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# PyLoT
Python picking and Location Tool
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

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#!/usr/bin/python
# -*- coding: utf-8 -*-
from __future__ import print_function
import argparse
import glob
import os
from obspy import read_events
import pylot.core.loc.hsat as hsat
import pylot.core.loc.nll as nll
from pylot.core.analysis.magnitude import MomentMagnitude, RichterMagnitude
from pylot.core.io.data import Data
from pylot.core.io.inputs import AutoPickParameter
from pylot.core.pick.autopick import autopickevent, iteratepicker
from pylot.core.util.dataprocessing import restitute_data, read_metadata, \
remove_underscores
from pylot.core.util.structure import DATASTRUCTURE
from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
def autoPyLoT(inputfile):
"""
Determine phase onsets automatically utilizing the automatic picking
algorithms by Kueperkoch et al. 2010/2012.
:param inputfile: path to the input file containing all parameter
information for automatic picking (for formatting details, see.
`~pylot.core.io.inputs.AutoPickParameter`
:type inputfile: str
:return:
.. rubric:: Example
"""
splash = '''************************************\n
*********autoPyLoT starting*********\n
The Python picking and Location Tool\n
Version {version} 2015\n
\n
Authors:\n
S. Wehling-Benatelli (Ruhr-Universität Bochum)\n
L. Küperkoch (BESTEC GmbH, Landau i. d. Pfalz)\n
K. Olbert (Christian-Albrechts Universität zu Kiel)\n
***********************************'''.format(version=_getVersionString())
print(splash)
# reading parameter file
parameter = AutoPickParameter(inputfile)
data = Data()
evt = None
# getting information on data structure
if parameter.hasParam('datastructure'):
datastructure = DATASTRUCTURE[parameter.get('datastructure')]()
dsfields = {'root': parameter.get('rootpath'),
'dpath': parameter.get('datapath'),
'dbase': parameter.get('database')}
exf = ['root', 'dpath', 'dbase']
if parameter.hasParam('eventID'):
dsfields['eventID'] = parameter.get('eventID')
exf.append('eventID')
datastructure.modifyFields(**dsfields)
datastructure.setExpandFields(exf)
# check if default location routine NLLoc is available
if parameter.hasParam('nllocbin'):
locflag = 1
# get NLLoc-root path
nllocroot = parameter.get('nllocroot')
# get path to NLLoc executable
nllocbin = parameter.get('nllocbin')
nlloccall = '%s/NLLoc' % nllocbin
# get name of phase file
phasef = parameter.get('phasefile')
phasefile = '%s/obs/%s' % (nllocroot, phasef)
# get name of NLLoc-control file
ctrf = parameter.get('ctrfile')
ctrfile = '%s/run/%s' % (nllocroot, ctrf)
# pattern of NLLoc ttimes from location grid
ttpat = parameter.get('ttpatter')
# pattern of NLLoc-output file
nllocoutpatter = parameter.get('outpatter')
maxnumit = 3 # maximum number of iterations for re-picking
else:
locflag = 0
print(" !!! ")
print("!!No location routine available, autoPyLoT is running in non-location mode!!")
print("!!No source parameter estimation possible!!")
print(" !!! ")
datapath = datastructure.expandDataPath()
if not parameter.hasParam('eventID'):
# multiple event processing
# read each event in database
events = [events for events in glob.glob(os.path.join(datapath, '*')) if os.path.isdir(events)]
else:
# single event processing
events = glob.glob(os.path.join(datapath, parameter.get('eventID')))
for event in events:
data.setWFData(glob.glob(os.path.join(datapath, event, '*')))
evID = os.path.split(event)[-1]
print('Working on event %s' % event)
print(data)
wfdat = data.getWFData() # all available streams
wfdat = remove_underscores(wfdat)
metadata = read_metadata(parameter.get('invdir'))
corr_dat, rest_flag = restitute_data(wfdat.copy(), *metadata)
##########################################################
# !automated picking starts here!
picks = autopickevent(wfdat, parameter)
##########################################################
# locating
if locflag == 1:
# write phases to NLLoc-phase file
nll.export(picks, phasefile)
# For locating the event the NLLoc-control file has to be modified!
nllocout = '%s_%s' % (evID, nllocoutpatter)
# create comment line for NLLoc-control file
nll.modify_inputs(ctrf, nllocroot, nllocout, phasef,
ttpat)
# locate the event
nll.locate(ctrfile)
# !iterative picking if traces remained unpicked or occupied with bad picks!
# get theoretical onset times for picks with weights >= 4
# in order to reprocess them using smaller time windows around theoretical onset
# get stations with bad onsets
badpicks = []
for key in picks:
if picks[key]['P']['weight'] >= 4 or picks[key]['S']['weight'] >= 4:
badpicks.append([key, picks[key]['P']['mpp']])
# TODO keep code DRY (Don't Repeat Yourself) the following part is written twice
# suggestion: delete block and modify the later similar block to work properly
if len(badpicks) == 0:
print("autoPyLoT: No bad onsets found, thus no iterative picking necessary!")
# get NLLoc-location file
locsearch = '%s/loc/%s.????????.??????.grid?.loc.hyp' % (nllocroot, nllocout)
if len(glob.glob(locsearch)) > 0:
# get latest NLLoc-location file if several are available
nllocfile = max(glob.glob(locsearch), key=os.path.getctime)
evt = read_events(nllocfile)[0]
# calculating seismic moment Mo and moment magnitude Mw
moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'),
parameter.get('Qp'),
parameter.get('rho'), True, 0)
# update pick with moment property values (w0, fc, Mo)
for station, props in moment_mag.moment_props.items():
picks[station]['P'].update(props)
evt = moment_mag.updated_event()
local_mag = RichterMagnitude(corr_dat, evt,
parameter.get('sstop'), True, 0)
for station, amplitude in local_mag.amplitudes.items():
picks[station]['S']['Ao'] = amplitude.generic_amplitude
evt = local_mag.updated_event()
else:
print("autoPyLoT: No NLLoc-location file available!")
print("No source parameter estimation possible!")
else:
# get theoretical P-onset times from NLLoc-location file
locsearch = '%s/loc/%s.????????.??????.grid?.loc.hyp' % (nllocroot, nllocout)
if len(glob.glob(locsearch)) > 0:
# get latest file if several are available
nllocfile = max(glob.glob(locsearch), key=os.path.getctime)
nlloccounter = 0
while len(badpicks) > 0 and nlloccounter <= maxnumit:
nlloccounter += 1
if nlloccounter > maxnumit:
print("autoPyLoT: Number of maximum iterations reached, stop iterative picking!")
break
print("autoPyLoT: Starting with iteration No. %d ..." % nlloccounter)
picks = iteratepicker(wfdat, nllocfile, picks, badpicks, parameter)
# write phases to NLLoc-phase file
nll.export(picks, phasefile)
# remove actual NLLoc-location file to keep only the last
os.remove(nllocfile)
# locate the event
nll.locate(ctrfile)
print("autoPyLoT: Iteration No. %d finished." % nlloccounter)
# get updated NLLoc-location file
nllocfile = max(glob.glob(locsearch), key=os.path.getctime)
# check for bad picks
badpicks = []
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, \
len(badpicks)))
if len(badpicks) == 0:
print("autoPyLoT: No more bad onsets found, stop iterative picking!")
nlloccounter = maxnumit
evt = read_events(nllocfile)[0]
# calculating seismic moment Mo and moment magnitude Mw
moment_mag = MomentMagnitude(corr_dat, evt, parameter.get('vp'),
parameter.get('Qp'),
parameter.get('rho'), True, 0)
# update pick with moment property values (w0, fc, Mo)
for station, props in moment_mag.moment_props.items():
picks[station]['P'].update(props)
evt = moment_mag.updated_event()
local_mag = RichterMagnitude(corr_dat, evt,
parameter.get('sstop'), True, 0)
for station, amplitude in local_mag.amplitudes.items():
picks[station]['S']['Ao'] = amplitude.generic_amplitude
evt = local_mag.updated_event()
net_mw = moment_mag.net_magnitude()
print("Network moment magnitude: %4.1f" % net_mw.mag)
else:
print("autoPyLoT: No NLLoc-location file available! Stop iteration!")
##########################################################
# write phase files for various location routines
# HYPO71
hypo71file = '%s/autoPyLoT_HYPO71.pha' % event
hsat.export(picks, hypo71file)
data.applyEVTData(picks)
if evt is not None:
data.applyEVTData(evt, 'event')
fnqml = '%s/autoPyLoT' % event
data.exportEvent(fnqml)
endsplash = '''------------------------------------------\n'
-----Finished event %s!-----\n'
------------------------------------------'''.format \
(version=_getVersionString()) % evID
print(endsplash)
if locflag == 0:
print("autoPyLoT was running in non-location mode!")
endsp = '''####################################\n
************************************\n
*********autoPyLoT terminates*******\n
The Python picking and Location Tool\n
************************************'''.format(version=_getVersionString())
print(endsp)
if __name__ == "__main__":
from pylot.core.util.defaults import AUTOMATIC_DEFAULTS
# parse arguments
parser = argparse.ArgumentParser(
description='''autoPyLoT automatically picks phase onset times using higher order statistics,
autoregressive prediction and AIC''')
parser.add_argument('-i', '-I', '--inputfile', type=str,
action='store',
help='''full path to the file containing the input
parameters for autoPyLoT''',
default=AUTOMATIC_DEFAULTS
)
parser.add_argument('-v', '-V', '--version', action='version',
version='autoPyLoT ' + __version__,
help='show version information and exit')
cla = parser.parse_args()
autoPyLoT(str(cla.inputfile))

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<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>
<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>
</ul>
<p>Read more on the
<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>

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<RCC>
<qresource>
<file>icons/pylot.ico</file>
<file>icons/pylot.png</file>
<file>icons/locate.png</file>
<file>icons/printer.png</file>
<file>icons/delete.png</file>
<file>icons/compare.png</file>
<file>icons/key_E.png</file>
<file>icons/key_N.png</file>
<file>icons/key_P.png</file>
<file>icons/key_Q.png</file>
<file>icons/key_R.png</file>
<file>icons/key_S.png</file>
<file>icons/key_T.png</file>
<file>icons/key_U.png</file>
<file>icons/key_V.png</file>
<file>icons/key_W.png</file>
<file>icons/key_Z.png</file>
<file>icons/filter.png</file>
<file>icons/sync.png</file>
<file>icons/zoom_0.png</file>
<file>icons/zoom_in.png</file>
<file>icons/zoom_out.png</file>
<file>splash/splash.png</file>
</qresource>
<qresource prefix="/help">
<file>help/index.html</file>
</qresource>
</RCC>

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## default time errors for old PILOT phases
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

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%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

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%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

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%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

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P bandpass 4 2.0 20.0
S bandpass 4 2.0 15.0

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%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

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0 1.4
10 1.5
20 1.7
25 1.9
30 2.1
35 2.3
40 2.4
45 2.5
50 2.6
60 2.8
70 2.8
75 2.9
85 2.9
90 3.0
100 3.0
110 3.1
120 3.1
130 3.2
140 3.2
150 3.3
160 3.3
170 3.4
180 3.4
190 3.5
200 3.5
210 3.6
230 3.7
240 3.7
250 3.8
260 3.8
270 3.9
280 3.9
290 4.0
300 4.0
310 4.1
320 4.2
330 4.2
340 4.2
350 4.3
360 4.3
370 4.3
380 4.4
390 4.4
400 4.5
430 4.6
470 4.7
510 4.8
560 4.9
600 5.1
700 5.2
800 5.4
900 5.5
1000 5.7

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#!/usr/bin/env python
# encoding: utf-8
from __future__ import print_function
"""
makePyLoT -- build and install PyLoT
makePyLoT is a python make file in order to establish the folder structure and
meet requisites
It defines
:class CLIError:
:method main:
:author: Sebastian Wehling-Benatelli
:copyright: 2014 MAGS2 EP3 Working Group. All rights reserved.
:license: GNU Lesser General Public License, Version 3
(http://www.gnu.org/copyleft/lesser.html)
:contact: sebastian.wehling@rub.de
updated: Updated
"""
import glob
import os
import sys
import shutil
import copy
from argparse import ArgumentParser
from argparse import RawDescriptionHelpFormatter
__all__ = []
__version__ = 0.1
__date__ = '2014-11-26'
__updated__ = '2016-04-28'
DEBUG = 0
TESTRUN = 0
PROFILE = 0
class CLIError(Exception):
"""Generic exception to raise and log different fatal errors."""
def __init__(self, msg):
super(CLIError).__init__(type(self))
self.msg = "E: %s" % msg
def __str__(self):
return self.msg
def __unicode__(self):
return self.msg
def main(argv=None): # IGNORE:C0111
'''Command line options.'''
if argv is None:
argv = sys.argv
else:
sys.argv.extend(argv)
program_name = os.path.basename(sys.argv[0])
program_version = "v%s" % __version__
program_build_date = str(__updated__)
program_version_message = 'makePyLoT %s (%s)' % (
program_version, program_build_date)
program_shortdesc = __import__('__main__').__doc__.split("\n")[1]
program_license = '''{0:s}
Created by Sebastian Wehling-Benatelli on {1:s}.
Copyright 2014 MAGS2 EP3 Working Group. All rights reserved.
GNU Lesser General Public License, Version 3
(http://www.gnu.org/copyleft/lesser.html)
Distributed on an "AS IS" basis without warranties
or conditions of any kind, either express or implied.
USAGE
'''.format(program_shortdesc, str(__date__))
try:
# Setup argument parser
parser = ArgumentParser(description=program_license,
formatter_class=RawDescriptionHelpFormatter)
parser.add_argument("-b", "--build", dest="build", action="store_true",
help="build PyLoT")
parser.add_argument("-v", "--verbose", dest="verbose", action="count",
help="set verbosity level")
parser.add_argument("-i", "--install", dest="install",
action="store_true",
help="install PyLoT on the system")
parser.add_argument("-d", "--directory", dest="directory",
help="installation directory", metavar="RE")
parser.add_argument('-V', '--version', action='version',
version=program_version_message)
# Process arguments
args = parser.parse_args()
verbose = args.verbose
build = args.build
install = args.install
directory = args.directory
if verbose > 0:
print("Verbose mode on")
if install and not directory:
raise CLIError("""Trying to install without appropriate
destination; please specify an installation
directory!""")
if build and install:
print("Building and installing PyLoT ...\n")
buildPyLoT(verbose)
installPyLoT(verbose)
elif build and not install:
print("Building PyLoT without installing! Please wait ...\n")
buildPyLoT(verbose)
cleanUp()
return 0
except KeyboardInterrupt:
cleanUp(1)
return 0
except Exception as e:
if DEBUG or TESTRUN:
raise e
indent = len(program_name) * " "
sys.stderr.write(program_name + ": " + repr(e) + "\n")
sys.stderr.write(indent + " for help use --help")
return 2
def buildPyLoT(verbosity=None):
system = sys.platform
if verbosity > 1:
msg = ("... on system: {0}\n"
"\n"
" Current working directory: {1}\n"
).format(system, os.getcwd())
print(msg)
if system.startswith(('win', 'microsoft')):
raise CLIError(
"building on Windows system not tested yet; implementation pending")
elif system == 'darwin':
# create a symbolic link to the desired python interpreter in order to
# display the right application name
for path in os.getenv('PATH').split(':'):
found = glob.glob(os.path.join(path, 'python'))
if found:
os.symlink(found, './PyLoT')
break
def installPyLoT(verbosity=None):
files_to_copy = {'autoPyLoT_local.in':['~', '.pylot'],
'autoPyLoT_regional.in':['~', '.pylot'],
'filter.in':['~', '.pylot']}
if verbosity > 0:
print ('starting installation of PyLoT ...')
if verbosity > 1:
print ('copying input files into destination folder ...')
ans = input('please specify scope of interest '
'([0]=local, 1=regional) :') or 0
if not isinstance(ans, int):
ans = int(ans)
ans = 'local' if ans is 0 else 'regional'
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 = os.path.join(*link_dest)
destination.append(file)
destination = os.path.join(*destination)
srcfile = os.path.join('input', file)
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))
shutil.copyfile(srcfile, destination)
if link_file:
if verbosity:
print('linking input file for autoPyLoT ...')
os.symlink(destination, link_dest)
def cleanUp(verbosity=None):
if verbosity >= 1:
print('cleaning up build files...')
if sys.platform == 'darwin':
os.remove('./PyLoT')
if __name__ == "__main__":
if DEBUG:
sys.argv.append("-h")
sys.argv.append("-v")
if TESTRUN:
import doctest
doctest.testmod()
if PROFILE:
import cProfile
import pstats
profile_filename = 'makePyLoT_profile.txt'
cProfile.run('main()', profile_filename)
statsfile = open("profile_stats.txt", "wb")
p = pstats.Stats(profile_filename, stream=statsfile)
stats = p.strip_dirs().sort_stats('cumulative')
stats.print_stats()
statsfile.close()
sys.exit(0)
sys.exit(main())

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# -*- coding: utf-8 -*-
# --------------------------------------------------------
# Purpose: Convience imports for PyLoT
#
'''
================================================
PyLoT - the Python picking and Localization Tool
================================================
This python library contains a graphical user interfaces for picking
seismic phases. This software needs ObsPy (http://github.com/obspy/obspy/wiki)
and the Qt4 libraries to be installed first.
PILOT has been developed in Mathworks' MatLab. In order to distribute
PILOT without facing portability problems, it has been decided to re-
develop 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.
:copyright:
The PyLoT Development Team
:license:
GNU Lesser General Public License, Version 3
(http://www.gnu.org/copyleft/lesser.html)
'''

