Python：用np.array数据覆盖shapefile

我有一个美国的形状文件，我有一个m x n笛卡尔数据数组，表示每个像素的温度。我可以加载shapefile并对其进行打印：

import shapefile as shp
import matplotlib.pyplot as plt

sf = shp.Reader("/path/to/USA.shp")

plt.figure()
for shape in sf.shapeRecords():
    for i in range(len(shape.shape.parts)):
        i_start = shape.shape.parts[i]
        if i==len(shape.shape.parts)-1:
            i_end = len(shape.shape.points)
        else:
            i_end = shape.shape.parts[i+1]
        x = [i[0] for i in shape.shape.points[i_start:i_end]]
        y = [i[1] for i in shape.shape.points[i_start:i_end]]
        plt.plot(x,y, color = 'black')
plt.show()

import pickle
from matplotlib import pyplot as mp
Tfile = '/path/to/file.pkl'
with open(Tfile) as f:
    reshapeT = pickle.load(f)
mp.matshow(reshapeT)

我能够读入我的数据并绘制它：

import shapefile as shp
import matplotlib.pyplot as plt

sf = shp.Reader("/path/to/USA.shp")

plt.figure()
for shape in sf.shapeRecords():
    for i in range(len(shape.shape.parts)):
        i_start = shape.shape.parts[i]
        if i==len(shape.shape.parts)-1:
            i_end = len(shape.shape.points)
        else:
            i_end = shape.shape.parts[i+1]
        x = [i[0] for i in shape.shape.points[i_start:i_end]]
        y = [i[1] for i in shape.shape.points[i_start:i_end]]
        plt.plot(x,y, color = 'black')
plt.show()

import pickle
from matplotlib import pyplot as mp
Tfile = '/path/to/file.pkl'
with open(Tfile) as f:
    reshapeT = pickle.load(f)
mp.matshow(reshapeT)

问题是，Reformet的维数为536 x 592，是美国的子域。但是，我有关于重塑栅格lat/long的左上角以及每个像素之间的间距0.01的信息

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我的问题是：如何将整形数据覆盖到shapefile域的顶部？

如果我理解正确，您希望将536x592 numpy数组覆盖到打印的shapefile的特定部分上。我建议您使用Matplotlib的imwshow方法，并使用extent参数，这允许您将图像放置在绘图中

绘制形状文件的方法很好，但是，如果可以使用geopandas，它将大大简化事情。打印形状文件将减少到以下行：

import geopandas as gpd
sf = gpd.read_file("/path/to/USA.shp")
ax1 = sf.plot(edgecolor='black', facecolor='none')

如前所述，现在让我们加载阵列数据：

import pickle
Tfile = '/path/to/file.pkl'
with open(Tfile) as f:
    reshapeT = pickle.load(f)

现在为了能够在正确的位置绘制numpy数组，我们首先需要计算它的范围，它将覆盖的区域，用坐标表示。您提到您有关于左上角和分辨率0.01的信息-这就是我们所需要的。在下文中，我假设关于左上角的lat/lon信息保存在top_left_lat和top_left_lon变量中。数据块需要以元组的形式传递，每个边的值按左、右、下、上的顺序排列

因此，我们的范围可以计算如下：

extent_mat = (top_left_lon, top_left_lon + reshapeT.shape[1] * 0.01, top_left_lat - reshapeT.shape[0] * 0.01, top_left_lat)

最后，我们将矩阵绘制到同一个Axis对象ax1上，在该对象上我们已经绘制了计算范围内的形状文件：

# Let's turn off autoscale first. This prevents
# the view of the plot to be limited to the image
# dimensions (instead of the entire shapefile). If you prefer
# that behaviour, just remove the following line
ax1.autoscale(False)

# Finally, let's plot!
ax1.imshow(reshapeT, extent=extent_mat)

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这正是我想要的！