map_builder

Map Builder

This directory contains scripts and modules for visualising the results as interactive maps.

The maps are generated using the Folium library and can display the chosen devices along with their paths and connections.

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📂 Folder Structure

📂 map_builder/ — Scripts and modules for generating network maps

• 📁 map_modules/ — Core modules for map building

  • 🐍 config.py — Configuration (colours, thresholds, dataset toggle)
  • 🐍 helper_functions.py — Utility functions shared by scripts
  • 🐍 build_map_legend.py — Generates a map legend with RU, DU, CU markers
  • 🐍 plot_results_graph.py — Plots results from graph-based models (Genetic, Local Search, CPLEX)
  • 🐍 plot_results_short.py — Plots results from shortest path models (Greedy, CPLEX)
  • 🐍 __init__.py — Makes map_modules importable as a package

• 📄 README.md — Documentation of scripts, usage, and customisation

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Scripts

1. Shortest Path Results Map

File: plot_results_short.py This script visualises the results of Greedy or CPLEX shortest path models. It highlights selected/unselected RUs and DUs and overlays the chosen shortest-path connections.

How to Use

1. Populate the following variables at the top of the script with your results:

   • selected_rus, not_selected_rus
   • selected_dus, not_selected_dus

2. Set dataset to either "small-dataset" or "full-dataset".

3. Run the script. The output will be saved in the maps/ directory as:

   short_{dataset}.html
   

Example

python plot_results_short.py

Customisation

• Update colour thresholds in get_color_by_demand (inside config.py).
• Switch dataset via the dataset variable.
• Adjust map centre and zoom in create_map_with_layers if working with a new region.

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2. Graph Model Results Map

File: plot_results_graph.py This script visualises results from Genetic Algorithm, Local Search, or CPLEX graph models. It draws full graph-based solutions with their selected network segments.

How to Use

1. Set selected_rus, not_selected_rus, selected_dus, not_selected_dus.
2. Set dataset to "small-dataset" or "full-dataset".
3. Provide the segments file (e.g., {run_id}_segments.json) generated during optimisation.
4. Run the script. The HTML map will be saved in the maps/ directory.

Example

python plot_results_graph.py

Customisation

• Adjust colour thresholds in get_color_by_demand.
• Switch dataset with the dataset variable.
• Update map centre/zoom if visualising a new region.

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3. Map Legend

File: build_map_legend.py This script builds a map legend to visually distinguish between different network components, including RUs, DUs, and CUs. It produces a stand-alone legend map for clarity.

How to Use

1. Set dataset to "small-dataset" or "full-dataset".
2. Run the script. It generates a legend map HTML file in the maps/ directory.

Example

python build_map_legend.py

Customisation

• Update get_color_by_demand thresholds in config.py.
• Switch dataset using the dataset variable.
• Adjust map centre and zoom if using a different geographic region.

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Supporting Modules

• config.py – Configuration file for colour schemes, demand thresholds, and dataset selection.
• helper_functions.py – Shared utilities for processing JSON data, creating feature groups, and styling maps.

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General Notes

• All maps are generated as HTML files and saved in the maps/ directory. You can open them in any web browser.
• The scripts assume a dataset structure consistent with small-dataset and full-dataset. If you build your own dataset, replicate this structure.
• Always check the centre coordinates and zoom level for correct geographic alignment.
• For larger datasets, you may want to tune demand thresholds and colour mappings for clarity.