ewstools
ewstools: A Python package for early warning signals of bifurcations in time series data - Published in JOSS (2023)
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Published in Journal of Open Source Software
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Python package for early warning signals (EWS) of bifurcations in time series data.
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- Stars: 90
- Watchers: 4
- Forks: 19
- Open Issues: 6
- Releases: 7
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README.md
ewstools
A Python package for early warning signals (EWS) of bifurcations in time series data.
Overview
Many systems in nature and society can undergo critical transitions—sudden, often irreversible shifts in dynamics. Examples include the outbreak of disease, ecosystem collapse, and cardiac arrhythmias. Mathematically, such transitions often correspond to bifurcations (tipping points) in an underlying dynamical system.
Scheffer et al. (2009) proposed early warning signals (EWS) for bifurcations based on noisy fluctuations in time series data, sparking a surge of related ways to predict bifurcations (see Dakos et al. (2024) for a recent review). More recently, deep learing has shown great potential for predicting bifurcations and their type (Bury et al. 2021).
ewstools is a Python package for computing and visualizing EWS in time series. It complements the R package by (Dakos et al. 2012) and meets growing demand for Python-based tools (PYPL, 2022).
Features include: - An intuitive, object-oriented framework for computing EWS - Detrending methods: - Gaussian kernel smoothing - LOWESS (Locally Weighted Scatterplot Smoothing) - Computation of critical slowing down (CSD) indicators: - Variance, standard deviation, coefficient of variation - Autocorrelation (at specified lags) - Skewness, kurtosis - Power spectra metrics - Entropy measures - Kendall tau statistics to quantify trends - Deep learning classifiers for bifurcation prediction (Bury et al. 2021) - Visualization tools - Built-in dynamical system models for testing EWS
ewstools uses pandas for dataframe handling, numpy for fast numerical computing, plotly for visualisation, lmfit for least-squares minimisation, arch for bootstrapping methods, EntropyHub for entropy computations, statsmodels and scipy for detrending methods, and TensorFlow for deep learning.
Install
Requires Python 3.8–3.11. Install via:
bash
pip install --upgrade pip
pip install ewstools
For tutorials, install Jupyter notebook:
bash
pip install jupyter notebook
To enable deep learning features, install TensorFlow:
bash
pip install ewstools[tf]
Note: TensorFlow for ewstools is currently supported on Linux and macOS only.
To install the latest development version:
bash
pip install git+https://github.com/thomasmbury/ewstools.git#egg=ewstools
(Development versions may be unstable.)
Tutorials
Quick demo
Code available in ./quick_demo.ipynb. Import ewstools and simulate a time series (e.g., the Ricker model):
python
import ewstools
from ewstools.models import simulate_ricker
series = simulate_ricker(tmax=500, F=[0,2.7])
series.plot();
Create a TimeSeries object:
python
ts = ewstools.TimeSeries(data=series, transition=440)
Detrend, compute EWS, and calculate trends:
python
ts.detrend(method='Lowess', span=0.2)
ts.compute_var(rolling_window=0.5)
ts.compute_auto(lag=1, rolling_window=0.5)
ts.compute_auto(lag=2, rolling_window=0.5)
ts.compute_ktau()
Get predictions from deep learning classifiers
python
for idx, classifier in enumerate(list_classifiers):
ts.apply_classifier_inc(classifier, inc=10, verbose=0, name=str(idx))
Make an interactive visualization in Plotly:
python
ts.make_plotly()
For detailed demonstrations, see the tutorials.
Documentation
Full documentation available on ReadTheDocs.
Issues
Found a bug or have a suggestion? Please post it on the issue tracker.
Contributions are welcome! Feel free to reach out or submit a pull request.
Acknowledgements
This work is supported by an FRQNT postdoctoral research scholarship awarded to Dr. Thomas Bury. Previous support was provided by NSERC Discovery Grants awarded to Dr. Chris Bauch and Dr. Madhur Anand.
Citation info
If you find ewstools useful, please consider starring the repository ⭐ and citing:
Bury, Thomas M. "ewstools: A Python package for early warning signals of bifurcations in time series data." Journal of Open Source Software 8.82 (2023): 5038.
Owner
- Name: Thomas Bury
- Login: ThomasMBury
- Kind: user
- Location: Canada
- Company: McGill University
- Website: https://www.thomasbury.net/
- Twitter: ThomasMBury
- Repositories: 4
- Profile: https://github.com/ThomasMBury
Analysing tipping points in complex systems through data science and dynamical systems.
JOSS Publication
ewstools: A Python package for early warning signals of bifurcations in time series data
Authors
Department of Physiology, McGill University, Montréal, Canada, Department of Applied Mathematics, University of Waterloo, Waterloo, Canada
Editor
Øystein Sørensen
Tags
time series early warning signal tipping point dynamical system bifurcationPapers & Mentions
Total mentions: 1
Detecting and distinguishing tipping points using spectral early warning signals
- DOI: 10.1098/rsif.2020.0482
- OpenAlex ID: https://openalex.org/W3090635531
- Published: September 2020
GitHub Events
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- Watch event: 13
- Delete event: 2
- Issue comment event: 3
- Push event: 26
- Pull request event: 8
- Fork event: 3
- Create event: 5
Last Year
- Watch event: 13
- Delete event: 2
- Issue comment event: 3
- Push event: 26
- Pull request event: 8
- Fork event: 3
- Create event: 5
Committers
Last synced: 7 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Thomas Bury | t****2@g****m | 520 |
| Thomas Bury | 3****2 | 10 |
| pyup-bot | g****t@p****o | 6 |
| dependabot[bot] | 4****] | 5 |
| Snaptraks | s****s@g****m | 3 |
| Daniel S. Katz | d****z@i****g | 2 |
| Emma Southall | e****l@w****k | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 7
- Total pull requests: 114
- Average time to close issues: about 1 month
- Average time to close pull requests: about 1 month
- Total issue authors: 6
- Total pull request authors: 5
- Average comments per issue: 1.29
- Average comments per pull request: 0.6
- Merged pull requests: 17
- Bot issues: 0
- Bot pull requests: 20
Past Year
- Issues: 0
- Pull requests: 11
- Average time to close issues: N/A
- Average time to close pull requests: 3 days
- Issue authors: 0
- Pull request authors: 2
- Average comments per issue: 0
- Average comments per pull request: 0.09
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 9
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- denisthenichita (2)
- harishankar1014 (1)
- jjvaldes (1)
- gabosantos (1)
- ThomasMBury (1)
- alxgom (1)
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- pyup-bot (88)
- dependabot[bot] (22)
- ThomasMBury (6)
- Snaptraks (2)
- danielskatz (1)
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Packages
- Total packages: 1
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Total downloads:
- pypi 95 last-month
- Total dependent packages: 0
- Total dependent repositories: 3
- Total versions: 17
- Total maintainers: 1
pypi.org: ewstools
Python package to compute early warning signals (EWS)
- Homepage: https://github.com/ThomasMBury/ewstools
- Documentation: https://ewstools.readthedocs.io/
- License: MIT License
-
Latest release: 2.1.2
published almost 2 years ago
Rankings
Maintainers (1)
Dependencies
- arch ==5.3.1
- black ==22.6.0
- numpy ==1.23.0
- pandas ==1.4.3
- pip ==22.1.2
- pytest ==7.1.2
- pytest-cov ==3.0.0
- setuptools ==62.6.0
- sphinx ==5.0.2
- twine ==4.0.1
- wheel ==0.37.1