Switched off warnings.
This commit is contained in:
Ludger Küperkoch
parent
4a911a4ac9
commit
f2510ff400
@ -3,18 +3,18 @@
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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,
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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., 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,
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Geophys. J. Int., 188, 687-702.
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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
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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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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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@ -22,46 +22,43 @@ 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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import warnings
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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
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determined using AIC, HOS, or AR prediction.
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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,
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Pick1=None):
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warnings.simplefilter('ignore')
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def __init__(self, cf, TSNR, PickWindow, iplot=None, aus=None, Tsmooth=None, Pick1=None):
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'''
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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: 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 for SNR determination - [s]
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:type TSNR: tuple (T_noise, T_gap, T_signal)
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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 PickWindow: float
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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 iplot: integer
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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: 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: 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 window to calculate smoothed CF - [s]
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:type Tsmooth: 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
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PragPicker
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:type Pick1: 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,
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CharacteristicFunction), "%s is of wrong type" % str(
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cf)
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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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@ -89,6 +86,7 @@ class AutoPicking(object):
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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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@ -153,14 +151,14 @@ class AICPicker(AutoPicking):
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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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#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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@ -170,68 +168,63 @@ class AICPicker(AutoPicking):
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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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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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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
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# point
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#get maximum of 1st derivative of AIC-CF (more stable!) as starting point
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diffcf = np.diff(aicsmooth)
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# find NaN's
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#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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#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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#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 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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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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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 *= 1000000
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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]))) / \
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max(abs(aic[inoise] - np.mean(aic[inoise])))
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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(
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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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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 ' \
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'the beginning of the window!'
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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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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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@ -253,10 +246,8 @@ class AICPicker(AutoPicking):
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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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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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@ -267,29 +258,24 @@ class AICPicker(AutoPicking):
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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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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],
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['Data', 'Noise Window', 'Signal Window',
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'Slope Window', 'Slope'],
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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' % (
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self.Data[0].stats.station,
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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,
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self.Tcf[isignal[0][len(isignal) - 1]] + 5])
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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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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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if self.Pick == None:
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print 'AICPicker: Could not find minimum, picking window too short?'
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@ -301,13 +287,12 @@ 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 ' \
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'pragmatic picking algorithm ...'
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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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# smooth CF
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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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@ -316,40 +301,36 @@ class PragPicker(AutoPicking):
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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[
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ii1]) / 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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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 >=
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(self.getpick1() - self.PickWindow / 2)) and
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(self.Tcf <=
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(self.getpick1() + self.PickWindow / 2)))
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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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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 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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#check trend of CF, i.e. differences of CF and adjust aus 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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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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#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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flagpick = 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)),
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min([ipick1 + lpickwindow + 1, len(self.cf) - 1])):
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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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@ -359,7 +340,7 @@ class PragPicker(AutoPicking):
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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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#now we look to the left
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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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@ -370,7 +351,7 @@ class PragPicker(AutoPicking):
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cfpick_l = self.cf[i]
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break
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# now decide which pick: left or right?
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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 <= cfpick_r:
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self.Pick = pick_l
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elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
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@ -378,10 +359,9 @@ class PragPicker(AutoPicking):
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if self.getiplot() > 1:
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p = plt.figure(self.getiplot())
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p1, = plt.plot(Tcfpick, cfipick, 'k')
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p2, = plt.plot(Tcfpick, cfsmoothipick, 'r')
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p3, = plt.plot([self.Pick, self.Pick],
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[min(cfipick), max(cfipick)], 'b', linewidth=2)
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p1, = plt.plot(Tcfpick,cfipick, 'k')
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p2, = plt.plot(Tcfpick,cfsmoothipick, 'r')
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p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2)
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plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick'])
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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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@ -393,3 +373,4 @@ class PragPicker(AutoPicking):
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else:
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self.Pick = None
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print 'PragPicker: No initial onset time given! Check input!'
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return
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