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        "author": "Carlos O. POCASANGRE JIMENEZ",
        "author_email": "carlos.pocasangre@mine.kyushu-u.ac.jp",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5"
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        "description": "TOPIC\r\n===============================\r\nA Python-based stochastic library for assessing geothermal power potential using the volumetric\r\nmethod in a liquid-dominated reservoir.\r\n\r\nABSTRACT\r\n===============================\r\nWe present a Python-based stochastic library for assessing geothermal power\r\npotential using the volumetric method in a liquid-dominated reservoir.\r\nThe specific aims of this study are to use the volumetric method, ``heat in\r\nplace,`` to estimate electrical energy production ability from a geothermal\r\nliquid-dominated reservoir, and to build a Python-based stochastic library\r\nwith useful methods for running such simulations. Although licensed\r\nsoftware is available, we selected the open-source programming language\r\nPython for this task. The Geothermal Power Potential Evaluation stochastic\r\nlibrary (``gppeval``) is structured as three essential objects including a\r\ngeothermal power plant module, a Monte Carlo simulation module, and a tools\r\nmodule. In this study, we use hot spring data from the municipality of\r\nNombre de Jesus, El Salvador, to demonstrate how the ``gppeval`` can be used to\r\nassess geothermal power potential. Frequency distribution results from the\r\nstochastic simulation shows that this area could initially support a\r\n9.16-MWe power plant for 25 years, with a possible expansion to 17.1 MWe.\r\nFurther investigations into the geothermal power potential will be\r\nconducted to validate the new data.\r\n\r\nFor testing the application, a **Jupyter Notebook** example has been included in the `example\r\nfolder`_.\r\n\r\n*HINT*: **Now, this application is available for Python 3.5**\r\n\r\nReference\r\n--------------\r\nPocasangre, C., & Fujimitsu, Y. (2018). *A Python-based stochastic library for assessing\r\ngeothermal power potential using the volumetric method in a liquid-dominated reservoir*.\r\n**Geothermics**, 76, 164-176.\r\nhttps://doi.org/10.1016/J.GEOTHERMICS.2018.07.009\r\n\r\nINSTALLATION\r\n============\r\n\r\nRequired Packages\r\n-----------------\r\n\r\nThe following packages should be installed automatically (if using 'pip'\r\nor 'easy_install'), otherwise they will need to be installed manually:\r\n\r\n- NumPy_ : Numeric Python\r\n- SciPy_ : Scientific Python\r\n- Matplotlib_ : Python plotting library\r\n- Mcerp_ : Monte Carlo Error Propagation\r\n- Beautifultable_ : Utility package to print visually appealing ASCII tables to terminal\r\n\r\nHow to install\r\n--------------\r\n\r\nYou have **several easy, convenient options** to install the 'gppeval'\r\npackage (administrative privileges may be required).\r\n\r\n#. Simply copy the unzipped 'gppeval folder' directory to any other location that\r\n   python can find it and rename it 'gppeval'.\r\n\r\n#. From the command-line, do one of the following:\r\n\r\n   a. Manually download the package files below, unzip to any directory, and\r\n      run:\r\n\r\n       $ [sudo] python setup.py install\r\n\r\n   b. If 'pip' is installed, run the follow command (stable version and internet connection is required)\r\n\r\n       $ [sudo] pip install [--upgrade] gppeval\r\n\r\nCHANGES OF NEW ISSUE\r\n====================\r\n\r\n#. gppeval (2019.4.17.0.3.dev1).\r\n    Fixed bugs using \"print\" on Python 2.7\r\n\r\n#. gppeval (2019.4.17.0.2.dev1).\r\n    Python 3.5 available\r\n\r\n#. gppeval (2018.10.11.0.1.dev1).\r\n    The input file csv has been modified. It includes the possibility of using volume as a input\r\n    reservoir parameter. Using the word ``none`` is possible to exchange between either to use\r\n    **Area** and **Thickness** or to use only **Volume** as a reservoir geometric parameter.\r\n\r\n    Example: Using Area and Thickness\r\n\r\n        0,Name,14.00061,-88.73744,ReservoirArea,A,km2,5,6,7,0,0,T\r\n        1,,,,Thickness,h,m,450,500,600,0,0,T\r\n        2,,,,Volume,v,km3,4,6,8.2,0,0,none\r\n\r\n    Example: Using only Volume\r\n\r\n        0,Name,14.00061,-88.73744,ReservoirArea,A,km2,5,6,7,0,0,None\r\n        1,,,,Thickness,h,m,450,500,600,0,0,None\r\n        2,,,,Volume,v,km3,4,6,8.2,0,0,T\r\n\r\n#. gppeval (2018.4.6.0.1.dev1).\r\n    Original issue after have been upload as a stable.\r\n\r\nCONTACT\r\n=======\r\n\r\nPlease send **feature requests, bug reports, or feedback** to: `Carlos O. POCASANGRE JIMENEZ`_\r\n\r\n.. _Monte Carlo methods: http://en.wikipedia.org/wiki/Monte_Carlo_method\r\n.. _latin-hypercube sampling: http://en.wikipedia.org/wiki/Latin_hypercube_sampling\r\n.. _error propagation: http://en.wikipedia.org/wiki/Propagation_of_uncertainty\r\n.. _math: http://docs.python.org/library/math.html\r\n.. _NumPy: http://www.numpy.org/\r\n.. _SciPy: http://scipy.org\r\n.. _Matplotlib: http://matplotlib.org/\r\n.. _scipy.stats: http://docs.scipy.org/doc/scipy/reference/stats.html\r\n.. _uncertainties: http://pypi.python.org/pypi/uncertainties\r\n.. _Mcerp: http://github.com/tisimst/mcerp\r\n.. _Beautifultable: https://github.com/pri22296/beautifultable\r\n.. _Gppeval: http://github.com/cpocasangre/gppeval\r\n.. _example folder: https://github.com/cpocasangre/gppeval\r\n.. _Carlos O. POCASANGRE JIMENEZ: mailto:carlos.pocasangre@mine.kyushu-u.ac.jp\r\n",
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        "keywords": "monte carlo latin hypercube geothermal power potential volumetric method geothermal reservoir",
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