updates

wafo/misc.py

@@ -989,7 +989,8 @@ def findextrema(x):
     findcross
     crossdef
     '''
-    return findcross(diff(x), 0.0) + 1
+    x1 = np.atleast_1d(x)
+    return findcross(diff(x1), 0.0) + 1
 
 
 def findpeaks(data, n=2, min_h=None, min_p=0.0):

wafo/objects.py

@@ -2491,7 +2491,7 @@ class TimeSeries(PlotData):
             plt.title('Surface elevation from mean water level (MWL).')
             for ix in range(nsub):
                 if nsub > 1:
-                    subplot(nsub, 1, ix)
+                    subplot(nsub, 1, ix+1)
                 h_scale = array([tn[ind[0]], tn[ind[-1]]])
                 ind2 = where((h_scale[0] <= tn2) & (tn2 <= h_scale[1]))[0]
                 plot(tn[ind] * dT, xn[ind], sym1)

wafo/spectrum/core.py

@@ -14,30 +14,30 @@ from scipy.special import erf
 from scipy.linalg import toeplitz
 import scipy.interpolate as interpolate
 from scipy.interpolate.interpolate import interp1d, interp2d
-from ..objects import TimeSeries, mat2timeseries
-from ..interpolate import stineman_interp
-from ..wave_theory.dispersion_relation import w2k  # , k2w
-from ..containers import PlotData, now
-from ..misc import sub_dict_select, nextpow2, discretize, JITImport
-from ..misc import meshgrid, gravity, cart2polar, polar2cart
-from ..markov import mctp2rfc, mctp2tc
-from ..kdetools import qlevels
+from wafo.objects import TimeSeries, mat2timeseries
+from wafo.interpolate import stineman_interp
+from wafo.wave_theory.dispersion_relation import w2k  # , k2w
+from wafo.containers import PlotData, now
+from wafo.misc import (sub_dict_select, nextpow2, discretize, JITImport,
+                       meshgrid, cart2polar, polar2cart, gravity as _gravity)
+from wafo.markov import mctp2rfc, mctp2tc
+from wafo.kdetools import qlevels
 
 # from wafo.transform import TrData
-from ..transform.models import TrLinear
-from ..plotbackend import plotbackend
+from wafo.transform.models import TrLinear
+from wafo.plotbackend import plotbackend
 
 try:
-    from ..gaussian import Rind
+    from wafo.gaussian import Rind
 except ImportError:
     Rind = None
 try:
-    from .. import c_library
+    from wafo import c_library
 except ImportError:
     warnings.warn('Compile the c_library.pyd again!')
     c_library = None
 try:
-    from .. import cov2mod
+    from wafo import cov2mod
 except ImportError:
     warnings.warn('Compile the cov2mod.pyd again!')
     cov2mod = None
@@ -102,7 +102,7 @@ def qtf(w, h=inf, g=9.81):
     k1p2 = (k_1 + k_2)
     k1m2 = abs(k_1 - k_2)
 
-    if 0:  # Langley
+    if False:  # Langley
         p_1 = (-2 * w1p2 * (k12 * g ** 2. - w12 ** 2.) +
                w_1 * (w_2 ** 4. - g ** 2 * k_2 ** 2) +
                w_2 * (w_1 ** 4 - g * 2. * k_1 ** 2)) / (4. * w12)
@@ -585,7 +585,7 @@ class SpecData1D(PlotData):
         freq = self.args
         checkdt = 1.2 * min(diff(freq)) / 2. / pi
         if self.freqtype in 'f':
-                checkdt *= 2 * pi
+            checkdt *= 2 * pi
         if (checkdt < 2. ** -16 / dt):
             print('Step dt = %g in computation of the density is ' +
                   'too small.' % dt)
@@ -914,8 +914,8 @@ class SpecData1D(PlotData):
 #          ftype = 'w'
 #          S = ttspec(S,ftype)
 #        end
-        Hm0 = self.characteristic('Hm0')
-        Tm02 = self.characteristic('Tm02')
+        Hm0 = self.characteristic('Hm0')[0]
+        Tm02 = self.characteristic('Tm02')[0]
 
         if iseed is not None:
             _set_seed(iseed)  # set the the seed
@@ -937,8 +937,7 @@ class SpecData1D(PlotData):
         num_waves = 10000  # Typical number of waves in 30 hour seastate
         Amax = sqrt(2 * log(num_waves)) * Hm0 / 4
 
-        fLimitLo = sqrt(
-            gravity * tanh(kbar * water_depth) * Amax / water_depth ** 3)
+        fLimitLo = sqrt(gravity * tanh(kbar * water_depth) * Amax / water_depth ** 3)
 
         freq = S.args
         eps = finfo(float).eps
@@ -955,7 +954,7 @@ class SpecData1D(PlotData):
         # Fs = 2*freq(end)+eps  # sampling frequency
 
         for ix in range(max_sim):
-            x2, x1 = self.sim_nl(ns=np, cases=cases, dt=None, iseed=iseed,
+            x2, x1 = self.sim_nl(ns=ns, cases=cases, dt=None, iseed=iseed,
                                  method=method, fnlimit=fn_limit,
                                  output='timeseries')
             x2.data -= x1.data  # x2(:,2:end) = x2(:,2:end) -x1(:,2:end)
@@ -968,10 +967,12 @@ class SpecData1D(PlotData):
 # %[tf21,fi] = tfe(x2(:,2),x1(:,2),1024,Fs,[],512)
 # %Hw11 = interp1q(fi,tf21.*conj(tf21),freq)
             if True:
-                Hw1 = exp(interp1d(log(abs(S1.data / S2.data)), S2.args)(freq))
+                Hw1 = exp(interp1d(log(abs(S1.data / (S2.data + 1e-5))), S2.args,
+                                   fill_value=0, bounds_error=False)(freq))
             else:
                 # Geometric mean
-                fun = interp1d(log(abs(S1.data / S2.data)), S2.args)
+                fun = interp1d(log(abs(S1.data / (S2.data + 1e-5))), S2.args,
+                               fill_value=0, bounds_error=False)
                 Hw1 = exp((fun(freq) + log(Hw2)) / 2)
                 # end
             # Hw1  = (interp1q( S2.w,abs(S1.S./S2.S),freq)+Hw2)/2
@@ -1011,6 +1012,8 @@ class SpecData1D(PlotData):
                 plotbackend.figure(2)
                 plotbackend.semilogy(freq, abs(Hw12), 'b')
                 plotbackend.title('Hw-HwOld')
+                plotbackend.show('hold')
+
                 # figtile
             # end
 
@@ -3068,9 +3071,9 @@ class SpecData1D(PlotData):
 
         >>> S = S0.copy()
         >>> me, va, sk, ku = S.stats_nl(moments='mvsk')
-        >>> S.tr = wtm.TrHermite(mean=me, sigma=Hs/4, skew=sk, kurt=ku,
-        ...                        ysigma=Hs/4)
+        >>> S.tr = wtm.TrHermite(mean=me, sigma=Hs/4, skew=sk, kurt=ku, ysigma=Hs/4)
         >>> ys = wo.mat2timeseries(S.sim(ns=2**13))
+        >>> ys
         >>> g0, gemp = ys.trdata()
         >>> t0 = g0.dist2gauss()
         >>> t1 = S0.testgaussian(ns=2**13, cases=50)
@@ -3363,6 +3366,11 @@ class SpecData1D(PlotData):
                 self.data = intfun(self.args)
             self.data = self.data.clip(0)  # clip negative values to 0
 
+    def interp(self, dt):
+        S = self.copy()
+        S.resample(dt)
+        return S
+
     def normalize(self, gravity=9.81):
         '''
         Normalize a spectral density such that m0=m2=1
@@ -4036,7 +4044,7 @@ class SpecData2D(PlotData):
 
         if nr > 0:
             vec = []
-            g = np.atleast_1d(S1.__dict__.get('g', gravity()))
+            g = np.atleast_1d(S1.__dict__.get('g', _gravity()))
             # maybe different normalization in x and y => diff. g
             kx = w ** 2 / g[0]
             ky = w ** 2 / g[-1]
@@ -4231,7 +4239,8 @@ def test_mm_pdf():
     S = sm.SpecData1D(Sj(w), w)  # Alternatively do it manually
     S0 = S.to_linspec()
     mm = S.to_mm_pdf()
-
+    mm.plot()
+    plotbackend.show()
 
 def test_docstrings():
     import doctest
@@ -4239,6 +4248,6 @@ def test_docstrings():
 
 
 if __name__ == '__main__':
-    test_docstrings()
-    # test_mm_pdf()
+    # test_docstrings()
+    test_mm_pdf()
     # main()

wafo/spectrum/models.py

@@ -36,9 +36,9 @@ import scipy.integrate as integrate
 import scipy.special as sp
 from scipy.fftpack import fft
 import numpy as np
-from numpy import (inf, atleast_1d, newaxis, any, minimum, maximum, array,
+from numpy import (inf, atleast_1d, newaxis, minimum, maximum, array,
                    asarray, exp, log, sqrt, where, pi, arange, linspace, sin,
-                   cos, abs, sinh, isfinite, mod, expm1, tanh, cosh, finfo,
+                   cos, sinh, isfinite, mod, expm1, tanh, cosh, finfo,
                    ones, ones_like, isnan, zeros_like, flatnonzero, sinc,
                    hstack, vstack, real, flipud, clip)
 from ..wave_theory.dispersion_relation import w2k, k2w  # @UnusedImport
@@ -498,7 +498,7 @@ class Jonswap(ModelSpectrum):
     >>> S = wsm.Jonswap(Hm0=7, Tp=11,gamma=1)
     >>> S2 = wsm.Bretschneider(Hm0=7, Tp=11)
     >>> w = plb.linspace(0,5)
-    >>> all(abs(S(w)-S2(w))<1.e-7)
+    >>> all(np.abs(S(w)-S2(w))<1.e-7)
     True
 
     h = plb.plot(w,S(w))
@@ -1313,7 +1313,7 @@ class Wallop(Bretschneider):
             wp = 2. * pi / Tp
             kp = w2k(wp, 0, inf)[0]  # wavenumber at peak frequency
             Lp = 2. * pi / kp  # wave length at the peak frequency
-            N = abs((log(2. * pi ** 2.) + 2 * log(Hm0 / 4) -
+            N = np.abs((log(2. * pi ** 2.) + 2 * log(Hm0 / 4) -
                      2.0 * log(Lp)) / log(2))
 
         super(Wallop, self).__init__(Hm0, Tp, N, M, chk_seastate)
@@ -1796,7 +1796,7 @@ class Spreading(object):
         """ Returns the solution of r1 = x.
         """
         X = r1
-        if any(X >= 1):
+        if np.any(X >= 1):
             raise ValueError('POISSON spreading: X value must be less than 1')
         return X
 
@@ -1822,9 +1822,9 @@ class Spreading(object):
             A[ix] = Ai + 0.5 * (da[ix] - Ai) * (Ai <= 0.0)
 
             ix = flatnonzero(
-                (abs(da) > sqrt(_EPS) * abs(A)) * (abs(da) > sqrt(_EPS)))
+                (np.abs(da) > sqrt(_EPS) * np.abs(A)) * (np.abs(da) > sqrt(_EPS)))
             if ix.size == 0:
-                if any(A > pi):
+                if np.any(A > pi):
                     raise ValueError(
                         'BOX-CAR spreading: The A value must be less than pi')
                 return A.clip(min=1e-16, max=pi)
@@ -1872,7 +1872,7 @@ class Spreading(object):
         def fun(x):
             return 0.5 * pi / (sinh(.5 * pi / x)) - x * r1[ix]
         for ix in ix0:
-            B[ix] = abs(optimize.fsolve(fun, B0[ix]))
+            B[ix] = np.abs(optimize.fsolve(fun, B0[ix]))
         return B
 
     def fourier2d(self, r1):
@@ -1953,14 +1953,14 @@ class Spreading(object):
                             self._frequency_independent_spread)
         s = spread(wn)
 
-        if any(s < 0):
+        if np.any(s < 0):
             raise ValueError('The COS2S spread parameter, S(w), ' +
                              'value must be larger than 0')
         if self.type[0] == 'c':  # cos2s
             s_par = s
         else:
             # First Fourier coefficient of the directional spreading function.
-            r1 = abs(s / (s + 1))
+            r1 = np.abs(s / (s + 1))
             # Find distribution parameter from first Fourier coefficient.
             s_par = self.fourier2distpar(r1)
         if self.method is not None:
@@ -1986,8 +1986,8 @@ class Spreading(object):
 
     @staticmethod
     def _check_theta(theta):
-        L = abs(theta[-1] - theta[0])
-        if abs(L - 2 * np.pi) > _EPS:
+        L = np.abs(theta[-1] - theta[0])
+        if np.abs(L - 2 * np.pi) > _EPS:
             raise ValueError('theta must cover all angles -pi -> pi')
         nt = len(theta)
         if nt < 40: