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README.md

@ -1,90 +1,96 @@
# PyLoT
version: 0.1a
The Python picking and Localisation Tool
This python library contains a graphical user interfaces for picking
seismic phases. This software needs [ObsPy][ObsPy]
and the PySide Qt4 bindings for python to be installed first.
PILOT has originally been developed in Mathworks' MatLab. In order to
distribute PILOT without facing portability problems, it has been decided
to redevelop the software package in Python. The great work of the ObsPy
group allows easy handling of a bunch of seismic data and PyLoT will
benefit a lot compared to the former MatLab version.
The development of PyLoT is part of the joint research project MAGS2.
## Installation
At the moment there is no automatic installation procedure available for PyLoT.
Best way to install is to clone the repository and add the path to your Python path.
#### Prerequisites:
In order to run PyLoT you need to install:
- python
- scipy
- numpy
- matplotlib
- obspy
- pyside
#### Some handwork:
PyLoT needs a properties folder on your system to work. It should be situated in your home directory:
mkdir ~/.pylot
In the next step you have to copy some files to this directory:
cp path-to-pylot/inputs/pylot.in ~/.pylot/
for local distance seismicity
cp path-to-pylot/inputs/autoPyLoT_local.in ~/.pylot/autoPyLoT.in
for regional distance seismicity
cp path-to-pylot/inputs/autoPyLoT_regional.in ~/.pylot/autoPyLoT.in
and some extra information on filtering, error estimates (just needed for reading old PILOT data) and the Richter magnitude scaling relation
cp path-to-pylot/inputs/filter.in path-to-pylot/inputs/PILOT_TimeErrors.in path-to-pylot/inputs/richter_scaling.data ~/.pylot/
You may need to do some modifications to these files. Especially folder names should be reviewed.
PyLoT has been tested on Mac OSX (10.11) and Debian Linux 8.
## 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)
We hope to solve these with the next release.
## Staff
Original author(s): L. Kueperkoch, S. Wehling-Benatelli, M. Bischoff (PILOT)
Developer(s): S. Wehling-Benatelli, L. Kueperkoch, K. Olbert, M. Bischoff,
C. Wollin, M. Rische, M. Paffrath
Others: A. Bruestle, T. Meier, W. Friederich
[ObsPy]: http://github.com/obspy/obspy/wiki
October 2016
# PyLoT
version: 0.2
The Python picking and Localisation Tool
This python library contains a graphical user interfaces for picking
seismic phases. This software needs [ObsPy][ObsPy]
and the PySide Qt4 bindings for python to be installed first.
PILOT has originally been developed in Mathworks' MatLab. In order to
distribute PILOT without facing portability problems, it has been decided
to redevelop the software package in Python. The great work of the ObsPy
group allows easy handling of a bunch of seismic data and PyLoT will
benefit a lot compared to the former MatLab version.
The development of PyLoT is part of the joint research project MAGS2 and AlpArray.
## Installation
At the moment there is no automatic installation procedure available for PyLoT.
Best way to install is to clone the repository and add the path to your Python path.
#### Prerequisites:
In order to run PyLoT you need to install:
- python 2 or 3
- scipy
- numpy
- matplotlib
- obspy
- pyside
#### Some handwork:
PyLoT needs a properties folder on your system to work. It should be situated in your home directory
(on Windows usually C:/Users/*username*):
mkdir ~/.pylot
In the next step you have to copy some files to this directory:
*for local distance seismicity*
cp path-to-pylot/inputs/pylot_local.in ~/.pylot/pylot.in
*for regional distance seismicity*
cp path-to-pylot/inputs/pylot_regional.in ~/.pylot/pylot.in
*for global distance seismicity*
cp path-to-pylot/inputs/pylot_global.in ~/.pylot/pylot.in
and some extra information on error estimates (just needed for reading old PILOT data) and the Richter magnitude scaling relation
cp path-to-pylot/inputs/PILOT_TimeErrors.in path-to-pylot/inputs/richter_scaling.data ~/.pylot/
You may need to do some modifications to these files. Especially folder names should be reviewed.
PyLoT has been tested on Mac OSX (10.11), Debian Linux 8 and on Windows 10.
## Release notes
#### Features:
- event organisation in project files and waveform visualisation
- consistent manual phase picking through predefined SNR dependant zoom level
- consistent automatic phase picking routines using Higher Order Statistics, AIC and Autoregression
- interactive tuning of auto-pick parameters
- 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:
- Sometimes an error might occur when using Qt
We hope to solve these with the next release.
## Staff
Original author(s): L. Kueperkoch, S. Wehling-Benatelli, M. Bischoff (PILOT)
Developer(s): S. Wehling-Benatelli, L. Kueperkoch, K. Olbert, M. Bischoff,
C. Wollin, M. Rische, M. Paffrath
Others: A. Bruestle, T. Meier, W. Friederich
[ObsPy]: http://github.com/obspy/obspy/wiki
September 2017

@ -4,31 +4,37 @@
from __future__ import print_function
import argparse
import datetime
import glob
import os
import datetime
from obspy import read_events
import pylot.core.loc.hyposat as hyposat
import pylot.core.loc.hypo71 as hypo71
import pylot.core.loc.velest as velest
import pylot.core.loc.hypodd as hypodd
import pylot.core.loc.focmec as focmec
import pylot.core.loc.hash as hash
import pylot.core.loc.hypo71 as hypo71
import pylot.core.loc.hypodd as hypodd
import pylot.core.loc.hyposat as hyposat
import pylot.core.loc.nll as nll
#from PySide.QtGui import QWidget, QInputDialog
import pylot.core.loc.velest as velest
from obspy import read_events
from obspy.core.event import ResourceIdentifier
# from PySide.QtGui import QWidget, QInputDialog
from pylot.core.analysis.magnitude import MomentMagnitude, LocalMagnitude
from pylot.core.io.data import Data
from pylot.core.io.inputs import PylotParameter
from pylot.core.pick.autopick import autopickevent, iteratepicker
from pylot.core.util.dataprocessing import restitute_data, read_metadata, \
remove_underscores
from pylot.core.util.dataprocessing import restitute_data, read_metadata
from pylot.core.util.defaults import SEPARATOR
from pylot.core.util.event import Event
from pylot.core.util.structure import DATASTRUCTURE
from pylot.core.util.utils import real_None, remove_underscores, trim_station_components, check4gaps, check4doubled, \
check4rotated
from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, eventid=None, savepath=None, station='all', iplot=0):
def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, eventid=None, savepath=None,
savexml=True, station='all', iplot=0, ncores=0):
"""
Determine phase onsets automatically utilizing the automatic picking
algorithms by Kueperkoch et al. 2010/2012.
@ -42,40 +48,68 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
.. rubric:: Example
"""
if ncores == 1:
sp_info = 'autoPyLoT is running serial on 1 cores.'
else:
if ncores == 0:
ncores_readable = 'all available'
else:
ncores_readable = ncores
sp_info = 'autoPyLoT is running in parallel on {} cores.'.format(ncores_readable)
splash = '''************************************\n
*********autoPyLoT starting*********\n
The Python picking and Location Tool\n
Version {version} 2015\n
Version {version} 2017\n
\n
Authors:\n
S. Wehling-Benatelli (Ruhr-Universitaet Bochum)\n
L. Kueperkoch (BESTEC GmbH, Landau i. d. Pfalz)\n
K. Olbert (Christian-Albrechts Universitaet zu Kiel)\n
***********************************'''.format(version=_getVersionString())
M. Paffrath (Ruhr-Universitaet Bochum)\n
S. Wehling-Benatelli (Ruhr-Universitaet Bochum)\n
{sp}
***********************************'''.format(version=_getVersionString(),
sp=sp_info)
print(splash)
parameter = real_None(parameter)
inputfile = real_None(inputfile)
eventid = real_None(eventid)
fig_dict = None
fig_dict_wadatijack = None
locflag = 1
if input_dict and isinstance(input_dict, dict):
if input_dict.has_key('parameter'):
if 'parameter' in input_dict:
parameter = input_dict['parameter']
if input_dict.has_key('fig_dict'):
if 'fig_dict' in input_dict:
fig_dict = input_dict['fig_dict']
if input_dict.has_key('station'):
if 'fig_dict_wadatijack' in input_dict:
fig_dict_wadatijack = input_dict['fig_dict_wadatijack']
if 'station' in input_dict:
station = input_dict['station']
if input_dict.has_key('fnames'):
if 'fnames' in input_dict:
fnames = input_dict['fnames']
if input_dict.has_key('iplot'):
if 'eventid' in input_dict:
eventid = input_dict['eventid']
if 'iplot' in input_dict:
iplot = input_dict['iplot']
if input_dict.has_key('locflag'):
if 'locflag' in input_dict:
locflag = input_dict['locflag']
if 'savexml' in input_dict:
savexml = input_dict['savexml']
if not parameter:
if inputfile:
parameter = PylotParameter(inputfile)
iplot = parameter['iplot']
#iplot = parameter['iplot']
else:
print('No parameters set and no input file given. Choose either of both.')
return
infile = os.path.join(os.path.expanduser('~'), '.pylot', 'pylot.in')
print('Using default input file {}'.format(infile))
parameter = PylotParameter(infile)
else:
if not type(parameter) == PylotParameter:
print('Wrong input type for parameter: {}'.format(type(parameter)))
@ -83,8 +117,6 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
if inputfile:
print('Parameters set and input file given. Choose either of both.')
return
data = Data()
evt = None
@ -97,7 +129,7 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
'dbase': parameter.get('database')}
exf = ['root', 'dpath', 'dbase']
if parameter['eventID'] is not '*' and fnames == 'None':
dsfields['eventID'] = parameter['eventID']
exf.append('eventID')
@ -106,7 +138,7 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
datastructure.setExpandFields(exf)
# check if default location routine NLLoc is available
if parameter['nllocbin'] and locflag:
if real_None(parameter['nllocbin']) and locflag:
# get NLLoc-root path
nllocroot = parameter.get('nllocroot')
# get path to NLLoc executable
@ -137,9 +169,14 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
# multiple event processing
# read each event in database
events = [events for events in glob.glob(os.path.join(datapath, '*')) if os.path.isdir(events)]
elif fnames == 'None' and parameter['eventID'] is not '*':
elif fnames == 'None' and parameter['eventID'] is not '*' and not type(parameter['eventID']) == list:
# single event processing
events = glob.glob(os.path.join(datapath, parameter['eventID']))
elif fnames == 'None' and type(parameter['eventID']) == list:
# multiple event processing
events = []
for eventID in parameter['eventID']:
events.append(os.path.join(datapath, eventID))
else:
# autoPyLoT was initialized from GUI
events = []
@ -147,22 +184,41 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
evID = os.path.split(eventid)[-1]
locflag = 2
else:
# started in tune mode
# started in tune or interactive mode
datapath = os.path.join(parameter['rootpath'],
parameter['datapath'])
events = []
events.append(os.path.join(datapath,
parameter['database'],
parameter['eventID']))
for eventID in eventid:
events.append(os.path.join(datapath,
parameter['database'],
eventID))
if not events:
print('autoPyLoT: No events given. Return!')
return
for event in events:
# transform system path separator to '/'
for index, eventpath in enumerate(events):
eventpath = eventpath.replace(SEPARATOR, '/')
events[index] = eventpath
allpicks = {}
glocflag = locflag
for eventpath in events:
evID = os.path.split(eventpath)[-1]
fext = '.xml'
filename = os.path.join(eventpath, 'PyLoT_' + evID + fext)
try:
data = Data(evtdata=filename)
data.get_evt_data().path = eventpath
print('Reading event data from filename {}...'.format(filename))
except Exception as e:
print('Could not read event from file {}: {}'.format(filename, e))
data = Data()
pylot_event = Event(eventpath) # event should be path to event directory
data.setEvtData(pylot_event)
if fnames == 'None':
data.setWFData(glob.glob(os.path.join(datapath, event, '*')))
evID = os.path.split(event)[-1]
data.setWFData(glob.glob(os.path.join(datapath, eventpath, '*')))
# the following is necessary because within
# multiple event processing no event ID is provided
# in autopylot.in
@ -178,10 +234,10 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
parameter.setParam(eventID=eventID)
else:
data.setWFData(fnames)
event = events[0]
#now = datetime.datetime.now()
#evID = '%d%02d%02d%02d%02d' % (now.year,
eventpath = events[0]
# now = datetime.datetime.now()
# evID = '%d%02d%02d%02d%02d' % (now.year,
# now.month,
# now.day,
# now.hour,
@ -192,22 +248,31 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
wfdat = wfdat.select(station=station)
if not wfdat:
print('Could not find station {}. STOP!'.format(station))
return
return
wfdat = remove_underscores(wfdat)
metadata = read_metadata(parameter.get('invdir'))
print("Restitute data ...")
corr_dat = restitute_data(wfdat.copy(), *metadata)
print('Working on event %s. Stations: %s' % (event, station))
# trim components for each station to avoid problems with different trace starttimes for one station
wfdat = check4gaps(wfdat)
wfdat = check4doubled(wfdat)
wfdat = trim_station_components(wfdat, trim_start=True, trim_end=False)
metadata = read_metadata(parameter.get('invdir'))
# rotate stations to ZNE
wfdat = check4rotated(wfdat, metadata)
corr_dat = None
if locflag:
print("Restitute data ...")
corr_dat = restitute_data(wfdat.copy(), *metadata, ncores=ncores)
if not corr_dat and locflag:
locflag = 2
print('Working on event %s. Stations: %s' % (eventpath, station))
print(wfdat)
##########################################################
# !automated picking starts here!
if input_dict:
if input_dict.has_key('fig_dict'):
fig_dict = input_dict['fig_dict']
picks = autopickevent(wfdat, parameter, iplot=iplot, fig_dict=fig_dict)
else:
picks = autopickevent(wfdat, parameter, iplot=iplot)
fdwj = None
if fig_dict_wadatijack:
fdwj = fig_dict_wadatijack[evID]
picks = autopickevent(wfdat, parameter, iplot=iplot, fig_dict=fig_dict,
fig_dict_wadatijack=fdwj,
ncores=ncores, metadata=metadata, origin=data.get_evt_data().origins)
##########################################################
# locating
if locflag > 0:
@ -246,11 +311,11 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
# calculate seismic moment Mo and moment magnitude Mw
moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'),
parameter.get('Qp'),
parameter.get('rho'), True, \
parameter.get('rho'), True,
iplot)
# update pick with moment property values (w0, fc, Mo)
for station, props in moment_mag.moment_props.items():
picks[station]['P'].update(props)
for stats, props in moment_mag.moment_props.items():
picks[stats]['P'].update(props)
evt = moment_mag.updated_event()
net_mw = moment_mag.net_magnitude()
print("Network moment magnitude: %4.1f" % net_mw.mag)
@ -258,12 +323,12 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
WAscaling = parameter.get('WAscaling')
magscaling = parameter.get('magscaling')
local_mag = LocalMagnitude(corr_dat, evt,
parameter.get('sstop'),
parameter.get('sstop'),
WAscaling, True, iplot)
for station, amplitude in local_mag.amplitudes.items():
picks[station]['S']['Ao'] = amplitude.generic_amplitude
for stats, amplitude in local_mag.amplitudes.items():
picks[stats]['S']['Ao'] = amplitude.generic_amplitude
print("Local station magnitudes scaled with:")
print("log(Ao) + %f * log(r) + %f * r + %f" % (WAscaling[0],
print("log(Ao) + %f * log(r) + %f * r + %f" % (WAscaling[0],
WAscaling[1],
WAscaling[2]))
evt = local_mag.updated_event(magscaling)
@ -289,9 +354,10 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
break
print("autoPyLoT: Starting with iteration No. %d ..." % nlloccounter)
if input_dict:
if input_dict.has_key('fig_dict'):
if 'fig_dict' in input_dict:
fig_dict = input_dict['fig_dict']
picks = iteratepicker(wfdat, nllocfile, picks, badpicks, parameter, fig_dict=fig_dict)
picks = iteratepicker(wfdat, nllocfile, picks, badpicks, parameter,
fig_dict=fig_dict)
else:
picks = iteratepicker(wfdat, nllocfile, picks, badpicks, parameter)
# write phases to NLLoc-phase file
@ -308,7 +374,7 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
for key in picks:
if picks[key]['P']['weight'] >= 4 or picks[key]['S']['weight'] >= 4:
badpicks.append([key, picks[key]['P']['mpp']])
print("autoPyLoT: After iteration No. %d: %d bad onsets found ..." % (nlloccounter, \
print("autoPyLoT: After iteration No. %d: %d bad onsets found ..." % (nlloccounter,
len(badpicks)))
if len(badpicks) == 0:
print("autoPyLoT: No more bad onsets found, stop iterative picking!")
@ -318,11 +384,12 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
# calculate seismic moment Mo and moment magnitude Mw
moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'),
parameter.get('Qp'),
parameter.get('rho'), True, \
parameter.get('rho'), True,
iplot)
# update pick with moment property values (w0, fc, Mo)
for station, props in moment_mag.moment_props.items():
picks[station]['P'].update(props)
for stats, props in moment_mag.moment_props.items():
if picks.has_key(stats):
picks[stats]['P'].update(props)
evt = moment_mag.updated_event()
net_mw = moment_mag.net_magnitude()
print("Network moment magnitude: %4.1f" % net_mw.mag)
@ -330,12 +397,13 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
WAscaling = parameter.get('WAscaling')
magscaling = parameter.get('magscaling')
local_mag = LocalMagnitude(corr_dat, evt,
parameter.get('sstop'),
parameter.get('sstop'),
WAscaling, True, iplot)
for station, amplitude in local_mag.amplitudes.items():
picks[station]['S']['Ao'] = amplitude.generic_amplitude
for stats, amplitude in local_mag.amplitudes.items():
if picks.has_key(stats):
picks[stats]['S']['Ao'] = amplitude.generic_amplitude
print("Local station magnitudes scaled with:")
print("log(Ao) + %f * log(r) + %f * r + %f" % (WAscaling[0],
print("log(Ao) + %f * log(r) + %f * r + %f" % (WAscaling[0],
WAscaling[1],
WAscaling[2]))
evt = local_mag.updated_event(magscaling)
@ -350,46 +418,57 @@ def autoPyLoT(input_dict=None, parameter=None, inputfile=None, fnames=None, even
# write phase files for various location
# and fault mechanism calculation routines
# ObsPy event object
data.applyEVTData(picks)
if evt is not None:
event_id = eventpath.split('/')[-1]
evt.resource_id = ResourceIdentifier('smi:local/' + event_id)
data.applyEVTData(evt, 'event')
fnqml = '%s/PyLoT_%s' % (event, evID)
data.exportEvent(fnqml, fnext='.xml', fcheck='manual')
data.applyEVTData(picks)
if savexml:
if savepath == 'None' or savepath == None:
saveEvtPath = eventpath
else:
saveEvtPath = savepath
fnqml = '%s/PyLoT_%s' % (saveEvtPath, evID)
data.exportEvent(fnqml, fnext='.xml', fcheck=['auto', 'magnitude', 'origin'])
if locflag == 1:
# HYPO71
hypo71file = '%s/PyLoT_%s_HYPO71_phases' % (event, evID)
hypo71file = '%s/PyLoT_%s_HYPO71_phases' % (eventpath, evID)
hypo71.export(picks, hypo71file, parameter)
# HYPOSAT
hyposatfile = '%s/PyLoT_%s_HYPOSAT_phases' % (event, evID)
hyposatfile = '%s/PyLoT_%s_HYPOSAT_phases' % (eventpath, evID)
hyposat.export(picks, hyposatfile, parameter)
# VELEST
velestfile = '%s/PyLoT_%s_VELEST_phases.cnv' % (event, evID)
velest.export(picks, velestfile, parameter, evt)
# hypoDD
hypoddfile = '%s/PyLoT_%s_hypoDD_phases.pha' % (event, evID)
hypodd.export(picks, hypoddfile, parameter, evt)
# FOCMEC
focmecfile = '%s/PyLoT_%s_FOCMEC.in' % (event, evID)
focmec.export(picks, focmecfile, parameter, evt)
# HASH
hashfile = '%s/PyLoT_%s_HASH' % (event, evID)
hash.export(picks, hashfile, parameter, evt)
# VELEST
velestfile = '%s/PyLoT_%s_VELEST_phases.cnv' % (eventpath, evID)
velest.export(picks, velestfile, evt, parameter)
# hypoDD
hypoddfile = '%s/PyLoT_%s_hypoDD_phases.pha' % (eventpath, evID)
hypodd.export(picks, hypoddfile, parameter, evt)
# FOCMEC
focmecfile = '%s/PyLoT_%s_FOCMEC.in' % (eventpath, evID)
focmec.export(picks, focmecfile, parameter, evt)
# HASH
hashfile = '%s/PyLoT_%s_HASH' % (eventpath, evID)
hash.export(picks, hashfile, parameter, evt)
endsplash = '''------------------------------------------\n'
-----Finished event %s!-----\n'
------------------------------------------'''.format \
(version=_getVersionString()) % evID
print(endsplash)
locflag = glocflag
if locflag == 0:
print("autoPyLoT was running in non-location mode!")
# save picks for current event ID to dictionary with ALL picks
allpicks[evID] = picks
endsp = '''####################################\n
************************************\n
*********autoPyLoT terminates*******\n
The Python picking and Location Tool\n
************************************'''.format(version=_getVersionString())
print(endsp)
return picks
return allpicks
if __name__ == "__main__":
@ -399,30 +478,28 @@ if __name__ == "__main__":
autoregressive prediction and AIC followed by locating the seismic events using
NLLoc''')
#parser.add_argument('-d', '-D', '--input_dict', type=str,
# action='store',
# help='''optional, dictionary containing processing parameters''')
#parser.add_argument('-p', '-P', '--parameter', type=str,
# action='store',
# help='''parameter file, default=None''')
parser.add_argument('-i', '-I', '--inputfile', type=str,
action='store',
help='''full path to the file containing the input
parameters for autoPyLoT''')
parameters for autoPyLoT''')
parser.add_argument('-p', '-P', '--iplot', type=int,
action='store',
help='''optional, logical variable for plotting: 0=none, 1=partial, 2=all''')
parser.add_argument('-f', '-F', '--fnames', type=str,
action='store',
help='''optional, list of data file names''')
parser.add_argument('-e', '-E', '--eventid', type=str,
parser.add_argument('-e', '--eventid', type=str,
action='store',
help='''optional, event path incl. event ID''')
parser.add_argument('-s', '-S', '--spath', type=str,
action='store',
help='''optional, save path for autoPyLoT output''')
#parser.add_argument('-v', '-V', '--version', action='version',
# version='autoPyLoT ' + __version__,
# help='show version information and exit')
parser.add_argument('-c', '-C', '--ncores', type=int,
action='store', default=0,
help='''optional, number of CPU cores used for parallel processing (default: all available(=0))''')
cla = parser.parse_args()
picks = autoPyLoT(inputfile=str(cla.inputfile), fnames=str(cla.fnames),
eventid=str(cla.eventid), savepath=str(cla.spath))
picks = autoPyLoT(inputfile=str(cla.inputfile), fnames=str(cla.fnames),
eventid=str(cla.eventid), savepath=str(cla.spath),
ncores=cla.ncores, iplot=int(cla.iplot))

@ -1,8 +0,0 @@
git pull
Entferne qrc_resources.py
KONFLIKT (ändern/löschen): pylot/core/pick/getSNR.py gelöscht in HEAD und geändert in 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324. Stand 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324 von pylot/core/pick/getSNR.py wurde im Arbeitsbereich gelassen.
KONFLIKT (ändern/löschen): pylot/core/pick/fmpicker.py gelöscht in HEAD und geändert in 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324. Stand 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324 von pylot/core/pick/fmpicker.py wurde im Arbeitsbereich gelassen.
KONFLIKT (ändern/löschen): pylot/core/pick/earllatepicker.py gelöscht in HEAD und geändert in 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324. Stand 67dd66535a213ba5c7cfe2be52aa6d5a7e8b7324 von pylot/core/pick/earllatepicker.py wurde im Arbeitsbereich gelassen.
Automatisches Zusammenfügen von icons.qrc
Automatischer Merge fehlgeschlagen; beheben Sie die Konflikte und committen Sie dann das Ergebnis.

@ -1,17 +1,19 @@
<html><head><title>PyLoT - the Python picking and Localisation Tool</title></head>
<html>
<head><title>PyLoT - the Python picking and Localisation Tool</title></head>
<body>
<p><b>PyLoT</b> is a program which is capable of picking seismic phases,
exporting these as numerous standard phase format and localize the corresponding
seismic event with external software as, e.g.:</p>
exporting these as numerous standard phase format and localize the corresponding
seismic event with external software as, e.g.:</p>
<ul type="circle">
<li><a href="http://alomax.free.fr/nlloc/index.html">NonLinLoc</a></li>
<li>HypoInvers</li>
<li>HypoSat</li>
<li>whatever you want ...</li>
<li><a href="http://alomax.free.fr/nlloc/index.html">NonLinLoc</a></li>
<li>HypoInvers</li>
<li>HypoSat</li>
<li>whatever you want ...</li>
</ul>
<p>Read more on the
<a href="https://ariadne.geophysik.rub.de/trac/PyLoT/wiki/">PyLoT WikiPage</a>.</p>
<a href="https://ariadne.geophysik.rub.de/trac/PyLoT/wiki/">PyLoT WikiPage</a>.</p>
<p>Bug reports are very much appreciated and can also be delivered on our
<a href="https://ariadne.geophysik.rub.de/trac/PyLoT">PyLoT TracPage</a> after
successful registration.</p>
</body></html>
<a href="https://ariadne.geophysik.rub.de/trac/PyLoT">PyLoT TracPage</a> after
successful registration.</p>
</body>
</html>

@ -2,6 +2,8 @@
<qresource>
<file>icons/pylot.ico</file>
<file>icons/pylot.png</file>
<file>icons/back.png</file>
<file>icons/home.png</file>
<file>icons/newfile.png</file>
<file>icons/open.png</file>
<file>icons/openproject.png</file>
@ -23,6 +25,8 @@
<file>icons/savepicks.png</file>
<file>icons/preferences.png</file>
<file>icons/parameter.png</file>
<file>icons/inventory.png</file>
<file>icons/map.png</file>
<file>icons/openloc.png</file>
<file>icons/compare_button.png</file>
<file>icons/locate_button.png</file>

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@ -1,100 +0,0 @@
%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

@ -1,99 +0,0 @@
%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

@ -1,100 +0,0 @@
%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

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

@ -1,98 +0,0 @@
%This is a example parameter input file for PyLoT.
%All main and special settings regarding data handling
%and picking are to be set here!
%Parameters shown here are optimized for local data sets!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#main settings#
/data/Geothermie/Insheim #rootpath# %project path
EVENT_DATA/LOCAL #datapath# %data path
2013.02_Insheim #database# %name of data base
e0019.048.13 #eventID# %event ID for single event processing
/data/Geothermie/Insheim/STAT_INFO #invdir# %full path to inventory or dataless-seed file
PILOT #datastructure# %choose data structure
0 #iplot# %flag for plotting: 0 none, 1 partly, >1 everything
True #apverbose# %choose 'True' or 'False' for terminal output
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#NLLoc settings#
/progs/bin #nllocbin# %path to NLLoc executable
/data/Geothermie/Insheim/LOCALISATION/NLLoc #nllocroot# %root of NLLoc-processing directory
AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
%(in nllocroot/obs)
Insheim_min1d2015.in #ctrfile# %name of PyLoT-output control file for NLLoc
%(in nllocroot/run)
ttime #ttpatter# %pattern of NLLoc ttimes from grid
%(in nllocroot/times)
AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
%(returns 'eventID_outpatter')
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#parameters for seismic moment estimation#
3530 #vp# %average P-wave velocity
2500 #rho# %average rock density [kg/m^3]
300 0.8 #Qp# %quality factor for P waves (Qp*f^a); list(Qp, a)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
AUTOFOCMEC_AIC_HOS4_ARH.in #focmecin# %name of focmec input file containing derived polarities
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#common settings picker#
15.0 #pstart# %start time [s] for calculating CF for P-picking
60.0 #pstop# %end time [s] for calculating CF for P-picking
-1.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
10.0 #sstop# %end time [s] after P-onset for calculating CF for S-picking
2 20 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
2 30 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
2 15 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
2 20 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz]
#special settings for calculating CF#
%!!Edit the following only if you know what you are doing!!%
#Z-component#
HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
7.0 #tlta# %for HOS-/AR-AIC-picker, length of LTA window [s]
4 #hosorder# %for HOS-picker, order of Higher Order Statistics
2 #Parorder# %for AR-picker, order of AR process of Z-component
1.2 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
0.4 #tpred1z# %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
0.6 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
0.2 #tpred2z# %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
0.001 #addnoise# %add noise to seismogram for stable AR prediction
3 0.1 0.5 0.5 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
3.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
6.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
0.2 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
0.1 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
0.001 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
1.3 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
#H-components#
ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
0.8 #tdet1h# %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
0.4 #tpred1h# %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
0.6 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
0.3 #tpred2h# %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
4 #Sarorder# %for AR-picker, order of AR process of H-components
5.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
3.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
2 0.2 1.5 0.5 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise,tsafetey,tsignal,tslope] [s]
0.5 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
0.7 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
0.9 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
1.5 #nfacS# %for AR-picker, noise factor for noise level determination (S)
%first-motion picker%
1 #minfmweight# %minimum required P weight for first-motion determination
2 #minFMSNR# %miniumum required SNR for first-motion determination
0.2 #fmpickwin# %pick window around P onset for calculating zero crossings
%quality assessment%
#inital AIC onset#
0.01 0.02 0.04 0.08 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
0.04 0.08 0.16 0.32 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
4 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
1.2 #minAICPSNR# %below this SNR the initial P pick is rejected
2 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
1.5 #minAICSSNR# %below this SNR the initial S pick is rejected
#check duration of signal using envelope function#
3 #minsiglength# %minimum required length of signal [s]
1.0 #noisefactor# %noiselevel*noisefactor=threshold
40 #minpercent# %required percentage of samples higher than threshold
#check for spuriously picked S-onsets#
2.0 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
#check statistics of P onsets#
2.5 #mdttolerance# %maximum allowed deviation of P picks from median [s]
#wadati check#
1.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram

100
inputs/pylot_global.in Normal file

@ -0,0 +1,100 @@
%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#
#rootpath# %project path
#datapath# %data path
#database# %name of data base
#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#
0.01 0.01 #minfreq# %Lower filter frequency [P, S]
0.3 0.3 #maxfreq# %Upper filter frequency [P, S]
3 3 #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")
-150.0 #pstart# %start time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
600.0 #pstop# %end time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
200.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
1150.0 #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
iasp91 #taup_model# %define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
0.05 0.5 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
0.001 0.5 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
0.05 0.5 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
0.001 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)
150.0 #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 10.0 40.0 10.0 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
150.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
35.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
6.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
4.0 #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)
1.1 #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
30.0 #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]
100.0 10.0 45.0 10.0 #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 for smoothing of AIC-function [s]
10.0 #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)
1.2 #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 around P onset for calculating zero crossings
#quality assessment#
1.0 2.0 4.0 8.0 #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
0.5 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
1.0 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
1.3 #minAICSSNR# %below this SNR the initial S pick is rejected
5.0 #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
1.2 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
25.0 #mdttolerance# %maximum allowed deviation of P picks from median [s]
50.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
5.0 #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

100
inputs/pylot_local.in Normal file

@ -0,0 +1,100 @@
%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#
#rootpath# %project path
#datapath# %data path
#database# %name of data base
#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#
1.11 0.0009 -2.0 #WAscaling# %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] If zeros are set, original Richter magnitude is calculated!
1.0382 -0.447 #magscaling# %Scaling relation for derived local magnitude [a*Ml+b]. If zeros are set, no scaling of network magnitude is applied!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#filter settings#
1.0 1.0 #minfreq# %Lower filter frequency [P, S]
10.0 10.0 #maxfreq# %Upper filter frequency [P, S]
2 2 #filter_order# %filter order [P, S]
bandpass bandpass #filter_type# %filter type (bandpass, bandstop, lowpass, highpass) [P, S]
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#common settings picker#
local #extent# %extent of array ("local", "regional" or "global")
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
True #use_taup# %use estimated traveltimes from TauPy for calculating windows for CF
iasp91 #taup_model# %define TauPy model for traveltime estimation
2.0 10.0 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
2.0 12.0 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
2.0 8.0 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
2.0 10.0 #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 0.1 0.5 1.0 #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)
4.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
2.0 0.3 1.5 1.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
1.0 #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
2.0 #minFMSNR# %miniumum required SNR for first-motion determination
0.2 #fmpickwin# %pick window around P onset for calculating zero crossings
#quality assessment#
0.02 0.04 0.08 0.16 #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
0.8 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
1.0 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
1.5 #minAICSSNR# %below this SNR the initial S pick is rejected
1.0 #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
1.5 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
6.0 #mdttolerance# %maximum allowed deviation of P picks from median [s]
1.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
5.0 #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

100
inputs/pylot_regional.in Normal file

@ -0,0 +1,100 @@
%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#
#rootpath# %project path
#datapath# %data path
#database# %name of data base
#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#
1.11 0.0009 -2.0 #WAscaling# %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] If zeros are set, original Richter magnitude is calculated!
1.0382 -0.447 #magscaling# %Scaling relation for derived local magnitude [a*Ml+b]. If zeros are set, no scaling of network magnitude is applied!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#filter settings#
1.0 1.0 #minfreq# %Lower filter frequency [P, S]
10.0 10.0 #maxfreq# %Upper filter frequency [P, S]
2 2 #filter_order# %filter order [P, S]
bandpass bandpass #filter_type# %filter type (bandpass, bandstop, lowpass, highpass) [P, S]
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#common settings picker#
local #extent# %extent of array ("local", "regional" or "global")
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
True #use_taup# %use estimated traveltimes from TauPy for calculating windows for CF
iasp91 #taup_model# %define TauPy model for traveltime estimation
2.0 10.0 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
2.0 12.0 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
2.0 8.0 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
2.0 10.0 #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 0.1 0.5 1.0 #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)
4.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
2.0 0.3 1.5 1.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
1.0 #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
2.0 #minFMSNR# %miniumum required SNR for first-motion determination
0.2 #fmpickwin# %pick window around P onset for calculating zero crossings
#quality assessment#
0.02 0.04 0.08 0.16 #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
0.8 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
1.0 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
1.5 #minAICSSNR# %below this SNR the initial S pick is rejected
1.0 #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
1.5 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
6.0 #mdttolerance# %maximum allowed deviation of P picks from median [s]
1.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
5.0 #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

@ -158,24 +158,29 @@ def buildPyLoT(verbosity=None):
def installPyLoT(verbosity=None):
files_to_copy = {'autoPyLoT_local.in':['~', '.pylot'],
'autoPyLoT_regional.in':['~', '.pylot'],
'filter.in':['~', '.pylot']}
files_to_copy = {'pylot_local.in': ['~', '.pylot'],
'pylot_regional.in': ['~', '.pylot'],
'pylot_global.in': ['~', '.pylot']}
if verbosity > 0:
print ('starting installation of PyLoT ...')
print('starting installation of PyLoT ...')
if verbosity > 1:
print ('copying input files into destination folder ...')
print('copying input files into destination folder ...')
ans = input('please specify scope of interest '
'([0]=local, 1=regional) :') or 0
'([0]=local, 1=regional, 2=global) :') or 0
if not isinstance(ans, int):
ans = int(ans)
ans = 'local' if ans is 0 else 'regional'
if ans == 0:
ans = 'local'
elif ans == 1:
ans = 'regional'
elif ans == 2:
ans = 'global'
link_dest = []
for file, destination in files_to_copy.items():
link_file = ans in file
if link_file:
link_dest = copy.deepcopy(destination)
link_dest.append('autoPyLoT.in')
link_dest.append('pylot.in')
link_dest = os.path.join(*link_dest)
destination.append(file)
destination = os.path.join(*destination)
@ -183,7 +188,7 @@ def installPyLoT(verbosity=None):
assert not os.path.isabs(srcfile), 'source files seem to be ' \
'corrupted ...'
if verbosity > 1:
print ('copying file {file} to folder {dest}'.format(file=file, dest=destination))
print('copying file {file} to folder {dest}'.format(file=file, dest=destination))
shutil.copyfile(srcfile, destination)
if link_file:
if verbosity:
@ -191,8 +196,6 @@ def installPyLoT(verbosity=None):
os.symlink(destination, link_dest)
def cleanUp(verbosity=None):
if verbosity >= 1:
print('cleaning up build files...')

