[cleanup] remove old files pt.2
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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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True #apverbose#
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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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@ -1,99 +0,0 @@
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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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%Parameters are optimized for local data sets!
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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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2016.08_Insheim #database# %name of data base
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e0007.224.16 #eventID# %event ID for single event processing
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/DATA/Insheim/STAT_INFO #invdir# %full path to inventory or dataless-seed file
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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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True #apverbose# %choose 'True' or 'False' for terminal output
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#NLLoc settings#
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/home/ludger/NLLOC #nllocbin# %path to NLLoc executable
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/home/ludger/NLLOC/Insheim #nllocroot# %root of NLLoc-processing directory
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AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
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%(in nllocroot/obs)
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Insheim_min1d032016_auto.in #ctrfile# %name of autoPyLoT-output control file for NLLoc
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%(in nllocroot/run)
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ttime #ttpatter# %pattern of NLLoc ttimes from grid
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%(in nllocroot/times)
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AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
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%(returns 'eventID_outpatter')
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#parameters for seismic moment estimation#
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3530 #vp# %average P-wave velocity
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2500 #rho# %average rock density [kg/m^3]
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300 0.8 #Qp# %quality factor for P waves ([Qp, ap], Qp*f^a)
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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AUTOFOCMEC_AIC_HOS4_ARH.in #focmecin# %name of focmec input file containing derived polarities
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#common settings picker#
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15.0 #pstart# %start time [s] for calculating CF for P-picking
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60.0 #pstop# %end time [s] for calculating CF for P-picking
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-1.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
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10.0 #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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%!!Edit the following only if you know what you are doing!!%
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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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7.0 #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 0.5 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
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3.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
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6.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
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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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5.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
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3.0 #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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0.5 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
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0.7 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
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0.9 #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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0.05 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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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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3 #minsiglength# %minimum required length of signal [s]
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1.0 #noisefactor# %noiselevel*noisefactor=threshold
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40 #minpercent# %required percentage of samples higher than threshold
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#check for spuriously picked S-onsets#
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2.0 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
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#check statistics of P onsets#
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2.5 #mdttolerance# %maximum allowed deviation of P picks from median [s]
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#wadati check#
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1.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
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@ -1,100 +0,0 @@
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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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%Parameters are optimized for regional data sets!
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#main settings#
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/DATA/Egelados #rootpath# %project path
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EVENT_DATA/LOCAL #datapath# %data path
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2006.01_Nisyros #database# %name of data base
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e1412.008.06 #eventID# %event ID for single event processing
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/DATA/Egelados/STAT_INFO #invdir# %full path to inventory or dataless-seed file
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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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True #apverbose# %choose 'True' or 'False' for terminal output
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#NLLoc settings#
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/home/ludger/NLLOC #nllocbin# %path to NLLoc executable
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/home/ludger/NLLOC/Insheim #nllocroot# %root of NLLoc-processing directory
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AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
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%(in nllocroot/obs)
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Insheim_min1d2015_auto.in #ctrfile# %name of autoPyLoT-output control file for NLLoc
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%(in nllocroot/run)
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ttime #ttpatter# %pattern of NLLoc ttimes from grid
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%(in nllocroot/times)
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AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
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%(returns 'eventID_outpatter')
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#parameters for seismic moment estimation#
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3530 #vp# %average P-wave velocity
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2700 #rho# %average rock density [kg/m^3]
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1000f**0.8 #Qp# %quality factor for P waves (Qp*f^a)
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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AUTOFOCMEC_AIC_HOS4_ARH.in #focmecin# %name of focmec input file containing derived polarities
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#common settings picker#
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20 #pstart# %start time [s] for calculating CF for P-picking
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100 #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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100 #sstop# %end time [s] after P-onset for calculating CF for S-picking
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3 10 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
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3 12 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
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3 8 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
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3 6 #bph2# %lower/upper corner freq. of second band pass filter H-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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5 0.2 3.0 1.5 #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 and refined 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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1.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
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0.3 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
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0.3 #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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10 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
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25 #pickwinS# %for initial AIC and refined pick, length of S-pick window [s]
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5 0.2 3.0 3.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
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3.5 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
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1.0 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
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0.2 #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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6.0 #fmpickwin# %pick window around P onset for calculating zero crossings
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%quality assessment%
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#inital AIC onset#
|
||||
0.04 0.08 0.16 0.32 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
|
||||
0.04 0.08 0.16 0.32 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
|
||||
3 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
|
||||
1.2 #minAICPSNR# %below this SNR the initial P pick is rejected
|
||||
5 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
|
||||
2.5 #minAICSSNR# %below this SNR the initial S pick is rejected
|
||||
#check duration of signal using envelope function#
|
||||
30 #minsiglength# %minimum required length of signal [s]
|
||||
2.5 #noisefactor# %noiselevel*noisefactor=threshold
|
||||
60 #minpercent# %required percentage of samples higher than threshold
|
||||
#check for spuriously picked S-onsets#
|
||||
0.5 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
|
||||
#check statistics of P onsets#
|
||||
45 #mdttolerance# %maximum allowed deviation of P picks from median [s]
|
||||
#wadati check#
|
||||
3.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
|
||||
|
||||
@ -158,8 +158,8 @@ def buildPyLoT(verbosity=None):
|
||||
|
||||
|
||||
def installPyLoT(verbosity=None):
|
||||
files_to_copy = {'autoPyLoT_local.in': ['~', '.pylot'],
|
||||
'autoPyLoT_regional.in': ['~', '.pylot']}
|
||||
files_to_copy = {'pylot.in': ['~', '.pylot'],
|
||||
'pylot_global.in': ['~', '.pylot']}
|
||||
if verbosity > 0:
|
||||
print('starting installation of PyLoT ...')
