diff --git a/wafo/spectrum/core.py b/wafo/spectrum/core.py index 051bae5..119f9e9 100644 --- a/wafo/spectrum/core.py +++ b/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 @@ -311,19 +313,20 @@ def plotspec(specdata, linetype='b-', flag=1): # plotbackend.subplot(2, 1, 2) # # if (flag == 2) or (flag == 3) : # Plot in logaritmic scale -# ind = np.flatnonzero(data > 0) +# ind = np.flatnonzero(data > 0) # -# plotbackend.plot(np.vstack([Fp, Fp]), -# np.vstack((min(10 * log10(data.take(ind) / maxS)).repeat(len(Fp)), -# 10 * log10(data.take(indm) / maxS))), ':',label=txt) +# plotbackend.plot(np.vstack([Fp, 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'), # plot(freq(ind),10*log10(S.S(ind)*S.CI(1)/maxS), 'r:' ) # plot(freq(ind),10*log10(S.S(ind)*S.CI(2)/maxS), 'r:' ) # end -# plotbackend.plot(freq[ind], 10 * log10(data[ind] / maxS), linetype) +# plotbackend.plot(freq[ind], 10 * log10(data[ind] / maxS), linetype) # -# a = plotbackend.axis() +# a = plotbackend.axis() # # a1 = Fn # if (Fp > 0): @@ -1910,12 +1913,13 @@ class SpecData1D(PlotData): # opt0 = {SCIS,XcScale,ABSEPS,RELEPS,COVEPS,MAXPTS,MINPTS,seed,NIT1} if (Nx > 1): - # (M,m) or (M,m)v distribution wanted - 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 - asize = [Nx1, Nx1, Ntime] + # (M,m) or (M,m)v distribution wanted + 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 + asize = [Nx1, Nx1, Ntime] elif (def_nr > 3): # Conditional distribution for (TMd,TMm)v or (Tdm,TMm)v given (M,m) # wanted @@ -1937,7 +1941,7 @@ class SpecData1D(PlotData): a_up = zeros(1, NI - 1) a_lo = zeros(1, NI - 1) - # INFIN = INTEGER, array of integration limits flags: size 1 x Nb (in) + # INFIN = INTEGER, array of integration limits flags: size 1 x Nb (in) # if INFIN(I) < 0, Ith limits are (-infinity, infinity) # if INFIN(I) = 0, Ith limits are (-infinity, Hup(I)] # if INFIN(I) = 1, Ith limits are [Hlo(I), infinity) @@ -1957,15 +1961,15 @@ class SpecData1D(PlotData): xc[3, IJ:J] = hg[:I].T IJ = J else: - # Level u separated Max2min - xc[Nc, :] = u - # Hg(1) = Hg(Nx1+1)= u => start do loop at I=2 since by definition - # we must have: minimum u - xc[3, IJ:J] = hg[Nx1 + 2: 2 * Nx1].T # Min < u - IJ = J + # Level u separated Max2min + xc[Nc, :] = u + # Hg(1) = Hg(Nx1+1)= u => start do loop at I=2 since by definition + # we must have: minimum u + xc[3, IJ:J] = hg[Nx1 + 2: 2 * Nx1].T # Min < u + IJ = J if (def_nr <= 3): # h11 = fwaitbar(0,[],sprintf('Please wait ...(start at: # %s)',datestr(now))) @@ -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: diff --git a/wafo/spectrum/tests/test_specdata1d.py b/wafo/spectrum/tests/test_specdata1d.py index d50e4c7..3a7e900 100644 --- a/wafo/spectrum/tests/test_specdata1d.py +++ b/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) diff --git a/wafo/tests/test_gaussian.py b/wafo/tests/test_gaussian.py index b984e33..336a9a1 100644 --- a/wafo/tests/test_gaussian.py +++ b/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)