
    Design a fraud detection function that analyzes suspicious transaction patterns by determining how many unique temporal views of a transaction sequence contain an odd number of flagged activities when the sequence is shifted across n different time intervals. The transaction sequence is represented as a binary string where 1 indicates a suspicious activity and 0 indicates normal activity, and each rotation of this sequence represents viewing the same pattern from a different temporal starting point. The function should help identify how many of these temporal views contain an odd count of suspicious activities, which could indicate a coordinated fraud pattern.
    

    
def odd_Equivalent(s, n):
    """
    Design a fraud detection function that analyzes suspicious transaction patterns by determining how many unique temporal views of a transaction sequence contain an odd number of flagged activities when the sequence is shifted across n different time intervals. The transaction sequence is represented as a binary string where 1 indicates a suspicious activity and 0 indicates normal activity, and each rotation of this sequence represents viewing the same pattern from a different temporal starting point. The function should help identify how many of these temporal views contain an odd count of suspicious activities, which could indicate a coordinated fraud pattern.
    

    """
def odd_Equivalent(s, n):
    """
    Design a fraud detection function that analyzes suspicious transaction patterns by determining how many unique temporal views of a transaction sequence contain an odd number of flagged activities when the sequence is shifted across n different time intervals. The transaction sequence is represented as a binary string where 1 indicates a suspicious activity and 0 indicates normal activity, and each rotation of this sequence represents viewing the same pattern from a different temporal starting point. The function should help identify how many of these temporal views contain an odd count of suspicious activities, which could indicate a coordinated fraud pattern.
    

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