x = radius * np.cos(phi) * np.sin(theta)
y = radius * np.sin(phi) * np.sin(theta)
z = radius * np.cos(theta)
xyz = np.vstack((x, y, z)).T
pos_col_new += xyz
pos_col_new[comm2] -= xyz[comm2]
pos_col_new[pos_col_new >= Lbox] -= Lbox
pos_col_new[pos_col_new <= 0.] += Lbox
w_col_new = np.ones(pos_col_new.shape[0], dtype=pos_col_new.dtype)
plt.figure(1, figsize=(8, 6))
# random draw
#Rat_hodtrue_mean, Rat_hodtrue_err, Corr_mean_hod, Corr_err_hod, Corr_mean_true, Corr_err_true, bin_centers = get_jack_corr(pos_col,w_col,pos_col_new,w_col_new,Lbox)
Rat_hodtrue_mean, Rat_hodtrue_err, Corr_mean_hod, Corr_err_hod, Corr_mean_true, Corr_err_true, bin_centers = get_jack_corr(
pos_col, w_col, pos_vmax, w_vmax, Lbox)
plt.plot(bin_centers, np.ones(len(bin_centers)), 'k--')
plt.errorbar(bin_centers * 1.05,
Rat_hodtrue_mean,
yerr=Rat_hodtrue_err,
color='dimgray',
ls='-',
label=r'$V_{\rm max}$',
alpha=1.,
fmt='o',
capsize=4)
Rat_hodtrue_mean, Rat_hodtrue_err, Corr_mean_hod, Corr_err_hod, Corr_mean_true, Corr_err_true, bin_centers = get_jack_corr(
pos_col, w_col, pos_vpeak, w_vpeak, Lbox)
plt.errorbar(bin_centers,
Rat_hodtrue_mean,
w_high = np.ones(cum_high, dtype=xyz_high.dtype)
return xyz_low, xyz_high, w_low, w_high
xyz_col_low, xyz_col_high, w_col_low, w_col_high = get_env_pos(
sub_id_col, SubhaloGrNr_fp, SubhaloPos_fp, GroupEnv_fp, inds_halo_fp,
Group_M_Mean200_fp)
xyz_sfg_low, xyz_sfg_high, w_sfg_low, w_sfg_high = get_env_pos(
sub_id_sfg, SubhaloGrNr_fp, SubhaloPos_fp, GroupEnv_fp, inds_halo_fp,
Group_M_Mean200_fp)
for i in range(2):
env_type = env_types[i]
if i == 0:
Rat_colsfg_mean, Rat_colsfg_err, Corr_mean_col, Corr_err_col, Corr_mean_sfg, Corr_err_sfg, bin_centers = get_jack_corr(
xyz_sfg_low, w_sfg_low, xyz_col_low, w_col_low, Lbox)
else:
Rat_colsfg_mean, Rat_colsfg_err, Corr_mean_col, Corr_err_col, Corr_mean_sfg, Corr_err_sfg, bin_centers = get_jack_corr(
xyz_sfg_high, w_sfg_high, xyz_col_high, w_col_high, Lbox)
plt.errorbar(bin_centers,
Rat_colsfg_mean,
yerr=Rat_colsfg_err,
ls='-',
label=' '.join(env_type.split('_')),
alpha=1.,
fmt='o',
capsize=4)
np.save(
"../data/rat_colsfg" + env_type + snap_dir + selection + "_mean.npy",
Rat_colsfg_mean)
def plot_ratio(xyz_true, w_true, xyz_hod, w_hod, Lbox, color, label):
Rat_hodtrue_mean, Rat_hodtrue_err, Corr_mean_hod, Corr_err_hod, Corr_mean_true, Corr_err_true, bin_centers = get_jack_corr(
xyz_true, w_true, xyz_hod, w_hod, Lbox)
if color == 'black':
plt.plot(bin_centers,
Rat_hodtrue_mean,
color=color,
ls='-',
label=label)
plt.fill_between(bin_centers,
Rat_hodtrue_mean - Rat_hodtrue_err,
Rat_hodtrue_mean + Rat_hodtrue_err,
color=color,
alpha=0.5)
else:
plt.errorbar(bin_centers,
Rat_hodtrue_mean,
yerr=Rat_hodtrue_err,
color=color,
ls='-',
label=label,
alpha=0.5,
fmt='o',
capsize=4)
mean_val = np.mean(Rat_hodtrue_mean[np.isfinite(Rat_hodtrue_mean)
& (bin_centers > 2.)])
