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Showing 2983 open source projects for "javascript open source"
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DynamicHMC
Implementation of robust dynamic Hamiltonian Monte Carlo methods
Implementation of robust dynamic Hamiltonian Monte Carlo methods in Julia. In contrast to frameworks that utilize a directed acyclic graph to build a posterior for a Bayesian model from small components, this package requires that you code a log-density function of the posterior in Julia. Derivatives can be provided manually, or using automatic differentiation. Consequently, this package requires that the user is comfortable with the basics of the theory of Bayesian inference, to the extent... -
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RCall.jl
Call R from Julia
R is a language for statistical computing and graphics that has been around for a couple of decades and it has one of the most impressive collections of scientific and statistical packages of any environment. Recently, the Julia language has become an attractive alternative because it provides the remarkable performance of a low-level language without sacrificing the readability and ease of use of high-level languages. However, Julia still lacks the depth and scale of the R package... -
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Infiltrator.jl
No-overhead breakpoints in Julia
This package provides the @infiltrate macro, which acts as a breakpoint with negligible runtime performance overhead. Note that you cannot access other function scopes or step into further calls. Use an actual debugger if you need that level of flexibility. Running code that ends up triggering the @infiltrate REPL mode via inline evaluation in VS Code or Juno can cause issues, so it's recommended to always use the REPL directly. When the infiltration point is hit, it will drop you into an... -
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BinaryBuilder
Binary Dependency Builder for Julia
Binary Dependency Builder for Julia. Building binary packages is a pain. BinaryBuilder follows a philosophy that is similar to that of building Julia itself; when you want something done right, you do it yourself. To that end, BinaryBuilder is designed from the ground up to facilitate the building of packages within an easily reproducible and reliable Linux environment, ensuring that the built libraries and executables are deployable to every platform that Julia itself will run on. Packages... -
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ChainRules.jl
Forward and reverse mode automatic differentiation primitives
The ChainRules package provides a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse--, and mixed-mode primitives. The core logic of ChainRules is implemented in ChainRulesCore.jl. To add ChainRules support to your package, by defining new rules or frules, you only need to depend on the very light-weight package ChainRulesCore.jl. This repository contains ChainRules.jl, which is what people actually use... -
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The PyPlot module for Julia
Plotting for Julia based on matplotlib.pyplot
This module provides a Julia interface to the Matplotlib plotting library from Python, and specifically to the matplotlib.pyplot module. PyPlot uses the Julia PyCall package to call Matplotlib directly from Julia with little or no overhead (arrays are passed without making a copy). (See also PythonPlot.jl for a version of PyPlot.jl using the alternative PythonCall.jl package.) This package takes advantage of Julia's multimedia I/O API to display plots in any Julia graphical backend,... -
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F2
An elegant, interactive and flexible charting library for mobile
F2 is an out-of-the-box visualization engine focused on the mobile terminal, oriented to conventional statistical charts, perfectly supporting the H5 environment and compatible with multiple environments (Node, applet), complete graphics grammar theory, to meet your various visualization needs , professional mobile design guidelines to bring you the best mobile graphics experience. Best practices for moving side charts around design, performance and heterogeneous environments. Based on the... -
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Obsidian Visual Skills Pack
Generate Canvas, Excalidraw, and Mermaid diagrams from text
LLM-TLDR is a Python-based tool designed to dramatically reduce the amount of code a large language model needs to read by extracting the essential structure and context from a codebase and presenting only the most relevant parts to the model. Traditional approaches often dump entire files into a model’s context, which quickly exceeds token limits; LLM-TLDR instead indexes project structure, traces dependencies, and summarizes code in a way that preserves semantic relevance while shrinking... -
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InteractiveViz.jl
Interactive visualization tools for Julia
Julia already has a rich set of plotting tools in the form of the Plots and Makie ecosystems, and various backends for these. So why another plotting package? InteractiveViz is not a replacement for Plots or Makie, but rather a graphics pipeline system developed on top of Makie. It has a few objectives. To provide a simple API to visualize large or possibly infinite datasets (tens of millions of data points) easily. To enable interactivity, and be responsive even with large amounts of data.... -
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LoggingExtras.jl
Composable Loggers for the Julia Logging StdLib
LoggingExtras allows routing logged information to different places when constructing complicated "log plumbing" systems. Built upon the concept of simple parts composed together, subtyping AbstractLogger provides a powerful and flexible definition for your logging system without a need to define any custom loggers. When we talk about composability, the composition of any set of Loggers is itself a Logger, and LoggingExtras is a composable logging system. -
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ImplicitGlobalGrid.jl
Distributed parallelization of stencil-based GPU and CPU applications
ImplicitGlobalGrid is an outcome of a collaboration of the Swiss National Supercomputing Centre, ETH Zurich (Dr. Samuel Omlin) with Stanford University (Dr. Ludovic Räss) and the Swiss Geocomputing Centre (Prof. Yuri Podladchikov). It renders the distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid almost trivial and enables close to ideal weak scaling of real-world applications on thousands of GPUs [1, 2, 3]. ImplicitGlobalGrid relies on the... -
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Wavelets.jl
A Julia package for fast discrete wavelet transforms and utilities
A Julia package for fast wavelet transforms (1-D, 2-D, 3-D, by filtering or lifting). The package includes discrete wavelet transforms, column-wise discrete wavelet transforms, and wavelet packet transforms. 1st generation wavelets using filter banks (periodic and orthogonal). Filters are included for the following types: Haar, Daubechies, Coiflet, Symmlet, Battle-Lemarie, Beylkin, Vaidyanathan. 2nd generation wavelets by lifting (periodic and general type including orthogonal and... -
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LinearSolve.jl
High-Performance Unified Interface for Linear Solvers in Julia
LinearSolve.jl is a unified interface for the linear solving packages of Julia. It interfaces with other packages of the Julia ecosystem to make it easy to test alternative solver packages and pass small types to control algorithm swapping. It also interfaces with the ModelingToolkit.jl world of symbolic modeling to allow for automatically generating high-performance code. Performance is key: the current methods are made to be highly performant on scalar and statically sized small problems,... -
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jlrs
Julia bindings for Rust
jlrs is a crate that provides access to most of the Julia C API, it can be used to embed Julia in Rust applications and to use functionality it provides when writing ccallable functions in Rust. Currently, this crate is only tested in combination with Julia 1.6 and 1.9, but also supports Julia 1.7, 1.8, and 1.10. Using the current stable version is highly recommended. The minimum supported Rust version is currently 1.65. Julia must be installed before jlrs can be used, jlrs is compatible... -
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Mixed-effects models in Julia
A Julia package for fitting (statistical) mixed-effects models
This package defines linear mixed models (LinearMixedModel) and generalized linear mixed models (GeneralizedLinearMixedModel). Users can use the abstraction for statistical model API to build, fit (fit/fit!), and query the fitted models. A mixed-effects model is a statistical model for a response variable as a function of one or more covariates. For a categorical covariate the coefficients associated with the levels of the covariate are sometimes called effects, as in "the effect of using... -
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Optimization.jl
Mathematical Optimization in Julia
Optimization.jl provides the easiest way to create an optimization problem and solve it. It enables rapid prototyping and experimentation with minimal syntax overhead by providing a uniform interface to >25 optimization libraries, hence 100+ optimization solvers encompassing almost all classes of optimization algorithms such as global, mixed-integer, non-convex, second-order local, constrained, etc. It allows you to choose an Automatic Differentiation (AD) backend by simply passing an... -
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ForwardDiff.jl
Forward Mode Automatic Differentiation for Julia
ForwardDiff implements methods to take derivatives, gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really) using forward mode automatic differentiation (AD). While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms (such as finite-differencing) in both speed and accuracy. Functions like f which map a vector to a scalar are the best case... -
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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... -
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HyperTools
A Python toolbox for gaining geometric insights
HyperTools is a library for visualizing and manipulating high-dimensional data in Python. It is built on top of matplotlib (for plotting), seaborn (for plot styling), and scikit-learn (for data manipulation). Functions for plotting high-dimensional datasets in 2/3D. Static and animated plots. Simple API for customizing plot styles. Set of powerful data manipulation tools including hyperalignment, k-means clustering, normalizing and more. Support for lists of Numpy arrays, Pandas dataframes,... -
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React Chart.js
React components for Chart.js, the most popular charting library
React components for Chart.js, the most popular charting library. With v4, this library introduces a number of breaking changes. In order to improve performance, offer new features, and improve maintainability, it was necessary to break backwards compatibility, but we aimed to do so only when worth the benefit. You will find that any event which causes the chart to re-render, such as hover tooltips, etc., will cause the first dataset to be copied over to other datasets, causing your lines... -
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Sysdig
Linux system exploration and troubleshooting tool
Continuously assess cloud security posture by flagging misconfigurations and suspicious activity. Consolidate container and host scanning in a single workflow. Automate scanning locally in your CI/CD tools without images leaving your environment and block vulnerabilities pre-deployment. Visualize all network communication across apps and services. Apply microsegmentation by automating Kubernetes-native network policies. Unify threat detection and incident response across containers,... -
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see
Visualisation toolbox for beautiful and publication-ready figures
see is an R package that serves as the visualization component of the easystats ecosystem, providing plotting utilities to produce publication-ready visualizations of statistical model parameters, diagnostics, predictions, and performance metrics. It works in conjunction with other easystats packages (such as parameters, performance, modelbased, bayestestR, etc.) to convert model outputs or summary objects into visual forms (dot-and-whisker plots, diagnostic plots, residual plots, etc.). It... -
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ggrepel
epel overlapping text labels away from each other in your ggplot2
ggrepel is an R package that provides “smart” repulsion for text and label geoms in ggplot2. When placing text labels on a plot (e.g. labeling points), the labels can often overlap; ggrepel ensures labels don’t overlap (or overlap less) by repelling labels / pushing them away, adding connecting lines or nudges, etc. It improves the readability of plots, especially when many labels are present. Support for point and segment geoms (so labels can be connected by lines when moved). Supports both... -
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nb-clean
Clean Jupyter notebooks of outputs, metadata, and empty cells
nb-clean cleans Jupyter notebooks of cell execution counts, metadata, outputs, and (optionally) empty cells, preparing them for committing to version control. It provides both a Git filter and pre-commit hook to automatically clean notebooks before they're staged, and can also be used with other version control systems, as a command line tool, and as a Python library. It can determine if a notebook is clean or not, which can be used as a check in your continuous integration pipelines.... -
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ProbabilisticCircuits.jl
Probabilistic Circuits from the Juice library
This module provides a Julia implementation of Probabilistic Circuits (PCs), tools to learn structure and parameters of PCs from data, and tools to do tractable exact inference with them. Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models. PCs are represented as computational graphs that define a joint probability distribution as recursive mixtures (sum units) and factorizations (product units) of simpler distributions (input units)....
