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from __future__ import absolute_import, division
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import warnings
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import os
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import numpy as np
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from numpy import (pi, inf, zeros, ones, where, nonzero,
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flatnonzero, ceil, sqrt, exp, log, arctan2,
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tanh, cosh, sinh, random, atleast_1d,
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minimum, diff, isnan, r_, conj, mod,
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hstack, vstack, interp, ravel, finfo, linspace,
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arange, array, nan, newaxis, sign)
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from numpy.fft import fft
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from scipy.integrate import simps, trapz
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from scipy.special import erf
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from scipy.linalg import toeplitz
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import scipy.interpolate as interpolate
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from scipy.interpolate.interpolate import interp1d, interp2d
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from ..objects import TimeSeries, mat2timeseries
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from ..interpolate import stineman_interp
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from ..wave_theory.dispersion_relation import w2k # , k2w
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from ..containers import PlotData, now
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from ..misc import sub_dict_select, nextpow2, discretize, JITImport, mctp2tc
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from ..misc import meshgrid, gravity, cart2polar, polar2cart, mctp2rfc
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from ..kdetools import qlevels
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# from wafo.transform import TrData
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from ..transform.models import TrLinear
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from ..plotbackend import plotbackend
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try:
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from ..gaussian import Rind
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except ImportError:
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Rind = None
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try:
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from .. import c_library
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except ImportError:
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warnings.warn('Compile the c_library.pyd again!')
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c_library = None
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try:
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from .. import cov2mod
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except ImportError:
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warnings.warn('Compile the cov2mod.pyd again!')
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cov2mod = None
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# Trick to avoid error due to circular import
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_WAFOCOV = JITImport('wafo.covariance')
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__all__ = ['SpecData1D', 'SpecData2D', 'plotspec']
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_EPS = np.finfo(float).eps
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def _set_seed(iseed):
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'''Set seed of random generator'''
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if iseed is not None:
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try:
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random.set_state(iseed)
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except (KeyError, TypeError):
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random.seed(iseed)
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def qtf(w, h=inf, g=9.81):
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"""
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Return Quadratic Transfer Function
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Parameters
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------------
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w : array-like
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angular frequencies
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h : scalar
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water depth
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g : scalar
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acceleration of gravity
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Returns
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-------
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h_s = sum frequency effects
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h_d = difference frequency effects
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h_dii = diagonal of h_d
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"""
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w = atleast_1d(w)
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num_w = w.size
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k_w = w2k(w, theta=0, h=h, g=g)[0]
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k_1, k_2 = meshgrid(k_w, k_w)
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if h == inf: # go here for faster calculations
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h_s = 0.25 * (abs(k_1) + abs(k_2))
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h_d = -0.25 * abs(abs(k_1) - abs(k_2))
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h_dii = zeros(num_w)
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return h_s, h_d, h_dii
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[w_1, w_2] = meshgrid(w, w)
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w12 = (w_1 * w_2)
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w1p2 = (w_1 + w_2)
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w1m2 = (w_1 - w_2)
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k12 = (k_1 * k_2)
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k1p2 = (k_1 + k_2)
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k1m2 = abs(k_1 - k_2)
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if 0: # Langley
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p_1 = (-2 * w1p2 * (k12 * g ** 2. - w12 ** 2.) +
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w_1 * (w_2 ** 4. - g ** 2 * k_2 ** 2) +
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w_2 * (w_1 ** 4 - g * 2. * k_1 ** 2)) / (4. * w12)
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p_2 = w1p2 ** 2. * cosh((k1p2) * h) - g * (k1p2) * sinh((k1p2) * h)
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h_s = (-p_1 / p_2 * w1p2 * cosh((k1p2) * h) / g -
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(k12 * g ** 2 - w12 ** 2.) / (4 * g * w12) +
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(w_1 ** 2 + w_2 ** 2) / (4 * g))
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p_3 = (-2 * w1m2 * (k12 * g ** 2 + w12 ** 2) -
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w_1 * (w_2 ** 4 - g ** 2 * k_2 ** 2) +
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w_2 * (w_1 ** 4 - g ** 2 * k_1 ** 2)) / (4. * w12)
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p_4 = w1m2 ** 2. * cosh(k1m2 * h) - g * (k1m2) * sinh((k1m2) * h)
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h_d = (-p_3 / p_4 * (w1m2) * cosh((k1m2) * h) / g -
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(k12 * g ** 2 + w12 ** 2) / (4 * g * w12) +
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(w_1 ** 2. + w_2 ** 2.) / (4. * g))
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else: # Marthinsen & Winterstein
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tmp1 = 0.5 * g * k12 / w12
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tmp2 = 0.25 / g * (w_1 ** 2. + w_2 ** 2. + w12)
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h_s = (tmp1 - tmp2 + 0.25 * g * (w_1 * k_2 ** 2. + w_2 * k_1 ** 2) /
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(w12 * (w1p2))) / (1. - g * (k1p2) / (w1p2) ** 2. *
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tanh((k1p2) * h)) + tmp2 - 0.5 * tmp1 # OK
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tmp2 = 0.25 / g * (w_1 ** 2 + w_2 ** 2 - w12) # OK
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h_d = (tmp1 - tmp2 - 0.25 * g * (w_1 * k_2 ** 2 - w_2 * k_1 ** 2) /
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(w12 * (w1m2))) / (1. - g * (k1m2) / (w1m2) ** 2. *
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tanh((k1m2) * h)) + tmp2 - 0.5 * tmp1 # OK
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# tmp1 = 0.5*g*k_w./(w.*sqrt(g*h))
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# tmp2 = 0.25*w.^2/g
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# Wave group velocity
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c_g = 0.5 * g * (tanh(k_w * h) + k_w * h * (1.0 - tanh(k_w * h) ** 2)) / w
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h_dii = (0.5 * (0.5 * g * (k_w / w) ** 2. - 0.5 * w ** 2 / g +
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g * k_w / (w * c_g)) /
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(1. - g * h / c_g ** 2.) - 0.5 * k_w / sinh(2 * k_w * h)) # OK
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h_d.flat[0::num_w + 1] = h_dii
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# k = find(w_1==w_2)
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# h_d(k) = h_dii
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# The NaN's occur due to division by zero. => Set the isnans to zero
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h_dii = where(isnan(h_dii), 0, h_dii)
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h_d = where(isnan(h_d), 0, h_d)
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h_s = where(isnan(h_s), 0, h_s)
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return h_s, h_d, h_dii
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def plotspec(specdata, linetype='b-', flag=1):
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'''
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PLOTSPEC Plot a spectral density
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Parameters
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----------
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S : SpecData1D or SpecData2D object
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defining spectral density.
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linetype : string
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defining color and linetype, see plot for possibilities
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flag : scalar integer
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defining the type of plot
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1D:
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1 plots the density, S, (default)
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2 plot 10log10(S)
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3 plots both the above plots
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2D:
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Directional spectra: S(w,theta), S(f,theta)
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1 polar plot S (default)
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2 plots spectral density and the directional
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spreading, int S(w,theta) dw or int S(f,theta) df
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3 plots spectral density and the directional
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spreading, int S(w,theta)/S(w) dw or int S(f,theta)/S(f) df
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4 mesh of S
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5 mesh of S in polar coordinates
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6 contour plot of S
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7 filled contour plot of S
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Wavenumber spectra: S(k1,k2)
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1 contour plot of S (default)
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2 filled contour plot of S
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Example
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-------
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>>> import numpy as np
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>>> import wafo.spectrum as ws
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>>> Sj = ws.models.Jonswap(Hm0=3, Tp=7)
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>>> S = Sj.tospecdata()
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>>> ws.plotspec(S,flag=1)
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S = demospec('dir'); S2 = mkdspec(jonswap,spreading);
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plotspec(S,2), hold on
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# Same as previous fig. due to frequency independent spreading
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plotspec(S,3,'g')
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# Not the same as previous figs. due to frequency dependent spreading
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plotspec(S2,2,'r')
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plotspec(S2,3,'m')
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# transform from angular frequency and radians to frequency and degrees
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Sf = ttspec(S,'f','d'); clf
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plotspec(Sf,2),
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See also
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dat2spec, createspec, simpson
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'''
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pass
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# # label the contour levels
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# txtFlag = 0
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# LegendOn = 1
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#
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# ftype = specdata.freqtype # options are 'f' and 'w' and 'k'
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# data = specdata.data
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# if data.ndim == 2:
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# freq = specdata.args[1]
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# theta = specdata.args[0]
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# else:
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# freq = specdata.args
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# if isinstance(specdata.args, (list, tuple)):
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#
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# if ftype == 'w':
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# xlbl_txt = 'Frequency [rad/s]'
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# ylbl1_txt = 'S(w) [m^2 s / rad]'
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# ylbl3_txt = 'Directional Spectrum'
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# zlbl_txt = 'S(w,\theta) [m^2 s / rad^2]'
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# funit = ' [rad/s]'
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# Sunit = ' [m^2 s / rad]'
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# elif ftype == 'f':
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# xlbl_txt = 'Frequency [Hz]'
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# ylbl1_txt = 'S(f) [m^2 s]'
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# ylbl3_txt = 'Directional Spectrum'
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# zlbl_txt = 'S(f,\theta) [m^2 s / rad]'
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# funit = ' [Hz]'
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# Sunit = ' [m^2 s ]'
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# elif ftype == 'k':
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# xlbl_txt = 'Wave number [rad/m]'
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# ylbl1_txt = 'S(k) [m^3/ rad]'
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# funit = ' [rad/m]'
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# Sunit = ' [m^3 / rad]'
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# ylbl4_txt = 'Wave Number Spectrum'
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#
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# else:
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# raise ValueError('Frequency type unknown')
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#
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#
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# if hasattr(specdata, 'norm') and specdata.norm :
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# Sunit=[]
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# funit = []
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# ylbl1_txt = 'Normalized Spectral density'
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# ylbl3_txt = 'Normalized Directional Spectrum'
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# ylbl4_txt = 'Normalized Wave Number Spectrum'
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# if ftype == 'k':
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# xlbl_txt = 'Normalized Wave number'
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# else:
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# xlbl_txt = 'Normalized Frequency'
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#
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# ylbl2_txt = 'Power spectrum (dB)'
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#
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# phi = specdata.phi
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#
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# spectype = specdata.type.lower()
