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Python3 Program for Queries to find maximum sum contiguous subarrays of given length in a rotating array

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  • Last Updated : 27 Jan, 2022

Given an array arr[] of N integers and Q queries of the form {X, Y} of the following two types:

  • If X = 1, rotate the given array to the left by Y positions.
  • If X = 2, print the maximum sum subarray of length Y in the current state of the array.

Examples: 

Input: N = 5, arr[] = {1, 2, 3, 4, 5}, Q = 2, Query[][] = {{1, 2}, {2, 3}}
Output:
Query 1: 3 4 5 1 2
Query 2: 12
Explanation:
Query 1: Shift array to the left 2 times: {1, 2, 3, 4, 5} -> {2, 3, 4, 5, 1} -> {3, 4, 5, 1, 2}
Query 2: Maximum sum subarray of length 3 is {3, 4, 5} and the sum is 12

Input: N = 5, arr[] = {3, 4, 5, 1, 2}, Q = 3, Query[][] = {{1, 3}, {1, 1}, {2, 4}}
Output: 
Query 1: 1 2 3 4 5
Query 2: 2 3 4 5 1
Query 3: 14
Explanation:
Query 1: Shift array to the left 3 times: {3, 4, 5, 1, 2} -> {4, 5, 1, 2, 3} -> {5, 1, 2, 3, 4} -> {1, 2, 3, 4, 5}
Query 2: Shift array to the left 1 time: {1, 2, 3, 4, 5} -> {2, 3, 4, 5, 1}
Query 3: Maximum sum subarray of length 4 is {2, 3, 4, 5} and sum is 14

Naive Approach: The simplest approach is to rotate the array by shifting elements one by one up to distance Y for queries is of type 1 and generating the sum of all the subarrays of length Y and print the maximum sum if the query is of type 2
Time Complexity: O(Q*N*Y)
Auxiliary Space: O(N)

Efficient Approach: To optimize the above approach, the idea is to use the Juggling Algorithm for array rotation and for finding the maximum sum subarray of length Y, use the Sliding Window Technique. Follow the steps below to solve the problem:

  1. If X = 1, rotate the array by Y, using the Juggling Algorithm.
  2. Otherwise, if X = 2, find the maximum sum subarray of length Y using the Sliding Window Technique.
  3. Print the array if query X is 1.
  4. Otherwise, print the maximum sum subarray of size Y.

Below is the implementation of the above approach:

Python3




# Python3 program for the above approach
  
# Function to calculate the maximum
# sum of length k
def MaxSum(arr, n, k):
      
    i, max_sum = 0, 0
    sum = 0
  
    # Calculating the max sum for
    # the first k elements
    while i < k:
        sum += arr[i]
        i += 1
          
    max_sum = sum
  
    # Find subarray with maximum sum
    while (i < n):
  
        # Update the sum
        sum = sum - arr[i - k] + arr[i]
          
        if (max_sum < sum):
            max_sum = sum
              
        i += 1
  
    # Return maximum sum
    return max_sum
  
# Function to calculate gcd of the
# two numbers n1 and n2
def gcd(n1, n2):
      
    # Base Case
    if (n2 == 0):
        return n1
  
    # Recursively find the GCD
    else:
        return gcd(n2, n1 % n2)
  
# Function to rotate the array by Y
def RotateArr(arr, n, d):
      
    # For handling k >= N
    i = 0
    j = 0
    d = d % n
  
    # Dividing the array into
    # number of sets
    no_of_sets = gcd(d, n)
  
    for i in range(no_of_sets):
        temp = arr[i]
        j = i
  
        # Rotate the array by Y
        while (True):
            k = j + d
  
            if (k >= n):
                k = k - n
            if (k == i):
                break
  
            arr[j] = arr[k]
            j = k
  
        # Update arr[j]
        arr[j] = temp
  
    # Return the rotated array
    return arr
  
# Function that performs the queries
# on the given array
def performQuery(arr, Q, q):
  
    N = len(arr)
  
    # Traverse each query
    for i in range(q):
  
        # If query of type X = 1
        if (Q[i][0] == 1):
            arr = RotateArr(arr, N, Q[i][1])
  
            # Print the array
            for t in arr:
                print(t, end = " ")
                  
            print()
  
        # If query of type X = 2
        else:
            print(MaxSum(arr, N, Q[i][1]))
  
# Driver Code
if __name__ == '__main__':
      
    # Given array arr[]
    arr = [ 1, 2, 3, 4, 5 ]
  
    q = 5
  
    # Given Queries
    Q = [ [ 1, 2 ], [ 2, 3 ],
          [ 1, 3 ], [ 1, 1 ],
          [ 2, 4 ] ]
  
    # Function call
    performQuery(arr, Q, q)
  
# This code is contributed by mohit kumar 29

Output: 

3 4 5 1 2 
12
1 2 3 4 5 
2 3 4 5 1 
14

Time Complexity: O(Q*N), where Q is the number of queries, and N is the size of the given array.
Auxiliary Space: O(N)

Please refer complete article on Queries to find maximum sum contiguous subarrays of given length in a rotating array for more details!


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