made reader more flexible

Author: timtreis

src/spatialdata_io/converters/legacy_anndata.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
-import warnings
+import logging
+from collections.abc import Mapping
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -239,6 +240,7 @@ def from_legacy_anndata(adata: AnnData) -> SpatialData:
     -----
     The SpatialData object will have one hires and one lowres image for each dataset in the AnnData object.
     """
+    logger = logging.getLogger(__name__)
     # AnnData keys
     SPATIAL = "spatial"
 
@@ -250,6 +252,7 @@ def from_legacy_anndata(adata: AnnData) -> SpatialData:
     IMAGES = "images"
     HIRES = "hires"
     LOWRES = "lowres"
+    DEFAULT_SCALE_FACTOR = 1.0
 
     # SpatialData keys
     REGION = "locations"
@@ -265,72 +268,77 @@ def from_legacy_anndata(adata: AnnData) -> SpatialData:
     if SPATIAL in adata.uns:
         dataset_ids = list(adata.uns[SPATIAL].keys())
         for dataset_id in dataset_ids:
-            # read the image data and the scale factors for the shapes
-            keys = set(adata.uns[SPATIAL][dataset_id].keys())
-            tissue_hires_scalef = None
-            tissue_lowres_scalef = None
-            hires = None
-            lowres = None
-            if SCALEFACTORS in keys:
-                scalefactors = adata.uns[SPATIAL][dataset_id][SCALEFACTORS]
-                if TISSUE_HIRES_SCALEF in scalefactors:
-                    tissue_hires_scalef = scalefactors[TISSUE_HIRES_SCALEF]
-                if TISSUE_LOWRES_SCALEF in scalefactors:
-                    tissue_lowres_scalef = scalefactors[TISSUE_LOWRES_SCALEF]
-                if SPOT_DIAMETER_FULLRES in scalefactors:
-                    spot_diameter_fullres_list.append(scalefactors[SPOT_DIAMETER_FULLRES])
-            if IMAGES in keys:
-                image_data = adata.uns[SPATIAL][dataset_id][IMAGES]
-                if HIRES in image_data:
-                    hires = image_data[HIRES]
-                if LOWRES in image_data:
-                    lowres = image_data[LOWRES]
-
-            # construct the spatialdata elements
-            if hires is not None:
-                # prepare the hires image
-                assert tissue_hires_scalef is not None, (
-                    "tissue_hires_scalef is required when an the hires image is present"
+            dataset_data = adata.uns[SPATIAL][dataset_id]
+            keys = set(dataset_data.keys())
+            scalefactors_raw = dataset_data.get(SCALEFACTORS, {}) if isinstance(dataset_data, Mapping) else {}
+            scalefactors = scalefactors_raw if isinstance(scalefactors_raw, Mapping) else {}
+
+            if SPOT_DIAMETER_FULLRES in scalefactors:
+                spot_diameter_fullres_list.append(scalefactors[SPOT_DIAMETER_FULLRES])
+
+            image_data = dataset_data.get(IMAGES, {}) if IMAGES in keys and isinstance(dataset_data, Mapping) else {}
+            if not isinstance(image_data, Mapping):
+                image_data = {}
+            for image_key, image_value in image_data.items():
+                if image_key not in (HIRES, LOWRES):
+                    logger.warning(
+                        "Found non-standard image key '%s' in dataset '%s' - attempting to parse.",
+                        image_key,
+                        dataset_id,
+                    )
+                # prefer scalefactors keyed by the image name, fall back to legacy hires/lowres names,
+                # then to tissue_<image_key>_scalef, finally default to 1.0.
+                scalefactor = None
+                scalefactor_source = None
+                if image_key in scalefactors:
+                    scalefactor = scalefactors[image_key]
+                    scalefactor_source = image_key
+                elif f"tissue_{image_key}_scalef" in scalefactors:
+                    scalefactor = scalefactors[f"tissue_{image_key}_scalef"]
+                    scalefactor_source = f"tissue_{image_key}_scalef"
+                elif image_key == HIRES and TISSUE_HIRES_SCALEF in scalefactors:
+                    scalefactor = scalefactors[TISSUE_HIRES_SCALEF]
+                    scalefactor_source = TISSUE_HIRES_SCALEF
+                elif image_key == LOWRES and TISSUE_LOWRES_SCALEF in scalefactors:
+                    scalefactor = scalefactors[TISSUE_LOWRES_SCALEF]
+                    scalefactor_source = TISSUE_LOWRES_SCALEF
+
+                if scalefactor is None:
+                    scalefactor = DEFAULT_SCALE_FACTOR
+                    logger.warning(
+                        "Scale factor missing for image '%s' in dataset '%s'; defaulting to %s",
+                        image_key,
+                        dataset_id,
+                        DEFAULT_SCALE_FACTOR,
+                    )
+                else:
+                    logger.warning(
+                        "Using scalefactor '%s' for image '%s' in dataset '%s'",
+                        scalefactor_source,
+                        image_key,
+                        dataset_id,
+                    )
+
+                transform_name = f"{dataset_id}_{image_key}"
+                image_name = f"{dataset_id}_{image_key}"
+                images[image_name] = Image2DModel.parse(
+                    image_value, dims=("y", "x", "c"), transformations={transform_name: Identity()}
                 )
-                hires_image = Image2DModel.parse(
-                    hires, dims=("y", "x", "c"), transformations={f"{dataset_id}_downscaled_hires": Identity()}
-                )
-                images[f"{dataset_id}_hires_image"] = hires_image
-
-                # prepare the transformation to the hires image for the shapes
-                scale_hires = Scale([tissue_hires_scalef, tissue_hires_scalef], axes=("x", "y"))
-                shapes_transformations[f"{dataset_id}_downscaled_hires"] = scale_hires
-            if lowres is not None:
-                # prepare the lowres image
-                assert tissue_lowres_scalef is not None, (
-                    "tissue_lowres_scalef is required when an the lowres image is present"
-                )
-                lowres_image = Image2DModel.parse(
-                    lowres, dims=("y", "x", "c"), transformations={f"{dataset_id}_downscaled_lowres": Identity()}
-                )
-                images[f"{dataset_id}_lowres_image"] = lowres_image
-
-                # prepare the transformation to the lowres image for the shapes
-                scale_lowres = Scale([tissue_lowres_scalef, tissue_lowres_scalef], axes=("x", "y"))
-                shapes_transformations[f"{dataset_id}_downscaled_lowres"] = scale_lowres
+                shapes_transformations[transform_name] = Scale([scalefactor, scalefactor], axes=("x", "y"))
 
     # validate the spot_diameter_fullres value
     if len(spot_diameter_fullres_list) > 0:
         d = np.array(spot_diameter_fullres_list)
         if not np.allclose(d, d[0]):
-            warnings.warn(
+            logger.warning(
                 "spot_diameter_fullres is not constant across datasets. Using the average value.",
-                UserWarning,
-                stacklevel=2,
             )
             spot_diameter_fullres = d.mean()
         else:
             spot_diameter_fullres = d[0]
     else:
-        warnings.warn(
+        logger.warning(
             f"spot_diameter_fullres is not present. Using {SPOT_DIAMETER_FULLRES_DEFAULT} as default value.",
-            UserWarning,
-            stacklevel=2,
         )
         spot_diameter_fullres = SPOT_DIAMETER_FULLRES_DEFAULT