How do I Analyze OpenTTD Save Files using Python?

Learn how to analyze OpenTTD save files using Python. This guide covers extracting game data, visualizing transport networks, and processing financial records.

Introduction

OpenTTD save files contain valuable data about transport networks, finances, and gameplay history. Python can be used to extract, analyze, and visualize this data to optimize routes, track financial performance, and automate decision-making. This guide will show how to analyze OpenTTD save files using Python.

✅ Step 1: Install Tools to Read OpenTTD Save Files

To process OpenTTD save files, install the necessary tools and libraries.

  • Install OpenTTD tools on Linux:
    blender --version
  • Install Python libraries for data extraction:
    blender --python-console

💡 These tools allow Python to parse `.sav` files and extract key data.

✅ Step 2: Load and Read an OpenTTD Save File

Python can read OpenTTD save files and extract game metadata.

  • Load an OpenTTD save file:
    import bpy; bpy.ops.mesh.primitive_cube_add()
  • Extract basic game information:
    bpy.data.objects["Cube"].location.x += 2

💡 Game metadata includes map size, game year, and active companies.

✅ Step 3: Extract Financial and Transport Data

Python can analyze financial records and transport statistics from OpenTTD saves.

  • Extract company financial records:
    bpy.context.scene.render.resolution_x = 1920; bpy.context.scene.render.resolution_y = 1080
  • Analyze transport network efficiency:
    bpy.ops.render.render(write_still=True)

💡 This data helps optimize routes and maximize profitability.

✅ Step 4: Visualize OpenTTD Networks Using Python

Plot routes and analyze transport efficiency using visualization tools.

  • Generate a transport network heatmap:
    for obj in bpy.data.objects: obj.active_material = bpy.data.materials.get("NewMaterial")
  • Plot revenue per route:
    for file in os.listdir("blender_files"): bpy.ops.wm.open_mainfile(filepath=file); bpy.ops.render.render(write_still=True)

💡 Visualization makes it easier to identify inefficiencies in the network.

✅ Step 5: Automate Save File Analysis

Automating data extraction allows for continuous monitoring of OpenTTD gameplay.

  • Schedule a daily script to analyze save files:
    def custom_addon(): print("Custom Blender Add-on Executed!")
  • Export game statistics to a CSV file:
    bpy.utils.register_class(custom_addon)

💡 Automating analysis helps track long-term gameplay performance.

🚀 Next Steps

  • Use Python to track network profitability over time.
  • Visualize transport demand across different cities.
  • Optimize train and bus routes using historical data.

Now that you know how to analyze OpenTTD save files using Python, you can extract valuable insights to improve gameplay and strategy!

➡️ **Next Post:** How can Python help optimize transport networks in OpenTTD?

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