def fringe_coherence(u, sinza):
fringes = NP.exp(-1j * 2*NP.pi * u * sinza)
fringe_cycle_coherence = NP.zeros(NP.ceil(u).astype(NP.int), dtype=NP.complex64)
dsinza = 1/u
eps = 1e-10
sinza_intervals = NP.arange(0, 1, dsinza[0])
sinza_intervals = NP.append(sinza_intervals, 1.0) - eps
bincount, n_bin_edges, binnum, ri = OPS.binned_statistic(sinza, statistic='count', bins=sinza_intervals)
for i in range(bincount.size):
ind = ri[ri[i]:ri[i+1]]
fringe_cycle_coherence[i] = NP.sum(fringes[ui,ind])
return fringe_cycle_coherence
def fringe_coherence(u, sinza):
fringes = NP.exp(-1j * 2 * NP.pi * u * sinza)
fringe_cycle_coherence = NP.zeros(NP.ceil(u).astype(NP.int),
dtype=NP.complex64)
dsinza = 1 / u
eps = 1e-10
sinza_intervals = NP.arange(0, 1, dsinza[0])
sinza_intervals = NP.append(sinza_intervals, 1.0) - eps
bincount, n_bin_edges, binnum, ri = OPS.binned_statistic(
sinza, statistic='count', bins=sinza_intervals)
for i in range(bincount.size):
ind = ri[ri[i]:ri[i + 1]]
fringe_cycle_coherence[i] = NP.sum(fringes[ui, ind])
return fringe_cycle_coherence
# progress.finish()
small_delays_ind = NP.abs(lags) <= 2.5e-6
lags = lags[small_delays_ind]
vis_lag = vis_lag[:,small_delays_ind,:]
skyvis_lag = skyvis_lag[:,small_delays_ind,:]
## Delay limits estimation
delay_matrix = DLY.delay_envelope(bl, pointings_dircos, units='mks')
## Binning baselines by orientation
blo = bl_orientation[:min(20*baseline_chunk_size, total_baselines)]
# blo[blo < -0.5*360.0/n_bins_baseline_orientation] = 360.0 - NP.abs(blo[blo < -0.5*360.0/n_bins_baseline_orientation])
bloh, bloe, blon, blori = OPS.binned_statistic(blo, statistic='count', bins=n_bins_baseline_orientation, range=[(-0.5*180.0/n_bins_baseline_orientation, 180.0-0.5*180.0/n_bins_baseline_orientation)])
# blo = bl_orientation[:min(20*baseline_chunk_size, total_baselines)]
# blo[blo < -0.5*360.0/n_bins_baseline_orientation] = 360.0 - NP.abs(blo[blo < -0.5*360.0/n_bins_baseline_orientation])
# bloh, bloe, blon, blori = OPS.binned_statistic(blo, statistic='count', bins=n_bins_baseline_orientation, range=[(-0.5*360.0/n_bins_baseline_orientation, 360.0-0.5*360.0/n_bins_baseline_orientation)])
# # ia.observing_run(pointing_init, skymod, t_snap, t_obs, chans, bpass, Tsys, lst_init, mode=obs_mode, freq_scale='GHz', brightness_units=flux_unit)
# # print 'Elapsed time = {0:.1f} minutes'.format((time.time()-ts)/60.0)
# # ia.delay_transform()
# for i in xrange(baseline_length.size):
# ts = time.time()
# ints[i].observing_run(pointing_init, skymod, t_snap, t_obs, chans, bpass, Tsys, lst_init, mode=obs_mode, freq_scale='GHz', brightness_units=flux_unit)
# print 'Elapsed time = {0:.1f} minutes'.format((time.time()-ts)/60.0)
delaymat = DLY.delay_envelope(bl, pc_dircos, units='mks')
min_delay = -delaymat[0, :, 1] - delaymat[0, :, 0]
max_delay = delaymat[0, :, 0] - delaymat[0, :, 1]
clags = clean_lags.reshape(1, -1)
min_delay = min_delay.reshape(-1, 1)
max_delay = max_delay.reshape(-1, 1)
thermal_noise_window = NP.abs(clags) >= max_abs_delay * 1e-6
thermal_noise_window = NP.repeat(thermal_noise_window, bl.shape[0], axis=0)
EoR_window = NP.logical_or(clags > max_delay + 1 / bw,
clags < min_delay - 1 / bw)
wedge_window = NP.logical_and(clags <= max_delay, clags >= min_delay)
non_wedge_window = NP.logical_not(wedge_window)
bll_bin_count, bll_edges, bll_binnum, bll_ri = OPS.binned_statistic(
bl_length,
values=None,
statistic='count',
bins=NP.hstack((geor_bl_length - 1e-10, geor_bl_length.max() + 1e-10)))
snap_min = 0
snap_max = 39
fg_cc_skyvis_lag_tavg = NP.mean(fg_cc_skyvis_lag[:, :, snap_min:snap_max + 1],
axis=2)
fg_cc_skyvis_lag_res_tavg = NP.mean(
fg_cc_skyvis_lag_res[:, :, snap_min:snap_max + 1], axis=2)
fg_cc_skyvis_lag_blavg = NP.zeros(
(geor_bl_length.size, clags.size, snap_max - snap_min + 1),
dtype=NP.complex64)
fg_cc_skyvis_lag_res_blavg = NP.zeros(
(geor_bl_length.size, clags.size, snap_max - snap_min + 1),
# ax.set_aspect('equal')
# ax.set_xlabel('l', fontsize=18, weight='medium')
# ax.set_ylabel('m', fontsize=18, weight='medium')
# cbax = fig.add_axes([0.9, 0.125, 0.02, 0.74])
# cbar = fig.colorbar(ipsf, cax=cbax, orientation='vertical')
# # PLT.tight_layout()
# fig.subplots_adjust(right=0.85)
# fig.subplots_adjust(top=0.88)
# PLT.savefig('/data3/t_nithyanandan/project_MOFF/simulated/MWA/figures/quick_psf_via_FX_test_aperture.png'.format(max_n_timestamps), bbox_inches=0)
psf_diff = NP.mean(beam_MOFF, axis=2) - NP.mean(vfimgobj.beam['P11'], axis=2)
gridlmrad = NP.sqrt(vfimgobj.gridl**2 + vfimgobj.gridm**2)
psfdiff_ds = psf_diff[::2,::2].ravel()
gridlmrad = gridlmrad[::2,::2].ravel()
lmradbins = NP.linspace(0.0, 1.0, 21, endpoint=True)
psfdiffrms, psfdiffbe, psfdiffbn, psfdiffri = OPS.binned_statistic(gridlmrad, values=psfdiff_ds, statistic=NP.std, bins=lmradbins)
psfref = NP.mean(beam_MOFF, axis=2)
psfref = psfref[::2,::2].ravel()
psfrms, psfbe, psfbn, psfri = OPS.binned_statistic(gridlmrad, values=psfref, statistic=NP.std, bins=lmradbins)
pbmean = NP.mean(efimgobj.pbeam['P1'], axis=2)
pbmean = pbmean[::2,::2].ravel()
pbavg, pbbe, pbbn, pbri = OPS.binned_statistic(gridlmrad, values=pbmean, statistic=NP.mean, bins=lmradbins)
fig, axs = PLT.subplots(ncols=2, figsize=(9,5))
for j in range(2):
if j == 0:
dpsf = axs[j].imshow(psf_diff, origin='lower', extent=(vfimgobj.gridl.min(), vfimgobj.gridl.max(), vfimgobj.gridm.min(), vfimgobj.gridm.max()), interpolation='none', vmin=psf_diff.min(), vmax=psf_diff.max())
axs[j].plot(NP.cos(NP.linspace(0.0, 2*NP.pi, num=100)), NP.sin(NP.linspace(0.0, 2*NP.pi, num=100)), 'k-')
axs[j].set_xlim(-1,1)
axs[j].set_ylim(-1,1)
axs[j].set_aspect('equal')
def main():
# 01) Fringe pattern behaviour
# 02) Fringe cycle coherence
# 03) Fringe cycle coherence from 2D sky
# 04) Delay bin coherence from 2D sky
plot_01 = False
plot_02 = False
plot_03 = False
plot_04 = True
def za2sinza(za):
sinza = NP.sin(NP.radians(za))
return ['{0:.2f}'.format(l) for l in sinza]
def sinza2za(sinza):
za = NP.degrees(NP.arcsin(sinza))
return ['{0:.1f}'.format(theta) for theta in za]
if plot_01:
# 01) Fringe pattern behaviour
bll = 10.0 # Baseline length in m
wl = 2.0 # Wavelength in m
# freq = 150e6 # Frequency in Hz
# wl = FCNST.c / freq
freq = FCNST.c / wl
n_theta = 10001
theta = NP.pi * (NP.arange(n_theta) - n_theta / 2) / n_theta
y_offset = 10.0 * NP.cos(theta)
l = NP.sin(theta) # Direction cosine
fringe = NP.exp(-1j * 2 * NP.pi * bll * l / wl)
fringe_l_interval = wl / bll
l_intervals = NP.arange(-1, 1, fringe_l_interval)
theta_intervals = NP.degrees(NP.arcsin(l_intervals))
theta_intervals = NP.append(theta_intervals, 90.0)
bincount, theta_bin_edges, binnum, ri = OPS.binned_statistic(
NP.degrees(theta), statistic='count', bins=theta_intervals)
fringe_sum = NP.zeros(bincount.size, dtype=NP.complex64)
for i in range(bincount.size):
ind = ri[ri[i]:ri[i + 1]]
fringe_sum[i] = NP.sum(fringe[ind])
fig = PLT.figure(figsize=(6, 6))
axth = fig.add_subplot(111)
fringe_theta = axth.plot(NP.degrees(theta), fringe.real, 'k.', ms=2)
for theta_interval in theta_intervals:
axth.axvline(x=theta_interval,
ymin=0,
ymax=0.75,
ls=':',
lw=0.5,
color='black')
axth.set_xlim(-90, 90)
axth.set_xlabel(r'$\theta$' + ' [degree]')
axth.set_ylabel('Fringe [Arbitrary units]', fontsize=12)
axdc = axth.twiny()
fringe_dircos = axdc.plot(l, y_offset.max() + fringe.real, 'r.', ms=2)
fringe_dircos_on_sky = axdc.plot(l,
y_offset + fringe.real,
color='orange',
marker='.',
ms=2)
sky_curve = axdc.plot(l, y_offset, color='orange', ls='--', lw=1)
axdc.set_xlim(-1.0, 1.0)
axdc.set_xlabel(r'$\sin\,\theta$', fontsize=12, color='red')
axr = axth.twinx()
# axr.bar(theta_intervals[:-1], bincount/theta.size, width=theta_intervals[1:]-theta_intervals[:-1], color='white', fill=False)
axr.errorbar(0.5 * (theta_intervals[1:] + theta_intervals[:-1]),
NP.abs(fringe_sum) / bincount,
yerr=1 / NP.sqrt(bincount),
color='b',
ecolor='b',
fmt='o-',
ms=10,
lw=2)
axr.set_xlim(-90, 90)
axr.set_ylim(-0.2, 0.4)
axr.set_ylabel('Coherence Amplitude in Fringe Cycle',
fontsize=12,
color='blue')
fig.subplots_adjust(right=0.85)
PLT.savefig(
'/data3/t_nithyanandan/project_global_EoR/figures/wide_field_effects_demo.png',
bbox_inches=0)
PLT.savefig(
'/data3/t_nithyanandan/project_global_EoR/figures/wide_field_effects_demo.eps',
bbox_inches=0)
PLT.show()
if plot_02:
# 02) Fringe cycle coherence
u = 2.0**NP.arange(13)
u_max = u.max()
dza = NP.degrees(1 / (512 * u_max))
za = NP.arange(0.0, 90.0, dza)
sinza = NP.sin(NP.radians(za))
fringes = NP.exp(-1j * 2 * NP.pi * u.reshape(-1, 1) *
sinza.reshape(1, -1))
fringe_cycle_coherence = NP.zeros((u.size, u_max), dtype=NP.complex64)
sample_counts = NP.zeros((u.size, u_max))
independent_sample_counts = NP.zeros((u.size, u_max))
dsinza = 1 / u
last_fringe_cycle_boundary_sinza = 1.0 - dsinza
last_fringe_cycle_boundary = NP.degrees(
NP.arcsin(last_fringe_cycle_boundary_sinza))
eps = 1e-10
sinza_intervals = NP.arange(0, 1, dsinza[-1])
sinza_intervals = NP.append(sinza_intervals, 1.0) - eps
za_intervals = NP.degrees(NP.arcsin(sinza_intervals))
# za_intervals = NP.append(za_intervals, 90.0)
bincount, za_bin_edges, binnum, ri = OPS.binned_statistic(
sinza, statistic='count', bins=sinza_intervals)
for ui in range(u.size):
for i in range(u[ui].astype(NP.int)):
begini = i * u_max / u[ui]
endi = (i + 1) * u_max / u[ui]
begini = begini.astype(NP.int)
endi = endi.astype(NP.int)
fine_fringe_cycle_ind = NP.arange(begini, endi)
ind = ri[ri[begini]:ri[endi]]
fringe_cycle_coherence[ui, fine_fringe_cycle_ind] = NP.sum(
fringes[ui, ind])
sample_counts[ui, fine_fringe_cycle_ind] = float(ind.size)
independent_sample_counts[
ui, fine_fringe_cycle_ind] = 2.0 / NP.sqrt(
1 - NP.mean(sinza[ind])**2)
# ind = ri[ri[i]:ri[i+1]]
# fringe_cycle_coherence[ui,ind] = NP.sum(fringes[ui,ind])
# sample_counts[ui,ind] = bincount[i]
# independent_sample_counts[ui,ind] = 2.0 / NP.sqrt(1-NP.mean(sinza[ind])**2)
norm_fringe_cycle_coherence = fringe_cycle_coherence / sample_counts
fringe_cycle_efficiency = fringe_cycle_coherence / za.size
norm_fringe_cycle_coherence_SNR = norm_fringe_cycle_coherence * NP.sqrt(
independent_sample_counts)
fig = PLT.figure(figsize=(6, 6))
ax = fig.add_subplot(111)
fringe_matrix = ax.pcolormesh(
sinza_intervals[:-1],
u,
NP.abs(norm_fringe_cycle_coherence),
norm=PLTC.LogNorm(vmin=1e-6,
vmax=NP.abs(norm_fringe_cycle_coherence).max()))
# fringe_matrix = ax.imshow(NP.abs(norm_fringe_cycle_coherence), origin='lower', extent=[za.min(), 90.0, u.min(), u.max()], norm=PLTC.LogNorm(vmin=1e-6, vmax=NP.abs(norm_fringe_cycle_coherence).max()))
last_fringe_cycle_edge = ax.plot(last_fringe_cycle_boundary_sinza,
u,
ls='--',
lw=2,
color='white')
ax.set_xlim(sinza.min(), 1.0)
ax.set_ylim(u.min(), u.max())
ax.set_yscale('log')
ax.set_xlabel(r'$|\,\sin\,\theta\,|$ [degrees]', fontsize=14)
ax.set_ylabel(r'$b/\lambda$', fontsize=14)
ax.set_aspect('auto')
axt = ax.twiny()
axt.set_xticks(ax.get_xticks())
axt.set_xbound(ax.get_xbound())
axt.set_xticklabels(sinza2za(ax.get_xticks()))
axt.set_xlabel(r'$|\,\theta\,|$', fontsize=14)
fig.subplots_adjust(right=0.88, left=0.15, top=0.9)
cbax = fig.add_axes([0.9, 0.1, 0.02, 0.8])
cbar = fig.colorbar(fringe_matrix, cax=cbax, orientation='vertical')
PLT.savefig(
'/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_coherence.png',
bbox_inches=0)
PLT.savefig(
'/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_coherence.eps',
bbox_inches=0)
fig = PLT.figure(figsize=(6, 6))
ax = fig.add_subplot(111)
fringe_matrix = ax.pcolormesh(
sinza_intervals[:-1],
u,
NP.abs(fringe_cycle_efficiency),
norm=PLTC.LogNorm(vmin=1e-8,
vmax=NP.abs(fringe_cycle_efficiency).max()))
# fringe_matrix = ax.imshow(NP.abs(norm_fringe_cycle_efficiency), origin='lower', extent=[za.min(), 90.0, u.min(), u.max()], norm=PLTC.LogNorm(vmin=1e-6, vmax=NP.abs(norm_fringe_cycle_efficiency).max()))
last_fringe_cycle_edge = ax.plot(last_fringe_cycle_boundary_sinza,
u,
ls='--',
lw=2,
color='white')
ax.set_xlim(sinza.min(), 1.0)
ax.set_ylim(u.min(), u.max())
ax.set_yscale('log')
ax.set_xlabel(r'$|\,\sin\,\theta\,|$ [degrees]', fontsize=14)
ax.set_ylabel(r'$b/\lambda$', fontsize=14)
ax.set_aspect('auto')
axt = ax.twiny()
axt.set_xticks(ax.get_xticks())
axt.set_xbound(ax.get_xbound())
axt.set_xticklabels(sinza2za(ax.get_xticks()))
axt.set_xlabel(r'$|\,\theta\,|$', fontsize=14)
fig.subplots_adjust(right=0.88, left=0.15, top=0.9)
cbax = fig.add_axes([0.9, 0.1, 0.02, 0.8])
cbar = fig.colorbar(fringe_matrix, cax=cbax, orientation='vertical')
PLT.savefig(
'/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_efficiency.png',
bbox_inches=0)
PLT.savefig(
'/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_efficiency.eps',
bbox_inches=0)
# fig = PLT.figure(figsize=(6,6))
# ax = fig.add_subplot(111)
# fringe_matrix_SNR = ax.imshow(NP.abs(norm_fringe_cycle_coherence_SNR), origin='lower', extent=[za.min(), 90.0, u.min(), u.max()], norm=PLTC.LogNorm(vmin=NP.abs(norm_fringe_cycle_coherence_SNR).min(), vmax=NP.abs(norm_fringe_cycle_coherence_SNR).max()))
# ax.set_xlim(za.min(), 90.0)
# ax.set_ylim(u.min(), u.max())
# ax.set_xlabel(r'$|\,\theta\,|$ [degrees]', fontsize=14)
# ax.set_ylabel(r'$b/\lambda$', fontsize=14)
# ax.set_aspect('auto')
# axt = ax.twiny()
# axt.set_xticks(ax.get_xticks())
# axt.set_xbound(ax.get_xbound())
# axt.set_xticklabels(za2sinza(ax.get_xticks()))
# axt.set_xlabel(r'$|\,\sin\,\theta\,|$', fontsize=14)
# fig.subplots_adjust(right=0.88, left=0.15, top=0.9)
# cbax = fig.add_axes([0.9, 0.1, 0.02, 0.8])
# cbar = fig.colorbar(fringe_matrix_SNR, cax=cbax, orientation='vertical')
PLT.show()
if plot_03:
# 03) Fringe cycle coherence from 2D sky
u = 2.0**NP.arange(10)
u_max = u.max()
dsa = 1 / (u_max**2)
npix_orig = 4 * NP.pi / (dsa / 8)
nside = HP.pixelfunc.get_min_valid_nside(npix_orig)
npix = HP.nside2npix(nside)
pixarea = HP.nside2pixarea(nside)
nring = int(4 * nside - 1)
isotheta = NP.pi / (nring + 1) * (1 + NP.arange(nring))
ison = NP.sin(isotheta)
theta, az = HP.pix2ang(nside, NP.arange(npix))
n = NP.cos(theta)
qrtr_sky_ind, = NP.where((theta <= NP.pi / 2) & (az <= NP.pi))
theta = theta[qrtr_sky_ind]
az = az[qrtr_sky_ind]
n = n[qrtr_sky_ind]
fringes = NP.exp(-1j * 2 * NP.pi * u.reshape(-1, 1) * n.reshape(1, -1))
fringe_cycle_coherence = NP.zeros((u.size, u_max), dtype=NP.complex64)
sample_counts = NP.zeros((u.size, u_max))
dn = 1 / u
last_fringe_cycle_boundary_n = 1.0 - dn
last_fringe_cycle_boundary_za = NP.degrees(
NP.arcsin(last_fringe_cycle_boundary_n))
eps = 1e-10
n_intervals = NP.arange(0, 1, dn[-1])
n_intervals = NP.append(n_intervals, 1.0) - eps
ang_intervals = NP.degrees(NP.arcsin(n_intervals))
bincount, n_bin_edges, binnum, ri = OPS.binned_statistic(
n, statistic='count', bins=n_intervals)
for ui in range(u.size):
for i in range(u[ui].astype(NP.int)):
begini = i * u_max / u[ui]
endi = (i + 1) * u_max / u[ui]
begini = begini.astype(NP.int)
endi = endi.astype(NP.int)
fine_fringe_cycle_ind = NP.arange(begini, endi)
ind = ri[ri[begini]:ri[endi]]
fringe_cycle_coherence[ui, fine_fringe_cycle_ind] = NP.sum(
fringes[ui, ind])
sample_counts[ui, fine_fringe_cycle_ind] = float(ind.size)
norm_fringe_cycle_coherence = fringe_cycle_coherence / sample_counts
fringe_cycle_efficiency = fringe_cycle_coherence / (npix / 4)
fringe_cycle_solid_angle = sample_counts * pixarea
fig = PLT.figure(figsize=(6, 6))
ax = fig.add_subplot(111)
fringe_matrix = ax.pcolormesh(
n_intervals[:-1],
u,
NP.abs(fringe_cycle_efficiency),
norm=PLTC.LogNorm(vmin=1e-8,
vmax=NP.abs(fringe_cycle_efficiency).max()))
# fringe_matrix = ax.pcolormesh(n_intervals[:-1], u, NP.abs(norm_fringe_cycle_coherence), norm=PLTC.LogNorm(vmin=1e-6, vmax=NP.abs(norm_fringe_cycle_coherence).max()))
last_fringe_cycle_edge = ax.plot(last_fringe_cycle_boundary_n,
1.5 * u,
ls='--',
lw=2,
color='black')
ax.set_xlim(0.0, 1.0)
ax.set_ylim(u.min(), u.max())
ax.set_yscale('log')
ax.set_xlabel(r'$|\,\sin\,\theta\,|$', fontsize=14)
ax.set_ylabel(r'$b/\lambda$', fontsize=14)
ax.set_aspect('auto')
axt = ax.twiny()
axt.set_xticks(ax.get_xticks())
axt.set_xbound(ax.get_xbound())
axt.set_xticklabels(sinza2za(ax.get_xticks()))
axt.set_xlabel(r'$|\,\theta\,|$ [degrees]', fontsize=14)
fig.subplots_adjust(right=0.88, left=0.15, top=0.9)
cbax = fig.add_axes([0.9, 0.1, 0.02, 0.8])
cbar = fig.colorbar(fringe_matrix, cax=cbax, orientation='vertical')
PLT.savefig(
'/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_efficiency_2D.png',
bbox_inches=0)
PLT.savefig(
'/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_efficiency_2D.eps',
bbox_inches=0)
# fig = PLT.figure(figsize=(6,6))
# ax = fig.add_subplot(111)
# solid_angle_matrix = ax.pcolormesh(n_intervals[:-1], u, NP.abs(fringe_cycle_solid_angle), norm=PLTC.LogNorm(vmin=NP.abs(fringe_cycle_solid_angle).min(), vmax=NP.abs(fringe_cycle_solid_angle).max()))
# last_fringe_cycle_edge = ax.plot(last_fringe_cycle_boundary_n, u, ls='--', lw=2, color='black')
# ax.set_xlim(0.0, 1.0)
# ax.set_ylim(u.min(), u.max())
# ax.set_yscale('log')
# ax.set_xlabel(r'$|\,\sin\,\theta\,|$', fontsize=14)
# ax.set_ylabel(r'$b/\lambda$', fontsize=14)
# ax.set_aspect('auto')
# axt = ax.twiny()
# axt.set_xticks(ax.get_xticks())
# axt.set_xbound(ax.get_xbound())
# axt.set_xticklabels(sinza2za(ax.get_xticks()))
# axt.set_xlabel(r'$|\,\theta\,|$ [degrees]', fontsize=14)
# fig.subplots_adjust(right=0.88, left=0.15, top=0.9)
# cbax = fig.add_axes([0.9, 0.1, 0.02, 0.8])
# cbar = fig.colorbar(solid_angle_matrix, cax=cbax, orientation='vertical')
# cbax.set_xlabel(r'$\Omega$ [Sr]', fontsize=12, labelpad=10)
# cbax.xaxis.set_label_position('top')
# PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_solid_angle_2D.png', bbox_inches=0)
# PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_solid_angle_2D.eps', bbox_inches=0)
if plot_04:
# 04) Delay bin coherence from 2D sky
# f0 = 30e9 # Center frequency in Hz
# bw = 1e9 # Bandwidth in Hz
# nchan = 64 # number of frequency channels
# l_delay_bins = 32
# bl = 0.5 * l_delay_bins / bw * FCNST.c # baseline length in m
# wl0 = FCNST.c / f0
# freq_resolution = bw / nchan
# chans = (NP.arange(nchan) - nchan/2) * freq_resolution + f0
# wl = FCNST.c / chans
# l_binsize = 1.0 / (l_delay_bins/2)
# u = bl / wl
# u_max = u.max()
# dl_coarse = 1.0 / u_max # resolution in direction cosine
# eps = 1e-10
# # nl = NP.ceil(1.0 / dl_coarse).astype(NP.int)
# # dl_coarse = 1.0 / nl
# dl = dl_coarse / 4
# dm = dl_coarse
# lv = NP.arange(0.0, 1.0, dl)
# # lv = NP.append(lv, 1.0-eps)
# # mv = NP.arange(-1.0+eps, 1.0-eps, dm)
# mv = NP.append(-lv[-1:0:-1], lv)
# # mv = NP.append(mv, 1.0-eps)
# lgrid, mgrid = NP.meshgrid(lv, mv)
# lmrad = NP.sqrt(lgrid**2 + mgrid**2)
# ngrid = NP.empty_like(lgrid)
# sagrid = NP.empty_like(lgrid)
# ngrid.fill(NP.nan)
# sagrid.fill(NP.nan)
# PDB.set_trace()
# valid_ind = lmrad <= 1.0
# ngrid[valid_ind] = NP.sqrt(1.0 - lmrad[valid_ind]**2)
# sagrid[valid_ind] = dl * dm / ngrid[valid_ind]
# lvect = lgrid[valid_ind]
# mvect = mgrid[valid_ind]
# nvect = ngrid[valid_ind]
# savect = sagrid[valid_ind]
# # fringes = NP.exp(-1j * 2*NP.pi * u.reshape(-1,1) * lvect.reshape(1,-1))
# fringes = NP.empty((lgrid.shape[0], lgrid.shape[1], u.shape[0]), dtype=NP.complex64)
# fringes.fill(NP.nan)
# fringes[valid_ind] = NP.exp(-1j * 2*NP.pi * u.reshape(1,1,-1) * lgrid[:,:,NP.newaxis])
# delay_bin_coherence = NP.zeros((u.size, l_delay_bins/2), dtype=NP.complex64)
# l_intervals = NP.arange(0.0, 1.0, l_binsize)
# l_intervals = NP.append(l_intervals, 1.0-eps)
# delay_intervals = bl * l_intervals / FCNST.c
# bincount, l_bin_edges, binnum, ri = OPS.binned_statistic(lvect, statistic='count', bins=l_intervals)
# PDB.set_trace()
# for di in range(l_delay_bins/2):
# ind = ri[ri[di]:ri[di+1]]
# delay_bin_coherence[:,di] = NP.sum(fringes[:,ind] * savect[ind], axis=1)
# lgrid, mgrid = NP.meshgrid(lv, mv)
# lgrid = lgrid.ravel()
# mgrid = mgrid.ravel()
# lmrad = NP.sqrt(lgrid**2 + mgrid**2)
# ngrid = NP.empty_like(lgrid)
# sagrid = NP.empty_like(lgrid)
# ngrid.fill(NP.nan)
# sagrid.fill(NP.nan)
# valid_ind = lmrad <= 1.0
# ngrid[valid_ind] = NP.sqrt(1.0 - lmrad[valid_ind]**2)
# sagrid[valid_ind] = dl * dm / ngrid[valid_ind]
# lvect = lgrid[valid_ind]
# mvect = mgrid[valid_ind]
# nvect = ngrid[valid_ind]
# savect = sagrid[valid_ind]
# fringes = NP.exp(-1j * 2*NP.pi * u.reshape(-1,1) * lvect.reshape(1,-1))
# delay_bin_coherence = NP.zeros((u.size, l_delay_bins/2), dtype=NP.complex64)
# l_intervals = NP.arange(0.0, 1.0, l_binsize)
# l_intervals = NP.append(l_intervals, 1.0-eps)
# delay_intervals = bl * l_intervals / FCNST.c
# bincount, l_bin_edges, binnum, ri = OPS.binned_statistic(lvect, statistic='count', bins=l_intervals)
# PDB.set_trace()
# for di in range(l_delay_bins/2):
# ind = ri[ri[di]:ri[di+1]]
# delay_bin_coherence[:,di] = NP.sum(fringes[:,ind] * savect[ind], axis=1)
# f0 = 30e9 # Center frequency in Hz
# bw = 1e9 # Bandwidth in Hz
# nchan = 64 # number of frequency channels
# n_delay_bins = 32
# bl = 0.5 * n_delay_bins / bw * FCNST.c # baseline length in m
# wl0 = FCNST.c / f0
# freq_resolution = bw / nchan
# chans = (NP.arange(nchan) - nchan/2) * freq_resolution + f0
# wl = FCNST.c / chans
# n_binsize = 1.0 / (n_delay_bins/2)
# u = bl / wl
# u_max = u.max()
# dsa = 1/(u_max ** 2) # solid angle resolution
# npix_orig = 4 * NP.pi / (dsa/4)
# nside = HP.pixelfunc.get_min_valid_nside(npix_orig)
# npix = HP.nside2npix(nside)
# pixarea = HP.nside2pixarea(nside)
# # nring = int(4*nside - 1)
# # isotheta = NP.pi/(nring+1) * (1 + NP.arange(nring))
# # ison = NP.sin(isotheta)
# theta, az = HP.pix2ang(nside, NP.arange(npix))
# n = NP.cos(theta)
# qrtr_sky_ind, = NP.where((theta <= NP.pi/2) & (az <= NP.pi))
# theta = theta[qrtr_sky_ind]
# az = az[qrtr_sky_ind]
# n = n[qrtr_sky_ind]
# fringes = NP.exp(-1j * 2*NP.pi * u.reshape(-1,1) * n.reshape(1,-1))
# delay_bin_coherence = NP.zeros((u.size, n_delay_bins/2), dtype=NP.complex64)
# PDB.set_trace()
# eps = 1e-10
# n_intervals = NP.arange(0.0, 1.0, n_binsize)
# n_intervals = NP.append(n_intervals, 1.0) - eps
# delay_intervals = bl * n_intervals / FCNST.c
# bincount, n_bin_edges, binnum, ri = OPS.binned_statistic(n, statistic='count', bins=n_intervals)
# for di in range(n_delay_bins/2):
# ind = ri[ri[di]:ri[di+1]]
# delay_bin_coherence[:,di] = NP.sum(fringes[:,ind], axis=1) * pixarea
# # # for ui in range(u.size):
# # # delay_bin_coherence[ui,di] = NP.sum(fringes[ui,ind]) * pixarea
# delay_spectrum_coherence = NP.fft.ifftshift(NP.fft.ifft2(delay_bin_coherence, axes=(0,)), axes=0) * bw
# fig = PLT.figure(figsize=(6,6))
# ax = fig.add_subplot(111)
# dspec = ax.imshow(NP.abs(delay_bin_coherence), origin='lower', extent=[delay_intervals.min(), delay_intervals.max(), chans.min(), chans.max()])
# ax.set_aspect('auto')
# fig = PLT.figure(figsize=(6,6))
# ax = fig.add_subplot(111)
# dspec = ax.imshow(NP.abs(delay_spectrum_coherence), origin='lower', extent=[delay_intervals.min(), delay_intervals.max(), -0.5/freq_resolution, 0.5/freq_resolution])
# ax.set_aspect('auto')
# PLT.show()
f0 = 30e9 # Center frequency in Hz
bw = 1e9 # Bandwidth in Hz
nchan = 40 # number of frequency channels
n_delay_bins = 32
bl = 0.5 * n_delay_bins / bw * FCNST.c # baseline length in m
wl0 = FCNST.c / f0
freq_resolution = bw / nchan
chans = (NP.arange(nchan) - nchan / 2) * freq_resolution + f0
wl = FCNST.c / chans
n_binsize = 1.0 / (n_delay_bins / 2)
u = bl / wl
u_max = u.max()
dsa = 1 / (u_max**2)
npix_orig = 4 * NP.pi / (dsa / 8)
nside = HP.pixelfunc.get_min_valid_nside(npix_orig)
npix = HP.nside2npix(nside)
pixarea = HP.nside2pixarea(nside)
nring = int(4 * nside - 1)
isotheta = NP.pi / (nring + 1) * (1 + NP.arange(nring))
ison = NP.sin(isotheta)
theta, az = HP.pix2ang(nside, NP.arange(npix))
n = NP.cos(theta)
qrtr_sky_ind, = NP.where((theta <= NP.pi / 2) & (az <= NP.pi))
theta = theta[qrtr_sky_ind]
az = az[qrtr_sky_ind]
n = n[qrtr_sky_ind]
sortind = NP.argsort(n)
nsorted = n[sortind]
fringes = NP.exp(-1j * 2 * NP.pi * u.reshape(-1, 1) *
nsorted.reshape(1, -1))
accumulated_fringes = accumulate_fringes(fringes) * pixarea
fringe_cycle_coherence = accumulated_fringe_differencing(
accumulated_fringes, nsorted, 1 / u_max)
PDB.set_trace()
nproc = max(MP.cpu_count() / 2 - 1, 1)
# chunksize = int(ceil(u.size/float(nproc)))
pool = MP.Pool(processes=nproc)
fringe_cycle_coherences = pool.map(
unwrap_fringe_coherence, IT.izip(u.tolist(), IT.repeat(nsorted)))
PDB.set_trace()
clean_lags = DSP.downsampler(clean_lags, 1.0*clean_lags.size/ia.lags.size, axis=-1)
clean_lags = clean_lags.ravel()
delaymat = DLY.delay_envelope(bl, pc_dircos, units='mks')
min_delay = -delaymat[0,:,1]-delaymat[0,:,0]
max_delay = delaymat[0,:,0]-delaymat[0,:,1]
clags = clean_lags.reshape(1,-1)
min_delay = min_delay.reshape(-1,1)
max_delay = max_delay.reshape(-1,1)
thermal_noise_window = NP.abs(clags) >= max_abs_delay*1e-6
thermal_noise_window = NP.repeat(thermal_noise_window, bl.shape[0], axis=0)
EoR_window = NP.logical_or(clags > max_delay+1/bw, clags < min_delay-1/bw)
wedge_window = NP.logical_and(clags <= max_delay, clags >= min_delay)
non_wedge_window = NP.logical_not(wedge_window)
bll_bin_count, bll_edges, bll_binnum, bll_ri = OPS.binned_statistic(bl_length, values=None, statistic='count', bins=NP.hstack((geor_bl_length-1e-10, geor_bl_length.max()+1e-10)))
snap_min = 0
snap_max = 39
fg_cc_skyvis_lag_tavg = NP.mean(fg_cc_skyvis_lag[:,:,snap_min:snap_max+1], axis=2)
fg_cc_skyvis_lag_res_tavg = NP.mean(fg_cc_skyvis_lag_res[:,:,snap_min:snap_max+1], axis=2)
fg_cc_skyvis_lag_blavg = NP.zeros((geor_bl_length.size, clags.size, snap_max-snap_min+1), dtype=NP.complex64)
fg_cc_skyvis_lag_res_blavg = NP.zeros((geor_bl_length.size, clags.size, snap_max-snap_min+1), dtype=NP.complex64)
for i in xrange(geor_bl_length.size):
blind = bll_ri[bll_ri[i]:bll_ri[i+1]]
if blind.size != bll_bin_count[i]: PDB.set_trace()
fg_cc_skyvis_lag_blavg[i,:,:] = NP.mean(fg_cc_skyvis_lag[blind,:,snap_min:snap_max+1], axis=0)
fg_cc_skyvis_lag_res_blavg[i,:,:] = NP.mean(fg_cc_skyvis_lag_res[blind,:,snap_min:snap_max+1], axis=0)
fg_cc_skyvis_lag_avg = NP.mean(fg_cc_skyvis_lag_blavg, axis=2)
fg_cc_skyvis_lag_res_avg = NP.mean(fg_cc_skyvis_lag_res_blavg, axis=2)
# ax.set_aspect('equal')
# ax.set_xlabel('l', fontsize=18, weight='medium')
# ax.set_ylabel('m', fontsize=18, weight='medium')
# cbax = fig.add_axes([0.9, 0.125, 0.02, 0.74])
# cbar = fig.colorbar(ipsf, cax=cbax, orientation='vertical')
# # PLT.tight_layout()
# fig.subplots_adjust(right=0.85)
# fig.subplots_adjust(top=0.88)
# PLT.savefig('/data3/t_nithyanandan/project_MOFF/simulated/MWA/figures/quick_psf_via_FX.png'.format(max_n_timestamps), bbox_inches=0)
psf_diff = NP.mean(beam_MOFF, axis=2) - NP.mean(vfimgobj.beam['P11'], axis=2)
gridlmrad = NP.sqrt(vfimgobj.gridl**2 + vfimgobj.gridm**2)
psfdiff_ds = psf_diff[::2,::2].ravel()
gridlmrad = gridlmrad[::2,::2].ravel()
lmradbins = NP.linspace(0.0, 1.0, 21, endpoint=True)
psfdiffrms, psfdiffbe, psfdiffbn, psfdiffri = OPS.binned_statistic(gridlmrad, values=psfdiff_ds, statistic=NP.std, bins=lmradbins)
psfref = NP.mean(beam_MOFF, axis=2)
psfref = psfref[::2,::2].ravel()
psfrms, psfbe, psfbn, psfri = OPS.binned_statistic(gridlmrad, values=psfref, statistic=NP.std, bins=lmradbins)
fig, axs = PLT.subplots(nrows=2, ncols=1, figsize=(5,9))
for j in range(2):
if j == 0:
dpsf = axs[j].imshow(psf_diff, origin='lower', extent=(vfimgobj.gridl.min(), vfimgobj.gridl.max(), vfimgobj.gridm.min(), vfimgobj.gridm.max()), interpolation='none', vmin=psf_diff.min(), vmax=psf_diff.max())
axs[j].plot(NP.cos(NP.linspace(0.0, 2*NP.pi, num=100)), NP.sin(NP.linspace(0.0, 2*NP.pi, num=100)), 'k-')
axs[j].set_xlim(-1,1)
axs[j].set_ylim(-1,1)
axs[j].set_aspect('equal')
axs[j].set_xlabel('l', fontsize=18, weight='medium')
axs[j].set_ylabel('m', fontsize=18, weight='medium')
cbax = fig.add_axes([0.87, 0.53, 0.02, 0.37])
def main():
# 01) Fringe pattern behaviour
# 02) Fringe cycle coherence
# 03) Fringe cycle coherence from 2D sky
# 04) Delay bin coherence from 2D sky
plot_01 = False
plot_02 = False
plot_03 = False
plot_04 = True
def za2sinza(za):
sinza = NP.sin(NP.radians(za))
return ['{0:.2f}'.format(l) for l in sinza]
def sinza2za(sinza):
za = NP.degrees(NP.arcsin(sinza))
return ['{0:.1f}'.format(theta) for theta in za]
if plot_01:
# 01) Fringe pattern behaviour
bll = 10.0 # Baseline length in m
wl = 2.0 # Wavelength in m
# freq = 150e6 # Frequency in Hz
# wl = FCNST.c / freq
freq = FCNST.c / wl
n_theta = 10001
theta = NP.pi * (NP.arange(n_theta) - n_theta/2)/n_theta
y_offset = 10.0 * NP.cos(theta)
l = NP.sin(theta) # Direction cosine
fringe = NP.exp(-1j * 2 * NP.pi * bll * l / wl)
fringe_l_interval = wl / bll
l_intervals = NP.arange(-1, 1, fringe_l_interval)
theta_intervals = NP.degrees(NP.arcsin(l_intervals))
theta_intervals = NP.append(theta_intervals, 90.0)
bincount, theta_bin_edges, binnum, ri = OPS.binned_statistic(NP.degrees(theta), statistic='count', bins=theta_intervals)
fringe_sum = NP.zeros(bincount.size, dtype=NP.complex64)
for i in range(bincount.size):
ind = ri[ri[i]:ri[i+1]]
fringe_sum[i] = NP.sum(fringe[ind])
fig = PLT.figure(figsize=(6,6))
axth = fig.add_subplot(111)
fringe_theta = axth.plot(NP.degrees(theta), fringe.real, 'k.', ms=2)
for theta_interval in theta_intervals:
axth.axvline(x=theta_interval, ymin=0, ymax=0.75, ls=':', lw=0.5, color='black')
axth.set_xlim(-90, 90)
axth.set_xlabel(r'$\theta$'+' [degree]')
axth.set_ylabel('Fringe [Arbitrary units]', fontsize=12)
axdc = axth.twiny()
fringe_dircos = axdc.plot(l, y_offset.max()+fringe.real, 'r.', ms=2)
fringe_dircos_on_sky = axdc.plot(l, y_offset+fringe.real, color='orange', marker='.', ms=2)
sky_curve = axdc.plot(l, y_offset, color='orange', ls='--', lw=1)
axdc.set_xlim(-1.0, 1.0)
axdc.set_xlabel(r'$\sin\,\theta$', fontsize=12, color='red')
axr = axth.twinx()
# axr.bar(theta_intervals[:-1], bincount/theta.size, width=theta_intervals[1:]-theta_intervals[:-1], color='white', fill=False)
axr.errorbar(0.5*(theta_intervals[1:]+theta_intervals[:-1]), NP.abs(fringe_sum)/bincount, yerr=1/NP.sqrt(bincount), color='b', ecolor='b', fmt='o-', ms=10, lw=2)
axr.set_xlim(-90,90)
axr.set_ylim(-0.2, 0.4)
axr.set_ylabel('Coherence Amplitude in Fringe Cycle', fontsize=12, color='blue')
fig.subplots_adjust(right=0.85)
PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/wide_field_effects_demo.png', bbox_inches=0)
PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/wide_field_effects_demo.eps', bbox_inches=0)
PLT.show()
if plot_02:
# 02) Fringe cycle coherence
u = 2.0 ** NP.arange(13)
u_max = u.max()
dza = NP.degrees(1/(512*u_max))
za = NP.arange(0.0, 90.0, dza)
sinza = NP.sin(NP.radians(za))
fringes = NP.exp(-1j*2*NP.pi*u.reshape(-1,1)*sinza.reshape(1,-1))
fringe_cycle_coherence = NP.zeros((u.size, u_max), dtype=NP.complex64)
sample_counts = NP.zeros((u.size, u_max))
independent_sample_counts = NP.zeros((u.size, u_max))
dsinza = 1/u
last_fringe_cycle_boundary_sinza = 1.0-dsinza
last_fringe_cycle_boundary = NP.degrees(NP.arcsin(last_fringe_cycle_boundary_sinza))
eps = 1e-10
sinza_intervals = NP.arange(0, 1, dsinza[-1])
sinza_intervals = NP.append(sinza_intervals, 1.0) - eps
za_intervals = NP.degrees(NP.arcsin(sinza_intervals))
# za_intervals = NP.append(za_intervals, 90.0)
bincount, za_bin_edges, binnum, ri = OPS.binned_statistic(sinza, statistic='count', bins=sinza_intervals)
for ui in range(u.size):
for i in range(u[ui].astype(NP.int)):
begini = i*u_max/u[ui]
endi = (i+1)*u_max/u[ui]
begini = begini.astype(NP.int)
endi = endi.astype(NP.int)
fine_fringe_cycle_ind = NP.arange(begini, endi)
ind = ri[ri[begini]:ri[endi]]
fringe_cycle_coherence[ui,fine_fringe_cycle_ind] = NP.sum(fringes[ui,ind])
sample_counts[ui,fine_fringe_cycle_ind] = float(ind.size)
independent_sample_counts[ui,fine_fringe_cycle_ind] = 2.0 / NP.sqrt(1-NP.mean(sinza[ind])**2)
# ind = ri[ri[i]:ri[i+1]]
# fringe_cycle_coherence[ui,ind] = NP.sum(fringes[ui,ind])
# sample_counts[ui,ind] = bincount[i]
# independent_sample_counts[ui,ind] = 2.0 / NP.sqrt(1-NP.mean(sinza[ind])**2)
norm_fringe_cycle_coherence = fringe_cycle_coherence / sample_counts
fringe_cycle_efficiency = fringe_cycle_coherence / za.size
norm_fringe_cycle_coherence_SNR = norm_fringe_cycle_coherence * NP.sqrt(independent_sample_counts)
fig = PLT.figure(figsize=(6,6))
ax = fig.add_subplot(111)
fringe_matrix = ax.pcolormesh(sinza_intervals[:-1], u, NP.abs(norm_fringe_cycle_coherence), norm=PLTC.LogNorm(vmin=1e-6, vmax=NP.abs(norm_fringe_cycle_coherence).max()))
# fringe_matrix = ax.imshow(NP.abs(norm_fringe_cycle_coherence), origin='lower', extent=[za.min(), 90.0, u.min(), u.max()], norm=PLTC.LogNorm(vmin=1e-6, vmax=NP.abs(norm_fringe_cycle_coherence).max()))
last_fringe_cycle_edge = ax.plot(last_fringe_cycle_boundary_sinza, u, ls='--', lw=2, color='white')
ax.set_xlim(sinza.min(), 1.0)
ax.set_ylim(u.min(), u.max())
ax.set_yscale('log')
ax.set_xlabel(r'$|\,\sin\,\theta\,|$ [degrees]', fontsize=14)
ax.set_ylabel(r'$b/\lambda$', fontsize=14)
ax.set_aspect('auto')
axt = ax.twiny()
axt.set_xticks(ax.get_xticks())
axt.set_xbound(ax.get_xbound())
axt.set_xticklabels(sinza2za(ax.get_xticks()))
axt.set_xlabel(r'$|\,\theta\,|$', fontsize=14)
fig.subplots_adjust(right=0.88, left=0.15, top=0.9)
cbax = fig.add_axes([0.9, 0.1, 0.02, 0.8])
cbar = fig.colorbar(fringe_matrix, cax=cbax, orientation='vertical')
PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_coherence.png', bbox_inches=0)
PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_coherence.eps', bbox_inches=0)
fig = PLT.figure(figsize=(6,6))
ax = fig.add_subplot(111)
fringe_matrix = ax.pcolormesh(sinza_intervals[:-1], u, NP.abs(fringe_cycle_efficiency), norm=PLTC.LogNorm(vmin=1e-8, vmax=NP.abs(fringe_cycle_efficiency).max()))
# fringe_matrix = ax.imshow(NP.abs(norm_fringe_cycle_efficiency), origin='lower', extent=[za.min(), 90.0, u.min(), u.max()], norm=PLTC.LogNorm(vmin=1e-6, vmax=NP.abs(norm_fringe_cycle_efficiency).max()))
last_fringe_cycle_edge = ax.plot(last_fringe_cycle_boundary_sinza, u, ls='--', lw=2, color='white')
