Marginal changes.
This commit is contained in:
Ludger Küperkoch
parent
5bb616ffc5
commit
3e81adfec6
@ -13,8 +13,6 @@ import scipy as sc
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import matplotlib.pyplot as plt
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from obspy.core import Stream, UTCDateTime
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import warnings
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import pdb
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def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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'''
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@ -257,6 +255,8 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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elif P1[0] > 0 and P2[0] <= 0:
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FM = '+'
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print 'fmpicker: Found polarity %s' % FM
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if iplot > 1:
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plt.figure(iplot)
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plt.subplot(2, 1, 1)
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@ -301,48 +301,34 @@ def crossings_nonzero_all(data):
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return ((pos[:-1] & npos[1:]) | (npos[:-1] & pos[1:])).nonzero()[0]
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def getSNR(st, TSNR, t0):
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"""
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returns the maximum signal to noise ratio SNR (also in dB) and the
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corresponding noise level for a given data stream ST ,initial time T0 and
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time window parameter tuple TSNR
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def getSNR(X, TSNR, t1):
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'''
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Function to calculate SNR of certain part of seismogram relative to
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given time (onset) out of given noise and signal windows. A safety gap
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between noise and signal part can be set. Returns SNR and SNR [dB] and
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noiselevel.
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:param: st, time series (seismogram)
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:param: X, time series (seismogram)
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:type: `~obspy.core.stream.Stream`
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:param: TSNR, length of time windows [s] around t0 (onset) used to determine
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SNR
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:param: TSNR, length of time windows [s] around t1 (onset) used to determine SNR
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:type: tuple (T_noise, T_gap, T_signal)
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:param: t0, initial time (onset) from which noise and signal windows are calculated
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:param: t1, initial time (onset) from which noise and signal windows are calculated
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:type: float
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:return: SNR, SNRdB, noiselevel
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'''
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..examples:
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assert isinstance(X, Stream), "%s is not a stream object" % str(X)
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>>> from obspy.core import read
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>>> st = read()
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>>> result = getSNR(st, (6., .3, 3.), 4.67)
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>>> print result
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(5.1267717641040758, 7.0984398375666435, 132.89370192191919)
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>>> result = getSNR(st, (8., .2, 5.), 4.67)
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>>> print result
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(4.645441835797703, 6.6702702677384131, 133.03562794665109)
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"""
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assert isinstance(st, Stream), "%s is not a stream object" % str(st)
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SNR = None
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noiselevel = None
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for tr in st:
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x = tr.data
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t = np.arange(0, tr.stats.npts / tr.stats.sampling_rate,
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tr.stats.delta)
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x = X[0].data
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t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
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X[0].stats.delta)
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# get noise window
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inoise = getnoisewin(t, t0, TSNR[0], TSNR[1])
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inoise = getnoisewin(t, t1, TSNR[0], TSNR[1])
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# get signal window
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isignal = getsignalwin(t, t0, TSNR[2])
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isignal = getsignalwin(t, t1, TSNR[2])
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if np.size(inoise) < 1:
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print 'getSNR: Empty array inoise, check noise window!'
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return
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@ -354,18 +340,9 @@ def getSNR(st, TSNR, t0):
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x = x - np.mean(x[inoise])
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# calculate ratios
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new_noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
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noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
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signallevel = np.sqrt(np.mean(np.square(x[isignal])))
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newSNR = signallevel / new_noiselevel
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if not SNR or newSNR > SNR:
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SNR = newSNR
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noiselevel = new_noiselevel
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if not SNR or not noiselevel:
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raise ValueError('signal to noise ratio could not be calculated:\n'
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'noiselevel: {0}\n'
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'SNR: {1}'.format(noiselevel, SNR))
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SNR = signallevel / noiselevel
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SNRdB = 10 * np.log10(SNR)
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return SNR, SNRdB, noiselevel
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@ -496,6 +473,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
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Spicks.append(UTCSpick.timestamp)
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SPtimes.append(spt)
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if len(SPtimes) >= 3:
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# calculate slope
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p1 = np.polyfit(Ppicks, SPtimes, 1)
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@ -527,8 +505,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
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checkedPpicks.append(checkedPpick.timestamp)
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checkedSpick = UTCDateTime(pickdic[key]['S']['mpp'])
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checkedSpicks.append(checkedSpick.timestamp)
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checkedSPtime = pickdic[key]['S']['mpp'] - \
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pickdic[key]['P']['mpp']
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checkedSPtime = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
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checkedSPtimes.append(checkedSPtime)
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pickdic[key]['S']['marked'] = marker
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@ -563,8 +540,7 @@ def wadaticheck(pickdic, dttolerance, iplot):
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plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
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'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
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plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1', \
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'Reliable S-Picks', 'Wadati 2'],
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loc='best')
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'Reliable S-Picks', 'Wadati 2'], loc='best')
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else:
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plt.title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
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@ -649,8 +625,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
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[minsiglevel, minsiglevel], 'g')
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p5, = plt.plot([pick, pick], [min(x), max(x)], 'b', linewidth=2)
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plt.legend([p1, p2, p3, p4, p5], ['Data', 'Envelope Noise Window', \
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'Envelope Signal Window',
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'Minimum Signal Level', \
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'Envelope Signal Window', 'Minimum Signal Level', \
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'Onset'], loc='best')
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plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
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plt.ylabel('Counts')
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@ -713,8 +688,7 @@ def checkPonsets(pickdic, dttolerance, iplot):
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badstations = np.array(stations)[ibad]
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print 'checkPonset: Skipped %d P onsets out of %d' % (len(badstations) \
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+ len(badjkstations),
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len(stations))
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+ len(badjkstations), len(stations))
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goodmarker = 'goodPonsetcheck'
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badmarker = 'badPonsetcheck'
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@ -819,7 +793,108 @@ def jackknife(X, phi, h):
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return PHI_jack, PHI_pseudo, PHI_sub
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def checkZ4S(X, pick, zfac, checkwin, iplot):
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'''
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Function to compare energy content of vertical trace with
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energy content of horizontal traces to detect spuriously
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picked S onsets instead of P onsets. Usually, P coda shows
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larger longitudal energy on vertical trace than on horizontal
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traces, where the transversal energy is larger within S coda.
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Be careful: there are special circumstances, where this is not
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the case!
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: param: X, fitered(!) time series, three traces
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: type: `~obspy.core.stream.Stream`
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: param: pick, initial (AIC) P onset time
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: type: float
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: param: zfac, factor for threshold determination,
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vertical energy must exceed coda level times zfac
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to declare a pick as P onset
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: type: float
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: param: checkwin, window length [s] for calculating P-coda
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energy content
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: type: float
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: param: iplot, if iplot > 1, energy content and threshold
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are shown
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: type: int
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'''
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assert isinstance(X, Stream), "%s is not a stream object" % str(X)
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print 'Check for spuriously picked S onset instead of P onset ...'
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returnflag = 0
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# split components
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zdat = X.select(component="Z")
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edat = X.select(component="E")
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if len(edat) == 0: # check for other components
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edat = X.select(component="2")
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ndat = X.select(component="N")
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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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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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# 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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# get signal window
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isignal = getsignalwin(tz, pick, checkwin)
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# calculate energy levels
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zcodalevel = max(absz[isignal])
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encodalevel = max(absen[isignal])
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# calculate threshold
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minsiglevel = encodalevel * 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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print 'checkZ4S: Maybe S onset? Skip this P pick!'
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else:
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print 'checkZ4S: P onset passed 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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plt.plot(tz, z / max(z), 'k')
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plt.plot(tz[isignal], z[isignal] / max(z), 'r')
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plt.plot(te, edat[0].data / max(edat[0].data) + 1, 'k')
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plt.plot(te[isignal], edat[0].data[isignal] / max(edat[0].data) + 1, 'r')
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plt.plot(tn, ndat[0].data / max(ndat[0].data) + 2, 'k')
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plt.plot(tn[isignal], ndat[0].data[isignal] / max(ndat[0].data) + 2, 'r')
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plt.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)
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plt.xlabel('Time [s] since %s' % zdat[0].stats.starttime)
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plt.ylabel('Normalized Counts')
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plt.yticks([0, 1, 2], [zdat[0].stats.channel, edat[0].stats.channel, \
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ndat[0].stats.channel])
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plt.title('CheckZ4S, Station %s' % zdat[0].stats.station)
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plt.show()
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raw_input()
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return returnflag
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if __name__ == '__main__':
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import doctest
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doctest.testmod()
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