evfuncs 0.3.5

Functions for working with files created by the EvTAF program and the evsonganaly GUI

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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

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