Better 3D and 2.5D support for raster and vector data

.gitignore

@@ -117,3 +117,4 @@ tests/plots/generated
 
 # Local temp script for testing user bugs (Luca)
 temp/
+uv.lock

docs/contributing.md

@@ -1,3 +1,25 @@
 # Contributing guide
 
 Please refer to the [contribution guide from the `spatialdata` repository](https://github.com/scverse/spatialdata/blob/main/docs/contributing.md).
+
+## Debugging napari GUI tests
+
+To visually inspect what a test is rendering in napari:
+
+1. Change `make_napari_viewer()` to `make_napari_viewer(show=True)`
+2. Add `napari.run()` before the end of the test (before the assertions)
+
+Example:
+
+```python
+import napari
+
+
+def test_my_visualization(make_napari_viewer):
+    viewer = make_napari_viewer(show=True)
+    # ... setup code ...
+    napari.run()
+    # assertions...
+```
+
+Remember to revert these changes before committing.

setup.cfg

@@ -54,7 +54,7 @@ install_requires =
     scipy
     shapely
     scikit-learn
-    spatialdata>=0.7.0dev0
+    spatialdata>=0.7.0dev1
     superqt
     typing_extensions>=4.8.0
     vispy

src/napari_spatialdata/_viewer.py

@@ -18,12 +18,11 @@
 from spatialdata import get_element_annotators, get_element_instances
 from spatialdata._core.query.relational_query import _left_join_spatialelement_table
 from spatialdata._types import ArrayLike
-from spatialdata.models import PointsModel, ShapesModel, TableModel, force_2d, get_channel_names
+from spatialdata.models import PointsModel, ShapesModel, TableModel, force_2d, get_axes_names, get_channel_names
 from spatialdata.transformations import Affine, Identity
 
 from napari_spatialdata._model import DataModel
 from napari_spatialdata.constants import config
-from napari_spatialdata.constants.config import CIRCLES_AS_POINTS
 from napari_spatialdata.utils._utils import (
     _adjust_channels_order,
     _get_ellipses_from_circles,
@@ -470,7 +469,7 @@ def get_sdata_image(self, sdata: SpatialData, key: str, selected_cs: str, multi:
         if multi:
             original_name = original_name[: original_name.rfind("_")]
 
-        affine = _get_transform(sdata.images[original_name], selected_cs)
+        affine = _get_transform(sdata.images[original_name], selected_cs, include_z=True)
         rgb_image, rgb = _adjust_channels_order(element=sdata.images[original_name])
 
         channels = ("RGB(A)",) if rgb else get_channel_names(sdata.images[original_name])
@@ -517,6 +516,7 @@ def get_sdata_circles(self, sdata: SpatialData, key: str, selected_cs: str, mult
         df = sdata.shapes[original_name]
         affine = _get_transform(sdata.shapes[original_name], selected_cs)
 
+        # 2.5D circles not supported yet
         xy = np.array([df.geometry.x, df.geometry.y]).T
         yx = np.fliplr(xy)
         radii = df.radius.to_numpy()
@@ -541,10 +541,10 @@ def get_sdata_circles(self, sdata: SpatialData, key: str, selected_cs: str, mult
         version = get_napari_version()
         kwargs: dict[str, Any] = (
             {"edge_width": 0.0}
-            if version <= packaging.version.parse("0.4.20") or not CIRCLES_AS_POINTS
+            if version <= packaging.version.parse("0.4.20") or not config.CIRCLES_AS_POINTS
             else {"border_width": 0.0}
         )
-        if CIRCLES_AS_POINTS:
+        if config.CIRCLES_AS_POINTS:
             layer = Points(
                 yx,
                 name=key,
@@ -556,7 +556,7 @@ def get_sdata_circles(self, sdata: SpatialData, key: str, selected_cs: str, mult
             assert affine is not None
             self._adjust_radii_of_points_layer(layer=layer, affine=affine)
         else:
-            if version <= packaging.version.parse("0.4.20") or not CIRCLES_AS_POINTS:
+            if version <= packaging.version.parse("0.4.20") or not config.CIRCLES_AS_POINTS:
                 kwargs |= {"edge_color": "white"}
             else:
                 kwargs |= {"border_color": "white"}
@@ -597,7 +597,8 @@ def get_sdata_shapes(self, sdata: SpatialData, key: str, selected_cs: str, multi
             original_name = original_name[: original_name.rfind("_")]
 
         df = sdata.shapes[original_name]
-        affine = _get_transform(sdata.shapes[original_name], selected_cs)
+        include_z = not config.PROJECT_2_5D_SHAPES_TO_2D
+        affine = _get_transform(sdata.shapes[original_name], selected_cs, include_z=include_z)
 
