TST: test multivariate_normal check_valid kw

Test that warnings are or are not raised when check_valid kwarg is used.

1 files changed, 28 insertions, 12 deletions

diff --git a/numpy/random/tests/test_random.py b/numpy/random/tests/test_random.py
index 596c218a2..f73991267 100644
--- a/numpy/random/tests/test_random.py
+++ b/numpy/random/tests/test_random.py
@@ -1,4 +1,5 @@
 from __future__ import division, absolute_import, print_function
+import warnings
 
 import numpy as np
 from numpy.testing import (
@@ -460,27 +461,42 @@ class TestRandomDist(TestCase):
     def test_multivariate_normal(self):
         np.random.seed(self.seed)
         mean = (.123456789, 10)
-        # Hmm... not even symmetric.
-        cov = [[1, 0], [1, 0]]
+        cov = [[1, 0], [0, 1]]
         size = (3, 2)
         actual = np.random.multivariate_normal(mean, cov, size)
-        desired = np.array([[[-1.47027513018564449, 10.],
-                             [-1.65915081534845532, 10.]],
-                            [[-2.29186329304599745, 10.],
-                             [-1.77505606019580053, 10.]],
-                            [[-0.54970369430044119, 10.],
-                             [0.29768848031692957, 10.]]])
+        desired = np.array([[[1.463620246718631, 11.73759122771936 ],
+                             [1.622445133300628, 9.771356667546383]],
+                            [[2.154490787682787, 12.170324946056553],
+                             [1.719909438201865, 9.230548443648306]],
+                            [[0.689515026297799, 9.880729819607714],
+                             [-0.023054015651998, 9.201096623542879]]])
+
         np.testing.assert_array_almost_equal(actual, desired, decimal=15)
 
         # Check for default size, was raising deprecation warning
         actual = np.random.multivariate_normal(mean, cov)
-        desired = np.array([-0.79441224511977482, 10.])
+        desired = np.array([0.895289569463708, 9.17180864067987])
         np.testing.assert_array_almost_equal(actual, desired, decimal=15)
 
-        # Check that non positive-semidefinite covariance raises warning
+        # Check that non positive-semidefinite covariance warns with
+        # RuntimeWarning
         mean = [0, 0]
-        cov = [[1, 1 + 1e-10], [1 + 1e-10, 1]]
-        assert_warns(RuntimeWarning, np.random.multivariate_normal, mean, cov)
+        cov = [[1, 2], [3, 4]]
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            np.random.multivariate_normal(mean, cov)
+            assert len(w) == 1
+            assert issubclass(w[0].category, RuntimeWarning)
+
+        # and that it doesn't warn with RuntimeWarning check_valid='ignore'
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            np.random.multivariate_normal(mean, cov, check_valid='ignore')
+            assert len(w) == 0
+
+        # and that it raises with RuntimeWarning check_valid='raises'
+        assert_raises(ValueError, np.random.multivariate_normal, mean, cov,
+                      check_valid='raise')
 
     def test_negative_binomial(self):
         np.random.seed(self.seed)