def main():
#img = fitsio.read('347736-S5.fits')
img = fitsio.read('392772-N29.fits')
print(img.shape)
img = img.T.copy()
mm = np.median(img)
img -= mm
ps = PlotSequence('sky')
lo,hi = np.percentile(img, [20,80])
ima = dict(vmin=lo, vmax=hi, interpolation='nearest', origin='lower',
cmap='gray')
plt.clf()
plt.imshow(img, **ima)
ps.savefig()
# PAD
padimg = np.zeros((2048, 4096))
padimg[1:-1, 1:-1] = img
img = padimg
from tractor.splinesky import SplineSky
from scipy.ndimage.morphology import binary_dilation
from astrometry.util.util import median_smooth
# # Estimate per-pixel noise via Blanton's 5-pixel MAD
slice1 = (slice(0,-5,10),slice(0,-5,10))
slice2 = (slice(5,None,10),slice(5,None,10))
mad = np.median(np.abs(img[slice1] - img[slice2]).ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print('sig1 estimate:', sig1)
mask = np.zeros(img.shape, bool)
mask[binary_dilation(img > 5*sig1, iterations=5)] = True
for mm in [None, mask]:
notmm = None
if mm is not None:
notmm = np.logical_not(mm)
sky = SplineSky.BlantonMethod(img, notmm, 512)
skyfn = 'sky-%s.fits' % (mm is not None and 'mask' or 'nomask')
sky.write_fits(skyfn)
from tractor.utils import get_class_from_name
print('Reading sky model from', skyfn)
hdr = fitsio.read_header(skyfn)
skyclass = hdr['SKY']
clazz = get_class_from_name(skyclass)
if getattr(clazz, 'from_fits'):
fromfits = getattr(clazz, 'from_fits')
skyobj = fromfits(skyfn, hdr)
else:
fromfits = getattr(clazz, 'fromFitsHeader')
skyobj = fromfits(hdr, prefix='SKY_')
sky2 = skyobj
print('sky2', sky2)
sky2.write_fits(skyfn.replace('sky', 'sky2'))
mod = np.zeros_like(img)
sky.addTo(mod)
plt.clf()
plt.imshow(mod, **ima)
plt.title('Blanton method')
ps.savefig()
plt.clf()
plt.imshow(img - mod, **ima)
plt.title('Blanton method (subtracted)')
ps.savefig()
grid = 512
#grid = 256
img = img.astype(np.float32)
med = np.zeros_like(img)
median_smooth(img, mm, grid/2, med)
plt.clf()
plt.imshow(med, **ima)
plt.title('dmedsmooth')
ps.savefig()
plt.clf()
plt.imshow(img - med, **ima)
plt.title('dmedsmooth (subtracted)')
ps.savefig()
sys.exit(0)
med2 = np.zeros_like(img)
mask = np.zeros(img.shape, bool)
mask[binary_dilation(img > 5*sig1, iterations=5)] = True
median_smooth(img, mask, grid/2, med2)
# UN-PAD
img = img[1:-1, 1:-1]
med = med[1:-1, 1:-1]
med2 = med2[1:-1, 1:-1]
mask = mask[1:-1, 1:-1]
sub = img - med
plt.clf()
plt.imshow(sub, **ima)
ps.savefig()
plt.clf()
plt.imshow(img - med2, **ima)
ps.savefig()
plt.clf()
plt.imshow(img * (1-mask), **ima)
ps.savefig()
plt.clf()
plt.imshow(med2, **ima)
ps.savefig()
lo2,hi2 = np.percentile(img, [5,95])
ha = dict(bins=100, range=(lo2,hi2), log=True,
histtype='step')
plt.clf()
n1,b,p = plt.hist(img.ravel(), color='r', **ha)
n2,b,p = plt.hist(sub.ravel(), color='b', **ha)
mx = max(max(n1), max(n2))
plt.ylim(mx*0.1, mx)
ps.savefig()
ha = dict(bins=100, range=(lo2,hi2), histtype='step')
plt.clf()
n1,b,p = plt.hist(img.ravel(), color='r', **ha)
n2,b,p = plt.hist(sub.ravel(), color='b', **ha)
