def main():
ps = PlotSequence('cov')
survey = LegacySurveyData()
ra,dec = 242.0, 10.2
fn = 'coverage-ccds.fits'
if not os.path.exists(fn):
ccds = survey.get_ccds()
ccds.cut(ccds.filter == 'r')
ccds.cut(ccds.propid == '2014B-0404')
ccds.cut(np.hypot(ccds.ra_bore - ra, ccds.dec_bore - dec) < 2.5)
print(np.unique(ccds.expnum), 'unique exposures')
print('propids', np.unique(ccds.propid))
ccds.writeto(fn)
else:
ccds = fits_table(fn)
plt.clf()
for e in np.unique(ccds.expnum):
I = np.flatnonzero(ccds.expnum == e)
plt.plot(ccds.ra[I], ccds.dec[I], '.')
ps.savefig()
degw = 3.0
pixscale = 10.
W = degw * 3600 / 10.
H = W
hi = 6
cmap = cmap_discretize('jet', hi+1)
wcs = Tan(ra, dec, W/2.+0.5, H/2.+0.5,
-pixscale/3600., 0., 0., pixscale/3600., float(W), float(H))
r0,d0 = wcs.pixelxy2radec(1,1)
r1,d1 = wcs.pixelxy2radec(W,H)
extent = [min(r0,r1),max(r0,r1), min(d0,d1),max(d0,d1)]
for expnums in [ [348666], [348666,348710, 348686],
[348659, 348667, 348658, 348666, 348665, 348669, 348668],
None,
[348683, 348687, 347333, 348686, 348685, 348692, 348694,
348659, 348667, 348658, 348666, 348665, 348669, 348668,
348707, 348709, 348708, 348710, 348711, 348716, 348717],
]:
nexp = np.zeros((H,W), np.uint8)
for ccd in ccds:
if expnums is not None and not ccd.expnum in expnums:
continue
ccdwcs = survey.get_approx_wcs(ccd)
r,d = ccdwcs.pixelxy2radec(1, 1)
ok,x0,y0 = wcs.radec2pixelxy(r, d)
r,d = ccdwcs.pixelxy2radec(ccd.width, ccd.height)
ok,x1,y1 = wcs.radec2pixelxy(r, d)
xlo = np.clip(int(np.round(min(x0,x1))) - 1, 0, W-1)
xhi = np.clip(int(np.round(max(x0,x1))) - 1, 0, W-1)
ylo = np.clip(int(np.round(min(y0,y1))) - 1, 0, H-1)
yhi = np.clip(int(np.round(max(y0,y1))) - 1, 0, H-1)
nexp[ylo:yhi+1, xlo:xhi+1] += 1
plt.clf()
plt.imshow(nexp, interpolation='nearest', origin='lower',
vmin=-0.5, vmax=hi+0.5, cmap=cmap, extent=extent)
plt.colorbar(ticks=np.arange(hi+1))
ps.savefig()
O = fits_table('obstatus/decam-tiles_obstatus.fits')
O.cut(np.hypot(O.ra - ra, O.dec - dec) < 2.5)
for p in [1,2,3]:
print('Pass', p, 'exposures:', O.r_expnum[O.get('pass') == p])
O.cut(O.get('pass') == 2)
print(len(O), 'pass 2 nearby')
d = np.hypot(O.ra - ra, O.dec - dec)
print('Dists:', d)
I = np.flatnonzero(d < 0.5)
assert(len(I) == 1)
ocenter = O[I[0]]
print('Center expnum', ocenter.r_expnum)
I = np.flatnonzero(d >= 0.5)
O.cut(I)
#center = ccds[ccds.expnum == ocenter.r_expnum]
#p2 = ccds[ccds.
ok,xc,yc = wcs.radec2pixelxy(ocenter.ra, ocenter.dec)
xx,yy = np.meshgrid(np.arange(W)+1, np.arange(H)+1)
c_d2 = (xc - xx)**2 + (yc - yy)**2
best = np.ones((H,W), bool)
for o in O:
ok,x,y = wcs.radec2pixelxy(o.ra, o.dec)
d2 = (x - xx)**2 + (y - yy)**2
best[d2 < c_d2] = False
del d2
del c_d2,xx,yy
# plt.clf()
# plt.imshow(best, interpolation='nearest', origin='lower', cmap='gray',
# vmin=0, vmax=1)
# ps.savefig()
plt.clf()
plt.imshow(nexp * best, interpolation='nearest', origin='lower',
vmin=-0.5, vmax=hi+0.5, cmap=cmap, extent=extent)
plt.colorbar(ticks=np.arange(hi+1))
ps.savefig()
plt.clf()
n,b,p = plt.hist(np.clip(nexp[best], 0, hi), range=(-0.5,hi+0.5), bins=hi+1)
plt.xlim(-0.5, hi+0.5)
ps.savefig()
print('b', b)
print('n', n)
print('fracs', np.array(n) / np.sum(n))
print('pcts', ', '.join(['%.1f' % f for f in 100. * np.array(n)/np.sum(n)]))
def main():
ps = PlotSequence('cov')
survey = LegacySurveyData()
ra, dec = 242.0, 10.2
fn = 'coverage-ccds.fits'
if not os.path.exists(fn):
ccds = survey.get_ccds()
ccds.cut(ccds.filter == 'r')
ccds.cut(ccds.propid == '2014B-0404')
ccds.cut(np.hypot(ccds.ra_bore - ra, ccds.dec_bore - dec) < 2.5)
print(np.unique(ccds.expnum), 'unique exposures')
print('propids', np.unique(ccds.propid))
ccds.writeto(fn)
else:
ccds = fits_table(fn)
plt.clf()
for e in np.unique(ccds.expnum):
I = np.flatnonzero(ccds.expnum == e)
plt.plot(ccds.ra[I], ccds.dec[I], '.')
