skmap.parallel.blocks.RasterBlockAggregator#

class RasterBlockAggregator(reader=None)[source]#

Bases: object

Class for aggregating results of block wise raster processing into a single result.

Parameters:

reader (RasterBlockReader) – RasterBlockReader instance to use for reading rasters.

For full usage examples please refer to the block processing tutorial notebook [1].

References

[1] Raster block processing tutorial

Examples

>>> from skmap.parallel.blocks import RasterBlockReader, RasterBlockAggregator
>>>
>>> fp = 'https://s3.eu-central-1.wasabisys.com/skmap/lcv/lcv_landcover.hcl_lucas.corine.rf_p_30m_0..0cm_2019_skmap_epsg3035_v0.1.tif'
>>>
>>> reader = RasterBlockReader(fp)
>>> aggregator = RasterBlockAggregator(reader)

Methods

aggregate

Aggregates results of block wise raster processing into a single result.
aggregate(src_path, geometry, block_func, agg_func=<function mean>, **kwargs)[source]#

Aggregates results of block wise raster processing into a single result.

Parameters:

  • src_path (Union[str, Iterable[str]]) – Path(s) (or URLs) of the raster file(s) to read. If aggregator is initialized with reader=None, the first file in src_path will be used to initialize a new reader.

  • geometry (dict) – The bounding geometry within which to read raster blocks, given as a dictionary (with the GeoJSON geometry schema).

  • block_func (Callable) – Callable to perform on the data for each block.

  • agg_func (Callable) – Callable to produce an aggregation of block-wise results.

  • **kwargs –

    Additional keyword arguments passed to RasterBlockReader.read_overlay().

Returns:

The result of agg_func called with block-wise block_func results as the argument.

For full usage examples please refer to the block processing tutorial notebook [1].

References

[1] Raster block processing tutorial

Examples

>>> from skmap.parallel import blocks
>>> geom = {
...     'type': 'Polygon',
...     'coordinates': [[
...         [4765389, 2441103],
...         [4764441, 2439352],
...         [4767369, 2438696],
...         [4761659, 2441949],
...         [4765389, 2441103],
...     ]],
... }
>>>
>>> def urban_fabric_area(lc):
...     return (lc==1) * 9e-4 # spatial resolution is 30x30 m
>>>
>>> result = aggregator.aggregate(
...     fp, geom,
...     block_func=urban_fabric_area,
...     agg_func=np.sum,
... )