import numpy as np

import matplotlib.pyplot as plt

 mean1=[5,5]

 cov1=[[1,1],[1,1.5]]

 data=np.random.multivariate_normal(mean1,cov1,100)

 x1,y1=data.T

 x2=np.random.choice(10,10)

 y2=x2

 plt.plot(x1,y1,c='b')

 plt.scatter(x2,y2,'r-')

 plt.xlabel('x')

 plt.ylabel('y')

 new_xtick=[1,2,3,4,5]

 new_ytick=[1,2,3,4,5]

 plt.xticks(new_xtick)

 plt.yticks(new_ytick)

 ax=plt.gca()

 ax = plt.gca()

 ax.spines['right'].set_color('none')

 ax.spines['top'].set_color('none')
13 plt.show()

 ax.spines['left'].set_position(('data',0))

 ax.spines['right'].set_position(('data',0))

 ax.spines['bottom'].set_position(('data',0))

 ax.spines['top'].set_position(('data',0))

 dot_in=np.array([[2**(0.5)/2,-2**(0.5)/2],[2**(0.5)/2,2**(0.5)/2]])

 dot_inv=np.linalg.inv(dot_in)

 x3,y3=x1-mean1[0],y1-mean1[1]

 x4,y4=np.dot(dot_inv,np.array([x3,y3]))

 plt.scatter(x1,y1,c='r')

 plt.scatter(x4,y4,c='b')

 ax=plt.gca()

 ax.spines['bottom'].set_position(('data',0))

 ax.spines['top'].set_position(('data',0))

 ax.spines['left'].set_position(('data',0))

 ax.spines['right'].set_position(('data',0))

 ax.spines['right'].set_color(('none'))

 ax.spines['top'].set_color(('none'))

  plt.show()