Incomplete changes to the documentation: this needs finishing

README.md

@@ -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/make_fake_data.py).
 
 * [Making fake data with and without glitches](docs/make_fake_data.md)
-* [Fully coherent MCMC search](docs/fully_coherent_search.md)
-* [Fully coherent MCMC search on data containing glitching signals](docs/fully_coherent_search_on_glitching_data.md)
+* [Fully coherent MCMC search](docs/fully_coherent_search_using_MCMC).md)
+* [Fully coherent MCMC search on data containing glitching signals](docs/fully_coherent_search_using_MCMC_on_glitching_data.md)
 
 ## Installation
 

docs/fully_coherent_search_using_MCMC.md

@@ -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.

examples/make_fake_data.py

@@ -24,6 +24,7 @@ data = Writer(
     F2=F2, duration=duration, Alpha=Alpha, Delta=Delta, h0=h0, sqrtSX=sqrtSX)
 data.make_data()
 
+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')
 glitch_data.make_data()
 
 
 # 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