def high_res_plot_img(array,
filename=None,
down=None,
verbose=True,
overwrite=True,
crange=None,
cmap="planck"):
if not (overwrite):
if os.path.isfile(filename): return
try:
from enlib import enmap, enplot
except:
traceback.print_exc()
cprint(
"Could not produce plot " + filename +
". High resolution plotting requires enlib, which couldn't be imported. Continuing without plotting.",
color='fail')
return
if (down is not None) and (down != 1):
downmap = enmap.downgrade(enmap.enmap(array)[None], down)
else:
downmap = enmap.enmap(array)[None]
img = enplot.draw_map_field(downmap,
enplot.parse_args("-c " + cmap + " -vvvg moo"),
crange=crange)
#img = enplot.draw_map_field(downmap,enplot.parse_args("--grid 1"),crange=crange)
if filename is None:
img.show()
else:
img.save(filename)
if verbose:
print(bcolors.OKGREEN + "Saved high-res plot to",
filename + bcolors.ENDC)
def highResPlot2d(array,
outPath,
down=None,
verbose=True,
overwrite=True,
crange=None):
if not (overwrite):
if os.path.isfile(outPath): return
try:
from enlib import enmap, enplot
except:
traceback.print_exc()
printC(
"Could not produce plot " + outPath +
". High resolution plotting requires enlib, which couldn't be imported. Continuing without plotting.",
color='fail')
return
if (down is not None) and (down != 1):
downmap = enmap.downgrade(enmap.enmap(array)[None], down)
else:
downmap = enmap.enmap(array)[None]
img = enplot.draw_map_field(downmap,
enplot.parse_args("-vvvg moo"),
crange=crange)
img.save(outPath)
if verbose:
print(bcolors.OKGREEN + "Saved high-res plot to",
outPath + bcolors.ENDC)
def get_shear(self, do_m=True, do_c=True):
rms = self.cat.data['ishape_hsm_regauss_derived_rms_e']
m = self.cat.data['ishape_hsm_regauss_derived_bias_m']
e1 = self.cat.data['ishape_hsm_regauss_e1']
e2 = self.cat.data['ishape_hsm_regauss_e2']
c1 = self.cat.data['ishape_hsm_regauss_derived_bias_c1']
c2 = self.cat.data['ishape_hsm_regauss_derived_bias_c2']
hsc_wts = self.hsc_wts
wts = self.wts
hsc_resp = 1. - np.nan_to_num(
self.get_map(weights=(wts * (rms**2.))) / hsc_wts)
hsc_m = np.nan_to_num(self.get_map(weights=(wts * (m))) /
hsc_wts) if do_m else hsc_wts * 0.
hsc_e1 = self.get_map(weights=e1 * wts)
hsc_e2 = self.get_map(weights=e2 * wts)
hsc_c1 = np.nan_to_num(self.get_map(weights=c1 * wts) /
hsc_wts) if do_c else hsc_wts * 0.
hsc_c2 = np.nan_to_num(self.get_map(weights=c2 * wts) /
hsc_wts) if do_c else hsc_wts * 0.
g1map = np.nan_to_num(hsc_e1 / 2. / hsc_resp / (1. + hsc_m) /
hsc_wts) - np.nan_to_num(hsc_c1 / (1. + hsc_m))
g2map = np.nan_to_num(hsc_e2 / 2. / hsc_resp / (1. + hsc_m) /
hsc_wts) - np.nan_to_num(hsc_c2 / (1. + hsc_m))
if not self.curved:
g1map = enmap.enmap(g1map, self.wcs)
g2map = enmap.enmap(g2map, self.wcs)
return g1map, g2map
def lens(ulensed,convergence):
posmap = ulensed.posmap()
kmask = maps.mask_kspace(ulensed.shape,ulensed.wcs,lmin=10,lmax=8000)
phi,_ = lensing.kappa_to_phi(enmap.enmap(maps.filter_map(enmap.enmap(convergence,wcs),kmask),wcs),ulensed.modlmap(),return_fphi=True)
grad_phi = enmap.grad(phi)
pos = posmap + grad_phi
alpha_pix = ulensed.sky2pix(pos, safe=False)
lensed = enlensing.displace_map(ulensed, alpha_pix, order=5)
return lensed
def update_mask(self, mask_threshold):
mask = np.zeros(self.shape)
mask[self.mean_wt > mask_threshold] = 1
self.mask = mask
if not self.curved:
self.mask = enmap.enmap(self.mask, self.wcs)
self._counts()
def __init__(self,
ras_deg,
decs_deg,
shape=None,
wcs=None,
nside=None,
verbose=True):
self.verbose = verbose
if nside is not None:
if verbose: print("Calculating pixels...")
