"""Measure spatial relationships between neighboring colonies in grid cells."""
from __future__ import annotations
from typing import ClassVar, TYPE_CHECKING
if TYPE_CHECKING:
from phenotypic._core._grid_image import GridImage
import numpy as np
import pandas as pd
from scipy import ndimage as ndi
from phenotypic.abc_ import GridMeasureFeatures
from phenotypic.schema import OBJECT
from phenotypic.schema import NEIGHBOR_DIST, GRID
[docs]
class MeasureNeighborDist(GridMeasureFeatures):
"""Measure pixel-to-pixel distances to neighbors in adjacent grid cells.
For each detected colony, identify the nearest object in the left,
right, above, and below grid cells and report the minimum Euclidean
distance between their pixel masks. Distances are computed via a
per-section distance transform over a local window covering the target
cell and its immediate neighbors, so round colonies are not
over-estimated by their bounding boxes. Edge and corner colonies report
``NaN`` for directions beyond the plate boundary.
Returns:
pd.DataFrame: Object-level neighbor measurements with columns:
- Label: unique object identifier.
- LeftNeighborObjLabel, LeftDistance.
- RightNeighborObjLabel, RightDistance.
- AboveNeighborObjLabel, AboveDistance.
- UnderNeighborObjLabel, UnderDistance.
- ``NaN`` where no neighbor exists (out of plate, empty
neighbor cell, or the colony is shielded by another colony
in the same cell).
Best For:
- Quantifying colony spacing and crowding to assess nutrient
competition risk on arrayed plates, especially for round or
irregular colony shapes where bounding-box geometry overstates
proximity.
- Flagging closely spaced colonies that may cross-contaminate.
- Enabling neighbor-aware paired statistical comparisons for
competition or cooperation studies.
Consider Also:
- :class:`MeasureGridLinRegStats` for regression-based positional
quality metrics.
- :class:`MeasureGridSpread` for within-well colony dispersion
rather than between-well distances.
- :class:`MeasureBounds` for raw bounding boxes and centroids
without neighbor lookups.
See Also:
:doc:`/tutorials/notebooks/07_measuring_and_exporting` for a
walkthrough of grid-level measurements.
"""
_measurement_infoclass: ClassVar[type] = NEIGHBOR_DIST
# (d_row, d_col, label_col, dist_col)
_DIRECTIONS: ClassVar[tuple] = (
(0, -1, NEIGHBOR_DIST.LEFT_NEIGHBOR_OBJ_LABEL,
NEIGHBOR_DIST.LEFT_DISTANCE),
(0, +1, NEIGHBOR_DIST.RIGHT_NEIGHBOR_OBJ_LABEL,
NEIGHBOR_DIST.RIGHT_DISTANCE),
(-1, 0, NEIGHBOR_DIST.ABOVE_NEIGHBOR_OBJ_LABEL,
NEIGHBOR_DIST.ABOVE_DISTANCE),
(+1, 0, NEIGHBOR_DIST.UNDER_NEIGHBOR_OBJ_LABEL,
NEIGHBOR_DIST.UNDER_DISTANCE),
)
def _operate(self, image: GridImage) -> pd.DataFrame:
grid_info = image.grid.info(include_metadata=False)
# Densify once; subsequent windowing is pure numpy slicing on this view.
objmap_full = image.objmap[:]
nrows, ncols = image.grid.nrows, image.grid.ncols
labels = grid_info[OBJECT.LABEL].to_numpy()
n_objs = len(labels)
label_to_row = {int(label): i for i, label in enumerate(labels)}
results: dict = {
NEIGHBOR_DIST.LEFT_NEIGHBOR_OBJ_LABEL : np.full(n_objs, np.nan),
NEIGHBOR_DIST.LEFT_DISTANCE : np.full(n_objs, np.nan),
NEIGHBOR_DIST.RIGHT_NEIGHBOR_OBJ_LABEL: np.full(n_objs, np.nan),
NEIGHBOR_DIST.RIGHT_DISTANCE : np.full(n_objs, np.nan),
NEIGHBOR_DIST.ABOVE_NEIGHBOR_OBJ_LABEL: np.full(n_objs, np.nan),
NEIGHBOR_DIST.ABOVE_DISTANCE : np.full(n_objs, np.nan),
NEIGHBOR_DIST.UNDER_NEIGHBOR_OBJ_LABEL: np.full(n_objs, np.nan),
NEIGHBOR_DIST.UNDER_DISTANCE : np.full(n_objs, np.nan),
}
# Build (row, col) -> section_df once so neighbour lookups are O(1)
# instead of an O(n_objects) DataFrame scan per direction per target.
section_groups: dict[tuple[int, int], pd.DataFrame] = {}
for (g_row, g_col), sec_df in grid_info.groupby(
[GRID.ROW_NUM, GRID.COL_NUM], observed=True
):
if pd.isna(g_row) or pd.isna(g_col):
continue
section_groups[(int(g_row), int(g_col))] = sec_df
for (target_row, target_col), section_df in section_groups.items():
target_idx = int(image.grid._idx_ref_matrix[target_row, target_col])
target_labels = section_df[OBJECT.LABEL].to_numpy().astype(np.int64)
if target_labels.size == 0:
continue
target_bbox = self._section_bbox(image, target_idx, grid_info)
valid_neighbors = self._collect_neighbors(
image, grid_info, section_groups,
target_row, target_col, nrows, ncols,
)
if not valid_neighbors:
continue
# Include target bbox so any target-object pixels that spilled past
# the section's grid edges are still seeded into target_mask (the
# object-fitting bounds may extend beyond the naive grid edges).
all_bboxes = [target_bbox] + [n[2] for n in valid_neighbors]
win = self._window_bbox(all_bboxes)
objmap_win = objmap_full[win[0]:win[1] + 1, win[2]:win[3] + 1]
target_mask = np.isin(objmap_win, target_labels)
if not target_mask.any():
continue
# Distance from each window pixel to the nearest target-section
# object pixel, with index back-pointers so we can attribute every
# pixel to a specific target object.
dt, indices = ndi.distance_transform_edt(
~target_mask, return_indices=True
)
nearest_target_label = objmap_win[indices[0], indices[1]]
for (_n_row, _n_col, n_bbox, n_labels,
label_col, dist_col) in valid_neighbors:
lo_rr = n_bbox[0] - win[0]
hi_rr = n_bbox[1] - win[0] + 1
lo_cc = n_bbox[2] - win[2]
hi_cc = n_bbox[3] - win[2] + 1
objmap_n = objmap_win[lo_rr:hi_rr, lo_cc:hi_cc]
neighbor_pixel_mask = np.isin(objmap_n, n_labels)
if not neighbor_pixel_mask.any():
continue
dt_n = dt[lo_rr:hi_rr, lo_cc:hi_cc]
nearest_n = nearest_target_label[lo_rr:hi_rr, lo_cc:hi_cc]
for target_label in target_labels:
cand = neighbor_pixel_mask & (nearest_n == target_label)
if not cand.any():
continue
cand_dt = dt_n[cand]
cand_obj = objmap_n[cand]
argmin = int(np.argmin(cand_dt))
row_idx = label_to_row[int(target_label)]
results[label_col][row_idx] = int(cand_obj[argmin])
results[dist_col][row_idx] = float(cand_dt[argmin])
df = pd.DataFrame(results)
df.insert(0, OBJECT.LABEL, labels)
return df
@staticmethod
def _section_bbox(
image: GridImage, idx: int, grid_info: pd.DataFrame
) -> tuple[int, int, int, int]:
"""Return integer (min_rr, max_rr, min_cc, max_cc) for a grid section.
Uses the object-fitting section slices so colonies that spill past
their grid line are not cropped at the boundary.
"""
(min_rr, min_cc), (max_rr, max_cc) = (
image.grid._adv_get_grid_section_slices(idx, grid_info)
)
return (
int(np.asarray(min_rr).item()),
int(np.asarray(max_rr).item()),
int(np.asarray(min_cc).item()),
int(np.asarray(max_cc).item()),
)
@staticmethod
def _window_bbox(
bboxes: list[tuple[int, int, int, int]]
) -> tuple[int, int, int, int]:
"""Union of (min_rr, max_rr, min_cc, max_cc) bboxes."""
arr = np.asarray(bboxes, dtype=np.int64)
return (
int(arr[:, 0].min()),
int(arr[:, 1].max()),
int(arr[:, 2].min()),
int(arr[:, 3].max()),
)
def _collect_neighbors(
self,
image: GridImage,
grid_info: pd.DataFrame,
section_groups: dict[tuple[int, int], pd.DataFrame],
target_row: int,
target_col: int,
nrows: int,
ncols: int,
) -> list[tuple[int, int, tuple[int, int, int, int], np.ndarray, str, str]]:
"""Enumerate valid (in-bounds, non-empty) neighbor sections."""
valid: list[tuple[
int, int, tuple[int, int, int, int], np.ndarray, str, str
]] = []
for d_row, d_col, label_col, dist_col in self._DIRECTIONS:
n_row, n_col = target_row + d_row, target_col + d_col
if not (0 <= n_row < nrows and 0 <= n_col < ncols):
continue
n_section_df = section_groups.get((n_row, n_col))
if n_section_df is None or n_section_df.empty:
continue
n_idx = int(image.grid._idx_ref_matrix[n_row, n_col])
n_bbox = self._section_bbox(image, n_idx, grid_info)
n_labels = n_section_df[OBJECT.LABEL].to_numpy().astype(np.int64)
valid.append(
(n_row, n_col, n_bbox, n_labels, label_col, dist_col)
)
return valid
MeasureNeighborDist.__doc__ = NEIGHBOR_DIST.append_rst_to_doc(MeasureNeighborDist)
