Search Results for "big data visualization" - Page 13

Showing 2499 open source projects for "big data visualization"

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  • 1
    Automa.jl

    Automa.jl

    A julia code generator for regular expressions

    Automa is a regex-to-Julia compiler. By compiling regex to Julia code in the form of Expr objects, Automa provides facilities to create efficient and robust regex-based lexers, tokenizers and parsers using Julia's metaprogramming capabilities. You can view Automa as a regex engine that can insert arbitrary Julia code into its input-matching process, which will be executed when certain parts of the regex match an input.
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  • 2
    JuliaSyntax

    JuliaSyntax

    A Julia frontend, written in Julia

    A Julia compiler frontend, written in Julia. Read the documentation for more information. JuliaSyntax.jl is used as the new default Julia parser in Julia 1.10. It's highly compatible with Julia's older femtoliter-based parser - It parses all of Base, the standard libraries and the General registry. Some minor differences remain where we've decided to fix bugs or strange behaviors in the reference parser. The AST and tree data structures are usable but their APIs will evolve as we try out...
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  • 3
    ComponentArrays.jl

    ComponentArrays.jl

    Arrays with arbitrarily nested named components

    The main export of this package is the ComponentArray type. "Components" of ComponentArrays are really just array blocks that can be accessed through a named index. This will create a new ComponentArray whose data is a view into the original, allowing for standalone models to be composed together by simple function composition. In essence, ComponentArrays allow you to do the things you would usually need a modeling language for, but without actually needing a modeling language. The main targets...
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  • 4
    Surrogates.jl

    Surrogates.jl

    Surrogate modeling and optimization for scientific machine learning

    A surrogate model is an approximation method that mimics the behavior of a computationally expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model g which approximates f by training on previous data collected from evaluations...
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    ScikitLearn.jl

    ScikitLearn.jl

    Julia implementation of the scikit-learn API

    The scikit-learn Python library has proven very popular with machine learning researchers and data scientists in the last five years. It provides a uniform interface for training and using models, as well as a set of tools for chaining (pipelines), evaluating, and tuning model hyperparameters. ScikitLearn.jl brings these capabilities to Julia. Its primary goal is to integrate both Julia- and Python-defined models together into the scikit-learn framework.
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  • 6
    PyCall.jl

    PyCall.jl

    Package to call Python functions from the Julia language

    Package to call Python functions from the Julia language. 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.
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  • 7
    VisualDL

    VisualDL

    Deep Learning Visualization Toolkit

    VisualDL, a visualization analysis tool of PaddlePaddle, provides a variety of charts to show the trends of parameters and visualizes model structures, data samples, histograms of tensors, PR curves , ROC curves and high-dimensional data distributions. It enables users to understand the training process and the model structure more clearly and intuitively so as to optimize models efficiently. VisualDL provides various visualization functions, including tracking metrics in real-time, visualizing...
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  • 8
    Run Page

    Run Page

    Make your own running home page

    GitHub Actions manages automatic synchronization of runs and generation of new pages. Gatsby-generated static pages, fast. Support for Vercel (recommended) and GitHub Pages automated deployment. React Hooks. Mapbox for map display. Supports most sports apps such as nike strava. Automatically backup gpx data for easy backup and uploading to other software.
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  • 9
    TOAST UI Chart

    TOAST UI Chart

    Beautiful chart for data visualization

    The Chart makes your data pop, and it is easy to use. It provides you with multiple charts like Bar, Column, Line, and more. The functionality of TOAST UI Chart is available when using the Plain JavaScript, React, Vue Component. TOAST UI Chart makes your data pop and presents it in a manner that is easy to understand. Furthermore, it provides a wide range of theme options for customizing the charts to be suitable for all of your services. Chart components like the title, axes, legends, tooltips...
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  • 10
    WordCloud.jl

    WordCloud.jl

    Word cloud generator in julia

    Word cloud (tag cloud or wordle) is a novelty visual representation of text data. The importance of each word is shown with its font size, position, or color. WordCloud.jl is the perfect tool for generating word clouds, offering several advantages. You have control over every aspect of generating a word cloud. You can customize the shape, color, angle, position, distribution, density, and spacing to align with your preferences and artistic style. This visualization solution guarantees precise...
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  • 11
    manticoresearch

    manticoresearch

    Easy to use open source fast database for search

    Manticore Search is an easy to use open source fast database for search. Modern MPP architecture and smart query parallelization capabilities allow to fully utilize all your CPU cores to lower response time as much as possible, when needed. Powerful and fast full-text searching which works fine for small and big datasets. Columnar storage support via the Manticore Columnar Library for bigger datasets (much bigger than can fit in RAM). SQL-first: Manticore's native syntax is SQL. It speaks SQL...
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  • 12
    electricityMap

    electricityMap

    A real-time visualisation of the CO2 emissions of electricity

    Real-time visualization of the Greenhouse Gas (in terms of CO2 equivalent) footprint of electricity consumption built with d3.js and mapbox GL. Real-time data is defined as a data source with an hourly (or better) frequency, delayed by less than 2hrs. It should provide a breakdown by generation type. Often fossil fuel generation (coal/gas/oil) is combined under a single heading like 'thermal' or 'conventional', this is not a problem. Citizens should not be responsible for the emissions...
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  • 13
    onlook

