Commit c6b4c6b8 authored by Gregory Ashton's avatar Gregory Ashton
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Updates to docsumantion to get all links and images displaying

parent 92956a08
......@@ -12,8 +12,10 @@ 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_using_MCMC.md)
* [Fully coherent MCMC search on data containing glitching signals](docs/fully_coherent_search_using_MCMC_on_glitching_data.md)
* [Fully-coherent MCMC search](docs/fully_coherent_search_using_MCMC.md)
* [Fully-coherent MCMC search on data containing a single glitch](docs/fully_coherent_search_using_MCMC_on_glitching_data.md)
* [Semi-coherent MCMC glitch-search on data containing a single glitch](docs/semi_coherent_glitch_search_using_MCMC_on_glitching_data.md)
* [Semi-coherent MCMC glitch-search on data containing two glitches](docs/semi_coherent_glitch_search_with_two_glitches_using_MCMC_on_glitching_data.md)
## Installation
......
......@@ -55,5 +55,9 @@ mcmc.plot_corner(add_prior=True)
mcmc.print_summary()
```
The posterior for this search demonstrates that we recover the input parameters:
![](img/semi_coherent_search_using_MCMC_corner.png)
The output png's for the initialisation and burnin/production steps:
![](img/semi_coherent_glitch_search_using_MCMC_init_0_walkers.png)
![](img/semi_coherent_glitch_search_using_MCMC_walkers.png)
and the final posterior estimates:
![](img/semi_coherent_glitch_search_using_MCMC_corner.png)
# Semi-coherent glitch search on data with two-glitches using MCMC
In this example, based on [this
script](../examples/semi_coherent_twoglitch_search_using_MCMC.py), we show the
basic setup for a two-glitch search. We begin by defining the prior:
```python
import pyfstat
F0 = 30.0
F1 = -1e-10
F2 = 0
Alpha = 5e-3
Delta = 6e-2
tref = 362750407.0
tstart = 1000000000
duration = 100*86400
tend = tstart + duration
theta_prior = {'F0': {'type': 'norm', 'loc': F0, 'scale': abs(1e-6*F0)},
'F1': {'type': 'norm', 'loc': F1, 'scale': abs(1e-6*F1)},
'F2': F2,
'Alpha': Alpha,
'Delta': Delta,
'delta_F0_0': {'type': 'halfnorm', 'loc': 0,
'scale': 1e-7*F0},
'delta_F0_1': {'type': 'halfnorm', 'loc': 0,
'scale': 1e-7*F0},
'delta_F1_0': 0,
'delta_F1_1': 0,
'tglitch_0': {'type': 'unif',
'lower': tstart+0.01*duration,
'upper': tstart+0.5*duration},
'tglitch_1': {'type': 'unif',
'lower': tstart+0.5*duration,
'upper': tstart+0.99*duration},
}
```
Note that, in this case, we define a prior for each of the two glitches.
Alternatively, one can provide a prior (with no indexing) which is applied to
all glitches. The sampler has a prior specification to sort the glitches such
that `tglitch_0 < tglitch_1 < ...`.
The outputs plots:
![](img/semi_coherent_twoglitch_search_init_0_walkers.png)
![](img/semi_coherent_twoglitch_search_walkers.png)
![](img/semi_coherent_twoglitch_search_corner.png)
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