from __future__ import absolute_import, division import warnings import os import numpy as np from numpy import (pi, inf, zeros, ones, where, nonzero, flatnonzero, ceil, sqrt, exp, log, arctan2, tanh, cosh, sinh, random, atleast_1d, minimum, diff, isnan, r_, conj, mod, hstack, vstack, interp, ravel, finfo, linspace, arange, array, nan, newaxis, sign) from numpy.fft import fft from scipy.integrate import simps, trapz from scipy.special import erf from scipy.linalg import toeplitz import scipy.interpolate as interpolate from scipy.interpolate.interpolate import interp1d, interp2d 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 wafo.transform.models import TrLinear from wafo.plotbackend import plotbackend try: from wafo.gaussian import Rind except ImportError: Rind = None # try: # from wafo import c_library # except ImportError: # warnings.warn('Compile the c_library.pyd again!') # c_library = None # try: # from wafo import cov2mod # except ImportError: # warnings.warn('Compile the cov2mod.pyd again!') # cov2mod = None # Trick to avoid error due to circular import _WAFOCOV = JITImport('wafo.covariance') __all__ = ['SpecData1D', 'SpecData2D', 'plotspec'] _EPS = np.finfo(float).eps _TINY = np.finfo(float).tiny def _set_seed(iseed): '''Set seed of random generator''' if iseed is not None: try: random.set_state(iseed) except (KeyError, TypeError): random.seed(iseed) def qtf(w, h=inf, g=9.81, method='winterstein', rtol=1e-7, atol=0): """ Return Quadratic Transfer Function Parameters ------------ w : array-like angular frequencies h : scalar water depth g : scalar acceleration of gravity Returns ------- h_s = sum frequency effects h_d = difference frequency effects h_dii = diagonal of h_d Example ------- >>> w = np.r_[0.1, 1./3, 2./3, 1] >>> hs, hd, hdi = qtf(w, h=np.inf, g=9.81) >>> np.allclose(hs, [[ 0.00050968, 0.00308642, 0.01158115, 0.02573904], ... [ 0.00308642, 0.00566316, 0.01415789, 0.02831578], ... [ 0.01158115, 0.01415789, 0.02265262, 0.03681051], ... [ 0.02573904, 0.02831578, 0.03681051, 0.0509684 ]]) True >>> np.allclose(hd, [[-0. , -0.00257674, -0.01107147, -0.02522936], ... [-0.00257674, -0. , -0.00849473, -0.02265262], ... [-0.01107147, -0.00849473, -0. , -0.01415789], ... [-0.02522936, -0.02265262, -0.01415789, -0. ]]) True >>> hs2, hd2, hdi2 = qtf(w, h=1e+6, g=9.81, method='winterstein') >>> np.allclose(hs2, [[0.00050968, 0.00308642, 0.01158115, 0.02573904], ... [0.00308642, 0.00566316, 0.01415789, 0.02831578], ... [0.01158115, 0.01415789, 0.02265262, 0.03681051], ... [0.02573904, 0.02831578, 0.03681051, 0.0509684 ]]) True >>> np.allclose(hd2, [[-2.50061328e-07, 1.38729557e-03, 8.18314621e-03, 2.06421189e-02], ... [1.38729557e-03, -2.50005518e-07, 2.83135545e-03, 1.13261230e-02], ... [8.18314621e-03, 2.83135545e-03, -2.50001380e-07, 2.83133750e-03], ... [2.06421189e-02, 1.13261230e-02, 2.83133750e-03, -2.50000613e-07]]) True >>> w = np.r_[0, 1e-10, 1e-5, 1e-1] >>> hs, hd, hdi = qtf(w, h=np.inf, g=9.81) >>> np.allclose(hs, [[0.00000000e+00, 2.54841998e-22, 2.54841998e-12, 2.54841998e-04], ... [2.54841998e-22, 5.09683996e-22, 2.54841998e-12, 2.54841998e-04], ... [2.54841998e-12, 2.54841998e-12, 5.09683996e-12, 2.54842001e-04], ... [2.54841998e-04, 2.54841998e-04, 2.54842001e-04, 5.09683996e-04]]) True >>> np.allclose(hd, [[-0.00000000e+00, -2.54841998e-22, -2.54841998e-12, -2.54841998e-04], ... [-2.54841998e-22, -0.00000000e+00, -2.54841998e-12, -2.54841998e-04], ... [-2.54841998e-12, -2.54841998e-12, -0.00000000e+00, -2.54841995e-04], ... [-2.54841998e-04, -2.54841998e-04, -2.54841995e-04, -0.00000000e+00]]) True >>> hs2, hd2, hdi2 = qtf(w, h=1e+100, g=9.81, method='winterstein') >>> np.allclose(hs2, [[1.50234572e-63, 2.54841998e-22, 2.54841998e-12, 2.54841998e-04], ... [2.54841998e-22, 5.09683996e-22, 2.54841998e-12, 2.54841998e-04], ... [2.54841998e-12, 2.54841998e-12, 5.09683996e-12, 2.54842001e-04], ... [2.54841998e-04, 2.54841998e-04, 2.54842001e-04, 5.09683996e-04]], ... atol=0) True >>> np.allclose(hd2, [[-2.50000000e-101, 2.54841998e-022, 2.54841998e-012, 2.54841998e-004], ... [2.54841998e-022, -2.50000000e-101, 2.54836901e-012, 2.54841997e-004], ... [2.54841998e-012, 2.54836901e-012, -2.50000000e-101, 2.54791032e-004], ... [2.54841998e-004, 2.54841997e-004, 2.54791032e-004, -2.500000e-101]], ... atol=0) True References: ----------- Langley, RS (1987) 'A statistical analysis of nonlinear random waves' Ocean Engineering, Vol 14, No 5, pp 389-407 Marthinsen, T. and Winterstein, S.R (1992) 'On the skewness of random surface waves' In proceedings of the 2nd ISOPE Conference, San Francisco, 14-19 june. """ # >>> hs3, hd3, hdi3 = qtf(w, h=200, g=9.81, method='winterstein') # >>> hs3 # # >>> hd3 # # >>> np.allclose(hs3, [[ 0. , 0.00283158, 0.01132631, 0.0254842 ], # ... [ 0.00283158, 0.00566316, 0.01415789, 0.02831578], # ... [ 0.01132631, 0.01415789, 0.02265262, 0.03681051], # ... [ 0.0254842 , 0.02831578, 0.03681051, 0.0509684 ]]) # # >>> np.allclose(hd3, [[-0. , -0.00283158, -0.01132631, -0.0254842 ], # ... [-0.00283158, -0. , -0.00849473, -0.02265262], # ... [-0.01132631, -0.00849473, -0. , -0.01415789], # ... [-0.0254842 , -0.02265262, -0.01415789, -0. ]]) w = atleast_1d(w) num_w = w.size if h == inf: # go here for faster calculations k_w = w2k(w, theta=0, h=h, g=g, rtol=rtol, atol=atol)[0] k_1, k_2 = meshgrid(k_w, k_w, sparse=True) h_s = 0.25 * (abs(k_1) + abs(k_2)) h_d = -0.25 * abs(abs(k_1) - abs(k_2)) h_dii = zeros(num_w) return h_s, h_d, h_dii w1 = w + _TINY ** (1. / 10) * (np.sign(w) * np.int_(np.abs(w) < _EPS) + np.int_(w == 0)) w = w1 # k_w += _TINY ** (1./3) * (np.sign(k_w) * np.int_(np.abs(k_w) < _EPS) + np.int_(k_w==0)) k_w = w2k(w, theta=0, h=h, g=g, rtol=rtol, atol=atol)[0] k_1, k_2 = meshgrid(k_w, k_w, sparse=True) w_1, w_2 = meshgrid(w, w, sparse=True) w12 = w_1 * w_2 w1p2 = w_1 + w_2 w1m2 = w_1 - w_2 k12 = k_1 * k_2 k1p2 = k_1 + k_2 k1m2 = abs(k_1 - k_2) if method.startswith('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 + _TINY) p_2 = w1p2 ** 2. * cosh((k1p2) * h) - g * (k1p2) * sinh((k1p2) * h) p_2 += _TINY * np.int_(p_2 == 0) h_s = (-p_1 / p_2 * w1p2 * cosh((k1p2) * h) / g - (k12 * g ** 2 - w12 ** 2.) / (4 * g * w12 + _TINY) + (w_1 ** 2 + w_2 ** 2) / (4 * g)) p_3 = (-2 * w1m2 * (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 + _TINY) p_4 = w1m2 ** 2. * cosh(k1m2 * h) - g * (k1m2) * sinh((k1m2) * h) p_4 += _TINY * np.int_(p_4 == 0) h_d = (-p_3 / p_4 * (w1m2) * cosh((k1m2) * h) / g - (k12 * g ** 2 + w12 ** 2) / (4 * g * w12 + _TINY) + (w_1 ** 2. + w_2 ** 2.) / (4. * g)) else: # Marthinsen & Winterstein tmp1 = 2.0 * g * k12 / (w12 + 0) tmp2 = (w_1 ** 2. + w_2 ** 2. + w12) / g h_s = 0.25 * ((tmp1 - tmp2 + g * (w_1 * k_2 ** 2. + w_2 * k_1 ** 2) / (w12 * w1p2 + 0)) / (1. - g * h * k1p2 / (w1p2 ** 2. + 0) * tanh(k1p2)) + tmp2 - 0.5 * tmp1) # OK tiny_diag = _TINY * np.diag(np.ones(num_w)) # Avoid division by zero on diagonal tmp3 = (w_1 ** 2 + w_2 ** 2 - w12) / g # OK numerator = (tmp1 - tmp3 - g * (w_1 * k_2 ** 2 - w_2 * k_1 ** 2) / (w12 * w1m2 + tiny_diag)) h_d = 0.25 * (numerator / (1. - g * h * k1m2 / (w1m2 ** 2. + tiny_diag) * tanh(k1m2)) + tmp3 - 0.5 * tmp1) # OK # h_d = 0.25 * ((tmp1 - tmp3 # - g * (w_1 * k_2 ** 2 - w_2 * k_1 ** 2) / (w12 * w1m2 + tiny_diag)) # / (1. - g * h * k1m2 / (w1m2 ** 2. + tiny_diag) * tanh(k1m2)) # + tmp3 - 0.5 * tmp1) # OK # tmp1 = 2 * g * (k_w./w)^2 # tmp2 = w.^2/g # Wave group velocity k_h = k_w * h c_g = 0.5 * g * (tanh(k_h) + k_h / np.cosh(k_h) ** 2) / w numerator2 = (g * (k_w / w) ** 2. - w ** 2 / g + 2 * g * k_w / (w * c_g + 0)) h_dii = 0.25 * (numerator2 / (1. - g * h / (c_g ** 2. + 0)) - 2 * k_w / sinh(2 * k_h)) # OK # 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 + 0)) # / (1. - g * h / (c_g ** 2. + 0)) # - 0.5 * k_w / sinh(2 * k_w * h)) # OK h_d.flat[0::num_w + 1] = h_dii # infinite water # >>> np.allclose(hs, [[ 0. , 0.00283158, 0.01132631, 0.0254842 ], # ... [ 0.00283158, 0.00566316, 0.01415789, 0.02831578], # ... [ 0.01132631, 0.01415789, 0.02265262, 0.03681051], # ... [ 0.0254842 , 0.02831578, 0.03681051, 0.0509684 ]]) # True # # >>> np.allclose(hd, [[-0. , -0.00283158, -0.01132631, -0.0254842 ], # ... [-0.00283158, -0. , -0.00849473, -0.02265262], # ... [-0.01132631, -0.00849473, -0. , -0.01415789], # ... [-0.0254842 , -0.02265262, -0.01415789, -0. ]]) # True # winterstein # h_s = # [[ 0.00000000e+00 -1.64775418e+00 -6.95612056e-01 -4.18817231e-01 -3.15690232e-01] # [ -1.64775418e+00 -7.98574421e-01 -2.21051428e-01 -6.73808482e-02 -1.69373060e-02] # [ -6.95612056e-01 -2.21051428e-01 -1.29139936e-01 -4.11797418e-02 1.12063541e-03] # [ -4.18817231e-01 -6.73808482e-02 -4.11797418e-02 -3.51718594e-03 2.44489725e-02] # h_d = # [[ 0. -1.64775418 -0.69561206 -0.41881723 -0.31569023] # [-1.6103978 0.0130494 -0.12128861 -0.05785645 -0.02806048] # [-0.65467824 -0.12128861 0.01494093 -0.04402996 -0.02595442] # [-0.35876732 -0.05785645 -0.04402996 0.01905565 -0.02218373] # [ inf -0.02806048 -0.02595442 -0.02218373 0.02705736]] # langley # h_d = [[ 0.00000000e+00 -8.87390092e+14 -3.87924869e+14 -1.66844106e+15 inf] # [ -8.87390092e+14 -1.14566397e-02 -1.50113192e-01 -1.11791139e-01 -1.13090565e-01] # [ -3.87924869e+14 -1.50113192e-01 -8.56987798e-03 -5.10233013e-02 -4.93936523e-02] # [ -1.66844106e+15 -1.11791139e-01 -5.10233013e-02 -4.72078473e-03 -2.74040590e-02] # [ inf -1.13090565e-01 -4.93936523e-02 -2.74040590e-02 -1.57316125e-03]] # h_s = # [[ 0.00000000e+00 -8.62422934e+14 -3.76136070e+14 -1.61053099e+15 inf] # [ -8.87390092e+14 2.59936788e-01 1.22409408e-01 7.97392657e-02 6.16999831e-02] # [ -3.87924869e+14 1.46564082e-01 7.02793126e-02 4.62059958e-02 3.58607610e-02] # [ -1.66844106e+15 1.18356989e-01 5.82970744e-02 3.92688958e-02 3.13685586e-02] # [ inf 1.25606419e-01 6.35218804e-02 4.41902963e-02 3.69195895e-02]] # k = find(w_1==w_2) # h_d(k) = h_dii # The NaN's occur due to division by zero. => Set the isnans to zero h_dii = where(isnan(h_dii), 0, h_dii) h_d = where(isnan(h_d), 0, h_d) h_s = where(isnan(h_s), 0, h_s) return h_s, h_d, h_dii def plotspec(specdata, linetype='b-', flag=1): ''' PLOTSPEC Plot a spectral density Parameters ---------- S : SpecData1D or SpecData2D object defining spectral density. linetype : string defining color and linetype, see plot for possibilities flag : scalar integer defining the type of plot 1D: 1 plots the density, S, (default) 2 plot 10log10(S) 3 plots both the above plots 2D: Directional spectra: S(w,theta), S(f,theta) 1 polar plot S (default) 2 plots spectral density and the directional spreading, int S(w,theta) dw or int S(f,theta) df 3 plots spectral density and the directional spreading, int S(w,theta)/S(w) dw or int S(f,theta)/S(f) df 4 mesh of S 5 mesh of S in polar coordinates 6 contour plot of S 7 filled contour plot of S Wavenumber spectra: S(k1,k2) 1 contour plot of S (default) 2 filled contour plot of S Example ------- >>> import numpy as np >>> import wafo.spectrum as ws >>> Sj = ws.models.Jonswap(Hm0=3, Tp=7) >>> S = Sj.tospecdata() >>> ws.plotspec(S,flag=1) S = demospec('dir'); S2 = mkdspec(jonswap,spreading); plotspec(S,2), hold on # 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 plotspec(Sf,2), See also dat2spec, createspec, simpson ''' pass # # label the contour levels # txtFlag = 0 # LegendOn = 1 # # ftype = specdata.freqtype # options are 'f' and 'w' and 'k' # data = specdata.data # if data.ndim == 2: # freq = specdata.args[1] # theta = specdata.args[0] # else: # freq = specdata.args # if isinstance(specdata.args, (list, tuple)): # # if ftype == 'w': # xlbl_txt = 'Frequency [rad/s]' # ylbl1_txt = 'S(w) [m^2 s / rad]' # ylbl3_txt = 'Directional Spectrum' # zlbl_txt = 'S(w,\theta) [m^2 s / rad^2]' # funit = ' [rad/s]' # Sunit = ' [m^2 s / rad]' # elif ftype == 'f': # xlbl_txt = 'Frequency [Hz]' # ylbl1_txt = 'S(f) [m^2 s]' # ylbl3_txt = 'Directional Spectrum' # zlbl_txt = 'S(f,\theta) [m^2 s / rad]' # funit = ' [Hz]' # Sunit = ' [m^2 s ]' # elif ftype == 'k': # xlbl_txt = 'Wave number [rad/m]' # ylbl1_txt = 'S(k) [m^3/ rad]' # funit = ' [rad/m]' # Sunit = ' [m^3 / rad]' # ylbl4_txt = 'Wave Number Spectrum' # # else: # raise ValueError('Frequency type unknown') # # # if hasattr(specdata, 'norm') and specdata.norm : # Sunit=[] # funit = [] # ylbl1_txt = 'Normalized Spectral density' # ylbl3_txt = 'Normalized Directional Spectrum' # ylbl4_txt = 'Normalized Wave Number Spectrum' # if ftype == 'k': # xlbl_txt = 'Normalized Wave number' # else: # xlbl_txt = 'Normalized Frequency' # # ylbl2_txt = 'Power spectrum (dB)' # # phi = specdata.phi # # spectype = specdata.type.lower() # stype = spectype[-3::] # if stype in ('enc', 'req', 'k1d') : #1D plot # Fn = freq[-1] # Nyquist frequency # indm = findpeaks(data, n=4) # maxS = data.max() # if isfield(S,'CI') && ~isempty(S.CI): # maxS = maxS*S.CI(2) # txtCI = [num2str(100*S.p), '% CI'] # #end # # Fp = freq[indm]# %peak frequency/wave number # # if len(indm) == 1: # txt = [('fp = %0.2g' % Fp) + funit] # else: # txt = [] # for i, fp in enumerate(Fp.tolist()): # txt.append(('fp%d = %0.2g' % (i, fp)) + funit) # # txt = ''.join(txt) # if (flag == 3): # plotbackend.subplot(2, 1, 1) # if (flag == 1) or (flag == 3):# Plot in normal scale # plotbackend.plot(np.vstack([Fp, Fp]), # np.vstack([zeros(len(indm)), data.take(indm)]), # ':', label=txt) # plotbackend.plot(freq, data, linetype) # specdata.labels.labelfig() # if isfield(S,'CI'): # plot(freq,S.S*S.CI(1), 'r:' ) # plot(freq,S.S*S.CI(2), 'r:' ) # # a = plotbackend.axis() # # a1 = Fn # if (Fp > 0): # a1 = max(min(Fn, 10 * max(Fp)), a[1]) # # plotbackend.axis([0, a1 , 0, max(1.01 * maxS, a[3])]) # plotbackend.title('Spectral