spotpy 1.2.9

A Statistical Parameter Optimization Tool

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)

• Wide range of objective 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

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

Version 1.1.0

• Changed likelihood to objectivefunction. Checkout new example spotpy_setup files.

Version 1.1.1

• Minor bugfixes

Version 1.1.2

• Minor bugfixes

Version 1.1.3

• Minor bugfixes

Version 1.1.4

• Added a new functionality to sample parameters from a given list. Checkout the corresponding parameter example tutorial and parameter example code

Version 1.2.0

• Added Python 3 support

Version 1.2.1

• Spotpy supports now userdefined databases. Checkout the corresponding database example tutorial and database example code

Version 1.2.2

• Minor bugfix in Monte Carlo sampler