modlmap = enmap.modlmap(shape, wcs)
comm = mpi.MPI.COMM_WORLD
rank = comm.Get_rank()
numcores = comm.Get_size()
Njobs = int(narrays * (narrays + 1.) / 2.)
num_each, each_tasks = mpi.mpi_distribute(Njobs, numcores)
if rank == 0: print("At most ", max(num_each), " tasks...")
my_tasks = each_tasks[rank]
ainds = []
for aindex1 in range(narrays):
for aindex2 in range(aindex1, narrays):
ainds.append((aindex1, aindex2))
io.mkdir("data/spectra/%s" % version, comm)
for task in my_tasks:
aindex1, aindex2 = ainds[task]
proot = "data/spectra/%s/%s_%s_" % (version, arrays[aindex1],
arrays[aindex2])
print("Noise calc...")
scov, _, _, ncov = ncalc(
aindex1,
aindex2) # raw power spectra in full 2d space before any downsampling
enmap.write_map(proot + "signal.fits", scov)
enmap.write_map(proot + "noise.fits", ncov)
io.plot_img(tutils.tpower(scov), proot + "signal.png")
io.plot_img(tutils.tpower(ncov), proot + "noise.png")
parser.add_argument("-n", "--noise", type=float, default=3.0,help="Noise (uK-arcmin).")
#parser.add_argument("-f", "--flag", action='store_true',help='A flag.')
args = parser.parse_args()
# MPI
comm = mpi.MPI.COMM_WORLD
rank = comm.Get_rank()
numcores = comm.Get_size()
# Paths
PathConfig = io.load_path_config()
pout_dir = PathConfig.get("paths","plots")+"qest_hdv_"+str(args.noise)+"_"
io.mkdir(pout_dir,comm)
# Theory
theory_file_root = "../alhazen/data/Aug6_highAcc_CDM"
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)
# Lens grid
amin = args.GridMin
amax = args.GridMax
num_amps = args.GridNum
kamps = np.linspace(amin,amax,num_amps)
# MPI calculate set up
Nsims = num_amps
Njobs = Nsims
num_each,each_tasks = mpi.mpi_distribute(Njobs,numcores)
if rank==0: print ("At most ", max(num_each) , " tasks...")
my_tasks = each_tasks[rank]
# File I/O
io.mkdir(GridName,comm)
cov_name = lambda x: GridName+"/cov_"+str(x)+".npy"
if rank==0: print("Rank 0 starting ...")
for k,my_task in enumerate(my_tasks):
kamp = kamps[my_task]
kappa_template = lensing.nfw_kappa(kamp*1e15,bmodrmap,cc,overdensity=200.,critical=True,atClusterZ=True)
phi,_ = lensing.kappa_to_phi(kappa_template,bmodlmap,return_fphi=True)
grad_phi = enmap.grad(phi)
pos = posmap + grad_phi
alpha_pix = enmap.sky2pix(bshape,bwcs,pos, safe=False)
def do_the_thing():