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Ludger Küperkoch
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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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Created Mar 2015
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Function to derive first motion (polarity) for given phase onset based on zero crossings.
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:author: MAGS2 EP3 working group / Ludger Kueperkoch
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from obspy.core import Stream
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import argparse
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def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
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'''
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Function to derive first motion (polarity) of given phase onset Pick.
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Calculation is based on zero crossings determined within time window pickwin
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after given onset time.
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:param: Xraw, unfiltered time series (seismogram)
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:type: `~obspy.core.stream.Stream`
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:param: Xfilt, filtered time series (seismogram)
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:type: `~obspy.core.stream.Stream`
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:param: pickwin, time window after onset Pick within zero crossings are calculated
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:type: float
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:param: Pick, initial (most likely) onset time, starting point for fmpicker
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:type: float
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:param: iplot, if given, results are plotted in figure(iplot)
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:type: int
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'''
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assert isinstance(Xraw, Stream), "%s is not a stream object" % str(Xraw)
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assert isinstance(Xfilt, Stream), "%s is not a stream object" % str(Xfilt)
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FM = None
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if Pick is not None:
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print 'fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...'
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xraw = Xraw[0].data
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xfilt = Xfilt[0].data
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t = np.arange(0, Xraw[0].stats.npts / Xraw[0].stats.sampling_rate, Xraw[0].stats.delta)
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#get pick window
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ipick = np.where((t <= min([Pick + pickwin, len(Xraw[0])])) & (t >= Pick))
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#remove mean
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xraw[ipick] = xraw[ipick] - np.mean(xraw[ipick])
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xfilt[ipick] = xfilt[ipick] - np.mean(xfilt[ipick])
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#get next zero crossing after most likely pick
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#initial onset is assumed to be the first zero crossing
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#first from unfiltered trace
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zc1 = []
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zc1.append(Pick)
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index1 = []
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i = 0
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for j in range(ipick[0][1],ipick[0][len(t[ipick]) - 1]):
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i = i+ 1
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if xraw[j-1] <= 0 and xraw[j] >= 0:
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zc1.append(t[ipick][i])
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index1.append(i)
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elif xraw[j-1] > 0 and xraw[j] <= 0:
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zc1.append(t[ipick][i])
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index1.append(i)
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if len(zc1) == 3:
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break
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#if time difference betweeen 1st and 2cnd zero crossing
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#is too short, get time difference between 1st and 3rd
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#to derive maximum
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if zc1[1] - zc1[0] <= Xraw[0].stats.delta:
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li1 = index1[1]
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else:
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li1 = index1[0]
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if np.size(xraw[ipick[0][1]:ipick[0][li1]]) == 0:
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print 'earllatepicker: Onset on unfiltered trace too emergent for first motion determination!'
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P1 = 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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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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xslope1 = np.arange(0, len(xraw[islope1]), 1)
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P1 = np.polyfit(xslope1, xraw[islope1], 1)
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datafit1 = np.polyval(P1, xslope1)
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#now using filterd trace
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#next zero crossing after most likely pick
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zc2 = []
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zc2.append(Pick)
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index2 = []
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i = 0
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for j in range(ipick[0][1],ipick[0][len(t[ipick]) - 1]):
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i = i+ 1
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if xfilt[j-1] <= 0 and xfilt[j] >= 0:
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zc2.append(t[ipick][i])
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index2.append(i)
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elif xfilt[j-1] > 0 and xfilt[j] <= 0:
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zc2.append(t[ipick][i])
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index2.append(i)
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if len(zc2) == 3:
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break
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#if time difference betweeen 1st and 2cnd zero crossing
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#is too short, get time difference between 1st and 3rd
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#to derive maximum
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if zc2[1] - zc2[0] <= Xfilt[0].stats.delta:
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li2 = index2[1]
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else:
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li2 = index2[0]
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if np.size(xfilt[ipick[0][1]:ipick[0][li2]]) == 0:
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print 'earllatepicker: Onset on filtered trace too emergent for first motion determination!'
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P2 = 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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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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xslope2 = np.arange(0, len(xfilt[islope2]), 1)
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P2 = np.polyfit(xslope2, xfilt[islope2], 1)
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datafit2 = np.polyval(P2, xslope2)
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#compare results
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if P1 is not None and P2 is not None:
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if P1[0] < 0 and P2[0] < 0:
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FM = 'D'
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elif P1[0] >= 0 and P2[0] < 0:
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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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elif P1[0] > 0 and P2[0] > 0:
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FM = 'U'
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elif P1[0] <= 0 and P2[0] > 0:
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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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if iplot is not None:
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plt.figure(iplot)
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plt.subplot(2,1,1)
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plt.plot(t, xraw, 'k')
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p1, = plt.plot([Pick, Pick], [max(xraw), -max(xraw)], 'b', linewidth=2)
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if P1 is not None:
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p2, = plt.plot(t[islope1], xraw[islope1])
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p3, = plt.plot(zc1, np.zeros(len(zc1)), '*g', markersize=14)
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p4, = plt.plot(t[islope1], datafit1, '--g', linewidth=2)
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plt.legend([p1, p2, p3, p4], ['Pick', 'Slope Window', 'Zero Crossings', 'Slope'], \
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loc='best')
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plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
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ax = plt.gca()
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ax.set_xlim([t[islope1[0][0]] - 0.1, t[islope1[0][len(islope1) - 1]] + 0.3])
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plt.yticks([])
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plt.title('First-Motion Determination, %s, Unfiltered Data' % Xraw[0].stats.station)
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plt.subplot(2,1,2)
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plt.title('First-Motion Determination, Filtered Data')
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plt.plot(t, xfilt, 'k')
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p1, = plt.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b', linewidth=2)
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if P2 is not None:
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p2, = plt.plot(t[islope2], xfilt[islope2])
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p3, = plt.plot(zc2, np.zeros(len(zc2)), '*g', markersize=14)
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p4, = plt.plot(t[islope2], datafit2, '--g', linewidth=2)
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plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
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ax = plt.gca()
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ax.set_xlim([t[islope2[0][0]] - 0.1, t[islope2[0][len(islope2) - 1]] + 0.3])
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plt.xlabel('Time [s] since %s' % Xraw[0].stats.starttime)
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plt.yticks([])
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plt.show()
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raw_input()
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plt.close(iplot)
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return FM
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--Xraw', type=~obspy.core.stream.Stream, help='unfiltered time series (seismogram) read with obspy module read')
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parser.add_argument('--Xfilt', type=~obspy.core.stream.Stream, help='filtered time series (seismogram) read with obspy module read')
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parser.add_argument('--pickwin', type=float, help='length of pick window [s] for first motion determination')
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parser.add_argument('--Pick', type=float, help='Onset time of most likely pick')
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parser.add_argument('--iplot', type=int, help='if set, figure no. iplot occurs')
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args = parser.parse_args()
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earllatepicker(args.Xraw, args.Xfilt, args.pickwin, args.Pick, args.iplot)
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@ -1,66 +0,0 @@
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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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Created Mar/Apr 2015
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Function to calculate SNR of certain part of seismogram relativ
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to given time. Returns SNR and SNR [dB].
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:author: Ludger Kueperkoch /MAGS EP3 working group
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"""
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from obspy.core import Stream
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import numpy as np
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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].
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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 t1 (onset) used to determine SNR
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:type: tuple (T_noise, T_gap, T_signal)
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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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'''
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assert isinstance(X, Stream), "%s is not a stream object" % str(X)
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SNR = None
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SNRdB = None
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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, X[0].stats.delta)
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#some parameters needed:
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tnoise = TSNR[0] #noise window length for calculating noise level
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tsignal = TSNR[2] #signal window length
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tsafety = TSNR[1] #safety gap between signal onset and noise window
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#get noise window
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inoise = np.where((t <= max([t1 - tsafety, 0])) \
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& (t >= max([t1 - tnoise - tsafety, 0])))
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#get signal window
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isignal = np.where((t <= min([t1 + tsignal + tsafety, len(x)])) \
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& (t >= t1))
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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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#calculate ratios
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SNR = max(abs(x[isignal])) / np.mean(abs(x[inoise]))
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SNRdB = 20 * np.log10(SNR)
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return SNR, SNRdB
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--X', type=~obspy.core.stream.Stream, help='time series (seismogram) read with obspy module read')
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parser.add_argument('--TSNR', type=tuple, help='length of time windows around pick used to determine SNR \
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[s] (Tnoise, Tgap, Tsignal)')
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parser.add_argument('--t1', type=float, help='initial time from which noise and signal windows are calculated')
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args = parser.parse_args()
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getSNR(args.X, args.TSNR, args.t1)
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