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
That installs everything needed to read a file. Four things are separate, because most callers never reach them:
| Extra | Install | What it adds |
|---|---|---|
plot |
pip install rainbow-api[plot] |
matplotlib, for DataFile.plot |
waters |
pip install rainbow-api[waters] |
pandas, for the Waters compound transition table |
hrms |
pip install rainbow-api[hrms] |
python-lzf, for LZF-compressed MassHunter MSProfile.bin |
validate |
pip install rainbow-api[validate] |
jsonschema and rfc3339-validator, for the opt-in ASM conformance tests. Both: without the second, timestamp formats are not actually checked |
Each says which extra to install if you call it without one, so nothing fails mysteriously.
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("Caffeine 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 timesdatafile.ylabels- 1D numpy array with wavelengthsdatafile.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.
A whole sequence at once
A sequence directory, one injection subdirectory per sample, reads in one call. Injections come back in sorted-name order, which for the names ChemStation writes by default is the order they ran.
sequence = rb.read_sequence("Caffeine_Stability")
first = sequence[0]
one = sequence.get_injection("008-D1F-A1-sample_01.D")
See Sequences.
Getting the data out of the vendor's format
Any run, or any sequence, exports to the
Allotrope Simple Model: an open, published JSON
format that other tools read, so the data stops depending on rainbow or on the
instrument vendor. It reads back, and it validates against the published
Allotrope schemas once the run's UTC offset is known: most vendor formats record
local wall clock with no offset, and rainbow will not invent one, so pass
utc_offset= if you know where the instrument was.
datadir.export_asm("caffeine.asm.json") # one run
sequence.export_asm("stability.asm.json") # the whole sequence
back = rb.from_asm(json.load(open("caffeine.asm.json")))
A full-scan MS channel is a 2D grid the format cannot hold faithfully, so ask
for the ions you want: datadir.to_asm(ions=[195.1]). See
ASM export.
The metadata the normal read leaves behind
A vendor run ships sidecar files the data parsers never touch, holding the
acquisition context: module serials, firmware versions, the operator, method
details, vial positions, timestamps. rainbow.debug decodes them on demand,
and costs nothing if you never call it.
from rainbow import debug
debug.fields("mydata.D") # one merged record of the whole run
debug.inspect("mydata.D") # every recognized sidecar, in full
See Debug metadata.
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 the packages indocs/requirements.txt(pip install -r docs/requirements.txt);myst-parseris required as well assphinxandsphinx-rtd-theme, since part of the documentation is Markdown. Then, move to thedocs/directory and runmake html. The docpages will be generated underdocs/_build.tests/contains unit tests for the library. These can be run withpytestfrom the repository root (install the test dependency withpip 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.
3 maintainers
Metadata
3 maintainers
Release files for rainbow-api 1.5.3
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| Tags | CPython 3.8 Linux glibc 2.17+ x86-64 |
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