def residual_image(coadd, coadd_col_n, label='all'):
res = (coadd - coadd_col_n)/(coadd_col_n + 1) # the constant should fade noise
for i, band in enumerate(bands):
pyfits.writeto('stackres_%s_%s.fits'%(label, band), res[i], clobber=True)
res *= 3.0
res += 0.5
colimg = RGBImage(res[0], res[1], res[2], beta=0.0001)
colimg.save_as('stackres_%s.png'%label)
colimg = numpy.asarray(colimg.img)
for i, band in enumerate(bands):
pyfits.writeto('stackrescol_%s_%s.fits'%(label, band), colimg[:,:,i], clobber=True)
return res
def stack_galfit_images(infns, label='all', par='n', debug=False):
#fns = random.sample(infns, min(len(infns),maxstack))
fns = infns[:min(len(infns),maxstack)]
#for j in range(len(fns)):
# fns[j] = fns[j].replace('/home/boris/gama/galapagos/galapagos_2.0.3_galfit_0.1.2.1_GAMA_9', '/Users/spb/gala9')
bandscales = numpy.array([0.06, 0.1, 0.4])/10.0
beta=2.0
outsize = size
n = len(fns)
if n == 0:
return None
if maxstack > 5 and n < 5:
return None
print label, len(infns), n
if n < 5:
print fns
nbands = len(bands)
coadd = []
modelcoadd = []
for i, band in enumerate(bands):
valueoffsets = []
valuescales = []
xcentres = []
ycentres = []
radii = []
angles = []
axisratios = []
for j, fn in enumerate(fns):
p = pyfits.open(fn)
results = p['final_band'].data
# the following scales to the largest image
# not suitable when building residual images (need same shape for all stacked images)
#outsize = max(outsize, max(p[0].shape))
# index to scale according to the parameters in this band
bandidx = (results.field('BAND')==band).nonzero()[0][0]
# index to scale according to r-band parameters
rbandidx = (results.field('BAND')=='r').nonzero()[0][0]
valueoffsets.append(-results.field('COMP1_SKY')[bandidx])
valuescales.append(10**(0.4*(results.field('COMP2_MAG')[rbandidx]-18)))
xcentres.append(results.field('COMP2_YC')[rbandidx])
ycentres.append(results.field('COMP2_XC')[rbandidx])
if par == 're':
radii.append(results.field('COMP2_RE')[rbandidx])
elif par == 'n':
radii.append(results.field('COMP2_RE')[bandidx])
angles.append(results.field('COMP2_PA')[rbandidx])
axisratios.append(results.field('COMP2_AR')[rbandidx])
axisratios = 1.0
coaddi, stacki = stack_images(fns, outsize, 'input_%s'%band, valueoffsets, valuescales,
xcentres, ycentres, scales, angles, axisratios, clip=3.0)
coadd.append(coaddi)
pyfits.writeto('stack_%s_%s.fits'%(label, band), coaddi, clobber=True)
for si in stacki:
pyfits.append('stackall_%s_%s.fits'%(label, band), si)
# stack the models to verify
coaddi, stacki = stack_images(fns, outsize, 'model_%s'%band, valueoffsets, valuescales,
xcentres, ycentres, scales, angles, axisratios, clip=3.0)
modelcoadd.append(coaddi)
pyfits.writeto('stackmodel_%s_%s.fits'%(label, band), coaddi, clobber=True)
for si in stacki:
pyfits.append('stackmodelall_%s_%s.fits'%(label, band), si)
coadd = numpy.array(coadd)
for i, b in enumerate(bandscales):
coadd[i] *= b
colimg = RGBImage(coadd[0], coadd[1], coadd[2], beta=beta)
colimg.save_as('stack_%s.png'%label)
coadd = numpy.array(modelcoadd)
for i, b in enumerate(bandscales):
coadd[i] *= b
colimg = RGBImage(coadd[0], coadd[1], coadd[2], beta=beta)
colimg.save_as('stackmodel_%s.png'%label)
return coadd
def plotnonparamcolimg(fns,
rgb='Hzg',
ids=None,
name=None,
label=False,
desaturate=True,
pedestal=0,
beta=2.5,
scales=np.array([0.04, 0.055, 0.2]),
offsets=np.array([75.0, 40.0, 8.0]) * 0.5):
nbands = len(bands)
fns = np.atleast_1d(fns)
nid = len(fns)
fig = pyplot.figure(figsize=(15.0 / nbands * (1 + nid * 2), 15))
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.05,
right=0.95,
hspace=0.0,
wspace=0.0)
original_iid = None
for i, fn in enumerate(fns):
if ids is not None:
iid = ids[i]
else:
iid = os.path.splitext(os.path.basename(fn))[0]
img = fit_images(fn, rgb)
# Second row, results with nonparam
img = fit_images(iid, rgb)
img[0] = [img[0][j] - offsets[j] for j in range(3)]
img[1] = [img[1][j] - offsets[j] for j in range(3)]
img[2] = [img[2][j] + scales[j] * 2 * offsets.mean() for j in range(3)]
ax = fig.add_subplot(nbands, 1 + 2 * nid, 1 + 2 * nid + 2 + i * 2)
ticksoff(ax)
colimg = RGBImage(*img[1],
scales=scales,
beta=beta,
desaturate=False,
pedestal=pedestal).img
pyplot.imshow(colimg, interpolation='nearest', origin='lower')
ax = fig.add_subplot(nbands, 1 + 2 * nid, 1 + 2 * nid + 3 + i * 2)
ticksoff(ax)
colimg = RGBImage(*img[2],
scales=scales,
beta=beta,
desaturate=desaturate).img
pyplot.imshow(colimg, interpolation='nearest', origin='lower')
# Third row, nonparam diagnostics
nonparam = nonparam_images(iid, rgb)
datasub = [img[0][j] - nonparam[j] for j in range(3)]
nonparam = [nonparam - offsets[j] for j in range(3)]
datasub = [datasub[j] - offsets[j] for j in range(3)]
ax = fig.add_subplot(nbands, 1 + 2 * nid, 2 + 4 * nid + 2 + i * 2)
ticksoff(ax)
ax.set_xlabel('nonparam %s' % iid, fontsize=labelsize)
colimg = RGBImage(*nonparam,
scales=scales,
beta=beta,
desaturate=desaturate).img
pyplot.imshow(colimg, interpolation='nearest', origin='lower')
ax = fig.add_subplot(nbands, 1 + 2 * nid, 2 + 4 * nid + 3 + i * 2)
ticksoff(ax)
ax.set_xlabel('datasub %s' % iid, fontsize=labelsize)
colimg = RGBImage(*datasub,
scales=scales,
beta=beta,
desaturate=desaturate).img
pyplot.imshow(colimg, interpolation='nearest', origin='lower')
fig.savefig('plots/nonparamcolimages_%s.pdf' % name)
pyplot.close('all')
def plotcolimg(fns,
rgb='Hzg',
ids=None,
name=None,
label=False,
desaturate=True,
pedestal=0):
nbands = len(bands)
fns = np.atleast_1d(fns)
nid = len(fns)
beta = 2.5
scales = np.array((0.04, 0.055, 0.2))
# offsets not so necessary now have nice desaturation feature working
offsets = np.array([75.0, 40.0, 8.0]) * 0.5
rows = 1 + 2 * nid
fig = pyplot.figure(figsize=(15.0 / nbands * rows, 15))
fig.subplots_adjust(bottom=0.05,
top=0.95,
left=0.05,
right=0.95,
hspace=0.0,
wspace=0.0)
for i, fn in enumerate(fns):
if ids is not None:
iid = ids[i]
else:
iid = os.path.splitext(os.path.basename(fn))[0]
img = fit_images(fn, rgb)
img[0] = [img[0][j] - offsets[j] for j in range(3)]
img[1] = [img[1][j] - offsets[j] for j in range(3)]
img[2] = [img[2][j] + scales[j] * 2 * offsets.mean() for j in range(3)]
if i == 0:
ax = fig.add_subplot(nbands, rows, 1 + i * 2)
ticksoff(ax)
ax.set_xlabel('image', fontsize=labelsize)
colimg = RGBImage(*img[0],
scales=scales,
beta=beta,
desaturate=desaturate,
pedestal=pedestal).img
pyplot.imshow(colimg, interpolation='nearest', origin='lower')
ax = fig.add_subplot(nbands, rows, 2 + i * 2)
ticksoff(ax)
if label:
ax.set_xlabel('model {}'.format(iid), fontsize=labelsize)
colimg = RGBImage(*img[1],
scales=scales,
beta=beta,
desaturate=False,
pedestal=pedestal).img
pyplot.imshow(colimg, interpolation='nearest', origin='lower')
ax = fig.add_subplot(nbands, rows, 3 + i * 2)
ticksoff(ax)
if label:
ax.set_xlabel('residual {}'.format(iid), fontsize=labelsize)
colimg = RGBImage(*img[2],
scales=scales,
beta=beta,
desaturate=desaturate).img
pyplot.imshow(colimg, interpolation='nearest', origin='lower')
if name is None:
name = os.path.splitext(os.path.basename(fns[0]))[0]
fig.savefig('plots/colimages_{}.pdf'.format(name))
pyplot.close('all')