Python 使用 matplotlib 更有效地绘制多边形
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Draw polygons more efficiently with matplotlib
提问by HyperCube
I have a dateset of around 60000 shapes (with lat/lon coordinates of each corner) which I want to draw on a map using matplotlib and basemap.
我有一个大约 60000 个形状的日期集（每个角的纬度/经度坐标），我想使用 matplotlib 和底图在地图上绘制它们。
This is the way I am doing it at the moment:
这是我目前的做法：
for ii in range(len(data)):
lons = np.array([data['lon1'][ii],data['lon3'][ii],data['lon4'][ii],data['lon2'][ii]],'f2')
lats = np.array([data['lat1'][ii],data['lat3'][ii],data['lat4'][ii],data['lat2'][ii]],'f2')
x,y = m(lons,lats)
poly = Polygon(zip(x,y),facecolor=colorval[ii],edgecolor='none')
plt.gca().add_patch(poly)
However, this takes around 1.5 minutes on my machine and I was thinking whether it is possible to speed things up a little. Is there a more efficient way to draw polygons and add them to the map?
但是，这在我的机器上大约需要 1.5 分钟，我在想是否可以稍微加快速度。有没有更有效的方法来绘制多边形并将它们添加到地图中？
采纳答案by pelson
You could consider creating Collections of polygons instead of individual polygons.
您可以考虑创建多边形集合而不是单个多边形。
The relevant docs can be found here: http://matplotlib.org/api/collections_api.htmlWith a example worth picking appart here: http://matplotlib.org/examples/api/collections_demo.html
相关文档可以在这里找到：http://matplotlib.org/api/collections_api.html 这里有一个值得选择的例子：http: //matplotlib.org/examples/api/collections_demo.html
As an example:
举个例子：
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
import matplotlib as mpl
# Generate data. In this case, we'll make a bunch of center-points and generate
# verticies by subtracting random offsets from those center-points
numpoly, numverts = 100, 4
centers = 100 * (np.random.random((numpoly,2)) - 0.5)
offsets = 10 * (np.random.random((numverts,numpoly,2)) - 0.5)
verts = centers + offsets
verts = np.swapaxes(verts, 0, 1)
# In your case, "verts" might be something like:
# verts = zip(zip(lon1, lat1), zip(lon2, lat2), ...)
# If "data" in your case is a numpy array, there are cleaner ways to reorder
# things to suit.
# Color scalar...
# If you have rgb values in your "colorval" array, you could just pass them
# in as "facecolors=colorval" when you create the PolyCollection
z = np.random.random(numpoly) * 500
fig, ax = plt.subplots()
# Make the collection and add it to the plot.
coll = PolyCollection(verts, array=z, cmap=mpl.cm.jet, edgecolors='none')
ax.add_collection(coll)
ax.autoscale_view()
# Add a colorbar for the PolyCollection
fig.colorbar(coll, ax=ax)
plt.show()
HTH,
哈，
回答by HyperCube
I adjusted my code and now it is working flawlessly :)
我调整了我的代码，现在它完美地工作了:)
Here is the working example:
这是工作示例：
lons = np.array([data['lon1'],data['lon3'],data['lon4'],data['lon2']])
lats = np.array([data['lat1'],data['lat3'],data['lat4'],data['lat2']])
x,y = m(lons,lats)
pols = zip(x,y)
pols = np.swapaxes(pols,0,2)
pols = np.swapaxes(pols,1,2)
coll = PolyCollection(pols,facecolor=colorval,cmap=jet,edgecolor='none',zorder=2)
plt.gca().add_collection(coll)
