just cleaning up the code to meet coding conventions
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@ -3,58 +3,65 @@
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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., Lee, J., Friederich, W., & Egelados Working Group,
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2010: Automated determination of P-phase arrival times at regional and local
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distances 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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autoregressive prediction: application ot local and regional distances,
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Geophys. J. Int., 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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The picks with the above described algorithms are assumed to be the most likely
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picks. For each most likely pick the corresponding earliest and latest possible
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picks are calculated after Diehl & Kissling (2009).
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:author: MAGS2 EP3 working group / Ludger Kueperkoch
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:author: MAGS2 EP3 working group / Ludger Kueperkoch
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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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'''
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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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def __init__(self, cf, TSNR, PickWindow, iplot=None, aus=None, Tsmooth=None, Pick1=None):
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Superclass of different, automated picking algorithms applied on a CF
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determined using AIC, HOS, or AR prediction.
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'''
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def __init__(self, cf, TSNR, PickWindow, iplot=None, aus=None, Tsmooth=None,
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Pick1=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 cf: characteristic function, on which the picking algorithm is
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applied
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:type cf: `~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 TSNR: length of time windows for SNR determination - [s]
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:type TSNR: 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 PickWindow: length of pick window - [s]
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:type PickWindow: 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 iplot: no. of figure window for plotting interims results
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:type iplot: 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 aus: aus ("artificial uplift of samples"), find local minimum at
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i if aic(i-1)*(1+aus) >= aic(i)
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:type aus: 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 Tsmooth: length of moving window to calculate smoothed CF - [s]
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:type Tsmooth: 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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:param Pick1: initial (prelimenary) onset time, starting point for
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PragPicker and EarlLatePicker
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:type Pick1: 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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assert isinstance(cf,
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CharacteristicFunction), "%s is of wrong type" % str(
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cf)
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self.cf = cf.getCF()
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self.Tcf = cf.getTimeArray()
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@ -80,9 +87,8 @@ class AutoPicking(object):
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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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Pick1=self.getpick1())
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def getTSNR(self):
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return self.TSNR
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@ -112,7 +118,7 @@ class AutoPicking(object):
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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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@ -136,144 +142,156 @@ class AICPicker(AutoPicking):
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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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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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#find NaN's
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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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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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# 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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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
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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[
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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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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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# get maximum of 1st derivative of AIC-CF (more stable!) as starting
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# point
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diffcf = np.diff(aicsmooth)
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#find NaN's
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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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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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#find minimum in AIC-CF front of maximum
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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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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):
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self.Data[0].data = self.Data[0].data * 1000000
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#get signal window
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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(aic[isignal] - np.mean(aic[isignal]))) / max(abs(aic[inoise] \
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- 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])
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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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return
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islope = islope[0][0 :imax]
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dataslope = self.Data[0].data[islope]
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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 / tslope * datafit[len(dataslope) - 1] - datafit[0]
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# find minimum in AIC-CF front of maximum
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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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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],
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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):
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self.Data[0].data *= 1000000
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# get signal window
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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(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(
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(self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)]))
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and (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])
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if imax == 0:
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print 'AICPicker: Maximum for slope determination right at ' \
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'the beginning of the window!'
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print 'Choose longer slope determination window!'
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return
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islope = islope[0][0:imax]
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dataslope = self.Data[0].data[islope]
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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 / tslope * 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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self.SNR = None
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self.slope = None
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if self.iplot > 1:
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p = plt.figure(self.iplot)
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x = self.Data[0].data
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p1, = plt.plot(self.Tcf, x / max(x), 'k')
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p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
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if self.Pick is not None:
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p3, = plt.plot([self.Pick, self.Pick], [-0.1 , 0.5], 'b', linewidth=2)
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plt.legend([p1, p2, p3], ['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick'])
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else:
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plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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plt.yticks([])
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plt.title(self.Data[0].stats.station)
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p = plt.figure(self.iplot)
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x = self.Data[0].data
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p1, = plt.plot(self.Tcf, x / max(x), 'k')
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p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
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if self.Pick is not None:
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p3, = plt.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b',
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linewidth=2)
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plt.legend([p1, p2, p3],
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['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick'])
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else:
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plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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plt.yticks([])
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plt.title(self.Data[0].stats.station)
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if self.Pick is not None:
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plt.figure(self.iplot + 1)
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p11, = plt.plot(self.Tcf, x, 'k')
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p12, = plt.plot(self.Tcf[inoise], self.Data[0].data[inoise])
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p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r')
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p14, = plt.plot(self.Tcf[islope], dataslope, 'g--')
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p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2)
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plt.legend([p11, p12, p13, p14, p15], ['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'], \
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loc='best')
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plt.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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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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plt.ylabel('Counts')
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ax = plt.gca()
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plt.yticks([])
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ax.set_xlim([self.Tcf[inoise[0][0]] - 5, self.Tcf[isignal[0][len(isignal) - 1]] + 5])
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if self.Pick is not None:
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plt.figure(self.iplot + 1)
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p11, = plt.plot(self.Tcf, x, 'k')
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p12, = plt.plot(self.Tcf[inoise], self.Data[0].data[inoise])
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p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal],
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'r')
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p14, = plt.plot(self.Tcf[islope], dataslope, 'g--')
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p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2)
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plt.legend([p11, p12, p13, p14, p15],
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['Data', 'Noise Window', 'Signal Window',
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'Slope Window', 'Slope'],
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loc='best')
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plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (
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self.Data[0].stats.station,
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self.SNR, self.slope))
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plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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plt.ylabel('Counts')
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ax = plt.gca()
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plt.yticks([])
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ax.set_xlim([self.Tcf[inoise[0][0]] - 5,
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self.Tcf[isignal[0][len(isignal) - 1]] + 5])
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plt.show()
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raw_input()
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plt.close(p)
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plt.show()
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raw_input()
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plt.close(p)
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if self.Pick is None:
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print 'AICPicker: Could not find minimum, picking window too short?'
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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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class PragPicker(AutoPicking):
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'''
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@ -283,90 +301,95 @@ class PragPicker(AutoPicking):
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def calcPick(self):
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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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print 'PragPicker: Get most likely pick from HOS- or AR-CF using ' \
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'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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#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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self.Pick = None
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self.SNR = None
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self.slope = None
|
||||
# smooth CF
|
||||
ismooth = int(round(self.Tsmooth / self.dt))
|
||||
cfsmooth = np.zeros(len(self.cf))
|
||||
if len(self.cf) < ismooth:
|
||||
print 'PragPicker: Tsmooth larger than CF!'
|
||||
return
|
||||
else:
|
||||
for i in range(1, len(self.cf)):
|
||||
if i > ismooth:
|
||||
ii1 = i - ismooth
|
||||
cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[
|
||||
ii1]) / ismooth
|
||||
else:
|
||||
cfsmooth[i] = np.mean(self.cf[1: i])
|
||||
|
||||
#select picking window
|
||||
#which is centered around tpick1
|
||||
ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \
|
||||
& (self.Tcf <= self.getpick1() + self.PickWindow / 2))
|
||||
cfipick = self.cf[ipick] - np.mean(self.cf[ipick])
|
||||
Tcfpick = self.Tcf[ipick]
|
||||
cfsmoothipick = cfsmooth[ipick]- np.mean(self.cf[ipick])
|
||||
ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
|
||||
cfpick1 = 2 * self.cf[ipick1]
|
||||
# select picking window
|
||||
# which is centered around tpick1
|
||||
ipick = np.where((self.Tcf >=
|
||||
(self.getpick1() - self.PickWindow / 2)) and
|
||||
(self.Tcf <=
|
||||
(self.getpick1() + self.PickWindow / 2)))
|
||||
cfipick = self.cf[ipick] - np.mean(self.cf[ipick])
|
||||
Tcfpick = self.Tcf[ipick]
|
||||
cfsmoothipick = cfsmooth[ipick] - np.mean(self.cf[ipick])
|
||||
ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
|
||||
cfpick1 = 2 * self.cf[ipick1]
|
||||
|
||||
#check trend of CF, i.e. differences of CF and adjust aus regarding this trend
|
||||
#prominent trend: decrease aus
|
||||
#flat: use given aus
|
||||
cfdiff = np.diff(cfipick);
|
||||
i0diff = np.where(cfdiff > 0)
|
||||
cfdiff = cfdiff[i0diff]
|
||||
minaus = min(cfdiff * (1 + self.aus));
|
||||
aus1 = max([minaus, self.aus]);
|
||||
# check trend of CF, i.e. differences of CF and adjust aus regarding this trend
|
||||
# prominent trend: decrease aus
|
||||
# flat: use given aus
|
||||
cfdiff = np.diff(cfipick)
|
||||
i0diff = np.where(cfdiff > 0)
|
||||
cfdiff = cfdiff[i0diff]
|
||||
minaus = min(cfdiff * (1 + self.aus))
|
||||
aus1 = max([minaus, self.aus])
|
||||
|
||||
#at first we look to the right until the end of the pick window is reached
|
||||
flagpick_r = 0
|
||||
flagpick_l = 0
|
||||
flagpick = 0
|
||||
lpickwindow = int(round(self.PickWindow / self.dt))
|
||||
for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
|
||||
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
|
||||
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
|
||||
if cfpick1 >= self.cf[i]:
|
||||
pick_r = self.Tcf[i]
|
||||
self.Pick = pick_r
|
||||
flagpick_l = 1
|
||||
cfpick_r = self.cf[i]
|
||||
break
|
||||
# at first we look to the right until the end of the pick window is reached
|
||||
flagpick_r = 0
|
||||
flagpick_l = 0
|
||||
flagpick = 0
|
||||
lpickwindow = int(round(self.PickWindow / self.dt))
|
||||
for i in range(max(np.insert(ipick, 0, 2)),
|
||||
min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
|
||||
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
|
||||
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
|
||||
if cfpick1 >= self.cf[i]:
|
||||
pick_r = self.Tcf[i]
|
||||
self.Pick = pick_r
|
||||
flagpick_l = 1
|
||||
cfpick_r = self.cf[i]
|
||||
break
|
||||
|
||||
#now we look to the left
|
||||
for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
|
||||
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
|
||||
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
|
||||
if cfpick1 >= self.cf[i]:
|
||||
pick_l = self.Tcf[i]
|
||||
self.Pick = pick_l
|
||||
flagpick_r = 1
|
||||
cfpick_l = self.cf[i]
|
||||
break
|
||||
# now we look to the left
|
||||
for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
|
||||
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
|
||||
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
|
||||
if cfpick1 >= self.cf[i]:
|
||||
pick_l = self.Tcf[i]
|
||||
self.Pick = pick_l
|
||||
flagpick_r = 1
|
||||
cfpick_l = self.cf[i]
|
||||
break
|
||||
|
||||
#now decide which pick: left or right?
|
||||
if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= cfpick_r:
|
||||
self.Pick = pick_l
|
||||
elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
|
||||
self.Pick = pick_r
|
||||
# now decide which pick: left or right?
|
||||
if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= cfpick_r:
|
||||
self.Pick = pick_l
|
||||
elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
|
||||
self.Pick = pick_r
|
||||
|
||||
if self.getiplot() > 1:
|
||||
p = plt.figure(self.getiplot())
|
||||
p1, = plt.plot(Tcfpick,cfipick, 'k')
|
||||
p2, = plt.plot(Tcfpick,cfsmoothipick, 'r')
|
||||
p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2)
|
||||
plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick'])
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.title(self.Data[0].stats.station)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(p)
|
||||
if self.getiplot() > 1:
|
||||
p = plt.figure(self.getiplot())
|
||||
p1, = plt.plot(Tcfpick, cfipick, 'k')
|
||||
p2, = plt.plot(Tcfpick, cfsmoothipick, 'r')
|
||||
p3, = plt.plot([self.Pick, self.Pick],
|
||||
[min(cfipick), max(cfipick)], 'b', linewidth=2)
|
||||
plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick'])
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.title(self.Data[0].stats.station)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(p)
|
||||
|
||||
else:
|
||||
self.Pick = None
|
||||
print 'PragPicker: No initial onset time given! Check input!'
|
||||
return
|
||||
else:
|
||||
self.Pick = None
|
||||
print 'PragPicker: No initial onset time given! Check input!'
|
||||
|
||||
@ -3,43 +3,38 @@
|
||||
|
||||
"""
|
||||
Function to run automated picking algorithms using AIC,
|
||||
HOS and AR prediction. Uses object CharFuns and Picker and
|
||||
HOS and AR prediction. Uses object CharFuns and Picker and
|
||||
function conglomerate utils.
|
||||
|
||||
:author: MAGS2 EP3 working group / Ludger Kueperkoch
|
||||
"""
|
||||
|
||||
from obspy.core import read
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from pylot.core.pick.CharFuns import *
|
||||
from pylot.core.pick.Picker import *
|
||||
from pylot.core.pick.CharFuns import *
|
||||
from pylot.core.pick import utils
|
||||
|
||||
|
||||
def run_autopicking(wfstream, pickparam):
|
||||
|
||||
'''
|
||||
"""
|
||||
param: wfstream
|
||||
:type: `~obspy.core.stream.Stream`
|
||||
|
||||
param: pickparam
|
||||
:type: container of picking parameters from input file,
|
||||
usually autoPyLoT.in
|
||||
'''
|
||||
"""
|
||||
|
||||
# declaring pickparam variables (only for convenience)
|
||||
# read your autoPyLoT.in for details!
|
||||
|
||||
#special parameters for P picking
|
||||
algoP = pickparam.getParam('algoP')
|
||||
# special parameters for P picking
|
||||
algoP = pickparam.getParam('algoP')
|
||||
iplot = pickparam.getParam('iplot')
|
||||
pstart = pickparam.getParam('pstart')
|
||||
pstop = pickparam.getParam('pstop')
|
||||
thosmw = pickparam.getParam('tlta')
|
||||
hosorder = pickparam.getParam('hosorder')
|
||||
tsnrz = pickparam.getParam('tsnrz')
|
||||
tsnrz = pickparam.getParam('tsnrz')
|
||||
hosorder = pickparam.getParam('hosorder')
|
||||
bpz1 = pickparam.getParam('bpz1')
|
||||
bpz2 = pickparam.getParam('bpz2')
|
||||
@ -55,13 +50,13 @@ def run_autopicking(wfstream, pickparam):
|
||||
minAICPslope = pickparam.getParam('minAICPslope')
|
||||
minAICPSNR = pickparam.getParam('minAICPSNR')
|
||||
timeerrorsP = pickparam.getParam('timeerrorsP')
|
||||
#special parameters for S picking
|
||||
algoS = pickparam.getParam('algoS')
|
||||
# special parameters for S picking
|
||||
algoS = pickparam.getParam('algoS')
|
||||
sstart = pickparam.getParam('sstart')
|
||||
sstop = pickparam.getParam('sstop')
|
||||
bph1 = pickparam.getParam('bph1')
|
||||
bph2 = pickparam.getParam('bph2')
|
||||
tsnrh = pickparam.getParam('tsnrh')
|
||||
tsnrh = pickparam.getParam('tsnrh')
|
||||
pickwinS = pickparam.getParam('pickwinS')
|
||||
tpred1h = pickparam.getParam('tpred1h')
|
||||
tdet1h = pickparam.getParam('tdet1h')
|
||||
@ -76,7 +71,7 @@ def run_autopicking(wfstream, pickparam):
|
||||
Srecalcwin = pickparam.getParam('Srecalcwin')
|
||||
nfacS = pickparam.getParam('nfacS')
|
||||
timeerrorsS = pickparam.getParam('timeerrorsS')
|
||||
#parameters for first-motion determination
|
||||
# parameters for first-motion determination
|
||||
minFMSNR = pickparam.getParam('minFMSNR')
|
||||
fmpickwin = pickparam.getParam('fmpickwin')
|
||||
minfmweight = pickparam.getParam('minfmweight')
|
||||
@ -84,164 +79,192 @@ def run_autopicking(wfstream, pickparam):
|
||||
# split components
|
||||
zdat = wfstream.select(component="Z")
|
||||
edat = wfstream.select(component="E")
|
||||
if len(edat) == 0: #check for other components
|
||||
if len(edat) == 0: # check for other components
|
||||
edat = wfstream.select(component="2")
|
||||
ndat = wfstream.select(component="N")
|
||||
if len(ndat) == 0: #check for other components
|
||||
if len(ndat) == 0: # check for other components
|
||||
ndat = wfstream.select(component="1")
|
||||
|
||||
if algoP == 'HOS' or algoP == 'ARZ' and zdat is not None:
|
||||
print '##########################################'
|
||||
print 'run_autopicking: Working on P onset of station %s' % zdat[0].stats.station
|
||||
print 'run_autopicking: Working on P onset of station %s' % zdat[
|
||||
0].stats.station
|
||||
print 'Filtering vertical trace ...'
|
||||
print zdat
|
||||
z_copy = zdat.copy()
|
||||
#filter and taper data
|
||||
# filter and taper data
|
||||
tr_filt = zdat[0].copy()
|
||||
tr_filt.filter('bandpass', freqmin=bpz1[0], freqmax=bpz1[1], zerophase=False)
|
||||
tr_filt.filter('bandpass', freqmin=bpz1[0], freqmax=bpz1[1],
|
||||
zerophase=False)
|
||||
tr_filt.taper(max_percentage=0.05, type='hann')
|
||||
z_copy[0].data = tr_filt.data
|
||||
##############################################################
|
||||
#check length of waveform and compare with cut times
|
||||
# check length of waveform and compare with cut times
|
||||
Lc = pstop - pstart
|
||||
Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
|
||||
Ldiff = Lwf - Lc
|
||||
if Ldiff < 0:
|
||||
print 'run_autopicking: Cutting times are too large for actual waveform!'
|
||||
print 'Use entire waveform instead!'
|
||||
pstart = 0
|
||||
pstop = len(zdat[0].data) * zdat[0].stats.delta
|
||||
print 'run_autopicking: Cutting times are too large for actual ' \
|
||||
'waveform!'
|
||||
print 'Use entire waveform instead!'
|
||||
pstart = 0
|
||||
pstop = len(zdat[0].data) * zdat[0].stats.delta
|
||||
cuttimes = [pstart, pstop]
|
||||
if algoP == 'HOS':
|
||||
#calculate HOS-CF using subclass HOScf of class CharacteristicFunction
|
||||
cf1 = HOScf(z_copy, cuttimes, thosmw, hosorder) #instance of HOScf
|
||||
# calculate HOS-CF using subclass HOScf of class
|
||||
# CharacteristicFunction
|
||||
cf1 = HOScf(z_copy, cuttimes, thosmw, hosorder) # instance of HOScf
|
||||
elif algoP == 'ARZ':
|
||||
#calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
|
||||
cf1 = ARZcf(z_copy, cuttimes, tpred1z, Parorder, tdet1z, addnoise) #instance of ARZcf
|
||||
# calculate ARZ-CF using subclass ARZcf of class
|
||||
# CharcteristicFunction
|
||||
cf1 = ARZcf(z_copy, cuttimes, tpred1z, Parorder, tdet1z,
|
||||
addnoise) # instance of ARZcf
|
||||
##############################################################
|
||||
#calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
|
||||
#class needs stream object => build it
|
||||
# calculate AIC-HOS-CF using subclass AICcf of class
|
||||
# CharacteristicFunction
|
||||
# class needs stream object => build it
|
||||
tr_aic = tr_filt.copy()
|
||||
tr_aic.data =cf1.getCF()
|
||||
tr_aic.data = cf1.getCF()
|
||||
z_copy[0].data = tr_aic.data
|
||||
aiccf = AICcf(z_copy, cuttimes) #instance of AICcf
|
||||
aiccf = AICcf(z_copy, cuttimes) # instance of AICcf
|
||||
##############################################################
|
||||
#get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
|
||||
# get prelimenary onset time from AIC-HOS-CF using subclass AICPicker
|
||||
# of class AutoPicking
|
||||
aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP)
|
||||
##############################################################
|
||||
#go on with processing if AIC onset passes quality control
|
||||
if aicpick.getSlope() >= minAICPslope and aicpick.getSNR() >= minAICPSNR:
|
||||
aicPflag = 1
|
||||
print 'AIC P-pick passes quality control: Slope: %f, SNR: %f' % \
|
||||
# go on with processing if AIC onset passes quality control
|
||||
if (aicpick.getSlope() >= minAICPslope and
|
||||
aicpick.getSNR() >= minAICPSNR):
|
||||
aicPflag = 1
|
||||
print 'AIC P-pick passes quality control: Slope: %f, SNR: %f' % \
|
||||
(aicpick.getSlope(), aicpick.getSNR())
|
||||
print 'Go on with refined picking ...'
|
||||
#re-filter waveform with larger bandpass
|
||||
print 'run_autopicking: re-filtering vertical trace ...'
|
||||
z_copy = zdat.copy()
|
||||
tr_filt = zdat[0].copy()
|
||||
tr_filt.filter('bandpass', freqmin=bpz2[0], freqmax=bpz2[1], zerophase=False)
|
||||
tr_filt.taper(max_percentage=0.05, type='hann')
|
||||
z_copy[0].data = tr_filt.data
|
||||
#############################################################
|
||||
#re-calculate CF from re-filtered trace in vicinity of initial onset
|
||||
cuttimes2 = [round(max([aicpick.getpick() - Precalcwin, 0])), \
|
||||
round(min([len(zdat[0].data) * zdat[0].stats.delta, \
|
||||
aicpick.getpick() + Precalcwin]))]
|
||||
if algoP == 'HOS':
|
||||
#calculate HOS-CF using subclass HOScf of class CharacteristicFunction
|
||||
cf2 = HOScf(z_copy, cuttimes2, thosmw, hosorder) #instance of HOScf
|
||||
elif algoP == 'ARZ':
|
||||
#calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
|
||||
cf2 = ARZcf(z_copy, cuttimes2, tpred1z, Parorder, tdet1z, addnoise) #instance of ARZcf
|
||||
##############################################################
|
||||
#get refined onset time from CF2 using class Picker
|
||||
refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP, aicpick.getpick())
|
||||
#############################################################
|
||||
#quality assessment
|
||||
#get earliest and latest possible pick and symmetrized uncertainty
|
||||
[lpickP, epickP, Perror] = earllatepicker(z_copy, nfacP, tsnrz, refPpick.getpick(), iplot)
|
||||
print 'Go on with refined picking ...'
|
||||
# re-filter waveform with larger bandpass
|
||||
print 'run_autopicking: re-filtering vertical trace ...'
|
||||
z_copy = zdat.copy()
|
||||
tr_filt = zdat[0].copy()
|
||||
tr_filt.filter('bandpass', freqmin=bpz2[0], freqmax=bpz2[1],
|
||||
zerophase=False)
|
||||
tr_filt.taper(max_percentage=0.05, type='hann')
|
||||
z_copy[0].data = tr_filt.data
|
||||
#############################################################
|
||||
# re-calculate CF from re-filtered trace in vicinity of initial
|
||||
# onset
|
||||
cuttimes2 = [round(max([aicpick.getpick() - Precalcwin, 0])),
|
||||
round(min([len(zdat[0].data) * zdat[0].stats.delta,
|
||||
aicpick.getpick() + Precalcwin]))]
|
||||
if algoP == 'HOS':
|
||||
# calculate HOS-CF using subclass HOScf of class
|
||||
# CharacteristicFunction
|
||||
cf2 = HOScf(z_copy, cuttimes2, thosmw,
|
||||
hosorder) # instance of HOScf
|
||||
elif algoP == 'ARZ':
|
||||
# calculate ARZ-CF using subclass ARZcf of class
|
||||
# CharcteristicFunction
|
||||
cf2 = ARZcf(z_copy, cuttimes2, tpred1z, Parorder, tdet1z,
|
||||
addnoise) # instance of ARZcf
|
||||
##############################################################
|
||||
# get refined onset time from CF2 using class Picker
|
||||
refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP,
|
||||
aicpick.getpick())
|
||||
#############################################################
|
||||
# quality assessment
|
||||
# get earliest and latest possible pick and symmetrized uncertainty
|
||||
[lpickP, epickP, Perror] = earllatepicker(z_copy, nfacP, tsnrz,
|
||||
refPpick.getpick(), iplot)
|
||||
|
||||
#get SNR
|
||||
[SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz, refPpick.getpick())
|
||||
# get SNR
|
||||
[SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz,
|
||||
refPpick.getpick())
|
||||
|
||||
#weight P-onset using symmetric error
|
||||
if Perror <= timeerrorsP[0]:
|
||||
Pweight = 0
|
||||
elif Perror > timeerrorsP[0] and Perror <= timeerrorsP[1]:
|
||||
Pweight = 1
|
||||
elif Perror > timeerrorsP[1] and Perror <= timeerrorsP[2]:
|
||||
Pweight = 2
|
||||
elif Perror > timeerrorsP[2] and Perror <= timeerrorsP[3]:
|
||||
Pweight = 3
|
||||
elif Perror > timeerrorsP[3]:
|
||||
Pweight = 4
|
||||
# weight P-onset using symmetric error
|
||||
if Perror <= timeerrorsP[0]:
|
||||
Pweight = 0
|
||||
elif timeerrorsP[0] < Perror <= timeerrorsP[1]:
|
||||
Pweight = 1
|
||||
elif timeerrorsP[1] < Perror <= timeerrorsP[2]:
|
||||
Pweight = 2
|
||||
elif timeerrorsP[2] < Perror <= timeerrorsP[3]:
|
||||
Pweight = 3
|
||||
elif Perror > timeerrorsP[3]:
|
||||
Pweight = 4
|
||||
|
||||
##############################################################
|
||||
# get first motion of P onset
|
||||
# certain quality required
|
||||
if Pweight <= minfmweight and SNRP >= minFMSNR:
|
||||
FM = fmpicker(zdat, z_copy, fmpickwin, refPpick.getpick(),
|
||||
iplot)
|
||||
else:
|
||||
FM = 'N'
|
||||
|
||||
print 'run_autopicking: P-weight: %d, SNR: %f, SNR[dB]: %f, ' \
|
||||
'Polarity: %s' % (Pweight, SNRP, SNRPdB, FM)
|
||||
|
||||
##############################################################
|
||||
#get first motion of P onset
|
||||
#certain quality required
|
||||
if Pweight <= minfmweight and SNRP >= minFMSNR:
|
||||
FM = fmpicker(zdat, z_copy, fmpickwin, refPpick.getpick(), iplot)
|
||||
else:
|
||||
FM = 'N'
|
||||
|
||||
print 'run_autopicking: P-weight: %d, SNR: %f, SNR[dB]: %f, Polarity: %s' % (Pweight, SNRP, SNRPdB, FM)
|
||||
|
||||
else:
|
||||
print 'Bad initial (AIC) P-pick, skip this onset!'