|
||||
if verbosity > 1:
|
||||
@ -174,7 +174,7 @@ def installPyLoT(verbosity=None):
|
||||
link_file = ans in file
|
||||
if link_file:
|
||||
link_dest = copy.deepcopy(destination)
|
||||
link_dest.append('autoPyLoT.in')
|
||||
link_dest.append('pylot.in')
|
||||
link_dest = os.path.join(*link_dest)
|
||||
destination.append(file)
|
||||
destination = os.path.join(*destination)
|
||||
|
||||
@ -38,7 +38,7 @@ def autopickevent(data, param, iplot=0, fig_dict=None, fig_dict_wadatijack=None,
|
||||
|
||||
|
||||
# get some parameters for quality control from
|
||||
# parameter input file (usually autoPyLoT.in).
|
||||
# parameter input file (usually pylot.in).
|
||||
wdttolerance = param.get('wdttolerance')
|
||||
mdttolerance = param.get('mdttolerance')
|
||||
jackfactor = param.get('jackfactor')
|
||||
@ -113,7 +113,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
|
||||
:type wfstream: obspy.core.stream.Stream
|
||||
|
||||
:param pickparam: container of picking parameters from input file,
|
||||
usually autoPyLoT.in
|
||||
usually pylot.in
|
||||
:type pickparam: PylotParameter
|
||||
:param verbose:
|
||||
:type verbose: bool
|
||||
@ -121,7 +121,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
|
||||
"""
|
||||
|
||||
# declaring pickparam variables (only for convenience)
|
||||
# read your autoPyLoT.in for details!
|
||||
# read your pylot.in for details!
|
||||
plt_flag = 0
|
||||
|
||||
# special parameters for P picking
|
||||
|
||||
@ -1,13 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import sys
|
||||
|
||||
from PySide.QtGui import QApplication
|
||||
from pylot.core.util.widgets import HelpForm
|
||||
|
||||
app = QApplication(sys.argv)
|
||||
|
||||
win = HelpForm()
|
||||
win.show()
|
||||
app.exec_()
|
||||
@ -1,20 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import sys
|
||||
|
||||
import matplotlib
|
||||
|
||||
matplotlib.use('Qt4Agg')
|
||||
matplotlib.rcParams['backend.qt4'] = 'PySide'
|
||||
|
||||
from PySide.QtGui import QApplication
|
||||
from obspy.core import read
|
||||
from pylot.core.util.widgets import PickDlg
|
||||
|
||||
app = QApplication(sys.argv)
|
||||
|
||||
data = read()
|
||||
win = PickDlg(data=data)
|
||||
win.show()
|
||||
app.exec_()
|
||||
@ -1,13 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import sys
|
||||
|
||||
from PySide.QtGui import QApplication
|
||||
from pylot.core.util.widgets import PropertiesDlg
|
||||
|
||||
app = QApplication(sys.argv)
|
||||
|
||||
win = PropertiesDlg()
|
||||
win.show()
|
||||
app.exec_()
|
||||
@ -1,19 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
|
||||
import sys
|
||||
import time
|
||||
|
||||
from PySide.QtGui import QApplication
|
||||
from pylot.core.util.widgets import FilterOptionsDialog, PropertiesDlg, HelpForm
|
||||
|
||||
dialogs = [FilterOptionsDialog, PropertiesDlg, HelpForm]
|
||||
|
||||
app = QApplication(sys.argv)
|
||||
|
||||
for dlg in dialogs:
|
||||
win = dlg()
|
||||
win.show()
|
||||
time.sleep(1)
|
||||
win.destroy()
|
||||
@ -1,23 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
'''
|
||||
Created on 10.11.2014
|
||||
|
||||
@author: sebastianw
|
||||
'''
|
||||
import unittest
|
||||
|
||||
|
||||
class Test(unittest.TestCase):
|
||||
def setUp(self):
|
||||
pass
|
||||
|
||||
def tearDown(self):
|
||||
pass
|
||||
|
||||
def testName(self):
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# import sys;sys.argv = ['', 'Test.testName']
|
||||
unittest.main()
|
||||
@ -1,17 +0,0 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
'''
|
||||
Created on 10.11.2014
|
||||
|
||||
@author: sebastianw
|
||||
'''
|
||||
import unittest
|
||||
|
||||
|
||||
class Test(unittest.TestCase):
|
||||
def testName(self):
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# import sys;sys.argv = ['', 'Test.testName']
|
||||
unittest.main()
|
||||
@ -1,311 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""
|
||||
Script to run autoPyLoT-script "makeCF.py".
|
||||
Only for test purposes!
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import glob
|
||||
|
||||
from obspy.core import read
|
||||
from pylot.core.pick.charfuns import *
|
||||
from pylot.core.pick.picker import *
|
||||
|
||||
|
||||
def run_makeCF(project, database, event, iplot, station=None):
|
||||
# parameters for CF calculation
|
||||
t2 = 7 # length of moving window for HOS calculation [sec]
|
||||
p = 4 # order of HOS
|
||||
cuttimes = [10, 50] # start and end time for CF calculation
|
||||
bpz = [2, 30] # corner frequencies of bandpass filter, vertical component
|
||||
bph = [2, 15] # corner frequencies of bandpass filter, horizontal components
|
||||
tdetz = 1.2 # length of AR-determination window [sec], vertical component
|
||||
tdeth = 0.8 # length of AR-determination window [sec], horizontal components
|
||||
tpredz = 0.4 # length of AR-prediction window [sec], vertical component
|
||||
tpredh = 0.4 # length of AR-prediction window [sec], horizontal components
|
||||
addnoise = 0.001 # add noise to seismogram for stable AR prediction
|
||||
arzorder = 2 # chosen order of AR process, vertical component
|
||||
arhorder = 4 # chosen order of AR process, horizontal components
|
||||
TSNRhos = [5, 0.5, 1, 0.1] # window lengths [s] for calculating SNR for earliest/latest pick and quality assessment
|
||||
# from HOS-CF [noise window, safety gap, signal window, slope determination window]
|
||||
TSNRarz = [5, 0.5, 1, 0.5] # window lengths [s] for calculating SNR for earliest/lates pick and quality assessment
|
||||
# from ARZ-CF
|
||||
# get waveform data
|
||||
if station:
|
||||
dpz = '/data/%s/EVENT_DATA/LOCAL/%s/%s/%s*HZ.msd' % (project, database, event, station)
|
||||
dpe = '/data/%s/EVENT_DATA/LOCAL/%s/%s/%s*HE.msd' % (project, database, event, station)
|
||||
dpn = '/data/%s/EVENT_DATA/LOCAL/%s/%s/%s*HN.msd' % (project, database, event, station)
|
||||
# dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_z.gse' % (project, database, event, station)
|
||||
# dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_e.gse' % (project, database, event, station)
|
||||
# dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_n.gse' % (project, database, event, station)
|
||||
else:
|
||||
# dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*_z.gse' % (project, database, event)
|
||||
# dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*_e.gse' % (project, database, event)
|
||||
# dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*_n.gse' % (project, database, event)
|
||||
dpz = '/data/%s/EVENT_DATA/LOCAL/%s/%s/*HZ.msd' % (project, database, event)
|
||||
dpe = '/data/%s/EVENT_DATA/LOCAL/%s/%s/*HE.msd' % (project, database, event)
|
||||
dpn = '/data/%s/EVENT_DATA/LOCAL/%s/%s/*HN.msd' % (project, database, event)
|
||||
wfzfiles = glob.glob(dpz)
|
||||
wfefiles = glob.glob(dpe)
|
||||
wfnfiles = glob.glob(dpn)
|
||||
if wfzfiles:
|
||||
for i in range(len(wfzfiles)):
|
||||
print
|
||||
'Vertical component data found ...'
