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Rates_original.py
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Rates_original.py
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import numpy as np
import matplotlib.pyplot as plt
import os
from scipy.signal import savgol_filter
from time_conversion import read_units
from astropy import units as u
from astropy.cosmology import Planck15, z_at_value
#---- Important functions
def inspiral_time_peters(a0,e0,m1,m2,af=0):
"""
Computes the inspiral time, in Gyr, for a binary
a0 in Au, and masses in solar masses
if different af is given, computes the time from a0,e0
to that af
for af=0, just returns inspiral time
for af!=0, returns (t_insp,af,ef)
"""
coef = 6.086768e-11 #G^3 / c^5 in au, gigayear, solar mass units
beta = (64./5.) * coef * m1 * m2 * (m1+m2)
if e0 == 0:
print(e0,a0)
if not af == 0:
print("ERROR: doesn't work for circular binaries")
return 0
return a0**4 / (4*beta)
c0 = a0 * (1.-e0**2.) * e0**(-12./19.) * (1.+(121./304.)*e0**2.)**(-870./2299.)
if af == 0:
eFinal = 0.
else:
r = ode(deda_peters)
r.set_integrator('lsoda')
r.set_initial_value(e0,a0)
r.integrate(af)
if not r.successful():
print("ERROR, Integrator failed!")
else:
eFinal = r.y[0]
time_integrand = lambda e: e**(29./19.)*(1.+(121./304.)*e**2.)**(1181./2299.) / (1.-e**2.)**1.5
integral,abserr = integrate.quad(time_integrand,eFinal,e0)
if af==0:
return integral * (12./19.) * c0**4. / beta
else:
return (integral * (12./19.) * c0**4. / beta), af, eFinal
def comovingDistance(z):
h = 0.679
omegaM = 0.306
omegaK = 0.
omegaL = 1 - 0.306
dh = 3000. / h
e = lambda zp: 1./np.sqrt(omegaM*(1+zp)**3 + omegaL)
return dh*integrate.quad(e,0,z)[0]
def lookbackTime(z):
h = 0.679
omegaM = 0.306
omegaK = 0.
omegaL = 1 - 0.306
th = 9.78/h
e = lambda zp: 1./(np.sqrt(omegaM*(1+zp)**3 + omegaL)*(1+zp))
return th*integrate.quad(e,0,z)[0]
def zAtLookbackTime(t):
zero = lambda z: lookbackTime(z) - t
return brentq(zero,0,10)
#---This function gets the merger times for all In-cluster mergers (adapated to my runs, modify as needed)
def get_t_merge(m_min, m_max):
fbh = ['0.50', '0.75', '1.0']
m_times = []
m_m1 = []
m_m2 = []
for h in range(len(fbh)):
end = 1
# end = 15
if fbh[h] == '0.75':
end = 25
for i in range(0,end):
model = 'FBH_HS1.0_rv1rg20z0.002N8e5_primMS_alpha1_fb0.05_hbf' + fbh[h]+ '_' + str(i) + '_long/'
path = '/projects/b1095/egp8636/elena_cmc/rundir/triples/' + model
units=read_units(path+'initial')
time_units = units[0]['t_myr']
print(model)
flag = 'inactive'
# if os.path.isfile(path + 'restarted.bhmerger.dat'):
# flag, rt, _ = restart_time(path)
if flag == 'inactive':
bh_merger = np.genfromtxt(path + 'initial.bhmerger.dat')
if (len(bh_merger))>0:
bh_merger_time = bh_merger[:,0]
bh_merger_m1 = bh_merger[:,5]
bh_merger_m2 = bh_merger[:,6]
for j in range(len(bh_merger_time)):
if m_max == 40.5:
if (bh_merger_m1[j] >= m_min and bh_merger_m1[j] < m_max) and (bh_merger_m2[j] >= m_min and bh_merger_m2[j] < m_max):
m_times.append(bh_merger_time[j]*time_units)
m_m1.append(bh_merger_m1[j])