self.pixs = hp.ang2pix(nside, ras_deg, decs_deg, lonlat=True)
if verbose: print("Done with pixels...")
self.nside = nside
self.shape = hp.nside2npix(nside)
self.curved = True
else:
coords = np.vstack((decs_deg, ras_deg)) * np.pi / 180.
self.shape = shape
self.wcs = wcs
if verbose: print("Calculating pixels...")
self.pixs = enmap.sky2pix(shape, wcs, coords,
corner=True) # should corner=True?!
if verbose: print("Done with pixels...")
self.curved = False
self.counts = self.get_map()
if not self.curved:
self.counts = enmap.enmap(self.counts, self.wcs)
def get_patch(coords, sim_idx, map_type, cmb_type, pixel_fac = 2):
shape,wcs = enmap.fullsky_geometry(res=1.0*np.pi/180./60.)
def _process_coords(coords):
coords = np.array(coords)
if coords[1,1] > 180. : coords[1,1] -= 360.
if coords[0,1] > 180. : coords[0,1] -= 360.
coords *= np.pi/180.
return coords
zpsim_idx = '%05d' %sim_idx
coords = _process_coords(coords)
file_name = sim_file_temp.format(zpsim_idx, map_type, cmb_type, zpsim_idx)
emap = enmap.read_fits(file_name, box=coords, wcs_override=wcs)
nshape = tuple(np.array([i for i in emap.shape])*2)
data = simTools.resample_fft_withbeam(emap, nshape, doBeam=False, beamData=None)
oshape, owcs = enmap.scale_geometry(emap.shape, emap.wcs, pixel_fac)
assert(oshape == nshape)
pmap = enmap.enmap(data, owcs)
return emap.to_flipper()
def getKappaSZ(bSims,snap,massIndex,px,thetaMapshape):
b = bSims
PIX = 2048
maps, z, kappaSimDat, szMapuKDat, projectedM500, trueM500, trueR500, pxInRad, pxInRad = b.getMaps(snap,massIndex,freqGHz=150.)
pxIn = pxInRad * 180.*60./np.pi
hwidth = PIX*pxIn/2.
print "At redshift ", z , " stamp is ", hwidth ," arcminutes wide."
# input pixelization
shapeSim, wcsSim = enmap.geometry(pos=[[-hwidth*arcmin,-hwidth*arcmin],[hwidth*arcmin,hwidth*arcmin]], res=pxIn*arcmin, proj="car")
kappaSim = enmap.enmap(kappaSimDat,wcsSim)
szMapuK = enmap.enmap(szMapuKDat,wcsSim)
# downgrade to native
shapeOut, wcsOut = enmap.geometry(pos=[[-hwidth*arcmin,-hwidth*arcmin],[hwidth*arcmin,hwidth*arcmin]], res=px*arcmin, proj="car")
kappaMap = enmap.project(kappaSim,shapeOut,wcsOut)
szMap = enmap.project(szMapuK,shapeOut,wcsOut)
print thetaMapshape
print szMap.shape
if szMap.shape[0]>thetaMapshape[0]:
kappaMap = enmap.project(kappaSim,thetaMapshape,wcsOut)
szMap = enmap.project(szMapuK,thetaMapshape,wcsOut)
else:
diffPad = ((np.array(thetaMapshape) - np.array(szMap.shape))/2.+0.5).astype(int)
apodWidth = 25
kappaMap = enmap.pad(enmap.apod(kappaMap,apodWidth),diffPad)[:-1,:-1]
szMap = enmap.pad(enmap.apod(szMap,apodWidth),diffPad)[:-1,:-1]
# print szMap.shape
assert szMap.shape==thetaMap.shape
# print z, projectedM500
# pl = Plotter()
# pl.plot2d(kappaMap)
# pl.done("output/kappasim.png")
# pl = Plotter()
# pl.plot2d(szMap)
# pl.done("output/szsim.png")
# sys.exit()
# print "kappaint ", kappaMap[thetaMap*60.*180./np.pi<10.].mean()
return kappaMap,szMap
def tqu2teb(tmap, qmap, umap):
tqu = np.zeros((3, ) + tmap.shape)
tqu[0], tqu[1], tqu[2] = (tmap, qmap, umap)
tqu = enmap.enmap(tqu, tmap.wcs)
alm = curvedsky.map2alm(tqu, lmax=lmax)
teb = curvedsky.alm2map(alm[:, None], tqu.copy()[:, None], spin=0)[:, 0]
del tqu
return (teb[0], teb[1], teb[2])
def get_sim(sim_idx, beam_fwhm=beam_fwhm, noise_level=noise_level):
ret = act_sim.getActpolCmbSim(None,
coords,
sim_idx,
cmb_dir,
doBeam=False,
pixelFac=1)
np.random.seed(sim_idx)
# handle mean
for i in range(len(ret)):
ret[i].data -= np.mean(ret[i].data)
ret[i] = enmap.from_flipper(ret[i])
# beam convolve
owcs, shape = (ret[0].wcs.copy(), ret[0].shape)
tqu = np.zeros((3, ) + ret[0].shape)
tqu[0], tqu[1], tqu[2] = (ret[0], ret[1], ret[2])
tqu = enmap.enmap(tqu, owcs)
alm = curvedsky.map2alm(tqu, lmax=lmax)
del ret
_, beam = cmblens.theory.get_gauss_beam(np.arange(lmax + 1), beam_fwhm)
fl = np.sqrt(beam)
log.info("beam convolution")
for i in range(alm.shape[0]):
alm[i] = hp.sphtfunc.almxfl(alm[i], fl)
tqu = curvedsky.alm2map(alm[:, None], tqu.copy()[:, None])[:, 0]
if noise_level > 0:
log.info("adding noise")
for i in range(len(tqu)):
noise = enmap.to_flipper(tqu[i]).getTemplate()
l_n = np.arange(lmax + 1000)
noise_fact = 1. if i == 0 else np.sqrt(2)
nl = cmblens.theory.get_white_noise_power(l_n,
noise_level * noise_fact)
ps = nl.reshape((1, 1, nl.size))
noise = curvedsky.rand_map(shape, owcs, ps, lmax=(lmax + 1000))
#noise.fillWithGaussianRandomField(l_n, nl, bufferFactor=1)
tqu[i] += noise
del noise
# beam deconvolve
log.info("beam deconvolution")
alm = curvedsky.map2alm(tqu, lmax=lmax)
for i in range(alm.shape[0]):
alm[i] = hp.sphtfunc.almxfl(alm[i], 1. / fl)
tqu = curvedsky.alm2map(alm[:, None], tqu.copy()[:, None])[:, 0]
