1 unstable release
0.3.1 | Aug 24, 2022 |
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#841 in Science
23KB
281 lines
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:
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<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
~8–13MB
~177K SLoC