Merge branch 'develop' of ariadne.geophysik.ruhr-uni-bochum.de:/data/git/pylot into develop
This commit is contained in:
commit
1f1d0aa118
51
autoPyLoT.py
51
autoPyLoT.py
@ -6,11 +6,11 @@ import argparse
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import glob
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import glob
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import subprocess
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import subprocess
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import string
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import string
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from obspy.core import read
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from obspy.core import read, UTCDateTime
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from pylot.core.read.data import Data
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from pylot.core.read.data import Data
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from pylot.core.read.inputs import AutoPickParameter
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from pylot.core.read.inputs import AutoPickParameter
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from pylot.core.util.structure import DATASTRUCTURE
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from pylot.core.util.structure import DATASTRUCTURE
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from pylot.core.pick.autopick import autopickevent
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from pylot.core.pick.autopick import autopickevent, iteratepicker
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from pylot.core.loc.nll import *
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from pylot.core.loc.nll import *
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from pylot.core.util.version import get_git_version as _getVersionString
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from pylot.core.util.version import get_git_version as _getVersionString
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@ -121,7 +121,28 @@ def autoPyLoT(inputfile):
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# !iterative picking if traces remained unpicked or occupied with bad picks!
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# !iterative picking if traces remained unpicked or occupied with bad picks!
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# get theoretical onset times for picks with weights >= 4
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# get theoretical onset times for picks with weights >= 4
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# in order to reprocess them using smaller time windows
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# in order to reprocess them using smaller time windows around theoretical onset
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# get stations with bad onsets
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badpicks = []
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for key in picks:
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if picks[key]['P']['weight'] >= 4 or picks[key]['S']['weight'] >= 4:
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badpicks.append([key, picks[key]['P']['mpp']])
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if len(badpicks) == 0:
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print("autoPyLoT: No bad onsets found, thus no iterative picking necessary!")
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else:
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# get theoretical P-onset times from NLLoc-location file
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locsearch = '%s/loc/%s.????????.??????.grid?.loc.hyp' % (nllocroot, nllocout)
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# get latest file if several are available
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nllocfile = max(glob.glob(locsearch), key=os.path.getctime)
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if os.path.isfile(nllocfile):
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picks = iteratepicker(wfdat, nllocfile, picks, badpicks, parameter)
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# write phases to NLLoc-phase file
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picksExport(picks, 'NLLoc', phasefile)
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# locate the event
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locate(nlloccall, ctrfile)
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else:
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print("autoPyLoT: No NLLoc-location file available! Stop iteration!")
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##########################################################
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##########################################################
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# write phase files for various location routines
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# write phase files for various location routines
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# HYPO71
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# HYPO71
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@ -160,10 +181,30 @@ def autoPyLoT(inputfile):
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# locate the event
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# locate the event
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locate(nlloccall, ctrfile)
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locate(nlloccall, ctrfile)
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# !iterative picking if traces remained unpicked or occupied with bad picks!
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# !iterative picking if traces remained unpicked or occupied with bad picks!
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# get theoretical onset times for picks with weights >= 4
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# get theoretical onset times for picks with weights >= 4
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# in order to reprocess them using smaller time windows
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# in order to reprocess them using smaller time windows around theoretical onset
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# get stations with bad onsets
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badpicks = []
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for key in picks:
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if picks[key]['P']['weight'] >= 4 or picks[key]['S']['weight'] >= 4:
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badpicks.append([key, picks[key]['P']['mpp']])
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if len(badpicks) == 0:
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print("autoPyLoT: No bad onsets found, thus no iterative picking necessary!")
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else:
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# get theoretical P-onset times from NLLoc-location file
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locsearch = '%s/loc/%s.????????.??????.grid?.loc.hyp' % (nllocroot, nllocout)
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# get latest file if several are available
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nllocfile = max(glob.glob(locsearch), key=os.path.getctime)
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if os.path.isfile(nllocfile):
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picks = iteratepicker(wfdat, nllocfile, picks, badpicks, parameter)
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# write phases to NLLoc-phase file
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picksExport(picks, 'NLLoc', phasefile)
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# locate the event
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locate(nlloccall, ctrfile)
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else:
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print("autoPyLoT: No NLLoc-location file available! Stop iteration!")
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##########################################################
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##########################################################
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# write phase files for various location routines
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# write phase files for various location routines
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# HYPO71
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# HYPO71
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@ -17,7 +17,7 @@ PILOT #datastructure#%choose data structure
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/home/ludger/NLLOC/Insheim #nllocroot# %root of NLLoc-processing directory
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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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AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
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%(in nllocroot/obs)
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%(in nllocroot/obs)
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Insheim_min1d2015_auto.in #locfile# %name of autoPyLoT-output control file for NLLoc
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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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%(in nllocroot/run)
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ttime #ttpatter# %pattern of NLLoc ttimes from grid
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ttime #ttpatter# %pattern of NLLoc ttimes from grid
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%(in nllocroot/times)
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%(in nllocroot/times)
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@ -41,7 +41,7 @@ AUTOFOCMEC_AIC_HOS4_ARH.in #focmecin# %name of focmec input file co
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#common settings picker#
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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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15 #pstart# %start time [s] for calculating CF for P-picking
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60 #pstop# %end time [s] for calculating CF for P-picking
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60 #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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-1.0 #sstart# %start time [s] relative to 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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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 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 30 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
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@ -51,7 +51,7 @@ AUTOFOCMEC_AIC_HOS4_ARH.in #focmecin# %name of focmec input file co
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%!!Be careful when editing the following!!
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%!!Be careful when editing the following!!
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#Z-component#
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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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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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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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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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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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1.2 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
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@ -60,8 +60,8 @@ HOS #algoP# %choose algorithm for P-onset
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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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0.2 #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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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 0.1 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 #pickwinP# %for initial AIC pick, length of P-pick window [s]
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3.0 #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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8.0 #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 #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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0.2 #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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0.1 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
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@ -74,8 +74,8 @@ ARH #algoS# %choose algorithm for S-onset
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0.6 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
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0.6 #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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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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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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6.0 #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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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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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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0.05 #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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0.02 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
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@ -83,7 +83,7 @@ ARH #algoS# %choose algorithm for S-onset
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0.4 #ausS# %for HOS/AR, artificial uplift of samples (aus) 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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1.5 #nfacS# %for AR-picker, noise factor for noise level determination (S)
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%first-motion picker%
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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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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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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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0.2 #fmpickwin# %pick window around P onset for calculating zero crossings
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%quality assessment%
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%quality assessment%
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@ -92,7 +92,7 @@ ARH #algoS# %choose algorithm for S-onset
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0.04 0.08 0.16 0.32 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
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0.04 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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10 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
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10 #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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1.2 #minAICPSNR# %below this SNR the initial P pick is rejected
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6 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
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3 #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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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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#check duration of signal using envelope function#
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5 #minsiglength# %minimum required length of signal [s]
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5 #minsiglength# %minimum required length of signal [s]
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@ -341,7 +341,7 @@ def addCheckerboard(spacing = 10., pertubation = 0.1, inputfile = 'vgrids.in',
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_update_progress(progress)
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_update_progress(progress)
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print('Added checkerboard to the grid in file %s with a spacing of %s and a pertubation of %s %%. '
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print('Added checkerboard to the grid in file %s with a spacing of %s and a pertubation of %s %%. '
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'Outputfile: %s.'%(inputfile, spacing, pertubation, outputfile))
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'Outputfile: %s.'%(inputfile, spacing, pertubation*100, outputfile))
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outfile.close()
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outfile.close()
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def addBox(x = (None, None), y = (None, None), z = (None, None),
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def addBox(x = (None, None), y = (None, None), z = (None, None),
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@ -381,14 +381,34 @@ class SeisArray(object):
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return surface
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return surface
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def generateFMTOMOinputFromArray(self, nRP, nThetaP, nPhiP, nRI, nThetaI, nPhiI,
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def generateFMTOMOinputFromArray(self, nPointsPropgrid, nPointsInvgrid,
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Rbt, cushionfactor, interpolationMethod = 'linear',
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zBotTop, cushionfactor, interpolationMethod = 'linear',
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customgrid = 'mygrid.in', writeVTK = True):
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customgrid = 'mygrid.in', writeVTK = True):
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'''
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Generate FMTOMO input files from the SeisArray dimensions.
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Generates: vgrids.in, interfaces.in, propgrid.in
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:param: nPointsPropgrid, number of points in each direction of the propagation grid (z, y, x)
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:type: tuple
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:param: nPointsInvgrid, number of points in each direction of the inversion grid (z, y, x)
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:type: tuple
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:param: zBotTop, (bottom, top) dimensions of the model
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:type: tuple
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:param: cushionfactor, adds cushioning around the model (0.1 = 10%)
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:type: float
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'''
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nRP, nThetaP, nPhiP = nPointsPropgrid
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nRI, nThetaI, nPhiI = nPointsInvgrid
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print('\n------------------------------------------------------------')
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print('\n------------------------------------------------------------')
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print('Automatically generating input for FMTOMO from array size.')
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print('Automatically generating input for FMTOMO from array size.')
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print('Propgrid: nR = %s, nTheta = %s, nPhi = %s'%(nRP, nThetaP, nPhiP))
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print('Propgrid: nR = %s, nTheta = %s, nPhi = %s'%(nRP, nThetaP, nPhiP))
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print('Interpolation Grid and Interfaces Grid: nR = %s, nTheta = %s, nPhi = %s'%(nRI, nThetaI, nPhiI))
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print('Interpolation Grid and Interfaces Grid: nR = %s, nTheta = %s, nPhi = %s'%(nRI, nThetaI, nPhiI))
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print('Bottom and Top of model: (%s, %s)'%(Rbt[0], Rbt[1]))
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print('Bottom and Top of model: (%s, %s)'%(zBotTop[0], zBotTop[1]))
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print('Method: %s, customgrid = %s'%(interpolationMethod, customgrid))
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print('Method: %s, customgrid = %s'%(interpolationMethod, customgrid))
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print('------------------------------------------------------------')
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print('------------------------------------------------------------')
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@ -398,13 +418,13 @@ class SeisArray(object):
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z.append(point[2])
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z.append(point[2])
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return min(z)
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return min(z)
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self.generatePropgrid(nThetaP, nPhiP, nRP, Rbt, cushionfactor = cushionfactor,
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self.generatePropgrid(nThetaP, nPhiP, nRP, zBotTop, cushionfactor = cushionfactor,
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cushionpropgrid = 0.05)
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cushionpropgrid = 0.05)
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surface = self.generateVgrid(nThetaI, nPhiI, nRI, Rbt, method = interpolationMethod,
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surface = self.generateVgrid(nThetaI, nPhiI, nRI, zBotTop, method = interpolationMethod,
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cushionfactor = cushionfactor, infilename = customgrid,
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cushionfactor = cushionfactor, infilename = customgrid,
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returnTopo = True)
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returnTopo = True)
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depthmax = abs(Rbt[0] - getZmin(surface)) - 1.0 # cushioning for the bottom interface
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depthmax = abs(zBotTop[0] - getZmin(surface)) - 1.0 # cushioning for the bottom interface
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interf1, interf2 = self.generateInterfaces(nThetaI, nPhiI, depthmax, cushionfactor = cushionfactor,
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interf1, interf2 = self.generateInterfaces(nThetaI, nPhiI, depthmax, cushionfactor = cushionfactor,
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returnInterfaces = True, method = interpolationMethod)
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returnInterfaces = True, method = interpolationMethod)
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@ -3,7 +3,7 @@
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"""
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"""
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Function to run automated picking algorithms using AIC,
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Function to run automated picking algorithms using AIC,
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HOS and AR prediction. Uses object CharFuns and Picker and
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HOS and AR prediction. Uses objects CharFuns and Picker and
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function conglomerate utils.
