2D Smoothing Gaussian Python

#!/usr/bin/python

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

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)

####plot

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

fig=plt.figure()

ax=fig.add_subplot(1,2,1)

plt.contour(xi, yi, zi, 20, linewidths = 0.5, colors = 'k')

plt.contourf(xi,yi,zis)

ax=fig.add_subplot(1,2,2)

plt.contour(xi, yi, zis, 20, linewidths = 0.5, colors = 'k')

plt.contourf(xi,yi,zis)

plt.colorbar()

plt.show()