Fixed failing tests.

wafo/spectrum/core.py

@@ -2040,7 +2040,7 @@ class SpecData1D(PlotData):
                 # end %ENDIF
                 # waitTxt = sprintf('%s Ready: %d of %d',datestr(now),Ntd,Ntime)
                 # fwaitbar(Ntd/Ntime,h11,waitTxt)
-              
+
             # end %do
             # close(h11)
             err = sqrt(err)
@@ -2102,7 +2102,7 @@ class SpecData1D(PlotData):
                             else:
                                 #  5,  gives level u separated Max2min and wave period from
                                 #  the crossing of level u to the min (M,m,Tdm).
-                                  
+
                                 IJ = 0
                                 for i in range(1, Nx1):  # = 2:Nx1
                                     J = IJ + Nx1
@@ -2282,9 +2282,9 @@ class SpecData1D(PlotData):
             # Cov(Xd,Xc)
             BIG[tn - 1, N] = R2[ts]  # %cov(X''(t1),X(ts))
             BIG[tn, N] = R2[tn - ts]  # %cov(X''(tn),X(ts))
-           
+
             # ADD a level u crossing  at ts
-           
+
             # Cov(Xt,Xd)
             # for
             i = np.arange(tn - 2)  # 1:tn-2
@@ -2326,17 +2326,17 @@ class SpecData1D(PlotData):
             # N = tn+4
             shft = 0
         # end %if
-                  
+
         if (tn > 2):
             # for i=1:tn-2
             # cov(Xt)
             #    for j=i:tn-2
             #      BIG(i,j) = -R2(j-i+1)              % cov(X'(ti+1),X'(tj+1))
             #   end %do
-           
+
             # % cov(Xt) =   % cov(X'(ti+1),X'(tj+1))
             BIG[:tn - 2, :tn - 2] = toeplitz(-R2[:tn - 2])
-        
+
             # cov(Xt,Xc)
             BIG[:tn - 2, tn + shft] = -R2[1:tn - 1]  # cov(X'(ti+1),X'(t1))
             # cov(X'(ti+1),X'(tn))
@@ -2344,7 +2344,7 @@ class SpecData1D(PlotData):
             BIG[:tn - 2, tn + shft + 2] = R1[1:tn - 1]  # cov(X'(ti+1),X(t1))
             # cov(X'(ti+1),X(tn))
             BIG[:tn - 2, tn + shft + 3] = -R1[tn - 2:0:-1]
-          
+
             # Cov(Xt,Xd)
             BIG[:tn - 2, tn - 2] = R3[1:tn - 1]     # cov(X'(ti+1),X''(t1))
             BIG[:tn - 2, tn - 1] = -R3[tn - 2:0:-1]  # cov(X'(ti+1),X''(tn))
@@ -2354,7 +2354,7 @@ class SpecData1D(PlotData):
         BIG[tn - 2, tn - 2] = R4[0]
         BIG[tn - 2, tn - 1] = R4[tn - 1]  # cov(X''(t1),X''(tn))
         BIG[tn - 1, tn - 1] = R4[0]
-        
+
         # cov(Xc)
         BIG[tn + shft + 2, tn + shft + 2] = R0[0]        # cov(X(t1),X(t1))
         # cov(X(t1),X(tn))
@@ -2929,8 +2929,8 @@ class SpecData1D(PlotData):
         # 1'st order + 2'nd order component.
         x2[:, 1::] = x[:, 1::] + x2o[0:ns, :].real
         if output == 'timeseries':
-            xx2 = mat2timeseries(x2[:, 1::], x2[:, 0].ravel())
+            xx2 = mat2timeseries(x2)
-            xx = mat2timeseries(x[:, 1::], x[:, 0].ravel())
+            xx = mat2timeseries(x)
             return xx2, xx
         return x2, x
 

wafo/spectrum/test/test_specdata1d.py

@@ -3,6 +3,7 @@ import wafo.transform.models as wtm
 import wafo.objects as wo
 from wafo.spectrum import SpecData1D
 import numpy as np
+from numpy.testing import assert_array_almost_equal
 import unittest
 
 
@@ -93,10 +94,10 @@ def test_sim_nl():
 
     funs = [np.mean, np.std, st.skew, st.kurtosis]
     for fun, trueval in zip(funs, truth1):
-        res = fun(x2[:, 1::], axis=0)
+        res = fun(x2.data, axis=0)
         m = res.mean()
         sa = res.std()
-        #trueval, m, sa
+        # trueval, m, sa
         assert(np.abs(m - trueval) < 2 * sa)
 
 
@@ -107,9 +108,9 @@ def test_stats_nl():
     S = Sj.tospecdata()
     me, va, sk, ku = S.stats_nl(moments='mvsk')
     assert(me == 0.0)
-    assert(va == 3.0608203389019537)
+    assert_array_almost_equal(va, 3.0608203389019537)
-    assert(sk == 0.18673120577479801)
+    assert_array_almost_equal(sk, 0.18673120577479801)
-    assert(ku == 3.0619885212624176)
+    assert_array_almost_equal(ku, 3.0619885212624176)
 
 
 def test_testgaussian():
@@ -127,7 +128,7 @@ def test_testgaussian():
     ys = wo.mat2timeseries(S.sim(ns=2 ** 13))
     g0, _gemp = ys.trdata()
     t0 = g0.dist2gauss()
-    t1 = S0.testgaussian(ns=2 ** 13, t0=t0, cases=50)
+    t1 = S0.testgaussian(ns=2 ** 13, test0=t0, cases=50)
     assert(sum(t1 > t0) < 5)
 
 
@@ -138,9 +139,9 @@ def test_moment():
     true_vals = [1.5614600345079888, 0.95567089481941048]
     true_txt = ['m0', 'm0tt']
     for tv, v in zip(true_vals, vals):
-        assert(tv == v)
+        assert_array_almost_equal(tv, v)
     for tv, v in zip(true_txt, txt):
-        assert(tv == v)
+        assert(tv==v)
 
 
 def test_nyquist_freq():
@@ -163,7 +164,7 @@ def test_normalize():
     vals, _txt = S.moment(2)
     true_vals = [1.5614600345079888, 0.95567089481941048]
     for tv, v in zip(true_vals, vals):
-        assert(tv == v)
+        assert_array_almost_equal(tv, v)
 
     Sn = S.copy()
     Sn.normalize()