Skip to main content

Dynamical Components Analysis

Actions Status Documentation Status codecov

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

Installation

To install, you can clone the repository and cd into the DynamicalComponentsAnalysis folder.

# use ssh
$ git clone git@github.com:BouchardLab/DynamicalComponentsAnalysis.git
# or use https
$ git clone https://github.com/BouchardLab/DynamicalComponentsAnalysis.git
$ cd DynamicalComponentsAnalysis

If 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.txt

Note: DCA only requires a CPU-only pytorch. If you wish to, before installing DCA as in the above instructions, you can install a CPU-only pytorch by following the pytorch installation guide.

$ pip install torch --index-url https://download.pytorch.org/whl/cpu

Datasets

The 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

Copyright

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 IPO@lbl.gov.

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.

Release files for DynamicalComponentsAnalysis 1.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for DynamicalComponentsAnalysis 1.1.0
File Size Uploaded
dynamicalcomponentsanalysis-1.1.0.tar.gz 48.5 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for DynamicalComponentsAnalysis 1.1.0
File Interpreter ABI Platform
dynamicalcomponentsanalysis-1.1.0-py3-none-any.whl Python 3 none any Details

Total release size: 100.0 kB

Release files / dynamicalcomponentsanalysis-1.1.0.tar.gz

Download URL dynamicalcomponentsanalysis-1.1.0.tar.gz
Size 48.5 kB
Tags Source
SHA-256 checksum
How to use checksums
190a3c471f04c15db62afad238d584a9a49c92ca9ccaeaf054654ef6290a5ba8
BLAKE2b-256 checksum
How to use checksums
9ae46bf1184f51d26b2ee4ef3c1d9989d4731d67bb8ec2c434b48fae5fcbb1c9
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.12.9

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.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 28, 2025.

Transparency log

Release files / dynamicalcomponentsanalysis-1.1.0-py3-none-any.whl

Download URL dynamicalcomponentsanalysis-1.1.0-py3-none-any.whl
Size 51.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
aee342dca327e0a69f7ca880f9848164b44dea0c82837c919281651a24df3b86
BLAKE2b-256 checksum
How to use checksums
64bff9c2a5aba873c2457fcd1624232505663e008528b1633fc9adec57afb7eb
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.12.9

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.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 28, 2025.

Transparency log

Release history Release notifications | RSS feed

This release

1.1.0 This release

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page