squidpy.im.ImageContainer.generate_spot_crops

ImageContainer.generate_spot_crops(adata, spatial_key='spatial', library_id=None, spot_diameter_key='spot_diameter_fullres', spot_scale=1.0, obs_names=None, as_array=False, squeeze=True, return_obs=False, **kwargs)

Iterate over anndata.AnnData.obs_names and extract crops.

Implemented for 10X spatial datasets. For Z-stacks, the specified library_id or list of library_id need to match the name of the Z-dimension. Always extracts 2D crops from the specified Z-dimension.

Parameters:

• adata (AnnData) – Annotated data object.

• spatial_key (str) – Key in anndata.AnnData.obsm where spatial coordinates are stored.

• library_id (Sequence[str] | str | None) –

  • If None, there should only exist one entry in anndata.AnnData.uns ['{spatial_key}'].

  • If a str, first search anndata.AnnData.obs ['{library_id}'] which contains the mapping from observations to library ids, then search anndata.AnnData.uns ['{spatial_key}'].

• spot_diameter_key (str) – Key in anndata.AnnData.uns ['{spatial_key}']['{library_id}']['scalefactors'] where the spot diameter is stored.

• spot_scale (float) – Scaling factor for the spot diameter. Larger values mean more context.

• obs_names (Iterable[Any] | None) – Observations from anndata.AnnData.obs_names for which to generate the crops. If None, all observations are used.

• as_array (str | bool) –

  • If True, yields a dict where keys are layers and values are numpy.ndarray.

  • If a str, yields one numpy.ndarray for the specified layer.

  • If a typing.Sequence, yields a tuple of numpy.ndarray for the specified layers.

  • Otherwise, yields squidpy.im.ImageContainer.

• squeeze (bool) – Remove singleton dimensions from the results if as_array = True.

• return_obs (bool) – Whether to also yield names from obs_names.

• kwargs (Any) – Keyword arguments for crop_center().

Yields:

• If return_obs = True, yields a tuple (crop, obs_name). Otherwise, yields just the crops.

• The type of the crops depends on as_array and the number of dimensions on squeeze.

Return type:

Iterator[ImageContainer] | Iterator[ndarray[tuple[Any, ...], dtype[Any]]] | Iterator[tuple[ndarray[tuple[Any, ...], dtype[Any]], ...]] | Iterator[dict[str, ndarray[tuple[Any, ...], dtype[Any]]]]