import cartopy.crs as ccrs
import matplotlib.pyplot as plt
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import matplotlib.ticker as mticker
import numpy as np
import pandas as pd
import requests
from bs4 import BeautifulSoup
 
def get_catalog():
    url = 'http://news.ceic.ac.cn/index.html'
    web_data = requests.get(url)
    web_data.encoding = web_data.apparent_encoding
    web_data = web_data.text
    f = open('earthquake.csv', 'w')
    Date = []
    Latitude = []
    Longitude = []
    Magnitude = []
    soup = BeautifulSoup(web_data, 'lxml')
 
    rows = soup.find_all('tr')
    for i in rows:
        cols = i.find_all('td')
        if len(cols) != 0:
            Magnitude.append(cols[0].text)
            Date.append(cols[1].text.split()[0])
            Latitude.append(cols[2].text)
            Longitude.append(cols[3].text)
    f.write('Date,Latitude,Longitude,Magnitude\n')
    for i in range(len(Date)):
        line = Date[i] + ',' + Latitude[i] + ',' + Longitude[i] + ',' + Magnitude[i]
        print(Date[i] + ',' + Latitude[i] + ',' + Longitude[i] + ',' + Magnitude[i])
        f.write(line + '\n')
    f.close()
 
def plot_map():
    plt.figure(figsize=(12, 8))
    plt.rcParams['font.sans-serif'] = 'FangSong' # 设置中文字体为仿宋
    plt.rcParams['axes.unicode_minus'] = False  # 正常显示坐标轴上的铀号
    ax = plt.axes(projection=ccrs.PlateCarree())
    ax.coastlines()
    ax.stock_img()  # 将参考底图图像添加到地图,如果没有这条命令，底图是没有背景色的
    # 画经纬度网格
    gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=False, linewidth=1.2, color='k', alpha=0.3, linestyle='--')
    gl.xlabels_top = False  # 关闭顶端的经纬度标签
    gl.ylabels_right = False  # 关闭右侧的经纬度标签
    gl.xformatter = LONGITUDE_FORMATTER  # x轴设为经度的格式
    gl.yformatter = LATITUDE_FORMATTER  # y轴设为纬度的格式
 
    #设置经纬度网格的间隔
    gl.xlocator = mticker.FixedLocator(np.arange(-180, 180, 30))
    gl.ylocator = mticker.FixedLocator(np.arange(-90, 90, 30))
    # 设置显示范围
    ax.set_extent([-180, 180, -90, 90],crs=ccrs.PlateCarree())
    #设置坐标标签
    ax.set_xticks(list(range(-180,180,60)), crs=ccrs.PlateCarree())
    ax.set_yticks(list(range(-90,90,30)), crs=ccrs.PlateCarree())
    plt.xticks(fontsize = 20)
    plt.yticks(fontsize = 20)
 
    # 填加大地测量座标系下的线条
    # ny_lon, ny_lat = -75, 43
    # delhi_lon, delhi_lat = 77.23, 28.61
    # plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
    #         color='blue', linewidth=2, marker='o',
    #         transform=ccrs.Geodetic(),
    #         )
    # 填加直角座标系下的线条
    # plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
    #           color='red', linestyle='--',
    #           transform=ccrs.PlateCarree(),
    #           )
    # 填加文字
    # plt.text(ny_lon - 3, ny_lat - 12, '纽约',
    #           horizontalalignment='right', color = 'red',
    #           transform=ccrs.Geodetic())
    # plt.text(delhi_lon + 3, delhi_lat - 12, 'Delhi',
    #           horizontalalignment='left', color = 'red',
    #           transform=ccrs.Geodetic())
 
    # 画震中分布
    data = pd.read_csv('earthquake.csv')
    scatter = ax.scatter(data.Longitude, data.Latitude,
               s= (0.2* 2 ** data.Magnitude)**2,
               c='red', alpha=0.8,
               # c=data.depth / data.depth.max(), alpha=0.8,
               transform=ccrs.PlateCarree())
    # 填加图例
    kw = dict(prop="sizes", num=5, color='red', fmt="M {x:.1f}",
              func=lambda s: np.log2(np.sqrt(s)/0.2))
    legend2 = ax.legend(*scatter.legend_elements(**kw),
                        loc="lower left", title="Mag")
    ax.add_artist(legend2)
 
    plt.tight_layout()
    plt.savefig('world.png',dpi = 600)
    # plt.show()
 
#################主程序###################
get_catalog()
plot_map()