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function conglomerate utils.
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:author: MAGS2 EP3 working group / Ludger Kueperkoch
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:author: MAGS2 EP3 working group / Ludger Kueperkoch
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@ -16,6 +16,7 @@ from pylot.core.pick.Picker import AICPicker, PragPicker
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from pylot.core.pick.CharFuns import HOScf, AICcf, ARZcf, ARHcf, AR3Ccf
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from pylot.core.pick.CharFuns import HOScf, AICcf, ARZcf, ARHcf, AR3Ccf
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from pylot.core.pick.utils import checksignallength, checkZ4S, earllatepicker,\
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from pylot.core.pick.utils import checksignallength, checkZ4S, earllatepicker,\
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getSNR, fmpicker, checkPonsets, wadaticheck, crossings_nonzero_all
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getSNR, fmpicker, checkPonsets, wadaticheck, crossings_nonzero_all
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from pylot.core.util.utils import getPatternLine
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from pylot.core.read.data import Data
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from pylot.core.read.data import Data
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from pylot.core.analysis.magnitude import WApp, DCfc
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from pylot.core.analysis.magnitude import WApp, DCfc
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@ -316,12 +317,11 @@ def autopickstation(wfstream, pickparam):
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# from P pulse
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# from P pulse
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# initialize Data object
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# initialize Data object
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data = Data()
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data = Data()
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[corzdat, restflag] = data.restituteWFData(invdir, zdat)
|
z_copy = zdat.copy()
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[corzdat, restflag] = data.restituteWFData(invdir, z_copy)
|
||||||
if restflag == 1:
|
if restflag == 1:
|
||||||
# integrate to displacement
|
# integrate to displacement
|
||||||
corintzdat = integrate.cumtrapz(corzdat[0], None, corzdat[0].stats.delta)
|
corintzdat = integrate.cumtrapz(corzdat[0], None, corzdat[0].stats.delta)
|
||||||
# class needs stream object => build it
|
|
||||||
z_copy = zdat.copy()
|
|
||||||
z_copy[0].data = corintzdat
|
z_copy[0].data = corintzdat
|
||||||
# largest detectable period == window length
|
# largest detectable period == window length
|
||||||
# after P pulse for calculating source spectrum
|
# after P pulse for calculating source spectrum
|
||||||
@ -799,3 +799,77 @@ def autopickstation(wfstream, pickparam):
|
|||||||
picks[phase]['Ao'] = Ao
|
picks[phase]['Ao'] = Ao
|
||||||
|
|
||||||
return picks
|
return picks
|
||||||
|
|
||||||
|
def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter):
|
||||||
|
'''
|
||||||
|
Repicking of bad onsets. Uses theoretical onset times from NLLoc-location file.
