diff options
Diffstat (limited to 'numpy/lib/tests/test_function_base.py')
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 11 |
1 files changed, 7 insertions, 4 deletions
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 8e2de7ace..4df13e5f9 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -565,7 +565,8 @@ class TestHistogram(TestCase): area = sum(a * diff(b)) assert_almost_equal(area, 1) - warnings.simplefilter('ignore', Warning) + warnings.filterwarnings('ignore', + message="\s*This release of NumPy fixes a normalization bug") # Check with non-constant bin widths v = np.arange(10) bins = [0,1,3,6,10] @@ -584,7 +585,7 @@ class TestHistogram(TestCase): # mailing list Aug. 6, 2010. counts, dmy = np.histogram([1,2,3,4], [0.5,1.5,np.inf], normed=True) assert_equal(counts, [.25, 0]) - warnings.resetwarnings() + warnings.filters.pop(0) def test_outliers(self): # Check that outliers are not tallied @@ -647,12 +648,14 @@ class TestHistogram(TestCase): wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], normed=True) assert_array_almost_equal(wa, array([4, 5, 0, 1]) / 10. / 3. * 4) - warnings.simplefilter('ignore', Warning) + warnings.filterwarnings('ignore', \ + message="\s*This release of NumPy fixes a normalization bug") # Check weights with non-uniform bin widths a,b = histogram(np.arange(9), [0,1,3,6,10], \ weights=[2,1,1,1,1,1,1,1,1], normed=True) assert_almost_equal(a, [.2, .1, .1, .075]) - warnings.resetwarnings() + warnings.filters.pop(0) + class TestHistogramdd(TestCase): def test_simple(self): |