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Modified to handle new module utils.

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This commit is contained in:
Ludger Küperkoch 2015-03-30 16:25:04 +02:00
parent a015b0c90d
commit f6f1c9cf2f
1 changed files with 4 additions and 9 deletions
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13
pylot/core/pick/Picker.py
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@ -20,6 +20,7 @@ calculated after Diehl & Kissling (2009).
""" """
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from pylot.core.pick.utils import *
from pylot.core.pick.CharFuns import CharacteristicFunction from pylot.core.pick.CharFuns import CharacteristicFunction
class AutoPicking(object): class AutoPicking(object):
@ -194,22 +195,16 @@ class AICPicker(AutoPicking):
#quality assessment using SNR and slope from CF #quality assessment using SNR and slope from CF
if self.Pick is not None: if self.Pick is not None:
#some parameters needed:
tnoise = self.TSNR[0] #noise window length for calculating noise level
tsignal = self.TSNR[2] #signal window length
tsafety = self.TSNR[1] #safety gap between signal onset and noise window
tslope = self.TSNR[3] #slope determination window
#get noise window #get noise window
inoise = np.where((self.Tcf <= max([self.Pick - tsafety, 0])) \ inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
& (self.Tcf >= max([self.Pick - tnoise - tsafety, 0])))
#get signal window #get signal window
isignal = np.where((self.Tcf <= min([self.Pick + tsignal + tsafety, len(self.Data[0].data)])) \ isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
& (self.Tcf >= self.Pick))
#calculate SNR from CF #calculate SNR from CF
self.SNR = max(abs(self.cf[isignal] - np.mean(self.cf[isignal]))) / max(abs(self.cf[inoise] \ self.SNR = max(abs(self.cf[isignal] - np.mean(self.cf[isignal]))) / max(abs(self.cf[inoise] \
- np.mean(self.cf[inoise]))) - np.mean(self.cf[inoise])))
#calculate slope from CF after initial pick #calculate slope from CF after initial pick
#get slope window #get slope window
tslope = self.TSNR[3] #slope determination window
islope = np.where((self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)])) \ islope = np.where((self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)])) \
& (self.Tcf >= self.Pick)) & (self.Tcf >= self.Pick))
#find maximum within slope determination window #find maximum within slope determination window