spotpy 1.5.2

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)
• 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)
  • 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/