"""ds003392: hMT+ Localizer (OTP + Maxwell filter).

See [OpenNeuro](https://openneuro.org/datasets/ds003392) for more information.
This config tests implementation of oversampled temporal projection followed by maxwell
filtering and subsequent preprocessing steps.
"""

from mne.transforms import translation

bids_root = "~/mne_data/ds003392"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds003392_otp_mxw"
ignore_warnings = [
    "Internal Active Shielding data",  # until MNE-BIDS releases a fix for ERM finding
]
subjects = ["01"]

task = "localizer"

# use oversampled temporal projection to clean sensor noise
use_otp_denoising = True
otp_duration = 10.0
# for OTP testing purposes, set to True
find_flat_channels_meg = True
find_noisy_channels_meg = True
use_maxwell_filter = True
mf_extra_kws = {"bad_condition": "warning"}
ch_types = ["meg"]
mf_esss = 1
# translation args should be x, y, z as scalar (int or float)
mf_destination = translation(z=0.04)

mf_cal_missing = "warn"
mf_ctc_missing = "warn"

l_freq = 1.0
h_freq = 40.0
raw_resample_sfreq = 250
crop_runs = (0, 20)

# Artifact correction
spatial_filter = "ica"
process_raw_clean = False
ica_algorithm = "picard-extended_infomax"
ica_max_iterations = 1000
ica_l_freq = 1.0
ica_n_components = 0.99

# Epochs
epochs_tmin = -0.2
epochs_tmax = 1.0
baseline = (None, 0)

# Conditions / events to consider when epoching
conditions = ["coherent", "incoherent"]

  Platform             Linux-7.0.0-1004-aws-x86_64-with-glibc2.43 (X11)
Python               3.14.4 (main, May 12 2026, 13:57:53) [GCC 15.2.0]
Executable           /home/circleci/python_env/bin/python3
CPU                  AMD EPYC 9R45 (2 cores)
Memory               7.6 GiB

Core
├☑ mne               1.14.0.dev112+g8f498626a (development, latest release is 1.13.2)
├☑ numpy             2.5.4 (OpenBLAS 0.3.34.106.0 with 2 threads via pthreads)
├☑ scipy             1.18.1
└☑ matplotlib        3.11.2 (backend=agg)

Numerical (optional)
├☑ scikit-learn      1.9.1
├☑ threadpoolctl     3.7.0
├☑ numba             0.68.0
├☑ nibabel           5.4.2
├☑ pandas            3.0.6
├☑ h5io              0.2.5
├☑ h5py              3.16.0
└☐ unavailable       nilearn, dipy, openmeeg, python-picard, jamica, cupy

Visualization (optional)
├☑ pyvista           0.49.1 (OpenGL 4.5 (Core Profile) Mesa 26.0.8-1ubuntu0.3 via llvmpipe (LLVM 21.1.8, 256 bits))
├☑ pyvistaqt         0.13.1
├☑ vtk               9.7.1
├☑ qtpy              2.4.3 (PySide6=6.12.0)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame, trame_client, trame_server, trame_pyvista, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.21.0.dev10+ga582be8f8
├☑ mne-icalabel      0.10.0
├☑ mne-bids-pipeline 1.11.0.dev86+ge2a34cf44
├☑ autoreject        0.6.0.dev2+g2f72b264
├☑ eeglabio          0.1.4
├☑ edfio             0.4.18
├☑ 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