config

tit.opt.config

Configuration dataclasses for TI optimization.

Pure Python — no SimNIBS, numpy, or heavy dependencies. Mirrors the tit.sim.config pattern.

FlexConfig dataclass

FlexConfig(subject_id: str, project_dir: str, goal: OptGoal, postproc: FieldPostproc, current_mA: float, electrode: ElectrodeConfig, roi: SphericalROI | AtlasROI | SubcorticalROI, anisotropy_type: str = 'scalar', aniso_maxratio: float = 10.0, aniso_maxcond: float = 2.0, non_roi_method: NonROIMethod | None = None, non_roi: SphericalROI | AtlasROI | SubcorticalROI | None = None, thresholds: str | None = None, eeg_net: str | None = None, enable_mapping: bool = False, disable_mapping_simulation: bool = False, output_folder: str | None = None, run_final_electrode_simulation: bool = False, n_multistart: int = 1, max_iterations: int | None = None, population_size: int | None = None, tolerance: float | None = None, mutation: str | None = None, recombination: float | None = None, cpus: int | None = None, detailed_results: bool = False, visualize_valid_skin_region: bool = False, skin_visualization_net: str | None = None)

Full configuration for flex-search optimization.

OptGoal

Bases: StrEnum

Optimization goal.

FieldPostproc

Bases: StrEnum

Field post-processing method.

NonROIMethod

Bases: StrEnum

Non-ROI specification method for focality optimization.

SphericalROI dataclass

SphericalROI(x: float, y: float, z: float, radius: float = 10.0, use_mni: bool = False)

Spherical region of interest defined by center + radius.

AtlasROI dataclass

AtlasROI(atlas_path: str, label: int, hemisphere: str = 'lh')

Cortical surface ROI from a FreeSurfer annotation atlas.

SubcorticalROI dataclass

SubcorticalROI(atlas_path: str, label: int, tissues: str = 'GM')

Subcortical volume ROI from a volumetric atlas.

ElectrodeConfig dataclass

ElectrodeConfig(shape: str = 'ellipse', dimensions: list[float] = (lambda: [8.0, 8.0])(), gel_thickness: float = 4.0)

Electrode geometry for flex-search.

Only gel_thickness is needed here — the optimization leadfield uses point electrodes; gel_thickness is recorded in the manifest for downstream simulation.

FlexResult dataclass

FlexResult(success: bool, output_folder: str, function_values: list[float], best_value: float, best_run_index: int)

Result from a flex-search optimization run.

ExConfig dataclass

ExConfig(subject_id: str, project_dir: str, leadfield_hdf: str, roi_name: str, electrodes: BucketElectrodes | PoolElectrodes, total_current: float = 2.0, current_step: float = 0.5, channel_limit: float | None = None, roi_radius: float = 3.0, run_name: str | None = None)

Full configuration for exhaustive search optimization.

BucketElectrodes dataclass

BucketElectrodes(e1_plus: list[str], e1_minus: list[str], e2_plus: list[str], e2_minus: list[str])

Separate electrode lists for each bipolar channel position.

PoolElectrodes dataclass

PoolElectrodes(electrodes: list[str])

Single electrode pool — all positions draw from the same set.

ExResult dataclass

ExResult(success: bool, output_dir: str, n_combinations: int, results_csv: str | None = None, config_json: str | None = None)

Result from an exhaustive search run.