def setUp(self):
random.seed(0)
self.ImageSet = afwImage.ImagePcaF()
def imagePca(mosaics,
visits=None,
nComponent=3,
log=False,
rng=30,
showEigen=True,
showResiduals=False,
showOriginal=True,
showRecon=False,
normalizeEimages=True,
scale=False,
frame0=0,
verbose=False):
if showOriginal and showRecon:
raise RuntimeError(
"You may not specify both showOriginal and showRecon")
try:
rng[0]
except TypeError:
rng = [rng, rng]
if not visits:
visits = sorted(mosaics.keys())
mosaics = mosaics.copy()
for v in visits:
mosaics[v] = mosaics[v].clone()
pca = afwImage.ImagePcaF()
mask = None
for v in visits:
im = mosaics[v]
if not mask:
mask = im.Factory(im.getDimensions())
maska = mask.getArray()
X, Y = np.meshgrid(np.arange(mask.getWidth()),
np.arange(mask.getHeight()))
maska[np.where(np.hypot(X - 571, Y - 552) > 531)] = np.nan
del maska
mask[168:184, 701:838] = np.nan
mask[667:733, 420:556] = np.nan
mask[653:677, 548:570] = np.nan
mask[1031:1047, 274:414] = np.nan
if False:
mask[866:931, 697:828] = np.nan
mask[267:334, 145:276] = np.nan
im += mask
pca.addImage(im, afwMath.makeStatistics(im, afwMath.SUM).getValue())
pca.analyze()
eValues = np.array(pca.getEigenValues())
eValues /= eValues[0]
eImages = pca.getEigenImages()
#
# Fiddle eigen images (we don't care about orthogonality)
#
if False:
f10 = 0.1
f20 = -0.3
f30 = 0.55
eImages[1].getArray()[:] += f10 * eImages[0].getArray()
eImages[2].getArray()[:] += f20 * eImages[0].getArray()
eImages[3].getArray()[:] += f30 * eImages[0].getArray()
if nComponent:
eImages = eImages[:nComponent]
else:
nComponent = len(eImages)
#
# Normalize
#
good = np.where(np.isfinite(eImages[0].getArray()))
if normalizeEimages:
for eim in eImages:
eima = eim.getArray()
eima /= 1e-3 * np.sqrt(np.sum(eima[good]**2))
#
# Plot/display eigen values/images
#
frame = frame0
if showEigen:
for i in range(len(eImages)):
ds9.mtv(eImages[i], frame=frame, title="%d %.2g" % (i, eValues[i]))
ds9.ds9Cmd("scale linear; scale mode zscale")
frame += 1
pyplot.clf()
if log:
eValues = np.log10(eValues)
pyplot.plot(eValues)
pyplot.plot(eValues, marker='o')
pyplot.xlim(-0.5, len(eValues))
pyplot.ylim(max(-5, pyplot.ylim()[0]), 0.05 if log else 1.05)
pyplot.xlabel("n")
pyplot.ylabel(r"$lg(\lambda)$" if log else r"$\lambda$")
pyplot.show()
if showOriginal or showRecon or showResiduals:
#
# Expand input images in the (modified) eImages
#
A = np.empty(nComponent * nComponent).reshape((nComponent, nComponent))
b = np.empty(nComponent)
for v in visits:
im = mosaics[v]
if scale:
im /= afwMath.makeStatistics(im, afwMath.MEANCLIP).getValue()
for i in range(nComponent):
b[i] = np.dot(eImages[i].getArray()[good], im.getArray()[good])
for j in range(i, nComponent):
A[i, j] = np.dot(eImages[i].getArray()[good],
eImages[j].getArray()[good])
A[j, i] = A[i, j]
x = np.linalg.solve(A, b)
#print v, A, b, x
print "%d [%s] %s" % (v, ", ".join(["%9.2e" % _ for _ in x / x[0]
]), labels.get(v, ""))
recon = eImages[0].clone()
recon *= x[0]
recona = recon.getArray()
for i in range(1, nComponent):
recona += x[i] * eImages[i].getArray()
mtv = True
if showOriginal or showRecon:
if mtv:
ds9.mtv(im if showOriginal else recon,
frame=frame,
title=v)
else:
ds9.erase(frame=frame)
s0 = afwMath.makeStatistics(recon, afwMath.MEDIAN).getValue()
s0 -= 0.5 * rng[0]
ds9.ds9Cmd("scale linear; scale limits %g %g" %
(s0, s0 + rng[0]),
frame=frame)
ds9.dot("%s %d" % ("orig" if showOriginal else "resid", v),
int(0.5 * im.getWidth()),
int(0.15 * im.getHeight()),
frame=frame,
ctype=ds9.RED)
if labels.has_key(v):
ds9.dot(labels[v],
int(0.5 * im.getWidth()),
int(0.85 * im.getHeight()),
frame=frame,
ctype=ds9.RED)
frame += 1
if showResiduals:
recon -= im
if mtv:
ds9.mtv(recon, frame=frame)
else:
ds9.erase(frame=frame)
s0 = 0
s0 -= 0.5 * rng[1]
ds9.ds9Cmd("scale linear; scale limits %g %g" %
(s0, s0 + rng[1]),
frame=frame)
frame += 1
return eImages