def heatmap(post, ax, color_map='OrRd', colorbar=False, colorbar_label=''):
'''
generate mollweide projection of heatmap with requested annotations
'''
plt.sca(ax)
lalinf_plot.healpix_heatmap(post, cmap=plt.get_cmap(
color_map)) ### is this buggy when projection=="mollweide"?
if colorbar:
cb = plt.colorbar(
orientation='horizontal', fraction=0.15, pad=0.03,
shrink=0.8) ### FIXME: hard-coded options are a bit fragile...
cb.set_label(colorbar_label)
def plot_skymap(output, skypost, pixresol=np.pi/180.0, nest=True,inj=None, fast=True):
nside = 1
while hp.nside2resol(nside) > pixresol:
nside *= 2
pix_post = skypost.as_healpix(nside, nest=nest, fast=fast)
fig = pp.figure(frameon=False)
ax = pp.subplot(111, projection='astro mollweide')
ax.cla()
ax.grid()
plot.healpix_heatmap(pix_post, nest=nest, vmin=0.0, vmax=np.max(pix_post), cmap=pp.get_cmap('cylon'))
if inj is not None:
# If using an injection file, also plot an X at the true position
pp.plot(inj['ra'], inj['dec'], 'kx', ms=30, mew=1)
pp.savefig(output)
ax.grid()
skymap, metadata = fits.read_sky_map(opts.input.name, nest=None)
nside = hp.npix2nside(len(skymap))
if opts.geo:
dlon = -lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(metadata['gps_time'])) % (2*np.pi)
else:
dlon = 0
# 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)
skymap, metadata = fits.read_sky_map(infilename, nest=None)
nside = hp.npix2nside(len(skymap))
if opts.geo:
dlon = -lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(metadata['gps_time'])) % (2*np.pi)
else:
dlon = 0
# 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, 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(
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]
nside = hp.npix2nside(len(skymap))
if opts.geo:
dlon = -lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(
metadata['gps_time'])) % (2 * np.pi)
else:
dlon = 0
# 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',
ax.grid()
skymap, metadata = fits.read_sky_map(opts.input.name, nest=None)
nside = hp.npix2nside(len(skymap))
if opts.geo:
dlon = -lal.GreenwichMeanSiderealTime(lal.LIGOTimeGPS(metadata["gps_time"])) % (2 * np.pi)
else:
dlon = 0
# 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:
def make_skyview(directory='.',
mdc=None,
NSIDE=128,
ra=None,
dec=None,
results=None,
npost=5000):
# -- Close any open figures
plt.close('all')
# ----------------
# Read S6 MDC log file
# -----------------
if mdc is None:
print "No MDC log provided."
# -- Save PWD for future reference
topdir = os.getcwd()
# -- Enter requested directory
os.chdir(directory)
print "Entered", os.getcwd()
jobname = glob.glob('*bayeswave.run')[0]
# ---------------------------------------
# Extract information from bayeswave.run
# Note: We may only need the trigger time from this
# ---------------------------------------
bayeswave = open(jobname, 'r')
ifoNames = []
for line in bayeswave:
spl = line.split()
if len(spl) < 1: continue
# Determine names of IFOs and MDC scale factor
if spl[0] == 'Command' and spl[1] == 'line:':
for index, splElement in enumerate(spl):
if splElement == '--ifo':
ifoNames.append(spl[index + 1])
del spl[-1]
# Determine number of IFOs
if ' '.join(spl) == 'number of detectors':
spl = line.split()
ifoNum = spl[3]
continue
# Determine gps trigger time
if ' '.join(spl) == 'trigger time (s)':
spl = line.split()
gps = spl[3]
break
bayeswave.close()
# --------------------------------
# Create skymap summary statistics
# --------------------------------
# -- Get run name
params = BwbParams()
jobname = params.jobname
# -- Input skymap data
# num, L, ralist, sin_dec, psi, e, dA, dphi, dt = np.loadtxt(filename, unpack=True)
print "Extracting RA/DEC samples"
filename = './chains/' + jobname + 'signal_params.dat.0'
data = np.loadtxt(filename, unpack=True, usecols=(0, 1, 2))
ralist = data[1]
sin_dec = data[2]
print "Total samples are {0}".format(ralist.size)
# -- Remove burn in samples
burnin = ralist.size / 4
ralist = ralist[burnin:]
sin_dec = sin_dec[burnin:]
print "After removing burn-in samples are {0}".format(ralist.size)
declist = np.arcsin(sin_dec)
thetalist = np.pi / 2.0 - declist
radec = np.column_stack((ralist, declist))
if radec.shape[0] > npost:
radec = np.random.permutation(radec)[:npost, :]
kde = sky.ClusteredSkyKDEPosterior(radec)
# -- Get the sidereal time
print "Finding sidereal time"
print "Using GPS {0}".format(gps)
trigtime = float(gps)
lalgps = LIGOTimeGPS(trigtime)
sidtime = XLALGreenwichSiderealTime(lalgps, 0)
sidtime = sidtime % (np.pi * 2)
# -- Get the injection location
print "GOT MDC?"
