Metadata-Version: 2.1
Name: MCRLLM
Version: 0.0.33
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.    
        
        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.    
        
        # Input data    
        Algorithm is designed to treat 2D data X(nxk) matrices where n spectra acquired over k energy levels.    
        3D spectral image X(n1,n2,k) can be unfolded to 2D matrix X(n1xn2,k) prior to MCRLLM analysis. Composition maps can then be obtained by folding C(n1xn2,a) matrices into 2D chemical maps C(n1,n2,a).    
        # 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 optimize phi: 'phi' or to set phi to 1: 'standard'    
        decomposition = mcr.mcrllm(X,nb,'phi')    
        # Results    
        Show S and C for each iteration (all) or only for final results (final).    
        S_all = decomposition.allS    
        S_final = decomposition.S    
        C_all = decomposition.allC    
        C_final = decomposition.C    
        # Example:    
        Compute MCRLLM using 7 components and optimizing phi exponent.    
        import mcrllm as mcr    
        decomposition = mcr.mcrllm(X,7,'phi')    
        #Iterate each component 20 times    
        decomposition.iterate(20)    
        S_final = decomposition.S    
        C_final = decomposition.C    
        plt.figure()    
        plt.plot(S_final.T)    
        plt.title('S',fontsize=16)    
        plt.figure()    
        plt.plot(C_final)    
        plt.title('C',fontsize=16)
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
