Merge pull request #12 from fractal-analytics-platform/expand_testing

Expand testing

docs/notebooks/basic_usage.ipynb

@@ -185,7 +185,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "dev2",
+   "display_name": "ngio",
    "language": "python",
    "name": "python3"
   },

docs/notebooks/image.ipynb

@@ -301,15 +301,15 @@
     "\n",
     "print(f\"New list of feature table: {ngff_image.table.list(table_type='feature_table')}\")\n",
     "feat_table.set_table(feat_df)\n",
-    "feat_table.write()\n",
+    "feat_table.consolidate()\n",
     "\n",
     "feat_table.table"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "dev2",
+   "display_name": "ngio",
    "language": "python",
    "name": "python3"
   },

docs/notebooks/processing.ipynb

@@ -167,7 +167,7 @@
     "    roi_list.append(roi)\n",
     "\n",
     "mip_roi_table.set_rois(roi_list, overwrite=True)\n",
-    "mip_roi_table.write()\n",
+    "mip_roi_table.consolidate()\n",
     "\n",
     "mip_roi_table.table"
    ]
@@ -303,7 +303,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.10.15"
   }
  },
  "nbformat": 4,

pyproject.toml

@@ -50,11 +50,12 @@ core = ["zarr<3", "dask[distributed]", "dask-image"]
 
 test = ["zarr<3", "pytest", "pytest-cov"]
 
-# add anything else you like to have in your dev environment here
 dev2 = [
     "zarr<3",
     "dask[distributed]",
     "dask-image",
+    "napari",
+    "pyqt5",
     "scikit-image",
     "matplotlib",
     "ipython",
@@ -66,7 +67,7 @@ dev2 = [
     "pre-commit",
     "rich",              # https://github.com/Textualize/rich
     "ruff",
-]
+] # add anything else you like to have in your dev environment here
 
 dev3 = [
     "zarr==v3.0.0-alpha.4",

src/ngio/core/dimensions.py

@@ -4,6 +4,8 @@
 but it is based on the actual metadata of the image data.
 """
 
+from collections import OrderedDict
+
 
 class Dimensions:
     """Dimension metadata."""
@@ -37,7 +39,7 @@ def __init__(
         self._axes_order = axes_order
 
         self._shape = [self._on_disk_shape[i] for i in axes_order]
-        self._shape_dict = dict(zip(axes_names, self._shape, strict=True))
+        self._shape_dict = OrderedDict(zip(axes_names, self._shape, strict=True))
 
     def __str__(self) -> str:
         """Return the string representation of the object."""
@@ -64,33 +66,6 @@ def ad_dict(self) -> dict[str, int]:
         """Return the shape as a dictionary."""
         return self._shape_dict
 
-    @property
-    def t(self) -> int | None:
-        """Return the time dimension."""
-        return self._shape_dict.get("t", None)
-
-    @property
-    def c(self) -> int | None:
-        """Return the channel dimension."""
-        return self._shape_dict.get("c", None)
-
-    @property
-    def z(self) -> int | None:
-        """Return the z dimension."""
-        return self._shape_dict.get("z", None)
-
-    @property
-    def y(self) -> int:
-        """Return the y dimension."""
-        assert "y" in self._shape_dict
-        return self._shape_dict["y"]
-
-    @property
-    def x(self) -> int:
-        """Return the x dimension."""
-        assert "x" in self._shape_dict
-        return self._shape_dict["x"]
-
     def get(self, ax_name: str, default: int = 1) -> int:
         """Return the dimension of the given axis name."""
         return self._shape_dict.get(ax_name, default)
@@ -103,14 +78,16 @@ def on_disk_ndim(self) -> int:
     @property
     def is_time_series(self) -> bool:
         """Return whether the data is a time series."""
-        if (self.t is None) or (self.t == 1):
+        t = self._shape_dict.get("t", 1)
+        if t == 1:
             return False
         return True
 
     @property
     def is_2d(self) -> bool:
         """Return whether the data is 2D."""
-        if (self.z is not None) and (self.z > 1):
+        z = self._shape_dict.get("z", 1)
+        if z != 1:
             return False
         return True
 
@@ -122,9 +99,7 @@ def is_2d_time_series(self) -> bool:
     @property
     def is_3d(self) -> bool:
         """Return whether the data is 3D."""
-        if (self.z is None) or (self.z == 1):
-            return False
-        return True
+        return not self.is_2d
 
     @property
     def is_3d_time_series(self) -> bool:
@@ -134,6 +109,14 @@ def is_3d_time_series(self) -> bool:
     @property
     def is_multi_channels(self) -> bool:
         """Return whether the data has multiple channels."""
-        if (self.c is None) or (self.c == 1):
+        c = self._shape_dict.get("c", 1)
+        if c == 1:
             return False
         return True
+
+    def find_axis(self, ax_name: str) -> int | None:
+        """Return the index of the axis name."""
+        for i, ax in enumerate(self._axes_names):
+            if ax == ax_name:
+                return i
+        return None

src/ngio/core/image_like_handler.py

@@ -2,7 +2,6 @@
 
 from pathlib import Path
 from typing import Any, Literal
-from warnings import warn
 
 import dask.array as da
 import numpy as np
@@ -21,6 +20,7 @@
     get_ngff_image_meta_handler,
 )
 from ngio.pipes import DataTransformPipe, NaiveSlicer, RoiSlicer, on_disk_zoom
+from ngio.utils import ngio_logger
 from ngio.utils._common_types import ArrayLike
 
 
@@ -62,7 +62,7 @@ def __init__(
         _label_group (LabelGroup): The group containing the label data (internal use).
         """
         if not strict:
-            warn("Strict mode is not fully supported yet.", UserWarning, stacklevel=2)
+            ngio_logger.warning("Strict mode is not fully supported yet.")
 
         self._mode = mode
         if not isinstance(store, zarr.Group):
@@ -227,6 +227,10 @@ def on_disk_shape(self) -> tuple[int, ...]:
         """Return the shape of the image."""
         return self.dimensions.on_disk_shape
 
+    def find_axis(self, axis_name: str) -> int | None:
+        """Return the index of the given axis name."""
+        return self.dimensions.find_axis(axis_name)
+
     # Methods to get the image data in the canonical order
     def init_lock(self, lock_id: str | None = None) -> None:
         """Set the lock for the Dask array."""

src/ngio/core/label_handler.py

@@ -1,5 +1,6 @@
 """A module to handle OME-NGFF images stored in Zarr format."""
 
+import builtins
 from typing import Any, Literal
 
 import zarr
@@ -285,13 +286,18 @@ def get_label(
     def derive(
         self,
         name: str,
+        reference: ImageLike | None = None,
+        levels: int | builtins.list[str] = 5,
         overwrite: bool = False,
         **kwargs: dict,
     ) -> Label:
         """Derive a new label from an existing label.
 
         Args:
             name (str): The name of the new label.
+            reference (ImageLike | None): The reference image to use for the new label.
+            levels (int | list[str]): The number of levels to create or
+                a list of paths names.
             overwrite (bool): If True, the label will be overwritten if it exists.
                 Default is False.
             **kwargs: Additional keyword arguments to pass to the new label.
@@ -311,44 +317,46 @@ def derive(
         # create the new label
         new_label_group = self._label_group.create_group(name, overwrite=overwrite)
 
