I have a numpy 2d-array representing the geometrical height of a specific area where a street will be build. I can visualize this using scipy.misc.toimage
. However I would like to get a simple 3D view of the area. Is there a simple way to plot or render this data as an 3d-image?
Perhaps use matplotlib's plot_surface or plot_wireframe:
import matplotlib.pyplot as plt
import numpy as np
import mpl_toolkits.mplot3d.axes3d as axes3d
np.random.seed(1)
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
N = 100
X, Y = np.meshgrid(np.arange(N), np.arange(N))
heights = np.sin(2*np.pi*np.sqrt(X**2+Y**2)/N)
ax.plot_surface(X, Y, heights, cmap=plt.get_cmap('jet'))
plt.show()
These functions require three 2D-arrays: X, Y, Z.
You have the heights, Z
. To generate the standard X
and Y
locations associated with those Z
s, you could use np.meshgrid
.
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