Modified to handle new module utils.
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@ -20,6 +20,7 @@ calculated after Diehl & Kissling (2009).
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from pylot.core.pick.utils import *
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from pylot.core.pick.CharFuns import CharacteristicFunction
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class AutoPicking(object):
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@ -194,22 +195,16 @@ class AICPicker(AutoPicking):
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#quality assessment using SNR and slope from CF
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if self.Pick is not None:
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#some parameters needed:
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tnoise = self.TSNR[0] #noise window length for calculating noise level
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tsignal = self.TSNR[2] #signal window length
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tsafety = self.TSNR[1] #safety gap between signal onset and noise window
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tslope = self.TSNR[3] #slope determination window
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#get noise window
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inoise = np.where((self.Tcf <= max([self.Pick - tsafety, 0])) \
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& (self.Tcf >= max([self.Pick - tnoise - tsafety, 0])))
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inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
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#get signal window
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isignal = np.where((self.Tcf <= min([self.Pick + tsignal + tsafety, len(self.Data[0].data)])) \
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& (self.Tcf >= self.Pick))
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isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
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#calculate SNR from CF
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self.SNR = max(abs(self.cf[isignal] - np.mean(self.cf[isignal]))) / max(abs(self.cf[inoise] \
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- np.mean(self.cf[inoise])))
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#calculate slope from CF after initial pick
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#get slope window
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tslope = self.TSNR[3] #slope determination window
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islope = np.where((self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)])) \
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& (self.Tcf >= self.Pick))
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#find maximum within slope determination window
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