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@ -6,27 +6,27 @@ Revised/extended summer 2017.
:author: Ludger Küperkoch / MAGS2 EP3 working group
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import obspy.core.event as ope
from obspy.geodetics import degrees2kilometers
from scipy import integrate, signal
from scipy.optimize import curve_fit
from pylot.core.pick.utils import getsignalwin, crossings_nonzero_all, \
select_for_phase
from pylot.core.util.utils import common_range, fit_curve
from scipy import integrate, signal
from scipy.optimize import curve_fit
def richter_magnitude_scaling(delta):
distance = np.array([0, 10, 20, 25, 30, 35,40, 45, 50, 60, 70, 75, 85, 90, 100, 110,
distance = np.array([0, 10, 20, 25, 30, 35, 40, 45, 50, 60, 70, 75, 85, 90, 100, 110,
120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 230, 240, 250,
260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380,
390, 400, 430, 470, 510, 560, 600, 700, 800, 900, 1000])
richter_scaling = np.array([1.4, 1.5, 1.7, 1.9, 2.1, 2.3, 2.4, 2.5, 2.6, 2.8, 2.8, 2.9,
2.9, 3.0, 3.1, 3.1, 3.2, 3.2, 3.3, 3.3, 3.4, 3.4, 3.5, 3.5,
3.6, 3.7, 3.7, 3.8, 3.8, 3.9, 3.9, 4.0, 4.0, 4.1, 4.2, 4.2,
4.2, 4.2, 4.3, 4.3, 4.3, 4.4, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9,
5.1, 5.2, 5.4, 5.5, 5.7])
richter_scaling = np.array([1.4, 1.5, 1.7, 1.9, 2.1, 2.3, 2.4, 2.5, 2.6, 2.8, 2.8, 2.9,
2.9, 3.0, 3.1, 3.1, 3.2, 3.2, 3.3, 3.3, 3.4, 3.4, 3.5, 3.5,
3.6, 3.7, 3.7, 3.8, 3.8, 3.9, 3.9, 4.0, 4.0, 4.1, 4.2, 4.2,
4.2, 4.2, 4.3, 4.3, 4.3, 4.4, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9,
5.1, 5.2, 5.4, 5.5, 5.7])
# prepare spline interpolation to calculate return value
func, params = fit_curve(distance, richter_scaling)
return func(delta, params)
@ -47,7 +47,7 @@ class Magnitude(object):
def __str__(self):
print(
'number of stations used: {0}\n'.format(len(self.magnitudes.values())))
'number of stations used: {0}\n'.format(len(self.magnitudes.values())))
print('\tstation\tmagnitude')
for s, m in self.magnitudes.items(): print('\t{0}\t{1}'.format(s, m))
@ -126,8 +126,8 @@ class Magnitude(object):
# scaling necessary
print("Scaling network magnitude ...")
mag = ope.Magnitude(
mag=np.median([M.mag for M in self.magnitudes.values()]) *\
magscaling[0] + magscaling[1],
mag=np.median([M.mag for M in self.magnitudes.values()]) * \
magscaling[0] + magscaling[1],
magnitude_type=self.type,
origin_id=self.origin_id,
station_count=len(self.magnitudes),
@ -192,6 +192,14 @@ class LocalMagnitude(Magnitude):
def peak_to_peak(self, st, t0):
try:
iplot = int(self.plot_flag)
except:
if self.plot_flag == True or self.plot_flag == 'True':
iplot = 2
else:
iplot = 0
# simulate Wood-Anderson response
st.simulate(paz_remove=None, paz_simulate=self._paz)
@ -215,7 +223,7 @@ class LocalMagnitude(Magnitude):
th = np.arange(0, len(sqH) * dt, dt)
# get maximum peak within pick window
iwin = getsignalwin(th, t0 - stime, self.calc_win)
ii = min([iwin[len(iwin)-1], len(th)])
ii = min([iwin[len(iwin) - 1], len(th)])
iwin = iwin[0:ii]
wapp = np.max(sqH[iwin])
if self.verbose:
@ -224,7 +232,7 @@ class LocalMagnitude(Magnitude):
# check for plot flag (for debugging only)
fig = None
if self.plot_flag > 1:
if iplot > 1:
st.plot()
fig = plt.figure()
ax = fig.add_subplot(111)
@ -250,8 +258,8 @@ class LocalMagnitude(Magnitude):
if not wf:
if self.verbose:
print(
'WARNING: no waveform data found for station {0}'.format(
station))
'WARNING: no waveform data found for station {0}'.format(
station))
continue
delta = degrees2kilometers(a.distance)
onset = pick.time
@ -270,13 +278,14 @@ class LocalMagnitude(Magnitude):
if str(self.wascaling) == '[0.0, 0.0, 0.0]':
print("Calculating original Richter magnitude ...")
magnitude = ope.StationMagnitude(mag=np.log10(a0) \
+ richter_magnitude_scaling(delta))
+ richter_magnitude_scaling(delta))
else:
print("Calculating scaled local magnitude ...")
a0 = a0 * 1e03 # mm to nm (see Havskov & Ottemöller, 2010)
a0 = a0 * 1e03 # mm to nm (see Havskov & Ottemöller, 2010)
magnitude = ope.StationMagnitude(mag=np.log10(a0) \
+ self.wascaling[0] * np.log10(delta) + self.wascaling[1]
* delta + self.wascaling[2])
+ self.wascaling[0] * np.log10(delta) + self.wascaling[1]
* delta + self.wascaling[
2])
magnitude.origin_id = self.origin_id
magnitude.waveform_id = pick.waveform_id
magnitude.amplitude_id = amplitude.resource_id
@ -397,8 +406,8 @@ def calcMoMw(wfstream, w0, rho, vp, delta, verbosity=False):
if verbosity:
print(
"calcMoMw: Calculating seismic moment Mo and moment magnitude Mw for station {0} ...".format(
tr.stats.station))
"calcMoMw: Calculating seismic moment Mo and moment magnitude Mw for station {0} ...".format(
tr.stats.station))
# additional common parameters for calculating Mo
rP = 2 / np.sqrt(
@ -412,8 +421,8 @@ def calcMoMw(wfstream, w0, rho, vp, delta, verbosity=False):
if verbosity:
print(
"calcMoMw: Calculated seismic moment Mo = {0} Nm => Mw = {1:3.1f} ".format(
Mo, Mw))
"calcMoMw: Calculated seismic moment Mo = {0} Nm => Mw = {1:3.1f} ".format(
Mo, Mw))
return Mo, Mw
@ -452,7 +461,15 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
:type: integer
'''
if verbosity:
print ("Calculating source spectrum for station %s ...." % wfstream[0].stats.station)
print("Calculating source spectrum for station %s ...." % wfstream[0].stats.station)
try:
iplot = int(iplot)
except:
if iplot == True or iplot == 'True':
iplot = 2
else:
iplot = 0
# get Q value
Q, A = qp
@ -509,9 +526,9 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
zc = crossings_nonzero_all(wfzc)
if np.size(zc) == 0 or len(zc) <= 3:
if verbosity:
print ("calcsourcespec: Something is wrong with the waveform, "
"no zero crossings derived!\n")
print ("No calculation of source spectrum possible!")
print("calcsourcespec: Something is wrong with the waveform, "
"no zero crossings derived!\n")
print("No calculation of source spectrum possible!")
plotflag = 0
else:
plotflag = 1
@ -558,22 +575,22 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
[optspecfit, _] = curve_fit(synthsourcespec, F, YYcor, [w0in, Fcin])
w0 = optspecfit[0]
fc = optspecfit[1]
#w01 = optspecfit[0]
#fc1 = optspecfit[1]
# w01 = optspecfit[0]
# fc1 = optspecfit[1]
if verbosity:
print ("calcsourcespec: Determined w0-value: %e m/Hz, \n"
"calcsourcespec: Determined corner frequency: %f Hz" % (w0, fc))
print("calcsourcespec: Determined w0-value: %e m/Hz, \n"
"calcsourcespec: Determined corner frequency: %f Hz" % (w0, fc))
# use of conventional fitting
# [w02, fc2] = fitSourceModel(F, YYcor, Fcin, iplot, verbosity)
# use of conventional fitting
# [w02, fc2] = fitSourceModel(F, YYcor, Fcin, iplot, verbosity)
# get w0 and fc as median of both
# source spectrum fits
#w0 = np.median([w01, w02])
#fc = np.median([fc1, fc2])
#if verbosity:
# print("calcsourcespec: Using w0-value = %e m/Hz and fc = %f Hz" % (
# w0, fc))
# get w0 and fc as median of both
# source spectrum fits
# w0 = np.median([w01, w02])
# fc = np.median([fc1, fc2])
# if verbosity:
# print("calcsourcespec: Using w0-value = %e m/Hz and fc = %f Hz" % (
# w0, fc))
if iplot > 1:
f1 = plt.figure()
@ -600,9 +617,9 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
p3, = plt.loglog(F, YYcor, 'r')
p4, = plt.loglog(F, fit, 'g')
plt.loglog([fc, fc], [w0 / 100, w0], 'g')
plt.legend([p1, p2, p3, p4], ['Raw Spectrum', \
'Used Raw Spectrum', \
'Q-Corrected Spectrum', \
plt.legend([p1, p2, p3, p4], ['Raw Spectrum',
'Used Raw Spectrum',
'Q-Corrected Spectrum',
'Fit to Spectrum'])
plt.title('Source Spectrum from P Pulse, w0=%e m/Hz, fc=%6.2f Hz' \
% (w0, fc))
@ -650,6 +667,14 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
:type: float
'''
try:
iplot = int(iplot)
except:
if iplot == True or iplot == 'True':
iplot = 2
else:
iplot = 0
w0 = []
stdw0 = []
fc = []
@ -659,9 +684,9 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
# left side of initial corner frequency
fcstopl = max(f[0], fc0 - max(1, fc0 / 2))
il = np.where(f <= fcstopl)
il = il[0][np.size(il) - 1]
il = il[0][np.size(il) - 1]
# right side of initial corner frequency
fcstopr = min(fc0 + (fc0 / 2), f[len(f) - 1])
fcstopr = min(fc0 + (fc0 / 2), f[len(f) - 1])
ir = np.where(f >= fcstopr)
# check, if fcstopr is available
if np.size(ir) == 0:
@ -672,16 +697,16 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
# vary corner frequency around initial point
print("fitSourceModel: Varying corner frequency "
"around initial corner frequency ...")
"around initial corner frequency ...")
# check difference of il and ir in order to
# keep calculation time acceptable
idiff = ir - il
if idiff > 10000:
increment = 100
increment = 100
elif idiff <= 20:
increment = 1
increment = 1
else:
increment = 10
increment = 10
for i in range(il, ir, increment):
FC = f[i]
@ -707,10 +732,10 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
w0 = max(S)
if verbosity:
print(
"fitSourceModel: best fc: {0} Hz, best w0: {1} m/Hz".format(fc, w0))
"fitSourceModel: best fc: {0} Hz, best w0: {1} m/Hz".format(fc, w0))
if iplot > 1:
plt.figure()#iplot)
plt.figure() # iplot)
plt.loglog(f, S, 'k')
plt.loglog([f[0], fc], [w0, w0], 'g')
plt.loglog([fc, fc], [w0 / 100, w0], 'g')
@ -719,7 +744,7 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude [m/Hz]')
plt.grid()
plt.figure()#iplot + 1)
plt.figure() # iplot + 1)
plt.subplot(311)
plt.plot(f[il:ir], STD, '*')
plt.title('Common Standard Deviations')

@ -1,242 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created August/September 2015.
:author: Ludger Küperkoch / MAGS2 EP3 working group
"""
import matplotlib.pyplot as plt
import numpy as np
from obspy.core import Stream
from pylot.core.pick.utils import getsignalwin
from scipy.optimize import curve_fit
class Magnitude(object):
'''
Superclass for calculating Wood-Anderson peak-to-peak
amplitudes, local magnitudes and moment magnitudes.
'''
def __init__(self, wfstream, To, pwin, iplot):
'''
:param: wfstream
:type: `~obspy.core.stream.Stream
:param: To, onset time, P- or S phase
:type: float
:param: pwin, pick window [To To+pwin] to get maximum
peak-to-peak amplitude (WApp) or to calculate
source spectrum (DCfc)
:type: float
:param: iplot, no. of figure window for plotting interims results
:type: integer
'''
assert isinstance(wfstream, Stream), "%s is not a stream object" % str(wfstream)
self.setwfstream(wfstream)
self.setTo(To)
self.setpwin(pwin)
self.setiplot(iplot)
self.calcwapp()
self.calcsourcespec()
def getwfstream(self):
return self.wfstream
def setwfstream(self, wfstream):
self.wfstream = wfstream
def getTo(self):
return self.To
def setTo(self, To):
self.To = To
def getpwin(self):
return self.pwin
def setpwin(self, pwin):
self.pwin = pwin
def getiplot(self):
return self.iplot
def setiplot(self, iplot):
self.iplot = iplot
def getwapp(self):
return self.wapp
def getw0(self):
return self.w0
def getfc(self):
return self.fc
def calcwapp(self):
self.wapp = None
def calcsourcespec(self):
self.sourcespek = None
class WApp(Magnitude):
'''
Method to derive peak-to-peak amplitude as seen on a Wood-Anderson-
seismograph. Has to be derived from instrument corrected traces!
'''
def calcwapp(self):
print ("Getting Wood-Anderson peak-to-peak amplitude ...")
print ("Simulating Wood-Anderson seismograph ...")
self.wapp = None
stream = self.getwfstream()
# poles, zeros and sensitivity of WA seismograph
# (see Uhrhammer & Collins, 1990, BSSA, pp. 702-716)
paz_wa = {
'poles': [5.6089 - 5.4978j, -5.6089 - 5.4978j],
'zeros': [0j, 0j],
'gain': 2080,
'sensitivity': 1}
stream.simulate(paz_remove=None, paz_simulate=paz_wa)
trH1 = stream[0].data
trH2 = stream[1].data
ilen = min([len(trH1), len(trH2)])
# get RMS of both horizontal components
sqH = np.sqrt(np.power(trH1[0:ilen], 2) + np.power(trH2[0:ilen], 2))
# get time array
th = np.arange(0, len(sqH) * stream[0].stats.delta, stream[0].stats.delta)
# get maximum peak within pick window
iwin = getsignalwin(th, self.getTo(), self.getpwin())
self.wapp = np.max(sqH[iwin])
print ("Determined Wood-Anderson peak-to-peak amplitude: %f mm") % self.wapp
if self.getiplot() > 1:
stream.plot()
f = plt.figure(2)
plt.plot(th, sqH)
plt.plot(th[iwin], sqH[iwin], 'g')
plt.plot([self.getTo(), self.getTo()], [0, max(sqH)], 'r', linewidth=2)
plt.title('Station %s, RMS Horizontal Traces, WA-peak-to-peak=%4.1f mm' \
% (stream[0].stats.station, self.wapp))
plt.xlabel('Time [s]')
plt.ylabel('Displacement [mm]')
plt.show()
raw_input()
plt.close(f)
class DCfc(Magnitude):
'''
Method to calculate the source spectrum and to derive from that the plateau
(so-called DC-value) and the corner frequency assuming Aki's omega-square
source model. Has to be derived from instrument corrected displacement traces!
'''
def calcsourcespec(self):
print ("Calculating source spectrum ....")
self.w0 = None # DC-value
self.fc = None # corner frequency
stream = self.getwfstream()
tr = stream[0]
# get time array
t = np.arange(0, len(tr) * tr.stats.delta, tr.stats.delta)
iwin = getsignalwin(t, self.getTo(), self.getpwin())
xdat = tr.data[iwin]
# fft
fny = tr.stats.sampling_rate / 2
l = len(xdat) / tr.stats.sampling_rate
n = tr.stats.sampling_rate * l # number of fft bins after Bath
# find next power of 2 of data length
m = pow(2, np.ceil(np.log(len(xdat)) / np.log(2)))
N = int(np.power(m, 2))
y = tr.stats.delta * np.fft.fft(xdat, N)
Y = abs(y[: N/2])
L = (N - 1) / tr.stats.sampling_rate
f = np.arange(0, fny, 1/L)
# remove zero-frequency and frequencies above
# corner frequency of seismometer (assumed
# to be 100 Hz)
fi = np.where((f >= 1) & (f < 100))
F = f[fi]
YY = Y[fi]
# get plateau (DC value) and corner frequency
# initial guess of plateau
DCin = np.mean(YY[0:100])
# initial guess of corner frequency
# where spectral level reached 50% of flat level
iin = np.where(YY >= 0.5 * DCin)
Fcin = F[iin[0][np.size(iin) - 1]]
fit = synthsourcespec(F, DCin, Fcin)
[optspecfit, pcov] = curve_fit(synthsourcespec, F, YY.real, [DCin, Fcin])
self.w0 = optspecfit[0]
self.fc = optspecfit[1]
print ("DCfc: Determined DC-value: %e m/Hz, \n" \
"Determined corner frequency: %f Hz" % (self.w0, self.fc))
#if self.getiplot() > 1:
iplot=2
if iplot > 1:
print ("DCfc: Determined DC-value: %e m/Hz, \n"
"Determined corner frequency: %f Hz" % (self.w0, self.fc))
if self.getiplot() > 1:
f1 = plt.figure()
plt.subplot(2,1,1)
# show displacement in mm
plt.plot(t, np.multiply(tr, 1000), 'k')
plt.plot(t[iwin], np.multiply(xdat, 1000), 'g')
plt.title('Seismogram and P pulse, station %s' % tr.stats.station)
plt.xlabel('Time since %s' % tr.stats.starttime)
plt.ylabel('Displacement [mm]')
plt.subplot(2,1,2)
plt.loglog(f, Y.real, 'k')
plt.loglog(F, YY.real)
plt.loglog(F, fit, 'g')
plt.title('Source Spectrum from P Pulse, DC=%e m/Hz, fc=%4.1f Hz' \
% (self.w0, self.fc))
plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude [m/Hz]')
plt.grid()
plt.show()
raw_input()
plt.close(f1)
def synthsourcespec(f, omega0, fcorner):
'''
Calculates synthetic source spectrum from given plateau and corner
frequency assuming Akis omega-square model.
:param: f, frequencies
:type: array
:param: omega0, DC-value (plateau) of source spectrum
:type: float
:param: fcorner, corner frequency of source spectrum
:type: float
'''
#ssp = omega0 / (pow(2, (1 + f / fcorner)))
ssp = omega0 / (1 + pow(2, (f / fcorner)))
return ssp

@ -3,15 +3,18 @@
import copy
import os
from obspy import read_events
from obspy.core import read, Stream, UTCDateTime
from obspy.io.sac import SacIOError
from obspy.core.event import Event as ObsPyEvent
from obspy.io.sac import SacIOError
from pylot.core.io.phases import readPILOTEvent, picks_from_picksdict, \
picksdict_from_pilot, merge_picks
from pylot.core.util.errors import FormatError, OverwriteError
from pylot.core.util.utils import fnConstructor, full_range
from pylot.core.util.event import Event
from pylot.core.util.utils import fnConstructor, full_range
import pylot.core.loc.velest as velest
class Data(object):
"""
@ -39,7 +42,7 @@ class Data(object):
elif isinstance(evtdata, dict):
evt = readPILOTEvent(**evtdata)
evtdata = evt
elif isinstance(evtdata, basestring):
elif isinstance(evtdata, str):
try:
cat = read_events(evtdata)
if len(cat) is not 1:
@ -75,7 +78,7 @@ class Data(object):
def __add__(self, other):
assert isinstance(other, Data), "operands must be of same type 'Data'"
rs_id = self.get_evt_data().get('resource_id')
rs_id_other = other.get_evt_data().get('resource_id')
rs_id_other = other.get_evt_data().get('resource_id')
if other.isNew() and not self.isNew():
picks_to_add = other.get_evt_data().picks
old_picks = self.get_evt_data().picks
@ -98,7 +101,7 @@ class Data(object):
def getPicksStr(self):
picks_str = ''
for pick in self.get_evt_data().picks:
picks_str += str(PyLoT) + '\n'
picks_str += str(pick) + '\n'
return picks_str
def getParent(self):
@ -147,89 +150,158 @@ class Data(object):
# handle forbidden filenames especially on windows systems
return fnConstructor(str(ID))
def exportEvent(self, fnout, fnext='.xml', fcheck='auto'):
def checkEvent(self, event, fcheck, forceOverwrite=False):
if 'origin' in fcheck:
self.replaceOrigin(event, forceOverwrite)
if 'magnitude' in fcheck:
self.replaceMagnitude(event, forceOverwrite)
if 'auto' in fcheck:
self.replacePicks(event, 'auto')
if 'manual' in fcheck:
self.replacePicks(event, 'manual')
def replaceOrigin(self, event, forceOverwrite=False):
if self.get_evt_data().origins or forceOverwrite:
if event.origins:
print("Found origin, replace it by new origin.")
event.origins = self.get_evt_data().origins
def replaceMagnitude(self, event, forceOverwrite=False):
if self.get_evt_data().magnitudes or forceOverwrite:
if event.magnitudes:
print("Found magnitude, replace it by new magnitude")
event.magnitudes = self.get_evt_data().magnitudes
def replacePicks(self, event, picktype):
checkflag = 0
picks = event.picks
# remove existing picks
for j, pick in reversed(list(enumerate(picks))):
if picktype in str(pick.method_id.id):
picks.pop(j)
checkflag = 1
if checkflag:
print("Found %s pick(s), remove them and append new picks to catalog." % picktype)
# append new picks
for pick in self.get_evt_data().picks:
if picktype in str(pick.method_id.id):
picks.append(pick)
def exportEvent(self, fnout, fnext='.xml', fcheck='auto', upperErrors=None):
"""
:param fnout:
:param fnext:
:param fcheck:
:raise KeyError:
:param fnout: basename of file
:param fnext: file extension
:param fcheck: check and delete existing information
can be a str or a list of strings of ['manual', 'auto', 'origin', 'magnitude']
"""
from pylot.core.util.defaults import OUTPUTFORMATS
if not type(fcheck) == list:
fcheck = [fcheck]
try:
evtformat = OUTPUTFORMATS[fnext]
except KeyError as e:
errmsg = '{0}; selected file extension {1} not ' \
'supported'.format(e, fnext)
raise FormatError(errmsg)
# check for already existing xml-file
if fnext == '.xml':
if os.path.isfile(fnout + fnext):
print("xml-file already exists! Check content ...")
cat_old = read_events(fnout + fnext)
checkflag = 0
for j in range(len(cat_old.events[0].picks)):
if cat_old.events[0].picks[j].method_id.id.split('/')[1] == fcheck:
print("Found %s pick(s), append to new catalog." % fcheck)
checkflag = 1
break
if checkflag == 1:
self.get_evt_data().write(fnout + fnext, format=evtformat)
cat_new = read_events(fnout + fnext)
cat_new.append(cat_old.events[0])
cat_new.write(fnout + fnext, format=evtformat)
else:
self.get_evt_data().write(fnout + fnext, format=evtformat)
else:
self.get_evt_data().write(fnout + fnext, format=evtformat)
# try exporting event via ObsPy
cat = read_events(fnout + fnext)
if len(cat) > 1:
raise IOError('Ambigious event information in file {}'.format(fnout + fnext))
if len(cat) < 1:
raise IOError('No event information in file {}'.format(fnout + fnext))
event = cat[0]
if not event.resource_id == self.get_evt_data().resource_id:
raise IOError("Missmatching event resource id's: {} and {}".format(event.resource_id,
self.get_evt_data().resource_id))
self.checkEvent(event, fcheck)
self.setEvtData(event)
self.get_evt_data().write(fnout + fnext, format=evtformat)
# try exporting event
else:
evtdata_org = self.get_evt_data()
picks = evtdata_org.picks
eventpath = evtdata_org.path
picks_copy = copy.deepcopy(picks)
evtdata_copy = Event(eventpath)
evtdata_copy.picks = picks_copy
# check for stations picked automatically as well as manually
# Prefer manual picks!
evtdata_copy = self.get_evt_data().copy()
evtdata_org = self.get_evt_data()
for i in range(len(evtdata_org.picks)):
if evtdata_org.picks[i].method_id == 'manual':
mstation = evtdata_org.picks[i].waveform_id.station_code
mstation_ext = mstation + '_'
for k in range(len(evtdata_copy.picks)):
if evtdata_copy.picks[k].waveform_id.station_code == mstation or \
evtdata_copy.picks[k].waveform_id.station_code == mstation_ext and \
evtdata_copy.picks[k].method_id == 'auto':
del evtdata_copy.picks[k]
break
lendiff = len(evtdata_org.picks) - len(evtdata_copy.picks)
for i in range(len(picks)):
if picks[i].method_id == 'manual':
mstation = picks[i].waveform_id.station_code
mstation_ext = mstation + '_'
for k in range(len(picks_copy)):
if ((picks_copy[k].waveform_id.station_code == mstation) or
(picks_copy[k].waveform_id.station_code == mstation_ext)) and \
(picks_copy[k].method_id == 'auto'):
del picks_copy[k]
break
lendiff = len(picks) - len(picks_copy)
if lendiff is not 0:
print("Manual as well as automatic picks available. Prefered the {} manual ones!".format(lendiff))
print("Manual as well as automatic picks available. Prefered the {} manual ones!".format(lendiff))
if upperErrors:
# check for pick uncertainties exceeding adjusted upper errors
# Picks with larger uncertainties will not be saved in output file!
for j in range(len(picks)):
for i in range(len(picks_copy)):
if picks_copy[i].phase_hint[0] == 'P':
if (picks_copy[i].time_errors['upper_uncertainty'] >= upperErrors[0]) or \
(picks_copy[i].time_errors['uncertainty'] == None):
print("Uncertainty exceeds or equal adjusted upper time error!")
print("Adjusted uncertainty: {}".format(upperErrors[0]))
print("Pick uncertainty: {}".format(picks_copy[i].time_errors['uncertainty']))
print("{1} P-Pick of station {0} will not be saved in outputfile".format(
picks_copy[i].waveform_id.station_code,
picks_copy[i].method_id))
print("#")
del picks_copy[i]
break
if picks_copy[i].phase_hint[0] == 'S':
if (picks_copy[i].time_errors['upper_uncertainty'] >= upperErrors[1]) or \
(picks_copy[i].time_errors['uncertainty'] == None):
print("Uncertainty exceeds or equal adjusted upper time error!")
print("Adjusted uncertainty: {}".format(upperErrors[1]))
print("Pick uncertainty: {}".format(picks_copy[i].time_errors['uncertainty']))
print("{1} S-Pick of station {0} will not be saved in outputfile".format(
picks_copy[i].waveform_id.station_code,
picks_copy[i].method_id))
print("#")
del picks_copy[i]
break
if fnext == '.obs':
try:
evtdata_copy.write(fnout + fnext, format=evtformat)
# write header afterwards
evid = str(evtdata_org.resource_id).split('/')[1]
header = '# EQEVENT: Label: EQ%s Loc: X 0.00 Y 0.00 Z 10.00 OT 0.00 \n' % evid
nllocfile = open(fnout + fnext)
l = nllocfile.readlines()
nllocfile.close()
l.insert(0, header)
nllocfile = open(fnout + fnext, 'w')
nllocfile.write("".join(l))
nllocfile.close()
except KeyError as e:
raise KeyError('''{0} export format
try:
evtdata_copy.write(fnout + fnext, format=evtformat)
# write header afterwards
evid = str(evtdata_org.resource_id).split('/')[1]
header = '# EQEVENT: Label: EQ%s Loc: X 0.00 Y 0.00 Z 10.00 OT 0.00 \n' % evid
nllocfile = open(fnout + fnext)
l = nllocfile.readlines()
nllocfile.close()
l.insert(0, header)
nllocfile = open(fnout + fnext, 'w')
nllocfile.write("".join(l))
nllocfile.close()
except KeyError as e:
raise KeyError('''{0} export format
not implemented: {1}'''.format(evtformat, e))
if fnext == '.cnv':
try:
evtdata_org.write(fnout + fnext, format=evtformat)
except KeyError as e:
raise KeyError('''{0} export format
try:
velest.export(picks_copy, fnout + fnext, eventinfo=self.get_evt_data())
except KeyError as e:
raise KeyError('''{0} export format
not implemented: {1}'''.format(evtformat, e))
def getComp(self):
"""
@ -294,7 +366,7 @@ class Data(object):
except Exception as e:
warnmsg += '{0}\n{1}\n'.format(fname, e)
except SacIOError as se:
warnmsg += '{0}\n{1}\n'.format(fname, se)
warnmsg += '{0}\n{1}\n'.format(fname, se)
if warnmsg:
warnmsg = 'WARNING: unable to read\n' + warnmsg
print(warnmsg)
@ -359,21 +431,20 @@ class Data(object):
:raise OverwriteError: raises an OverwriteError if the picks list is
not empty. The GUI will then ask for a decision.
"""
#firstonset = find_firstonset(picks)
# firstonset = find_firstonset(picks)
# check for automatic picks
print("Writing phases to ObsPy-quakeml file")
for key in picks:
if picks[key]['P']['picker'] == 'auto':
print("Existing picks will be overwritten!")
picks = picks_from_picksdict(picks)
break
print("Existing picks will be overwritten!")
picks = picks_from_picksdict(picks)
break
else:
if self.get_evt_data().picks:
raise OverwriteError('Existing picks would be overwritten!')
break
else:
picks = picks_from_picksdict(picks)
break
if self.get_evt_data().picks:
raise OverwriteError('Existing picks would be overwritten!')
else:
picks = picks_from_picksdict(picks)
break
self.get_evt_data().picks = picks
# if 'smi:local' in self.getID() and firstonset:
# fonset_str = firstonset.strftime('%Y_%m_%d_%H_%M_%S')
@ -381,7 +452,6 @@ class Data(object):
# ID.convertIDToQuakeMLURI(authority_id=authority_id)
# self.get_evt_data().resource_id = ID
def applyEvent(event):
"""
takes an `obspy.core.event.Event` object and applies all new
@ -393,13 +463,13 @@ class Data(object):
else:
# prevent overwriting original pick information
event_old = self.get_evt_data()
print(event_old.resource_id, event.resource_id)
if not event_old.resource_id == event.resource_id:
print("WARNING: Missmatch in event resource id's: {} and {}".format(
event_old.resource_id,
event.resource_id))
picks = copy.deepcopy(event_old.picks)
event = merge_picks(event, picks)
else:
picks = copy.deepcopy(event_old.picks)
event = merge_picks(event, picks)
# apply event information from location
event_old.update(event)
@ -408,7 +478,6 @@ class Data(object):
applydata[typ](data)
self._new = False
class GenericDataStructure(object):

@ -3,293 +3,401 @@
defaults = {'rootpath': {'type': str,
'tooltip': 'project path',
'value': ''},
'value': '',
'namestring': 'Root path'},
'datapath': {'type': str,
'tooltip': 'data path',
'value': ''},
'value': '',
'namestring': 'Data path'},
'database': {'type': str,
'tooltip': 'name of data base',
'value': ''},
'value': '',
'namestring': 'Database path'},
'eventID': {'type': str,
'tooltip': 'event ID for single event processing (* for all events found in database)',
'value': ''},
'value': '',
'namestring': 'Event ID'},
'extent': {'type': str,
'tooltip': 'extent of array ("local", "regional" or "global")',
'value': 'local'},
'value': 'local',
'namestring': 'Array extent'},
'invdir': {'type': str,
'tooltip': 'full path to inventory or dataless-seed file',
'value': ''},
'value': '',
'namestring': 'Inversion dir'},
'datastructure': {'type': str,
'tooltip': 'choose data structure',
'value': 'PILOT'},
'value': 'PILOT',
'namestring': 'Datastructure'},
'apverbose': {'type': bool,
'tooltip': "choose 'True' or 'False' for terminal output",
'value': True},
'value': True,
'namestring': 'App. verbosity'},
'nllocbin': {'type': str,
'tooltip': 'path to NLLoc executable',
'value': ''},
'value': '',
'namestring': 'NLLoc bin path'},
'nllocroot': {'type': str,
'tooltip': 'root of NLLoc-processing directory',
'value': ''},
'value': '',
'namestring': 'NLLoc root path'},
'phasefile': {'type': str,
'tooltip': 'name of autoPyLoT-output phase file for NLLoc',
'value': 'AUTOPHASES.obs'},
'value': 'AUTOPHASES.obs',
'namestring': 'Phase filename'},
'ctrfile': {'type': str,
'tooltip': 'name of autoPyLoT-output control file for NLLoc',
'value': 'Insheim_min1d2015_auto.in'},
'value': 'Insheim_min1d2015_auto.in',
'namestring': 'Control filename'},
'ttpatter': {'type': str,
'tooltip': 'pattern of NLLoc ttimes from grid',
'value': 'ttime'},
'value': 'ttime',
'namestring': 'Traveltime pattern'},
'outpatter': {'type': str,
'tooltip': 'pattern of NLLoc-output file',
'value': 'AUTOLOC_nlloc'},
'value': 'AUTOLOC_nlloc',
'namestring': 'NLLoc output pattern'},
'vp': {'type': float,
'tooltip': 'average P-wave velocity',
'value': 3530.},
'value': 3530.,
'namestring': 'P-velocity'},
'rho': {'type': float,
'tooltip': 'average rock density [kg/m^3]',
'value': 2500.},
'value': 2500.,
'namestring': 'Density'},
'Qp': {'type': (float, float),
'tooltip': 'quality factor for P waves (Qp*f^a); list(Qp, a)',
'value': (300., 0.8)},
'value': (300., 0.8),
'namestring': ('Quality factor', 'Qp1', 'Qp2')},
'pstart': {'type': float,
'tooltip': 'start time [s] for calculating CF for P-picking',
'value': 15.0},
'tooltip': 'start time [s] for calculating CF for P-picking (if TauPy:'
' seconds relative to estimated onset)',
'value': 15.0,
'namestring': 'P start'},
'pstop': {'type': float,
'tooltip': 'end time [s] for calculating CF for P-picking',
'value': 60.0},
'tooltip': 'end time [s] for calculating CF for P-picking (if TauPy:'
' seconds relative to estimated onset)',
'value': 60.0,
'namestring': 'P stop'},
'sstart': {'type': float,
'tooltip': 'start time [s] relative to P-onset for calculating CF for S-picking',
'value': -1.0},
'value': -1.0,
'namestring': 'S start'},
'sstop': {'type': float,
'tooltip': 'end time [s] after P-onset for calculating CF for S-picking',
'value': 10.0},
'value': 10.0,
'namestring': 'S stop'},
'bpz1': {'type': (float, float),
'tooltip': 'lower/upper corner freq. of first band pass filter Z-comp. [Hz]',
'value': (2, 20)},
'value': (2, 20),
'namestring': ('Z-bandpass 1', 'Lower', 'Upper')},
'bpz2': {'type': (float, float),
'tooltip': 'lower/upper corner freq. of second band pass filter Z-comp. [Hz]',
'value': (2, 30)},
'value': (2, 30),
'namestring': ('Z-bandpass 2', 'Lower', 'Upper')},
'bph1': {'type': (float, float),
'tooltip': 'lower/upper corner freq. of first band pass filter H-comp. [Hz]',
'value': (2, 15)},
'value': (2, 15),
'namestring': ('H-bandpass 1', 'Lower', 'Upper')},
'bph2': {'type': (float, float),
'tooltip': 'lower/upper corner freq. of second band pass filter z-comp. [Hz]',
'value': (2, 20)},
'value': (2, 20),
'namestring': ('H-bandpass 2', 'Lower', 'Upper')},
'algoP': {'type': str,
'tooltip': 'choose algorithm for P-onset determination (HOS, ARZ, or AR3)',
'value': 'HOS'},
'value': 'HOS',
'namestring': 'P algorithm'},
'tlta': {'type': float,
'tooltip': 'for HOS-/AR-AIC-picker, length of LTA window [s]',
'value': 7.0},
'value': 7.0,
'namestring': 'LTA window'},
'hosorder': {'type': int,
'tooltip': 'for HOS-picker, order of Higher Order Statistics',
'value': 4},
'value': 4,
'namestring': 'HOS order'},
'Parorder': {'type': int,
'tooltip': 'for AR-picker, order of AR process of Z-component',
'value': 2},
'value': 2,
'namestring': 'AR order P'},
'tdet1z': {'type': float,
'tooltip': 'for AR-picker, length of AR determination window [s] for Z-component, 1st pick',
'value': 1.2},
'value': 1.2,
'namestring': 'AR det. window Z 1'},
'tpred1z': {'type': float,
'tooltip': 'for AR-picker, length of AR prediction window [s] for Z-component, 1st pick',
'value': 0.4},
'value': 0.4,
'namestring': 'AR pred. window Z 1'},
'tdet2z': {'type': float,
'tooltip': 'for AR-picker, length of AR determination window [s] for Z-component, 2nd pick',
'value': 0.6},
'value': 0.6,
'namestring': 'AR det. window Z 2'},
'tpred2z': {'type': float,
'tooltip': 'for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick',
'value': 0.2},
'value': 0.2,
'namestring': 'AR pred. window Z 2'},
'addnoise': {'type': float,
'tooltip': 'add noise to seismogram for stable AR prediction',
'value': 0.001},
'value': 0.001,
'namestring': 'Add noise'},
'tsnrz': {'type': (float, float, float, float),
'tooltip': 'for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]',
'value': (3, 0.1, 0.5, 1.0)},
'value': (3, 0.1, 0.5, 1.0),
'namestring': ('SNR windows P', 'Noise', 'Safety', 'Signal', 'Slope')},
'pickwinP': {'type': float,
'tooltip': 'for initial AIC pick, length of P-pick window [s]',
'value': 3.0},
'value': 3.0,
'namestring': 'AIC window P'},
'Precalcwin': {'type': float,
'tooltip': 'for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)',
'value': 6.0},
'value': 6.0,
'namestring': 'Recal. window P'},
'aictsmooth': {'type': float,
'tooltip': 'for HOS/AR, take average of samples for smoothing of AIC-function [s]',
'value': 0.2},
'value': 0.2,
'namestring': 'AIC smooth P'},
'tsmoothP': {'type': float,
'tooltip': 'for HOS/AR, take average of samples for smoothing CF [s]',
'value': 0.1},
'value': 0.1,
'namestring': 'CF smooth P'},
'ausP': {'type': float,
'tooltip': 'for HOS/AR, artificial uplift of samples (aus) of CF (P)',
'value': 0.001},
'value': 0.001,
'namestring': 'Artificial uplift P'},
'nfacP': {'type': float,
'tooltip': 'for HOS/AR, noise factor for noise level determination (P)',
'value': 1.3},
'value': 1.3,
'namestring': 'Noise factor P'},
'algoS': {'type': str,
'tooltip': 'choose algorithm for S-onset determination (ARH or AR3)',
'value': 'ARH'},
'value': 'ARH',
'namestring': 'S algorithm'},
'tdet1h': {'type': float,
'tooltip': 'for HOS/AR, length of AR-determination window [s], H-components, 1st pick',
'value': 0.8},
'value': 0.8,
'namestring': 'AR det. window H 1'},
'tpred1h': {'type': float,
'tooltip': 'for HOS/AR, length of AR-prediction window [s], H-components, 1st pick',
'value': 0.4},
'value': 0.4,
'namestring': 'AR pred. window H 1'},
'tdet2h': {'type': float,
'tooltip': 'for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick',
'value': 0.6},
'value': 0.6,
'namestring': 'AR det. window H 2'},
'tpred2h': {'type': float,
'tooltip': 'for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick',
'value': 0.3},
'value': 0.3,
'namestring': 'AR pred. window H 2'},
'Sarorder': {'type': int,
'tooltip': 'for AR-picker, order of AR process of H-components',
'value': 4},
'value': 4,
'namestring': 'AR order S'},
'Srecalcwin': {'type': float,
'tooltip': 'for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)',
'value': 5.0},
'value': 5.0,
'namestring': 'Recal. window S'},
'pickwinS': {'type': float,
'tooltip': 'for initial AIC pick, length of S-pick window [s]',
'value': 3.0},
'value': 3.0,
'namestring': 'AIC window S'},
'tsnrh': {'type': (float, float, float, float),
'tooltip': 'for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]',
'value': (2, 0.2, 1.5, 0.5)},
'value': (2, 0.2, 1.5, 0.5),
'namestring': ('SNR windows S', 'Noise', 'Safety', 'Signal', 'Slope')},
'aictsmoothS': {'type': float,
'tooltip': 'for AIC-picker, take average of samples for smoothing of AIC-function [s]',
'value': 0.5},
'value': 0.5,
'namestring': 'AIC smooth S'},
'tsmoothS': {'type': float,
'tooltip': 'for AR-picker, take average of samples for smoothing CF [s] (S)',
'value': 0.7},
'value': 0.7,
'namestring': 'CF smooth S'},
'ausS': {'type': float,
'tooltip': 'for HOS/AR, artificial uplift of samples (aus) of CF (S)',
'value': 0.9},
'value': 0.9,
'namestring': 'Artificial uplift S'},
'nfacS': {'type': float,
'tooltip': 'for AR-picker, noise factor for noise level determination (S)',
'value': 1.5},
'value': 1.5,
'namestring': 'Noise factor S'},
'minfmweight': {'type': int,
'tooltip': 'minimum required P weight for first-motion determination',
'value': 1},
'value': 1,
'namestring': 'Min. P weight'},
'minFMSNR': {'type': float,
'tooltip': 'miniumum required SNR for first-motion determination',
'value': 2.},
'value': 2.,
'namestring': 'Min SNR'},
'fmpickwin': {'type': float,
'tooltip': 'pick window around P onset for calculating zero crossings',
'value': 0.2},
'value': 0.2,
'namestring': 'Zero crossings window'},
'timeerrorsP': {'type': (float, float, float, float),
'tooltip': 'discrete time errors [s] corresponding to picking weights [0 1 2 3] for P',
'value': (0.01, 0.02, 0.04, 0.08)},
'value': (0.01, 0.02, 0.04, 0.08),
'namestring': ('Time errors P', '0', '1', '2', '3')},
'timeerrorsS': {'type': (float, float, float, float),
'tooltip': 'discrete time errors [s] corresponding to picking weights [0 1 2 3] for S',
'value': (0.04, 0.08, 0.16, 0.32)},
'value': (0.04, 0.08, 0.16, 0.32),
'namestring': ('Time errors S', '0', '1', '2', '3')},
'minAICPslope': {'type': float,
'tooltip': 'below this slope [counts/s] the initial P pick is rejected',
'value': 0.8},
'value': 0.8,
'namestring': 'Min. slope P'},
'minAICPSNR': {'type': float,
'tooltip': 'below this SNR the initial P pick is rejected',
'value': 1.1},
'value': 1.1,
'namestring': 'Min. SNR P'},
'minAICSslope': {'type': float,
'tooltip': 'below this slope [counts/s] the initial S pick is rejected',
'value': 1.},
'value': 1.,
'namestring': 'Min. slope S'},
'minAICSSNR': {'type': float,
'tooltip': 'below this SNR the initial S pick is rejected',
'value': 1.5},
'value': 1.5,
'namestring': 'Min. SNR S'},
'minsiglength': {'type': float,
'tooltip': 'length of signal part for which amplitudes must exceed noiselevel [s]',
'value': 1.},
'value': 1.,
'namestring': 'Min. signal length'},
'noisefactor': {'type': float,
'tooltip': 'noiselevel*noisefactor=threshold',
'value': 1.0},
'value': 1.0,
'namestring': 'Noise factor'},
'minpercent': {'type': float,
'tooltip': 'required percentage of amplitudes exceeding threshold',
'value': 10.},
'value': 10.,
'namestring': 'Min amplitude [%]'},
'zfac': {'type': float,
'tooltip': 'P-amplitude must exceed at least zfac times RMS-S amplitude',
'value': 1.5},
'value': 1.5,
'namestring': 'Z factor'},
'mdttolerance': {'type': float,
'tooltip': 'maximum allowed deviation of P picks from median [s]',
'value': 6.0},
'value': 6.0,
'namestring': 'Median tolerance'},
'wdttolerance': {'type': float,
'tooltip': 'maximum allowed deviation from Wadati-diagram',
'value': 1.0},
'value': 1.0,
'namestring': 'Wadati tolerance'},
'jackfactor': {'type': float,
'tooltip': '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',
'value': 5.0,
'namestring': 'Jackknife safety factor'},
'WAscaling': {'type': (float, float, float),
'tooltip': 'Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] \
If zeros are set, original Richter magnitude is calculated!',
'value': (0., 0., 0.)},
'tooltip': 'Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] \
If zeros are set, original Richter magnitude is calculated!',
'value': (0., 0., 0.),
'namestring': ('Wood-Anderson scaling', '', '', '')},
'magscaling': {'type': (float, float),
'tooltip': 'Scaling relation for derived local magnitude [a*Ml+b]. \
If zeros are set, no scaling of network magnitude is applied!',
'value': (0., 0.)}
}
'tooltip': 'Scaling relation for derived local magnitude [a*Ml+b]. \
If zeros are set, no scaling of network magnitude is applied!',
'value': (0., 0.),
'namestring': ('Local mag. scaling', '', '')},
settings_main={
'dirs':[
'minfreq': {'type': (float, float),
'tooltip': 'Lower filter frequency [P, S]',
'value': (1.0, 1.0),
'namestring': ('Lower freq.', 'P', 'S')},
'maxfreq': {'type': (float, float),
'tooltip': 'Upper filter frequency [P, S]',
'value': (10.0, 10.0),
'namestring': ('Upper freq.', 'P', 'S')},
'filter_order': {'type': (int, int),
'tooltip': 'filter order [P, S]',
'value': (2, 2),
'namestring': ('Order', 'P', 'S')},
'filter_type': {'type': (str, str),
'tooltip': 'filter type (bandpass, bandstop, lowpass, highpass) [P, S]',
'value': ('bandpass', 'bandpass'),
'namestring': ('Type', 'P', 'S')},
'use_taup': {'type': bool,
'tooltip': 'use estimated traveltimes from TauPy for calculating windows for CF',
'value': True,
'namestring': 'Use TauPy'},
'taup_model': {'type': str,
'tooltip': 'define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6',
'value': 'iasp91',
'namestring': 'TauPy model'}
}
settings_main = {
'dirs': [
'rootpath',
'datapath',
'database',
@ -297,34 +405,41 @@ settings_main={
'invdir',
'datastructure',
'apverbose'],
'nlloc':[
'nlloc': [
'nllocbin',
'nllocroot',
'phasefile',
'ctrfile',
'ttpatter',
'outpatter'],
'smoment':[
'smoment': [
'vp',
'rho',
'Qp'],
'localmag':[
'localmag': [
'WAscaling',
'magscaling'],
'pick':[
'filter': [
'minfreq',
'maxfreq',
'filter_order',
'filter_type'],
'pick': [
'extent',
'pstart',
'pstop',
'sstart',
'sstop',
'use_taup',
'taup_model',
'bpz1',
'bpz2',
'bph1',
'bph2']
}
settings_special_pick={
'z':[
settings_special_pick = {
'z': [
'algoP',
'tlta',
'hosorder',
@ -341,7 +456,7 @@ settings_special_pick={
'tsmoothP',
'ausP',
'nfacP'],
'h':[
'h': [
'algoS',
'tdet1h',
'tpred1h',
@ -355,11 +470,11 @@ settings_special_pick={
'tsmoothS',
'ausS',
'nfacS'],
'fm':[
'fm': [
'minfmweight',
'minFMSNR',
'fmpickwin'],
'quality':[
'quality': [
'timeerrorsP',
'timeerrorsS',
'minAICPslope',
@ -371,5 +486,6 @@ settings_special_pick={
'minpercent',
'zfac',
'mdttolerance',
'wdttolerance']
'wdttolerance',
'jackfactor'],
}

