Verified Commit 25be03e2 authored by Daniel Williams's avatar Daniel Williams
Browse files

Added draft of the data release.

parents

JSD_calculation.py

0 → 100644
+605 −0
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#!/usr/bin/env python

# coding: utf-8



import numpy as np

import matplotlib.pyplot as plt

import bilby





num_samples=100





m1_BBH=np.zeros((num_samples,10000))

m2_BBH=np.zeros((num_samples,10000))

t0_BBH=np.zeros((num_samples,10000))

m1_1=np.zeros((num_samples,10000))

m2_1=np.zeros((num_samples,10000))

t0_1=np.zeros((num_samples,10000))

m1_4=np.zeros((num_samples,10000))

m2_4=np.zeros((num_samples,10000))

t0_4=np.zeros((num_samples,10000))

m1_8=np.zeros((num_samples,10000))

m2_8=np.zeros((num_samples,10000))

t0_8=np.zeros((num_samples,10000))

m1_16=np.zeros((num_samples,10000))

m2_16=np.zeros((num_samples,10000))

t0_16=np.zeros((num_samples,10000))



m1_recover_1=np.zeros((num_samples,10000))

m2_recover_1=np.zeros((num_samples,10000))

t0_recover_1=np.zeros((num_samples,10000))

m1_recover_4=np.zeros((num_samples,10000))

m2_recover_4=np.zeros((num_samples,10000))

t0_recover_4=np.zeros((num_samples,10000))

m1_recover_8=np.zeros((num_samples,10000))

m2_recover_8=np.zeros((num_samples,10000))

t0_recover_8=np.zeros((num_samples,10000))

m1_recover_16=np.zeros((num_samples,10000))

m2_recover_16=np.zeros((num_samples,10000))

t0_recover_16=np.zeros((num_samples,10000))





for i in range(num_samples):

    m1_BBH[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/BBH/BBH_results/samples/vitamin_sample_timeseries-{i}_parameter-0.txt')  

    m2_BBH[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/BBH/BBH_results/samples/vitamin_sample_timeseries-{i}_parameter-1.txt')  

    t0_BBH[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/BBH/BBH_results/samples/vitamin_sample_timeseries-{i}_parameter-3.txt')  

for i in range(num_samples):

    m1_1[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m1/encounter_results/samples/vitamin_sample_timeseries-{i}_parameter-0.txt')  

    m2_1[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m1/encounter_results/samples/vitamin_sample_timeseries-{i}_parameter-1.txt')  

    t0_1[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m1/encounter_results/samples/vitamin_sample_timeseries-{i}_parameter-3.txt')  

for i in range(num_samples):

    m1_4[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m4/encounter_results/20211013/samples/vitamin_sample_timeseries-{i}_parameter-0.txt')  

    m2_4[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m4/encounter_results/20211013/samples/vitamin_sample_timeseries-{i}_parameter-1.txt')  

    t0_4[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m4/encounter_results/20211013/samples/vitamin_sample_timeseries-{i}_parameter-3.txt')  

for i in range(num_samples):

    m1_8[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m8/encounter_results/20211013/samples/vitamin_sample_timeseries-{i}_parameter-0.txt')  

    m2_8[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m8/encounter_results/20211013/samples/vitamin_sample_timeseries-{i}_parameter-1.txt')  

    t0_8[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m8/encounter_results/20211013/samples/vitamin_sample_timeseries-{i}_parameter-3.txt')  

for i in range(num_samples):

    m1_16[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m16/encounter_results/20211013/samples/vitamin_sample_timeseries-{i}_parameter-0.txt')  

    m2_16[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m16/encounter_results/20211013/samples/vitamin_sample_timeseries-{i}_parameter-1.txt')  

    t0_16[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter/m16/encounter_results/20211013/samples/vitamin_sample_timeseries-{i}_parameter-3.txt')  





for i in range(num_samples):

    m1_recover_1[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m1/results/samples/vitamin_sample_timeseries-{i}_parameter-0.txt')  

    m2_recover_1[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m1/results/samples/vitamin_sample_timeseries-{i}_parameter-1.txt')  

    t0_recover_1[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m1/results/samples/vitamin_sample_timeseries-{i}_parameter-3.txt')  

for i in range(num_samples):

    m1_recover_4[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m4/results/samples/vitamin_sample_timeseries-{i}_parameter-0.txt')  

    m2_recover_4[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m4/results/samples/vitamin_sample_timeseries-{i}_parameter-1.txt')  

    t0_recover_4[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m4/results/samples/vitamin_sample_timeseries-{i}_parameter-3.txt')  

for i in range(num_samples):

    m1_recover_8[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m8/results/samples/vitamin_sample_timeseries-{i}_parameter-0.txt')  

    m2_recover_8[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m8/results/samples/vitamin_sample_timeseries-{i}_parameter-1.txt')  

    t0_recover_8[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m8/results/samples/vitamin_sample_timeseries-{i}_parameter-3.txt')  

for i in range(num_samples):

    m1_recover_16[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m16/results/samples/vitamin_sample_timeseries-{i}_parameter-0.txt')  

    m2_recover_16[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m16/results/samples/vitamin_sample_timeseries-{i}_parameter-1.txt')  

    t0_recover_16[i] = np.loadtxt(f'/mnt/d/Colony/vitamin/encounter-like_BBH/m16/results/samples/vitamin_sample_timeseries-{i}_parameter-3.txt')  





jsd_m1_BBH=np.zeros((int(num_samples/2)))

jsd_m2_BBH=np.zeros((int(num_samples/2)))

jsd_t0_BBH=np.zeros((int(num_samples/2)))

jsd_m1_ref_1=np.zeros((num_samples))

jsd_m2_ref_1=np.zeros((num_samples))

jsd_t0_ref_1=np.zeros((num_samples))

jsd_m1_ref_4=np.zeros((num_samples))

jsd_m2_ref_4=np.zeros((num_samples))

jsd_t0_ref_4=np.zeros((num_samples))

jsd_m1_ref_8=np.zeros((num_samples))

jsd_m2_ref_8=np.zeros((num_samples))

jsd_t0_ref_8=np.zeros((num_samples))

jsd_m1_ref_16=np.zeros((num_samples))

jsd_m2_ref_16=np.zeros((num_samples))

jsd_t0_ref_16=np.zeros((num_samples))





jsd_m1_recover_1=np.zeros((num_samples))

jsd_m2_recover_1=np.zeros((num_samples))

jsd_t0_recover_1=np.zeros((num_samples))

jsd_m1_recover_4=np.zeros((num_samples))

jsd_m2_recover_4=np.zeros((num_samples))

jsd_t0_recover_4=np.zeros((num_samples))

jsd_m1_recover_8=np.zeros((num_samples))

jsd_m2_recover_8=np.zeros((num_samples))

jsd_t0_recover_8=np.zeros((num_samples))

jsd_m1_recover_16=np.zeros((num_samples))

jsd_m2_recover_16=np.zeros((num_samples))

jsd_t0_recover_16=np.zeros((num_samples))





# # JSD calculation function



from scipy.spatial import distance

from scipy.stats import gaussian_kde

from collections import namedtuple





def calc_median_error(jsvalues, quantiles=(0.16, 0.84)):



    quants_to_compute = np.array([quantiles[0], 0.5, quantiles[1]])

    quants = np.percentile(jsvalues, quants_to_compute * 100)

    summary = namedtuple("summary", ["median", "lower", "upper"])

    summary.median = quants[1]

    summary.plus = quants[2] - summary.median

    summary.minus = summary.median - quants[0]



    return summary





def calculate_js(samplesA, samplesB, ntests=100, xsteps=100):

    

    js_array = np.zeros(ntests)



    for j in range(ntests):

        nsamples = min([len(samplesA), len(samplesB)])

        A = np.random.choice(samplesA, size=nsamples, replace=False)

        B = np.random.choice(samplesB, size=nsamples, replace=False)

        xmin = np.min([np.min(A), np.min(B)])

        xmax = np.max([np.max(A), np.max(B)])

        x = np.linspace(xmin, xmax, xsteps)

        A_pdf = gaussian_kde(A)(x)

        B_pdf = gaussian_kde(B)(x)

        js_array[j] = np.nan_to_num(np.power(distance.jensenshannon(A_pdf, B_pdf), 2))



#    return calc_median_error(js_array)

    return js_array





for i in range(int(num_samples/2)):

    jsd_t0_BBH[i] = calculate_js(t0_BBH[2*i],t0_BBH[2*i+1]).mean()

    jsd_m1_BBH[i] = calculate_js(m1_BBH[2*i],m1_BBH[2*i+1]).mean()

    jsd_m2_BBH[i] = calculate_js(m2_BBH[2*i],m2_BBH[2*i+1]).mean()

for i in range(num_samples):

    jsd_m1_ref_1[i] = calculate_js(m1_BBH[i],m1_1[i]).mean()

    jsd_m2_ref_1[i] = calculate_js(m2_BBH[i],m2_1[i]).mean()

    jsd_t0_ref_1[i] = calculate_js(t0_BBH[i],t0_1[i]).mean()

for i in range(num_samples):

    jsd_m1_ref_4[i] = calculate_js(m1_BBH[i],m1_4[i]).mean()

    jsd_m2_ref_4[i] = calculate_js(m2_BBH[i],m2_4[i]).mean()

    jsd_t0_ref_4[i] = calculate_js(t0_BBH[i],t0_4[i]).mean()

for i in range(num_samples):

    jsd_m1_ref_8[i] = calculate_js(m1_BBH[i],m1_8[i]).mean()

    jsd_m2_ref_8[i] = calculate_js(m2_BBH[i],m2_8[i]).mean()

    jsd_t0_ref_8[i] = calculate_js(t0_BBH[i],t0_8[i]).mean()

for i in range(num_samples):

    jsd_m1_ref_16[i] = calculate_js(m1_BBH[i],m1_16[i]).mean()

    jsd_m2_ref_16[i] = calculate_js(m2_BBH[i],m2_16[i]).mean()

    jsd_t0_ref_16[i] = calculate_js(t0_BBH[i],t0_16[i]).mean()





for i in range(num_samples):

    jsd_m1_recover_1[i] = calculate_js(m1_1[i],m1_recover_1[i]).mean()

    jsd_m2_recover_1[i] = calculate_js(m2_1[i],m2_recover_1[i]).mean()

    jsd_t0_recover_1[i] = calculate_js(t0_1[i],t0_recover_1[i]).mean()

for i in range(num_samples):

    jsd_m1_recover_4[i] = calculate_js(m1_4[i],m1_recover_4[i]).mean()

    jsd_m2_recover_4[i] = calculate_js(m2_4[i],m2_recover_4[i]).mean()

    jsd_t0_recover_4[i] = calculate_js(t0_4[i],t0_recover_4[i]).mean()

for i in range(num_samples):

    jsd_m1_recover_8[i] = calculate_js(m1_8[i],m1_recover_8[i]).mean()

    jsd_m2_recover_8[i] = calculate_js(m2_8[i],m2_recover_8[i]).mean()

    jsd_t0_recover_8[i] = calculate_js(t0_8[i],t0_recover_8[i]).mean()

for i in range(num_samples):

    jsd_m1_recover_16[i] = calculate_js(m1_16[i],m1_recover_16[i]).mean()

    jsd_m2_recover_16[i] = calculate_js(m2_16[i],m2_recover_16[i]).mean()

    jsd_t0_recover_16[i] = calculate_js(t0_16[i],t0_recover_16[i]).mean()





# # histogram weights





weights = np.ones_like(jsd_m1_ref_1)/float(len(jsd_m1_1))

weights_BBH = np.ones_like(jsd_m1_BBH)/float(len(jsd_m1_BBH))



font = {'family': 'Times New Roman', 'weight': 'normal', 'size': 13}





# # 1. JSD of m1



# ## 1.1 BH encounter & BBH reference





def max_jsd(a,b,c,d,e):



    max_jsd=np.max([np.max(a),np.max(b),np.max(c),np.max(d),np.max(e)])

    

    return max_jsd



max_jsd_m1_ref=0.75

max_jsd_m2_ref=0.75

max_jsd_t0_ref=0.75

max_jsd_m1_inj=0.75

max_jsd_m2_inj=0.75

max_jsd_t0_inj=0.75





bins=10



font = {'family': 'Times New Roman', 'weight': 'normal', 'size': 20}



fig,ax=plt.subplots(4,1,figsize=(7,6),sharex=True)

ax[0].hist(jsd_m1_1,weights=weights,range=(0,max_jsd_m1_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=1')

ax[1].hist(jsd_m1_4,weights=weights,range=(0,max_jsd_m1_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=4')

ax[2].hist(jsd_m1_8,weights=weights,range=(0,max_jsd_m1_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=8')

ax[3].hist(jsd_m1_16,weights=weights,range=(0,max_jsd_m1_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=16')

ax[0].hist(jsd_m1_BBH,weights=weights_BBH,range=(0,max_jsd_m1_ref),bins=bins,alpha=0.7,color='grey')

ax[1].hist(jsd_m1_BBH,weights=weights_BBH,range=(0,max_jsd_m1_ref),bins=bins,alpha=0.7,color='grey')

ax[2].hist(jsd_m1_BBH,weights=weights_BBH,range=(0,max_jsd_m1_ref),bins=bins,alpha=0.7,color='grey')

ax[3].hist(jsd_m1_BBH,weights=weights_BBH,range=(0,max_jsd_m1_ref),bins=bins,alpha=0.7,color='grey')

#ax[0]=plt.axvline(0.121,linestyle='dashdot',color='black')

#ax[1]=plt.axvline(0.121,linestyle='dashdot',color='black')



ax[3].set_xlabel('JSD of m1',fontdict=font)

#ax[3].set_ylabel('Rate',fontdict=font)

#ax[2].set_ylabel('Fraction of injections',fontdict=font)

#ax[1].set_ylabel('Rate',fontdict=font)

#ax[0].set_ylabel('Rate',fontdict=font)

ax[0].legend(prop={'size':18})

ax[1].legend(prop={'size':18})

ax[2].legend(prop={'size':18})

ax[3].legend(prop={'size':18})

ax[0].tick_params(labelsize=14)

ax[1].tick_params(labelsize=14)

ax[2].tick_params(labelsize=14)

ax[3].tick_params(labelsize=14)

ax[0].axvline(0.121,linestyle='dashdot',color='black')

ax[1].axvline(0.121,linestyle='dashdot',color='black')

ax[2].axvline(0.121,linestyle='dashdot',color='black')

ax[3].axvline(0.121,linestyle='dashdot',color='black')

#fig.text(0.5, 0, 'JSD of m1', ha='center',fontdict=font)

fig.text(0, 0.5, 'Fraction of injections', va='center', rotation='vertical',fontdict=font)

fig.savefig('m1_be_ref.pdf')





m1_be_1_90=np.percentile(jsd_m1_1,10)

m1_be_1_90



m1_be_4_90=np.percentile(jsd_m1_4,10)

m1_be_4_90



m1_be_8_90=np.percentile(jsd_m1_8,10)

m1_be_8_90



m1_be_16_90=np.percentile(jsd_m1_16,10)

m1_be_16_90



m1_BBH_90=np.percentile(jsd_m1_BBH,95)

m1_BBH_90



m1_bem1_90=np.sum(jsd_m1_1>=m1_BBH_90)     

m1_bem1_90



m1_bem4_90=np.sum(jsd_m1_4>=m1_BBH_90)     

m1_bem4_90



m1_bem8_90=np.sum(jsd_m1_8>=m1_BBH_90)     

m1_bem8_90



m1_bem16_90=np.sum(jsd_m1_16>=m1_BBH_90)     

m1_bem16_90





# ## 1.2 BH encounter & injected BBH 



fig,ax=plt.subplots(4,1,figsize=(7,6),sharex=True)

ax[0].hist(jsd_m1_recover_1,weights=weights,range=(0,max_jsd_m1_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=1')

ax[1].hist(jsd_m1_recover_4,weights=weights,range=(0,max_jsd_m1_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=4')

ax[2].hist(jsd_m1_recover_8,weights=weights,range=(0,max_jsd_m1_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=8')

ax[3].hist(jsd_m1_recover_16,weights=weights,range=(0,max_jsd_m1_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=16')

ax[0].hist(jsd_m1_BBH,weights=weights_BBH,range=(0,max_jsd_m1_inj),bins=bins,alpha=0.7,color='grey')

ax[1].hist(jsd_m1_BBH,weights=weights_BBH,range=(0,max_jsd_m1_inj),bins=bins,alpha=0.7,color='grey')

ax[2].hist(jsd_m1_BBH,weights=weights_BBH,range=(0,max_jsd_m1_inj),bins=bins,alpha=0.7,color='grey')

ax[3].hist(jsd_m1_BBH,weights=weights_BBH,range=(0,max_jsd_m1_inj),bins=bins,alpha=0.7,color='grey')

#ax[0]=plt.axvline(0.121,linestyle='dashdot',color='black')

#ax[1]=plt.axvline(0.121,linestyle='dashdot',color='black')



ax[3].set_xlabel('JSD of m1',fontdict=font)

#ax[3].set_ylabel('Rate',fontdict=font)

#ax[2].set_ylabel('Rate',fontdict=font)

#ax[1].set_ylabel('Rate',fontdict=font)

#ax[0].set_ylabel('Rate',fontdict=font)

ax[0].legend(prop={'size':18})

ax[1].legend(prop={'size':18})

ax[2].legend(prop={'size':18})

ax[3].legend(prop={'size':18})

ax[0].tick_params(labelsize=14)

ax[1].tick_params(labelsize=14)

ax[2].tick_params(labelsize=14)

ax[3].tick_params(labelsize=14)

ax[0].axvline(0.121,linestyle='dashdot',color='black')

ax[1].axvline(0.121,linestyle='dashdot',color='black')

ax[2].axvline(0.121,linestyle='dashdot',color='black')

ax[3].axvline(0.121,linestyle='dashdot',color='black')

#fig.text(0.5, 0, 'JSD of m1', ha='center',fontdict=font)

fig.text(0, 0.5, 'Fraction of injections', va='center', rotation='vertical',fontdict=font)



fig.savefig('m1_be_inj.pdf')



m1_eb_1_90=np.percentile(jsd_eb_m1_1,10)

m1_eb_1_90



m1_eb_4_90=np.percentile(jsd_eb_m1_4,10)

m1_eb_4_90



m1_eb_8_90=np.percentile(jsd_eb_m1_8,10)

m1_eb_8_90



m1_eb_16_90=np.percentile(jsd_eb_m1_16,10)

m1_eb_16_90



m1_BBH_90=np.percentile(jsd_m1_BBH,90)

m1_BBH_90



m1_ebm1_90=np.sum(jsd_eb_m1_1>=m1_BBH_90)     

m1_ebm1_90



m1_ebm4_90=np.sum(jsd_eb_m1_4>=m1_BBH_90)     

m1_ebm4_90



m1_ebm8_90=np.sum(jsd_eb_m1_8>=m1_BBH_90)     

m1_ebm8_90



m1_ebm16_90=np.sum(jsd_eb_m1_16>=m1_BBH_90)     

m1_ebm16_90





# # 2. JSD of m2



# ## 2.1 BH encounter & BBH reference





fig,ax=plt.subplots(4,1,figsize=(7,6),sharex=True)

ax[0].hist(jsd_m2_1,weights=weights,range=(0,max_jsd_m2_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=1')

ax[1].hist(jsd_m2_4,weights=weights,range=(0,max_jsd_m2_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=4')

ax[2].hist(jsd_m2_8,weights=weights,range=(0,max_jsd_m2_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=8')

ax[3].hist(jsd_m2_16,weights=weights,range=(0,max_jsd_m2_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=16')

ax[0].hist(jsd_m2_BBH,weights=weights_BBH,range=(0,max_jsd_m2_ref),bins=bins,alpha=0.7,color='grey')

ax[1].hist(jsd_m2_BBH,weights=weights_BBH,range=(0,max_jsd_m2_ref),bins=bins,alpha=0.7,color='grey')

ax[2].hist(jsd_m2_BBH,weights=weights_BBH,range=(0,max_jsd_m2_ref),bins=bins,alpha=0.7,color='grey')

ax[3].hist(jsd_m2_BBH,weights=weights_BBH,range=(0,max_jsd_m2_ref),bins=bins,alpha=0.7,color='grey')

#ax[0]=plt.axvline(0.121,linestyle='dashdot',color='black')

#ax[1]=plt.axvline(0.121,linestyle='dashdot',color='black')



ax[3].set_xlabel('JSD of m2',fontdict=font)

#ax[3].set_ylabel('Rate',fontdict=font)

#ax[2].set_ylabel('Rate',fontdict=font)

#ax[1].set_ylabel('Rate',fontdict=font)

#ax[0].set_ylabel('Rate',fontdict=font)

ax[0].legend(prop={'size':18})

ax[1].legend(prop={'size':18})

ax[2].legend(prop={'size':18})

ax[3].legend(prop={'size':18})

ax[0].tick_params(labelsize=14)

ax[1].tick_params(labelsize=14)

ax[2].tick_params(labelsize=14)

ax[3].tick_params(labelsize=14)

ax[0].axvline(0.121,linestyle='dashdot',color='black')

ax[1].axvline(0.121,linestyle='dashdot',color='black')

ax[2].axvline(0.121,linestyle='dashdot',color='black')

ax[3].axvline(0.121,linestyle='dashdot',color='black')

#fig.text(0.5, 0, 'JSD of m2', ha='center',fontdict=font)

fig.text(0, 0.5, 'Fraction of injections', va='center', rotation='vertical',fontdict=font)



fig.savefig('m2_be_ref.pdf')







m2_BBH_90=np.percentile(jsd_m2_BBH,95)

m2_BBH_90





m2_bem1_90=np.sum(jsd_m2_1>=m2_BBH_90)     

m2_bem1_90









m2_bem4_90=np.sum(jsd_m2_4>=m2_BBH_90)     

m2_bem4_90







m2_bem8_90=np.sum(jsd_m2_8>=m2_BBH_90)     

m2_bem8_90









m2_bem16_90=np.sum(jsd_m2_16>=m2_BBH_90)     

m2_bem16_90





# ## 2.2 BH encounter & injected BBH



fig,ax=plt.subplots(4,1,figsize=(7,6),sharex=True)

ax[0].hist(jsd_m2_recover_1,weights=weights,range=(0,max_jsd_m2_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=1')

ax[1].hist(jsd_m2_recover_4,weights=weights,range=(0,max_jsd_m2_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=4')

ax[2].hist(jsd_m2_recover_8,weights=weights,range=(0,max_jsd_m2_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=8')

ax[3].hist(jsd_m2_recover_16,weights=weights,range=(0,max_jsd_m2_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=16')

ax[0].hist(jsd_m2_BBH,weights=weights_BBH,range=(0,max_jsd_m2_inj),bins=bins,alpha=0.7,color='grey')

ax[1].hist(jsd_m2_BBH,weights=weights_BBH,range=(0,max_jsd_m2_inj),bins=bins,alpha=0.7,color='grey')

ax[2].hist(jsd_m2_BBH,weights=weights_BBH,range=(0,max_jsd_m2_inj),bins=bins,alpha=0.7,color='grey')

ax[3].hist(jsd_m2_BBH,weights=weights_BBH,range=(0,max_jsd_m2_inj),bins=bins,alpha=0.7,color='grey')

#ax[0]=plt.axvline(0.121,linestyle='dashdot',color='black')

#ax[1]=plt.axvline(0.121,linestyle='dashdot',color='black')



ax[3].set_xlabel('JSD of m2',fontdict=font)

#ax[3].set_ylabel('Rate',fontdict=font)

#ax[2].set_ylabel('Rate',fontdict=font)

#ax[1].set_ylabel('Rate',fontdict=font)

