chore: add in accessors for deprecation warnings

Author: ilan-gold

src/scanpy/plotting/_anndata.py

@@ -13,11 +13,12 @@
 from matplotlib import colormaps, gridspec, patheffects, rcParams
 from matplotlib import pyplot as plt
 from matplotlib.colors import is_color_like
+from packaging.version import Version
 from pandas.api.types import CategoricalDtype, is_numeric_dtype
 
 from .. import get
 from .. import logging as logg
-from .._compat import CSBase, old_positionals
+from .._compat import CSBase, old_positionals, pkg_version
 from .._settings import settings
 from .._utils import (
     _doc_params,
@@ -55,13 +56,7 @@
     from seaborn.matrix import ClusterGrid
 
     from .._utils import Empty
-    from ._utils import (
-        ColorLike,
-        DensityNorm,
-        _FontSize,
-        _FontWeight,
-        _LegendLoc,
-    )
+    from ._utils import ColorLike, DensityNorm, _FontSize, _FontWeight, _LegendLoc
 
 # TODO: is that all?
 type _Basis = Literal["pca", "tsne", "umap", "diffmap", "draw_graph_fr"]
@@ -319,6 +314,9 @@ def _scatter_obs(  # noqa: PLR0912, PLR0913, PLR0915
     if title is not None and isinstance(title, str):
         title = [title]
     highlights = adata.uns.get("highlights", [])
+    is_anndata_13 = pkg_version("anndata") >= Version("0.13.0rc0")
+    if is_anndata_13:
+        from anndata.acc import A
     if basis is not None:
         try:
             # ignore the '0th' diffusion component
@@ -334,16 +332,28 @@ def _scatter_obs(  # noqa: PLR0912, PLR0913, PLR0915
     elif x is not None and y is not None:
         if use_raw:
             if x in adata.obs.columns:
-                x_arr = adata.obs_vector(x)
+                x_arr = adata[A.obs[x]] if is_anndata_13 else adata.obs_vector(x)
             else:
-                x_arr = adata.raw.obs_vector(x)
+                x_arr = (
+                    adata.raw[A.X[:, x]] if is_anndata_13 else adata.raw.obs_vector(x)
+                )
             if y in adata.obs.columns:
-                y_arr = adata.obs_vector(y)
+                y_arr = adata[A.obs[y]] if is_anndata_13 else adata.obs_vector(y)
             else:
-                y_arr = adata.raw.obs_vector(y)
+                y_arr = (
+                    adata.raw[A.X[:, y]] if is_anndata_13 else adata.raw.obs_vector(y)
+                )
         else:
-            x_arr = adata.obs_vector(x, layer=layers[0])
-            y_arr = adata.obs_vector(y, layer=layers[1])
+            x_arr = (
+                adata[A.layers[layers[0]][:, x]]
+                if is_anndata_13
+                else adata.obs_vector(x, layer=layers[0])
+            )
+            y_arr = (
+                adata[A.layers[layers[1]][:, y]]
+                if is_anndata_13
+                else adata.obs_vector(y, layer=layers[1])
+            )
 
         xy = np.c_[x_arr, y_arr]
     else:
@@ -400,9 +410,13 @@ def _scatter_obs(  # noqa: PLR0912, PLR0913, PLR0915
                 c = adata.obs[key].to_numpy()
         # coloring according to gene expression
         elif use_raw and adata.raw is not None and key in adata.raw.var_names:
-            c = adata.raw.obs_vector(key)
+            c = adata[A.obs[key]] if is_anndata_13 else adata.raw.obs_vector(key)
         elif key in adata.var_names:
-            c = adata.obs_vector(key, layer=layers[2])
+            c = (
+                adata[A.layers[layers[2][:, key]]]
+                if is_anndata_13
+                else adata.obs_vector(key, layer=layers[2])
+            )
         elif is_color_like(key):  # a flat color
             c = key
             colorbar = False

src/scanpy/plotting/_tools/scatterplots.py

@@ -16,9 +16,10 @@
 from matplotlib import pyplot as plt
 from matplotlib.colors import Normalize
 from matplotlib.markers import MarkerStyle
+from packaging.version import Version
 
 from ... import logging as logg
-from ..._compat import deprecated
+from ..._compat import deprecated, pkg_version
 from ..._settings import settings
 from ..._utils import _doc_params, _empty, sanitize_anndata
 from ..._utils._doctests import doctest_internet
@@ -1221,10 +1222,21 @@ def _get_color_source_vector(
         # We should probably just make an index for this, and share it over runs
         # TODO: Throw helpful error if this doesn't work
         value_to_plot = adata.var.index[adata.var[gene_symbols] == value_to_plot][0]
+    is_anndata_13 = pkg_version("anndata") >= Version("0.13.0rc0")
+    if is_anndata_13:
+        from anndata.acc import A
     if use_raw and value_to_plot not in adata.obs.columns:
-        values = adata.raw.obs_vector(value_to_plot)
+        values = (
+            adata.raw[A.X[:, value_to_plot]]
+            if is_anndata_13
+            else adata.raw.obs_vector(value_to_plot)
+        )
     else:
-        values = adata.obs_vector(value_to_plot, layer=layer)
+        values = (
+            adata[A.layers[layer][:, value_to_plot]]
+            if is_anndata_13
+            else adata.obs_vector(value_to_plot, layer=layer)
+        )
     if mask_obs is not None:
         values = values.copy()
         values[~mask_obs] = np.nan

tests/test_get.py

@@ -209,15 +209,15 @@ def test_column_content():
     for col in query:
         assert col in df
         np.testing.assert_array_equal(query, df.columns)
-        np.testing.assert_array_equal(df[col].values, adata.obs_vector(col))
+        np.testing.assert_array_equal(df[col].values, adata.obs[col].values)
 
     # test that columns content is correct for var_df
     cell_ids = list(adata.obs.sample(5).index)
     query = [*cell_ids, "highly_variable", "dispersions_norm", "dispersions"]
     df = sc.get.var_df(adata, query)
     np.testing.assert_array_equal(query, df.columns)
     for col in query:
-        np.testing.assert_array_equal(df[col].values, adata.var_vector(col))
+        np.testing.assert_array_equal(df[col].values, adata.var[col].values)
 
 
 def test_var_df(adata: AnnData):

tests/test_metrics.py

@@ -68,7 +68,9 @@ def test_consistency(metric) -> None:
     )
 
     all_genes = metric(pbmc, layer="raw")
-    first_gene = metric(pbmc, vals=pbmc.obs_vector(pbmc.var_names[0], layer="raw"))
+    first_gene = metric(
+        pbmc, vals=pbmc[:, pbmc.var_names[0]].layers["raw"].toarray().ravel()
+    )
 
     np.testing.assert_allclose(all_genes[0], first_gene, rtol=1e-9)
 

tests/test_plotting_embedded/conftest.py

@@ -58,7 +58,7 @@ def adata():
     adata.obs["label_missing"] = adata.obs["label"].copy()
     adata.obs.loc[::2, "label_missing"] = np.nan
 
-    adata.obs["1_missing"] = adata.obs_vector("1")
+    adata.obs["1_missing"] = adata.obs["1"]
     adata.obs.loc[
         adata.obsm["spatial"][:, 0] < adata.obsm["spatial"][:, 0].mean(), "1_missing"
     ] = np.nan