update prior tests to include uniform in log

test/prior_tests.py

@@ -92,8 +92,9 @@ class TestPriorClasses(unittest.TestCase):
             tupak.prior.DeltaFunction(name='test', peak=1),
             tupak.prior.Gaussian(name='test', mu=0, sigma=1),
             tupak.prior.PowerLaw(name='test', alpha=0, minimum=0, maximum=1),
-            tupak.prior.PowerLaw(name='test', alpha=-1, minimum=1, maximum=1e2),
+            tupak.prior.PowerLaw(name='test', alpha=2, minimum=1, maximum=1e2),
             tupak.prior.Uniform(name='test', minimum=0, maximum=1),
+            tupak.prior.LogUniform(name='test', minimum=5e0, maximum=1e2),
             tupak.prior.UniformComovingVolume(name='test', minimum=2e2, maximum=5e3),
             tupak.prior.Sine(name='test'),
             tupak.prior.Cosine(name='test'),
@@ -102,26 +103,42 @@ class TestPriorClasses(unittest.TestCase):
             tupak.prior.TruncatedGaussian(name='test', mu=1, sigma=0.4, minimum=-1, maximum=1)
         ]
 
-    def test_rescaling(self):
-        for prior in self.priors:
+    def test_minimum_rescaling(self):
         """Test the the rescaling works as expected."""
+        for prior in self.priors:
             minimum_sample = prior.rescale(0)
             self.assertAlmostEqual(minimum_sample, prior.minimum)
+
+    def test_maximum_rescaling(self):
+        """Test the the rescaling works as expected."""
+        for prior in self.priors:
             maximum_sample = prior.rescale(1)
             self.assertAlmostEqual(maximum_sample, prior.maximum)
+
+    def test_many_sample_rescaling(self):
+        """Test the the rescaling works as expected."""
+        for prior in self.priors:
             many_samples = prior.rescale(np.random.uniform(0, 1, 1000))
             self.assertTrue(all((many_samples >= prior.minimum) & (many_samples <= prior.maximum)))
+
+    def test_out_of_bounds_rescaling(self):
+        """Test the the rescaling works as expected."""
+        for prior in self.priors:
             self.assertRaises(ValueError, lambda: prior.rescale(-1))
 
-    def test_sampling(self):
+    def test_sampling_single(self):
         """Test that sampling from the prior always returns values within its domain."""
         for prior in self.priors:
             single_sample = prior.sample()
             self.assertTrue((single_sample >= prior.minimum) & (single_sample <= prior.maximum))
+
+    def test_sampling_many(self):
+        """Test that sampling from the prior always returns values within its domain."""
+        for prior in self.priors:
             many_samples = prior.sample(1000)
             self.assertTrue(all((many_samples >= prior.minimum) & (many_samples <= prior.maximum)))
 
-    def test_prob(self):
+    def test_probability_above_domain(self):
         """Test that the prior probability is non-negative in domain of validity and zero outside."""
         for prior in self.priors:
             # skip delta function prior in this case
@@ -130,15 +147,36 @@ class TestPriorClasses(unittest.TestCase):
             if prior.maximum != np.inf:
                 outside_domain = np.linspace(prior.maximum + 1, prior.maximum + 1e4, 1000)
                 self.assertTrue(all(prior.prob(outside_domain) == 0))
+
+    def test_probability_below_domain(self):
+        """Test that the prior probability is non-negative in domain of validity and zero outside."""
+        for prior in self.priors:
+            # skip delta function prior in this case
+            if isinstance(prior, tupak.prior.DeltaFunction):
+                continue
             if prior.minimum != -np.inf:
                 outside_domain = np.linspace(prior.minimum - 1e4, prior.minimum - 1, 1000)
                 self.assertTrue(all(prior.prob(outside_domain) == 0))
+
+    def test_probability_in_domain(self):
+        """Test that the prior probability is non-negative in domain of validity and zero outside."""
+        for prior in self.priors:
+            # skip delta function prior in this case
+            if isinstance(prior, tupak.prior.DeltaFunction):
+                continue
             if prior.minimum == -np.inf:
                 prior.minimum = -1e5
             if prior.maximum == np.inf:
                 prior.maximum = 1e5
             domain = np.linspace(prior.minimum, prior.maximum, 1000)
             self.assertTrue(all(prior.prob(domain) >= 0))
+
+    def test_probability_surrounding_domain(self):
+        """Test that the prior probability is non-negative in domain of validity and zero outside."""
+        for prior in self.priors:
+            # skip delta function prior in this case
+            if isinstance(prior, tupak.prior.DeltaFunction):
+                continue
             surround_domain = np.linspace(prior.minimum - 1, prior.maximum + 1, 1000)
             prior.prob(surround_domain)
 
@@ -151,6 +189,7 @@ class TestPriorClasses(unittest.TestCase):
                 domain = np.linspace(-1e2, 1e2, 1000)
             else:
                 domain = np.linspace(prior.minimum, prior.maximum, 1000)
+            print(prior.minimum, prior.maximum)
             self.assertAlmostEqual(np.trapz(prior.prob(domain), domain), 1, 3)
 
 
@@ -189,6 +228,5 @@ class TestFillPrior(unittest.TestCase):
         self.assertIsInstance(self.priors['ra'], tupak.prior.Uniform)
 
 
-
 if __name__ == '__main__':
     unittest.main()