def view_mbobs(mbobs, **kw):
import images
if len(mbobs) == 3:
rgb = make_rgb(mbobs)
plt = images.view(rgb, **kw)
else:
#rgb=fake_rgb(mbobs)
# just show the first one
#plt = images.view(mbobs[0][0].image, **kw)
#plt=images.view(rgb, **kw)
#imc = mbobs[0][0].image.clip(min=1.0e-6)
imin = -95.0
imax = 985.0
imc = mbobs[0][0].image.copy()
#imin, imax = imc.min(), imc.max()
#print('min:',imin,'max:',imax)
imc -= imin
imc *= 1.0 / imax
plt = images.view(imc, nonlinear=0.3)
#imstd=imc.std()
#imc.clip(min=-, out=imc)
#logim=np.log10(imc)
#imc -= imc.min() + 1.0e-6
#logim=np.log10(imc)
#imc *= 1.0/imc.max()
#logim *= 1.0/logim.max()
#plt = images.view(logim, **kw)
return plt
def show(self):
import biggles
levels=7
tab = biggles.Table(2,2)
tab[0,0] = images.view(self.image0, show=False, levels=levels, min=0)
tab[0,1] = images.view(self.psf, show=False, levels=levels, min=0)
tab[1,0] = images.view(self.image, show=False, levels=levels, min=0)
# cross-section across rows
cen = self['cen']
imrows = self.image[:, cen[1]]
imcols = self.image[cen[0], :]
crossplt = biggles.FramedPlot()
hrows = biggles.Histogram(imrows, color='blue')
hrows.label = 'Center rows'
hcols = biggles.Histogram(imcols, color='red')
hcols.label = 'Center columns'
key = biggles.PlotKey(0.1, 0.9, [hrows, hcols])
crossplt.add(hrows, hcols, key)
crossplt.aspect_ratio=1
yr = crossplt._limits1().yrange()
yrange = (yr[0], yr[1]*1.2)
crossplt.yrange = yrange
tab[1,1] = crossplt
tab.show()
def _show_epoch(self, iobj, icut, im, seg, wt, bmask, extra=None):
import biggles
import images
tab=biggles.Table(2,2,aspect_ratio=1.0)
tab[0,0] = images.view(im, title='im', show=False)
tab[0,1] = images.view(seg, title='seg', show=False)
tab[1,0] = images.view(wt, title='weight', show=False)
tab[1,1] = images.view(bmask, title='bmask', show=False)
d='plots'
if not os.path.exists(d):
try:
os.makedirs(d)
except:
pass
fname=[
'object',
'%06d' % iobj,
'%02d' % icut,
]
if extra is not None:
fname += [extra]
fname = '-'.join(fname)
fname += '.png'
fname=os.path.join(d, fname)
#tab.show()
print(" writing:",fname)
tab.write_img(1500,1500,fname)
def view2(im1,im2):
import biggles
import images
tab = biggles.Table(1,2)
p1 = images.view(im1,show=False)
p2 = images.view(im2,show=False)
tab[0,0] = p1
tab[0,1] = p2
tab.show()
def _plot_compare_model(gmix, tobs):
import biggles
import images
model_im = gmix.make_image(tobs.image.shape, jacobian=tobs.jacobian)
imdiff = model_im - tobs.image
tab = biggles.Table(2, 2, aspect_ratio=1.0)
tab[0, 0] = images.view(tobs.image, show=False, title='im')
tab[0, 1] = images.view(model_im, show=False, title='model')
tab[1, 0] = images.view(imdiff, show=False, title='diff')
tab.show()
if input('hit a key (q to quit): ') == 'q':
stop
def find_centroid(image, rng, offset=None, maxiter=200, ntry=4):
"""
use AM to fit a single gaussian
"""
dims = numpy.array(image.shape)
row0, col0 = (dims-1)/2.0
if offset is not None:
row0 += offset['row_offset']
col0 += offset['col_offset']
if False:
import images
images.multiview(image, width=800,height=800)
if 'q'==raw_input('hit a key: '):
stop
j=ngmix.UnitJacobian(row=row0, col=col0)
obs = ngmix.Observation(image, jacobian=j)
guess_T = 4.0
for i in xrange(ntry):
fitter = ngmix.admom.run_admom(obs, guess_T, rng=rng)
res=fitter.get_result()
if res['flags']==0:
break
if res['flags'] != 0:
if False:
import images
images.view(image, width=800,height=800)
if 'q'==raw_input('hit a key: '):
stop
raise TryAgainError("could not fit 1 gauss to get centroid")
pars=res['pars']
row=pars[0]
col=pars[1]
row = row0 + row
col = col0 + col
return row,col
def view_mbobs_list(fofid, mbobs_list, **kw):
import biggles
import images
import plotting
show = kw.get('show', False)
save = kw.get('save', False)
for i, mbobs in enumerate(mbobs_list):
id = mbobs[0][0].meta['id']
for band, obslist in enumerate(mbobs):
grid = plotting.Grid(len(obslist))
plt = biggles.Table(
grid.nrow,
grid.ncol,
)
aratio = grid.nrow/(grid.ncol*2)
plt.aspect_ratio = aratio
plt.title = 'FoF: %d id: %d band: %d' % (fofid, id, band)
for iobs, obs in enumerate(obslist):
im = obs.image
wt = obs.weight
im = im/im.max()
row, col = grid(iobs)
implt = images.view(im, nonlinear=0.4, show=False)
wtplt = images.view(wt, show=False)
tab = biggles.Table(1, 2)
tab[0, 0] = implt
tab[0, 1] = wtplt
tab.title = 'id: %d band: %d obs: %d' % (id, band, iobs)
plt[row, col] = tab
if save:
pltname = 'images-fof%06d-id%06d-band%d.png' % \
(fofid, id, band)
logger.info('writing: %s' % pltname)
plt.write_img(3000, 3000*aratio, pltname)
if show:
plt.show(width=2000, height=2000*aratio)
return plt
def _get_mof_obs(self):
im, psf_im, coords, dims, dx1, dy1, noises = self.sim()
bg_rms = self.sim['Image']['Bgrms'] / np.sqrt(len(im))
bg_rms_psf = self.sim['Psf']['Bgrms_psf']
psf_ccen = (np.array(psf_im.shape) - 1.0) / 2.0
psf_jacob = ngmix.UnitJacobian(
row=psf_ccen[0],
col=psf_ccen[1],
)
psf_weight = psf_im * 0 + 1.0 / bg_rms_psf**2
psf_obs = ngmix.Observation(
psf_im,
weight=psf_weight,
jacobian=psf_jacob,
)
psf_gmix = self._fit_psf_admom(psf_obs)
psf_obs.set_gmix(psf_gmix)
mb = MultiBandObsList()
for i in range(len(im)):
if self.show:
import images
tim = im[i] / im[i].max()
tim = np.log10(tim - tim.min() + 1.0)
images.view(tim)
if 'q' == input('hit a key: (q to quit) '):
stop
weight = im[i] * 0 + 1.0 / bg_rms**2
jacobian = ngmix.UnitJacobian(
row=0,
col=0,
)
obs = ngmix.Observation(
im[i],
weight=weight,
jacobian=jacobian,
psf=psf_obs,
)
obs.noise = noises[i]
olist = ObsList()
olist.append(obs)
mb.append(olist)
return mb, coords
def plot_seg(segin, width=1000, rng=None, show=False, **kw):
"""
plot the seg map with randomized colors for better display
"""
import images
if rng is None:
rng = np.random.RandomState()
seg = np.transpose(segin)
max_seg = seg.max()
low = 50 / 255
high = 255 / 255
n = (max_seg + 1) * 3
colors = rng.uniform(low=low, high=high, size=n).reshape(
(max_seg + 1, 3), )
cseg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype='f4')
_make_color_seg(seg, cseg, colors)
plt = images.view(cseg, show=False, **kw)
if show:
srat = seg.shape[1] / seg.shape[0]
plt.show(width=width, height=width * srat)
return plt
def view_peaks(*,
image,
noise,
objects,
show=False,
color='red',
type='filled circle',
width=800,
plt=None):
"""
view the image with peak positions overplotted
"""
import biggles
import images
tim = image.copy()
if plt is None:
aim = images.asinh_scale(image, noise=noise)
plt = images.view(aim, show=False)
# the viewer transposes the image
points = biggles.Points(
objects['col'],
objects['row'],
color=color,
type=type,
)
plt.add(points)
if show:
arat = image.shape[0] / image.shape[1]
plt.show(width=width, height=width * arat)
return plt
def get_mof_model(self):
mb, coords = self._get_mof_obs()
prior = self._get_mof_prior(coords, len(mb))
nobj = len(coords)
mm = minimof.MOF(mb, "bdf", nobj, prior=prior)
print(mm)
for i in range(2):
guess = self._get_mof_guess(coords, mb)
ngmix.print_pars(guess, front=" guess:")
mm.go(guess)
res = mm.get_result()
if res['flags'] == 0:
break
if res['flags'] == 0:
ngmix.print_pars(res['pars'], front=" fit:")
center_obs = mm.make_corrected_obs(0,
band=None,
obsnum=None,
recenter=True)
for i in range(len(center_obs)):
center_obs[i][0].noise = mb[i][0].noise
if self.show:
import images
cim = center_obs.image
s = cim.shape
tim = np.zeros((s[0], 2 * s[1]))
tim[:, 0:s[1]] = obs.image
tim[:, s[1]:] = cim
tim *= 1.0 / tim.max()
tim = np.log10(tim - tim.min() + 1.0)
images.view(
#tim-tim.min(),
tim,
title='MOF',
)
if 'q' == input('hit a key: (q to quit) '):
stop
else:
center_obs = None
return center_obs
def view_rgb(imlist, wtlist, scale=2, show=False, **kw):
"""
view rgb data
"""
import images
rgb = make_rgb(imlist, wtlist, scale=scale)
plt = images.view(rgb, show=show, **kw)
return plt
def plot_hist2d(x, y, **kw):
"""
create a 2-d histogram of the data and view it
parameters
----------
x: array
x data
y: array
y data
log: bool
If true, plot the log of the histogram, default
True
xlabel: string
label for x axis
ylabel: string
label for y axis
**kw:
keywords for the histogram2d routine
dependencies
------------
esutil
images
"""
import images
dolog=kw.pop('log',True)
kw['more']=True
hdict = eu.stat.histogram2d(x, y, **kw)
hist = hdict['hist']
if dolog:
hist = numpy.log10( hist.clip(min=0.1) )
kw['transpose'] = False
kw['ranges'] = hdict['ranges']
# we want exact ranges here, to avoid confusing areas
# of black where there is no histogram
kw['xrange'] = [hdict['ranges'][0][0], hdict['ranges'][1][0]]
kw['yrange'] = [hdict['ranges'][0][1], hdict['ranges'][1][1]]
plt = images.view(hist, **kw)
return plt
def plot_seg(segin, title=None, width=1000, rng=None, show=False):
"""
plot the seg map with randomized colors
"""
import images
seg = np.transpose(segin)
cseg = np.zeros((seg.shape[0], seg.shape[1], 3))
if rng is None:
rng = np.random.RandomState()
useg = np.unique(seg)[1:]
low = 50 / 255
high = 255 / 255
for i, segval in enumerate(useg):
w = np.where(seg == segval)
r, g, b = rng.uniform(low=low, high=high, size=3)
cseg[w[0], w[1], 0] = r
cseg[w[0], w[1], 1] = g
cseg[w[0], w[1], 2] = b
plt = images.view(cseg, show=False)
if title is not None:
plt.title = title
if show:
srat = seg.shape[1] / seg.shape[0]
fname = tempfile.mktemp(suffix='.png')
plt.write_img(width, width * srat, fname)
show_image(fname)
return plt
def show(self, min=0, **keys):
import fimage
import biggles
plt=images.view(self.im, show=False, min=min, **keys)
s=self.rg['imstats']
if s['whyflag'] == 0:
levels=7
wrow = s['wrow']
wcol = s['wcol']
model = fimage.model_image('gauss',
self.im.shape,
[wrow,wcol],
[s['Irr'],s['Irc'],s['Icc']],
counts=self.im.sum())
cmod = biggles.Contours(model.transpose(), color='grey')
cmod.levels = levels
plt.add(cmod)
plt.show()
def _plot_gmix_thresh(im, cen, thresh, prompt=False):
import images
import biggles
tab=biggles.Table(1,2)
plt=biggles.FramedPlot()
im_cenrows = im[:,cen[1]]
im_cencols = im[cen[0],:]
wrow,=numpy.where(im_cenrows >= thresh)
wcol,=numpy.where(im_cencols >= thresh)
hrow=biggles.Histogram(im_cenrows, x0=0, binsize=1,color='blue')
hcol=biggles.Histogram(im_cencols, x0=0, binsize=1,color='red')
plt.add(hrow,hcol)
if wrow.size > 0:
hrow_t=biggles.Histogram(im_cenrows[wrow], x0=wrow[0], binsize=1,
color='blue',width=2)
plt.add(hrow_t)
if wcol.size > 0:
hcol_t=biggles.Histogram(im_cencols[wcol], x0=wcol[0], binsize=1,
color='red',width=2)
plt.add(hcol_t)
plt.title='thresh: %.2f' % thresh
plt.aspect_ratio=1
implt=images.view(im,show=False,type='dens-cont')
tab[0,0]= implt
tab[0,1] = plt
tab.show()
if prompt:
key=raw_input('hit a key (q to quit): ')
if key=='q':
stop
def main():
args = get_args()
o1, o2 = get_objects(args)
psf = get_psf()
if args.no_full_scene:
name = 'toy-no-full-scene'
allo = galsim.Add(
o1.shift(dx=-SHIFT, dy=0.0),
o2.shift(dx=+SHIFT, dy=0.0),
)
allo_pre = galsim.Convolve(
allo,
psf,
)
o1_pre = galsim.Convolve(o1, psf).shear(
g1=SHEAR[0],
g2=SHEAR[1],
)
o2_pre = galsim.Convolve(o2, psf).shear(
g1=SHEAR[0],
g2=SHEAR[1],
)
o1_pre = o1_pre.shift(dx=-SHIFT, dy=0.0)
