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Edit and run



In [1]:

    
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_style('whitegrid')



In [2]:

    
from hybridpy.learning import dynamicprogramming
from hybridpy.dataset import triploader



In [3]:



In [4]:



In [24]:

    
trip = triploader.load('/Users/astyler/projects/ChargeCarData/thor/thor20100305_1.csv')
controls = [0, 5000, 10000, 15000, 20000, 25000, 40000]
v, q, p, tp = dynamicprogramming.compute(trip=trip, controls=controls, soc_states=100)



In [25]:

    
plt.figure(figsize=(10,8))
sns.heatmap(v.T[::-1], linewidth=0, robust=True, cmap='RdBu_r')









    Out[25]:





<matplotlib.axes._subplots.AxesSubplot at 0x10f2c7750>



In [8]:

    
plt.plot(v.T[0:10].T)









    Out[8]:





[<matplotlib.lines.Line2D at 0x111e96cd0>,
 <matplotlib.lines.Line2D at 0x111e964d0>,
 <matplotlib.lines.Line2D at 0x111e8de10>,
 <matplotlib.lines.Line2D at 0x111e8db90>,
 <matplotlib.lines.Line2D at 0x111e8d4d0>,
 <matplotlib.lines.Line2D at 0x111e80150>,
 <matplotlib.lines.Line2D at 0x112efa710>,
 <matplotlib.lines.Line2D at 0x111e804d0>,
 <matplotlib.lines.Line2D at 0x111e77850>,
 <matplotlib.lines.Line2D at 0x111e77d90>]

maybe interp1d with a inf in y values, one inf at 1137, and all before are now NAN, including at other SOCS. no at 1136, 0 is nan but full soc still has value

one nan at the bottom is back propping up, ~1 soc per timestep from interpolation

check interp1d with nan values... check min q t is correct?



In [13]:

    
from scipy.signal import butter, filtfilt
import numpy as np



In [23]:

    
plt.subplot(111)
plt.plot(trip.Acceleration)
plt.figure(figsize=(12,8))
#plt.plot(trip.Power)
plt.plot(p)









    Out[23]:





[<matplotlib.lines.Line2D at 0x110e50fd0>]



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