release version 0.2
release notes: ============== Features: - centralize all functionalities of PyLoT and control them from within the main GUI - handling multiple events inside GUI with project files (save and load work progress) - GUI based adjustments of pick parameters and I/O - interactive tuning of parameters from within the GUI - call automatic picking algorithm from within the GUI - comparison of automatic with manual picks for multiple events using clear differentiation of manual picks into 'tune' and 'test-set' (beta) - manual picking of different (user defined) phase types - phase onset estimation with ObsPy TauPy - interactive zoom/scale functionalities in all plots (mousewheel, pan, pan-zoom) - array map to visualize stations and control onsets (beta feature, switch to manual picks not implemented) Platform support: - python 3 support - Windows support Performance: - multiprocessing for automatic picking and restitution of multiple stations - use pyqtgraph library for better performance on main waveform plot Visualization: - pick uncertainty (quality classes) visualization with gradients - pick color unification for all plots - new icons and stylesheets Known Issues:
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QtPyLoT.py
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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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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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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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0.01 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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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#
|
||||
%!!Be careful when editing the following!!
|
||||
#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)
|
||||
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
|
||||
0.6 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
|
||||
0.3 #tpred2h# %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
|
||||
4 #Sarorder# %for AR-picker, order of AR process of H-components
|
||||
10 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
|
||||
25 #pickwinS# %for initial AIC and refined pick, length of S-pick window [s]
|
||||
5 0.2 3.0 3.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
|
||||
3.5 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
|
||||
1.0 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
|
||||
0.2 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
|
||||
1.5 #nfacS# %for AR-picker, noise factor for noise level determination (S)
|
||||
%first-motion picker%
|
||||
1 #minfmweight# %minimum required p weight for first-motion determination
|
||||
2 #minFMSNR# %miniumum required SNR for first-motion determination
|
||||
6.0 #fmpickwin# %pick window around P onset for calculating zero crossings
|
||||
%quality assessment%
|
||||
#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
|
||||
|
||||
@ -1,2 +0,0 @@
|
||||
P bandpass 4 2.0 20.0
|
||||
S bandpass 4 2.0 15.0
|
||||
@ -1,98 +0,0 @@
|
||||
%This is a example parameter input file for PyLoT.
|
||||
%All main and special settings regarding data handling
|
||||
%and picking are to be set here!
|
||||
%Parameters shown here are optimized for local data sets!
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#main settings#
|
||||
/data/Geothermie/Insheim #rootpath# %project path
|
||||
EVENT_DATA/LOCAL #datapath# %data path
|
||||
2013.02_Insheim #database# %name of data base
|
||||
e0019.048.13 #eventID# %event ID for single event processing
|
||||
/data/Geothermie/Insheim/STAT_INFO #invdir# %full path to inventory or dataless-seed file
|
||||
PILOT #datastructure# %choose data structure
|
||||
0 #iplot# %flag for plotting: 0 none, 1 partly, >1 everything
|
||||
True #apverbose# %choose 'True' or 'False' for terminal output
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#NLLoc settings#
|
||||
/progs/bin #nllocbin# %path to NLLoc executable
|
||||
/data/Geothermie/Insheim/LOCALISATION/NLLoc #nllocroot# %root of NLLoc-processing directory
|
||||
AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
|
||||
%(in nllocroot/obs)
|
||||
Insheim_min1d2015.in #ctrfile# %name of PyLoT-output control file for NLLoc
|
||||
%(in nllocroot/run)
|
||||
ttime #ttpatter# %pattern of NLLoc ttimes from grid
|
||||
%(in nllocroot/times)
|
||||
AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
|
||||
%(returns 'eventID_outpatter')
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#parameters for seismic moment estimation#
|
||||
3530 #vp# %average P-wave velocity
|
||||
2500 #rho# %average rock density [kg/m^3]
|
||||