@ -1,8 +1,9 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pylot.core.io import default_parameters
from pylot.core.util.errors import ParameterError
import default_parameters
class PylotParameter(object):
'''
@ -69,13 +70,13 @@ class PylotParameter(object):
# Set default values of parameter names
def __init_default_paras(self):
parameters=default_parameters.defaults
parameters = default_parameters.defaults
self.__defaults = parameters
def __init_subsettings(self):
self._settings_main=default_parameters.settings_main
self._settings_special_pick=default_parameters.settings_special_pick
self._settings_main = default_parameters.settings_main
self._settings_special_pick = default_parameters.settings_special_pick
# String representation of the object
def __repr__(self):
return "PylotParameter('%s')" % self.__filename
@ -107,7 +108,7 @@ class PylotParameter(object):
yield key, value
def hasParam(self, parameter):
if self.__parameter.has_key(parameter):
if parameter in self.__parameter.keys():
return True
return False
@ -136,12 +137,13 @@ class PylotParameter(object):
return self._settings_special_pick
def get_all_para_names(self):
all_names=[]
all_names = []
all_names += self.get_main_para_names()['dirs']
all_names += self.get_main_para_names()['nlloc']
all_names += self.get_main_para_names()['smoment']
all_names += self.get_main_para_names()['localmag']
all_names += self.get_main_para_names()['pick']
all_names += self.get_main_para_names()['pick']
all_names += self.get_main_para_names()['filter']
all_names += self.get_special_para_names()['z']
all_names += self.get_special_para_names()['h']
all_names += self.get_special_para_names()['fm']
@ -155,14 +157,14 @@ class PylotParameter(object):
message = 'Type check failed for param: {}, is type: {}, expected type:{}'
message = message.format(param, is_type, expect_type)
print(Warning(message))
def setParamKV(self, param, value):
self.__setitem__(param, value)
def setParam(self, **kwargs):
for key in kwargs:
self.__setitem__(key, kwargs[key])
@staticmethod
def _printParameterError(errmsg):
print('ParameterError:\n non-existent parameter %s' % errmsg)
@ -171,7 +173,7 @@ class PylotParameter(object):
defaults = self.get_defaults()
for param in defaults:
self.setParamKV(param, defaults[param]['value'])
def from_file(self, fnin=None):
if not fnin:
if self.__filename is not None:
@ -208,7 +210,10 @@ class PylotParameter(object):
vallist = value.strip().split(' ')
val = []
for val0 in vallist:
val0 = float(val0)
try:
val0 = float(val0)
except:
pass
val.append(val0)
else:
val = str(value.strip())
@ -221,9 +226,9 @@ class PylotParameter(object):
# for key, value in self.iteritems():
# lines.append('{key}\t{value}\n'.format(key=key, value=value))
# fid_out.writelines(lines)
header = ('%This is a parameter input file for PyLoT/autoPyLoT.\n'+
'%All main and special settings regarding data handling\n'+
'%and picking are to be set here!\n'+
header = ('%This is a parameter input file for PyLoT/autoPyLoT.\n' +
'%All main and special settings regarding data handling\n' +
'%and picking are to be set here!\n' +
'%Parameters are optimized for %{} data sets!\n'.format(self.get_main_para_names()['pick'][0]))
separator = '%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n'
@ -236,16 +241,18 @@ class PylotParameter(object):
'parameters for seismic moment estimation', separator)
self.write_section(fid_out, self.get_main_para_names()['localmag'],
'settings local magnitude', separator)
self.write_section(fid_out, self.get_main_para_names()['filter'],
'filter settings', separator)
self.write_section(fid_out, self.get_main_para_names()['pick'],
'common settings picker', separator)
fid_out.write(('#special settings for calculating CF#\n'+
fid_out.write(('#special settings for calculating CF#\n' +
'%!!Edit the following only if you know what you are doing!!%\n'))
self.write_section(fid_out, self.get_special_para_names()['z'],
'Z-component', None)
self.write_section(fid_out, self.get_special_para_names()['h'],
'H-components', None)
self.write_section(fid_out, self.get_special_para_names()['fm'],
'first-motion picker', None)
'first-motion picker', None)
self.write_section(fid_out, self.get_special_para_names()['quality'],
'quality assessment', None)
@ -261,7 +268,7 @@ class PylotParameter(object):
if type(value) == list or type(value) == tuple:
value_tmp = ''
for vl in value:
value_tmp+= '{} '.format(vl)
value_tmp += '{} '.format(vl)
value = value_tmp
tooltip = self.get_defaults()[name]['tooltip']
if not len(str(value)) > l_val:
@ -277,7 +284,7 @@ class PylotParameter(object):
ttip = '%{:<{}}\n'.format(tooltip, l_ttip)
else:
ttip = '%{}\n'.format(tooltip)
line = value+name+ttip
line = value + name + ttip
fid.write(line)
@ -335,12 +342,13 @@ class FilterOptions(object):
def parseFilterOptions(self):
if self:
robject = {'type': self.getFilterType(), 'corners': self.getOrder()}
if len(self.getFreq()) > 1:
if not self.getFilterType() in ['highpass', 'lowpass']:
robject['freqmin'] = self.getFreq()[0]
robject['freqmax'] = self.getFreq()[1]
else:
robject['freq'] = self.getFreq() if type(self.getFreq()) is \
float else self.getFreq()[0]
elif self.getFilterType() == 'highpass':
robject['freq'] = self.getFreq()[0]
elif self.getFilterType() == 'lowpass':
robject['freq'] = self.getFreq()[1]
return robject
return None

@ -2,19 +2,23 @@
# -*- coding: utf-8 -*-
import glob
import obspy.core.event as ope
import os
import scipy.io as sio
import warnings
from obspy.core import UTCDateTime
from obspy.core.util import AttribDict
import matplotlib.pyplot as plt
import numpy as np
import obspy.core.event as ope
import scipy.io as sio
from obspy.core import UTCDateTime
from obspy.core.event import read_events
from obspy.core.util import AttribDict
from pylot.core.io.inputs import PylotParameter
from pylot.core.io.location import create_arrival, create_event, \
create_magnitude, create_origin, create_pick
from pylot.core.io.location import create_event, \
create_magnitude
from pylot.core.pick.utils import select_for_phase
from pylot.core.util.utils import getOwner, full_range, four_digits
def add_amplitudes(event, amplitudes):
amplitude_list = []
for pick in event.picks:
@ -33,6 +37,7 @@ def add_amplitudes(event, amplitudes):
event.amplitudes = amplitude_list
return event
def readPILOTEvent(phasfn=None, locfn=None, authority_id='RUB', **kwargs):
"""
readPILOTEvent - function
@ -189,26 +194,35 @@ def picksdict_from_picks(evt):
PyLoT
:param evt: Event object contain all available information
:type evt: `~obspy.core.event.Event`
:return: pick dictionary
:return: pick dictionary (auto and manual)
"""
picks = {}
picksdict = {
'manual': {},
'auto': {}
}
for pick in evt.picks:
phase = {}
station = pick.waveform_id.station_code
channel = pick.waveform_id.channel_code
network = pick.waveform_id.network_code
try:
onsets = picks[station]
except KeyError as e:
#print(e)
onsets = {}
mpp = pick.time
spe = pick.time_errors.uncertainty
try:
picker = str(pick.method_id)
if picker.startswith('smi:local/'):
picker = picker.split('smi:local/')[1]
except IndexError:
picker = 'manual' # MP MP TODO maybe improve statement
try:
onsets = picksdict[picker][station]
except KeyError as e:
# print(e)
onsets = {}
try:
lpp = mpp + pick.time_errors.upper_uncertainty
epp = mpp - pick.time_errors.lower_uncertainty
except TypeError as e:
msg = e.message + ',\n falling back to symmetric uncertainties'
msg = e + ',\n falling back to symmetric uncertainties'
warnings.warn(msg)
lpp = mpp + spe
epp = mpp - spe
@ -218,17 +232,12 @@ def picksdict_from_picks(evt):
phase['spe'] = spe
phase['channel'] = channel
phase['network'] = network
try:
picker = str(pick.method_id)
if picker.startswith('smi:local/'):
picker = picker.split('smi:local/')[1]
phase['picker'] = picker
except IndexError:
pass
phase['picker'] = picker
onsets[pick.phase_hint] = phase.copy()
picks[station] = onsets.copy()
return picks
picksdict[picker][station] = onsets.copy()
return picksdict
def picks_from_picksdict(picks, creation_info=None):
picks_list = list()
@ -238,8 +247,8 @@ def picks_from_picksdict(picks, creation_info=None):
continue
onset = phase['mpp']
try:
ccode = phase['channel']
ncode = phase['network']
ccode = phase['channel']
ncode = phase['network']
except:
continue
pick = ope.Pick()
@ -253,8 +262,8 @@ def picks_from_picksdict(picks, creation_info=None):
lpp = phase['lpp']
pick.time_errors.lower_uncertainty = onset - epp
pick.time_errors.upper_uncertainty = lpp - onset
except KeyError as e:
warnings.warn(e.message, RuntimeWarning)
except (KeyError, TypeError) as e:
warnings.warn(str(e), RuntimeWarning)
try:
picker = phase['picker']
except KeyError as e:
@ -263,8 +272,8 @@ def picks_from_picksdict(picks, creation_info=None):
pick.phase_hint = label
pick.method_id = ope.ResourceIdentifier(id=picker)
pick.waveform_id = ope.WaveformStreamID(station_code=station,
channel_code=ccode,
network_code=ncode)
channel_code=ccode,
network_code=ncode)
try:
polarity = phase['fm']
if polarity == 'U' or '+':
@ -274,7 +283,7 @@ def picks_from_picksdict(picks, creation_info=None):
else:
pick.polarity = 'undecidable'
except KeyError as e:
if 'fm' in e.message: # no polarity information found for this phase
if 'fm' in str(e): # no polarity information found for this phase
pass
else:
raise e
@ -286,7 +295,7 @@ def reassess_pilot_db(root_dir, db_dir, out_dir=None, fn_param=None, verbosity=0
import glob
db_root = os.path.join(root_dir, db_dir)
evt_list = glob.glob1(db_root,'e????.???.??')
evt_list = glob.glob1(db_root, 'e????.???.??')
for evt in evt_list:
if verbosity > 0:
@ -294,7 +303,6 @@ def reassess_pilot_db(root_dir, db_dir, out_dir=None, fn_param=None, verbosity=0
reassess_pilot_event(root_dir, db_dir, evt, out_dir, fn_param, verbosity)
def reassess_pilot_event(root_dir, db_dir, event_id, out_dir=None, fn_param=None, verbosity=0):
from obspy import read
@ -302,7 +310,6 @@ def reassess_pilot_event(root_dir, db_dir, event_id, out_dir=None, fn_param=None
from pylot.core.pick.utils import earllatepicker
if fn_param is None:
import pylot.core.util.defaults as defaults
fn_param = defaults.AUTOMATIC_DEFAULTS
default = PylotParameter(fn_param, verbosity)
@ -336,7 +343,8 @@ def reassess_pilot_event(root_dir, db_dir, event_id, out_dir=None, fn_param=None
except Exception as e:
if 'No file matching file pattern:' in e.message:
if verbosity > 0:
warnings.warn('no waveform data found for station {station}'.format(station=station), RuntimeWarning)
warnings.warn('no waveform data found for station {station}'.format(station=station),
RuntimeWarning)
datacheck.append(fn_pattern + ' (no data)\n')
continue
else:
@ -360,7 +368,7 @@ def reassess_pilot_event(root_dir, db_dir, event_id, out_dir=None, fn_param=None
default.get('nfac{0}'.format(phase)),
default.get('tsnrz' if phase == 'P' else 'tsnrh'),
Pick1=rel_pick,
iplot=None,
iplot=0,
verbosity=0)
if epp is None or lpp is None:
continue
@ -392,10 +400,10 @@ def reassess_pilot_event(root_dir, db_dir, event_id, out_dir=None, fn_param=None
os.makedirs(out_dir)
fnout_prefix = os.path.join(out_dir, 'PyLoT_{0}.'.format(event_id))
evt.write(fnout_prefix + 'xml', format='QUAKEML')
#evt.write(fnout_prefix + 'cnv', format='VELEST')
# evt.write(fnout_prefix + 'cnv', format='VELEST')
def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
def writephases(arrivals, fformat, filename, parameter=None, eventinfo=None):
"""
Function of methods to write phases to the following standard file
formats used for locating earthquakes:
@ -421,10 +429,10 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
:param: eventinfo, optional, needed for VELEST-cnv file
and FOCMEC- and HASH-input files
:type: `obspy.core.event.Event` object
"""
"""
if fformat == 'NLLoc':
print ("Writing phases to %s for NLLoc" % filename)
print("Writing phases to %s for NLLoc" % filename)
fid = open("%s" % filename, 'w')
# write header
fid.write('# EQEVENT: %s Label: EQ%s Loc: X 0.00 Y 0.00 Z 10.00 OT 0.00 \n' %
@ -448,7 +456,7 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
ss = onset.second
ms = onset.microsecond
ss_ms = ss + ms / 1000000.0
pweight = 1 # use pick
pweight = 1 # use pick
try:
if arrivals[key]['P']['weight'] >= 4:
pweight = 0 # do not use pick
@ -475,7 +483,7 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
ss = onset.second
ms = onset.microsecond
ss_ms = ss + ms / 1000000.0
sweight = 1 # use pick
sweight = 1 # use pick
try:
if arrivals[key]['S']['weight'] >= 4:
sweight = 0 # do not use pick
@ -493,15 +501,15 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
fid.close()
elif fformat == 'HYPO71':
print ("Writing phases to %s for HYPO71" % filename)
print("Writing phases to %s for HYPO71" % filename)
fid = open("%s" % filename, 'w')
# write header
fid.write(' %s\n' %
parameter.get('eventID'))
parameter.get('eventID'))
for key in arrivals:
if arrivals[key]['P']['weight'] < 4:
stat = key
if len(stat) > 4: # HYPO71 handles only 4-string station IDs
if len(stat) > 4: # HYPO71 handles only 4-string station IDs
stat = stat[1:5]
Ponset = arrivals[key]['P']['mpp']
Sonset = arrivals[key]['S']['mpp']
@ -541,41 +549,39 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
elif sweight >= 2:
sstr = 'E'
fid.write('%-4s%sP%s%d %02d%02d%02d%02d%02d%5.2f %s%sS %d %s\n' % (stat,
pstr,
fm,
pweight,
year,
month,
day,
hh,
mm,
ss_ms,
Sss_ms,
sstr,
sweight,
Ao))
pstr,
fm,
pweight,
year,
month,
day,
hh,
mm,
ss_ms,
Sss_ms,
sstr,
sweight,
Ao))
else:
fid.write('%-4s%sP%s%d %02d%02d%02d%02d%02d%5.2f %s\n' % (stat,
pstr,
fm,
pweight,
year,
month,
day,
hh,
mm,
ss_ms,
Ao))
pstr,
fm,
pweight,
year,
month,
day,
hh,
mm,
ss_ms,
Ao))
fid.close()
elif fformat == 'HYPOSAT':
print ("Writing phases to %s for HYPOSAT" % filename)
print("Writing phases to %s for HYPOSAT" % filename)
fid = open("%s" % filename, 'w')
# write header
fid.write('%s, event %s \n' % (parameter.get('database'), parameter.get('eventID')))
errP = parameter.get('timeerrorsP')
errS = parameter.get('timeerrorsS')
for key in arrivals:
# P onsets
if arrivals[key].has_key('P'):
@ -592,7 +598,7 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
# use symmetrized picking error as std
# (read the HYPOSAT manual)
pstd = arrivals[key]['P']['spe']
fid.write('%-5s P1 %4.0f %02d %02d %02d %02d %05.02f %5.3f -999. 0.00 -999. 0.00\n'
fid.write('%-5s P1 %4.0f %02d %02d %02d %02d %05.02f %5.3f -999. 0.00 -999. 0.00\n'
% (key, pyear, pmonth, pday, phh, pmm, Pss, pstd))
# S onsets
if arrivals[key].has_key('S') and arrivals[key]['S']:
@ -607,16 +613,20 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
sms = Sonset.microsecond
Sss = sss + sms / 1000000.0
sstd = arrivals[key]['S']['spe']
fid.write('%-5s S1 %4.0f %02d %02d %02d %02d %05.02f %5.3f -999. 0.00 -999. 0.00\n'
fid.write('%-5s S1 %4.0f %02d %02d %02d %02d %05.02f %5.3f -999. 0.00 -999. 0.00\n'
% (key, syear, smonth, sday, shh, smm, Sss, sstd))
fid.close()
elif fformat == 'VELEST':
print ("Writing phases to %s for VELEST" % filename)
print("Writing phases to %s for VELEST" % filename)
fid = open("%s" % filename, 'w')
# get informations needed in cnv-file
# check, whether latitude is N or S and longitude is E or W
eventsource = eventinfo.origins[0]
try:
eventsource = eventinfo.origins[0]
except:
print("No source origin calculated yet, thus no cnv-file creation possible!")
return
if eventsource['latitude'] < 0:
cns = 'S'
else:
@ -628,14 +638,14 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
# get last two integers of origin year
stime = eventsource['time']
if stime.year - 2000 >= 0:
syear = stime.year - 2000
syear = stime.year - 2000
else:
syear = stime.year - 1900
ifx = 0 # default value, see VELEST manual, pp. 22-23
syear = stime.year - 1900
ifx = 0 # default value, see VELEST manual, pp. 22-23
# write header
fid.write('%s%02d%02d %02d%02d %05.2f %7.4f%c %8.4f%c %7.2f %6.2f %02.0f 0.0 0.03 1.0 1.0\n' % (
syear, stime.month, stime.day, stime.hour, stime.minute, stime.second, eventsource['latitude'],
cns, eventsource['longitude'], cew, eventsource['depth'],eventinfo.magnitudes[0]['mag'], ifx))
syear, stime.month, stime.day, stime.hour, stime.minute, stime.second, eventsource['latitude'],
cns, eventsource['longitude'], cew, eventsource['depth'], eventinfo.magnitudes[0]['mag'], ifx))
n = 0
for key in arrivals:
# P onsets
@ -643,33 +653,33 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
if arrivals[key]['P']['weight'] < 4:
n += 1
stat = key
if len(stat) > 4: # VELEST handles only 4-string station IDs
if len(stat) > 4: # VELEST handles only 4-string station IDs
stat = stat[1:5]
Ponset = arrivals[key]['P']['mpp']
Pweight = arrivals[key]['P']['weight']
Prt = Ponset - stime # onset time relative to source time
Prt = Ponset - stime # onset time relative to source time
if n % 6 is not 0:
fid.write('%-4sP%d%6.2f' % (stat, Pweight, Prt))
fid.write('%-4sP%d%6.2f' % (stat, Pweight, Prt))
else:
fid.write('%-4sP%d%6.2f\n' % (stat, Pweight, Prt))
# S onsets
fid.write('%-4sP%d%6.2f\n' % (stat, Pweight, Prt))
# S onsets
if arrivals[key].has_key('S'):
if arrivals[key]['S']['weight'] < 4:
n += 1
stat = key
if len(stat) > 4: # VELEST handles only 4-string station IDs
if len(stat) > 4: # VELEST handles only 4-string station IDs
stat = stat[1:5]
Sonset = arrivals[key]['S']['mpp']
Sweight = arrivals[key]['S']['weight']
Srt = Ponset - stime # onset time relative to source time
Srt = Ponset - stime # onset time relative to source time
if n % 6 is not 0:
fid.write('%-4sS%d%6.2f' % (stat, Sweight, Srt))
fid.write('%-4sS%d%6.2f' % (stat, Sweight, Srt))
else:
fid.write('%-4sS%d%6.2f\n' % (stat, Sweight, Srt))
fid.write('%-4sS%d%6.2f\n' % (stat, Sweight, Srt))
fid.close()
elif fformat == 'hypoDD':
print ("Writing phases to %s for hypoDD" % filename)
print("Writing phases to %s for hypoDD" % filename)
fid = open("%s" % filename, 'w')
# get event information needed for hypoDD-phase file
eventsource = eventinfo.origins[0]
@ -678,59 +688,62 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
hddID = event.split('.')[0][1:5]
# write header
fid.write('# %d %d %d %d %d %5.2f %7.4f +%6.4f %7.4f %4.2f 0.1 0.5 %4.2f %s\n' % (
stime.year, stime.month, stime.day, stime.hour, stime.minute, stime.second,
eventsource['latitude'], eventsource['longitude'], eventsource['depth'] / 1000,
eventinfo.magnitudes[0]['mag'], eventsource['quality']['standard_error'], hddID))
stime.year, stime.month, stime.day, stime.hour, stime.minute, stime.second,
eventsource['latitude'], eventsource['longitude'], eventsource['depth'] / 1000,
eventinfo.magnitudes[0]['mag'], eventsource['quality']['standard_error'], hddID))
for key in arrivals:
if arrivals[key].has_key('P'):
# P onsets
if arrivals[key]['P']['weight'] < 4:
Ponset = arrivals[key]['P']['mpp']
Prt = Ponset - stime # onset time relative to source time
fid.write('%s %6.3f 1 P\n' % (key, Prt))
# S onsets
Prt = Ponset - stime # onset time relative to source time
fid.write('%s %6.3f 1 P\n' % (key, Prt))
# S onsets
if arrivals[key]['S']['weight'] < 4:
Sonset = arrivals[key]['S']['mpp']
Srt = Sonset - stime # onset time relative to source time
fid.write('%-5s %6.3f 1 S\n' % (key, Srt))
Srt = Sonset - stime # onset time relative to source time
fid.write('%-5s %6.3f 1 S\n' % (key, Srt))
fid.close()
elif fformat == 'FOCMEC':
print ("Writing phases to %s for FOCMEC" % filename)
print("Writing phases to %s for FOCMEC" % filename)
fid = open("%s" % filename, 'w')
# get event information needed for FOCMEC-input file
eventsource = eventinfo.origins[0]
stime = eventsource['time']
# write header line including event information
fid.write('%s %d%02d%02d%02d%02d%02.0f %7.4f %6.4f %3.1f %3.1f\n' % (parameter.get('eventID'),
stime.year, stime.month, stime.day, stime.hour, stime.minute, stime.second,
eventsource['latitude'], eventsource['longitude'], eventsource['depth'] / 1000,
eventinfo.magnitudes[0]['mag']))
stime.year, stime.month, stime.day,
stime.hour, stime.minute, stime.second,
eventsource['latitude'],
eventsource['longitude'],
eventsource['depth'] / 1000,
eventinfo.magnitudes[0]['mag']))
picks = eventinfo.picks
for key in arrivals:
if arrivals[key].has_key('P'):
if arrivals[key]['P']['weight'] < 4 and arrivals[key]['P']['fm'] is not None:
stat = key
for i in range(len(picks)):
station = picks[i].waveform_id.station_code
if station == stat:
# get resource ID
resid_picks = picks[i].get('resource_id')
# find same ID in eventinfo
# there it is the pick_id!!
for j in range(len(eventinfo.origins[0].arrivals)):
resid_eventinfo = eventinfo.origins[0].arrivals[j].get('pick_id')
if resid_eventinfo == resid_picks and eventinfo.origins[0].arrivals[j].phase == 'P':
if len(stat) > 4: # FOCMEC handles only 4-string station IDs
stat = stat[1:5]
az = eventinfo.origins[0].arrivals[j].get('azimuth')
inz = eventinfo.origins[0].arrivals[j].get('takeoff_angle')
fid.write('%-4s %6.2f %6.2f%s \n' % (stat,
az,
inz,
arrivals[key]['P']['fm']))
break
station = picks[i].waveform_id.station_code
if station == stat:
# get resource ID
resid_picks = picks[i].get('resource_id')
# find same ID in eventinfo
# there it is the pick_id!!
for j in range(len(eventinfo.origins[0].arrivals)):
resid_eventinfo = eventinfo.origins[0].arrivals[j].get('pick_id')
if resid_eventinfo == resid_picks and eventinfo.origins[0].arrivals[j].phase == 'P':
if len(stat) > 4: # FOCMEC handles only 4-string station IDs
stat = stat[1:5]
az = eventinfo.origins[0].arrivals[j].get('azimuth')
inz = eventinfo.origins[0].arrivals[j].get('takeoff_angle')
fid.write('%-4s %6.2f %6.2f%s \n' % (stat,
az,
inz,
arrivals[key]['P']['fm']))
break
fid.close()
@ -739,9 +752,9 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
# HASH-driver 1 and 2 (see HASH manual!)
filename1 = filename + 'drv1' + '.phase'
filename2 = filename + 'drv2' + '.phase'
print ("Writing phases to %s for HASH for HASH-driver 1" % filename1)
print("Writing phases to %s for HASH for HASH-driver 1" % filename1)
fid1 = open("%s" % filename1, 'w')
print ("Writing phases to %s for HASH for HASH-driver 2" % filename2)
print("Writing phases to %s for HASH for HASH-driver 2" % filename2)
fid2 = open("%s" % filename2, 'w')
# get event information needed for HASH-input file
eventsource = eventinfo.origins[0]
@ -756,26 +769,32 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
erz = eventsource.depth_errors['uncertainty']
stime = eventsource['time']
if stime.year - 2000 >= 0:
syear = stime.year - 2000
syear = stime.year - 2000
else:
syear = stime.year - 1900
syear = stime.year - 1900
picks = eventinfo.picks
# write header line including event information
# for HASH-driver 1
fid1.write('%s%02d%02d%02d%02d%5.2f%2dN%5.2f%3dE%5.2f%6.3f%4.2f%5.2f%5.2f%s\n' % (syear,
stime.month, stime.day, stime.hour, stime.minute, stime.second,
latdeg, latmin, londeg, lonmin, eventsource['depth'],
eventinfo.magnitudes[0]['mag'], erh, erz,
hashID))
stime.month, stime.day,
stime.hour, stime.minute,
stime.second,
latdeg, latmin, londeg,
lonmin, eventsource['depth'],
eventinfo.magnitudes[0][
'mag'], erh, erz,
hashID))
# write header line including event information
# for HASH-driver 2
fid2.write('%d%02d%02d%02d%02d%5.2f%dN%5.2f%3dE%6.2f%5.2f %d %5.2f %5.2f %4.2f %s \n' % (syear, stime.month, stime.day,
stime.hour, stime.minute, stime.second,
latdeg,latmin,londeg, lonmin,
eventsource['depth'],
eventsource['quality']['used_phase_count'],
erh, erz, eventinfo.magnitudes[0]['mag'],
hashID))
fid2.write(
'%d%02d%02d%02d%02d%5.2f%dN%5.2f%3dE%6.2f%5.2f %d %5.2f %5.2f %4.2f %s \n' % (
syear, stime.month, stime.day,
stime.hour, stime.minute, stime.second,
latdeg, latmin, londeg, lonmin,
eventsource['depth'],
eventsource['quality']['used_phase_count'],
erh, erz, eventinfo.magnitudes[0]['mag'],
hashID))
# write phase lines
for key in arrivals:
@ -786,36 +805,38 @@ def writephases(arrivals, fformat, filename, parameter, eventinfo=None):
ncode = arrivals[key]['P']['network']
if arrivals[key]['P']['weight'] < 2:
Pqual='I'
Pqual = 'I'
else:
Pqual='E'
Pqual = 'E'
for i in range(len(picks)):
station = picks[i].waveform_id.station_code
if station == stat:
# get resource ID
resid_picks = picks[i].get('resource_id')
# find same ID in eventinfo
# there it is the pick_id!!
for j in range(len(eventinfo.origins[0].arrivals)):
resid_eventinfo = eventinfo.origins[0].arrivals[j].get('pick_id')
if resid_eventinfo == resid_picks and eventinfo.origins[0].arrivals[j].phase == 'P':
if len(stat) > 4: # HASH handles only 4-string station IDs
stat = stat[1:5]
az = eventinfo.origins[0].arrivals[j].get('azimuth')
inz = eventinfo.origins[0].arrivals[j].get('takeoff_angle')
dist = eventinfo.origins[0].arrivals[j].get('distance')
# write phase line for HASH-driver 1
fid1.write('%-4s%sP%s%d 0 %3.1f %03d %03d 2 1 %s\n' % (stat, Pqual, arrivals[key]['P']['fm'], arrivals[key]['P']['weight'],
dist, inz, az, ccode))
# write phase line for HASH-driver 2
fid2.write('%-4s %s %s %s %s \n' % (
stat,
ncode,
ccode,
Pqual,
arrivals[key]['P']['fm']))
break
station = picks[i].waveform_id.station_code
if station == stat:
# get resource ID
resid_picks = picks[i].get('resource_id')
# find same ID in eventinfo
# there it is the pick_id!!
for j in range(len(eventinfo.origins[0].arrivals)):
resid_eventinfo = eventinfo.origins[0].arrivals[j].get('pick_id')
if resid_eventinfo == resid_picks and eventinfo.origins[0].arrivals[j].phase == 'P':
if len(stat) > 4: # HASH handles only 4-string station IDs
stat = stat[1:5]
az = eventinfo.origins[0].arrivals[j].get('azimuth')
inz = eventinfo.origins[0].arrivals[j].get('takeoff_angle')
dist = eventinfo.origins[0].arrivals[j].get('distance')
# write phase line for HASH-driver 1
fid1.write(
'%-4s%sP%s%d 0 %3.1f %03d %03d 2 1 %s\n' % (
stat, Pqual, arrivals[key]['P']['fm'], arrivals[key]['P']['weight'],
dist, inz, az, ccode))
# write phase line for HASH-driver 2
fid2.write('%-4s %s %s %s %s \n' % (
stat,
ncode,
ccode,
Pqual,
arrivals[key]['P']['fm']))
break
fid1.write(' %s' % hashID)
fid1.close()
@ -841,7 +862,155 @@ def merge_picks(event, picks):
network = pick.waveform_id.network_code
method = pick.method_id
for p in event.picks:
if p.waveform_id.station_code == station and p.phase_hint == phase:
if p.waveform_id.station_code == station\
and p.waveform_id.network_code == network\
and p.phase_hint == phase\
and (str(p.method_id) in str(method)
or str(method) in str(p.method_id)):
p.time, p.time_errors, p.waveform_id.network_code, p.method_id = time, err, network, method
del time, err, phase, station, network, method
return event
def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
"""
Script to get onset uncertainties from Quakeml.xml files created by PyLoT.
Uncertainties are tranformed into quality classes and visualized via histogram if desired.
Ludger Küperkoch, BESTEC GmbH, 07/2017
"""
from pylot.core.pick.utils import getQualityFromUncertainty
from pylot.core.util.utils import loopIdentifyPhase, identifyPhase
# read all onset weights
Pw0 = []
Pw1 = []
Pw2 = []
Pw3 = []
Pw4 = []
Sw0 = []
Sw1 = []
Sw2 = []
Sw3 = []
Sw4 = []
for names in xmlnames:
print("Getting onset weights from {}".format(names))
cat = read_events(names)
cat_copy = cat.copy()
arrivals = cat.events[0].picks
arrivals_copy = cat_copy.events[0].picks
# Prefere manual picks if qualities are sufficient!
for Pick in arrivals:
if (Pick.method_id.id).split('/')[1] == 'manual':
mstation = Pick.waveform_id.station_code
mstation_ext = mstation + '_'
for mpick in arrivals_copy:
phase = identifyPhase(loopIdentifyPhase(Pick.phase_hint))
if phase == 'P':
if ((mpick.waveform_id.station_code == mstation) or
(mpick.waveform_id.station_code == mstation_ext)) and \
((mpick.method_id).split('/')[1] == 'auto') and \
(mpick.time_errors['uncertainty'] <= ErrorsP[3]):
del mpick
break
elif phase == 'S':
if ((mpick.waveform_id.station_code == mstation) or
(mpick.waveform_id.station_code == mstation_ext)) and \
((mpick.method_id).split('/')[1] == 'auto') and \
(mpick.time_errors['uncertainty'] <= ErrorsS[3]):
del mpick
break
lendiff = len(arrivals) - len(arrivals_copy)
if lendiff is not 0:
print("Found manual as well as automatic picks, prefered the {} manual ones!".format(lendiff))
for Pick in arrivals_copy:
phase = identifyPhase(loopIdentifyPhase(Pick.phase_hint))
if phase == 'P':
Pqual = getQualityFromUncertainty(Pick.time_errors.uncertainty, ErrorsP)
if Pqual == 0:
Pw0.append(Pick.time_errors.uncertainty)
elif Pqual == 1:
Pw1.append(Pick.time_errors.uncertainty)
elif Pqual == 2:
Pw2.append(Pick.time_errors.uncertainty)
elif Pqual == 3:
Pw3.append(Pick.time_errors.uncertainty)
elif Pqual == 4:
Pw4.append(Pick.time_errors.uncertainty)
elif phase == 'S':
Squal = getQualityFromUncertainty(Pick.time_errors.uncertainty, ErrorsS)
if Squal == 0:
Sw0.append(Pick.time_errors.uncertainty)
elif Squal == 1:
Sw1.append(Pick.time_errors.uncertainty)
elif Squal == 2:
Sw2.append(Pick.time_errors.uncertainty)
elif Squal == 3:
Sw3.append(Pick.time_errors.uncertainty)
elif Squal == 4:
Sw4.append(Pick.time_errors.uncertainty)
else:
print("Phase hint not defined for picking!")
pass
if plotflag == 0:
Punc = [Pw0, Pw1, Pw2, Pw3, Pw4]
Sunc = [Sw0, Sw1, Sw2, Sw3, Sw4]
return Punc, Sunc
else:
# get percentage of weights
numPweights = np.sum([len(Pw0), len(Pw1), len(Pw2), len(Pw3), len(Pw4)])
numSweights = np.sum([len(Sw0), len(Sw1), len(Sw2), len(Sw3), len(Sw4)])
if len(Pw0) > 0:
P0perc = 100 / numPweights * len(Pw0)
else:
P0perc = 0
if len(Pw1) > 0:
P1perc = 100 / numPweights * len(Pw1)
else:
P1perc = 0
if len(Pw2) > 0:
P2perc = 100 / numPweights * len(Pw2)
else:
P2perc = 0
if len(Pw3) > 0:
P3perc = 100 / numPweights * len(Pw3)
else:
P3perc = 0
if len(Pw4) > 0:
P4perc = 100 / numPweights * len(Pw4)
else:
P4perc = 0
if len(Sw0) > 0:
S0perc = 100 / numSweights * len(Sw0)
else:
S0perc = 0
if len(Sw1) > 0:
S1perc = 100 / numSweights * len(Sw1)
else:
S1perc = 0
if len(Sw2) > 0:
S2perc = 100 / numSweights * len(Sw2)
else:
S2perc = 0
if len(Sw3) > 0:
S3perc = 100 / numSweights * len(Sw3)
else:
S3perc = 0
if len(Sw4) > 0:
S4perc = 100 / numSweights * len(Sw4)
else:
S4perc = 0
weights = ('0', '1', '2', '3', '4')
y_pos = np.arange(len(weights))
width = 0.34
plt.bar(y_pos - width, [P0perc, P1perc, P2perc, P3perc, P4perc], width, color='black')
plt.bar(y_pos, [S0perc, S1perc, S2perc, S3perc, S4perc], width, color='red')
plt.ylabel('%')
plt.xticks(y_pos, weights)
plt.xlim([-0.5, 4.5])
plt.xlabel('Qualities')
plt.title('{0} P-Qualities, {1} S-Qualities'.format(numPweights, numSweights))
plt.show()

