spotpy

A Statistical Parameter Optimization Tool

https://github.com/thouska/spotpy

Keywords from Contributors

mesh hydrology transformation

Last synced: 7 months ago · JSON representation

Repository

A Statistical Parameter Optimization Tool

Basic Info

Host: GitHub
Owner: thouska
License: mit
Language: Python
Default Branch: master
Homepage: https://spotpy.readthedocs.io/en/latest/
Size: 19.2 MB

Statistics

Stars: 265
Watchers: 22
Forks: 157
Open Issues: 54
Releases: 11

Created over 10 years ago · Last pushed 10 months ago

Metadata Files

Readme Changelog Contributing Funding License Code of conduct

spotpy

A Statistical Parameter Optimization Tool for Python

Purpose

https://github.com/user-attachments/assets/4df71cb2-dcf9-4a24-bb78-6e4e1c43356a

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 published 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, 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)
- extended Fourier Amplitude Sensitivity Test (eFAST)
- Artificial Bee Colony (ABC)
- Fitness Scaled Chaotic Artificial Bee Colony (FSCABC)
- Dynamically Dimensioned Search algorithm (DDS)
- Pareto Archived - Dynamically 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
- log-normal
- Chi-Square
- Exponential
- Gamma
- Wald
- Weibull
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:

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.
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!

Fork it ( http://github.com/thouska/spotpy/fork )
Create your feature branch (git checkout -b my-new-feature)
Add your modifications
Add short summary of your modifications on CHANGELOG.md
Commit your changes (git commit -m "Add some feature")
Push to the branch (git push origin my-new-feature)
Create new Pull Request

Papers citing SPOTPY

See Google Scholar for a continuously updated list.

Owner

Login: thouska
Kind: user
Location: Giessen
Company: Justus-Liebig-University

Repositories: 1
Profile: https://github.com/thouska

GitHub Events

Total

Create event: 3
Release event: 3
Issues event: 7
Watch event: 12
Delete event: 1
Issue comment event: 12
Push event: 14
Pull request event: 9
Fork event: 7

Last Year

Create event: 3
Release event: 3
Issues event: 7
Watch event: 12
Delete event: 1
Issue comment event: 12
Push event: 14
Pull request event: 9
Fork event: 7

Committers

Last synced: over 2 years ago

All Time

Total Commits: 1,041
Total Committers: 27
Avg Commits per committer: 38.556
Development Distribution Score (DDS): 0.477

Past Year

Commits: 75
Committers: 3
Avg Commits per committer: 25.0
Development Distribution Score (DDS): 0.453

Top Committers

Name	Email	Commits
thouska	t**a@u**e	544
bees4ever	b**r@g**e	163
kraft-p	o**z@g**e	79
Havan	h**l@g**m	63
Sebastian Müller	m**b@p**e	33
philippkraft	p**t@u**e	28
Florian Jehn	f**n@u**e	24
Florian Jehn	u**0@g**e	19
Patrick Lauer	l**r@u**e	17
Karlson	k**y@k**v	14
iacopoff	i**f@g**m	9
Ashish Vinayak	a**k@g**m	7
Philipp Kraft	o**z@g**m	6
kbstn	k**n@u**e	6
lu-kas		5
gitporst	R**e@h**l	5
Juan Carlos Castilla-Rho	j**o@g**m	4
Trista Hehnen	t**n@c**h	3
Robert Schweppe	s**t@g**m	3
Stephan Thober	s**r@g**m	2
Ashish Vinayak	a**k@u**m	1
Cecile	K**C@u**m	1
Jinadelee	5**e@u**m	1
Christian Krause	c**e@i**e	1
J-M	j**d@c**u	1
ThibHlln	t**n@u**e	1
Yantong Wu	4**y@u**m	1

Committer Domains (Top 20 + Academic)

umwelt.uni-giessen.de: 4 gmx.de: 3 ucdconnect.ie: 1 csiro.au: 1 idiv.de: 1 cern.ch: 1 kla.tv: 1 uni-wuppertal.de: 1 posteo.de: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time

Total issues: 88
Total pull requests: 46
Average time to close issues: 4 months
Average time to close pull requests: 4 months
Total issue authors: 64
Total pull request authors: 18
Average comments per issue: 2.73
Average comments per pull request: 2.24
Merged pull requests: 34
Bot issues: 0
Bot pull requests: 2

Past Year

Issues: 6
Pull requests: 8
Average time to close issues: 7 days
Average time to close pull requests: about 2 months
Issue authors: 5
Pull request authors: 4
Average comments per issue: 0.5
Average comments per pull request: 1.25
Merged pull requests: 6
Bot issues: 0
Bot pull requests: 2

View more stats

Top Authors

Issue Authors

MuellerSeb (8)
dingxinjun (3)
ZhiqiangD (3)
monuronen (3)
arthur-e (3)
jds485 (2)
TheodorB (2)
BeingHapppy (2)
SunCeZong (2)
lwq-star (2)
florianjehn (2)
lu-kas (2)
monbiola (2)
ssheeder (1)
kiufta (1)

Pull Request Authors

bees4ever (9)
thouska (6)
philippkraft (5)
Zeitsperre (4)
Herzog-A (3)
cpwnd (2)
cheginit (2)
lqxyz (2)
dependabot[bot] (2)
MuellerSeb (2)
lu-kas (2)
wookietreiber (1)
hpsone (1)
florianjehn (1)
iacopoff (1)

Top Labels

Issue Labels

bug (1) enhancement (1)

Pull Request Labels

dependencies (2)

Packages

Total packages: 2
Total downloads:
- pypi 5,230 last-month
Total docker downloads: 5,728

Total dependent packages: 10
(may contain duplicates)
Total dependent repositories: 24
(may contain duplicates)
Total versions: 125
Total maintainers: 1

pypi.org: spotpy

A Statistical Parameter Optimization Tool.

Homepage: https://github.com/thouska/spotpy
Documentation: https://spotpy.readthedocs.io
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.
Latest release: 1.6.6
published about 1 year ago

Versions: 112
Dependent Packages: 7
Dependent Repositories: 20
Downloads: 5,230 Last month
Docker Downloads: 5,728

Rankings

Docker downloads count: 1.7%

Dependent packages count: 2.4%

Dependent repos count: 3.2%

Average: 4.0%

Forks count: 4.2%

Stargazers count: 4.3%

Downloads: 8.0%

Maintainers (1)

thouska

Last synced: 7 months ago

conda-forge.org: spotpy

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 published 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, 2015.

Homepage: https://spotpy.readthedocs.io/en/latest/
License: MIT
Latest release: 1.6.1
published over 3 years ago

Versions: 13
Dependent Packages: 3
Dependent Repositories: 4

Rankings

Dependent packages count: 15.6%

Dependent repos count: 16.2%

Forks count: 16.2%

Average: 18.4%

Stargazers count: 25.5%

Last synced: 7 months ago

spotpy

Science Score: 46.0%

Keywords from Contributors

Repository

Basic Info

Statistics

Metadata Files

README.md

spotpy

Purpose

Features

Install

Support

Getting started

Contributing

Papers citing SPOTPY

Owner

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: spotpy

Rankings

Maintainers (1)

conda-forge.org: spotpy

Rankings