Fixed doctests

setup.cfg

@@ -76,8 +76,10 @@ addopts =
 norecursedirs =
     .*
     _build
+    doc
     tmp*
     source
+    stats  # postpone testing of stats
 
 pep8ignore =
     tests/*.py ALL

wafo/conftest.py

@@ -1,2 +1,3 @@
 # TODO Fix doctests in fig.py
-collect_ignore = ["fig.py", "MSO.py", "MSPPT.py"]
+collect_ignore = ["fig.py", "MSO.py", "MSPPT.py", "powerpoint.py",
+                  "win32_utils.py"]

wafo/containers.py

@@ -130,8 +130,9 @@ class PlotData(object):
         >>> d = PlotData(np.sin(x), x, xlab='x', ylab='sin', title='sinus',
         ...                plot_args=['r.'])
         >>> di = PlotData(d.eval_points(xi), xi)
-        >>> hi = di.plot()
-        >>> h = d.plot()
+
+        hi = di.plot()
+        h = d.plot()
 
         See also
         --------

wafo/data/info.py

@@ -19,6 +19,7 @@ This module gives gives detailed information and easy access to all datasets
 included in WAFO
 
 """
+import numpy as np
 from numpy import (loadtxt, nan)
 import os
 __path2data = os.path.dirname(os.path.realpath(__file__))
@@ -73,7 +74,10 @@ def atlantic():
     >>> import pylab
     >>> import wafo
     >>> Hs = wafo.data.atlantic()
-    >>> h = pylab.plot(Hs)
+    >>> np.allclose(Hs[:3], [ 5.48296296,  4.3615    ,  5.26023256])
+    True
+
+    h = pylab.plot(Hs)
 
     Acknowledgement:
     ---------------
@@ -121,7 +125,10 @@ def gfaks89():
     >>> import pylab
     >>> import wafo
     >>> x = wafo.data.gfaks89()
-    >>> h = pylab.plot(x[:,0],x[:,1])
+    >>> np.allclose(x[:3, 1], [-0.19667949, -0.46667949, -0.38667949])
+    True
+
+    h = pylab.plot(x[:,0],x[:,1])
 
     Acknowledgement:
     ---------------
@@ -216,12 +223,15 @@ def japansea():
     >>> import pylab
     >>> import wafo
     >>> map1 = wafo.data.japansea()
-    >>> h = pylab.plot(map1[:,0],map1[:,1])
-    >>> lon_loc = [131,132,132,135,139.5,139]
-    >>> lat_loc = [46, 43, 40, 35, 38.3, 35.7]
-    >>> loc = ['China','Vladivostok','Japan Sea', 'Japan', 'Yura','Tokyo']
-    >>> algn = 'right'
-    >>> for lon, lat, name in zip(lon_loc,lat_loc,loc):
+    >>> np.allclose(map1[1:4, 0], [ 141.960057,  142.058624,  142.103214])
+    True
+
+    h = pylab.plot(map1[:,0],map1[:,1])
+    lon_loc = [131,132,132,135,139.5,139]
+    lat_loc = [46, 43, 40, 35, 38.3, 35.7]
+    loc = ['China','Vladivostok','Japan Sea', 'Japan', 'Yura','Tokyo']
+    algn = 'right'
+    for lon, lat, name in zip(lon_loc,lat_loc,loc):
         pylab.text(lon,lat,name,horizontalalignment=algn)
 
 
@@ -263,39 +273,42 @@ def northsea():
     >>> import pylab
     >>> import wafo
     >>> map1 = wafo.data.northsea()
-    >>> h = pylab.plot(map1[:,0],map1[:,1])
-    >>> lon_pltfrm = [1.8,   2.3,  2.,  1.9, 2.6]
-    >>> lat_pltfrm = [61.2, 61.2, 59.9, 58.4, 57.7]
-    >>> pltfrm = ['Statfjord A', 'Gullfaks C', 'Frigg', 'Sleipner', 'Draupner']
-    >>> h = pylab.scatter(lon_pltfrm,lat_pltfrm);
-    >>> algn = 'right'
-    >>> for lon, lat, name in zip(lon_pltfrm,lat_pltfrm,pltfrm):
+    >>> np.allclose(map1[1:4, 0], [ 1.261996,  1.264064,  1.268171])
+    True
+
+    h = pylab.plot(map1[:,0],map1[:,1])
+    lon_pltfrm = [1.8,   2.3,  2.,  1.9, 2.6]
+    lat_pltfrm = [61.2, 61.2, 59.9, 58.4, 57.7]
+    pltfrm = ['Statfjord A', 'Gullfaks C', 'Frigg', 'Sleipner', 'Draupner']
+    h = pylab.scatter(lon_pltfrm,lat_pltfrm);
+    algn = 'right'
+    for lon, lat, name in zip(lon_pltfrm,lat_pltfrm,pltfrm):
         pylab.text(lon,lat,name,horizontalalignment=algn); algn = 'left'
 
 
-    >>> lon_city = [10.8, 10.8, 5.52, 5.2]
-    >>> lat_city = [59.85, 63.4, 58.9, 60.3]
-    >>> city = ['Oslo','Trondheim','Stavanger', 'Bergen']
-    >>> h = pylab.scatter(lon_city,lat_city);
-    >>> algn = 'right'
-    >>> for lon, lat, name in zip(lon_city,lat_city,city):
+    lon_city = [10.8, 10.8, 5.52, 5.2]
+    lat_city = [59.85, 63.4, 58.9, 60.3]
+    city = ['Oslo','Trondheim','Stavanger', 'Bergen']
+    h = pylab.scatter(lon_city,lat_city);
+    algn = 'right'
+    for lon, lat, name in zip(lon_city,lat_city,city):
          pylab.text(lon,lat,name,horizontalalignment=algn)
 
     # If you have the mpl_toolkits.basemap installed
-    >>> from mpl_toolkits.basemap import Basemap
-    >>> import matplotlib.pyplot as plt
+    from mpl_toolkits.basemap import Basemap
+    import matplotlib.pyplot as plt
 
     # setup Lambert Conformal basemap.
-    >>> m = Basemap(width=1200000,height=900000,projection='lcc',
+    m = Basemap(width=1200000,height=900000,projection='lcc',
             resolution='f',lat_1=56.,lat_2=64,lat_0=58,lon_0=5.)
-    >>> m.drawcoastlines()
-    >>> h = m.scatter(lon_pltfrm,lat_pltfrm);
-    >>> algn = 'right'
-    >>> for lon, lat, name in zip(lon_pltfrm,lat_pltfrm,pltfrm):
+    m.drawcoastlines()
+    h = m.scatter(lon_pltfrm,lat_pltfrm);
+    algn = 'right'
+    for lon, lat, name in zip(lon_pltfrm,lat_pltfrm,pltfrm):
          m.text(lon,lat,name,horizontalalignment=algn); algn = 'left'
-    >>> m.scatter(lon_city,lat_city)
-    >>> algn = 'right'
-    >>> for lon, lat, name in zip(lon_city,lat_city,city):
+    m.scatter(lon_city,lat_city)
+    algn = 'right'
+    for lon, lat, name in zip(lon_city,lat_city,city):
          m.text(lon,lat,name,horizontalalignment=algn)
     """
     return _loadnan('northsea.dat')
@@ -324,7 +337,10 @@ def sea():
     >>> import pylab
     >>> import wafo
     >>> x = wafo.data.sea()
-    >>> h = pylab.plot(x[:,0],x[:,1])
+    >>> np.allclose(x[:3,1],[-1.2004945 , -1.0904945 , -0.79049454])
+    True
+
+    h = pylab.plot(x[:,0],x[:,1])
     """
     return _load('sea.dat')
 
@@ -459,6 +475,8 @@ def yura87():
     japansea
     """
     return _load('yura87.dat')
+
+
 if __name__ == '__main__':
     import doctest
     doctest.testmod()

wafo/demo_sg.py

@@ -4,39 +4,43 @@ from numpy import random, arange, sin
 from sg_filter import SavitzkyGolay, smoothn  # calc_coeff, smooth
 
 
-figure(figsize=(7, 12))
+def example_reconstruct_noisy_chirp():
+    figure(figsize=(7, 12))
 
+    # generate chirp signal
+    tvec = arange(0, 6.28, .02)
+    true_signal = sin(tvec * (2.0 + tvec))
 
-# generate chirp signal
-tvec = arange(0, 6.28, .02)
-true_signal = sin(tvec * (2.0 + tvec))
+    # add noise to signal
+    noise = random.normal(size=true_signal.shape)
+    signal = true_signal + .15 * noise
 
