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</pre><pre class="rust">
<span class="comment">// Copyright 2014-2016 bluss and ndarray developers.</span>
<span class="comment">//</span>
<span class="comment">// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or</span>
<span class="comment">// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license</span>
<span class="comment">// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your</span>
<span class="comment">// option. This file may not be copied, modified, or distributed</span>
<span class="comment">// except according to those terms.</span>
<span class="attribute">#![<span class="ident">crate_name</span><span class="op">=</span><span class="string">"ndarray"</span>]</span>
<span class="attribute">#![<span class="ident">doc</span>(<span class="ident">html_root_url</span> <span class="op">=</span> <span class="string">"https://docs.rs/ndarray/0.10/"</span>)]</span>
<span class="doccomment">//! The `ndarray` crate provides an *n*-dimensional container for general elements</span>
<span class="doccomment">//! and for numerics.</span>
<span class="doccomment">//!</span>
<span class="doccomment">//! In *n*-dimensional we include for example 1-dimensional rows or columns,</span>
<span class="doccomment">//! 2-dimensional matrices, and higher dimensional arrays. If the array has *n*</span>
<span class="doccomment">//! dimensions, then an element in the array is accessed by using that many indices.</span>
<span class="doccomment">//! Each dimension is also called an *axis*.</span>
<span class="doccomment">//!</span>
<span class="doccomment">//! - **[`ArrayBase`](struct.ArrayBase.html)**:</span>
<span class="doccomment">//! The *n*-dimensional array type itself.<br></span>
<span class="doccomment">//! It is used to implement both the owned arrays and the views; see its docs</span>
<span class="doccomment">//! for an overview of all array features.<br></span>
<span class="doccomment">//! - The main specific array type is **[`Array`](type.Array.html)**, which owns</span>
<span class="doccomment">//! its elements.</span>
<span class="doccomment">//!</span>
<span class="doccomment">//! ## Highlights</span>
<span class="doccomment">//!</span>
<span class="doccomment">//! - Generic *n*-dimensional array</span>
<span class="doccomment">//! - Slicing, also with arbitrary step size, and negative indices to mean</span>
<span class="doccomment">//! elements from the end of the axis.</span>
<span class="doccomment">//! - Views and subviews of arrays; iterators that yield subviews.</span>
<span class="doccomment">//! - Higher order operations and arithmetic are performant</span>
<span class="doccomment">//! - Array views can be used to slice and mutate any `[T]` data using</span>
<span class="doccomment">//! `ArrayView::from` and `ArrayViewMut::from`.</span>
<span class="doccomment">//! - `Zip` for lock step function application across two or more arrays or other</span>
<span class="doccomment">//! item producers (`NdProducer` trait).</span>
<span class="doccomment">//!</span>
<span class="doccomment">//! ## Crate Status</span>
<span class="doccomment">//!</span>
<span class="doccomment">//! - Still iterating on and evolving the crate</span>
<span class="doccomment">//! + The crate is continuously developing, and breaking changes are expected</span>
<span class="doccomment">//! during evolution from version to version. We adopt the newest stable</span>
<span class="doccomment">//! rust features if we need them.</span>
<span class="doccomment">//! - Performance:</span>
<span class="doccomment">//! + Prefer higher order methods and arithmetic operations on arrays first,</span>
<span class="doccomment">//! then iteration, and as a last priority using indexed algorithms.</span>
<span class="doccomment">//! + The higher order functions like ``.map()``, ``.map_inplace()``, </span>
<span class="doccomment">//! ``.zip_mut_with()``, ``Zip`` and ``azip!()`` are the most efficient ways</span>
<span class="doccomment">//! to perform single traversal and lock step traversal respectively.</span>
<span class="doccomment">//! + Performance of an operation depends on the memory layout of the array</span>
<span class="doccomment">//! or array view. Especially if it's a binary operation, which</span>
<span class="doccomment">//! needs matching memory layout to be efficient (with some exceptions).</span>
<span class="doccomment">//! + Efficient floating point matrix multiplication even for very large</span>
<span class="doccomment">//! matrices; can optionally use BLAS to improve it further.</span>
<span class="doccomment">//! + See also the [`ndarray-parallel`] crate for integration with rayon.</span>
<span class="doccomment">//! - **Requires Rust 1.18**</span>
<span class="doccomment">//!</span>
<span class="doccomment">//! [`ndarray-parallel`]: https://docs.rs/ndarray-parallel</span>
<span class="doccomment">//!</span>
<span class="doccomment">//! ## Crate Feature Flags</span>
<span class="doccomment">//!</span>
<span class="doccomment">//! The following crate feature flags are available. They are configured in your</span>
<span class="doccomment">//! `Cargo.toml`.</span>
<span class="doccomment">//!</span>
<span class="doccomment">//! - `rustc-serialize`</span>
<span class="doccomment">//! - Optional, compatible with Rust stable</span>
<span class="doccomment">//! - Enables serialization support for rustc-serialize 0.3</span>
<span class="doccomment">//! - `serde-1`</span>
<span class="doccomment">//! - Optional, compatible with Rust stable</span>
<span class="doccomment">//! - Enables serialization support for serde 1.0</span>
<span class="doccomment">//! - `blas`</span>
<span class="doccomment">//! - Optional and experimental, compatible with Rust stable</span>
<span class="doccomment">//! - Enable transparent BLAS support for matrix multiplication.</span>
<span class="doccomment">//! Uses ``blas-sys`` for pluggable backend, which needs to be configured</span>
<span class="doccomment">//! separately.</span>
<span class="doccomment">//!</span>
<span class="attribute">#[<span class="ident">cfg</span>(<span class="ident">feature</span> <span class="op">=</span> <span class="string">"serde-1"</span>)]</span>
<span class="kw">extern</span> <span class="kw">crate</span> <span class="ident">serde</span>;
<span class="attribute">#[<span class="ident">cfg</span>(<span class="ident">feature</span> <span class="op">=</span> <span class="string">"rustc-serialize"</span>)]</span>
<span class="kw">extern</span> <span class="kw">crate</span> <span class="ident">rustc_serialize</span> <span class="kw">as</span> <span class="ident">serialize</span>;
<span class="attribute">#[<span class="ident">cfg</span>(<span class="ident">feature</span><span class="op">=</span><span class="string">"blas"</span>)]</span>
<span class="kw">extern</span> <span class="kw">crate</span> <span class="ident">blas_sys</span>;
<span class="kw">extern</span> <span class="kw">crate</span> <span class="ident">matrixmultiply</span>;
<span class="attribute">#[<span class="ident">macro_use</span>(<span class="ident">izip</span>)]</span> <span class="kw">extern</span> <span class="kw">crate</span> <span class="ident">itertools</span>;
<span class="kw">extern</span> <span class="kw">crate</span> <span class="ident">num_traits</span> <span class="kw">as</span> <span class="ident">libnum</span>;
<span class="kw">extern</span> <span class="kw">crate</span> <span class="ident">num_complex</span>;
<span class="kw">use</span> <span class="ident">std::marker::PhantomData</span>;
<span class="kw">use</span> <span class="ident">std::rc::Rc</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">dimension</span>::{
<span class="ident">Dimension</span>,
<span class="ident">IntoDimension</span>,
