Go to their editor. Paste in a well-formed JSON document. Watch it generate a graph for you out of the data. You can even download the generated image. No API yet.
Go from graph data to a secure and interactive visual graph app in 15 minutes. Batteries-included self-hosting of graph data apps with Streamlit, Graphistry, RAPIDS, and more!
This open source effort puts together patterns the Graphistry team has reused across many graph projects as teams go from code-heavy Jupyter notebook experiments to deploying streamlined analyst tools. Whether building your first graph app, trying an idea, or wanting to check a reference, this project aims to simplify that process. It covers pieces like: Easy code editing and deployment, a project stucture ready for teams, built-in authentication, no need for custom JS/CSS at the start, batteries-included data + library dependencies, and fast loading & visualization of large graphs.
GPU enabled.
Seems to be Docker-only.
Bloxs is a simple python package that helps you display information in an attractive way (formed in blocks). Perfect for building dashboards, reports and apps in the notebook.
It works with Jupyter Notebook, Google Colab, Deepnote, Kaggle Notebook, and Mercury.
A sparkline generator implemented as a shell script.
Just run spark and pass it a list of numbers (comma-delimited, spaces, whatever you'd like) and it generates a text-mode bar graph, where the heights are relative to one another but reflect the numbers given. It's designed to be used in conjunction with other scripts that can output in that format.
Github: https://github.com/holman/spark
Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Tulip aims to provide the developer with a complete library, supporting the design of interactive information visualization applications for relational data that can be tailored to the problems he or she is addressing.
Written in C++ the framework enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. One of the goal of Tulip is to ease the reuse of components and allows the developers to focus on programming their application. This development pipeline makes the framework efficient for research prototyping as well as the development of end-user applications.
Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs and networks. Contrary to most other Python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library. Conveniently draw your graphs, using a variety of algorithms and output formats (including to the screen). Graph-tool has its own layout algorithms and versatile, interactive drawing routines based on cairo and GTK+, but it can also work as a very comfortable interface to the excellent graphviz package.
Source code: https://git.skewed.de/count0/graph-tool
Dynamic web based reports/dashboards in Python. Write a little bit of code to define the dashboard and populate the layout, and treat the rest like Python. Seems pretty straightforward. Has a built-in DSL to make defining some parts of a dashboard easier to do. Also has a CLI tool that you can use to interactively build dashboards without having to stop and start the server again and again. Even has a REST API that can be used to update the page's widgets in the background, so you can push instead of pull.
A curated list of amazingly awesome dashboards/visualization resources.
H2O Wave is a software stack for building beautiful, low-latency, realtime, browser-based applications and dashboards entirely in Python without using HTML, Javascript, or CSS. H2O Wave excels at capturing information from multiple sources and broadcasting them live over the web, letting you build and deploy realtime analytics with dramatically less effort.
The server is written in Go, which is weird, why are they calling it a Python app?
A data visualization framework written in CSS. Uses the semantic HTML5 tags to identify data to process, the data goes inside the HTML markup in the form of tables. No Javascript is needed to pull data out of APIs for processing (unless you want to roll that way, I guess). The core CSS file can be downloaded and put to use more or less immediately.
CygnusRFI is an easy-to-use open-source Radio Frequency Interference (RFI) analysis tool, based on Python and GNU Radio Companion (GRC) that is conveniently applicable to any ground station/radio telescope working with a GRC-supported software-defined radio (SDR). In addition to data acquisition, CygnusRFI also carries out automated analysis of the recorded data, producing a series of averaged spectra covering a wide range of frequencies of interest. CygnusRFI is built for ground station operators, radio astronomers, amateur radio operators and anyone who wishes to get an idea of how "radio-quiet" their environment is, using inexpensive instruments like SDRs.
The CLI tool is used to set up scanning runs. Data is graphed as output.
Chartbrew is an open-source web application that can connect directly to databases and APIs and use the data to create beautiful charts. It features a chart builder, editable dashboards, embedable charts, query & requests editor, and team capabilities. Can pull data from MySQL, Postgres, MongoDB, and any API that returns JSON documents. Interactive graph and chart builder.
Written in node.js. Requires MySQL on the back-end.
If you use the service (https://chartbrew.com/) there's a free tier.
Pyrgg is an easy-to-use synthetic random graph generator written in Python which supports various graph file formats including DIMACS .gr files. Pyrgg has the ability to generate graphs of different sizes and is designed to provide input files for broad range of graph-based research applications, including but not limited to testing, benchmarking and performance-analysis of graph processing frameworks. Pyrgg target audiences are computer scientists who study graph algorithms and graph processing frameworks.
The Image-Charts API returns a chart image in response to a URL GET or POST request. The API can generate many kinds of charts, from pie or line charts to bar charts and radars. All the information about the chart that you want, such as chart data, size, colors, and labels, are part of the URL.
To make the simplest chart possible, all your URL needs to specify is the chart type, data, and size. You can type this URL directly in your browser, or point to it with an <img> tag in your web page.
Graphs for dump1090 (based on dump1090-tools by mutability). Message rate, aircraft seen, tracks seen, range, signal strength... front end to rrdtool and collectd.
A Python framework for doing graphical data analysis without needing to know Javascript. Visualizations are updated in realtime as they are interacted with. You'll have to write some code to set it up, it would appear mostly to get the data into the application to begin with.
Docs: https://dash.plotly.com/
Here's a tutorial for how to use it: https://mymasterdesigner.com/2021/12/13/visualization-dashboards-with-python-dash/
Gallery of Dash dashboards: https://dash.gallery/Portal/
A CSS file that lets you develop various types of plots and graphs without having to resort to Javascript.
Analyze spectrograms created by rtl_power.
Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Tulip aims to provide the developer with a complete library, supporting the design of interactive information visualization applications for relational data that can be tailored to the problems he or she is addressing.
Comes with Python embedded to interact with the data.
Versions for multiple OSes are available. Might be worth grabbing the .appimage to save time.l