return (tqu[0], tqu[1], tqu[2]) # TQU
def A(self, x):
m = enmap.enmap(x.reshape(self.shape), self.wcs, copy=False)
res = m * 0
for dataset in self.datasets:
for split in dataset.splits:
if split.data.empty or not split.active: continue
w = split.data.H * m
w = map_ifft(map_fft(w) * dataset.iN)
w *= split.data.H
res += w
# Apply resolution mask
res *= self.highres_mask
return res.reshape(-1)
def get_map(self,component,mass_index,snap,shape=None,wcs=None):
if shape is None:
assert wcs is None
shape,wcs = self.get_geometry(snap)
assert np.all(shape==(self.PIX,self.PIX))
filen = self.files[component](mass_index,snap)
with open(filen, 'rb') as fd:
temp = np.fromfile(file=fd, dtype=np.float32)
dat = np.reshape(temp,shape) * self.h # THIS NEEDS DEBUGGING
imap = enmap.enmap(dat,wcs)
return imap
def read_maps(fmt, n, ntot=4):
try:
maps = en.read_map(fmt)
if maps.ndim == ntot - 1: maps = en.enmap([maps] * n, maps.wcs)
if maps.ndim != ntot:
raise ValueError("Map %s must have %d dimensions" % (fmt, ntot))
return maps
except (IOError, OSError):
maps = [en.read_map(fmt % i) for i in range(n)]
maps = en.ndmap(maps, maps[0].wcs)
if maps.ndim != ntot:
maps = maps.reshape(maps.shape[:-2] + (1, ) * (maps.ndim - ntot) +
maps.shape[-2:])
return maps
def update_mask(self,rand_sigma_arcmin=2.,rand_threshold=1e-3):
if rand_sigma_arcmin>1.e-3:
if self.verbose: print( "Smoothing...")
if self.curved:
smap = hp.smoothing(self.rand_map,sigma=rand_sigma_arcmin*np.pi/180./60.)
else:
smap = enmap.smooth_gauss(self.rand_map,rand_sigma_arcmin*np.pi/180./60.)
if self.verbose: print( "Done smoothing...")
else:
if self.verbose: smap = self.rand_map
self.mask = np.zeros(self.shape)
self.mask[smap>rand_threshold] = 1
if not self.curved:
self.mask = enmap.enmap(self.mask,self.wcs)
self._counts()
def __init__(self,ras_deg,decs_deg,shape=None,wcs=None,nside=None,verbose=True,hp_coords="equatorial"):
self.verbose = verbose
if nside is not None:
eq_coords = ['fk5','j2000','equatorial']
gal_coords = ['galactic']
if verbose: print( "Calculating pixels...")
if hp_coords in gal_coords:
if verbose: print( "Transforming coords...")
from astropy.coordinates import SkyCoord
import astropy.units as u
gc = SkyCoord(ra=ras_deg*u.degree, dec=decs_deg*u.degree, frame='fk5')
gc = gc.transform_to('galactic')
phOut = gc.l.deg * np.pi/180.
thOut = gc.b.deg * np.pi/180.
thOut = np.pi/2. - thOut #polar angle is 0 at north pole
self.pixs = hp.ang2pix( nside, thOut, phOut )
elif hp_coords in eq_coords:
ras_out = ras_deg
decs_out = decs_deg
lonlat = True
self.pixs = hp.ang2pix(nside,ras_out,decs_out,lonlat=lonlat)
else:
raise ValueError
if verbose: print( "Done with pixels...")
self.nside = nside
self.shape = hp.nside2npix(nside)
self.curved = True
else:
coords = np.vstack((decs_deg,ras_deg))*np.pi/180.
self.shape = shape
self.wcs = wcs
if verbose: print( "Calculating pixels...")
self.pixs = enmap.sky2pix(shape,wcs,coords,corner=True) # should corner=True?!
if verbose: print( "Done with pixels...")
