H MCRLLM: Hierarchical Multivariate Curve Resolution by Log-Likelihood Maximization
X = CS
where
X(nxk): Spectroscopic data where n spectra acquired over k energy levels
C(nxa): Composition map based on a MCRLLM components
S(axk): Spectra of the a components as computed by MCRLLM
Method first presented in
Lavoie F.B., Braidy N. and Gosselin R. (2016) Including Noise Characteristics in MCR to improve Mapping and Component Extraction from Spectral Images, Chemometrics and Intelligent Laboratory Systems, 153, 40-50.
Dataset
XPS dataset of Titanium, Vanadium and Chromium. Please refer to Lavoie et al. (2016) for further details on the sample.
1 maintainer
Release files for H-MCRLLM 0.0.24
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| H_MCRLLM-0.0.24.tar.gz | 10.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| H_MCRLLM-0.0.24-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 25.9 kB
Release files / H_MCRLLM-0.0.24.tar.gz
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| Size | 10.7 kB |
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Release files / H_MCRLLM-0.0.24-py3-none-any.whl
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