def det_weights(m1, m2, N=1):
"""Compute detection weights for distributions of CBC systems."""
pdet = gwdet.detectability() # Instantiate detection weight class
m1_many = np.array([m1 for x in range(N)]).flatten() # bootstrap binaries
m2_many = np.array([m2 for x in range(N)]).flatten()
z_many = np.array([generate_redshift()
for x in m1_many]) # compute redshifts
det_many = pdet(m1_many, m2_many, z_many) # output weights
return (compute_chirpmass(m1_many, m2_many), z_many, det_many)
#m1 = input()
#print('please input m2')
#m2 = input()
#print('please input z')
#z = input()
#p=gwdet.detectability()
#m1=10. # Component mass in Msun
#m2=10. # Component mass in Msun
#z=0.1 # Redshift
#print(p(m1,m2,z)) # Fraction of detectabile sources
#p_det is completed!
p = gwdet.detectability()
def dR_det(m_1, m_2, z):
if m_1 < 0.0:
return 0.0
elif m_2 < 0.0:
return 0.0
elif z < 0.0:
return 0.0
else:
return dVdz(z) * p(m_1, m_2, z) * R_th(m_1, m_2, z) / (1 + z)
#return 1 / ( 1 + z ) * 1 * p( m_1 ,m_2 ,z ) * R_th( m_1 ,m_2 , z )
print('hello world!')
import gwdet
import numpy as np
import math
import scipy
from scipy import integrate
from scipy import stats
print('please select sensitivity mode.')
x = input()
if x == 1:
p = gwdet.detectability()
print('the sensitibity mode is design sensitibity.')
else:
p = gwdet.detectability(psd='aLIGOEarlyHighSensitivityP1200087')
print('the sensitibity mode is aLIGOEarlyHighSensitivityP1200087.')
#p = gwdet.detectability(psd = 'aLIGOEarlyHighSensitivityP1200087')
#p = gwdet.detectability()
import time
t1 = time.time()
N = 1000
for i in range(0, N):
print(p(0.05 * i, 0.05 * i, 0.1))
