Struct ndarray::ArrayBase [−] [src]

pub struct ArrayBase<S, D> where S: Data {
    // some fields omitted
}

An N-dimensional array.

The array is a general container of elements. It can be of numerical use too, supporting all mathematical operators by applying them elementwise. It cannot grow or shrink, but can be sliced into views of parts of its data.

The ArrayBase<S, D> is parameterized by:

  • S for the data storage
  • D for the number of dimensions

Type aliases Array, OwnedArray, ArrayView, and ArrayViewMut refer to ArrayBase with different types for the data storage.

Array

Array<A, D> is a an array with reference counted data and copy-on-write mutability.

The Array is both a view and a shared owner of its data. Some methods, for example slice(), merely change the view of the data, while methods like iadd() allow mutating the element values.

Calling a method for mutating elements, for example get_mut(), iadd() or iter_mut() will break sharing and require a clone of the data (if it is not uniquely held).

Method Conventions

Methods mutating the view or array elements in place use an i prefix, for example slice vs. islice and add vs iadd.

Note that all ArrayBase variants can change their view (slicing) of the data freely, even when the data can’t be mutated.

Indexing

Array indexes are represented by the types Ix and Ixs (signed). Note: A future version will switch from u32 to usize.

Slicing

You can use slicing to create a view of a subset of the data in the array. Slicing methods include .slice(), .islice(), .slice_mut().

The 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. For the 2D array this means that indices are (row, column), and the order of the elements is (0, 0), (0, 1), (0, 2), ... (1, 0), (1, 1), (1, 2) ... etc.

The slicing specification is passed as a function argument as a fixed size array with elements of type Si with fields Si(begin, end, stride), where the values are signed integers, and end is an Option<Ixs>. The constant S is a shorthand for the full range of an axis. For example, if the array has two axes, the slice argument is passed as type &[Si; 2].

The macro s![] is however a much more convenient way to specify the slicing argument, so it will be used in all examples.

// import the s![] macro
#[macro_use(s)]
extern crate ndarray;

use ndarray::arr3;

fn main() {

// 2 submatrices of 2 rows with 3 elements per row, means a shape of `[2, 2, 3]`.

let a = arr3(&[[[ 1,  2,  3],     // -- 2 rows  \_
                [ 4,  5,  6]],    // --         /  
               [[ 7,  8,  9],     //            \_ 2 submatrices
                [10, 11, 12]]]);  //            /
//  3 columns ..../.../.../

assert_eq!(a.shape(), &[2, 2, 3]);

// Let’s create a slice with
//
// - Both of the submatrices of the greatest dimension: `..`
// - Only the first row in each submatrix: `0..1`
// - Every element in each row: `..`

let b = a.slice(s![.., 0..1, ..]);
// without the macro, the explicit argument is `&[S, Si(0, Some(1), 1), S]`

let c = arr3(&[[[ 1,  2,  3]],
               [[ 7,  8,  9]]]);
assert_eq!(b, c);
assert_eq!(b.shape(), &[2, 1, 3]);

// Let’s create a slice with
// 
// - Both submatrices of the greatest dimension: `..`
// - The last row in each submatrix: `-1..`
// - Row elements in reverse order: `..;-1`
let d = a.slice(s![.., -1.., ..;-1]);
let e = arr3(&[[[ 6,  5,  4]],
               [[12, 11, 10]]]);
assert_eq!(d, e);
}

Subviews

Subview methods allow you to restrict the array view while removing one axis from the array. Subview methods include .subview(), .isubview(), .subview_mut().

Subview takes two arguments: axis and index.

use ndarray::{arr3, aview2};

// 2 submatrices of 2 rows with 3 elements per row, means a shape of `[2, 2, 3]`.

let a = arr3(&[[[ 1,  2,  3],    // \ axis 0, submatrix 0
                [ 4,  5,  6]],   // /
               [[ 7,  8,  9],    // \ axis 0, submatrix 1
                [10, 11, 12]]]); // /
        //        \ 
        //         axis 2, column 0

assert_eq!(a.shape(), &[2, 2, 3]);

// Let’s take a subview along the greatest dimension (axis 0),
// taking submatrix 0, then submatrix 1

let sub_0 = a.subview(0, 0);
let sub_1 = a.subview(0, 1);

assert_eq!(sub_0, aview2(&[[ 1,  2,  3],
                           [ 4,  5,  6]]));
assert_eq!(sub_1, aview2(&[[ 7,  8,  9],
                           [10, 11, 12]]));
assert_eq!(sub_0.shape(), &[2, 3]);

// This is the subview picking only axis 2, column 0
let sub_col = a.subview(2, 0);

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

.isubview() modifies the view in the same way as subview(), but since it is in place, it cannot remove the collapsed axis. It becomes an axis of length 1.

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

let c = arr2(&[[1., 2.],
               [1., 3.]]);
// We can add because the shapes are compatible even if not equal.
assert!(
    c == a + b
);

Methods

impl<S> ArrayBase<S, Ix> where S: DataOwned
[src]

Constructor methods for single dimensional ArrayBase.

fn from_vec(v: Vec<S::Elem>) -> ArrayBase<S, Ix>

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

fn from_iter<I: IntoIterator<Item=S::Elem>>(iterable: I) -> ArrayBase<S, Ix>

Create a one-dimensional array from an iterable.

fn linspace<F>(start: F, end: F, n: usize) -> ArrayBase<S, Ix> where S: Data<Elem=F>, F: Float, usize: ToFloat<F>

Create a one-dimensional array from inclusive interval [start, end] with n elements. F must be a floating point type.

fn range(start: f32, end: f32) -> ArrayBase<S, Ix> where S: Data<Elem=f32>

Deprecated

: use ArrayBase::linspace() instead

Create a one-dimensional array from interval [start, end)

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

Constructor methods for ArrayBase.

fn from_elem(dim: D, elem: A) -> ArrayBase<S, D> where A: Clone

Construct an array with copies of elem, dimension dim.

use ndarray::Array;
use ndarray::arr3;

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

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

fn zeros(dim: D) -> ArrayBase<S, D> where A: Clone + Zero

Construct an array with zeros, dimension dim.

fn default(dim: D) -> ArrayBase<S, D> where A: Default

Construct an array with default values, dimension dim.

unsafe fn from_vec_dim(dim: D, v: Vec<A>) -> ArrayBase<S, D>

Create an array from a vector (with no allocation needed).

Unsafe because dimension is unchecked, and must be correct.

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

fn len(&self) -> usize

Return the total number of elements in the Array.

fn dim(&self) -> D

Return the shape of the array.

fn shape(&self) -> &[Ix]

Return the shape of the array as a slice.

fn strides(&self) -> &[Ixs]

Return the strides of the array

fn view(&self) -> ArrayView<A, D>

Return a read-only view of the array

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

Return a read-write view of the array

fn to_owned(&self) -> OwnedArray<A, D> where A: Clone

Return an uniquely owned copy of the array

fn to_shared(&self) -> Array<A, D> where A: Clone

Return a shared ownership (copy on write) array.

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

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

fn iter(&self) -> Elements<A, D>

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

Iterator element type is &A.

fn indexed_iter(&self) -> Indexed<A, D>

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

Iterator element type is (D, &A).

fn iter_mut(&mut self) -> ElementsMut<A, D> where S: DataMut

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

Iterator element type is &mut A.

fn indexed_iter_mut(&mut self) -> IndexedMut<A, D> where S: DataMut

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

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

fn slice(&self, indexes: &D::SliceArg) -> Self where S: DataShared

Return a sliced array.

See Slicing for full documentation.

D::SliceArg is typically a fixed size array of Si, with one element per axis.

Panics if an index is out of bounds or stride is zero.
(Panics if D is Vec and indexes does not match the number of array axes.)

fn islice(&mut self, indexes: &D::SliceArg)

Slice the array’s view in place.

D::SliceArg is typically a fixed size array of Si, with one element per axis.

Panics if an index is out of bounds or stride is zero.
(Panics if D is Vec and indexes does not match the number of array axes.)

fn slice_iter(&self, indexes: &D::SliceArg) -> Elements<A, D>

Return an iterator over a sliced view.

D::SliceArg is typically a fixed size array of Si, with one element per axis.

Panics if an index is out of bounds or stride is zero.
(Panics if D is Vec and indexes does not match the number of array axes.)

fn slice_mut(&mut self, indexes: &D::SliceArg) -> ArrayViewMut<A, D> where S: DataMut

Return a sliced read-write view of the array.

D::SliceArg is typically a fixed size array of Si, with one element per axis.

Panics if an index is out of bounds or stride is zero.
(Panics if D is Vec and indexes does not match the number of array axes.)

fn slice_iter_mut(&mut self, indexes: &D::SliceArg) -> ElementsMut<A, D> where S: DataMut

Deprecated

: use .slice_mut() instead

Deprecated: use .slice_mut()

fn get(&self, index: D) -> Option<&A>

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

fn at(&self, index: D) -> Option<&A>

Deprecated

: use .get() instead

Deprecated: use .get(i)

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

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

fn at_mut(&mut self, index: D) -> Option<&mut A> where S: DataMut

Deprecated

: use .get_mut() instead

Deprecated: use .get_mut(i)

unsafe fn uget(&self, index: D) -> &A

Perform unchecked array indexing.

Return a reference to the element at index.

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

unsafe fn uchk_at(&self, index: D) -> &A

Deprecated

: use .uget() instead

Deprecated: use .uget()

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

Perform unchecked array indexing.

Return a mutable reference to the element at index.

Note: Only unchecked for non-debug builds of ndarray.
Note: The array must be uniquely held when mutating it.

unsafe fn uchk_at_mut(&mut self, index: D) -> &mut A where S: DataMut

Deprecated

: use .uget_mut() instead

Deprecated: use .uget_mut()

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

Swap axes ax and bx.

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.]])
);

fn subview(&self, axis: usize, index: Ix) -> ArrayBase<S, D::Smaller> where D: RemoveAxis, S: DataShared

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

See Subviews for full documentation.

Panics if axis or index is out of bounds.

use ndarray::{arr1, arr2};

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.subview(0, 1) == arr1(&[3., 4.]) &&
    a.subview(1, 1) == arr1(&[2., 4., 6.])
);

fn isubview(&mut self, axis: usize, index: Ix)

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.

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

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.

use ndarray::{arr2, aview2};

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

a.subview_mut(1, 1).iadd_scalar(&10.);

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

fn sub_iter_mut(&mut self, axis: usize, index: Ix) -> ElementsMut<A, D> where S: DataMut

Deprecated

: use .subview_mut() instead

Deprecated: use .subview_mut()

fn inner_iter(&self) -> InnerIter<A, D>

Return an iterator that traverses over all dimensions but the innermost, and yields each inner row.

Iterator element is ArrayView<A, Ix> (1D array view).

use ndarray::arr3;
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
// `inner_iter` yields the four inner rows of the 3D array.
let mut row_sums = a.inner_iter().map(|v| v.scalar_sum());
assert_eq!(row_sums.collect::<Vec<_>>(), vec![3, 12, 21, 30]);

fn inner_iter_mut(&mut self) -> InnerIterMut<A, D> where S: DataMut

Return an iterator that traverses over all dimensions but the innermost, and yields each inner row.

