I have a large .tiff file (4.4gB, 79530 x 54980 values) with 1 band. Since only 16% of the values are valid, I was thinking it’s better to import the file as sparse matrix, to save RAM. When I first open it as
np.array and then transform it into a sparse matrix using
csr_matrix(), my kernel already crashes. See code below.
from osgeo import gdal import numpy as np from scipy.sparse import csr_matrix ds = gdal.Open("file.tif") band = ds.GetRasterBand(1) array = np.array(band.ReadAsArray()) csr_matrix(array)
Is there a better way to work with this file? In the end I have to make calculations based on the values in the raster. (Unfortunately, due to confidentiality, I cannot attach the relevant file.)
Can you tell where the crash occurs?
band = ds.GetRasterBand(1) temp = band.ReadAsArray() array = np.array(temp) # if temp is already an array, you don't need this csr_matrix(array)
array is 4.4gB, (79530, 54980)
In : (79530 * 54980) / 1e9 Out: 4.3725594 # 4.4gB makes sense for 1 byte/element In : (79530 * 54980) * 0.16 # 16% density Out: 699609504.0 # number of nonzero values
csr requires doing
np.nonzero(array) to get the indices. That will produce 2 arrays of this 0.7 * 8 Gb size (indices are 8 byte ints).
coo format actually requires those 2 arrays plus 0.7 for the nonzero values – about 12 Gb . Converted to
row attribute is reduced to 79530 elements – so about 7 Gb . (corrected for 8 bytes/element)
So at 16% density, the sparse format is, at it’s best, is still larger than the dense version.
is a recent case of a memory error – which occurred in