def bis_resid_analyze(obs, image):
"""Examines the residuals of image compared to the obs"""
biarr = obs.bispectra(mode='all')
uv1 = np.hstack((biarr['u1'].reshape(-1,1), biarr['v1'].reshape(-1,1)))
uv2 = np.hstack((biarr['u2'].reshape(-1,1), biarr['v2'].reshape(-1,1)))
uv3 = np.hstack((biarr['u3'].reshape(-1,1), biarr['v3'].reshape(-1,1)))
bis = biarr['bispec']
A3 = (vb.ftmatrix(image.psize, image.xdim, image.ydim, uv1),
vb.ftmatrix(image.psize, image.xdim, image.ydim, uv2),
vb.ftmatrix(image.psize, image.xdim, image.ydim, uv3)
)
sig = biarr['sigmab']
# get model bispectra
bis_model = np.dot(A3[0], image.imvec)*np.dot(A3[1], image.imvec)*np.dot(A3[2], image.imvec)
# residuals
resid = (bis - bis_model)/sig
# chi - squared
chisq = np.sum(np.abs(resid)**2)
print "Bispectrum Chi^2/2N: %f" % (chisq/(2*len(bis)))
print "Bispectrum Chi^2/2(N-p): %f" % (chisq/(2*(len(bis)-len(image.imvec))))
# plot the histograms
plt.figure()
plt.subplot(121)
n,bins,patches = plt.hist(np.real(resid), normed=1, range=(-5,5), bins=50, color='b', alpha=0.5)
n,bins,patches = plt.hist(np.imag(resid), normed=1, range=(-5,5), bins=50, color='r', alpha=0.5)
y = scipy.stats.norm.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('Normalized Bispectrum Residual Re/Imag')
plt.ylabel('p')
plt.subplot(122)
n,bins,patches = plt.hist(np.abs(resid), normed=1, range=(-5,5), bins=50, color='g')
y = scipy.stats.rayleigh.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('Normalized Bispectrum Residual Amp.')
plt.ylabel('p')
# plt.subplot(133)
# n,bins,patches = plt.hist(np.angle(resid)/vb.DEGREE, normed=1, bins=100, color='g')
# y = scipy.stats.vonmises.pdf(bins)
# plt.plot(bins, y, 'k--', linewidth=1.5)
# plt.xlabel('Normalized Residual Phase (deg)')
# plt.ylabel('p')
plt.show()
def vis_resid_analyze(obs, image):
"""Examines the residuals of image compared to the obs"""
data = obs.unpack(['u','v','vis','phase','sigma'])
vis = data['vis']
phase = data['phase']
sig = data['sigma']
sigphase = sig / np.abs(vis)
uv = data[['u','v']].view(('f8',2))
# get model visibilities
mat = vb.ftmatrix(image.psize, image.xdim, image.ydim, uv)
vis_model = np.dot(mat, image.imvec)
# residuals
resid = (vis - vis_model)/sig
residamp = (np.abs(vis) - np.abs(vis_model))/sig
#residphase = (vis/np.abs(vis) - vis_model/np.abs(vis_model))/sigphase
# chi - squared
chisq = np.sum(np.abs(resid)**2)
print "Chi^2/2N: %f" % (chisq/(2*len(vis)))
# plot the histograms
plt.figure()
plt.clf()
plt.subplot(121)
n,bins,patches = plt.hist(np.real(resid), normed=1, bins=50,range=(-5,5), color='b', alpha=0.5)
n,bins,patches = plt.hist(np.imag(resid), normed=1, bins=50,range=(-5,5), color='r', alpha=0.5)
y = scipy.stats.norm.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('Normalized Residual Re/Imag')
plt.ylabel('p')
plt.subplot(122)
n,bins,patches = plt.hist(residamp, normed=1, bins=50,range=(-5,5), color='b', alpha=0.5)
y = scipy.stats.norm.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('Normalized Amplitude Residual')
plt.ylabel('p')
