
    Given a sequence of transaction flags representing various fraud indicators in binary format, determine how many unique fraud patterns emerge when shifting the sequence through n different time windows. Each shift represents a different temporal analysis window, and a pattern is considered suspicious if its decimal value is odd when interpreted as a binary number.
    

    
def odd_Equivalent(s, n):
    """
    Given a sequence of transaction flags representing various fraud indicators in binary format, determine how many unique fraud patterns emerge when shifting the sequence through n different time windows. Each shift represents a different temporal analysis window, and a pattern is considered suspicious if its decimal value is odd when interpreted as a binary number.
    

    """
def odd_Equivalent(s, n):
    """
    Given a sequence of transaction flags representing various fraud indicators in binary format, determine how many unique fraud patterns emerge when shifting the sequence through n different time windows. Each shift represents a different temporal analysis window, and a pattern is considered suspicious if its decimal value is odd when interpreted as a binary number.
    

    """
    count = 0
    for i in range(0, n):
        if s[i] == '1':
            count = count + 1
    return count