Using rasterMath with 3d block or 2d block

Test notebook to validate code.

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=True)

import time
t0=time.time()
for return_3d in [True,False]:

    rM = RasterMath(raster,in_image_mask='/tmp/mask.tif',return_3d=return_3d)

    rM.custom_block_size(128,128) # block of  128x128

    x = rM.get_block()

    # Returns with only 1 dimension
    returnFlatten = lambda x : x[...,0]

    # Returns 3x the original last dimension
    addOneBand = lambda x : np.repeat(x,3,axis=x.ndim-1)
    # Add functions to rasterMath
    rM.add_function(addOneBand,'/tmp/x_repeat_{}.tif'.format(str(return_3d)))
    rM.add_function(returnFlatten,'/tmp/x_flatten_{}.tif'.format(str(return_3d)))

    rM.run()
print(time.time()-t0)

from osgeo import gdal
dst = gdal.Open('/tmp/x_flatten_True.tif')
arr = dst.GetRasterBand(1).ReadAsArray()
plt.imshow(np.ma.masked_where(arr == np.min(arr), arr))

Out:

Total number of blocks : 15
Total number of blocks : 45
Using datatype from numpy table : uint8.
Detected 9 bands for function <lambda>.
No data is set to : 0.
Using datatype from numpy table : uint8.
Detected 1 band for function <lambda>.
No data is set to : 0.
Batch processing (45 blocks using 10Mo of ram)

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Total number of blocks : 15
Total number of blocks : 45
Using datatype from numpy table : uint8.
Detected 9 bands for function <lambda>.
No data is set to : 0.
Using datatype from numpy table : uint8.
Detected 1 band for function <lambda>.
No data is set to : 0.
Batch processing (45 blocks using 10Mo of ram)

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