def contour(post,
ax,
levels=[0.1, 0.5, 0.9],
alpha=1.0,
colors='b',
linewidths=1):
'''
generate mollweide projection of contours with requested annotations
'''
cpost = np.empty(post.shape)
indecies = np.argsort(post)[::-1]
cpost[indecies] = np.cumsum(post[indecies])
plt.sca(ax)
lalinf_plot.healpix_contour(cpost,
levels=levels,
alpha=alpha,
colors=colors,
linewidths=linewidths)
# 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)
radecs = np.deg2rad(opts.radec)
vmin=0., vmax=vmax, cmap=plt.get_cmap(opts.colormap))
if opts.colorbar:
# Plot colorbar.
cb = plot.colorbar()
# Set colorbar label.
cb.set_label(r'prob. per deg$^2$')
# Add contours.
if opts.contour:
indices = np.argsort(-skymap)
region = np.empty(skymap.shape)
region[indices] = 100 * np.cumsum(skymap[indices])
cs = plot.healpix_contour(
region, 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)
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)
# Add markers (e.g., for injections or external triggers).
for ra, dec in np.deg2rad(opts.radec):
if opts.colormap is None:
colors = ['k'] * len(opts.fitsfilenames)
else:
colors = matplotlib.cm.get_cmap(opts.colormap)
colors = colors(np.linspace(0, 1, len(opts.fitsfilenames)))
for count_records, (color,
fitsfilename) in enumerate(zip(colors,
opts.fitsfilenames)):
progress.update(count_records, fitsfilename)
skymap, metadata = fits.read_sky_map(fitsfilename, nest=None)
nside = hp.npix2nside(len(skymap))
gmst = lal.GreenwichMeanSiderealTime(metadata['gps_time']) % (2 * np.pi)
indices = np.argsort(-skymap)
region = np.empty(skymap.shape)
region[indices] = 100 * np.cumsum(skymap[indices])
plot.healpix_contour(region,
nest=metadata['nest'],
dlon=-gmst,
colors=[color],
linewidths=0.5,
levels=[opts.contour],
alpha=opts.alpha)
progress.update(-1, 'saving figure')
# Add a white outline to all text to make it stand out from the background.
plot.outline_text(ax)
opts.output()
def mollweideScaffold( post, post_masked, mask=None, projection="astro mollweide", contours=False, color_map='OrRd', contour_color='grey', LatLon=None):
"""
a standardized plotting routine for the mollweide projections needed in simPlot.py
each mask supplied via *masks is overlayed on the masked plot
LatLon will be marked with a red dot iff projection=="mollweide" (assumed EarthFixed coords)
"""
### set up figure and axis objects
figwidth = 10
figheight = 10
fig = plt.figure(figsize=(figwidth,figheight))
ax1 = plt.subplot(2,1,1, projection=projection)
ax2 = plt.subplot(2,1,2, projection=projection)
### establish color map
cmap = plt.get_cmap(color_map)
### establish range for color map
vmin = min(np.min(post), np.min(post_masked))
vmax = max(np.max(post), np.max(post_masked))
### plot heatmaps
plt.sca( ax1 )
lalinf_plot.healpix_heatmap( post, cmap=cmap, vmin=vmin, vmax=vmax, nest=False )
plt.sca( ax2 )
lalinf_plot.healpix_heatmap( post_masked, cmap=cmap, vmin=vmin, vmax=vmax, nest=False )
if mask!=None: ### plot the mask as a shaded region
mask = np.ma.masked_where(mask==1, mask)
lalinf_plot.healpix_heatmap( mask, cmap=plt.get_cmap('Greys_r'), vmin=0, vmax=1, nest=False, alpha=0.1 )
if projection=="mollweide": ### add continents
geojson_filename = os.path.join(os.path.dirname(lalinf_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'])
color='k'
ax1.plot(verts[:, 0], verts[:, 1], color=color, linewidth=0.5)
ax2.plot(verts[:, 0], verts[:, 1], color=color, linewidth=0.5)
### plot contours
if contours:
if np.sum(post):
cpost = np.empty(post.shape)
indecies = np.argsort(post)[::-1]
cpost[indecies] = np.cumsum(post[indecies])
plt.sca( ax1 )
lalinf_plot.healpix_contour( cpost, alpha=0.25, levels=[0.1, 0.5, 0.9], colors=contour_color )
if np.sum(post_masked):
cpost = np.empty(post_masked.shape)
indecies = np.argsort(post_masked)[::-1]
cpost[indecies] = np.cumsum(post_masked[indecies])
plt.sca( ax2 )
lalinf_plot.healpix_contour( cpost, alpha=0.25, levels=[0.1, 0.5, 0.9], colors=contour_color )
### set titles
ax1.set_title('posterior')
ax2.set_title('masked posterior')
### decoration
for ax in [ax1, ax2]:
if LatLon!=None:
lat, lon = LatLon
ax.plot( lon, lat, marker='o', markeredgecolor='r', markerfacecolor='r', markersize=3, linestyle='none')
ax.patch.set_alpha(0.)
ax.set_alpha(0.)
### return fig and ax handles
return fig, [ax1, ax2]
# 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=0.0, 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)
radecs = np.deg2rad(opts.radec)
if opts.inj_database:
query = """SELECT longitude, latitude FROM sim_inspiral AS si
progress.max = len(fitsfilenames)
matplotlib.rc('path', simplify=True, simplify_threshold=1)
for count_records, fitsfilename in enumerate(fitsfilenames):
progress.update(count_records, fitsfilename)
skymap, metadata = fits.read_sky_map(fitsfilename, nest=None)
nside = hp.npix2nside(len(skymap))
gmst = lal.GreenwichMeanSiderealTime(metadata['gps_time']) % (2*np.pi)
indices = np.argsort(-skymap)
region = np.empty(skymap.shape)
region[indices] = 100 * np.cumsum(skymap[indices])
plot.healpix_contour(
region, nest=metadata['nest'], dlon=-gmst, colors='k', linewidths=0.5,
levels=[opts.contour], alpha=opts.alpha)
progress.update(-1, 'saving figure')
# If we are using a new enough version of matplotlib, then
# add a white outline to all text to make it stand out from the background.
plot.outline_text(ax)
if opts.transparent:
fig.patch.set_alpha(0.)
ax.patch.set_alpha(0.)
ax.set_alpha(0.)
if opts.output is None:
plt.show()
