Add example for using multivariate Gaussian prior

examples/other_examples/multivariate_gaussian_prior.py

+#!/usr/bin/env python
+"""
+An example of how to use bilby with a (multi-modal) multivariate
+Gaussian prior distribution.
+"""
+
+from __future__ import division
+import bilby
+import numpy as np
+import matplotlib.pyplot as plt
+
+from bilby.core.likelihood import GaussianLikelihood
+
+# A few simple setup steps
+label = 'multivariate_gaussian_prior'
+outdir = 'outdir'
+bilby.utils.check_directory_exists_and_if_not_mkdir(outdir)
+
+
+# First, we define our "signal model", in this case a simple linear function
+def model(time, m, c):
+    return time * m + c
+
+
+# Now we define the injection parameters which we make simulated data with
+injection_parameters = dict(m=0.0, c=0.0)
+
+# For this example, we'll use standard Gaussian noise
+sigma = 200.
+
+# These lines of code generate the fake data. Note the ** just unpacks the
+# contents of the injection_parameters when calling the model function.
+sampling_frequency = 10
+time_duration = 10
+time = np.arange(0, time_duration, 1 / sampling_frequency)
+N = len(time)
+data = model(time, **injection_parameters) + np.random.normal(0, sigma, N)
+
+# We quickly plot the data to check it looks sensible
+fig, ax = plt.subplots()
+ax.plot(time, data, 'o', label='data')
+ax.plot(time, model(time, **injection_parameters), '--r', label='signal')
+ax.set_xlabel('time')
+ax.set_ylabel('y')
+ax.legend()
+fig.savefig('{}/{}_data.png'.format(outdir, label))
+
+# Now lets instantiate a version of our GaussianLikelihood, giving it
+# the time, data and signal model
+likelihood = GaussianLikelihood(time, data, model, sigma=sigma)
+
+# Create a Multivariate Gaussian prior distribution with two modes
+names = ['m', 'c']
+mus = [[-3., -3.], [5., 5.]]  # means of two modes
+corrcoefs = [[[1., 0.], [0., 1.]], [[1., 0.95], [0.95, 1.]]]  # correlation coefficients of the modes
+sigmas = [[1., 1.], [2., 2.]]  # standard deviations of the modes
+weights = [0.5, 0.5]  # weights of each mode
+nmodes = 2
+mvg = bilby.core.prior.MultivariateGaussianDist(names, nmodes=2, mus=mus,
+                                                corrcoefs=corrcoefs,
+                                                sigmas=sigmas, weights=weights)
+priors = dict()
+priors['m'] = bilby.core.prior.MultivariateGaussian(mvg, 'm')
+priors['c'] = bilby.core.prior.MultivariateGaussian(mvg, 'c')
+
+# And run sampler
+#result = bilby.run_sampler(
+#    likelihood=likelihood, priors=priors, sampler='pymc3',
+#    injection_parameters=injection_parameters, outdir=outdir,
+#    draws=2000, label=label)
+result = bilby.run_sampler(
+    likelihood=likelihood, priors=priors, sampler='dynesty', nlive=2000,
+    outdir=outdir, label=label)
+#result = bilby.run_sampler(
+#    likelihood=likelihood, priors=priors, sampler='emcee', nsteps=100,
+#    nwalkers=100, outdir=outdir, label=label)
+result.plot_corner()