From 61c1d8e650a38bfe082836a6aeba8c340a7fb140 Mon Sep 17 00:00:00 2001 From: Per A Brodtkorb Date: Fri, 16 Mar 2018 19:54:32 +0100 Subject: [PATCH] Added wavemodels.py --- wafo/wavemodels.py | 268 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 268 insertions(+) create mode 100644 wafo/wavemodels.py diff --git a/wafo/wavemodels.py b/wafo/wavemodels.py new file mode 100644 index 0000000..36a77c1 --- /dev/null +++ b/wafo/wavemodels.py @@ -0,0 +1,268 @@ +''' +Created on 13. mar. 2018 + +@author: pab +''' +import numpy as np +from numpy import pi, sqrt +import wafo.transform.estimation as te +import wafo.transform as wt +from wafo.containers import PlotData +from wafo.kdetools.kernels import qlevels +from wafo.misc import tranproc +import warnings + + + +def _set_default_t_h_g(t, h, g, m0, m2): + if g is None: + y = np.linspace(-5, 5) + x = sqrt(m0) * y + 0 + g = wt.TrData(y, x) + if t is None: + tt1 = 2 * pi * sqrt(m0 / m2) + t = np.linspace(0, 1.7 * tt1, 51) + if h is None: + px = g.gauss2dat([0, 4.]) + px = abs(px[1] - px[0]) + h = np.linspace(0, 1.3 * px, 41) + return h, t, g + +def lh83pdf(t=None, h=None, mom=None, g=None): + """ + LH83PDF Longuet-Higgins (1983) approximation of the density (Tc,Ac) + in a stationary Gaussian transform process X(t) where + Y(t) = g(X(t)) (Y zero-mean Gaussian, X non-Gaussian). + + CALL: f = lh83pdf(t,h,[m0,m1,m2],g); + + f = density of wave characteristics of half-wavelength + in a stationary Gaussian transformed process X(t), + where Y(t) = g(X(t)) (Y zero-mean Gaussian) + t,h = vectors of periods and amplitudes, respectively. + default depending on the spectral moments + m0,m1,m2 = the 0'th,1'st and 2'nd moment of the spectral density + with angular frequency. + g = space transformation, Y(t)=g(X(t)), default: g is identity + transformation, i.e. X(t) = Y(t) is Gaussian, + The transformation, g, can be estimated using lc2tr + or dat2tr or given apriori by ochi. + + Example + ------- + >>> import wafo.spectrum.models as sm + >>> Sj = sm.Jonswap() + >>> w = np.linspace(0,4,256) + >>> S = Sj.tospecdata(w) #Make spectrum object from numerical values + >>> S = sm.SpecData1D(Sj(w),w) # Alternatively do it manually + >>> mom, mom_txt = S.moment(nr=2, even=False) + >>> f = lh83pdf(mom=mom) + >>> f.plot() + + See also + -------- + cav76pdf, lc2tr, dat2tr + + References + ---------- + Longuet-Higgins, M.S. (1983) + "On the joint distribution wave periods and amplitudes in a + random wave field", Proc. R. Soc. A389, pp 24--258 + + Longuet-Higgins, M.S. (1975) + "On the joint distribution wave periods and amplitudes of sea waves", + J. geophys. Res. 80, pp 2688--2694 + """ + + # tested on: matlab 5.3 + # History: + # Revised pab 01.04.2001 + # - Added example + # - Better automatic scaling for h,t + # revised by IR 18.06.2000, fixing transformation and transposing t and h to fit simpson req. + # revised by pab 28.09.1999 + # made more efficient calculation of f + # by Igor Rychlik + + m0, m1, m2 = mom + h, t, g = _set_default_t_h_g(t, h, g, m0, m2) + + L0 = m0 + L1 = m1 / (2 * pi) + L2 = m2 / (2 * pi)**2 + eps2 = sqrt((L2 * L0) / (L1**2) - 1) + + if np.any(~np.isreal(eps2)): + raise ValueError('input moments are not correct') + + const = 4 / sqrt(pi) / eps2 / (1 + 1 / sqrt(1 + eps2**2)) + + a = len(h) + b = len(t) + der = np.ones((a, 1)) + + h_lh = g.dat2gauss(h.ravel(), der.ravel()) + + der = abs(h_lh[1]) # abs(h_lh[:, 1]) + h_lh = h_lh[0] + + # Normalization + transformation of t and h ??????? + # Without any transformation + + t_lh = t / (L0 / L1) + #h_lh = h_lh/sqrt(2*L0) + h_lh = h_lh / sqrt(2) + t_lh = 2 * t_lh + + # Computation of the distribution + T, H = np.meshgrid(t_lh[1:b], h_lh) + f_th = np.zeros((a, b)) + tmp = const * der[:, None] * (H / T)**2 * np.exp(-H**2. * + (1 + ((1 - 1. / T) / eps2)**2)) / ((L0 / L1) * sqrt(2) / 2) + f_th[:, 1:b] = tmp + + f = PlotData(f_th, (t, h), + xlab='Tc', ylab='Ac', + title='Joint density of (Tc,Ac) - Longuet-Higgins (1983)', + plot_kwds=dict(plotflag=1)) + + return _add_contour_levels(f) + + +def cav76pdf(t=None, h=None, mom=None, g=None): + """ + CAV76PDF Cavanie et al. (1976) approximation of the density (Tc,Ac) + in a stationary Gaussian transform process X(t) where + Y(t) = g(X(t)) (Y zero-mean Gaussian, X non-Gaussian). + + CALL: f = cav76pdf(t,h,[m0,m2,m4],g); + + f = density of wave characteristics