print mdc
if mdc is None:
injtheta = 0
injphi = 0
injdec = 0
injra = 0
else:
injtheta, injphi = mdc.get_theta_phi(trigtime)
injra = injphi + sidtime
injdec = np.pi / 2 - injtheta
print "GOT INJECTION PARAMTERS"
print injtheta, injra
# -- Special handling for injections drawn from prior
if params.ra is not None:
injdec = params.dec
injra = params.ra
injtheta = np.pi / 2 - injdec
mdc = True
# -- Make plots directory, if needed
plotsDir = './plots'
if not os.path.exists(plotsDir):
os.makedirs(plotsDir)
# Add markers (e.g., for injections or external triggers).
if (ra is not None and dec is not None):
# Convert the right ascension to either a reference angle from -pi to pi
# or a wrapped angle from 0 to 2 pi, depending on the version of Matplotlib.
#for rarad, decrad in [np.deg2rad([ra, dec])]:
(rarad, decrad) = np.deg2rad([ra, dec])
if geo:
dlon = -sidtime # + or -?
#rarad = lalinference.plot.reference_angle(rarad + dlon)
rarad = rarad + dlon
while rarad > np.pi:
rarad = rarad - 2 * np.pi
while rarad < -np.pi:
rarad = rarad + 2 * np.pi
else:
#rarad = lalinference.plot.wrapped_angle(rarad)
while rarad > 2 * np.pi:
rarad = rarad - 2 * np.pi
while rarad < 0:
rarad = rarad + 2 * np.pi
# Shift the declination to match hp.projscatter conventions
decrad = np.pi * 0.5 - decrad
# -- Plot the skymap and injection location
skymap = kde.as_healpix(NSIDE, nest=True)
#fig = plt.figure(frameon=False)
fig = plt.figure(figsize=(8, 6), frameon=False)
ax = plt.subplot(111, projection='astro mollweide')
ax.cla()
ax.grid()
lp.healpix_heatmap(skymap,
nest=True,
vmin=0.0,
vmax=np.max(skymap),
cmap=plt.get_cmap('cylon'))
if (ra is not None and dec is not None):
plt.plot(rarad, dec, 'kx', ms=30, mew=1)
if mdc is not None:
plt.plot(injra, injdec, 'kx', ms=30, mew=1)
plt.savefig(plotsDir + '/skymap.png')
plt.close()
lf.write_sky_map('skymap_{0}.fits'.format(gps),
skymap,
nest=True,
gps_time=trigtime)
# -- Calculate the 50 and 90% credible intervals
sq_deg = (180.0 / np.pi)**2
print "Calculating sky area ..."
(area50, area90) = kde.sky_area([0.5, 0.9]) * sq_deg
print "Got area50 of {0}".format(area50)
# -- Get the found contour and searched areas
if mdc is not None:
print "Calculating p value of injection"
injcontour = kde.p_values(np.array([[injra, injdec]]))[0]
print "Got contour of {0}".format(injcontour)
print "Calculating searched area..."
searcharea = (kde.searched_area(np.array([[injra, injdec]])) *
sq_deg)[0]
print "Got searched area of {0}".format(searcharea)
else:
injcontour = 0
searcharea = 0
pixarea = hp.nside2pixarea(NSIDE, degrees=True)
print("here")
if results is not None:
for name in ['injcontour', 'area50', 'area90', 'searcharea']:
if name not in results: results[name] = []
results[name].append(eval(name))
# -- Make an output file with key statistics
outfile = open('skystats.txt', 'w')
outfile.write("# area50 area90 searcharea injcontour \n")
outfile.write("{0} {1} {2} {3}".format(area50, area90, searcharea,
injcontour))
outfile.close()
def make_skyview(directory='.', mdc=None, NSIDE=128, ra=None, dec=None, results=None, npost=5000):
# -- Close any open figures
plt.close('all')
# ----------------
# Read S6 MDC log file
# -----------------
if mdc is None:
print "No MDC log provided."