density') # plotbackend.xlabel(xlbl_txt) # plotbackend.ylabel(ylbl1_txt) # # # if (flag == 3): # plotbackend.subplot(2, 1, 2) # # if (flag == 2) or (flag == 3) : # Plot in logaritmic scale # 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) # 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:' ) # # plotbackend.plot(freq[ind], 10 * log10(data[ind] / maxS), linetype) # # a = plotbackend.axis() # # a1 = Fn # if (Fp > 0): # a1 = max(min(Fn, 10 * max(Fp)), a[1]) # # plotbackend.axis([0, a1 , -20, max(1.01 * 10 * log10(1), a[3])]) # # specdata.labels.labelfig() # plotbackend.title('Spectral density') # plotbackend.xlabel(xlbl_txt) # plotbackend.ylabel(ylbl2_txt) # # if LegendOn: # plotbackend.legend() # if isfield(S,'CI'), # legend(txt{:},txtCI,1) # else # legend(txt{:},1) # end # end # case {'k2d'} # if plotflag==1, # [c, h] = contour(freq,S.k2,S.S,'b') # z_level = clevels(c) # # # if txtFlag==1 # textstart_x=0.05; textstart_y=0.94 # cltext1(z_level,textstart_x,textstart_y) # else # cltext(z_level,0) # end # else # [c,h] = contourf(freq,S.k2,S.S) # %clabel(c,h), colorbar(c,h) # fcolorbar(c) % alternative # end # rotate(h,[0 0 1],-phi*180/pi) # # # # xlabel(xlbl_txt) # ylabel(xlbl_txt) # title(ylbl4_txt) # # return # km=max([-freq(1) freq(end) S.k2(1) -S.k2(end)]) # axis([-km km -km km]) # hold on # plot([0 0],[ -km km],':') # plot([-km km],[0 0],':') # axis('square') # # # # cltext(z_level) # # axis('square') # if ~ih, hold off,end # case {'dir'} # thmin = S.theta(1)-phi;thmax=S.theta(end)-phi # if plotflag==1 % polar plot # if 0, % alternative but then z_level must be chosen beforehand # h = polar([0 2*pi],[0 freq(end)]) # delete(h);hold on # [X,Y]=meshgrid(S.theta,freq) # [X,Y]=polar2cart(X,Y) # contour(X,Y,S.S',lintype) # else # if (abs(thmax-thmin)<3*pi), % angle given in radians # theta = S.theta # else # theta = S.theta*pi/180 % convert to radians # phi = phi*pi/180 # end # c = contours(theta,freq,S.S')%,Nlevel) % calculate levels # if isempty(c) # c = contours(theta,freq,S.S)%,Nlevel); % calculate levels # end # [z_level c] = clevels(c); % find contour levels # h = polar(c(1,:),c(2,:),lintype); # rotate(h,[0 0 1],-phi*180/pi) # end # title(ylbl3_txt) # % label the contour levels # # if txtFlag==1 # textstart_x = -0.1; textstart_y=1.00; # cltext1(z_level,textstart_x,textstart_y); # else # cltext(z_level,0) # end # # elseif (plotflag==2) || (plotflag==3), # %ih = ishold; # # subplot(211) # # if ih, hold on, end # # Sf = spec2spec(S,'freq'); % frequency spectrum # plotspec(Sf,1,lintype) # # subplot(212) # # Dtf = S.S; # [Nt,Nf] = size(S.S); # Sf = Sf.S(:).'; # ind = find(Sf); # # if plotflag==3, %Directional distribution D(theta,freq)) # Dtf(:,ind) = Dtf(:,ind)./Sf(ones(Nt,1),ind); # end # Dtheta = simpson(freq,Dtf,2); %Directional spreading, D(theta) # Dtheta = Dtheta/simpson(S.theta,Dtheta); # int D(theta)dtheta = 1 # [y,ind] = max(Dtheta); # Wdir = S.theta(ind)-phi; % main wave direction # txtwdir = ['\theta_p=' num2pistr(Wdir,3)]; % convert to text string # # plot([1 1]*S.theta(ind)-phi,[0 Dtheta(ind)],':'), hold on # if LegendOn # lh=legend(txtwdir,0); # end # plot(S.theta-phi,Dtheta,lintype) # # fixthetalabels(thmin,thmax,'x',2) # ylabel('D(\theta)') # title('Spreading function') # if ~ih, hold off, end # %legend(lh) % refresh current legend # elseif plotflag==4 % mesh # mesh(freq,S.theta-phi,S.S) # xlabel(xlbl_txt); # fixthetalabels(thmin,thmax,'y',3) # zlabel(zlbl_txt) # title(ylbl3_txt) # elseif plotflag==5 % mesh # %h=polar([0 2*pi],[0 freq(end)]); # %delete(h);hold on # [X,Y]=meshgrid(S.theta-phi,freq); # [X,Y]=polar2cart(X,Y); # mesh(X,Y,S.S') # % display the unit circle beneath the surface # hold on, mesh(X,Y,zeros(size(S.S'))),hold off # zlabel(zlbl_txt) # title(ylbl3_txt) # set(gca,'xticklabel','','yticklabel','') # lighting phong # %lighting gouraud # %light # elseif (plotflag==6) || (plotflag==7), # theta = S.theta-phi; # [c, h] = contour(freq,theta,S.S); %,Nlevel); % calculate levels # fixthetalabels(thmin,thmax,'y',2) # if plotflag==7, # hold on # [c,h] = contourf(freq,theta,S.S); %,Nlevel); # %hold on # end # # title(ylbl3_txt) # xlabel(xlbl_txt); # if 0, # [z_level] = clevels(c); % find contour levels # % label the contour levels # if txtFlag==1 # textstart_x = 0.06; textstart_y=0.94; # cltext1(z_level,textstart_x,textstart_y) #local: cltext # else # cltext(z_level) # end # else # colormap('jet') # # if plotflag==7, # fcolorbar(c) # else # %clabel(c,h), # hcb = colorbar; # end # grid on # end # else # error('Unknown plot option') # end # otherwise, error('unknown spectral type') # end # # if ~ih, hold off, end # # # The following two commands install point-and-click editing of # # all the text objects (title, xlabel, ylabel) of the current figure: # # #set(findall(gcf,'type','text'),'buttondownfcn','edtext') # #set(gcf,'windowbuttondownfcn','edtext(''hide'')') # # return class SpecData1D(PlotData): """ Container class for 1D spectrum data objects in WAFO Member variables ---------------- data : array-like One sided Spectrum values, size nf args : array-like freguency/wave-number-lag values of freqtype, size nf type : String spectrum type, one of 'freq', 'k1d', 'enc' (default 'freq') freqtype : letter frequency type, one of: 'f', 'w' or 'k' (default 'w') tr : Transformation function (default (none)). h : real scalar Water depth (default inf). v : real scalar Ship speed, if type = 'enc'. norm : bool Normalization flag, True if S is normalized, False if not date : string Date and time of creation or change. Examples -------- >>> import numpy as np >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap(Hm0=3) >>> w = np.linspace(0,4,256) >>> S1 = Sj.tospecdata(w) #Make spectrum object from numerical values >>> S = sm.SpecData1D(Sj(w),w) # Alternatively do it manually See also -------- PlotData CovData """ def __init__(self, *args, **kwds): super(SpecData1D, self).__init__(*args, **kwds) self.name_ = kwds.pop('name', 'WAFO Spectrum Object') self.type = kwds.pop('type', 'freq') self._freqtype = kwds.pop('freqtype', 'w') self.angletype = '' self.h = kwds.pop('h', inf) self.tr = kwds.pop('tr', None) # TrLinear() self.phi = kwds.pop('phi', 0.0) self.v = kwds.pop('v', 0.0) self.norm = kwds.pop('norm', False) self.setlabels() @property def freqtype(self): return self._freqtype @freqtype.setter def freqtype(self, freqtype): if self._freqtype == freqtype: return # do nothind if freqtype == 'w' and self._freqtype == 'f': self.args *= 2 * np.pi self.data /= 2 * np.pi self._freqtype = 'w' self.setlabels() elif freqtype == 'f' and self._freqtype == 'w': self.args /= 2 * np.pi self.data *= 2 * np.pi self._freqtype = 'f' self.setlabels() def _get_default_dt_and_rate(self, dt): dt_old = self.sampling_period() if dt is None: return dt_old, 1 rate = max(round(dt_old * 1. / dt), 1.) return dt, int(rate) def _check_dt(self, dt): freq = self.args checkdt = 1.2 * min(diff(freq)) / 2. / pi if self.freqtype in 'f': checkdt *= 2 * pi if (checkdt < 2. ** -16 / dt): print('Step dt = %g in computation of the density is ' + 'too small.' % dt) print('The computed covariance (by FFT(2^K)) may differ from the') print('theoretical. Solution:') raise ValueError('use larger dt or sparser grid for spectrum.') @staticmethod def _check_cov_matrix(acfmat, nt, dt): eps0 = 0.0001 if nt + 1 >= 5: cc2 = acfmat[0, 0] - acfmat[4, 0] * (acfmat[4, 0] / acfmat[0, 0]) if (cc2 < eps0): warnings.warn('Step dt = %g in computation of the density ' + 'is too small.' % dt) cc1 = acfmat[0, 0] - acfmat[1, 0] * (acfmat[1, 0] / acfmat[0, 0]) if (cc1 < eps0): warnings.warn('Step dt = %g is small, and may cause numerical ' + 'inaccuracies.' % dt) @property def lagtype(self): if self.freqtype in 'k': # options are 'f' and 'w' and 'k' return 'x' return 't' def tocov_matrix(self, nr=0, nt=None, dt=None): ''' Computes covariance function and its derivatives, alternative version Parameters ---------- nr : scalar integer number of derivatives in output, nr<=4 (default 0) nt : scalar integer number in time grid, i.e., number of time-lags. (default rate*(n_f-1)) where rate = round(1/(2*f(end)*dt)) or rate = round(pi/(w(n_f)*dt)) depending on S. dt : real scalar time spacing for acfmat Returns ------- acfmat : [R0, R1,...Rnr], shape Nt+1 x Nr+1 matrix with autocovariance and its derivatives, i.e., Ri (i=1:nr) are column vectors with the 1'st to nr'th derivatives of R0. NB! This routine requires that the spectrum grid is equidistant starting from zero frequency. Example ------- >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap() >>> S = Sj.tospecdata() >>> acfmat = S.tocov_matrix(nr=3, nt=256, dt=0.1) >>> np.round(acfmat[:2,:],3) array([[ 3.061, 0. , -1.677, 0. ], [ 3.052, -0.167, -1.668, 0.187]]) See also -------- cov, resample, objects ''' dt, rate = self._get_default_dt_and_rate(dt) self._check_dt(dt) freq = self.args n_f = len(freq) if nt is None: nt = rate * (n_f - 1) else: # check if Nt is ok nt = minimum(nt, rate * (n_f - 1)) spec = self.copy() spec.resample(dt) acf = spec.tocovdata(nr, nt, rate=1) acfmat = zeros((nt + 1, nr + 1), dtype=float) acfmat[:, 0] = acf.data[0:nt + 1] fieldname = 'R' + self.lagtype * nr for i in range(1, nr + 1): fname = fieldname[:i + 1] r_i = getattr(acf, fname) acfmat[:, i] = r_i[0:nt + 1] self._check_cov_matrix(acfmat, nt, dt) return acfmat def tocovdata(self, nr=0, nt=None, rate=None): ''' Computes covariance function and its derivatives Parameters ---------- nr : number of derivatives in output, nr<=4 (default = 0). nt : number in time grid, i.e., number of time-lags (default rate*(length(S.data)-1)). rate = 1,2,4,8...2**r, interpolation rate for R (default = 1, no interpolation) Returns ------- R : CovData1D auto covariance function The input 'rate' with the spectrum gives the time-grid-spacing: dt=pi/(S.w[-1]*rate), S.w[-1] is the Nyquist freq. This results in the time-grid: 0:dt:Nt*dt. What output is achieved with different S and choices of Nt, Nx and Ny: 1) S.type='freq' or 'dir', Nt set, Nx,Ny not set => R(time) (one-dim) 2) S.type='k1d' or 'k2d', Nt set, Nx,Ny not set: => R(x) (one-dim) 3) Any type, Nt and Nx set => R(x,time); Nt and Ny set => R(y,time) 4) Any type, Nt, Nx and Ny set => R(x,y,time) 5) Any type, Nt not set, Nx and/or Ny set => Nt set to default, goto 3) or 4) NB! This routine requires that the spectrum grid is equidistant starting from zero frequency. NB! If you are using a model spectrum, spec, with sharp edges to calculate covariances then you should probably round off the sharp edges like this: Example: >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap() >>> S = Sj.tospecdata() >>> S.data[0:40] = 0.0 >>> S.data[100:-1] = 0.0 >>> Nt = len(S.data)-1 >>> acf = S.tocovdata(nr=0, nt=Nt) >>> S1 = acf.tospecdata() h = S.plot('r') h1 = S1.plot('b:') R = spec2cov(spec,0,Nt) win = parzen(2*Nt+1) R.data = R.data.*win(Nt+1:end) S1 = cov2spec(acf) R2 = spec2cov(S1) figure(1) plotspec(S),hold on, plotspec(S1,'r') figure(2) covplot(R), hold on, covplot(R2,[],[],'r') figure(3) semilogy(abs(R2.data-R.data)), hold on, semilogy(abs(S1.data-S.data)+1e-7,'r') See also -------- cov2spec ''' freq = self.args n_f = len(freq) if freq[0] > 0: txt = '''Spectrum does not start at zero frequency/wave number. Correct it with resample, for example.''' raise ValueError(txt) d_w = abs(diff(freq, n=2, axis=0)) if np.any(d_w > 1.0e-8): txt = '''Not equidistant frequencies/wave numbers in spectrum. Correct it with resample, for example.''' raise ValueError(txt) if rate is None: rate = 1 # interpolation rate elif rate > 16: rate = 16 else: # make sure rate is a power of 2 rate = 2 ** nextpow2(rate) if nt is None: nt = int(rate * (n_f - 1)) else: # check if Nt is ok nt = int(minimum(nt, rate * (n_f - 1))) spec = self.copy() lagtype = self.lagtype d_t = spec.sampling_period() # normalize spec so that sum(specn)/(n_f-1)=acf(0)=var(X) specn = spec.data * freq[-1] if spec.freqtype in 'f': w = freq * 2 * pi else: w = freq nfft = rate * 2 ** nextpow2(2 * n_f - 2) # periodogram rper = r_[ specn, zeros(nfft - (2 * n_f) + 2), conj(specn[n_f - 2:0:-1])] time = r_[0:nt + 1] * d_t * (2 * n_f - 2) / nfft r = fft(rper, nfft).real / (2 * n_f - 2) acf = _WAFOCOV.CovData1D(r[0:nt + 1], time, lagtype=lagtype) acf.tr = spec.tr acf.h = spec.h acf.norm = spec.norm if nr > 0: w = r_[w, zeros(nfft - 2 * n_f + 2), -w[n_f - 2:0:-1]] fieldname = 'R' + lagtype[0] * nr for i in range(1, nr + 1): rper = -1j * w * rper d_acf = fft(rper, nfft).real / (2 * n_f - 2) setattr(acf, fieldname[0:i + 1], d_acf[0:nt + 1]) return acf def to_linspec(self, ns=None, dt=None, cases=20, iseed=None, fn_limit=sqrt(2), gravity=9.81): ''' Split the linear and non-linear component from the Spectrum according to 2nd order wave theory Returns ------- SL, SN : SpecData1D objects with linear and non-linear components only, respectively. Parameters ---------- ns : scalar integer giving ns load points. (default length(S)-1=n-1). If np>n-1 it is assummed that S(k)=0 for all k>n-1 cases : scalar integer number of cases (default=20) dt : real scalar step in grid (default dt is defined by the Nyquist freq) iseed : scalar integer starting seed number for the random number generator (default none is set) fn_limit : real scalar normalized upper frequency limit of spectrum for 2'nd order components. The frequency is normalized with sqrt(gravity*tanh(kbar*water_depth)/a_max)/(2*pi) (default sqrt(2), i.e., Convergence criterion). Generally this should be the same as used in the final non-linear simulation (see example below). SPEC2LINSPEC separates the linear and non-linear component of the spectrum according to 2nd order wave theory. This is useful when simulating non-linear waves because: If the spectrum does not decay rapidly enough towards zero, the contribution from the 2nd order wave components at the upper tail can be very large and unphysical. Another option to ensure convergence of the perturbation series in the simulation, is to truncate the upper tail of the spectrum at FNLIMIT in the calculation of the 2nd order wave components, i.e., in the calculation of sum and difference frequency effects. Example: -------- np = 10000 iseed = 1 pflag = 2 S = jonswap(10) fnLimit = inf [SL,SN] = spec2linspec(S,np,[],[],fnLimit) x0 = spec2nlsdat(SL,8*np,[],iseed,[],fnLimit) x1 = spec2nlsdat(S,8*np,[],iseed,[],fnLimit) x2 = spec2nlsdat(S,8*np,[],iseed,[],sqrt(2)) Se0 = dat2spec(x0) Se1 = dat2spec(x1) Se2 = dat2spec(x2) clf plotspec(SL,'r',pflag), % Linear components hold on plotspec(S,'b',pflag) % target spectrum for simulated data plotspec(Se0,'m',pflag), % approx. same as S plotspec(Se1,'g',pflag) % unphysical spectrum plotspec(Se2,'k',pflag) % approx. same as S axis([0 10 -80 0]) hold off See also -------- spec2nlsdat References ---------- P. A. Brodtkorb (2004), The probability of Occurrence of dangerous Wave Situations at Sea. Dr.Ing thesis, Norwegian University of Science and Technolgy, NTNU, Trondheim, Norway. Nestegaard, A and Stokka T (1995) A Third Order Random Wave model. In proc.ISOPE conf., Vol III, pp 136-142. R. S Langley (1987) A statistical analysis of non-linear random waves. Ocean Engng, Vol 14, pp 389-407 Marthinsen, T. and Winterstein, S.R (1992) 'On the skewness of random surface waves' In proc. ISOPE Conf., San Francisco, 14-19 june. ''' # by pab 13.08.2002 # TODO % Can be improved further. method = 'apstochastic' trace = 1 # % trace the convergence max_sim = 30 tolerance = 5e-4 max_lag = 200 # maximum lag size of the window function used in estimate # ftype = self.freqtype #options are 'f' and 'w' and 'k' # switch ftype # case 'f', # ftype = 'w' # S = ttspec(S,ftype) # end Hm0 = self.characteristic('Hm0')[0] Tm02 = self.characteristic('Tm02')[0] if iseed is not None: _set_seed(iseed) # set the the seed n = len(self.data) if ns is None: ns = max(n - 1, 5000) if dt is None: S = self.interp(dt) # interpolate spectrum else: S = self.copy() ns = ns + mod(ns, 2) # make sure np is even water_depth = abs(self.h) kbar = w2k(2 * pi / Tm02, 0, water_depth)[0] # Expected maximum amplitude for 10000 waves seastate num_waves = 10000 # Typical number of waves in 30 hour seastate a_max = sqrt(2 * log(num_waves)) * Hm0 / 4 f_limit_lo = sqrt(gravity * tanh(kbar * water_depth) * a_max / water_depth ** 3) freq = S.args eps = finfo(float).eps freq[-1] = freq[-1] - sqrt(eps) Hw2 = 0 SL = S ind_zero = nonzero(freq < f_limit_lo)[0] if len(ind_zero): SL.data[ind_zero] = 0 max_spec = max(S.data) # Fs = 2*freq(end)+eps # sampling frequency for ix in range(max_sim): 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) S2 = x2.tospecdata(max_lag) S1 = x1.tospecdata(max_lag) # TODO: Finish spec.to_linspec # S2 = dat2spec(x2, max_lag) # S1 = dat2spec(x1, max_lag) # %[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 + 1e-5))), S2.args, fill_value=0, bounds_error=False)(freq)) else: # Geometric mean 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 # plot(freq, abs(Hw11-Hw1),'g') # title('diff') # pause # clf # d1 = interp1q( S2.w,S2.S,freq) SL.data = (Hw1 * S.data) if len(ind_zero): SL.data[ind_zero] = 0 # end k = nonzero(SL.data < 0)[0] if len(k): # Make sure that the current guess is larger than zero # k # Hw1(k) Hw1[k] = min(S1.data[k] * 0.9, S.data[k]) SL.data[k] = max(Hw1[k] * S.data[k], eps) # end Hw12 = Hw1 - Hw2 maxHw12 = max(abs(Hw12)) if trace == 1: plotbackend.figure(1) plotbackend.semilogy(freq, Hw1, 'r') plotbackend.title('Hw') plotbackend.figure(2) plotbackend.semilogy(freq, abs(Hw12), 'r') plotbackend.title('Hw-HwOld') # pause(3) plotbackend.figure(1) plotbackend.semilogy(freq, Hw1, 'b') plotbackend.title('Hw') plotbackend.figure(2) plotbackend.semilogy(freq, abs(Hw12), 'b') plotbackend.title('Hw-HwOld') plotbackend.show('hold') # figtile # end print('Iteration : %d, Hw12 : %g Hw12/max_spec : %g' % (ix, maxHw12, (maxHw12 / max_spec))) if (maxHw12 < max_spec * tolerance) and (Hw1[-1] < Hw2[-1]): break # end Hw2 = Hw1 # end # Hw1(end) # max_spec*1e-3 # if Hw1[-1]*S.data>max_spec*1e-3, # warning('The Nyquist frequency of the spectrum may be too low') # end SL.date = now() # datestr(now) # if nargout>1 SN = SL.copy() SN.data = S.data - SL.data SN.note = SN.note + ' non-linear component (spec2linspec)' # end SL.note = SL.note + ' linear component (spec2linspec)' return SL, SN def to_mm_pdf(self, paramt=None, paramu=None, utc=None, nit=2, EPS=5e-5, EPSS=1e-6, C=4.5, EPS0=1e-5, IAC=1, ISQ=0, verbose=False): ''' nit = order of numerical integration: 0,1,2,3,4,5. paramu = parameter vector defining discretization of min/max values. t = grid of time points between maximum and minimum (to integrate out). interval between maximum and the following minimum, The variable ISQ marks which type of conditioning will be used ISQ=0 means random time where the probability is minimum, ISQ=1 is the time where the variance of the residual process is minimal(ISQ=1 is faster). NIT, IAC are described in CROSSPACK paper, EPS0 is the accuracy constant used in choosing the number of nodes in numerical integrations (XX1, H1 vectors). The nodes and weights and other parameters are read in the subroutine INITINTEG from files Z.DAT, H.DAT and ACCUR.DAT. NIT=0, IAC=1 then one uses RIND0 - subroutine, all other cases goes through RIND1, ...,RIND5. NIT=0, here means explicite formula approximation for XIND=E[Y^+1{ HH>> import numpy as np >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap(Hm0=3) >>> w = np.linspace(0,4,256) >>> S1 = Sj.tospecdata(w) #Make spectrum object from numerical values >>> S = sm.SpecData1D(Sj(w),w) # Alternatively do it manually mm = S.to_mm_pdf() mm.plot() mm.plot(plotflag=1) ''' S = self.copy() S.normalize() m = self.moment(nr=4, even=True)[0] A = sqrt(m[0] / m[1]) if paramt is None: # (2.5 * mean distance between extremes) distance_between_extremes = 5 * pi * sqrt(m[1] / m[2]) paramt = [0, distance_between_extremes, 43] if paramu is None: paramu = [-5 * sqrt(m[0]), 5 * sqrt(m[0]), 41] if self.tr is None: g = TrLinear(var=m[0]) else: g = self.tr if utc is None: utc = g.gauss2dat(0) # most frequent crossed level # transform reference level into Gaussian level u = g.dat2gauss(utc) if verbose: print('The level u for Gaussian process = %g' % u) tn, Nt = paramt[1:] t = linspace(0, tn / A, Nt) # normalized times # Transform amplitudes to Gaussian levels: h = linspace(*paramu) dt = t[1] - t[0] nr = 4 R = S.tocov_matrix(nr, Nt - 1, dt) # ulev = linspace(*paramu) # vlev = linspace(*paramu) trdata = g.trdata() Tg = trdata.args Xg = trdata.data cov2mod.initinteg(EPS, EPSS, EPS0, C, IAC, ISQ) uvdens = cov2mod.cov2mmpdfreg(t, R, h, h, Tg, Xg, nit) uvdens = np.rot90(uvdens, -2) dh = h[1] - h[0] uvdens *= dh * dh mmpdf = PlotData(uvdens, args=(h, h), xlab='max [m]', ylab='min [m]', title='Joint density of maximum and minimum') try: pl = [10, 30, 50, 70, 90, 95, 99, 99.9] mmpdf.cl = qlevels(uvdens, pl, xi=(h, h)) mmpdf.pl = pl except Exception: pass return mmpdf def to_t_pdf(self, u=None, kind='Tc', paramt=None, **options): ''' Density of crest/trough- period or length, version 2. Parameters ---------- u : real scalar reference level (default the most frequently crossed level). kind : string, 'Tc', Tt', 'Lc' or 'Lt' 'Tc', gives half wave period, Tc (default). 'Tt', gives half wave period, Tt 'Lc' and 'Lt' ditto for wave length. paramt : [t0, tn, nt] where t0, tn and nt is the first value, last value and the number of points, respectively, for which the density will be computed. paramt= [5, 5, 51] implies that the density is computed only for T=5 and using 51 equidistant points in the interval [0,5]. options : optional parameters controlling the performance of the integration. See Rind for details. Notes ----- SPEC2TPDF2 calculates pdf of halfperiods Tc, Tt, Lc or Lt in a stationary Gaussian transform process X(t), where Y(t) = g(X(t)) (Y zero-mean Gaussian with spectrum given in S). The transformation, g, can be estimated using LC2TR, DAT2TR, HERMITETR or OCHITR. Example ------- The density of Tc is computed by: >>> import pylab as plb >>> from wafo.spectrum import models as sm >>> w = np.linspace(0,3,100) >>> Sj = sm.Jonswap() >>> S = Sj.tospecdata() >>> f = S.to_t_pdf(pdef='Tc', paramt=(0, 10, 51), speed=7) h = f.plot() # estimated error bounds h2 = plb.plot(f.args, f.data+f.err, 'r', f.args, f.data-f.err, 'r') plb.close('all') See also -------- Rind, spec2cov2, specnorm, dat2tr, dat2gaus, definitions.wave_periods, definitions.waves ''' opts = dict(speed=9) opts.update(options) if kind[0] in ('l', 'L'): if self.type != 'k1d': raise ValueError('Must be spectrum of type: k1d') elif kind[0] in ('t', 'T'): if self.type != 'freq': raise ValueError('Must be spectrum of type: freq') else: raise ValueError('pdef must be Tc,Tt or Lc, Lt') # if strncmpi('l',kind,1) # spec=spec2spec(spec,'k1d') # elseif strncmpi('t',kind,1) # spec=spec2spec(spec,'freq') # else # error('Unknown kind') # end kind2defnr = dict(tc=1, lc=1, tt=-1, lt=-1) defnr = kind2defnr[kind.lower()] S = self.copy() S.normalize() m = self.moment(nr=2, even=True)[0] A = sqrt(m[0] / m[1]) if self.tr is None: g = TrLinear(var=m[0]) else: g = self.tr if u is None: u = g.gauss2dat(0) # % most frequently crossed level # transform reference level into Gaussian level un = g.dat2gauss(u) # disp(['The level u for Gaussian process = ', num2str(u)]) if paramt is None: # z2 = u^2/2 z = -sign(defnr) * un / sqrt(2) expectedMaxPeriod = 2 * \ ceil(2 * pi * A * exp(z) * (0.5 + erf(z) / 2)) paramt = [0, expectedMaxPeriod, 51] t0 = paramt[0] tn = paramt[1] Ntime = paramt[2] t = linspace(0, tn / A, Ntime) # normalized times # index to starting point to evaluate Nstart = max(round(t0 / tn * (Ntime - 1)), 1) dt = t[1] - t[0] nr = 2 R = S.tocov_matrix(nr, Ntime - 1, dt) # R = spec2cov2(S,nr,Ntime-1,dt) xc = vstack((un, un)) indI = -ones(4, dtype=int) Nd = 2 Nc = 2 XdInf = 100.e0 * sqrt(-R[0, 2]) XtInf = 100.e0 * sqrt(R[0, 0]) B_up = hstack([un + XtInf, XdInf, 0]) B_lo = hstack([un, 0, -XdInf]) # INFIN = [1 1 0] # BIG = zeros((Ntime+2,Ntime+2)) ex = zeros(Ntime + 2, dtype=float) # CC = 2*pi*sqrt(-R(1,1)/R(1,3))*exp(un^2/(2*R(1,1))) # XcScale = log(CC) opts['xcscale'] = log( 2 * pi * sqrt(-R[0, 0] / R[0, 2])) + (un ** 2 / (2 * R[0, 0])) f = zeros(Ntime, dtype=float) err = zeros(Ntime, dtype=float) rind = Rind(**opts) # h11 = fwaitbar(0,[],sprintf('Please wait ...(start at: %s)', # datestr(now))) for pt in range(Nstart, Ntime): Nt = pt - Nd + 1 Ntd = Nt + Nd Ntdc = Ntd + Nc indI[1] = Nt - 1 indI[2] = Nt indI[3] = Ntd - 1 # positive wave period BIG = self._covinput_t_pdf(pt, R) tmp = rind(BIG, ex[:Ntdc], B_lo, B_up, indI, xc, Nt) f[pt], err[pt] = tmp[:2] # fwaitbar(pt/Ntime,h11,sprintf('%s Ready: %d of %d', # datestr(now),pt,Ntime)) # end # close(h11) titledict = dict( tc='Density of Tc', tt='Density of Tt', lc='Density of Lc', lt='Density of Lt') Htxt = titledict.get(kind.lower()) if kind[0].lower() == 'l': xtxt = 'wave length [m]' else: xtxt = 'period [s]' Htxt = '%s_{v =%2.5g}' % (Htxt, u) pdf = PlotData(f / A, t * A, title=Htxt, xlab=xtxt) pdf.err = err / A pdf.u = u pdf.options = opts return pdf @staticmethod def _covinput_t_pdf(pt, R): """ Return covariance matrix for Tc or Tt period problems Parameters ---------- pt : scalar integer time R : array-like, shape Ntime x 3 [R0,R1,R2] column vectors with autocovariance and its derivatives, i.e., R1 and R2 are vectors with the 1'st and 2'nd derivatives of R0, respectively. The order of the variables in the covariance matrix are organized as follows: For pt>1: ||X(t2)..X(ts),..X(tn-1)|| X'(t1) X'(tn)|| X(t1) X(tn) || = [Xt Xd Xc] where Xt = time points in the indicator function Xd = derivatives Xc=variables to condition on Computations of all covariances follows simple rules: Cov(X(t),X(s))=r(t,s), then Cov(X'(t),X(s))=dr(t,s)/dt. Now for stationary X(t) we have a function r(tau) such that Cov(X(t),X(s))=r(s-t) (or r(t-s) will give the same result). Consequently Cov(X'(t),X(s)) = -r'(s-t) = -sign(s-t)*r'(|s-t|) Cov(X'(t),X'(s)) = -r''(s-t) = -r''(|s-t|) Cov(X''(t),X'(s)) = r'''(s-t) = sign(s-t)*r'''(|s-t|) Cov(X''(t),X(s)) = r''(s-t) = r''(|s-t|) Cov(X''(t),X''(s)) = r''''(s-t) = r''''(|s-t|) """ # cov(Xd) Sdd = -toeplitz(R[[0, pt], 2]) # cov(Xc) Scc = toeplitz(R[[0, pt], 0]) # cov(Xc,Xd) Scd = array([[0, R[pt, 1]], [-R[pt, 1], 0]]) if pt > 1: # cov(Xt) # Cov(X(tn),X(ts)) = r(ts-tn) = r(|ts-tn|) Stt = toeplitz(R[:pt - 1, 0]) # cov(Xc,Xt) # Cov(X(tn),X(ts)) = r(ts-tn) = r(|ts-tn|) Sct = R[1:pt, 0] Sct = vstack((Sct, Sct[::-1])) # Cov(Xd,Xt) # Cov(X'(t1),X(ts)) = -r'(ts-t1) = r(|s-t|) Sdt = -R[1:pt, 1] Sdt = vstack((Sdt, -Sdt[::-1])) # N = pt + 3 big = vstack((hstack((Stt, Sdt.T, Sct.T)), hstack((Sdt, Sdd, Scd.T)), hstack((Sct, Scd, Scc)))) else: # N = 4 big = vstack((hstack((Sdd, Scd.T)), hstack((Scd, Scc)))) return big def to_mmt_pdf(self, paramt=None, paramu=None, utc=None, kind='mm', verbose=False, **options): ''' Returns joint density of Maximum, minimum and period. Parameters ---------- u = reference level (default the most frequently crossed level). kind : string defining density returned 'Mm' : maximum and the following minimum. (M,m) (default) 'rfc' : maximum and the rainflow minimum height. 'AcAt' : (crest,trough) heights. 