from __future__ import print_function
import pylab as plt
import numpy as np
from astrometry.util.ttime import Time
def _detmap(X):
from scipy.ndimage.filters import gaussian_filter
from legacypipe.survey import tim_get_resamp
(tim, targetwcs, H, W) = X
R = tim_get_resamp(tim, targetwcs)
if R is None:
return None,None,None,None,None
ie = tim.getInvvar()
assert(tim.psf_sigma > 0)
psfnorm = 1./(2. * np.sqrt(np.pi) * tim.psf_sigma)
detim = tim.getImage().copy()
tim.getSky().addTo(detim, scale=-1.)
detim[ie == 0] = 0.
# Patch SATURATED pixels with the value saturated pixels would have??
#detim[(tim.dq & tim.dq_bits['satur']) > 0] = tim.satval
detim = gaussian_filter(detim, tim.psf_sigma) / psfnorm**2
detsig1 = tim.sig1 / psfnorm
subh,subw = tim.shape
detiv = np.zeros((subh,subw), np.float32) + (1. / detsig1**2)
detiv[ie == 0] = 0.
(Yo,Xo,Yi,Xi) = R
if tim.dq is None:
sat = None
else:
sat = ((tim.dq[Yi,Xi] & tim.dq_saturation_bits) > 0)
return Yo, Xo, detim[Yi,Xi], detiv[Yi,Xi], sat
def detection_maps(tims, targetwcs, bands, mp):
# Render the detection maps
H,W = targetwcs.shape
H,W = np.int(H), np.int(W)
ibands = dict([(b,i) for i,b in enumerate(bands)])
detmaps = [np.zeros((H,W), np.float32) for b in bands]
detivs = [np.zeros((H,W), np.float32) for b in bands]
satmaps = [np.zeros((H,W), bool) for b in bands]
for tim, (Yo,Xo,incmap,inciv,sat) in zip(
tims, mp.map(_detmap, [(tim, targetwcs, H, W) for tim in tims])):
if Yo is None:
continue
ib = ibands[tim.band]
detmaps[ib][Yo,Xo] += incmap * inciv
detivs [ib][Yo,Xo] += inciv
if sat is not None:
satmaps[ib][Yo,Xo] |= sat
for detmap,detiv in zip(detmaps, detivs):
detmap /= np.maximum(1e-16, detiv)
return detmaps, detivs, satmaps
[docs]def sed_matched_filters(bands):
'''
Determines which SED-matched filters to run based on the available
bands.
Returns
-------
SEDs : list of (name, sed) tuples
'''
# single-band filters
SEDs = []
for i,band in enumerate(bands):
sed = np.zeros(len(bands))
sed[i] = 1.
SEDs.append((band, sed))
# Reverse the order -- run z-band detection filter *first*.
SEDs = list(reversed(SEDs))
if len(bands) > 1:
flat = dict(g=1., r=1., i=1., z=1.)
SEDs.append(('Flat', [flat[b] for b in bands]))
red = dict(g=2.5, r=1., i=0.4, z=0.4)
SEDs.append(('Red', [red[b] for b in bands]))
print('SED-matched filters:', SEDs)
return SEDs
[docs]def run_sed_matched_filters(SEDs, bands, detmaps, detivs, omit_xy,
targetwcs, nsigma=5, saturated_pix=None,
plots=False, ps=None, mp=None):
'''
Runs a given set of SED-matched filters.
Parameters
----------
SEDs : list of (name, sed) tuples
The SEDs to run. The `sed` values are lists the same length
as `bands`.
bands : list of string
The band names of `detmaps` and `detivs`.
detmaps : numpy array, float
Detection maps for each of the listed `bands`.
detivs : numpy array, float
Inverse-variances of the `detmaps`.
omit_xy : None, or (xx,yy,rr) tuple
Existing sources to avoid: x, y, radius.
targetwcs : WCS object
WCS object to use to convert pixel values into RA,Decs for the
returned Tractor PointSource objects.
nsigma : float, optional
Detection threshold
saturated_pix : None or list of numpy arrays, booleans
Passed through to sed_matched_detection.
