Metadata-Version: 1.1
Name: pyMCR
Version: 0.2.0
Summary: Multivariate Curve Resolution in Python
Home-page: https://github.com/CCampJr/pyMCR
Author: Charles H. Camp Jr.
Author-email: charles.camp@nist.gov
License: Public Domain
Description-Content-Type: UNKNOWN
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        pyMCR: Multivariate Curve Resolution in Python
        ===============================================================
        
        **Note: Verion 0.2.* is a total re-write from Verion 0.1.* and is not
        compatible.**
        
        pyMCR is a small package for performing multivariate curve resolution.
        Currently, it implements a simple alternating least squares method
        (i.e., MCR-ALS).
        
        MCR-ALS, in general, is a constrained implementation of alternating
        least squares (ALS) nonnegative matrix factorization (NMF). Historically,
        other names were used for MCR as well:
        
        -   Self modeling mixture analysis (SMMA)
        -   Self modeling curve resolution (SMCR)
        
        Available methods:
        
        -   Regressors:
        
            -   `Ordinary least squares <https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.lstsq.html>`_ (default)
            -   `Non-negatively constrained least squares 
                <https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.nnls.html>`_
            -   Native support for `scikit-learn linear model regressors 
                <http://scikit-learn.org/stable/modules/linear_model.html>`_
                (e.g., `LinearRegression <http://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares>`_, 
                `RidgeRegression <http://scikit-learn.org/stable/modules/linear_model.html#ridge-regression>`_, 
                `Lasso <http://scikit-learn.org/stable/modules/linear_model.html#lasso>`_)
        
        -   Constraints
        
            -   Non-negativity
            -   Normalization
        
        -   Error metrics / Loss function
        
            -   Mean-squared error
        
        What it **does** do:
        
        -   Approximate the concentration and spectral matrices via minimization routines. 
            This is the core the MCR-ALS methods.
        -   Enable the application of certain constraints (currently): sum-to-one, 
            non-negativity, normalization, maximum limits (closure)
        
        What it **does not** do:
        
        -   Estimate the number of components in the sample. This is a bonus feature in 
            some more-advanced MCR-ALS packages.
        
            - In MATLAB: https://mcrals.wordpress.com/
            - In R: https://cran.r-project.org/web/packages/ALS/index.html
        
        Dependencies
        ------------
        
        **Note**: These are the developmental system specs. Older versions of certain
        packages may work.
        
        -   python >= 3.4
            
            - Tested with 3.4.6, 3.5.4, 3.6.3
        
        -   numpy (1.9.3)
            
            - Tested with 1.12.1, 1.13.1, 1.13.3
        
        -   scipy (1.0.0)
        
            - Tested with 1.0.0
        
        Known Issues
        ------------
        
        
        Installation
        ------------
        
        Using pip (hard install)
        ~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code::
        
            # Only Python 3.* installed
            pip install pyMCR
        
            # If you have both Python 2.* and 3.* you may need
            pip3 install pyMCR
        
        Using pip (soft install [can update with git])
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        .. code::
            
            # Make new directory for pyMCR and enter it
            # Clone from github
            git clone https://github.com/CCampJr/pyMCR
        
            # Only Python 3.* installed
            pip install -e .
        
            # If you have both Python 2.* and 3.* you may need instead
            pip3 install -e .
        
            # To update in the future
            git pull
        
        Using setuptools
        ~~~~~~~~~~~~~~~~
        
        You will need to `download the repository <https://github.com/CCampJr/pyMCR>`_
        or clone the repository with git:
        
        .. code::
            
            # Make new directory for pyMCR and enter it
            # Clone from github
            git clone https://github.com/CCampJr/pyMCR
        
        Perform the install:
        
        .. code::
        
            python setup.py install
        
        Usage
        -----
        
        .. code:: python
        
            from pymcr.mcr import McrAls
            mcrals = McrAls()
            
            # MCR assumes a system of the form: D = CS^T
            #
            # Data that you will provide (hyperspectral context):
            # D [n_pixels, n_frequencies]  # Hyperspectral image unraveled in space (2D)
            #
            # initial_spectra [n_components, n_frequencies]  ## S^T in the literature
            # OR
            # initial_conc [n_pixels, n_components]   ## C in the literature
        
            # If you have an initial estimate of the spectra
            mcrals.fit(D, ST=initial_spectra)
        
            # Otherwise, if you have an initial estimate of the concentrations
            mcrals.fit(D, C=initial_conc)
        
        Examples
        --------
        
        Command line and Jupyter notebook examples are provided in the ``Examples/`` folder.
        
        From ``Examples/Demo.ipynb``:
        
        .. image:: ./Examples/mcr_spectra_retr.png
        
        .. image:: ./Examples/mcr_conc_retr.png
            
        References
        ----------
        
        -   `W. H. Lawton and E. A. Sylvestre, "Self Modeling Curve Resolution", 
            Technometrics 13, 617â€“633 (1971). <https://www.jstor.org/stable/1267173>`_
        -   https://mcrals.wordpress.com/theory/
        -   `J. Jaumot, R. Gargallo, A. de Juan, and R. Tauler, "A graphical user-friendly 
            interface for MCR-ALS: a new tool for multivariate curve resolution in
            MATLAB", Chemometrics and Intelligent Laboratory Systems 76, 101-110 
            (2005). <http://www.sciencedirect.com/science/article/pii/S0169743904002874>`_
        -   `J. Felten, H. Hall, J. Jaumot, R. Tauler, A. de Juan, and A. GorzsÃ¡s, 
            "Vibrational spectroscopic image analysis of biological material using 
            multivariate curve resolutionâ€“alternating least squares (MCR-ALS)", Nature Protocols 
            10, 217-240 (2015). <https://www.nature.com/articles/nprot.2015.008>`_
            
        
        LICENSE
        ----------
        This software was developed by employees of the National Institute of Standards 
        and Technology (NIST), an agency of the Federal Government. Pursuant to 
        `title 17 United States Code Section 105 <http://www.copyright.gov/title17/92chap1.html#105>`_, 
        works of NIST employees are not subject to copyright protection in the United States and are 
        considered to be in the public domain. Permission to freely use, copy, modify, 
        and distribute this software and its documentation without fee is hereby granted, 
        provided that this notice and disclaimer of warranty appears in all copies.
        
        THE SOFTWARE IS PROVIDED 'AS IS' WITHOUT ANY WARRANTY OF ANY KIND, EITHER 
        EXPRESSED, IMPLIED, OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTY 
        THAT THE SOFTWARE WILL CONFORM TO SPECIFICATIONS, ANY IMPLIED WARRANTIES OF 
        MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND FREEDOM FROM INFRINGEMENT, 
        AND ANY WARRANTY THAT THE DOCUMENTATION WILL CONFORM TO THE SOFTWARE, OR ANY 
        WARRANTY THAT THE SOFTWARE WILL BE ERROR FREE. IN NO EVENT SHALL NIST BE LIABLE 
        FOR ANY DAMAGES, INCLUDING, BUT NOT LIMITED TO, DIRECT, INDIRECT, SPECIAL OR 
        CONSEQUENTIAL DAMAGES, ARISING OUT OF, RESULTING FROM, OR IN ANY WAY CONNECTED 
        WITH THIS SOFTWARE, WHETHER OR NOT BASED UPON WARRANTY, CONTRACT, TORT, OR 
        OTHERWISE, WHETHER OR NOT INJURY WAS SUSTAINED BY PERSONS OR PROPERTY OR 
        OTHERWISE, AND WHETHER OR NOT LOSS WAS SUSTAINED FROM, OR AROSE OUT OF THE 
        RESULTS OF, OR USE OF, THE SOFTWARE OR SERVICES PROVIDED HEREUNDER.
        
        Contact
        -------
        Charles H Camp Jr: `charles.camp@nist.gov <mailto:charles.camp@nist.gov>`_
        
        Contributors
        -------------
        Charles H Camp Jr
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
