475 lines
18 KiB
Python
475 lines
18 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created Dec 2014 to Feb 2015
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Implementation of the automated picking algorithms published and described in:
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Kueperkoch, L., Meier, T., Lee, J., Friederich, W., & Egelados Working Group, 2010:
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Automated determination of P-phase arrival times at regional and local distances
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using higher order statistics, Geophys. J. Int., 181, 1159-1170
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Kueperkoch, L., Meier, T., Bruestle, A., Lee, J., Friederich, W., & Egelados
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Working Group, 2012: Automated determination of S-phase arrival times using
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autoregressive prediction: application ot local and regional distances, Geophys. J. Int.,
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188, 687-702.
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The picks with the above described algorithms are assumed to be the most likely picks.
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For each most likely pick the corresponding earliest and latest possible picks are
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calculated after Diehl & Kissling (2009).
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:author: MAGS2 EP3 working group / Ludger Kueperkoch
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"""
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import warnings
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import matplotlib.pyplot as plt
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import numpy as np
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from pylot.core.pick.charfuns import CharacteristicFunction
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from pylot.core.pick.utils import getnoisewin, getsignalwin
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class AutoPicker(object):
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'''
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Superclass of different, automated picking algorithms applied on a CF determined
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using AIC, HOS, or AR prediction.
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'''
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warnings.simplefilter('ignore')
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def __init__(self, cf, TSNR, PickWindow, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None):
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'''
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:param: cf, characteristic function, on which the picking algorithm is applied
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:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
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:param: TSNR, length of time windows around pick used to determine SNR [s]
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:type: tuple (T_noise, T_gap, T_signal)
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:param: PickWindow, length of pick window [s]
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:type: float
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:param: iplot, no. of figure window for plotting interims results
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:type: integer
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:param: aus ("artificial uplift of samples"), find local minimum at i if aic(i-1)*(1+aus) >= aic(i)
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:type: float
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:param: Tsmooth, length of moving smoothing window to calculate smoothed CF [s]
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:type: float
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:param: Pick1, initial (prelimenary) onset time, starting point for PragPicker and
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EarlLatePicker
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:type: float
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'''
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assert isinstance(cf, CharacteristicFunction), "%s is not a CharacteristicFunction object" % str(cf)
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self.cf = cf.getCF()
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self.Tcf = cf.getTimeArray()
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self.Data = cf.getXCF()
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self.dt = cf.getIncrement()
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self.setTSNR(TSNR)
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self.setPickWindow(PickWindow)
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self.setiplot(iplot)
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self.setaus(aus)
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self.setTsmooth(Tsmooth)
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self.setpick1(Pick1)
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self.fig = fig
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self.calcPick()
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def __str__(self):
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return '''\n\t{name} object:\n
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TSNR:\t\t\t{TSNR}\n
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PickWindow:\t{PickWindow}\n
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aus:\t{aus}\n
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Tsmooth:\t{Tsmooth}\n
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Pick1:\t{Pick1}\n
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'''.format(name=type(self).__name__,
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TSNR=self.getTSNR(),
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PickWindow=self.getPickWindow(),
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aus=self.getaus(),
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Tsmooth=self.getTsmooth(),
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Pick1=self.getpick1())
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def getTSNR(self):
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return self.TSNR
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def setTSNR(self, TSNR):
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self.TSNR = TSNR
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def getPickWindow(self):
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return self.PickWindow
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def setPickWindow(self, PickWindow):
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self.PickWindow = PickWindow
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def getaus(self):
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return self.aus
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def setaus(self, aus):
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self.aus = aus
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def setTsmooth(self, Tsmooth):
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self.Tsmooth = Tsmooth
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def getTsmooth(self):
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return self.Tsmooth
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def getpick(self):
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return self.Pick
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def getSNR(self):
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return self.SNR
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def getSlope(self):
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return self.slope
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def getiplot(self):
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return self.iplot
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def setiplot(self, iplot):
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self.iplot = iplot
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def getpick1(self):
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return self.Pick1
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def setpick1(self, Pick1):
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self.Pick1 = Pick1
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def calcPick(self):
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self.Pick = None
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class AICPicker(AutoPicker):
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'''
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Method to derive the onset time of an arriving phase based on CF
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derived from AIC. In order to get an impression of the quality of this inital pick,
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a quality assessment is applied based on SNR and slope determination derived from the CF,
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from which the AIC has been calculated.
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'''
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def calcPick(self):
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print('AICPicker: Get initial onset time (pick) from AIC-CF ...')
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self.Pick = None
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self.slope = None
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self.SNR = None
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plt_flag = 0
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try:
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iplot = int(self.iplot)
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except:
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if self.iplot == True or self.iplot == 'True':
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iplot = 2
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else:
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iplot = 0
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# find NaN's
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nn = np.isnan(self.cf)
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if len(nn) > 1:
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self.cf[nn] = 0
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# taper AIC-CF to get rid off side maxima
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tap = np.hanning(len(self.cf))
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aic = tap * self.cf + max(abs(self.cf))
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# smooth AIC-CF
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ismooth = int(round(self.Tsmooth / self.dt))
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aicsmooth = np.zeros(len(aic))
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if len(aic) < ismooth:
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print('AICPicker: Tsmooth larger than CF!')
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return
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else:
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for i in range(1, len(aic)):
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if i > ismooth:
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ii1 = i - ismooth
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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 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 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 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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self.Pick = self.Tcf[i]
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break
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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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break
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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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# get noise window
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inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
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# check, if these are counts or m/s, important for slope estimation!
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# this is quick and dirty, better solution?
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if max(self.Data[0].data < 1e-3) and max(self.Data[0].data >= 1e-6):
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self.Data[0].data = self.Data[0].data * 1000000
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elif max(self.Data[0].data < 1e-6):
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self.Data[0].data = self.Data[0].data * 1e13
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# get signal window
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isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
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ii = min([isignal[len(isignal) - 1], len(self.Tcf)])
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isignal = isignal[0:ii]
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try:
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aic[isignal]
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except IndexError as e:
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msg = "Time series out of bounds! {}".format(e)
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print(msg)
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return
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# calculate SNR from CF
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self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / \
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max(abs(aic[inoise] - np.mean(aic[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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# 'cause slope should be calculated up to first local minimum only!
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imax = np.argmax(self.Data[0].data[islope[0][0]:islope[0][len(islope[0])-1]])
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iislope = islope[0][0:imax+1]
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if len(iislope) <= 2:
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# calculate slope from initial onset to maximum of AIC function
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print("AICPicker: Not enough data samples left for slope calculation!")
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print("Calculating slope from initial onset to maximum of AIC function ...")
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imax = np.argmax(aicsmooth[islope[0][0]:islope[0][len(islope[0])-1]])
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if imax == 0:
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print("AICPicker: Maximum for slope determination right at the beginning of the window!")
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print("Choose longer slope determination window!")
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if self.iplot > 1:
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if self.fig == None or self.fig == 'None':
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fig = plt.figure() # self.iplot) ### WHY? MP MP
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plt_flag = 1
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else:
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fig = self.fig
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ax = fig.add_subplot(111)
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x = self.Data[0].data
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ax.plot(self.Tcf, x / max(x), 'k', label='(HOS-/AR-) Data')
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ax.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
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ax.legend()
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ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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ax.set_yticks([])
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ax.set_title(self.Data[0].stats.station)
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if plt_flag == 1:
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fig.show()
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raw_input()
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plt.close(fig)
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return
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iislope = islope[0][0:imax+1]
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dataslope = self.Data[0].data[iislope]
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# calculate slope as polynomal fit of order 1
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xslope = np.arange(0, len(dataslope), 1)
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P = np.polyfit(xslope, dataslope, 1)
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datafit = np.polyval(P, xslope)
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if datafit[0] >= datafit[len(datafit) - 1]:
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print('AICPicker: Negative slope, bad onset skipped!')
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return
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self.slope = 1 / (len(dataslope) * self.Data[0].stats.delta) * (datafit[len(dataslope) - 1] - datafit[0])
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else:
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self.SNR = None
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self.slope = None
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if iplot > 1:
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if self.fig == None or self.fig == 'None':
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fig = plt.figure() # self.iplot)
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plt_flag = 1
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else:
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fig = self.fig
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ax1 = fig.add_subplot(211)
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x = self.Data[0].data
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if len(self.Tcf) > len(self.Data[0].data): # why? LK
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self.Tcf = self.Tcf[0:len(self.Tcf)-1]
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ax1.plot(self.Tcf, x / max(x), 'k', label='(HOS-/AR-) Data')
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ax1.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
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if self.Pick is not None:
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ax1.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2, label='AIC-Pick')
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ax1.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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ax1.set_yticks([])
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ax1.legend()
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if self.Pick is not None:
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ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
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ax2.plot(self.Tcf, x, 'k', label='Data')
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ax1.axvspan(self.Tcf[inoise[0]], self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
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ax1.axvspan(self.Tcf[isignal[0]], self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0,
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label='Signal Window')
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ax1.axvspan(self.Tcf[iislope[0]], self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0,
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label='Slope Window')
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ax2.axvspan(self.Tcf[inoise[0]], self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
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ax2.axvspan(self.Tcf[isignal[0]], self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0,
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label='Signal Window')
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ax2.axvspan(self.Tcf[iislope[0]], self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0,
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label='Slope Window')
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ax2.plot(self.Tcf[iislope], datafit, 'g', linewidth=2, label='Slope')
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ax1.set_title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station,
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self.SNR, self.slope))
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ax2.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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ax2.set_ylabel('Counts')
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ax2.set_yticks([])
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ax2.legend()
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if plt_flag == 1:
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fig.show()
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raw_input()
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plt.close(fig)
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else:
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ax1.set_title(self.Data[0].stats.station)
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if plt_flag == 1:
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fig.show()
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raw_input()
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plt.close(fig)
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if self.Pick == None:
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print('AICPicker: Could not find minimum, picking window too short?')
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return
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class PragPicker(AutoPicker):
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'''
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Method of pragmatic picking exploiting information given by CF.
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'''
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def calcPick(self):
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try:
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iplot = int(self.getiplot())
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except:
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if self.getiplot() == True or self.getiplot() == 'True':
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iplot = 2
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else:
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iplot = 0
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if self.getpick1() is not None:
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print('PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...')
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self.Pick = None
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self.SNR = None
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self.slope = None
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pickflag = 0
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plt_flag = 0
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# smooth CF
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ismooth = int(round(self.Tsmooth / self.dt))
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cfsmooth = np.zeros(len(self.cf))
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if len(self.cf) < ismooth:
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print('PragPicker: Tsmooth larger than CF!')
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return
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else:
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for i in range(1, len(self.cf)):
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if i > ismooth:
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ii1 = i - ismooth
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cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[ii1]) / ismooth
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else:
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cfsmooth[i] = np.mean(self.cf[1: i])
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# select picking window
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# which is centered around tpick1
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ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \
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& (self.Tcf <= self.getpick1() + self.PickWindow / 2))
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cfipick = self.cf[ipick] - np.mean(self.cf[ipick])
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Tcfpick = self.Tcf[ipick]
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cfsmoothipick = cfsmooth[ipick] - np.mean(self.cf[ipick])
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ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
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cfpick1 = 2 * self.cf[ipick1]
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# check trend of CF, i.e. differences of CF and adjust aus ("artificial uplift
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# of picks") regarding this trend
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# prominent trend: decrease aus
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# flat: use given aus
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cfdiff = np.diff(cfipick)
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i0diff = np.where(cfdiff > 0)
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cfdiff = cfdiff[i0diff]
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minaus = min(cfdiff * (1 + self.aus))
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aus1 = max([minaus, self.aus])
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# at first we look to the right until the end of the pick window is reached
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flagpick_r = 0
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flagpick_l = 0
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cfpick_r = 0
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cfpick_l = 0
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lpickwindow = int(round(self.PickWindow / self.dt))
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for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
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if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
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if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
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if cfpick1 >= self.cf[i]:
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pick_r = self.Tcf[i]
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self.Pick = pick_r
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flagpick_l = 1
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cfpick_r = self.cf[i]
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break
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# now we look to the left
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if len(self.cf) > ipick1 +1:
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for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
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if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
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if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
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if cfpick1 >= self.cf[i]:
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pick_l = self.Tcf[i]
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self.Pick = pick_l
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flagpick_r = 1
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cfpick_l = self.cf[i]
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break
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else:
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msg ='PragPicker: Initial onset too close to start of CF! \
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Stop finalizing pick to the left.'
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print(msg)
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# now decide which pick: left or right?
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if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= 3 * cfpick_r:
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self.Pick = pick_l
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pickflag = 1
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elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
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self.Pick = pick_r
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pickflag = 1
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elif flagpick_l == 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
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self.Pick = pick_l
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pickflag = 1
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else:
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print("PragPicker: Could not find reliable onset!")
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self.Pick = None
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pickflag = 0
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if iplot > 1:
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if self.fig == None or self.fig == 'None':
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fig = plt.figure() # self.getiplot())
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plt_flag = 1
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else:
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fig = self.fig
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ax = fig.add_subplot(111)
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ax.plot(Tcfpick, cfipick, 'k', label='CF')
|
|
ax.plot(Tcfpick, cfsmoothipick, 'r', label='Smoothed CF')
|
|
if pickflag > 0:
|
|
ax.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2, label='Pick')
|
|
ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
|
ax.set_yticks([])
|
|
ax.set_title(self.Data[0].stats.station)
|
|
ax.legend()
|
|
if plt_flag == 1:
|
|
fig.show()
|
|
raw_input()
|
|
plt.close(fig)
|
|
return
|
|
|
|
else:
|
|
print("PragPicker: No initial onset time given! Check input!")
|
|
self.Pick = None
|
|
return
|