set_galaxy_cache_size(100)
labels.append((len(mem), 'tractor'))
mem.append(memuse())
mod = tractor.getModelImage(im)
labels.append((len(mem), 'model'))
mem.append(memuse())
track('mod')
tractor.freezeParam('images')
for x in range(10):
#for x in range(2):
tractor.optimize(alphas=[1e-3, 1e-2, 0.1, 1])
labels.append((len(mem), 'opt %i' % x))
mem.append(memuse())
nun = gc.collect()
print nun, 'unreachable objects'
track('opt')
if False:
for x in range(10):
p0 = np.array(tractor.getParams())
ss = np.array(tractor.getStepSizes())
p0 += ss * np.random.normal(0., 1e-3, size=len(p0))
tractor.setParams(p0)
mod = tractor.getModelImage(im)
resids2 = []
chis2 = []
stars.xfit = stars.xx.copy()
stars.yfit = stars.yy.copy()
alphas = [0.1, 0.3, 1.0]
for i,tr in enumerate(tractors):
print tr
src = tr.catalog[0]
print 'Initial position:', src.pos
x,y = src.pos.x, src.pos.y
tr.freezeParam('images')
tr.printThawedParams()
for step in range(50):
dlnp,X,alpha = tr.optimize(priors=False, shared_params=False,
alphas=alphas)
print 'dlnp', dlnp
print 'pos', src.pos.x, src.pos.y
print 'Delta position:', src.pos.x - x, src.pos.y - y
#if dlnp < 0.1:
if dlnp == 0.:
break
print 'Final position:', src.pos
pos = src.getPosition()
stars.xfit[i] = stars.x0[i] + pos.x
stars.yfit[i] = stars.y0[i] + pos.y
mod = tr.getModelImage(0)
mods2.append(mod)
subtim = tr.images[0]
def psf_residuals(expnum,
ccdname,
stampsize=35,
nstar=30,
magrange=(13, 17),
verbose=0,
splinesky=False):
# Set the debugging level.
if verbose == 0:
lvl = logging.INFO
else:
lvl = logging.DEBUG
logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
pngprefix = 'qapsf-{}-{}'.format(expnum, ccdname)
# Gather all the info we need about this CCD.
decals = Decals()
ccd = decals.find_ccds(expnum=expnum, ccdname=ccdname)[0]
band = ccd.filter
ps1band = dict(g=0, r=1, i=2, z=3, Y=4)
print('Band {}'.format(band))
#scales = dict(g=0.0066, r=0.01, z=0.025)
#vmin, vmax = np.arcsinh(-1), np.arcsinh(100)
#print(scales[band])
im = decals.get_image_object(ccd)
iminfo = im.get_image_info()
H, W = iminfo['dims']
wcs = im.get_wcs()
# Choose a uniformly selected subset of PS1 stars on this CCD.
ps1 = ps1cat(ccdwcs=wcs)
cat = ps1.get_stars(band=band, magrange=magrange)
rand = np.random.RandomState(seed=expnum * ccd.ccdnum)
these = rand.choice(len(cat) - 1, nstar, replace=False)
#these = rand.random_integers(0,len(cat)-1,nstar)
cat = cat[these]
cat = cat[np.argsort(cat.median[:, ps1band[band]])] # sort by magnitude
#print(cat.nmag_ok)
get_tim_kwargs = dict(const2psf=True, splinesky=splinesky)
# Make a QAplot of the positions of all the stars.
tim = im.get_tractor_image(**get_tim_kwargs)
img = tim.getImage()
#img = tim.getImage()/scales[band]
fig = plt.figure(figsize=(5, 10))
ax = fig.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)
#ax.imshow(np.arcsinh(img),cmap='gray',interpolation='nearest',
# origin='lower',vmin=vmax,vmax=vmax)
ax.imshow(img, **tim.ima)
ax.axis('off')
ax.set_title('{}: {}/{} AM={:.2f} Seeing={:.3f}"'.format(
band, expnum, ccdname, ccd.airmass, ccd.seeing))
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ax.text(xpos,
ypos,
'{:2d}'.format(istar + 1),
color='red',
horizontalalignment='left')
circ = plt.Circle((xpos, ypos), radius=30, color='g', fill=False, lw=1)
ax.add_patch(circ)
#radec = wcs.radec_bounds()
#ax.scatter(cat.ra,cat.dec)
#ax.set_xlim([radec[1],radec[0]])#*[1.0002,0.9998])
#ax.set_ylim([radec[2],radec[3]])#*[0.985,1.015])
#ax.set_xlabel('$RA\ (deg)$',fontsize=18)
#ax.set_ylabel('$Dec\ (deg)$',fontsize=18)
fig.savefig(pngprefix + '-ccd.png', bbox_inches='tight')
# Initialize the many-stamp QAplot
ncols = 3
nrows = np.ceil(nstar / ncols).astype('int')
inchperstamp = 2.0
fig = plt.figure(figsize=(inchperstamp * 3 * ncols, inchperstamp * nrows))
irow = 0
icol = 0
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
mag = ps1star.median[ps1band[band]] # r-band
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ix, iy = int(xpos), int(ypos)
# create a little tractor Image object around the star
slc = (slice(max(iy - stampsize, 0), min(iy + stampsize + 1, H)),
slice(max(ix - stampsize, 0), min(ix + stampsize + 1, W)))
# The PSF model 'const2Psf' is the one used in DR1: a 2-component
# Gaussian fit to PsfEx instantiated in the image center.
tim = im.get_tractor_image(slc=slc, **get_tim_kwargs)
stamp = tim.getImage()
ivarstamp = tim.getInvvar()
# Initialize a tractor PointSource from PS1 measurements
flux = NanoMaggies.magToNanomaggies(mag)
star = PointSource(RaDecPos(ra, dec), NanoMaggies(**{band: flux}))
# Fit just the source RA,Dec,flux.
tractor = Tractor([tim], [star])
tractor.freezeParam('images')
print('2-component MOG:', tim.psf)
tractor.printThawedParams()
for step in range(50):
dlnp, X, alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_mog = tractor.getModelImage(0)
chi2_mog = -2.0 * tractor.getLogLikelihood()
mag_mog = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
# Now change the PSF model to a pixelized PSF model from PsfEx instantiated
# at this place in the image.
psf = PixelizedPsfEx(im.psffn)
tim.psf = psf.constantPsfAt(xpos, ypos)
#print('PSF model:', tim.psf)
#tractor.printThawedParams()
for step in range(50):
dlnp, X, alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_psfex = tractor.getModelImage(0)
chi2_psfex = -2.0 * tractor.getLogLikelihood()
mag_psfex = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
#mn, mx = np.percentile((stamp-model_psfex)[ivarstamp>0],[1,95])
sig = np.std((stamp - model_psfex)[ivarstamp > 0])
mn, mx = [-2.0 * sig, 5 * sig]
# Generate a QAplot.
if (istar > 0) and (istar % (ncols) == 0):
irow = irow + 1
icol = 3 * istar - 3 * ncols * irow
#print(istar, irow, icol, icol+1, icol+2)
ax1 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol),
aspect='equal')
ax1.axis('off')
#ax1.imshow(stamp, **tim.ima)
ax1.imshow(stamp,
cmap='gray',
interpolation='nearest',
origin='lower',
vmin=mn,
vmax=mx)
ax1.text(0.1,
0.9,
'{:2d}'.format(istar + 1),
color='white',
horizontalalignment='left',
verticalalignment='top',
transform=ax1.transAxes)
ax2 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol + 1),
aspect='equal')
ax2.axis('off')
#ax2.imshow(stamp-model_mog, **tim.ima)
ax2.imshow(stamp - model_mog,
cmap='gray',
interpolation='nearest',
origin='lower',
vmin=mn,
vmax=mx)
ax2.text(0.1,
0.9,
'MoG',
color='white',
horizontalalignment='left',
verticalalignment='top',
transform=ax2.transAxes)
ax2.text(0.08,
0.08,
'{:.3f}'.format(mag_mog),
color='white',
horizontalalignment='left',
verticalalignment='bottom',
transform=ax2.transAxes)
#ax2.set_title('{:.3f}, {:.2f}'.format(mag_psfex,chi2_psfex),fontsize=14)
#ax2.set_title('{:.3f}, $\chi^{2}$={:.2f}'.format(mag_psfex,chi2_psfex))
ax3 = plt.subplot2grid((nrows, 3 * ncols), (irow, icol + 2),
aspect='equal')
ax3.axis('off')
#ax3.imshow(stamp-model_psfex, **tim.ima)
ax3.imshow(stamp - model_psfex,
cmap='gray',
interpolation='nearest',
origin='lower',
vmin=mn,
vmax=mx)
ax3.text(0.1,
0.9,
'PSFEx',
color='white',
horizontalalignment='left',
verticalalignment='top',
transform=ax3.transAxes)
ax3.text(0.08,
0.08,
'{:.3f}'.format(mag_psfex),
color='white',
horizontalalignment='left',
verticalalignment='bottom',
transform=ax3.transAxes)
if istar == (nstar - 1):
break
fig.savefig(pngprefix + '-stargrid.png', bbox_inches='tight')
def psf_residuals(expnum,ccdname,stampsize=35,nstar=30,
magrange=(13,17),verbose=0):
# Set the debugging level.
if verbose==0:
lvl = logging.INFO
else:
lvl = logging.DEBUG
logging.basicConfig(level=lvl,format='%(message)s',stream=sys.stdout)
pngprefix = 'qapsf-{}-{}'.format(expnum,ccdname)
# Gather all the info we need about this CCD.
decals = Decals()
ccd = decals.find_ccds(expnum=expnum,ccdname=ccdname)[0]
band = ccd.filter
ps1band = dict(g=0,r=1,i=2,z=3,Y=4)
print('Band {}'.format(band))
#scales = dict(g=0.0066, r=0.01, z=0.025)
#vmin, vmax = np.arcsinh(-1), np.arcsinh(100)
#print(scales[band])
im = DecamImage(decals,ccd)
iminfo = im.get_image_info()
H,W = iminfo['dims']
wcs = im.get_wcs()
# Choose a uniformly selected subset of PS1 stars on this CCD.
ps1 = ps1cat(ccdwcs=wcs)
cat = ps1.get_stars(band=band,magrange=magrange)
rand = np.random.RandomState(seed=expnum*ccd.ccdnum)
these = rand.choice(len(cat)-1,nstar,replace=False)
#these = rand.random_integers(0,len(cat)-1,nstar)
cat = cat[these]
cat = cat[np.argsort(cat.median[:,ps1band[band]])] # sort by magnitude
#print(cat.nmag_ok)
# Make a QAplot of the positions of all the stars.
tim = im.get_tractor_image(const2psf=True)
img = tim.getImage()
#img = tim.getImage()/scales[band]
fig = plt.figure(figsize=(5,10))
ax = fig.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)
#ax.imshow(np.arcsinh(img),cmap='gray',interpolation='nearest',
# origin='lower',vmin=vmax,vmax=vmax)
ax.imshow(img, **tim.ima)
ax.axis('off')
ax.set_title('{}: {}/{} AM={:.2f} Seeing={:.3f}"'.
format(band,expnum,ccdname,ccd.airmass,ccd.seeing))
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ax.text(xpos,ypos,'{:2d}'.format(istar+1),color='red',
horizontalalignment='left')
circ = plt.Circle((xpos,ypos),radius=30,color='g',fill=False,lw=1)
ax.add_patch(circ)
#radec = wcs.radec_bounds()
#ax.scatter(cat.ra,cat.dec)
#ax.set_xlim([radec[1],radec[0]])#*[1.0002,0.9998])
#ax.set_ylim([radec[2],radec[3]])#*[0.985,1.015])
#ax.set_xlabel('$RA\ (deg)$',fontsize=18)
#ax.set_ylabel('$Dec\ (deg)$',fontsize=18)
fig.savefig(pngprefix+'-ccd.png',bbox_inches='tight')
# Initialize the many-stamp QAplot
ncols = 3
nrows = np.ceil(nstar/ncols).astype('int')
inchperstamp = 2.0
fig = plt.figure(figsize=(inchperstamp*3*ncols,inchperstamp*nrows))
irow = 0
icol = 0
for istar, ps1star in enumerate(cat):
ra, dec = (ps1star.ra, ps1star.dec)
mag = ps1star.median[ps1band[band]] # r-band
ok, xpos, ypos = wcs.radec2pixelxy(ra, dec)
ix,iy = int(xpos), int(ypos)
# create a little tractor Image object around the star
slc = (slice(max(iy-stampsize, 0), min(iy+stampsize+1, H)),
slice(max(ix-stampsize, 0), min(ix+stampsize+1, W)))
# The PSF model 'const2Psf' is the one used in DR1: a 2-component
# Gaussian fit to PsfEx instantiated in the image center.
tim = im.get_tractor_image(slc=slc, const2psf=True)
stamp = tim.getImage()
ivarstamp = tim.getInvvar()
# Initialize a tractor PointSource from PS1 measurements
flux = NanoMaggies.magToNanomaggies(mag)
star = PointSource(RaDecPos(ra,dec), NanoMaggies(**{band: flux}))
# Fit just the source RA,Dec,flux.
tractor = Tractor([tim], [star])
tractor.freezeParam('images')
print('2-component MOG:', tim.psf)
tractor.printThawedParams()
for step in range(50):
dlnp,X,alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_mog = tractor.getModelImage(0)
chi2_mog = -2.0*tractor.getLogLikelihood()
mag_mog = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
# Now change the PSF model to a pixelized PSF model from PsfEx instantiated
# at this place in the image.
psf = PixelizedPsfEx(im.psffn)
tim.psf = psf.constantPsfAt(xpos, ypos)
#print('PSF model:', tim.psf)
#tractor.printThawedParams()
for step in range(50):
dlnp,X,alpha = tractor.optimize()
if dlnp < 0.1:
break
print('Fit:', star)
model_psfex = tractor.getModelImage(0)
chi2_psfex = -2.0*tractor.getLogLikelihood()
mag_psfex = NanoMaggies.nanomaggiesToMag(star.brightness)[0]
#mn, mx = np.percentile((stamp-model_psfex)[ivarstamp>0],[1,95])
sig = np.std((stamp-model_psfex)[ivarstamp>0])
mn, mx = [-2.0*sig,5*sig]
# Generate a QAplot.
if (istar>0) and (istar%(ncols)==0):
irow = irow+1
icol = 3*istar - 3*ncols*irow
#print(istar, irow, icol, icol+1, icol+2)
ax1 = plt.subplot2grid((nrows,3*ncols), (irow,icol), aspect='equal')
ax1.axis('off')
#ax1.imshow(stamp, **tim.ima)
ax1.imshow(stamp,cmap='gray',interpolation='nearest',
origin='lower',vmin=mn,vmax=mx)
ax1.text(0.1,0.9,'{:2d}'.format(istar+1),color='white',
horizontalalignment='left',verticalalignment='top',
transform=ax1.transAxes)
ax2 = plt.subplot2grid((nrows,3*ncols), (irow,icol+1), aspect='equal')
ax2.axis('off')
#ax2.imshow(stamp-model_mog, **tim.ima)
ax2.imshow(stamp-model_mog,cmap='gray',interpolation='nearest',
origin='lower',vmin=mn,vmax=mx)
ax2.text(0.1,0.9,'MoG',color='white',
horizontalalignment='left',verticalalignment='top',
transform=ax2.transAxes)
ax2.text(0.08,0.08,'{:.3f}'.format(mag_mog),color='white',
horizontalalignment='left',verticalalignment='bottom',
transform=ax2.transAxes)
#ax2.set_title('{:.3f}, {:.2f}'.format(mag_psfex,chi2_psfex),fontsize=14)
#ax2.set_title('{:.3f}, $\chi^{2}$={:.2f}'.format(mag_psfex,chi2_psfex))
ax3 = plt.subplot2grid((nrows,3*ncols), (irow,icol+2), aspect='equal')
ax3.axis('off')
#ax3.imshow(stamp-model_psfex, **tim.ima)
ax3.imshow(stamp-model_psfex,cmap='gray',interpolation='nearest',
origin='lower',vmin=mn,vmax=mx)
ax3.text(0.1,0.9,'PSFEx',color='white',
horizontalalignment='left',verticalalignment='top',
transform=ax3.transAxes)
ax3.text(0.08,0.08,'{:.3f}'.format(mag_psfex),color='white',
horizontalalignment='left',verticalalignment='bottom',
transform=ax3.transAxes)
if istar==(nstar-1):
break
fig.savefig(pngprefix+'-stargrid.png',bbox_inches='tight')
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)
continue
plt.clf()
plt.imshow(derivs[i].patch, interpolation='nearest', origin='lower')
plt.title('MoG galaxy derivative for parameter %s' % p)
plt.savefig('deriv-%02i.png' % i)
# import sys
# import logging
# lvl = logging.DEBUG
# logging.basicConfig(level=lvl, format='%(message)s', stream=sys.stdout)
# Optimize the model.
for step in range(50):
print('Tractor params:')
tractor.printThawedParams()
dlnp,X,alpha = tractor.optimize(damp=1.)
print('dlnp', dlnp)
print('galaxy:', moggal)
#print('Mog', moggal.mog.getParams())
if dlnp == 0:
break
# Plot the model as we're optimizing...
mod = tractor.getModelImage(0)
chi = (tim.getImage() - mod) * tim.getInvError()
plt.clf()
plt.subplot(1,2,1)
plt.imshow(mod, interpolation='nearest', origin='lower')
plt.title('Model')
plt.subplot(1,2,2)
mx = np.abs(chi).max()
# #plt.contour(pix, contours, color='k')
# plt.title('%s-pixel Median filtered W1' % fsize)
#
# lbticks(wcs, xlo,ylo)
#
# ps.savefig()
#
# contours = np.percentile(w1, [50, 70, 85, 90, 95])
# #plt.contour(pix, contours, colors='k')
# plt.contour(pix, contours, colors='r')
# ps.savefig()
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
mod1 = tractor.getModelImage(0)
resid1 = w1masked - mod1
mod2 = tractor.getModelImage(1)
resid2 = w2masked - mod2
#plt.figure(2)
