def detection_plots_2(tims, bands, targetwcs, refstars, Tnew, hot,
saturated_pix, ps):
coimgs, _ = quick_coadds(tims, bands, targetwcs)
crossa = dict(ms=10, mew=1.5)
plt.clf()
dimshow(get_rgb(coimgs, bands))
plt.title('Detections')
ps.savefig()
ax = plt.axis()
if len(refstars):
I, = np.nonzero([len(r) and r[0] == 'T' for r in refstars.ref_cat])
if len(I):
plt.plot(refstars.ibx[I],
refstars.iby[I],
'+',
color=(0, 1, 1),
label='Tycho-2',
**crossa)
I, = np.nonzero([len(r) and r[0] == 'G' for r in refstars.ref_cat])
if len(I):
plt.plot(refstars.ibx[I],
refstars.iby[I],
'+',
color=(0.2, 0.2, 1),
label='Gaia',
**crossa)
I, = np.nonzero([len(r) and r[0] == 'L' for r in refstars.ref_cat])
if len(I):
plt.plot(refstars.ibx[I],
refstars.iby[I],
'+',
color=(0.6, 0.6, 0.2),
label='Large Galaxy',
**crossa)
plt.plot(Tnew.ibx,
Tnew.iby,
'+',
color=(0, 1, 0),
label='New SED-matched detections',
**crossa)
plt.axis(ax)
plt.title('Detections')
plt.legend(loc='upper left')
ps.savefig()
plt.clf()
plt.subplot(1, 2, 1)
dimshow(hot, vmin=0, vmax=1, cmap='hot')
plt.title('hot')
plt.subplot(1, 2, 2)
H, W = targetwcs.shape
rgb = np.zeros((H, W, 3))
for i, satpix in enumerate(saturated_pix):
rgb[:, :, 2 - i] = satpix
dimshow(rgb)
plt.title('saturated_pix')
ps.savefig()
def halo_plots_before(tims, bands, targetwcs, halostars, ps):
coimgs, _ = quick_coadds(tims, bands, targetwcs)
plt.clf()
dimshow(get_rgb(coimgs, bands))
ax = plt.axis()
plt.plot(halostars.ibx, halostars.iby, 'o', mec='r', ms=15, mfc='none')
plt.axis(ax)
plt.title('Before star halo subtraction')
ps.savefig()
return coimgs
def halo_plots_after(tims, bands, targetwcs, halostars, coimgs, ps):
coimgs2, _ = quick_coadds(tims, bands, targetwcs)
plt.clf()
dimshow(get_rgb(coimgs2, bands))
ax = plt.axis()
plt.plot(halostars.ibx, halostars.iby, 'o', mec='r', ms=15, mfc='none')
plt.axis(ax)
plt.title('After star halo subtraction')
ps.savefig()
plt.clf()
dimshow(get_rgb([co - co2 for co, co2 in zip(coimgs, coimgs2)], bands))
ax = plt.axis()
plt.plot(halostars.ibx, halostars.iby, 'o', mec='r', ms=15, mfc='none')
plt.axis(ax)
plt.title('Subtracted halos')
ps.savefig()
for g in halostars[:10]:
plt.clf()
pixscale = targetwcs.pixel_scale()
pixrad = int(g.radius * 3600. / pixscale)
ax = [g.ibx - pixrad, g.ibx + pixrad, g.iby - pixrad, g.iby + pixrad]
ima = dict(interpolation='nearest', origin='lower')
plt.subplot(2, 2, 1)
plt.imshow(get_rgb(coimgs, bands), **ima)
plt.plot(halostars.ibx, halostars.iby, 'o', mec='r', ms=15, mfc='none')
plt.axis(ax)
plt.subplot(2, 2, 2)
plt.imshow(get_rgb(coimgs2, bands), **ima)
plt.axis(ax)
plt.subplot(2, 2, 3)
plt.imshow(
get_rgb([co - co2 for co, co2 in zip(coimgs, coimgs2)], bands),
**ima)
plt.axis(ax)
ps.savefig()
def showresid(tims,mods,wcs,bands='grz'):
co,_ = quick_coadds(tims, bands, wcs)
comod,_ = quick_coadds(tims, bands, wcs, images=mods)