o2_pre = o2_pre.shift(dx=+SHIFT, dy=0.0)
allo_pre_sheared = galsim.Add(o1_pre, o2_pre)
o1_post = o1.shear(
g1=SHEAR[0],
g2=SHEAR[1],
)
o2_post = o2.shear(
g1=SHEAR[0],
g2=SHEAR[1],
)
o1_post = galsim.Convolve(o1_post, psf)
o2_post = galsim.Convolve(o2_post, psf)
o1_post = o1_post.shift(dx=-SHIFT, dy=0.0)
o2_post = o2_post.shift(dx=+SHIFT, dy=0.0)
allo_post_sheared = galsim.Add(o1_post, o2_post)
else:
name = 'toy'
allo = galsim.Add(o1, o2)
allo_pre = galsim.Convolve(
allo,
psf,
)
allo_pre = galsim.Convolve(
allo,
psf,
)
allo_pre_sheared = allo_pre.shear(
g1=SHEAR[0],
g2=SHEAR[1],
)
allo_post = galsim.Add(o1, o2).shear(
g1=SHEAR[0],
g2=SHEAR[1],
)
allo_post_sheared = galsim.Convolve(
allo_post,
psf,
)
im_pre = get_image(allo_pre)
im_pre_sheared = get_image(allo_pre_sheared)
im_post_sheared = get_image(allo_post_sheared)
plt_pre = images.view(
im_pre,
type='dens-cont',
ccolor=CCOLOR,
levels=LEVELS,
zrange=np.percentile(im_pre, ZPERC),
invert=True,
show=False,
)
plt_pre_sheared = images.view(
im_pre_sheared,
type='dens-cont',
ccolor=CCOLOR,
levels=LEVELS,
zrange=np.percentile(im_pre_sheared, ZPERC),
invert=True,
show=False,
)
plt_post_sheared = images.view(
im_post_sheared,
type='dens-cont',
ccolor=CCOLOR,
levels=LEVELS,
zrange=np.percentile(im_post_sheared, ZPERC),
invert=True,
show=False,
)
plt_pre.add(
biggles.PlotLabel(0.9, 0.9, '(a)', halign='right', color='black')
)
plt_pre_sheared.add(
biggles.PlotLabel(0.9, 0.9, '(b)', halign='right', color='black')
)
plt_post_sheared.add(
biggles.PlotLabel(0.9, 0.9, '(c)', halign='right', color='black')
)
tab = biggles.Table(1, 3, aspect_ratio=1/3)
tab[0, 0] = plt_pre
tab[0, 1] = plt_pre_sheared
tab[0, 2] = plt_post_sheared
tab.show()
tab.write('%s.pdf' % name)
tab.write('%s.png' % name, dpi=150)
def main():
import biggles
biggles.configure('screen','width',1920)
biggles.configure('screen','height',1200)
tiledata=read_tiles()
image, ra, dec = get_map_image()
#images.view(image-image.min(),transpose=True)
plt=images.view(image-image.min(),
xdr=[ra.min(), ra.max()],
ydr=[dec.min(), dec.max()],
transpose=False,
show=False)
pts=biggles.Points(tiledata['ra'], tiledata['dec'],
type='filled circle', color='steel blue',
size=0.25)
plt.add(pts)
shoff=0.01
#shoff=0.008
for i in xrange(tiledata.size):
ra=tiledata['ra'][i]
dec=tiledata['dec'][i]
tilename=tiledata['tilename'][i]
if ( (spte_ra_range[0] < ra < spte_ra_range[1])
and (spte_dec_range[0] < dec < spte_dec_range[1]) ):
if tilename.strip() in testbed_tiles:
color='green'
else:
color='red'
dark_lab=biggles.DataLabel(ra,
dec+0.05,
tilename,
color='black',
size=0.3)
plt.add(dark_lab)
light_lab=biggles.DataLabel(ra-shoff,
dec+0.05+shoff,
tilename,
color=color,
size=0.3)
plt.add(light_lab)
plt.xrange=spte_ra_range
plt.yrange=spte_dec_range
plt.x1.ticks_style['color']='white'
plt.x1.subticks_style['color']='white'
plt.x1.spine_style['color']='white'
plt.x2.ticks_style['color']='white'
plt.x2.subticks_style['color']='white'
plt.x2.spine_style['color']='white'
plt.y1.ticks_style['color']='white'
plt.y1.subticks_style['color']='white'
plt.y1.spine_style['color']='white'
plt.y2.ticks_style['color']='white'
plt.y2.subticks_style['color']='white'
plt.y2.spine_style['color']='white'
epsname="map.eps"
print("writing:",epsname)
plt.write_eps(epsname)
def _test_nobj(nobj, seed, show=False):
"""
simulate one object
"""
detband = 0
conf = _get_conf(nobj)
sim = Sim(conf, seed)
sim.make_obs()
if show:
import images
images.view(sim.obs[0][0].image)
if raw_input('hit a key: (q to quit): ') == 'q':
raise KeyboardInterrupt('stopping')
fitrng = np.random.RandomState(sim.rng.randint(0, 2**15))
# this runs sextractor
medser = sim.get_medsifier()
m2 = medser.get_meds(detband)
objects = m2.get_cat()
nobj = len(objects)
if nobj == 0:
print('found no objects')
return
m = medser.get_multiband_meds()
list_of_obs = []
for iobj in range(objects.size):
mbo = m.get_mbobs(iobj, weight_type='weight')
for olist in mbo:
for o in olist:
o.set_psf(sim.psf_obs)
list_of_obs.append(mbo)
# first do a fit
prior = get_mof_stamps_prior(
list_of_obs,
conf['fit_model'],
fitrng,
)
fitter = MOFStamps(
list_of_obs,
conf['fit_model'],
prior=prior,
lm_pars=LM_PARS,
)
guess = get_stamp_guesses(
list_of_obs,
detband,
conf['fit_model'],
fitrng,
)
for itry in range(2):
fitter.go(guess)
res = fitter.get_result()
if res['flags'] == 0:
break
if res['flags'] != 0:
raise RuntimeError('didnt find a fit')
print('-' * 70)
print('best fit pars for all objects')
print('object pars')
for i in range(nobj):
ores = fitter.get_object_result(i)
s2n = ores['s2n']
ngmix.print_pars(ores['pars'],
front='%d s2n: %.1f pars: ' % (i + 1, s2n))
ffitter = MOFFlux(list_of_obs, conf['fit_model'], res['pars'])
ffitter.go()
fres = ffitter.get_result()
if np.any(fres['flags'] != 0):
print('didnt find a linear fit')
return np.zeros(0), np.zeros(0)
print('-' * 70)
print('flux comparison')
print('object flux flux_err fflux fflux_err')
fluxes = np.zeros(nobj)
flux_errs = np.zeros(nobj)
for i in range(nobj):
ores = fitter.get_object_result(i)
fres = ffitter.get_object_result(i)
print(
'object:',
i + 1,
'flux:',
ores['flux'][0],
'flux_err:',
ores['flux_err'][0],
'fflux:',
fres['flux'][0],
'fflux_err:',
fres['flux_err'][0],
)
fluxes[i] = fres['flux'][0]
flux_errs[i] = fres['flux_err'][0]
return fluxes, flux_errs
def _get_star_data(self, iobj, icutout):
"""
We use a fixed size apertures for all stars but only process stars for
which there are no neighbors in that aperture and for which there are
not zero weights in that aperture.
The smallest cutout we do is 32x32 so we will use a sub region
of that to allow for pixel shifts, 25x25. Then the region is
rowcen-r1, colcen+r1 where r1 is (25-1)/2 = 12
"""
defres=None,None,[]
box_size=29
rsub=(box_size-1)//2
image0=self.meds.get_cutout(iobj,icutout)
wt0=self.meds.get_cutout(iobj,icutout,type='weight')
seg0=self.meds.get_cutout(iobj,icutout,type='seg')
rowcen=self.meds['cutout_row'][iobj,icutout]
colcen=self.meds['cutout_col'][iobj,icutout]
sid=seg0[rowcen,colcen]
# fix up compression issue
"""
w=numpy.where(seg == sid)
if w[0].size ==0:
return defres
rowmin=w[0].min()
rowmax=w[0].max()
colmin=w[1].min()
colmax=w[1].max()
"""
rowmin = int(rowcen) - rsub
rowmax = int(rowcen) + rsub
colmin = int(colcen) - rsub
colmax = int(colcen) + rsub
if ((rowmin < 0)
or (rowmax > image0.shape[0])
or (colmin < 0)
or (colmax > image0.shape[1]) ):
print >>stderr,' hit bounds'
return defres
rowcen -= rowmin
colcen -= colmin
image = image0[rowmin:rowmax+1, colmin:colmax+1]
wt = wt0[rowmin:rowmax+1, colmin:colmax+1]
seg = seg0[rowmin:rowmax+1, colmin:colmax+1]
if False:
import images
import biggles
tab=biggles.Table(1,2)
w0=numpy.where(wt0 < 0.2*wt0.max())
if w0[0].size != 0:
image0[w0] = 0
w=numpy.where(wt < 0.2*wt.max())
if w[0].size != 0:
image[w] = 0
nl=0.05
tab[0,0]=images.view(image0,nonlinear=nl,title='raw',show=False)
tab[0,1]=images.view(image,nonlinear=nl,title='cut',show=False)
tab.show()
resp=raw_input('hit enter (q to quit): ')
if resp.lower() == 'q':
stop
# we don't work with images that have zero weight
# in the image anywere because the EM code doesn't
# have that feature
# have to deal with noisy "zero" weights
# also no pixels from other objects in our box
wt_logic = ( wt < 0.2*wt.max() )
seg_logic = ( (seg != sid) & (seg != 0) )
w_wt = numpy.where(wt_logic)
w_seg = numpy.where(seg_logic)
if w_wt[0].size > 0:
print >>stderr,' ',iobj,'has',w_wt[0].size,'zero weight pixels'
return None,None,[]
if w_seg[0].size > 0:
print >>stderr,' ',iobj,'has',w_seg[0].size,'neighbor pixels'
return None,None,[]
ivar=numpy.median(wt)
return image, ivar, [rowcen,colcen]
def _show_coadd(self):
import images
images.view(self.coadd)
def admom_atlas(type, run, camcol, field, id, filter,
objs=None,
show=False, showmin=0, **keys):
"""
Objs must be for this camcol!
"""
import admom
import sdsspy
c = sdsspy.FILTERNUM[filter]
if objs is None:
sc=SweepCache()
obj = sc.readobj(type, run, camcol, field, id)
else:
w=where1( (objs['field'] == field) & (objs['id'] == id) )
if w.size == 0:
raise ValueError("Object not found objs: "
"%06i-%i-%04i" % (run,camcol,id))
obj = objs[w[0]]
atls = sdsspy.read('fpAtlas',run,camcol,field,id, trim=True)
im = numpy.array( atls['images'][c], dtype='f8') - atls['SOFT_BIAS']
psfield = sdsspy.read('psField', run, camcol, field, lower=True)
rowc=obj['rowc'][c]
colc=obj['colc'][c]
row=rowc - atls['row0'][c] - 0.5
col=colc - atls['col0'][c] - 0.5
sigsky = psfield['skysig'][0,c]
Tguess_psf=admom.fwhm2mom( obj['psf_fwhm'][c], pixscale=0.4)
if obj['m_rr_cc'][c] > Tguess_psf:
Tguess=obj['m_rr_cc'][c]
else:
Tguess=Tguess_psf
out = admom.admom(im,
row,
col,
sky=0.0,
sigsky=sigsky,
Tguess=Tguess)
if show:
import fimage
import biggles
plt=images.view(im, min=showmin, show=False, **keys)
if out['whyflag'] == 0:
levels=7
wrow = out['wrow']
wcol = out['wcol']
model = fimage.model_image('gauss',
im.shape,
[wrow,wcol],
[out['Irr'],out['Irc'],out['Icc']],
counts=im.sum())
cmod = biggles.Contours(model.transpose(), color='grey')
cmod.levels = levels
plt.add(cmod)
plt.show()
return out
def _doplots(self):
import mcmc
import biggles
import esutil as eu
biggles.configure("default","fontsize_min",1.2)
tab=biggles.Table(6,2)
cen1vals=self._trials[:,0]
cen2vals=self._trials[:,1]
Tvals=self._trials[:,4]
g1vals=self._trials[:,2]
g2vals=self._trials[:,3]
g1lab=r'$g_1$'
g2lab=r'$g_2$'
ampvals=self._trials[:,5]
ind=numpy.arange(g1vals.size)
burn_cen=biggles.FramedPlot()
cen1p=biggles.Curve(ind, cen1vals, color='blue')
cen2p=biggles.Curve(ind, cen2vals, color='red')
cen1p.label=r'$x_1$'
cen2p.label=r'$x_2$'
burn_cen.add(cen1p)
burn_cen.add(cen2p)
key=biggles.PlotKey(0.9,0.9,[cen1p,cen2p],halign='right')
burn_cen.add(key)
burn_cen.ylabel='cen'
