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test_fsig.py
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Daniel Brown authoredadding in fsig command (not parsing yet). See example test_fsig.py in bin folder. Also made component variable an optional argument for xaxis and x2axis which will break previous scripts. Did this as when setting the parameter to tune, the Param object contains whatever component owns that parameter so no need to pass it twice. Also stops someone passing a parameter not for the component stated.
Daniel Brown authoredadding in fsig command (not parsing yet). See example test_fsig.py in bin folder. Also made component variable an optional argument for xaxis and x2axis which will break previous scripts. Did this as when setting the parameter to tune, the Param object contains whatever component owns that parameter so no need to pass it twice. Also stops someone passing a parameter not for the component stated.
generate_table.py 2.25 KiB
import pyfstat
import numpy as np
outdir = 'data'
label = 'allsky_setup'
data_label = '{}_data'.format(label)
# Properties of the GW data
sqrtSX = 2e-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 = 0.05
DeltaDelta = 0.05
depth = 100
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 = sqrtSX / float(depth)
r = np.random.uniform(0, 1)
theta = np.random.uniform(0, 2*np.pi)
F0 = F0_center + 3*np.sqrt(r)*np.cos(theta)/(np.pi**2 * Tspan**2)
F1 = F1_center + 45*np.sqrt(r)*np.sin(theta)/(4*np.pi**2 * Tspan**4)
Alpha = 0
Delta = 0
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()
predicted_twoF = data.predict_fstat()
theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2.,
'upper': F0+DeltaF0/2.},
'F1': {'type': 'unif',
'lower': F1-DeltaF1/2.,
'upper': F1+DeltaF1/2.},
'F2': F2,
'Alpha': {'type': 'unif',
'lower': Alpha-DeltaAlpha/2.,
'upper': Alpha+DeltaAlpha/2.},
'Delta': {'type': 'unif',
'lower': Delta-DeltaDelta/2.,
'upper': Delta+DeltaDelta/2.},
}
ntemps = 1
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)