Feature Inclusion

There may be a case where an original dataset has multiple features, but only a subset of these features are wanted to be included in the segmentation process.

>>> import numpy as np
>>> import matplotlib.pylab as plt
>>> import seg1d 

>>> #retrieve the sample reference, target, and weight data
>>> r,t,w = seg1d.sampleData(c=0.5)

Note: The reference data shown here is centered at 0 on the y axis (vertical). As the algorithm process is based on the shape of the curve, it is irrelevant what this offset is.

>>> # plot reference data
>>> plt_r = np.asarray( [ x for y in r for x in y.values()  ]  ).T
>>> plt.figure(figsize=(3,3)) 
>>> plt.plot(plt_r,alpha=0.3) 
>>> plt.show() 

(Source code)

>>> # plot target data
>>> plt_t = np.asarray( [ x for x in t.values() ] )
>>> plt.figure(figsize=(15,4)) 
>>> plt.plot(plt_t.T,alpha=0.5) 
>>> plt.show() 

(Source code)

>>> #Make an instance of the segmenter
>>> s = seg1d.Segmenter()
>>> #set scaling parameters
>>> s.minW,s.maxW,s.step = 98, 105, 1
>>> #Set target and reference data
>>> s.t, s.r, s.w = t,r,w
>>> #call the segmentation algorithm
>>> segments = s.segment()
>>> print(np.around(segments,decimals=7))
[[204.        245.          0.7128945]
 [ 70.        112.          0.6670482]
 [340.        382.          0.6630886]]

>>> plt_t = s.t_masked #get a NaN masked array of the target data

>>> # plot masked target
>>> plt.figure(figsize=(15,4)) 
>>> plt.plot(plt_t.T,alpha=0.5) 
>>> plt.show() 

(Source code)

To use a subset of the features, the weights can be redefined, which may result in a different segmentation result

>>> sub = [('C7','z'),('T10','z'),('CLAV','z')]
>>> s.w = { x: w[x] for x in sub }
>>> segments = s.segment()

>>> print(np.around(segments,decimals=7))
[[  2.         44.          0.9648465]
 [341.        383.          0.9646419]
 [203.        244.          0.9644605]
 [273.        314.          0.9640178]
 [ 72.        113.          0.9632458]
 [139.        180.          0.9624551]]

>>> plt_t = s.t_masked #get a NaN masked array of the target data

>>> # plot masked target
>>> plt.figure(figsize=(15,4)) 
>>> plt.plot(plt_t.T,alpha=0.5) 
>>> plt.show() 

(Source code)