BrainMaze - Utils

BrainMaze - Utils#

BrainMaze: Brain Electrophysiology, Behavior and Dynamics Analysis Toolbox - Utils#

This toolbox provides a generic tools for the BrainMaze package. This tool was separated from the BrainMaze toolbox to support a convenient and lightweight sharing of these tools across projects.

This project was originally developed as a part of the BEhavioral STate Analysis Toolbox (BEST) project. However, the development has transferred to the BrainMaze project.

Documentation#

Documentation is available here.

Installation#

pip install brainmaze-utils

Supported dependency versions: NumPy 1.24+ (1.x and 2.x), SciPy 1.10+, pandas 2.x and 3.x.

Signal-processing conventions#

  • Multichannel arrays are (n_channels, n_samples): time runs along the last axis. Frequencies are in Hz, durations in seconds. Functions return new arrays and do not modify their inputs.

  • NaN marks missing data. Every function documents what it does with NaN; resampling functions re-apply the gaps to their output instead of silently filling them.

  • Which functions filter?

    • signal.decimate: anti-aliasing low-pass included (16th-order Butterworth in second-order sections, zero-phase, default cutoff fs_new / 3), then resampling to exactly k / fs_new (any ratio, including non-round rates such as 30000.5 Hz; it also upsamples). Use this to change the sampling rate.

    • signal.resample: no anti-aliasing filter, by design. It only interpolates. When downsampling, low-pass the signal below the new Nyquist frequency first, or use decimate. Otherwise content above fs_new / 2 aliases into the output.

    • signal.nandecimate: weak 30-tap FIR anti-aliasing (legacy); prefer decimate.

    • signal.LowFrequencyFilter: zero-phase low-/high-pass for very low cutoffs (e.g. a 0.5 Hz high-pass on 8 kHz data) via a decimate/filter/upsample cascade. DC offset and linear drift are handled exactly at the record edges, so there are no start/end jumps. NaN gaps are handled (short ones bridged, long ones split into segments with the same edge handling); the output is NaN exactly where the input is, so it can follow decimate in a cascade.

    • signal.fft_filter: brick-wall FFT filter (rings at transients; record edges periodic by default, edges='extend' for the edge handling above). NaN gaps handled as above.

import scipy.signal as ss
from brainmaze_utils.signal import decimate, resample

y = decimate(x, fs=2000, fs_new=250)               # filtered + downsampled, NaN-aware

sos = ss.butter(8, 100, 'lp', fs=2000, output='sos')
y = resample(ss.sosfiltfilt(sos, x), 2000, 250)    # resample: you low-pass first

See the Changes page of the documentation for behaviour changes after v2.0.0 (bug fixes that change numerical results).

How to contribute#

The project has 2 main protected branches main that contains official software releases and dev that contains the latest feature implementations shared with developers. To implement a new feature a new branch should be created from the dev branch with name pattern of developer_identifier/feature_name.

After the feature is implemented, a pull request can be created to merge the feature branch into the dev branch with. Pull requests need to be reviewed by a maintainer.

Releasing#

Releases are automated and go through a reviewed pull request. To cut a release, a maintainer runs the Prepare release GitHub Action (Actions tab, workflow_dispatch) and selects the bump (patch / minor / major). This opens a small Release vX.Y.Z pull request that bumps [project].version in pyproject.toml on a release/bump-* branch off main. A reviewer checks that it changes only that line and squash-merges it into main; the merge triggers the Release workflow, which tests, builds, publishes to PyPI (Trusted Publishing), and then tags the version and creates the GitHub release. The build reads the version from pyproject.toml, which remains the single source of truth.

Promotion of features from dev to main is independent of releases and must not change [project].version – a Version guard CI check flags any pull request outside the release flow that edits it (the check is advisory, so reviewers must not merge a flagged pull request). The version line is owned solely by the release automation on main; this is what keeps dev -> main merges free of version conflicts under the squash-merge policy (a version edited on both branches would otherwise conflict every release cycle).

The Prepare release action requires the repository/organization setting Allow GitHub Actions to create and approve pull requests to be enabled, so it can open the bump pull request. See RELEASING.md and the family guide bnelair/brainmaze-sphinx (including what to do if a release fails part-way).

New functions need to be implemented with Sphinx compatible docstrings. The documentation is automatically generated from the docstrings using Sphinx.

Building Documentation#

Documentation source is in docs_src/ and generated HTML output goes to docs/. The docs/ directory is excluded from version control (added to .gitignore) to keep the repository clean. To build documentation locally:

# Install documentation dependencies
pip install -r docs_src/requirements.txt

# Build documentation
sphinx-build -b html docs_src/source docs

# Or use the provided script
./make_docs.sh

The generated documentation can be hosted separately (e.g., GitHub Pages, ReadTheDocs) without committing build artifacts to the repository.

License#

This software is licensed under BSD-3Clause license. For details see the LICENSE file in the root directory of this project.

Acknowledgment#

This code was developed and originally published for the first time with by (Mivalt 2022, and Sladky 2022). We appreciate you citing these papers when utilizing this toolbox in further research work.

F. Mivalt et V. Kremen et al., “Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans,” J. Neural Eng., vol. 19, no. 1, p. 016019, Feb. 2022, doi: 10.1088/1741-2552/ac4bfd.

V. Sladky et al., “Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation,” Brain Commun., vol. 4, no. 3, May 2022, doi: 10.1093/braincomms/fcac115.

BrainMaze Packages#

BrainMaze - Python Packages

BrainMaze Utils Modules#

BrainMaze Utils - Modules