将坐标向量转换为numpy二维矩阵 [英] Converting coordinates vector to numpy 2D matrix

问题描述

我有一组3D坐标点:[纬度，经度，高程]([X，Y，Z])，是从LIDAR数据得出的. 这些点没有排序，并且这些点之间的步长或多或少是随机的.

I have a set of 3D coordinates points: [lat,long,elevation] ([X,Y,Z]), derived from LIDAR data. The points are not sorted and the steps size between the points is more or less random.

我的目标是构建一个函数，将这组点转换为具有恒定像素数的2D numpy矩阵，其中每个(X，Y)单元格都保留Z值，然后将其绘制为高程热图.

My goal is to build a function that converts this set of points to a 2D numpy matrix of a constant number of pixels where each (X,Y) cell hold the Z value, then plot it as elevations heatmap.

• 比例尺必须保持现实，X和Y应该具有相同的步长.
• 矩阵不必捕获精确的高程图像，显然，为了使像素数恒定，需要降低某种分辨率.

我想到的解决方案是为每个像素构建一个存储桶，遍历所有点，然后根据其(X，Y)值将其放入存储桶中.最后，创建一个矩阵，其中每个卖出均在相应的存储区中保存Z值的平均值.

The solution I was thinking of is to build a bucket for each pixel, iterate over the points and put each in a bucket according to it's (X,Y) values. At last create a matrix where each sell holds the mean of the Z values in the corresponding bucket.

1. 由于我在这一领域没有太多经验，所以我很想听听一些技巧，特别是如果有更好的方法来解决此任务.
2. 是否有一个numpy函数将我的点集转换为所需的矩阵? (也许是网格网格具有恒定值的步长?)

3. 如果我构建的矩阵非常稀疏，步长为

1. Since I don't have lots of experience in this field I would love to hear some tips and specially if there are better ways to address this task.
2. Is there a numpy function for converting my set of points to the desired matrix? (maybe meshgrid with steps of a constant value?)

3. If I build very sparse matrix, where the step size is

min [min {Xi，Xj}，min {Yk，Yl}]

min[min{Xi,Xj} , min{Yk,Yl}] for all i,j,k,l

有没有一种方法可以降低"分辨率并将其转换为具有所需大小的矩阵?

is there a way to "reduce" the resolution and convert it to a matrix with the required size?

谢谢！

推荐答案

您不需要重新发明自行车.

You don't need to reinvent the bicycle.

from matplotlib.mlab import griddata
import numpy as np

#-- Your coordinates
x = np.random.random(100)
y = np.random.random(100)
z = np.random.random(100)*10
#--

#-- Your new grid
xsteps=200    # resolution in x
ysteps=200    # resolution in y
xi = linspace(min(x), max(x), xsteps)
yi = linspace(min(y), max(y), ysteps)
Z = griddata(x, y, z, xi, yi)  # interpolates between points in your data
#--

plt.pcolormesh(xi, yi, Z, cmap=plt.cm.hot)   # plot your elevation map :D
plt.show()

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