Search Results for "c-like" - Page 3
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Showing 1218 open source projects for "c-like"
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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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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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Plots
Powerful convenience for Julia visualizations and data analysis
...Some packages make a display and never change it, while others make updates in real-time. Plots is a visualization interface and toolset. It sits above other backends, like GR, PythonPlot, PGFPlotsX, or Plotly, connecting commands with implementation. If one backend does not support your desired features or make the right trade-offs, you can just switch to another backend with one command. No need to change your code. No need to learn a new syntax. Plots might be the last plotting package you ever learn. -
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ggforce
Accelerating ggplot2
ggforce is an extension package for ggplot2 that introduces specialized statistical transforms, geoms, and layout utilities to enhance and complement the built-in ggplot2 offerings. It enables more advanced visualization techniques such as faceting enhancements, hulls, annotation marks, and novel layouts for network data and marked regions. -
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Gradle Task Tree
Gradle plugin that adds a 'taskTree' task that prints task dependency
A Gradle plugin that adds a taskTree task to your build. Running it prints out a hierarchical, easy‑to‑read task dependency tree, helping you visualize the build execution order. -
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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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HCubature.jl
Pure-Julia multidimensional h-adaptive integration
The HCubature module is a pure-Julia implementation of multidimensional "h-adaptive" integration. then hcubature(f, a, b) computes the integral, adaptively subdividing the integration volume into smaller and smaller pieces until convergence is achieved to the desired tolerance (specified by optional rtol and atol keyword arguments. Because hcubature is written purely in Julia, the integrand f(x) can return any vector-like object (technically, any type supporting +, -, * real, and norm: a Banach space). You can integrate real, complex, and matrix-valued integrands, for example. Note that HCubature assumes that your function f(x) can be computed at arbitrary points in the integration domain. (This is the ideal way to do numerical integration.) If you instead have f(x) precomputed at a fixed set of points, such as a Cartesian grid, you will need to use some other method (e.g. ... -
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vue-chartjs
Vue.js wrapper for Chart.js
vue-chartjs is a wrapper for Chart.js in vue. You can easily create reusable chart components. You can use vue-chartjs directly in the browser without any build setup. Like in this codepen. For this case, please use the vue-chartjs.min.js which is the minified version. You also need to add the Chart.js CDN script. Chart.js does not update or re-render the chart if new data is passed. However, you can simply implement this on your own or use one of the two mixins which are included. The mixins automatically create chartData as a prop or data. ... -
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Perspective
A data visualization and analytics component
Perspective is a high-performance data visualization library for building real-time, interactive analytics dashboards. Developed by FINOS, it supports WebAssembly-powered pivot tables and can handle large streaming datasets with speed and flexibility. Perspective is ideal for fintech, trading, and IoT applications where insights from live data need to be visualized, sliced, and explored quickly in a browser. -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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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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Conda.jl
https://github.com/JuliaPy/Conda.jl
This package allows one to use conda as a cross-platform binary provider for Julia for other Julia packages, especially to install binaries that have complicated dependencies like Python. conda is a package manager that started as the binary package manager for the Anaconda Python distribution, but it also provides arbitrary packages. Instead of the full Anaconda distribution, Conda.jl uses the miniconda Python environment, which only includes conda and its dependencies. -
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ResumableFunctions.jl
C# style generators a.k.a. semi-coroutines for Julia
C# has a convenient way to create iterators using the yield return statement. The package ResumableFunctions provides the same functionality for the Julia language by introducing the @resumable and the @yield macros. These macros can be used to replace the Task switching functions produce and consume which were deprecated in Julia v0.6. -
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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. ... -
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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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BAT.jl
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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Chromatone
Chromatone is a digital garden of visual music theory
