Browse free open source Python Data Visualization Software and projects below. Use the toggles on the left to filter open source Python Data Visualization Software by OS, license, language, programming language, and project status.
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An intuitive molecular editor and visualization tool
Avogadro is an advanced molecular editor designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science and related areas. It offers a flexible rendering framework and a powerful plugin architecture.
FreeImage is a library project for developers who would like to support popular graphics image formats (PNG, JPEG, TIFF, BMP and others). Some highlights are: extremely simple in use, not limited to the local PC (unique FreeImageIO) and Plugin driven!
SciDAVis is a user-friendly data analysis and visualization program primarily aimed at high-quality plotting of scientific data. It strives to combine an intuitive, easy-to-use graphical user interface with powerful features such as Python scriptability.
The General Mission Analysis Tool (GMAT) is an open-source tool for space mission design and navigation. GMAT is developed by a team of NASA, private industry, and public and private contributors.
The GMAT development team is pleased to announce the release of GMAT version R2026a. For a complete list of new features, compatibility changes, and bug fixes, see the R2026a Release Notes in the Users Guide.
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Data Analysis, Simulations and Visualization on the Sphere
Software for pixelization, hierarchical indexation, synthesis, analysis, and visualization of data on the sphere. Please acknowledge HEALPix by quoting the web page http://healpix.sourceforge.net (or https://healpix.sourceforge.io) and publication: K.M. Gorski et al., 2005, Ap.J., 622, p.759
Full software documentation available at https://healpix.sourceforge.io/documentation.php
Wiki Pages: https://sourceforge.net/p/healpix/wiki/Home
Exchanging Data with HEALPix (in FITS files): https://sourceforge.net/p/healpix/wiki/Exchanging%20Data%20with%20HEALPix/
GDL and FL users should read https://sourceforge.net/p/healpix/wiki/HEALPix%20and%20GDL/
Open source machine learning and data visualization. Build data analysis workflows visually, with a large, diverse toolbox. Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. Place widgets on the canvas, connect them, load your datasets and harvest the insight! When teaching data mining, we like to illustrate rather than only explain.
Matplotlib is a python library for making publication quality plots using a syntax familiar to MATLAB users. Matplotlib uses numpy for numerics. Output formats include PDF, Postscript, SVG, and PNG, as well as screen display.
As of matplotlib version 1.5, we are no longer making file releases available on SourceForge. Please visit http://matplotlib.org/users/installing.html for help obtaining matplotlib.
Asymptote is a powerful descriptive vector graphics language for technical drawing, inspired by MetaPost but with an improved C++-like syntax. Asymptote provides for figures the same high-quality typesetting that LaTeX does for scientific text.
TexGen is a geometric textile modelling software package to be used for obtaining engineering properties of woven textiles and textile composites.
Citing TexGen
We would be grateful if you could acknowledge use of TexGen where appropriate and suggest using one of the following references:
L P Brown and A C Long. "Modelling the geometry of textile reinforcements for composites: TexGen", Chapter 8 in "Composite reinforcements for optimum performance (Second Edition)", ed. P Boisse, Woodhead Publishing Ltd, 2021, ISBN: 978-0-12-819005-0. https://doi.org/10.1016/B978-0-12-819005-0.00008-3
Lin, H., Brown, L. P. & Long, A. C. 2011. Modelling and Simulating Textile Structures using TexGen. Advanced Materials Research, 331, 44-47.
To reference version 3.13.0 please use: Louise Brown, mike-matveev, & georgespackman. (2023). louisepb/TexGen: TexGen v3.13.1 (v3.13.1). Zenodo. https://doi.org/10.5281/zenodo.8221491
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Stand-alone application and Python tools for interactive and/or batch processing analysis of X-Ray Fluorescence Spectra. Graphical user interface (GUI) and batch processing capabilities provided.
PLplot is a cross-platform, scientific graphics plotting library that supports math symbols and human languages (via UTF-8 user input strings); plot capabilities for multiple non-interactive plot file formats and in multiple interactive environments; and bindings for multiple computer languages.
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. Matplotlib is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. Matplotlib has support for visualizing information with a wide array of colors and colormaps.
DXF2GCODE: converting 2D dxf drawings to CNC machine compatible G-Code
DXF2GCODE converts 2D drawings (DXF, PDF, PS) into G-code for CNC machines.
**Development has moved to GitHub!**
The next version of dxf2gcode is being developed at:
https://github.com/dxf2gcode/dxf2gcode
Please use GitHub Issues for bug reports and feature requests.
SourceForge remains available for previous releases and historical tickets.
For user support and discussions, please visit our community forum:
https://www.ltspiceusers.ch/forums/english-section.67/
DXF2GCODE is a tool for converting 2D (dxf, pdf, ps) drawings to CNC machine compatible GCode.
Windows, Linux, and Mac support by using python scripting language.
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. Behind the scenes, seaborn uses matplotlib to draw its plots. For interactive work, it’s recommended to use a Jupyter/IPython interface in matplotlib mode, or else you’ll have to call matplotlib.pyplot.show() when you want to see the plot.
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 imaging data such as connectomics datasets. Neuroglancer operates entirely client-side, fetching data over HTTP in a variety of supported formats including Neuroglancer precomputed, N5, Zarr, and NIfTI, among others. The viewer is built with a multi-threaded architecture, separating rendering and data processing to ensure smooth performance even with massive datasets. Extensively used in neuroscience research, Neuroglancer supports integration with tools.
A free, industrial quality library for simulating articulated rigid body dynamics - for example ground vehicles, legged creatures, and moving objects in VR environments. It's fast, flexible & robust. Built-in collision detection.
Veusz is a scientific plotting application for creating publication-quality two-dimensional and three-dimensional graphs. It combines a graphical editor with Python scripting and command-line interfaces for automated visualization. Users can create line graphs, histograms, contour plots, vector fields, polar charts, and three-dimensional surfaces. Its widget-based architecture supports complex figure layouts, nested plots, and multiple axes. Data can be imported from CSV, HDF5, FITS, NumPy, and other scientific formats. Built-in tools provide dataset manipulation, mathematical functions, and curve fitting. Figures support LaTeX-style labels and customizable annotations. Completed graphs can be exported to PDF, SVG, PNG, EPS, and EMF for reports and publications.
Converts text input or URL into knowledge graph and displays
InstaGraph is a Flask application that converts text input or a URL into a visual knowledge graph. It uses an LLM to identify entities, relationships, labels, and node types from the provided content. The generated graph is displayed visually, helping users understand relationships that may be harder to see in plain text. The project includes Graphviz-based visualization logic and supports updating an existing graph through follow-up instructions. It is designed to be simple, colorful, and easy to use rather than a complex enterprise knowledge graph system. It is useful for learners, researchers, writers, and builders who want to experiment with LLM-assisted relationship mapping.
PyTorch Implementation for "TransPose, Keypoint localization
TransPose is a human pose estimation model based on a CNN feature extractor, a Transformer Encoder, and a prediction head. Given an image, the attention layers built in Transformer can efficiently capture long-range spatial relationships between keypoints and explain what dependencies the predicted keypoints locations highly rely on.
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 associated with all the products they export, but only for what they consume. Consumption-based accounting (CBA) is a very important aspect of climate policy and allows assigning responsibility to consumers instead of producers. Furthermore, this method is robust to governments relocating dirty production to neighboring countries in order to green their image while still importing from it.
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