###############################################################################
### Preprocessing pipeline settings for mne-bids-pipeline |
###############################################################################
from collections.abc import Callable, Sequence
from typing import Annotated, Any, Literal

from annotated_types import Ge, Interval, Len, MinLen
from mne import Covariance
from mne_bids import BIDSPath

from mne_bids_pipeline.typing import (
  # ArbitraryContrast,
  # DigMontageType,
  # FloatArrayLike,
  PathLike,
)

# %%05
# # General settings
# Configuration file for mne-bids-pipeline

from typing import Sequence, Literal, Optional
from mne import Covariance
from mne_bids import BIDSPath

# %%
# General Settings

bids_root: PathLike | None = '/data/fastreplay/Fast-Replay-MEG-bids/'
deriv_root: PathLike = f"{bids_root}/derivatives/"  # Save all processed data under /derivatives/
subjects_dir: Optional[PathLike] = f"{deriv_root}/freesurfer/subjects/"  # Path to FreeSurfer subject reconstructions
interactive: bool = False  # Disable interactive elements
# sessions: Literal["all"] = "all"  # Process all sessions
# task: str = ""  # Process all tasks by leaving empty
task_is_rest: bool = True  # Treat data as resting-state, disable epoching
# runs: Literal["all"] = "all"  # Process all runs
exclude_runs: Optional[dict[str, list[str]]] = None  # No excluded runs
subjects: Sequence[str] | Literal["all"] = "all"  # Analyze all subjects
exclude_subjects: Sequence[str] = ['23']  # No excluded subjects
process_empty_room: bool = True  # Preprocess empty-room data
process_rest: bool = True  # Preprocess resting-state data
ch_types: Sequence[Literal["meg"]] = ["meg"]  # Include MEG and EEG channels
data_type: Literal["meg", "eeg"] = "meg"  # Data type is MEG
eog_channels: Sequence[str] = ["BIO0002", "BIO0003"]  # Specify EOG channels
ecg_channel: str = "BIO0001"  # Specify ECG channel
spatial_filter: Literal["ica"] = "ica"  # Use ICA for artifact removal
ica_n_components: int = 50  # Number of ICA components
ica_algorithm: str = 'picard'

# %%
# Preprocessing

raw_resample_sfreq: float = 100.0  # Resample data to 100 Hz
l_freq: float = 0.1  # Apply high-pass filter at 0.1 Hz
h_freq: Optional[float] = None  # Disable low-pass filter
notch_freq: Sequence[float] = [50.0]  # Apply notch filter at 50 Hz
notch_trans_bandwidth: float = 1.0  # Set notch filter transition bandwidth to 1 Hz

# %%
# Artifact Removal via ICA

# The pipeline will automatically identify and remove ICA components related to EOG and ECG.

# %%
# Source-level Analysis

run_source_estimation: bool = True  # Enable source-level analysis
inverse_method: Literal["dSPM"] = "dSPM"  # Use dSPM as the inverse solution method
loose: float = 0.2  # Weigh parallel dipole components by 0.2
depth: float = 0.8  # Set depth weighting exponent to 0.8
noise_cov = "emptyroom"  # Use resting-state recording for noise covariance

# %%
# FreeSurfer recon-all Settings

recon_all: bool = True  # Enable FreeSurfer's recon-all
freesurfer_verbose: bool = True  # print the complete recon-all pipeline

# %%
# Parallelization

n_jobs: int = 4  # Use all available CPU cores
parallel_backend: Literal["loky"] = "loky"  # Use 'loky' backend for parallel processing

# %%
# Logging

log_level: Literal["info"] = "info"  # Set pipeline logging verbosity to 'info'
mne_log_level: Literal["error"] = "error"  # Set MNE-Python logging verbosity to 'error'

# %%
# Error Handling

on_error: Literal["abort"] = "abort"  # Abort processing on errors
config_validation: Literal["raise"] = "raise"  # Raise exceptions on config validation issues