n3,b,p = plt.hist((img - sub).ravel(), color='m', **ha)
#mx = max(max(n1), max(n2))
#plt.ylim(0, mx)
ps.savefig()
def stage_1(expnum=431202,
extname='S19',
plotprefix='lsb',
plots=False,
brightstars='bright.fits',
pixscale=0.27,
**kwa):
if plots:
ps = PlotSequence(plotprefix)
else:
ps = None
survey = LegacySurveyData()
C = survey.find_ccds(expnum=expnum, ccdname=extname)
print len(C), 'CCDs'
im = survey.get_image_object(C[0])
print 'im', im
#(x0,x1,y0,y1) = opt.zoom
#zoomslice = (slice(y0,y1), slice(x0,x1))
zoomslice = None
#tim = im.get_tractor_image(gaussPsf=True, splinesky=True, slc=zoomslice)
tim = im.get_tractor_image(hybridPsf=True, splinesky=True, slc=zoomslice)
print 'Tim', tim
cats = []
bricks = bricks_touching_wcs(tim.subwcs, survey=survey)
bricknames = bricks.brickname
for b in bricknames:
fn = survey.find_file('tractor', brick=b)
if not os.path.exists(fn):
print 'WARNING: file does not exist:', fn
continue
print 'Reading', fn
cat = fits_table(fn)
print 'Read', len(cat), 'sources'
if cat is None or len(cat) == 0:
continue
cats.append(cat)
if len(cats):
T = merge_tables(cats)
T._header = cats[0]._header
# margin
M = 20
ok, x, y = tim.subwcs.radec2pixelxy(T.ra, T.dec)
x -= 1.
y -= 1.
T.x = x
T.y = y
H, W = tim.shape
T.cut((x > -M) * (x < (W + M)) * (y > -M) * (y < (H + M)))
print 'Cut to', len(T), 'within image bounds'
T.cut(T.brick_primary)
print 'Cut to', len(T), 'brick_primary'
T.cut((T.out_of_bounds == False) * (T.left_blob == False))
print 'Cut to', len(T), 'not out-of-bound or left-blob'
print 'Brightest z-band:', np.max(T.decam_flux[:, 4])
print 'Brightest r-band:', np.max(T.decam_flux[:, 2])
orig_catalog = T.copy()
# Cut to compact sources
T.cut(np.maximum(T.shapeexp_r, T.shapedev_r) < 3.)
print 'Cut to', len(T), 'compact catalog objects'
cat = read_fits_catalog(T, allbands='ugrizY')
else:
cat = []
orig_catalog = fits_table()
print len(cat), 'catalog objects'
if plots:
plt.clf()
img = tim.getImage()
mn, mx = np.percentile(img.ravel(), [25, 99])
print('Image plot range:', mn, mx)
tim.ima = dict(interpolation='nearest',
origin='lower',
vmin=mn,
vmax=mx)
plt.imshow(tim.getImage(), **tim.ima)
plt.title('Orig data')
ps.savefig()
# Mask out bright pixels.
mask = np.zeros(tim.shape, np.bool)
bright = fits_table(brightstars)
print 'Read', len(bright), 'SDSS bright stars'
ok, bx, by = tim.subwcs.radec2pixelxy(bright.ra, bright.dec)
bx = np.round(bx).astype(int)
by = np.round(by).astype(int)
H, W = mask.shape
bright.modelmag = 22.5 - 2.5 * np.log10(bright.modelflux)
mag = bright.modelmag[:, 2]
radius = (10.**(3.5 - 0.15 * mag) / pixscale).astype(np.int)
I = np.flatnonzero(ok * (radius > 0) * (bx + radius > 0) *
(bx - radius < W) * (by + radius > 0) *
(by - radius < H))
print len(I), 'bright stars are near the image'
xx, yy = np.meshgrid(np.arange(W), np.arange(H))
for x, y, r in zip(bx[I], by[I], radius[I]):
mask[(xx - x)**2 + (yy - y)**2 < r**2] = True
mask[tim.inverr == 0] = True
tim.inverr[mask] = 0.
