class datacube.api.GridWorkflow(index, grid_spec=None, product=None)[source]

GridWorkflow deals with cell- and tile-based processing using a grid defining a projection and resolution.

Use GridWorkflow to specify your desired output grid. The methods list_cells() and list_tiles() query the index and return a dictionary of cell or tile keys, each mapping to a Tile object.

The Tile object can then be used to load the data without needing the index, and can be serialized for use with the distributed package.

__init__(index, grid_spec=None, product=None)[source]

Create a grid workflow tool.

Either grid_spec or product must be supplied.

  • index (datacube.index.Index) – The database index to use.
  • grid_spec (GridSpec) – The grid projection and resolution
  • product (str) – The name of an existing product, if no grid_spec is supplied.


__init__(index[, grid_spec, product]) Create a grid workflow tool.
cell_observations([cell_index, geopolygon, …]) List datasets, grouped by cell.
cell_sources(observations, group_by)
group_into_cells(observations, group_by) Group observations into a stack of source tiles.
list_cells([cell_index]) List cells that match the query.
list_tiles([cell_index]) List tiles of data, sorted by cell.
load(tile[, measurements, dask_chunks, …]) Load data for a cell/tile.
tile_sources(observations, group_by) Split observations into tiles and group into source tiles