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pywafo/wafo/wave_theory/dispersion_relation.py

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6.1 KiB
Python

"""
Dispersion relation module
--------------------------
k2w - Translates from wave number to frequency
w2k - Translates from frequency to wave number
"""
import warnings
import numpy as np
from wafo.misc import lazywhere
from numpy import (atleast_1d, sqrt, ones_like, zeros_like, arctan2, where,
tanh, sin, cos, sign, inf,
flatnonzero, finfo, cosh, abs)
__all__ = ['k2w', 'w2k']
def k2w(k1, k2=0e0, h=inf, g=9.81, u1=0e0, u2=0e0):
''' Translates from wave number to frequency
using the dispersion relation
Parameters
----------
k1 : array-like
wave numbers [rad/m].
k2 : array-like, optional
second dimension wave number
h : real scalar, optional
water depth [m].
g : real scalar, optional
acceleration of gravity, see gravity
u1, u2 : real scalars, optional
current velocity [m/s] along dimension 1 and 2.
note: when u1!=0 | u2!=0 then theta is not calculated correctly
Returns
-------
w : ndarray
angular frequency [rad/s].
theta : ndarray
direction [rad].
Dispersion relation
-------------------
w = sqrt(g*K*tanh(K*h)) ( 0 < w < inf)
theta = arctan2(k2,k1) (-pi < theta < pi)
where
K = sqrt(k1**2+k2**2)
The shape of w and theta is the common shape of k1 and k2 according to the
numpy broadcasting rules.
See also
--------
w2k
Example
-------
>>> from numpy import arange
>>> import wafo.wave_theory.dispersion_relation as wsd
>>> wsd.k2w(arange(0.01,.5,0.2))[0]
array([ 0.3132092 , 1.43530485, 2.00551739])
>>> wsd.k2w(arange(0.01,.5,0.2),h=20)[0]
array([ 0.13914927, 1.43498213, 2.00551724])
'''
k1i, k2i, hi, gi, u1i, u2i = atleast_1d(k1, k2, h, g, u1, u2)
if k1i.size == 0:
return zeros_like(k1i)
ku1 = k1i * u1i
ku2 = k2i * u2i
theta = arctan2(k2, k1)
k = sqrt(k1i ** 2 + k2i ** 2)
w = where(k > 0, ku1 + ku2 + sqrt(gi * k * tanh(k * hi)), 0.0)
cond = (w < 0)
if np.any(cond):
txt0 = '''
Waves and current are in opposite directions
making some of the frequencies negative.
Here we are forcing the negative frequencies to zero.
'''
warnings.warn(txt0)
w = where(cond, 0.0, w) # force w to zero
return w, theta
def w2k(w, theta=0.0, h=inf, g=9.81, count_limit=100):
'''
Translates from frequency to wave number
using the dispersion relation
Parameters
----------
w : array-like
angular frequency [rad/s].
theta : array-like, optional
direction [rad].
h : real scalar, optional
water depth [m].
g : real scalar or array-like of size 2.
constant of gravity [m/s**2] or 3D normalizing constant
Returns
-------
k1, k2 : ndarray
wave numbers [rad/m] along dimension 1 and 2.
Description
-----------
Uses Newton Raphson method to find the wave number k in the dispersion
relation
w**2= g*k*tanh(k*h).
The solution k(w) => k1 = k(w)*cos(theta)
k2 = k(w)*sin(theta)
The size of k1,k2 is the common shape of w and theta according to numpy
broadcasting rules. If w or theta is scalar it functions as a constant
matrix of the same shape as the other.
Example
-------
>>> import pylab as plb
>>> import wafo.wave_theory.dispersion_relation as wsd
>>> w = plb.linspace(0,3);
>>> wsd.w2k(range(4))[0]
array([ 0. , 0.1019368 , 0.4077472 , 0.91743119])
>>> wsd.w2k(range(4),h=20)[0]
array([ 0. , 0.10503601, 0.40774726, 0.91743119])
h = plb.plot(w,w2k(w)[0])
plb.close('all')
See also
--------
k2w
'''
wi, th, hi, gi = atleast_1d(w, theta, h, g)
if wi.size == 0:
return zeros_like(wi)
k = 1.0 * sign(wi) * wi ** 2.0 / gi[0] # deep water
if (hi > 10. ** 25).all():
k2 = k * sin(th) * gi[0] / gi[-1] # size np x nf
k1 = k * cos(th)
return k1, k2
if gi.size > 1:
raise ValueError('Finite depth in combination with 3D normalization' +
' (len(g)=2) is not implemented yet.')
find = flatnonzero
eps = finfo(float).eps
oshape = k.shape
wi, k, hi = wi.ravel(), k.ravel(), hi.ravel()
# Newton's Method
# Permit no more than count_limit iterations.
hi = hi * ones_like(k)
hn = zeros_like(k)
ix = find((wi < 0) | (0 < wi))
# Break out of the iteration loop for three reasons:
# 1) the last update is very small (compared to x)
# 2) the last update is very small (compared to sqrt(eps))
# 3) There are more than 100 iterations. This should NEVER happen.
count = 0
while (ix.size > 0 and count < count_limit):
ki = k[ix]
kh = ki * hi[ix]
coshkh2 = lazywhere(np.abs(kh) < 350, (kh, ),
lambda kh: cosh(kh) ** 2.0, fillvalue=np.inf)
hn[ix] = (ki * tanh(kh) - wi[ix] ** 2.0 / gi) / \
(tanh(kh) + kh / coshkh2)
knew = ki - hn[ix]
# Make sure that the current guess is not zero.
# When Newton's Method suggests steps that lead to zero guesses
# take a step 9/10ths of the way to zero:
ksmall = find(np.abs(knew) == 0)
if ksmall.size > 0:
knew[ksmall] = ki[ksmall] / 10.0
hn[ix[ksmall]] = ki[ksmall] - knew[ksmall]
k[ix] = knew
# disp(['Iteration ',num2str(count),' Number of points left: '
# num2str(length(ix)) ]),
ix = find((np.abs(hn) > sqrt(eps) * np.abs(k)) * np.abs(hn) > sqrt(eps))
count += 1
if count == count_limit:
warnings.warn('W2K did not converge. The maximum error in the ' +
'last step was: %13.8f' % max(hn[ix]))
k.shape = oshape
k2 = k * sin(th)
k1 = k * cos(th)
return k1, k2
def test_docstrings():
import doctest
print('Testing docstrings in %s' % __file__)
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
if __name__ == '__main__':
test_docstrings()