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https://github.com/DynamicsAndNeuralSystems/pycatch22
python implementation of catch22
https://github.com/DynamicsAndNeuralSystems/pycatch22
Last synced: 6 days ago
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python implementation of catch22
- Host: GitHub
- URL: https://github.com/DynamicsAndNeuralSystems/pycatch22
- Owner: DynamicsAndNeuralSystems
- License: gpl-3.0
- Created: 2022-07-05T03:58:34.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-04T02:58:51.000Z (2 months ago)
- Last Synced: 2024-11-08T06:48:42.496Z (9 days ago)
- Language: C
- Homepage: https://time-series-features.gitbook.io/catch22/python
- Size: 213 KB
- Stars: 76
- Watchers: 3
- Forks: 16
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
pycatch22: CAnonical Time-series CHaracteristics in python
## About
[_catch22_](https://github.com/DynamicsAndNeuralSystems/catch22) is a collection of 22 time-series features coded in C that can be run from Python, as well as [R](https://github.com/hendersontrent/Rcatch22), [Matlab](https://github.com/DynamicsAndNeuralSystems/catch22), and [Julia](https://github.com/brendanjohnharris/Catch22.jl).
This package provides a python implementation as the module _pycatch22_, licensed under the [GNU GPL v3 license](http://www.gnu.org/licenses/gpl-3.0.html) (or later).
### What do the features do?
This [GitBooks website](https://time-series-features.gitbook.io/catch22/feature-descriptions) is dedicated to describing the features.
For their implementation in code, see the [main _catch22_ repository](https://github.com/DynamicsAndNeuralSystems/catch22).
There is also information in the associated paper [📗 Lubba et al. (2019).](https://doi.org/10.1007/s10618-019-00647-x).
### Acknowledgement :+1:
If you use this software, please read and cite this open-access article:
- 📗 Lubba et al. [_catch22_: CAnonical Time-series CHaracteristics](https://doi.org/10.1007/s10618-019-00647-x), _Data Min Knowl Disc_ __33__, 1821 (2019).
## Installation
Using `pip` for [`pycatch22`](https://pypi.org/project/pycatch22/):
```
pip install pycatch22
```
If this doesn't work, make sure you are using the latest `setuptools`: `pip install setuptools --upgrade`.
If you come across errors with version resolution, you should try something like: `pip install pycatch22==0.4.2 --use-deprecated=legacy-resolver`.
It is also a [package on anaconda](https://anaconda.org/conda-forge/pycatch22) thanks to [@rpanai](https://github.com/rpanai), which you can install via `conda`:
```
conda install -c conda-forge pycatch22
```
or `mamba`:
```
mamba install -c conda-forge pycatch22
```
[A manual install (bottom of this page) is a last resort.]
## Usage
Each feature function can be accessed individually and takes arrays as tuple or lists (not `numpy` arrays).
For example, for loaded data `tsData` in Python:
```python3
import pycatch22
tsData = [1,2,4,3] # (or more interesting data!)
pycatch22.CO_f1ecac(tsData)
```
All features are bundled in the method `catch22_all`, which also accepts `numpy` arrays and gives back a dictionary containing the entries `catch22_all['names']` for feature names and `catch22_all['values']` for feature outputs.
Usage (computing 22 features: _catch22_):
```python3
pycatch22.catch22_all(tsData)
```
Usage (computing 24 features: _catch24_ = _catch22_ + mean + standard deviation):
```python3
pycatch22.catch22_all(tsData,catch24=True)
```
We also include a 'short name' for each feature for easier reference (as outlined in the GitBook [Feature overview table](https://time-series-features.gitbook.io/catch22/feature-descriptions/feature-overview-table)).
These short names can be included in the output from `catch22_all()` by setting `short_names=True` as follows:
```python3
pycatch22.catch22_all(tsData,catch24=True,short_names=True)
```
### Template analysis script
Thanks to [@jmoo2880](https://github.com/jmoo2880) for putting together a [demonstration notebook](https://github.com/jmoo2880/c22-usage-examples/) for using pycatch22 to extract features from a time-series dataset.
### Usage notes
- When presenting results using _catch22_, you must identify the version used to allow clear reproduction of your results. For example, `CO_f1ecac` was altered from an integer-valued output to a linearly interpolated real-valued output from v0.3.
- __Important Note:__ _catch22_ features only evaluate _dynamical_ properties of time series and do not respond to basic differences in the location (e.g., mean) or spread (e.g., variance).
- From _catch22_ v0.3, If the location and spread of the raw time-series distribution may be important for your application, we suggest applying the function argument `catch24 = True` to your call to the _catch22_ function in the language of your choice.
This will result in 24 features being calculated: the _catch22_ features in addition to mean and standard deviation.
### Manual install
If you find issues with the `pip` install, you can also install using `setuptools`:
```
python3 setup.py build
python3 setup.py install
```