def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-o',
'--out',
dest='outfn',
help='Output filename',
default='TMP/nexp.fits')
parser.add_argument('--merge',
action='store_true',
help='Merge sub-tables')
parser.add_argument('--plot', action='store_true', help='Plot results')
parser.add_argument('files',
metavar='nexp-file.fits.gz',
nargs='+',
help='List of nexp files to process')
opt = parser.parse_args()
fns = opt.files
if opt.merge:
from astrometry.util.fits import merge_tables
TT = []
for fn in fns:
T = fits_table(fn)
print(fn, '->', len(T))
TT.append(T)
T = merge_tables(TT)
T.writeto(opt.outfn)
print('Wrote', opt.outfn)
if opt.plot:
T = fits_table(opt.files[0])
import pylab as plt
import matplotlib
ax = [360, 0, -21, 36]
def radec_plot():
plt.axis(ax)
plt.xlabel('RA (deg)')
plt.xticks(np.arange(0, 361, 45))
plt.ylabel('Dec (deg)')
gl = np.arange(361)
gb = np.zeros_like(gl)
from astrometry.util.starutil_numpy import lbtoradec
rr, dd = lbtoradec(gl, gb)
plt.plot(rr, dd, 'k-', alpha=0.5, lw=1)
rr, dd = lbtoradec(gl, gb + 10)
plt.plot(rr, dd, 'k-', alpha=0.25, lw=1)
rr, dd = lbtoradec(gl, gb - 10)
plt.plot(rr, dd, 'k-', alpha=0.25, lw=1)
plt.figure(figsize=(8, 5))
plt.subplots_adjust(left=0.1, right=0.98, top=0.93)
# Map of the tile centers we want to observe...
O = fits_table('obstatus/decam-tiles_obstatus.fits')
O.cut(O.in_desi == 1)
rr, dd = np.meshgrid(np.linspace(ax[1], ax[0], 700),
np.linspace(ax[2], ax[3], 200))
from astrometry.libkd.spherematch import match_radec
I, J, d = match_radec(O.ra, O.dec, rr.ravel(), dd.ravel(), 1.)
desimap = np.zeros(rr.shape, bool)
desimap.flat[J] = True
def desi_map():
# Show the DESI tile map in the background.
from astrometry.util.plotutils import antigray
plt.imshow(desimap,
origin='lower',
interpolation='nearest',
extent=[ax[1], ax[0], ax[2], ax[3]],
aspect='auto',
cmap=antigray,
vmax=8)
for band in 'grz':
plt.clf()
desi_map()
N = T.get('nexp_%s' % band)
I = np.flatnonzero(N > 0)
#cm = matplotlib.cm.get_cmap('jet', 6)
#cm = matplotlib.cm.get_cmap('winter', 5)
cm = matplotlib.cm.viridis
cm = matplotlib.cm.get_cmap(cm, 5)
plt.scatter(T.ra[I],
T.dec[I],
c=N[I],
s=2,
edgecolors='none',
vmin=0.5,
vmax=5.5,
cmap=cm)
radec_plot()
cax = colorbar_axes(plt.gca(), frac=0.06)
plt.colorbar(cax=cax, ticks=range(6))
#plt.colorbar(ticks=range(6))
plt.title('DECaLS DR3: Number of exposures in %s' % band)
plt.savefig('nexp-%s.png' % band)
plt.clf()
desi_map()
plt.scatter(T.ra,
T.dec,
c=T.get('nexp_%s' % band),
s=2,
edgecolors='none',
vmin=0,
vmax=2.)
radec_plot()
plt.colorbar()
plt.title('DECaLS DR3: PSF size, band %s' % band)
plt.savefig('psfsize-%s.png' % band)
return 0
for col in ['nobjs', 'npsf', 'nsimp', 'nexp', 'ndev', 'ncomp']:
plt.clf()
desi_map()
N = T.get(col)
mx = np.percentile(N, 99.5)
plt.scatter(T.ra,
T.dec,
c=N,
s=2,
edgecolors='none',
vmin=0,
vmax=mx)
radec_plot()
plt.colorbar()
plt.title('DECaLS DR3: Number of objects of type %s' % col[1:])
plt.savefig('nobjs-%s.png' % col[1:])
Ntot = T.nobjs
for col in ['npsf', 'nsimp', 'nexp', 'ndev', 'ncomp']:
plt.clf()
desi_map()
N = T.get(col) / Ntot.astype(np.float32)
mx = np.percentile(N, 99.5)
plt.scatter(T.ra,
T.dec,
c=N,
s=2,
edgecolors='none',
vmin=0,
vmax=mx)
radec_plot()
plt.colorbar()
plt.title('DECaLS DR3: Fraction of objects of type %s' % col[1:])
plt.savefig('fobjs-%s.png' % col[1:])
return 0
# fnpats = opt.files
# fns = []
# for pat in fnpats:
# pfns = glob(pat)
# fns.extend(pfns)
# print('Pattern', pat, '->', len(pfns), 'files')
#fns = glob('coadd/*/*/*-nexp*')
#fns = glob('coadd/000/*/*-nexp*')
#fns = glob('coadd/000/0001*/*-nexp*')
fns.sort()
print(len(fns), 'nexp files')
brickset = set()
bricklist = []
gn = []
rn = []
zn = []
gnhist = []
rnhist = []
znhist = []
nnhist = 6
gdepth = []
rdepth = []
zdepth = []
ibricks = []
nsrcs = []
npsf = []
nsimp = []
nexp = []
ndev = []
ncomp = []
gpsfsize = []
rpsfsize = []
zpsfsize = []
ebv = []
gtrans = []
rtrans = []
ztrans = []
bricks = fits_table('survey-bricks.fits.gz')
#sfd = SFDMap()
W = H = 3600
# H=3600
# xx,yy = np.meshgrid(np.arange(W), np.arange(H))
unique = np.ones((H, W), bool)
tlast = 0
for fn in fns:
print('File', fn)
words = fn.split('/')
dirprefix = '/'.join(words[:-4])
print('Directory prefix:', dirprefix)
words = words[-4:]
brick = words[2]
print('Brick', brick)
if not brick in brickset:
brickset.add(brick)
bricklist.append(brick)
gn.append(0)
rn.append(0)
zn.append(0)
gnhist.append([0 for i in range(nnhist)])
rnhist.append([0 for i in range(nnhist)])
znhist.append([0 for i in range(nnhist)])
index = -1
ibrick = np.nonzero(bricks.brickname == brick)[0][0]
ibricks.append(ibrick)
tfn = os.path.join(dirprefix, 'tractor', brick[:3],
'tractor-%s.fits' % brick)
print('Tractor filename', tfn)
T = fits_table(tfn,
columns=[
'brick_primary', 'type', 'decam_psfsize', 'ebv',
'decam_mw_transmission'
])
T.cut(T.brick_primary)
nsrcs.append(len(T))
types = Counter([t.strip() for t in T.type])
npsf.append(types['PSF'])
nsimp.append(types['SIMP'])
nexp.append(types['EXP'])
ndev.append(types['DEV'])
ncomp.append(types['COMP'])
print('N sources', nsrcs[-1])
gpsfsize.append(np.median(T.decam_psfsize[:, 1]))
rpsfsize.append(np.median(T.decam_psfsize[:, 2]))
zpsfsize.append(np.median(T.decam_psfsize[:, 4]))
ebv.append(np.median(T.ebv))
gtrans.append(np.median(T.decam_mw_transmission[:, 1]))
rtrans.append(np.median(T.decam_mw_transmission[:, 2]))
ztrans.append(np.median(T.decam_mw_transmission[:, 4]))
br = bricks[ibrick]
print('Computing unique brick pixels...')
