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add smale scales script by @NicolettaK
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import healpy as hp | ||
import numpy as np | ||
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def create_high_pass_filter(l1, l2, lmax): | ||
ell = np.arange(l1, l2) | ||
wl = np.zeros(lmax+1) | ||
wl[l2:] = 1.0 | ||
wl[ell] = 0.5 * (1 - np.cos(np.pi * (ell - l1) / (l2 - l1))) | ||
return wl | ||
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def create_low_pass_filter(l1, l2, lmax): | ||
ell = np.arange(l1, l2) | ||
wl = np.zeros(lmax+1) | ||
wl[0:l1] = 1.0 | ||
wl[ell] = 0.5 * (1 - np.cos(np.pi * (l2 - ell) / (l2 - l1))) | ||
return wl | ||
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def apply_filter(hmap, filt): | ||
import healpy as hp | ||
nside = hp.get_nside(hmap) | ||
print('apply_filter', nside) | ||
lmax = 3*nside | ||
filt = filt[0:lmax] | ||
alm = hp.map2alm(hmap, lmax=lmax) | ||
almf = np.array([hp.almxfl(alm[i], filt) for i in range(len(alm))]) | ||
hmap_out = hp.alm2map(almf, nside) | ||
return hmap_out | ||
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def add_gaussian_small_scales(map_in, nside_out, pol=False): | ||
cl_in = hp.anafast(map_in) | ||
map_in_ns_out = hp.ud_grade(map_in, nside_out) | ||
map_in_ns_out_smt = hp.smoothing(map_in_ns_out, fwhm=np.radians(180./1000.)) | ||
print('map smoothed') | ||
ell_to_fit = np.arange(100, 500) | ||
hpf = create_high_pass_filter(300, 1000, 4*nside_out) | ||
ell_hell = np.arange(len(hpf)) | ||
if pol==False: | ||
cl_T_to_fit = cl_in[ell_to_fit] | ||
else: | ||
cl_T_to_fit = cl_in[0][ell_to_fit] | ||
fit_cl_T = np.polyfit(np.log(ell_to_fit), np.log(cl_T_to_fit), 1) | ||
cl_hell_T = np.exp(fit_cl_T[0]*np.log(ell_hell)+fit_cl_T[1])*hpf | ||
cl_hell_T[0] = 0 | ||
cl_hell_zero = np.zeros(len(cl_hell_T)) | ||
if pol: | ||
cl_E_to_fit = cl_in[1][ell_to_fit] | ||
cl_B_to_fit = cl_in[2][ell_to_fit] | ||
fit_cl_E = np.polyfit(np.log(ell_to_fit), np.log(cl_E_to_fit), 1) | ||
fit_cl_B = np.polyfit(np.log(ell_to_fit), np.log(cl_B_to_fit), 1) | ||
cl_hell_E = np.exp(fit_cl_E[0]*np.log(ell_hell)+fit_cl_E[1])*hpf | ||
cl_hell_B = np.exp(fit_cl_B[0]*np.log(ell_hell)+fit_cl_B[1])*hpf | ||
cl_hell_E[0] = 0 | ||
cl_hell_B[0] = 0 | ||
cl_hell = np.array([cl_hell_T, cl_hell_E, cl_hell_B, cl_hell_zero, cl_hell_zero, cl_hell_zero]) | ||
map_ss = hp.synfast(cl_hell, nside_out, lmax=nside_out*3, pol=True, new=True) | ||
else: | ||
map_ss = hp.synfast(cl_hell_T, nside_out, lmax=nside_out*3) | ||
print('map small scales computed') | ||
map_ss_mod = map_ss*map_in_ns_out_smt | ||
if pol==False: | ||
coeff_T = np.std(map_ss_mod)/np.std(map_ss) | ||
map_out_T = map_ss_mod/coeff_T+map_in_ns_out_smt | ||
else: | ||
coeff_T = np.std(map_ss_mod[0])/np.std(map_ss[0]) | ||
map_out_T = map_ss_mod[0]/coeff_T+map_in_ns_out_smt[0] | ||
if np.any(map_out_T<0): | ||
negative_pix = np.where(map_out_T<0)[0] | ||
print('negative pixels ', len(negative_pix)) | ||
if pol==False: | ||
map_out_T[negative_pix] = map_in_ns_out[negative_pix] | ||
else: | ||
map_out_T[negative_pix] = map_in_ns_out[0][negative_pix] | ||
if pol: | ||
coeff_Q = np.std(map_ss_mod[1])/np.std(map_ss[1]) | ||
coeff_U = np.std(map_ss_mod[2])/np.std(map_ss[2]) | ||
coeff_P = (coeff_Q+coeff_U)/2. | ||
map_out_Q = map_ss_mod[1]/coeff_P+map_in_ns_out_smt[1] | ||
map_out_U = map_ss_mod[2]/coeff_P+map_in_ns_out_smt[2] | ||
map_out = np.array([map_out_T, map_out_Q, map_out_U]) | ||
else: | ||
map_out = map_out_T | ||
return map_out |