Cartopy 绘图示例库

Cartopy 是为了向 Python 添加地图制图功能而开发的扩展库。该项目致力于以 matplotlib 包为基础,用简单直观的方式操作各类地理要素的成图。Cartopy 官网的画廊页面已经提供了很多绘图的例子,它们和官方文档一起,是学习该工具的主要材料。

本文亦提供一些例子,演示 Cartopy 在测量学等领域的应用,包括绘制中国政区图、IGS 站点分布、GNSS 控制网、地球板块分布、GNSS 速度场、电子含量分布以及突出显示某些地理要素等,旨在提供大地测量学方面的补充。

绘制中国政区图

本图使用兰伯特等积投影。Cartopy 默认使用的由 Natural Earth 提供的国界数据不符合我国的领土主张,本文的中国政区边界数据来自 GMT 中文社区,包含中国国界、省界、十段线以及南海诸岛。你也可以在 GMT 中文社区网站上下载单独的国界和十段线数据。在此向 GMT 中文社区的维护和贡献者表示感谢!

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:

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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
# https://gmt-china.org/data/CN-border-La.dat
with open('CN-border-La.dat') as src:
context = src.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], '-', lw=1, color='k',
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], '-', lw=1, color='k',
transform=ccrs.Geodetic())
# Set figure extent
sub_ax.set_extent([105, 125, 0, 25])
# Show figure
plt.show()

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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。我已经将其整理成为 igs-coremgex 两个 CSV 文件,你可以直接下载。

IGS 核心站与 MGEX 站点分布图

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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
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()

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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

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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
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()

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绘制板块分布图

板块构造理论将地球的岩石圈分为十数个大小不等的板块。本图使用的 Nuvel 板块边界数据来自 EarthByte 网站,我已经将其整理为一个压缩文件,你可以直接下载使用。

Nuvel 板块分布图

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import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

# The plate boundary files
files = ['African.txt', 'Antarctic.txt', 'Arabian.txt', 'Australian.txt',
'Caribbean.txt', 'Cocos.txt', 'Eurasian.txt', 'Indian.txt', 'Juan.txt',
'Nazca.txt', 'North_Am.txt', 'Pacific.txt', 'Philippine.txt',
'Scotia.txt', 'South_Am.txt']
# Read boundaries into numpy
borders = []
for f in files:
border = np.genfromtxt(f, names=['lon', 'lat'], dtype=float, comments=':')
borders.append(border)
# Plate names
plates = ['African', 'Antarctic', 'Arabian', 'Australian', 'Caribbean', 'Cocos',
'Eurasian', 'Indian', 'Juan', 'Nazca', ' North\nAmerican', 'Pacific',
'Philippine', 'Scotia', ' South\nAmerican']
# Central point for every plate, just for text positioning
central = [(17, -5), (90, -80), (40, 21), (120, -28), (270, 12), (260, 6),
(60, 50), (70, 13), (230, 45), (260, -21), (250, 36), (190, 0),
(123, 17), (304, -59), (315, -27)]

# Start plot
fig = plt.figure(figsize=(12, 7))
ax = plt.axes(projection=ccrs.Mollweide(central_longitude=120))
# Plot a image as background
ax.stock_img()
# Plot boundaries
for plate, center, border in zip(plates, central, borders):
ax.plot(border['lon'], border['lat'], color='coral',
transform=ccrs.Geodetic())
ax.text(center[0], center[1], plate, transform=ccrs.Geodetic())

plt.show()

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绘制 GNSS 速度场

本图使用美国西部的 GNSS 测站速度场数据,数据来自加州大学圣迭戈分校。在使用 ax.quiver() 方法绘制站速度矢量时,由于 matplotlib 自动确定的矢量长度比较短,不能够表现足够的细节。因此使用 scale_units 属性指定矢量长度单位,然后使用 scale 设置缩放程度。加州西部是著名的圣安德列斯断层,从下图中可以看出其两侧迥异的地质变化。

美国西部 GNSS 速度场

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import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature

# Download the GPS velocity field from
# https://topex.ucsd.edu/CGM/01_GPS/wus_gps_final_names.dat
velo = np.genfromtxt('wus_gps_final_names.dat', usecols=range(1, 5),
names=('lat', 'lon', 'Vn', 'Ve'), skip_header=1)
# Coordinates of Los Angeles & San Francisco
l_a, s_f = (-118.30, 34.12), (-122.43, 37.74)

fig = plt.figure(figsize=(12, 12))
# Set projection
ax = plt.axes(projection=ccrs.Miller(central_longitude=-120))
# Add ocean, land, rivers, lakes & boundary of states
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'))
ax.add_feature(cfeature.STATES.with_scale('50m'))
# Plot quivers
qs = ax.quiver(velo['lon'], velo['lat'], velo['Ve'], velo['Vn'], width=0.0015,
scale_units='inches', scale=85, color='darkred',
transform=ccrs.PlateCarree())
# Plot a legend for quiver
ax.quiverkey(qs, 0.04, 0.1, 20, '20 mm/yr', labelpos='E')
# Plot Los Angeles & San Francisco, as reference
ax.scatter(s_f[0], s_f[1], s=90, label='San Francisco', color='darkcyan',
transform=ccrs.Geodetic())
ax.scatter(l_a[0], l_a[1], s=210, label='Los Angeles', color='olivedrab',
transform=ccrs.Geodetic())
ax.legend(loc='lower left')
# Plot grid lines
ax.gridlines(linestyle='--')
ax.set_extent([-126, -113, 32, 40])

plt.show()

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绘制电子含量分布图

Cartopy 以 matplotlib 包作为基础,可以使用 matplotlib 中的方法来绘制等值线图,只需在绘制时使用 Cartopy 处理地图投影变形。这里以绘制全球电离层电子含量图为例,模型来自北京航空航天大学·前沿电离层实验室,但只截取其产品文件 whug1420.18i 中第一个时段的数据,即使用的 1st-tecmap.dat 文件。

全球电子含量分布图

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import numpy as np
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

# Read VTEC data from a file
with open('1st-tecmap.dat') as src:
data = [line for line in src if not line.endswith('LAT/LON1/LON2/DLON/H\n')]
tec = np.fromstring(''.join(data), dtype=float, sep=' ')

# Generate a geodetic grid
nlats, nlons = 71, 73

lats = np.linspace(-87.5, 87.5, nlats)
lons = np.linspace(-180, 180, nlons)
lons, lats = np.meshgrid(lons, lats)

tec.shape = nlats, nlons

fig = plt.figure(figsize=(8, 3))
# Set projection
ax = plt.axes(projection=ccrs.PlateCarree())
# Plot contour
cp = plt.contourf(lons, lats, tec, transform=ccrs.PlateCarree(), cmap='jet')
ax.coastlines()
# Add a color bar
plt.colorbar(cp)
# Set figure extent & ticks
ax.set_extent([-180, 180, -87.5, 87.5])
ax.set_xticks([-180, -150, -120, -90, -60, -30, 0, 30, 60, 90, 120, 150, 180])
ax.set_yticks([-80, -60, -40, -20, 0, 20, 40, 60, 80])

plt.show()

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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

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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()

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