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✓ Uses Rust 2018 edition

0.2.1 Jan 14, 2020
0.2.0 Nov 14, 2019
0.1.3 Nov 13, 2019

#64 in Science

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BSD-2-Clause

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nalgebra-numpy

This crate provides conversion between nalgebra and numpy. It is intended to be used when you want to share nalgebra matrices between Python and Rust code, for example with inline-python.

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_numpy to copy the data into a new matrix, or one of matrix_slice_from_numpy or matrix_slice_mut_from_numpy to create a view. If a numpy array is not compatible with the requested matrix type, an error is returned.

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.

Conversion from nalgebra to numpy.

A nalgebra matrix can also be converted to a numpy array, using matrix_to_numpy. This function always creates a copy. Since all nalgebra arrays can be represented as a numpy array, this directly returns a pyo3::PyObject rather than a Result.

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_numpy};

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_numpy(gil.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;
#

let matrix : DMatrix<f64> = matrix_from_numpy(gil.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};

let matrix : MatrixMN<f64, U3, Dynamic> = matrix_from_numpy(gil.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,
]));

A conversion to python object looks as follows:

use nalgebra_numpy::matrix_to_numpy;
use nalgebra::Matrix3;
use inline_python::python;

let gil = pyo3::Python::acquire_gil();
let matrix = matrix_to_numpy(gil.python(), &Matrix3::<i32>::new(
    0, 1, 2,
    3, 4, 5,
    6, 7, 8,
));

python! {
    from numpy import array_equal
    assert array_equal('matrix, [
        [0, 1, 2],
        [3, 4, 5],
        [6, 7, 8],
    ])
}

Dependencies

~6MB
~123K SLoC