of half-wavelength + in a stationary Gaussian transformed process X(t), + where Y(t) = g(X(t)) (Y zero-mean Gaussian) + t,h = vectors of periods and amplitudes, respectively. + default depending on the spectral moments + m0,m2,m4 = the 0'th, 2'nd and 4'th moment of the spectral density + with angular frequency. + g = space transformation, Y(t)=g(X(t)), default: g is identity + transformation, i.e. X(t) = Y(t) is Gaussian, + The transformation, g, can be estimated using lc2tr + or dat2tr or given a priori by ochi. + [] = default values are used. + + Example + ------- + >>> import wafo.spectrum.models as sm + >>> Sj = sm.Jonswap() + >>> w = np.linspace(0,4,256) + >>> S = Sj.tospecdata(w) #Make spectrum object from numerical values + >>> S = sm.SpecData1D(Sj(w),w) # Alternatively do it manually + >>> mom, mom_txt = S.moment(nr=4, even=True) + >>> f = cav76pdf(mom=mom) + >>> f.plot() + + See also + -------- + lh83pdf, lc2tr, dat2tr + + References + ---------- + Cavanie, A., Arhan, M. and Ezraty, R. (1976) + "A statistical relationship between individual heights and periods of + storm waves". + In Proceedings Conference on Behaviour of Offshore Structures, + Trondheim, pp. 354--360 + Norwegian Institute of Technology, Trondheim, Norway + + Lindgren, G. and Rychlik, I. (1982) + Wave Characteristics Distributions for Gaussian Waves -- + Wave-lenght, Amplitude and Steepness, Ocean Engng vol 9, pp. 411-432. + """ + # tested on: matlab 5.3 NB! note + # History: + # revised pab 04.11.2000 + # - fixed xlabels i.e. f.labx={'Tc','Ac'} + # revised by IR 4 X 2000. fixed transform and normalisation + # using Lindgren & Rychlik (1982) paper. + # At the end of the function there is a text with derivation of the density. + # + # revised by jr 21.02.2000 + # - Introduced cell array for f.x for use with pdfplot + # by pab 28.09.1999 + + m0, m2, m4 = mom + h, t, g = _set_default_t_h_g(t, h, g, m0, m2) + + eps4 = 1.0 - m2**2 / (m0 * m4) + alfa = m2 / sqrt(m0 * m4) + if np.any(~np.isreal(eps4)): + raise ValueError('input moments are not correct') + + a = len(h) + b = len(t) + der = np.ones((a, 1)) + + h_lh = g.dat2gauss(h.ravel(), der.ravel()) + der = abs(h_lh[1]) + h_lh = h_lh[0] + + # Normalization + transformation of t and h + + pos = 2 / (1 + alfa) # inverse of a fraction of positive maxima + cons = 2 * pi**4 * pos / sqrt(2 * pi) / m4 / sqrt((1 - alfa**2)) + # Tm=2*pi*sqrt(m0/m2)/alpha; #mean period between positive maxima + + t_lh = t + h_lh = sqrt(m0) * h_lh + + # Computation of the distribution + T, H = np.meshgrid(t_lh[1:b], h_lh) + f_th = np.zeros((a, b)) + + f_th[:, 1:b] = cons * der[:, None] * (H**2 / (T**5)) * np.exp(-0.5 * ( + H / T**2)**2. * ((T**2 - pi**2 * m2 / m4)**2 / (m0 * (1 - alfa**2)) + pi**4 / m4)) + f = PlotData(f_th, (t, h), + xlab='Tc', ylab='Ac', + title='Joint density of (Tc,Ac) - Cavanie et al. (1976)', + plot_kwds=dict(plotflag=1)) + return _add_contour_levels(f) + +def _add_contour_levels(f): + p_levels = np.r_[10:90:20, 95, 99, 99.9] + try: + c_levels = qlevels(f.data, p=p_levels) + f.clevels = c_levels + f.plevels = p_levels + except ValueError as e: + msg = "Could not calculate contour levels!. ({})".format(str(e)) + warnings.warn(msg) + return f + + # Let U,Z be the height and second derivative (curvature) at a local maximum in a Gaussian proces + # with spectral moments m0,m2,m4. The conditional density ($U>0$) has the following form + #$$ + # f(z,u)=c \frac{1}{\sqrt{2\pi}}\frac{1}{\sqrt{m0(1-\alpha^2)}}\exp(-0.5\left(\frac{u-z(m2/m4)} + # {\sqrt{m0(1-\alpha^2)}}\right)^2)\frac{|z|}{m4}\exp(-0.5z^2/m4), \quad z<0, + #$$ + # where $c=2/(1+\alpha)$, $\alpha=m2/\sqrt{m0\cdot m4}$. + # + # The cavanie approximation is based on the model $X(t)=U \cos(\pi t/T)$, consequently + # we have $U=H$ and by twice differentiation $Z=-U(\pi^2/T)^2\cos(0)$. The variable change has Jacobian + # $2\pi^2 H/T^3$ giving the final formula for the density of $T,H$ + #$$ + # f(t,h)=c \frac{2\pi^4}{\sqrt{2\pi}}\frac{1}{m4\sqrt{m0(1-\alpha^2)}}\frac{h^2}{t^5} + # \exp(-0.5\frac{h^2}{t^4}\left(\left(\frac{t^2-\pi^2(m2/m4)} + # {\sqrt{m0(1-\alpha^2)}}\right)^2+\frac{\pi^4}{m4}\right)). + #$$ + # + # + + +def test_docstrings(): + import doctest + print('Testing docstrings in %s' % __file__) + doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE) + + +if __name__ == '__main__': + test_docstrings()