@ -6,6 +6,7 @@ from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
def export(picks, fnout, parameter, eventinfo):
'''
Take <picks> dictionary and exports picking data to a focmec

@ -6,6 +6,7 @@ from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
def export(picks, fnout, parameter, eventinfo):
'''
Take <picks> dictionary and exports picking data to a HASH

@ -6,6 +6,7 @@ from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
def export(picks, fnout, parameter):
'''
Take <picks> dictionary and exports picking data to a HYPO71

@ -6,6 +6,7 @@ from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
def export(picks, fnout, parameter, eventinfo):
'''
Take <picks> dictionary and exports picking data to a hypoDD

@ -6,6 +6,7 @@ from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
def export(picks, fnout, parameter):
'''
Take <picks> dictionary and exports picking data to a HYPOSAT

@ -1,9 +1,10 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import subprocess
import os
import glob
import os
import subprocess
from obspy import read_events
from pylot.core.io.phases import writephases
from pylot.core.util.utils import getPatternLine, runProgram, which
@ -11,9 +12,11 @@ from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
class NLLocError(EnvironmentError):
pass
def export(picks, fnout, parameter):
'''
Take <picks> dictionary and exports picking data to a NLLOC-obs
@ -58,7 +61,7 @@ def modify_inputs(ctrfn, root, nllocoutn, phasefn, tttn):
locfiles = 'LOCFILES %s NLLOC_OBS %s %s 0\n' % (phasefile, tttable, nllocout)
# modification of NLLoc-control file
print ("Modifying NLLoc-control file %s ..." % ctrfile)
print("Modifying NLLoc-control file %s ..." % ctrfile)
curlocfiles = getPatternLine(ctrfile, 'LOCFILES')
nllfile = open(ctrfile, 'r')
filedata = nllfile.read()
@ -94,7 +97,7 @@ def locate(fnin, infile=None):
def read_location(fn):
path, file = os.path.split(fn)
file = glob.glob1(path, file + '.[0-9]*.grid0.loc.hyp')
file = glob.glob1(path, file + '.[0-9]*.grid0.loc.hyp')
if len(file) > 1:
raise IOError('ambiguous location name {0}'.format(file))
fn = os.path.join(path, file[0])

@ -6,7 +6,8 @@ from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
def export(picks, fnout, parameter, eventinfo):
def export(picks, fnout, eventinfo, parameter=None):
'''
Take <picks> dictionary and exports picking data to a VELEST-cnv
<phasefile> without creating an ObsPy event object.
@ -17,11 +18,11 @@ def export(picks, fnout, parameter, eventinfo):
:param fnout: complete path to the exporting obs file
:type fnout: str
:param: parameter, all input information
:type: object
:param: eventinfo, source time needed for VELEST-cnv format
:type: list object
:param: parameter, all input information
:type: object
'''
# write phases to VELEST-phase file
writephases(picks, 'VELEST', fnout, parameter, eventinfo)

@ -11,25 +11,37 @@ function conglomerate utils.
import matplotlib.pyplot as plt
import numpy as np
from pylot.core.io.data import Data
from pylot.core.io.inputs import PylotParameter
from pylot.core.pick.picker import AICPicker, PragPicker
from pylot.core.pick.charfuns import CharacteristicFunction
from pylot.core.pick.charfuns import HOScf, AICcf, ARZcf, ARHcf, AR3Ccf
from pylot.core.pick.picker import AICPicker, PragPicker
from pylot.core.pick.utils import checksignallength, checkZ4S, earllatepicker, \
getSNR, fmpicker, checkPonsets, wadaticheck
from pylot.core.util.utils import getPatternLine, gen_Pool
from pylot.core.io.data import Data
from pylot.core.util.utils import getPatternLine, gen_Pool,\
real_Bool, identifyPhaseID
from obspy.taup import TauPyModel
def autopickevent(data, param, iplot=0, fig_dict=None):
def autopickevent(data, param, iplot=0, fig_dict=None, fig_dict_wadatijack=None, ncores=0, metadata=None, origin=None):
stations = []
all_onsets = {}
input_tuples = []
try:
iplot = int(iplot)
except:
if iplot == True or iplot == 'True':
iplot = 2
else:
iplot = 0
# get some parameters for quality control from
# parameter input file (usually autoPyLoT.in).
# parameter input file (usually pylot.in).
wdttolerance = param.get('wdttolerance')
mdttolerance = param.get('mdttolerance')
jackfactor = param.get('jackfactor')
apverbose = param.get('apverbose')
for n in range(len(data)):
station = data[n].stats.station
@ -41,46 +53,67 @@ def autopickevent(data, param, iplot=0, fig_dict=None):
for station in stations:
topick = data.select(station=station)
if not iplot:
input_tuples.append((topick, param, apverbose))
if iplot>0:
all_onsets[station] = autopickstation(topick, param, verbose=apverbose, iplot=iplot, fig_dict=fig_dict)
if iplot == None or iplot == 'None' or iplot == 0:
input_tuples.append((topick, param, apverbose, metadata, origin))
if iplot > 0:
all_onsets[station] = autopickstation(topick, param, verbose=apverbose,
iplot=iplot, fig_dict=fig_dict,
metadata=metadata, origin=origin)
if iplot>0:
if iplot > 0:
print('iPlot Flag active: NO MULTIPROCESSING possible.')
return all_onsets
pool = gen_Pool()
# rename str for ncores in case ncores == 0 (use all cores)
ncores_str = ncores if ncores != 0 else 'all available'
print('Autopickstation: Distribute autopicking for {} '
'stations on {} cores.'.format(len(input_tuples), ncores_str))
pool = gen_Pool(ncores)
result = pool.map(call_autopickstation, input_tuples)
pool.close()
for pick in result:
station = pick['station']
pick.pop('station')
all_onsets[station] = pick
if pick:
station = pick['station']
pick.pop('station')
all_onsets[station] = pick
return all_onsets
#return all_onsets
# quality control
# median check and jackknife on P-onset times
jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, iplot)
jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, jackfactor, 1, fig_dict_wadatijack)
#return jk_checked_onsets
# check S-P times (Wadati)
return wadaticheck(jk_checked_onsets, wdttolerance, iplot)
wadationsets = wadaticheck(jk_checked_onsets, wdttolerance, 1, fig_dict_wadatijack)
return wadationsets
def call_autopickstation(input_tuple):
wfstream, pickparam, verbose = input_tuple
#multiprocessing not possible with interactive plotting
return autopickstation(wfstream, pickparam, verbose, iplot=0)
wfstream, pickparam, verbose, metadata, origin = input_tuple
# multiprocessing not possible with interactive plotting
return autopickstation(wfstream, pickparam, verbose, iplot=0, metadata=metadata, origin=origin)
def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
def get_source_coords(parser, station_id):
station_coords = None
try:
station_coords = parser.get_coordinates(station_id)
except Exception as e:
print('Could not get source coordinates for station {}: {}'.format(station_id, e))
return station_coords
def autopickstation(wfstream, pickparam, verbose=False,
iplot=0, fig_dict=None, metadata=None, origin=None):
"""
:param wfstream: `~obspy.core.stream.Stream` containing waveform
:type wfstream: obspy.core.stream.Stream
:param pickparam: container of picking parameters from input file,
usually autoPyLoT.in
usually pylot.in
:type pickparam: PylotParameter
:param verbose:
:type verbose: bool
@ -88,11 +121,10 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
"""
# declaring pickparam variables (only for convenience)
# read your autoPyLoT.in for details!
# read your pylot.in for details!
plt_flag = 0
# special parameters for P picking
iplot = iplot
algoP = pickparam.get('algoP')
pstart = pickparam.get('pstart')
pstop = pickparam.get('pstop')
@ -102,6 +134,7 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
bpz1 = pickparam.get('bpz1')
bpz2 = pickparam.get('bpz2')
pickwinP = pickparam.get('pickwinP')
aictsmoothP = pickparam.get('aictsmooth')
tsmoothP = pickparam.get('tsmoothP')
ausP = pickparam.get('ausP')
nfacP = pickparam.get('nfacP')
@ -117,6 +150,8 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
algoS = pickparam.get('algoS')
sstart = pickparam.get('sstart')
sstop = pickparam.get('sstop')
use_taup = real_Bool(pickparam.get('use_taup'))
taup_model = pickparam.get('taup_model')
bph1 = pickparam.get('bph1')
bph2 = pickparam.get('bph2')
tsnrh = pickparam.get('tsnrh')
@ -182,25 +217,82 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
if len(ndat) == 0: # check for other components
ndat = wfstream.select(component="1")
if not zdat:
print('No z-component found for station {}. STOP'.format(wfstream[0].stats.station))
return
if algoP == 'HOS' or algoP == 'ARZ' and zdat is not None:
msg = '##################################################\nautopickstation:' \
' Working on P onset of station {station}\nFiltering vertical ' \
'trace ...\n{data}'.format(station=zdat[0].stats.station,
'trace ...\n{data}'.format(station=wfstream[0].stats.station,
data=str(zdat))
if verbose: print(msg)
z_copy = zdat.copy()
# filter and taper data
tr_filt = zdat[0].copy()
#remove constant offset from data to avoid unwanted filter response
tr_filt.detrend(type='demean')
# filter and taper data
tr_filt.filter('bandpass', freqmin=bpz1[0], freqmax=bpz1[1],
zerophase=False)
tr_filt.taper(max_percentage=0.05, type='hann')
z_copy[0].data = tr_filt.data
##############################################################
# check length of waveform and compare with cut times
Lc = pstop - pstart
# for global seismology: use tau-p method for estimating travel times (needs source and station coords.)
# if not given: sets Lc to infinity to use full stream
if use_taup == True:
Lc = np.inf
print('autopickstation: use_taup flag active.')
if not metadata[1]:
print('Warning: Could not use TauPy to estimate onsets as there are no metadata given.')
else:
station_id = wfstream[0].get_id()
parser = metadata[1]
station_coords = get_source_coords(parser, station_id)
if station_coords and origin:
source_origin = origin[0]
model = TauPyModel(taup_model)
arrivals = model.get_travel_times_geo(
source_origin.depth,
source_origin.latitude,
source_origin.longitude,
station_coords['latitude'],
station_coords['longitude']
)
phases = {'P': [],
'S': []}
for arr in arrivals:
phases[identifyPhaseID(arr.phase.name)].append(arr)
# get first P and S onsets from arrivals list
arrP, estFirstP = min([(arr, arr.time) for arr in phases['P']], key = lambda t: t[1])
arrS, estFirstS = min([(arr, arr.time) for arr in phases['S']], key = lambda t: t[1])
print('autopick: estimated first arrivals for P: {} s, S:{} s after event'
' origin time using TauPy'.format(estFirstP, estFirstS))
# modifiy pstart and pstop relative to estimated first P arrival (relative to station time axis)
pstart += (source_origin.time + estFirstP) - zdat[0].stats.starttime
pstop += (source_origin.time + estFirstP) - zdat[0].stats.starttime
print('autopick: CF calculation times respectively:'
' pstart: {} s, pstop: {} s'.format(pstart, pstop))
elif not origin:
print('No source origins given!')
# make sure pstart and pstop are inside zdat[0]
pstart = max(pstart, 0)
pstop = min(pstop, len(zdat[0])*zdat[0].stats.delta)
if not use_taup == True or origin:
Lc = pstop - pstart
Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
Ldiff = Lwf - Lc
if Ldiff < 0:
if not Lwf > 0:
print('autopickstation: empty trace! Return!')
return
Ldiff = Lwf - abs(Lc)
if Ldiff < 0 or pstop <= pstart:
msg = 'autopickstation: Cutting times are too large for actual ' \
'waveform!\nUsing entire waveform instead!'
if verbose: print(msg)
@ -235,9 +327,17 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
key = 'aicFig'
if fig_dict:
fig = fig_dict[key]
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
else:
fig = None
aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP, fig=fig)
linecolor = 'k'
aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, aictsmoothP, fig=fig, linecolor=linecolor)
# add pstart and pstop to aic plot
if fig:
for ax in fig.axes:
ax.vlines(pstart, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P start')
ax.vlines(pstop, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P stop')
ax.legend(loc=1)
##############################################################
if aicpick.getpick() is not None:
# check signal length to detect spuriously picked noise peaks
@ -254,16 +354,21 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
key = 'slength'
if fig_dict:
fig = fig_dict[key]
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
else:
fig = None
linecolor = 'k'
Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
minsiglength / 2,
nfacsl, minpercent, iplot,
fig)
fig, linecolor)
else:
# filter and taper horizontal traces
trH1_filt = edat.copy()
trH2_filt = ndat.copy()
# remove constant offset from data to avoid unwanted filter response
trH1_filt.detrend(type='demean')
trH2_filt.detrend(type='demean')
trH1_filt.filter('bandpass', freqmin=bph1[0],
freqmax=bph1[1],
zerophase=False)
@ -276,12 +381,14 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
zne += trH2_filt
if fig_dict:
fig = fig_dict['slength']
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
else:
fig = None
linecolor = 'k'
Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
minsiglength,
nfacsl, minpercent, iplot,
fig)
fig, linecolor)
if Pflag == 1:
# check for spuriously picked S onset
@ -291,13 +398,15 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
'Skipping control function checkZ4S.'
if verbose: print(msg)
else:
if iplot>1:
if iplot > 1:
if fig_dict:
fig = fig_dict['checkZ4s']
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
else:
fig = None
linecolor = 'k'
Pflag = checkZ4S(zne, aicpick.getpick(), zfac,
tsnrz[3], iplot, fig)
tsnrz[2], iplot, fig, linecolor)
if Pflag == 0:
Pmarker = 'SinsteadP'
Pweight = 9
@ -306,8 +415,11 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
Pweight = 9
##############################################################
# go on with processing if AIC onset passes quality control
if (aicpick.getSlope() >= minAICPslope and
aicpick.getSNR() >= minAICPSNR and Pflag == 1):
slope = aicpick.getSlope()
if not slope:
slope = 0
if (slope >= minAICPslope and
aicpick.getSNR() >= minAICPSNR and Pflag == 1):
aicPflag = 1
msg = 'AIC P-pick passes quality control: Slope: {0} counts/s, ' \
'SNR: {1}\nGo on with refined picking ...\n' \
@ -317,6 +429,7 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
# re-filter waveform with larger bandpass
z_copy = zdat.copy()
tr_filt = zdat[0].copy()
tr_filt.detrend(type='demean')
tr_filt.filter('bandpass', freqmin=bpz2[0], freqmax=bpz2[1],
zerophase=False)
tr_filt.taper(max_percentage=0.05, type='hann')
@ -346,10 +459,12 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
algoP=algoP)
if fig_dict:
fig = fig_dict['refPpick']
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
else:
fig = None
linecolor = 'k'
refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP,
aicpick.getpick(), fig)
aicpick.getpick(), fig, linecolor)
mpickP = refPpick.getpick()
#############################################################
if mpickP is not None:
@ -358,28 +473,34 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
if iplot:
if fig_dict:
fig = fig_dict['el_Ppick']
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
else:
fig = None
linecolor = 'k'
epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
mpickP, iplot, fig=fig)
mpickP, iplot, fig=fig,
linecolor=linecolor)
else:
epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
mpickP, iplot)
mpickP, iplot)
# get SNR
[SNRP, SNRPdB, Pnoiselevel] = getSNR(z_copy, tsnrz, mpickP)
# weight P-onset using symmetric error
if Perror <= timeerrorsP[0]:
Pweight = 0
elif timeerrorsP[0] < Perror <= timeerrorsP[1]:
Pweight = 1
elif timeerrorsP[1] < Perror <= timeerrorsP[2]:
Pweight = 2
elif timeerrorsP[2] < Perror <= timeerrorsP[3]:
Pweight = 3
elif Perror > timeerrorsP[3]:
if Perror == None:
Pweight = 4
else:
if Perror <= timeerrorsP[0]:
Pweight = 0
elif timeerrorsP[0] < Perror <= timeerrorsP[1]:
Pweight = 1
elif timeerrorsP[1] < Perror <= timeerrorsP[2]:
Pweight = 2
elif timeerrorsP[2] < Perror <= timeerrorsP[3]:
Pweight = 3
elif Perror > timeerrorsP[3]:
Pweight = 4
##############################################################
# get first motion of P onset
@ -388,11 +509,12 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
if iplot:
if fig_dict:
fig = fig_dict['fm_picker']
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
else:
fig = None
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot, fig)
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot, fig, linecolor)
else:
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
else:
FM = 'N'
@ -402,6 +524,8 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
SNRPdB,
FM)
print(msg)
msg = 'autopickstation: Refined P-Pick: {} s | P-Error: {} s'.format(mpickP, Perror)
print(msg)
Sflag = 1
else:
@ -419,16 +543,46 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
print('autopickstation: No vertical component data available!, '
'Skipping station!')
if edat is not None and ndat is not None and len(edat) > 0 and len(
ndat) > 0 and Pweight < 4:
if ((len(edat) > 0 and len(ndat) == 0) or (
len(ndat) > 0 and len(edat) == 0)) and Pweight < 4:
msg = 'Go on picking S onset ...\n' \
'##################################################\n' \
'Only one horizontal component available!\n' \
'ARH prediction requires at least 2 components!\n' \
'Copying existing horizontal component ...'
if verbose: print(msg)
# check which component is missing
if len(edat) == 0:
edat = ndat
else:
ndat = edat
pickSonset = (edat is not None and ndat is not None and len(edat) > 0 and len(
ndat) > 0 and Pweight < 4)
if pickSonset:
# determine time window for calculating CF after P onset
cuttimesh = [
round(max([mpickP + sstart, 0])), # MP MP relative time axis
round(min([
mpickP + sstop,
edat[0].stats.endtime-edat[0].stats.starttime,
ndat[0].stats.endtime-ndat[0].stats.starttime
]))
]
if not cuttimesh[1] >= cuttimesh[0]:
print('Cut window for horizontal phases too small! Will not pick S onsets.')
pickSonset = False
if pickSonset:
msg = 'Go on picking S onset ...\n' \
'##################################################\n' \
'Working on S onset of station {0}\nFiltering horizontal ' \
'traces ...'.format(edat[0].stats.station)
if verbose: print(msg)
# determine time window for calculating CF after P onset
cuttimesh = [round(max([mpickP + sstart, 0])),
round(min([mpickP + sstop, Lwf]))]
if algoS == 'ARH':
# re-create stream object including both horizontal components
@ -438,6 +592,8 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
# filter and taper data
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH1_filt.detrend(type='demean')
trH2_filt.detrend(type='demean')
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
@ -456,6 +612,9 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH3_filt = hdat[2].copy()
trH1_filt.detrend(type='demean')
trH2_filt.detrend(type='demean')
trH3_filt.detrend(type='demean')
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
@ -493,15 +652,20 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
# of class AutoPicking
if fig_dict:
fig = fig_dict['aicARHfig']
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
else:
fig = None
linecolor = 'k'
aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None,
aictsmoothS, fig=fig)
aictsmoothS, fig=fig, linecolor=linecolor)
###############################################################
# go on with processing if AIC onset passes quality control
if (aicarhpick.getSlope() >= minAICSslope and
aicarhpick.getSNR() >= minAICSSNR and
aicarhpick.getpick() is not None):
slope = aicarhpick.getSlope()
if not slope:
slope = 0
if (slope >= minAICSslope and
aicarhpick.getSNR() >= minAICSSNR and
aicarhpick.getpick() is not None):
aicSflag = 1
msg = 'AIC S-pick passes quality control: Slope: {0} counts/s, ' \
'SNR: {1}\nGo on with refined picking ...\n' \
@ -518,6 +682,8 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
if algoS == 'ARH':
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH1_filt.detrend(type='demean')
trH2_filt.detrend(type='demean')
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
@ -533,6 +699,9 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH3_filt = hdat[2].copy()
trH1_filt.detrend(type='demean')
trH2_filt.detrend(type='demean')
trH3_filt.detrend(type='demean')
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
@ -552,10 +721,12 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
# get refined onset time from CF2 using class Picker
if fig_dict:
fig = fig_dict['refSpick']
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
else:
fig = None
linecolor = 'k'
refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS,
tsmoothS, aicarhpick.getpick(), fig)
tsmoothS, aicarhpick.getpick(), fig, linecolor)
mpickS = refSpick.getpick()
#############################################################
if mpickS is not None:
@ -565,27 +736,33 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
if iplot:
if fig_dict:
fig = fig_dict['el_S1pick']
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
else:
fig = None
epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
tsnrh,
mpickS, iplot,
fig=fig)
linecolor = 'k'
epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
tsnrh,
mpickS, iplot,
fig=fig,
linecolor=linecolor)
else:
epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
tsnrh,
mpickS, iplot)
h_copy[0].data = trH2_filt.data
if iplot:
if fig_dict:
fig = fig_dict['el_S2pick']
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
else:
fig = None
linecolor = ''
epickS2, lpickS2, Serror2 = earllatepicker(h_copy, nfacS,
tsnrh,
mpickS, iplot,
fig=fig)
fig=fig,
linecolor=linecolor)
else:
epickS2, lpickS2, Serror2 = earllatepicker(h_copy, nfacS,
tsnrh,
@ -629,6 +806,9 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
lpickS = lpick[ipick]
Serror = pickerr[ipick]
msg = 'autopickstation: Refined S-Pick: {} s | S-Error: {} s'.format(mpickS, Serror)
print(msg)
# get SNR
[SNRS, SNRSdB, Snoiselevel] = getSNR(h_copy, tsnrh, mpickS)
@ -651,7 +831,6 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
################################################################
# get Wood-Anderson peak-to-peak amplitude
# initialize Data object
data = Data()
# re-create stream object including both horizontal components
hdat = edat.copy()
hdat += ndat
@ -670,21 +849,33 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
############################################################
# get Wood-Anderson peak-to-peak amplitude
# initialize Data object
data = Data()
# re-create stream object including both horizontal components
hdat = edat.copy()
hdat += ndat
else:
print('autopickstation: No horizontal component data available or ' \
print('autopickstation: No horizontal component data available or '
'bad P onset, skipping S picking!')
##############################################################
try:
iplot = int(iplot)
except:
if iplot == True or iplot == 'True':
iplot = 2
else:
iplot = 0
if iplot > 0:
# plot vertical trace
if not fig_dict:
if fig_dict == None or fig_dict == 'None':
fig = plt.figure()
plt_flag = 1
linecolor = 'k'
else:
fig = fig_dict['mainFig']
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
fig._tight = True
ax1 = fig.add_subplot(311)
tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate,
tr_filt.stats.delta)
@ -692,7 +883,7 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
wfldiff = len(tr_filt.data) - len(tdata)
if wfldiff < 0:
tdata = tdata[0:len(tdata) - abs(wfldiff)]
ax1.plot(tdata, tr_filt.data / max(tr_filt.data), 'k', label='Data')
ax1.plot(tdata, tr_filt.data / max(tr_filt.data), color=linecolor, linewidth=0.7, label='Data')
if Pweight < 4:
ax1.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()),
'b', label='CF1')
@ -706,7 +897,7 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
ax1.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
[-1, -1], 'r')
ax1.plot([refPpick.getpick(), refPpick.getpick()],
[-1.3, 1.3], 'r', linewidth=2, label='Final P Pick')
[-1.3, 1.3], 'r', linewidth=2, label='Final P Pick')
ax1.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
[1.3, 1.3], 'r', linewidth=2)
ax1.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
@ -714,28 +905,35 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
ax1.plot([lpickP, lpickP], [-1.1, 1.1], 'r--', label='lpp')
ax1.plot([epickP, epickP], [-1.1, 1.1], 'r--', label='epp')
ax1.set_title('%s, %s, P Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f '
'Polarity: %s' % (tr_filt.stats.station,
tr_filt.stats.channel,
Pweight,
SNRP,
SNRPdB,
FM))
'Polarity: %s' % (tr_filt.stats.station,
tr_filt.stats.channel,
Pweight,
SNRP,
SNRPdB,
FM))
else:
ax1.set_title('%s, P Weight=%d, SNR=None, '
'SNRdB=None' % (tr_filt.stats.channel, Pweight))
'SNRdB=None' % (tr_filt.stats.channel, Pweight))
else:
ax1.set_title('%s, %s, P Weight=%d' % (tr_filt.stats.station,
tr_filt.stats.channel,
Pweight))
ax1.legend()
tr_filt.stats.channel,
Pweight))
ax1.legend(loc=1)
ax1.set_yticks([])
ax1.set_ylim([-1.5, 1.5])
ax1.set_ylabel('Normalized Counts')
#fig.suptitle(tr_filt.stats.starttime)
# fig.suptitle(tr_filt.stats.starttime)
try:
len(edat[0])
except:
edat = ndat
try:
len(ndat[0])
except:
ndat = edat
if len(edat[0]) > 1 and len(ndat[0]) > 1 and Sflag == 1:
# plot horizontal traces
ax2 = fig.add_subplot(3,1,2,sharex=ax1)
ax2 = fig.add_subplot(3, 1, 2, sharex=ax1)
th1data = np.arange(0,
trH1_filt.stats.npts /
trH1_filt.stats.sampling_rate,
@ -744,13 +942,13 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
wfldiff = len(trH1_filt.data) - len(th1data)
if wfldiff < 0:
th1data = th1data[0:len(th1data) - abs(wfldiff)]
ax2.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k', label='Data')
ax2.plot(th1data, trH1_filt.data / max(trH1_filt.data), color=linecolor, linewidth=0.7, label='Data')
if Pweight < 4:
ax2.plot(arhcf1.getTimeArray(),
arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
if aicSflag == 1:
if aicSflag == 1 and Sweight < 4:
ax2.plot(arhcf2.getTimeArray(),
arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
arhcf2.getCF() / max(arhcf2.getCF()), 'm', label='CF2')
ax2.plot(
[aicarhpick.getpick(), aicarhpick.getpick()],
[-1, 1], 'g', label='Initial S Onset')
@ -778,13 +976,13 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
else:
ax2.set_title('%s, S Weight=%d, SNR=None, SNRdB=None' % (
trH1_filt.stats.channel, Sweight))
ax2.legend()
ax2.legend(loc=1)
ax2.set_yticks([])
ax2.set_ylim([-1.5, 1.5])
ax2.set_ylabel('Normalized Counts')
#fig.suptitle(trH1_filt.stats.starttime)
# fig.suptitle(trH1_filt.stats.starttime)
ax3 = fig.add_subplot(3,1,3, sharex=ax1)
ax3 = fig.add_subplot(3, 1, 3, sharex=ax1)
th2data = np.arange(0,
trH2_filt.stats.npts /
trH2_filt.stats.sampling_rate,
@ -793,7 +991,7 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
wfldiff = len(trH2_filt.data) - len(th2data)
if wfldiff < 0:
th2data = th2data[0:len(th2data) - abs(wfldiff)]
ax3.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k', label='Data')
ax3.plot(th2data, trH2_filt.data / max(trH2_filt.data), color=linecolor, linewidth=0.7, label='Data')
if Pweight < 4:
p22, = ax3.plot(arhcf1.getTimeArray(),
arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
@ -821,18 +1019,23 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
[-1.3, -1.3], 'g', linewidth=2)
ax3.plot([lpickS, lpickS], [-1.1, 1.1], 'g--', label='lpp')
ax3.plot([epickS, epickS], [-1.1, 1.1], 'g--', label='epp')
ax3.legend()
ax3.legend(loc=1)
ax3.set_yticks([])
ax3.set_ylim([-1.5, 1.5])
ax3.set_xlabel('Time [s] after %s' % tr_filt.stats.starttime)
ax3.set_ylabel('Normalized Counts')
ax3.set_title(trH2_filt.stats.channel)
if plt_flag == 1:
fig.show()
try: input()
except SyntaxError: pass
plt.close(fig)
##########################################################################
# calculate "real" onset times
if lpickP is not None and lpickP == mpickP:
lpickP += timeerrorsP[0]
lpickP += zdat[0].stats.delta
if epickP is not None and epickP == mpickP:
epickP -= timeerrorsP[0]
epickP -= zdat[0].stats.delta
if mpickP is not None and epickP is not None and lpickP is not None:
lpickP = zdat[0].stats.starttime + lpickP
epickP = zdat[0].stats.starttime + epickP
@ -844,20 +1047,27 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
epickP = zdat[0].stats.starttime - timeerrorsP[3]
mpickP = zdat[0].stats.starttime
if edat:
hdat = edat[0]
elif ndat:
hdat = ndat[0]
else:
return
if lpickS is not None and lpickS == mpickS:
lpickS += timeerrorsS[0]
lpickS += hdat.stats.delta
if epickS is not None and epickS == mpickS:
epickS -= timeerrorsS[0]
epickS -= hdat.stats.delta
if mpickS is not None and epickS is not None and lpickS is not None:
lpickS = edat[0].stats.starttime + lpickS
epickS = edat[0].stats.starttime + epickS
mpickS = edat[0].stats.starttime + mpickS
lpickS = hdat.stats.starttime + lpickS
epickS = hdat.stats.starttime + epickS
mpickS = hdat.stats.starttime + mpickS
else:
# dummy values (start of seismic trace) in order to derive
# theoretical onset times for iteratve picking
lpickS = edat[0].stats.starttime + timeerrorsS[3]
epickS = edat[0].stats.starttime - timeerrorsS[3]
mpickS = edat[0].stats.starttime
lpickS = hdat.stats.starttime + timeerrorsS[3]
epickS = hdat.stats.starttime - timeerrorsS[3]
mpickS = hdat.stats.starttime
# create dictionary
# for P phase
@ -867,8 +1077,8 @@ def autopickstation(wfstream, pickparam, verbose=False, iplot=0, fig_dict=None):
snrdb=SNRPdB, weight=Pweight, fm=FM, w0=None, fc=None, Mo=None,
Mw=None, picker=picker, marked=Pmarker)
# add S phase
ccode = edat[0].stats.channel
ncode = edat[0].stats.network
ccode = hdat.stats.channel
ncode = hdat.stats.network
spick = dict(channel=ccode, network=ncode, lpp=lpickS, epp=epickS, mpp=mpickS, spe=Serror, snr=SNRS,
snrdb=SNRSdB, weight=Sweight, fm=None, picker=picker, Ao=Ao)
# merge picks into returning dictionary
@ -923,17 +1133,21 @@ def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter, fig_dict=None):
Precalcwin_old = pickparameter.get('Precalcwin')
noisefactor_old = pickparameter.get('noisefactor')
zfac_old = pickparameter.get('zfac')
pickparameter.setParam(
pstart=max([0, badpicks[i][1] - wf2pick[0].stats.starttime \
- pickparameter.get('tlta')]))
pickparameter.setParam(pstop=pickparameter.get('pstart') + \
(3 * pickparameter.get('tlta')))
pickparameter.setParam(sstop=pickparameter.get('sstop') / 2)
pickparameter.setParam(pickwinP=pickparameter.get('pickwinP') / 2)
pickparameter.setParam(
Precalcwin=pickparameter.get('Precalcwin') / 2)
pickparameter.setParam(noisefactor=1.0)
pickparameter.setParam(zfac=1.0)
twindows = pickparameter.get('tsnrz')
tsafety = twindows[1]
pstart = max([0, badpicks[i][1] - wf2pick[0].stats.starttime - pickparameter.get('tlta')])
if abs(float(res)) <= tsafety / 2 or pstart == 0:
print("iteratepicker: Small residuum, leave parameters unchanged for this phase!")
else:
pickparameter.setParam(pstart=pstart)
pickparameter.setParam(pstop=pickparameter.get('pstart') + \
(pickparameter.get('Precalcwin')))
pickparameter.setParam(sstop=pickparameter.get('sstop') / 2)
pickparameter.setParam(pickwinP=pickparameter.get('pickwinP') / 2)
pickparameter.setParam(Precalcwin=pickparameter.get('Precalcwin') / 2)
pickparameter.setParam(noisefactor=1.0)
pickparameter.setParam(zfac=1.0)
print(
"iteratepicker: The following picking parameters have been modified for iterative picking:")
print(

@ -17,7 +17,6 @@ autoregressive prediction: application ot local and regional distances, Geophys.
:author: MAGS2 EP3 working group
"""
import matplotlib.pyplot as plt
import numpy as np
from obspy.core import Stream
@ -27,7 +26,7 @@ class CharacteristicFunction(object):
SuperClass for different types of characteristic functions.
'''
def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=None, stealthMode=False):
def __init__(self, data, cut, t2=None, order=None, t1=None, fnoise=None):
'''
Initialize data type object with information from the original
Seismogram.
@ -64,7 +63,6 @@ class CharacteristicFunction(object):
self.calcCF(self.getDataArray())
self.arpara = np.array([])
self.xpred = np.array([])
self._stealthMode = stealthMode
def __str__(self):
return '''\n\t{name} object:\n
@ -138,9 +136,6 @@ class CharacteristicFunction(object):
def getXCF(self):
return self.xcf
def _getStealthMode(self):
return self._stealthMode()
def getDataArray(self, cut=None):
'''
If cut times are given, time series is cut from cut[0] (start time)
@ -225,13 +220,11 @@ class AICcf(CharacteristicFunction):
def calcCF(self, data):
# if self._getStealthMode() is False:
# print 'Calculating AIC ...'
x = self.getDataArray()
xnp = x[0].data
nn = np.isnan(xnp)
if len(nn) > 1:
xnp[nn] = 0
ind = np.where(~np.isnan(xnp))[0]
if ind.size:
xnp[:ind[0]] = xnp[ind[0]]
datlen = len(xnp)
k = np.arange(1, datlen)
cf = np.zeros(datlen)
@ -265,13 +258,9 @@ class HOScf(CharacteristicFunction):
if len(nn) > 1:
xnp[nn] = 0
if self.getOrder() == 3: # this is skewness
# if self._getStealthMode() is False:
# print 'Calculating skewness ...'
y = np.power(xnp, 3)
y1 = np.power(xnp, 2)
elif self.getOrder() == 4: # this is kurtosis
# if self._getStealthMode() is False:
# print 'Calculating kurtosis ...'
y = np.power(xnp, 4)
y1 = np.power(xnp, 2)
@ -297,9 +286,12 @@ class HOScf(CharacteristicFunction):
elif self.getOrder() == 4:
LTA[j] = lta / np.power(lta1, 2)
nn = np.isnan(LTA)
if len(nn) > 1:
LTA[nn] = 0
# remove NaN's with first not-NaN-value,
# so autopicker doesnt pick discontinuity at start of the trace
ind = np.where(~np.isnan(LTA))[0]
if ind.size:
first = ind[0]
LTA[:first] = LTA[first]
self.cf = LTA
self.xcf = x
@ -307,7 +299,7 @@ class HOScf(CharacteristicFunction):
class ARZcf(CharacteristicFunction):
def calcCF(self, data):
print 'Calculating AR-prediction error from single trace ...'
print('Calculating AR-prediction error from single trace ...')
x = self.getDataArray(self.getCut())
xnp = x[0].data
nn = np.isnan(xnp)
@ -343,7 +335,8 @@ class ARZcf(CharacteristicFunction):
cf = tap * cf
io = np.where(cf == 0)
ino = np.where(cf > 0)
cf[io] = cf[ino[0][0]]
if np.size(ino):
cf[io] = cf[ino[0][0]]
self.cf = cf
self.xcf = x
@ -430,7 +423,7 @@ class ARZcf(CharacteristicFunction):
class ARHcf(CharacteristicFunction):
def calcCF(self, data):
print 'Calculating AR-prediction error from both horizontal traces ...'
print('Calculating AR-prediction error from both horizontal traces ...')
xnp = self.getDataArray(self.getCut())
n0 = np.isnan(xnp[0].data)
@ -466,7 +459,7 @@ class ARHcf(CharacteristicFunction):
# prediction error = CF
cf[i + lpred] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
+ np.power(self.xpred[1][i:i + lpred] - xnp[1][i:i + lpred], 2)) / (
2 * lpred))
2 * lpred))
nn = np.isnan(cf)
if len(nn) > 1:
cf[nn] = 0
@ -475,7 +468,8 @@ class ARHcf(CharacteristicFunction):
cf = tap * cf
io = np.where(cf == 0)
ino = np.where(cf > 0)
cf[io] = cf[ino[0][0]]
if np.size(ino):
cf[io] = cf[ino[0][0]]
self.cf = cf
self.xcf = xnp
@ -567,7 +561,7 @@ class ARHcf(CharacteristicFunction):
class AR3Ccf(CharacteristicFunction):
def calcCF(self, data):
print 'Calculating AR-prediction error from all 3 components ...'
print('Calculating AR-prediction error from all 3 components ...')
xnp = self.getDataArray(self.getCut())
n0 = np.isnan(xnp[0].data)
@ -608,7 +602,7 @@ class AR3Ccf(CharacteristicFunction):
cf[i + lpred] = np.sqrt(np.sum(np.power(self.xpred[0][i:i + lpred] - xnp[0][i:i + lpred], 2) \
+ np.power(self.xpred[1][i:i + lpred] - xnp[1][i:i + lpred], 2) \
+ np.power(self.xpred[2][i:i + lpred] - xnp[2][i:i + lpred], 2)) / (
3 * lpred))
3 * lpred))
nn = np.isnan(cf)
if len(nn) > 1:
cf[nn] = 0
@ -617,7 +611,8 @@ class AR3Ccf(CharacteristicFunction):
cf = tap * cf
io = np.where(cf == 0)
ino = np.where(cf > 0)
cf[io] = cf[ino[0][0]]
if np.size(ino):
cf[io] = cf[ino[0][0]]
self.cf = cf
self.xcf = xnp

@ -4,11 +4,11 @@
import copy
import operator
import os
import numpy as np
import glob
import matplotlib.pyplot as plt
from obspy import read_events
import matplotlib.pyplot as plt
import numpy as np
from obspy import read_events
from obspy.core import AttribDict
from pylot.core.io.phases import picksdict_from_picks
from pylot.core.util.pdf import ProbabilityDensityFunction
from pylot.core.util.utils import find_in_list
@ -27,16 +27,8 @@ class Comparison(object):
"""
def __init__(self, **kwargs):
names = list()
self._pdfs = dict()
for name, fn in kwargs.items():
if isinstance(fn, PDFDictionary):
self._pdfs[name] = fn
elif isinstance(fn, dict):
self._pdfs[name] = PDFDictionary(fn)
else:
self._pdfs[name] = PDFDictionary.from_quakeml(fn)
names.append(name)
names = self.iter_kwargs(kwargs)
if len(names) > 2:
raise ValueError('Comparison is only defined for two '
'arguments!')
@ -48,6 +40,40 @@ class Comparison(object):
return False
return True
def iter_kwargs(self, kwargs):
names = list()
for name, fn in kwargs.items():
if name == 'eventlist':
names = self.init_by_eventlist(fn)
break
if isinstance(fn, PDFDictionary):
self._pdfs[name] = fn
elif isinstance(fn, dict) or isinstance(fn, AttribDict):
self._pdfs[name] = PDFDictionary(fn)
else:
self._pdfs[name] = PDFDictionary.from_quakeml(fn)
names.append(name)
return names
def init_by_eventlist(self, eventlist):
# create one dictionary containing all picks for all events (therefore modify station key)
global_picksdict = {}
for event in eventlist:
automanu = {'manu': event.pylot_picks,
'auto': event.pylot_autopicks}
for method, picksdict in automanu.items():
if not method in global_picksdict.keys():
global_picksdict[method] = {}
for station, picks in picksdict.items():
new_picksdict = global_picksdict[method]
# new id combining event and station in one dictionary for all events
id = '{}_{}'.format(event.pylot_id, station)
new_picksdict[id] = picks
for method, picksdict in global_picksdict.items():
self._pdfs[method] = PDFDictionary(picksdict)
names = list(global_picksdict.keys())
return names
def get(self, name):
return self._pdfs[name]
@ -92,8 +118,8 @@ class Comparison(object):
"""
compare_pdfs = dict()
pdf_a = self.get(self.names[0]).generate_pdf_data(type)
pdf_b = self.get(self.names[1]).generate_pdf_data(type)
pdf_a = self.get('auto').generate_pdf_data(type)
pdf_b = self.get('manu').generate_pdf_data(type)
for station, phases in pdf_a.items():
if station in pdf_b.keys():
@ -154,7 +180,7 @@ class Comparison(object):
def get_array(self, phase, method_name):
method = operator.methodcaller(method_name)
pdf_list = self.get_all(phase)
rarray = map(method, pdf_list)
rarray = list(map(method, pdf_list))
return np.array(rarray)
def get_expectation_array(self, phase):
@ -252,11 +278,7 @@ class PDFDictionary(object):
@classmethod
def from_quakeml(self, fn):
cat = read_events(fn)
if len(cat) > 1:
raise NotImplementedError('reading more than one event at the same '
'time is not implemented yet! Sorry!')
return PDFDictionary(picksdict_from_picks(cat[0]))
return PDFDictionary(fn)
def get_all(self, phase):
rlist = list()
@ -334,7 +356,7 @@ class PDFDictionary(object):
axarr[l].set_title(phase)
if l is 0:
axann = axarr[l].annotate(station, xy=(.05, .5),
xycoords='axes fraction')
xycoords='axes fraction')
bbox_props = dict(boxstyle='round', facecolor='lightgrey',
alpha=.7)
axann.set_bbox(bbox_props)
@ -352,7 +374,6 @@ class PDFstatistics(object):
Takes a path as argument.
"""
def __init__(self, directory):
"""Initiates some values needed when dealing with pdfs later"""
self._rootdir = directory
@ -449,7 +470,7 @@ class PDFstatistics(object):
else:
raise ValueError("for call to method {0} value has to be "
"defined but is 'None' ".format(method_options[
property.upper()]))
property.upper()]))
for pdf_dict in self:
# create worklist
@ -459,7 +480,7 @@ class PDFstatistics(object):
return rlist
def writeThetaToFile(self,array,out_dir):
def writeThetaToFile(self, array, out_dir):
"""
Method to write array like data to file. Useful since acquiring can take
serious amount of time when dealing with large databases.
@ -471,16 +492,16 @@ class PDFstatistics(object):
"""
fid = open(os.path.join(out_dir), 'w')
for val in array:
fid.write(str(val)+'\n')
fid.write(str(val) + '\n')
fid.close()
def main():
root_dir ='/home/sebastianp/Codetesting/xmls/'
root_dir = '/home/sebastianp/Codetesting/xmls/'
Insheim = PDFstatistics(root_dir)
Insheim.curphase = 'p'
qdlist = Insheim.get('qdf', 0.2)
print qdlist
print(qdlist)
if __name__ == "__main__":

@ -19,12 +19,14 @@ calculated after Diehl & Kissling (2009).
:author: MAGS2 EP3 working group / Ludger Kueperkoch
"""
import numpy as np
import matplotlib.pyplot as plt
from pylot.core.pick.utils import getnoisewin, getsignalwin
from pylot.core.pick.charfuns import CharacteristicFunction
import warnings
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import argrelmax
from pylot.core.pick.charfuns import CharacteristicFunction
from pylot.core.pick.utils import getnoisewin, getsignalwin
class AutoPicker(object):
'''
@ -34,7 +36,7 @@ class AutoPicker(object):
warnings.simplefilter('ignore')
def __init__(self, cf, TSNR, PickWindow, iplot=None, aus=None, Tsmooth=None, Pick1=None, fig=None):
def __init__(self, cf, TSNR, PickWindow, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None, linecolor='k'):
'''
:param: cf, characteristic function, on which the picking algorithm is applied
:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
@ -61,7 +63,8 @@ class AutoPicker(object):
'''
assert isinstance(cf, CharacteristicFunction), "%s is not a CharacteristicFunction object" % str(cf)
self._linecolor = linecolor
self._pickcolor_p = 'b'
self.cf = cf.getCF()
self.Tcf = cf.getTimeArray()
self.Data = cf.getXCF()
@ -153,6 +156,15 @@ class AICPicker(AutoPicker):
self.Pick = None
self.slope = None
self.SNR = None
plt_flag = 0
try:
iplot = int(self.iplot)
except:
if self.iplot == True or self.iplot == 'True':
iplot = 2
else:
iplot = 0
# find NaN's
nn = np.isnan(self.cf)
if len(nn) > 1:
@ -208,106 +220,146 @@ class AICPicker(AutoPicker):
inoise = getnoisewin(self.Tcf, self.Pick, self.TSNR[0], self.TSNR[1])
# check, if these are counts or m/s, important for slope estimation!
# this is quick and dirty, better solution?
if max(self.Data[0].data < 1e-3):
self.Data[0].data = self.Data[0].data * 1000000
if max(self.Data[0].data < 1e-3) and max(self.Data[0].data >= 1e-6):
self.Data[0].data = self.Data[0].data * 1000000.
elif max(self.Data[0].data < 1e-6):
self.Data[0].data = self.Data[0].data * 1e13
# get signal window
isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
ii = min([isignal[len(isignal)-1], len(self.Tcf)])
if len(isignal) == 0:
return
ii = min([isignal[len(isignal) - 1], len(self.Tcf)])
isignal = isignal[0:ii]
try:
aic[isignal]
self.Data[0].data[isignal]
except IndexError as e:
msg = "Time series out of bounds! {}".format(e)
print(msg)
return
msg = "Time series out of bounds! {}".format(e)
print(msg)
return
# calculate SNR from CF
self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / \
max(abs(aic[inoise] - np.mean(aic[inoise])))
self.SNR = max(abs(self.Data[0].data[isignal] - np.mean(self.Data[0].data[isignal]))) / \
max(abs(self.Data[0].data[inoise] - np.mean(self.Data[0].data[inoise])))
# calculate slope from CF after initial pick
# get slope window
tslope = self.TSNR[3] # slope determination window
islope = np.where((self.Tcf <= min([self.Pick + tslope, len(self.Data[0].data)])) \
& (self.Tcf >= self.Pick))
islope = np.where((self.Tcf <= min([self.Pick + tslope, self.Tcf[-1]])) \
& (self.Tcf >= self.Pick)) # TODO: put this in a seperate function like getsignalwin
# find maximum within slope determination window
# 'cause slope should be calculated up to first local minimum only!
imax = np.argmax(self.Data[0].data[islope])
iislope = islope[0][0:imax]
if len(iislope) <= 2:
try:
dataslope = self.Data[0].data[islope[0][0:-1]]
except IndexError:
print("Slope Calculation: empty array islope, check signal window")
return
if len(dataslope) < 1:
print('No data in slope window found!')
return
imaxs, = argrelmax(dataslope)
if imaxs.size:
imax = imaxs[0]
else:
imax = np.argmax(dataslope)
iislope = islope[0][0:imax + 1]
if len(iislope) < 2:
# calculate slope from initial onset to maximum of AIC function
print("AICPicker: Not enough data samples left for slope calculation!")
print("Calculating slope from initial onset to maximum of AIC function ...")
imax = np.argmax(aicsmooth[islope])
imax = np.argmax(aicsmooth[islope[0][0:-1]])
if imax == 0:
print("AICPicker: Maximum for slope determination right at the beginning of the window!")
print("Choose longer slope determination window!")
if self.iplot > 1:
if not self.fig:
fig = plt.figure() #self.iplot) ### WHY? MP MP
if self.fig == None or self.fig == 'None':
fig = plt.figure()
plt_flag = 1
else:
fig = self.fig
ax = fig.add_subplot(111)
x = self.Data[0].data
ax.plot(self.Tcf, x / max(x), 'k', label='(HOS-/AR-) Data')
ax.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
ax.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
ax.legend()
ax.legend(loc=1)
ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
ax.set_yticks([])
ax.set_title(self.Data[0].stats.station)
if plt_flag == 1:
fig.show()
try: input()
except SyntaxError: pass
plt.close(fig)
return
iislope = islope[0][0:imax]
iislope = islope[0][0:imax+1]
dataslope = self.Data[0].data[iislope]
# calculate slope as polynomal fit of order 1
xslope = np.arange(0, len(dataslope), 1)
P = np.polyfit(xslope, dataslope, 1)
datafit = np.polyval(P, xslope)
if datafit[0] >= datafit[len(datafit) - 1]:
if datafit[0] >= datafit[-1]:
print('AICPicker: Negative slope, bad onset skipped!')
return
self.slope = 1 / tslope * (datafit[len(dataslope) - 1] - datafit[0])
self.slope = 1 / (len(dataslope) * self.Data[0].stats.delta) * (datafit[-1] - datafit[0])
else:
self.SNR = None
self.slope = None
if self.iplot > 1:
if not self.fig:
fig = plt.figure()#self.iplot)
if iplot > 1:
if self.fig == None or self.fig == 'None':
fig = plt.figure() # self.iplot)
plt_flag = 1
else:
fig = self.fig
fig._tight = True
ax1 = fig.add_subplot(211)
x = self.Data[0].data
ax1.plot(self.Tcf, x / max(x), 'k', label='(HOS-/AR-) Data')
if len(self.Tcf) > len(self.Data[0].data): # why? LK
self.Tcf = self.Tcf[0:len(self.Tcf)-1]
ax1.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
ax1.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
if self.Pick is not None:
ax1.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2, label='AIC-Pick')
ax1.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
ax1.set_yticks([])
ax1.legend()
ax1.legend(loc=1)
if self.Pick is not None:
ax2 = fig.add_subplot(2,1,2, sharex=ax1)
ax2.plot(self.Tcf, x, 'k', label='Data')
ax1.axvspan(self.Tcf[inoise[0]],self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax1.axvspan(self.Tcf[isignal[0]],self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
ax1.axvspan(self.Tcf[iislope[0]],self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0, label='Slope Window')
ax2.axvspan(self.Tcf[inoise[0]],self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax2.axvspan(self.Tcf[isignal[0]],self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
ax2.axvspan(self.Tcf[iislope[0]],self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0, label='Slope Window')
ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
ax2.plot(self.Tcf, x, color=self._linecolor, linewidth=0.7, label='Data')
ax1.axvspan(self.Tcf[inoise[0]], self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax1.axvspan(self.Tcf[isignal[0]], self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0,
label='Signal Window')
ax1.axvspan(self.Tcf[iislope[0]], self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0,
label='Slope Window')
ax2.axvspan(self.Tcf[inoise[0]], self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax2.axvspan(self.Tcf[isignal[0]], self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0,
label='Signal Window')
ax2.axvspan(self.Tcf[iislope[0]], self.Tcf[iislope[-1]], color='g', alpha=0.2, lw=0,
label='Slope Window')
ax2.plot(self.Tcf[iislope], datafit, 'g', linewidth=2, label='Slope')
ax1.set_title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station,
self.SNR, self.slope))
self.SNR, self.slope))
ax2.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
ax2.set_ylabel('Counts')
ax2.set_yticks([])
ax2.legend()
ax2.legend(loc=1)
if plt_flag == 1:
fig.show()
try: input()
except SyntaxError: pass
plt.close(fig)
else:
ax1.set_title(self.Data[0].stats.station)
if plt_flag == 1:
fig.show()
try: input()
except SyntaxError: pass
plt.close(fig)
if self.Pick == None:
print('AICPicker: Could not find minimum, picking window too short?')
return
@ -317,7 +369,15 @@ class PragPicker(AutoPicker):
'''
def calcPick(self):
try:
iplot = int(self.getiplot())
except:
if self.getiplot() == True or self.getiplot() == 'True':
iplot = 2
else:
iplot = 0
if self.getpick1() is not None:
print('PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...')
@ -325,6 +385,7 @@ class PragPicker(AutoPicker):
self.SNR = None
self.slope = None
pickflag = 0
plt_flag = 0
# smooth CF
ismooth = int(round(self.Tsmooth / self.dt))
cfsmooth = np.zeros(len(self.cf))
@ -349,12 +410,19 @@ class PragPicker(AutoPicker):
ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
cfpick1 = 2 * self.cf[ipick1]
# check trend of CF, i.e. differences of CF and adjust aus regarding this trend
# check trend of CF, i.e. differences of CF and adjust aus ("artificial uplift
# of picks") regarding this trend
# prominent trend: decrease aus
# flat: use given aus
cfdiff = np.diff(cfipick)
if len(cfdiff)<20:
print('PragPicker: Very few samples for CF. Check LTA window dimensions!')
i0diff = np.where(cfdiff > 0)
cfdiff = cfdiff[i0diff]
if len(cfdiff)<1:
print('PragPicker: Negative slope for CF. Check LTA window dimensions! STOP')
self.Pick = None
return
minaus = min(cfdiff * (1 + self.aus))
aus1 = max([minaus, self.aus])
@ -375,15 +443,20 @@ class PragPicker(AutoPicker):
break
# now we look to the left
for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
if cfpick1 >= self.cf[i]:
pick_l = self.Tcf[i]
self.Pick = pick_l
flagpick_r = 1
cfpick_l = self.cf[i]
break
if len(self.cf) > ipick1 +1:
for i in range(ipick1, max([ipick1 - lpickwindow + 1, 2]), -1):
if self.cf[i + 1] > self.cf[i] and self.cf[i - 1] >= self.cf[i]:
if cfsmooth[i - 1] * (1 + aus1) >= cfsmooth[i]:
if cfpick1 >= self.cf[i]:
pick_l = self.Tcf[i]
self.Pick = pick_l
flagpick_r = 1
cfpick_l = self.cf[i]
break
else:
msg ='PragPicker: Initial onset too close to start of CF! \
Stop finalizing pick to the left.'
print(msg)
# now decide which pick: left or right?
if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= 3 * cfpick_r:
@ -396,27 +469,34 @@ class PragPicker(AutoPicker):
self.Pick = pick_l
pickflag = 1
else:
print('PragPicker: Could not find reliable onset!')
print("PragPicker: Could not find reliable onset!")
self.Pick = None
pickflag = 0
if self.getiplot() > 1:
if not self.fig:
fig = plt.figure()#self.getiplot())
if iplot > 1:
if self.fig == None or self.fig == 'None':
fig = plt.figure() # self.getiplot())
plt_flag = 1
else:
fig = self.fig
fig._tight = True
ax = fig.add_subplot(111)
ax.plot(Tcfpick, cfipick, 'k', label='CF')
ax.plot(Tcfpick, cfipick, color=self._linecolor, linewidth=0.7, label='CF')
ax.plot(Tcfpick, cfsmoothipick, 'r', label='Smoothed CF')
if pickflag > 0:
ax.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2, label='Pick')
ax.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], self._pickcolor_p, linewidth=2, label='Pick')
ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
ax.set_yticks([])
ax.set_title(self.Data[0].stats.station)
ax.legend()
ax.legend(loc=1)
if plt_flag == 1:
fig.show()
try: input()
except SyntaxError: pass
plt.close(fig)
return
else:
print('PragPicker: No initial onset time given! Check input!')
print("PragPicker: No initial onset time given! Check input!")
self.Pick = None
return

@ -9,12 +9,13 @@
"""
import warnings
import matplotlib.pyplot as plt
import numpy as np
from obspy.core import Stream, UTCDateTime
def earllatepicker(X, nfac, TSNR, Pick1, iplot=None, verbosity=1, fig=None):
def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecolor='k'):
'''
Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
as reasonable uncertainties. Latest possible pick is based on noise level,
@ -41,14 +42,23 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None, verbosity=1, fig=None):
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
if verbosity == 2:
print('earllatepicker:')
print('earllatepicker:')
print('nfac:', nfac)
print('Init pick:', Pick1)
print('TSNR (T_noise, T_gap, T_signal):', TSNR)
LPick = None
EPick = None
PickError = None
plt_flag = 0
try:
iplot = int(iplot)
except:
if iplot == True or iplot == 'True':
iplot = 2
else:
iplot = 0
if verbosity:
print('earllatepicker: Get earliest and latest possible pick'
' relative to most likely pick ...')
@ -69,14 +79,14 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None, verbosity=1, fig=None):
print('x_inoise:', x[inoise])
print('x_isignal:', x[isignal])
print('nlevel:', nlevel)
# get time where signal exceeds nlevel
ilup, = np.where(x[isignal] > nlevel)
ildown, = np.where(x[isignal] < -nlevel)
if not ilup.size and not ildown.size:
if verbosity:
print ("earllatepicker: Signal lower than noise level!\n"
"Skip this trace!")
print("earllatepicker: Signal lower than noise level!\n"
"Skip this trace!")
return LPick, EPick, PickError
il = min(np.min(ilup) if ilup.size else float('inf'),
np.min(ildown) if ildown.size else float('inf'))
@ -84,7 +94,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None, verbosity=1, fig=None):
# get earliest possible pick
EPick = np.nan;
EPick = np.nan
count = 0
pis = isignal
@ -117,34 +127,41 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None, verbosity=1, fig=None):
PickError = symmetrize_error(diffti_te, diffti_tl)
if iplot > 1:
if not fig:
fig = plt.figure()#iplot)
if fig == None or fig == 'None':
fig = plt.figure() # iplot)
plt_flag = 1
fig._tight = True
ax = fig.add_subplot(111)
ax.plot(t, x, 'k', label='Data')
ax.plot(t, x, color=linecolor, linewidth=0.7, label='Data')
ax.axvspan(t[inoise[0]], t[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax.axvspan(t[isignal[0]], t[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
ax.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k', label='Noise Level')
ax.plot(t[isignal[zc]], np.zeros(len(zc)), '*g',
ax.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], color=linecolor, linewidth=0.7, linestyle='dashed', label='Noise Level')
ax.plot(t[pis[zc]], np.zeros(len(zc)), '*g',
markersize=14, label='Zero Crossings')
ax.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
ax.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], color=linecolor, linewidth=0.7, linestyle='dashed')
ax.plot([Pick1, Pick1], [max(x), -max(x)], 'b', linewidth=2, label='mpp')
ax.plot([LPick, LPick], [max(x) / 2, -max(x) / 2], '--k', label='lpp')
ax.plot([EPick, EPick], [max(x) / 2, -max(x) / 2], '--k', label='epp')
ax.plot([LPick, LPick], [max(x) / 2, -max(x) / 2], color=linecolor, linewidth=0.7, linestyle='dashed', label='lpp')
ax.plot([EPick, EPick], [max(x) / 2, -max(x) / 2], color=linecolor, linewidth=0.7, linestyle='dashed', label='epp')
ax.plot([Pick1 + PickError, Pick1 + PickError],
[max(x) / 2, -max(x) / 2], 'r--', label='spe')
[max(x) / 2, -max(x) / 2], 'r--', label='spe')
ax.plot([Pick1 - PickError, Pick1 - PickError],
[max(x) / 2, -max(x) / 2], 'r--')
[max(x) / 2, -max(x) / 2], 'r--')
ax.set_xlabel('Time [s] since %s' % X[0].stats.starttime)
ax.set_yticks([])
ax.set_title(
'Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' %
X[0].stats.station)
ax.legend()
ax.legend(loc=1)
if plt_flag == 1:
fig.show()
try: input()
except SyntaxError: pass
plt.close(fig)
return EPick, LPick, PickError
def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None, fig=None):
def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
'''
Function to derive first motion (polarity) of given phase onset Pick.
Calculation is based on zero crossings determined within time window pickwin
@ -166,6 +183,15 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None, fig=None):
:type: int
'''
plt_flag = 0
try:
iplot = int(iplot)
except:
if iplot == True or iplot == 'True':
iplot = 2
else:
iplot = 0
warnings.simplefilter('ignore', np.RankWarning)
assert isinstance(Xraw, Stream), "%s is not a stream object" % str(Xraw)
@ -173,15 +199,17 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None, fig=None):
FM = None
if Pick is not None:
print ("fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...")
print("fmpicker: Get first motion (polarity) of onset using unfiltered seismogram...")
xraw = Xraw[0].data
xfilt = Xfilt[0].data
t = np.arange(0, Xraw[0].stats.npts / Xraw[0].stats.sampling_rate,
Xraw[0].stats.delta)
# get pick window
ipick = np.where(
(t <= min([Pick + pickwin, len(Xraw[0])])) & (t >= Pick))
ipick = np.where((t <= min([Pick + pickwin, len(Xraw[0])])) & (t >= Pick))
if len(ipick[0]) <= 1:
print('fmpicker: Zero crossings window to short!')
return
# remove mean
xraw[ipick] = xraw[ipick] - np.mean(xraw[ipick])
xfilt[ipick] = xfilt[ipick] - np.mean(xfilt[ipick])
@ -204,6 +232,10 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None, fig=None):
if len(zc1) == 3:
break
if len(zc1) < 3:
print('fmpicker: Could not determine zero crossings!')
return
# if time difference betweeen 1st and 2cnd zero crossing
# is too short, get time difference between 1st and 3rd
# to derive maximum
@ -211,16 +243,16 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None, fig=None):
li1 = index1[1]
else:
li1 = index1[0]
if np.size(xraw[ipick[0][1]:ipick[0][li1]]) == 0:
print ("fmpicker: Onset on unfiltered trace too emergent for first motion determination!")
if np.size(xraw[ipick[0][1]:ipick[0][li1]]) == 0 or len(index1) <= 1:
print("fmpicker: Onset on unfiltered trace too emergent for first motion determination!")
P1 = None
else:
imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][li1]]))
if imax1 == 0:
imax1 = np.argmax(abs(xraw[ipick[0][1]:ipick[0][index1[1]]]))
if imax1 == 0:
print ("fmpicker: Zero crossings too close!")
print ("Skip first motion determination!")
print("fmpicker: Zero crossings too close!")
print("Skip first motion determination!")
return FM
islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
@ -253,16 +285,16 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None, fig=None):
li2 = index2[1]
else:
li2 = index2[0]
if np.size(xfilt[ipick[0][1]:ipick[0][li2]]) == 0:
print ("fmpicker: Onset on filtered trace too emergent for first motion determination!")
if np.size(xfilt[ipick[0][1]:ipick[0][li2]]) == 0 or len(index2) <= 1:
print("fmpicker: Onset on filtered trace too emergent for first motion determination!")
P2 = None
else:
imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][li2]]))
if imax2 == 0:
imax2 = np.argmax(abs(xfilt[ipick[0][1]:ipick[0][index2[1]]]))
if imax2 == 0:
print ("fmpicker: Zero crossings too close!")
print ("Skip first motion determination!")
print("fmpicker: Zero crossings too close!")
print("Skip first motion determination!")
return FM
islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
@ -286,29 +318,31 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None, fig=None):
elif P1[0] > 0 >= P2[0]:
FM = '+'
print ("fmpicker: Found polarity %s" % FM)
print("fmpicker: Found polarity %s" % FM)
if iplot > 1:
if not fig:
fig = plt.figure()#iplot)
if fig == None or fig == 'None':
fig = plt.figure() # iplot)
plt_flag = 1
fig._tight = True
ax1 = fig.add_subplot(211)
ax1.plot(t, xraw, 'k')
ax1.plot(t, xraw, color=linecolor, linewidth=0.7)
ax1.plot([Pick, Pick], [max(xraw), -max(xraw)], 'b', linewidth=2, label='Pick')
if P1 is not None:
ax1.plot(t[islope1], xraw[islope1], label='Slope Window')
ax1.plot(zc1, np.zeros(len(zc1)), '*g', markersize=14, label='Zero Crossings')
ax1.plot(t[islope1], datafit1, '--g', linewidth=2)
ax1.legend()
ax1.legend(loc=1)
ax1.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
ax1.set_yticks([])
ax1.set_title('First-Motion Determination, %s, Unfiltered Data' % Xraw[
0].stats.station)
ax2=fig.add_subplot(2,1,2, sharex=ax1)
ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
ax2.set_title('First-Motion Determination, Filtered Data')
ax2.plot(t, xfilt, 'k')
ax2.plot(t, xfilt, color=linecolor, linewidth=0.7)
ax2.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b',
linewidth=2)
linewidth=2)
if P2 is not None:
ax2.plot(t[islope2], xfilt[islope2])
ax2.plot(zc2, np.zeros(len(zc2)), '*g', markersize=14)
@ -316,6 +350,11 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None, fig=None):
ax2.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
ax2.set_xlabel('Time [s] since %s' % Xraw[0].stats.starttime)
ax2.set_yticks([])
if plt_flag == 1:
fig.show()
try: input()
except SyntaxError: pass
plt.close(fig)
return FM
@ -357,9 +396,9 @@ def getSNR(X, TSNR, t1, tracenum=0):
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
SNR = None
SNRdb = None
SNRdB = None
noiselevel = None
x = X[tracenum].data
npts = X[tracenum].stats.npts
sr = X[tracenum].stats.sampling_rate
@ -372,7 +411,7 @@ def getSNR(X, TSNR, t1, tracenum=0):
# get signal window
isignal = getsignalwin(t, t1, TSNR[2])
if np.size(inoise) < 1:
print ("getSNR: Empty array inoise, check noise window!")
print("getSNR: Empty array inoise, check noise window!")
return SNR, SNRdB, noiselevel
# demean over entire waveform
@ -380,13 +419,13 @@ def getSNR(X, TSNR, t1, tracenum=0):
# calculate ratios
noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
#signallevel = np.sqrt(np.mean(np.square(x[isignal])))
# signallevel = np.sqrt(np.mean(np.square(x[isignal])))
if np.size(isignal) < 1:
print ("getSNR: Empty array isignal, check signal window!")
print("getSNR: Empty array isignal, check signal window!")
return SNR, SNRdB, noiselevel
#noiselevel = np.abs(x[inoise]).max()
# noiselevel = np.abs(x[inoise]).max()
signallevel = np.abs(x[isignal]).max()
SNR = signallevel / noiselevel
@ -418,9 +457,9 @@ def getnoisewin(t, t1, tnoise, tgap):
inoise, = np.where((t <= max([t1 - tgap, 0])) \
& (t >= max([t1 - tnoise - tgap, 0])))
if np.size(inoise) < 1:
inoise, = np.where((t>=t[0]) & (t<=t1))
inoise, = np.where((t >= t[0]) & (t <= t1))
if np.size(inoise) < 1:
print ("getnoisewin: Empty array inoise, check noise window!")
print("getnoisewin: Empty array inoise, check noise window!")
return inoise
@ -441,10 +480,10 @@ def getsignalwin(t, t1, tsignal):
'''
# get signal window
isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \
isignal, = np.where((t <= min([t1 + tsignal, t[-1]])) \
& (t >= t1))
if np.size(isignal) < 1:
print ("getsignalwin: Empty array isignal, check signal window!")
print("getsignalwin: Empty array isignal, check signal window!")
return isignal
@ -473,24 +512,25 @@ def getResolutionWindow(snr, extent):
>>> getResolutionWindow(2)
2.5
"""
res_wins = {
'regional': {'HRW': 2., 'MRW': 5., 'LRW': 10., 'VLRW': 15.},
'local': {'HRW': 2., 'MRW': 5., 'LRW': 10., 'VLRW': 15.},
'global': {'HRW': 40., 'MRW': 100., 'LRW': 200., 'VLRW': 300.}
}
if snr < 1.5:
time_resolution = res_wins[extent]['VLRW']
elif snr < 2.:
time_resolution = res_wins[extent]['LRW']
elif snr < 3.:
time_resolution = res_wins[extent]['MRW']
elif snr >3.:
time_resolution = res_wins[extent]['HRW']
if snr:
if snr < 1.5:
time_resolution = res_wins[extent]['VLRW']
elif snr < 2.:
time_resolution = res_wins[extent]['LRW']
elif snr < 3.:
time_resolution = res_wins[extent]['MRW']
elif snr > 3.:
time_resolution = res_wins[extent]['HRW']
else:
time_resolution = res_wins[extent]['VLRW']
return time_resolution / 2
@ -528,7 +568,7 @@ def select_for_phase(st, phase):
return sel_st
def wadaticheck(pickdic, dttolerance, iplot):
def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
'''
Function to calculate Wadati-diagram from given P and S onsets in order
to detect S pick outliers. If a certain S-P time deviates by dttolerance
@ -551,7 +591,10 @@ def wadaticheck(pickdic, dttolerance, iplot):
Ppicks = []
Spicks = []
SPtimes = []
for key in pickdic:
stations = []
ibad = 0
for key in list(pickdic.keys()):
if pickdic[key]['P']['weight'] < 4 and pickdic[key]['S']['weight'] < 4:
# calculate S-P time
spt = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
@ -572,25 +615,29 @@ def wadaticheck(pickdic, dttolerance, iplot):
# calculate vp/vs ratio before check
vpvsr = p1[0] + 1
print ("###############################################")
print ("wadaticheck: Average Vp/Vs ratio before check: %f" % vpvsr)
print("###############################################")
print("wadaticheck: Average Vp/Vs ratio before check: %f" % vpvsr)
checkedPpicks = []
checkedSpicks = []
checkedSPtimes = []
badstations = []
# calculate deviations from Wadati regression
ii = 0
ibad = 0
for key in pickdic:
if pickdic[key].has_key('SPt'):
for key in list(pickdic.keys()):
if 'SPt' in pickdic[key]:
stations.append(key)
wddiff = abs(pickdic[key]['SPt'] - wdfit[ii])
ii += 1
# check, if deviation is larger than adjusted
if wddiff > dttolerance:
# mark onset and downgrade S-weight to 9
# (not used anymore)
marker = 'badWadatiCheck'
pickdic[key]['S']['weight'] = 9
# remove pick from dictionary
pickdic.pop(key)
# # mark onset and downgrade S-weight to 9
# # (not used anymore)
# marker = 'badWadatiCheck'
# pickdic[key]['S']['weight'] = 9
badstations.append(key)
ibad += 1
else:
marker = 'goodWadatiCheck'
@ -601,7 +648,10 @@ def wadaticheck(pickdic, dttolerance, iplot):
checkedSPtime = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
checkedSPtimes.append(checkedSPtime)
pickdic[key]['S']['marked'] = marker
pickdic[key]['S']['marked'] = marker
#pickdic[key]['S']['marked'] = marker
print("wadaticheck: the following stations failed the check:")
print(badstations)
if len(checkedPpicks) >= 3:
# calculate new slope
@ -610,42 +660,67 @@ def wadaticheck(pickdic, dttolerance, iplot):
# calculate vp/vs ratio after check
cvpvsr = p2[0] + 1
print ("wadaticheck: Average Vp/Vs ratio after check: %f" % cvpvsr)
print ("wadatacheck: Skipped %d S pick(s)" % ibad)
print("wadaticheck: Average Vp/Vs ratio after check: %f" % cvpvsr)
print("wadatacheck: Skipped %d S pick(s)" % ibad)
else:
print ("###############################################")
print ("wadatacheck: Not enough checked S-P times available!")
print ("Skip Wadati check!")
print("###############################################")
print("wadaticheck: Not enough checked S-P times available!")
print("Skip Wadati check!")
wfitflag = 1
wdfit2 = None
checkedonsets = pickdic
else:
print ("wadaticheck: Not enough S-P times available for reliable regression!")
print ("Skip wadati check!")
print("wadaticheck: Not enough S-P times available for reliable regression!")
print("Skip wadati check!")
wfitflag = 1
# plot results
if iplot > 0:
plt.figure()#iplot)
f1, = plt.plot(Ppicks, SPtimes, 'ro')
if wfitflag == 0:
f2, = plt.plot(Ppicks, wdfit, 'k')
f3, = plt.plot(checkedPpicks, checkedSPtimes, 'ko')
f4, = plt.plot(checkedPpicks, wdfit2, 'g')
plt.title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
plt.legend([f1, f2, f3, f4], ['Skipped S-Picks', 'Wadati 1',
'Reliable S-Picks', 'Wadati 2'], loc='best')
if fig_dict:
fig = fig_dict['wadati']
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
plt_flag = 0
else:
plt.title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
fig = plt.figure()
linecolor = 'k'
plt_flag = 1
ax = fig.add_subplot(111)
if ibad > 0:
ax.plot(Ppicks, SPtimes, 'ro', label='Skipped S-Picks')
if wfitflag == 0:
ax.plot(Ppicks, wdfit, color=linecolor, linewidth=0.7, label='Wadati 1')
ax.plot(Ppicks, wdfit+dttolerance, color='0.9', linewidth=0.5, label='Wadati 1 Tolerance')
ax.plot(Ppicks, wdfit-dttolerance, color='0.9', linewidth=0.5)
ax.plot(checkedPpicks, wdfit2, 'g', label='Wadati 2')
ax.plot(checkedPpicks, checkedSPtimes, color=linecolor,
linewidth=0, marker='o', label='Reliable S-Picks')
for Ppick, SPtime, station in zip(Ppicks, SPtimes, stations):
ax.text(Ppick, SPtime + 0.01, '{0}'.format(station), color='0.25')
plt.ylabel('S-P Times [s]')
plt.xlabel('P Times [s]')
ax.set_title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
ax.legend(loc=1, numpoints=1)
else:
ax.set_title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
ax.set_ylabel('S-P Times [s]')
ax.set_xlabel('P Times [s]')
if plt_flag:
fig.show()
return checkedonsets
def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fig=None):
def RMS(X):
'''
Function returns root mean square of a given array X
'''
return np.sqrt(np.sum(np.power(X, 2)) / len(X))
def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fig=None, linecolor='k'):
'''
Function to detect spuriously picked noise peaks.
Uses RMS trace of all 3 components (if available) to determine,
@ -676,9 +751,18 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
: type: int
'''
plt_flag = 0
try:
iplot = int(iplot)
except:
if iplot == True or iplot == 'True':
iplot = 2
else:
iplot = 0
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print ("Checking signal length ...")
print("Checking signal length ...")
if len(X) > 1:
# all three components available
@ -691,51 +775,59 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
rms = np.sqrt((np.power(x1, 2) + np.power(x2, 2) + np.power(x3, 2)) / 3)
else:
x1 = X[0].data
rms = np.sqrt(np.power(2, x1))
ilen = len(x1)
rms = abs(x1)
t = np.arange(0, ilen / X[0].stats.sampling_rate,
X[0].stats.delta)
# get noise window in front of pick plus saftey gap
inoise = getnoisewin(t, pick - 0.5, TSNR[0], TSNR[1])
inoise = getnoisewin(t, pick, TSNR[0], TSNR[1])
# get signal window
isignal = getsignalwin(t, pick, minsiglength)
# calculate minimum adjusted signal level
minsiglevel = max(rms[inoise]) * nfac
minsiglevel = np.mean(rms[inoise]) * nfac
# minimum adjusted number of samples over minimum signal level
minnum = len(isignal) * minpercent / 100
# get number of samples above minimum adjusted signal level
numoverthr = len(np.where(rms[isignal] >= minsiglevel)[0])
if numoverthr >= minnum:
print ("checksignallength: Signal reached required length.")
print("checksignallength: Signal reached required length.")
returnflag = 1
else:
print ("checksignallength: Signal shorter than required minimum signal length!")
print ("Presumably picked noise peak, pick is rejected!")
print ("(min. signal length required: %s s)" % minsiglength)
print("checksignallength: Signal shorter than required minimum signal length!")
print("Presumably picked noise peak, pick is rejected!")
print("(min. signal length required: %s s)" % minsiglength)
returnflag = 0
if iplot == 2:
if not fig:
fig = plt.figure()#iplot)
if iplot > 1:
if fig == None or fig == 'None':
fig = plt.figure() # iplot)
plt_flag = 1
fig._tight = True
ax = fig.add_subplot(111)
ax.plot(t, rms, 'k', label='RMS Data')
ax.axvspan(t[inoise[0]], t[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax.axvspan(t[isignal[0]], t[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
ax.plot(t, rms, color=linecolor, linewidth=0.7, label='RMS Data')
ax.axvspan(t[inoise[0]], t[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
ax.axvspan(t[isignal[0]], t[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
ax.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]],
[minsiglevel, minsiglevel], 'g', linewidth=2, label='Minimum Signal Level')
ax.plot([pick, pick], [min(rms), max(rms)], 'b', linewidth=2, label='Onset')
ax.legend()
ax.legend(loc=1)
ax.set_xlabel('Time [s] since %s' % X[0].stats.starttime)
ax.set_ylabel('Counts')
ax.set_title('Check for Signal Length, Station %s' % X[0].stats.station)
ax.set_yticks([])
if plt_flag == 1:
fig.show()
try: input()
except SyntaxError: pass
plt.close(fig)
return returnflag
def checkPonsets(pickdic, dttolerance, iplot):
def checkPonsets(pickdic, dttolerance, jackfactor=5, iplot=0, fig_dict=None):
'''
Function to check statistics of P-onset times: Control deviation from
median (maximum adjusted deviation = dttolerance) and apply pseudo-
@ -757,24 +849,26 @@ def checkPonsets(pickdic, dttolerance, iplot):
# search for good quality P picks
Ppicks = []
stations = []
for key in pickdic:
if pickdic[key]['P']['weight'] < 4:
for station in pickdic:
if pickdic[station]['P']['weight'] < 4:
# add P onsets to list
UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
UTCPpick = UTCDateTime(pickdic[station]['P']['mpp'])
Ppicks.append(UTCPpick.timestamp)
stations.append(key)
stations.append(station)
# apply jackknife bootstrapping on variance of P onsets
print ("###############################################")
print ("checkPonsets: Apply jackknife bootstrapping on P-onset times ...")
print("###############################################")
print("checkPonsets: Apply jackknife bootstrapping on P-onset times ...")
[xjack, PHI_pseudo, PHI_sub] = jackknife(Ppicks, 'VAR', 1)
if not xjack:
return
# get pseudo variances smaller than average variances
# (times safety factor), these picks passed jackknife test
ij = np.where(PHI_pseudo <= 5 * xjack)
ij = np.where(PHI_pseudo <= jackfactor * xjack)
# these picks did not pass jackknife test
badjk = np.where(PHI_pseudo > 5 * xjack)
badjk = np.where(PHI_pseudo > jackfactor * xjack)
badjkstations = np.array(stations)[badjk]
print ("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
print("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
print(badjkstations)
# calculate median from these picks
@ -787,9 +881,10 @@ def checkPonsets(pickdic, dttolerance, iplot):
goodstations = np.array(stations)[igood]
badstations = np.array(stations)[ibad]
print ("checkPonsets: %d pick(s) deviate too much from median!" % len(ibad))
print ("checkPonsets: Skipped %d P pick(s) out of %d" % (len(badstations) \
+ len(badjkstations), len(stations)))
print("checkPonsets: %d pick(s) deviate too much from median!" % len(ibad))
print(badstations)
print("checkPonsets: Skipped %d P pick(s) out of %d" % (len(badstations) \
+ len(badjkstations), len(stations)))
goodmarker = 'goodPonsetcheck'
badmarker = 'badPonsetcheck'
@ -798,34 +893,52 @@ def checkPonsets(pickdic, dttolerance, iplot):
# mark P onset as checked and keep P weight
pickdic[goodstations[i]]['P']['marked'] = goodmarker
for i in range(0, len(badstations)):
# mark P onset and downgrade P weight to 9
# (not used anymore)
pickdic[badstations[i]]['P']['marked'] = badmarker
pickdic[badstations[i]]['P']['weight'] = 9
# remove pick from dictionary
pickdic.pop(badstations[i])
for i in range(0, len(badjkstations)):
# mark P onset and downgrade P weight to 9
# (not used anymore)
pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
pickdic[badjkstations[i]]['P']['weight'] = 9
# remove pick from dictionary
pickdic.pop(badjkstations[i])
# for i in range(0, len(badstations)):
# # mark P onset and downgrade P weight to 9
# # (not used anymore)
# pickdic[badstations[i]]['P']['marked'] = badmarker
# pickdic[badstations[i]]['P']['weight'] = 9
# for i in range(0, len(badjkstations)):
# # mark P onset and downgrade P weight to 9
# # (not used anymore)
# pickdic[badjkstations[i]]['P']['marked'] = badjkmarker
# pickdic[badjkstations[i]]['P']['weight'] = 9
checkedonsets = pickdic
if iplot > 0:
p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'ro', markersize=14)
if len(badstations) < 1 and len(badjkstations) < 1:
p2, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'go', markersize=14)
if fig_dict:
fig = fig_dict['jackknife']
plt_flag = 0
else:
p2, = plt.plot(igood, np.array(Ppicks)[igood], 'go', markersize=14)
p3, = plt.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g',
linewidth=2)
for i in range(0, len(Ppicks)):
plt.text(i, Ppicks[i] + 0.01, '{0}'.format(stations[i]))
fig = plt.figure()
plt_flag = 1
ax = fig.add_subplot(111)
plt.xlabel('Number of P Picks')
plt.ylabel('Onset Time [s] from 1.1.1970')
plt.legend([p1, p2, p3], ['Skipped P Picks', 'Good P Picks', 'Median'],
loc='best')
plt.title('Jackknifing and Median Tests on P Onsets')
if len(badstations) > 0:
ax.plot(ibad, np.array(Ppicks)[ibad], marker ='o', markerfacecolor='orange', markersize=14,
linestyle='None', label='Median Skipped P Picks')
if len(badjkstations) > 0:
ax.plot(badjk[0], np.array(Ppicks)[badjk], 'ro', markersize=14, label='Jackknife Skipped P Picks')
ax.plot(igood, np.array(Ppicks)[igood], 'go', markersize=14, label='Good P Picks')
ax.plot([0, len(Ppicks) - 1], [pmedian, pmedian], 'g', linewidth=2, label='Median')
ax.plot([0, len(Ppicks) - 1], [pmedian + dttolerance, pmedian + dttolerance], 'g--', linewidth=1.2,
dashes=[25, 25], label='Median Tolerance')
ax.plot([0, len(Ppicks) - 1], [pmedian - dttolerance, pmedian - dttolerance], 'g--', linewidth=1.2,
dashes=[25, 25])
for index, pick in enumerate(Ppicks):
ax.text(index, pick + 0.01, '{0}'.format(stations[index]), color='0.25')
ax.set_xlabel('Number of P Picks')
ax.set_ylabel('Onset Time [s] from 1.1.1970') # MP MP Improve this?
ax.legend(loc=1, numpoints=1)
ax.set_title('Jackknifing and Median Tests on P Onsets')
if plt_flag:
fig.show()
return checkedonsets
@ -854,13 +967,13 @@ def jackknife(X, phi, h):
PHI_sub = None
# determine number of subgroups
g = len(X) / h
if type(g) is not int:
print ("jackknife: Cannot divide quantity X in equal sized subgroups!")
print ("Choose another size for subgroups!")
if len(X) % h:
print("jackknife: Cannot divide quantity X in equal sized subgroups!")
print("Choose another size for subgroups!")
return PHI_jack, PHI_pseudo, PHI_sub
else:
g = int(len(X) / h)
# estimator of undisturbed spot check
if phi == 'MEA':
phi_sc = np.mean(X)
@ -894,7 +1007,7 @@ def jackknife(X, phi, h):
return PHI_jack, PHI_pseudo, PHI_sub
def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
'''
Function to compare energy content of vertical trace with
energy content of horizontal traces to detect spuriously
@ -923,10 +1036,20 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
are shown
: type: int
'''
plt_flag = 0
try:
iplot = int(iplot)
except:
if iplot == True or iplot == 'True':
iplot = 2
else:
iplot = 0
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print ("Check for spuriously picked S onset instead of P onset ...")
print("Check for spuriously picked S onset instead of P onset ...")
returnflag = 0
@ -941,74 +1064,122 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
if len(ndat) == 0: # check for other components
ndat = X.select(component="1")
z = zdat[0].data
# get earliest time of all 3 traces
min_t = min(zdat[0].stats.starttime, edat[0].stats.starttime, ndat[0].stats.starttime)
# generate time arrays for all 3 traces
tz = np.arange(0, zdat[0].stats.npts / zdat[0].stats.sampling_rate,
zdat[0].stats.delta)
tn = np.arange(0, ndat[0].stats.npts / ndat[0].stats.sampling_rate,
ndat[0].stats.delta)
te = np.arange(0, edat[0].stats.npts / edat[0].stats.sampling_rate,
edat[0].stats.delta)
# calculate RMS trace from vertical component
absz = np.sqrt(np.power(z, 2))
# calculate RMS trace from both horizontal traces
# make sure, both traces have equal lengths
lene = len(edat[0].data)
lenn = len(ndat[0].data)
minlen = min([lene, lenn])
absen = np.sqrt(np.power(edat[0].data[0:minlen - 1], 2) \
+ np.power(ndat[0].data[0:minlen - 1], 2))
zdiff = (zdat[0].stats.starttime - min_t)
ndiff = (ndat[0].stats.starttime - min_t)
ediff = (edat[0].stats.starttime - min_t)
# get signal window
isignal = getsignalwin(tz, pick, checkwin)
# get signal windows
isignalz = getsignalwin(tz, pick - zdiff, checkwin)
isignaln = getsignalwin(tn, pick - ndiff, checkwin)
isignale = getsignalwin(te, pick - ediff, checkwin)
# calculate energy levels
try:
zcodalevel = max(absz[isignal])
except:
ii = np.where(isignal <= len(absz))
isignal = isignal[ii]
zcodalevel = max(absz[isignal - 1])
try:
encodalevel = max(absen[isignal])
except:
ii = np.where(isignal <= len(absen))
isignal = isignal[ii]
encodalevel = max(absen[isignal - 1])
# calculate RMS of traces
rmsz = RMS(zdat[0].data[isignalz])
rmsn = RMS(ndat[0].data[isignaln])
rmse = RMS(edat[0].data[isignale])
# calculate threshold
minsiglevel = encodalevel * zfac
minsiglevel = (rmsn + rmse) / 2 * zfac
# vertical P-coda level must exceed horizontal P-coda level
# zfac times encodalevel
if zcodalevel < minsiglevel:
print ("checkZ4S: Maybe S onset? Skip this P pick!")
if rmsz < minsiglevel:
print("checkZ4S: Maybe S onset? Skip this P pick!")
else:
print ("checkZ4S: P onset passes checkZ4S test!")
print("checkZ4S: P onset passes checkZ4S test!")
returnflag = 1
if iplot > 1:
te = np.arange(0, edat[0].stats.npts / edat[0].stats.sampling_rate,
edat[0].stats.delta)
tn = np.arange(0, ndat[0].stats.npts / ndat[0].stats.sampling_rate,
ndat[0].stats.delta)
if not fig:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(tz, z / max(z), 'k')
ax.axvspan(tz[isignal[0]], tz[isignal[-1]], color='b', alpha=0.2,
lw=0, label='Signal Window')
ax.plot(te, edat[0].data / max(edat[0].data) + 1, 'k')
ax.plot(tn, ndat[0].data / max(ndat[0].data) + 2, 'k')
ax.plot([tz[isignal[0]], tz[isignal[len(isignal) - 1]]],
[minsiglevel / max(z), minsiglevel / max(z)], 'g',
linewidth=2, label='Minimum Signal Level')
ax.set_xlabel('Time [s] since %s' % zdat[0].stats.starttime)
ax.set_ylabel('Normalized Counts')
ax.set_yticks([0, 1, 2], [zdat[0].stats.channel, edat[0].stats.channel,
ndat[0].stats.channel])
ax.set_title('CheckZ4S, Station %s' % zdat[0].stats.station)
ax.legend()
rms_dict = {'Z': rmsz,
'N': rmsn,
'E': rmse}
traces_dict = {'Z': zdat[0],
'N': ndat[0],
'E': edat[0]}
diff_dict = {'Z': zdiff,
'N': ndiff,
'E': ediff}
signal_dict = {'Z': isignalz,
'N': isignaln,
'E': isignale}
for i, key in enumerate(['Z', 'N', 'E']):
rms = rms_dict[key]
trace = traces_dict[key]
t = np.arange(diff_dict[key], trace.stats.npts / trace.stats.sampling_rate + diff_dict[key],
trace.stats.delta)
if i == 0:
if fig == None or fig == 'None':
fig = plt.figure() # self.iplot) ### WHY? MP MP
plt_flag = 1
ax1 = fig.add_subplot(3, 1, i + 1)
ax = ax1
ax.set_title('CheckZ4S, Station %s' % zdat[0].stats.station)
else:
if fig == None or fig == 'None':
fig = plt.figure() # self.iplot) ### WHY? MP MP
plt_flag = 1
ax = fig.add_subplot(3, 1, i + 1, sharex=ax1)
fig._tight = True
ax.plot(t, abs(trace.data), color='b', label='abs')
ax.plot(t, trace.data, color=linecolor, linewidth=0.7)
name = str(trace.stats.channel) + ': {}'.format(rms)
ax.plot([pick, pick + checkwin], [rms, rms], 'r', label='RMS {}'.format(name))
ax.plot([pick, pick], ax.get_ylim(), 'm', label='Pick')
ax.set_ylabel('Normalized Counts')
ax.axvspan(pick, pick + checkwin, color='c', alpha=0.2,
lw=0)
ax.legend(loc=1)
ax.set_xlabel('Time [s] since %s' % zdat[0].stats.starttime)
if plt_flag == 1:
fig.show()
try: input()
except SyntaxError: pass
plt.close(fig)
return returnflag
def getQualityFromUncertainty(uncertainty, Errors):
'''Script to transform uncertainty into quality classes 0-4
regarding adjusted time errors Errors.
'''
# set initial quality to 4 (worst) and change only if one condition is hit
quality = 4
if uncertainty == None or uncertainty == 'None':
return quality
if uncertainty <= Errors[0]:
quality = 0
elif (uncertainty > Errors[0]) and \
(uncertainty < Errors[1]):
quality = 1
elif (uncertainty > Errors[1]) and \
(uncertainty < Errors[2]):
quality = 2
elif (uncertainty > Errors[2]) and \
(uncertainty < Errors[3]):
quality = 3
elif uncertainty > Errors[3]:
quality = 4
return quality
if __name__ == '__main__':
import doctest

File diff suppressed because it is too large Load Diff

@ -1,13 +1,16 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import urllib2
try:
from urllib2 import urlopen
except:
from urllib.request import urlopen
def checkurl(url='https://ariadne.geophysik.rub.de/trac/PyLoT'):
def checkurl(url='https://ariadne.geophysik.ruhr-uni-bochum.de/trac/PyLoT/'):
try:
urllib2.urlopen(url, timeout=1)
urlopen(url, timeout=1)
return True
except urllib2.URLError:
except:
pass
return False

@ -1,13 +1,11 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import glob
import os
import sys
from obspy.io.xseed import Parser
import numpy as np
from obspy import UTCDateTime, read_inventory, read
from obspy.io.xseed import Parser
from pylot.core.util.utils import key_for_set_value, find_in_list, \
@ -117,7 +115,7 @@ def make_time_line(line, datetime):
return newline
def evt_head_check(root_dir, out_dir = None):
def evt_head_check(root_dir, out_dir=None):
"""
A function to make sure that an arbitrary number of .gse files have correct values in their header.
:param root_dir: a directory leading to the .gse files.
@ -170,7 +168,7 @@ def read_metadata(path_to_inventory):
invfile = list()
respfile = list()
# possible file extensions specified here:
inv = dict(dless=dlfile, xml=invfile, resp=respfile, dseed=dlfile)
inv = dict(dless=dlfile, xml=invfile, resp=respfile, dseed=dlfile[:])
if os.path.isfile(path_to_inventory):
ext = os.path.splitext(path_to_inventory)[1].split('.')[1]
inv[ext] += [path_to_inventory]
@ -184,7 +182,7 @@ def read_metadata(path_to_inventory):
print("Neither dataless-SEED file, inventory-xml file nor "
"RESP-file found!")
print("!!WRONG CALCULATION OF SOURCE PARAMETERS!!")
robj = None,
robj = None,
elif invtype == 'dless': # prevent multiple read of large dlsv
print("Reading metadata information from dataless-SEED file ...")
if len(inv[invtype]) == 1:
@ -202,7 +200,7 @@ def restitute_trace(input_tuple):
tr, invtype, inobj, unit, force = input_tuple
remove_trace = False
seed_id = tr.get_id()
# check, whether this trace has already been corrected
if 'processing' in tr.stats.keys() \
@ -245,14 +243,14 @@ def restitute_trace(input_tuple):
remove_trace = True
# apply restitution to data
print("Correcting instrument at station %s, channel %s" \
% (tr.stats.station, tr.stats.channel))
% (tr.stats.station, tr.stats.channel))
try:
if invtype in ['resp', 'dless']:
try:
tr.simulate(**kwargs)
tr.simulate(**kwargs)
except ValueError as e:
vmsg = '{0}'.format(e)
print(vmsg)
vmsg = '{0}'.format(e)
print(vmsg)
else:
tr.attach_response(inventory)
@ -271,7 +269,7 @@ def restitute_trace(input_tuple):
return tr, remove_trace
def restitute_data(data, invtype, inobj, unit='VEL', force=False):
def restitute_data(data, invtype, inobj, unit='VEL', force=False, ncores=0):
"""
takes a data stream and a path_to_inventory and returns the corrected
waveform data stream
@ -294,15 +292,15 @@ def restitute_data(data, invtype, inobj, unit='VEL', force=False):
for tr in data:
input_tuples.append((tr, invtype, inobj, unit, force))
data.remove(tr)
pool = gen_Pool()
pool = gen_Pool(ncores)
result = pool.map(restitute_trace, input_tuples)
pool.close()
for tr, remove_trace in result:
if not remove_trace:
data.traces.append(tr)
# check if ALL traces could be restituted, take care of large datasets
# better try restitution for smaller subsets of data (e.g. station by
# station)
@ -344,8 +342,8 @@ def get_prefilt(trace, tlow=(0.5, 0.9), thi=(5., 2.), verbosity=0):
trace.stats.station, trace.stats.channel))
# get corner frequencies for pre-filtering
fny = trace.stats.sampling_rate / 2
fc21 = fny - (fny * thi[0]/100.)
fc22 = fny - (fny * thi[1]/100.)
fc21 = fny - (fny * thi[0] / 100.)
fc22 = fny - (fny * thi[1] / 100.)
return (tlow[0], tlow[1], fc21, fc22)

@ -7,43 +7,29 @@ Created on Wed Feb 26 12:31:25 2014
"""
import os
from pylot.core.loc import nll
from pylot.core.loc import hyposat
import platform
from pylot.core.util.utils import readDefaultFilterInformation
from pylot.core.loc import hypo71
from pylot.core.loc import hypodd
from pylot.core.loc import hyposat
from pylot.core.loc import nll
from pylot.core.loc import velest
def readFilterInformation(fname):
def convert2FreqRange(*args):
if len(args) > 1:
return [float(arg) for arg in args]
elif len(args) == 1:
return float(args[0])
return None
# determine system dependent path separator
system_name = platform.system()
if system_name in ["Linux", "Darwin"]:
SEPARATOR = '/'
elif system_name == "Windows":
SEPARATOR = '\\'
filter_file = open(fname, 'r')
filter_information = dict()
for filter_line in filter_file.readlines():
filter_line = filter_line.split(' ')
for n, pos in enumerate(filter_line):
if pos == '\n':
filter_line[n] = ''
filter_information[filter_line[0]] = {'filtertype': filter_line[1]
if filter_line[1]
else None,
'order': int(filter_line[2])
if filter_line[1]
else None,
'freq': convert2FreqRange(*filter_line[3:])
if filter_line[1]
else None}
return filter_information
# suffix for phase name if not phase identified by last letter (P, p, etc.)
ALTSUFFIX = ['diff', 'n', 'g', '1', '2', '3']
FILTERDEFAULTS = readFilterInformation(os.path.join(os.path.expanduser('~'),
'.pylot',
'filter.in'))
FILTERDEFAULTS = readDefaultFilterInformation(os.path.join(os.path.expanduser('~'),
'.pylot',
'pylot.in'))
TIMEERROR_DEFAULTS = os.path.join(os.path.expanduser('~'),
'.pylot',
@ -59,14 +45,14 @@ LOCTOOLS = dict(nll=nll, hyposat=hyposat, velest=velest, hypo71=hypo71, hypodd=h
class SetChannelComponents(object):
def __init__(self):
self.setDefaultCompPosition()
def setDefaultCompPosition(self):
# default component order
self.compPosition_Map = dict(Z=2, N=1, E=0)
self.compName_Map = {'3': 'Z',
'1': 'N',
'2': 'E'}
def _getCurrentPosition(self, component):
for key, value in self.compName_Map.items():
if value == component:
@ -85,10 +71,10 @@ class SetChannelComponents(object):
def setCompPosition(self, component_alter, component, switch=True):
component_alter = str(component_alter)
if not component_alter in self.compName_Map.keys():
errMsg='setCompPosition: Unrecognized alternative component {}. Expecting one of {}.'
errMsg = 'setCompPosition: Unrecognized alternative component {}. Expecting one of {}.'
raise ValueError(errMsg.format(component_alter, self.compName_Map.keys()))
if not component in self.compPosition_Map.keys():
errMsg='setCompPosition: Unrecognized target component {}. Expecting one of {}.'
errMsg = 'setCompPosition: Unrecognized target component {}. Expecting one of {}.'
raise ValueError(errMsg.format(component, self.compPosition_Map.keys()))
print('setCompPosition: set component {} to {}'.format(component_alter, component))
if switch:
@ -97,7 +83,7 @@ class SetChannelComponents(object):
def getCompPosition(self, component):
return self._getCurrentPosition(component)[0]
def getPlotPosition(self, component):
component = str(component)
if component in self.compPosition_Map.keys():
@ -105,6 +91,5 @@ class SetChannelComponents(object):
elif component in self.compName_Map.keys():
return self.compPosition_Map[self.compName_Map[component]]
else:
errMsg='getCompPosition: Unrecognized component {}. Expecting one of {} or {}.'
errMsg = 'getCompPosition: Unrecognized component {}. Expecting one of {} or {}.'
raise ValueError(errMsg.format(component, self.compPosition_Map.keys(), self.compName_Map.keys()))

@ -25,5 +25,6 @@ class OverwriteError(IOError):
class ParameterError(Exception):
pass
class ProcessingError(RuntimeError):
pass

@ -5,8 +5,7 @@ import os
from obspy import UTCDateTime
from obspy.core.event import Event as ObsPyEvent
from obspy.core.event import Origin, Magnitude, ResourceIdentifier
from obspy.core.event import Origin, ResourceIdentifier
from pylot.core.io.phases import picks_from_picksdict
@ -14,10 +13,11 @@ class Event(ObsPyEvent):
'''
Pickable class derived from ~obspy.core.event.Event containing information on a single event.
'''
def __init__(self, path):
self.pylot_id = path.split('/')[-1]
# initialize super class
super(Event, self).__init__(resource_id=ResourceIdentifier('smi:local/'+self.pylot_id))
super(Event, self).__init__(resource_id=ResourceIdentifier('smi:local/' + self.pylot_id))
self.path = path
self.database = path.split('/')[-2]
self.datapath = path.split('/')[-3]
@ -32,13 +32,13 @@ class Event(ObsPyEvent):
def get_notes_path(self):
notesfile = os.path.join(self.path, 'notes.txt')
return notesfile
def get_notes(self):
notesfile = self.get_notes_path()
if os.path.isfile(notesfile):
with open(notesfile) as infile:
path = str(infile.readlines()[0].split('\n')[0])
text = '[eventInfo: '+path+']'
text = '[eventInfo: ' + path + ']'
self.addNotes(text)
try:
datetime = UTCDateTime(path.split('/')[-1])
@ -67,31 +67,47 @@ class Event(ObsPyEvent):
self._testEvent = bool
if bool: self._refEvent = False
def clearObsPyPicks(self, picktype):
for index, pick in reversed(list(enumerate(self.picks))):
if picktype in str(pick.method_id):
self.picks.pop(index)
def addPicks(self, picks):
'''
add pylot picks and overwrite existing
add pylot picks and overwrite existing ones
'''
for station in picks:
self.pylot_picks[station] = picks[station]
#add ObsPy picks
self.picks = picks_from_picksdict(self.pylot_picks)
# add ObsPy picks (clear old manual and copy all new manual from pylot)
self.clearObsPyPicks('manual')
self.picks += picks_from_picksdict(self.pylot_picks)
def addAutopicks(self, autopicks):
for station in autopicks:
self.pylot_autopicks[station] = autopicks[station]
# add ObsPy picks (clear old auto and copy all new auto from pylot)
self.clearObsPyPicks('auto')
self.picks += picks_from_picksdict(self.pylot_autopicks)
def setPick(self, station, pick):
if pick:
self.pylot_picks[station] = pick
self.picks = picks_from_picksdict(self.pylot_picks)
else:
try:
self.pylot_picks.pop(station)
except Exception as e:
print('Could not remove pick {} from station {}: {}'.format(pick, station, e))
self.clearObsPyPicks('manual')
self.picks += picks_from_picksdict(self.pylot_picks)
def setPicks(self, picks):
'''
set pylot picks and delete and overwrite all existing
'''
self.pylot_picks = picks
self.picks = picks_from_picksdict(self.pylot_picks)
self.clearObsPyPicks('manual')
self.picks += picks_from_picksdict(self.pylot_picks)
def getPick(self, station):
if station in self.pylot_picks.keys():
return self.pylot_picks[station]
@ -99,13 +115,25 @@ class Event(ObsPyEvent):
def getPicks(self):
return self.pylot_picks
def setAutopick(self, station, autopick):
if autopick:
self.pylot_autopicks[station] = autopick
def setAutopick(self, station, pick):
if pick:
self.pylot_autopicks[station] = pick
else:
try:
self.pylot_autopicks.pop(station)
except Exception as e:
print('Could not remove pick {} from station {}: {}'.format(pick, station, e))
self.clearObsPyPicks('auto')
self.picks += picks_from_picksdict(self.pylot_autopicks)
def setAutopicks(self, picks):
'''
set pylot picks and delete and overwrite all existing
'''
self.pylot_autopicks = picks
self.clearObsPyPicks('auto')
self.picks += picks_from_picksdict(self.pylot_autopicks)
def setAutopicks(self, autopicks):
self.pylot_autopicks = autopicks
def getAutopick(self, station):
if station in self.pylot_autopicks.keys():
return self.pylot_autopicks[station]

@ -1,24 +1,28 @@
from mpl_toolkits.basemap import Basemap
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
import obspy
from matplotlib import cm
from scipy.interpolate import griddata
from PySide import QtGui
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
from PySide import QtCore, QtGui
from mpl_toolkits.basemap import Basemap
from pylot.core.util.widgets import PickDlg
from scipy.interpolate import griddata
plt.interactive(False)
class map_projection(QtGui.QWidget):
def __init__(self, parent, figure=None):
'''
:param: picked, can be False, auto, manual
:value: str
'''
QtGui.QWidget.__init__(self)
self._parent = parent
self.metadata = parent.metadata
self.parser = parent.metadata[1]
self.picks = None
self.picks_dict = None
@ -28,7 +32,7 @@ class map_projection(QtGui.QWidget):
self.init_stations()
self.init_basemap(resolution='l')
self.init_map()
#self.show()
# self.show()
def init_map(self):
self.init_lat_lon_dimensions()
@ -36,7 +40,7 @@ class map_projection(QtGui.QWidget):
self.init_x_y_dimensions()
self.connectSignals()
self.draw_everything()
def onpick(self, event):
ind = event.ind
button = event.mouseevent.button
@ -44,7 +48,7 @@ class map_projection(QtGui.QWidget):
return
data = self._parent.get_data().getWFData()
for index in ind:
station=str(self.station_names[index].split('.')[-1])
station = str(self.station_names[index].split('.')[-1])
try:
pickDlg = PickDlg(self, parameter=self._parent._inputs,
data=data.select(station=station),
@ -89,7 +93,7 @@ class map_projection(QtGui.QWidget):
else:
self.figure = self._parent.am_figure
self.toolbar = self._parent.am_toolbar
self.main_ax = self.figure.add_subplot(111)
self.canvas = self.figure.canvas
@ -105,29 +109,29 @@ class map_projection(QtGui.QWidget):
self.comboBox_am = QtGui.QComboBox()
self.comboBox_am.insertItem(0, 'auto')
self.comboBox_am.insertItem(1, 'manual')
self.comboBox_am.insertItem(1, 'manual')
self.top_row.addWidget(QtGui.QLabel('Select a phase: '))
self.top_row.addWidget(self.comboBox_phase)
self.top_row.setStretch(1,1) #set stretch of item 1 to 1
self.top_row.setStretch(1, 1) # set stretch of item 1 to 1
self.main_box.addWidget(self.canvas)
self.main_box.addWidget(self.toolbar)
def init_stations(self):
def get_station_names_lat_lon(parser):
station_names=[]
lat=[]
lon=[]
station_names = []
lat = []
lon = []
for station in parser.stations:
station_name=station[0].station_call_letters
network=station[0].network_code
station_name = station[0].station_call_letters
network = station[0].network_code
if not station_name in station_names:
station_names.append(network+'.'+station_name)
station_names.append(network + '.' + station_name)
lat.append(station[0].latitude)
lon.append(station[0].longitude)
return station_names, lat, lon
station_names, lat, lon = get_station_names_lat_lon(self.parser)
self.station_names = station_names
self.lat = lat
@ -135,52 +139,53 @@ class map_projection(QtGui.QWidget):
def init_picks(self):
phase = self.comboBox_phase.currentText()
def get_picks(station_names):
picks=[]
picks = []
for station in station_names:
try:
station=station.split('.')[-1]
station = station.split('.')[-1]
picks.append(self.picks_dict[station][phase]['mpp'])
except:
picks.append(np.nan)
return picks
def get_picks_rel(picks):
picks_rel=[]
picks_rel = []
picks_utc = []
for pick in picks:
if type(pick) is obspy.core.utcdatetime.UTCDateTime:
picks_utc.append(pick)
minp = min(picks_utc)
for pick in picks:
if type(pick) is obspy.core.utcdatetime.UTCDateTime:
if type(pick) is obspy.core.utcdatetime.UTCDateTime:
pick -= minp
picks_rel.append(pick)
return picks_rel
self.picks = get_picks(self.station_names)
self.picks_rel = get_picks_rel(self.picks)
def init_picks_active(self):
def remove_nan_picks(picks):
picks_no_nan=[]
picks_no_nan = []
for pick in picks:
if not np.isnan(pick):
picks_no_nan.append(pick)
return picks_no_nan
self.picks_no_nan = remove_nan_picks(self.picks_rel)
def init_stations_active(self):
def remove_nan_lat_lon(picks, lat, lon):
lat_no_nan=[]
lon_no_nan=[]
lat_no_nan = []
lon_no_nan = []
for index, pick in enumerate(picks):
if not np.isnan(pick):
lat_no_nan.append(lat[index])
lon_no_nan.append(lon[index])
return lat_no_nan, lon_no_nan
self.lat_no_nan, self.lon_no_nan = remove_nan_lat_lon(self.picks_rel, self.lat, self.lon)
def init_lat_lon_dimensions(self):
@ -188,7 +193,7 @@ class map_projection(QtGui.QWidget):
londim = max(lon) - min(lon)
latdim = max(lat) - min(lat)
return londim, latdim
self.londim, self.latdim = get_lon_lat_dim(self.lon, self.lat)
def init_x_y_dimensions(self):
@ -196,30 +201,30 @@ class map_projection(QtGui.QWidget):
xdim = max(x) - min(x)
ydim = max(y) - min(y)
return xdim, ydim
self.x, self.y = self.basemap(self.lon, self.lat)
self.xdim, self.ydim = get_x_y_dim(self.x, self.y)
def init_basemap(self, resolution='l'):
#basemap = Basemap(projection=projection, resolution = resolution, ax=self.main_ax)
basemap = Basemap(projection='lcc', resolution = resolution, ax=self.main_ax,
# basemap = Basemap(projection=projection, resolution = resolution, ax=self.main_ax)
basemap = Basemap(projection='lcc', resolution=resolution, ax=self.main_ax,
width=5e6, height=2e6,
lat_0=(min(self.lat)+max(self.lat))/2.,
lon_0=(min(self.lon)+max(self.lon))/2.)
#basemap.fillcontinents(color=None, lake_color='aqua',zorder=1)
basemap.drawmapboundary(zorder=2)#fill_color='darkblue')
lat_0=(min(self.lat) + max(self.lat)) / 2.,
lon_0=(min(self.lon) + max(self.lon)) / 2.)
# basemap.fillcontinents(color=None, lake_color='aqua',zorder=1)
basemap.drawmapboundary(zorder=2) # fill_color='darkblue')
basemap.shadedrelief(zorder=3)
basemap.drawcountries(zorder=4)
basemap.drawstates(zorder=5)
basemap.drawcoastlines(zorder=6)
self.basemap = basemap
self.figure.tight_layout()
def init_lat_lon_grid(self):
def get_lat_lon_axis(lat, lon):
steplat = (max(lat)-min(lat))/250
steplon = (max(lon)-min(lon))/250
steplat = (max(lat) - min(lat)) / 250
steplon = (max(lon) - min(lon)) / 250
lataxis = np.arange(min(lat), max(lat), steplat)
lonaxis = np.arange(min(lon), max(lon), steplon)
@ -234,7 +239,8 @@ class map_projection(QtGui.QWidget):
def init_picksgrid(self):
self.picksgrid_no_nan = griddata((self.lat_no_nan, self.lon_no_nan),
self.picks_no_nan, (self.latgrid, self.longrid), method='linear') ##################
self.picks_no_nan, (self.latgrid, self.longrid),
method='linear') ##################
def draw_contour_filled(self, nlevel='50'):
levels = np.linspace(min(self.picks_no_nan), max(self.picks_no_nan), nlevel)
@ -243,7 +249,7 @@ class map_projection(QtGui.QWidget):
def scatter_all_stations(self):
self.sc = self.basemap.scatter(self.lon, self.lat, s=50, facecolor='none', latlon=True,
zorder=10, picker=True, edgecolor='m', label='Not Picked')
zorder=10, picker=True, edgecolor='m', label='Not Picked')
self.cid = self.canvas.mpl_connect('pick_event', self.onpick)
if self.eventLoc:
lat, lon = self.eventLoc
@ -254,11 +260,11 @@ class map_projection(QtGui.QWidget):
lon = self.lon_no_nan
lat = self.lat_no_nan
#workaround because of an issue with latlon transformation of arrays with len <3
# workaround because of an issue with latlon transformation of arrays with len <3
if len(lon) <= 2 and len(lat) <= 2:
self.sc_picked = self.basemap.scatter(lon[0], lat[0], s=50, facecolor='white',
c=self.picks_no_nan[0], latlon=True, zorder=11, label='Picked')
if len(lon) == 2 and len(lat) == 2:
if len(lon) == 2 and len(lat) == 2:
self.sc_picked = self.basemap.scatter(lon[1], lat[1], s=50, facecolor='white',
c=self.picks_no_nan[1], latlon=True, zorder=11)
else:
@ -266,11 +272,11 @@ class map_projection(QtGui.QWidget):
c=self.picks_no_nan, latlon=True, zorder=11, label='Picked')
def annotate_ax(self):
self.annotations=[]
self.annotations = []
for index, name in enumerate(self.station_names):
self.annotations.append(self.main_ax.annotate(' %s' % name, xy=(self.x[index], self.y[index]),
fontsize='x-small', color='white', zorder=12))
self.legend=self.main_ax.legend()
self.legend = self.main_ax.legend(loc=1)
def add_cbar(self, label):
cbar = self.main_ax.figure.colorbar(self.sc_picked, fraction=0.025)
@ -306,19 +312,19 @@ class map_projection(QtGui.QWidget):
def remove_drawings(self):
if hasattr(self, 'sc_picked'):
self.sc_picked.remove()
del(self.sc_picked)
del (self.sc_picked)
if hasattr(self, 'sc_event'):
self.sc_event.remove()
del(self.sc_event)
del (self.sc_event)
if hasattr(self, 'cbar'):
self.cbar.remove()
del(self.cbar)
del (self.cbar)
if hasattr(self, 'contourf'):
self.remove_contourf()
del(self.contourf)
del (self.contourf)
if hasattr(self, 'cid'):
self.canvas.mpl_disconnect(self.cid)
del(self.cid)
del (self.cid)
try:
self.sc.remove()
except Exception as e:
@ -342,18 +348,18 @@ class map_projection(QtGui.QWidget):
xlim = map.ax.get_xlim()
ylim = map.ax.get_ylim()
x, y = event.xdata, event.ydata
zoom = {'up': 1./2.,
zoom = {'up': 1. / 2.,
'down': 2.}
if not event.xdata or not event.ydata:
return
if event.button in zoom:
factor = zoom[event.button]
xdiff = (xlim[1]-xlim[0])*factor
xdiff = (xlim[1] - xlim[0]) * factor
xl = x - 0.5 * xdiff
xr = x + 0.5 * xdiff
ydiff = (ylim[1]-ylim[0])*factor
ydiff = (ylim[1] - ylim[0]) * factor
yb = y - 0.5 * ydiff
yt = y + 0.5 * ydiff
@ -363,10 +369,8 @@ class map_projection(QtGui.QWidget):
map.ax.set_xlim(xl, xr)
map.ax.set_ylim(yb, yt)
map.ax.figure.canvas.draw()
def _warn(self, message):
self.qmb = QtGui.QMessageBox(QtGui.QMessageBox.Icon.Warning,
'Warning', message)
self.qmb.show()
self.qmb.show()

@ -2,20 +2,23 @@
# -*- coding: utf-8 -*-
import warnings
import numpy as np
from obspy import UTCDateTime
from pylot.core.util.utils import fit_curve, find_nearest, clims
from pylot.core.util.utils import fit_curve, clims
from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
__author__ = 'sebastianw'
def create_axis(x0, incr, npts):
ax = np.zeros(npts)
for i in range(npts):
ax[i] = x0 + incr * i
return ax
def gauss_parameter(te, tm, tl, eta):
'''
takes three onset times and returns the parameters sig1, sig2, a1 and a2
@ -59,7 +62,7 @@ def exp_parameter(te, tm, tl, eta):
return tm, sig1, sig2, a
def gauss_branches(k, (mu, sig1, sig2, a1, a2)):
def gauss_branches(k, param_tuple):
'''
function gauss_branches takes an axes x, a center value mu, two sigma
values sig1 and sig2 and two scaling factors a1 and a2 and return a
@ -79,6 +82,9 @@ def gauss_branches(k, (mu, sig1, sig2, a1, a2)):
:returns fun_vals: list with function values along axes x
'''
# python 3 workaround
mu, sig1, sig2, a1, a2 = param_tuple
def _func(k, mu, sig1, sig2, a1, a2):
if k < mu:
rval = a1 * 1 / (np.sqrt(2 * np.pi) * sig1) * np.exp(-((k - mu) / sig1) ** 2 / 2)
@ -93,7 +99,7 @@ def gauss_branches(k, (mu, sig1, sig2, a1, a2)):
return _func(k, mu, sig1, sig2, a1, a2)
def exp_branches(k, (mu, sig1, sig2, a)):
def exp_branches(k, param_tuple):
'''
function exp_branches takes an axes x, a center value mu, two sigma
values sig1 and sig2 and a scaling factor a and return a
@ -107,6 +113,9 @@ def exp_branches(k, (mu, sig1, sig2, a)):
:returns fun_vals: list with function values along axes x:
'''
# python 3 workaround
mu, sig1, sig2, a = param_tuple
def _func(k, mu, sig1, sig2, a):
mu = float(mu)
if k < mu:
@ -239,7 +248,7 @@ class ProbabilityDensityFunction(object):
self._x = np.array(x)
@classmethod
def from_pick(self, lbound, barycentre, rbound, incr=0.001, decfact=0.01,
def from_pick(self, lbound, barycentre, rbound, incr=0.1, decfact=0.01,
type='exp'):
'''
Initialize a new ProbabilityDensityFunction object.
@ -307,14 +316,14 @@ class ProbabilityDensityFunction(object):
:return float: rval
'''
#rval = 0
#for x in self.axis:
# rval = 0
# for x in self.axis:
# rval += x * self.data(x)
rval = self.mu
# Not sure about this! That might not be the barycentre.
# However, for std calculation (next function)
# self.mu is also used!! (LK, 02/2017)
return rval
return rval
def standard_deviation(self):
mu = self.mu
@ -388,7 +397,6 @@ class ProbabilityDensityFunction(object):
qu = self.quantile(1 - prob_value)
return qu - ql
def quantile_dist_frac(self, x):
"""
takes a probability value and returns the fraction of two
@ -405,8 +413,7 @@ class ProbabilityDensityFunction(object):
"""
if x <= 0 or x >= 0.25:
raise ValueError('Value out of range.')
return self.quantile_distance(0.5-x)/self.quantile_distance(x)
return self.quantile_distance(0.5 - x) / self.quantile_distance(x)
def plot(self, label=None):
import matplotlib.pyplot as plt
@ -480,4 +487,3 @@ class ProbabilityDensityFunction(object):
x0, npts = self.commonlimits(incr, other)
return x0, incr, npts

@ -3,6 +3,7 @@
import matplotlib.pyplot as plt
def create_bin_list(l_boundary, u_boundary, nbins=100):
"""
takes two boundaries and a number of bins and creates a list of bins for
@ -54,4 +55,4 @@ def histplot(array, binlist, xlab='Values',
if fnout:
plt.savefig(fnout)
else:
plt.show()
plt.show()

@ -1,82 +1,167 @@
# -*- coding: utf-8 -*-
import sys, os
from PySide.QtCore import QThread, Signal, Qt
from PySide.QtGui import QDialog, QProgressBar, QLabel, QHBoxLayout
class AutoPickThread(QThread):
message = Signal(str)
finished = Signal()
def __init__(self, parent, func, infile, fnames, eventid, savepath):
super(AutoPickThread, self).__init__()
self.setParent(parent)
self.func = func
self.infile = infile
self.fnames = fnames
self.eventid = eventid
self.savepath = savepath
def run(self):
sys.stdout = self
picks = self.func(None, None, self.infile, self.fnames, self.eventid, self.savepath)
print("Autopicking finished!\n")
try:
for station in picks:
self.parent().addPicks(station, picks[station], type='auto')
except AttributeError:
print(picks)
sys.stdout = sys.__stdout__
self.finished.emit()
def write(self, text):
self.message.emit(text)
def flush(self):
pass
import sys, os, traceback
import multiprocessing
from PySide.QtCore import QThread, Signal, Qt, Slot, QRunnable, QObject
from PySide.QtGui import QDialog, QProgressBar, QLabel, QHBoxLayout, QPushButton
class Thread(QThread):
message = Signal(str)
def __init__(self, parent, func, arg=None, progressText=None, pb_widget=None, redirect_stdout=False):
def __init__(self, parent, func, arg=None, progressText=None,
pb_widget=None, redirect_stdout=False, abortButton=False):
QThread.__init__(self, parent)
self.func = func
self.arg = arg
self.progressText = progressText
self.pb_widget = pb_widget
self.redirect_stdout = redirect_stdout
self.abortButton = abortButton
self.finished.connect(self.hideProgressbar)
self.showProgressbar()
def run(self):
if self.redirect_stdout:
sys.stdout = self
sys.stdout = self
try:
if self.arg:
self.data = self.func(self.arg)
else:
self.data = self.func()
self._executed = True
except Exception as e:
self._executed = False
self._executedError = e
traceback.print_exc()
exctype, value = sys.exc_info ()[:2]
self._executedErrorInfo = '{} {} {}'.\
format(exctype, value, traceback.format_exc())
sys.stdout = sys.__stdout__
def showProgressbar(self):
if self.progressText:
# generate widget if not given in init
if not self.pb_widget:
self.pb_widget = QDialog(self.parent())
self.pb_widget.setWindowFlags(Qt.SplashScreen)
self.pb_widget.setModal(True)
# add button
delete_button = QPushButton('X')
delete_button.clicked.connect(self.exit)
hl = QHBoxLayout()
pb = QProgressBar()
pb.setRange(0, 0)
hl.addWidget(pb)
hl.addWidget(QLabel(self.progressText))
if self.abortButton:
hl.addWidget(delete_button)
self.pb_widget.setLayout(hl)
self.pb_widget.show()
def hideProgressbar(self):
if self.pb_widget:
self.pb_widget.hide()
def write(self, text):
self.message.emit(text)
def flush(self):
pass
class Worker(QRunnable):
'''
Worker class to be run by MultiThread(QThread).
'''
def __init__(self, fun, args,
progressText=None,
pb_widget=None,
redirect_stdout=False):
super(Worker, self).__init__()
self.fun = fun
self.args = args
#self.kwargs = kwargs
self.signals = WorkerSignals()
self.progressText = progressText
self.pb_widget = pb_widget
self.redirect_stdout = redirect_stdout
@Slot()
def run(self):
if self.redirect_stdout:
sys.stdout = self
try:
result = self.fun(self.args)
except:
exctype, value = sys.exc_info ()[:2]
print(exctype, value, traceback.format_exc())
self.signals.error.emit ((exctype, value, traceback.format_exc ()))
else:
self.signals.result.emit(result)
finally:
self.signals.finished.emit('Done')
sys.stdout = sys.__stdout__
def write(self, text):
self.signals.message.emit(text)
def flush(self):
pass
class WorkerSignals(QObject):
'''
Class to provide signals for Worker Class
'''
finished = Signal(str)
message = Signal(str)
error = Signal(tuple)
result = Signal(object)
class MultiThread(QThread):
finished = Signal(str)
message = Signal(str)
def __init__(self, parent, func, args, ncores=1,
progressText=None, pb_widget=None, redirect_stdout=False):
QThread.__init__(self, parent)
self.func = func
self.args = args
self.ncores = ncores
self.progressText = progressText
self.pb_widget = pb_widget
self.redirect_stdout = redirect_stdout
self.finished.connect(self.hideProgressbar)
self.showProgressbar()
def run(self):
if self.redirect_stdout:
sys.stdout = self
try:
if not self.ncores:
self.ncores = multiprocessing.cpu_count()
pool = multiprocessing.Pool(self.ncores)
self.data = pool.map_async(self.func, self.args, callback=self.emitDone)
#self.data = pool.apply_async(self.func, self.shotlist, callback=self.emitDone) #emit each time returned
pool.close()
self._executed = True
except Exception as e:
self._executed = False
self._executedError = e
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
print('Exception: {}, file: {}, line: {}'.format(exc_type, fname, exc_tb.tb_lineno))
sys.stdout = sys.__stdout__
def __del__(self):
self.wait()
sys.stdout = sys.__stdout__
def showProgressbar(self):
if self.progressText:
if not self.pb_widget:
self.pb_widget = QDialog(self.parent())
self.pb_widget.setWindowFlags(Qt.SplashScreen)
self.pb_widget.setWindowFlags(Qt.SplashScreen)
self.pb_widget.setModal(True)
hl = QHBoxLayout()
pb = QProgressBar()
@ -95,4 +180,8 @@ class Thread(QThread):
def flush(self):
pass
def emitDone(self, result):
print('emitDone!')
self.finished.emit('Done thread!')
self.hideProgressbar()

@ -2,26 +2,57 @@
# -*- coding: utf-8 -*-
import hashlib
import numpy as np
from scipy.interpolate import splrep, splev
import os
import pwd
import platform
import re
import warnings
import subprocess
from obspy import UTCDateTime, read
from pylot.core.io.inputs import PylotParameter
import numpy as np
from obspy import UTCDateTime, read
from obspy.core import AttribDict
from obspy.signal.rotate import rotate2zne
from obspy.io.xseed.utils import SEEDParserException
from pylot.core.io.inputs import PylotParameter
from pylot.styles import style_settings
from scipy.interpolate import splrep, splev
from PySide import QtCore, QtGui
try:
import pyqtgraph as pg
except Exception as e:
print('PyLoT: Could not import pyqtgraph. {}'.format(e))
pg = None
def _pickle_method(m):
if m.im_self is None:
return getattr, (m.im_class, m.im_func.func_name)
else:
return getattr, (m.im_self, m.im_func.func_name)
def readDefaultFilterInformation(fname):
pparam = PylotParameter(fname)
return readFilterInformation(pparam)
def readFilterInformation(pylot_parameter):
p_filter = {'filtertype': pylot_parameter['filter_type'][0],
'freq': [pylot_parameter['minfreq'][0], pylot_parameter['maxfreq'][0]],
'order': int(pylot_parameter['filter_order'][0])}
s_filter = {'filtertype': pylot_parameter['filter_type'][1],
'freq': [pylot_parameter['minfreq'][1], pylot_parameter['maxfreq'][1]],
'order': int(pylot_parameter['filter_order'][1])}
filter_information = {'P': p_filter,
'S': s_filter}
return filter_information
def fit_curve(x, y):
return splev, splrep(x, y)
def getindexbounds(f, eta):
mi = f.argmax()
m = max(f)
@ -31,16 +62,55 @@ def getindexbounds(f, eta):
return mi, l, u
def gen_Pool(ncores='max'):
def gen_Pool(ncores=0):
'''
:param ncores: number of CPU cores for multiprocessing.Pool, if ncores == 0 use all available
:return: multiprocessing.Pool object
'''
import multiprocessing
if ncores=='max':
ncores=multiprocessing.cpu_count()
if ncores == 0:
ncores = multiprocessing.cpu_count()
print('gen_Pool: Generated multiprocessing Pool with {} cores\n'.format(ncores))
pool = multiprocessing.Pool(ncores)
return pool
def excludeQualityClasses(picks, qClasses, timeerrorsP, timeerrorsS):
'''
takes PyLoT picks dictionary and returns a new dictionary with certain classes excluded.
:param picks: PyLoT picks dictionary
:param qClasses: list (or int) of quality classes (0-4) to exclude
:param timeerrorsP: time errors for classes (0-4) for P
:param timeerrorsS: time errors for classes (0-4) for S
:return: new picks dictionary
'''
from pylot.core.pick.utils import getQualityFromUncertainty
if type(qClasses) in [int, float]:
qClasses = [qClasses]
picksdict_new = {}
phaseError = {'P': timeerrorsP,
'S': timeerrorsS}
for station, phases in picks.items():
for phase, pick in phases.items():
if not type(pick) in [AttribDict, dict]:
continue
pickerror = phaseError[identifyPhaseID(phase)]
quality = getQualityFromUncertainty(pick['spe'], pickerror)
if not quality in qClasses:
if not station in picksdict_new:
picksdict_new[station] = {}
picksdict_new[station][phase] = pick
return picksdict_new
def clims(lim1, lim2):
"""
takes two pairs of limits and returns one pair of common limts
@ -106,6 +176,7 @@ def findComboBoxIndex(combo_box, val):
"""
return combo_box.findText(val) if combo_box.findText(val) is not -1 else 0
def find_in_list(list, str):
"""
takes a list of strings and a string and returns the first list item
@ -135,6 +206,7 @@ def find_in_list(list, str):
return rlist[0]
return None
def find_nearest(array, value):
'''
function find_nearest takes an array and a value and returns the
@ -182,6 +254,22 @@ def fnConstructor(s):
return fn
def real_None(value):
if value == 'None':
return None
else:
return value
def real_Bool(value):
if value == 'True':
return True
elif value == 'False':
return False
else:
return value
def four_digits(year):
"""
takes a two digit year integer and returns the correct four digit equivalent
@ -258,7 +346,7 @@ def getLogin():
returns the actual user's login ID
:return: login ID
'''
return pwd.getpwuid(os.getuid())[0]
return os.getlogin()
def getOwner(fn):
@ -268,7 +356,15 @@ def getOwner(fn):
:type fn: str
:return: login ID of the file's owner
'''
return pwd.getpwuid(os.stat(fn).st_uid).pw_name
system_name = platform.system()
if system_name in ["Linux", "Darwin"]:
import pwd
return pwd.getpwuid(os.stat(fn).st_uid).pw_name
elif system_name == "Windows":
import win32security
f = win32security.GetFileSecurity(fn, win32security.OWNER_SECURITY_INFORMATION)
(username, domain, sid_name_use) = win32security.LookupAccountSid(None, f.GetSecurityDescriptorOwner())
return username
def getPatternLine(fn, pattern):
@ -294,6 +390,7 @@ def getPatternLine(fn, pattern):
return None
def is_executable(fn):
"""
takes a filename and returns True if the file is executable on the system
@ -362,7 +459,7 @@ def key_for_set_value(d):
return r
def prepTimeAxis(stime, trace):
def prepTimeAxis(stime, trace, verbosity=0):
'''
takes a starttime and a trace object and returns a valid time axis for
plotting
@ -376,16 +473,18 @@ def prepTimeAxis(stime, trace):
etime = stime + nsamp / srate
time_ax = np.arange(stime, etime, tincr)
if len(time_ax) < nsamp:
print('elongate time axes by one datum')
if verbosity:
print('elongate time axes by one datum')
time_ax = np.arange(stime, etime + tincr, tincr)
elif len(time_ax) > nsamp:
print('shorten time axes by one datum')
if verbosity:
print('shorten time axes by one datum')
time_ax = np.arange(stime, etime - tincr, tincr)
if len(time_ax) != nsamp:
print('Station {0}, {1} samples of data \n '
'{2} length of time vector \n'
'delta: {3}'.format(trace.stats.station,
nsamp, len(time_ax), tincr))
'{2} length of time vector \n'
'delta: {3}'.format(trace.stats.station,
nsamp, len(time_ax), tincr))
time_ax = None
return time_ax
@ -413,6 +512,91 @@ def find_horizontals(data):
return rval
def make_pen(picktype, phase, key, quality):
if pg:
rgba = pick_color(picktype, phase, quality)
linestyle, width = pick_linestyle_pg(picktype, key)
pen = pg.mkPen(rgba, width=width, style=linestyle)
return pen
def pick_color(picktype, phase, quality=0):
min_quality = 3
bpc = base_phase_colors(picktype, phase)
rgba = bpc['rgba']
modifier = bpc['modifier']
intensity = 255.*quality/min_quality
rgba = modify_rgba(rgba, modifier, intensity)
return rgba
def pick_color_plt(picktype, phase, quality=0):
rgba = list(pick_color(picktype, phase, quality))
for index, val in enumerate(rgba):
rgba[index] /= 255.
return rgba
def pick_linestyle_plt(picktype, key):
linestyles_manu = {'mpp': ('solid', 2.),
'epp': ('dashed', 1.),
'lpp': ('dashed', 1.),
'spe': ('dashed', 1.)}
linestyles_auto = {'mpp': ('dotted', 2.),
'epp': ('dashdot', 1.),
'lpp': ('dashdot', 1.),
'spe': ('dashdot', 1.)}
linestyles = {'manual': linestyles_manu,
'auto': linestyles_auto}
return linestyles[picktype][key]
def pick_linestyle_pg(picktype, key):
linestyles_manu = {'mpp': (QtCore.Qt.SolidLine, 2.),
'epp': (QtCore.Qt.DashLine, 1.),
'lpp': (QtCore.Qt.DashLine, 1.),
'spe': (QtCore.Qt.DashLine, 1.)}
linestyles_auto = {'mpp': (QtCore.Qt.DotLine, 2.),
'epp': (QtCore.Qt.DashDotLine, 1.),
'lpp': (QtCore.Qt.DashDotLine, 1.),
'spe': (QtCore.Qt.DashDotLine, 1.)}
linestyles = {'manual': linestyles_manu,
'auto': linestyles_auto}
return linestyles[picktype][key]
def modify_rgba(rgba, modifier, intensity):
rgba = list(rgba)
index = {'r': 0,
'g': 1,
'b': 2}
val = rgba[index[modifier]] + intensity
if val > 255.:
val = 255.
elif val < 0.:
val = 0
rgba[index[modifier]] = val
return tuple(rgba)
def base_phase_colors(picktype, phase):
phasecolors = style_settings.phasecolors
return phasecolors[picktype][phase]
def transform_colors_mpl_str(colors, no_alpha=False):
colors = list(colors)
colors_mpl = tuple([color / 255. for color in colors])
if no_alpha:
colors_mpl = '({}, {}, {})'.format(*colors_mpl)
else:
colors_mpl = '({}, {}, {}, {})'.format(*colors_mpl)
return colors_mpl
def transform_colors_mpl(colors):
colors = list(colors)
colors_mpl = tuple([color / 255. for color in colors])
return colors_mpl
def remove_underscores(data):
"""
takes a `obspy.core.stream.Stream` object and removes all underscores
@ -427,6 +611,176 @@ def remove_underscores(data):
return data
def trim_station_components(data, trim_start=True, trim_end=True):
'''
cut a stream so only the part common to all three traces is kept to avoid dealing with offsets
:param data: stream of seismic data
:type data: `obspy.core.stream.Stream`
:param trim_start: trim start of stream
:type trim_start: bool
:param trim_end: trim end of stream
:type trim_end: bool
:return: data stream
'''
starttime = {False: None}
endtime = {False: None}
stations = get_stations(data)
print('trim_station_components: Will trim stream for trim_start: {} and for '
'trim_end: {}.'.format(trim_start, trim_end))
for station in stations:
wf_station = data.select(station=station)
starttime[True] = max([trace.stats.starttime for trace in wf_station])
endtime[True] = min([trace.stats.endtime for trace in wf_station])
wf_station.trim(starttime=starttime[trim_start], endtime=endtime[trim_end])
return data
def check4gaps(data):
'''
check for gaps in Stream and remove them
:param data: stream of seismic data
:return: data stream
'''
stations = get_stations(data)
for station in stations:
wf_station = data.select(station=station)
if wf_station.get_gaps():
for trace in wf_station:
data.remove(trace)
print('check4gaps: Found gaps and removed station {} from waveform data.'.format(station))
return data
def check4doubled(data):
'''
check for doubled stations for same channel in Stream and take only the first one
:param data: stream of seismic data
:return: data stream
'''
stations = get_stations(data)
for station in stations:
wf_station = data.select(station=station)
# create list of all possible channels
channels = []
for trace in wf_station:
channel = trace.stats.channel
if not channel in channels:
channels.append(channel)
else:
print('check4doubled: removed the following trace for station {}, as there is'
' already a trace with the same channel given:\n{}'.format(
station, trace
))
data.remove(trace)
return data
def get_stations(data):
stations = []
for tr in data:
station = tr.stats.station
if not station in stations:
stations.append(station)
return stations
def check4rotated(data, metadata=None, verbosity=1):
def rotate_components(wfstream, metadata=None):
"""rotates components if orientation code is numeric.
azimut and dip are fetched from metadata"""
try:
# indexing fails if metadata is None
metadata[0]
except:
if verbosity:
msg = 'Warning: could not rotate traces since no metadata was given\nset Inventory file!'
print(msg)
return wfstream
if metadata[0] is None:
# sometimes metadata is (None, (None,))
if verbosity:
msg = 'Warning: could not rotate traces since no metadata was given\nCheck inventory directory!'
print(msg)
return wfstream
else:
parser = metadata[1]
def get_dip_azimut(parser, trace_id):
"""gets azimut and dip for a trace out of the metadata parser"""
dip = None
azimut = None
try:
blockettes = parser._select(trace_id)
except SEEDParserException as e:
print(e)
raise ValueError
for blockette_ in blockettes:
if blockette_.id != 52:
continue
dip = blockette_.dip
azimut = blockette_.azimuth
break
if dip is None or azimut is None:
error_msg = 'Dip and azimuth not available for trace_id {}'.format(trace_id)
raise ValueError(error_msg)
return dip, azimut
trace_ids = [trace.id for trace in wfstream]
for trace_id in trace_ids:
orientation = trace_id[-1]
if orientation.isnumeric():
# misaligned channels have a number as orientation
azimuts = []
dips = []
for trace_id in trace_ids:
try:
dip, azimut = get_dip_azimut(parser, trace_id)
except ValueError as e:
print(e)
print('Failed to rotate station {}, no azimuth or dip available in metadata'.format(trace_id))
return wfstream
azimuts.append(azimut)
dips.append(dip)
# to rotate all traces must have same length
wfstream = trim_station_components(wfstream, trim_start=True, trim_end=True)
z, n, e = rotate2zne(wfstream[0], azimuts[0], dips[0],
wfstream[1], azimuts[1], dips[1],
wfstream[2], azimuts[2], dips[2])
print('check4rotated: rotated station {} to ZNE'.format(trace_id))
z_index = dips.index(min(dips)) # get z-trace index (dip is measured from 0 to -90
wfstream[z_index].data = z
wfstream[z_index].stats.channel = wfstream[z_index].stats.channel[0:-1] + 'Z'
del trace_ids[z_index]
for trace_id in trace_ids:
dip, az = get_dip_azimut(parser, trace_id)
trace = wfstream.select(id=trace_id)[0]
if az > 315 and az <= 45 or az > 135 and az <= 225:
trace.data = n
trace.stats.channel = trace.stats.channel[0:-1] + 'N'
elif az > 45 and az <= 135 or az > 225 and az <= 315:
trace.data = e
trace.stats.channel = trace.stats.channel[0:-1] + 'E'
break
else:
continue
return wfstream
stations = get_stations(data)
for station in stations:
wf_station = data.select(station=station)
wf_station = rotate_components(wf_station, metadata)
return data
def scaleWFData(data, factor=None, components='all'):
"""
produce scaled waveforms from given waveform data and a scaling factor,
@ -477,6 +831,7 @@ def runProgram(cmd, parameter=None):
subprocess.check_output('{} | tee /dev/stderr'.format(cmd), shell=True)
def which(program, infile=None):
"""
takes a program name and returns the full path to the executable or None
@ -495,7 +850,7 @@ def which(program, infile=None):
bpath = os.path.join(os.path.expanduser('~'), '.pylot', 'pylot.in')
else:
bpath = os.path.join(os.path.expanduser('~'), '.pylot', infile)
if os.path.exists(bpath):
nllocpath = ":" + PylotParameter(bpath).get('nllocbin')
os.environ['PATH'] += nllocpath
@ -523,6 +878,61 @@ def which(program, infile=None):
return None
def loopIdentifyPhase(phase):
'''
Loop through phase string and try to recognize its type (P or S wave).
Global variable ALTSUFFIX gives alternative suffix for phases if they do not end with P, p or S, s.
If ALTSUFFIX is not given, the function will cut the last letter of the phase string until string ends
with P or S.
:param phase: phase name (str)
:return:
'''
from pylot.core.util.defaults import ALTSUFFIX
phase_copy = phase
while not identifyPhase(phase_copy):
identified = False
for alt_suf in ALTSUFFIX:
if phase_copy.endswith(alt_suf):
phase_copy = phase_copy.split(alt_suf)[0]
identified = True
if not identified:
phase_copy = phase_copy[:-1]
if len(phase_copy) < 1:
print('Warning: Could not identify phase {}!'.format(phase))
return
return phase_copy
def identifyPhase(phase):
'''
Returns capital P or S if phase string is identified by last letter. Else returns False.
:param phase: phase name (str)
:return: 'P', 'S' or False
'''
# common phase suffix for P and S
common_P = ['P', 'p']
common_S = ['S', 's']
if phase[-1] in common_P:
return 'P'
if phase[-1] in common_S:
return 'S'
else:
return False
def identifyPhaseID(phase):
return identifyPhase(loopIdentifyPhase(phase))
def has_spe(pick):
if not 'spe' in pick.keys():
return None
else:
return pick['spe']
if __name__ == "__main__":
import doctest

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319295
pylot/os

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1
pylot/styles/__init__.py Normal file

@ -0,0 +1 @@
# -*- coding: utf-8 -*-

259
pylot/styles/bright.qss Normal file

@ -0,0 +1,259 @@
QMainWindow{
background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(255, 255, 255, 255));
color: rgba(0, 0, 0, 255);
}
QWidget{
background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(235, 235, 235, 255), stop:1 rgba(230, 230, 230, 255));
color: rgba(0, 0, 0, 255);
}
QToolBar QWidget:checked{
background-color: transparent;
border-color: rgba(230, 230, 230, 255);
border-width: 2px;
border-style:inset;
}
QComboBox{
background-color: rgba(255, 255, 255, 255);
color: rgba(0, 0, 0, 255);
min-height: 1.5em;
selection-background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 55, 140, 150), stop:1 rgba(0, 70, 180, 150));
}
QComboBox *{
background-color: rgba(255, 255, 255, 255);
color: rgba(0, 0, 0, 255);
selection-background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 55, 140, 150), stop:1 rgba(0, 70, 180, 150));
selection-color: rgba(255, 255, 255, 255);
}
QMenuBar{
background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:2, stop:0 rgba(240, 240, 240, 255), stop:1 rgba(230, 230, 230, 255));
padding:1px;
}
QMenuBar::item{
background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:2, stop:0 rgba(240, 240, 240, 255), stop:1 rgba(230, 230, 230, 255));
color: rgba(0, 0, 0, 255);
padding:3px;
padding-left:5px;
padding-right:5px;
}
QMenu{
background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(230, 230, 230 255));
color: rgba(0, 0, 0, 255);
padding:0;
}
*::item:selected{
color: rgba(0, 0, 0, 255);
background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 55, 140, 150), stop:1 rgba(0, 70, 180, 150));
}
QToolBar{
background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(255, 255, 255, 255));
border-style:solid;
border-color:rgba(200, 200, 200, 150);
border-width:1px;
}
QToolBar *{
background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(255, 255, 255, 255));
}
QMessageBox{
background-color: rgba(255, 255, 255, 255);
color: rgba(0, 0, 0, 255);
}
QTableWidget{
background-color: rgba(255, 255, 255, 255);
color:rgba(0, 0, 0, 255);
border-color:rgba(0, 0, 0, 255);
selection-background-color: rgba(200, 210, 230, 255);
}
QTableCornerButton::section{
border: none;
background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(255, 255, 255, 255), stop:1 rgba(230, 230, 230, 255));
}
QHeaderView::section{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(255, 255, 255, 255), stop:1 rgba(230, 230, 230, 255));
border:none;
border-top-style:solid;
border-width:1px;
border-top-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(255, 255, 255, 255), stop:1 rgba(230, 230, 230, 255));
color:rgba(0, 0, 0, 255);
padding:5px;
}
QHeaderView::section:checked{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 55, 140, 150), stop:1 rgba(0, 70, 180, 150));
border-top-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 55, 140, 150), stop:1 rgba(0, 70, 180, 150));
}
QHeaderView{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(255, 255, 255, 255), stop:1 rgba(230, 230, 230, 255));
border:none;
border-top-style:solid;
border-width:1px;
border-top-color:rgba(230, 230, 230, 255);
color:rgba(0, 0, 0, 255);
}
QListWidget{
background-color:rgba(230, 230, 230, 255);
color:rgba(0, 0, 0, 255);
}
QStatusBar{
background-color:rgba(255, 255, 255, 255);
color:rgba(0, 0, 0, 255);
}
QPushButton{
background-color:qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(245, 245, 245, 255));
color:rgba(0, 0, 0, 255);
border-style: outset;
border-width: 1px;
border-color: rgba(100, 100, 120, 255);
min-width: 6em;
padding: 4px;
padding-left:5px;
padding-right:5px;
border-radius: 2px;
}
QPushButton:pressed{
background-color: rgba(230, 230, 230, 255);
border-style: inset;
}
QPushButton:checked{
background-color: rgba(230, 230, 230, 255);
border-style: inset;
}
*:disabled{
color:rgba(100, 100, 120, 255);
}
QTabBar{
background-color:transparent;
}
QTabBar::tab{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(210, 210, 210, 255));
color: rgba(0, 0, 0, 255);
border-style:solid;
border-color:rgba(210, 210, 210 255);
border-bottom-color: transparent;
border-width:1px;
padding:5px;
}
QTabBar::tab:selected{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(255, 255, 255, 255), stop:1 rgba(245, 245, 245, 255));
color: rgba(0, 0, 0, 255);
border-style:solid;
border-color:rgba(245, 245, 245, 255);
border-bottom-color: transparent;
border-width:1px;
padding:5px;
}
QTabBar::tab:disabled{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(230, 230, 230, 255), stop:1 rgba(210, 210, 210, 255));
color: rgba(100, 100, 120, 255);
}
QTabWidget{
background-color:transparent;
}
QTabWidget::pane{
background-color:rgba(0, 0, 0, 255);
border-style:solid;
border-color:rgba(245, 245, 245, 255);
border-width:1px;
}
QTabWidget::tab{
background-color:rgba(255, 255, 255, 255);
}
QTabWidget > QWidget{
background-color: rgba(245, 245, 245, 255);
color: rgba(0, 0, 0, 255);
}
QScrollArea{
background: transparent;
}
QScrollArea>QWidget>QWidget{
background: transparent;
}
QLabel{
color: rgba(0, 0, 0, 255);
background-color: transparent;
}
QTextEdit{
color: rgba(0, 0, 0, 255);
background-color: rgba(255, 255, 255, 255);
}
QSpinBox{
color: rgba(0, 0, 0, 255);
background-color: rgba(255, 255, 255, 255);
}
QDoubleSpinBox{
color: rgba(0, 0, 0, 255);
background-color: rgba(255, 255, 255, 255);
}
QCheckBox{
background-color:transparent;
border:none;
}
QLineEdit{
background-color: rgba(255, 255, 255, 255);
border: 1px inset;
border-radius:0;
border-color: rgba(100, 100, 120, 255);
}
QLineEdit:disabled{
background-color: rgba(255, 255, 255, 255);
border: 1px inset;
border-radius:0;
border-color: rgba(200, 200, 200, 255);
}
QListWidget{
background-color:rgba(255, 255, 255, 255)
}
QProgressBar{
background-color:rgba(230, 230, 230, 255);
}
QProgressBar::chunk{
background-color:qlineargradient(spread:reflect, x1:0, y1:0, x2:0.5, y2:0, stop:0 transparent, stop:1 rgba(0, 70, 180, 150));
}
QStatusBar{
background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(235, 235, 235, 255), stop:1 rgba(230, 230, 230, 255));
color: rgba(0, 0, 0, 255);
}

258
pylot/styles/dark.qss Normal file

@ -0,0 +1,258 @@
QMainWindow{
background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
color: rgba(255, 255, 255, 255);
}
QWidget{
background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
color: rgba(255, 255, 255, 255);
}
QToolBar QWidget:checked{
background-color: transparent;
border-color: rgba(100, 100, 120, 255);
border-width: 2px;
border-style:inset;
}
QComboBox{
background-color: rgba(90, 90, 100, 255);
color: rgba(255, 255, 255, 255);
min-height: 1.5em;
selection-background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 144, 180, 255), stop:1 rgba(0, 150, 190, 255));
}
QComboBox *{
background-color: rgba(90, 90, 100, 255);
color: rgba(255, 255, 255, 255);
selection-background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 144, 180, 255), stop:1 rgba(0, 150, 190, 255));
selection-color: rgba(255, 255, 255, 255);
}
QMenuBar{
background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
padding:1px;
}
QMenuBar::item{
background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
color: rgba(255, 255, 255, 255);
padding:3px;
padding-left:5px;
padding-right:5px;
}
QMenu{
background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
color: rgba(255, 255, 255, 255);
padding:0;
}
*::item:selected{
color: rgba(255, 255, 255, 255);
background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 144, 180, 255), stop:1 rgba(0, 150, 190, 255));
}
QToolBar{
background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
border-style:solid;
border-color:rgba(80, 80, 90, 255);
border-width:1px;
}
QToolBar *{
background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
}
QMessageBox{
background-color: rgba(60, 60, 70, 255);
color: rgba(255, 255, 255, 255);
}
QTableWidget{
background-color: rgba(80, 80, 90, 255);
color:rgba(255, 255, 255, 255);
border-color:rgba(255, 255, 255, 255);
selection-background-color: rgba(200, 210, 230, 255);
}
QTableCornerButton::section{
border: none;
background-color: qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(60, 60, 70, 255), stop:1 rgba(70, 70, 80, 255));
}
QHeaderView::section{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(60, 60, 70, 255), stop:1 rgba(70, 70, 80, 255));
border:none;
border-top-style:solid;
border-width:1px;
border-top-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(60, 60, 70, 255), stop:1 rgba(70, 70, 80, 255));
color:rgba(255, 255, 255, 255);
padding:5px;
}
QHeaderView::section:checked{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 120, 150, 255), stop:1 rgba(0, 150, 190, 255));
border-top-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(0, 120, 150, 255), stop:1 rgba(0, 150, 190, 255));
}
QHeaderView{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:1, y2:0, stop:0 rgba(60, 60, 70, 255), stop:1 rgba(70, 70, 80, 255));
border:none;
border-top-style:solid;
border-width:1px;
border-top-color:rgba(70, 70, 80, 255);
color:rgba(255, 255, 255, 255);
}
QListWidget{
background-color:rgba(200, 200, 200, 255);
color:rgba(255, 255, 255, 255);
}
QStatusBar{
background-color:rgba(60, 60, 70, 255);
color:rgba(255, 255, 255, 255);
}
QPushButton{
background-color:qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
color:rgba(255, 255, 255, 255);
border-style: outset;
border-width: 2px;
border-color: rgba(50, 50, 60, 255);
min-width: 6em;
padding: 4px;
padding-left:5px;
padding-right:5px;
border-radius: 2px;
}
QPushButton:pressed{
background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(80, 80, 90, 255), stop:1 rgba(70, 70, 80, 255));
border-style: inset;
}
QPushButton:checked{
background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(80, 80, 90, 255), stop:1 rgba(70, 70, 80, 255));
border-style: inset;
}
*:disabled{
color: rgba(130, 130, 130, 255);
}
QTabBar{
background-color:transparent;
}
QTabBar::tab{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
color: rgba(255, 255, 255, 255);
border-style:solid;
border-color:rgba(70, 70, 80, 255);
border-bottom-color: transparent;
border-width:1px;
padding:5px;
}
QTabBar::tab:selected{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(80, 80, 90, 255), stop:1 rgba(70, 70, 80, 255));
color: rgba(255, 255, 255, 255);
border-style:solid;
border-color:rgba(70, 70, 80, 255);
border-bottom-color: transparent;
border-width:1px;
padding:5px;
}
QTabBar::tab:disabled{
background-color:qlineargradient(spread:pad, x1:0, y1:0, x2:0, y2:1, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
color: rgba(100, 100, 120, 255);
}
QTabWidget{
background-color:transparent;
}
QTabWidget::pane{
background-color:rgba(70, 70, 80, 255);
border-style:solid;
border-color:rgba(70, 70, 80, 255);
border-width:1px;
}
QTabWidget::tab{
background-color:rgba(70, 70, 80, 255);
}
QTabWidget > QWidget{
background-color: rgba(70, 70, 80, 255);
color: rgba(255, 255, 255, 255);
}
QScrollArea{
background: transparent;
}
QScrollArea>QWidget>QWidget{
background: transparent;
}
QLabel{
color: rgba(255, 255, 255, 255);
background-color: transparent;
}
QTextEdit{
color: rgba(255, 255, 255, 255);
background-color: rgba(90, 90, 100, 255);
}
QSpinBox{
color: rgba(255, 255, 255, 255);
background-color: rgba(90, 90, 100, 255);
}
QDoubleSpinBox{
color: rgba(255, 255, 255, 255);
background-color: rgba(90, 90, 100, 255);
}
QCheckBox{
background-color:transparent;
border:none;
}
QLineEdit{
background-color: rgba(90, 90, 100, 255);
border: 1px inset;
border-radius:0;
border-color: rgba(100, 100, 120, 255);
}
QLineEdit:disabled{
background-color: rgba(90, 90, 100, 255);
border: 1px inset;
border-radius:0;
border-color: rgba(200, 200, 200, 255);
}
QListWidget{
background-color:rgba(60, 60, 70, 255)
}
QProgressBar{
background-color:rgba(60, 60, 70, 255);
}
QProgressBar::chunk{
background-color:qlineargradient(spread:reflect, x1:0, y1:0, x2:0.5, y2:0, stop:0 transparent, stop:1 rgba(0, 150, 190, 255));
}
QStatusBar{
background-color: qlineargradient(spread:reflect, x1:0, y1:0, x2:0, y2:0.5, stop:0 rgba(70, 70, 80, 255), stop:1 rgba(60, 60, 70, 255));
color: rgba(255, 255, 255, 255);
}

