datacube.api.GridWorkflow¶

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.

Parameters:	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.

Methods

`__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
`update_tile_lineage`(tile)