#wcs = Tan(fn, 0)
#W,H = int(wcs.get_width()), int(wcs.get_height())
pixscale = 0.262 / 3600.
wcs = Tan(br.ra, br.dec, W / 2. + 0.5, H / 2. + 0.5, -pixscale, 0.,
0., pixscale, float(W), float(H))
import time
t0 = time.clock()
unique[:, :] = True
find_unique_pixels(wcs, W, H, unique, br.ra1, br.ra2, br.dec1,
br.dec2)
# for i in range(W/2):
# allin = True
# lo,hi = i, W-i-1
# # one slice per side
# side = slice(lo,hi+1)
# top = (lo, side)
# bot = (hi, side)
# left = (side, lo)
# right = (side, hi)
# for slc in [top, bot, left, right]:
# #print('xx,yy', xx[slc], yy[slc])
# rr,dd = wcs.pixelxy2radec(xx[slc]+1, yy[slc]+1)
# U = (rr >= br.ra1 ) * (rr < br.ra2 ) * (dd >= br.dec1) * (dd < br.dec2)
# #print('Pixel', i, ':', np.sum(U), 'of', len(U), 'pixels are unique')
# allin *= np.all(U)
# unique[slc] = U
# if allin:
# print('Scanned to pixel', i)
# break
t1 = time.clock()
U = np.flatnonzero(unique)
t2 = time.clock()
print(len(U), 'of', W * H, 'pixels are unique to this brick')
# #t3 = time.clock()
#rr,dd = wcs.pixelxy2radec(xx+1, yy+1)
# #t4 = time.clock()
# #u = (rr >= br.ra1 ) * (rr < br.ra2 ) * (dd >= br.dec1) * (dd < br.dec2)
# #t5 = time.clock()
# #U2 = np.flatnonzero(u)
#U2 = np.flatnonzero((rr >= br.ra1 ) * (rr < br.ra2 ) *
# (dd >= br.dec1) * (dd < br.dec2))
#assert(np.all(U == U2))
#assert(len(U) == len(U2))
# #t6 = time.clock()
# print(len(U2), 'of', W*H, 'pixels are unique to this brick')
#
#print(t0-tlast, 'other time')
#tlast = time.clock() #t2
#print('t1:', t1-t0, 't2', t2-t1)
# #print('t4:', t4-t3, 't5', t5-t4, 't6', t6-t5)
#
else:
index = bricklist.index(brick)
assert (index == len(bricklist) - 1)
index = bricklist.index(brick)
assert (index == len(bricklist) - 1)
filepart = words[-1]
filepart = filepart.replace('.fits.gz', '')
print('File:', filepart)
band = filepart[-1]
assert (band in 'grz')
nlist, nhist = dict(g=(gn, gnhist), r=(rn, rnhist),
z=(zn, znhist))[band]
upix = fitsio.read(fn).flat[U]
med = np.median(upix)
print('Band', band, ': Median', med)
nlist[index] = med
hist = nhist[index]
for i in range(nnhist):
if i < nnhist - 1:
hist[i] = np.sum(upix == i)
else:
hist[i] = np.sum(upix >= i)
assert (sum(hist) == len(upix))
print('Number of exposures histogram:', hist)
ibricks = np.array(ibricks)
print('Maximum number of sources:', max(nsrcs))
T = fits_table()
T.brickname = np.array(bricklist)
T.ra = bricks.ra[ibricks]
T.dec = bricks.dec[ibricks]
T.nexp_g = np.array(gn).astype(np.int16)
T.nexp_r = np.array(rn).astype(np.int16)
T.nexp_z = np.array(zn).astype(np.int16)
T.nexphist_g = np.array(gnhist).astype(np.int32)
T.nexphist_r = np.array(rnhist).astype(np.int32)
T.nexphist_z = np.array(znhist).astype(np.int32)
T.nobjs = np.array(nsrcs).astype(np.int16)
T.npsf = np.array(npsf).astype(np.int16)
T.nsimp = np.array(nsimp).astype(np.int16)
T.nexp = np.array(nexp).astype(np.int16)
T.ndev = np.array(ndev).astype(np.int16)
T.ncomp = np.array(ncomp).astype(np.int16)
T.psfsize_g = np.array(gpsfsize).astype(np.float32)
T.psfsize_r = np.array(rpsfsize).astype(np.float32)
T.psfsize_z = np.array(zpsfsize).astype(np.float32)
T.ebv = np.array(ebv).astype(np.float32)
T.trans_g = np.array(gtrans).astype(np.float32)
T.trans_r = np.array(rtrans).astype(np.float32)
T.trans_z = np.array(ztrans).astype(np.float32)
T.writeto(opt.outfn)
def sky_fibers_for_brick(
survey,
brickname,
bands=['g', 'r', 'z'],
apertures_arcsec=[0.5, 0.75, 1., 1.5, 2., 3.5, 5., 7.]):
'''
Produces a table of possible DESI sky fiber locations for a given
"brickname" (eg, "0001p000") read from the given LegacySurveyData object *survey*.
