Local analysis tool for Pokercraft in GGNetwork
This project has been archived.
The maintainers of this project have marked this project as archived. No new releases are expected.
Project description
Pokercraft Local
This is a customized visualization tool using downloaded data from Pokercraft in GGNetwork.
Here is working demo.
Dependencies
- Python 3.12
- plotly, pandas
I develop most stuffs on WSL and did not tested on other operating systems yet.
How to run
1. Installation
Clone this git repo and install dependencies with pip install -r requirements.txt, optionally on virtual environment.
If you want the library files only, you can download from PyPI. Run pip install pokercraft-local.
pip install -r requirements.txt # When you cloned the whole repo
pip install pokercraft-local # When you install library only via pip
Or alternatively, you can just download compiled binaries from Releases page. This is the best decision when you don't know programming.
2. Collect your data files from Pokercraft
Download "Game summaries" file by pressing green button on your pokercraft tournament section. If there are too many tournament records on your account, GGNetwork will prevent you from bulk downloading, therefore you may have to download separately monthly or weekly records.
After you downloaded, just put all .zip files in single folder.
The unzipping is not necessary anymore from v1.7.0.
The library finds all GG(blabla).txt files from both directory and .zip files recursively by default.
3. Running a program
For GUI, if you successfully run the program, you will be able to view something like following image.
3A. When you cloned this whole repo
Run run_cli.py with some arguments.
Make sure you installed all dependencies before running.
python run_cli.py --help
Or alternatively, you can run run_gui.py instead.
python run_gui.py
3B. When you installed libraries via pip
Run following Python code to start GUI, and you are good to go.
from pokercraft_local.gui import PokerCraftLocalGUI
if __name__ == "__main__":
PokerCraftLocalGUI().run_gui()
To do something programatic, check run_cli.py for example references.
3C. When you directly downloaded releases
Execute run_gui-(YOUR_OS)/run_gui/run_gui.exe from downloaded zip file on your local machine.
4. Check results
Go to your output folder and open generated .html file.
Note that plotly javascripts are included by CDN, so you need working internet connection to properly view it.
Features
Each plots are now providing documentations in .html,
so please read when you generated a file.
There are following sections;
- Historical Performances
- RRE Heatmaps
- Bankroll Analysis with Monte-Carlo simulation
- Your Prizes
- RR by Rank Percentiles
Project details
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