MOCCA2 is a Python package for automatic processing of HPLC chromatograms.
To automate your workflow and get accurate results, MOCCA2 features:
- support for raw data files from Agilent, Shimadzu and Waters
- automatic baseline correction
- adaptive peak picking
- automatic purity checking and peak deconvolution
- compound tracking across chromatograms
- fully automatic processing of any number of chromatograms
Examples and detailed documentation are documented at https://bayer-group.github.io/MOCCA.
The latest version of MOCCA2 can be installed simply using pip:
pip install mocca2
Example data can be then downloaded using the following command:
python -m mocca2 --download-data
Now you are ready to process your first chromatogram!
from mocca2 import example_data
from matplotlib import pyplot as plt
# Load example data
chromatogram = example_data.example_1()
# Correct the baseline
chromatogram.correct_baseline()
# Crop the chromatogram to the region of interest, 1.4 to 1.8 minutes
chromatogram.extract_time(1.4, 1.8, inplace=True)
# Exclude low wavelengths that tend to be noisy - ignore everything below 220 nm
chromatogram.extract_wavelength(220, None, inplace=True)
# Find peaks in the chromatogram
chromatogram.find_peaks(min_height=2)
# Deconvolve the peaks
print("Deconvolving peaks, this migth take a minute...")
chromatogram.deconvolve_peaks(
model="FraserSuzuki", min_r2=0.999, relaxe_concs=False, max_comps=5
)
print("Deconvolved!")
# Plot the chromatogram
chromatogram.plot()
plt.show()
This package is based on MOCCA package by HaasCP. This work has been published by Christian Haas et al. in 2023.
Inspired by MOCCA, MOCCA2 features more Pythonic interface as well as adaptive and more accurate algorithms.
Publication featuring MOCCA2 is coming soon!
This repository automates numerous workflows:
On push to main, all tests in the tests directory are automatically run. Currently, MOCCA2 is tested on Ubuntu with Python 3.10, 3.11 and 3.12.
On push to main, the Sphinx docs are automatically compiled and published to GitHub pages.
The repository contains various example datasets:
- Knoevenagel condensation (Christian Haas et al., 2023)
- Cyanation screening (Christian Haas et al., 2023)
- Diterpene esters from coffee extracts (Erny et al., 2021)
- and various standalone chromatograms
Since these datasets don't fit into the PyPI package size limit, they are automatically compressed and published onto example-data branch on push to main.
The data can be automatically downloaded using python -m mocca2 --download-data.
On push to main, the MOCCA2 package is automatically published to PyPI and GitHub Releases.
The process for contributing is outlined in CONTRIBUTING.md.