def load_scf_coefficients(coeff_files, cov_files, nmax, lmax, mmax, min_sample,
max_sample, pmass, sn):
"""
Load coefficients.
TODO : REMOVE THIS FUNCTION WHEN SCRIPT IS FULLY WORKING
"""
ncoeff_sample = max_sample - min_sample
print(pmass)
S, T = coefficients_smoothing.read_coeff_matrix(coeff_files,
ncoeff_sample,
nmax,
lmax,
mmax,
min_sample,
max_sample,
snaps=90)
SS, TT, ST = coefficients_smoothing.read_cov_elements(cov_files,
ncoeff_sample,
nmax,
lmax,
mmax,
min_sample,
max_sample,
snaps=90)
S_smooth, T_smooth, N_smooth = coefficients_smoothing.smooth_coeff_matrix(
S, T, SS, TT, ST, pmass, nmax, lmax, mmax, sn, sn_out=0)
return S_smooth, T_smooth
def smooth_coeff(coeff_path, cov_path, ni, nf, nmax, lmax, mmax, sn, pmass, snap, sn_out=0):
nfiles = nf-ni
S, T = coefficients_smoothing.read_coeff_matrix(coeff_path, nfiles, nmax, \
lmax, mmax, n_min=ni,\
n_max=nf, snaps=snap)
SS, TT, ST = coefficients_smoothing.read_cov_elements(cov_path, nfiles, nmax,\
lmax, mmax, n_min=ni,\
n_max=nf, snaps=snap)
if sn_out==0:
S_smooth, T_smooth, N_smooth = coefficients_smoothing.smooth_coeff_matrix(S, T, SS, TT, ST, pmass, nmax, lmax, mmax, sn, sn_out)
return S_smooth, T_smooth, N_smooth
elif sn_out==1:
S_smooth, T_smooth, N_smooth, SN_coeff = coefficients_smoothing.smooth_coeff_matrix(S, T, SS, TT, ST, pmass, nmax, lmax, mmax, sn, sn_out)
return S_smooth, T_smooth, N_smooth, SN_coeff
def rho_square(i, sn):
"""
Computes the `True' density square as defined
in equation 5 of the companion doc.
It use the coefficients computed with the particle distribution
in each particle batch i.
"""
path = '/home/u9/jngaravitoc/codes/Kullback-Leibler/data/'
t1 = time_now()
S, T = coefficients_smoothing.read_coeff_matrix(
path + 'mwlmc_hal_sn_test_coeff_sample_',
1,
nmax,
lmax,
lmax,
i,
i + 1,
snaps=1)
SS, TT, ST = coefficients_smoothing.read_cov_elements(
path + 'mwlmc_hal_sn_test_covmat_sample_',
1,
nmax,
lmax,
lmax,
i,
i + 1,
snaps=1)
S_smooth, T_smooth, N_coeff = coefficients_smoothing.smooth_coeff_matrix(
S, T, SS, TT, ST, mass, nmax, lmax, lmax, sn_range[sn])
rho2 = coefficients_sum_fast(S_smooth, T_smooth, rs)
t2 = time_now()
time_diff(t1, t2)
return rho2
def kpq_sn_estimator(i):
pos = np.loadtxt('./MW_100M_b1_dm_part_1e6_300.txt')
N_particles = len(pos)
n_batches = 100
particles_in_batch = int(N_particles / n_batches)
sn_range = np.arange(0, 11, 0.2)
Hp = np.zeros(len(sn_range))
Kpq = np.zeros(len(sn_range))
Kpq_rho = np.zeros(len(sn_range))
N_part = 990000 # len(pos_grid)
m_p = 1 / N_part
rho_factor = 1e4 / N_part
m_p_sim = 1E-4
N_coeff = np.zeros(len(sn_range))
path = '/home/xzk/work/github/MW-LMC-SCF/code/data_KL_SN/'
