Skip to main content

A Statistical Parameter Optimization Tool

Project description

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
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 likelihood functions to validate the sampled results. Available Lieklihoods are Bias, Nash-Sutcliff (NSE), log 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:

Linux:

sudo python setup.py install

Windows (after making sure python is in your path):

python setup.py install

Project details


Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for spotpy, version 1.0.9
Filename, size File type Python version Upload date Hashes
Filename, size spotpy-1.0.9-py2-none-any.whl (71.1 kB) File type Wheel Python version 2.7 Upload date Hashes View hashes
Filename, size spotpy-1.0.9.win32.exe (266.2 kB) File type Windows Installer Python version any Upload date Hashes View hashes
Filename, size spotpy-1.0.9.zip (66.4 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page