Struct PooledArray 

pub struct PooledArray<'a> { /* private fields */ }

A scoped handle that automatically returns an array to the pool

Implementations

impl<'a> PooledArray<'a>

pub fn new(pool: &'a ArrayPool) -> Self

Create a new pooled array handle

pub fn zeros(pool: &'a ArrayPool) -> Self

Create a new pooled array filled with zeros

pub fn as_array(&self) -> &Array1<f32>

Get a reference to the underlying array

pub fn as_array_mut(&mut self) -> &mut Array1<f32>

Get a mutable reference to the underlying array

pub fn take(self) -> Array1<f32>

Take ownership of the array, preventing automatic return

Methods from Deref<Target = Array1<f32>>

pub fn as_layout_ref(&self) -> &LayoutRef<A, D>

Cheaply convert a reference to the array to an &LayoutRef

pub fn as_layout_ref_mut(&mut self) -> &mut LayoutRef<A, D>

Cheaply and mutably convert a reference to the array to an &LayoutRef

pub fn as_raw_ref(&self) -> &RawRef<A, D>

Cheaply convert a reference to the array to an &RawRef

pub fn as_raw_ref_mut(&mut self) -> &mut RawRef<A, D>

where S: RawDataMut<Elem = A>,

Cheaply and mutably convert a reference to the array to an &RawRef

pub fn to_owned(&self) -> ArrayBase<OwnedRepr<A>, D>

where A: Clone, S: Data,

Return an uniquely owned copy of the array.

If the input array is contiguous, 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 collectan 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) -> ArrayBase<OwnedArcRepr<A>, D>

where A: Clone, S: Data,

Return a shared ownership (copy on write) array, cloning the array elements if necessary.

pub fn as_mut_ptr(&mut self) -> *mut A

where S: RawDataMut,

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

This method attempts to unshare the data. If S: DataMut, then the data is guaranteed to be uniquely held on return.

Warning

When accessing elements through this pointer, make sure to use strides obtained after calling this method, since the process of unsharing the data may change the strides.

pub fn raw_view_mut(&mut self) -> ArrayBase<RawViewRepr<*mut A>, D>

where S: RawDataMut,

Return a raw mutable view of the array.

This method attempts to unshare the data. If S: DataMut, then the data is guaranteed to be uniquely held on return.

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_mut(&mut self) -> Option<&mut [A]>

where S: DataMut,

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

In the contiguous case, in order to return a unique reference, this method unshares the data if necessary, but it preserves the existing strides.

pub fn reshape<E>(&self, shape: E) -> ArrayBase<S, <E as IntoDimension>::Dim>

where S: DataShared + DataOwned, A: Clone, E: IntoDimension,

👎Deprecated since 0.16.0: Use .into_shape_with_order() or .to_shape()

Note: Reshape is for ArcArray only. Use .into_shape_with_order() 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.

This method is obsolete, because it is inflexible in how logical order of the array is handled. See ArrayRef::to_shape().

use ndarray::{rcarr1, rcarr2};

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

pub fn permute_axes<T>(&mut self, axes: T)

where T: IntoDimension<Dim = D>,

Permute the axes in-place.

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’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.

Example

use ndarray::{arr2, Array3};

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

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

pub fn reverse_axes(&mut self)

Reverse the axes of the array in-place.

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

pub fn get_ptr<I>(&self, index: I) -> Option<*const A>

where I: NdIndex<D>,

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

use ndarray::arr2;

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

let v = a.raw_view();
let p = a.get_ptr((0, 1)).unwrap();

assert_eq!(unsafe { *p }, 2.);

pub fn get_mut_ptr<I>(&mut self, index: I) -> Option<*mut A>

where S: RawDataMut<Elem = A>, I: NdIndex<D>,

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

use ndarray::arr2;

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

let v = a.raw_view_mut();
let p = a.get_mut_ptr((0, 1)).unwrap();

unsafe {
    *p = 5.;
}

assert_eq!(a.get((0, 1)), Some(&5.));

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 raw_view(&self) -> ArrayBase<RawViewRepr<*const <S as RawData>::Elem>, D>

Return a raw view of the array.

pub fn slice_collapse<I>(&mut self, info: I)

where I: SliceArg<D>,

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

In particular, if an axis is sliced with an index, the axis is collapsed, as in .collapse_axis(), rather than removed, as in .slice_move() or .index_axis_move().

See Slicing for full documentation. See also s!, SliceArg, and SliceInfo.

Panics in the following cases:

• if an index is out of bounds
• if a step size is zero
• if NewAxis is in info, e.g. if NewAxis was used in the s! macro
• if D is IxDyn and info does not match the number of array axes

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 slice_each_axis_inplace<F>(&mut self, f: F)

where F: FnMut(AxisDescription) -> Slice,

Slice the array in place, with a closure specifying the slice for each axis.

This is especially useful for code which is generic over the dimensionality of the array.

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

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 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 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 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 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 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 as Dimension>::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’s 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 push_row( &mut self, row: ArrayBase<ViewRepr<&A>, Dim<[usize; 1]>>, ) -> Result<(), ShapeError>

where A: Clone,

Append a row to an array

The elements from row are cloned and added as a new row in the array.

Errors with a shape error if the length of the row does not match the length of the rows in the array.

The memory layout of the self array matters for ensuring that the append is efficient. Appending automatically changes memory layout of the array so that it is appended to along the “growing axis”. However, if the memory layout needs adjusting, the array must reallocate and move memory.

The operation leaves the existing data in place and is most efficient if one of these is true:

• The axis being appended to is the longest stride axis, i.e the array is in row major (“C”) layout.
• The array has 0 or 1 rows (It is converted to row major)

Ensure appending is efficient by, for example, appending to an empty array and then always pushing/appending along the same axis. For pushing rows, ndarray’s default layout (C order) is efficient.

When repeatedly appending to a single axis, the amortized average complexity of each append is O(m), where m is the length of the row.

use ndarray::{Array, ArrayView, array};

// create an empty array and append
let mut a = Array::zeros((0, 4));
a.push_row(ArrayView::from(&[ 1.,  2.,  3.,  4.])).unwrap();
a.push_row(ArrayView::from(&[-1., -2., -3., -4.])).unwrap();

assert_eq!(
    a,
    array![[ 1.,  2.,  3.,  4.],
           [-1., -2., -3., -4.]]);

pub fn push_column( &mut self, column: ArrayBase<ViewRepr<&A>, Dim<[usize; 1]>>, ) -> Result<(), ShapeError>

where A: Clone,

Append a column to an array

The elements from column are cloned and added as a new column in the array.

Errors with a shape error if the length of the column does not match the length of the columns in the array.

The memory layout of the self array matters for ensuring that the append is efficient. Appending automatically changes memory layout of the array so that it is appended to along the “growing axis”. However, if the memory layout needs adjusting, the array must reallocate and move memory.

The operation leaves the existing data in place and is most efficient if one of these is true:

• The axis being appended to is the longest stride axis, i.e the array is in column major (“F”) layout.
• The array has 0 or 1 columns (It is converted to column major)

Ensure appending is efficient by, for example, appending to an empty array and then always pushing/appending along the same axis. For pushing columns, column major layout (F order) is efficient.

When repeatedly appending to a single axis, the amortized average complexity of each append is O(m), where m is the length of the column.

use ndarray::{Array, ArrayView, array};

// create an empty array and append
let mut a = Array::zeros((2, 0));
a.push_column(ArrayView::from(&[1., 2.])).unwrap();
a.push_column(ArrayView::from(&[-1., -2.])).unwrap();

assert_eq!(
    a,
    array![[1., -1.],
           [2., -2.]]);

pub fn reserve_rows(&mut self, additional: usize) -> Result<(), ShapeError>

Reserve capacity to grow array by at least additional rows.

Existing elements of array are untouched and the backing storage is grown by calling the underlying reserve method of the OwnedRepr.

This is useful when pushing or appending repeatedly to an array to avoid multiple allocations.

Errors with a shape error if the resultant capacity is larger than the addressable bounds; that is, the product of non-zero axis lengths once axis has been extended by additional exceeds isize::MAX.

use ndarray::Array2;
let mut a = Array2::<i32>::zeros((2,4));
a.reserve_rows(1000).unwrap();
assert!(a.into_raw_vec().capacity() >= 4*1002);

pub fn reserve_columns(&mut self, additional: usize) -> Result<(), ShapeError>

Reserve capacity to grow array by at least additional columns.

Existing elements of array are untouched and the backing storage is grown by calling the underlying reserve method of the OwnedRepr.

This is useful when pushing or appending repeatedly to an array to avoid multiple allocations.

Errors with a shape error if the resultant capacity is larger than the addressable bounds; that is, the product of non-zero axis lengths once axis has been extended by additional exceeds isize::MAX.

use ndarray::Array2;
let mut a = Array2::<i32>::zeros((2,4));
a.reserve_columns(1000).unwrap();
assert!(a.into_raw_vec().capacity() >= 2*1002);

pub fn push( &mut self, axis: Axis, array: ArrayBase<ViewRepr<&A>, <D as Dimension>::Smaller>, ) -> Result<(), ShapeError>

where A: Clone, D: RemoveAxis,

Append an array to the array along an axis.

The elements of array are cloned and extend the axis axis in the present array; self will grow in size by 1 along axis.

Append to the array, where the array being pushed to the array has one dimension less than the self array. This method is equivalent to append in this way: self.append(axis, array.insert_axis(axis)).

Errors with a shape error if the shape of self does not match the array-to-append; all axes except the axis along which it being appended matter for this check: the shape of self with axis removed must be the same as the shape of array.

The memory layout of the self array matters for ensuring that the append is efficient. Appending automatically changes memory layout of the array so that it is appended to along the “growing axis”. However, if the memory layout needs adjusting, the array must reallocate and move memory.

The operation leaves the existing data in place and is most efficient if axis is a “growing axis” for the array, i.e. one of these is true:

• The axis is the longest stride axis, for example the 0th axis in a C-layout or the n-1th axis in an F-layout array.
• The axis has length 0 or 1 (It is converted to the new growing axis)

Ensure appending is efficient by for example starting from an empty array and/or always appending to an array along the same axis.

The amortized average complexity of the append, when appending along its growing axis, is O(m) where m is the number of individual elements to append.

The memory layout of the argument array does not matter to the same extent.

use ndarray::{Array, ArrayView, array, Axis};

// create an empty array and push rows to it
let mut a = Array::zeros((0, 4));
let ones  = ArrayView::from(&[1.; 4]);
let zeros = ArrayView::from(&[0.; 4]);
a.push(Axis(0), ones).unwrap();
a.push(Axis(0), zeros).unwrap();
a.push(Axis(0), ones).unwrap();

assert_eq!(
    a,
    array![[1., 1., 1., 1.],
           [0., 0., 0., 0.],
           [1., 1., 1., 1.]]);

pub fn append( &mut self, axis: Axis, array: ArrayBase<ViewRepr<&A>, D>, ) -> Result<(), ShapeError>

where A: Clone, D: RemoveAxis,

Append an array to the array along an axis.

The elements of array are cloned and extend the axis axis in the present array; self will grow in size by array.len_of(axis) along axis.

Errors with a shape error if the shape of self does not match the array-to-append; all axes except the axis along which it being appended matter for this check: the shape of self with axis removed must be the same as the shape of array with axis removed.

The memory layout of the self array matters for ensuring that the append is efficient. Appending automatically changes memory layout of the array so that it is appended to along the “growing axis”. However, if the memory layout needs adjusting, the array must reallocate and move memory.

The operation leaves the existing data in place and is most efficient if axis is a “growing axis” for the array, i.e. one of these is true:

• The axis is the longest stride axis, for example the 0th axis in a C-layout or the n-1th axis in an F-layout array.
• The axis has length 0 or 1 (It is converted to the new growing axis)

Ensure appending is efficient by for example starting from an empty array and/or always appending to an array along the same axis.

The amortized average complexity of the append, when appending along its growing axis, is O(m) where m is the number of individual elements to append.

The memory layout of the argument array does not matter to the same extent.

use ndarray::{Array, ArrayView, array, Axis};

// create an empty array and append two rows at a time
let mut a = Array::zeros((0, 4));
let ones  = ArrayView::from(&[1.; 8]).into_shape_with_order((2, 4)).unwrap();
let zeros = ArrayView::from(&[0.; 8]).into_shape_with_order((2, 4)).unwrap();
a.append(Axis(0), ones).unwrap();
a.append(Axis(0), zeros).unwrap();
a.append(Axis(0), ones).unwrap();

assert_eq!(
    a,
    array![[1., 1., 1., 1.],
           [1., 1., 1., 1.],
           [0., 0., 0., 0.],
           [0., 0., 0., 0.],
           [1., 1., 1., 1.],
           [1., 1., 1., 1.]]);

pub fn reserve( &mut self, axis: Axis, additional: usize, ) -> Result<(), ShapeError>

where D: RemoveAxis,

Reserve capacity to grow array along axis by at least additional elements.

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

Existing elements of array are untouched and the backing storage is grown by calling the underlying reserve method of the OwnedRepr.

This is useful when pushing or appending repeatedly to an array to avoid multiple allocations.

Panics if the axis is out of bounds.

Errors with a shape error if the resultant capacity is larger than the addressable bounds; that is, the product of non-zero axis lengths once axis has been extended by additional exceeds isize::MAX.

use ndarray::{Array3, Axis};
let mut a = Array3::<i32>::zeros((0,2,4));
a.reserve(Axis(0), 1000).unwrap();
assert!(a.into_raw_vec().capacity() >= 2*4*1000);

pub fn nrows(&self) -> usize

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

use ndarray::{array, Axis};

let array = array![[1., 2.],
                   [3., 4.],
                   [5., 6.]];
assert_eq!(array.nrows(), 3);

// equivalent ways of getting the dimensions
// get nrows, ncols by using dim:
let (m, n) = array.dim();
assert_eq!(m, array.nrows());
// get length of any particular axis with .len_of()
assert_eq!(m, array.len_of(Axis(0)));

pub fn ncols(&self) -> usize

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

use ndarray::{array, Axis};

let array = array![[1., 2.],
                   [3., 4.],
                   [5., 6.]];
assert_eq!(array.ncols(), 2);

// equivalent ways of getting the dimensions
// get nrows, ncols by using dim:
let (m, n) = array.dim();
assert_eq!(n, array.ncols());
// get length of any particular axis with .len_of()
assert_eq!(n, array.len_of(Axis(1)));

pub fn is_square(&self) -> bool

Return true if the array is square, false otherwise.

Examples

Square:

use ndarray::array;
let array = array![[1., 2.], [3., 4.]];
assert!(array.is_square());

Not square:

use ndarray::array;
let array = array![[1., 2., 5.], [3., 4., 6.]];
assert!(!array.is_square());

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]);

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.

Note that while the method is similar to ArrayRef::as_slice(), this method transfers the view’s lifetime to the slice, so it is a bit more powerful.

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

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

Note that while the method is similar to ArrayRef::as_slice_memory_order(), this method transfers the view’s lifetime to the slice, so it is a bit more powerful.

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.

pub fn is_unique(&self) -> bool

Returns true iff the inner Arc is not shared. If you want to ensure the Arc is not concurrently cloned, you need to provide a &mut self to this function.

Methods from Deref<Target = ArrayRef<<S as RawData>::Elem, D>>

pub fn view(&self) -> ArrayBase<ViewRepr<&A>, D>

Return a read-only view of the array

pub fn view_mut(&mut self) -> ArrayBase<ViewRepr<&mut A>, D>

Return a read-write view of the array

pub fn cell_view(&mut self) -> ArrayBase<ViewRepr<&MathCell<A>>, D>

Return a shared view of the array with elements as if they were embedded in cells.

The cell view requires a mutable borrow of the array. Once borrowed the cell view itself can be copied and accessed without exclusivity.

The view acts “as if” the elements are temporarily in cells, and elements can be changed through shared references using the regular cell methods.

pub fn to_owned(&self) -> ArrayBase<OwnedRepr<A>, D>

where A: Clone,

Return an uniquely owned copy of the array.

