diff --git a/Paper/paper_cw_mcmc.tex b/Paper/paper_cw_mcmc.tex
index 2c91873c1670b28e57ec714147d70b58930987de..c0ed37eac78a5607bb926407875988e518332093 100644
--- a/Paper/paper_cw_mcmc.tex
+++ b/Paper/paper_cw_mcmc.tex
@@ -792,7 +792,7 @@ We now provide an illustrative example of the follow-up method. We consider a
directed search over the sky position and frequency in 100 days of data from a
single detector, with $\sqrt{\Sn}=10^{-23}$~Hz$^{-1/2}$ (at the fiducial
frequency of the signal). The simulated signal has an amplitude
-$h_0=1.4\times10^{25}$ such that the signal has a depth of $\sqrt{\Sn}/h_0=70$
+$h_0=2\times10^{-25}$ such that the signal has a depth of $\sqrt{\Sn}/h_0=50$
in the noise.
First, we must define the setup for the run. Using $\mathcal{R}=10$ and
@@ -834,14 +834,28 @@ are listed in Table~\ref{tab_weak_signal_follow_up}.}
\end{figure}
The key advantage to note here is that all walkers succefully convereged to the
-signal peak, which occupies $\approx 10^{-6}$ of the initial volume. While it
-is possible for this to occur during an ordinary MCMC simulation (with $\Tcoh$
-fixed at $\Tspan$), it would take much longer to converge as the chains explore
-the other `noise peaks' in the data.
+signal peak, which occupies $\sim 10^{-6}$ of the initial volume. While it is
+possible for this to occur during an ordinary MCMC simulation (with $\Tcoh$
+fixed at $\Tspan$), it would take substantially longer to converge as the
+chains explore the other `noise peaks' in the data.
\section{Alternative waveform models: transients}
\label{sec_transients}
+\begin{figure}[htb]
+\centering
+\includegraphics[width=0.5\textwidth]{transient_search_initial_stage_twoFcumulative}
+\caption{}
+\label{fig:}
+\end{figure}
+
+\begin{figure}[htb]
+\centering
+\includegraphics[width=0.5\textwidth]{transient_search_corner}
+\caption{}
+\label{fig:}
+\end{figure}
+
\section{Alternative waveform models: glitches}
\label{sec_glitches}
diff --git a/Paper/transient_search_corner.png b/Paper/transient_search_corner.png
new file mode 100644
index 0000000000000000000000000000000000000000..7507dd26a823a3a5776dcd2db0d23e5458fb719a
Binary files /dev/null and b/Paper/transient_search_corner.png differ
diff --git a/Paper/transient_search_initial_stage_twoFcumulative.png b/Paper/transient_search_initial_stage_twoFcumulative.png
new file mode 100644
index 0000000000000000000000000000000000000000..460e9da5b93382f17fe017898678aed340a8f658
Binary files /dev/null and b/Paper/transient_search_initial_stage_twoFcumulative.png differ
diff --git a/Paper/weak_signal_follow_up_run_setup.tex b/Paper/weak_signal_follow_up_run_setup.tex
index 6a77ddc95edd2997fc634a628bd434651bca561a..a43fb4a45d09ee85d526acbd63a7e59264aa5d92 100644
--- a/Paper/weak_signal_follow_up_run_setup.tex
+++ b/Paper/weak_signal_follow_up_run_setup.tex
@@ -1,9 +1,9 @@
\begin{tabular}{c|cccccc}
Stage & $\Nseg$ & $\Tcoh^{\rm days}$ &$\Nsteps$ & $\V$ & $\Vsky$ & $\Vpe$ \\ \hline
-0 & 93 & 1.1 & 100 & 10.0 & 1.0 & 10.0 \\
-1 & 43 & 2.3 & 100 & $1{\times}10^{2}$ & 6.0 & 20.0 \\
-2 & 20 & 5.0 & 100 & $1{\times}10^{3}$ & 30.0 & 50.0 \\
-3 & 9 & 11.1 & 100 & $1{\times}10^{4}$ & $1{\times}10^{2}$ & $1{\times}10^{2}$ \\
-4 & 4 & 25.0 & 100 & $1{\times}10^{5}$ & $6{\times}10^{2}$ & $2{\times}10^{2}$ \\
-5 & 1 & 100.0 & 100,100 & $1{\times}10^{6}$ & $1{\times}10^{3}$ & $9{\times}10^{2}$ \\
+0 & 93 & 1.1 & 100 & 20.0 & 2.0 & 10.0 \\
+1 & 43 & 2.3 & 100 & $2{\times}10^{2}$ & 10.0 & 20.0 \\
+2 & 20 & 5.0 & 100 & $2{\times}10^{3}$ & 50.0 & 50.0 \\
+3 & 9 & 11.1 & 100 & $2{\times}10^{4}$ & $2{\times}10^{2}$ & $1{\times}10^{2}$ \\
+4 & 4 & 25.0 & 100 & $2{\times}10^{5}$ & $1{\times}10^{3}$ & $2{\times}10^{2}$ \\
+5 & 1 & 100.0 & 100,100 & $2{\times}10^{6}$ & $3{\times}10^{3}$ & $9{\times}10^{2}$ \\
\end{tabular}
diff --git a/Paper/weak_signal_follow_up_walkers.png b/Paper/weak_signal_follow_up_walkers.png
index f389425ce0d15083b02cf310a0463e0db2a74cfd..d901ea5dce2cc0b572741a0c426c12d435ebe792 100644
Binary files a/Paper/weak_signal_follow_up_walkers.png and b/Paper/weak_signal_follow_up_walkers.png differ
diff --git a/examples/make_fake_data.py b/examples/make_fake_data.py
index 62c9d95cd54970ac278b148d63290ba36f23f5a3..e645dbd31facb55b5105a371513c372fe26db8ea 100644
--- a/examples/make_fake_data.py
+++ b/examples/make_fake_data.py
@@ -53,14 +53,3 @@ two_glitch_data = Writer(
dtglitch=dtglitch, delta_phi=delta_phi, delta_F0=delta_F0,
delta_F1=delta_F1, delta_F2=delta_F2)
two_glitch_data.make_data()
-
-
-# Making transient data in the middle third
-data_tstart = tstart - duration
-data_duration = 3 * duration
-transient = 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,
- data_tstart=data_tstart, data_duration=data_duration)
-transient.make_data()
-
diff --git a/examples/transient_search_using_MCMC.py b/examples/transient_search_using_MCMC.py
index 7fc7202bd39f2b3a0c23f64b784a335f1952fad2..5a0738a80544b69f468a7a125798c7ad308c56f2 100644
--- a/examples/transient_search_using_MCMC.py
+++ b/examples/transient_search_using_MCMC.py
@@ -1,35 +1,83 @@
-from pyfstat import MCMCTransientSearch
+import pyfstat
+import numpy as np
F0 = 30.0
F1 = -1e-10
F2 = 0
Alpha = 5e-3
Delta = 6e-2
-tref = 362750407.0
tstart = 1000000000
duration = 100*86400
-tstart = 1000000000 - duration
-tend = tstart + 3*duration
+data_tstart = tstart - duration
+data_tend = data_tstart + 3*duration
+tref = .5*(data_tstart+data_tend)
-theta_prior = {'F0': {'type': 'unif', 'lower': F0*(1-1e-6), 'upper': F0*(1+1e-6)},
- 'F1': {'type': 'unif', 'lower': F1*(1+1e-2), 'upper': F1*(1-1e-2)},
+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()
+
+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,
- 'transient_tstart': {'type': 'unif', 'lower': tstart, 'upper': tend},
- 'transient_duration': {'type': 'halfnorm', 'loc':0, 'scale': duration}
+ 'Delta': Delta
}
-ntemps = 4
+ntemps = 3
log10temperature_min = -1
nwalkers = 100
-nsteps = [1000, 1000]
+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()
+
+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,
+ 'transient_tstart': {'type': 'unif',
+ 'lower': data_tstart,
+ 'upper': data_tend},
+ 'transient_duration': {'type': 'halfnorm',
+ 'loc': 0,
+ 'scale': 0.5*duration}
+ }
+
+nwalkers = 500
+nsteps = [200, 200]
-mcmc = MCMCTransientSearch(
- label='transient_search_using_MCMC', outdir='data',
+mcmc = pyfstat.MCMCTransientSearch(
+ label='transient_search', outdir='data',
sftfilepath='data/*transient*sft', theta_prior=theta_prior, tref=tref,
- tstart=tstart, tend=tend, nsteps=nsteps, nwalkers=nwalkers, ntemps=ntemps,
+ minStartTime=data_tstart, maxStartTime=data_tend, nsteps=nsteps,
+ nwalkers=nwalkers, ntemps=ntemps,
log10temperature_min=log10temperature_min)
mcmc.run()
mcmc.plot_corner(add_prior=True)
diff --git a/examples/weak_signal_follow_up.py b/examples/weak_signal_follow_up.py
index 488fe1a402e2e534071e57a3ed8b2fab8ecc042b..38bb9937ff44e64a8650f2f1a191ce3b1ac94f0d 100644
--- a/examples/weak_signal_follow_up.py
+++ b/examples/weak_signal_follow_up.py
@@ -13,7 +13,7 @@ duration = 100*86400
tend = tstart+duration
tref = .5*(tstart+tend)
-depth = 70
+depth = 50
data_label = 'weak_signal_follow_up_depth_{:1.0f}'.format(depth)
h0 = sqrtSX / depth
@@ -41,8 +41,8 @@ theta_prior = {'F0': {'type': 'unif', 'lower': F0*(1-1e-6),
}
ntemps = 3
-log10temperature_min = -1
-nwalkers = 200
+log10temperature_min = -0.5
+nwalkers = 100
scatter_val = 1e-10
nsteps = [100, 100]
@@ -52,7 +52,6 @@ mcmc = pyfstat.MCMCFollowUpSearch(
minStartTime=tstart, maxStartTime=tend, nwalkers=nwalkers, nsteps=nsteps,
ntemps=ntemps, log10temperature_min=log10temperature_min,
scatter_val=scatter_val)
-mcmc.run(R0=10, Vmin=100)
+mcmc.run(R0=10, Vmin=100, subtractions=[F0, Alpha, Delta], context='paper')
mcmc.plot_corner(add_prior=True)
mcmc.print_summary()
-#mcmc.generate_loudest()
diff --git a/pyfstat.py b/pyfstat.py
index 14561909ba43ae4de1962dd2e159b703835af02f..234f22a954db0098e9b35532fbfc3fc68259fc93 100755
--- a/pyfstat.py
+++ b/pyfstat.py
@@ -655,7 +655,8 @@ class ComputeFstat(object):
else:
ax.set_ylabel(r'$\widetilde{2\mathcal{F}}_{\rm cumulative}$')
ax.set_xlim(0, taus[-1]/86400)
- ax.set_title(title)
+ if title:
+ ax.set_title(title)
if savefig:
plt.savefig('{}/{}_twoFcumulative.png'.format(outdir, label))
return taus, twoFs