def dummy_legus():
return mass, mass_err_lo, mass_err_hi, r_eff, r_eff_err_lo, r_eff_err_hi
funcs = [
dummy_legus, mru_d.get_mw_open_clusters,
mru_d.get_mw_ymc_krumholz_19_clusters
]
mass_total = np.concatenate([func()[0] for func in funcs])
mass_err_lo_total = np.concatenate([func()[1] for func in funcs])
mass_err_hi_total = np.concatenate([func()[2] for func in funcs])
r_eff_total = np.concatenate([func()[3] for func in funcs])
r_eff_err_lo_total = np.concatenate([func()[4] for func in funcs])
r_eff_err_hi_total = np.concatenate([func()[5] for func in funcs])
# Then we can do the fit
fit, fit_history = mru_mle.fit_mass_size_relation(
mass_total,
mass_err_lo_total,
mass_err_hi_total,
r_eff_total,
r_eff_err_lo_total,
r_eff_err_hi_total,
fit_mass_upper_limit=1e5,
)
mru.write_fit_results(fit_out_file, "LEGUS + MW", fit, fit_history, mass_total)
# finalize output file
fit_out_file.close()
r_eff[age_mask],
color,
zorder=zorder,
alpha=0.25,
)
mru_p.add_percentile_lines(
ax,
mass[age_mask],
r_eff[age_mask],
color=color,
percentiles=[50],
label_percents=False,
label_legend=f"{name.replace('--', '-')}, N={np.sum(age_mask)}",
)
mru_p.plot_best_fit_line(
ax,
fit,
1,
1e5,
color,
fill=False,
label="",
ls=":",
)
mru.write_fit_results(fit_out_file, name, fit, fit_history, mass[age_mask])
mru_p.format_mass_size_plot(ax, legend_fontsize=13)
fig.savefig(plot_name)
# finalize output file
fit_out_file.close()
funcs = [
dummy_legus,
mru_d.get_m31_open_clusters,
mru_d.get_m83_clusters,
]
mass_total = np.concatenate([func()[0] for func in funcs])
mass_err_lo_total = np.concatenate([func()[1] for func in funcs])
mass_err_hi_total = np.concatenate([func()[2] for func in funcs])
r_eff_total = np.concatenate([func()[3] for func in funcs])
r_eff_err_lo_total = np.concatenate([func()[4] for func in funcs])
r_eff_err_hi_total = np.concatenate([func()[5] for func in funcs])
# Then we can do the fit
fit, fit_history = mru_mle.fit_mass_size_relation(
mass_total,
mass_err_lo_total,
mass_err_hi_total,
r_eff_total,
r_eff_err_lo_total,
r_eff_err_hi_total,
fit_mass_upper_limit=1e5,
)
mru.write_fit_results(fit_out_file, "LEGUS + External Galaxies", fit,
fit_history, mass_total)
# finalize output file
fit_out_file.close()
fit, fit_history = mru_mle.fit_mass_size_relation(
mass,
mass_err_lo,
mass_err_hi,
r_eff,
r_eff_err_lo,
r_eff_err_hi,
fit_mass_upper_limit=1e5,
)
# then plot the dataset
fig, ax = bpl.subplots()
mru_p.plot_mass_size_dataset_scatter(
ax,
mass,
mass_err_lo,
mass_err_hi,
r_eff,
r_eff_err_lo,
r_eff_err_hi,
bpl.color_cycle[0],
)
mru_p.add_percentile_lines(ax, mass, r_eff)
mru_p.plot_best_fit_line(ax, fit, 1e2, 1e5, color=bpl.color_cycle[1])
mru_p.format_mass_size_plot(ax)
fig.savefig(plot_name)
mru.write_fit_results(fit_out_file, "1 Myr -- 1 Gyr", fit, fit_history, mass)
# finalize output file
fit_out_file.close()
mass_err_lo,
mass_err_hi,
r_eff,
r_eff_err_lo,
r_eff_err_hi,
fit_mass_upper_limit=1e5,
)
# then plot the dataset
fig, ax = bpl.subplots()
mru_p.plot_mass_size_dataset_scatter(
ax,
mass,
mass_err_lo,
mass_err_hi,
r_eff,
r_eff_err_lo,
r_eff_err_hi,
bpl.color_cycle[0],
)
mru_p.add_percentile_lines(ax, mass, r_eff)
mru_p.plot_best_fit_line(ax, fit, 1e2, 1e5, color=bpl.color_cycle[1])
mru_p.format_mass_size_plot(ax)
fig.savefig(plot_name)
mru.write_fit_results(fit_out_file, "Full LEGUS Sample", fit, fit_history,
mass)
# finalize output file
fit_out_file.close()