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 tools to express a design pattern for dealing with large/ nested structures, as in machine learning and optimisation. For large machine learning models it can be cumbersome or inefficient to work with parameters as one big, flat vector, and structs help in managing complexity; but it is also desirable to easily operate over all parameters at once, e.g. for changing precision or applying an optimiser update step.

This package supports representing banded matrices by only the entries on the bands.

This package is an implementation of tropical (min-plus) arithmetic in Julia.

Astronomical https://www.atnf.csiro.au/people/mcalabre/WCS/ library for Julia. This package wraps the WCSLIB C library.

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.

Optim.jl is a package for univariate and multivariate optimization of functions.

FileIO aims to provide a common framework for detecting file formats and dispatching to appropriate readers/writers. The two core functions in this package are called load and save, and offer high-level support for formatted files (in contrast with Julia's low-level read and write).

Aqua.jl, provides functions to run a few automatable checks for Julia packages.

This package will provide a library of functions useful for machine learning, such as softmax, sigmoid, convolutions and pooling. It doesn't provide any other "high-level" functionality like layers or AD.

Quaternions are best known for their suitability as representations of 3D rotational orientation. They can also be viewed as an extension of complex numbers.

Various special functions based on log and exp moved from StatsFuns.jl into a separate package, to minimize dependencies. These functions only use native Julia code, so there is no need to depend on librmath or similar libraries.

This package provides a collection of tools for metaprogramming on Julia Expr, the meta programming standard library for MLStyle.

A block array is a partition of an array into blocks or subarrays. This package has two purposes. Firstly, it defines an interface for an AbstractBlockArray block arrays that can be shared among types representing different types of block arrays. The advantage to this is that it provides a consistent API for block arrays. Secondly, it also implements two different type of block arrays that follow the AbstractBlockArray interface. The type BlockArray stores each block contiguously while the type PseudoBlockArray stores the full matrix contiguously. This means that BlockArray supports fast non copying extraction and insertion of blocks while PseudoBlockArray supports fast access to the full matrix to use in in for example a linear solver.

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 lazily represents matrices filled with a single entry, as well as identity matrices. This package exports the following types: Eye, Fill, Ones, Zeros, Trues and Falses.

This package provides an implementation of an associative container mapping (K,V) pairs via the type IntervalTreeK, V. The type K may be any ordered type.

ExprTools provides tooling for working with Julia expressions during metaprogramming. This package aims to provide light-weight performant tooling without requiring additional package dependencies.

A Julia package for evaluating distances(metrics) between vectors. This package also provides optimized functions to compute column-wise and pairwise distances, which are often substantially faster than a straightforward loop implementation.

This package provides reader/writer for delimited text data, as comma-delimited (csv), tab-delimited (tsv), or otherwise.

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.

libwhich is like which, but for dynamic libraries. It is also a bit like ldd and otool -L.

This package provides a set of widgets to help facilitate reuse of large datasets across widgets.

Jupyter Events enables Jupyter Python Applications (e.g. Jupyter Server, JupyterLab Server, JupyterHub, etc.) to emit events—structured data describing things happening inside the application. Other software (e.g. client applications like JupyterLab) can listen and respond to these events.