# Plot sky map.
vmax = probperdeg2.max()
plot.healpix_heatmap(probperdeg2,
dlon=dlon,
nest=metadata['nest'],
vmin=0.,
vmax=vmax)
# Add colorbar.
if opts.colorbar:
cb = plot.colorbar()
cb.set_label(r'prob. per deg$^2$')
# Add contours.
if opts.contour:
cls = 100 * postprocess.find_greedy_credible_levels(skymap)
cs = plot.healpix_contour(cls,
dlon=dlon,
nest=metadata['nest'],
colors='k',
linewidths=0.5,
levels=opts.contour)
fmt = r'%g\%%' if rcParams['text.usetex'] else '%g%%'
plt.clabel(cs, fmt=fmt, fontsize=6, inline=True)
# Add continents.
if opts.geo:
geojson_filename = os.path.join(os.path.dirname(plot.__file__),
'ne_simplified_coastline.json')
with open(geojson_filename, 'r') as geojson_file:
geojson = json.load(geojson_file)
# Convert sky map from probability to probability per square degree.
probperdeg2 = skymap / hp.nside2pixarea(nside, degrees=True)
# Plot sky map.
vmax = probperdeg2.max()
plot.healpix_heatmap(
probperdeg2, dlon=dlon, nest=metadata['nest'], vmin=0., vmax=vmax)
# Add colorbar.
if opts.colorbar:
cb = plot.colorbar()
cb.set_label(r'prob. per deg$^2$')
# Add contours.
if opts.contour:
cls = 100 * postprocess.find_greedy_credible_levels(skymap)
cs = plot.healpix_contour(
cls, dlon=dlon, nest=metadata['nest'],
colors='k', linewidths=0.5, levels=opts.contour)
fmt = r'%g\%%' if rcParams['text.usetex'] else '%g%%'
plt.clabel(cs, fmt=fmt, fontsize=6, inline=True)
# Add continents.
if opts.geo:
geojson_filename = os.path.join(os.path.dirname(plot.__file__),
'ne_simplified_coastline.json')
with open(geojson_filename, 'r') as geojson_file:
geojson = json.load(geojson_file)
for shape in geojson['geometries']:
verts = np.deg2rad(shape['coordinates'])
plt.plot(verts[:, 0], verts[:, 1], color='0.5', linewidth=0.5)
CDELT2=1./deg_per_pix,
CTYPE1='RA---AIT',
CTYPE2='DEC--AIT',
RADESYS='ICRS'))
wcs = WCS(header)
fig = pl.figure(figsize=[8,6])
ax = pl.axes(projection=wcs, frame_class=RectangularFrame, aspect=1)
world_transform = ax.get_transform('world')
ax.set_xlim((180 - args.size) * deg_per_pix,
(180 + args.size) * deg_per_pix)
ax.set_ylim((90 - args.size) * deg_per_pix,
(90 + args.size) * deg_per_pix)
cls = postprocess.find_greedy_credible_levels(optprob)
cls, _ = reproject_from_healpix((cls, 'icrs'), header)
cs = ax.contour(cls, levels=[0.5, 0.9], colors='C0')
ax.plot([],[], '-', color='C0', label='Exact masses')
## Work on the suboptimal skymap
prob, _ = read_sky_map(args.fits_file, nest=False, distances=False)
nside = hp.npix2nside(len(prob))
deg2perpix = hp.nside2pixarea(nside, degrees=True)
radec = [float(x) for x in args.radec]
deg_per_pix = 4
header = Header(dict(