rasterMath with custom block size, mask, and in 3 dimensions

Tips to use rasterMath by defining its block size and to receive a full block (not a array with one pixel per row.)

Tips : A function readBlockPerBlock() yields each block, without saving results to a new raster.

Import librairies

from museotoolbox.processing import RasterMath,image_mask_from_vector
from museotoolbox import datasets
from matplotlib import pyplot as plt
import numpy as np

Load HistoricalMap dataset

raster,vector = datasets.load_historical_data()

Initialize rasterMath with raster

# Set return_3d to True to have full block size (not one pixel per row)
# Create raster mask to only keep pixel inside polygons.

image_mask_from_vector(vector,raster,'/tmp/mask.tif',invert=False)

rM = RasterMath(raster,in_image_mask='/tmp/mask.tif',return_3d=True)
#rM.addInputRaster('/tmp/mask.tif')
print(rM.get_random_block().shape)

Out:

Total number of blocks : 15
(256, 256, 3)

Plot blocks

x = rM.get_random_block()

rM.add_function(np.mean,'/tmp/mean.tif',axis=2,out_np_dt=np.int16)

rM.run()

from osgeo import gdal
dst = gdal.Open('/tmp/mean.tif')
arr = dst.GetRasterBand(1).ReadAsArray()
plt.imshow(np.ma.masked_where(arr == rM._outputs[0]['nodata'], arr))

Out:

Detected 1 band for function mean.
No data is set to : -32768.
Batch processing (15 blocks using 11Mo of ram)

rasterMath... [##......................................]6%

rasterMath... [#####...................................]13%

rasterMath... [########................................]20%

rasterMath... [##########..............................]26%

rasterMath... [#############...........................]33%

rasterMath... [################........................]40%

rasterMath... [##################......................]46%

rasterMath... [#####################...................]53%

rasterMath... [########################................]60%

rasterMath... [##########################..............]66%

rasterMath... [#############################...........]73%

rasterMath... [################################........]80%

rasterMath... [##################################......]86%

rasterMath... [#####################################...]93%

rasterMath... [########################################]100%

<matplotlib.image.AxesImage object at 0x7f7fdf87d690>