If the input array is contiguous, 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 first(&self) -> Option<&A>

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

Example

use ndarray::Array3;

let mut a = Array3::<f64>::zeros([3, 4, 2]);
a[[0, 0, 0]] = 42.;
assert_eq!(a.first(), Some(&42.));

let b = Array3::<f64>::zeros([3, 0, 5]);
assert_eq!(b.first(), None);

pub fn first_mut(&mut self) -> Option<&mut A>

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

Example

use ndarray::Array3;

let mut a = Array3::<f64>::zeros([3, 4, 2]);
*a.first_mut().unwrap() = 42.;
assert_eq!(a[[0, 0, 0]], 42.);

let mut b = Array3::<f64>::zeros([3, 0, 5]);
assert_eq!(b.first_mut(), None);

pub fn last(&self) -> Option<&A>

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

Example

use ndarray::Array3;

let mut a = Array3::<f64>::zeros([3, 4, 2]);
a[[2, 3, 1]] = 42.;
assert_eq!(a.last(), Some(&42.));

let b = Array3::<f64>::zeros([3, 0, 5]);
assert_eq!(b.last(), None);

pub fn last_mut(&mut self) -> Option<&mut A>

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

Example

use ndarray::Array3;

let mut a = Array3::<f64>::zeros([3, 4, 2]);
*a.last_mut().unwrap() = 42.;
assert_eq!(a[[2, 3, 1]], 42.);

let mut b = Array3::<f64>::zeros([3, 0, 5]);
assert_eq!(b.last_mut(), None);

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

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>

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>

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>

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<I>( &self, info: I, ) -> ArrayBase<ViewRepr<&A>, <I as SliceArg<D>>::OutDim>

where I: SliceArg<D>,

Return a sliced view of the array.

See Slicing for full documentation. See also s!, SliceArg, and SliceInfo.

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<I>( &mut self, info: I, ) -> ArrayBase<ViewRepr<&mut A>, <I as SliceArg<D>>::OutDim>

where I: SliceArg<D>,

Return a sliced read-write view of the array.

See Slicing for full documentation. See also s!, SliceArg, and SliceInfo.

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 as MultiSliceArg<'a, A, D>>::Output

where M: MultiSliceArg<'a, A, D>,

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

See Slicing for full documentation. See also MultiSliceArg, s!, SliceArg, and SliceInfo.

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_axis( &self, axis: Axis, indices: Slice, ) -> ArrayBase<ViewRepr<&A>, D>

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, ) -> ArrayBase<ViewRepr<&mut A>, D>

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_each_axis<F>(&self, f: F) -> ArrayBase<ViewRepr<&A>, D>

where F: FnMut(AxisDescription) -> Slice,

Return a view of a slice of the array, with a closure specifying the slice for each axis.

This is especially useful for code which is generic over the dimensionality of the array.

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

pub fn slice_each_axis_mut<F>(&mut self, f: F) -> ArrayBase<ViewRepr<&mut A>, D>

where F: FnMut(AxisDescription) -> Slice,

Return a mutable view of a slice of the array, with a closure specifying the slice for each axis.

This is especially useful for code which is generic over the dimensionality of the array.

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

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

where I: NdIndex<D>,

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 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 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 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 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 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, ) -> ArrayBase<ViewRepr<&A>, <D as Dimension>::Smaller>

where 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, ) -> ArrayBase<ViewRepr<&mut A>, <D as Dimension>::Smaller>

where 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 select( &self, axis: Axis, indices: &[usize], ) -> ArrayBase<OwnedRepr<A>, D>

where A: Clone, D: RemoveAxis,

Along axis, select arbitrary subviews corresponding to indices 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 rows(&self) -> Lanes<'_, A, <D as Dimension>::Smaller>

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;

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

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

pub fn rows_mut(&mut self) -> LanesMut<'_, A, <D as Dimension>::Smaller>

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 columns(&self) -> Lanes<'_, A, <D as Dimension>::Smaller>

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;

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

pub fn columns_mut(&mut self) -> LanesMut<'_, A, <D as Dimension>::Smaller>

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 as Dimension>::Smaller>

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

When 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 as Dimension>::Smaller>

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 as Dimension>::Smaller>

where 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 as Dimension>::Smaller>

where 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 as Dimension>::Smaller>

where 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 as Dimension>::Smaller>

where 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>

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};

let a = Array::from_iter(0..28).into_shape_with_order((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>

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>,

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>,

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>,

Return a window producer and iterable.

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

This is essentially equivalent to ArrayRef::windows_with_stride() with unit stride.

pub fn windows_with_stride<E>( &self, window_size: E, stride: E, ) -> Windows<'_, A, D>

where E: IntoDimension<Dim = D>,

Return a window producer and iterable.

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

The stride is ordered by the outermost axis.
Hence, a (x₀, x₁, …, xₙ) stride will be applied to (A₀, A₁, …, Aₙ) where Aₓ stands for Axis(x).

This produces all windows that fit within the array for the given stride, assuming the window size is not larger than the array size.

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

Note that passing a stride of only ones is similar to calling ArrayRef::windows().

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

This is the same illustration found in ArrayRef::windows(), 2×2 windows in a 3×4 array, but now with a (1, 2) stride:

         ──▶ Axis(1)

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

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

pub fn axis_windows( &self, axis: Axis, window_size: usize, ) -> AxisWindows<'_, A, D>

Returns a producer which traverses over all windows of a given length along an axis.

The windows are all distinct, possibly-overlapping views. The shape of each window is the shape of self, with the length of axis replaced with window_size.

Panics if axis is out-of-bounds or if window_size is zero.

use ndarray::{Array3, Axis, s};

let arr = Array3::from_shape_fn([4, 5, 2], |(i, j, k)| i * 100 + j * 10 + k);
let correct = vec![
    arr.slice(s![.., 0..3, ..]),
    arr.slice(s![.., 1..4, ..]),
    arr.slice(s![.., 2..5, ..]),
];
for (window, correct) in arr.axis_windows(Axis(1), 3).into_iter().zip(&correct) {
    assert_eq!(window, correct);
    assert_eq!(window.shape(), &[4, 3, 2]);
}

pub fn axis_windows_with_stride( &self, axis: Axis, window_size: usize, stride_size: usize, ) -> AxisWindows<'_, A, D>

Returns a producer which traverses over windows of a given length and stride along an axis.

Note that a calling this method with a stride of 1 is equivalent to calling ArrayRef::axis_windows().

pub fn diag(&self) -> ArrayBase<ViewRepr<&A>, Dim<[usize; 1]>>

Return a 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) -> ArrayBase<ViewRepr<&mut A>, Dim<[usize; 1]>>

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

pub fn as_standard_layout(&self) -> ArrayBase<CowRepr<'_, A>, D>

where 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_slice(&self) -> Option<&[A]>

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]>

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]>

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.

pub fn as_slice_memory_order_mut(&mut self) -> Option<&mut [A]>

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

In the contiguous case, in order to return a unique reference, this method unshares the data if necessary, but it preserves the existing strides.

pub fn to_shape<E>( &self, new_shape: E, ) -> Result<ArrayBase<CowRepr<'_, A>, <E as ShapeArg>::Dim>, ShapeError>

where E: ShapeArg, A: Clone,

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

order specifies the logical order in which the array is to be read and reshaped. The array is returned as a CowArray; a view if possible, otherwise an owned array.

For example, when starting from the one-dimensional sequence 1 2 3 4 5 6, it would be understood as a 2 x 3 array in row major (“C”) order this way:

1 2 3
4 5 6

and as 2 x 3 in column major (“F”) order this way:

1 3 5
2 4 6

This example should show that any time we “reflow” the elements in the array to a different number of rows and columns (or more axes if applicable), it is important to pick an index ordering, and that’s the reason for the function parameter for order.

The new_shape parameter should be a dimension and an optional order like these examples:

(3, 4)                          // Shape 3 x 4 with default order (RowMajor)
((3, 4), Order::RowMajor))      // use specific order
((3, 4), Order::ColumnMajor))   // use specific order
((3, 4), Order::C))             // use shorthand for order - shorthands C and F

Errors if the new shape doesn’t have the same number of elements as the array’s current shape.

Example

use ndarray::array;
use ndarray::Order;

assert!(
    array![1., 2., 3., 4., 5., 6.].to_shape(((2, 3), Order::RowMajor)).unwrap()
    == array![[1., 2., 3.],
              [4., 5., 6.]]
);

assert!(
    array![1., 2., 3., 4., 5., 6.].to_shape(((2, 3), Order::ColumnMajor)).unwrap()
    == array![[1., 3., 5.],
              [2., 4., 6.]]
);

pub fn flatten(&self) -> ArrayBase<CowRepr<'_, A>, Dim<[usize; 1]>>

where A: Clone,

Flatten the array to a one-dimensional array.

The array is returned as a CowArray; a view if possible, otherwise an owned array.

use ndarray::{arr1, arr3};

let array = arr3(&[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]);
let flattened = array.flatten();
assert_eq!(flattened, arr1(&[1, 2, 3, 4, 5, 6, 7, 8]));

pub fn flatten_with_order( &self, order: Order, ) -> ArrayBase<CowRepr<'_, A>, Dim<[usize; 1]>>

where A: Clone,

Flatten the array to a one-dimensional array.

order specifies the logical order in which the array is to be read and reshaped. The array is returned as a CowArray; a view if possible, otherwise an owned array.

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

let array = arr2(&[[1, 2], [3, 4], [5, 6], [7, 8]]);
let flattened = array.flatten_with_order(Order::RowMajor);
assert_eq!(flattened, arr1(&[1, 2, 3, 4, 5, 6, 7, 8]));
let flattened = array.flatten_with_order(Order::ColumnMajor);
assert_eq!(flattened, arr1(&[1, 3, 5, 7, 2, 4, 6, 8]));

pub fn broadcast<E>( &self, dim: E, ) -> Option<ArrayBase<ViewRepr<&A>, <E as IntoDimension>::Dim>>

where E: IntoDimension,

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 t(&self) -> ArrayBase<ViewRepr<&A>, D>

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 assign<E>(&mut self, rhs: &ArrayRef<A, E>)

where E: Dimension, A: Clone,

Perform an elementwise assignment to self from rhs.

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

Panics if broadcasting isn’t possible.

pub fn assign_to<P>(&self, to: P)

where P: IntoNdProducer<Dim = D>, <P as IntoNdProducer>::Item: AssignElem<A>, A: Clone,

Perform an elementwise assignment of values cloned from self into array or producer to.

The destination to can be another array or a producer of assignable elements. AssignElem determines how elements are assigned.

Panics if shapes disagree.

pub fn fill(&mut self, x: A)

where A: Clone,

Perform an elementwise assignment to self from element x.

pub fn zip_mut_with<B, E, F>(&mut self, rhs: &ArrayRef<B, E>, f: F)

where 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,

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) -> ArrayBase<OwnedRepr<B>, D>

where F: FnMut(&'a A) -> B, A: 'a,

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) -> ArrayBase<OwnedRepr<B>, D>

where F: FnMut(&'a mut A) -> B, A: 'a,

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) -> ArrayBase<OwnedRepr<B>, D>

where F: FnMut(A) -> B, A: Clone,

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 map_inplace<'a, F>(&'a mut self, f: F)

where A: 'a, F: FnMut(&'a 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 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 for_each<'a, F>(&'a self, f: F)

where F: FnMut(&'a A), A: 'a,

Call f for each element in the array.