ax.set_xlim(sinza.min(), 1.0)
ax.set_ylim(u.min(), u.max())
ax.set_yscale('log')
ax.set_xlabel(r'$|\,\sin\,\theta\,|$ [degrees]', fontsize=14)
ax.set_ylabel(r'$b/\lambda$', fontsize=14)
ax.set_aspect('auto')
axt = ax.twiny()
axt.set_xticks(ax.get_xticks())
axt.set_xbound(ax.get_xbound())
axt.set_xticklabels(sinza2za(ax.get_xticks()))
axt.set_xlabel(r'$|\,\theta\,|$', fontsize=14)
fig.subplots_adjust(right=0.88, left=0.15, top=0.9)
cbax = fig.add_axes([0.9, 0.1, 0.02, 0.8])
cbar = fig.colorbar(fringe_matrix, cax=cbax, orientation='vertical')
PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_efficiency.png', bbox_inches=0)
PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_efficiency.eps', bbox_inches=0)
# fig = PLT.figure(figsize=(6,6))
# ax = fig.add_subplot(111)
# fringe_matrix_SNR = ax.imshow(NP.abs(norm_fringe_cycle_coherence_SNR), origin='lower', extent=[za.min(), 90.0, u.min(), u.max()], norm=PLTC.LogNorm(vmin=NP.abs(norm_fringe_cycle_coherence_SNR).min(), vmax=NP.abs(norm_fringe_cycle_coherence_SNR).max()))
# ax.set_xlim(za.min(), 90.0)
# ax.set_ylim(u.min(), u.max())
# ax.set_xlabel(r'$|\,\theta\,|$ [degrees]', fontsize=14)
# ax.set_ylabel(r'$b/\lambda$', fontsize=14)
# ax.set_aspect('auto')
# axt = ax.twiny()
# axt.set_xticks(ax.get_xticks())
# axt.set_xbound(ax.get_xbound())
# axt.set_xticklabels(za2sinza(ax.get_xticks()))
# axt.set_xlabel(r'$|\,\sin\,\theta\,|$', fontsize=14)
# fig.subplots_adjust(right=0.88, left=0.15, top=0.9)
# cbax = fig.add_axes([0.9, 0.1, 0.02, 0.8])
# cbar = fig.colorbar(fringe_matrix_SNR, cax=cbax, orientation='vertical')
PLT.show()
if plot_03:
# 03) Fringe cycle coherence from 2D sky
u = 2.0 ** NP.arange(10)
u_max = u.max()
dsa = 1/(u_max ** 2)
npix_orig = 4 * NP.pi / (dsa/8)
nside = HP.pixelfunc.get_min_valid_nside(npix_orig)
npix = HP.nside2npix(nside)
pixarea = HP.nside2pixarea(nside)
nring = int(4*nside - 1)
isotheta = NP.pi/(nring+1) * (1 + NP.arange(nring))
ison = NP.sin(isotheta)
theta, az = HP.pix2ang(nside, NP.arange(npix))
n = NP.cos(theta)
qrtr_sky_ind, = NP.where((theta <= NP.pi/2) & (az <= NP.pi))
theta = theta[qrtr_sky_ind]
az = az[qrtr_sky_ind]
n = n[qrtr_sky_ind]
fringes = NP.exp(-1j * 2*NP.pi * u.reshape(-1,1) * n.reshape(1,-1))
fringe_cycle_coherence = NP.zeros((u.size, u_max), dtype=NP.complex64)
sample_counts = NP.zeros((u.size, u_max))
dn = 1/u
last_fringe_cycle_boundary_n = 1.0-dn
last_fringe_cycle_boundary_za = NP.degrees(NP.arcsin(last_fringe_cycle_boundary_n))
eps = 1e-10
n_intervals = NP.arange(0, 1, dn[-1])
n_intervals = NP.append(n_intervals, 1.0) - eps
ang_intervals = NP.degrees(NP.arcsin(n_intervals))
bincount, n_bin_edges, binnum, ri = OPS.binned_statistic(n, statistic='count', bins=n_intervals)
for ui in range(u.size):
for i in range(u[ui].astype(NP.int)):
begini = i*u_max/u[ui]
endi = (i+1)*u_max/u[ui]
begini = begini.astype(NP.int)
endi = endi.astype(NP.int)
fine_fringe_cycle_ind = NP.arange(begini, endi)
ind = ri[ri[begini]:ri[endi]]
fringe_cycle_coherence[ui,fine_fringe_cycle_ind] = NP.sum(fringes[ui,ind])
sample_counts[ui,fine_fringe_cycle_ind] = float(ind.size)
norm_fringe_cycle_coherence = fringe_cycle_coherence / sample_counts
fringe_cycle_efficiency = fringe_cycle_coherence / (npix/4)
fringe_cycle_solid_angle = sample_counts * pixarea
fig = PLT.figure(figsize=(6,6))
ax = fig.add_subplot(111)
fringe_matrix = ax.pcolormesh(n_intervals[:-1], u, NP.abs(fringe_cycle_efficiency), norm=PLTC.LogNorm(vmin=1e-8, vmax=NP.abs(fringe_cycle_efficiency).max()))
# fringe_matrix = ax.pcolormesh(n_intervals[:-1], u, NP.abs(norm_fringe_cycle_coherence), norm=PLTC.LogNorm(vmin=1e-6, vmax=NP.abs(norm_fringe_cycle_coherence).max()))
last_fringe_cycle_edge = ax.plot(last_fringe_cycle_boundary_n, 1.5*u, ls='--', lw=2, color='black')
ax.set_xlim(0.0, 1.0)
ax.set_ylim(u.min(), u.max())
ax.set_yscale('log')
ax.set_xlabel(r'$|\,\sin\,\theta\,|$', fontsize=14)
ax.set_ylabel(r'$b/\lambda$', fontsize=14)
ax.set_aspect('auto')
axt = ax.twiny()
axt.set_xticks(ax.get_xticks())
axt.set_xbound(ax.get_xbound())
axt.set_xticklabels(sinza2za(ax.get_xticks()))
axt.set_xlabel(r'$|\,\theta\,|$ [degrees]', fontsize=14)
fig.subplots_adjust(right=0.88, left=0.15, top=0.9)
cbax = fig.add_axes([0.9, 0.1, 0.02, 0.8])
cbar = fig.colorbar(fringe_matrix, cax=cbax, orientation='vertical')
PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_efficiency_2D.png', bbox_inches=0)
PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_efficiency_2D.eps', bbox_inches=0)
# fig = PLT.figure(figsize=(6,6))
# ax = fig.add_subplot(111)
# solid_angle_matrix = ax.pcolormesh(n_intervals[:-1], u, NP.abs(fringe_cycle_solid_angle), norm=PLTC.LogNorm(vmin=NP.abs(fringe_cycle_solid_angle).min(), vmax=NP.abs(fringe_cycle_solid_angle).max()))
# last_fringe_cycle_edge = ax.plot(last_fringe_cycle_boundary_n, u, ls='--', lw=2, color='black')
# ax.set_xlim(0.0, 1.0)
# ax.set_ylim(u.min(), u.max())
# ax.set_yscale('log')
# ax.set_xlabel(r'$|\,\sin\,\theta\,|$', fontsize=14)
# ax.set_ylabel(r'$b/\lambda$', fontsize=14)
# ax.set_aspect('auto')
# axt = ax.twiny()
# axt.set_xticks(ax.get_xticks())
# axt.set_xbound(ax.get_xbound())
# axt.set_xticklabels(sinza2za(ax.get_xticks()))
# axt.set_xlabel(r'$|\,\theta\,|$ [degrees]', fontsize=14)
# fig.subplots_adjust(right=0.88, left=0.15, top=0.9)
# cbax = fig.add_axes([0.9, 0.1, 0.02, 0.8])
# cbar = fig.colorbar(solid_angle_matrix, cax=cbax, orientation='vertical')
# cbax.set_xlabel(r'$\Omega$ [Sr]', fontsize=12, labelpad=10)
# cbax.xaxis.set_label_position('top')
# PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_solid_angle_2D.png', bbox_inches=0)
# PLT.savefig('/data3/t_nithyanandan/project_global_EoR/figures/fringe_cycle_solid_angle_2D.eps', bbox_inches=0)
if plot_04:
