Basics to use rasterMath

Compute substract and addition between two raster bands.

Import librairies

from museotoolbox.processing import RasterMath
from museotoolbox import datasets
import numpy as np

Load HistoricalMap dataset

raster,vector = datasets.load_historical_data()

Initialize rasterMath with raster

rM = RasterMath(raster)

print(rM.get_random_block())

Out:

Total number of blocks : 15
[[160 140 115]
 [207 187 162]
 [193 173 148]
 ...
 [136 109 100]
 [108  75  68]
 [130 135 112]]

Let’s suppose you want compute the difference between blue and green band. I suggest you to define type in numpy array to save space while creating the raster!

X = rM.get_random_block()


sub = lambda X : np.array((X[:,0]-X[:,1])).astype(np.int16)


rM.add_function(sub,out_image='/tmp/sub_lambda.tif')

Out:

Using datatype from numpy table : int16.
Detected 1 band for function <lambda>.

Use a python function to use arguments

def sub(X,band1=0,band2=1):
    outX = np.array((X[:,band1]-X[:,band2])).astype(np.int16)
    return outX

We can add keyword argument in the addFunction. This function is going to substract band2 from band 1

import time
t=time.time()
rM = RasterMath(raster)
rM.add_function(sub,out_image='/tmp/sub.tif',band1=1,band2=0,compress='high')

Out:

Total number of blocks : 15
Using datatype from numpy table : int16.
Detected 1 band for function sub.

Run the script

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

Out:

rasterMath... [........................................]0%

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%
Saved /tmp/sub.tif using function sub
0.3649485111236572

Plot result

#from osgeo import gdal
#from matplotlib import pyplot as plt
#src = gdal.Open('/tmp/sub.tif')
#plt.imshow(src.ReadAsArray())