m_m2.append(bh_merger_m2[j])
else:#to consider cases with only one component
if (bh_merger_m1[j] >= m_min and bh_merger_m1[j] < m_max) or (bh_merger_m2[j] >= m_min and bh_merger_m2[j] < m_max):
m_times.append(bh_merger_time[j]*time_units)
m_m1.append(bh_merger_m1[j])
m_m2.append(bh_merger_m2[j])
# elif flag == 'active':
# bh_merger_ini = np.genfromtxt(path + 'initial.bhmerger.dat')
# bh_merger_res = np.genfromtxt(path + 'restarted.bhmerger.dat')
# if (len(bh_merger_ini))>0:
# bh_merger_time = bh_merger_ini[:,0]
# bh_merger_m1 = bh_merger_ini[:,5]
# bh_merger_m2 = bh_merger_ini[:,6]
# for j in range(len(bh_merger_time)):
# if m_max == 40.5:
# if bh_merger_time[j] < rt :
# if (bh_merger_m1[j] >= m_min and bh_merger_m1[j] < m_max) and (bh_merger_m2[j] >= m_min and bh_merger_m2[j] < m_max):
# m_times.append(bh_merger_time[j]*time_units)
# m_m1.append(bh_merger_m1[j])
# m_m2.append(bh_merger_m2[j])
# else:#to consider cases with only one component
# if bh_merger_time[j] < rt :
# if ((bh_merger_m1[j] >= m_min and bh_merger_m1[j] < m_max) or (bh_merger_m2[j] >= m_min and bh_merger_m2[j] < m_max)):
# m_times.append(bh_merger_time[j]*time_units)
# m_m1.append(bh_merger_m1[j])
# m_m2.append(bh_merger_m2[j])
# if (len(bh_merger_res))>0:
# bh_merger_time = bh_merger_res[:,0]
# bh_merger_m1 = bh_merger_res[:,5]
# bh_merger_m2 = bh_merger_res[:,6]
# for j in range(len(bh_merger_time)):
# if m_max == 40.5:
# if bh_merger_time[j] >= rt:
# if (bh_merger_m1[j] >= m_min and bh_merger_m1[j] < m_max) and (bh_merger_m2[j] >= m_min and bh_merger_m2[j] < m_max):
# m_times.append(bh_merger_time[j]*time_units)
# m_m1.append(bh_merger_m1[j])
# m_m2.append(bh_merger_m2[j])
# else:
# if bh_merger_time[j] >= rt:
# if ((bh_merger_m1[j] >= m_min and bh_merger_m1[j] < m_max) or (bh_merger_m2[j] >= m_min and bh_merger_m2[j] < m_max)):
# m_times.append(bh_merger_time[j]*time_units)
# m_m1.append(bh_merger_m1[j])
# m_m2.append(bh_merger_m2[j])
return m_times, m_m1, m_m2
#---This function gets the merger times for all out-cluster mergers (adapated to my runs, modify as needed)
def out_t_merge(m_min, m_max):
from ipynb.fs.full.LISA_calculations import inspiral_time_peters
times = []
m1 = []
m2 = []
fbh = ['0.50', '0.75', '1.0']
for hmbf in fbh:
end = 1
# end = 15
if hmbf == '0.75':
end = 25
for e in range(0,end):
model = 'FBH_HS1.0_rv1rg20z0.002N8e5_primMS_alpha1_fb0.05_hbf' + hmbf + '_' + str(e)
path = '/projects/b1095/egp8636/elena_cmc/rundir/triples/'+model+'_long/'
print(model)
units=read_units(path+'initial')
time_units = units[0]['t_myr']
flag ='inactive'
# if os.path.isfile(path + 'restarted.esc.dat'):
# flag, rt, _ = restart_time(path)
if flag == 'inactive':
data = np.genfromtxt(path + 'initial.esc.dat')
binflag = data[:,14]
for j in range(len(binflag)):
if binflag[j] == 1:
t = data[j,1]
t = t
type1= int(data[j,22])
type2 = int(data[j,23])
mass1 = (data[j,15])
mass2 = (data[j,16])
a = (data[j,19])
e = (data[j,20])
if (type1 == 14 and type2 == 14):
if m_max == 40.5:
if (mass1 >= m_min and mass1 < m_max) and (mass2 >= m_min and mass2 < m_max):
