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A Statistical Parameter Optimization Tool

Project description


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; _doi:10.1371/journal.pone.0145180; 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 algorithms bring complex tasks to link them with a 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 Adaptive Metropolis Algorithm (DE-MCz)
    • RObust Parameter Estimation (ROPE).
    • Fourier Amplitude Sensitivity Test (FAST)
  • Wide range of objective functions (also known as loss function, fitness function or energy function) to validate the sampled results. Available functions are
    • 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).
  • 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.
  • 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


Installing SPOTPY is easy. Just use:

pip install spotpy

Or, after downloading the source code and making sure python is in your path:

python install


  • Feel free to contact the authors of this tool for any support questions.
  • Please contact the authors in case of any bug.
  • If you use this package for a scientific research paper, please cite SPOTPY.
  • Patches/enhancements and any other contributions to this package are very welcome!
  • GitHub:

Version 1.1.0

  • Changed likelihood to objectivefunction. Checkout new example spotpy_setup files.

Version 1.1.1

  • Minor bugfixes

Version 1.1.2

  • Minor bugfixes

Version 1.1.3

  • Minor bugfixes

Version 1.1.4

Version 1.2.0

  • Added Python 3 support

Version 1.2.1

Version 1.2.2

  • Minor bugfix in Monte Carlo sampler

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