Multidimensional Arrays
ndarray-pixels
Common operations for ndarray arrays
TypeScript definitions for ndarray
Packs an array-of-arrays into a single ndarray
Resize an ndarray with Lanczos resampling
Computes the determinant of an ndarray
Fills an ndarray with function
Get a view of the diagonal entries of an ndarray
Validates if a value is ndarray-like.
Finds the gradient of an ndarray using finite differences
ndarray image warping
List of ndarray orders.
Default ndarray settings.
List of ndarray data types.
TypeScript definitions for ndarray-ops
Applies a homograph to an ndarray
List of ndarray index modes.
Reads the pixels of an image as an ndarray
Converts an ndarray into an array-of-arrays
ndarray data buffer constructors.
List of ndarray data type strings.
List of ndarray casting modes.
Return the number of ndarray dimensions.
An n-dimensional array for general elements and for numerics. Lightweight array views and slicing; views support chunking and splitting.
NDArray Package for dendritic
ndarray integration for OxiBLAS
Very small multi-dimensional-array implementation
N-Dimension convolution (with FFT) lib for ndarray.
A numpy-like library for Rust
Zero-copy bridge between Apache Arrow and ndarray
Zero-copy implementations for the Image crate to convert to and from ndarrays
Statistical routines for ArrayBase, the n-dimensional array data structure provided by ndarray.
Statistical routines for the n-dimensional array data structures provided by ndarray.
High-performance computer graphics mathematics library based on ndarray with vectors, matrices, and transformations
High-performance Machine Learning, Auto-Differentiation and Tensor Algebra crate for Rust
`MemoryViewTestHelper::NDArray` provides simple multi-dimensional numeric array that can export MemoryView.
Ignis is the foundation of a CUDA-backed deep-learning ecosystem for Ruby that actually targets native Windows. It provides a GPU n-dimensional array (Ignis::NDArray), CUDA memory/device management, a runtime kernel compiler (NVRTC) with a batteries-included kernel library, fp16/bf16 conversion, and cuBLAS GEMM. Kernels are compiled at runtime and libraries are bound via FFI — there are NO C extensions, so installation needs no compiler or devkit (the usual Windows native-gem killer). Requires an NVIDIA GPU + CUDA toolkit/runtime.
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