update example on specifying the prior

examples/injection_examples/how_to_specify_the_prior.py

@@ -33,20 +33,13 @@ IFOs = [tupak.gw.detector.get_interferometer_with_fake_noise_and_injection(
     sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
 
 # Set up prior
-priors = dict()
+# This loads in a predefined set of priors for BBHs.
+priors = tupak.gw.prior.BBHPriorSet()
 # These parameters will not be sampled
 for key in ['tilt_1', 'tilt_2', 'phi_12', 'phi_jl', 'phase', 'iota', 'ra', 'dec', 'geocent_time', 'psi']:
     priors[key] = injection_parameters[key]
-# We can assign a default prior distribution, note this only works for certain parameters.
-priors['mass_1'] = tupak.core.prior.create_default_prior(name='mass_1')
 # We can make uniform distributions.
 priors['mass_2'] = tupak.core.prior.Uniform(name='mass_2', minimum=20, maximum=40)
-# We can load a prior distribution from a file, e.g., a uniform in comoving volume distribution.
-# If no path is given it will look in it's directory of known distributions.
-# Note: that this file is used for the default prior distribution for distance.
-# Also note: this special case is coded in as tupak.prior.UniformComovingVolume.
-priors['luminosity_distance'] = tupak.core.prior.FromFile('comoving.txt', name='luminosity_distance',
-                                                          minimum=1e3, maximum=5e3)
 # We can make a power-law distribution, p(x) ~ x^{alpha}
 # Note: alpha=0 is a uniform distribution, alpha=-1 is uniform-in-log
 priors['a_1'] = tupak.core.prior.PowerLaw(name='a_1', alpha=-1, minimum=1e-2, maximum=1)
@@ -56,6 +49,7 @@ a_2 = np.linspace(0, 1, 1001)
 p_a_2 = a_2 ** 4
 priors['a_2'] = tupak.core.prior.Interped(name='a_2', xx=a_2, yy=p_a_2, minimum=0, maximum=0.5)
 # Additionally, we have Gaussian, TruncatedGaussian, Sine and Cosine.
+# It's also possible to load an interpolate a prior from a file.
 # Finally, if you don't specify any necessary parameters it will be filled in from the default when the sampler starts.
 # Enjoy.