-        if self._image_ref is None:
-            label_0 = self.get_label(list_of_labels[0])
-            metadata = label_0.metadata
-            on_disk_shape = label_0.on_disk_shape
-            chunks = label_0.on_disk_array.chunks
-            dataset = label_0.dataset
-        else:
-            label_0 = self._image_ref
-            metadata = label_0.metadata
-            channel_index = metadata.index_mapping.get("c", None)
-            if channel_index is not None:
-                on_disk_shape = (
-                    label_0.on_disk_shape[:channel_index]
-                    + label_0.on_disk_shape[channel_index + 1 :]
-                )
-                chunks = (
-                    label_0.on_disk_array.chunks[:channel_index]
-                    + label_0.on_disk_array.chunks[channel_index + 1 :]
-                )
-            else:
-                on_disk_shape = label_0.on_disk_shape
-                chunks = label_0.on_disk_array.chunks
+        ref_0 = self._image_ref if reference is None else reference
+        assert isinstance(ref_0, ImageLike)
+
+        if isinstance(levels, int):
+            paths = [str(i) for i in range(levels)]
+        elif isinstance(levels, list):
+            if not all(isinstance(level, str) for level in levels):
+                raise ValueError(f"All levels must be strings. Got: {levels}")
+            paths = levels
+
+        on_disk_ch_index = ref_0.find_axis("c")
+        metadata = ref_0.metadata
 
+        if on_disk_ch_index is None:
+            on_disk_shape = ref_0.on_disk_shape
+            chunks = ref_0.on_disk_array.chunks
+        else:
             metadata = metadata.remove_axis("c")
-            dataset = metadata.get_highest_resolution_dataset()
+            on_disk_shape = (
+                ref_0.on_disk_shape[:on_disk_ch_index]
+                + ref_0.on_disk_shape[on_disk_ch_index + 1 :]
+            )
+            chunks = (
+                ref_0.on_disk_array.chunks[:on_disk_ch_index]
+                + ref_0.on_disk_array.chunks[on_disk_ch_index + 1 :]
+            )
+        dataset = metadata.get_dataset(path=paths[0])
 
         default_kwargs = {
             "store": new_label_group,
             "shape": on_disk_shape,
             "chunks": chunks,
-            "dtype": label_0.on_disk_array.dtype,
+            "dtype": ref_0.on_disk_array.dtype,
             "on_disk_axis": dataset.on_disk_axes_names,
             "pixel_sizes": dataset.pixel_size,
             "xy_scaling_factor": metadata.xy_scaling_factor,
             "z_scaling_factor": metadata.z_scaling_factor,
             "time_spacing": dataset.time_spacing,
             "time_units": dataset.time_axis_unit,
-            "num_levels": metadata.num_levels,
+            "levels": paths,
             "name": name,
             "overwrite": overwrite,
             "version": metadata.version,
@@ -357,7 +365,20 @@ def derive(
         default_kwargs.update(kwargs)
 
         create_empty_ome_zarr_label(
-            **default_kwargs,
+            store=new_label_group,
+            on_disk_shape=on_disk_shape,
+            chunks=chunks,
+            dtype=ref_0.on_disk_array.dtype,
+            on_disk_axis=dataset.on_disk_axes_names,
+            pixel_sizes=dataset.pixel_size,
+            xy_scaling_factor=metadata.xy_scaling_factor,
+            z_scaling_factor=metadata.z_scaling_factor,
+            time_spacing=dataset.time_spacing,
+            time_units=dataset.time_axis_unit,
+            levels=paths,
+            name=name,
+            overwrite=overwrite,
+            version=metadata.version,
         )
 
         if name not in self.list():

src/ngio/core/ngff_image.py

@@ -178,7 +178,7 @@ def derive_new_image(
 
         default_kwargs = {
             "store": store,
-            "shape": image_0.on_disk_shape,
+            "on_disk_shape": image_0.on_disk_shape,
             "chunks": image_0.on_disk_array.chunks,
             "dtype": image_0.on_disk_array.dtype,
             "on_disk_axis": image_0.dataset.on_disk_axes_names,
@@ -187,7 +187,7 @@ def derive_new_image(
             "z_scaling_factor": self.image_meta.z_scaling_factor,
             "time_spacing": image_0.dataset.time_spacing,
             "time_units": image_0.dataset.time_axis_unit,
-            "num_levels": self.num_levels,
+            "levels": self.num_levels,
             "name": name,
             "channel_labels": image_0.channel_labels,
             "channel_wavelengths": [ch.wavelength_id for ch in channels],