spotpy

A Statistical Parameter Optimization Tool for Python

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!

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 CHANGELOG.rst
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.

License

iacopoff/spotpy

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