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        "author": "Tobias Houska, Philipp Kraft, Alejandro Chamorro-Chavez and Lutz Breuer",
        "author_email": "tobias.houska@umwelt.uni-giessen.de",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "SPOTPY\n========\n**A Statistical Parameter Optimization Tool for Python**\n\nSPOTPY is a Python tool that enables the use of Computational optimization techniques for calibration, uncertainty \nand sensitivity analysis techniques of almost every (environmental-) model. The package is puplished in the open source journal PLoS One\n\nHouska, T, Kraft, P, Chamorro-Chavez, A and Breuer, L; `SPOTting Model Parameters Using a Ready-Made Python Package <http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0145180>`_; PLoS ONE; 2015\n\nThe simplicity and flexibility enables the use and test of different \nalgorithms without the need of complex codes::\n\n\tsampler = spotpy.algorithms.sceua(model_setup())     # Initialize your model with a setup file\n\tsampler.sample(10000)                                # Run the model\n\tresults = sampler.getdata()                          # Load the results\n\tspotpy.analyser.plot_parametertrace(results)         # Show the results\n\n\nFeatures\n--------\n\nComplex formal Bayesian informal Bayesian and non-Bayesian algorithms bring complex tasks to link them with a given model. \nWe want to make this task as easy as possible. Some features you can use with the SPOTPY package are:\n\n* Fitting models to evaluation data with different algorithms. \n  Available algorithms are: \n\n  * Monte Carlo (`MC`)\n  * Markov-Chain Monte-Carlo (`MCMC`)\n  * Maximum Likelihood Estimation (`MLE`)\n  * Latin-Hypercube Sampling (`LHS`) \n  * Simulated Annealing (`SA`)\n  * Shuffled Complex Evolution Algorithm (`SCE-UA`)\n  * Differential Evolution Markov Chain Algorithm (`DE-MCz`) \n  * Differential Evolution Adaptive Metropolis Algorithm (`DREAM`) \n  * RObust Parameter Estimation (`ROPE`)\n  * Fourier Amplitude Sensitivity Test (`FAST`)\n  * Artificial Bee Colony (`ABC`)\n  * Fitness Scaled Chaotic Artificial Bee Colony (`FSCABC`)\n  * Dynamically Dimensioned Search algorithm (`DDS`)\n  * Pareto Archived - Dynamicallly Dimensioned Search algorithm (`PA-DDS`)\n  \n* Wide range of objective functions (also known as loss function, fitness function or energy function) to validate the sampled results. Available functions are\n\n  * Bias\n  * Procentual Bias (`PBias`)\n  * Nash-Sutcliffe (`NSE`)\n  * logarithmic Nash-Sutcliffe (`logNSE`)\n  * logarithmic probability (`logp`)\n  * Correlation Coefficient (`r`)\n  * Coefficient of Determination (`r^2`)\n  * Mean Squared Error (`MSE`)\n  * Root Mean Squared Error (`RMSE`)\n  * Mean Absolute Error (`MAE`)\n  * Relative Root Mean Squared Error (`RRMSE`)\n  * Agreement Index (`AI`)\n  * Covariance, Decomposed MSE (`dMSE`)\n  * Kling-Gupta Efficiency (`KGE`)\n  * Non parametric Kling-Gupta Efficiency (`KGE_non_parametric`)\n\n* Wide range of likelihood functions to validate the sampled results:\n\n  * logLikelihood\n  * Gaussian Likelihood to account for Measurement Errors\n  * Gaussian Likelihood to account for Heteroscedasticity\n  * Likelihood to accounr for Autocorrelation\n  * Generalized Likelihood Function\n  * Lapacian Likelihood\n  * Skewed Student Likelihood assuming homoscedasticity\n  * Skewed Student Likelihood assuming heteroscedasticity\n  * Skewed Student Likelihood assuming heteroscedasticity and Autocorrelation\n  * Noisy ABC Gaussian Likelihood\n  * ABC Boxcar Likelihood\n  * Limits Of Acceptability\n  * Inverse Error Variance Shaping Factor\n  * Nash Sutcliffe Efficiency Shaping Factor\n  * Exponential Transform Shaping Factor\n  * Sum of Absolute Error Residuals\n\n* Wide range of hydrological signatures functions to validate the sampled results:\n\n  * Slope\n  * Flooding/Drought events\n  * Flood/Drought frequency\n  * Flood/Drought duration\n  * Flood/Drought variance\n  * Mean flow\n  * Median flow\n  * Skewness\n  * compare percentiles of discharge\n  \n* Prebuild parameter distribution functions:\n\n  * Uniform\n  * Normal\n  * logNormal\n  * Chisquare\n  * Exponential\n  * Gamma\n  * Wald\n  * Weilbull\n\n* Wide range to adapt algorithms to perform uncertainty-, sensitivity analysis or calibration\n  of a model.\n\n* Multi-objective support\n \n* MPI support for fast parallel computing\n\n* A progress bar monitoring the sampling loops. Enables you to plan your coffee brakes.\n\n* Use of NumPy functions as often as possible. This makes your coffee brakes short.\n\n* Different databases solutions: `ram` storage for fast sampling a simple , `csv` tables\n  the save solution for long duration samplings and a `sql` database for larger projects.\n\n* Automatic best run selecting and plotting\n\n* Parameter trace plotting\n\n* Parameter interaction plot including the Gaussian-kde function\n\n* Regression analysis between simulation and evaluation data\n\n* Posterior distribution plot\n\n* Convergence diagnostics with Gelman-Rubin and the Geweke plot\n\n\nDocumentation\n-------------\n\nDocumentation is available at `<http://fb09-pasig.umwelt.uni-giessen.de/spotpy>`__\n\n\nInstall\n-------\n\nInstalling SPOTPY is easy. Just use:\n\n\tpip install spotpy\n\nOr, after downloading the source code and making sure python is in your path:\n\n\tpython setup.py install\n\n\t\nPapers citing SPOTPY\n--------------------\nSee `Google Scholar <https://scholar.google.de/scholar?cites=17155001516727704728&as_sdt=2005&sciodt=0,5&hl=de>`__ for a continuously updated list.\n\n\nSupport\n-------\n\n* Feel free to contact the authors of this tool for any support questions.\n\n* If you use this package for a scientific research paper, please `cite <http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0145180>`_ SPOTPY.\n\n* Please report any bug through mail or GitHub: https://github.com/thouska/spotpy.\n\n* If you want to share your code with others, you are welcome to do this through GitHub: https://github.com/thouska/spotpy.\n\n\nContributing\n------------\nPatches/enhancements/new algorithms and any other contributions to this package are very welcome!\n\n1. Fork it ( http://github.com/thouska/spotpy/fork )\n2. Create your feature branch (``git checkout -b my-new-feature``)\n3. Add your modifications\n4. Add short summary of your modifications on ``CHANGELOG.rst``\n5. Commit your changes (``git commit -m \"Add some feature\"``)\n6. Push to the branch (``git push origin my-new-feature``)\n7. Create new Pull Request\n\n\nGetting started\n---------------\n\nHave a look at https://github.com/thouska/spotpy/tree/master/spotpy/examples and http://fb09-pasig.umwelt.uni-giessen.de/spotpy/Tutorial/2-Rosenbrock/\n",
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