A map of pixels that are always considered "hot" when
determining whether a new source touches hot pixels of an
existing source.
plots : boolean, optional
Create plots?
ps : PlotSequence object
Create plots?
mp : multiproc object
Multiprocessing
Returns
-------
Tnew : fits_table
Table of new sources detected
newcat : list of PointSource objects
Newly detected objects, with positions and fluxes, as Tractor
PointSource objects.
hot : numpy array of bool
"Hot pixels" containing sources.
See also
--------
sed_matched_detection : run a single SED-matched filter.
'''
from astrometry.util.fits import fits_table
from tractor import PointSource, RaDecPos, NanoMaggies
if omit_xy is not None:
xx,yy,rr = omit_xy
n0 = len(xx)
else:
xx,yy,rr = [],[],[]
n0 = 0
H,W = detmaps[0].shape
hot = np.zeros((H,W), bool)
peaksn = []
apsn = []
for sedname,sed in SEDs:
print('SED', sedname)
if plots:
pps = ps
else:
pps = None
t0 = Time()
sedhot,px,py,peakval,apval = sed_matched_detection(
sedname, sed, detmaps, detivs, bands, xx, yy, rr,
nsigma=nsigma, saturated_pix=saturated_pix, ps=pps)
print('SED took', Time()-t0)
if sedhot is None:
continue
print(len(px), 'new peaks')
hot |= sedhot
# With an empty xx, np.append turns it into a double!
xx = np.append(xx, px).astype(int)
yy = np.append(yy, py).astype(int)
peaksn.extend(peakval)
apsn.extend(apval)
# New peaks:
peakx = xx[n0:]
peaky = yy[n0:]
if len(peakx) == 0:
return None,None,None
# Add sources for the new peaks we found
pr,pd = targetwcs.pixelxy2radec(peakx+1, peaky+1)
print('Adding', len(pr), 'new sources')
# Also create FITS table for new sources
Tnew = fits_table()
Tnew.ra = pr
Tnew.dec = pd
Tnew.ibx = peakx
Tnew.iby = peaky
assert(len(peaksn) == len(Tnew))
assert(len(apsn) == len(Tnew))
Tnew.peaksn = np.array(peaksn)
Tnew.apsn = np.array(apsn)
newcat = []
for i,(r,d,x,y) in enumerate(zip(pr,pd,peakx,peaky)):
fluxes = dict([(band, detmap[y, x])
for band,detmap in zip(bands,detmaps)])
newcat.append(PointSource(RaDecPos(r,d),
NanoMaggies(order=bands, **fluxes)))
return Tnew, newcat, hot
def plot_boundary_map(X, rgb=(0,255,0), extent=None):
from scipy.ndimage.morphology import binary_dilation
H,W = X.shape
#bounds = np.logical_xor(binary_dilation(X), X)
padded = np.zeros((H+2, W+2), bool)
padded[1:-1, 1:-1] = X.astype(bool)
bounds = np.logical_xor(binary_dilation(padded), padded)
#rgba = np.zeros((H,W,4), np.uint8)
rgba = np.zeros((H+2,W+2,4), np.uint8)
rgba[:,:,0] = bounds*rgb[0]
rgba[:,:,1] = bounds*rgb[1]
rgba[:,:,2] = bounds*rgb[2]
rgba[:,:,3] = bounds*255
if extent is None:
extent = [-1, W+1, -1, H+1]
else:
x0,x1,y0,y1 = extent
extent = [x0-1, x1+1, y0-1, y1+1]
plt.imshow(rgba, interpolation='nearest', origin='lower', extent=extent)
[docs]def sed_matched_detection(sedname, sed, detmaps, detivs, bands,
xomit, yomit, romit,
nsigma=5.,
saturated_pix=None,
saddle=2.,
cutonaper=True,
ps=None):
'''
Runs a single SED-matched detection filter.
Avoids creating sources close to existing sources.
Parameters
----------
sedname : string
Name of this SED; only used for plots.
sed : list of floats
The SED -- a list of floats, one per band, of this SED.
detmaps : list of numpy arrays
The per-band detection maps. These must all be the same size, the
brick image size.
detivs : list of numpy arrays
The inverse-variance maps associated with `detmaps`.
bands : list of strings
The band names of the `detmaps` and `detivs` images.
xomit, yomit, romit : iterables (lists or numpy arrays) of int
Previously known sources that are to be avoided; x,y +- radius
nsigma : float, optional
Detection threshold.
saturated_pix : None or list of numpy arrays, boolean
A map of pixels that are always considered "hot" when
determining whether a new source touches hot pixels of an
existing source.
saddle : float, optional
Saddle-point depth from existing sources down to new sources.
cutonaper : bool, optional
Apply a cut that the source's detection strength must be greater
than `nsigma` above the 16th percentile of the detection strength in
an annulus (from 10 to 20 pixels) around the source.
ps : PlotSequence object, optional
Create plots?
