HealPixPrior (Resloves Issue #419)

Hi all, This MR introduces a HealPixMapPriorDist object to be used to create a 2D or 3D joint prior in ra/dec (ra/dec/distance) according to a passed in HealPix map or .fits file to address and resolve issue #419 (closed).

This PriorObject inherits from BaseJointPriorDist and needs to overwrite three methods (_rescale(), _sample(), and _lnprob()) To illustrate this is working I sample 5000 samples from the prior and show below that these samples are distribvuted according to whats in the HealPix Map .fits file.

Here I show that the _rescale() method is also working by generating 5000 samples uniformly on [0,1] then rescale to the HealPixPrior which also matches with what is in the map file.

bilby/core/prior/joint.py

@@ -284,7 +284,7 @@ class BaseJointPriorDist(object):
             An vector sample drawn from the multivariate Gaussian
             distribution.
         """
-        samp = np.asarray(value)
+        samp = np.array(value)
         if len(samp.shape) == 1:
             samp = samp.reshape(1, self.num_vars)
 
@@ -360,8 +360,8 @@ class MultivariateGaussianDist(BaseJointPriorDist):
             +/- infinity.
         """
         super(MultivariateGaussianDist, self).__init__(names=names, bounds=bounds)
-        for name in names:
-            bound = bounds[name]
+        for name in self.names:
+            bound = self.bounds[name]
             if bound[0] != -np.inf or bound[1] != np.inf:
                 logger.warning("If using bounded ranges on the multivariate "
                                "Gaussian this will lead to biased posteriors "
@@ -378,10 +378,6 @@ class MultivariateGaussianDist(BaseJointPriorDist):
         self.sqeigvalues = []  # square root of the eigenvalues
         self.mvn = []  # list of multivariate normal distributions
 
-        self._current_sample = {}  # initialise empty sample
-        self._uncorrelated = None
-        self._current_lnprob = None
-
         # put values in lists if required
         if nmodes == 1:
             if mus is not None: