config

tit.plotting.nilearn.config

Configuration dataclass for the Nilearn Visuals panel (owner: B4).

Mirrors the fields that panel collects, so :mod:tit.config_io can generate a JSON Schema for it and a future POST /api/jobs (kind "nilearn") can validate a request body before submitting the job. Pure Python -- no numpy/nibabel/nilearn dependency.

Public API

NilearnSubjectSimulation One subject/simulation pair to average and visualise. NilearnConfig Configuration for one Nilearn Visuals run.

See Also

tit.plotting.nilearn.main : Runner entry point that consumes this config (simnibs_python -m tit.plotting.nilearn config.json).

NilearnSubjectSimulation dataclass

NilearnSubjectSimulation(subject_id: str, simulation_name: str)

One subject/simulation pair to group-average before visualising.

NilearnConfig dataclass

NilearnConfig(subject_simulation_pairs: list[NilearnSubjectSimulation], min_cutoff: float | None = None, max_cutoff: float | None = None, atlas_name: str = 'harvard_oxford_sub', selected_regions: list[int] | None = None, subdir_name: str = 'nilearn_visuals', use_percentiles: bool = False, create_glass_brain: bool = False, glass_brain_cmap: str = 'hot')

Configuration for one Nilearn Visuals run.

Attributes

subject_simulation_pairs : list of NilearnSubjectSimulation Subjects/simulations to group-average before visualising. min_cutoff : float or None, optional Minimum threshold (V/m, or a percentile when use_percentiles is true). None -- the default -- is data-driven: the 95th percentile of the averaged field's non-zero voxels, the display floor the former simulation report derived from real TI fields. A fixed default in V/m cannot work here: a real TI field peaks around 0.1 V/m, so the former 0.3 was above every voxel of every run and the job died inside matplotlib. max_cutoff : float or None, optional Maximum threshold; None (the default) uses the 99.9th percentile of the averaged data. atlas_name : str, optional Atlas used for region contours on the slice PDF. selected_regions : list of int or None, optional 0-indexed region indices to include; None includes all. subdir_name : str, optional Output subdirectory under derivatives/ti-toolbox/nilearn_visuals/. use_percentiles : bool, optional Interpret min_cutoff/max_cutoff as percentiles (0-100) of the averaged data's non-zero voxels rather than absolute V/m values. create_glass_brain : bool, optional Also render a glass-brain PDF alongside the multi-slice one. glass_brain_cmap : str, optional Colormap for the glass-brain render.

Raises

ValueError If subject_simulation_pairs is empty, or use_percentiles is set with a cutoff outside [0, 100].

See Also

tit.plotting.nilearn.main.main : Consumes this config. tit.plotting.nilearn.cutoffs.resolve_cutoffs : Turns these fields into the two absolute V/m numbers the renderers take, and refuses a cutoff that is above the data with a message naming the range.