Static.jl defines a limited set of statically parameterized types and a common interface that is shared between them.

This repository implements the scratch spaces API for package-specific mutable containers of data. These spaces can contain datasets, text, binaries, or any other kind of data that would be convenient to store in a location specific to your package. As compared to Artifacts, these containers of data are mutable. Because the scratch space location on disk is not very user-friendly, scratch spaces should, in general, not be used for a storing files that the user must interact with through a file browser.

When visualizing images, it is not uncommon to provide a 2D view of different image sources. For example, comparing multiple images of different sizes, getting a preview of machine learning dataset. This package aims to provide easy-to-use tools for such tasks.

This package provides support for image resizing, image rotation, and other spatial transformations of arrays.

This package provides a pooled representation of arrays for purposes of compression when there are few unique elements.

This package provides a combinatorics library for Julia, focusing mostly (as of now) on enumerative combinatorics and permutations.

This package offers Python-style general formatting and c-style numerical formatting.

This package provides a collection of useful extensions for Julia's built-in docsystem. These are features that are not yet mature enough to be considered for inclusion in Base, or that have sufficiently niche use cases that including them with the default Julia installation is not seen as valuable enough at this time.

This package implements OrderedDicts and OrderedSets, which are similar to containers in base Julia. However, during iteration the Ordered* containers return items in the order in which they were added to the collection.

This package for the Julia language provides an array type (the AxisArray) that knows about its dimension names and axis values. This allows for indexing by name without incurring any runtime overhead. This permits one to implement algorithms that are oblivious to the storage order of the underlying arrays. AxisArrays can also be indexed by the values along their axes, allowing column names or interval selections.

Extents.jl is a small package that defines an Extent object that can be used by the different Julia spatial data packages. Extent is a wrapper for a NamedTuple of tuples holding the lower and upper bounds for each dimension of a object.

This package provides the ability to directly call and fully interoperate with Python from the Julia language. You can import arbitrary Python modules from Julia, call Python functions (with automatic conversion of types between Julia and Python), define Python classes from Julia methods, and share large data structures between Julia and Python without copying them.

This package provides the type DataValue that is used to represent missing data.

This package provides number datatypes which store their values in type parameters, making them runtime constants.

This package is the counterpart of AbstractArray interface, but for GPU array types. It provides functionality and tooling to speed-up development of new GPU array types. This package is not intended for end users; instead, you should use one of the packages that builds on GPUArrays.jl, such as CUDA.jl, oneAPI.jl or AMDGPU.jl.

This package provides representations for infinity and negative infinity in Julia.

An IndirectArray is one that encodes data using a combination of an index and a value table. Each element is assigned its own index, which is used to retrieve the value from the value table. Among other uses, IndirectArrays can represent indexed images, sometimes called "colormap images" or "paletted images."

This package supports SI units and also many other unit system.

The DualNumbers Julia package defines the Dual type to represent dual numbers, and supports standard mathematical operations on them. Conversions and promotions are defined to allow performing operations on combinations of dual numbers with predefined Julia numeric types.

Implementations of basic math functions which return NaN instead of throwing a DomainError.

This package takes a string or buffer containing Julia code, performs lexical analysis and returns a stream of tokens.

This package provides support for the Woodbury matrix identity for the Julia programming language. This is a generalization of the Sherman-Morrison formula. Note that the Woodbury matrix identity is notorious for floating-point roundoff errors, so be prepared for a certain amount of inaccuracy in the result.

This package provides a summary of available CPU features in Julia.

This package contains the testset from Julia, packaged into a loadable module.