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# stype = spectype[-3::]
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# if stype in ('enc', 'req', 'k1d') : #1D plot
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# Fn = freq[-1] # Nyquist frequency
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# indm = findpeaks(data, n=4)
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# maxS = data.max()
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# if isfield(S,'CI') && ~isempty(S.CI):
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# maxS = maxS*S.CI(2)
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# txtCI = [num2str(100*S.p), '% CI']
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# #end
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#
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# Fp = freq[indm]# %peak frequency/wave number
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#
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# if len(indm) == 1:
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# txt = [('fp = %0.2g' % Fp) + funit]
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# else:
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# txt = []
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# for i, fp in enumerate(Fp.tolist()):
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# txt.append(('fp%d = %0.2g' % (i, fp)) + funit)
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#
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# txt = ''.join(txt)
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# if (flag == 3):
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# plotbackend.subplot(2, 1, 1)
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# if (flag == 1) or (flag == 3):# Plot in normal scale
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# plotbackend.plot(np.vstack([Fp, Fp]),
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# np.vstack([zeros(len(indm)), data.take(indm)]),
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# ':', label=txt)
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# plotbackend.plot(freq, data, linetype)
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# specdata.labels.labelfig()
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# if isfield(S,'CI'):
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# plot(freq,S.S*S.CI(1), 'r:' )
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# plot(freq,S.S*S.CI(2), 'r:' )
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#
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# a = plotbackend.axis()
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#
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# a1 = Fn
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|
# 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):
|
|
|
|
|
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)
|
|
|
|
|
super(SpecData1D, self).__init__(*args, **kwds)
|
|
|
|
|
|
|
|
|
|
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
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def lagtype(self):
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if self.freqtype in 'k': # options are 'f' and 'w' and 'k'
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return 'x'
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return 't'
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def tocov_matrix(self, nr=0, nt=None, dt=None):
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'''
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Computes covariance function and its derivatives, alternative version
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Parameters
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----------
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nr : scalar integer
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number of derivatives in output, nr<=4 (default 0)
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nt : scalar integer
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number in time grid, i.e., number of time-lags.
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(default rate*(n_f-1)) where rate = round(1/(2*f(end)*dt)) or
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rate = round(pi/(w(n_f)*dt)) depending on S.
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dt : real scalar
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time spacing for acfmat
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Returns
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-------
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acfmat : [R0, R1,...Rnr], shape Nt+1 x Nr+1
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matrix with autocovariance and its derivatives, i.e., Ri (i=1:nr)
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are column vectors with the 1'st to nr'th derivatives of R0.
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NB! This routine requires that the spectrum grid is equidistant
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starting from zero frequency.
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Example
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-------
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>>> import wafo.spectrum.models as sm
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>>> Sj = sm.Jonswap()
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>>> S = Sj.tospecdata()
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>>> acfmat = S.tocov_matrix(nr=3, nt=256, dt=0.1)
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>>> np.round(acfmat[:2,:],3)
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array([[ 3.061, 0. , -1.677, 0. ],
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[ 3.052, -0.167, -1.668, 0.187]])
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See also
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--------
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cov,
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resample,
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objects
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'''
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dt, rate = self._get_default_dt_and_rate(dt)
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self._check_dt(dt)
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freq = self.args
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n_f = len(freq)
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if nt is None:
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nt = rate * (n_f - 1)
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else: # check if Nt is ok
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nt = minimum(nt, rate * (n_f - 1))
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# nr, nt = int(nr), int(nt)
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spec = self.copy()
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spec.resample(dt)
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acf = spec.tocovdata(nr, nt, rate=1)
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acfmat = zeros((nt + 1, nr + 1), dtype=float)
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acfmat[:, 0] = acf.data[0:nt + 1]
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fieldname = 'R' + self.lagtype * nr
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for i in range(1, nr + 1):
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fname = fieldname[:i + 1]
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r_i = getattr(acf, fname)
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acfmat[:, i] = r_i[0:nt + 1]
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self._check_cov_matrix(acfmat, nt, dt)
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return acfmat
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def tocovdata(self, nr=0, nt=None, rate=None):
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'''
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Computes covariance function and its derivatives
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Parameters
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----------
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nr : number of derivatives in output, nr<=4 (default = 0).
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nt : number in time grid, i.e., number of time-lags
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(default rate*(length(S.data)-1)).
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rate = 1,2,4,8...2**r, interpolation rate for R
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(default = 1, no interpolation)
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Returns
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-------
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R : CovData1D
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auto covariance function
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The input 'rate' with the spectrum gives the time-grid-spacing:
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dt=pi/(S.w[-1]*rate),
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S.w[-1] is the Nyquist freq.
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This results in the time-grid: 0:dt:Nt*dt.
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What output is achieved with different S and choices of Nt, Nx and Ny:
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1) S.type='freq' or 'dir', Nt set, Nx,Ny not set => R(time) (one-dim)
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2) S.type='k1d' or 'k2d', Nt set, Nx,Ny not set: => R(x) (one-dim)
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3) Any type, Nt and Nx set => R(x,time); Nt and Ny set => R(y,time)
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4) Any type, Nt, Nx and Ny set => R(x,y,time)
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5) Any type, Nt not set, Nx and/or Ny set
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=> Nt set to default, goto 3) or 4)
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NB! This routine requires that the spectrum grid is equidistant
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starting from zero frequency.
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NB! If you are using a model spectrum, spec, with sharp edges
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to calculate covariances then you should probably round off the sharp
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edges like this:
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Example:
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>>> import wafo.spectrum.models as sm
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>>> Sj = sm.Jonswap()
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>>> S = Sj.tospecdata()
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>>> S.data[0:40] = 0.0
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>>> S.data[100:-1] = 0.0
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>>> Nt = len(S.data)-1
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>>> acf = S.tocovdata(nr=0, nt=Nt)
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>>> S1 = acf.tospecdata()
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h = S.plot('r')
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h1 = S1.plot('b:')
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R = spec2cov(spec,0,Nt)
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win = parzen(2*Nt+1)
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R.data = R.data.*win(Nt+1:end)
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S1 = cov2spec(acf)
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R2 = spec2cov(S1)
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figure(1)
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plotspec(S),hold on, plotspec(S1,'r')
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figure(2)
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covplot(R), hold on, covplot(R2,[],[],'r')
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figure(3)
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semilogy(abs(R2.data-R.data)), hold on,
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semilogy(abs(S1.data-S.data)+1e-7,'r')
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See also
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--------
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cov2spec
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'''
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freq = self.args
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n_f = len(freq)
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if freq[0] > 0:
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txt = '''Spectrum does not start at zero frequency/wave number.
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Correct it with resample, for example.'''
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raise ValueError(txt)
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d_w = abs(diff(freq, n=2, axis=0))
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if np.any(d_w > 1.0e-8):
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txt = '''Not equidistant frequencies/wave numbers in spectrum.
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Correct it with resample, for example.'''
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raise ValueError(txt)
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if rate is None:
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rate = 1 # interpolation rate
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elif rate > 16:
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rate = 16
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else: # make sure rate is a power of 2
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rate = 2 ** nextpow2(rate)
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if nt is None:
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nt = int(rate * (n_f - 1))
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else: # check if Nt is ok
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nt = int(minimum(nt, rate * (n_f - 1)))
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spec = self.copy()
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if self.freqtype in 'k':
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lagtype = 'x'
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else:
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lagtype = 't'
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d_t = spec.sampling_period()
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# normalize spec so that sum(specn)/(n_f-1)=acf(0)=var(X)
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specn = spec.data * freq[-1]
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if spec.freqtype in 'f':
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w = freq * 2 * pi
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else:
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w = freq
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nfft = rate * 2 ** nextpow2(2 * n_f - 2)
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# periodogram
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rper = r_[
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specn, zeros(nfft - (2 * n_f) + 2), conj(specn[n_f - 2:0:-1])]
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time = r_[0:nt + 1] * d_t * (2 * n_f - 2) / nfft
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r = fft(rper, nfft).real / (2 * n_f - 2)
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acf = _WAFOCOV.CovData1D(r[0:nt + 1], time, lagtype=lagtype)
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acf.tr = spec.tr
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acf.h = spec.h
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acf.norm = spec.norm
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if nr > 0:
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w = r_[w, zeros(nfft - 2 * n_f + 2), -w[n_f - 2:0:-1]]
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fieldname = 'R' + lagtype[0] * nr
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for i in range(1, nr + 1):
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rper = -1j * w * rper
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d_acf = fft(rper, nfft).real / (2 * n_f - 2)
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setattr(acf, fieldname[0:i + 1], d_acf[0:nt + 1])
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return acf
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def to_linspec(self, ns=None, dt=None, cases=20, iseed=None,
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fn_limit=sqrt(2), gravity=9.81):
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'''
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Split the linear and non-linear component from the Spectrum
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according to 2nd order wave theory
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Returns
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-------
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SL, SN : SpecData1D objects
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with linear and non-linear components only, respectively.
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Parameters
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----------
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ns : scalar integer
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giving ns load points. (default length(S)-1=n-1).
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If np>n-1 it is assummed that S(k)=0 for all k>n-1
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cases : scalar integer
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number of cases (default=20)
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dt : real scalar
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step in grid (default dt is defined by the Nyquist freq)
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iseed : scalar integer
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starting seed number for the random number generator
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(default none is set)
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fnLimit : real scalar
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normalized upper frequency limit of spectrum for 2'nd order
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components. The frequency is normalized with
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sqrt(gravity*tanh(kbar*water_depth)/Amax)/(2*pi)
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(default sqrt(2), i.e., Convergence criterion).
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Generally this should be the same as used in the final
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non-linear simulation (see example below).
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SPEC2LINSPEC separates the linear and non-linear component of the
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spectrum according to 2nd order wave theory. This is useful when
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simulating non-linear waves because:
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If the spectrum does not decay rapidly enough towards zero, the
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contribution from the 2nd order wave components at the upper tail can
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be very large and unphysical. Another option to ensure convergence of
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the perturbation series in the simulation, is to truncate the upper
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tail of the spectrum at FNLIMIT in the calculation of the 2nd order
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wave components, i.e., in the calculation of sum and difference
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frequency effects.
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Example:
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--------
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np = 10000
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iseed = 1
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pflag = 2
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S = jonswap(10)
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fnLimit = inf
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[SL,SN] = spec2linspec(S,np,[],[],fnLimit)
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x0 = spec2nlsdat(SL,8*np,[],iseed,[],fnLimit)
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x1 = spec2nlsdat(S,8*np,[],iseed,[],fnLimit)
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x2 = spec2nlsdat(S,8*np,[],iseed,[],sqrt(2))
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Se0 = dat2spec(x0)
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Se1 = dat2spec(x1)
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Se2 = dat2spec(x2)
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clf
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plotspec(SL,'r',pflag), % Linear components
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hold on
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plotspec(S,'b',pflag) % target spectrum for simulated data
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plotspec(Se0,'m',pflag), % approx. same as S
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plotspec(Se1,'g',pflag) % unphysical spectrum
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plotspec(Se2,'k',pflag) % approx. same as S
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axis([0 10 -80 0])
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hold off
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See also
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--------
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spec2nlsdat
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References
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----------
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|
P. A. Brodtkorb (2004),
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The probability of Occurrence of dangerous Wave Situations at Sea.
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Dr.Ing thesis, Norwegian University of Science and Technolgy, NTNU,
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Trondheim, Norway.
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Nestegaard, A and Stokka T (1995)
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A Third Order Random Wave model.
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In proc.ISOPE conf., Vol III, pp 136-142.
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R. S Langley (1987)
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A statistical analysis of non-linear random waves.
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Ocean Engng, Vol 14, pp 389-407
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Marthinsen, T. and Winterstein, S.R (1992)
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'On the skewness of random surface waves'
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In proc. ISOPE Conf., San Francisco, 14-19 june.
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'''
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# by pab 13.08.2002
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# TODO % Replace inputs with options structure
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# TODO % Can be improved further.
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method = 'apstochastic'
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trace = 1 # % trace the convergence
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max_sim = 30
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tolerance = 5e-4
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L = 200 # maximum lag size of the window function used in estimate
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# ftype = self.freqtype #options are 'f' and 'w' and 'k'
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# switch ftype
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# case 'f',
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# ftype = 'w'
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|
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# S = ttspec(S,ftype)
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# end
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Hm0 = self.characteristic('Hm0')
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Tm02 = self.characteristic('Tm02')
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if iseed is not None:
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|
|
_set_seed(iseed) # set the the seed
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n = len(self.data)
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|
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if ns is None:
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|
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ns = max(n - 1, 5000)
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|
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if dt is None:
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|
|
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
|
|
|
|
|
Amax = sqrt(2 * log(num_waves)) * Hm0 / 4
|
|
|
|
|
|
|
|
|
|
fLimitLo = sqrt(
|
|
|
|
|
gravity * tanh(kbar * water_depth) * Amax / water_depth ** 3)
|
|
|
|
|
|
|
|
|
|
freq = S.args
|
|
|
|
|
eps = finfo(float).eps
|
|
|
|
|
freq[-1] = freq[-1] - sqrt(eps)
|
|
|
|
|
Hw2 = 0
|
|
|
|
|
|
|
|
|
|
SL = S
|
|
|
|
|
|
|
|
|
|
indZero = nonzero(freq < fLimitLo)[0]
|
|
|
|
|
if len(indZero):
|
|
|
|
|
SL.data[indZero] = 0
|
|
|
|
|
|
|
|
|
|
maxS = max(S.data)
|
|
|
|
|
# Fs = 2*freq(end)+eps # sampling frequency
|
|
|
|
|
|
|
|
|
|
for ix in range(max_sim):
|
|
|
|
|
x2, x1 = self.sim_nl(ns=np, cases=cases, dt=None, iseed=iseed,
|
|
|
|
|
method=method, fnlimit=fn_limit,
|
|
|
|
|
output='timeseries')
|
|
|
|
|
x2.data -= x1.data # x2(:,2:end) = x2(:,2:end) -x1(:,2:end)
|
|
|
|
|
S2 = x2.tospecdata(L)
|
|
|
|
|
S1 = x1.tospecdata(L)
|
|
|
|
|
|
|
|
|
|
# TODO: Finish spec.to_linspec
|
|
|
|
|
# S2 = dat2spec(x2, L)
|
|
|
|
|
# S1 = dat2spec(x1, L)
|
|
|
|
|
# %[tf21,fi] = tfe(x2(:,2),x1(:,2),1024,Fs,[],512)
|
|
|
|
|
# %Hw11 = interp1q(fi,tf21.*conj(tf21),freq)
|
|
|
|
|
if True:
|
|
|
|
|
Hw1 = exp(interp1d(log(abs(S1.data / S2.data)), S2.args)(freq))
|
|
|
|
|
else:
|
|
|
|
|
# Geometric mean
|
|
|
|
|
fun = interp1d(log(abs(S1.data / S2.data)), S2.args)
|
|
|
|
|
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(indZero):
|
|
|
|
|
SL.data[indZero] = 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')
|
|
|
|
|
# figtile
|
|
|
|
|
# end
|
|
|
|
|
|
|
|
|
|
print('Iteration : %d, Hw12 : %g Hw12/maxS : %g' %
|
|
|
|
|
(ix, maxHw12, (maxHw12 / maxS)))
|
|
|
|
|
if (maxHw12 < maxS * tolerance) and (Hw1[-1] < Hw2[-1]):
|
|
|
|
|
break
|
|
|
|
|
# end
|
|
|
|
|
Hw2 = Hw1
|
|
|
|
|
# end
|
|
|
|
|
|
|
|
|
|
# Hw1(end)
|
|
|
|
|
# maxS*1e-3
|
|
|
|
|
# if Hw1[-1]*S.data>maxS*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<BU(I)<0 for all I, I=1,...,N}], where
|
|
|
|
|
BU(I) is deterministic function.
|
|
|
|
|
|
|
|
|
|
NIT=1, leads tp call RIND1, IAC=0 is also explicit form approximation,
|
|
|
|
|
while IAC=1 leads to maximum one dimensional integral.
|
|
|
|
|
.......
|
|
|
|
|
NIT=5, leads tp call RIND5, IAC is maximally 4-dimensional integral,
|
|
|
|
|
while IAC=1 leads to maximum 5 dimensional integral.
|
|
|
|
|
|
|
|
|
|
>>> 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)
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
|
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:
|
|
|
|
|
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.f, [10, 30, 50, 70, 90, 95, 99, 99.9],
|
|
|
|
|
f.args[0], f.args[1])
|
|
|
|
|
except:
|
|
|
|
|
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-level<Maximum
|
|
|
|
|
for i in range(1, Nx1):
|
|
|
|
|
J = IJ + Nx1
|
|
|
|
|
xc[2, IJ:J] = hg[i] # Max > 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)<sa
|
|
|
|
|
True
|
|
|
|
|
array([ True], dtype=bool)
|
|
|
|
|
True
|
|
|
|
|
True
|
|
|
|
|
|
|
|
|
|
waveplot(x1,'r',x2,'g',1,1)
|
|
|
|
|
|
|
|
|
|
See also
|
|
|
|
|
--------
|
|
|
|
|
cov2sdat, gaus2dat
|
|
|
|
|
|
|
|
|
|
Reference
|
|
|
|
|
-----------
|
|
|
|
|
C.S Dietrich and G. N. Newsam (1997)
|
|
|
|
|
"Fast and exact simulation of stationary
|
|
|
|
|
Gaussian process through circulant embedding
|
|
|
|
|
of the Covariance matrix"
|
|
|
|
|
SIAM J. SCI. COMPT. Vol 18, No 4, pp. 1088-1107
|
|
|
|
|
|
|
|
|
|
Hudspeth, S.T. and Borgman, L.E. (1979)
|
|
|
|
|
"Efficient FFT simulation of Digital Time sequences"
|
|
|
|
|
Journal of the Engineering Mechanics Division, ASCE, Vol. 105, No. EM2,
|
|
|
|
|
|
|
|
|
|
'''
|
|
|
|
|
|
|
|
|
|
spec = self.copy()
|
|
|
|
|
if dt is not None:
|
|
|
|
|
spec.resample(dt)
|
|
|
|
|
|
|
|
|
|
ftype = spec.freqtype
|
|
|
|
|
freq = spec.args
|
|
|
|
|
|
|
|
|
|
d_t = spec.sampling_period()
|
|
|
|
|
Nt = freq.size
|
|
|
|
|
|
|
|
|
|
if ns is None:
|
|
|
|
|
ns = Nt - 1
|
|
|
|
|
|
|
|
|
|
if method in 'exact':
|
|
|
|
|
|
|
|
|
|
# nr=0,Nt=None,dt=None
|
|
|
|
|
acf = spec.tocovdata(nr=0)
|
|
|
|
|
T = Nt * d_t
|
|
|
|
|
i = flatnonzero(acf.args > 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)<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)<sa
|
|
|
|
|
True
|
|
|
|
|
True
|
|
|
|
|
True
|
|
|
|
|
True
|
|
|
|
|
|
|
|
|
|
assert(np.abs(m-trueval)<sa, fun.__name__)
|
|
|
|
|
|
|
|
|
|
np =100; dt = .2
|
|
|
|
|
[x1, x2] = spec2nlsdat(jonswap,np,dt)
|
|
|
|
|
waveplot(x1,'r',x2,'g',1,1)
|
|
|
|
|
|
|
|
|
|
See also
|
|
|
|
|
--------
|
|
|
|
|
spec2linspec, spec2sdat, cov2sdat
|
|
|
|
|
|
|
|
|
|
References
|
|
|
|
|
----------
|
|
|
|
|
.. [1] Nestegaard, amp and Stokka T (1995)
|
|
|
|
|
amp Third Order Random Wave model.
|
|
|
|
|
In proc.ISOPE conf., Vol III, pp 136-142.
|
|
|
|
|
|
|
|
|
|
.. [2] R. spec Langley (1987)
|
|
|
|
|
amp statistical analysis of non-linear random waves.
|
|
|
|
|
Ocean Engng, Vol 14, pp 389-407
|
|
|
|
|
|
|
|
|
|
.. [3] Marthinsen, T. and Winterstein, spec.R (1992)
|
|
|
|
|
'On the skewness of random surface waves'
|
|
|
|
|
In proc. ISOPE Conf., San Francisco, 14-19 june.
