MCRLLM
Multivariate Curve Resolution by Log-Likelihood Maximization

Available at www.pypi.org


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.


EXAMPLES
Two full examples, along with datasets, are provided in 'Download Files'.
Please refer to 'MCRLLM_example.pdf' for full details.
- Example 1: 1D spectral linescan of EELS data.
- Example 2: 2D spectral image of XPS data.


COMPATIBILITY
MCRLLM tested on Python 3.7 using the following modules:
- Numpy 1.17.2
- Scipy 1.3.1
- Sklearn 0.21.3
- Pysptools 0.15.0
- Tqdm 4.36.1


CONTACT INFO
Ryan Gosselin, Université de Sherbrooke, ryan.gosselin@usherbrooke.ca




-- Ryan Gosselin, May 1st, 2020