PyCatRobin

Python module to analyze time-on-stream Catalyst testing results from Round Robin test

Requirements

• All specified in setup.py

Getting started

1. Make a virtual environment (e.g., when using conda):

conda create -n pycatrobin python=3.12
conda activate pycatrobin

2. Installation

• choice1) Directly install using pip

  pip install git+https://github.com/dongjae-shin/PyCatRobin.git

• choice2) Clone repository & install using pip

  git clone https://github.com/dongjae-shin/PyCatRobin.git
  cd PyCatRobin
  pip install .

3. Run example codes (under development)

• Example python codes to use pycatrobin are in examples/ directory.
• In the examples/, run as follows:

  python ./extract_from_gc_data_snr.py
  python ./Welchs_t_test.py
  python ./fANOVA.py

• See the instructions in the examples/ folder.
• Currently, Welch's t-test and fANOVA codes are separate scripts from pycatrobin. They will be incorporated into the main package in the near future.

Related publication

• Quantifying Experimental Uncertainty in Catalyst Deactivation: Round-Robin Testing and Implications for Machine-Learned Prediction, S. Bac, D. Shin, S. Hong, J. Heinlein, A. Khan, G. Barber, Z. Chen, M. M. Albrechtsen, C. Tassone*, R. M. Rioux*, M. Cargnello*, S. R. Bare*, K. Winther*, P. Christopher*, A. S. Hoffman*, submitted (2025).

Acknowledgement

• Original codes for t-test and fANOVA analyses were written by Dr. Selin Bac (UCSB) and Michael Albrechtsen (DTU), respectively.