Implementation of the methods and analyses in Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis.
Documentation can be found at https://dynamicalcomponentsanalysis.readthedocs.io/en/latest/index.html
To install, you can clone the repository and cd into the DynamicalComponentsAnalysis folder.
# use ssh
$ git clone [email protected]:BouchardLab/DynamicalComponentsAnalysis.git
# or use https
$ git clone https://github.com/BouchardLab/DynamicalComponentsAnalysis.git
$ cd DynamicalComponentsAnalysisIf you are installing into an active conda environment, you can run
$ conda env update --file environment.yml
$ pip install -e .If you are installing with pip you can run
$ pip install -e . -r requirements.txtThe 4 datasets used in the DCA paper can be found in the following locations
- M1 - We used indy_20160627_01.mat
- HC - See link to the datasets in the README
- Temperature - We used the 30 US cities from temperature.csv.
- Accelerometer - We used std_6/sub_19.csv from A_DeviceMotion_data.zip
Dynamical Components Analysis (DCA) Copyright (c) 2021, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.
If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at [email protected].
NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.