Hi all, As part of the ongoing work on NEP 52 we are getting close to merging the pull request that changes numpy.core to numpy._core.
While working on this we realized that numpy pickle files include paths to np.core in the pickle data. If we do nothing, switching np.core to np._core will generate deprecation warnings when loading pickle files generated by Numpy 1.x in Numpy 2.x and Numpy 1.x will be unable to read Numpy 2.x pickle files. Eventually, when Numpy 2.x completely removes the stub np.core module, loading old pickle files will break. The fix we have come up with is to add a new public NumpyUnpickler class to both the main branch and the Numpy 1.26 maintenance branch. This allows loading pickle files that were generated by Numpy 1.x and 2.x in either version without any warnings or errors. Users who are loading old pickle files will need to update their code to use NumpyUnpickler or create new pickle files and users who generate pickles with numpy 2.x will need to use NumpyUnpickler to read the files in numpy 1.x. We are using NumpyUnpickler internally for loading files in the npy file format. Users with pickle data saved in npy files won't see warnings. Only users who are storing data in pickle files directly and who want pickle files written in one numpy version to load correctly in another numpy version will run into trouble. The I/O docs already explicitly discourage using pickles to share data files between people and organizations like this. An alternate approach which would require less work for users would be to leave a limited subset of functionality in `np.core` needed for loading pickle files undeprecated. We would prefer to avoid doing this both because it would leave behind a publicly visible `np.core` module in NumPy's public API and because we're not sure if we can come up with a complete set of imports that should be allowed without warning from `np.core` without missing some corner cases and users will see deprecation warnings when loading pickles anyway. See https://github.com/numpy/numpy/pull/24866, https://github.com/numpy/numpy/issues/24844, and the discussion in https://github.com/numpy/numpy/pull/24634 for more context. -Nathan
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