Add Numpy 2 compatibility
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- Dominant language
- Python
- Stars
- 279
- Forks
- 177
- PR merge metrics
- No merged PRs in 30d
Description
We would like to use py4DSTEM in Python environments with numpy 2.x.
The core packages seem to all be available to be used with numpy 2.x. Tested with this command:
conda create -n trial_dependencies -c conda-forge numpy scipy hdf5 hdf5plugin ncempy matplotlib scikit-image scikit-learn scikit-optimize tqdm dill gdown dask distributed emdfile mpire threadpoolctl pylops colorspacious
Some of the extensions dependencies may not yet available on conda-forge, but are available on PyPI (tensorflow requires 2.18 for numpy 2 compatibility, 2.18 isn't available using conda-forge on macOS arm64 yet). I'm on a macOS machine right now so I couldn't try everything (i.e. cupy).
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by recreating the reported conda environment with NumPy 2.x and the listed core dependencies. Check whether py4DSTEM imports and its core and optional extensions work, including the TensorFlow and CuPy cases where available; done means supported environments run successfully under NumPy 2.x.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- numpy, python, tensorflow
- Domain
- data
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 28/100