import ast
import sys
import numpy as np
import json
import time
import redis
from collections import deque

radiusLen = None

# 将字符串转换为浮点型数组
def convert_to_float_array(str_array):
    if isinstance(str_array, str):
        return np.array(ast.literal_eval(str_array), dtype=float)
    return str_array

def getWaveScopeByCode(x, y):
    code = codeMatrix[x, y]
    includeList = []
    existNodes = set()
    spreadWaveNode({"x": x, "y": y}, {"x": x, "y": y}, existNodes, includeList)
    return includeList

def spreadWaveNode(originNode, currNode, existNodes, includeList):
    x, y = currNode['x'], currNode['y']
    neighbors = [(x + 1, y), (x - 1, y), (x, y + 1), (x, y - 1)]
    for neighbor in neighbors:
        extendNeighborNodes(originNode, {"x": neighbor[0], "y": neighbor[1]}, existNodes, includeList)

def extendNeighborNodes(originNode, nextNode, existNodes, includeList):
    x, y = nextNode['x'], nextNode['y']
    if (x < 0 or x >= codeMatrix.shape[0] or y < 0 or y >= codeMatrix.shape[1]):
        return

    if (x, y) in existNodes:
        return

    existNodes.add((x, y))

    nextNodeCodeData = codeMatrix[x, y]

    if nextNodeCodeData == 'NONE':
        spreadWaveNode(originNode, nextNode, existNodes, includeList)
    else:
        o1Cda = convert_to_float_array(cdaMatrix[originNode['x'], originNode['y']])
        o2Cda = convert_to_float_array(cdaMatrix[x, y])

        num1 = (o1Cda[0] - o2Cda[0]) ** 2
        num2 = (o1Cda[1] - o2Cda[1]) ** 2
        if num1 + num2 <= radiusLen ** 2:
            includeList.append({"x": int(x), "y": int(y), "code": str(codeMatrix[x, y])})
            spreadWaveNode(originNode, nextNode, existNodes, includeList)

# 找到某个值对应的 x, y 下标
def find_value_in_matrix(value):
    indices = np.where(codeMatrix == value)
    return list(zip(indices[0], indices[1]))

def initWaveMatrix():
    lev = 1
    waveMatrix = np.empty_like(codeMatrix, dtype=object)

    for x in range(codeMatrix.shape[0]):
        for y in range(codeMatrix.shape[1]):
            if codeMatrix[x][y] == 'NONE':
                waveMatrix[x][y] = "-"
            else:
                waveMatrix[x][y] = '[]'

    return waveMatrix

# 优化版本：使用集合来提高性能
def mergeWave(originWave, vehicle):
    # 将字符串解析为集合
    set_data = set(ast.literal_eval(originWave))
    # 如果 vehicle 不在集合中，则添加
    set_data.add(vehicle)
    # 返回序列化后的字符串
    return json.dumps(list(set_data))

# 将 dynamicMatrix 转换为 numpy 结构化数组
def convert_to_structured_array(dynamicMatrix):
    # 定义结构化数组的 dtype
    dtype = [('serial', int), ('vehicle', 'U2')]
    # 将嵌套的列表转换为结构化数组
    structured_array = np.array([tuple(d.values()) for row in dynamicMatrix for d in row], dtype=dtype)
    # 重塑为原始的二维形状
    return structured_array.reshape(len(dynamicMatrix), -1)

# 使用 numpy 加速的代码
def process_dynamic_matrix(dynamicMatrix, codeMatrix):
    # 将 dynamicMatrix 转换为结构化数组
    dynamicMatrix = convert_to_structured_array(dynamicMatrix)

    # 获取 dynamicMatrix 的形状
    rows, cols = dynamicMatrix.shape

    # 创建一个布尔掩码，用于筛选出 vehicle 不为 '0' 和 '-1' 的元素
    mask = (dynamicMatrix['vehicle'] != '0') & (dynamicMatrix['vehicle'] != '-1')

    # 获取满足条件的 x 和 y 坐标
    x_indices, y_indices = np.where(mask)

    # 遍历满足条件的坐标
    for x, y in zip(x_indices, y_indices):
        # print(code)
        data = dynamicMatrix[x][y]
        vehicle = data['vehicle']
        includeList = getWaveScopeByCode(x,y)
        for include in includeList:
            originWave = waveMatrix[include['x']][include['y']]
            waveMatrix[include['x']][include['y']] = mergeWave(originWave, vehicle)

radiusLenStr = sys.argv[1]
radiusLen = float(radiusLenStr)

redisHost = sys.argv[2]
redisPwd = sys.argv[3]
redisPort = sys.argv[4]
redisIdx = sys.argv[5]

startTime = time.perf_counter()

# 创建一个连接池
pool = redis.ConnectionPool(host=redisHost, port=int(redisPort), password=redisPwd, db=int(redisIdx))
r = redis.Redis(connection_pool=pool)

codeMatrixStr = r.get('KV.AGV_MAP_ASTAR_CODE_FLAG.1')
codeMatrix = np.array(json.loads(codeMatrixStr))

cdaMatrixStr = r.get('KV.AGV_MAP_ASTAR_CDA_FLAG.1')
cdaMatrix = np.array(json.loads(cdaMatrixStr))

dynamicMatrixStr = r.get('KV.AGV_MAP_ASTAR_DYNAMIC_FLAG.1')
dynamicMatrix = np.array(json.loads(dynamicMatrixStr))

waveMatrix = initWaveMatrix()

# # 使用 numpy 加速的代码
process_dynamic_matrix(dynamicMatrix, codeMatrix)

# for x in range(dynamicMatrix.shape[0]):
#     for y in range(dynamicMatrix.shape[1]):
#         data = dynamicMatrix[x, y]
#         vehicle = data['vehicle']
#         if vehicle != '0' and vehicle != '-1':
#             getWaveScopeByCode(x, y)

# 将 numpy.ndarray 转换为嵌套列表
waveMatrixList = waveMatrix.tolist()
# 将嵌套列表转换为 JSON 字符串
waveMatrixJsonStr = json.dumps(waveMatrixList)

r.set("KV.AGV_MAP_ASTAR_WAVE_FLAG.1",waveMatrixJsonStr)

end = time.perf_counter()
# print('程序运行时间为: %s Seconds' % (end - startTime))
print("1")