itasc.contact_analysis.contacts.neighborhood

Neighborhood & density derivations over the cell–cell contact graph.

Two headless, Qt-free, label-agnostic derivations on an already-read PositionContactAnalysis, each returning a tidy column-major table (dict[str, np.ndarray]):

  • cell_neighbor_counts() — adjacency degree per cell (how many neighbors).

  • cell_density() — cells per unit field-of-view area. (Counts straight off a frame -> [cell_id, …] map, so it needs only the cell labels, not the contact graph.)

The cell_cell edges of the contacts edges table are the adjacency graph; degree is the count of incident cell_cell edges, deduped per neighbor (a boundary split across several edge rows still counts as one neighbor).

The classification-dependent derivations (neighbour enrichment; contact-type z-score vs a label-shuffle null) are not here — they consume a per-cell subpopulation class_label and so live with the dataset that defines it, not in this label-agnostic library.

Like contacts.signed_contact_length and contacts.contact_labels, these operate on the in-memory analysis object only: they never open HDF5, so they run unchanged in scripts, notebooks, and the napari plugin.

Functions

cell_density(frame_cells, *, fov_area_mm2)

Per frame cell count and density = n_cells / fov_area_mm2.

cell_neighbor_counts(analysis)

Per (frame, cell_id) adjacency degree — the headline neighbor count.
itasc.contact_analysis.contacts.neighborhood.cell_neighbor_counts(analysis)[source]

Per (frame, cell_id) adjacency degree — the headline neighbor count.

One row per cell present in the cells table for that frame, with n_neighbors its number of distinct cell_cell neighbors (0 for an isolated cell). This count is also the numerator for density.

Columns (column-major, equal length): frame, cell_id, n_neighbors (int).

Return type:

dict[str, ndarray]

Parameters:

analysis (PositionContactAnalysis)

itasc.contact_analysis.contacts.neighborhood.cell_density(frame_cells, *, fov_area_mm2)[source]

Per frame cell count and density = n_cells / fov_area_mm2.

Emits one label="all" row per frame that counts every cell in the frame. density is in cells/mm²; fov_area_mm2 is the user’s field-of-view area and is required (a positive number) — there is no silent image-area fallback.

frame_cells maps frame -> [cell_id, …] (the cell labels present in that frame), so this counts straight off the cell labels with no contacts dependency.

Columns: frame, label (str, always "all"), n_cells (int), density (float, cells/mm²).

Return type:

dict[str, ndarray]

Parameters: