Merge branch 'develop' of 134.147.164.251:/data/git/pylot into develop
This commit is contained in:
commit
e6e38dbb95
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autoPyLoT.in
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99
autoPyLoT.in
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@ -0,0 +1,99 @@
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%This is a parameter input file for autoPyLoT.
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%All main and special settings regarding data handling
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%and picking are to be set here!
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#main settings#
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/DATA/Insheim #rootpath# %project path
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EVENT_DATA/LOCAL #datapath# %data path
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2013.02_Insheim #database# %name of data base
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e0019.048.13 #eventID# %certain evnt ID for processing
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PILOT #datastructure# %choose data structure
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0 #iplot# %flag for plotting: 0 none, 1, partly, >1 everything
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AUTOPHASES_AIC_HOS4_ARH #phasefile# %name of autoPILOT output phase file
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AUTOLOC_AIC_HOS4_ARH #locfile# %name of autoPILOT output location file
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AUTOFOCMEC_AIC_HOS4_ARH.in #focmecin# %name of focmec input file containing polarities
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HYPOSAT #locrt# %location routine used ("HYPOINVERSE" or "HYPOSAT")
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6 #pmin# %minimum required P picks for location
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4 #p0min# %minimum required P picks for location if at least
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%3 excellent P picks are found
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2 #smin# %minimum required S picks for location
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/home/ludger/bin/run_HYPOSAT4autoPILOT.csh #cshellp# %path and name of c-shell script to run location routine
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7.6 8.5 #blon# %longitude bounding for location map
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49 49.4 #blat# %lattitude bounding for location map
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#parameters for moment magnitude estimation#
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5000 #vp# %average P-wave velocity
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2800 #vs# %average S-wave velocity
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2200 #rho# %rock density [kg/m^3]
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300 #Qp# %quality factor for P waves
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100 #Qs# %quality factor for S waves
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#common settings picker#
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15 #pstart# %start time [s] for calculating CF for P-picking
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40 #pstop# %end time [s] for calculating CF for P-picking
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-1.0 #sstart# %start time [s] after or before(-) P-onset for calculating CF for S-picking
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7 #sstop# %end time [s] after P-onset for calculating CF for S-picking
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2 20 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
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2 30 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
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2 15 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
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2 20 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz]
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#special settings for calculating CF#
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%!!Be careful when editing the following!!
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#Z-component#
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HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
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7 #tlta# %for HOS-/AR-AIC-picker, length of LTA window [s]
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4 #hosorder# %for HOS-picker, order of Higher Order Statistics
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2 #Parorder# %for AR-picker, order of AR process of Z-component
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1.2 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
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0.4 #tpred1z# %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
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0.6 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
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0.2 #tpred2z# %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
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0.001 #addnoise# %add noise to seismogram for stable AR prediction
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3 0.1 0.5 0.1 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
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3 #pickwinP# %for initial AIC pick, length of P-pick window [s]
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8 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
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0 #peps4aic# %for HOS/AR, artificial uplift of samples of AIC-function (P)
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0.2 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
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0.1 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
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0.001 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
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1.3 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
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#H-components#
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ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
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0.8 #tdet1h# %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
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0.4 #tpred1h# %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
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0.6 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
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0.3 #tpred2h# %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
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4 #Sarorder# %for AR-picker, order of AR process of H-components
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6 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
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3 #pickwinS# %for initial AIC pick, length of S-pick window [s]
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2 0.2 1.5 0.5 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
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0.05 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
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0.02 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
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0.2 #pepsS# %for AR-picker, artificial uplift of samples of CF (S)
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0.4 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
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1.5 #nfacS# %for AR-picker, noise factor for noise level determination (S)
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%first-motion picker%
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1 #minfmweight# %minimum required p weight for first-motion determination
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2 #minFMSNR# %miniumum required SNR for first-motion determination
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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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80 #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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50 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
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1.5 #minAICSSNR# %below this SNR the initial S pick is rejected
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#check duration of signal using envelope function#
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1.5 #prepickwin# %pre-signal window length [s] for noise level estimation
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0.7 #minsiglength# %minimum required length of signal [s]
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0.2 #sgap# %safety gap between noise and signal window [s]
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2 #noisefactor# %noiselevel*noisefactor=threshold
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60 #minpercent# %per cent of samples required higher than threshold
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#check for spuriously picked S-onsets#
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3.0 #zfac# %P-amplitude must exceed zfac times RMS-S amplitude
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#jackknife-processing for P-picks#
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3 #thresholdweight#%minimum required weight of picks
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3 #dttolerance# %maximum allowed deviation of P picks from median [s]
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4 #minstats# %minimum number of stations with reliable P picks
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3 #Sdttolerance# %maximum allowed deviation from Wadati-diagram
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69
autoPyLoT.py
69
autoPyLoT.py
@ -6,15 +6,17 @@ import os
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import argparse
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import glob
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import matplotlib.pyplot as plt
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from obspy.core import read
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from pylot.core.util import _getVersionString
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from pylot.core.read import Data, AutoPickParameter
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from pylot.core.pick.CharFuns import HOScf, AICcf
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from pylot.core.pick.run_autopicking import run_autopicking
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from pylot.core.util.structure import DATASTRUCTURE
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__version__ = _getVersionString()
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METHOD = {'HOS':HOScf, 'AIC':AICcf}
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#METHOD = {'HOS':HOScf, 'AIC':AICcf}
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def autoPyLoT(inputfile):
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'''
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@ -37,16 +39,6 @@ def autoPyLoT(inputfile):
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data = Data()
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# declaring parameter variables (only for convenience)
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meth = parameter.getParam('algoP')
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tsnr1 = parameter.getParam('tsnr1')
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tsnr2 = parameter.getParam('tsnr2')
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tnoise = parameter.getParam('pnoiselen')
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tsignal = parameter.getParam('tlim')
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order = parameter.getParam('hosorder')
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thosmw = parameter.getParam('tlta')
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# getting information on data structure
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if parameter.hasParam('datastructure'):
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@ -60,30 +52,63 @@ def autoPyLoT(inputfile):
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if parameter.hasParam('eventID'):
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dsfields['eventID'] = parameter.getParam('eventID')
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exf.append('eventID')
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datastructure.modifyFields(**dsfields)
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datastructure.modifyFields(**dsfields)
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datastructure.setExpandFields(exf)
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# process each event in database
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# process each event in database
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# get streams
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# read each event in database
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datapath = datastructure.expandDataPath()
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if not parameter.hasParam('eventID'):
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for event in [events for events in
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glob.glob(os.path.join(datapath, '*'))
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if os.path.isdir(events)]:
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for event in [events for events in glob.glob(os.path.join(datapath, '*')) if os.path.isdir(events)]:
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data.setWFData(glob.glob(os.path.join(datapath, event, '*')))
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print 'Working on event %s' %event
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print data
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wfdat = data.getWFData() # all available streams
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##########################################################
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# !automated picking starts here!
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procstats = []
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for i in range(len(wfdat)):
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stationID = wfdat[i].stats.station
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#check if station has already been processed
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if stationID not in procstats:
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procstats.append(stationID)
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#find corresponding streams
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statdat = wfdat.select(station=stationID)
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run_autopicking(statdat, parameter)
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print '------------------------------------------'
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print '-----Finished event %s!-----' % event
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print '------------------------------------------'
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#for single event processing
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else:
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data.setWFData(glob.glob(os.path.join(datapath,
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parameter.getParam('eventID'),
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'*')))
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data.setWFData(glob.glob(os.path.join(datapath, parameter.getParam('eventID'), '*')))
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print 'Working on event ', parameter.getParam('eventID')
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print data
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wfdat = data.getWFData() # all available streams
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##########################################################
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# !automated picking starts here!
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procstats = []
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for i in range(len(wfdat)):
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stationID = wfdat[i].stats.station
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#check if station has already been processed
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if stationID not in procstats:
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procstats.append(stationID)
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#find corresponding streams
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statdat = wfdat.select(station=stationID)
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run_autopicking(statdat, parameter)
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print '------------------------------------------'
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print '-------Finished event %s!-------' % parameter.getParam('eventID')
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print '------------------------------------------'
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if __name__ == "__main__":
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# parse arguments
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parser = argparse.ArgumentParser(
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description='''This program ''')
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description='''autoPyLoT automatically picks phase onset times using higher order statistics,
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autoregressive prediction and AIC''')
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parser.add_argument('-i', '-I', '--inputfile', type=str,
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action='store',
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@ -218,13 +218,12 @@ class AICcf(CharacteristicFunction):
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nn = np.isnan(xnp)
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if len(nn) > 1:
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xnp[nn] = 0
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i0 = np.where(xnp == 0)
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i = np.where(xnp > 0)
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xnp[i0] = xnp[i[0][0]]
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datlen = len(xnp)
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k = np.arange(1, datlen)
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cf = np.zeros(datlen)
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cumsumcf = np.cumsum(np.power(xnp, 2))
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i = np.where(cumsumcf == 0)
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cumsumcf[i] = np.finfo(np.float64).eps
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cf[k] = ((k - 1) * np.log(cumsumcf[k] / k) + (datlen - k + 1) * \
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np.log((cumsumcf[datlen - 1] - cumsumcf[k - 1]) / (datlen - k + 1)))
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cf[0] = cf[1]
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@ -236,7 +235,6 @@ class AICcf(CharacteristicFunction):
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self.cf = cf - np.mean(cf)
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self.xcf = x
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class HOScf(CharacteristicFunction):
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'''
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Function to calculate skewness (statistics of order 3) or kurtosis
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@ -310,8 +308,8 @@ class ARZcf(CharacteristicFunction):
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cf = np.zeros(len(xnp))
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loopstep = self.getARdetStep()
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arcalci = ldet + self.getOrder() - 1 #AR-calculation index
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for i in range(ldet + self.getOrder() - 1, tend - 2 * lpred + 1):
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arcalci = ldet + self.getOrder() #AR-calculation index
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for i in range(ldet + self.getOrder(), tend - lpred - 1):
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if i == arcalci:
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#determination of AR coefficients
