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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 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 onjective functions (also known as loss function, fitness function or energy function) to validate the sampled results. Available functions are

* Bias
* 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`).

* 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 OS 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!

Version 1.1.0 (2015-11-13)

* Changed `likelihood` to `objectivefunction`. Checkout new example spotpy_setup files.

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