Metadata-Version: 1.1
Name: spotpy
Version: 1.3.11
Summary: A Statistical Parameter Optimization Tool
Home-page: http://www.uni-giessen.de/cms/faculties/f09/institutes/ilr/hydro/download/spotpy
Author: Tobias Houska, Philipp Kraft, Alejandro Chamorro-Chavez and Lutz Breuer
Author-email: tobias.houska@umwelt.uni-giessen.de
License: MIT
Description: .. image:: https://img.shields.io/pypi/v/spotpy.png
          :target: https://pypi.python.org/pypi/spotpy
        .. image:: https://img.shields.io/travis/thouska/spotpy/master.png
          :target: https://travis-ci.org/thouska/spotpy
        .. image:: https://img.shields.io/badge/license-MIT-blue.png
          :target: http://opensource.org/licenses/MIT
        
        
        Purpose
        -------
        
        SPOTPY is a Python tool that enables the use of Computational optimization techniques for calibration, uncertainty 
        and sensitivity analysis techniques of almost every (environmental-) model. The package is puplished in the open source journal PLoS One
        
        Houska, 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
        
        The simplicity and flexibility enables the use and test of different 
        algorithms without the need of complex codes::
        
        	sampler = spotpy.algorithms.sceua(model_setup())     # Initialize your model with a setup file
        	sampler.sample(10000)                                # Run the model
        	results = sampler.getdata()                          # Load the results
        	spotpy.analyser.plot_parametertrace(results)         # Show the results
        
        
        Features
        --------
        
        Complex formal Bayesian informal Bayesian and non-Bayesian algorithms bring complex tasks to link them with a given model. 
        We want to make this task as easy as possible. Some features you can use with the SPOTPY package are:
        
        * Fitting models to evaluation data with different algorithms. 
          Available algorithms are: 
        
          * Monte Carlo (`MC`)
          * Markov-Chain Monte-Carlo (`MCMC`)
          * Maximum Likelihood Estimation (`MLE`)
          * Latin-Hypercube Sampling (`LHS`) 
          * Simulated Annealing (`SA`)
          * Shuffled Complex Evolution Algorithm (`SCE-UA`)
          * Differential Evolution Markov Chain Algorithm (`DE-MCz`) 
          * Differential Evolution Adaptive Metropolis Algorithm (`DREAM`) 
          * RObust Parameter Estimation (`ROPE`)
          * Fourier Amplitude Sensitivity Test (`FAST`)
          * Artificial Bee Colony (`ABC`)
          * Fitness Scaled Chaotic Artificial Bee Colony (`FSCABC`)
        
        * Wide range of objective functions (also known as loss function, fitness function or energy function) to validate the sampled results. Available functions are
        
          * Bias
          * Procentual Bias (`PBias`)
          * Nash-Sutcliff (`NSE`)
          * logarithmic Nash-Sutcliff (`logNSE`)
          * logarithmic probability (`logp`)
          * Correlation Coefficient (`r`)
          * Coefficient of Determination (`r^2`)
          * Mean Squared Error (`MSE`)
          * Root Mean Squared Error (`RMSE`)
          * Mean Absolute Error (`MAE`)
          * Relative Root Mean Squared Error (`RRMSE`)
          * Agreement Index (`AI`)
          * Covariance, Decomposed MSE (`dMSE`)
          * Kling-Gupta Efficiency (`KGE`)
        
        * Wide range of likelihood functions to validate the sampled results:
        
          * logLikelihood
          * Gaussian Likelihood to account for Measurement Errors
          * Gaussian Likelihood to account for Heteroscedasticity
          * Likelihood to accounr for Autocorrelation
          * Generalized Likelihood Function
          * Lapacian Likelihood
          * Skewed Student Likelihood assuming homoscedasticity
          * Skewed Student Likelihood assuming heteroscedasticity
          * Skewed Student Likelihood assuming heteroscedasticity and Autocorrelation
          * Noisy ABC Gaussian Likelihood
          * ABC Boxcar Likelihood
          * Limits Of Acceptability
          * Inverse Error Variance Shaping Factor
          * Nash Sutcliffe Efficiency Shaping Factor
          * Exponential Transform Shaping Factor
          * Sum of Absolute Error Residuals
        
        * Wide range of hydrological signatures functions to validate the sampled results:
        
          * Slope
          * Flooding/Drought events
          * Flood/Drought frequency
          * Flood/Drought duration
          * Flood/Drought variance
          * Mean flow
          * Median flow
          * Skewness
          * compare percentiles of discharge
          
        * Prebuild parameter distribution functions:
        
          * Uniform
          * Normal
          * logNormal
          * Chisquare
          * Exponential
          * Gamma
          * Wald
          * Weilbull
        
        * Wide range to adapt algorithms to perform uncertainty-, sensitivity analysis or calibration
          of a model.
        
        * Multi-objective support
         
        * MPI support for fast parallel computing
        
        * A progress bar monitoring the sampling loops. Enables you to plan your coffee brakes.
        
        * Use of NumPy functions as often as possible. This makes your coffee brakes short.
        
        * Different databases solutions: `ram` storage for fast sampling a simple , `csv` tables
          the save solution for long duration samplings and a `sql` database for larger projects.
        
        * Automatic best run selecting and plotting
        
        * Parameter trace plotting
        
        * Parameter interaction plot including the Gaussian-kde function
        
        * Regression analysis between simulation and evaluation data
        
        * Posterior distribution plot
        
        * Convergence diagnostics with Gelman-Rubin and the Geweke plot
        
        
        Documentation
        -------------
        
        Documentation is available at `<http://fb09-pasig.umwelt.uni-giessen.de/spotpy>`__
        
        
        Install
        -------
        
        Installing SPOTPY is easy. Just use:
        
        	pip install spotpy
        
        Or, after downloading the source code and making sure python is in your path:
        
        	python setup.py install
        
        
        Support
        -------
        
        * Feel free to contact the authors of this tool for any support questions.
        
        * 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.
        
        * Please report any bug through mail or GitHub: https://github.com/thouska/spotpy.
        
        * If you want to share your code with others, you are welcome to do this through GitHub: https://github.com/thouska/spotpy.
        
        
        Contributing
        ------------
        Patches/enhancements/new algorithms and any other contributions to this package are very welcome!
        
        1. Fork it ( http://github.com/thouska/spotpy/fork )
        2. Create your feature branch (``git checkout -b my-new-feature``)
        3. Add your modifications
        4. Add short summary of your modifications on ``CHANGELOG.rst``
        5. Commit your changes (``git commit -m "Add some feature"``)
        6. Push to the branch (``git push origin my-new-feature``)
        7. Create new Pull Request
        
        
        Getting started
        ---------------
        
        Have a look at https://github.com/thouska/spotpy/tree/master/spotpy/examples and http://fb09-pasig.umwelt.uni-giessen.de/spotpy/Tutorial/2-Rosenbrock/
Keywords: Monte Carlo,MCMC,MLE,SCE-UA,Simulated Annealing,DE-MCz,DREAM,ROPE,Artifical Bee Colony,Uncertainty,Calibration,Model,Signatures
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Software Development :: Libraries :: Python Modules
