import pandas as pd
import json
from pyproj import Proj
from math import ceil, sqrt

# 建筑工地CSV通过json模板转为json形式

def wgs84toutm(lon, lat):
    '''
    :param lon:
    :param lat:
    :return:UTM投影带号和相应UTM坐标,及相对距离坐标
    '''
    zone = ceil((lon + 180) / 6.0000001)
    utm_pj = Proj("+proj=utm +zone=" + str(zone) + ", +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
    utm_x, utm_y = utm_pj(lon, lat, inverse=False)  # 从经纬度转平面坐标
    return zone, utm_x, utm_y


def wgs84toutm_reverse(zone, utm_x, utm_y):
    '''
    :param lon:
    :param lat:
    :return:UTM投影带号和相应UTM坐标,及相对距离坐标
    '''
    # zone = ceil((lon + 180) / 6.0000001)
    utm_pj = Proj("+proj=utm +zone=" + str(zone) + ", +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
    lon, lat = utm_pj(utm_x, utm_y, inverse=True)  # 从经纬度转平面坐标
    return lon, lat


def get_locationX_locationY(lon_center, lat_center, area):
    '''
    求正方形建组工地的经纬度范围
    :param lon_center: 中心经度
    :param lat_center: 中心纬度
    :param area: 面积
    :return: 经纬度范围列表
    '''
    # 1.求中心经纬度对应的x和y
    zone, utm_x, utm_y = wgs84toutm(lon_center, lat_center)
    # 2.求四个角的x和y
    print(utm_x, utm_y)
    # 将建筑工地看似一个正方形
    side_length = sqrt(area)
    utm_x1 = utm_x - side_length / 2
    utm_x2 = utm_x - side_length / 2
    utm_x3 = utm_x + side_length / 2
    utm_x4 = utm_x + side_length / 2
    utm_y1 = utm_y - side_length / 2
    utm_y2 = utm_y + side_length / 2
    utm_y3 = utm_y + side_length / 2
    utm_y4 = utm_y - side_length / 2

    # 3.求四个角的经纬度
    lon1, lat1 = wgs84toutm_reverse(zone, utm_x1, utm_y1)
    lon2, lat2 = wgs84toutm_reverse(zone, utm_x2, utm_y2)
    lon3, lat3 = wgs84toutm_reverse(zone, utm_x3, utm_y3)
    lon4, lat4 = wgs84toutm_reverse(zone, utm_x4, utm_y4)

    # 4.整理结果返回
    locationx = [lon1, lon2, lon3, lon4, lon1]
    locationy = [lat1, lat2, lat3, lat4, lat1]
    for i in range(len(locationx)):
        locationx[i] = round(locationx[i], 6)
        locationy[i] = round(locationy[i], 6)
    return locationx, locationy


# 建筑工地面源csv转json
def csv_to_json(csv_path, json_path, json_back_path, relhgt, szinit):
    '''
    由确定好字段的建筑工地json模板，生成有具体建筑工地信息的json
    :param csv_path: 建筑工地信息的csv
    :param json_path: 生成有具体建筑工地信息的json的路径
    :param json_back_path: 确定好字段的建筑工地json模板的路径
    relhgt # 排放离地高度 m
    szinit # 面源烟羽的初始垂直尺度 m
    :return:
    '''
    with open(json_back_path, 'r', encoding='utf-8') as f:
        d = json.loads(f.read())
    print(d['project_data']['basedata']['sources'])
    print(d['project_data']['basedata']['emissionCase'][0]['select_Source'])
    print(d['project_data']['basedata']['emissionCase'][0]['select_Source_ID'])
    sources = []
    select_Source = []
    select_Source_ID = []
    df = pd.read_csv(csv_path)
    for i in range(df.shape[0]):
        print(df.iloc[i, :])
        sr = df.iloc[i, :]
        source = {}
        project_name = sr['项目名称']
        lon_center = sr['中心经度']
        lat_center = sr['中心纬度']
        area = sr['建筑施工面积（平方米）']
        tsp = sr['排放强度 kg/h']

        locationX, locationY = get_locationX_locationY(lon_center, lat_center, area)


        source['name'] = project_name
        source['ID'] = i + 1
        source["type"] = "AREAPOLY"
        source['locationY'] = locationY
        source['locationX'] = locationX

        source["pollutant_unit"] = "kg/h"
        pollutants = {}
        pollutants['TSP'] = round(tsp, 7)
        source["pollutants"] = pollutants
        source["Relhgt"] = relhgt
        source["Szinit"] = szinit
        sources.append(source)
        select_Source.append(project_name)
        select_Source_ID.append(i + 1)


    d['project_data']['basedata']['sources'] = sources
    d['project_data']['basedata']['emissionCase'][0]['select_Source'] = select_Source
    d['project_data']['basedata']['emissionCase'][0]['select_Source_ID'] = select_Source_ID


    with open(json_path, 'w', encoding='utf-8') as f:
        f.write(json.dumps(d, ensure_ascii=False))


# 工业点源csv转json
def csv_to_json_point(csv_path, json_path, json_back_path, stkvel, stkdia, stktmp, stkhat):
    with open(json_back_path, 'r', encoding='utf-8') as f:
        d = json.loads(f.read())
    print(d['project_data']['basedata']['sources'])
    print(d['project_data']['basedata']['emissionCase'][0]['select_Source'])
    print(d['project_data']['basedata']['emissionCase'][0]['select_Source_ID'])
    sources = []
    select_Source = []
    select_Source_ID = []
    df = pd.read_csv(csv_path)
    for i in range(df.shape[0]):
        print(df.iloc[i, :])
        sr = df.iloc[i, :]
        source = {}
        project_name = sr['企业']
        lon_center = round(sr['经度'], 7)
        lat_center = round(sr['纬度'], 7)
        tsp = sr['小时排放量（kg/h）']

        source['name'] = project_name
        source['ID'] = i + 1
        source['Stkvel'] = stkvel
        source['Stkdia'] = stkdia
        source["type"] = "POINT"
        source['heat_loss'] = ""
        source['emiss_units'] = "----------   要求单位是 kg/h ,与onesky 一致"

        pollutants = {}
        pollutants['SO2'] = round(tsp, 7)
        source["pollutants"] = pollutants
        source["totle_heat_release"] = ""
        source["emission_condition"] = "normal"
        source["emission_hours_num"] = 8760
        source["Stktmp"] = stktmp
        source["location"] = [lon_center, lat_center]
        source["Stkhat"] = stkhat
        sources.append(source)
        select_Source.append(project_name)
        select_Source_ID.append(i + 1)

    d['project_data']['basedata']['sources'] = sources
    d['project_data']['basedata']['emissionCase'][0]['select_Source'] = select_Source
    d['project_data']['basedata']['emissionCase'][0]['select_Source_ID'] = select_Source_ID

    with open(json_path, 'w', encoding='utf-8') as f:
        f.write(json.dumps(d, ensure_ascii=False))


if __name__ == '__main__':
    # # 建筑工地面源
    # relhgt = 2.0  # 排放离地高度 m
    # szinit = 4.0  # 面源烟羽的初始垂直尺度 m
    # csv_path = r'D:\PycharmProjects\aermod\real_time\aermod_stastic\datong\大同建筑工地清单-规整2.csv'
    # json_path = r'D:\PycharmProjects\aermod\real_time\aermod_stastic\datong\datong_jianzhu.json'
    # json_back_path = r'D:\PycharmProjects\aermod\real_time\aermod_stastic\datong\datong_jianzhu_back.json'
    # csv_to_json(csv_path, json_path, json_back_path, relhgt, szinit)
    # # get_locationY_locationX(104.132, 30.7273, 251794)
    # print(get_locationX_locationY(114.835208, 34.798364, 400000000))

    # 工业点源
    stkvel, stkdia, stktmp, stkhat = 10, 6, 100, 80.0
    csv_path = r'D:\PycharmProjects\aermod\real_time\aermod_stastic\datong\大同主要工业企业排放-30km以内-top50.csv'
    json_path = r'D:\PycharmProjects\aermod\real_time\aermod_stastic\datong\datong_point.json'
    json_back_path = r'D:\PycharmProjects\aermod\real_time\aermod_stastic\datong\datong_gongye_back.json'
    csv_to_json_point(csv_path, json_path, json_back_path, stkvel, stkdia, stktmp, stkhat)