def run_ptf(src, tag, opt):
ps = PlotSequence('ptf-%s' % (tag))
stari = src.srci
fns = glob('ptf-%i/*.fits' % stari)
print('Found files:', len(fns))
stamps = []
for fn in fns:
print(' ', fn)
wcs = Sip(fn, 0)
x, y = wcs.radec2pixelxy(src.ra, src.dec)
print(' source at', x, y)
img = pyfits.open(fn)[0].data
print(' image', img.shape)
H, W = img.shape
ix = int(np.round(x))
iy = int(np.round(y))
S = 20
stamp = img[max(0, iy - S):min(H, iy + S),
max(0, ix - S):min(W, ix + S)]
stamps.append(stamp)
N = len(stamps)
cols = int(np.ceil(np.sqrt(N)))
rows = int(np.ceil(N / float(cols)))
plt.clf()
for i, stamp in enumerate(stamps):
plt.subplot(rows, cols, i + 1)
plt.imshow(stamp, interpolation='nearest', origin='lower')
ps.savefig()
def run_ptf(src, tag, opt):
ps = PlotSequence('ptf-%s' % (tag))
stari = src.srci
fns = glob('ptf-%i/*.fits' % stari)
print 'Found files:', len(fns)
stamps = []
for fn in fns:
print ' ', fn
wcs = Sip(fn, 0)
x,y = wcs.radec2pixelxy(src.ra, src.dec)
print ' source at', x, y
img = pyfits.open(fn)[0].data
print ' image', img.shape
H,W = img.shape
ix = int(np.round(x))
iy = int(np.round(y))
S = 20
stamp = img[max(0, iy - S) : min(H, iy+S),
max(0, ix - S) : min(W, ix+S)]
stamps.append(stamp)
N = len(stamps)
cols = int(np.ceil(np.sqrt(N)))
rows = int(np.ceil(N / float(cols)))
plt.clf()
for i,stamp in enumerate(stamps):
plt.subplot(rows, cols, i+1)
plt.imshow(stamp, interpolation='nearest', origin='lower')
ps.savefig()
def read_pv_wcs(self):
'''
Reads the WCS header, returning an `astrometry.util.util.Sip` object.
'''
from astrometry.util.util import Sip
print('Reading WCS from', self.pvwcsfn)
wcs = Sip(self.pvwcsfn)
dra, ddec = self.decals.get_astrometric_zeropoint_for(self)
r, d = wcs.get_crval()
print('Applying astrometric zeropoint:', (dra, ddec))
wcs.set_crval((r + dra, d + ddec))
hdr = fitsio.read_header(self.pvwcsfn)
wcs.version = hdr.get('LEGPIPEV', '')
if len(wcs.version) == 0:
wcs.version = hdr.get('TRACTORV', '').strip()
if len(wcs.version) == 0:
wcs.version = str(os.stat(self.pvwcsfn).st_mtime)
wcs.plver = hdr.get('PLVER', '').strip()
return wcs
def read_pv_wcs(self):
'''
Reads the WCS header, returning an `astrometry.util.util.Sip` object.
'''
from astrometry.util.util import Sip
print('Reading WCS from', self.pvwcsfn)
wcs = Sip(self.pvwcsfn)
dra,ddec = self.decals.get_astrometric_zeropoint_for(self)
r,d = wcs.get_crval()
print('Applying astrometric zeropoint:', (dra,ddec))
wcs.set_crval((r + dra, d + ddec))
hdr = fitsio.read_header(self.pvwcsfn)
wcs.version = hdr.get('LEGPIPEV', '')
if len(wcs.version) == 0:
wcs.version = hdr.get('TRACTORV', '').strip()
if len(wcs.version) == 0:
wcs.version = str(os.stat(self.pvwcsfn).st_mtime)
wcs.plver = hdr.get('PLVER', '').strip()
return wcs
plt.savefig('p2-%02i.png' % i)
sys.exit(0)
ra, dec = 219.577111, 54.52
pixscale = 2.75 / 3600.
#W,H = 2048, 2048
W, H = 512, 512
#W,H = 100,100
cowcs = Tan(ra, dec, (W + 1) / 2., (H + 1) / 2., -pixscale, 0., 0.,
pixscale, W, H)
cowcs.write_to('co.wcs')
if True:
#intfn = '05579a167-w1-int-1b.fits'
intfn = 'wise-frames/9a/05579a/167/05579a167-w1-int-1b.fits'
wcs = Sip(intfn)
pix = fitsio.read(intfn)
pix[np.logical_not(np.isfinite(pix))] = 0.