Elements are visited in arbitrary order.

pub fn fold_axis<B, F>( &self, axis: Axis, init: B, fold: F, ) -> ArrayBase<OwnedRepr<B>, <D as Dimension>::Smaller>

where D: RemoveAxis, F: FnMut(&B, &A) -> B, B: Clone,

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, ) -> ArrayBase<OwnedRepr<B>, <D as Dimension>::Smaller>

where D: RemoveAxis, F: FnMut(ArrayBase<ViewRepr<&'a A>, Dim<[usize; 1]>>) -> B, A: 'a,

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, ) -> ArrayBase<OwnedRepr<B>, <D as Dimension>::Smaller>

where D: RemoveAxis, F: FnMut(ArrayBase<ViewRepr<&'a mut A>, Dim<[usize; 1]>>) -> B, A: 'a,

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 remove_index(&mut self, axis: Axis, index: usize)

Remove the indexth elements along axis and shift down elements from higher indexes.

Note that this “removes” the elements by swapping them around to the end of the axis and shortening the length of the axis; the elements are not deinitialized or dropped by this, just moved out of view (this only matters for elements with ownership semantics). It’s similar to slicing an owned array in place.

Decreases the length of axis by one.

Panics if axis is out of bounds
Panics if not index < self.len_of(axis).

pub fn accumulate_axis_inplace<F>(&mut self, axis: Axis, f: F)

where F: FnMut(&A, &mut A),

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]],
    ],
);

pub fn partition(&self, kth: usize, axis: Axis) -> ArrayBase<OwnedRepr<A>, D>

where A: Clone + Ord + Zero, D: Dimension,

Return a partitioned copy of the array.

Creates a copy of the array and partially sorts it around the k-th element along the given axis. The k-th element will be in its sorted position, with:

• All elements smaller than the k-th element to its left
• All elements equal or greater than the k-th element to its right
• The ordering within each partition is undefined

Empty arrays (i.e., those with any zero-length axes) are considered partitioned already, and will be returned unchanged.

Panics if k is out of bounds for a non-zero axis length.

Parameters

• kth - Index to partition by. The k-th element will be in its sorted position.
• axis - Axis along which to partition.

Examples

use ndarray::prelude::*;

let a = array![7, 1, 5, 2, 6, 0, 3, 4];
let p = a.partition(3, Axis(0));

// The element at position 3 is now 3, with smaller elements to the left
// and greater elements to the right
assert_eq!(p[3], 3);
assert!(p.slice(s![..3]).iter().all(|&x| x <= 3));
assert!(p.slice(s![4..]).iter().all(|&x| x >= 3));

pub fn par_map_inplace<F>(&mut self, f: F)

where F: Fn(&mut A) + Sync + Send,

Parallel version of map_inplace.

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

Elements are visited in arbitrary order.

pub fn par_mapv_inplace<F>(&mut self, f: F)

where F: Fn(A) -> A + Sync + Send, A: Clone,

Parallel version of mapv_inplace.

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.

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 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 cumprod(&self, axis: Axis) -> ArrayBase<OwnedRepr<A>, D>

where A: Clone + Mul<Output = A> + MulAssign, D: Dimension + RemoveAxis,

Return the cumulative product of elements along a given axis.

use ndarray::{arr2, Axis};

let a = arr2(&[[1., 2., 3.],
               [4., 5., 6.]]);

// Cumulative product along rows (axis 0)
assert_eq!(
    a.cumprod(Axis(0)),
    arr2(&[[1., 2., 3.],
          [4., 10., 18.]])
);

// Cumulative product along columns (axis 1)
assert_eq!(
    a.cumprod(Axis(1)),
    arr2(&[[1., 2., 6.],
          [4., 20., 120.]])
);

Panics if axis is out of bounds.

pub fn var(&self, ddof: A) -> A

where A: Float + FromPrimitive,

Return variance of elements in the array.

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 array.

Panics if ddof is less than zero or greater than n

Example

use ndarray::array;
use approx::assert_abs_diff_eq;

let a = array![1., -4.32, 1.14, 0.32];
let var = a.var(1.);
assert_abs_diff_eq!(var, 6.7331, epsilon = 1e-4);

pub fn std(&self, ddof: A) -> A

where A: Float + FromPrimitive,

Return standard deviation of elements in the array.

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 array.

Panics if ddof is less than zero or greater than n

Example

use ndarray::array;
use approx::assert_abs_diff_eq;

let a = array![1., -4.32, 1.14, 0.32];
let stddev = a.std(1.);
assert_abs_diff_eq!(stddev, 2.59483, epsilon = 1e-4);

pub fn sum_axis( &self, axis: Axis, ) -> ArrayBase<OwnedRepr<A>, <D as Dimension>::Smaller>

where A: Clone + Zero<Output = A> + Add, 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 product_axis( &self, axis: Axis, ) -> ArrayBase<OwnedRepr<A>, <D as Dimension>::Smaller>

where A: Clone + One<Output = A> + Mul, D: RemoveAxis,

Return product along axis.

The product of an empty array is 1.

