Skip to main content

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This project has been archived.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.

pokercraft_download

After you downloaded, unzip your downloaded .zip files, and put all of them under single folder. The library finds all GG(blabla).txt files recursively by default, so it's ok to make multiple folders inside to make better organization of files.

3. Running a program

For GUI, if you successfully run the program, you will be able to view something like following image.

gui_screen

3A. When you cloned this whole repo

For CLI, run run_cli.py with some arguments. If you installed dependencies in your virtual environment, make sure you enabled it before.

python run_cli.py -d (YOUR_DATA_FOLDER) -o (OUTPUT_FOLDER) -n (YOUR_GG_NICKNAME)

For GUI, simply run run_gui.py or you can directly download binaries from Releases to execute compiled program directly. When you start the GUI program, then you will be able to view something like above image. Choose data directory and output directory, input your nickname, then run the process by clicking the bottom button.

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)/dist/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

  • Net Profit & Rake chart
  • Profitable Tournaments Ratio chart (This is different from ITM; If you made profits by bounty killing without being ITM, then it is also counted as "profitable")
  • Average Buy In Amount chart
  • Relative Prize Return chart
  • ITM Scatters chart

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pokercraft_local-1.2.0.tar.gz (14.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pokercraft_local-1.2.0-py3-none-any.whl (14.2 kB view details)

Uploaded Python 3

File details

Details for the file pokercraft_local-1.2.0.tar.gz.

File metadata

  • Download URL: pokercraft_local-1.2.0.tar.gz
  • Upload date:
  • Size: 14.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for pokercraft_local-1.2.0.tar.gz
Algorithm Hash digest
SHA256 49e45fb981646f2cf53d37dc19da3f01e7ac330aa268b7e61771fef0954b64ac
MD5 62432a2842c687266dfd3e5b29a6a829
BLAKE2b-256 a211a3a85203af6161f8ffca8a1a02065b8ad0a85524783e477651373fce9a96

See more details on using hashes here.

File details

Details for the file pokercraft_local-1.2.0-py3-none-any.whl.

File metadata

File hashes

Hashes for pokercraft_local-1.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 1115d3455a6c3df3d42f5ecaea693eba7483cf08e9214e3f5f4dd99559bf1253
MD5 5ed3c4cd176f56646b7f82a7c909e541
BLAKE2b-256 23dc732e24f95e115c217e71b6a7cb69ceee70fe54d61517abbdc33bef60562e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page