DTU Pectin Yield example data
=============================

Source
------
https://github.com/DTUComputeStatisticsAndDataAnalysis/MBPLS
File: mbpls/data/MBPLS_intro/{ftir1, ftir2, ftir3, extraction1,
extraction2, extraction3}.pkl

Reference
---------
Baum, A. & Vermue, L. *Multiblock PLS: Block dependent prediction
modeling for Python.* Journal of Open Source Software, 4(34), 1190
(2019). https://doi.org/10.21105/joss.01190

Contents
--------
Three pectin extractions; each extraction has two measured X-blocks
(FTIR spectra, 148 wavenumbers; carbohydrate microarray, 30 antibody
probes) and a single Y (pectin yield in grams). The yield is encoded
as the index of every CSV. The three extractions concatenate along
the row axis to give 37 samples in total.

- ftir1.csv, ftir2.csv, ftir3.csv: FTIR features, shape (23, 148),
  (7, 148), (7, 148). Column names are wavenumbers (cm^-1).
- extraction1.csv, extraction2.csv, extraction3.csv: microarray
  features, shape (23, 30), (7, 30), (7, 30). Column names are
  antibody / probe identifiers (e.g. JIM5, JIM7, LM18).

The CSVs are exact value-preserving exports of the upstream pickles
(written via pandas with float_format='%.6f' for the FTIR / microarray
values; index preserved as the yield in grams).

License (upstream)
------------------
BSD 3-Clause License

Copyright (c) 2018, Andreas Baum and Laurent Vermue
All rights reserved.

Redistribution and use in source and binary forms, with or without
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  list of conditions and the following disclaimer.

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  this list of conditions and the following disclaimer in the documentation
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