A Statistical Parameter Optimization Tool for Python
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
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 (
- Markov-Chain Monte-Carlo (
- Maximum Likelihood Estimation (
- Latin-Hypercube Sampling (
- Simulated Annealing (
- Shuffled Complex Evolution Algorithm (
- Differential Evolution Markov Chain Algorithm (
- Differential Evolution Adaptive Metropolis Algorithm (
- RObust Parameter Estimation (
- Fourier Amplitude Sensitivity Test (
- Artificial Bee Colony (
- Fitness Scaled Chaotic Artificial Bee Colony (
- Monte Carlo (
Wide range of objective functions (also known as loss function, fitness function or energy function) to validate the sampled results. Available functions are
- Nash-Sutcliff (
- logarithmic Nash-Sutcliff (
- logarithmic probability (
- Correlation Coefficient (
- Coefficient of Determination (
- Mean Squared Error (
- Root Mean Squared Error (
- Mean Absolute Error (
- Relative Root Mean Squared Error (
- Agreement Index (
- Covariance, Decomposed MSE (
- Kling-Gupta Efficiency (
Prebuild parameter distribution functions:
Wide range to adapt algorithms to perform uncertainty-, sensitivity analysis or calibration of a model.
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:
ramstorage for fast sampling a simple ,
csvtables 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 OS path:
python setup.py 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. It is peer-reviewed.
Patches/enhancements and any other contributions to this package are very welcome!
Patches/enhancements/new algorithms and any other contributions to this package are very welcome!
- Fork it ( http://github.com/thouska/spotpy/fork )
- Create your feature branch (
git checkout -b my-new-feature)
- Add your modifications
- Add short summary of your modifications on
- Commit your changes (
git commit -m "Add some feature")
- Push to the branch (
git push origin my-new-feature)
- Create new Pull Request
Papers citing SPOTPY
See Google Scholar for a continuously updated list.