spotpy 1.3.1

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 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 (\(FAST\))
  • 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 (\(KGE\)).
• 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

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.
• 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: https://github.com/thouska/spotpy

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/