diff options
Diffstat (limited to 'numpy/lib')
| -rw-r--r-- | numpy/lib/histograms.py | 7 | ||||
| -rw-r--r-- | numpy/lib/tests/test_histograms.py | 36 |
2 files changed, 40 insertions, 3 deletions
diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py index b7d9c4406..337957dd5 100644 --- a/numpy/lib/histograms.py +++ b/numpy/lib/histograms.py @@ -572,8 +572,8 @@ def histogram(a, bins=10, range=None, normed=None, weights=None, bins : int or sequence of scalars or str, optional If `bins` is an int, it defines the number of equal-width bins in the given range (10, by default). If `bins` is a - sequence, it defines the bin edges, including the rightmost - edge, allowing for non-uniform bin widths. + sequence, it defines a monotonically increasing array of bin edges, + including the rightmost edge, allowing for non-uniform bin widths. .. versionadded:: 1.11.0 @@ -833,7 +833,8 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): bins : sequence or int, optional The bin specification: - * A sequence of arrays describing the bin edges along each dimension. + * A sequence of arrays describing the monotonically increasing bin + edges along each dimension. * The number of bins for each dimension (nx, ny, ... =bins) * The number of bins for all dimensions (nx=ny=...=bins). diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py index adaa7e3bd..9bea2aca8 100644 --- a/numpy/lib/tests/test_histograms.py +++ b/numpy/lib/tests/test_histograms.py @@ -703,3 +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]) |