iota2.common.raster_utils

Functions

CreateBandMathApplication(OtbParameters)	IN: parameter consistency are not tested here (done in otb's applications) every value could be string
CreateBandMathXApplication(OtbParameters)	IN: parameter consistency are not tested here (done in otb's applications) every value could be string
compress_raster(raster_in, raster_out[, ...])	compress a raster thanks to gdal_translate
extract_raster_bands(in_raster, out_raster, ...)	use gdal_translate to extract bands of interest, band's index start at 1.
get_boundary_with_padding(boundary, padding)
get_chunks_boundaries(shape, chunk_config)
get_padding(chunk_config, boundary, size_x, ...)
get_raster_n_bands(raster)	usage get raster's number of bands
get_rasterio_datasets(array_proj[, ...])	transform numpy arrays (containing projection data) to rasterio datasets it works only with 3D arrays
insert_external_function_to_pipeline(...[, ...])	Apply a python function to an otb pipeline
memory_usage_psutil([unit])	Return the memory usage :param unit: the expect unit for return ram (MB or GB)
merge_rasters(rasters, output_path, epsg_code)	merge geo-referenced rasters thanks to rasterio.merge
process_function(function, padding[, ...])	apply python function to the output of an otbApplication
raster_to_array(InRaster)	convert a raster to an array
re_encode_raster(in_raster, out_raster, ...)	according to a conversion table, re_encode the monoband raster values
reorder_proba_map(proba_map_path_in, ...[, ...])	reorder the probability map
roi_raster(in_raster, out_raster, roi_coord)	use rasterio to extract raster region of interest
run(cmd[, desc, env, logger])	Launch a system command and raise an execption if fail
split_raster(otb_pipelines, chunk_config, ...)	extract regions of interest over the otbApplication

Classes

ChunkConfig(chunk_size_mode, ...[, ...])
partial	partial(func, *args, **keywords) - new function with partial application of the given arguments and keywords.