我有一些Python代码，可以使用matplotlib从一些海洋模型数据中绘制大量非规则多边形。
我通过创建4个形状为（N，2）的numpy数组来定义每个补丁的角，其中N是一个很大的数字（比如500，000）
然后，我为每组角创建matplotlib Patch对象，并将其添加到列表中。最后，我从Patch列表中创建matplotlib PatchCollection对象。
问题是补丁生成很慢，因为它是在for循环中。我一直在想一种方法来加快这与numpy广播，但不能完全破解它。
下面是一些示例代码，带有一个小的测试数据集（显然运行起来很快）。

import numpy as np
from matplotlib.collections import PatchCollection
import matplotlib.pyplot as plt

# Cell lat/lon centers:
lons = np.array([ 323.811,  323.854,  323.811,  323.723, 324.162,  324.206,  323.723,  324.162, 323.635,  323.679])
lats = np.array([-54.887, -54.887, -54.858, -54.829, -54.829, -54.829, -54.799, -54.799, -54.770, -54.770])

# Cell size scaling factors:
cx = np.array([1,1,1,2,2,2,4,1,2,1]) 
cy = np.array([1,1,1,1,2,2,2,1,2,1])

# Smallest cell sizes:
min_dlon = 0.0439453  
min_dlat = 0.0292969 

# Calculate cell sizes based on cell scaling factor and smallest cell size
dlon = cx * min_dlon
dlat = cy * min_dlat

# calculate cell extnets....
x1 = lons - 0.5 * dlon
x2 = lons + 0.5 * dlon
y1 = lats - 0.5 * dlat
y2 = lats + 0.5 * dlat

# ... and corners
c1 = np.array([x1,y1]).T
c2 = np.array([x2,y1]).T
c3 = np.array([x2,y2]).T
c4 = np.array([x1,y2]).T

# Now loop over cells and create Patch objects from the cell corners.
# This is the bottleneck as it using a slow Python loop instead of 
# fast numpy broadcasting. How can I speed this up?
ncel = np.alen(lons)
patches = []
for i in np.arange(ncel):
    verts = np.vstack([c1[i], c2[i], c3[i], c4[i]])
    p = plt.Polygon(verts)
    patches.append(p)

# Create patch collection from list of Patches
p = PatchCollection(patches, match_original=True)

有什么办法能让我快点吗？