[bugfix/improvement] checkZ4s independent of different trace starttimes and sampling rates
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176f13db09
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@ -1 +1 @@
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2633-dirty
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176f-dirty
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@ -645,6 +645,11 @@ def wadaticheck(pickdic, dttolerance, iplot):
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return checkedonsets
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def RMS(X):
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'''
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Function returns root mean square of a given array X
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'''
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return np.sqrt(np.sum(np.power(X, 2))/len(X))
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def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fig=None):
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'''
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@ -942,71 +947,80 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
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if len(ndat) == 0: # check for other components
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ndat = X.select(component="1")
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z = zdat[0].data
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# get earliest time of all 3 traces
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min_t = min(zdat[0].stats.starttime, edat[0].stats.starttime, ndat[0].stats.starttime)
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# generate time arrays for all 3 traces
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tz = np.arange(0, zdat[0].stats.npts / zdat[0].stats.sampling_rate,
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zdat[0].stats.delta)
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tn = np.arange(0, ndat[0].stats.npts / ndat[0].stats.sampling_rate,
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ndat[0].stats.delta)
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te = np.arange(0, edat[0].stats.npts / edat[0].stats.sampling_rate,
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edat[0].stats.delta)
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# calculate RMS trace from vertical component
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absz = np.sqrt(np.power(z, 2))
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# calculate RMS trace from both horizontal traces
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# make sure, both traces have equal lengths
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lene = len(edat[0].data)
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lenn = len(ndat[0].data)
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minlen = min([lene, lenn])
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absen = np.sqrt(np.power(edat[0].data[0:minlen - 1], 2) \
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+ np.power(ndat[0].data[0:minlen - 1], 2))
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zdiff = (zdat[0].stats.starttime - min_t)
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ndiff = (ndat[0].stats.starttime - min_t)
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ediff = (edat[0].stats.starttime - min_t)
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# get signal window
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isignal = getsignalwin(tz, pick, checkwin)
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# get signal windows
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isignalz = getsignalwin(tz, pick-zdiff, checkwin)
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isignaln = getsignalwin(tn, pick-ndiff, checkwin)
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isignale = getsignalwin(te, pick-ediff, checkwin)
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# calculate energy levels
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try:
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zcodalevel = max(absz[isignal])
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except:
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ii = np.where(isignal <= len(absz))
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isignal = isignal[ii]
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zcodalevel = max(absz[isignal - 1])
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try:
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encodalevel = max(absen[isignal])
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except:
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ii = np.where(isignal <= len(absen))
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isignal = isignal[ii]
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encodalevel = max(absen[isignal - 1])
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# calculate RMS of traces
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rmsz = RMS(zdat[0].data[isignalz])
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rmsn = RMS(ndat[0].data[isignaln])
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rmse = RMS(edat[0].data[isignale])
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# calculate threshold
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minsiglevel = encodalevel * zfac
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minsiglevel = (rmsn + rmse) * zfac
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# vertical P-coda level must exceed horizontal P-coda level
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# zfac times encodalevel
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if zcodalevel < minsiglevel:
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if rmsz < minsiglevel:
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print ("checkZ4S: Maybe S onset? Skip this P pick!")
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else:
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print ("checkZ4S: P onset passes checkZ4S test!")
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returnflag = 1
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if iplot > 1:
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te = np.arange(0, edat[0].stats.npts / edat[0].stats.sampling_rate,
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edat[0].stats.delta)
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tn = np.arange(0, ndat[0].stats.npts / ndat[0].stats.sampling_rate,
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ndat[0].stats.delta)
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if not fig:
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(tz, z / max(z), 'k')
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ax.axvspan(tz[isignal[0]], tz[isignal[-1]], color='b', alpha=0.2,
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lw=0, label='Signal Window')
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ax.plot(te, edat[0].data / max(edat[0].data) + 1, 'k')
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ax.plot(tn, ndat[0].data / max(ndat[0].data) + 2, 'k')
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ax.plot([tz[isignal[0]], tz[isignal[len(isignal) - 1]]],
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[minsiglevel / max(z), minsiglevel / max(z)], 'g',
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linewidth=2, label='Minimum Signal Level')
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ax.set_xlabel('Time [s] since %s' % zdat[0].stats.starttime)
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ax.set_ylabel('Normalized Counts')
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ax.set_yticks([0, 1, 2], [zdat[0].stats.channel, edat[0].stats.channel,
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ndat[0].stats.channel])
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ax.set_title('CheckZ4S, Station %s' % zdat[0].stats.station)
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ax.legend()
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rms_dict = {'Z': rmsz,
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'N': rmsn,
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'E': rmse}
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traces_dict = {'Z': zdat[0],
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'N': ndat[0],
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'E': edat[0]}
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diff_dict = {'Z': zdiff,
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'N': ndiff,
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'E': ediff}
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signal_dict = {'Z': isignalz,
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'N': isignaln,
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'E': isignale}
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for i, key in enumerate(['Z', 'N', 'E']):
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rms = rms_dict[key]
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trace = traces_dict[key]
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t = np.arange(diff_dict[key], trace.stats.npts / trace.stats.sampling_rate+diff_dict[key],
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trace.stats.delta)
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if i == 0:
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ax1 = fig.add_subplot(3, 1, i+1)
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ax = ax1
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ax.set_title('CheckZ4S, Station %s' % zdat[0].stats.station)
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else:
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ax = fig.add_subplot(3,1,i+1, sharex=ax1)
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ax.plot(t, abs(trace.data), color='b', label='abs')
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ax.plot(t, trace.data, color='k')
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name = str(trace.stats.channel) + ': {}'.format(rms)
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ax.plot([pick, pick+checkwin], [rms, rms], 'r', label='RMS {}'.format(name))
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ax.plot([pick, pick], ax.get_ylim(), 'm', label='Pick')
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ax.set_ylabel('Normalized Counts')
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ax.axvspan(pick, pick+checkwin, color='c', alpha=0.2,
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lw=0)
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ax.legend()
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ax.set_xlabel('Time [s] since %s' % zdat[0].stats.starttime)
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return returnflag
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