def load_snr(fcode=None, box=None, pol=False, downgrade=1, use='coadd'):
# load map and ivar
imap = load_map(filedb[fcode][use], fcode=fcode, box=box)
ivar = load_ivar(filedb[fcode][f"{use}_ivar"], fcode=fcode, box=box)
if not pol: # total intensity
snr = imap[0] * ivar[0]**0.5
else: # polarization intensity
P = np.sum(imap[1:]**2, axis=0)**0.5
P_err = lib.P_error(imap, ivar)
snr = P / P_err
return snr
# optionally downgrade
if downgrade > 1:
snr = snr.downgrade(downgrade) * downgrade
if box is not None: return snr.submap(box)
else: return snr
'norm': colors.LogNorm(vmin=1e-5, vmax=1e-2),
}
plotstyle.setup_axis(ax, nticks=[5, 5], yticks=False, fmt=None)
irmap[irmap < 0] = 1e-6
im = ax.imshow(irmap, **opts)
# polarization angle plot
# reload imap to get the original resolution
theta = lib.Bangle(imap[1], imap[2], toIAU=True)
theta += (np.pi / 2) # this gets the B-field angle corrected
# x- and y-components of magnetic field
Bx = np.cos(theta)
By = np.sin(theta)
# mask by polarization intensity
if args.mask:
P = np.sum(imap[1:]**2, axis=0)**0.5
P_err = lib.P_error(imap, ivar * s**2)
Psnr = P / P_err
mask = Psnr < 3
# Pangle_err = lib.Pangle_error(imap, ivar*s**2, deg=True)
# mask = Pangle_err > 10
cmap_ = plt.get_cmap('binary') # actual cmap doesn't matter
color = cmap_(np.ones_like(X))
# color[ mask,-1] = 0.2
# color[~mask,-1] = 1
val = np.min([Psnr, np.ones_like(Psnr) * 3], axis=0)
val /= 3
color[..., -1] = val
color = color.reshape(color.shape[0] * color.shape[1], 4)
else:
color = 'k'
ax.quiver(X,
else:
Y_, X_ = imap.posmap()/np.pi*180
im = ax.contourf(back, colors=color, levels=level)
xmin, xmax = box2extent(box)[:2]/np.pi*180
ax.set_xlim([xmin, xmax]) # revert x axis
theta = lib.Bangle(imap_ds[1], imap_ds[2], toIAU=True)
theta += (np.pi/2.)
# x- and y-components of magnetic field
Bx = np.cos(theta)
By = np.sin(theta)
# optionally mask regions above certain angular uncertainty level
# and high the segments through alpha
if args.mask == 'Psnr':
# mask by Polarization intensity SNR
P = np.sum(imap_ds[1:]**2, axis=0)**0.5
P_err = lib.P_error(imap_ds, ivar_ds*s**2)
mask = (P/P_err) <= 3
elif args.mask == 'Pangle':
Pa_err= lib.Pangle_error(imap_ds, ivar_ds*s**2, deg=True)
mask = Pa_err >= 20
else:
print("No mask applied")
mask = np.zeros_like(imap_ds)
cmap_ = plt.get_cmap('binary') # actual cmap doesn't matter
color = cmap_(np.ones_like(X))
color[mask,-1] = 0.3
color[~mask,-1] = args.alpha
color=color.reshape(color.shape[0]*color.shape[1],4)
ax.quiver(X,Y,Bx,By,pivot='middle', headlength=0,
headaxislength=0, color=color, scale=args.scale,
transform=ax.get_transform('world'))
imap = enmap.smooth_gauss(imap, args.smooth * u.fwhm * u.arcmin)
ivar = enmap.smooth_gauss(ivar, args.smooth * u.fwhm * u.arcmin)
s = sfactor(args.freq, args.smooth)
else:
s = 1
# optionally apply downgrade
if args.downgrade > 1:
imap = imap.downgrade(args.downgrade)
ivar = ivar.downgrade(args.downgrade)
# decide whether to look at I or P
if not args.pol:
noise = ivar[0]**-0.5 / s
label = "Noise (I)"
if title: label = f'{title}: ' + label
else:
noise = lib.P_error(imap, ivar * s**2)
label = "Noise (P)"
if title: label = f'{title}: ' + label
# start plotting
opts = {'cmap': 'gray', 'vmin': args.min, 'vmax': args.max}
fig = plt.figure()
ax = plt.subplot(111, projection=imap.wcs)
plotstyle.setup_axis(ax, nticks=[5, 5], fmt=None)
im = ax.imshow(noise, **opts)
plt.xlabel('$l$')
plt.ylabel('$b$')
cax = plotstyle.add_colorbar_hpad(ax)
fig.colorbar(im, cax=cax, orientation='horizontal').set_label(
texify(f"{label} {fcode} [MJy/sr]"), fontsize=12)
# imap = load_map(filedb[fcode]['coadd'], fcode=fcode, mJy=False)
# ivar = load_ivar(filedb[fcode]['coadd_ivar'], fcode=fcode, mJy=False)
# optionally apply smoothing
if args.smooth > 0:
imap = enmap.smooth_gauss(imap, args.smooth*u.fwhm*u.arcmin)
ivar = enmap.smooth_gauss(ivar, args.smooth*u.fwhm*u.arcmin)
# optionally apply downgrade
if args.downgrade > 1:
imap = imap.downgrade(args.downgrade)
ivar = ivar.downgrade(args.downgrade)*args.downgrade**2
# decide whether to look at I or P
if not args.pol:
snr = imap[0] * ivar[0]**0.5
else:
perr = lib.P_error(imap, ivar, method=args.method)
snr = np.sum(imap[1:]**2,axis=0)**0.5/perr
# start plotting
opts = {
'origin': 'lower',
'cmap': 'jet',
'extent': box2extent(box)/np.pi*180,
'vmin': args.min,
'vmax': args.max
}
if args.log: plt.imshow(np.log10(snr), **opts)
else: plt.imshow(snr, **opts)
plt.xlabel('l [deg]')
plt.ylabel('b [deg]')
if not args.pol:
plt.colorbar(shrink=0.55).set_label('Total intensity S/N')