300 0.8 #Qp# %quality factor for P waves (Qp*f^a); list(Qp, a)
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
AUTOFOCMEC_AIC_HOS4_ARH.in #focmecin# %name of focmec input file containing derived polarities
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#common settings picker#
|
||||
15.0 #pstart# %start time [s] for calculating CF for P-picking
|
||||
60.0 #pstop# %end time [s] for calculating CF for P-picking
|
||||
-1.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
|
||||
10.0 #sstop# %end time [s] after P-onset for calculating CF for S-picking
|
||||
2 20 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
|
||||
2 30 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
|
||||
2 15 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
|
||||
2 20 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz]
|
||||
#special settings for calculating CF#
|
||||
%!!Edit the following only if you know what you are doing!!%
|
||||
#Z-component#
|
||||
HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
|
||||
7.0 #tlta# %for HOS-/AR-AIC-picker, length of LTA window [s]
|
||||
4 #hosorder# %for HOS-picker, order of Higher Order Statistics
|
||||
2 #Parorder# %for AR-picker, order of AR process of Z-component
|
||||
1.2 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
|
||||
0.4 #tpred1z# %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
|
||||
0.6 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
|
||||
0.2 #tpred2z# %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
|
||||
0.001 #addnoise# %add noise to seismogram for stable AR prediction
|
||||
3 0.1 0.5 0.5 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
|
||||
3.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
|
||||
6.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
|
||||
0.2 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
|
||||
0.1 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
|
||||
0.001 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
|
||||
1.3 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
|
||||
#H-components#
|
||||
ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
|
||||
0.8 #tdet1h# %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
|
||||
0.4 #tpred1h# %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
|
||||
0.6 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
|
||||
0.3 #tpred2h# %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
|
||||
4 #Sarorder# %for AR-picker, order of AR process of H-components
|
||||
5.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
|
||||
3.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
|
||||
2 0.2 1.5 0.5 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
|
||||
0.5 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
|
||||
0.7 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
|
||||
0.9 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
|
||||
1.5 #nfacS# %for AR-picker, noise factor for noise level determination (S)
|
||||
%first-motion picker%
|
||||
1 #minfmweight# %minimum required P weight for first-motion determination
|
||||
2 #minFMSNR# %miniumum required SNR for first-motion determination
|
||||
0.2 #fmpickwin# %pick window around P onset for calculating zero crossings
|
||||
%quality assessment%
|
||||
#inital AIC onset#
|
||||
0.01 0.02 0.04 0.08 #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
|
||||
4 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
|
||||
1.2 #minAICPSNR# %below this SNR the initial P pick is rejected
|
||||
2 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
|
||||
1.5 #minAICSSNR# %below this SNR the initial S pick is rejected
|
||||
#check duration of signal using envelope function#
|
||||
3 #minsiglength# %minimum required length of signal [s]
|
||||
1.0 #noisefactor# %noiselevel*noisefactor=threshold
|
||||
40 #minpercent# %required percentage of samples higher than threshold
|
||||
#check for spuriously picked S-onsets#
|
||||
2.0 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
|
||||
#check statistics of P onsets#
|
||||
2.5 #mdttolerance# %maximum allowed deviation of P picks from median [s]
|
||||
#wadati check#
|
||||
1.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
|
||||
BIN
pylot/PyLoT.ico
BIN
pylot/PyLoT.ico
Binary file not shown.
|
Before Width: | Height: | Size: 2.2 KiB |
@ -1 +0,0 @@
|
||||
0.1a
|
||||
@ -1,21 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
from pylot.core.io.phases import writephases
|
||||
from pylot.core.util.version import get_git_version as _getVersionString
|
||||
|
||||
__version__ = _getVersionString()
|
||||
|
||||
def export(picks, fnout):
|
||||
'''
|
||||
Take <picks> dictionary and exports picking data to a NLLOC-obs
|
||||
<phasefile> without creating an ObsPy event object.
