Fix inplace arithmetic operators for numpy>=2 #271
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
…on and division with NumPy 2.x The reason for the failure is that with NumPy 1.25, `super().__imul__(other)` calls `self.__array_prepare__(self, context)`, but `_array_prepare__` was removed in NumPy 2.0. Long-term, we should probably implement `__array_ufunc__`. This inelegant fix will hopefully be enough to get a release out quickly, then we can revisit this when we have more time and fix it properly
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
The reason for the broken in-place behaviour reported in #254 and #258 is that with NumPy 1.x,
super().__imul__(other)callsself.__array_prepare__(self, context), which calculates the correct units, but_array_prepare__was removed from NumPy in NumPy 2.0.This is an inelegant quick fix, calling
self.__array_prepare__(self, context)explicitly. It's inelegant because we have to reconstructcontext, and we have similar code in multiple places. This will hopefully be enough to get a release out quickly, then we can revisit this when we have more time and fix it properly, probably by implementing__array_ufunc__.See: