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