    onlook

    The open source, local-first Webflow alternative

    Seamlessly integrate with any website or web app running on React + TailwindCSS, and make live edits directly in the browser DOM. Customize your design, control your codebase, and push changes your changes without compromise. Link Onlook to your React project with just one command. Run this command on your project's root folder to get set up in seconds. Onlook writes reliable code you can trust, exactly where it needs to go. Adjust layouts, change colors, modify text, and more.
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  • 14
    ipychart

    ipychart

    The power of Chart.js with Python

    Create charts with Python in a very similar way to creating charts using Chart.js. The charts created are fully configurable, interactive, and modular and are displayed directly in the output of the cells of your jupyter notebook environment. Charts are fully interactive, you can hover it to display tooltips and select the information you want to see directly from the output cell of your notebook. All the types of charts present in Chart.js are exposed in ipychart. Even complex features such...
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  • 15
    miepython

    miepython

    Mie scattering of light by perfect spheres

    miepython is a pure Python module to calculate light scattering for non-absorbing, partially-absorbing, or perfectly-conducting spheres. Mie theory is used, following the procedure described by Wiscombe. This code has been validated against his results. This code provides functions for calculating the extinction efficiency, scattering efficiency, backscattering, and scattering asymmetry. Moreover, a set of angles can be given to calculate the scattering for a sphere at each of those angles.
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  • 16
    ReactiveMP.jl

    ReactiveMP.jl

    High-performance reactive message-passing based Bayesian engine

    ReactiveMP.jl is a Julia package that provides an efficient reactive message passing based Bayesian inference engine on a factor graph. The package is a part of the bigger and user-friendly ecosystem for automatic Bayesian inference called RxInfer. While ReactiveMP.jl exports only the inference engine, RxInfer provides convenient tools for model and inference constraints specification as well as routines for running efficient inference both for static and real-time datasets.
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  • 17
    WhereTraits.jl

    WhereTraits.jl

    Traits for julia: dispatch on whatever you want using where syntax

    Welcome to WhereTraits.jl. This package exports one powerful macro @traits with which you can extend Julia's where syntax in order to support traits definitions. In addition, WhereTraits comes with a standardized way how to resolve ambiguities among traits, by defining an order among the traits with @traits_order. Under the hood @traits uses normal function dispatch to achieve the speed and flexibility, however, julia function dispatch can lead to ambiguities. With traits these can easily...
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  • 18
    ProxSDP.jl

    ProxSDP.jl

    Semidefinite programming optimization solver

    ProxSDP is an open-source semidefinite programming (SDP) solver based on the paper "Exploiting Low-Rank Structure in Semidefinite Programming by Approximate Operator Splitting". The main advantage of ProxSDP over other state-of-the-art solvers is the ability to exploit the low-rank structure inherent to several SDP problems.
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  • 19
    ExponentialUtilities.jl

    ExponentialUtilities.jl

    Fast and differentiable implementations of matrix exponentials

    ExponentialUtilities is a package of utility functions for matrix functions of exponential type, including functionality for the matrix exponential and phi-functions. These methods are more numerically stable, generic (thus support a wider range of number types), and faster than the matrix exponentiation tools in Julia's Base. The tools are used by the exponential integrators in OrdinaryDiffEq. The package has no external dependencies, so it can also be used independently.
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  • 20
    Braket.jl

    Braket.jl

    Experimental Julia implementation of the Amazon Braket SDK

    Braket.jl is not an officially supported AWS product. This package is a Julia implementation of the Amazon Braket SDK allowing customers to access Quantum Hardware and Simulators. This is experimental software, and support may be discontinued in the future. For a fully supported SDK, please use the Python SDK. We may change, remove, or deprecate parts of the API when making new releases. Please review the CHANGELOG for information about changes in each release.
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  • 21
    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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  • 22
    JUDI.jl

    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...
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  • 23
    RuntimeGeneratedFunctions.jl

    RuntimeGeneratedFunctions.jl

    Functions generated at runtime without world-age issues or overhead

    RuntimeGeneratedFunctions are functions generated at runtime without world-age issues and with the full performance of a standard Julia anonymous function. This builds functions in a way that avoids eval. For technical reasons, RuntimeGeneratedFunctions needs to cache the function expression in a global variable within some module. This is normally transparent to the user, but if the RuntimeGeneratedFunction is evaluated during module precompilation, the cache module must be explicitly set...
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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
    DynamicQuantities.jl

    DynamicQuantities.jl

    Lightweight + fast physical quantities in Julia

    DynamicQuantities defines a simple statically-typed Quantity type for Julia. Physical dimensions are stored as a value, as opposed to a parametric type, as in Unitful.jl. This can greatly improve both runtime performance, by avoiding type instabilities, and startup time, as it avoids overspecializing methods.
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