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bns_detection_plot.py
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bns_detection_plot.py
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import sys
import numpy as np
import pandas as pd
import argparse
import matplotlib.pyplot as plt
import seaborn as sns
GW170817_chirp = 1.1977
GW170817_dist = 43
def get_options(argv=None):
'''
Get commandline options
'''
parser = argparse.ArgumentParser()
parser.add_argument('--trials_dir', required=True, help='Directory to store simulation results')
parser.add_argument('--obs_run', required=True, choices=['O4','O5'], help='Observing run')
args = parser.parse_args(args=argv)
return args
def getChirpMass(m1, m2):
m_chirp = (m1 * m2)**(3/5) / (m1 + m2)**(1/5)
return m_chirp
if __name__=='__main__':
argv = sys.argv[1:]
args = get_options(argv=argv)
trials_dir = args.trials_dir
obs_run = args.obs_run
df = pd.read_csv(f'{trials_dir}/trials_df.csv')
# Fig 1 - Number of BNS mergers detected by LVK
gw1 = df.groupby('trial_number')['gw1'].sum()
gw2 = df.groupby('trial_number')['gw2'].sum()
gw3 = df.groupby('trial_number')['gw3'].sum()
gw4 = df.groupby('trial_number')['gw4'].sum()
gw_mergers = gw1 + gw2 + gw3 + gw4
gw_mean = np.mean(gw_mergers)
gw_median = np.median(gw_mergers)
gw_5 = np.percentile(gw_mergers, 5)
gw_95 = np.percentile(gw_mergers, 95)
print('BNS mergers detected: ${', gw_median, "}_{-", gw_median - gw_5, "}^{+", gw_95 - gw_median, "}$")
bins = np.arange(0, np.max(gw_mergers))
plt.hist(gw_mergers, histtype='step', bins=bins)
plt.axvline(gw_mean, label = r'$\langle N_{{mergers}} \rangle = {:.1f}$'.format(gw_mean), c = 'black', linestyle='--')
plt.axvline(gw_5, label = r'$\langle N_{{mergers}} \rangle_{5} = %.1f$ ' % (gw_5), c = 'red', linestyle='dotted')
plt.axvline(gw_95, label = r'$\langle N_{{mergers}} \rangle_{95} = %.1f$ ' % (gw_95), c = 'red', linestyle='dotted')
plt.xlabel(r'Number of BNS mergers detected ($N_{mergers}$)', fontsize='x-large')
plt.ylabel('Count', fontsize='x-large')
#plt.yscale('log')
plt.tight_layout()
plt.legend()
plt.savefig(f'{trials_dir}/BNS_mergers_hist.pdf')
plt.show()
# Fig 2 - Chirp masses distribution
gw1_df = df[df['gw1'] == True]
gw2_df = df[df['gw2'] == True]
gw3_df = df[df['gw3'] == True]
gw4_df = df[df['gw4'] == True]
chirp_1 = getChirpMass(gw1_df['m1'],gw1_df['m2']).to_numpy()
chirp_2 = getChirpMass(gw2_df['m1'],gw2_df['m2']).to_numpy()
chirp_3 = getChirpMass(gw3_df['m1'],gw3_df['m2']).to_numpy()
chirp_4 = getChirpMass(gw4_df['m1'],gw4_df['m2']).to_numpy()
chirp_masses = np.concatenate((chirp_1, chirp_2, chirp_3, chirp_4))
bins = np.arange(min(chirp_masses) - 0.1, max(chirp_masses)+ 0.1, 0.01)
plt.hist(chirp_masses, histtype='step', bins = bins)
plt.axvline(np.mean(chirp_masses), label = r'$\langle M_{{chirp}} \rangle = %.1f {M}_{\odot }$ ' % (np.mean(chirp_masses)), c = 'black', linestyle='--')
plt.axvline(1.1977, label= r'GW170817 $M_{chirp} \sim {1.2}\,{M}_{\odot }$', c = 'red', linestyle='dotted')
plt.xlabel(r'$M_{chirp} ({M}_{\odot})$',fontsize='x-large')
plt.ylabel('Count', fontsize='x-large')
plt.legend()
plt.tight_layout()
plt.savefig(f'{trials_dir}/chirp_masses.pdf')
plt.show()
# Figure 3 - Inclination vs chirp mass
cos_theta1 = gw1_df['cos_theta']
cos_theta2 = gw2_df['cos_theta']
cos_theta3 = gw3_df['cos_theta']
cos_theta4 = gw4_df['cos_theta']
cos_theta = np.concatenate((cos_theta1, cos_theta2, cos_theta3, cos_theta4))
thetas = np.rad2deg(np.arccos(cos_theta))
omegas = np.minimum(thetas, 180 - thetas)
sns.kdeplot(chirp_masses, omegas , cmap="Reds", levels=15)
plt.xlabel(r'$M_{chirp} ({M}_{\odot})$', fontsize='x-large')
plt.ylabel(r'Inclination (degrees)',fontsize='x-large')
plt.tight_layout()
plt.savefig(f'{trials_dir}/chirp_inclination.pdf')
plt.show()
# Figure 4 - dist vs chirp mass
dist1 = gw1_df['dist']
dist2 = gw2_df['dist']
dist3 = gw3_df['dist']
dist4 = gw4_df['dist']
dist = np.concatenate((dist1, dist2, dist3, dist4))
sns.kdeplot(chirp_masses, dist, cmap="Reds", levels=15)
plt.scatter(GW170817_chirp, GW170817_dist, marker='*',c='black', label='GW170817')
plt.xlabel(r'$M_{chirp} ({M}_{\odot})$', fontsize='x-large')
plt.ylabel(r'Distance (Mpc)',fontsize='x-large')
plt.legend()
plt.tight_layout()
plt.savefig(f'{trials_dir}/chirp_distance.pdf')
plt.show()
# Figure 5 - ejecta mass histogram
no_mej = df.groupby('trial_number')
no_mej_fracs = []
for i in range(len(no_mej)):
temp = df[df['trial_number'] == i]
N = len(temp)
M = len(temp.loc[(temp['mej_wind']==0) & (temp['mej_dyn']==0)])
f = M/N * 100
no_mej_fracs.append(f)
print(r"%","of mergers producing zero ejecta ${", np.mean(no_mej_fracs), "}_{-", np.mean(no_mej_fracs) - np.percentile(no_mej_fracs, 5) ,"}^{+", np.percentile(no_mej_fracs, 95) - np.mean(no_mej_fracs), "}$" )
peak_u = df['peak_u'].to_numpy()
peak_g = df['peak_g'].to_numpy()
peak_r = df['peak_r'].to_numpy()
peak_i = df['peak_i'].to_numpy()
peak_z = df['peak_z'].to_numpy()
peak_y = df['peak_y'].to_numpy()
peak_u[peak_u==np.inf] = np.nan
peak_g[peak_g==np.inf] = np.nan
peak_r[peak_r==np.inf] = np.nan
peak_i[peak_i==np.inf] = np.nan
peak_z[peak_z==np.inf] = np.nan
peak_y[peak_y==np.inf] = np.nan
print(max(peak_u),max(peak_i), max(peak_y))
bins = np.arange(min(min(peak_u),min(peak_i), min(peak_z), min(peak_y)) - 1, max(max(peak_u),max(peak_g),max(peak_r),max(peak_i), max(peak_z), max(peak_y)) + 1, 0.5)
plt.hist(peak_u, label = 'LSST u', histtype='step', color='C1', lw=3, bins=bins)
#plt.hist(peak_g, label = 'LSST g', histtype='step', color='C2', lw=3, bins=bins)
#plt.hist(peak_r, label = 'LSST r', histtype='step', color='C3', lw=3, bins=bins)
plt.hist(peak_i, label = 'LSST i', histtype='step', color='C4', lw=3, bins=bins)
#plt.hist(peak_z, label = 'LSST z', histtype='step', color='C5', lw=3, bins=bins)
plt.hist(peak_y, label = 'LSST y', histtype='step', color='C6', lw=3, bins=bins)
plt.hist(peak_u, histtype='stepfilled', color='C1', alpha=0.3, bins=bins)
#plt.hist(peak_g, histtype='stepfilled', color='C2', alpha=0.5, bins=bins)
#plt.hist(peak_r, histtype='stepfilled', color='C3', alpha=0.3, bins=bins)
plt.hist(peak_i, histtype='stepfilled', color='C4', alpha=0.3, bins=bins)
#plt.hist(peak_z, histtype='stepfilled', color='C5', alpha=0.5, bins=bins)
plt.hist(peak_y, histtype='stepfilled', color='C6', alpha=0.3, bins=bins)
plt.xlabel("Peak mag (AB)", fontsize='x-large')
plt.ylabel("Count",fontsize='x-large')
plt.yscale('log')
plt.xlim(12, 40)
plt.legend()
plt.tight_layout()
plt.savefig(f'{trials_dir}/peak_mags.pdf')
plt.show()