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#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
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@ -320,10 +318,17 @@ class ARZcf(CharacteristicFunction):
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#AR prediction of waveform using calculated AR coefficients
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self.arPredZ(xnp, self.arpara, i + 1, lpred)
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#prediction error = CF
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cf[i + lpred] = np.sqrt(np.sum(np.power(self.xpred[i:i + lpred] - xnp[i:i + lpred], 2)) / lpred)
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cf[i + lpred-1] = np.sqrt(np.sum(np.power(self.xpred[i:i + lpred-1] - xnp[i:i + lpred-1], 2)) / lpred)
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nn = np.isnan(cf)
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if len(nn) > 1:
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cf[nn] = 0
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#remove zeros and artefacts
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tap = np.hanning(len(cf))
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cf = tap * cf
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io = np.where(cf == 0)
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ino = np.where(cf > 0)
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cf[io] = cf[ino[0][0]]
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self.cf = cf
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self.xcf = x
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@ -350,17 +355,18 @@ class ARZcf(CharacteristicFunction):
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#recursive calculation of data vector (right part of eq. 6.5 in Kueperkoch et al. (2012)
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rhs = np.zeros(self.getOrder())
|
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for k in range(0, self.getOrder()):
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for i in range(rind, ldet):
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rhs[k] = rhs[k] + data[i] * data[i - k]
|
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for i in range(rind, ldet+1):
|
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ki = k + 1
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rhs[k] = rhs[k] + data[i] * data[i - ki]
|
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|
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#recursive calculation of data array (second sum at left part of eq. 6.5 in Kueperkoch et al. 2012)
|
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A = np.zeros((2,2))
|
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A = np.zeros((self.getOrder(),self.getOrder()))
|
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for k in range(1, self.getOrder() + 1):
|
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for j in range(1, k + 1):
|
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for i in range(rind, ldet):
|
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for i in range(rind, ldet+1):
|
||||
ki = k - 1
|
||||
ji = j - 1
|
||||
A[ki,ji] = A[ki,ji] + data[i - ji] * data[i - ki]
|
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A[ki,ji] = A[ki,ji] + data[i - j] * data[i - k]
|
||||
|
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A[ji,ki] = A[ki,ji]
|
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@ -387,20 +393,20 @@ class ARZcf(CharacteristicFunction):
|
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Output: predicted waveform z
|
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'''
|
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#be sure of the summation indeces
|
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if rind < len(arpara) + 1:
|
||||
rind = len(arpara) + 1
|
||||
if rind > len(data) - lpred + 1:
|
||||
rind = len(data) - lpred + 1
|
||||
if rind < len(arpara):
|
||||
rind = len(arpara)
|
||||
if rind > len(data) - lpred :
|
||||
rind = len(data) - lpred
|
||||
if lpred < 1:
|
||||
lpred = 1
|
||||
if lpred > len(data) - 1:
|
||||
lpred = len(data) - 1
|
||||
if lpred > len(data) - 2:
|
||||
lpred = len(data) - 2
|
||||
|
||||
z = np.append(data[0:rind], np.zeros(lpred))
|
||||
for i in range(rind, rind + lpred):
|
||||
for j in range(1, len(arpara) + 1):
|
||||
ji = j - 1
|
||||
z[i] = z[i] + arpara[ji] * z[i - ji]
|
||||
z[i] = z[i] + arpara[ji] * z[i - j]
|
||||
|
||||
self.xpred = z
|
||||
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||||
@ -432,8 +438,9 @@ class ARHcf(CharacteristicFunction):
|
||||
|
||||
cf = np.zeros(len(xenoise))
|
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loopstep = self.getARdetStep()
|
||||
arcalci = ldet + self.getOrder() - 1 #AR-calculation index
|
||||
for i in range(ldet + self.getOrder() - 1, tend - 2 * lpred + 1):
|
||||
arcalci = lpred + self.getOrder() - 1 #AR-calculation index
|
||||
#arcalci = ldet + self.getOrder() - 1 #AR-calculation index
|
||||
for i in range(lpred + self.getOrder() - 1, tend - 2 * lpred + 1):
|
||||
if i == arcalci:
|
||||
#determination of AR coefficients
|
||||
#to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
|
||||
@ -447,6 +454,13 @@ class ARHcf(CharacteristicFunction):
|
||||
nn = np.isnan(cf)
|
||||
if len(nn) > 1:
|
||||
cf[nn] = 0
|
||||
#remove zeros and artefacts
|
||||
tap = np.hanning(len(cf))
|
||||
cf = tap * cf
|
||||
io = np.where(cf == 0)
|
||||
ino = np.where(cf > 0)
|
||||
cf[io] = cf[ino[0][0]]
|
||||
|
||||
self.cf = cf
|
||||
self.xcf = xnp
|
||||
|
||||
@ -581,6 +595,13 @@ class AR3Ccf(CharacteristicFunction):
|
||||
nn = np.isnan(cf)
|
||||
if len(nn) > 1:
|
||||
cf[nn] = 0
|
||||
#remove zeros and artefacts
|
||||
tap = np.hanning(len(cf))
|
||||
cf = tap * cf
|
||||
io = np.where(cf == 0)
|
||||
ino = np.where(cf > 0)
|
||||
cf[io] = cf[ino[0][0]]
|
||||
|
||||
self.cf = cf
|
||||
self.xcf = xnp
|
||||
|
||||
|
||||
@ -145,6 +145,8 @@ class AICPicker(AutoPicking):
|
||||
print 'AICPicker: Get initial onset time (pick) from AIC-CF ...'
|
||||
|
||||
self.Pick = None
|
||||
self.slope = None
|
||||
self.SNR = None
|
||||
#find NaN's
|
||||
nn = np.isnan(self.cf)
|
||||
if len(nn) > 1:
|
||||
@ -197,11 +199,15 @@ class AICPicker(AutoPicking):
|
||||
if self.Pick is not None:
|
||||
#get noise window
|
||||
inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
|
||||
#check, if these are counts or m/s, important for slope estimation!
|
||||
#this is quick and dirty, better solution?