'''
import fitsio
from astrometry.util.fits import fits_table
from astrometry.util.util import Tan
import photutils
from legacypipe.utils import find_unique_pixels
fn = survey.find_file('blobmap', brick=brickname)
blobs = fitsio.read(fn)
print('Blobs:', blobs.min(), blobs.max())
header = fitsio.read_header(fn)
wcs = Tan(header)
goodpix = (blobs == -1)
for band in bands:
fn = survey.find_file('nexp', brick=brickname, band=band)
if not os.path.exists(fn):
# Skip
continue
nexp = fitsio.read(fn)
goodpix[nexp == 0] = False
# Cut to unique brick area... required since the blob map drops
# blobs that are completely outside the brick's unique area, thus
# those locations are not masked.
brick = survey.get_brick_by_name(brickname)
H, W = wcs.shape
U = find_unique_pixels(wcs, W, H, None, brick.ra1, brick.ra2, brick.dec1,
brick.dec2)
goodpix[U == 0] = False
del U
x, y, blobdist = sky_fiber_locations(goodpix)
skyfibers = fits_table()
skyfibers.brickid = np.zeros(len(x), np.int32) + brick.brickid
skyfibers.brickname = np.array([brickname] * len(x))
skyfibers.x = x.astype(np.int16)
skyfibers.y = y.astype(np.int16)
skyfibers.blobdist = blobdist
skyfibers.ra, skyfibers.dec = wcs.pixelxy2radec(x + 1, y + 1)
pixscale = wcs.pixel_scale()
apertures = np.array(apertures_arcsec) / pixscale
# Now, do aperture photometry at these points in the coadd images
for band in bands:
imfn = survey.find_file('image', brick=brickname, band=band)
ivfn = survey.find_file('invvar', brick=brickname, band=band)
if not (os.path.exists(imfn) and os.path.exists(ivfn)):
continue
coimg = fitsio.read(imfn)
coiv = fitsio.read(ivfn)
apflux = np.zeros((len(skyfibers), len(apertures)), np.float32)
apiv = np.zeros((len(skyfibers), len(apertures)), np.float32)
skyfibers.set('apflux_%s' % band, apflux)
skyfibers.set('apflux_ivar_%s' % band, apiv)
with np.errstate(divide='ignore', invalid='ignore'):
imsigma = 1. / np.sqrt(coiv)
imsigma[coiv == 0] = 0
apxy = np.vstack((skyfibers.x, skyfibers.y)).T
for irad, rad in enumerate(apertures):
aper = photutils.CircularAperture(apxy, rad)
p = photutils.aperture_photometry(coimg, aper, error=imsigma)
apflux[:, irad] = p.field('aperture_sum')
err = p.field('aperture_sum_err')
apiv[:, irad] = 1. / err**2
header = fitsio.FITSHDR()
for i, ap in enumerate(apertures_arcsec):
header.add_record(
dict(name='AP%i' % i, value=ap,
comment='Aperture radius (arcsec)'))
skyfibers._header = header
return skyfibers
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-o', '--out', dest='outfn', help='Output filename',
default='TMP/nexp.fits')
parser.add_argument('--merge', action='store_true', help='Merge sub-tables')
parser.add_argument('--plot', action='store_true', help='Plot results')
parser.add_argument('files', metavar='nexp-file.fits.gz', nargs='+',
help='List of nexp files to process')
opt = parser.parse_args()
fns = opt.files
if opt.merge:
from astrometry.util.fits import merge_tables
TT = []
for fn in fns:
T = fits_table(fn)
print(fn, '->', len(T))
TT.append(T)
T = merge_tables(TT)
T.writeto(opt.outfn)
print('Wrote', opt.outfn)
if opt.plot:
T = fits_table(opt.files[0])
import pylab as plt
import matplotlib
ax = [360, 0, -21, 36]
def radec_plot():
plt.axis(ax)
plt.xlabel('RA (deg)')
plt.xticks(np.arange(0, 361, 45))
plt.ylabel('Dec (deg)')
gl = np.arange(361)
gb = np.zeros_like(gl)
from astrometry.util.starutil_numpy import lbtoradec
rr,dd = lbtoradec(gl, gb)
plt.plot(rr, dd, 'k-', alpha=0.5, lw=1)
rr,dd = lbtoradec(gl, gb+10)
plt.plot(rr, dd, 'k-', alpha=0.25, lw=1)
rr,dd = lbtoradec(gl, gb-10)
plt.plot(rr, dd, 'k-', alpha=0.25, lw=1)
plt.figure(figsize=(8,5))
plt.subplots_adjust(left=0.1, right=0.98, top=0.93)
# Map of the tile centers we want to observe...
O = fits_table('obstatus/decam-tiles_obstatus.fits')
O.cut(O.in_desi == 1)
rr,dd = np.meshgrid(np.linspace(ax[1],ax[0], 700),
np.linspace(ax[2],ax[3], 200))
from astrometry.libkd.spherematch import match_radec
I,J,d = match_radec(O.ra, O.dec, rr.ravel(), dd.ravel(), 1.)
desimap = np.zeros(rr.shape, bool)
desimap.flat[J] = True
def desi_map():
# Show the DESI tile map in the background.
from astrometry.util.plotutils import antigray
plt.imshow(desimap, origin='lower', interpolation='nearest',
extent=[ax[1],ax[0],ax[2],ax[3]], aspect='auto',
cmap=antigray, vmax=8)
for band in 'grz':
plt.clf()
desi_map()
N = T.get('nexp_%s' % band)
I = np.flatnonzero(N > 0)
#cm = matplotlib.cm.get_cmap('jet', 6)
#cm = matplotlib.cm.get_cmap('winter', 5)
cm = matplotlib.cm.viridis
cm = matplotlib.cm.get_cmap(cm, 5)
plt.scatter(T.ra[I], T.dec[I], c=N[I], s=2,
edgecolors='none',
vmin=0.5, vmax=5.5, cmap=cm)
radec_plot()
cax = colorbar_axes(plt.gca(), frac=0.06)
plt.colorbar(cax=cax, ticks=range(6))
#plt.colorbar(ticks=range(6))
plt.title('DECaLS DR3: Number of exposures in %s' % band)
plt.savefig('nexp-%s.png' % band)
plt.clf()
desi_map()
plt.scatter(T.ra, T.dec, c=T.get('nexp_%s' % band), s=2,
edgecolors='none', vmin=0, vmax=2.)
radec_plot()
plt.colorbar()
plt.title('DECaLS DR3: PSF size, band %s' % band)
plt.savefig('psfsize-%s.png' % band)
return 0
for col in ['nobjs', 'npsf', 'nsimp', 'nexp', 'ndev', 'ncomp']:
plt.clf()
desi_map()
N = T.get(col)
mx = np.percentile(N, 99.5)
plt.scatter(T.ra, T.dec, c=N, s=2,
edgecolors='none', vmin=0, vmax=mx)
radec_plot()
plt.colorbar()
plt.title('DECaLS DR3: Number of objects of type %s' % col[1:])
plt.savefig('nobjs-%s.png' % col[1:])
Ntot = T.nobjs
for col in ['npsf', 'nsimp', 'nexp', 'ndev', 'ncomp']:
plt.clf()
desi_map()
N = T.get(col) / Ntot.astype(np.float32)
mx = np.percentile(N, 99.5)
plt.scatter(T.ra, T.dec, c=N, s=2,
edgecolors='none', vmin=0, vmax=mx)
radec_plot()
plt.colorbar()
plt.title('DECaLS DR3: Fraction of objects of type %s' % col[1:])
plt.savefig('fobjs-%s.png' % col[1:])
return 0
# fnpats = opt.files
# fns = []
# for pat in fnpats:
# pfns = glob(pat)
# fns.extend(pfns)
# print('Pattern', pat, '->', len(pfns), 'files')
#fns = glob('coadd/*/*/*-nexp*')
#fns = glob('coadd/000/*/*-nexp*')
#fns = glob('coadd/000/0001*/*-nexp*')
fns.sort()
print(len(fns), 'nexp files')
brickset = set()
bricklist = []
gn = []
rn = []
zn = []
gnhist = []
rnhist = []
znhist = []
nnhist = 6
gdepth = []
rdepth = []
zdepth = []
ibricks = []
nsrcs = []
npsf = []
nsimp = []
nexp = []
ndev = []
ncomp = []
gpsfsize = []
rpsfsize = []
zpsfsize = []
ebv = []
gtrans = []
rtrans = []
ztrans = []
bricks = fits_table('survey-bricks.fits.gz')
#sfd = SFDMap()
W = H = 3600
# H=3600
# xx,yy = np.meshgrid(np.arange(W), np.arange(H))
unique = np.ones((H,W), bool)
tlast = 0
for fn in fns:
print('File', fn)
words = fn.split('/')
dirprefix = '/'.join(words[:-4])
print('Directory prefix:', dirprefix)
words = words[-4:]
brick = words[2]
print('Brick', brick)
if not brick in brickset:
brickset.add(brick)
bricklist.append(brick)
gn.append(0)
rn.append(0)
zn.append(0)
gnhist.append([0 for i in range(nnhist)])
rnhist.append([0 for i in range(nnhist)])
znhist.append([0 for i in range(nnhist)])
index = -1
ibrick = np.nonzero(bricks.brickname == brick)[0][0]
ibricks.append(ibrick)
tfn = os.path.join(dirprefix, 'tractor', brick[:3], 'tractor-%s.fits'%brick)
print('Tractor filename', tfn)
T = fits_table(tfn, columns=['brick_primary', 'type', 'decam_psfsize',
'ebv', 'decam_mw_transmission'])
T.cut(T.brick_primary)
nsrcs.append(len(T))
types = Counter([t.strip() for t in T.type])
npsf.append(types['PSF'])
nsimp.append(types['SIMP'])
nexp.append(types['EXP'])
ndev.append(types['DEV'])
ncomp.append(types['COMP'])
print('N sources', nsrcs[-1])
gpsfsize.append(np.median(T.decam_psfsize[:,1]))
rpsfsize.append(np.median(T.decam_psfsize[:,2]))
zpsfsize.append(np.median(T.decam_psfsize[:,4]))
ebv.append(np.median(T.ebv))
gtrans.append(np.median(T.decam_mw_transmission[:,1]))
rtrans.append(np.median(T.decam_mw_transmission[:,2]))
ztrans.append(np.median(T.decam_mw_transmission[:,4]))
br = bricks[ibrick]
print('Computing unique brick pixels...')
#wcs = Tan(fn, 0)
#W,H = int(wcs.get_width()), int(wcs.get_height())
pixscale = 0.262/3600.
wcs = Tan(br.ra, br.dec, W/2.+0.5, H/2.+0.5,
-pixscale, 0., 0., pixscale,
float(W), float(H))
import time
t0 = time.clock()
unique[:,:] = True
find_unique_pixels(wcs, W, H, unique,
br.ra1, br.ra2, br.dec1, br.dec2)
# for i in range(W/2):
# allin = True
# lo,hi = i, W-i-1
# # one slice per side
# side = slice(lo,hi+1)
# top = (lo, side)
# bot = (hi, side)
# left = (side, lo)
# right = (side, hi)
# for slc in [top, bot, left, right]:
# #print('xx,yy', xx[slc], yy[slc])
# rr,dd = wcs.pixelxy2radec(xx[slc]+1, yy[slc]+1)
# U = (rr >= br.ra1 ) * (rr < br.ra2 ) * (dd >= br.dec1) * (dd < br.dec2)
# #print('Pixel', i, ':', np.sum(U), 'of', len(U), 'pixels are unique')
# allin *= np.all(U)
# unique[slc] = U
# if allin:
# print('Scanned to pixel', i)
# break
t1 = time.clock()
U = np.flatnonzero(unique)
t2 = time.clock()
print(len(U), 'of', W*H, 'pixels are unique to this brick')
# #t3 = time.clock()
#rr,dd = wcs.pixelxy2radec(xx+1, yy+1)
# #t4 = time.clock()
# #u = (rr >= br.ra1 ) * (rr < br.ra2 ) * (dd >= br.dec1) * (dd < br.dec2)
# #t5 = time.clock()
# #U2 = np.flatnonzero(u)
#U2 = np.flatnonzero((rr >= br.ra1 ) * (rr < br.ra2 ) *
# (dd >= br.dec1) * (dd < br.dec2))
#assert(np.all(U == U2))
#assert(len(U) == len(U2))
# #t6 = time.clock()
# print(len(U2), 'of', W*H, 'pixels are unique to this brick')
#
#print(t0-tlast, 'other time')
#tlast = time.clock() #t2
#print('t1:', t1-t0, 't2', t2-t1)
# #print('t4:', t4-t3, 't5', t5-t4, 't6', t6-t5)
#
else:
index = bricklist.index(brick)
assert(index == len(bricklist)-1)
index = bricklist.index(brick)
assert(index == len(bricklist)-1)
filepart = words[-1]
filepart = filepart.replace('.fits.gz', '')
print('File:', filepart)
band = filepart[-1]
assert(band in 'grz')
nlist,nhist = dict(g=(gn,gnhist), r=(rn,rnhist), z=(zn,znhist))[band]
upix = fitsio.read(fn).flat[U]
med = np.median(upix)
print('Band', band, ': Median', med)
nlist[index] = med
hist = nhist[index]
for i in range(nnhist):
if i < nnhist-1:
hist[i] = np.sum(upix == i)
else:
hist[i] = np.sum(upix >= i)
assert(sum(hist) == len(upix))
print('Number of exposures histogram:', hist)
ibricks = np.array(ibricks)
print('Maximum number of sources:', max(nsrcs))
T = fits_table()
T.brickname = np.array(bricklist)
T.ra = bricks.ra [ibricks]
T.dec = bricks.dec[ibricks]
T.nexp_g = np.array(gn).astype(np.int16)
T.nexp_r = np.array(rn).astype(np.int16)
T.nexp_z = np.array(zn).astype(np.int16)
T.nexphist_g = np.array(gnhist).astype(np.int32)
T.nexphist_r = np.array(rnhist).astype(np.int32)
T.nexphist_z = np.array(znhist).astype(np.int32)
T.nobjs = np.array(nsrcs).astype(np.int16)
T.npsf = np.array(npsf ).astype(np.int16)
T.nsimp = np.array(nsimp).astype(np.int16)
T.nexp = np.array(nexp ).astype(np.int16)
T.ndev = np.array(ndev ).astype(np.int16)
T.ncomp = np.array(ncomp).astype(np.int16)
T.psfsize_g = np.array(gpsfsize).astype(np.float32)
T.psfsize_r = np.array(rpsfsize).astype(np.float32)
T.psfsize_z = np.array(zpsfsize).astype(np.float32)
T.ebv = np.array(ebv).astype(np.float32)
T.trans_g = np.array(gtrans).astype(np.float32)
T.trans_r = np.array(rtrans).astype(np.float32)
T.trans_z = np.array(ztrans).astype(np.float32)
T.writeto(opt.outfn)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-o',
'--out',
dest='outfn',
help='Output filename',
default='TMP/nexp.fits')
parser.add_argument('--merge',
action='store_true',
help='Merge sub-tables')
parser.add_argument('--north',
action='store_true',
default=False,
help='Northern survey?')
