# generate our signal only sim
split = sph_tools.alm2map(alms_beamed, template[sv])
# compute the alms of the sim
master_alms[sv, ar, "nofilter"] = sph_tools.get_alms(
split, window_tuple, niter, lmax, dtype=sim_alm_dtype)
# apply the k-space filter
binary = so_map.read_map("%s/binary_%s_%s.fits" %
(window_dir, sv, ar))
norm, split = data_analysis_utils.get_filtered_map(
split,
binary,
filter[sv],
weighted_filter=ks_f["weighted"])
# compute the alms of the filtered sim
master_alms[sv, ar,
"filter"] = sph_tools.get_alms(split,
window_tuple,
niter,
lmax,
dtype=sim_alm_dtype)
master_alms[sv, ar, "filter"] /= (split.data.shape[1] *
split.data.shape[2])**norm
print(scenario, sv, ar, time.time() - t0)
print("%s split of survey: %s, array %s"%(nsplit[sv], sv, ar))
t = time.time()
for k, map in enumerate(maps):
if win_T.pixel == "CAR":
split = so_map.read_map(map, geometry=win_T.data.geometry)
if d["src_free_maps"] == True:
point_source_map = so_map.read_map(map.replace(".fits", "_model.fits"))
point_source_mask = so_map.read_map(d["ps_mask"])
split = data_analysis_utils.get_coadded_map(split, point_source_map, point_source_mask)
if d["use_kspace_filter"]:
print("apply kspace filter on %s" %map)
binary = so_map.read_map("%s/binary_%s_%s.fits" % (window_dir, sv, ar))
split = data_analysis_utils.get_filtered_map(
split, binary, vk_mask=d["vk_mask"], hk_mask=d["hk_mask"], normalize=False)
elif win_T.pixel == "HEALPIX":
split = so_map.read_map(map)
split.data *= cal
if d["remove_mean"] == True:
split = data_analysis_utils.remove_mean(split, window_tuple, ncomp)
#split.plot(file_name="%s/split_%d_%s_%s" % (plot_dir, k, sv, ar), color_range=[250, 100, 100])
master_alms[sv, ar, k] = sph_tools.get_alms(split, window_tuple, niter, lmax)
if d["use_kspace_filter"]:
# there is an extra normalisation for the FFT/IFFT bit
# note that we apply it here rather than at the FFT level because correcting the alm is faster than correcting the maps
master_alms[sv, ar, k] /= (split.data.shape[1]*split.data.shape[2])
if point_source_map_name == map:
raise ValueError(
"No model map is provided! Check map names!")
point_source_map = so_map.read_map(point_source_map_name)
point_source_mask = so_map.read_map(d["ps_mask_%s_%s" %
(sv, ar)])
split = data_analysis_utils.get_coadded_map(
split, point_source_map, point_source_mask)
if ks_f["apply"]:
print("apply kspace filter on %s" % map)
binary = so_map.read_map("%s/binary_%s_%s.fits" %
(window_dir, sv, ar))
norm, split = data_analysis_utils.get_filtered_map(
split,
binary,
filter[sv],
inv_pixwin_lxly=inv_pixwin_lxly,
weighted_filter=ks_f["weighted"])
else:
print("WARNING: no kspace filter is applied")
if deconvolve_pixwin:
binary = so_map.read_map("%s/binary_%s_%s.fits" %
(window_dir, sv, ar))
norm, split = data_analysis_utils.deconvolve_pixwin_CAR(
split, binary, inv_pixwin_lxly)
elif window_tuple[0].pixel == "HEALPIX":
split = so_map.read_map(map)
split.data *= cal
