Hotfix for dynesty bounds

bilby/core/sampler/dynesty.py

@@ -10,7 +10,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 from pandas import DataFrame
 
-from ..utils import logger, check_directory_exists_and_if_not_mkdir
+from ..utils import logger, check_directory_exists_and_if_not_mkdir, reflect
 from .base_sampler import Sampler, NestedSampler
 
 
@@ -216,6 +216,7 @@ class Dynesty(NestedSampler):
 
     def run_sampler(self):
         import dynesty
+        logger.info("Using dynesty version {}".format(dynesty.__version__))
         if self.kwargs['live_points'] is None:
             self.kwargs['live_points'] = (
                 self.get_initial_points_from_prior(
@@ -509,4 +510,5 @@ class Dynesty(NestedSampler):
         |theta| - 1 (i.e. wrap around).
 
         """
+        theta[self._reflective] = reflect(theta[self._reflective])
         return self.priors.rescale(self._search_parameter_keys, theta)

bilby/core/utils.py

@@ -1004,6 +1004,33 @@ def decode_astropy_quantity(dct):
         return dct
 
 
+def reflect(u):
+    """
+    Iteratively reflect a number until it is contained in [0, 1].
+
+    This is for priors with a reflective boundary condition, all numbers in the set `u = 2n +/- x` should be mapped to x.
+
+    For the `+` case we just take `u % 1`.
+    For the `-` case we take `1 - (u % 1)`.
+
+    E.g., -0.9, 1.1, and 2.9 should all map to 0.9.
+
+    Parameters
+    ----------
+    u: array-like
+        The array of points to map to the unit cube
+
+    Returns
+    -------
+    u: array-like
+       The input array, modified in place.
+    """
+    idxs_even = np.mod(u, 2) < 1
+    u[idxs_even] = np.mod(u[idxs_even], 1)
+    u[~idxs_even] = 1 - np.mod(u[~idxs_even], 1)
+    return u
+
+
 class IllegalDurationAndSamplingFrequencyException(Exception):
     pass
 

test/utils_test.py

@@ -165,5 +165,37 @@ class TestTimeAndFrequencyArrays(unittest.TestCase):
                                          starting_time=0)
 
 
+class TestReflect(unittest.TestCase):
+
+    def test_in_range(self):
+        xprime = np.array([0.1, 0.5, 0.9])
+        x = np.array([0.1, 0.5, 0.9])
+        self.assertTrue(
+            np.testing.assert_allclose(utils.reflect(xprime),  x) is None)
+
+    def test_in_one_to_two(self):
+        xprime = np.array([1.1, 1.5, 1.9])
+        x = np.array([0.9, 0.5, 0.1])
+        self.assertTrue(
+            np.testing.assert_allclose(utils.reflect(xprime),  x) is None)
+
+    def test_in_two_to_three(self):
+        xprime = np.array([2.1, 2.5, 2.9])
+        x = np.array([0.1, 0.5, 0.9])
+        self.assertTrue(
+            np.testing.assert_allclose(utils.reflect(xprime),  x) is None)
+
+    def test_in_minus_one_to_zero(self):
+        xprime = np.array([-0.9, -0.5, -0.1])
+        x = np.array([0.9, 0.5, 0.1])
+        self.assertTrue(
+            np.testing.assert_allclose(utils.reflect(xprime),  x) is None)
+
+    def test_in_minus_two_to_minus_one(self):
+        xprime = np.array([-1.9, -1.5, -1.1])
+        x = np.array([0.1, 0.5, 0.9])
+        self.assertTrue(
+            np.testing.assert_allclose(utils.reflect(xprime),  x) is None)
+
 if __name__ == '__main__':
     unittest.main()