"""ERP CORE.

This example demonstrates how to process 5 participants from the
[ERP CORE](https://erpinfo.org/erp-core) dataset. It shows how to obtain 7 ERP
components from a total of 6 experimental tasks:

- N170 (face perception)
- MMN (passive auditory oddball)
- N2pc (visual search)
- N400 (word pair judgment)
- P3b (active visual oddball)
- LRP and ERN (flankers task)

## Dataset information

- **Authors:** Emily S. Kappenman, Jaclyn L. Farrens, Wendy Zhang,
                       Andrew X. Stewart, and Steven J. Luck
- **License:** CC-BY-4.0
- **URL:** [https://erpinfo.org/erp-core](https://erpinfo.org/erp-core)
- **Citation:** Kappenman, E., Farrens, J., Zhang, W., Stewart, A. X.,
                & Luck, S. J. (2021). ERP CORE: An open resource for human
                event-related potential research. *NeuroImage* 225: 117465.
                [https://doi.org/10.1016/j.neuroimage.2020.117465](https://doi.org/10.1016/j.neuroimage.2020.117465)
"""

import argparse
import sys

import mne

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

# Find the --task option
args = [arg for arg in sys.argv if arg.startswith("--task") or not arg.startswith("-")]
parser = argparse.ArgumentParser()
parser.add_argument("ignored", nargs="*")
parser.add_argument(
    "--task", choices=("N400", "ERN", "LRP", "MMN", "N2pc", "N170", "P3"), required=True
)
task = parser.parse_args(args).task
sessions = [task]

subjects = ["015", "016", "017", "018", "019"]

ch_types = ["eeg"]
interactive = False

raw_resample_sfreq = 128
# Suppress "Data file name in EEG.data (sub-019_task-ERN_eeg.fdt) is incorrect..."
read_raw_bids_verbose = "error"

eeg_template_montage = mne.channels.make_standard_montage("standard_1005")
eeg_bipolar_channels = {
    "HEOG": ("HEOG_left", "HEOG_right"),
    "VEOG": ("VEOG_lower", "FP2"),
}
drop_channels = ["HEOG_left", "HEOG_right", "VEOG_lower"]
eog_channels = ["HEOG", "VEOG"]

l_freq = 0.1
h_freq = None
notch_freq = 60

decode = True
decoding_time_generalization = True
decoding_time_generalization_decim = 2

find_breaks = True
min_break_duration = 10
t_break_annot_start_after_previous_event = 3.0
t_break_annot_stop_before_next_event = 1.5

if task == "N400":  # test autoreject local without ICA
    spatial_filter = None
    reject = "autoreject_local"
    autoreject_n_interpolate = [2, 4]
elif task == "N170":  # test autoreject local before ICA
    spatial_filter = "ica"
    ica_reject = "autoreject_local"
    reject = "autoreject_global"
    autoreject_n_interpolate = [2, 4]
else:
    spatial_filter = "ica"
    ica_reject = dict(eeg=350e-6, eog=500e-6)
    reject = "autoreject_global"

# These settings are only used for the cases where spatial_filter="ica"
ica_max_iterations = 1000
ica_eog_threshold = 2
ica_decim = 2  # speed up ICA fitting

run_source_estimation = False
on_rename_missing_events = "ignore"

parallel_backend = "dask"
dask_worker_memory_limit = "2.5G"
n_jobs = 4

if task == "N400":
    dask_open_dashboard = True

    rename_events = {
        "response/201": "response/correct",
        "response/202": "response/error",
        "stimulus/111": "stimulus/prime/related",
        "stimulus/112": "stimulus/prime/related",
        "stimulus/121": "stimulus/prime/unrelated",
        "stimulus/122": "stimulus/prime/unrelated",
        "stimulus/211": "stimulus/target/related",
        "stimulus/212": "stimulus/target/related",
        "stimulus/221": "stimulus/target/unrelated",
        "stimulus/222": "stimulus/target/unrelated",
    }

    eeg_reference = ["P9", "P10"]
    epochs_tmin = -0.2
    epochs_tmax = 0.8
    epochs_metadata_tmin = 0
    epochs_metadata_tmax = 1.5
    epochs_metadata_keep_first = ["stimulus/target", "response"]
    baseline = (None, 0)