         # when mulitpolygons are present, we select the largest ones
         if "MultiPolygon" in np.unique(df.geometry.type):
@@ -609,7 +610,7 @@ def get_sdata_shapes(self, sdata: SpatialData, key: str, selected_cs: str, multi
             df = df.sort_index()  # reset the index to the first order
 
         simplify = len(df) > config.POLYGON_THRESHOLD
-        polygons, indices = _get_polygons_properties(df, simplify)
+        polygons, indices = _get_polygons_properties(df, simplify, include_z=include_z)
 
         # this will only work for polygons and not for multipolygons
         polygons = _transform_coordinates(polygons, f=lambda x: x[::-1])
@@ -662,7 +663,7 @@ def get_sdata_labels(self, sdata: SpatialData, key: str, selected_cs: str, multi
             original_name = original_name[: original_name.rfind("_")]
 
         indices = get_element_instances(sdata.labels[original_name])
-        affine = _get_transform(sdata.labels[original_name], selected_cs)
+        affine = _get_transform(sdata.labels[original_name], selected_cs, include_z=True)
         rgb_labels, _ = _adjust_channels_order(element=sdata.labels[original_name])
 
         adata, table_name, table_names = self._get_table_data(sdata, original_name)
@@ -706,8 +707,10 @@ def get_sdata_points(self, sdata: SpatialData, key: str, selected_cs: str, multi
         if multi:
             original_name = original_name[: original_name.rfind("_")]
 
+        axes = get_axes_names(sdata.points[original_name])
         points = sdata.points[original_name].compute()
-        affine = _get_transform(sdata.points[original_name], selected_cs)
+        include_z = "z" in axes and not config.PROJECT_3D_POINTS_TO_2D
+        affine = _get_transform(sdata.points[original_name], selected_cs, include_z=include_z)
         adata, table_name, table_names = self._get_table_data(sdata, original_name)
 
         if len(points) < config.POINT_THRESHOLD:
@@ -727,14 +730,16 @@ def get_sdata_points(self, sdata: SpatialData, key: str, selected_cs: str, multi
             _, adata = _left_join_spatialelement_table(
                 {"points": {original_name: subsample_points}}, sdata[table_name], match_rows="left"
             )
-        xy = subsample_points[["y", "x"]].values
-        np.fliplr(xy)
+        axes = sorted(axes, reverse=True)
+        if not include_z and "z" in axes:
+            axes.remove("z")
+        coords = subsample_points[axes].values
         # radii_size = _calc_default_radii(self.viewer, sdata, selected_cs)
         radii_size = 3
         version = get_napari_version()
         kwargs = {"edge_width": 0.0} if version <= packaging.version.parse("0.4.20") else {"border_width": 0.0}
         layer = Points(
-            xy,
+            coords,
             name=key,
             size=radii_size * 2,
             affine=affine,

src/napari_spatialdata/constants/config.py

@@ -4,3 +4,5 @@
 N_SHAPES_WARNING_THRESHOLD = 10000
 POINT_SIZE_SCATTERPLOT_WIDGET = 6
 CIRCLES_AS_POINTS = True
+PROJECT_3D_POINTS_TO_2D = True
+PROJECT_2_5D_SHAPES_TO_2D = True

src/napari_spatialdata/utils/_utils.py

@@ -181,15 +181,20 @@ def _transform_coordinates(data: list[Any], f: Callable[..., Any]) -> list[Any]:
     return [[f(xy) for xy in sublist] for sublist in data]
 
 
-def _get_transform(element: SpatialElement, coordinate_system_name: str | None = None) -> None | ArrayLike:
+def _get_transform(
+    element: SpatialElement, coordinate_system_name: str | None = None, include_z: bool | None = None
+) -> None | ArrayLike:
     if not isinstance(element, DataArray | DataTree | DaskDataFrame | GeoDataFrame):
         raise RuntimeError("Cannot get transform for {type(element)}")
 
     transformations = get_transformation(element, get_all=True)
     cs = transformations.keys().__iter__().__next__() if coordinate_system_name is None else coordinate_system_name
     ct = transformations.get(cs)
     if ct:
-        return ct.to_affine_matrix(input_axes=("y", "x"), output_axes=("y", "x"))
+        axes_element = get_axes_names(element)
+        include_z = include_z and "z" in axes_element
+        axes_transformation = ("z", "y", "x") if include_z else ("y", "x")
+        return ct.to_affine_matrix(input_axes=axes_transformation, output_axes=axes_transformation)
     return None
 
 

src/napari_spatialdata/utils/_viewer_utils.py

@@ -1,18 +1,37 @@
 from geopandas import GeoDataFrame
+from spatialdata.models import get_axes_names
 
+# type aliases, only used in this module
+Coord2D = tuple[float, float]
+Coord3D = tuple[float, float, float]
+Polygon2D = list[Coord2D]
+Polygon3D = list[Coord3D]
+Polygon = Polygon2D | Polygon3D
 
-def _get_polygons_properties(df: GeoDataFrame, simplify: bool) -> tuple[list[list[tuple[float, float]]], list[int]]:
-    indices = []
-    polygons = []
 