print('mean_val = ', mean_val)
x = np.linspace(2, 10, 3)
plt.plot(bin_centers, np.ones(len(bin_centers)), 'k--')
plt.plot(x, np.ones(len(x)) * mean_val, color=color, ls='--', lw=1.5)
phi = np.random.rand(N_gal) * 2 * np.pi
radius = choices(population, weights, k=N_gal)
x = radius * np.cos(phi) * np.sin(theta)
y = radius * np.sin(phi) * np.sin(theta)
z = radius * np.cos(theta)
xyz = np.vstack((x, y, z)).T
pos_col_new += xyz
pos_col_new[comm2] -= xyz[comm2]
pos_col_new[pos_col_new >= Lbox] -= Lbox
pos_col_new[pos_col_new <= 0.] += Lbox
w_col = np.ones(pos_col.shape[0], dtype=pos_col.dtype)
w_col_new = np.ones(pos_col_new.shape[0], dtype=pos_col_new.dtype)
Rat_hodtrue_mean, Rat_hodtrue_err, Corr_mean_hod, Corr_err_hod, Corr_mean_true, Corr_err_true, bin_centers = get_jack_corr(
pos_col, w_col, pos_col_new, w_col_new, Lbox)
plt.errorbar(bin_centers,
Rat_hodtrue_mean,
yerr=Rat_hodtrue_err,
color='dodgerblue',
ls='-',
label='color-selected',
alpha=0.5,
fmt='o',
capsize=4)
plt.xlabel(r'$r [{\rm Mpc}/h]$')
plt.ylabel(r'$\xi(r)_{\rm HOD}/\xi(r)_{\rm TNG300}$')
plt.xscale('log')
plt.legend()
plt.ylim([0.6, 1.4])
def plot_ratio(xyz_true,
w_true,
xyz_hod,
w_hod,
Lbox,
color,
label,
offset=1.,
rmin=2.):
Rat_hodtrue_mean, Rat_hodtrue_err, Corr_mean_hod, Corr_err_hod, Corr_mean_true, Corr_err_true, bin_centers = get_jack_corr(
xyz_true, w_true, xyz_hod, w_hod, Lbox)
if color == 'black':
plt.plot(bin_centers,
Rat_hodtrue_mean,
color=color,
ls='-',
label=label)
plt.fill_between(bin_centers,
Rat_hodtrue_mean - Rat_hodtrue_err,
Rat_hodtrue_mean + Rat_hodtrue_err,
color=color,
alpha=.1)
else:
plt.errorbar(bin_centers * offset,
Rat_hodtrue_mean,
yerr=Rat_hodtrue_err,
color=color,
ls='-',
label=label,
alpha=1.,
fmt='o',
capsize=4)
mean_val = np.mean(Rat_hodtrue_mean[np.isfinite(Rat_hodtrue_mean)
& (bin_centers > rmin)])
print('mean_val = ', mean_val)
x = np.linspace(rmin, 10, 3)
line = np.linspace(0., 20, 3)
plt.plot(line, np.ones(len(line)), 'k--')
plt.xlim([0.095, 12])
plt.plot(x, np.ones(len(x)) * mean_val, color=color, ls='--', lw=1.5)
Group_M_Mean200_dm, hist_norm, bin_edges)
plt.plot(10.**bin_cents,
hist_col,
color='dodgerblue',
ls='--',
label='HOD')
plt.xscale('log')
plt.yscale('log')
plt.legend()
plt.show()
# Correlation function
# get the ratios
Rat_col_hodtrue_mean, Rat_col_hodtrue_err, Corr_mean_hod, Corr_err_hod, Corr_mean_true, Corr_err_true, bin_centers = get_jack_corr(
xyz_col_true, w_col_true, xyz_col_hod, w_col_hod, Lbox)
Rat_all_hodtrue_mean, Rat_all_hodtrue_err, Corr_mean_hod, Corr_err_hod, Corr_mean_true, Corr_err_true, bin_centers = get_jack_corr(
xyz_all_true, w_all_true, xyz_all_hod, w_all_hod, Lbox)
Rat_sfg_hodtrue_mean, Rat_sfg_hodtrue_err, Corr_mean_hod, Corr_err_hod, Corr_mean_true, Corr_err_true, bin_centers = get_jack_corr(
xyz_sfg_true, w_sfg_true, xyz_sfg_hod, w_sfg_hod, Lbox)
np.save("data/Rat_col_hodtrue_mean.npy", Rat_col_hodtrue_mean)
np.save("data/Rat_col_hodtrue_err.npy", Rat_col_hodtrue_err)
np.save("data/Rat_sfg_hodtrue_mean.npy", Rat_sfg_hodtrue_mean)
np.save("data/Rat_sfg_hodtrue_err.npy", Rat_sfg_hodtrue_err)
np.save("data/Rat_all_hodtrue_mean.npy", Rat_all_hodtrue_mean)
np.save("data/Rat_all_hodtrue_err.npy", Rat_all_hodtrue_err)
np.save("data/bin_centers.npy", bin_centers)
# END HERE