【GNSS】【绘图】【python】Cartopy 绘图示例库，绘制站点分布图

 摘自于，GNSS Helper

http://www.gnss.help/2018/04/24/cartopy-gallery/

具体绘制的时候，还可以参考https://scitools.org.uk/cartopy/docs/latest/gallery/index.html

绘制中国政区图

中国政区图

本图使用的中国国界、省界、十段线数据中，边界线数据块使用 “>” 号分隔。因此首先将其内容按照 “>” 切块，然后加载到 NumPy 中。绘图使用的代码如下：

import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
# Load the border data, CN-border-La.dat is downloaded from
# http://gmt-china.org/datas/CN-border-La.dat
with open('CN-border-La.dat') as datf:
context = datf.read()
blocks = [cnt for cnt in context.split('>') if len(cnt) > 0]
borders = [np.fromstring(block, dtype=float, sep=' ') for block in blocks]
# Set figure size
fig = plt.figure(figsize=[10, 8])
# Set projection and plot the main figure
ax = plt.axes(projection=ccrs.LambertConformal(central_latitude=90,
central_longitude=105))
# Add ocean, land, rivers and lakes
ax.add_feature(cfeature.OCEAN.with_scale('50m'))
ax.add_feature(cfeature.LAND.with_scale('50m'))
ax.add_feature(cfeature.RIVERS.with_scale('50m'))
ax.add_feature(cfeature.LAKES.with_scale('50m'))
# Plot border lines
for line in borders:
ax.plot(line[0::2], line[1::2], '-', color='gray',
transform=ccrs.Geodetic())
# Plot gridlines
ax.gridlines(linestyle='--')
# Set figure extent
ax.set_extent([80, 130, 13, 55])
# Plot South China Sea as a subfigure
sub_ax = fig.add_axes([0.741, 0.11, 0.14, 0.155],
projection=ccrs.LambertConformal(central_latitude=90,
central_longitude=115))
# Add ocean, land, rivers and lakes
sub_ax.add_feature(cfeature.OCEAN.with_scale('50m'))
sub_ax.add_feature(cfeature.LAND.with_scale('50m'))
sub_ax.add_feature(cfeature.RIVERS.with_scale('50m'))
sub_ax.add_feature(cfeature.LAKES.with_scale('50m'))
# Plot border lines
for line in borders:
sub_ax.plot(line[0::2], line[1::2], '-', color='gray',
transform=ccrs.Geodetic())
# Set figure extent
sub_ax.set_extent([105, 125, 0, 25])
# Show figure
plt.show()

绘制 IGS 站点分布图

本图使用的 IGS 核心站与 MGEX 项目站点，及其坐标均来自 IGS 网站。

IGS 核心站与 MGEX 站点分布图

import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
# Load the coordinate of IGS Core & MGEX sites, The CSV files are
# exported from: http://www.igs.org/network
# Download igs-core.csv & mgex.csv from
# http://ocijpaoi3.bkt.clouddn.com/igs-core.csv
# http://ocijpaoi3.bkt.clouddn.com/mgex.csv
igs_core = np.recfromcsv('igs-core.csv', names=True, encoding='utf-8')
mgex = np.recfromcsv('mgex.csv', names=True, encoding='utf-8')
fig = plt.figure(figsize=[9, 6])
# Set projection
ax = plt.axes(projection=ccrs.Robinson())
# Add ocean and land
ax.add_feature(cfeature.LAND)
ax.add_feature(cfeature.OCEAN)
# Add MGEX & IGS core sites
ax.plot(mgex['longitude'], mgex['latitude'], 'o', color='tomato',
label='MGEX', transform=ccrs.Geodetic())
ax.plot(igs_core['longitude'], igs_core['latitude'], '*', color='darkmagenta',
label='IGS Core', transform=ccrs.Geodetic())
# Plot gridlines
ax.gridlines(linestyle='--')
# Set figure extent
ax.set_global()
# Set legend location
plt.legend(loc='lower right')
# Show figure
plt.show()

绘制 GNSS 控制网

这里使用的 IGS 站点坐标数据同样来自 IGS 网站。我已将其整理成一个 CSV 格式的文件：euro-igs，你可以直接下载使用。这里使用 matplotlib.tri 中的 Triangulation 来根据输入的点位坐标来创建 Delaunay 三角网，然后使用 plt.triplot() 方法绘制。

GNSS 控制网

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as tri
import cartopy.crs as ccrs
import cartopy.feature as cfeature
# Load coordinate of the IGS sites in Europe, this CSV file is
# exported from: http://www.igs.org/network
# Download euro-igs.csv from:
# http://ocijpaoi3.bkt.clouddn.com/euro-igs.csv
igs_sites = np.recfromcsv('euro-igs.csv', names=True, encoding='utf-8')
# Generate Delaunay triangles
triangles = tri.Triangulation(igs_sites['longitude'], igs_sites['latitude'])
fig = plt.figure(figsize=[6, 8])
# Set projection
ax = plt.axes(projection=ccrs.LambertConformal(central_latitude=90,
central_longitude=10))
# Add ocean, land, rivers and lakes
ax.add_feature(cfeature.OCEAN.with_scale('50m'))
ax.add_feature(cfeature.LAND.with_scale('50m'))
ax.add_feature(cfeature.RIVERS.with_scale('50m'))
ax.add_feature(cfeature.LAKES.with_scale('50m'))
# Plot triangles
plt.triplot(triangles, transform=ccrs.Geodetic(), marker='o', linestyle='-')
# Plot gridlines
ax.gridlines(linestyle='--')
# Set figure extent
ax.set_extent([-10, 30, 30, 73])
# Show figure
plt.show()

突出显示某区域

Cartopy 默认对所有地物使用相同的外观，如果需要突出显示某些地物，就必须进行筛选。这里从 Natural Earth 提供的小比例尺国界数据中，提取出欧盟国家，然后使用 ax.add_geometries() 方法将它们加入到绘图元素中。

欧洲联盟

import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader
# Country names in Europe Union, exported from Wikipedia:
# https://en.wikipedia.org/wiki/European_Union
eu_country_names = ('Austria', 'Belgium', 'Bulgaria', 'Croatia', 'Cyprus',
'Czechia', 'Denmark', 'Estonia', 'Finland', 'France',
'Germany', 'Greece', 'Hungary', 'Ireland', 'Italy',
'Latvia', 'Lithuania', 'Luxembourg', 'Malta',
'Netherlands', 'Poland', 'Portugal', 'Romania', 'Slovakia',
'Slovenia', 'Spain', 'Sweden', 'United Kingdom')
# Create a .shp file reader
shp_file = shpreader.natural_earth(resolution='110m', category='cultural',
name='admin_0_countries')
shp_reader = shpreader.Reader(shp_file)
# Reader the shape file
records = shp_reader.records()
# Collect the European Union countries
eu_countries = []
for rec in records:
if rec.attributes['NAME'] in eu_country_names:
eu_countries.append(rec)
# Start ploting
fig = plt.figure(figsize=[6, 6])
# Set projection
ax = plt.axes(projection=ccrs.Orthographic(central_latitude=50,
central_longitude=10))
# Add land
ax.add_feature(cfeature.LAND, facecolor='lightgray')
# Plot the European Union countries
for country in eu_countries:
ax.add_geometries(country.geometry, crs=ccrs.PlateCarree(),
facecolor='darkgreen')
# Plot gridlines
ax.gridlines(linestyle='--')
# Show figure
plt.show（）