'vMm' : level v separated Maximum and minimum (M,m)_v 'MmTMm' : maximum, minimum and period between (M,m,TMm) 'vMmTMm': level v separated Maximum, minimum and period between (M,m,TMm)_v 'MmTMd' : level v separated Maximum, minimum and the period from Max to level v-down-crossing (M,m,TMd)_v. 'MmTdm' : level v separated Maximum, minimum and the period from level v-down-crossing to min. (M,m,Tdm)_v NB! All 'T' above can be replaced by 'L' to get wave length instead. paramt : [0 tn Nt] defines discretization of half period: tn is the longest period considered while Nt is the number of points, i.e. (Nt-1)/tn is the sampling frequnecy. paramt= [0 10 51] implies that the halfperiods are considered at 51 linearly spaced points in the interval [0,10], i.e. sampling frequency is 5 Hz. paramu : [u, v, N] defines discretization of maxima and minima ranges: u is the lowest minimum considered, v the highest maximum and N is the number of levels (u,v) included. options : rind-options structure containing optional parameters controlling the performance of the integration. See rindoptset for details. [] = default values are used. Returns ------- f = pdf (density structure) of crests (trough) heights TO_MMT_PDF calculates densities of wave characteristics in a stationary Gaussian transform process X(t) where Y(t) = g(X(t)) (Y zero-mean Gaussian with spectrum given in input spec) The tr.g can be estimated using lc2tr, dat2tr, hermitetr or ochitr. Examples -------- The joint density of zero separated Max2min cycles in time (a); in space (b); AcAt in time for nonlinear sea model (c): >>> from wafo.spectrum import models as sm >>> w = np.linspace(0,3,100) >>> Sj = sm.Jonswap() >>> S = Sj.tospecdata() >>> f = S.to_t_pdf(pdef='Tc', paramt=(0, 10, 51), speed=7) >>> S = sm.Jonswap(wnc=2, Hm0=7, Tp=11) Sk = spec2spec(S,'k1d') L0 = spec2mom(S,1) paramu = [sqrt(L0)*[-4 4] 41] ft = spec2mmtpdf(S,0,'vmm',[],paramu); pdfplot(ft) % a) fs = spec2mmtpdf(Sk,0,'vmm'); figure, pdfplot(fs) % b) [sk, ku, me]=spec2skew(S) g = hermitetr([],[sqrt(L0) sk ku me]) Snorm=S; Snorm.S=S.S/L0; Snorm.tr=g ftg=spec2mmtpdf(Snorm,0,'AcAt',[],paramu); pdfplot(ftg) % c) See also -------- rindoptset, dat2tr, datastructures, wavedef, perioddef References --------- Podgorski et al. (2000) "Exact distributions for apparent waves in irregular seas" Ocean Engineering, Vol 27, no 1, pp979-1016. P. A. Brodtkorb (2004), Numerical evaluation of multinormal expectations In Lund university report series and in the Dr.Ing thesis: The probability of Occurrence of dangerous Wave Situations at Sea. Dr.Ing thesis, Norwegian University of Science and Technolgy, NTNU, Trondheim, Norway. Per A. Brodtkorb (2006) "Evaluating Nearly Singular Multinormal Expectations with Application to Wave Distributions", Methodology And Computing In Applied Probability, Volume 8, Number 1, pp. 65-91(27) ''' opts = dict(speed=4, nit=2, method=0) opts.update(**options) ftype = self.freqtype kind2defnr = dict(ac=-2, at=-2, rfc=-1, mm=0, mmtmm=1, mmlmm=1, vmm=2, vmmtmm=3, vmmlmm=3, mmtmd=4, vmmtmd=4, mmlmd=4, vmmlmd=4, mmtdm=5, vmmtdm=5, mmldm=5, vmmldm=5) defnr = kind2defnr.get(kind, 0) in_space = (ftype == 'k') # distribution in space or time if defnr >= 3 or defnr == 1: in_space = (kind[-2].upper() == 'L') if in_space: # spec = spec2spec(spec,'k1d') ptxt = 'space' else: # spec = spec2spec(spec,'freq') ptxt = 'time' S = self.copy() S.normalize() m, unused_mtxt = self.moment(nr=4, even=True) L0, L2, L4 = m A = sqrt(m[0] / m[1]) if paramt is None: # (2.5 * mean distance between extremes) distanceBetweenExtremes = 5 * pi * sqrt(m[1] / m[2]) paramt = [0, distanceBetweenExtremes, 43] if paramu is None: paramu = [-5 * sqrt(m[0]), 5 * sqrt(m[0]), 41] if self.tr is None: g = TrLinear(var=m[0]) else: g = self.tr if utc is None: utc = g.gauss2dat(0) # most frequent crossed level # transform reference level into Gaussian level u = g.dat2gauss(utc) if verbose: print('The level u for Gaussian process = %g' % u) t0, tn, Nt = paramt t = np.linspace(0, tn / A, Nt) # normalized times # the starting point to evaluate Nstart = 1 + round(t0 / tn * (Nt - 1)) Nx = paramu[2] if (defnr > 1): paramu[0] = max(0, paramu[0]) if (paramu[1] < 0): raise ValueError( 'Discretization levels must be larger than zero') # Transform amplitudes to Gaussian levels: h = linspace(*paramu) if defnr > 1: # level v separated Max2min densities hg = np.hstack((utc + h, utc - h)) hg, der = g.dat2gauss(utc + h, ones(Nx)) hg1, der1 = g.dat2gauss(utc - h, ones(Nx)) der, der1 = np.abs(der), np.abs(der1) hg = np.hstack((hg, hg1)) else: # Max2min densities hg, der = np.abs(g.dat2gauss(h, ones(Nx))) der = der1 = np.abs(der) dt = t[1] - t[0] nr = 4 R = S.tocov_matrix(nr, Nt - 1, dt) # NB!!! the spec2XXpdf.exe programmes are very sensitive to how you # interpolate the covariances, especially where the process is very # dependent and the covariance matrix is nearly singular. (i.e. for # small t and high levels of u if Tc and low levels of u if Tt) # The best is to interpolate the spectrum linearly so that S.S>=0 # This makes sure that the covariance matrix is positive # semi-definitt, since the circulant spectrum are the eigenvalues of # the circulant covariance matrix. # callFortran = 0 # %options.method<0 # if callFortran, % call fortran # ftmp = cov2mmtpdfexe(R,dt,u,defnr,Nstart,hg,options) # err = repmat(nan,size(ftmp)) # else ftmp, err, _terr, options = self._cov2mmtpdf(R, dt, u, defnr, Nstart, hg, options) # end note = '' if hasattr(self, 'note'): note = note + self.note tmp = 'L' if in_space else 'T' title = '' labx = '' laby = '' args = None if Nx > 2: titledict = { '-2': 'Joint density of (Ac,At) in %s' % ptxt, '-1': 'Joint density of (M,m_{rfc}) in %s' % ptxt, '0': 'Joint density of (M,m) in %s' % ptxt, '1': 'Joint density of (M,m,%sMm) in %s' % (tmp, ptxt), '2': 'Joint density of (M,m)_{v=%2.5g} in %s' % (utc, ptxt), '3': 'Joint density of (M,m,%sMm)_{v=%2.5g} in %s' % (tmp, utc, ptxt), '4': 'Joint density of (M,m,%sMd)_{v=%2.5g} in %s' % (tmp, utc, ptxt), '5': 'Joint density of (M,m,%sdm)_{v=%2.5g} in %s' % (tmp, utc, ptxt)} title = titledict[defnr] labx = 'Max [m]' laby = 'min [m]' args = (h, h) else: note = note + 'Density is not scaled to unity' if defnr in (-2, -1, 0, 1): title_txt = 'Density of (%sMm, M = %2.5g, m = %2.5g)' title = title_txt % (tmp, h[1], h[0]) elif defnr in (2, 3): title_txt = 'Density of (%sMm, M = %2.5g, m = %2.5g)_{v=%2.5g}' title = title_txt % (tmp, h[1], -h[1], utc) elif defnr == 4: txt = 'Density of (%sMd, %sMm, M = %2.5g, m = %2.5g)_{v=%2.5g}' title = txt % (tmp, tmp, h[1], -h[1], utc) elif defnr == 5: txt = 'Density of (%sdm, %sMm, M = %2.5g, m = %2.5g)_{v=%2.5g}' title = txt % (tmp, tmp, h[1], -h[1], utc) f = PlotData(args=args, title=title, labx=labx, laby=laby) f.options = options if defnr > 1 or defnr == -2: f.u = utc # save level u if Nx > 2: # amplitude distributions wanted # f.x{2} = h # f.labx{2} = 'min [m]' if defnr > 2 or defnr == 1: der0 = der1[:, None] * der[None, :] shape = (Nx, Nx, Nt) ftmp = np.reshape(ftmp, shape) * der0[:, :, None] / A err = np.reshape(err, shape) * der0[:, :, None] / A f.args[2] = t[:] * A _labz = 'wave length [m]' if in_space else 'period [sec]' else: der0 = der[:, None] * der[None, :] ftmp = np.reshape(ftmp, [Nx, Nx]) * der0 err = np.reshape(err, [Nx, Nx]) * der0 if (defnr == -1): ftmp0 = np.fliplr(mctp2rfc(np.fliplr(ftmp))) err = np.abs(ftmp0 - np.fliplr(mctp2rfc(np.fliplr(ftmp + err)))) ftmp = ftmp0 elif (defnr == -2): ftmp0 = np.fliplr(mctp2tc(np.fliplr(ftmp), utc, paramu)) * sqrt(L4 * L0) / L2 err = np.abs(ftmp0 - np.fliplr(mctp2tc(np.fliplr(ftmp + err), utc, paramu)) * sqrt(L4 * L0) / L2) index1 = np.flatnonzero(f.args[0] > 0) index2 = np.flatnonzero(f.args[1] < 0) ftmp = np.flipud(ftmp0[index2, index1]) err = np.flipud(err[index2, index1]) f.args[0] = f.args[0][index1] f.args[1] = np.abs(np.flipud(f.args[1][index2])) # end # end f.data = ftmp f.err = err else: # Only time or wave length distributions wanted f.data = ftmp / A f.err = err / A f.args[0] = A * t # if def_[0] == 't': # f.labx{1} = 'period [sec]' # else: # f.labx{1} = 'wave length [m]' # end if defnr > 3: f.data = np.reshape(f.data, [Nt, Nt]) f.err = np.reshape(f.err, [Nt, Nt]) f.args[1] = A * t # if def_[0] == 't': # f.labx{2} = 'period [sec]' # else: # f.labx{2} = 'wave length [m]' # end # end # end try: f.cl, f.pl = qlevels(f.data, [10, 30, 50, 70, 90, 95, 99, 99.9], f.args[0], f.args[1]) except Exception: warnings.warn('Singularity likely in pdf') # Test of spec2mmtpdf # cd f:\matlab\matlab\wafo\source\sp2thpdfalan # addpath f:\matlab\matlab\wafo ,initwafo, # addpath f:\matlab\matlab\graphutil # Hm0=7;Tp=11; S = jonswap(4*pi/Tp,[Hm0 Tp]) # ft = spec2mmtpdf(S,0,'vMmTMm',[0.3,.4,11],[0 .00005 2]) return f def _cov2mmtpdf(self, R, dt, u, def_nr, Nstart, hg, options): ''' COV2MMTPDF Joint density of Maximum, minimum and period. CALL [pdf, err, options] = cov2mmtpdf(R,dt,u,def,Nstart,hg,options) pdf = calculated pdf size Nx x Ntime err = error estimate terr = truncation error options = requested and actual rindoptions used in integration. R = [R0,R1,R2,R3,R4] column vectors with autocovariance and its derivatives, i.e., Ri (i=1:4) are vectors with the 1'st to 4'th derivatives of R0. size Ntime x Nr+1 dt = time spacing between covariance samples, i.e., between R0(1),R0(2). u = crossing level def = integer defining pdf calculated: 0 : maximum and the following minimum. (M,m) (default) 1 : level v separated Maximum and minimum (M,m)_v 2 : maximum, minimum and period between (M,m,TMm) 3 : level v separated Maximum, minimum and period between (M,m,TMm)_v 4 : level v separated Maximum, minimum and the period from Max to level v-down-crossing (M,m,TMd)_v. 5 : level v separated Maximum, minimum and the period from level v-down-crossing to min. (M,m,Tdm)_v Nstart = index to where to start calculation, i.e., t0 = t(Nstart) hg = vector of amplitudes length Nx or 0 options = rind options structure defining the integration parameters COV2MMTPDF computes joint density of the maximum and the following minimum or level u separated maxima and minima + period/wavelength For DEF = 0,1 : (Maxima, Minima and period/wavelength) = 2,3 : (Level v separated Maxima and Minima and period/wavelength between them) If Nx==1 then the conditional density for period/wavelength between Maxima and Minima given the Max and Min is returned Y = X'(t2)..X'(ts)..X'(tn-1)|| X''(t1) X''(tn)|| X'(t1) X'(tn) X(t1) X(tn) = [ Xt Xd Xc ] Nt = tn-2, Nd = 2, Nc = 4 Xt = contains Nt time points in the indicator function Xd = " Nd derivatives in Jacobian Xc = " Nc variables to condition on There are 3 (NI=4) regions with constant barriers: (indI[0]=0); for i in (indI[0],indI[1]] Y[i]<0. (indI[1]=Nt); for i in (indI[1]+1,indI[2]], Y[i]<0 (deriv. X''(t1)) (indI[2]=Nt+1); for i\in (indI[2]+1,indI[3]], Y[i]>0 (deriv. X''(tn)) For DEF = 4,5 (Level v separated Maxima and Minima and period/wavelength from Max to crossing) If Nx==1 then the conditional joint density for period/wavelength between Maxima, Minima and Max to level v crossing given the Max and the min is returned Y = [Xt, Xd, Xc] where Xt = X'(t2)..X'(ts)..X'(tn-1) Xd = ||X''(t1) X''(tn) X'(ts)|| Xc = X'(t1) X'(tn) X(t1) X(tn) X(ts) Nt = tn-2, Nd = 3, Nc = 5 Xt = contains Nt time points in the indicator function Xd = " Nd derivatives Xc = " Nc variables to condition on There are 4 (NI=5) regions with constant barriers: (indI(1)=0); for i\in (indI(1),indI(2)] Y(i)<0. (indI(2)=Nt) ; for i\in (indI(2)+1,indI(3)], Y(i)<0 (deriv. X''(t1)) (indI(3)=Nt+1); for i\in (indI(3)+1,indI(4)], Y(i)>0 (deriv. X''(tn)) (indI(4)=Nt+2); for i\in (indI(4)+1,indI(5)], Y(i)<0 (deriv. X'(ts)) ''' R0, R2, R4 = R[:, :5:2].T covinput = self._covinput_mmt_pdf Ntime = len(R0) Nx0 = max(1, len(hg)) Nx1 = Nx0 # Nx0 = Nx1 #just plain Mm if def_nr > 1: Nx1 = Nx0 // 2 # Nx0 = 2*Nx1 # level v separated max2min densities wanted # print('def = %d' % def_nr)) # The bound 'infinity' is set to 100*sigma XdInf = 100.0 * sqrt(R4[0]) XtInf = 100.0 * sqrt(-R2[0]) Nc = 4 NI = 4 Nd = 2 # Mb = 1 # Nj = 0 Nstart = max(2, Nstart) symmetry = 0 is_odd = np.mod(Nx1, 2) if def_nr <= 1: # just plain Mm Nx = Nx1 * (Nx1 - 1) / 2 IJ = (Nx1 + is_odd) / 2 if (hg[0] + hg[Nx1 - 1] == 0 and (hg[IJ - 1] == 0 or hg[IJ - 1] + hg[IJ] == 0)): symmetry = 0 print(' Integration region symmetric') # May save Nx1-is_odd integrations in each time step # This is not implemented yet. # Nx = Nx1*(Nx1-1)/2-Nx1+is_odd # normalizing constant: # CC = 1/ expected number of zero-up-crossings of X' # CC = 2*pi*sqrt(-R2[0]/R4[0]) # XcScale = log(CC) XcScale = log(2 * pi * sqrt(-R2[0] / R4[0])) else: # level u separated Mm Nx = (Nx1 - 1) * (Nx1 - 1) if (abs(u) <= _EPS and (hg[0] + hg[Nx1] == 0) and (hg[Nx1 - 1] + hg[2 * Nx1 - 1] == 0)): symmetry = 0 print(' Integration region symmetric') # Not implemented for DEF <= 3 # IF (DEF.LE.3) Nx = (Nx1-1)*(Nx1-2)/2 if def_nr > 3: Nstart = max(Nstart, 3) Nc = 5 NI = 5 Nd = 3 # CC = 1/ expected number of u-up-crossings of X # CC = 2*pi*sqrt(-R0(1)/R2(1))*exp(0.5D0*u*u/R0(1)) XcScale = log(2 * pi * sqrt(-R0[0] / R2[0])) + 0.5 * u * u / R0[0] options['xcscale'] = XcScale # opt0 = [options[n] for n in ('SCIS', 'XcScale', 'ABSEPS', 'RELEPS', # 'COVEPS', 'MAXPTS', 'MINPTS', 'seed', # 'NIT1')] dt2 = dt ** 2 rind = Rind(**options) if (Nx > 1): # (M,m) or (M,m)v distribution wanted if def_nr in [0, 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 asize = [1, Ntime, Ntime] else: # Conditional distribution for (TMm) or (TMm)v given (M,m) wanted asize = [1, 1, Ntime] # Initialization pdf = zeros(asize) err = zeros(asize) terr = zeros(asize) BIG = zeros(Ntime + Nc + 1, Ntime + Nc + 1) ex = zeros(1, Ntime + Nc + 1) # fxind = zeros(Nx,1) xc = zeros(Nc, Nx) indI = zeros(1, NI) a_up = zeros(1, NI - 1) a_lo = zeros(1, NI - 1) # 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) # if INFIN(I) = 2, Ith limits are [Hlo(I), Hup(I)]. # INFIN = repmat(0,1,NI-1) # INFIN(3) = 1 a_up[0, 2] = +XdInf a_lo[0, :2] = [-XtInf, -XdInf] if (def_nr > 3): a_lo[0, 3] = -XtInf IJ = 0 if (def_nr <= 1): # Max2min and period/wavelength for I in range(1, Nx1): J = IJ + I xc[2, IJ:J] = hg[I] 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 if (def_nr <= 3): # h11 = fwaitbar(0,[],sprintf('Please wait ...