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A Statistical Parameter Optimization Tool

Project description

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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; 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 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/

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