python 形状区分市控国控，颜色表示aqi等级

# 任务需求 从数据库取重点时刻前后各两小时的aqi求平均，画点图、面图

import pandas as pd
from datetime import timedelta, datetime
import numpy as np
from sqlalchemy import create_engine
import matplotlib as mpl

mpl.use("Agg")
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import matplotlib.font_manager as fm

zhfont1 = fm.FontProperties(fname='/home/gzblue/xgx/xx_copy_files/SIMKAI.TTF')


def get_station_aqi_mean(station, t_start, t_end):
    """求站点重点时刻平均AQI

    :param station: 站点ID，例如'51010101001'
    :param t_start: 开始时间，time类型或类似时间str
    :param t_end: 结束时间，time类型或类似时间str
    :return: 重点时刻平均AQI
    """

    sql_engine = create_engine('mssql+pymssql://xxxx:xxxx@xxxx:xxxx/AQI_HKY')
    data = pd.read_sql_query(
        "SELECT MN_, DATA_TIME_,AQI_ FROM MS_HOUR_DATA where MN_='%s' and DATA_TIME_>='%s' and DATA_TIME_<='%s'"
        % (station, t_start, t_end), sql_engine)
    # print(data)
    # print(data['AQI_'].mean())
    return data['AQI_'].mean()


def main(station, goal_time):
    titlename = str(goal_time)
    savepath = r'/data01/home/gzblue/xgx/1205/chengduguoshi_plot/data/' + str(goal_time) + 'new_.png'
    time_mid = datetime.strptime(goal_time, '%Y%m%d%H')
    time_start = time_mid - timedelta(minutes=120)
    time_end = time_mid + timedelta(minutes=120)
    print(time_start)
    print(time_end)
    # 取数据
    station['AQI'] = None
    for mn in station['MN_'].values:
        station.loc[mn, 'AQI'] = get_station_aqi_mean(mn, time_start, time_end)
    print(station)
    # 画图
    station_list = ['51010101001', '51010401001', '51010501001', '51010501002',
                    '51010601002', '51010801001', '51010901001']
    map = Basemap(llcrnrlon=102.8, llcrnrlat=30, urcrnrlon=105, urcrnrlat=31.5, projection='lcc',
                  lat_1=30, lat_2=60, lat_0=33.3, lon_0=103.3, resolution='l')
    station.index = range(len(station))
    for ss in station_list:
        for i in range(40):
            if station.loc[i, 'MN_'] == ss:
                lons1 = station.loc[i, 'LONG_']
                lats1 = station.loc[i, 'LAT_']
                x1, y1 = map(lons1, lats1)
                if station.loc[i, 'AQI'] < 50:
                    map.scatter(x1, y1, marker='s', color='limegreen', s=20)
                elif station.loc[i, 'AQI'] < 100:
                    map.scatter(x1, y1, marker='s', color='yellow', s=20)
                elif station.loc[i, 'AQI'] < 150:
                    map.scatter(x1, y1, marker='s', color='darkorange', s=20)
                elif station.loc[i, 'AQI'] < 200:
                    map.scatter(x1, y1, marker='s', color='red', s=20)
                elif station.loc[i, 'AQI'] < 300:
                    map.scatter(x1, y1, marker='s', color='purple', s=20)
                elif station.loc[i, 'AQI'] >= 300:
                    map.scatter(x1, y1, marker='s', color='darkred', s=20)
                else:
                    pass
            else:
                lons1 = station.loc[i, 'LONG_']
                lats1 = station.loc[i, 'LAT_']
                x1, y1 = map(lons1, lats1)
                if station.loc[i, 'AQI'] < 50:
                    map.scatter(x1, y1, marker='v', color='limegreen', s=20, alpha=0.5)
                elif station.loc[i, 'AQI'] < 100:
                    map.scatter(x1, y1, marker='v', color='yellow', s=20, alpha=0.5)
                elif station.loc[i, 'AQI'] < 150:
                    map.scatter(x1, y1, marker='v', color='darkorange', s=20, alpha=0.5)
                elif station.loc[i, 'AQI'] < 200:
                    map.scatter(x1, y1, marker='v', color='red', s=20, alpha=0.5)
                elif station.loc[i, 'AQI'] < 300:
                    map.scatter(x1, y1, marker='v', color='purple', s=20, alpha=0.5)
                elif station.loc[i, 'AQI'] >= 300:
                    map.scatter(x1, y1, marker='v', color='darkred', s=20, alpha=0.5)
                else:
                    pass
    map.scatter(0, 0, marker='v', color='k', s=35, label='市控站点')
    map.scatter(0, 0, marker='s', color='k', s=35, label='国控站点')
    map.scatter(0, 0, marker='8', color='limegreen', s=35, label='优')
    map.scatter(0, 0, marker='8', color='yellow', s=35, label='良')
    map.scatter(0, 0, marker='8', color='darkorange', s=35, label='轻度污染')
    map.scatter(0, 0, marker='8', color='red', s=35, label='中度污染')    map.
scatter(0, 0, marker='8', color='purple', s=35, label='重度污染')    map.
scatter(0, 0, marker='8', color='darkred', s=35, label='严重污染')    map.
scatter(0, 0, marker='8', color='w', s=70)    plt.
legend(prop=zhfont1, fontsize=8, ncol=2, loc=0, columnspacing=0.2, handletextpad=0.2)    map.
readshapefile("/data01/home/share/chengdu_map_new/chengdu_city_merge_new", 'states', drawbounds=True,                      
linewidth=0.3, color='gray')    map.
readshapefile("/data01/home/share/OnlyChengdu_Map/chengdu_xzqh_city", 'states', drawbounds=True, linewidth=0.8)    plt.
title(titlename, fontsize=15, fontproperties=zhfont1)    plt.
savefig(savepath, format='png', dpi=300)    
# plt.show()
    plt.close()    
print('完成')


if __name__ == '__main__':
# 获得国控与市控的列表
    station_data = pd.read_csv(r'/home/gzblue/xgx/1205/chengduguoshi_plot/data/guoshikong.csv')    
# print(station_data)
    # print(station)
    # print(station.loc['51010101001','NAME_'])

    # goal_time_list = ['2018121612']  # '2018112011'
    goal_time_list = ['2018121512','2018121612','2018121712','2018121812','2018121912','2018122011']  # '2018112011'
for goal_time in goal_time_list:
        station_new = pd.DataFrame()        station_new[
'MN_'] = station_data['MN_']        station_new[
'NAME_'] = station_data['NAME_']        station_new[
'LONG_'] = station_data['LONG_']        station_new[
'LAT_'] = station_data['LAT_']        station_new.
set_index('MN_', drop=False, inplace=True)        
print(goal_time)        
main(station_new, goal_time)