8000 BUG: Added proper handling of median and percentile when nan's are prese... by empeeu · Pull Request #5753 · numpy/numpy · GitHub
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BUG: Added proper handling of median and percentile when nan's are prese... #5753

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Merged
merged 3 commits into from
Jun 23, 2015
Merged

BUG: Added proper handling of median and percentile when nan's are prese... #5753

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a320fd7
BUG: Added proper handling of median and percentile when nan's are pr…
empeeu Mar 8, 2014
3e82d6d
Moving warning outside of for loop.
empeeu Jun 22, 2015
f5d3d96
Updating unit tests for removed warning.
empeeu Jun 22, 2015
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8 changes: 8 additions & 0 deletions doc/release/1.10.0-notes.rst
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Expand Up @@ -128,6 +128,14 @@ Masked arrays containing objects with arrays
For such (rare) masked arrays, getting a single masked item no longer returns a
corrupted masked array, but a fully masked version of the item.

Median warns and returns nan when invalid values are encountered
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Similar to mean, median and percentile now emits a Runtime warning and
returns `NaN` in slices where a `NaN` is present.
To compute the median or percentile while ignoring invalid values use the
new `nanmedian` or `nanpercentile` functions.


New Features
============

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10000
118 changes: 92 additions & 26 deletions numpy/lib/function_base.py
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Expand Up @@ -3029,51 +3029,71 @@ def _median(a, axis=None, out=None, overwrite_input=False):
# can't be reasonably be implemented in terms of percentile as we have to
# call mean to not break astropy
a = np.asanyarray(a)
if axis is not None and axis >= a.ndim:
raise IndexError(
"axis %d out of bounds (%d)" % (axis, a.ndim))

# Set the partition indexes
if axis is None:
sz = a.size
else:
sz = a.shape[axis]
if sz % 2 == 0:
szh = sz // 2
kth = [szh - 1, szh]
else:
kth = [(sz - 1) // 2]
# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
kth.append(-1)

if overwrite_input:
if axis is None:
part = a.ravel()
sz = part.size
if sz % 2 == 0:
szh = sz // 2
part.partition((szh - 1, szh))
else:
part.partition((sz - 1) // 2)
part.partition(kth)
else:
sz = a.shape[axis]
if sz % 2 == 0:
szh = sz // 2
a.partition((szh - 1, szh), axis=axis)
else:
a.partition((sz - 1) // 2, axis=axis)
a.partition(kth, axis=axis)
part = a
else:
if axis is None:
sz = a.size
else:
sz = a.shape[axis]
if sz % 2 == 0:
part = partition(a, ((sz // 2) - 1, sz // 2), axis=axis)
else:
part = partition(a, (sz - 1) // 2, axis=axis)
part = partition(a, kth, axis=axis)

if part.shape == ():
# make 0-D arrays work
return part.item()
if axis is None:
axis = 0

indexer = [slice(None)] * part.ndim
index = part.shape[axis] // 2
if part.shape[axis] % 2 == 1:
# index with slice to allow mean (below) to work
indexer[axis] = slice(index, index+1)
else:
indexer[axis] = slice(index-1, index+1)
# Use mean in odd and even case to coerce data type
# and check, use out array.
return mean(part[indexer], axis=axis, out=out)

# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
# warn and return nans like mean would
rout = mean(part[indexer], axis=axis, out=out)
part = np.rollaxis(part, axis, part.ndim)
n = np.isnan(part[..., -1])
if rout.ndim == 0:
if n == True:
warnings.warn("Invalid value encountered in median",
RuntimeWarning)
if out is not None:
out[...] = a.dtype.type(np.nan)
rout = out
else:
rout = a.dtype.type(np.nan)
elif np.count_nonzero(n.ravel()) > 0:
warnings.warn("Invalid value encountered in median for" +
" %d results" % np.count_nonzero(n.ravel()),
RuntimeWarning)
rout[n] = np.nan
return rout
else:
# if there are no nans
# Use mean in odd and even case to coerce data type
# and check, use out array.
return mean(part[indexer], axis=axis, out=out)


def percentile(a, q, axis=None, out=None,
Expand Down Expand Up @@ -3249,20 +3269,36 @@ def _percentile(a, q, axis=None, out=None,
"interpolation can only be 'linear', 'lower' 'higher', "
"'midpoint', or 'nearest'")

n = np.array(False, dtype=bool) # check for nan's flag
if indices.dtype == intp: # take the points along axis
# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
indices = concatenate((indices, [-1]))

ap.partition(indices, axis=axis)
# ensure axis with qth is first
ap = np.rollaxis(ap, axis, 0)
axis = 0

# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
indices = indices[:-1]
n = np.isnan(ap[-1:, ...])

if zerod:
indices = indices[0]
r = take(ap, indices, axis=axis, out=out)


else: # weight the points above and below the indices
indices_below = floor(indices).astype(intp)
indices_above = indices_below + 1
indices_above[indices_above > Nx - 1] = Nx - 1

# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
indices_above = concatenate((indices_above, [-1]))

weights_above = indices - indices_below
weights_below = 1.0 - weights_above

Expand All @@ -3272,6 +3308,18 @@ def _percentile(a, q, axis=None, out=None,
weights_above.shape = weights_shape

ap.partition(concatenate((indices_below, indices_above)), axis=axis)

# ensure axis with qth is first
ap = np.rollaxis(ap, axis, 0)
weights_below = np.rollaxis(weights_below, axis, 0)
weights_above = np.rollaxis(weights_above, axis, 0)
axis = 0

# Check if the array contains any nan's
if np.issubdtype(a.dtype, np.inexact):
indices_above = indices_above[:-1]
n = np.isnan(ap[-1:, ...])

x1 = take(ap, indices_below, axis=axis) * weights_below
x2 = take(ap, indices_above, axis=axis) * weights_above

Expand All @@ -3288,6 +3336,24 @@ def _percentile(a, q, axis=None, out=None,
else:
r = add(x1, x2)

if np.any(n):
warnings.warn("Invalid value encountered in median",
RuntimeWarning)
if zerod:
if ap.ndim == 1:
if out is not None:
out[...] = a.dtype.type(np.nan)
r = out
else:
r = a.dtype.type(np.nan)
else:
r[..., n.squeeze(0)] = a.dtype.type(np.nan)
else:
if r.ndim == 1:
r[:] = a.dtype.type(np.nan)
else:
r[..., n.repeat(q.size, 0)] = a.dtype.type(np.nan)

return r


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