-    if simplify:
-        for i in range(0, len(df)):
-            indices.append(df.iloc[i].name)
-            # This can be removed once napari is sped up in the plotting. It changes the shapes only very slightly
-            polygons.append(list(df.geometry.iloc[i].exterior.simplify(tolerance=2).coords))
-    else:
-        for i in range(0, len(df)):
-            indices.append(df.iloc[i].name)
-            polygons.append(list(df.geometry.iloc[i].exterior.coords))
+def _get_polygons_properties(df: GeoDataFrame, simplify: bool, include_z: bool) -> tuple[list[Polygon], list[int]]:
+    # assumes no "Polygon Z": z is in separate column if present
+    indices: list[int] = []
+    polygons: list[Polygon] = []
+
+    axes = get_axes_names(df)
+    add_z = include_z and "z" in axes
+
+    for i in range(len(df)):
+        indices.append(int(df.index[i]))
+
+        if simplify:
+            xy = list(df.geometry.iloc[i].exterior.simplify(tolerance=2).coords)
+        else:
+            xy = list(df.geometry.iloc[i].exterior.coords)
+
+        coords: Polygon2D | Polygon3D
+        if add_z:
+            z_val = float(df.iloc[i].z.item() if hasattr(df.iloc[i].z, "item") else df.iloc[i].z)
+            coords = [(x, y, z_val) for x, y in xy]
+        else:
+            coords = xy
+
+        polygons.append(coords)
 
     return polygons, indices

tests/conftest.py

@@ -9,19 +9,23 @@
 from pathlib import Path
 from typing import Any
 
+import geopandas as gpd
 import napari
 import numpy as np
 import pandas as pd
 import pytest
 from anndata import AnnData
+from dask.dataframe import from_pandas
 from loguru import logger
 from matplotlib.testing.compare import compare_images
 from scipy import ndimage as ndi
+from shapely import MultiPolygon, Polygon
 from skimage import data
 from spatialdata import SpatialData
 from spatialdata._types import ArrayLike
 from spatialdata.datasets import blobs
-from spatialdata.models import TableModel
+from spatialdata.models import PointsModel, ShapesModel, TableModel
+from spatialdata.transformations import Identity, set_transformation
 
 from napari_spatialdata.utils._test_utils import export_figure, save_image
 
@@ -259,3 +263,61 @@ def caplog(caplog):
 def always_sync(monkeypatch, request):
     if request.node.get_closest_marker("use_thread_loader") is None:
         monkeypatch.setattr("napari_spatialdata._sdata_widgets.PROBLEMATIC_NUMPY_MACOS", True)
+
+
+@pytest.fixture
+def sdata_3d_points() -> SpatialData:
+    """Create a SpatialData object with 3D points (x, y, z coordinates)."""
+    n_points = 10
+    rng = np.random.default_rng(SEED)
+    df = pd.DataFrame(
+        {
+            "x": rng.uniform(0, 100, n_points),
+            "y": rng.uniform(0, 100, n_points),
+            "z": rng.uniform(0, 50, n_points),
+        }
+    )
+    dask_df = from_pandas(df, npartitions=1)
+    points = PointsModel.parse(dask_df)
+    set_transformation(points, {"global": Identity()}, set_all=True)
+
+    return SpatialData(points={"points_3d": points})
+
+
+@pytest.fixture
+def sdata_2_5d_shapes() -> SpatialData:
+    """Create a SpatialData object with 2.5D shapes (3 layers at different z, polygons + multipolygons)."""
+    shapes = {}
+
+    geometries = []
+    z_values = []
+    indices = []
+    for i, z_val in enumerate([0.0, 10.0, 20.0]):
+        # Add simple polygons (triangles and quadrilaterals)
+        poly1 = Polygon([(10 + i * 5, 10), (20 + i * 5, 10), (15 + i * 5, 20)])
+        poly2 = Polygon([(30 + i * 5, 30), (40 + i * 5, 30), (40 + i * 5, 40), (30 + i * 5, 40)])
+        geometries.extend([poly1, poly2])
+        indices.extend([0, 1])
+        z_values.extend([z_val] * 2)
+
+        # Add a multipolygon (two separate polygon parts)
+        multi_poly = MultiPolygon(
+            [
+                Polygon([(50 + i * 5, 10), (60 + i * 5, 10), (55 + i * 5, 20)]),
+                Polygon([(50 + i * 5, 30), (60 + i * 5, 30), (60 + i * 5, 40), (50 + i * 5, 40)]),
+            ]
+        )
+        geometries.append(multi_poly)
+        indices.append(2)
+        z_values.append(z_val)
+
+    gdf = gpd.GeoDataFrame(
+        {"z": z_values, "geometry": geometries},
+        index=indices,
+    )
+
+    shape_element = ShapesModel.parse(gdf)
+    set_transformation(shape_element, {"global": Identity()}, set_all=True)
+    shapes["shapes_2.5d"] = shape_element
+
+    return SpatialData(shapes=shapes)