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Gregory Ashton authoredPreviously, we used the emcee.PTSampler, but this has been removed from the master branch of emcee (see discussion here https://github.com/dfm/emcee/issues/236) and a fork, ptemcee has been developed. Testing shows this is equivalent (after some changes in the interface) and perhaps better as it contains the adaptive temperature ladders developed by Will Vousden (the maintainer of ptemcee)
Gregory Ashton authoredPreviously, we used the emcee.PTSampler, but this has been removed from the master branch of emcee (see discussion here https://github.com/dfm/emcee/issues/236) and a fork, ptemcee has been developed. Testing shows this is equivalent (after some changes in the interface) and perhaps better as it contains the adaptive temperature ladders developed by Will Vousden (the maintainer of ptemcee)
generate_data.py 2.85 KiB
import pyfstat
import numpy as np
import os
import sys
import time
ID = sys.argv[1]
outdir = sys.argv[2]
label = 'run_{}'.format(ID)
data_label = '{}_data'.format(label)
results_file_name = '{}/NoiseOnlyMCResults_{}.txt'.format(outdir, ID)
# Properties of the GW data
sqrtSX = 1e-23
tstart = 1000000000
Tspan = 100*86400
tend = tstart + Tspan
# Fixed properties of the signal
F0_center = 30
F1_center = -1e-10
F2 = 0
tref = .5*(tstart+tend)
VF0 = VF1 = 100
DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
DeltaAlpha = 0.02
DeltaDelta = 0.02
nsteps = 50
run_setup = [((nsteps, 0), 20, False),
((nsteps, 0), 11, False),
((nsteps, 0), 6, False),
((nsteps, 0), 3, False),
((nsteps, nsteps), 1, False)]
h0 = 0
F0 = F0_center + np.random.uniform(-0.5, 0.5)*DeltaF0
F1 = F1_center + np.random.uniform(-0.5, 0.5)*DeltaF1
Alpha_center = np.random.uniform(DeltaAlpha, 2*np.pi-DeltaAlpha)
Delta_center = np.arccos(2*np.random.uniform(0, 1)-1)-np.pi/2
Alpha = Alpha_center + np.random.uniform(-0.5, 0.5)*DeltaAlpha
Delta = Delta_center + np.random.uniform(-0.5, 0.5)*DeltaDelta
psi = np.random.uniform(-np.pi/4, np.pi/4)
phi = np.random.uniform(0, 2*np.pi)
cosi = np.random.uniform(-1, 1)
data = pyfstat.Writer(
label=data_label, outdir=outdir, tref=tref,
tstart=tstart, F0=F0, F1=F1, F2=F2, duration=Tspan, Alpha=Alpha,
Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
detector='H1,L1')
data.make_data()
startTime = time.time()
theta_prior = {'F0': {'type': 'unif',
'lower': F0_center-DeltaF0,
'upper': F0_center+DeltaF0},
'F1': {'type': 'unif',
'lower': F1_center-DeltaF1,
'upper': F1_center+DeltaF1},
'F2': F2,
'Alpha': {'type': 'unif',
'lower': Alpha_center-DeltaAlpha,
'upper': Alpha_center+DeltaAlpha},
'Delta': {'type': 'unif',
'lower': Delta_center-DeltaDelta,
'upper': Delta_center+DeltaDelta},
}
ntemps = 2
log10temperature_min = -1
nwalkers = 100
mcmc = pyfstat.MCMCFollowUpSearch(
label=label, outdir=outdir,
sftfilepath='{}/*{}*sft'.format(outdir, data_label),
theta_prior=theta_prior,
tref=tref, minStartTime=tstart, maxStartTime=tend,
nwalkers=nwalkers, ntemps=ntemps,
log10temperature_min=log10temperature_min)
mcmc.run(run_setup=run_setup, create_plots=False, log_table=False,
gen_tex_table=False)
d, maxtwoF = mcmc.get_max_twoF()
dF0 = F0 - d['F0']
dF1 = F1 - d['F1']
runTime = time.time() - startTime
with open(results_file_name, 'a') as f:
f.write('{:1.8e} {:1.8e} {:1.8e} {}\n'
.format(dF0, dF1, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label))