Open Source Julia Data Management Systems - Page 4
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Julia Data Management Systems
View 4117 business solutionsBrowse free open source Julia Data Management Systems and projects below. Use the toggles on the left to filter open source Julia Data Management Systems by OS, license, language, programming language, and project status.
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ImageFiltering.jl
Julia implementations of multidimensional array convolution
Julia implementations of multidimensional array convolution and nonlinear stencil operations. ImageFiltering implements blurring, sharpening, gradient computation, and other linear filtering operations, as well nonlinear filters like min/max. -
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InteractiveViz.jl
Interactive visualization tools for Julia
Julia already has a rich set of plotting tools in the form of the Plots and Makie ecosystems, and various backends for these. So why another plotting package? InteractiveViz is not a replacement for Plots or Makie, but rather a graphics pipeline system developed on top of Makie. It has a few objectives. To provide a simple API to visualize large or possibly infinite datasets (tens of millions of data points) easily. To enable interactivity, and be responsive even with large amounts of data. To render perceptually accurate summaries at large scale, allowing drill down to individual data points. To allow generation of data points on demand through a graphics pipeline, requiring computation only at a level of detail appropriate for display at the viewing resolution. Additional data points can be generated on demand when zooming or panning. This package was partly inspired by the excellent Datashader package available in the Python ecosystem. -
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Iterative Solvers
Iterative algorithms for solving linear systems, eigensystems
IterativeSolvers is a Julia package that provides iterative algorithms for solving linear systems, eigensystems, and singular value problems. -
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JuliaConnectoR
A functionally oriented interface for calling Julia from R
This R-package provides a functionally oriented interface between R and Julia. The goal is to call functions from Julia packages directly as R functions. Julia functions imported via the JuliaConnectoR can accept and return R variables. It is also possible to pass R functions as arguments in place of Julia functions, which allows callbacks from Julia to R. From a technical perspective, R data structures are serialized with an optimized custom streaming format, sent to a (local) Julia TCP server, and translated to Julia data structures by Julia. The results of function calls are likewise translated back to R. Complex Julia structures can either be used by reference via proxy objects in R or fully translated to R data structures. -
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JuliaFEM.jl
The JuliaFEM software library is a framework
The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The JuliaFEM software library is a framework that allows for the distributed processing of large Finite Element Models across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. The basic design principle is: that everything is nonlinear. All physics models are nonlinear from which the linearization are made as special cases. -
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JuliaWorkshop
Intensive Julia workshop that takes you from zero to hero
This is an intensive workshop for the Julia language, composed out of three 2-hour segments. It targets people already familiar with programming, so that the established basics such as for-loops are skipped through quickly and efficiently. Nevertheless, it assumes only rudimentary programming familiarity and does explain concepts that go beyond the basics. The goal of the workshop is to take you from zero to hero (regarding Julia): even if you know nothing about Julia, by the end you should be able to use it like a pro. The material has been updated during July-December 2023 to Julia v1.9+ and the corresponding latest stable versions of used packages -
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LineSearches.jl
Line search methods for optimization and root-finding
Line search methods for optimization and root-finding. This package provides an interface to line search algorithms implemented in Julia. The code was originally written as part of Optim, but has now been separated out to its own package. -
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LinearSolve.jl
High-Performance Unified Interface for Linear Solvers in Julia
LinearSolve.jl is a unified interface for the linear solving packages of Julia. It interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code. Performance is key: the current methods are made to be highly performant on scalar and statically sized small problems, with options for large-scale systems. If you run into any performance issues, please file an issue. -
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LoggingExtras.jl
Composable Loggers for the Julia Logging StdLib
LoggingExtras allows routing logged information to different places when constructing complicated "log plumbing" systems. Built upon the concept of simple parts composed together, subtyping AbstractLogger provides a powerful and flexible definition for your logging system without a need to define any custom loggers. When we talk about composability, the composition of any set of Loggers is itself a Logger, and LoggingExtras is a composable logging system. -
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LossFunctions.jl
Julia package of loss functions for machine learning
This package represents a community effort to centralize the definition and implementation of loss functions in Julia. As such, it is a part of the JuliaML ecosystem. The sole purpose of this package is to provide an efficient and extensible implementation of various loss functions used throughout Machine Learning (ML). It is thus intended to serve as a special purpose back-end for other ML libraries that require losses to accomplish their tasks. To that end we provide a considerable amount of carefully implemented loss functions, as well as an API to query their properties (e.g. convexity). Furthermore, we expose methods to compute their values, derivatives, and second derivatives for single observations as well as arbitrarily sized arrays of observations. In the case of arrays a user additionally has the ability to define if and how element-wise results are averaged or summed over. -
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MIRT.jl
MIRT: Michigan Image Reconstruction Toolbox (Julia version)
MIRT.jl is a collection of Julia functions for performing image reconstruction and solving related inverse problems. It is very much still under construction, although there are already enough tools to solve useful problems like compressed sensing MRI reconstruction. Trying the demos is a good way to get started. The documentation is even more still under construction. -
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MPI.jl
MPI wrappers for Julia
This is a basic Julia wrapper for the portable message-passing system Message Passing Interface (MPI). Inspiration is taken from mpi4py, although we generally follow the C and not the C++ MPI API. (The C++ MPI API is deprecated.) MPI is based on a single program, multiple data (SPMD) model, where multiple processes are launched running independent programs, which then communicate as necessary via messages. As the main entry point for users, MPI.jl provides a high-level interface which loosely follows the MPI C API and is described in details in the following sections. The syntax should look familiar if you know MPI already, but some arguments may not be needed (e.g. the type or the number of elements of arrays, which are inferred automatically), others may be placed slightly differently, and others may be optional keyword arguments (e.g. for the index of the root process, or the source and destination of point-to-point communication functions). -
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MathLink.jl
Julia language interface for Mathematica/Wolfram Engine
This package provides access to Mathematica/Wolfram Engine via the MathLink library, now renamed to Wolfram Symbolic Transfer Protocol (WSTP). -
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Measurements.jl
Error propagation calculator and library for physical measurements
Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration. Physical measures are typically reported with an error, a quantification of the uncertainty of the accuracy of the measurement. Whenever you perform mathematical operations involving these quantities you have also to propagate the uncertainty, so that the resulting number will also have an attached error to quantify the confidence about its accuracy. Measurements.jl relieves you from the hassle of propagating uncertainties coming from physical measurements, when performing mathematical operations involving them. The linear error propagation theory is employed to propagate the errors. -
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Memento.jl
A flexible logging library for Julia
Memento is a flexible hierarchical logging library for Julia. -
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Metalhead.jl
Computer vision models for Flux
Metalhead.jl provides standard machine learning vision models for use with Flux.jl. The architectures in this package make use of pure Flux layers, and they represent the best practices for creating modules like residual blocks, inception blocks, etc. in Flux. Metalhead also provides some building blocks for more complex models in the Layers module. -
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Mixed-effects models in Julia
A Julia package for fitting (statistical) mixed-effects models
This package defines linear mixed models (LinearMixedModel) and generalized linear mixed models (GeneralizedLinearMixedModel). Users can use the abstraction for statistical model API to build, fit (fit/fit!), and query the fitted models. A mixed-effects model is a statistical model for a response variable as a function of one or more covariates. For a categorical covariate the coefficients associated with the levels of the covariate are sometimes called effects, as in "the effect of using Treatment 1 versus the placebo". If the potential levels of the covariate are fixed and reproducible, e.g. the levels for Sex could be "F" and "M", they are modeled with fixed-effects parameters. If the levels constitute a sample from a population, e.g. the Subject or the Item at a particular observation, they are modeled as random effects. -
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MultivariatePolynomials.jl
Multivariate polynomials interface
MultivariatePolynomials.jl is an implementation-independent library for manipulating multivariate polynomials. It defines abstract types and an API for multivariate monomials, terms, and polynomials and gives default implementation for common operations on them using the API. On the one hand, This packages allows you to implement algorithms on multivariate polynomials that will be independant on the representation of the polynomial that will be chosen by the user. On the other hand, it allows the user to easily switch between different representations of polynomials to see which one is faster for the algorithm that he is using. -
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NCDatasets.jl
Load and create NetCDF files in Julia
NCDatasets allows one to read and create netCDF files. NetCDF data set and attribute list behave like Julia dictionaries and variables like Julia arrays. This package implements the CommonDataModel.jl interface, which means that the datasets can be accessed in the same way as GRIB files opened with GRIBDatasets.jl. -
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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function spaces. The kernel can be trained on different geometry, which is learned from a graph. Fourier neural operator learns a neural operator with Dirichlet kernel to form a Fourier transformation. It performs Fourier transformation across infinite-dimensional function spaces and learns better than neural operators. Markov neural operator learns a neural operator with Fourier operators. -
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OnlineStats.jl
Single-pass algorithms for statistics
OnlineStats does statistics and data visualization for big/streaming data via online algorithms. High-performance single-pass algorithms for statistics and data viz. Updated one observation at a time. Algorithms use O(1) memory. Algorithms use O(1) memory. -
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Oxygen.jl
A breath of fresh air for programming web apps in Julia
A breath of fresh air for programming web apps in Julia. Oxygen is a micro-framework built on top of the HTTP.jl library. Breathe easy knowing you can quickly spin up a web server with abstractions you're already familiar with. -
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PartitionedArrays.jl
Vectors and sparse matrices partitioned into pieces
This package provides distributed (a.k.a. partitioned) vectors and sparse matrices in Julia. See the documentation for further details. -
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Pkg Julia
Package manager for the Julia programming language
Unlike traditional package managers, which install and manage a single global set of packages, Pkg is designed around “environments”: independent sets of packages that can be local to an individual project or shared and selected by name. The exact set of packages and versions in an environment is captured in a manifest file which can be checked into a project repository and tracked in version control, significantly improving reproducibility of projects. If you’ve ever tried to run code you haven’t used in a while only to find that you can’t get anything to work because you’ve updated or uninstalled some of the packages your project was using, you’ll understand the motivation for this approach. In Pkg, since each project maintains its own independent set of package versions, you’ll never have this problem again. Moreover, if you check out a project on a new system, you can simply materialize the environment described by its manifest file and immediately be up and running. -
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PlotlyJS
Julia library for plotting with plotly.js
Julia interface to plotly.js visualization library. This package constructs plotly graphics using all local resources. To interact or save graphics to the Plotly cloud, use the Plotly package.