def insert_ts_list(ts_map, ts_cut, nside):
ts_px = numpy.argwhere(ts_map>ts_cut)
table = ""
for px in ts_px.T[0]:
ts = round(ts_map[px],2)
ra, dec = pix_to_sky(px,nside)
#ra, dec = hp.pix2ang(nside, px, lonlat=True)
ra = round(ra, 2)
dec = round(dec, 2)
table += content.format(ts, ra, dec)
return table
def max_ts(map_path, ts_cut):
ts_map, header = hp.read_map(map_path, h=True)
header = dict(header)
nside = header['NSIDE']
ts_max = round(numpy.nanmax(ts_map),2)
px_max = numpy.nanargmax(ts_map)
ra_max, dec_max = pix_to_sky(px_max,nside)
# hp.pix2ang(nside, px_max, lonlat=True)
ra_max = round(ra_max, 2)
dec_max = round(dec_max, 2)
ts_list = insert_ts_list(ts_map, ts_cut, nside)
return ts_max, ra_max, dec_max, ts_list, nside
def get_coverage(ft2file, ligo_map_file, met_t1, met_t2, theta_cut,
zenith_cut):
ft2data = pyfits.getdata(ft2file)
ligo_map = hp.read_map(ligo_map_file)
ligo_ra, ligo_dec = pix_to_sky(range(ligo_map.shape[0]), 512)
# Filter out pixels with too low probability to gain speed
interesting_idx = (ligo_map > 1e-7)
print("Kept %s pixels for considerations in LIGO map" %
np.sum(interesting_idx))
# Get entries from the FT2 file every 10 s (cadence)
start, ra_scz, dec_scz, ra_zenith, dec_zenith = _gtmktime(ft2data,
met_t1,
met_t2,
cadence=30)
print("\nCoverage will be computed from %.1f to %.1f\n" %
(start.min(), start.max()))
coverage = np.zeros_like(start)
for i, (t, rz, dz, rz2, dz2) in enumerate(
zip(start, ra_scz, dec_scz, ra_zenith, dec_zenith)):
# Get the angular distance between the current pointing and
# all the LIGO pixels
d = getAngularDistance(rz, dz, ligo_ra[interesting_idx],
ligo_dec[interesting_idx])
zenith = getAngularDistance(rz2, dz2, ligo_ra[interesting_idx],
ligo_dec[interesting_idx])
# Select the LIGO pixels within the LAT FOV
idx = (d < theta_cut) & (zenith < zenith_cut)
coverage[i] = np.sum(ligo_map[interesting_idx][idx])
sys.stdout.write("\r%.1f percent completed" %
((i + 1) / float(coverage.shape[0]) * 100.0))
sys.stdout.write("\n")
return start, coverage
else:
mmin = hpx_ul.min()
if args.max_percentile != 100:
mmax = np.percentile(hpx_ul[idx],
args.max_percentile,
interpolation='nearest')
mmax_idx = (hpx_ul == mmax).nonzero()[0][0]
#print mmax_idx
ra, dec = pix_to_sky(mmax_idx, nside)
print("%s percentile is %s, at %s, %s" %
(args.max_percentile, mmax, ra, dec))
else:
mmax = hpx_ul.max()
# Now set to nan all negative or zero pixels
idx = hpx_ul < 0
hpx_ul[idx] = np.nan
if args.cmap == 'jet':
background = 'w'
background = 'ivory'
ra = ra_dec.ra.degree
dec = ra_dec.dec.degree
l = l_b.l.degree
b = l_b.b.degree
if hpmap is not None:
nside = 32
my_finder = ContourFinder(hpmap, nside)
hpx = my_finder.map
idx = sky_to_pix(ra, dec, nside)
prob = hpx[idx]
indexes = my_finder.find_contour(0.9)
cra, cdec = pix_to_sky(indexes, nside)
cel = SkyCoord(ra=cra * u.degree,
dec=cdec * u.degree,
frame='fk5')
gal = cel.transform_to('galactic')
cl, cb = gal.l.degree, gal.b.degree
print 'plotting countour...%d points' % len(cl)
galactic(f,
ax,
cl,
cb,
symbol='circle',
size=1,
color='magenta',
prob=None)
z_title = r'Flux Upper Bound (0.1-1 GeV) [10$^{%.0f}$ cm$^{-2}$ s$^{-1}$]' % np.log10(
magnitude)
elif args.map_type == 'TS':
z_title = r'TS '
magnitude = 1
elif args.map_type == 'SIG':
z_title = r'$\sigma$ '
magnitude = 1
else:
print 'Unrecognized map type %s. Use EFLUX, FLUX or TS' % args.map_type
exit()
pass
MAXVALUE = round(np.nanmax(hpx_ul), 2)
px_max = np.nanargmax(hpx_ul)
ra_max, dec_max = pix_to_sky(px_max, nside)
print 'Minimum Value = ', hpx_ul[idx].min()
print 'Maximum Value = ', MAXVALUE
print 'RA,DEC=%f %f MAX= %f' % (ra_max, dec_max, MAXVALUE)
norm = args.zscale
ticks = np.logspace(np.log10(mmin / magnitude), np.log10(mmax / magnitude),
4),
if norm == 'linear':
norm = None
ticks = np.linspace(mmin / magnitude, mmax / magnitude, 4),
#mmin=0
pass
print 'Normalization of the axis:', norm
if args.zoom is None:
