def star_profiles(ps):
# Run an example CCD, 292604-N4, with fairly large difference vs PS1.
# python -c "from astrometry.util.fits import *; T = merge_tables([fits_table('/project/projectdirs/desiproc/dr3/tractor/244/tractor-244%s.fits' % b) for b in ['2p065','4p065', '7p065']]); T.writeto('tst-cat.fits')"
# python legacypipe/forced_photom_decam.py --save-data tst-data.fits --save-model tst-model.fits 292604 N4 tst-cat.fits tst-phot.fits
# -> tst-{model,data,phot}.fits
datafn = 'tst-data.fits'
modfn = 'tst-model.fits'
photfn = 'tst-phot.fits'
catfn = 'tst-cat.fits'
img = fitsio.read(datafn)
mod = fitsio.read(modfn)
phot = fits_table(photfn)
cat = fits_table(catfn)
print(len(phot), 'forced-photometry results')
margin = 25
phot.cut((phot.x > 0 + margin) * (phot.x < 2046 - margin) *
(phot.y > 0 + margin) * (phot.y < 4096 - margin))
print(len(phot), 'in bounds')
cmap = dict([((b, o), i)
for i, (b, o) in enumerate(zip(cat.brickname, cat.objid))])
I = np.array(
[cmap.get((b, o), -1) for b, o in zip(phot.brickname, phot.objid)])
print(np.sum(I >= 0), 'forced-phot matched cat')
phot.type = cat.type[I]
wcs = Sip(datafn)
phot.ra, phot.dec = wcs.pixelxy2radec(phot.x + 1, phot.y + 1)
phot.cut(np.argsort(phot.flux))
phot.sn = phot.flux * np.sqrt(phot.flux_ivar)
phot.cut(phot.sn > 5)
print(len(phot), 'with S/N > 5')
ps1 = ps1cat(ccdwcs=wcs)
stars = ps1.get_stars()
print(len(stars), 'PS1 sources')
# Now cut to just *stars* with good colors
stars.gicolor = stars.median[:, 0] - stars.median[:, 2]
keep = (stars.gicolor > 0.4) * (stars.gicolor < 2.7)
stars.cut(keep)
print(len(stars), 'PS1 stars with good colors')
stars.cut(np.minimum(stars.stdev[:, 1], stars.stdev[:, 2]) < 0.05)
print(len(stars), 'PS1 stars with min stdev(r,i) < 0.05')
I, J, d = match_radec(phot.ra, phot.dec, stars.ra, stars.dec, 1. / 3600.)
print(len(I), 'matches')
plt.clf()
ha = dict(histtype='step', bins=20, range=(0, 100), normed=True)
plt.hist(phot.flux, color='b', **ha)
plt.hist(phot.flux[I], color='r', **ha)
ps.savefig()
plt.clf()
plt.hist(phot.flux * np.sqrt(phot.flux_ivar), bins=100, range=(-10, 50))
plt.xlabel('Flux S/N')
ps.savefig()
K = np.argsort(phot.flux[I])
I = I[K]
J = J[K]
ix = np.round(phot.x).astype(int)
iy = np.round(phot.y).astype(int)
sz = 10
P = np.flatnonzero(phot.type == 'PSF ')
print(len(P), 'PSFs')
imed = len(P) / 2
i1 = int(len(P) * 0.75)
i2 = int(len(P) * 0.25)
N = 401
allmods = []
allimgs = []
for II, tt in [ #(I[:len(I)/2], 'faint matches to PS1'),
#(I[len(I)/2:], 'bright matches to PS1'),
#(P[i2: i2+N], '25th pct PSFs'),
#(P[imed: imed+N], 'median PSFs'),
#(P[i1: i1+N], '75th pct PSFs'),
#(P[-25:], 'brightest PSFs'),
(P[i2:imed], '2nd quartile of PSFs'),
(P[imed:i1], '3rd quartile of PSFs'),
#(P[:len(P)/2], 'faint half of PSFs'),
#(P[len(P)/2:], 'bright half of PSFs'),
]:
imgs = []
mods = []
shimgs = []
shmods = []
imgsum = modsum = 0
#plt.clf()
for i in II:
from astrometry.util.util import lanczos_shift_image
dy = phot.y[i] - iy[i]
dx = phot.x[i] - ix[i]
sub = img[iy[i] - sz:iy[i] + sz + 1, ix[i] - sz:ix[i] + sz + 1]
shimg = lanczos_shift_image(sub, -dx, -dy)
sub = mod[iy[i] - sz:iy[i] + sz + 1, ix[i] - sz:ix[i] + sz + 1]
shmod = lanczos_shift_image(sub, -dx, -dy)
iyslice = img[iy[i], ix[i] - sz:ix[i] + sz + 1]
myslice = mod[iy[i], ix[i] - sz:ix[i] + sz + 1]
ixslice = img[iy[i] - sz:iy[i] + sz + 1, ix[i]]
mxslice = mod[iy[i] - sz:iy[i] + sz + 1, ix[i]]
mx = iyslice.max()
# plt.plot(iyslice/mx, 'b-', alpha=0.1)
# plt.plot(myslice/mx, 'r-', alpha=0.1)
# plt.plot(ixslice/mx, 'b-', alpha=0.1)
# plt.plot(mxslice/mx, 'r-', alpha=0.1)
siyslice = shimg[sz, :]
sixslice = shimg[:, sz]
smyslice = shmod[sz, :]
smxslice = shmod[:, sz]
shimgs.append(siyslice / mx)
shimgs.append(sixslice / mx)
shmods.append(smyslice / mx)
shmods.append(smxslice / mx)
imgs.append(iyslice / mx)
imgs.append(ixslice / mx)
mods.append(myslice / mx)
mods.append(mxslice / mx)
imgsum = imgsum + ixslice + iyslice
modsum = modsum + mxslice + myslice
# plt.ylim(-0.1, 1.1)
# plt.title(tt)
# ps.savefig()
mimg = np.median(np.array(imgs), axis=0)
mmod = np.median(np.array(mods), axis=0)
mshim = np.median(np.array(shimgs), axis=0)
mshmo = np.median(np.array(shmods), axis=0)
allmods.append(mshmo)
allimgs.append(mshim)
plt.clf()
# plt.plot(mimg, 'b-')
# plt.plot(mmod, 'r-')
plt.plot(mshim, 'g-')
plt.plot(mshmo, 'm-')
plt.ylim(-0.1, 1.1)
plt.title(tt + ': median; sums %.3f/%.3f' %
(np.sum(mimg), np.sum(mmod)))
ps.savefig()
# plt.clf()
# mx = imgsum.max()
# plt.plot(imgsum/mx, 'b-')
# plt.plot(modsum/mx, 'r-')
# plt.ylim(-0.1, 1.1)
# plt.title(tt + ': sum')
# ps.savefig()
plt.clf()
plt.plot((mimg + 0.01) / (mmod + 0.01), 'k-')
plt.plot((imgsum / mx + 0.01) / (modsum / mx + 0.01), 'g-')
plt.plot((mshim + 0.01) / (mshmo + 0.01), 'm-')
plt.ylabel('(img + 0.01) / (mod + 0.01)')
plt.title(tt)
ps.savefig()
iq2, iq3 = allimgs
mq2, mq3 = allmods
plt.clf()
plt.plot(iq2, 'r-')
plt.plot(mq2, 'm-')
plt.plot(iq3, 'b-')
plt.plot(mq3, 'g-')
plt.title('Q2 vs Q3')
ps.savefig()
def star_profiles(ps):
# Run an example CCD, 292604-N4, with fairly large difference vs PS1.
# python -c "from astrometry.util.fits import *; T = merge_tables([fits_table('/project/projectdirs/desiproc/dr3/tractor/244/tractor-244%s.fits' % b) for b in ['2p065','4p065', '7p065']]); T.writeto('tst-cat.fits')"
# python legacypipe/forced_photom_decam.py --save-data tst-data.fits --save-model tst-model.fits 292604 N4 tst-cat.fits tst-phot.fits
# -> tst-{model,data,phot}.fits
datafn = 'tst-data.fits'
modfn = 'tst-model.fits'
photfn = 'tst-phot.fits'
catfn = 'tst-cat.fits'
img = fitsio.read(datafn)
mod = fitsio.read(modfn)
phot = fits_table(photfn)
cat = fits_table(catfn)
print(len(phot), 'forced-photometry results')
margin = 25
phot.cut((phot.x > 0+margin) * (phot.x < 2046-margin) *
(phot.y > 0+margin) * (phot.y < 4096-margin))
print(len(phot), 'in bounds')
cmap = dict([((b,o),i) for i,(b,o) in enumerate(zip(cat.brickname, cat.objid))])
I = np.array([cmap.get((b,o), -1) for b,o in zip(phot.brickname, phot.objid)])
print(np.sum(I >= 0), 'forced-phot matched cat')
phot.type = cat.type[I]
wcs = Sip(datafn)
phot.ra,phot.dec = wcs.pixelxy2radec(phot.x+1, phot.y+1)
phot.cut(np.argsort(phot.flux))
phot.sn = phot.flux * np.sqrt(phot.flux_ivar)
phot.cut(phot.sn > 5)
print(len(phot), 'with S/N > 5')
ps1 = ps1cat(ccdwcs=wcs)
stars = ps1.get_stars()
print(len(stars), 'PS1 sources')
# Now cut to just *stars* with good colors
stars.gicolor = stars.median[:,0] - stars.median[:,2]
keep = (stars.gicolor > 0.4) * (stars.gicolor < 2.7)
stars.cut(keep)
print(len(stars), 'PS1 stars with good colors')
stars.cut(np.minimum(stars.stdev[:,1], stars.stdev[:,2]) < 0.05)
print(len(stars), 'PS1 stars with min stdev(r,i) < 0.05')
I,J,d = match_radec(phot.ra, phot.dec, stars.ra, stars.dec, 1./3600.)
print(len(I), 'matches')
plt.clf()
ha=dict(histtype='step', bins=20, range=(0,100), normed=True)
plt.hist(phot.flux, color='b', **ha)
plt.hist(phot.flux[I], color='r', **ha)
ps.savefig()
plt.clf()
plt.hist(phot.flux * np.sqrt(phot.flux_ivar), bins=100,
range=(-10, 50))
plt.xlabel('Flux S/N')
ps.savefig()
K = np.argsort(phot.flux[I])
I = I[K]
J = J[K]
ix = np.round(phot.x).astype(int)
iy = np.round(phot.y).astype(int)
sz = 10
P = np.flatnonzero(phot.type == 'PSF ')
print(len(P), 'PSFs')
imed = len(P)/2
i1 = int(len(P) * 0.75)
i2 = int(len(P) * 0.25)
N = 401
allmods = []
allimgs = []
for II,tt in [#(I[:len(I)/2], 'faint matches to PS1'),
#(I[len(I)/2:], 'bright matches to PS1'),
#(P[i2: i2+N], '25th pct PSFs'),
#(P[imed: imed+N], 'median PSFs'),
#(P[i1: i1+N], '75th pct PSFs'),
#(P[-25:], 'brightest PSFs'),
(P[i2:imed], '2nd quartile of PSFs'),
(P[imed:i1], '3rd quartile of PSFs'),
#(P[:len(P)/2], 'faint half of PSFs'),
#(P[len(P)/2:], 'bright half of PSFs'),
]:
imgs = []
mods = []
shimgs = []
shmods = []
imgsum = modsum = 0
#plt.clf()
for i in II:
from astrometry.util.util import lanczos_shift_image
dy = phot.y[i] - iy[i]
dx = phot.x[i] - ix[i]
sub = img[iy[i]-sz : iy[i]+sz+1, ix[i]-sz : ix[i]+sz+1]
shimg = lanczos_shift_image(sub, -dx, -dy)
sub = mod[iy[i]-sz : iy[i]+sz+1, ix[i]-sz : ix[i]+sz+1]
shmod = lanczos_shift_image(sub, -dx, -dy)
iyslice = img[iy[i], ix[i]-sz : ix[i]+sz+1]
myslice = mod[iy[i], ix[i]-sz : ix[i]+sz+1]
ixslice = img[iy[i]-sz : iy[i]+sz+1, ix[i]]
mxslice = mod[iy[i]-sz : iy[i]+sz+1, ix[i]]
mx = iyslice.max()
# plt.plot(iyslice/mx, 'b-', alpha=0.1)
# plt.plot(myslice/mx, 'r-', alpha=0.1)
# plt.plot(ixslice/mx, 'b-', alpha=0.1)
# plt.plot(mxslice/mx, 'r-', alpha=0.1)
siyslice = shimg[sz, :]
sixslice = shimg[:, sz]
smyslice = shmod[sz, :]
smxslice = shmod[:, sz]
shimgs.append(siyslice/mx)
shimgs.append(sixslice/mx)
shmods.append(smyslice/mx)
shmods.append(smxslice/mx)
imgs.append(iyslice/mx)
imgs.append(ixslice/mx)
mods.append(myslice/mx)
mods.append(mxslice/mx)
imgsum = imgsum + ixslice + iyslice
modsum = modsum + mxslice + myslice
# plt.ylim(-0.1, 1.1)
# plt.title(tt)
# ps.savefig()
mimg = np.median(np.array(imgs), axis=0)
mmod = np.median(np.array(mods), axis=0)
mshim = np.median(np.array(shimgs), axis=0)
mshmo = np.median(np.array(shmods), axis=0)
allmods.append(mshmo)
allimgs.append(mshim)
plt.clf()
# plt.plot(mimg, 'b-')
# plt.plot(mmod, 'r-')
plt.plot(mshim, 'g-')
plt.plot(mshmo, 'm-')
plt.ylim(-0.1, 1.1)
plt.title(tt + ': median; sums %.3f/%.3f' % (np.sum(mimg), np.sum(mmod)))
ps.savefig()
# plt.clf()
# mx = imgsum.max()
# plt.plot(imgsum/mx, 'b-')
# plt.plot(modsum/mx, 'r-')
# plt.ylim(-0.1, 1.1)
# plt.title(tt + ': sum')
# ps.savefig()
plt.clf()
plt.plot((mimg + 0.01) / (mmod + 0.01), 'k-')
plt.plot((imgsum/mx + 0.01) / (modsum/mx + 0.01), 'g-')
plt.plot((mshim + 0.01) / (mshmo + 0.01), 'm-')
plt.ylabel('(img + 0.01) / (mod + 0.01)')
plt.title(tt)
ps.savefig()
iq2,iq3 = allimgs
mq2,mq3 = allmods
plt.clf()
plt.plot(iq2, 'r-')
plt.plot(mq2, 'm-')
plt.plot(iq3, 'b-')
plt.plot(mq3, 'g-')
plt.title('Q2 vs Q3')
ps.savefig()
ps.savefig()
# Subsample the PSF via resampling
from astrometry.util.util import lanczos_shift_image
scale = 2
sh, sw = ph * scale, pw * scale
subpixpsf = np.zeros((sh, sw))
for ix in np.arange(scale):
for iy in np.arange(scale):
dx = ix / float(scale)
dy = iy / float(scale)
if ix == 0 and iy == 0:
subpixpsf[0::scale, 0::scale] = pixpsf
continue
shift = lanczos_shift_image(pixpsf, -dx, -dy, order=5)
subpixpsf[iy::scale, ix::scale] = shift
# Evaluate a R_e = 1 pixel deVauc on the native pixel grid, using
# the Gaussian approximation
xx, yy = np.meshgrid(np.arange(pw), np.arange(ph))
center = pw / 2
r2 = (xx - center)**2 + (yy - center)**2
pixgal = np.zeros_like(pixpsf)
from demo import DevGalaxy
gal = DevGalaxy()
for a, v in zip(gal.amp, gal.var):
vv = v * gal_re**2
## FIXME ??? prefactors?
def lopass(psfex, ps3):
### Lopass
#plt.figure(2)
fig_rect = 3
fig_square = 2
H, W = 256, 256
theta = 110
psfim = psfex.instantiateAt(0, 0)
T2 = 15
psfim = psfim[T2:-T2, T2:-T2]
ph, pw = psfim.shape
cx, cy = pw / 2, ph / 2
egal = EllipseE.fromRAbPhi(4., 0.3, theta)
gal = ExpGalaxy(PixPos(0, 0), Flux(100.), egal)
pixpsf = PixelizedPSF(psfim)
halfsize = 10.
P, (px0, py0), (pH,
pW), (w, v) = pixpsf.getFourierTransform(0., 0., halfsize)
data = np.zeros((H, W), np.float32)
tinypsf = NCircularGaussianPSF([1e-6], [1.])
img = Image(data=data, invvar=np.ones_like(data), psf=tinypsf)
#amix = gal._getAffineProfile(img, cx, cy)
amix = gal._getAffineProfile(img, 0, 0)
Fmine = amix.getFourierTransform(w, v)
print('Amix amps:', amix.amp, 'sum', amix.amp.sum())
ima = dict(ticks=False, cmap=antigray)
fima = dict(ticks=False, cmap='Blues')
rima = dict(ticks=False, cmap='Greens')
iima = dict(ticks=False, cmap='Reds')
def diffshow(D, **ima):
mx = np.max(np.abs(D))
dimshow(D, vmin=-mx, vmax=mx, **ima)
# Move galaxy to center of image.
gal.pos.x = pH / 2.
gal.pos.y = pW / 2.
print('tiny PSF conv galaxy')
print('pH,pW:', pH, pW)
tinyp = gal.getModelPatch(img)
tinypad = np.zeros((pH, pW))
tinyp.addTo(tinypad)
# #print('w range:', np.min(w), np.max(w))
# #print('v range:', np.min(v), np.max(v))
# w2 = np.linspace(2.*np.min(w), 2.*np.max(w), len(w)*2-1)
# v2 = np.linspace(2.*np.min(v), 2.*np.max(v), len(v)*2-1)
# # print('w:', w)
# # print('w2:', w2)
# # print('v:', v)
# # print('v2:', v2)
# # [v2,w2]
# Fmine2 = amix.getFourierTransform(w2, v2)
#
# # print('w2', len(w2), 'v2', len(v2), 'Fmine2', Fmine2.shape)
#
# I = np.flatnonzero((w2 >= np.min(w)) * (w2 <= np.max(w)))
# J = np.flatnonzero((v2 >= np.min(v)) * (v2 <= np.max(v)))
# # print('w', len(w), 'v', len(v), 'Fmine', Fmine.shape)
# # print('I', len(I), 'J', len(J))
#
# print('Sub-sum Fmine2:', Fmine2[J,:][:,I].real.sum())
#
# # w3 = np.linspace(3.*np.min(w), 3.*np.max(w), len(w)*3-2)
# # v3 = np.linspace(3.*np.min(v), 3.*np.max(v), len(v)*3-2)
# # print('w:', w)
# # print('w3:', w3)
# # print('v:', v)
# # print('v3:', v3)
# # # [v2,w2]
# # Fmine3 = amix.getFourierTransform(w3, v3)
# # print('Fmine3.real sum', Fmine3.real.sum())
# #
# # print('Folded Fmine3.real sum', Fmine3.real.sum() + Fmine3[:,1:].real.sum())
#
# #print('amix:', amix)
# #print('amix means:', amix.mean)
#
# # My method, Fourier transform with twice the frequency range
# plt.clf()
# dimshow(np.hypot(Fmine2.real, Fmine2.imag), **fima)
# plt.title('Fmine2')
# ps3.savefig()
#
# #for va in amix.var:
# # e = EllipseE.fromCovariance(va)
# ax = plt.axis()
# for k in range(amix.K):
# Cinv = np.linalg.inv(amix.var[k,:,:])
# Cinv *= (4. * np.pi**2)
# e = EllipseE.fromCovariance(Cinv)
# B = e.getRaDecBasis() * 3600.
# angle = np.linspace(0, 2.*np.pi, 90)
# cc = np.cos(angle)
# ss = np.sin(angle)
# xx = B[0,0] * cc + B[0,1] * ss
# yy = B[1,0] * cc + B[1,1] * ss
# f2H,f2W = Fmine2.shape
# plt.plot(xx, f2H/2. + yy, 'r-', lw=2)
#
# plt.plot(xx, 1.5 * f2H + yy, 'r--', lw=2)
# plt.plot(xx, -0.5 * f2H + yy, 'r--', lw=2)
#
# plt.axis(ax)
# ps3.savefig()
#
# # plt.clf()
# # dimshow(Fmine2.real, **rima)
# # print('Real range:', Fmine2.real.min(), Fmine2.real.max())
# # ps3.savefig()
# # plt.clf()
# # dimshow(Fmine2.imag, **iima)
# # print('Imag range:', Fmine2.imag.min(), Fmine2.imag.max())
# # ps3.savefig()
#
# print('Fmine2.real sum', Fmine2.real.sum())
# print('Fmine.real sum', Fmine.real.sum())
# plt.clf()
# dimshow(tinypad, **ima)
# ps3.savefig()
#
# Rotated to be zero-centered.
tinypad2 = np.fft.fftshift(tinypad)
# plt.clf()
# dimshow(tinypad2, **ima)
# ps3.savefig()
#
# my = np.fft.irfft2(Fmine, s=(pH,pW))
# plt.clf()
# dimshow(my, **ima)
# ps3.savefig()
# Galaxy conv tiny PSF
#Ftiny = np.fft.rfft2(tinypad)
Ftiny = np.fft.rfft2(tinypad2)
#print('Tinypad shape', tinypad.shape)
tH, tW = tinypad.shape
Ftiny /= (tH * np.pi)
print('Ftiny.real sum', Ftiny.real.sum())
#print('Folded Ftiny.real sum', Ftiny.real.sum() + Ftiny[:,1:].real.sum())
plt.clf()
dimshow(np.fft.fftshift(np.hypot(Ftiny.real, Ftiny.imag), axes=(0, )),
vmin=0,
vmax=1.1,
**fima)
#plt.colorbar()
#ps3.savefig()
plt.savefig('lopass-naive.pdf')
# Ftiny2 = np.fft.fft2(tinypad2)
# Ftiny2 /= (tH * np.pi)
# print('Ftiny2.real sum', Ftiny2.real.sum())
#
# plt.clf()
# dimshow(np.fft.fftshift(np.hypot(Ftiny2.real, Ftiny2.imag)),
# **fima)
# plt.colorbar()
# ps3.savefig()
# plt.clf()
# dimshow(np.fft.fftshift(Ftiny.real, axes=(0,)), **rima)
# ps3.savefig()
# plt.clf()
# dimshow(np.fft.fftshift(Ftiny.imag, axes=(0,)), **iima)
# ps3.savefig()
print('Ftiny Real range:', Ftiny.real.min(), Ftiny.real.max())
print('Ftiny Imag range:', Ftiny.imag.min(), Ftiny.imag.max())
# Mine, at regular frequencies
plt.clf()
dimshow(np.fft.fftshift(np.hypot(Fmine.real, Fmine.imag), axes=(0, )),
vmin=0,
vmax=1.1,
**fima)
#plt.colorbar()
plt.savefig('lopass-mine.pdf')
plt.figure(fig_square)
# Pixel-space galaxy plots
PM = np.fft.irfft2(Fmine, s=(pH, pW))
PT = np.fft.irfft2(Ftiny, s=(tH, tW))
mx = max(PM.max(), PT.max())
print('PM sum', PM.sum())
print('Max', PM.max())
PM = np.fft.fftshift(PM)
plt.clf()
dimshow(PM, vmin=0, vmax=mx, **ima)
plt.savefig('lopass-mine-pix.pdf')
#ps3.savefig()
print('PT sum', PT.sum())
PT = np.fft.fftshift(PT)
plt.clf()
dimshow(PT, vmin=0, vmax=mx, **ima)
#ps3.savefig()
plt.savefig('lopass-naive-pix.pdf')
plt.figure(fig_rect)
# plt.clf()
# dimshow(np.fft.fftshift(Fmine.real, axes=(0,)), **rima)
# ps3.savefig()
#
# plt.clf()
# dimshow(np.fft.fftshift(Fmine.imag, axes=(0,)), **iima)
# ps3.savefig()
print('Fmine Real range:', Fmine.real.min(), Fmine.real.max())
print('Fmine Imag range:', Fmine.imag.min(), Fmine.imag.max())
plt.clf()
dimshow(
np.fft.fftshift(np.hypot(Ftiny.real - Fmine.real,
Ftiny.imag - Fmine.imag),
axes=(0, )), **fima)
#plt.colorbar()
plt.savefig('lopass-diff.pdf')
# plt.clf()
# dimshow(np.fft.fftshift(Ftiny.real - Fmine.real, axes=(0,)), **rima)
# ps3.savefig()
#
# plt.clf()
# dimshow(np.fft.fftshift(Ftiny.imag - Fmine.imag, axes=(0,)), **iima)
# ps3.savefig()
diff = Ftiny - Fmine
print('diff Real range:', diff.real.min(), diff.real.max())
print('diff Imag range:', diff.imag.min(), diff.imag.max())
print('Fmine sum:', np.hypot(Fmine.real, Fmine.imag).sum())
print('Ftiny sum:', np.hypot(Ftiny.real, Ftiny.imag).sum())
ax = plt.axis()
## HACK -- 5th contour is confusing-looking
for k in range(2, amix.K):
Cinv = np.linalg.inv(amix.var[k, :, :])
Cinv *= (4. * np.pi**2)
e = EllipseE.fromCovariance(Cinv)
B = e.getRaDecBasis() * 3600.
angle = np.linspace(0, 2. * np.pi, 90)
cc = np.cos(angle)
ss = np.sin(angle)
xx = B[0, 0] * cc + B[0, 1] * ss
yy = B[1, 0] * cc + B[1, 1] * ss
fsH, fsW = Fmine.shape
plt.plot(xx, fsH / 2. + yy, 'r-', lw=2)
plt.plot(xx, 1.5 * fsH + yy, 'r--', lw=2)
plt.plot(xx, -0.5 * fsH + yy, 'r--', lw=2)
plt.axis(ax)
plt.savefig('lopass-diff-ell.pdf')
#ps3.savefig()
plt.clf()
dimshow(np.log10(
np.maximum(
np.fft.fftshift(np.hypot(Ftiny.real, Ftiny.imag), axes=(0, )),
1e-6)),
vmin=-3,
vmax=0,
**fima)
#plt.colorbar()
#plt.title('log Ftiny')
#ps3.savefig()
plt.savefig('lopass-naive-log.pdf')
plt.clf()
dimshow(np.log10(
np.maximum(
np.fft.fftshift(np.hypot(Fmine.real, Fmine.imag), axes=(0, )),
1e-6)),
vmin=-3,
vmax=0,
**fima)
#plt.colorbar()
#plt.title('log Fmine')
#ps3.savefig()
plt.savefig('lopass-mine-log.pdf')
# plt.clf()
# dimshow(np.log10(np.maximum(
# np.hypot(Fmine2.real, Fmine2.imag),
# 1e-6)),
# vmin=-3, vmax=0, **fima)
# plt.colorbar()
# plt.title('log Fmine2')
# ps3.savefig()
# Subsample the PSF via resampling
from astrometry.util.util import lanczos_shift_image
# dxlist,dylist = [],[]
# scales = [2,4,8,16]
# for scale in scales:
#
# sh,sw = ph*scale, pw*scale
# subpsfim = np.zeros((sh,sw))
# step = 1./float(scale)
# for ix in np.arange(scale):
# for iy in np.arange(scale):
# dx = ix * step
# dy = iy * step
# #dx = 0.5 + (ix - 0.5) * step
# #dy = 0.5 + (iy - 0.5) * step
# #if ix == 0 and iy == 0:
# # subpsfim[0::scale, 0::scale] = psfim
# # continue
# shift = lanczos_shift_image(psfim, -dx, -dy, order=5)
# subpsfim[iy::scale, ix::scale] = shift
#
# # HACK -clamp edges to zero to counter possible weird lanczos edge issues?
# subpsfim[:2,:] = 0
# subpsfim[:,:2] = 0
# subpsfim[-2:,:] = 0
# subpsfim[:,-2:] = 0
#
# print('ph,pw, scale', ph,pw,scale)
# print('sh,sw', sh,sw)
# print('Subpsfim shape', subpsfim.shape)
# subpixpsf = PixelizedPSF(subpsfim[:-1,:-1])
# SP,(px0,py0),(spH,spW),(sw,sv) = subpixpsf.getFourierTransform(
# 0., 0., scale*halfsize)
#
# wcs = NullWCS()
# wcs.pixscale /= scale
# print('subsampling image: set pixscale', wcs.pixscale)
# print('WCS:', wcs)
#
# subdata=np.zeros((scale*H,scale*W), np.float32)
# subimg = Image(data=subdata, invvar=np.ones_like(subdata), psf=tinypsf,
# wcs=wcs)
#
# # Move galaxy to center of image.
# gal.pos.x = scale*pH/2.
# gal.pos.y = scale*pW/2.
#
# subtinyp = gal.getModelPatch(subimg)
# subtinypad = np.zeros((scale*pH,scale*pW))
# subtinyp.addTo(subtinypad)
# # Rotated to be zero-centered.
# subtinypad2 = np.fft.fftshift(subtinypad)
#
# Fsub = np.fft.rfft2(subtinypad2)
# tH,tW = subtinypad.shape
# Fsub /= (tH * np.pi)
# Fsub *= scale
#
# sz = scale * 32
# SG = np.fft.irfft2(Fsub * SP, s=(sz,sz))
#
# # Bin the subsampled ones...
# BSG = bin_image(SG, scale)
# BSG /= (scale**2)
#
# sz1 = 32
# MG = np.fft.irfft2(Fmine * P, s=(sz1,sz1))
#
# print('MG:')
# x1,y1 = measure(MG)
# #mx.append(x1)
# #my.append(y1)
# print('BSG:')
# x2,y2 = measure(BSG)
# #sx.append(x2)
# #sy.append(y2)
# dxlist.append(x2 - x1)
# dylist.append(y2 - y1)
#
# print('shift', x2-x1, y2-y1)
# shift = lanczos_shift_image(BSG, -(x2-x1), -(y2-y1), order=5)
# x3,y3 = measure(shift)
# print('shifted:', x3-x1, y3-y1)
#
#
# plt.clf()
# plt.plot(scales, dxlist, 'bo-')
# plt.plot(scales, dylist, 'ro-')
# ps3.savefig()
# print('scales = np.array(', scales, ')')
# print('dx = np.array(', dxlist, ')')
# print('dy = np.array(', dylist, ')')
#
# return
scale = 8
sh, sw = ph * scale, pw * scale
subpsfim = np.zeros((sh, sw))
step = 1. / float(scale)
for ix in np.arange(scale):
for iy in np.arange(scale):
dx = ix * step
dy = iy * step
#dx = 0.5 + (ix - 0.5) * step
#dy = 0.5 + (iy - 0.5) * step
#if ix == 0 and iy == 0:
# subpsfim[0::scale, 0::scale] = psfim
# continue
shift = lanczos_shift_image(psfim, -dx, -dy, order=5)
subpsfim[iy::scale, ix::scale] = shift
# HACK -clamp edges to zero to counter possible weird lanczos edge issues?
subpsfim[:2, :] = 0
subpsfim[:, :2] = 0
subpsfim[-2:, :] = 0
subpsfim[:, -2:] = 0
plt.clf()
plt.subplot(1, 2, 1)
dimshow(psfim)
plt.subplot(1, 2, 2)
dimshow(subpsfim)
ps3.savefig()
print('SubPSF image:', subpsfim.shape)
subpixpsf = PixelizedPSF(subpsfim[:-1, :-1])
SP, (px0, py0), (spH, spW), (sw, sv) = subpixpsf.getFourierTransform(
0., 0., scale * halfsize)
print('SP shape', SP.shape)
wcs = NullWCS()
wcs.pixscale /= scale
print('subsampling image: set pixscale', wcs.pixscale)
print('WCS:', wcs)
subdata = np.zeros((scale * H, scale * W), np.float32)
subimg = Image(data=subdata,
invvar=np.ones_like(subdata),
psf=tinypsf,
wcs=wcs)
# Move galaxy to center of image.
gal.pos.x = scale * pH / 2.
gal.pos.y = scale * pW / 2.
subtinyp = gal.getModelPatch(subimg)
subtinypad = np.zeros((scale * pH, scale * pW))
subtinyp.addTo(subtinypad)
# Rotated to be zero-centered.
subtinypad2 = np.fft.fftshift(subtinypad)
plt.clf()
dimshow(subtinypad2, **ima)
plt.title('subtinypad2')
ps3.savefig()
plt.clf()
dimshow(tinypad2, **ima)
plt.title('tinypad2')
ps3.savefig()
Fsub = np.fft.rfft2(subtinypad2)
tH, tW = subtinypad.shape
Fsub /= (tH * np.pi)
Fsub *= scale
fima2 = fima.copy()
fima2.update(vmin=0, vmax=1.1)
Fsub_orig = Fsub.copy()
# Trim Fsub to same size (63,33) vs (64,33)
#h1,w1 = Fsub.shape
#hm2,wm2 = Fmine2.shape
#Fsub = Fsub[:hm2,:]
plt.clf()
dimshow(np.fft.fftshift(np.hypot(Fsub.real, Fsub.imag), axes=(0, )),
**fima2)
plt.colorbar()
plt.title('Fsub')
ps3.savefig()
print('Fsub sum:', np.hypot(Fsub.real, Fsub.imag).sum())
print('Fsub Real range:', Fsub.real.min(), Fsub.real.max())
print('Fsub Imag range:', Fsub.imag.min(), Fsub.imag.max())
pH, pW = subtinypad2.shape
wt = np.fft.rfftfreq(pW)
vt = np.fft.fftfreq(pH)
# print('wsub:', len(wt), 'min/max', wt.min(), wt.max())
# print('vsub:', len(vt), 'min/max', vt.min(), vt.max())
df = np.abs(w[1] - w[0])
w2b = np.arange(scale * (len(w) - 1) + 1) * df + scale * np.min(w)
v2b = np.arange(scale * len(v)) * df + scale * np.min(v)
# print('w2:', w2)
# print('w2b:', w2b)
# print('v2:', v2)
# print('v2b:', v2b)
Fmine2b = amix.getFourierTransform(w2b, v2b)
# -> shape len(v2b),len(w2b)
print('Fmine2b shape', Fmine2b.shape)
print('Fmine2b sum:', np.hypot(Fmine2b.real, Fmine2b.imag).sum())
print('Fmine2b Real range:', Fmine2b.real.min(), Fmine2b.real.max())
print('Fmine2b Imag range:', Fmine2b.imag.min(), Fmine2b.imag.max())
print('Fsub_orig shape:', Fsub_orig.shape)
# # My method, Fourier transform with twice the frequency range
# plt.clf()
# dimshow(np.hypot(Fmine2.real, Fmine2.imag), **fima2)
# plt.colorbar()
# plt.title('Fmine2')
# ps3.savefig()
#
# diff = np.fft.fftshift(Fsub, axes=(0,)) - Fmine2
# print('diff Real range:', diff.real.min(), diff.real.max())
# print('diff Imag range:', diff.imag.min(), diff.imag.max())
#
# plt.clf()
# dimshow(np.hypot(diff.real, diff.imag), **fima)
# plt.colorbar()
# plt.title('Fsub - Fmine2')
# ps3.savefig()
diff2 = np.fft.fftshift(Fsub_orig, axes=(0, )) - Fmine2b
print('diff2 Real range:', diff.real.min(), diff.real.max())
print('diff2 Imag range:', diff.imag.min(), diff.imag.max())
plt.clf()
dimshow(np.hypot(diff2.real, diff2.imag), **fima)
plt.colorbar()
plt.title('Fsub - Fmine2b')
ps3.savefig()
plt.clf()
dimshow(diff2.real, **rima)
plt.colorbar()
plt.title('real(Fsub - Fmine2b)')
ps3.savefig()
print('Ftiny:', Ftiny.shape)
print('P:', P.shape)
print('Fsub:', Fsub.shape)
print('SP:', SP.shape)
print('Fmine2b:', Fmine2b.shape)
#sz1 = 32
#sz = 64
sz1 = 32
sz = scale * 32
plt.clf()
plt.subplot(2, 2, 1)
dimshow(Ftiny.real, **rima)
plt.colorbar()
plt.subplot(2, 2, 2)
dimshow(Ftiny.imag, **iima)
plt.colorbar()
plt.subplot(2, 2, 3)
dimshow(P.real, **rima)
plt.colorbar()
plt.subplot(2, 2, 4)
dimshow(P.imag, **iima)
plt.colorbar()
plt.suptitle('Ftiny, P')
ps3.savefig()
PG = np.fft.irfft2(Ftiny * P, s=(sz1, sz1))
print('PG', PG.dtype, PG.shape)
plt.clf()
dimshow(PG, **ima)
plt.colorbar()
plt.title('PG')
ps3.savefig()
#P,(px0,py0),(pH,pW),(w,v) = pixpsf.getFourierTransform(0., 0., halfsize)
#Fmine = amix.getFourierTransform(w, v)
plt.clf()
plt.subplot(2, 2, 1)
dimshow(Fmine.real, **rima)
plt.colorbar()
plt.subplot(2, 2, 2)
dimshow(Fmine.imag, **iima)
plt.colorbar()
plt.subplot(2, 2, 3)
dimshow(P.real, **rima)
plt.colorbar()
plt.subplot(2, 2, 4)
dimshow(P.imag, **iima)
plt.colorbar()
plt.suptitle('Fmine, P')
ps3.savefig()
MG = np.fft.irfft2(Fmine * P, s=(sz1, sz1))
print('MG', MG.dtype, MG.shape)
plt.clf()
dimshow(MG, **ima)
plt.colorbar()
plt.title('MG')
ps3.savefig()
plt.clf()
diffshow(PG - MG, **ima)
plt.colorbar()
plt.title('PG - MG')
ps3.savefig()
plt.clf()
plt.subplot(2, 2, 1)
dimshow(Fsub.real, **rima)
plt.colorbar()
plt.subplot(2, 2, 2)
dimshow(Fsub.imag, **iima)
plt.colorbar()
plt.subplot(2, 2, 3)
dimshow(SP.real, **rima)
plt.colorbar()
plt.subplot(2, 2, 4)
dimshow(SP.imag, **iima)
plt.colorbar()
plt.suptitle('Fsub, SP')
ps3.savefig()
SG = np.fft.irfft2(Fsub * SP, s=(sz, sz))
print('SG', SG.dtype, SG.shape)
plt.clf()
dimshow(SG, **ima)
plt.colorbar()
plt.title('SG')
ps3.savefig()
Fmine2c = np.fft.fftshift(Fmine2b, axes=(0, ))
plt.clf()
plt.subplot(2, 2, 1)
dimshow(Fmine2c.real, **rima)
plt.colorbar()
plt.subplot(2, 2, 2)
dimshow(Fmine2c.imag, **iima)
plt.colorbar()
plt.subplot(2, 2, 3)
dimshow(SP.real, **rima)
plt.colorbar()
plt.subplot(2, 2, 4)
dimshow(SP.imag, **iima)
plt.colorbar()
plt.suptitle('Fmine2c, SP')
ps3.savefig()
MG2 = np.fft.irfft2(Fmine2c * SP, s=(sz, sz))
print('MG2', MG2.dtype, MG2.shape)
plt.clf()
dimshow(MG2, **ima)
plt.colorbar()
plt.title('MG2')
ps3.savefig()
plt.clf()
diffshow(SG - MG2, **ima)
plt.colorbar()
plt.title('SG - MG2')
ps3.savefig()
# Bin the subsampled ones...
BSG = bin_image(SG, scale)
BSG /= (scale**2)
print('MG:')
x1, y1 = measure(MG)
print('BSG:')
x2, y2 = measure(BSG)
shiftBSG = lanczos_shift_image(BSG, -(x2 - x1), -(y2 - y1), order=5)
plt.clf()
dimshow(BSG, **ima)
plt.colorbar()
plt.title('BSG')
ps3.savefig()
plt.clf()
diffshow(BSG - MG, **ima)
plt.colorbar()
plt.title('BSG - MG')
ps3.savefig()
plt.clf()
diffshow(shiftBSG - MG, **ima)
plt.colorbar()
plt.title('shiftBSG - MG')
ps3.savefig()
ps.savefig()
# Subsample the PSF via resampling
from astrometry.util.util import lanczos_shift_image
scale = 2
sh,sw = ph*scale, pw*scale
subpixpsf = np.zeros((sh,sw))
for ix in np.arange(scale):
for iy in np.arange(scale):
dx = ix / float(scale)
dy = iy / float(scale)
if ix == 0 and iy == 0:
subpixpsf[0::scale, 0::scale] = pixpsf
continue
shift = lanczos_shift_image(pixpsf, -dx, -dy, order=5)
subpixpsf[iy::scale, ix::scale] = shift
# Evaluate a R_e = 1 pixel deVauc on the native pixel grid, using
# the Gaussian approximation
xx,yy = np.meshgrid(np.arange(pw), np.arange(ph))
center = pw/2
r2 = (xx - center)**2 + (yy - center)**2
pixgal = np.zeros_like(pixpsf)
from demo import DevGalaxy
gal = DevGalaxy()
for a,v in zip(gal.amp, gal.var):
vv = v * gal_re**2
## FIXME ??? prefactors?
def lopass(psfex, ps3):
### Lopass
#plt.figure(2)
fig_rect = 3
fig_square = 2
H,W = 256,256
theta = 110
psfim = psfex.instantiateAt(0,0)
T2 = 15
psfim = psfim[T2:-T2,T2:-T2]
ph,pw = psfim.shape
cx,cy = pw/2, ph/2
egal = EllipseE.fromRAbPhi(4., 0.3, theta)
gal = ExpGalaxy(PixPos(0,0), Flux(100.), egal)
pixpsf = PixelizedPSF(psfim)
halfsize = 10.