|
||||
if max(self.Data[0].data < 1e-3):
|
||||
self.Data[0].data = self.Data[0].data * 1000000
|
||||
#get signal window
|
||||
isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
|
||||
#calculate SNR from CF
|
||||
self.SNR = max(abs(self.cf[isignal] - np.mean(self.cf[isignal]))) / max(abs(self.cf[inoise] \
|
||||
- np.mean(self.cf[inoise])))
|
||||
self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / max(abs(aic[inoise] \
|
||||
- np.mean(aic[inoise])))
|
||||
#calculate slope from CF after initial pick
|
||||
#get slope window
|
||||
tslope = self.TSNR[3] #slope determination window
|
||||
@ -230,8 +236,8 @@ class AICPicker(AutoPicking):
|
||||
self.SNR = None
|
||||
self.slope = None
|
||||
|
||||
if self.iplot is not None:
|
||||
plt.figure(self.iplot)
|
||||
if self.iplot > 1:
|
||||
p = plt.figure(self.iplot)
|
||||
x = self.Data[0].data
|
||||
p1, = plt.plot(self.Tcf, x / max(x), 'k')
|
||||
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
|
||||
@ -243,7 +249,6 @@ class AICPicker(AutoPicking):
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.yticks([])
|
||||
plt.title(self.Data[0].stats.station)
|
||||
plt.show()
|
||||
|
||||
if self.Pick is not None:
|
||||
plt.figure(self.iplot + 1)
|
||||
@ -259,11 +264,12 @@ class AICPicker(AutoPicking):
|
||||
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
|
||||
plt.ylabel('Counts')
|
||||
ax = plt.gca()
|
||||
ax.set_ylim([-10, max(self.Data[0].data)])
|
||||
plt.yticks([])
|
||||
ax.set_xlim([self.Tcf[inoise[0][0]] - 5, self.Tcf[isignal[0][len(isignal) - 1]] + 5])
|
||||
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(self.iplot)
|
||||
plt.close(p)
|
||||
|
||||
if self.Pick == None:
|
||||
print 'AICPicker: Could not find minimum, picking window too short?'
|
||||
@ -347,8 +353,8 @@ class PragPicker(AutoPicking):
|
||||
elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
|
||||
self.Pick = pick_r
|
||||
|
||||
if self.getiplot() is not None:
|
||||
plt.figure(self.getiplot())
|
||||
if self.getiplot() > 1:
|
||||
p = plt.figure(self.getiplot())
|
||||
p1, = plt.plot(Tcfpick,cfipick, 'k')
|
||||
p2, = plt.plot(Tcfpick,cfsmoothipick, 'r')
|
||||
p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2)
|
||||
@ -358,7 +364,7 @@ class PragPicker(AutoPicking):
|
||||
plt.title(self.Data[0].stats.station)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(self.getiplot())
|
||||
plt.close(p)
|
||||
|
||||
else:
|
||||
self.Pick = None
|
||||
|
||||
459
pylot/core/pick/run_autopicking.py
Executable file
459
pylot/core/pick/run_autopicking.py
Executable file
@ -0,0 +1,459 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""
|
||||
Function to run automated picking algorithms using AIC,
|
||||
HOS and AR prediction. Uses object CharFuns and Picker and
|
||||
function conglomerate utils.
|
||||
|
||||
:author: MAGS2 EP3 working group / Ludger Kueperkoch
|
||||
"""
|
||||
|
||||
from obspy.core import read
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from pylot.core.pick.CharFuns import *
|
||||
from pylot.core.pick.Picker import *
|
||||
from pylot.core.pick.CharFuns import *
|
||||
from pylot.core.pick import utils
|
||||
|
||||
|
||||
def run_autopicking(wfstream, pickparam):
|
||||
|
||||
'''
|
||||
param: wfstream
|
||||
:type: `~obspy.core.stream.Stream`
|
||||
|
||||
param: pickparam
|
||||
:type: container of picking parameters from input file,
|
||||
usually autoPyLoT.in
|
||||
'''
