[update] added test for AutoPyLoT, added test files for correlation picker as well
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*~
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.idea
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pylot/RELEASE-VERSION
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/tests/test_autopicker/dmt_database_test/
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29
PyLoT.py
29
PyLoT.py
@ -1031,22 +1031,23 @@ class MainWindow(QMainWindow):
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if not fname:
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return
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qmb = QMessageBox(self, icon=QMessageBox.Question,
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text='Do you want to overwrite this data?',)
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overwrite_button = qmb.addButton('Overwrite', QMessageBox.YesRole)
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merge_button = qmb.addButton('Merge', QMessageBox.NoRole)
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qmb.exec_()
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if qmb.clickedButton() == overwrite_button:
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merge_strategy = 'Overwrite'
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elif qmb.clickedButton() == merge_button:
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merge_strategy = 'Merge'
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else:
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return
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if not event:
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event = self.get_current_event()
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if event.picks:
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qmb = QMessageBox(self, icon=QMessageBox.Question,
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text='Do you want to overwrite the data?',)
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overwrite_button = qmb.addButton('Overwrite', QMessageBox.YesRole)
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merge_button = qmb.addButton('Merge', QMessageBox.NoRole)
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qmb.exec_()
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if qmb.clickedButton() == overwrite_button:
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merge_strategy = 'Overwrite'
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elif qmb.clickedButton() == merge_button:
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merge_strategy = 'Merge'
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else:
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return
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data = Data(self, event)
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try:
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data_new = Data(self, evtdata=str(fname))
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15
autoPyLoT.py
15
autoPyLoT.py
@ -184,15 +184,15 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
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if not input_dict:
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# started in production mode
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datapath = datastructure.expandDataPath()
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if fnames == 'None' and parameter['eventID'] == '*':
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if fnames in [None, 'None'] and parameter['eventID'] == '*':
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# multiple event processing
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# read each event in database
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events = [event for event in glob.glob(os.path.join(datapath, '*')) if
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(os.path.isdir(event) and not event.endswith('EVENTS-INFO'))]
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elif fnames == 'None' and parameter['eventID'] != '*' and not type(parameter['eventID']) == list:
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elif fnames in [None, 'None'] and parameter['eventID'] != '*' and not type(parameter['eventID']) == list:
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# single event processing
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events = glob.glob(os.path.join(datapath, parameter['eventID']))
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elif fnames == 'None' and type(parameter['eventID']) == list:
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elif fnames in [None, 'None'] and type(parameter['eventID']) == list:
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# multiple event processing
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events = []
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for eventID in parameter['eventID']:
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@ -234,12 +234,15 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
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data.get_evt_data().path = eventpath
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print('Reading event data from filename {}...'.format(filename))
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except Exception as e:
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print('Could not read event from file {}: {}'.format(filename, e))
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if type(e) == FileNotFoundError:
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print('Creating new event file.')
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else:
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print('Could not read event from file {}: {}'.format(filename, e))
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data = Data()
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pylot_event = Event(eventpath) # event should be path to event directory
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data.setEvtData(pylot_event)
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if fnames == 'None':
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data.setWFData(glob.glob(os.path.join(datapath, event_datapath, '*')))
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if fnames in [None, 'None']:
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data.setWFData(glob.glob(os.path.join(event_datapath, '*')))
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# the following is necessary because within
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# multiple event processing no event ID is provided
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# in autopylot.in
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18672
tests/test_autopicker/PyLoT_20171010_063224.a_autopylot.xml
Normal file
18672
tests/test_autopicker/PyLoT_20171010_063224.a_autopylot.xml
Normal file
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
18672
tests/test_autopicker/PyLoT_20171010_063224.a_saved_from_GUI.xml
Normal file
18672
tests/test_autopicker/PyLoT_20171010_063224.a_saved_from_GUI.xml
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File diff suppressed because it is too large
Load Diff
@ -0,0 +1,99 @@
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%This is a parameter input file for PyLoT/autoPyLoT.
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%All main and special settings regarding data handling
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%and picking are to be set here!
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%Parameters are optimized for %extent data sets!
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#main settings#
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dmt_database_test #datapath# %data path
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20171010_063224.a #eventID# %event ID for single event processing (* for all events found in database)
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#invdir# %full path to inventory or dataless-seed file
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PILOT #datastructure# %choose data structure
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True #apverbose# %choose 'True' or 'False' for terminal output
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#NLLoc settings#
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None #nllocbin# %path to NLLoc executable
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None #nllocroot# %root of NLLoc-processing directory
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None #phasefile# %name of autoPyLoT-output phase file for NLLoc
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None #ctrfile# %name of autoPyLoT-output control file for NLLoc
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ttime #ttpatter# %pattern of NLLoc ttimes from grid
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AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#parameters for seismic moment estimation#
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3530.0 #vp# %average P-wave velocity
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2500.0 #rho# %average rock density [kg/m^3]
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300.0 0.8 #Qp# %quality factor for P waves (Qp*f^a); list(Qp, a)
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#settings local magnitude#
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1.0 1.0 1.0 #WAscaling# %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] If zeros are set, original Richter magnitude is calculated!
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1.0 1.0 #magscaling# %Scaling relation for derived local magnitude [a*Ml+b]. If zeros are set, no scaling of network magnitude is applied!
