import numpy as np
import time
import matplotlib
matplotlib.use('Agg')
sys.path.append("../pyBSE/")
import pybse
import dart_board
from dart_board import sf_history
LMC_metallicity = 0.008
pub = dart_board.DartBoard("HMXB",
evolve_binary=pybse.evolve,
ln_prior_pos=sf_history.lmc.prior_lmc,
metallicity=LMC_metallicity,
nwalkers=320,
threads=20)
pub.aim_darts()
start_time = time.time()
pub.throw_darts(nburn=2, nsteps=120000)
print("Simulation took", time.time() - start_time, "seconds.")
# Acceptance fraction
print("Acceptance fractions:", pub.sampler.acceptance_fraction)
# Autocorrelation length
try:
print("Autocorrelation length:", pub.sample.acor)
sys.path.append("../pyBSE/")
import pybse
import dart_board
from dart_board import sf_history
# Values for mock system 1
# Input values: 11.77 8.07 4850.81 0.83 153.04 2.05 2.33 34.74
# Output values: 1.48 8.09 179.89 0.68 39.65 1.32e-12 22.12 13 1
LMC_metallicity = 0.008
system_kwargs = {"M2": 7.7, "M2_err": 0.5, "ecc": 0.69, "ecc_err": 0.05}
pub = dart_board.DartBoard("NSHMXB",
evolve_binary=pybse.evolve,
nwalkers=320,
threads=20,
metallicity=LMC_metallicity,
thin=100,
system_kwargs=system_kwargs)
pub.aim_darts(N_iterations=100000, a_set='high')
start_time = time.time()
pub.throw_darts(nburn=2, nsteps=150000)
print("Simulation took", time.time() - start_time, "seconds.")
# Acceptance fraction
print("Acceptance fractions:", pub.sampler.acceptance_fraction)
# Autocorrelation length
try:
"M2": 7.84,
"M2_err": 0.25,
"P_orb": 14.11,
"P_orb_err": 1.0,
"ecc": 0.47,
"ecc_err": 0.05,
"L_x": 1.94e33,
"L_x_err": 1.0e32,
"ra": 83.5744461,
"dec": -69.4876344
}
pub = dart_board.DartBoard("HMXB",
evolve_binary=pybse.evolve,
ln_prior_pos=sf_history.lmc.prior_lmc,
nwalkers=320,
threads=20,
ntemps=10,
metallicity=LMC_metallicity,
thin=100,
system_kwargs=system_kwargs)
# Darts need to be in ln
pub.aim_darts(N_iterations=200000, a_set='low')
start_time = time.time()
pub.throw_darts(nburn=2, nsteps=150000)
print("Simulation took", time.time() - start_time, "seconds.")
# Since emcee_PT does not have a blobs function, we must include the following calculation
if pub.chains.ndim == 4:
dec_J0513 = -65.7885278
# Restrict size of viable region to within 2 degrees of J0513
if np.abs(ra - ra_J0513)*np.cos(dec*np.pi/180.0) > 2.0: return -np.inf
if np.abs(dec - dec_J0513) > 2.0: return -np.inf
return sf_history.lmc.prior_lmc(ra, dec, ln_t_b)
# Values for Swift J0513.4-6547 from Coe et al. 2015, MNRAS, 447, 1630
pub = dart_board.DartBoard("NSHMXB", evolve_binary=pybse.evolve, metallicity=LMC_metallicity,
ln_prior_pos=lmc_sfh_J0513,
nwalkers=320, threads=20, thin=10)
pub.aim_darts(N_iterations=10000)
start_time = time.time()
pub.throw_darts(nburn=2, nsteps=150000)
print("Simulation took",time.time()-start_time,"seconds.")
# Since emcee_PT does not have a blobs function, we must include the following calculation
if pub.ntemps is not None:
import sys
import numpy as np
import time
import matplotlib
matplotlib.use('Agg')
sys.path.append("../pyBSE/")
import pybse
import dart_board
pub = dart_board.DartBoard("HMXB", evolve_binary=pybse.evolve, nwalkers=320, threads=20)
pub.aim_darts()
start_time = time.time()
pub.throw_darts(nburn=2, nsteps=120000)
print("Simulation took",time.time()-start_time,"seconds.")
# Acceptance fraction
print("Acceptance fractions:",pub.sampler.acceptance_fraction)
# Autocorrelation length
try:
print("Autocorrelation length:", pub.sample.acor)
except:
print("Acceptance fraction is too low.")
# Save outputs
LMC_metallicity = 0.008
system_kwargs = {
"P_orb": 27.405,
"P_orb_err": 0.5,
"ecc_max": 0.17,
"m_f": 9.9,
"m_f_err": 2.0,
"ra": 78.36775,
"dec": -65.7885278
}
pub = dart_board.DartBoard("NSHMXB",
evolve_binary=pybse.evolve,
metallicity=LMC_metallicity,
ln_prior_pos=sf_history.lmc.prior_lmc,
generate_pos=sf_history.lmc.get_random_positions,
nwalkers=320,
system_kwargs=system_kwargs)
# pub.aim_darts()
start_time = time.time()
pub.scatter_darts(seconds=467648)
# pub.throw_darts(nburn=50000, nsteps=50000)
print("Simulation took", time.time() - start_time, "seconds.")
# Save outputs
np.save("../data/J0513_trad_chain.npy", pub.chains)
np.save("../data/J0513_trad_derived.npy", pub.derived)
np.save("../data/J0513_trad_lnprobability.npy", pub.lnprobability)