feat: accessors #2290

flying-sheep · 2026-01-08T13:32:44Z

Closes Reference paths / accessors #2189
Tests added
Release note added (or unnecessary)

User API design

create AdPath instances by descending into a central accessor constant A:

Lines 25 to 42 in 61fc920

    
           (A[:, :], lambda ad: ad.X), 
        
           (A[:, "gene-3"], lambda ad: ad[:, "gene-3"].X.flatten()), 
        
           (A["cell-5", :], lambda ad: ad["cell-5"].X.flatten()), 
        
           (A.obs["type"], lambda ad: ad.obs["type"]), 
        
           (A.obs.index, lambda ad: ad.obs.index.values), 
        
           (A.layers["a"][:, :], lambda ad: ad.layers["a"].copy().toarray()), 
        
           ( 
        
               A.layers["a"][:, "gene-18"], 
        
               lambda ad: ad[:, "gene-18"].layers["a"].copy().toarray().flatten(), 
        
           ), 
        
           ( 
        
               A.layers["a"]["cell-77", :], 
        
               lambda ad: ad["cell-77"].layers["a"].copy().toarray().flatten(), 
        
           ), 
        
           (A.obsm["umap"][0], lambda ad: ad.obsm["umap"][:, 0]), 
        
           (A.obsm["umap"][1], lambda ad: ad.obsm["umap"][:, 1]), 
        
           (A.varp["cons"]["gene-46", :], lambda ad: ad.varp["cons"][46, :].toarray()), 
        
           (A.varp["cons"][:, "gene-46"], lambda ad: ad.varp["cons"][:, 46].toarray()),

Inspect AdPath instance, e.g. to figure out which axes the resulting vector spans:

anndata/tests/test_accessors.py

Lines 101 to 107 in 61fc920

    
                   pytest.param(A.obsm["c"][:, 0], {"obs"}, id="obsm"), 
        
                   pytest.param(A.varp["d"][:, :], ("var", "var"), id="varp"), 
        
                   pytest.param(A.varp["d"][:, "c2"], {"var"}, id="varp-col"), 
        
               ], 
        
           ) 
        
           def test_axes(ad_path: AdPath, axes: Collection[Literal["obs", "var"]]) -> None: 
        
               assert ad_path.axes == axes

Call AdPath instance to extract a vector (see 1. for examples)

subclassing

… is a direct goal of this, as people should be able to use AdPath subclasses. This means that

the AdPath API is minimal and inspection can be done through .acc (could be even more minimal by just putting it all into a container?):

anndata/src/anndata/acc/__init__.py

Lines 56 to 57 in 61fc920

acc: VecAcc[Self, I]

idx: I

anndata/src/anndata/acc/__init__.py

Lines 63 to 64 in 61fc920

@cached_property

def axes(self) -> Axes:
It’s trivial to create a new AdAcc constant that produces your own AdPath subclass:

anndata/src/anndata/acc/__init__.py

Line 415 in 61fc920

A = AdAcc(path_class=AdPath)
The data flow is easy to understand:
1. User uses VecAcc.__getitem__ to get an AdPath or a list of them. In that process,
  1. __getitem__ calls process_idx which verifies and simplifies the index
  2. axes, __repr__, and idx_repr get called too to validate things work
2. The public API of AdPath can be used, which basically just delegates to VecAcc’s axes, __repr__, idx_repr, and __call__

So in the end everything except for __call__ is validated, i.e. VecAcc.__getitem__ raises exceptions on misuse

anndata/src/anndata/acc/__init__.py

Lines 101 to 119 in 61fc920

    
           class VecAcc[P: AdPath[I], I](abc.ABC):  # type: ignore 
        
               path_class: type[P] 
        
               def process_idx(self, idx: Any, /) -> I: 
        
                   self.axes(idx) 
        
                   return idx 
        
               def __getitem__(self, idx: Any, /) -> P: 
        
                   idx = self.process_idx(idx) 
        
                   return self.path_class(self, idx)  # type: ignore 
        
               @abc.abstractmethod 
        
               def axes(self, idx: I, /) -> Axes: ... 
        