(start at: # %s)',datestr(now))) for Ntd in range(Nstart, Ntime): # Ntd=tn Ntdc = Ntd + Nc Nt = Ntd - Nd indI[1] = Nt indI[2] = Nt + 1 indI[3] = Ntd # positive wave period # self._covinput_mmt_pdf(BIG, R, tn, ts, tnold) BIG[:Ntdc, :Ntdc] = covinput(BIG[:Ntdc, :Ntdc], R, Ntd, 0) fxind, err0, terr0 = rind(BIG[:Ntdc, :Ntdc], ex[:Ntdc], a_lo, a_up, indI, xc, Nt) err0 = err0 ** 2 # fxind = CC*rind(BIG(1:Ntdc,1:Ntdc),ex(1:Ntdc),xc,Nt,NIT1, # speed1,indI,a_lo,a_up) if (Nx < 2): # Density of TMm given the Max and the Min. Note that the # density is not scaled to unity pdf[0, 0, Ntd] = fxind[0] err[0, 0, Ntd] = err0[0] terr[0, 0, Ntd] = terr0[0] # GOTO 100 else: IJ = 0 # joint density of (Ac,At),(M,m_rfc) or (M,m). if def_nr in [-2, -1, 0]: for i in range(1, Nx1): J = IJ + i pdf[:i, i, 0] += fxind[IJ:J].T * dt # *CC err[:i, i, 0] += err0[IJ + 1:J].T * dt2 terr[:i, i, 0] += terr0[IJ:J].T * dt IJ = J elif def_nr == 1: # joint density of (M,m,TMm) for i in range(1, Nx1): J = IJ + i pdf[:i, i, Ntd] = fxind[IJ:J].T # *CC err[:i, i, Ntd] = err0[IJ:J].T # *CC terr[:i, i, Ntd] = terr0[IJ:J].T # *CC IJ = J # end do # joint density of level v separated (M,m)v elif def_nr == 2: for i in range(1, Nx1): J = IJ + Nx1 pdf[1:Nx1, i, 0] += fxind[IJ:J].T * dt # *CC err[1:Nx1, i, 0] += err0[IJ:J].T * dt2 terr[1:Nx1, i, 0] += terr0[IJ:J].T * dt IJ = J # end %do elif def_nr == 3: # joint density of level v separated (M,m,TMm)v for i in range(1, Nx1): J = IJ + Nx1 pdf[1:Nx1, i, Ntd] += fxind[IJ:J].T # %*CC err[1:Nx1, i, Ntd] += err0[IJ:J].T terr[1:Nx1, i, Ntd] += terr0[IJ:J].T IJ = J # end do # end SELECT # end ENDIF # waitTxt = '%s Ready: %d of %d' % (datestr(now),Ntd,Ntime) # fwaitbar(Ntd/Ntime,h11,waitTxt) # end %do # close(h11) err = sqrt(err) # goto 800 else: # def_nr>3 # 200 continue # waitTxt = sprintf('Please wait ...(start at: %s)',datestr(now)) # h11 = fwaitbar(0,[],waitTxt) tnold = -1 for tn in range(Nstart, Ntime): Ntd = tn + 1 Ntdc = Ntd + Nc Nt = Ntd - Nd indI[1] = Nt indI[2] = Nt + 1 indI[3] = Nt + 2 indI[4] = Ntd if not symmetry: # IF (SYMMETRY) GOTO 300 for ts in range(1, tn - 1): # = 2:tn-1: # positive wave period BIG[:Ntdc, :Ntdc] = covinput(BIG[:Ntdc, :Ntdc], R, tn, ts, tnold) fxind, err0, terr0 = rind(BIG[:Ntdc, :Ntdc], ex[:Ntdc], a_lo, a_up, indI, xc, Nt) err0 = err0 ** 2 # tnold = tn tns = tn - ts if def_nr in [3, 4]: if (Nx == 1): # Joint density (TMd,TMm) given the Max and min # Note the density is not scaled to unity pdf[0, ts, tn] = fxind[0] # *CC err[0, ts, tn] = err0[0] # *CC terr[0, ts, tn] = terr0[0] # *CC else: # level u separated Max2min and wave period # from Max to the crossing of level u # (M,m,TMd). IJ = 0 for i in range(1, Nx1): J = IJ + Nx1 pdf[1:Nx1, i, ts] += fxind[IJ:J].T * dt err[1:Nx1, i, ts] += err0[IJ:J].T * dt2 terr[1:Nx1, i, ts] += terr0[IJ:J].T * dt IJ = J # end %do # end elif def_nr == 5: if (Nx == 1): # Joint density (Tdm,TMm) given the Max and min # Note the density is not scaled to unity pdf[0, tns, tn] = fxind[0] # *CC err[0, tns, tn] = err0[0] terr[0, tns, tn] = terr0[0] else: # 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 # *CC pdf[1:Nx1, i, tns] += fxind[IJ:J].T * dt err[1:Nx1, i, tns] += err0[IJ:J].T * dt2 terr[1:Nx1, i, tns] += terr0[IJ:J].T * dt IJ = J # end %do # end # end % SELECT # end% enddo else: # % exploit symmetry # 300 Symmetry for ts in range(1, Ntd // 2): # = 2:floor(Ntd//2) # Using the symmetry since U = 0 and the # transformation is linear. # positive wave period BIG[:Ntdc, :Ntdc] = covinput(BIG[:Ntdc, :Ntdc], R, tn, ts, tnold) fxind, err0, terr0 = rind(BIG[:Ntdc, :Ntdc], ex[:Ntdc], a_lo, a_up, indI, xc, Nt) # [fxind,err0] = rind(BIG(1:Ntdc,1:Ntdc),ex,a_lo,a_up, # indI, xc,Nt,opt0{:}) # tnold = tn tns = tn - ts if (Nx == 1): # % THEN # Joint density of (TMd,TMm),(Tdm,TMm) given # the max and the min. # Note that the density is not scaled to unity pdf[0, ts, tn] = fxind[0] # %*CC err[0, ts, tn] = err0[0] err[0, ts, tn] = terr0[0] if (ts < tns): # %THEN pdf[0, tns, tn] = fxind[0] # *CC err[0, tns, tn] = err0[0] ** 2 terr[0, tns, tn] = terr0[0] # end # GOTO 350 else: IJ = 0 if def_nr == 4: # level u separated Max2min and wave period # from Max to the crossing of level u (M,m,TMd) for i in range(1, Nx1): J = IJ + Nx1 # *CC pdf[1:Nx1, i, ts] += fxind[IJ:J] * dt err[1:Nx1, i, ts] += err0[IJ:J] * dt2 terr[1:Nx1, i, ts] += terr0[IJ:J] * dt if (ts < tns): # exploiting the symmetry # *CC pdf[i, 1:Nx1, tns] += fxind[IJ:J] * dt err[i, 1:Nx1, tns] += err0[IJ:J] * dt2 terr[i, 1:Nx1, tns] += terr0[IJ:J] * dt # end IJ = J # end do elif def_nr == 5: # level u separated Max2min and wave period # from the crossing of level u to min (M,m,Tdm) for i in range(1, Nx1): # = 2:Nx1, J = IJ + Nx1 pdf[1:Nx1, i, tns] += fxind[IJ:J] * dt err[1:Nx1, i, tns] += err0[IJ:J] * dt2 terr[1:Nx1, i, tns] += terr0[IJ:J] * dt if (ts < tns + 1): # exploiting the symmetry pdf[i, 1:Nx1, ts] += fxind[IJ:J] * dt err[i, 1:Nx1, ts] += err0[IJ:J] * dt2 terr[i, 1:Nx1, ts] += terr0[IJ:J] * dt # end %ENDIF IJ = J # end do # end %END SELECT # end # 350 # end %do # end # waitTxt = sprintf('%s Ready: %d of %d',datestr(now),tn,Ntime) # fwaitbar(tn/Ntime,h11,waitTxt) # 400 print *,'Ready: ',tn,' of ',Ntime # end %do # close(h11) err = sqrt(err) # end % if # Nx1,size(pdf) def Ntime if (Nx > 1): # % THEN IJ = 1 if (def_nr > 2 or def_nr == 1): IJ = Ntime # end pdf = pdf[:Nx1, :Nx1, :IJ] err = err[:Nx1, :Nx1, :IJ] terr = terr[:Nx1, :Nx1, :IJ] else: IJ = 1 if (def_nr > 3): IJ = Ntime # end pdf = np.squeeze(pdf[0, :IJ, :Ntime]) err = np.squeeze(err[0, :IJ, :Ntime]) terr = np.squeeze(terr[0, :IJ, :Ntime]) # end return pdf, err, terr, options @staticmethod def _covinput_mmt_pdf(BIG, R, tn, ts, tnold=-1): """ COVINPUT Sets up the covariance matrix CALL BIG = covinput(BIG, R0,R1,R2,R3,R4,tn,ts) BIG = covariance matrix for X = [Xt,Xd,Xc] in spec2mmtpdf problems. The order of the variables in the covariance matrix are organized as follows: for ts <= 1: Xt = X'(t2)..X'(ts),...,X'(tn-1) Xd = X''(t1), X''(tn), X'(t1), X'(tn) Xc = X(t1),X(tn) for ts > =2: Xt = X'(t2)..X'(ts),...,X'(tn-1) Xd = X''(t1), X''(tn), X'(ts), X'(t1), X'(tn), Xc = X(t1),X(tn) X(ts) where Xt = time points in the indicator function Xd = derivatives Xc = variables to condition on Computations of all covariances follows simple rules: Cov(X(t),X(s)) = r(t,s), then Cov(X'(t),X(s))=dr(t,s)/dt. Now for stationary X(t) we have a function r(tau) such that Cov(X(t),X(s))=r(s-t) (or r(t-s) will give the same result). Consequently Cov(X'(t),X(s)) = -r'(s-t) = -sign(s-t)*r'(|s-t|) Cov(X'(t),X'(s)) = -r''(s-t) = -r''(|s-t|) Cov(X''(t),X'(s)) = r'''(s-t) = sign(s-t)*r'''(|s-t|) Cov(X''(t),X(s)) = r''(s-t) = r''(|s-t|) Cov(X''(t),X''(s)) = r''''(s-t) = r''''(|s-t|) """ R0, R1, R2, R3, R4 = R[:, :5].T if (ts > 1): shft = 1 N = tn + 5 + shft # Cov(Xt,Xc) # for i = np.arange(tn - 2) # 1:tn-2 # j = abs(i+1-ts) # BIG(i,N) = -sign(R1(j+1),R1(j+1)*dble(ts-i-1)) j = i + 1 - ts tau = abs(j) # BIG(i,N) = abs(R1(tau)).*sign(R1(tau).*j.') BIG[i, N] = R1[tau] * sign(j) # cov(X'(ti+1),X(ts)) # end do # Cov(Xc) BIG[N, N] = R0[0] # cov(X(ts),X(ts)) BIG[tn + shft + 1, N] = -R1[ts] # cov(X'(t1),X(ts)) BIG[tn + shft + 2, N] = R1[tn - ts] # cov(X'(tn),X(ts)) BIG[tn + shft + 3, N] = R0[ts] # cov(X(t1),X(ts)) BIG[tn + shft + 4, N] = R0[tn - ts] # cov(X(tn),X(ts)) # 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 j = abs(i + 1 - ts) BIG[i, tn + shft] = -R2[j] # cov(X'(ti+1),X'(ts)) # end do # Cov(Xd) BIG[tn + shft, tn + shft] = -R2[0] # cov(X'(ts),X'(ts)) BIG[tn - 1, tn + shft] = R3[ts] # cov(X''(t1),X'(ts)) BIG[tn, tn + shft] = -R3[tn - ts] # cov(X''(tn),X'(ts)) # Cov(Xd,Xc) BIG[tn + shft, N] = 0.0 # %cov(X'(ts),X(ts)) # % cov(X'(ts),X'(t1)) BIG[tn + shft, tn + shft + 1] = -R2[ts] # % cov(X'(ts),X'(tn)) BIG[tn + shft, tn + shft + 2] = -R2[tn - ts] BIG[tn + shft, tn + shft + 3] = R1[ts] # % cov(X'(ts),X(t1)) # % cov(X'(ts),X(tn)) BIG[tn + shft, tn + shft + 4] = -R1[tn - ts] if (tnold == tn): # A previous call to covinput with tn==tnold has been made # need only to update row and column N and tn+1 of big: return BIG # % make lower triangular part equal to upper and then return # for j=1:tn+shft # BIG(N,j) = BIG(j,N) # BIG(tn+shft,j) = BIG(j,tn+shft) # end # for j=tn+shft+1:N-1 # BIG(N,j) = BIG(j,N) # BIG(j,tn+shft) = BIG(tn+shft,j) # end # return # end %if # %tnold = tn else: # 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)) BIG[:tn - 2, tn + shft + 1] = -R2[tn - 2:0:-1] 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)) # end %do # end # cov(Xd) 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)) BIG[tn + shft + 2, tn + shft + 3] = R0[tn - 1] BIG[tn + shft + 1, tn + shft + 2] = 0.0 # cov(X(t1),X'(t1)) # cov(X(t1),X'(tn)) BIG[tn + shft + 1, tn + shft + 2] = R1[tn - 1] BIG[tn + shft + 3, tn + shft + 3] = R0[0] # cov(X(tn),X(tn)) BIG[tn + shft, tn + shft + 3] = -R1[tn - 1] # cov(X(tn),X'(t1)) BIG[tn + shft + 1, tn + shft + 3] = 0.0 # cov(X(tn),X'(tn)) BIG[tn + shft, tn + shft] = -R2[0] # cov(X'(t1),X'(t1)) BIG[tn + shft, tn + shft + 1] = -R2[tn - 1] # cov(X'(t1),X'(tn)) BIG[tn + shft + 1, tn + shft + 1] = -R2[0] # cov(X'(tn),X'(tn)) # Xc=X(t1),X(tn),X'(t1),X'(tn) # Xd=X''(t1),X''(tn) # cov(Xd,Xc) BIG[tn - 2, tn + shft + 2] = R2[0] # cov(X''(t1),X(t1)) BIG[tn - 2, tn + shft + 3] = R2[tn - 1] # cov(X''(t1),X(tn)) BIG[tn - 2, tn + shft] = 0.0 # cov(X''(t1),X'(t1)) BIG[tn - 2, tn + shft + 1] = R3[tn - 1] # cov(X''(t1),X'(tn)) BIG[tn - 1, tn + shft + 2] = R2[tn - 1] # cov(X''(tn),X(t1)) BIG[tn - 1, tn + shft + 3] = R2[0] # cov(X''(tn),X(tn)) BIG[tn - 1, tn + shft] = -R3[tn - 1] # cov(X''(tn),X'(t1)) BIG[tn - 1, tn + shft + 1] = 0.0 # cov(X''(tn),X'(tn)) # make lower triangular part equal to upper # for j=1:N-1 # for i=j+1:N # BIG(i,j) = BIG(j,i) # end #do # end #do # indices to lower triangular part: lp = np.flatnonzero(np.tril(ones(BIG.shape))) BIGT = BIG.T BIG[lp] = BIGT[lp] return BIG # END SUBROUTINE COV_INPUT def _cov2mmtpdfexe(self, R, dt, u, defnr, Nstart, hg, options): # Write parameters to file Nx = max(1, len(hg)) if defnr > 1: Nx = Nx // 2 # level v separated max2min densities wanted Ntime = R.shape[0] filenames = ['h.in', 'reflev.in'] self._cleanup(*filenames) with open('h.in', 'wt') as f: f.write('%12.10f\n', hg) # XSPLT = options.xsplit nit = options.nit speed = options.speed seed = options.seed SCIS = abs(options.method) # method<=0 with open('reflev.in', 'wt') as fid: fid.write('%2.0f \n', Ntime) fid.write('%2.0f \n', Nstart) fid.write('%2.0f \n', nit) fid.write('%2.0f \n', speed) fid.write('%2.0f \n', SCIS) fid.write('%2.0f \n', seed) fid.write('%2.0f \n', Nx) fid.write('%12.10E \n', dt) fid.write('%12.10E \n', u) fid.write('%2.0f \n', defnr) filenames2 = self._writecov(R) print(' Starting Fortran executable.') # compiled cov2mmtpdf.f with rind70.f # dos([ wafoexepath 'cov2mmtpdf.exe']) dens = 1 # load('dens.out') self._cleanup(*filenames) self._cleanup(*filenames2) return dens @staticmethod def _cleanup(*files): '''Removes files from harddisk if they exist''' for f in files: if os.path.exists(f): os.remove(f) def to_specnorm(self): S = self.copy() S.normalize() return S def sim(self, ns=None, cases=1, dt=None, iseed=None, method='random', derivative=False): ''' Simulates a Gaussian process and its derivative from spectrum Parameters ---------- ns : scalar number of simulated points. (default length(spec)-1=n-1). If ns>n-1 it is assummed that acf(k)=0 for all k>n-1 cases : scalar number of replicates (default=1) dt : scalar step in grid (default dt is defined by the Nyquist freq) iseed : int or state starting state/seed number for the random number generator (default none is set) method : string if 'exact' : simulation using cov2sdat if 'random' : random phase and amplitude simulation (default) derivative : bool if true : return derivative of simulated signal as well otherwise Returns ------- xs = a cases+1 column matrix ( t,X1(t) X2(t) ...). xsder = a cases+1 column matrix ( t,X1'(t) X2'(t) ...). Details ------- Performs a fast and exact simulation of stationary zero mean Gaussian process through circulant embedding of the covariance matrix or by summation of sinus functions with random amplitudes and random phase angle. If the spectrum has a non-empty field .tr, then the transformation is applied to the simulated data, the result is a simulation of a transformed Gaussian process. Note: The method 'exact' simulation may give high frequency ripple when used with a small dt. In this case the method 'random' works better. Example: >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap();S = Sj.tospecdata() >>> ns =100; dt = .2 >>> x1 = S.sim(ns,dt=dt) >>> import numpy as np >>> import scipy.stats as st >>> x2 = S.sim(20000,20) >>> truth1 = [0,np.sqrt(S.moment(1)[0]),0., 0.] >>> funs = [np.mean,np.std,st.skew,st.kurtosis] >>> for fun,trueval in zip(funs,truth1): ... res = fun(x2[:,1::],axis=0) ... m = res.mean() ... sa = res.std() ... #trueval, m, sa ... np.abs(m-trueval) T) # Trick to avoid adding high frequency noise to the spectrum if i.size > 0: acf.data[i[0]::] = 0.0 return