.. PySptools documentation master file, created by
   sphinx-quickstart on Sun Sep 29 09:27:46 2013.
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.. meta::
	:description: Python tools for hyperspectral imaging
	:keywords: python, hyperspectral imaging, signal processing, library, software, endmembers, unmixing, pysptools, sam, sid, atgp, N-FINDR, NFINDR, spectroscopy, target detection, georessources, geoimaging, chemical imaging, pharmaceutical, pharma, minerals, spectral, remote sensing, hyperspectral drill core imaging

Welcome to the PySptools Documentation
**************************************

Tools for hyperspectral imaging ::

	Documentation at 2018-10-11.

.. figure:: ./pic/pic_burner1.png
   :scale: 100 %
   :align: center
   :alt: stacked abundance maps

Hyperspectral imaging is used to **visualize chemistry**, the spatial relation between chemicals and the proportion of them. PySptools is a python module that implements spectral and hyperspectral algorithms. Specializations of the library are the endmembers extraction, unmixing process, supervised classification, target detection, noise reduction, convex hull removal and features extraction at spectrum level. The library is designed to be easy to use and almost all functionality has a plot function to save you time with the data analysis process. The actual sources of the algorithms are the Matlab Hyperspectral Toolbox of Isaac Gerg, the pwctools of M. A. Little, the Endmember Induction Algorithms toolbox (EIA), the HySime Matlab module of José Bioucas-Dias and José Nascimento and research papers. Starting with version 0.14.0, PySptools add a bridge to the scikit-learn library. You can download PySptools from the PySptools
Project Page hosted by Sourceforge.net or from the pypi packages repository.

`My personal web site <http://ctherien.netlify.com/>`_ 

Version 0.15.0 is released
==========================

This version have an experimental module named **ml** (see the Machine Learning section below). 
This module implement an interface to **XGBoost** and **LightGBM**. The interface is scikit-learn and PySptools friendly.
The machine learning part of the project work very well but there is many glitches on the cross validation side and it 
will take time to fix. If you like hyper-parameters, you will be served!

Documentation
=============

.. toctree::
   :maxdepth: 2

   introduction
   installation
   examples_front
   abundance_maps
   classification
   noise
   detection
   distance
   eea
   ml
   material_count
   sigproc
   skl
   spectro
   util
   glossary
   links


Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`