def test__replace_nan():
""" Test that _replace_nan returns the original array if there are no
NaNs, not a copy.
"""
for dtype in [np.bool, np.int32, np.int64]:
arr = np.array([0, 1], dtype=dtype)
result, mask = _replace_nan(arr, 0)
assert mask is None
# do not make a copy if there are no nans
assert result is arr
for dtype in [np.float32, np.float64]:
arr = np.array([0, 1], dtype=dtype)
result, mask = _replace_nan(arr, 2)
assert (mask == False).all()
# mask is not None, so we make a copy
assert result is not arr
assert_equal(result, arr)
arr_nan = np.array([0, 1, np.nan], dtype=dtype)
result_nan, mask_nan = _replace_nan(arr_nan, 2)
assert_equal(mask_nan, np.array([False, False, True]))
assert result_nan is not arr_nan
assert_equal(result_nan, np.array([0, 1, 2]))
assert np.isnan(arr_nan[-1])
def nanargmin(a, axis):
from numpy.lib.nanfunctions import _replace_nan
a, mask = _replace_nan(a, np.inf)
res = np.argmin(a, axis=axis)
return res
