diff options
Diffstat (limited to 'numpy/lib/tests/test_function_base.py')
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 31 |
1 files changed, 31 insertions, 0 deletions
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 00d9f36c8..cd126fe71 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -1381,6 +1381,37 @@ class TestHistogramOptimBinNums(TestCase): a, b = np.histogram(outlier_dataset, estimator) assert_equal(len(a), numbins) + def test_simple_range(self): + """ + Straightforward testing with a mixture of linspace data (for + consistency). Adding in a 3rd mixture that will then be + completely ignored. All test values have been precomputed and + the shouldn't change. + """ + # some basic sanity checking, with some fixed data. Checking for the correct number of bins + basic_test = {50: {'fd': 4, 'scott': 4, 'rice': 8, 'sturges': 7, 'auto': 7}, + 500: {'fd': 8, 'scott': 8, 'rice': 16, 'sturges': 10, 'auto': 10}, + 5000: {'fd': 17, 'scott': 17, 'rice': 35, 'sturges': 14, 'auto': 17}} + + for testlen, expectedResults in basic_test.items(): + # create some sort of non uniform data to test with (2 peak uniform mixture) + x1 = np.linspace(-10, -1, testlen/5 * 2) + x2 = np.linspace(1, 10, testlen/5 * 3) + x3 = np.linspace(-100, -50, testlen) + x = np.hstack((x1, x2, x3)) + for estimator, numbins in expectedResults.items(): + a, b = np.histogram(x, estimator, range = (-20, 20)) + msg = "For the {0} estimator with datasize of {1}".format(estimator, testlen) + assert_equal(len(a), numbins, err_msg=msg) + + def test_simple_weighted(self): + """ + Check that weighted data raises a TypeError + """ + estimator_list = ['fd', 'scott', 'rice', 'sturges', 'auto'] + for estimator in estimator_list: + assert_raises(TypeError, histogram, [1, 2, 3], estimator, weights=[1, 2, 3]) + class TestHistogramdd(TestCase): |