diff options
Diffstat (limited to 'numpy/lib/tests')
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 27 |
1 files changed, 19 insertions, 8 deletions
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 6e80b0438..ea3ca4000 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -565,10 +565,25 @@ class TestHistogram(TestCase): area = sum(a * diff(b)) assert_almost_equal(area, 1) + # Check with non-constant bin widths (buggy but backwards compatible) + v = np.arange(10) + bins = [0, 1, 5, 9, 10] + a, b = histogram(v, bins, normed=True) + area = sum(a * diff(b)) + assert_almost_equal(area, 1) + + def test_density(self): + # Check that the integral of the density equals 1. + n = 100 + v = rand(n) + a, b = histogram(v, density=True) + area = sum(a * diff(b)) + assert_almost_equal(area, 1) + # Check with non-constant bin widths v = np.arange(10) bins = [0,1,3,6,10] - a, b = histogram(v, bins, normed=True) + a, b = histogram(v, bins, density=True) assert_array_equal(a, .1) assert_equal(sum(a*diff(b)), 1) @@ -576,14 +591,13 @@ class TestHistogram(TestCase): # infinities. v = np.arange(10) bins = [0,1,3,6,np.inf] - a, b = histogram(v, bins, normed=True) + a, b = histogram(v, bins, density=True) assert_array_equal(a, [.1,.1,.1,0.]) # Taken from a bug report from N. Becker on the numpy-discussion # mailing list Aug. 6, 2010. - counts, dmy = np.histogram([1,2,3,4], [0.5,1.5,np.inf], normed=True) + counts, dmy = np.histogram([1,2,3,4], [0.5,1.5,np.inf], density=True) assert_equal(counts, [.25, 0]) - warnings.filters.pop(0) def test_outliers(self): # Check that outliers are not tallied @@ -646,13 +660,10 @@ 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.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) + weights=[2,1,1,1,1,1,1,1,1], density=True) assert_almost_equal(a, [.2, .1, .1, .075]) - warnings.filters.pop(0) def test_empty(self): a, b = histogram([], bins=([0,1])) |