This project is no longer actively maintained, although the code uses core, stable functions from Python, Numpy, and SciPy, so it is likely to work.
If you need to load cbin (audio) files from evsonganaly, there are maintained, updated versions of the functions in this package in the vocalpy library: https://vocalpy.readthedocs.io
If you need to load .not.mat (annotation) files from evsonganaly, there are maintained, updated versions of that function in crowsetta: https://crowsetta.readthedocs.io
(Note that if you install vocalpy, you will already have crowsetta installed.)

[

evfuncs

Functions for working with files created by EvTAF and the evsonganaly GUI.
In case you need to work with those files in Python 😊😊😊 (see "Usage" below).

The first work published with data collected using EvTAF and evsonganaly is in this paper:
Tumer, Evren C., and Michael S. Brainard.
"Performance variability enables adaptive plasticity of ‘crystallized’adult birdsong."
Nature 450.7173 (2007): 1240.
https://www.nature.com/articles/nature06390

These functions are translations to Python of the original functions written in MATLAB (copyright Mathworks) by Evren Tumer (shown below).

Installation

with pip

$ pip install evfuncs

with conda

$ conda install evfuncs -c conda-forge

Usage

The main purpose for developing these functions in Python was to work with files of Bengalese finch song in this data repository: https://figshare.com/articles/Bengalese_Finch_song_repository/4805749

Using evfuncs with that repository, you can load the .cbin audio files ...

>>> import evfuncs

>>> rawsong, samp_freq = evfuncs.load_cbin('gy6or6_baseline_230312_0808.138.cbin')

... and the annotation in the .not.mat files ...

>>> notmat_dict = evfuncs.load_notmat('gy6or6_baseline_230312_0808.138.cbin')

(or, using the .not.mat filename directly)

>>> notmat_dict = evfuncs.load_notmat('gy6or6_baseline_230312_0808.138.not.mat')

...and you should be able to reproduce the segmentation of the raw audio files of birdsong into syllables and silent periods, using the segmenting parameters from a .not.mat file and the simple algorithm applied by the SegmentNotes.m function.

>>> smooth = evfuncs.smooth_data(rawsong, samp_freq)
>>> threshold = notmat_dict['threshold']
>>> min_syl_dur = notmat_dict['min_dur'] / 1000
>>> min_silent_dur = notmat_dict['min_int'] / 1000
>>> onsets, offsets = evfuncs.segment_song(smooth, samp_freq, threshold, min_syl_dur, min_silent_dur)
>>> import numpy as np
>>> np.allclose(onsets, notmat_dict['onsets'])
True

(Note that this test would return False if the onsets and offsets in the .not.mat annotation file had been modified, e.g., a user of the evsonganaly GUI had edited them, after they were originally computed by the SegmentNotes.m function.)

evfuncs is used to load annotations by
'crowsetta', a data-munging tool for building datasets of vocalizations that can be used to train machine learning models. Two machine learning libraries that can use those datasets are: hybrid-vocal-classifier, and vak.

Getting Help

Please feel free to raise an issue here:
https://github.com/NickleDave/evfuncs/issues

License

BSD License.

Citation

Please cite this software as shown below. To get the most up-to-date, automatically-generated citation, please click "Cite this repository" on the upper right side of the page.

bibtex:

@software{Nicholson_evfuncs_2021,
author = {Nicholson, David},
doi = {10.5281/zenodo.4584209},
license = {BSD-3-Clause},
month = {3},
title = {{evfuncs}},
url = {https://github.com/NickleDave/evfuncs},
version = {0.3.2.post1},
year = {2021}

APA:

Nicholson, D. (2021). evfuncs (Version 0.3.2.post1) [Computer software]. https://doi.org/10.5281/zenodo.4584209

Build Status