diff options
Diffstat (limited to 'numpy/ma/tests/test_extras.py')
| -rw-r--r-- | numpy/ma/tests/test_extras.py | 186 |
1 files changed, 186 insertions, 0 deletions
diff --git a/numpy/ma/tests/test_extras.py b/numpy/ma/tests/test_extras.py index 0a6de4eba..faee4f599 100644 --- a/numpy/ma/tests/test_extras.py +++ b/numpy/ma/tests/test_extras.py @@ -659,6 +659,15 @@ class TestMedian(TestCase): r = np.ma.median([[np.inf, np.inf], [np.inf, np.inf]], axis=-1) assert_equal(r, np.inf) + def test_inf(self): + # test that even which computes handles inf / x = masked + r = np.ma.median(np.ma.masked_array([[np.inf, np.inf], + [np.inf, np.inf]]), axis=-1) + assert_equal(r, np.inf) + r = np.ma.median(np.ma.masked_array([[np.inf, np.inf], + [np.inf, np.inf]]), axis=None) + assert_equal(r, np.inf) + def test_non_masked(self): x = np.arange(9) assert_equal(np.ma.median(x), 4.) @@ -799,6 +808,183 @@ class TestMedian(TestCase): assert_array_equal(np.ma.median(masked_arr, axis=0), expected) + def test_nan(self): + with suppress_warnings() as w: + w.record(RuntimeWarning) + w.filter(DeprecationWarning, message=r"in 3\.x, __getslice__") + for mask in (False, np.zeros(6, dtype=np.bool)): + dm = np.ma.array([[1, np.nan, 3], [1, 2, 3]]) + dm.mask = mask + + # scalar result + r = np.ma.median(dm, axis=None) + assert_(np.isscalar(r)) + assert_array_equal(r, np.nan) + r = np.ma.median(dm.ravel(), axis=0) + assert_(np.isscalar(r)) + assert_array_equal(r, np.nan) + + r = np.ma.median(dm, axis=0) + assert_equal(type(r), MaskedArray) + assert_array_equal(r, [1, np.nan, 3]) + r = np.ma.median(dm, axis=1) + assert_equal(type(r), MaskedArray) + assert_array_equal(r, [np.nan, 2]) + r = np.ma.median(dm, axis=-1) + assert_equal(type(r), MaskedArray) + assert_array_equal(r, [np.nan, 2]) + + dm = np.ma.array([[1, np.nan, 3], [1, 2, 3]]) + dm[:, 2] = np.ma.masked + assert_array_equal(np.ma.median(dm, axis=None), np.nan) + assert_array_equal(np.ma.median(dm, axis=0), [1, np.nan, 3]) + assert_array_equal(np.ma.median(dm, axis=1), [np.nan, 1.5]) + assert_equal([x.category is RuntimeWarning for x in w.log], + [True]*13) + + def test_out_nan(self): + with warnings.catch_warnings(record=True): + warnings.filterwarnings('always', '', RuntimeWarning) + o = np.ma.masked_array(np.zeros((4,))) + d = np.ma.masked_array(np.ones((3, 4))) + d[2, 1] = np.nan + d[2, 2] = np.ma.masked + assert_equal(np.ma.median(d, 0, out=o), o) + o = np.ma.masked_array(np.zeros((3,))) + assert_equal(np.ma.median(d, 1, out=o), o) + o = np.ma.masked_array(np.zeros(())) + assert_equal(np.ma.median(d, out=o), o) + + def test_nan_behavior(self): + a = np.ma.masked_array(np.arange(24, dtype=float)) + a[::3] = np.ma.masked + a[2] = np.nan + with suppress_warnings() as w: + w.record(RuntimeWarning) + w.filter(DeprecationWarning, message=r"in 3\.x, __getslice__") + assert_array_equal(np.ma.median(a), np.nan) + assert_array_equal(np.ma.median(a, axis=0), np.nan) + assert_(w.log[0].category is RuntimeWarning) + assert_(w.log[1].category is RuntimeWarning) + + a = np.ma.masked_array(np.arange(24, dtype=float).reshape(2, 3, 4)) + a.mask = np.arange(a.size) % 2 == 1 + aorig = a.copy() + a[1, 2, 3] = np.nan + a[1, 1, 2] = np.nan + + # no axis + with suppress_warnings() as w: + w.record(RuntimeWarning) + w.filter(DeprecationWarning, message=r"in 3\.x, __getslice__") + warnings.filterwarnings('always', '', RuntimeWarning) + assert_array_equal(np.ma.median(a), np.nan) + assert_(np.isscalar(np.ma.median(a))) + assert_(w.log[0].category is RuntimeWarning) + + # axis0 + b = np.ma.median(aorig, axis=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.ma.median(a, 0), b) + assert_equal(len(w), 1) + + # axis1 + b = np.ma.median(aorig, axis=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.ma.median(a, 1), b) + assert_equal(len(w), 1) + + # axis02 + b = np.ma.median(aorig, axis=(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.ma.median(a, (0, 2)), b) + assert_equal(len(w), 1) + + def test_ambigous_fill(self): + # 255 is max value, used as filler for sort + a = np.array([[3, 3, 255], [3, 3, 255]], dtype=np.uint8) + a = np.ma.masked_array(a, mask=a == 3) + assert_array_equal(np.ma.median(a, axis=1), 255) + assert_array_equal(np.ma.median(a, axis=1).mask, False) + assert_array_equal(np.ma.median(a, axis=0), a[0]) + assert_array_equal(np.ma.median(a), 255) + + def test_special(self): + for inf in [np.inf, -np.inf]: + a = np.array([[inf, np.nan], [np.nan, np.nan]]) + a = np.ma.masked_array(a, mask=np.isnan(a)) + assert_equal(np.ma.median(a, axis=0), [inf, np.nan]) + assert_equal(np.ma.median(a, axis=1), [inf, np.nan]) + assert_equal(np.ma.median(a), inf) + + a = np.array([[np.nan, np.nan, inf], [np.nan, np.nan, inf]]) + a = np.ma.masked_array(a, mask=np.isnan(a)) + assert_array_equal(np.ma.median(a, axis=1), inf) + assert_array_equal(np.ma.median(a, axis=1).mask, False) + assert_array_equal(np.ma.median(a, axis=0), a[0]) + assert_array_equal(np.ma.median(a), inf) + + # no mask + a = np.array([[inf, inf], [inf, inf]]) + assert_equal(np.ma.median(a), inf) + assert_equal(np.ma.median(a, axis=0), inf) + assert_equal(np.ma.median(a, axis=1), inf) + + for i in range(0, 10): + for j in range(1, 10): + a = np.array([([np.nan] * i) + ([inf] * j)] * 2) + a = np.ma.masked_array(a, mask=np.isnan(a)) + assert_equal(np.ma.median(a), inf) + assert_equal(np.ma.median(a, axis=1), inf) + assert_equal(np.ma.median(a, axis=0), + ([np.nan] * i) + [inf] * j) + + def test_empty(self): + # empty arrays + a = np.ma.masked_array(np.array([], dtype=float)) + with suppress_warnings() as w: + w.record(RuntimeWarning) + w.filter(DeprecationWarning, message=r"in 3\.x, __getslice__") + assert_array_equal(np.ma.median(a), np.nan) + assert_(w.log[0].category is RuntimeWarning) + + # multiple dimensions + a = np.ma.masked_array(np.array([], dtype=float, ndmin=3)) + # no axis + with suppress_warnings() as w: + w.record(RuntimeWarning) + w.filter(DeprecationWarning, message=r"in 3\.x, __getslice__") + warnings.filterwarnings('always', '', RuntimeWarning) + assert_array_equal(np.ma.median(a), np.nan) + assert_(w.log[0].category is RuntimeWarning) + + # axis 0 and 1 + b = np.ma.masked_array(np.array([], dtype=float, ndmin=2)) + assert_equal(np.median(a, axis=0), b) + assert_equal(np.median(a, axis=1), b) + + # axis 2 + b = np.ma.masked_array(np.array(np.nan, dtype=float, ndmin=2)) + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings('always', '', RuntimeWarning) + assert_equal(np.median(a, axis=2), b) + assert_(w[0].category is RuntimeWarning) + + def test_object(self): + o = np.ma.masked_array(np.arange(7.)) + assert_(type(np.ma.median(o.astype(object))), float) + o[2] = np.nan + assert_(type(np.ma.median(o.astype(object))), float) + class TestCov(TestCase): |