odc.stac.bench.BenchmarkContext

class odc.stac.bench.BenchmarkContext(cluster_info, npix, nbytes, dtype, shape, chunks, crs, transform, scenario='', temporal_id='', method='undefined', extras=<factory>)[source]

Benchmark Context Metadata.

Normalized representation of the task being benchmarked and the environment it is benchmarked in.

__init__(cluster_info, npix, nbytes, dtype, shape, chunks, crs, transform, scenario='', temporal_id='', method='undefined', extras=<factory>)

Methods

`__init__`(cluster_info, npix, nbytes, dtype, ...)
`render_timing_info`(times[, col_width])	Render textual representation of timing result.
`render_txt`([col_width])	Render textual representation for human consumption.
`to_pandas_dict`()	Extract parts one would need for analysis of results.

Attributes

`chunk_signature`	Render textual representation of chunk shapes.
`data_signature`	Render textual representation of data shape and type.
`method`	Some human name for method, (stackstac, odc-stac, rio-xarray)
`resolution`	Extract resolution.
`scenario`	Some human name for this benchmark data
`temporal_id`	Time period covered by data in human readable form
`cluster_info`	client.scheduler_info().copy()
`nworkers`	Number of workers in the cluster
`nthreads`	Number of threads in the cluster across all workers
`total_ram`	Total RAM across all workers of the cluster
`npix`	Total number of output pixels across all bands/timeslices
`nbytes`	Total number of output bytes across bands/timeslices
`dtype`	Data type of a pixel, assumed to be the same across bands
`shape`	Normalized shape in time,band,y,x order
`chunks`	Normalized chunk size in time,band,y,x order
`crs`	Projection used for output
`transform`	Linear mapping from pixel coordinates to CRS coordinates
`extras`	Any other parameters to capture