Metadata-Version: 2.1
Name: mcrllm
Version: 0.0.23
Summary: MCRLLM: Multivariate Curve Resolution by Log-Likelihood Maximization
Home-page: UNKNOWN
Author: Ryan Gosselin
Author-email: ryan.gosselin@usherbrooke.ca
License: UNKNOWN
Description: MCRLLM: Multivariate Curve Resolution by Log-Likelihood Maximization.    
        
        #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.    
        
        #Input variable    
        X(nxk): 2D spectral matrix : n spectra acquired over k energy levels    
        Note: 3D spectral image can be unfold to 2D matrix prior to analysis.    
        # Input and output arguments    
        MCRLLM requires 3 inputs : X dat, number of components to compute (nb) and use of phi exponent.    
         Refer to paper above for use of phi. To use it: 'phi', if not: 'standard'    
        decomposition = mcr.mcrllm(X,nb,'phi')    
        decomposition.iterate(20)    
        # Results    
        allS = decomposition.allS    
        S_final = decomposition.S    
        allC = decomposition.allC    
        C_final = decomposition.C    
        Sini = decomposition.Sini    
        allphi = decomposition.allphi
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
