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A Statistical Parameter Optimization Tool

Project description

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


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)
    • Fast and Elitist Multiobjective Genetic Algorithm (NSGA-II)
  • 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 is available at


Classical Python options exist to install SPOTPY:

From PyPi:

pip install spotpy

From Conda-Forge:

conda config –add channels conda-forge conda config –set channel_priority strict conda install spotpy

From [Source](

python install

Papers citing SPOTPY

See Google Scholar for a continuously updated list.


  • 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:
  • If you want to share your code with others, you are welcome to do this through GitHub:


Patches/enhancements/new algorithms and any other contributions to this package are very welcome!

  1. Fork it ( )
  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

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