Open Source Julia Data Management Systems - Page 2
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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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EAGO.jl
A development environment for robust and global optimization
EAGO is an open-source development environment for robust and global optimization in Julia. EAGO is a deterministic global optimizer designed to address a wide variety of optimization problems, emphasizing nonlinear programs (NLPs), by propagating McCormick relaxations along the factorable structure of each expression in the NLP. Most operators supported by modern automatic differentiation (AD) packages (e.g., +, sin, cosh) are supported by EAGO and a number of utilities for sanitizing native Julia code and generating relaxations on a wide variety of user-defined functions have been included. Currently, EAGO supports problems that have a priori variable bounds defined and have differentiable constraints. -
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Enzyme.jl
Julia bindings for the Enzyme automatic differentiator
This is a package containing the Julia bindings for Enzyme. This is very much a work in progress and bug reports/discussion is greatly appreciated. Enzyme is a plugin that performs automatic differentiation (AD) of statically analyzable LLVM. It is highly-efficient and its ability perform AD on optimized code allows Enzyme to meet or exceed the performance of state-of-the-art AD tools. -
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EzXML.jl
XML/HTML handling tools for primates
EzXML.jl is a package to handle XML/HTML documents for primates. This package depends on libxml2, which will be automatically installed as an artifact via XML2_jll.jl if you use Julia 1.3 or later. Currently, Windows, Linux, macOS, and FreeBSD are now supported. -
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Flux3D.jl
3D computer vision library in Julia
Flux3D.jl is a 3D vision library, written completely in Julia. This package utilizes Flux.jl and Zygote.jl as its building blocks for training 3D vision models and for supporting differentiation. This package also have support of CUDA GPU acceleration with CUDA.jl. -
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GDAL.jl
Thin Julia wrapper for GDAL - Geospatial Data Abstraction Library
Julia wrapper for GDAL - Geospatial Data Abstraction Library. This package is a binding to the C API of GDAL/OGR. It provides only a C style usage, where resources must be closed manually, and datasets are pointers. Other packages can build on top of this to provide a more Julian user experience. See for example ArchGDAL.jl. Most users will want to use ArchGDAL.jl instead of using GDAL.jl directly. -
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IntervalArithmetic.jl
Library for validated numerics using interval arithmetic
IntervalArithmetic.jl is a Julia package for validated numerics in Julia. All calculations are carried out using interval arithmetic where quantities are treated as intervals. The final result is a rigorous enclosure of the true value. We are working towards having the IntervalArithmetic library be conformant with the IEEE 1788-2015 Standard for Interval Arithmetic. To do so, we have incorporated tests from the ITF1788 test suite. -
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JUDI.jl
Julia Devito inversion
JUDI is a framework for large-scale seismic modeling and inversion and is designed to enable rapid translations of algorithms to fast and efficient code that scales to industry-size 3D problems. The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling operators can also be used as layers in (convolutional) neural networks to implement physics-augmented deep learning algorithms thanks to its implementation of ChainRules's rrule for the linear operators representing the discre wave equation. -
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KernelDensity.jl
Kernel density estimators for Julia
Kernel density estimators for Julia. -
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Latexify.jl
Convert julia objects to LaTeX equations, arrays or other environments
This is a package for generating LaTeX maths from Julia objects. This package utilizes Julia's homoiconicity to convert expressions to LaTeX-formatted strings. Latexify.jl supplies functionalities for converting a range of different Julia objects. -
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Makie
Interactive data visualizations and plotting in Julia
Makie is an interactive data visualization and plotting ecosystem for the Julia programming language, available on Windows, Linux, and Mac. The backend packages GLMakie, WGLMakie, CairoMakie and RPRMakie add different functionalities: You can use Makie to interactively explore your data and create simple GUIs in native Windows or web browsers, export high-quality vector graphics or even raytrace with physically accurate lighting. Choose one or more backend packages: GLMakie (interactive OpenGL in native OS windows), WGLMakie (interactive WebGL in browsers, IDEs, notebooks), CairoMakie (static 2D vector graphics and images), and RPRMakie (raytracing). Each backend re-exports all of Makie.jl so you don't have to install or load it explicitly. -
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Molly.jl
Molecular simulation in Julia
Much of science can be explained by the movement and interaction of molecules. Molecular dynamics (MD) is a computational technique used to explore these phenomena, from noble gases to biological macromolecules. Molly.jl is a pure Julia package for MD, and for the simulation of physical systems more broadly. The package is described in a talk at Enzyme Conference 2023 and an earlier talk at the JuliaMolSim minisymposium at JuliaCon 2022. Slides are also available for a tutorial in September 2023. -
