spotpy

A Statistical Parameter Optimization Tool for Python

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Changes for SPOTPY-gprMax

• worker processes return and do not exit in sample function. This way, more than one sample call can be carried out by SPOTPY-gprMax

• SCEUA input useoptguess. Mimics Matlab behavior, where the optimal guess is used in the initial / "burn-in" population of SCEUA.

• MPI: possibility for input MPI-communicator that replaces MPI.COMM_WORLD. MPI-communicator is introduced as input comm to class _algorithm. This feature is needed for Two-level-MPI (parallel SPOTPY, as well as parallel forward model)

Purpose

SPOTPY is a Python framework 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, 10(12), e0145180, doi:10.1371/journal.pone.0145180, 2015

The simplicity and flexibility enables the use and test of different algorithms of almost any model, 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 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
  • 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 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.

• 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

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 setup.py install

Support

• Documentation: https://spotpy.readthedocs.io/en/latest/

• 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. It is peer-reviewed.

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

Getting started

Have a look at https://github.com/thouska/spotpy/tree/master/spotpy/examples and https://spotpy.readthedocs.io/en/latest/getting_started/

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.md
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

Papers citing SPOTPY

See Google Scholar for a continuously updated list.