python - Smoothing results from scipy griddata interpolatioin

I am using the griddata interpolation package in scipy, and an extrapolation function pulled from fatiando:

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


def extrapolate_nans(x, y, v):
    '''  
    Extrapolate the NaNs or masked values in a grid INPLACE using nearest
    value.

    .. warning:: Replaces the NaN or masked values of the original array!

    Parameters:


    * x, y : 1D arrays
        Arrays with the x and y coordinates of the data points.
    * v : 1D array
        Array with the scalar value assigned to the data points.

    Returns:

    * v : 1D array
        The array with NaNs or masked values extrapolated.
    '''


    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



grid_x, grid_y = np.mgrid[0:1.5:50j, 0:1.2:50j]
points = np.random.rand(50, 2)
values = np.random.random_integers(1,10,50)

x = [] 
y = []
for i in points:
    x.append(i[0])
    y.append(i[1])

n = plt.Normalize(values.min(), values.max())


grid_z = griddata(points, values, (grid_x, grid_y), method='cubic')
extrapolate_nans(grid_x,grid_y,grid_z)
plt.contourf(grid_x,grid_y,grid_z)
plt.scatter(x,y,c=values)
plt.colorbar()

plt.show()

I think the results are decent, but the problem is that the extrapolated values have a distinct zigzag pattern:

How do I smooth these lines? Or is there an alternative interpolation method in python that will generate smoother results?