def map_stats(cosmo_tomo_cone):
'''for fits file fn, generate ps, peaks, minima, pdf, MFs
fn: input file name, including full path
tomo=1, 2,..5: int, for tomographic bins
cone=1, 2,..5: int, for light cones'''
if len(cosmo_tomo_cone) == 3:
cosmo, tomo, cone = cosmo_tomo_cone
ipz = ''
else:
cosmo, tomo, cone, ipz = cosmo_tomo_cone
##################################
### generate random see, such that it is the same for all cosmology
### but different for tomo and cone
##################################
if cosmo == 'cov':
iseed = int(10000 + cone * 10 + tomo)
out_dir = dir_cov
fn = cov_fn_gen(tomo, cone)
elif cosmo == 'bias':
iseed = int(20000 + cone * 10 + tomo) #20000
out_dir = dir_bias
fn = bias_fn_gen(tomo, ipz, cone)
else: ## all cosmologies
if cosmo[-1] == 'a':
iseed = int(cone * 10 +
tomo) ## cone goes from 1 to 25, so 10 to 250
#else:#
elif cosmo[
-1] == 'f': ##'f' starts with a different seed from the 'a' cosmology
iseed = int(1000 + cone * 10 + tomo)
out_dir = dir_cosmos
fn = cosmo_fn_gen(cosmo, tomo, cone)
print(fn)
##################################
#### check if the map and comoputed stats files are there
##################################
############ check fits file exist
if not os.path.isfile(fn):
print(fn, 'fits file does not exist \n')
return 0
out_fn_arr = [
out_dir + cosmo + '_tomo%i_cone%s_s%i.npy' % (tomo, cone, theta_g)
for theta_g in theta_g_arr
]
############# check if stats files exist; if yes, skip computation
if np.prod(array([os.path.isfile(out_fn) for out_fn in out_fn_arr])):
### check if the product of boolean elements in the array = 1 (meaning for all smoothing scales)
print(fn, 'stats files exist; skip computation.\n')
return 0 ### all files already exist, no need to process
##################################
########## map operations
##################################
imap = fits.open(fn)[0].data ## open the file
### add noise
seed(iseed)
noise_map = np.random.normal(loc=0.0,
scale=sigma_pix_arr[tomo - 1],
size=(map_pix, map_pix))
kappa_map = ConvergenceMap(data=imap + noise_map, angle=map_side_deg)
noise_map = 0 ## release the memory
### compute stats
## 3 smoothing
## 9 cols: ell, ps, kappa, peak, minima, pdf, v0, v1, v2
ps_noiseless = ConvergenceMap(data=imap,
angle=map_side_deg).powerSpectrum(l_edges)
ps_unsmoothed = kappa_map.powerSpectrum(
l_edges) ## power spectrum should be computed on unsmoothed maps
s = 0
for theta_g in theta_g_arr:
out_fn = out_dir + cosmo + '%s_tomo%i_cone%s_s%i.npy' % (ipz, tomo,
cone, theta_g)
imap = kappa_map.smooth(theta_g * u.arcmin)
out = zeros(shape=(11, Nbin))
kappa_bins = kappa_bin_edges[s][tomo - 1]
ps = imap.powerSpectrum(l_edges)
peak = imap.peakCount(kappa_bins)
minima = ConvergenceMap(data=-imap.data,
angle=map_side_deg).peakCount(kappa_bins)
pdf = imap.pdf(kappa_bins)
mfs = imap.minkowskiFunctionals(kappa_bins)
out[0] = ps[0]
out[1] = ps_noiseless[1]
out[2] = ps_unsmoothed[1]
out[3] = ps[1]
out[4] = peak[0]
out[5] = peak[1]
out[6] = minima[1][::-1]
out[7] = pdf[1]
out[8] = mfs[1]
out[9] = mfs[2]
out[10] = mfs[3]
save(out_fn, out) ### save the file
s += 1