print('pix', pix.shape, pix.dtype)
for i in range(5):
t0 = time.clock()
Yo, Xo, Yi, Xi, ims = resample_with_wcs(cowcs, wcs, [pix], 3)
t1 = time.clock() - t0
print('C resampling took', t1)
t0 = time.clock()
Yo2, Xo2, Yi2, Xi2, ims2 = resample_with_wcs(cowcs,
wcs, [pix],
3,
cinterp=False,
def plotDistortionFile(sipfn, ext, ncells, **kwargs):
wcs = Sip(sipfn, ext)
if wcs is None:
raise RuntimeError('Failed to open WCS file %s' % sipfn)
plotDistortion(wcs, wcs.get_width(), wcs.get_height(), ncells, **kwargs)
flt1 = gst1.replace('.gst.fits', '.fits').replace('.st.fits', '.fits')
flt2 = gst2.replace('.gst.fits', '.fits').replace('.st.fits', '.fits')
fltout = gstout.replace('.gst.fits', '.fits').replace('.st.fits', '.fits')
print('Reading', gst1)
T1 = fits_table(gst1)
print('Read', len(T1))
T1.about()
print('Reading', gst2)
T2 = fits_table(gst2)
print('Read', len(T2))
T2.about()
print('Reading WCS from', flt1)
wcs1 = Sip(flt1, 0)
wcs1.ensure_inverse_polynomials()
# print('Reading WCS from', flt2)
# wcs2 = Sip(flt2, 0)
# wcs2.ensure_inverse_polynomials()
print('T1 X,Y ranges', T1.x.min(), T1.x.max(), T1.y.min(), T1.y.max())
print('T2 X,Y ranges', T2.x.min(), T2.x.max(), T2.y.min(), T2.y.max())
# ~ 1e-6, 0.0006
# ok,x,y = wcs2.radec2pixelxy(T2.ra, T2.dec)
# print('Scatter wcs x vs catalog x:', np.mean(x - T2.x), np.std(x - T2.x))
# print('Scatter wcs y vs catalog y:', np.mean(y - T2.y), np.std(y - T2.y))
ok, x, y = wcs1.radec2pixelxy(T2.ra, T2.dec)
print('Converted X,Y ranges:', x.min(), x.max(), y.min(), y.max())
def plotDistortionFile(sipfn, ext, ncells, **kwargs):
wcs = Sip(sipfn, ext)
if wcs is None:
raise RuntimeError('Failed to open WCS file %s' % sipfn)
plotDistortion(wcs, wcs.get_width(), wcs.get_height(), ncells, **kwargs)
def main():
# In LSST meas-deblend (on lsst6):
# python examples/suprimePlot.py --data ~dstn/lsst/ACT-data -v 126969 -c 5 --data-range -100 300 --roi 0 500 0 500 --psf psf.fits --image img.fits --sources srcs.fits
from optparse import OptionParser
import sys
parser = OptionParser(usage=('%prog <img> <psf> <srcs>'))
parser.add_option('-v',
'--verbose',
dest='verbose',
action='count',
default=0,
help='Make more verbose')
opt, args = parser.parse_args()
if len(args) != 3:
parser.print_help()
sys.exit(-1)
if opt.verbose == 0:
lvl = logging.INFO
else:
lvl = logging.DEBUG
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
imgfn, psffn, srcfn = args
pimg = pyfits.open(imgfn)
if len(pimg) != 4:
print('Image must have 3 extensions')
sys.exit(-1)
img = pimg[1].data
mask = pimg[2].data
maskhdr = pimg[2].header
var = pimg[3].data
del pimg
print('var', var.shape)
#print var
print('mask', mask.shape)
#print mask
print('img', img.shape)
#print img
mask = mask.astype(np.int16)
for bit in range(16):
on = ((mask & (1 << bit)) != 0)
print('Bit', bit, 'has', np.sum(on), 'pixels set')
'''
MP_BAD = 0
Bit 0 has 2500 pixels set
MP_SAT = 1
Bit 1 has 5771 pixels set
MP_INTRP= 2
Bit 2 has 11269 pixels set
MP_CR = 3
Bit 3 has 136 pixels set
MP_EDGE = 4
Bit 4 has 11856 pixels set
HIERARCH MP_DETECTED = 5
Bit 5 has 37032 pixels set
'''
print('Mask header:', maskhdr)
maskplanes = {}
print('Mask planes:')
for card in maskhdr.ascardlist():
if not card.key.startswith('MP_'):
continue
print(card.value, card.key)
maskplanes[card.key[3:]] = card.value
print('Variance range:', var.min(), var.max())
print('Image median:', np.median(img.ravel()))
invvar = 1. / var
invvar[var == 0] = 0.
invvar[var < 0] = 0.
sig = np.sqrt(np.median(var))
H, W = img.shape
for k, v in maskplanes.items():
plt.clf()
I = ((mask & (1 << v)) != 0)
rgb = np.zeros((H, W, 3))
clipimg = np.clip((img - (-3. * sig)) / (13. * sig), 0, 1)
cimg = clipimg.copy()
cimg[I] = 1
rgb[:, :, 0] = cimg
cimg = clipimg.copy()
cimg[I] = 0
rgb[:, :, 1] = cimg
rgb[:, :, 2] = cimg
plt.imshow(rgb, interpolation='nearest', origin='lower')
plt.title(k)
plt.savefig('mask-%s.png' % k.lower())
badmask = sum([(1 << maskplanes[k])
for k in ['BAD', 'SAT', 'INTRP', 'CR']])
# HACK -- left EDGE sucks
badmask += (1 << maskplanes['EDGE'])
#badmask = (1 << 0) | (1 << 1) | (1 << 2) | (1 << 3)
#badmask |= (1 << 4)
print('Masking out: 0x%x' % badmask)
invvar[(mask & badmask) != 0] = 0.
assert (all(np.isfinite(img.ravel())))
assert (all(np.isfinite(invvar.ravel())))
psf = pyfits.open(psffn)[0].data
print('psf', psf.shape)
psf /= psf.sum()
from tractor.emfit import em_fit_2d
from tractor.fitpsf import em_init_params
# Create Gaussian mixture model PSF approximation.
S = psf.shape[0]
# number of Gaussian components
K = 3
w, mu, sig = em_init_params(K, None, None, None)
II = psf.copy()
II /= II.sum()
# HIDEOUS HACK
II = np.maximum(II, 0)
print('Multi-Gaussian PSF fit...')
xm, ym = -(S / 2), -(S / 2)
em_fit_2d(II, xm, ym, w, mu, sig)
print('w,mu,sig', w, mu, sig)
mypsf = tractor.GaussianMixturePSF(w, mu, sig)
P = mypsf.getPointSourcePatch(S / 2, S / 2)
mn, mx = psf.min(), psf.max()
ima = dict(interpolation='nearest', origin='lower', vmin=mn, vmax=mx)
plt.clf()
plt.subplot(1, 2, 1)
plt.imshow(psf, **ima)
plt.subplot(1, 2, 2)
pimg = np.zeros_like(psf)
P.addTo(pimg)
plt.imshow(pimg, **ima)
plt.savefig('psf.png')
sig = np.sqrt(np.median(var))
plt.clf()
plt.hist(img.ravel(), 100, range=(-3. * sig, 3. * sig))
plt.savefig('imghist.png')
srcs = fits_table(srcfn)
print('Initial:', len(srcs), 'sources')
# Trim sources with x=0 or y=0
srcs = srcs[(srcs.x != 0) * (srcs.y != 0)]
print('Trim on x,y:', len(srcs), 'sources left')
# Zero out nans & infs
for c in ['theta', 'a', 'b']:
I = np.logical_not(np.isfinite(srcs.get(c)))
srcs.get(c)[I] = 0.
# Set sources with flux=NaN to something more sensible...
I = np.logical_not(np.isfinite(srcs.flux))
srcs.flux[I] = 1.
# Sort sources by flux.
srcs = srcs[np.argsort(-srcs.flux)]
# Trim sources that are way outside the image.
margin = 8. * np.maximum(srcs.a, srcs.b)
H, W = img.shape
srcs = srcs[(srcs.x > -margin) * (srcs.y > -margin) *
(srcs.x < (W + margin) * (srcs.y < (H + margin)))]
print('Trim out-of-bounds:', len(srcs), 'sources left')
wcs = tractor.FitsWcs(Sip(imgfn, 1))
#wcs = tractor.NullWCS()
timg = tractor.Image(data=img,
invvar=invvar,
psf=mypsf,
wcs=wcs,
sky=tractor.ConstantSky(0.),
photocal=tractor.NullPhotoCal(),
name='image')
inverr = timg.getInvError()
assert (all(np.isfinite(inverr.ravel())))
tsrcs = []
for s in srcs:
#pos = tractor.PixPos(s.x, s.y)
pos = tractor.RaDecPos(s.ra, s.dec)
if s.a > 0 and s.b > 0:
eflux = tractor.Flux(s.flux / 2.)
dflux = tractor.Flux(s.flux / 2.)
re, ab, phi = s.a, s.b / s.a, 90. - s.theta
eshape = gal.GalaxyShape(re, ab, phi)
dshape = gal.GalaxyShape(re, ab, phi)
print('Fluxes', eflux, dflux)
tsrc = gal.CompositeGalaxy(pos, eflux, eshape, dflux, dshape)
else:
flux = tractor.Flux(s.flux)
print('Flux', flux)
tsrc = tractor.PointSource(pos, flux)
tsrcs.append(tsrc)
chug = tractor.Tractor([timg])
for src in tsrcs:
if chug.getModelPatch(timg, src) is None:
print('Dropping non-overlapping source:', src)
continue
chug.addSource(src)
print('Kept a total of', len(chug.catalog), 'sources')
ima = dict(interpolation='nearest',
origin='lower',
vmin=-3. * sig,
vmax=10. * sig)
chia = dict(interpolation='nearest', origin='lower', vmin=-5., vmax=5.)
plt.clf()
plt.imshow(img, **ima)
plt.colorbar()
plt.savefig('img.png')
plt.clf()
plt.imshow(invvar, interpolation='nearest', origin='lower')
plt.colorbar()
plt.savefig('invvar.png')
mod = chug.getModelImages()[0]
plt.clf()
plt.imshow(mod, **ima)
plt.colorbar()
plt.savefig('mod-0.png')
chi = chug.getChiImage(0)
plt.clf()
plt.imshow(chi, **chia)
plt.colorbar()
plt.savefig('chi-0.png')
for step in range(5):
cat = chug.getCatalog()
for src in cat:
if chug.getModelPatch(timg, src) is None:
print('Dropping non-overlapping source:', src)
chug.removeSource(src)
print('Kept a total of', len(chug.catalog), 'sources')
#cat = chug.getCatalog()
#for i,src in enumerate([]):
#for i,src in enumerate(chug.getCatalog()):
#for i in range(len(cat)):
i = 0
while i < len(cat):
src = cat[i]
#print 'Step', i
#for j,s in enumerate(cat):
# x,y = timg.getWcs().positionToPixel(s, s.getPosition())
# print ' ',
# if j == i:
# print '*',
# print '(%6.1f, %6.1f)'%(x,y), s
print('Optimizing source', i, 'of', len(cat))
x, y = timg.getWcs().positionToPixel(src.getPosition(), src)
print('(%6.1f, %6.1f)' % (x, y), src)
# pre = src.getModelPatch(timg)
s1 = str(src)
print('src1 ', s1)
dlnp1, X, a = chug.optimizeCatalogFluxes(srcs=[src])
s2 = str(src)
dlnp2, X, a = chug.optimizeCatalogAtFixedComplexityStep(srcs=[src],
sky=False)
s3 = str(src)
#post = src.getModelPatch(timg)
print('src1 ', s1)
print('src2 ', s2)
print('src3 ', s3)
print('dlnp', dlnp1, dlnp2)
if chug.getModelPatch(timg, src) is None:
print('After optimizing, no overlap!')
print('Removing source', src)
chug.removeSource(src)
i -= 1
i += 1
# plt.clf()
# plt.subplot(2,2,1)
# img = timg.getImage()
# (x0,x1,y0,y1) = pre.getExtent()
# plt.imshow(img, **ima)
# ax = plt.axis()
# plt.plot([x0,x0,x1,x1,x0], [y0,y1,y1,y0,y0], 'k-', lw=2)
# plt.axis(ax)
# plt.subplot(2,2,3)
# plt.imshow(pre.getImage(), **ima)
# plt.subplot(2,2,4)
# plt.imshow(post.getImage(), **ima)
# plt.savefig('prepost-s%i-s%03i.png' % (step, i))
#
# mod = chug.getModelImages()[0]
# plt.clf()
# plt.imshow(mod, **ima)
# plt.colorbar()
# plt.savefig('mod-s%i-s%03i.png' % (step, i))
# chi = chug.getChiImage(0)
# plt.clf()
# plt.imshow(chi, **chia)
# plt.colorbar()
# plt.savefig('chi-s%i-s%03i.png' % (step, i))
#dlnp,x,a = chug.optimizeCatalogFluxes()
#print 'fluxes: dlnp', dlnp
#dlnp,x,a = chug.optimizeCatalogAtFixedComplexityStep()
#print 'opt: dlnp', dlnp
mod = chug.getModelImages()[0]
plt.clf()
plt.imshow(mod, **ima)
plt.colorbar()
plt.savefig('mod-%i.png' % (step + 1))
chi = chug.getChiImage(0)
plt.clf()
plt.imshow(chi, **chia)
plt.colorbar()
plt.savefig('chi-%i.png' % (step + 1))
return
for step in range(5):
chug.optimizeCatalogFluxes()
mod = chug.getModelImages()[0]
plt.clf()
plt.imshow(mod, **ima)
plt.colorbar()
plt.savefig('mod-s%i.png' % step)
chi = chug.getChiImage(0)
plt.clf()
plt.imshow(chi, **chia)
plt.colorbar()
plt.savefig('chi-s%i.png' % step)
def main():
decals = Decals()
catpattern = 'pipebrick-cats/tractor-phot-b%06i.fits'
ra,dec = 242, 7
# Region-of-interest, in pixels: x0, x1, y0, y1
#roi = None
roi = [500, 1000, 500, 1000]
if roi is not None:
x0,x1,y0,y1 = roi
#expnum = 346623
#ccdname = 'N12'
#chips = decals.find_ccds(expnum=expnum, extname=ccdname)
#print 'Found', len(chips), 'chips for expnum', expnum, 'extname', ccdname
#if len(chips) != 1:
#return False
chips = decals.get_ccds()
D = np.argsort(np.hypot(chips.ra - ra, chips.dec - dec))
print('Closest chip:', chips[D[0]])
chips = [chips[D[0]]]
im = DecamImage(decals, chips[0])
print('Image:', im)
targetwcs = Sip(im.wcsfn)
if roi is not None:
targetwcs = targetwcs.get_subimage(x0, y0, x1-x0, y1-y0)
r0,r1,d0,d1 = targetwcs.radec_bounds()
# ~ 30-pixel margin
margin = 2e-3
if r0 > r1:
# RA wrap-around
TT = [brick_catalog_for_radec_box(ra,rb, d0-margin,d1+margin,
decals, catpattern)
for (ra,rb) in [(0, r1+margin), (r0-margin, 360.)]]
T = merge_tables(TT)
T._header = TT[0]._header
else:
T = brick_catalog_for_radec_box(r0-margin,r1+margin,d0-margin,
d1+margin, decals, catpattern)
print('Got', len(T), 'catalog entries within range')
cat = read_fits_catalog(T, T._header)
print('Got', len(cat), 'catalog objects')
print('Switching ellipse parameterizations')
switch_to_soft_ellipses(cat)
keepcat = []
for src in cat:
if not np.all(np.isfinite(src.getParams())):
print('Src has infinite params:', src)
continue
if isinstance(src, FixedCompositeGalaxy):
f = src.fracDev.getClippedValue()
if f == 0.:
src = ExpGalaxy(src.pos, src.brightness, src.shapeExp)
elif f == 1.:
src = DevGalaxy(src.pos, src.brightness, src.shapeDev)
keepcat.append(src)
cat = keepcat
slc = None
if roi is not None:
slc = slice(y0,y1), slice(x0,x1)
tim = im.get_tractor_image(slc=slc)
print('Got', tim)
tim.psfex.fitSavedData(*tim.psfex.splinedata)
tim.psfex.radius = 20
tim.psf = CachingPsfEx.fromPsfEx(tim.psfex)
tractor = Tractor([tim], cat)
print('Created', tractor)
mod = tractor.getModelImage(0)
plt.clf()
dimshow(tim.getImage(), **tim.ima)
plt.title('Image')
plt.savefig('1.png')
plt.clf()
dimshow(mod, **tim.ima)
plt.title('Model')
plt.savefig('2.png')
ok,x,y = targetwcs.radec2pixelxy([src.getPosition().ra for src in cat],
[src.getPosition().dec for src in cat])
ax = plt.axis()
plt.plot(x, y, 'rx')
#plt.savefig('3.png')
plt.axis(ax)
plt.title('Sources')
plt.savefig('3.png')
bands = [im.band]
import runbrick
runbrick.photoobjdir = '.'
scat,T = get_sdss_sources(bands, targetwcs, local=False)
print('Got', len(scat), 'SDSS sources in bounds')
stractor = Tractor([tim], scat)
print('Created', stractor)
smod = stractor.getModelImage(0)
plt.clf()
dimshow(smod, **tim.ima)
plt.title('SDSS model')
plt.savefig('4.png')
def map_sdss(req,
ver,
zoom,
x,
y,
savecache=None,
tag='sdss',
get_images=False,
ignoreCached=False,
wcs=None,
forcecache=False,
forcescale=None,
bestOnly=False,
bands='gri',
**kwargs):
from decals import settings
if savecache is None:
savecache = settings.SAVE_CACHE
zoom = int(zoom)
zoomscale = 2.**zoom
x = int(x)
y = int(y)
if zoom < 0 or x < 0 or y < 0 or x >= zoomscale or y >= zoomscale:
raise RuntimeError('Invalid zoom,x,y %i,%i,%i' % (zoom, x, y))
ver = int(ver)
if not ver in tileversions[tag]:
raise RuntimeError('Invalid version %i for tag %s' % (ver, tag))
basedir = settings.DATA_DIR
tilefn = os.path.join(basedir, 'tiles', tag,
'%i/%i/%i/%i.jpg' % (ver, zoom, x, y))
if os.path.exists(tilefn) and not ignoreCached:
if get_images:
return None
return send_file(tilefn,
'image/jpeg',
expires=oneyear,
modsince=req.META.get('HTTP_IF_MODIFIED_SINCE'))
if not savecache:
import tempfile
f, tilefn = tempfile.mkstemp(suffix='.jpg')
os.close(f)
if wcs is None:
try:
wcs, W, H, zoomscale, zoom, x, y = get_tile_wcs(zoom, x, y)
except RuntimeError as e:
if get_images:
return None
return HttpResponse(e.strerror)
else:
W = wcs.get_width()
H = wcs.get_height()
from astrometry.util.fits import fits_table
import numpy as np
from astrometry.libkd.spherematch import tree_build_radec, tree_search_radec
from astrometry.util.starutil_numpy import degrees_between, arcsec_between
from astrometry.util.resample import resample_with_wcs, OverlapError
from astrometry.util.util import Tan, Sip
import fitsio
print('Tile wcs: center', wcs.radec_center(), 'pixel scale',
wcs.pixel_scale())
global w_flist
global w_flist_tree
if w_flist is None:
w_flist = fits_table(
os.path.join(settings.DATA_DIR, 'sdss', 'window_flist.fits'),
columns=['run', 'rerun', 'camcol', 'field', 'ra', 'dec', 'score'])
print('Read', len(w_flist), 'window_flist entries')
w_flist.cut(w_flist.rerun == '301')
print('Cut to', len(w_flist), 'in rerun 301')
w_flist_tree = tree_build_radec(w_flist.ra, w_flist.dec)
# SDSS field size
radius = 1.01 * np.hypot(10., 14.) / 2. / 60.
