Update the multivariate Gaussian likelihood prior example

examples/other_examples/multivariate_gaussian_prior.py

@@ -7,6 +7,8 @@ Gaussian prior distribution.
 from __future__ import division
 import bilby
 import numpy as np
+from scipy import linalg, stats
+import matplotlib as mpl
 
 from bilby.core.likelihood import GaussianLikelihood
 
@@ -34,16 +36,15 @@ N = len(time)
 data = model(time, 0., 0.)  # noiseless data
 
 
-# Now lets instantiate a version of our GaussianLikelihood, giving it
-# the time, data, signal model and standard deviation
+# instantiate the GaussianLikelihood
 likelihood = GaussianLikelihood(time, data, model, sigma=sigma)
 
 # Create a Multivariate Gaussian prior distribution with two modes
 names = ['m', 'c']
-mus = [[-5., -5.], [5., 5.]]  # means of two modes
-corrcoefs = [[[1., -0.7], [-0.7, 1.]], [[1., -0.7], [-0.7, 1.]]]  # correlation coefficients of the modes
-sigmas = [[1.5, 1.5], [1.5, 1.5]]  # standard deviations of the modes
-weights = [0.5, 0.5]  # weights of each mode
+mus = [[-5., -5.], [5., 5.]]  # means of the two modes
+corrcoefs = [[[1., -0.7], [-0.7, 1.]], [[1., 0.7], [0.7, 1.]]]  # correlation coefficients of the two modes
+sigmas = [[1.5, 1.5], [2.1, 2.1]]  # standard deviations of the two modes
+weights = [1., 3.]  # relative weights of each mode
 nmodes = 2
 mvg = bilby.core.prior.MultivariateGaussianDist(names, nmodes=2, mus=mus,
                                                 corrcoefs=corrcoefs,
@@ -52,20 +53,34 @@ 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',
-#    outdir=outdir, draws=2000, label=label)
-
 result = bilby.run_sampler(
     likelihood=likelihood, priors=priors, sampler='dynesty', nlive=4000,
     outdir=outdir, label=label)
 
-# result = bilby.run_sampler(
-#     likelihood=likelihood, priors=priors, sampler='nestle', nlive=4000,
-#     outdir=outdir, label=label)
+fig = result.plot_corner(save=False)
+
+# plot the priors (to show that they look correct)
+axs = fig.get_axes()
+
+# plot the 1d marginal distributions
+x = np.linspace(-12, 12, 5000)
+aidx = [0, 3]
+for j in range(2):  # loop over parameters
+    gp = np.zeros(len(x))
+    for i in range(nmodes):  # loop over modes
+        gp += weights[i]*stats.norm.pdf(x, loc=mus[i][j], scale=mvg.sigmas[i][j])
+    gp = gp/np.trapz(gp, x)  # renormalise
+    
+    axs[aidx[j]].plot(x, gp, 'k--', lw=2)
+
+# plot the 2d distribution
+for i in range(nmodes):
+    v, w = linalg.eigh(mvg.covs[i])
+    v = 2. * np.sqrt(2.) * np.sqrt(v)
+    u = w[0] / linalg.norm(w[0])
+    angle = np.arctan(u[1] / u[0])
+    angle = 180. * angle / np.pi  # convert to degrees
+    ell = mpl.patches.Ellipse(mus[i], v[0], v[1], 180. + angle, edgecolor='black', facecolor='none', lw=2, ls='--')
+    axs[2].add_artist(ell)
 
-# result = bilby.run_sampler(
-#     likelihood=likelihood, priors=priors, sampler='emcee', nsteps=1000,
-#     nwalkers=200, nburn=500, outdir=outdir, label=label)
-result.plot_corner()
\ No newline at end of file
+fig.savefig('{}/{}_corner.png'.format(outdir, label), dpi=300)

sampler_requirements.txt

@@ -3,5 +3,5 @@ dynesty
 emcee
 nestle
 ptemcee
-pymc3
+pymc3>=3.6
 pymultinest
\ No newline at end of file