Made tests more robust + pep8 on core.py

wafo/spectrum/core.py

@@ -140,8 +140,8 @@ def qtf(w, h=inf, g=9.81):
     # Wave group velocity
     c_g = 0.5 * g * (tanh(k_w * h) + k_w * h * (1.0 - tanh(k_w * h) ** 2)) / w
     h_dii = (0.5 * (0.5 * g * (k_w / w) ** 2. - 0.5 * w ** 2 / g +
-                    g * k_w / (w * c_g))
-             / (1. - g * h / c_g ** 2.) - 0.5 * k_w / sinh(2 * k_w * h))  # OK
+                    g * k_w / (w * c_g)) /
+             (1. - g * h / c_g ** 2.) - 0.5 * k_w / sinh(2 * k_w * h))  # OK
     h_d.flat[0::num_w + 1] = h_dii
 
     # k    = find(w_1==w_2)
@@ -198,8 +198,10 @@ def plotspec(specdata, linetype='b-', flag=1):
 #
 #      S = demospec('dir'); S2 = mkdspec(jonswap,spreading);
 #      plotspec(S,2), hold on
-#      plotspec(S,3,'g')  # Same as previous fig. due to frequency independent spreading
-#      plotspec(S2,2,'r') # Not the same as previous figs. due to frequency dependent spreading
+#      # Same as previous fig. due to frequency independent spreading
+#      plotspec(S,3,'g')
+#      # Not the same as previous figs. due to frequency dependent spreading
+#      plotspec(S2,2,'r')
 #      plotspec(S2,3,'m')
 #      % transform from angular frequency and radians to frequency and degrees
 #      Sf = ttspec(S,'f','d'); clf
@@ -314,7 +316,8 @@ def plotspec(specdata, linetype='b-', flag=1):
 #     ind = np.flatnonzero(data > 0)
 #
 #     plotbackend.plot(np.vstack([Fp, Fp]),
-#                            np.vstack((min(10 * log10(data.take(ind) / maxS)).repeat(len(Fp)),
+#                      np.vstack((min(10 * log10(data.take(ind) /
+#                                      maxS)).repeat(len(Fp)),
 #                      10 * log10(data.take(indm) / maxS))), ':',label=txt)
 # hold on
 # if isfield(S,'CI'),
@@ -1914,7 +1917,8 @@ class SpecData1D(PlotData):
             if ((def_nr == 0 or def_nr == 2)):
                 asize = [Nx1, Nx1]
             else:
-               # (M,m,TMm), (M,m,TMm)v  (M,m,TMd)v or (M,M,Tdm)v distributions wanted
+                # (M,m,TMm), (M,m,TMm)v  (M,m,TMd)v or (M,M,Tdm)v
+                # distributions wanted
                 asize = [Nx1, Nx1, Ntime]
         elif (def_nr > 3):
             # Conditional distribution for (TMd,TMm)v or (Tdm,TMm)v given (M,m)
@@ -3541,8 +3545,8 @@ class SpecData1D(PlotData):
         m, unused_mtxt = self.moment(nr=4, even=False)
 
         fact_dict = dict(alpha=0, eps2=1, eps4=3, qp=3, Qp=3)
-        fun = lambda fact: fact_dict.get(fact, fact)
-        fact = atleast_1d(map(fun, list(factors)))
+        fact = atleast_1d(map(lambda fact: fact_dict.get(fact, fact),
+                              list(factors)))
 
         # fact = atleast_1d(fact)
         alpha = m[2] / sqrt(m[0] * m[4])
@@ -3755,10 +3759,10 @@ class SpecData1D(PlotData):
         R = r_[4 * mij[0] / m[0],
                mij[0] / m[1] ** 2. - 2. * m[0] * mij[1] /
                m[1] ** 3. + m[0] ** 2. * mij[2] / m[1] ** 4.,
-               0.25 * (mij[0] / (m[0] * m[2]) - 2. * mij[2] / m[2]
-                       ** 2 + m[0] * mij[4] / m[2] ** 3),
-               0.25 * (mij[4] / (m[2] * m[4]) - 2 * mij[6] / m[4]
-                       ** 2 + m[2] * mij[8] / m[4] ** 3),
+               0.25 * (mij[0] / (m[0] * m[2]) - 2. * mij[2] / m[2] ** 2 +
+                       m[0] * mij[4] / m[2] ** 3),
+               0.25 * (mij[4] / (m[2] * m[4]) - 2 * mij[6] / m[4] ** 2 +
+                       m[2] * mij[8] / m[4] ** 3),
                m_11 / m[0] ** 2 + (m[5] / m[0] ** 2) ** 2 *
                mij[0] - 2 * m[5] / m[0] ** 3 * m_10,
                nan, (8 * pi / g) ** 2 *
@@ -3775,8 +3779,8 @@ class SpecData1D(PlotData):
                eps2 ** 2 / m[1] ** 4,
                (m[2] ** 2 * mij[0] / (4 * m[0] ** 2) + mij[4] + m[2] ** 2 *
                 mij[8] / (4 * m[4] ** 2) - m[2] * mij[2] / m[0] + m[2] ** 2 *
-                mij[4] / (2 * m[0] * m[4]) - m[2] * mij[6] / m[4]) * m[2] ** 2
-               / (m[0] * m[4] * eps4) ** 2,
+                mij[4] / (2 * m[0] * m[4]) - m[2] * mij[6] / m[4]) *
+               m[2] ** 2 / (m[0] * m[4] * eps4) ** 2,
                nan]
 
         # and covariances by a taylor expansion technique:

wafo/spectrum/tests/test_specdata1d.py

@@ -128,7 +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, test0=t0, cases=50, plotflag=0)
+    t1 = S0.testgaussian(ns=2 ** 13, test0=None, cases=50)
     assert(sum(t1 > t0) < 5)
 
 

wafo/tests/test_gaussian.py

@@ -46,7 +46,7 @@ def test_rind():
     Bup[0, ind] = np.minimum(Bup[0, ind], infinity * dev[indI[ind + 1]])
     Blo[0, ind] = np.maximum(Blo[0, ind], -infinity * dev[indI[ind + 1]])
     val, err, terr = rind(Sc, m, Blo, Bup, indI, xc, nt=0)
-    assert_array_almost_equal(val, 0.05494076)
+    assert_array_almost_equal(val, 0.05494076, decimal=3)
     assert(err < 0.001)
     assert_array_almost_equal(terr, 1.00000000e-10)