Commit 115bcbac authored by Gregory Ashton's avatar Gregory Ashton
Browse files

Incomplete changes to the documentation: this needs finishing

parent 98dbe528
......@@ -12,8 +12,8 @@ output which we list below. Before running any of the search examples, be sure
to have run the [script to generate fake data](examples/
* [Making fake data with and without glitches](docs/
* [Fully coherent MCMC search](docs/
* [Fully coherent MCMC search on data containing glitching signals](docs/
* [Fully coherent MCMC search](docs/fully_coherent_search_using_MCMC).md)
* [Fully coherent MCMC search on data containing glitching signals](docs/
## Installation
......@@ -88,7 +88,7 @@ This shows (in red) the position of the walkers during the burn-in stage. They
are initially defuse (they start from positions randomly picked from the prior),
but eventually converge to a single stable solution. The black is the production
period from which posterior estimates are made. The bottom panel is a histogram
of `twoF`, split into production and burn-in. Note that, early on there are
of `twoF`, split for the production period. Note that, early on there are
multiple modes corresponding to other peaks, by using the parallel tempering,
we allow the walkers to explore all of these peaks and opt for the strong
central candidate.
......@@ -24,6 +24,7 @@ data = Writer(
F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX)
print 'Predicted fstat value:', data.predict_fstat()
# Next, taking the same signal parameters, we include a glitch half way through
dtglitch = duration/2.0
......@@ -33,13 +34,13 @@ delta_F1 = 0
glitch_data = Writer(
label='glitch', outdir='data', tref=tref, tstart=tstart, F0=F0, F1=F1,
F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX,
dtglitch=dtglitch, delta_F0=delta_F0, delta_F1=delta_F1, detector='H1,L1')
dtglitch=dtglitch, delta_F0=delta_F0, delta_F1=delta_F1, detector='L1')
# The predicted twoF, given by lalapps_predictFstat can be accessed by
print data.predict_fstat()
print 'Predicted fstat value:', data.predict_fstat()
# Making data with two glitches
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