<span class="ident">RemoveAxis</span>,
<span class="ident">Axis</span>,
<span class="ident">AxisDescription</span>,
};
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">dimension::dim</span>::<span class="kw-2">*</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">dimension::NdIndex</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">dimension::IxDynImpl</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">indexes</span>::{<span class="ident">indices</span>, <span class="ident">indices_of</span>};
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">error</span>::{<span class="ident">ShapeError</span>, <span class="ident">ErrorKind</span>};
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">si</span>::{<span class="ident">Si</span>, <span class="ident">S</span>};
<span class="kw">use</span> <span class="ident">iterators::Baseiter</span>;
<span class="kw">use</span> <span class="ident">iterators</span>::{<span class="ident">ElementsBase</span>, <span class="ident">ElementsBaseMut</span>, <span class="ident">Iter</span>, <span class="ident">IterMut</span>};
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">arraytraits::AsArray</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">linalg_traits</span>::{<span class="ident">LinalgScalar</span>, <span class="ident">NdFloat</span>};
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">stacking::stack</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">shape_builder</span>::{ <span class="ident">ShapeBuilder</span>};
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">impl_views::IndexLonger</span>;
<span class="attribute">#[<span class="ident">macro_use</span>]</span> <span class="kw">mod</span> <span class="ident">macro_utils</span>;
<span class="attribute">#[<span class="ident">macro_use</span>]</span> <span class="kw">mod</span> <span class="ident">private</span>;
<span class="kw">mod</span> <span class="ident">aliases</span>;
<span class="kw">mod</span> <span class="ident">arraytraits</span>;
<span class="attribute">#[<span class="ident">cfg</span>(<span class="ident">feature</span> <span class="op">=</span> <span class="string">"serde-1"</span>)]</span>
<span class="kw">mod</span> <span class="ident">array_serde</span>;
<span class="attribute">#[<span class="ident">cfg</span>(<span class="ident">feature</span> <span class="op">=</span> <span class="string">"rustc-serialize"</span>)]</span>
<span class="kw">mod</span> <span class="ident">array_serialize</span>;
<span class="kw">mod</span> <span class="ident">arrayformat</span>;
<span class="kw">mod</span> <span class="ident">data_traits</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">aliases</span>::<span class="kw-2">*</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">data_traits</span>::{
<span class="ident">Data</span>,
<span class="ident">DataMut</span>,
<span class="ident">DataOwned</span>,
<span class="ident">DataShared</span>,
<span class="ident">DataClone</span>,
};
<span class="kw">mod</span> <span class="ident">dimension</span>;
<span class="kw">mod</span> <span class="ident">free_functions</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">free_functions</span>::<span class="kw-2">*</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">iterators::iter</span>;
<span class="kw">mod</span> <span class="ident">si</span>;
<span class="kw">mod</span> <span class="ident">layout</span>;
<span class="kw">mod</span> <span class="ident">indexes</span>;
<span class="kw">mod</span> <span class="ident">iterators</span>;
<span class="kw">mod</span> <span class="ident">linalg_traits</span>;
<span class="kw">mod</span> <span class="ident">linspace</span>;
<span class="kw">mod</span> <span class="ident">numeric_util</span>;
<span class="kw">mod</span> <span class="ident">error</span>;
<span class="kw">mod</span> <span class="ident">shape_builder</span>;
<span class="kw">mod</span> <span class="ident">stacking</span>;
<span class="kw">mod</span> <span class="ident">zip</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">zip</span>::{
<span class="ident">Zip</span>,
<span class="ident">NdProducer</span>,
<span class="ident">IntoNdProducer</span>,
<span class="ident">FoldWhile</span>,
};
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">layout::Layout</span>;
<span class="doccomment">/// Implementation's prelude. Common types used everywhere.</span>
<span class="kw">mod</span> <span class="ident">imp_prelude</span> {
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">prelude</span>::<span class="kw-2">*</span>;
<span class="kw">pub</span> <span class="kw">use</span> {
<span class="ident">RemoveAxis</span>,
<span class="ident">Data</span>,
<span class="ident">DataMut</span>,
<span class="ident">DataOwned</span>,
<span class="ident">DataShared</span>,
<span class="ident">ViewRepr</span>,
<span class="ident">Ix</span>, <span class="ident">Ixs</span>,
};
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">dimension::DimensionExt</span>;
}
<span class="kw">pub</span> <span class="kw">mod</span> <span class="ident">prelude</span>;
<span class="doccomment">/// Array index type</span>
<span class="kw">pub</span> <span class="kw">type</span> <span class="ident">Ix</span> <span class="op">=</span> <span class="ident">usize</span>;
<span class="doccomment">/// Array index type (signed)</span>
<span class="kw">pub</span> <span class="kw">type</span> <span class="ident">Ixs</span> <span class="op">=</span> <span class="ident">isize</span>;
<span class="doccomment">/// An *n*-dimensional array.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The array is a general container of elements. It cannot grow or shrink, but</span>
<span class="doccomment">/// can be sliced into subsets of its data.</span>
<span class="doccomment">/// The array supports arithmetic operations by applying them elementwise.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// In *n*-dimensional we include for example 1-dimensional rows or columns,</span>
<span class="doccomment">/// 2-dimensional matrices, and higher dimensional arrays. If the array has *n*</span>
<span class="doccomment">/// dimensions, then an element is accessed by using that many indices.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The `ArrayBase<S, D>` is parameterized by `S` for the data container and</span>
<span class="doccomment">/// `D` for the dimensionality.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Type aliases [`Array`], [`RcArray`], [`ArrayView`], and [`ArrayViewMut`] refer</span>
<span class="doccomment">/// to `ArrayBase` with different types for the data container.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [`Array`]: type.Array.html</span>
<span class="doccomment">/// [`RcArray`]: type.RcArray.html</span>
<span class="doccomment">/// [`ArrayView`]: type.ArrayView.html</span>
<span class="doccomment">/// [`ArrayViewMut`]: type.ArrayViewMut.html</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ## Contents</span>
<span class="doccomment">///</span>
<span class="doccomment">/// + [Array](#array)</span>
<span class="doccomment">/// + [RcArray](#rcarray)</span>
<span class="doccomment">/// + [Array Views](#array-views)</span>
<span class="doccomment">/// + [Indexing and Dimension](#indexing-and-dimension)</span>
<span class="doccomment">/// + [Loops, Producers and Iterators](#loops-producers-and-iterators)</span>
<span class="doccomment">/// + [Slicing](#slicing)</span>
<span class="doccomment">/// + [Subviews](#subviews)</span>
<span class="doccomment">/// + [Arithmetic Operations](#arithmetic-operations)</span>
<span class="doccomment">/// + [Broadcasting](#broadcasting)</span>
<span class="doccomment">/// + [Constructor Methods for Owned Arrays](#constructor-methods-for-owned-arrays)</span>
<span class="doccomment">/// + [Methods For All Array Types](#methods-for-all-array-types)</span>
<span class="doccomment">///</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ## `Array`</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [`Array`](type.Array.html) is an owned array that ows the underlying array</span>
<span class="doccomment">/// elements directly (just like a `Vec`) and it is the default way to create and</span>
<span class="doccomment">/// store n-dimensional data. `Array<A, D>` has two type parameters: `A` for</span>
<span class="doccomment">/// the element type, and `D` for the dimensionality. A particular</span>
<span class="doccomment">/// dimensionality's type alias like `Array3<A>` just has the type parameter</span>
<span class="doccomment">/// `A` for element type.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// An example:</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">/// // Create a three-dimensional f64 array, initialized with zeros</span>
<span class="doccomment">/// use ndarray::Array3;</span>
<span class="doccomment">/// let mut temperature = Array3::<f64>::zeros((3, 4, 5));</span>
<span class="doccomment">/// // Increase the temperature in this location</span>
<span class="doccomment">/// temperature[[2, 2, 2]] += 0.5;</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ## `RcArray`</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [`RcArray`](type.RcArray.html) is an owned array with reference counted</span>
<span class="doccomment">/// data (shared ownership).</span>
<span class="doccomment">/// Sharing requires that it uses copy-on-write for mutable operations.</span>
<span class="doccomment">/// Calling a method for mutating elements on `RcArray`, for example</span>
<span class="doccomment">/// [`view_mut()`](#method.view_mut) or [`get_mut()`](#method.get_mut),</span>
<span class="doccomment">/// will break sharing and require a clone of the data (if it is not uniquely held).</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ## Array Views</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [`ArrayView`] and [`ArrayViewMut`] are read-only and read-write array views</span>
<span class="doccomment">/// respectively. They use dimensionality, indexing, and almost all other</span>
<span class="doccomment">/// methods the same was as the other array types.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Methods for `ArrayBase` apply to array views too, when the trait bounds</span>
<span class="doccomment">/// allow.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Please see the documentation for the respective array view for an overview</span>
<span class="doccomment">/// of methods specific to array views: [`ArrayView`], [`ArrayViewMut`].</span>
<span class="doccomment">///</span>
<span class="doccomment">/// A view is created from an array using `.view()`, `.view_mut()`, using</span>
<span class="doccomment">/// slicing (`.slice()`, `.slice_mut()`) or from one of the many iterators</span>
<span class="doccomment">/// that yield array views.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// You can also create an array view from a regular slice of data not</span>
<span class="doccomment">/// allocated with `Array` — see array view methods or their `From` impls.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Note that all `ArrayBase` variants can change their view (slicing) of the</span>
<span class="doccomment">/// data freely, even when their data can’t be mutated.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ## Indexing and Dimension</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The dimensionality of the array determines the number of *axes*, for example</span>
<span class="doccomment">/// a 2D array has two axes. These are listed in “big endian” order, so that</span>
<span class="doccomment">/// the greatest dimension is listed first, the lowest dimension with the most</span>
<span class="doccomment">/// rapidly varying index is the last.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// In a 2D array the index of each element is `[row, column]` as seen in this</span>
<span class="doccomment">/// 4 × 3 example:</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ```ignore</span>
<span class="doccomment">/// [[ [0, 0], [0, 1], [0, 2] ], // row 0</span>
<span class="doccomment">/// [ [1, 0], [1, 1], [1, 2] ], // row 1</span>
<span class="doccomment">/// [ [2, 0], [2, 1], [2, 2] ], // row 2</span>
<span class="doccomment">/// [ [3, 0], [3, 1], [3, 2] ]] // row 3</span>
<span class="doccomment">/// // \ \ \</span>
<span class="doccomment">/// // column 0 \ column 2</span>
<span class="doccomment">/// // column 1</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The number of axes for an array is fixed by its `D` type parameter: `Ix1`</span>
<span class="doccomment">/// for a 1D array, `Ix2` for a 2D array etc. The dimension type `IxDyn` allows</span>
<span class="doccomment">/// a dynamic number of axes.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// A fixed size array (`[usize; N]`) of the corresponding dimensionality is</span>
<span class="doccomment">/// used to index the `Array`, making the syntax `array[[` i, j, ...`]]`</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">/// use ndarray::Array2;</span>
<span class="doccomment">/// let mut array = Array2::zeros((4, 3));</span>
<span class="doccomment">/// array[[1, 1]] = 7;</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Important traits and types for dimension and indexing:</span>
<span class="doccomment">///</span>
<span class="doccomment">/// - A [`Dim`](Dim.t.html) value represents a dimensionality or index.</span>
<span class="doccomment">/// - Trait [`Dimension`](Dimension.t.html) is implemented by all</span>
<span class="doccomment">/// dimensionalities. It defines many operations for dimensions and indices.</span>
<span class="doccomment">/// - Trait [`IntoDimension`](IntoDimension.t.html) is used to convert into a</span>
<span class="doccomment">/// `Dim` value.</span>
<span class="doccomment">/// - Trait [`ShapeBuilder`](ShapeBuilder.t.html) is an extension of</span>
<span class="doccomment">/// `IntoDimension` and is used when constructing an array. A shape describes</span>
<span class="doccomment">/// not just the extent of each axis but also their strides.</span>
<span class="doccomment">/// - Trait [`NdIndex`](NdIndex.t.html) is an extension of `Dimension` and is</span>
<span class="doccomment">/// for values that can be used with indexing syntax.</span>
<span class="doccomment">///</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The default memory order of an array is *row major* order (a.k.a “c” order),</span>
<span class="doccomment">/// where each row is contiguous in memory.</span>
<span class="doccomment">/// A *column major* (a.k.a. “f” or fortran) memory order array has</span>
<span class="doccomment">/// columns (or, in general, the outermost axis) with contiguous elements.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The logical order of any array’s elements is the row major order </span>
<span class="doccomment">/// (the rightmost index is varying the fastest).</span>
<span class="doccomment">/// The iterators `.iter(), .iter_mut()` always adhere to this order, for example.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ## Loops, Producers and Iterators</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Using [`Zip`](struct.Zip.html) is the most general way to apply a procedure</span>
<span class="doccomment">/// across one or several arrays or *producers*.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [`NdProducer`](trait.NdProducer.html) is like an iterable but for</span>
<span class="doccomment">/// multidimensional data. All producers have dimensions and axes, like an</span>