|
||||
|
||||
:param picks: picking data dictionary
|
||||
:type picks: dict
|
||||
|
||||
:param fnout: complete path to the exporting obs file
|
||||
:type fnout: str
|
||||
'''
|
||||
# write phases to NLLoc-phase file
|
||||
writephases(picks, 'HYPO71', fnout)
|
||||
376
pylot/core/util/map_projection.py
Normal file
376
pylot/core/util/map_projection.py
Normal file
@ -0,0 +1,376 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import obspy
|
||||
from PySide import QtGui
|
||||
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
|
||||
from mpl_toolkits.basemap import Basemap
|
||||
from pylot.core.util.widgets import PickDlg
|
||||
from scipy.interpolate import griddata
|
||||
|
||||
plt.interactive(False)
|
||||
|
||||
|
||||
class map_projection(QtGui.QWidget):
|
||||
def __init__(self, parent, figure=None):
|
||||
'''
|
||||
|
||||
:param: picked, can be False, auto, manual
|
||||
:value: str
|
||||
'''
|
||||
QtGui.QWidget.__init__(self)
|
||||
self._parent = parent
|
||||
self.metadata = parent.metadata
|
||||
self.parser = parent.metadata[1]
|
||||
self.picks = None
|
||||
self.picks_dict = None
|
||||
self.eventLoc = None
|
||||
self.figure = figure
|
||||
self.init_graphics()
|
||||
self.init_stations()
|
||||
self.init_basemap(resolution='l')
|
||||
self.init_map()
|
||||
# self.show()
|
||||
|
||||
def init_map(self):
|
||||
self.init_lat_lon_dimensions()
|
||||
self.init_lat_lon_grid()
|
||||
self.init_x_y_dimensions()
|
||||
self.connectSignals()
|
||||
self.draw_everything()
|
||||
|
||||
def onpick(self, event):
|
||||
ind = event.ind
|
||||
button = event.mouseevent.button
|
||||
if ind == [] or not button == 1:
|
||||
return
|
||||
data = self._parent.get_data().getWFData()
|
||||
for index in ind:
|
||||
station = str(self.station_names[index].split('.')[-1])
|
||||
try:
|
||||
pickDlg = PickDlg(self, parameter=self._parent._inputs,
|
||||
data=data.select(station=station),
|
||||
station=station,
|
||||
picks=self._parent.get_current_event().getPick(station),
|
||||
autopicks=self._parent.get_current_event().getAutopick(station))
|
||||
except Exception as e:
|
||||
message = 'Could not generate Plot for station {st}.\n {er}'.format(st=station, er=e)
|
||||
self._warn(message)
|
||||
print(message, e)
|
||||
return
|
||||
pyl_mw = self._parent
|
||||
try:
|
||||
if pickDlg.exec_():
|
||||
pyl_mw.setDirty(True)
|
||||
pyl_mw.update_status('picks accepted ({0})'.format(station))
|
||||
replot = pyl_mw.get_current_event().setPick(station, pickDlg.getPicks())
|
||||
self._refresh_drawings()
|
||||
if replot:
|
||||
pyl_mw.plotWaveformData()
|
||||
pyl_mw.drawPicks()
|
||||
pyl_mw.draw()
|
||||
else:
|
||||
pyl_mw.drawPicks(station)
|
||||
pyl_mw.draw()
|
||||
else:
|
||||
pyl_mw.update_status('picks discarded ({0})'.format(station))
|
||||
except Exception as e:
|
||||
message = 'Could not save picks for station {st}.\n{er}'.format(st=station, er=e)
|
||||
self._warn(message)
|
||||
print(message, e)
|
||||
|
||||
def connectSignals(self):
|
||||
self.comboBox_phase.currentIndexChanged.connect(self._refresh_drawings)
|
||||
self.zoom_id = self.basemap.ax.figure.canvas.mpl_connect('scroll_event', self.zoom)
|
||||
|
||||
def init_graphics(self):
|
||||