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# -*- coding: utf-8 -*-

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created autumn/winter 2015.
: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
def richter_magnitude_scaling(delta):
relation = np.loadtxt(os.path.join(os.path.expanduser('~'),
'.pylot', 'richter_scaling.data'))
# prepare spline interpolation to calculate return value
func, params = fit_curve(relation[:, 0], relation[:, 1])
return func(delta, params)
class Magnitude(object):
"""
Base class object for Magnitude calculation within PyLoT.
"""
def __init__(self, stream, event, verbosity=False, iplot=0):
self._type = "M"
self._plot_flag = iplot
self._verbosity = verbosity
self._event = event
self._stream = stream
self._magnitudes = dict()
def __str__(self):
print(
'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))
def __nonzero__(self):
return bool(self.magnitudes)
@property
def type(self):
return self._type
@property
def plot_flag(self):
return self._plot_flag
@plot_flag.setter
def plot_flag(self, value):
self._plot_flag = value
@property
def verbose(self):
return self._verbosity
@verbose.setter
def verbose(self, value):
if not isinstance(value, bool):
print('WARNING: only boolean values accepted...\n')
value = bool(value)
self._verbosity = value
@property
def stream(self):
return self._stream
@stream.setter
def stream(self, value):
self._stream = value
@property
def event(self):
return self._event
@property
def origin_id(self):
return self._event.origins[0].resource_id
@property
def arrivals(self):
return self._event.origins[0].arrivals
@property
def magnitudes(self):
return self._magnitudes
@magnitudes.setter
def magnitudes(self, value):
"""
takes a tuple and saves the key value pair to private
attribute _magnitudes
:param value: station, magnitude value pair
:type value: tuple or list
:return:
"""
station, magnitude = value
self._magnitudes[station] = magnitude
def calc(self):
pass
def updated_event(self):
self.event.magnitudes.append(self.net_magnitude())
return self.event
def net_magnitude(self):
if self:
# TODO if an average Magnitude instead of the median is calculated
# StationMagnitudeContributions should be added to the returned
# Magnitude object
# mag_error => weights (magnitude error estimate from peak_to_peak, calcsourcespec?)
# weights => StationMagnitdeContribution
mag = ope.Magnitude(
mag=np.median([M.mag for M in self.magnitudes.values()]),
magnitude_type=self.type,
origin_id=self.origin_id,
station_count=len(self.magnitudes),
azimuthal_gap=self.origin_id.get_referred_object().quality.azimuthal_gap)
return mag
return None
class RichterMagnitude(Magnitude):
"""
Method to derive peak-to-peak amplitude as seen on a Wood-Anderson-
seismograph. Has to be derived from instrument corrected traces!
"""
# poles, zeros and sensitivity of WA seismograph
# (see Uhrhammer & Collins, 1990, BSSA, pp. 702-716)
_paz = {
'poles': [5.6089 - 5.4978j, -5.6089 - 5.4978j],
'zeros': [0j, 0j],
'gain': 2080,
'sensitivity': 1
}
_amplitudes = dict()
def __init__(self, stream, event, calc_win, verbosity=False, iplot=0):
super(RichterMagnitude, self).__init__(stream, event, verbosity, iplot)
self._calc_win = calc_win
self._type = 'ML'
self.calc()
@property
def calc_win(self):
return self._calc_win
@calc_win.setter
def calc_win(self, value):
self._calc_win = value
@property
def amplitudes(self):
return self._amplitudes
@amplitudes.setter
def amplitudes(self, value):
station, a0 = value
self._amplitudes[station] = a0
def peak_to_peak(self, st, t0):
# simulate Wood-Anderson response
st.simulate(paz_remove=None, paz_simulate=self._paz)
# trim waveform to common range
stime, etime = common_range(st)
st.trim(stime, etime)
# get time delta from waveform data
dt = st[0].stats.delta
power = [np.power(tr.data, 2) for tr in st if tr.stats.channel[-1] not
in 'Z3']
if len(power) != 2:
raise ValueError('Wood-Anderson amplitude defintion only valid for '
'two horizontals: {0} given'.format(len(power)))
power_sum = power[0] + power[1]
#
sqH = np.sqrt(power_sum)
# get time array
th = np.arange(0, len(sqH) * dt, dt)
# get maximum peak within pick window
iwin = getsignalwin(th, t0 - stime, self.calc_win)
wapp = np.max(sqH[iwin])
if self.verbose:
print("Determined Wood-Anderson peak-to-peak amplitude: {0} "
"mm".format(wapp))
# check for plot flag (for debugging only)
if self.plot_flag > 1:
st.plot()
f = plt.figure(2)
plt.plot(th, sqH)
plt.plot(th[iwin], sqH[iwin], 'g')
plt.plot([t0, t0], [0, max(sqH)], 'r', linewidth=2)
plt.title(
'Station %s, RMS Horizontal Traces, WA-peak-to-peak=%4.1f mm' \
% (st[0].stats.station, wapp))
plt.xlabel('Time [s]')
plt.ylabel('Displacement [mm]')
plt.show()
raw_input()
plt.close(f)
return wapp
def calc(self):
for a in self.arrivals:
if a.phase not in 'sS':
continue
pick = a.pick_id.get_referred_object()
station = pick.waveform_id.station_code
wf = select_for_phase(self.stream.select(
station=station), a.phase)
if not wf:
if self.verbose:
print(
'WARNING: no waveform data found for station {0}'.format(
station))
continue
delta = degrees2kilometers(a.distance)
onset = pick.time
a0 = self.peak_to_peak(wf, onset)
amplitude = ope.Amplitude(generic_amplitude=a0 * 1e-3)
amplitude.unit = 'm'
amplitude.category = 'point'
amplitude.waveform_id = pick.waveform_id
amplitude.magnitude_hint = self.type
amplitude.pick_id = pick.resource_id
amplitude.type = 'AML'
self.event.amplitudes.append(amplitude)
self.amplitudes = (station, amplitude)
# using standard Gutenberg-Richter relation
# TODO make the ML calculation more flexible by allowing
# use of custom relation functions
magnitude = ope.StationMagnitude(
mag=np.log10(a0) + richter_magnitude_scaling(delta))
magnitude.origin_id = self.origin_id
magnitude.waveform_id = pick.waveform_id
magnitude.amplitude_id = amplitude.resource_id
magnitude.station_magnitude_type = self.type
self.event.station_magnitudes.append(magnitude)
self.magnitudes = (station, magnitude)
class MomentMagnitude(Magnitude):
'''
Method to calculate seismic moment Mo and moment magnitude Mw.
Requires results of class calcsourcespec for calculating plateau w0
and corner frequency fc of source spectrum, respectively. Uses
subfunction calcMoMw.py. Returns modified dictionary of picks including
Dc-value, corner frequency fc, seismic moment Mo and
corresponding moment magntiude Mw.
'''
_props = dict()
def __init__(self, stream, event, vp, Qp, density, verbosity=False,
iplot=False):
super(MomentMagnitude, self).__init__(stream, event, verbosity, iplot)
self._vp = vp
self._Qp = Qp
self._density = density
self._type = 'Mw'
self.calc()
@property
def p_velocity(self):
return self._vp
@property
def p_attenuation(self):
return self._Qp
@property
def rock_density(self):
return self._density
@property
def moment_props(self):
return self._props
@moment_props.setter
def moment_props(self, value):
station, props = value
self._props[station] = props
@property
def seismic_moment(self):
return self._m0
@seismic_moment.setter
def seismic_moment(self, value):
self._m0 = value
def calc(self):
for a in self.arrivals:
if a.phase not in 'pP':
continue
pick = a.pick_id.get_referred_object()
station = pick.waveform_id.station_code
wf = select_for_phase(self.stream.select(
station=station), a.phase)
if not wf:
continue
onset = pick.time
distance = degrees2kilometers(a.distance)
azimuth = a.azimuth
incidence = a.takeoff_angle
w0, fc = calcsourcespec(wf, onset, self.p_velocity, distance,
azimuth,
incidence, self.p_attenuation,
self.plot_flag, self.verbose)
if w0 is None or fc is None:
if self.verbose:
print("WARNING: insufficient frequency information")
continue
wf = select_for_phase(wf, "P")
m0, mw = calcMoMw(wf, w0, self.rock_density, self.p_velocity,
distance, self.verbose)
self.moment_props = (station, dict(w0=w0, fc=fc, Mo=m0))
magnitude = ope.StationMagnitude(mag=mw)
magnitude.origin_id = self.origin_id
magnitude.waveform_id = pick.waveform_id
magnitude.station_magnitude_type = self.type
self.event.station_magnitudes.append(magnitude)
self.magnitudes = (station, magnitude)
def calcMoMw(wfstream, w0, rho, vp, delta, verbosity=False):
'''
Subfunction of run_calcMoMw to calculate individual
seismic moments and corresponding moment magnitudes.
:param: wfstream
:type: `~obspy.core.stream.Stream`
:param: w0, height of plateau of source spectrum
:type: float
:param: rho, rock density [kg/m³]
:type: integer
:param: delta, hypocentral distance [km]
:type: integer
:param: inv, name/path of inventory or dataless-SEED file
:type: string
'''
tr = wfstream[0]
delta = delta * 1000 # hypocentral distance in [m]
if verbosity:
print(
"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(
15) # average radiation pattern of P waves (Aki & Richards, 1980)
freesurf = 2.0 # free surface correction, assuming vertical incidence
Mo = w0 * 4 * np.pi * rho * np.power(vp, 3) * delta / (rP * freesurf)
# Mw = np.log10(Mo * 1e07) * 2 / 3 - 10.7 # after Hanks & Kanamori (1979), defined for [dyn*cm]!
Mw = np.log10(Mo) * 2 / 3 - 6.7 # for metric units
if verbosity:
print(
"calcMoMw: Calculated seismic moment Mo = {0} Nm => Mw = {1:3.1f} ".format(
Mo, Mw))
return Mo, Mw
def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
qp, iplot=0, verbosity=False):
'''
Subfunction to calculate the source spectrum and to derive from that the plateau
(usually called omega0) and the corner frequency assuming Aki's omega-square
source model. Has to be derived from instrument corrected displacement traces,
thus restitution and integration necessary! Integrated traces are rotated
into ray-coordinate system ZNE => LQT using Obspy's rotate modul!
:param: wfstream (corrected for instrument)
:type: `~obspy.core.stream.Stream`
:param: onset, P-phase onset time
:type: float
:param: vp, Vp-wave velocity
:type: float
:param: delta, hypocentral distance [km]
:type: integer
:param: azimuth
:type: integer
:param: incidence
:type: integer
:param: Qp, quality factor for P-waves
:type: integer
:param: iplot, show results (iplot>1) or not (iplot<1)
:type: integer
'''
if verbosity:
print ("Calculating source spectrum ....")
# get Q value
Q, A = qp
dist = delta * 1000 # hypocentral distance in [m]
fc = None
w0 = None
zdat = select_for_phase(wfstream, "P")
dt = zdat[0].stats.delta
freq = zdat[0].stats.sampling_rate
# trim traces to common range (for rotation)
trstart, trend = common_range(wfstream)
wfstream.trim(trstart, trend)
# rotate into LQT (ray-coordindate-) system using Obspy's rotate
# L: P-wave direction
# Q: SV-wave direction
# T: SH-wave direction
LQT = wfstream.rotate('ZNE->LQT', azimuth, incidence)
ldat = LQT.select(component="L")
if len(ldat) == 0:
# if horizontal channels are 2 and 3
# no azimuth information is available and thus no
# rotation is possible!
if verbosity:
print("calcsourcespec: Azimuth information is missing, "
"no rotation of components possible!")
ldat = LQT.select(component="Z")
# integrate to displacement
# unrotated vertical component (for comparison)
inttrz = signal.detrend(integrate.cumtrapz(zdat[0].data, None, dt))
# rotated component Z => L
Ldat = signal.detrend(integrate.cumtrapz(ldat[0].data, None, dt))
# get window after P pulse for
# calculating source spectrum
rel_onset = onset - trstart
impickP = int(rel_onset * freq)
wfzc = Ldat[impickP: len(Ldat) - 1]
# get time array
t = np.arange(0, len(inttrz) * dt, dt)
# calculate spectrum using only first cycles of
# waveform after P onset!
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!")
plotflag = 0
else:
plotflag = 1
index = min([3, len(zc) - 1])
calcwin = (zc[index] - zc[0]) * dt
iwin = getsignalwin(t, rel_onset, calcwin)
xdat = Ldat[iwin]
# fft
fny = freq / 2
l = len(xdat) / freq
# number of fft bins after Bath
n = freq * l
# 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 = dt * np.fft.fft(xdat, N)
Y = abs(y[: N / 2])
L = (N - 1) / freq
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]
# correction for attenuation
wa = 2 * np.pi * F # angular frequency
D = np.exp((wa * dist) / (2 * vp * Q * F ** A))
YYcor = YY.real * D
# get plateau (DC value) and corner frequency
# initial guess of plateau
w0in = np.mean(YYcor[0:100])
# initial guess of corner frequency
# where spectral level reached 50% of flat level
iin = np.where(YYcor >= 0.5 * w0in)
Fcin = F[iin[0][np.size(iin) - 1]]
# use of implicit scipy otimization function
fit = synthsourcespec(F, w0in, Fcin)
[optspecfit, _] = curve_fit(synthsourcespec, F, YYcor, [w0in, Fcin])
w01 = optspecfit[0]
fc1 = optspecfit[1]
if verbosity:
print ("calcsourcespec: Determined w0-value: %e m/Hz, \n"
"Determined corner frequency: %f Hz" % (w01, fc1))
# 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))
if iplot > 1:
f1 = plt.figure()
tLdat = np.arange(0, len(Ldat) * dt, dt)
plt.subplot(2, 1, 1)
# show displacement in mm
p1, = plt.plot(t, np.multiply(inttrz, 1000), 'k')
p2, = plt.plot(tLdat, np.multiply(Ldat, 1000))
plt.legend([p1, p2], ['Displacement', 'Rotated Displacement'])
if plotflag == 1:
plt.plot(t[iwin], np.multiply(xdat, 1000), 'g')
plt.title('Seismogram and P Pulse, Station %s-%s' \
% (zdat[0].stats.station, zdat[0].stats.channel))
else:
plt.title('Seismogram, Station %s-%s' \
% (zdat[0].stats.station, zdat[0].stats.channel))
plt.xlabel('Time since %s' % zdat[0].stats.starttime)
plt.ylabel('Displacement [mm]')
if plotflag == 1:
plt.subplot(2, 1, 2)
p1, = plt.loglog(f, Y.real, 'k')
p2, = plt.loglog(F, YY.real)
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', \
'Fit to Spectrum'])
plt.title('Source Spectrum from P Pulse, w0=%e m/Hz, fc=%6.2f Hz' \
% (w0, fc))
plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude [m/Hz]')
plt.grid()
plt.show()
raw_input()
plt.close(f1)
return w0, fc
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
def fitSourceModel(f, S, fc0, iplot, verbosity=False):
'''
Calculates synthetic source spectrum by varying corner frequency fc.
Returns best approximated plateau omega0 and corner frequency, i.e. with least
common standard deviations.
:param: f, frequencies
:type: array
:param: S, observed source spectrum
:type: array
:param: fc0, initial corner frequency
:type: float
'''
w0 = []
stdw0 = []
fc = []
stdfc = []
STD = []
# get window around initial corner frequency for trials
fcstopl = fc0 - max(1, len(f) / 10)
il = np.argmin(abs(f - fcstopl))
fcstopl = f[il]
fcstopr = fc0 + min(len(f), len(f) / 10)
ir = np.argmin(abs(f - fcstopr))
fcstopr = f[ir]
iF = np.where((f >= fcstopl) & (f <= fcstopr))
# vary corner frequency around initial point
for i in range(il, ir):
FC = f[i]
indexdc = np.where((f > 0) & (f <= FC))
dc = np.mean(S[indexdc])
stddc = np.std(dc - S[indexdc])
w0.append(dc)
stdw0.append(stddc)
fc.append(FC)
# slope
indexfc = np.where((f >= FC) & (f <= fcstopr))
yi = dc / (1 + (f[indexfc] / FC) ** 2)
stdFC = np.std(yi - S[indexfc])
stdfc.append(stdFC)
STD.append(stddc + stdFC)
# get best found w0 anf fc from minimum
if len(STD) > 0:
fc = fc[np.argmin(STD)]
w0 = w0[np.argmin(STD)]
elif len(STD) == 0:
fc = fc0
w0 = max(S)
if verbosity:
print(
"fitSourceModel: best fc: {0} Hz, best w0: {1} m/Hz".format(fc, w0))
if iplot > 1:
plt.figure(iplot)
plt.loglog(f, S, 'k')
plt.loglog([f[0], fc], [w0, w0], 'g')
plt.loglog([fc, fc], [w0 / 100, w0], 'g')
plt.title('Calculated Source Spectrum, Omega0=%e m/Hz, fc=%6.2f Hz' \
% (w0, fc))
plt.xlabel('Frequency [Hz]')
plt.ylabel('Amplitude [m/Hz]')
plt.grid()
plt.figure(iplot + 1)
plt.subplot(311)
plt.plot(f[il:ir], STD, '*')
plt.title('Common Standard Deviations')
plt.xticks([])
plt.subplot(312)
plt.plot(f[il:ir], stdw0, '*')
plt.title('Standard Deviations of w0-Values')
plt.xticks([])
plt.subplot(313)
plt.plot(f[il:ir], stdfc, '*')
plt.title('Standard Deviations of Corner Frequencies')
plt.xlabel('Corner Frequencies [Hz]')
plt.show()
raw_input()
plt.close()
return w0, fc

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pylot/core/io/__init__.py Normal file
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# -*- coding: utf-8 -*-

601
pylot/core/io/data.py Normal file
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import copy
import os
from obspy import read_events
from obspy.core import read, Stream, UTCDateTime
from obspy.core.event import Event
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
class Data(object):
"""
Data container with attributes wfdata holding ~obspy.core.stream.
:type parent: PySide.QtGui.QWidget object, optional
:param parent: A PySide.QtGui.QWidget object utilized when
called by a GUI to display a PySide.QtGui.QMessageBox instead of printing
to standard out.
:type evtdata: ~obspy.core.event.Event object, optional
:param evtdata ~obspy.core.event.Event object containing all derived or
loaded event. Container object holding, e.g. phase arrivals, etc.
"""
def __init__(self, parent=None, evtdata=None):
self._parent = parent
if self.getParent():
self.comp = parent.getComponent()
else:
self.comp = 'Z'
self.wfdata = Stream()
self._new = False
if isinstance(evtdata, Event):
pass
elif isinstance(evtdata, dict):
evt = readPILOTEvent(**evtdata)
evtdata = evt
elif isinstance(evtdata, basestring):
try:
cat = read_events(evtdata)
if len(cat) is not 1:
raise ValueError('ambiguous event information for file: '
'{file}'.format(file=evtdata))
evtdata = cat[0]
except TypeError as e:
if 'Unknown format for file' in e.message:
if 'PHASES' in evtdata:
picks = picksdict_from_pilot(evtdata)
evtdata = Event()
evtdata.picks = picks_from_picksdict(picks)
elif 'LOC' in evtdata:
raise NotImplementedError('PILOT location information '
'read support not yet '
'implemeted.')
else:
raise e
else:
raise e
else: # create an empty Event object
self.setNew()
evtdata = Event()
evtdata.picks = []
self.evtdata = evtdata
self.wforiginal = None
self.cuttimes = None
self.dirty = False
def __str__(self):
return str(self.wfdata)
def __add__(self, other):
assert isinstance(other, Data), "operands must be of same type 'Data'"
if other.isNew() and not self.isNew():
picks_to_add = other.get_evt_data().picks
old_picks = self.get_evt_data().picks
for pick in picks_to_add:
if pick not in old_picks:
old_picks.append(pick)
elif not other.isNew() and self.isNew():
new = other + self
self.evtdata = new.get_evt_data()
elif self.isNew() and other.isNew():
pass
elif self.get_evt_data().get('id') == other.get_evt_data().get('id'):
other.setNew()
return self + other
else:
raise ValueError("both Data objects have differing "
"unique Event identifiers")
return self
def getPicksStr(self):
picks_str = ''
for pick in self.get_evt_data().picks:
picks_str += str(pick) + '\n'
return picks_str
def getParent(self):
"""
:return:
"""
return self._parent
def isNew(self):
"""
:return:
"""
return self._new
def setNew(self):
self._new = True
def getCutTimes(self):
"""
:return:
"""
if self.cuttimes is None:
self.updateCutTimes()
return self.cuttimes
def updateCutTimes(self):
"""
"""
self.cuttimes = full_range(self.getWFData())
def getEventFileName(self):
"""
:return:
"""
ID = self.getID()
# handle forbidden filenames especially on windows systems
return fnConstructor(str(ID))
def exportEvent(self, fnout, fnext='.xml'):
"""
:param fnout:
:param fnext:
:raise KeyError:
"""
from pylot.core.util.defaults import OUTPUTFORMATS
try:
evtformat = OUTPUTFORMATS[fnext]
except KeyError as e:
errmsg = '{0}; selected file extension {1} not ' \
'supported'.format(e, fnext)
raise FormatError(errmsg)
# try exporting event via ObsPy
try:
self.get_evt_data().write(fnout + fnext, format=evtformat)
except KeyError as e:
raise KeyError('''{0} export format
not implemented: {1}'''.format(evtformat, e))
def getComp(self):
"""
:return:
"""
return self.comp
def getID(self):
"""
:return:
"""
try:
return self.evtdata.get('resource_id').id
except:
return None
def filterWFData(self, kwargs):
"""
:param kwargs:
"""
self.getWFData().filter(**kwargs)
self.dirty = True
def setWFData(self, fnames):
"""
:param fnames:
"""
self.wfdata = Stream()
self.wforiginal = None
if fnames is not None:
self.appendWFData(fnames)
else:
return False
self.wforiginal = self.getWFData().copy()
self.dirty = False
return True
def appendWFData(self, fnames):
"""
:param fnames:
"""
assert isinstance(fnames, list), "input parameter 'fnames' is " \
"supposed to be of type 'list' " \
"but is actually" \
" {0}".format(type(fnames))
if self.dirty:
self.resetWFData()
warnmsg = ''
for fname in fnames:
try:
self.wfdata += read(fname)
except TypeError:
try:
self.wfdata += read(fname, format='GSE2')
except Exception as e:
warnmsg += '{0}\n{1}\n'.format(fname, e)
if warnmsg:
warnmsg = 'WARNING: unable to read\n' + warnmsg
print(warnmsg)
def getWFData(self):
"""
:return:
"""
return self.wfdata
def getOriginalWFData(self):
"""
:return:
"""
return self.wforiginal
def resetWFData(self):
"""
"""
self.wfdata = self.getOriginalWFData().copy()
self.dirty = False
def resetPicks(self):
"""
"""
self.get_evt_data().picks = []
def get_evt_data(self):
"""
:return:
"""
return self.evtdata
def setEvtData(self, event):
self.evtdata = event
def applyEVTData(self, data, type='pick', authority_id='rub'):
"""
:param data:
:param type:
:param authority_id:
:raise OverwriteError:
"""
def applyPicks(picks):
"""
Creates ObsPy pick objects and append it to the picks list from the
PyLoT dictionary contain all picks.
:param picks:
:raise OverwriteError: raises an OverwriteError if the picks list is
not empty. The GUI will then ask for a decision.
"""
#firstonset = find_firstonset(picks)
if self.get_evt_data().picks:
raise OverwriteError('Actual picks would be overwritten!')
else:
picks = picks_from_picksdict(picks)
self.get_evt_data().picks = picks
# if 'smi:local' in self.getID() and firstonset:
# fonset_str = firstonset.strftime('%Y_%m_%d_%H_%M_%S')
# ID = ResourceIdentifier('event/' + fonset_str)
# 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
information on the event to the actual data
:param event:
"""
if not self.isNew():
self.setEvtData(event)
else:
# prevent overwriting original pick information
picks = copy.deepcopy(self.get_evt_data().picks)
event = merge_picks(event, picks)
# apply event information from location
self.get_evt_data().update(event)
applydata = {'pick': applyPicks,
'event': applyEvent}
applydata[type](data)
class GenericDataStructure(object):
"""
GenericDataBase type holds all information about the current data-
base working on.
"""
def __init__(self, **kwargs):
self.allowedFields = []
self.expandFields = ['root']
self.dsFields = {}
self.modifyFields(**kwargs)
def modifyFields(self, **kwargs):
"""
:param kwargs:
"""
assert isinstance(kwargs, dict), 'dictionary type object expected'
if not self.extraAllowed():
kwargs = self.updateNotAllowed(kwargs)
for key, value in kwargs.items():
key = str(key).lower()
if value is not None:
if type(value) not in (str, int, float):
for n, val in enumerate(value):
value[n] = str(val)
else:
value = str(value)
try:
self.setFieldValue(key, value)
except KeyError as e:
errmsg = ''
errmsg += 'WARNING:\n'
errmsg += 'unable to set values for datastructure fields\n'
errmsg += '%s; desired value was: %s\n' % (e, value)
print(errmsg)
def isField(self, key):
"""
:param key:
:return:
"""
return key in self.getFields().keys()
def getFieldValue(self, key):
"""
:param key:
:return:
"""
if self.isField(key):
return self.getFields()[key]
else:
return
def setFieldValue(self, key, value):
"""
:param key:
:param value:
:raise KeyError:
"""
if not self.extraAllowed() and key not in self.getAllowed():
raise KeyError
else:
if not self.isField(key):
print('creating new field "%s"' % key)
self.getFields()[key] = value
def getFields(self):
"""
:return:
"""
return self.dsFields
def getExpandFields(self):
"""
:return:
"""
return self.expandFields
def setExpandFields(self, keys):
"""
:param keys:
"""
expandFields = []
for key in keys:
if self.isField(key):
expandFields.append(key)
self.expandFields = expandFields
def getAllowed(self):
"""
:return:
"""
return self.allowedFields
def extraAllowed(self):
"""
:return:
"""
return not self.allowedFields
def updateNotAllowed(self, kwargs):
"""
:param kwargs:
:return:
"""
for key in kwargs:
if key not in self.getAllowed():
kwargs.__delitem__(key)
return kwargs
def hasSuffix(self):
"""
:return:
"""
try:
self.getFieldValue('suffix')
except KeyError:
return False
else:
if self.getFieldValue('suffix'):
return True
return False
def expandDataPath(self):
"""
:return:
"""
expandList = []
for item in self.getExpandFields():
expandList.append(self.getFieldValue(item))
if self.hasSuffix():
expandList.append('*%s' % self.getFieldValue('suffix'))
return os.path.join(*expandList)
def getCatalogName(self):
"""
:return:
"""
return os.path.join(self.getFieldValue('root'), 'catalog.qml')
class PilotDataStructure(GenericDataStructure):
"""
Object containing the data access information for the old PILOT data
structure.
"""
def __init__(self, **fields):
if not fields:
fields = {'database': '2006.01',
'root': '/data/Egelados/EVENT_DATA/LOCAL'}
GenericDataStructure.__init__(self, **fields)
self.setExpandFields(['root', 'database'])
class SeiscompDataStructure(GenericDataStructure):
"""
Dictionary containing the data access information for an SDS data archive:
:param str dataType: Desired data type. Default: ``'waveform'``
:param sdate, edate: Either date string or an instance of
:class:`obspy.core.utcdatetime.UTCDateTime. Default: ``None``
:type sdate, edate: str or UTCDateTime or None
"""
def __init__(self, rootpath='/data/SDS', dataformat='MSEED',
filesuffix=None, **kwargs):
super(GenericDataStructure, self).__init__()
edate = UTCDateTime()
halfyear = UTCDateTime('1970-07-01')
sdate = UTCDateTime(edate - halfyear)
del halfyear
year = ''
if not edate.year == sdate.year:
nyears = edate.year - sdate.year
for yr in range(nyears):
year += '{0:04d},'.format(sdate.year + yr)
year = '{' + year[:-1] + '}'
else:
year = '{0:04d}'.format(sdate.year)
# SDS fields' default values
# definitions from
# http://www.seiscomp3.org/wiki/doc/applications/slarchive/SDS
self.dsFields = {'root': '/data/SDS', 'YEAR': year, 'NET': '??',
'STA': '????', 'CHAN': 'HH?', 'TYPE': 'D', 'LOC': '',
'DAY': '{0:03d}'.format(sdate.julday)
}
self.modifiyFields(**kwargs)
def modifiyFields(self, **kwargs):
"""
:param kwargs:
"""
if kwargs and isinstance(kwargs, dict):
for key, value in kwargs.iteritems():
key = str(key)
if type(value) not in (str, int, float):
for n, val in enumerate(value):
value[n] = str(val)
else:
value = str(value)
try:
self.setFieldValue(key, value)
except KeyError as e:
errmsg = ''
errmsg += 'WARNING:\n'
errmsg += 'unable to set values for SDS fields\n'
errmsg += '%s; desired value was: %s\n' % (e, value)
print(errmsg)
def setFieldValue(self, key, value):
"""
:param key:
:param value:
"""
if self.isField(key):
self.getFields()[key] = value
else:
print('Warning: trying to set value of non-existent field '
'{field}'.format(field=key))
def expandDataPath(self):
"""
:return:
"""
fullChan = '{0}.{1}'.format(self.getFields()['CHAN'], self.getType())
dataPath = os.path.join(self.getFields()['SDSdir'],
self.getFields()['YEAR'],
self.getFields()['NET'],
self.getFields()['STA'],
fullChan,
'*{0}'.format(self.getFields()['DAY']))
return dataPath

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pylot.core.util.errors import ParameterError
class AutoPickParameter(object):
'''
AutoPickParameters is a parameter type object capable to read and/or write
parameter ASCII.
:param fn str: Filename of the input file
Parameters are given for example as follows:
========== ========== =======================================
Name Value Comment
========== ========== =======================================
phl S # phaselabel
ff1 0.1 # freqmin
ff2 0.5 # freqmax
tdet 6.875 # det-window_(s)_for_ar
tpred 2.5 # pred-window_(s)_for_ar
order 4 # order_of_ar
fnoise 0 # noise_level_for_ar
suppp 7 # envelopecoeff
tolt 300 # (s)time around arrival time
f1tpwt 4 # propfact_minfreq_secondtaper
pickwindow 9 # length_of_pick_window
w1 1 # length_of_smoothing_window
w2 0.37 # cf(i-1)*(1+peps)_for_local_min
w3 0.25 # cf(i-1)*(1+peps)_for_local_min
tslope 0.8;2 # slope_det_window_loc_glob
aerr 30;60 # adjusted_error_slope_fitting_loc_glob
tsn 20;5;20;10 # length_signal_window_S/N
proPh Sn # nextprominentphase
========== ========== =======================================
'''
def __init__(self, fnin=None, fnout=None, verbosity=0, **kwargs):
'''
Initialize parameter object:
io content of an ASCII file an form a type consistent dictionary
contain all parameters.
'''
self.__filename = fnin
parFileCont = {}
# io from parsed arguments alternatively
for key, val in kwargs.items():
parFileCont[key] = val
if self.__filename is not None:
inputFile = open(self.__filename, 'r')
else:
return
try:
lines = inputFile.readlines()
for line in lines:
parspl = line.split('\t')[:2]
parFileCont[parspl[0].strip()] = parspl[1]
except IndexError as e:
if verbosity > 0:
self._printParameterError(e)
inputFile.seek(0)
lines = inputFile.readlines()
for line in lines:
if not line.startswith(('#', '%', '\n', ' ')):
parspl = line.split('#')[:2]
parFileCont[parspl[1].strip()] = parspl[0].strip()
for key, value in parFileCont.items():
try:
val = int(value)
except:
try:
val = float(value)
except:
if len(value.split(' ')) > 1:
vallist = value.strip().split(' ')
val = []
for val0 in vallist:
val0 = float(val0)
val.append(val0)
else:
val = str(value.strip())
parFileCont[key] = val
self.__parameter = parFileCont
if fnout:
self.export2File(fnout)
# Human-readable string representation of the object
def __str__(self):
string = ''
string += 'Automated picking parameter:\n\n'
if self.__parameter:
for key, value in self.iteritems():
string += '%s:\t\t%s\n' % (key, value)
else:
string += 'Empty parameter dictionary.'
return string
# String representation of the object
def __repr__(self):
return "AutoPickParameter('%s')" % self.__filename
# Boolean test
def __nonzero__(self):
return self.__parameter
def __getitem__(self, key):
return self.__parameter[key]
def __setitem__(self, key, value):
self.__parameter[key] = value
def __delitem__(self, key):
del self.__parameter[key]
def __iter__(self):
return iter(self.__parameter)
def __len__(self):
return len(self.__parameter.keys())
def iteritems(self):
for key, value in self.__parameter.items():
yield key, value
def hasParam(self, parameter):
if self.__parameter.has_key(parameter):
return True
return False
def get(self, *args):
try:
for param in args:
try:
return self.__getitem__(param)
except KeyError as e:
self._printParameterError(e)
raise ParameterError(e)
except TypeError:
try:
return self.__getitem__(args)
except KeyError as e:
self._printParameterError(e)
raise ParameterError(e)
def setParam(self, **kwargs):
for param, value in kwargs.items():
self.__setitem__(param, value)
# print(self)
@staticmethod
def _printParameterError(errmsg):
print('ParameterError:\n non-existent parameter %s' % errmsg)
def export2File(self, fnout):
fid_out = open(fnout, 'w')
lines = []
for key, value in self.iteritems():
lines.append('{key}\t{value}'.format(key=key, value=value))
fid_out.writelines(lines)
class FilterOptions(object):
'''
FilterOptions is a parameter object type providing Butterworth filter
option parameter for PyLoT. Its easy to access properties helps to manage
file based as well as parameter manipulation within the GUI.
:type filtertype: str, optional
:param filtertype: String containing the desired filtertype For information
about the supported filter types see _`Supported Filter` section .
:type freq: list, optional
:param freq: list of float(s) describing the cutoff limits of the filter
:type order: int, optional
:param order: Integer value describing the order of the desired Butterworth
filter.
.. rubric:: _`Supported Filter`
``'bandpass'``
Butterworth-Bandpass
``'bandstop'``
Butterworth-Bandstop
``'lowpass'``
Butterworth-Lowpass
``'highpass'``
Butterworth-Highpass
'''
def __init__(self, filtertype='bandpass', freq=[2., 5.], order=3,
**kwargs):
self._order = order
self._filtertype = filtertype
self._freq = freq
def __str__(self):
hrs = '''\n\tFilter parameter:\n
Type:\t\t{ftype}\n
Frequencies:\t{freq}\n
Order:\t\t{order}\n
'''.format(ftype=self.getFilterType(),
freq=self.getFreq(),
order=self.getOrder())
return hrs
def __nonzero__(self):
return bool(self.getFilterType())
def parseFilterOptions(self):
if self:
robject = {'type': self.getFilterType(), 'corners': self.getOrder()}
if len(self.getFreq()) > 1:
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]
return robject
return None
def getFreq(self):
return self.__getattribute__('_freq')
def setFreq(self, freq):
self.__setattr__('_freq', freq)
def getOrder(self):
return self.__getattribute__('_order')
def setOrder(self, order):
self.__setattr__('_order', order)
def getFilterType(self):
return self.__getattribute__('_filtertype')
def setFilterType(self, filtertype):
self.__setattr__('_filtertype', filtertype)

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pylot/core/io/location.py Normal file
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from obspy import UTCDateTime
from obspy.core import event as ope
from pylot.core.util.utils import getLogin, getHash
def create_amplitude(pickID, amp, unit, category, cinfo):
'''
:param pickID:
:param amp:
:param unit:
:param category:
:param cinfo:
:return:
'''
amplitude = ope.Amplitude()
amplitude.creation_info = cinfo
amplitude.generic_amplitude = amp
amplitude.unit = ope.AmplitudeUnit(unit)
amplitude.type = ope.AmplitudeCategory(category)
amplitude.pick_id = pickID
return amplitude
def create_arrival(pickresID, cinfo, phase, azimuth=None, dist=None):
'''
create_arrival - function to create an Obspy Arrival
:param pickresID: Resource identifier of the created pick
:type pickresID: :class: `~obspy.core.event.ResourceIdentifier` object
:param cinfo: An ObsPy :class: `~obspy.core.event.CreationInfo` object
holding information on the creation of the returned object
:type cinfo: :class: `~obspy.core.event.CreationInfo` object
:param phase: name of the arrivals seismic phase
:type phase: str
:param azimuth: azimuth between source and receiver
:type azimuth: float or int, optional
:param dist: distance between source and receiver
:type dist: float or int, optional
:return: An ObsPy :class: `~obspy.core.event.Arrival` object
'''
arrival = ope.Arrival()
arrival.creation_info = cinfo
arrival.pick_id = pickresID
arrival.phase = phase
if azimuth is not None:
arrival.azimuth = float(azimuth) if azimuth > -180 else azimuth + 360.
else:
arrival.azimuth = azimuth
arrival.distance = dist
return arrival
def create_creation_info(agency_id=None, creation_time=None, author=None):
'''
:param agency_id:
:param creation_time:
:param author:
:return:
'''
if author is None:
author = getLogin()
if creation_time is None:
creation_time = UTCDateTime()
return ope.CreationInfo(agency_id=agency_id, author=author,
creation_time=creation_time)
def create_event(origintime, cinfo, originloc=None, etype='earthquake',
resID=None, authority_id=None):
'''
create_event - funtion to create an ObsPy Event
:param origintime: the events origintime
:type origintime: :class: `~obspy.core.utcdatetime.UTCDateTime` object
:param cinfo: An ObsPy :class: `~obspy.core.event.CreationInfo` object
holding information on the creation of the returned object
:type cinfo: :class: `~obspy.core.event.CreationInfo` object
:param originloc: tuple containing the location of the origin
(LAT, LON, DEP) affiliated with the event which is created
:type originloc: tuple, list
:param etype: Event type str object. converted via ObsPy to a valid event
type string.
:type etype: str
:param resID: Resource identifier of the created event
:type resID: :class: `~obspy.core.event.ResourceIdentifier` object, str
:param authority_id: name of the institution carrying out the processing
:type authority_id: str
:return: An ObsPy :class: `~obspy.core.event.Event` object
'''
if originloc is not None:
o = create_origin(origintime, cinfo,
originloc[0], originloc[1], originloc[2])
else:
o = None
if not resID:
resID = create_resourceID(origintime, etype, authority_id)
elif isinstance(resID, str):
resID = create_resourceID(origintime, etype, authority_id, resID)
elif not isinstance(resID, ope.ResourceIdentifier):
raise TypeError("unsupported type(resID) for resource identifier "
"generation: %s" % type(resID))
event = ope.Event(resource_id=resID)
event.creation_info = cinfo
event.event_type = etype
if o:
event.origins = [o]
return event
def create_magnitude(originID, cinfo):
'''
create_magnitude - function to create an ObsPy Magnitude object
:param originID:
:type originID:
:param cinfo:
:type cinfo:
:return:
'''
magnitude = ope.Magnitude()
magnitude.creation_info = cinfo
magnitude.origin_id = originID
return magnitude
def create_origin(origintime, cinfo, latitude, longitude, depth):
'''
create_origin - function to create an ObsPy Origin
:param origintime: the origins time of occurence
:type origintime: :class: `~obspy.core.utcdatetime.UTCDateTime` object
:param cinfo:
:type cinfo:
:param latitude: latitude in decimal degree of the origins location
:type latitude: float
:param longitude: longitude in decimal degree of the origins location
:type longitude: float
:param depth: hypocentral depth of the origin
:type depth: float
:return: An ObsPy :class: `~obspy.core.event.Origin` object
'''
assert isinstance(origintime, UTCDateTime), "origintime has to be " \
"a UTCDateTime object, but " \
"actually is of type " \
"'%s'" % type(origintime)
origin = ope.Origin()
origin.time = origintime
origin.creation_info = cinfo
origin.latitude = latitude
origin.longitude = longitude
origin.depth = depth
return origin
def create_pick(origintime, picknum, picktime, eventnum, cinfo, phase, station,
wfseedstr, authority_id):
'''
create_pick - function to create an ObsPy Pick
:param origintime:
:type origintime:
:param picknum: number of the created pick
:type picknum: int
:param picktime:
:type picktime:
:param eventnum: human-readable event identifier
:type eventnum: str
:param cinfo: An ObsPy :class: `~obspy.core.event.CreationInfo` object
holding information on the creation of the returned object
:type cinfo: :class: `~obspy.core.event.CreationInfo` object
:param phase: name of the arrivals seismic phase
:type phase: str
:param station: name of the station at which the seismic phase has been
picked
:type station: str
:param wfseedstr: A SEED formatted string of the form
network.station.location.channel in order to set a referenced waveform
:type wfseedstr: str, SEED formatted
:param authority_id: name of the institution carrying out the processing
:type authority_id: str
:return: An ObsPy :class: `~obspy.core.event.Pick` object
'''
pickID = eventnum + '_' + station.strip() + '/{0:03d}'.format(picknum)
pickresID = create_resourceID(origintime, 'pick', authority_id, pickID)
pick = ope.Pick()
pick.resource_id = pickresID
pick.time = picktime
pick.creation_info = cinfo
pick.phase_hint = phase
pick.waveform_id = ope.ResourceIdentifier(id=wfseedstr, prefix='file:/')
return pick
def create_resourceID(timetohash, restype, authority_id=None, hrstr=None):
'''
:param timetohash:
:type timetohash
:param restype: type of the resource, e.g. 'orig', 'earthquake' ...
:type restype: str
:param authority_id: name of the institution carrying out the processing
:type authority_id: str, optional
:param hrstr:
:type hrstr:
:return:
'''
assert isinstance(timetohash, UTCDateTime), "'timetohash' is not an ObsPy" \
"UTCDateTime object"
hid = getHash(timetohash)
if hrstr is None:
resID = ope.ResourceIdentifier(restype + '/' + hid[0:6])
else:
resID = ope.ResourceIdentifier(restype + '/' + hrstr)
if authority_id is not None:
resID.convertIDToQuakeMLURI(authority_id=authority_id)
return resID

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#!/usr/bin/env python
# -*- 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 pylot.core.io.inputs import AutoPickParameter
from pylot.core.io.location import create_arrival, create_event, \
create_magnitude, create_origin, create_pick
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:
try:
a0 = amplitudes[pick.waveform_id.station_code]
amplitude = ope.Amplitude(generic_amplitude=a0 * 1e-3)
amplitude.unit = 'm'
amplitude.category = 'point'
amplitude.waveform_id = pick.waveform_id
amplitude.magnitude_hint = 'ML'
amplitude.pick_id = pick.resource_id
amplitude.type = 'AML'
amplitude_list.append(amplitude)
except KeyError:
continue
event.amplitudes = amplitude_list
return event
def readPILOTEvent(phasfn=None, locfn=None, authority_id='RUB', **kwargs):
"""
readPILOTEvent - function
Reads Matlab PHASES and LOC files written by Matlab versions of PILOT and
converts the data into an ObsPy Event object which is returned to the
calling program.
:rtype : ~obspy.core.event.Event
:param eventID:
:param authority:
:param kwargs:
:param phasfn: filename of the old PILOT Matlab PHASES file
:param locfn: filename of the old PILOT Matlab LOC file
:return event: event object containing event and phase information
"""
if phasfn is not None and os.path.isfile(phasfn):
phases = sio.loadmat(phasfn)
phasctime = UTCDateTime(os.path.getmtime(phasfn))
phasauthor = getOwner(phasfn)
else:
phases = None
phasctime = None
phasauthor = None
if locfn is not None and os.path.isfile(locfn):
loc = sio.loadmat(locfn)
locctime = UTCDateTime(os.path.getmtime(locfn))
locauthor = getOwner(locfn)
else:
loc = None
locctime = None
locauthor = None
pickcinfo = ope.CreationInfo(agency_id=authority_id,
author=phasauthor,
creation_time=phasctime)
loccinfo = ope.CreationInfo(agency_id=authority_id,
author=locauthor,
creation_time=locctime)
eventNum = str(loc['ID'][0])
# retrieve eventID for the actual database
idsplit = eventNum.split('.')
# retrieve date information
julday = int(idsplit[1])
year = int(idsplit[2])
hour = int(loc['hh'])
minute = int(loc['mm'])
second = int(loc['ss'])
year = four_digits(year)
eventDate = UTCDateTime(year=year, julday=julday, hour=hour,
minute=minute, second=second)
stations = [stat for stat in phases['stat'][0:-1:3]]
lat = float(loc['LAT'])
lon = float(loc['LON'])
dep = float(loc['DEP'])
event = create_event(eventDate, loccinfo, originloc=(lat, lon, dep),
etype='earthquake', resID=eventNum,
authority_id=authority_id)
picks = picksdict_from_pilot(phasfn)
event.picks = picks_from_picksdict(picks, creation_info=pickcinfo)
if event.origins:
origin = event.origins[0]
magnitude = create_magnitude(origin.get('id'), loccinfo)
magnitude.mag = float(loc['Mnet'])
magnitude.magnitude_type = 'Ml'
event.magnitudes.append(magnitude)
return event
def picksdict_from_pilot(fn):
from pylot.core.util.defaults import TIMEERROR_DEFAULTS
picks = dict()
phases_pilot = sio.loadmat(fn)
stations = stations_from_pilot(phases_pilot['stat'])
params = AutoPickParameter(TIMEERROR_DEFAULTS)
timeerrors = dict(P=params.get('timeerrorsP'),
S=params.get('timeerrorsS'))
for n, station in enumerate(stations):
phases = dict()
for onset_name in 'PS':
onset_label = '{0}time'.format(onset_name)
pick = phases_pilot[onset_label][n]
if not pick[0]:
continue
pick = convert_pilot_times(pick)
uncertainty_label = '{0}weight'.format(onset_name.lower())
ierror = phases_pilot[uncertainty_label][0, n]
try:
spe = timeerrors[onset_name][ierror]
except IndexError as e:
print(e.message + '\ntake two times the largest default error value')
spe = timeerrors[onset_name][-1] * 2
phases[onset_name] = dict(mpp=pick, spe=spe, weight=ierror)
picks[station] = phases
return picks
def stations_from_pilot(stat_array):
stations = list()
cur_stat = None
for stat in stat_array:
stat = stat.strip()
if stat == cur_stat:
continue
cur_stat = stat
if stat not in stations:
stations.append(stat)
else:
warnings.warn('station {0} listed at least twice, might corrupt '
'phase times', RuntimeWarning)
return stations
def convert_pilot_times(time_array):
times = [int(time) for time in time_array]
microseconds = int((time_array[-1] - times[-1]) * 1e6)
times.append(microseconds)
return UTCDateTime(*times)
def picksdict_from_obs(fn):
picks = dict()
station_name = str()
for line in open(fn, 'r'):
if line.startswith('#'):
continue
else:
phase_line = line.split()
if not station_name == phase_line[0]:
phase = dict()
station_name = phase_line[0]
phase_name = phase_line[4].upper()
pick = UTCDateTime(phase_line[6] + phase_line[7] + phase_line[8])
phase[phase_name] = dict(mpp=pick, fm=phase_line[5])
picks[station_name] = phase
return picks
def picksdict_from_picks(evt):
"""
Takes an Event object and return the pick dictionary commonly used within
PyLoT
:param evt: Event object contain all available information
:type evt: `~obspy.core.event.Event`
:return: pick dictionary
"""
picks = {}
for pick in evt.picks:
phase = {}
station = pick.waveform_id.station_code
try:
onsets = picks[station]
except KeyError as e:
#print(e)
onsets = {}
mpp = pick.time
spe = pick.time_errors.uncertainty
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'
warnings.warn(msg)
lpp = mpp + spe
epp = mpp - spe
phase['mpp'] = mpp
phase['epp'] = epp
phase['lpp'] = lpp
phase['spe'] = spe
try:
picker = str(pick.method_id)
if picker.startswith('smi:local/'):
picker = picker.split('smi:local/')[1]
phase['picker'] = picker
except IndexError:
pass
onsets[pick.phase_hint] = phase.copy()
picks[station] = onsets.copy()
return picks
def picks_from_picksdict(picks, creation_info=None):
picks_list = list()
for station, onsets in picks.items():
for label, phase in onsets.items():
if not isinstance(phase, dict):
continue
onset = phase['mpp']
pick = ope.Pick()
if creation_info:
pick.creation_info = creation_info
pick.time = onset
error = phase['spe']
pick.time_errors.uncertainty = error
try:
epp = phase['epp']
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)
try:
picker = phase['picker']
except KeyError as e:
warnings.warn(e.message, RuntimeWarning)
picker = 'Unknown'
pick.phase_hint = label
pick.method_id = ope.ResourceIdentifier(id=picker)
pick.waveform_id = ope.WaveformStreamID(station_code=station)
try:
polarity = phase['fm']
if polarity == 'U' or '+':
pick.polarity = 'positive'
elif polarity == 'D' or '-':
pick.polarity = 'negative'
else:
pick.polarity = 'undecidable'
except KeyError as e:
if 'fm' in e.message: # no polarity information found for this phase
pass
else:
raise e
picks_list.append(pick)
return picks_list
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????.???.??')
for evt in evt_list:
if verbosity > 0:
print('Reassessing event {0}'.format(evt))
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
from pylot.core.io.inputs import AutoPickParameter
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 = AutoPickParameter(fn_param, verbosity)
search_base = os.path.join(root_dir, db_dir, event_id)
phases_file = glob.glob(os.path.join(search_base, 'PHASES.mat'))
if not phases_file:
return
if verbosity > 1:
print('Opening PILOT phases file: {fn}'.format(fn=phases_file[0]))
picks_dict = picksdict_from_pilot(phases_file[0])
if verbosity > 0:
print('Dictionary read from PHASES.mat:\n{0}'.format(picks_dict))
datacheck = list()
info = None
for station in picks_dict.keys():
fn_pattern = os.path.join(search_base, '{0}*'.format(station))
try:
st = read(fn_pattern)
except TypeError as e:
if 'Unknown format for file' in e.message:
try:
st = read(fn_pattern, format='GSE2')
except ValueError as e:
if e.message == 'second must be in 0..59':
info = 'A known Error was raised. Please find the list of corrupted files and double-check these files.'
datacheck.append(fn_pattern + ' (time info)\n')
continue
else:
raise ValueError(e.message)
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)
datacheck.append(fn_pattern + ' (no data)\n')
continue
else:
raise e
else:
raise e
for phase in picks_dict[station].keys():
try:
mpp = picks_dict[station][phase]['mpp']
except KeyError as e:
print(e.message, station)
continue
sel_st = select_for_phase(st, phase)
if not sel_st:
msg = 'no waveform data found for station {station}'.format(station=station)
warnings.warn(msg, RuntimeWarning)
continue
stime, etime = full_range(sel_st)
rel_pick = mpp - stime
epp, lpp, spe = earllatepicker(sel_st,
default.get('nfac{0}'.format(phase)),
default.get('tsnrz' if phase == 'P' else 'tsnrh'),
Pick1=rel_pick,
iplot=None,
stealth_mode=True)
if epp is None or lpp is None:
continue
epp = stime + epp
lpp = stime + lpp
min_diff = 3 * st[0].stats.delta
if lpp - mpp < min_diff:
lpp = mpp + min_diff
if mpp - epp < min_diff:
epp = mpp - min_diff
picks_dict[station][phase] = dict(epp=epp, mpp=mpp, lpp=lpp, spe=spe)
if datacheck:
if info:
if verbosity > 0:
print(info + ': {0}'.format(search_base))
fncheck = open(os.path.join(search_base, 'datacheck_list'), 'w')
fncheck.writelines(datacheck)
fncheck.close()
del datacheck
# create Event object for export
evt = ope.Event(resource_id=event_id)
evt.picks = picks_from_picksdict(picks_dict)
# write phase information to file
if not out_dir:
fnout_prefix = os.path.join(root_dir, db_dir, event_id, '{0}.'.format(event_id))
else:
out_dir = os.path.join(out_dir, db_dir)
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
fnout_prefix = os.path.join(out_dir, '{0}.'.format(event_id))
evt.write(fnout_prefix + 'xml', format='QUAKEML')
#evt.write(fnout_prefix + 'cnv', format='VELEST')
def writephases(arrivals, fformat, filename):
"""
Function of methods to write phases to the following standard file
formats used for locating earthquakes:
HYPO71, NLLoc, VELEST, HYPOSAT, and hypoDD
:param: arrivals
:type: dictionary containing all phase information including
station ID, phase, first motion, weight (uncertainty),
....
:param: fformat
:type: string, chosen file format (location routine),
choose between NLLoc, HYPO71, HYPOSAT, VELEST,
HYPOINVERSE, and hypoDD
:param: filename, full path and name of phase file
:type: string
"""
if fformat == 'NLLoc':
print ("Writing phases to %s for NLLoc" % filename)
fid = open("%s" % filename, 'w')
# write header
fid.write('# EQEVENT: Label: EQ001 Loc: X 0.00 Y 0.00 Z 10.00 OT 0.00 \n')
for key in arrivals:
# P onsets
if arrivals[key]['P']:
try:
fm = arrivals[key]['P']['fm']
except KeyError as e:
print(e)
fm = None
if fm == None:
fm = '?'
onset = arrivals[key]['P']['mpp']
year = onset.year
month = onset.month
day = onset.day
hh = onset.hour
mm = onset.minute
ss = onset.second
ms = onset.microsecond
ss_ms = ss + ms / 1000000.0
pweight = 1 # use pick
try:
if arrivals[key]['P']['weight'] >= 4:
pweight = 0 # do not use pick
except KeyError as e:
print(e.message + '; no weight set during processing')
fid.write('%s ? ? ? P %s %d%02d%02d %02d%02d %7.4f GAU 0 0 0 0 %d \n' % (key,
fm,
year,
month,
day,
hh,
mm,
ss_ms,
pweight))
# S onsets
if arrivals[key].has_key('S') and arrivals[key]['S']:
fm = '?'
onset = arrivals[key]['S']['mpp']
year = onset.year
month = onset.month
day = onset.day
hh = onset.hour
mm = onset.minute
ss = onset.second
ms = onset.microsecond
ss_ms = ss + ms / 1000000.0
sweight = 1 # use pick
try:
if arrivals[key]['S']['weight'] >= 4:
sweight = 0 # do not use pick
except KeyError as e:
print(str(e) + '; no weight set during processing')
fid.write('%s ? ? ? S %s %d%02d%02d %02d%02d %7.4f GAU 0 0 0 0 %d \n' % (key,
fm,
year,
month,
day,
hh,
mm,
ss_ms,
sweight))
fid.close()
elif fformat == 'HYPO71':
print ("Writing phases to %s for HYPO71" % filename)
fid = open("%s" % filename, 'w')
# write header
fid.write(' EQ001\n')
for key in arrivals:
if arrivals[key]['P']['weight'] < 4:
Ponset = arrivals[key]['P']['mpp']
Sonset = arrivals[key]['S']['mpp']
pweight = arrivals[key]['P']['weight']
sweight = arrivals[key]['S']['weight']
fm = arrivals[key]['P']['fm']
if fm is None:
fm = '-'
Ao = arrivals[key]['S']['Ao']
if Ao is None:
Ao = ''
else:
Ao = str('%7.2f' % Ao)
year = Ponset.year
if year >= 2000:
year = year - 2000
else:
year = year - 1900
month = Ponset.month
day = Ponset.day
hh = Ponset.hour
mm = Ponset.minute
ss = Ponset.second
ms = Ponset.microsecond
ss_ms = ss + ms / 1000000.0
if pweight < 2:
pstr = 'I'
elif pweight >= 2:
pstr = 'E'
if arrivals[key]['S']['weight'] < 4:
Sss = Sonset.second
Sms = Sonset.microsecond
Sss_ms = Sss + Sms / 1000000.0
Sss_ms = str('%5.02f' % Sss_ms)
if sweight < 2:
sstr = 'I'
elif sweight >= 2:
sstr = 'E'
fid.write('%s%sP%s%d %02d%02d%02d%02d%02d%5.2f %s%sS %d %s\n' % (key,
pstr,
fm,
pweight,
year,
month,
day,
hh,
mm,
ss_ms,
Sss_ms,
sstr,
sweight,
Ao))
else:
fid.write('%s%sP%s%d %02d%02d%02d%02d%02d%5.2f %s\n' % (key,
pstr,
fm,
pweight,
year,
month,
day,
hh,
mm,
ss_ms,
Ao))
fid.close()
def merge_picks(event, picks):
"""
takes an event object and a list of picks and searches for matching
entries by comparing station name and phase_hint and overwrites the time
and time_errors value of the event picks' with those from the picks
without changing the resource identifiers
:param event: `obspy.core.event.Event` object (e.g. from NLLoc output)
:param picks: list of `obspy.core.event.Pick` objects containing the
original time and time_errors values
:return: merged `obspy.core.event.Event` object
"""
for pick in picks:
time = pick.time
err = pick.time_errors
phase = pick.phase_hint
station = pick.waveform_id.station_code
for p in event.picks:
if p.waveform_id.station_code == station and p.phase_hint == phase:
p.time, p.time_errors = time, err
del time, err, phase, station
return event

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

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pylot/core/loc/hsat.py Normal file
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pylot.core.io.phases import writephases
from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
def export(picks, fnout):
'''
Take <picks> dictionary and exports picking data to a NLLOC-obs
<phasefile> without creating an ObsPy event object.
:param picks: picking data dictionary
:type picks: dict
:param fnout: complete path to the exporting obs file
:type fnout: str
'''
# write phases to NLLoc-phase file
writephases(picks, 'HYPO71', fnout)

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pylot/core/loc/nll.py Normal file
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import subprocess
import os
import glob
from obspy import read_events
from pylot.core.io.phases import writephases
from pylot.core.util.utils import getPatternLine, runProgram, which
from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
class NLLocError(EnvironmentError):
pass
def export(picks, fnout):
'''
Take <picks> dictionary and exports picking data to a NLLOC-obs
<phasefile> without creating an ObsPy event object.
:param picks: picking data dictionary
:type picks: dict
:param fnout: complete path to the exporting obs file
:type fnout: str
'''
# write phases to NLLoc-phase file
writephases(picks, 'NLLoc', fnout)
def modify_inputs(ctrfn, root, nllocoutn, phasefn, tttn):
'''
:param ctrfn: name of NLLoc-control file
:type: str
:param root: root path to NLLoc working directory
:type: str
:param nllocoutn: name of NLLoc-location output file
:type: str
:param phasefn: name of NLLoc-input phase file
:type: str
:param tttn: pattern of precalculated NLLoc traveltime tables
:type: str
'''
# For locating the event the NLLoc-control file has to be modified!
# create comment line for NLLoc-control file NLLoc-output file
ctrfile = os.path.join(root, 'run', ctrfn)
nllocout = os.path.join(root, 'loc', nllocoutn)
phasefile = os.path.join(root, 'obs', phasefn)
tttable = os.path.join(root, 'time', 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)
curlocfiles = getPatternLine(ctrfile, 'LOCFILES')
nllfile = open(ctrfile, 'r')
filedata = nllfile.read()
if filedata.find(locfiles) < 0:
# replace old command
filedata = filedata.replace(curlocfiles, locfiles)
nllfile = open(ctrfile, 'w')
nllfile.write(filedata)
nllfile.close()
def locate(fnin):
"""
takes an external program name
:param fnin:
:return:
"""
exe_path = which('NLLoc')
if exe_path is None:
raise NLLocError('NonLinLoc executable not found; check your '
'environment variables')
# locate the event utilizing external NonLinLoc installation
try:
runProgram(exe_path, fnin)
except subprocess.CalledProcessError as e:
raise RuntimeError(e.output)
def read_location(fn):
path, file = os.path.split(fn)
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])
return read_events(fn)[0]
if __name__ == '__main__':
pass

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

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pylot/core/pick/__init__.py Normal file
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# -*- coding: utf-8 -*-
#

