Rename variables

Author: melonora

src/spatialdata/datasets.py

@@ -31,7 +31,7 @@
 
 
 def blobs(
-    length: int = 512, n_points: int = 200, n_shapes: int = 5, extra_coord_space: Optional[str] = None
+    length: int = 512, n_points: int = 200, n_shapes: int = 5, extra_coord_system: Optional[str] = None
 ) -> SpatialData:
     """
     Blobs dataset.
@@ -45,7 +45,7 @@ def blobs(
     n_shapes
         Number of max shapes to generate.
         At most, as if overlapping they will be discarded
-    extra_coord_space
+    extra_coord_system
             Extra coordinate space on top of the standard global coordinate space. Will have only identity transform.
 
 
@@ -55,7 +55,7 @@ def blobs(
         SpatialData object with blobs dataset.
     """
     return BlobsDataset(
-        length=length, n_points=n_points, n_shapes=n_shapes, extra_coord_space=extra_coord_space
+        length=length, n_points=n_points, n_shapes=n_shapes, extra_coord_system=extra_coord_system
     ).blobs()
 
 
@@ -87,7 +87,7 @@ class BlobsDataset:
     """Blobs dataset."""
 
     def __init__(
-        self, length: int = 512, n_points: int = 200, n_shapes: int = 5, extra_coord_space: Optional[str] = None
+        self, length: int = 512, n_points: int = 200, n_shapes: int = 5, extra_coord_system: Optional[str] = None
     ) -> None:
         """
         Blobs dataset.
@@ -101,28 +101,28 @@ def __init__(
         n_shapes
             Number of max shapes to generate.
             At most, as if overlapping they will be discarded
-        extra_coord_space
+        extra_coord_system
             Extra coordinate space on top of the standard global coordinate space. Will have only identity transform.
         """
         self.length = length
         self.n_points = n_points
         self.n_shapes = n_shapes
-        self.transforms = {"global": Identity()}
-        if extra_coord_space:
-            self.transforms[extra_coord_space] = Identity()
+        self.transformations = {"global": Identity()}
+        if extra_coord_system:
+            self.transformations[extra_coord_system] = Identity()
 
     def blobs(
         self,
     ) -> SpatialData:
         """Blobs dataset."""
-        image = self._image_blobs(self.transforms, self.length)
-        multiscale_image = self._image_blobs(self.transforms, self.length, multiscale=True)
-        labels = self._labels_blobs(self.transforms, self.length)
-        multiscale_labels = self._labels_blobs(self.transforms, self.length, multiscale=True)
-        points = self._points_blobs(self.transforms, self.length, self.n_points)
-        circles = self._circles_blobs(self.transforms, self.length, self.n_shapes)
-        polygons = self._polygons_blobs(self.transforms, self.length, self.n_shapes)
-        multipolygons = self._polygons_blobs(self.transforms, self.length, self.n_shapes, multipolygons=True)
+        image = self._image_blobs(self.transformations, self.length)
+        multiscale_image = self._image_blobs(self.transformations, self.length, multiscale=True)
+        labels = self._labels_blobs(self.transformations, self.length)
+        multiscale_labels = self._labels_blobs(self.transformations, self.length, multiscale=True)
+        points = self._points_blobs(self.transformations, self.length, self.n_points)
+        circles = self._circles_blobs(self.transformations, self.length, self.n_shapes)
+        polygons = self._polygons_blobs(self.transformations, self.length, self.n_shapes)
+        multipolygons = self._polygons_blobs(self.transformations, self.length, self.n_shapes, multipolygons=True)
         adata = aggregate(image, labels)
         adata.obs["region"] = pd.Categorical(["blobs_labels"] * len(adata))
         adata.obs["instance_id"] = adata.obs_names.astype(int)
@@ -138,7 +138,7 @@ def blobs(
 