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#filter settings#
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0.03 0.03 #minfreq# %Lower filter frequency [P, S]
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0.5 0.5 #maxfreq# %Upper filter frequency [P, S]
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4 4 #filter_order# %filter order [P, S]
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bandpass bandpass #filter_type# %filter type (bandpass, bandstop, lowpass, highpass) [P, S]
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#common settings picker#
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global #extent# %extent of array ("local", "regional" or "global")
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-100.0 #pstart# %start time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
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50.0 #pstop# %end time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
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-50.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
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50.0 #sstop# %end time [s] after P-onset for calculating CF for S-picking
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True #use_taup# %use estimated traveltimes from TauPy for calculating windows for CF
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ak135 #taup_model# %Define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
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P,Pdiff,S,SKS #taup_phases# %Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.
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0.03 0.5 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
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0.01 0.5 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
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0.03 0.5 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
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0.01 0.5 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz]
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#special settings for calculating CF#
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%!!Edit the following only if you know what you are doing!!%
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#Z-component#
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HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
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300.0 #tlta# %for HOS-/AR-AIC-picker, length of LTA window [s]
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4 #hosorder# %for HOS-picker, order of Higher Order Statistics
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2 #Parorder# %for AR-picker, order of AR process of Z-component
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16.0 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
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10.0 #tpred1z# %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
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12.0 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
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6.0 #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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60.0 5.0 20.0 12.0 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
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50.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
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30.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
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2.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
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2.0 #tsmoothP# %for HOS/AR, take average of samples in this time window for smoothing CF [s]
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0.006 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
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2.0 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
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#H-components#
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ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
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12.0 #tdet1h# %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
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6.0 #tpred1h# %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
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8.0 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
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4.0 #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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100.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
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195.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
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60.0 10.0 30.0 12.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
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22.0 #aictsmoothS# %for AIC-picker, take average of samples in this time window for smoothing of AIC-function [s]
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20.0 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
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0.001 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
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2.0 #nfacS# %for AR-picker, noise factor for noise level determination (S)
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#first-motion picker#
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1 #minfmweight# %minimum required P weight for first-motion determination
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3.0 #minFMSNR# %miniumum required SNR for first-motion determination
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10.0 #fmpickwin# %pick window [s] around P onset for calculating zero crossings
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#quality assessment#
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0.1 0.2 0.4 0.8 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
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4.0 8.0 16.0 32.0 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
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0.005 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
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1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
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0.002 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
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1.3 #minAICSSNR# %below this SNR the initial S pick is rejected
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20.0 #minsiglength# %length of signal part for which amplitudes must exceed noiselevel [s]
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1.0 #noisefactor# %noiselevel*noisefactor=threshold
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10.0 #minpercent# %required percentage of amplitudes exceeding threshold
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0.1 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
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100.0 #mdttolerance# %maximum allowed deviation of P picks from median [s]
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50.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
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25.0 #jackfactor# %pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor
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67
tests/test_autopicker/test_autopylot.py
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67
tests/test_autopicker/test_autopylot.py
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import os
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import pytest
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from obspy import read_events
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from autoPyLoT import autoPyLoT
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class TestAutopickerGlobal():
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def init(self):
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self.params_infile = 'pylot_alparray_mantle_corr_stack_0.03-0.5.in'
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self.test_event_dir = 'dmt_database_test'
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self.fname_outfile_xml = os.path.join(
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self.test_event_dir, '20171010_063224.a', 'PyLoT_20171010_063224.a_autopylot.xml'
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)
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# check if the input files exist
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if not os.path.isfile(self.params_infile):
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print(f'Test input file {os.path.abspath(self.params_infile)} not found.')
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return False
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if not os.path.exists(self.test_event_dir):
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print(
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f'Test event directory not found at location "{os.path.abspath(self.test_event_dir)}". '
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f'Make sure to load it from the website first.'
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)
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return False
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return True
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def test_autopicker(self):
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assert self.init(), 'Initialization failed due to missing input files.'
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# check for output file in test directory and remove it if necessary
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if os.path.isfile(self.fname_outfile_xml):
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os.remove(self.fname_outfile_xml)
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autoPyLoT(inputfile=self.params_infile, eventid='20171010_063224.a', obspyDMT_wfpath='processed')
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# test for different known output files if they are identical or not
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compare_pickfiles(self.fname_outfile_xml, 'PyLoT_20171010_063224.a_autopylot.xml', True)
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compare_pickfiles(self.fname_outfile_xml, 'PyLoT_20171010_063224.a_saved_from_GUI.xml', True)
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compare_pickfiles(self.fname_outfile_xml, 'PyLoT_20171010_063224.a_corrected_taup_times_0.03-0.5_P.xml', False)
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def compare_pickfiles(pickfile1: str, pickfile2: str, samefile: bool = True) -> None:
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"""
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Compare the pick times and errors from two pick files.
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Parameters:
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pickfile1 (str): The path to the first pick file.
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pickfile2 (str): The path to the second pick file.
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samefile (bool): A flag indicating whether the two files are expected to be the same. Defaults to True.
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Returns:
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None
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"""
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cat1 = read_events(pickfile1)
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cat2 = read_events(pickfile2)
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picks1 = sorted(cat1[0].picks, key=lambda pick: str(pick.waveform_id))
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picks2 = sorted(cat2[0].picks, key=lambda pick: str(pick.waveform_id))
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pick_times1 = [pick.time for pick in picks1]
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pick_times2 = [pick.time for pick in picks2]
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pick_terrs1 = [pick.time_errors for pick in picks1]
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pick_terrs2 = [pick.time_errors for pick in picks2]
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# check if times and errors are identical or not depending on the samefile flag
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assert (pick_times1 == pick_times2) is samefile, 'Pick times error'
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assert (pick_terrs1 == pick_terrs2) is samefile, 'Pick time errors errors'
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