"""ds000117: Faces MEG.

See [OpenNeuro](https://openneuro.org/datasets/ds000117) for more information.
"""

bids_root = "~/mne_data/ds000117"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds000117"

task = "facerecognition"
ch_types = ["meg"]
runs = ["01", "02"]
sessions = ["meg"]
subjects = ["01"]

raw_resample_sfreq = 125.0
crop_runs = (0, 300)  # Reduce memory usage on CI system

ignore_warnings = (
    "The number of channels in the channels.tsv sidecar file",
    'contains a "stim_type" column. This column should be renamed to "trial_type"',
    "Cannot set channel type for the following channels",
    "Unable to map the following column",
    "more than 20 mm from head frame origin",
    r"Did not find any (channels\.tsv|meg\.json) associated with sub-emptyroom_ses",
)

find_flat_channels_meg = True
find_noisy_channels_meg = True
use_maxwell_filter = True
process_empty_room = True

mf_reference_run = "02"
mf_cal_fname = bids_root + "/derivatives/meg_derivatives/sss_cal.dat"
mf_ctc_fname = bids_root + "/derivatives/meg_derivatives/ct_sparse.fif"
mf_int_order = 9
mf_ext_order = 2

reject = {"grad": 4000e-13, "mag": 4e-12}
conditions = ["Famous", "Unfamiliar", "Scrambled"]
contrasts = [
    ("Famous", "Scrambled"),
    ("Unfamiliar", "Scrambled"),
    ("Famous", "Unfamiliar"),
]

decode = True
decoding_time_generalization = True

run_source_estimation = False

  Platform             Linux-6.8.0-1053-aws-x86_64-with-glibc2.39
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.dev54+g692eccee2 (development, latest release is 1.12.1)
├☑ numpy             2.4.4 (OpenBLAS 0.3.31.188.0 with 2 threads)
├☑ scipy             1.17.1
└☑ matplotlib        3.10.9 (backend=agg)

Numerical (optional)
├☑ sklearn           1.8.0
├☑ numba             0.65.1
├☑ nibabel           5.4.2
├☑ pandas            3.0.3
├☑ h5io              0.2.5
├☑ h5py              3.16.0
└☐ unavailable       nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.48.2 (OpenGL 4.5 (Core Profile) Mesa 25.2.8-0ubuntu0.24.04.1 via llvmpipe (LLVM 20.1.2, 256 bits))
├☑ pyvistaqt         0.11.4
├☑ vtk               9.6.1
├☑ qtpy              2.4.3 (PySide6=6.11.1)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_pyvista, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.19.0.dev68+gf25498449
├☑ mne-icalabel      0.8.1
├☑ mne-bids-pipeline 1.11.0.dev23+gb5c8b98b1
├☑ eeglabio          0.1.3
├☑ edfio             0.4.13
├☑ curryreader       0.1.2
├☑ pybv              0.7.6
├☑ defusedxml        0.7.1
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, neo, mffpy, pymef, antio