def demedian(arr, axis=None):
"""
Subtract the median along the specified axis.
Parameters
----------
arr : ndarray
Input array.
axis : {int, None}, optional
The axis along which to remove the median. The default (None) is
to subtract the median of the flattened array.
Returns
-------
y : ndarray
A copy with the median along the specified axis removed.
Examples
--------
>>> arr = np.array([1, np.nan, 2, 10])
>>> demedian(arr)
array([ -1., NaN, 0., 8.])
"""
# Adapted from pylab.demean
if axis != 0 and not axis is None:
ind = [slice(None)] * arr.ndim
ind[axis] = np.newaxis
arr = arr - nanmedian(arr, axis)[ind]
else:
arr = arr - nanmedian(arr, axis)
return arr
def group_median(x, groups, axis=0):
"""
Median with groups along an axis.
Parameters
----------
x : ndarray
Input data.
groups : list
List of group membership of each element along the given axis.
axis : int, {default: 0}
axis along which the ranking is calculated.
Returns
-------
idx : ndarray
The group median of the data along axis 0.
"""
# Find set of unique groups
ugroups = unique_group(groups)
# Convert groups to a numpy array
groups = np.asarray(groups)
# Loop through unique groups and normalize
xmedian = np.nan * np.zeros(x.shape)
for group in ugroups:
idx = groups == group
idxall = [slice(None)] * x.ndim
idxall[axis] = idx
if idx.sum() > 0:
ns = nanmedian(x[idxall], axis=axis)
xmedian[idxall] = np.expand_dims(ns, axis)
return xmedian
def group_median(x, groups, axis=0):
"""
Median with groups along an axis.
Parameters
----------
x : ndarray
Input data.
groups : list
List of group membership of each element along the given axis.
axis : int, {default: 0}
axis along which the ranking is calculated.
Returns
-------
idx : ndarray
The group median of the data along axis 0.
"""
# Find set of unique groups
ugroups = unique_group(groups)
# Convert groups to a numpy array
groups = np.asarray(groups)
# Loop through unique groups and normalize
xmedian = np.nan * np.zeros(x.shape)
for group in ugroups:
idx = groups == group
idxall = [slice(None)] * x.ndim
idxall[axis] = idx
if idx.sum() > 0:
ns = nanmedian(x[idxall], axis=axis)
xmedian[idxall] = np.expand_dims(ns, axis)
return xmedian
def test_nanmedian_all(self):
"""Check nanmedian when all values are nan."""
m = nanmedian(self.Xall)
assert np.isnan(m)
def test_nanmedian_some(self):
"""Check nanmedian when some values only are nan."""
m = nanmedian(self.Xsome)
assert_approx_equal(m, np.median(self.Xsomet))
def test_nanmedian_none(self):
"""Check nanmedian when no values are nan."""
m = nanmedian(self.X)
assert_approx_equal(m, np.median(self.X))
def test_nanmedian_all(self):
"""Check nanmedian when all values are nan."""
m = nanmedian(self.Xall)
assert np.isnan(m)
def test_nanmedian_some(self):
"""Check nanmedian when some values only are nan."""
m = nanmedian(self.Xsome)
assert_approx_equal(m, np.median(self.Xsomet))
def test_nanmedian_none(self):
"""Check nanmedian when no values are nan."""
m = nanmedian(self.X)
assert_approx_equal(m, np.median(self.X))