pyMCR: Multivariate Curve Resolution for Python
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Documentation available online at https://pages.nist.gov/pyMCR
Software DOI: https://doi.org/10.18434/M32064
Manuscript DOI: https://doi.org/10.6028/jres.124.018
pyMCR is a small package for performing multivariate curve resolution. Currently, it implements a simple alternating regression scheme (MCR-AR). The most common implementation is with ordinary least-squares regression, MCR-ALS.
MCR with non-negativity constraints on both matrices is the same as non-negative matrix factorization (NMF). Historically, other names were used for MCR as well:
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>
_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
Error metrics / Loss function
Other options
What it does do:
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.
Note: These are the developmental system specs. Older versions of certain packages may work.
python >= 3.4
numpy (1.9.3)
scipy (1.0.0)
scikit-learn, optional (0.2.0)
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/usnistgov/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/usnistgov/pyMCR>`_
or clone the repository with git:
.. code::
# Make new directory for pyMCR and enter it
# Clone from github
git clone https://github.com/usnistgov/pyMCR
Perform the install:
.. code::
python setup.py install
Logging
--------
**New in pyMCR 0.4.*, the logging module is now automatically loaded and setup during import (via __init__.py) to print messages**. You do not need to do the logger setup below.
**New in pyMCR 0.3.1**, Python's native logging module is now used to capture messages. Though this is not as
convenient as print() statements, it has many advantages.
- Logging module docs: https://docs.python.org/3.7/library/logging.html
- Logging tutorial: https://docs.python.org/3.7/howto/logging.html#logging-basic-tutorial
- Logging cookbook: https://docs.python.org/3.7/howto/logging-cookbook.html#logging-cookbook
A simple example that prints simplified logging messages to the stdout (command line):
.. code:: python
import sys
import logging
# Need to import pymcr or mcr prior to setting up the logger
from pymcr.mcr import McrAR
logger = logging.getLogger('pymcr')
logger.setLevel(logging.DEBUG)
# StdOut is a "stream"; thus, StreamHandler
stdout_handler = logging.StreamHandler(stream=sys.stdout)
# Set the message format. Simple and removing log level or date info
stdout_format = logging.Formatter('%(message)s') # Just a basic message akin to print statements
stdout_handler.setFormatter(stdout_format)
logger.addHandler(stdout_handler)
# Begin your code for pyMCR below
Usage
-----
.. code:: python
from pymcr.mcr import McrAR
mcrar = McrAR()
# 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
mcrar.fit(D, ST=initial_spectra)
# Otherwise, if you have an initial estimate of the concentrations
mcrar.fit(D, C=initial_conc)
Example Results
---------------
Command line and Jupyter notebook examples are provided in the ``Examples/`` folder. Examples of instantiating
the McrAR class with different regressors available in the `documentation <https://pages.nist.gov/pyMCR>`_ .
From ``Examples/Demo.ipynb``:
.. image:: ./Examples/mcr_spectra_retr.png
.. image:: ./Examples/mcr_conc_retr.png
Citing this Software
--------------------
If you use *pyMCR*, citing the following article is much appreciated:
- `C. H. Camp Jr., "pyMCR: A Python Library for Multivariate Curve Resolution
Analysis with Alternating Regression (MCR-AR)", Journal of Research of
National Institute of Standards and Technology 124, 1-10 (2019)
<https://doi.org/10.6028/jres.124.018>`_.
References
----------
- `W. H. Lawton and E. A. Sylvestre, "Self Modeling Curve Resolution",
Technometrics 13, 617–633 (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. Gorzsás,
"Vibrational spectroscopic image analysis of biological material using
multivariate curve resolution–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: `[email protected] <mailto:[email protected]>`_
Contributors
-------------
- Charles H Camp Jr
- Charles Le Losq ([email protected])
- Robert Kern ([email protected])
- Joshua Taillon ([email protected])