plt.imshow(np.flipud(get_rgb([i-m for i,m in zip(co,comod)], bands)))
plt.xticks([]); plt.yticks([])
return co,comod
def fitblobs_plots(tims, bands, targetwcs, blobslices, blobsrcs, cat, blobs,
ps):
coimgs, _ = quick_coadds(tims, bands, targetwcs)
plt.clf()
dimshow(get_rgb(coimgs, bands))
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,
ha='center',
va='bottom',
color='r')
plt.axis(ax)
plt.title('Blobs')
ps.savefig()
for i, Isrcs in enumerate(blobsrcs):
for isrc in Isrcs:
src = cat[isrc]
ra, dec = src.getPosition().ra, src.getPosition().dec
_, x, y = targetwcs.radec2pixelxy(ra, dec)
plt.text(x,
y,
'b%i/s%i' % (i, isrc),
ha='center',
va='bottom',
color='r')
plt.axis(ax)
plt.title('Blobs + Sources')
ps.savefig()
plt.clf()
dimshow(blobs)
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,
ha='center',
va='bottom',
color='r')
plt.axis(ax)
plt.title('Blobs')
ps.savefig()
plt.clf()
dimshow(blobs != -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,
ha='center',
va='bottom',
color='r')
plt.axis(ax)
plt.title('Blobs')
ps.savefig()
def detection_plots(detmaps, detivs, bands, saturated_pix, tims, targetwcs,
refstars, large_galaxies, gaia_stars, ps):
rgb = get_rgb(detmaps, bands)
plt.clf()
dimshow(rgb)
plt.title('detmaps')
ps.savefig()
for i, satpix in enumerate(saturated_pix):
rgb[:, :, 2 - i][satpix] = 1
plt.clf()
dimshow(rgb)
plt.title('detmaps & saturated')
ps.savefig()
coimgs, _ = quick_coadds(tims, bands, targetwcs, fill_holes=False)
if refstars:
plt.clf()
dimshow(get_rgb(coimgs, bands))
ax = plt.axis()
lp, lt = [], []
tycho = refstars[refstars.isbright]
if len(tycho):
_, ix, iy = targetwcs.radec2pixelxy(tycho.ra, tycho.dec)
p = plt.plot(ix - 1,
iy - 1,
'o',
mew=3,
ms=14,
mec='r',
mfc='none')
lp.append(p)
lt.append('Tycho-2 only')
if gaia_stars:
gaia = refstars[refstars.isgaia]
if gaia_stars and len(gaia):
_, ix, iy = targetwcs.radec2pixelxy(gaia.ra, gaia.dec)
p = plt.plot(ix - 1,
iy - 1,
'o',
mew=3,
ms=10,
mec='c',
mfc='none')
for x, y, g in zip(ix, iy, gaia.phot_g_mean_mag):
plt.text(x,
y,
'%.1f' % g,
color='k',
bbox=dict(facecolor='w', alpha=0.5))
lp.append(p)
lt.append('Gaia')
# star_clusters?
if large_galaxies:
galaxies = refstars[refstars.islargegalaxy]
if large_galaxies and len(galaxies):
_, ix, iy = targetwcs.radec2pixelxy(galaxies.ra, galaxies.dec)
p = plt.plot(ix - 1,
iy - 1,
'o',
mew=3,
ms=14,
mec=(0, 1, 0),
mfc='none')
lp.append(p)
lt.append('Galaxies')
plt.axis(ax)
plt.title('Ref sources')
plt.figlegend([p[0] for p in lp], lt)
ps.savefig()
for band, detmap, detiv in zip(bands, detmaps, detivs):
plt.clf()
plt.subplot(2, 1, 1)
plt.hist((detmap * np.sqrt(detiv))[detiv > 0],
bins=50,
range=(-5, 8),
log=True)
plt.title('Detection map pixel values (sigmas): band %s' % band)
plt.subplot(2, 1, 2)
plt.hist((detmap * np.sqrt(detiv))[detiv > 0], bins=50, range=(-5, 8))
ps.savefig()
