text: import numpy as np
  import matplotlib.pyplot as plt
  from scipy.interpolate import griddata
  import matplotlib.mlab as ml
  filename = 'highMT_l_10000_04af4a428f8144b79276665ed6689189'
  dat = np.loadtxt('data/'+filename+'.txt' ,  delimiter=',', skiprows=1, unpack=False)
  
  qfail = dat[:,6]
  numsamples = 10000
  
  # Load data from CSV
  
  x = dat[:,1]
  y = dat[:,3]
  z = qfail
  xmin = 4
  xmax = 7
  nx = 12 #numer of steps
  ymin = 5
  ymax = 15
  ny = 10
  xi = np.linspace(xmin, xmax, nx)
  yi = np.linspace(ymin, ymax, ny)
  zi = ml.griddata(x, y, z, xi, yi, 'linear')
  plt.xlabel('expected distance ($\overline{w}$)')
  plt.ylabel('variacne ($\sigma^2$)')
  #CS = plt.contour(xi, yi, zi, 15, linewidths=0.5, colors='k')
  CS = plt.pcolormesh(xi, yi, zi, cmap = plt.get_cmap('rainbow'))
  plt.colorbar(CS, orientation='vertical', shrink=0.8)
  plt.suptitle('Variance Vs Gaussian location')
  plt.title('L = 40, N = 32, Anyons = 15')
  #plt.grid(True)
  #plt.colorbar()
  plt.savefig('figs/'+'variancevslocation_'+filename+'.pdf')
  plt.savefig('figs/'+'variancevslocation_'+filename+'.png')
  plt.show()
  