Struct ndarray::ArrayBase

pub struct ArrayBase<S, D> where
    S: RawData,  { /* fields omitted */ }

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, ArcArray, CowArray, ArrayView, and ArrayViewMut refer to ArrayBase with different types for the data container.

Contents

• Array
• ArcArray
• CowArray
• Array Views
• Indexing and Dimension
• Loops, Producers and Iterators
• Slicing
• Subviews
• Arithmetic Operations
• Broadcasting
• Conversions
• Constructor Methods for Owned Arrays
• Methods For All Array Types
• Methods For 1-D Arrays
• Methods For 2-D Arrays
• Methods for Dynamic-Dimensional Arrays
• Numerical Methods for Arrays

Array

Array is an owned array that owns 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;

ArcArray

ArcArray 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 ArcArray, for example view_mut() or get_mut(), will break sharing and require a clone of the data (if it is not uniquely held).

CowArray

CowArray is analogous to std::borrow::Cow. It can represent either an immutable view or a uniquely owned array. If a CowArray instance is the immutable view variant, then calling a method for mutating elements in the array will cause it to be converted into the owned variant (by cloning all the elements) before the modification is performed.

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:

[[ [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 value represents a dimensionality or index.
• Trait Dimension is implemented by all dimensionalities. It defines many operations for dimensions and indices.
• Trait IntoDimension is used to convert into a Dim value.
• Trait ShapeBuilder 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 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 is the most general way to apply a procedure across one or several arrays or producers.

NdProducer 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() 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.nrows());

Zip::from(a.genrows())
    .and(&mut b)
    .apply(|a_row, b_elt| {
        *b_elt = a_row[a.ncols() - 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(), .genrows_mut(), .gencolumns(), .gencolumns_mut(), .lanes(axis), .lanes_mut(axis).

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(), .slice_mut(), .slice_move(), and .slice_collapse().

The slicing argument can be passed using the macro s![], which will be used in all examples. (The explicit form is an instance of &SliceInfo; see its docs for more information.)

If a range is used, the axis is preserved. If an index is used, that index is selected and the axis is removed; this selects a subview. See Subviews for more information about subviews. Note that .slice_collapse() behaves like .collapse_axis() by preserving the number of dimensions.

It’s possible to take multiple simultaneous mutable slices with .multi_slice_mut() or (for ArrayViewMut only) .multi_slice_move().

use ndarray::{arr2, arr3, s};

// 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, ..]);
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);
assert_eq!(d.shape(), &[2, 1, 3]);

// Let’s create a slice while selecting a subview with
//
// - Both submatrices of the greatest dimension: `..`
// - The last row in each submatrix, removing that axis: `-1`
// - Row elements in reverse order: `..;-1`
let f = a.slice(s![.., -1, ..;-1]);
let g = arr2(&[[ 6,  5,  4],
               [12, 11, 10]]);
assert_eq!(f, g);
assert_eq!(f.shape(), &[2, 3]);

// Let's take two disjoint, mutable slices of a matrix with
//
// - One containing all the even-index columns in the matrix
// - One containing all the odd-index columns in the matrix
let mut h = arr2(&[[0, 1, 2, 3],
                   [4, 5, 6, 7]]);
let (s0, s1) = h.multi_slice_mut((s![.., ..;2], s![.., 1..;2]));
let i = arr2(&[[0, 2],
               [4, 6]]);
let j = arr2(&[[1, 3],
               [5, 7]]);
assert_eq!(s0, i);
assert_eq!(s1, j);

Subviews

Subview methods allow you to restrict the array view while removing one axis from the array. Methods for selecting individual subviews include .index_axis(), .index_axis_mut(), .index_axis_move(), and .index_axis_inplace(). You can also select a subview by using a single index instead of a range when slicing. Some other methods, such as .fold_axis(), .axis_iter(), .axis_iter_mut(), .outer_iter(), and .outer_iter_mut() operate on all the subviews along an axis.

A related method is .collapse_axis(), which modifies the view in the same way as .index_axis() except for removing the collapsed axis, since it operates in place. The length of the axis becomes 1.

Methods for selecting an individual subview take two arguments: axis and index.

use ndarray::{arr3, aview1, aview2, s, 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.index_axis(Axis(0), 0);
let sub_1 = a.index_axis(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.index_axis(Axis(2), 0);

assert_eq!(sub_col, aview2(&[[ 1,  4],
                             [ 7, 10]]));

// You can take multiple subviews at once (and slice at the same time)
let double_sub = a.slice(s![1, .., 0]);
assert_eq!(double_sub, aview1(&[7, 10]));

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.

Binary Operators with Two Arrays

Let A be an array or view of any kind. Let B be an array with owned storage (either Array or ArcArray). Let C be an array with mutable data (either Array, ArcArray 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

Note that the element type needs to implement the operator trait and the Clone trait.

use ndarray::{array, ArrayView1};

let owned1 = array![1, 2];
let owned2 = array![3, 4];
let view1 = ArrayView1::from(&[5, 6]);
let view2 = ArrayView1::from(&[7, 8]);
let mut mutable = array![9, 10];

let sum1 = &view1 + &view2;   // Allocates a new array. Note the explicit `&`.
// let sum2 = view1 + &view2; // This doesn't work because `view1` is not an owned array.
let sum3 = owned1 + view1;    // Consumes `owned1`, updates it, and returns it.
let sum4 = owned2 + &view2;   // Consumes `owned2`, updates it, and returns it.
mutable += &view2;            // Updates `mutable` in-place.

Binary Operators with Array and Scalar

The trait ScalarOperand 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

Unary Operators

Let A be an array or view of any kind. Let B be an array with owned storage (either Array or ArcArray). The following operands are supported for an arbitrary unary operator denoted by @ (it can be - or !).

• @&A which produces a new Array
• @B which consumes B, updates it with the result, and returns it

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() 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
);

Conversions

Conversions Between Array Types

This table is a summary of the conversions between arrays of different ownership, dimensionality, and element type. All of the conversions in this table preserve the shape of the array.

Output	Input
	Array<A, D>	ArcArray<A, D>	CowArray<'a, A, D>	ArrayView<'a, A, D>	ArrayViewMut<'a, A, D>
Array<A, D>	no-op	a.into_owned()	a.into_owned()	a.to_owned()	a.to_owned()
ArcArray<A, D>	a.into_shared()	no-op	a.into_owned().into_shared()	a.to_owned().into_shared()	a.to_owned().into_shared()
CowArray<'a, A, D>	CowArray::from(a)	CowArray::from(a.into_owned())	no-op	CowArray::from(a)	CowArray::from(a.view())
ArrayView<'b, A, D>	a.view()	a.view()	a.view()	a.view() or a.reborrow()	a.view()
ArrayViewMut<'b, A, D>	a.view_mut()	a.view_mut()	a.view_mut()	illegal	a.view_mut() or a.reborrow()
equivalent with dim D2 (e.g. converting from dynamic dim to const dim)	a.into_dimensionality::<D2>()
equivalent with dim IxDyn	a.into_dyn()
Array<B, D> (new element type)	a.map(|x| x.do_your_conversion())

Conversions Between Arrays and Vecs/Slices/Scalars

This is a table of the safe conversions between arrays and Vecs/slices/scalars. Note that some of the return values are actually Result/Option wrappers around the indicated output types.

Input	Output	Methods
Vec<A>	ArrayBase<S: DataOwned, Ix1>	::from_vec()
Vec<A>	ArrayBase<S: DataOwned, D>	::from_shape_vec()
&[A]	ArrayView1<A>	::from()
&[A]	ArrayView<A, D>	::from_shape()
&mut [A]	ArrayViewMut1<A>	::from()
&mut [A]	ArrayViewMut<A, D>	::from_shape()
&ArrayBase<S, Ix1>	Vec<A>	.to_vec()
Array<A, D>	Vec<A>	.into_raw_vec()1
&ArrayBase<S, D>	&[A]	.as_slice()2, .as_slice_memory_order()3
&mut ArrayBase<S: DataMut, D>	&mut [A]	.as_slice_mut()2, .as_slice_memory_order_mut()3
ArrayView<A, D>	&[A]	.to_slice()2
ArrayViewMut<A, D>	&mut [A]	.into_slice()2
Array0<A>	A	.into_scalar()

¹Returns the data in memory order.

²Works only if the array is contiguous and in standard order.

³Works only if the array is contiguous.

The table above does not include all the constructors; it only shows conversions to/from Vecs/slices. See below for more constructors.

Conversions from Nested Vecs/Arrays

It’s generally a good idea to avoid nested Vec/Array types, such as Vec<Vec<A>> or Vec<Array2<A>> because:

• they require extra heap allocations compared to a single Array,

• they can scatter data all over memory (because of multiple allocations),

• they cause unnecessary indirection (traversing multiple pointers to reach the data),

• they don’t enforce consistent shape within the nested Vecs/ArrayBases, and

• they are generally more difficult to work with.

The most common case where users might consider using nested Vecs/Arrays is when creating an array by appending rows/subviews in a loop, where the rows/subviews are computed within the loop. However, there are better ways than using nested Vecs/Arrays.

If you know ahead-of-time the shape of the final array, the cleanest solution is to allocate the final array before the loop, and then assign the data to it within the loop, like this:

use ndarray::{array, Array2, Axis};

let mut arr = Array2::zeros((2, 3));
for (i, mut row) in arr.axis_iter_mut(Axis(0)).enumerate() {
    // Perform calculations and assign to `row`; this is a trivial example:
    row.fill(i);
}
assert_eq!(arr, array![[0, 0, 0], [1, 1, 1]]);

If you don’t know ahead-of-time the shape of the final array, then the cleanest solution is generally to append the data to a flat Vec, and then convert it to an Array at the end with ::from_shape_vec(). You just have to be careful that the layout of the data (the order of the elements in the flat Vec) is correct.

use ndarray::{array, Array2};

let ncols = 3;
let mut data = Vec::new();
let mut nrows = 0;
for i in 0..2 {
    // Compute `row` and append it to `data`; this is a trivial example:
    let row = vec![i; ncols];
    data.extend_from_slice(&row);
    nrows += 1;
}
let arr = Array2::from_shape_vec((nrows, ncols), data)?;
assert_eq!(arr, array![[0, 0, 0], [1, 1, 1]]);

If neither of these options works for you, and you really need to convert nested Vec/Array instances to an Array, the cleanest solution is generally to use Iterator::flatten() to get a flat Vec, and then convert the Vec to an Array with ::from_shape_vec(), like this:

use ndarray::{array, Array2, Array3};

let nested: Vec<Array2<i32>> = vec![
    array![[1, 2, 3], [4, 5, 6]],
    array![[7, 8, 9], [10, 11, 12]],
];
let inner_shape = nested[0].dim();
let shape = (nested.len(), inner_shape.0, inner_shape.1);
let flat: Vec<i32> = nested.iter().flatten().cloned().collect();
let arr = Array3::from_shape_vec(shape, flat)?;
assert_eq!(arr, array![
    [[1, 2, 3], [4, 5, 6]],
    [[7, 8, 9], [10, 11, 12]],
]);

Note that this implementation assumes that the nested Vecs are all the same shape and that the Vec is non-empty. Depending on your application, it may be a good idea to add checks for these assumptions and possibly choose a different way to handle the empty case.

Implementations

impl<S, A> ArrayBase<S, Ix1> where
    S: DataOwned<Elem = A>, 

Constructor Methods for Owned Arrays

Note that the constructor methods apply to Array and ArcArray, the two array types that have owned storage.

Constructor methods for one-dimensional arrays.

pub fn from_vec(v: Vec<A>) -> Self

👎 Deprecated since 0.13.0:

use standard from

Create a one-dimensional array from a vector (no copying needed).

Panics if the length is greater than isize::MAX.

use ndarray::Array;

let array = Array::from(vec![1., 2., 3., 4.]);

pub fn linspace(start: A, end: A, n: usize) -> Self where
    A: Float, 

Create a one-dimensional array with n evenly spaced elements from start to end (inclusive). A must be a floating point type.

Note that if start > end, the first element will still be start, and the following elements will be decreasing. This is different from the behavior of std::ops::RangeInclusive, which interprets start > end to mean that the range is empty.

Panics if n is greater than isize::MAX or if converting n - 1 to type A fails.

use ndarray::{Array, arr1};

let array = Array::linspace(0., 1., 5);
assert!(array == arr1(&[0.0, 0.25, 0.5, 0.75, 1.0]))

pub fn range(start: A, end: A, step: A) -> Self where
    A: Float, 

Create a one-dimensional array with elements from start to end (exclusive), incrementing by step. A must be a floating point type.

Panics if the length is greater than isize::MAX.

use ndarray::{Array, arr1};

let array = Array::range(0., 5., 1.);
assert!(array == arr1(&[0., 1., 2., 3., 4.]))

pub fn logspace(base: A, start: A, end: A, n: usize) -> Self where
    A: Float, 

Create a one-dimensional array with n logarithmically spaced elements, with the starting value being base.powf(start) and the final one being base.powf(end). A must be a floating point type.

If base is negative, all values will be negative.

Panics if n is greater than isize::MAX or if converting n - 1 to type A fails.

use approx::assert_abs_diff_eq;
use ndarray::{Array, arr1};

let array = Array::logspace(10.0, 0.0, 3.0, 4);
assert_abs_diff_eq!(array, arr1(&[1e0, 1e1, 1e2, 1e3]));

let array = Array::logspace(-10.0, 3.0, 0.0, 4);
assert_abs_diff_eq!(array, arr1(&[-1e3, -1e2, -1e1, -1e0]));

pub fn geomspace(start: A, end: A, n: usize) -> Option<Self> where
    A: Float, 

Create a one-dimensional array with n geometrically spaced elements from start to end (inclusive). A must be a floating point type.

Returns None if start and end have different signs or if either one is zero. Conceptually, this means that in order to obtain a Some result, end / start must be positive.

Panics if n is greater than isize::MAX or if converting n - 1 to type A fails.

use approx::assert_abs_diff_eq;
use ndarray::{Array, arr1};

let array = Array::geomspace(1e0, 1e3, 4)?;
assert_abs_diff_eq!(array, arr1(&[1e0, 1e1, 1e2, 1e3]), epsilon = 1e-12);

let array = Array::geomspace(-1e3, -1e0, 4)?;
assert_abs_diff_eq!(array, arr1(&[-1e3, -1e2, -1e1, -1e0]), epsilon = 1e-12);

impl<S, A> ArrayBase<S, Ix2> where
    S: DataOwned<Elem = A>, 

Constructor methods for two-dimensional arrays.

pub fn eye(n: Ix) -> Self where
    S: DataMut,
    A: Clone + Zero + One, 

Create an identity matrix of size n (square 2D array).

Panics if n * n would overflow isize.

pub fn from_diag<S2>(diag: &ArrayBase<S2, Ix1>) -> Self where
    A: Clone + Zero,
    S: DataMut,
    S2: Data<Elem = A>, 

Create a 2D matrix from its diagonal

Panics if diag.len() * diag.len() would overflow isize.

use ndarray::{Array2, arr1, arr2};

let diag = arr1(&[1, 2]);
let array = Array2::from_diag(&diag);
assert_eq!(array, arr2(&[[1, 0], [0, 2]]));

impl<S, A, D> ArrayBase<S, D> where
    S: DataOwned<Elem = A>,
    D: Dimension, 

Constructor methods for n-dimensional arrays.

The shape argument can be an integer or a tuple of integers to specify a static size. For example 10 makes a length 10 one-dimensional array (dimension type Ix1) and (5, 6) a 5 × 6 array (dimension type Ix2).

With the trait ShapeBuilder in scope, there is the method .f() to select column major (“f” order) memory layout instead of the default row major. For example Array::zeros((5, 6).f()) makes a column major 5 × 6 array.

Use IxDyn for the shape to create an array with dynamic number of axes.

Finally, the few constructors that take a completely general Into<StrideShape> argument optionally support custom strides, for example a shape given like (10, 2, 2).strides((1, 10, 20)) is valid.

pub fn from_elem<Sh>(shape: Sh, elem: A) -> Self where
    A: Clone,
    Sh: ShapeBuilder<Dim = D>, 

Create an array with copies of elem, shape shape.

Panics if the product of non-zero axis lengths overflows isize.

use ndarray::{Array, arr3, ShapeBuilder};

let a = Array::from_elem((2, 2, 2), 1.);

assert!(
    a == arr3(&[[[1., 1.],
                 [1., 1.]],
                [[1., 1.],
                 [1., 1.]]])
);
assert!(a.strides() == &[4, 2, 1]);

let b = Array::from_elem((2, 2, 2).f(), 1.);
assert!(b.strides() == &[1, 2, 4]);

pub fn zeros<Sh>(shape: Sh) -> Self where
    A: Clone + Zero,
    Sh: ShapeBuilder<Dim = D>, 

Create an array with zeros, shape shape.

Panics if the product of non-zero axis lengths overflows isize.

pub fn ones<Sh>(shape: Sh) -> Self where
    A: Clone + One,
    Sh: ShapeBuilder<Dim = D>, 

Create an array with ones, shape shape.

Panics if the product of non-zero axis lengths overflows isize.

pub fn default<Sh>(shape: Sh) -> Self where
    A: Default,
    Sh: ShapeBuilder<Dim = D>, 

Create an array with default values, shape shape

Panics if the product of non-zero axis lengths overflows isize.

pub fn from_shape_simple_fn<Sh, F>(shape: Sh, f: F) -> Self where
    Sh: ShapeBuilder<Dim = D>,
    F: FnMut() -> A, 

Create an array with values created by the function f.

f is called with no argument, and it should return the element to create. If the precise index of the element to create is needed, use from_shape_fn instead.

This constructor can be useful if the element order is not important, for example if they are identical or random.

Panics if the product of non-zero axis lengths overflows isize.

pub fn from_shape_fn<Sh, F>(shape: Sh, f: F) -> Self where
    Sh: ShapeBuilder<Dim = D>,
    F: FnMut(D::Pattern) -> A, 

Create an array with values created by the function f.

f is called with the index of the element to create; the elements are visited in arbitrary order.

Panics if the product of non-zero axis lengths overflows isize.

use ndarray::{Array, arr2};

// Create a table of i × j (with i and j from 1 to 3)
let ij_table = Array::from_shape_fn((3, 3), |(i, j)| (1 + i) * (1 + j));

assert_eq!(
    ij_table,
    arr2(&[[1, 2, 3],
           [2, 4, 6],
           [3, 6, 9]])
);

pub fn from_shape_vec<Sh>(shape: Sh, v: Vec<A>) -> Result<Self, ShapeError> where
    Sh: Into<StrideShape<D>>, 

Create an array with the given shape from a vector. (No cloning of elements needed.)

---

For a contiguous c- or f-order shape, the following applies:

Errors if shape does not correspond to the number of elements in v or if the shape/strides would result in overflowing isize.

---

For custom strides, the following applies:

Errors if strides and dimensions can point out of bounds of v, if strides allow multiple indices to point to the same element, or if the shape/strides would result in overflowing isize.

use ndarray::Array;
use ndarray::ShapeBuilder; // Needed for .strides() method
use ndarray::arr2;

let a = Array::from_shape_vec((2, 2), vec![1., 2., 3., 4.]);
assert!(a.is_ok());

let b = Array::from_shape_vec((2, 2).strides((1, 2)),
                              vec![1., 2., 3., 4.]).unwrap();
assert!(
    b == arr2(&[[1., 3.],
                [2., 4.]])
);

pub unsafe fn from_shape_vec_unchecked<Sh>(shape: Sh, v: Vec<A>) -> Self where
    Sh: Into<StrideShape<D>>, 

Creates an array from a vector and interpret it according to the provided shape and strides. (No cloning of elements needed.)

Safety

The caller must ensure that the following conditions are met:

1. The ndim of dim and strides must be the same.

2. The product of non-zero axis lengths must not exceed isize::MAX.

3. For axes with length > 1, the stride must be nonnegative.

4. If the array will be empty (any axes are zero-length), the difference between the least address and greatest address accessible by moving along all axes must be ≤ v.len().

   If the array will not be empty, the difference between the least address and greatest address accessible by moving along all axes must be < v.len().

5. The strides must not allow any element to be referenced by two different indices.

pub unsafe fn uninitialized<Sh>(shape: Sh) -> Self where
    A: Copy,
    Sh: ShapeBuilder<Dim = D>, 

Create an array with uninitalized elements, shape shape.

Prefer to use maybe_uninit() if possible, because it is easier to use correctly.

Panics if the number of elements in shape would overflow isize.

Safety

Accessing uninitalized values is undefined behaviour. You must overwrite all the elements in the array after it is created; for example using raw_view_mut or other low-level element access.

The contents of the array is indeterminate before initialization and it is an error to perform operations that use the previous values. For example it would not be legal to use a += 1.; on such an array.

This constructor is limited to elements where A: Copy (no destructors) to avoid users shooting themselves too hard in the foot.

(Also note that the constructors from_shape_vec and from_shape_vec_unchecked allow the user yet more control, in the sense that Arrays can be created from arbitrary vectors.)

impl<S, A, D> ArrayBase<S, D> where
    S: DataOwned<Elem = MaybeUninit<A>>,
    D: Dimension, 

pub fn maybe_uninit<Sh>(shape: Sh) -> Self where
    Sh: ShapeBuilder<Dim = D>, 

Create an array with uninitalized elements, shape shape.

The uninitialized elements of type A are represented by the type MaybeUninit<A>, an easier way to handle uninit values correctly.

Only when the array is completely initialized with valid elements, can it be converted to an array of A elements using .assume_init().

Panics if the number of elements in shape would overflow isize.

Safety

The whole of the array must be initialized before it is converted using .assume_init() or otherwise traversed.

Examples

It is possible to assign individual values through *elt = MaybeUninit::new(value) and so on.

use ndarray::{s, Array2};
use ndarray::Zip;
use ndarray::Axis;

// Example Task: Let's create a column shifted copy of the input

fn shift_by_two(a: &Array2<f32>) -> Array2<f32> {
    // create an uninitialized array
    let mut b = Array2::maybe_uninit(a.dim());

    // two first columns in b are two last in a
    // rest of columns in b are the initial columns in a

    assign_to(a.slice(s![.., -2..]), b.slice_mut(s![.., ..2]));
    assign_to(a.slice(s![.., 2..]), b.slice_mut(s![.., ..-2]));

    // Now we can promise that `b` is safe to use with all operations
    unsafe {
        b.assume_init()
    }
}

use ndarray::{IntoNdProducer, AssignElem};

// This function clones elements from the first input to the second;
// the two producers must have the same shape
fn assign_to<'a, P1, P2, A>(from: P1, to: P2)
    where P1: IntoNdProducer<Item = &'a A>,
          P2: IntoNdProducer<Dim = P1::Dim>,
          P2::Item: AssignElem<A>,
          A: Clone + 'a
{
    Zip::from(from)
        .apply_assign_into(to, A::clone);
}

impl<A, S, D> ArrayBase<S, D> where
    S: RawData<Elem = A>,
    D: Dimension, 

Methods For All Array Types

pub fn len(&self) -> usize

Return the total number of elements in the array.

pub fn len_of(&self, axis: Axis) -> usize

Return the length of axis.

The axis should be in the range Axis( 0 .. n ) where n is the number of dimensions (axes) of the array.

Panics if the axis is out of bounds.

pub fn is_empty(&self) -> bool

Return whether the array has any elements

pub fn ndim(&self) -> usize

Return the number of dimensions (axes) in the array

pub fn dim(&self) -> D::Pattern

Return the shape of the array in its “pattern” form, an integer in the one-dimensional case, tuple in the n-dimensional cases and so on.

pub fn raw_dim(&self) -> D

Return the shape of the array as it stored in the array.

This is primarily useful for passing to other ArrayBase functions, such as when creating another array of the same shape and dimensionality.

use ndarray::Array;

let a = Array::from_elem((2, 3), 5.);

// Create an array of zeros that's the same shape and dimensionality as `a`.
let b = Array::<f64, _>::zeros(a.raw_dim());

pub fn shape(&self) -> &[usize]

Return the shape of the array as a slice.

Note that you probably don’t want to use this to create an array of the same shape as another array because creating an array with e.g. Array::zeros() using a shape of type &[usize] results in a dynamic-dimensional array. If you want to create an array that has the same shape and dimensionality as another array, use .raw_dim() instead:

use ndarray::{Array, Array2};

let a = Array2::<i32>::zeros((3, 4));
let shape = a.shape();
assert_eq!(shape, &[3, 4]);

// Since `a.shape()` returned `&[usize]`, we get an `ArrayD` instance:
let b = Array::zeros(shape);
assert_eq!(a.clone().into_dyn(), b);

// To get the same dimension type, use `.raw_dim()` instead:
let c = Array::zeros(a.raw_dim());
assert_eq!(a, c);

pub fn strides(&self) -> &[isize]

Return the strides of the array as a slice.

pub fn stride_of(&self, axis: Axis) -> isize

Return the stride of axis.

The axis should be in the range Axis( 0 .. n ) where n is the number of dimensions (axes) of the array.

Panics if the axis is out of bounds.

pub fn view(&self) -> ArrayView<'_, A, D> where
    S: Data, 

Return a read-only view of the array

pub fn view_mut(&mut self) -> ArrayViewMut<'_, A, D> where
    S: DataMut, 

Return a read-write view of the array

pub fn to_owned(&self) -> Array<A, D> where
    A: Clone,
    S: Data, 

Return an uniquely owned copy of the array.

If the input array is contiguous and its strides are positive, then the output array will have the same memory layout. Otherwise, the layout of the output array is unspecified. If you need a particular layout, you can allocate a new array with the desired memory layout and .assign() the data. Alternatively, you can collect an iterator, like this for a result in standard layout:

Array::from_shape_vec(arr.raw_dim(), arr.iter().cloned().collect()).unwrap()

or this for a result in column-major (Fortran) layout:

Array::from_shape_vec(arr.raw_dim().f(), arr.t().iter().cloned().collect()).unwrap()

pub fn to_shared(&self) -> ArcArray<A, D> where
    A: Clone,
    S: Data, 

Return a shared ownership (copy on write) array.

pub fn into_owned(self) -> Array<A, D> where
    A: Clone,
    S: Data, 

Turn the array into a uniquely owned array, cloning the array elements if necessary.

pub fn into_shared(self) -> ArcArray<A, D> where
    S: DataOwned, 

Turn the array into a shared ownership (copy on write) array, without any copying.

pub fn first(&self) -> Option<&A> where
    S: Data, 

Returns a reference to the first element of the array, or None if it is empty.

pub fn first_mut(&mut self) -> Option<&mut A> where
    S: DataMut, 

Returns a mutable reference to the first element of the array, or None if it is empty.

pub fn iter(&self) -> Iter<'_, A, D> where
    S: Data, 

Return an iterator of references to the elements of the array.

Elements are visited in the logical order of the array, which is where the rightmost index is varying the fastest.

Iterator element type is &A.

pub fn iter_mut(&mut self) -> IterMut<'_, A, D> where
    S: DataMut, 

Return an iterator of mutable references to the elements of the array.

Elements are visited in the logical order of the array, which is where the rightmost index is varying the fastest.

Iterator element type is &mut A.

pub fn indexed_iter(&self) -> IndexedIter<'_, A, D> where
    S: Data, 

Return an iterator of indexes and references to the elements of the array.

Elements are visited in the logical order of the array, which is where the rightmost index is varying the fastest.

Iterator element type is (D::Pattern, &A).

See also Zip::indexed

pub fn indexed_iter_mut(&mut self) -> IndexedIterMut<'_, A, D> where
    S: DataMut, 

Return an iterator of indexes and mutable references to the elements of the array.

Elements are visited in the logical order of the array, which is where the rightmost index is varying the fastest.

Iterator element type is (D::Pattern, &mut A).

pub fn slice<Do>(
    &self,
    info: &SliceInfo<D::SliceArg, Do>
) -> ArrayView<'_, A, Do> where
    Do: Dimension,
    S: Data, 

Return a sliced view of the array.

See Slicing for full documentation. See also SliceInfo and D::SliceArg.

Panics if an index is out of bounds or step size is zero.
(Panics if D is IxDyn and info does not match the number of array axes.)

pub fn slice_mut<Do>(
    &mut self,
    info: &SliceInfo<D::SliceArg, Do>
) -> ArrayViewMut<'_, A, Do> where
    Do: Dimension,
    S: DataMut, 

Return a sliced read-write view of the array.

See Slicing for full documentation. See also SliceInfo and D::SliceArg.

Panics if an index is out of bounds or step size is zero.
(Panics if D is IxDyn and info does not match the number of array axes.)

pub fn multi_slice_mut<'a, M>(&'a mut self, info: M) -> M::Output where
    M: MultiSlice<'a, A, D>,
    S: DataMut, 

Return multiple disjoint, sliced, mutable views of the array.

See Slicing for full documentation. See also SliceInfo and D::SliceArg.

Panics if any of the following occur:

• if any of the views would intersect (i.e. if any element would appear in multiple slices)
• if an index is out of bounds or step size is zero
• if D is IxDyn and info does not match the number of array axes

Example

use ndarray::{arr2, s};

let mut a = arr2(&[[1, 2, 3], [4, 5, 6]]);
let (mut edges, mut middle) = a.multi_slice_mut((s![.., ..;2], s![.., 1]));
edges.fill(1);
middle.fill(0);
assert_eq!(a, arr2(&[[1, 0, 1], [1, 0, 1]]));

pub fn slice_move<Do>(
    self,
    info: &SliceInfo<D::SliceArg, Do>
) -> ArrayBase<S, Do> where
    Do: Dimension, 

Slice the array, possibly changing the number of dimensions.

See Slicing for full documentation. See also SliceInfo and D::SliceArg.

Panics if an index is out of bounds or step size is zero.
(Panics if D is IxDyn and info does not match the number of array axes.)

pub fn slice_collapse(&mut self, indices: &D::SliceArg)

Slice the array in place without changing the number of dimensions.

Note that &SliceInfo (produced by the s![] macro) will usually coerce into &D::SliceArg automatically, but in some cases (e.g. if D is IxDyn), you may need to call .as_ref().

See Slicing for full documentation. See also D::SliceArg.

Panics if an index is out of bounds or step size is zero.
(Panics if D is IxDyn and indices does not match the number of array axes.)

pub fn slice_inplace(&mut self, indices: &D::SliceArg)

👎 Deprecated since 0.12.1:

renamed to slice_collapse

Slice the array in place without changing the number of dimensions.

Panics if an index is out of bounds or step size is zero.
(Panics if D is IxDyn and indices does not match the number of array axes.)

pub fn slice_axis(&self, axis: Axis, indices: Slice) -> ArrayView<'_, A, D> where
    S: Data, 

Return a view of the array, sliced along the specified axis.

Panics if an index is out of bounds or step size is zero.
Panics if axis is out of bounds.

pub fn slice_axis_mut(
    &mut self,
    axis: Axis,
    indices: Slice
) -> ArrayViewMut<'_, A, D> where
    S: DataMut, 

Return a mutable view of the array, sliced along the specified axis.

Panics if an index is out of bounds or step size is zero.
Panics if axis is out of bounds.

pub fn slice_axis_inplace(&mut self, axis: Axis, indices: Slice)

Slice the array in place along the specified axis.

Panics if an index is out of bounds or step size is zero.
Panics if axis is out of bounds.

pub fn get<I>(&self, index: I) -> Option<&A> where
    I: NdIndex<D>,
    S: Data, 

Return a reference to the element at index, or return None if the index is out of bounds.

Arrays also support indexing syntax: array[index].

use ndarray::arr2;

let a = arr2(&[[1., 2.],
               [3., 4.]]);

assert!(
    a.get((0, 1)) == Some(&2.) &&
    a.get((0, 2)) == None &&
    a[(0, 1)] == 2. &&
    a[[0, 1]] == 2.
);

pub fn get_mut<I>(&mut self, index: I) -> Option<&mut A> where
    S: DataMut,
    I: NdIndex<D>, 

Return a mutable reference to the element at index, or return None if the index is out of bounds.

pub unsafe fn uget<I>(&self, index: I) -> &A where
    S: Data,
    I: NdIndex<D>, 

Perform unchecked array indexing.

Return a reference to the element at index.

Note: only unchecked for non-debug builds of ndarray.

Safety

The caller must ensure that the index is in-bounds.

pub unsafe fn uget_mut<I>(&mut self, index: I) -> &mut A where
    S: DataMut,
    I: NdIndex<D>, 

Perform unchecked array indexing.

Return a mutable reference to the element at index.

Note: Only unchecked for non-debug builds of ndarray.

Safety

The caller must ensure that:

1. the index is in-bounds and

2. the data is uniquely held by the array. (This property is guaranteed for Array and ArrayViewMut, but not for ArcArray or CowArray.)

pub fn swap<I>(&mut self, index1: I, index2: I) where
    S: DataMut,
    I: NdIndex<D>, 

Swap elements at indices index1 and index2.

Indices may be equal.

Panics if an index is out of bounds.

pub unsafe fn uswap<I>(&mut self, index1: I, index2: I) where
    S: DataMut,
    I: NdIndex<D>, 

Swap elements unchecked at indices index1 and index2.

Indices may be equal.

Note: only unchecked for non-debug builds of ndarray.

Safety

The caller must ensure that:

1. both index1 and index2` are in-bounds and

2. the data is uniquely held by the array. (This property is guaranteed for Array and ArrayViewMut, but not for ArcArray or CowArray.)

pub fn index_axis(
    &self,
    axis: Axis,
    index: usize
) -> ArrayView<'_, A, D::Smaller> where
    S: Data,
    D: RemoveAxis, 

Returns a view restricted to index along the axis, with the axis removed.

See Subviews for full documentation.

Panics if axis or index is out of bounds.

use ndarray::{arr2, ArrayView, Axis};

let a = arr2(&[[1., 2. ],    // ... axis 0, row 0
               [3., 4. ],    // --- axis 0, row 1
               [5., 6. ]]);  // ... axis 0, row 2
//               .   \
//                .   axis 1, column 1
//                 axis 1, column 0
assert!(
    a.index_axis(Axis(0), 1) == ArrayView::from(&[3., 4.]) &&
    a.index_axis(Axis(1), 1) == ArrayView::from(&[2., 4., 6.])
);

pub fn index_axis_mut(
    &mut self,
    axis: Axis,
    index: usize
) -> ArrayViewMut<'_, A, D::Smaller> where
    S: DataMut,
    D: RemoveAxis, 

Returns a mutable view restricted to index along the axis, with the axis removed.

Panics if axis or index is out of bounds.

use ndarray::{arr2, aview2, Axis};

let mut a = arr2(&[[1., 2. ],
                   [3., 4. ]]);
//                   .   \
//                    .   axis 1, column 1
//                     axis 1, column 0

{
    let mut column1 = a.index_axis_mut(Axis(1), 1);
    column1 += 10.;
}

assert!(
    a == aview2(&[[1., 12.],
                  [3., 14.]])
);

pub fn index_axis_move(
    self,
    axis: Axis,
    index: usize
) -> ArrayBase<S, D::Smaller> where
    D: RemoveAxis, 

Collapses the array to index along the axis and removes the axis.

See .index_axis() and Subviews for full documentation.

Panics if axis or index is out of bounds.

pub fn collapse_axis(&mut self, axis: Axis, index: usize)

Selects index along the axis, collapsing the axis into length one.

Panics if axis or index is out of bounds.

pub fn subview(&self, axis: Axis, index: Ix) -> ArrayView<'_, A, D::Smaller> where
    S: Data,
    D: RemoveAxis, 

👎 Deprecated since 0.12.1:

renamed to index_axis

Along axis, select the subview index and return a view with that axis removed.

Panics if axis or index is out of bounds.

pub fn subview_mut(
    &mut self,
    axis: Axis,
    index: Ix
) -> ArrayViewMut<'_, A, D::Smaller> where
    S: DataMut,
    D: RemoveAxis, 

👎 Deprecated since 0.12.1:

renamed to index_axis_mut

Along axis, select the subview index and return a read-write view with the axis removed.

Panics if axis or index is out of bounds.

pub fn subview_inplace(&mut self, axis: Axis, index: Ix)

👎 Deprecated since 0.12.1:

renamed to collapse_axis

Collapse dimension axis into length one, and select the subview of index along that axis.

Panics if index is past the length of the axis.

pub fn into_subview(self, axis: Axis, index: Ix) -> ArrayBase<S, D::Smaller> where
    D: RemoveAxis, 

👎 Deprecated since 0.12.1:

renamed to index_axis_move

Along axis, select the subview index and return self with that axis removed.

pub fn select(&self, axis: Axis, indices: &[Ix]) -> Array<A, D> where
    A: Copy,
    S: Data,
    D: RemoveAxis, 

Along axis, select arbitrary subviews corresponding to indices and and copy them into a new array.

Panics if axis or an element of indices is out of bounds.

use ndarray::{arr2, Axis};

let x = arr2(&[[0., 1.],
               [2., 3.],
               [4., 5.],
               [6., 7.],
               [8., 9.]]);

let r = x.select(Axis(0), &[0, 4, 3]);
assert!(
        r == arr2(&[[0., 1.],
                    [8., 9.],
                    [6., 7.]])
);

pub fn genrows(&self) -> Lanes<'_, A, D::Smaller> where
    S: Data, 

Return a producer and iterable that traverses over the generalized rows of the array. For a 2D array these are the regular rows.

This is equivalent to .lanes(Axis(n - 1)) where n is self.ndim().

For an array of dimensions a × b × c × … × l × m it has a × b × c × … × l rows each of length m.

For example, in a 2 × 2 × 3 array, each row is 3 elements long and there are 2 × 2 = 4 rows in total.

Iterator element is ArrayView1<A> (1D array view).

use ndarray::{arr3, Axis, arr1};

let a = arr3(&[[[ 0,  1,  2],    // -- row 0, 0
                [ 3,  4,  5]],   // -- row 0, 1
               [[ 6,  7,  8],    // -- row 1, 0
                [ 9, 10, 11]]]); // -- row 1, 1

// `genrows` will yield the four generalized rows of the array.
for row in a.genrows() {
    /* loop body */
}

pub fn genrows_mut(&mut self) -> LanesMut<'_, A, D::Smaller> where
    S: DataMut, 

Return a producer and iterable that traverses over the generalized rows of the array and yields mutable array views.

Iterator element is ArrayView1<A> (1D read-write array view).

pub fn gencolumns(&self) -> Lanes<'_, A, D::Smaller> where
    S: Data, 

Return a producer and iterable that traverses over the generalized columns of the array. For a 2D array these are the regular columns.

This is equivalent to .lanes(Axis(0)).

For an array of dimensions a × b × c × … × l × m it has b × c × … × l × m columns each of length a.

For example, in a 2 × 2 × 3 array, each column is 2 elements long and there are 2 × 3 = 6 columns in total.

Iterator element is ArrayView1<A> (1D array view).

use ndarray::{arr3, Axis, arr1};

// The generalized columns of a 3D array:
// are directed along the 0th axis: 0 and 6, 1 and 7 and so on...
let a = arr3(&[[[ 0,  1,  2], [ 3,  4,  5]],
               [[ 6,  7,  8], [ 9, 10, 11]]]);

// Here `gencolumns` will yield the six generalized columns of the array.
for row in a.gencolumns() {
    /* loop body */
}

pub fn gencolumns_mut(&mut self) -> LanesMut<'_, A, D::Smaller> where
    S: DataMut, 

Return a producer and iterable that traverses over the generalized columns of the array and yields mutable array views.

Iterator element is ArrayView1<A> (1D read-write array view).

pub fn lanes(&self, axis: Axis) -> Lanes<'_, A, D::Smaller> where
    S: Data, 

Return a producer and iterable that traverses over all 1D lanes pointing in the direction of axis.

When the pointing in the direction of the first axis, they are columns, in the direction of the last axis rows; in general they are all lanes and are one dimensional.

Iterator element is ArrayView1<A> (1D array view).

use ndarray::{arr3, aview1, Axis};

let a = arr3(&[[[ 0,  1,  2],
                [ 3,  4,  5]],
               [[ 6,  7,  8],
                [ 9, 10, 11]]]);

let inner0 = a.lanes(Axis(0));
let inner1 = a.lanes(Axis(1));
let inner2 = a.lanes(Axis(2));

// The first lane for axis 0 is [0, 6]
assert_eq!(inner0.into_iter().next().unwrap(), aview1(&[0, 6]));
// The first lane for axis 1 is [0, 3]
assert_eq!(inner1.into_iter().next().unwrap(), aview1(&[0, 3]));
// The first lane for axis 2 is [0, 1, 2]
assert_eq!(inner2.into_iter().next().unwrap(), aview1(&[0, 1, 2]));

pub fn lanes_mut(&mut self, axis: Axis) -> LanesMut<'_, A, D::Smaller> where
    S: DataMut, 

Return a producer and iterable that traverses over all 1D lanes pointing in the direction of axis.

Iterator element is ArrayViewMut1<A> (1D read-write array view).

pub fn outer_iter(&self) -> AxisIter<'_, A, D::Smaller> where
    S: Data,
    D: RemoveAxis, 

Return an iterator that traverses over the outermost dimension and yields each subview.

This is equivalent to .axis_iter(Axis(0)).

Iterator element is ArrayView<A, D::Smaller> (read-only array view).

pub fn outer_iter_mut(&mut self) -> AxisIterMut<'_, A, D::Smaller> where
    S: DataMut,
    D: RemoveAxis, 

Return an iterator that traverses over the outermost dimension and yields each subview.

This is equivalent to .axis_iter_mut(Axis(0)).

Iterator element is ArrayViewMut<A, D::Smaller> (read-write array view).

pub fn axis_iter(&self, axis: Axis) -> AxisIter<'_, A, D::Smaller> where
    S: Data,
    D: RemoveAxis, 

Return an iterator that traverses over axis and yields each subview along it.

For example, in a 3 × 4 × 5 array, with axis equal to Axis(2), the iterator element is a 3 × 4 subview (and there are 5 in total), as shown in the picture below.

Iterator element is ArrayView<A, D::Smaller> (read-only array view).

See Subviews for full documentation.

Panics if axis is out of bounds.

pub fn axis_iter_mut(&mut self, axis: Axis) -> AxisIterMut<'_, A, D::Smaller> where
    S: DataMut,
    D: RemoveAxis, 

Return an iterator that traverses over axis and yields each mutable subview along it.

Iterator element is ArrayViewMut<A, D::Smaller> (read-write array view).

Panics if axis is out of bounds.

pub fn axis_chunks_iter(
    &self,
    axis: Axis,
    size: usize
) -> AxisChunksIter<'_, A, D> where
    S: Data, 

Return an iterator that traverses over axis by chunks of size, yielding non-overlapping views along that axis.

Iterator element is ArrayView<A, D>

The last view may have less elements if size does not divide the axis’ dimension.

Panics if axis is out of bounds or if size is zero.

use ndarray::Array;
use ndarray::{arr3, Axis};
use std::iter::FromIterator;

let a = Array::from_iter(0..28).into_shape((2, 7, 2)).unwrap();
let mut iter = a.axis_chunks_iter(Axis(1), 2);

// first iteration yields a 2 × 2 × 2 view
assert_eq!(iter.next().unwrap(),
           arr3(&[[[ 0,  1], [ 2, 3]],
                  [[14, 15], [16, 17]]]));

// however the last element is a 2 × 1 × 2 view since 7 % 2 == 1
assert_eq!(iter.next_back().unwrap(), arr3(&[[[12, 13]],
                                             [[26, 27]]]));

pub fn axis_chunks_iter_mut(
    &mut self,
    axis: Axis,
    size: usize
) -> AxisChunksIterMut<'_, A, D> where
    S: DataMut, 

Return an iterator that traverses over axis by chunks of size, yielding non-overlapping read-write views along that axis.

Iterator element is ArrayViewMut<A, D>

Panics if axis is out of bounds or if size is zero.

pub fn exact_chunks<E>(&self, chunk_size: E) -> ExactChunks<'_, A, D> where
    E: IntoDimension<Dim = D>,
    S: Data, 

Return an exact chunks producer (and iterable).

It produces the whole chunks of a given n-dimensional chunk size, skipping the remainder along each dimension that doesn’t fit evenly.

The produced element is a ArrayView<A, D> with exactly the dimension chunk_size.

Panics if any dimension of chunk_size is zero
(Panics if D is IxDyn and chunk_size does not match the number of array axes.)

pub fn exact_chunks_mut<E>(&mut self, chunk_size: E) -> ExactChunksMut<'_, A, D> where
    E: IntoDimension<Dim = D>,
    S: DataMut, 

Return an exact chunks producer (and iterable).

It produces the whole chunks of a given n-dimensional chunk size, skipping the remainder along each dimension that doesn’t fit evenly.

The produced element is a ArrayViewMut<A, D> with exactly the dimension chunk_size.

Panics if any dimension of chunk_size is zero
(Panics if D is IxDyn and chunk_size does not match the number of array axes.)

use ndarray::Array;
use ndarray::arr2;
let mut a = Array::zeros((6, 7));

// Fill each 2 × 2 chunk with the index of where it appeared in iteration
for (i, mut chunk) in a.exact_chunks_mut((2, 2)).into_iter().enumerate() {
    chunk.fill(i);
}

// The resulting array is:
assert_eq!(
  a,
  arr2(&[[0, 0, 1, 1, 2, 2, 0],
         [0, 0, 1, 1, 2, 2, 0],
         [3, 3, 4, 4, 5, 5, 0],
         [3, 3, 4, 4, 5, 5, 0],
         [6, 6, 7, 7, 8, 8, 0],
         [6, 6, 7, 7, 8, 8, 0]]));

pub fn windows<E>(&self, window_size: E) -> Windows<'_, A, D> where
    E: IntoDimension<Dim = D>,
    S: Data, 

Return a window producer and iterable.

The windows are all distinct overlapping views of size window_size that fit into the array’s shape.

Will yield over no elements if window size is larger than the actual array size of any dimension.

The produced element is an ArrayView<A, D> with exactly the dimension window_size.

Panics if any dimension of window_size is zero.
(Panics if D is IxDyn and window_size does not match the number of array axes.)

This is an illustration of the 2×2 windows in a 3×4 array:

         ──▶ Axis(1)

     │   ┏━━━━━┳━━━━━┱─────┬─────┐   ┌─────┲━━━━━┳━━━━━┱─────┐   ┌─────┬─────┲━━━━━┳━━━━━┓
     ▼   ┃ a₀₀ ┃ a₀₁ ┃     │     │   │     ┃ a₀₁ ┃ a₀₂ ┃     │   │     │     ┃ a₀₂ ┃ a₀₃ ┃
Axis(0)  ┣━━━━━╋━━━━━╉─────┼─────┤   ├─────╊━━━━━╋━━━━━╉─────┤   ├─────┼─────╊━━━━━╋━━━━━┫
         ┃ a₁₀ ┃ a₁₁ ┃     │     │   │     ┃ a₁₁ ┃ a₁₂ ┃     │   │     │     ┃ a₁₂ ┃ a₁₃ ┃
         ┡━━━━━╇━━━━━╃─────┼─────┤   ├─────╄━━━━━╇━━━━━╃─────┤   ├─────┼─────╄━━━━━╇━━━━━┩
         │     │     │     │     │   │     │     │     │     │   │     │     │     │     │
         └─────┴─────┴─────┴─────┘   └─────┴─────┴─────┴─────┘   └─────┴─────┴─────┴─────┘

         ┌─────┬─────┬─────┬─────┐   ┌─────┬─────┬─────┬─────┐   ┌─────┬─────┬─────┬─────┐
         │     │     │     │     │   │     │     │     │     │   │     │     │     │     │
         ┢━━━━━╈━━━━━╅─────┼─────┤   ├─────╆━━━━━╈━━━━━╅─────┤   ├─────┼─────╆━━━━━╈━━━━━┪
         ┃ a₁₀ ┃ a₁₁ ┃     │     │   │     ┃ a₁₁ ┃ a₁₂ ┃     │   │     │     ┃ a₁₂ ┃ a₁₃ ┃
         ┣━━━━━╋━━━━━╉─────┼─────┤   ├─────╊━━━━━╋━━━━━╉─────┤   ├─────┼─────╊━━━━━╋━━━━━┫
         ┃ a₂₀ ┃ a₂₁ ┃     │     │   │     ┃ a₂₁ ┃ a₂₂ ┃     │   │     │     ┃ a₂₂ ┃ a₂₃ ┃
         ┗━━━━━┻━━━━━┹─────┴─────┘   └─────┺━━━━━┻━━━━━┹─────┘   └─────┴─────┺━━━━━┻━━━━━┛

pub fn diag(&self) -> ArrayView1<'_, A> where
    S: Data, 

Return an view of the diagonal elements of the array.

The diagonal is simply the sequence indexed by (0, 0, .., 0), (1, 1, …, 1) etc as long as all axes have elements.

pub fn diag_mut(&mut self) -> ArrayViewMut1<'_, A> where
    S: DataMut, 

Return a read-write view over the diagonal elements of the array.

pub fn into_diag(self) -> ArrayBase<S, Ix1>

Return the diagonal as a one-dimensional array.

pub fn is_standard_layout(&self) -> bool

Return true if the array data is laid out in contiguous “C order” in memory (where the last index is the most rapidly varying).

Return false otherwise, i.e the array is possibly not contiguous in memory, it has custom strides, etc.

pub fn as_standard_layout(&self) -> CowArray<'_, A, D> where
    S: Data<Elem = A>,
    A: Clone, 

Return a standard-layout array containing the data, cloning if necessary.

If self is in standard layout, a COW view of the data is returned without cloning. Otherwise, the data is cloned, and the returned array owns the cloned data.

use ndarray::Array2;

let standard = Array2::<f64>::zeros((3, 4));
assert!(standard.is_standard_layout());
let cow_view = standard.as_standard_layout();
assert!(cow_view.is_view());
assert!(cow_view.is_standard_layout());

let fortran = standard.reversed_axes();
assert!(!fortran.is_standard_layout());
let cow_owned = fortran.as_standard_layout();
assert!(cow_owned.is_owned());
assert!(cow_owned.is_standard_layout());

pub fn as_ptr(&self) -> *const A

Return a pointer to the first element in the array.

Raw access to array elements needs to follow the strided indexing scheme: an element at multi-index I in an array with strides S is located at offset

Σ_{0 ≤ k < d} I_k × S_k

where d is self.ndim().

pub fn as_mut_ptr(&mut self) -> *mut A where
    S: RawDataMut, 

Return a mutable pointer to the first element in the array.

pub fn raw_view(&self) -> RawArrayView<A, D>

Return a raw view of the array.

pub fn raw_view_mut(&mut self) -> RawArrayViewMut<A, D> where
    S: RawDataMut, 

Return a raw mutable view of the array.

pub fn as_slice(&self) -> Option<&[A]> where
    S: Data, 

Return the array’s data as a slice, if it is contiguous and in standard order. Return None otherwise.

If this function returns Some(_), then the element order in the slice corresponds to the logical order of the array’s elements.

pub fn as_slice_mut(&mut self) -> Option<&mut [A]> where
    S: DataMut, 

Return the array’s data as a slice, if it is contiguous and in standard order. Return None otherwise.

pub fn as_slice_memory_order(&self) -> Option<&[A]> where
    S: Data, 

Return the array’s data as a slice if it is contiguous, return None otherwise.

If this function returns Some(_), then the elements in the slice have whatever order the elements have in memory.

Implementation notes: Does not yet support negatively strided arrays.

pub fn as_slice_memory_order_mut(&mut self) -> Option<&mut [A]> where
    S: DataMut, 

Return the array’s data as a slice if it is contiguous, return None otherwise.

pub fn into_shape<E>(self, shape: E) -> Result<ArrayBase<S, E::Dim>, ShapeError> where
    E: IntoDimension, 

Transform the array into shape; any shape with the same number of elements is accepted, but the source array or view must be in standard or column-major (Fortran) layout.

Errors if the shapes don’t have the same number of elements.
Errors if the input array is not c- or f-contiguous.

use ndarray::{aview1, aview2};

assert!(
    aview1(&[1., 2., 3., 4.]).into_shape((2, 2)).unwrap()
    == aview2(&[[1., 2.],
                [3., 4.]])
);

pub fn reshape<E>(&self, shape: E) -> ArrayBase<S, E::Dim> where
    S: DataShared + DataOwned,
    A: Clone,
    E: IntoDimension, 

Note: Reshape is for ArcArray only. Use .into_shape() for other arrays and array views.

Transform the array into shape; any shape with the same number of elements is accepted.

May clone all elements if needed to arrange elements in standard layout (and break sharing).

Panics if shapes are incompatible.

use ndarray::{rcarr1, rcarr2};

assert!(
    rcarr1(&[1., 2., 3., 4.]).reshape((2, 2))
    == rcarr2(&[[1., 2.],
                [3., 4.]])
);

pub fn into_dyn(self) -> ArrayBase<S, IxDyn>

Convert any array or array view to a dynamic dimensional array or array view (respectively).

use ndarray::{arr2, ArrayD};

let array: ArrayD<i32> = arr2(&[[1, 2],
                                [3, 4]]).into_dyn();

pub fn into_dimensionality<D2>(self) -> Result<ArrayBase<S, D2>, ShapeError> where
    D2: Dimension, 

Convert an array or array view to another with the same type, but different dimensionality type. Errors if the dimensions don’t agree.

use ndarray::{ArrayD, Ix2, IxDyn};

// Create a dynamic dimensionality array and convert it to an Array2
// (Ix2 dimension type).

let array = ArrayD::<f64>::zeros(IxDyn(&[10, 10]));

assert!(array.into_dimensionality::<Ix2>().is_ok());

pub fn broadcast<E>(&self, dim: E) -> Option<ArrayView<'_, A, E::Dim>> where
    E: IntoDimension,
    S: Data, 

Act like a larger size and/or shape array by broadcasting into a larger shape, if possible.

Return None if shapes can not be broadcast together.

Background

• Two axes are compatible if they are equal, or one of them is 1.
• In this instance, only the axes of the smaller side (self) can be 1.

Compare axes beginning with the last axis of each shape.

For example (1, 2, 4) can be broadcast into (7, 6, 2, 4) because its axes are either equal or 1 (or missing); while (2, 2) can not be broadcast into (2, 4).

The implementation creates a view with strides set to zero for the axes that are to be repeated.

The broadcasting documentation for Numpy has more information.

use ndarray::{aview1, aview2};

assert!(
    aview1(&[1., 0.]).broadcast((10, 2)).unwrap()
    == aview2(&[[1., 0.]; 10])
);

pub fn swap_axes(&mut self, ax: usize, bx: usize)

Swap axes ax and bx.

This does not move any data, it just adjusts the array’s dimensions and strides.

Panics if the axes are out of bounds.

use ndarray::arr2;

let mut a = arr2(&[[1., 2., 3.]]);
a.swap_axes(0, 1);
assert!(
    a == arr2(&[[1.], [2.], [3.]])
);

pub fn permuted_axes<T>(self, axes: T) -> ArrayBase<S, D> where
    T: IntoDimension<Dim = D>, 

Permute the axes.

This does not move any data, it just adjusts the array’s dimensions and strides.

i in the j-th place in the axes sequence means self’s i-th axis becomes self.permuted_axes()’s j-th axis

Panics if any of the axes are out of bounds, if an axis is missing, or if an axis is repeated more than once.

Examples

use ndarray::{arr2, Array3};

let a = arr2(&[[0, 1], [2, 3]]);
assert_eq!(a.view().permuted_axes([1, 0]), a.t());

let b = Array3::<u8>::zeros((1, 2, 3));
assert_eq!(b.permuted_axes([1, 0, 2]).shape(), &[2, 1, 3]);

pub fn reversed_axes(self) -> ArrayBase<S, D>

Transpose the array by reversing axes.

Transposition reverses the order of the axes (dimensions and strides) while retaining the same data.

pub fn t(&self) -> ArrayView<'_, A, D> where
    S: Data, 

Return a transposed view of the array.

This is a shorthand for self.view().reversed_axes().

See also the more general methods .reversed_axes() and .swap_axes().

pub fn axes(&self) -> Axes<'_, D>

Return an iterator over the length and stride of each axis.

pub fn max_stride_axis(&self) -> Axis

Return the axis with the greatest stride (by absolute value), preferring axes with len > 1.

pub fn invert_axis(&mut self, axis: Axis)

Reverse the stride of axis.

Panics if the axis is out of bounds.

pub fn merge_axes(&mut self, take: Axis, into: Axis) -> bool

If possible, merge in the axis take to into.

Returns true iff the axes are now merged.

This method merges the axes if movement along the two original axes (moving fastest along the into axis) can be equivalently represented as movement along one (merged) axis. Merging the axes preserves this order in the merged axis. If take and into are the same axis, then the axis is “merged” if its length is ≤ 1.

If the return value is true, then the following hold:

• The new length of the into axis is the product of the original lengths of the two axes.

• The new length of the take axis is 0 if the product of the original lengths of the two axes is 0, and 1 otherwise.

If the return value is false, then merging is not possible, and the original shape and strides have been preserved.

Note that the ordering constraint means that if it’s possible to merge take into into, it’s usually not possible to merge into into take, and vice versa.

use ndarray::Array3;
use ndarray::Axis;

let mut a = Array3::<f64>::zeros((2, 3, 4));
assert!(a.merge_axes(Axis(1), Axis(2)));
assert_eq!(a.shape(), &[2, 1, 12]);

Panics if an axis is out of bounds.

pub fn insert_axis(self, axis: Axis) -> ArrayBase<S, D::Larger>

Insert new array axis at axis and return the result.

use ndarray::{Array3, Axis, arr1, arr2};

// Convert a 1-D array into a row vector (2-D).
let a = arr1(&[1, 2, 3]);
let row = a.insert_axis(Axis(0));
assert_eq!(row, arr2(&[[1, 2, 3]]));

// Convert a 1-D array into a column vector (2-D).
let b = arr1(&[1, 2, 3]);
let col = b.insert_axis(Axis(1));
assert_eq!(col, arr2(&[[1], [2], [3]]));

// The new axis always has length 1.
let b = Array3::<f64>::zeros((3, 4, 5));
assert_eq!(b.insert_axis(Axis(2)).shape(), &[3, 4, 1, 5]);

Panics if the axis is out of bounds.

pub fn remove_axis(self, axis: Axis) -> ArrayBase<S, D::Smaller> where
    D: RemoveAxis, 

👎 Deprecated since 0.12.1:

use .index_axis_move(Axis(_), 0) instead

Remove array axis axis and return the result.

Panics if the axis is out of bounds or its length is zero.

pub fn assign<E: Dimension, S2>(&mut self, rhs: &ArrayBase<S2, E>) where
    S: DataMut,
    A: Clone,
    S2: Data<Elem = A>, 

Perform an elementwise assigment to self from rhs.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

pub fn fill(&mut self, x: A) where
    S: DataMut,
    A: Clone, 

Perform an elementwise assigment to self from element x.

pub fn zip_mut_with<B, S2, E, F>(&mut self, rhs: &ArrayBase<S2, E>, f: F) where
    S: DataMut,
    S2: Data<Elem = B>,
    E: Dimension,
    F: FnMut(&mut A, &B), 

Traverse two arrays in unspecified order, in lock step, calling the closure f on each element pair.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

pub fn fold<'a, F, B>(&'a self, init: B, f: F) -> B where
    F: FnMut(B, &'a A) -> B,
    A: 'a,
    S: Data, 

Traverse the array elements and apply a fold, returning the resulting value.

Elements are visited in arbitrary order.

pub fn map<'a, B, F>(&'a self, f: F) -> Array<B, D> where
    F: FnMut(&'a A) -> B,
    A: 'a,
    S: Data, 

Call f by reference on each element and create a new array with the new values.

Elements are visited in arbitrary order.

Return an array with the same shape as self.

use ndarray::arr2;

let a = arr2(&[[ 0., 1.],
               [-1., 2.]]);
assert!(
    a.map(|x| *x >= 1.0)
    == arr2(&[[false, true],
              [false, true]])
);

pub fn map_mut<'a, B, F>(&'a mut self, f: F) -> Array<B, D> where
    F: FnMut(&'a mut A) -> B,
    A: 'a,
    S: DataMut, 

Call f on a mutable reference of each element and create a new array with the new values.

Elements are visited in arbitrary order.

Return an array with the same shape as self.

pub fn mapv<B, F>(&self, f: F) -> Array<B, D> where
    F: FnMut(A) -> B,
    A: Clone,
    S: Data, 

Call f by value on each element and create a new array with the new values.

Elements are visited in arbitrary order.

Return an array with the same shape as self.

use ndarray::arr2;

let a = arr2(&[[ 0., 1.],
               [-1., 2.]]);
assert!(
    a.mapv(f32::abs) == arr2(&[[0., 1.],
                               [1., 2.]])
);

pub fn mapv_into<F>(self, f: F) -> Self where
    S: DataMut,
    F: FnMut(A) -> A,
    A: Clone, 

Call f by value on each element, update the array with the new values and return it.

Elements are visited in arbitrary order.

pub fn map_inplace<F>(&mut self, f: F) where
    S: DataMut,
    F: FnMut(&mut A), 

Modify the array in place by calling f by mutable reference on each element.

Elements are visited in arbitrary order.

pub fn mapv_inplace<F>(&mut self, f: F) where
    S: DataMut,
    F: FnMut(A) -> A,
    A: Clone, 

Modify the array in place by calling f by value on each element. The array is updated with the new values.

Elements are visited in arbitrary order.

use approx::assert_abs_diff_eq;
use ndarray::arr2;

let mut a = arr2(&[[ 0., 1.],
                   [-1., 2.]]);
a.mapv_inplace(f32::exp);
assert_abs_diff_eq!(
    a,
    arr2(&[[1.00000, 2.71828],
           [0.36788, 7.38906]]),
    epsilon = 1e-5,
);

pub fn visit<'a, F>(&'a self, f: F) where
    F: FnMut(&'a A),
    A: 'a,
    S: Data, 

Visit each element in the array by calling f by reference on each element.

Elements are visited in arbitrary order.

pub fn fold_axis<B, F>(
    &self,
    axis: Axis,
    init: B,
    fold: F
) -> Array<B, D::Smaller> where
    D: RemoveAxis,
    F: FnMut(&B, &A) -> B,
    B: Clone,
    S: Data, 

Fold along an axis.

Combine the elements of each subview with the previous using the fold function and initial value init.

Return the result as an Array.

Panics if axis is out of bounds.

pub fn map_axis<'a, B, F>(
    &'a self,
    axis: Axis,
    mapping: F
) -> Array<B, D::Smaller> where
    D: RemoveAxis,
    F: FnMut(ArrayView1<'a, A>) -> B,
    A: 'a,
    S: Data, 

Reduce the values along an axis into just one value, producing a new array with one less dimension.

Elements are visited in arbitrary order.

Return the result as an Array.

Panics if axis is out of bounds.

pub fn map_axis_mut<'a, B, F>(
    &'a mut self,
    axis: Axis,
    mapping: F
) -> Array<B, D::Smaller> where
    D: RemoveAxis,
    F: FnMut(ArrayViewMut1<'a, A>) -> B,
    A: 'a,
    S: DataMut, 

Reduce the values along an axis into just one value, producing a new array with one less dimension. 1-dimensional lanes are passed as mutable references to the reducer, allowing for side-effects.

Elements are visited in arbitrary order.

Return the result as an Array.

Panics if axis is out of bounds.

pub fn accumulate_axis_inplace<F>(&mut self, axis: Axis, f: F) where
    F: FnMut(&A, &mut A),
    S: DataMut, 

Iterates over pairs of consecutive elements along the axis.

The first argument to the closure is an element, and the second argument is the next element along the axis. Iteration is guaranteed to proceed in order along the specified axis, but in all other respects the iteration order is unspecified.

Example

For example, this can be used to compute the cumulative sum along an axis:

use ndarray::{array, Axis};

let mut arr = array![
    [[1, 2], [3, 4], [5, 6]],
    [[7, 8], [9, 10], [11, 12]],
];
arr.accumulate_axis_inplace(Axis(1), |&prev, curr| *curr += prev);
assert_eq!(
    arr,
    array![
        [[1, 2], [4, 6], [9, 12]],
        [[7, 8], [16, 18], [27, 30]],
    ],
);

impl<A> ArrayBase<OwnedRepr<A>, D>

Methods specific to Array0.

See also all methods for ArrayBase

pub fn into_scalar(self) -> A

Returns the single element in the array without cloning it.

use ndarray::{arr0, Array0};

// `Foo` doesn't implement `Clone`.
#[derive(Debug, Eq, PartialEq)]
struct Foo;

let array: Array0<Foo> = arr0(Foo);
let scalar: Foo = array.into_scalar();
assert_eq!(scalar, Foo);

impl<A, D> ArrayBase<OwnedRepr<A>, D> where
    D: Dimension, 

Methods specific to Array.

See also all methods for ArrayBase

pub fn into_raw_vec(self) -> Vec<A>

Return a vector of the elements in the array, in the way they are stored internally.

If the array is in standard memory layout, the logical element order of the array (.iter() order) and of the returned vector will be the same.

impl<A, S, D> ArrayBase<S, D> where
    S: RawDataSubst<A, Elem = MaybeUninit<A>>,
    D: Dimension, 

Methods specific to arrays with MaybeUninit elements.

See also all methods for ArrayBase

pub unsafe fn assume_init(self) -> ArrayBase<<S as RawDataSubst<A>>::Output, D>

Promise that the array’s elements are all fully initialized, and convert the array from element type MaybeUninit<A> to A.

For example, it can convert an Array<MaybeUninit<f64>, D> to Array<f64, D>.

Safety

Safe to use if all the array’s elements have been initialized.

Note that for owned and shared ownership arrays, the promise must include all of the array’s storage; it is for example possible to slice these in place, but that must only be done after all elements have been initialized.

impl<A, S> ArrayBase<S, Ix1> where
    S: RawData<Elem = A>, 

Methods For 1-D Arrays

pub fn to_vec(&self) -> Vec<A> where
    A: Clone,
    S: Data, 

Return an vector with the elements of the one-dimensional array.

impl<A, S> ArrayBase<S, Ix2> where
    S: RawData<Elem = A>, 

Methods For 2-D Arrays

pub fn row(&self, index: Ix) -> ArrayView1<'_, A> where
    S: Data, 

Return an array view of row index.

Panics if index is out of bounds.

pub fn row_mut(&mut self, index: Ix) -> ArrayViewMut1<'_, A> where
    S: DataMut, 

Return a mutable array view of row index.

Panics if index is out of bounds.

pub fn nrows(&self) -> usize

Return the number of rows (length of Axis(0)) in the two-dimensional array.

pub fn rows(&self) -> usize

👎 Deprecated:

Renamed to .nrows(), please use the new name

Return the number of rows (length of Axis(0)) in the two-dimensional array.

pub fn column(&self, index: Ix) -> ArrayView1<'_, A> where
    S: Data, 

Return an array view of column index.

Panics if index is out of bounds.

pub fn column_mut(&mut self, index: Ix) -> ArrayViewMut1<'_, A> where
    S: DataMut, 

Return a mutable array view of column index.

Panics if index is out of bounds.

pub fn ncols(&self) -> usize

Return the number of columns (length of Axis(1)) in the two-dimensional array.

pub fn cols(&self) -> usize

👎 Deprecated:

Renamed to .ncols(), please use the new name

Return the number of columns (length of Axis(1)) in the two-dimensional array.

pub fn is_square(&self) -> bool

Return true if the array is square, false otherwise.

impl<A, S> ArrayBase<S, IxDyn> where
    S: Data<Elem = A>, 

Methods for Dynamic-Dimensional Arrays

pub fn insert_axis_inplace(&mut self, axis: Axis)

Insert new array axis of length 1 at axis, modifying the shape and strides in-place.

Panics if the axis is out of bounds.

use ndarray::{Axis, arr2, arr3};

let mut a = arr2(&[[1, 2, 3], [4, 5, 6]]).into_dyn();
assert_eq!(a.shape(), &[2, 3]);

a.insert_axis_inplace(Axis(1));
assert_eq!(a, arr3(&[[[1, 2, 3]], [[4, 5, 6]]]).into_dyn());
assert_eq!(a.shape(), &[2, 1, 3]);

pub fn index_axis_inplace(&mut self, axis: Axis, index: usize)

Collapses the array to index along the axis and removes the axis, modifying the shape and strides in-place.

Panics if axis or index is out of bounds.

use ndarray::{Axis, arr1, arr2};

let mut a = arr2(&[[1, 2, 3], [4, 5, 6]]).into_dyn();
assert_eq!(a.shape(), &[2, 3]);

a.index_axis_inplace(Axis(1), 1);
assert_eq!(a, arr1(&[2, 5]).into_dyn());
assert_eq!(a.shape(), &[2]);

impl<A, S, D> ArrayBase<S, D> where
    S: Data<Elem = A>,
    D: Dimension, 

Numerical Methods for Arrays

pub fn sum(&self) -> A where
    A: Clone + Add<Output = A> + Zero, 

Return the sum of all elements in the array.

use ndarray::arr2;

let a = arr2(&[[1., 2.],
               [3., 4.]]);
assert_eq!(a.sum(), 10.);

pub fn mean(&self) -> Option<A> where
    A: Clone + FromPrimitive + Add<Output = A> + Div<Output = A> + Zero, 

Returns the arithmetic mean x̅ of all elements in the array:

    1   n
x̅ = ―   ∑ xᵢ
    n  i=1

If the array is empty, None is returned.

Panics if A::from_usize() fails to convert the number of elements in the array.

pub fn scalar_sum(&self) -> A where
    A: Clone + Add<Output = A> + Zero, 

Return the sum of all elements in the array.

This method has been renamed to .sum() and will be deprecated in the next version.

pub fn product(&self) -> A where
    A: Clone + Mul<Output = A> + One, 

Return the product of all elements in the array.

use ndarray::arr2;

let a = arr2(&[[1., 2.],
               [3., 4.]]);
assert_eq!(a.product(), 24.);

pub fn sum_axis(&self, axis: Axis) -> Array<A, D::Smaller> where
    A: Clone + Zero + Add<Output = A>,
    D: RemoveAxis, 

Return sum along axis.

use ndarray::{aview0, aview1, arr2, Axis};

let a = arr2(&[[1., 2., 3.],
               [4., 5., 6.]]);
assert!(
    a.sum_axis(Axis(0)) == aview1(&[5., 7., 9.]) &&
    a.sum_axis(Axis(1)) == aview1(&[6., 15.]) &&

    a.sum_axis(Axis(0)).sum_axis(Axis(0)) == aview0(&21.)
);

Panics if axis is out of bounds.

pub fn mean_axis(&self, axis: Axis) -> Option<Array<A, D::Smaller>> where
    A: Clone + Zero + FromPrimitive + Add<Output = A> + Div<Output = A>,
    D: RemoveAxis, 

Return mean along axis.

Return None if the length of the axis is zero.

Panics if axis is out of bounds or if A::from_usize() fails for the axis length.

use ndarray::{aview0, aview1, arr2, Axis};

let a = arr2(&[[1., 2., 3.],
               [4., 5., 6.]]);
assert!(
    a.mean_axis(Axis(0)).unwrap() == aview1(&[2.5, 3.5, 4.5]) &&
    a.mean_axis(Axis(1)).unwrap() == aview1(&[2., 5.]) &&

    a.mean_axis(Axis(0)).unwrap().mean_axis(Axis(0)).unwrap() == aview0(&3.5)
);

pub fn var_axis(&self, axis: Axis, ddof: A) -> Array<A, D::Smaller> where
    A: Float + FromPrimitive,
    D: RemoveAxis, 

Return variance along axis.

The variance is computed using the Welford one-pass algorithm.

The parameter ddof specifies the “delta degrees of freedom”. For example, to calculate the population variance, use ddof = 0, or to calculate the sample variance, use ddof = 1.

The variance is defined as:

              1       n
variance = ――――――――   ∑ (xᵢ - x̅)²
           n - ddof  i=1

where

    1   n
x̅ = ―   ∑ xᵢ
    n  i=1

and n is the length of the axis.

Panics if ddof is less than zero or greater than n, if axis is out of bounds, or if A::from_usize() fails for any any of the numbers in the range 0..=n.

Example

use ndarray::{aview1, arr2, Axis};

let a = arr2(&[[1., 2.],
               [3., 4.],
               [5., 6.]]);
let var = a.var_axis(Axis(0), 1.);
assert_eq!(var, aview1(&[4., 4.]));

pub fn std_axis(&self, axis: Axis, ddof: A) -> Array<A, D::Smaller> where
    A: Float + FromPrimitive,
    D: RemoveAxis, 

Return standard deviation along axis.

The standard deviation is computed from the variance using the Welford one-pass algorithm.

The parameter ddof specifies the “delta degrees of freedom”. For example, to calculate the population standard deviation, use ddof = 0, or to calculate the sample standard deviation, use ddof = 1.

The standard deviation is defined as:

              ⎛    1       n          ⎞
stddev = sqrt ⎜ ――――――――   ∑ (xᵢ - x̅)²⎟
              ⎝ n - ddof  i=1         ⎠

where

    1   n
x̅ = ―   ∑ xᵢ
    n  i=1

and n is the length of the axis.

Panics if ddof is less than zero or greater than n, if axis is out of bounds, or if A::from_usize() fails for any any of the numbers in the range 0..=n.

Example

use ndarray::{aview1, arr2, Axis};

let a = arr2(&[[1., 2.],
               [3., 4.],
               [5., 6.]]);
let stddev = a.std_axis(Axis(0), 1.);
assert_eq!(stddev, aview1(&[2., 2.]));

pub fn all_close<S2, E>(&self, rhs: &ArrayBase<S2, E>, tol: A) -> bool where
    A: Float,
    S2: Data<Elem = A>,
    E: Dimension, 

👎 Deprecated since 0.13.0:

Use abs_diff_eq - it requires the approx crate feature

Return true if the arrays’ elementwise differences are all within the given absolute tolerance, false otherwise.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting to the same shape isn’t possible.

impl<A, S> ArrayBase<S, Ix1> where
    S: Data<Elem = A>, 

pub fn dot<Rhs>(&self, rhs: &Rhs) -> Self::Output where
    Self: Dot<Rhs>, 

Perform dot product or matrix multiplication of arrays self and rhs.

Rhs may be either a one-dimensional or a two-dimensional array.

If Rhs is one-dimensional, then the operation is a vector dot product, which is the sum of the elementwise products (no conjugation of complex operands, and thus not their inner product). In this case, self and rhs must be the same length.

If Rhs is two-dimensional, then the operation is matrix multiplication, where self is treated as a row vector. In this case, if self is shape M, then rhs is shape M × N and the result is shape N.

Panics if the array shapes are incompatible.
Note: If enabled, uses blas dot for elements of f32, f64 when memory layout allows.

impl<A, S> ArrayBase<S, Ix2> where
    S: Data<Elem = A>, 

pub fn dot<Rhs>(&self, rhs: &Rhs) -> Self::Output where
    Self: Dot<Rhs>, 

Perform matrix multiplication of rectangular arrays self and rhs.

Rhs may be either a one-dimensional or a two-dimensional array.

If Rhs is two-dimensional, they array shapes must agree in the way that if self is M × N, then rhs is N × K.

Return a result array with shape M × K.

Panics if shapes are incompatible or the number of elements in the result would overflow isize.

Note: If enabled, uses blas gemv/gemm for elements of f32, f64 when memory layout allows. The default matrixmultiply backend is otherwise used for f32, f64 for all memory layouts.

use ndarray::arr2;

let a = arr2(&[[1., 2.],
               [0., 1.]]);
let b = arr2(&[[1., 2.],
               [2., 3.]]);

assert!(
    a.dot(&b) == arr2(&[[5., 8.],
                        [2., 3.]])
);

impl<A, S, D> ArrayBase<S, D> where
    S: Data<Elem = A>,
    D: Dimension, 

pub fn scaled_add<S2, E>(&mut self, alpha: A, rhs: &ArrayBase<S2, E>) where
    S: DataMut,
    S2: Data<Elem = A>,
    A: LinalgScalar,
    E: Dimension, 

Perform the operation self += alpha * rhs efficiently, where alpha is a scalar and rhs is another array. This operation is also known as axpy in BLAS.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

impl<'a, A, D> ArrayBase<ViewRepr<&'a A>, D> where
    D: Dimension, 

Methods for read-only array views.

pub fn from_shape<Sh>(shape: Sh, xs: &'a [A]) -> Result<Self, ShapeError> where
    Sh: Into<StrideShape<D>>, 

Create a read-only array view borrowing its data from a slice.

Checks whether shape are compatible with the slice’s length, returning an Err if not compatible.

use ndarray::ArrayView;
use ndarray::arr3;
use ndarray::ShapeBuilder;

let s = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
let a = ArrayView::from_shape((2, 3, 2).strides((1, 4, 2)),
                              &s).unwrap();

assert!(
    a == arr3(&[[[0, 2],
                 [4, 6],
                 [8, 10]],
                [[1, 3],
                 [5, 7],
                 [9, 11]]])
);
assert!(a.strides() == &[1, 4, 2]);

pub unsafe fn from_shape_ptr<Sh>(shape: Sh, ptr: *const A) -> Self where
    Sh: Into<StrideShape<D>>, 

Create an ArrayView<A, D> from shape information and a raw pointer to the elements.

Safety

The caller is responsible for ensuring all of the following:

• The elements seen by moving ptr according to the shape and strides must live at least as long as 'a and must not be not mutably aliased for the duration of 'a.

• ptr must be non-null and aligned, and it must be safe to .offset() ptr by zero.

• It must be safe to .offset() the pointer repeatedly along all axes and calculate the counts for the .offset() calls without overflow, even if the array is empty or the elements are zero-sized.

  In other words,

  • All possible pointers generated by moving along all axes must be in bounds or one byte past the end of a single allocation with element type A. The only exceptions are if the array is empty or the element type is zero-sized. In these cases, ptr may be dangling, but it must still be safe to .offset() the pointer along the axes.

  • The offset in units of bytes between the least address and greatest address by moving along all axes must not exceed isize::MAX. This constraint prevents the computed offset, in bytes, from overflowing isize regardless of the starting point due to past offsets.

  • The offset in units of A between the least address and greatest address by moving along all axes must not exceed isize::MAX. This constraint prevents overflow when calculating the count parameter to .offset() regardless of the starting point due to past offsets.

• The product of non-zero axis lengths must not exceed isize::MAX.

impl<'a, A, D> ArrayBase<ViewRepr<&'a mut A>, D> where
    D: Dimension, 

Methods for read-write array views.

pub fn from_shape<Sh>(shape: Sh, xs: &'a mut [A]) -> Result<Self, ShapeError> where
    Sh: Into<StrideShape<D>>, 

Create a read-write array view borrowing its data from a slice.

