8000 BUG: masked-array median of 1d array should be scalar by AmitAronovitch · Pull Request #7592 · numpy/numpy · GitHub
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BUG: masked-array median of 1d array should be scalar #7592

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BUG: masked-array median of 1d array should be scalar #7592

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cf1689d
test that ma.median of 1d array returns a scalar
AmitAronovitch Apr 30, 2016
51887f4
ma.median: fix 1d behavior: avoid triggering advanced indexing's spec…
AmitAronovitch Apr 30, 2016
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13 changes: 9 additions & 4 deletions numpy/ma/extras.py
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Original file line number Diff line number Diff line change
Expand Up @@ -654,19 +654,21 @@ def _median(a, axis=None, out=None, overwrite_input=False):
axis += a.ndim

counts = asorted.shape[axis] - (asorted.mask).sum(axis=axis)
h = counts // 2
h = np.asarray(counts // 2)
# create indexing mesh grid for all but reduced axis
axes_grid = [np.arange(x) for i, x in enumerate(asorted.shape)
if i != axis]
ind = np.meshgrid(*axes_grid, sparse=True, indexing='ij')
# insert indices of low and high median
ind.insert(axis, h - 1)
ind[axis] = np.asarray(h-1)
low = asorted[ind]
low._sharedmask = False
if hasattr(low,"mask"):
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I would prefer if isinstance(low, MaskedArray): I think.

low._sharedmask = False
ind[axis] = h
high = asorted[ind]
# duplicate high if odd number of elements so mean does nothing
odd = counts % 2 == 1
odd = np.asarray( counts % 2 == 1 )
if asorted.ndim == 1:
if odd:
low = high
Expand All @@ -676,7 +678,10 @@ def _median(a, axis=None, out=None, overwrite_input=False):
if np.issubdtype(asorted.dtype, np.inexact):
# avoid inf / x = masked
s = np.ma.sum([low, high], axis=0, out=out)
np.true_divide(s.data, 2., casting='unsafe', out=s.data)
if hasattr(s,"mask"):
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isinstance here too

np.true_divide(s.data, 2., casting='unsafe', out=s.data)
else:
s = np.true_divide(s,2.0)
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is it all right to simply write s = s/2 here?

else:
s = np.ma.mean([low, high], axis=0, out=out)
return s
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13 changes: 13 additions & 0 deletions numpy/ma/tests/test_extras.py
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Expand Up @@ -662,6 +662,19 @@ def test_non_masked(self):
assert_equal(np.ma.median(np.arange(9)), 4.)
assert_equal(np.ma.median(range(9)), 4)

def test_masked_1d(self):
"test the examples given in the docstring of ma.median"
x = array(np.arange(8), mask=[0]*4 + [1]*4)
assert_equal(np.ma.median(x), 1.5)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")
x = array(np.arange(10).reshape(2, 5), mask=[0]*6 + [1]*4)
assert_equal(np.ma.median(x), 2.5)
assert_equal(np.ma.median(x).shape, (), "shape mismatch")

def test_1d_shape_consistency(self):
assert_equal(np.ma.median(array([1,2,3],mask=[0,0,0])).shape,
np.ma.median(array([1,2,3],mask=[0,1,0])).shape )

def test_2d(self):
# Tests median w/ 2D
(n, p) = (101, 30)
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