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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.
semi_coherent_directed_follow_up.py 2.04 KiB
import pyfstat
import numpy as np
import matplotlib.pyplot as plt
F0 = 30.0
F1 = -1e-10
F2 = 0
Alpha = np.radians(83.6292)
Delta = np.radians(22.0144)
# Properties of the GW data
sqrtSX = 1e-23
tstart = 1000000000
duration = 100*86400
tend = tstart+duration
tref = .5*(tstart+tend)
depth = 40
label = 'semicoherent_directed_follow_up'
outdir = 'data'
h0 = sqrtSX / depth
data = pyfstat.Writer(
label=label, outdir=outdir, tref=tref, tstart=tstart, F0=F0, F1=F1,
F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX)
data.make_data()
# The predicted twoF, given by lalapps_predictFstat can be accessed by
twoF = data.predict_fstat()
print 'Predicted twoF value: {}\n'.format(twoF)
# Search
VF0 = VF1 = 1e5
DeltaF0 = np.sqrt(VF0) * np.sqrt(3)/(np.pi*duration)
DeltaF1 = np.sqrt(VF1) * np.sqrt(180)/(np.pi*duration**2)
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': Alpha,
'Delta': Delta
}
ntemps = 3
log10beta_min = -0.5
nwalkers = 100
nsteps = [100, 100]
mcmc = pyfstat.MCMCFollowUpSearch(
label=label, outdir=outdir,
sftfilepattern='{}/*{}*sft'.format(outdir, label),
theta_prior=theta_prior, tref=tref, minStartTime=tstart, maxStartTime=tend,
nwalkers=nwalkers, nsteps=nsteps, ntemps=ntemps,
log10beta_min=log10beta_min)
NstarMax = 1000
Nsegs0 = 100
fig, axes = plt.subplots(nrows=2, figsize=(3.4, 3.5))
fig, axes = mcmc.run(
NstarMax=NstarMax, Nsegs0=Nsegs0, labelpad=0.01,
plot_det_stat=False, return_fig=True, fig=fig,
axes=axes)
for ax in axes:
ax.grid()
ax.set_xticks(np.arange(0, 600, 100))
ax.set_xticklabels([str(s) for s in np.arange(0, 700, 100)])
axes[-1].set_xlabel(r'$\textrm{Number of steps}$', labelpad=0.1)
fig.tight_layout()
fig.savefig('{}/{}_walkers.png'.format(mcmc.outdir, mcmc.label), dpi=400)