Efficient way for mapping array values-CodePudding

I have numpy array, and I do want to map the ranges of the array values into specific values:

import numpy as np

array = [
[328, 124, 146, 117, 147, 109, 139, 100, 140, 98, 128, 101, 121, 95, 111, 89, 114, 82, 106, 77, 97, 71, 273], 
[132, 121, 121, 117, 114, 110, 99, 163, 89, 81, 83, 81, 91, 91, 89, 144, 89, 81, 81, 75, 69, 68, 68], 
[164, 114, 117, 110, 105, 105, 97, 91, 142, 80, 131, 77, 122, 81, 108, 74, 77, 75, 69, 71, 68, 63, 87], 
[121, 116, 110, 103, 104, 99, 90, 84, 77, 137, 77, 125, 99, 128, 79, 73, 69, 71, 68, 70, 60, 53, 64], 
[151, 100, 103, 99, 379, 88, 264, 78, 135, 77, 121, 97, 113, 75, 100, 62, 232, 65, 350, 54, 56, 50, 75], 
[101, 93, 121, 90, 84, 92, 86, 78, 74, 113, 66, 115, 72, 100, 63, 67, 60, 57, 57, 47, 47, 44, 43], 
[146, 89, 116, 86, 87, 85, 79, 73, 101, 59, 98, 63, 107, 64, 62, 56, 56, 52, 48, 50, 46, 38, 68], 
[89, 113, 108, 83, 82, 78, 74, 125, 69, 67, 57, 65, 67, 56, 58, 105, 49, 52, 69, 59, 63, 34, 43], 
[127, 87, 78, 79, 105, 74, 163, 64, 61, 61, 56, 235, 61, 48, 51, 43, 134, 40, 63, 28, 31, 26, 55], 
[76, 79, 75, 70, 72, 69, 62, 58, 53, 52, 49, 50, 53, 43, 39, 37, 41, 36, 31, 29, 26, 18, 17], 
[274, 73, 96, 71, 92, 64, 87, 57, 82, 53, 81, 30, 73, 41, 66, 37, 52, 31, 49, 22, 50, 15, 211]]

array = np.array(array)

def mapVal(val, table = [140, 195, 250, 350, 450]):
    """
    Function: mapVal, to quantify depth values manually.
    ---
    Parameters:
    @param: val, nd-array.
    @param: table, list, mapping table.

    ---
    @return: None.
    """
    
    val[val <= table[0]]  = 0
    val[val >  table[0] & val <=  table[1]]  = 1
    val[val >  table[1] & val <=  table[2]]  = 2
    val[val >  table[2] & val <=  table[3]]  = 3
    val[val >  table[3] & val <=  table[4]]  = 4
    val[val >  table[4]]  = 5

But I'm getting this Error:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

I'm awar that I can use inefficient loops, but I do like to ask if there is a better solution, thanks in advance.

CodePudding user response：

As mentioned in the comments, first solve your ValueError by using parenthesis around your conditional checks to avoid having the bit-wise and operator & be evaluated first, which is the default behaviour.

For example:

val[(val > table[0]) & (val <= table[1])]

Next, for your search for a more efficient method, you can use the built-in np.digitize function to help you do what you're doing:

np.digitize(array, table)

CodePudding user response：

There is a much simpler and much faster way to do what you want:

table = np.array([140, 195, 250, 350, 450])
result = np.searchsorted(table, array)

This assume that the table is sorted though which is the case here. If it is not, then you can use np.argsort to reorder the table and np.digitize to remap the values.