Algorithm for guessing MV grid network based on NTL. An adaptation of this work from Facebook.
gridfinder uses NASA night time lights imagery to as an indicator of settlements/towns with grid electricity access. Then a minimum spanning tree is calculated for these connect points, using the Dijkstra algorithm and using existing road networks as a cost function.
The algorithm looks as follows in process, guessing the grid network for Uganda:
gridfinder requires the following data sources:
To get to grips with the API and steps in the model, open the Jupyter notebook
example.ipynb. This repository includes the input data needed to do a test run for Burundi, so it should be a matter of openening the notebook and running all cells.
gridfinder requires Python >= 3.5 with the following packages installed:
These additional packages may be necessary depending on your configuration:
And these for using an interactive notebook:
Install with pip
pip install gridfinder
Install from GitHub
Download or clone the repository and install the required packages (preferably in a virtual environment):
git clone https://github.com/carderne/gridfinder.git cd gridfinder pip install -r requirements.txt
You can run
./test.sh in the directory, which will do an entire run through using the test data and confirm whether everything is set up properly. (It will fail if jupyter isn’t installed!)