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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@ -61,7 +59,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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ilup, = np.where(x[isignal] > nlevel)
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ildown, = np.where(x[isignal] < -nlevel)
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if not ilup.size and not ildown.size:
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print 'earllatepicker: Signal lower than noise level!'
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print 'earllatepicker: Signal lower than noise level!'
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print 'Skip this trace!'
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return LPick, EPick, PickError
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il = min(np.min(ilup) if ilup.size else float('inf'),
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@ -188,11 +186,11 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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else:
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imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]]))
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if imax1 == 0:
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imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]]))
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imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]]))
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if imax1 == 0:
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print 'fmpicker: Zero crossings too close!'
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print 'Skip first motion determination!'
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return FM
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print 'fmpicker: Zero crossings too close!'
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print 'Skip first motion determination!'
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return FM
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islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
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# calculate slope as polynomal fit of order 1
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@ -230,11 +228,11 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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else:
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imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]]))
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if imax2 == 0:
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imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]]))
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imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]]))
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if imax1 == 0:
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print 'fmpicker: Zero crossings too close!'
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print 'Skip first motion determination!'
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return FM
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print 'fmpicker: Zero crossings too close!'
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print 'Skip first motion determination!'
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return FM
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islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
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# calculate slope as polynomal fit of order 1
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@ -256,6 +254,8 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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FM = '+'
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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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@ -301,71 +301,48 @@ 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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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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assert isinstance(st, Stream), "%s is not a stream object" % str(st)
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# get noise window
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inoise = getnoisewin(t, t1, TSNR[0], TSNR[1])
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SNR = None
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noiselevel = None
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# get signal window
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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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elif np.size(isignal) < 1:
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print 'getSNR: Empty array isignal, check signal window!'
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return
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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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# get noise window
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inoise = getnoisewin(t, t0, 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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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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elif np.size(isignal) < 1:
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print 'getSNR: Empty array isignal, check signal window!'
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return
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# demean over entire waveform
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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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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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# demean over entire waveform
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x = x - np.mean(x[inoise])
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# calculate ratios
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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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SNR = signallevel / noiselevel
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SNRdB = 10 * np.log10(SNR)
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return SNR, SNRdB, noiselevel
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@ -392,7 +369,7 @@ def getnoisewin(t, t1, tnoise, tgap):
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# get noise window
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inoise, = np.where((t <= max([t1 - tgap, 0])) \
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& (t >= max([t1 - tnoise - tgap, 0])))
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& (t >= max([t1 - tnoise - tgap, 0])))
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if np.size(inoise) < 1:
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print 'getnoisewin: Empty array inoise, check noise window!'
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@ -416,7 +393,7 @@ def getsignalwin(t, t1, tsignal):
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# get signal window
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isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \
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& (t >= t1))
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& (t >= t1))
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if np.size(isignal) < 1:
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print 'getsignalwin: Empty array isignal, check signal window!'
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@ -457,7 +434,7 @@ def getResolutionWindow(snr):
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else:
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time_resolution = res_wins['HRW']
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return time_resolution / 2
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return time_resolution/2
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def wadaticheck(pickdic, dttolerance, iplot):
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@ -485,21 +462,22 @@ def wadaticheck(pickdic, dttolerance, iplot):
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SPtimes = []
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for key in pickdic:
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if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4:
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# calculate S-P time
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spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
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# add S-P time to dictionary
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pickdic[key]['SPt'] = spt
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# add P onsets and corresponding S-P times to list
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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
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UTCSpick = UTCDateTime(pickdic[key]['S']['mpp'])
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Ppicks.append(UTCPpick.timestamp)
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Spicks.append(UTCSpick.timestamp)
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SPtimes.append(spt)
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# calculate S-P time
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spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
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# add S-P time to dictionary
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pickdic[key]['SPt'] = spt
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# add P onsets and corresponding S-P times to list
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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
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UTCSpick = UTCDateTime(pickdic[key]['S']['mpp'])
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Ppicks.append(UTCPpick.timestamp)
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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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wdfit = np.polyval(p1, Ppicks)
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# calculate slope
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p1 = np.polyfit(Ppicks, SPtimes, 1)
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wdfit = np.polyval(p1, Ppicks)
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wfitflag = 0
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# calculate vp/vs ratio before check
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@ -523,50 +501,48 @@ def wadaticheck(pickdic, dttolerance, iplot):
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pickdic[key]['S']['weight'] = 9
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else:
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marker = 'goodWadatiCheck'
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checkedPpick = UTCDateTime(pickdic[key]['P']['mpp'])
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checkedPpick = UTCDateTime(pickdic[key]['P']['mpp'])
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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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if len(checkedPpicks) >= 3:
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# calculate new slope
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p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
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wdfit2 = np.polyval(p2, checkedPpicks)
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# calculate new slope
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p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
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wdfit2 = np.polyval(p2, checkedPpicks)
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# calculate vp/vs ratio after check
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cvpvsr = p2[0] + 1
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print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr
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# calculate vp/vs ratio after check
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cvpvsr = p2[0] + 1
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print 'wadaticheck: Average Vp/Vs ratio after check:', cvpvsr
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else:
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print 'wadatacheck: Not enough checked S-P times available!'
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print 'Skip Wadati check!'
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print 'wadatacheck: Not enough checked S-P times available!'
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print 'Skip Wadati check!'
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checkedonsets = pickdic
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else:
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print 'wadaticheck: Not enough S-P times available for reliable regression!'
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print 'wadaticheck: Not enough S-P times available for reliable regression!'
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print 'Skip wadati check!'
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wfitflag = 1
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iplot = 2
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iplot=2
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# plot results
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if iplot > 1:
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plt.figure(iplot)
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f1, = plt.plot(Ppicks, SPtimes, 'ro')
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plt.figure(iplot)
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f1, = plt.plot(Ppicks, SPtimes, 'ro')
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if wfitflag == 0:
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f2, = plt.plot(Ppicks, wdfit, 'k')
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f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
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f4, = plt.plot(checkedPpicks, wdfit2, 'g')
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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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f2, = plt.plot(Ppicks, wdfit, 'k')
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f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
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f4, = plt.plot(checkedPpicks, wdfit2, 'g')
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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'], 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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plt.title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
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plt.ylabel('S-P Times [s]')
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plt.xlabel('P Times [s]')
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@ -626,12 +602,12 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
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# calculate minimum adjusted signal level
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minsiglevel = max(e[inoise]) * nfac
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# minimum adjusted number of samples over minimum signal level
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minnum = len(isignal) * minpercent / 100
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minnum = len(isignal) * minpercent/100
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# get number of samples above minimum adjusted signal level
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numoverthr = len(np.where(e[isignal] >= minsiglevel)[0])
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if numoverthr >= minnum:
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print 'checksignallength: Signal reached required length.'
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print 'checksignallength: Signal reached required length.'
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returnflag = 1
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else:
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print 'checksignallength: Signal shorter than required minimum signal length!'
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@ -640,18 +616,17 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
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if iplot == 2:
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plt.figure(iplot)
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p1, = plt.plot(t, x, 'k')
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p1, = plt.plot(t,x, 'k')
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p2, = plt.plot(t[inoise], e[inoise], 'c')
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p3, = plt.plot(t[isignal], e[isignal], 'r')
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p3, = plt.plot(t[isignal],e[isignal], 'r')
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p2, = plt.plot(t[inoise], e[inoise])
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p3, = plt.plot(t[isignal], e[isignal], 'r')
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p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]], \
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[minsiglevel, minsiglevel], 'g')
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p3, = plt.plot(t[isignal],e[isignal], 'r')
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p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal)-1]]], \
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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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'Onset'], loc='best')
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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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plt.title('Check for Signal Length, Station %s' % X[0].stats.station)
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@ -665,7 +640,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
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def checkPonsets(pickdic, dttolerance, iplot):
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'''
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Function to check statistics of P-onset times: Control deviation from
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Function to check statistics of P-onset times: Control deviation from
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median (maximum adjusted deviation = dttolerance) and apply pseudo-
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bootstrapping jackknife.
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@ -687,14 +662,14 @@ def checkPonsets(pickdic, dttolerance, iplot):
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stations = []
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for key in pickdic:
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if pickdic[key]['P']['weight'] < 4:
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# add P onsets to list
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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
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Ppicks.append(UTCPpick.timestamp)
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stations.append(key)
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# add P onsets to list
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UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
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Ppicks.append(UTCPpick.timestamp)
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stations.append(key)
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# apply jackknife bootstrapping on variance of P onsets
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print 'checkPonsets: Apply jackknife bootstrapping on P-onset times ...'
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[xjack, PHI_pseudo, PHI_sub] = jackknife(Ppicks, 'VAR', 1)
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[xjack,PHI_pseudo,PHI_sub] = jackknife(Ppicks, 'VAR', 1)
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# get pseudo variances smaller than average variances
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# these picks passed jackknife test
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ij = np.where(PHI_pseudo <= xjack)
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@ -713,41 +688,40 @@ 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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badjkmarker = 'badjkcheck'
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for i in range(0, len(goodstations)):
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# mark P onset as checked and keep P weight
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pickdic[goodstations[i]]['P']['marked'] = goodmarker
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pickdic[goodstations[i]]['P']['marked'] = goodmarker
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for i in range(0, len(badstations)):
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# mark P onset and downgrade P weight to 9
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# (not used anymore)
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pickdic[badstations[i]]['P']['marked'] = badmarker
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pickdic[badstations[i]]['P']['weight'] = 9
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# mark P onset and downgrade P weight to 9
|
||||
# (not used anymore)
|
||||
pickdic[badstations[i]]['P']['marked'] = badmarker
|
||||
pickdic[badstations[i]]['P']['weight'] = 9
|
||||
for i in range(0, len(badjkstations)):
|
||||
# mark P onset and downgrade P weight to 9
|
||||
# (not used anymore)
|
||||
pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
|
||||
pickdic[badjkstations[i]]['P']['weight'] = 9
|
||||
# mark P onset and downgrade P weight to 9
|
||||
# (not used anymore)
|
||||
pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
|
||||
pickdic[badjkstations[i]]['P']['weight'] = 9
|
||||
|
||||
checkedonsets = pickdic
|
||||
|
||||
iplot = 2
|
||||
if iplot > 1:
|
||||
p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14)
|
||||
p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14)
|
||||
p2, = plt.plot(igood, np.array(Ppicks)[igood], 'g*', markersize=14)
|
||||
p3, = plt.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g', \
|
||||
linewidth=2)
|
||||
linewidth=2)
|
||||
for i in range(0, len(Ppicks)):
|
||||
plt.text(i, Ppicks[i] + 0.2, stations[i])
|
||||
plt.text(i, Ppicks[i] + 0.2, stations[i])
|
||||
|
||||
plt.xlabel('Number of P Picks')
|
||||
plt.xlabel('Number of P Picks')
|
||||
plt.ylabel('Onset Time [s] from 1.1.1970')
|
||||
plt.legend([p1, p2, p3], ['Skipped P Picks', 'Good P Picks', 'Median'], \
|
||||
loc='best')
|
||||
loc='best')
|
||||
plt.title('Check P Onsets')
|
||||
plt.show()
|
||||
raw_input()
|
||||
@ -773,7 +747,7 @@ def jackknife(X, phi, h):
|
||||
: param: h, size of subgroups, optinal, default = 1
|
||||
: type: integer
|
||||
'''
|
||||
|
||||
|
||||
PHI_jack = None
|
||||
PHI_pseudo = None
|
||||
PHI_sub = None
|
||||
@ -782,44 +756,145 @@ def jackknife(X, phi, h):
|
||||
g = len(X) / h
|
||||
|
||||
if type(g) is not int:
|
||||
print 'jackknife: Cannot divide quantity X in equal sized subgroups!'
|
||||
print 'jackknife: Cannot divide quantity X in equal sized subgroups!'
|
||||
print 'Choose another size for subgroups!'
|
||||
return PHI_jack, PHI_pseudo, PHI_sub
|
||||
else:
|
||||
# estimator of undisturbed spot check
|
||||
if phi == 'MEA':
|
||||
phi_sc = np.mean(X)
|
||||
# estimator of undisturbed spot check
|
||||
if phi == 'MEA':
|
||||
phi_sc = np.mean(X)
|
||||
elif phi == 'VAR':
|
||||
phi_sc = np.var(X)
|
||||
phi_sc = np.var(X)
|
||||
elif phi == 'MED':
|
||||
phi_sc = np.median(X)
|
||||
phi_sc = np.median(X)
|
||||
|
||||
# estimators of subgroups
|
||||
# estimators of subgroups
|
||||
PHI_pseudo = []
|
||||
PHI_sub = []
|
||||
for i in range(0, g - 1):
|
||||
# subgroup i, remove i-th sample
|
||||
xx = X[:]
|
||||
del xx[i]
|
||||
# calculate estimators of disturbed spot check
|
||||
if phi == 'MEA':
|
||||
phi_sub = np.mean(xx)
|
||||
elif phi == 'VAR':
|
||||
phi_sub = np.var(xx)
|
||||
elif phi == 'MED':
|
||||
phi_sub = np.median(xx)
|
||||
|
||||
PHI_sub.append(phi_sub)
|
||||
# pseudo values
|
||||
phi_pseudo = g * phi_sc - ((g - 1) * phi_sub)
|
||||
PHI_pseudo.append(phi_pseudo)
|
||||
# subgroup i, remove i-th sample
|
||||
xx = X[:]
|
||||
del xx[i]
|
||||
# calculate estimators of disturbed spot check
|
||||
if phi == 'MEA':
|
||||
phi_sub = np.mean(xx)
|
||||
elif phi == 'VAR':
|
||||
phi_sub = np.var(xx)
|
||||
elif phi == 'MED':
|
||||
phi_sub = np.median(xx)
|
||||
|
||||
PHI_sub.append(phi_sub)
|
||||
# pseudo values
|
||||
phi_pseudo = g * phi_sc - ((g - 1) * phi_sub)
|
||||
PHI_pseudo.append(phi_pseudo)
|
||||
# jackknife estimator
|
||||
PHI_jack = np.mean(PHI_pseudo)
|
||||
|
||||
return PHI_jack, PHI_pseudo, PHI_sub
|
||||
|
||||
|
||||
def checkZ4S(X, pick, zfac, checkwin, iplot):
|
||||
'''
|
||||
Function to compare energy content of vertical trace with
|
||||
energy content of horizontal traces to detect spuriously
|
||||
picked S onsets instead of P onsets. Usually, P coda shows
|
||||
larger longitudal energy on vertical trace than on horizontal
|
||||
traces, where the transversal energy is larger within S coda.
|
||||
Be careful: there are special circumstances, where this is not
|
||||
the case!
|
||||
|
||||
: param: X, fitered(!) time series, three traces
|
||||
: type: `~obspy.core.stream.Stream`
|
||||
|
||||
: param: pick, initial (AIC) P onset time
|
||||
: type: float
|
||||
|
||||
: param: zfac, factor for threshold determination,
|
||||
vertical energy must exceed coda level times zfac
|
||||
to declare a pick as P onset
|
||||
: type: float
|
||||
|
||||
: param: checkwin, window length [s] for calculating P-coda
|
||||
energy content
|
||||
: type: float
|
||||
|
||||
: param: iplot, if iplot > 1, energy content and threshold
|
||||
are shown
|
||||
: type: int
|
||||
'''
|
||||
|
||||
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
|
||||
|
||||
print 'Check for spuriously picked S onset instead of P onset ...'
|
||||
|
||||
returnflag = 0
|
||||
|
||||
# split components
|
||||
zdat = X.select(component="Z")
|
||||
edat = X.select(component="E")
|
||||
if len(edat) == 0: # check for other components
|
||||
edat = X.select(component="2")
|
||||
ndat = X.select(component="N")
|
||||
if len(ndat) == 0: # check for other components
|
||||
ndat = X.select(component="1")
|
||||
|
||||
|
||||
z = zdat[0].data
|
||||
tz = np.arange(0, zdat[0].stats.npts / zdat[0].stats.sampling_rate,
|
||||
zdat[0].stats.delta)
|
||||
|
||||
# calculate RMS trace from vertical component
|
||||
absz = np.sqrt(np.power(z, 2))
|
||||
# calculate RMS trace from both horizontal traces
|
||||
# make sure, both traces have equal lengths
|
||||
lene = len(edat[0].data)
|
||||
lenn = len(ndat[0].data)
|
||||
minlen = min([lene, lenn])
|
||||
absen = np.sqrt(np.power(edat[0].data[0:minlen - 1], 2) \
|
||||
+ np.power(ndat[0].data[0:minlen - 1], 2))
|
||||
|
||||
# get signal window
|
||||
isignal = getsignalwin(tz, pick, checkwin)
|
||||
|
||||
# calculate energy levels
|
||||
zcodalevel = max(absz[isignal])
|
||||
encodalevel = max(absen[isignal])
|
||||
|
||||
# calculate threshold
|
||||
minsiglevel = encodalevel * zfac
|
||||
|
||||
# vertical P-coda level must exceed horizontal P-coda level
|
||||
# zfac times encodalevel
|
||||
if zcodalevel < minsiglevel:
|
||||
print 'checkZ4S: Maybe S onset? Skip this P pick!'
|
||||
else:
|
||||
print 'checkZ4S: P onset passed checkZ4S test!'
|
||||
returnflag = 1
|
||||
|
||||
if iplot > 1:
|
||||
te = np.arange(0, edat[0].stats.npts / edat[0].stats.sampling_rate,
|
||||
edat[0].stats.delta)
|
||||
tn = np.arange(0, ndat[0].stats.npts / ndat[0].stats.sampling_rate,
|
||||
ndat[0].stats.delta)
|
||||
plt.plot(tz, z / max(z), 'k')
|
||||
plt.plot(tz[isignal], z[isignal] / max(z), 'r')
|
||||
plt.plot(te, edat[0].data / max(edat[0].data) + 1, 'k')
|
||||
plt.plot(te[isignal], edat[0].data[isignal] / max(edat[0].data) + 1, 'r')
|
||||
plt.plot(tn, ndat[0].data / max(ndat[0].data) + 2, 'k')
|
||||
plt.plot(tn[isignal], ndat[0].data[isignal] / max(ndat[0].data) + 2, 'r')
|
||||
plt.plot([tz[isignal[0]], tz[isignal[len(isignal) - 1]]], \
|
||||
[minsiglevel / max(z), minsiglevel / max(z)], 'g', \
|
||||
linewidth=2)
|
||||
plt.xlabel('Time [s] since %s' % zdat[0].stats.starttime)
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.yticks([0, 1, 2], [zdat[0].stats.channel, edat[0].stats.channel, \
|
||||
ndat[0].stats.channel])
|
||||
plt.title('CheckZ4S, Station %s' % zdat[0].stats.station)
|
||||
plt.show()
|
||||
raw_input()
|
||||
|
||||
return returnflag
|
||||
|
||||
if __name__ == '__main__':
|
||||
import doctest
|
||||
|
||||
doctest.testmod()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user