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)
- Monte Carlo (
-
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:
ramstorage for fast sampling a simple ,csvtables 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.
1 maintainer
Metadata
1 maintainer
Release files for spotpy 1.6.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| spotpy-1.6.7.tar.gz | 7.1 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| spotpy-1.6.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 14.1 MB
Release files / spotpy-1.6.7.tar.gz
| Download URL | spotpy-1.6.7.tar.gz |
|---|---|
| Size | 7.1 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
50cc0a53c4518d81f5b02580a6eee98c16da48a21dde59a90fe2ed40a1413edd
|
|
BLAKE2b-256 checksum How to use checksums |
af84ac8c24992425eb8eb0163181ef1afdada5c64aac333f50291aaf010c283a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Release files / spotpy-1.6.7-py3-none-any.whl
| Download URL | spotpy-1.6.7-py3-none-any.whl |
|---|---|
| Size | 7.0 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
add77c4e17f49ecf54865a0f04008aee375d0c64c09a0fe6b3be392f7a280808
|
|
BLAKE2b-256 checksum How to use checksums |
5bb2a7f0d059c664b5fcdf4529df0e15d5075289146e04007fe8c7236051b7c7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
Security for spotpy 1.6.7
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