-# add noise to signal
-noise = random.normal(size=true_signal.shape)
-signal = true_signal + .15 * noise
+    # plot signal
+    subplot(311)
+    plot(signal)
+    title('signal')
 
-# plot signal
-subplot(311)
-plot(signal)
-title('signal')
+    # smooth and plot signal
+    subplot(312)
+    savgol = SavitzkyGolay(n=8, degree=4)
+    s_signal = savgol.smooth(signal)
+    s2 = smoothn(signal, robust=True)
+    plot(s_signal)
+    plot(s2)
+    plot(true_signal, 'r--')
+    title('smoothed signal')
 
-# smooth and plot signal
-subplot(312)
-savgol = SavitzkyGolay(n=8, degree=4)
-s_signal = savgol.smooth(signal)
-s2 = smoothn(signal, robust=True)
-plot(s_signal)
-plot(s2)
-plot(true_signal, 'r--')
-title('smoothed signal')
+    # smooth derivative of signal and plot it
+    subplot(313)
+    savgol1 = SavitzkyGolay(n=8, degree=1, diff_order=1)
 
-# smooth derivative of signal and plot it
-subplot(313)
-savgol1 = SavitzkyGolay(n=8, degree=1, diff_order=1)
+    d_signal = savgol1.smooth(signal)
 
-d_signal = savgol1.smooth(signal)
+    plot(d_signal)
+    title('smoothed derivative of signal')
 
-plot(d_signal)
-title('smoothed derivative of signal')
+    plt.show('hold')  # show plot
 
-plt.show('hold')  # show plot
+
+if __name__ == '__main__':
+    example_reconstruct_noisy_chirp()

wafo/gaussian.py

@@ -361,7 +361,7 @@ class Rind(object):
         Blo[0, ind] = maximum(Blo[0, ind], -infinity * dev[indI[ind + 1]])
         ind2 = indI + 1
 
-        return rindmod.rind(BIG, Ex, xc, nt, ind2, Blo, Bup, infin, seed)  # @UndefinedVariable @IgnorePep8
+        return rindmod.rind(BIG, Ex, xc, nt, ind2, Blo, Bup, infin, seed)
 
 
 def test_rind():
@@ -440,7 +440,8 @@ def cdflomax(x, alpha, m0):
     >>> alpha = S.characteristic('alpha')[0]
     >>> x = np.linspace(-10,10,200);
     >>> mcdf = ws.edf(mM.data)
-    >>> h = mcdf.plot(), pylab.plot(x,wg.cdflomax(x,alpha,m0))
+
+    h = mcdf.plot(), pylab.plot(x,wg.cdflomax(x,alpha,m0))
 
     See also
     --------
@@ -533,7 +534,7 @@ def prbnormtndpc(rho, a, b, D=None, df=0, abseps=1e-4, IERC=0, HNC=0.24):
     A = np.clip(a - D, -100, 100)
     B = np.clip(b - D, -100, 100)
 
-    return mvnprdmod.prbnormtndpc(rho, A, B, df, abseps, IERC, HNC)  # @UndefinedVariable @IgnorePep8
+    return mvnprdmod.prbnormtndpc(rho, A, B, df, abseps, IERC, HNC)
 
 
 def prbnormndpc(rho, a, b, abserr=1e-4, relerr=1e-4, usesimpson=True,
@@ -696,8 +697,10 @@ def prbnormnd(correl, a, b, abseps=1e-4, releps=1e-3, maxpts=None, method=0):
 
     >>> A = np.repeat(Blo,n)
     >>> B = np.repeat(Bup,n)-m
-    >>> [val,err,inform] = prbnormnd(Sc,A,B);[val, err, inform]
-    [0.0019456719705212067, 1.0059406844578488e-05, 0]
+    >>> val, err, inform = prbnormnd(Sc,A,B)
+    >>> np.allclose([val, err, inform],
+    ...    [0.0019456719705212067, 1.0059406844578488e-05, 0])
+    True
 
     >>> np.abs(val-Et)< err0+terr0
     array([ True], dtype=bool)
@@ -738,7 +741,7 @@ def prbnormnd(correl, a, b, abseps=1e-4, releps=1e-3, maxpts=None, method=0):
     infinity = 37
     infin = np.repeat(2, n) - (B > infinity) - 2 * (A < -infinity)
 
-    err, val, inform = mvn.mvndst(A, B, infin, L, maxpts, abseps, releps)  # @UndefinedVariable @IgnorePep8
+    err, val, inform = mvn.mvndst(A, B, infin, L, maxpts, abseps, releps)
 
     return val, err, inform
 
@@ -910,8 +913,9 @@ def cdfnorm2d(b1, b2, r):
             if len(k5) > 0:
                 #               b = sqrt(bs);
                 k135 = k13[k5]
-                bvn[k135] = bvn[k135] - exp(-hk[k135] / 2) * sqrt(twopi) * fi(-b[k5] / a[k5]) * \
-                    b[k5] * (1 - c[k5] * bs[k5] * (1 - d[k5] * bs[k5] / 5) / 3)
+                bvn[k135] = bvn[k135] - exp(-hk[k135] / 2) * sqrt(twopi) * \
+                    fi(-b[k5] / a[k5]) * b[k5] * \
+                    (1 - c[k5] * bs[k5] * (1 - d[k5] * bs[k5] / 5) / 3)
 
             a /= two
             for i in range(10):
@@ -971,8 +975,8 @@ def prbnorm2d(a, b, r):
     >>> a = [-1, -2]
     >>> b = [1, 1]
     >>> r = 0.3
-    >>> wg.prbnorm2d(a,b,r)
-    array([ 0.56659121])
+    >>> np.allclose(wg.prbnorm2d(a,b,r), 0.56659121350428077)
+    True
 
     See also
     --------

wafo/graphutil.py

@@ -105,9 +105,10 @@ def cltext(levels, percent=False, n=4, xs=0.036, ys=0.94, zs=0, figure=None,
     >>> import wafo.demos as wd
     >>> import pylab as plt
     >>> x,y,z  = wd.peaks();
-    >>> h = plt.contour(x,y,z)
-    >>> h = wg.cltext(h.levels)
-    >>> plt.show()
+
+    h = plt.contour(x,y,z)
+    h = wg.cltext(h.levels)
+    plt.show()
     '''
     # TODO : Make it work like legend does (but without the box): include
     # position options etc...
@@ -189,8 +190,9 @@ def tallibing(*args, **kwds):
     >>> import wafo.graphutil as wg
     >>> import wafo.demos as wd
     >>> [x,y,z] = wd.peaks(n=20)
-    >>> h0 = wg.pcolor(x,y,z)
-    >>> h1 = wg.tallibing(x,y,z)
+
+    h0 = wg.pcolor(x,y,z)
+    h1 = wg.tallibing(x,y,z)
 
     See also
     --------

wafo/integrate.py

@@ -156,8 +156,9 @@ def romberg(fun, a, b, releps=1e-3, abseps=1e-3):
     -------
     >>> import numpy as np
     >>> [q,err] = romberg(np.sqrt,0,10,0,1e-4)
-    >>> q,err
-    (array([ 21.0818511]), array([  6.6163547e-05]))
+    >>> np.allclose([q,err],
+    ...    [ 21.08185107,   6.61635466e-05])
+    True
     '''
     h = b - a
     hMin = 1.0e-9

wafo/interpolate.py

@@ -51,14 +51,21 @@ def savitzky_golay(y, window_size, order, deriv=0):
     Examples
     --------
     >>> t = np.linspace(-4, 4, 500)
-    >>> y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
+    >>> noise = np.random.normal(0, 0.05, t.shape)
+    >>> noise = 0.4*np.sin(100*t)
+    >>> y = np.exp( -t**2 ) + noise
     >>> ysg = savitzky_golay(y, window_size=31, order=4)
-    >>> import matplotlib.pyplot as plt
-    >>> h=plt.plot(t, y, label='Noisy signal')
-    >>> h=plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
-    >>> h=plt.plot(t, ysg, 'r', label='Filtered signal')
-    >>> h=plt.legend()
-    >>> plt.show()
+    >>> np.allclose(ysg[:10],
+    ... [-0.00127789, -0.02390299, -0.04444364, -0.01738837,  0.00585856,
+    ...  -0.01675704, -0.03140276,  0.00010455,  0.02099063, -0.00380031])
+    True
+
+    import matplotlib.pyplot as plt
+    h=plt.plot(t, y, label='Noisy signal')
+    h=plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
+    h=plt.plot(t, ysg, 'r', label='Filtered signal')
+    h=plt.legend()
+    plt.show()
 
