Compare commits

...

3 Commits

13 changed files with 65934 additions and 22 deletions

1
.gitignore vendored
View File

@ -2,3 +2,4 @@
*~ *~
.idea .idea
pylot/RELEASE-VERSION pylot/RELEASE-VERSION
/tests/test_autopicker/dmt_database_test/

View File

@ -1031,11 +1031,14 @@ class MainWindow(QMainWindow):
if not fname: if not fname:
return return
if not event:
event = self.get_current_event()
if event.picks:
qmb = QMessageBox(self, icon=QMessageBox.Question, qmb = QMessageBox(self, icon=QMessageBox.Question,
text='Do you want to overwrite this data?',) text='Do you want to overwrite the data?',)
overwrite_button = qmb.addButton('Overwrite', QMessageBox.YesRole) overwrite_button = qmb.addButton('Overwrite', QMessageBox.YesRole)
merge_button = qmb.addButton('Merge', QMessageBox.NoRole) merge_button = qmb.addButton('Merge', QMessageBox.NoRole)
qmb.exec_() qmb.exec_()
if qmb.clickedButton() == overwrite_button: if qmb.clickedButton() == overwrite_button:
@ -1045,8 +1048,6 @@ class MainWindow(QMainWindow):
else: else:
return return
if not event:
event = self.get_current_event()
data = Data(self, event) data = Data(self, event)
try: try:
data_new = Data(self, evtdata=str(fname)) data_new = Data(self, evtdata=str(fname))

View File

@ -184,15 +184,15 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
if not input_dict: if not input_dict:
# started in production mode # started in production mode
datapath = datastructure.expandDataPath() datapath = datastructure.expandDataPath()
if fnames == 'None' and parameter['eventID'] == '*': if fnames in [None, 'None'] and parameter['eventID'] == '*':
# multiple event processing # multiple event processing
# read each event in database # read each event in database
events = [event for event in glob.glob(os.path.join(datapath, '*')) if events = [event for event in glob.glob(os.path.join(datapath, '*')) if
(os.path.isdir(event) and not event.endswith('EVENTS-INFO'))] (os.path.isdir(event) and not event.endswith('EVENTS-INFO'))]
elif fnames == 'None' and parameter['eventID'] != '*' and not type(parameter['eventID']) == list: elif fnames in [None, 'None'] and parameter['eventID'] != '*' and not type(parameter['eventID']) == list:
# single event processing # single event processing
events = glob.glob(os.path.join(datapath, parameter['eventID'])) events = glob.glob(os.path.join(datapath, parameter['eventID']))
elif fnames == 'None' and type(parameter['eventID']) == list: elif fnames in [None, 'None'] and type(parameter['eventID']) == list:
# multiple event processing # multiple event processing
events = [] events = []
for eventID in parameter['eventID']: for eventID in parameter['eventID']:
@ -234,12 +234,15 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
data.get_evt_data().path = eventpath data.get_evt_data().path = eventpath
print('Reading event data from filename {}...'.format(filename)) print('Reading event data from filename {}...'.format(filename))
except Exception as e: except Exception as e:
if type(e) == FileNotFoundError:
print('Creating new event file.')
else:
print('Could not read event from file {}: {}'.format(filename, e)) print('Could not read event from file {}: {}'.format(filename, e))
data = Data() data = Data()
pylot_event = Event(eventpath) # event should be path to event directory pylot_event = Event(eventpath) # event should be path to event directory
data.setEvtData(pylot_event) data.setEvtData(pylot_event)
if fnames == 'None': if fnames in [None, 'None']:
data.setWFData(glob.glob(os.path.join(datapath, event_datapath, '*'))) data.setWFData(glob.glob(os.path.join(event_datapath, '*')))
# the following is necessary because within # the following is necessary because within
# multiple event processing no event ID is provided # multiple event processing no event ID is provided
# in autopylot.in # in autopylot.in

