for id_ar1, ar1 in enumerate(arrays_1):
for id_sv2, sv2 in enumerate(surveys):
arrays_2 = d["arrays_%s" % sv2]
for id_ar2, ar2 in enumerate(arrays_2):
# This ensures that we do not repeat redundant computations
if (id_sv1 == id_sv2) & (id_ar1 > id_ar2): continue
if (id_sv1 > id_sv2): continue
sv1_list += [sv1]
ar1_list += [ar1]
sv2_list += [sv2]
ar2_list += [ar2]
n_mcms += 1
print("number of mcm matrices to compute : %s" % n_mcms)
so_mpi.init(True)
subtasks = so_mpi.taskrange(imin=0, imax=n_mcms - 1)
print(subtasks)
for task in subtasks:
task = int(task)
sv1, ar1, sv2, ar2 = sv1_list[task], ar1_list[task], sv2_list[
task], ar2_list[task]
print("%s_%s x %s_%s" % (sv1, ar1, sv2, ar2))
l, bl1 = pspy_utils.read_beam_file(d["beam_%s_%s" % (sv1, ar1)])
win1_T = so_map.read_map(d["window_T_%s_%s" % (sv1, ar1)])
win1_pol = so_map.read_map(d["window_pol_%s_%s" % (sv1, ar1)])
l, bl2 = pspy_utils.read_beam_file(d["beam_%s_%s" % (sv2, ar2)])
win2_T = so_map.read_map(d["window_T_%s_%s" % (sv2, ar2)])
win2_pol = so_map.read_map(d["window_pol_%s_%s" % (sv2, ar2)])
pspy_utils.create_directory(specDir)
spectra = ["TT", "TE", "TB", "ET", "BT", "EE", "EB", "BE", "BB"]
spin_pairs = ["spin0xspin0", "spin0xspin2", "spin2xspin0", "spin2xspin2"]
all_freqs = [freq for exp in experiments for freq in d["freqs_%s" % exp]]
ncomp = 3
ps_cmb = powspec.read_spectrum(d["clfile"])[:ncomp, :ncomp]
if include_fg == True:
l, ps_fg = maps_to_params_utils.get_foreground_matrix(
fg_dir, fg_components, all_freqs, lmax_simu + 1)
so_mpi.init(True)
subtasks = so_mpi.taskrange(imin=d["iStart"], imax=d["iStop"])
for iii in subtasks:
#First we will generate our simulations and take their harmonics transforms
t0 = time.time()
alms = curvedsky.rand_alm(ps_cmb, lmax=lmax_simu)
if include_fg == True:
fglms = curvedsky.rand_alm(ps_fg, lmax=lmax_simu)
master_alms = {}
fcount = 0
for exp in experiments:
freqs = d["freqs_%s" % exp]
# here we list the different windows that need to be computed, we will then do a MPI loops over this list
sv_list, ar_list = [], []
n_wins = 0
for sv in surveys:
arrays = d["arrays_%s" % sv]
for ar in arrays:
sv_list += [sv]
ar_list += [ar]
n_wins += 1
print("number of windows to compute : %s" % n_wins)
so_mpi.init(True)
subtasks = so_mpi.taskrange(imin=0, imax=n_wins - 1)
print(subtasks)
for task in subtasks:
task = int(task)
sv, ar = sv_list[task], ar_list[task]
survey_mask = gal_mask.copy()
survey_mask.data[:] = 1
maps = d["maps_%s_%s" % (sv, ar)]
for k, map in enumerate(maps):
print(map)
map = so_map.read_map(map)
survey_mask.data[map.data[0] == 0.0] = 0.0
for sid2, spec2 in enumerate(spec_name):
if sid1 > sid2: continue
print(spec1, spec2)
na, nb = spec1.split("x")
nc, nd = spec2.split("x")
na_list += [na]
nb_list += [nb]
nc_list += [nc]
nd_list += [nd]
ncovs += 1
nspecs = len(spec_name)
print("number of covariance matrices to compute : %s" % ncovs)
so_mpi.init(True)
subtasks = so_mpi.taskrange(imin=0, imax=ncovs - 1)
for task in subtasks:
task = int(task)
na, nb, nc, nd = na_list[task], nb_list[task], nc_list[task], nd_list[task]
win = {}
win["Ta"] = so_map.read_map("%s/window_%s.fits" % (window_dir, na))
win["Tb"] = so_map.read_map("%s/window_%s.fits" % (window_dir, nb))
win["Tc"] = so_map.read_map("%s/window_%s.fits" % (window_dir, nc))
win["Td"] = so_map.read_map("%s/window_%s.fits" % (window_dir, nd))
win["Pa"] = so_map.read_map("%s/window_%s.fits" % (window_dir, na))
win["Pb"] = so_map.read_map("%s/window_%s.fits" % (window_dir, nb))
win["Pc"] = so_map.read_map("%s/window_%s.fits" % (window_dir, nc))
win["Pd"] = so_map.read_map("%s/window_%s.fits" % (window_dir, nd))
freqs += len(files) * [ar[1:]]
# Survey mask
survey = so_map.read_map(d.get("survey_planck", raise_error=True))
survey.ncomp = 3
survey.data = np.tile(survey.data, (survey.ncomp, 1, 1))
# Mask dir for removing mon/dipole
masks_dir = os.path.join(d["data_dir"], "planck/likelihood_mask/")
mask_tmpl = os.path.join(masks_dir,
"COM_Mask_Likelihood-{}-{}-{}_2048_R3.00.fits")
nmaps = len(map_files)
print(f"number of map to project : {nmaps}")
so_mpi.init(True)
subtasks = so_mpi.taskrange(imin=0, imax=nmaps - 1)
print(subtasks)
for task in subtasks:
task = int(task)
map_file = map_files[task]
print(f"Reading {map_file}...")
npipe_map = so_map.read_map(map_file,
fields_healpix=(0, 1, 2),
coordinate="gal")
npipe_map.data *= 10**6
if d.get("remove_mono_dipo_t", True):
mask_hm1 = so_map.read_map(
mask_tmpl.format("temperature", freqs[task], "hm1"))
mask_hm2 = so_map.read_map(
nSplits = len(splits)
l, Nl_T, Nl_P = planck_utils.get_noise_matrix_spin0and2(
ps_model_dir,
experiment,
freqs,
lmax,
nSplits,
lcut=0,
use_noise_th=use_noise_th)
pixwin = hp.pixwin(nside)
so_mpi.init(True)
subtasks = so_mpi.taskrange(imin=d['iStart'], imax=d['iStop'])
for iii in subtasks:
t0 = time.time()
alms = {}
sim_alm = curvedsky.rand_alm(
ps_th, lmax=lmax - 1
) #the nlms and sim_alm lmax need to be investigated, there is a mismatch of 1 that is not understood at the moment
nlms = planck_utils.generate_noise_alms(Nl_T, Nl_P, lmax, nSplits, ncomp)
for freq_id, freq in enumerate(freqs):
maps = d['map_%s' % freq]
