The DISSTANS Python package

Welcome to the package repository for the Decomposition and Inference of Sources through Spatiotemporal Analysis of Network Signals (DISSTANS) toolbox.

With DISSTANS, you can:

Fit GNSS displacement (or any type of) timeseries using a variety of functional models. These range from common ones such as a polynomial, a sinusoid, or a logarithm, to more complex ones as logarithms with unknown timescales, modulated sinusoids, or dictionaries of splines.
Solve for model parameters using least squares with no regularization or using any of the L2, L1, and L0 norms. Spatial L0 allows to use expected spatial coherence in the data to improve local fits.
Take advantage of multiprocessor systems using multiple threads for the most computationally heavy steps.
Perform PCA/ICA-based common mode estimation and timeseries basis decomposition.
Clean data from outliers.
Visualize timeseries, network maps, dictionary scalograms, station motions, station observation availabilities, model parameter correlations, and more.
Manage databases of raw RINEX files, including availability plots.
Download GNSS timeseries from public sites.
Use catalogued seismic and maintenance events to inform the model setup.
Detect jumps in the data using a simple step detector.
Run the MIDAS algorithm.
Generate synthetic timeseries.
Load timeseries in JPL's .(t)series, UNR's .tenv3 and .kenv, and Japan's F5 .pos formats natively, or load standard NumPy and pandas data.

All from within your Python shell, and everything in standard Python object-oriented programming style, allowing you to easily subclass existing code to suit your individual needs.

Documentation

A peer-reviewed study has been published (see Using and citing this work below) that explains the concept, inner workings, goals, and successes of DISSTANS in detail. You can find the final version here, and the accepted preprint here.

Furthermore, DISSTANS contains full code annotation, an API documentation, as well as tutorials and real-data examples that show the usage of the package.

The documentation can be found in the docs/ folder. It is hosted on GitHub publicly at tobiscode.github.io/disstans, but you can also read it locally, e.g., by running python -m http.server 8080 --bind 127.0.0.1 from with the documentation folder and then opening a browser.

Installation

The full installation instructions, including necessary prerequisites, can be found in the documentation.

If you're happy with a minimal installation (no local documentation, not suited for modifications, without experimental newest commits), then the short answer is:

# download the environment file
wget https://raw.githubusercontent.com/tobiscode/disstans/main/environment.yml
# create the environment, including all prerequisites
conda env create -f environment.yml
# activate the environment
conda activate disstans
# install DISSTANS from the Python Package Index (PyPI)
pip install disstans

Updating the code is then just:

pip install --upgrade disstans

Using and citing this work

Please note that this work is under a GPL-3.0 License.

If you're using this code or any parts of it, please cite the following study:

Köhne, T., Riel, B., & Simons, M. (2023). Decomposition and Inference of Sources through Spatiotemporal Analysis of Network Signals: The DISSTANS Python package. Computers & Geosciences, 170, 105247. DOI: 10.1016/j.cageo.2022.105247

You can find the accepted preprint here.

A poster introducing the code was presented at the AGU Fall Meeting 2021, you can find it here.

Acknowledgments

This code would not be possible without the work of others, such as:

The inspiration for this code, pygeodesy by Bryan V. Riel
The MIDAS code by Geoff Blewitt
The powerlaw noise generation code colorednoise by Felix Patzelt
The wrapper for the Okada elastic dislocation model okada_wrapper by Thomas Ben Thompson

Reporting bugs and getting involved

If you find a bug or have a question about the code, please raise an issue on GitHub. If you have any other comment, feedback, or suggestion, feel free to send me an email to tkoehne@caltech.edu. Similarly, if you want to contribute to any part of the code (functions, classes, documentation, examples, etc.), please send me an email - contributions of all kinds are always welcome!