title: Resampling irregularly spaced data to a regular grid in Python
body: <p>I need to resample 2D-data to a regular grid.</p>
  
  <p>This is what my code looks like:</p>
  
  <pre><code>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)
  </code></pre>
  
  <p>(512,115) is the shape of the 2D data, and I already installed mpl_toolkits.natgrid.</p>
  
  <p>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.</p>
  
  <p>Could someone point me to what I am doing wrong?</p>
  
  <p>Thanks!</p>