self.curved = False
self.counts = self.get_map()
if not self.curved:
self.counts = enmap.enmap(self.counts,self.wcs)
self.mask = np.ones(shape)
self._counts()
def __call__(fself, x):
params = param_zipper.unzip(x)
chisqs, amps, adiv = self.calc_chisqs_amps_cached(
params.poss, params.taus)
#print amps[433], adiv[433]
chisq = np.sum(chisqs)
if verbosity > 0 and fself.i % 10 == 0:
if verbosity == 1:
print("%6d %12.4f" % (fself.i, chisq / self.ndata))
else:
print("%6d %12.4f" % (fself.i, chisq / self.ndata) +
" %6.3f" * len(x) % tuple(x))
sys.stdout.flush()
if False and fself.i % 10000 == 0:
model = self.model(params.poss, amps, params.taus)
map = enmap.enmap(
[self.rdata.tod, model, self.rdata.tod - model])
enmap.write_map(args.odir + "/foo%06d.fits" % fself.i,
map)
fself.i += 1
return chisq
tweight = None for sub in range(nsub): pre = args.idir + "/" + tag % sub opre = args.odir + "/" + tag % sub # Weight map if div: weight = vread(pre + "div.fits") if weight.ndim == 4: weight = weight[0,0] elif noise: weight = vread(pre + "noise.fits") weight[weight>0] = weight[weight>0]**-2 else: weight = vread(pre + "weights_I.fits") # CMB map map = [vread(pre + args.wpoly + "%d_%s.fits" % (step, comp)) for comp in ["I","Q","U"]] map = enmap.enmap(map, map[0].wcs) # Input used, if available if iused and not args.wpoly: for i, comp in enumerate(["I","Q","U"]): base = vread(pre + "input_used_%s.fits" % comp) map[i,weight>0] += base[weight>0] nosrc = map.copy() # Point source template, if available if beam: srcs = vread(pre + "beam_test.fits") map[0][weight>0] += srcs[weight>0] if args.single % 2 > 0: vwrite(opre + "nosrc_%04d.fits" % step, nosrc) if args.single % 2 > 0: vwrite(opre + "map_%04d.fits" % step, map) vwrite(opre + "div.fits", weight)
coords,
sim_idx,
cmb_dir,
doBeam=False,
pixelFac=2)
for i in range(len(ret)):
ret[i] = enmap.from_flipper(ret[i])
ret[i] -= np.mean(ret[i])
return (ret[0], ret[1], ret[2]) # TQU
temp_map, _, _ = get_sim(0)
shape, wcs = temp_map.shape, temp_map.wcs
taper, _ = maps.get_taper(shape)
taper = enmap.enmap(taper, wcs=wcs)
# initialize cusps
overwrite = False
mcm_identifier = "%lsd_le%d_nb%d_lm%d_%s_%s" % (0, lmax, nbin, lmax,
coords_str, postfix)
cusps_fc = cusps.power.CUSPS(mcm_identifier, taper, taper, bin_edges, lmax,
None, overwrite)
binner = cusps_fc.binner
# bin the theory
theo_bin = {}
lbin_th, theo_bin['dltt'] = cusps_fc.bin_theory_scalarxscalar(
l_th, theo['dltt'])
lbin_th, theo_bin['dlte'] = cusps_fc.bin_theory_scalarxscalar(
l_th, theo['dlte'])
# Overwrite with copies of first half-stroke n = ndelay/4 x = np.linspace(0, 1, n+1, endpoint=False) apod = (1+(x/0.99)**2000)**-1 moo = (d[:,:,:,:n+1]-d[:,:,:,n,None])*apod + d[:,:,:,n,None] print apod d[:,:,:,2*n:3*n] = moo[:,:,:,0*n:1*n] d[:,:,:,1*n:2*n] = moo[:,:,:,n:0:-1] d[:,:,:,3*n:4*n] = moo[:,:,:,n:0:-1] # Go from stroke to spectrum. This takes us to W/sr/m^2/Hz d = fft.rfft(d).real[...,::2]*2/ndelay/dfreq #dump(pre+"_4", d) # Unapply the frequency filter freqs = np.arange(d.shape[-1])*dfreq d /= filter(freqs) #dump(pre+"_5", d) # Reorder from [stokes,det,theta,phi,freq] to [det,freq,stokes,theta,phi] d = d[:,:,:,None,:] d = utils.moveaxes(d, (1,4,0,2,3), (0,1,2,3,4)) # Apply the detector responses. for di, det in enumerate(dets): d[di] = change_response(det.response, [1,1,0], d[di]) d[di] = change_horn(det.horn, 0, d[di]) # If we are in double barrel mode, then we're seeing # the sky double up. Compensate for this. if args.barrel_mode == "double": d /= 2 # Output as a ring file m = enmap.enmap(d, wcs, copy=False) enmap.write_map(pre + ".fits", m)
def M(self, x):
m = enmap.enmap(x.reshape(self.shape), self.wcs, copy=False)
res = m * self.tot_idiv
return res.reshape(-1)
posmap = enmap.posmap(gshape, gwcs)
pos = posmap + grad_phi
alpha_pix = enmap.sky2pix(gshape, gwcs, pos, safe=False)
kbeam = maps.gauss_beam(args.beam, gmodlmap)
mstats = stats.Stats()
for i in range(args.Nclusters):
if (i + 1) % 100 == 0: print(i + 1)
unlensed = mg.get_map()
noise_map = ng.get_map()
lensed = maps.filter_map(
enlensing.displace_map(unlensed.copy(), alpha_pix, order=lens_order),
kbeam)
fdownsampled = enmap.enmap(resample.resample_fft(lensed, bshape), bwcs)
stamp = fdownsampled + noise_map
#cutout = lensed + noise_map
cutout = stamp[int(bshape[0] / 2. - shape[0] / 2.):int(bshape[0] / 2. +
shape[0] / 2.),
int(bshape[0] / 2. - shape[0] / 2.):int(bshape[0] / 2. +
shape[0] / 2.)]
# print(cinvs[k].shape,cutout.shape)
totlnlikes = []
for k, kamp in enumerate(kamps):
lnlike = maps.get_lnlike(cinvs[k], cutout) + logdets[k]
totlnlike = lnlike #+ lnprior[k]
totlnlikes.append(totlnlike)
beam2d=okbeam,
kmask=tmask,
kmask_K=kmask,
pol=False,
grad_cut=2000,
unlensed_equals_lensed=False)
for i, task in enumerate(my_tasks):
if (i + 1) % 10 == 0 and rank == 0: print(i + 1)
# Sim
unlensed = mg.get_map()
noise_map = ng.get_map()
lensed = maps.filter_map(
enlensing.displace_map(unlensed, alpha_pix, order=lens_order), kbeam)
fdownsampled = enmap.enmap(resample.resample_fft(lensed, oshape), owcs)
stamp = fdownsampled + noise_map
# Bayesian
cutout = stamp[int(oshape[0] / 2. - bshape[0] / 2.):int(oshape[0] / 2. +
bshape[0] / 2.),
int(oshape[0] / 2. - bshape[0] / 2.):int(oshape[0] / 2. +
bshape[0] / 2.)]