               @abc.abstractmethod 
        
               def __repr__(self, /) -> str: ... 
        
               @abc.abstractmethod 
        
               def idx_repr(self, idx: I, /) -> str: ... 
        
               @abc.abstractmethod 
        
               def __call__(self, adata: AnnData, idx: I, /) -> Vector: ...

TODO:

serialization
docs (especially data flow)
- nomenclature: “vector” vs “array”, “accessor”, “vector accessor”, “reference”, “path”, … get it straight!
  Especially since we already call these accessors! Instance namespaces (adata.custom) #1869
Array types: fix typing and test with all array types we support
improve __contains__ so it doesn’t try to access the array
add JSON schema
- also add it to docs (linked from from_json/to_json)
- and test it
terminology: axis → dim
(maybe) runtime autocompletion (__dir__)

codecov · 2026-01-08T13:33:44Z

❌ 6 Tests Failed:

Tests completed	Failed	Passed	Skipped
5281	6	5275	2572

View the top 3 failed test(s) by shortest run time

tests.test_base::test_1d_slice_dtypes

Stack Traces | 0.006s run time

#x1B[0m#x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mtest_1d_slice_dtypes#x1B[39;49;00m() -> #x1B[94mNone#x1B[39;49;00m:#x1B[90m#x1B[39;49;00m
        N, M = #x1B[94m10#x1B[39;49;00m, #x1B[94m20#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        obs_df = pd.DataFrame(#x1B[90m#x1B[39;49;00m
            #x1B[96mdict#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
                cat=pd.Categorical(np.arange(N, dtype=#x1B[96mint#x1B[39;49;00m)),#x1B[90m#x1B[39;49;00m
                #x1B[96mint#x1B[39;49;00m=np.arange(N, dtype=#x1B[96mint#x1B[39;49;00m),#x1B[90m#x1B[39;49;00m
                #x1B[96mfloat#x1B[39;49;00m=np.arange(N, dtype=#x1B[96mfloat#x1B[39;49;00m),#x1B[90m#x1B[39;49;00m
                obj=[#x1B[96mstr#x1B[39;49;00m(i) #x1B[94mfor#x1B[39;49;00m i #x1B[95min#x1B[39;49;00m np.arange(N, dtype=#x1B[96mint#x1B[39;49;00m)],#x1B[90m#x1B[39;49;00m
            ),#x1B[90m#x1B[39;49;00m
            index=[#x1B[33mf#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[33mcell#x1B[39;49;00m#x1B[33m{#x1B[39;49;00mi#x1B[33m}#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[94mfor#x1B[39;49;00m i #x1B[95min#x1B[39;49;00m np.arange(N, dtype=#x1B[96mint#x1B[39;49;00m)],#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        var_df = pd.DataFrame(#x1B[90m#x1B[39;49;00m
            #x1B[96mdict#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
                cat=pd.Categorical(np.arange(M, dtype=#x1B[96mint#x1B[39;49;00m)),#x1B[90m#x1B[39;49;00m
                #x1B[96mint#x1B[39;49;00m=np.arange(M, dtype=#x1B[96mint#x1B[39;49;00m),#x1B[90m#x1B[39;49;00m
                #x1B[96mfloat#x1B[39;49;00m=np.arange(M, dtype=#x1B[96mfloat#x1B[39;49;00m),#x1B[90m#x1B[39;49;00m
                obj=[#x1B[96mstr#x1B[39;49;00m(i) #x1B[94mfor#x1B[39;49;00m i #x1B[95min#x1B[39;49;00m np.arange(M, dtype=#x1B[96mint#x1B[39;49;00m)],#x1B[90m#x1B[39;49;00m
            ),#x1B[90m#x1B[39;49;00m
            index=[#x1B[33mf#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[33mgene#x1B[39;49;00m#x1B[33m{#x1B[39;49;00mi#x1B[33m}#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m #x1B[94mfor#x1B[39;49;00m i #x1B[95min#x1B[39;49;00m np.arange(M, dtype=#x1B[96mint#x1B[39;49;00m)],#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        adata = AnnData(X=np.random.random((N, M)), obs=obs_df, var=var_df)#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        new_obs_df = pd.DataFrame(index=adata.obs_names)#x1B[90m#x1B[39;49;00m
        #x1B[94mfor#x1B[39;49;00m k #x1B[95min#x1B[39;49;00m obs_df.columns:#x1B[90m#x1B[39;49;00m
>           new_obs_df[k] = A.obs[k](adata)#x1B[90m#x1B[39;49;00m
                            ^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           TypeError: 'AdRef' object is not callable#x1B[0m