View File

@ -17,7 +17,10 @@ autoregressive prediction: application ot local and regional distances, Geophys.
:author: MAGS2 EP3 working group :author: MAGS2 EP3 working group
""" """
import numpy as np import numpy as np
from scipy import signal try:
from scipy.signal import tukey
except ImportError:
from scipy.signal.windows import tukey
from obspy.core import Stream from obspy.core import Stream
from pylot.core.pick.utils import PickingFailedException from pylot.core.pick.utils import PickingFailedException
@ -227,7 +230,7 @@ class AICcf(CharacteristicFunction):
ind = np.where(~np.isnan(xnp))[0] ind = np.where(~np.isnan(xnp))[0]
if ind.size: if ind.size:
xnp[:ind[0]] = xnp[ind[0]] xnp[:ind[0]] = xnp[ind[0]]
xnp = signal.tukey(len(xnp), alpha=0.05) * xnp xnp = tukey(len(xnp), alpha=0.05) * xnp
xnp = xnp - np.mean(xnp) xnp = xnp - np.mean(xnp)
datlen = len(xnp) datlen = len(xnp)
k = np.arange(1, datlen) k = np.arange(1, datlen)

View File

@ -27,6 +27,10 @@ class Metadata(object):
# saves which metadata files are from obspy dmt # saves which metadata files are from obspy dmt
self.obspy_dmt_invs = [] self.obspy_dmt_invs = []
if inventory: if inventory:
# make sure that no accidental backslashes mess up the path
if isinstance(inventory, str):
inventory = inventory.replace('\\', '/')
inventory = os.path.abspath(inventory)
if os.path.isdir(inventory): if os.path.isdir(inventory):
self.add_inventory(inventory) self.add_inventory(inventory)
if os.path.isfile(inventory): if os.path.isfile(inventory):

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

View File

@ -0,0 +1,99 @@
%This is a parameter input file for PyLoT/autoPyLoT.
%All main and special settings regarding data handling
%and picking are to be set here!
%Parameters are optimized for %extent data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#
dmt_database_test #datapath# %data path
20171010_063224.a #eventID# %event ID for single event processing (* for all events found in database)
#invdir# %full path to inventory or dataless-seed file
PILOT #datastructure# %choose data structure
True #apverbose# %choose 'True' or 'False' for terminal output
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#NLLoc settings#
None #nllocbin# %path to NLLoc executable
None #nllocroot# %root of NLLoc-processing directory
None #phasefile# %name of autoPyLoT-output phase file for NLLoc
None #ctrfile# %name of autoPyLoT-output control file for NLLoc
ttime #ttpatter# %pattern of NLLoc ttimes from grid
AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#parameters for seismic moment estimation#
3530.0 #vp# %average P-wave velocity
2500.0 #rho# %average rock density [kg/m^3]
300.0 0.8 #Qp# %quality factor for P waves (Qp*f^a); list(Qp, a)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#settings local magnitude#
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!
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!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#filter settings#
0.03 0.03 #minfreq# %Lower filter frequency [P, S]
0.5 0.5 #maxfreq# %Upper filter frequency [P, S]
4 4 #filter_order# %filter order [P, S]
bandpass bandpass #filter_type# %filter type (bandpass, bandstop, lowpass, highpass) [P, S]
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#common settings picker#
global #extent# %extent of array ("local", "regional" or "global")
-100.0 #pstart# %start time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
50.0 #pstop# %end time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
-50.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
50.0 #sstop# %end time [s] after P-onset for calculating CF for S-picking
True #use_taup# %use estimated traveltimes from TauPy for calculating windows for CF
ak135 #taup_model# %Define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
P,Pdiff,S,SKS #taup_phases# %Specify possible phases for TauPy (comma separated). See Obspy TauPy documentation for possible values.
0.03 0.5 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
0.01 0.5 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
0.03 0.5 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
0.01 0.5 #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)
300.0 #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
16.0 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
10.0 #tpred1z# %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
12.0 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
6.0 #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
60.0 5.0 20.0 12.0 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
50.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
30.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
2.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
2.0 #tsmoothP# %for HOS/AR, take average of samples in this time window for smoothing CF [s]
0.006 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
2.0 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
#H-components#
ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
12.0 #tdet1h# %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
6.0 #tpred1h# %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
8.0 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
4.0 #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
100.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
195.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
60.0 10.0 30.0 12.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
22.0 #aictsmoothS# %for AIC-picker, take average of samples in this time window for smoothing of AIC-function [s]
20.0 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
0.001 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
2.0 #nfacS# %for AR-picker, noise factor for noise level determination (S)
#first-motion picker#
1 #minfmweight# %minimum required P weight for first-motion determination
3.0 #minFMSNR# %miniumum required SNR for first-motion determination
10.0 #fmpickwin# %pick window [s] around P onset for calculating zero crossings
#quality assessment#
0.1 0.2 0.4 0.8 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
4.0 8.0 16.0 32.0 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
0.005 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
0.002 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
1.3 #minAICSSNR# %below this SNR the initial S pick is rejected
20.0 #minsiglength# %length of signal part for which amplitudes must exceed noiselevel [s]
1.0 #noisefactor# %noiselevel*noisefactor=threshold
10.0 #minpercent# %required percentage of amplitudes exceeding threshold
0.1 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
100.0 #mdttolerance# %maximum allowed deviation of P picks from median [s]
50.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
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

View File

@ -0,0 +1,67 @@
import os
import pytest
from obspy import read_events
from autoPyLoT import autoPyLoT
class TestAutopickerGlobal():
def init(self):
self.params_infile = 'pylot_alparray_mantle_corr_stack_0.03-0.5.in'
self.test_event_dir = 'dmt_database_test'
self.fname_outfile_xml = os.path.join(
self.test_event_dir, '20171010_063224.a', 'PyLoT_20171010_063224.a_autopylot.xml'
)
# check if the input files exist
if not os.path.isfile(self.params_infile):
print(f'Test input file {os.path.abspath(self.params_infile)} not found.')
return False
if not os.path.exists(self.test_event_dir):
print(
f'Test event directory not found at location "{os.path.abspath(self.test_event_dir)}". '
f'Make sure to load it from the website first.'
)
return False
return True
def test_autopicker(self):
assert self.init(), 'Initialization failed due to missing input files.'
# check for output file in test directory and remove it if necessary
if os.path.isfile(self.fname_outfile_xml):
os.remove(self.fname_outfile_xml)
autoPyLoT(inputfile=self.params_infile, eventid='20171010_063224.a', obspyDMT_wfpath='processed')
# test for different known output files if they are identical or not
compare_pickfiles(self.fname_outfile_xml, 'PyLoT_20171010_063224.a_autopylot.xml', True)
compare_pickfiles(self.fname_outfile_xml, 'PyLoT_20171010_063224.a_saved_from_GUI.xml', True)
compare_pickfiles(self.fname_outfile_xml, 'PyLoT_20171010_063224.a_corrected_taup_times_0.03-0.5_P.xml', False)
def compare_pickfiles(pickfile1: str, pickfile2: str, samefile: bool = True) -> None:
"""
Compare the pick times and errors from two pick files.
Parameters:
pickfile1 (str): The path to the first pick file.
pickfile2 (str): The path to the second pick file.
samefile (bool): A flag indicating whether the two files are expected to be the same. Defaults to True.
Returns:
None
"""
cat1 = read_events(pickfile1)
cat2 = read_events(pickfile2)
picks1 = sorted(cat1[0].picks, key=lambda pick: str(pick.waveform_id))
picks2 = sorted(cat2[0].picks, key=lambda pick: str(pick.waveform_id))
pick_times1 = [pick.time for pick in picks1]
pick_times2 = [pick.time for pick in picks2]
pick_terrs1 = [pick.time_errors for pick in picks1]
pick_terrs2 = [pick.time_errors for pick in picks2]
# check if times and errors are identical or not depending on the samefile flag
assert (pick_times1 == pick_times2) is samefile, 'Pick times error'
assert (pick_terrs1 == pick_terrs2) is samefile, 'Pick time errors errors'