882
pylot/core/pick/autopick.py Executable file
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Function to run automated picking algorithms using AIC,
HOS and AR prediction. Uses objects CharFuns and Picker and
function conglomerate utils.
:author: MAGS2 EP3 working group / Ludger Kueperkoch
"""
import matplotlib.pyplot as plt
import numpy as np
from pylot.core.io.inputs import AutoPickParameter
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.utils import checksignallength, checkZ4S, earllatepicker, \
getSNR, fmpicker, checkPonsets, wadaticheck
from pylot.core.util.utils import getPatternLine
from pylot.core.io.data import Data
def autopickevent(data, param):
stations = []
all_onsets = {}
# get some parameters for quality control from
# parameter input file (usually autoPyLoT.in).
wdttolerance = param.get('wdttolerance')
mdttolerance = param.get('mdttolerance')
iplot = param.get('iplot')
apverbose = param.get('apverbose')
for n in range(len(data)):
station = data[n].stats.station
if station not in stations:
stations.append(station)
else:
continue
for station in stations:
topick = data.select(station=station)
all_onsets[station] = autopickstation(topick, param, verbose=apverbose)
# quality control
# median check and jackknife on P-onset times
jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, iplot)
# check S-P times (Wadati)
return wadaticheck(jk_checked_onsets, wdttolerance, iplot)
def autopickstation(wfstream, pickparam, verbose=False):
"""
: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
:type pickparam: AutoPickParameter
:param verbose:
:type verbose: bool
"""
# declaring pickparam variables (only for convenience)
# read your autoPyLoT.in for details!
# special parameters for P picking
algoP = pickparam.get('algoP')
iplot = pickparam.get('iplot')
pstart = pickparam.get('pstart')
pstop = pickparam.get('pstop')
thosmw = pickparam.get('tlta')
tsnrz = pickparam.get('tsnrz')
hosorder = pickparam.get('hosorder')
bpz1 = pickparam.get('bpz1')
bpz2 = pickparam.get('bpz2')
pickwinP = pickparam.get('pickwinP')
tsmoothP = pickparam.get('tsmoothP')
ausP = pickparam.get('ausP')
nfacP = pickparam.get('nfacP')
tpred1z = pickparam.get('tpred1z')
tdet1z = pickparam.get('tdet1z')
Parorder = pickparam.get('Parorder')
addnoise = pickparam.get('addnoise')
Precalcwin = pickparam.get('Precalcwin')
minAICPslope = pickparam.get('minAICPslope')
minAICPSNR = pickparam.get('minAICPSNR')
timeerrorsP = pickparam.get('timeerrorsP')
# special parameters for S picking
algoS = pickparam.get('algoS')
sstart = pickparam.get('sstart')
sstop = pickparam.get('sstop')
bph1 = pickparam.get('bph1')
bph2 = pickparam.get('bph2')
tsnrh = pickparam.get('tsnrh')
pickwinS = pickparam.get('pickwinS')
tpred1h = pickparam.get('tpred1h')
tdet1h = pickparam.get('tdet1h')
tpred2h = pickparam.get('tpred2h')
tdet2h = pickparam.get('tdet2h')
Sarorder = pickparam.get('Sarorder')
aictsmoothS = pickparam.get('aictsmoothS')
tsmoothS = pickparam.get('tsmoothS')
ausS = pickparam.get('ausS')
minAICSslope = pickparam.get('minAICSslope')
minAICSSNR = pickparam.get('minAICSSNR')
Srecalcwin = pickparam.get('Srecalcwin')
nfacS = pickparam.get('nfacS')
timeerrorsS = pickparam.get('timeerrorsS')
# parameters for first-motion determination
minFMSNR = pickparam.get('minFMSNR')
fmpickwin = pickparam.get('fmpickwin')
minfmweight = pickparam.get('minfmweight')
# parameters for checking signal length
minsiglength = pickparam.get('minsiglength')
minpercent = pickparam.get('minpercent')
nfacsl = pickparam.get('noisefactor')
# parameter to check for spuriously picked S onset
zfac = pickparam.get('zfac')
# path to inventory-, dataless- or resp-files
# initialize output
Pweight = 4 # weight for P onset
Sweight = 4 # weight for S onset
FM = 'N' # first motion (polarity)
SNRP = None # signal-to-noise ratio of P onset
SNRPdB = None # signal-to-noise ratio of P onset [dB]
SNRS = None # signal-to-noise ratio of S onset
SNRSdB = None # signal-to-noise ratio of S onset [dB]
mpickP = None # most likely P onset
lpickP = None # latest possible P onset
epickP = None # earliest possible P onset
mpickS = None # most likely S onset
lpickS = None # latest possible S onset
epickS = None # earliest possible S onset
Perror = None # symmetrized picking error P onset
Serror = None # symmetrized picking error S onset
aicSflag = 0
aicPflag = 0
Pflag = 0
Sflag = 0
Pmarker = []
Ao = None # Wood-Anderson peak-to-peak amplitude
picker = 'autoPyLoT' # name of the picking programm
# split components
zdat = wfstream.select(component="Z")
if len(zdat) == 0: # check for other components
zdat = wfstream.select(component="3")
edat = wfstream.select(component="E")
if len(edat) == 0: # check for other components
edat = wfstream.select(component="2")
ndat = wfstream.select(component="N")
if len(ndat) == 0: # check for other components
ndat = wfstream.select(component="1")
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,
data=str(zdat))
if verbose: print(msg)
z_copy = zdat.copy()
# filter and taper data
tr_filt = zdat[0].copy()
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
Lwf = zdat[0].stats.endtime - zdat[0].stats.starttime
Ldiff = Lwf - Lc
if Ldiff < 0:
msg = 'autopickstation: Cutting times are too large for actual ' \
'waveform!\nUsing entire waveform instead!'
if verbose: print(msg)
pstart = 0
pstop = len(zdat[0].data) * zdat[0].stats.delta
cuttimes = [pstart, pstop]
cf1 = None
if algoP == 'HOS':
# calculate HOS-CF using subclass HOScf of class
# CharacteristicFunction
cf1 = HOScf(z_copy, cuttimes, thosmw, hosorder) # instance of HOScf
elif algoP == 'ARZ':
# calculate ARZ-CF using subclass ARZcf of class
# CharcteristicFunction
cf1 = ARZcf(z_copy, cuttimes, tpred1z, Parorder, tdet1z,
addnoise) # instance of ARZcf
##############################################################
# calculate AIC-HOS-CF using subclass AICcf of class
# CharacteristicFunction
# class needs stream object => build it
assert isinstance(cf1, CharacteristicFunction), 'cf2 is not set ' \
'correctly: maybe the algorithm name ({algoP}) is ' \
'corrupted'.format(
algoP=algoP)
tr_aic = tr_filt.copy()
tr_aic.data = cf1.getCF()
z_copy[0].data = tr_aic.data
aiccf = AICcf(z_copy, cuttimes) # instance of AICcf
##############################################################
# get prelimenary onset time from AIC-HOS-CF using subclass AICPicker
# of class AutoPicking
aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, tsmoothP)
##############################################################
if aicpick.getpick() is not None:
# check signal length to detect spuriously picked noise peaks
# use all available components to avoid skipping correct picks
# on vertical traces with weak P coda
z_copy[0].data = tr_filt.data
zne = z_copy
if len(ndat) == 0 or len(edat) == 0:
msg = 'One or more horizontal component(s) missing!\nSignal ' \
'length only checked on vertical component!\n' \
'Decreasing minsiglengh from {0} to ' \
'{1}'.format(minsiglength, minsiglength / 2)
if verbose: print(msg)
Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
minsiglength / 2,
nfacsl, minpercent, iplot)
else:
# filter and taper horizontal traces
trH1_filt = edat.copy()
trH2_filt = ndat.copy()
trH1_filt.filter('bandpass', freqmin=bph1[0],
freqmax=bph1[1],
zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph1[0],
freqmax=bph1[1],
zerophase=False)
trH1_filt.taper(max_percentage=0.05, type='hann')
trH2_filt.taper(max_percentage=0.05, type='hann')
zne += trH1_filt
zne += trH2_filt
Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
minsiglength,
nfacsl, minpercent, iplot)
if Pflag == 1:
# check for spuriously picked S onset
# both horizontal traces needed
if len(ndat) == 0 or len(edat) == 0:
msg = 'One or more horizontal components missing!\n' \
'Skipping control function checkZ4S.'
if verbose: print(msg)
else:
Pflag = checkZ4S(zne, aicpick.getpick(), zfac,
tsnrz[3], iplot)
if Pflag == 0:
Pmarker = 'SinsteadP'
Pweight = 9
else:
Pmarker = 'shortsignallength'
Pweight = 9
##############################################################
# go on with processing if AIC onset passes quality control
if (aicpick.getSlope() >= 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' \
'autopickstation: re-filtering vertical trace ' \
'...'.format(aicpick.getSlope(), aicpick.getSNR())
if verbose: print(msg)
# re-filter waveform with larger bandpass
z_copy = zdat.copy()
tr_filt = zdat[0].copy()
tr_filt.filter('bandpass', freqmin=bpz2[0], freqmax=bpz2[1],
zerophase=False)
tr_filt.taper(max_percentage=0.05, type='hann')
z_copy[0].data = tr_filt.data
#############################################################
# re-calculate CF from re-filtered trace in vicinity of initial
# onset
cuttimes2 = [round(max([aicpick.getpick() - Precalcwin, 0])),
round(min([len(zdat[0].data) * zdat[0].stats.delta,
aicpick.getpick() + Precalcwin]))]
cf2 = None
if algoP == 'HOS':
# calculate HOS-CF using subclass HOScf of class
# CharacteristicFunction
cf2 = HOScf(z_copy, cuttimes2, thosmw,
hosorder) # instance of HOScf
elif algoP == 'ARZ':
# calculate ARZ-CF using subclass ARZcf of class
# CharcteristicFunction
cf2 = ARZcf(z_copy, cuttimes2, tpred1z, Parorder, tdet1z,
addnoise) # instance of ARZcf
##############################################################
# get refined onset time from CF2 using class Picker
assert isinstance(cf2, CharacteristicFunction), 'cf2 is not set ' \
'correctly: maybe the algorithm name ({algoP}) is ' \
'corrupted'.format(
algoP=algoP)
refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP,
aicpick.getpick())
mpickP = refPpick.getpick()
#############################################################
if mpickP is not None:
# quality assessment
# get earliest/latest possible pick and symmetrized uncertainty
[epickP, lpickP, Perror] = earllatepicker(z_copy, nfacP, tsnrz,
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]:
Pweight = 4
##############################################################
# get first motion of P onset
# certain quality required
if Pweight <= minfmweight and SNRP >= minFMSNR:
FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
else:
FM = 'N'
msg = "autopickstation: P-weight: {0}, " \
"SNR: {1}, SNR[dB]: {2}, Polarity: {3}".format(Pweight,
SNRP,
SNRPdB,
FM)
print(msg)
Sflag = 1
else:
msg = 'Bad initial (AIC) P-pick, skipping this onset!\n' \
'AIC-SNR={0}, AIC-Slope={1}counts/s\n' \
'(min. AIC-SNR={2}, ' \
'min. AIC-Slope={3}counts/s)'.format(aicpick.getSNR(),
aicpick.getSlope(),
minAICPSNR,
minAICPslope)
if verbose: print(msg)
Sflag = 0
else:
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:
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
hdat = edat.copy()
hdat += ndat
h_copy = hdat.copy()
# filter and taper data
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[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
elif algoS == 'AR3':
# re-create stream object including all components
hdat = zdat.copy()
hdat += edat
hdat += ndat
h_copy = hdat.copy()
# filter and taper data
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH3_filt = hdat[2].copy()
trH1_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
zerophase=False)
trH3_filt.filter('bandpass', freqmin=bph1[0], freqmax=bph1[1],
zerophase=False)
trH1_filt.taper(max_percentage=0.05, type='hann')
trH2_filt.taper(max_percentage=0.05, type='hann')
trH3_filt.taper(max_percentage=0.05, type='hann')
h_copy[0].data = trH1_filt.data
h_copy[1].data = trH2_filt.data
h_copy[2].data = trH3_filt.data
##############################################################
if algoS == 'ARH':
# calculate ARH-CF using subclass ARHcf of class
# CharcteristicFunction
arhcf1 = ARHcf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h,
addnoise) # instance of ARHcf
elif algoS == 'AR3':
# calculate ARH-CF using subclass AR3cf of class
# CharcteristicFunction
arhcf1 = AR3Ccf(h_copy, cuttimesh, tpred1h, Sarorder, tdet1h,
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 = arhcf1.getCF()
h_copy[0].data = tr_arhaic.data
# calculate ARH-AIC-CF
haiccf = AICcf(h_copy, cuttimesh) # instance of AICcf
##############################################################
# get prelimenary onset time from AIC-HOS-CF using subclass AICPicker
# of class AutoPicking
aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None,
aictsmoothS)
###############################################################
# go on with processing if AIC onset passes quality control
if (aicarhpick.getSlope() >= 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' \
'autopickstation: re-filtering horizontal traces ' \
'...'.format(aicarhpick.getSlope(), aicarhpick.getSNR())
if verbose: print(msg)
# re-calculate CF from re-filtered trace in vicinity of initial
# onset
cuttimesh2 = [round(aicarhpick.getpick() - Srecalcwin),
round(aicarhpick.getpick() + Srecalcwin)]
# re-filter waveform with larger bandpass
h_copy = hdat.copy()
# filter and taper data
if algoS == 'ARH':
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[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
#############################################################
arhcf2 = ARHcf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h,
addnoise) # instance of ARHcf
elif algoS == 'AR3':
trH1_filt = hdat[0].copy()
trH2_filt = hdat[1].copy()
trH3_filt = hdat[2].copy()
trH1_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
zerophase=False)
trH2_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
zerophase=False)
trH3_filt.filter('bandpass', freqmin=bph2[0], freqmax=bph2[1],
zerophase=False)
trH1_filt.taper(max_percentage=0.05, type='hann')
trH2_filt.taper(max_percentage=0.05, type='hann')
trH3_filt.taper(max_percentage=0.05, type='hann')
h_copy[0].data = trH1_filt.data
h_copy[1].data = trH2_filt.data
h_copy[2].data = trH3_filt.data
#############################################################
arhcf2 = AR3Ccf(h_copy, cuttimesh2, tpred2h, Sarorder, tdet2h,
addnoise) # instance of ARHcf
# get refined onset time from CF2 using class Picker
refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS,
tsmoothS, aicarhpick.getpick())
mpickS = refSpick.getpick()
#############################################################
if mpickS is not None:
# quality assessment
# get earliest/latest possible pick and symmetrized uncertainty
h_copy[0].data = trH1_filt.data
[epickS1, lpickS1, Serror1] = earllatepicker(h_copy, nfacS,
tsnrh,
mpickS, iplot)
h_copy[0].data = trH2_filt.data
[epickS2, lpickS2, Serror2] = earllatepicker(h_copy, nfacS,
tsnrh,
mpickS, iplot)
if epickS1 is not None and epickS2 is not None:
if algoS == 'ARH':
# get earliest pick of both earliest possible picks
epick = [epickS1, epickS2]
lpick = [lpickS1, lpickS2]
pickerr = [Serror1, Serror2]
if epickS1 is None and epickS2 is not None:
ipick = 1
elif epickS1 is not None and epickS2 is None:
ipick = 0
elif epickS1 is not None and epickS2 is not None:
ipick = np.argmin([epickS1, epickS2])
elif algoS == 'AR3':
[epickS3, lpickS3, Serror3] = earllatepicker(h_copy,
nfacS,
tsnrh,
mpickS,
iplot)
# get earliest pick of all three picks
epick = [epickS1, epickS2, epickS3]
lpick = [lpickS1, lpickS2, lpickS3]
pickerr = [Serror1, Serror2, Serror3]
if epickS1 is None and epickS2 is not None \
and epickS3 is not None:
ipick = np.argmin([epickS2, epickS3])
elif epickS1 is not None and epickS2 is None \
and epickS3 is not None:
ipick = np.argmin([epickS2, epickS3])
elif epickS1 is not None and epickS2 is not None \
and epickS3 is None:
ipick = np.argmin([epickS1, epickS2])
elif epickS1 is not None and epickS2 is not None \
and epickS3 is not None:
ipick = np.argmin([epickS1, epickS2, epickS3])
epickS = epick[ipick]
lpickS = lpick[ipick]
Serror = pickerr[ipick]
# get SNR
[SNRS, SNRSdB, Snoiselevel] = getSNR(h_copy, tsnrh, mpickS)
# weight S-onset using symmetric error
if Serror <= timeerrorsS[0]:
Sweight = 0
elif timeerrorsS[0] < Serror <= timeerrorsS[1]:
Sweight = 1
elif Perror > timeerrorsS[1] and Serror <= timeerrorsS[2]:
Sweight = 2
elif timeerrorsS[2] < Serror <= timeerrorsS[3]:
Sweight = 3
elif Serror > timeerrorsS[3]:
Sweight = 4
print('autopickstation: S-weight: {0}, SNR: {1}, '
'SNR[dB]: {2}\n'
'################################################'
''.format(Sweight, SNRS, SNRSdB))
################################################################
# 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:
msg = 'Bad initial (AIC) S-pick, skipping this onset!\n' \
'AIC-SNR={0}, AIC-Slope={1}counts/s\n' \
'(min. AIC-SNR={2}, ' \
'min. AIC-Slope={3}counts/s)\n' \
'################################################' \
''.format(aicarhpick.getSNR(),
aicarhpick.getSlope(),
minAICSSNR,
minAICSslope)
if verbose: print(msg)
############################################################
# 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 ' \
'bad P onset, skipping S picking!')
##############################################################
if iplot > 0:
# plot vertical trace
plt.figure()
plt.subplot(3, 1, 1)
tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate,
tr_filt.stats.delta)
# check equal length of arrays, sometimes they are different!?
wfldiff = len(tr_filt.data) - len(tdata)
if wfldiff < 0:
tdata = tdata[0:len(tdata) - abs(wfldiff)]
p1, = plt.plot(tdata, tr_filt.data / max(tr_filt.data), 'k')
if Pweight < 4:
p2, = plt.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()),
'b')
if aicPflag == 1:
p3, = plt.plot(cf2.getTimeArray(),
cf2.getCF() / max(cf2.getCF()), 'm')
p4, = plt.plot([aicpick.getpick(), aicpick.getpick()], [-1, 1],
'r')
plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
[1, 1], 'r')
plt.plot([aicpick.getpick() - 0.5, aicpick.getpick() + 0.5],
[-1, -1], 'r')
p5, = plt.plot([refPpick.getpick(), refPpick.getpick()],
[-1.3, 1.3], 'r', linewidth=2)
plt.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
[1.3, 1.3], 'r', linewidth=2)
plt.plot([refPpick.getpick() - 0.5, refPpick.getpick() + 0.5],
[-1.3, -1.3], 'r', linewidth=2)
plt.plot([lpickP, lpickP], [-1.1, 1.1], 'r--')
plt.plot([epickP, epickP], [-1.1, 1.1], 'r--')
plt.legend([p1, p2, p3, p4, p5],
['Data', 'CF1', 'CF2', 'Initial P Onset',
'Final P Pick'])
plt.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))
else:
plt.legend([p1, p2], ['Data', 'CF1'])
plt.title('%s, P Weight=%d, SNR=None, '
'SNRdB=None' % (tr_filt.stats.channel, Pweight))
else:
plt.title('%s, %s, P Weight=%d' % (tr_filt.stats.station,
tr_filt.stats.channel,
Pweight))
plt.yticks([])
plt.ylim([-1.5, 1.5])
plt.ylabel('Normalized Counts')
plt.suptitle(tr_filt.stats.starttime)
if len(edat[0]) > 1 and len(ndat[0]) > 1 and Sflag == 1:
# plot horizontal traces
plt.subplot(3, 1, 2)
th1data = np.arange(0,
trH1_filt.stats.npts /
trH1_filt.stats.sampling_rate,
trH1_filt.stats.delta)
# check equal length of arrays, sometimes they are different!?
wfldiff = len(trH1_filt.data) - len(th1data)
if wfldiff < 0:
th1data = th1data[0:len(th1data) - abs(wfldiff)]
p21, = plt.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k')
if Pweight < 4:
p22, = plt.plot(arhcf1.getTimeArray(),
arhcf1.getCF() / max(arhcf1.getCF()), 'b')
if aicSflag == 1:
p23, = plt.plot(arhcf2.getTimeArray(),
arhcf2.getCF() / max(arhcf2.getCF()), 'm')
p24, = plt.plot(
[aicarhpick.getpick(), aicarhpick.getpick()],
[-1, 1], 'g')
plt.plot(
[aicarhpick.getpick() - 0.5,
aicarhpick.getpick() + 0.5],
[1, 1], 'g')
plt.plot(
[aicarhpick.getpick() - 0.5,
aicarhpick.getpick() + 0.5],
[-1, -1], 'g')
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()],
[-1.3, 1.3], 'g', linewidth=2)
plt.plot(
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
[1.3, 1.3], 'g', linewidth=2)
plt.plot(
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
[-1.3, -1.3], 'g', linewidth=2)
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
plt.legend([p21, p22, p23, p24, p25],
['Data', 'CF1', 'CF2', 'Initial S Onset',
'Final S Pick'])
plt.title('%s, S Weight=%d, SNR=%7.2f, SNR[dB]=%7.2f' % (
trH1_filt.stats.channel,
Sweight, SNRS, SNRSdB))
else:
plt.legend([p21, p22], ['Data', 'CF1'])
plt.title('%s, S Weight=%d, SNR=None, SNRdB=None' % (
trH1_filt.stats.channel, Sweight))
plt.yticks([])
plt.ylim([-1.5, 1.5])
plt.ylabel('Normalized Counts')
plt.suptitle(trH1_filt.stats.starttime)
plt.subplot(3, 1, 3)
th2data = np.arange(0,
trH2_filt.stats.npts /
trH2_filt.stats.sampling_rate,
trH2_filt.stats.delta)
# check equal length of arrays, sometimes they are different!?
wfldiff = len(trH2_filt.data) - len(th2data)
if wfldiff < 0:
th2data = th2data[0:len(th2data) - abs(wfldiff)]
plt.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k')
if Pweight < 4:
p22, = plt.plot(arhcf1.getTimeArray(),
arhcf1.getCF() / max(arhcf1.getCF()), 'b')
if aicSflag == 1:
p23, = plt.plot(arhcf2.getTimeArray(),
arhcf2.getCF() / max(arhcf2.getCF()), 'm')
p24, = plt.plot(
[aicarhpick.getpick(), aicarhpick.getpick()],
[-1, 1], 'g')
plt.plot(
[aicarhpick.getpick() - 0.5,
aicarhpick.getpick() + 0.5],
[1, 1], 'g')
plt.plot(
[aicarhpick.getpick() - 0.5,
aicarhpick.getpick() + 0.5],
[-1, -1], 'g')
p25, = plt.plot([refSpick.getpick(), refSpick.getpick()],
[-1.3, 1.3], 'g', linewidth=2)
plt.plot(
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
[1.3, 1.3], 'g', linewidth=2)
plt.plot(
[refSpick.getpick() - 0.5, refSpick.getpick() + 0.5],
[-1.3, -1.3], 'g', linewidth=2)
plt.plot([lpickS, lpickS], [-1.1, 1.1], 'g--')
plt.plot([epickS, epickS], [-1.1, 1.1], 'g--')
plt.legend([p21, p22, p23, p24, p25],
['Data', 'CF1', 'CF2', 'Initial S Onset',
'Final S Pick'])
else:
plt.legend([p21, p22], ['Data', 'CF1'])
plt.yticks([])
plt.ylim([-1.5, 1.5])
plt.xlabel('Time [s] after %s' % tr_filt.stats.starttime)
plt.ylabel('Normalized Counts')
plt.title(trH2_filt.stats.channel)
plt.show()
raw_input()
plt.close()
##########################################################################
# calculate "real" onset times
if lpickP is not None and lpickP == mpickP:
lpickP += timeerrorsP[0]
if epickP is not None and epickP == mpickP:
epickP -= timeerrorsP[0]
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
mpickP = zdat[0].stats.starttime + mpickP
else:
# dummy values (start of seismic trace) in order to derive
# theoretical onset times for iteratve picking
lpickP = zdat[0].stats.starttime + timeerrorsP[3]
epickP = zdat[0].stats.starttime - timeerrorsP[3]
mpickP = zdat[0].stats.starttime
if lpickS is not None and lpickS == mpickS:
lpickS += timeerrorsS[0]
if epickS is not None and epickS == mpickS:
epickS -= timeerrorsS[0]
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
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
# create dictionary
# for P phase
ppick = dict(lpp=lpickP, epp=epickP, mpp=mpickP, spe=Perror, snr=SNRP,
snrdb=SNRPdB, weight=Pweight, fm=FM, w0=None, fc=None, Mo=None,
Mw=None, picker=picker, marked=Pmarker)
# add S phase
spick = dict(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
picks = dict(P=ppick, S=spick)
return picks
def iteratepicker(wf, NLLocfile, picks, badpicks, pickparameter):
'''
Repicking of bad onsets. Uses theoretical onset times from NLLoc-location file.
:param wf: waveform, obspy stream object
:param NLLocfile: path/name of NLLoc-location file
:param picks: dictionary of available onset times
:param badpicks: picks to be repicked
:param pickparameter: picking parameters from autoPyLoT-input file
'''
msg = '#######################################################\n' \
'autoPyLoT: Found {0} bad onsets at station(s) {1}, ' \
'starting re-picking them ...'.format(len(badpicks), badpicks)
print(msg)
newpicks = {}
for i in range(0, len(badpicks)):
if len(badpicks[i][0]) > 4:
Ppattern = '%s ? ? ? P' % badpicks[i][0]
elif len(badpicks[i][0]) == 4:
Ppattern = '%s ? ? ? P' % badpicks[i][0]
elif len(badpicks[i][0]) < 4:
Ppattern = '%s ? ? ? P' % badpicks[i][0]
nllocline = getPatternLine(NLLocfile, Ppattern)
res = nllocline.split(None)[16]
# get theoretical P-onset time from residuum
badpicks[i][1] = picks[badpicks[i][0]]['P']['mpp'] - float(res)
# get corresponding waveform stream
msg = '#######################################################\n' \
'iteratepicker: Re-picking station {0}'.format(badpicks[i][0])
print(msg)
wf2pick = wf.select(station=badpicks[i][0])
# modify some picking parameters
pstart_old = pickparameter.get('pstart')
pstop_old = pickparameter.get('pstop')
sstop_old = pickparameter.get('sstop')
pickwinP_old = pickparameter.get('pickwinP')
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)
print(
"iteratepicker: The following picking parameters have been modified for iterative picking:")
print(
"pstart: %fs => %fs" % (pstart_old, pickparameter.get('pstart')))
print(
"pstop: %fs => %fs" % (pstop_old, pickparameter.get('pstop')))
print(
"sstop: %fs => %fs" % (sstop_old, pickparameter.get('sstop')))
print("pickwinP: %fs => %fs" % (
pickwinP_old, pickparameter.get('pickwinP')))
print("Precalcwin: %fs => %fs" % (
Precalcwin_old, pickparameter.get('Precalcwin')))
print("noisefactor: %f => %f" % (
noisefactor_old, pickparameter.get('noisefactor')))
print("zfac: %f => %f" % (zfac_old, pickparameter.get('zfac')))
# repick station
newpicks = autopickstation(wf2pick, pickparameter)
# replace old dictionary with new one
picks[badpicks[i][0]] = newpicks
# reset temporary change of picking parameters
print("iteratepicker: Resetting picking parameters ...")
pickparameter.setParam(pstart=pstart_old)
pickparameter.setParam(pstop=pstop_old)
pickparameter.setParam(sstop=sstop_old)
pickparameter.setParam(pickwinP=pickwinP_old)
pickparameter.setParam(Precalcwin=Precalcwin_old)
pickparameter.setParam(noisefactor=noisefactor_old)
pickparameter.setParam(zfac=zfac_old)
return picks

