chromatograpy 0.1.0

Library to analyze chromatograms

Chromatograpy

A Python library to analyze chromatograms with support for multiple instrument formats and comprehensive analysis tools.

Features

• File I/O: Read chromatograms from multiple instrument formats
  • Thermo Chromeleon CSV exports
  • Waters MassLynx RAW files
• Peak Detection: Detect peaks using prominence-based algorithms
• Baseline Correction: Apply airPLS baseline correction algorithm
• Peak Identification: Identify peaks by retention time matching
• Quantification: Quantify analytes using calibration curves
• Visualization: Plot chromatograms with peak annotations

Installation

pip install chromatograpy

Requirements

• Python >= 3.13
• pandas
• numpy
• scipy
• matplotlib
• rainbow-api

Quick Start

import chromatograpy as chm

# Read a chromatogram file
chrom = chm.open("path/to/chromatogram.csv")

# Detect peaks
peaks = chm.peaks.by_prominence(chrom, prominence=0.1)

# Identify peaks using a retention time database
ids = chm.identify.by_retention_time(peaks, db)

# Quantify peaks
quantities = chm.quantify_peaks(peaks, ids, calibration_db)

# Plot the chromatogram with peaks
chm.plot(chrom, peaks=peaks, identification=ids, quantification=quantities)

Supported File Formats

Thermo Chromeleon

Read CSV exports from Chromeleon:

chrom = chm.open("sample.csv")

Waters MassLynx

Read MassLynx RAW files:

# Read all detectors
chroms = chm.open("sample.raw")

# Read specific detector
chrom = chm.open("sample.raw", detector="UV")

# Read with metadata filtering
chrom = chm.open("sample.raw", metadata={"polarity": "+"})

Core Classes

• Chromatogram1D: Single channel chromatogram (pandas Series subclass)
• Chromatogram2D: Multi-channel chromatogram (pandas DataFrame subclass)
• Peak1D: A detected peak with properties like area, height, and retention time

API Overview

Reading Files

• chromatograpy.open(path, *args, **kwargs): Universal file reader that auto-detects format

Peak Detection

• chromatograpy.peaks.by_prominence(chromatogram, prominence=0.1): Detect peaks based on prominence

Baseline Correction

• chromatograpy.baseline.airPLS(x, lambda_=100, porder=1, itermax=15): Apply airPLS baseline correction

Peak Identification

• chromatograpy.identify.by_retention_time(peaks, db): Identify peaks using retention time database

Quantification

• chromatograpy.quantify_peaks(peaks, ids, db): Quantify individual peaks
• chromatograpy.quantify_analytes(peaks, ids, db): Quantify all analytes in database

Visualization

• chromatograpy.plot(chrom, peaks=None, identification=None, quantification=None, ...): Plot chromatogram with optional annotations

License

MIT

Author

[e-mail]