Cards and short overviews on the physics and physiology of vision and hearing and their intersection at visual music research, exploration, practice, and self-expression. Useful tools to have in the pocket like a pack of interactive cards to learn and use in everyday music practice. These are open source web experiments with different aspects of sound and color. Chromatone is an open source research and design project to explore, develop and implement the scientific way of visual music education, communication and performance. We've discovered complete and coherent Visual Music Language there. ... -
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Java Tablesaw
Java dataframe and visualization library
...If you work with data in Java, it may save you time and effort. Tablesaw also supports descriptive statistics and can be used to prepare data for working with machine learning libraries like Smile, Tribuo, H20.ai, DL4J. Import data from RDBMS, Excel, CSV, TSV, JSON, HTML, or Fixed Width text files, whether they are local or remote (http, S3, etc.) Tablesaw supports data visualization by providing a wrapper for the Plot.ly JavaScript plotting library. Here are a few examples of the new library in action. Descriptive stats: mean, min, max, median, sum, product, standard deviation, variance, percentiles, geometric mean, skewness, kurtosis, etc. ... -
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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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SimpleTraits.jl
Simple Traits for Julia
This package provides a macro-based implementation of traits, using Tim Holy's trait trick. The main idea behind traits is to group types outside the type-hierarchy and to make dispatch work with that grouping. The difference to Union-types is that types can be added to a trait after the creation of the trait, whereas Union types are fixed after creation. The cool thing about Tim's trick is that there is no performance impact compared to using ordinary dispatch. For a bit of background and a... -
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Neuroglancer
WebGL-based viewer for volumetric data
Neuroglancer is a WebGL-based visualization tool designed for exploring large-scale volumetric and neuroimaging datasets directly in the browser. It allows users to interactively view arbitrary 2D and 3D cross-sections of volumetric data alongside 3D meshes and skeleton models, enabling precise examination of neural structures and biological imaging results. Its multi-pane interface synchronizes multiple orthogonal views with a central 3D viewport, making it ideal for analyzing complex brain... -
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geemap
A Python package for interactive geospaital analysis and visualization
A Python package for interactive geospatial analysis and visualization with Google Earth Engine. Geemap is a Python package for geospatial analysis and visualization with Google Earth Engine (GEE), which is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. During the past few years, GEE has become very popular in the geospatial community and it has empowered numerous environmental applications at local, regional, and global scales. GEE... -
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RevoGrid
Powerful virtual data grid smartsheet with advanced customization
Support Millions of cells and thousands of columns easy and efficiently for fast data rendering. Easy to use. For large organizations managing massive datasets. Performance and scalability to handle even the most complex data tables. Rapid prototyping with intuitive and extendable codebase. Agility to build robust data-driven applications in no time. Quickly build elegant and efficient data grids. Lightweight yet powerful architecture lets you easily scale as your needs grow. -
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PlutoSliderServer.jl
Web server to run just the `@bind` parts of a Pluto.jl notebook
...For this, take a look at our template repository that used GitHub Actions and PlutoSliderServer to generate a website on every commit. Many input elements only have a finite number of possible values, for example, PlutoUI.Slider(5:15) can only have 11 values. For finite inputs like the slider, PlutoSliderServer can run the slider server in advance, and precompute the results to all possible inputs (in other words: precompute the response to all possible requests). -
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Tullio.jl
Tullio is a very flexible einsum macro
...It understands many array operations written in index notation -- not just matrix multiplication and permutations, but also convolutions, stencils, scatter/gather, and broadcasting. Used by itself the macro writes ordinary nested loops much like Einsum.@einsum. One difference is that it can parse more expressions, and infer ranges for their indices. Another is that it will use multi-threading (via Threads.@spawn) and recursive tiling, on large enough arrays. -
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forecast
Forecasting Functions for Time Series and Linear Models
The forecast package is a comprehensive R package for time series analysis and forecasting. It provides functions for building, assessing, and using univariate forecasting models (e.g. ARIMA, exponential smoothing, etc.), tools for automatic model selection, diagnostics, plotting, forecasting future values, etc. It's widely used in statistics, economics, business forecasting, environmental science, etc. Exponential smoothing state space models (ETS) including seasonal components. Residual...
