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Import large .tiff file as sparse matrix

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())

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

If array is 4.4gB, (79530, 54980)

In [62]: (79530 * 54980) / 1e9
Out[62]: 4.3725594    # 4.4gB makes sense for 1 byte/element
In [63]: (79530 * 54980) * 0.16        # 16% density
Out[63]: 699609504.0                # number of nonzero values

creating 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 csr, the 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.

Memory error when converting matrix to sparse matrix, specified dtype is invalid

is a recent case of a memory error – which occurred in nonzero step.