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batch1_23.py
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import h5py as h5
import numpy as np
import healpy as hp
import pickle
from Moments_analysis import gk_inv
from Moments_analysis import apply_random_rotation,addSourceEllipticity,convert_to_pix_coord
from Moments_analysis import moments_map
import copy
import tqdm
nside_out = 512
nside = 512
lmax = nside_out*2
std_e = 0.28 # typical values for DES Y3 between 0.26-0.33, depending on the tomographic bin.
n_density = 5.51/4. # gal/arcmin^2; 5.51 is the full des y3 catalog, divide by 4 because we have 4 tomographc bins.
n_gal_per_pixel = np.int(n_density*hp.nside2resol(nside_out, arcmin = True)**2)
def load_obj(name):
with open(name + '.pkl', 'rb') as f:
return pickle.load(f)#, encoding='latin1')
def save_obj(name, obj):
with open(name + '.pkl', 'wb') as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
f.close()
def rotate_map_approx(mask,rot_angles, nside=512, flip=False):
alpha, delta = hp.pix2ang(nside, np.arange(len(mask)))
rot = hp.rotator.Rotator(rot=rot_angles, deg=True)
rot_alpha, rot_delta = rot(alpha, delta)
if not flip:
rot_i = hp.ang2pix(nside, rot_alpha, rot_delta)
else:
rot_i = hp.ang2pix(nside, np.pi-rot_alpha, rot_delta)
rot_map = mask*0.
rot_map[rot_i] = mask[np.arange(len(mask))]
return rot_map
mask = load_obj("./Covariance/mask_DES_y3_py2")
mask = hp.ud_grade(mask, nside_out = 512)
masks = [mask, rotate_map_approx(mask,[ 180 ,0 , 0], flip=False), rotate_map_approx(mask,[ 90 ,0 , 0], flip=True), rotate_map_approx(mask,[ 270 ,0 , 0], flip=True)]
conf = dict()
# conf['smoothing_scales'] = np.array([8.2, 13.125, 21.0,33.6,54.,86., 137.6, 220.16]) # arcmin
conf['smoothing_scales'] = np.array([21.0,33.6,54.,86., 137.6, 220.16]) # arcmin
conf['nside'] = nside
conf['lmax'] = conf['nside']*2
conf['verbose'] = False
conf['output_folder'] = './simulated_moments'
mcal_moments = moments_map(conf)
momentsEE_2_2 = []
momentsEE_2_3 = []
momentsEE_3_3 = []
momentsEEE_2_2_2 = []
momentsEEE_3_2_2 = []
momentsEEE_2_3_3 = []
momentsEEE_3_3_3 = []
maps1 = load_obj('./maps/kappa_2ndbin_512_1batch')
maps2 = load_obj('./maps/kappa_3rdbin_512_1batch')
label = 1
for i in tqdm.tqdm(range(25), ascii=True):
map1 = maps1[i]
g1_map,g2_map = gk_inv(map1,map1*0.,conf['nside'],conf['nside']*2)
e1_noisemap = np.random.normal(0,std_e/np.sqrt(n_gal_per_pixel),len(g1_map))
e2_noisemap = np.random.normal(0,std_e/np.sqrt(n_gal_per_pixel),len(g2_map))
e1_noisemap_ = np.random.normal(0,std_e/np.sqrt(n_gal_per_pixel),len(g1_map))
e2_noisemap_ = np.random.normal(0,std_e/np.sqrt(n_gal_per_pixel),len(g2_map))
# add noise
e1_map,e2_map = addSourceEllipticity({'shear1':g1_map,'shear2':g2_map},{'e1':e1_noisemap,'e2':e2_noisemap},es_colnames=("e1","e2"))
map2 = maps2[i]
g1_map2,g2_map2 = gk_inv(map2,map2*0.,conf['nside'],conf['nside']*2)
e1_noisemap2 = np.random.normal(0,std_e/np.sqrt(n_gal_per_pixel),len(g1_map))
e2_noisemap2 = np.random.normal(0,std_e/np.sqrt(n_gal_per_pixel),len(g2_map))
e1_noisemap_2 = np.random.normal(0,std_e/np.sqrt(n_gal_per_pixel),len(g1_map))
e2_noisemap_2 = np.random.normal(0,std_e/np.sqrt(n_gal_per_pixel),len(g2_map))
# add noise
e1_map2,e2_map2 = addSourceEllipticity({'shear1':g1_map2,'shear2':g2_map2},{'e1':e1_noisemap2,'e2':e2_noisemap2},es_colnames=("e1","e2"))
for rot in range(4):
mask_r = masks[rot]
mcal_moments.add_map(e1_map*mask_r, field_label = 'e1', tomo_bin = 2)
mcal_moments.add_map(e2_map*mask_r, field_label = 'e2', tomo_bin = 2)
mcal_moments.add_map(e1_noisemap_*mask_r, field_label = 'e1r', tomo_bin = 2)
mcal_moments.add_map(e2_noisemap_*mask_r, field_label = 'e2r', tomo_bin = 2)
mcal_moments.add_map(e1_map2*mask_r, field_label = 'e1', tomo_bin = 3)
mcal_moments.add_map(e2_map2*mask_r, field_label = 'e2', tomo_bin = 3)
mcal_moments.add_map(e1_noisemap_2*mask_r, field_label = 'e1r', tomo_bin = 3)
mcal_moments.add_map(e2_noisemap_2*mask_r, field_label = 'e2r', tomo_bin = 3)
mcal_moments.transform_and_smooth('convergence'+str(label),'e1' ,'e2' , shear = True, tomo_bins = [2, 3], overwrite = True , skip_conversion_toalm = False)
mcal_moments.transform_and_smooth('noise'+str(label), 'e1r','e2r', shear = True, tomo_bins = [2, 3], overwrite = True , skip_conversion_toalm = False)
mcal_moments.compute_moments_gen( label_moments='kEkE_', field_label1 ='convergence'+str(label)+'_kE', denoise1 = 'noise'+str(label)+'_kE', tomo_bins1 = [2, 3])
# needed to denoise 3rd moments
mcal_moments.compute_moments_gen( label_moments='kEkN_', field_label1 ='convergence'+str(label)+'_kE', field_label2 = 'noise'+str(label)+'_kE', denoise1 = 'noise'+str(label)+'_kE', tomo_bins1 = [2, 3])
momentsEE_2_2.append(copy.deepcopy(mcal_moments.moments["kEkE_"]['2_2']))
momentsEE_2_3.append(copy.deepcopy(mcal_moments.moments["kEkE_"]['2_3']))
momentsEE_3_3.append(copy.deepcopy(mcal_moments.moments["kEkE_"]['3_3']))
# I think the transposes are right here, but think hard about how to transpose when you are denoising.
# IE, which redshift-smoothing combination is the noise at any given transpose
EEEdenoise = mcal_moments.moments["kEkN_"]['2_2_2']
momentsEEE_2_2_2.append(copy.deepcopy(mcal_moments.moments["kEkE_"]['2_2_2'] - EEEdenoise - np.transpose(EEEdenoise, [2,1,0]) - np.transpose(EEEdenoise, [1,0,2]) ))
EEEdenoise = mcal_moments.moments["kEkN_"]['3_2_2']
momentsEEE_3_2_2.append(copy.deepcopy(mcal_moments.moments["kEkE_"]['3_2_2'] - EEEdenoise - np.transpose(EEEdenoise, [2,1,0]) - np.transpose(EEEdenoise, [1,0,2]) ))
EEEdenoise = mcal_moments.moments["kEkN_"]['2_3_3']
momentsEEE_2_3_3.append(copy.deepcopy(mcal_moments.moments["kEkE_"]['2_3_3'] - EEEdenoise - np.transpose(EEEdenoise, [2,1,0]) - np.transpose(EEEdenoise, [1,0,2]) ))
EEEdenoise = mcal_moments.moments["kEkN_"]['3_3_3']
momentsEEE_3_3_3.append(copy.deepcopy(mcal_moments.moments["kEkE_"]['3_3_3'] - EEEdenoise - np.transpose(EEEdenoise, [2,1,0]) - np.transpose(EEEdenoise, [1,0,2]) ))
momentsEE_2_2 = np.array(momentsEE_2_2)
momentsEE_2_3 = np.array(momentsEE_2_3)
momentsEE_3_3 = np.array(momentsEE_3_3)
momentsEEE_2_2_2 = np.array(momentsEEE_2_2_2)
momentsEEE_3_2_2 = np.array(momentsEEE_3_2_2)
momentsEEE_2_3_3 = np.array(momentsEEE_2_3_3)
momentsEEE_3_3_3 = np.array(momentsEEE_3_3_3)
save_obj('./masked_noised_cov/EE_2_2_0', momentsEE_2_2)
save_obj('./masked_noised_cov/EE_2_3_0', momentsEE_2_3)
save_obj('./masked_noised_cov/EE_3_3_0', momentsEE_3_3)
save_obj('./masked_noised_cov/EEE_2_2_2_0', momentsEEE_2_2_2)
save_obj('./masked_noised_cov/EEE_3_2_2_0', momentsEEE_3_2_2)
save_obj('./masked_noised_cov/EEE_2_3_3_0', momentsEEE_2_3_3)
save_obj('./masked_noised_cov/EEE_3_3_3_0', momentsEEE_3_3_3)