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Per A Brodtkorb 7 years ago
parent d16d175190
commit 36f9b69a69
4 changed files with 52 additions and 42 deletions
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3
wafo/misc.py
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@ -989,7 +989,8 @@ def findextrema(x):
findcross
crossdef
'''
return findcross(diff(x), 0.0) + 1
x1 = np.atleast_1d(x)
return findcross(diff(x1), 0.0) + 1
def findpeaks(data, n=2, min_h=None, min_p=0.0):

2
wafo/objects.py
Unescape Escape View File

@ -2491,7 +2491,7 @@ class TimeSeries(PlotData):
plt.title('Surface elevation from mean water level (MWL).')
for ix in range(nsub):
if nsub > 1:
subplot(nsub, 1, ix)
subplot(nsub, 1, ix+1)
h_scale = array([tn[ind[0]], tn[ind[-1]]])
ind2 = where((h_scale[0] <= tn2) & (tn2 <= h_scale[1]))[0]
plot(tn[ind] * dT, xn[ind], sym1)

63
wafo/spectrum/core.py
Unescape Escape View File

@ -14,30 +14,30 @@ from scipy.special import erf
from scipy.linalg import toeplitz
import scipy.interpolate as interpolate
from scipy.interpolate.interpolate import interp1d, interp2d
from ..objects import TimeSeries, mat2timeseries
from ..interpolate import stineman_interp
from ..wave_theory.dispersion_relation import w2k # , k2w
from ..containers import PlotData, now
from ..misc import sub_dict_select, nextpow2, discretize, JITImport
from ..misc import meshgrid, gravity, cart2polar, polar2cart
from ..markov import mctp2rfc, mctp2tc
from ..kdetools import qlevels
from wafo.objects import TimeSeries, mat2timeseries
from wafo.interpolate import stineman_interp
from wafo.wave_theory.dispersion_relation import w2k # , k2w
from wafo.containers import PlotData, now
from wafo.misc import (sub_dict_select, nextpow2, discretize, JITImport,
meshgrid, cart2polar, polar2cart, gravity as _gravity)
from wafo.markov import mctp2rfc, mctp2tc
from wafo.kdetools import qlevels
# from wafo.transform import TrData
from ..transform.models import TrLinear
from ..plotbackend import plotbackend
from wafo.transform.models import TrLinear
from wafo.plotbackend import plotbackend
try:
from ..gaussian import Rind
from wafo.gaussian import Rind
except ImportError:
Rind = None
try:
from .. import c_library
from wafo import c_library
except ImportError:
warnings.warn('Compile the c_library.pyd again!')
c_library = None
try:
from .. import cov2mod
from wafo import cov2mod
except ImportError:
warnings.warn('Compile the cov2mod.pyd again!')
cov2mod = None
@ -102,7 +102,7 @@ def qtf(w, h=inf, g=9.81):
k1p2 = (k_1 + k_2)
k1m2 = abs(k_1 - k_2)
if 0: # Langley
if False: # Langley
p_1 = (-2 * w1p2 * (k12 * g ** 2. - w12 ** 2.) +
w_1 * (w_2 ** 4. - g ** 2 * k_2 ** 2) +
w_2 * (w_1 ** 4 - g * 2. * k_1 ** 2)) / (4. * w12)
@ -914,8 +914,8 @@ class SpecData1D(PlotData):
# ftype = 'w'
# S = ttspec(S,ftype)
# end
Hm0 = self.characteristic('Hm0')
Tm02 = self.characteristic('Tm02')
Hm0 = self.characteristic('Hm0')[0]
Tm02 = self.characteristic('Tm02')[0]
if iseed is not None:
_set_seed(iseed) # set the the seed
@ -937,8 +937,7 @@ class SpecData1D(PlotData):
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)
fLimitLo = sqrt(gravity * tanh(kbar * water_depth) * Amax / water_depth ** 3)
freq = S.args
eps = finfo(float).eps
@ -955,7 +954,7 @@ class SpecData1D(PlotData):
# 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,
x2, x1 = self.sim_nl(ns=ns, 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)
@ -968,10 +967,12 @@ class SpecData1D(PlotData):
# %[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))
Hw1 = exp(interp1d(log(abs(S1.data / (S2.data + 1e-5))), S2.args,
fill_value=0, bounds_error=False)(freq))
else:
# Geometric mean
fun = interp1d(log(abs(S1.data / S2.data)), S2.args)
fun = interp1d(log(abs(S1.data / (S2.data + 1e-5))), S2.args,
fill_value=0, bounds_error=False)
Hw1 = exp((fun(freq) + log(Hw2)) / 2)
# end
# Hw1 = (interp1q( S2.w,abs(S1.S./S2.S),freq)+Hw2)/2
@ -1011,6 +1012,8 @@ class SpecData1D(PlotData):
plotbackend.figure(2)
plotbackend.semilogy(freq, abs(Hw12), 'b')
plotbackend.title('Hw-HwOld')
plotbackend.show('hold')
# figtile
# end
@ -3068,9 +3071,9 @@ class SpecData1D(PlotData):
>>> 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)
>>> S.tr = wtm.TrHermite(mean=me, sigma=Hs/4, skew=sk, kurt=ku, ysigma=Hs/4)
>>> ys = wo.mat2timeseries(S.sim(ns=2**13))
>>> ys
>>> g0, gemp = ys.trdata()
>>> t0 = g0.dist2gauss()
>>> t1 = S0.testgaussian(ns=2**13, cases=50)
@ -3363,6 +3366,11 @@ class SpecData1D(PlotData):
self.data = intfun(self.args)
self.data = self.data.clip(0) # clip negative values to 0
def interp(self, dt):
S = self.copy()
S.resample(dt)
return S
def normalize(self, gravity=9.81):
'''
Normalize a spectral density such that m0=m2=1
@ -4036,7 +4044,7 @@ class SpecData2D(PlotData):
if nr > 0:
vec = []
g = np.atleast_1d(S1.__dict__.get('g', gravity()))
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]
@ -4231,7 +4239,8 @@ def test_mm_pdf():
S = sm.SpecData1D(Sj(w), w) # Alternatively do it manually
S0 = S.to_linspec()
mm = S.to_mm_pdf()
mm.plot()
plotbackend.show()
def test_docstrings():
import doctest
@ -4239,6 +4248,6 @@ def test_docstrings():
if __name__ == '__main__':
test_docstrings()
# test_mm_pdf()
# test_docstrings()
test_mm_pdf()
# main()

