#rfv = cb[2]
#del cb
#tf = df + vf
#tp, kpar, kperp = ps_estimation.ps_azimuth(df, width=[64.0, 64.0, 64.0], kmodes = True)
#kvec = np.rollaxis(np.array(np.meshgrid(kpar, kperp)), 0, 3)
#pst = ps((kvec**2).sum(axis=2)**0.5)
#mi = np.fft.irfftn(rfv._kweight* 2**0.5)
#tpm, kparm, kperpm = ps_estimation.ps_azimuth(mi, width=[64.0, 64.0, 64.0], kmodes = True)
rf = cr.realisation(z1, z2, thetax, thetay, nf, nx, ny)[::-1,...]
psnw, kpar, kperp = ps_estimation.ps_azimuth(rf, window = False)
psww, kpar, kperp = ps_estimation.ps_azimuth(rf, window = True)
lag0 = np.cov(rf.reshape((256, 128*128)))
bc = bincov(lag0)
a = algebra.make_vect(rf, axis_names = ('freq', 'ra', 'dec'))
a.set_axis_info('freq', (f1+f2)/2.0, (f1-f2)/nf)
a.set_axis_info('ra', 0.0, thetax / nx)
a.set_axis_info('dec', 0.0, thetay / ny)
b = beam.GaussianBeam(width = [0.25, 0.25*f2/f1], freq = [f2, f1])
ab = b.apply(a)
# Adjust angular and frequency ranges to make close to a cube. cr.x_width = 10.0 # degrees cr.y_width = 10.0 # degrees cr.nu_lower = 500.0 # MHz cr.nu_upper = 600.0 # MHz # Generate two realisations f1 = cr.getfield() f2 = cr.getfield() # Calculate 2D statistics (that is treating kpar and kperp # separately). # 2D powerspectra of each field ps1 = ps_estimation.ps_azimuth(f1, window=True, kmodes=False) ps2 = ps_estimation.ps_azimuth(f2, window=True, kmodes=False) # 2D cross power spectrum ps12 = ps_estimation.crossps_azimuth(f1, f2, window=True, kmodes=False) # Rescale to make differences more obvious ch1 = ps12 / (ps1 * ps2)**0.5 # 2D mutual coherence of the two fields coh = np.abs(ch1)**2 ## Calculate 1D radially average statistics. ## These do not account for anisotropy (but small as angular and ## frequency ranges chosen to be near a cube)
# Adjust angular and frequency ranges to make close to a cube. cr.x_width = 10.0 # degrees cr.y_width = 10.0 # degrees cr.nu_lower = 500.0 # MHz cr.nu_upper = 600.0 # MHz # Generate two realisations f1 = cr.getfield() f2 = cr.getfield() # Calculate 2D statistics (that is treating kpar and kperp # separately). # 2D powerspectra of each field ps1 = ps_estimation.ps_azimuth(f1, window = True, kmodes = False) ps2 = ps_estimation.ps_azimuth(f2, window = True, kmodes = False) # 2D cross power spectrum ps12 = ps_estimation.crossps_azimuth(f1, f2, window = True, kmodes = False) # Rescale to make differences more obvious ch1 = ps12 / (ps1 * ps2)**0.5 # 2D mutual coherence of the two fields coh = np.abs(ch1)**2 ## Calculate 1D radially average statistics. ## These do not account for anisotropy (but small as angular and ## frequency ranges chosen to be near a cube)