inspiral_time = inspiral_time_peters(a,e,mass1,mass2)
t_tot = t*time_units + inspiral_time*10**3
print(t,inspiral_time*10**3,t_tot)
if t_tot <= 13700.0:
times.append(t_tot)
else:
if (mass1 >= m_min and mass1 < m_max) or (mass2 >= m_min and mass2 < m_max):
inspiral_time = inspiral_time_peters(a,e,mass1,mass2)
t_tot = t*time_units + inspiral_time*10**3
if t_tot<= 13700.0:
times.append(t_tot)
# if flag == 'active':
# data_ini = np.genfromtxt(path + 'initial.esc.dat')
# binflag = data_ini[:,14]
# for j in range(len(binflag)):
# if binflag[j] == 1:
# t = data_ini[j,1]
# type1= int(data_ini[j,22])
# type2 = int(data_ini[j,23])
# mass1 = (data_ini[j,15])
# mass2 = (data_ini[j,16])
# a = (data_ini[j,19])
# e = (data_ini[j,20])
# if (type1 == 14 and type2 == 14):
# if m_max == 40.5:
# if t < rt :
# if (mass1 >= m_min and mass1 < m_max) and (mass2 >= m_min and mass2 < m_max):
# inspiral_time = inspiral_time_peters(a,e,mass1,mass2)
# t_tot = t*time_units + inspiral_time*10**3
# if t_tot <= 13700.0:
# times.append(t_tot)
# else:
# if t < rt :
# if (mass1 >= m_min and mass1 < m_max) or (mass2 >= m_min and mass2 < m_max):
# inspiral_time = inspiral_time_peters(a,e,mass1,mass2)
# t_tot = t*time_units + inspiral_time*10**3
# if t_tot<= 13700.0:
# times.append(t_tot)
# data_res = np.genfromtxt(path + 'restarted.esc.dat')
# binflag = data_res[:,14]
# for j in range(len(binflag)):
# if binflag[j] == 1:
# t = data_res[j,1]
# t = t
# type1= int(data_res[j,22])
# type2 = int(data_res[j,23])
# mass1 = (data_res[j,15])
# mass2 = (data_res[j,16])
# a = (data_res[j,19])
# e = (data_res[j,20])
# if (type1 == 14 and type2 == 14):
# if m_max == 40.5:
# if t >= rt:
# if (mass1 >= m_min and mass1 < m_max) and (mass2 >= m_min and mass2 < m_max):
# inspiral_time = inspiral_time_peters(a,e,mass1,mass2)
# t_tot = t*time_units + inspiral_time*10**3
# if t_tot <= 13700.0:
# times.append(t_tot)
# else:
# if t >= rt:
# if (mass1 >= m_min and mass1 < m_max) or (mass2 >= m_min and mass2 < m_max):
# inspiral_time = inspiral_time_peters(a,e,mass1,mass2)
# t_tot = t*time_units + inspiral_time*10**3
# if t_tot <= 13700.0:
# times.append(t_tot)
return times
in_cluster_merger_times=open("TEST_in_cluster_merger_times.txt",'w')
m_times, m_m1, m_m2 = get_t_merge(0, 100000000)
for m in m_times:
m = str(m)
in_cluster_merger_times.write(f'{m}\n')
in_cluster_merger_times.close()
# Write out-of-cluster merger times to a separate .txt file ## NEED TO FIGURE OUT STRUCTURE OF VAR 'times'
out_cluster_merger_times=open("TEST_out_cluster_merger_times.txt",'w')
times = get_t_merge(0, 100000000)
temp_count = 0
print(f'type(times) = {type(times)}')
print(f'len(times) = {len(times)}')
for t in times:
# if temp_count == 0:
print(f'type(t) = {type(t)}')
print(f'len(t) = {len(t)}')
# temp_count += 1
t = str(t)
out_cluster_merger_times.write(f'{t}\n')
out_cluster_merger_times.close()
files = ["TEST_in_cluster_merger_times.txt", "TEST_out_cluster_merger_times.txt"]
#--- I saved all of the merger times to data files. I deleted this part not to confuse you.