<span class="doccomment">/// array view, and they can be split and used with parallelization using `Zip`.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// For example, `ArrayView<A, D>` is a producer, it has the same dimensions</span>
<span class="doccomment">/// as the array view and for each iteration it produces a reference to</span>
<span class="doccomment">/// the array element (`&A` in this case).</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Another example, if we have a 10 × 10 array and use `.exact_chunks((2, 2))`</span>
<span class="doccomment">/// we get a producer of chunks which has the dimensions 5 × 5 (because</span>
<span class="doccomment">/// there are *10 / 2 = 5* chunks in either direction). The 5 × 5 chunks producer</span>
<span class="doccomment">/// can be paired with any other producers of the same dimension with `Zip`, for</span>
<span class="doccomment">/// example 5 × 5 arrays.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ### `.iter()` and `.iter_mut()`</span>
<span class="doccomment">///</span>
<span class="doccomment">/// These are the element iterators of arrays and they produce an element</span>
<span class="doccomment">/// sequence in the logical order of the array, that means that the elements</span>
<span class="doccomment">/// will be visited in the sequence that corresponds to increasing the </span>
<span class="doccomment">/// last index first: *0, ..., 0, 0*; *0, ..., 0, 1*; *0, ...0, 2* and so on.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ### `.outer_iter()` and `.axis_iter()`</span>
<span class="doccomment">///</span>
<span class="doccomment">/// These iterators produce array views of one smaller dimension.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// For example, for a 2D array, `.outer_iter()` will produce the 1D rows.</span>
<span class="doccomment">/// For a 3D array, `.outer_iter()` produces 2D subviews.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// `.axis_iter()` is like `outer_iter()` but allows you to pick which</span>
<span class="doccomment">/// axis to traverse.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The `outer_iter` and `axis_iter` are one dimensional producers.</span>
<span class="doccomment">/// </span>
<span class="doccomment">/// ## `.genrows()`, `.gencolumns()` and `.lanes()`</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [`.genrows()`][gr] is a producer (and iterable) of all rows in an array.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">/// use ndarray::Array;</span>
<span class="doccomment">///</span>
<span class="doccomment">/// // 1. Loop over the rows of a 2D array</span>
<span class="doccomment">/// let mut a = Array::zeros((10, 10));</span>
<span class="doccomment">/// for mut row in a.genrows_mut() {</span>
<span class="doccomment">/// row.fill(1.);</span>
<span class="doccomment">/// }</span>
<span class="doccomment">///</span>
<span class="doccomment">/// // 2. Use Zip to pair each row in 2D `a` with elements in 1D `b`</span>
<span class="doccomment">/// use ndarray::Zip;</span>
<span class="doccomment">/// let mut b = Array::zeros(a.rows());</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Zip::from(a.genrows())</span>
<span class="doccomment">/// .and(&mut b)</span>
<span class="doccomment">/// .apply(|a_row, b_elt| {</span>
<span class="doccomment">/// *b_elt = a_row[a.cols() - 1] - a_row[0];</span>
<span class="doccomment">/// });</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The *lanes* of an array are 1D segments along an axis and when pointed</span>
<span class="doccomment">/// along the last axis they are *rows*, when pointed along the first axis</span>
<span class="doccomment">/// they are *columns*.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// A *m* × *n* array has *m* rows each of length *n* and conversely</span>
<span class="doccomment">/// *n* columns each of length *m*.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// To generalize this, we say that an array of dimension *a* × *m* × *n*</span>
<span class="doccomment">/// has *a m* rows. It's composed of *a* times the previous array, so it</span>
<span class="doccomment">/// has *a* times as many rows.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// All methods: [`.genrows()`][gr], [`.genrows_mut()`][grm],</span>
<span class="doccomment">/// [`.gencolumns()`][gc], [`.gencolumns_mut()`][gcm],</span>
<span class="doccomment">/// [`.lanes(axis)`][l], [`.lanes_mut(axis)`][lm].</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [gr]: #method.genrows</span>
<span class="doccomment">/// [grm]: #method.genrows_mut</span>
<span class="doccomment">/// [gc]: #method.gencolumns</span>
<span class="doccomment">/// [gcm]: #method.gencolumns_mut</span>
<span class="doccomment">/// [l]: #method.lanes</span>
<span class="doccomment">/// [lm]: #method.lanes_mut</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Yes, for 2D arrays `.genrows()` and `.outer_iter()` have about the same</span>
<span class="doccomment">/// effect:</span>
<span class="doccomment">///</span>
<span class="doccomment">/// + `genrows()` is a producer with *n* - 1 dimensions of 1 dimensional items</span>
<span class="doccomment">/// + `outer_iter()` is a producer with 1 dimension of *n* - 1 dimensional items</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ## Slicing</span>
<span class="doccomment">///</span>
<span class="doccomment">/// You can use slicing to create a view of a subset of the data in</span>
<span class="doccomment">/// the array. Slicing methods include `.slice()`, `.islice()`,</span>
<span class="doccomment">/// `.slice_mut()`.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The slicing argument can be passed using the macro [`s![]`](macro.s!.html),</span>
<span class="doccomment">/// which will be used in all examples. (The explicit form is a reference</span>
<span class="doccomment">/// to a fixed size array of [`Si`]; see its docs for more information.)</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [`Si`]: struct.Si.html</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">/// // import the s![] macro</span>
<span class="doccomment">/// #[macro_use(s)]</span>
<span class="doccomment">/// extern crate ndarray;</span>
<span class="doccomment">///</span>
<span class="doccomment">/// use ndarray::arr3;</span>
<span class="doccomment">///</span>
<span class="doccomment">/// fn main() {</span>
<span class="doccomment">///</span>
<span class="doccomment">/// // 2 submatrices of 2 rows with 3 elements per row, means a shape of `[2, 2, 3]`.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// let a = arr3(&[[[ 1, 2, 3], // -- 2 rows \_</span>
<span class="doccomment">/// [ 4, 5, 6]], // -- /</span>
<span class="doccomment">/// [[ 7, 8, 9], // \_ 2 submatrices</span>
<span class="doccomment">/// [10, 11, 12]]]); // /</span>
<span class="doccomment">/// // 3 columns ..../.../.../</span>
<span class="doccomment">///</span>
<span class="doccomment">/// assert_eq!(a.shape(), &[2, 2, 3]);</span>
<span class="doccomment">///</span>
<span class="doccomment">/// // Let’s create a slice with</span>
<span class="doccomment">/// //</span>
<span class="doccomment">/// // - Both of the submatrices of the greatest dimension: `..`</span>
<span class="doccomment">/// // - Only the first row in each submatrix: `0..1`</span>
<span class="doccomment">/// // - Every element in each row: `..`</span>
<span class="doccomment">///</span>
<span class="doccomment">/// let b = a.slice(s![.., 0..1, ..]);</span>
<span class="doccomment">/// // without the macro, the explicit argument is `&[S, Si(0, Some(1), 1), S]`</span>
<span class="doccomment">///</span>
<span class="doccomment">/// let c = arr3(&[[[ 1, 2, 3]],</span>
<span class="doccomment">/// [[ 7, 8, 9]]]);</span>
<span class="doccomment">/// assert_eq!(b, c);</span>
<span class="doccomment">/// assert_eq!(b.shape(), &[2, 1, 3]);</span>
<span class="doccomment">///</span>
<span class="doccomment">/// // Let’s create a slice with</span>
<span class="doccomment">/// //</span>
<span class="doccomment">/// // - Both submatrices of the greatest dimension: `..`</span>
<span class="doccomment">/// // - The last row in each submatrix: `-1..`</span>
<span class="doccomment">/// // - Row elements in reverse order: `..;-1`</span>
<span class="doccomment">/// let d = a.slice(s![.., -1.., ..;-1]);</span>
<span class="doccomment">/// let e = arr3(&[[[ 6, 5, 4]],</span>
<span class="doccomment">/// [[12, 11, 10]]]);</span>
<span class="doccomment">/// assert_eq!(d, e);</span>
<span class="doccomment">/// }</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ## Subviews</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Subview methods allow you to restrict the array view while removing</span>
<span class="doccomment">/// one axis from the array. Subview methods include `.subview()`,</span>
<span class="doccomment">/// `.isubview()`, `.subview_mut()`.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Subview takes two arguments: `axis` and `index`.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">/// use ndarray::{arr3, aview2, Axis};</span>
<span class="doccomment">///</span>
<span class="doccomment">/// // 2 submatrices of 2 rows with 3 elements per row, means a shape of `[2, 2, 3]`.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// let a = arr3(&[[[ 1, 2, 3], // \ axis 0, submatrix 0</span>
<span class="doccomment">/// [ 4, 5, 6]], // /</span>
<span class="doccomment">/// [[ 7, 8, 9], // \ axis 0, submatrix 1</span>
<span class="doccomment">/// [10, 11, 12]]]); // /</span>
<span class="doccomment">/// // \</span>
<span class="doccomment">/// // axis 2, column 0</span>
<span class="doccomment">///</span>
<span class="doccomment">/// assert_eq!(a.shape(), &[2, 2, 3]);</span>
<span class="doccomment">///</span>
<span class="doccomment">/// // Let’s take a subview along the greatest dimension (axis 0),</span>
<span class="doccomment">/// // taking submatrix 0, then submatrix 1</span>
<span class="doccomment">///</span>
<span class="doccomment">/// let sub_0 = a.subview(Axis(0), 0);</span>
<span class="doccomment">/// let sub_1 = a.subview(Axis(0), 1);</span>
<span class="doccomment">///</span>
<span class="doccomment">/// assert_eq!(sub_0, aview2(&[[ 1, 2, 3],</span>
<span class="doccomment">/// [ 4, 5, 6]]));</span>
<span class="doccomment">/// assert_eq!(sub_1, aview2(&[[ 7, 8, 9],</span>
<span class="doccomment">/// [10, 11, 12]]));</span>
<span class="doccomment">/// assert_eq!(sub_0.shape(), &[2, 3]);</span>
<span class="doccomment">///</span>
<span class="doccomment">/// // This is the subview picking only axis 2, column 0</span>
<span class="doccomment">/// let sub_col = a.subview(Axis(2), 0);</span>
<span class="doccomment">///</span>
<span class="doccomment">/// assert_eq!(sub_col, aview2(&[[ 1, 4],</span>
<span class="doccomment">/// [ 7, 10]]));</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">///</span>
<span class="doccomment">/// `.isubview()` modifies the view in the same way as `subview()`, but</span>
<span class="doccomment">/// since it is *in place*, it cannot remove the collapsed axis. It becomes</span>
<span class="doccomment">/// an axis of length 1.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// `.outer_iter()` is an iterator of every subview along the zeroth (outer)</span>
<span class="doccomment">/// axis, while `.axis_iter()` is an iterator of every subview along a</span>
<span class="doccomment">/// specific axis.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ## Arithmetic Operations</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Arrays support all arithmetic operations the same way: they apply elementwise.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Since the trait implementations are hard to overview, here is a summary.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ### Binary Operators with Two Arrays</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Let `A` be an array or view of any kind. Let `B` be an array</span>
<span class="doccomment">/// with owned storage (either `Array` or `RcArray`).</span>
<span class="doccomment">/// Let `C` be an array with mutable data (either `Array`, `RcArray`</span>
<span class="doccomment">/// or `ArrayViewMut`).</span>
<span class="doccomment">/// The following combinations of operands</span>
<span class="doccomment">/// are supported for an arbitrary binary operator denoted by `@` (it can be</span>
<span class="doccomment">/// `+`, `-`, `*`, `/` and so on).</span>
<span class="doccomment">///</span>
<span class="doccomment">/// - `&A @ &A` which produces a new `Array`</span>
<span class="doccomment">/// - `B @ A` which consumes `B`, updates it with the result, and returns it</span>
<span class="doccomment">/// - `B @ &A` which consumes `B`, updates it with the result, and returns it</span>
<span class="doccomment">/// - `C @= &A` which performs an arithmetic operation in place</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ### Binary Operators with Array and Scalar</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The trait [`ScalarOperand`](trait.ScalarOperand.html) marks types that can be used in arithmetic</span>
<span class="doccomment">/// with arrays directly. For a scalar `K` the following combinations of operands</span>
<span class="doccomment">/// are supported (scalar can be on either the left or right side, but</span>
<span class="doccomment">/// `ScalarOperand` docs has the detailed condtions).</span>
<span class="doccomment">///</span>
<span class="doccomment">/// - `&A @ K` or `K @ &A` which produces a new `Array`</span>
<span class="doccomment">/// - `B @ K` or `K @ B` which consumes `B`, updates it with the result and returns it</span>
<span class="doccomment">/// - `C @= K` which performs an arithmetic operation in place</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ### Unary Operators</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Let `A` be an array or view of any kind. Let `B` be an array with owned</span>
<span class="doccomment">/// storage (either `Array` or `RcArray`). The following operands are supported</span>
<span class="doccomment">/// for an arbitrary unary operator denoted by `@` (it can be `-` or `!`).</span>
<span class="doccomment">///</span>
<span class="doccomment">/// - `@&A` which produces a new `Array`</span>
<span class="doccomment">/// - `@B` which consumes `B`, updates it with the result, and returns it</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ## Broadcasting</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Arrays support limited *broadcasting*, where arithmetic operations with</span>
<span class="doccomment">/// array operands of different sizes can be carried out by repeating the</span>
<span class="doccomment">/// elements of the smaller dimension array. See</span>
<span class="doccomment">/// [`.broadcast()`](#method.broadcast) for a more detailed</span>
<span class="doccomment">/// description.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">/// use ndarray::arr2;</span>
<span class="doccomment">///</span>
<span class="doccomment">/// let a = arr2(&[[1., 1.],</span>
<span class="doccomment">/// [1., 2.],</span>
<span class="doccomment">/// [0., 3.],</span>
<span class="doccomment">/// [0., 4.]]);</span>
<span class="doccomment">///</span>
<span class="doccomment">/// let b = arr2(&[[0., 1.]]);</span>
<span class="doccomment">///</span>
<span class="doccomment">/// let c = arr2(&[[1., 2.],</span>
<span class="doccomment">/// [1., 3.],</span>
<span class="doccomment">/// [0., 4.],</span>