|
||||
print 'AIC-SNR=', aicpick.getSNR(), 'AIC-Slope=', aicpick.getSlope()
|
||||
Pweight = 4
|
||||
Sweight = 4
|
||||
FM = 'N'
|
||||
SNRP = None
|
||||
SNRPdB = None
|
||||
SNRS = None
|
||||
SNRSdB = None
|
||||
aicSflag = 0
|
||||
aicPflag = 0
|
||||
print 'Bad initial (AIC) P-pick, skip this onset!'
|
||||
print 'AIC-SNR=', aicpick.getSNR(), 'AIC-Slope=', aicpick.getSlope()
|
||||
Pweight = 4
|
||||
Sweight = 4
|
||||
FM = 'N'
|
||||
SNRP = None
|
||||
SNRPdB = None
|
||||
SNRS = None
|
||||
SNRSdB = None
|
||||
aicSflag = 0
|
||||
aicPflag = 0
|
||||
else:
|
||||
print 'run_autopicking: No vertical component data available, skipping station!'
|
||||
return
|
||||
print 'run_autopicking: No vertical component data available, ' \
|
||||
'skipping station!'
|
||||
return
|
||||
|
||||
if edat is not None and ndat is not None and len(edat) > 0 and len(ndat) > 0 and Pweight < 4:
|
||||
print 'Go on picking S onset ...'
|
||||
if edat is not None and ndat is not None and len(edat) > 0 and len(
|
||||
ndat) > 0 and Pweight < 4:
|
||||
print 'Go on picking S onset ...'
|
||||
print '##################################################'
|
||||
print 'Working on S onset of station %s' % edat[0].stats.station
|
||||
print 'Filtering horizontal traces ...'
|
||||
|
||||
#determine time window for calculating CF after P onset
|
||||
#cuttimesh = [round(refPpick.getpick() + sstart), round(refPpick.getpick() + sstop)]
|
||||
cuttimesh = [round(max([refPpick.getpick() + sstart, 0])), \
|
||||
# determine time window for calculating CF after P onset
|
||||
# cuttimesh = [round(refPpick.getpick() + sstart),
|
||||
# round(refPpick.getpick() + sstop)]
|
||||
cuttimesh = [round(max([refPpick.getpick() + sstart, 0])),
|
||||
round(min([refPpick.getpick() + sstop, Lwf]))]
|
||||
|
||||
if algoS == 'ARH':
|
||||
print edat, ndat
|
||||
#re-create stream object including both horizontal components
|
||||
# re-create stream object including both horizontal components
|
||||
hdat = edat.copy()
|
||||
hdat += ndat
|
||||
h_copy = hdat.copy()
|
||||
#filter and taper data
|
||||
# filter and taper data
|
||||
trH1_filt = hdat[0].copy()
|
||||
trH2_filt = hdat[1].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
|
||||
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
|
||||
zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
|
||||
zerophase=False)
|
||||
trH1_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH2_filt.taper(max_percentage=0.05, type='hann')
|
||||
h_copy[0].data = trH1_filt.data
|
||||
h_copy[1].data = trH2_filt.data
|
||||
elif algoS == 'AR3':
|
||||
print zdat, edat, ndat
|
||||
#re-create stream object including both horizontal components
|
||||
# re-create stream object including both horizontal components
|
||||
hdat = zdat.copy()
|
||||
hdat += edat
|
||||
hdat += ndat
|
||||
h_copy = hdat.copy()
|
||||
#filter and taper data
|
||||
# filter and taper data
|
||||
trH1_filt = hdat[0].copy()
|
||||
trH2_filt = hdat[1].copy()
|
||||
trH3_filt = hdat[2].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
|
||||
trH3_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
|
||||
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
|
||||
zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
|
||||
zerophase=False)
|
||||
trH3_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
|
||||
zerophase=False)
|
||||
trH1_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH2_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH3_filt.taper(max_percentage=0.05, type='hann')
|
||||
@ -250,210 +273,289 @@ def run_autopicking(wfstream, pickparam):
|
||||
h_copy[2].data = trH3_filt.data
|
||||
##############################################################
|
||||
if algoS == 'ARH':
|
||||
#calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
|
||||
arhcf1 = ARHcf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h, addnoise) #instance of ARHcf
|
||||
# calculate ARH-CF using subclass ARHcf of class
|
||||
# CharcteristicFunction
|
||||
arhcf1 = ARHcf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h,
|
||||
addnoise) # instance of ARHcf
|
||||
elif algoS == 'AR3':
|
||||
#calculate ARH-CF using subclass AR3cf of class CharcteristicFunction
|
||||
arhcf1 = AR3Ccf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h, addnoise) #instance of ARHcf
|
||||
# calculate ARH-CF using subclass AR3cf of class
|
||||
# CharcteristicFunction
|
||||
arhcf1 = AR3Ccf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h,
|
||||
addnoise) # instance of ARHcf
|
||||
##############################################################
|
||||
#calculate AIC-ARH-CF using subclass AICcf of class CharacteristicFunction
|
||||
#class needs stream object => build it
|
||||
# calculate AIC-ARH-CF using subclass AICcf of class
|
||||
# CharacteristicFunction
|
||||
# class needs stream object => build it
|
||||
tr_arhaic = trH1_filt.copy()
|
||||
tr_arhaic.data = arhcf1.getCF()
|
||||
h_copy[0].data = tr_arhaic.data
|
||||
#calculate ARH-AIC-CF
|
||||
haiccf = AICcf(h_copy, cuttimesh) #instance of AICcf
|
||||
# calculate ARH-AIC-CF
|
||||
haiccf = AICcf(h_copy, cuttimesh) # instance of AICcf
|
||||
##############################################################
|
||||
#get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
|
||||
aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None, aictsmoothS)
|
||||
# get prelimenary onset time from AIC-HOS-CF using subclass AICPicker
|
||||
# of class AutoPicking
|
||||
aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None,
|
||||
aictsmoothS)
|
||||
###############################################################
|
||||
#go on with processing if AIC onset passes quality control
|
||||
if aicarhpick.getSlope() >= minAICSslope and aicarhpick.getSNR() >= minAICSSNR:
|
||||
aicSflag = 1
|
||||
print 'AIC S-pick passes quality control: Slope: %f, SNR: %f' \
|
||||
% (aicarhpick.getSlope(), aicarhpick.getSNR())
|
||||
print 'Go on with refined picking ...'