|
||||
|
||||
# declaring pickparam variables (only for convenience)
|
||||
# read your autoPyLoT.in for details!
|
||||
|
||||
#special parameters for P picking
|
||||
algoP = pickparam.getParam('algoP')
|
||||
iplot = pickparam.getParam('iplot')
|
||||
pstart = pickparam.getParam('pstart')
|
||||
pstop = pickparam.getParam('pstop')
|
||||
thosmw = pickparam.getParam('tlta')
|
||||
hosorder = pickparam.getParam('hosorder')
|
||||
tsnrz = pickparam.getParam('tsnrz')
|
||||
hosorder = pickparam.getParam('hosorder')
|
||||
bpz1 = pickparam.getParam('bpz1')
|
||||
bpz2 = pickparam.getParam('bpz2')
|
||||
pickwinP = pickparam.getParam('pickwinP')
|
||||
tsmoothP = pickparam.getParam('tsmoothP')
|
||||
ausP = pickparam.getParam('ausP')
|
||||
nfacP = pickparam.getParam('nfacP')
|
||||
tpred1z = pickparam.getParam('tpred1z')
|
||||
tdet1z = pickparam.getParam('tdet1z')
|
||||
Parorder = pickparam.getParam('Parorder')
|
||||
addnoise = pickparam.getParam('addnoise')
|
||||
Precalcwin = pickparam.getParam('Precalcwin')
|
||||
minAICPslope = pickparam.getParam('minAICPslope')
|
||||
minAICPSNR = pickparam.getParam('minAICPSNR')
|
||||
timeerrorsP = pickparam.getParam('timeerrorsP')
|
||||
#special parameters for S picking
|
||||
algoS = pickparam.getParam('algoS')
|
||||
sstart = pickparam.getParam('sstart')
|
||||
sstop = pickparam.getParam('sstop')
|
||||
bph1 = pickparam.getParam('bph1')
|
||||
bph2 = pickparam.getParam('bph2')
|
||||
tsnrh = pickparam.getParam('tsnrh')
|
||||
pickwinS = pickparam.getParam('pickwinS')
|
||||
tpred1h = pickparam.getParam('tpred1h')
|
||||
tdet1h = pickparam.getParam('tdet1h')
|
||||
tpred2h = pickparam.getParam('tpred2h')
|
||||
tdet2h = pickparam.getParam('tdet2h')
|
||||
Sarorder = pickparam.getParam('Sarorder')
|
||||
aictsmoothS = pickparam.getParam('aictsmoothS')
|
||||
tsmoothS = pickparam.getParam('tsmoothS')
|
||||
ausS = pickparam.getParam('ausS')
|
||||
minAICSslope = pickparam.getParam('minAICSslope')
|
||||
minAICSSNR = pickparam.getParam('minAICSSNR')
|
||||
Srecalcwin = pickparam.getParam('Srecalcwin')
|
||||
nfacS = pickparam.getParam('nfacS')
|
||||
timeerrorsS = pickparam.getParam('timeerrorsS')
|
||||
#parameters for first-motion determination
|
||||
minFMSNR = pickparam.getParam('minFMSNR')
|
||||
fmpickwin = pickparam.getParam('fmpickwin')
|
||||
minfmweight = pickparam.getParam('minfmweight')
|
||||
|
||||
# split components
|
||||
zdat = wfstream.select(component="Z")
|
||||
edat = wfstream.select(component="E")
|
||||
if len(edat) == 0: #check for other components
|
||||
edat = wfstream.select(component="2")
|
||||
ndat = wfstream.select(component="N")
|
||||
if len(ndat) == 0: #check for other components
|
||||
ndat = wfstream.select(component="1")
|
||||
|
||||
if algoP == 'HOS' or algoP == 'ARZ' and zdat is not None:
|
||||
print '##########################################'
|
||||
print 'run_autopicking: Working on P onset of station %s' % zdat[0].stats.station
|
||||
print 'Filtering vertical trace ...'
|
||||
print zdat
|
||||
z_copy = zdat.copy()
|
||||
#filter and taper data
|
||||
tr_filt = zdat[0].copy()
|
||||
tr_filt.filter('bandpass', freqmin=bpz1[0], freqmax=bpz1[1], zerophase=False)
|
||||
tr_filt.taper(max_percentage=0.05, type='hann')
|
||||
z_copy[0].data = tr_filt.data
|
||||
##############################################################
|
||||
#check length of waveform and compare with cut times
|
||||
Lc = pstop - pstart
|
||||
Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
|
||||
Ldiff = Lwf - Lc
|
||||
if Ldiff < 0:
|
||||
print 'run_autopicking: Cutting times are too large for actual waveform!'
|
||||
print 'Use entire waveform instead!'
|
||||
pstart = 0
|
||||
pstop = len(zdat[0].data) * zdat[0].stats.delta
|
||||
cuttimes = [pstart, pstop]
|
||||
if algoP == 'HOS':
|
||||
#calculate HOS-CF using subclass HOScf of class CharacteristicFunction
|
||||
cf1 = HOScf(z_copy, cuttimes, thosmw, hosorder) #instance of HOScf
|
||||
elif algoP == 'ARZ':
|
||||
#calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
|
||||
cf1 = ARZcf(z_copy, cuttimes, tpred1z, Parorder, tdet1z, addnoise) #instance of ARZcf
|
||||
##############################################################
|
||||
#calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
|
||||
#class needs stream object => build it
|
||||
tr_aic = tr_filt.copy()
|
||||
tr_aic.data =cf1.getCF()
|
||||
z_copy[0].data = tr_aic.data
|
||||
aiccf = AICcf(z_copy, cuttimes) #instance of AICcf
|
||||
##############################################################
|
||||
#get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
|
||||
aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP)
|
||||
##############################################################
|
||||
#go on with processing if AIC onset passes quality control
|
||||
if aicpick.getSlope() >= minAICPslope and aicpick.getSNR() >= minAICPSNR:
|
||||
aicPflag = 1
|
||||
print 'AIC P-pick passes quality control: Slope: %f, SNR: %f' % \
|
||||
(aicpick.getSlope(), aicpick.getSNR())
|
||||
print 'Go on with refined picking ...'
|
||||
#re-filter waveform with larger bandpass
|
||||
print 'run_autopicking: re-filtering vertical trace ...'
|
||||
z_copy = zdat.copy()
|
||||
tr_filt = zdat[0].copy()
|
||||
tr_filt.filter('bandpass', freqmin=bpz2[0], freqmax=bpz2[1], zerophase=False)
|
||||
tr_filt.taper(max_percentage=0.05, type='hann')
|
||||
z_copy[0].data = tr_filt.data
|
||||
#############################################################
|
||||
#re-calculate CF from re-filtered trace in vicinity of initial onset
|
||||
cuttimes2 = [round(max([aicpick.getpick() - Precalcwin, 0])), \
|
||||
round(min([len(zdat[0].data) * zdat[0].stats.delta, \
|
||||
aicpick.getpick() + Precalcwin]))]
|
||||
if algoP == 'HOS':
|
||||
#calculate HOS-CF using subclass HOScf of class CharacteristicFunction
|
||||
cf2 = HOScf(z_copy, cuttimes2, thosmw, hosorder) #instance of HOScf
|
||||
elif algoP == 'ARZ':
|
||||
#calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
|
||||
cf2 = ARZcf(z_copy, cuttimes2, tpred1z, Parorder, tdet1z, addnoise) #instance of ARZcf
|
||||
##############################################################
|
||||
#get refined onset time from CF2 using class Picker
|
||||
refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP, aicpick.getpick())
|
||||
#############################################################
|
||||
#quality assessment
|
||||
#get earliest and latest possible pick and symmetrized uncertainty
|
||||
[lpickP, epickP, Perror] = earllatepicker(z_copy, nfacP, tsnrz, refPpick.getpick(), iplot)
|
||||
|
||||
#get SNR
|
||||
[SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz, refPpick.getpick())
|
||||
|
||||
#weight P-onset using symmetric error
|
||||
if Perror <= timeerrorsP[0]:
|
||||
Pweight = 0
|
||||
elif Perror > timeerrorsP[0] and Perror <= timeerrorsP[1]:
|
||||
Pweight = 1
|
||||
elif Perror > timeerrorsP[1] and Perror <= timeerrorsP[2]:
|
||||
Pweight = 2
|
||||
elif Perror > timeerrorsP[2] and Perror <= timeerrorsP[3]:
|
||||
Pweight = 3
|
||||
elif Perror > timeerrorsP[3]:
|
||||
Pweight = 4
|
||||
|
||||
##############################################################
|
||||
#get first motion of P onset
|
||||
#certain quality required
|
||||
if Pweight <= minfmweight and SNRP >= minFMSNR:
|
||||
FM = fmpicker(zdat, z_copy, fmpickwin, refPpick.getpick(), iplot)
|
||||
else:
|
||||
FM = 'N'
|
||||
|
||||
print 'run_autopicking: P-weight: %d, SNR: %f, SNR[dB]: %f, Polarity: %s' % (Pweight, SNRP, SNRPdB, FM)
|
||||
|
||||
else:
|
||||
print 'Bad initial (AIC) P-pick, skip this onset!'
|
||||
print 'AIC-SNR=', aicpick.getSNR(), 'AIC-Slope=', aicpick.getSlope()
|
||||
Pweight = 4
|
||||
Sweight = 4
|
||||
FM = 'N'
|
||||
SNRP = None
|
||||
SNRPdB = None
|
||||
SNRS = None
|
||||
SNRSdB = None
|
||||
aicSflag = 0
|
||||
aicPflag = 0
|
||||
else:
|
||||
print 'run_autopicking: No vertical component data available, skipping station!'
|
||||
return
|
||||
|
||||
if edat is not None and ndat is not None and len(edat) > 0 and len(ndat) > 0 and Pweight < 4:
|
||||
print 'Go on picking S onset ...'
|
||||
print '##################################################'
|
||||
print 'Working on S onset of station %s' % edat[0].stats.station
|
||||
print 'Filtering horizontal traces ...'
|
||||
|
||||
#determine time window for calculating CF after P onset
|
||||
#cuttimesh = [round(refPpick.getpick() + sstart), round(refPpick.getpick() + sstop)]
|
||||
cuttimesh = [round(max([refPpick.getpick() + sstart, 0])), \
|
||||
round(min([refPpick.getpick() + sstop, Lwf]))]
|
||||
|
||||
if algoS == 'ARH':
|
||||
print edat, ndat
|
||||
#re-create stream object including both horizontal components
|
||||
hdat = edat.copy()
|
||||
hdat += ndat
|
||||
h_copy = hdat.copy()
|
||||
#filter and taper data
|
||||
trH1_filt = hdat[0].copy()
|
||||
trH2_filt = hdat[1].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
|
||||
trH1_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH2_filt.taper(max_percentage=0.05, type='hann')
|
||||
h_copy[0].data = trH1_filt.data
|
||||
h_copy[1].data = trH2_filt.data
|
||||
elif algoS == 'AR3':
|
||||
print zdat, edat, ndat
|
||||
#re-create stream object including both horizontal components
|
||||
hdat = zdat.copy()
|
||||
hdat += edat
|
||||
hdat += ndat
|
||||
h_copy = hdat.copy()
|
||||
#filter and taper data
|
||||
trH1_filt = hdat[0].copy()
|
||||
trH2_filt = hdat[1].copy()
|
||||
trH3_filt = hdat[2].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
|
||||
trH3_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1], zerophase=False)
|
||||
trH1_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH2_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH3_filt.taper(max_percentage=0.05, type='hann')
|
||||
h_copy[0].data = trH1_filt.data
|
||||
h_copy[1].data = trH2_filt.data
|
||||
h_copy[2].data = trH3_filt.data
|
||||
##############################################################
|
||||
if algoS == 'ARH':
|
||||
#calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
|
||||
arhcf1 = ARHcf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h, addnoise) #instance of ARHcf
|
||||
elif algoS == 'AR3':
|
||||
#calculate ARH-CF using subclass AR3cf of class CharcteristicFunction
|
||||
arhcf1 = AR3Ccf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h, addnoise) #instance of ARHcf
|
||||
##############################################################
|
||||
#calculate AIC-ARH-CF using subclass AICcf of class CharacteristicFunction
|
||||
#class needs stream object => build it
|
||||
tr_arhaic = trH1_filt.copy()
|
||||
tr_arhaic.data = arhcf1.getCF()
|
||||
h_copy[0].data = tr_arhaic.data
|
||||
#calculate ARH-AIC-CF
|
||||
haiccf = AICcf(h_copy, cuttimesh) #instance of AICcf
|
||||
##############################################################
|
||||
#get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
|
||||
aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None, aictsmoothS)
|
||||
###############################################################
|
||||
#go on with processing if AIC onset passes quality control
|
||||
if aicarhpick.getSlope() >= minAICSslope and aicarhpick.getSNR() >= minAICSSNR:
|
||||
aicSflag = 1
|
||||
print 'AIC S-pick passes quality control: Slope: %f, SNR: %f' \
|
||||
% (aicarhpick.getSlope(), aicarhpick.getSNR())
|
||||
print 'Go on with refined picking ...'