# leaflet tile size
ra, dec = wcs.pixelxy2radec(W / 2., H / 2.)[-2:]
r0, d0 = wcs.pixelxy2radec(1, 1)[-2:]
r1, d1 = wcs.pixelxy2radec(W, H)[-2:]
radius = radius + max(degrees_between(ra, dec, r0, d0),
degrees_between(ra, dec, r1, d1))
J = tree_search_radec(w_flist_tree, ra, dec, radius)
print(len(J), 'overlapping SDSS fields found')
if len(J) == 0:
if get_images:
return None
if forcecache:
# create symlink to blank.jpg!
trymakedirs(tilefn)
src = os.path.join(settings.STATIC_ROOT, 'blank.jpg')
if os.path.exists(tilefn):
os.unlink(tilefn)
os.symlink(src, tilefn)
print('Symlinked', tilefn, '->', src)
from django.http import HttpResponseRedirect
return HttpResponseRedirect(settings.STATIC_URL + 'blank.jpg')
ww = [1, W * 0.25, W * 0.5, W * 0.75, W]
hh = [1, H * 0.25, H * 0.5, H * 0.75, H]
r, d = wcs.pixelxy2radec(
[1] * len(hh) + ww + [W] * len(hh) + list(reversed(ww)),
hh + [1] * len(ww) + list(reversed(hh)) + [H] * len(ww))[-2:]
scaled = 0
scalepat = None
scaledir = 'sdss'
if zoom <= 13 and forcescale is None:
# Get *actual* pixel scales at the top & bottom
r1, d1 = wcs.pixelxy2radec(W / 2., H)[-2:]
r2, d2 = wcs.pixelxy2radec(W / 2., H - 1.)[-2:]
r3, d3 = wcs.pixelxy2radec(W / 2., 1.)[-2:]
r4, d4 = wcs.pixelxy2radec(W / 2., 2.)[-2:]
# Take the min = most zoomed-in
scale = min(arcsec_between(r1, d1, r2, d2),
arcsec_between(r3, d3, r4, d4))
native_scale = 0.396
scaled = int(np.floor(np.log2(scale / native_scale)))
print('Zoom:', zoom, 'x,y', x, y, 'Tile pixel scale:', scale,
'Scale step:', scaled)
scaled = np.clip(scaled, 1, 7)
dirnm = os.path.join(basedir, 'scaled', scaledir)
scalepat = os.path.join(
dirnm, '%(scale)i%(band)s', '%(rerun)s', '%(run)i', '%(camcol)i',
'sdss-%(run)i-%(camcol)i-%(field)i-%(band)s.fits')
if forcescale is not None:
scaled = forcescale
rimgs = [np.zeros((H, W), np.float32) for band in bands]
rns = [np.zeros((H, W), np.float32) for band in bands]
from astrometry.sdss import AsTransWrapper, DR9
sdss = DR9(basedir=settings.SDSS_DIR)
sdss.saveUnzippedFiles(settings.SDSS_DIR)
#sdss.setFitsioReadBZ2()
if settings.SDSS_PHOTOOBJS:
sdss.useLocalTree(photoObjs=settings.SDSS_PHOTOOBJS,
resolve=settings.SDSS_RESOLVE)
for jnum, j in enumerate(J):
print('SDSS field', jnum, 'of', len(J), 'for zoom', zoom, 'x', x, 'y',
y)
im = w_flist[j]
if im.score >= 0.5:
weight = 1.
else:
weight = 0.001
for band, rimg, rn in zip(bands, rimgs, rns):
if im.rerun != '301':
continue
tmpsuff = '.tmp%08i' % np.random.randint(100000000)
basefn = sdss.retrieve('frame',
im.run,
im.camcol,
field=im.field,
band=band,
rerun=im.rerun,
tempsuffix=tmpsuff)
if scaled > 0:
fnargs = dict(band=band,
rerun=im.rerun,
run=im.run,
camcol=im.camcol,
field=im.field)
fn = get_scaled(scalepat,
fnargs,
scaled,
basefn,
read_base_wcs=read_astrans,
read_wcs=read_sip_wcs)
print('get_scaled:', fn)
else:
fn = basefn
frame = None
if fn == basefn:
frame = sdss.readFrame(im.run,
im.camcol,
im.field,
band,
filename=fn)
h, w = frame.getImageShape()
# Trim off the overlapping top of the image
# Wimp out and instead of trimming 128 pix, trim 124!
trim = 124
subh = h - trim
astrans = frame.getAsTrans()
fwcs = AsTransWrapper(astrans, w, subh)
fullimg = frame.getImage()
fullimg = fullimg[:-trim, :]
else:
fwcs = Sip(fn)
fitsimg = fitsio.FITS(fn)[0]
h, w = fitsimg.get_info()['dims']
fullimg = fitsimg.read()
try:
#Yo,Xo,Yi,Xi,nil = resample_with_wcs(wcs, fwcs, [], 3)
Yo, Xo, Yi, Xi, [resamp
] = resample_with_wcs(wcs, fwcs, [fullimg], 2)
except OverlapError:
continue
if len(Xi) == 0:
#print 'No overlap'
continue
if sdssps is not None:
x0 = Xi.min()
x1 = Xi.max()
y0 = Yi.min()
y1 = Yi.max()
slc = (slice(y0, y1 + 1), slice(x0, x1 + 1))
if frame is not None:
img = frame.getImageSlice(slc)
else:
img = fitsimg[slc]
#rimg[Yo,Xo] += img[Yi-y0, Xi-x0]
if bestOnly:
K = np.flatnonzero(weight > rn[Yo, Xo])
print('Updating', len(K), 'of', len(Yo), 'pixels')
if len(K):
rimg[Yo[K], Xo[K]] = resamp[K] * weight
rn[Yo[K], Xo[K]] = weight
else:
rimg[Yo, Xo] += resamp * weight
rn[Yo, Xo] += weight
if sdssps is not None:
# goodpix = np.ones(img.shape, bool)
# fpM = sdss.readFpM(im.run, im.camcol, im.field, band)
# for plane in [ 'INTERP', 'SATUR', 'CR', 'GHOST' ]:
# fpM.setMaskedPixels(plane, goodpix, False, roi=[x0,x1,y0,y1])
plt.clf()
#ima = dict(vmin=-0.05, vmax=0.5)
#ima = dict(vmin=-0.5, vmax=2.)
