Metadata-Version: 2.1
Name: MCRLLM
Version: 0.0.68
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) where n spectra acquired over k energy levels.    
        A 3D spectral image X(n1,n2,k) can be reshaped to a 2D matrix X(n1xn2,k) prior to MCRLLM analysis. Composition maps can then be obtained by reshaping C(n1xn2,a) into 2D chemical maps C(n1,n2,a).    
        # Input and output arguments    
        MCRLLM requires 2 inputs : X data and number of MCRLLM components to compute (a).    
        decomposition = mcr.mcrllm(X,a)    
        S = decomposition.S    
        C = decomposition.C    
        # Example    
        #Compute MCRLLM on X using 7 components.    
        
        #First obtain X data. Spectroscopic data is available in the zip file of this Pypi module (data_EELS.txt). It represents EELS (Electron Energy Loss Spectroscopy). This data consists of 100 spectra acquired over 2048 energy levels.    
        
        import MCRLLM as mcr    
        import matplotlib.pyplot as plt    
        import numpy as np    
        
        X = np.loadtxt('data_EELS.txt', delimiter=',')    
        
        decomposition = mcr.mcrllm(X,7)    
        #Iterate each component 10 times    
        decomposition.iterate(10)    
        S = decomposition.S    
        C = decomposition.C    
        plt.figure();plt.plot(S.T)    
        plt.figure();plt.plot(C)    
        # 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
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