|
||||
#re-calculate CF from re-filtered trace in vicinity of initial onset
|
||||
cuttimesh2 = [round(aicarhpick.getpick() - Srecalcwin), \
|
||||
round(aicarhpick.getpick() + Srecalcwin)]
|
||||
#re-filter waveform with larger bandpass
|
||||
print 'run_autopicking: re-filtering horizontal traces...'
|
||||
h_copy = hdat.copy()
|
||||
#filter and taper data
|
||||
if algoS == 'ARH':
|
||||
trH1_filt = hdat[0].copy()
|
||||
trH2_filt = hdat[1].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
|
||||
trH1_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH2_filt.taper(max_percentage=0.05, type='hann')
|
||||
h_copy[0].data = trH1_filt.data
|
||||
h_copy[1].data = trH2_filt.data
|
||||
#############################################################
|
||||
arhcf2 = ARHcf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h, addnoise) #instance of ARHcf
|
||||
elif algoS == 'AR3':
|
||||
trH1_filt = hdat[0].copy()
|
||||
trH2_filt = hdat[1].copy()
|
||||
trH3_filt = hdat[2].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
|
||||
trH3_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1], zerophase=False)
|
||||
trH1_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH2_filt.taper(max_percentage=0.05, type='hann')
|
||||
trH3_filt.taper(max_percentage=0.05, type='hann')
|
||||
h_copy[0].data = trH1_filt.data
|
||||
h_copy[1].data = trH2_filt.data
|
||||
h_copy[2].data = trH3_filt.data
|
||||
#############################################################
|
||||
arhcf2 = AR3Ccf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h, addnoise) #instance of ARHcf
|
||||
|
||||
#get refined onset time from CF2 using class Picker
|
||||
refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS, tsmoothS, aicarhpick.getpick())
|
||||
#############################################################
|
||||
#quality assessment
|
||||
#get earliest and latest possible pick and symmetrized uncertainty
|
||||
h_copy[0].data = trH1_filt.data
|
||||
[lpickS1, epickS1, Serror1] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
|
||||
h_copy[0].data = trH2_filt.data
|
||||
[lpickS2, epickS2, Serror2] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
|
||||
if algoS == 'ARH':
|
||||
#get earliest pick of both earliest possible picks
|
||||
epick = [epickS1, epickS2]
|
||||
lpick = [lpickS1, lpickS2]
|
||||
pickerr = [Serror1, Serror2]
|
||||
ipick =np.argmin([epickS1, epickS2])
|
||||
elif algoS == 'AR3':
|
||||
[lpickS3, epickS3, Serror3] = earllatepicker(h_copy, nfacS, tsnrh, refSpick.getpick(), iplot)
|
||||
#get earliest pick of all three picks
|
||||
epick = [epickS1, epickS2, epickS3]
|
||||
lpick = [lpickS1, lpickS2, lpickS3]
|
||||
pickerr = [Serror1, Serror2, Serror3]
|
||||
ipick =np.argmin([epickS1, epickS2, epickS3])
|
||||
epickS = epick[ipick]
|
||||
lpickS = lpick[ipick]
|
||||
Serror = pickerr[ipick]
|
||||
|
||||
#get SNR
|
||||
[SNRS, SNRSdB, Snoiselevel] = getSNR(h_copy, tsnrh, refSpick.getpick())
|
||||
|
||||
#weight S-onset using symmetric error
|
||||
if Serror <= timeerrorsS[0]:
|
||||
Sweight = 0
|
||||
elif Serror > timeerrorsS[0] and Serror <= timeerrorsS[1]:
|
||||
Sweight = 1
|
||||
elif Perror > timeerrorsS[1] and Serror <= timeerrorsS[2]:
|
||||
Sweight = 2
|
||||
elif Serror > timeerrorsS[2] and Serror <= timeerrorsS[3]:
|
||||
Sweight = 3
|
||||
elif Serror > timeerrorsS[3]:
|
||||
Sweight = 4
|
||||
|
||||
print 'run_autopicking: S-weight: %d, SNR: %f, SNR[dB]: %f' % (Sweight, SNRS, SNRSdB)
|
||||
|
||||
else:
|
||||
print 'Bad initial (AIC) S-pick, skip this onset!'
|
||||
print 'AIC-SNR=', aicarhpick.getSNR(), 'AIC-Slope=', aicarhpick.getSlope()
|
||||
Sweight = 4
|
||||
SNRS = None
|
||||
SNRSdB = None
|
||||
aicSflag = 0
|
||||
|
||||
else:
|
||||
print 'run_autopicking: No horizontal component data available or bad P onset, skipping S picking!'
|
||||
return
|
||||
|
||||
##############################################################
|
||||
if iplot > 0:
|
||||
#plot vertical trace
|
||||
plt.figure()
|
||||
plt.subplot(3,1,1)
|
||||
tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate, tr_filt.stats.delta)
|
||||
#check equal length of arrays, sometimes they are different!?
