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//! This crate provides conversion between [`nalgebra`] and [`numpy`](https://numpy.org/).
//!
//! 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};
//!
//! # fn main() -> Result<(), nalgebra_numpy::Error> {
//! 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,
//! ));
//! # Ok(())
//! # }
//! ```
//!
//! Dynamic matrices are also supported:
//!
//! ```
//! # #![feature(proc_macro_hygiene)]
//! # use inline_python::{Context, python};
//! # use nalgebra_numpy::{matrix_from_python};
//! use nalgebra::DMatrix;
//! # fn main() -> Result<(), nalgebra_numpy::Error> {
//! # 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();
//!
//! // <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,
//! ]));
//! # Ok(())
//! # }
//! ```
//!
//! And so are partially dynamic matrices:
//!
//! ```
//! # #![feature(proc_macro_hygiene)]
//! # use inline_python::{Context, python};
//! # use nalgebra_numpy::{matrix_from_python};
//! use nalgebra::{MatrixMN, Dynamic, U3};
//! # fn main() -> Result<(), nalgebra_numpy::Error> {
//! # 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();
//!
//! // <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,
//! ]));
//! # Ok(())
//! # }
//! ```

mod from_numpy;
pub use from_numpy::*;