     References
     ----------
@@ -114,10 +121,16 @@ def savitzky_golay_piecewise(xvals, data, kernel=11, order=4):
     # As an example, this figure shows the effect of an additive noise with a
     # variance of 0.2 (original signal (black), noisy signal (red) and filtered
     # signal (blue dots)).
-
-    >>> yn = y + np.sqrt(0.2)*np.random.randn(*x.shape)
+    >>> noise = np.sqrt(0.2)*np.random.randn(*x.shape)
+    >>> noise = np.sqrt(0.2)*np.sin(1000*x)
+    >>> yn = y + noise
     >>> yr = savitzky_golay_piecewise(x, yn, kernel=11, order=4)
-    >>> h=plt.plot(x, yn, 'r', x, y, 'k', x, yr, 'b.')
+    >>> np.allclose(yr[:10],
+    ...    [-0.02708216, -0.04295155, -0.08522043, -0.13995016, -0.1908162 ,
+    ...     -0.22938387, -0.26932722, -0.30614865, -0.33942134, -0.3687596 ])
+    True
+
+    h=plt.plot(x, yn, 'r', x, y, 'k', x, yr, 'b.')
     '''
     turnpoint = 0
     last = len(xvals)
@@ -171,16 +184,23 @@ def sgolay2d(z, window_size, order, derivative=None):
     >>> Z = np.exp( -(X**2+Y**2))
 
     # add noise
-    >>> Zn = Z + np.random.normal( 0, 0.2, Z.shape )
+    >>> noise = np.random.normal( 0, 0.2, Z.shape )
+    >>> noise = np.sqrt(0.2) * np.sin(100*X)*np.sin(100*Y)
+    >>> Zn = Z + noise
 
     # filter it
     >>> Zf = sgolay2d( Zn, window_size=29, order=4)
+    >>> np.allclose(Zf[:3,:5],
+    ...  [[ 0.29304073,  0.29749652,  0.29007645,  0.2695685 ,  0.23541966],
+    ...    [ 0.29749652,  0.29819304,  0.28766723,  0.26524542,  0.23081572],
+    ...    [ 0.29007645,  0.28766723,  0.27483445,  0.25141198,  0.21769662]])
+    True
 
     # do some plotting
-    >>> import matplotlib.pyplot as plt
-    >>> h=plt.matshow(Z)
-    >>> h=plt.matshow(Zn)
-    >>> h=plt.matshow(Zf)
+    import matplotlib.pyplot as plt
+    h=plt.matshow(Z)
+    h=plt.matshow(Zn)
+    h=plt.matshow(Zf)
     """
     # number of terms in the polynomial expression
     n_terms = (order + 1) * (order + 2) / 2.0
@@ -290,8 +310,14 @@ class PPform(object):
     >>> coef = np.array([[1,1],[1,1],[0,2]]) # linear from 0 to 2
     >>> breaks = [0,1,2]
     >>> self = PPform(coef, breaks)
-    >>> x = linspace(-1,3)
-    >>> h=plt.plot(x,self(x))
+    >>> x = linspace(-1, 3, 21)
+    >>> y = self(x)
+    >>> np.allclose(y, [ 0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.24,  0.56,
+    ...    0.96, 1.44,  2.  ,  2.24,  2.56,  2.96,  3.44,  4.  ,  0.  ,  0.  ,
+    ...     0.  ,  0.  ,  0.  ])
+    True
+
+    h=plt.plot(x, y)
     """
 
     def __init__(self, coeffs, breaks, fill=0.0, sort=False, a=None, b=None):
@@ -468,11 +494,37 @@ class SmoothSpline(PPform):
     -------
     >>> import numpy as np
     >>> import matplotlib.pyplot as plt
-    >>> x = np.linspace(0,1)
-    >>> y = np.exp(x)+1e-1*np.random.randn(x.size)
+    >>> x = np.linspace(0, 1, 21)
+    >>> noise = 1e-1*np.random.randn(x.size)
+    >>> noise = np.array(
+    ...    [-0.03298601, -0.08164429, -0.06845745, -0.20718593,  0.08666282,
+    ...     0.04702094,  0.08208645, -0.1017021 , -0.03031708,  0.22871709,
+    ...    -0.10302486, -0.17724316, -0.05885157, -0.03875947, -0.1102984 ,
+    ...    -0.05542001, -0.12717549,  0.14337697, -0.02637848, -0.10353976,
+    ...    -0.0618834 ])
+
+    >>> y = np.exp(x) + noise
     >>> pp9 = SmoothSpline(x, y, p=.9)
     >>> pp99 = SmoothSpline(x, y, p=.99, var=0.01)
-    >>> h=plt.plot(x,y, x,pp99(x),'g', x,pp9(x),'k', x,np.exp(x),'r')
+
+    >>> y99 = pp99(x); y9 = pp9(x)
+    >>> np.allclose(y9,
+    ...    [ 0.8754795 ,  0.95285289,  1.03033239,  1.10803792,  1.18606854,
+    ...     1.26443234,  1.34321265,  1.42258227,  1.5027733 ,  1.58394785,
+    ...     1.66625727,  1.74998243,  1.8353173 ,  1.92227431,  2.01076693,
+    ...     2.10064087,  2.19164551,  2.28346334,  2.37573696,  2.46825194,
+    ...     2.56087699])
+    True
+    >>> np.allclose(y99,
+    ...     [ 0.95227461,  0.97317995,  1.01159244,  1.08726908,  1.21260587,
+    ...     1.31545644,  1.37829108,  1.42719649,  1.51308685,  1.59669367,
+    ...     1.61486217,  1.64481078,  1.72970022,  1.83208819,  1.93312796,
+    ...     2.05164767,  2.19326122,  2.34608425,  2.45023567,  2.5357288 ,
+    ...     2.6357401 ])
+    True
+
+
+    h=plt.plot(x,y, x,pp99(x),'g', x,pp9(x),'k', x,np.exp(x),'r')
 
     See also
     --------
@@ -882,14 +934,19 @@ class StinemanInterp(object):
     >>> y = np.sin(x); yp = np.cos(x)
     >>> xi = np.linspace(0,2*pi,40);
     >>> yi = wi.StinemanInterp(x,y)(xi)
+    >>> np.allclose(yi[:10],
+    ...    [ 0.,  0.16258231,  0.31681338,  0.46390886,  0.60091421,
+    ...      0.7206556 ,  0.82314953,  0.90304148,  0.96059538,  0.99241945])
+    True
     >>> yi1 = wi.CubicHermiteSpline(x,y, yp)(xi)
     >>> yi2 = wi.Pchip(x,y, method='parabola')(xi)
-    >>> h=plt.subplot(211)
-    >>> h=plt.plot(x,y,'o',xi,yi,'r', xi,yi1, 'g', xi,yi1, 'b')
-    >>> h=plt.subplot(212)
-    >>> h=plt.plot(xi,np.abs(sin(xi)-yi), 'r',
-    ...            xi,  np.abs(sin(xi)-yi1), 'g',
-    ...            xi, np.abs(sin(xi)-yi2), 'b')
+
+    h=plt.subplot(211)
+    h=plt.plot(x,y,'o',xi,yi,'r', xi,yi1, 'g', xi,yi1, 'b')
+    h=plt.subplot(212)
+    h=plt.plot(xi,np.abs(sin(xi)-yi), 'r',
+               xi,  np.abs(sin(xi)-yi1), 'g',
+               xi, np.abs(sin(xi)-yi2), 'b')
 
     References
     ----------
@@ -1012,36 +1069,58 @@ class Pchip(PiecewisePolynomial):
     >>> y = np.array([-1.0, -1,-1,0,1,1,1])
 
     # Interpolate using monotonic piecewise Hermite cubic spline
-    >>> xvec = np.arange(599.)/100. - 3.0
+    >>> n = 20.
+    >>> xvec = np.arange(n)/10. - 1.0
     >>> yvec = wi.Pchip(x, y)(xvec)
+    >>> np.allclose(yvec, [-1.   , -0.981, -0.928, -0.847, -0.744, -0.625,
+    ...    -0.496, -0.363, -0.232, -0.109,  0.   ,  0.109,  0.232,  0.363,
+    ...    0.496,  0.625, 0.744,  0.847,  0.928,  0.981])
+    True
 
     # Call the Scipy cubic spline interpolator
     >>> from scipy.interpolate import interpolate
     >>> function = interpolate.interp1d(x, y, kind='cubic')
     >>> yvec1 = function(xvec)
+    >>> np.allclose(yvec1, [-1.00000000e+00, -9.41911765e-01, -8.70588235e-01,
+    ...        -7.87500000e-01,  -6.94117647e-01,  -5.91911765e-01,
+    ...        -4.82352941e-01,  -3.66911765e-01,  -2.47058824e-01,
+    ...        -1.24264706e-01,   2.49800181e-16,   1.24264706e-01,
+    ...         2.47058824e-01,   3.66911765e-01,   4.82352941e-01,
+    ...         5.91911765e-01,   6.94117647e-01,   7.87500000e-01,
+    ...         8.70588235e-01,   9.41911765e-01])
+    True
+
 
     # Non-montonic cubic Hermite spline interpolator using
     # Catmul-Rom method for computing slopes...
     >>> yvec2 = wi.CubicHermiteSpline(x,y)(xvec)
-
     >>> yvec3 = wi.StinemanInterp(x, y)(xvec)
 
+    >>> np.allclose(yvec2, [-1., -0.9405, -0.864 , -0.7735, -0.672 , -0.5625,
+    ...    -0.448 , -0.3315, -0.216 , -0.1045,  0.    ,  0.1045,  0.216 ,
+    ...    0.3315, 0.448 ,  0.5625,  0.672 ,  0.7735,  0.864 ,  0.9405])
+    True
+
+    >>> np.allclose(yvec3, [-1. , -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3,
+    ... -0.2, -0.1,  0. , 0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9])
+    True
+
     # Plot the results
-    >>> import matplotlib.pyplot as plt
-    >>> h=plt.plot(x,    y,     'ro')
-    >>> h=plt.plot(xvec, yvec,  'b')
-    >>> h=plt.plot(xvec, yvec1, 'k')
-    >>> h=plt.plot(xvec, yvec2, 'g')
-    >>> h=plt.plot(xvec, yvec3, 'm')
-    >>> h=plt.title("pchip() step function test")
-
-    >>> h=plt.xlabel("X")
-    >>> h=plt.ylabel("Y")
-    >>> txt = "Comparing pypchip() vs. Scipy interp1d() vs. non-monotonic CHS"
-    >>> h=plt.title(txt)
-    >>> legends = ["Data", "pypchip()", "interp1d","CHS", 'SI']
-    >>> h=plt.legend(legends, loc="upper left")
-    >>> plt.show()
+    import matplotlib.pyplot as plt
+    h=plt.plot(x,    y,     'ro')
+    h=plt.plot(xvec, yvec,  'b')
+    h=plt.plot(xvec, yvec1, 'k')
+    h=plt.plot(xvec, yvec2, 'g')
+    h=plt.plot(xvec, yvec3, 'm')
+    h=plt.title("pchip() step function test")
+
+    h=plt.xlabel("X")
+    h=plt.ylabel("Y")
+    txt = "Comparing pypchip() vs. Scipy interp1d() vs. non-monotonic CHS"
+    h=plt.title(txt)
+    legends = ["Data", "pypchip()", "interp1d","CHS", 'SI']
+    h=plt.legend(legends, loc="upper left")
+    plt.show()
 
     """
 
@@ -1264,8 +1343,8 @@ def test_docstrings():
 
 if __name__ == '__main__':
     # test_func()
-    # test_doctstrings()
-    test_smoothing_spline()
+    test_docstrings()
+    # test_smoothing_spline()
     # compare_methods()
     # demo_monoticity()
     # test_interp3()

wafo/kdetools.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 # -------------------------------------------------------------------------
 # Name:        kdetools
 # Purpose:
@@ -8,7 +9,7 @@
 # Copyright:   (c) pab 2008
 # Licence:     LGPL
 # -------------------------------------------------------------------------
-#!/usr/bin/env python  # @IgnorePep8
+
 from __future__ import absolute_import, division
 import copy
 import numpy as np
@@ -19,12 +20,12 @@ from scipy import interpolate, linalg, optimize, sparse, special, stats
 from scipy.special import gamma
 from numpy import pi, sqrt, atleast_2d, exp, newaxis  # @UnresolvedImport
 
-from .misc import meshgrid, nextpow2, tranproc  # , trangood
-from .containers import PlotData
-from .dctpack import dct, dctn, idctn
-from .plotbackend import plotbackend as plt
+from wafo.misc import meshgrid, nextpow2, tranproc  # , trangood
+from wafo.containers import PlotData
+from wafo.dctpack import dct, dctn, idctn
+from wafo.plotbackend import plotbackend as plt
 try:
-    from . import fig
+    from wafo import fig
 except ImportError:
     warnings.warn('fig import only supported on Windows')
 
@@ -841,8 +842,8 @@ class KDE(_KDE):
     ...   [ 0.20397743,  0.40252228,  0.54594119,  0.52219025,  0.39062189,
     ...     0.2638171 ,  0.16407487,  0.08270755,  0.04784434,  0.04784434])
     True
-    >>> h = f1.plot()
 
+    h = f1.plot()
     import pylab as plb
     h1 = plb.plot(x, f) #  1D probability density plot
     t = np.trapz(f, x)
@@ -1080,16 +1081,20 @@ class KRegression(_KDE):
 
     Example
     -------
+    >>> import wafo.kdetools as wk
     >>> N = 100
-    >>> ei = np.random.normal(loc=0, scale=0.075, size=(N,))
-
     >>> x = np.linspace(0, 1, N)
-    >>> import wafo.kdetools as wk
+    >>> ei = np.random.normal(loc=0, scale=0.075, size=(N,))
+    >>> ei = np.sqrt(0.075) * np.sin(100*x)
 
     >>> y = 2*np.exp(-x**2/(2*0.3**2))+3*np.exp(-(x-1)**2/(2*0.7**2)) + ei
     >>> kreg = wk.KRegression(x, y)
     >>> f = kreg(output='plotobj', title='Kernel regression', plotflag=1)
-    >>> h = f.plot(label='p=0')
+    >>> np.allclose(f.data[:5],
+    ...     [ 3.18381052,  3.18362269,  3.18343648,  3.1832536 ,  3.1830757 ])
+    True
+
+    h = f.plot(label='p=0')
     """
 
     def __init__(self, data, y, p=0, hs=None, kernel=None, alpha=0.0,
@@ -3105,18 +3110,26 @@ def gridcount(data, X, y=1):
     >>> import numpy as np
     >>> import wafo.kdetools as wk
     >>> import pylab as plb
-    >>> N = 200
+    >>> N = 20
     >>> data  = np.random.rayleigh(1,N)
+    >>> data = np.array(
+    ...    [ 1.07855907,  1.51199717,  1.54382893,  1.54774808,  1.51913566,
+    ...     1.11386486,  1.49146216,  1.51127214,  2.61287913,  0.94793051,
+    ...     2.08532731,  1.35510641,  0.56759888,  1.55766981,  0.77883602,
+    ...     0.9135759 ,  0.81177855,  1.02111483,  1.76334202,  0.07571454])
     >>> x = np.linspace(0,max(data)+1,50)
     >>> dx = x[1]-x[0]
 
-    >>> c = wk.gridcount(data,x)
+    >>> c = wk.gridcount(data, x)
+    >>> np.allclose(c[:5], [ 0.,  0.9731147,  0.0268853,  0.,  0.])
+    True
 
-    >>> h = plb.plot(x,c,'.')   # 1D histogram
     >>> pdf = c/dx/N
-    >>> h1 = plb.plot(x, pdf) #  1D probability density plot
-    >>> '%1.2f' % np.trapz(pdf, x)
-    '1.00'
+    >>> np.allclose(np.trapz(pdf, x), 1)
+    True
+
+    h = plb.plot(x,c,'.')   # 1D histogram
+    h1 = plb.plot(x, pdf) #  1D probability density plot
 
     See also
     --------

wafo/misc.py

@@ -17,10 +17,10 @@ from scipy.integrate import trapz, simps
 import warnings
 from time import strftime, gmtime
 from numdifftools.extrapolation import dea3  # @UnusedImport
-from .plotbackend import plotbackend
+from wafo.plotbackend import plotbackend
 from collections import Callable
 try:
-    from . import c_library as clib  # @UnresolvedImport
+    from wafo import c_library as clib  # @UnresolvedImport
 except ImportError:
     warnings.warn('c_library not found. Check its compilation.')
     clib = None
@@ -635,15 +635,22 @@ def detrendma(x, L):
 
     Examples
     --------
+    >>> import numpy as np
     >>> import wafo.misc as wm
-    >>> import pylab as plt
-    >>> exp = plt.exp; cos = plt.cos; randn = plt.randn
-    >>> x = plt.linspace(0,1,200)
-    >>> y = exp(x)+cos(5*2*pi*x)+1e-1*randn(x.size)
-    >>> y0 = wm.detrendma(y,20); tr = y-y0
-    >>> h = plt.plot(x, y, x, y0, 'r', x, exp(x), 'k', x, tr, 'm')
+    >>> exp = np.exp; cos = np.cos; randn = np.random.randn
+    >>> x = np.linspace(0,1,200)
+    >>> noise = 0.1*randn(x.size)
+    >>> noise = 0.1*np.sin(100*x)
+    >>> y = exp(x)+cos(5*2*pi*x) + noise
+    >>> y0 = wm.detrendma(y,20)
+    >>> tr = y-y0
+    >>> np.allclose(tr[:5],
+    ...    [ 1.14134814,  1.14134814,  1.14134814,  1.14134814,  1.14134814])
+    True
 
-    >>> plt.close('all')
+    import pylab as plt
+    h = plt.plot(x, y, x, y0, 'r', x, exp(x), 'k', x, tr, 'm')
+    plt.close('all')
 
     See also
     --------
@@ -703,16 +710,16 @@ def ecross(t, f, ind, v=0):
     >>> t = np.linspace(0,7*np.pi,250)
     >>> x = np.sin(t)
     >>> ind = wm.findcross(x,0.75)
-    >>> ind
-    array([  9,  25,  80,  97, 151, 168, 223, 239])
+    >>> np.allclose(ind, [  9,  25,  80,  97, 151, 168, 223, 239])
+    True
     >>> t0 = wm.ecross(t,x,ind,0.75)
-    >>> np.abs(t0 - np.array([0.84910514, 2.2933879 , 7.13205663, 8.57630119,
-    ...        13.41484739, 14.85909194, 19.69776067, 21.14204343]))<1e-7
-    array([ True,  True,  True,  True,  True,  True,  True,  True], dtype=bool)
+    >>> np.allclose(t0, [0.84910514, 2.2933879 , 7.13205663, 8.57630119,
+    ...        13.41484739, 14.85909194, 19.69776067, 21.14204343])
+    True
 
-    >>> a = plt.plot(t, x, '.', t[ind], x[ind], 'r.', t, ones(t.shape)*0.75,
-    ...              t0, ones(t0.shape)*0.75, 'g.')
-    >>> plt.close('all')
+    a = plt.plot(t, x, '.', t[ind], x[ind], 'r.', t, ones(t.shape)*0.75,
+                  t0, ones(t0.shape)*0.75, 'g.')
+    plt.close('all')
 
     See also
     --------
@@ -797,22 +804,21 @@ def findcross(x, v=0.0, kind=None):
     >>> from matplotlib import pylab as plt
     >>> import wafo.misc as wm
     >>> ones = np.ones
-    >>> findcross([0, 1, -1, 1],0)
-    array([0, 1, 2])
+    >>> np.allclose(findcross([0, 1, -1, 1], 0), [0, 1, 2])
+    True
     >>> v = 0.75
     >>> t = np.linspace(0,7*np.pi,250)
     >>> x = np.sin(t)
     >>> ind = wm.findcross(x,v) # all crossings
-    >>> ind
-    array([  9,  25,  80,  97, 151, 168, 223, 239])
-
-    >>> t0 = plt.plot(t,x,'.',t[ind],x[ind],'r.', t, ones(t.shape)*v)
+    >>> np.allclose(ind, [  9,  25,  80,  97, 151, 168, 223, 239])
+    True
     >>> ind2 = wm.findcross(x,v,'u')
-    >>> ind2
-    array([  9,  80, 151, 223])
+    >>> np.allclose(ind2, [  9,  80, 151, 223])
+    True
 
-    >>> t0 = plt.plot(t[ind2],x[ind2],'o')
-    >>> plt.close('all')
+    t0 = plt.plot(t,x,'.',t[ind],x[ind],'r.', t, ones(t.shape)*v)
+    t0 = plt.plot(t[ind2],x[ind2],'o')
+    plt.close('all')
 
     See also
     --------
@@ -873,9 +879,11 @@ def findextrema(x):
     >>> t = np.linspace(0,7*np.pi,250)
     >>> x = np.sin(t)
     >>> ind = wm.findextrema(x)
+    >>> np.allclose(ind, [ 18,  53,  89, 125, 160, 196, 231])
+    True
 
-    >>> a = plt.plot(t,x,'.',t[ind],x[ind],'r.')
-    >>> plt.close('all')
+    a = plt.plot(t,x,'.',t[ind],x[ind],'r.')
+    plt.close('all')
 
     See also
     --------
@@ -912,8 +920,9 @@ def findpeaks(data, n=2, min_h=None, min_p=0.0):
     >>> import wafo.misc as wm
     >>> x = np.arange(0,10,0.01)
     >>> data = x**2+10*np.sin(3*x)+0.5*np.sin(50*x)
-    >>> wm.findpeaks(data, n=8, min_h=5, min_p=0.3)
-    array([908, 694, 481])
+    >>> np.allclose(wm.findpeaks(data, n=8, min_h=5, min_p=0.3),
+    ...            [908, 694, 481])
+    True
 
     See also
     --------
@@ -1031,13 +1040,16 @@ def findrfc(tp, h=0.0, method='clib'):
     >>> ind = wm.findextrema(x)
     >>> ti, tp = t[ind], x[ind]
 
-    >>> a = plt.plot(t,x,'.',ti,tp,'r.')
-    >>> ind1 = wm.findrfc(tp,0.3); ind1
-    array([  0,   9,  32,  53,  74,  95, 116, 137])
-    >>> ind2 = wm.findrfc(tp,0.3, method=''); ind2
-    array([  0,   9,  32,  53,  74,  95, 116, 137])
-    >>> a = plt.plot(ti[ind1],tp[ind1])
-    >>> plt.close('all')
+    >>> ind1 = wm.findrfc(tp,0.3)
+    >>> np.allclose(ind1, [  0,   9,  32,  53,  74,  95, 116, 137])
+    True
+    >>> ind2 = wm.findrfc(tp,0.3, method='')
+    >>> np.allclose(ind2, [  0,   9,  32,  53,  74,  95, 116, 137])
+    True
+
+    a = plt.plot(t,x,'.',ti,tp,'r.')
+    a = plt.plot(ti[ind1],tp[ind1])
+    plt.close('all')
 
     See also
     --------
@@ -1436,14 +1448,15 @@ def findtp(x, h=0.0, kind=None):
     >>> itph = wm.findtp(x1[:,1],0.3,'Mw')
     >>> tp = x1[itp,:]
     >>> tph = x1[itph,:]
-    >>> a = plt.plot(x1[:,0],x1[:,1],
-    ...             tp[:,0],tp[:,1],'ro',
-    ...             tph[:,0],tph[:,1],'k.')
-    >>> plt.close('all')
-    >>> itp
-    array([ 5, 18, 24, 38, 46, 57, 70, 76, 91, 98, 99])
-    >>> itph
-    array([91])
+    >>> np.allclose(itp, [ 5, 18, 24, 38, 46, 57, 70, 76, 91, 98, 99])
+    True
+    >>> np.allclose(itph, 91)
+    True
+
+    a = plt.plot(x1[:,0],x1[:,1],
+                 tp[:,0],tp[:,1],'ro',
+                 tph[:,0],tph[:,1],'k.')
+    plt.close('all')
 
     See also
     ---------
@@ -1535,8 +1548,11 @@ def findtc(x_in, v=None, kind=None):
     >>> x1 = x[0:200,:]
     >>> itc, iv = wm.findtc(x1[:,1],0,'dw')
     >>> tc = x1[itc,:]
-    >>> a = plt.plot(x1[:,0],x1[:,1],tc[:,0],tc[:,1],'ro')
-    >>> plt.close('all')
+    >>> np.allclose(itc, [ 52, 105])
+    True
+
+    a = plt.plot(x1[:,0],x1[:,1],tc[:,0],tc[:,1],'ro')
+    plt.close('all')
 
