This is the codebase for the pipeline used to produce the catchaMouse16 feature set licensed under the GNU GPL v3 license (or later).
The pipeline to reproduce to create the catchaMouse16 feature set is an adaptation of the general pipeline from C.H. Lubba, S.S. Sethi, P. Knaute, S.R. Schultz, B.D. Fulcher, N.S. Jones. catch22: CAnonical Time-series CHaracteristics. Data Mining and Knowledge Discovery (2019) for a specific fMRI mouse data set.
The repo also reproduces the figures from the pre-print [paper][addlink here]. The pipeline analyses the performance of all features from hctsa against a specific time series classification task and produces a highly representative subset.
For information on the full set of over 7000 features, see the following (open) publications:
- B.D. Fulcher and N.S. Jones. hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell Systems 5, 527 (2017).
- B.D. Fulcher, M.A. Little, N.S. Jones Highly comparative time-series analysis: the empirical structure of time series and their methods. J. Roy. Soc. Interface 10, 83 (2013).
There is comprehensive documentation for catchaMouse16, including:
- Installation instructions (across C, python, and Matlab)
- Information about the theory behind and behavior of each of the features,
- A list of publications that have used the catchaMouse16 feature set or specific time series pipeline
- And more 😋
If you use this software, please read and cite this open-access article:
- 📗 PREPRINT OF THE PAPER.