<span class="doccomment">/// [0., 5.]]);</span>
<span class="doccomment">/// // We can add because the shapes are compatible even if not equal.</span>
<span class="doccomment">/// // The `b` array is shape 1 × 2 but acts like a 4 × 2 array.</span>
<span class="doccomment">/// assert!(</span>
<span class="doccomment">/// c == a + b</span>
<span class="doccomment">/// );</span>
<span class="doccomment">/// ```</span>
<span class="doccomment">///</span>
<span class="kw">pub</span> <span class="kw">struct</span> <span class="ident">ArrayBase</span><span class="op"><</span><span class="ident">S</span>, <span class="ident">D</span><span class="op">></span>
<span class="kw">where</span> <span class="ident">S</span>: <span class="ident">Data</span>
{
<span class="doccomment">/// Rc data when used as view, Uniquely held data when being mutated</span>
<span class="ident">data</span>: <span class="ident">S</span>,
<span class="doccomment">/// A pointer into the buffer held by data, may point anywhere</span>
<span class="doccomment">/// in its range.</span>
<span class="ident">ptr</span>: <span class="kw-2">*</span><span class="kw-2">mut</span> <span class="ident">S::Elem</span>,
<span class="doccomment">/// The size of each axis</span>
<span class="ident">dim</span>: <span class="ident">D</span>,
<span class="doccomment">/// The element count stride per axis. To be parsed as `isize`.</span>
<span class="ident">strides</span>: <span class="ident">D</span>,
}
<span class="doccomment">/// An array where the data has shared ownership and is copy on write.</span>
<span class="doccomment">/// It can act as both an owner as the data as well as a shared reference (view</span>
<span class="doccomment">/// like).</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The `RcArray<A, D>` is parameterized by `A` for the element type and `D` for</span>
<span class="doccomment">/// the dimensionality.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [**`ArrayBase`**](struct.ArrayBase.html) is used to implement both the owned</span>
<span class="doccomment">/// arrays and the views; see its docs for an overview of all array features. </span>
<span class="doccomment">///</span>
<span class="doccomment">/// See also:</span>
<span class="doccomment">///</span>
<span class="doccomment">/// + [Constructor Methods for Owned Arrays](struct.ArrayBase.html#constructor-methods-for-owned-arrays)</span>
<span class="doccomment">/// + [Methods For All Array Types](struct.ArrayBase.html#methods-for-all-array-types)</span>
<span class="kw">pub</span> <span class="kw">type</span> <span class="ident">RcArray</span><span class="op"><</span><span class="ident">A</span>, <span class="ident">D</span><span class="op">></span> <span class="op">=</span> <span class="ident">ArrayBase</span><span class="op"><</span><span class="ident">OwnedRcRepr</span><span class="op"><</span><span class="ident">A</span><span class="op">></span>, <span class="ident">D</span><span class="op">></span>;
<span class="doccomment">/// An array that owns its data uniquely.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// `Array` is the main n-dimensional array type, and it owns all its array</span>
<span class="doccomment">/// elements.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The `Array<A, D>` is parameterized by `A` for the element type and `D` for</span>
<span class="doccomment">/// the dimensionality.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [**`ArrayBase`**](struct.ArrayBase.html) is used to implement both the owned</span>
<span class="doccomment">/// arrays and the views; see its docs for an overview of all array features. </span>
<span class="doccomment">///</span>
<span class="doccomment">/// See also:</span>
<span class="doccomment">///</span>
<span class="doccomment">/// + [Constructor Methods for Owned Arrays](struct.ArrayBase.html#constructor-methods-for-owned-arrays)</span>
<span class="doccomment">/// + [Methods For All Array Types](struct.ArrayBase.html#methods-for-all-array-types)</span>
<span class="doccomment">/// + Dimensionality-specific type alises</span>
<span class="doccomment">/// [`Array1`](Array1.t.html),</span>
<span class="doccomment">/// [`Array2`](Array2.t.html),</span>
<span class="doccomment">/// [`Array3`](Array3.t.html), ...,</span>
<span class="doccomment">/// [`ArrayD`](ArrayD.t.html),</span>
<span class="doccomment">/// and so on.</span>
<span class="kw">pub</span> <span class="kw">type</span> <span class="ident">Array</span><span class="op"><</span><span class="ident">A</span>, <span class="ident">D</span><span class="op">></span> <span class="op">=</span> <span class="ident">ArrayBase</span><span class="op"><</span><span class="ident">OwnedRepr</span><span class="op"><</span><span class="ident">A</span><span class="op">></span>, <span class="ident">D</span><span class="op">></span>;
<span class="doccomment">/// A read-only array view.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// An array view represents an array or a part of it, created from</span>
<span class="doccomment">/// an iterator, subview or slice of an array.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The `ArrayView<'a, A, D>` is parameterized by `'a` for the scope of the</span>
<span class="doccomment">/// borrow, `A` for the element type and `D` for the dimensionality.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Array views have all the methods of an array (see [`ArrayBase`][ab]).</span>
<span class="doccomment">///</span>
<span class="doccomment">/// See also [`ArrayViewMut`](type.ArrayViewMut.html).</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [ab]: struct.ArrayBase.html</span>
<span class="kw">pub</span> <span class="kw">type</span> <span class="ident">ArrayView</span><span class="op"><</span><span class="lifetime">'a</span>, <span class="ident">A</span>, <span class="ident">D</span><span class="op">></span> <span class="op">=</span> <span class="ident">ArrayBase</span><span class="op"><</span><span class="ident">ViewRepr</span><span class="op"><</span><span class="kw-2">&</span><span class="lifetime">'a</span> <span class="ident">A</span><span class="op">></span>, <span class="ident">D</span><span class="op">></span>;
<span class="doccomment">/// A read-write array view.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// An array view represents an array or a part of it, created from</span>
<span class="doccomment">/// an iterator, subview or slice of an array.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// The `ArrayViewMut<'a, A, D>` is parameterized by `'a` for the scope of the</span>
<span class="doccomment">/// borrow, `A` for the element type and `D` for the dimensionality.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Array views have all the methods of an array (see [`ArrayBase`][ab]).</span>
<span class="doccomment">///</span>
<span class="doccomment">/// See also [`ArrayView`](type.ArrayView.html).</span>
<span class="doccomment">///</span>
<span class="doccomment">/// [ab]: struct.ArrayBase.html</span>
<span class="kw">pub</span> <span class="kw">type</span> <span class="ident">ArrayViewMut</span><span class="op"><</span><span class="lifetime">'a</span>, <span class="ident">A</span>, <span class="ident">D</span><span class="op">></span> <span class="op">=</span> <span class="ident">ArrayBase</span><span class="op"><</span><span class="ident">ViewRepr</span><span class="op"><</span><span class="kw-2">&</span><span class="lifetime">'a</span> <span class="kw-2">mut</span> <span class="ident">A</span><span class="op">></span>, <span class="ident">D</span><span class="op">></span>;
<span class="doccomment">/// Array's representation.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// *Don’t use this type directly—use the type alias</span>
<span class="doccomment">/// [`Array`](type.Array.html) for the array type!*</span>
<span class="attribute">#[<span class="ident">derive</span>(<span class="ident">Clone</span>, <span class="ident">Debug</span>)]</span>
<span class="kw">pub</span> <span class="kw">struct</span> <span class="ident">OwnedRepr</span><span class="op"><</span><span class="ident">A</span><span class="op">></span>(<span class="ident">Vec</span><span class="op"><</span><span class="ident">A</span><span class="op">></span>);
<span class="doccomment">/// RcArray's representation.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// *Don’t use this type directly—use the type alias</span>
<span class="doccomment">/// [`RcArray`](type.RcArray.html) for the array type!*</span>
<span class="attribute">#[<span class="ident">derive</span>(<span class="ident">Debug</span>)]</span>
<span class="kw">pub</span> <span class="kw">struct</span> <span class="ident">OwnedRcRepr</span><span class="op"><</span><span class="ident">A</span><span class="op">></span>(<span class="ident">Rc</span><span class="op"><</span><span class="ident">Vec</span><span class="op"><</span><span class="ident">A</span><span class="op">></span><span class="op">></span>);
<span class="kw">impl</span><span class="op"><</span><span class="ident">A</span><span class="op">></span> <span class="ident">Clone</span> <span class="kw">for</span> <span class="ident">OwnedRcRepr</span><span class="op"><</span><span class="ident">A</span><span class="op">></span> {
<span class="kw">fn</span> <span class="ident">clone</span>(<span class="kw-2">&</span><span class="self">self</span>) <span class="op">-</span><span class="op">></span> <span class="self">Self</span> {
<span class="ident">OwnedRcRepr</span>(<span class="self">self</span>.<span class="number">0</span>.<span class="ident">clone</span>())
}
}
<span class="doccomment">/// Array view’s representation.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// *Don’t use this type directly—use the type aliases</span>
<span class="doccomment">/// [`ArrayView`](type.ArrayView.html)</span>
<span class="doccomment">/// / [`ArrayViewMut`](type.ArrayViewMut.html) for the array type!*</span>
<span class="attribute">#[<span class="ident">derive</span>(<span class="ident">Copy</span>, <span class="ident">Clone</span>)]</span>
<span class="comment">// This is just a marker type, to carry the lifetime parameter.</span>
<span class="kw">pub</span> <span class="kw">struct</span> <span class="ident">ViewRepr</span><span class="op"><</span><span class="ident">A</span><span class="op">></span> {
<span class="ident">life</span>: <span class="ident">PhantomData</span><span class="op"><</span><span class="ident">A</span><span class="op">></span>,
}
<span class="kw">impl</span><span class="op"><</span><span class="ident">A</span><span class="op">></span> <span class="ident">ViewRepr</span><span class="op"><</span><span class="ident">A</span><span class="op">></span> {
<span class="attribute">#[<span class="ident">inline</span>(<span class="ident">always</span>)]</span>
<span class="kw">fn</span> <span class="ident">new</span>() <span class="op">-</span><span class="op">></span> <span class="self">Self</span> {
<span class="ident">ViewRepr</span> { <span class="ident">life</span>: <span class="ident">PhantomData</span> }
}
}
<span class="kw">mod</span> <span class="ident">impl_clone</span>;
<span class="kw">mod</span> <span class="ident">impl_constructors</span>;
<span class="kw">mod</span> <span class="ident">impl_methods</span>;
<span class="kw">mod</span> <span class="ident">impl_owned_array</span>;
<span class="doccomment">/// Private Methods</span>
<span class="kw">impl</span><span class="op"><</span><span class="ident">A</span>, <span class="ident">S</span>, <span class="ident">D</span><span class="op">></span> <span class="ident">ArrayBase</span><span class="op"><</span><span class="ident">S</span>, <span class="ident">D</span><span class="op">></span>
<span class="kw">where</span> <span class="ident">S</span>: <span class="ident">Data</span><span class="op"><</span><span class="ident">Elem</span><span class="op">=</span><span class="ident">A</span><span class="op">></span>, <span class="ident">D</span>: <span class="ident">Dimension</span>
{
<span class="attribute">#[<span class="ident">inline</span>]</span>
<span class="kw">fn</span> <span class="ident">broadcast_unwrap</span><span class="op"><</span><span class="ident">E</span><span class="op">></span>(<span class="kw-2">&</span><span class="self">self</span>, <span class="ident">dim</span>: <span class="ident">E</span>) <span class="op">-</span><span class="op">></span> <span class="ident">ArrayView</span><span class="op"><</span><span class="ident">A</span>, <span class="ident">E</span><span class="op">></span>
<span class="kw">where</span> <span class="ident">E</span>: <span class="ident">Dimension</span>,
{
<span class="attribute">#[<span class="ident">cold</span>]</span>
<span class="attribute">#[<span class="ident">inline</span>(<span class="ident">never</span>)]</span>
<span class="kw">fn</span> <span class="ident">broadcast_panic</span><span class="op"><</span><span class="ident">D</span>, <span class="ident">E</span><span class="op">></span>(<span class="ident">from</span>: <span class="kw-2">&</span><span class="ident">D</span>, <span class="ident">to</span>: <span class="kw-2">&</span><span class="ident">E</span>) <span class="op">-</span><span class="op">></span> <span class="op">!</span>
<span class="kw">where</span> <span class="ident">D</span>: <span class="ident">Dimension</span>,
<span class="ident">E</span>: <span class="ident">Dimension</span>,
{
<span class="macro">panic!</span>(<span class="string">"ndarray: could not broadcast array from shape: {:?} to: {:?}"</span>,
<span class="ident">from</span>.<span class="ident">slice</span>(), <span class="ident">to</span>.<span class="ident">slice</span>())
}
<span class="kw">match</span> <span class="self">self</span>.<span class="ident">broadcast</span>(<span class="ident">dim</span>.<span class="ident">clone</span>()) {
<span class="prelude-val">Some</span>(<span class="ident">it</span>) <span class="op">=</span><span class="op">></span> <span class="ident">it</span>,
<span class="prelude-val">None</span> <span class="op">=</span><span class="op">></span> <span class="ident">broadcast_panic</span>(<span class="kw-2">&</span><span class="self">self</span>.<span class="ident">dim</span>, <span class="kw-2">&</span><span class="ident">dim</span>),
}
}
<span class="comment">// Broadcast to dimension `E`, without checking that the dimensions match</span>
<span class="comment">// (Checked in debug assertions).</span>
<span class="attribute">#[<span class="ident">inline</span>]</span>
<span class="kw">fn</span> <span class="ident">broadcast_assume</span><span class="op"><</span><span class="ident">E</span><span class="op">></span>(<span class="kw-2">&</span><span class="self">self</span>, <span class="ident">dim</span>: <span class="ident">E</span>) <span class="op">-</span><span class="op">></span> <span class="ident">ArrayView</span><span class="op"><</span><span class="ident">A</span>, <span class="ident">E</span><span class="op">></span>
<span class="kw">where</span> <span class="ident">E</span>: <span class="ident">Dimension</span>,
{
<span class="kw">let</span> <span class="ident">dim</span> <span class="op">=</span> <span class="ident">dim</span>.<span class="ident">into_dimension</span>();
<span class="macro">debug_assert_eq!</span>(<span class="self">self</span>.<span class="ident">shape</span>(), <span class="ident">dim</span>.<span class="ident">slice</span>());
<span class="kw">let</span> <span class="ident">ptr</span> <span class="op">=</span> <span class="self">self</span>.<span class="ident">ptr</span>;
