nalgebra-numpy

This crate provides conversion between nalgebra and numpy.

Currently, only the conversion from numpy to nalgebra is implemented, but the other direction will be added soon.

Conversion from numpy to nalgebra.

It is possible to create either a view or a copy of a numpy array. You can use matrix_from_python to copy the data into a new matrix, or one of matrix_slice_from_python or matrix_slice_mut_from_python to create a view.

Keep in mind though that the borrow checker can not enforce rules on data managed by a Python object. You could potentially keep an immutable view around in Rust, and then modify the data from Python. For this reason, creating any view -- even an immutable one -- is unsafe.

Examples.

Copy a numpy array to a new fixed size matrix:

#![feature(proc_macro_hygiene)]
use inline_python::{Context, python};
use nalgebra_numpy::{matrix_from_python};

let gil = pyo3::Python::acquire_gil();
let context = Context::new_with_gil(gil.python()).unwrap();
python! {
    #![context = &context]
    import numpy as np
    matrix = np.array([
        [1.0, 2.0, 3.0],
        [4.0, 5.0, 6.0],
        [7.0, 8.0, 9.0],
    ])
}

let matrix = context.globals(gil.python()).get_item("matrix").unwrap();
let matrix : nalgebra::Matrix3<f64> = matrix_from_python(matrix)?;

assert_eq!(matrix, nalgebra::Matrix3::new(
    1.0, 2.0, 3.0,
    4.0, 5.0, 6.0,
    7.0, 8.0, 9.0,
));

Dynamic matrices are also supported:

use nalgebra::DMatrix;
#

// <snip>

let matrix : DMatrix<f64> = matrix_from_python(matrix)?;
assert_eq!(matrix, DMatrix::from_row_slice(3, 3, &[
    1.0, 2.0, 3.0,
    4.0, 5.0, 6.0,
    7.0, 8.0, 9.0,
]));

And so are partially dynamic matrices:

use nalgebra::{MatrixMN, Dynamic, U3};

// <snip>

let matrix : MatrixMN<f64, U3, Dynamic> = matrix_from_python(matrix)?;
assert_eq!(matrix, MatrixMN::<f64, U3, Dynamic>::from_row_slice(&[
    1.0, 2.0, 3.0,
    4.0, 5.0, 6.0,
    7.0, 8.0, 9.0,
]));