def main():
parser = argparse.ArgumentParser(prog='galfast_err_dist.py', description='Test distribution of errors in absolute magnitudes from galfast.', add_help=True)
parser.add_argument('catalog', type=str, help='Galfast catalog (as a FITS file)')
parser.add_argument('--maxmag', type=float, nargs='+', help='Faintest magnitude to include')
if sys.argv[0] == 'python':
offset = 2
else:
offset = 1
values = parser.parse_args(sys.argv[offset:])
# Set up the magnitude cutoff
mag_cutoff = None
if values.maxmag != None:
if len(values.maxmag) == 1:
mag_cutoff = np.empty(5, dtype=float)
mag_cutoff.fill(values.maxmag)
elif len(values.maxmag) == 5:
mag_cutoff = np.array(values.maxmag)
else:
print 'Invalid # of arguments for maxmag. Enter either a unform magnitude limit, or a separate limit for each band (5).'
return 1
# Load apparent magnitudes (w/ and w/o photometric errors) and theoretical errors
fname = abspath(values.catalog)
ra_dec, obs_mags, LSST_mags, errs, params = get_objects(fname)
N = len(obs_mags)
# Load the SED catalog
SED_catalog = SED()
#for j in range(N):
# print calc_abs_mag(LSST_mags[j], params[j]) - SED_catalog(params[j][2], params[j][3])[:-1]
# Determined normalized errors
score = [np.empty(N, dtype=float) for i in range(5)] # Normalized difference between observed and true apparent magnitudes
mag_diff = [np.empty(N, dtype=float) for i in range(5)] # Difference between galfast and catalog magnitudes
for i in range(N):
tmp_diff = calc_abs_mag(LSST_mags[i], params[i]) - SED_catalog(params[i][2], params[i][3])[:-1]
for j in range(5):
score[j][i] = (obs_mags[i,j] - LSST_mags[i,j]) / errs[i,j]
mag_diff[j][i] = tmp_diff[j]
# Filter out faint stars
if values.maxmag != None:
for i in range(5):
filtmag = np.empty(N, dtype=bool)
filtmag.fill(True)
for k in range(N):
filtmag[k] = (LSST_mags[k,:].max() <= mag_cutoff[i])
score[i] = score[i][filtmag]
mag_diff[i] = mag_diff[i][filtmag]
# Take log of differences
log_mag_diff = []
for i in range(5):
log_mag_diff.append(np.log(mag_diff[i]))
idx = np.isfinite(log_mag_diff[i])
#idx = np.isneginf(log_mag_diff[i])
#idx = np.logical_or(idx, np.isnan(log_mag_diff[i]))
#idx = np.logical_not(idx)
min_log_diff = min(log_mag_diff[i][idx])
print min_log_diff
for j in range(len(idx)):
if not idx[j]:
log_mag_diff[i][j] = min_log_diff
# Set matplotlib style attributes
mplib.rc('text',usetex=True)
mplib.rc('xtick.major', size=6)
mplib.rc('xtick.minor', size=4)
mplib.rc('axes', grid=False)
# Create histogram of scores
fig_score = plt.figure(figsize=(8.5,11))
fig_score.suptitle(r'$\mathrm{Distribution \ of \ Scores}$', fontsize=22, y=0.96)
fig_diff = plt.figure(figsize=(8.5,11))
fig_diff.suptitle(r'$\mathrm{Distribution \ of \ Log \ Differences}$', fontsize=22, y=0.96)
name = ['u', 'g', 'r', 'i', 'z']
color = ['cyan', 'g', 'r', 'magenta', 'purple']
for i in range(5):
ax = fig_score.add_subplot(3,2,i+1)
ax.set_title(r'$%s$' % name[i], fontsize=20)
ax.set_xlabel(r'$z$', fontsize=18)
ax.set_ylabel(r'$N$', fontsize=18)
ax.hist(score[i], bins=30, color=color[i])
#
ax = fig_diff.add_subplot(3,2,i+1)
ax.set_title(r'$%s$' % name[i], fontsize=20)
ax.set_xlabel(r'$\Delta m$', fontsize=18)
ax.set_ylabel(r'$N$', fontsize=18)
ax.hist(log_mag_diff[i], bins=30, color=color[i])
fig_score.subplots_adjust(hspace=0.3, wspace=0.25)
fig_diff.subplots_adjust(hspace=0.3, wspace=0.25)
outfile = fname.replace('.fits', '')
fig_score.savefig(outfile + '_score.png', dpi=150)
fig_diff.savefig(outfile + '_diff.png', dpi=150)
plt.show()
# Intitialize the SED catalog
#ugrizy = SED()
# Load the absolute magnitudes from galfast
#abs_mags = get_abs_mags(values.catalog)
'''for i in range(10):
Mr = -1. + 29.*random()
FeH = -2.5 + 2.5*random()
tmp = ugrizy(Mr, FeH)
print '(%.3f, %.2f): %.3f %.3f %.3f %.3f %.3f %.3f' % (Mr, FeH, tmp[0], tmp[1], tmp[2], tmp[3], tmp[4], tmp[5])'''
return 0
def main():
parser = argparse.ArgumentParser(prog='read_sdss', description='Translate galfast output to ASCII input for galstar', add_help=True)
parser.add_argument('inputs', nargs='+', type=str, help='FITS files')
parser.add_argument('--filterr', type=float, default=None, help='Filter objects with errors greater than the specified amount')
parser.add_argument('--maxmag', type=float, nargs='+', default=None, help='Maximum absolute magnitude to allow')
parser.add_argument('--minmag', type=float, nargs='+', default=None, help='Minimum absolute magnitude to allow')
parser.add_argument('--toscreen', action='store_true', help='Print results to screen, rather than outputting to ASCII files')
if sys.argv[0] == 'python':
offset = 2
else:
offset = 1
values = parser.parse_args(sys.argv[offset:])
# Set up the magnitude cutoffs
mag_max = None
if values.maxmag != None:
if len(values.maxmag) == 1:
mag_max = np.empty(5, dtype=float)
mag_max.fill(values.maxmag)
elif len(values.maxmag) == 5:
mag_max = np.array(values.maxmag)
else:
print 'Invalid # of arguments for maxmag. Enter either a unform magnitude limit, or a separate limit for each band (5).'
return 1
mag_min = None
if values.minmag != None:
if len(values.minmag) == 1:
mag_min = np.empty(5, dtype=float)
mag_min.fill(values.minmag)
elif len(values.maxmag) == 5:
mag_min = np.array(values.minmag)
else:
print 'Invalid # of arguments for minmag. Enter either a unform magnitude limit, or a separate limit for each band (5).'
return 1
for fn in values.inputs:
print 'Processing %s ...' % fn
# Load data from FITS file
ra_dec, mags, LSSTmags, absmags, errs, params = get_objects(fn)
# Construct filters
N = len(ra_dec)
filterr = np.empty(N, dtype=bool)
filterr.fill(True)
filtmin = np.empty(N, dtype=bool)
filtmin.fill(True)
filtmax = np.empty(N, dtype=bool)
filtmax.fill(True)
# Stars with large errors
if values.filterr != None:
for i in range(N):
filterr[i] = (errs[i,:].max() <= values.filterr)
# Faint stars
if values.minmag != None:
for i in range(N):
filtmin[i] = (mags[i] >= mag_min).all()
# Bright stars
if values.maxmag != None:
for i in range(N):
filtmax[i] = (mags[i] <= mag_max).all()
print filtmax
print filtmin
print filterr
# Combine and apply filters
idx = np.logical_and(filterr, filtmin)
idx = np.logical_and(idx, filtmax)
print idx
ra_dec = ra_dec[idx]
mags = mags[idx]
errs = errs[idx]
params = params[idx]
N_filt = len(ra_dec)
# Produce input for galstar
if N_filt != 0:
count, output, truth = print_for_galstar(ra_dec, mags, errs, params)
if values.toscreen:
print output
else:
tmp = abspath(fn).split('.')
output_fn = ''.join(tmp[:-1]) + '.in'
f = open(output_fn, 'w')
f.write(output)
f.close()
truth_fn = ''.join(tmp[:-1]) + '.truth'
f = open(truth_fn, 'w')
f.write(truth)
f.close()
print 'Written to %s' % output_fn
print '-> %d objects (of %d total).' % (count, N)
else:
print 'No stars match specified criteria.'
print 'Done.'
return 0
