def get_fit_param(param_fname):
fit_fname = get_fit_limits_fname(param_fname)
print(fit_fname)
fit_param = load_fit_limits(fit_fname)
fit_mi = 10**fit_param['mi']
fit_rd = fit_param['rd'] * 1e-3
return fit_mi, fit_rd
def get_survival_rate(param_fname, fit, mass_bins):
param = dtk.Param(param_fname)
cluster_loc = param.get_string('cluster_loc')
cluster_load_num = param.get_int('cluster_load_num')
cluster_data = load_clusters(cluster_loc)
# getting the Ngal with and without disruption
model_fit_fname = "figs/"+param_fname+"/calc_likelihood_bounds.py/grid_fit_param.txt"
model_fit = load_fit_limits(model_fit_fname)
return get_survival_rate_cluster(cluster_data, cluster_load_num, mass_bins, model_fit, )
def plot_radii_effects(param_files, radii, params, xlabel=None, plot_reference=None):
parameter_labels = {'mi': r"M$_{infall}$"+"\n"+r"[log10 h$^{-1}$ M$_\odot$]",
'rd': r"R$_{disrupt}$"+"\n"+r"[h$^{-1}$ kpc]",
'rm': r"R$_{merge}$"+"\n"+r"[h$^{-1}$ Mpc]",
'x2': r"$\chi^2_{reduced}$",}
param_fits = []
for pfile in param_files:
print(pfile)
fit = load_fit_limits(pfile)
param_fits.append(fit)
size = len(params)
gs = gridspec.GridSpec(size, 1)
sharex_ax = None
fig = plt.figure()
for i, param in enumerate(params):
if sharex_ax is None:
ax = plt.subplot(gs[i, 0])
else:
ax = plt.subplot(gs[i, 0], sharex=sharex_ax)
y, y_err_lower, y_err_upper = get_param_line(param_fits, param, param!='x2')
ax.plot(radii, y)
if param != 'x2':
print(param)
ax.fill_between(radii, y-y_err_lower, y+y_err_upper, alpha=0.3)
if plot_reference is not None:
ax.axhline(y[plot_reference], ls='--', color='k')
ax.axhline(y[plot_reference]-y_err_lower[plot_reference], ls=':', color='k')
ax.axhline(y[plot_reference]+y_err_upper[plot_reference], ls=':', color='k')
ax.set_ylabel(parameter_labels[param])
if param == 'x2' :
if xlabel is not None: #x2 is always last
ax.set_xlabel(xlabel)
else:
ax.xaxis.set_ticklabels([])
gs.tight_layout(fig)
def load_fit_limits_set(param_fnames):
return [ load_fit_limits("figs/"+param_fname+"/calc_likelihood_bounds.py/grid_fit_param.txt") for param_fname in param_fnames]
def plot_miscentering_effects(fname_pattern, distances_str, distances_val):
param_files_rd = [
fname_pattern.replace("${displacement}", distance)
for distance in distances_str
]
# [print(pfd) for pfd in param_files_rd]
rds = []
rds_ue = []
rds_le = []
mis = []
mis_ue = []
mis_le = []
chi2 = []
for pfd in param_files_rd:
fit = load_fit_limits(pfd)
print(fit)
mis.append(fit['mi'])
mis_ue.append(fit['mi_limits'][0])
mis_le.append(fit['mi_limits'][1])
rds.append(fit['rd'])
rds_ue.append(fit['rd_limits'][0])
rds_le.append(fit['rd_limits'][1])
chi2.append(fit['x2'])
# Convert Mpc to kpc
distances_val *= 1000
distances_val += 1
# plt.figure()
# plt.plot(distances_val+0.01, mis, '-o')
# plt.fill_between(distances_val+0.01, mis_le, mis_ue, alpha=0.3)
# plt.xlabel('Center Displacement')
# plt.ylabel('M$_{infall}$ [Msun/h]')
# plt.xscale('log')
# plt.figure()
# plt.plot(distances_val+0.01, rds, '-o')
# plt.fill_between(distances_val+0.01, rds_le, rds_ue, alpha=0.3)
# plt.xlabel('Center Displacement')
# plt.ylabel('R$_{disrupt}$ [kpc/h]')
# plt.xscale('log')
gs = gridspec.GridSpec(3, 1)
sharex_ax = None
fig = plt.figure(figsize=(6, 10))
ax1 = plt.subplot(gs[0, 0])
ax1.plot(distances_val, mis, '-o')
ax1.fill_between(distances_val, mis_le, mis_ue, alpha=0.3)
ax1.set_xscale('log')
ax1.set_ylabel("M$_{infall}$" + "\n" + r"[log10 h$^{-1}$ M$_\odot$]")
ax1.xaxis.set_ticklabels([])
# ax1.plot(distances_val+0.01, mis, '-o')
# ax1.fill_between(distances_val+0.01, mis_le, mis_ue, alpha=0.3)
ax2 = plt.subplot(gs[1, 0])
ax2.plot(distances_val, rds, '-o')
ax2.fill_between(distances_val, rds_le, rds_ue, alpha=0.3)
ax2.set_ylabel("R$_{disrupt}$" + "\n" + r"[h$^{-1}$ kpc]")
ax2.set_xscale('log')
ax2.xaxis.set_ticklabels([])
ax3 = plt.subplot(gs[2, 0])
ax3.plot(distances_val, chi2, '-o')
ax3.set_ylabel("$\widetilde\chi^2$")
ax3.set_xscale('log')
ax3.set_yscale('log')
# ax3.fill_between(distances_val+0.01, rds_le, rds_ue, alpha=0.3)
ax3.set_xlabel('Center Displacement + 1 [h$^{-1}$ kpc]')
gs.tight_layout(fig)