Fix dependency update and test issues (#190)

* fix dependencies and tests

* [pre-commit.ci lite] apply automatic fixes

* add comments for conditionals

Co-Authored-By: Jenna Tomkinson <[email protected]>

---------

Co-authored-by: pre-commit-ci-lite[bot] <117423508+pre-commit-ci-lite[bot]@users.noreply.github.com>
Co-authored-by: Jenna Tomkinson <[email protected]>

Author: 3 people

.pre-commit-config.yaml

@@ -54,7 +54,7 @@ repos:
     hooks:
     -   id: actionlint
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.15.0"
+    rev: "v0.15.1"
     hooks:
     -   id: ruff-format
     -   id: ruff-check

media/coverage-badge.svg

@@ -58,6 +58,8 @@ sqlalchemy = ">=1.3.6,<3"
 coverage = "^7.6.0"
 jupyterlab = "^4.3.0"
 jupytext = "^1.16.4"
+# jupyterlab/httpx requires a modern httpcore; avoid resolver selecting 0.13.x
+httpcore = ">=0.18,<0.19"
 black = ">=24.10,<27.0"
 isort = ">=5.13.2,<8.0.0"
 jupyterlab-code-formatter = "^3.0.2"

poetry.lock

@@ -716,6 +716,10 @@ def to_ome_parquet(  # noqa: PLR0915, PLR0912, C901
                 "CytoDataFrame.to_ome_parquet requires the optional 'ome-arrow' "
                 "dependency. Install it via `pip install ome-arrow`."
             ) from exc
+        try:
+            from ome_arrow import from_numpy as ome_from_numpy  # type: ignore
+        except ImportError:
+            ome_from_numpy = None
 
         try:
             import importlib.metadata as importlib_metadata
@@ -907,24 +911,64 @@ def to_ome_parquet(  # noqa: PLR0915, PLR0912, C901
                             column_values[col_name].append(None)
                             continue
 
-                        temp_path = (
-                            tmpdir_path
-                            / f"{sanitized_col}_{layer_key}_{uuid.uuid4().hex}.tiff"
-                        )
                         try:
-                            with warnings.catch_warnings():
-                                warnings.simplefilter("ignore", UserWarning)
-                                imageio.imwrite(temp_path, layer_array, format="tiff")
-                        except Exception as exc:
-                            logger.error(
-                                "Failed to write temporary TIFF for OMEArrow (%s): %s",
-                                layer_key,
-                                exc,
-                            )
-                            column_values[col_name].append(None)
-                            continue
-                        try:
-                            ome_struct = OMEArrow(data=str(temp_path)).data
+                            # Prefer direct in-memory conversion when available.
+                            # This avoids TIFF round-trips and keeps channel
+                            # layout explicit.
+                            if (
+                                ome_from_numpy is not None
+                                and layer_array.ndim < MIN_VOLUME_NDIM
+                            ):
+                                ome_struct = ome_from_numpy(
+                                    np.asarray(layer_array),
+                                    dim_order="YX",
+                                )
+                            elif (
+                                ome_from_numpy is not None
+                                and layer_array.ndim == MIN_VOLUME_NDIM
+                                and layer_array.shape[-1] in RGB_LIKE_CHANNEL_COUNTS
+                            ):
+                                # OME-Arrow expects channels-first for
+                                # 2D multi-channel arrays.
+                                channel_first = np.moveaxis(
+                                    np.asarray(layer_array), -1, 0
+                                )
+                                ome_struct = ome_from_numpy(
+                                    channel_first,
+                                    dim_order="CYX",
+                                )
+                            elif (
+                                layer_array.ndim == MIN_VOLUME_NDIM
+                                and layer_array.shape[-1] in RGB_LIKE_CHANNEL_COUNTS
+                            ):
+                                # Compatibility fallback for environments where
+                                # `ome_arrow.from_numpy` is not available.
+                                temp_path = tmpdir_path / (
+                                    f"{sanitized_col}_{layer_key}_"
+                                    f"{uuid.uuid4().hex}.tiff"
+                                )
+                                with warnings.catch_warnings():
+                                    warnings.simplefilter("ignore", UserWarning)
+                                    imageio.imwrite(
+                                        temp_path,
+                                        layer_array,
+                                        format="tiff",
+                                    )
+                                ome_struct = OMEArrow(data=str(temp_path)).data
+                            else:
+                                # Generic fallback for all other array shapes.
+                                temp_path = tmpdir_path / (
+                                    f"{sanitized_col}_{layer_key}_"
+                                    f"{uuid.uuid4().hex}.tiff"
+                                )
+                                with warnings.catch_warnings():
+                                    warnings.simplefilter("ignore", UserWarning)
+                                    imageio.imwrite(
+                                        temp_path,
+                                        layer_array,
+                                        format="tiff",
+                                    )
+                                ome_struct = OMEArrow(data=str(temp_path)).data
                             if hasattr(ome_struct, "as_py"):
                                 ome_struct = ome_struct.as_py()
                         except Exception as exc:
@@ -1255,7 +1299,7 @@ def search_for_mask_or_outline(  # noqa: PLR0913, PLR0911, C901
 
