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Commit 5d735435 authored by Gregory Ashton's avatar Gregory Ashton
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New examples on using the glitch_robust search

parent a09a1bbe
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examples/glitch_robust_search.py 0 → 100644
View file @ 5d735435
import numpy as np
import pyfstat
outdir = 'data'
label = 'glitch_robust_search'
# Properties of the GW data
tstart = 1000000000
Tspan = 60 * 86400
# Fixed properties of the signal
F0s = 30
F1s = -1e-8
F2s = 0
Alpha = np.radians(83.6292)
Delta = np.radians(22.0144)
tref = tstart + .5 * Tspan
sftfilepath = 'data/*glitching_signal*sft'
F0_width = np.sqrt(3)/(np.pi*Tspan)
F1_width = np.sqrt(45/4.)/(np.pi*Tspan**2)
DeltaF0 = 50 * F0_width
DeltaF1 = 50 * F1_width
theta_prior = {'F0': {'type': 'unif',
'lower': F0s-DeltaF0,
'upper': F0s+DeltaF0},
'F1': {'type': 'unif',
'lower': F1s-DeltaF1,
'upper': F1s+DeltaF1},
'F2': F2s,
'delta_F0': {'type': 'unif',
'lower': 0,
'upper': 1e-5},
'delta_F1': {'type': 'unif',
'lower': -1e-11,
'upper': 1e-11},
'tglitch': {'type': 'unif',
'lower': tstart+0.1*Tspan,
'upper': tstart+0.9*Tspan},
'Alpha': Alpha,
'Delta': Delta,
}
search = pyfstat.MCMCGlitchSearch(
label=label, outdir=outdir, sftfilepath=sftfilepath,
theta_prior=theta_prior, nglitch=1, tref=tref, nsteps=[500, 500],
ntemps=3, log10temperature_min=-0.5, minStartTime=tstart,
maxStartTime=tstart+Tspan)
search.run()
search.plot_corner(label_offset=0.8, add_prior=True)
search.print_summary()
examples/glitch_robust_search_make_simulated_data.py 0 → 100644
View file @ 5d735435
import numpy as np
import pyfstat
outdir = 'data'
label = 'simulated_glitching_signal'
# Properties of the GW data
tstart = 1000000000
Tspan = 60 * 86400
tref = tstart + .5 * Tspan
# Fixed properties of the signal
F0s = 30
F1s = -1e-8
F2s = 0
Alpha = np.radians(83.6292)
Delta = np.radians(22.0144)
h0 = 1e-25
sqrtSX = 1e-24
psi = -0.1
phi = 0
cosi = 0.5
# Glitch properties
dtglitch = 0.45 * Tspan # time (in secs) after minStartTime
dF0 = 5e-6
dF1 = 1e-12
detectors = 'H1'
glitch_data = pyfstat.Writer(
label=label, outdir=outdir, tref=tref, tstart=tstart,
F0=F0s, F1=F1s, F2=F2s, duration=Tspan, Alpha=Alpha,
Delta=Delta, sqrtSX=sqrtSX, dtglitch=dtglitch,
h0=h0, cosi=cosi, phi=phi, psi=psi,
delta_F0=dF0, delta_F1=dF1, add_noise=True)
glitch_data.make_data()
examples/transient_search_using_MCMC.py
View file @ 5d735435
import pyfstat
import numpy as np
import matplotlib.pyplot as plt
#!/usr/bin/env python
plt.style.use('thesis')
import pyfstat
F0 = 30.0
F1 = -1e-10
F2 = 0
Alpha = 5e-3
Delta = 6e-2
tstart = 1000000000
duration = 100*86400
data_tstart = tstart - duration
data_tend = data_tstart + 3*duration
tref = .5*(data_tstart+data_tend)
h0 = 1e-23
sqrtSX = 1e-22
transient = pyfstat.Writer(
label='transient', outdir='data', tref=tref, tstart=tstart, F0=F0, F1=F1,
F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX,
minStartTime=data_tstart, maxStartTime=data_tend)
transient.make_data()
print transient.predict_fstat()
Alpha = 0.5
Delta = 1
minStartTime = 1000000000
maxStartTime = minStartTime + 200*86400
Tspan = maxStartTime - minStartTime
tref = minStartTime
DeltaF0 = 6e-7
DeltaF1 = 1e-13
VF0 = (np.pi * duration * DeltaF0)**2 / 3.0
VF1 = (np.pi * duration**2 * DeltaF1)**2 * 4/45.
print '\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1)
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
log10temperature_min = -1
nwalkers = 100
nsteps = [750, 250]
mcmc = pyfstat.MCMCSearch(
label='transient_search_initial_stage', outdir='data',
sftfilepath='data/*transient*sft', theta_prior=theta_prior, tref=tref,
minStartTime=data_tstart, maxStartTime=data_tend, nsteps=nsteps,
nwalkers=nwalkers, ntemps=ntemps,
log10temperature_min=log10temperature_min)
mcmc.run()
mcmc.plot_cumulative_max()
mcmc.print_summary()
theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2.,
......@@ -70,22 +26,24 @@ theta_prior = {'F0': {'type': 'unif',
'Alpha': Alpha,
'Delta': Delta,
'transient_tstart': {'type': 'unif',
'lower': data_tstart,
'upper': data_tend},
'lower': minStartTime,
'upper': maxStartTime},
'transient_duration': {'type': 'halfnorm',
'loc': 0,
'scale': 0.5*duration}
'loc': 0.001*Tspan,
'scale': 0.5*Tspan}
}
nwalkers = 500
nsteps = [200, 200]
ntemps = 2
log10temperature_min = -1
nwalkers = 100
nsteps = [100, 100]
mcmc = pyfstat.MCMCTransientSearch(
label='transient_search', outdir='data',
sftfilepath='data/*transient*sft', theta_prior=theta_prior, tref=tref,
minStartTime=data_tstart, maxStartTime=data_tend, nsteps=nsteps,
nwalkers=nwalkers, ntemps=ntemps,
sftfilepath='data/*simulated_transient_signal*sft',
theta_prior=theta_prior, tref=tref, minStartTime=minStartTime,
maxStartTime=maxStartTime, nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
log10temperature_min=log10temperature_min)
mcmc.run()
mcmc.plot_corner(add_prior=True)
mcmc.plot_corner(label_offset=0.7)
mcmc.print_summary()
examples/transient_search_using_MCMC_make_simulated_data.py 0 → 100644
View file @ 5d735435
#!/usr/bin/env python
import pyfstat
F0 = 30.0
F1 = -1e-10
F2 = 0
Alpha = 0.5
Delta = 1
minStartTime = 1000000000
maxStartTime = minStartTime + 200*86400
transient_tstart = minStartTime + 50*86400
transient_duration = 100*86400
tref = minStartTime
h0 = 1e-23
sqrtSX = 1e-22
transient = pyfstat.Writer(
label='simulated_transient_signal', outdir='data', tref=tref,
tstart=transient_tstart, F0=F0, F1=F1, F2=F2, duration=transient_duration,
Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX, minStartTime=minStartTime,
maxStartTime=maxStartTime)
transient.make_data()
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