Added filter or nearby spots for ToF data.

src/dials/algorithms/spot_finding/finder.py

@@ -9,7 +9,11 @@
 import pickle
 from typing import Iterable, Tuple
 
+import numpy as np
+from scipy.spatial import distance
+
 import libtbx
+from dxtbx import flumpy
 from dxtbx.format.image import ImageBool
 from dxtbx.imageset import ImageSequence, ImageSet
 from dxtbx.model import ExperimentList, Scan
@@ -790,10 +794,62 @@ def find_spots(self, experiments: ExperimentList) -> flex.reflection_table:
  # If any of the experiments are ToF experiments, add wavelength data
  if experiments.contains_tof_experiments():
  reflections.add_beam_data(experiments)
+ reflections = self.filter_nearby_spots(reflections, experiments)
 
  # Return the reflections
  return reflections
 
+ def filter_nearby_spots(self, reflections, experiments, threshold=0.015, k=20):
+
+ if "rlp" not in reflections:
+ reflections.centroid_px_to_mm(experiments)
+ reflections.map_centroids_to_reciprocal_space(experiments)
+
+ points = reflections["rlp"]
+ points = flumpy.to_numpy(points)
+
+ # Compute pairwise distances
+ pairwise_distances = distance.cdist(points, points)
+
+ # Exclude self-distances
+ np.fill_diagonal(pairwise_distances, np.inf)
+
+ new_points = np.copy(points)
+ used_indices = set()
+
+ for i, point in enumerate(points):
+ if i not in used_indices:
+ # Sort distances for the current point
+ sorted_indices = np.argsort(pairwise_distances[i])
+ k_nearest_indices = sorted_indices[
+ 1 : k + 1
+ ] # Exclude self, consider k-nearest neighbors
+
+ # Filter distances based on threshold
+ close_neighbors_indices = [
+ idx
+ for idx in k_nearest_indices
+ if pairwise_distances[i][idx] < threshold
+ ]
+
+ if close_neighbors_indices:
+ close_neighbors_coords = points[close_neighbors_indices]
+ avg_coord = np.mean(close_neighbors_coords, axis=0)
+ new_points[i] = avg_coord
+
+ # Mark points as used
+ for idx in close_neighbors_indices:
+ used_indices.add(idx)
+ new_points[idx] = [0, 0, 0] # Set coordinates to (0,0,0)
+
+ new_points = flumpy.vec_from_numpy(new_points)
+ sel = new_points.norms() > 1e-7
+ original_len = len(reflections)
+ reflections = reflections.select(sel)
+ new_len = len(reflections)
+ logger.info(f"Removed {original_len - new_len} spots due to close proximity.")
+ return reflections
+
  def _get_spot_finding_algorithm(self, imageset):
 
  # The input mask