Returns
-------
hotblobs : numpy array of bool
A map of the blobs yielding sources in this SED.
px, py : numpy array of int
The new sources found.
aper : numpy array of float
The detection strength in the annulus around the source, if
`cutonaper` is set; else -1.
peakval : numpy array of float
The detection strength.
See also
--------
sed_matched_filters : creates the `(sedname, sed)` pairs used here
run_sed_matched_filters : calls this method
'''
from scipy.ndimage.measurements import label, find_objects
from scipy.ndimage.morphology import binary_dilation, binary_fill_holes
t0 = Time()
H,W = detmaps[0].shape
allzero = True
for iband,band in enumerate(bands):
if sed[iband] == 0:
continue
if np.all(detivs[iband] == 0):
continue
allzero = False
break
if allzero:
print('SED', sedname, 'has all zero weight')
return None,None,None,None,None
sedmap = np.zeros((H,W), np.float32)
sediv = np.zeros((H,W), np.float32)
if saturated_pix is not None:
satur = np.zeros((H,W), bool)
for iband,band in enumerate(bands):
if sed[iband] == 0:
continue
# We convert the detmap to canonical band via
# detmap * w
# And the corresponding change to sig1 is
# sig1 * w
# So the invvar-weighted sum is
# (detmap * w) / (sig1**2 * w**2)
# = detmap / (sig1**2 * w)
sedmap += detmaps[iband] * detivs[iband] / sed[iband]
sediv += detivs [iband] / sed[iband]**2
if saturated_pix is not None:
satur |= saturated_pix[iband]
sedmap /= np.maximum(1e-16, sediv)
sedsn = sedmap * np.sqrt(sediv)
del sedmap
peaks = (sedsn > nsigma)
print('SED sn:', Time()-t0)
t0 = Time()
def saddle_level(Y):
# Require a saddle that drops by (the larger of) "saddle"
# sigma, or 10% of the peak height.
# ("saddle" is passed in as an argument to the
# sed_matched_detection function)
drop = max(saddle, Y * 0.1)
return Y - drop
lowest_saddle = nsigma - saddle
# zero out the edges -- larger margin here?
peaks[0 ,:] = 0
peaks[:, 0] = 0
peaks[-1,:] = 0
peaks[:,-1] = 0
# Label the N-sigma blobs at this point... we'll use this to build
# "sedhot", which in turn is used to define the blobs that we will
# optimize simultaneously. This also determines which pixels go
# into the fitting!
dilate = 8
hotblobs,nhot = label(binary_fill_holes(
binary_dilation(peaks, iterations=dilate)))
# find pixels that are larger than their 8 neighbors
peaks[1:-1, 1:-1] &= (sedsn[1:-1,1:-1] >= sedsn[0:-2,1:-1])
peaks[1:-1, 1:-1] &= (sedsn[1:-1,1:-1] >= sedsn[2: ,1:-1])
peaks[1:-1, 1:-1] &= (sedsn[1:-1,1:-1] >= sedsn[1:-1,0:-2])
peaks[1:-1, 1:-1] &= (sedsn[1:-1,1:-1] >= sedsn[1:-1,2: ])
peaks[1:-1, 1:-1] &= (sedsn[1:-1,1:-1] >= sedsn[0:-2,0:-2])
peaks[1:-1, 1:-1] &= (sedsn[1:-1,1:-1] >= sedsn[0:-2,2: ])
peaks[1:-1, 1:-1] &= (sedsn[1:-1,1:-1] >= sedsn[2: ,0:-2])
peaks[1:-1, 1:-1] &= (sedsn[1:-1,1:-1] >= sedsn[2: ,2: ])
print('Peaks:', Time()-t0)
t0 = Time()
if ps is not None:
from astrometry.util.plotutils import dimshow
crossa = dict(ms=10, mew=1.5)
green = (0,1,0)
plt.clf()
plt.subplot(1,2,2)
dimshow(sedsn, vmin=-2, vmax=100, cmap='hot', ticks=False)
plt.subplot(1,2,1)
dimshow(sedsn, vmin=-2, vmax=10, cmap='hot', ticks=False)
above = (sedsn > nsigma)
plot_boundary_map(above)
ax = plt.axis()
y,x = np.nonzero(peaks)
plt.plot(xomit, yomit, 'm.')
plt.plot(x, y, 'r+')
plt.axis(ax)
plt.title('SED %s: S/N & peaks' % sedname)
ps.savefig()
# plt.clf()
# plt.imshow(sedsn, vmin=-2, vmax=10, interpolation='nearest',
# origin='lower', cmap='hot')
# plot_boundary_map(sedsn > lowest_saddle)
# plt.title('SED %s: S/N & lowest saddle point bounds' % sedname)
# ps.savefig()
# For each new source, compute the saddle value, segment at that
# level, and drop the source if it is in the same blob as a
# previously-detected source.
# We dilate the blobs a bit too, to
# catch slight differences in centroid positions.
dilate = 1
# For efficiency, segment at the minimum saddle level to compute
# slices; the operations described above need only happen within
# the slice.
saddlemap = (sedsn > lowest_saddle)
saddlemap = binary_dilation(saddlemap, iterations=dilate)
if saturated_pix is not None:
saddlemap |= satur
allblobs,nblobs = label(saddlemap)
allslices = find_objects(allblobs)
ally0 = [sy.start for sy,sx in allslices]
allx0 = [sx.start for sy,sx in allslices]
# brightest peaks first
py,px = np.nonzero(peaks)
I = np.argsort(-sedsn[py,px])
py = py[I]
px = px[I]
keep = np.zeros(len(px), bool)
peakval = []
aper = []
apin = 10
apout = 20
# Map of pixels that are vetoed by sources found so far. The veto
# area is based on saddle height. We go from brightest to
# faintest pixels. Thus the saddle level decreases, and the
# saddlemap areas become larger; the saddlemap when a source is
# found is a lower bound on the pixels that it will veto based on
# the saddle heights of fainter sources. Thus the vetomap isn't
# the final word, it is just a quick veto of pixels we know for
# sure will be vetoed.
vetomap = np.zeros(sedsn.shape, bool)
for x,y,r in zip(xomit, yomit, romit):
xlo = int(np.clip(np.floor(x - r), 0, W-1))
xhi = int(np.clip(np.ceil (x + r), 0, W-1))
ylo = int(np.clip(np.floor(y - r), 0, H-1))
yhi = int(np.clip(np.ceil (y + r), 0, H-1))
vetomap[ylo:yhi+1, xlo:xhi+1] |= (np.hypot(
(x - np.arange(xlo, xhi+1))[np.newaxis, :],
(y - np.arange(ylo, yhi+1))[:, np.newaxis]) < r)
# For each peak, determine whether it is isolated enough --
# separated by a low enough saddle from other sources. Need only
# search within its "allblob", which is defined by the lowest
# saddle.
print('Found', len(px), 'potential peaks')
for i,(x,y) in enumerate(zip(px, py)):
# one plot per peak is a little excessive!
if False and ps is not None:
level = saddle_level(sedsn[y,x])
_peak_plot_1(vetomap, x, y, px, py, keep, i, xomit, yomit, sedsn, allblobs,
level, dilate, saturated_pix, satur, ps)
if vetomap[y,x]:
#print(' in veto map!')
continue
level = saddle_level(sedsn[y,x])
ablob = allblobs[y,x]
index = int(ablob - 1)
slc = allslices[index]
#print('source', i, 'of', len(px), 'at', x,y, 'S/N', sedsn[y,x], 'saddle', level)
#print(' allblobs slice', slc)
saddlemap = (sedsn[slc] > level)
saddlemap = binary_dilation(saddlemap, iterations=dilate)
if saturated_pix is not None:
saddlemap |= satur[slc]
saddlemap *= (allblobs[slc] == ablob)
saddlemap = binary_fill_holes(saddlemap)
blobs,nblobs = label(saddlemap)
x0,y0 = allx0[index], ally0[index]
thisblob = blobs[y-y0, x-x0]
saddlemap *= (blobs == thisblob)
# previously found sources:
ox = np.append(xomit, px[:i][keep[:i]]) - x0
oy = np.append(yomit, py[:i][keep[:i]]) - y0
h,w = blobs.shape
cut = False
if len(ox):
ox = ox.astype(int)
oy = oy.astype(int)
cut = any((ox >= 0) * (ox < w) * (oy >= 0) * (oy < h) *
(blobs[np.clip(oy,0,h-1), np.clip(ox,0,w-1)] ==
thisblob))
if False and cut and ps is not None:
_peak_plot_2(ox, oy, w, h, blobs, thisblob, sedsn, x0, y0,
x, y, level, ps)
if False and (not cut) and ps is not None:
_peak_plot_3(sedsn, nsigma, x, y, x0, y0, slc, saddlemap,
xomit, yomit, px, py, keep, i, ps)
if cut:
# in same blob as previously found source.
#print(' cut')
# update vetomap
vetomap[slc] |= saddlemap
#print('Added to vetomap:', np.sum(saddlemap), 'pixels set; now total of', np.sum(vetomap), 'pixels set')
continue
# Measure in aperture...
ap = sedsn[max(0, y-apout):min(H,y+apout+1),
max(0, x-apout):min(W,x+apout+1)]
apiv = (sediv[max(0, y-apout):min(H,y+apout+1),
max(0, x-apout):min(W,x+apout+1)] > 0)
aph,apw = ap.shape
apx0, apy0 = max(0, x - apout), max(0, y - apout)
R2 = ((np.arange(aph)+apy0 - y)[:,np.newaxis]**2 +
(np.arange(apw)+apx0 - x)[np.newaxis,:]**2)
ap = ap[apiv * (R2 >= apin**2) * (R2 <= apout**2)]
if len(ap):
# 16th percentile ~ -1 sigma point.
m = np.percentile(ap, 16.)
else:
# fake
m = -1.
if cutonaper:
if sedsn[y,x] - m < nsigma:
continue
aper.append(m)
peakval.append(sedsn[y,x])
keep[i] = True
vetomap[slc] |= saddlemap
#print('Added to vetomap:', np.sum(saddlemap), 'pixels set; now total of', np.sum(vetomap), 'pixels set')
if False and ps is not None:
plt.clf()
plt.subplot(1,2,1)
dimshow(ap, vmin=-2, vmax=10, cmap='hot',
extent=[apx0,apx0+apw,apy0,apy0+aph])
plt.subplot(1,2,2)
dimshow(ap * ((R2 >= apin**2) * (R2 <= apout**2)),
vmin=-2, vmax=10, cmap='hot',
extent=[apx0,apx0+apw,apy0,apy0+aph])
plt.suptitle('peak %.1f vs ap %.1f' % (sedsn[y,x], m))
ps.savefig()
print('New sources:', Time()-t0)
t0 = Time()
if ps is not None:
pxdrop = px[np.logical_not(keep)]
pydrop = py[np.logical_not(keep)]
py = py[keep]
px = px[keep]
# Which of the hotblobs yielded sources? Those are the ones to keep.
hbmap = np.zeros(nhot+1, bool)
hbmap[hotblobs[py,px]] = True
if len(xomit):
h,w = hotblobs.shape
hbmap[hotblobs[np.clip(yomit, 0, h-1), np.clip(xomit, 0, w-1)]] = True
# in case a source is (somehow) not in a hotblob?
hbmap[0] = False
hotblobs = hbmap[hotblobs]
if ps is not None:
plt.clf()
dimshow(vetomap, vmin=0, vmax=1, cmap='hot')
plt.title('SED %s: veto map' % sedname)
ps.savefig()
plt.clf()
dimshow(hotblobs, vmin=0, vmax=1, cmap='hot')
ax = plt.axis()
p1 = plt.plot(px, py, 'g+', ms=8, mew=2)
p2 = plt.plot(pxdrop, pydrop, 'm+', ms=8, mew=2)
p3 = plt.plot(xomit, yomit, 'r+', ms=8, mew=2)
plt.axis(ax)
plt.title('SED %s: hot blobs' % sedname)
plt.figlegend((p3[0],p1[0],p2[0]), ('Existing', 'Keep', 'Drop'),
'upper left')
ps.savefig()
return hotblobs, px, py, aper, peakval
def _peak_plot_1(vetomap, x, y, px, py, keep, i, xomit, yomit, sedsn, allblobs,
level, dilate, saturated_pix, satur, ps):
from scipy.ndimage.morphology import binary_dilation, binary_fill_holes
from scipy.ndimage.measurements import label, find_objects
plt.clf()
plt.suptitle('Peak at %i,%i' % (x,y))
plt.subplot(2,2,1)
plt.imshow(vetomap, interpolation='nearest', origin='lower',
cmap='gray', vmin=0, vmax=1)
ax = plt.axis()
prevx = px[:i][keep[:i]]
prevy = py[:i][keep[:i]]
plt.plot(prevx, prevy, 'o', mec='r', mfc='none')
plt.plot(xomit, yomit, 'm.')
plt.plot(x, y, '+', mec='r', mfc='r')
plt.axis(ax)
plt.title('veto map')
ablob = allblobs[y,x]
saddlemap = (sedsn > level)
saddlemap = binary_dilation(saddlemap, iterations=dilate)
if saturated_pix is not None:
saddlemap |= satur
saddlemap *= (allblobs == ablob)
# plt.subplot(2,2,2)
# plt.imshow(saddlemap, interpolation='nearest', origin='lower',
# vmin=0, vmax=1, cmap='gray')
# ax = plt.axis()
# plt.plot(x, y, '+', mec='r', mfc='r')
# plt.plot(prevx, prevy, 'o', mec='r', mfc='none')
# plt.plot(xomit, yomit, 'm.')
# plt.axis(ax)
# plt.title('saddle map (1)')
plt.subplot(2,2,2)
saddlemap = binary_fill_holes(saddlemap)
plt.imshow(saddlemap, interpolation='nearest', origin='lower',
vmin=0, vmax=1, cmap='gray')
ax = plt.axis()
plt.plot(x, y, '+', mec='r', mfc='r')
plt.plot(prevx, prevy, 'o', mec='r', mfc='none')
plt.plot(xomit, yomit, 'm.')
plt.axis(ax)
plt.title('saddle map (fill holes)')
blobs,nblobs = label(saddlemap)
thisblob = blobs[y, x]
saddlemap *= (blobs == thisblob)
plt.subplot(2,2,3)
plt.imshow(saddlemap, interpolation='nearest', origin='lower',
vmin=0, vmax=1, cmap='gray')
ax = plt.axis()
plt.plot(x, y, '+', mec='r', mfc='r')
plt.plot(prevx, prevx, 'o', mec='r', mfc='none')
plt.plot(xomit, yomit, 'm.')
plt.axis(ax)
plt.title('saddle map (this blob)')
nzy,nzx = np.nonzero(saddlemap)
plt.subplot(2,2,4)
plt.imshow(saddlemap, interpolation='nearest', origin='lower',
vmin=0, vmax=1, cmap='gray')
plt.plot(x, y, '+', mec='r', mfc='r')
plt.plot(prevx, prevx, 'o', mec='r', mfc='none')
plt.plot(xomit, yomit, 'm.')