parser.add_argument('--plot', action='store_true', help='Plot results')
parser.add_argument('--depth-hist',
action='store_true',
help='Depth histograms')
parser.add_argument('files',
metavar='nexp-file.fits.gz',
nargs='+',
help='List of nexp files to process')
opt = parser.parse_args()
fns = opt.files
if opt.merge:
from astrometry.util.fits import merge_tables
TT = []
for fn in fns:
T = fits_table(fn)
print(fn, '->', len(T))
TT.append(T)
T = merge_tables(TT)
add_brick_data(T, opt.north)
T.writeto(opt.outfn)
print('Wrote', opt.outfn)
return
if opt.plot:
plots(opt)
depth_hist(opt)
return
if opt.depth_hist:
depth_hist(opt)
return
fns.sort()
print(len(fns), 'nexp files')
if len(fns) == 1:
if not os.path.exists(fns[0]):
print('No such file.')
return 0
brickset = set()
bricklist = []
gn = []
rn = []
zn = []
gnhist = []
rnhist = []
znhist = []
nnhist = 6
ibricks = []
nsrcs = []
npsf = []
nsimp = []
nrex = []
nexp = []
ndev = []
ncomp = []
nser = []
ndup = []
gpsfsize = []
rpsfsize = []
zpsfsize = []
gpsfdepth = []
rpsfdepth = []
zpsfdepth = []
ggaldepth = []
rgaldepth = []
zgaldepth = []
wise_nobs = []
wise_trans = []
ebv = []
gtrans = []
rtrans = []
ztrans = []
gcosky = []
rcosky = []
zcosky = []
bricks = fits_table('survey-bricks.fits.gz')
W = H = 3600
unique = np.ones((H, W), bool)
tlast = 0
for ifn, fn in enumerate(fns):
print('File', (ifn + 1), 'of', len(fns), ':', fn)
words = fn.split('/')
dirprefix = '/'.join(words[:-4])
#print('Directory prefix:', dirprefix)
words = words[-4:]
brick = words[2]
#print('Brick', brick)
if not brick in brickset:
try:
tfn = os.path.join(dirprefix, 'tractor', brick[:3],
'tractor-%s.fits' % brick)
print('Tractor filename', tfn)
T = fits_table(
tfn,
columns=[
'brick_primary', 'type', 'psfsize_g', 'psfsize_r',
'psfsize_z', 'psfdepth_g', 'psfdepth_r', 'psfdepth_z',
'galdepth_g', 'galdepth_r', 'galdepth_z', 'ebv',
'mw_transmission_g', 'mw_transmission_r',
'mw_transmission_z', 'nobs_w1', 'nobs_w2', 'nobs_w3',
'nobs_w4', 'mw_transmission_w1', 'mw_transmission_w2',
'mw_transmission_w3', 'mw_transmission_w4'
])
# we need tho primary header, not the table-hdu header!
#Thdr = T.get_header()
Thdr = fitsio.read_header(tfn)
except:
print('Failed to read FITS table', tfn)
import traceback
traceback.print_exc()
print('Carrying on.')
continue
brickset.add(brick)
bricklist.append(brick)
gn.append(0)
rn.append(0)
zn.append(0)
gnhist.append([0 for i in range(nnhist)])
rnhist.append([0 for i in range(nnhist)])
znhist.append([0 for i in range(nnhist)])
index = -1
ibrick = np.nonzero(bricks.brickname == brick)[0][0]
ibricks.append(ibrick)
T.cut(T.brick_primary)
nsrcs.append(len(T))
types = Counter([t.strip() for t in T.type])
npsf.append(types['PSF'])
nsimp.append(types['SIMP'])
nrex.append(types['REX'])
nexp.append(types['EXP'])
ndev.append(types['DEV'])
ncomp.append(types['COMP'])
nser.append(types['SER'])
ndup.append(types['DUP'])
print('N sources', nsrcs[-1])
gpsfsize.append(np.median(T.psfsize_g))
rpsfsize.append(np.median(T.psfsize_r))
zpsfsize.append(np.median(T.psfsize_z))
gpsfdepth.append(np.median(T.psfdepth_g))
rpsfdepth.append(np.median(T.psfdepth_r))
zpsfdepth.append(np.median(T.psfdepth_z))
ggaldepth.append(np.median(T.galdepth_g))
rgaldepth.append(np.median(T.galdepth_r))
zgaldepth.append(np.median(T.galdepth_z))
wise_nobs.append(
np.median(np.vstack(
(T.nobs_w1, T.nobs_w2, T.nobs_w3, T.nobs_w4)).T,
axis=0))
wise_trans.append(
np.median(np.vstack(
(T.mw_transmission_w1, T.mw_transmission_w2,
T.mw_transmission_w3, T.mw_transmission_w4)).T,
axis=0))
gtrans.append(np.median(T.mw_transmission_g))
rtrans.append(np.median(T.mw_transmission_r))
ztrans.append(np.median(T.mw_transmission_z))
gcosky.append(Thdr.get('COSKY_G', 0.))