P,(px0,py0),(pH,pW),(w,v) = pixpsf.getFourierTransform(0., 0., halfsize)
data=np.zeros((H,W), np.float32)
tinypsf = NCircularGaussianPSF([1e-6], [1.])
img = Image(data=data, invvar=np.ones_like(data), psf=tinypsf)
#amix = gal._getAffineProfile(img, cx, cy)
amix = gal._getAffineProfile(img, 0, 0)
Fmine = amix.getFourierTransform(w, v)
print('Amix amps:', amix.amp, 'sum', amix.amp.sum())
ima = dict(ticks=False, cmap=antigray)
fima = dict(ticks=False, cmap='Blues')
rima = dict(ticks=False, cmap='Greens')
iima = dict(ticks=False, cmap='Reds')
def diffshow(D, **ima):
mx = np.max(np.abs(D))
dimshow(D, vmin=-mx, vmax=mx, **ima)
# Move galaxy to center of image.
gal.pos.x = pH/2.
gal.pos.y = pW/2.
print('tiny PSF conv galaxy')
print('pH,pW:', pH,pW)
tinyp = gal.getModelPatch(img)
tinypad = np.zeros((pH,pW))
tinyp.addTo(tinypad)
# #print('w range:', np.min(w), np.max(w))
# #print('v range:', np.min(v), np.max(v))
# w2 = np.linspace(2.*np.min(w), 2.*np.max(w), len(w)*2-1)
# v2 = np.linspace(2.*np.min(v), 2.*np.max(v), len(v)*2-1)
# # print('w:', w)
# # print('w2:', w2)
# # print('v:', v)
# # print('v2:', v2)
# # [v2,w2]
# Fmine2 = amix.getFourierTransform(w2, v2)
#
# # print('w2', len(w2), 'v2', len(v2), 'Fmine2', Fmine2.shape)
#
# I = np.flatnonzero((w2 >= np.min(w)) * (w2 <= np.max(w)))
# J = np.flatnonzero((v2 >= np.min(v)) * (v2 <= np.max(v)))
# # print('w', len(w), 'v', len(v), 'Fmine', Fmine.shape)
# # print('I', len(I), 'J', len(J))
#
# print('Sub-sum Fmine2:', Fmine2[J,:][:,I].real.sum())
#
# # w3 = np.linspace(3.*np.min(w), 3.*np.max(w), len(w)*3-2)
# # v3 = np.linspace(3.*np.min(v), 3.*np.max(v), len(v)*3-2)
# # print('w:', w)
# # print('w3:', w3)
# # print('v:', v)
# # print('v3:', v3)
# # # [v2,w2]
# # Fmine3 = amix.getFourierTransform(w3, v3)
# # print('Fmine3.real sum', Fmine3.real.sum())
# #
# # print('Folded Fmine3.real sum', Fmine3.real.sum() + Fmine3[:,1:].real.sum())
#
# #print('amix:', amix)
# #print('amix means:', amix.mean)
#
# # My method, Fourier transform with twice the frequency range
# plt.clf()
# dimshow(np.hypot(Fmine2.real, Fmine2.imag), **fima)
# plt.title('Fmine2')
# ps3.savefig()
#
# #for va in amix.var:
# # e = EllipseE.fromCovariance(va)
# ax = plt.axis()
# for k in range(amix.K):
# Cinv = np.linalg.inv(amix.var[k,:,:])
# Cinv *= (4. * np.pi**2)
# e = EllipseE.fromCovariance(Cinv)
# B = e.getRaDecBasis() * 3600.
# angle = np.linspace(0, 2.*np.pi, 90)
# cc = np.cos(angle)
# ss = np.sin(angle)
# xx = B[0,0] * cc + B[0,1] * ss
# yy = B[1,0] * cc + B[1,1] * ss
# f2H,f2W = Fmine2.shape
# plt.plot(xx, f2H/2. + yy, 'r-', lw=2)
#
# plt.plot(xx, 1.5 * f2H + yy, 'r--', lw=2)
# plt.plot(xx, -0.5 * f2H + yy, 'r--', lw=2)
#
# plt.axis(ax)
# ps3.savefig()
#
# # plt.clf()
# # dimshow(Fmine2.real, **rima)
# # print('Real range:', Fmine2.real.min(), Fmine2.real.max())
# # ps3.savefig()
# # plt.clf()
# # dimshow(Fmine2.imag, **iima)
# # print('Imag range:', Fmine2.imag.min(), Fmine2.imag.max())
# # ps3.savefig()
#
# print('Fmine2.real sum', Fmine2.real.sum())
# print('Fmine.real sum', Fmine.real.sum())
# plt.clf()
# dimshow(tinypad, **ima)
# ps3.savefig()
#
# Rotated to be zero-centered.
tinypad2 = np.fft.fftshift(tinypad)
# plt.clf()
# dimshow(tinypad2, **ima)
# ps3.savefig()
#
# my = np.fft.irfft2(Fmine, s=(pH,pW))
# plt.clf()
# dimshow(my, **ima)
# ps3.savefig()
# Galaxy conv tiny PSF
#Ftiny = np.fft.rfft2(tinypad)
Ftiny = np.fft.rfft2(tinypad2)
#print('Tinypad shape', tinypad.shape)
tH,tW = tinypad.shape
Ftiny /= (tH * np.pi)
print('Ftiny.real sum', Ftiny.real.sum())
#print('Folded Ftiny.real sum', Ftiny.real.sum() + Ftiny[:,1:].real.sum())
plt.clf()
dimshow(np.fft.fftshift(np.hypot(Ftiny.real, Ftiny.imag), axes=(0,)),
vmin=0, vmax=1.1, **fima)
#plt.colorbar()
#ps3.savefig()
plt.savefig('lopass-naive.pdf')
# Ftiny2 = np.fft.fft2(tinypad2)
# Ftiny2 /= (tH * np.pi)
# print('Ftiny2.real sum', Ftiny2.real.sum())
#
# plt.clf()
# dimshow(np.fft.fftshift(np.hypot(Ftiny2.real, Ftiny2.imag)),
# **fima)
# plt.colorbar()
# ps3.savefig()
# plt.clf()
# dimshow(np.fft.fftshift(Ftiny.real, axes=(0,)), **rima)
# ps3.savefig()
# plt.clf()
# dimshow(np.fft.fftshift(Ftiny.imag, axes=(0,)), **iima)
# ps3.savefig()
print('Ftiny Real range:', Ftiny.real.min(), Ftiny.real.max())
print('Ftiny Imag range:', Ftiny.imag.min(), Ftiny.imag.max())
# Mine, at regular frequencies
plt.clf()
dimshow(np.fft.fftshift(np.hypot(Fmine.real, Fmine.imag), axes=(0,)),
vmin=0, vmax=1.1, **fima)
#plt.colorbar()
plt.savefig('lopass-mine.pdf')
plt.figure(fig_square)
# Pixel-space galaxy plots
PM = np.fft.irfft2(Fmine, s=(pH,pW))
PT = np.fft.irfft2(Ftiny, s=(tH,tW))
mx = max(PM.max(), PT.max())
print('PM sum', PM.sum())
print('Max', PM.max())
PM = np.fft.fftshift(PM)
plt.clf()
dimshow(PM, vmin=0, vmax=mx, **ima)
plt.savefig('lopass-mine-pix.pdf')
#ps3.savefig()
print('PT sum', PT.sum())
PT = np.fft.fftshift(PT)
plt.clf()
dimshow(PT, vmin=0, vmax=mx, **ima)
#ps3.savefig()
plt.savefig('lopass-naive-pix.pdf')
plt.figure(fig_rect)
# plt.clf()
# dimshow(np.fft.fftshift(Fmine.real, axes=(0,)), **rima)
# ps3.savefig()
#
# plt.clf()
# dimshow(np.fft.fftshift(Fmine.imag, axes=(0,)), **iima)
# ps3.savefig()
print('Fmine Real range:', Fmine.real.min(), Fmine.real.max())
print('Fmine Imag range:', Fmine.imag.min(), Fmine.imag.max())
plt.clf()
dimshow(np.fft.fftshift(np.hypot(Ftiny.real - Fmine.real,
Ftiny.imag - Fmine.imag), axes=(0,)), **fima)
#plt.colorbar()
plt.savefig('lopass-diff.pdf')
# plt.clf()
# dimshow(np.fft.fftshift(Ftiny.real - Fmine.real, axes=(0,)), **rima)
# ps3.savefig()
#
# plt.clf()
# dimshow(np.fft.fftshift(Ftiny.imag - Fmine.imag, axes=(0,)), **iima)
# ps3.savefig()
diff = Ftiny - Fmine
print('diff Real range:', diff.real.min(), diff.real.max())
print('diff Imag range:', diff.imag.min(), diff.imag.max())
print('Fmine sum:', np.hypot(Fmine.real, Fmine.imag).sum())
print('Ftiny sum:', np.hypot(Ftiny.real, Ftiny.imag).sum())
ax = plt.axis()
## HACK -- 5th contour is confusing-looking
for k in range(2, amix.K):
Cinv = np.linalg.inv(amix.var[k,:,:])
Cinv *= (4. * np.pi**2)
e = EllipseE.fromCovariance(Cinv)
B = e.getRaDecBasis() * 3600.
