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06b8c8413f
@ -80,8 +80,8 @@ ARH #algoS# %choose algorithm for S-onset
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0.2 #fmpickwin# %pick window around P onset for calculating zero crossings
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%quality assessment%
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#inital AIC onset#
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0.01 0.02 0.04 0.08 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
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0.04 0.08 0.16 0.32 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
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0.05 0.10 0.20 0.40 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
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0.10 0.20 0.40 0.80 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
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4 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
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1.2 #minAICPSNR# %below this SNR the initial P pick is rejected
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2 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
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@ -3,8 +3,11 @@
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import glob
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import obspy.core.event as ope
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from obspy.core.event import read_events
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import os
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import scipy.io as sio
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import matplotlib.pyplot as plt
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import numpy as np
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import warnings
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from obspy.core import UTCDateTime
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from obspy.core.util import AttribDict
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@ -845,3 +848,150 @@ def merge_picks(event, picks):
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p.time, p.time_errors, p.waveform_id.network_code, p.method_id = time, err, network, method
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del time, err, phase, station, network, method
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return event
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def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
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"""
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Script to get onset uncertainties from Quakeml.xml files created by PyLoT.
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Uncertainties are tranformed into quality classes and visualized via histogram if desired.
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Ludger Küperkoch, BESTEC GmbH, 07/2017
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"""
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# read all onset weights
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Pw0 = []
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Pw1 = []
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Pw2 = []
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Pw3 = []
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Pw4 = []
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Sw0 = []
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Sw1 = []
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Sw2 = []
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Sw3 = []
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Sw4 = []
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for names in xmlnames:
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print("Getting onset weights from {}".format(names))
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cat = read_events(names)
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cat_copy = cat.copy()
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arrivals = cat.events[0].picks
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arrivals_copy = cat_copy.events[0].picks
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# Prefere manual picks if qualities are sufficient!
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for Pick in arrivals:
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if Pick.method_id.id == 'manual':
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mstation = Pick.waveform_id.station_code
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mstation_ext = mstation + '_'
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for mpick in arrivals_copy:
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if mpick.phase_hint[0] == 'P':
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if ((mpick.waveform_id.station_code == mstation) or \
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(mpick.waveform_id.station_code == mstation_ext)) and \
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(mpick.method_id == 'auto') and \
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(mpick.time_errors['uncertainty'] <= ErrorsP[3]):
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del mpick
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break
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elif mpick.phase_hint[0] == 'S':
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if ((mpick.waveform_id.station_code == mstation) or \
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(mpick.waveform_id.station_code == mstation_ext)) and \
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(mpick.method_id == 'auto') and \
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(mpick.time_errors['uncertainty'] <= ErrorsS[3]):
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del mpick
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break
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lendiff = len(arrivals) - len(arrivals_copy)
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if lendiff is not 0:
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print("Found manual as well as automatic picks, prefered the {} manual ones!".format(lendiff))
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for Pick in arrivals_copy:
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if Pick.phase_hint[0] == 'P':
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if Pick.time_errors.uncertainty <= ErrorsP[0]:
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Pw0.append(Pick.time_errors.uncertainty)
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elif (Pick.time_errors.uncertainty > ErrorsP[0]) and \
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(Pick.time_errors.uncertainty <= ErrorsP[1]):
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Pw1.append(Pick.time_errors.uncertainty)
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elif (Pick.time_errors.uncertainty > ErrorsP[1]) and \
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(Pick.time_errors.uncertainty <= ErrorsP[2]):
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Pw2.append(Pick.time_errors.uncertainty)
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elif (Pick.time_errors.uncertainty > ErrorsP[2]) and \
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(Pick.time_errors.uncertainty <= ErrorsP[3]):
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Pw3.append(Pick.time_errors.uncertainty)
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elif Pick.time_errors.uncertainty > ErrorsP[3]:
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Pw4.append(Pick.time_errors.uncertainty)
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else:
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pass
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elif Pick.phase_hint[0] == 'S':
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if Pick.time_errors.uncertainty <= ErrorsS[0]:
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Sw0.append(Pick.time_errors.uncertainty)
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elif (Pick.time_errors.uncertainty > ErrorsS[0]) and \
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(Pick.time_errors.uncertainty <= ErrorsS[1]):
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Sw1.append(Pick.time_errors.uncertainty)
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elif (Pick.time_errors.uncertainty > ErrorsS[1]) and \
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(Pick.time_errors.uncertainty <= ErrorsS[2]):
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Sw2.append(Pick.time_errors.uncertainty)
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elif (Pick.time_errors.uncertainty > ErrorsS[2]) and \
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(Pick.time_errors.uncertainty <= ErrorsS[3]):
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Sw3.append(Pick.time_errors.uncertainty)
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elif Pick.time_errors.uncertainty > ErrorsS[3]:
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Sw4.append(Pick.time_errors.uncertainty)
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else:
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pass
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else:
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print("Phase hint not defined for picking!")
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pass
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if plotflag == 0:
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Punc = [Pw0, Pw1, Pw2, Pw3, Pw4]
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Sunc = [Sw0, Sw1, Sw2, Sw3, Sw4]
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return Puns, Sunc
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else:
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# get percentage of weights
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numPweights = np.sum([len(Pw0), len(Pw1), len(Pw2), len(Pw3), len(Pw4)])
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numSweights = np.sum([len(Sw0), len(Sw1), len(Sw2), len(Sw3), len(Sw4)])
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try:
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P0perc = 100 / numPweights * len(Pw0)
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except:
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P0perc = 0
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try:
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P1perc = 100 / numPweights * len(Pw1)
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except:
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P1perc = 0
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try:
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P2perc = 100 / numPweights * len(Pw2)
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except:
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P2perc = 0
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try:
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P3perc = 100 / numPweights * len(Pw3)
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except:
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P3perc = 0
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try:
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P4perc = 100 / numPweights * len(Pw4)
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except:
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P4perc = 0
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try:
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S0perc = 100 / numSweights * len(Sw0)
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except:
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Soperc = 0
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try:
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S1perc = 100 / numSweights * len(Sw1)
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except:
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S1perc = 0
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try:
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S2perc = 100 / numSweights * len(Sw2)
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except:
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S2perc = 0
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try:
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S3perc = 100 / numSweights * len(Sw3)
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except:
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S3perc = 0
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try:
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S4perc = 100 / numSweights * len(Sw4)
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except:
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S4perc = 0
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weights = ('0', '1', '2', '3', '4')
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y_pos = np.arange(len(weights))
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width = 0.34
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plt.bar(y_pos - width, [P0perc, P1perc, P2perc, P3perc, P4perc], width, color='black')
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plt.bar(y_pos, [S0perc, S1perc, S2perc, S3perc, S4perc], width, color='red')
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plt.ylabel('%')
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plt.xticks(y_pos, weights)
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plt.xlim([-0.5, 4.5])
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plt.xlabel('Qualities')
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plt.title('{0} P-Qualities, {1} S-Qualities'.format(numPweights, numSweights))
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plt.show()
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