|
||||||
|
|
||||||
|
:param wf: waveform, obspy stream object
|
||||||
|
|
||||||
|
:param NLLocfile: path/name of NLLoc-location file
|
||||||
|
|
||||||
|
:param picks: dictionary of available onset times
|
||||||
|
|
||||||
|
:param badpicks: picks to be repicked
|
||||||
|
|
||||||
|
:param pickparameter: picking parameters from autoPyLoT-input file
|
||||||
|
'''
|
||||||
|
|
||||||
|
print("#######################################################")
|
||||||
|
print("autoPyLoT: Found bad onsets at station(s) %s, starting re-picking them ...") \
|
||||||
|
% badpicks
|
||||||
|
|
||||||
|
newpicks = {}
|
||||||
|
for i in range(0, len(badpicks)):
|
||||||
|
if len(badpicks[i][0]) > 4:
|
||||||
|
Ppattern = '%s ? ? ? P' % badpicks[i][0]
|
||||||
|
elif len(badpicks[i][0]) == 4:
|
||||||
|
Ppattern = '%s ? ? ? P' % badpicks[i][0]
|
||||||
|
elif len(badpicks[i][0]) < 4:
|
||||||
|
Ppattern = '%s ? ? ? P' % badpicks[i][0]
|
||||||
|
nllocline = getPatternLine(NLLocfile, Ppattern)
|
||||||
|
res = nllocline.split(None)[16]
|
||||||
|
# get theoretical P-onset time from residuum
|
||||||
|
badpicks[i][1] = picks[badpicks[i][0]]['P']['mpp'] - float(res)
|
||||||
|
|
||||||
|
# get corresponding waveform stream
|
||||||
|
wf2pick = wf.select(station=badpicks[i][0])
|
||||||
|
|
||||||
|
# modify some picking parameters
|
||||||
|
pstart_old = pickparameter.getParam('pstart')
|
||||||
|
pstop_old = pickparameter.getParam('pstop')
|
||||||
|
pickwinP_old = pickparameter.getParam('pickwinP')
|
||||||
|
Precalcwin_old = pickparameter.getParam('Precalcwin')
|
||||||
|
pickparameter.setParam(pstart=badpicks[i][1] - wf2pick[0].stats.starttime \
|
||||||
|
- pickparameter.getParam('tlta'))
|
||||||
|
pickparameter.setParam(pstop=pickparameter.getParam('pstart') + \
|
||||||
|
(3 * pickparameter.getParam('tlta')))
|
||||||
|
pickparameter.setParam(pickwinP=pickparameter.getParam('pickwinP') / 2)
|
||||||
|
pickparameter.setParam(Precalcwin=pickparameter.getParam('Precalcwin') / 2)
|
||||||
|
print("iteratepicker: The following picking parameters have been modified for iterative picking:")
|
||||||
|
print("pstart: %fs => %fs" % (pstart_old, pickparameter.getParam('pstart')))
|
||||||
|
print("pstop: %fs => %fs" % (pstop_old, pickparameter.getParam('pstop')))
|
||||||
|
print("pickwinP: %fs => %fs" % (pickwinP_old, pickparameter.getParam('pickwinP')))
|
||||||
|
print("Precalcwin: %fs => %fs" % (Precalcwin_old, pickparameter.getParam('Precalcwin')))
|
||||||
|
|
||||||
|
# repick station
|
||||||
|
newpicks = autopickstation(wf2pick, pickparameter)
|
||||||
|
|
||||||
|
# replace old dictionary with new one
|
||||||
|
picks[badpicks[i][0]] = newpicks
|
||||||
|
|
||||||
|
# reset temporary change of picking parameters
|
||||||
|
print("iteratepicker: Resetting picking parameters ...")
|
||||||
|
pickparameter.setParam(pstart=pstart_old)
|
||||||
|
pickparameter.setParam(pstop=pstop_old)
|
||||||
|
pickparameter.setParam(pickwinP=pickwinP_old)
|
||||||
|
pickparameter.setParam(Precalcwin=Precalcwin_old)
|
||||||
|
|
||||||
|
return picks
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
@ -145,7 +145,7 @@ class AutoPickParameter(object):
|
|||||||
def setParam(self, **kwargs):
|
def setParam(self, **kwargs):
|
||||||
for param, value in kwargs.items():
|
for param, value in kwargs.items():
|
||||||
self.__setitem__(param, value)
|
self.__setitem__(param, value)
|
||||||
print(self)
|
#print(self)
|
||||||
|
|
||||||
@staticmethod
|
@staticmethod
|
||||||
def _printParameterError(errmsg):
|
def _printParameterError(errmsg):
|
||||||
|
Loading…
Reference in New Issue
Block a user