|
||||
print
|
||||
wfzfiles[i]
|
||||
st = read('%s' % wfzfiles[i])
|
||||
st_copy = st.copy()
|
||||
# filter and taper data
|
||||
tr_filt = st[0].copy()
|
||||
tr_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
|
||||
tr_filt.taper(max_percentage=0.05, type='hann')
|
||||
st_copy[0].data = tr_filt.data
|
||||
##############################################################
|
||||
# calculate HOS-CF using subclass HOScf of class CharacteristicFunction
|
||||
hoscf = HOScf(st_copy, cuttimes, t2, p) # instance of HOScf
|
||||
##############################################################
|
||||
# calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
|
||||
# class needs stream object => build it
|
||||
tr_aic = tr_filt.copy()
|
||||
tr_aic.data = hoscf.getCF()
|
||||
st_copy[0].data = tr_aic.data
|
||||
aiccf = AICcf(st_copy, cuttimes) # instance of AICcf
|
||||
##############################################################
|
||||
# get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
|
||||
aicpick = AICPicker(aiccf, None, TSNRhos, 3, 10, None, 0.1)
|
||||
##############################################################
|
||||
# get refined onset time from HOS-CF using class Picker
|
||||
hospick = PragPicker(hoscf, None, TSNRhos, 2, 10, 0.001, 0.2, aicpick.getpick())
|
||||
# get earliest and latest possible picks
|
||||
hosELpick = EarlLatePicker(hoscf, 1.5, TSNRhos, None, 10, None, None, hospick.getpick())
|
||||
##############################################################
|
||||
# calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
|
||||
# get stream object of filtered data
|
||||
st_copy[0].data = tr_filt.data
|
||||
arzcf = ARZcf(st_copy, cuttimes, tpredz, arzorder, tdetz, addnoise) # instance of ARZcf
|
||||
##############################################################
|
||||
# calculate AIC-ARZ-CF using subclass AICcf of class CharacteristicFunction
|
||||
# class needs stream object => build it
|
||||
tr_arzaic = tr_filt.copy()
|
||||
tr_arzaic.data = arzcf.getCF()
|
||||
st_copy[0].data = tr_arzaic.data
|
||||
araiccf = AICcf(st_copy, cuttimes, tpredz, 0, tdetz) # instance of AICcf
|
||||
##############################################################
|
||||
# get onset time from AIC-ARZ-CF using subclass AICPicker of class AutoPicking
|
||||
aicarzpick = AICPicker(araiccf, 1.5, TSNRarz, 2, 10, None, 0.1)
|
||||
##############################################################
|
||||
# get refined onset time from ARZ-CF using class Picker
|
||||
arzpick = PragPicker(arzcf, 1.5, TSNRarz, 2.0, 10, 0.1, 0.05, aicarzpick.getpick())
|
||||
# get earliest and latest possible picks
|
||||
arzELpick = EarlLatePicker(arzcf, 1.5, TSNRarz, None, 10, None, None, arzpick.getpick())
|
||||
elif not wfzfiles:
|
||||
print
|
||||
'No vertical component data found!'
|
||||
|
||||
if wfefiles and wfnfiles:
|
||||
for i in range(len(wfefiles)):
|
||||
print
|
||||
'Horizontal component data found ...'
|
||||
print
|
||||
wfefiles[i]
|
||||
print
|
||||
wfnfiles[i]
|
||||
# merge streams
|
||||
H = read('%s' % wfefiles[i])
|
||||
H += read('%s' % wfnfiles[i])
|
||||
H_copy = H.copy()
|
||||
# filter and taper data
|
||||
trH1_filt = H[0].copy()
|
||||
trH2_filt = H[1].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[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
|
||||
|
||||
##############################################################
|
||||
# calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
|
||||
arhcf = ARHcf(H_copy, cuttimes, tpredh, arhorder, tdeth, 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 = arhcf.getCF()
|
||||
H_copy[0].data = tr_arhaic.data
|
||||
# calculate ARH-AIC-CF
|
||||
arhaiccf = AICcf(H_copy, cuttimes, tpredh, 0, tdeth) # instance of AICcf
|
||||
##############################################################
|
||||
# get onset time from AIC-ARH-CF using subclass AICPicker of class AutoPicking
|
||||
aicarhpick = AICPicker(arhaiccf, 1.5, TSNRarz, 4, 10, None, 0.1)
|
||||
###############################################################
|
||||
# get refined onset time from ARH-CF using class Picker
|
||||
arhpick = PragPicker(arhcf, 1.5, TSNRarz, 2.5, 10, 0.1, 0.05, aicarhpick.getpick())
|
||||
# get earliest and latest possible picks
|
||||
arhELpick = EarlLatePicker(arhcf, 1.5, TSNRarz, None, 10, None, None, arhpick.getpick())
|
||||
|
||||
# create stream with 3 traces
|
||||
# merge streams
|
||||
AllC = read('%s' % wfefiles[i])
|
||||
AllC += read('%s' % wfnfiles[i])
|
||||
AllC += read('%s' % wfzfiles[i])
|
||||
# filter and taper data
|
||||
All1_filt = AllC[0].copy()
|
||||
All2_filt = AllC[1].copy()
|
||||
All3_filt = AllC[2].copy()
|
||||
All1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
|
||||
All2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
|
||||
All3_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
|
||||
All1_filt.taper(max_percentage=0.05, type='hann')
|
||||
All2_filt.taper(max_percentage=0.05, type='hann')
|
||||
All3_filt.taper(max_percentage=0.05, type='hann')
|
||||
AllC[0].data = All1_filt.data
|
||||
AllC[1].data = All2_filt.data
|
||||
AllC[2].data = All3_filt.data
|
||||
# calculate AR3C-CF using subclass AR3Ccf of class CharacteristicFunction
|
||||
ar3ccf = AR3Ccf(AllC, cuttimes, tpredz, arhorder, tdetz, addnoise) # instance of AR3Ccf
|
||||
# get earliest and latest possible pick from initial ARH-pick
|
||||
ar3cELpick = EarlLatePicker(ar3ccf, 1.5, TSNRarz, None, 10, None, None, arhpick.getpick())
|
||||
##############################################################
|
||||
if iplot:
|
||||
# plot vertical trace
|
||||
plt.figure()
|
||||
tr = st[0]
|
||||
tdata = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)
|
||||
p1, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k')
|
||||
p2, = plt.plot(hoscf.getTimeArray(), hoscf.getCF() / max(hoscf.getCF()), 'r')
|
||||
p3, = plt.plot(aiccf.getTimeArray(), aiccf.getCF() / max(aiccf.getCF()), 'b')
|
||||
p4, = plt.plot(arzcf.getTimeArray(), arzcf.getCF() / max(arzcf.getCF()), 'g')
|
||||
p5, = plt.plot(araiccf.getTimeArray(), araiccf.getCF() / max(araiccf.getCF()), 'y')
|
||||
plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'b--')
|
||||
plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([hospick.getpick(), hospick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
|
||||
plt.plot([hospick.getpick() - 0.5, hospick.getpick() + 0.5], [1.3, 1.3], 'r')
