refactor: tidy up for gridscan analysis switching

src/aarecommon/math/find_xtal.py

@@ -318,29 +318,38 @@ def raster_centre_of_mass(result_array, images) -> CenterOfMassModel | None:
     return _to_com_model(com, images, "Center of mass")
 
 
-def raster_highest_score(images, min_low_res_spots: float = 10.0) -> CenterOfMassModel | None:
+def center_of_grid(n_x: int, n_y: int) -> CenterOfMassModel:
+    # get_com_mm maps index i -> (i+0.5)*step, so index (N-1)/2 is the true
+    # geometric centre of the grid for both odd and even N (and N==1).
+    y_pos = (n_y - 1) / 2.0
+    x_pos = (n_x - 1) / 2.0
+    return CenterOfMassModel(n_x=x_pos, n_y=y_pos, max_image=int(y_pos * n_x + x_pos))
+
+
+def raster_highest_score(images, min_low_res_spots: float = 10.0) -> CenterOfMassModel:
     """Target the grid cell with the highest crystal score.
 
     If the whole grid is too weak to hold a crystal — max spots_low_res below
     min_low_res_spots — target the geometric centre of the grid instead of
     collecting at a noisy cell, so a 'nothing here' result is centred and
     deliberate rather than random noise.
-
     """
+
     score_array = compute_crystal_score_array(images)
     max_low_res = max((getattr(img, "spots_low_res", 0) or 0 for img in images), default=0)
     if max_low_res < min_low_res_spots:
-        n_nx, n_ny = score_array.shape
         logger.info(
-            f"No crystal in loop (max spots_low_res={max_low_res} < {min_low_res_spots}); "
-            f"targeting grid centre of {n_nx}x{n_ny} grid"
+            f"No crystal in loop (max spots_low_res={max_low_res} < {min_low_res_spots}); targeting grid centre"
         )
-        # get_com_mm maps index i -> (i+0.5)*step, so index (N-1)/2 is the true
-        # geometric centre of the grid for both odd and even N (and N==1).
-        y_pos = (n_ny - 1) / 2.0
-        x_pos = (n_nx - 1) / 2.0
-        return CenterOfMassModel(n_x=x_pos, n_y=y_pos, max_image=int(y_pos * n_nx + x_pos))
-    return _to_com_model(_max_cell(score_array), images, "Highest score")
+        return center_of_grid(*score_array.shape)
+    com = _to_com_model(_max_cell(score_array), images, "Highest score")
+    if com is None:
+        logger.error(
+            "Error in computing centre of mass for gridscan result, images contained invalid data. Returning center image of grid."
+        )
+        return center_of_grid(*score_array.shape)
+    else:
+        return com
 
 
 def has_sufficient_low_res_spots(result_array: np.ndarray, min_spots_low_res: float) -> bool:

src/aarecommon/math/jfjoch_gridscan_union.py

@@ -51,24 +51,13 @@ from pydantic import BaseModel, Field
 # The structures this exchanges with AareDAQ and Jungfraujoch. Verified against
 # jfjoch-client 1.0.0-rc.165. The fallbacks let the module import and its self-test
 # run outside AareDAQ; inside it the real classes are always the ones used.
-try:
-    from jfjoch_client.models.grid_scan import GridScan
-    from jfjoch_client.models.scan_result import ScanResult
-    from jfjoch_client.models.scan_result_images_inner import ScanResultImagesInner
-    from jfjoch_client.models.unit_cell import UnitCell
-except ImportError:  # pragma: no cover
-    GridScan = ScanResult = ScanResultImagesInner = UnitCell = Any
+from jfjoch_client.models.grid_scan import GridScan
+from jfjoch_client.models.scan_result import ScanResult
+from jfjoch_client.models.scan_result_images_inner import ScanResultImagesInner
+from jfjoch_client.models.unit_cell import UnitCell
 
-try:
-    from aarecommon.math.coordinate import Coordinate
-    from aarecommon.math.sample_geometry import SampleGeometryModel
-except ImportError:  # pragma: no cover
-    SampleGeometryModel = Any
-
-    class Coordinate(BaseModel):  # same shape as aarecommon's
-        x: float = 0.0
-        y: float = 0.0
-        z: float = 0.0
+from aarecommon.math.coordinate import Coordinate
+from aarecommon.math.sample_geometry import SampleGeometryModel
 
 
 __all__ = ["Centre", "Counts", "GridScanResult", "Size", "Thresholds", "analyse"]