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0.15.3 | Jun 5, 2021 |
0.3.0-alpha.1 | Dec 27, 2015 |
#9 in Data structures
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Used in 2,026 crates
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ndarray
implements 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.
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::from
andArrayViewMut::from
. Zip
for lock step function application across two or more arrays or other item producers (NdProducer
trait).
lib.rs
:
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::from
andArrayViewMut::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
] andazip!()
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 your
Cargo.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, theparallel
module andpar_azip!
.approx
Implementations of traits from theapprox
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 thes![]
andazip!()
macros. -
If you have experience with NumPy, you may also be interested in
ndarray_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
and
image
check out nshare
.
Dependencies
~0.4–1.3MB
~26K SLoC