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NNlib.jl
Neural Network primitives with multiple backends
This package provides a library of functions useful for neural networks, such as softmax, sigmoid, batched multiplication, convolutions and pooling. Many of these are used by Flux.jl, which loads this package, but they may be used independently. -
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Nonlinear Dynamics
A concise introduction interlaced with code
This repository holds material related with the textbook Nonlinear Dynamics: A Concise Introduction Interlaced with code, co-authored by George Datseris and Ulrich Parlitz. The textbook will be published by Springer-Nature, in the series Undergraduate Lecture Notes in Physics. -
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OpenCL.jl
OpenCL Julia bindings
Julia interface for the OpenCL parallel computation API. This package aims to be a complete solution for OpenCL programming in Julia, similar in scope to PyOpenCL for Python. It provides a high level API for OpenCL to make programing hardware accelerators, such as GPUs, FPGAs, and DSPs, as well as multicore CPUs much less onerous. -
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Parquet.jl
Julia implementation of Parquet columnar file format reader
A parquet file or dataset can be loaded using the read_parquet function. A parquet dataset is a directory with multiple parquet files, each of which is a partition belonging to the dataset. -
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PowerSimulations.jl
Julia for optimization simulation and modeling of PowerSystems
PowerSimulations.jl is a Julia package for power system modeling and simulation of Power Systems operations. Provide a flexible modeling framework that can accommodate problems of different complexity and at different time scales. Streamline the construction of large-scale optimization problems to avoid repetition of work when adding/modifying model details. Exploit Julia's capabilities to improve computational performance of large-scale power system quasi-static simulations. The flexible modeling framework is enabled through a modular set of capabilities that enable scalable power system analysis and exploration of new analysis methods. The modularity of PowerSimulations results from the structure of the simulations enabled by the package. Simulations define a set of problems that can be solved using numerical techniques. -
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SCIP.jl
Julia interface to SCIP solver
SCIP.jl is a Julia interface to the SCIP solver. This wrapper is maintained by the SCIP project with the help of the JuMP community. -
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SatelliteToolbox.jl
A toolbox for satellite analysis written in julia language
The SatelliteToolbox.jl contains a set of packages with functions to perform analysis and build simulations related to satellites. It is used on a daily basis on projects at the Brazilian National Institute for Space Research (INPE). -
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SymbolicNumericIntegration.jl
SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
SymbolicNumericIntegration.jl is a hybrid symbolic/numerical integration package that works on the Julia Symbolics expressions. -
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SymbolicRegression.jl
Distributed High-Performance Symbolic Regression in Julia
SymbolicRegression.jl searches for symbolic expressions which optimize a particular objective. -
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AbstractFFTs.jl
A Julia framework for implementing FFTs
A general framework for fast Fourier transforms (FFTs) in Julia. This package is mainly not intended to be used directly. Instead, developers of packages that implement FFTs (such as FFTW.jl or FastTransforms.jl) extend the types/functions defined in AbstractFFTs. This allows multiple FFT packages to co-exist with the same underlying fft(x) and plan_fft(x) interface. -
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AbstractGPs.jl
Abstract types and methods for Gaussian Processes
AbstractGPs.jl is a package that defines a low-level API for working with Gaussian processes (GPs), and basic functionality for working with them in the simplest cases. As such it is aimed more at developers and researchers who are interested in using it as a building block than end-users of GPs. You may want to go through the main API design documentation. -
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BAT.jl
A Bayesian Analysis Toolkit in Julia
Welcome to BAT, a Bayesian analysis toolkit in Julia. BAT.jl offers a variety of posterior sampling, mode estimation and integration algorithms, supplemented by plotting recipes and I/O functionality. BAT.jl originated as a rewrite/redesign of BAT, the Bayesian Analysis Toolkit in C++. BAT.jl now offer a different set of functionality and a wider variety of algorithms than its C++ predecessor. -
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BasicBSpline.jl
Basic (mathematical) operations for B-spline functions
Basic (mathematical) operations for B-spline functions and related things with Julia. -
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Bayesian Julia
Bayesian Statistics using Julia and Turing
Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from classical inferential statistics that prohibits probability statements about parameters and is based on asymptotically sampling infinite samples from a theoretical population and finding parameter values that maximize the likelihood function. Mostly notorious is null-hypothesis significance testing (NHST) based on p-values. Bayesian statistics incorporate uncertainty (and prior knowledge) by allowing probability statements about parameters.