Showing 391 open source projects for "qt data visualization"

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  • 1
    PowerSimulationsDynamics.jl

    PowerSimulationsDynamics.jl

    Julia package to run Dynamic Power System simulations

    PowerSimulationsDynamics.jl is a Julia package for power system modeling and simulation of Power Systems dynamics.
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  • 2
    Nemo.jl

    Nemo.jl

    Julia bindings for various mathematical libraries (including flint2)

    Nemo is a computer algebra package for the Julia programming language. It aims to cover commutative algebra, number theory and group theory. Julia bindings for various mathematical libraries (including flint2)
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  • 3
    VoronoiFVM.jl

    VoronoiFVM.jl

    Solution of nonlinear multiphysics partial differential equations

    Solver for coupled nonlinear partial differential equations (elliptic-parabolic conservation laws) based on the Voronoi finite volume method. It uses automatic differentiation via ForwardDiff.jl and DiffResults.jl to evaluate user functions along with their jacobians and calculate derivatives of solutions with respect to their parameters.
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  • 4
    Winston.jl

    Winston.jl

    2D plotting for Julia

    2D plotting for Julia. Winston offers an easy-to-use plot command to create figures without any fuss. After Winston is loaded by typing using Winston, the most basic plot can be created.
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  • 5
    Calculus.jl

    Calculus.jl

    Calculus functions in Julia

    The Calculus package provides tools for working with the basic calculus operations of differentiation and integration. You can use the Calculus package to produce approximate derivatives by several forms of finite differencing or to produce exact derivatives using symbolic differentiation. You can also compute definite integrals by different numerical methods.
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  • 6
    MAT.jl

    MAT.jl

    Julia module for reading MATLAB files

    Julia module for reading MATLAB files. Read and write MATLAB files in Julia. This library can read MATLAB .mat files, both in the older v4/v5/v6/v7 format, as well as the newer v7.3 format.
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  • 7
    FastGaussQuadrature.jl

    FastGaussQuadrature.jl

    Julia package for Gaussian quadrature

    A Julia package to compute n-point Gauss quadrature nodes and weights to 16-digit accuracy and in O(n) time. So far the package includes gausschebyshev(), gausslegendre(), gaussjacobi(), gaussradau(), gausslobatto(), gausslaguerre(), and gausshermite(). This package is heavily influenced by Chebfun.
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  • 8
    Oxygen.jl

    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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  • 9
    DFTK.jl

    DFTK.jl

    Density-functional toolkit

    The density-functional toolkit, DFTK for short, is a collection of Julia routines for experimentation with plane-wave density-functional theory (DFT). The unique feature of this code is its emphasis on simplicity and flexibility with the goal of facilitating algorithmic and numerical developments as well as interdisciplinary collaboration in solid-state research.
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  • 10
    Parameters.jl

    Parameters.jl

    Types w/ default field values, keyword constructors, (un-)pack macros

    This is a package I use to handle numerical-model parameters, thus the name. However, it should be useful otherwise too.
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  • 11
    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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  • 12
    FinEtools.jl

    FinEtools.jl

    Finite Element tools in Julia

    FinEtools is a package for basic operations on finite element meshes: Construction, modification, selection, and evaluation of quantities defined on a mesh. Utilities are provided for maintaining mesh-based data (fields), for defining normals and loads, for working with physical units and coordinate systems, and for integrating over finite element meshes.
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  • 13
    EllipsisNotation.jl

    EllipsisNotation.jl

    Julia-based implementation of ellipsis array indexing notation

    Julia-based implementation of ellipsis array indexing notation. This implements the notation .. for indexing arrays. It's similar to Python, in that it means "all the columns before (or after)". Note: .. slurps dimensions greedily, meaning that the first occurrence of .. in an index expression creates as many slices as possible. Other instances of .. afterward are treated simply as slices. Usually, you should only use one instance of .. in an indexing expression to avoid possible confusion.
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  • 14
    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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  • 15
    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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  • 16
    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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  • 17
    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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  • 18
    LineSearches.jl

    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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  • 19
    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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  • 20
    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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  • 21
    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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  • 22
    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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  • 23
    oneAPI.jl

    oneAPI.jl

    Julia support for the oneAPI programming toolkit.

    Julia support for the oneAPI programming toolkit. oneAPI.jl provides support for working with the oneAPI unified programming model. The package is verified to work with the (currently) only implementation of this interface that is part of the Intel Compute Runtime, only available on Linux. This package is still under significant development, so expect bugs and missing features.
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  • 24
    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.
    Downloads: 0 This Week
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  • 25
    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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