scanpy.pp.log1p

scanpy.pp.log1p(X, *, base=None, copy=False, chunked=None, chunk_size=None, layer=None, obsm=None)

scanpy.pp.log1p(X: scipy.sparse._base.spmatrix, *, base: Optional[numbers.Number] = None, copy: bool = False)

scanpy.pp.log1p(X: numpy.ndarray, *, base: Optional[numbers.Number] = None, copy: bool = False)

scanpy.pp.log1p(adata: anndata._core.anndata.AnnData, *, base: Optional[numbers.Number] = None, copy: bool = False, chunked: bool = False, chunk_size: Optional[int] = None, layer: Optional[str] = None, obsm: Optional[str] = None) → Optional[anndata._core.anndata.AnnData]

Logarithmize the data matrix.

Computes \(X = \log(X + 1)\), where \(log\) denotes the natural logarithm unless a different base is given.

Parameters

X : Union[AnnData, ndarray, spmatrix]

The (annotated) data matrix of shape n_obs × n_vars. Rows correspond to cells and columns to genes.

base : Optional[Number] (default: None)

Base of the logarithm. Natural logarithm is used by default.

copy : bool (default: False)

If an AnnData is passed, determines whether a copy is returned.

chunked : Optional[bool] (default: None)

Process the data matrix in chunks, which will save memory. Applies only to AnnData.

chunk_size : Optional[int] (default: None)

n_obs of the chunks to process the data in.

layer : Optional[str] (default: None)

Entry of layers to tranform.

obsm : Optional[str] (default: None)

Entry of obsm to transform.

Returns

Returns or updates data, depending on copy.