def get_coadded_tile(mapinfo, box, obeam=None, ncomp=1, dump_dir=None, verbose=False): if not overlaps_any(box, boxes): return None mapset = mapinfo.read(box, pad=pad, dtype=dtype, verbose=verbose, ncomp=ncomp) if mapset is None: return None if all([d.insufficient for d in mapset.datasets]): return None jointmap.sanitize_maps(mapset) jointmap.build_noise_model(mapset) if len(mapset.datasets) == 0: return None if all([d.insufficient for d in mapset.datasets]): return None jointmap.setup_beams(mapset) jointmap.setup_target_beam(mapset, obeam) jointmap.setup_filter(mapset, mode=args.filter_mode) jointmap.setup_background_spectrum(mapset) mask = jointmap.get_mask_insufficient(mapset) coadder = jointmap.Coadder(mapset) rhs = coadder.calc_rhs() if dump_dir: enmap.write_map(dump_dir + "/rhs.fits", rhs) enmap.write_map(dump_dir + "/ps_rhs.fits", np.abs(enmap.fft(rhs.preflat[0]))**2) map = coadder.calc_coadd(rhs, dump_dir=dump_dir, verbose=verbose)#, maxiter=1) if dump_dir: enmap.write_map(dump_dir + "/ps_map.fits", np.abs(enmap.fft(mapdiag(map)))**2) div = coadder.tot_div #C = 1/mapset.datasets[0].iN res = bunch.Bunch(rhs=rhs*mask, map=map*mask, div=div*mask)#, C=C) #res = bunch.Bunch(rhs=rhs, map=map, div=div)#, C=C) return res
def get_coadded_tile(mapinfo, box, obeam=None, ncomp=1, dump_dir=None, verbose=False): if not overlaps_any(box, boxes): return None mapset = mapinfo.read(box, pad=pad, dtype=dtype, verbose=verbose, ncomp=ncomp) if mapset is None: return None if all([d.insufficient for d in mapset.datasets]): return None jointmap.sanitize_maps(mapset) jointmap.build_noise_model(mapset) if len(mapset.datasets) == 0: return None if all([d.insufficient for d in mapset.datasets]): return None jointmap.setup_beams(mapset) jointmap.setup_target_beam(mapset, obeam) jointmap.setup_filter(mapset, mode=args.filter_mode) jointmap.setup_background_spectrum(mapset) mask = jointmap.get_mask_insufficient(mapset) if args.wiener: coadder = jointmap.Wiener(mapset) else: coadder = jointmap.Coadder(mapset) rhs = coadder.calc_rhs() if dump_dir: enmap.write_map(dump_dir + "/rhs.fits", rhs) enmap.write_map(dump_dir + "/ps_rhs.fits", np.abs(enmap.fft(rhs.preflat[0]))**2) map = coadder.calc_map(rhs, dump_dir=dump_dir, verbose=verbose, cg_tol=args.cg_tol)#, maxiter=1) if dump_dir: enmap.write_map(dump_dir + "/ps_map.fits", np.abs(enmap.fft(mapdiag(map)))**2) div = coadder.tot_div #C = 1/mapset.datasets[0].iN res = bunch.Bunch(rhs=rhs*mask, map=map*mask, div=div*mask)#, C=C) #res = bunch.Bunch(rhs=rhs, map=map, div=div)#, C=C) return res
def get_coadded_tile(mapinfo,
box,
obeam=None,
ncomp=1,
dump_dir=None,
verbose=False):
if not overlaps_any(np.sort(box, 0), boxes): return None
mapset = mapinfo.read(box,
pad=pad,
dtype=dtype,
verbose=verbose,
ncomp=ncomp)
if mapset is None: return None
if all([d.insufficient for d in mapset.datasets]): return None
jointmap.sanitize_maps(mapset, detrend=args.detrend)
jointmap.build_noise_model(mapset)
if len(mapset.datasets) == 0: return None
if all([d.insufficient for d in mapset.datasets]): return None
jointmap.setup_beams(mapset)
jointmap.setup_target_beam(mapset, obeam)
jointmap.setup_filter(mapset, mode=args.filter_mode)
jointmap.setup_background_spectrum(mapset)
mask = jointmap.get_mask_insufficient(mapset)
if args.wiener: coadder = jointmap.Wiener(mapset)
else: coadder = jointmap.Coadder(mapset)
rhs = coadder.calc_rhs()
if dump_dir:
enmap.write_map(dump_dir + "/rhs.fits", rhs)
enmap.write_map(dump_dir + "/ps_rhs.fits",
np.abs(enmap.fft(rhs.preflat[0]))**2)
with open(dump_dir + "/names.txt", "w") as nfile:
for name in coadder.names:
nfile.write(name + "\n")
ls, weights = coadder.calc_debug_weights()
np.savetxt(
dump_dir + "/weights_1d.txt",
np.concatenate(
[ls[None], weights.reshape(-1, weights.shape[-1])], 0).T,
fmt="%15.7e")
ls, noisespecs = coadder.calc_debug_noise()
np.savetxt(
dump_dir + "/noisespecs_1d.txt",
np.concatenate(
[ls[None],
noisespecs.reshape(-1, noisespecs.shape[-1])], 0).T,
fmt="%15.7e")
map = coadder.calc_map(rhs,
dump_dir=dump_dir,
verbose=verbose,
cg_tol=args.cg_tol) #, maxiter=1)
if dump_dir:
enmap.write_map(dump_dir + "/ps_map.fits",
np.abs(enmap.fft(mapdiag(map)))**2)
div = coadder.tot_div
#C = 1/mapset.datasets[0].iN
res = bunch.Bunch(rhs=rhs * mask, map=map * mask, div=div * mask) #, C=C)
#res = bunch.Bunch(rhs=rhs, map=map, div=div)#, C=C)
return res
def smooth_tophat(map, rad): # Will use flat sky approximation here. It's not a good approximation for # our big maps, but this doesn't need to be accurate anyway ny,nx = map.shape[-2:] refy, refx = ny/2,nx/2 pos = map.posmap() pos[0] -= pos[0,refy,refx] pos[1] -= pos[1,refy,refx] r2 = np.sum(pos**2,0) kernel = (r2 < rad**2).astype(dtype) / (np.pi*rad**2) / map.size**0.5 * map.area() kernel = np.roll(kernel,-refy,0) kernel = np.roll(kernel,-refx,1) res = enmap.ifft(enmap.fft(map)*np.conj(enmap.fft(kernel))).real return res
def smooth_pix(map, pixrad):
fmap = enmap.fft(map)
ky = np.fft.fftfreq(map.shape[-2])
kx = np.fft.fftfreq(map.shape[-1])
kr2 = ky[:, None]**2 + kx[None, :]**2
fmap *= np.exp(-0.5 * kr2 * pixrad**2)
map = enmap.ifft(fmap).real
return map
def sim_points(shape, wcs, info): # Simulate the point amplitudes N = enmap.area(shape, wcs) * info.density*(180/np.pi)**2 n = N*(info.minamp/info.amp)**(info.alpha+1) amps = info.minamp*np.random.uniform(0,1,n)**(1/(info.alpha+1)) amps = np.maximum(-100*np.abs(info.amp),np.minimum(100*np.abs(info.amp), amps)) # Simulate the polarization psi = np.random.uniform(0,np.pi,n) amps = amps[None,:] * np.array([psi*0+1,np.cos(2*psi)*info.pol,np.sin(2*psi)*info.pol]) # Simulate positions uniformly in pixels ipos = np.array([np.random.uniform(0,shape[-2],n),np.random.uniform(0,shape[-1],n)]) pos = enmap.pix2sky(wcs, ipos) # Draw the points on a canvas using convolution. This requires all points # to have integer pixel positions and the same radius. rawmap = np.zeros(shape) for i in range(shape[0]): rawmap[i][tuple(ipos.astype(int))] = amps[i] l = np.sum(enmap.lmap(shape,wcs)**2,0)**0.5 kernel = np.exp(-0.5*l**2*info.rad**2) # actually perform the convolution pmap = enmap.ifft(enmap.fft(rawmap)*kernel[None]).real print np.max(pmap), np.min(pmap) return pmap