# 04) Delay bin coherence from 2D sky
# f0 = 30e9 # Center frequency in Hz
# bw = 1e9 # Bandwidth in Hz
# nchan = 64 # number of frequency channels
# l_delay_bins = 32
# bl = 0.5 * l_delay_bins / bw * FCNST.c # baseline length in m
# wl0 = FCNST.c / f0
# freq_resolution = bw / nchan
# chans = (NP.arange(nchan) - nchan/2) * freq_resolution + f0
# wl = FCNST.c / chans
# l_binsize = 1.0 / (l_delay_bins/2)
# u = bl / wl
# u_max = u.max()
# dl_coarse = 1.0 / u_max # resolution in direction cosine
# eps = 1e-10
# # nl = NP.ceil(1.0 / dl_coarse).astype(NP.int)
# # dl_coarse = 1.0 / nl
# dl = dl_coarse / 4
# dm = dl_coarse
# lv = NP.arange(0.0, 1.0, dl)
# # lv = NP.append(lv, 1.0-eps)
# # mv = NP.arange(-1.0+eps, 1.0-eps, dm)
# mv = NP.append(-lv[-1:0:-1], lv)
# # mv = NP.append(mv, 1.0-eps)
# lgrid, mgrid = NP.meshgrid(lv, mv)
# lmrad = NP.sqrt(lgrid**2 + mgrid**2)
# ngrid = NP.empty_like(lgrid)
# sagrid = NP.empty_like(lgrid)
# ngrid.fill(NP.nan)
# sagrid.fill(NP.nan)
# PDB.set_trace()
# valid_ind = lmrad <= 1.0
# ngrid[valid_ind] = NP.sqrt(1.0 - lmrad[valid_ind]**2)
# sagrid[valid_ind] = dl * dm / ngrid[valid_ind]
# lvect = lgrid[valid_ind]
# mvect = mgrid[valid_ind]
# nvect = ngrid[valid_ind]
# savect = sagrid[valid_ind]
# # fringes = NP.exp(-1j * 2*NP.pi * u.reshape(-1,1) * lvect.reshape(1,-1))
# fringes = NP.empty((lgrid.shape[0], lgrid.shape[1], u.shape[0]), dtype=NP.complex64)
# fringes.fill(NP.nan)
# fringes[valid_ind] = NP.exp(-1j * 2*NP.pi * u.reshape(1,1,-1) * lgrid[:,:,NP.newaxis])
# delay_bin_coherence = NP.zeros((u.size, l_delay_bins/2), dtype=NP.complex64)
# l_intervals = NP.arange(0.0, 1.0, l_binsize)
# l_intervals = NP.append(l_intervals, 1.0-eps)
# delay_intervals = bl * l_intervals / FCNST.c
# bincount, l_bin_edges, binnum, ri = OPS.binned_statistic(lvect, statistic='count', bins=l_intervals)
# PDB.set_trace()
# for di in range(l_delay_bins/2):
# ind = ri[ri[di]:ri[di+1]]
# delay_bin_coherence[:,di] = NP.sum(fringes[:,ind] * savect[ind], axis=1)
# lgrid, mgrid = NP.meshgrid(lv, mv)
# lgrid = lgrid.ravel()
# mgrid = mgrid.ravel()
# lmrad = NP.sqrt(lgrid**2 + mgrid**2)
# ngrid = NP.empty_like(lgrid)
# sagrid = NP.empty_like(lgrid)
# ngrid.fill(NP.nan)
# sagrid.fill(NP.nan)
# valid_ind = lmrad <= 1.0
# ngrid[valid_ind] = NP.sqrt(1.0 - lmrad[valid_ind]**2)
# sagrid[valid_ind] = dl * dm / ngrid[valid_ind]
# lvect = lgrid[valid_ind]
# mvect = mgrid[valid_ind]
# nvect = ngrid[valid_ind]
# savect = sagrid[valid_ind]
# fringes = NP.exp(-1j * 2*NP.pi * u.reshape(-1,1) * lvect.reshape(1,-1))
# delay_bin_coherence = NP.zeros((u.size, l_delay_bins/2), dtype=NP.complex64)
# l_intervals = NP.arange(0.0, 1.0, l_binsize)
# l_intervals = NP.append(l_intervals, 1.0-eps)
# delay_intervals = bl * l_intervals / FCNST.c
# bincount, l_bin_edges, binnum, ri = OPS.binned_statistic(lvect, statistic='count', bins=l_intervals)
# PDB.set_trace()
# for di in range(l_delay_bins/2):
# ind = ri[ri[di]:ri[di+1]]
# delay_bin_coherence[:,di] = NP.sum(fringes[:,ind] * savect[ind], axis=1)
# f0 = 30e9 # Center frequency in Hz
# bw = 1e9 # Bandwidth in Hz
# nchan = 64 # number of frequency channels
# n_delay_bins = 32
# bl = 0.5 * n_delay_bins / bw * FCNST.c # baseline length in m
# wl0 = FCNST.c / f0
# freq_resolution = bw / nchan
# chans = (NP.arange(nchan) - nchan/2) * freq_resolution + f0
# wl = FCNST.c / chans
# n_binsize = 1.0 / (n_delay_bins/2)
# u = bl / wl
# u_max = u.max()
# dsa = 1/(u_max ** 2) # solid angle resolution
# npix_orig = 4 * NP.pi / (dsa/4)
# nside = HP.pixelfunc.get_min_valid_nside(npix_orig)
# npix = HP.nside2npix(nside)
# pixarea = HP.nside2pixarea(nside)
# # nring = int(4*nside - 1)
# # isotheta = NP.pi/(nring+1) * (1 + NP.arange(nring))
# # ison = NP.sin(isotheta)
# theta, az = HP.pix2ang(nside, NP.arange(npix))
# n = NP.cos(theta)
# qrtr_sky_ind, = NP.where((theta <= NP.pi/2) & (az <= NP.pi))
# theta = theta[qrtr_sky_ind]
# az = az[qrtr_sky_ind]
# n = n[qrtr_sky_ind]
# fringes = NP.exp(-1j * 2*NP.pi * u.reshape(-1,1) * n.reshape(1,-1))
# delay_bin_coherence = NP.zeros((u.size, n_delay_bins/2), dtype=NP.complex64)
# PDB.set_trace()
# eps = 1e-10
# n_intervals = NP.arange(0.0, 1.0, n_binsize)
# n_intervals = NP.append(n_intervals, 1.0) - eps
# delay_intervals = bl * n_intervals / FCNST.c
# bincount, n_bin_edges, binnum, ri = OPS.binned_statistic(n, statistic='count', bins=n_intervals)
# for di in range(n_delay_bins/2):
# ind = ri[ri[di]:ri[di+1]]
# delay_bin_coherence[:,di] = NP.sum(fringes[:,ind], axis=1) * pixarea
# # # for ui in range(u.size):
# # # delay_bin_coherence[ui,di] = NP.sum(fringes[ui,ind]) * pixarea
# delay_spectrum_coherence = NP.fft.ifftshift(NP.fft.ifft2(delay_bin_coherence, axes=(0,)), axes=0) * bw
# fig = PLT.figure(figsize=(6,6))
# ax = fig.add_subplot(111)
# dspec = ax.imshow(NP.abs(delay_bin_coherence), origin='lower', extent=[delay_intervals.min(), delay_intervals.max(), chans.min(), chans.max()])
# ax.set_aspect('auto')
# fig = PLT.figure(figsize=(6,6))
# ax = fig.add_subplot(111)
# dspec = ax.imshow(NP.abs(delay_spectrum_coherence), origin='lower', extent=[delay_intervals.min(), delay_intervals.max(), -0.5/freq_resolution, 0.5/freq_resolution])
# ax.set_aspect('auto')
# PLT.show()
f0 = 30e9 # Center frequency in Hz
bw = 1e9 # Bandwidth in Hz
nchan = 40 # number of frequency channels
n_delay_bins = 32
bl = 0.5 * n_delay_bins / bw * FCNST.c # baseline length in m
wl0 = FCNST.c / f0
freq_resolution = bw / nchan
chans = (NP.arange(nchan) - nchan/2) * freq_resolution + f0
wl = FCNST.c / chans
n_binsize = 1.0 / (n_delay_bins/2)
u = bl / wl
u_max = u.max()
dsa = 1/(u_max ** 2)
npix_orig = 4 * NP.pi / (dsa/8)
nside = HP.pixelfunc.get_min_valid_nside(npix_orig)
npix = HP.nside2npix(nside)
pixarea = HP.nside2pixarea(nside)
nring = int(4*nside - 1)
isotheta = NP.pi/(nring+1) * (1 + NP.arange(nring))
ison = NP.sin(isotheta)
theta, az = HP.pix2ang(nside, NP.arange(npix))
n = NP.cos(theta)
qrtr_sky_ind, = NP.where((theta <= NP.pi/2) & (az <= NP.pi))
theta = theta[qrtr_sky_ind]
az = az[qrtr_sky_ind]
n = n[qrtr_sky_ind]
sortind = NP.argsort(n)
nsorted = n[sortind]
fringes = NP.exp(-1j * 2*NP.pi * u.reshape(-1,1) * nsorted.reshape(1,-1))
accumulated_fringes = accumulate_fringes(fringes) * pixarea
fringe_cycle_coherence = accumulated_fringe_differencing(accumulated_fringes, nsorted, 1/u_max)
PDB.set_trace()
nproc = max(MP.cpu_count()/2-1, 1)
# chunksize = int(ceil(u.size/float(nproc)))
pool = MP.Pool(processes=nproc)
fringe_cycle_coherences = pool.map(unwrap_fringe_coherence, IT.izip(u.tolist(), IT.repeat(nsorted)))
PDB.set_trace()
def to_healpix(self, freq, nside, in_units='Jy', out_coords='equatorial',
out_units='K', outfile=None, outfmt='fits'):
"""
-------------------------------------------------------------------------
Convert catalog to a healpix format of given nside at specified
frequencies.
Inputs:
freq [scalar or numpy array] Frequencies at which HEALPIX output
maps are to be generated
nside [integer] HEALPIX nside parameter for the output map(s)
in_units [string] Units of input map or catalog. Accepted values are
'K' for Temperature of 'Jy' for flux density. Default='Jy'
out_coords [string] Output coordinate system. Accepted values are
'galactic' and 'equatorial' (default)
out_units [string] Units of output map. Accepted values are
'K' for Temperature of 'Jy' for flux density. Default='K'
outfile [string] Filename with full path to save the output HEALPIX
map(s) to. Default=None
outfmt [string] File format for output file. Accepted values are
'fits' (default) and 'ascii'
Output(s):
A dictionary with the following keys and values:
'filename' Pull path to the output file. Set only if input parameter
outfile is set. Default=None.
