Interpolation of unstructured 3-dimensional data?

Question

Python

I already asked about this, here . But then it was not possible to ask a very specific question. So, I repeat. There is a set of points, 3D:

x	y	z
0.8316467632710912	3.9125904029352112	4.2460486461065035e+009
1.1025494232680673	3.8450467837532551	4.2460486461065035e+009
1.6269465723033014	3.6541818305703586	4.2460486461065035e+009
...	...	...
0.8370776849271087	-11.9707686031180580	4.2380933897388258e+009
-0.0000000000024552	-12.0000000000000000	4.2359237743658228e+009
-0.8370776849320072	-11.9707686031177150	4.2337541589928198e+009
...	...	...

It's kind of an irregular surface. You need to display it. C++ is used.
To clearly show what is needed, here is an example in Python (I don’t know the language):

import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from scipy.interpolate import griddata

fig = plt.figure(figsize=plt.figaspect(0.5))
ax = fig.gca(projection='3d')
my_data = np.genfromtxt('data.csv', delimiter=',',skiprows=0)
X = my_data[:,0]
Y = my_data[:,1]
Z = my_data[:,2]

xi = np.linspace(X.min(),X.max(),1000)
yi = np.linspace(Y.min(),Y.max(),1000)

zi = griddata((X, Y), Z, (xi[None,:], yi[:,None]), method='cubic')

xig, yig = np.meshgrid(xi, yi)

surf = ax.plot_surface(xig, yig, zi,linewidth=0.5,color='DarkKhaki',alpha=0.50)

plt.show()

And a few surfaces:



Prompt the library in C / C ++ (?). Or a method to implement it. The number of points can reach 10,000, but on average - up to 5,000 points.
PS : Isn't this Nearest neighbor weighted interpolation by any chance ?

Answer 1

Nothing needs to be interpolated. You need a 3d graph.
gnuplot should work.