Showing 2997 open source projects for "gnu/linux"

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  • 1
    GeoInterface.jl

    GeoInterface.jl

    A Julia Protocol for Geospatial Data

    This Package describe a set of traits based on the Simple Features standard (SF) for geospatial vector data, including the SQL/MM extension with support for circular geometry. Using these traits, it should be easy to parse, serialize and use different geometries in the Julia ecosystem, without knowing the specifics of each individual package. In that regard it is similar to Tables.jl, but for geometries instead of tables.
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  • 2
    Bumper.jl

    Bumper.jl

    Bring Your Own Stack

    Bumper.jl is a package that aims to make working with bump allocators (also known as arena allocators) easier and safer. You can dynamically allocate memory to these bump allocators, and reset them at the end of a code block, just like Julia's stack. Allocating to a bump allocator with Bumper.jl can be just as efficient as stack allocation. Bumper.jl is still a young package, and may have bugs. Let me know if you find any.
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  • 3
    ExplainableAI.jl

    ExplainableAI.jl

    Explainable AI in Julia

    This package implements interpretability methods for black box models, with a focus on local explanations and attribution maps in input space. It is similar to Captum and Zennit for PyTorch and iNNvestigate for Keras models. Most of the implemented methods only require the model to be differentiable with Zygote. Layerwise Relevance Propagation (LRP) is implemented for use with Flux.jl models.
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  • 4
    101-0250-00

    101-0250-00

    ETH course - Solving PDEs in parallel on GPUs

    This course aims to cover state-of-the-art methods in modern parallel Graphical Processing Unit (GPU) computing, supercomputing and code development with applications to natural sciences and engineering.
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  • 5
    InferOpt.jl

    InferOpt.jl

    Combinatorial optimization layers for machine learning pipelines

    InferOpt.jl is a toolbox for using combinatorial optimization algorithms within machine learning pipelines. It allows you to create differentiable layers from optimization oracles that do not have meaningful derivatives. Typical examples include mixed integer linear programs or graph algorithms.
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  • 6
    MultilayerGraphs.jl

    MultilayerGraphs.jl

    Julia package for the creation and analysis of multilayer graphs

    MultilayerGraphs.jl is a Julia package for the creation, manipulation and analysis of the structure, dynamics and functions of multilayer graphs. A multilayer graph is a graph consisting of multiple standard subgraphs called layers which can be interconnected through bipartite graphs called interlayers composed of the vertex sets of two different layers and the edges between them. The vertices in each layer represent a single set of nodes, although not all nodes have to be represented in...
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  • 7
    NetCDF.jl

    NetCDF.jl

    NetCDF support for the julia programming language

    NetCDF support for the Julia programming language, there is a high-level and a medium-level interface for writing and reading netcdf files. The dimensions "x1" and "t" of the variable are called "x1" and "t" in this example. If the dimensions do not exist yet in the file, they will be created. The dimension "x1" will be of length 10 and have the values 11..20, and the dimension "t" will have length 20 and the attribute "units" with the value "s".
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  • 8
    LLVM.jl

    LLVM.jl

    Julia wrapper for the LLVM C API

    A Julia wrapper for the LLVM C API. The LLVM.jl package is a Julia wrapper for the LLVM C API, and can be used to work with the LLVM compiler framework from Julia. You can use the package to work with LLVM code generated by Julia, to interoperate with the Julia compiler, or to create your own compiler. It is heavily used by the different GPU compilers for the Julia programming language.
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  • 9
    AbstractAlgebra.jl

    AbstractAlgebra.jl

    Generic abstract algebra functionality in pure Julia

    AbstractAlgebra is a pure Julia package for computational abstract algebra. It grew out of the Nemo project and provides all of the abstract types and generic implementations that Nemo relies on. It was originally developed by William Hart, Tommy Hofmann, Fredrik Johansson and Claus Fieker with contributions from others. Current maintainers are Claus Fieker, Tommy Hofmann and Max Horn.
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  • 10
    Clapeyron

    Clapeyron

    Framework for the development and use of fluid-thermodynamic models

    Welcome to Clapeyron! This module provides both a large library of thermodynamic models and a framework for one to easily implement their own models. Clapeyron provides a framework for the development and use of fluid-thermodynamic models, including SAFT, cubic, activity, multi-parameter, and COSMO-SAC.
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  • 11
    Polyhedra

    Polyhedra

    Polyhedral Computation Interface

    Polyhedra provides an unified interface for Polyhedral Computation Libraries such as CDDLib.jl. This manipulation notably includes the transformation from (resp. to) an inequality representation of a polyhedron to (resp. from) its generator representation (convex hull of points + conic hull of rays) and projection/elimination of a variable with e.g. Fourier-Motzkin.
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  • 12
    EvoTrees.jl

    EvoTrees.jl

    Boosted trees in Julia

    A Julia implementation of boosted trees with CPU and GPU support. Efficient histogram-based algorithms with support for multiple loss functions, including various regressions, multi-classification and Gaussian max likelihood.
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  • 13
    BAT.jl

    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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  • 14
    Sundials.jl

    Sundials.jl

    Julia interface to Sundials, including a nonlinear solver

    This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations.
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  • 15
    Clang.jl

    Clang.jl

    C binding generator and Julia interface to libclang

    This package provides a Julia language wrapper for libclang: the stable, C-exported interface to the LLVM Clang compiler. The libclang API documentation provides background on the functionality available through libclang, and thus through the Julia wrapper. The repository also hosts related tools built on top of libclang functionality.
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  • 16
    Compose.jl

    Compose.jl

    Declarative vector graphics

    Compose is a vector graphics library for Julia. It forms the basis for the statistical graphics system Gadfly. Compose is a declarative vector graphics system written in Julia. It's designed to simplify the creation of complex graphics and serves as the basis of the Gadfly data visualization package.
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  • 17
    OpenCL.jl

    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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  • 18
    Manopt.jl

    Manopt.jl

    Optimization on Manifolds in Julia

    Optimization Algorithm on Riemannian Manifolds. A framework to implement arbitrary optimization algorithms on Riemannian Manifolds. Library of optimization algorithms on Riemannian manifolds. Easy-to-use interface for (debug) output and recording values during an algorithm run. Several tools to investigate the algorithms, gradients, and optimality criteria.
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  • 19
    PowerSystems.jl

    PowerSystems.jl

    Data structures in Julia to enable power systems analysis

    The PowerSystems.jl package provides a rigorous data model using Julia structures to enable power systems analysis and modeling. In addition to stand-alone system analysis tools and data model building, the PowerSystems.jl package is used as the foundational data container for the PowerSimulations.jl and PowerSimulationsDynamics.jl packages. PowerSystems.jl supports a limited number of data file formats for parsing.
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  • 20
    FriendsDon'tLetFriends

    FriendsDon'tLetFriends

    Friends don't let friends make certain types of data visualization

    Friends don't let friends make certain types of data visualization - What are they and why are they bad.
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  • 21
    pydna

    pydna

    Clone with Python! Data structures for double stranded DNA

    Clone with Python! Data structures for double stranded DNA & simulation of homologous recombination, Gibson assembly, cut & paste cloning. Planning genetic constructs with many parts and assembly steps, such as recombinant metabolic pathways, are often difficult to properly document as is evident from the poor state of documentation in the scientific literature. The pydna python package provide a human-readable formal description of cloning and genetic assembly strategies in Python which...
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  • 22
    QuantumClifford.jl

    QuantumClifford.jl

    Clifford circuits, graph states, and other quantum Stabilizer tools

    A Julia package for working with quantum stabilizer states and Clifford circuits that act on them. Graphs states are also supported. The package is already very fast for the majority of common operations, but there are still many low-hanging fruits performance-wise. See the detailed suggested readings & references page for background on the various algorithms.
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  • 23
    CounterfactualExplanations.jl

    CounterfactualExplanations.jl

    A package for Counterfactual Explanations and Algorithmic Recourse

    CounterfactualExplanations.jl is a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box algorithms. Both CE and AR are related tools for explainable artificial intelligence (XAI). While the package is written purely in Julia, it can be used to explain machine learning algorithms developed and trained in other popular programming languages like Python and R. See below for a short introduction and other resources or dive straight into the docs.
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  • 24
    MIRT.jl

    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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  • 25
    SciMLBase.jl

    SciMLBase.jl

    The Base interface of the SciML ecosystem

    SciMLBase.jl is the core interface definition of the SciML ecosystem. It is a low-dependency library made to be depended on by the downstream libraries to supply the common interface and allow for the interexchange of mathematical problems. The SciML common interface ties together the numerical solvers of the Julia package ecosystem into a single unified interface. It is designed for maximal efficiency and parallelism, while incorporating essential features for large-scale scientific machine...
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