8000 BUG: fixed failure of np.ma.median for 1-D even arrays. by mhvk · Pull Request #8414 · numpy/numpy · GitHub
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BUG: fixed failure of np.ma.median for 1-D even arrays. #8414

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Merged
merged 2 commits into from
Dec 25, 2016
Merged

BUG: fixed failure of np.ma.median for 1-D even arrays. #8414

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44e086d
BUG: fixed failure of np.ma.median for 1-D even arrays.
mhvk Dec 23, 2016
6d52633
TST: extend ma.median testing and fix inconsistent out return
juliantaylor Dec 24, 2016
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9 changes: 7 additions & 2 deletions numpy/ma/extras.py
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Original file line number Diff line number Diff line change
Expand Up @@ -723,11 +723,16 @@ def _median(a, axis=None, out=None, overwrite_input=False):
if asorted.ndim == 1:
counts = count(asorted)
idx, odd = divmod(count(asorted), 2)
mid = asorted[idx + odd - 1 : idx + 1]
mid = asorted[idx + odd - 1:idx + 1]
if np.issubdtype(asorted.dtype, np.inexact) and asorted.size > 0:
# avoid inf / x = masked
s = mid.sum(out=out)
np.true_divide(s, 2., casting='unsafe')
if not odd:
s = np.true_divide(s, 2., casting='safe', out=out)
# masked ufuncs do not fullfill `returned is out` (gh-8416)
# fix this to return the same in the nd path
if out is not None:
s = out
s = np.lib.utils._median_nancheck(asorted, s, axis, out)
else:
s = mid.mean(out=out)
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69 changes: 61 additions & 8 deletions numpy/ma/tests/test_extras.py
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Expand Up @@ -672,12 +672,22 @@ def test_non_masked(self):
x = np.arange(9)
assert_equal(np.ma.median(x), 4.)
assert_(type(np.ma.median(x)) is not MaskedArray)
x = range(9)
assert_equal(np.ma.median(x), 4.)
x = range(8)
assert_equal(np.ma.median(x), 3.5)
assert_(type(np.ma.median(x)) is not MaskedArray)
x = 5
assert_equal(np.ma.median(x), 5.)
assert_(type(np.ma.median(x)) is not MaskedArray)
# integer
x = np.arange(9 * 8).reshape(9, 8)
assert_equal(np.ma.median(x, axis=0), np.median(x, axis=0))
assert_equal(np.ma.median(x, axis=1), np.median(x, axis=1))
assert_(np.ma.median(x, axis=1) is not MaskedArray)
# float
x = np.arange(9 * 8.).reshape(9, 8)
assert_equal(np.ma.median(x, axis=0), np.median(x, axis=0))
assert_equal(np.ma.median(x, axis=1), np.median(x, axis=1))
assert_(np.ma.median(x, axis=1) is not MaskedArray)

def test_docstring_examples(self):
"test the examples given in the docstring of ma.median"
Expand Down Expand Up @@ -742,6 +752,26 @@ def test_masked_1d(self):
assert_equal(np.ma.median(x), 0.)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
# integer
x = array(np.arange(5), mask=[0,1,1,0,0])
assert_equal(np.ma.median(x), 3.)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
# float
x = array(np.arange(5.), mask=[0,1,1,0,0])
assert_equal(np.ma.median(x), 3.)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
# integer
x = array(np.arange(6), mask=[0,1,1,1,1,0])
assert_equal(np.ma.median(x), 2.5)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)
# float
x = array(np.arange(6.), mask=[0,1,1,1,1,0])
assert_equal(np.ma.median(x), 2.5)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
assert_(type(np.ma.median(x)) is not MaskedArray)

def test_1d_shape_consistency(self):
assert_equal(np.ma.median(array([1,2,3],mask=[0,0,0])).shape,
Expand Down Expand Up @@ -791,13 +821,36 @@ def test_neg_axis(self):
x[:3] = x[-3:] = masked
assert_equal(median(x, axis=-1), median(x, axis=1))

def test_out_1d(self):
# integer float even odd
for v in (30, 30., 31, 31.):
x = masked_array(np.arange(v))
x[:3] = x[-3:] = masked
out = masked_array(np.ones(()))
r = median(x, out=out)
if v == 30:
assert_equal(out, 14.5)
else:
assert_equal(out, 15.)
assert_(r is out)
assert_(type(r) is MaskedArray)

def test_out(self):
x = masked_array(np.arange(30).reshape(10, 3))
x[:3] = x[-3:] = masked
out = masked_array(np.ones(10))
r = median(x, axis=1, out=out)
assert_equal(r, out)
assert_(type(r) == MaskedArray)
# integer float even odd
for v in (40, 40., 30, 30.):
x = masked_array(np.arange(v).reshape(10, -1))
x[:3] = x[-3:] = masked
out = masked_array(np.ones(10))
r = median(x, axis=1, out=out)
if v == 30:
e = masked_array([0.]*3 + [10, 13, 16, 19] + [0.]*3,
mask=[True] * 3 + [False] * 4 + [True] * 3)
else:
e = masked_array([0.]*3 + [13.5, 17.5, 21.5, 25.5] + [0.]*3,
mask=[True]*3 + [False]*4 + [True]*3)
assert_equal(r, e)
assert_(r is out)
assert_(type(r) is MaskedArray)

def test_single_non_masked_value_on_axis(self):
data = [[1., 0.],
Expand Down
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