YodasLib

World Data League 2022 team YODAS - Python package

YODAS Custom Python Package (YodasLib)

This Python package includes a module for wildlife dark corridors optimization, one of the outputs of the YODAS team in 2022 edition of WDL.

YODAS Application

Get installer here

YodasLib is used in the custom application developed by YODAS for the WDL 2022 finals. This application allows the user to customize and visualize in real time the optimization of wildlife dark corridors and is flexibility to any animal and city.

Installation

The package was only tested for Python 3, so there is no guarantee that it works with Python 2.

[OPTIONAL] Create a virtual environment before the installation

Windows

1 - Go to the project folder and open a Command Prompt

2 - Run the following command to create the virtual environment

virtualenv <name of the virtual environment> 

3 - Activate the virtual environment with the following command (always in the creation folder)

<name of virtual environment>\Scripts\activate.bat

Ubuntu

1 - Enter as root with the following command

sudo -i

2 - Go to the project folder and run the following command to create the virtual environment

python3 -m venv <name of virtual environment>

3 - Activate the virtual environment with the following command (always in the creation folder)

source <name of virtual environment>/bin/activate

Install the package with pip in your personal computer

pip install git+https://github.com/joao-afonso-pereira/YodasLib.git

Usage example

# Necessary data:
#  - City grid with the following columns: zone, geometry, animal_score, human_score, contains_cluster, nearest_cluster, cluster_score
#  - GeoJSON with city map
#  - CSV with city's street lights

import pandas as pd
import geopandas as gpd
from YODAS.DarkCorridors import LightManager

optimization_df = pd.read_csv('grid.csv')
#optimization_df.rename(columns = {'Unnamed: 0': 'zone'}, inplace = True)
optimization_df.geometry = gpd.GeoSeries.from_wkt(optimization_df.geometry)
optimization_df = gpd.GeoDataFrame(optimization_df, geometry='geometry')

city_map = 'https://martinjc.github.io/UK-GeoJSON/json/eng/wpc_by_lad/topo_E06000023.json'
street_lights = 'streetlights.csv' 

manager = LightManager(optimization_df, city_map, street_lights)
manager.plot_clusters()

manager.load_cost_matrix('cost_matrix.csv')

paths = manager.find_cluster_paths()
manager.plot_paths()
corridors = manager.create_dark_corridors()
new_lights = manager.update_lighting()