710
pylot/core/pick/charfuns.py Normal file
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@ -0,0 +1,710 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created Oct/Nov 2014
Implementation of the Characteristic Functions (CF) published and described in:
Kueperkoch, L., Meier, T., Lee, J., Friederich, W., & EGELADOS Working Group, 2010:
Automated determination of P-phase arrival times at regional and local distances
using higher order statistics, Geophys. J. Int., 181, 1159-1170
Kueperkoch, L., Meier, T., Bruestle, A., Lee, J., Friederich, W., & EGELADOS
Working Group, 2012: Automated determination of S-phase arrival times using
autoregressive prediction: application ot local and regional distances, Geophys. J. Int.,
188, 687-702.
:author: MAGS2 EP3 working group
"""
import matplotlib.pyplot as plt
import numpy as np
from obspy.core import Stream
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):
'''
Initialize data type object with information from the original
Seismogram.
:param: data
:type: `~obspy.core.stream.Stream`
:param: cut
:type: tuple
:param: t2
:type: float
:param: order
:type: int
:param: t1
:type: float (optional, only for AR)
:param: fnoise
:type: float (optional, only for AR)
'''
assert isinstance(data, Stream), "%s is not a stream object" % str(data)
self.orig_data = data
self.dt = self.orig_data[0].stats.delta
self.setCut(cut)
self.setTime1(t1)
self.setTime2(t2)
self.setOrder(order)
self.setFnoise(fnoise)
self.setARdetStep(t2)
self.calcCF(self.getDataArray())
self.arpara = np.array([])
self.xpred = np.array([])
self._stealthMode = stealthMode
def __str__(self):
return '''\n\t{name} object:\n
Cut:\t\t{cut}\n
t1:\t{t1}\n
t2:\t{t2}\n
Order:\t\t{order}\n
Fnoise:\t{fnoise}\n
ARdetStep:\t{ardetstep}\n
'''.format(name=type(self).__name__,
cut=self.getCut(),
t1=self.getTime1(),
t2=self.getTime2(),
order=self.getOrder(),
fnoise=self.getFnoise(),
ardetstep=self.getARdetStep[0]())
def getCut(self):
return self.cut
def setCut(self, cut):
self.cut = cut
def getTime1(self):
return self.t1
def setTime1(self, t1):
self.t1 = t1
def getTime2(self):
return self.t2
def setTime2(self, t2):
self.t2 = t2
def getARdetStep(self):
return self.ARdetStep
def setARdetStep(self, t1):
if t1:
self.ARdetStep = []
self.ARdetStep.append(t1 / 4)
self.ARdetStep.append(int(np.ceil(self.getTime2() / self.getIncrement()) / 4))
def getOrder(self):
return self.order
def setOrder(self, order):
self.order = order
def getIncrement(self):
"""
:rtype : int
"""
return self.dt
def getTimeArray(self):
incr = self.getIncrement()
self.TimeArray = np.arange(0, len(self.getCF()) * incr, incr) + self.getCut()[0]
return self.TimeArray
def getFnoise(self):
return self.fnoise
def setFnoise(self, fnoise):
self.fnoise = fnoise
def getCF(self):
return self.cf
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)
till cut[1] (stop time) in order to calculate CF for certain part
only where you expect the signal!
input: cut (tuple) ()
cutting window
'''
if cut is not None:
if len(self.orig_data) == 1:
if self.cut[0] == 0 and self.cut[1] == 0:
start = 0
stop = len(self.orig_data[0])
elif self.cut[0] == 0 and self.cut[1] is not 0:
start = 0
stop = self.cut[1] / self.dt
else:
start = self.cut[0] / self.dt
stop = self.cut[1] / self.dt
zz = self.orig_data.copy()
z1 = zz[0].copy()
zz[0].data = z1.data[int(start):int(stop)]
data = zz
return data
elif len(self.orig_data) == 2:
if self.cut[0] == 0 and self.cut[1] == 0:
start = 0
stop = min([len(self.orig_data[0]), len(self.orig_data[1])])
elif self.cut[0] == 0 and self.cut[1] is not 0:
start = 0
stop = min([self.cut[1] / self.dt, len(self.orig_data[0]),
len(self.orig_data[1])])
else:
start = max([0, self.cut[0] / self.dt])
stop = min([self.cut[1] / self.dt, len(self.orig_data[0]),
len(self.orig_data[1])])
hh = self.orig_data.copy()
h1 = hh[0].copy()
h2 = hh[1].copy()
hh[0].data = h1.data[int(start):int(stop)]
hh[1].data = h2.data[int(start):int(stop)]
data = hh
return data
elif len(self.orig_data) == 3:
if self.cut[0] == 0 and self.cut[1] == 0:
start = 0
stop = min([self.cut[1] / self.dt, len(self.orig_data[0]),
len(self.orig_data[1]), len(self.orig_data[2])])
elif self.cut[0] == 0 and self.cut[1] is not 0:
start = 0
stop = self.cut[1] / self.dt
else:
start = max([0, self.cut[0] / self.dt])
stop = min([self.cut[1] / self.dt, len(self.orig_data[0]),
len(self.orig_data[1]), len(self.orig_data[2])])
hh = self.orig_data.copy()
h1 = hh[0].copy()
h2 = hh[1].copy()
h3 = hh[2].copy()
hh[0].data = h1.data[int(start):int(stop)]
hh[1].data = h2.data[int(start):int(stop)]
hh[2].data = h3.data[int(start):int(stop)]
data = hh
return data
else:
data = self.orig_data.copy()
return data
def calcCF(self, data=None):
self.cf = data
class AICcf(CharacteristicFunction):
'''
Function to calculate the Akaike Information Criterion (AIC) after
Maeda (1985).
:param: data, time series (whether seismogram or CF)
:type: tuple
Output: AIC function
'''
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
datlen = len(xnp)
k = np.arange(1, datlen)
cf = np.zeros(datlen)
cumsumcf = np.cumsum(np.power(xnp, 2))
i = np.where(cumsumcf == 0)
cumsumcf[i] = np.finfo(np.float64).eps
cf[k] = ((k - 1) * np.log(cumsumcf[k] / k) + (datlen - k + 1) *
np.log((cumsumcf[datlen - 1] - cumsumcf[k - 1]) / (datlen - k + 1)))
cf[0] = cf[1]
inf = np.isinf(cf)
ff = np.where(inf == True)
if len(ff) >= 1:
cf[ff] = 0
self.cf = cf - np.mean(cf)
self.xcf = x
class HOScf(CharacteristicFunction):
'''
Function to calculate skewness (statistics of order 3) or kurtosis
(statistics of order 4), using one long moving window, as published
in Kueperkoch et al. (2010).
'''
def calcCF(self, data):
x = self.getDataArray(self.getCut())
xnp = x[0].data
nn = np.isnan(xnp)
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)
# Initialisation
# t2: long term moving window
ilta = int(round(self.getTime2() / self.getIncrement()))
lta = y[0]
lta1 = y1[0]
# moving windows
LTA = np.zeros(len(xnp))
for j in range(0, len(xnp)):
if j < 4:
LTA[j] = 0
elif j <= ilta:
lta = (y[j] + lta * (j - 1)) / j
lta1 = (y1[j] + lta1 * (j - 1)) / j
else:
lta = (y[j] - y[j - ilta]) / ilta + lta
lta1 = (y1[j] - y1[j - ilta]) / ilta + lta1
# define LTA
if self.getOrder() == 3:
LTA[j] = lta / np.power(lta1, 1.5)
elif self.getOrder() == 4:
LTA[j] = lta / np.power(lta1, 2)
nn = np.isnan(LTA)
if len(nn) > 1:
LTA[nn] = 0
self.cf = LTA
self.xcf = x
class ARZcf(CharacteristicFunction):
def calcCF(self, data):
print 'Calculating AR-prediction error from single trace ...'
x = self.getDataArray(self.getCut())
xnp = x[0].data
nn = np.isnan(xnp)
if len(nn) > 1:
xnp[nn] = 0
# some parameters needed
# add noise to time series
xnoise = xnp + np.random.normal(0.0, 1.0, len(xnp)) * self.getFnoise() * max(abs(xnp))
tend = len(xnp)
# Time1: length of AR-determination window [sec]
# Time2: length of AR-prediction window [sec]
ldet = int(round(self.getTime1() / self.getIncrement())) # length of AR-determination window [samples]
lpred = int(np.ceil(self.getTime2() / self.getIncrement())) # length of AR-prediction window [samples]
cf = np.zeros(len(xnp))
loopstep = self.getARdetStep()
arcalci = ldet + self.getOrder() # AR-calculation index
for i in range(ldet + self.getOrder(), tend - lpred - 1):
if i == arcalci:
# determination of AR coefficients
# to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
self.arDetZ(xnoise, self.getOrder(), i - ldet, i)
arcalci = arcalci + loopstep[1]
# AR prediction of waveform using calculated AR coefficients
self.arPredZ(xnp, self.arpara, i + 1, lpred)
# prediction error = CF
cf[i + lpred - 1] = np.sqrt(np.sum(np.power(self.xpred[i:i + lpred - 1] - xnp[i:i + lpred - 1], 2)) / lpred)
nn = np.isnan(cf)
if len(nn) > 1:
cf[nn] = 0
# remove zeros and artefacts
tap = np.hanning(len(cf))
cf = tap * cf
io = np.where(cf == 0)
ino = np.where(cf > 0)
cf[io] = cf[ino[0][0]]
self.cf = cf
self.xcf = x
def arDetZ(self, data, order, rind, ldet):
'''
Function to calculate AR parameters arpara after Thomas Meier (CAU), published
in Kueperkoch et al. (2012). This function solves SLE using the Moore-
Penrose inverse, i.e. the least-squares approach.
:param: data, time series to calculate AR parameters from
:type: array
:param: order, order of AR process
:type: int
:param: rind, first running summation index
:type: int
:param: ldet, length of AR-determination window (=end of summation index)
:type: int
Output: AR parameters arpara
'''
# recursive calculation of data vector (right part of eq. 6.5 in Kueperkoch et al. (2012)
rhs = np.zeros(self.getOrder())
for k in range(0, self.getOrder()):
for i in range(rind, ldet + 1):
ki = k + 1
rhs[k] = rhs[k] + data[i] * data[i - ki]
# recursive calculation of data array (second sum at left part of eq. 6.5 in Kueperkoch et al. 2012)
A = np.zeros((self.getOrder(), self.getOrder()))
for k in range(1, self.getOrder() + 1):
for j in range(1, k + 1):
for i in range(rind, ldet + 1):
ki = k - 1
ji = j - 1
A[ki, ji] = A[ki, ji] + data[i - j] * data[i - k]
A[ji, ki] = A[ki, ji]
# apply Moore-Penrose inverse for SVD yielding the AR-parameters
self.arpara = np.dot(np.linalg.pinv(A), rhs)
def arPredZ(self, data, arpara, rind, lpred):
'''
Function to predict waveform, assuming an autoregressive process of order
p (=size(arpara)), with AR parameters arpara calculated in arDet. After
Thomas Meier (CAU), published in Kueperkoch et al. (2012).
:param: data, time series to be predicted
:type: array
:param: arpara, AR parameters
:type: float
:param: rind, first running summation index
:type: int
:param: lpred, length of prediction window (=end of summation index)
:type: int
Output: predicted waveform z
'''
# be sure of the summation indeces
if rind < len(arpara):
rind = len(arpara)
if rind > len(data) - lpred:
rind = len(data) - lpred
if lpred < 1:
lpred = 1
if lpred > len(data) - 2:
lpred = len(data) - 2
z = np.append(data[0:rind], np.zeros(lpred))
for i in range(rind, rind + lpred):
for j in range(1, len(arpara) + 1):
ji = j - 1
z[i] = z[i] + arpara[ji] * z[i - j]
self.xpred = z
class ARHcf(CharacteristicFunction):
def calcCF(self, data):
print 'Calculating AR-prediction error from both horizontal traces ...'
xnp = self.getDataArray(self.getCut())
n0 = np.isnan(xnp[0].data)
if len(n0) > 1:
xnp[0].data[n0] = 0
n1 = np.isnan(xnp[1].data)
if len(n1) > 1:
xnp[1].data[n1] = 0
# some parameters needed
# add noise to time series
xenoise = xnp[0].data + np.random.normal(0.0, 1.0, len(xnp[0].data)) * self.getFnoise() * max(abs(xnp[0].data))
xnnoise = xnp[1].data + np.random.normal(0.0, 1.0, len(xnp[1].data)) * self.getFnoise() * max(abs(xnp[1].data))
Xnoise = np.array([xenoise.tolist(), xnnoise.tolist()])
tend = len(xnp[0].data)
# Time1: length of AR-determination window [sec]
# Time2: length of AR-prediction window [sec]
ldet = int(round(self.getTime1() / self.getIncrement())) # length of AR-determination window [samples]
lpred = int(np.ceil(self.getTime2() / self.getIncrement())) # length of AR-prediction window [samples]
cf = np.zeros(len(xenoise))
loopstep = self.getARdetStep()
arcalci = lpred + self.getOrder() - 1 # AR-calculation index
# arcalci = ldet + self.getOrder() - 1 #AR-calculation index
for i in range(lpred + self.getOrder() - 1, tend - 2 * lpred + 1):
if i == arcalci:
# determination of AR coefficients
# to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
self.arDetH(Xnoise, self.getOrder(), i - ldet, i)
arcalci = arcalci + loopstep[1]
# AR prediction of waveform using calculated AR coefficients
self.arPredH(xnp, self.arpara, i + 1, lpred)
# 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))
nn = np.isnan(cf)
if len(nn) > 1:
cf[nn] = 0
# remove zeros and artefacts
tap = np.hanning(len(cf))
cf = tap * cf
io = np.where(cf == 0)
ino = np.where(cf > 0)
cf[io] = cf[ino[0][0]]
self.cf = cf
self.xcf = xnp
def arDetH(self, data, order, rind, ldet):
'''
Function to calculate AR parameters arpara after Thomas Meier (CAU), published
in Kueperkoch et al. (2012). This function solves SLE using the Moore-
Penrose inverse, i.e. the least-squares approach. "data" is a structured array.
AR parameters are calculated based on both horizontal components in order
to account for polarization.
:param: data, horizontal component seismograms to calculate AR parameters from
:type: structured array
:param: order, order of AR process
:type: int
:param: rind, first running summation index
:type: int
:param: ldet, length of AR-determination window (=end of summation index)
:type: int
Output: AR parameters arpara
'''
# recursive calculation of data vector (right part of eq. 6.5 in Kueperkoch et al. (2012)
rhs = np.zeros(self.getOrder())
for k in range(0, self.getOrder()):
for i in range(rind, ldet):
rhs[k] = rhs[k] + data[0, i] * data[0, i - k] + data[1, i] * data[1, i - k]
# recursive calculation of data array (second sum at left part of eq. 6.5 in Kueperkoch et al. 2012)
A = np.zeros((4, 4))
for k in range(1, self.getOrder() + 1):
for j in range(1, k + 1):
for i in range(rind, ldet):
ki = k - 1
ji = j - 1
A[ki, ji] = A[ki, ji] + data[0, i - ji] * data[0, i - ki] + data[1, i - ji] * data[1, i - ki]
A[ji, ki] = A[ki, ji]
# apply Moore-Penrose inverse for SVD yielding the AR-parameters
self.arpara = np.dot(np.linalg.pinv(A), rhs)
def arPredH(self, data, arpara, rind, lpred):
'''
Function to predict waveform, assuming an autoregressive process of order
p (=size(arpara)), with AR parameters arpara calculated in arDet. After
Thomas Meier (CAU), published in Kueperkoch et al. (2012).
:param: data, horizontal component seismograms to be predicted
:type: structured array
:param: arpara, AR parameters
:type: float
:param: rind, first running summation index
:type: int
:param: lpred, length of prediction window (=end of summation index)
:type: int
Output: predicted waveform z
:type: structured array
'''
# be sure of the summation indeces
if rind < len(arpara) + 1:
rind = len(arpara) + 1
if rind > len(data[0]) - lpred + 1:
rind = len(data[0]) - lpred + 1
if lpred < 1:
lpred = 1
if lpred > len(data[0]) - 1:
lpred = len(data[0]) - 1
z1 = np.append(data[0][0:rind], np.zeros(lpred))
z2 = np.append(data[1][0:rind], np.zeros(lpred))
for i in range(rind, rind + lpred):
for j in range(1, len(arpara) + 1):
ji = j - 1
z1[i] = z1[i] + arpara[ji] * z1[i - ji]
z2[i] = z2[i] + arpara[ji] * z2[i - ji]
z = np.array([z1.tolist(), z2.tolist()])
self.xpred = z
class AR3Ccf(CharacteristicFunction):
def calcCF(self, data):
print 'Calculating AR-prediction error from all 3 components ...'
xnp = self.getDataArray(self.getCut())
n0 = np.isnan(xnp[0].data)
if len(n0) > 1:
xnp[0].data[n0] = 0
n1 = np.isnan(xnp[1].data)
if len(n1) > 1:
xnp[1].data[n1] = 0
n2 = np.isnan(xnp[2].data)
if len(n2) > 1:
xnp[2].data[n2] = 0
# some parameters needed
# add noise to time series
xenoise = xnp[0].data + np.random.normal(0.0, 1.0, len(xnp[0].data)) * self.getFnoise() * max(abs(xnp[0].data))
xnnoise = xnp[1].data + np.random.normal(0.0, 1.0, len(xnp[1].data)) * self.getFnoise() * max(abs(xnp[1].data))
xznoise = xnp[2].data + np.random.normal(0.0, 1.0, len(xnp[2].data)) * self.getFnoise() * max(abs(xnp[2].data))
Xnoise = np.array([xenoise.tolist(), xnnoise.tolist(), xznoise.tolist()])
tend = len(xnp[0].data)
# Time1: length of AR-determination window [sec]
# Time2: length of AR-prediction window [sec]
ldet = int(round(self.getTime1() / self.getIncrement())) # length of AR-determination window [samples]
lpred = int(np.ceil(self.getTime2() / self.getIncrement())) # length of AR-prediction window [samples]
cf = np.zeros(len(xenoise))
loopstep = self.getARdetStep()
arcalci = ldet + self.getOrder() - 1 # AR-calculation index
for i in range(ldet + self.getOrder() - 1, tend - 2 * lpred + 1):
if i == arcalci:
# determination of AR coefficients
# to speed up calculation, AR-coefficients are calculated only every i+loopstep[1]!
self.arDet3C(Xnoise, self.getOrder(), i - ldet, i)
arcalci = arcalci + loopstep[1]
# AR prediction of waveform using calculated AR coefficients
self.arPred3C(xnp, self.arpara, i + 1, lpred)
# 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) \
+ np.power(self.xpred[2][i:i + lpred] - xnp[2][i:i + lpred], 2)) / (
3 * lpred))
nn = np.isnan(cf)
if len(nn) > 1:
cf[nn] = 0
# remove zeros and artefacts
tap = np.hanning(len(cf))
cf = tap * cf
io = np.where(cf == 0)
ino = np.where(cf > 0)
cf[io] = cf[ino[0][0]]
self.cf = cf
self.xcf = xnp
def arDet3C(self, data, order, rind, ldet):
'''
Function to calculate AR parameters arpara after Thomas Meier (CAU), published
in Kueperkoch et al. (2012). This function solves SLE using the Moore-
Penrose inverse, i.e. the least-squares approach. "data" is a structured array.
AR parameters are calculated based on both horizontal components and vertical
componant.
:param: data, horizontal component seismograms to calculate AR parameters from
:type: structured array
:param: order, order of AR process
:type: int
:param: rind, first running summation index
:type: int
:param: ldet, length of AR-determination window (=end of summation index)
:type: int
Output: AR parameters arpara
'''
# recursive calculation of data vector (right part of eq. 6.5 in Kueperkoch et al. (2012)
rhs = np.zeros(self.getOrder())
for k in range(0, self.getOrder()):
for i in range(rind, ldet):
rhs[k] = rhs[k] + data[0, i] * data[0, i - k] + data[1, i] * data[1, i - k] \
+ data[2, i] * data[2, i - k]
# recursive calculation of data array (second sum at left part of eq. 6.5 in Kueperkoch et al. 2012)
A = np.zeros((4, 4))
for k in range(1, self.getOrder() + 1):
for j in range(1, k + 1):
for i in range(rind, ldet):
ki = k - 1
ji = j - 1
A[ki, ji] = A[ki, ji] + data[0, i - ji] * data[0, i - ki] + data[1, i - ji] * data[1, i - ki] \
+ data[2, i - ji] * data[2, i - ki]
A[ji, ki] = A[ki, ji]
# apply Moore-Penrose inverse for SVD yielding the AR-parameters
self.arpara = np.dot(np.linalg.pinv(A), rhs)
def arPred3C(self, data, arpara, rind, lpred):
'''
Function to predict waveform, assuming an autoregressive process of order
p (=size(arpara)), with AR parameters arpara calculated in arDet3C. After
Thomas Meier (CAU), published in Kueperkoch et al. (2012).
:param: data, horizontal and vertical component seismograms to be predicted
:type: structured array
:param: arpara, AR parameters
:type: float
:param: rind, first running summation index
:type: int
:param: lpred, length of prediction window (=end of summation index)
:type: int
Output: predicted waveform z
:type: structured array
'''
# be sure of the summation indeces
if rind < len(arpara) + 1:
rind = len(arpara) + 1
if rind > len(data[0]) - lpred + 1:
rind = len(data[0]) - lpred + 1
if lpred < 1:
lpred = 1
if lpred > len(data[0]) - 1:
lpred = len(data[0]) - 1
z1 = np.append(data[0][0:rind], np.zeros(lpred))
z2 = np.append(data[1][0:rind], np.zeros(lpred))
z3 = np.append(data[2][0:rind], np.zeros(lpred))
for i in range(rind, rind + lpred):
for j in range(1, len(arpara) + 1):
ji = j - 1
z1[i] = z1[i] + arpara[ji] * z1[i - ji]
z2[i] = z2[i] + arpara[ji] * z2[i - ji]
z3[i] = z3[i] + arpara[ji] * z3[i - ji]
z = np.array([z1.tolist(), z2.tolist(), z3.tolist()])
self.xpred = z

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import copy
import operator
import os
import numpy as np
import glob
import matplotlib.pyplot as plt
from obspy import read_events
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
from pylot.core.util.version import get_git_version as _getVersionString
__version__ = _getVersionString()
__author__ = 'sebastianw'
class Comparison(object):
"""
A Comparison object contains information on the evaluated picks' probability
density function and compares these in terms of building the difference of
compared pick sets. The results can be displayed as histograms showing its
properties.
"""
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)
if len(names) > 2:
raise ValueError('Comparison is only defined for two '
'arguments!')
self._names = names
self._compare = self.compare_picksets()
def __nonzero__(self):
if not len(self.names) == 2 or not self._pdfs:
return False
return True
def get(self, name):
return self._pdfs[name]
@property
def names(self):
return self._names
@names.setter
def names(self, names):
assert isinstance(names, list) and len(names) == 2, 'variable "names"' \
' is either not a' \
' list or its ' \
'length is not 2:' \
'names : {names}'.format(
names=names)
self._names = names
@property
def comparison(self):
return self._compare
@property
def stations(self):
return self.comparison.keys()
@property
def nstations(self):
return len(self.stations)
def compare_picksets(self, type='exp'):
"""
Compare two picksets A and B and return a dictionary compiling the results.
Comparison is carried out with the help of pdf representation of the picks
and a probabilistic approach to the time difference of two onset
measurements.
:param a: filename for pickset A
:type a: str
:param b: filename for pickset B
:type b: str
:return: dictionary containing the resulting comparison pdfs for all picks
:rtype: dict
"""
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)
for station, phases in pdf_a.items():
if station in pdf_b.keys():
compare_pdf = dict()
for phase in phases:
if phase in pdf_b[station].keys():
compare_pdf[phase] = phases[phase] - pdf_b[station][
phase]
if compare_pdf is not None:
compare_pdfs[station] = compare_pdf
return compare_pdfs
def plot(self, stations=None):
if stations is None:
nstations = self.nstations
stations = self.stations
else:
nstations = len(stations)
istations = range(nstations)
fig, axarr = plt.subplots(nstations, 2, sharex='col', sharey='row')
for n in istations:
station = stations[n]
if station not in self.comparison.keys():
continue
compare_pdf = self.comparison[station]
for l, phase in enumerate(compare_pdf.keys()):
axarr[n, l].plot(compare_pdf[phase].axis,
compare_pdf[phase].data)
if n is 0:
axarr[n, l].set_title(phase)
if l is 0:
axann = axarr[n, l].annotate(station, xy=(.05, .5),
xycoords='axes fraction')
bbox_props = dict(boxstyle='round', facecolor='lightgrey',
alpha=.7)
axann.set_bbox(bbox_props)
if n == int(np.median(istations)) and l is 0:
label = 'probability density (qualitative)'
axarr[n, l].set_ylabel(label)
plt.setp([a.get_xticklabels() for a in axarr[0, :]], visible=False)
plt.setp([a.get_yticklabels() for a in axarr[:, 1]], visible=False)
plt.setp([a.get_yticklabels() for a in axarr[:, 0]], visible=False)
plt.show()
def get_all(self, phasename):
pdf_dict = self.comparison
rlist = list()
for phases in pdf_dict.values():
try:
rlist.append(phases[phasename])
except KeyError:
continue
return rlist
def get_array(self, phase, method_name):
method = operator.methodcaller(method_name)
pdf_list = self.get_all(phase)
rarray = map(method, pdf_list)
return np.array(rarray)
def get_expectation_array(self, phase):
return self.get_array(phase, 'expectation')
def get_std_array(self, phase):
return self.get_array(phase, 'standard_deviation')
def hist_expectation(self, phases='all', bins=20, normed=False):
phases.strip()
if phases.find('all') is 0:
phases = 'ps'
phases = phases.upper()
nsp = len(phases)
fig, axarray = plt.subplots(1, nsp, sharey=True)
for n, phase in enumerate(phases):
ax = axarray[n]
data = self.get_expectation_array(phase)
xlims = [min(data), max(data)]
ax.hist(data, range=xlims, bins=bins, normed=normed)
title_str = 'phase: {0}, samples: {1}'.format(phase, len(data))
ax.set_title(title_str)
ax.set_xlabel('expectation [s]')
if n is 0:
ax.set_ylabel('abundance [-]')
plt.setp([a.get_yticklabels() for a in axarray[1:]], visible=False)
plt.show()
def hist_standard_deviation(self, phases='all', bins=20, normed=False):
phases.strip()
if phases.find('all') == 0:
phases = 'ps'
phases = phases.upper()
nsp = len(phases)
fig, axarray = plt.subplots(1, nsp, sharey=True)
for n, phase in enumerate(phases):
ax = axarray[n]
data = self.get_std_array(phase)
xlims = [min(data), max(data)]
ax.hist(data, range=xlims, bins=bins, normed=normed)
title_str = 'phase: {0}, samples: {1}'.format(phase, len(data))
ax.set_title(title_str)
ax.set_xlabel('standard deviation [s]')
if n is 0:
ax.set_ylabel('abundance [-]')
plt.setp([a.get_yticklabels() for a in axarray[1:]], visible=False)
plt.show()
def hist(self, type='std'):
pass
class PDFDictionary(object):
"""
A PDFDictionary is a dictionary like object containing structured data on
the probability density function of seismic phase onsets.
"""
def __init__(self, data):
self._pickdata = data
self._pdfdata = self.generate_pdf_data()
def __nonzero__(self):
if len(self.pick_data) < 1:
return False
else:
return True
def __getitem__(self, item):
return self.pdf_data[item]
@property
def pdf_data(self):
return self._pdfdata
@pdf_data.setter
def pdf_data(self, data):
self._pdfdata = data
@property
def pick_data(self):
return self._pickdata
@pick_data.setter
def pick_data(self, data):
self._pickdata = data
@property
def stations(self):
return self.pick_data.keys()
@property
def nstations(self):
return len(self.stations)
@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]))
def get_all(self, phase):
rlist = list()
for phases in self.pdf_data.values():
try:
rlist.append(phases[phase])
except KeyError:
continue
return rlist
def generate_pdf_data(self, type='exp'):
"""
Returns probabiliy density function dictionary containing the
representation of the actual pick_data.
:param type: type of the returned
`~pylot.core.util.pdf.ProbabilityDensityFunction` object
:type type: str
:return: a dictionary containing the picks represented as pdfs
"""
pdf_picks = copy.deepcopy(self.pick_data)
for station, phases in pdf_picks.items():
for phase, values in phases.items():
if phase not in 'PS':
continue
phases[phase] = ProbabilityDensityFunction.from_pick(
values['epp'],
values['mpp'],
values['lpp'],
type=type)
return pdf_picks
def plot(self, stations=None):
'''
plots the all probability density function for either desired STATIONS
or all available date
:param stations: list of stations to be plotted
:type stations: list
:return: matplotlib figure object containing the plot
'''
assert stations is not None or not isinstance(stations, list), \
'parameter stations should be a list not {0}'.format(type(stations))
if not stations:
nstations = self.nstations
stations = self.stations
else:
nstations = len(stations)
istations = range(nstations)
fig, axarr = plt.subplots(nstations, 2, sharex='col', sharey='row')
hide_labels = True
for n in istations:
station = stations[n]
pdfs = self.pdf_data[station]
for l, phase in enumerate(pdfs.keys()):
try:
axarr[n, l].plot(pdfs[phase].axis, pdfs[phase].data())
if n is 0:
axarr[n, l].set_title(phase)
if l is 0:
axann = axarr[n, l].annotate(station, xy=(.05, .5),
xycoords='axes fraction')
bbox_props = dict(boxstyle='round', facecolor='lightgrey',
alpha=.7)
axann.set_bbox(bbox_props)
if n == int(np.median(istations)) and l is 0:
label = 'probability density (qualitative)'
axarr[n, l].set_ylabel(label)
except IndexError as e:
print('trying aligned plotting\n{0}'.format(e))
hide_labels = False
axarr[l].plot(pdfs[phase].axis, pdfs[phase].data())
axarr[l].set_title(phase)
if l is 0:
axann = axarr[l].annotate(station, xy=(.05, .5),
xycoords='axes fraction')
bbox_props = dict(boxstyle='round', facecolor='lightgrey',
alpha=.7)
axann.set_bbox(bbox_props)
if hide_labels:
plt.setp([a.get_xticklabels() for a in axarr[0, :]], visible=False)
plt.setp([a.get_yticklabels() for a in axarr[:, 1]], visible=False)
plt.setp([a.get_yticklabels() for a in axarr[:, 0]], visible=False)
return fig
class PDFstatistics(object):
"""
This object can be used to get various statistic values from probabillity density functions.
Takes a path as argument.
"""
def __init__(self, directory):
"""Initiates some values needed when dealing with pdfs later"""
self._rootdir = directory
self._evtlist = list()
self._rphase = None
self.make_fnlist()
def make_fnlist(self, fn_pattern='*.xml'):
"""
Takes a file pattern and searches for that recursively in the set path for the object.
:param fn_pattern: A pattern that can identify all datafiles. Default Value = '*.xml'
:type fn_pattern: string
:return: creates a list of events saved in the PDFstatistics object.
"""
evtlist = list()
for root, _, files in os.walk(self.root):
for file in files:
if file.endswith(fn_pattern[1:]):
evtlist.append(os.path.join(root, file))
self._evtlist = evtlist
def __iter__(self):
for evt in self._evtlist:
yield PDFDictionary.from_quakeml(evt)
def __getitem__(self, item):
evt = find_in_list(self._evtlist, item)
if evt:
return PDFDictionary.from_quakeml(evt)
return None
@property
def root(self):
return self._rootdir
@root.setter
def root(self, value):
if os.path.exists(value):
self._rootdir = value
else:
raise ValueError("path doesn't exist: %s" % value)
@property
def curphase(self):
"""
return the current phase type of interest
:return: current phase
"""
return self._rphase
@curphase.setter
def curphase(self, type):
"""
setter method for property curphase
:param type: specify the phase type of interest
:type type: string ('p' or 's')
:return: -
"""
if type.upper() not in 'PS':
raise ValueError("phase type must be either 'P' or 'S'!")
else:
self._rphase = type.upper()
def get(self, property='std', value=None):
"""
takes a property str and a probability value and returns all
property's values for the current phase of interest
:func:`self.curphase`
:param property: property name (default: 'std')
:type property: str
:param value: probability value :math:\alpha
:type value: float
:return: list containing all property's values
"""
assert isinstance(self.curphase,
str), 'phase has to be set before being ' \
'able to iterate over items...'
rlist = []
method_options = dict(STD='standard_deviation',
Q='quantile',
QD='quantile_distance',
QDF='quantile_dist_frac')
# create method caller for easy mapping
if property.upper() == 'STD':
method = operator.methodcaller(method_options[property.upper()])
elif value is not None:
try:
method = operator.methodcaller(method_options[property.upper()],
value)
except KeyError:
raise KeyError('unknwon property: {0}'.format(property.upper()))
else:
raise ValueError("for call to method {0} value has to be "
"defined but is 'None' ".format(method_options[
property.upper()]))
for pdf_dict in self:
# create worklist
wlist = pdf_dict.get_all(self.curphase)
# map method calls to object in worklist
rlist += map(method, wlist)
return rlist
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.
:param array: List of values.
:type array: list
:param out_dir: Path to save file to including file name.
:type out_dir: str
:return: Saves a file at given output directory.
"""
fid = open(os.path.join(out_dir), 'w')
for val in array:
fid.write(str(val)+'\n')
fid.close()
def main():
root_dir ='/home/sebastianp/Codetesting/xmls/'
Insheim = PDFstatistics(root_dir)
Insheim.curphase = 'p'
qdlist = Insheim.get('qdf', 0.2)
print qdlist
if __name__ == "__main__":
import cProfile
pr = cProfile.Profile()
pr.enable()
main()
pr.disable()
# after your program ends
pr.print_stats(sort="calls")

