def nanargmax(a, axis=None):
""" Returns the indices of the maximum values in the specified axis ignoring NaNs.
For all-NaN slices ``ValueError`` is raised. Warning: the results cannot be trusted
if a slice contains only NaNs and -Infs.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which to operate. By default flattened input is used.
Returns
-------
index_array : ndarray
An array of indices or a single index value.
See Also
--------
argmax : Returns the indices of the maximum values along an axis.
nanargmin : Returns the indices of the minimum values in the specified axis
ignoring NaNs.
Examples
--------
>>> import nlcpy as vp
>>> a = vp.array([[vp.nan, 4], [2, 3]])
>>> vp.argmax(a)
array(0)
>>> vp.nanargmax(a)
array(1)
>>> vp.nanargmax(a, axis=0)
array([1, 0])
>>> vp.nanargmax(a, axis=1)
array([1, 1])
"""
a = nlcpy.array(a)
if a.dtype.kind not in 'fc':
return nlcpy.argmax(a, axis=axis)
mask = nlcpy.isnan(a)
if nlcpy.any(nlcpy.all(mask, axis=axis)):
raise ValueError("All-NaN slice encountered")
nlcpy.copyto(a, -nlcpy.inf, where=mask)
return nlcpy.argmax(a, axis=axis)
def testinge_case_6():
a = vp.array([[10, 7, 4], [3, 2, 1]])
b = a.copy()
vp.quantile(b, 0.5, axis=1, overwrite_input=True)
return vp.all(a == b)
def testinge_case_6():
a = vp.array([[10., 7., 4.], [3., 2., 1.]])
a[0][1] = vp.nan
b = a.copy()
vp.nanpercentile(b, 50, axis=1, overwrite_input=True)
return vp.all(a == b)
def test_me_case_2():
a = nlcpy.array([[10, 7, 4], [3, 2, 1]])
b = a.copy()
ny.median(b, axis=None, overwrite_input=True)
assert not ny.all(a == b)
def test_me_case_1():
a = ny.array([[10, 7, 4], [3, 2, 1]])
b = a.copy()
ny.median(b, overwrite_input=False)
print("a={} b={}".format(a, b))
assert ny.all(a == b)