https://github.com/fusion-engineering/nalgebra-numpy
Convert between nalgebra and numpy types.
https://github.com/fusion-engineering/nalgebra-numpy
Last synced: about 1 year ago
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Convert between nalgebra and numpy types.
- Host: GitHub
- URL: https://github.com/fusion-engineering/nalgebra-numpy
- Owner: fusion-engineering
- License: bsd-2-clause
- Created: 2019-11-12T22:30:41.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-11-01T02:26:48.000Z (over 3 years ago)
- Last Synced: 2025-03-27T20:51:18.399Z (over 1 year ago)
- Language: Rust
- Size: 81.1 KB
- Stars: 22
- Watchers: 3
- Forks: 8
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# nalgebra-numpy
This crate provides conversion between [`nalgebra`] and [`numpy`](https://numpy.org/).
It is intended to be used when you want to share nalgebra matrices between Python and Rust code,
for example with [`inline-python`](https://docs.rs/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:
```rust
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());
context.run(python! {
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 = 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:
```rust
use nalgebra::DMatrix;
#
let matrix : DMatrix = 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:
```rust
use nalgebra::{MatrixMN, Dynamic, U3};
let matrix : MatrixMN = matrix_from_numpy(gil.python(), matrix)?;
assert_eq!(matrix, MatrixMN::::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:
```rust
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::::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],
])
}
```
[`nalgebra`]: https://docs.rs/nalgebra
[`matrix_from_numpy`]: https://docs.rs/nalgebra-numpy/latest/nalgebra_numpy/fn.matrix_from_numpy.html
[`matrix_slice_from_numpy`]: https://docs.rs/nalgebra-numpy/latest/nalgebra_numpy/fn.matrix_slice_from_numpy.html
[`matrix_slice_mut_from_numpy`]: https://docs.rs/nalgebra-numpy/latest/nalgebra_numpy/fn.matrix_slice_mut_from_numpy.html
[`matrix_to_numpy`]: https://docs.rs/nalgebra-numpy/latest/nalgebra_numpy/fn.matrix_to_numpy.html
[`pyo3::PyObject`]: https://docs.rs/pyo3/latest/pyo3/type.PyObject.html