I have multiple arrays of thousands of elements. I need to open all of them and create an output array using multiple conditions. Using the instructions from this question (Iterating over a numpy array), I managed to create a solution, but it runs very slowly on my large arrays.
The code is this, run on a test sample with only two arrays (i can have more than two):
import numpy as np
from random import randint
import random
def declare_arrays():
random.seed(1)
w, h = 10, 10
mat1 = np.array([[randint(0, 100) for x in range(w)] for y in range(h)])
print (mat1, type(mat1))
random.seed(2)
mat2 = np.array([[randint(0, 100) for i in range(w)] for j in range(h)])
print (mat2, type(mat2))
return mat1, mat2
if __name__=="__main__":
arr1, arr2 = declare_arrays()
arr_out = np.zeros(arr1.shape)
for (i, j), val1 in np.ndenumerate(arr1):
val2 = arr2[i, j]
if (val1 > val2) and (val2 > 5):
arr_out[i, j] = val2
else:
arr_out[i, j] = val1
print("Arr out: ", arr_out)
This gives the result:
Arr out:
[[ 7. 11. 10. 8. 21. 15. 63. 39. 32. 60.]
[ 27. 48. 100. 26. 12. 20. 3. 49. 50. 77.]
[ 65. 47. 0. 56. 57. 34. 92. 29. 46. 13.]
[ 40. 3. 2. 3. 21. 69. 1. 30. 29. 27.]
[ 22. 41. 3. 17. 28. 65. 46. 63. 70. 29.]
[ 23. 29. 53. 28. 67. 58. 37. 2. 45. 46.]
[ 57. 12. 23. 51. 91. 37. 15. 83. 42. 31.]
[ 62. 35. 54. 64. 65. 24. 38. 36. 59. 44.]
[ 64. 50. 71. 4. 58. 31. 84. 28. 41. 85.]
[ 21. 46. 34. 89. 61. 39. 38. 47. 11. 56.]]
But this was run on a 10x10 array. If I run it on a 10000x10000 array, it takes enormous time. Is there a way to make this faster? Thanks for any help!
