comparison novelty.py @ 7:294f66d285af

some previous changes

 
 import sys, os
 import numpy as np
 from scipy.signal import correlate2d, convolve2d
 import matplotlib.pyplot as plt
+from utils.PeakPickerUtil import PeakPicker
 
-# from utils.PeakPickerUtil import PeakPicker
-
+	
+peak_picker = PeakPicker()
+peak_picker.params.alpha = 9.0 # Alpha norm
+peak_picker.params.delta = 0.0 # Adaptive thresholding delta
+peak_picker.params.QuadThresh_a = (100 - 20.0) / 1000.0
+peak_picker.params.QuadThresh_b = 0.0
+peak_picker.params.QuadThresh_c = (100 - 20.0) / 1500.0
+peak_picker.params.rawSensitivity = 20
+peak_picker.params.aCoeffs = [1.0000, -0.5949, 0.2348]
+peak_picker.params.bCoeffs = [0.1600,	 0.3200, 0.1600]
+peak_picker.params.preWin = 5
+peak_picker.params.postWin = 5 + 1
+peak_picker.params.LP_on = True
+peak_picker.params.Medfilt_on = True
+peak_picker.params.Polyfit_on = True
+peak_picker.params.isMedianPositive = False
+	
 def getNoveltyCurve(ssm, kernel_size, normalise=False):
 	'''Return novelty score from ssm.'''
 
 	kernel_size = int(np.floor(kernel_size/2.0) + 1)
 	stripe = getDiagonalSlice(ssm, kernel_size)

 			else:
 				kernel[i-1,j-1] = gauss
 				
 	return kernel	
 	
-def process(ssm, kernel_size, peak_picker, normalise=False, plot=False):
+def segmentation(ssm, peak_picker=peak_picker, kernel_size=48, normalise=False, plot=False):
 	'''Detect segment boundaries in the ssm.'''
+	# peak_picker for the 1st round boudary detection
+
+	
 	novelty = getNoveltyCurve(ssm, kernel_size, normalise=False)
 	smoothed_novelty, novelty_peaks = peak_picker.process(novelty)
 	
 	if plot:
 		plot_detection(smoothed_novelty, novelty_peaks)