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)