|
||||
plt.plot([hospick.getpick() - 0.5, hospick.getpick() + 0.5], [-1.3, -1.3], 'r')
|
||||
plt.plot([hosELpick.getLpick(), hosELpick.getLpick()], [-1.1, 1.1], 'r--')
|
||||
plt.plot([hosELpick.getEpick(), hosELpick.getEpick()], [-1.1, 1.1], 'r--')
|
||||
plt.plot([aicarzpick.getpick(), aicarzpick.getpick()], [-1.2, 1.2], 'y', linewidth=2)
|
||||
plt.plot([aicarzpick.getpick() - 0.5, aicarzpick.getpick() + 0.5], [1.2, 1.2], 'y')
|
||||
plt.plot([aicarzpick.getpick() - 0.5, aicarzpick.getpick() + 0.5], [-1.2, -1.2], 'y')
|
||||
plt.plot([arzpick.getpick(), arzpick.getpick()], [-1.4, 1.4], 'g', linewidth=2)
|
||||
plt.plot([arzpick.getpick() - 0.5, arzpick.getpick() + 0.5], [1.4, 1.4], 'g')
|
||||
plt.plot([arzpick.getpick() - 0.5, arzpick.getpick() + 0.5], [-1.4, -1.4], 'g')
|
||||
plt.plot([arzELpick.getLpick(), arzELpick.getLpick()], [-1.2, 1.2], 'g--')
|
||||
plt.plot([arzELpick.getEpick(), arzELpick.getEpick()], [-1.2, 1.2], 'g--')
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.xlabel('Time [s]')
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title('%s, %s, CF-SNR=%7.2f, CF-Slope=%12.2f' % (tr.stats.station,
|
||||
tr.stats.channel, aicpick.getSNR(),
|
||||
aicpick.getSlope()))
|
||||
plt.suptitle(tr.stats.starttime)
|
||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'HOS-CF', 'HOSAIC-CF', 'ARZ-CF', 'ARZAIC-CF'])
|
||||
# plot horizontal traces
|
||||
plt.figure(2)
|
||||
plt.subplot(2, 1, 1)
|
||||
tsteph = tpredh / 4
|
||||
th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
|
||||
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
|
||||
tarhcf = np.arange(0, len(arhcf.getCF()) * tsteph, tsteph) + cuttimes[0] + tdeth + tpredh
|
||||
p21, = plt.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k')
|
||||
p22, = plt.plot(arhcf.getTimeArray(), arhcf.getCF() / max(arhcf.getCF()), 'r')
|
||||
p23, = plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF() / max(arhaiccf.getCF()))
|
||||
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'r')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'r')
|
||||
plt.plot([arhELpick.getLpick(), arhELpick.getLpick()], [-0.8, 0.8], 'r--')
|
||||
plt.plot([arhELpick.getEpick(), arhELpick.getEpick()], [-0.8, 0.8], 'r--')
|
||||
plt.plot([arhpick.getpick() + arhELpick.getPickError(), arhpick.getpick() + arhELpick.getPickError()],
|
||||
[-0.2, 0.2], 'r--')
|
||||
plt.plot([arhpick.getpick() - arhELpick.getPickError(), arhpick.getpick() - arhELpick.getPickError()],
|
||||
[-0.2, 0.2], 'r--')
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
|
||||
plt.suptitle(trH1_filt.stats.starttime)
|
||||
plt.legend([p21, p22, p23], ['Data', 'ARH-CF', 'ARHAIC-CF'])
|
||||
plt.subplot(2, 1, 2)
|
||||
plt.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k')
|
||||
plt.plot(arhcf.getTimeArray(), arhcf.getCF() / max(arhcf.getCF()), 'r')
|
||||
plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF() / max(arhaiccf.getCF()))
|
||||
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'r')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'r')
|
||||
plt.plot([arhELpick.getLpick(), arhELpick.getLpick()], [-0.8, 0.8], 'r--')
|
||||
plt.plot([arhELpick.getEpick(), arhELpick.getEpick()], [-0.8, 0.8], 'r--')
|
||||
plt.plot([arhpick.getpick() + arhELpick.getPickError(), arhpick.getpick() + arhELpick.getPickError()],
|
||||
[-0.2, 0.2], 'r--')
|
||||
plt.plot([arhpick.getpick() - arhELpick.getPickError(), arhpick.getpick() - arhELpick.getPickError()],
|
||||
[-0.2, 0.2], 'r--')
|
||||
plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.xlabel('Time [s]')
|
||||
plt.ylabel('Normalized Counts')
|
||||
# plot 3-component window
|
||||
plt.figure(3)
|
||||
plt.subplot(3, 1, 1)
|
||||
p31, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k')
|
||||
p32, = plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
|
||||
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.plot([ar3cELpick.getLpick(), ar3cELpick.getLpick()], [-0.8, 0.8], 'b--')
|
||||
plt.plot([ar3cELpick.getEpick(), ar3cELpick.getEpick()], [-0.8, 0.8], 'b--')
|
||||
plt.yticks([])
|
||||
plt.xticks([])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title([tr.stats.station, tr.stats.channel])
|
||||
plt.suptitle(trH1_filt.stats.starttime)
|
||||
plt.legend([p31, p32], ['Data', 'AR3C-CF'])
|
||||
plt.subplot(3, 1, 2)
|
||||
plt.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k')
|
||||
plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
|
||||
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.plot([ar3cELpick.getLpick(), ar3cELpick.getLpick()], [-0.8, 0.8], 'b--')
|
||||
plt.plot([ar3cELpick.getEpick(), ar3cELpick.getEpick()], [-0.8, 0.8], 'b--')
|
||||
plt.yticks([])
|
||||
plt.xticks([])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
|
||||
plt.subplot(3, 1, 3)
|
||||
plt.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k')
|
||||
plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
|
||||
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.plot([ar3cELpick.getLpick(), ar3cELpick.getLpick()], [-0.8, 0.8], 'b--')
|
||||
plt.plot([ar3cELpick.getEpick(), ar3cELpick.getEpick()], [-0.8, 0.8], 'b--')
|
||||
plt.yticks([])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
|
||||
plt.xlabel('Time [s]')
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--project', type=str, help='project name (e.g. Insheim)')
|
||||
parser.add_argument('--database', type=str, help='event data base (e.g. 2014.09_Insheim)')
|
||||
parser.add_argument('--event', type=str, help='event ID (e.g. e0010.015.14)')
|
||||
parser.add_argument('--iplot', help='anything, if set, figure occurs')
|
||||
parser.add_argument('--station', type=str, help='Station ID (e.g. INS3) (optional)')
|
||||
args = parser.parse_args()
|
||||
|
||||
run_makeCF(args.project, args.database, args.event, args.iplot, args.station)
|
||||
@ -1,8 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
from pylot.core.util.pdf import ProbabilityDensityFunction
|
||||
|
||||
pdf = ProbabilityDensityFunction.from_pick(0.34, 0.5, 0.54, type='exp')
|
||||
pdf2 = ProbabilityDensityFunction.from_pick(0.34, 0.5, 0.54, type='exp')
|
||||
diff = pdf - pdf2
|
||||
@ -1,16 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import argparse
|
||||
|
||||
import numpy
|
||||
from pylot.core.pick.utils import getnoisewin
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--t', type=numpy.array, help='numpy array of time stamps')
