marcel/pylot
3
0
Fork 0
Code Issues 7 Pull Requests 1 Releases Wiki Activity

release version: 0.1a

Browse Source 
release notes:
==============
Features
- consistent manual phase picking through predefined SNR dependant zoom level
- uniform uncertainty estimation from waveform's properties for automatic and manual picks
- pdf representation and comparison of picks taking the uncertainty intrinsically into account
- Richter and moment magnitude estimation
- location determination with external installation of [NonLinLoc](http://alomax.free.fr/nlloc/index.html)
Known issues
- Magnitude estimation from manual PyLoT takes some time (instrument correction)
This commit is contained in:
Marc S. Boxberg 2016-10-04 09:36:11 +02:00 committed by Sebastian Wehling-Benatelli
parent 889e632198
commit 417316a8bb
29 changed files with 2533 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1177
QtPyLoT.py Executable file
View File

File diff suppressed because it is too large Load Diff

BIN
icons/compare.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 3.0 KiB

BIN
icons/locate.png Executable file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 7.1 KiB

21
icons_rc.py Normal file
View File

File diff suppressed because one or more lines are too long

100
inputs/autoPyLoT.in Normal file
View File

@ -0,0 +1,100 @@
%This is a parameter input file for autoPyLoT.
%All main and special settings regarding data handling
%and picking are to be set here!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#
/DATA/Insheim #rootpath# %project path
EVENT_DATA/LOCAL #datapath# %data path
2013.02_Insheim #database# %name of data base
e0019.048.13 #eventID# %certain evnt ID for processing
True #apverbose#
PILOT #datastructure# %choose data structure
0 #iplot# %flag for plotting: 0 none, 1, partly, >1 everything
AUTOPHASES_AIC_HOS4_ARH #phasefile# %name of autoPILOT output phase file
AUTOLOC_AIC_HOS4_ARH #locfile# %name of autoPILOT output location file
AUTOFOCMEC_AIC_HOS4_ARH.in #focmecin# %name of focmec input file containing polarities
HYPOSAT #locrt# %location routine used ("HYPOINVERSE" or "HYPOSAT")
6 #pmin# %minimum required P picks for location
4 #p0min# %minimum required P picks for location if at least
%3 excellent P picks are found
2 #smin# %minimum required S picks for location
/home/ludger/bin/run_HYPOSAT4autoPILOT.csh #cshellp# %path and name of c-shell script to run location routine
7.6 8.5 #blon# %longitude bounding for location map
49 49.4 #blat# %lattitude bounding for location map
#parameters for moment magnitude estimation#
5000 #vp# %average P-wave velocity
2800 #vs# %average S-wave velocity
2200 #rho# %rock density [kg/m^3]
300 #Qp# %quality factor for P waves
100 #Qs# %quality factor for S waves
#common settings picker#
15 #pstart# %start time [s] for calculating CF for P-picking
40 #pstop# %end time [s] for calculating CF for P-picking
-1.0 #sstart# %start time [s] after or before(-) P-onset for calculating CF for S-picking
7 #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#
%!!Be careful when editing the following!!
#Z-component#
HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
7 #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 0.1 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
3 #pickwinP# %for initial AIC pick, length of P-pick window [s]
8 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
0 #peps4aic# %for HOS/AR, artificial uplift of samples of AIC-function (P)
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
6 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
3 #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]
0.05 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
0.02 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
0.2 #pepsS# %for AR-picker, artificial uplift of samples of CF (S)
0.4 #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
80 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
1.2 #minAICPSNR# %below this SNR the initial P pick is rejected
50 #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#
1.5 #prepickwin# %pre-signal window length [s] for noise level estimation
0.7 #minsiglength# %minimum required length of signal [s]
0.2 #sgap# %safety gap between noise and signal window [s]
2 #noisefactor# %noiselevel*noisefactor=threshold
60 #minpercent# %per cent of samples required higher than threshold
#check for spuriously picked S-onsets#
3.0 #zfac# %P-amplitude must exceed zfac times RMS-S amplitude
#jackknife-processing for P-picks#
3 #thresholdweight#%minimum required weight of picks
3 #dttolerance# %maximum allowed deviation of P picks from median [s]
4 #minstats# %minimum number of stations with reliable P picks
3 #Sdttolerance# %maximum allowed deviation from Wadati-diagram

99
inputs/autoPyLoT_local.in Normal file
View File

@ -0,0 +1,99 @@
%This is a parameter input file for autoPyLoT.
%All main and special settings regarding data handling
%and picking are to be set here!
%Parameters are optimized for local data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#
/DATA/Insheim #rootpath# %project path
EVENT_DATA/LOCAL #datapath# %data path
2016.08_Insheim #database# %name of data base
e0007.224.16 #eventID# %event ID for single event processing
/DATA/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#
/home/ludger/NLLOC #nllocbin# %path to NLLoc executable
/home/ludger/NLLOC/Insheim #nllocroot# %root of NLLoc-processing directory
AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
%(in nllocroot/obs)
Insheim_min1d032016_auto.in #ctrfile# %name of autoPyLoT-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, ap], Qp*f^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

100
inputs/autoPyLoT_regional.in Normal file
View File

@ -0,0 +1,100 @@
%This is a parameter input file for autoPyLoT.
%All main and special settings regarding data handling
%and picking are to be set here!
%Parameters are optimized for regional data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#
/DATA/Egelados #rootpath# %project path
EVENT_DATA/LOCAL #datapath# %data path
2006.01_Nisyros #database# %name of data base
e1412.008.06 #eventID# %event ID for single event processing
/DATA/Egelados/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#
/home/ludger/NLLOC #nllocbin# %path to NLLoc executable
/home/ludger/NLLOC/Insheim #nllocroot# %root of NLLoc-processing directory
AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
%(in nllocroot/obs)
Insheim_min1d2015_auto.in #ctrfile# %name of autoPyLoT-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
2700 #rho# %average rock density [kg/m^3]
1000f**0.8 #Qp# %quality factor for P waves (Qp*f^a)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
AUTOFOCMEC_AIC_HOS4_ARH.in #focmecin# %name of focmec input file containing derived polarities
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#common settings picker#
20 #pstart# %start time [s] for calculating CF for P-picking
100 #pstop# %end time [s] for calculating CF for P-picking
1.0 #sstart# %start time [s] after or before(-) P-onset for calculating CF for S-picking
100 #sstop# %end time [s] after P-onset for calculating CF for S-picking
3 10 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
3 12 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
3 8 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
3 6 #bph2# %lower/upper corner freq. of second band pass filter H-comp. [Hz]
#special settings for calculating CF#
%!!Be careful when editing the following!!
#Z-component#
HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
7 #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
5 0.2 3.0 1.5 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
3 #pickwinP# %for initial AIC and refined pick, length of P-pick window [s]
8 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
1.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
0.3 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
0.3 #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
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

2
inputs/filter.in Normal file
View File

@ -0,0 +1,2 @@
P bandpass 4 2.0 20.0
S bandpass 4 2.0 15.0

98
inputs/pylot.in Normal file
View File

@ -0,0 +1,98 @@
%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 Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 2.2 KiB

1
pylot/RELEASE-VERSION Normal file
View File

@ -0,0 +1 @@
0.1a

21
pylot/core/loc/hsat.py Normal file
View File

@ -0,0 +1,21 @@
#!/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)

0
pylot/testing/__init__.py Normal file
View File

12
pylot/testing/testHelpForm.py Executable file
View File

@ -0,0 +1,12 @@
#!/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_()

20
pylot/testing/testPickDlg.py Executable file
View File

@ -0,0 +1,20 @@
#!/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_()

12
pylot/testing/testPropDlg.py Executable file
View File

@ -0,0 +1,12 @@
#!/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_()

17
pylot/testing/testUIcomponents.py Executable file
View File

@ -0,0 +1,17 @@
#!/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()

27
pylot/testing/testUtils.py Normal file
View File

@ -0,0 +1,27 @@
# -*- 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()

19
pylot/testing/testWidgets.py Normal file
View File

@ -0,0 +1,19 @@
# -*- 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()

303
pylot/testing/test_autopick.py Executable file
View File

@ -0,0 +1,303 @@
#!/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)

7
pylot/testing/test_pdf.py Normal file
View File

@ -0,0 +1,7 @@
#!/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

15
scripts/pylot-noisewindow.py Executable file
View File

@ -0,0 +1,15 @@
#!/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)

28
scripts/pylot-pick-earliest-latest.py Executable file
View File

@ -0,0 +1,28 @@
#!/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)

24
scripts/pylot-pick-firstmotion.py Executable file
View File

@ -0,0 +1,24 @@
#!/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)

41
scripts/pylot-reasses-pilot-db.py Executable file
View File

@ -0,0 +1,41 @@
#!/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)

38
scripts/pylot-reasses-pilot-event.py Executable file
View File

@ -0,0 +1,38 @@
#!/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)

14
scripts/pylot-signalwindow.py Executable file
View File

@ -0,0 +1,14 @@
#!/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)

30
scripts/pylot-snr.py Executable file
View File

@ -0,0 +1,30 @@
#!/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)

307
scripts/run_makeCF.py Executable file
View File

@ -0,0 +1,307 @@
#!/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)