> std = 7./4
+    >>> g = TrHermite(sigma=std, ysigma=std)
+    >>> g.dist2gauss()
+    3.9858776379926808
+    
+    >>> g.mean
+    0.0
+    >>> g.sigma
+    1.75
+    >>> g.dat2gauss([0,1,2,3])
+    array([ 0.04654321,  1.03176393,  1.98871279,  2.91930895])
+    
+    '''
+def test_trochi():
+    '''
+    >>> std = 7./4
+    >>> g = TrOchi(sigma=std, ysigma=std)
+    >>> g.dist2gauss()
+    5.9322684525265501
+    >>> g.mean
+    0.0
+    >>> g.sigma
+    1.75
+    >>> g.dat2gauss([0,1,2,3])
+    array([  6.21927960e-04,   9.90237621e-01,   1.96075606e+00,
+             2.91254576e+00])
+    '''
+def test_trlinear():
+    '''
+    >>> std = 7./4
+    >>> g = TrLinear(sigma=std, ysigma=std)
+    >>> g.dist2gauss()
+    0.0
+    >>> g.mean
+    0.0
+    >>> g.sigma
+    1.75
+    >>> g.dat2gauss([0,1,2,3])
+    array([ 0.,  1.,  2.,  3.])
+    '''
+if __name__=='__main__':
+    import doctest
+    doctest.testmod()

pywafo/src/wafo/transform/test/test_trdata.py

@@ -0,0 +1,32 @@
+from wafo.transform import TrData
+
+def test_trdata():
+    '''
+    Construct a linear transformation model
+    >>> import numpy as np
+    >>> sigma = 5; mean = 1
+    >>> u = np.linspace(-5,5); x = sigma*u+mean; y = u
+    >>> g = TrData(y,x)
+    >>> g.mean
+    array([ 1.])
+    >>> g.sigma
+    array([ 5.])
+    
+    >>> g = TrData(y,x,mean=1,sigma=5)
+    >>> g.mean
+    1
+    >>> g.sigma
+    5
+    >>> g.dat2gauss(1,2,3)
+    [array([ 0.]), array([ 0.4]), array([ 0.6])]
+    >>> g.dat2gauss([0,1,2,3])
+    array([-0.2,  0. ,  0.2,  0.4])
+    
+    Check that the departure from a Gaussian model is zero
+    >>> g.dist2gauss() < 1e-16
+    True
+    '''
+    
+if __name__=='__main__':
+    import doctest
+    doctest.testmod()