A Dash application in Python 3 with a D backend to create graphs, digraphs, and networks, and run common optimization algorithms on them. The split between the front-end in Python and the back-end in D is a long story, but it grew out of an experiment I did during an undergraduate course.
All the data structures and algorithms are located in the directory app/lib.
To compile them you need rdmd.
The Dash application needs the following dependencies:
- dash
- dash_bootstrap_components
- dash_cytoscape
- numpy
- networkx
There are different ways to run this program, depending on whether you have (or want to have) D installed.
If you have both languages, clone this repository and run the install.sh script.
This script will create a Python virtual environment with all dependencies needed, and compile the complete D library.
After that, you can run run.sh to display the application locally.
If you don't have, or don't want to use, D, you can download a version of nagui with the D library precompiled here.
After that, enter the main directory of the application and run run.sh.
If you don't already have a Python virtual environment, it will automatically create it.
Of course, you can still manage everything manually.
The D library has a makefile to compile it.
The entry point for the Dash application is the file index.py.