new_contents: #Read sabple data
  
  import csv
  from pygeocoder import Geocoder
  
  
  # open samle data with per row dictionary
  data = csv.DictReader(open("sampledata.csv"))
  
  # GeoCode from address
  latlon = []
  propvalue = []
  for row in data:
      propvalue.append(row["VALACT"])
      addr = [row["PRPSTRNUM"], row["PRPSTRDIR"], row["PRPSTRNAM"], row["PRPSTRTYP"], row["PRPSTRSFX"], 
              row["PRPSTRUNT"], row["PRPCTYNAM"], row["PRPSTENAM"], row["PRPZIP5"]]
      addr = ' '.join(addr)
      results = Geocoder.geocode(addr)
      latlon.append(results[0].coordinates)
  
  # for coor in latlon:
  #     print(coor)
  # for val in propvalue:
  #     print(val)
  
  #########################
  
  import scipy.sparse.sparsetools
  import matplotlib.pyplot as plt
  from scipy.spatial import Delaunay
  
  points = numpy.asarray(latlon)
  
  tri = Delaunay(points)
  
  plt.triplot(points[:,0], points[:,1], tri.simplices.copy())
  plt.plot(points[:,0], points[:,1], 'o')
  plt.show()
  
  #########################
  # Fails
  #########################
  from matplotlib.tri import Triangulation, UniformTriRefiner, CubicTriInterpolator
  import numpy
  
  propvalue = numpy.asarray(propvalue)
  x = 100*points[:,0]
  y = 100*points[:,1]
  triang = Triangulation(x, y)
  
  tci = CubicTriInterpolator(triang, propvalue)
  (Ex, Ey) = tci.gradient(triang.x, triang.y)
  E_norm = np.sqrt(Ex**2 + Ey**2)
  
subject: Create misc.py