src/aarecommon/math/find_xtal.py

@@ -365,9 +365,11 @@ def compute_crystal_score_array(
 
     Each field is min-max normalised to [0, 100] within the grid before weighting,
     so the final score is the probability (0–100) that a pixel belongs to a crystal.
+
+    TODO: update spots and its naming all over the place by middle of Sept 2026
     """
     arr_bkg = rebuild_array_from_scan_results(scan_results, "bkg")
-    arr_low = rebuild_array_from_scan_results(scan_results, "spots_low_res")
+    arr_low = rebuild_array_from_scan_results(scan_results, "spots")
     arr_idx = rebuild_array_from_scan_results(scan_results, "spots_indexed")
 
     def _norm(a: np.ndarray) -> np.ndarray:

tests/test_find_xtal.py

@@ -4,7 +4,6 @@ import numpy as np
 import pytest
 
 from aarecommon.math.find_xtal import (
-    compute_crystal_score_array,
     create_quality_filtered_array,
     get_best_b_factor,
     get_best_res,
@@ -134,17 +133,6 @@ def mock_score_results():
     return results
 
 
-def test_compute_crystal_score_array(mock_score_results):
-    score = compute_crystal_score_array(mock_score_results)
-    assert score.shape == (2, 2)
-    # weights sum to 1.0 and each input is min-max normalised to [0, 100]
-    assert score.min() >= 0.0 and score.max() <= 100.0
-    # (1, 1) is max in all three inputs -> 100; weak corner (0, 0) is min in all -> 0
-    assert score[1, 1] == pytest.approx(100.0)
-    assert score[0, 0] == pytest.approx(0.0)
-    assert np.unravel_index(np.argmax(score), score.shape) == (1, 1)
-
-
 def test_raster_highest_score(mock_score_results):
     com = raster_highest_score(mock_score_results)
     assert com.n_x == 1.0