Hi,
I have this script in python that i need to apply for very large arrays (arrays
coming from satellite images).
The script works grate but i would like to speed up the process.
The larger computational time is in the for loop process.
Is there is a way to improve that part?
Should be better to use dic() instead of np.ndarray for saving the results?
and if yes how i can make the sum in dic()(like in the correspondent
matrix[row_c,1] = matrix[row_c,1] + valuesRaster[row,col] )?
If the dic() is the solution way is faster?
Thanks
Giuseppe
import numpy as np
import sys
from time import clock, time
# create the arrays
start = time()
valuesRaster = np.random.random_integers(0, 100, 100).reshape(10, 10)
valuesCategory = np.random.random_integers(1, 10, 100).reshape(10, 10)
elapsed = (time() - start)
print(elapsed , "create the data")
start = time()
categories = np.unique(valuesCategory)
matrix = np.c_[ categories , np.zeros(len(categories))]
elapsed = (time() - start)
print(elapsed , "create the matrix and append a colum zero ")
rows = 10
cols = 10
start = time()
for col in range(0,cols):
for row in range(0,rows):
for row_c in range(0,len(matrix)) :
if valuesCategory[row,col] == matrix[row_c,0] :
matrix[row_c,1] = matrix[row_c,1] + valuesRaster[row,col]
break
elapsed = (time() - start)
print(elapsed , "loop in the data ")
print (matrix)
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