    conditions = {
        "related": '`first_stimulus/target` == "related" and '
        'first_response == "correct"',
        "unrelated": '`first_stimulus/target` == "unrelated" and '
        'first_response == "correct"',
    }
    contrasts = [("unrelated", "related")]
    cluster_forming_t_threshold = 1.5  # Only for testing!
    cluster_permutation_p_threshold = 0.2  # Only for testing!
elif task == "ERN":
    rename_events = {
        "stimulus/11": "compatible/left",
        "stimulus/12": "compatible/right",
        "stimulus/21": "incompatible/left",
        "stimulus/22": "incompatible/right",
        "response/111": "response/correct",
        "response/112": "response/incorrect",
        "response/121": "response/correct",
        "response/122": "response/incorrect",
        "response/211": "response/incorrect",
        "response/212": "response/correct",
        "response/221": "response/incorrect",
        "response/222": "response/correct",
    }

    eeg_reference = ["P9", "P10"]
    epochs_tmin = -0.6
    epochs_tmax = 0.4
    baseline = (-0.4, -0.2)
    conditions = ["response/correct", "response/incorrect"]
    contrasts = [("response/incorrect", "response/correct")]
    cluster_forming_t_threshold = 5  # Only for testing!
    cluster_permutation_p_threshold = 0.2  # Only for testing!
    decoding_csp = True
    decoding_csp_freqs = {
        "theta": [4, 7],
        "alpha": [8, 12],
        "beta": [13, 20, 30],
        "gamma": [50, 63],
    }
    decoding_csp_times = [-0.2, 0.0, 0.2, 0.4]
elif task == "LRP":
    rename_events = {
        "stimulus/11": "compatible/left",
        "stimulus/12": "compatible/right",
        "stimulus/21": "incompatible/left",
        "stimulus/22": "incompatible/right",
        "response/111": "response/left/correct",
        "response/112": "response/left/incorrect",
        "response/121": "response/left/correct",
        "response/122": "response/left/incorrect",
        "response/211": "response/right/incorrect",
        "response/212": "response/right/correct",
        "response/221": "response/right/incorrect",
        "response/222": "response/right/correct",
    }

    eeg_reference = ["P9", "P10"]
    epochs_tmin = -0.8
    epochs_tmax = 0.2
    baseline = (None, -0.6)
    conditions = ["response/left", "response/right"]
    contrasts = [("response/right", "response/left")]  # contralateral vs ipsi
elif task == "MMN":
    rename_events = {
        "stimulus/70": "stimulus/deviant",
        "stimulus/80": "stimulus/standard",
    }

    eeg_reference = ["P9", "P10"]
    epochs_tmin = -0.2
    epochs_tmax = 0.8
    baseline = (None, 0)
    conditions = ["stimulus/standard", "stimulus/deviant"]
    contrasts = [("stimulus/deviant", "stimulus/standard")]
elif task == "N2pc":
    rename_events = {
        "response/201": "response/correct",
        "response/202": "response/error",
        "stimulus/111": "stimulus/blue/left",
        "stimulus/112": "stimulus/blue/left",
        "stimulus/121": "stimulus/blue/right",
        "stimulus/122": "stimulus/blue/right",
        "stimulus/211": "stimulus/pink/left",
        "stimulus/212": "stimulus/pink/left",
        "stimulus/221": "stimulus/pink/right",
        "stimulus/222": "stimulus/pink/right",
    }

    eeg_reference = ["P9", "P10"]
    # Analyze all EEG channels -- we only specify the channels here for the purpose of
    # demonstration
    analyze_channels = [
        "FP1",
        "F3",
        "F7",
        "FC3",
        "C3",
        "C5",
        "P3",
        "P7",
        "P9",
        "PO7",
        "PO3",
        "O1",
        "Oz",
        "Pz",
        "CPz",
        "FP2",
        "Fz",
        "F4",
        "F8",
        "FC4",
        "FCz",
        "Cz",
        "C4",
        "C6",
        "P4",
        "P8",
        "P10",
        "PO8",
        "PO4",
        "O2",
    ]

    epochs_tmin = -0.2
    epochs_tmax = 0.8
    baseline = (None, 0)
    conditions = ["stimulus/right", "stimulus/left"]
    contrasts = [("stimulus/right", "stimulus/left")]  # Contralteral vs ipsi
elif task == "N170":
    rename_events = {
        "response/201": "response/correct",
        "response/202": "response/error",
    }

    eeg_reference = "average"
    # Analyze all EEG channels -- we only specify the channels here for the purpose of
    # demonstration
    analyze_channels = [
        "FP1",
        "F3",
        "F7",
        "FC3",
        "C3",
        "C5",
        "P3",
        "P7",
        "P9",
        "PO7",
        "PO3",
        "O1",
        "Oz",
        "Pz",
        "CPz",
        "FP2",
        "Fz",
        "F4",
        "F8",
        "FC4",
        "FCz",
        "Cz",
        "C4",
        "C6",
        "P4",
        "P8",
        "P10",
        "PO8",
        "PO4",
        "O2",
    ]

