import pandas as pd
import numpy as np


'''
工作量方差
sizeWeight 区域站点数量权重
distanceWeight 区域调度距离权重
这两个权重构成了工作量指标
'''

def get_workload_variance(partition, size_weight, distance_weight):
        distance_filepath = "D:\\JetBrains\\PcharmWorkSpace\\Paper2\\NYC\\data\\distanceMatrix.csv"
        distance_matrix = pd.read_csv(distance_filepath).as_matrix()
        workload = []
        areasizelist = []
        maxdistancelist = []
        for i in range(0, len(partition)):
            areaSize = len(partition[i])
            # Maximum Spanning Star算法
            maxdistance = 0
            for root in partition[i]:
                distance = 0
                for tmp in partition[i]:
                    if (root != tmp):
                        distance = distance + distance_matrix[root][tmp]
                    maxdistance = max(maxdistance, distance)
            areasizelist.append(areaSize)
            maxdistancelist.append(maxdistance)
        for i in range(0, len(areasizelist)):
            areaworkload = size_weight * areasizelist[i] + distance_weight * (maxdistancelist[i]/1000) #以千米为单位
            workload.append(areaworkload)
        return np.var(workload)

'''
各区域间的站点的方差
'''
def get_areasize_variance(partition):
    areasizelist = []
    for i in range(0, len(partition)):
        areaSize = len(partition[i])
        areasizelist.append(areaSize)
    return np.var(areasizelist)

'''
各区域间的调度距离的方差
'''


def get_distance_variance(partition):
    distance_filepath = "D:\\JetBrains\\PcharmWorkSpace\\Paper2\\NYC\\data\\distanceMatrix.csv"
    distance_matrix = pd.read_csv(distance_filepath, header=None).as_matrix()
    maxdistancelist = []
    distanceSum = 0
    for i in range(0, len(partition)):
        # Maximum Spanning Star算法
        maxdistance = 0
        for root in partition[i]:
            distance = 0
            for tmp in partition[i]:
                if (root != tmp):
                    distance = distance + distance_matrix[root][tmp]
                    maxdistance = max(maxdistance, distance)/1000
                    distanceSum = distanceSum + distance
        maxdistancelist.append(maxdistance)
    return np.var(maxdistancelist), distanceSum