diff options
Diffstat (limited to 'numpy/lib/tests')
| -rw-r--r-- | numpy/lib/tests/test_histograms.py | 82 |
1 files changed, 63 insertions, 19 deletions
diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py index e16ae12c2..9bea2aca8 100644 --- a/numpy/lib/tests/test_histograms.py +++ b/numpy/lib/tests/test_histograms.py @@ -40,20 +40,28 @@ class TestHistogram(object): assert_allclose(e, np.array([1., 2.])) def test_normed(self): - # Check that the integral of the density equals 1. - n = 100 - v = np.random.rand(n) - a, b = histogram(v, normed=True) - area = np.sum(a * np.diff(b)) - assert_almost_equal(area, 1) + sup = suppress_warnings() + with sup: + rec = sup.record(np.VisibleDeprecationWarning, '.*normed.*') + # Check that the integral of the density equals 1. + n = 100 + v = np.random.rand(n) + a, b = histogram(v, normed=True) + area = np.sum(a * np.diff(b)) + assert_almost_equal(area, 1) + assert_equal(len(rec), 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 = np.sum(a * np.diff(b)) - assert_almost_equal(area, 1) + sup = suppress_warnings() + with sup: + rec = sup.record(np.VisibleDeprecationWarning, '.*normed.*') + # 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 = np.sum(a * np.diff(b)) + assert_almost_equal(area, 1) + assert_equal(len(rec), 1) def test_density(self): # Check that the integral of the density equals 1. @@ -96,12 +104,12 @@ class TestHistogram(object): assert_equal(h.sum(), 9) # Normalization - h, b = histogram(a, range=[1, 9], normed=True) + h, b = histogram(a, range=[1, 9], density=True) assert_almost_equal((h * np.diff(b)).sum(), 1, decimal=15) # Weights w = np.arange(10) + .5 - h, b = histogram(a, range=[1, 9], weights=w, normed=True) + h, b = histogram(a, range=[1, 9], weights=w, density=True) assert_equal((h * np.diff(b)).sum(), 1) h, b = histogram(a, bins=8, range=[1, 9], weights=w) @@ -113,7 +121,7 @@ class TestHistogram(object): h, b = histogram(a) assert_(np.issubdtype(h.dtype, np.integer)) - h, b = histogram(a, normed=True) + h, b = histogram(a, density=True) assert_(np.issubdtype(h.dtype, np.floating)) h, b = histogram(a, weights=np.ones(10, int)) @@ -133,9 +141,9 @@ class TestHistogram(object): v = np.random.rand(100) w = np.ones(100) * 5 a, b = histogram(v) - na, nb = histogram(v, normed=True) + na, nb = histogram(v, density=True) wa, wb = histogram(v, weights=w) - nwa, nwb = histogram(v, weights=w, normed=True) + nwa, nwb = histogram(v, weights=w, density=True) assert_array_almost_equal(a * 5, wa) assert_array_almost_equal(na, nwa) @@ -149,7 +157,7 @@ class TestHistogram(object): wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1]) assert_array_equal(wa, [4, 5, 0, 1]) wa, wb = histogram( - [1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], normed=True) + [1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], density=True) assert_array_almost_equal(wa, np.array([4, 5, 0, 1]) / 10. / 3. * 4) # Check weights with non-uniform bin widths @@ -695,3 +703,39 @@ class TestHistogramdd(object): hist, edges = histogramdd((x, y), bins=(x_edges, y_edges)) assert_equal(hist[0, 0], 1) + + def test_normed_non_uniform_2d(self): + # Defines the following grid: + # + # 0 2 8 + # 0+-+-----+ + # + | + + # + | + + # 6+-+-----+ + # 8+-+-----+ + x_edges = np.array([0, 2, 8]) + y_edges = np.array([0, 6, 8]) + relative_areas = np.array([ + [3, 9], + [1, 3]]) + + # ensure the number of points in each region is proportional to its area + x = np.array([1] + [1]*3 + [7]*3 + [7]*9) + y = np.array([7] + [1]*3 + [7]*3 + [1]*9) + + # sanity check that the above worked as intended + hist, edges = histogramdd((y, x), bins=(y_edges, x_edges)) + assert_equal(hist, relative_areas) + + # resulting histogram should be uniform, since counts and areas are propotional + hist, edges = histogramdd((y, x), bins=(y_edges, x_edges), normed=True) + assert_equal(hist, 1 / (8*8)) + + def test_normed_non_uniform_1d(self): + # compare to histogram to show the results are the same + v = np.arange(10) + bins = np.array([0, 1, 3, 6, 10]) + hist, edges = histogram(v, bins, density=True) + hist_dd, edges_dd = histogramdd((v,), (bins,), normed=True) + assert_equal(hist, hist_dd) + assert_equal(edges, edges_dd[0]) |