Resampling irregularly spaced data to a regular grid in Python

I need to resample 2D-data to a regular grid.

This is what my code looks like:

import matplotlib.mlab as ml
import numpy as np

y = np.zeros((512,115))
x = np.zeros((512,115))

# Just random data for this test:
data = np.random.randn(512,115)

# filling the grid coordinates:    
for i in range(512):
    y[i,:]=np.arange(380,380+4*115,4)

for i in range(115):
    x[:,i] = np.linspace(-8,8,512)
    y[:,i] -=  np.linspace(-0.1,0.2,512)

# Defining the regular grid
y_i = np.arange(380,380+4*115,4)
x_i = np.linspace(-8,8,512)

resampled_data = ml.griddata(x,y,data,x_i,y_i)

(512,115) is the shape of the 2D data, and I already installed mpl_toolkits.natgrid.

My issue is that I get back a masked array, where most of the entries are nan, instead of an array that is mostly composed of regular entries and just nan at the borders.

Could someone point me to what I am doing wrong?

Thanks!