def map_fft(x):
return enmap.fft(x)
def smooth_gauss(m, sigma):
l = np.sum(m.lmap()**2, 0)**0.5
return np.real(en.ifft(en.fft(m) * np.exp(-0.5 * (l * sigma)**2)))
import numpy as np, argparse
from scipy import ndimage
from enlib import enmap
parser = argparse.ArgumentParser()
parser.add_argument("ifile")
parser.add_argument("ofile")
parser.add_argument("-r", "--apod-radius", type=int, default=64)
args = parser.parse_args()
def make_apod(shape, rad):
mask = np.zeros(shape[-2:])
mask[rad:-rad, rad:-rad] = 1
w = np.maximum(1 - ndimage.distance_transform_edt(1 - mask) / rad, 0) ** 3
return w
teb = enmap.read_map(args.ifile)
tqu = enmap.harm2map(enmap.fft(teb))
mask = make_apod(teb.shape, args.apod_radius)
tqu_mask = tqu * mask[None]
teb_mask = enmap.ifft(enmap.map2harm(tqu_mask)).real
res = enmap.samewcs([teb, tqu, tqu_mask, teb_mask], teb)
enmap.write_map(args.ofile, res)
pix = thetaMap.sky2pix(pos, safe=False)
lensedTQU = lensing.displace_map(map, pix,order=5)
lensedMapX = enmap.ifft(enmap.map2harm(lensedTQU)).real
lensedMapY = lensedMapX.copy()
if szX:
lensedMapX += (szMap/TCMB)
if szY:
lensedMapY += (szMap/TCMB)
fotX = enmap.fft(lensedMapX,normalize=False)
fotY = enmap.fft(lensedMapY,normalize=False)
print "Reconstructing" , i , " ..."
qest.updateTEB_X(fotX,alreadyFTed=True)
qest.updateTEB_Y(fotY,alreadyFTed=True)
kappa = enmap.samewcs(qest.getKappa(polCombList[0]).real,thetaMap)
enmap.write_map(saveName+"_kappa_"+str(i)+"_"+str(snap)+".hdf",kappa)
enmap.write_map(saveName+"_inpkappa_"+str(i)+"_"+str(snap)+".hdf",inputKappaMap)
enmap.write_map(saveName+"_sz_"+str(i)+"_"+str(snap)+".hdf",szMap)
rgrad_phitt = enmap.grad(rphitt)
# delens original lensed with current model
delensed = lensing.delens_map(lensed.copy(),
rgrad_phitt,
nstep=delens_steps,
order=lens_order)
delensed = enmap.ndmap(
fmaps.filter_map(delensed,
delensed * 0. + 1.,
parray_sim.modlmap,
lowPass=tellmax,
highPass=tellmin), wcs_sim)
# get fft of delensed map and reconstruct
llteb = enmap.fft(delensed, normalize=False)
qest.updateTEB_X(llteb, alreadyFTed=True)
qest.updateTEB_Y(llteb, alreadyFTed=True)
with io.nostdout():
rawkappa = qest.getKappa("TT").real
kappa_recon = enmap.ndmap(rawkappa, wcs_dat)
if j == 0:
# ps_noise = np.zeros((1,1,modlmap_dat.shape[0],modlmap_dat.shape[1]))
# ps_noise[0,0] = qest.N.Nlkk['TT']
# ngen = enmap.MapGen(shape_dat,wcs_dat,ps_noise)
# cents,kp1d = lbinner_dat.bin(qest.N.Nlkk['TT'])
# # pl.add(cents,kp1d,ls="-")
# #cluster_power = theory.gCl('kk',modlmap_dat)
