This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (pypi.python.org).
Help us improve Python packaging - Donate today!

A Statistical Parameter Optimization Tool

Project Description

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 (\(FAST\))
    • Artificial Bee Colony (\(ABC\))
    • Fitness Scaled Chaotic Artificial Bee Colony (\(FSCABC\))
  • Wide range of objective functions (also known as loss function, fitness function or energy function) to validate the sampled results. Available functions are
    • Bias
    • Procentual Bias (\(PBias\))
    • 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 (\(dMSE\))
    • Kling-Gupta Efficiency (\(KGE\)).
  • 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
Release History

Release History

This version
History Node

1.3.1

History Node

1.3.0

History Node

1.2.38

History Node

1.2.37

History Node

1.2.36

History Node

1.2.35

History Node

1.2.34

History Node

1.2.33

History Node

1.2.32

History Node

1.2.31

History Node

1.2.30

History Node

1.2.29

History Node

1.2.27

History Node

1.2.26

History Node

1.2.25

History Node

1.2.24

History Node

1.2.23

History Node

1.2.22

History Node

1.2.21

History Node

1.2.20

History Node

1.2.19

History Node

1.2.18

History Node

1.2.17

History Node

1.2.16

History Node

1.2.15

History Node

1.2.14

History Node

1.2.13

History Node

1.2.12

History Node

1.2.11

History Node

1.2.10

History Node

1.2.9

History Node

1.2.8

History Node

1.2.7

History Node

1.2.6

History Node

1.2.5

History Node

1.2.4

History Node

1.2.3

History Node

1.2.2

History Node

1.2.1

History Node

1.2.0

History Node

1.1.5

History Node

1.1.4

History Node

1.1.3

History Node

1.1.2

History Node

1.1.1

History Node

1.1.0

History Node

1.0.13

History Node

1.0.12

History Node

1.0.11

History Node

1.0.10

History Node

1.0.9

History Node

1.0.8

History Node

1.0.7

History Node

1.0.6

History Node

1.0.5

History Node

1.0.4

History Node

1.0.3

History Node

1.0.2

Download Files

Download Files

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

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
spotpy-1.3.1-py2-none-any.whl (93.1 kB) Copy SHA256 Checksum SHA256 2.7 Wheel Jun 22, 2017
spotpy-1.3.1.win32.exe (287.9 kB) Copy SHA256 Checksum SHA256 any Windows Installer Jun 22, 2017
spotpy-1.3.1.zip (95.1 kB) Copy SHA256 Checksum SHA256 Source Jun 22, 2017

Supported By

WebFaction WebFaction Technical Writing Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Rackspace Rackspace Cloud Servers DreamHost DreamHost Log Hosting