@ -0,0 +1,70 @@
# -*- coding: utf-8 -*-
# Set base phase colors for manual and automatic picks
# together with a modifier (r, g, or b) used to alternate
# the base color
phasecolors = {
'manual': {
'P':{
'rgba': (0, 0, 255, 255),
'modifier': 'g'},
'S':{
'rgba': (255, 0, 0, 255),
'modifier': 'b'}
},
'auto':{
'P':{
'rgba': (140, 0, 255, 255),
'modifier': 'g'},
'S':{
'rgba': (255, 140, 0, 255),
'modifier': 'b'}
}
}
# Set plot colors and stylesheet for each style
stylecolors = {
'default':{
'linecolor':{
'rgba': (0, 0, 0, 255)},
'background': {
'rgba': (255, 255, 255, 255)},
'multicursor': {
'rgba': (255, 190, 0, 255)},
'ref': {
'rgba': (200, 210, 230, 255)},
'test': {
'rgba': (200, 230, 200, 255)},
'stylesheet': {
'filename': None}
},
'dark': {
'linecolor': {
'rgba': (230, 230, 230, 255)},
'background': {
'rgba': (50, 50, 60, 255)},
'multicursor': {
'rgba': (0, 150, 190, 255)},
'ref': {
'rgba': (80, 110, 170, 255)},
'test': {
'rgba': (130, 190, 100, 255)},
'stylesheet': {
'filename': 'dark.qss'}
},
'bright': {
'linecolor': {
'rgba': (0, 0, 0, 255)},
'background': {
'rgba': (255, 255, 255, 255)},
'multicursor': {
'rgba': (100, 100, 190, 255)},
'ref': {
'rgba': (200, 210, 230, 255)},
'test': {
'rgba': (200, 230, 200, 255)},
'stylesheet': {
'filename': 'bright.qss'}
}
}

@ -1,12 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, time
from PySide.QtGui import QApplication
from pylot.core.util.widgets import HelpForm
app = QApplication(sys.argv)
win = HelpForm()
win.show()
app.exec_()

@ -1,20 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import matplotlib
matplotlib.use('Qt4Agg')
matplotlib.rcParams['backend.qt4'] = 'PySide'
from PySide.QtGui import QApplication
from obspy.core import read
from pylot.core.util.widgets import PickDlg
import icons_rc
app = QApplication(sys.argv)
data = read()
win = PickDlg(data=data)
win.show()
app.exec_()

@ -1,12 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, time
from PySide.QtGui import QApplication
from pylot.core.util.widgets import PropertiesDlg
app = QApplication(sys.argv)
win = PropertiesDlg()
win.show()
app.exec_()

@ -1,17 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, time
from PySide.QtGui import QApplication
from pylot.core.util.widgets import FilterOptionsDialog, PropertiesDlg, HelpForm
dialogs = [FilterOptionsDialog, PropertiesDlg, HelpForm]
app = QApplication(sys.argv)
for dlg in dialogs:
win = dlg()
win.show()
time.sleep(1)
win.destroy()

@ -1,27 +0,0 @@
# -*- coding: utf-8 -*-
'''
Created on 10.11.2014
@author: sebastianw
'''
import unittest
class Test(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def testName(self):
pass
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']
unittest.main()

@ -1,19 +0,0 @@
# -*- coding: utf-8 -*-
'''
Created on 10.11.2014
@author: sebastianw
'''
import unittest
class Test(unittest.TestCase):
def testName(self):
pass
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']
unittest.main()

@ -1,303 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Script to run autoPyLoT-script "makeCF.py".
Only for test purposes!
"""
from obspy.core import read
import matplotlib.pyplot as plt
import numpy as np
from pylot.core.pick.charfuns import *
from pylot.core.pick.picker import *
import glob
import argparse
def run_makeCF(project, database, event, iplot, station=None):
#parameters for CF calculation
t2 = 7 #length of moving window for HOS calculation [sec]
p = 4 #order of HOS
cuttimes = [10, 50] #start and end time for CF calculation
bpz = [2, 30] #corner frequencies of bandpass filter, vertical component
bph = [2, 15] #corner frequencies of bandpass filter, horizontal components
tdetz= 1.2 #length of AR-determination window [sec], vertical component
tdeth= 0.8 #length of AR-determination window [sec], horizontal components
tpredz = 0.4 #length of AR-prediction window [sec], vertical component
tpredh = 0.4 #length of AR-prediction window [sec], horizontal components
addnoise = 0.001 #add noise to seismogram for stable AR prediction
arzorder = 2 #chosen order of AR process, vertical component
arhorder = 4 #chosen order of AR process, horizontal components
TSNRhos = [5, 0.5, 1, 0.1] #window lengths [s] for calculating SNR for earliest/latest pick and quality assessment
#from HOS-CF [noise window, safety gap, signal window, slope determination window]
TSNRarz = [5, 0.5, 1, 0.5] #window lengths [s] for calculating SNR for earliest/lates pick and quality assessment
#from ARZ-CF
#get waveform data
if station:
dpz = '/data/%s/EVENT_DATA/LOCAL/%s/%s/%s*HZ.msd' % (project, database, event, station)
dpe = '/data/%s/EVENT_DATA/LOCAL/%s/%s/%s*HE.msd' % (project, database, event, station)
dpn = '/data/%s/EVENT_DATA/LOCAL/%s/%s/%s*HN.msd' % (project, database, event, station)
#dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_z.gse' % (project, database, event, station)
#dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_e.gse' % (project, database, event, station)
#dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_n.gse' % (project, database, event, station)
else:
# dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*_z.gse' % (project, database, event)
# dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*_e.gse' % (project, database, event)
# dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*_n.gse' % (project, database, event)
dpz = '/data/%s/EVENT_DATA/LOCAL/%s/%s/*HZ.msd' % (project, database, event)
dpe = '/data/%s/EVENT_DATA/LOCAL/%s/%s/*HE.msd' % (project, database, event)
dpn = '/data/%s/EVENT_DATA/LOCAL/%s/%s/*HN.msd' % (project, database, event)
wfzfiles = glob.glob(dpz)
wfefiles = glob.glob(dpe)
wfnfiles = glob.glob(dpn)
if wfzfiles:
for i in range(len(wfzfiles)):
print 'Vertical component data found ...'
print wfzfiles[i]
st = read('%s' % wfzfiles[i])
st_copy = st.copy()
#filter and taper data
tr_filt = st[0].copy()
tr_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
tr_filt.taper(max_percentage=0.05, type='hann')
st_copy[0].data = tr_filt.data
##############################################################
#calculate HOS-CF using subclass HOScf of class CharacteristicFunction
hoscf = HOScf(st_copy, cuttimes, t2, p) #instance of HOScf
##############################################################
#calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
#class needs stream object => build it
tr_aic = tr_filt.copy()
tr_aic.data = hoscf.getCF()
st_copy[0].data = tr_aic.data
aiccf = AICcf(st_copy, cuttimes) #instance of AICcf
##############################################################
#get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
aicpick = AICPicker(aiccf, None, TSNRhos, 3, 10, None, 0.1)
##############################################################
#get refined onset time from HOS-CF using class Picker
hospick = PragPicker(hoscf, None, TSNRhos, 2, 10, 0.001, 0.2, aicpick.getpick())
#get earliest and latest possible picks
hosELpick = EarlLatePicker(hoscf, 1.5, TSNRhos, None, 10, None, None, hospick.getpick())
##############################################################
#calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
#get stream object of filtered data
st_copy[0].data = tr_filt.data
arzcf = ARZcf(st_copy, cuttimes, tpredz, arzorder, tdetz, addnoise) #instance of ARZcf
##############################################################
#calculate AIC-ARZ-CF using subclass AICcf of class CharacteristicFunction
#class needs stream object => build it
tr_arzaic = tr_filt.copy()
tr_arzaic.data = arzcf.getCF()
st_copy[0].data = tr_arzaic.data
araiccf = AICcf(st_copy, cuttimes, tpredz, 0, tdetz) #instance of AICcf
##############################################################
#get onset time from AIC-ARZ-CF using subclass AICPicker of class AutoPicking
aicarzpick = AICPicker(araiccf, 1.5, TSNRarz, 2, 10, None, 0.1)
##############################################################
#get refined onset time from ARZ-CF using class Picker
arzpick = PragPicker(arzcf, 1.5, TSNRarz, 2.0, 10, 0.1, 0.05, aicarzpick.getpick())
#get earliest and latest possible picks
arzELpick = EarlLatePicker(arzcf, 1.5, TSNRarz, None, 10, None, None, arzpick.getpick())
elif not wfzfiles:
print 'No vertical component data found!'
if wfefiles and wfnfiles:
for i in range(len(wfefiles)):
print 'Horizontal component data found ...'
print wfefiles[i]
print wfnfiles[i]
#merge streams
H = read('%s' % wfefiles[i])
H += read('%s' % wfnfiles[i])
H_copy = H.copy()
#filter and taper data
trH1_filt = H[0].copy()
trH2_filt = H[1].copy()
trH1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
trH1_filt.taper(max_percentage=0.05, type='hann')
trH2_filt.taper(max_percentage=0.05, type='hann')
H_copy[0].data = trH1_filt.data
H_copy[1].data = trH2_filt.data
##############################################################
#calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
arhcf = ARHcf(H_copy, cuttimes, tpredh, arhorder, tdeth, addnoise) #instance of ARHcf
##############################################################
#calculate AIC-ARH-CF using subclass AICcf of class CharacteristicFunction
#class needs stream object => build it
tr_arhaic = trH1_filt.copy()
tr_arhaic.data = arhcf.getCF()
H_copy[0].data = tr_arhaic.data
#calculate ARH-AIC-CF
arhaiccf = AICcf(H_copy, cuttimes, tpredh, 0, tdeth) #instance of AICcf
##############################################################
#get onset time from AIC-ARH-CF using subclass AICPicker of class AutoPicking
aicarhpick = AICPicker(arhaiccf, 1.5, TSNRarz, 4, 10, None, 0.1)
###############################################################
#get refined onset time from ARH-CF using class Picker
arhpick = PragPicker(arhcf, 1.5, TSNRarz, 2.5, 10, 0.1, 0.05, aicarhpick.getpick())
#get earliest and latest possible picks
arhELpick = EarlLatePicker(arhcf, 1.5, TSNRarz, None, 10, None, None, arhpick.getpick())
#create stream with 3 traces
#merge streams
AllC = read('%s' % wfefiles[i])
AllC += read('%s' % wfnfiles[i])
AllC += read('%s' % wfzfiles[i])
#filter and taper data
All1_filt = AllC[0].copy()
All2_filt = AllC[1].copy()
All3_filt = AllC[2].copy()
All1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
All2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
All3_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
All1_filt.taper(max_percentage=0.05, type='hann')
All2_filt.taper(max_percentage=0.05, type='hann')
All3_filt.taper(max_percentage=0.05, type='hann')
AllC[0].data = All1_filt.data
AllC[1].data = All2_filt.data
AllC[2].data = All3_filt.data
#calculate AR3C-CF using subclass AR3Ccf of class CharacteristicFunction
ar3ccf = AR3Ccf(AllC, cuttimes, tpredz, arhorder, tdetz, addnoise) #instance of AR3Ccf
#get earliest and latest possible pick from initial ARH-pick
ar3cELpick = EarlLatePicker(ar3ccf, 1.5, TSNRarz, None, 10, None, None, arhpick.getpick())
##############################################################
if iplot:
#plot vertical trace
plt.figure()
tr = st[0]
tdata = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)
p1, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p2, = plt.plot(hoscf.getTimeArray(), hoscf.getCF() / max(hoscf.getCF()), 'r')
p3, = plt.plot(aiccf.getTimeArray(), aiccf.getCF()/max(aiccf.getCF()), 'b')
p4, = plt.plot(arzcf.getTimeArray(), arzcf.getCF()/max(arzcf.getCF()), 'g')
p5, = plt.plot(araiccf.getTimeArray(), araiccf.getCF()/max(araiccf.getCF()), 'y')
plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'b--')
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'b')
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([hospick.getpick(), hospick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
plt.plot([hospick.getpick()-0.5, hospick.getpick()+0.5], [1.3, 1.3], 'r')
plt.plot([hospick.getpick()-0.5, hospick.getpick()+0.5], [-1.3, -1.3], 'r')
plt.plot([hosELpick.getLpick(), hosELpick.getLpick()], [-1.1, 1.1], 'r--')
plt.plot([hosELpick.getEpick(), hosELpick.getEpick()], [-1.1, 1.1], 'r--')
plt.plot([aicarzpick.getpick(), aicarzpick.getpick()], [-1.2, 1.2], 'y', linewidth=2)
plt.plot([aicarzpick.getpick()-0.5, aicarzpick.getpick()+0.5], [1.2, 1.2], 'y')
plt.plot([aicarzpick.getpick()-0.5, aicarzpick.getpick()+0.5], [-1.2, -1.2], 'y')
plt.plot([arzpick.getpick(), arzpick.getpick()], [-1.4, 1.4], 'g', linewidth=2)
plt.plot([arzpick.getpick()-0.5, arzpick.getpick()+0.5], [1.4, 1.4], 'g')
plt.plot([arzpick.getpick()-0.5, arzpick.getpick()+0.5], [-1.4, -1.4], 'g')
plt.plot([arzELpick.getLpick(), arzELpick.getLpick()], [-1.2, 1.2], 'g--')
plt.plot([arzELpick.getEpick(), arzELpick.getEpick()], [-1.2, 1.2], 'g--')
plt.yticks([])
plt.ylim([-1.5, 1.5])
plt.xlabel('Time [s]')
plt.ylabel('Normalized Counts')
plt.title('%s, %s, CF-SNR=%7.2f, CF-Slope=%12.2f' % (tr.stats.station, \
tr.stats.channel, aicpick.getSNR(), aicpick.getSlope()))
plt.suptitle(tr.stats.starttime)
plt.legend([p1, p2, p3, p4, p5], ['Data', 'HOS-CF', 'HOSAIC-CF', 'ARZ-CF', 'ARZAIC-CF'])
#plot horizontal traces
plt.figure(2)
plt.subplot(2,1,1)
tsteph = tpredh / 4
th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
tarhcf = np.arange(0, len(arhcf.getCF()) * tsteph, tsteph) + cuttimes[0] + tdeth +tpredh
p21, = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
p22, = plt.plot(arhcf.getTimeArray(), arhcf.getCF()/max(arhcf.getCF()), 'r')
p23, = plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF()/max(arhaiccf.getCF()))
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [1, 1], 'b')
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [1, 1], 'r')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [-1, -1], 'r')
plt.plot([arhELpick.getLpick(), arhELpick.getLpick()], [-0.8, 0.8], 'r--')
plt.plot([arhELpick.getEpick(), arhELpick.getEpick()], [-0.8, 0.8], 'r--')
plt.plot([arhpick.getpick() + arhELpick.getPickError(), arhpick.getpick() + arhELpick.getPickError()], \
[-0.2, 0.2], 'r--')
plt.plot([arhpick.getpick() - arhELpick.getPickError(), arhpick.getpick() - arhELpick.getPickError()], \
[-0.2, 0.2], 'r--')
plt.yticks([])
plt.ylim([-1.5, 1.5])
plt.ylabel('Normalized Counts')
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
plt.suptitle(trH1_filt.stats.starttime)
plt.legend([p21, p22, p23], ['Data', 'ARH-CF', 'ARHAIC-CF'])
plt.subplot(2,1,2)
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
plt.plot(arhcf.getTimeArray(), arhcf.getCF()/max(arhcf.getCF()), 'r')
plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF()/max(arhaiccf.getCF()))
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [1, 1], 'b')
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [1, 1], 'r')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [-1, -1], 'r')
plt.plot([arhELpick.getLpick(), arhELpick.getLpick()], [-0.8, 0.8], 'r--')
plt.plot([arhELpick.getEpick(), arhELpick.getEpick()], [-0.8, 0.8], 'r--')
plt.plot([arhpick.getpick() + arhELpick.getPickError(), arhpick.getpick() + arhELpick.getPickError()], \
[-0.2, 0.2], 'r--')
plt.plot([arhpick.getpick() - arhELpick.getPickError(), arhpick.getpick() - arhELpick.getPickError()], \
[-0.2, 0.2], 'r--')
plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
plt.yticks([])
plt.ylim([-1.5, 1.5])
plt.xlabel('Time [s]')
plt.ylabel('Normalized Counts')
#plot 3-component window
plt.figure(3)
plt.subplot(3,1,1)
p31, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p32, = plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [1, 1], 'b')
plt.plot([ar3cELpick.getLpick(), ar3cELpick.getLpick()], [-0.8, 0.8], 'b--')
plt.plot([ar3cELpick.getEpick(), ar3cELpick.getEpick()], [-0.8, 0.8], 'b--')
plt.yticks([])
plt.xticks([])
plt.ylabel('Normalized Counts')
plt.title([tr.stats.station, tr.stats.channel])
plt.suptitle(trH1_filt.stats.starttime)
plt.legend([p31, p32], ['Data', 'AR3C-CF'])
plt.subplot(3,1,2)
plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [1, 1], 'b')
plt.plot([ar3cELpick.getLpick(), ar3cELpick.getLpick()], [-0.8, 0.8], 'b--')
plt.plot([ar3cELpick.getEpick(), ar3cELpick.getEpick()], [-0.8, 0.8], 'b--')
plt.yticks([])
plt.xticks([])
plt.ylabel('Normalized Counts')
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
plt.subplot(3,1,3)
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [1, 1], 'b')
plt.plot([ar3cELpick.getLpick(), ar3cELpick.getLpick()], [-0.8, 0.8], 'b--')
plt.plot([ar3cELpick.getEpick(), ar3cELpick.getEpick()], [-0.8, 0.8], 'b--')
plt.yticks([])
plt.ylabel('Normalized Counts')
plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
plt.xlabel('Time [s]')
plt.show()
raw_input()
plt.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--project', type=str, help='project name (e.g. Insheim)')
parser.add_argument('--database', type=str, help='event data base (e.g. 2014.09_Insheim)')
parser.add_argument('--event', type=str, help='event ID (e.g. e0010.015.14)')
parser.add_argument('--iplot', help='anything, if set, figure occurs')
parser.add_argument('--station', type=str, help='Station ID (e.g. INS3) (optional)')
args = parser.parse_args()
run_makeCF(args.project, args.database, args.event, args.iplot, args.station)

@ -1,7 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pylot.core.util.pdf import ProbabilityDensityFunction
pdf = ProbabilityDensityFunction.from_pick(0.34, 0.5, 0.54, type='exp')
pdf2 = ProbabilityDensityFunction.from_pick(0.34, 0.5, 0.54, type='exp')
diff = pdf - pdf2

@ -1,15 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import numpy
from pylot.core.pick.utils import getnoisewin
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--t', type=numpy.array, help='numpy array of time stamps')
parser.add_argument('--t1', type=float, help='time from which relativ to it noise window is extracted')
parser.add_argument('--tnoise', type=float, help='length of time window [s] for noise part extraction')
parser.add_argument('--tgap', type=float, help='safety gap between signal (t1=onset) and noise')
args = parser.parse_args()
getnoisewin(args.t, args.t1, args.tnoise, args.tgap)

@ -1,28 +0,0 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Created Mar 2015
Transcription of the rezipe of Diehl et al. (2009) for consistent phase
picking. For a given inital (the most likely) pick, the corresponding earliest
and latest possible pick is calculated based on noise measurements in front of
the most likely pick and signal wavelength derived from zero crossings.
:author: Ludger Kueperkoch / MAGS2 EP3 working group
"""
import argparse
import obspy
from pylot.core.pick.utils import earllatepicker
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--X', type=~obspy.core.stream.Stream,
help='time series (seismogram) read with obspy module read')
parser.add_argument('--nfac', type=int,
help='(noise factor), nfac times noise level to calculate latest possible pick')
parser.add_argument('--TSNR', type=tuple, help='length of time windows around pick used to determine SNR \
[s] (Tnoise, Tgap, Tsignal)')
parser.add_argument('--Pick1', type=float, help='Onset time of most likely pick')
parser.add_argument('--iplot', type=int, help='if set, figure no. iplot occurs')
args = parser.parse_args()
earllatepicker(args.X, args.nfac, args.TSNR, args.Pick1, args.iplot)

@ -1,24 +0,0 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Created Mar 2015
Function to derive first motion (polarity) for given phase onset based on zero crossings.
:author: MAGS2 EP3 working group / Ludger Kueperkoch
"""
import argparse
import obspy
from pylot.core.pick.utils import fmpicker
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--Xraw', type=obspy.core.stream.Stream,
help='unfiltered time series (seismogram) read with obspy module read')
parser.add_argument('--Xfilt', type=obspy.core.stream.Stream,
help='filtered time series (seismogram) read with obspy module read')
parser.add_argument('--pickwin', type=float, help='length of pick window [s] for first motion determination')
parser.add_argument('--Pick', type=float, help='Onset time of most likely pick')
parser.add_argument('--iplot', type=int, help='if set, figure no. iplot occurs')
args = parser.parse_args()
fmpicker(args.Xraw, args.Xfilt, args.pickwin, args.Pick, args.iplot)

@ -1,41 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
from pylot.core.util.version import get_git_version as _getVersionString
from pylot.core.io.phases import reassess_pilot_db
__version__ = _getVersionString()
__author__ = 'S. Wehling-Benatelli'
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='reassess old PILOT event data base in terms of consistent '
'automatic uncertainty estimation',
epilog='Script written by {author} belonging to PyLoT version'
' {version}\n'.format(author=__author__,
version=__version__)
)
parser.add_argument(
'root', type=str, help='specifies the root directory'
)
parser.add_argument(
'db', type=str, help='specifies the database name'
)
parser.add_argument(
'--output', '-o', type=str, help='path to the output directory',
dest='output'
)
parser.add_argument(
'--parameterfile', '-p', type=str,
help='full path to the parameterfile', dest='parfile'
)
parser.add_argument(
'--verbosity', '-v', action='count', help='increase output verbosity',
default=0, dest='verbosity'
)
args = parser.parse_args()
reassess_pilot_db(args.root, args.db, args.output, args.parfile, args.verbosity)

@ -1,38 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
from pylot.core.util.version import get_git_version as _getVersionString
from pylot.core.io.phases import reassess_pilot_event
__version__ = _getVersionString()
__author__ = 'S. Wehling-Benatelli'
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='reassess old PILOT event data in terms of consistent '
'automatic uncertainty estimation',
epilog='Script written by {author} belonging to PyLoT version'
' {version}\n'.format(author=__author__,
version=__version__)
)
parser.add_argument(
'root', type=str, help='specifies the root directory'
)
parser.add_argument(
'db', type=str, help='specifies the database name'
)
parser.add_argument(
'id', type=str, help='PILOT event identifier'
)
parser.add_argument(
'--output', '-o', type=str, help='path to the output directory', dest='output'
)
parser.add_argument(
'--parameterfile', '-p', type=str, help='full path to the parameterfile', dest='parfile'
)
args = parser.parse_args()
reassess_pilot_event(args.root, args.db, args.id, args.output, args.parfile)

@ -1,14 +0,0 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import numpy
from pylot.core.pick.utils import getsignalwin
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--t', type=numpy.array, help='numpy array of time stamps')
parser.add_argument('--t1', type=float, help='time from which relativ to it signal window is extracted')
parser.add_argument('--tsignal', type=float, help='length of time window [s] for signal part extraction')
args = parser.parse_args()
getsignalwin(args.t, args.t1, args.tsignal)

@ -1,30 +0,0 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Created Mar/Apr 2015
Function to calculate SNR of certain part of seismogram relative
to given time. Returns SNR and SNR [dB].
:author: Ludger Kueperkoch /MAGS EP3 working group
"""
import argparse
import obspy
from pylot.core.pick.utils import getSNR
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--data', '-d', type=obspy.core.stream.Stream,
help='time series (seismogram) read with obspy module '
'read',
dest='data')
parser.add_argument('--tsnr', '-s', type=tuple,
help='length of time windows around pick used to '
'determine SNR [s] (Tnoise, Tgap, Tsignal)',
dest='tsnr')
parser.add_argument('--time', '-t', type=float,
help='initial time from which noise and signal windows '
'are calculated',
dest='time')
args = parser.parse_args()
print getSNR(args.data, args.tsnr, args.time)

@ -1,307 +0,0 @@
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Script to run autoPyLoT-script "run_makeCF.py".
Only for test purposes!
"""
from obspy.core import read
import matplotlib.pyplot as plt
import numpy as np
from pylot.core.pick.charfuns import CharacteristicFunction
from pylot.core.pick.picker import AutoPicker
from pylot.core.pick.utils import *
import glob
import argparse
def run_makeCF(project, database, event, iplot, station=None):
#parameters for CF calculation
t2 = 7 #length of moving window for HOS calculation [sec]
p = 4 #order of HOS
cuttimes = [10, 50] #start and end time for CF calculation
bpz = [2, 30] #corner frequencies of bandpass filter, vertical component
bph = [2, 15] #corner frequencies of bandpass filter, horizontal components
tdetz= 1.2 #length of AR-determination window [sec], vertical component
tdeth= 0.8 #length of AR-determination window [sec], horizontal components
tpredz = 0.4 #length of AR-prediction window [sec], vertical component
tpredh = 0.4 #length of AR-prediction window [sec], horizontal components
addnoise = 0.001 #add noise to seismogram for stable AR prediction
arzorder = 2 #chosen order of AR process, vertical component
arhorder = 4 #chosen order of AR process, horizontal components
TSNRhos = [5, 0.5, 1, .6] #window lengths [s] for calculating SNR for earliest/latest pick and quality assessment
#from HOS-CF [noise window, safety gap, signal window, slope determination window]
TSNRarz = [5, 0.5, 1, 1.0] #window lengths [s] for calculating SNR for earliest/lates pick and quality assessment
#from ARZ-CF
#get waveform data
if station:
dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*HZ.msd' % (project, database, event, station)
dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*HE.msd' % (project, database, event, station)
dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*HN.msd' % (project, database, event, station)
#dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_z.gse' % (project, database, event, station)
#dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_e.gse' % (project, database, event, station)
#dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/%s*_n.gse' % (project, database, event, station)
else:
dpz = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*HZ.msd' % (project, database, event)
dpe = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*HE.msd' % (project, database, event)
dpn = '/DATA/%s/EVENT_DATA/LOCAL/%s/%s/*HN.msd' % (project, database, event)
wfzfiles = glob.glob(dpz)
wfefiles = glob.glob(dpe)
wfnfiles = glob.glob(dpn)
if wfzfiles:
for i in range(len(wfzfiles)):
print 'Vertical component data found ...'
print wfzfiles[i]
st = read('%s' % wfzfiles[i])
st_copy = st.copy()
#filter and taper data
tr_filt = st[0].copy()
tr_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
tr_filt.taper(max_percentage=0.05, type='hann')
st_copy[0].data = tr_filt.data
##############################################################
#calculate HOS-CF using subclass HOScf of class CharacteristicFunction
hoscf = HOScf(st_copy, cuttimes, t2, p) #instance of HOScf
##############################################################
#calculate AIC-HOS-CF using subclass AICcf of class CharacteristicFunction
#class needs stream object => build it
tr_aic = tr_filt.copy()
tr_aic.data = hoscf.getCF()
st_copy[0].data = tr_aic.data
aiccf = AICcf(st_copy, cuttimes) #instance of AICcf
##############################################################
#get prelimenary onset time from AIC-HOS-CF using subclass AICPicker of class AutoPicking
aicpick = AICPicker(aiccf, TSNRhos, 3, 10, None, 0.1)
##############################################################
#get refined onset time from HOS-CF using class Picker
hospick = PragPicker(hoscf, TSNRhos, 2, 10, 0.001, 0.2, aicpick.getpick())
#############################################################
#get earliest and latest possible picks
st_copy[0].data = tr_filt.data
[lpickhos, epickhos, pickerrhos] = earllatepicker(st_copy, 1.5, TSNRhos, hospick.getpick(), 10)
#############################################################
#get SNR
[SNR, SNRdB] = getSNR(st_copy, TSNRhos, hospick.getpick())
print 'SNR:', SNR, 'SNR[dB]:', SNRdB
##########################################################
#get first motion of onset
hosfm = fmpicker(st, st_copy, 0.2, hospick.getpick(), 11)
##############################################################
#calculate ARZ-CF using subclass ARZcf of class CharcteristicFunction
arzcf = ARZcf(st, cuttimes, tpredz, arzorder, tdetz, addnoise) #instance of ARZcf
##############################################################
#calculate AIC-ARZ-CF using subclass AICcf of class CharacteristicFunction
#class needs stream object => build it
tr_arzaic = tr_filt.copy()
tr_arzaic.data = arzcf.getCF()
st_copy[0].data = tr_arzaic.data
araiccf = AICcf(st_copy, cuttimes, tpredz, 0, tdetz) #instance of AICcf
##############################################################
#get onset time from AIC-ARZ-CF using subclass AICPicker of class AutoPicking
aicarzpick = AICPicker(araiccf, TSNRarz, 2, 10, None, 0.1)
##############################################################
#get refined onset time from ARZ-CF using class Picker
arzpick = PragPicker(arzcf, TSNRarz, 2.0, 10, 0.1, 0.05, aicarzpick.getpick())
#get earliest and latest possible picks
st_copy[0].data = tr_filt.data
[lpickarz, epickarz, pickerrarz] = earllatepicker(st_copy, 1.5, TSNRarz, arzpick.getpick(), 10)
elif not wfzfiles:
print 'No vertical component data found!'
if wfefiles and wfnfiles:
for i in range(len(wfefiles)):
print 'Horizontal component data found ...'
print wfefiles[i]
print wfnfiles[i]
#merge streams
H = read('%s' % wfefiles[i])
H += read('%s' % wfnfiles[i])
H_copy = H.copy()
#filter and taper data
trH1_filt = H[0].copy()
trH2_filt = H[1].copy()
trH1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
trH1_filt.taper(max_percentage=0.05, type='hann')
trH2_filt.taper(max_percentage=0.05, type='hann')
H_copy[0].data = trH1_filt.data
H_copy[1].data = trH2_filt.data
##############################################################
#calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
arhcf = ARHcf(H_copy, cuttimes, tpredh, arhorder, tdeth, addnoise) #instance of ARHcf
##############################################################
#calculate AIC-ARH-CF using subclass AICcf of class CharacteristicFunction
#class needs stream object => build it
tr_arhaic = trH1_filt.copy()
tr_arhaic.data = arhcf.getCF()
H_copy[0].data = tr_arhaic.data
#calculate ARH-AIC-CF
arhaiccf = AICcf(H_copy, cuttimes, tpredh, 0, tdeth) #instance of AICcf
##############################################################
#get onset time from AIC-ARH-CF using subclass AICPicker of class AutoPicking
aicarhpick = AICPicker(arhaiccf, TSNRarz, 4, 10, None, 0.1)
###############################################################
#get refined onset time from ARH-CF using class Picker
arhpick = PragPicker(arhcf, TSNRarz, 2.5, 10, 0.1, 0.05, aicarhpick.getpick())
#get earliest and latest possible picks
H_copy[0].data = trH1_filt.data
[lpickarh1, epickarh1, pickerrarh1] = earllatepicker(H_copy, 1.5, TSNRarz, arhpick.getpick(), 10)
H_copy[0].data = trH2_filt.data
[lpickarh2, epickarh2, pickerrarh2] = earllatepicker(H_copy, 1.5, TSNRarz, arhpick.getpick(), 10)
#get earliest pick of both earliest possible picks
epick = [epickarh1, epickarh2]
lpick = [lpickarh1, lpickarh2]
pickerr = [pickerrarh1, pickerrarh2]
ipick =np.argmin([epickarh1, epickarh2])
epickarh = epick[ipick]
lpickarh = lpick[ipick]
pickerrarh = pickerr[ipick]
#create stream with 3 traces
#merge streams
AllC = read('%s' % wfefiles[i])
AllC += read('%s' % wfnfiles[i])
AllC += read('%s' % wfzfiles[i])
#filter and taper data
All1_filt = AllC[0].copy()
All2_filt = AllC[1].copy()
All3_filt = AllC[2].copy()
All1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
All2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
All3_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
All1_filt.taper(max_percentage=0.05, type='hann')
All2_filt.taper(max_percentage=0.05, type='hann')
All3_filt.taper(max_percentage=0.05, type='hann')
AllC[0].data = All1_filt.data
AllC[1].data = All2_filt.data
AllC[2].data = All3_filt.data
#calculate AR3C-CF using subclass AR3Ccf of class CharacteristicFunction
ar3ccf = AR3Ccf(AllC, cuttimes, tpredz, arhorder, tdetz, addnoise) #instance of AR3Ccf
##############################################################
if iplot:
#plot vertical trace
plt.figure()
tr = st[0]
tdata = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)
p1, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p2, = plt.plot(hoscf.getTimeArray(), hoscf.getCF() / max(hoscf.getCF()), 'r')
p3, = plt.plot(aiccf.getTimeArray(), aiccf.getCF()/max(aiccf.getCF()), 'b')
p4, = plt.plot(arzcf.getTimeArray(), arzcf.getCF()/max(arzcf.getCF()), 'g')
p5, = plt.plot(araiccf.getTimeArray(), araiccf.getCF()/max(araiccf.getCF()), 'y')
plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1], 'b--')
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [1, 1], 'b')
plt.plot([aicpick.getpick()-0.5, aicpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([hospick.getpick(), hospick.getpick()], [-1.3, 1.3], 'r', linewidth=2)
plt.plot([hospick.getpick()-0.5, hospick.getpick()+0.5], [1.3, 1.3], 'r')
plt.plot([hospick.getpick()-0.5, hospick.getpick()+0.5], [-1.3, -1.3], 'r')
plt.plot([lpickhos, lpickhos], [-1.1, 1.1], 'r--')
plt.plot([epickhos, epickhos], [-1.1, 1.1], 'r--')
plt.plot([aicarzpick.getpick(), aicarzpick.getpick()], [-1.2, 1.2], 'y', linewidth=2)
plt.plot([aicarzpick.getpick()-0.5, aicarzpick.getpick()+0.5], [1.2, 1.2], 'y')
plt.plot([aicarzpick.getpick()-0.5, aicarzpick.getpick()+0.5], [-1.2, -1.2], 'y')
plt.plot([arzpick.getpick(), arzpick.getpick()], [-1.4, 1.4], 'g', linewidth=2)
plt.plot([arzpick.getpick()-0.5, arzpick.getpick()+0.5], [1.4, 1.4], 'g')
plt.plot([arzpick.getpick()-0.5, arzpick.getpick()+0.5], [-1.4, -1.4], 'g')
plt.plot([lpickarz, lpickarz], [-1.2, 1.2], 'g--')
plt.plot([epickarz, epickarz], [-1.2, 1.2], 'g--')
plt.yticks([])
plt.ylim([-1.5, 1.5])
plt.xlabel('Time [s]')
plt.ylabel('Normalized Counts')
plt.title('%s, %s, CF-SNR=%7.2f, CF-Slope=%12.2f' % (tr.stats.station,
tr.stats.channel, aicpick.getSNR(), aicpick.getSlope()))
plt.suptitle(tr.stats.starttime)
plt.legend([p1, p2, p3, p4, p5], ['Data', 'HOS-CF', 'HOSAIC-CF', 'ARZ-CF', 'ARZAIC-CF'])
#plot horizontal traces
plt.figure(2)
plt.subplot(2,1,1)
tsteph = tpredh / 4
th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
tarhcf = np.arange(0, len(arhcf.getCF()) * tsteph, tsteph) + cuttimes[0] + tdeth +tpredh
p21, = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
p22, = plt.plot(arhcf.getTimeArray(), arhcf.getCF()/max(arhcf.getCF()), 'r')
p23, = plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF()/max(arhaiccf.getCF()))
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [1, 1], 'b')
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [1, 1], 'r')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [-1, -1], 'r')
plt.plot([lpickarh, lpickarh], [-0.8, 0.8], 'r--')
plt.plot([epickarh, epickarh], [-0.8, 0.8], 'r--')
plt.plot([arhpick.getpick() + pickerrarh, arhpick.getpick() + pickerrarh], [-0.2, 0.2], 'r--')
plt.plot([arhpick.getpick() - pickerrarh, arhpick.getpick() - pickerrarh], [-0.2, 0.2], 'r--')
plt.yticks([])
plt.ylim([-1.5, 1.5])
plt.ylabel('Normalized Counts')
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
plt.suptitle(trH1_filt.stats.starttime)
plt.legend([p21, p22, p23], ['Data', 'ARH-CF', 'ARHAIC-CF'])
plt.subplot(2,1,2)
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
plt.plot(arhcf.getTimeArray(), arhcf.getCF()/max(arhcf.getCF()), 'r')
plt.plot(arhaiccf.getTimeArray(), arhaiccf.getCF()/max(arhaiccf.getCF()))
plt.plot([aicarhpick.getpick(), aicarhpick.getpick()], [-1, 1], 'b')
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [1, 1], 'b')
plt.plot([aicarhpick.getpick()-0.5, aicarhpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'r')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [1, 1], 'r')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [-1, -1], 'r')
plt.plot([lpickarh, lpickarh], [-0.8, 0.8], 'r--')
plt.plot([epickarh, epickarh], [-0.8, 0.8], 'r--')
plt.plot([arhpick.getpick() + pickerrarh, arhpick.getpick() + pickerrarh], [-0.2, 0.2], 'r--')
plt.plot([arhpick.getpick() - pickerrarh, arhpick.getpick() - pickerrarh], [-0.2, 0.2], 'r--')
plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
plt.yticks([])
plt.ylim([-1.5, 1.5])
plt.xlabel('Time [s]')
plt.ylabel('Normalized Counts')
#plot 3-component window
plt.figure(3)
plt.subplot(3,1,1)
p31, = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p32, = plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [1, 1], 'b')
plt.yticks([])
plt.xticks([])
plt.ylabel('Normalized Counts')
plt.title([tr.stats.station, tr.stats.channel])
plt.suptitle(trH1_filt.stats.starttime)
plt.legend([p31, p32], ['Data', 'AR3C-CF'])
plt.subplot(3,1,2)
plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [1, 1], 'b')
plt.yticks([])
plt.xticks([])
plt.ylabel('Normalized Counts')
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
plt.subplot(3,1,3)
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
plt.plot(ar3ccf.getTimeArray(), ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.plot([arhpick.getpick(), arhpick.getpick()], [-1, 1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [-1, -1], 'b')
plt.plot([arhpick.getpick()-0.5, arhpick.getpick()+0.5], [1, 1], 'b')
plt.yticks([])
plt.ylabel('Normalized Counts')
plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
plt.xlabel('Time [s]')
plt.show()
raw_input()
plt.close()
parser = argparse.ArgumentParser()
parser.add_argument('--project', type=str, help='project name (e.g. Insheim)')
parser.add_argument('--database', type=str, help='event data base (e.g. 2014.09_Insheim)')
parser.add_argument('--event', type=str, help='event ID (e.g. e0010.015.14)')
parser.add_argument('--iplot', help='anything, if set, figure occurs')
parser.add_argument('--station', type=str, help='Station ID (e.g. INS3) (optional)')
args = parser.parse_args()
run_makeCF(args.project, args.database, args.event, args.iplot, args.station)

@ -4,11 +4,11 @@ from distutils.core import setup
setup(
name='PyLoT',
version='0.1a1',
version='0.2',
packages=['pylot', 'pylot.core', 'pylot.core.loc', 'pylot.core.pick',
'pylot.core.io', 'pylot.core.util', 'pylot.core.active',
'pylot.core.analysis', 'pylot.testing'],
requires=['obspy', 'PySide'],
requires=['obspy', 'PySide', 'matplotlib', 'numpy'],
url='dummy',
license='LGPLv3',
author='Sebastian Wehling-Benatelli',