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

let a = arr2(&[[1., 2., 3.],
               [4., 5., 6.]]);

assert!(
    a.product_axis(Axis(0)) == aview1(&[4., 10., 18.]) &&
    a.product_axis(Axis(1)) == aview1(&[6., 120.]) &&

    a.product_axis(Axis(0)).product_axis(Axis(0)) == aview0(&720.)
);

Panics if axis is out of bounds.

pub fn mean_axis( &self, axis: Axis, ) -> Option<ArrayBase<OwnedRepr<A>, <D as Dimension>::Smaller>>

where A: Clone + Zero<Output = A> + FromPrimitive + Add + 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, ) -> ArrayBase<OwnedRepr<A>, <D as Dimension>::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, ) -> ArrayBase<OwnedRepr<A>, <D as Dimension>::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 diff(&self, n: usize, axis: Axis) -> ArrayBase<OwnedRepr<A>, D>

where A: Sub<Output = A> + Zero + Clone,

Calculates the (forward) finite differences of order n, along the axis. For the 1D-case, n==1, this means: diff[i] == arr[i+1] - arr[i]

For n>=2, the process is iterated:

use ndarray::{array, Axis};
let arr = array![1.0, 2.0, 5.0];
assert_eq!(arr.diff(2, Axis(0)), arr.diff(1, Axis(0)).diff(1, Axis(0)))

Panics if axis is out of bounds

Panics if n is too big / the array is to short:

use ndarray::{array, Axis};
array![1.0, 2.0, 3.0].diff(10, Axis(0));

pub fn is_nan(&self) -> ArrayBase<OwnedRepr<bool>, D>

If the number is NaN (not a number), then true is returned for each element.

pub fn is_all_nan(&self) -> bool

Return true if all elements are NaN (not a number).

pub fn is_any_nan(&self) -> bool

Return true if any element is NaN (not a number).

pub fn is_infinite(&self) -> ArrayBase<OwnedRepr<bool>, D>

If the number is infinity, then true is returned for each element.

pub fn is_all_infinite(&self) -> bool

Return true if all elements are infinity.

pub fn is_any_infinite(&self) -> bool

Return true if any element is infinity.

pub fn floor(&self) -> ArrayBase<OwnedRepr<A>, D>

The largest integer less than or equal to each element.

pub fn ceil(&self) -> ArrayBase<OwnedRepr<A>, D>

The smallest integer less than or equal to each element.

pub fn round(&self) -> ArrayBase<OwnedRepr<A>, D>

The nearest integer of each element.

pub fn trunc(&self) -> ArrayBase<OwnedRepr<A>, D>

The integer part of each element.

pub fn fract(&self) -> ArrayBase<OwnedRepr<A>, D>

The fractional part of each element.

pub fn abs(&self) -> ArrayBase<OwnedRepr<A>, D>

Absolute of each element.

pub fn signum(&self) -> ArrayBase<OwnedRepr<A>, D>

Sign number of each element.

• 1.0 for all positive numbers.
• -1.0 for all negative numbers.
• NaN for all NaN (not a number).

pub fn recip(&self) -> ArrayBase<OwnedRepr<A>, D>

The reciprocal (inverse) of each element, 1/x.

pub fn sqrt(&self) -> ArrayBase<OwnedRepr<A>, D>

Square root of each element.

pub fn exp(&self) -> ArrayBase<OwnedRepr<A>, D>

e^x of each element (exponential function).

pub fn exp2(&self) -> ArrayBase<OwnedRepr<A>, D>

2^x of each element.

pub fn exp_m1(&self) -> ArrayBase<OwnedRepr<A>, D>

e^x - 1 of each element.

pub fn ln(&self) -> ArrayBase<OwnedRepr<A>, D>

Natural logarithm of each element.

pub fn log2(&self) -> ArrayBase<OwnedRepr<A>, D>

Base 2 logarithm of each element.

pub fn log10(&self) -> ArrayBase<OwnedRepr<A>, D>

Base 10 logarithm of each element.

pub fn ln_1p(&self) -> ArrayBase<OwnedRepr<A>, D>

ln(1 + x) of each element.

pub fn cbrt(&self) -> ArrayBase<OwnedRepr<A>, D>

Cubic root of each element.

pub fn sin(&self) -> ArrayBase<OwnedRepr<A>, D>

Sine of each element (in radians).

pub fn cos(&self) -> ArrayBase<OwnedRepr<A>, D>

Cosine of each element (in radians).

pub fn tan(&self) -> ArrayBase<OwnedRepr<A>, D>

Tangent of each element (in radians).

pub fn asin(&self) -> ArrayBase<OwnedRepr<A>, D>

Arcsine of each element (return in radians).

pub fn acos(&self) -> ArrayBase<OwnedRepr<A>, D>

Arccosine of each element (return in radians).

pub fn atan(&self) -> ArrayBase<OwnedRepr<A>, D>

Arctangent of each element (return in radians).

pub fn sinh(&self) -> ArrayBase<OwnedRepr<A>, D>

Hyperbolic sine of each element.

pub fn cosh(&self) -> ArrayBase<OwnedRepr<A>, D>

Hyperbolic cosine of each element.

pub fn tanh(&self) -> ArrayBase<OwnedRepr<A>, D>

Hyperbolic tangent of each element.

pub fn asinh(&self) -> ArrayBase<OwnedRepr<A>, D>

Inverse hyperbolic sine of each element.

pub fn acosh(&self) -> ArrayBase<OwnedRepr<A>, D>

Inverse hyperbolic cosine of each element.

pub fn atanh(&self) -> ArrayBase<OwnedRepr<A>, D>

Inverse hyperbolic tangent of each element.

pub fn to_degrees(&self) -> ArrayBase<OwnedRepr<A>, D>

Converts radians to degrees for each element.

pub fn to_radians(&self) -> ArrayBase<OwnedRepr<A>, D>

Converts degrees to radians for each element.

pub fn powi(&self, rhs: i32) -> ArrayBase<OwnedRepr<A>, D>

Integer power of each element.