|
||||
parser.add_argument('--t1', type=float, help='time from which relativ to it noise window is extracted')
|
||||
parser.add_argument('--tnoise', type=float, help='length of time window [s] for noise part extraction')
|
||||
parser.add_argument('--tgap', type=float, help='safety gap between signal (t1=onset) and noise')
|
||||
args = parser.parse_args()
|
||||
getnoisewin(args.t, args.t1, args.tnoise, args.tgap)
|
||||
@ -1,29 +0,0 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created Mar 2015
|
||||
Transcription of the rezipe of Diehl et al. (2009) for consistent phase
|
||||
picking. For a given inital (the most likely) pick, the corresponding earliest
|
||||
and latest possible pick is calculated based on noise measurements in front of
|
||||
the most likely pick and signal wavelength derived from zero crossings.
|
||||
|
||||
:author: Ludger Kueperkoch / MAGS2 EP3 working group
|
||||
"""
|
||||
|
||||
import argparse
|
||||
|
||||
import obspy
|
||||
from pylot.core.pick.utils import earllatepicker
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--X', type=~obspy.core.stream.Stream,
|
||||
help='time series (seismogram) read with obspy module read')
|
||||
parser.add_argument('--nfac', type=int,
|
||||
help='(noise factor), nfac times noise level to calculate latest possible pick')
|
||||
parser.add_argument('--TSNR', type=tuple, help='length of time windows around pick used to determine SNR \
|
||||
[s] (Tnoise, Tgap, Tsignal)')
|
||||
parser.add_argument('--Pick1', type=float, help='Onset time of most likely pick')
|
||||
parser.add_argument('--iplot', type=int, help='if set, figure no. iplot occurs')
|
||||
args = parser.parse_args()
|
||||
earllatepicker(args.X, args.nfac, args.TSNR, args.Pick1, args.iplot)
|
||||
@ -1,25 +0,0 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created Mar 2015
|
||||
Function to derive first motion (polarity) for given phase onset based on zero crossings.
|
||||
|
||||
:author: MAGS2 EP3 working group / Ludger Kueperkoch
|
||||
"""
|
||||
|
||||
import argparse
|
||||
|
||||
import obspy
|
||||
from pylot.core.pick.utils import fmpicker
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--Xraw', type=obspy.core.stream.Stream,
|
||||
help='unfiltered time series (seismogram) read with obspy module read')
|
||||
parser.add_argument('--Xfilt', type=obspy.core.stream.Stream,
|
||||
help='filtered time series (seismogram) read with obspy module read')
|
||||
parser.add_argument('--pickwin', type=float, help='length of pick window [s] for first motion determination')
|
||||
parser.add_argument('--Pick', type=float, help='Onset time of most likely pick')
|
||||
parser.add_argument('--iplot', type=int, help='if set, figure no. iplot occurs')
|
||||
args = parser.parse_args()
|
||||
fmpicker(args.Xraw, args.Xfilt, args.pickwin, args.Pick, args.iplot)
|
||||
@ -1,41 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import argparse
|
||||
|
||||
from pylot.core.io.phases import reassess_pilot_db
|
||||
from pylot.core.util.version import get_git_version as _getVersionString
|
||||
|
||||
__version__ = _getVersionString()
|
||||
__author__ = 'S. Wehling-Benatelli'
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(
|
||||
description='reassess old PILOT event data base in terms of consistent '
|
||||
'automatic uncertainty estimation',
|
||||
epilog='Script written by {author} belonging to PyLoT version'
|
||||
' {version}\n'.format(author=__author__,
|
||||
version=__version__)
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'root', type=str, help='specifies the root directory'
|
||||
)
|
||||
parser.add_argument(
|
||||
'db', type=str, help='specifies the database name'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--output', '-o', type=str, help='path to the output directory',
|
||||
dest='output'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--parameterfile', '-p', type=str,
|
||||
help='full path to the parameterfile', dest='parfile'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--verbosity', '-v', action='count', help='increase output verbosity',
|
||||
default=0, dest='verbosity'
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
reassess_pilot_db(args.root, args.db, args.output, args.parfile, args.verbosity)
|
||||
@ -1,38 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import argparse
|
||||
|
||||
from pylot.core.io.phases import reassess_pilot_event
|
||||
from pylot.core.util.version import get_git_version as _getVersionString
|
||||
|
||||
__version__ = _getVersionString()
|
||||
__author__ = 'S. Wehling-Benatelli'
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(
|
||||
description='reassess old PILOT event data in terms of consistent '
|
||||
'automatic uncertainty estimation',
|
||||
epilog='Script written by {author} belonging to PyLoT version'
|
||||
' {version}\n'.format(author=__author__,
|
||||
version=__version__)
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
'root', type=str, help='specifies the root directory'
|
||||
)
|
||||
parser.add_argument(
|
||||
'db', type=str, help='specifies the database name'
|
||||
)
|
||||
parser.add_argument(
|
||||
'id', type=str, help='PILOT event identifier'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--output', '-o', type=str, help='path to the output directory', dest='output'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--parameterfile', '-p', type=str, help='full path to the parameterfile', dest='parfile'
|
||||
)
|
||||
|
||||
args = parser.parse_args()
|
||||
reassess_pilot_event(args.root, args.db, args.id, args.output, args.parfile)
|
||||
@ -1,15 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import argparse
|
||||
|
||||
import numpy
|
||||
from pylot.core.pick.utils import getsignalwin
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--t', type=numpy.array, help='numpy array of time stamps')
|
||||
parser.add_argument('--t1', type=float, help='time from which relativ to it signal window is extracted')
|
||||
parser.add_argument('--tsignal', type=float, help='length of time window [s] for signal part extraction')
|
||||
args = parser.parse_args()
|
||||
getsignalwin(args.t, args.t1, args.tsignal)
|
||||
@ -1,32 +0,0 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created Mar/Apr 2015
|
||||
Function to calculate SNR of certain part of seismogram relative
|
||||
to given time. Returns SNR and SNR [dB].
|
||||
|
||||
:author: Ludger Kueperkoch /MAGS EP3 working group
|
||||
"""
|
||||
|
||||
import argparse
|
||||
|
||||
import obspy
|
||||
from pylot.core.pick.utils import getSNR
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--data', '-d', type=obspy.core.stream.Stream,
|
||||
help='time series (seismogram) read with obspy module '
|
||||
'read',
|
||||
dest='data')
|
||||
parser.add_argument('--tsnr', '-s', type=tuple,
|
||||
help='length of time windows around pick used to '
|
||||
'determine SNR [s] (Tnoise, Tgap, Tsignal)',
|
||||
dest='tsnr')
|
||||
parser.add_argument('--time', '-t', type=float,
|
||||
help='initial time from which noise and signal windows '
|
||||
'are calculated',
|
||||
dest='time')
|
||||
args = parser.parse_args()
|
||||
print
|
||||
getSNR(args.data, args.tsnr, args.time)
|
||||
@ -1,314 +0,0 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""
|
||||
Script to run autoPyLoT-script "run_makeCF.py".
|
||||
Only for test purposes!
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import glob
|
||||
|
||||
from obspy.core import read
|
||||
from pylot.core.pick.utils import *
|
||||
|
||||
|
||||
def run_makeCF(project, database, event, iplot, station=None):
|
||||
# parameters for CF calculation
|
||||
t2 = 7 # length of moving window for HOS calculation [sec]
|
||||
p = 4 # order of HOS
|
||||
cuttimes = [10, 50] # start and end time for CF calculation
|
||||
bpz = [2, 30] # corner frequencies of bandpass filter, vertical component
|
||||
bph = [2, 15] # corner frequencies of bandpass filter, horizontal components
|
||||
tdetz = 1.2 # length of AR-determination window [sec], vertical component
|
||||
tdeth = 0.8 # length of AR-determination window [sec], horizontal components
|
||||
tpredz = 0.4 # length of AR-prediction window [sec], vertical component
|
||||
tpredh = 0.4 # length of AR-prediction window [sec], horizontal components
|
||||
addnoise = 0.001 # add noise to seismogram for stable AR prediction
|
||||
arzorder = 2 # chosen order of AR process, vertical component
|
||||
arhorder = 4 # chosen order of AR process, horizontal components
|
||||
TSNRhos = [5, 0.5, 1, .6] # window lengths [s] for calculating SNR for earliest/latest pick and quality assessment
|
||||
# from HOS-CF [noise window, safety gap, signal window, slope determination window]
|
||||
TSNRarz = [5, 0.5, 1, 1.0] # window lengths [s] for calculating SNR for earliest/lates pick and quality assessment
|
||||
# from ARZ-CF
|
||||
# get waveform data
|
||||
if station:
|
||||
dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*HZ.msd' % (project, database, event, station)
|
||||
dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*HE.msd' % (project, database, event, station)
|
||||
dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*HN.msd' % (project, database, event, station)
|
||||
# dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_z.gse' % (project, database, event, station)
|
||||
# dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_e.gse' % (project, database, event, station)
|
||||
# dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_n.gse' % (project, database, event, station)
|
||||
else:
|
||||
dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*HZ.msd' % (project, database, event)
|
||||
dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*HE.msd' % (project, database, event)
|
||||
dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*HN.msd' % (project, database, event)
|
||||
wfzfiles = glob.glob(dpz)
|
||||
wfefiles = glob.glob(dpe)
|
||||
wfnfiles = glob.glob(dpn)
|
||||
if wfzfiles:
|
||||
for i in range(len(wfzfiles)):
|
||||
print
|
||||
'Vertical component data found ...'
|
||||
print
|
||||
wfzfiles[i]
|
||||
st = read('%s' % wfzfiles[i])
|
||||
st_copy = st.copy()
|
||||
# filter and taper data
|
||||
tr_filt = st[0].copy()
|
||||
tr_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
|
||||
tr_filt.taper(max_percentage=0.05, type='hann')
|
||||
st_copy[0].data = tr_filt.data
|
||||
##############################################################
|
||||
# calculate HOS-CF using subclass HOScf of class CharacteristicFunction
|
||||
hoscf = HOScf(st_copy, cuttimes, t2, p) # instance of HOScf
|
||||
##############################################################
|
||||
# calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
|
||||
# class needs stream object => build it
|
||||
tr_aic = tr_filt.copy()
|
||||
tr_aic.data = hoscf.getCF()
|
||||
st_copy[0].data = tr_aic.data
|
||||
aiccf = AICcf(st_copy, cuttimes) # instance of AICcf
|
||||
##############################################################
|
||||
# get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
|
||||
aicpick = AICPicker(aiccf, TSNRhos, 3, 10, None, 0.1)
|
||||
##############################################################
|
||||
# get refined onset time from HOS-CF using class Picker
|
||||
hospick = PragPicker(hoscf, TSNRhos, 2, 10, 0.001, 0.2, aicpick.getpick())
|
||||
#############################################################
|
||||
# get earliest and latest possible picks
|
||||
st_copy[0].data = tr_filt.data
|
||||
[lpickhos, epickhos, pickerrhos] = earllatepicker(st_copy, 1.5, TSNRhos, hospick.getpick(), 10)
|
||||
#############################################################
|
||||
# get SNR
|
||||
[SNR, SNRdB] = getSNR(st_copy, TSNRhos, hospick.getpick())
|
||||
print
|
||||
'SNR:', SNR, 'SNR[dB]:', SNRdB
|
||||
##########################################################
|
||||
# get first motion of onset
|
||||
hosfm = fmpicker(st, st_copy, 0.2, hospick.getpick(), 11)
|
||||
##############################################################
|
||||
# calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
|
||||
arzcf = ARZcf(st, cuttimes, tpredz, arzorder, tdetz, addnoise) # instance of ARZcf
|
||||
##############################################################
|
||||
# calculate AIC-ARZ-CF using subclass AICcf of class CharacteristicFunction
|
||||
# class needs stream object => build it
|
||||
tr_arzaic = tr_filt.copy()
|
||||
tr_arzaic.data = arzcf.getCF()
|
||||
st_copy[0].data = tr_arzaic.data
|
||||
araiccf = AICcf(st_copy, cuttimes, tpredz, 0, tdetz) # instance of AICcf
|
||||
##############################################################
|
||||
# get onset time from AIC-ARZ-CF using subclass AICPicker of class AutoPicking
|
||||
aicarzpick = AICPicker(araiccf, TSNRarz, 2, 10, None, 0.1)
|
||||
##############################################################
|
||||
# get refined onset time from ARZ-CF using class Picker
|
||||
arzpick = PragPicker(arzcf, TSNRarz, 2.0, 10, 0.1, 0.05, aicarzpick.getpick())
|
||||
# get earliest and latest possible picks
|
||||
st_copy[0].data = tr_filt.data
|
||||
[lpickarz, epickarz, pickerrarz] = earllatepicker(st_copy, 1.5, TSNRarz, arzpick.getpick(), 10)
|
||||
elif not wfzfiles:
|
||||
print
|
||||
'No vertical component data found!'
|
||||
|
||||
if wfefiles and wfnfiles:
|
||||
for i in range(len(wfefiles)):
|
||||
print
|
||||
'Horizontal component data found ...'
|
||||
print
|
||||
wfefiles[i]
|
||||
print
|
||||
wfnfiles[i]
|
||||
# merge streams
|
||||
H = read('%s' % wfefiles[i])
|
||||
H += read('%s' % wfnfiles[i])
|
||||
H_copy = H.copy()
|
||||
# filter and taper data
|
||||
trH1_filt = H[0].copy()
|
||||
trH2_filt = H[1].copy()
|
||||
trH1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
|
||||
trH2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[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
|
||||
|
||||
##############################################################
|
||||
# calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
|
||||
arhcf = ARHcf(H_copy, cuttimes, tpredh, arhorder, tdeth, 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 = arhcf.getCF()
|
||||
H_copy[0].data = tr_arhaic.data
|
||||
# calculate ARH-AIC-CF
|
||||
arhaiccf = AICcf(H_copy, cuttimes, tpredh, 0, tdeth) # instance of AICcf
|
||||
##############################################################
|
||||
# get onset time from AIC-ARH-CF using subclass AICPicker of class AutoPicking
|
||||
aicarhpick = AICPicker(arhaiccf, TSNRarz, 4, 10, None, 0.1)
|
||||
###############################################################
|
||||
# get refined onset time from ARH-CF using class Picker
|
||||
arhpick = PragPicker(arhcf, TSNRarz, 2.5, 10, 0.1, 0.05, aicarhpick.getpick())
|
||||
# get earliest and latest possible picks
|
||||
H_copy[0].data = trH1_filt.data
|
||||
[lpickarh1, epickarh1, pickerrarh1] = earllatepicker(H_copy, 1.5, TSNRarz, arhpick.getpick(), 10)
|
||||
H_copy[0].data = trH2_filt.data
|
||||
[lpickarh2, epickarh2, pickerrarh2] = earllatepicker(H_copy, 1.5, TSNRarz, arhpick.getpick(), 10)
|
||||
# get earliest pick of both earliest possible picks
|
||||
epick = [epickarh1, epickarh2]
|
||||
lpick = [lpickarh1, lpickarh2]
|
||||
pickerr = [pickerrarh1, pickerrarh2]
|
||||
ipick = np.argmin([epickarh1, epickarh2])
|
||||
epickarh = epick[ipick]
|
||||
lpickarh = lpick[ipick]
|
||||
pickerrarh = pickerr[ipick]
|
||||
|
||||
# create stream with 3 traces
|
||||
# merge streams
|
||||
AllC = read('%s' % wfefiles[i])
|
||||
AllC += read('%s' % wfnfiles[i])
|
||||
AllC += read('%s' % wfzfiles[i])
|
||||
# filter and taper data
|
||||
All1_filt = AllC[0].copy()
|
||||
All2_filt = AllC[1].copy()
|
||||
All3_filt = AllC[2].copy()
|
||||
All1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
|
||||
All2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
|
||||
All3_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
|
||||
All1_filt.taper(max_percentage=0.05, type='hann')
|
||||
All2_filt.taper(max_percentage=0.05, type='hann')
|
||||
All3_filt.taper(max_percentage=0.05, type='hann')
|
||||
AllC[0].data = All1_filt.data
|
||||
AllC[1].data = All2_filt.data
|
||||
AllC[2].data = All3_filt.data
|
||||
# calculate AR3C-CF using subclass AR3Ccf of class CharacteristicFunction
|
||||
ar3ccf = AR3Ccf(AllC, cuttimes, tpredz, arhorder, tdetz, addnoise) # instance of AR3Ccf
|
||||
