HLTB Linear & Temporal Breakdown

Warning

I'm retiring HLTB in favor of local database powered by Datasette. As a result, I no longer have the intention to maintain this repo. While this is still useful for reference, it contains many errors I don't bother to backport fixes (especially the SQL queries) so you should use it at your own risk (which is also why it's licensed under GLWTPL).

Intro

HowLongToBeat is a website that helps your track play times, which is also in collaboration with Microsoft to show approximate competition time for the Xbox app on PC. The best thing is you can export your records to a CSV file so you really own your data, instead of relying on a third party.

This is a linear and temporal data analysis & visualization based on the exported CSV using Python libraries, notably numpy, pandas and matplotlib, and only tested on GNU/Linux with Python 3.12.

Install

Tip

For a complete list of available commands, run make help.

It's really straightforward though:

# Clone repo
git clone https://github.com/Vinfall/hltb
cd hltb

# Set up venv and install the project
python -m venv .venv
source .venv/bin/activate
pip install -e .

# Detect problematic lines in CSV
python debug.py

Alternative, if you use uv it's even easier:

git clone https://github.com/Vinfall/hltb
cd hltb

make

Now you are good to go ahead.

To uninstall:

# Clean up
make clean
# Only needed if you set up venv
deactivate
rm -rf .venv

Usage

0. Export your HLTB data in options. Alternatively, you can use provided HLTB_Games_example.csv in example folder for tests.

1. Run the scripts.

The following information may be useful to you:

• hltb_sanitizer.py: sanitize exported data
  • Edit BLOCK_TAGS to excluded games with certain tags
  • Edit CUSTOM_TAGS to recognize your custom tab names
  • Change SCORE_MAX to 100 if you prefer Percentage System
• hltb_visualizer.py: generate a few ugly charts from sanitized data
  • Control plot behavior via SHOW_PLOT: patch CSV to meet the criteria of batchartrace
• hltb_barchartrace.py
  • Adjust DATE_COL if you don't always add a completion date
  • Set DATE_RANGE to False if you want full data
  • Edit BLOCK_DIVS and DIVISION to preconfigure plot layout
• debug.py: figure out the real line number of invalid lines, the one pandas reported is not trustworthy

That's it. You can find example charts and other information on my blog, or just read code comments.

License

GLWTPL (Good Luck With That Public License)