diff --git a/README.md b/README.md index 4a82e683aae2f696417c5e5fd3e181ae952d738b..aca4d7c8a800905e6b8fcd76c013265d15909e90 100644 --- a/README.md +++ b/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 diff --git a/docs/fully_coherent_search_using_MCMC.md b/docs/fully_coherent_search_using_MCMC.md index 3e0de8cd9e4df903243c3017cddada426f419f91..b27a195fe5e80776e02438e1f636f68f0984f12e 100644 --- a/docs/fully_coherent_search_using_MCMC.md +++ b/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. diff --git a/docs/img/fully_coherent_search_using_MCMC_walkers.png b/docs/img/fully_coherent_search_using_MCMC_walkers.png index af2d507c69f5c0880b58a19a470112dd54c28745..aed498584cf8492b930492798e54aa0c3a688b0d 100644 Binary files a/docs/img/fully_coherent_search_using_MCMC_walkers.png and b/docs/img/fully_coherent_search_using_MCMC_walkers.png differ diff --git a/examples/make_fake_data.py b/examples/make_fake_data.py index 1cc195420991a170af1cce1c3e61c86eac09f5c6..eeadca9108ea1cea366e786e0029b384ad48eab0 100644 --- a/examples/make_fake_data.py +++ b/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