# nalgebra-numpy 0.2.1

conversions between nalgebra and numpy
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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],
])
}
``````