Ongoing work
Per.Andreas.Brodtkorb
12 years ago
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8242a74b60
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"""
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Dispersion relation module
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--------------------------
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k2w - Translates from wave number to frequency
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w2k - Translates from frequency to wave number
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"""
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import warnings
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#import numpy as np
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from numpy import (atleast_1d, sqrt, ones_like, zeros_like, arctan2, where, tanh, any, #@UnresolvedImport
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sin, cos, sign, inf, flatnonzero, finfo, cosh, abs) #@UnresolvedImport
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__all__ = ['k2w', 'w2k']
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def k2w(k1, k2=0e0, h=inf, g=9.81, u1=0e0, u2=0e0):
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''' Translates from wave number to frequency
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using the dispersion relation
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Parameters
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----------
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k1 : array-like
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wave numbers [rad/m].
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k2 : array-like, optional
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second dimension wave number
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h : real scalar, optional
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water depth [m].
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g : real scalar, optional
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acceleration of gravity, see gravity
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u1, u2 : real scalars, optional
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current velocity [m/s] along dimension 1 and 2.
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note: when u1!=0 | u2!=0 then theta is not calculated correctly
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Returns
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-------
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w : ndarray
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angular frequency [rad/s].
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theta : ndarray
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direction [rad].
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Dispersion relation
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-------------------
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w = sqrt(g*K*tanh(K*h)) ( 0 < w < inf)
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theta = arctan2(k2,k1) (-pi < theta < pi)
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where
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K = sqrt(k1**2+k2**2)
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The shape of w and theta is the common shape of k1 and k2 according to the
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numpy broadcasting rules.
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See also
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--------
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w2k
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Example
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-------
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>>> from numpy import arange
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>>> import wafo.spectrum.dispersion_relation as wsd
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>>> wsd.k2w(arange(0.01,.5,0.2))[0]
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array([ 0.3132092 , 1.43530485, 2.00551739])
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>>> wsd.k2w(arange(0.01,.5,0.2),h=20)[0]
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array([ 0.13914927, 1.43498213, 2.00551724])
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'''
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k1i, k2i, hi, gi, u1i, u2i = atleast_1d(k1, k2, h, g, u1, u2)
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if k1i.size == 0:
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return zeros_like(k1i)
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ku1 = k1i*u1i
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ku2 = k2i*u2i
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theta = arctan2(k2, k1)
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k = sqrt(k1i**2+k2i**2)
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w = where(k>0, ku1+ku2+sqrt(gi*k*tanh(k*hi)), 0.0)
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cond = (w<0)
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if any(cond):
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txt0 = '''
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Waves and current are in opposite directions
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making some of the frequencies negative.
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Here we are forcing the negative frequencies to zero.
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'''
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warnings.warn(txt0)
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w = where(cond, 0.0, w) # force w to zero
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return w, theta
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def w2k(w, theta=0.0, h=inf, g=9.81, count_limit=100):
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'''
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Translates from frequency to wave number
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using the dispersion relation
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Parameters
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----------
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w : array-like
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angular frequency [rad/s].
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theta : array-like, optional
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direction [rad].
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h : real scalar, optional
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water depth [m].
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g : real scalar or array-like of size 2.
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constant of gravity [m/s**2] or 3D normalizing constant
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Returns
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-------
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k1, k2 : ndarray
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wave numbers [rad/m] along dimension 1 and 2.
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Description
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-----------
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Uses Newton Raphson method to find the wave number k in the dispersion relation
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w**2= g*k*tanh(k*h).
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The solution k(w) => k1 = k(w)*cos(theta)
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k2 = k(w)*sin(theta)
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The size of k1,k2 is the common shape of w and theta according to numpy
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broadcasting rules. If w or theta is scalar it functions as a constant
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matrix of the same shape as the other.
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Example
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-------
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>>> import pylab as plb
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>>> import wafo.spectrum.dispersion_relation as wsd
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>>> w = plb.linspace(0,3);
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>>> h = plb.plot(w,w2k(w)[0])
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>>> wsd.w2k(range(4))[0]
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array([ 0. , 0.1019368 , 0.4077472 , 0.91743119])
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>>> wsd.w2k(range(4),h=20)[0]
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array([ 0. , 0.10503601, 0.40774726, 0.91743119])
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>>> plb.close('all')
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See also
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--------
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k2w
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'''
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wi, th, hi, gi = atleast_1d(w, theta, h, g)
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if wi.size == 0:
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return zeros_like(wi)
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k = 1.0*sign(wi)*wi**2.0 / gi[0] # deep water
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if (hi > 10. ** 25).all():
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k2 = k*sin(th)*gi[0]/gi[-1] #size np x nf
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k1 = k*cos(th)
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return k1, k2
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if gi.size > 1:
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txt0 = '''
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Finite depth in combination with 3D normalization (len(g)=2) is not implemented yet.
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'''
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raise ValueError(txt0)
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find = flatnonzero
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eps = finfo(float).eps
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oshape = k.shape
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wi, k, hi = wi.ravel(), k.ravel(), hi.ravel()
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# Newton's Method
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# Permit no more than count_limit iterations.
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hi = hi * ones_like(k)
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hn = zeros_like(k)
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ix = find((wi<0) | (0<wi))
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# Break out of the iteration loop for three reasons:
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# 1) the last update is very small (compared to x)
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# 2) the last update is very small (compared to sqrt(eps))
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# 3) There are more than 100 iterations. This should NEVER happen.
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count = 0
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while (ix.size>0 and count < count_limit):
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ki = k[ix]
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kh = ki * hi[ix]
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hn[ix] = (ki*tanh(kh)-wi[ix]**2.0/gi)/(tanh(kh)+kh/(cosh(kh)**2.0))
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knew = ki - hn[ix]
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# Make sure that the current guess is not zero.
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# When Newton's Method suggests steps that lead to zero guesses
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# take a step 9/10ths of the way to zero:
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ksmall = find(abs(knew)==0)
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if ksmall.size>0:
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knew[ksmall] = ki[ksmall] / 10.0
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hn[ix[ksmall]] = ki[ksmall]-knew[ksmall]
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k[ix] = knew
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# disp(['Iteration ',num2str(count),' Number of points left: ' num2str(length(ix)) ]),
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ix = find((abs(hn) > sqrt(eps)*abs(k)) * abs(hn) > sqrt(eps))
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count += 1
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if count == count_limit:
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txt1 = ''' W2K did not converge.
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The maximum error in the last step was: %13.8f''' % max(hn[ix])
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warnings.warn(txt1)
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k.shape = oshape
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k2 = k*sin(th)
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k1 = k*cos(th)
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return k1, k2
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def main():
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import doctest
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doctest.testmod()
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if __name__ == '__main__':
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main()
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