|
||||
wfldiff = len(tr_filt.data) - len(tdata)
|
||||
if wfldiff < 0:
|
||||
tdata = tdata[0:len(tdata) - abs(wfldiff)]
|
||||
p1, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
|
||||
if Pweight < 4:
|
||||
p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()), 'b')
|
||||
if aicPflag == 1:
|
||||
p3, = plt.plot(cf2.getTimeArray(), cf2.getCF() / max(cf2.getCF()), 'm')
|
||||
p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'r')
|
||||
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'r')
|
||||
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'r')
|
||||
p5, = plt.plot([refPpick.getpick(), refPpick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
|
||||
plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [1.3, 1.3], 'r', linewidth=2)
|
||||
plt.plot([refPpick.getpick()-0.5, refPpick.getpick()+0.5], [-1.3, -1.3], 'r', linewidth=2)
|
||||
plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--')
|
||||
plt.plot([epickP, epickP], [-1.1, 1.1], 'r--')
|
||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'CF1', 'CF2', 'Initial P Onset', 'Final P Pick'])
|
||||
plt.title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f Polarity: %s' % (tr_filt.stats.station, \
|
||||
tr_filt.stats.channel, Pweight, SNRP, SNRPdB, FM))
|
||||
else:
|
||||
plt.legend([p1, p2], ['Data', 'CF1'])
|
||||
plt.title('%s, P Weight=%d, SNR=None, SNRdB=None' % (tr_filt.stats.channel, Pweight))
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.suptitle(tr_filt.stats.starttime)
|
||||
|
||||
#plot horizontal traces
|
||||
plt.subplot(3,1,2)
|
||||
th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
|
||||
#check equal length of arrays, sometimes they are different!?
|
||||
wfldiff = len(trH1_filt.data) - len(th1data)
|
||||
if wfldiff < 0:
|
||||
th1data = th1data[0:len(th1data) - abs(wfldiff)]
|
||||
p21, = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
|
||||
if Pweight < 4:
|
||||
p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
|
||||
if aicSflag == 1:
|
||||
p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
|
||||
p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
|
||||
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
|
||||
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
|
||||
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
|
||||
plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
|
||||
plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (trH1_filt.stats.channel, \
|
||||
Sweight, SNRS, SNRSdB))
|
||||
else:
|
||||
plt.legend([p21, p22], ['Data', 'CF1'])
|
||||
plt.title('%s, S Weight=%d, SNR=None, SNRdB=None' % (trH1_filt.stats.channel, Sweight))
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.suptitle(trH1_filt.stats.starttime)
|
||||
|
||||
plt.subplot(3,1,3)
|
||||
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
|
||||
#check equal length of arrays, sometimes they are different!?
|
||||
wfldiff = len(trH2_filt.data) - len(th2data)
|
||||
if wfldiff < 0:
|
||||
th2data = th2data[0:len(th2data) - abs(wfldiff)]
|
||||
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
|
||||
if Pweight < 4:
|
||||
p22, = plt.plot(arhcf1.getTimeArray(), arhcf1.getCF()/max(arhcf1.getCF()), 'b')
|
||||
if aicSflag == 1:
|
||||
p23, = plt.plot(arhcf2.getTimeArray(), arhcf2.getCF()/max(arhcf2.getCF()), 'm')
|
||||
p24, = plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'g')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'g')
|
||||
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()], [-1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [1.3, 1.3], 'g', linewidth=2)
|
||||
plt.plot([refSpick.getpick() - 0.5, refSpick.getpick() + 0.5], [-1.3, -1.3], 'g', linewidth=2)
|
||||
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
|
||||
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
|
||||
plt.legend([p21, p22, p23, p24, p25], ['Data', 'CF1', 'CF2', 'Initial S Onset', 'Final S Pick'])
|
||||
else:
|
||||
plt.legend([p21, p22], ['Data', 'CF1'])
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.xlabel('Time [s] after %s' % tr_filt.stats.starttime)
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title(trH2_filt.stats.channel)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close()
|
||||
@ -11,7 +11,6 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from obspy.core import Stream
|
||||
import pdb
|
||||
|
||||
|
||||
def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
|
||||
@ -81,8 +80,8 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
|
||||
diffti_te = Pick1 - EPick
|
||||
PickError = (diffti_te + 2 * diffti_tl) / 3
|
||||
|
||||
if iplot is not None:
|
||||
plt.figure(iplot)
|
||||
if iplot > 1:
|
||||
p = plt.figure(iplot)
|
||||
p1, = plt.plot(t, x, 'k')
|
||||
p2, = plt.plot(t[inoise], x[inoise])
|
||||
p3, = plt.plot(t[isignal], x[isignal], 'r')
|
||||
@ -109,7 +108,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
|
||||
X[0].stats.station)
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(iplot)
|
||||
plt.close(p)
|
||||
|
||||
return EPick, LPick, PickError
|
||||
|
||||
@ -240,7 +239,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
|
||||
elif P1[0] > 0 and P2[0] <= 0:
|
||||
FM = '+'
|
||||
|
||||
if iplot is not None:
|
||||
if iplot > 1:
|
||||
plt.figure(iplot)
|
||||
plt.subplot(2, 1, 1)
|
||||
plt.plot(t, xraw, 'k')
|
||||
|
||||
Loading…
Reference in New Issue
Block a user