squidpy.im.ImageContainer.interactive

ImageContainer.interactive(adata, spatial_key='spatial', library_key=None, library_id=None, cmap='viridis', palette=None, blending='opaque', symbol='disc', key_added='shapes')[source]

Launch napari viewer.

Parameters:

  • adata (AnnData) – Annotated data object.

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

  • library_key (Optional[str]) – Key in adata.AnnData.obs specifying mapping between observations and library ids. Required if the container has more than 1 Z-dimension.

  • library_id (Union[str, Sequence[str], None]) – Subset of library ids to visualize. If None, visualize all library ids.

  • cmap (str) – Colormap for continuous variables.

  • palette (Optional[str]) – Colormap for categorical variables in anndata.AnnData.obs. If None, use scanpy’s default.

  • blending (Literal['opaque', 'translucent', 'additive']) – Method which determines how RGB and alpha values of napari.layers.Shapes are mixed.

  • symbol (Literal['disc', 'square']) –

    Symbol to use for the spots. Valid options are:

    • ’disc’ - circle.

    • ’square’ - square.

  • key_added (str) –

    Key where to store napari.layers.Shapes, which can be exported by pressing SHIFT-E:

    • anndata.AnnData.obs ['{layer_name}_{key_added}'] - boolean mask containing the selected cells.

    • anndata.AnnData.uns ['{layer_name}_{key_added}']['meshes'] - list of numpy.array, defining a mesh in the spatial coordinates.

    See napari’s tutorial for more information about different mesh types, such as circles, squares etc.

Return type:

TypeVar(Interactive)

Returns:

: Interactive view of this container. Screenshot of the canvas can be taken by squidpy.pl.Interactive.screenshot().