#x1B[1m#x1B[31mtests/test_base.py#x1B[0m:707: TypeError

tests.accessors.test_get::test_get_values[A.obsm['umap'][:, 0]-sp.csr_array]

Stack Traces | 0.007s run time

adata = AnnData object with n_obs × n_vars = 100 × 50
    obs: 'type'
    var: 'grp'
    obsm: 'umap'
    layers: 'a'
    varp: 'cons'
request = <SubRequest 'get_test_params' for <Function test_get_values[A.obsm['umap'][:, 0]-sp.csr_array]>>

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.fixture(#x1B[90m#x1B[39;49;00m
        params=[#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[90m#x1B[39;49;00m
                (ad_ref, ad_expected, *typ.values, convert),#x1B[90m#x1B[39;49;00m
                marks=typ.marks,#x1B[90m#x1B[39;49;00m
                #x1B[96mid#x1B[39;49;00m=#x1B[33mf#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[33m{#x1B[39;49;00mad_ref#x1B[33m}#x1B[39;49;00m#x1B[33m-#x1B[39;49;00m#x1B[33m{#x1B[39;49;00mtyp.id#x1B[33m}#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m paths, types, convert #x1B[95min#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
                (ND_PATHS, ND_TYPES, convert_ndarrays),#x1B[90m#x1B[39;49;00m
                (DF_PATHS, DF_TYPES, convert_dataframes),#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m ad_ref, ad_expected #x1B[95min#x1B[39;49;00m paths#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m typ #x1B[95min#x1B[39;49;00m types#x1B[90m#x1B[39;49;00m
        ]#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mget_test_params#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        adata: AnnData, request: pytest.FixtureRequest#x1B[90m#x1B[39;49;00m
    ) -> #x1B[96mtuple#x1B[39;49;00m[AnnData, AdRef, #x1B[96mtype#x1B[39;49;00m[InMemoryArray], InMemoryArray]:#x1B[90m#x1B[39;49;00m
        ad_ref, ad_expected, convert_array, type_expected_1d, type_expected_2d, convert = (#x1B[90m#x1B[39;49;00m
            request.param#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        convert(adata, convert_array)#x1B[90m#x1B[39;49;00m
        #x1B[94mreturn#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
            adata,#x1B[90m#x1B[39;49;00m
            ad_ref,#x1B[90m#x1B[39;49;00m
            type_expected_1d #x1B[94mif#x1B[39;49;00m #x1B[96mlen#x1B[39;49;00m(ad_ref.dims) == #x1B[94m1#x1B[39;49;00m #x1B[94melse#x1B[39;49;00m type_expected_2d,#x1B[90m#x1B[39;49;00m
>           _expected2np(ad_expected(adata), ad_ref),#x1B[90m#x1B[39;49;00m
                         ^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/accessors/test_get.py#x1B[0m:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31mtests/accessors/test_get.py#x1B[0m:48: in <lambda>
    #x1B[0m(A.obsm[#x1B[33m"#x1B[39;49;00m#x1B[33mumap#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m][#x1B[94m0#x1B[39;49;00m], #x1B[94mlambda#x1B[39;49;00m ad: ad.obsm[#x1B[33m"#x1B[39;49;00m#x1B[33mumap#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m][:, #x1B[94m0#x1B[39;49;00m]),#x1B[90m#x1B[39;49;00m
                                   ^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../anndata/88DSKUwu/hatch-test.min/lib/python3.12.../scipy/sparse/_index.py#x1B[0m:69: in __getitem__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._raise_on_1d_array_slice()#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <100x2 sparse array of type '<class 'numpy.float64'>'
	with 200 stored elements in Compressed Sparse Row format>

    #x1B[0m#x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_raise_on_1d_array_slice#x1B[39;49;00m(#x1B[96mself#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""We do not currently support 1D sparse arrays.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    This function is called each time that a 1D array would#x1B[39;49;00m
    #x1B[33m    result, raising an error instead.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    Once 1D sparse arrays are implemented, it should be removed.#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfrom#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[04m#x1B[96mscipy#x1B[39;49;00m#x1B[04m#x1B[96m.#x1B[39;49;00m#x1B[04m#x1B[96msparse#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[94mimport#x1B[39;49;00m sparray#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(#x1B[96mself#x1B[39;49;00m, sparray):#x1B[90m#x1B[39;49;00m
>           #x1B[94mraise#x1B[39;49;00m #x1B[96mNotImplementedError#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
                #x1B[33m'#x1B[39;49;00m#x1B[33mWe have not yet implemented 1D sparse slices; #x1B[39;49;00m#x1B[33m'#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
                #x1B[33m'#x1B[39;49;00m#x1B[33mplease index using explicit indices, e.g. `x[:, [0]]`#x1B[39;49;00m#x1B[33m'#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           NotImplementedError: We have not yet implemented 1D sparse slices; please index using explicit indices, e.g. `x[:, [0]]`#x1B[0m

#x1B[1m#x1B[31m../../../..../anndata/88DSKUwu/hatch-test.min/lib/python3.12.../scipy/sparse/_index.py#x1B[0m:46: NotImplementedError

tests.accessors.test_get::test_get_values[A.obsm['umap'][:, 1]-sp.csr_array]

Stack Traces | 0.007s run time

adata = AnnData object with n_obs × n_vars = 100 × 50
    obs: 'type'
    var: 'grp'
    obsm: 'umap'
    layers: 'a'
    varp: 'cons'
request = <SubRequest 'get_test_params' for <Function test_get_values[A.obsm['umap'][:, 1]-sp.csr_array]>>

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.fixture(#x1B[90m#x1B[39;49;00m
        params=[#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[90m#x1B[39;49;00m
                (ad_ref, ad_expected, *typ.values, convert),#x1B[90m#x1B[39;49;00m
                marks=typ.marks,#x1B[90m#x1B[39;49;00m
                #x1B[96mid#x1B[39;49;00m=#x1B[33mf#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[33m{#x1B[39;49;00mad_ref#x1B[33m}#x1B[39;49;00m#x1B[33m-#x1B[39;49;00m#x1B[33m{#x1B[39;49;00mtyp.id#x1B[33m}#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m paths, types, convert #x1B[95min#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
                (ND_PATHS, ND_TYPES, convert_ndarrays),#x1B[90m#x1B[39;49;00m
                (DF_PATHS, DF_TYPES, convert_dataframes),#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m ad_ref, ad_expected #x1B[95min#x1B[39;49;00m paths#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m typ #x1B[95min#x1B[39;49;00m types#x1B[90m#x1B[39;49;00m
        ]#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mget_test_params#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        adata: AnnData, request: pytest.FixtureRequest#x1B[90m#x1B[39;49;00m
    ) -> #x1B[96mtuple#x1B[39;49;00m[AnnData, AdRef, #x1B[96mtype#x1B[39;49;00m[InMemoryArray], InMemoryArray]:#x1B[90m#x1B[39;49;00m
        ad_ref, ad_expected, convert_array, type_expected_1d, type_expected_2d, convert = (#x1B[90m#x1B[39;49;00m
            request.param#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        convert(adata, convert_array)#x1B[90m#x1B[39;49;00m
        #x1B[94mreturn#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
            adata,#x1B[90m#x1B[39;49;00m
            ad_ref,#x1B[90m#x1B[39;49;00m
            type_expected_1d #x1B[94mif#x1B[39;49;00m #x1B[96mlen#x1B[39;49;00m(ad_ref.dims) == #x1B[94m1#x1B[39;49;00m #x1B[94melse#x1B[39;49;00m type_expected_2d,#x1B[90m#x1B[39;49;00m
>           _expected2np(ad_expected(adata), ad_ref),#x1B[90m#x1B[39;49;00m
                         ^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/accessors/test_get.py#x1B[0m:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31mtests/accessors/test_get.py#x1B[0m:49: in <lambda>
    #x1B[0m(A.obsm[#x1B[33m"#x1B[39;49;00m#x1B[33mumap#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m][#x1B[94m1#x1B[39;49;00m], #x1B[94mlambda#x1B[39;49;00m ad: ad.obsm[#x1B[33m"#x1B[39;49;00m#x1B[33mumap#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m][:, #x1B[94m1#x1B[39;49;00m]),#x1B[90m#x1B[39;49;00m
                                   ^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../anndata/88DSKUwu/hatch-test.min/lib/python3.12.../scipy/sparse/_index.py#x1B[0m:69: in __getitem__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._raise_on_1d_array_slice()#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <100x2 sparse array of type '<class 'numpy.float64'>'
	with 200 stored elements in Compressed Sparse Row format>

    #x1B[0m#x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_raise_on_1d_array_slice#x1B[39;49;00m(#x1B[96mself#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""We do not currently support 1D sparse arrays.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    This function is called each time that a 1D array would#x1B[39;49;00m
    #x1B[33m    result, raising an error instead.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    Once 1D sparse arrays are implemented, it should be removed.#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfrom#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[04m#x1B[96mscipy#x1B[39;49;00m#x1B[04m#x1B[96m.#x1B[39;49;00m#x1B[04m#x1B[96msparse#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[94mimport#x1B[39;49;00m sparray#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(#x1B[96mself#x1B[39;49;00m, sparray):#x1B[90m#x1B[39;49;00m
>           #x1B[94mraise#x1B[39;49;00m #x1B[96mNotImplementedError#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
                #x1B[33m'#x1B[39;49;00m#x1B[33mWe have not yet implemented 1D sparse slices; #x1B[39;49;00m#x1B[33m'#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
                #x1B[33m'#x1B[39;49;00m#x1B[33mplease index using explicit indices, e.g. `x[:, [0]]`#x1B[39;49;00m#x1B[33m'#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           NotImplementedError: We have not yet implemented 1D sparse slices; please index using explicit indices, e.g. `x[:, [0]]`#x1B[0m

#x1B[1m#x1B[31m../../../..../anndata/88DSKUwu/hatch-test.min/lib/python3.12.../scipy/sparse/_index.py#x1B[0m:46: NotImplementedError

tests.accessors.test_get::test_get_values[A.varp['cons']['gene-46', :]-sp.csr_array]

Stack Traces | 0.009s run time

adata = AnnData object with n_obs × n_vars = 100 × 50
    obs: 'type'
    var: 'grp'
    obsm: 'umap'
    layers: 'a'
    varp: 'cons'
request = <SubRequest 'get_test_params' for <Function test_get_values[A.varp['cons']['gene-46', :]-sp.csr_array]>>