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

fn diag_iter(&self) -> Elements<A, Ix>

Return an iterator over 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.

fn diag(&self) -> ArrayBase<S, Ix> where S: DataShared

Return the diagonal as a one-dimensional array.

fn diag_mut(&mut self) -> ArrayViewMut<A, Ix> where S: DataMut

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

fn diag_iter_mut(&mut self) -> ElementsMut<A, Ix> where S: DataMut

Deprecated

: use .diag_mut() instead

Deprecated: use .diag_mut()

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.

fn as_slice(&self) -> Option<&[A]>

Return the array’s data as a slice, if it is contiguous and the element order corresponds to the memory order. Return None otherwise.

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 the element order corresponds to the memory order. Return None otherwise.

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

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::{arr1, arr2};

assert!(
    arr1(&[1., 2., 3., 4.]).reshape((2, 2))
    == arr2(&[[1., 2.],
              [3., 4.]])
);

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

Transform the array into shape; any shape with the same number of elements is accepted, but the source array or view must be contiguous, otherwise we cannot rearrange the dimension.

Errors if the shapes don't have the same number of elements.
Errors if the input array is not c-contiguous (this will be slightly improved in the future).

use ndarray::{aview1, aview2};

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

fn broadcast<E>(&self, dim: E) -> Option<ArrayView<A, E>> where E: Dimension

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::arr1;

assert!(
    arr1(&[1., 0.]).broadcast((10, 2)).unwrap().dim()
    == (10, 2)
);

fn broadcast_iter<E>(&self, dim: E) -> Option<Elements<A, E>> where E: Dimension

Deprecated

: use .broadcast() instead

Deprecated: Use .broadcast() instead.

fn raw_data(&self) -> &[A]

Return a slice of the array’s backing data in memory order.

Note: Data memory order may not correspond to the index order of the array. Neither is the raw data slice is restricted to just the Array’s view.
Note: the slice may be empty.

fn raw_data_mut(&mut self) -> &mut [A] where S: DataMut

Return a mutable slice of the array’s backing data in memory order.

Note: Data memory order may not correspond to the index order of the array. Neither is the raw data slice is restricted to just the Array’s view.
Note: the slice may be empty.

Note: The data is uniquely held and nonaliased while it is mutably borrowed.

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.

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

Perform an elementwise assigment to self from scalar x.

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.

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

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

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

Apply f elementwise and return a new array with the results.

Return an array with the same shape as self.

use ndarray::arr2;

let a = arr2(&[[1., 2.],
               [3., 4.]]);
assert!(
    a.map(|&x| (x / 2.) as i32)
    == arr2(&[[0, 1], [1, 2]])
);

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

fn sum(&self, axis: usize) -> OwnedArray<A, D::Smaller> where A: Clone + Add<Output=A>, D: RemoveAxis

Return sum along axis.

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

let a = arr2(&[[1., 2.],
               [3., 4.]]);
assert!(
    a.sum(0) == aview1(&[4., 6.]) &&
    a.sum(1) == aview1(&[3., 7.]) &&

    a.sum(0).sum(0) == aview0(&10.)
);

Panics if axis is out of bounds.

fn scalar_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.scalar_sum(), 10.);

fn mean(&self, axis: usize) -> OwnedArray<A, D::Smaller> where A: Copy + Field, D: RemoveAxis

Return mean along axis.

use ndarray::{aview1, arr2};

let a = arr2(&[[1., 2.],
               [3., 4.]]);
assert!(
    a.mean(0) == aview1(&[2.0, 3.0]) &&
    a.mean(1) == aview1(&[1.5, 3.5])
);

Panics if axis is out of bounds.

fn allclose<S2>(&self, rhs: &ArrayBase<S2, D>, tol: A) -> bool where A: Float + PartialOrd, S2: Data<Elem=A>

Return true if the arrays' elementwise differences are all within the given absolute tolerance.
Return false otherwise, or if the shapes disagree.

impl<A, S> ArrayBase<S, (Ix, Ix)> where S: Data<Elem=A>
[src]

fn row_iter(&self, index: Ix) -> Elements<A, Ix>

Return an iterator over the elements of row index.

Panics if index is out of bounds.

fn col_iter(&self, index: Ix) -> Elements<A, Ix>

Return an iterator over the elements of column index.

Panics if index is out of bounds.

fn mat_mul(&self, rhs: &ArrayBase<S, (Ix, Ix)>) -> Array<A, (Ix, Ix)> where A: Copy + Ring

Perform matrix multiplication of rectangular arrays self and rhs.

The array sizes 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 sizes are incompatible.

use ndarray::arr2;

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

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

fn mat_mul_col(&self, rhs: &ArrayBase<S, Ix>) -> Array<A, Ix> where A: Copy + Ring

Perform the matrix multiplication of the rectangular array self and column vector rhs.

The array sizes must agree in the way that if self is M × N, then rhs is N.

Return a result array with shape M.

Panics if sizes are incompatible.

impl<A, S, D> ArrayBase<S, D> where S: DataMut<Elem=A>, D: Dimension
[src]

In-place arithmetic operations.

fn iadd<E: Dimension, S2>(&mut self, rhs: &ArrayBase<S2, E>) where A: Clone + Add<A, Output=A>, S2: Data<Elem=A>

Perform elementwise Addition between self and rhs, in place.

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

Panics if broadcasting isn’t possible.

fn iadd_scalar(&mut self, x: &A) where A: Clone + Add<A, Output=A>

Perform elementwise Addition between self and the scalar x, in place.

fn isub<E: Dimension, S2>(&mut self, rhs: &ArrayBase<S2, E>) where A: Clone + Sub<A, Output=A>, S2: Data<Elem=A>

Perform elementwise Subtraction between self and rhs, in place.

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

Panics if broadcasting isn’t possible.

fn isub_scalar(&mut self, x: &A) where A: Clone + Sub<A, Output=A>

Perform elementwise Subtraction between self and the scalar x, in place.

fn imul<E: Dimension, S2>(&mut self, rhs: &ArrayBase<S2, E>) where A: Clone + Mul<A, Output=A>, S2: Data<Elem=A>

Perform elementwise Multiplication between self and rhs, in place.

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

Panics if broadcasting isn’t possible.

fn imul_scalar(&mut self, x: &A) where A: Clone + Mul<A, Output=A>

Perform elementwise Multiplication between self and the scalar x, in place.

fn idiv<E: Dimension, S2>(&mut self, rhs: &ArrayBase<S2, E>) where A: Clone + Div<A, Output=A>, S2: Data<Elem=A>

Perform elementwise Divsion between self and rhs, in place.

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

Panics if broadcasting isn’t possible.

fn idiv_scalar(&mut self, x: &A) where A: Clone + Div<A, Output=A>

Perform elementwise Divsion between self and the scalar x, in place.

fn irem<E: Dimension, S2>(&mut self, rhs: &ArrayBase<S2, E>) where A: Clone + Rem<A, Output=A>, S2: Data<Elem=A>

Perform elementwise Remainder between self and rhs, in place.

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

Panics if broadcasting isn’t possible.

fn irem_scalar(&mut self, x: &A) where A: Clone + Rem<A, Output=A>

Perform elementwise Remainder between self and the scalar x, in place.

fn ibitand<E: Dimension, S2>(&mut self, rhs: &ArrayBase<S2, E>) where A: Clone + BitAnd<A, Output=A>, S2: Data<Elem=A>

Perform elementwise Bit and between self and rhs, in place.

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

Panics if broadcasting isn’t possible.

fn ibitand_scalar(&mut self, x: &A) where A: Clone + BitAnd<A, Output=A>

Perform elementwise Bit and between self and the scalar x, in place.

fn ibitor<E: Dimension, S2>(&mut self, rhs: &ArrayBase<S2, E>) where A: Clone + BitOr<A, Output=A>, S2: Data<Elem=A>

Perform elementwise Bit or between self and rhs, in place.

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

Panics if broadcasting isn’t possible.

fn ibitor_scalar(&mut self, x: &A) where A: Clone + BitOr<A, Output=A>

Perform elementwise Bit or between self and the scalar x, in place.

fn ibitxor<E: Dimension, S2>(&mut self, rhs: &ArrayBase<S2, E>) where A: Clone + BitXor<A, Output=A>, S2: Data<Elem=A>

Perform elementwise Bit xor between self and rhs, in place.

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

Panics if broadcasting isn’t possible.

fn ibitxor_scalar(&mut self, x: &A) where A: Clone + BitXor<A, Output=A>

Perform elementwise Bit xor between self and the scalar x, in place.

fn ishl<E: Dimension, S2>(&mut self, rhs: &ArrayBase<S2, E>) where A: Clone + Shl<A, Output=A>, S2: Data<Elem=A>

Perform elementwise Left shift between self and rhs, in place.

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

Panics if broadcasting isn’t possible.

fn ishl_scalar(&mut self, x: &A) where A: Clone + Shl<A, Output=A>

Perform elementwise Left shift between self and the scalar x, in place.

fn ishr<E: Dimension, S2>(&mut self, rhs: &ArrayBase<S2, E>) where A: Clone + Shr<A, Output=A>, S2: Data<Elem=A>

Perform elementwise Right shift between self and rhs, in place.

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

Panics if broadcasting isn’t possible.

fn ishr_scalar(&mut self, x: &A) where A: Clone + Shr<A, Output=A>

Perform elementwise Right shift between self and the scalar x, in place.

fn ineg(&mut self) where A: Clone + Neg<Output=A>

Perform an elementwise negation of self, in place.

fn inot(&mut self) where A: Clone + Not<Output=A>

Perform an elementwise unary not of self, in place.

Trait Implementations

impl<S, D> Index<D> for ArrayBase<S, D> where D: Dimension, S: Data
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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: D) -> &S::Elem

The method for the indexing (Foo[Bar]) operation

impl<S, D> IndexMut<D> for ArrayBase<S, D> where D: Dimension, S: DataMut
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Access the element at index mutably.

Panics if index is out of bounds.

fn index_mut(&mut self, index: D) -> &mut S::Elem

The method for the indexing (Foo[Bar]) operation

impl<S, S2, D> PartialEq<ArrayBase<S2, D>> for ArrayBase<S, D> where D: Dimension, S: Data, S2: Data<Elem=S::Elem>, S::Elem: PartialEq
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fn eq(&self, rhs: &ArrayBase<S2, D>) -> bool

Return true if the array shapes and all elements of self and rhs are equal. Return false otherwise.

fn ne(&self, other: &Rhs) -> bool
1.0.0

This method tests for !=.

impl<S, D> Eq for ArrayBase<S, D> where D: Dimension, S: Data, S::Elem: Eq
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impl<A, S> FromIterator<A> for ArrayBase<S, Ix> where S: DataOwned<Elem=A>
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fn from_iter<I>(iterable: I) -> ArrayBase<S, Ix> where I: IntoIterator<Item=A>

Creates a value from an iterator. Read more

impl<'a, S, D> IntoIterator for &'a ArrayBase<S, D> where D: Dimension, S: Data
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type Item = &'a S::Elem

The type of the elements being iterated over.

type IntoIter = Elements<'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. Read more

impl<'a, S, D> IntoIterator for &'a mut ArrayBase<S, D> where D: Dimension, S: DataMut
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type Item = &'a mut S::Elem

The type of the elements being iterated over.

type IntoIter = ElementsMut<'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. Read more

impl<'a, S, D> Hash for ArrayBase<S, D> where D: Dimension, S: Data, S::Elem: Hash
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fn hash<H: Hasher>(&self, state: &mut H)

Feeds this value into the state given, updating the hasher as necessary.

fn hash_slice<H>(data: &[Self], state: &mut H) where H: Hasher
1.3.0

Feeds a slice of this type into the state provided.

impl<S, D> Sync for ArrayBase<S, D> where S: Sync + Data, D: Sync
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ArrayBase is Sync when the storage type is.

impl<S, D> Send for ArrayBase<S, D> where S: Send + Data, D: Send
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ArrayBase is Send when the storage type is.

impl<'a, A: Display, S, D: Dimension> Display for ArrayBase<S, D> where S: Data<Elem=A>
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Format the array using Display and apply the formatting parameters used to each element.