totlnlikes = []
for k, kamp in enumerate(bkamps):
lnlike = maps.get_lnlike(cinvs[k], cutout) + logdets[k]
totlnlike = lnlike #+ lnprior[k]
totlnlikes.append(totlnlike)
nlnlikes = -0.5 * np.array(totlnlikes)
mstats.add_to_stats("totlikes", nlnlikes)
import numpy as np, argparse
from enlib import enmap
from scipy import ndimage
parser = argparse.ArgumentParser()
parser.add_argument("pos")
parser.add_argument("template")
parser.add_argument("ofile")
parser.add_argument("-r", "--radius", type=float, default=3)
parser.add_argument("-c", "--columns", type=str, default="3,5,2")
parser.add_argument("-t", "--threshold", type=float, default=0)
args = parser.parse_args()
cols = [int(w) for w in args.columns.split(",")]
srcinfo = np.loadtxt(args.pos)[:, cols]
pos = srcinfo[np.abs(srcinfo[:, 2]) >= args.threshold][:, :2] * np.pi / 180
map = enmap.read_map(args.template)
pix = map.sky2pix(pos.T).T.astype(int)
pixrad = (map.area() / map.npix)**0.5
mrad = args.radius * np.pi / 180 / 60 / pixrad
mask = enmap.zeros(map.shape[-2:], map.wcs) + 1
mask[pix[:, 0], pix[:, 1]] = 0
mask = enmap.enmap(1.0 * (ndimage.distance_transform_edt(mask) > mrad),
map.wcs)
enmap.write_map(args.ofile, mask)
import numpy as np, argparse
from enlib import enmap
from scipy import ndimage
parser = argparse.ArgumentParser()
parser.add_argument("pos")
parser.add_argument("template")
parser.add_argument("ofile")
parser.add_argument("-r", "--radius", type=float, default=3)
parser.add_argument("-c", "--columns", type=str, default="3,5,2")
parser.add_argument("-t", "--threshold", type=float, default=0)
args = parser.parse_args()
cols = [int(w) for w in args.columns.split(",")]
srcinfo = np.loadtxt(args.pos)[:,cols]
pos = srcinfo[np.abs(srcinfo[:,2])>=args.threshold][:,:2] * np.pi/180
map = enmap.read_map(args.template)
pix = map.sky2pix(pos.T).T.astype(int)
pixrad = (map.area()/map.npix)**0.5
mrad = args.radius*np.pi/180/60/pixrad
mask = enmap.zeros(map.shape[-2:], map.wcs)+1
mask[pix[:,0],pix[:,1]] = 0
mask = enmap.enmap(1.0*(ndimage.distance_transform_edt(mask) > mrad), map.wcs)
enmap.write_map(args.ofile, mask)
opre = args.odir + "/" + tag % sub
# Weight map
if div:
weight = vread(pre + "div.fits")
if weight.ndim == 4: weight = weight[0, 0]
elif noise:
weight = vread(pre + "noise.fits")
weight[weight > 0] = weight[weight > 0]**-2
else:
weight = vread(pre + "weights_I.fits")
# CMB map
map = [
vread(pre + args.wpoly + "%d_%s.fits" % (step, comp))
for comp in ["I", "Q", "U"]
]
map = enmap.enmap(map, map[0].wcs)
# Input used, if available
if iused and not args.wpoly:
for i, comp in enumerate(["I", "Q", "U"]):
base = vread(pre + "input_used_%s.fits" % comp)
map[i, weight > 0] += base[weight > 0]
nosrc = map.copy()
# Point source template, if available
if beam:
srcs = vread(pre + "beam_test.fits")
map[0][weight > 0] += srcs[weight > 0]
if args.single % 2 > 0:
vwrite(opre + "nosrc_%04d.fits" % step, nosrc)
if args.single % 2 > 0:
vwrite(opre + "map_%04d.fits" % step, map)
vwrite(opre + "div.fits", weight)
mass = 2.e14
fkappa = lens_func(fmodrmap)
phi, _ = lensing.kappa_to_phi(fkappa, fmodlmap, return_fphi=True)
grad_phi = enmap.grad(phi)
pos = enmap.posmap(fshape, fwcs) + grad_phi
alpha_pix = enmap.sky2pix(fshape, fwcs, pos, safe=False)
lmax = cmodlmap.max()
ells = np.arange(0, lmax, 1)
cls = theory.uCl('TT', ells)
ps = cls.reshape((1, 1, ells.size))
mg = maps.MapGen(cshape, cwcs, ps)
unlensed = mg.get_map()
hunlensed = enmap.enmap(resample.resample_fft(unlensed.copy(), fshape), fwcs)
hlensed = enlensing.displace_map(hunlensed,
alpha_pix,
order=lens_order,
mode=mode)
lensed = enmap.enmap(resample.resample_fft(hlensed, cshape), cwcs)
rkappa = lens_func(rmodrmap)
phi, _ = lensing.kappa_to_phi(rkappa, rmodlmap, return_fphi=True)
grad_phi = enmap.grad(phi)
pos = enmap.posmap(rshape, rwcs) + grad_phi
ralpha_pix = enmap.sky2pix(rshape, rwcs, pos, safe=False)
hunlensed = enmap.enmap(resample.resample_fft(unlensed.copy(), rshape), rwcs)
enmap.write_map("unlensed_0.001arc.fits", hunlensed)
def get_delta(self):
delta = (self.counts / self.nmean - 1.)
