add more tests

bento/geometry/_ops.py

@@ -61,7 +61,6 @@ def overlay(
     )
 
 
-
 @singledispatch
 def labels_to_shapes(labels: np.ndarray, attrs: dict, bg_value: int = 0):
     """
@@ -80,7 +79,7 @@ def labels_to_shapes(labels: np.ndarray, attrs: dict, bg_value: int = 0):
     -------
     GeoPandas DataFrame
         GeoPandas DataFrame containing the polygons extracted from the labeled image.
-    
+
     """
     import rasterio as rio
     import shapely.geometry
@@ -91,8 +90,8 @@ def labels_to_shapes(labels: np.ndarray, attrs: dict, bg_value: int = 0):
     shapes = gpd.GeoDataFrame(
         polygons[:, 1], geometry=gpd.GeoSeries(polygons[:, 0]).T, columns=["id"]
     )
-    shapes = shapes[shapes["id"] != bg_value] # Ignore background
-    
+    shapes = shapes[shapes["id"] != bg_value]  # Ignore background
+
     # Validate for SpatialData
     sd_shape = ShapesModel.parse(shapes)
     sd_shape.attrs = attrs
@@ -112,13 +111,13 @@ def _(labels: SpatialImage, attrs: dict, bg_value: int = 0):
         Dictionary of attributes to set for the SpatialData object.
     bg_value : int, optional
         Value of the background pixels, by default 0
-    
+
     Returns
     -------
     GeoPandas DataFrame
         GeoPandas DataFrame containing the polygons extracted from the labeled image.
     """
-        
+
     # Convert spatial_image.SpatialImage to np.ndarray
     labels = labels.values
-    return labels_to_shapes(labels, attrs, bg_value)
+    return labels_to_shapes(labels, attrs, bg_value)

bento/tools/_composition.py

@@ -73,7 +73,7 @@ def comp(sdata: SpatialData, points_key: str, shape_names: list):
     sdata : spatialdata.SpatialData
         Updates `sdata.table.uns` with average gene compositions for each shape.
     """
-    points = get_points(sdata,points_key=points_key, astype="pandas")
+    points = get_points(sdata, points_key=points_key, astype="pandas")
 
     instance_key = get_instance_key(sdata)
     feature_key = get_feature_key(sdata)
@@ -86,7 +86,9 @@ def comp(sdata: SpatialData, points_key: str, shape_names: list):
     sdata.table.uns["comp_stats"] = comp_stats
 
 
-def comp_diff(sdata: SpatialData, points_key: str, shape_names: list, groupby: str, ref_group: str):
+def comp_diff(
+    sdata: SpatialData, points_key: str, shape_names: list, groupby: str, ref_group: str
+):
     """Calculate the average difference in gene composition for shapes across batches of cells. Uses the Wasserstein distance.
 
     Parameters
@@ -109,7 +111,9 @@ def comp_diff(sdata: SpatialData, points_key: str, shape_names: list, groupby: s
     # Get average gene compositions for each batch
     comp_stats = dict()
     for group, pt_group in points.groupby(groupby):
-        comp_stats[group] = _get_compositions(pt_group, shape_names, instance_key=instance_key, feature_key=feature_key)
+        comp_stats[group] = _get_compositions(
+            pt_group, shape_names, instance_key=instance_key, feature_key=feature_key
+        )
 
     ref_comp = comp_stats[ref_group]
 

bento/tools/_point_features.py

@@ -133,12 +133,12 @@ def analyze_points(
     for shape in list(
         set(obs_attrs).intersection(set([x for x in shape_keys if x != instance_key]))
     ):
-        points_df = (
-            points_df.join(
-                sdata.shapes[shape].set_index(instance_key), on=instance_key, lsuffix="", rsuffix=f"_{shape}"
-            )
-            .rename(columns={shape: f"{shape}_index", f"geometry_{shape}": shape})
-        )
+        points_df = points_df.join(
+            sdata.shapes[shape].set_index(instance_key),
+            on=instance_key,
+            lsuffix="",
+            rsuffix=f"_{shape}",
+        ).rename(columns={shape: f"{shape}_index", f"geometry_{shape}": shape})
 
     # Pull cell_boundaries shape features into the points dataframe
     points_df = (

pyproject.toml

@@ -27,16 +27,10 @@ dependencies = [
     "xgboost>=2.0.3",
     "statsmodels>=0.14.1",
     "scikit-learn>=1.4.2",
-    "pytest>=8.2.1",
 ]
 license = "BSD-2-Clause"
 readme = "README.md"
 requires-python = ">= 3.9"
-include = [
-    "bento/datasets/datasets.csv",
-    "bento/models/**/*",
-    "bento/tools/gene_sets/*",
-]
 
 [project.optional-dependencies]
 docs = [
@@ -61,7 +55,19 @@ build-backend = "hatchling.build"
 
 [tool.rye]
 managed = true
-dev-dependencies = []
+dev-dependencies = [
+    "pytest>=8.2.2",
+    "pytest-cov>=5.0.0",
+    "pytest-watcher>=0.4.2",
+    "pytest-mock>=3.14.0",
+]
+
+[tool.hatch.build]
+include = [
+    "bento/datasets/datasets.csv",
+    "bento/models/**/*",
+    "bento/tools/gene_sets/*",
+]
 
 [tool.hatch.metadata]
 allow-direct-references = true

tests/test_composition.py

@@ -0,0 +1,22 @@
+import bento as bt
+import pandas as pd
+
+
+def test_comp(small_data):
+    # Set up test data
+    shape_names = ["cell_boundaries", "nucleus_boundaries"]
+    points_key = "transcripts"
+
+    # Call the comp function
+    bt.tl.comp(sdata=small_data, points_key=points_key, shape_names=shape_names)
+
+    # Check if comp_stats is updated in sdata.table.uns
+    assert "comp_stats" in small_data.table.uns
+
+    # Check the type of comp_stats
+    assert type(small_data.table.uns["comp_stats"]) == pd.DataFrame
+
+    # Check if the shape_names are present in comp_stats
+    assert all(
+        shape_name in small_data.table.uns["comp_stats"] for shape_name in shape_names
+    )

tests/test_datasets.py

@@ -0,0 +1,31 @@
+import bento as bt
+import pandas as pd
+import geopandas as gpd
+import spatialdata as sd
+import dask.dataframe as dd
+
+
+def test_sample_data():
+    sdata = bt.ds.sample_data()
+
+    # Check if the returned object is an instance of `bento.SpatialData`
+    assert isinstance(sdata, sd.SpatialData)
+
+    # Check if the required keys are present in the `sdata` object
+    assert "transcripts" in sdata.points
+    assert "cell_boundaries" in sdata.shapes
+    assert "nucleus_boundaries" in sdata.shapes
+
+    # Check if the types of the keys are correct
+    assert isinstance(sdata.points["transcripts"], dd.DataFrame)
+    assert isinstance(sdata.shapes["cell_boundaries"], gpd.GeoDataFrame)
+    assert isinstance(sdata.shapes["nucleus_boundaries"], gpd.GeoDataFrame)
+
+    # Check if the `feature_name` column is present in the `transcripts` DataFrame
+    assert "feature_name" in sdata.points["transcripts"]
+
+    # Check if the `cell_boundaries` and `nucleus_boundaries` shapes are present
+    assert "cell_boundaries" in sdata.shapes
+    assert "nucleus_boundaries" in sdata.shapes
+    assert isinstance(sdata.shapes["cell_boundaries"], gpd.GeoDataFrame)
+    assert isinstance(sdata.shapes["nucleus_boundaries"], gpd.GeoDataFrame)

tests/test_geometry.py

@@ -0,0 +1,24 @@
+import bento as bt
+import geopandas as gpd
+
+
+def test_overlay_intersection(small_data):
+    s1 = "nucleus_boundaries"
+    s2 = "cell_boundaries"
+    name = "overlay_result"
+
+    # Perform overlay operation using GeoDataFrame.overlay()
+    shape1 = small_data.shapes[s1]
+    shape2 = small_data.shapes[s2]
+    expected_result = shape1.overlay(shape2, how="intersection", make_valid=True)
+
+    # Perform overlay operation using bento.geo.overlay()
+    bt.geo.overlay(small_data, s1, s2, name, how="intersection")
+
+    assert name in small_data.shapes
+    assert isinstance(small_data.shapes[name], gpd.GeoDataFrame)
+    assert (
+        small_data[name]
+        .geom_almost_equals(expected_result, decimal=1, align=True)
+        .all()
+    )