Use maximum of HOS/AR-CF instead of AIC-CF to start searching for global minimum in AIC function.
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@ -172,21 +172,14 @@ class AICPicker(AutoPicker):
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aicsmooth[i] = aicsmooth[i - 1] + (aic[i] - aic[ii1]) / ismooth
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else:
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aicsmooth[i] = np.mean(aic[1: i])
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# remove offset
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# remove offset in AIC function
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offset = abs(min(aic) - min(aicsmooth))
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aicsmooth = aicsmooth - offset
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# get maximum of 1st derivative of AIC-CF (more stable!) as starting point
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diffcf = np.diff(aicsmooth)
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# find NaN's
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nn = np.isnan(diffcf)
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if len(nn) > 1:
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diffcf[nn] = 0
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# taper CF to get rid off side maxima
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tap = np.hanning(len(diffcf))
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diffcf = tap * diffcf * max(abs(aicsmooth))
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icfmax = np.argmax(diffcf)
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# get maximum of HOS/AR-CF as startimg point for searching
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# minimum in AIC function
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icfmax = np.argmax(self.Data[0].data)
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# find minimum in AIC-CF front of maximum
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# find minimum in AIC-CF front of maximum of HOS/AR-CF
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lpickwindow = int(round(self.PickWindow / self.dt))
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for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
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if aicsmooth[i - 1] >= aicsmooth[i]:
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@ -195,6 +188,14 @@ class AICPicker(AutoPicker):
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# if no minimum could be found:
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# search in 1st derivative of AIC-CF
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if self.Pick is None:
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diffcf = np.diff(aicsmooth)
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# find NaN's
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nn = np.isnan(diffcf)
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if len(nn) > 1:
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diffcf[nn] = 0
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# taper CF to get rid off side maxima
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tap = np.hanning(len(diffcf))
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diffcf = tap * diffcf * max(abs(aicsmooth))
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for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
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if diffcf[i - 1] >= diffcf[i]:
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self.Pick = self.Tcf[i]
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