def main():
parser = argparse.ArgumentParser(prog='sdss2ps.py',
description='Transform template library and '
'luminosity function from SDSS '
'bandpasses to PanSTARRS bandpasses.',
add_help=True)
parser.add_argument('SDSScolors', type=str, help="Mario's SDSS template library.")
parser.add_argument('PScolors', type=str,
help='Output filename for PanSTARRS template library.')
parser.add_argument('SDSSlf', type=str, help="Mario's SDSS luminosity function.")
parser.add_argument('PSlf', type=str,
help='Output filename for PanSTARRS luminosity function.')
parser.add_argument('--Delta-grizy', '-D', type=float, nargs=5, default=[0.,0.,0.,0.,0.],
help='Offset to add to each PS1 band (in millimags)')
if 'python' in sys.argv[0]:
offset = 2
else:
offset = 1
args = parser.parse_args(sys.argv[offset:])
SDSSugriz, MrFeH = load_SDSS_templates(args.SDSScolors)
PSgrizy = ps.pssdsstransformall(SDSSugriz)
for i in xrange(5):
PSgrizy[:,i] += 0.001 * args.Delta_grizy[i]
save_PS_templates(args.PScolors, PSgrizy, MrFeH)
SDSSlf = np.loadtxt(abspath(args.SDSSlf), usecols=(0,1), dtype='f4')
save_PS_lf(args.PSlf, SDSSlf, MrFeH, PSgrizy[:,1])
return 0
def main():
parser = argparse.ArgumentParser(prog='sdss2ps.py', description='Transform template library and luminosity function from SDSS bandpasses to PanSTARRS bandpasses.', add_help=True)
parser.add_argument('SDSStemp', type=str, help="Mario's SDSS template library.")
parser.add_argument('PStemp', type=str, help='Output filename for PanSTARRS template library.')
parser.add_argument('SDSSlf', type=str, help="Mario's SDSS luminosity function.")
parser.add_argument('PSlf', type=str, help='Output filename for PanSTARRS luminosity function.')
if 'python' in sys.argv[0]:
offset = 2
else:
offset = 1
values = parser.parse_args(sys.argv[offset:])
SDSSugriz, MrFeH = load_SDSS_templates(values.SDSStemp)
PSgrizy = ps.pssdsstransformall(SDSSugriz)
save_PS_templates(values.PStemp, PSgrizy, MrFeH)
SDSSlf = np.loadtxt(abspath(values.SDSSlf), usecols=(0,1), dtype=np.float32)
save_PS_lf(values.PSlf, SDSSlf, MrFeH, PSgrizy[:,1])
return 0
def main():
parser = argparse.ArgumentParser(
prog='s82_completeness.py',
description='Estimate PS1 completeness by comparison with deep SDSS Stripe 82 photometry.',
add_help=True)
#parser.add_argument('out', type=str, help='Filename for query output.')
parser.add_argument('-b', '--bounds', type=float, nargs=4, default=None,
help='Restrict pixels to region enclosed by: RA_min, RA_max, Dec_min, Dec_max.')
parser.add_argument('--n-bands', type=int, default=4,
help='Min. # of PS1 passbands with detection.')
parser.add_argument('--n-det', type=int, default=4,
help='Min. # of PS1 detections.')
parser.add_argument('-w', '--n-workers', type=int, default=5,
help='# of workers for LSD to use.')
if 'python' in sys.argv[0]:
offset = 2
else:
offset = 1
args = parser.parse_args(sys.argv[offset:])
mplib.rc('text', usetex=True)
n_pointlike = args.n_bands - 1
if n_pointlike == 0:
n_pointlike = 1
# Determine the query bounds
query_bounds = None
if args.bounds != None:
query_bounds = lsd.bounds.rectangle(args.bounds[0], args.bounds[2],
args.bounds[1], args.bounds[3],
coordsys='equ')
query_bounds = lsd.bounds.make_canonical(query_bounds)
# Set up the query
db = lsd.DB(os.environ['LSD_DB'])
query = ("select s82coadd.l as l, s82coadd.b as b, "
"s82coadd.ra as ra_s82, "
"s82coadd.dec as dec_s82, "
"s82coadd.psfcounts as s82_counts, "
"s82coadd.psfcountserr as s82_counts_err, "
"ucal_magsqx_noref.ra as ra_ps1, "
"ucal_magsqx_noref.dec as dec_ps1, "
"ucal_magsqx_noref.mean as ps1_mean, "
"ucal_magsqx_noref.err as ps1_err, "
"ucal_magsqx_noref.mean_ap as ps1_mean_ap, "
"ucal_magsqx_noref.maglimit as ps1_maglim, "
"ucal_magsqx_noref.nmag_ok as ps1_nmag_ok "
"from s82coadd, ucal_magsqx_noref(outer, matchedto=s82coadd, nmax=1, dmax=30) "
"where (s82coadd.objc_type == 6)")
query = db.query(query)
# Execute query
rows = query.fetch(bounds=query_bounds, nworkers=args.n_workers)
# Transform from luptitudes to AB magnitudes
b_s82 = [1.0e-11, 0.43e-11, 0.81e-11, 1.4e-11, 3.7e-11] # Stripe-82 ugriz softening parameters
sdss_bands = 'ugriz'
dtype = [(b, 'f8') for b in sdss_bands]
s82_mags = np.empty(rows['s82_counts'].shape[0], dtype=dtype)
for band, (name, b) in enumerate(zip(sdss_bands, b_s82)):
s82_mags[name][:] = luptitudes2mags(rows['s82_counts'][:, band], b)
# Transform SDSS magnitudes to synthetic PS1 magnitudes
s82_ps1_mags = pssdsstransformall(s82_mags)
# Filter objects which do not have 5-band Stripe-82 detections
idx = np.isfinite(s82_ps1_mags) & (s82_ps1_mags > 0.) & (s82_ps1_mags < 30.)