     def _image_blobs(
         self,
-        transforms: Optional[dict[str, Any]] = None,
+        transformations: Optional[dict[str, Any]] = None,
         length: int = 512,
         multiscale: bool = False,
     ) -> Union[SpatialImage, MultiscaleSpatialImage]:
@@ -151,11 +151,11 @@ def _image_blobs(
         x = np.stack(masks, axis=0)
         dims = ["c", "y", "x"]
         if not multiscale:
-            return Image2DModel.parse(x, transformations=transforms, dims=dims)
-        return Image2DModel.parse(x, transformations=transforms, dims=dims, scale_factors=[2, 2])
+            return Image2DModel.parse(x, transformations=transformations, dims=dims)
+        return Image2DModel.parse(x, transformations=transformations, dims=dims, scale_factors=[2, 2])
 
     def _labels_blobs(
-        self, transforms: Optional[dict[str, Any]] = None, length: int = 512, multiscale: bool = False
+        self, transformations: Optional[dict[str, Any]] = None, length: int = 512, multiscale: bool = False
     ) -> Union[SpatialImage, MultiscaleSpatialImage]:
         """Create a 2D labels."""
         from scipy.ndimage import watershed_ift
@@ -180,8 +180,8 @@ def _labels_blobs(
                 out[out == val[idx]] = i
         dims = ["y", "x"]
         if not multiscale:
-            return Labels2DModel.parse(out, transformations=transforms, dims=dims)
-        return Labels2DModel.parse(out, transformations=transforms, dims=dims, scale_factors=[2, 2])
+            return Labels2DModel.parse(out, transformations=transformations, dims=dims)
+        return Labels2DModel.parse(out, transformations=transformations, dims=dims, scale_factors=[2, 2])
 
     def _generate_blobs(self, length: int = 512, seed: Optional[int] = None) -> ArrayLike:
         from scipy.ndimage import gaussian_filter
@@ -197,7 +197,7 @@ def _generate_blobs(self, length: int = 512, seed: Optional[int] = None) -> Arra
         return mask
 
     def _points_blobs(
-        self, transforms: Optional[dict[str, Any]] = None, length: int = 512, n_points: int = 200
+        self, transformations: Optional[dict[str, Any]] = None, length: int = 512, n_points: int = 200
     ) -> DaskDataFrame:
         rng = default_rng(42)
         arr = rng.integers(10, length - 10, size=(n_points, 2)).astype(np.int64)
@@ -211,11 +211,11 @@ def _points_blobs(
             },
         )
         return PointsModel.parse(
-            arr, transformations=transforms, annotation=annotation, feature_key="genes", instance_key="instance_id"
+            arr, transformations=transformations, annotation=annotation, feature_key="genes", instance_key="instance_id"
         )
 
     def _circles_blobs(
-        self, transforms: Optional[dict[str, Any]] = None, length: int = 512, n_shapes: int = 5
+        self, transformations: Optional[dict[str, Any]] = None, length: int = 512, n_shapes: int = 5
     ) -> GeoDataFrame:
         midpoint = length // 2
         halfmidpoint = midpoint // 2
@@ -226,11 +226,11 @@ def _circles_blobs(
                 "radius": radius,
             }
         )
-        return ShapesModel.parse(circles, transformations=transforms)
+        return ShapesModel.parse(circles, transformations=transformations)
 
     def _polygons_blobs(
         self,
-        transforms: Optional[dict[str, Any]] = None,
+        transformations: Optional[dict[str, Any]] = None,
         length: int = 512,
         n_shapes: int = 5,
         multipolygons: bool = False,
@@ -244,7 +244,7 @@ def _polygons_blobs(
                 )
             }
         )
-        return ShapesModel.parse(poly, transformations=transforms)
+        return ShapesModel.parse(poly, transformations=transformations)
 
     # function that generates random shapely polygons given a bounding box
     def _generate_random_polygons(

tests/datasets/test_datasets.py

@@ -3,7 +3,7 @@
 
 def test_datasets() -> None:
     extra_cs = "test"
-    sdata_blobs = blobs(extra_coord_space=extra_cs)
+    sdata_blobs = blobs(extra_coord_system=extra_cs)
 
     assert len(sdata_blobs.table) == 26
     assert len(sdata_blobs.shapes["blobs_circles"]) == 5