https://github.com/douglasdavis/tdub

tW analysis tools
https://github.com/douglasdavis/tdub

analysis analysis-framework machine-learning matplotlib pandas plotting python python3

Last synced: 3 months ago
JSON representation

tW analysis tools

• Host: GitHub
• URL: https://github.com/douglasdavis/tdub
• Owner: douglasdavis
• License: bsd-3-clause
• Created: 2019-08-13T03:14:43.000Z (almost 6 years ago)
• Default Branch: main
• Last Pushed: 2023-03-27T16:29:13.000Z (about 2 years ago)
• Last Synced: 2025-02-23T12:44:41.339Z (3 months ago)
• Topics: analysis, analysis-framework, machine-learning, matplotlib, pandas, plotting, python, python3
• Language: Python
• Homepage: https://tdub.readthedocs.io
• Size: 2 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# tdub

[![Actions Status](https://github.com/douglasdavis/tdub/workflows/Linux/macOS/badge.svg)](https://github.com/douglasdavis/tdub/actions)

[![Documentation Status](https://readthedocs.org/projects/tdub/badge/?version=latest)](https://tdub.readthedocs.io/)

[![PyPI](https://img.shields.io/pypi/v/tdub?color=teal)](https://pypi.org/project/tdub/)

[![Python Version](https://img.shields.io/pypi/pyversions/tdub)](https://pypi.org/project/tdub/)

`tdub` is a Python project for handling some downstsream steps in the

ATLAS Run 2 *tW* inclusive cross section analysis. The project provides

a simple command line interface for performing standard analysis tasks

including:

- BDT feature selection and hyperparameter optimization.

- Training BDT models on our Monte Carlo.

- Applying trained BDT models to our data and Monte Carlo.

- Generating plots from various raw sources (our ROOT files and

  Classifier training output).

- Generating plots from the output of

  [`TRExFitter`](https://gitlab.cern.ch/TRExStats/TRExFitter/).

For potentially finer-grained tasks the API is fully documented. The

API mainly provides quick and easy access to pythonic representations

(i.e. dataframes or NumPy arrays) of our datasets (which of course

originate from [ROOT](https://root.cern/) files), modularized ML

tasks, and a set of utilities tailored for interacting with our

specific datasets.