rcosky.append(Thdr.get('COSKY_R', 0.))
zcosky.append(Thdr.get('COSKY_Z', 0.))
ebv.append(np.median(T.ebv))
br = bricks[ibrick]
#print('Computing unique brick pixels...')
pixscale = 0.262 / 3600.
wcs = Tan(br.ra, br.dec, W / 2. + 0.5, H / 2. + 0.5, -pixscale, 0.,
0., pixscale, float(W), float(H))
unique[:, :] = True
find_unique_pixels(wcs, W, H, unique, br.ra1, br.ra2, br.dec1,
br.dec2)
U = np.flatnonzero(unique)
#print(len(U), 'of', W*H, 'pixels are unique to this brick')
else:
index = bricklist.index(brick)
assert (index == len(bricklist) - 1)
index = bricklist.index(brick)
assert (index == len(bricklist) - 1)
filepart = words[-1]
filepart = filepart.replace('.fits.gz', '')
filepart = filepart.replace('.fits.fz', '')
print('File:', filepart)
band = filepart[-1]
assert (band in 'grz')
nlist, nhist = dict(g=(gn, gnhist), r=(rn, rnhist),
z=(zn, znhist))[band]
upix = fitsio.read(fn).flat[U]
med = np.median(upix)
print('Band', band, ': Median', med)
nlist[index] = med
hist = nhist[index]
for i in range(nnhist):
if i < nnhist - 1:
hist[i] = np.sum(upix == i)
else:
hist[i] = np.sum(upix >= i)
assert (sum(hist) == len(upix))
print('Number of exposures histogram:', hist)
ibricks = np.array(ibricks)
T = fits_table()
T.brickname = np.array(bricklist)
T.ra = bricks.ra[ibricks]
T.dec = bricks.dec[ibricks]
T.nexp_g = np.array(gn).astype(np.int16)
T.nexp_r = np.array(rn).astype(np.int16)
T.nexp_z = np.array(zn).astype(np.int16)
T.nexphist_g = np.array(gnhist).astype(np.int32)
T.nexphist_r = np.array(rnhist).astype(np.int32)
T.nexphist_z = np.array(znhist).astype(np.int32)
T.nobjs = np.array(nsrcs).astype(np.int32)
T.npsf = np.array(npsf).astype(np.int32)
T.nsimp = np.array(nsimp).astype(np.int32)
T.nrex = np.array(nrex).astype(np.int32)
T.nexp = np.array(nexp).astype(np.int32)
T.ndev = np.array(ndev).astype(np.int32)
T.ncomp = np.array(ncomp).astype(np.int32)
T.nser = np.array(nser).astype(np.int32)
T.ndup = np.array(ndup).astype(np.int32)
T.psfsize_g = np.array(gpsfsize).astype(np.float32)
T.psfsize_r = np.array(rpsfsize).astype(np.float32)
T.psfsize_z = np.array(zpsfsize).astype(np.float32)
with np.errstate(divide='ignore'):
T.psfdepth_g = (
-2.5 *
(-9. + np.log10(5. * np.sqrt(1. / np.array(gpsfdepth))))).astype(
np.float32)
T.psfdepth_r = (
-2.5 *
(-9. + np.log10(5. * np.sqrt(1. / np.array(rpsfdepth))))).astype(
np.float32)
T.psfdepth_z = (
-2.5 *
(-9. + np.log10(5. * np.sqrt(1. / np.array(zpsfdepth))))).astype(
np.float32)
T.galdepth_g = (
-2.5 *
(-9. + np.log10(5. * np.sqrt(1. / np.array(ggaldepth))))).astype(
np.float32)
T.galdepth_r = (
-2.5 *
(-9. + np.log10(5. * np.sqrt(1. / np.array(rgaldepth))))).astype(
np.float32)
T.galdepth_z = (
-2.5 *
(-9. + np.log10(5. * np.sqrt(1. / np.array(zgaldepth))))).astype(
np.float32)
for k in [
'psfdepth_g', 'psfdepth_r', 'psfdepth_z', 'galdepth_g',
'galdepth_r', 'galdepth_z'
]:
v = T.get(k)
v[np.logical_not(np.isfinite(v))] = 0.
T.ebv = np.array(ebv).astype(np.float32)
T.trans_g = np.array(gtrans).astype(np.float32)
T.trans_r = np.array(rtrans).astype(np.float32)
T.trans_z = np.array(ztrans).astype(np.float32)
T.cosky_g = np.array(gcosky).astype(np.float32)
T.cosky_r = np.array(rcosky).astype(np.float32)
T.cosky_z = np.array(zcosky).astype(np.float32)
T.ext_g = -2.5 * np.log10(T.trans_g)
T.ext_r = -2.5 * np.log10(T.trans_r)
T.ext_z = -2.5 * np.log10(T.trans_z)
T.wise_nobs = np.array(wise_nobs).astype(np.int16)
T.trans_wise = np.array(wise_trans).astype(np.float32)
T.ext_w1 = -2.5 * np.log10(T.trans_wise[:, 0])
T.ext_w2 = -2.5 * np.log10(T.trans_wise[:, 1])
T.ext_w3 = -2.5 * np.log10(T.trans_wise[:, 2])
T.ext_w4 = -2.5 * np.log10(T.trans_wise[:, 3])
T.writeto(opt.outfn)
def make_depth_cut(survey, ccds, bands, targetrd, brick, W, H, pixscale,
plots, ps, splinesky, gaussPsf, pixPsf, normalizePsf, do_calibs,
gitver, targetwcs, old_calibs_ok, get_depth_maps=False, margin=0.5,
use_approx_wcs=False):
if plots:
import pylab as plt
# Add some margin to our DESI depth requirements
target_depth_map = dict(g=24.0 + margin, r=23.4 + margin, z=22.5 + margin)
# List extra (redundant) target percentiles so that increasing the depth at
# any of these percentiles causes the image to be kept.
target_percentiles = np.array(list(range(2, 10)) +
list(range(10, 30, 5)) +
list(range(30, 101, 10)))
target_ddepths = np.zeros(len(target_percentiles), np.float32)
target_ddepths[target_percentiles < 10] = -0.3
target_ddepths[target_percentiles < 5] = -0.6
#print('Target percentiles:', target_percentiles)
#print('Target ddepths:', target_ddepths)
cH,cW = H//10, W//10
coarsewcs = targetwcs.scale(0.1)
coarsewcs.imagew = cW
coarsewcs.imageh = cH
# Unique pixels in this brick (U: cH x cW boolean)
U = find_unique_pixels(coarsewcs, cW, cH, None,
brick.ra1, brick.ra2, brick.dec1, brick.dec2)
pixscale = 3600. * np.sqrt(np.abs(ccds.cd1_1*ccds.cd2_2 - ccds.cd1_2*ccds.cd2_1))
seeing = ccds.fwhm * pixscale
# Compute the rectangle in *coarsewcs* covered by each CCD
slices = []
overlapping_ccds = np.zeros(len(ccds), bool)
for i,ccd in enumerate(ccds):
wcs = survey.get_approx_wcs(ccd)
hh,ww = wcs.shape
rr,dd = wcs.pixelxy2radec([1,ww,ww,1], [1,1,hh,hh])
ok,xx,yy = coarsewcs.radec2pixelxy(rr, dd)
y0 = int(np.round(np.clip(yy.min(), 0, cH-1)))
y1 = int(np.round(np.clip(yy.max(), 0, cH-1)))
x0 = int(np.round(np.clip(xx.min(), 0, cW-1)))
x1 = int(np.round(np.clip(xx.max(), 0, cW-1)))
if y0 == y1 or x0 == x1:
slices.append(None)
continue
# Check whether this CCD overlaps the unique area of this brick...
if not np.any(U[y0:y1+1, x0:x1+1]):
info('No overlap with unique area for CCD', ccd.expnum, ccd.ccdname)
slices.append(None)
continue
overlapping_ccds[i] = True
slices.append((slice(y0, y1+1), slice(x0, x1+1)))
keep_ccds = np.zeros(len(ccds), bool)
depthmaps = []
for band in bands:
# scalar
target_depth = target_depth_map[band]
# vector
target_depths = target_depth + target_ddepths
depthiv = np.zeros((cH,cW), np.float32)
depthmap = np.zeros_like(depthiv)
depthvalue = np.zeros_like(depthiv)
last_pcts = np.zeros_like(target_depths)
# indices of CCDs we still want to look at in the current band
b_inds = np.where(ccds.filter == band)[0]
info(len(b_inds), 'CCDs in', band, 'band')
if len(b_inds) == 0:
continue
b_inds = np.array([i for i in b_inds if slices[i] is not None])
info(len(b_inds), 'CCDs in', band, 'band overlap target')
if len(b_inds) == 0:
continue
# CCDs that we will try before searching for good ones -- CCDs
# from the same exposure number as CCDs we have chosen to
# take.
try_ccds = set()
# Try DECaLS data first!
Idecals = np.where(ccds.propid[b_inds] == '2014B-0404')[0]
if len(Idecals):
try_ccds.update(b_inds[Idecals])
debug('Added', len(try_ccds), 'DECaLS CCDs to try-list')
plot_vals = []
if plots:
plt.clf()
for i in b_inds:
sy,sx = slices[i]
x0,x1 = sx.start, sx.stop
y0,y1 = sy.start, sy.stop
plt.plot([x0,x0,x1,x1,x0], [y0,y1,y1,y0,y0], 'b-', alpha=0.5)
plt.title('CCDs overlapping brick: %i in %s band' % (len(b_inds), band))
ps.savefig()
nccds = np.zeros((cH,cW), np.int16)
plt.clf()
for i in b_inds:
nccds[slices[i]] += 1
plt.imshow(nccds, interpolation='nearest', origin='lower', vmin=0)
plt.colorbar()
plt.title('CCDs overlapping brick: %i in %s band (%i / %i / %i)' %
(len(b_inds), band, nccds.min(), np.median(nccds), nccds.max()))
ps.savefig()
#continue
while len(b_inds):
if len(try_ccds) == 0:
# Choose the next CCD to look at in this band.
# A rough point-source depth proxy would be:
# metric = np.sqrt(ccds.extime[b_inds]) / seeing[b_inds]
# If we want to put more weight on choosing good-seeing images, we could do:
#metric = np.sqrt(ccds.exptime[b_inds]) / seeing[b_inds]**2
# depth would be ~ 1 / (sig1 * seeing); we privilege good seeing here.
metric = 1. / (ccds.sig1[b_inds] * seeing[b_inds]**2)
# This metric is *BIG* for *GOOD* ccds!
# Here, we try explicitly to include CCDs that cover
# pixels that are still shallow by the largest amount
# for the largest number of percentiles of interest;
# note that pixels with no coverage get depth 0, so
# score high in this metric.
#
# The value is the depth still required to hit the
# target, summed over percentiles of interest
# (for pixels unique to this brick)
depthvalue[:,:] = 0.
active = (last_pcts < target_depths)
for d in target_depths[active]:
depthvalue += U * np.maximum(0, d - depthmap)
ccdvalue = np.zeros(len(b_inds), np.float32)
for j,i in enumerate(b_inds):
#ccdvalue[j] = np.sum(depthvalue[slices[i]])
# mean -- we want the most bang for the buck per pixel?
ccdvalue[j] = np.mean(depthvalue[slices[i]])
metric *= ccdvalue
# *ibest* is an index into b_inds
ibest = np.argmax(metric)
# *iccd* is an index into ccds.
iccd = b_inds[ibest]
ccd = ccds[iccd]
debug('Chose best CCD: seeing', seeing[iccd], 'exptime', ccds.exptime[iccd], 'with value', ccdvalue[ibest])
else:
iccd = try_ccds.pop()
ccd = ccds[iccd]
debug('Popping CCD from use_ccds list')
# remove *iccd* from b_inds
b_inds = b_inds[b_inds != iccd]
im = survey.get_image_object(ccd)
debug('Band', im.band, 'expnum', im.expnum, 'exptime', im.exptime, 'seeing', im.fwhm*im.pixscale, 'arcsec, propid', im.propid)
im.check_for_cached_files(survey)
debug(im)
if do_calibs:
kwa = dict(git_version=gitver, old_calibs_ok=old_calibs_ok)
if gaussPsf:
kwa.update(psfex=False)
if splinesky:
kwa.update(splinesky=True)
im.run_calibs(**kwa)
if use_approx_wcs:
debug('Using approximate (TAN) WCS')
wcs = survey.get_approx_wcs(ccd)
else:
debug('Reading WCS from', im.imgfn, 'HDU', im.hdu)
wcs = im.get_wcs()
x0,x1,y0,y1,slc = im.get_image_extent(wcs=wcs, radecpoly=targetrd)
if x0==x1 or y0==y1:
debug('No actual overlap')
continue
wcs = wcs.get_subimage(int(x0), int(y0), int(x1-x0), int(y1-y0))
if 'galnorm_mean' in ccds.get_columns():
galnorm = ccd.galnorm_mean
debug('Using galnorm_mean from CCDs table:', galnorm)
else:
psf = im.read_psf_model(x0, y0, gaussPsf=gaussPsf, pixPsf=pixPsf,
normalizePsf=normalizePsf)
psf = psf.constantPsfAt((x1-x0)//2, (y1-y0)//2)
# create a fake tim to compute galnorm
from tractor import PixPos, Flux, ModelMask, Image, NullWCS
from legacypipe.survey import SimpleGalaxy
h,w = 50,50
gal = SimpleGalaxy(PixPos(w//2,h//2), Flux(1.))
tim = Image(data=np.zeros((h,w), np.float32),
psf=psf, wcs=NullWCS(pixscale=im.pixscale))
mm = ModelMask(0, 0, w, h)
galmod = gal.getModelPatch(tim, modelMask=mm).patch
galmod = np.maximum(0, galmod)
galmod /= galmod.sum()
galnorm = np.sqrt(np.sum(galmod**2))
detiv = 1. / (im.sig1 / galnorm)**2
galdepth = -2.5 * (np.log10(5. * im.sig1 / galnorm) - 9.)
debug('Galnorm:', galnorm, 'sig1:', im.sig1, 'galdepth', galdepth)
# Add this image the the depth map...
from astrometry.util.resample import resample_with_wcs, OverlapError
try:
Yo,Xo,_,_,_ = resample_with_wcs(coarsewcs, wcs)
debug(len(Yo), 'of', (cW*cH), 'pixels covered by this image')
except OverlapError:
debug('No overlap')
continue
depthiv[Yo,Xo] += detiv
# compute the new depth map & percentiles (including the proposed new CCD)
depthmap[:,:] = 0.
depthmap[depthiv > 0] = 22.5 - 2.5*np.log10(5./np.sqrt(depthiv[depthiv > 0]))
depthpcts = np.percentile(depthmap[U], target_percentiles)
for i,(p,d,t) in enumerate(zip(target_percentiles, depthpcts, target_depths)):
info(' pct % 3i, prev %5.2f -> %5.2f vs target %5.2f %s' % (p, last_pcts[i], d, t, ('ok' if d >= t else '')))
keep = False
# Did we increase the depth of any target percentile that did not already exceed its target depth?
if np.any((depthpcts > last_pcts) * (last_pcts < target_depths)):
keep = True
# Add any other CCDs from this same expnum to the try_ccds list.
# (before making the plot)
I = np.where(ccd.expnum == ccds.expnum[b_inds])[0]
try_ccds.update(b_inds[I])
debug('Adding', len(I), 'CCDs with the same expnum to try_ccds list')
if plots:
cc = '1' if keep else '0'
xx = [Xo.min(), Xo.min(), Xo.max(), Xo.max(), Xo.min()]
yy = [Yo.min(), Yo.max(), Yo.max(), Yo.min(), Yo.min()]
plot_vals.append(((xx,yy,cc),(last_pcts,depthpcts,keep),im.ccdname))
if plots and (
(len(try_ccds) == 0) or np.all(depthpcts >= target_depths)):
plt.clf()
plt.subplot2grid((2,2),(0,0))
plt.imshow(depthvalue, interpolation='nearest', origin='lower',
vmin=0)
plt.xticks([]); plt.yticks([])
plt.colorbar()
plt.title('heuristic value')
plt.subplot2grid((2,2),(0,1))
plt.imshow(depthmap, interpolation='nearest', origin='lower',
vmin=target_depth - 2, vmax=target_depth + 0.5)
ax = plt.axis()
for (xx,yy,cc) in [p[0] for p in plot_vals]:
plt.plot(xx,yy, '-', color=cc, lw=3)
plt.axis(ax)
plt.xticks([]); plt.yticks([])
plt.colorbar()
plt.title('depth map')
plt.subplot2grid((2,2),(1,0), colspan=2)
ax = plt.gca()
plt.plot(target_percentiles, target_depths, 'ro', label='Target')
plt.plot(target_percentiles, target_depths, 'r-')
for (lp,dp,k) in [p[1] for p in plot_vals]:
plt.plot(target_percentiles, lp, 'k-',
label='Previous percentiles')
for (lp,dp,k) in [p[1] for p in plot_vals]:
cc = 'b' if k else 'r'
plt.plot(target_percentiles, dp, '-', color=cc,
label='Depth percentiles')
ccdnames = ','.join([p[2] for p in plot_vals])
plot_vals = []
plt.ylim(target_depth - 2, target_depth + 0.5)
plt.xscale('log')
plt.xlabel('Percentile')
plt.ylabel('Depth')
plt.title('depth percentiles')
plt.suptitle('%s %i-%s, exptime %.0f, seeing %.2f, band %s' %
(im.camera, im.expnum, ccdnames, im.exptime,
im.pixscale * im.fwhm, band))
ps.savefig()
if keep:
info('Keeping this exposure')
else:
info('Not keeping this exposure')
depthiv[Yo,Xo] -= detiv
continue
keep_ccds[iccd] = True
last_pcts = depthpcts
if np.all(depthpcts >= target_depths):
info('Reached all target depth percentiles for band', band)
break
if get_depth_maps:
if np.any(depthiv > 0):
depthmap[:,:] = 0.
depthmap[depthiv > 0] = 22.5 -2.5*np.log10(5./np.sqrt(depthiv[depthiv > 0]))
depthmap[np.logical_not(U)] = np.nan
depthmaps.append((band, depthmap.copy()))
if plots:
I = np.where(ccds.filter == band)[0]
plt.clf()
plt.plot(seeing[I], ccds.exptime[I], 'k.')
# which CCDs from this band are we keeping?
kept, = np.nonzero(keep_ccds)
if len(kept):
kept = kept[ccds.filter[kept] == band]
plt.plot(seeing[kept], ccds.exptime[kept], 'ro')
plt.xlabel('Seeing (arcsec)')
plt.ylabel('Exptime (sec)')
plt.title('CCDs kept for band %s' % band)
plt.ylim(0, np.max(ccds.exptime[I]) * 1.1)
ps.savefig()
if get_depth_maps:
return (keep_ccds, overlapping_ccds, depthmaps)
return keep_ccds, overlapping_ccds