    ica_n_components = 30 - 1
    for i in range(1, 180 + 1):
        orig_name = f"stimulus/{i}"

        if 1 <= i <= 40:
            new_name = "stimulus/face/normal"
        elif 41 <= i <= 80:
            new_name = "stimulus/car/normal"
        elif 101 <= i <= 140:
            new_name = "stimulus/face/scrambled"
        elif 141 <= i <= 180:
            new_name = "stimulus/car/scrambled"
        else:
            continue

        rename_events[orig_name] = new_name

    epochs_tmin = -0.2
    epochs_tmax = 0.8
    baseline = (None, 0)
    conditions = ["stimulus/face/normal", "stimulus/car/normal"]
    contrasts = [("stimulus/face/normal", "stimulus/car/normal")]
elif task == "P3":
    rename_events = {
        "response/201": "response/correct",
        "response/202": "response/incorrect",
        "stimulus/11": "stimulus/target/11",
        "stimulus/22": "stimulus/target/22",
        "stimulus/33": "stimulus/target/33",
        "stimulus/44": "stimulus/target/44",
        "stimulus/55": "stimulus/target/55",
        "stimulus/21": "stimulus/non-target/21",
        "stimulus/31": "stimulus/non-target/31",
        "stimulus/41": "stimulus/non-target/41",
        "stimulus/51": "stimulus/non-target/51",
        "stimulus/12": "stimulus/non-target/12",
        "stimulus/32": "stimulus/non-target/32",
        "stimulus/42": "stimulus/non-target/42",
        "stimulus/52": "stimulus/non-target/52",
        "stimulus/13": "stimulus/non-target/13",
        "stimulus/23": "stimulus/non-target/23",
        "stimulus/43": "stimulus/non-target/43",
        "stimulus/53": "stimulus/non-target/53",
        "stimulus/14": "stimulus/non-target/14",
        "stimulus/24": "stimulus/non-target/24",
        "stimulus/34": "stimulus/non-target/34",
        "stimulus/54": "stimulus/non-target/54",
        "stimulus/15": "stimulus/non-target/15",
        "stimulus/25": "stimulus/non-target/25",
        "stimulus/35": "stimulus/non-target/35",
        "stimulus/45": "stimulus/non-target/45",
    }

    eeg_reference = ["P9", "P10"]
    epochs_tmin = -0.2
    epochs_tmax = 0.8
    baseline = (None, 0)
    conditions = ["stimulus/target", "stimulus/non-target"]
    contrasts = [("stimulus/target", "stimulus/non-target")]
    cluster_forming_t_threshold = 0.8  # Only for testing!
    cluster_permutation_p_threshold = 0.2  # Only for testing!
else:
    raise RuntimeError(f"Task {task} not currently supported")

  Platform             Linux-5.15.0-1057-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 8223CL CPU @ 3.00GHz (36 cores)
Memory               69.1 GiB

Core
├☑ mne               1.9.0.dev59+ged933b8d9 (devel, latest release is 1.8.0)
├☑ numpy             2.0.2 (OpenBLAS 0.3.27 with 2 threads)
├☑ scipy             1.14.1
└☑ matplotlib        3.9.2 (backend=agg)

Numerical (optional)
├☑ sklearn           1.5.2
├☑ numba             0.60.0
├☑ nibabel           5.2.1
├☑ pandas            2.2.3
├☑ h5io              0.2.4
├☑ h5py              3.12.1
└☐ unavailable       nilearn, dipy, openmeeg, cupy

Visualization (optional)
├☑ pyvista           0.44.1 (OpenGL 4.5 (Core Profile) Mesa 23.2.1-1ubuntu3.1~22.04.2 via llvmpipe (LLVM 15.0.7, 256 bits))
├☑ pyvistaqt         0.11.1
├☑ vtk               9.3.1
├☑ qtpy              2.4.1 (PyQt6=6.7.1)
└☐ unavailable       ipympl, pyqtgraph, mne-qt-browser, ipywidgets, trame_client, trame_server, trame_vtk, trame_vuetify

Ecosystem (optional)
├☑ mne-bids          0.16.0.dev44+g65da1ae78
├☑ mne-bids-pipeline 1.10.0.dev27+g02cdc20
├☑ eeglabio          0.0.2-4
├☑ edfio             0.4.4
├☑ pybv              0.7.5
└☐ unavailable       mne-nirs, mne-features, mne-connectivity, mne-icalabel, neo, mffpy