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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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mitian@1
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13 import matplotlib.pyplot as plt
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mitian@7
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14 from utils.PeakPickerUtil import PeakPicker
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15
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mitian@7
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16
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mitian@7
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17 peak_picker = PeakPicker()
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mitian@7
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18 peak_picker.params.alpha = 9.0 # Alpha norm
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mitian@7
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19 peak_picker.params.delta = 0.0 # Adaptive thresholding delta
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mitian@7
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20 peak_picker.params.QuadThresh_a = (100 - 20.0) / 1000.0
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mitian@7
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21 peak_picker.params.QuadThresh_b = 0.0
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22 peak_picker.params.QuadThresh_c = (100 - 20.0) / 1500.0
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23 peak_picker.params.rawSensitivity = 20
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24 peak_picker.params.aCoeffs = [1.0000, -0.5949, 0.2348]
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25 peak_picker.params.bCoeffs = [0.1600, 0.3200, 0.1600]
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26 peak_picker.params.preWin = 5
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27 peak_picker.params.postWin = 5 + 1
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28 peak_picker.params.LP_on = True
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29 peak_picker.params.Medfilt_on = True
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30 peak_picker.params.Polyfit_on = True
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31 peak_picker.params.isMedianPositive = False
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mitian@7
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32
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33 def getNoveltyCurve(ssm, kernel_size, normalise=False):
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34 '''Return novelty score from ssm.'''
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35
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36 kernel_size = int(np.floor(kernel_size/2.0) + 1)
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37 stripe = getDiagonalSlice(ssm, kernel_size)
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38 kernel = gaussian_kernel(kernel_size)
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39 xc = convolve2d(stripe,kernel,mode='same')
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40 xc[abs(xc)>1e+10]=0.00001
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41
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42 novelty = xc[int(np.floor(xc.shape[0]/2.0)),:]
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43 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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44
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45 if normalise:
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46 novelty = (novelty - np.min(novelty)) / (np.max(novelty) - np.min(novelty))
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47 return novelty
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48
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49 def getDiagonalSlice(ssm, width):
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50 ''' Return a diagonal stripe of the ssm given its width, with 45 degrees rotation.
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51 Note: requres 45 degrees rotated kernel also.'''
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52 w = int(np.floor(width/2.0))
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53 length = len(np.diagonal(ssm))
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54 stripe = np.zeros((2*w+1,length))
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55 # print 'diagonal', length, w, stripe.shape
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56 for i in xrange(-w, w+1) :
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57 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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58 return stripe
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59
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60 def gaussian_kernel(size):
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61 '''Create a gaussian tapered 45 degrees rotated checkerboard kernel.
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62 TODO: Unit testing: Should produce this with kernel size 3:
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63 0.1353 -0.3679 0.1353
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64 0.3679 1.0000 0.3679
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65 0.1353 -0.3679 0.1353
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66 '''
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67 n = float(np.ceil(size / 2.0))
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68 kernel = np.zeros((size,size))
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69 for i in xrange(1,size+1) :
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70 for j in xrange(1,size+1) :
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71 gauss = np.exp( -4.0 * (np.square( (i-n)/n ) + np.square( (j-n)/n )) )
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72 # gauss = 1
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73 if np.logical_xor( j - n > np.floor((i-n) / 2.0), j - n > np.floor((n-i) / 2.0) ) :
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74 kernel[i-1,j-1] = -gauss
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75 else:
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76 kernel[i-1,j-1] = gauss
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77
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78 return kernel
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79
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mitian@7
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80 def segmentation(ssm, peak_picker=peak_picker, kernel_size=48, normalise=False, plot=False):
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81 '''Detect segment boundaries in the ssm.'''
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mitian@7
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82 # peak_picker for the 1st round boudary detection
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mitian@7
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83
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mitian@7
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84
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85 novelty = getNoveltyCurve(ssm, kernel_size, normalise=False)
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86 smoothed_novelty, novelty_peaks = peak_picker.process(novelty)
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87
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88 if plot:
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89 plot_detection(smoothed_novelty, novelty_peaks)
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90 return novelty, smoothed_novelty, novelty_peaks
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91
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92 def plot_detection(smoothed_novelty, novelty_peaks):
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93 pass |
