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2d_spec_estimate_w.py
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2d_spec_estimate_w.py
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def spec_est2(A,d1,d2,win=True):
""" computes 2D spectral estimate of A
obs: the returned array is fftshifted
and consistent with the f1,f2 arrays
d1,d2 are the sampling rates in rows,columns """
import numpy as np
l1,l2,l3 = A.shape
df1 = 1./(l1*d1)
df2 = 1./(l2*d2)
f1Ny = 1./(2*d1)
f2Ny = 1./(2*d2)
f1 = np.arange(-f1Ny,f1Ny,df1)
f2 = np.arange(-f2Ny,f2Ny,df2)
if win == True:
wx = np.matrix(np.hanning(l1))
wy = np.matrix(np.hanning(l2))
window_s = np.repeat(np.array(wx.T*wy),l3).reshape(l1,l2,l3)
else:
window_s = np.ones((l1,l2,l3))
an = np.fft.fft2(A*window_s,axes=(0,1))
E = (an*an.conjugate()) / (df1*df2) / ((l1*l2)**2)
E = np.fft.fftshift(E)
E = E.mean(axis=2)
return np.real(E),f1,f2,df1,df2,f1Ny,f2Ny
def spec_est2_2(A,d1,d2,win=True):
""" computes 2D spectral estimate of A
obs: the returned array is fftshifted
and consistent with the f1,f2 arrays
d1,d2 are the sampling rates in rows,columns """
import numpy as np
l1,l2 = A.shape
df1 = 1./(l1*d1)
df2 = 1./(l2*d2)
f1Ny = 1./(2*d1)
f2Ny = 1./(2*d2)
f1 = np.arange(-f1Ny,f1Ny-df1,df1)
f2 = np.arange(-f2Ny,f2Ny,df2)
if win == True:
wx = np.matrix(np.hanning(l1))
wy = np.matrix(np.hanning(l2))
window_s = np.array(wx.T*wy)
else:
window_s = np.ones((l1,l2))
an = np.fft.fft2(A*window_s,axes=(0,1))
E = (an*an.conjugate()) / (df1*df2) / ((l1*l2)**2)
E = np.fft.fftshift(E)
return np.real(E),f1,f2,df1,df2,f1Ny,f2Ny
if __name__=='__main__':
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal
import scipy as sp
import glob, os
import seawater.csiro as sw
import aux_func_3dfields as my
plt.close('all')
plt.rcParams.update({'font.size': 24, 'legend.handlelength' : 1.5
, 'legend.markerscale': 14., 'legend.linewidth': 3.})
iz = 3500 # vertical level [m]
data_path = '/Users/crocha/Data/llc4320/w/'+str(iz)+'m/*'
grid_path= '/Users/crocha/Data/llc4320/uv/'
grid = np.load(grid_path+'grid.npz')
lons = grid['lon'][300,:]
lats = grid['lat'][:,300]
# projection onto regular grid
lati = np.linspace(lats.min(),lats.max(),lats.size)
loni = np.linspace(lons.min(),lons.max(),lons.size)
loni,lati = np.meshgrid(loni,lati)
dist,ang = sw.dist(loni[300,:],lati[300,:])
dx = dist.mean() # [km], about 1 km
dist,ang = sw.dist(loni[:,300],lati[:,300])
dy = dist.mean() # [km], about 1 km
files = sorted(glob.glob(data_path), key=os.path.getmtime)
E = np.zeros((lats.size,lons.size,np.array(files).size))
Ef = np.zeros((lats.size,lons.size,np.array(files).size))
kk = 0
for file in sorted(files[0:-1]):
data = np.load(file)
print kk
ix,jx,kx = data['w'].shape
wi = np.zeros((ix,jx,kx))
for i in range(kx):
interp_w = sp.interpolate.interp2d(lons,lats,data['w'][:,:,i],kind='linear')
wi[:,:,i] = interp_w(loni[300,:],lati[:,300])
E[:,:,kk],l,k,dl,dk,flNy,fkNy = spec_est2(wi,dy,dx,win=True)
Ef[:,:,kk],lf,kf,dlf,dkf,flNyf,fkNyf = spec_est2_2(wi.mean(axis=2),dy,dx,win=True)
kk = kk + 1
del data, wi
E = E.mean(axis=2)
Ef = Ef.mean(axis=2)
# isotropic spectral estimate
ki,li = np.meshgrid(k,l)
K = np.sqrt(ki**2+li**2)
K = np.ma.masked_array(K,K<1.e-10)
phi = np.math.atan2(dl,dk)
dK = dk*np.cos(phi)
Ki = np.arange(K.min(),K.max(),dK)
Kw = (Ki[1:]+Ki[0:-1])/2.
dK2 = dK/2.
Eiso = np.zeros(Kw.size)
for i in range(Kw.size):
f = (K>=Kw[i]-dK2)&(K<Kw[i]+dK2)
dtheta = (2*np.pi)/np.float(f.sum())
Eiso[i] = ((E[f].sum()))*Kw[i]*dtheta
ki,li = np.meshgrid(kf,lf)
K = np.sqrt(ki**2+li**2)
K = np.ma.masked_array(K,K<1.e-10)
phi = np.math.atan2(dlf,dkf)
dK = dkf*np.cos(phi)
Ki = np.arange(K.min(),K.max(),dK)
Kwf = (Ki[1:]+Ki[0:-1])/2.
dK2 = dK/2.
Eisof = np.zeros(Kwf.size)
for i in range(Kwf.size):
f = (K>=Kwf[i]-dK2)&(K<Kwf[i]+dK2)
dtheta = (2*np.pi)/np.float(f.sum())
Eisof[i] = ((Ef[f].sum()))*Kwf[i]*dtheta
fno='outputs/Eiso_w_'+str(iz)+'m'
np.savez(fno,Eiso=Eiso,Eisof=Eisof,Kw=Kw,Kwf=Kwf)