#--- This gets the cluster t_ages from an El-Badry paper
gc_ages = np.genfromtxt('Mvir1e14.txt')
gc_age = gc_ages[:,0]
gc_met = gc_ages[:,1]
#get only age values with metallicities between -0.8 and -1.2
gc_age_met = [gc_age[i]*10**3 for i in range(len(gc_age)) if gc_met[i] >= -1.3 and gc_met[i] <= -0.7]
#create files of effective times
for file in files:
t_mergers = np.genfromtxt('merger_times/' + file)
f1 = open('effective_times/eff_' + file, 'w')
for t_merger in t_mergers:
#for each merger, draw 100 cluster ages
t_ages = np.random.choice(gc_age_met, size = 100)
#compute the effective merger time
for tage in t_ages:
t_eff = 13.7 * 10**3 - tage + t_merger #in Myr
f1.write(str(t_eff))
f1.write('\n')
f1.close()
#--- This function calculates the cumulative merger rate and volumetric merger rates.
def rate(t_effs):
'''This is used to calculate the merger rate
PARAMETERS
---------------
t_effs : list of effective merger times in Myr
OUTPUT
---------------
rates: rates in units of merger/Gpc^3/yr
'''
n = 55 #number of models (personal to my project)
m = 100
rates = []
d_mins = []
d_maxs = []
zs = []
#divide the list of effective times by into separate redshift bins
t_bins = np.linspace(10,13200, num = 300) #300 bins in Myr
for j in range(1,len(t_bins)-1):
t_lower = t_bins[j]
t_upper = t_bins[j+1]
count = 0
for teff in t_effs:
if teff >= t_lower and teff < t_upper:
count += 1
delta_t = (t_upper-t_lower)*10**6 #in years
rho = 2.31*10**9 #Gpc^-3
weight = (m*n)
R = ((count*rho)/weight)/(delta_t)*4.65
rates.append(R)
t_lb1 = (13.3*10**9- t_lower*10**6)*u.yr #lookback time
t_lb2 = (13.3*10**9- t_upper*10**6)*u.yr
t_mid = (13.3*10**9- (t_upper*10**6+t_lower*10**6)/2)*u.yr
zmin = z_at_value(Planck15.lookback_time,t_lb1)
zmax = z_at_value(Planck15.lookback_time,t_lb2)
zmid = z_at_value(Planck15.lookback_time,t_mid)
d_min = (Planck15.comoving_distance(zmin)).to(u.Gpc)
d_max = (Planck15.comoving_distance(zmax)).to(u.Gpc)
zs.append(zmid.value)
d_mins.append(d_min.value)
d_maxs.append(d_max.value)
#calculating cumulative rates
rc = cumulative_rate(rates, zs, d_maxs,d_mins)
return rates, rc, zs
def cumulative_rate(rates,zmid, d_max,d_min):
cumulative_rates = []
r_c = 0
for i in range(len(rates)-1,-1,-1):
r = rates[i]
r_c += r * (1 + zmid[i])**(-1) * (4/3) * np.pi * abs((d_max[i]**3 - d_min[i]**3))
cumulative_rates.append(r_c)
return cumulative_rates