From 05bd9620f2d99970080e3d24d44938c065dcc1c0 Mon Sep 17 00:00:00 2001
From: Colm Talbot <colm.talbot@ligo.org>
Date: Mon, 11 Jun 2018 14:55:24 +1000
Subject: [PATCH] update example on specifying the prior
---
.../injection_examples/how_to_specify_the_prior.py | 12 +++---------
1 file changed, 3 insertions(+), 9 deletions(-)
diff --git a/examples/injection_examples/how_to_specify_the_prior.py b/examples/injection_examples/how_to_specify_the_prior.py
index e6d3aa8c1..67e3e209b 100644
--- a/examples/injection_examples/how_to_specify_the_prior.py
+++ b/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.
--
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