plt.axis([min(nzx)-1, max(nzx)+1, min(nzy)-1, max(nzy)+1])
plt.title('saddle map (this blob)')
ps.savefig()
def _peak_plot_2(ox, oy, w, h, blobs, thisblob, sedsn, x0, y0,
x, y, level, ps):
I = np.flatnonzero((ox >= 0) * (ox < w) * (oy >= 0) * (oy < h) *
(blobs[np.clip(oy,0,h-1), np.clip(ox,0,w-1)] ==
thisblob))
j = I[0]
plt.clf()
plt.subplot(1,2,1)
plt.imshow(sedsn, cmap='hot', interpolation='nearest', origin='lower')
ax = plt.axis()
plt.plot([ox[j]+x0, x], [oy[j]+y0, y], 'g-')
plt.axis(ax)
dx = ox[j]+x0 - x
dy = oy[j]+y0 - y
dist = max(1, np.hypot(dx, dy))
ss = []
steps = int(np.ceil(dist))
H,W = sedsn.shape
for s in range(-3, steps+3):
ix = int(np.round(x + (dx / dist) * s))
iy = int(np.round(y + (dy / dist) * s))
ss.append(sedsn[np.clip(iy, 0, H-1), np.clip(ix, 0, W-1)])
plt.subplot(1,2,2)
plt.plot(ss)
plt.axhline(sedsn[y,x], color='k')
plt.axhline(sedsn[oy[j],ox[j]], color='r')
plt.axhline(level)
plt.xticks([])
plt.title('S/N')
ps.savefig()
def _peak_plot_3(sedsn, nsigma, x, y, x0, y0, slc, saddlemap,
xomit, yomit, px, py, keep, i, cut, ps):
green = (0,1,0)
plt.clf()
plt.subplot(1,2,1)
plt.imshow(sedsn, vmin=-2, vmax=10, cmap='hot', interpolation='nearest',
origin='lower')
plot_boundary_map((sedsn > nsigma))
ax = plt.axis()
plt.plot(x, y, 'm+', ms=12, mew=2)
plt.axis(ax)
plt.subplot(1,2,2)
y1,x1 = [s.stop for s in slc]
ext = [x0,x1,y0,y1]
plt.imshow(saddlemap, extent=ext, interpolation='nearest', origin='lower')
#plt.plot([x0,x0,x1,x1,x0], [y0,y1,y1,y0,y0], 'c-')
#ax = plt.axis()
#plt.plot(ox+x0, oy+y0, 'rx')
plt.plot(xomit, yomit, 'rx', ms=8, mew=2)
plt.plot(px[:i][keep[:i]], py[:i][keep[:i]], '+',
color=green, ms=8, mew=2)
plt.plot(x, y, 'mo', mec='m', mfc='none', ms=12, mew=2)
plt.axis(ax)
if cut:
plt.suptitle('Cut')
else:
plt.suptitle('Keep')
ps.savefig()
[docs]def segment_and_group_sources(image, T, name=None, ps=None, plots=False):
'''
*image*: binary image that defines "blobs"
*T*: source table; only ".ibx" and ".iby" elements are used (x,y integer
pix pos). Note: ".blob" field is added.
*name*: for debugging only
Returns: (blobs, blobsrcs, blobslices)
*blobs*: image, values -1 = no blob, integer blob indices
*blobsrcs*: list of np arrays of integers, elements in T within each blob
*blobslices*: list of slice objects for blob bounding-boxes.
'''
from scipy.ndimage.morphology import binary_fill_holes
from scipy.ndimage.measurements import label, find_objects
emptyblob = 0
image = binary_fill_holes(image)
blobs,nblobs = label(image)
print('N detected blobs:', nblobs)
H,W = image.shape
del image
blobslices = find_objects(blobs)
T.blob = blobs[T.iby, T.ibx]
if plots:
import pylab as plt
from astrometry.util.plotutils import dimshow
plt.clf()
dimshow(blobs > 0, vmin=0, vmax=1)
ax = plt.axis()
for i,bs in enumerate(blobslices):
sy,sx = bs
by0,by1 = sy.start, sy.stop
bx0,bx1 = sx.start, sx.stop
plt.plot([bx0, bx0, bx1, bx1, bx0], [by0, by1, by1, by0, by0], 'r-')
plt.text((bx0+bx1)/2., by0, '%i' % (i+1),
ha='center', va='bottom', color='r')
plt.plot(T.ibx, T.iby, 'rx')
for i,t in enumerate(T):
plt.text(t.ibx, t.iby, 'src %i' % i, color='red',
ha='left', va='center')
plt.axis(ax)
plt.title('Blobs')
ps.savefig()
# Find sets of sources within blobs
blobsrcs = []
keepslices = []
blobmap = {}
dropslices = {}
for blob in range(1, nblobs+1):
Isrcs = np.flatnonzero(T.blob == blob)
if len(Isrcs) == 0:
#print('Blob', blob, 'has no sources')
blobmap[blob] = -1
dropslices[blob] = blobslices[blob-1]
continue
blobmap[blob] = len(blobsrcs)
blobsrcs.append(Isrcs)
bslc = blobslices[blob-1]
keepslices.append(bslc)
blobslices = keepslices
# Find sources that do not belong to a blob and add them as
# singleton "blobs"; otherwise they don't get optimized.
# for sources outside the image bounds, what should we do?
inblobs = np.zeros(len(T), bool)
for Isrcs in blobsrcs:
inblobs[Isrcs] = True
noblobs = np.flatnonzero(np.logical_not(inblobs))
del inblobs
# Add new fake blobs!
for ib,i in enumerate(noblobs):
#S = 3
S = 5
bslc = (slice(np.clip(T.iby[i] - S, 0, H-1),
np.clip(T.iby[i] + S+1, 0, H)),
slice(np.clip(T.ibx[i] - S, 0, W-1),
np.clip(T.ibx[i] + S+1, 0, W)))
# Does this new blob overlap existing blob(s)?
oblobs = np.unique(blobs[bslc])
oblobs = oblobs[oblobs != emptyblob]
#print('This blob overlaps existing blobs:', oblobs)
if len(oblobs) > 1:
print('WARNING: not merging overlapping blobs like maybe we should')
if len(oblobs):
blob = oblobs[0]
#print('Adding source to existing blob', blob)
blobs[bslc][blobs[bslc] == emptyblob] = blob
blobindex = blobmap[blob]
if blobindex == -1:
# the overlapping blob was going to be dropped -- restore it.
blobindex = len(blobsrcs)
blobmap[blob] = blobindex
blobslices.append(dropslices[blob])
blobsrcs.append(np.array([], np.int64))
# Expand the existing blob slice to encompass this new source
oldslc = blobslices[blobindex]
sy,sx = oldslc
oy0,oy1, ox0,ox1 = sy.start,sy.stop, sx.start,sx.stop
sy,sx = bslc
ny0,ny1, nx0,nx1 = sy.start,sy.stop, sx.start,sx.stop
newslc = slice(min(oy0,ny0), max(oy1,ny1)), slice(min(ox0,nx0), max(ox1,nx1))
blobslices[blobindex] = newslc
# Add this source to the list of source indices for the existing blob.
blobsrcs[blobindex] = np.append(blobsrcs[blobindex], np.array([i]))
else:
# Set synthetic blob number
blob = nblobs+1 + ib
blobs[bslc][blobs[bslc] == emptyblob] = blob
blobmap[blob] = len(blobsrcs)
blobslices.append(bslc)
blobsrcs.append(np.array([i]))
#print('Added', len(noblobs), 'new fake singleton blobs')
# Remap the "blobs" image so that empty regions are = -1 and the blob values
# correspond to their indices in the "blobsrcs" list.
if len(blobmap):
maxblob = max(blobmap.keys())
else:
maxblob = 0
maxblob = max(maxblob, blobs.max())
bm = np.zeros(maxblob + 1, int)
for k,v in blobmap.items():
bm[k] = v
bm[0] = -1
# DEBUG
if plots:
import fitsio
fitsio.write('blobs-before-%s.fits' % name, blobs, clobber=True)
# Remap blob numbers
blobs = bm[blobs]
if plots:
import fitsio
fitsio.write('blobs-after-%s.fits' % name, blobs, clobber=True)
if plots:
import pylab as plt
from astrometry.util.plotutils import dimshow
plt.clf()
dimshow(blobs > -1, vmin=0, vmax=1)
ax = plt.axis()
for i,bs in enumerate(blobslices):
sy,sx = bs
by0,by1 = sy.start, sy.stop
bx0,bx1 = sx.start, sx.stop
plt.plot([bx0, bx0, bx1, bx1, bx0], [by0, by1, by1, by0, by0], 'r-')
plt.text((bx0+bx1)/2., by0, '%i' % (i+1),
ha='center', va='bottom', color='r')
plt.plot(T.ibx, T.iby, 'rx')
for i,t in enumerate(T):
plt.text(t.ibx, t.iby, 'src %i' % i, color='red',
ha='left', va='center')
plt.axis(ax)
plt.title('Blobs')
ps.savefig()
for j,Isrcs in enumerate(blobsrcs):
for i in Isrcs:
if (blobs[T.iby[i], T.ibx[i]] != j):
print('---------------------------!!!-------------------------')
print('Blob', j, 'sources', Isrcs)
print('Source', i, 'coords x,y', T.ibx[i], T.iby[i])
print('Expected blob value', j, 'but got',
blobs[T.iby[i], T.ibx[i]])
T.blob = blobs[T.iby, T.ibx]
assert(len(blobsrcs) == len(blobslices))
return blobs, blobsrcs, blobslices
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