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//! Parallelization features for ndarray.
//!
//! Parallelization features are based on the crate [rayon] and its parallel
//! iterators. Ndarray implements the parallel iterable traits for arrays
//! and array views, for some of its iterators and for [Zip].
//! There are also directly parallelized methods on arrays and on [Zip].
//!
//! This requires the crate feature `rayon` to be enabled.
//!
//! The following types implement parallel iterators, accessed using these
//! methods:
//!
//! - [`Array`], [`ArcArray`]: `.par_iter()` and `.par_iter_mut()`
//! - [`ArrayView`](ArrayView): `.into_par_iter()`
//! - [`ArrayViewMut`](ArrayViewMut): `.into_par_iter()`
//! - [`AxisIter`](iter::AxisIter), [`AxisIterMut`](iter::AxisIterMut): `.into_par_iter()`
//! - [`AxisChunksIter`](iter::AxisChunksIter), [`AxisChunksIterMut`](iter::AxisChunksIterMut): `.into_par_iter()`
//! - [`Zip`] `.into_par_iter()`
//!
//! The following other parallelized methods exist:
//!
//! - [`ArrayBase::par_map_inplace()`]
//! - [`ArrayBase::par_mapv_inplace()`]
//! - [`Zip::par_apply()`] (all arities)
//!
//! Note that you can use the parallel iterator for [Zip] to access all other
//! rayon parallel iterator methods.
//!
//! Only the axis iterators are indexed parallel iterators, the rest are all
//! “unindexed”. Use ndarray’s [Zip] for lock step parallel iteration of
//! multiple arrays or producers at a time.
//!
//! # Examples
//!
//! ## Arrays and array views
//!
//! Compute the exponential of each element in an array, parallelized.
//!
//! ```
//! extern crate ndarray;
//!
//! use ndarray::Array2;
//! use ndarray::parallel::prelude::*;
//!
//! fn main() {
//! let mut a = Array2::<f64>::zeros((128, 128));
//!
//! // Parallel versions of regular array methods
//! a.par_map_inplace(|x| *x = x.exp());
//! a.par_mapv_inplace(f64::exp);
//!
//! // You can also use the parallel iterator directly
//! a.par_iter_mut().for_each(|x| *x = x.exp());
//! }
//! ```
//!
//! ## Axis iterators
//!
//! Use the parallel `.axis_iter()` to compute the sum of each row.
//!
//! ```
//! extern crate ndarray;
//!
//! use ndarray::Array;
//! use ndarray::Axis;
//! use ndarray::parallel::prelude::*;
//!
//! fn main() {
//! let a = Array::linspace(0., 63., 64).into_shape((4, 16)).unwrap();
//! let mut sums = Vec::new();
//! a.axis_iter(Axis(0))
//! .into_par_iter()
//! .map(|row| row.sum())
//! .collect_into_vec(&mut sums);
//!
//! assert_eq!(sums, [120., 376., 632., 888.]);
//! }
//! ```
//!
//! ## Axis chunks iterators
//!
//! Use the parallel `.axis_chunks_iter()` to process your data in chunks.
//!
//! ```
//! extern crate ndarray;
//!
//! use ndarray::Array;
//! use ndarray::Axis;
//! use ndarray::parallel::prelude::*;
//!
//! fn main() {
//! let a = Array::linspace(0., 63., 64).into_shape((4, 16)).unwrap();
//! let mut shapes = Vec::new();
//! a.axis_chunks_iter(Axis(0), 3)
//! .into_par_iter()
//! .map(|chunk| chunk.shape().to_owned())
//! .collect_into_vec(&mut shapes);
//!
//! assert_eq!(shapes, [vec![3, 16], vec![1, 16]]);
//! }
//! ```
//!
//! ## Zip
//!
//! Use zip for lock step function application across several arrays
//!
//! ```
//! extern crate ndarray;
//!
//! use ndarray::Array3;
//! use ndarray::Zip;
//!
//! type Array3f64 = Array3<f64>;
//!
//! fn main() {
//! const N: usize = 128;
//! let a = Array3f64::from_elem((N, N, N), 1.);
//! let b = Array3f64::from_elem(a.dim(), 2.);
//! let mut c = Array3f64::zeros(a.dim());
//!
//! Zip::from(&mut c)
//! .and(&a)
//! .and(&b)
//! .par_apply(|c, &a, &b| {
//! *c += a - b;
//! });
//! }
//! ```
/// Into- traits for creating parallelized iterators and/or using [`par_azip!`]
pub use Parallel;
pub use cratepar_azip;