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
# TODO % Check the methods: 'apdeterministic' and 'adeterministic'
|
|
|
|
|
Hm0, Tm02 = self.characteristic(['Hm0', 'Tm02'])[0].tolist()
|
|
|
|
|
|
|
|
|
|
_set_seed(iseed)
|
|
|
|
|
|
|
|
|
|
spec = self.copy()
|
|
|
|
|
if dt is not None:
|
|
|
|
|
spec.resample(dt)
|
|
|
|
|
|
|
|
|
|
ftype = spec.freqtype
|
|
|
|
|
freq = spec.args
|
|
|
|
|
|
|
|
|
|
d_t = spec.sampling_period()
|
|
|
|
|
Nt = freq.size
|
|
|
|
|
|
|
|
|
|
if ns is None:
|
|
|
|
|
ns = Nt - 1
|
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
s_max = max(s_i)
|
|
|
|
|
water_depth = min(abs(spec.h), 10. ** 30)
|
|
|
|
|
|
|
|
|
|
x = zeros((ns, cases + 1))
|
|
|
|
|
|
|
|
|
|
df = 1 / (ns * d_t)
|
|
|
|
|
|
|
|
|
|
# interpolate for freq. [1:(N/2)-1]*df and create 2-sided, uncentered
|
|
|
|
|
# spectra
|
|
|
|
|
ns2 = ns // 2
|
|
|
|
|
f = arange(1, ns2) * df
|
|
|
|
|
f_u = hstack((0., f_i, df * ns2))
|
|
|
|
|
w = 2. * pi * hstack((0., f, df * ns2))
|
|
|
|
|
kw = w2k(w, 0., water_depth, g)[0]
|
|
|
|
|
s_u = hstack((0., abs(s_i) / 2., 0.))
|
|
|
|
|
|
|
|
|
|
s_i = interp(f, f_u, s_u)
|
|
|
|
|
nmin = (s_i > 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 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<newtheta<pi
|
|
|
|
|
For spectrum in Cartesian representation:
|
|
|
|
|
If the grid is rotated physically, the size of it is preserved
|
|
|
|
|
(maybe it must be increased such that no nonzero points are
|
|
|
|
|
affected, but this is not implemented yet: i.e. corners are cut off)
|
|
|
|
|
The spectrum is assumed to be zero outside original grid.
|
|
|
|
|
NB! The routine does not change the type of spectrum, use spec2spec
|
|
|
|
|
for this.
|
|
|
|
|
|
|
|
|
|
Example
|
|
|
|
|
-------
|
|
|
|
|
S=demospec('dir');
|
|
|
|
|
plotspec(S), hold on
|
|
|
|
|
plotspec(rotspec(S,pi/2),'r'), hold off
|
|
|
|
|
|
|
|
|
|
See also
|
|
|
|
|
--------
|
|
|
|
|
spec2spec
|
|
|
|
|
'''