This function is generally faster than using float power.

pub fn powf(&self, rhs: A) -> ArrayBase<OwnedRepr<A>, D>

Float power of each element.

pub fn log(&self, rhs: A) -> ArrayBase<OwnedRepr<A>, D>

Logarithm of each element with respect to an arbitrary base.

pub fn abs_sub(&self, rhs: A) -> ArrayBase<OwnedRepr<A>, D>

The positive difference between given number and each element.

pub fn hypot(&self, rhs: A) -> ArrayBase<OwnedRepr<A>, D>

Length of the hypotenuse of a right-angle triangle of each element

pub fn pow2(&self) -> ArrayBase<OwnedRepr<A>, D>

Square (two powers) of each element.

pub fn clamp(&self, min: A, max: A) -> ArrayBase<OwnedRepr<A>, D>

Limit the values for each element, similar to NumPy’s clip function.

use ndarray::array;

let a = array![0., 1., 2., 3., 4., 5., 6., 7., 8., 9.];
assert_eq!(a.clamp(1., 8.), array![1., 1., 2., 3., 4., 5., 6., 7., 8., 8.]);
assert_eq!(a.clamp(3., 6.), array![3., 3., 3., 3., 4., 5., 6., 6., 6., 6.]);

Panics

Panics if !(min <= max).

pub fn scaled_add<E>(&mut self, alpha: A, rhs: &ArrayRef<A, E>)

where 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.

pub fn abs_diff_eq<B>( &self, other: &ArrayRef<B, D>, epsilon: <A as AbsDiffEq<B>>::Epsilon, ) -> bool

where A: AbsDiffEq<B>, <A as AbsDiffEq<B>>::Epsilon: Clone,

A test for equality that uses the elementwise absolute difference to compute the approximate equality of two arrays.

pub fn relative_eq<B>( &self, other: &ArrayRef<B, D>, epsilon: <A as AbsDiffEq<B>>::Epsilon, max_relative: <A as AbsDiffEq<B>>::Epsilon, ) -> bool

where A: RelativeEq<B>, <A as AbsDiffEq<B>>::Epsilon: Clone,

A test for equality that uses an elementwise relative comparison if the values are far apart; and the absolute difference otherwise.

pub fn triu(&self, k: isize) -> ArrayBase<OwnedRepr<A>, D>

Upper triangular of an array.

Return a copy of the array with elements below the k-th diagonal zeroed. For arrays with ndim exceeding 2, triu will apply to the final two axes. For 0D and 1D arrays, triu will return an unchanged clone.

See also ArrayRef::tril

use ndarray::array;

let arr = array![
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
];
assert_eq!(
    arr.triu(0),
    array![
        [1, 2, 3],
        [0, 5, 6],
        [0, 0, 9]
    ]
);

pub fn tril(&self, k: isize) -> ArrayBase<OwnedRepr<A>, D>

Lower triangular of an array.

Return a copy of the array with elements above the k-th diagonal zeroed. For arrays with ndim exceeding 2, tril will apply to the final two axes. For 0D and 1D arrays, tril will return an unchanged clone.

See also ArrayRef::triu

use ndarray::array;

let arr = array![
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
];
assert_eq!(
    arr.tril(0),
    array![
        [1, 0, 0],
        [4, 5, 0],
        [7, 8, 9]
    ]
);

Methods from Deref<Target = RawRef<A, D>>

pub fn get_ptr<I>(&self, index: I) -> Option<*const A>

where I: NdIndex<D>,

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

use ndarray::arr2;

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

let v = a.raw_view();
let p = a.get_ptr((0, 1)).unwrap();

assert_eq!(unsafe { *p }, 2.);

pub fn get_mut_ptr<I>(&mut self, index: I) -> Option<*mut A>

where I: NdIndex<D>,

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

use ndarray::arr2;

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

let v = a.raw_view_mut();
let p = a.get_mut_ptr((0, 1)).unwrap();

unsafe {
    *p = 5.;
}

assert_eq!(a.get((0, 1)), Some(&5.));

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

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

pub fn raw_view(&self) -> ArrayBase<RawViewRepr<*const A>, D>

Return a raw view of the array.

pub fn raw_view_mut(&mut self) -> ArrayBase<RawViewRepr<*mut A>, D>

Return a raw mutable view of the array.

Methods from Deref<Target = LayoutRef<A, D>>

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 as Dimension>::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’s 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 slice_collapse<I>(&mut self, info: I)

where I: SliceArg<D>,

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

In particular, if an axis is sliced with an index, the axis is collapsed, as in .collapse_axis(), rather than removed, as in .slice_move() or .index_axis_move().

See Slicing for full documentation. See also s!, SliceArg, and SliceInfo.

Panics in the following cases:

• if an index is out of bounds
• if a step size is zero
• if SliceInfoElem::NewAxis is in info, e.g. if NewAxis was used in the s! macro
• if D is IxDyn and info does not match the number of array axes

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 slice_each_axis_inplace<F>(&mut self, f: F)

where F: FnMut(AxisDescription) -> Slice,

Slice the array in place, with a closure specifying the slice for each axis.

This is especially useful for code which is generic over the dimensionality of the array.

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

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 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 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 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.

Trait Implementations

impl Deref for PooledArray<'_>

type Target = ArrayBase<OwnedRepr<f32>, Dim<[usize; 1]>>

The resulting type after dereferencing.

fn deref(&self) -> &Self::Target

Dereferences the value.

impl DerefMut for PooledArray<'_>

fn deref_mut(&mut self) -> &mut Self::Target

Mutably dereferences the value.

impl Drop for PooledArray<'_>

fn drop(&mut self)

Executes the destructor for this type.