The array is shown in multiline style, unless the alternate form is used, {:#}.

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

Formats the value using the given formatter.

impl<'a, A: Debug, S, D: Dimension> Debug for ArrayBase<S, D> where S: Data<Elem=A>
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Format the array using Debug and apply the formatting parameters used to each element.

The array is shown in multiline style, unless the alternate form is used, {:#}.

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

Formats the value using the given formatter.

impl<'a, A: LowerExp, S, D: Dimension> LowerExp for ArrayBase<S, D> where S: Data<Elem=A>
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Format the array using LowerExp and apply the formatting parameters used to each element.

The array is shown in multiline style, unless the alternate form is used, {:#e}.

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

Formats the value using the given formatter.

impl<'a, A: UpperExp, S, D: Dimension> UpperExp for ArrayBase<S, D> where S: Data<Elem=A>
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Format the array using UpperExp and apply the formatting parameters used to each element.

The array is shown in multiline style, unless the alternate form is used, {:#E}.

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

The array is shown in multiline style, unless the alternate form is used, {:#x}.

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

Formats the value using the given formatter.

impl<A, S, S2, D, E> Add<ArrayBase<S2, E>> for ArrayBase<S, D> where A: Clone + Add<A, Output=A>, S: DataMut<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Addition between self and rhs, and return the result.

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>

The method for the + operator

impl<'a, A, S, S2, D, E> Add<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where A: Clone + Add<A, Output=A>, S: Data<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Addition between self and rhs, and return the result.

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

Panics if broadcasting isn’t possible.

type Output = OwnedArray<A, D>

The resulting type after applying the + operator

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

The method for the + operator

impl<A, S, S2, D, E> Sub<ArrayBase<S2, E>> for ArrayBase<S, D> where A: Clone + Sub<A, Output=A>, S: DataMut<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Subtraction between self and rhs, and return the result.

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>

The method for the - operator

impl<'a, A, S, S2, D, E> Sub<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where A: Clone + Sub<A, Output=A>, S: Data<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Subtraction between self and rhs, and return the result.

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

Panics if broadcasting isn’t possible.

type Output = OwnedArray<A, D>

The resulting type after applying the - operator

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

The method for the - operator

impl<A, S, S2, D, E> Mul<ArrayBase<S2, E>> for ArrayBase<S, D> where A: Clone + Mul<A, Output=A>, S: DataMut<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Multiplication between self and rhs, and return the result.

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>

The method for the * operator

impl<'a, A, S, S2, D, E> Mul<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where A: Clone + Mul<A, Output=A>, S: Data<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Multiplication between self and rhs, and return the result.

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

Panics if broadcasting isn’t possible.

type Output = OwnedArray<A, D>

The resulting type after applying the * operator

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

The method for the * operator

impl<A, S, S2, D, E> Div<ArrayBase<S2, E>> for ArrayBase<S, D> where A: Clone + Div<A, Output=A>, S: DataMut<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Divsion between self and rhs, and return the result.

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>

The method for the / operator

impl<'a, A, S, S2, D, E> Div<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where A: Clone + Div<A, Output=A>, S: Data<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Divsion between self and rhs, and return the result.

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

Panics if broadcasting isn’t possible.

type Output = OwnedArray<A, D>

The resulting type after applying the / operator

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

The method for the / operator

impl<A, S, S2, D, E> Rem<ArrayBase<S2, E>> for ArrayBase<S, D> where A: Clone + Rem<A, Output=A>, S: DataMut<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Remainder between self and rhs, and return the result.

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>

The method for the % operator

impl<'a, A, S, S2, D, E> Rem<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where A: Clone + Rem<A, Output=A>, S: Data<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Remainder between self and rhs, and return the result.

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

Panics if broadcasting isn’t possible.

type Output = OwnedArray<A, D>

The resulting type after applying the % operator

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

The method for the % operator

impl<A, S, S2, D, E> BitAnd<ArrayBase<S2, E>> for ArrayBase<S, D> where A: Clone + BitAnd<A, Output=A>, S: DataMut<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Bit and between self and rhs, and return the result.

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>

The method for the & operator

impl<'a, A, S, S2, D, E> BitAnd<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where A: Clone + BitAnd<A, Output=A>, S: Data<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Bit and between self and rhs, and return the result.

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

Panics if broadcasting isn’t possible.

type Output = OwnedArray<A, D>

The resulting type after applying the & operator

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

The method for the & operator

impl<A, S, S2, D, E> BitOr<ArrayBase<S2, E>> for ArrayBase<S, D> where A: Clone + BitOr<A, Output=A>, S: DataMut<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Bit or between self and rhs, and return the result.

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>

The method for the | operator

impl<'a, A, S, S2, D, E> BitOr<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where A: Clone + BitOr<A, Output=A>, S: Data<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Bit or between self and rhs, and return the result.

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

Panics if broadcasting isn’t possible.

type Output = OwnedArray<A, D>

The resulting type after applying the | operator

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

The method for the | operator

impl<A, S, S2, D, E> BitXor<ArrayBase<S2, E>> for ArrayBase<S, D> where A: Clone + BitXor<A, Output=A>, S: DataMut<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Bit xor between self and rhs, and return the result.

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>

The method for the ^ operator

impl<'a, A, S, S2, D, E> BitXor<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where A: Clone + BitXor<A, Output=A>, S: Data<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Bit xor between self and rhs, and return the result.

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

Panics if broadcasting isn’t possible.

type Output = OwnedArray<A, D>

The resulting type after applying the ^ operator

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

The method for the ^ operator

impl<A, S, S2, D, E> Shl<ArrayBase<S2, E>> for ArrayBase<S, D> where A: Clone + Shl<A, Output=A>, S: DataMut<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Left shift between self and rhs, and return the result.

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>

The method for the << operator

impl<'a, A, S, S2, D, E> Shl<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where A: Clone + Shl<A, Output=A>, S: Data<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Left shift between self and rhs, and return the result.

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

Panics if broadcasting isn’t possible.

type Output = OwnedArray<A, D>

The resulting type after applying the << operator

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

The method for the << operator

impl<A, S, S2, D, E> Shr<ArrayBase<S2, E>> for ArrayBase<S, D> where A: Clone + Shr<A, Output=A>, S: DataMut<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Right shift between self and rhs, and return the result.

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>

The method for the >> operator

impl<'a, A, S, S2, D, E> Shr<&'a ArrayBase<S2, E>> for &'a ArrayBase<S, D> where A: Clone + Shr<A, Output=A>, S: Data<Elem=A>, S2: Data<Elem=A>, D: Dimension, E: Dimension
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Perform elementwise Right shift between self and rhs, and return the result.

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

Panics if broadcasting isn’t possible.

type Output = OwnedArray<A, D>

The resulting type after applying the >> operator

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

The method for the >> operator

impl<A, S, D> Neg for ArrayBase<S, D> where A: Clone + Neg<Output=A>, S: DataMut<Elem=A>, D: Dimension
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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, S, D> Not for ArrayBase<S, D> where A: Clone + Not<Output=A>, S: DataMut<Elem=A>, D: Dimension
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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<S: DataClone, D: Clone> Clone for ArrayBase<S, D>
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fn clone(&self) -> ArrayBase<S, D>

Returns a copy of the value. Read more

fn clone_from(&mut self, source: &Self)
1.0.0

Performs copy-assignment from source. Read more

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