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Use NgffMultiscales from ome-zarr-py #5

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Use NgffMultiscales from ome-zarr-py #5

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9ab5358
Use NgffMultiscales from ome-zarr-py
will-moore Apr 7, 2026
bb97489
Fix ngffMultiscalse typo
will-moore Apr 7, 2026
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57 changes: 21 additions & 36 deletions napari_ome_zarr/ome_zarr_reader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
import numpy as np
import zarr
from napari.utils.colormaps import AVAILABLE_COLORMAPS, Colormap
from ome_zarr.image import NgffMultiscales
from zarr import Group
from zarr.core.buffer import default_buffer_prototype
from zarr.core.sync import SyncMixin
Expand Down Expand Up @@ -86,55 +87,39 @@ class Multiscales(Spec):
def matches(group: Group) -> bool:
return "multiscales" in Spec.get_attrs(group)

def __init__(self, group: Group) -> None:
super().__init__(group)
self.ngffMultiscales = NgffMultiscales.from_ome_zarr(group)

def children(self) -> list[Spec]:
ch: list[Spec] = []
# test for child "labels"
try:
# We use labels keys, but not the lables NgffMultiscales...
if self.ngffMultiscales.labels is not None:
grp = self.group["labels"]
attrs = Spec.get_attrs(grp)
if "labels" in attrs:
for name in attrs["labels"]:
g = grp[name]
labels_paths = self.ngffMultiscales.labels.keys()
for label_path in labels_paths:
try:
g = grp[label_path]
if Label.matches(g):
ch.append(Label(g))
except KeyError:
pass
except KeyError:
pass
return ch

def data(self) -> list[da.core.Array]:
attrs = Spec.get_attrs(self.group)
paths = [ds["path"] for ds in attrs["multiscales"][0]["datasets"]]
return [da.from_zarr(self.group[path]) for path in paths]
return [img.data for img in self.ngffMultiscales.images]

def metadata(self) -> Dict[str, Any]:
rsp: dict = {}
attrs = Spec.get_attrs(self.group)
# No axes (v0.1, v0.2), assume 5D (t,c,z,y,x)
axes = attrs["multiscales"][0].get("axes", AXES_5D)
atypes = []
for axis in axes:
if isinstance(axis, str):
# v0.3
atypes.append(AXES_TYPES.get(axis.lower(), "space"))
else:
atypes.append(axis.get("type", "space"))
dataset_0 = attrs["multiscales"][0]["datasets"][0]
channel_axis = None
if "channel" in atypes:
channel_axis = atypes.index("channel")
scales = self.ngffMultiscales.images[0].scale
axes_names = self.ngffMultiscales.images[0].axes
rsp["scale"] = [scales[ax] for ax in axes_names if ax != "c"]
# We don't know "type" of axes, but can use name:
if "c" in axes_names:
channel_axis = axes_names.index("c")
rsp["channel_axis"] = channel_axis
if "coordinateTransformations" in dataset_0:
for transf in dataset_0["coordinateTransformations"]:
if "scale" in transf:
scale = transf["scale"]
if channel_axis is not None:
scale.pop(channel_axis)
rsp["scale"] = tuple(scale)
if "translation" in transf:
translate = transf["translation"]
if channel_axis is not None:
translate.pop(channel_axis)
rsp["translate"] = tuple(translate)

if "omero" in attrs:
colormaps = []
ch_names = []
Expand Down