# -- Save PWD for future reference
topdir = os.getcwd()
# -- Enter requested directory
os.chdir(directory)
print "Entered", os.getcwd()
jobname = glob.glob('*bayeswave.run')[0]
# ---------------------------------------
# Extract information from bayeswave.run
# Note: We may only need the trigger time from this
# ---------------------------------------
bayeswave = open(jobname, 'r')
ifoNames = []
for line in bayeswave:
spl = line.split()
if len(spl) < 1: continue
# Determine names of IFOs and MDC scale factor
if spl[0] == 'Command' and spl[1] == 'line:':
for index, splElement in enumerate(spl):
if splElement == '--ifo':
ifoNames.append(spl[index+1])
del spl[-1]
# Determine number of IFOs
if ' '.join(spl) == 'number of detectors':
spl = line.split()
ifoNum = spl[3]
continue
# Determine gps trigger time
if ' '.join(spl) == 'trigger time (s)':
spl = line.split()
gps = spl[3]
break
bayeswave.close()
# --------------------------------
# Create skymap summary statistics
# --------------------------------
# -- Get run name
params = BwbParams()
jobname = params.jobname
# -- Input skymap data
# num, L, ralist, sin_dec, psi, e, dA, dphi, dt = np.loadtxt(filename, unpack=True)
print "Extracting RA/DEC samples"
filename = './chains/' + jobname + 'signal_params.dat.0'
data = np.loadtxt(filename, unpack=True,usecols=(0,1,2))
ralist = data[1]
sin_dec = data[2]
print "Total samples are {0}".format(ralist.size)
# -- Remove burn in samples
burnin = ralist.size/4
ralist = ralist[burnin:]
sin_dec = sin_dec[burnin:]
print "After removing burn-in samples are {0}".format(ralist.size)
declist = np.arcsin(sin_dec)
thetalist = np.pi/2.0 - declist
radec = np.column_stack((ralist, declist))
if radec.shape[0] > npost:
radec = np.random.permutation(radec)[:npost,:]
kde = sky.ClusteredSkyKDEPosterior(radec)
# -- Get the sidereal time
print "Finding sidereal time"
print "Using GPS {0}".format(gps)
trigtime = float(gps)
lalgps = LIGOTimeGPS(trigtime)
sidtime = XLALGreenwichSiderealTime(lalgps, 0)
sidtime = sidtime % (np.pi*2)
# -- Get the injection location
print "GOT MDC?"
print mdc
if mdc is None:
injtheta = 0
injphi = 0
injdec = 0
injra = 0
else:
injtheta, injphi = mdc.get_theta_phi(trigtime)
injra = injphi + sidtime
injdec = np.pi/2 - injtheta
print "GOT INJECTION PARAMTERS"
print injtheta, injra
# -- Special handling for injections drawn from prior
if params.ra is not None:
injdec = params.dec
injra = params.ra
injtheta = np.pi/2 - injdec
mdc = True
# -- Make plots directory, if needed
plotsDir = './plots'
if not os.path.exists(plotsDir):
os.makedirs(plotsDir)
# Add markers (e.g., for injections or external triggers).
if (ra is not None and dec is not None):
# Convert the right ascension to either a reference angle from -pi to pi
# or a wrapped angle from 0 to 2 pi, depending on the version of Matplotlib.
#for rarad, decrad in [np.deg2rad([ra, dec])]:
(rarad, decrad) = np.deg2rad([ra, dec])
if geo:
dlon = -sidtime # + or -?
#rarad = lalinference.plot.reference_angle(rarad + dlon)
rarad = rarad + dlon
while rarad > np.pi:
rarad = rarad - 2*np.pi
while rarad < -np.pi:
rarad = rarad + 2*np.pi
else:
#rarad = lalinference.plot.wrapped_angle(rarad)
while rarad > 2*np.pi:
rarad = rarad - 2*np.pi
while rarad < 0:
rarad = rarad + 2*np.pi
# Shift the declination to match hp.projscatter conventions
decrad = np.pi*0.5 - decrad
# -- Plot the skymap and injection location
skymap = kde.as_healpix(NSIDE, nest=True)
#fig = plt.figure(frameon=False)
fig = plt.figure(figsize=(8,6), frameon=False)
ax = plt.subplot(111, projection='astro mollweide')
ax.cla()
ax.grid()
lp.healpix_heatmap(skymap, nest=True, vmin=0.0, vmax=np.max(skymap), cmap=plt.get_cmap('cylon'))
if (ra is not None and dec is not None):
plt.plot(rarad, dec, 'kx', ms=30, mew=1)
if mdc is not None:
plt.plot(injra, injdec, 'kx', ms=30, mew=1)
plt.savefig(plotsDir+'/skymap.png')
plt.close()
lf.write_sky_map('skymap_{0}.fits'.format(gps), skymap, nest=True, gps_time=trigtime)
# -- Calculate the 50 and 90% credible intervals
sq_deg = (180.0/np.pi)**2
print "Calculating sky area ..."
(area50, area90) = kde.sky_area( [0.5, 0.9] )*sq_deg
print "Got area50 of {0}".format(area50)
# -- Get the found contour and searched areas
if mdc is not None:
print "Calculating p value of injection"
injcontour = kde.p_values( np.array([[injra, injdec]]) )[0]
print "Got contour of {0}".format(injcontour)
print "Calculating searched area..."
searcharea = (kde.searched_area( np.array([[injra, injdec]]) )*sq_deg)[0]
print "Got searched area of {0}".format(searcharea)
else:
injcontour = 0
searcharea = 0
pixarea = hp.nside2pixarea(NSIDE, degrees=True)
print("here")
if results is not None:
for name in ['injcontour', 'area50', 'area90', 'searcharea']:
if name not in results: results[name] = []
results[name].append(eval(name))
# -- Make an output file with key statistics
outfile = open('skystats.txt', 'w')
outfile.write("# area50 area90 searcharea injcontour \n")
outfile.write("{0} {1} {2} {3}".format(area50, area90, searcharea, injcontour))
outfile.close()
