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.

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.

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rainbow-api 1.4.0

rainbow

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