|
||||
#re-calculate CF from re-filtered trace in vicinity of initial onset
|
||||
cuttimesh2 = [round(aicarhpick.getpick() - Srecalcwin), \
|
||||
round(aicarhpick.getpick() + Srecalcwin)]
|
||||
#re-filter waveform with larger bandpass
|
||||
print 'run_autopicking: re-filtering horizontal traces...'
|
||||
h_copy = hdat.copy()
|
||||
#filter and taper data
|
||||
if algoS == 'ARH':
|
||||
trH1_filt = hdat[0].copy()
|
||||
trH2_filt = hdat[1].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
|
||||
trH1_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH2_filt.taper(max_percentage=0.05, type='hann')
|
||||
h_copy[0].data = trH1_filt.data
|
||||
h_copy[1].data = trH2_filt.data
|
||||
#############################################################
|
||||
arhcf2 = ARHcf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h, addnoise) #instance of ARHcf
|
||||
elif algoS == 'AR3':
|
||||
trH1_filt = hdat[0].copy()
|
||||
trH2_filt = hdat[1].copy()
|
||||
trH3_filt = hdat[2].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
|
||||
trH3_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
|
||||
trH1_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH2_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH3_filt.taper(max_percentage=0.05, type='hann')
|
||||
h_copy[0].data = trH1_filt.data
|
||||
h_copy[1].data = trH2_filt.data
|
||||
h_copy[2].data = trH3_filt.data
|
||||
#############################################################
|
||||
arhcf2 = AR3Ccf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h, addnoise) #instance of ARHcf
|
||||
# go on with processing if AIC onset passes quality control
|
||||
if (aicarhpick.getSlope() >= minAICSslope and
|
||||
aicarhpick.getSNR() >= minAICSSNR):
|
||||
aicSflag = 1
|
||||
print 'AIC S-pick passes quality control: Slope: %f, SNR: %f' \
|
||||
% (aicarhpick.getSlope(), aicarhpick.getSNR())
|
||||
print 'Go on with refined picking ...'
|
||||
# re-calculate CF from re-filtered trace in vicinity of initial
|
||||
# onset
|
||||
cuttimesh2 = [round(aicarhpick.getpick() - Srecalcwin),
|
||||
round(aicarhpick.getpick() + Srecalcwin)]
|
||||
# re-filter waveform with larger bandpass
|
||||
print 'run_autopicking: re-filtering horizontal traces...'
|
||||
h_copy = hdat.copy()
|
||||
# filter and taper data
|
||||
if algoS == 'ARH':
|
||||
trH1_filt = hdat[0].copy()
|
||||
trH2_filt = hdat[1].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
|
||||
zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
|
||||
zerophase=False)
|
||||
trH1_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH2_filt.taper(max_percentage=0.05, type='hann')
|
||||
h_copy[0].data = trH1_filt.data
|
||||
h_copy[1].data = trH2_filt.data
|
||||
#############################################################
|
||||
arhcf2 = ARHcf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h,
|
||||
addnoise) # instance of ARHcf
|
||||
elif algoS == 'AR3':
|
||||
trH1_filt = hdat[0].copy()
|
||||
trH2_filt = hdat[1].copy()
|
||||
trH3_filt = hdat[2].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
|
||||
zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
|
||||
zerophase=False)
|
||||
trH3_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
|
||||
zerophase=False)
|
||||
trH1_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH2_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH3_filt.taper(max_percentage=0.05, type='hann')
|
||||
h_copy[0].data = trH1_filt.data
|
||||
h_copy[1].data = trH2_filt.data
|
||||
h_copy[2].data = trH3_filt.data
|
||||
#############################################################
|
||||
arhcf2 = AR3Ccf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h,
|
||||
addnoise) # instance of ARHcf
|
||||
|
||||
#get refined onset time from CF2 using class Picker
|
||||
refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS, tsmoothS, aicarhpick.getpick())
|
||||
#############################################################
|
||||
#quality assessment
|
||||
#get earliest and latest possible pick and symmetrized uncertainty
|
||||
h_copy[0].data = trH1_filt.data
|
||||
[lpickS1, epickS1, Serror1] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
|
||||
h_copy[0].data = trH2_filt.data
|
||||
[lpickS2, epickS2, Serror2] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
|
||||
if algoS == 'ARH':
|
||||
#get earliest pick of both earliest possible picks
|
||||
epick = [epickS1, epickS2]
|
||||
lpick = [lpickS1, lpickS2]
|
||||
pickerr = [Serror1, Serror2]
|
||||
ipick =np.argmin([epickS1, epickS2])
|
||||
elif algoS == 'AR3':
|
||||
[lpickS3, epickS3, Serror3] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
|
||||
#get earliest pick of all three picks
|
||||
epick = [epickS1, epickS2, epickS3]
|
||||
lpick = [lpickS1, lpickS2, lpickS3]
|
||||
pickerr = [Serror1, Serror2, Serror3]
|
||||
ipick =np.argmin([epickS1, epickS2, epickS3])
|
||||
epickS = epick[ipick]
|
||||
lpickS = lpick[ipick]
|
||||
Serror = pickerr[ipick]
|
||||
# get refined onset time from CF2 using class Picker
|
||||
refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS,
|
||||
tsmoothS, aicarhpick.getpick())
|
||||
#############################################################
|
||||
# quality assessment
|
||||
# get earliest and latest possible pick and symmetrized uncertainty
|
||||
h_copy[0].data = trH1_filt.data
|
||||
[lpickS1, epickS1, Serror1] = earllatepicker(h_copy, nfacS, tsnrh,
|
||||
refSpick.getpick(),
|
||||
iplot)
|
||||
h_copy[0].data = trH2_filt.data
|
||||
[lpickS2, epickS2, Serror2] = earllatepicker(h_copy, nfacS, tsnrh,
|
||||
refSpick.getpick(),
|
||||
iplot)
|
||||
if algoS == 'ARH':
|
||||
# get earliest pick of both earliest possible picks
|
||||
epick = [epickS1, epickS2]
|
||||
lpick = [lpickS1, lpickS2]
|
||||
pickerr = [Serror1, Serror2]
|
||||
ipick = np.argmin([epickS1, epickS2])
|
||||
elif algoS == 'AR3':
|
||||
[lpickS3, epickS3, Serror3] = earllatepicker(h_copy, nfacS,
|
||||
tsnrh,
|
||||
refSpick.getpick(),
|
||||
iplot)
|
||||
# get earliest pick of all three picks
|
||||
epick = [epickS1, epickS2, epickS3]
|
||||
lpick = [lpickS1, lpickS2, lpickS3]
|
||||
pickerr = [Serror1, Serror2, Serror3]
|
||||
ipick = np.argmin([epickS1, epickS2, epickS3])
|
||||
epickS = epick[ipick]
|
||||
lpickS = lpick[ipick]
|
||||
Serror = pickerr[ipick]
|
||||
|
||||
#get SNR
|
||||
[SNRS, SNRSdB, Snoiselevel] = getSNR(h_copy, tsnrh, refSpick.getpick())
|
||||
# get SNR
|
||||
[SNRS, SNRSdB, Snoiselevel] = getSNR(h_copy, tsnrh,
|
||||
refSpick.getpick())
|
||||
|
||||
#weight S-onset using symmetric error
|
||||
if Serror <= timeerrorsS[0]:
|
||||
Sweight = 0
|
||||
elif Serror > timeerrorsS[0] and Serror <= timeerrorsS[1]:
|
||||
Sweight = 1
|
||||
elif Perror > timeerrorsS[1] and Serror <= timeerrorsS[2]:
|
||||
Sweight = 2
|
||||
elif Serror > timeerrorsS[2] and Serror <= timeerrorsS[3]:
|
||||
Sweight = 3
|
||||
elif Serror > timeerrorsS[3]:
|
||||
Sweight = 4
|
||||
# weight S-onset using symmetric error
|
||||
if Serror <= timeerrorsS[0]:
|
||||
Sweight = 0
|
||||
elif timeerrorsS[0] < Serror <= timeerrorsS[1]:
|
||||
Sweight = 1
|
||||
elif Perror > timeerrorsS[1] and Serror <= timeerrorsS[2]:
|
||||
Sweight = 2
|
||||
elif timeerrorsS[2] < Serror <= timeerrorsS[3]:
|
||||
Sweight = 3
|
||||
elif Serror > timeerrorsS[3]:
|
||||
Sweight = 4
|
||||
|
||||
print 'run_autopicking: S-weight: %d, SNR: %f, SNR[dB]: %f' % (Sweight, SNRS, SNRSdB)
|
||||
print 'run_autopicking: S-weight: %d, SNR: %f, SNR[dB]: %f' % (
|
||||
Sweight, SNRS, SNRSdB)
|
||||
|
||||
else:
|
||||
print 'Bad initial (AIC) S-pick, skip this onset!'
|
||||
print 'AIC-SNR=', aicarhpick.getSNR(), 'AIC-Slope=', aicarhpick.getSlope()
|
||||
Sweight = 4
|
||||
SNRS = None
|
||||
SNRSdB = None
|
||||
aicSflag = 0
|
||||
|
||||
print 'Bad initial (AIC) S-pick, skip this onset!'
|
||||
print 'AIC-SNR=', aicarhpick.getSNR(), \
|
||||
'AIC-Slope=', aicarhpick.getSlope()
|
||||
Sweight = 4
|
||||
SNRS = None
|
||||
SNRSdB = None
|
||||
aicSflag = 0
|
||||
|
||||
else:
|
||||
print 'run_autopicking: No horizontal component data available or bad P onset, skipping S picking!'
|
||||
return
|
||||
print 'run_autopicking: No horizontal component data available or ' \
|
||||
'bad P onset, skipping S picking!'
|
||||
return
|
||||
|
||||
##############################################################
|
||||
if iplot > 0:
|
||||
#plot vertical trace
|
||||
plt.figure()
|
||||
plt.subplot(3,1,1)
|
||||
tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate, tr_filt.stats.delta)
|
||||
#check equal length of arrays, sometimes they are different!?
|
||||
wfldiff = len(tr_filt.data) - len(tdata)
|
||||
if wfldiff < 0:
|
||||
tdata = tdata[0:len(tdata) - abs(wfldiff)]
|
||||
p1, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
|
||||
if Pweight < 4:
|
||||
p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()), 'b')
|
||||
if aicPflag == 1:
|
||||
p3, = plt.plot(cf2.getTimeArray(), cf2.getCF() / max(cf2.getCF()), 'm')
|
||||
p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'r')
|
||||
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'r')
|
||||
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'r')
|
||||
p5, = plt.plot([refPpick.getpick(), refPpick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
|
||||
plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [1.3, 1.3], 'r', linewidth=2)
|
||||
plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [-1.3, -1.3], 'r', linewidth=2)
|
||||
plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--')
|
||||
plt.plot([epickP, epickP], [-1.1, 1.1], 'r--')
|
||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'CF1', 'CF2', 'Initial P Onset', 'Final P Pick'])
|
||||
plt.title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f Polarity: %s' % (tr_filt.stats.station, \
|
||||
tr_filt.stats.channel, Pweight, SNRP, SNRPdB, FM))
|
||||
else:
|
||||
plt.legend([p1, p2], ['Data', 'CF1'])
|
||||
plt.title('%s, P Weight=%d, SNR=None, SNRdB=None' % (tr_filt.stats.channel, Pweight))
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.suptitle(tr_filt.stats.starttime)
|
||||
# plot vertical trace
|
||||
plt.figure()
|
||||
plt.subplot(3, 1, 1)
|
||||
tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate,
|
||||
tr_filt.stats.delta)
|
||||
# check equal length of arrays, sometimes they are different!?
|
||||
wfldiff = len(tr_filt.data) - len(tdata)
|
||||
if wfldiff < 0:
|
||||
tdata = tdata[0:len(tdata) - abs(wfldiff)]
|
||||
p1, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k')
|
||||
if Pweight < 4:
|
||||
p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()),
|
||||
'b')
|
||||
if aicPflag == 1:
|
||||
p3, = plt.plot(cf2.getTimeArray(),
|
||||
cf2.getCF() / max(cf2.getCF()), 'm')
|
||||
p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1],
|
||||
'r')
|
||||
plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
|
||||
[1, 1], 'r')
|
||||
plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
|
||||
[-1, -1], 'r')
|
||||
p5, = plt.plot([refPpick.getpick(), refPpick.getpick()],
|
||||
[-1.3, 1.3], 'r', linewidth=2)
|
||||
plt.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
|
||||
[1.3, 1.3], 'r', linewidth=2)
|
||||
plt.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
|
||||
[-1.3, -1.3], 'r', linewidth=2)
|
||||
plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--')
|
||||
plt.plot([epickP, epickP], [-1.1, 1.1], 'r--')
|
||||
plt.legend([p1, p2, p3, p4, p5],
|
||||
['Data', 'CF1', 'CF2', 'Initial P Onset',
|
||||
'Final P Pick'])
|
||||
plt.title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f '
|
||||
'Polarity: %s' % (tr_filt.stats.station,
|
||||
tr_filt.stats.channel,
|
||||
Pweight,
|
||||
SNRP,
|
||||
SNRPdB,
|
||||
FM))
|
||||
else:
|
||||
plt.legend([p1, p2], ['Data', 'CF1'])
|
||||
plt.title('%s, P Weight=%d, SNR=None, '
|
||||
'SNRdB=None' % (tr_filt.stats.channel, Pweight))
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.suptitle(tr_filt.stats.starttime)
|
||||
|
||||
#plot horizontal traces
|
||||
plt.subplot(3,1,2)
|
||||
th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
|
||||
#check equal length of arrays, sometimes they are different!?
|
||||
wfldiff = len(trH1_filt.data) - len(th1data)
|
||||
if wfldiff < 0:
|
||||
th1data = th1data[0:len(th1data) - abs(wfldiff)]
|
||||
p21, = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
|
||||
if Pweight < 4:
|
||||
p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
|
||||
if aicSflag == 1:
|
||||
p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
|
||||
p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
|
||||
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
|
||||
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
|
||||
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
|
||||
plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
|
||||
plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (trH1_filt.stats.channel, \
|
||||
Sweight, SNRS, SNRSdB))
|
||||
else:
|
||||
plt.legend([p21, p22], ['Data', 'CF1'])
|
||||
plt.title('%s, S Weight=%d, SNR=None, SNRdB=None' % (trH1_filt.stats.channel, Sweight))
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.suptitle(trH1_filt.stats.starttime)
|
||||
# plot horizontal traces
|
||||
plt.subplot(3, 1, 2)
|
||||
th1data = np.arange(0,
|
||||
trH1_filt.stats.npts /
|
||||
trH1_filt.stats.sampling_rate,
|
||||
trH1_filt.stats.delta)
|
||||
# check equal length of arrays, sometimes they are different!?
|
||||
wfldiff = len(trH1_filt.data) - len(th1data)
|
||||
if wfldiff < 0:
|
||||
th1data = th1data[0:len(th1data) - abs(wfldiff)]
|
||||
p21, = plt.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k')
|
||||
if Pweight < 4:
|
||||
p22, = plt.plot(arhcf1.getTimeArray(),
|
||||
arhcf1.getCF() / max(arhcf1.getCF()), 'b')
|
||||
if aicSflag == 1:
|
||||
p23, = plt.plot(arhcf2.getTimeArray(),
|
||||
arhcf2.getCF() / max(arhcf2.getCF()), 'm')
|
||||
p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()],
|
||||
[-1, 1], 'g')
|
||||
plt.plot(
|
||||
[aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5],
|
||||
[1, 1], 'g')
|
||||
plt.plot(
|
||||
[aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5],
|
||||
[-1, -1], 'g')
|
||||
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()],
|
||||
[-1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[-1.3, -1.3], 'g', linewidth=2)
|
||||
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
|
||||
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
|
||||
plt.legend([p21, p22, p23, p24, p25],
|
||||
['Data', 'CF1', 'CF2', 'Initial S Onset',
|
||||
'Final S Pick'])
|
||||
plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (
|
||||
trH1_filt.stats.channel,
|
||||
Sweight, SNRS, SNRSdB))
|
||||
else:
|
||||
plt.legend([p21, p22], ['Data', 'CF1'])
|
||||
plt.title('%s, S Weight=%d, SNR=None, SNRdB=None' % (
|
||||
trH1_filt.stats.channel, Sweight))
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.suptitle(trH1_filt.stats.starttime)
|
||||
|
||||
plt.subplot(3,1,3)
|
||||
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
|
||||
#check equal length of arrays, sometimes they are different!?
|
||||
wfldiff = len(trH2_filt.data) - len(th2data)
|
||||
if wfldiff < 0:
|
||||
th2data = th2data[0:len(th2data) - abs(wfldiff)]
|
||||
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
|
||||
if Pweight < 4:
|
||||
p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
|
||||
if aicSflag == 1:
|
||||
p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
|
||||
p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
|
||||
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
|
||||
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
|
||||
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
|
||||
plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
|
||||
else:
|
||||
plt.legend([p21, p22], ['Data', 'CF1'])
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.xlabel('Time [s] after %s' % tr_filt.stats.starttime)
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title(trH2_filt.stats.channel)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close()
|
||||
plt.subplot(3, 1, 3)
|
||||
th2data = np.arange(0,
|
||||
trH2_filt.stats.npts /
|
||||
trH2_filt.stats.sampling_rate,
|
||||
trH2_filt.stats.delta)
|
||||
# check equal length of arrays, sometimes they are different!?
|
||||
wfldiff = len(trH2_filt.data) - len(th2data)
|
||||
if wfldiff < 0:
|
||||
th2data = th2data[0:len(th2data) - abs(wfldiff)]
|
||||
plt.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k')
|
||||
if Pweight < 4:
|
||||
p22, = plt.plot(arhcf1.getTimeArray(),
|
||||
arhcf1.getCF() / max(arhcf1.getCF()), 'b')
|
||||
if aicSflag == 1:
|
||||
p23, = plt.plot(arhcf2.getTimeArray(),
|
||||
arhcf2.getCF() / max(arhcf2.getCF()), 'm')
|
||||
p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()],
|
||||
[-1, 1], 'g')
|
||||
plt.plot(
|
||||
[aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5],
|
||||
[1, 1], 'g')
|
||||
plt.plot(
|
||||
[aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5],
|
||||
[-1, -1], 'g')
|
||||
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()],
|
||||
[-1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
|
||||
[-1.3, -1.3], 'g', linewidth=2)
|
||||
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
|
||||
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
|
||||
plt.legend([p21, p22, p23, p24, p25],
|
||||
['Data', 'CF1', 'CF2', 'Initial S Onset',
|
||||
'Final S Pick'])
|
||||
else:
|
||||
plt.legend([p21, p22], ['Data', 'CF1'])
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.xlabel('Time [s] after %s' % tr_filt.stats.starttime)
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title(trH2_filt.stats.channel)
|
||||
plt.show()
|
||||
|
||||
|
||||
raw_input()
|
||||
plt.close()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user