[](https://pypi.org/project/mocca2/)
[](https://bayer-group.github.io/MOCCA/)
[](https://github.com/bayer-group/MOCCA/tree/example-data)
# Welcome to MOCCA2
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
## Documentation
Examples and detailed documentation are documented at [https://bayer-group.github.io/MOCCA](https://bayer-group.github.io/MOCCA).
## Getting Started
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()
```
## Publications and MOCCA
This package is based on [MOCCA](https://github.com/HaasCP/mocca) package by [HaasCP](https://github.com/HaasCP). This work has been published by [Christian Haas et al. in 2023](https://doi.org/10.1021/acscentsci.2c01042).
Inspired by MOCCA, MOCCA2 features more Pythonic interface as well as adaptive and more accurate algorithms.
Publication featuring MOCCA2 is coming soon!
## Repository Details
This repository automates numerous workflows:
### Automatic testing
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.
### Docs
On push to `main`, the Sphinx docs are automatically compiled and published to [GitHub pages](https://bayer-group.github.io/mocca).
### Example data
The repository contains various example datasets:
- Knoevenagel condensation ([Christian Haas et al., 2023](https://doi.org/10.1021/acscentsci.2c01042))
- Cyanation screening ([Christian Haas et al., 2023](https://doi.org/10.1021/acscentsci.2c01042))
- Diterpene esters from coffee extracts ([Erny et al., 2021](https://doi.org/10.5281/zenodo.5412345))
- 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``.
### Publishing to PyPI and GitHub
On push to `main`, the MOCCA2 package is automatically published to [PyPI](https://pypi.org/project/mocca2/) and [GitHub Releases](https://github.com/bayer-group/MOCCA/releases).
## Contributing
The process for contributing is outlined in [CONTRIBUTING.md](https://github.com/bayer-group/MOCCA/blob/main/CONTRIBUTING.md).
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