SCC plotting

(c) Jonathan Goodman, Richard Lewis, Benji Rowlands
2024
jmg11@cam.ac.uk
University of Cambridge

Overview

This repository contains scripts to reproduce the SCC plots from the paper "Towards automatically verifying chemical structures: the powerful combination of ¹H NMR and IR spectroscopy".

The main plotting script is contained in scc.py. An example input file is contained in config.yaml.

Requirements

• Operating system: this software has been tested on macOS Sonoma 14.5. It should work on other operating systems, but has not been tested on them.
• Python version: this software was developed using Python 3.12.

Installation

To run this software, ensure you have Python 3.12 installed along with the following packages:

• matplotlib (version 3.9.0)
• pandas (version 2.2.2)
• numpy (version 2.0.1)
• scipy (version 1.14)
• scikit-learn (version 1.5.1)
• scienceplots (version 2.1.1)
• pyyaml (version 6.0.2)
• PyQt5 (version 5.15.11)

Installation time will depend on how long it takes to download the necessary packages, but should be less than a minute.

To configure the environment correctly, follow the following steps:

1. Install Python: Ensure you have Python 3.12 installed. You can download it from the official Python website.

2. Create a virtual environment:

   python3.12 -m venv .venv
   source .venv/bin/activate 
   

3. Activate the virtual environment and install the required package:

   source .venv/bin/activate
   pip install matplotlib==3.9.0 pandas==2.2.2 numpy==2.0.1 scipy==1.14 scikit-learn==1.5.1 scienceplots==2.1.1 pyyaml==6.0.2 pyqt5==5.15.11
   

Usage

SCC plots

1. Prepare your input file: The input file contains the settings for the plot, and should be named config.yaml. An example input file which reproduces the plots in the paper is provided.

2. Run the script: Execute the script from the command line. With no modification to the input file provided, a plot will be generated using data from the data/ directory.

python scc.py config.yaml --outdir ./plots

Execution time should be a few seconds.

Output

scc.py will save the plots to the plots/ directory.

Data

data contains 9 files:

• DP4_H.csv contains DP4* scores for the molecules in the test set
• ACD.csv contains ACD scores for the molecules in the test set
• ACD_auto.csv contains ACD scores for the molecules in the test set, obtained using ACD's automatic peak-picking tool
• Six CSV files (IR_high_fwhm_8.csv, IR_high_fwhm_10.csv, IR_high_fwhm_12.csv, IR_low_fwhm_8.csv, IR_low_fwhm_10.csv, IR_low_fwhm_12.csv) contain IR.Cai scores for the test set molecules.

The filename format is: IR_<theory>_fwhm_<value>.csv where:

• <theory> is high or low for high- or low-level theory, and
• <value> is the full width at half maximum (FWHM, in cm⁻¹) used in the calculation.

The data are in csv format. The 'Molecule' column identifies the molecule in question from the test set. The 'Comparison' column identifies which isomers are being compared. The '0', '1', '2', and '3' columns contain the scores for the relevant isomer for each molecule. As throughout this work, the score in the '0' column is the score for the correct isomer; the other columns contain scores for incorrect isomers.

Contact

For any questions or issues, please contact Jonathan Goodman at jmg11@cam.ac.uk.