idx = np.all(idx, axis=1)
print 'Stripe-82 objects filtered: %d of %d' % (np.sum(~idx), len(idx))
rows = rows[idx]
s82_mags = s82_mags[idx]
s82_ps1_mags = s82_ps1_mags[idx]
# Which Stripe-82 objects have PS1 matches
ps1_mags = rows['ps1_mean']
ps1_mask = (ps1_mags > 0.)
match_dist = gc_dist(rows['ra_s82'], rows['dec_s82'], rows['ra_ps1'], rows['dec_ps1'])
max_dist = 1. * np.pi / 180. / 3600. # One arcsecond
idx = (match_dist < max_dist)
for band in xrange(5):
ps1_mask[:, band] = ps1_mask[:, band] & idx
'''
ps1_row_mask = ( (rows['ps1_nmag_ok'][:,0] > 0)
& (np.sum(rows['ps1_nmag_ok'] > 0, axis=1) >= args.n_bands)
& (np.sum(rows['ps1_nmag_ok'], axis=1) >= args.n_det)
& (np.sum(rows['ps1_mean'] - rows['ps1_mean_ap'] < 0.1, axis=1) >= n_pointlike)
)
for band in xrange(ps1_mask.shape[1]):
ps1_mask[:, band] = ps1_mask[:, band] & ps1_row_mask
'''
print 'Stripe-82 detections:', len(rows)
print 'PS1 griz matches:', np.sum(ps1_mask, axis=0)
# PS1 completeness in each magnitude bin
mag_bins = np.linspace(-2., 4., 60)
bin_min = mag_bins[:-1]
bin_max = mag_bins[1:]
mag_bin_center = 0.5 * (bin_min + bin_max)
'''
ps1_pct = np.empty((mag_bins.size-1, 5), dtype='f8')
for band in xrange(5):
for i, (mag_min, mag_max) in enumerate(zip(bin_min, bin_max)):
idx = (s82_ps1_mags[:, band] >= mag_min) & (s82_ps1_mags[:, band] < mag_max)
ps1_pct[i, band] = np.sum(ps1_mask[idx, band], axis=0) / float(np.sum(idx))
'''
# PS1 completeness in different spatial pixels
pix_idx = lb2pix(128, rows['ra_s82'], rows['dec_s82'])
pix_idx_unique = np.unique(pix_idx)
n_pix = pix_idx_unique.size
ps1_pct_area = np.empty((n_pix, mag_bins.size-1, 5), dtype='f8')
maglim_area = np.empty((n_pix, 5), dtype='f8')
n_stars = np.empty(n_pix)
for k, p_idx in enumerate(pix_idx_unique):
in_pix = (pix_idx == p_idx)
n_stars[k] = np.sum(in_pix)
print '%d stars in pixel %d.' % (n_stars[k], k + 1)
s82_ps1_tmp = s82_ps1_mags[in_pix]
ps1_mask_tmp = ps1_mask[in_pix]
for band in xrange(5):
maglim = rows['ps1_maglim'][:, band]
idx = (maglim > 0.)
maglim_area[k, band] = np.median(maglim[idx])
maglim = maglim_area[k, band]
for i, (mag_min, mag_max) in enumerate(zip(bin_min, bin_max)):
idx = (s82_ps1_tmp[:, band] - maglim >= mag_min) & (s82_ps1_tmp[:, band] - maglim < mag_max)
ps1_pct_area[k, i, band] = np.sum(ps1_mask_tmp[idx, band], axis=0) / float(np.sum(idx))
idx = (n_stars > 1000)
ps1_pct_area = np.percentile(ps1_pct_area[idx], [15.87, 50., 84.13], axis=0)
ps1_pct_area = np.array(ps1_pct_area)
idx = (ps1_pct_area[0, :, :] < 1.e-10)
ps1_pct_area[0, idx] = 1.e-10 * (0.5 + np.random.random(ps1_pct_area[0, idx].shape))
idx = (ps1_pct_area[1, :, :] < 1.e-9)
ps1_pct_area[1, idx] = 1.e-9 * (0.5 + np.random.random(ps1_pct_area[0, idx].shape))
idx = (ps1_pct_area[2, :, :] < 1.e-8)
ps1_pct_area[2, idx] = 1.e-8 * (0.5 + np.random.random(ps1_pct_area[0, idx].shape))
# PS1 completeness as a function of mag - maglimit
mag_diff_bins = np.linspace(-6., 4., 100)
bin_min = mag_diff_bins[:-1]
bin_max = mag_diff_bins[1:]
diff_bin_center = 0.5 * (bin_min + bin_max)
ps1_pct_diff = np.empty((mag_diff_bins.size-1, 5), dtype='f8')
s82_ps1_diff = s82_ps1_mags - rows['ps1_maglim']
for band in xrange(5):
idx_maglim = (rows['ps1_maglim'][:, band] > 0.)
print band, np.median(rows['ps1_maglim'][idx_maglim, band])
for i, (mag_min, mag_max) in enumerate(zip(bin_min, bin_max)):
idx_bin = (s82_ps1_diff[:, band] >= mag_min) & (s82_ps1_diff[:, band] < mag_max)
idx = idx_maglim & idx_bin
ps1_pct_diff[i, band] = np.sum(ps1_mask[idx, band], axis=0) / float(np.sum(idx))
# Completeness parameterization
tmp_pct = ps1_pct_area[1, -20:, :]
idx = (tmp_pct > 1.e-5) & np.isfinite(tmp_pct)
comp_floor = np.median(tmp_pct[idx])
print 'Completeness floor: %.2g' % comp_floor
#dm_0 = [0.15, 0.23, 0.17, 0.12, 0.15]
dm_0 = [0.16, 0.16, 0.16, 0.16, 0.16]
dm_1 = 0.20
'''
comp_fit = 0.5 * (1. - scipy.special.erf((dm - 0.15) / 0.47))
ax.plot(dm, comp_fit, lw=2., alpha=0.3, c='orange')
comp_fit = (1. - comp_floor) * comp_fit + comp_floor
'''
#comp_fit = 1. / (1. + np.exp((dm - 0.13) / 0.20))
#comp_fit_floor = (1. - comp_floor) * comp_fit + comp_floor
# Plot completeness
fig = plt.figure(figsize=(9,6), dpi=150)
band_names = ['g', 'r', 'i', 'z', 'y']
plt_colors = ['c', 'b', 'g', 'r', 'gray']
for band, (name, color) in enumerate(zip(band_names, plt_colors)):
maglim = rows['ps1_maglim'][:, band]
idx = (maglim > 0.)
maglim = np.percentile(maglim[idx], [15.87, 50., 84.13])
print 'maglim_%d: %.2f + %.2f - %.2f' % (band,
maglim[1],
maglim[1] - maglim[0],
maglim[2] - maglim[1])
ax = fig.add_subplot(2, 3, band+1)
ax.axvline(x=0., c='k', ls=':', lw=1., alpha=0.2)
ax.axhline(y=1., c='k', ls=':', lw=1., alpha=0.2)
pos = np.all(ps1_pct_area[:, :, band] > 0, axis=0)
ax.fill_between(mag_bin_center,
ps1_pct_area[0, :, band],
ps1_pct_area[2, :, band],
where=pos,
color=color,
edgecolor=color,
alpha=0.5,
label=r'$%s_{\mathrm{P1}}$' % name)
ax.semilogy(mag_bin_center, ps1_pct_area[1, :, band],
c=color, alpha=0.5, label=r'$%s_{\mathrm{P1}}$' % name)
tmp_pct = ps1_pct_area[1, -25:, band]
idx = (tmp_pct > 1.e-5) & np.isfinite(tmp_pct)
comp_floor = np.median(tmp_pct[idx])
print 'Completeness floor %d: %.2g' % (band, comp_floor)
comp_floor = 0.01
dm = np.linspace(-2., 4., 1000)
comp_fit = 1. / (1. + np.exp((dm - dm_0[band]) / dm_1))
comp_fit_floor = (1. - comp_floor) * comp_fit + comp_floor
ax.semilogy(dm, comp_fit, lw=2., alpha=0.5,
c='k', ls='-',
label=r'$\mathrm{Fit}$')
ax.semilogy(dm, comp_fit_floor, lw=1., alpha=0.25,
c='k', ls='--')
ax.set_yscale('log')
ax.set_xlim(-1., 2.)