24
wafo/spectrum/models.py
Unescape Escape View File

@ -36,9 +36,9 @@ import scipy.integrate as integrate
import scipy.special as sp
from scipy.fftpack import fft
import numpy as np
from numpy import (inf, atleast_1d, newaxis, any, minimum, maximum, array,
from numpy import (inf, atleast_1d, newaxis, minimum, maximum, array,
asarray, exp, log, sqrt, where, pi, arange, linspace, sin,
cos, abs, sinh, isfinite, mod, expm1, tanh, cosh, finfo,
cos, sinh, isfinite, mod, expm1, tanh, cosh, finfo,
ones, ones_like, isnan, zeros_like, flatnonzero, sinc,
hstack, vstack, real, flipud, clip)
from ..wave_theory.dispersion_relation import w2k, k2w # @UnusedImport
@ -498,7 +498,7 @@ class Jonswap(ModelSpectrum):
>>> S = wsm.Jonswap(Hm0=7, Tp=11,gamma=1)
>>> S2 = wsm.Bretschneider(Hm0=7, Tp=11)
>>> w = plb.linspace(0,5)
>>> all(abs(S(w)-S2(w))<1.e-7)
>>> all(np.abs(S(w)-S2(w))<1.e-7)
True
h = plb.plot(w,S(w))
@ -1313,7 +1313,7 @@ class Wallop(Bretschneider):
wp = 2. * pi / Tp
kp = w2k(wp, 0, inf)[0] # wavenumber at peak frequency
Lp = 2. * pi / kp # wave length at the peak frequency
N = abs((log(2. * pi ** 2.) + 2 * log(Hm0 / 4) -
N = np.abs((log(2. * pi ** 2.) + 2 * log(Hm0 / 4) -
2.0 * log(Lp)) / log(2))
super(Wallop, self).__init__(Hm0, Tp, N, M, chk_seastate)
@ -1796,7 +1796,7 @@ class Spreading(object):
""" Returns the solution of r1 = x.
"""
X = r1
if any(X >= 1):
if np.any(X >= 1):
raise ValueError('POISSON spreading: X value must be less than 1')
return X
@ -1822,9 +1822,9 @@ class Spreading(object):
A[ix] = Ai + 0.5 * (da[ix] - Ai) * (Ai <= 0.0)
ix = flatnonzero(
(abs(da) > sqrt(_EPS) * abs(A)) * (abs(da) > sqrt(_EPS)))
(np.abs(da) > sqrt(_EPS) * np.abs(A)) * (np.abs(da) > sqrt(_EPS)))
if ix.size == 0:
if any(A > pi):
if np.any(A > pi):
raise ValueError(
'BOX-CAR spreading: The A value must be less than pi')
return A.clip(min=1e-16, max=pi)
@ -1872,7 +1872,7 @@ class Spreading(object):
def fun(x):
return 0.5 * pi / (sinh(.5 * pi / x)) - x * r1[ix]
for ix in ix0:
B[ix] = abs(optimize.fsolve(fun, B0[ix]))
B[ix] = np.abs(optimize.fsolve(fun, B0[ix]))
return B
def fourier2d(self, r1):
@ -1953,14 +1953,14 @@ class Spreading(object):
self._frequency_independent_spread)
s = spread(wn)
if any(s < 0):
if np.any(s < 0):
raise ValueError('The COS2S spread parameter, S(w), ' +
'value must be larger than 0')
if self.type[0] == 'c': # cos2s
s_par = s
else:
# First Fourier coefficient of the directional spreading function.
r1 = abs(s / (s + 1))
r1 = np.abs(s / (s + 1))
# Find distribution parameter from first Fourier coefficient.
s_par = self.fourier2distpar(r1)
if self.method is not None:
@ -1986,8 +1986,8 @@ class Spreading(object):
@staticmethod
def _check_theta(theta):
L = abs(theta[-1] - theta[0])
if abs(L - 2 * np.pi) > _EPS:
L = np.abs(theta[-1] - theta[0])
if np.abs(L - 2 * np.pi) > _EPS:
raise ValueError('theta must cover all angles -pi -> pi')
nt = len(theta)
if nt < 40:

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