SC_46 bump spatialdata

docs/tutorials/general/Harpy_feature_calculation.ipynb

@@ -214,11 +214,11 @@
     "#Hotfix: Due to an issue with passing z_axis to model.eval inside the Cellpose library, it appears to be ignored and is therefore automatically inferred incorrectly\n",
     "#Temporary fix is to write to intermediate slot \"__image__\", with only one channel dimension.\n",
     "img_layer = \"HumanLiverH35\"\n",
-    "sdata=harpy.im.add_image_layer( sdata, arr= sdata[ \"HumanLiverH35\" ].sel( c=\"R0 DAPI\" ).data[ None, ... ], output_layer=\"__image__\", overwrite=True  )\n",
+    "sdata=harpy.im.add_image_layer( sdata, arr= sdata[ \"HumanLiverH35\" ].sel( c=\"R0 DAPI\" ).data[ None, ... ], output_layer=\"_image_\", overwrite=True  )\n",
     "\n",
     "sdata = harpy.im.segment(\n",
     "    sdata,\n",
-    "    img_layer=\"__image__\",\n",
+    "    img_layer=\"_image_\",\n",
     "    chunks=1000,\n",
     "    depth=200,\n",
     "    model=cellpose_callable,  # can be any callable. GPU will be used for segmentation if it could be found by torch (torch.cuda.is_available())\n",

src/harpy/plot/_flowsom.py

@@ -89,7 +89,7 @@ def pixel_clusters(
             target_coordinate_system=to_coordinate_system,
         )
         if se_crop is not None:
-            labels_layer_crop = f"__labels_{uuid.uuid4()}__"
+            labels_layer_crop = f"_labels_{uuid.uuid4()}_"
             sdata[labels_layer_crop] = se_crop
             se = se_crop
         else:
@@ -100,7 +100,7 @@ def pixel_clusters(
 
     cluster_ids = labels
 
-    intermediate_table_key = f"__value_clusters__{uuid.uuid4()}"
+    intermediate_table_key = f"_value_clusters_{uuid.uuid4()}"
 
     # create a dummy anndata object, so we can plot cluster ID's spatially using spatialdata plot
     obs = pd.DataFrame({_INSTANCE_KEY: cluster_ids}, index=cluster_ids)