#ax[0].set_ylabel('Rate',fontdict=font)

ax[0].legend(prop={'size':18})

ax[1].legend(prop={'size':18})

ax[2].legend(prop={'size':18})

ax[3].legend(prop={'size':18})

ax[0].tick_params(labelsize=14)

ax[1].tick_params(labelsize=14)

ax[2].tick_params(labelsize=14)

ax[3].tick_params(labelsize=14)

ax[0].axvline(0.121,linestyle='dashdot',color='black')

ax[1].axvline(0.121,linestyle='dashdot',color='black')

ax[2].axvline(0.121,linestyle='dashdot',color='black')

ax[3].axvline(0.121,linestyle='dashdot',color='black')

#fig.text(0.5, 0, 'JSD of m2', ha='center',fontdict=font)

fig.text(0, 0.5, 'Fraction of injections', va='center', rotation='vertical',fontdict=font)



fig.savefig('m2_be_inj.pdf')









m2_eb_1_90=np.percentile(jsd_eb_m2_1,10)

m2_eb_1_90





m2_eb_4_90=np.percentile(jsd_eb_m2_4,10)

m2_eb_4_90





m2_eb_8_90=np.percentile(jsd_eb_m2_8,10)

m2_eb_8_90



m2_eb_16_90=np.percentile(jsd_eb_m2_16,10)

m2_eb_16_90





m2_BBH_90=np.percentile(jsd_m2_BBH,90)

m2_BBH_90



m2_ebm1_90=np.sum(jsd_eb_m2_1>=m2_BBH_90)     

m2_ebm1_90



m2_ebm4_90=np.sum(jsd_eb_m2_4>=m2_BBH_90)     

m2_ebm4_90



m2_ebm8_90=np.sum(jsd_eb_m2_8>=m2_BBH_90)     

m2_ebm8_90



m2_ebm16_90=np.sum(jsd_eb_m2_16>=m2_BBH_90)     

m2_ebm16_90





# # 3. JSD of t0



# ## 3.1 BH encounter & BBH reference





fig,ax=plt.subplots(4,1,figsize=(7,6),sharex=True)

ax[0].hist(jsd_t0_1,weights=weights,range=(0,max_jsd_t0_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=1')

ax[1].hist(jsd_t0_4,weights=weights,range=(0,max_jsd_t0_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=4')

ax[2].hist(jsd_t0_8,weights=weights,range=(0,max_jsd_t0_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=8')

ax[3].hist(jsd_t0_16,weights=weights,range=(0,max_jsd_t0_ref),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=16')

ax[0].hist(jsd_t0_BBH,weights=weights_BBH,range=(0,max_jsd_t0_ref),bins=bins,alpha=0.7,color='grey')

ax[1].hist(jsd_t0_BBH,weights=weights_BBH,range=(0,max_jsd_t0_ref),bins=bins,alpha=0.7,color='grey')

ax[2].hist(jsd_t0_BBH,weights=weights_BBH,range=(0,max_jsd_t0_ref),bins=bins,alpha=0.7,color='grey')

ax[3].hist(jsd_t0_BBH,weights=weights_BBH,range=(0,max_jsd_t0_ref),bins=bins,alpha=0.7,color='grey')



ax[3].set_xlabel('JSD of t0',fontdict=font)

#ax[3].set_ylabel('Rate',fontdict=font)

#ax[2].set_ylabel('Rate',fontdict=font)

#ax[1].set_ylabel('Rate',fontdict=font)

#ax[0].set_ylabel('Rate',fontdict=font)

ax[0].legend(prop={'size':18},loc='upper center')

ax[1].legend(prop={'size':18})

ax[2].legend(prop={'size':18},loc='upper center')

ax[3].legend(prop={'size':18},loc='upper center')

ax[0].tick_params(labelsize=14)

ax[1].tick_params(labelsize=14)

ax[2].tick_params(labelsize=14)

ax[3].tick_params(labelsize=14)

ax[0].axvline(0.121,linestyle='dashdot',color='black')

ax[1].axvline(0.121,linestyle='dashdot',color='black')

ax[2].axvline(0.121,linestyle='dashdot',color='black')

ax[3].axvline(0.121,linestyle='dashdot',color='black')

#fig.text(0.5, 0, 'JSD of t0', ha='center',fontdict=font)

fig.text(0, 0.5, 'Fraction of injections', va='center', rotation='vertical',fontdict=font)



fig.savefig('t0_be_ref.pdf')





t0_BBH_90=np.percentile(jsd_t0_BBH,95)

t0_BBH_90



t0_bem1_90=np.sum(jsd_t0_1>=t0_BBH_90)     

t0_bem1_90



t0_bem4_90=np.sum(jsd_t0_4>=t0_BBH_90)     

t0_bem4_90



t0_bem8_90=np.sum(jsd_t0_8>=t0_BBH_90)     

t0_bem8_90



t0_bem16_90=np.sum(jsd_t0_16>=t0_BBH_90)     

t0_bem16_90





# ## 3.2 BH encounter & injected BBH



fig,ax=plt.subplots(4,1,figsize=(7,6),sharex=True)

ax[0].hist(jsd_t0_recover_1,weights=weights,range=(0,max_jsd_t0_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=1')

ax[1].hist(jsd_t0_recover_4,weights=weights,range=(0,max_jsd_t0_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=4')

ax[2].hist(jsd_t0_recover_8,weights=weights,range=(0,max_jsd_t0_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=8')

ax[3].hist(jsd_t0_recover_16,weights=weights,range=(0,max_jsd_t0_inj),bins=bins,alpha=0.7,histtype = 'step',color='r',label='q=16')

ax[0].hist(jsd_t0_BBH,weights=weights_BBH,range=(0,max_jsd_t0_inj),bins=bins,alpha=0.7,color='grey')

ax[1].hist(jsd_t0_BBH,weights=weights_BBH,range=(0,max_jsd_t0_inj),bins=bins,alpha=0.7,color='grey')

ax[2].hist(jsd_t0_BBH,weights=weights_BBH,range=(0,max_jsd_t0_inj),bins=bins,alpha=0.7,color='grey')

ax[3].hist(jsd_t0_BBH,weights=weights_BBH,range=(0,max_jsd_t0_inj),bins=bins,alpha=0.7,color='grey')



ax[3].set_xlabel('JSD of t0',fontdict=font)

#ax[3].set_ylabel('Rate',fontdict=font)

#ax[2].set_ylabel('Rate',fontdict=font)

#ax[1].set_ylabel('Rate',fontdict=font)

#ax[0].set_ylabel('Rate',fontdict=font)

ax[0].legend(prop={'size':18})

ax[1].legend(prop={'size':18})

ax[2].legend(prop={'size':18})

ax[3].legend(prop={'size':18})

ax[0].tick_params(labelsize=14)

ax[1].tick_params(labelsize=14)

ax[2].tick_params(labelsize=14)

ax[3].tick_params(labelsize=14)

ax[0].axvline(0.121,linestyle='dashdot',color='black')

ax[1].axvline(0.121,linestyle='dashdot',color='black')

ax[2].axvline(0.121,linestyle='dashdot',color='black')

ax[3].axvline(0.121,linestyle='dashdot',color='black')

#fig.text(0.5, 0, 'JSD of t0', ha='center',fontdict=font)

fig.text(0, 0.5, 'Fraction of injections', va='center', rotation='vertical',fontdict=font)



fig.savefig('t0_be_inj.pdf')





t0_eb_1_90=np.percentile(jsd_eb_t0_1,10)

t0_eb_1_90



t0_eb_4_90=np.percentile(jsd_eb_t0_4,10)

t0_eb_4_90



t0_eb_8_90=np.percentile(jsd_eb_t0_8,10)

t0_eb_8_90



t0_eb_16_90=np.percentile(jsd_eb_t0_16,10)

t0_eb_16_90



t0_BBH_90=np.percentile(jsd_t0_BBH,95)

t0_BBH_90



t0_ebm1_90=np.sum(jsd_eb_t0_1>=t0_BBH_90)     

t0_ebm1_90



t0_ebm4_90=np.sum(jsd_eb_t0_4>=t0_BBH_90)     

t0_ebm4_90



t0_ebm8_90=np.sum(jsd_eb_t0_8>=t0_BBH_90)     

t0_ebm8_90



t0_ebm16_90=np.sum(jsd_eb_t0_16>=t0_BBH_90)     

t0_ebm16_90

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