In [1]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

fulltraj = load('enlair.npy')

fulltraj = fulltraj[:, [1, 2, 4, 5]]
traj = fulltraj[415:442]

plot(traj)


    

    
Out[2]:





[<matplotlib.lines.Line2D at 0x105b3eb10>,
 <matplotlib.lines.Line2D at 0x105b3ed90>,
 <matplotlib.lines.Line2D at 0x105b3efd0>,
 <matplotlib.lines.Line2D at 0x105b4b1d0>]






    

In [3]:

    

plot(concatenate((traj, traj, traj)))


    

    
Out[3]:





[<matplotlib.lines.Line2D at 0x105c9c190>,
 <matplotlib.lines.Line2D at 0x105c9c410>,
 <matplotlib.lines.Line2D at 0x105c9c650>,
 <matplotlib.lines.Line2D at 0x105c9c810>]






    

In [4]:

    

from pydmps.dmp_rhythmic import DMPs_rhythmic


    

In [5]:

    

BFS = 10


    

In [6]:

    

dmp = DMPs_rhythmic(dmps=traj.shape[1], bfs=BFS)


    

In [7]:

    

reconstructed = dmp.imitate_path(traj.T).T


    

In [8]:

    

plot(linspace(0, len(traj), len(reconstructed)), reconstructed)
plot(traj)


    

    
Out[8]:





[<matplotlib.lines.Line2D at 0x106c60e90>,
 <matplotlib.lines.Line2D at 0x106bc5250>,
 <matplotlib.lines.Line2D at 0x106bc5410>,
 <matplotlib.lines.Line2D at 0x106bff350>]






    

In [9]:

    

dmp = DMPs_rhythmic(dmps=traj.shape[1], bfs=BFS)
reconstructed = dmp.imitate_path(traj.T).T

dmp.timesteps *= 2
y, _, _ = dmp.rollout()

i = 1

plot(linspace(0, 2, 2*len(traj)), concatenate((traj[:, i], traj[:, i])))
plot(linspace(0, 2, len(y)), y[:, i])

legend(('fit', 'rollout'))


    

    
Out[9]:





<matplotlib.legend.Legend at 0x106da8450>






    

In [10]:

    

from __init__ import save

with open('base_dmp.json', 'w') as f:
    save(dmp, f)


    

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