"""ds001810: tDCS EEG.

See [OpenNeuro](https://openneuro.org/datasets/ds001810) for more information.
"""

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-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               8.0 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 1 thread 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