Backend-native Implementation

pyproject.toml

@@ -99,19 +99,28 @@ test-min = [
     "pyarrow<21",        # https://github.com/scikit-hep/awkward/issues/3579
     "anndata[dask]",
 ]
-test = [ "anndata[test-min,lazy]", "fast-array-utils>=1.2.3" ]
+test = [
+    "anndata[test-min,lazy]",
+    "anndata[jax]",
+    "fast-array-utils>=1.2.3",
+] # JAX is separate to avoid forcing installation for all users
 gpu = [ "cupy" ]
 cu12 = [ "cupy-cuda12x" ]
 cu11 = [ "cupy-cuda11x" ]
 # requests and aiohttp needed for zarr remote data
 lazy = [ "xarray>=2025.06.1", "aiohttp", "requests", "anndata[dask]" ]
-# https://github.com/dask/dask/issues/11290
-# https://github.com/dask/dask/issues/11752
+
 dask = [
     "dask[array]>=2023.5.1,!=2024.8.*,!=2024.9.*,!=2025.2.*,!=2025.3.*,!=2025.4.*,!=2025.5.*,!=2025.6.*,!=2025.7.*,!=2025.8.*",
 ]
+jax = [
+    "jax>=0.6.0",
+    "jaxlib>=0.6.0",
+]
 
 [tool.hatch.version]
+# https://github.com/dask/dask/issues/11290
+# https://github.com/dask/dask/issues/11752
 source = "vcs"
 raw-options.version_scheme = "release-branch-semver"
 [tool.hatch.build.targets.wheel]

src/anndata/_core/anndata.py

@@ -663,6 +663,12 @@ def X(self, value: XDataType | None):  # noqa: PLR0912
                     ImplicitModificationWarning,
                     stacklevel=2,
                 )
+                dest = self._adata_ref._X
+                # Handles read-only NumPy views from backend arrays like JAX by
+                # making a writable copy so in-place assignment on views can succeed.
+                if isinstance(dest, np.ndarray) and not dest.flags.writeable:
+                    dest = np.array(dest, copy=True)  # make a fresh, writable buffer
+                    self._adata_ref._X = dest
                 self._adata_ref._X[oidx, vidx] = value
             else:
                 self._X = value

src/anndata/_core/index.py

@@ -10,7 +10,7 @@
 import pandas as pd
 from scipy.sparse import issparse
 
-from ..compat import AwkArray, CSArray, CSMatrix, DaskArray, XDataArray
+from ..compat import AwkArray, CSArray, CSMatrix, DaskArray, XDataArray, has_xp
 from .xarray import Dataset2D
 
 if TYPE_CHECKING:
@@ -37,10 +37,11 @@ def _normalize_indices(
     return ax0, ax1
 
 
-def _normalize_index(  # noqa: PLR0911, PLR0912
+def _normalize_index(  # noqa: PLR0911, PLR0912, PLR0915
     indexer: Index1D, index: pd.Index
 ) -> Index1DNorm | int | np.integer:
     # TODO: why is this here? All tests pass without it and it seems at the minimum not strict enough.
+    # protect aroound weird numeric index
     if not isinstance(index, pd.RangeIndex) and index.dtype in (np.float64, np.int64):
         msg = f"Don’t call _normalize_index with non-categorical/string names and non-range index {index}"
         raise TypeError(msg)
@@ -52,6 +53,7 @@ def name_idx(i):
             i = index.get_loc(i)
         return i
 
+    # converting start and stop of the slide to the integer positions if they are strings
     if isinstance(indexer, slice):
         start = name_idx(indexer.start)
         stop = name_idx(indexer.stop)
@@ -67,17 +69,21 @@ def name_idx(i):
     elif isinstance(
         indexer, Sequence | np.ndarray | pd.Index | CSMatrix | np.matrix | CSArray
     ):
+        # convert to the 1D if it's accidentally 2D column/row vector
+        # convert sparse into dense arrays if needed
         if hasattr(indexer, "shape") and (
             (indexer.shape == (index.shape[0], 1))
             or (indexer.shape == (1, index.shape[0]))
         ):
             if isinstance(indexer, CSMatrix | CSArray):
                 indexer = indexer.toarray()
             indexer = np.ravel(indexer)
+        # if it is something else, convert it to numpy
         if not isinstance(indexer, np.ndarray | pd.Index):
             indexer = np.array(indexer)
             if len(indexer) == 0:
                 indexer = indexer.astype(int)
+        # if it is a float array or something along those lines, convert it to integers
         if isinstance(indexer, np.ndarray) and np.issubdtype(
             indexer.dtype, np.floating
         ):
@@ -96,7 +102,7 @@ def name_idx(i):
                 )
                 raise IndexError(msg)
             return indexer
-        else:  # indexer should be string array
+        else:
             positions = index.get_indexer(indexer)
             if np.any(positions < 0):
                 not_found = indexer[positions < 0]
@@ -110,8 +116,65 @@ def name_idx(i):
         if isinstance(indexer.data, DaskArray):
             return indexer.data.compute()
         return indexer.data
+
+    elif has_xp(indexer):
+        # getting array's namespace
+        xp = indexer.__array_namespace__()
+
+        # Flatten to 1D
+        if hasattr(indexer, "shape") and (
+            indexer.shape == (index.shape[0], 1) or indexer.shape == (1, index.shape[0])
+        ):
+            indexer = xp.ravel(
+                indexer
+            )  # flattening to 1D, jax.numpy has it, not sure about cubed
+
+        # Get dtype in array-api-style
+        dtype = getattr(indexer, "dtype", None)
+
+        # if we have like a jax boolean mask array
+        if xp.issubdtype(dtype, xp.bool_):
+            if indexer.shape != index.shape:
+                msg = (
+                    f"Boolean index does not match AnnData’s shape along this dimension. "
+                    f"Boolean index has shape {indexer.shape}, expected {index.shape}"
+                )
+                raise IndexError(msg)
+            return indexer
+
+        # all good, you can return it
+        elif xp.issubdtype(dtype, xp.integer):
+            return indexer
+        # float number case
+        elif xp.issubdtype(dtype, xp.floating):
+            indexer_int = xp.astype(indexer, xp.int32)  # jax default to it
+            # If all floats were “safe” (like 0.0, 1.0, 2.0), return them cast to integers.
+            is_fractional = xp.not_equal(indexer, xp.astype(indexer_int, xp.floating))
+            if xp.any(is_fractional):
+                msg = f"Indexer {indexer!r} has non-integer floating point values."
+                raise IndexError(msg)
+            return indexer_int
+
+        else:
+            try:
+                values = indexer.tolist()  # converting to the list
+            except Exception as err:
+                msg = f"Could not convert {indexer!r} to list for string lookup."
+                raise IndexError(msg) from err
+            positions = index.get_indexer(values)
+            if np.any(positions < 0):
+                not_found = [
+                    v for v, p in zip(values, positions, strict=False) if p < 0
+                ]
+                msg = (
+                    f"Values {not_found}, from {values}, "
+                    "are not valid obs/ var names or indices."
+                )
+                raise KeyError(msg)
+            return positions
+
     msg = f"Unknown indexer {indexer!r} of type {type(indexer)}"
-    raise IndexError()
+    raise IndexError(msg)
 
 
 def _fix_slice_bounds(s: slice, length: int) -> slice: