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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
    • 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

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

Version 1.2.0

  • Added Python 3 support

Version 1.2.1

Version 1.2.2

  • Minor bugfix in Monte Carlo sampler
Release History

Release History

1.2.35

This version

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1.2.34

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1.2.33

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1.2.32

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1.2.31

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1.2.30

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1.2.29

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1.2.27

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1.2.26

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1.2.25

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1.2.24

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1.2.23

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1.2.22

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1.2.21

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1.2.20

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1.2.19

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1.2.18

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1.2.17

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1.2.16

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1.2.15

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1.2.14

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1.2.13

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1.2.12

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1.2.11

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1.2.10

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1.2.9

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1.2.8

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1.2.7

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1.2.6

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1.2.5

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1.2.4

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1.2.3

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1.2.2

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1.2.1

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1.2.0

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1.1.5

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1.1.4

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1.1.3

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1.1.2

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1.1.1

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1.1.0

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1.0.13

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1.0.12

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1.0.11

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1.0.10

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1.0.9

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1.0.8

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1.0.7

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1.0.6

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1.0.5

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1.0.4

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1.0.3

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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1.0.2

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

Donec et mollis dolor. Praesent et diam eget libero egestas mattis sit amet vitae augue. Nam tincidunt congue enim, ut porta lorem lacinia consectetur. Donec ut libero sed arcu vehicula ultricies a non tortor. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
spotpy-1.2.35-py2-none-any.whl (78.6 kB) Copy SHA256 Checksum SHA256 2.7 Wheel Feb 20, 2017
spotpy-1.2.35.win32.exe (273.5 kB) Copy SHA256 Checksum SHA256 any Windows Installer Feb 20, 2017
spotpy-1.2.35.zip (81.1 kB) Copy SHA256 Checksum SHA256 Source Feb 20, 2017

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