<span class="kw">let</span> <span class="kw-2">mut</span> <span class="ident">strides</span> <span class="op">=</span> <span class="ident">dim</span>.<span class="ident">clone</span>();
<span class="ident">strides</span>.<span class="ident">slice_mut</span>().<span class="ident">copy_from_slice</span>(<span class="self">self</span>.<span class="ident">strides</span>.<span class="ident">slice</span>());
<span class="kw">unsafe</span> {
<span class="ident">ArrayView::new_</span>(<span class="ident">ptr</span>, <span class="ident">dim</span>, <span class="ident">strides</span>)
}
}
<span class="kw">fn</span> <span class="ident">raw_strides</span>(<span class="kw-2">&</span><span class="self">self</span>) <span class="op">-</span><span class="op">></span> <span class="ident">D</span> {
<span class="self">self</span>.<span class="ident">strides</span>.<span class="ident">clone</span>()
}
<span class="doccomment">/// Apply closure `f` to each element in the array, in whatever</span>
<span class="doccomment">/// order is the fastest to visit.</span>
<span class="kw">fn</span> <span class="ident">unordered_foreach_mut</span><span class="op"><</span><span class="ident">F</span><span class="op">></span>(<span class="kw-2">&</span><span class="kw-2">mut</span> <span class="self">self</span>, <span class="kw-2">mut</span> <span class="ident">f</span>: <span class="ident">F</span>)
<span class="kw">where</span> <span class="ident">S</span>: <span class="ident">DataMut</span>,
<span class="ident">F</span>: <span class="ident">FnMut</span>(<span class="kw-2">&</span><span class="kw-2">mut</span> <span class="ident">A</span>)
{
<span class="kw">if</span> <span class="kw">let</span> <span class="prelude-val">Some</span>(<span class="ident">slc</span>) <span class="op">=</span> <span class="self">self</span>.<span class="ident">as_slice_memory_order_mut</span>() {
<span class="comment">// FIXME: Use for loop when slice iterator is perf is restored</span>
<span class="kw">for</span> <span class="ident">i</span> <span class="kw">in</span> <span class="number">0</span>..<span class="ident">slc</span>.<span class="ident">len</span>() {
<span class="ident">f</span>(<span class="kw-2">&</span><span class="kw-2">mut</span> <span class="ident">slc</span>[<span class="ident">i</span>]);
}
<span class="kw">return</span>;
}
<span class="kw">for</span> <span class="ident">row</span> <span class="kw">in</span> <span class="self">self</span>.<span class="ident">inner_rows_mut</span>() {
<span class="ident">row</span>.<span class="ident">into_iter_</span>().<span class="ident">fold</span>((), <span class="op">|</span>(), <span class="ident">elt</span><span class="op">|</span> <span class="ident">f</span>(<span class="ident">elt</span>));
}
}
<span class="doccomment">/// Remove array axis `axis` and return the result.</span>
<span class="kw">fn</span> <span class="ident">try_remove_axis</span>(<span class="self">self</span>, <span class="ident">axis</span>: <span class="ident">Axis</span>) <span class="op">-</span><span class="op">></span> <span class="ident">ArrayBase</span><span class="op"><</span><span class="ident">S</span>, <span class="ident">D::Smaller</span><span class="op">></span>
{
<span class="kw">let</span> <span class="ident">d</span> <span class="op">=</span> <span class="self">self</span>.<span class="ident">dim</span>.<span class="ident">try_remove_axis</span>(<span class="ident">axis</span>);
<span class="kw">let</span> <span class="ident">s</span> <span class="op">=</span> <span class="self">self</span>.<span class="ident">strides</span>.<span class="ident">try_remove_axis</span>(<span class="ident">axis</span>);
<span class="ident">ArrayBase</span> {
<span class="ident">ptr</span>: <span class="self">self</span>.<span class="ident">ptr</span>,
<span class="ident">data</span>: <span class="self">self</span>.<span class="ident">data</span>,
<span class="ident">dim</span>: <span class="ident">d</span>,
<span class="ident">strides</span>: <span class="ident">s</span>,
}
}
<span class="doccomment">/// n-d generalization of rows, just like inner iter</span>
<span class="kw">fn</span> <span class="ident">inner_rows</span>(<span class="kw-2">&</span><span class="self">self</span>) <span class="op">-</span><span class="op">></span> <span class="ident">iterators::Lanes</span><span class="op"><</span><span class="ident">A</span>, <span class="ident">D::Smaller</span><span class="op">></span>
{
<span class="kw">let</span> <span class="ident">n</span> <span class="op">=</span> <span class="self">self</span>.<span class="ident">ndim</span>();
<span class="ident">iterators::new_lanes</span>(<span class="self">self</span>.<span class="ident">view</span>(), <span class="ident">Axis</span>(<span class="ident">n</span>.<span class="ident">saturating_sub</span>(<span class="number">1</span>)))
}
<span class="doccomment">/// n-d generalization of rows, just like inner iter</span>
<span class="kw">fn</span> <span class="ident">inner_rows_mut</span>(<span class="kw-2">&</span><span class="kw-2">mut</span> <span class="self">self</span>) <span class="op">-</span><span class="op">></span> <span class="ident">iterators::LanesMut</span><span class="op"><</span><span class="ident">A</span>, <span class="ident">D::Smaller</span><span class="op">></span>
<span class="kw">where</span> <span class="ident">S</span>: <span class="ident">DataMut</span>
{
<span class="kw">let</span> <span class="ident">n</span> <span class="op">=</span> <span class="self">self</span>.<span class="ident">ndim</span>();
<span class="ident">iterators::new_lanes_mut</span>(<span class="self">self</span>.<span class="ident">view_mut</span>(), <span class="ident">Axis</span>(<span class="ident">n</span>.<span class="ident">saturating_sub</span>(<span class="number">1</span>)))
}
}
<span class="kw">mod</span> <span class="ident">impl_1d</span>;
<span class="kw">mod</span> <span class="ident">impl_2d</span>;
<span class="kw">mod</span> <span class="ident">numeric</span>;
<span class="kw">pub</span> <span class="kw">mod</span> <span class="ident">linalg</span>;
<span class="kw">mod</span> <span class="ident">impl_ops</span>;
<span class="kw">pub</span> <span class="kw">use</span> <span class="ident">impl_ops::ScalarOperand</span>;
<span class="comment">// Array view methods</span>
<span class="kw">mod</span> <span class="ident">impl_views</span>;
<span class="doccomment">/// A contiguous array shape of n dimensions.</span>
<span class="doccomment">///</span>
<span class="doccomment">/// Either c- or f- memory ordered (*c* a.k.a *row major* is the default).</span>
<span class="attribute">#[<span class="ident">derive</span>(<span class="ident">Copy</span>, <span class="ident">Clone</span>, <span class="ident">Debug</span>)]</span>
<span class="kw">pub</span> <span class="kw">struct</span> <span class="ident">Shape</span><span class="op"><</span><span class="ident">D</span><span class="op">></span> {
<span class="ident">dim</span>: <span class="ident">D</span>,
<span class="ident">is_c</span>: <span class="ident">bool</span>,
}
<span class="doccomment">/// An array shape of n dimensions in c-order, f-order or custom strides.</span>
<span class="attribute">#[<span class="ident">derive</span>(<span class="ident">Copy</span>, <span class="ident">Clone</span>, <span class="ident">Debug</span>)]</span>
<span class="kw">pub</span> <span class="kw">struct</span> <span class="ident">StrideShape</span><span class="op"><</span><span class="ident">D</span><span class="op">></span> {
<span class="ident">dim</span>: <span class="ident">D</span>,
<span class="ident">strides</span>: <span class="ident">D</span>,
<span class="ident">custom</span>: <span class="ident">bool</span>,
}
</pre></div>
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