Numpy version 1.24.0.
The current behavior of `np.testing.assert_allclose` and `np.allclose` are
still inconsistent with the docs, which specifies that `atol` and `rtol` must
be a scalar. However, the current implementation of `np.allclose` allows
passing in `atol` and `rtol` as an array, while `np.testing.assert_allclose`
only raises error because of a string formatting line `header = f'Not equal to
tolerance rtol={rtol:g}, atol={atol:g}'`. To make things less confusing, for
BOTH `np.allclose()` and `np.testing.assert_allclose()`, we should either
perform a check for the correct type of `atol` and `rtol`, or to actually
support an array as `atol`.
Indeed there are real use-cases for array `atol` and `rtol`. For example for
`atol`: let's say we want to compare two 3D vectors x and y where the first two
axis should have absolute tolerance of 0.1 while the third axis should have
absolute tolerance of 1, because there are more stringent requirements on x and
y. I believe this is the use case that many of us run into, and it is quite
natural to write
```python3
import numpy as np
x = [1, 2, 3]
y = [1.1, 2.1, 5]
np.allclose(x, y, atol=[0.1, 0.1, 1])
```
[I have posted this as a comment on an
issue](https://github.com/numpy/numpy/issues/14320#issuecomment-1482201277)
[And there is already a proposed PR to support this
change](https://github.com/numpy/numpy/pull/14343)
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