{
    "info": {
        "author": "Thibault Hallouin",
        "author_email": "thibault.hallouin@ucdconnect.ie",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX :: Linux",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering :: Hydrology"
        ],
        "description": "[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)\n[![PyPI Version](https://badge.fury.io/py/hydroeval.svg)](https://pypi.python.org/pypi/hydroeval)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2591217.svg)](https://doi.org/10.5281/zenodo.2591217)\n\n# HydroEval - An efficient evaluator for streamflow time series in Python\n\nHydroEval is an open-source evaluator for streamflow time series in Python. It is licensed under GNU GPL-3.0 (see [licence file](https://github.com/ThibHlln/hydroeval/blob/master/LICENCE.md) provided). The purpose of this evaluator is to compare observed and simulated hydrographs using one or more objective functions. HydroEval is designed to calculate all objective functions in a vectorised manner (using [numpy](https://github.com/numpy/numpy), and therefore C code in the background) which makes for very efficient computation of the objective functions.\n\n## How to Install\n\nHydroEval is available on PyPI, so you can simply use pip and the name of the package:\n\n    python -m pip install hydroeval\n\nYou can also use pip and a link to the GitHub repository directly:\n\n\tpython -m pip install git+https://github.com/ThibHlln/hydroeval.git\n\nAlternatively, you can download the source code (*i.e.* the GitHub repository) and, from the downloaded directory itself, run the command:\n\n    python setup.py install\n\n## How to Use\n\nA tutorial in the form of a [Jupyter notebook](https://github.com/ThibHlln/hydroeval/blob/master/examples/api_usage_example.ipynb) is available to get started with the usage of HydroEval's API. The input files required for the tutorial are all provided in the `examples/` folder.\n\n## How to Cite\n\nIf you are using HydroEval, please consider citing the software as follows (click on the link to get the DOI of a specific version):\n* Hallouin, T. (XXXX). HydroEval: Streamflow Simulations Evaluator (Version X.X.X). Zenodo. https://doi.org/10.5281/zenodo.2591217\n\n## Objective Functions Available\n\nThe objective functions currently available in HydroEval to evaluate the fit between observed and simulated stream flow time series are as follows:\n* [Nash-Sutcliffe Efficiency](https://doi.org/10.1016/0022-1694(70)90255-6) (`nse`)\n* [Original Kling-Gupta Efficiency](https://doi.org/10.1016/j.jhydrol.2009.08.003) (`kge`) and its three components (r, \u03b1, \u03b2)\n* [Modified Kling-Gupta Efficiency](https://doi.org/10.1016/j.jhydrol.2012.01.011) (`kgeprime`) and its three components (r, \u03b3, \u03b2)\n* [Non-Parametric Kling-Gupta Efficiency](https://doi.org/10.1080/02626667.2018.1552002) (`kgenp`) and its three components (r, \u03b1, \u03b2)\n* Root Mean Square Error (`rmse`)\n* Mean Absolute Relative Error (`mare`)\n* Percent Bias (`pbias`)\n\nMoreover, KGE and NSE can be calculated in a bounded version following [Mathevet et al. (2006)](https://iahs.info/uploads/dms/13614.21--211-219-41-MATHEVET.pdf):\n\n* Bounded Nash-Sutcliffe Efficiency (`nse_c2m`)\n* Bounded Original Kling-Gupta Efficiency (`kge_c2m`)\n* Bounded Modified Kling-Gupta Efficiency (`kgeprime_c2m`)\n* Bounded Non-Parametric Kling-Gupta Efficiency (`kgenp_c2m`)\n\nFinally, any of the objective functions can take an optional argument `transform`. This argument allows to apply a transformation on both the observed and the simulated streamflow time series prior the calculation of the objective function. The possible transformations are as follows:\n* Inverted flows (using `transform='inv'`)\n* Square Root-transformed flows (using `transform='sqrt'`)\n* Natural Logarithm-transformed flows (using `transform='log'`)\n\n## Dependencies\n\nHydroEval requires the Python package `numpy` to be installed on the Python interpreter where `hydroeval` is installed.\n\n## Version History\n\n* 0.0.3 [09 Sep 2019]: [General enhancements](https://github.com/ThibHlln/hydroeval/releases/tag/v0.0.3)\n* 0.0.2 [29 Nov 2018]: [General improvements](https://github.com/ThibHlln/hydroeval/releases/tag/v0.0.2)\n* 0.0.1 [26 Oct 2018]: [First version of HydroEval](https://github.com/ThibHlln/hydroeval/releases/tag/v0.0.1)\n\n## Acknowledgment\n\nThis tool was developed with the financial support of Ireland's Environmental Protection Agency (Grant Number 2014-W-LS-5).\n\n\n",
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