Improves the documentation

- Add doc file on CBC PE
- Add introduction text

docs/basics-of-parameter-estimation.txt

@@ -87,7 +87,6 @@ a likelihood and prior, and running nested sampling using `tupak`.
 
 .. literalinclude:: /../examples/other_examples/linear_regression.py
    :language: python
-   :emphasize-lines: 12,15-18
    :linenos:
 
 Running the script above will make a few images. Firstly, the plot of the data:

docs/index.txt

@@ -9,6 +9,7 @@ Welcome to tupak's documentation!
    :caption: Contents:
 
    basics-of-parameter-estimation
+   compact-binary-coalescence-parameter-estimation
    tupak-output
    likelihood
    samplers

examples/injection_examples/basic_tutorial.py

@@ -47,12 +47,15 @@ priors = dict()
 # so for this example we will set almost all of the priors to be equall to their injected values.  This implies the
 # prior is a delta function at the true, injected value.  In reality, the sampler implementation is smart enough to
 # not sample any parameter that has a delta-function prior.
-for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi', 'ra', 'dec', 'geocent_time']:
+for key in ['a_1', 'a_2', 'tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'psi', 'ra', 'dec', 'luminosity_distance', 'iota']:
     priors[key] = injection_parameters[key]
 
 # The above list does *not* include mass_1, mass_2, iota and luminosity_distance, which means those are the parameters
 # that will be included in the sampler.  If we do nothing, then the default priors get used.
-priors['luminosity_distance'] = tupak.prior.create_default_prior(name='luminosity_distance')
+#priors['luminosity_distance'] = tupak.prior.create_default_prior(name='luminosity_distance')
+priors['geocent_time'] = tupak.prior.Uniform(injection_parameters['geocent_time'] - 1,
+                                            injection_parameters['geocent_time'] + 1,
+                                            'geocent_time')
 
 # Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
 likelihood = tupak.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)

tupak/__init__.py

@@ -4,7 +4,12 @@ tupak
 
 Tupak is The User friendly Parameter estimAtion Kode
 
-FILL IN THE REST
+The aim of tupak is to provide user friendly interface to perform parameter
+estimation. It is primarily designed and built for inference of compact
+binary coalesence events in interferometric data, but it can also be used for
+more general problems.
+
+For installation instructions see https://git.ligo.org/Monash/tupak
 
 """