NuMojo V0.2 Release: Simplified Type Handling, New Features, and Enhanced Compatibility
We’re excited to announce NuMojo V0.2, featuring significant improvements and new features. This update simplifies data type management by removing
in_dtype and out_dtype parameters and introduces Rust-like data type aliases (e.g., f64). Function overloads were implemented in arithmetic to allow easy symmetric usage of NDArray and Scalar types.
Array operations have seen major enhancements, including the introduction of the diagflat() method for creating diagonal arrays, improved slicing functionality that aligns with NumPy, and added boolean masking for NDArrays. The NDArray constructor now supports string array initialization, making array creation easier.
Documentation has been expanded with an updated README, improved function docstrings, and a new style guide. We’ve also introduced new test files compatible with the mojo test framework, improving NuMojo’s reliability.
NuMojo V0.2 is compatible with Mojo 24.4 and requires no external dependencies.
For more details, check out the full changelog.GitHub
GitHub - Mojo-Numerics-and-Algorithms-group/NuMojo: NuMojo is a lib...
NuMojo is a library for numerical computing in Mojo 🔥 similar to numpy in Python. - Mojo-Numerics-and-Algorithms-group/NuMojo
4 Replies
While magic is getting worked on, is the recommendation for development to build a mojopkg of the dependency and place it in the source tree?
That has been what I have been doing, apparently there are some issues with slices from mojopkgs on Mac computers.
Probably git submodules for now.
I've had some luck with rattler build and using prefix to host conda packages if you're interested, you're able to set up dependencies and have them installed along with your package via magic install.
I created this channel to share later for whomever was interested. Waiting for Mojo 24.5 so things stabilize a little bit.
https://prefix.dev/channels/mojo-community