burn_g1=biggles.FramedPlot()
burn_g1.add(biggles.Curve(ind, g1vals))
burn_g1.ylabel=r'$g_1$'
burn_g2=biggles.FramedPlot()
burn_g2.add(biggles.Curve(ind, g2vals))
burn_g2.ylabel=r'$g_2$'
burn_T=biggles.FramedPlot()
burn_T.add(biggles.Curve(ind, Tvals))
burn_T.ylabel='T'
burn_amp=biggles.FramedPlot()
burn_amp.add(biggles.Curve(ind, ampvals))
burn_amp.ylabel='Amplitide'
likep = biggles.FramedPlot()
likep.add( biggles.Curve(ind, self.lnprobs) )
likep.ylabel='ln( prob )'
g = self._result['g']
gcov = self._result['gcov']
errs = sqrt(diag(gcov))
res=self.get_result()
print 's2n weighted:',res['s2n_w']
print 'acceptance rate:',res['arate'],'mca_a',self.mca_a
print 'T: %.16g +/- %.16g' % (Tvals.mean(), Tvals.std())
print_pars(self._result['pars'])
print_pars(sqrt(diag(self._result['pcov'])))
print 'g1: %.16g +/- %.16g' % (g[0],errs[0])
print 'g2: %.16g +/- %.16g' % (g[1],errs[1])
print 'chi^2/dof: %.3f/%i = %f' % (res['chi2per']*res['dof'],res['dof'],res['chi2per'])
print 'probrand:',res['fit_prob']
cenw = cen1vals.std()
cen_bsize=cenw*0.2
hplt_cen0 = eu.plotting.bhist(cen1vals,binsize=cen_bsize,
color='blue',
show=False)
hplt_cen = eu.plotting.bhist(cen2vals,binsize=cen_bsize,
color='red',
show=False, plt=hplt_cen0)
hplt_cen.add(key)
bsize1=g1vals.std()*0.2 #errs[0]*0.2
bsize2=g2vals.std()*0.2 # errs[1]*0.2
hplt_g1 = eu.plotting.bhist(g1vals,binsize=bsize1,
show=False)
hplt_g2 = eu.plotting.bhist(g2vals,binsize=bsize2,
show=False)
Tsdev = Tvals.std()
Tbsize=Tsdev*0.2
#hplt_T = eu.plotting.bhist(Tvals,binsize=Tbsize,
# show=False)
logTvals=log10(Tvals)
Tsdev = logTvals.std()
Tbsize=Tsdev*0.2
hplt_T = eu.plotting.bhist(logTvals,binsize=Tbsize,
show=False)
amp_sdev = ampvals.std()
amp_bsize=amp_sdev*0.2
hplt_amp = eu.plotting.bhist(ampvals,binsize=amp_bsize,
show=False)
hplt_cen.xlabel='center'
hplt_g1.xlabel=g1lab
hplt_g2.xlabel=g2lab
hplt_T.xlabel=r'$log_{10}T$'
hplt_amp.xlabel='Amplitude'
tab[0,0] = burn_cen
tab[1,0] = burn_g1
tab[2,0] = burn_g2
tab[3,0] = burn_T
tab[4,0] = burn_amp
tab[0,1] = hplt_cen
tab[1,1] = hplt_g1
tab[2,1] = hplt_g2
tab[3,1] = hplt_T
tab[4,1] = hplt_amp
tab[5,0] = likep
tab.show()
if True:
import images
nx = ny = 20
levels=8
h2d = eu.stat.histogram2d(g1vals, g2vals, nx=nx, ny=ny,more=True)
images.view(h2d['hist'],
xdr=[h2d['xcenter'][0], h2d['xcenter'][-1]],
ydr=[h2d['ycenter'][0], h2d['ycenter'][-1]],
xlabel='g1', ylabel='g2', levels=levels)
if True:
gmm=self.get_like_mixture(ngauss=3)
print gmm.weights_
print gmm.means_
covmean=0*gmm.covars_[0].copy()
for i,cov in enumerate(gmm.covars_):
covmean += gmm.weights_[i]*cov
"""
covmean=0*gmm.precs_[0].copy()
for i,prec in enumerate(gmm.precs_):
cov = numpy.linalg.inv(prec)
covmean += gmm.weights_[i]*cov
"""
wsum=gmm.weights_.sum()
covmean /= wsum
print 'weighted means'
print (gmm.means_[:,0]*gmm.weights_).sum()/wsum,
print (gmm.means_[:,1]*gmm.weights_).sum()/wsum
print 'weighted error'
print sqrt(diag(covmean))
rnd=gmm.sample(g1vals.size*10)
print rnd.shape
rh2d = eu.stat.histogram2d(rnd[:,0], rnd[:,1], nx=nx, ny=ny,
xmin=h2d['xlow'][0], xmax=h2d['xhigh'][-1],
ymin=h2d['ylow'][0], ymax=h2d['yhigh'][-1],
more=True)
rh2d['hist'] = rh2d['hist']*float(g1vals.size)/float(rh2d['hist'].sum())
images.compare_images(h2d['hist'], rh2d['hist'])
if False:
import images
model=self.get_maxprob_model()
images.compare_images(self.image,model,
label1='image [%d,%d]' % self.image.shape,
label2='model')
key=raw_input('hit a key (q to quit): ')
if key=='q':
stop
print
def compare_images(im1_in, im2_in, wt_in, **keys):
import biggles
import copy
import images
"""
wt = wt_in.copy()
maxwt = wt.max()
noiseval = np.sqrt(1.0/maxwt)
w = np.where(wt <= 0.0)
if w[0].size > 0:
wt[w] = maxwt
noise = np.random.normal(size=wt.shape)
noise *= np.sqrt(1.0/wt)
"""
if im1_in.shape != im2_in.shape:
raise ValueError("images must be the same shape")
color1 = keys.get('color1', 'blue')
color2 = keys.get('color2', 'orange')
colordiff = keys.get('colordiff', 'red')
label1 = keys.get('label1', 'im1')
label2 = keys.get('label2', 'im2')
resid = (im1_in - im2_in)
im1 = im1_in
im2 = im2_in
cen = [(im1.shape[0]-1)/2., (im1.shape[1]-1)/2.]
labelres = '%s-%s' % (label1, label2)
biggles.configure('default', 'fontsize_min', 1.)
# will only be used if type is contour
tab = biggles.Table(2, 3)
if 'title' in keys:
tab.title = keys['title']
tkeys = copy.deepcopy(keys)
tkeys.pop('title', None)
tkeys['show'] = False
tkeys['file'] = None
autoscale = True
tab[0, 0] = images.view(im1, autoscale=autoscale, **tkeys)
tab[0, 1] = images.view(im2, autoscale=autoscale, **tkeys)
tab[0, 2] = residplt = images.view(
resid*np.sqrt(wt_in.clip(min=0)), **tkeys
)
wgood = np.where(wt_in > 0.0)
dof = wgood[0].size
chi2per = (resid**2 * wt_in).sum()/dof
lab = biggles.PlotLabel(0.9, 0.9,
r'$\chi^2/dof$: %.2f' % chi2per,
color='red',
halign='right')
residplt.add(lab)
cen0 = int(cen[0])
cen1 = int(cen[1])
im1rows = im1_in[:, cen1]
im1cols = im1_in[cen0, :]
im2rows = im2_in[:, cen1]
im2cols = im2_in[cen0, :]
resrows = resid[:, cen1]
rescols = resid[cen0, :]
him1rows = biggles.Histogram(im1rows, color=color1)
him1cols = biggles.Histogram(im1cols, color=color1)
him2rows = biggles.Histogram(im2rows, color=color2)
him2cols = biggles.Histogram(im2cols, color=color2)
hresrows = biggles.Histogram(resrows, color=colordiff)
hrescols = biggles.Histogram(rescols, color=colordiff)
him1rows.label = label1
him2rows.label = label2
hresrows.label = labelres
key = biggles.PlotKey(0.1, 0.9,
[him1rows, him2rows, hresrows])
rplt = biggles.FramedPlot()
rplt.add(him1rows, him2rows, hresrows, key)
rplt.xlabel = 'Center Rows'
cplt = biggles.FramedPlot()
cplt.add(him1cols, him2cols, hrescols)
cplt.xlabel = 'Center Columns'
rplt.aspect_ratio = 1
cplt.aspect_ratio = 1
tab[1, 0] = rplt
tab[1, 1] = cplt
images._writefile_maybe(tab, **keys)
images._show_maybe(tab, **keys)
return tab
def plot_images(**keys):
"""
Plot a random subset of objects, potentially with
some selection
parameters
----------
run: keyword, string
The run identifier
model: keyword, string
Model name, e.g. 'exp'
imtype: keyword, string
'mosaic' or 'composite'
num: keyword, int
Number to make images for.
type: keyword, string
'bad-center' or 'any'
"""
import images
model=keys['model']
num=keys['num']
seed=keys.get('seed',35)
keys['type']=keys.get('type','any')
keys['imtype']=keys.get('imtype','mosaic')
numpy.random.seed(seed)
data=files.read_output(**keys)
m=files.open_meds()
if keys['type']=='bad-center':
w=select_bad_center(data, model)
else:
w=numpy.arange(data.size)
r=numpy.random.random(w.size)
s=r.argsort()
wplot=w[s[0:num]]
d=files.get_plot_dir(**keys)
pf=path.join(d,'%(imtype)s-%(type)s-%(run)s-%(index)06d.png')
if not os.path.exists(d):
os.makedirs(d)
for i in xrange(num):
index=wplot[i]
im=m.get_mosaic(index)
if keys['imtype']=='composite':
cw=m.get_cseg_mosaic(index)
im *= cw
elif keys['imtype'] == 'interp':
iseg=m.interpolate_coadd_seg_mosaic(index)
w=where( (iseg != (index+1)) & (iseg != 0) )
im[w] = 0.0
plt=images.view(im, title='%s' % index,show=False)
keys['index'] = index
fname=pf % keys
print(fname)
xsize=1800
ysize=xsize*float(im.shape[1])/im.shape[0]
plt.write_img(xsize,ysize,fname)
def test_admom_residuals(image, cen, guess, sky=0.0, sigsky=1.0):
"""
Fit adaptive moments to the input image and display the residuals
"""
import images
import fimage
import biggles
res = admom(image, cen[0], cen[1], guess=guess, sky=sky, sigsky=sigsky,
nsub=8)
counts = image.sum()
wcen=[res['wrow'],res['wcol']]
fake = fimage.model_image('gauss',image.shape,wcen,res['Irr'],res['Irc'],res['Icc'],
counts=counts)
resid = fake-image
maxval = max( image.max(), fake.max() )
minval = 0.0
levels=7
tab=biggles.Table(2,3)
#tab=biggles.Table(3,2)
implt=images.view(image, levels=levels, show=False, min=minval, max=maxval)
fakeplt=images.view(fake, levels=levels, show=False, min=minval, max=maxval)
residplt=images.view(resid, show=False, min=minval, max=maxval)
sigma = numpy.sqrt((res['Irr']+res['Icc'])/2.0)
lab = biggles.PlotLabel(0.1,0.9,r'$\sigma$: %0.2f' % sigma, fontsize=4, halign='left')
fakeplt.add(lab)
implt.title='original'
fakeplt.title='gaussian model'
residplt.title='residuals'
# cross-sections
imrows = image[:,wcen[1]]
imcols = image[wcen[0],:]
fakerows = fake[:,wcen[1]]
fakecols = fake[wcen[0],:]
resrows = resid[:,wcen[1]]
rescols = resid[wcen[0],:]
himrows = biggles.Histogram(imrows, color='blue')
himcols = biggles.Histogram(imcols, color='blue')
hfakerows = biggles.Histogram(fakerows, color='orange')
hfakecols = biggles.Histogram(fakecols, color='orange')
hresrows = biggles.Histogram(resrows, color='red')
hrescols = biggles.Histogram(rescols, color='red')
himrows.label = 'image'
hfakerows.label = 'model'
hresrows.label = 'resid'
key = biggles.PlotKey(0.1,0.9,[himrows,hfakerows,hresrows])
rplt=biggles.FramedPlot()
rplt.add( himrows, hfakerows, hresrows,key )
cplt=biggles.FramedPlot()
cplt.add( himcols, hfakecols, hrescols )
rplt.aspect_ratio=1
cplt.aspect_ratio=1
tab[0,0] = implt
tab[0,1] = fakeplt
tab[0,2] = residplt
tab[1,0] = rplt
tab[1,1] = cplt
#tab[0,0] = implt
#tab[0,1] = fakeplt
#tab[1,0] = residplt
#tab[1,1] = rplt
#tab[2,0] = cplt
tab.show()
def psfield_compare_model(rsp=None, generator='filter', next=False):
"""
compare psf reconstructions with the best fit models in the 6th header.
"""
import images
import biggles
from scipy.ndimage.filters import gaussian_filter
import fimage
filter='r'
fnum=2
if rsp is None:
rsp=RandomSDSSPSF(1.3,1.5,filter,verbose=True)
image,meta = rsp.next(meta=True)
else:
if rsp.psf is None:
image,meta = rsp.current(meta=True)
else:
if next:
image,meta = rsp.next(meta=True)
else:
image,meta = rsp.current(meta=True)
cen = [(image.shape[0]-1)/2, (image.shape[1]-1)/2]
extra='_2G'
a = 1.0
b = meta['psf_b'+extra][0,fnum]
s1 = meta['psf_sigma1'+extra][0,fnum]
s2 = meta['psf_sigma2'+extra][0,fnum]
if generator == 'fimage':
fake1 = fimage.makeimage('gauss',image.shape,cen,s1**2,0,s1**2,counts=1)
fake2 = fimage.makeimage('gauss',image.shape,cen,s2**2,0,s2**2,counts=1)
a /= (s1**2 + b*s2**2)
fake = a*( s1**2*fake1 + b*s2**2*fake2 )
elif generator == 'imsim':
import imsim
fake1 = imsim.mom2disk('gauss',s1**2,0,s1**2,image.shape,cen=cen,counts=1)
fake2 = imsim.mom2disk('gauss',s2**2,0,s2**2,image.shape,cen=cen,counts=1)
a /= (s1**2 + b*s2**2)
fake = a*( s1**2*fake1 + b*s2**2*fake2 )
elif generator == 'filter':
a /= (s1**2 + b*s2**2)
fake1 = numpy.zeros_like(image)
# convolve delta function with gaussians
fake1[cen[0],cen[1]] = 1
fake = a * (s1**2 * gaussian_filter(fake1, (s1,s1)) + b*s2**2*gaussian_filter(fake1, (s2,s2)))
else:
raise ValueError("unknown generator type: '%s'" % generator)
wlog("image counts:",image.sum(),"model counts:",fake.sum())
resid = fake-image
wlog("summed residuals:",resid.sum())
maxval = max( image.max(), fake.max() )
minval = 0.0
levels=7
tab=biggles.Table(2,3)
#tab=biggles.Table(3,2)
implt=images.view(image, levels=levels, show=False, min=minval, max=maxval)
fakeplt=images.view(fake, levels=levels, show=False, min=minval, max=maxval)
residplt=images.view(resid, show=False, min=minval, max=maxval)
#sigma = numpy.sqrt((res['Irr']+res['Icc'])/2.0)
#lab = biggles.PlotLabel(0.1,0.9,r'$\sigma$: %0.3f' % sigma, fontsize=4, halign='left')
#fakeplt.add(lab)
implt.title='original'
fakeplt.title='gaussian '+generator
residplt.title='residuals'
# cross-sections
imrows = image[:,cen[1]]
imcols = image[cen[0],:]
fakerows = fake[:,cen[1]]
fakecols = fake[cen[0],:]
resrows = resid[:,cen[1]]
rescols = resid[cen[0],:]
himrows = biggles.Histogram(imrows, color='blue')
himcols = biggles.Histogram(imcols, color='blue')
hfakerows = biggles.Histogram(fakerows, color='orange')
hfakecols = biggles.Histogram(fakecols, color='orange')
hresrows = biggles.Histogram(resrows, color='red')
hrescols = biggles.Histogram(rescols, color='red')
himrows.label = 'image'
hfakerows.label = 'model'
hresrows.label = 'resid'
key = biggles.PlotKey(0.1,0.9,[himrows,hfakerows,hresrows])
rplt=biggles.FramedPlot()
rplt.add( himrows, hfakerows, hresrows,key )
cplt=biggles.FramedPlot()
cplt.add( himcols, hfakecols, hrescols )
rplt.aspect_ratio=1
cplt.aspect_ratio=1
tab[0,0] = implt
tab[0,1] = fakeplt
tab[0,2] = residplt
tab[1,0] = rplt
tab[1,1] = cplt
#tab[0,0] = implt
#tab[0,1] = fakeplt
#tab[1,0] = residplt
#tab[1,1] = rplt
#tab[2,0] = cplt
tab.show()
return rsp
def _show_coadd(self, iobj, obs, original_coadd, original_wt, original_seg):
import biggles
import images
tab=biggles.Table(3,3,aspect_ratio=1.0)
nl=None
tab[0,0] = images.view(
np.rot90( obs.image.transpose(),k=2),
#obs.image,
nonlinear=nl,
title='coadd (trans/rot)',
show=False,
)
tab[0,1] = images.view(original_coadd, nonlinear=nl, title='orig coadd', show=False)
diff = np.rot90( obs.image.transpose(),k=2) - original_coadd
tab[0,2] = images.view(diff, title='diff', show=False)
cmin,cmax = obs.weight.min(), obs.weight.max()
tab[1,0] = images.view(
np.rot90( obs.weight.transpose(),k=2),
#obs.weight,
nonlinear=nl,
title='weight (trans/rot) minmax: %.3g/%.3g' % (cmin,cmax),
show=False,
)
cmin,cmax = original_wt.min(), original_wt.max()
tab[1,1] = images.view(original_wt,
nonlinear=nl,
title='orig weight minmax: %.3g/%.3g' % (cmin,cmax),
show=False)
tab[1,2] = images.view(obs.noise, title='noise', show=False)
#tab[0,2] = images.view(obs.seg, title='seg', show=False)
tab[2,0] = images.view(
np.rot90( obs.seg.transpose(),k=2),
#obs.seg,
title='seg (trans/rot)',
show=False,
)
tab[2,1] = images.view(original_seg, title='orig seg', show=False)
tab[2,2] = images.view(obs.psf.image, title='coadded psf', show=False)
d='plots'
if not os.path.exists(d):
try:
os.makedirs(d)
except:
pass
fname=[
'object',
'%06d' % iobj,
'coadd',
]
fname = '-'.join(fname)
fname += '.png'
fname=os.path.join(d, fname)
#tab.show()
print(" writing:",fname)
tab.write_img(1500,1500,fname)
def compare_rgb_images(image,
model,
diffim,
seg=None,
weight=None,
width=1000,
chi2per=None,
rng=None,
title=None,
show=False):
"""
make a comparison of the image with the model
"""
import biggles
import images
if chi2per is not None:
diff_title = 'chi2/dof: %.2f' % chi2per
else:
diff_title = None
imrow, imcol = 0, 0
modrow, modcol = 0, 1
nrows = 2
if seg is not None and weight is not None:
ncols = 3
arat = image.shape[1] / image.shape[0] * 2 / 3
diffrow, diffcol = 0, 2
segrow, segcol = 1, 0
wtrow, wtcol = 1, 1
else:
ncols = 2
arat = image.shape[1] / image.shape[0]
diffrow, diffcol = 1, 0
if seg is not None:
segrow, segcol = 1, 1
elif weight is not None:
wtrow, wtcol = 1, 1
tab = biggles.Table(nrows, ncols, aspect_ratio=arat)
tab[imrow, imcol] = images.view(
image, # /maxval,
show=False,
title='image',
)
tab[modrow, modcol] = images.view(
model, # /maxval,
show=False,
title='model',
)
tab[diffrow, diffcol] = images.view(
diffim,
show=False,
title=diff_title,
)
if seg is not None:
tab[segrow, segcol] = plot_seg(seg, rng=rng, width=width, title='seg')
if weight is not None:
tab[wtrow, wtcol] = images.view(weight, show=False, title='weight')
if title is not None:
tab.title = title
if show:
fname = tempfile.mktemp(suffix='.png')
tab.write_img(width, width * arat, fname)
show_image(fname)
return tab
def compare_images_mosaic(im1, im2, **keys):
import biggles
import copy
import images
show = keys.get('show', True)
ymin = keys.get('min', None)
ymax = keys.get('max', None)
color1 = keys.get('color1', 'blue')
color2 = keys.get('color2', 'orange')
colordiff = keys.get('colordiff', 'red')
nrow = 2
ncol = 3
label1 = keys.get('label1', 'im1')
label2 = keys.get('label2', 'im2')
cen = keys.get('cen', None)
if cen is None:
cen = [(im1.shape[0] - 1) / 2., (im1.shape[1] - 1) / 2.]
labelres = '%s-%s' % (label1, label2)
biggles.configure('default', 'fontsize_min', 1.)
if im1.shape != im2.shape:
raise ValueError("images must be the same shape")
#resid = im2-im1
resid = im1 - im2
# will only be used if type is contour
tab = biggles.Table(2, 1)
if 'title' in keys:
tab.title = keys['title']
tkeys = copy.deepcopy(keys)
tkeys.pop('title', None)
tkeys['show'] = False
tkeys['file'] = None
tkeys['nonlinear'] = None
# this has no effect
tkeys['min'] = resid.min()
tkeys['max'] = resid.max()
mosaic = np.zeros((im1.shape[0], 3 * im1.shape[1]))
ncols = im1.shape[1]
mosaic[:, 0:ncols] = im1
mosaic[:, ncols:2 * ncols] = im2
mosaic[:, 2 * ncols:3 * ncols] = resid
residplt = images.view(mosaic, **tkeys)
dof = im1.size
chi2per = (resid**2).sum() / dof
lab = biggles.PlotLabel(0.9,
0.9,
r'$\chi^2/npix$: %.3e' % chi2per,
color='red',
halign='right')
residplt.add(lab)
cen0 = int(cen[0])
cen1 = int(cen[1])
im1rows = im1[:, cen1]
im1cols = im1[cen0, :]
im2rows = im2[:, cen1]
im2cols = im2[cen0, :]
resrows = resid[:, cen1]
rescols = resid[cen0, :]
him1rows = biggles.Histogram(im1rows, color=color1)
him1cols = biggles.Histogram(im1cols, color=color1)
him2rows = biggles.Histogram(im2rows, color=color2)
him2cols = biggles.Histogram(im2cols, color=color2)
hresrows = biggles.Histogram(resrows, color=colordiff)
hrescols = biggles.Histogram(rescols, color=colordiff)
him1rows.label = label1
him2rows.label = label2
hresrows.label = labelres
key = biggles.PlotKey(0.1, 0.9, [him1rows, him2rows, hresrows])
rplt = biggles.FramedPlot()
rplt.add(him1rows, him2rows, hresrows, key)
rplt.xlabel = 'Center Rows'
cplt = biggles.FramedPlot()
cplt.add(him1cols, him2cols, hrescols)
cplt.xlabel = 'Center Columns'
rplt.aspect_ratio = 1
cplt.aspect_ratio = 1
ctab = biggles.Table(1, 2)
ctab[0, 0] = rplt
ctab[0, 1] = cplt
tab[0, 0] = residplt
tab[1, 0] = ctab
images._writefile_maybe(tab, **keys)
images._show_maybe(tab, **keys)
return tab
def view_atlas(**keys):
"""
View atlas images for the input object
parameters
----------
cat: optional
A full structure with run,rerun,camcol,field,id
run: optional
Run id
rerun: optional
rerun id
camcol: optional
camcol id
field: opt
field id
id: opt
id in field
band: optional
Only display this band
shift:
Apply sub-pixel astronometric shifts, default False
"""
import images
import biggles
import sdsspy
biggles.configure( 'default', 'fontsize_min', 1)
band=keys.get('band',None)
cat=keys.get('cat',None)
show=keys.get('show',True)
color=keys.get('color',False)
shift=keys.get('shift',False)
idinfo=get_id_info(**keys)
imdict=read_atlas(**idinfo)
nonlinear=keys.get('nonlinear', _default_nonlinear)
if band is not None:
bnum=sdsspy.util.FILTERNUM[band]
bchar=sdsspy.util.FILTERCHAR[band]
idinfo['bchar']=bchar
title='%(run)06d-%(camcol)d-%(field)04d-%(id)05d-%(bchar)s' % idinfo
im=imdict['images'][bnum]
plt=images.view(im, nonlinear=nonlinear,show=False)
plt.title=title
lab=biggles.PlotLabel(0.9,0.9,bchar,color='yellow')
plt.add(lab)
if show:
plt.show()
return plt
elif color:
img=imdict['images'][1].transpose()
imr=imdict['images'][2].transpose()
imi=imdict['images'][3].transpose()
if shift:
img,imi=shift_images(imdict, img, imi)
colorim=images.get_color_image(imi,imr,img,
satval=30.,
nonlinear=nonlinear)
s=imdict['images'][0].shape
ranges = ((-0.5, -0.5), (s[1]-0.5, s[0]-0.5))
d = biggles.Density(colorim, ranges)
plt=biggles.FramedPlot()
plt.add(d)
plt.aspect_ratio=s[0]/float(s[1])
title='%(run)06d-%(camcol)d-%(field)04d-%(id)05d' % idinfo
plt.title=title
if show:
plt.show()
return plt
imslist=scale_image_list(imdict['images'], nonlinear)
nrow=2
ncol=3
plt=biggles.FramedArray(nrow,ncol)
title='%(run)06d-%(camcol)d-%(field)04d-%(id)05d' % idinfo
plt.title=title
s=imslist[0].shape
plt.aspect_ratio = s[0]/float(s[1])*2./3.
for i in xrange(5):
bchar=sdsspy.util.FILTERCHAR[i]
row=i/ncol
col=i % ncol
im=imslist[i]
s=im.shape
im = im.transpose()
ranges = ((-0.5, -0.5), (s[1]-0.5, s[0]-0.5))
d = biggles.Density(im, ranges)
#labs=get_shaded_label(0.9,0.9,bchar)
lab=biggles.PlotLabel(0.9,0.9,bchar,color='yellow')
plt[row,col].add(d)
plt[row,col].add(lab)
if show:
plt.show()
return plt