ps.savefig()
degw = 3.0
pixscale = 10.
W = degw * 3600 / 10.
H = W
hi = 6
cmap = cmap_discretize('jet', hi + 1)
wcs = Tan(ra, dec, W / 2. + 0.5, H / 2. + 0.5, -pixscale / 3600., 0., 0.,
pixscale / 3600., float(W), float(H))
r0, d0 = wcs.pixelxy2radec(1, 1)
r1, d1 = wcs.pixelxy2radec(W, H)
extent = [min(r0, r1), max(r0, r1), min(d0, d1), max(d0, d1)]
for expnums in [
[348666],
[348666, 348710, 348686],
[348659, 348667, 348658, 348666, 348665, 348669, 348668],
None,
[
348683, 348687, 347333, 348686, 348685, 348692, 348694, 348659,
348667, 348658, 348666, 348665, 348669, 348668, 348707, 348709,
348708, 348710, 348711, 348716, 348717
],
]:
nexp = np.zeros((H, W), np.uint8)
for ccd in ccds:
if expnums is not None and not ccd.expnum in expnums:
continue
ccdwcs = survey.get_approx_wcs(ccd)
r, d = ccdwcs.pixelxy2radec(1, 1)
ok, x0, y0 = wcs.radec2pixelxy(r, d)
r, d = ccdwcs.pixelxy2radec(ccd.width, ccd.height)
ok, x1, y1 = wcs.radec2pixelxy(r, d)
xlo = np.clip(int(np.round(min(x0, x1))) - 1, 0, W - 1)
xhi = np.clip(int(np.round(max(x0, x1))) - 1, 0, W - 1)
ylo = np.clip(int(np.round(min(y0, y1))) - 1, 0, H - 1)
yhi = np.clip(int(np.round(max(y0, y1))) - 1, 0, H - 1)
nexp[ylo:yhi + 1, xlo:xhi + 1] += 1
plt.clf()
plt.imshow(nexp,
interpolation='nearest',
origin='lower',
vmin=-0.5,
vmax=hi + 0.5,
cmap=cmap,
extent=extent)
plt.colorbar(ticks=np.arange(hi + 1))
ps.savefig()
O = fits_table('obstatus/decam-tiles_obstatus.fits')
O.cut(np.hypot(O.ra - ra, O.dec - dec) < 2.5)
for p in [1, 2, 3]:
print('Pass', p, 'exposures:', O.r_expnum[O.get('pass') == p])
O.cut(O.get('pass') == 2)
print(len(O), 'pass 2 nearby')
d = np.hypot(O.ra - ra, O.dec - dec)
print('Dists:', d)
I = np.flatnonzero(d < 0.5)
assert (len(I) == 1)
ocenter = O[I[0]]
print('Center expnum', ocenter.r_expnum)
I = np.flatnonzero(d >= 0.5)
O.cut(I)
#center = ccds[ccds.expnum == ocenter.r_expnum]
#p2 = ccds[ccds.
ok, xc, yc = wcs.radec2pixelxy(ocenter.ra, ocenter.dec)
xx, yy = np.meshgrid(np.arange(W) + 1, np.arange(H) + 1)
c_d2 = (xc - xx)**2 + (yc - yy)**2
best = np.ones((H, W), bool)
for o in O:
ok, x, y = wcs.radec2pixelxy(o.ra, o.dec)
d2 = (x - xx)**2 + (y - yy)**2
best[d2 < c_d2] = False
del d2
del c_d2, xx, yy
# plt.clf()
# plt.imshow(best, interpolation='nearest', origin='lower', cmap='gray',
# vmin=0, vmax=1)
# ps.savefig()
plt.clf()
plt.imshow(nexp * best,
interpolation='nearest',
origin='lower',
vmin=-0.5,
vmax=hi + 0.5,
cmap=cmap,
extent=extent)
plt.colorbar(ticks=np.arange(hi + 1))
ps.savefig()
plt.clf()
n, b, p = plt.hist(np.clip(nexp[best], 0, hi),
range=(-0.5, hi + 0.5),
bins=hi + 1)
plt.xlim(-0.5, hi + 0.5)
ps.savefig()
print('b', b)
print('n', n)
print('fracs', np.array(n) / np.sum(n))
print('pcts',
', '.join(['%.1f' % f for f in 100. * np.array(n) / np.sum(n)]))
def one_brick(X):
(ibrick, brick) = X
bands = ['g', 'r', 'z']
print('Brick', brick.brickname)
wcs = wcs_for_brick(brick, W=94, H=94, pixscale=10.)
BH, BW = wcs.shape
targetrd = np.array([
wcs.pixelxy2radec(x, y)
for x, y in [(1, 1), (BW, 1), (BW, BH), (1, BH), (1, 1)]
])
survey = LegacySurveyData()
C = survey.ccds_touching_wcs(wcs)
if C is None:
print('No CCDs touching brick')
return None
I = np.flatnonzero(C.ccd_cuts == 0)
if len(I) == 0:
print('No good CCDs touching brick')
return None
C.cut(I)
print(len(C), 'CCDs touching brick')
depths = {}
for band in bands:
d = np.zeros((BH, BW), np.float32)
depths[band] = d
npix = dict([(band, 0) for band in bands])
nexps = dict([(band, 0) for band in bands])
# survey.get_approx_wcs(ccd)
for ccd in C:
#im = survey.get_image_object(ccd)
awcs = survey.get_approx_wcs(ccd)
imh, imw = ccd.height, ccd.width
x0, y0 = 0, 0
x1 = x0 + imw
y1 = y0 + imh
imgpoly = [(1, 1), (1, imh), (imw, imh), (imw, 1)]
ok, tx, ty = awcs.radec2pixelxy(targetrd[:-1, 0], targetrd[:-1, 1])
tpoly = list(zip(tx, ty))
clip = clip_polygon(imgpoly, tpoly)
clip = np.array(clip)
if len(clip) == 0:
continue
x0, y0 = np.floor(clip.min(axis=0)).astype(int)
x1, y1 = np.ceil(clip.max(axis=0)).astype(int)
#slc = slice(y0,y1+1), slice(x0,x1+1)
awcs = awcs.get_subimage(x0, y0, x1 - x0, y1 - y0)
ah, aw = awcs.shape
#print('Image', ccd.expnum, ccd.ccdname, ccd.filter, 'overlap', x0,x1, y0,y1, '->', (1+x1-x0),'x',(1+y1-y0))
# Find bbox in brick space
r, d = awcs.pixelxy2radec([1, 1, aw, aw], [1, ah, ah, 1])
ok, bx, by = wcs.radec2pixelxy(r, d)
bx0 = np.clip(np.round(bx.min()).astype(int) - 1, 0, BW - 1)
bx1 = np.clip(np.round(bx.max()).astype(int) - 1, 0, BW - 1)
by0 = np.clip(np.round(by.min()).astype(int) - 1, 0, BH - 1)
by1 = np.clip(np.round(by.max()).astype(int) - 1, 0, BH - 1)
#print('Brick', bx0,bx1,by0,by1)
band = ccd.filter[0]
assert (band in bands)
ccdzpt = ccd.ccdzpt + 2.5 * np.log10(ccd.exptime)
psf_sigma = ccd.fwhm / 2.35
psfnorm = 1. / (2. * np.sqrt(np.pi) * psf_sigma)
orig_zpscale = zpscale = NanoMaggies.zeropointToScale(ccdzpt)
sig1 = ccd.sig1 / orig_zpscale
detsig1 = sig1 / psfnorm
# print('Image', ccd.expnum, ccd.ccdname, ccd.filter,
# 'PSF depth', -2.5 * (np.log10(5.*detsig1) - 9), 'exptime', ccd.exptime,
# 'sig1', ccd.sig1, 'zpt', ccd.ccdzpt, 'fwhm', ccd.fwhm,
# 'filename', ccd.image_filename.strip())
depths[band][by0:by1 + 1, bx0:bx1 + 1] += (1. / detsig1**2)
npix[band] += (y1 + 1 - y0) * (x1 + 1 - x0)
nexps[band] += 1
for band in bands:
det = np.median(depths[band])
# compute stats for 5-sigma detection
with np.errstate(divide='ignore'):
depth = 5. / np.sqrt(det)
# that's flux in nanomaggies -- convert to mag
depth = -2.5 * (np.log10(depth) - 9)
if not np.isfinite(depth):
depth = 0.
depths[band] = depth
#bricks.get('psfdepth_' + band)[ibrick] = depth
print(brick.brickname, 'median PSF depth', band, ':', depth, 'npix',
npix[band], 'nexp', nexps[band])
#'npix', bricks.get('npix_'+band)[ibrick],
#'nexp', bricks.get('nexp_'+band)[ibrick])
return (npix, nexps, depths)