##############################################################
|
||||
if iplot:
|
||||
# plot vertical trace
|
||||
plt.figure()
|
||||
tr = st[0]
|
||||
tdata = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)
|
||||
p1, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k')
|
||||
p2, = plt.plot(hoscf.getTimeArray(), hoscf.getCF() / max(hoscf.getCF()), 'r')
|
||||
p3, = plt.plot(aiccf.getTimeArray(), aiccf.getCF() / max(aiccf.getCF()), 'b')
|
||||
p4, = plt.plot(arzcf.getTimeArray(), arzcf.getCF() / max(arzcf.getCF()), 'g')
|
||||
p5, = plt.plot(araiccf.getTimeArray(), araiccf.getCF() / max(araiccf.getCF()), 'y')
|
||||
plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'b--')
|
||||
plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([hospick.getpick(), hospick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
|
||||
plt.plot([hospick.getpick() - 0.5, hospick.getpick() + 0.5], [1.3, 1.3], 'r')
|
||||
plt.plot([hospick.getpick() - 0.5, hospick.getpick() + 0.5], [-1.3, -1.3], 'r')
|
||||
plt.plot([lpickhos, lpickhos], [-1.1, 1.1], 'r--')
|
||||
plt.plot([epickhos, epickhos], [-1.1, 1.1], 'r--')
|
||||
plt.plot([aicarzpick.getpick(), aicarzpick.getpick()], [-1.2, 1.2], 'y', linewidth=2)
|
||||
plt.plot([aicarzpick.getpick() - 0.5, aicarzpick.getpick() + 0.5], [1.2, 1.2], 'y')
|
||||
plt.plot([aicarzpick.getpick() - 0.5, aicarzpick.getpick() + 0.5], [-1.2, -1.2], 'y')
|
||||
plt.plot([arzpick.getpick(), arzpick.getpick()], [-1.4, 1.4], 'g', linewidth=2)
|
||||
plt.plot([arzpick.getpick() - 0.5, arzpick.getpick() + 0.5], [1.4, 1.4], 'g')
|
||||
plt.plot([arzpick.getpick() - 0.5, arzpick.getpick() + 0.5], [-1.4, -1.4], 'g')
|
||||
plt.plot([lpickarz, lpickarz], [-1.2, 1.2], 'g--')
|
||||
plt.plot([epickarz, epickarz], [-1.2, 1.2], 'g--')
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.xlabel('Time [s]')
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title('%s, %s, CF-SNR=%7.2f, CF-Slope=%12.2f' % (tr.stats.station,
|
||||
tr.stats.channel, aicpick.getSNR(),
|
||||
aicpick.getSlope()))
|
||||
plt.suptitle(tr.stats.starttime)
|
||||
plt.legend([p1, p2, p3, p4, p5], ['Data', 'HOS-CF', 'HOSAIC-CF', 'ARZ-CF', 'ARZAIC-CF'])
|
||||
# plot horizontal traces
|
||||
plt.figure(2)
|
||||
plt.subplot(2, 1, 1)
|
||||
tsteph = tpredh / 4
|
||||
th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
|
||||
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
|
||||
tarhcf = np.arange(0, len(arhcf.getCF()) * tsteph, tsteph) + cuttimes[0] + tdeth + tpredh
|
||||
p21, = plt.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k')
|
||||
p22, = plt.plot(arhcf.getTimeArray(), arhcf.getCF() / max(arhcf.getCF()), 'r')
|
||||
p23, = plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF() / max(arhaiccf.getCF()))
|
||||
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'r')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'r')
|
||||
plt.plot([lpickarh, lpickarh], [-0.8, 0.8], 'r--')
|
||||
plt.plot([epickarh, epickarh], [-0.8, 0.8], 'r--')
|
||||
plt.plot([arhpick.getpick() + pickerrarh, arhpick.getpick() + pickerrarh], [-0.2, 0.2], 'r--')
|
||||
plt.plot([arhpick.getpick() - pickerrarh, arhpick.getpick() - pickerrarh], [-0.2, 0.2], 'r--')
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
|
||||
plt.suptitle(trH1_filt.stats.starttime)
|
||||
plt.legend([p21, p22, p23], ['Data', 'ARH-CF', 'ARHAIC-CF'])
|
||||
plt.subplot(2, 1, 2)
|
||||
plt.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k')
|
||||
plt.plot(arhcf.getTimeArray(), arhcf.getCF() / max(arhcf.getCF()), 'r')
|
||||
plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF() / max(arhaiccf.getCF()))
|
||||
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.plot([aicarhpick.getpick() - 0.5, aicarhpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'r')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'r')
|
||||
plt.plot([lpickarh, lpickarh], [-0.8, 0.8], 'r--')
|
||||
plt.plot([epickarh, epickarh], [-0.8, 0.8], 'r--')
|
||||
plt.plot([arhpick.getpick() + pickerrarh, arhpick.getpick() + pickerrarh], [-0.2, 0.2], 'r--')
|
||||
plt.plot([arhpick.getpick() - pickerrarh, arhpick.getpick() - pickerrarh], [-0.2, 0.2], 'r--')
|
||||
plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
|
||||
plt.yticks([])
|
||||
plt.ylim([-1.5, 1.5])
|
||||
plt.xlabel('Time [s]')
|
||||
plt.ylabel('Normalized Counts')
|
||||
# plot 3-component window
|
||||
plt.figure(3)
|
||||
plt.subplot(3, 1, 1)
|
||||
p31, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k')
|
||||
p32, = plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
|
||||
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.yticks([])
|
||||
plt.xticks([])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title([tr.stats.station, tr.stats.channel])
|
||||
plt.suptitle(trH1_filt.stats.starttime)
|
||||
plt.legend([p31, p32], ['Data', 'AR3C-CF'])
|
||||
plt.subplot(3, 1, 2)
|
||||
plt.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k')
|
||||
plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
|
||||
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.yticks([])
|
||||
plt.xticks([])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
|
||||
plt.subplot(3, 1, 3)
|
||||
plt.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k')
|
||||
plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF() / max(ar3ccf.getCF()), 'r')
|
||||
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [-1, -1], 'b')
|
||||
plt.plot([arhpick.getpick() - 0.5, arhpick.getpick() + 0.5], [1, 1], 'b')
|
||||
plt.yticks([])
|
||||
plt.ylabel('Normalized Counts')
|
||||
plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
|
||||
plt.xlabel('Time [s]')
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close()
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--project', type=str, help='project name (e.g. Insheim)')
|
||||
parser.add_argument('--database', type=str, help='event data base (e.g. 2014.09_Insheim)')
|
||||
parser.add_argument('--event', type=str, help='event ID (e.g. e0010.015.14)')
|
||||
parser.add_argument('--iplot', help='anything, if set, figure occurs')
|
||||
parser.add_argument('--station', type=str, help='Station ID (e.g. INS3) (optional)')
|
||||
args = parser.parse_args()
|
||||
|
||||
run_makeCF(args.project, args.database, args.event, args.iplot, args.station)
|
||||
Loading…
Reference in New Issue
Block a user