3D CartoPy 类似于 Matplotlib-Basemap [英] 3D CartoPy similar to Matplotlib-Basemap

问题描述

我是 Python 新手，有一个关于 Cartopy 能够用于 3D 绘图的问题.下面是一个使用 matplotlibBasemap 的例子.

I'm new to Python with a question about Cartopy being able to be used in a 3D plot. Below is an example using matplotlibBasemap.

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.basemap import Basemap

m = Basemap(projection='merc',
            llcrnrlat=52.0,urcrnrlat=58.0,
            llcrnrlon=19.0,urcrnrlon=40.0,
            rsphere=6371200.,resolution='h',area_thresh=10)

fig = plt.figure()
ax = Axes3D(fig)
ax.add_collection3d(m.drawcoastlines(linewidth=0.25))
ax.add_collection3d(m.drawcountries(linewidth=0.35))
ax.add_collection3d(m.drawrivers(color='blue'))

ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Height')

fig.show()

这会在 3D 轴内创建地图，以便您可以在表面上绘制对象.但是 Cartopy 返回一个 matplotlib.axes.GeoAxesSubplot.不清楚如何使用 matplotlib-basemap 将其添加到 3D 图形/轴，如上所述.

This creates a map within a 3D axis so that you can plot objects over the surface. But with Cartopy returns a matplotlib.axes.GeoAxesSubplot. Not clear how to take this and add to a 3D figure/axis as above with matplotlib-basemap.

那么，有人可以就如何使用 Cartopy 进行类似的 3D 绘图提供任何指示吗?

So, can someone give any pointers on how to do a similar 3D plot with Cartopy?

推荐答案

底图 mpl3d 是一个非常巧妙的 hack，但它没有被设计为以描述的方式运行.因此，除了简单的海岸线之外，您目前无法使用相同的技术.例如，填充的大陆在 AFAICT 中不起作用.

The basemap mpl3d is a pretty neat hack, but it hasn't been designed to function in the described way. As a result, you can't currently use the same technique for much other than simple coastlines. For example, filled continents just don't work AFAICT.

也就是说，在使用 cartopy 时可以使用类似的 hack.由于我们可以通用地访问 shapefile 信息，因此该解决方案应该适用于任何折线 shapefile，例如海岸线.

That said, a similar hack is available when using cartopy. Since we can access shapefile information generically, this solution should work for any poly-line shapefile such as coastlines.

第一步是获取 shapefile 和相应的几何图形:

The first step is to get hold of the shapefile, and the respective geometries:

feature = cartopy.feature.NaturalEarthFeature('physical', 'coastline', '110m')
geoms = feature.geometries()

接下来，我们可以将这些转换为所需的投影:

Next, we can convert these to the desired projection:

target_projection = ccrs.PlateCarree()
geoms = [target_projection.project_geometry(geom, feature.crs)
         for geom in geoms]

由于这些是匀称的几何图形，因此我们希望将它们转换为 matplotlib 路径:

Since these are shapely geometries, we then want to convert them to matplotlib paths with:

from cartopy.mpl.patch import geos_to_path
import itertools

paths = list(itertools.chain.from_iterable(geos_to_path(geom)
                                             for geom in geoms))

对于路径，我们应该能够在 matplotlib 中创建一个 PathCollection，并将其添加到坐标区，但遗憾的是，Axes3D 似乎无法处理 PathCollection 实例，因此我们需要通过构建 LineCollection 来解决此问题(就像底图一样).遗憾的是 LineCollections 不采用路径，而是采用段，我们可以用它来计算:

With paths, we should be able to just create a PathCollection in matplotlib, and add it to the axes, but sadly, Axes3D doesn't seem to cope with PathCollection instances, so we need to workaround this by constructing a LineCollection (as basemap does). Sadly LineCollections don't take paths, but segments, which we can compute with:

segments = []
for path in paths:
    vertices = [vertex for vertex, _ in path.iter_segments()]
    vertices = np.asarray(vertices)
    segments.append(vertices)

将所有这些放在一起，我们最终得到与您的代码生成的底图相似的结果:

Pulling this all together, we end up with a similar result to the basemap plot which your code produces:

import itertools

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np

import cartopy.feature
from cartopy.mpl.patch import geos_to_path
import cartopy.crs as ccrs


fig = plt.figure()
ax = Axes3D(fig, xlim=[-180, 180], ylim=[-90, 90])
ax.set_zlim(bottom=0)


target_projection = ccrs.PlateCarree()

feature = cartopy.feature.NaturalEarthFeature('physical', 'coastline', '110m')
geoms = feature.geometries()

geoms = [target_projection.project_geometry(geom, feature.crs)
         for geom in geoms]

paths = list(itertools.chain.from_iterable(geos_to_path(geom) for geom in geoms))

# At this point, we start working around mpl3d's slightly broken interfaces.
# So we produce a LineCollection rather than a PathCollection.
segments = []
for path in paths:
    vertices = [vertex for vertex, _ in path.iter_segments()]
    vertices = np.asarray(vertices)
    segments.append(vertices)

lc = LineCollection(segments, color='black')

ax.add_collection3d(lc)

ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Height')

plt.show()

最重要的是，mpl3d 似乎可以很好地处理 PolyCollection，这将是我研究填充几何图形的路线，例如陆地轮廓(与海岸线相反，严格来说是轮廓).

On top of this, mpl3d seems to handle PolyCollection well, which would be the route I would investigate for filled geometries, such as the land outline (as opposed to the coastline, which is strictly an outline).

重要的一步是将路径转换为多边形，并在 PolyCollection 对象中使用它们:

The important step is to convert the paths to polygons, and use these in a PolyCollection object:

concat = lambda iterable: list(itertools.chain.from_iterable(iterable))

polys = concat(path.to_polygons() for path in paths)
lc = PolyCollection(polys, edgecolor='black',
                    facecolor='green', closed=False)

此案例的完整代码如下所示:

The complete code for this case would look something like:

import itertools

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection, PolyCollection
import numpy as np

import cartopy.feature
from cartopy.mpl.patch import geos_to_path
import cartopy.crs as ccrs


fig = plt.figure()
ax = Axes3D(fig, xlim=[-180, 180], ylim=[-90, 90])
ax.set_zlim(bottom=0)


concat = lambda iterable: list(itertools.chain.from_iterable(iterable))

target_projection = ccrs.PlateCarree()

feature = cartopy.feature.NaturalEarthFeature('physical', 'land', '110m')
geoms = feature.geometries()

geoms = [target_projection.project_geometry(geom, feature.crs)
         for geom in geoms]

paths = concat(geos_to_path(geom) for geom in geoms)

polys = concat(path.to_polygons() for path in paths)

lc = PolyCollection(polys, edgecolor='black',
                    facecolor='green', closed=False)

ax.add_collection3d(lc)

ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Height')

plt.show()

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