Plot 3D

#!/usr/bin/python

from mpl_toolkits.mplot3d import Axes3D

import numpy as np

import scipy

from scipy.interpolate import griddata

import matplotlib.pyplot as plt

import matplotlib.mlab as ml

import scipy.ndimage as ndimage

from matplotlib import mlab, cm

def extrapolate_nans(x, y, v):

if np.ma.is_masked(v):

nans = v.mask

else:

nans = np.isnan(v)

notnans = np.logical_not(nans)

v[nans] = scipy.interpolate.griddata((x[notnans], y[notnans]), v[notnans],

(x[nans], y[nans]), method='nearest').ravel()

return v

data = np.genfromtxt('input2d.txt')

x1 = data[:,0]

y1 = data[:,1]

z1 = data[:,2]

numcols, numrows = 500, 500

xi = np.linspace(min(x1), max(x1), numcols)

yi = np.linspace(min(y1), max(y1), numrows)

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

x, y, z = x1, y1, z1

zi = ml.griddata(x, y, z, xi, yi, interp='nn')

extrapolate_nans(xi,yi,zi)

levels = np.arange(-1200, -200, 25) # Boost the upper limit to avoid truncation errors.

norm = cm.colors.Normalize(vmax=abs(z1).max(), vmin=-abs(z1).max())

cmap = cm.hsv

####plot

zis = ndimage.gaussian_filter(zi, sigma=25.0, order=0)

fig=plt.figure()

ax = fig.gca(projection='3d')

surf = ax.plot_surface(xi, yi, zis, rstride=20, cstride=20, cmap=cm.get_cmap(cmap, len(levels) - 1), linewidth=0, antialiased=True)

ax.set_zlim3d(np.min(zis), np.max(zis))

fig.colorbar(surf)

plt.show()