anndata.acc.MultiAcc

class anndata.acc.MultiAcc(dim, k, *, ref_class)

Bases: RefAcc[R, int, MuData | AnnData]

Reference accessor for arrays from multi-dimensional containers (A.obsm/A.varm).

Examples

>>> from anndata.acc import A, MultiAcc
>>> assert isinstance(A.obsm["pca"], MultiAcc)
>>> A.obsm["pca"][:, 0]
A.obsm['pca'][:, 0]

Note that something like this is invalid, as it references an array that isn’t aligned to an anndata dimension.

A.varm["PCs"]["cell-0", :]

Attributes

dim: Literal['obs', 'var']

Dimension this accessor refers to, e.g. A.obsm[k].dim == 'obs'.

k: str

Key this accessor refers to, e.g. A.varm['x'].k == 'x'.

ref_class: type[AdRef[Hashable, MuData | AnnData]]

Methods

dims(i, /)

Get along which dimensions the referenced array is.

Return type:

Collection[Literal['obs', 'var']]

get(data, i=Return Full Array, /)

Overloads:

• self, data (MuData | AnnData) → FullArray

• self, data (MuData | AnnData), i (int) → InMemoryArray

Get the indexed array from the AnnData object at idx.

When idx is omitted, return the full array one level up instead. This has the same semantics as the AdRef path but one level up: adata[A.obs] returns the full DataFrame and adata[A.obsm["pca"]] the full numpy.ndarray. These both have defined shape-like properties (or awkward.Array), unlike, for example, obsm or similar.

idx_repr(i)

Get a string representation of the index.

Return type:

str

isin(data, idx=None)

Check if the referenced array is in the AnnData object.

Return type:

bool

static process_idx(i, /)

Return type:

int | list[int] | pd.Index[int]