     See also
     --------
@@ -2164,10 +2180,14 @@ def discretize(fun, a, b, tol=0.005, n=5, method='linear'):
     >>> import pylab as plt
     >>> x,y = wm.discretize(np.cos, 0, np.pi)
     >>> xa,ya = wm.discretize(np.cos, 0, np.pi, method='adaptive')
-    >>> t = plt.plot(x, y, xa, ya, 'r.')
+    >>> np.allclose(xa[:5],
+    ... [ 0.        ,  0.19634954,  0.39269908,  0.58904862,  0.78539816])
+    True
+
 
+    t = plt.plot(x, y, xa, ya, 'r.')
     plt.show()
-    >>> plt.close('all')
+    plt.close('all')
 
     '''
     if method.startswith('a'):
@@ -2426,12 +2446,15 @@ def tranproc(x, f, x0, *xi):
     >>> x = linspace(-5,5,501)
     >>> g = tr(x)
     >>> gder = wm.tranproc(x, g, x, ones(g.shape[0]))
-    >>> h = plt.plot(x, g, x, gder[1])
+    >>> np.allclose(gder[1][:5],
+    ... [ 1.09938766,  1.39779849,  1.39538745,  1.39298656,  1.39059575])
+    True
 
+    h = plt.plot(x, g, x, gder[1])
     plt.plot(x,pdfnorm(g)*gder[1],x,pdfnorm(x))
     plt.legend('Transformed model','Gaussian model')
 
-    >>> plt.close('all')
+    plt.close('all')
 
     See also
     --------
@@ -2615,10 +2638,11 @@ def plot_histgrm(data, bins=None, range=None,  # @ReservedAssignment
     >>> import wafo.stats as ws
     >>> R = ws.weibull_min.rvs(2,loc=0,scale=2, size=100)
 
-    >>> h0 = wm.plot_histgrm(R, 20, normed=True)
-    >>> x = np.linspace(-3,16,200)
-    >>> h1 = plt.plot(x,ws.weibull_min.pdf(x,2,0,2),'r')
-    >>> plt.close('all')
+    h0 = wm.plot_histgrm(R, 20, normed=True)
+    x = np.linspace(-3,16,200)
+
+    h1 = plt.plot(x,ws.weibull_min.pdf(x,2,0,2),'r')
+    plt.close('all')
 
     See also
     --------

wafo/objects.py

@@ -13,14 +13,14 @@
 
 
 from __future__ import absolute_import, division
-from .transform.core import TrData
-from .transform.estimation import TransformEstimator
-from .stats import distributions
-from .misc import (nextpow2, findtp, findrfc, findtc, findcross,
+from wafo.transform.core import TrData
+from wafo.transform.estimation import TransformEstimator
+from wafo.stats import distributions
+from wafo.misc import (nextpow2, findtp, findrfc, findtc, findcross,
                    ecross, JITImport, DotDict, gravity, findrfc_astm)
-from .interpolate import stineman_interp
-from .containers import PlotData
-from .plotbackend import plotbackend
+from wafo.interpolate import stineman_interp
+from wafo.containers import PlotData
+from wafo.plotbackend import plotbackend
 from scipy.integrate import trapz
 from scipy.signal import welch, lfilter
 from scipy.signal.windows import get_window  # @UnusedImport
@@ -76,7 +76,10 @@ class LevelCrossings(PlotData):
     >>> mm = tp.cycle_pairs()
 
     >>> lc = mm.level_crossings()
-    >>> h2 = lc.plot()
+    >>> np.allclose(lc.data[:5], [ 0.,  1.,  2.,  2.,  3.])
+    True
+
+    h2 = lc.plot()
     '''
 
     def __init__(self, *args, **kwds):
@@ -366,16 +369,16 @@ class LevelCrossings(PlotData):
         >>> np.abs(alpha-alpha2)<0.03
         array([ True], dtype=bool)
 
-        >>> h0 = S.plot('b')
-        >>> h1 = Se.plot('r')
-
         >>> lc2 = ts2.turning_points().cycle_pairs().level_crossings()
 
-        >>> import pylab as plt
-        >>> h = plt.subplot(211)
-        >>> h2 = lc2.plot()
-        >>> h = plt.subplot(212)
-        >>> h0 = lc.plot()
+        import pylab as plt
+        h0 = S.plot('b')
+        h1 = Se.plot('r')
+
+        h = plt.subplot(211)
+        h2 = lc2.plot()
+        h = plt.subplot(212)
+        h0 = lc.plot()
 
         """
 
@@ -544,7 +547,7 @@ class LevelCrossings(PlotData):
         >>> tp = ts.turning_points()
         >>> mm = tp.cycle_pairs()
         >>> lc = mm.level_crossings()
-        >>> g0, g0emp = lc.trdata(plotflag=1)
+        >>> g0, g0emp = lc.trdata(plotflag=0)
         >>> 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 ends
         >>> int(S.tr.dist2gauss()*100)
@@ -608,7 +611,11 @@ class CyclePairs(PlotData):
 
     >>> tp = ts.turning_points()
     >>> mm = tp.cycle_pairs()
-    >>> h1 = mm.plot(marker='x')
+    >>> np.allclose(mm.data[:5],
+    ...    [ 0.83950546, -0.02049454, -0.04049454,  0.25950546, -0.08049454])
+    True
+
+    h1 = mm.plot(marker='x')
     '''
 
     def __init__(self, *args, **kwds):
@@ -654,13 +661,15 @@ class CyclePairs(PlotData):
         >>> ts = wafo.objects.mat2timeseries(wafo.data.sea())
         >>> tp = ts.turning_points()
         >>> mm = tp.cycle_pairs()
-        >>> h = mm.plot(marker='.')
+
         >>> bv = range(3,9)
         >>> D = mm.damage(beta=bv)
         >>> D
         array([ 138.5238799 ,  117.56050788,  108.99265423,  107.86681126,
                 112.3791076 ,  122.08375071])
-        >>> h = plt.plot(bv,D,'x-')
+
+        h = mm.plot(marker='.')
+        h = plt.plot(bv,D,'x-')
 
         See also
         --------
@@ -708,9 +717,10 @@ class CyclePairs(PlotData):
         >>> ts = wafo.objects.mat2timeseries(wafo.data.sea())
         >>> tp = ts.turning_points()
         >>> mm = tp.cycle_pairs()
-        >>> h = mm.plot(marker='.')
         >>> lc = mm.level_crossings()
-        >>> h2 = lc.plot()
+
+        h = mm.plot(marker='.')
+        h2 = lc.plot()
 
         See also
         --------
@@ -785,7 +795,11 @@ class TurningPoints(PlotData):
     >>> ts = wo.mat2timeseries(x)
 
     >>> tp = ts.turning_points()
-    >>> h1 = tp.plot(marker='x')
+    >>> np.allclose(tp.data[:5],
+    ... [-1.2004945 ,  0.83950546, -0.09049454, -0.02049454, -0.09049454])
+    True
+
+    h1 = tp.plot(marker='x')
     '''
 
     def __init__(self, *args, **kwds):
@@ -828,9 +842,14 @@ class TurningPoints(PlotData):
         >>> ts1 = mat2timeseries(x1)
         >>> tp = ts1.turning_points(wavetype='Mw')
         >>> tph = tp.rainflow_filter(h=0.3)
-        >>> hs = ts1.plot()
-        >>> hp = tp.plot('ro')
-        >>> hph = tph.plot('k.')
+        >>> np.allclose(tph.data[:5],
+        ... [-0.16049454,  0.25950546, -0.43049454, -0.08049454, -0.42049454])
+        True
+
+
+        hs = ts1.plot()
+        hp = tp.plot('ro')
+        hph = tph.plot('k.')
 
         See also
         ---------
@@ -871,7 +890,11 @@ class TurningPoints(PlotData):
         >>> ts = wafo.objects.mat2timeseries(x)
         >>> tp = ts.turning_points()
         >>> mM = tp.cycle_pairs()
-        >>> h = mM.plot(marker='x')
+        >>> np.allclose(mM.data[:5], [ 0.83950546, -0.02049454, -0.04049454,
+        ...    0.25950546, -0.08049454])
+        True
+
+        h = mM.plot(marker='x')
 
 
         See also
@@ -980,15 +1003,15 @@ class TimeSeries(PlotData):
     >>> x = wafo.data.sea()
     >>> ts = wo.mat2timeseries(x)
     >>> rf = ts.tocovdata(lag=150)
-    >>> h = rf.plot()
 
     >>> S = ts.tospecdata()
     >>> tp = ts.turning_points()
     >>> mm = tp.cycle_pairs()
-    >>> h1 = mm.plot(marker='x')
-
     >>> lc = mm.level_crossings()
-    >>> h2 = lc.plot()
+
+    h = rf.plot()
+    h1 = mm.plot(marker='x')
+    h2 = lc.plot()
     '''
 
     def __init__(self, *args, **kwds):
@@ -1065,7 +1088,10 @@ class TimeSeries(PlotData):
          >>> x = wafo.data.sea()
          >>> ts = wo.mat2timeseries(x)
          >>> acf = ts.tocovdata(150)
-         >>> h = acf.plot()
+         >>> np.allclose(acf.data[:3], [ 0.22368637,  0.20838473,  0.17110733])
+         True
+
+         h = acf.plot()
         '''
         estimate_cov = _wafocov_estimation.CovarianceEstimator(
             lag=lag, tr=tr, detrend=detrend, window=window, flag=flag,
@@ -1328,7 +1354,7 @@ class TimeSeries(PlotData):
         ...        sigma=Hs/4, ysigma=Hs/4)
         >>> xs = S.sim(ns=2**16, iseed=10)
         >>> ts = mat2timeseries(xs)
-        >>> g0, g0emp = ts.trdata(plotflag=1)
+        >>> g0, g0emp = ts.trdata(plotflag=0)
         >>> g1, g1emp = ts.trdata(method='mnonlinear', gvar=0.5 )
         >>> g2, g2emp = ts.trdata(method='nonlinear', gvar=[3.5, 0.5, 3.5])
         >>> 100 < S.tr.dist2gauss()*100 < 200
@@ -1397,9 +1423,12 @@ class TimeSeries(PlotData):
         >>> ts1 = mat2timeseries(x1)
         >>> tp = ts1.turning_points(wavetype='Mw')
         >>> tph = ts1.turning_points(h=0.3,wavetype='Mw')
-        >>> hs = ts1.plot()
-        >>> hp = tp.plot('ro')
-        >>> hph = tph.plot('k.')
+        >>> np.allclose(tph.data[:3], [ 0.83950546, -0.16049454,  0.25950546])
+        True
+
+        hs = ts1.plot()
+        hp = tp.plot('ro')
+        hph = tph.plot('k.')
 
         See also
         ---------
@@ -1485,10 +1514,10 @@ class TimeSeries(PlotData):
         ('Tcf', array([ 0.42656819,  0.57361617]))
         ('Tcb', array([ 0.93355982,  1.04063638]))
 
-        >>> import pylab as plt
-        >>> h = plt.plot(wp['Td'],wp['Hd'],'.')
-        >>> h = plt.xlabel('Td [s]')
-        >>> h = plt.ylabel('Hd [m]')
+        import pylab as plt
+        h = plt.plot(wp['Td'],wp['Hd'],'.')
+        h = plt.xlabel('Td [s]')
+        h = plt.ylabel('Hd [m]')
 
 
         See also
@@ -1574,10 +1603,10 @@ class TimeSeries(PlotData):
         (array([ 0.10140867,  0.06141156]), array([ 0.42,  0.78]))
         (array([ 0.01821413,  0.01236672]), array([ 0.42,  0.78]))
 
-        >>> import pylab as plt
-        >>> h = plt.plot(S,H,'.')
-        >>> h = plt.xlabel('S')
-        >>> h = plt.ylabel('Hd [m]')
+        import pylab as plt
+        h = plt.plot(S,H,'.')
+        h = plt.xlabel('S')
+        h = plt.ylabel('Hd [m]')
 
         See also
         --------
@@ -1675,7 +1704,8 @@ class TimeSeries(PlotData):
 
     def _get_start_index(self, pdef, down_crossing_or_max):
         if down_crossing_or_max:
-            if pdef in ('d2t', 'M2m', 'c2t', 'd2u', 'M2M', 'c2c', 'd2d', 'all'):
+            if pdef in ('d2t', 'M2m', 'c2t', 'd2u', 'M2M', 'c2c', 'd2d',
+                        'all'):
                 start = 1
             elif pdef in ('t2u', 'm2M', 't2c', 'u2d', 'm2m', 't2t', 'u2u'):
                 start = 2
@@ -1698,13 +1728,12 @@ class TimeSeries(PlotData):
             raise ValueError('Unknown pdef option!')
         return start
 
-
     def _get_step(self, pdef):
         # determine the steps between wanted periods
         if pdef in ('d2t', 't2u', 'u2c', 'c2d'):
             step = 4
         elif pdef in ('all'):
-            step = 1 # % secret option!
+            step = 1  # secret option!
         else:
             step = 2
         return step
@@ -1773,7 +1802,10 @@ class TimeSeries(PlotData):
         >>> x = wd.sea()
         >>> ts = wo.mat2timeseries(x[0:400,:])
         >>> T, ix = ts.wave_periods(vh=0.0,pdef='c2c')
-        >>> h = plb.hist(T)
+        >>> np.allclose(T[:3,0], [-0.27, -0.08,  0.32])
+        True
+
+        h = plb.hist(T)
 
         See also:
         --------
@@ -2166,7 +2198,8 @@ class TimeSeries(PlotData):
         >>> import wafo
         >>> x = wafo.data.sea()
         >>> ts150 = wafo.objects.mat2timeseries(x[:150,:])
-        >>> h = ts150.plot_wave('r-', sym2='bo')
+
+        h = ts150.plot_wave('r-', sym2='bo')
 
         See also
         --------
@@ -2259,7 +2292,8 @@ class TimeSeries(PlotData):
         >>> import wafo
         >>> x = wafo.data.sea()
         >>> ts = wafo.objects.mat2timeseries(x[0:500,...])
-        >>> h = ts.plot_sp_wave(np.r_[6:9,12:18])
+
+        h = ts.plot_sp_wave(np.r_[6:9,12:18])
 
         See also
         --------

wafo/sg_filter.py

@@ -6,8 +6,8 @@ from numpy import (pi, abs, size, convolve, linalg, concatenate, sqrt)
 from scipy.sparse import spdiags
 from scipy.sparse.linalg import spsolve, expm
 from scipy.signal import medfilt
-from .dctpack import dctn, idctn
-from .plotbackend import plotbackend as plt
+from wafo.dctpack import dctn, idctn
+from wafo.plotbackend import plotbackend as plt
 import scipy.optimize as optimize
 from scipy.signal import _savitzky_golay
 from scipy.ndimage import convolve1d
@@ -91,14 +91,19 @@ class SavitzkyGolay(object):
     Examples
     --------
     >>> t = np.linspace(-4, 4, 500)
-    >>> y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
+    >>> noise = np.random.normal(0, 0.05, t.shape)
+    >>> noise = np.sqrt(0.05)*np.sin(100*t)
+    >>> y = np.exp( -t**2 ) + noise
     >>> ysg = SavitzkyGolay(n=20, degree=2).smooth(y)
-    >>> import matplotlib.pyplot as plt
-    >>> h = plt.plot(t, y, label='Noisy signal')
-    >>> h1 = plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
-    >>> h2 = plt.plot(t, ysg, 'r', label='Filtered signal')
-    >>> h3 = plt.legend()
-    >>> h4 = plt.title('Savitzky-Golay')
+    >>> np.allclose(ysg[:3], [ 0.01345312,  0.01164172,  0.00992839])
+    True
+
+    import matplotlib.pyplot as plt
+    h = plt.plot(t, y, label='Noisy signal')
+    h1 = plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
+    h2 = plt.plot(t, ysg, 'r', label='Filtered signal')
+    h3 = plt.legend()
+    h4 = plt.title('Savitzky-Golay')
     plt.show()
 
     References
@@ -232,10 +237,10 @@ def evar(y):
 
     3D function
     >>> yp = np.linspace(-2,2,50)
-    >>> [x,y,z] = meshgrid(yp,yp,yp, sparse=True)
-    >>> f = x*exp(-x**2-y**2-z**2)
+    >>> [x,y,z] = np.meshgrid(yp,yp,yp, sparse=True)
+    >>> f = x*np.exp(-x**2-y**2-z**2)
     >>> var0 = 0.5  # noise variance
-    >>> fn = f + sqrt(var0)*np.random.randn(*f.shape)
+    >>> fn = f + np.sqrt(var0)*np.random.randn(*f.shape)
     >>> s = evar(fn)  # estimated variance
     >>> np.abs(s-var0)/var0 < 3.5/np.sqrt(50)
     True
@@ -571,12 +576,13 @@ def smoothn(data, s=None, weight=None, robust=False, z0=None, tolz=1e-3,
     >>> y[np.r_[70, 75, 80]] = np.array([5.5, 5, 6])
     >>> z = smoothn(y) # Regular smoothing
     >>> zr = smoothn(y,robust=True) #  Robust smoothing
-    >>> h=plt.subplot(121),
-    >>> h = plt.plot(x,y,'r.',x,z,'k',linewidth=2)
-    >>> h=plt.title('Regular smoothing')
-    >>> h=plt.subplot(122)
-    >>> h=plt.plot(x,y,'r.',x,zr,'k',linewidth=2)
-    >>> h=plt.title('Robust smoothing')
+
+    h=plt.subplot(121),
+    h = plt.plot(x,y,'r.',x,z,'k',linewidth=2)
+    h=plt.title('Regular smoothing')
+    h=plt.subplot(122)
+    h=plt.plot(x,y,'r.',x,zr,'k',linewidth=2)
+    h=plt.title('Robust smoothing')
 
