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@ -43,6 +43,7 @@ from numpy import (inf, atleast_1d, newaxis, any, minimum, maximum, array,
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hstack, vstack, real, flipud, clip)
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from ..wave_theory.dispersion_relation import w2k, k2w # @UnusedImport
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from .core import SpecData1D, SpecData2D
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from dask.dataframe.core import meth
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__all__ = ['Bretschneider', 'Jonswap', 'Torsethaugen', 'Wallop', 'McCormick',
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@ -568,6 +569,14 @@ class Jonswap(ModelSpectrum):
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Gf = self.peak_e_factor(wn)
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return Gf * _gengamspec(wn, self.N, self.M)
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def _check_parametric_ag(self, N, M, gammai):
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parameters_ok = 3 <= N <= 50 or 2 <= M <= 9.5 and 1 <= gammai <= 20
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if not parameters_ok:
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raise ValueError('Not knowing the normalization because N, ' +
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'M or peakedness parameter is out of bounds!')
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if self.sigmaA != 0.07 or self.sigmaB != 0.09:
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warnings.warn('Use integration to calculate Ag when ' + 'sigmaA!=0.07 or sigmaB!=0.09')
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def _parametric_ag(self):
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"""
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Original normalization
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@ -581,23 +590,18 @@ class Jonswap(ModelSpectrum):
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N = self.N
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M = self.M
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gammai = self.gamma
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parameters_ok = (3 <= N <= 50 or 2 <= M <= 9.5 and 1 <= gammai <= 20)
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f1NM = 4.1 * (N - 2 * M ** 0.28 + 5.3) ** (-1.45 * M ** 0.1 + 0.96)
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f2NM = ((2.2 * M ** (-3.3) + 0.57) * N ** (-0.58 * M ** 0.37 + 0.53) -
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1.04 * M ** (-1.9) + 0.94)
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self.Ag = (1 + f1NM * log(gammai) ** f2NM) / gammai
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if not parameters_ok:
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raise ValueError('Not knowing the normalization because N, ' +
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'M or peakedness parameter is out of bounds!')
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# elseif N == 5 && M == 4,
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# if N == 5 && M == 4,
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# options.Ag = (1+1.0*log(gammai).**1.16)/gammai
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# options.Ag = (1-0.287*log(gammai))
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# options.normalizeMethod = 'Three'
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# elseif N == 4 && M == 4,
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# options.Ag = (1+1.1*log(gammai).**1.19)/gammai
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if self.sigmaA != 0.07 or self.sigmaB != 0.09:
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warnings.warn('Use integration to calculate Ag when ' +
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'sigmaA!=0.07 or sigmaB!=0.09')
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self._check_parametric_ag(N, M, gammai)
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def _custom_ag(self):
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self.method = 'custom'
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@ -1502,20 +1506,6 @@ class Spreading(object):
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self.wn_c = wn_c
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self.wn_up = wn_up
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methods = dict(n=None, m='mitsuyasu', d='donelan', b='banner')
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methodslist = (None, 'mitsuyasu', 'donelan', 'banner')
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if isinstance(self.method, str):
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if not self.method[0] in methods:
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raise ValueError('Unknown method')
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self.method = methods[self.method[0]]
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elif self.method is None:
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pass
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else:
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if method < 0 or 3 < method:
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method = 2
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self.method = methodslist[method]
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self._spreadfun = dict(c=self.cos2s, b=self.box, m=self.mises,
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v=self.mises,
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p=self.poisson, s=self.sech2, w=self.wrap_norm)
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@ -1524,6 +1514,22 @@ class Spreading(object):
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p=self.fourier2x, s=self.fourier2b,
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w=self.fourier2d)
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@property
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def method(self):
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return self._method
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@method.setter
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def method(self, method):
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methods = {'n': None, 'm': 'mitsuyasu', 'd': 'donelan', 'b':'banner',
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0: None, 1: 'mitsuyasu', 2: 'donelan', 3:'banner',
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None: None}
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m = method if not isinstance(self.method, str) else method[0].lower()
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try:
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self._method = methods[m]
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except KeyError:
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msg = 'Unknown method. Got {}, but expected one of {}!'
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raise ValueError(msg.format(method, str(methods.keys())))
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def __call__(self, theta, w=1, wc=1):
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spreadfun = self._spreadfun[self.type[0]]
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return spreadfun(theta, w, wc)
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@ -1979,7 +1985,16 @@ class Spreading(object):
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return where(x < 100., xk / sinh(xk),
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-2. * xk / (exp(xk) * expm1(-2. * xk)))
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def tospecdata2d(self, specdata=None, theta=None, wc=0.52, nt=51):
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def _check_theta(self, theta):
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L = abs(theta[-1] - theta[0])
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if abs(L - np.pi) > _EPS:
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raise ValueError('theta must cover all angles -pi -> pi')
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nt = len(theta)
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if nt < 40:
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warnings.warn('Number of angles is less than 40. ' +
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'Spreading too sparsely sampled!')
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def tospecdata2d(self, specdata, theta=None, wc=0.52, nt=51):
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"""
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MKDSPEC Make a directional spectrum
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frequency spectrum times spreading function
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@ -1991,7 +2006,6 @@ class Spreading(object):
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(default jonswap)
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D = spreading function (special struct)
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(default spreading([],'cos2s'))
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plotflag = 1: plot the spectrum, else: do not plot (default 0)
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Creates a directional spectrum through multiplication of a frequency
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spectrum and a spreading function: S(w,theta)=S(w)*D(w,theta)
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@ -2013,21 +2027,10 @@ class Spreading(object):
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See also spreading, rotspec, jonswap, torsethaugen
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"""
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if specdata is None:
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specdata = Jonswap().tospecdata()
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if theta is None:
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pi = np.pi
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theta = np.linspace(-pi, pi, nt)
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else:
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L = abs(theta[-1] - theta[0])
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if abs(L - pi) > _EPS:
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raise ValueError('theta must cover all angles -pi -> pi')
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nt = len(theta)
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theta = np.linspace(-np.pi, np.pi, nt)
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if nt < 40:
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warnings.warn('Number of angles is less than 40. ' +
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'Spreading too sparsely sampled!')
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self._check_theta(theta)
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w = specdata.args
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S = specdata.data
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Per A Brodtkorb
7 years ago