Checks whether dim and strides are compatible with the slice’s length, returning an Err if not compatible.

use ndarray::ArrayViewMut;
use ndarray::arr3;
use ndarray::ShapeBuilder;

let mut s = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12];
let mut a = ArrayViewMut::from_shape((2, 3, 2).strides((1, 4, 2)),
                                     &mut s).unwrap();

a[[0, 0, 0]] = 1;
assert!(
    a == arr3(&[[[1, 2],
                 [4, 6],
                 [8, 10]],
                [[1, 3],
                 [5, 7],
                 [9, 11]]])
);
assert!(a.strides() == &[1, 4, 2]);

pub unsafe fn from_shape_ptr<Sh>(shape: Sh, ptr: *mut A) -> Self where
    Sh: Into<StrideShape<D>>, 

Create an ArrayViewMut<A, D> from shape information and a raw pointer to the elements.

Safety

The caller is responsible for ensuring all of the following:

• The elements seen by moving ptr according to the shape and strides must live at least as long as 'a and must not be aliased for the duration of 'a.

• ptr must be non-null and aligned, and it must be safe to .offset() ptr by zero.

• It must be safe to .offset() the pointer repeatedly along all axes and calculate the counts for the .offset() calls without overflow, even if the array is empty or the elements are zero-sized.

  In other words,

  • All possible pointers generated by moving along all axes must be in bounds or one byte past the end of a single allocation with element type A. The only exceptions are if the array is empty or the element type is zero-sized. In these cases, ptr may be dangling, but it must still be safe to .offset() the pointer along the axes.

  • The offset in units of bytes between the least address and greatest address by moving along all axes must not exceed isize::MAX. This constraint prevents the computed offset, in bytes, from overflowing isize regardless of the starting point due to past offsets.

  • The offset in units of A between the least address and greatest address by moving along all axes must not exceed isize::MAX. This constraint prevents overflow when calculating the count parameter to .offset() regardless of the starting point due to past offsets.

• The product of non-zero axis lengths must not exceed isize::MAX.

pub fn reborrow<'b>(self) -> ArrayViewMut<'b, A, D> where
    'a: 'b, 

Convert the view into an ArrayViewMut<'b, A, D> where 'b is a lifetime outlived by 'a'.

impl<'a, A, D> ArrayBase<ViewRepr<&'a A>, D> where
    D: Dimension, 

Methods for read-only array views.

pub fn reborrow<'b>(self) -> ArrayView<'b, A, D> where
    'a: 'b, 

Convert the view into an ArrayView<'b, A, D> where 'b is a lifetime outlived by 'a'.

pub fn into_slice(&self) -> Option<&'a [A]>

👎 Deprecated since 0.13.0:

into_slice has been renamed to to_slice

Return the array’s data as a slice, if it is contiguous and in standard order. Return None otherwise.

pub fn to_slice(&self) -> Option<&'a [A]>

Return the array’s data as a slice, if it is contiguous and in standard order. Return None otherwise.

impl<'a, A> ArrayBase<ViewRepr<&'a A>, D>

Methods specific to ArrayView0.

See also all methods for ArrayView and ArrayBase

pub fn into_scalar(self) -> &'a A

Consume the view and return a reference to the single element in the array.

The lifetime of the returned reference matches the lifetime of the data the array view was pointing to.

use ndarray::{arr0, Array0};

// `Foo` doesn't implement `Clone`.
#[derive(Debug, Eq, PartialEq)]
struct Foo;

let array: Array0<Foo> = arr0(Foo);
let view = array.view();
let scalar: &Foo = view.into_scalar();
assert_eq!(scalar, &Foo);

impl<'a, A> ArrayBase<ViewRepr<&'a mut A>, D>

Methods specific to ArrayViewMut0.

See also all methods for ArrayViewMut and ArrayBase

pub fn into_scalar(self) -> &'a mut A

Consume the mutable view and return a mutable reference to the single element in the array.

The lifetime of the returned reference matches the lifetime of the data the array view was pointing to.

use ndarray::{arr0, Array0};

let mut array: Array0<f64> = arr0(5.);
let view = array.view_mut();
let mut scalar = view.into_scalar();
*scalar = 7.;
assert_eq!(scalar, &7.);
assert_eq!(array[()], 7.);

impl<'a, A, D> ArrayBase<ViewRepr<&'a mut A>, D> where
    D: Dimension, 

Methods for read-write array views.

pub fn into_slice(self) -> Option<&'a mut [A]>

Return the array’s data as a slice, if it is contiguous and in standard order. Return None otherwise.

impl<'a, A, D> ArrayBase<ViewRepr<&'a A>, D> where
    D: Dimension, 

Methods for read-only array views.

pub fn split_at(self, axis: Axis, index: Ix) -> (Self, Self)

Split the array view along axis and return one view strictly before the split and one view after the split.

Panics if axis or index is out of bounds.

Examples:

let a = aview2(&[[0, 1, 2, 3],
                 [4, 5, 6, 7],
                 [8, 9, 0, 1]]);

The array view a has two axes and shape 3 × 4:

         ──▶ Axis(1)
        ┌─────┬─────┬─────┬─────┐ 0
      │ │ a₀₀ │ a₀₁ │ a₀₂ │ a₀₃ │
      ▼ ├─────┼─────┼─────┼─────┤ 1
 Axis(0)│ a₁₀ │ a₁₁ │ a₁₂ │ a₁₃ │
        ├─────┼─────┼─────┼─────┤ 2
        │ a₂₀ │ a₂₁ │ a₂₂ │ a₂₃ │
        └─────┴─────┴─────┴─────┘ 3 ↑
        0     1     2     3     4 ← possible split_at indices.

Row indices increase along Axis(0), and column indices increase along Axis(1). Note that we split “before” an element index, and that both 0 and the endpoint are valid split indices.

Example 1: Split a along the first axis, in this case the rows, at index 2.
This produces views v1 and v2 of shapes 2 × 4 and 1 × 4:

let (v1, v2) = a.split_at(Axis(0), 1);

        ┌─────┬─────┬─────┬─────┐       0  ↓ indices
        │ a₀₀ │ a₀₁ │ a₀₂ │ a₀₃ │            along Axis(0)
        ├─────┼─────┼─────┼─────┤ v1    1
        │ a₁₀ │ a₁₁ │ a₁₂ │ a₁₃ │
        └─────┴─────┴─────┴─────┘
        ┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄┄       2
        ┌─────┬─────┬─────┬─────┐
        │ a₂₀ │ a₂₁ │ a₂₂ │ a₂₃ │ v2
        └─────┴─────┴─────┴─────┘       3

Example 2: Split a along the second axis, in this case the columns, at index 2.
This produces views u1 and u2 of shapes 3 × 2 and 3 × 2:

let (u1, u2) = a.split_at(Axis(1), 2);

             u1             u2
        ┌─────┬─────┐┊┌─────┬─────┐
        │ a₀₀ │ a₀₁ │┊│ a₀₂ │ a₀₃ │
        ├─────┼─────┤┊├─────┼─────┤
        │ a₁₀ │ a₁₁ │┊│ a₁₂ │ a₁₃ │
        ├─────┼─────┤┊├─────┼─────┤
        │ a₂₀ │ a₂₁ │┊│ a₂₂ │ a₂₃ │
        └─────┴─────┘┊└─────┴─────┘
        0     1      2      3     4  indices →
                                     along Axis(1)

impl<'a, A, D> ArrayBase<ViewRepr<&'a mut A>, D> where
    D: Dimension, 

Methods for read-write array views.

pub fn split_at(self, axis: Axis, index: Ix) -> (Self, Self)

Split the array view along axis and return one mutable view strictly before the split and one mutable view after the split.

Panics if axis or index is out of bounds.

pub fn multi_slice_move<M>(self, info: M) -> M::Output where
    M: MultiSlice<'a, A, D>, 

Split the view into multiple disjoint slices.

This is similar to .multi_slice_mut(), but .multi_slice_move() consumes self and produces views with lifetimes matching that of self.

See Slicing for full documentation. See also SliceInfo and D::SliceArg.

Panics if any of the following occur:

• if any of the views would intersect (i.e. if any element would appear in multiple slices)
• if an index is out of bounds or step size is zero
• if D is IxDyn and info does not match the number of array axes

impl<A, D> ArrayBase<RawViewRepr<*const A>, D> where
    D: Dimension, 

pub unsafe fn from_shape_ptr<Sh>(shape: Sh, ptr: *const A) -> Self where
    Sh: Into<StrideShape<D>>, 

Create an RawArrayView<A, D> from shape information and a raw pointer to the elements.

Safety

The caller is responsible for ensuring all of the following:

• ptr must be non-null, and it must be safe to .offset() ptr by zero.

• It must be safe to .offset() the pointer repeatedly along all axes and calculate the counts for the .offset() calls without overflow, even if the array is empty or the elements are zero-sized.

  In other words,

  • All possible pointers generated by moving along all axes must be in bounds or one byte past the end of a single allocation with element type A. The only exceptions are if the array is empty or the element type is zero-sized. In these cases, ptr may be dangling, but it must still be safe to .offset() the pointer along the axes.

  • The offset in units of bytes between the least address and greatest address by moving along all axes must not exceed isize::MAX. This constraint prevents the computed offset, in bytes, from overflowing isize regardless of the starting point due to past offsets.

  • The offset in units of A between the least address and greatest address by moving along all axes must not exceed isize::MAX. This constraint prevents overflow when calculating the count parameter to .offset() regardless of the starting point due to past offsets.

• The product of non-zero axis lengths must not exceed isize::MAX.

pub unsafe fn deref_into_view<'a>(self) -> ArrayView<'a, A, D>

Converts to a read-only view of the array.

Safety

From a safety standpoint, this is equivalent to dereferencing a raw pointer for every element in the array. You must ensure that all of the data is valid, ensure that the pointer is aligned, and choose the correct lifetime.

pub fn split_at(self, axis: Axis, index: Ix) -> (Self, Self)

Split the array view along axis and return one array pointer strictly before the split and one array pointer after the split.

Panics if axis or index is out of bounds.

pub fn cast<B>(self) -> RawArrayView<B, D>

Cast the raw pointer of the raw array view to a different type

Panics if element size is not compatible.

Lack of panic does not imply it is a valid cast. The cast works the same way as regular raw pointer casts.

While this method is safe, for the same reason as regular raw pointer casts are safe, access through the produced raw view is only possible in an unsafe block or function.

impl<A, D> ArrayBase<RawViewRepr<*mut A>, D> where
    D: Dimension, 

pub unsafe fn from_shape_ptr<Sh>(shape: Sh, ptr: *mut A) -> Self where
    Sh: Into<StrideShape<D>>, 

Create an RawArrayViewMut<A, D> from shape information and a raw pointer to the elements.

Safety

The caller is responsible for ensuring all of the following:

• ptr must be non-null, and it must be safe to .offset() ptr by zero.

• It must be safe to .offset() the pointer repeatedly along all axes and calculate the counts for the .offset() calls without overflow, even if the array is empty or the elements are zero-sized.

  In other words,

  • All possible pointers generated by moving along all axes must be in bounds or one byte past the end of a single allocation with element type A. The only exceptions are if the array is empty or the element type is zero-sized. In these cases, ptr may be dangling, but it must still be safe to .offset() the pointer along the axes.

  • The offset in units of bytes between the least address and greatest address by moving along all axes must not exceed isize::MAX. This constraint prevents the computed offset, in bytes, from overflowing isize regardless of the starting point due to past offsets.

  • The offset in units of A between the least address and greatest address by moving along all axes must not exceed isize::MAX. This constraint prevents overflow when calculating the count parameter to .offset() regardless of the starting point due to past offsets.

• The product of non-zero axis lengths must not exceed isize::MAX.

pub unsafe fn deref_into_view<'a>(self) -> ArrayView<'a, A, D>

Converts to a read-only view of the array.

Safety

From a safety standpoint, this is equivalent to dereferencing a raw pointer for every element in the array. You must ensure that all of the data is valid, ensure that the pointer is aligned, and choose the correct lifetime.

pub unsafe fn deref_into_view_mut<'a>(self) -> ArrayViewMut<'a, A, D>

Converts to a mutable view of the array.

Safety

From a safety standpoint, this is equivalent to dereferencing a raw pointer for every element in the array. You must ensure that all of the data is valid, ensure that the pointer is aligned, and choose the correct lifetime.

pub fn split_at(self, axis: Axis, index: Ix) -> (Self, Self)

Split the array view along axis and return one array pointer strictly before the split and one array pointer after the split.

Panics if axis or index is out of bounds.

pub fn cast<B>(self) -> RawArrayViewMut<B, D>

Cast the raw pointer of the raw array view to a different type

Panics if element size is not compatible.

Lack of panic does not imply it is a valid cast. The cast works the same way as regular raw pointer casts.

While this method is safe, for the same reason as regular raw pointer casts are safe, access through the produced raw view is only possible in an unsafe block or function.

impl<'a, A, D> ArrayBase<CowRepr<'a, A>, D> where
    D: Dimension, 

Methods specific to CowArray.

See also all methods for ArrayBase

pub fn is_view(&self) -> bool

Returns true iff the array is the view (borrowed) variant.

pub fn is_owned(&self) -> bool

Returns true iff the array is the owned variant.

Trait Implementations

impl<'a, A, B, S, S2, D, E> Add<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Add<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise addition between self and reference rhs, and return the result (based on self).

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the + operator.

fn add(self, rhs: &ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the + operation.

impl<'a, A, B, S, S2, D, E> Add<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where
    A: Clone + Add<B, Output = A>,
    B: Clone,
    S: Data<Elem = A>,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise addition between references self and rhs, and return the result as a new Array.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = Array<A, D>

The resulting type after applying the + operator.

fn add(self, rhs: &'a ArrayBase<S2, E>) -> Array<A, D>

Performs the + operation.

impl<A, B, S, S2, D, E> Add<ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Add<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise addition between self and rhs, and return the result (based on self).

self must be an Array or ArcArray.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the + operator.

fn add(self, rhs: ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the + operation.

impl<A, S, D, B> Add<B> for ArrayBase<S, D> where
    A: Clone + Add<B, Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise addition between self and the scalar x, and return the result (based on self).

self must be an Array or ArcArray.

type Output = ArrayBase<S, D>

The resulting type after applying the + operator.

fn add(self, x: B) -> ArrayBase<S, D>

Performs the + operation.

impl<'a, A, S, D, B> Add<B> for &'a ArrayBase<S, D> where
    A: Clone + Add<B, Output = A>,
    S: Data<Elem = A>,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise addition between the reference self and the scalar x, and return the result as a new Array.

type Output = Array<A, D>

The resulting type after applying the + operator.

fn add(self, x: B) -> Array<A, D>

Performs the + operation.

impl<'a, A, S, S2, D, E> AddAssign<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + AddAssign<A>,
    S: DataMut<Elem = A>,
    S2: Data<Elem = A>,
    D: Dimension,
    E: Dimension, 

Perform self += rhs as elementwise addition (in place). If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

fn add_assign(&mut self, rhs: &ArrayBase<S2, E>)

Performs the += operation.

impl<A, S, D> AddAssign<A> for ArrayBase<S, D> where
    A: ScalarOperand + AddAssign<A>,
    S: DataMut<Elem = A>,
    D: Dimension, 

Perform self += rhs as elementwise addition (in place).

fn add_assign(&mut self, rhs: A)

Performs the += operation.

impl<'a, A: Binary, S, D: Dimension> Binary for ArrayBase<S, D> where
    S: Data<Elem = A>, 

Format the array using Binary and apply the formatting parameters used to each element.

The array is shown in multiline style.

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'a, A, B, S, S2, D, E> BitAnd<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + BitAnd<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise bit and between self and reference rhs, and return the result (based on self).

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the & operator.

fn bitand(self, rhs: &ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the & operation.

impl<'a, A, B, S, S2, D, E> BitAnd<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where
    A: Clone + BitAnd<B, Output = A>,
    B: Clone,
    S: Data<Elem = A>,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise bit and between references self and rhs, and return the result as a new Array.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = Array<A, D>

The resulting type after applying the & operator.

fn bitand(self, rhs: &'a ArrayBase<S2, E>) -> Array<A, D>

Performs the & operation.

impl<A, B, S, S2, D, E> BitAnd<ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + BitAnd<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise bit and between self and rhs, and return the result (based on self).

self must be an Array or ArcArray.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the & operator.

fn bitand(self, rhs: ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the & operation.

impl<A, S, D, B> BitAnd<B> for ArrayBase<S, D> where
    A: Clone + BitAnd<B, Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise bit and between self and the scalar x, and return the result (based on self).

self must be an Array or ArcArray.

type Output = ArrayBase<S, D>

The resulting type after applying the & operator.

fn bitand(self, x: B) -> ArrayBase<S, D>

Performs the & operation.

impl<'a, A, S, D, B> BitAnd<B> for &'a ArrayBase<S, D> where
    A: Clone + BitAnd<B, Output = A>,
    S: Data<Elem = A>,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise bit and between the reference self and the scalar x, and return the result as a new Array.

type Output = Array<A, D>

The resulting type after applying the & operator.

fn bitand(self, x: B) -> Array<A, D>

Performs the & operation.

impl<'a, A, S, S2, D, E> BitAndAssign<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + BitAndAssign<A>,
    S: DataMut<Elem = A>,
    S2: Data<Elem = A>,
    D: Dimension,
    E: Dimension, 

Perform self &= rhs as elementwise bit and (in place). If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

fn bitand_assign(&mut self, rhs: &ArrayBase<S2, E>)

Performs the &= operation.

impl<A, S, D> BitAndAssign<A> for ArrayBase<S, D> where
    A: ScalarOperand + BitAndAssign<A>,
    S: DataMut<Elem = A>,
    D: Dimension, 

Perform self &= rhs as elementwise bit and (in place).

fn bitand_assign(&mut self, rhs: A)

Performs the &= operation.

impl<'a, A, B, S, S2, D, E> BitOr<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + BitOr<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise bit or between self and reference rhs, and return the result (based on self).

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the | operator.

fn bitor(self, rhs: &ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the | operation.

impl<'a, A, B, S, S2, D, E> BitOr<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where
    A: Clone + BitOr<B, Output = A>,
    B: Clone,
    S: Data<Elem = A>,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise bit or between references self and rhs, and return the result as a new Array.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = Array<A, D>

The resulting type after applying the | operator.

fn bitor(self, rhs: &'a ArrayBase<S2, E>) -> Array<A, D>

Performs the | operation.

impl<A, B, S, S2, D, E> BitOr<ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + BitOr<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise bit or between self and rhs, and return the result (based on self).

self must be an Array or ArcArray.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the | operator.

fn bitor(self, rhs: ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the | operation.

impl<A, S, D, B> BitOr<B> for ArrayBase<S, D> where
    A: Clone + BitOr<B, Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise bit or between self and the scalar x, and return the result (based on self).

self must be an Array or ArcArray.

type Output = ArrayBase<S, D>

The resulting type after applying the | operator.

fn bitor(self, x: B) -> ArrayBase<S, D>

Performs the | operation.

impl<'a, A, S, D, B> BitOr<B> for &'a ArrayBase<S, D> where
    A: Clone + BitOr<B, Output = A>,
    S: Data<Elem = A>,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise bit or between the reference self and the scalar x, and return the result as a new Array.

type Output = Array<A, D>

The resulting type after applying the | operator.

fn bitor(self, x: B) -> Array<A, D>

Performs the | operation.

impl<'a, A, S, S2, D, E> BitOrAssign<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + BitOrAssign<A>,
    S: DataMut<Elem = A>,
    S2: Data<Elem = A>,
    D: Dimension,
    E: Dimension, 

Perform self |= rhs as elementwise bit or (in place). If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

fn bitor_assign(&mut self, rhs: &ArrayBase<S2, E>)

Performs the |= operation.

impl<A, S, D> BitOrAssign<A> for ArrayBase<S, D> where
    A: ScalarOperand + BitOrAssign<A>,
    S: DataMut<Elem = A>,
    D: Dimension, 

Perform self |= rhs as elementwise bit or (in place).

fn bitor_assign(&mut self, rhs: A)

Performs the |= operation.

impl<'a, A, B, S, S2, D, E> BitXor<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + BitXor<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise bit xor between self and reference rhs, and return the result (based on self).

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the ^ operator.

fn bitxor(self, rhs: &ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the ^ operation.

impl<'a, A, B, S, S2, D, E> BitXor<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where
    A: Clone + BitXor<B, Output = A>,
    B: Clone,
    S: Data<Elem = A>,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise bit xor between references self and rhs, and return the result as a new Array.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = Array<A, D>

The resulting type after applying the ^ operator.

fn bitxor(self, rhs: &'a ArrayBase<S2, E>) -> Array<A, D>

Performs the ^ operation.

impl<A, B, S, S2, D, E> BitXor<ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + BitXor<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise bit xor between self and rhs, and return the result (based on self).

self must be an Array or ArcArray.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the ^ operator.

fn bitxor(self, rhs: ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the ^ operation.

impl<A, S, D, B> BitXor<B> for ArrayBase<S, D> where
    A: Clone + BitXor<B, Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise bit xor between self and the scalar x, and return the result (based on self).

self must be an Array or ArcArray.

type Output = ArrayBase<S, D>

The resulting type after applying the ^ operator.

fn bitxor(self, x: B) -> ArrayBase<S, D>

Performs the ^ operation.

impl<'a, A, S, D, B> BitXor<B> for &'a ArrayBase<S, D> where
    A: Clone + BitXor<B, Output = A>,
    S: Data<Elem = A>,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise bit xor between the reference self and the scalar x, and return the result as a new Array.

type Output = Array<A, D>

The resulting type after applying the ^ operator.

fn bitxor(self, x: B) -> Array<A, D>

Performs the ^ operation.

impl<'a, A, S, S2, D, E> BitXorAssign<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + BitXorAssign<A>,
    S: DataMut<Elem = A>,
    S2: Data<Elem = A>,
    D: Dimension,
    E: Dimension, 

Perform self ^= rhs as elementwise bit xor (in place). If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

fn bitxor_assign(&mut self, rhs: &ArrayBase<S2, E>)

Performs the ^= operation.

impl<A, S, D> BitXorAssign<A> for ArrayBase<S, D> where
    A: ScalarOperand + BitXorAssign<A>,
    S: DataMut<Elem = A>,
    D: Dimension, 

Perform self ^= rhs as elementwise bit xor (in place).

fn bitxor_assign(&mut self, rhs: A)

Performs the ^= operation.

impl<S: RawDataClone, D: Clone> Clone for ArrayBase<S, D>

fn clone(&self) -> ArrayBase<S, D>

Returns a copy of the value.

fn clone_from(&mut self, other: &Self)

Array implements .clone_from() to reuse an array’s existing allocation. Semantically equivalent to *self = other.clone(), but potentially more efficient.

impl<S: RawDataClone + Copy, D: Copy> Copy for ArrayBase<S, D>

impl<'a, A: Debug, S, D: Dimension> Debug for ArrayBase<S, D> where
    S: Data<Elem = A>, 

Format the array using Debug and apply the formatting parameters used to each element.

The array is shown in multiline style.

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<A, S, D> Default for ArrayBase<S, D> where
    S: DataOwned<Elem = A>,
    D: Dimension,
    A: Default, 

Create an owned array with a default state.

The array is created with dimension D::default(), which results in for example dimensions 0 and (0, 0) with zero elements for the one-dimensional and two-dimensional cases respectively.

The default dimension for IxDyn is IxDyn(&[0]) (array has zero elements). And the default for the dimension () is () (array has one element).

Since arrays cannot grow, the intention is to use the default value as placeholder.

fn default() -> Self

Returns the “default value” for a type.

impl<'a, A: Display, S, D: Dimension> Display for ArrayBase<S, D> where
    S: Data<Elem = A>, 

Format the array using Display and apply the formatting parameters used to each element.

The array is shown in multiline style.

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'a, A, B, S, S2, D, E> Div<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Div<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise division between self and reference rhs, and return the result (based on self).

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the / operator.

fn div(self, rhs: &ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the / operation.

impl<'a, A, B, S, S2, D, E> Div<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where
    A: Clone + Div<B, Output = A>,
    B: Clone,
    S: Data<Elem = A>,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise division between references self and rhs, and return the result as a new Array.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = Array<A, D>

The resulting type after applying the / operator.

fn div(self, rhs: &'a ArrayBase<S2, E>) -> Array<A, D>

Performs the / operation.

impl<A, B, S, S2, D, E> Div<ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Div<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise division between self and rhs, and return the result (based on self).

self must be an Array or ArcArray.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the / operator.

fn div(self, rhs: ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the / operation.

impl<A, S, D, B> Div<B> for ArrayBase<S, D> where
    A: Clone + Div<B, Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise division between self and the scalar x, and return the result (based on self).

self must be an Array or ArcArray.

type Output = ArrayBase<S, D>

The resulting type after applying the / operator.

fn div(self, x: B) -> ArrayBase<S, D>

Performs the / operation.

impl<'a, A, S, D, B> Div<B> for &'a ArrayBase<S, D> where
    A: Clone + Div<B, Output = A>,
    S: Data<Elem = A>,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise division between the reference self and the scalar x, and return the result as a new Array.

type Output = Array<A, D>

The resulting type after applying the / operator.

fn div(self, x: B) -> Array<A, D>

Performs the / operation.

impl<'a, A, S, S2, D, E> DivAssign<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + DivAssign<A>,
    S: DataMut<Elem = A>,
    S2: Data<Elem = A>,
    D: Dimension,
    E: Dimension, 

Perform self /= rhs as elementwise division (in place). If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

fn div_assign(&mut self, rhs: &ArrayBase<S2, E>)

Performs the /= operation.

impl<A, S, D> DivAssign<A> for ArrayBase<S, D> where
    A: ScalarOperand + DivAssign<A>,
    S: DataMut<Elem = A>,
    D: Dimension, 

Perform self /= rhs as elementwise division (in place).

fn div_assign(&mut self, rhs: A)

Performs the /= operation.

impl<A, S, S2> Dot<ArrayBase<S2, Dim<[usize; 1]>>> for ArrayBase<S, Ix1> where
    S: Data<Elem = A>,
    S2: Data<Elem = A>,
    A: LinalgScalar, 

type Output = A

The result of the operation.

fn dot(&self, rhs: &ArrayBase<S2, Ix1>) -> A

Compute the dot product of one-dimensional arrays.

The dot product is a sum of the elementwise products (no conjugation of complex operands, and thus not their inner product).

Panics if the arrays are not of the same length.
Note: If enabled, uses blas dot for elements of f32, f64 when memory layout allows.

impl<A, S, S2> Dot<ArrayBase<S2, Dim<[usize; 1]>>> for ArrayBase<S, Ix2> where
    S: Data<Elem = A>,
    S2: Data<Elem = A>,
    A: LinalgScalar, 

Perform the matrix multiplication of the rectangular array self and column vector rhs.

The array shapes must agree in the way that if self is M × N, then rhs is N.

Return a result array with shape M.

Panics if shapes are incompatible.

type Output = Array<A, Ix1>

The result of the operation.

fn dot(&self, rhs: &ArrayBase<S2, Ix1>) -> Array<A, Ix1>

impl<A, S, S2> Dot<ArrayBase<S2, Dim<[usize; 2]>>> for ArrayBase<S, Ix1> where
    S: Data<Elem = A>,
    S2: Data<Elem = A>,
    A: LinalgScalar, 

type Output = Array<A, Ix1>

The result of the operation.

fn dot(&self, rhs: &ArrayBase<S2, Ix2>) -> Array<A, Ix1>

Perform the matrix multiplication of the row vector self and rectangular matrix rhs.

The array shapes must agree in the way that if self is M, then rhs is M × N.

Return a result array with shape N.

Panics if shapes are incompatible.

impl<A, S, S2> Dot<ArrayBase<S2, Dim<[usize; 2]>>> for ArrayBase<S, Ix2> where
    S: Data<Elem = A>,
    S2: Data<Elem = A>,
    A: LinalgScalar, 

type Output = Array2<A>

The result of the operation.

fn dot(&self, b: &ArrayBase<S2, Ix2>) -> Array2<A>

impl<S, D> Eq for ArrayBase<S, D> where
    D: Dimension,
    S: Data,
    S::Elem: Eq, 

impl<'a, A, S, D> From<&'a ArrayBase<S, D>> for ArrayView<'a, A, D> where
    S: Data<Elem = A>,
    D: Dimension, 

Implementation of ArrayView::from(&A) where A is an array.

fn from(array: &'a ArrayBase<S, D>) -> Self

Create a read-only array view of the array.

impl<'a, A, S, D> From<&'a mut ArrayBase<S, D>> for ArrayViewMut<'a, A, D> where
    S: DataMut<Elem = A>,
    D: Dimension, 

Implementation of ArrayViewMut::from(&mut A) where A is an array.

fn from(array: &'a mut ArrayBase<S, D>) -> Self

Create a read-write array view of the array.

impl<'a, A, D> From<ArrayBase<OwnedRepr<A>, D>> for CowArray<'a, A, D> where
    D: Dimension, 

fn from(array: Array<A, D>) -> CowArray<'a, A, D>

Performs the conversion.

impl<'a, A, D> From<ArrayBase<ViewRepr<&'a A>, D>> for CowArray<'a, A, D> where
    D: Dimension, 

fn from(view: ArrayView<'a, A, D>) -> CowArray<'a, A, D>

Performs the conversion.

impl<A, S> From<Vec<A, Global>> for ArrayBase<S, Ix1> where
    S: DataOwned<Elem = A>, 

fn from(v: Vec<A>) -> Self

Create a one-dimensional array from a vector (no copying needed).

Panics if the length is greater than isize::MAX.

use ndarray::Array;

let array = Array::from(vec![1., 2., 3., 4.]);

impl<A, S> FromIterator<A> for ArrayBase<S, Ix1> where
    S: DataOwned<Elem = A>, 

fn from_iter<I>(iterable: I) -> ArrayBase<S, Ix1> where
    I: IntoIterator<Item = A>, 

Create a one-dimensional array from an iterable.

Panics if the length is greater than isize::MAX.

use ndarray::{Array, arr1};
use std::iter::FromIterator;

// Either use `from_iter` directly or use `Iterator::collect`.
let array = Array::from_iter((0..5).map(|x| x * x));
assert!(array == arr1(&[0, 1, 4, 9, 16]))

impl<'a, S, D> Hash for ArrayBase<S, D> where
    D: Dimension,
    S: Data,
    S::Elem: Hash, 

fn hash<H: Hasher>(&self, state: &mut H)

Feeds this value into the given Hasher.

pub fn hash_slice<H>(data: &[Self], state: &mut H) where
    H: Hasher, 

Feeds a slice of this type into the given Hasher.

impl<S, D, I> Index<I> for ArrayBase<S, D> where
    D: Dimension,
    I: NdIndex<D>,
    S: Data, 

Access the element at index.

Panics if index is out of bounds.

type Output = S::Elem

The returned type after indexing.

fn index(&self, index: I) -> &S::Elem

Performs the indexing (container[index]) operation.

impl<S, D, I> IndexMut<I> for ArrayBase<S, D> where
    D: Dimension,
    I: NdIndex<D>,
    S: DataMut, 

Access the element at index mutably.

Panics if index is out of bounds.

fn index_mut(&mut self, index: I) -> &mut S::Elem

Performs the mutable indexing (container[index]) operation.

impl<'a, S, D> IntoIterator for &'a ArrayBase<S, D> where
    D: Dimension,
    S: Data, 

type Item = &'a S::Elem

The type of the elements being iterated over.

type IntoIter = Iter<'a, S::Elem, D>

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.

impl<'a, S, D> IntoIterator for &'a mut ArrayBase<S, D> where
    D: Dimension,
    S: DataMut, 

type Item = &'a mut S::Elem

The type of the elements being iterated over.

type IntoIter = IterMut<'a, S::Elem, D>

Which kind of iterator are we turning this into?

fn into_iter(self) -> Self::IntoIter

Creates an iterator from a value.

impl<'a, A: 'a, S, D> IntoNdProducer for &'a ArrayBase<S, D> where
    D: Dimension,
    S: Data<Elem = A>, 

An array reference is an n-dimensional producer of element references (like ArrayView).

type Item = &'a A

The element produced per iteration.

type Dim = D

Dimension type of the producer

type Output = ArrayView<'a, A, D>

fn into_producer(self) -> Self::Output

Convert the value into an NdProducer.

impl<'a, A: 'a, S, D> IntoNdProducer for &'a mut ArrayBase<S, D> where
    D: Dimension,
    S: DataMut<Elem = A>, 

A mutable array reference is an n-dimensional producer of mutable element references (like ArrayViewMut).

type Item = &'a mut A

The element produced per iteration.

type Dim = D

Dimension type of the producer

type Output = ArrayViewMut<'a, A, D>

fn into_producer(self) -> Self::Output

Convert the value into an NdProducer.

impl<'a, A: LowerExp, S, D: Dimension> LowerExp for ArrayBase<S, D> where
    S: Data<Elem = A>, 

Format the array using LowerExp and apply the formatting parameters used to each element.

The array is shown in multiline style.

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'a, A: LowerHex, S, D: Dimension> LowerHex for ArrayBase<S, D> where
    S: Data<Elem = A>, 

Format the array using LowerHex and apply the formatting parameters used to each element.

The array is shown in multiline style.

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'a, A, B, S, S2, D, E> Mul<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Mul<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise multiplication between self and reference rhs, and return the result (based on self).

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the * operator.

fn mul(self, rhs: &ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the * operation.

impl<'a, A, B, S, S2, D, E> Mul<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where
    A: Clone + Mul<B, Output = A>,
    B: Clone,
    S: Data<Elem = A>,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise multiplication between references self and rhs, and return the result as a new Array.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = Array<A, D>

The resulting type after applying the * operator.

fn mul(self, rhs: &'a ArrayBase<S2, E>) -> Array<A, D>

Performs the * operation.

impl<A, B, S, S2, D, E> Mul<ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Mul<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise multiplication between self and rhs, and return the result (based on self).

self must be an Array or ArcArray.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the * operator.

fn mul(self, rhs: ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the * operation.

impl<A, S, D, B> Mul<B> for ArrayBase<S, D> where
    A: Clone + Mul<B, Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise multiplication between self and the scalar x, and return the result (based on self).

self must be an Array or ArcArray.

type Output = ArrayBase<S, D>

The resulting type after applying the * operator.

fn mul(self, x: B) -> ArrayBase<S, D>

Performs the * operation.

impl<'a, A, S, D, B> Mul<B> for &'a ArrayBase<S, D> where
    A: Clone + Mul<B, Output = A>,
    S: Data<Elem = A>,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise multiplication between the reference self and the scalar x, and return the result as a new Array.

type Output = Array<A, D>

The resulting type after applying the * operator.

fn mul(self, x: B) -> Array<A, D>

Performs the * operation.

impl<'a, A, S, S2, D, E> MulAssign<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + MulAssign<A>,
    S: DataMut<Elem = A>,
    S2: Data<Elem = A>,
    D: Dimension,
    E: Dimension, 

Perform self *= rhs as elementwise multiplication (in place). If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

fn mul_assign(&mut self, rhs: &ArrayBase<S2, E>)

Performs the *= operation.

impl<A, S, D> MulAssign<A> for ArrayBase<S, D> where
    A: ScalarOperand + MulAssign<A>,
    S: DataMut<Elem = A>,
    D: Dimension, 

Perform self *= rhs as elementwise multiplication (in place).

fn mul_assign(&mut self, rhs: A)

Performs the *= operation.

impl<A, S, D> Neg for ArrayBase<S, D> where
    A: Clone + Neg<Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension, 

type Output = Self

The resulting type after applying the - operator.

fn neg(self) -> Self

Perform an elementwise negation of self and return the result.

impl<'a, A, S, D> Neg for &'a ArrayBase<S, D> where
    &'a A: 'a + Neg<Output = A>,
    S: Data<Elem = A>,
    D: Dimension, 

type Output = Array<A, D>

The resulting type after applying the - operator.

fn neg(self) -> Array<A, D>

Perform an elementwise negation of reference self and return the result as a new Array.

impl<A, S, D> Not for ArrayBase<S, D> where
    A: Clone + Not<Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension, 

type Output = Self

The resulting type after applying the ! operator.

fn not(self) -> Self

Perform an elementwise unary not of self and return the result.

impl<'a, A, S, D> Not for &'a ArrayBase<S, D> where
    &'a A: 'a + Not<Output = A>,
    S: Data<Elem = A>,
    D: Dimension, 

type Output = Array<A, D>

The resulting type after applying the ! operator.

fn not(self) -> Array<A, D>

Perform an elementwise unary not of reference self and return the result as a new Array.

impl<A, B, S, S2, D> PartialEq<ArrayBase<S2, D>> for ArrayBase<S, D> where
    A: PartialEq<B>,
    S: Data<Elem = A>,
    S2: Data<Elem = B>,
    D: Dimension, 

Return true if the array shapes and all elements of self and rhs are equal. Return false otherwise.

fn eq(&self, rhs: &ArrayBase<S2, D>) -> bool

This method tests for self and other values to be equal, and is used by ==.

#[must_use]pub fn ne(&self, other: &Rhs) -> bool

This method tests for !=.

impl<'a, A, B, S, S2, D, E> Rem<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Rem<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise remainder between self and reference rhs, and return the result (based on self).

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the % operator.

fn rem(self, rhs: &ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the % operation.

impl<'a, A, B, S, S2, D, E> Rem<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where
    A: Clone + Rem<B, Output = A>,
    B: Clone,
    S: Data<Elem = A>,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise remainder between references self and rhs, and return the result as a new Array.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = Array<A, D>

The resulting type after applying the % operator.

fn rem(self, rhs: &'a ArrayBase<S2, E>) -> Array<A, D>

Performs the % operation.

impl<A, B, S, S2, D, E> Rem<ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Rem<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise remainder between self and rhs, and return the result (based on self).

self must be an Array or ArcArray.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the % operator.

fn rem(self, rhs: ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the % operation.

impl<A, S, D, B> Rem<B> for ArrayBase<S, D> where
    A: Clone + Rem<B, Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise remainder between self and the scalar x, and return the result (based on self).

self must be an Array or ArcArray.

type Output = ArrayBase<S, D>

The resulting type after applying the % operator.

fn rem(self, x: B) -> ArrayBase<S, D>

Performs the % operation.

impl<'a, A, S, D, B> Rem<B> for &'a ArrayBase<S, D> where
    A: Clone + Rem<B, Output = A>,
    S: Data<Elem = A>,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise remainder between the reference self and the scalar x, and return the result as a new Array.

type Output = Array<A, D>

The resulting type after applying the % operator.

fn rem(self, x: B) -> Array<A, D>

Performs the % operation.

impl<'a, A, S, S2, D, E> RemAssign<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + RemAssign<A>,
    S: DataMut<Elem = A>,
    S2: Data<Elem = A>,
    D: Dimension,
    E: Dimension, 

Perform self %= rhs as elementwise remainder (in place). If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

fn rem_assign(&mut self, rhs: &ArrayBase<S2, E>)

Performs the %= operation.

impl<A, S, D> RemAssign<A> for ArrayBase<S, D> where
    A: ScalarOperand + RemAssign<A>,
    S: DataMut<Elem = A>,
    D: Dimension, 

Perform self %= rhs as elementwise remainder (in place).

fn rem_assign(&mut self, rhs: A)

Performs the %= operation.

impl<S, D> Send for ArrayBase<S, D> where
    S: Send + Data,
    D: Send, 

ArrayBase is Send when the storage type is.

impl<'a, A, B, S, S2, D, E> Shl<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Shl<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise left shift between self and reference rhs, and return the result (based on self).

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the << operator.

fn shl(self, rhs: &ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the << operation.

impl<'a, A, B, S, S2, D, E> Shl<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where
    A: Clone + Shl<B, Output = A>,
    B: Clone,
    S: Data<Elem = A>,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise left shift between references self and rhs, and return the result as a new Array.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = Array<A, D>

The resulting type after applying the << operator.

fn shl(self, rhs: &'a ArrayBase<S2, E>) -> Array<A, D>

Performs the << operation.

impl<A, B, S, S2, D, E> Shl<ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Shl<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise left shift between self and rhs, and return the result (based on self).

self must be an Array or ArcArray.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the << operator.

fn shl(self, rhs: ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the << operation.

impl<A, S, D, B> Shl<B> for ArrayBase<S, D> where
    A: Clone + Shl<B, Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise left shift between self and the scalar x, and return the result (based on self).

self must be an Array or ArcArray.

type Output = ArrayBase<S, D>

The resulting type after applying the << operator.

fn shl(self, x: B) -> ArrayBase<S, D>

Performs the << operation.

impl<'a, A, S, D, B> Shl<B> for &'a ArrayBase<S, D> where
    A: Clone + Shl<B, Output = A>,
    S: Data<Elem = A>,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise left shift between the reference self and the scalar x, and return the result as a new Array.

type Output = Array<A, D>

The resulting type after applying the << operator.

fn shl(self, x: B) -> Array<A, D>

Performs the << operation.

impl<'a, A, S, S2, D, E> ShlAssign<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + ShlAssign<A>,
    S: DataMut<Elem = A>,
    S2: Data<Elem = A>,
    D: Dimension,
    E: Dimension, 

Perform self <<= rhs as elementwise left shift (in place). If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

fn shl_assign(&mut self, rhs: &ArrayBase<S2, E>)

Performs the <<= operation.

impl<A, S, D> ShlAssign<A> for ArrayBase<S, D> where
    A: ScalarOperand + ShlAssign<A>,
    S: DataMut<Elem = A>,
    D: Dimension, 

Perform self <<= rhs as elementwise left shift (in place).

fn shl_assign(&mut self, rhs: A)

Performs the <<= operation.

impl<'a, A, B, S, S2, D, E> Shr<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Shr<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise right shift between self and reference rhs, and return the result (based on self).

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the >> operator.

fn shr(self, rhs: &ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the >> operation.

impl<'a, A, B, S, S2, D, E> Shr<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where
    A: Clone + Shr<B, Output = A>,
    B: Clone,
    S: Data<Elem = A>,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise right shift between references self and rhs, and return the result as a new Array.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = Array<A, D>

The resulting type after applying the >> operator.

fn shr(self, rhs: &'a ArrayBase<S2, E>) -> Array<A, D>

Performs the >> operation.

impl<A, B, S, S2, D, E> Shr<ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Shr<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise right shift between self and rhs, and return the result (based on self).

self must be an Array or ArcArray.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the >> operator.

fn shr(self, rhs: ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the >> operation.

impl<A, S, D, B> Shr<B> for ArrayBase<S, D> where
    A: Clone + Shr<B, Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise right shift between self and the scalar x, and return the result (based on self).

self must be an Array or ArcArray.

type Output = ArrayBase<S, D>

The resulting type after applying the >> operator.

fn shr(self, x: B) -> ArrayBase<S, D>

Performs the >> operation.

impl<'a, A, S, D, B> Shr<B> for &'a ArrayBase<S, D> where
    A: Clone + Shr<B, Output = A>,
    S: Data<Elem = A>,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise right shift between the reference self and the scalar x, and return the result as a new Array.

type Output = Array<A, D>

The resulting type after applying the >> operator.

fn shr(self, x: B) -> Array<A, D>

Performs the >> operation.

impl<'a, A, S, S2, D, E> ShrAssign<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + ShrAssign<A>,
    S: DataMut<Elem = A>,
    S2: Data<Elem = A>,
    D: Dimension,
    E: Dimension, 

Perform self >>= rhs as elementwise right shift (in place). If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

fn shr_assign(&mut self, rhs: &ArrayBase<S2, E>)

Performs the >>= operation.

impl<A, S, D> ShrAssign<A> for ArrayBase<S, D> where
    A: ScalarOperand + ShrAssign<A>,
    S: DataMut<Elem = A>,
    D: Dimension, 

Perform self >>= rhs as elementwise right shift (in place).

fn shr_assign(&mut self, rhs: A)

Performs the >>= operation.

impl<'a, A, B, S, S2, D, E> Sub<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Sub<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise subtraction between self and reference rhs, and return the result (based on self).

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the - operator.

fn sub(self, rhs: &ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the - operation.

impl<'a, A, B, S, S2, D, E> Sub<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where
    A: Clone + Sub<B, Output = A>,
    B: Clone,
    S: Data<Elem = A>,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise subtraction between references self and rhs, and return the result as a new Array.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = Array<A, D>

The resulting type after applying the - operator.

fn sub(self, rhs: &'a ArrayBase<S2, E>) -> Array<A, D>

Performs the - operation.

impl<A, B, S, S2, D, E> Sub<ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + Sub<B, Output = A>,
    B: Clone,
    S: DataOwned<Elem = A> + DataMut,
    S2: Data<Elem = B>,
    D: Dimension,
    E: Dimension, 

Perform elementwise subtraction between self and rhs, and return the result (based on self).

self must be an Array or ArcArray.

If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

type Output = ArrayBase<S, D>

The resulting type after applying the - operator.

fn sub(self, rhs: ArrayBase<S2, E>) -> ArrayBase<S, D>

Performs the - operation.

impl<A, S, D, B> Sub<B> for ArrayBase<S, D> where
    A: Clone + Sub<B, Output = A>,
    S: DataOwned<Elem = A> + DataMut,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise subtraction between self and the scalar x, and return the result (based on self).

self must be an Array or ArcArray.

type Output = ArrayBase<S, D>

The resulting type after applying the - operator.

fn sub(self, x: B) -> ArrayBase<S, D>

Performs the - operation.

impl<'a, A, S, D, B> Sub<B> for &'a ArrayBase<S, D> where
    A: Clone + Sub<B, Output = A>,
    S: Data<Elem = A>,
    D: Dimension,
    B: ScalarOperand, 

Perform elementwise subtraction between the reference self and the scalar x, and return the result as a new Array.

type Output = Array<A, D>

The resulting type after applying the - operator.

fn sub(self, x: B) -> Array<A, D>

Performs the - operation.

impl<'a, A, S, S2, D, E> SubAssign<&'a ArrayBase<S2, E>> for ArrayBase<S, D> where
    A: Clone + SubAssign<A>,
    S: DataMut<Elem = A>,
    S2: Data<Elem = A>,
    D: Dimension,
    E: Dimension, 

Perform self -= rhs as elementwise subtraction (in place). If their shapes disagree, rhs is broadcast to the shape of self.

Panics if broadcasting isn’t possible.

fn sub_assign(&mut self, rhs: &ArrayBase<S2, E>)

Performs the -= operation.

impl<A, S, D> SubAssign<A> for ArrayBase<S, D> where
    A: ScalarOperand + SubAssign<A>,
    S: DataMut<Elem = A>,
    D: Dimension, 

Perform self -= rhs as elementwise subtraction (in place).

fn sub_assign(&mut self, rhs: A)

Performs the -= operation.

impl<S, D> Sync for ArrayBase<S, D> where
    S: Sync + Data,
    D: Sync, 

ArrayBase is Sync when the storage type is.

impl<'a, A: UpperExp, S, D: Dimension> UpperExp for ArrayBase<S, D> where
    S: Data<Elem = A>, 

Format the array using UpperExp and apply the formatting parameters used to each element.

The array is shown in multiline style.

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.