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Modify from_legacy_anndata so that it can read older ST datasets #347

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Modify from_legacy_anndata so that it can read older ST datasets #347

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5eac22c
made reader more flexible
timtreis Nov 26, 2025
3dd1cd9
adjusted logger import
timtreis Nov 26, 2025
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115 changes: 61 additions & 54 deletions src/spatialdata_io/converters/legacy_anndata.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
from __future__ import annotations

import warnings
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
Expand All @@ -12,6 +12,7 @@
to_circles,
)
from spatialdata._core.operations._utils import transform_to_data_extent
from spatialdata._logging import logger
from spatialdata.models import Image2DModel, ShapesModel, TableModel, get_table_keys
from spatialdata.transformations import Identity, Scale

Expand Down Expand Up @@ -250,6 +251,7 @@ def from_legacy_anndata(adata: AnnData) -> SpatialData:
IMAGES = "images"
HIRES = "hires"
LOWRES = "lowres"
DEFAULT_SCALE_FACTOR = 1.0

# SpatialData keys
REGION = "locations"
Expand All @@ -265,72 +267,77 @@ 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

Expand Down
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