acf.sim(ns=ns, cases=cases, iseed=iseed, derivative=derivative) _set_seed(iseed) ns = ns + mod(ns, 2) # make sure it is even f_i = freq[1:-1] s_i = spec.data[1:-1] if ftype in ('w', 'k'): fact = 2. * pi s_i = s_i * fact f_i = f_i / fact x = zeros((ns, cases + 1)) d_f = 1 / (ns * d_t) # interpolate for freq. [1:(N/2)-1]*d_f and create 2-sided, uncentered # spectra ns2 = ns // 2 f = arange(1, ns2) * d_f 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[ns2 - 2::-1])) del(s_i, f_u) # Generate standard normal random numbers for the simulations randn = random.randn z_r = randn(ns2 + 1, cases) z_i = vstack( (zeros((1, cases)), randn(ns2 - 1, cases), zeros((1, cases)))) amp = zeros((ns, cases), dtype=complex) amp[0:ns2 + 1, :] = z_r - 1j * z_i del(z_r, z_i) amp[ns2 + 1:ns, :] = amp[ns2 - 1:0:-1, :].conj() amp[0, :] = amp[0, :] * sqrt(2.) amp[ns2, :] = amp[ns2, :] * sqrt(2.) # Make simulated time series T = (ns - 1) * d_t Ssqr = sqrt(s_u * d_f / 2.) # stochastic amplitude amp = amp * Ssqr[:, newaxis] # Deterministic amplitude # amp = # sqrt[1]*Ssqr(:,ones(1,cases)) * \ # exp(sqrt(-1)*atan2(imag(amp),real(amp))) del(s_u, Ssqr) x[:, 1::] = fft(amp, axis=0).real x[:, 0] = linspace(0, T, ns) # ' %(0:d_t:(np-1)*d_t).' if derivative: xder = zeros(ns, cases + 1) w = 2. * pi * hstack((0, f, 0., -f[-1::-1])) amp = -1j * amp * w[:, newaxis] xder[:, 1:(cases + 1)] = fft(amp, axis=0).real xder[:, 0] = x[:, 0] if spec.tr is not None: # print(' Transforming data.') g = spec.tr if derivative: for i in range(cases): x[:, i + 1], xder[:, i + 1] = g.gauss2dat(x[:, i + 1], xder[:, i + 1]) else: for i in range(cases): x[:, i + 1] = g.gauss2dat(x[:, i + 1]) if derivative: return x, xder else: return x # function [x2,x,svec,dvec,amp]=spec2nlsdat(spec,np,dt,iseed,method, # truncationLimit) def sim_nl(self, ns=None, cases=1, dt=None, iseed=None, method='random', fnlimit=1.4142, reltol=1e-3, g=9.81, verbose=False, output='timeseries'): """ Simulates a Randomized 2nd order non-linear wave X(t) Parameters ---------- ns : scalar number of simulated points. (default length(spec)-1=n-1). If ns>n-1 it is assummed that R(k)=0 for all k>n-1 cases : scalar number of replicates (default=1) dt : scalar step in grid (default dt is defined by the Nyquist freq) iseed : int or state starting state/seed number for the random number generator (default none is set) method : string 'apStochastic' : Random amplitude and phase (default) 'aDeterministic' : Deterministic amplitude and random phase 'apDeterministic' : Deterministic amplitude and phase fnlimit : scalar normalized upper frequency limit of spectrum for 2'nd order components. The frequency is normalized with sqrt(gravity*tanh(kbar*water_depth)/amp_max)/(2*pi) (default sqrt(2), i.e., Convergence criterion [1]_). Other possible values are: sqrt(1/2) : No bump in trough criterion sqrt(pi/7) : Wave steepness criterion reltol : scalar relative tolerance defining where to truncate spectrum for the sum and difference frequency effects Returns ------- xs2 = a cases+1 column matrix ( t,X1(t) X2(t) ...). xs1 = a cases+1 column matrix ( t,X1'(t) X2'(t) ...). Details ------- Performs a Fast simulation of Randomized 2nd order non-linear waves by summation of sinus functions with random amplitudes and phase angles. The extent to which the simulated result are applicable to real seastates are dependent on the validity of the assumptions: 1. Seastate is unidirectional 2. Surface elevation is adequately represented by 2nd order random wave theory 3. The first order component of the surface elevation is a Gaussian random process. If the spectrum does not decay rapidly enough towards zero, the contribution from the 2nd order wave components at the upper tail can be very large and unphysical. To ensure convergence of the perturbation series, the upper tail of the spectrum is truncated at FNLIMIT in the calculation of the 2nd order wave components, i.e., in the calculation of sum and difference frequency effects. This may also be combined with the elimination of second order effects from the spectrum, i.e., extract the linear components from the spectrum. One way to do this is to use SPEC2LINSPEC. Example -------- >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap();S = Sj.tospecdata() >>> ns =100; dt = .2 >>> x1 = S.sim_nl(ns,dt=dt) >>> import numpy as np >>> import scipy.stats as st >>> x2, x1 = S.sim_nl(ns=20000,cases=20, output='data') >>> truth1 = [0,np.sqrt(S.moment(1)[0][0])] + S.stats_nl(moments='sk') >>> truth1[-1] = truth1[-1]-3 >>> np.round(truth1, 3) array([ 0. , 1.75 , 0.187, 0.062]) >>> funs = [np.mean,np.std,st.skew,st.kurtosis] >>> for fun,trueval in zip(funs,truth1): ... res = fun(x2[:,1::], axis=0) ... m = res.mean() ... sa = res.std() ... # trueval, m, sa ... np.abs(m-trueval) < 2*sa True True True True >>> x = [] >>> for i in range(20): ... x2, x1 = S.sim_nl(ns=20000,cases=1, output='data') ... x.append(x2[:,1::]) >>> x2 = np.hstack(x) >>> truth1 = [0,np.sqrt(S.moment(1)[0][0])] + S.stats_nl(moments='sk') >>> truth1[-1] = truth1[-1]-3 >>> np.round(truth1,3) array([ 0. , 1.75 , 0.187, 0.062]) >>> funs = [np.mean,np.std,st.skew,st.kurtosis] >>> for fun,trueval in zip(funs,truth1): ... res = fun(x2, axis=0) ... m = res.mean() ... sa = res.std() ... # trueval, m, sa ... np.abs(m-trueval) s_max * reltol).argmax() nmax = flatnonzero(s_i > 0).max() 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(ns2 + 1, cases) z_i = vstack((zeros((1, cases)), randn(ns2 - 1, cases), zeros((1, cases)))) amp = zeros((ns, cases), dtype=complex) amp[0:(ns2 + 1), :] = z_r - 1j * z_i del(z_r, z_i) amp[(ns2 + 1):ns, :] = amp[ns2 - 1:0:-1, :].conj() amp[0, :] = amp[0, :] * sqrt(2.) amp[(ns2), :] = amp[(ns2), :] * sqrt(2.) # Make simulated time series T = (ns - 1) * d_t Ssqr = sqrt(s_u * df / 2.) if method.startswith('apd'): # apdeterministic # Deterministic amplitude and phase amp[1:(ns2), :] = amp[1, 0] amp[(ns2 + 1):ns, :] = amp[1, 0].conj() amp = sqrt(2) * Ssqr[:, newaxis] * \ exp(1J * arctan2(amp.imag, amp.real)) elif method.startswith('ade'): # adeterministic # Deterministic amplitude and random phase amp = sqrt(2) * Ssqr[:, newaxis] * \ exp(1J * arctan2(amp.imag, amp.real)) else: # stochastic amplitude amp = amp * Ssqr[:, newaxis] # Deterministic amplitude # amp = # sqrt(2)*Ssqr(:,ones(1,cases))* \ # exp(sqrt(-1)*atan2(imag(amp),real(amp))) del(s_u, Ssqr) x[:, 1::] = fft(amp, axis=0).real x[:, 0] = linspace(0, T, ns) # ' %(0:d_t:(np-1)*d_t).' x2 = x.copy() # If the spectrum does not decay rapidly enough towards zero, the # contribution from the wave components at the upper tail can be very # large and unphysical. # To ensure convergence of the perturbation series, the upper tail of # the spectrum is truncated in the calculation of sum and difference # frequency effects. # Find the critical wave frequency to ensure convergence. num_waves = 1000. # Typical number of waves in 3 hour seastate kbar = w2k(2. * pi / Tm02, 0., water_depth)[0] # Expected maximum amplitude for 1000 waves seastate amp_max = sqrt(2 * log(num_waves)) * Hm0 / 4 f_limit_up = fnlimit * \ sqrt(g * tanh(kbar * water_depth) / amp_max) / (2 * pi) f_limit_lo = sqrt(g * tanh(kbar * water_depth) * amp_max / water_depth) / (2 * pi * water_depth) nmax = min(flatnonzero(f <= f_limit_up).max(), nmax) + 1 nmin = max(flatnonzero(f_limit_lo <= f).min(), nmin) + 1 # if isempty(nmax),nmax = np/2end # if isempty(nmin),nmin = 2end % Must always be greater than 1 f_limit_up = df * nmax f_limit_lo = df * nmin if verbose: print('2nd order frequency Limits = %g,%g' % (f_limit_lo, f_limit_up)) # if nargout>3, # #compute the sum and frequency effects separately # [svec, dvec] = disufq((amp.'),w,kw,min(h,10^30),g,nmin,nmax) # svec = svec.' # dvec = dvec.' ## # x2s = fft(svec) % 2'nd order sum frequency component # x2d = fft(dvec) % 2'nd order difference frequency component ## # # 1'st order + 2'nd order component. # x2(:,2:end) =x(:,2:end)+ real(x2s(1:np,:))+real(x2d(1:np,:)) # else if False: # TODO: disufq does not work for cases>1 amp = np.array(amp.T).ravel() rvec, ivec = c_library.disufq(amp.real, amp.imag, w, kw, water_depth, g, nmin, nmax, cases, ns) svec = rvec + 1J * ivec else: amp = amp.T svec = [] for i in range(cases): rvec, ivec = c_library.disufq(amp[i].real, amp[i].imag, w, kw, water_depth, g, nmin, nmax, 1, ns) svec.append(rvec + 1J * ivec) svec = np.hstack(svec) svec.shape = (cases, ns) x2o = fft(svec, axis=1).T # 2'nd order component # 1'st order + 2'nd order component. x2[:, 1::] = x[:, 1::] + x2o[0:ns, :].real if output == 'timeseries': xx2 = mat2timeseries(x2) xx = mat2timeseries(x) return xx2, xx return x2, x def stats_nl(self, h=None, moments='sk', method='approximate', g=9.81): """ Statistics of 2'nd order waves to the leading order. Parameters ---------- h : scalar water depth (default self.h) moments : string (default='sk') composed of letters ['mvsk'] specifying which moments to compute: 'm' = mean, 'v' = variance, 's' = skewness, 'k' = (Pearson's) kurtosis. method : string 'approximate' method due to Marthinsen & Winterstein (default) 'eigenvalue' method due to Kac and Siegert Skewness = kurtosis-3 = 0 for a Gaussian process. The mean, sigma, skewness and kurtosis are determined as follows: method == 'approximate': due to Marthinsen and Winterstein mean = 2 * int Hd(w1,w1)*S(w1) dw1 sigma = sqrt(int S(w1) dw1) skew = 6 * int int [Hs(w1,w2)+Hd(w1,w2)]*S(w1)*S(w2) dw1*dw2/m0^(3/2) kurt = (4*skew/3)^2 where Hs = sum frequency effects and Hd = difference frequency effects method == 'eigenvalue' mean = sum(E) sigma = sqrt(sum(C^2)+2*sum(E^2)) skew = sum((6*C^2+8*E^2).*E)/sigma^3 kurt = 3+48*sum((C^2+E^2).*E^2)/sigma^4 where h1 = sqrt(S*dw/2) C = (ctranspose(V)*[h1;h1]) and E and V is the eigenvalues and eigenvectors, respectively, of the 2'order transfer matrix. S is the spectrum and dw is the frequency spacing of S. Example: -------- # Simulate a Transformed Gaussian process: >>> import wafo.spectrum.models as sm >>> import wafo.transform.models as wtm >>> Hs = 7. >>> Sj = sm.Jonswap(Hm0=Hs, Tp=11) >>> S = Sj.tospecdata() >>> me, va, sk, ku = S.stats_nl(moments='mvsk') >>> g = wtm.TrHermite(mean=me, sigma=Hs/4, skew=sk, kurt=ku, ... ysigma=Hs/4) >>> ys = S.sim(15000) # Simulated in the Gaussian world >>> xs = g.gauss2dat(ys[:,1]) # Transformed to the real world See also --------- transform.TrHermite transform.TrOchi objects.LevelCrossings.trdata objects.TimeSeries.trdata References: ----------- Langley, RS (1987) 'A statistical analysis of nonlinear random waves' Ocean Engineering, Vol 14, No 5, pp 389-407 Marthinsen, T. and Winterstein, S.R (1992) 'On the skewness of random surface waves' In proceedings of the 2nd ISOPE Conference, San Francisco, 14-19 june. Winterstein, S.R, Ude, T.C. and Kleiven, G. (1994) 'Springing and slow drift responses: predicted extremes and fatigue vs. simulation' In Proc. 7th International behaviour of Offshore structures, (BOSS) Vol. 3, pp.1-15 """ # default options if h is None: h = self.h # S = ttspec(S,'w') w = ravel(self.args) S = ravel(self.data) if self.freqtype in ['f', 'w']: # vari = 't' if self.freqtype == 'f': w = 2. * pi * w S = S / (2. * pi) # m0 = self.moment(nr=0) m0 = simps(S, w) sa = sqrt(m0) # Nw = w.size Hs, Hd, Hdii = qtf(w, h, g) # return # skew=6/sqrt(m0)^3*simpson(S.w, # simpson(S.w,(Hs+Hd).*S1(:,ones(1,Nw))).*S1.') Hspd = trapz(trapz((Hs + Hd) * S[newaxis, :], w) * S, w) output = [] # %approx : Marthinsen, T. and Winterstein, S.R (1992) method if method[0] == 'a': if 'm' in moments: output.append(2. * trapz(Hdii * S, w)) if 'v' in moments: output.append(m0) skew = 6. / sa ** 3 * Hspd if 's' in moments: output.append(skew) if 'k' in moments: output.append((4. * skew / 3.) ** 2. + 3.) else: raise ValueError('Unknown option!') # elif method[0]== 'q': #, # quasi method # Fn = self.nyquist_freq() # dw = Fn/Nw # tmp1 =sqrt(S[:,newaxis]*S[newaxis,:])*dw # Hd = Hd*tmp1 # Hs = Hs*tmp1 # k = 6 # stop = 0 # while !stop: # E = eigs([Hd,Hs;Hs,Hd],[],k) # %stop = (length(find(abs(E)<1e-4))>0 | k>1200) # %stop = (any(abs(E(:))<1e-4) | k>1200) # stop = (any(abs(E(:))<1e-4) | k>=min(2*Nw,1200)) # k = min(2*k,2*Nw) # end ## ## # m02=2*sum(E.^2) % variance of 2'nd order contribution ## # %Hstd = 16*trapz(S.w,(Hdii.*S1).^2) # %Hstd = trapz(S.w,trapz(S.w,((Hs+Hd)+ 2*Hs.*Hd).*S1(:,ones(1,Nw))).*S1.') # ma = 2*trapz(S.w,Hdii.*S1) # %m02 = Hstd-ma^2% variance of second order part # sa = sqrt(m0+m02) # skew = 6/sa^3*Hspd # kurt = (4*skew/3).^2+3 # elif method[0]== 'e': #, % Kac and Siegert eigenvalue analysis # Fn = self.nyquist_freq() # dw = Fn/Nw # tmp1 =sqrt(S[:,newaxis]*S[newaxis,:])*dw # Hd = Hd*tmp1 # Hs = Hs*tmp1 # k = 6 # stop = 0 ## ## # while (not stop): # [V,D] = eigs([Hd,HsHs,Hd],[],k) # E = diag(D) # %stop = (length(find(abs(E)<1e-4))>0 | k>=min(2*Nw,1200)) # stop = (any(abs(E(:))<1e-4) | k>=min(2*Nw,1200)) # k = min(2*k,2*Nw) # end ## ## # h1 = sqrt(S*dw/2) # C = (ctranspose(V)*[h1;h1]) ## # E2 = E.^2 # C2 = C.^2 ## # ma = sum(E) % mean # sa = sqrt(sum(C2)+2*sum(E2)) % standard deviation # skew = sum((6*C2+8*E2).*E)/sa^3 % skewness # kurt = 3+48*sum((C2+E2).*E2)/sa^4 % kurtosis return output def testgaussian(self, ns, test0=None, cases=100, method='nonlinear', verbose=False, **opt): ''' TESTGAUSSIAN Test if a stochastic process is Gaussian. CALL: test1 = testgaussian(S,[ns,Ns],test0,def,options) Returns ------- test1 : array, simulated values of e(g)=int (g(u)-u)^2 du, where int limits is given by OPTIONS.PARAM. Parameters ---------- ns : int # of points simulated test0 : real scalar observed value of e(g)=int (g(u)-u)^2 du, cases : int # of independent simulations (default 100) method : string defines method of estimation of the transform nonlinear': from smoothed crossing intensity (default) 'mnonlinear': from smoothed marginal distribution options = options structure defining how the estimation of the transformation is done. (default troptset('dat2tr')) TESTGAUSSIAN simulates e(g(u)-u) = int (g(u)-u)^2 du for Gaussian processes given the spectral density, S. The result is plotted if test0 is given. This is useful for testing if the process X(t) is Gaussian. If 95% of TEST1 is less than TEST0 then X(t) is not Gaussian at a 5% level. Example: ------- >>> import wafo.spectrum.models as sm >>> import wafo.transform.models as wtm >>> import wafo.objects as wo >>> Hs = 7 >>> Sj = sm.Jonswap(Hm0=Hs) >>> S0 = Sj.tospecdata() >>> ns =100; dt = .2 >>> x1 = S0.sim(ns, dt=dt) >>> 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) >>> ys = wo.mat2timeseries(S.sim(ns=2**13)) >>> g0, gemp = ys.trdata() >>> t0 = g0.dist2gauss() >>> t1 = S0.testgaussian(ns=2**13, cases=50) >>> sum(t1 > t0) < 5 True See also -------- cov2sdat, dat2tr, troptset ''' maxsize = 200000 # must divide the computations due to limited memory # if nargin<5||isempty(opt): # opt = troptset('dat2tr') # opt = troptset(opt,'multip',1) plotflag = False if test0 is None else True if cases > 50: print(' ... be patient this may take a while') rep = int(ns * cases / maxsize) + 1 Nstep = int(cases / rep) acf = self.tocovdata() test1 = [] for ix in range(rep): xs = acf.sim(ns=ns, cases=Nstep) for iy in range(1, xs.shape[-1]): ts = TimeSeries(xs[:, iy], xs[:, 0].ravel()) g = ts.trdata(method, **opt)[0] test1.append(g.dist2gauss()) if verbose: print('finished %d of %d ' % (ix + 1, rep)) if rep > 1: xs = acf.sim(ns=ns, cases=np.remainder(cases, rep)) for iy in range(1, xs.shape[-1]): ts = TimeSeries(xs[:, iy], xs[:, 0].ravel()) g, _tmp = ts.trdata(method, **opt) test1.append(g.dist2gauss()) if plotflag: plotbackend.plot(test1, 'o') plotbackend.plot([1, cases], [test0, test0], '--') plotbackend.ylabel('e(g(u)-u)') plotbackend.xlabel('Simulation number') return test1 def moment(self, nr=2, even=True, j=0): ''' Calculates spectral moments from spectrum Parameters ---------- nr: int order of moments (recomended maximum 4) even : bool False for all moments, True for only even orders j: int 0 or 1 Returns ------- m : list of moments mtext : list of strings describing the elements of m, see below Details ------- Calculates spectral moments of up to order NR by use of Simpson-integration. / / mj_t^i = | w^i S(w)^(j+1) dw, or mj_x^i = | k^i S(k)^(j+1) dk / / where k=w^2/gravity, i=0,1,...,NR The strings in output mtext have the same position in the list as the corresponding numerical value has in output m Notation in mtext: 'm0' is the variance, 'm0x' is the first-order moment in x, 'm0xx' is the second-order moment in x, 'm0t' is the first-order moment in t, etc. For the calculation of moments see Baxevani et al. Example: >>> import numpy as np >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap(Hm0=3, Tp=7) >>> w = np.linspace(0,4,256) >>> S = SpecData1D(Sj(w),w) #Make spectrum object from numerical values >>> mom, mom_txt = S.moment() >>> np.allclose(mom, [0.5616342024616453, 0.7309966918203602]) True >>> mom_txt == ['m0', 'm0tt'] True References ---------- Baxevani A. et al. (2001) Velocities for Random Surfaces ''' one_dim_spectra = ['freq', 'enc', 'k1d'] if self.type not in one_dim_spectra: raise ValueError('Unknown spectrum type!') f = ravel(self.args) S = ravel(self.data) if self.freqtype in ['f', 'w']: vari = 't' if self.freqtype == 'f': f = 2. * pi * f S = S / (2. * pi) else: vari = 'x' S1 = abs(S) ** (j + 1.) m = [simps(S1, x=f)] mtxt = 'm%d' % j mtext = [mtxt] step = mod(even, 2) + 1 df = f ** step for i in range(step, nr + 1, step): S1 = S1 * df m.append(simps(S1, x=f)) mtext.append(mtxt + vari * i) return m, mtext def nyquist_freq(self): """ Return Nyquist frequency Example ------- >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap(Hm0=5) >>> S = Sj.tospecdata() #Make spectrum ob >>> S.nyquist_freq() 3.0 """ return self.args[-1] def sampling_period(self): ''' Returns sampling interval from Nyquist frequency of spectrum Returns --------- dT : scalar sampling interval, unit: [m] if wave number spectrum, [s] otherwise Let wm be maximum frequency/wave number in spectrum, then dT=pi/wm if angular frequency, dT=1/(2*wm) if natural frequency (Hz) Example ------- >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap(Hm0=5) >>> S = Sj.tospecdata() #Make spectrum ob >>> S.sampling_period() 1.0471975511965976 See also ''' if self.freqtype == 'f': wmdt = 0.5 # Nyquist to sampling interval factor else: # ftype == w og ftype == k wmdt = pi wm = self.args[-1] # Nyquist frequency dt = wmdt / wm # sampling interval = 1/Fs return dt def resample(self, dt=None, Nmin=0, Nmax=2 ** 13 + 1, method='stineman'): ''' Interpolate and zero-padd spectrum to change Nyquist freq. Parameters ---------- dt : real scalar wanted sampling interval (default as given by S, see spec2dt) unit: [s] if frequency-spectrum, [m] if wave number spectrum Nmin, Nmax : scalar integers minimum and maximum number of frequencies, respectively. method : string interpolation method (options are 'linear', 'cubic' or 'stineman') To be used before simulation (e.g. spec2sdat) or evaluation of covariance function (spec2cov) to get the wanted sampling interval. The input spectrum is interpolated and padded with zeros to reach the right max-frequency, w[-1]=pi/dt, f(end)=1/(2*dt), or k[-1]=pi/dt. The objective is that output frequency grid should be at least as dense as the input grid, have equidistant spacing and length equal to 2^k+1 (>=Nmin). If the max frequency is changed, the number of points in the spectrum is maximized to 2^13+1. Note: Also zero-padding down to zero freq, if S does not start there. If empty input dt, this is the only effect. See also -------- spec2cov, spec2sdat, covinterp, spec2dt ''' ftype = self.freqtype w = self.args.ravel() n = w.size # doInterpolate = 0 # Nyquist to sampling interval factor Cnf2dt = 0.5 if ftype == 'f' else pi # % ftype == w og ftype == k wnOld = w[-1] # Old Nyquist frequency dTold = Cnf2dt / wnOld # sampling interval=1/Fs # dTold = self.sampling_period() if dt is None: dt = dTold # Find how many points that is needed nfft = 2 ** nextpow2(max(n - 1, Nmin - 1)) dttest = dTold * (n - 1) / nfft while (dttest > dt) and (nfft < Nmax - 1): nfft = nfft * 2 dttest = dTold * (n - 1) / nfft nfft = nfft + 1 wnNew = Cnf2dt / dt # % New Nyquist frequency dWn = wnNew - wnOld doInterpolate = dWn > 0 or w[1] > 0 or ( nfft != n) or dt != dTold or np.any(abs(diff(w, axis=0)) > 1.0e-8) if doInterpolate > 0: S1 = self.data dw = min(diff(w)) if dWn > 0: # add a zero just above old max-freq, and a zero at new # max-freq to get correct interpolation there Nz = 1 + (dWn > dw) # % Number of zeros to add if Nz == 2: w = hstack((w, wnOld + dw, wnNew)) else: w = hstack((w, wnNew)) S1 = hstack((S1, zeros(Nz))) if w[0] > 0: # add a zero at freq 0, and, if there is space, a zero just # below min-freq Nz = 1 + (w[0] > dw) # % Number of zeros to add if Nz == 2: w = hstack((0, w[0] - dw, w)) else: w = hstack((0, w)) S1 = hstack((zeros(Nz), S1)) # Do a final check on spacing in order to check that the gridding # is sufficiently dense: # np1 = S1.size dwMin = finfo(float).max # wnc = min(wnNew,wnOld-1e-5) wnc = wnNew # specfun = lambda xi : stineman_interp(xi, w, S1) specfun = interpolate.interp1d(w, S1, kind='cubic') x = discretize(specfun, 0, wnc)[0] dwMin = minimum(min(diff(x)), dwMin) newNfft = 2 ** nextpow2(ceil(wnNew / dwMin)) + 1 if newNfft > nfft: # if (nfft <= 2 ** 15 + 1) and (newNfft > 2 ** 15 + 1): # warnings.warn('Spectrum matrix is very large (>33k). ' + # 'Memory problems may occur.') nfft = newNfft self.args = linspace(0, wnNew, nfft) if method == 'stineman': self.data = stineman_interp(self.args, w, S1) else: intfun = interpolate.interp1d(w, S1, kind=method) 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 Paramter -------- gravity=9.81 Notes ----- Normalization performed such that INT S(freq) dfreq = 1 INT freq^2 S(freq) dfreq = 1 where integration limits are given by freq and S(freq) is the spectral density; freq can be frequency or wave number. The normalization is defined by A=sqrt(m0/m2); B=1/A/m0; freq'=freq*A; S(freq')=S(freq)*B If S is a directional spectrum then a normalized gravity (.g) is added to Sn, such that mxx normalizes to 1, as well as m0 and mtt. (See spec2mom for notation of moments) If S is complex-valued cross spectral density which has to be normalized, then m0, m2 (suitable spectral moments) should be given. Example ------- >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap(Hm0=5) >>> S = Sj.tospecdata() #Make spectrum ob >>> np.allclose(S.moment(2)[0], ... [1.5614600345079888, 0.95567089481941048]) True >>> Sn = S.copy(); Sn.normalize() Now the moments should be one >>> np.allclose(Sn.moment(2)[0], [1.0, 1.0]) True ''' mom = self.moment(nr=4, even=True)[0] m0 = mom[0] m2 = mom[1] m4 = mom[2] SM0 = sqrt(m0) SM2 = sqrt(m2) A = SM0 / SM2 B = SM2 / (SM0 * m0) if self.freqtype == 'f': self.args = self.args * A / 2 / pi self.data = self.data * B * 2 * pi elif self.freqtype == 'w': self.args = self.args * A self.data = self.data * B m02 = m4 / gravity ** 2 m20 = m02 self.g = gravity * sqrt(m0 * m20) / m2 self.A = A self.norm = True self.date = now() def bandwidth(self, factors=0): ''' Return some spectral bandwidth and irregularity factors Parameters ----------- factors : array-like Input vector 'factors' correspondence: 0 alpha=m2/sqrt(m0*m4) (irregularity factor) 1 eps2 = sqrt(m0*m2/m1^2-1) (narrowness factor) 2 eps4 = sqrt(1-m2^2/(m0*m4))=sqrt(1-alpha^2) (broadness factor) 3 Qp=(2/m0^2)int_0^inf f*S(f)^2 df (peakedness factor) Returns -------- bw : arraylike vector of bandwidth factors Order of output is the same as order in 'factors' Example: >>> import numpy as np >>> import wafo.spectrum.models as sm >>> 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.bandwidth([0,'eps2',2,3]) array([ 0.73062845, 0.34476034, 0.68277527, 2.90817052]) ''' m = self.moment(nr=4, even=False)[0] if isinstance(factors, str): factors = [factors] fact_dict = dict(alpha=0, eps2=1, eps4=3, qp=3, Qp=3) fact = array([fact_dict.get(idx, idx) for idx in list(factors)], dtype=int) # fact = atleast_1d(fact) alpha = m[2] / sqrt(m[0] * m[4]) eps2 = sqrt(m[0] * m[2] / m[1] ** 2. - 1.) eps4 = sqrt(1. - m[2] ** 2. / m[0] / m[4]) f = self.args S = self.data Qp = 2 / m[0] ** 2. * simps(f * S ** 2, x=f) bw = array([alpha, eps2, eps4, Qp]) return bw[fact] def characteristic(self, fact='Hm0', T=1200, g=9.81): """ Returns spectral characteristics and their covariance Parameters ---------- fact : vector with factor integers or a string or a list of strings defining spectral characteristic, see description below. T : scalar recording time (sec) (default 1200 sec = 20 min) g : scalar acceleration of gravity [m/s^2] Returns ------- ch : vector of spectral characteristics R : matrix of the corresponding covariances given T chtext : a list of strings describing the elements of ch, see example. Description ------------ If input spectrum is of wave number type, output are factors for corresponding 'k1D', else output are factors for 'freq'. Input vector 'factors' correspondence: 1 Hm0 = 4*sqrt(m0) Significant wave height 2 Tm01 = 2*pi*m0/m1 Mean wave period 3 Tm02 = 2*pi*sqrt(m0/m2) Mean zero-crossing period 4 Tm24 = 2*pi*sqrt(m2/m4) Mean period between maxima 5 Tm_10 = 2*pi*m_1/m0 Energy period 6 Tp = 2*pi/{w | max(S(w))} Peak period 7 Ss = 2*pi*Hm0/(g*Tm02^2) Significant wave steepness 8 Sp = 2*pi*Hm0/(g*Tp^2) Average wave steepness 9 Ka = abs(int S(w)*exp(i*w*Tm02) dw ) /m0 Groupiness parameter 10 Rs = (S(0.092)+S(0.12)+S(0.15)/(3*max(S(w))) Quality control parameter 11 Tp1 = 2*pi*int S(w)^4 dw Peak Period ------------------ (robust estimate for Tp) int w*S(w)^4 dw 12 alpha = m2/sqrt(m0*m4) Irregularity factor 13 eps2 = sqrt(m0*m2/m1^2-1) Narrowness factor 14 eps4 = sqrt(1-m2^2/(m0*m4))=sqrt(1-alpha^2) Broadness factor 15 Qp = (2/m0^2)int_0^inf w*S(w)^2 dw Peakedness factor Order of output is same as order in 'factors' The covariances are computed with a Taylor expansion technique and is currently only available for factors 1, 2, and 3. Variances are also available for factors 4,5,7,12,13,14 and 15 Quality control: ---------------- Critical value for quality control parameter Rs is Rscrit = 0.02 for surface displacement records and Rscrit=0.0001 for records of surface acceleration or slope. If Rs > Rscrit then probably there are something wrong with the lower frequency part of S. Ss may be used as an indicator of major malfunction, by checking that it is in the range of 1/20 to 1/16 which is the usual range for locally generated wind seas. Examples: --------- >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap(Hm0=5) >>> S = Sj.tospecdata() #Make spectrum ob >>> S.characteristic(1) (array([ 8.59007646]), array([[ 0.03040216]]), ['Tm01']) >>> [ch, R, txt] = S.characteristic([1,2,3]) # fact vector of integers >>> S.characteristic('Ss') # fact a string (array([ 0.04963112]), array([[ 2.63624782e-06]]), ['Ss']) >>> S.characteristic(['Hm0','Tm02']) # fact a list of strings (array([ 4.99833578, 8.03139757]), array([[ 0.05292989, 0.02511371], [ 0.02511371, 0.0274645 ]]), ['Hm0', 'Tm02']) See also --------- bandwidth, moment References ---------- Krogstad, H.E., Wolf, J., Thompson, S.P., and Wyatt, L.R. (1999) 'Methods for intercomparison of wave measurements' Coastal Enginering, Vol. 37, pp. 235--257 Krogstad, H.E. (1982) 'On the covariance of the periodogram' Journal of time series analysis, Vol. 3, No. 3, pp. 195--207 Tucker, M.J. (1993) 'Recommended standard for wave data sampling and near-real-time processing' Ocean Engineering, Vol.20, No.5, pp. 459--474 Young, I.R. (1999) "Wind generated ocean waves" Elsevier Ocean Engineering Book Series, Vol. 2, pp 239 """ # TODO: Need more checking on computing the variances for Tm24,alpha, # eps2 and eps4 # TODO: Covariances between Tm24,alpha, eps2 and eps4 variables are # also needed tfact = dict(Hm0=0, Tm01=1, Tm02=2, Tm24=3, Tm_10=4, Tp=5, Ss=6, Sp=7, Ka=8, Rs=9, Tp1=10, Alpha=11, Eps2=12, Eps4=13, Qp=14) tfact1 = ('Hm0', 'Tm01', 'Tm02', 'Tm24', 'Tm_10', 'Tp', 'Ss', 'Sp', 'Ka', 'Rs', 'Tp1', 'Alpha', 'Eps2', 'Eps4', 'Qp') if isinstance(fact, str): fact = list((fact,)) if isinstance(fact, (list, tuple)): nfact = [] for k in fact: if isinstance(k, str): nfact.append(tfact.get(k.capitalize(), 15)) else: nfact.append(k) else: nfact = fact nfact = atleast_1d(nfact) if np.any((nfact > 14) | (nfact < 0)): raise ValueError('Factor outside range (0,...,14)') # vari = self.freqtype f = self.args.ravel() S1 = self.data.ravel() m, unused_mtxt = self.moment(nr=4, even=False) # moments corresponding to freq in Hz for k in range(1, 5): m[k] = m[k] / (2 * pi) ** k # pi = np.pi ind = flatnonzero(f > 0) m.append(simps(S1[ind] / f[ind], f[ind]) * 2. * pi) # = m_1 m_10 = simps(S1[ind] ** 2 / f[ind], f[ind]) * \ (2 * pi) ** 2 / T # = COV(m_1,m0|T=t0) m_11 = simps(S1[ind] ** 2. / f[ind] ** 2, f[ind]) * \ (2 * pi) ** 3 / T # = COV(m_1,m_1|T=t0) # sqrt = np.sqrt # Hm0 Tm01 Tm02 Tm24 Tm_10 Hm0 = 4. * sqrt(m[0]) Tm01 = m[0] / m[1] Tm02 = sqrt(m[0] / m[2]) Tm24 = sqrt(m[2] / m[4]) Tm_10 = m[5] / m[0] Tm12 = m[1] / m[2] ind = S1.argmax() maxS = S1[ind] # [maxS ind] = max(S1) Tp = 2. * pi / f[ind] # peak period /length Ss = 2. * pi * Hm0 / g / Tm02 ** 2 # Significant wave steepness Sp = 2. * pi * Hm0 / g / Tp ** 2 # Average wave steepness # groupiness factor Ka = abs(simps(S1 * exp(1J * f * Tm02), f)) / m[0] # Quality control parameter # critical value is approximately 0.02 for surface displacement records # If Rs>0.02 then there are something wrong with the lower frequency # part of S. Rs = np.sum( interp(r_[0.0146, 0.0195, 0.0244] * 2 * pi, f, S1)) / 3. / maxS Tp2 = 2 * pi * simps(S1 ** 4, f) / simps(f * S1 ** 4, f) alpha1 = Tm24 / Tm02 # m(3)/sqrt(m(1)*m(5)) eps2 = sqrt(Tm01 / Tm12 - 1.) # sqrt(m(1)*m(3)/m(2)^2-1) eps4 = sqrt(1. - alpha1 ** 2) # sqrt(1-m(3)^2/m(1)/m(5)) Qp = 2. / m[0] ** 2 * simps(f * S1 ** 2, f) ch = r_[Hm0, Tm01, Tm02, Tm24, Tm_10, Tp, Ss, Sp, Ka, Rs, Tp2, alpha1, eps2, eps4, Qp] # Select the appropriate values ch = ch[nfact] chtxt = [tfact1[i] for i in nfact] # if nargout>1, # covariance between the moments: # COV(mi,mj |T=t0) = int f^(i+j)*S(f)^2 df/T mij = self.moment(nr=8, even=False, j=1)[0] for ix, tmp in enumerate(mij): mij[ix] = tmp / T / ((2. * pi) ** (ix - 1.0)) # and the corresponding variances for # {'hm0', 'tm01', 'tm02', 'tm24', 'tm_10','tp','ss', 'sp', 'ka', 'rs', # 'tp1','alpha','eps2','eps4','qp'} 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), 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 * (m[2] ** 2 / (4 * m[0] ** 3) * mij[0] + mij[4] / m[0] - m[2] / m[0] ** 2 * mij[2]), nan * ones(4), m[2] ** 2 * mij[0] / (4 * m[0] ** 3 * m[4]) + mij[4] / (m[0] * m[4]) + mij[8] * m[2] ** 2 / (4 * m[0] * m[4] ** 3) - m[2] * mij[2] / (m[0] ** 2 * m[4]) + m[2] ** 2 * mij[4] / (2 * m[0] ** 2 * m[4] ** 2) - m[2] * mij[6] / m[0] / m[4] ** 2, (m[2] ** 2 * mij[0] / 4 + (m[0] * m[2] / m[1]) ** 2 * mij[2] + m[0] ** 2 * mij[4] / 4 - m[2] ** 2 * m[0] * mij[1] / m[1] + m[0] * m[2] * mij[2] / 2 - m[0] ** 2 * m[2] / m[1] * mij[3]) / 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, nan] # and covariances by a taylor expansion technique: # Cov(Hm0,Tm01) Cov(Hm0,Tm02) Cov(Tm01,Tm02) S0 = r_[2. / (sqrt(m[0]) * m[1]) * (mij[0] - m[0] * mij[1] / m[1]), 1. / sqrt(m[2]) * (mij[0] / m[0] - mij[2] / m[2]), 1. / (2 * m[1]) * sqrt(m[0] / m[2]) * (mij[0] / m[0] - mij[2] / m[2] - mij[1] / m[1] + m[0] * mij[3] / (m[1] * m[2]))] R1 = ones((15, 15)) R1[:, :] = nan for ix, Ri in enumerate(R): R1[ix, ix] = Ri R1[0, 2:4] = S0[:2] R1[1, 2] = S0[2] # make lower triangular equal to upper triangular part for ix in [0, 1]: R1[ix + 1:, ix] = R1[ix, ix + 1:] R = R[nfact] R1 = R1[nfact, :][:, nfact] # Needs further checking: # Var(Tm24)= 0.25*(mij[4]/(m[2]*m[4])- # 2*mij[6]/m[4]**2+m[2]*mij[8]/m[4]**3) return ch, R1, chtxt def setlabels(self): ''' Set automatic title, x-,y- and z- labels on SPECDATA object based on type, angletype, freqtype ''' N = len(self.type) if N == 0: raise ValueError( 'Object does not appear to be initialized, it is empty!') labels = ['', '', ''] if self.type.endswith('dir'): title = 'Directional Spectrum' if self.freqtype.startswith('w'): labels[0] = 'Frequency [rad/s]' labels[2] = r'S($\omega$,$\theta$) $[m^2 s / rad^2]$' else: labels[0] = 'Frequency [Hz]' labels[2] = r'S(f,$\theta$) $[m^2 s / rad]$' if self.angletype.startswith('r'): labels[1] = 'Wave directions [rad]' elif self.angletype.startswith('d'): labels[1] = 'Wave directions [deg]' elif self.type.endswith('freq'): title = 'Spectral density' if self.freqtype.startswith('w'): labels[0] = 'Frequency [rad/s]' labels[1] = r'S($\omega$) $[m^2 s/ rad]$' else: labels[0] = 'Frequency [Hz]' labels[1] = r'S(f) $[m^2 s]$' else: title = 'Wave Number Spectrum' labels[0] = 'Wave number [rad/m]' if self.type.endswith('k1d'): labels[1] = r'S(k) $[m^3/ rad]$' elif self.type.endswith('k2d'): labels[1] = labels[0] labels[2] = r'S(k1,k2) $[m^4/ rad^2]$' else: raise ValueError( 'Object does not appear to be initialized, it is empty!') if self.norm != 0: title = 'Normalized ' + title labels[0] = 'Normalized ' + labels[0].split('[')[0] if not self.type.endswith('dir'): labels[1] = labels[1].split('[')[0] labels[2] = labels[2].split('[')[0] self.labels.title = title self.labels.xlab = labels[0] self.labels.ylab = labels[1] self.labels.zlab = labels[2] class SpecData2D(PlotData): """ Container class for 2D spectrum data objects in WAFO Member variables ---------------- data : array_like args : vector for 1D, list of vectors for 2D, 3D, ... type : string spectrum type (default 'freq') freqtype : letter frequency type (default 'w') angletype : string angle type of directional spectrum (default 'radians') Examples -------- >>> import numpy as np >>> import wafo.spectrum.models as sm >>> Sj = sm.Jonswap(Hm0=3, Tp=7) >>> w = np.linspace(0,4,256) >>> S = SpecData1D(Sj(w),w) #Make spectrum object from numerical values See also -------- PlotData CovData """ def __init__(self, *args, **kwds): super(SpecData2D, self).__init__(*args, **kwds) self.name = 'WAFO Spectrum Object' self.type = 'dir' self.freqtype = 'w' self.angletype = '' self.h = inf self.tr = None self.phi = 0. self.v = 0. self.norm = 0 somekeys = ['angletype', 'phi', 'name', 'h', 'tr', 'freqtype', 'v', 'type', 'norm'] self.__dict__.update(sub_dict_select(kwds, somekeys)) if self.type.endswith('dir') and self.angletype == '': self.angletype = 'radians' self.setlabels() def toacf(self): pass def tospecdata(self, type=None): # @ReservedAssignment pass def sim(self): pass def sim_nl(self): pass def rotate(self, phi=0, rotateGrid=False, method='linear'): ''' Rotate spectrum clockwise around the origin. Parameters ---------- phi : real scalar rotation angle (default 0) rotateGrid : bool True if rotate grid of Snew physically (thus Snew.phi=0). False if rotate so that only Snew.phi is changed (the grid is not physically rotated) (default) method : string interpolation method to use when ROTATEGRID==1, (default 'linear') Rotates the spectrum clockwise around the origin. This equals a anti-clockwise rotation of the cordinate system (x,y). The spectrum can be of any of the two-dimensional types. For spectrum in polar representation: newtheta = theta-phi, but circulant such that -pi>> import wafo.spectrum.models as sm >>> D = sm.Spreading() >>> SD = D.tospecdata2d(sm.Jonswap().tospecdata(),nt=101) >>> m,mtext = SD.moment(nr=2,vari='xyt') >>> np.allclose(np.round(m,3), ... [ 3.061, 0.132, -0. , 2.13 , 0.011, 0.008, 1.677, -0., ... 0.109, 0.109]) True >>> mtext == ['m0', 'mx', 'my', 'mt', 'mxx', 'myy', 'mtt', 'mxy', ... 'mxt', 'myt'] True References ---------- Baxevani A. et al. (2001) Velocities for Random Surfaces ''' two_dim_spectra = ['dir', 'encdir', 'k2d'] if self.type not in two_dim_spectra: raise ValueError('Unknown 2D spectrum type!') if vari is None and nr <= 1: vari = 'x' elif vari is None: vari = 'xt' else: # secure the mutual order ('xyt') vari = ''.join(sorted(vari.lower())) Nv = len(vari) if vari[0] == 't' and Nv > 1: vari = vari[1::] + vari[0] Nv = len(vari) if not self.type.endswith('dir'): S1 = self.tospecdata(self.type[:-2] + 'dir') else: S1 = self w = ravel(S1.args[0]) theta = S1.args[1] - S1.phi S = S1.data Sw = simps(S, x=theta, axis=0) m = [simps(Sw, x=w)] mtext = ['m0'] if nr > 0: vec = [] 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] # nw = w.size if 'x' in vari: ct = np.cos(theta[:, None]) Sc = simps(S * ct, x=theta, axis=0) vec.append(kx * Sc) mtext.append('mx') if 'y' in vari: st = np.sin(theta[:, None]) Ss = simps(S * st, x=theta, axis=0) vec.append(ky * Ss) mtext.append('my') if 't' in vari: vec.append(w * Sw) mtext.append('mt') if nr > 1: if 'x' in vari: Sc2 = simps(S * ct ** 2, x=theta, axis=0) vec.append(kx ** 2 * Sc2) mtext.append('mxx') if 'y' in vari: Ss2 = simps(S * st ** 2, x=theta, axis=0) vec.append(ky ** 2 * Ss2) mtext.append('myy') if 't' in vari: vec.append(w ** 2 * Sw) mtext.append('mtt') if 'x' in vari and 'y' in vari: Scs = simps(S * ct * st, x=theta, axis=0) vec.append(kx * ky * Scs) mtext.append('mxy') if 'x' in vari and 't' in vari: vec.append(kx * w * Sc) mtext.append('mxt') if 'y' in vari and 't' in vari: vec.append(ky * w * Sc) mtext.append('myt') if nr > 3: if 'x' in vari: Sc3 = simps(S * ct ** 3, x=theta, axis=0) Sc4 = simps(S * ct ** 4, x=theta, axis=0) vec.append(kx ** 4 * Sc4) mtext.append('mxxxx') if 'y' in vari: Ss3 = simps(S * st ** 3, x=theta, axis=0) Ss4 = simps(S * st ** 4, x=theta, axis=0) vec.append(ky ** 4 * Ss4) mtext.append('myyyy') if 't' in vari: vec.append(w ** 4 * Sw) mtext.append('mtttt') if 'x' in vari and 'y' in vari: Sc2s = simps(S * ct ** 2 * st, x=theta, axis=0) Sc3s = simps(S * ct ** 3 * st, x=theta, axis=0) Scs2 = simps(S * ct * st ** 2, x=theta, axis=0) Scs3 = simps(S * ct * st ** 3, x=theta, axis=0) Sc2s2 = simps(S * ct ** 2 * st ** 2, x=theta, axis=0) vec.extend((kx ** 3 * ky * Sc3s, kx ** 2 * ky ** 2 * Sc2s2, kx * ky ** 3 * Scs3)) mtext.extend(('mxxxy', 'mxxyy', 'mxyyy')) if 'x' in vari and 't' in vari: vec.extend((kx ** 3 * w * Sc3, kx ** 2 * w ** 2 * Sc2, kx * w ** 3 * Sc)) mtext.extend(('mxxxt', 'mxxtt', 'mxttt')) if 'y' in vari and 't' in vari: vec.extend((ky ** 3 * w * Ss3, ky ** 2 * w ** 2 * Ss2, ky * w ** 3 * Ss)) mtext.extend(('myyyt', 'myytt', 'myttt')) if 'x' in vari and 'y' in vari and 't' in vari: vec.extend((kx ** 2 * ky * w * Sc2s, kx * ky ** 2 * w * Scs2, kx * ky * w ** 2 * Scs)) mtext.extend(('mxxyt', 'mxyyt', 'mxytt')) # end % if nr>1 m.extend([simps(vals, x=w) for vals in vec]) return np.asarray(m), mtext def interp(self): pass def normalize(self): pass def bandwidth(self): pass def setlabels(self): ''' Set automatic title, x-,y- and z- labels on SPECDATA object based on type, angletype, freqtype ''' N = len(self.type) if N == 0: raise ValueError( 'Object does not appear to be initialized, it is empty!') labels = ['', '', ''] if self.type.endswith('dir'): title = 'Directional Spectrum' if self.freqtype.startswith('w'): labels[0] = 'Frequency [rad/s]' labels[2] = r'$S(w,\theta) [m**2 s / rad**2]$' else: labels[0] = 'Frequency [Hz]' labels[2] = r'$S(f,\theta) [m**2 s / rad]$' if self.angletype.startswith('r'): labels[1] = 'Wave directions [rad]' elif self.angletype.startswith('d'): labels[1] = 'Wave directions [deg]' elif self.type.endswith('freq'): title = 'Spectral density' if self.freqtype.startswith('w'): labels[0] = 'Frequency [rad/s]' labels[1] = 'S(w) [m**2 s/ rad]' else: labels[0] = 'Frequency [Hz]' labels[1] = 'S(f) [m**2 s]' else: title = 'Wave Number Spectrum' labels[0] = 'Wave number [rad/m]' if self.type.endswith('k1d'): labels[1] = 'S(k) [m**3/ rad]' elif self.type.endswith('k2d'): labels[1] = labels[0] labels[2] = 'S(k1,k2) [m**4/ rad**2]' else: raise ValueError( 'Object does not appear to be initialized, it is empty!') if self.norm != 0: title = 'Normalized ' + title labels[0] = 'Normalized ' + labels[0].split('[')[0] if not self.type.endswith('dir'): labels[1] = labels[1].split('[')[0] labels[2] = labels[2].split('[')[0] self.labels.title = title self.labels.xlab = labels[0] self.labels.ylab = labels[1] self.labels.zlab = labels[2] def main(): import matplotlib matplotlib.interactive(True) from wafo.spectrum import models as sm Sj = sm.Jonswap() S = Sj.tospecdata() R = S.tocovdata(nr=1) Si = R.tospecdata() ns = 5000 dt = .2 x1 = S.sim_nl(ns=ns, dt=dt) x2 = TimeSeries(x1[:, 1], x1[:, 0]) R = x2.tocovdata(lag=100) R.plot() S.plot('ro') t = S.moment() t1 = S.bandwidth([0, 1, 2, 3]) S1 = S.copy() S1.resample(dt=0.3, method='cubic') S1.plot('k+') x = S1.sim(ns=100) import pylab pylab.clf() pylab.plot(x[:, 0], x[:, 1]) pylab.show() pylab.close('all') print('done') def test_mm_pdf(): import wafo.spectrum.models as sm Sj = sm.Jonswap(Hm0=7, Tp=11) w = np.linspace(0, 4, 256) S1 = Sj.tospecdata(w) # Make spectrum object from numerical values 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 doctest.testmod() if __name__ == '__main__': test_docstrings() # test_mm_pdf() # main()