ndarray - Rust (original) (raw)
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The ndarray crate provides an _n_-dimensional container for general elements and for numerics.
In _n_-dimensional we include, for example, 1-dimensional rows or columns, 2-dimensional matrices, and higher dimensional arrays. If the array has _n_dimensions, then an element in the array is accessed by using that many indices. Each dimension is also called an axis.
- ArrayBase: The _n_-dimensional array type itself.
It is used to implement both the owned arrays and the views; see its docs for an overview of all array features. - The main specific array type is Array, which owns its elements.
§Highlights
- Generic _n_-dimensional array
- Slicing, also with arbitrary step size, and negative indices to mean elements from the end of the axis.
- Views and subviews of arrays; iterators that yield subviews.
- Higher order operations and arithmetic are performant
- Array views can be used to slice and mutate any
[T]data usingArrayView::fromandArrayViewMut::from. - Zip for lock step function application across two or more arrays or other item producers (NdProducer trait).
§Crate Status
- Still iterating on and evolving the crate
- The crate is continuously developing, and breaking changes are expected during evolution from version to version. We adopt the newest stable rust features if we need them.
- Note that functions/methods/traits/etc. hidden from the docs are not considered part of the public API, so changes to them are not considered breaking changes.
- Performance:
- Prefer higher order methods and arithmetic operations on arrays first, then iteration, and as a last priority using indexed algorithms.
- The higher order functions like .map(),.map_inplace(), .zip_mut_with(),Zip and azip!() are the most efficient ways to perform single traversal and lock step traversal respectively.
- Performance of an operation depends on the memory layout of the array or array view. Especially if it’s a binary operation, which needs matching memory layout to be efficient (with some exceptions).
- Efficient floating point matrix multiplication even for very large matrices; can optionally use BLAS to improve it further.
- MSRV: Requires Rust 1.64 or later
§Crate Feature Flags
The following crate feature flags are available. They are configured in yourCargo.toml. See doc::crate_feature_flags for more information.
std: Rust standard library-using functionality (enabled by default)serde: serialization support for serde 1.xrayon: Parallel iterators, parallelized methods, the parallel module and par_azip!.approxImplementations of traits from the approx crate.blas: transparent BLAS support for matrix multiplication, needs configuration.matrixmultiply-threading: Use threading frommatrixmultiply.
§Documentation
- The docs for ArrayBase provide an overview of the _n_-dimensional array type. Other good pages to look at are the documentation for the s![] andazip!() macros.
- If you have experience with NumPy, you may also be interested inndarray_for_numpy_users.
§The ndarray ecosystem
ndarray provides a lot of functionality, but it’s not a one-stop solution.
ndarray includes matrix multiplication and other binary/unary operations out of the box. More advanced linear algebra routines (e.g. SVD decomposition or eigenvalue computation) can be found in ndarray-linalg.
The same holds for statistics: ndarray provides some basic functionalities (e.g. mean) but more advanced routines can be found in ndarray-stats.
If you are looking to generate random arrays instead, check out ndarray-rand.
For conversion between ndarray, nalgebra andimage check out nshare.
Standalone documentation pages.
Producers, iterables and iterators.
Linear algebra.
Parallelization features for ndarray.
ndarray prelude.
Create an Array with one, two, three, four, five, or six dimensions.
Array zip macro: lock step function application across several arrays and producers.
Concatenate arrays along the given axis.
Parallelized array zip macro: lock step function application across several arrays and producers.
Slice argument constructor.
Stack arrays along the new axis.
An _n_-dimensional array.
An axis index.
Description of the axis, its length and its stride.
Dimension description.
Dynamic dimension or index type.
An iterator of a sequence of evenly spaced floats.
An iterator of a sequence of logarithmically spaced number.
A transparent wrapper of Cell which is identical in every way, except it will implement arithmetic operators as well.
Token to represent a new axis in a slice description.
ArcArray’s representation.
Array’s representation.
Array pointer’s representation.
A contiguous array shape of n dimensions.
An error related to array shape or layout.
A slice (range with step size).
Represents all of the necessary information to perform a slice.
An array shape of n dimensions in c-order, f-order or custom strides.
Array view’s representation.
Lock step function application across several arrays or other producers.
CowArray’s representation.
Error code for an error related to array shape or layout.
Value controlling the execution of .fold_while on Zip.
Array order
A slice (range with step), an index, or a new axis token.
Argument conversion into an array view
A producer element that can be assigned to once
Array representation trait.
Array representation trait.
Array representation trait.
Array representation trait.
Adds the two dimensions at compile time.
Array shape and index trait.
Extra indexing methods for array views
Argument conversion a dimension.
Argument conversion into a producer.
Elements that support linear algebra operations.
Slicing information describing multiple mutable, disjoint slices.
Floating-point element types f32 and f64.
Tuple or fixed size arrays that can be used to index an array.
A producer of an n-dimensional set of elements; for example an array view, mutable array view or an iterator that yields chunks.
Array representation trait.
Array representation trait.
Array representation trait.
Array representation trait.
Array shape with a next smaller dimension.
Elements that can be used as direct operands in arithmetic with arrays.
Array shape argument with optional order parameter
A trait for Shape and D where D: Dimension that allows customizing the memory layout (strides) of an array shape.
A type that can slice an array of dimension D.
Create a new dimension value.
Create a zero-dimensional index
Create a one-dimensional index
Create a two-dimensional index
Create a three-dimensional index
Create a four-dimensional index
Create a five-dimensional index
Create a six-dimensional index
Create a dynamic-dimensional index
Create a zero-dimensional array with the element x.
Create a one-dimensional array with elements from xs.
Create a two-dimensional array with elements from xs.
Create a three-dimensional array with elements from xs.
Create a zero-dimensional array view borrowing x.
Create a one-dimensional array view with elements borrowing xs.
Create a two-dimensional array view with elements borrowing xs.
Create a one-dimensional read-write array view with elements borrowing xs.
Create a two-dimensional read-write array view with elements borrowing xs.
Concatenate arrays along the given axis.
Create an iterable of the array shape shape.
Return an iterable of the indices of the passed-in array.
Return an iterator of evenly spaced floats.
An iterator of a sequence of logarithmically spaced numbers.
Return an iterator of floats from a to b (exclusive), incrementing by step.
Create a one-dimensional array with elements from xs.
Create a two-dimensional array with elements from xs.
Create a three-dimensional array with elements from xs.
Stack arrays along the new axis.
An array where the data has shared ownership and is copy on write.
one-dimensional shared ownership array
two-dimensional shared ownership array
An array that owns its data uniquely.
zero-dimensional array
one-dimensional array
two-dimensional array
three-dimensional array
four-dimensional array
five-dimensional array
six-dimensional array
dynamic-dimensional array
A read-only array view.
zero-dimensional array view
one-dimensional array view
two-dimensional array view
three-dimensional array view
four-dimensional array view
five-dimensional array view
six-dimensional array view
dynamic-dimensional array view
A read-write array view.
zero-dimensional read-write array view
one-dimensional read-write array view
two-dimensional read-write array view
three-dimensional read-write array view
four-dimensional read-write array view
five-dimensional read-write array view
six-dimensional read-write array view
dynamic-dimensional read-write array view
An array with copy-on-write behavior.
Array index type
zero-dimensionial
one-dimensional
two-dimensional
three-dimensional
four-dimensional
five-dimensional
six-dimensional
dynamic-dimensional
Array index type (signed)
A read-only array view without a lifetime.
A mutable array view without a lifetime.