         return None, None
 
-    def _extract_array_from_ome_arrow(  # noqa: PLR0911
+    def _extract_array_from_ome_arrow(  # noqa: C901, PLR0911, PLR0912
         self: CytoDataFrame_type,
         data_value: Any,
     ) -> Optional[np.ndarray]:
@@ -1269,6 +1313,7 @@ def _extract_array_from_ome_arrow(  # noqa: PLR0911
             size_x = int(pixels_meta.get("size_x"))
             size_y = int(pixels_meta.get("size_y"))
             size_z = int(pixels_meta.get("size_z") or 1)
+            size_c = int(pixels_meta.get("size_c") or 1)
             planes = data_value.get("planes")
 
             if size_x <= 0 or size_y <= 0 or planes is None:
@@ -1284,21 +1329,43 @@ def _extract_array_from_ome_arrow(  # noqa: PLR0911
             if not plane_entries:
                 return None
 
-            plane = plane_entries[0]
-            pixels = plane.get("pixels")
-            if pixels is None:
-                return None
-
-            np_pixels = np.asarray(pixels)
             base = size_x * size_y
-            if base <= 0 or np_pixels.size == 0 or np_pixels.size % base != 0:
+            if base <= 0:
                 return None
 
-            channel_count = np_pixels.size // base
-            if channel_count == 1:
-                array = np_pixels.reshape((size_y, size_x))
+            if size_c > 1:
+                planes_by_c = {}
+                for plane in plane_entries:
+                    if int(plane.get("t") or 0) != 0 or int(plane.get("z") or 0) != 0:
+                        continue
+                    channel_index = int(plane.get("c") or 0)
+                    pixels = plane.get("pixels")
+                    if pixels is None:
+                        continue
+                    np_pixels = np.asarray(pixels)
+                    if np_pixels.size != base:
+                        continue
+                    planes_by_c[channel_index] = np_pixels.reshape((size_y, size_x))
+
+                if len(planes_by_c) != size_c:
+                    return None
+
+                array = np.stack([planes_by_c[c] for c in range(size_c)], axis=-1)
             else:
-                array = np_pixels.reshape((size_y, size_x, channel_count))
+                plane = plane_entries[0]
+                pixels = plane.get("pixels")
+                if pixels is None:
+                    return None
+
+                np_pixels = np.asarray(pixels)
+                if np_pixels.size == 0 or np_pixels.size % base != 0:
+                    return None
+
+                channel_count = np_pixels.size // base
+                if channel_count == 1:
+                    array = np_pixels.reshape((size_y, size_x))
+                else:
+                    array = np_pixels.reshape((size_y, size_x, channel_count))
 
             return self._ensure_uint8(array)
         except Exception as exc:

pyproject.toml

@@ -1124,6 +1124,25 @@ def test_is_3d_image_array_accepts_thin_small_volume_shapes() -> None:
     assert CytoDataFrame._is_3d_image_array(singleton_x) is True
 
 
+def test_extract_array_from_ome_arrow_rebuilds_multichannel_planes() -> None:
+    cdf = CytoDataFrame(pd.DataFrame({"A": [1]}))
+    data_value = {
+        "type": "ome.arrow",
+        "pixels_meta": {"size_x": 2, "size_y": 2, "size_z": 1, "size_c": 3},
+        "planes": [
+            {"t": 0, "c": 0, "z": 0, "pixels": [10, 20, 30, 40]},
+            {"t": 0, "c": 1, "z": 0, "pixels": [50, 60, 70, 80]},
+            {"t": 0, "c": 2, "z": 0, "pixels": [90, 100, 110, 120]},
+        ],
+    }
+
+    image = cdf._extract_array_from_ome_arrow(data_value)
+
+    assert image is not None
+    assert image.shape == (2, 2, 3)
+    assert np.array_equal(image[0, 0], np.array([10, 50, 90], dtype=np.uint8))
+
+
 def _install_fake_pyvista(  # noqa: C901
     monkeypatch: pytest.MonkeyPatch,
     screenshot_image: np.ndarray | None = None,