rainbow-api 1.3.0

Read chromatography and mass spectrometry binary files

rainbow

rainbow provides programmatic access to the raw data encoded in chromatography and mass spectrometry binary files. This library supports the following vendors and detectors:

Container	File	Data	Parse with
Agilent .D	.uv	UV spectrum (supports incomplete files)
	.ch	UV, FID, CAD, and ELSD channels
	.ms	MS (supports incomplete files)
	MSProfile.bin	HRMS and ICP-MS profile spectrum	hrms=True
	MSPeak.bin	centroid (peak-picked) spectrum	centroid=True
Agilent .dx (OpenLab CDS)	.UV	DAD spectrum
	.CH	single-wavelength UV/DAD signals
	.IT	instrument telemetry, as analog data	telemetry=True
Waters .raw	CHRO	CAD and ELSD, plus miscellaneous analog data
	FUNC	UV and MS

There is documentation for rainbow that also details the structure of each binary file format.

Installation

pip install rainbow-api

Prebuilt wheels include an optional compiled accelerator (see Performance); installation never requires a compiler, and rainbow works the same with or without it.

Usage

The easiest way to get started is to give rainbow a directory path. Assume that we have a directory mydata.D that contains a binary file DAD1.uv with UV data.

import rainbow as rb
datadir = rb.read("mydata.D")
datafile = datadir.get_file("DAD1A.uv")

Here, the datadir DataDirectory object contains a DataFile object for DAD1A.uv.

rainbow normally infers the vendor from the path suffix (.D/.dx for Agilent, .raw for Waters). A directory whose name lacks that suffix is identified from its contents instead, so renamed datasets still parse. To force a parser explicitly, pass format:

datadir = rb.read("Noscapine 3", format="waters")

The raw UV data is contained in numpy arrays that are attributes of datafile. Users may find the following particularly useful:

• datafile.xlabels - 1D numpy array with retention times
• datafile.ylabels - 1D numpy array with wavelengths
• datafile.data - 2D numpy array with absorbances

There is a tutorial available. There are also example snippets for basic tasks. Or just check out the full API.

Performance

A few inherently-sequential decode loops are sped up by optional compiled (Cython) extensions: roughly 100x faster, bit-identical, with a transparent pure-Python fallback when no compiler is available (prebuilt PyPI wheels include them). See the Performance page in the documentation for the optimization strategies behind rainbow and the considerations for adding a new format.

Contents

• rainbow/ contains the code of the Python library.
• docs/ contains code for generating documentation. To build documentation locally, you will need to install the sphinx and sphinx-rtd-theme packages. Then, move to the docs/ directory and run make html. The docpages will be generated under docs/_build.
• tests/ contains unit tests for the library. These can be run with pytest from the repository root (install the test dependency with pip install -e .[test]).

For development, an editable install (pip install -e .) compiles the optional accelerator in place if a C compiler and Cython are available; otherwise the pure-Python fallback is used. The parity between the two paths is checked by tests/test_accelerator.py.