Metadata-Version: 2.1
Name: gp_emulator
Version: 1.6.10
Summary: A Python Gaussian Process emulator software package
Home-page: http://github.com/jgomezdans/gp_emulator
Author: J Gomez-Dans
Author-email: j.gomez-dans@ucl.ac.uk
License: UNKNOWN
Description: # GP emulators
        
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        ## Gaussian process (GP) emulators for Python
        
        ### Author  
        J Gómez-Dans <j.gomez-dans@ucl.ac.uk>
        <p><img src="http://www.multiply-h2020.eu/wp-content/uploads/2018/08/multiply_banner_2018_klein.jpg" align="center" \></p>
        <p><img src="https://www.nceo.ac.uk/wp-content/themes/nceo/assets/images/logos/img_logo_purple.svg" align="left" />
        
        <img src="http://www.esa.int/esalogo/images/logotype/img_colorlogo_darkblue.gif" scale="20%" align="right" />
        </p>
        
        
        
        
        This repository contains an implementation of GPs for emulation of radiative transfer models in Python. This particular implementation is focused on emulating univariate output models (e.g. emulating reflectance or radiance for a single sensor band) and multivariate outputs (e.g. emulating reflectance/radiance over the entire solar reflective domain). The emulators also calculate the gradient of the emulated model and the Hessian.
        
        ### Documentation
        
        You can read the documentation in [http://http://gp-emulator.readthedocs.io]<http://gp-emulator.readthedocs.io>
        
        ### Installation
        
        
        You can install the software with either [conda]<https://docs.anaconda.com/anaconda/>
        
            conda install -c jgomezdans gp_emulators
        
        or using `pip`...
        
            pip install gp_emulator
        
        or just clone/download the repository and invoke the `setup.py` script:
        
            python setup.py install
        
        The only requirements are (if memory serves) numpy and scipy. 
        
        At some point, pointers to a library of emulators of popular vegetation and atmospheric RT codes will be provided.
        
        ### Citation
        
        
        If you use this code, we would be grateful if you cited the following paper:
        
            Gómez-Dans, J.L.; Lewis, P.E.; Disney, M. Efficient Emulation of Radiative Transfer Codes Using Gaussian Processes and Application to Land Surface Parameter Inferences. Remote Sens. 2016, 8, 119. <DOI:10.3390/rs8020119> <http://www.mdpi.com/2072-4292/8/2/119>
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Environment :: Console
Description-Content-Type: text/markdown
