[RFC]: add APIs for array equality to a scalar

首先查看现有的数组 API 相等性操作和链接的 Zarr issue，以了解提议的用例。完整的贡献需要为全相等、全不相等和可能不相等检查确定一个一致的 API，并进行相应的规范更改和测试。

Today, there are a few cases where people might want to check equality to a scalar.

import numpy as np

is_all_zero = np.all(arr == 0)

However, this code is terribly inefficient for out of memory array.

It can force them to go through the entire memory.

I think that we can do much better to define an operation that will check for

• All equal
• All not equal

and those can be implemented in more "streamed" fashion, that allow the underlying implementation to not create a full boolean array for arr == 0.

Second, I would like to propose an API for "likely not equal", where the strick inequality is not guaranteed.

For data compression, we might just be intrested in learning if the dataset is worth compressing or not:
Zarr for example does this:
https://github.com/zarr-developers/zarr-python/issues/3627

For example, consider the task to compress a 800MB array.
Zarr today:

1. Checks for equality to zero
2. If all zeros, it skips things
3. if not zeros, it compresses things

But if you have an out of memory data array, it may be hard to guarantee that things are not all zero, but the check likely doesn't matter, since with modern compression algorithm all zeros can be efficiently compressed.

>>> import numpy as np
>>> import numcodecs
>>> a = np.zeros((100, 1024, 1024), dtype='float64')
>>> len(numcodecs.blosc.compress(a, b'zstd', 7))
67216

On my computer, the equality check is roughly the same order of magnitude as the compression itself:

In [14]: %time numcodecs.blosc.compress(a, b'zstd', 7);
CPU times: user 2.04 s, sys: 903 μs, total: 2.04 s
Wall time: 266 ms

In [15]: %time np.all(a==0 );
CPU times: user 21.4 ms, sys: 109 ms, total: 130 ms
Wall time: 140 ms

so if zarr misses one of my images, because I think it has non-zero element, its likely not the end of the world in terms of system performance, but if I have to "guarantee that the images are non zero" that can be a costly operation that can't be done through my implementation easily "without checking every single element".

For not, without these APIs, today, zarr does something like:

1. Create an empty array
2. Check for equality with that empty array

---

Alternative:

I could likely check numpy metadata like strides and create my own fastpath for this:

>>> zero = np.zeros(1)
>>> a, zero = np.broadcast_arrays(a, zero)
>>> zero.strides
(0, 0, 0)

but this seems more like a hack and would be implicitely redefining "equality operation" to "likely equal" which isn't correct.