气温变化趋势

The provided solution aims to find the longest contiguous subarray where the temperature trends between two locations are the same. Firstly, it calculates the daily temperature change for each location and represents it as -1, 0, or 1 (decrease, no change, increase). These trends are stored in two lists (t1 for location A, t2 for location B). Then, by iterating over these trend lists, the solution counts the length of contiguous subarrays where the trends match. The maximum length found during this process is the output.

时间复杂度: O(n)

空间复杂度: O(n)

class Solution:
    def temperatureTrend(self, temperatureA: List[int], temperatureB: List[int]) -> int:
        t1, t2, l = [], [], len(temperatureA)
        # Compute trend differences for each day for both locations
        for i in range(1, l):
            n1, n2 = temperatureA[i] - temperatureA[i-1], temperatureB[i] - temperatureB[i-1]
            # Convert differences to -1, 0, 1 representing down, same, up
            t1.append(n1 // abs(n1) if n1 != 0 else 0)
            t2.append(n2 // abs(n2) if n2 != 0 else 0)
        
        ans, days = 0, 0
        # Compare trend arrays to find the longest matching subarray
        for i in range(l-1):
            if t1[i] != t2[i]:
                ans = max(ans, days)  # Update maximum length found
                days = 0  # Reset days counter when mismatch found
            else:
                days += 1  # Increment days counter when trends match
        ans = max(ans, days)  # Check last subarray
        return ans