8000 BUG: Fix numpy.ma.median. by charris · Pull Request #8016 · numpy/numpy · GitHub
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BUG: Fix numpy.ma.median. #8016

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Merged
merged 2 commits into from
Sep 6, 2016
Merged

BUG: Fix numpy.ma.median. #8016

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66f313f
BUG: Fix numpy.ma.median for various things.
charris Sep 4, 2016
ad5b13a
TST: Add ma.median tests for valid axis.
charris Sep 5, 2016
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19 changes: 11 additions & 8 deletions numpy/ma/extras.py
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Original file line number Diff line number Diff line change
Expand Up @@ -708,34 +708,37 @@ def _median(a, axis=None, out=None, overwrite_input=False):
asorted = a
else:
asorted = sort(a, axis=axis)

if axis is None:
axis = 0
elif axis < 0:
axis += a.ndim
axis += asorted.ndim

if asorted.ndim == 1:
idx, odd = divmod(count(asorted), 2)
return asorted[idx - (not odd) : idx + 1].mean()
return asorted[idx + odd - 1 : idx + 1].mean(out=out)

counts = asorted.shape[axis] - (asorted.mask).sum(axis=axis)
counts = count(asorted, axis=axis)
h = 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)
low = asorted[tuple(ind)]
low._sharedmask = False
ind[axis] = h
high = asorted[tuple(ind)]

# duplicate high if odd number of elements so mean does nothing
odd = counts % 2 == 1
if asorted.ndim == 1:
if odd:
low = high
else:
low[odd] = high[odd]
if asorted.ndim > 1:
np.copyto(low, high, where=odd)
elif odd:
low = high

if np.issubdtype(asorted.dtype, np.inexact):
# avoid inf / x = masked
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32 changes: 32 additions & 0 deletions numpy/ma/tests/test_extras.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@
from __future__ import division, absolute_import, print_function

import warnings
import itertools

import numpy as np
from numpy.testing import (
Expand Down Expand Up @@ -684,6 +685,37 @@ def test_docstring_examples(self):
assert_equal(ma_x.shape, (2,), "shape mismatch")
assert_(type(ma_x) is MaskedArray)

def test_axis_argument_errors(self):
msg = "mask = %s, ndim = %s, axis = %s, overwrite_input = %s"
for ndmin in range(5):
for mask in [False, True]:
x = array(1, ndmin=ndmin, mask=mask)

# Valid axis values should not raise exception
args = itertools.product(range(-ndmin, ndmin), [False, True])
for axis, over in args:
try:
np.ma.median(x, axis=axis, overwrite_input=over)
except:
raise AssertionError(msg % (mask, ndmin, axis, over))

# Invalid axis values should raise exception
args = itertools.product([-(ndmin + 1), ndmin], [False, True])
for axis, over in args:
try:
np.ma.median(x, axis=axis, overwrite_input=over)
except IndexError:
pass
else:
raise AssertionError(msg % (mask, ndmin, axis, over))

def test_masked_0d(self):
# Check values
x = array(1, mask=False)
assert_equal(np.ma.median(x), 1)
x = array(1, mask=True)
assert_equal(np.ma.median(x), np.ma.masked)

def test_masked_1d(self):
x = array(np.arange(5), mask=True)
assert_equal(np.ma.median(x), np.ma.masked)
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