"""tDCS EEG."""

import numpy as np
import pandas as pd

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"), ("letter=='a'", "letter=='b'")]
decode = True
decoding_n_splits = 3  # only for testing, use 5 otherwise
decoding_time_decim = 3  # for speed

l_freq = 0.3

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

interpolate_bads_grand_average = False
n_jobs = 4

epochs_custom_metadata = {
    "ses-anodalpost": pd.DataFrame(
        {
            "ones": np.ones(253),
            "letter": ["a" for x in range(150)] + ["b" for x in range(103)],
        }
    ),
    "ses-anodalpre": pd.DataFrame(
        {
            "ones": np.ones(268),
            "letter": ["a" for x in range(150)] + ["b" for x in range(118)],
        }
    ),
    "ses-anodaltDCS": pd.DataFrame(
        {
            "ones": np.ones(269),
            "letter": ["a" for x in range(150)] + ["b" for x in range(119)],
        }
    ),
    "ses-cathodalpost": pd.DataFrame(
        {
            "ones": np.ones(290),
            "letter": ["a" for x in range(150)] + ["b" for x in range(140)],
        }
    ),
    "ses-cathodalpre": pd.DataFrame(
        {
            "ones": np.ones(267),
            "letter": ["a" for x in range(150)] + ["b" for x in range(117)],
        }
    ),
    "ses-cathodaltDCS": pd.DataFrame(
        {
            "ones": np.ones(297),
            "letter": ["a" for x in range(150)] + ["b" for x in range(147)],
        }
    ),
}  # number of rows are hand-set

  Platform             Linux-6.8.0-1040-aws-x86_64-with-glibc2.35
Python               3.13.11 (main, Dec  6 2025, 01:10:48) [GCC 11.4.0]
Executable           /home/circleci/.pyenv/versions/3.13.11/bin/python3.13
CPU                  Intel(R) Xeon(R) Platinum 8124M CPU @ 3.00GHz (36 cores)
Memory               8.0 GiB

Core
├☑ mne               1.12.0.dev117+g39ee092e1 (development, latest release is 1.11.0)
├☑ numpy             2.4.3 (OpenBLAS 0.3.31.dev with 1 thread)
├☑ scipy             1.17.1
└☑ matplotlib        3.10.8 (backend=agg)

Numerical (optional)
├☑ sklearn           1.8.0
├☑ numba             0.64.0
├☑ nibabel           5.4.2
├☑ pandas            3.0.1
├☑ h5io              0.2.5
├☑ h5py              3.16.0
└☐ unavailable       nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.47.1 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.3 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt         0.11.3
├☑ vtk               9.6.0
├☑ qtpy              2.4.3 (PySide6=6.10.2)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.19.0.dev28+g6dcc45d4c
├☑ mne-icalabel      0.8.1
├☑ mne-bids-pipeline 1.10.0.dev171+g19a09516e
├☑ 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