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Fix sigma_clipped_stats to use the axis argument #4726

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Merged
pllim merged 1 commit into from
Apr 12, 2016
Merged

Fix sigma_clipped_stats to use the axis argument #4726

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4 changes: 3 additions & 1 deletion CHANGES.rst
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Expand Up @@ -278,7 +278,7 @@ Bug fixes
- ``NDArithmeticMixin`` does provide correct resulting uncertainties for
``divide`` and ``multiply`` if only one uncertainty was set. [#4152, #4272]

- ``astropy.stats``
- ``astropy.stats``

- ``astropy.table``

Expand Down Expand Up @@ -442,6 +442,8 @@ Bug Fixes

- ``astropy.stats``

- Fix ``sigma_clipped_stats`` to use the ``axis`` argument. [#4726]

- ``astropy.table``

- ``astropy.time``
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15 changes: 13 additions & 2 deletions astropy/stats/sigma_clipping.py
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Expand Up @@ -300,8 +300,19 @@ def sigma_clipped_stats(data, mask=None, mask_value=None, sigma=3.0,
data = np.ma.MaskedArray(data, mask)
if mask_value is not None:
data = np.ma.masked_values(data, mask_value)

data_clip = sigma_clip(data, sigma=sigma, sigma_lower=sigma_lower,
sigma_upper=sigma_upper, iters=iters,
cenfunc=cenfunc, stdfunc=stdfunc, axis=axis)
goodvals = np.ma.compressed(data_clip)
return np.mean(goodvals), np.median(goodvals), np.std(goodvals)

mean = np.ma.mean(data_clip, axis=axis)
median = np.ma.median(data_clip, axis=axis)
std = np.ma.std(data_clip, axis=axis)

if axis is None and np.ma.isMaskedArray(median):
# With Numpy 1.10 np.ma.median always return a MaskedArray, even with
# one element. So for compatibility with previous versions, we take the
# scalar value
median = median[0]

return mean, median, std
21 changes: 14 additions & 7 deletions astropy/stats/tests/test_sigma_clipping.py
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Expand Up @@ -95,16 +95,23 @@ def test_sigma_clipped_stats():
data = [0, 1]
mask = np.array([True, False])
result = sigma_clipped_stats(data, mask=mask)
assert result[0] == 1.
assert result[1] == 1.
assert result[2] == 0.
# Check that the result of np.ma.median was converted to a scalar
assert isinstance(result[1], float)
assert result == (1., 1., 0.)

# test list data with mask_value
result2 = sigma_clipped_stats(data, mask_value=0.)
assert result2[0] == 1.
assert result2[1] == 1.
assert result2[2] == 0.

assert isinstance(result[1], float)
assert result2 == (1., 1., 0.)

_data = np.arange(10)
data = np.ma.MaskedArray([_data, _data, 10 * _data])
mean = sigma_clip(data, axis=0, sigma=1).mean(axis=0)
assert_equal(mean, _data)
mean, median, stddev = sigma_clipped_stats(data, axis=0, sigma=1)
assert_equal(mean, _data)
assert_equal(median, _data)
assert_equal(stddev, np.zeros_like(_data))

def test_invalid_sigma_clip():
"""Test sigma_clip of data containing invalid values."""
Expand Down