Search Results for "plotting"
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Showing 274 open source projects for "plotting"
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PyVista
3D plotting and mesh analysis through a streamlined interface
...Intuitive plotting routines with matplotlib similar syntax. -
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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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PlotlyJS
Julia library for plotting with plotly.js
Julia interface to plotly.js visualization library. This package constructs plotly graphics using all local resources. To interact or save graphics to the Plotly cloud, use the Plotly package. -
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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. -
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Book2_Beauty-of-Data-Visualization
Machine Learning, Criticism and Correction
...By combining theory with hands-on plotting exercises, the book helps readers build both analytical and presentation skills. Overall, it is intended as a foundational guide for anyone seeking to produce professional-quality data visualizations. -
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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, including as inline graphics in IJulia. ... -
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Lets-Plot Kotlin API
Kotlin API for Lets-Plot Multiplatform plotting library
Lets-Plot Kotlin API is a Kotlin API for Lets-Plot Multiplatform plotting library, which is built on the principles of layered graphics first described in the Leland Wilkinson work The Grammar of Graphics. -
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UnicodePlots
Unicode-based scientific plotting for working in the terminal
Unicode-based scientific plotting for working in the terminal. -
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webgl-plot
A high-Performance real-time 2D plotting library based on native WebGL
webgl-plot is a high-performance plotting library for real-time data visualization in the browser using WebGL. Unlike traditional canvas or SVG-based charting libraries, webgl-plot is optimized for streaming and dynamic updates, making it ideal for oscilloscope-style data, biomedical signals, or any application where data updates hundreds of times per second. Its minimal memory footprint and GPU acceleration ensure excellent performance even with tens of thousands of data points, and its simple API allows developers to get started quickly. -
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IJulia.jl
Julia kernel for Jupyter
IJulia is a Julia-language backend (kernel) for Jupyter notebooks, allowing users to write and execute Julia code interactively in browser-based notebooks. It integrates seamlessly with Jupyter’s ecosystem, supporting markdown, plotting, multimedia, and inline output. IJulia is ideal for scientific computing, data analysis, and education, combining the power of Julia with the interactive capabilities of Jupyter. -
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Matplotlib
matplotlib: plotting with Python
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Matplotlib ships with several add-on toolkits, including 3D plotting with mplot3d, axes helpers in axes_grid1 and axis helpers in axisartist. A large number of third party packages extend and build on Matplotlib functionality, including several higher-level plotting interfaces (seaborn, HoloViews, ggplot, ...), and a projection and mapping toolkit (Cartopy). Matplotlib is the brainchild of John Hunter (1968-2012), who, along with its many contributors, have put an immeasurable amount of time and effort into producing a piece of software utilized by thousands of scientists worldwide. ... -
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Lets-Plot
Multiplatform plotting library based on Grammar of Graphics
Lets-Plot is a multiplatform plotting library based on the Grammar of Graphics. The library' design is heavily influenced by Leland Wilkinson work The Grammar of Graphics describing the deep features that underlie all statistical graphics. -
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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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LIFELINES
Survival analysis in Python
...The library includes estimators such as Kaplan-Meier, Nelson-Aalen, and regression-based survival models. It is designed to be accessible to Python users and works well with common scientific computing workflows. Built-in plotting methods and datasets help users explore survival curves and compare groups visually. It is a practical tool for analysts, researchers, and data scientists who need event-time modeling without leaving Python. -
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Gaston.jl
A julia front-end for gnuplot
Gaston is a Julia package for plotting. It provides an interface to gnuplot, a powerful plotting package available on all major platforms. The current stable release is v1.1.0, and it has been tested with Julia LTS (1.6) and stable (1.8), on Linux. Gaston should work on any platform that runs gnuplot. -
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MetBrewer
Color palette package inspired by Metropolitan Museum of Art in NY
...The package supports both discrete and continuous palette types, with interpolation when more colors are requested than originally defined. It also provides ggplot2-friendly scale functions (scale_color_met_c, scale_fill_met_d, etc.) so integration into typical R plotting workflows is smooth. Internally, the package includes functions to list available palettes, check which are colorblind-friendly, and visualize all palettes at once. -
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R Color Palettes
Comprehensive list of color palettes available in R
...The author also documents palette provenance and usage guidance (contrast, readability, colorblind friendliness). While not a full package in itself, it’s often used as a reference or source of palette definitions for other R plotting or theming packages. -
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forecast
Forecasting Functions for Time Series and Linear Models
...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 checks, model accuracy, plots, forecast error measures etc. -
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stdlib
Standard library for JavaScript and Node.js
A standard library for javascript and node.js. High performance, rigorous, and robust mathematical and statistical functions. Build advanced statistical models and machine learning libraries. Plotting and graphics functionality for data visualization and exploratory data analysis. Analyze and understand your data. Comprehensively tested utilities for application and library development. Functions to assert, group, filter, map, pluck, and transform your data both in browsers and on the server. Everything you would expect from a modern standard library. ... -
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almond
A Scala kernel for Jupyter
...This also makes it easy to copy some code from notebooks to Ammonite scripts, and vice versa. Almond exposes APIs to interact with Jupyter front-ends. Call them from notebooks… or from your own libraries. Several plotting libraries are already available to plot things from notebooks, such as plotly-scala or Vegas. Load the Spark version of your choice, create a Spark session, and start using it from your notebooks. -
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Plots
Powerful convenience for Julia visualizations and data analysis
...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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OxyPlot
A cross-platform plotting library for .NET
OxyPlot is a cross-platform plotting library for .NET. The code is licensed under the MIT license. This is a very permissive and corporate-friendly license. See open source and tl;dr for more details. The core library is a Portable Class Library that can be used on different platforms. Custom controls are implemented for WPF, Windows 8, Windows Phone, Windows Phone Silverlight, Windows Forms, Silverlight, GTK#, Xwt, Xamarin.iOS, Xamarin.Android, Xamarin.Forms and Xamarin.Mac. -
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CausalImpact
An R package for causal inference in time series
...It uses Bayesian modeling to fit a structural time series to the pre-period and extrapolate a counterfactual prediction for the post period, then compares observed vs predicted to infer the causal effect. The package supports plotting, summary tables, and verbal narratives for interpretive reports. -
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paletteer
Collection of most color palettes in a single R package
paletteer is an R package by Emil Hvitfeldt that aggregates color palettes from many other R packages, providing a unified, streamlined interface to access discrete, continuous, and dynamic palettes. It is intended to simplify choosing color schemes when plotting, remove the friction of remembering different palette package APIs, and make high‐quality color aesthetics more accessible. Some palettes change depending on the number of colors requested; the ability to reverse palettes. Support both discrete palettes (fixed number of colors) and continuous palettes (interpolated). -
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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.
