Merge branch 'add-posterior-probability-calculation' into 'master'

Adds two new methods to the result object: kde and posterior_probability

See merge request !278

bilby/core/result.py

@@ -8,6 +8,7 @@ import numpy as np
 import deepdish
 import pandas as pd
 import corner
+import scipy.stats
 import matplotlib
 import matplotlib.pyplot as plt
 
@@ -928,6 +929,44 @@ class Result(object):
                     return np.all(A == B)
         return False
 
+    @property
+    def kde(self):
+        """ Kernel density estimate built from the stored posterior
+
+        Uses `scipy.stats.gaussian_kde` to generate the kernel density
+        """
+        try:
+            return self._kde
+        except AttributeError:
+            self._kde = scipy.stats.gaussian_kde(
+                self.posterior[self.search_parameter_keys].values.T)
+            return self._kde
+
+    def posterior_probability(self, sample):
+        """ Calculate the posterior probabily for a new sample
+
+        This queries a Kernel Density Estimate of the posterior to calculate
+        the posterior probability density for the new sample.
+
+        Parameters
+        ----------
+        sample: dict, or list of dictionaries
+            A dictionary containing all the keys from
+            self.search_parameter_keys and corresponding values at which to
+            calculate the posterior probability
+
+        Returns
+        -------
+        p: array-like,
+            The posterior probability of the sample
+
+        """
+        if isinstance(sample, dict):
+            sample = [sample]
+        ordered_sample = [[s[key] for key in self.search_parameter_keys]
+                          for s in sample]
+        return self.kde(ordered_sample)
+
 
 def plot_multiple(results, filename=None, labels=None, colours=None,
                   save=True, evidences=False, **kwargs):

test/result_test.py

@@ -240,6 +240,30 @@ class TestResult(unittest.TestCase):
         with self.assertRaises(TypeError):
             self.result.get_all_injection_credible_levels()
 
+    def test_kde(self):
+        kde = self.result.kde
+        import scipy.stats
+        self.assertEqual(type(kde), scipy.stats.kde.gaussian_kde)
+        self.assertEqual(kde.d, 2)
+
+    def test_posterior_probability(self):
+        sample = dict(x=0, y=0.1)
+        self.assertTrue(
+            isinstance(self.result.posterior_probability(sample), np.ndarray))
+        self.assertTrue(
+            len(self.result.posterior_probability(sample)), 1)
+        self.assertEqual(
+            self.result.posterior_probability(sample)[0],
+            self.result.kde([0, 0.1]))
+
+    def test_multiple_posterior_probability(self):
+        sample = [dict(x=0, y=0.1), dict(x=0.8, y=0)]
+        self.assertTrue(
+            isinstance(self.result.posterior_probability(sample), np.ndarray))
+        self.assertTrue(
+            all(self.result.posterior_probability(sample)
+                == self.result.kde([[0, 0.1], [0.8, 0]])))
+
 
 if __name__ == '__main__':
     unittest.main()