      2-D example
     >>> xp = np.r_[0:1:.02]
@@ -584,10 +590,11 @@ def smoothn(data, s=None, weight=None, robust=False, z0=None, tolz=1e-3,
     >>> f = np.exp(x+y) + np.sin((x-2*y)*3);
     >>> fn = f + np.random.randn(*f.shape)*0.5;
     >>> fs = smoothn(fn);
-    >>> h=plt.subplot(121),
-    >>> h=plt.contourf(xp,xp,fn)
-    >>> h=plt.subplot(122)
-    >>> h=plt.contourf(xp,xp,fs)
+
+    h=plt.subplot(121),
+    h=plt.contourf(xp,xp,fn)
+    h=plt.subplot(122)
+    h=plt.contourf(xp,xp,fs)
 
      2-D example with missing data
     n = 256;
@@ -780,13 +787,14 @@ class HodrickPrescott(object):
     >>> t = np.linspace(-4, 4, 500)
     >>> y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
     >>> ysg = HodrickPrescott(w=10000)(y)
-    >>> import matplotlib.pyplot as plt
-    >>> h = plt.plot(t, y, label='Noisy signal')
-    >>> h1 = plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
-    >>> h2 = plt.plot(t, ysg, 'r', label='Filtered signal')
-    >>> h3 = plt.legend()
-    >>> h4 = plt.title('Hodrick-Prescott')
-    >>> plt.show()
+
+    import matplotlib.pyplot as plt
+    h = plt.plot(t, y, label='Noisy signal')
+    h1 = plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
+    h2 = plt.plot(t, ysg, 'r', label='Filtered signal')
+    h3 = plt.legend()
+    h4 = plt.title('Hodrick-Prescott')
+    plt.show()
 
     References
     ----------
@@ -944,15 +952,15 @@ class Kalman(object):
     >>> for i, zi in enumerate(z):
     ...     x[i] = filt(zi, u) #  perform a Kalman filter iteration
 
-    >>> import matplotlib.pyplot as plt
-    >>> hz = plt.plot(z,'r.', label='observations')
+    import matplotlib.pyplot as plt
+    hz = plt.plot(z,'r.', label='observations')
 
     # a-posteriori state estimates:
-    >>> hx = plt.plot(x,'b-', label='Kalman output')
-    >>> ht = plt.plot(truth,'g-', label='true voltage')
-    >>> h = plt.legend()
-    >>> h1 = plt.title('Automobile Voltimeter Example')
-    >>> plt.show()
+    hx = plt.plot(x,'b-', label='Kalman output')
+    ht = plt.plot(truth,'g-', label='true voltage')
+    h = plt.legend()
+    h1 = plt.title('Automobile Voltimeter Example')
+    plt.show()
 
     '''
 
@@ -1555,12 +1563,12 @@ def test_docstrings():
     doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
 
 if __name__ == '__main__':
-    # test_docstrings()
+    test_docstrings()
     # test_kalman_sine()
     # test_tide_filter()
     # demo_hampel()
     # test_kalman()
     # test_smooth()
     # test_hodrick_cardioid()
-    test_smoothn_1d()
+    # test_smoothn_1d()
     # test_smoothn_cardioid()

wafo/spectrum/core.py

@@ -722,8 +722,9 @@ class SpecData1D(PlotData):
         >>> Nt = len(S.data)-1
         >>> acf = S.tocovdata(nr=0, nt=Nt)
         >>> S1 = acf.tospecdata()
-        >>> h = S.plot('r')
-        >>> h1 = S1.plot('b:')
+
+        h = S.plot('r')
+        h1 = S1.plot('b:')
 
         R   = spec2cov(spec,0,Nt)
         win = parzen(2*Nt+1)
@@ -2449,27 +2450,28 @@ class SpecData1D(PlotData):
 
         # interpolate for freq.  [1:(N/2)-1]*d_f and create 2-sided, uncentered
         # spectra
-        f = arange(1, ns / 2.) * d_f
+        ns2 = ns // 2
+        f = arange(1, ns2) * d_f
 
-        f_u = hstack((0., f_i, d_f * ns / 2.))
-        s_u = hstack((0., abs(s_i) / 2., 0.))
+        f_u = hstack((0., f_i, d_f * ns2))
+        s_u = hstack((0., abs(s_i) / 2, 0.))
 
         s_i = interp(f, f_u, s_u)
-        s_u = hstack((0., s_i, 0, s_i[(ns / 2) - 2::-1]))
+        s_u = hstack((0., s_i, 0, s_i[ns2 - 2::-1]))
         del(s_i, f_u)
 
         # Generate standard normal random numbers for the simulations
         randn = random.randn
-        z_r = randn((ns / 2) + 1, cases)
+        z_r = randn(ns2 + 1, cases)
         z_i = vstack(
-            (zeros((1, cases)), randn((ns / 2) - 1, cases), zeros((1, cases))))
+            (zeros((1, cases)), randn(ns2 - 1, cases), zeros((1, cases))))
 
         amp = zeros((ns, cases), dtype=complex)
-        amp[0:(ns / 2 + 1), :] = z_r - 1j * z_i
+        amp[0:ns2 + 1, :] = z_r - 1j * z_i
         del(z_r, z_i)
-        amp[(ns / 2 + 1):ns, :] = amp[ns / 2 - 1:0:-1, :].conj()
+        amp[ns2 + 1:ns, :] = amp[ns2 - 1:0:-1, :].conj()
         amp[0, :] = amp[0, :] * sqrt(2.)
-        amp[(ns / 2), :] = amp[(ns / 2), :] * sqrt(2.)
+        amp[ns2, :] = amp[ns2, :] * sqrt(2.)
 
         # Make simulated time series
         T = (ns - 1) * d_t
@@ -3127,8 +3129,11 @@ class SpecData1D(PlotData):
         >>> Sj = sm.Jonswap(Hm0=3, Tp=7)
         >>> w = np.linspace(0,4,256)
         >>> S = SpecData1D(Sj(w),w) #Make spectrum object from numerical values
-        >>> S.moment()
-        ([0.5616342024616453, 0.7309966918203602], ['m0', 'm0tt'])
+        >>> mom, mom_txt = S.moment()
+        >>> np.allclose(mom, [0.5616342024616453, 0.7309966918203602])
+        True
+        >>> mom_txt == ['m0', 'm0tt']
+        True
 
         References
         ----------
@@ -3352,13 +3357,14 @@ class SpecData1D(PlotData):
         >>> import wafo.spectrum.models as sm
         >>> Sj = sm.Jonswap(Hm0=5)
         >>> S = Sj.tospecdata() #Make spectrum ob
-        >>> S.moment(2)
-        ([1.5614600345079888, 0.95567089481941048], ['m0', 'm0tt'])
+        >>> np.allclose(S.moment(2)[0],
+        ...    [1.5614600345079888, 0.95567089481941048])
+        True
         >>> Sn = S.copy(); Sn.normalize()
 
         Now the moments should be one
-        >>> Sn.moment(2)
-        ([1.0000000000000004, 0.99999999999999967], ['m0', 'm0tt'])
+        >>> np.allclose(Sn.moment(2)[0], [1.0, 1.0])
+        True
         '''
         mom, unused_mtext = self.moment(nr=4, even=True)
         m0 = mom[0]
@@ -4194,6 +4200,6 @@ def test_docstrings():
 
 
 if __name__ == '__main__':
-    #test_docstrings()
-    test_mm_pdf()
+    test_docstrings()
+    # test_mm_pdf()
         # main()

wafo/version.py

@@ -1,4 +0,0 @@
-# THIS FILE IS GENERATED FROM SETUP.PY
-short_version='0.1.2'
-version='0.1.2'
-release=False