ima = dict(vmax=np.percentile(img, 99))
plt.subplot(2, 3, 1)
dimshow(img, ticks=False, **ima)
plt.title('image')
rthis = np.zeros_like(rimg)
#rthis[Yo,Xo] += img[Yi-y0, Xi-x0]
rthis[Yo, Xo] += resamp
plt.subplot(2, 3, 2)
dimshow(rthis, ticks=False, **ima)
plt.title('resampled')
# plt.subplot(2,3,3)
# dimshow(goodpix, ticks=False, vmin=0, vmax=1)
# plt.title('good pix')
plt.subplot(2, 3, 4)
dimshow(rimg / np.maximum(rn, 1), ticks=False, **ima)
plt.title('coadd')
plt.subplot(2, 3, 5)
dimshow(rn, vmin=0, ticks=False)
plt.title('coverage: max %i' % rn.max())
plt.subplot(2, 3, 6)
rgb = sdss_rgb(
[rimg / np.maximum(rn, 1) for rimg, rn in zip(rimgs, rns)],
bands)
dimshow(rgb)
plt.suptitle('SDSS %s, R/C/F %i/%i/%i' %
(band, im.run, im.camcol, im.field))
sdssps.savefig()
for rimg, rn in zip(rimgs, rns):
rimg /= np.maximum(rn, 1e-3)
del rns
if get_images:
return rimgs
rgb = sdss_rgb(rimgs, bands)
trymakedirs(tilefn)
save_jpeg(tilefn, rgb)
print('Wrote', tilefn)
return send_file(tilefn, 'image/jpeg', unlink=(not savecache))
I = np.flatnonzero(T.flt == fltmerged)
if len(I) != 1:
print('Found %i match to merged FLT filename "%s"; expected 1' % (len(I), fltmerged))
sys.exit(-1)
i = I[0]
#aff = T[i]
affs = Affine.fromTable(T[I])
aff = affs[0]
print('Will write to', scriptout)
fscript = open(scriptout, 'w')
print('Reading WCS from', flt2)
wcsext = 0
wcs2 = Sip(flt2, wcsext)
wcs2.ensure_inverse_polynomials()
tan = wcs2.wcstan
aff.applyToWcsObject(tan)
tt = NamedTemporaryFile()
outfn = tt.name
wcs2.write_to(outfn)
info = parse_flt_filename(flt2)
name = info['name']
chip = info['chip']
extmap = {1:1, 2:4, 0:1}
scriptfn = '%s_flt.fits[%i]' % (name, extmap[chip])
write_update_script(flt2, outfn, scriptfn, fscript, inext=wcsext)
def main():
decals = Decals()
catpattern = 'pipebrick-cats/tractor-phot-b%06i.fits'
ra, dec = 242, 7
# Region-of-interest, in pixels: x0, x1, y0, y1
#roi = None
roi = [500, 1000, 500, 1000]
if roi is not None:
x0, x1, y0, y1 = roi
#expnum = 346623
#ccdname = 'N12'
#chips = decals.find_ccds(expnum=expnum, extname=ccdname)
#print 'Found', len(chips), 'chips for expnum', expnum, 'extname', ccdname
#if len(chips) != 1:
#return False
chips = decals.get_ccds()
D = np.argsort(np.hypot(chips.ra - ra, chips.dec - dec))
print('Closest chip:', chips[D[0]])
chips = [chips[D[0]]]
im = DecamImage(decals, chips[0])
print('Image:', im)
targetwcs = Sip(im.wcsfn)
if roi is not None:
targetwcs = targetwcs.get_subimage(x0, y0, x1 - x0, y1 - y0)
r0, r1, d0, d1 = targetwcs.radec_bounds()
# ~ 30-pixel margin
margin = 2e-3
if r0 > r1:
# RA wrap-around
TT = [
brick_catalog_for_radec_box(ra, rb, d0 - margin, d1 + margin,
decals, catpattern)
for (ra, rb) in [(0, r1 + margin), (r0 - margin, 360.)]
]
T = merge_tables(TT)
T._header = TT[0]._header
else:
T = brick_catalog_for_radec_box(r0 - margin, r1 + margin, d0 - margin,
d1 + margin, decals, catpattern)
print('Got', len(T), 'catalog entries within range')
cat = read_fits_catalog(T, T._header)
print('Got', len(cat), 'catalog objects')
print('Switching ellipse parameterizations')
switch_to_soft_ellipses(cat)
keepcat = []
for src in cat:
if not np.all(np.isfinite(src.getParams())):
print('Src has infinite params:', src)
continue
if isinstance(src, FixedCompositeGalaxy):
f = src.fracDev.getClippedValue()
if f == 0.:
src = ExpGalaxy(src.pos, src.brightness, src.shapeExp)
elif f == 1.:
src = DevGalaxy(src.pos, src.brightness, src.shapeDev)
keepcat.append(src)
cat = keepcat
slc = None
if roi is not None:
slc = slice(y0, y1), slice(x0, x1)
tim = im.get_tractor_image(slc=slc)
print('Got', tim)
tim.psfex.fitSavedData(*tim.psfex.splinedata)
tim.psfex.radius = 20
tim.psf = CachingPsfEx.fromPsfEx(tim.psfex)
tractor = Tractor([tim], cat)
print('Created', tractor)
mod = tractor.getModelImage(0)
plt.clf()
dimshow(tim.getImage(), **tim.ima)
plt.title('Image')
plt.savefig('1.png')
plt.clf()
dimshow(mod, **tim.ima)
plt.title('Model')
plt.savefig('2.png')
ok, x, y = targetwcs.radec2pixelxy([src.getPosition().ra for src in cat],
[src.getPosition().dec for src in cat])
ax = plt.axis()
plt.plot(x, y, 'rx')
#plt.savefig('3.png')
plt.axis(ax)
plt.title('Sources')
plt.savefig('3.png')
bands = [im.band]
import runbrick
runbrick.photoobjdir = '.'
scat, T = get_sdss_sources(bands, targetwcs, local=False)
print('Got', len(scat), 'SDSS sources in bounds')
stractor = Tractor([tim], scat)
print('Created', stractor)
smod = stractor.getModelImage(0)
plt.clf()
dimshow(smod, **tim.ima)
plt.title('SDSS model')
plt.savefig('4.png')
def read_wise_level1b(basefn, radecroi=None, radecrad=None, filtermap=None,
nanomaggies=False, mask_gz=False, unc_gz=False,
sipwcs=False, constantInvvar=False,
roi=None,
zrsigs=[-3, 10],
):
if filtermap is None:
filtermap = {}
intfn = basefn + '-int-1b.fits'
maskfn = basefn + '-msk-1b.fits'
if mask_gz:
maskfn = maskfn + '.gz'
uncfn = basefn + '-unc-1b.fits'
if unc_gz:
uncfn = uncfn + '.gz'
logger.debug('intensity image %s' % intfn)
logger.debug('mask image %s' % maskfn)
logger.debug('uncertainty image %s' % uncfn)
if sipwcs:
wcs = Sip(intfn, 0)
twcs = tractor.ConstantFitsWcs(wcs)
else:
twcs = tractor.ConstantFitsWcs(intfn)
# Read enough of the image to get its size
Fint = fitsio.FITS(intfn)
H, W = Fint[0].get_info()['dims']
if radecrad is not None:
r, d, rad = radecrad
x, y = twcs.positionToPixel(tractor.RaDecPos(r, d))
pixrad = rad / (twcs.pixel_scale() / 3600.)
print('Tractor WCS:', twcs)
print('RA,Dec,rad', r, d, rad, 'becomes x,y,pixrad', x, y, pixrad)
roi = (x - pixrad, x + pixrad + 1, y - pixrad, y + pixrad + 1)
if radecroi is not None:
ralo, rahi, declo, dechi = radecroi
xy = [twcs.positionToPixel(tractor.RaDecPos(r, d))
for r, d in [(ralo, declo), (ralo, dechi), (rahi, declo), (rahi, dechi)]]
xy = np.array(xy)
x0, x1 = xy[:, 0].min(), xy[:, 0].max()
y0, y1 = xy[:, 1].min(), xy[:, 1].max()
print('RA,Dec ROI', ralo, rahi, declo, dechi,
'becomes x,y ROI', x0, x1, y0, y1)
roi = (x0, x1 + 1, y0, y1 + 1)
if roi is not None:
x0, x1, y0, y1 = roi
x0 = int(np.floor(x0))
x1 = int(np.ceil(x1))
y0 = int(np.floor(y0))
y1 = int(np.ceil(y1))
roi = (x0, x1, y0, y1)
# Clip to image size...
x0 = np.clip(x0, 0, W)
x1 = np.clip(x1, 0, W)
y0 = np.clip(y0, 0, H)
y1 = np.clip(y1, 0, H)
if x0 == x1 or y0 == y1:
print('ROI is empty')
return None
assert(x0 < x1)
assert(y0 < y1)
#roi = (x0,x1,y0,y1)
twcs.setX0Y0(x0, y0)
else:
x0, x1, y0, y1 = 0, W, 0, H
roi = (x0, x1, y0, y1)
ihdr = Fint[0].read_header()
data = Fint[0][y0:y1, x0:x1]
logger.debug('Read %s intensity' % (str(data.shape)))
band = ihdr['BAND']
F = fitsio.FITS(uncfn)
assert(F[0].get_info()['dims'] == [H, W])
unc = F[0][y0:y1, x0:x1]
F = fitsio.FITS(maskfn)
assert(F[0].get_info()['dims'] == [H, W])
mask = F[0][y0:y1, x0:x1]
# HACK -- circular Gaussian PSF of fixed size...
# in arcsec
fwhms = {1: 6.1, 2: 6.4, 3: 6.5, 4: 12.0}
# -> sigma in pixels
sig = fwhms[band] / 2.35 / twcs.pixel_scale()
# print 'PSF sigma', sig, 'pixels'
tpsf = tractor.NCircularGaussianPSF([sig], [1.])
filter = 'w%i' % band
if filtermap:
filter = filtermap.get(filter, filter)
zp = ihdr['MAGZP']
if nanomaggies:
photocal = tractor.LinearPhotoCal(tractor.NanoMaggies.zeropointToScale(zp),
band=filter)
else:
photocal = tractor.MagsPhotoCal(filter, zp)
# print 'Image median:', np.median(data)
# print 'unc median:', np.median(unc)
sky = np.median(data)
tsky = tractor.ConstantSky(sky)
name = 'WISE ' + ihdr['FRSETID'] + ' W%i' % band
# Mask bits, from
# http://wise2.ipac.caltech.edu/docs/release/allsky/expsup/sec4_4a.html#maskdef
# 0 from static mask: excessively noisy due to high dark current alone
# 1 from static mask: generally noisy [includes bit 0]
# 2 from static mask: dead or very low responsivity
# 3 from static mask: low responsivity or low dark current
# 4 from static mask: high responsivity or high dark current
# 5 from static mask: saturated anywhere in ramp
# 6 from static mask: high, uncertain, or unreliable non-linearity
# 7 from static mask: known broken hardware pixel or excessively noisy responsivity estimate [may include bit 1]
# 8 reserved
# 9 broken pixel or negative slope fit value (downlink value = 32767)
# 10 saturated in sample read 1 (down-link value = 32753)
# 11 saturated in sample read 2 (down-link value = 32754)
# 12 saturated in sample read 3 (down-link value = 32755)
# 13 saturated in sample read 4 (down-link value = 32756)
# 14 saturated in sample read 5 (down-link value = 32757)
# 15 saturated in sample read 6 (down-link value = 32758)
# 16 saturated in sample read 7 (down-link value = 32759)
# 17 saturated in sample read 8 (down-link value = 32760)
# 18 saturated in sample read 9 (down-link value = 32761)
# 19 reserved
# 20 reserved
# 21 new/transient bad pixel from dynamic masking
# 22 reserved
# 23 reserved
# 24 reserved
# 25 reserved
# 26 non-linearity correction unreliable
# 27 contains cosmic-ray or outlier that cannot be classified (from temporal outlier rejection in multi-frame pipeline)
# 28 contains positive or negative spike-outlier
# 29 reserved
# 30 reserved
# 31 not used: sign bit
goodmask = ((mask & sum([1 << bit for bit in [0, 1, 2, 3, 4, 5, 6, 7, 9,
10, 11, 12, 13, 14, 15, 16, 17, 18,
21, 26, 27, 28]])) == 0)
sigma1 = np.median(unc[goodmask])
zr = np.array(zrsigs) * sigma1 + sky
# constant
cinvvar = np.zeros_like(data)
cinvvar[goodmask] = 1. / (sigma1**2)
# varying
vinvvar = np.zeros_like(data)
vinvvar[goodmask] = 1. / (unc[goodmask])**2
bad = np.flatnonzero(np.logical_not(np.isfinite(vinvvar)))
if len(bad):
vinvvar.flat[bad] = 0.
cinvvar.flat[bad] = 0.
data.flat[bad] = sky
if constantInvvar:
invvar = cinvvar
else:
invvar = vinvvar
# avoid NaNs
data[np.logical_not(goodmask)] = sky
mjd = ihdr['MJD_OBS']
time = TAITime(None, mjd=mjd)
tim = tractor.Image(data=data, invvar=invvar, psf=tpsf, wcs=twcs,
sky=tsky, photocal=photocal, time=time, name=name, zr=zr,
domask=False)
tim.extent = [x0, x1, y0, y1]
tim.sigma1 = sigma1
#tim.roi = roi
# FIXME
tim.maskplane = mask
tim.uncplane = unc
tim.goodmask = goodmask
# carry both around for retrofitting
tim.vinvvar = vinvvar
tim.cinvvar = cinvvar
return tim
chipnames = []
corrs = []
for im in ims:
if not os.path.exists(im.corrfn):
print 'NO SUCH FILE:', im.corrfn
continue
sdss = fits_table(im.sdssfn)
#sefn = im.morphfn
sefn = im.sefn
print 'Looking for', sefn
morph = fits_table(sefn, hdu=2)
wcs = Sip(im.wcsfn)
if len(sdss) == 0:
print 'EMPTY:', im.sdssfn
continue
if len(morph) == 0:
print 'EMPTY:', im.morphfn
continue
print len(sdss), 'SDSS sources from', im.sdssfn
print len(morph), 'SE sources from', sefn
morph.ra, morph.dec = wcs.pixelxy2radec(morph.x_image,
morph.y_image)
I, J, d = match_radec(morph.ra, morph.dec, sdss.ra, sdss.dec,
0.5 / 3600.)
corr = sdss[J]
corr.add_columns_from(morph[I])
chipnames = []
corrs = []
for im in ims:
if not os.path.exists(im.corrfn):
print 'NO SUCH FILE:', im.corrfn
continue
sdss = fits_table(im.sdssfn)
#sefn = im.morphfn
sefn = im.sefn
print 'Looking for', sefn
morph = fits_table(sefn, hdu=2)
wcs = Sip(im.wcsfn)
if len(sdss) == 0:
print 'EMPTY:', im.sdssfn
continue
if len(morph) == 0:
print 'EMPTY:', im.morphfn
continue
print len(sdss), 'SDSS sources from', im.sdssfn
print len(morph), 'SE sources from', sefn
morph.ra,morph.dec = wcs.pixelxy2radec(morph.x_image, morph.y_image)
I,J,d = match_radec(morph.ra, morph.dec, sdss.ra, sdss.dec, 0.5/3600.)
corr = sdss[J]
corr.add_columns_from(morph[I])
chipnames.append(im.extname)
flt1 = gst1.replace('.gst.fits', '.fits').replace('.st.fits', '.fits')
flt2 = gst2.replace('.gst.fits', '.fits').replace('.st.fits', '.fits')
fltout = gstout.replace('.gst.fits', '.fits').replace('.st.fits', '.fits')
print('Reading', gst1)
T1 = fits_table(gst1)
print('Read', len(T1))
T1.about()
print('Reading', gst2)
T2 = fits_table(gst2)
print('Read', len(T2))
T2.about()
print('Reading WCS from', flt1)
wcs1 = Sip(flt1, 0)
wcs1.ensure_inverse_polynomials()
# print('Reading WCS from', flt2)
# wcs2 = Sip(flt2, 0)
# wcs2.ensure_inverse_polynomials()
print('T1 X,Y ranges', T1.x.min(), T1.x.max(), T1.y.min(), T1.y.max())
print('T2 X,Y ranges', T2.x.min(), T2.x.max(), T2.y.min(), T2.y.max())
# ~ 1e-6, 0.0006
# ok,x,y = wcs2.radec2pixelxy(T2.ra, T2.dec)
# print('Scatter wcs x vs catalog x:', np.mean(x - T2.x), np.std(x - T2.x))
# print('Scatter wcs y vs catalog y:', np.mean(y - T2.y), np.std(y - T2.y))
ok,x,y = wcs1.radec2pixelxy(T2.ra, T2.dec)
print('Converted X,Y ranges:', x.min(), x.max(), y.min(), y.max())
