htfa.plot_network

htfa.plot_network(nodes, connectivity=None, *, sizes=None, widths=None, subject='global', mesh='ch2', snap=True, mask=None, scale_by_mass=False, fade_by_mass=False, node_mode='auto', lobe_level=0.05, lobe_negative_color='teal', out=None, jpeg_quality=None, **style)

Render nodes and thresholded edges BrainNet Viewer-style.

Parameters:

nodesHTFAResults, TFA, HTFA or array of shape (K, 3)

Node centers in MNI mm. For a fit, the centers of subject are used and its widths set the node sizes.

connectivityarray of shape (K, K), optional

Edge weights (upper triangle used). None draws nodes only.

sizesarray of shape (K,), optional

Node sizes; mapped linearly onto 2-7 mm radii (node_radius; see the radius_* style options). Defaults to the (effective, with snap) widths, or equal sizes (4 mm) when no widths are known.

widthsarray of shape (K,), optional

RBF widths of array nodes (a fit’s own widths are used otherwise).

subject“global” or int

Which centers to use when nodes is a fit.

meshstr, path, (vertices, faces) or None

Brain surface; see load_mesh. Default "ch2".

snapbool

Draw each node as its in-brain part (effective_nodes): the RBF-weighted centroid of the mask voxels, with width lambda_eff. Default True; False draws the fitted centers and widths as they are. The fit itself is never changed, and effective_nodes (or htfa.HTFAResults.display_nodes) returns the drawn values. Nodes without widths (a bare array without widths) are points: they stay if inside the mask and otherwise move to the nearest mask voxel.

maskarray of shape (V, 3), NIfTI image or (volume, affine), optional

The in-brain voxels for snap. Default: for an HTFAResults, the subject’s voxel coordinates (all subjects’ for "global"); otherwise nilearn’s 2 mm MNI152 brain mask (mni_mask), because the BrainNet meshes are in MNI space. Raises ImportError if that needs nilearn and nilearn cannot be installed.

scale_by_massbool

Multiply the node sizes by the in-mask RBF mass fraction before they are mapped to radii (needs widths and snap).

fade_by_massbool or float

Node opacity floor + (1 - floor) * mass_fraction with floor 0.15 (True) or the given floor, so nodes mostly outside the brain stay faintly visible (needs widths and snap).

node_mode{‘auto’, ‘components’, ‘summary’}

'auto' draws non-RBF kernels and mixtures as spatial component lobes; single RBFs retain the sphere view. 'components' always draws lobes. Every component is an edge anchor: each factor edge is repeated, unchanged, between all pairs of its components and those of the other factor. No within-factor edges are added. 'summary' draws one sphere per factor at its effective centroid.

lobe_levelfloat

Isosurface level as a fraction of the component’s unmasked peak (default 0.05); both positive and negative surfaces are drawn where they intersect mask. Lobes use fitted spatial dimensions, not the radius_* scaling of summary spheres. Mixing weights control opacity (0.2 + 0.6 times the weight; negative shells use 35% of that opacity so the positive core remains visible), never the edge value. snap changes edge anchors only; it does not move the component field.

lobe_negative_colorcolor

Color of negative component surfaces (default teal); positive surfaces use node_color. This sign is spatial loading, distinct from positive/negative connectivity colors.

outpath, optional

Save the image there: PNG, or JPEG for a .jpg/.jpeg suffix (quality jpeg_quality, default 75, 4:2:0 chroma, like MATLAB’s export).

jpeg_qualityint, optional

If given, the image is JPEG-compressed at this quality before it is returned or saved. JPEG at 75 makes thin edges paler.

**style

threshold (0.25; edges with |E| > threshold are drawn), view ("lateral" = "left", "right", "dorsal", "ventral", "anterior", "posterior"), size ((1600, 1000) pixels), edge_style ("intensity": diameter |E| mm at opacity 0.7; "flat": 0.5 mm, opaque), positive_color (“red”), negative_color (“blue”), node_color (0.502 gray), mesh_color (0.95 gray), mesh_opacity (0.2), background (“white”), radius_range ((2, 7) mm: radii of the smallest and largest size), radius_kb (a fixed (k, b): radius k * size + b mm, comparable across plots), radius_clip ((min, max) mm applied to the radii); see node_radius. See also config_style.

Returns:

PIL.Image.Image

The RGB image.