| Name | spotpy JSON |
| Version |
1.6.2
JSON |
| download |
| home_page | |
| Summary | A Statistical Parameter Optimization Tool. |
| upload_time | 2023-02-28 09:27:54 |
| maintainer | |
| docs_url | None |
| author | Philipp Kraft, Alejandro Chamorro-Chavez, Lutz Breuer |
| requires_python | >=3.7 |
| license | The MIT License (MIT) Copyright (c) 2015 Tobias Houska Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
monte carlo
mcmc
mle
sce-ua
simulated annealing
de-mcz
dream
rope
artifical bee colony
dds
pa-dds
nsgaii
uncertainty
calibration
model
signatures
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# spotpy
A Statistical Parameter Optimization Tool for Python
---
[![PyPI Version][pypi-v-image]][pypi-v-link]
[![Python Versions][pypi-pyv-image]][pypi-pyv-link]
[![Build Status][travis-image]][travis-link]
[![License][license-image]][license-link]
[](https://coveralls.io/github/thouska/spotpy?branch=master)
[](https://readthedocs.org/projects/spotpy/badge/?version=latest)
[](https://zenodo.org/badge/latestdoi/47562322)
[pypi-v-image]: https://img.shields.io/pypi/v/spotpy.png
[pypi-v-link]: https://pypi.python.org/pypi/spotpy
[pypi-pyv-image]: https://img.shields.io/pypi/pyversions/spotpy.png
[pypi-pyv-link]: https://img.shields.io/pypi/pyversions/spotpy
[travis-image]: https://img.shields.io/travis/thouska/spotpy/master.png
[travis-link]: https://travis-ci.org/thouska/spotpy
[license-image]: https://img.shields.io/badge/license-MIT-blue.png
[license-link]: http://opensource.org/licenses/MIT
Purpose
=================
SPOTPY is a Python framework 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,
10(12), e0145180, doi:[10.1371/journal.pone.0145180](http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0145180 "SPOTting Model Parameters Using a Ready-Made Python Package"), 2015
The simplicity and flexibility enables the use and test of different
algorithms of almost any model, 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 Markov Chain Algorithm (`DE-MCz`)
* Differential Evolution Adaptive Metropolis Algorithm (`DREAM`)
* RObust Parameter Estimation (`ROPE`)
* Fourier Amplitude Sensitivity Test (`FAST`)
* Artificial Bee Colony (`ABC`)
* Fitness Scaled Chaotic Artificial Bee Colony (`FSCABC`)
* Dynamically Dimensioned Search algorithm (`DDS`)
* Pareto Archived - Dynamicallly Dimensioned Search algorithm (`PA-DDS`)
* Fast and Elitist Multiobjective Genetic Algorithm (`NSGA-II`)
* 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-Sutcliffe (`NSE`)
* logarithmic Nash-Sutcliffe (`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`)
* Non parametric Kling-Gupta Efficiency (`KGE_non_parametric`)
* Wide range of hydrological signatures functions to validate the sampled results:
* Slope
* Flooding/Drought events
* Flood/Drought frequency
* Flood/Drought duration
* Flood/Drought variance
* Mean flow
* Median flow
* Skewness
* compare percentiles of discharge
* 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
=================
Classical Python options exist to install SPOTPY:
From PyPi:
pip install spotpy
From Conda-Forge:
conda config --add channels conda-forge
conda config --set channel_priority strict
conda install spotpy
From [Source](https://pypi.python.org/pypi/spotpy):
python setup.py install
Support
=================
* Documentation: https://spotpy.readthedocs.io/en/latest/
* 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. It is [peer-reviewed](http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0145180 "SPOTting Model Parameters Using a Ready-Made Python Package").
* Patches/enhancements and any other contributions to this package are very welcome!
Getting started
=================
Have a look at https://github.com/thouska/spotpy/tree/master/spotpy/examples and https://spotpy.readthedocs.io/en/latest/getting_started/
Contributing
=================
Patches/enhancements/new algorithms and any other contributions to this package are very welcome!
1. Fork it ( http://github.com/thouska/spotpy/fork )
2. Create your feature branch (``git checkout -b my-new-feature``)
3. Add your modifications
4. Add short summary of your modifications on ``CHANGELOG.md``
5. Commit your changes (``git commit -m "Add some feature"``)
6. Push to the branch (``git push origin my-new-feature``)
7. Create new Pull Request
Papers citing SPOTPY
=====================
See [Google Scholar](https://scholar.google.de/scholar?cites=17155001516727704728&as_sdt=2005&sciodt=0,5&hl=de) for a continuously updated list.
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"description": "# spotpy\nA Statistical Parameter Optimization Tool for Python\n\n---\n\n[![PyPI Version][pypi-v-image]][pypi-v-link]\n[![Python Versions][pypi-pyv-image]][pypi-pyv-link]\n[![Build Status][travis-image]][travis-link]\n[![License][license-image]][license-link]\n[](https://coveralls.io/github/thouska/spotpy?branch=master)\n[](https://readthedocs.org/projects/spotpy/badge/?version=latest)\n[](https://zenodo.org/badge/latestdoi/47562322)\n\n[pypi-v-image]: https://img.shields.io/pypi/v/spotpy.png\n[pypi-v-link]: https://pypi.python.org/pypi/spotpy\n[pypi-pyv-image]: https://img.shields.io/pypi/pyversions/spotpy.png\n[pypi-pyv-link]: https://img.shields.io/pypi/pyversions/spotpy\n[travis-image]: https://img.shields.io/travis/thouska/spotpy/master.png\n[travis-link]: https://travis-ci.org/thouska/spotpy\n[license-image]: https://img.shields.io/badge/license-MIT-blue.png\n[license-link]: http://opensource.org/licenses/MIT\n\n\n\nPurpose\n=================\n\nSPOTPY is a Python framework that enables the use of Computational optimization techniques for calibration, uncertainty\nand sensitivity analysis techniques of almost every (environmental-) model. The package is puplished in the open source journal PLoS One:\n\nHouska, T., Kraft, P., Chamorro-Chavez, A. and Breuer, L.: SPOTting Model Parameters Using a Ready-Made Python Package, PLoS ONE,\n10(12), e0145180, doi:[10.1371/journal.pone.0145180](http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0145180 \"SPOTting Model Parameters Using a Ready-Made Python Package\"), 2015\n\nThe simplicity and flexibility enables the use and test of different\nalgorithms of almost any model, without the need of complex codes::\n\n\tsampler = spotpy.algorithms.sceua(model_setup()) # Initialize your model with a setup file\n\tsampler.sample(10000) # Run the model\n\tresults = sampler.getdata() # Load the results\n\tspotpy.analyser.plot_parametertrace(results) # Show the results\n\n\n\nFeatures\n=================\n\nComplex algorithms bring complex tasks to link them with a model.\nWe want to make this task as easy as possible.\nSome features you can use with the SPOTPY package are:\n\n* Fitting models to evaluation data with different algorithms.\n Available algorithms are:\n\n * Monte Carlo (`MC`)\n * Markov-Chain Monte-Carlo (`MCMC`)\n * Maximum Likelihood Estimation (`MLE`)\n * Latin-Hypercube Sampling (`LHS`)\n * Simulated Annealing (`SA`)\n * Shuffled Complex Evolution Algorithm (`SCE-UA`)\n * Differential Evolution Markov Chain Algorithm (`DE-MCz`)\n * Differential Evolution Adaptive Metropolis Algorithm (`DREAM`)\n * RObust Parameter Estimation (`ROPE`)\n * Fourier Amplitude Sensitivity Test (`FAST`)\n * Artificial Bee Colony (`ABC`)\n * Fitness Scaled Chaotic Artificial Bee Colony (`FSCABC`)\n * Dynamically Dimensioned Search algorithm (`DDS`)\n * Pareto Archived - Dynamicallly Dimensioned Search algorithm (`PA-DDS`)\n * Fast and Elitist Multiobjective Genetic Algorithm (`NSGA-II`)\n\n* Wide range of objective functions (also known as loss function, fitness function or energy function) to validate the sampled results. Available functions are\n\n * Bias\n * PBias\n * Nash-Sutcliffe (`NSE`)\n * logarithmic Nash-Sutcliffe (`logNSE`)\n * logarithmic probability (`logp`)\n * Correlation Coefficient (`r`)\n * Coefficient of Determination (`r^2`)\n * Mean Squared Error (`MSE`)\n * Root Mean Squared Error (`RMSE`)\n * Mean Absolute Error (`MAE`)\n * Relative Root Mean Squared Error (`RRMSE`)\n * Agreement Index (`AI`)\n * Covariance, Decomposed MSE (`dMSE`)\n * Kling-Gupta Efficiency (`KGE`)\n * Non parametric Kling-Gupta Efficiency (`KGE_non_parametric`)\n\n* Wide range of hydrological signatures functions to validate the sampled results:\n\n * Slope\n * Flooding/Drought events\n * Flood/Drought frequency\n * Flood/Drought duration\n * Flood/Drought variance\n * Mean flow\n * Median flow\n * Skewness\n * compare percentiles of discharge\n\n* Prebuild parameter distribution functions:\n\n * Uniform\n * Normal\n * logNormal\n * Chisquare\n * Exponential\n * Gamma\n * Wald\n * Weilbull\n\n* Wide range to adapt algorithms to perform uncertainty-, sensitivity analysis or calibration\n of a model.\n\n* Multi-objective support\n\n* MPI support for fast parallel computing\n\n* A progress bar monitoring the sampling loops. 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It is [peer-reviewed](http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0145180 \"SPOTting Model Parameters Using a Ready-Made Python Package\").\n\n* Patches/enhancements and any other contributions to this package are very welcome!\n\n\nGetting started\n=================\nHave a look at https://github.com/thouska/spotpy/tree/master/spotpy/examples and https://spotpy.readthedocs.io/en/latest/getting_started/\n\n\nContributing\n=================\nPatches/enhancements/new algorithms and any other contributions to this package are very welcome!\n\n1. Fork it ( http://github.com/thouska/spotpy/fork )\n2. Create your feature branch (``git checkout -b my-new-feature``)\n3. Add your modifications\n4. Add short summary of your modifications on ``CHANGELOG.md``\n5. Commit your changes (``git commit -m \"Add some feature\"``)\n6. Push to the branch (``git push origin my-new-feature``)\n7. 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