def zscore(arr, axis=None):
"""
Z-score along the specified axis.
Parameters
----------
arr : ndarray
Input array.
axis : {int, None}, optional
The axis along which to take the z-score. The default (None) is
to find the z-score of the flattened array.
Returns
-------
y : ndarray
A copy normalized with the Z-score along the specified axis.
Examples
--------
>>> arr = np.array([1, np.nan, 2, 3])
>>> zscore(arr)
array([-1.22474487, NaN, 0. , 1.22474487])
"""
arr = demean(arr, axis)
if axis != 0 and not axis is None:
ind = [slice(None)] * arr.ndim
ind[axis] = np.newaxis
arr /= nanstd(arr, axis)[ind]
else:
arr /= nanstd(arr, axis)
return arr
def zscore(arr, axis=None):
"""
Z-score along the specified axis.
Parameters
----------
arr : ndarray
Input array.
axis : {int, None}, optional
The axis along which to take the z-score. The default (None) is
to find the z-score of the flattened array.
Returns
-------
y : ndarray
A copy normalized with the Z-score along the specified axis.
Examples
--------
>>> arr = np.array([1, np.nan, 2, 3])
>>> zscore(arr)
array([-1.22474487, NaN, 0. , 1.22474487])
"""
arr = demean(arr, axis)
if axis != 0 and not axis is None:
ind = [slice(None)] * arr.ndim
ind[axis] = np.newaxis
arr /= nanstd(arr, axis)[ind]
else:
arr /= nanstd(arr, axis)
return arr
def test_nanstd_all(self):
"""Check nanstd when all values are nan."""
s = nanstd(self.Xall)
assert np.isnan(s)
def test_nanstd_some(self):
"""Check nanstd when some values only are nan."""
s = nanstd(self.Xsome)
assert_approx_equal(s, np.std(self.Xsomet, ddof=0))
def test_nanstd_none(self):
"""Check nanstd when no values are nan."""
s = nanstd(self.X)
assert_approx_equal(s, np.std(self.X, ddof=0))
def test_nanstd_all(self):
"""Check nanstd when all values are nan."""
s = nanstd(self.Xall)
assert np.isnan(s)
def test_nanstd_some(self):
"""Check nanstd when some values only are nan."""
s = nanstd(self.Xsome)
assert_approx_equal(s, np.std(self.Xsomet, ddof=0))
def test_nanstd_none(self):
"""Check nanstd when no values are nan."""
s = nanstd(self.X)
assert_approx_equal(s, np.std(self.X, ddof=0))