[英]Surface plot with matplotlib, Python
I have a nx3
array, lets call it data
, where I want the first 2 columns to be x
and y
coordinates and the 3rd column to be a z
coordinate associated with the x
and y
coordinates in the same row. 我有一个
nx3
数组,我们称其为data
,在这里我希望前两列为x
和y
坐标,第三列为与同一行中x
和y
坐标关联的z
坐标。
I now want to draw a surface plot where the surface intersects all the z
coordinates. 我现在想绘制一个曲面图,其中曲面与所有
z
坐标相交。
I have seen this post but cannot figure it out. 我看过这篇文章,但无法弄清楚。
I know that I can use matplotlib's Axes3D
and fig.gca(projection='3d')
and that it takes 3 nxn
arrays, where I think the X
and Y
arrays can be obtained with X,Y = np.meshgrid(data[:,0],data[:,1])
, but I am not sure how to obtain an nxn
Z
array if there is only 1 Z
coordinate associated with each x
and y
. 我知道我可以使用matplotlib的
Axes3D
和fig.gca(projection='3d')
并且它需要3个nxn
数组,我认为X
和Y
数组可以通过X,Y = np.meshgrid(data[:,0],data[:,1])
,但是如果每个x
和y
只有1个Z
坐标,则我不确定如何获取nxn
Z
数组。
Then, I would like to smoothen the surface, as I am sure a surface with only a few data points will look ugly, and I am only looking to represent the general shape of the data and specific values aren't too important. 然后,我想对表面进行平滑处理,因为我确信只有几个数据点的表面看起来很难看,而且我只是想代表数据的一般形状,而具体的值并不是太重要。 Thus, is there a way to interpolate between the data points in 2 dimensions to smoothen the graph?
因此,是否有一种方法可以在2维数据点之间进行插值以使图形平滑?
Example data set: 数据集示例:
data = np.array([[4260,150,116]
[4204,149,1070]
[4204,188,470]
[4444,140,389]
[3255,149,69]
[6370,149,1109]
[5765,189,3531]])
Try it like this: 像这样尝试:
x, y, z = data[:,0], data[:,1], data[:,2]
grid_x, grid_y = np.mgrid[min(x):max(x):50j, min(y):max(y):50j]
z = griddata((x, y), z, (grid_x, grid_y), method='cubic')
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