S, T = coefficients_smoothing.read_coeff_matrix(
path + 'mwlmc_hal_sn_test_coeff_sample_',
i + 1,
20,
20,
20,
0,
i + 1,
snaps=1)
SS, TT, ST = coefficients_smoothing.read_cov_elements(
path + 'mwlmc_hal_sn_test_covmat_sample_',
i + 1,
20,
20,
20,
0,
i + 1,
snaps=1)
S_0, T_0, N_smooth_0 = coefficients_smoothing.smooth_coeff_matrix(
S, T, SS, TT, ST, m_p_sim, 20, 20, 20, 0) # sn
xyz1 = np.array([
pos[:i * (particles_in_batch), 0], pos[:i * (particles_in_batch), 1],
pos[:i * (particles_in_batch), 2]
]).T
xyz2 = np.array([
pos[(i + 1) * (particles_in_batch):,
0], pos[(i + 1) * (particles_in_batch):, 1],
pos[(i + 1) * (particles_in_batch):, 2]
]).T
xyz = np.concatenate((xyz1, xyz2))
assert (len(xyz == 990000))
rho_reference = biff.density(np.ascontiguousarray(xyz.astype(float)),
S_0,
T_0,
M=1,
r_s=40.85)
print('Here')
"""
for sn in range(len(sn_range)):
S_smooth, T_smooth, N_coeff[sn] = coefficients_smoothing.smooth_coeff_matrix(S, T, SS, TT, ST, m_p_sim, 20, 20, 20, sn_range[sn])
rho_estimate = biff.density(np.ascontiguousarray(xyz.astype(float)), S_smooth, T_smooth, M=1, r_s=40.85)
Hp[sn] = np.sum(m_p*np.log(rho_factor*np.abs(rho_estimate)))
Kpq[sn] = np.sum(m_p*np.log(rho_factor*np.abs(rho_reference))) - Hp[sn]
Kpq_rho[sn] = np.sum(rho_factor*rho_reference*np.log(np.abs(rho_reference/rho_estimate)))
"""
return Hp, Kpq, Kpq_rho, N_coeff
cbar = colorbar(im, cax=cb_ax)
fig.suptitle(
'Relative density of MW + LMC unbound particles {}'.format(title_name),
y=0.93)
cbar.set_label(r'$\Delta \rho$')
savefig(figname + '.pdf', bbox_inches='tight')
savefig(figname + '.png', bbox_inches='tight')
if __name__ == "__main__":
S_2T_V, T_2T_V = coefficients_smoothing.read_coeff_matrix(
'../data/MW/MW_lmc_unbound/mwlmc_unbound_BFE_2T_V_1e6_300_coeff_sample_0',
40, 20, 20, 20, 0, 40)
SS_2T_V, TT_2T_V, ST_2T_V = coefficients_smoothing.read_cov_elements(
'../data/MW/MW_lmc_unbound/mwlmc_unbound_BFE_2T_V_1e6_300_covmat_sample_0',
40, 20, 20, 20, 0, 40)
S_T_V, T_T_V = coefficients_smoothing.read_coeff_matrix(
'../data/MW/MW_lmc_unbound/mwlmc_unbound_BFE_T_V_1e6_300_coeff_sample_0',
40, 20, 20, 20, 0, 40)
SS_T_V, TT_T_V, ST_T_V = coefficients_smoothing.read_cov_elements(
'../data/MW/MW_lmc_unbound/mwlmc_unbound_BFE_T_V_1e6_300_covmat_sample_0',
40, 20, 20, 20, 0, 40)
S_T_2V, T_T_2V = coefficients_smoothing.read_coeff_matrix(
'../data/MW/MW_lmc_unbound/mwlmc_unbound_BFE_T_2V_1e6_300_coeff_sample_0',
40, 20, 20, 20, 0, 40)
SS_T_2V, TT_T_2V, ST_T_2V = coefficients_smoothing.read_cov_elements(
'../data/MW/MW_lmc_unbound/mwlmc_unbound_BFE_T_2V_1e6_300_covmat_sample_0',
40, 20, 20, 20, 0, 40)