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

e2e/features/image/test_image.py

@@ -5,12 +5,40 @@
 
 # Update this as new configs are added
 results = [
-    (siibra.features.get(siibra.get_template("big brain"), "CellbodyStainedSection"), 145),
-    (siibra.features.get(siibra.get_template("big brain"), "CellBodyStainedVolumeOfInterest"), 2),
-    (siibra.features.get(siibra.get_template("mni152"), "image", restrict_space=True), 4),
-    (siibra.features.get(siibra.get_template("mni152"), "image", restrict_space=False), 13),
-    (siibra.features.get(siibra.get_region('julich 3.1', 'hoc1 left'), "CellbodyStainedSection"), 45),
-    (siibra.features.get(siibra.get_region('julich 2.9', 'hoc1 left'), "CellbodyStainedSection"), 41)
+    (
+        siibra.features.get(siibra.get_template("big brain"), "CellbodyStainedSection"),
+        145,
+    ),
+    (
+        siibra.features.get(
+            siibra.get_template("big brain"), "CellBodyStainedVolumeOfInterest"
+        ),
+        2,
+    ),
+    (
+        siibra.features.get(
+            siibra.get_template("mni152"), "image", restrict_space=True
+        ),
+        4,
+    ),
+    (
+        siibra.features.get(
+            siibra.get_template("mni152"), "image", restrict_space=False
+        ),
+        14,
+    ),
+    (
+        siibra.features.get(
+            siibra.get_region("julich 3.1", "hoc1 left"), "CellbodyStainedSection"
+        ),
+        45,
+    ),
+    (
+        siibra.features.get(
+            siibra.get_region("julich 2.9", "hoc1 left"), "CellbodyStainedSection"
+        ),
+        41,
+    ),
 ]
 features = [f for fts, _ in results for f in fts]
 
@@ -21,10 +49,7 @@ def test_feature_has_datasets(feature: Image):
 
 
 @pytest.mark.parametrize("features, result_len", results)
-def test_image_query_results(
-    features: Image,
-    result_len: int
-):
+def test_image_query_results(features: Image, result_len: int):
     assert len(features) == result_len
 
 
@@ -33,18 +58,17 @@ def test_images_datasets_names():
     all_ds_names = {ds.name for f in features for ds in f.datasets}
     end = time.time()
     duration = start - end
-    assert len(all_ds_names) == 9, "expected 9 distinct names"
+    assert len(all_ds_names) == 10, "expected 9 distinct names"
     assert duration < 1, "Expected getting dataset names to be less than 1s"
 
 
 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}"