spotpy 1.5.7

A Statistical Parameter Optimization Tool

A Statistical Parameter Optimization Tool for Python

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)

  • Dynamically Dimensioned Search algorithm (DDS)

  • Pareto Archived - Dynamicallly Dimensioned Search algorithm (PA-DDS)

• 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-Sutcliffe (NSE)

  • logarithmic Nash-Sutcliffe (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)

  • Non parametric Kling-Gupta Efficiency (KGE_non_parametric)

• 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

Papers citing SPOTPY

See Google Scholar for a continuously updated list.

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/