angle = np.linspace(0, 2.*np.pi, 90)
cc = np.cos(angle)
ss = np.sin(angle)
xx = B[0,0] * cc + B[0,1] * ss
yy = B[1,0] * cc + B[1,1] * ss
fsH,fsW = Fmine.shape
plt.plot(xx, fsH/2. + yy, 'r-', lw=2)
plt.plot(xx, 1.5 * fsH + yy, 'r--', lw=2)
plt.plot(xx, -0.5 * fsH + yy, 'r--', lw=2)
plt.axis(ax)
plt.savefig('lopass-diff-ell.pdf')
#ps3.savefig()
plt.clf()
dimshow(np.log10(np.maximum(
np.fft.fftshift(np.hypot(Ftiny.real, Ftiny.imag), axes=(0,)),
1e-6)),
vmin=-3, vmax=0, **fima)
#plt.colorbar()
#plt.title('log Ftiny')
#ps3.savefig()
plt.savefig('lopass-naive-log.pdf')
plt.clf()
dimshow(np.log10(np.maximum(
np.fft.fftshift(np.hypot(Fmine.real, Fmine.imag), axes=(0,)),
1e-6)),
vmin=-3, vmax=0, **fima)
#plt.colorbar()
#plt.title('log Fmine')
#ps3.savefig()
plt.savefig('lopass-mine-log.pdf')
# plt.clf()
# dimshow(np.log10(np.maximum(
# np.hypot(Fmine2.real, Fmine2.imag),
# 1e-6)),
# vmin=-3, vmax=0, **fima)
# plt.colorbar()
# plt.title('log Fmine2')
# ps3.savefig()
# Subsample the PSF via resampling
from astrometry.util.util import lanczos_shift_image
# dxlist,dylist = [],[]
# scales = [2,4,8,16]
# for scale in scales:
#
# sh,sw = ph*scale, pw*scale
# subpsfim = np.zeros((sh,sw))
# step = 1./float(scale)
# for ix in np.arange(scale):
# for iy in np.arange(scale):
# dx = ix * step
# dy = iy * step
# #dx = 0.5 + (ix - 0.5) * step
# #dy = 0.5 + (iy - 0.5) * step
# #if ix == 0 and iy == 0:
# # subpsfim[0::scale, 0::scale] = psfim
# # continue
# shift = lanczos_shift_image(psfim, -dx, -dy, order=5)
# subpsfim[iy::scale, ix::scale] = shift
#
# # HACK -clamp edges to zero to counter possible weird lanczos edge issues?
# subpsfim[:2,:] = 0
# subpsfim[:,:2] = 0
# subpsfim[-2:,:] = 0
# subpsfim[:,-2:] = 0
#
# print('ph,pw, scale', ph,pw,scale)
# print('sh,sw', sh,sw)
# print('Subpsfim shape', subpsfim.shape)
# subpixpsf = PixelizedPSF(subpsfim[:-1,:-1])
# SP,(px0,py0),(spH,spW),(sw,sv) = subpixpsf.getFourierTransform(
# 0., 0., scale*halfsize)
#
# wcs = NullWCS()
# wcs.pixscale /= scale
# print('subsampling image: set pixscale', wcs.pixscale)
# print('WCS:', wcs)
#
# subdata=np.zeros((scale*H,scale*W), np.float32)
# subimg = Image(data=subdata, invvar=np.ones_like(subdata), psf=tinypsf,
# wcs=wcs)
#
# # Move galaxy to center of image.
# gal.pos.x = scale*pH/2.
# gal.pos.y = scale*pW/2.
#
# subtinyp = gal.getModelPatch(subimg)
# subtinypad = np.zeros((scale*pH,scale*pW))
# subtinyp.addTo(subtinypad)
# # Rotated to be zero-centered.
# subtinypad2 = np.fft.fftshift(subtinypad)
#
# Fsub = np.fft.rfft2(subtinypad2)
# tH,tW = subtinypad.shape
# Fsub /= (tH * np.pi)
# Fsub *= scale
#
# sz = scale * 32
# SG = np.fft.irfft2(Fsub * SP, s=(sz,sz))
#
# # Bin the subsampled ones...
# BSG = bin_image(SG, scale)
# BSG /= (scale**2)
#
# sz1 = 32
# MG = np.fft.irfft2(Fmine * P, s=(sz1,sz1))
#
# print('MG:')
# x1,y1 = measure(MG)
# #mx.append(x1)
# #my.append(y1)
# print('BSG:')
# x2,y2 = measure(BSG)
# #sx.append(x2)
# #sy.append(y2)
# dxlist.append(x2 - x1)
# dylist.append(y2 - y1)
#
# print('shift', x2-x1, y2-y1)
# shift = lanczos_shift_image(BSG, -(x2-x1), -(y2-y1), order=5)
# x3,y3 = measure(shift)
# print('shifted:', x3-x1, y3-y1)
#
#
# plt.clf()
# plt.plot(scales, dxlist, 'bo-')
# plt.plot(scales, dylist, 'ro-')
# ps3.savefig()
# print('scales = np.array(', scales, ')')
# print('dx = np.array(', dxlist, ')')
# print('dy = np.array(', dylist, ')')
#
# return
scale = 8
sh,sw = ph*scale, pw*scale
subpsfim = np.zeros((sh,sw))
step = 1./float(scale)
for ix in np.arange(scale):
for iy in np.arange(scale):
dx = ix * step
dy = iy * step
#dx = 0.5 + (ix - 0.5) * step
#dy = 0.5 + (iy - 0.5) * step
#if ix == 0 and iy == 0:
# subpsfim[0::scale, 0::scale] = psfim
# continue
shift = lanczos_shift_image(psfim, -dx, -dy, order=5)
subpsfim[iy::scale, ix::scale] = shift
# HACK -clamp edges to zero to counter possible weird lanczos edge issues?
subpsfim[:2,:] = 0
subpsfim[:,:2] = 0
subpsfim[-2:,:] = 0
subpsfim[:,-2:] = 0
plt.clf()
plt.subplot(1,2,1)
dimshow(psfim)
plt.subplot(1,2,2)
dimshow(subpsfim)
ps3.savefig()
print('SubPSF image:', subpsfim.shape)
subpixpsf = PixelizedPSF(subpsfim[:-1,:-1])
SP,(px0,py0),(spH,spW),(sw,sv) = subpixpsf.getFourierTransform(
0., 0., scale*halfsize)
print('SP shape', SP.shape)
wcs = NullWCS()
wcs.pixscale /= scale
print('subsampling image: set pixscale', wcs.pixscale)
print('WCS:', wcs)
subdata=np.zeros((scale*H,scale*W), np.float32)
subimg = Image(data=subdata, invvar=np.ones_like(subdata), psf=tinypsf,
wcs=wcs)
# Move galaxy to center of image.
gal.pos.x = scale*pH/2.
gal.pos.y = scale*pW/2.
subtinyp = gal.getModelPatch(subimg)
subtinypad = np.zeros((scale*pH,scale*pW))
subtinyp.addTo(subtinypad)
# Rotated to be zero-centered.
subtinypad2 = np.fft.fftshift(subtinypad)
plt.clf()
dimshow(subtinypad2, **ima)
plt.title('subtinypad2')
ps3.savefig()
plt.clf()
dimshow(tinypad2, **ima)
plt.title('tinypad2')
ps3.savefig()
Fsub = np.fft.rfft2(subtinypad2)
tH,tW = subtinypad.shape
Fsub /= (tH * np.pi)
Fsub *= scale
fima2 = fima.copy()
fima2.update(vmin=0, vmax=1.1)
Fsub_orig = Fsub.copy()
# Trim Fsub to same size (63,33) vs (64,33)
#h1,w1 = Fsub.shape
#hm2,wm2 = Fmine2.shape
#Fsub = Fsub[:hm2,:]
plt.clf()
dimshow(np.fft.fftshift(np.hypot(Fsub.real, Fsub.imag), axes=(0,)), **fima2)
plt.colorbar()
plt.title('Fsub')
ps3.savefig()
print('Fsub sum:', np.hypot(Fsub.real, Fsub.imag).sum())
print('Fsub Real range:', Fsub.real.min(), Fsub.real.max())
print('Fsub Imag range:', Fsub.imag.min(), Fsub.imag.max())
pH,pW = subtinypad2.shape
wt = np.fft.rfftfreq(pW)
vt = np.fft.fftfreq(pH)
# print('wsub:', len(wt), 'min/max', wt.min(), wt.max())
# print('vsub:', len(vt), 'min/max', vt.min(), vt.max())
df = np.abs(w[1] - w[0])
w2b = np.arange(scale * (len(w)-1) + 1) * df + scale*np.min(w)
v2b = np.arange(scale*len(v)) * df + scale*np.min(v)
# print('w2:', w2)
# print('w2b:', w2b)
# print('v2:', v2)
# print('v2b:', v2b)
Fmine2b = amix.getFourierTransform(w2b, v2b)
# -> shape len(v2b),len(w2b)
print('Fmine2b shape', Fmine2b.shape)
print('Fmine2b sum:', np.hypot(Fmine2b.real, Fmine2b.imag).sum())
print('Fmine2b Real range:', Fmine2b.real.min(), Fmine2b.real.max())
print('Fmine2b Imag range:', Fmine2b.imag.min(), Fmine2b.imag.max())
print('Fsub_orig shape:', Fsub_orig.shape)
# # My method, Fourier transform with twice the frequency range
# plt.clf()
# dimshow(np.hypot(Fmine2.real, Fmine2.imag), **fima2)
# plt.colorbar()
# plt.title('Fmine2')
# ps3.savefig()
#
# diff = np.fft.fftshift(Fsub, axes=(0,)) - Fmine2
# print('diff Real range:', diff.real.min(), diff.real.max())
# print('diff Imag range:', diff.imag.min(), diff.imag.max())
#
# plt.clf()
# dimshow(np.hypot(diff.real, diff.imag), **fima)
# plt.colorbar()
# plt.title('Fsub - Fmine2')
# ps3.savefig()
diff2 = np.fft.fftshift(Fsub_orig, axes=(0,)) - Fmine2b
print('diff2 Real range:', diff.real.min(), diff.real.max())
print('diff2 Imag range:', diff.imag.min(), diff.imag.max())
plt.clf()
dimshow(np.hypot(diff2.real, diff2.imag), **fima)
plt.colorbar()
plt.title('Fsub - Fmine2b')
ps3.savefig()
plt.clf()
dimshow(diff2.real, **rima)
plt.colorbar()
plt.title('real(Fsub - Fmine2b)')
ps3.savefig()
print('Ftiny:', Ftiny.shape)
print('P:', P.shape)
print('Fsub:', Fsub.shape)
print('SP:', SP.shape)
print('Fmine2b:', Fmine2b.shape)
#sz1 = 32
#sz = 64
sz1 = 32
sz = scale * 32
plt.clf()
plt.subplot(2,2,1)
dimshow(Ftiny.real, **rima)
plt.colorbar()
plt.subplot(2,2,2)
dimshow(Ftiny.imag, **iima)
plt.colorbar()
plt.subplot(2,2,3)
dimshow(P.real, **rima)
plt.colorbar()
plt.subplot(2,2,4)
dimshow(P.imag, **iima)
plt.colorbar()
plt.suptitle('Ftiny, P')
ps3.savefig()
PG = np.fft.irfft2(Ftiny * P, s=(sz1,sz1))
print('PG', PG.dtype, PG.shape)
plt.clf()
dimshow(PG, **ima)
plt.colorbar()
plt.title('PG')
ps3.savefig()
#P,(px0,py0),(pH,pW),(w,v) = pixpsf.getFourierTransform(0., 0., halfsize)
#Fmine = amix.getFourierTransform(w, v)
plt.clf()
plt.subplot(2,2,1)
dimshow(Fmine.real, **rima)
plt.colorbar()
plt.subplot(2,2,2)
dimshow(Fmine.imag, **iima)
plt.colorbar()
plt.subplot(2,2,3)
dimshow(P.real, **rima)
plt.colorbar()
plt.subplot(2,2,4)
dimshow(P.imag, **iima)
plt.colorbar()
plt.suptitle('Fmine, P')
ps3.savefig()
MG = np.fft.irfft2(Fmine * P, s=(sz1,sz1))
print('MG', MG.dtype, MG.shape)
plt.clf()
dimshow(MG, **ima)
plt.colorbar()
plt.title('MG')
ps3.savefig()
plt.clf()
diffshow(PG - MG, **ima)
plt.colorbar()
plt.title('PG - MG')
ps3.savefig()
plt.clf()
plt.subplot(2,2,1)
dimshow(Fsub.real, **rima)
plt.colorbar()
plt.subplot(2,2,2)
dimshow(Fsub.imag, **iima)
plt.colorbar()
plt.subplot(2,2,3)
dimshow(SP.real, **rima)
plt.colorbar()
plt.subplot(2,2,4)
dimshow(SP.imag, **iima)
plt.colorbar()
plt.suptitle('Fsub, SP')
ps3.savefig()
SG = np.fft.irfft2(Fsub * SP, s=(sz,sz))
print('SG', SG.dtype, SG.shape)
plt.clf()
dimshow(SG, **ima)
plt.colorbar()
plt.title('SG')
ps3.savefig()
Fmine2c = np.fft.fftshift(Fmine2b, axes=(0,))
plt.clf()
plt.subplot(2,2,1)
dimshow(Fmine2c.real, **rima)
plt.colorbar()
plt.subplot(2,2,2)
dimshow(Fmine2c.imag, **iima)
plt.colorbar()
plt.subplot(2,2,3)
dimshow(SP.real, **rima)
plt.colorbar()
plt.subplot(2,2,4)
dimshow(SP.imag, **iima)
plt.colorbar()
plt.suptitle('Fmine2c, SP')
ps3.savefig()
MG2 = np.fft.irfft2(Fmine2c * SP, s=(sz,sz))
print('MG2', MG2.dtype, MG2.shape)
plt.clf()
dimshow(MG2, **ima)
plt.colorbar()
plt.title('MG2')
ps3.savefig()
plt.clf()
diffshow(SG - MG2, **ima)
plt.colorbar()
plt.title('SG - MG2')
ps3.savefig()
# Bin the subsampled ones...
BSG = bin_image(SG, scale)
BSG /= (scale**2)
print('MG:')
x1,y1 = measure(MG)
print('BSG:')
x2,y2 = measure(BSG)
shiftBSG = lanczos_shift_image(BSG, -(x2-x1), -(y2-y1), order=5)
plt.clf()
dimshow(BSG, **ima)
plt.colorbar()
plt.title('BSG')
ps3.savefig()
plt.clf()
diffshow(BSG - MG, **ima)
plt.colorbar()
plt.title('BSG - MG')
ps3.savefig()
plt.clf()
diffshow(shiftBSG - MG, **ima)
plt.colorbar()
plt.title('shiftBSG - MG')
ps3.savefig()
