In [39]:

    

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline


    

In [40]:

    

test_signal = np.array([0,0,0,2,3,100,100,110,110,112,113,115,120,130,5,4,0])


    

In [41]:

    

plt.plot(test_signal);


    

In [42]:

    

from bayesianchangepoint import bcp


    

In [43]:

    

hazard_func = lambda r: bcp.constant_hazard(r, _lambda=200)


    

In [44]:

    

beliefs, maxes = bcp.inference(test_signal, hazard_func)


    

In [46]:

    

fig, ax = plt.subplots(nrows = 2, sharex = True)

ax[0].plot(test_signal)
ax[1].imshow(-np.log(beliefs), interpolation='none', aspect='auto',
               origin='lower', cmap=plt.cm.Blues)
ax[1].plot(maxes, color='r')
ax[1].set_xlim([0, len(test_signal)])
ax[1].set_ylim([0, ax[1].get_ylim()[1]])
ax[0].grid()
ax[1].grid()
index_changes = np.where(np.diff(maxes.T[0])<0)[0]
ax[0].scatter(index_changes, test_signal[index_changes]);