tim.data[mask] = 0.
if plots:
plt.clf()
plt.imshow(mask,
interpolation='nearest',
origin='lower',
vmin=0,
vmax=1,
cmap='gray')
plt.title('Mask')
ps.savefig()
plt.clf()
plt.imshow(tim.getImage(), **tim.ima)
plt.title('Masked')
ps.savefig()
tr = Tractor([tim], cat)
mod = tr.getModelImage(tim)
print('Model range:', mod.min(), mod.max())
# print('Model counts:', [tim.getPhotoCal().brightnessToCounts(src.getBrightness())
# for src in cat])
# print('Catalog:')
# for src in cat[:10]:
# print(' ', src)
if False:
# OLD DEBUGGING
print 'Model median:', np.median(mod)
rawimg = fitsio.read(
'decals-lsb/images/decam/CP20150407/c4d_150410_035040_ooi_z_v1.fits.fz',
ext=im.hdu)
print 'Image median:', np.median(rawimg)
print 'mid sky', tim.midsky
rawmod = mod * tim.zpscale + tim.midsky
print 'Model median:', np.median(rawmod)
fitsio.write('model.fits', rawmod, clobber=True)
if plots:
plt.clf()
plt.imshow(mod, **tim.ima)
plt.title('Model')
ps.savefig()
ax = plt.axis()
ok, xx, yy = tim.subwcs.radec2pixelxy(
[src.getPosition().ra for src in cat],
[src.getPosition().dec for src in cat])
plt.plot(xx, yy, 'r+')
plt.axis(ax)
ps.savefig()
mod[mask] = 0.
if plots:
plt.clf()
plt.imshow(mod, **tim.ima)
plt.title('Masked model')
ps.savefig()
imchi = dict(interpolation='nearest',
origin='lower',
vmin=-5,
vmax=5,
cmap='RdBu')
# plt.clf()
# plt.imshow((tim.getImage() - mod) * tim.getInvError(), **imchi)
# plt.title('Chi')
# plt.colorbar()
# ps.savefig()
plt.clf()
plt.imshow((tim.getImage() - mod), **tim.ima)
plt.title('Residuals')
ps.savefig()
resid = tim.getImage() - mod
sky = np.zeros_like(resid)
median_smooth(resid, mask, 256, sky)
if plots:
plt.clf()
plt.imshow(sky, **tim.ima)
plt.title('Smoothed residuals (sky)')
ps.savefig()
resid -= sky
# Re-apply mask
resid[mask] = 0.
if plots:
plt.clf()
plt.imshow(resid, **tim.ima)
plt.title('Residual - sky')
ps.savefig()
return dict(resid=resid,
sky=sky,
ps=ps,
tim=tim,
tr=tr,
mod=mod,
mask=mask,
orig_catalog=orig_catalog,
pixscale=pixscale)
def main():
#img = fitsio.read('347736-S5.fits')
img = fitsio.read('392772-N29.fits')
print(img.shape)
img = img.T.copy()
mm = np.median(img)
img -= mm
# PAD
padimg = np.zeros((2048, 4096))
padimg[1:-1, 1:-1] = img
img = padimg
ps = PlotSequence('sky')
lo,hi = np.percentile(img, [20,80])
ima = dict(vmin=lo, vmax=hi, interpolation='nearest', origin='lower',
cmap='gray')
plt.clf()
plt.imshow(img, **ima)
ps.savefig()
from astrometry.util.util import median_smooth
grid = 512
#grid = 256
img = img.astype(np.float32)
med = np.zeros_like(img)
median_smooth(img, None, grid/2, med)
plt.clf()
plt.imshow(med, **ima)
ps.savefig()
# # Estimate per-pixel noise via Blanton's 5-pixel MAD
slice1 = (slice(0,-5,10),slice(0,-5,10))
slice2 = (slice(5,None,10),slice(5,None,10))
mad = np.median(np.abs(img[slice1] - img[slice2]).ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print('sig1 estimate:', sig1)
from scipy.ndimage.morphology import binary_dilation
med2 = np.zeros_like(img)
mask = np.zeros(img.shape, bool)
mask[binary_dilation(img > 5*sig1, iterations=5)] = True
median_smooth(img, mask, grid/2, med2)
# UN-PAD
img = img[1:-1, 1:-1]
med = med[1:-1, 1:-1]
med2 = med2[1:-1, 1:-1]
mask = mask[1:-1, 1:-1]
sub = img - med
plt.clf()
plt.imshow(sub, **ima)
ps.savefig()
plt.clf()
plt.imshow(img - med2, **ima)
ps.savefig()
plt.clf()
plt.imshow(img * (1-mask), **ima)
ps.savefig()
plt.clf()
plt.imshow(med2, **ima)
ps.savefig()
lo2,hi2 = np.percentile(img, [5,95])
ha = dict(bins=100, range=(lo2,hi2), log=True,
histtype='step')
plt.clf()
n1,b,p = plt.hist(img.ravel(), color='r', **ha)
n2,b,p = plt.hist(sub.ravel(), color='b', **ha)
mx = max(max(n1), max(n2))
plt.ylim(mx*0.1, mx)
ps.savefig()
ha = dict(bins=100, range=(lo2,hi2), histtype='step')
plt.clf()
n1,b,p = plt.hist(img.ravel(), color='r', **ha)
n2,b,p = plt.hist(sub.ravel(), color='b', **ha)
n3,b,p = plt.hist((img - sub).ravel(), color='m', **ha)
#mx = max(max(n1), max(n2))
#plt.ylim(0, mx)
ps.savefig()
def main():
#img = fitsio.read('347736-S5.fits')
img = fitsio.read('392772-N29.fits')
print(img.shape)
img = img.T.copy()
mm = np.median(img)
img -= mm
# PAD
padimg = np.zeros((2048, 4096))
padimg[1:-1, 1:-1] = img
img = padimg
ps = PlotSequence('sky')
lo, hi = np.percentile(img, [20, 80])
ima = dict(vmin=lo,
vmax=hi,
interpolation='nearest',
origin='lower',
cmap='gray')
plt.clf()
plt.imshow(img, **ima)
ps.savefig()
from astrometry.util.util import median_smooth
grid = 512
#grid = 256
img = img.astype(np.float32)
med = np.zeros_like(img)
median_smooth(img, None, grid / 2, med)
plt.clf()
plt.imshow(med, **ima)
ps.savefig()
# # Estimate per-pixel noise via Blanton's 5-pixel MAD
slice1 = (slice(0, -5, 10), slice(0, -5, 10))
slice2 = (slice(5, None, 10), slice(5, None, 10))
mad = np.median(np.abs(img[slice1] - img[slice2]).ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print('sig1 estimate:', sig1)
from scipy.ndimage.morphology import binary_dilation
med2 = np.zeros_like(img)
mask = np.zeros(img.shape, bool)
mask[binary_dilation(img > 5 * sig1, iterations=5)] = True
median_smooth(img, mask, grid / 2, med2)
# UN-PAD
img = img[1:-1, 1:-1]
med = med[1:-1, 1:-1]
med2 = med2[1:-1, 1:-1]
mask = mask[1:-1, 1:-1]
sub = img - med
plt.clf()
plt.imshow(sub, **ima)
ps.savefig()
plt.clf()
plt.imshow(img - med2, **ima)
ps.savefig()
plt.clf()
plt.imshow(img * (1 - mask), **ima)
ps.savefig()
plt.clf()
plt.imshow(med2, **ima)
ps.savefig()
lo2, hi2 = np.percentile(img, [5, 95])
ha = dict(bins=100, range=(lo2, hi2), log=True, histtype='step')
plt.clf()
n1, b, p = plt.hist(img.ravel(), color='r', **ha)
n2, b, p = plt.hist(sub.ravel(), color='b', **ha)
mx = max(max(n1), max(n2))
plt.ylim(mx * 0.1, mx)
ps.savefig()
ha = dict(bins=100, range=(lo2, hi2), histtype='step')
plt.clf()
n1, b, p = plt.hist(img.ravel(), color='r', **ha)
n2, b, p = plt.hist(sub.ravel(), color='b', **ha)
n3, b, p = plt.hist((img - sub).ravel(), color='m', **ha)
#mx = max(max(n1), max(n2))
#plt.ylim(0, mx)
ps.savefig()
def main():
'''
This function generates the plots in the paper.
Some files and directories are assumed to exist in the current directory:
* WISE atlas tiles, from http://unwise.me/data/allsky-atlas.fits
* unwise-neo1-coadds, from http://unwise.me/data/neo1/
* unwise-neo1-coadds-half, unwise-neo1-coadds-quarter: directories
'''
# First, create the WCS into which we want to render
# degrees width to render in galactic coords
# |l| < 60
# |b| < 30
width = 120
# ~2 arcmin per pixel
W = int(width * 60.) / 2
H = W/2
zoom = 360. / width
wcs = anwcs_create_hammer_aitoff(0., 0., zoom, W, H, 0)
# Select WISE tiles that overlap. This atlas table is available
# from http://unwise.me/data/allsky-atlas.fits
# Select WISE tiles that overlap.
T = fits_table('allsky-atlas.fits')
print(len(T), 'tiles total')
T.ll,T.bb = radectolb(T.ra, T.dec)
I = np.flatnonzero(np.logical_or(T.ll < width+1,
T.ll > (360-width-1)) *
(T.bb > -width/2-1) * (T.bb < width/2+1))
T.cut(I)
print(len(I), 'tiles in L,B range')
# Create a coadd for each WISE band
lbpat = 'unwise-neo1-w%i-lb.fits'
imgs = []
for band in [1,2]:
outfn = lbpat % (band)
if os.path.exists(outfn):
print('Exists:', outfn)
img = fitsio.read(outfn)
imgs.append(img)
continue
coimg = np.zeros((H,W), np.float32)
conimg = np.zeros((H,W), np.float32)
for i,brick in enumerate(T.coadd_id):
# We downsample by 2, twice, just to make repeat runs a
# little faster.
# unWISE
fn = os.path.join('unwise-neo1-coadds', brick[:3], brick,
'unwise-%s-w%i-img-u.fits' % (brick, band))
qfn = os.path.join('unwise-neo1-coadds-quarter',
'unwise-%s-w%i.fits' % (brick, band))
hfn = os.path.join('unwise-neo1-coadds-half',
'unwise-%s-w%i.fits' % (brick, band))
if not os.path.exists(qfn):
if not os.path.exists(hfn):
print('Reading', fn)
halfsize(fn, hfn)
halfsize(hfn, qfn)
fn = qfn
print('Reading', fn)
img = fitsio.read(fn)
bwcs = Tan(fn, 0)
bh,bw = img.shape
# Coadd each unWISE pixel into the nearest target pixel.
xx,yy = np.meshgrid(np.arange(bw), np.arange(bh))
rr,dd = bwcs.pixelxy2radec(xx, yy)
ll,bb = radectolb(rr.ravel(), dd.ravel())
ll = ll.reshape(rr.shape)
bb = bb.reshape(rr.shape)
ok,ox,oy = wcs.radec2pixelxy(ll, bb)
ox = np.round(ox - 1).astype(int)
oy = np.round(oy - 1).astype(int)
K = (ox >= 0) * (ox < W) * (oy >= 0) * (oy < H) * ok
#print('ok:', np.unique(ok), 'x', ox.min(), ox.max(), 'y', oy.min(), oy.max())
assert(np.all(np.isfinite(img)))
if np.sum(K) == 0:
# no overlap
print('No overlap')
continue
np.add.at( coimg, (oy[K], ox[K]), img[K])
np.add.at(conimg, (oy[K], ox[K]), 1)
img = coimg / np.maximum(conimg, 1)
# Hack -- write and then read FITS WCS header.
fn = 'wiselb.wcs'
wcs.writeto(fn)
hdr = fitsio.read_header(fn)
hdr['CTYPE1'] = 'GLON-AIT'
hdr['CTYPE2'] = 'GLAT-AIT'
fitsio.write(outfn, img, header=hdr, clobber=True)
fitsio.write(outfn.replace('.fits', '-n.fits'), conimg,
header=hdr, clobber=True)
imgs.append(img)
w1,w2 = imgs
# Get/confirm L,B bounds...
H,W = w1.shape
print('Image size', W, 'x', H)
ok,l1,b1 = wcs.pixelxy2radec(1, (H+1)/2.)
ok,l2,b2 = wcs.pixelxy2radec(W, (H+1)/2.)
ok,l3,b3 = wcs.pixelxy2radec((W+1)/2., 1)
ok,l4,b4 = wcs.pixelxy2radec((W+1)/2., H)
print('L,B', (l1,b1), (l2,b2), (l3,b3), (l4,b4))
llo,lhi = l2,l1+360
blo,bhi = b3,b4
# Set plot sizes
plt.figure(1, figsize=(10,5))
plt.subplots_adjust(left=0.1, right=0.95, bottom=0.1, top=0.95)
plt.figure(2, figsize=(5,5))
plt.subplots_adjust(left=0.11, right=0.96, bottom=0.1, top=0.95)
suffix = '.pdf'
rgb = wise_rgb(w1, w2)
xlo,ylo = 0,0
plt.figure(1)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo, ylo, lticks=[60,30,0,330,300], bticks=[-30,-15,0,15,30])
plt.savefig('xbulge-00' + suffix)
# Compute the median of each row as a crude way of suppressing the
# Galactic plane
medy1 = np.median(w1, axis=1)
medy2 = np.median(w2, axis=1)
rgb = wise_rgb(w1 - medy1[:,np.newaxis],
w2 - medy2[:,np.newaxis])
# Zoom in a bit for Galactic plane subtracted version
lhi,llo,blo,bhi = 40, 320, -20, 20
okxy = np.array([wcs.radec2pixelxy(l,b) for l,b in [
(llo, blo), (llo, bhi), (lhi, blo), (lhi, bhi)]])
xlo = int(np.floor(min(okxy[:,-2])))
xhi = int(np.ceil (max(okxy[:,-2])))
ylo = int(np.floor(min(okxy[:,-1])))
yhi = int(np.ceil (max(okxy[:,-1])))
plt.clf()
plt.imshow(rgb[ylo:yhi, xlo:xhi, :],origin='lower', interpolation='nearest')
#lbticks(wcs, xlo, ylo, lticks=[40,20,0,340,320], bticks=[-20,-10,0,10,20])
lbticks(wcs, xlo, ylo, lticks=[30,15,0,345,330], bticks=[-20,-10,0,10,20])
plt.savefig('xbulge-01' + suffix)
# Zoom in on the core
lhi,llo,blo,bhi = 15, 345, -15, 15
ok,x1,y1 = wcs.radec2pixelxy(llo, blo)
ok,x2,y2 = wcs.radec2pixelxy(llo, bhi)
ok,x3,y3 = wcs.radec2pixelxy(lhi, blo)
ok,x4,y4 = wcs.radec2pixelxy(lhi, bhi)
xlo = int(np.floor(min(x1,x2,x3,x4)))
xhi = int(np.ceil (max(x1,x2,x3,x4)))
ylo = int(np.floor(min(y1,y2,y3,y4)))
yhi = int(np.ceil (max(y1,y2,y3,y4)))
print('xlo,ylo', xlo, ylo)
w1 = w1[ylo:yhi, xlo:xhi]
w2 = w2[ylo:yhi, xlo:xhi]
plt.figure(2)
# Apply color cut
w1mag = -2.5*(np.log10(w1) - 9.)
w2mag = -2.5*(np.log10(w2) - 9.)
cc = w1mag - w2mag
goodcolor = np.isfinite(cc)
mlo,mhi = np.percentile(cc[goodcolor], [5,95])
print('W1 - W2 color masks:', mlo,mhi)
mask = goodcolor * (cc > mlo) * (cc < mhi)
plt.clf()
rgb = wise_rgb(w1, w2)
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Data')
plt.savefig('xbulge-fit-data' + suffix)
plt.clf()
rgb = wise_rgb(w1 * mask, w2 * mask)
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Data (masked)')
plt.savefig('xbulge-fit-masked' + suffix)
ie = mask.astype(np.float32)
from tractor import (Image, NCircularGaussianPSF, LinearPhotoCal, Tractor,
PixPos, Fluxes)
from tractor.galaxy import ExpGalaxy, GalaxyShape
# Create Tractor images
tim1 = Image(data=w1 * mask, inverr=ie,
psf=NCircularGaussianPSF([1.],[1.]),
photocal=LinearPhotoCal(1., 'w1'))
tim2 = Image(data=w2 * mask, inverr=ie,
psf=NCircularGaussianPSF([1.],[1.]),
photocal=LinearPhotoCal(1., 'w2'))
H,W = w1.shape
gal = ExpGalaxy(PixPos(W/2, H/2), Fluxes(w1=w1.sum(), w2=w2.sum()),
GalaxyShape(200, 0.4, 90.))
tractor = Tractor([tim1, tim2],[gal])
# fitsio.write('data-w1.fits', w1 * mask, clobber=True)
# fitsio.write('data-w2.fits', w2 * mask, clobber=True)
# fitsio.write('mask.fits', mask.astype(np.uint8), clobber=True)
# Optimize galaxy model
tractor.freezeParam('images')
for step in range(50):
dlnp,x,alpha = tractor.optimize()
print('dlnp', dlnp)
print('x', x)
print('alpha', alpha)
print('Galaxy', gal)
if dlnp == 0:
break
# Get galaxy model images, compute residuals
mod1 = tractor.getModelImage(0)
resid1 = w1 - mod1
mod2 = tractor.getModelImage(1)
resid2 = w2 - mod2
rgb = wise_rgb(mod1, mod2)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Model')
plt.savefig('xbulge-fit-model' + suffix)
rgb = resid_rgb(resid1, resid2)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Residuals')
plt.savefig('xbulge-fit-resid' + suffix)
rgb = resid_rgb(resid1*mask, resid2*mask)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Residuals (masked)')
plt.savefig('xbulge-fit-residmasked' + suffix)
# fitsio.write('resid1.fits', resid1, clobber=True)
# fitsio.write('resid2.fits', resid2, clobber=True)
# Compute median-smoothed residuals
fr1 = np.zeros_like(resid1)
fr2 = np.zeros_like(resid2)
median_smooth(resid1, np.logical_not(mask), 25, fr1)
median_smooth(resid2, np.logical_not(mask), 25, fr2)
rgb = resid_rgb(fr1, fr2)
plt.clf()
plt.imshow(rgb, origin='lower', interpolation='nearest')
lbticks(wcs, xlo,ylo)
plt.title('Residuals (smoothed)')
plt.savefig('xbulge-fit-smooth2' + suffix)
def main():
#img = fitsio.read('347736-S5.fits')
img = fitsio.read('392772-N29.fits')
print(img.shape)
img = img.T.copy()
mm = np.median(img)
img -= mm
ps = PlotSequence('sky')
lo, hi = np.percentile(img, [20, 80])
ima = dict(vmin=lo,
vmax=hi,
interpolation='nearest',
origin='lower',
cmap='gray')
plt.clf()
plt.imshow(img, **ima)
ps.savefig()
# PAD
padimg = np.zeros((2048, 4096))
padimg[1:-1, 1:-1] = img
img = padimg
from tractor.splinesky import SplineSky
from scipy.ndimage.morphology import binary_dilation
from astrometry.util.util import median_smooth
# # Estimate per-pixel noise via Blanton's 5-pixel MAD
slice1 = (slice(0, -5, 10), slice(0, -5, 10))
slice2 = (slice(5, None, 10), slice(5, None, 10))
mad = np.median(np.abs(img[slice1] - img[slice2]).ravel())
sig1 = 1.4826 * mad / np.sqrt(2.)
