Fix writing of unit to ome-zarr

Author: constantinpape

scripts/cryo/cryo-et-portal/process_tomograms_on_the_fly.py

@@ -26,15 +26,20 @@ def get_tomograms(deposition_id, processing_type, number_of_tomograms=None):
     return tomograms
 
 
-def write_ome_zarr(output_file, segmentation, voxel_size):
+def write_ome_zarr(output_file, segmentation, voxel_size, unit="nanometer"):
     store = parse_url(output_file, mode="w").store
     root = zarr.group(store=store)
 
     scale = list(voxel_size.values())
     trafo = [
         [{"scale": scale, "type": "scale"}]
     ]
-    write_image(segmentation, root, axes="zyx", coordinate_transformations=trafo, scaler=None)
+    axes = [
+        {"name": "z", "type": "space", "unit": unit},
+        {"name": "y", "type": "space", "unit": unit},
+        {"name": "x", "type": "space", "unit": unit},
+    ]
+    write_image(segmentation, root, axes=axes, coordinate_transformations=trafo, scaler=None)
 
 
 def run_prediction(tomogram, deposition_id, processing_type):

synapse_net/file_utils.py

@@ -142,11 +142,16 @@ def parse_s3_uri(uri):
         # Read the axis and transformation metadata for this dataset, to determine the voxel size.
         axes = [axis["name"] for axis in multiscales["axes"]]
         assert set(axes) == set("xyz")
+        units = [axis.get("unit", "angstrom") for axis in multiscales["axes"]]
+        assert all(unit in ("angstrom", "nanometer") for unit in units)
+
         transformations = multiscales["datasets"][scale_level]["coordinateTransformations"]
         scale_transformation = [trafo["scale"] for trafo in transformations if trafo["type"] == "scale"][0]
 
-        # The voxel size is given in angstrom, we divide it by 10 to convert it to nanometer.
-        voxel_size = {axis: scale / 10.0 for axis, scale in zip(axes, scale_transformation)}
+        # Convert the given unit size to nanometer.
+        # (It is typically given in angstrom, and we have to divide by a factor of 10).
+        unit_factor = [10.0 if unit == "angstrom" else 1.0 for unit in units]
+        voxel_size = {axis: scale / factor for axis, scale, factor in zip(axes, scale_transformation, unit_factor)}
 
         # Get the internale path for the given scale and load the data.
         internal_path = multiscales["datasets"][scale_level]["path"]