ax.set_ylim(0.005, 1.5)
if band < 3:
ax.xaxis.set_label_position('top')
ax.xaxis.tick_top()
ax.set_xticks([-0.5, 0.0, 0.5, 1.0, 1.5])
ax.set_xlabel(r'$\Delta %s_{\mathrm{P1}} \ (\mathrm{mag})$' % name, fontsize=16)
ax.yaxis.set_major_formatter(FormatStrFormatter(r'$%.2f$'))
if band not in [0, 3]:
ax.set_yticklabels([])
fig.text(0.06, 0.5, r'$\mathrm{PS1 \ Completeness}$',
fontsize=16, va='center', ha='center', rotation='vertical')
# Legend
ax = fig.add_subplot(2, 3, 6)
ax.fill_between([0.1, 0.35], [0.7, 0.7], [0.8, 0.8],
color='orange', edgecolor='orange', alpha=0.5)
ax.plot([0.1, 0.35], [0.75, 0.75], color='orange', alpha=0.5)
ax.text(0.45, 0.75, r'$1 \sigma \ \mathrm{Region}$',
ha='left', va='center', fontsize=16)
ax.plot([0.1, 0.35], [0.5, 0.5], lw=2., alpha=0.5,
c='k', ls='-')
ax.text(0.45, 0.5, r'$\mathrm{Fit}$',
ha='left', va='center', fontsize=16)
ax.plot([0.1, 0.35], [0.3, 0.3], lw=1., alpha=0.25,
c='k', ls='--')
ax.text(0.45, 0.3, r'$\mathrm{Fit \ with \ floor}$',
ha='left', va='center', fontsize=16)
ax.set_xlim([0., 1.])
ax.set_ylim([0.05, 1.05])
ax.axis('off')
fig.subplots_adjust(top=0.85, bottom=0.13,
left=0.13, right=0.95,
hspace=0., wspace=0.)
fig.savefig('tmp.png', dpi=300)
#ax.set_title(r'$\mathrm{Absolute}$', fontsize=16)
#ax.set_xlabel(r'$\mathrm{\Delta m_{P1} \ (mag)}$', fontsize=16)
#ax.set_xlabel(r'$\mathrm{Stripe-82 \ asinh \ mag}$', fontsize=14)
#ax.set_ylabel(r'$\mathrm{PS1 \ Completeness}$', fontsize=16)
'''
ax = fig.add_subplot(2,1,2)
band_names = ['g', 'r', 'i', 'z', 'y']
plt_colors = ['c', 'b', 'g', 'r', 'gray']
for band, (name, color) in enumerate(zip(band_names, plt_colors)):
ax.semilogy(diff_bin_center, ps1_pct_diff[:, band], c=color, label=r'$%s$' % name)
ax.set_title(r'$\mathrm{Relative}$', fontsize=16)
ax.set_xlabel(r'$\mathrm{mag \ - \ maglim}$', fontsize=14)
#ax.set_xlabel(r'$\mathrm{Stripe-82 \ asinh \ mag}$', fontsize=14)
ax.set_ylabel(r'$\mathrm{PS1 \ Completeness}$', fontsize=14)
ax.legend()
ax.set_xlim(-1., 2.)
ax.set_ylim(0.005, 1.5)
fig.savefig('tmp.png', dpi=200)
'''
# Histograms of synthetic PS1 vs real PS1 magnitudes
fig = plt.figure()
diff = ps1_mags - s82_ps1_mags
for band, name in enumerate(band_names):
ax = fig.add_subplot(1, 5, band+1)
ax.hist(diff[ps1_mask[:, band], band], bins=50)
ax.set_title(r'$\Delta %s$' % name, fontsize=16)
ax.set_xlabel(r'$\Delta %s \ (\mathrm{mag})$' % name, fontsize=14)
# Color-color diagrams of synthetic PS1 photometry
fig = plt.figure()
for c2 in xrange(4):
for c1 in xrange(c2):
ax = fig.add_subplot(3, 4, 1 + 3*c1 + c2)
diff_1 = s82_ps1_mags[:, c1] - s82_ps1_mags[:, c1+1]
diff_2 = s82_ps1_mags[:, c2] - s82_ps1_mags[:, c2+1]
ax.scatter(diff_1, diff_2, s=1.5, alpha=0.05, edgecolor='none')
xlim = ax.get_xlim()
ylim = ax.get_ylim()
ax.set_xlim(xlim[1], xlim[0])
ax.set_ylim(ylim[1], ylim[0])
plt.show()
return 0
