plt.legend(loc='upper right')
plt.xlabel(r'${\rm ZP_{GCM} - ZP_{qSLR}}')
plt.ylabel("Number of CCDs")
gcm_bands = dict()
qslr_bands = dict()
diff_bands = dict()
for b in BANDS:
zp_g = gcm['MAG_ZERO'][gcm['BAND'] == b]
zp_q = qslr['MAG_ZERO'][qslr['BAND'] == b]
delta = zp_g - zp_q
labels = qslr[HPX][qslr['BAND'] == b]
index = np.unique(labels)
for data, out in [(zp_g, gcm_bands), (zp_q, qslr_bands),
(delta, diff_bands)]:
skymap = blank(nside)
skymap[index] = nd.median(data, labels=labels, index=index)
out[b] = skymap
diff_colors = dict()
for name, (b1, b2) in COLORS:
gcm_color = gcm_bands[b1] - gcm_bands[b2]
qslr_color = qslr_bands[b1] - qslr_bands[b2]
skymap = gcm_color - qslr_color
diff_colors[name] = skymap
for b in BANDS:
skymap = diff_bands[b]
plt.figure()
im = plotting.draw_footprint(skymap)
plt.colorbar(im, label=r'${\rm ZP_{GCM} - ZP_{qSLR}}$')
and len(glob.glob(y2q1dir + '/*%05d.fits' % p))
])
if len(pixels) == 0:
msg = "Invalid pixel: %s" % opts.pix
raise Exception(msg)
args = [pix for pix in pixels]
p = Pool(maxtasksperchild=1)
out = p.map(residuals, args)
median_skymaps = odict()
mean_skymaps = odict()
std_skymaps = odict()
for band in BANDS:
median_skymap = blank(nside)
mean_skymap = blank(nside)
std_skymap = blank(nside)
for pix, val in out:
median_skymap[pix] = val[band][0]
mean_skymap[pix] = val[band][1]
std_skymap[pix] = val[band][2]
median_skymaps[band] = median_skymap
mean_skymaps[band] = mean_skymap
std_skymaps[band] = std_skymap
for band in BANDS:
plt.figure()
im = plotting.draw_footprint(median_skymaps[band])
plt.colorbar(im, label=r'Median Offset (mag)')
plt.title(r'Median Magnitude Offset (%s-band)' % band)
if opts.type == 'gaia':
func = gaia_photometry
kwargs['version'] = 'edr3'
outbase += '_gaia_%(version)s'%kwargs
elif opts.type == 'rms':
func = rms_photometry
outbase += '_rms'
else:
msg = "Unrecognized type: %s"%args.type
raise Exception(msg)
results = utils.multiproc(func,args,kwargs)
#results = [func(*a,**kwargs) for a in args]
hpxmap = blank(nside)
if None in results:
print("WARNING: %i processes failed..."%results.count(None))
for pix,stat in [r for r in results if r is not None]:
hpxmap[pix] = stat
hpxmap = np.ma.MaskedArray(hpxmap,np.isnan(hpxmap),fill_value=np.nan)
hpxmaps[band] = hpxmap
outfile = join(outdir,outbase+'_%s_n%i.fits'%(band,nside))
print("Writing %s..."%outfile)
hp.write_map(outfile,hpxmap,overwrite=True)
q = [5,50,95]
p = np.percentile(hpxmap.compressed(),q)
config = yaml.load(open(args.config))
BANDS = config['bands']
NSIDE = args.nside
outdir = mkdir('release/depth')
infiles = sorted(glob.glob('cat/cat_hpx_*.fits'))
p = Pool(maxtasksperchild=1,processes=20)
out = p.map(depth,infiles)
skymaps = dict()
for b in BANDS:
logger.info("Filling %s-band..."%b)
skymap = blank(NSIDE)
for i,maglims in enumerate(out):
logger.info(str(i))
skymap[maglims[b][0]] = maglims[b][1]
skymaps[b] = np.ma.MaskedArray(skymap,np.isnan(skymap),fill_value=np.nan)
outfile = join(outdir,'y2q1_maglim_%s_n%i_ring.fits'%(b,NSIDE))
logger.info("Writing %s..."%outfile)
healpy.write_map(outfile,skymaps[b].data)
logger.info("Gzipping %s..."%outfile)
subprocess.call('gzip -f %s'%outfile,shell=True)
out = dict()
outstr = '|_. Band |_. Footprint |_. Distribution |_. Magnitude Limit |\n'
template = '|_. %(band)s |{{thumbnail(%(map)s, size=300)}}|{{thumbnail(%(hist)s, size=300)}}|_. %(maglim)s |\n'
for b in BANDS: