diff options
Diffstat (limited to 'numpy/lib/tests/test_histograms.py')
| -rw-r--r-- | numpy/lib/tests/test_histograms.py | 88 |
1 files changed, 75 insertions, 13 deletions
diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py index a2c684a20..e16ae12c2 100644 --- a/numpy/lib/tests/test_histograms.py +++ b/numpy/lib/tests/test_histograms.py @@ -2,13 +2,12 @@ from __future__ import division, absolute_import, print_function import numpy as np -from numpy.lib.histograms import histogram, histogramdd +from numpy.lib.histograms import histogram, histogramdd, histogram_bin_edges from numpy.testing import ( - run_module_suite, assert_, assert_equal, assert_array_equal, - assert_almost_equal, assert_array_almost_equal, assert_raises, - assert_allclose, assert_array_max_ulp, assert_warns, assert_raises_regex, - dec, suppress_warnings, HAS_REFCOUNT, -) + assert_, assert_equal, assert_array_equal, assert_almost_equal, + assert_array_almost_equal, assert_raises, assert_allclose, + assert_array_max_ulp, assert_warns, assert_raises_regex, suppress_warnings, + ) class TestHistogram(object): @@ -254,7 +253,7 @@ class TestHistogram(object): one_nan = np.array([0, 1, np.nan]) all_nan = np.array([np.nan, np.nan]) - # the internal commparisons with NaN give warnings + # the internal comparisons with NaN give warnings sup = suppress_warnings() sup.filter(RuntimeWarning) with sup: @@ -346,6 +345,20 @@ class TestHistogram(object): self.do_precision(np.single, np.longdouble) self.do_precision(np.double, np.longdouble) + def test_histogram_bin_edges(self): + hist, e = histogram([1, 2, 3, 4], [1, 2]) + edges = histogram_bin_edges([1, 2, 3, 4], [1, 2]) + assert_array_equal(edges, e) + + arr = np.array([0., 0., 0., 1., 2., 3., 3., 4., 5.]) + hist, e = histogram(arr, bins=30, range=(-0.5, 5)) + edges = histogram_bin_edges(arr, bins=30, range=(-0.5, 5)) + assert_array_equal(edges, e) + + hist, e = histogram(arr, bins='auto', range=(0, 1)) + edges = histogram_bin_edges(arr, bins='auto', range=(0, 1)) + assert_array_equal(edges, e) + class TestHistogramOptimBinNums(object): """ @@ -430,6 +443,24 @@ class TestHistogramOptimBinNums(object): assert_equal(len(a), numbins, err_msg="{0} estimator, " "No Variance test".format(estimator)) + def test_limited_variance(self): + """ + Check when IQR is 0, but variance exists, we return the sturges value + and not the fd value. + """ + lim_var_data = np.ones(1000) + lim_var_data[:3] = 0 + lim_var_data[-4:] = 100 + + edges_auto = histogram_bin_edges(lim_var_data, 'auto') + assert_equal(edges_auto, np.linspace(0, 100, 12)) + + edges_fd = histogram_bin_edges(lim_var_data, 'fd') + assert_equal(edges_fd, np.array([0, 100])) + + edges_sturges = histogram_bin_edges(lim_var_data, 'sturges') + assert_equal(edges_sturges, np.linspace(0, 100, 12)) + def test_outlier(self): """ Check the FD, Scott and Doane with outliers. @@ -582,8 +613,6 @@ class TestHistogramdd(object): assert_raises(ValueError, np.histogramdd, x, bins=[-1, 2, 4, 5]) assert_raises(ValueError, np.histogramdd, x, bins=[1, 0.99, 1, 1]) assert_raises( - ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 2, 3]]) - assert_raises( ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 3, -3]]) assert_(np.histogramdd(x, bins=[1, 1, 1, [1, 2, 3, 4]])) @@ -615,7 +644,7 @@ class TestHistogramdd(object): bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) - assert_(hist[1] == 1.) + assert_(hist[1] == 0.0) x = [1.0001] bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) @@ -630,6 +659,39 @@ class TestHistogramdd(object): assert_raises(ValueError, histogramdd, vals, range=[[0.0, 1.0], [np.nan, 0.75], [0.25, 0.5]]) - -if __name__ == "__main__": - run_module_suite() + def test_equal_edges(self): + """ Test that adjacent entries in an edge array can be equal """ + x = np.array([0, 1, 2]) + y = np.array([0, 1, 2]) + x_edges = np.array([0, 2, 2]) + y_edges = 1 + hist, edges = histogramdd((x, y), bins=(x_edges, y_edges)) + + hist_expected = np.array([ + [2.], + [1.], # x == 2 falls in the final bin + ]) + assert_equal(hist, hist_expected) + + def test_edge_dtype(self): + """ Test that if an edge array is input, its type is preserved """ + x = np.array([0, 10, 20]) + y = x / 10 + x_edges = np.array([0, 5, 15, 20]) + y_edges = x_edges / 10 + hist, edges = histogramdd((x, y), bins=(x_edges, y_edges)) + + assert_equal(edges[0].dtype, x_edges.dtype) + assert_equal(edges[1].dtype, y_edges.dtype) + + def test_large_integers(self): + big = 2**60 # Too large to represent with a full precision float + + x = np.array([0], np.int64) + x_edges = np.array([-1, +1], np.int64) + y = big + x + y_edges = big + x_edges + + hist, edges = histogramdd((x, y), bins=(x_edges, y_edges)) + + assert_equal(hist[0, 0], 1) |