Metadata-Version: 2.5
Name: mne-denoise
Version: 0.0.2
Summary: Artifact removal and signal denoising for EEG and MEG.
Project-URL: Bug Tracker, https://github.com/mne-tools/mne-denoise/issues
Project-URL: Changelog, https://github.com/mne-tools/mne-denoise/blob/main/CHANGELOG.md
Project-URL: Documentation, https://mne.tools/mne-denoise/
Project-URL: Homepage, https://mne.tools/mne-denoise/
Project-URL: Source Code, https://github.com/mne-tools/mne-denoise
Author-email: Sina Esmaeili <sina.esmaeili@umontreal.ca>, Hamza Abdelhedi <hamza.abdelhedi@umontreal.ca>
Maintainer-email: Sina Esmaeili <sina.esmaeili@umontreal.ca>, Hamza Abdelhedi <hamza.abdelhedi@umontreal.ca>
License-Expression: BSD-3-Clause
License-File: LICENSE
Keywords: artifact-removal,asr,denoising,dss,eeg,meg,mne,neuroscience,signal-processing,zapline
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.12
Requires-Dist: joblib>=1.4
Requires-Dist: numpy<3,>=1.26
Requires-Dist: scikit-learn>=1.5
Requires-Dist: scipy>=1.13
Provides-Extra: mne
Requires-Dist: mne>=1.13.0; extra == 'mne'
Provides-Extra: progress
Requires-Dist: tqdm>=4.66; extra == 'progress'
Provides-Extra: viz
Requires-Dist: matplotlib>=3.8; extra == 'viz'
Requires-Dist: seaborn>=0.13; extra == 'viz'
Description-Content-Type: text/markdown

# mne-denoise

[![Tests](https://github.com/mne-tools/mne-denoise/actions/workflows/tests.yml/badge.svg)](https://github.com/mne-tools/mne-denoise/actions/workflows/tests.yml)
[![codecov](https://codecov.io/gh/mne-tools/mne-denoise/branch/main/graph/badge.svg)](https://codecov.io/gh/mne-tools/mne-denoise)
[![PyPI version](https://img.shields.io/pypi/v/mne-denoise.svg)](https://pypi.org/project/mne-denoise/)
[![Python versions](https://img.shields.io/pypi/pyversions/mne-denoise.svg)](https://pypi.org/project/mne-denoise/)
[![License](https://img.shields.io/badge/License-BSD_3--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
[![Code style: ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://mne.tools/mne-denoise/)
[![Downloads](https://pepy.tech/badge/mne-denoise)](https://pepy.tech/project/mne-denoise)

`mne-denoise` provides artifact-suppression and signal-denoising methods for
EEG and MEG, with NumPy and MNE-Python integration.

The package contains several complementary methods for spatial, spectral,
statistical, and source-informed denoising. Many methods accept MNE `Raw`,
`Epochs`, and `Evoked` objects directly, and sklearn-style estimators are
provided where that interface fits the method.

See the [user guide](https://mne.tools/mne-denoise/getting-started.html) and
[API reference](https://mne.tools/mne-denoise/api.html) for method selection
and exact contracts. Experimental APIs are identified in the documentation.

## Installation

Python 3.12 or newer is required.

Base package — `pip install mne-denoise`

MNE-Python integration — `pip install "mne-denoise[mne]"`

Visualization (Matplotlib + Seaborn) — `pip install "mne-denoise[viz]"`

Progress bars (tqdm) — `pip install "mne-denoise[progress]"`

## Quick start

The example assumes that `raw` is an MNE `Raw` object loaded with
`preload=True`; install the `mne` extra to use it.

```python
from mne_denoise.spectrum_interpolation import SpectrumInterpolation

# `raw` is an mne.io.Raw object loaded with preload=True.
# Set line_freq to the mains frequency in your recording.
cleaner = SpectrumInterpolation(line_freq=60.0, n_harmonics=3)
clean_raw = cleaner.fit_transform(raw)
```

## Documentation

- [Documentation](https://mne.tools/mne-denoise/)
- [Getting started](https://mne.tools/mne-denoise/getting-started.html)
- [API reference](https://mne.tools/mne-denoise/api.html)
- [Example gallery](https://mne.tools/mne-denoise/auto_examples/index.html)

## Citing

When using mne-denoise in scientific work, cite both the software and the
primary publication(s) for the method(s) used in your analysis. See the
[citation guidance](https://mne.tools/mne-denoise/citing.html).

## Contributing

Contributions are welcome. See [CONTRIBUTING.md](CONTRIBUTING.md) for the
human contribution guide.

## License

BSD 3-Clause License. See [LICENSE](LICENSE) for details.
