https://github.com/jgraving/behavelet
a wavelet transform for mapping behavior
https://github.com/jgraving/behavelet
Last synced: 4 months ago
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a wavelet transform for mapping behavior
- Host: GitHub
- URL: https://github.com/jgraving/behavelet
- Owner: jgraving
- License: apache-2.0
- Created: 2019-08-25T09:29:40.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-04T10:34:02.000Z (about 1 year ago)
- Last Synced: 2024-12-28T19:38:55.430Z (4 months ago)
- Language: Python
- Homepage:
- Size: 809 KB
- Stars: 9
- Watchers: 1
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
behavelet: a wavelet transform for mapping behavior
============
behavelet is a Python implementation of the normalized Morlet wavelet transform for behavioral mapping from [Berman et al. (2014)](https://doi.org/10.1098/rsif.2014.0672).
It runs on the CPU using numpy and multiprocessing or on the GPU using [CuPy](https://github.com/cupy/cupy).
This code was adapted to Python using [the original MotionMapper code](https://github.com/gordonberman/MotionMapper) from Gordon Berman et al.
Quick Start
------------
Here is an example of how to use behavelet on the CPU with a randomly generated dataset:
```python
from behavelet import wavelet_transform
import numpy as np
n_samples = 10000
n_features = 10
X = np.random.normal(size=(n_samples, n_features))
freqs, power, X_new = wavelet_transform(X, n_freqs=25, fsample=100., fmin=1., fmax=50.)
```
use the `n_jobs` argument to parallelize the computations across multiple threads:
```python
freqs, power, X_new = wavelet_transform(X, n_freqs=25, fsample=100., fmin=1., fmax=50., n_jobs=-1)
```
and use the `gpu` argument to run it on the GPU with [CuPy](https://github.com/cupy/cupy):
```python
freqs, power, X_new = wavelet_transform(X, n_freqs=25, fsample=100., fmin=1., fmax=50., gpu=True)
```
- `freqs` is a `(n_freqs,)` shaped array of the frequencies used for the wavelet transform
- `power` is a `(n_samples,)` shaped array with the total power for the wavelet coefficients in each sample
- `X_new` is a `(n_samples, n_freqs*n_features)` shaped array of the wavelet coefficients.
Citation
---------
If you use behavelet for your research, please cite our DOI:
[](https://zenodo.org/badge/latestdoi/204273245)
@misc{graving2019behavelet,
title={behavelet: a wavelet transform for mapping behavior},
author={Graving, Jacob M},
month={aug},
year={2019},
doi={10.5281/zenodo.3376742},
url={https://doi.org/10.5281/zenodo.3376742}
}
for the original description of the normalized Morlet wavelet transform see the paper from [Berman et al. (2014)](https://doi.org/10.1098/rsif.2014.0672):
@article{berman2014mapping,
title={Mapping the stereotyped behaviour of freely moving fruit flies},
author={Berman, Gordon J and Choi, Daniel M and Bialek, William and Shaevitz, Joshua W},
journal={Journal of The Royal Society Interface},
volume={11},
number={99},
pages={20140672},
year={2014},
publisher={The Royal Society}
}
Installation
------------
Install the latest stable version with pip:
```bash
pip install behavelet
```
Install the development version with pip:
```bash
pip install git+https://www.github.com/jgraving/behavelet.git
```
You can also install from within Python rather than using the command line, either from within Jupyter or another IDE, to ensure it is installed in the correct working environment:
```python
import sys
!{sys.executable} -m pip install git+https://www.github.com/jgraving/behavelet.git
```
If you wish to use the GPU version, you must [install CuPy manually](https://github.com/cupy/cupy#installation).
Development
-------------
Please submit bugs or feature requests to the [GitHub issue tracker](https://github.com/jgraving/behavelet/issues/new). Please limit reported issues to the behavelet codebase and provide as much detail as you can with a minimal working example if possible.
If you experience problems with [CuPy](https://github.com/cupy/cupy), such as installing CUDA or other dependencies, then please direct issues to their development team.
Contributors
------------
behavelet was developed by [Jake Graving](https://github.com/jgraving), and is still being actively developed. Public contributions are welcome. If you wish to contribute, please [fork the repository](https://help.github.com/en/articles/fork-a-repo) to make your modifications and [submit a pull request](https://help.github.com/en/articles/creating-a-pull-request-from-a-fork).
License
------------
Released under a Apache 2.0 License. See [LICENSE](https://github.com/jgraving/behavelet/blob/master/LICENSE) for details.