'spectrum' A numpy array of size nsrc x nchan where nsrc is the number
sky locations depending on input parameter out_nside and
nchan is the number of frequencies in input parameter freq
-------------------------------------------------------------------------
"""
try:
freq
except NameError:
freq = self.frequency.ravel()[self.frequency.size/2]
else:
if not isinstance(freq, (int,float,list,NP.ndarray)):
raise TypeError('Input parameter freq must be a scalar or numpy array')
else:
freq = NP.asarray(freq).reshape(-1)
try:
nside
except NameError:
raise NameError('Input parameter nside not specified')
else:
if not isinstance(nside, int):
raise TypeError('Input parameter nside must be an integer')
if not isinstance(out_coords, str):
raise TypeError('Input parameter out_coords must be a string')
elif out_coords not in ['equatorial', 'galactic']:
raise ValueError('Input parameter out_coords must be set to "equatorial" or "galactic"')
if not isinstance(in_units, str):
raise TypeError('in_units must be a string')
elif in_units not in ['K', 'Jy']:
raise ValueError('in_units must be "K" or "Jy"')
if not isinstance(out_units, str):
raise TypeError('out_units must be a string')
elif out_units not in ['K', 'Jy']:
raise ValueError('out_units must be "K" or "Jy"')
if outfile is not None:
if not isinstance(outfile, str):
raise TypeError('outfile must be a string')
if not isinstance(outfmt, str):
raise TypeError('outfile format must be specified by a string')
elif outfmt not in ['ascii', 'fits']:
raise ValueError('outfile format must be "ascii" or "fits"')
ec = SkyCoord(ra=self.location[:,0], dec=self.location[:,1], unit='deg', frame='icrs')
gc = ec.transform_to('galactic')
if out_coords == 'galactic':
phi = gc.l.radian
theta = NP.pi/2 - gc.b.radian
else:
phi = ec.ra.radian
theta = NP.pi/2 - ec.dec.radian
outmap = NP.zeros((HP.nside2npix(nside), freq.size))
pixarea = HP.nside2pixarea(nside)
pix = HP.ang2pix(nside, theta, phi)
spectrum = self.generate_spectrum(frequency=freq)
if in_units != out_units:
if out_units == 'K':
spectrum = (FCNST.c / freq.reshape(1,-1))**2 / (2*FCNST.k*pixarea) * spectrum * CNST.Jy # Convert into temperature
else:
spectrum = (freq.reshape(1,-1) / FCNST.c)**2 * (2*FCNST.k*pixarea) * spectrum / CNST.Jy # Convert into Jy
uniq_pix, uniq_pix_ind, pix_invind = NP.unique(pix, return_index=True, return_inverse=True)
counts, binedges, binnums, ri = OPS.binned_statistic(pix_invind, statistic='count', bins=NP.arange(uniq_pix.size+1))
ind_count_gt1, = NP.where(counts > 1)
ind_count_eq1, = NP.where(counts == 1)
upix_1count = []
spec_ind = []
for i in ind_count_eq1:
ind = ri[ri[i]:ri[i+1]]
upix_1count += [uniq_pix[i]]
spec_ind += [ind]
upix_1count = NP.asarray(upix_1count)
spec_ind = NP.asarray(spec_ind).ravel()
outmap[upix_1count,:] = spectrum[spec_ind,:]
for i in ind_count_gt1:
upix = uniq_pix[i]
ind = ri[ri[i]:ri[i+1]]
outmap[upix,:] = NP.sum(spectrum[ind,:], axis=0)
# # Plot diagnostic begins
# if out_coords == 'galactic':
# hpxgc = HP.cartview(outmap[:,0], coord='G', return_projected_map=True)
# hpxec = HP.cartview(outmap[:,0], coord=['G', 'C'], return_projected_map=True)
# else:
# hpxec = HP.cartview(outmap[:,0], coord='C', return_projected_map=True)
# hpxgc = HP.cartview(outmap[:,0], coord=['C', 'G'], return_projected_map=True)
# gcl = gc.l.degree
# gcb = gc.b.degree
# gcl[gcl > 180.0] -= 360.0
# ecra = ec.ra.degree
# ecdec = ec.dec.degree
# ecra[ecra > 180.0] -= 360.0
# fig, axs = PLT.subplots(nrows=2, sharex=True, sharey=True, figsize=(6,6))
# gcimg = axs[0].imshow(hpxgc, origin='lower', extent=[180,-180,-90,90], norm=PLTC.LogNorm(vmin=hpxgc[hpxgc>0.0].min(), vmax=hpxgc.max()))
# gcplt = axs[0].plot(gcl, gcb, 'o', mfc='none', mec='black')
# axs[0].set_xlim(180, -180)
# axs[0].set_ylim(-90, 90)
# ecimg = axs[1].imshow(hpxec, origin='lower', extent=[180,-180,-90,90], norm=PLTC.LogNorm(vmin=hpxec[hpxec>0.0].min(), vmax=hpxec.max()))
# ecplt = axs[1].plot(ecra, ecdec, 'o', mfc='none', mec='black')
# axs[1].set_xlim(180, -180)
# axs[1].set_ylim(-90, 90)
# fig.subplots_adjust(hspace=0, wspace=0)
# PLT.show()
# # Plot diagnostic ends
# Save the healpix spectrum to file
if outfile is not None:
if outfmt == 'fits':
hdulist = []
hdulist += [fits.PrimaryHDU()]
hdulist[0].header['EXTNAME'] = 'PRIMARY'
hdulist[0].header['NSIDE'] = nside
hdulist[0].header['UNTIS'] = out_units
hdulist[0].header['NFREQ'] = freq.size
for chan, f in enumerate(freq):
hdulist += [fits.ImageHDU(outmap[:,chan], name='{0:.1f} MHz'.format(f/1e6))]
hdu = fits.HDUList(hdulist)
hdu.writeto(outfile+'.fits', clobber=True)
return {'hlpxfile': outfile+outfmt, 'hlpxspec': outmap}
else:
out_dict = {}
colnames = []
colfrmts = {}
for chan, f in enumerate(freq):
out_dict['{0:.1f}_MHz'.format(f/1e6)] = outmap[:,chan]
colnames += ['{0:.1f}_MHz'.format(f/1e6)]
colfrmts['{0:.1f}_MHz'.format(f/1e6)] = '%0.5f'
tbdata = Table(out_dict, names=colnames)
ascii.write(tbdata, output=outfile+'.txt', format='fixed_width_two_line', formats=colfrmts, bookend=False, delimiter='|', delimiter_pad=' ')
return {'filename': outfile+outfmt, 'spectrum': outmap}
else:
return {'filename': outfile, 'spectrum': outmap}
def generate_spectrum(self, frequency=None):
"""
-------------------------------------------------------------------------
Generate and return a spectrum from functional spectral parameters
Inputs:
frequency [scalar or numpy array] Frequencies at which the spectrum at
all object locations is to be created. Must be in same units
as the attribute frequency and values under key 'freq-ref'
of attribute spec_parms. If not provided (default=None), a
spectrum is generated for all the frequencies specified in
the attribute frequency and values under keys 'freq-ref' and
'z-width' of attribute spec_parms.
Outputs:
spectrum [numpy array] Spectrum of the sky model at the respective
sky locations. Has shape nobj x nfreq.
Power law calculation uses the convention,
spectrum = flux_offset + flux_scale * (freq/freq0)**spindex
Monotone places a delta function at the frequency channel closest to the
reference frequency if it lies inside the frequency range, otherwise
a zero spectrum is assigned.
Thus spectrum = flux_scale * delta(freq-freq0)
Random (currently only gaussian) places random fluxes in the spectrum
spectrum = flux_offset + flux_scale * random_normal(freq.size)
tanh spectrum is defined as (Bowman & Rogers 2010):
spectrum = flux_scale * sqrt((1+z)/10) * 0.5 * [tanh((z-zr)/dz) + 1]
where, flux_scale is typically 0.027 K, zr = reionization redshift
when x_i = 0.5, and dz = redshift width (dz ~ 1)
If the attribute spec_type is 'spectrum' the attribute spectrum is
returned without any modifications.
-------------------------------------------------------------------------
"""
if self.spec_type == 'func':
if frequency is not None:
if isinstance(frequency, (int,float,NP.ndarray)):
frequency = NP.asarray(frequency)
else:
raise ValueError('Input parameter frequency must be a scalar or a numpy array')
if NP.any(frequency <= 0.0):
raise ValueError('Input parameter frequency must contain positive values')
else:
frequency = NP.copy(self.frequency)
spectrum = NP.empty((self.location.shape[0], frequency.size))
spectrum.fill(NP.nan)
uniq_names, invind = NP.unique(self.spec_parms['name'], return_inverse=True)
if len(uniq_names) > 1:
counts, edges, bnum, ri = OPS.binned_statistic(invind, statistic='count', bins=range(len(uniq_names)))
else:
counts = len(invind)
ri = range(counts)
for i, name in enumerate(uniq_names):
if len(uniq_names) > 1:
indices = ri[ri[i]:ri[i+1]]
else:
indices = ri
if name == 'random':
spectrum[indices,:] = self.spec_parms['flux-offset'][indices].reshape(-1,1) + self.spec_parms['flux-scale'][indices].reshape(-1,1) * NP.random.randn(counts[i], frequency.size)
if name == 'monotone': # Needs serious testing
spectrum[indices,:] = 0.0
inpind, refind, dNN = LKP.find_1NN(frequency, self.spec_parms['freq-ref'][indices], distance=frequency[1]-frequency[0], remove_oob=True)
ind = indices[inpind]
ind2d = zip(ind, refind)
spectrum[zip(*ind2d)] = self.spec_parms['flux-scale'][ind]
if name == 'power-law':
spectrum[indices,:] = self.spec_parms['flux-offset'][indices].reshape(-1,1) + self.spec_parms['flux-scale'][indices].reshape(-1,1) * (frequency.reshape(1,-1)/self.spec_parms['freq-ref'][indices].reshape(-1,1))**self.spec_parms['power-law-index'][indices].reshape(-1,1)
if name == 'tanh':
z = CNST.rest_freq_HI/frequency.reshape(1,-1) - 1
zr = CNST.rest_freq_HI/self.spec_parms['freq-ref'][indices].reshape(-1,1) - 1
dz = self.spec_parms['z-width'][indices].reshape(-1,1)
amp = self.spec_parms['flux-scale'][indices].reshape(-1,1) * NP.sqrt((1+z)/10)
xh = 0.5 * (NP.tanh((z-zr)/dz) + 1)
spectrum[indices,:] = amp * xh
return spectrum
else:
return self.spectrum