old_contents: #!/usr/bin/env python
  
  import geostatsmodels.utilities as u
  import geostatsmodels.kriging as k
  import geostatsmodels.simulation as s
  from pylab import *
  import numpy as np
  
  # read cluster.dat
  d = u.readGeoEAS("cluster.dat")
  
  # take the first three columns
  d = d[:,:3]
  
  # define the lags and tolerance 
  # for the semivariogram modeling
  lags, tol = np.linspace( 10, 50, 10 ), 5
  
  # if the data is not normally distributed,
  # perfrom a z-score transformation
  d, inv = u.to_norm( d )
  
  # perform sequential Gaussian simulation using
  # a spherical model, on a 5x5 grid
  m = s.sgs( d, k.spherical, lags, tol, 5, 5 )
  
  # use the [:,::-1].T to a) reverse the order of the columns
  # and then b) transpose the data, this takes it from Python
  # conventions, back to the way we normally think of spatial data
  print m[:,::-1].T
  
  '''
  Printing m, you should see something like the following output.
  Notice that these values look like they have a mean of zero, and a
  standard deviation of one. This data has not been transformed back
  to the original distribution.
  
  array([[ 0.03499825, -0.56577758, -0.53807829,  0.45723896,  0.33959872],
         [-0.42029471, -0.27813954,  0.07044366,  0.39437271, -0.07625142],
         [-1.12918974, -0.36841119,  0.21116697, -1.11767303, -0.48140453],
         [ 0.39862306, -0.39414717, -0.95340368, -0.46121427, -0.08815428],
         [ 0.50258498, -0.91996145, -0.91149543, -0.61183492, -0.88997225]])
  '''
  
  # assuming you have matplotlib and  pylab installed, 
  # you can visulize the untransformed data
  matshow( m[:,::-1].T, cmap=u.YPcmap )
  
  # perform the back-transformation
  n = u.from_norm( m, inv )
  
  print n[:,::-1].T
  
  '''
  The back-transformed data should look something like this:
  
  array([[ 2.32771604,  0.89024448,  0.9233661 ,  3.63025147,  3.28306658],
         [ 1.01198912,  1.35326734,  2.33965581,  3.53342939,  1.94217347],
         [ 0.26234492,  1.10879666,  2.80242068,  0.26918933,  0.96348094],
         [ 3.54440162,  1.06344535,  0.33826991,  0.99125142,  1.89579485],
         [ 4.25739527,  0.36063006,  0.36684815,  0.82690577,  0.3828731 ]])
  '''
  
new_contents: #!/usr/bin/env python
  
  import geostatsmodels.utilities as u
  import geostatsmodels.kriging as k
  import geostatsmodels.simulation as s
  from pylab import *
  import numpy as np
  
  # read cluster.dat
  d = u.readGeoEAS("cluster.dat")
  
  # take the first three columns
  d = d[:,:3]
  
  # define the lags and tolerance 
  # for the semivariogram modeling
  lags, tol = np.linspace( 10, 50, 10 ), 5
  
  # if the data is not normally distributed,
  # perfrom a z-score transformation
  d, inv = u.to_norm( d )
  
  # perform sequential Gaussian simulation using
  # a spherical model, on a 5x5 grid
  m = s.sgs( d, k.spherical, lags, tol, 5, 5 )
  
  # use the [:,::-1].T to a) reverse the order of the columns
  # and then b) transpose the data, this takes it from Python
  # conventions, back to the way we normally think of spatial data
  print(m[:,::-1].T)
  
  '''
  Printing m, you should see something like the following output.
  Notice that these values look like they have a mean of zero, and a
  standard deviation of one. This data has not been transformed back
  to the original distribution.
  
  array([[ 0.03499825, -0.56577758, -0.53807829,  0.45723896,  0.33959872],
         [-0.42029471, -0.27813954,  0.07044366,  0.39437271, -0.07625142],
         [-1.12918974, -0.36841119,  0.21116697, -1.11767303, -0.48140453],
         [ 0.39862306, -0.39414717, -0.95340368, -0.46121427, -0.08815428],
         [ 0.50258498, -0.91996145, -0.91149543, -0.61183492, -0.88997225]])
  '''
  
  # assuming you have matplotlib and  pylab installed, 
  # you can visulize the untransformed data
  matshow( m[:,::-1].T, cmap=u.YPcmap )
  
  # perform the back-transformation
  n = u.from_norm( m, inv )
  
  print(n[:,::-1].T)
  
  '''
  The back-transformed data should look something like this:
  
  array([[ 2.32771604,  0.89024448,  0.9233661 ,  3.63025147,  3.28306658],
         [ 1.01198912,  1.35326734,  2.33965581,  3.53342939,  1.94217347],
         [ 0.26234492,  1.10879666,  2.80242068,  0.26918933,  0.96348094],
         [ 3.54440162,  1.06344535,  0.33826991,  0.99125142,  1.89579485],
         [ 4.25739527,  0.36063006,  0.36684815,  0.82690577,  0.3828731 ]])
  '''
  
subject: Add paren around print statements