Apply ruff/pyupgrade rule UP031

UP031 Use format specifiers instead of percent format

ome_zarr/axes.py

@@ -71,7 +71,7 @@ def _validate_axes_types(self) -> None:
         unknown_types = [atype for atype in axes_types if atype not in known_types]
         if len(unknown_types) > 1:
             raise ValueError(
-                "Too many unknown axes types. 1 allowed, found: %s" % unknown_types
+                f"Too many unknown axes types. 1 allowed, found: {unknown_types}"
             )
 
         def _last_index(item: str, item_list: list[Any]) -> int:
@@ -93,7 +93,7 @@ def _get_names(self) -> list[str]:
         axes_names = []
         for axis in self.axes:
             if "name" not in axis:
-                raise ValueError("Axis Dict %s has no 'name'" % axis)
+                raise ValueError(f"Axis Dict {axis} has no 'name'")
             axes_names.append(axis["name"])
         return axes_names
 
@@ -106,7 +106,7 @@ def _validate_03(self) -> None:
             if val_axes not in [("z", "y", "x"), ("c", "y", "x"), ("t", "y", "x")]:
                 raise ValueError(
                     "3D data must have axes ('z', 'y', 'x') or ('c', 'y', 'x')"
-                    " or ('t', 'y', 'x'), not %s" % (val_axes,)
+                    f" or ('t', 'y', 'x'), not {val_axes}"
                 )
         elif len(val_axes) == 4:
             if val_axes not in [

ome_zarr/format.py

@@ -290,14 +290,13 @@ def validate_coordinate_transformations(
         ct_count = len(coordinate_transformations)
         if ct_count != nlevels:
             raise ValueError(
-                "coordinate_transformations count: %s must match datasets %s"
-                % (ct_count, nlevels)
+                f"coordinate_transformations count: {ct_count} must match datasets {nlevels}"
             )
         for transformations in coordinate_transformations:
             assert isinstance(transformations, list)
             types = [t.get("type", None) for t in transformations]
             if any([t is None for t in types]):
-                raise ValueError("Missing type in: %s" % transformations)
+                raise ValueError(f"Missing type in: {transformations}")
             # validate scales...
             if sum(t == "scale" for t in types) != 1:
                 raise ValueError(
@@ -308,12 +307,11 @@ def validate_coordinate_transformations(
                 raise ValueError("First coordinate_transformations must be 'scale'")
             first = transformations[0]
             if "scale" not in transformations[0]:
-                raise ValueError("Missing scale argument in: %s" % first)
+                raise ValueError(f"Missing scale argument in: {first}")
             scale = first["scale"]
             if len(scale) != ndim:
                 raise ValueError(
-                    "'scale' list %s must match number of image dimensions: %s"
-                    % (scale, ndim)
+                    f"'scale' list {scale} must match number of image dimensions: {ndim}"
                 )
             for value in scale:
                 if not isinstance(value, (float, int)):
@@ -329,12 +327,11 @@ def validate_coordinate_transformations(
             elif sum(translation_types) == 1:
                 transformation = transformations[types.index("translation")]
                 if "translation" not in transformation:
-                    raise ValueError("Missing scale argument in: %s" % first)
+                    raise ValueError(f"Missing scale argument in: {first}")
                 translation = transformation["translation"]
                 if len(translation) != ndim:
                     raise ValueError(
-                        "'translation' list %s must match image dimensions count: %s"
-                        % (translation, ndim)
+                        f"'translation' list {translation} must match image dimensions count: {ndim}"
                     )
                 for value in translation:
                     if not isinstance(value, (float, int)):

ome_zarr/reader.py

@@ -301,7 +301,7 @@ def __init__(self, node: Node) -> None:
         for resolution in self.datasets:
             data: da.core.Array = self.array(resolution, version)
             chunk_sizes = [
-                str(c[0]) + (" (+ %s)" % c[-1] if c[-1] != c[0] else "")
+                str(c[0]) + (f" (+ {c[-1]})" if c[-1] != c[0] else "")
                 for c in data.chunks
             ]
             LOGGER.info("resolution: %s", resolution)
@@ -353,7 +353,7 @@ def __init__(self, node: Node) -> None:
 
             colormaps = []
             contrast_limits: Optional[list[Optional[Any]]] = [None for x in channels]
-            names: list[str] = [("channel_%d" % idx) for idx, ch in enumerate(channels)]
+            names: list[str] = [(f"channel_{idx}") for idx, ch in enumerate(channels)]
             visibles: list[bool] = [True for x in channels]
 
             for idx, ch in enumerate(channels):

ome_zarr/scale.py

@@ -147,7 +147,7 @@ def __create_group(
             if i == 0:
                 path = "base"
             else:
-                path = "%s" % i
+                path = str(i)
                 grp.create_dataset(path, data=pyramid[i])
             series.append({"path": path})
         return grp

ome_zarr/writer.py

@@ -918,8 +918,7 @@ def _create_mip(
     if scaler is not None:
         if image.shape[-1] == 1 or image.shape[-2] == 1:
             raise ValueError(
-                "Can't downsample if size of x or y dimension is 1. "
-                "Shape: %s" % (image.shape,)
+                f"Can't downsample if size of x or y dimension is 1. Shape: {image.shape}"
             )
         mip = scaler.func(image)
     else: