"""ds000248: MNE sample data (M/EEG).

See [OpenNeuro](https://openneuro.org/datasets/ds000248) for more information.
"""

import mne
import mne_bids

bids_root = "~/mne_data/ds000248"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds000248_base"
subjects_dir = f"{bids_root}/derivatives/freesurfer/subjects"

subjects = ["01"]
rename_events = {"Smiley": "Emoji", "Button": "Switch"}
conditions = ["Auditory", "Visual", "Auditory/Left", "Auditory/Right"]
epochs_metadata_query = "index > 0"  # Just for testing!
contrasts = [("Visual", "Auditory"), ("Auditory/Right", "Auditory/Left")]

time_frequency_conditions = ["Auditory", "Visual"]

ch_types = ["meg", "eeg"]
mf_reference_run = "01"
find_flat_channels_meg = True
find_noisy_channels_meg = True
use_maxwell_filter = True


def noise_cov(bp: mne_bids.BIDSPath) -> mne.Covariance:
    """Estimate the noise covariance."""
    # Use pre-stimulus period as noise source
    if not bp.fpath.exists():
        bp.update(split="01")
    epo = mne.read_epochs(bp)
    cov = mne.compute_covariance(epo, rank="info", tmax=0)
    return cov


spatial_filter = "ssp"
process_raw_clean = False
n_proj_eog = dict(n_mag=1, n_grad=1, n_eeg=1)
n_proj_ecg = dict(n_mag=1, n_grad=1, n_eeg=0)
ssp_meg = "combined"
ecg_proj_from_average = True
eog_proj_from_average = False
epochs_decim = 4

bem_mri_images = "FLASH"
recreate_bem = True

n_jobs = 2


def mri_t1_path_generator(bids_path: mne_bids.BIDSPath) -> mne_bids.BIDSPath:
    """Return the path to a T1 image."""
    # don't really do any modifications – just for testing!
    return bids_path

  Platform             Linux-7.0.0-1009-aws-x86_64-with-glibc2.39 (X11)
Python               3.14.5 (main, May 12 2026, 13:13:59) [GCC 13.3.0]
Executable           /home/circleci/.pyenv/versions/3.14.5/bin/python3.14
CPU                  Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz (36 cores)
Memory               4.0 GiB

Core
├☑ mne               1.13.0.dev294+ga0eb9250f (development, latest release is 1.12.1)
├☑ numpy             2.5.2 (OpenBLAS 0.3.34.0.0 with 2 threads via pthreads)
├☑ scipy             1.18.1
└☑ matplotlib        3.11.1 (backend=agg)

Numerical (optional)
├☑ scikit-learn      1.9.0
├☑ threadpoolctl     3.6.0
├☑ numba             0.67.0
├☑ nibabel           5.4.2
├☑ pandas            3.0.5
├☑ h5io              0.2.5
├☑ h5py              3.16.0
└☐ unavailable       nilearn, dipy, openmeeg, python-picard, cupy

Visualization (optional)
├☑ pyvista           0.48.4 (OpenGL 4.5 (Core Profile) Mesa 25.2.8-0ubuntu0.24.04.2 via llvmpipe (LLVM 20.1.2, 256 bits))
├☑ pyvistaqt         0.12.0
├☑ vtk               9.6.2
├☑ qtpy              2.4.3 (PySide6=6.11.2)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame, trame_client, trame_server, trame_pyvista, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.20.0.dev34+g25f78ddf4
├☑ mne-icalabel      0.9.0
├☑ mne-bids-pipeline 1.11.0.dev68+g1c2c2d8b2
├☑ autoreject        0.5.0.dev5+gb4e218e6
├☑ eeglabio          0.1.3
├☑ edfio             0.4.16
├☑ curryreader       0.1.2
├☑ mffpy             0.11.0
├☑ pybv              0.8.1
├☑ defusedxml        0.7.1
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, neo, pymef, antio