def rand_map_flat(shape,
wcs,
ps,
lmax=None,
lens=True,
aberrate=True,
beta=None,
dir=None,
seed=None,
dtype=None,
verbose=False,
recenter=False,
pad=0):
"""Simulate a random flat-sky map. The input spectrum should be
[{phi,T,E,B},{phi,T,E,b},nl] of lens is True, and just [{T,E,B},{T,E,B},nl]
otherwise."""
if dtype is None: dtype = np.float64
if dir is None: dir = aberration.dir_equ
if beta is None: beta = aberration.beta
ctype = np.result_type(dtype, 0j)
if verbose: print "Generating unlensed cmb"
# No position calculation necessary if we're not lensing or aberrating.
if not lens and not aberrate:
return enmap.rand_map(shape, wcs, ps, seed=seed)
# Otherwise we must deal with various displacements
if aberrate: pad += np.pi * beta * 1.2
pad_pix = int(pad / enmap.pixsize(shape, wcs)**0.5)
if pad_pix > 0:
if verbose: print "Padding"
template = enmap.zeros(shape, wcs, np.int16)
template, pslice = enmap.pad(template, pad_pix, return_slice=True)
pshape, pwcs = template.shape, template.wcs
else:
pshape, pwcs = shape, wcs
# Simulate (padded) lensing map
if lens:
maps = enmap.rand_map((ps.shape[0], ) + pshape[-2:], pwcs, ps)
phi, unlensed = maps[0], maps[1:]
if verbose: print "Lensing"
m = lensing.lens_map_flat(unlensed, phi)
else:
m = enmap.rand_map((ps.shape[0], ) + pshape[-2:], pwcs, ps)
# Then handle aberration if necessary
if aberrate:
if verbose: print "Computing aberration displacement"
pos = m.posmap()
pos = enmap.samewcs(
aberration.remap(pos[1::-1], dir=dir, beta=beta,
recenter=recenter), pos)
amp = pos[3]
pos = pos[1::-1]
if verbose: print "Interpolating aberration"
m = enmap.samewcs(m.at(pos, mask_nan=False), m)
if verbose: print "Applying modulation"
m *= amp
if pad_pix > 0:
if verbose: print "Unpadding"
m = m[pslice]
return m
cc = counts.ClusterCosmology(skipCls=True)
theory = cosmology.loadTheorySpectraFromCAMB(theory_file_root,unlensedEqualsLensed=False,
useTotal=False,TCMB = 2.7255e6,lpad=9000,get_dimensionless=False)
# Geometry
shape, wcs = maps.rect_geometry(width_arcmin=args.arc,px_res_arcmin=args.pix,pol=False)
modlmap = enmap.modlmap(shape,wcs)
modrmap = enmap.modrmap(shape,wcs)
# Binning
bin_edges = np.arange(0.,20.0,args.pix*2)
binner = stats.bin2D(modrmap*60.*180./np.pi,bin_edges)
# Noise model
noise_uK_rad = args.noise*np.pi/180./60.
normfact = np.sqrt(np.prod(enmap.pixsize(shape,wcs)))
kbeam = maps.gauss_beam(args.beam,modlmap)
# Simulate
lmax = int(modlmap.max()+1)
ells = np.arange(0,lmax,1)
ps = theory.uCl('TT',ells).reshape((1,1,lmax))
~ps_noise = np.array([(noise_uK_rad)**2.]*ells.size).reshape((1,1,ells.size))
mg = maps.MapGen(shape,wcs,ps)
ng = maps.MapGen(shape,wcs,ps_noise)
kamp_true = args.Amp
kappa = lensing.nfw_kappa(kamp_true*1e15,modrmap,cc,overdensity=200.,critical=True,atClusterZ=True)
phi,_ = lensing.kappa_to_phi(kappa,modlmap,return_fphi=True)
grad_phi = enmap.grad(phi)
posmap = enmap.posmap(shape,wcs)