if not self.figure:
|
||||
if not hasattr(self._parent, 'am_figure'):
|
||||
self.figure = plt.figure()
|
||||
self.toolbar = NavigationToolbar(self.figure.canvas, self)
|
||||
else:
|
||||
self.figure = self._parent.am_figure
|
||||
self.toolbar = self._parent.am_toolbar
|
||||
|
||||
self.main_ax = self.figure.add_subplot(111)
|
||||
self.canvas = self.figure.canvas
|
||||
|
||||
self.main_box = QtGui.QVBoxLayout()
|
||||
self.setLayout(self.main_box)
|
||||
|
||||
self.top_row = QtGui.QHBoxLayout()
|
||||
self.main_box.addLayout(self.top_row)
|
||||
|
||||
self.comboBox_phase = QtGui.QComboBox()
|
||||
self.comboBox_phase.insertItem(0, 'P')
|
||||
self.comboBox_phase.insertItem(1, 'S')
|
||||
|
||||
self.comboBox_am = QtGui.QComboBox()
|
||||
self.comboBox_am.insertItem(0, 'auto')
|
||||
self.comboBox_am.insertItem(1, 'manual')
|
||||
|
||||
self.top_row.addWidget(QtGui.QLabel('Select a phase: '))
|
||||
self.top_row.addWidget(self.comboBox_phase)
|
||||
self.top_row.setStretch(1, 1) # set stretch of item 1 to 1
|
||||
|
||||
self.main_box.addWidget(self.canvas)
|
||||
self.main_box.addWidget(self.toolbar)
|
||||
|
||||
def init_stations(self):
|
||||
def get_station_names_lat_lon(parser):
|
||||
station_names = []
|
||||
lat = []
|
||||
lon = []
|
||||
for station in parser.stations:
|
||||
station_name = station[0].station_call_letters
|
||||
network = station[0].network_code
|
||||
if not station_name in station_names:
|
||||
station_names.append(network + '.' + station_name)
|
||||
lat.append(station[0].latitude)
|
||||
lon.append(station[0].longitude)
|
||||
return station_names, lat, lon
|
||||
|
||||
station_names, lat, lon = get_station_names_lat_lon(self.parser)
|
||||
self.station_names = station_names
|
||||
self.lat = lat
|
||||
self.lon = lon
|
||||
|
||||
def init_picks(self):
|
||||
phase = self.comboBox_phase.currentText()
|
||||
|
||||
def get_picks(station_names):
|
||||
picks = []
|
||||
for station in station_names:
|
||||
try:
|
||||
station = station.split('.')[-1]
|
||||
picks.append(self.picks_dict[station][phase]['mpp'])
|
||||
except:
|
||||
picks.append(np.nan)
|
||||
return picks
|
||||
|
||||
def get_picks_rel(picks):
|
||||
picks_rel = []
|
||||
picks_utc = []
|
||||
for pick in picks:
|
||||
if type(pick) is obspy.core.utcdatetime.UTCDateTime:
|
||||
picks_utc.append(pick)
|
||||
minp = min(picks_utc)
|
||||
for pick in picks:
|
||||
if type(pick) is obspy.core.utcdatetime.UTCDateTime:
|
||||
pick -= minp
|
||||
picks_rel.append(pick)
|
||||
return picks_rel
|
||||
|
||||
self.picks = get_picks(self.station_names)
|
||||
self.picks_rel = get_picks_rel(self.picks)
|
||||
|
||||
def init_picks_active(self):
|
||||
def remove_nan_picks(picks):
|
||||
picks_no_nan = []
|
||||
for pick in picks:
|
||||
if not np.isnan(pick):
|
||||
picks_no_nan.append(pick)
|
||||
return picks_no_nan
|
||||
|
||||
self.picks_no_nan = remove_nan_picks(self.picks_rel)
|
||||
|
||||
def init_stations_active(self):
|
||||
def remove_nan_lat_lon(picks, lat, lon):
|
||||
lat_no_nan = []
|
||||
lon_no_nan = []
|
||||
for index, pick in enumerate(picks):
|
||||
if not np.isnan(pick):
|
||||
lat_no_nan.append(lat[index])
|
||||
lon_no_nan.append(lon[index])
|
||||
return lat_no_nan, lon_no_nan
|
||||
|
||||
self.lat_no_nan, self.lon_no_nan = remove_nan_lat_lon(self.picks_rel, self.lat, self.lon)
|
||||
|
||||
def init_lat_lon_dimensions(self):
|
||||
def get_lon_lat_dim(lon, lat):
|
||||
londim = max(lon) - min(lon)
|
||||
latdim = max(lat) - min(lat)
|
||||
return londim, latdim
|
||||
|
||||
self.londim, self.latdim = get_lon_lat_dim(self.lon, self.lat)
|
||||
|
||||
def init_x_y_dimensions(self):
|
||||
def get_x_y_dim(x, y):
|
||||
xdim = max(x) - min(x)
|
||||
ydim = max(y) - min(y)
|
||||
return xdim, ydim
|
||||
|
||||
self.x, self.y = self.basemap(self.lon, self.lat)
|
||||
self.xdim, self.ydim = get_x_y_dim(self.x, self.y)
|
||||
|
||||
def init_basemap(self, resolution='l'):
|
||||
# basemap = Basemap(projection=projection, resolution = resolution, ax=self.main_ax)
|
||||
basemap = Basemap(projection='lcc', resolution=resolution, ax=self.main_ax,
|
||||
width=5e6, height=2e6,
|
||||
lat_0=(min(self.lat) + max(self.lat)) / 2.,
|
||||
lon_0=(min(self.lon) + max(self.lon)) / 2.)
|
||||
|
||||
# basemap.fillcontinents(color=None, lake_color='aqua',zorder=1)
|
||||
basemap.drawmapboundary(zorder=2) # fill_color='darkblue')
|
||||
basemap.shadedrelief(zorder=3)
|
||||
basemap.drawcountries(zorder=4)
|
||||
basemap.drawstates(zorder=5)
|
||||
basemap.drawcoastlines(zorder=6)
|
||||
self.basemap = basemap
|
||||
self.figure.tight_layout()
|
||||
|
||||
def init_lat_lon_grid(self):
|
||||
def get_lat_lon_axis(lat, lon):
|
||||
steplat = (max(lat) - min(lat)) / 250
|
||||
steplon = (max(lon) - min(lon)) / 250
|
||||
|
||||
lataxis = np.arange(min(lat), max(lat), steplat)
|
||||
lonaxis = np.arange(min(lon), max(lon), steplon)
|
||||
return lataxis, lonaxis
|
||||
|
||||
def get_lat_lon_grid(lataxis, lonaxis):
|
||||
longrid, latgrid = np.meshgrid(lonaxis, lataxis)
|
||||
return latgrid, longrid
|
||||
|
||||
self.lataxis, self.lonaxis = get_lat_lon_axis(self.lat, self.lon)
|
||||
self.latgrid, self.longrid = get_lat_lon_grid(self.lataxis, self.lonaxis)
|
||||
|
||||
def init_picksgrid(self):
|
||||
self.picksgrid_no_nan = griddata((self.lat_no_nan, self.lon_no_nan),
|
||||
self.picks_no_nan, (self.latgrid, self.longrid),
|
||||
method='linear') ##################
|
||||
|
||||
def draw_contour_filled(self, nlevel='50'):
|
||||
levels = np.linspace(min(self.picks_no_nan), max(self.picks_no_nan), nlevel)
|
||||
self.contourf = self.basemap.contourf(self.longrid, self.latgrid, self.picksgrid_no_nan,
|
||||
levels, latlon=True, zorder=9, alpha=0.5)
|
||||
|
||||
def scatter_all_stations(self):
|
||||
self.sc = self.basemap.scatter(self.lon, self.lat, s=50, facecolor='none', latlon=True,
|
||||
zorder=10, picker=True, edgecolor='m', label='Not Picked')
|
||||
self.cid = self.canvas.mpl_connect('pick_event', self.onpick)
|
||||
if self.eventLoc:
|
||||
lat, lon = self.eventLoc
|
||||
self.sc_event = self.basemap.scatter(lon, lat, s=100, facecolor='red',
|
||||
latlon=True, zorder=11, label='Event (might be outside map region)')
|
||||
|
||||
def scatter_picked_stations(self):
|
||||
lon = self.lon_no_nan
|
||||
lat = self.lat_no_nan
|
||||
|
||||
# workaround because of an issue with latlon transformation of arrays with len <3
|
||||
if len(lon) <= 2 and len(lat) <= 2:
|
||||
self.sc_picked = self.basemap.scatter(lon[0], lat[0], s=50, facecolor='white',
|
||||
c=self.picks_no_nan[0], latlon=True, zorder=11, label='Picked')
|
||||
if len(lon) == 2 and len(lat) == 2:
|
||||
self.sc_picked = self.basemap.scatter(lon[1], lat[1], s=50, facecolor='white',
|
||||
c=self.picks_no_nan[1], latlon=True, zorder=11)
|
||||
else:
|
||||
self.sc_picked = self.basemap.scatter(lon, lat, s=50, facecolor='white',
|
||||
c=self.picks_no_nan, latlon=True, zorder=11, label='Picked')
|
||||
|
||||
def annotate_ax(self):
|
||||
self.annotations = []
|
||||
for index, name in enumerate(self.station_names):
|
||||
self.annotations.append(self.main_ax.annotate(' %s' % name, xy=(self.x[index], self.y[index]),
|
||||
fontsize='x-small', color='white', zorder=12))
|
||||
self.legend = self.main_ax.legend(loc=1)
|
||||
|
||||
def add_cbar(self, label):
|
||||
cbar = self.main_ax.figure.colorbar(self.sc_picked, fraction=0.025)
|
||||
cbar.set_label(label)
|
||||
return cbar
|
||||
|
||||
def refresh_drawings(self, picks=None):
|
||||
self.picks_dict = picks
|
||||
self._refresh_drawings()
|
||||
|
||||
def _refresh_drawings(self):
|
||||
self.remove_drawings()
|
||||
self.draw_everything()
|
||||
|
||||
def draw_everything(self):
|
||||
if self.picks_dict:
|
||||
self.init_picks()
|
||||
self.init_picks_active()
|
||||
self.init_stations_active()
|
||||
if len(self.picks_no_nan) >= 3:
|
||||
self.init_picksgrid()
|
||||
self.draw_contour_filled()
|
||||
self.scatter_all_stations()
|
||||
if self.picks_dict:
|
||||
self.scatter_picked_stations()
|
||||
self.cbar = self.add_cbar(label='Time relative to first onset [s]')
|
||||
self.comboBox_phase.setEnabled(True)
|
||||
else:
|
||||
self.comboBox_phase.setEnabled(False)
|
||||
self.annotate_ax()
|
||||
self.canvas.draw()
|
||||
|
||||
def remove_drawings(self):
|
||||
if hasattr(self, 'sc_picked'):
|
||||
self.sc_picked.remove()
|
||||
del (self.sc_picked)
|
||||
if hasattr(self, 'sc_event'):
|
||||
self.sc_event.remove()
|
||||
del (self.sc_event)
|
||||
if hasattr(self, 'cbar'):
|
||||
self.cbar.remove()
|
||||
del (self.cbar)
|
||||
if hasattr(self, 'contourf'):
|
||||
self.remove_contourf()
|
||||
del (self.contourf)
|
||||
if hasattr(self, 'cid'):
|
||||
self.canvas.mpl_disconnect(self.cid)
|
||||
del (self.cid)
|
||||
try:
|
||||
self.sc.remove()
|
||||
except Exception as e:
|
||||
print('Warning: could not remove station scatter plot.\nReason: {}'.format(e))
|
||||
try:
|
||||
self.legend.remove()
|
||||
except Exception as e:
|
||||
print('Warning: could not remove legend. Reason: {}'.format(e))
|
||||
self.canvas.draw()
|
||||
|
||||
def remove_contourf(self):
|
||||
for item in self.contourf.collections:
|
||||
item.remove()
|
||||
|
||||
def remove_annotations(self):
|
||||
for annotation in self.annotations:
|
||||
annotation.remove()
|
||||
|
||||
def zoom(self, event):
|
||||
map = self.basemap
|
||||
xlim = map.ax.get_xlim()
|
||||
ylim = map.ax.get_ylim()
|
||||
x, y = event.xdata, event.ydata
|
||||
zoom = {'up': 1. / 2.,
|
||||
'down': 2.}
|
||||
|
||||
if not event.xdata or not event.ydata:
|
||||
return
|
||||
|
||||
if event.button in zoom:
|
||||
factor = zoom[event.button]
|
||||
xdiff = (xlim[1] - xlim[0]) * factor
|
||||
xl = x - 0.5 * xdiff
|
||||
xr = x + 0.5 * xdiff
|
||||
ydiff = (ylim[1] - ylim[0]) * factor
|
||||
yb = y - 0.5 * ydiff
|
||||
yt = y + 0.5 * ydiff
|
||||
|
||||
if xl < map.xmin or yb < map.ymin or xr > map.xmax or yt > map.ymax:
|
||||
xl, xr = map.xmin, map.xmax
|
||||
yb, yt = map.ymin, map.ymax
|
||||
map.ax.set_xlim(xl, xr)
|
||||
map.ax.set_ylim(yb, yt)
|
||||
map.ax.figure.canvas.draw()
|
||||
|
||||
def _warn(self, message):
|
||||
self.qmb = QtGui.QMessageBox(QtGui.QMessageBox.Icon.Warning,
|
||||
'Warning', message)
|
||||
self.qmb.show()
|
||||
@ -1,12 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import sys, time
|
||||
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
|
||||
import icons_rc
|
||||
|
||||
app = QApplication(sys.argv)
|
||||
|
||||
data = read()
|
||||
win = PickDlg(data=data)
|
||||
win.show()
|
||||
app.exec_()
|
||||
@ -1,12 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import sys, time
|
||||
from PySide.QtGui import QApplication
|
||||
from pylot.core.util.widgets import PropertiesDlg
|
||||
|
||||
app = QApplication(sys.argv)
|
||||
|
||||
win = PropertiesDlg()
|
||||
win.show()
|
||||
app.exec_()
|
||||
@ -1,17 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
|
||||
import sys, 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()
|
||||
@ -1,27 +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()
|
||||
@ -1,19 +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()
|
||||
@ -1,303 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""
|
||||
Script to run autoPyLoT-script "makeCF.py".
|
||||
Only for test purposes!
|
||||
"""
|
||||
|
||||
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 *
|
||||
import glob
|
||||
import argparse
|
||||
|
||||
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)
|
||||
@ -1,7 +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
|
||||
@ -1,15 +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)
|
||||
@ -1,28 +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)
|
||||
@ -1,24 +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.util.version import get_git_version as _getVersionString
|
||||
from pylot.core.io.phases import reassess_pilot_db
|
||||
|
||||
__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.util.version import get_git_version as _getVersionString
|
||||
from pylot.core.io.phases import reassess_pilot_event
|
||||
|
||||
__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)
|
||||
@ -1,14 +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)
|
||||
@ -1,30 +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)
|
||||
@ -1,307 +0,0 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""
|
||||
Script to run autoPyLoT-script "run_makeCF.py".
|
||||
Only for test purposes!
|
||||
"""
|
||||
|
||||
from obspy.core import read
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from pylot.core.pick.charfuns import CharacteristicFunction
|
||||
from pylot.core.pick.picker import AutoPicker
|
||||
from pylot.core.pick.utils import *
|
||||
import glob
|
||||
import argparse
|
||||
|
||||
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…
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Reference in New Issue
Block a user