def _get_tile_data(self, tile: Tile, band: gdal.Band):
''' Loads the band data for the tile and converts it to the output
data type. Returns a numpy array
'''
band_data = np.array(
band.ReadAsArray(tile.min_x, tile.min_y, tile.max_x - tile.min_x,
tile.max_y - tile.min_y))
np_out_dt = gdal_array.GDALTypeCodeToNumericTypeCode(
self.output_datatype)
nodata = band.GetNoDataValue()
if nodata is None:
# convert the input data to the datatype we are going to use for
# output
band_data_converted = band_data.astype(np_out_dt)
else:
# conversion for data that includes nodata is done via a masked array
# to prevent issues of converting a nodata value, losing some precision,
# and having it not match the nodata value exactly (causing nodata to
# not be nodata!)
masked_band_data = np.ma.masked_where(band_data == nodata,
band_data)
masked_band_data_converted = masked_band_data.astype(np_out_dt)
masked_band_data_converted.fill_value = self.output_nodata
band_data_converted = masked_band_data_converted.filled()
return band_data_converted
def read_band(band: gdal.Band,
full=True,
offset=None,
chunk=None) -> List[int]:
'''
Returns an array of a raster band
Parameters:
band: a gdal band
full: to open the entirety of the raster
offset: row offset
chunk: chunk size of opening partially
Returns:
an array of pixels
'''
logging.info(f'reading raster band: full={full}, offset={offset}')
if full:
arr = band.ReadAsArray()
else:
nrows = band.YSize
ncols = band.XSize
chunk = (nrows - offset) if (nrows - offset) < chunk else chunk
arr = band.ReadAsArray(0, offset, ncols, chunk)
return arr
def _get_block_size(self, density: gdal.Band, depth: gdal.Band,
uncertainty: gdal.Band) -> Tuple[int, int]:
''' Gets the block size from the existing datasets. This is used to
get optimium performance when reading data.
'''
# For now it is assumed that all bands will share a common block
# size. This is likely if they all share the same overall size,
# and were all produced by the same piece of software.
bs = depth.GetBlockSize()
return (bs[0], bs[1])
def get_band_minimum(band: gdal.Band):
ret = band.GetMinimum()
if ret is None:
band.ComputeStatistics(0)
return band.GetMinimum()