Simplest way to plot 3d surface given 3d points

I have a lot (289) of 3d points with xyz coordinates which looks like:

With plotting simply 3d space with points is OK, but I have trouble with surface There are some points:

for i in range(30):
        output.write(str(X[i])+' '+str(Y[i])+' '+str(Z[i])+'\n')

-0.807237702464 0.904373229492 111.428744443
-0.802470821517 0.832159465335 98.572957317
-0.801052795982 0.744231916692 86.485869328
-0.802505546206 0.642324228721 75.279804677
-0.804158144115 0.52882485495 65.112895758
-0.806418040943 0.405733109371 56.1627277595
-0.808515314192 0.275100227689 48.508994388
-0.809879521648 0.139140394575 42.1027499025
-0.810645106092 -7.48279012695e-06 36.8668106345
-0.810676720161 -0.139773175337 32.714580273
-0.811308686707 -0.277276065449 29.5977405865
-0.812331692291 -0.40975978382 27.6210856615
-0.816075037319 -0.535615685086 27.2420699235
-0.823691366944 -0.654350489595 29.1823292975
-0.836688691603 -0.765630198427 34.2275056775
-0.854984518665 -0.86845932028 43.029581434
-0.879261949054 -0.961799684483 55.9594146815
-0.740499820944 0.901631050387 97.0261463995
-0.735011699497 0.82881933383 84.971061395
-0.733021568161 0.740454485354 73.733621269
-0.732821755233 0.638770044767 63.3815970475
-0.733876941678 0.525818698874 54.0655910105
-0.735055978521 0.403303715698 45.90859502
-0.736448900325 0.273425879041 38.935709456
-0.737556181137 0.13826504904 33.096106049
-0.738278724065 -9.73058423274e-06 28.359664343
-0.738507612286 -0.138781586244 24.627237837
-0.738539663773 -0.275090412979 21.857410904
-0.739099040189 -0.406068448513 20.1110519655
-0.741152200369 -0.529726022182 19.7019157715

There is no equal X's and Y's values. X is from -0.8 to 0.8, Y is from -0.9 to 0.9 and z from 0 to 111.

If it is possible, how to make 3d surface plot using these points?

Please have a look at Axes3D.plot_surface or at the other Axes3D methods. You can find examples and inspirations here, here, or here.

Edit:

Z-Data that is not on a regular X-Y-grid (equal distances between grid points in one dimension) is not trivial to plot as a triangulated surface. For a given set of irregular (X, Y) coordinates, there are multiple possible triangulations. One triangulation can be calculated via a "nearest neighbor" Delaunay algorithm. This can be done in matplotlib. However, it still is a bit tedious:

http://matplotlib.1069221.n5.nabble.com/Plotting-3D-Irregularly-Triangulated-Surfaces-An-Example-td9652.html

It looks like support will be improved:

http://matplotlib.org/examples/pylab_examples/tripcolor_demo.html http://matplotlib.1069221.n5.nabble.com/Custom-plot-trisurf-triangulations-tt39003.html

With the help of http://docs.enthought.com/mayavi/mayavi/auto/example_surface_from_irregular_data.html I was able to come up with a very simple solution based on mayavi:

import numpy as np
from mayavi import mlab

X = np.array([0, 1, 0, 1, 0.75])
Y = np.array([0, 0, 1, 1, 0.75])
Z = np.array([1, 1, 1, 1, 2])

# Define the points in 3D space
# including color code based on Z coordinate.
pts = mlab.points3d(X, Y, Z, Z)

# Triangulate based on X, Y with Delaunay 2D algorithm.
# Save resulting triangulation.
mesh = mlab.pipeline.delaunay2d(pts)

# Remove the point representation from the plot
pts.remove()

# Draw a surface based on the triangulation
surf = mlab.pipeline.surface(mesh)

# Simple plot.
mlab.xlabel("x")
mlab.ylabel("y")
mlab.zlabel("z")
mlab.show()

This is a very simple example based on 5 points. 4 of them are on z-level 1:

(0, 0) (0, 1) (1, 0) (1, 1)

One of them is on z-level 2:

(0.75, 0.75)

The Delaunay algorithm gets the triangulation right and the surface is drawn as expected:

I ran the above code on Windows after installing Python(x,y) with the command

ipython -wthread script.py