This package provides a functionality of files download with cURL, wget or HTTP.jl backends.

This package implements image show methods suitable for graphical platforms such as IJulia. It is intended to provide convenient inline presentation of greyscale or color images.

The aim of this package is to provide users with a set of small generic routines useful above all in astronomical and astrophysical context, written in Julia.

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.

The purpose of this package is partly to extend linear algebra functionality in base to cover generic element types, e.g. BigFloat and Quaternion, and partly to be a place to experiment with fast linear algebra routines written in Julia (except for optimized BLAS).

This module provides a Julia interface to GR, a framework for visualisation applications.

This package provides a collection of useful bit-twiddling tricks, ready to use as functions, with detailed documentation and example real-world use cases.

This package provides tools for transcoding data streams which are:

• fast: small overhead and specialized methods

• consistent: basic I/O operations work as expected

• generic: support any I/O objects like files, buffers, pipes, etc.

• extensible: easy definition for new codec to transcode data

This package defines the Bijection data type. A Bijection data structure behaves similar to a Dict, however it prevents assigning the same value to two different keys.

This package provides a canonical set of default initial values and identity elements for Julia.

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.

This package generates formatted output from timings made in different sections of a program.

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 just exports one type: the InvertedIndex, or Not for short. It can wrap any supported index type and may be used as an index into any AbstractArray subtype, including OffsetArrays.

This package provides a lightweight string parsing and representation of angles.

CommonSolve.jl provides solve, init, solve!, and step! commands. By using the same definition, solver libraries from other completely different ecosystems can extend the functions and thus not clash with SciML if both ecosystems export the solve command.

This package provides definitions for common functions that are useful for symbolic expression manipulation in Julia. Its purpose is to provide a shared interface between various symbolic programming packages, for example SymbolicUtils.jl, Symbolics.jl, and Metatheory.jl.

This package provides a namespace for data-related generic function definitions to solve the optional dependency problem; packages wishing to share and/or extend functions can avoid depending directly on each other by moving the function definition to DataAPI.jl and each package taking a dependency on it.

LeapSeconds provides a functionality to return the difference between TAI and UTC or vice versa for a given date. For dates after 1972-01-01, this is the number of leap seconds.

This package provides a display system which enables the user handle multiple input/output devices and decide what media types get displayed where.

Minimal package which enables to add custom gradients to Zygote, without depending on Zygote itself.

ReverseDiff.jl is a fast and compile-able tape-based reverse mode AD, that implements methods to take gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really).

Tracker.jl previously provided Flux.jl with automatic differentiation for its machine learning platform.

The Preferences package provides an integrated way for packages to store configuration switches to persistent TOML files, and use those pieces of information at both run time and compile time in Julia. This enables the user to modify the behavior of a package, and have that choice reflected in everything from run time algorithm choice to code generation at compile time.