Replacing Numpy elements if condition is met

Question

I have a large numpy array that I need to manipulate so that each element is changed to either a 1 or 0 if a condition is met  will be used as a pixel mask later   There are about 8 million elements in the array and my current method takes too long for the reduction pipeline   for  y x   value in numpy ndenumerate mask data         if mask data y x  lt 3   Good Pixel         mask data y x  1     elif mask data y x  gt 3   Bad Pixel         mask data y x  0   Is there a numpy function that would speed this up

User · Answer

I am not sure I understood your question, but if you write:

mask_data[:3, :3] = 1
mask_data[3:, 3:] = 0

This will make all values of mask data whose x and y indexes are less than 3 to be equal to 1 and all rest to be equal to 0

User · Answer

The quickest  and most flexible  way is to use np where  which chooses between two arrays according to a mask array of true and false values     import numpy as np a   np random randint 0  5  size  5  4   b   np where a lt 3 0 1  print  a   a  print   print  b   b    which will produce   a    1 4 0 1    1 3 2 4    1 0 2 1    3 1 0 0    1 4 0 1    b    0 1 0 0    0 1 0 1    0 0 0 0    1 0 0 0    0 1 0 0

User · Answer

gt  gt  gt  import numpy as np  gt  gt  gt  a   np random randint 0  5  size  5  4    gt  gt  gt  a array   4  2  1  1           3  0  1  2           2  0  1  1           4  0  2  3           0  0  0  2     gt  gt  gt  b   a  lt  3  gt  gt  gt  b array   False   True   True   True           False   True   True   True            True   True   True   True           False   True   True  False            True   True   True   True    dtype bool   gt  gt  gt    gt  gt  gt  c   b astype int   gt  gt  gt  c array   0  1  1  1           0  1  1  1           1  1  1  1           0  1  1  0           1  1  1  1      You can shorten this with    gt  gt  gt  c    a  lt  3  astype int

User · Answer

You can create your mask array in one step like this  mask data   input mask data  lt  3   This creates a boolean array which can then be used as a pixel mask  Note that we haven t changed the input array  as in your code  but have created a new array to hold the mask data - I would recommend doing it this way    gt  gt  gt  input mask data   np random randint 0  5   3  4    gt  gt  gt  input mask data array   1  3  4  0           4  1  2  2           1  2  3  0     gt  gt  gt  mask data   input mask data  lt  3  gt  gt  gt  mask data array    True  False  False   True           False   True   True   True            True   True  False   True    dtype bool   gt  gt  gt

User · Answer

gt  gt  gt  a   np random randint 0  5  size  5  4    gt  gt  gt  a array   0  3  3  2           4  1  1  2           3  4  2  4           2  4  3  0           1  2  3  4     gt  gt  gt    gt  gt  gt  a a  gt  3    -101  gt  gt  gt  a array      0     3     3     2           -101     1     1     2              3  -101     2  -101              2  -101     3     0              1     2     3  -101     gt  gt  gt    See  eg  Indexing with boolean arrays

[python] Replacing Numpy elements if condition is met

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