new_contents: """
  Function to plot the linear regression picture of behavial loss aversion
  and neural loss aversion(-betagain-betaloss) at a single coordinate of standard brain
  """
  def plot_neur_beh(x, y, z, betagains, betalosses, neural):
      """ 
       plot the linear regression picture of behavial loss aversion
       and neural loss aversion(-betagain-betaloss) of a single voxel's 
       coordinates of standard brain
       Parameters: 
       ----------- 
       x: the x coordinate of the voxel in standard brain
       y: the y coordinate of the voxel in standard brain 
       z: the z coordinate of the voxel in standard brain
       betagains: beta gain estimates from OLS regression of a single voxel,  
       4-d array (91*109*91*16)
       betalosses:beta loss estimates from OLS regression of a single voxel,  
       4-d array (91*109*91*16)
       neural: behavial loss aversion
        
       Returns: 
       -------- 
       A linear regression plot, the x axis is Neural loss aversion.
       The y axis is the Behavial loss aversion.The title is the coordinate of 
       the voxel in standrd brain.The plot is with the p value and r value of the
       linear regression.
       """ 
  
      X = -betagains[x, y, z, :] - betalosses[x, y, z, :]
      slope, intercept, r_value, p_value, std_err = stats.linregress(X,Y)
      plt.scatter(X,Y)
      X_plot = np.linspace(-75,130,100)
      plt.plot(X_plot, X_plot*slope + intercept)
      plt.xlabel(r'Neural loss aversion ($-\beta loss-\beta{gain}$)' , fontsize=12)
      plt.ylabel(r'Behavoiral aversion (In$\lambda$)', fontsize=12)
      plt.text(-80, 2.2, r'r = %.4f'%r_value +', p = %.4f'%p_value, fontsize=12)
      plt.title('('+str(x)+',  '+str(y)+',  '+str(z)+')', fontsize=18)
      plt.savefig('('+str(x)+',  '+str(y)+',  '+str(z)+').png')
subject: add the funtion plot_neural_behacior which is used to plot the linear regression plot of behavior loss aversion against neural loss aversion of a single voxel