Basemap上的国家标签

Question

我想在Basemap上绘制轨迹,并将国家标签(名称)显示为叠加层.

这是当前代码及其生成的映射:

import pandas as pd 
import matplotlib.pyplot as plt 
from mpl_toolkits.basemap import Basemap


path = "path\\to\\data"

animal_data = pd.DataFrame.from_csv(path, header=None)
animal_data.columns = ["date", "time", "gps_lat", "gps_long"]

# data cleaning omitted for clarity 

params = {
        'projection':'merc', 
        'lat_0':animal_data.gps_lat.mean(), 
        'lon_0':animal_data.gps_long.mean(), 
        'resolution':'h', 
        'area_thresh':0.1, 
        'llcrnrlon':animal_data.gps_long.min()-10, 
        'llcrnrlat':animal_data.gps_lat.min()-10, 
        'urcrnrlon':animal_data.gps_long.max()+10, 
        'urcrnrlat':animal_data.gps_lat.max()+10
}
map = Basemap(**params)

map.drawcoastlines()
map.drawcountries()
map.fillcontinents(color = 'coral')
map.drawmapboundary()          

x, y = map(animal_data.gps_long.values, animal_data.gps_lat.values)

map.plot(x, y, 'b-', linewidth=1)    
plt.show()

这导致地图:

这是迁徙鸟类的轨迹图.虽然这是一张非常好的地图(!),但我需要国家/地区名称标签,因此很容易确定这只鸟飞过的国家.

是否有直接添加国家/地区名称的方式？

Answer 1

我的解决方案依赖于将来可能会或可能不会提供的外部数据文件.但是,类似的数据可以在其他地方找到,所以这不应该是一个太大的问题.

首先,打印国家/地区名称标签的代码:

import pandas as pd 
import matplotlib.pyplot as plt 
from mpl_toolkits.basemap import Basemap

class MyBasemap(Basemap):     
    def printcountries(self, d=3, max_len=12):
        data = pd.io.parsers.read_csv("http://opengeocode.org/cude/download.php?file=/home/fashions/public_html/opengeocode.org/download/cow.txt", 
                                      sep=";", skiprows=28 )
        data = data[(data.latitude > self.llcrnrlat+d) & (data.latitude < self.urcrnrlat-d) & (data.longitude > self.llcrnrlon+d) & (data.longitude < self.urcrnrlon-d)]
        for ix, country in data.iterrows():                            
                plt.text(*self(country.longitude, country.latitude), s=country.BGN_name[:max_len]) 

所有这一切都是从这里下载国家/地区数据库,然后选择当前在地图上的国家/地区,并标记它们.

完整的代码:

import pandas as pd 
import matplotlib.pyplot as plt 
from mpl_toolkits.basemap import Basemap

class MyBasemap(Basemap):     
    def printcountries(self, d=3, max_len=12):
        data = pd.io.parsers.read_csv("http://opengeocode.org/cude/download.php?file=/home/fashions/public_html/opengeocode.org/download/cow.txt", 
                                      sep=";", skiprows=28 )
        data = data[(data.latitude > self.llcrnrlat+d) & (data.latitude < self.urcrnrlat-d) & (data.longitude > self.llcrnrlon+d) & (data.longitude < self.urcrnrlon-d)]
        for ix, country in data.iterrows():                            
                plt.text(*self(country.longitude, country.latitude), s=country.BGN_name[:max_len]) 


path = "path\\to\\data"

animal_data = pd.DataFrame.from_csv(path, header=None)
animal_data.columns = ["date", "time", "gps_lat", "gps_long"]

params = {
        'projection':'merc', 
        'lat_0':animal_data.gps_lat.mean(), 
        'lon_0':animal_data.gps_long.mean(), 
        'resolution':'h', 
        'area_thresh':0.1, 
        'llcrnrlon':animal_data.gps_long.min()-10, 
        'llcrnrlat':animal_data.gps_lat.min()-10, 
        'urcrnrlon':animal_data.gps_long.max()+10, 
        'urcrnrlat':animal_data.gps_lat.max()+10
}

plt.figure()
map = MyBasemap(**params)

map.drawcoastlines()
map.fillcontinents(color = 'coral')
map.drawmapboundary()          
map.drawcountries()
map.printcountries()

x, y = map(animal_data.gps_long.values, animal_data.gps_lat.values)

map.plot(x, y, 'b-', linewidth=1)    
plt.show()

最后,结果:

显然,这并没有像人们希望的那样仔细标注,并且应该实施一些关于国家规模,名称长度和地图大小的启发式方法,以使其完美,但这是一个很好的起点.