"""tDCS EEG."""

bids_root = "~/mne_data/ds001810"
deriv_root = "~/mne_data/derivatives/mne-bids-pipeline/ds001810"

task = "attentionalblink"
interactive = False
ch_types = ["eeg"]
eeg_template_montage = "biosemi64"
reject = dict(eeg=100e-6)
baseline = (None, 0)
conditions = ["61450", "61511"]
contrasts = [("61450", "61511")]
decode = True
decoding_n_splits = 3  # only for testing, use 5 otherwise

l_freq = 0.3

subjects = ["01"]
sessions = "all"

interpolate_bads_grand_average = False
n_jobs = 4

  Platform             Linux-5.15.0-1077-aws-x86_64-with-glibc2.35
Python               3.12.4 (main, Jun  8 2024, 23:40:19) [GCC 11.4.0]
Executable           /home/circleci/.pyenv/versions/3.12.4/bin/python3.12
CPU                  Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz (36 cores)
Memory               68.6 GiB

Core
├☑ mne               1.10.0.dev67+gbe27cf8dd (devel, latest release is 1.9.0)
├☑ numpy             2.1.3 (OpenBLAS 0.3.27 with 1 thread)
├☑ scipy             1.15.2
└☑ matplotlib        3.10.1 (backend=agg)

Numerical (optional)
├☑ sklearn           1.6.1
├☑ numba             0.61.0
├☑ nibabel           5.3.2
├☑ pandas            2.2.3
├☑ h5io              0.2.4
├☑ h5py              3.13.0
└☐ unavailable       nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.44.2 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.3 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt         0.11.2
├☑ vtk               9.3.1
├☑ qtpy              2.4.3 (PyQt6=6.8.2)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.17.0.dev32+g3a25345
├☑ mne-bids-pipeline 1.10.0.dev68+gb995b51
├☑ eeglabio          0.0.3
├☑ edfio             0.4.6
├☑ pybv              0.7.6
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, mffpy