# plt.subplot(133)
# n,bins,patches = plt.hist(np.real(residphase), normed=1, bins=50, color='b', alpha=0.5)
# n,bins,patches = plt.hist(np.imag(residphase), normed=1, bins=50, color='r', alpha=0.5)
# y = scipy.stats.norm.pdf(bins)
# plt.plot(bins, y, 'b--', linewidth=3)
# plt.xlabel('Normalized Phase Residual Re/Imag')
# plt.ylabel('p')
# plt.subplot(133)
# n,bins,patches = plt.hist(np.abs(resid), normed=1, bins=50, color='g')
# y = scipy.stats.rayleigh.pdf(bins)
# plt.plot(bins, y, 'b--', linewidth=3)
# plt.xlabel('Normalized Residual Amp.')
# plt.ylabel('p')
plt.show()
def pol_resid_analyze(obs, image):
"""Examines the residuals of image compared to the obs"""
data = obs.unpack(['u','v','vis','qvis','uvis','pvis','m','sigma'])
vis = data['vis']
qvis = data['qvis']
uvis = data['uvis']
pvis = data['pvis']
m = data['m']
sig = data['sigma']
psig = np.sqrt(2) * sig
msig = vb.merr(sig, vis, m)
uv = data[['u','v']].view(('f8',2))
# get model visibilities
mat = vb.ftmatrix(image.psize, image.xdim, image.ydim, uv)
vis_model = np.dot(mat, image.imvec)
qvis_model = np.dot(mat, image.qvec)
uvis_model = np.dot(mat, image.uvec)
pvis_model = np.dot(mat, image.qvec + 1j*image.uvec)
m_model = pvis_model/vis_model
# don't count measurements with SNR < 2:
mask = (np.abs(qvis)/sig > 5)
# debiased amps
qvis_d = np.real(np.sqrt(np.abs(qvis)**2 - sig**2 + 0j))
uvis_d = np.real(np.sqrt(np.abs(uvis)**2 - sig**2 + 0j))
pvis_d = np.real(np.sqrt(np.abs(pvis)**2 - psig**2 + 0j))
m_d = np.real(np.sqrt(np.abs(m)**2 - msig**2 + 0j))
# residuals
residq = (qvis[mask] - qvis_model[mask])/sig[mask]
residu = (uvis - uvis_model)/sig
residp = (pvis - pvis_model)/psig
residm = (m - m_model)/msig
residq_amp = (qvis_d[mask] - np.abs(qvis_model[mask]))/sig[mask]
residu_amp = (uvis_d - np.abs(uvis_model))/sig
residp_amp = (pvis_d - np.abs(pvis_model))/psig
residm_amp = (m_d - np.abs(m_model))/msig
# chi - squared
qchisq = np.sum(np.abs(residq)**2)
uchisq = np.sum(np.abs(residu)**2)
pchisq = np.sum(np.abs(residp)**2)
mchisq = np.sum(np.abs(residm)**2)
print "Q Chi^2/2N: %f" % (qchisq/(2*len(vis)))
print "U Chi^2/2N: %f" % (uchisq/(2*len(vis)))
print "P Chi^2/2N: %f" % (pchisq/(2*len(vis)))
print "m Chi^2/2N: %f" % (mchisq/(2*len(vis)))
# plot the histograms
plt.figure()
plt.subplot(241)
n,bins,patches = plt.hist(np.real(residq), normed=1, range=(-5,5), bins=50, color='b', alpha=0.5)
n,bins,patches = plt.hist(np.imag(residq), normed=1, range=(-5,5), bins=50, color='r', alpha=0.5)
y = scipy.stats.norm.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('Q Normalized Residual Re/Imag')
plt.ylabel('p')
plt.subplot(242)
n,bins,patches = plt.hist(np.real(residu), normed=1, range=(-5,5), bins=50, color='b', alpha=0.5)
n,bins,patches = plt.hist(np.imag(residu), normed=1, range=(-5,5), bins=50, color='r', alpha=0.5)
y = scipy.stats.norm.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('U Normalized Residual Re/Imag')
plt.ylabel('p')
plt.subplot(243)
n,bins,patches = plt.hist(np.real(residp), normed=1, range=(-5,5), bins=50, color='b', alpha=0.5)
n,bins,patches = plt.hist(np.imag(residp), normed=1, range=(-5,5), bins=50, color='r', alpha=0.5)
y = scipy.stats.norm.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('P Normalized Residual Re/Imag')
plt.ylabel('p')
plt.subplot(244)
n,bins,patches = plt.hist(np.real(residm), normed=1, range=(-5,5), bins=50, color='b', alpha=0.5)
n,bins,patches = plt.hist(np.imag(residm), normed=1, range=(-5,5), bins=50, color='r', alpha=0.5)
y = scipy.stats.norm.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('m Normalized Residual Re/Imag')
plt.ylabel('p')
plt.subplot(245)
n,bins,patches = plt.hist(residq_amp, normed=1,range=(-5,5), bins=50, color='g')
y = scipy.stats.norm.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('Q Normalized Amplitude Residual')
plt.ylabel('p')
plt.subplot(246)
n,bins,patches = plt.hist(residu_amp, normed=1,range=(-5,5), bins=50, color='g')
y = scipy.stats.norm.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('U Normalized AmplitudeResidual')
plt.ylabel('p')
plt.subplot(247)
n,bins,patches = plt.hist(residp_amp, normed=1,range=(-5,5), bins=50, color='g')
y = scipy.stats.norm.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('P Normalized Amplitude Residual')
plt.ylabel('p')
plt.subplot(248)
n,bins,patches = plt.hist(residm_amp, normed=1,range=(-5,5), bins=50, color='g')
y = scipy.stats.norm.pdf(bins)
plt.plot(bins, y, 'b--', linewidth=3)
plt.xlabel('m Normalized Amplitude Residual')
plt.ylabel('p')
# plt.subplot(245)
# n,bins,patches = plt.hist(np.abs(residq), normed=1, bins=50, color='g')
# y = scipy.stats.rayleigh.pdf(bins)
# plt.plot(bins, y, 'b--', linewidth=3)
# plt.xlabel('Q Normalized Residual Amp.')
# plt.ylabel('p')
#
# plt.subplot(246)
# n,bins,patches = plt.hist(np.abs(residu), normed=1, bins=50, color='g')
# y = scipy.stats.rayleigh.pdf(bins)
# plt.plot(bins, y, 'b--', linewidth=3)
# plt.xlabel('U Normalized Residual Amp.')
# plt.ylabel('p')
#
# plt.subplot(247)
# n,bins,patches = plt.hist(np.abs(residp), normed=1, bins=50, color='g')
# y = scipy.stats.rayleigh.pdf(bins)
# plt.plot(bins, y, 'b--', linewidth=3)
# plt.xlabel('P Normalized Residual Amp.')
# plt.ylabel('p')
#
# plt.subplot(248)
# n,bins,patches = plt.hist(np.abs(residm), normed=1, bins=50, color='g')
# y = scipy.stats.rayleigh.pdf(bins)
# plt.plot(bins, y, 'b--', linewidth=3)
# plt.xlabel('m Normalized Residual Amp.')
# plt.ylabel('p')
plt.show()
