

In [1]:

    
%matplotlib inline
%config InlineBackend.figure_format = 'retina'



In [2]:

    
import numpy as np

from matplotlib import pyplot as plt



In [3]:

    
import seaborn as sns

sns.set_style("ticks")
sns.set_palette("colorblind")
sns.set_context("notebook")

Measuring $\chi_\mathrm{eff}$ for individual events

This .csv file contains the 90% credible interval widths of the marginal posterior of $\chi_\mathrm{eff}$ (chi_eff_90cl) for 500 systems, along with the corresponding simulated mass ratio $q$ (q_sim) and $\chi_\mathrm{eff}$ (chi_eff_sim).



In [4]:

    
chi_eff_constraints = np.genfromtxt('../data/chi_eff_q_measurements.csv', delimiter=',', names=True)



In [5]:

    
plt.scatter(chi_eff_constraints['q_sim'], chi_eff_constraints['chi_eff_sim'], c=chi_eff_constraints['chi_eff_90cl'],
            alpha=0.3, cmap='viridis_r', lw=0)

plt.xlabel('$q_\mathrm{sim}$')
plt.ylabel('$\chi_\mathrm{eff,sim}$')
cbar = plt.colorbar()
cbar.set_label('width of 90% credible level of $\chi_\mathrm{sim}}$')
cbar.set_alpha(1)
cbar.draw_all()

plt.savefig('../paper/plots/chi-eff-90cl.pdf', dpi=500, bbox_inches='tight')