Use precalculated bounding boxes of image-features to speed up image feature query

e2e/features/image/test_image.py

@@ -29,6 +29,7 @@ def test_images_datasets_names():
 
 
 # Update this as new configs are added
+
 query_and_results = [
     (siibra.features.get(siibra.get_template("big brain"), "CellbodyStainedSection"), 145),
     (siibra.features.get(siibra.get_template("big brain"), "CellBodyStainedVolumeOfInterest"), 2),
@@ -51,10 +52,9 @@ def test_color_channel_fetching():
     dti_rgb_vol = [
         f
         for f in siibra.features.get(
-            siibra.get_template('mni152'),
-            siibra.features.fibres.DTIVolumeOfInterest
+            siibra.get_template("mni152"), siibra.features.fibres.DTIVolumeOfInterest
         )
-        if 'rgb' in f.name
+        if "rgb" in f.name
     ][0]
     _ = dti_rgb_vol.fetch(channel=0)
     _ = dti_rgb_vol.fetch(channel=1)

e2e/volumes/test_preconfigured_boundingbox.py

@@ -0,0 +1,24 @@
+import siibra
+from siibra.features.image.image import Image
+from siibra.volumes.volume import Volume
+import pytest
+from itertools import repeat
+
+map_vols = [v for m in siibra.maps for v in m.volumes]
+imagefeatures = [
+    feat
+    for ftype in siibra.features.Feature._SUBCLASSES[Image]
+    for feat in ftype._get_instances()
+]
+volumes = list(zip(map_vols, repeat(True))) + list(zip(imagefeatures, repeat(False)))
+
+
+@pytest.mark.parametrize("volume, clip_flag", volumes)
+def test_onthefly_and_preconfig_bboxes(volume: Volume, clip_flag: bool):
+    configured_bbox = volume._boundingbox
+    if configured_bbox is None:
+        pytest.skip(f"No preconfigured BoundingBox for {volume} is found. ")
+    volume._boundingbox = None
+    kwargs = {"clip": clip_flag}
+    bbox = volume.get_boundingbox(**kwargs)
+    assert configured_bbox == bbox, f" {volume}"