Mne-nirs

* Maintenance release to align with MNE-Python 1.0 changes

General

* MNE-NIRS now requires the latest MNE-Python 1.0 or later.


Enhancements

* Update SNIRF exporter to meet v1.0 validator requirements `mne_nirs.io.snirf.write_raw_snirf`.
* Add ability to provide custom channel weighting in `mne_nirs.statistics.RegressionResults.to_dataframe_region_of_interest` computation.
* Add function to plot montages on 3D brain with source, detector, and channel naming `mne_nirs.visualisation.plot_3d_montage`.

Fixes

* Fix bug when using no weighting in `mne_nirs.statistics.RegressionResults.to_dataframe_region_of_interest`.

General

* MNE-NIRS now uses the MNE-Python stable version v0.24 and no longer requires the development version.


Enhancements

* Add :meth:`mne_nirs.channels.list_sources`.
* Add :meth:`mne_nirs.channels.list_detectors`.
* Add :meth:`mne_nirs.channels.drop_sources`.
* Add :meth:`mne_nirs.channels.drop_detectors`.
* Add :meth:`mne_nirs.channels.pick_sources`.
* Add :meth:`mne_nirs.channels.pick_detectors`.
* Add :meth:`mne_nirs.preprocessing.quantify_mayer_fooof`.
* Add :func:`mne_nirs.io.fold_landmark_specificity`.
* Add :func:`mne_nirs.io.fold_channel_specificity`.
* Added fetchers for two more publicly available datasets.

API changes

- mne_nirs.channels.get_long_channels() maximum optode distance reduced from 5 to 4.5 cm.
- mne_nirs.experimental_design.create_boxcar() duration reduced from 5 to 1 second.

Enhancements

- Add mne_nirs.statistics.RegressionResults.save().
- Add mne_nirs.statistics.ContrastResults.save().
- Add mne_nirs.statistics.read_glm().
- Add mne_nirs.experimental_design.longest_inter_annotation_interval().
- Add mne_nirs.experimental_design.drift_high_pass().

Fixes

- Fix end values for windowed quality metrics.
- Fix snirf writer bug where it required the optional DateOfBirth field.

* Fix inconsistencies between files written via mne_nirs.io.snirf.write_raw_snirf() and the current version of the official SNIRF spec.
* Add RegressionResults and ContrastResults classes to store GLM results.
* Depreciate run_GLM and associated functions.
* Add an example of how to include second level covariates in the group level GLM tutorial

* Tutorial improvements and added a binder link
* Add ability to project GLM results on to the cortical surface for nice 3D plots.