Metadata-Version: 1.1
Name: gppeval
Version: 2019.4.17.0.4.dev1
Summary: Geothermal Power Potential assessment
Home-page: https://github.com/cpocasangre/gppeval
Author: Carlos O. POCASANGRE JIMENEZ
Author-email: carlos.pocasangre@fia.ues.edu.sv
License: MIT License
Description-Content-Type: UNKNOWN
Description: TOPIC
        ===============================
        A Python-based stochastic library for assessing geothermal power potential using the volumetric
        method in a liquid-dominated reservoir.
        
        ABSTRACT
        ===============================
        We present a Python-based stochastic library for assessing geothermal power
        potential using the volumetric method in a liquid-dominated reservoir.
        The specific aims of this study are to use the volumetric method, ``heat in
        place,`` to estimate electrical energy production ability from a geothermal
        liquid-dominated reservoir, and to build a Python-based stochastic library
        with useful methods for running such simulations. Although licensed
        software is available, we selected the open-source programming language
        Python for this task. The Geothermal Power Potential Evaluation stochastic
        library (``gppeval``) is structured as three essential objects including a
        geothermal power plant module, a Monte Carlo simulation module, and a tools
        module. In this study, we use hot spring data from the municipality of
        Nombre de Jesus, El Salvador, to demonstrate how the ``gppeval`` can be used to
        assess geothermal power potential. Frequency distribution results from the
        stochastic simulation shows that this area could initially support a
        9.16-MWe power plant for 25 years, with a possible expansion to 17.1 MWe.
        Further investigations into the geothermal power potential will be
        conducted to validate the new data.
        
        For testing the application, a **Jupyter Notebook** example has been included in the `example
        folder`_.
        
        *HINT*: **Now, this application is available for Python 3.5**
        
        Reference
        --------------
        Pocasangre, C., & Fujimitsu, Y. (2018). *A Python-based stochastic library for assessing
        geothermal power potential using the volumetric method in a liquid-dominated reservoir*.
        **Geothermics**, 76, 164-176.
        https://doi.org/10.1016/J.GEOTHERMICS.2018.07.009
        
        INSTALLATION
        ============
        
        Required Packages
        -----------------
        
        The following packages should be installed automatically (if using 'pip'
        or 'easy_install'), otherwise they will need to be installed manually:
        
        - NumPy_ : Numeric Python
        - SciPy_ : Scientific Python
        - Matplotlib_ : Python plotting library
        - Mcerp_ : Monte Carlo Error Propagation
        - Beautifultable_ : Utility package to print visually appealing ASCII tables to terminal
        
        How to install
        --------------
        
        You have **several easy, convenient options** to install the 'gppeval'
        package (administrative privileges may be required).
        
        #. Simply copy the unzipped 'gppeval folder' directory to any other location that
           python can find it and rename it 'gppeval'.
        
        #. From the command-line, do one of the following:
        
           a. Manually download the package files below, unzip to any directory, and
              run:
        
               $ [sudo] python setup.py install
        
           b. If 'pip' is installed, run the follow command (stable version and internet connection is required)
        
               $ [sudo] pip install [--upgrade] gppeval
        
        CHANGES OF NEW ISSUE
        ====================
        
        #. gppeval (2019.4.17.0.4.dev1).
            Fixed bugs using "print" on Python 2.7
        
        #. gppeval (2019.4.17.0.2.dev1).
            Python 3.5 available
        
        #. gppeval (2018.10.11.0.1.dev1).
            The input file csv has been modified. It includes the possibility of using volume as a input
            reservoir parameter. Using the word ``none`` is possible to exchange between either to use
            **Area** and **Thickness** or to use only **Volume** as a reservoir geometric parameter.
        
            Example: Using Area and Thickness
        
                0,Name,14.00061,-88.73744,ReservoirArea,A,km2,5,6,7,0,0,T
                1,,,,Thickness,h,m,450,500,600,0,0,T
                2,,,,Volume,v,km3,4,6,8.2,0,0,none
        
            Example: Using only Volume
        
                0,Name,14.00061,-88.73744,ReservoirArea,A,km2,5,6,7,0,0,None
                1,,,,Thickness,h,m,450,500,600,0,0,None
                2,,,,Volume,v,km3,4,6,8.2,0,0,T
        
        #. gppeval (2018.4.6.0.1.dev1).
            Original issue after have been upload as a stable.
        
        CONTACT
        =======
        
        Please send **feature requests, bug reports, or feedback** to: `Carlos O. POCASANGRE JIMENEZ`_
        
        .. _Monte Carlo methods: http://en.wikipedia.org/wiki/Monte_Carlo_method
        .. _latin-hypercube sampling: http://en.wikipedia.org/wiki/Latin_hypercube_sampling
        .. _error propagation: http://en.wikipedia.org/wiki/Propagation_of_uncertainty
        .. _math: http://docs.python.org/library/math.html
        .. _NumPy: http://www.numpy.org/
        .. _SciPy: http://scipy.org
        .. _Matplotlib: http://matplotlib.org/
        .. _scipy.stats: http://docs.scipy.org/doc/scipy/reference/stats.html
        .. _uncertainties: http://pypi.python.org/pypi/uncertainties
        .. _Mcerp: http://github.com/tisimst/mcerp
        .. _Beautifultable: https://github.com/pri22296/beautifultable
        .. _Gppeval: http://github.com/cpocasangre/gppeval
        .. _example folder: https://github.com/cpocasangre/gppeval
        .. _Carlos O. POCASANGRE JIMENEZ: mailto:carlos.pocasangre@fia.ues.edu.sv
        
Keywords: monte carlo latin hypercube geothermal power potential volumetric method geothermal reservoir
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
