diff options
Diffstat (limited to 'numpy/ma/tests/test_extras.py')
| -rw-r--r-- | numpy/ma/tests/test_extras.py | 107 |
1 files changed, 42 insertions, 65 deletions
diff --git a/numpy/ma/tests/test_extras.py b/numpy/ma/tests/test_extras.py index 5243cf714..afcfd126e 100644 --- a/numpy/ma/tests/test_extras.py +++ b/numpy/ma/tests/test_extras.py @@ -891,61 +891,51 @@ class TestMedian(object): expected) def test_nan(self): - with suppress_warnings() as w: - w.record(RuntimeWarning) - for mask in (False, np.zeros(6, dtype=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]) - + for mask in (False, np.zeros(6, dtype=bool)): 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) + 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]) 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) + 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) - 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) + assert_array_equal(np.ma.median(a), np.nan) + assert_array_equal(np.ma.median(a, axis=0), np.nan) a = np.ma.masked_array(np.arange(24, dtype=float).reshape(2, 3, 4)) a.mask = np.arange(a.size) % 2 == 1 @@ -954,39 +944,26 @@ class TestMedian(object): a[1, 1, 2] = np.nan # no axis - with suppress_warnings() as w: - w.record(RuntimeWarning) - 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) + assert_array_equal(np.ma.median(a), np.nan) + assert_(np.isscalar(np.ma.median(a))) # 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) + assert_equal(np.ma.median(a, 0), b) # 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) + assert_equal(np.ma.median(a, 1), b) # 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) + assert_equal(np.ma.median(a, (0, 2)), b) def test_ambigous_fill(self): # 255 is max value, used as filler for sort |