diff --git a/optimizer.py b/optimizer.py
index e797443..36f6d27 100644
--- a/optimizer.py
+++ b/optimizer.py
@@ -1,77 +1,78 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
 Created on Wed Oct 10 12:31:42 2018
 
 @author: alessia
 """
 
 import numpy as np
 import scipy.optimize as sopt
 from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.pyplot as plt
 from matplotlib import cm
 from matplotlib.ticker import LinearLocator, FormatStrFormatter
 import sys
 import argparse
 
 
 def S(x):
     x1 = x[0]
     x2 = x[1]
     return 2. * (x1 ** 2) + 3. / 2. * (x2 ** 2) + x1 * x2 - x1 - 2 * x2 + 6
 
 path = []
 def getIterationSteps(x):
     path.append(x)
     
 def plotFunc(S,path):
     
     fig = plt.figure()
     ax = fig.gca(projection='3d')
     
     # Plot the surface.
     X1 = np.arange(-3, 3, 0.01)
     X2 = np.arange(-3, 3, 0.01)
     X1, X2 = np.meshgrid(X1,X2)
     X3 = S((X1,X2))
     surf = ax.plot_surface(X1, X2, X3, cmap=cm.coolwarm,linewidth=0, antialiased=False)
     
     # Plot the path.
     path=np.array(path)
     x1 = path[:,0]
     x2 = path[:,1]
     x3 = S((x1,x2))
     ax.plot(x1,x2,x3,'--ko')
     
     plt.xlabel('x')
     plt.ylabel('y')
+    ax.view_init(45,140)
     fig.colorbar(surf, shrink=0.5, aspect=5)
     plt.show()       
     
 
 #-------------------------------------------------------------------------------------------------------------------
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description="optimizer.py finds the minimum of a given function with different methods")
 
     group = parser.add_mutually_exclusive_group()
     group.add_argument("-BFGS", "--BFGS", action="store_true", help="minimize with BFGS method")
     group.add_argument("-CG", "--CG", action="store_true", help="minimize with Conjugate Gradient method")
     args = parser.parse_args()
 
     if args.BFGS:
         min_method = 'BFGS'
     if args.CG:
         min_method = 'CG'
     else:
         sys.stderr.write("Choose a minimization method between BFGS and CG" )
     
 
 
     x = (np.random.rand(2)+2)
     path.append(x)
 
     minimum = sopt.minimize(S,x,method=min_method, tol=1e-14, callback=getIterationSteps)
 
     plotFunc(S,path)
\ No newline at end of file