|
|
|
|
|
# TODO: Make physical grid rotation of cartesian coordinates more
|
|
|
|
|
# robust.
|
|
|
|
|
|
|
|
|
|
# Snew=S;
|
|
|
|
|
|
|
|
|
|
self.phi = mod(self.phi + phi + pi, 2 * pi) - pi
|
|
|
|
|
stype = self.type.lower()[-3::]
|
|
|
|
|
if stype == 'dir':
|
|
|
|
|
# any of the directinal types
|
|
|
|
|
# Make sure theta is from -pi to pi
|
|
|
|
|
theta = self.args[0]
|
|
|
|
|
phi0 = theta[0] + pi
|
|
|
|
|
self.args[0] = theta - phi0
|
|
|
|
|
|
|
|
|
|
# make sure -pi<phi<pi
|
|
|
|
|
self.phi = mod(self.phi + phi0 + pi, 2 * pi) - pi
|
|
|
|
|
if (rotateGrid and (self.phi != 0)):
|
|
|
|
|
# Do a physical rotation of spectrum
|
|
|
|
|
# theta = Snew.args[0]
|
|
|
|
|
ntOld = len(theta)
|
|
|
|
|
if (mod(theta[0] - theta[-1], 2 * pi) == 0):
|
|
|
|
|
nt = ntOld - 1
|
|
|
|
|
else:
|
|
|
|
|
nt = ntOld
|
|
|
|
|
|
|
|
|
|
theta[0:nt] = mod(theta[0:nt] - self.phi + pi, 2 * pi) - pi
|
|
|
|
|
self.phi = 0
|
|
|
|
|
ind = theta.argsort()
|
|
|
|
|
self.data = self.data[ind, :]
|
|
|
|
|
self.args[0] = theta[ind]
|
|
|
|
|
if (nt < ntOld):
|
|
|
|
|
if (self.args[0][0] == -pi):
|
|
|
|
|
self.data[ntOld, :] = self.data[0, :]
|
|
|
|
|
else:
|
|
|
|
|
# ftype = self.freqtype
|
|
|
|
|
freq = self.args[1]
|
|
|
|
|
theta = linspace(-pi, pi, ntOld)
|
|
|
|
|
# [F, T] = meshgrid(freq, theta)
|
|
|
|
|
|
|
|
|
|
dtheta = self.theta[1] - self.theta[0]
|
|
|
|
|
self.theta[nt] = self.theta[nt - 1] + dtheta
|
|
|
|
|
self.data[nt, :] = self.data[0, :]
|
|
|
|
|
self.data = interp2d(freq,
|
|
|
|
|
np.vstack([self.theta[0] - dtheta,
|
|
|
|
|
self.theta]),
|
|
|
|
|
np.vstack([self.data[nt, :],
|
|
|
|
|
self.data]),
|
|
|
|
|
kind=method)(freq, theta)
|
|
|
|
|
self.args[0] = theta
|
|
|
|
|
|
|
|
|
|
elif stype == 'k2d':
|
|
|
|
|
# any of the 2D wave number types
|
|
|
|
|
# Snew.phi = mod(Snew.phi+phi+pi,2*pi)-pi
|
|
|
|
|
if (rotateGrid and (self.phi != 0)):
|
|
|
|
|
# Do a physical rotation of spectrum
|
|
|
|
|
|
|
|
|
|
[k, k2] = meshgrid(*self.args)
|
|
|
|
|
[th, r] = cart2polar(k, k2)
|
|
|
|
|
[k, k2] = polar2cart(th + self.phi, r)
|
|
|
|
|
ki1, ki2 = self.args
|
|
|
|
|
Sn = interp2d(ki1, ki2, self.data, kind=method)(k, k2)
|
|
|
|
|
self.data = np.where(np.isnan(Sn), 0, Sn)
|
|
|
|
|
self.phi = 0
|
|
|
|
|
else:
|
|
|
|
|
raise ValueError('Can only rotate two dimensional spectra')
|
|
|
|
|
return
|
|
|
|
|
|
|
|
|
|
def moment(self, nr=2, vari='xt'):
|
|
|
|
|
'''
|
|
|
|
|
Calculates spectral moments from spectrum
|
|
|
|
|
|
|
|
|
|
Parameters
|
|
|
|
|
----------
|
|
|
|
|
nr : int
|
|
|
|
|
order of moments (maximum 4)
|
|
|
|
|
vari : string
|
|
|
|
|
variables in model, optional when two-dim.spectrum,
|
|
|
|
|
string with 'x' and/or 'y' and/or 't'
|
|
|
|
|
Returns
|
|
|
|
|
-------
|
|
|
|
|
m : list of moments
|
|
|
|
|
mtext : list of strings describing the elements of m, see below
|
|
|
|
|
|
|
|
|
|
Details
|
|
|
|
|
-------
|
|
|
|
|
Calculates spectral moments of up to order four by use of
|
|
|
|
|
Simpson-integration.
|
|
|
|
|
|
|
|
|
|
//
|
|
|
|
|
m_jkl=|| k1^j*k2^k*w^l S(w,th) dw dth
|
|
|
|
|
//
|
|
|
|
|
|
|
|
|
|
where k1=w^2/gravity*cos(th-phi), k2=w^2/gravity*sin(th-phi)
|
|
|
|
|
and phi is the angle of the rotation in S.phi. If the spectrum
|
|
|
|
|
has field .g, gravity is replaced by S.g.
|
|
|
|
|
|
|
|
|
|
The strings in output mtext have the same position in the cell array
|
|
|
|
|
as the corresponding numerical value has in output m
|
|
|
|
|
Notation in mtext: 'm0' is the variance,
|
|
|
|
|
'mx' is the first-order moment in x,
|
|
|
|
|
'mxx' is the second-order moment in x,
|
|
|
|
|
'mxt' is the second-order cross moment between x and t,
|
|
|
|
|
'myyyy' is the fourth-order moment in y
|
|
|
|
|
etc.
|
|
|
|
|
For the calculation of moments see Baxevani et al.
|
|
|
|
|
|
|
|
|
|
Example:
|
|
|
|
|
>>> 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
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Sj = sm.Jonswap(Hm0=7, Tp=11)
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w = np.linspace(0, 4, 256)
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S1 = Sj.tospecdata(w) # Make spectrum object from numerical values
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S = sm.SpecData1D(Sj(w), w) # Alternatively do it manually
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S0 = S.to_linspec()
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mm = S.to_mm_pdf()
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def test_docstrings():
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import doctest
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doctest.testmod()
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if __name__ == '__main__':
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test_docstrings()
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# test_mm_pdf()
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# main()
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