spotpy 1.0.2

A Statistical Parameter Optimization Tool

Purpose

SPOTPY is a Python tool that enables the use of calibration, uncertainty and sensitivity analysis techniques for 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
results = sampler.sample(10000)                      # Run the model
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).

• 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