https://github.com/bayer-group/mocca
Science Score: 49.0%
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○CITATION.cff file
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✓DOI references
Found 8 DOI reference(s) in README -
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1 of 8 committers (12.5%) from academic institutions -
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○Scientific vocabulary similarity
Low similarity (15.0%) to scientific vocabulary
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Repository
Basic Info
- Host: GitHub
- Owner: Bayer-Group
- License: mit
- Language: Python
- Default Branch: main
- Homepage: https://bayer-group.github.io/MOCCA/
- Size: 195 MB
Statistics
- Stars: 29
- Watchers: 6
- Forks: 9
- Open Issues: 3
- Releases: 1
Topics
Metadata Files
readme.md
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.
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 = exampledata.example1()
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.findpeaks(minheight=2)
Deconvolve the peaks
print("Deconvolving peaks, this migth take a minute...")
chromatogram.deconvolvepeaks( model="FraserSuzuki", minr2=0.999, relaxeconcs=False, maxcomps=5 )
print("Deconvolved!")
Plot the chromatogram
chromatogram.plot() plt.show() ```
Publications and MOCCA
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!
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.
Example data
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.
Publishing to PyPI and GitHub
On push to main, the MOCCA2 package is automatically published to PyPI and GitHub Releases.
Contributing
The process for contributing is outlined in CONTRIBUTING.md.
Owner
- Name: Bayer Open Source
- Login: Bayer-Group
- Kind: organization
- Website: https://bayer.com/
- Repositories: 98
- Profile: https://github.com/Bayer-Group
Science for a better life
GitHub Events
Total
- Issues event: 1
- Watch event: 15
- Delete event: 1
- Issue comment event: 1
- Push event: 2
- Pull request review event: 4
- Pull request event: 6
- Fork event: 7
Last Year
- Issues event: 1
- Watch event: 15
- Delete event: 1
- Issue comment event: 1
- Push event: 2
- Pull request review event: 4
- Pull request event: 6
- Fork event: 7
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| m-lueb | 1****b | 7 |
| RachelNicholls1 | 9****1 | 7 |
| Jan Oboril | 9****l | 7 |
| lowprices | 8****s | 2 |
| dependabot[bot] | 4****] | 1 |
| Stanislav Bashkyrtsev | s****v@e****o | 1 |
| jpfolch | j****h@o****m | 1 |
| tpaul | t****l@e****h | 1 |
Issues and Pull Requests
Last synced: 8 months ago
All Time
- Total issues: 2
- Total pull requests: 20
- Average time to close issues: about 1 month
- Average time to close pull requests: 21 days
- Total issue authors: 2
- Total pull request authors: 8
- Average comments per issue: 0.0
- Average comments per pull request: 0.05
- Merged pull requests: 16
- Bot issues: 0
- Bot pull requests: 1
Past Year
- Issues: 2
- Pull requests: 10
- Average time to close issues: about 1 month
- Average time to close pull requests: about 2 months
- Issue authors: 2
- Pull request authors: 7
- Average comments per issue: 0.0
- Average comments per pull request: 0.1
- Merged pull requests: 7
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- IbkPLT (1)
- akirayou (1)
Pull Request Authors
- RachelNicholls1 (9)
- m-lueb (7)
- oboril (5)
- IbkPLT (2)
- dependabot[bot] (2)
- jpfolch (1)
- ctapobep (1)
- ivanmilevtues (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 1,077 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 27
- Total maintainers: 1
pypi.org: mocca2
MOCCA2 is an open-source Python project to analyze HPLC-DAD raw data
- Homepage: https://bayer-group.github.io/MOCCA/
- Documentation: https://mocca2.readthedocs.io/
- License: MIT License
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Latest release: 0.1.18
published 11 months ago