In [3]:

    

import matplotlib.pyplot as plt
import numpy as np


    

In [12]:

    

x = np.linspace(0,12,100)
print x


    

    




[  0.           0.12121212   0.24242424   0.36363636   0.48484848
   0.60606061   0.72727273   0.84848485   0.96969697   1.09090909
   1.21212121   1.33333333   1.45454545   1.57575758   1.6969697
   1.81818182   1.93939394   2.06060606   2.18181818   2.3030303
   2.42424242   2.54545455   2.66666667   2.78787879   2.90909091
   3.03030303   3.15151515   3.27272727   3.39393939   3.51515152
   3.63636364   3.75757576   3.87878788   4.           4.12121212
   4.24242424   4.36363636   4.48484848   4.60606061   4.72727273
   4.84848485   4.96969697   5.09090909   5.21212121   5.33333333
   5.45454545   5.57575758   5.6969697    5.81818182   5.93939394
   6.06060606   6.18181818   6.3030303    6.42424242   6.54545455
   6.66666667   6.78787879   6.90909091   7.03030303   7.15151515
   7.27272727   7.39393939   7.51515152   7.63636364   7.75757576
   7.87878788   8.           8.12121212   8.24242424   8.36363636
   8.48484848   8.60606061   8.72727273   8.84848485   8.96969697
   9.09090909   9.21212121   9.33333333   9.45454545   9.57575758
   9.6969697    9.81818182   9.93939394  10.06060606  10.18181818
  10.3030303   10.42424242  10.54545455  10.66666667  10.78787879
  10.90909091  11.03030303  11.15151515  11.27272727  11.39393939
  11.51515152  11.63636364  11.75757576  11.87878788  12.        ]

In [13]:

    

y = np.sin(x)


    

In [15]:

    

plt.figure()
plt.plot(x,y)


    

    
Out[15]:





[<matplotlib.lines.Line2D at 0x107d4ca10>]

In [8]:

    

%matplotlib


    

    




Using matplotlib backend: MacOSX

In [ ]: