diff options
Diffstat (limited to 'numpy/lib/tests/test_function_base.py')
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 178 |
1 files changed, 177 insertions, 1 deletions
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index ad71fd3fa..397505bd0 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -1785,6 +1785,12 @@ class TestScoreatpercentile(TestCase): assert_equal(np.percentile(x, 0), 0.) assert_equal(np.percentile(x, 100), 3.5) assert_equal(np.percentile(x, 50), 1.75) + x[1] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.percentile(x, 0), np.nan) + assert_equal(np.percentile(x, 0, interpolation='nearest'), np.nan) + assert_(w[0].category is RuntimeWarning) def test_api(self): d = np.ones(5) @@ -2080,7 +2086,107 @@ class TestScoreatpercentile(TestCase): keepdims=True).shape, (2, 1, 1, 7, 1)) assert_equal(np.percentile(d, [1, 7], axis=(0, 3), keepdims=True).shape, (2, 1, 5, 7, 1)) + def test_out(self): + o = np.zeros((4,)) + d = np.ones((3, 4)) + assert_equal(np.percentile(d, 0, 0, out=o), o) + assert_equal(np.percentile(d, 0, 0, interpolation='nearest', out=o), o) + o = np.zeros((3,)) + assert_equal(np.percentile(d, 1, 1, out=o), o) + assert_equal(np.percentile(d, 1, 1, interpolation='nearest', out=o), o) + + o = np.zeros(()) + assert_equal(np.percentile(d, 2, out=o), o) + assert_equal(np.percentile(d, 2, interpolation='nearest', out=o), o) + + + def test_out_nan(self): + with warnings.catch_warnings(record=True): + warnings.filterwarnings('always', '', RuntimeWarning) + o = np.zeros((4,)) + d = np.ones((3, 4)) + d[2, 1] = np.nan + assert_equal(np.percentile(d, 0, 0, out=o), o) + assert_equal(np.percentile(d, 0, 0, interpolation='nearest', out=o), o) + o = np.zeros((3,)) + assert_equal(np.percentile(d, 1, 1, out=o), o) + assert_equal(np.percentile(d, 1, 1, interpolation='nearest', out=o), o) + o = np.zeros(()) + assert_equal(np.percentile(d, 1, out=o), o) + assert_equal(np.percentile(d, 1, interpolation='nearest', out=o), o) + + def test_nan_behavior(self): + a = np.arange(24, dtype=float) + a[2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.percentile(a, 0.3), np.nan) + assert_equal(np.percentile(a, 0.3, axis=0), np.nan) + assert_equal(np.percentile(a, [0.3, 0.6], axis=0), + np.array([np.nan] * 2)) + assert_(w[0].category is RuntimeWarning) + assert_(w[1].category is RuntimeWarning) + + a = np.arange(24, dtype=float).reshape(2, 3, 4) + a[1, 2, 3] = np.nan + a[1, 1, 2] = np.nan + + # no axis + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.percentile(a, 0.3), np.nan) + assert_equal(np.percentile(a, 0.3).ndim, 0) + assert_(w[0].category is RuntimeWarning) + + # axis0 zerod + b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 0) + b[2, 3] = np.nan; b[1, 2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.percentile(a, 0.3, 0), b) + # axis0 not zerod + b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), + [0.3, 0.6], 0) + b[:, 2, 3] = np.nan; b[:, 1, 2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.percentile(a, [0.3, 0.6], 0), b) + + # axis1 zerod + b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 1) + b[1, 3] = np.nan; b[1, 2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.percentile(a, 0.3, 1), b) + # axis1 not zerod + b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], 1) + b[:, 1, 3] = np.nan; b[:, 1, 2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.percentile(a, [0.3, 0.6], 1), b) + + # axis02 zerod + b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, (0, 2)) + b[1] = np.nan; b[2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.percentile(a, 0.3, (0, 2)), b) + # axis02 not zerod + b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), + [0.3, 0.6], (0, 2)) + b[:, 1] = np.nan; b[:, 2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.percentile(a, [0.3, 0.6], (0, 2)), b) + # axis02 not zerod with nearest interpolation + b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), + [0.3, 0.6], (0, 2), interpolation='nearest') + b[:, 1] = np.nan; b[:, 2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.percentile( + a, [0.3, 0.6], (0, 2), interpolation='nearest'), b) class TestMedian(TestCase): def test_basic(self): @@ -2103,7 +2209,11 @@ class TestMedian(TestCase): # check array scalar result assert_equal(np.median(a).ndim, 0) a[1] = np.nan - assert_equal(np.median(a).ndim, 0) + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.median(a).ndim, 0) + assert_(w[0].category is RuntimeWarning) + def test_axis_keyword(self): a3 = np.array([[2, 3], @@ -2177,6 +2287,72 @@ class TestMedian(TestCase): a = MySubClass([1,2,3]) assert_equal(np.median(a), -7) + def test_out(self): + o = np.zeros((4,)) + d = np.ones((3, 4)) + assert_equal(np.median(d, 0, out=o), o) + o = np.zeros((3,)) + assert_equal(np.median(d, 1, out=o), o) + o = np.zeros(()) + assert_equal(np.median(d, out=o), o) + + def test_out_nan(self): + with warnings.catch_warnings(record=True): + warnings.filterwarnings('always', '', RuntimeWarning) + o = np.zeros((4,)) + d = np.ones((3, 4)) + d[2, 1] = np.nan + assert_equal(np.median(d, 0, out=o), o) + o = np.zeros((3,)) + assert_equal(np.median(d, 1, out=o), o) + o = np.zeros(()) + assert_equal(np.median(d, out=o), o) + + def test_nan_behavior(self): + a = np.arange(24, dtype=float) + a[2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.median(a), np.nan) + assert_equal(np.median(a, axis=0), np.nan) + assert_(w[0].category is RuntimeWarning) + assert_(w[1].category is RuntimeWarning) + + a = np.arange(24, dtype=float).reshape(2, 3, 4) + a[1, 2, 3] = np.nan + a[1, 1, 2] = np.nan + + #no axis + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.median(a), np.nan) + assert_equal(np.median(a).ndim, 0) + assert_(w[0].category is RuntimeWarning) + + #axis0 + b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 0) + b[2, 3] = np.nan; b[1, 2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.median(a, 0), b) + assert_equal(len(w), 2) + + #axis1 + b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 1) + b[1, 3] = np.nan; b[1, 2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.median(a, 1), b) + assert_equal(len(w), 2) + + #axis02 + b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), (0, 2)) + b[1] = np.nan; b[2] = np.nan + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.median(a, (0, 2)), b) + assert_equal(len(w), 2) + def test_object(self): o = np.arange(7.); assert_(type(np.median(o.astype(object))), float) |