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1 #!/usr/bin/env python
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2 # encoding: utf-8
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3 """
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4 novelty.py
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5
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6 Created by mi tian on 2015-04-02.
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7 Copyright (c) 2015 __MyCompanyName__. All rights reserved.
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8 """
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9
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10 import sys, os
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11 import numpy as np
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12 from scipy.signal import correlate2d, convolve2d
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13
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14 # from utils.PeakPickerUtil import PeakPicker
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15
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16 def getNoveltyCurve(ssm, kernel_size, normalise=False):
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17 '''Return novelty score from ssm.'''
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18
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19 kernel_size = int(np.floor(kernel_size/2.0) + 1)
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20 stripe = getDiagonalSlice(ssm, kernel_size)
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21 kernel = gaussian_kernel(kernel_size)
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22 xc = convolve2d(stripe,kernel,mode='same')
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23 xc[abs(xc)>1e+10]=0.00001
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24
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25 novelty = xc[int(np.floor(xc.shape[0]/2.0)),:]
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26 novelty = [0.0 if (np.isnan(x) or np.isinf(x) or x > 1e+100) else x for x in novelty]
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27
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28 if normalise:
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29 novelty = (novelty - np.min(novelty)) / (np.max(novelty) - np.min(novelty))
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30 return novelty
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31
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32 def getDiagonalSlice(ssm, width):
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33 ''' Return a diagonal stripe of the ssm given its width, with 45 degrees rotation.
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34 Note: requres 45 degrees rotated kernel also.'''
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35 w = int(np.floor(width/2.0))
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36 length = len(np.diagonal(ssm))
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37 stripe = np.zeros((2*w+1,length))
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38 # print 'diagonal', length, w, stripe.shape
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39 for i in xrange(-w, w+1) :
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40 stripe[w+i,:] = np.hstack(( np.zeros(int(np.floor(abs(i)/2.0))), np.diagonal(ssm,i), np.zeros(int(np.ceil(abs(i)/2.0))) ))
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41 return stripe
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42
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43 def gaussian_kernel(size):
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44 '''Create a gaussian tapered 45 degrees rotated checkerboard kernel.
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45 TODO: Unit testing: Should produce this with kernel size 3:
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46 0.1353 -0.3679 0.1353
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47 0.3679 1.0000 0.3679
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48 0.1353 -0.3679 0.1353
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49 '''
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50 n = float(np.ceil(size / 2.0))
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51 kernel = np.zeros((size,size))
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52 for i in xrange(1,size+1) :
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53 for j in xrange(1,size+1) :
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54 gauss = np.exp( -4.0 * (np.square( (i-n)/n ) + np.square( (j-n)/n )) )
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55 # gauss = 1
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56 if np.logical_xor( j - n > np.floor((i-n) / 2.0), j - n > np.floor((n-i) / 2.0) ) :
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57 kernel[i-1,j-1] = -gauss
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58 else:
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59 kernel[i-1,j-1] = gauss
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60
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61 return kernel
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62
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63 def getNoveltyPeaks(ssm, kernel_size, peak_picker, normalise=False):
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64 '''Detect segment boundaries in the ssm.'''
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65 novelty = getNoveltyCurve(ssm, kernel_size, normalise=False)
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66 smoothed_novelty, novelty_peaks = peak_picker.process(novelty)
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67
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68 return novelty, smoothed_novelty, novelty_peaks |