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.fixture(#x1B[90m#x1B[39;49;00m
        params=[#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[90m#x1B[39;49;00m
                (ad_ref, ad_expected, *typ.values, convert),#x1B[90m#x1B[39;49;00m
                marks=typ.marks,#x1B[90m#x1B[39;49;00m
                #x1B[96mid#x1B[39;49;00m=#x1B[33mf#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[33m{#x1B[39;49;00mad_ref#x1B[33m}#x1B[39;49;00m#x1B[33m-#x1B[39;49;00m#x1B[33m{#x1B[39;49;00mtyp.id#x1B[33m}#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m paths, types, convert #x1B[95min#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
                (ND_PATHS, ND_TYPES, convert_ndarrays),#x1B[90m#x1B[39;49;00m
                (DF_PATHS, DF_TYPES, convert_dataframes),#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m ad_ref, ad_expected #x1B[95min#x1B[39;49;00m paths#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m typ #x1B[95min#x1B[39;49;00m types#x1B[90m#x1B[39;49;00m
        ]#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mget_test_params#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        adata: AnnData, request: pytest.FixtureRequest#x1B[90m#x1B[39;49;00m
    ) -> #x1B[96mtuple#x1B[39;49;00m[AnnData, AdRef, #x1B[96mtype#x1B[39;49;00m[InMemoryArray], InMemoryArray]:#x1B[90m#x1B[39;49;00m
        ad_ref, ad_expected, convert_array, type_expected_1d, type_expected_2d, convert = (#x1B[90m#x1B[39;49;00m
            request.param#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        convert(adata, convert_array)#x1B[90m#x1B[39;49;00m
        #x1B[94mreturn#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
            adata,#x1B[90m#x1B[39;49;00m
            ad_ref,#x1B[90m#x1B[39;49;00m
            type_expected_1d #x1B[94mif#x1B[39;49;00m #x1B[96mlen#x1B[39;49;00m(ad_ref.dims) == #x1B[94m1#x1B[39;49;00m #x1B[94melse#x1B[39;49;00m type_expected_2d,#x1B[90m#x1B[39;49;00m
>           _expected2np(ad_expected(adata), ad_ref),#x1B[90m#x1B[39;49;00m
                         ^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/accessors/test_get.py#x1B[0m:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31mtests/accessors/test_get.py#x1B[0m:50: in <lambda>
    #x1B[0m(A.varp[#x1B[33m"#x1B[39;49;00m#x1B[33mcons#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m][#x1B[33m"#x1B[39;49;00m#x1B[33mgene-46#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m, :], #x1B[94mlambda#x1B[39;49;00m ad: ad.varp[#x1B[33m"#x1B[39;49;00m#x1B[33mcons#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m][#x1B[94m46#x1B[39;49;00m, :]),#x1B[90m#x1B[39;49;00m
                                              ^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../anndata/88DSKUwu/hatch-test.min/lib/python3.12.../scipy/sparse/_index.py#x1B[0m:59: in __getitem__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._raise_on_1d_array_slice()#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <50x50 sparse array of type '<class 'numpy.float64'>'
	with 25 stored elements in Compressed Sparse Row format>

    #x1B[0m#x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_raise_on_1d_array_slice#x1B[39;49;00m(#x1B[96mself#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""We do not currently support 1D sparse arrays.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    This function is called each time that a 1D array would#x1B[39;49;00m
    #x1B[33m    result, raising an error instead.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    Once 1D sparse arrays are implemented, it should be removed.#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfrom#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[04m#x1B[96mscipy#x1B[39;49;00m#x1B[04m#x1B[96m.#x1B[39;49;00m#x1B[04m#x1B[96msparse#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[94mimport#x1B[39;49;00m sparray#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(#x1B[96mself#x1B[39;49;00m, sparray):#x1B[90m#x1B[39;49;00m
>           #x1B[94mraise#x1B[39;49;00m #x1B[96mNotImplementedError#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
                #x1B[33m'#x1B[39;49;00m#x1B[33mWe have not yet implemented 1D sparse slices; #x1B[39;49;00m#x1B[33m'#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
                #x1B[33m'#x1B[39;49;00m#x1B[33mplease index using explicit indices, e.g. `x[:, [0]]`#x1B[39;49;00m#x1B[33m'#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           NotImplementedError: We have not yet implemented 1D sparse slices; please index using explicit indices, e.g. `x[:, [0]]`#x1B[0m

#x1B[1m#x1B[31m../../../..../anndata/88DSKUwu/hatch-test.min/lib/python3.12.../scipy/sparse/_index.py#x1B[0m:46: NotImplementedError

tests.accessors.test_get::test_get_values[A.varp['cons'][:, 'gene-46']-sp.csr_array]

Stack Traces | 0.009s run time

adata = AnnData object with n_obs × n_vars = 100 × 50
    obs: 'type'
    var: 'grp'
    obsm: 'umap'
    layers: 'a'
    varp: 'cons'
request = <SubRequest 'get_test_params' for <Function test_get_values[A.varp['cons'][:, 'gene-46']-sp.csr_array]>>

    #x1B[0m#x1B[37m@pytest#x1B[39;49;00m.fixture(#x1B[90m#x1B[39;49;00m
        params=[#x1B[90m#x1B[39;49;00m
            pytest.param(#x1B[90m#x1B[39;49;00m
                (ad_ref, ad_expected, *typ.values, convert),#x1B[90m#x1B[39;49;00m
                marks=typ.marks,#x1B[90m#x1B[39;49;00m
                #x1B[96mid#x1B[39;49;00m=#x1B[33mf#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m#x1B[33m{#x1B[39;49;00mad_ref#x1B[33m}#x1B[39;49;00m#x1B[33m-#x1B[39;49;00m#x1B[33m{#x1B[39;49;00mtyp.id#x1B[33m}#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m,#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m paths, types, convert #x1B[95min#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
                (ND_PATHS, ND_TYPES, convert_ndarrays),#x1B[90m#x1B[39;49;00m
                (DF_PATHS, DF_TYPES, convert_dataframes),#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m ad_ref, ad_expected #x1B[95min#x1B[39;49;00m paths#x1B[90m#x1B[39;49;00m
            #x1B[94mfor#x1B[39;49;00m typ #x1B[95min#x1B[39;49;00m types#x1B[90m#x1B[39;49;00m
        ]#x1B[90m#x1B[39;49;00m
    )#x1B[90m#x1B[39;49;00m
    #x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92mget_test_params#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
        adata: AnnData, request: pytest.FixtureRequest#x1B[90m#x1B[39;49;00m
    ) -> #x1B[96mtuple#x1B[39;49;00m[AnnData, AdRef, #x1B[96mtype#x1B[39;49;00m[InMemoryArray], InMemoryArray]:#x1B[90m#x1B[39;49;00m
        ad_ref, ad_expected, convert_array, type_expected_1d, type_expected_2d, convert = (#x1B[90m#x1B[39;49;00m
            request.param#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m
        convert(adata, convert_array)#x1B[90m#x1B[39;49;00m
        #x1B[94mreturn#x1B[39;49;00m (#x1B[90m#x1B[39;49;00m
            adata,#x1B[90m#x1B[39;49;00m
            ad_ref,#x1B[90m#x1B[39;49;00m
            type_expected_1d #x1B[94mif#x1B[39;49;00m #x1B[96mlen#x1B[39;49;00m(ad_ref.dims) == #x1B[94m1#x1B[39;49;00m #x1B[94melse#x1B[39;49;00m type_expected_2d,#x1B[90m#x1B[39;49;00m
>           _expected2np(ad_expected(adata), ad_ref),#x1B[90m#x1B[39;49;00m
                         ^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
        )#x1B[90m#x1B[39;49;00m

#x1B[1m#x1B[31mtests/accessors/test_get.py#x1B[0m:187: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
#x1B[1m#x1B[31mtests/accessors/test_get.py#x1B[0m:51: in <lambda>
    #x1B[0m(A.varp[#x1B[33m"#x1B[39;49;00m#x1B[33mcons#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m][:, #x1B[33m"#x1B[39;49;00m#x1B[33mgene-46#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m], #x1B[94mlambda#x1B[39;49;00m ad: ad.varp[#x1B[33m"#x1B[39;49;00m#x1B[33mcons#x1B[39;49;00m#x1B[33m"#x1B[39;49;00m][:, #x1B[94m46#x1B[39;49;00m]),#x1B[90m#x1B[39;49;00m
                                              ^^^^^^^^^^^^^^^^^^^^^^#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31m../../../..../anndata/88DSKUwu/hatch-test.min/lib/python3.12.../scipy/sparse/_index.py#x1B[0m:69: in __getitem__
    #x1B[0m#x1B[96mself#x1B[39;49;00m._raise_on_1d_array_slice()#x1B[90m#x1B[39;49;00m
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <50x50 sparse array of type '<class 'numpy.float64'>'
	with 25 stored elements in Compressed Sparse Row format>

    #x1B[0m#x1B[94mdef#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[92m_raise_on_1d_array_slice#x1B[39;49;00m(#x1B[96mself#x1B[39;49;00m):#x1B[90m#x1B[39;49;00m
    #x1B[90m    #x1B[39;49;00m#x1B[33m"""We do not currently support 1D sparse arrays.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    This function is called each time that a 1D array would#x1B[39;49;00m
    #x1B[33m    result, raising an error instead.#x1B[39;49;00m
    #x1B[33m#x1B[39;49;00m
    #x1B[33m    Once 1D sparse arrays are implemented, it should be removed.#x1B[39;49;00m
    #x1B[33m    """#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
        #x1B[94mfrom#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[04m#x1B[96mscipy#x1B[39;49;00m#x1B[04m#x1B[96m.#x1B[39;49;00m#x1B[04m#x1B[96msparse#x1B[39;49;00m#x1B[90m #x1B[39;49;00m#x1B[94mimport#x1B[39;49;00m sparray#x1B[90m#x1B[39;49;00m
    #x1B[90m#x1B[39;49;00m
        #x1B[94mif#x1B[39;49;00m #x1B[96misinstance#x1B[39;49;00m(#x1B[96mself#x1B[39;49;00m, sparray):#x1B[90m#x1B[39;49;00m
>           #x1B[94mraise#x1B[39;49;00m #x1B[96mNotImplementedError#x1B[39;49;00m(#x1B[90m#x1B[39;49;00m
                #x1B[33m'#x1B[39;49;00m#x1B[33mWe have not yet implemented 1D sparse slices; #x1B[39;49;00m#x1B[33m'#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
                #x1B[33m'#x1B[39;49;00m#x1B[33mplease index using explicit indices, e.g. `x[:, [0]]`#x1B[39;49;00m#x1B[33m'#x1B[39;49;00m#x1B[90m#x1B[39;49;00m
            )#x1B[90m#x1B[39;49;00m
#x1B[1m#x1B[31mE           NotImplementedError: We have not yet implemented 1D sparse slices; please index using explicit indices, e.g. `x[:, [0]]`#x1B[0m

#x1B[1m#x1B[31m../../../..../anndata/88DSKUwu/hatch-test.min/lib/python3.12.../scipy/sparse/_index.py#x1B[0m:46: NotImplementedError

docs.accessors.rst::accessors.rst

Stack Traces | 0.202s run time

028 
029     E.g. to check if `adata.varm["PCs"]` has at least 30 columns:
030 
031     >>> A.varm["PCs"][:, 30] in adata
032     True
033 
034     or to extract the referenced vector:
035 
036     >>> ref = A.obs["louvain"]
037     >>> adata[ref].categories[:2]
Expected:
    Index(['CD4 T cells', 'CD14+ Monocytes'], dtype='str')
Got:
    Index(['CD4 T cells', 'CD14+ Monocytes'], dtype='object')

#x1B[1m#x1B[.../anndata/docs/accessors.rst#x1B[0m:37: DocTestFailure

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flying-sheep · 2026-02-06T15:52:29Z

@ilan-gold I didn’t update the docs since your last review, but the rest should be finished now!

src/anndata/acc/__init__.py

WIP accessors

4c7767c

flying-sheep added 13 commits January 9, 2026 11:22

fix: AdPath oversight

5103b0d

chore: more API

6878fc3

tests

c24206a

fix equality

6ffcb29

move parsing out

05d0900

parse all

61fc920

tests

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Merge branch 'main' into pa/acc

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pattern matching

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simpler

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WIP docs

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fix tests

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warnings

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flying-sheep added the skip-gpu-ci label Jan 15, 2026

flying-sheep added 5 commits January 15, 2026 17:16

move types up

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adref

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mostly fix docs

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really fix docs

fa7f136

fix test

0aa542c

flying-sheep added this to the 0.13.0 milestone Jan 15, 2026

flying-sheep added 8 commits January 16, 2026 16:01

rename all the things

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bit more explicit

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Merge branch 'main' into pa/acc

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WIP

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Merge branch 'main' into pa/acc

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ext metadata

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works!

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Merge branch 'main' into pa/acc

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