print('sig1 estimate:', sig1)
mask = np.zeros(img.shape, bool)
mask[binary_dilation(img > 5 * sig1, iterations=5)] = True
for mm in [None, mask]:
notmm = None
if mm is not None:
notmm = np.logical_not(mm)
sky = SplineSky.BlantonMethod(img, notmm, 512)
skyfn = 'sky-%s.fits' % (mm is not None and 'mask' or 'nomask')
sky.write_fits(skyfn)
from tractor.utils import get_class_from_name
print('Reading sky model from', skyfn)
hdr = fitsio.read_header(skyfn)
skyclass = hdr['SKY']
clazz = get_class_from_name(skyclass)
if getattr(clazz, 'from_fits'):
fromfits = getattr(clazz, 'from_fits')
skyobj = fromfits(skyfn, hdr)
else:
fromfits = getattr(clazz, 'fromFitsHeader')
skyobj = fromfits(hdr, prefix='SKY_')
sky2 = skyobj
print('sky2', sky2)
sky2.write_fits(skyfn.replace('sky', 'sky2'))
mod = np.zeros_like(img)
sky.addTo(mod)
plt.clf()
plt.imshow(mod, **ima)
plt.title('Blanton method')
ps.savefig()
plt.clf()
plt.imshow(img - mod, **ima)
plt.title('Blanton method (subtracted)')
ps.savefig()
grid = 512
#grid = 256
img = img.astype(np.float32)
med = np.zeros_like(img)
median_smooth(img, mm, grid / 2, med)
plt.clf()
plt.imshow(med, **ima)
plt.title('dmedsmooth')
ps.savefig()
plt.clf()
plt.imshow(img - med, **ima)
plt.title('dmedsmooth (subtracted)')
ps.savefig()
sys.exit(0)
med2 = np.zeros_like(img)
mask = np.zeros(img.shape, bool)
mask[binary_dilation(img > 5 * sig1, iterations=5)] = True
median_smooth(img, mask, grid / 2, med2)
# UN-PAD
img = img[1:-1, 1:-1]
med = med[1:-1, 1:-1]
med2 = med2[1:-1, 1:-1]
mask = mask[1:-1, 1:-1]
sub = img - med
plt.clf()
plt.imshow(sub, **ima)
ps.savefig()
plt.clf()
plt.imshow(img - med2, **ima)
ps.savefig()
plt.clf()
plt.imshow(img * (1 - mask), **ima)
ps.savefig()
plt.clf()
plt.imshow(med2, **ima)
ps.savefig()
lo2, hi2 = np.percentile(img, [5, 95])
ha = dict(bins=100, range=(lo2, hi2), log=True, histtype='step')
plt.clf()
n1, b, p = plt.hist(img.ravel(), color='r', **ha)
n2, b, p = plt.hist(sub.ravel(), color='b', **ha)
mx = max(max(n1), max(n2))
plt.ylim(mx * 0.1, mx)
ps.savefig()
ha = dict(bins=100, range=(lo2, hi2), histtype='step')
plt.clf()
n1, b, p = plt.hist(img.ravel(), color='r', **ha)
n2, b, p = plt.hist(sub.ravel(), color='b', **ha)
n3, b, p = plt.hist((img - sub).ravel(), color='m', **ha)
#mx = max(max(n1), max(n2))
#plt.ylim(0, mx)
ps.savefig()
fr1 = median_filter(resid1*mask, size=50)
fr2 = median_filter(resid2*mask, size=50)
rgb = resid_rgb(fr1, fr2)
plt.clf()
dimshow(rgb)
plt.title('Residuals (smoothed)')
lbticks(wcs, xlo,ylo)
ps.savefig()
plt.savefig('xbulge-fit-smooth.pdf')
from astrometry.util.util import median_smooth
fr1 = np.zeros_like(resid1)
fr2 = np.zeros_like(resid2)
median_smooth(resid1, np.logical_not(mask), 25, fr1)
median_smooth(resid2, np.logical_not(mask), 25, fr2)
#median_smooth(resid1, np.logical_not(mask), 15, fr1)
#median_smooth(resid2, np.logical_not(mask), 15, fr2)
rgb = resid_rgb(fr1, fr2)
plt.clf()
dimshow(rgb)
plt.title('Residuals (smoothed)')
lbticks(wcs, xlo,ylo)
ps.savefig()
plt.savefig('xbulge-fit-smooth2.pdf')