if not self.curved:
delta = enmap.enmap(delta, self.wcs)
return delta
#fitter = SingleFitter(ref, maps[mi], divs[mi,:1,:1])
fitter = SingleFitter(ref_small, map_small, div_small)
p = fitter.fit(verbose=True)
params.append(p)
params = np.array(params)
rhss = divs[:, None] * maps
mrhss = apply_params(rhss, params, nooff=True)
mdivs = apply_params(divs, params, nooff=True)
mrhs = np.sum(mrhss, 0)
mdiv = np.sum(mdivs, 0)
ref = solve(mdiv, mrhs)
# Compute median too. This is more robust to glitches, but not optimally weighted.
smaps = apply_params(maps, params)
mask = np.logical_or(np.repeat(mdivs[:, None] == 0, smaps.shape[1], 1),
smaps == 0)
medmap = enmap.enmap(np.ma.median(np.ma.array(smaps, mask=mask), 0),
ref.wcs)
ref = remove_offset(ref)
medmap = remove_offset(medmap)
with open(args.odir + "/fit_%03d.txt" % ri, "w") as f:
for id, p in zip(ids, params):
f.write(("%7.4f %7.4f %7.4f" + " %12.4f" *
(params.shape[-1] - 3) + " %s\n") % (tuple(p) + (id, )))
enmap.write_map(args.odir + "/tot_map_%03d.fits" % ri, ref)
enmap.write_map(args.odir + "/tot_div_%03d.fits" % ri, mdiv)
enmap.write_map(args.odir + "/tot_med_%03d.fits" % ri, medmap)
# Output the individual best fits too
if args.individual:
for i, id in enumerate(ids):
div_small = eval("divs[mi]"+args.slice)
#fitter = SingleFitter(ref, maps[mi], divs[mi,:1,:1])
fitter = SingleFitter(ref_small, map_small, div_small)
p = fitter.fit(verbose=True)
params.append(p)
params = np.array(params)
rhss = divs[:,None]*maps
mrhss = apply_params(rhss, params, nooff=True)
mdivs = apply_params(divs, params, nooff=True)
mrhs = np.sum(mrhss,0)
mdiv = np.sum(mdivs,0)
ref = solve(mdiv, mrhs)
# Compute median too. This is more robust to glitches, but not optimally weighted.
smaps = apply_params(maps, params)
mask = np.logical_or(np.repeat(mdivs[:,None]==0,smaps.shape[1],1), smaps==0)
medmap= enmap.enmap(np.ma.median(np.ma.array(smaps,mask=mask),0),ref.wcs)
ref = remove_offset(ref)
medmap = remove_offset(medmap)
with open(args.odir + "/fit_%03d.txt"%ri, "w") as f:
for id, p in zip(ids,params):
f.write(("%7.4f %7.4f %7.4f" + " %12.4f"*(params.shape[-1]-3) + " %s\n") %
(tuple(p)+(id,)))
enmap.write_map(args.odir + "/tot_map_%03d.fits" % ri, ref)
enmap.write_map(args.odir + "/tot_div_%03d.fits" % ri, mdiv)
enmap.write_map(args.odir + "/tot_med_%03d.fits" % ri, medmap)
# Output the individual best fits too
if args.individual:
for i, id in enumerate(ids):