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# -*- coding: utf-8 -*-
"""
Created Dec 2014 to Feb 2015
Implementation of the automated picking algorithms published and described in:
Kueperkoch, L., Meier, T., Lee, J., Friederich, W., & Egelados Working Group, 2010:
Automated determination of P-phase arrival times at regional and local distances
using higher order statistics, Geophys. J. Int., 181, 1159-1170
Kueperkoch, L., Meier, T., Bruestle, A., Lee, J., Friederich, W., & Egelados
Working Group, 2012: Automated determination of S-phase arrival times using
autoregressive prediction: application ot local and regional distances, Geophys. J. Int.,
188, 687-702.
The picks with the above described algorithms are assumed to be the most likely picks.
For each most likely pick the corresponding earliest and latest possible picks are
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
class AutoPicker(object):
'''
Superclass of different, automated picking algorithms applied on a CF determined
using AIC, HOS, or AR prediction.
'''
warnings.simplefilter('ignore')
def __init__(self, cf, TSNR, PickWindow, iplot=None, aus=None, Tsmooth=None, Pick1=None):
'''
:param: cf, characteristic function, on which the picking algorithm is applied
:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
:param: TSNR, length of time windows around pick used to determine SNR [s]
:type: tuple (T_noise, T_gap, T_signal)
:param: PickWindow, length of pick window [s]
:type: float
:param: iplot, no. of figure window for plotting interims results
:type: integer
:param: aus ("artificial uplift of samples"), find local minimum at i if aic(i-1)*(1+aus) >= aic(i)
:type: float
:param: Tsmooth, length of moving smoothing window to calculate smoothed CF [s]
:type: float
:param: Pick1, initial (prelimenary) onset time, starting point for PragPicker and
EarlLatePicker
:type: float
'''
assert isinstance(cf, CharacteristicFunction), "%s is not a CharacteristicFunction object" % str(cf)
self.cf = cf.getCF()
self.Tcf = cf.getTimeArray()
self.Data = cf.getXCF()
self.dt = cf.getIncrement()
self.setTSNR(TSNR)
self.setPickWindow(PickWindow)
self.setiplot(iplot)
self.setaus(aus)
self.setTsmooth(Tsmooth)
self.setpick1(Pick1)
self.calcPick()
def __str__(self):
return '''\n\t{name} object:\n
TSNR:\t\t\t{TSNR}\n
PickWindow:\t{PickWindow}\n
aus:\t{aus}\n
Tsmooth:\t{Tsmooth}\n
Pick1:\t{Pick1}\n
'''.format(name=type(self).__name__,
TSNR=self.getTSNR(),
PickWindow=self.getPickWindow(),
aus=self.getaus(),
Tsmooth=self.getTsmooth(),
Pick1=self.getpick1())
def getTSNR(self):
return self.TSNR
def setTSNR(self, TSNR):
self.TSNR = TSNR
def getPickWindow(self):
return self.PickWindow
def setPickWindow(self, PickWindow):
self.PickWindow = PickWindow
def getaus(self):
return self.aus
def setaus(self, aus):
self.aus = aus
def setTsmooth(self, Tsmooth):
self.Tsmooth = Tsmooth
def getTsmooth(self):
return self.Tsmooth
def getpick(self):
return self.Pick
def getSNR(self):
return self.SNR
def getSlope(self):
return self.slope
def getiplot(self):
return self.iplot
def setiplot(self, iplot):
self.iplot = iplot
def getpick1(self):
return self.Pick1
def setpick1(self, Pick1):
self.Pick1 = Pick1
def calcPick(self):
self.Pick = None
class AICPicker(AutoPicker):
'''
Method to derive the onset time of an arriving phase based on CF
derived from AIC. In order to get an impression of the quality of this inital pick,
a quality assessment is applied based on SNR and slope determination derived from the CF,
from which the AIC has been calculated.
'''
def calcPick(self):
print('AICPicker: Get initial onset time (pick) from AIC-CF ...')
self.Pick = None
self.slope = None
self.SNR = None
# find NaN's
nn = np.isnan(self.cf)
if len(nn) > 1:
self.cf[nn] = 0
# taper AIC-CF to get rid off side maxima
tap = np.hanning(len(self.cf))
aic = tap * self.cf + max(abs(self.cf))
# smooth AIC-CF
ismooth = int(round(self.Tsmooth / self.dt))
aicsmooth = np.zeros(len(aic))
if len(aic) < ismooth:
print('AICPicker: Tsmooth larger than CF!')
return
else:
for i in range(1, len(aic)):
if i > ismooth:
ii1 = i - ismooth
aicsmooth[i] = aicsmooth[i - 1] + (aic[i] - aic[ii1]) / ismooth
else:
aicsmooth[i] = np.mean(aic[1: i])
# remove offset
offset = abs(min(aic) - min(aicsmooth))
aicsmooth = aicsmooth - offset
# get maximum of 1st derivative of AIC-CF (more stable!) as starting point
diffcf = np.diff(aicsmooth)
# find NaN's
nn = np.isnan(diffcf)
if len(nn) > 1:
diffcf[nn] = 0
# taper CF to get rid off side maxima
tap = np.hanning(len(diffcf))
diffcf = tap * diffcf * max(abs(aicsmooth))
icfmax = np.argmax(diffcf)
# find minimum in AIC-CF front of maximum
lpickwindow = int(round(self.PickWindow / self.dt))
for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
if aicsmooth[i - 1] >= aicsmooth[i]:
self.Pick = self.Tcf[i]
break
# if no minimum could be found:
# search in 1st derivative of AIC-CF
if self.Pick is None:
for i in range(icfmax - 1, max([icfmax - lpickwindow, 2]), -1):
if diffcf[i - 1] >= diffcf[i]:
self.Pick = self.Tcf[i]
break
# quality assessment using SNR and slope from CF
if self.Pick is not None:
# get noise window
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
# get signal window
isignal = getsignalwin(self.Tcf, self.Pick, self.TSNR[2])
# calculate SNR from CF
self.SNR = max(abs(aic[isignal] - np.mean(aic[isignal]))) / \
max(abs(aic[inoise] - np.mean(aic[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))
# 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])
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:
p = plt.figure(self.iplot)
x = self.Data[0].data
p1, = plt.plot(self.Tcf, x / max(x), 'k')
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.yticks([])
plt.title(self.Data[0].stats.station)
plt.show()
raw_input()
plt.close(p)
return
islope = islope[0][0:imax]
dataslope = self.Data[0].data[islope]
# 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]:
print('AICPicker: Negative slope, bad onset skipped!')
return
self.slope = 1 / tslope * (datafit[len(dataslope) - 1] - datafit[0])
else:
self.SNR = None
self.slope = None
if self.iplot > 1:
p = plt.figure(self.iplot)
x = self.Data[0].data
p1, = plt.plot(self.Tcf, x / max(x), 'k')
p2, = plt.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r')
if self.Pick is not None:
p3, = plt.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2)
plt.legend([p1, p2, p3], ['(HOS-/AR-) Data', 'Smoothed AIC-CF', 'AIC-Pick'])
else:
plt.legend([p1, p2], ['(HOS-/AR-) Data', 'Smoothed AIC-CF'])
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.yticks([])
plt.title(self.Data[0].stats.station)
if self.Pick is not None:
plt.figure(self.iplot + 1)
p11, = plt.plot(self.Tcf, x, 'k')
p12, = plt.plot(self.Tcf[inoise], self.Data[0].data[inoise])
p13, = plt.plot(self.Tcf[isignal], self.Data[0].data[isignal], 'r')
p14, = plt.plot(self.Tcf[islope], dataslope, 'g--')
p15, = plt.plot(self.Tcf[islope], datafit, 'g', linewidth=2)
plt.legend([p11, p12, p13, p14, p15],
['Data', 'Noise Window', 'Signal Window', 'Slope Window', 'Slope'],
loc='best')
plt.title('Station %s, SNR=%7.2f, Slope= %12.2f counts/s' % (self.Data[0].stats.station,
self.SNR, self.slope))
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.ylabel('Counts')
plt.yticks([])
plt.show()
raw_input()
plt.close(p)
if self.Pick == None:
print('AICPicker: Could not find minimum, picking window too short?')
class PragPicker(AutoPicker):
'''
Method of pragmatic picking exploiting information given by CF.
'''
def calcPick(self):
if self.getpick1() is not None:
print('PragPicker: Get most likely pick from HOS- or AR-CF using pragmatic picking algorithm ...')
self.Pick = None
self.SNR = None
self.slope = None
pickflag = 0
# smooth CF
ismooth = int(round(self.Tsmooth / self.dt))
cfsmooth = np.zeros(len(self.cf))
if len(self.cf) < ismooth:
print('PragPicker: Tsmooth larger than CF!')
return
else:
for i in range(1, len(self.cf)):
if i > ismooth:
ii1 = i - ismooth
cfsmooth[i] = cfsmooth[i - 1] + (self.cf[i] - self.cf[ii1]) / ismooth
else:
cfsmooth[i] = np.mean(self.cf[1: i])
# select picking window
# which is centered around tpick1
ipick = np.where((self.Tcf >= self.getpick1() - self.PickWindow / 2) \
& (self.Tcf <= self.getpick1() + self.PickWindow / 2))
cfipick = self.cf[ipick] - np.mean(self.cf[ipick])
Tcfpick = self.Tcf[ipick]
cfsmoothipick = cfsmooth[ipick] - np.mean(self.cf[ipick])
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
# prominent trend: decrease aus
# flat: use given aus
cfdiff = np.diff(cfipick)
i0diff = np.where(cfdiff > 0)
cfdiff = cfdiff[i0diff]
minaus = min(cfdiff * (1 + self.aus))
aus1 = max([minaus, self.aus])
# at first we look to the right until the end of the pick window is reached
flagpick_r = 0
flagpick_l = 0
cfpick_r = 0
cfpick_l = 0
lpickwindow = int(round(self.PickWindow / self.dt))
for i in range(max(np.insert(ipick, 0, 2)), min([ipick1 + lpickwindow + 1, len(self.cf) - 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_r = self.Tcf[i]
self.Pick = pick_r
flagpick_l = 1
cfpick_r = self.cf[i]
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
# now decide which pick: left or right?
if flagpick_l > 0 and flagpick_r > 0 and cfpick_l <= 3 * cfpick_r:
self.Pick = pick_l
pickflag = 1
elif flagpick_l > 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
self.Pick = pick_r
pickflag = 1
elif flagpick_l == 0 and flagpick_r > 0 and cfpick_l >= cfpick_r:
self.Pick = pick_l
pickflag = 1
else:
print('PragPicker: Could not find reliable onset!')
self.Pick = None
pickflag = 0
if self.getiplot() > 1:
p = plt.figure(self.getiplot())
p1, = plt.plot(Tcfpick, cfipick, 'k')
p2, = plt.plot(Tcfpick, cfsmoothipick, 'r')
if pickflag > 0:
p3, = plt.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2)
plt.legend([p1, p2, p3], ['CF', 'Smoothed CF', 'Pick'])
plt.xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
plt.yticks([])
plt.title(self.Data[0].stats.station)
plt.show()
raw_input()
plt.close(p)
else:
print('PragPicker: No initial onset time given! Check input!')
self.Pick = None
return

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# -*- coding: utf-8 -*-
"""
Created Mar/Apr 2015
Collection of helpful functions for manual and automatic picking.
:author: Ludger Kueperkoch / MAGS2 EP3 working group
"""
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, stealth_mode=False):
'''
Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
as reasonable uncertainties. Latest possible pick is based on noise level,
earliest possible pick is half a signal wavelength in front of most likely
pick given by PragPicker or manually set by analyst. Most likely pick
(initial pick Pick1) must be given.
:param: X, time series (seismogram)
:type: `~obspy.core.stream.Stream`
:param: nfac (noise factor), nfac times noise level to calculate latest possible pick
:type: int
:param: TSNR, length of time windows around pick used to determine SNR [s]
:type: tuple (T_noise, T_gap, T_signal)
:param: Pick1, initial (most likely) onset time, starting point for earllatepicker
:type: float
:param: iplot, if given, results are plotted in figure(iplot)
:type: int
'''
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
LPick = None
EPick = None
PickError = None
if stealth_mode is False:
print('earllatepicker: Get earliest and latest possible pick'
' relative to most likely pick ...')
x = X[0].data
t = np.arange(0, X[0].stats.npts / X[0].stats.sampling_rate,
X[0].stats.delta)
inoise = getnoisewin(t, Pick1, TSNR[0], TSNR[1])
# get signal window
isignal = getsignalwin(t, Pick1, TSNR[2])
# remove mean
x = x - np.mean(x[inoise])
# calculate noise level
nlevel = np.sqrt(np.mean(np.square(x[inoise]))) * nfac
# 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 stealth_mode is False:
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'))
LPick = t[isignal][il]
# get earliest possible pick
EPick = np.nan;
count = 0
pis = isignal
# if EPick stays NaN the signal window size will be doubled
while np.isnan(EPick):
if count > 0:
if stealth_mode is False:
print("\nearllatepicker: Doubled signal window size %s time(s) "
"because of NaN for earliest pick." % count)
isigDoubleWinStart = pis[-1] + 1
isignalDoubleWin = np.arange(isigDoubleWinStart,
isigDoubleWinStart + len(pis))
if (isigDoubleWinStart + len(pis)) < X[0].data.size:
pis = np.concatenate((pis, isignalDoubleWin))
else:
if stealth_mode is False:
print("Could not double signal window. Index out of bounds.")
break
count += 1
# determine all zero crossings in signal window (demeaned)
zc = crossings_nonzero_all(x[pis] - x[pis].mean())
# calculate mean half period T0 of signal as the average of the
T0 = np.mean(np.diff(zc)) * X[0].stats.delta # this is half wave length!
EPick = Pick1 - T0 # half wavelength as suggested by Diehl et al.
# get symmetric pick error as mean from earliest and latest possible pick
# by weighting latest possible pick two times earliest possible pick
diffti_tl = LPick - Pick1
diffti_te = Pick1 - EPick
PickError = symmetrize_error(diffti_te, diffti_tl)
if iplot > 1:
p = plt.figure(iplot)
p1, = plt.plot(t, x, 'k')
p2, = plt.plot(t[inoise], x[inoise])
p3, = plt.plot(t[isignal], x[isignal], 'r')
p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
p5, = plt.plot(t[isignal[zc]], np.zeros(len(zc)), '*g',
markersize=14)
plt.legend([p1, p2, p3, p4, p5],
['Data', 'Noise Window', 'Signal Window', 'Noise Level',
'Zero Crossings'],
loc='best')
plt.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
plt.plot([Pick1, Pick1], [max(x), -max(x)], 'b', linewidth=2)
plt.plot([LPick, LPick], [max(x) / 2, -max(x) / 2], '--k')
plt.plot([EPick, EPick], [max(x) / 2, -max(x) / 2], '--k')
plt.plot([Pick1 + PickError, Pick1 + PickError],
[max(x) / 2, -max(x) / 2], 'r--')
plt.plot([Pick1 - PickError, Pick1 - PickError],
[max(x) / 2, -max(x) / 2], 'r--')
plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
plt.yticks([])
plt.title(
'Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' %
X[0].stats.station)
plt.show()
raw_input()
plt.close(p)
return EPick, LPick, PickError
def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=None):
'''
Function to derive first motion (polarity) of given phase onset Pick.
Calculation is based on zero crossings determined within time window pickwin
after given onset time.
:param: Xraw, unfiltered time series (seismogram)
:type: `~obspy.core.stream.Stream`
:param: Xfilt, filtered time series (seismogram)
:type: `~obspy.core.stream.Stream`
:param: pickwin, time window after onset Pick within zero crossings are calculated
:type: float
:param: Pick, initial (most likely) onset time, starting point for fmpicker
:type: float
:param: iplot, if given, results are plotted in figure(iplot)
:type: int
'''
warnings.simplefilter('ignore', np.RankWarning)
assert isinstance(Xraw, Stream), "%s is not a stream object" % str(Xraw)
assert isinstance(Xfilt, Stream), "%s is not a stream object" % str(Xfilt)
FM = None
if Pick is not None:
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))
# remove mean
xraw[ipick] = xraw[ipick] - np.mean(xraw[ipick])
xfilt[ipick] = xfilt[ipick] - np.mean(xfilt[ipick])
# get zero crossings after most likely pick
# initial onset is assumed to be the first zero crossing
# first from unfiltered trace
zc1 = []
zc1.append(Pick)
index1 = []
i = 0
for j in range(ipick[0][1], ipick[0][len(t[ipick]) - 1]):
i = i + 1
if xraw[j - 1] <= 0 <= xraw[j]:
zc1.append(t[ipick][i])
index1.append(i)
elif xraw[j - 1] > 0 >= xraw[j]:
zc1.append(t[ipick][i])
index1.append(i)
if len(zc1) == 3:
break
# if time difference betweeen 1st and 2cnd zero crossing
# is too short, get time difference between 1st and 3rd
# to derive maximum
if zc1[1] - zc1[0] <= Xraw[0].stats.delta:
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!")
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!")
return FM
islope1 = np.where((t >= Pick) & (t <= Pick + t[imax1]))
# calculate slope as polynomal fit of order 1
xslope1 = np.arange(0, len(xraw[islope1]), 1)
P1 = np.polyfit(xslope1, xraw[islope1], 1)
datafit1 = np.polyval(P1, xslope1)
# now using filterd trace
# next zero crossings after most likely pick
zc2 = []
zc2.append(Pick)
index2 = []
i = 0
for j in range(ipick[0][1], ipick[0][len(t[ipick]) - 1]):
i = i + 1
if xfilt[j - 1] <= 0 <= xfilt[j]:
zc2.append(t[ipick][i])
index2.append(i)
elif xfilt[j - 1] > 0 >= xfilt[j]:
zc2.append(t[ipick][i])
index2.append(i)
if len(zc2) == 3:
break
# if time difference betweeen 1st and 2cnd zero crossing
# is too short, get time difference between 1st and 3rd
# to derive maximum
if zc2[1] - zc2[0] <= Xfilt[0].stats.delta:
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!")
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!")
return FM
islope2 = np.where((t >= Pick) & (t <= Pick + t[imax2]))
# calculate slope as polynomal fit of order 1
xslope2 = np.arange(0, len(xfilt[islope2]), 1)
P2 = np.polyfit(xslope2, xfilt[islope2], 1)
datafit2 = np.polyval(P2, xslope2)
# compare results
if P1 is not None and P2 is not None:
if P1[0] < 0 and P2[0] < 0:
FM = 'D'
elif P1[0] >= 0 > P2[0]:
FM = '-'
elif P1[0] < 0 <= P2[0]:
FM = '-'
elif P1[0] > 0 and P2[0] > 0:
FM = 'U'
elif P1[0] <= 0 < P2[0]:
FM = '+'
elif P1[0] > 0 >= P2[0]:
FM = '+'
print ("fmpicker: Found polarity %s" % FM)
if iplot > 1:
plt.figure(iplot)
plt.subplot(2, 1, 1)
plt.plot(t, xraw, 'k')
p1, = plt.plot([Pick, Pick], [max(xraw), -max(xraw)], 'b', linewidth=2)
if P1 is not None:
p2, = plt.plot(t[islope1], xraw[islope1])
p3, = plt.plot(zc1, np.zeros(len(zc1)), '*g', markersize=14)
p4, = plt.plot(t[islope1], datafit1, '--g', linewidth=2)
plt.legend([p1, p2, p3, p4],
['Pick', 'Slope Window', 'Zero Crossings', 'Slope'],
loc='best')
plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
ax = plt.gca()
plt.yticks([])
plt.title('First-Motion Determination, %s, Unfiltered Data' % Xraw[
0].stats.station)
plt.subplot(2, 1, 2)
plt.title('First-Motion Determination, Filtered Data')
plt.plot(t, xfilt, 'k')
p1, = plt.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b',
linewidth=2)
if P2 is not None:
p2, = plt.plot(t[islope2], xfilt[islope2])
p3, = plt.plot(zc2, np.zeros(len(zc2)), '*g', markersize=14)
p4, = plt.plot(t[islope2], datafit2, '--g', linewidth=2)
plt.text(Pick + 0.02, max(xraw) / 2, '%s' % FM, fontsize=14)
ax = plt.gca()
plt.xlabel('Time [s] since %s' % Xraw[0].stats.starttime)
plt.yticks([])
plt.show()
raw_input()
plt.close(iplot)
return FM
def crossings_nonzero_all(data):
pos = data > 0
npos = ~pos
return ((pos[:-1] & npos[1:]) | (npos[:-1] & pos[1:])).nonzero()[0]
def symmetrize_error(dte, dtl):
"""
takes earliest and latest possible pick and returns the symmetrized pick
uncertainty value
:param dte: relative lower uncertainty
:param dtl: relative upper uncertainty
:return: symmetrized error
"""
return (dte + 2 * dtl) / 3
def getSNR(X, TSNR, t1, tracenum=0):
'''
Function to calculate SNR of certain part of seismogram relative to
given time (onset) out of given noise and signal windows. A safety gap
between noise and signal part can be set. Returns SNR and SNR [dB] and
noiselevel.
:param: X, time series (seismogram)
:type: `~obspy.core.stream.Stream`
:param: TSNR, length of time windows [s] around t1 (onset) used to determine SNR
:type: tuple (T_noise, T_gap, T_signal)
:param: t1, initial time (onset) from which noise and signal windows are calculated
:type: float
'''
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
x = X[tracenum].data
npts = X[tracenum].stats.npts
sr = X[tracenum].stats.sampling_rate
dt = X[tracenum].stats.delta
t = np.arange(0, npts / sr, dt)
# get noise window
inoise = getnoisewin(t, t1, TSNR[0], TSNR[1])
# get signal window
isignal = getsignalwin(t, t1, TSNR[2])
if np.size(inoise) < 1:
print ("getSNR: Empty array inoise, check noise window!")
return
elif np.size(isignal) < 1:
print ("getSNR: Empty array isignal, check signal window!")
return
# demean over entire waveform
x = x - np.mean(x[inoise])
# calculate ratios
# noiselevel = np.sqrt(np.mean(np.square(x[inoise])))
# signallevel = np.sqrt(np.mean(np.square(x[isignal])))
noiselevel = np.abs(x[inoise]).max()
signallevel = np.abs(x[isignal]).max()
SNR = signallevel / noiselevel
SNRdB = 10 * np.log10(SNR)
return SNR, SNRdB, noiselevel
def getnoisewin(t, t1, tnoise, tgap):
'''
Function to extract indeces of data out of time series for noise calculation.
Returns an array of indeces.
:param: t, array of time stamps
:type: numpy array
:param: t1, time from which relativ to it noise window is extracted
:type: float
:param: tnoise, length of time window [s] for noise part extraction
:type: float
:param: tgap, safety gap between t1 (onset) and noise window to
ensure, that noise window contains no signal
:type: float
'''
# get noise window
inoise, = np.where((t <= max([t1 - tgap, 0])) \
& (t >= max([t1 - tnoise - tgap, 0])))
if np.size(inoise) < 1:
print ("getnoisewin: Empty array inoise, check noise window!")
return inoise
def getsignalwin(t, t1, tsignal):
'''
Function to extract data out of time series for signal level calculation.
Returns an array of indeces.
:param: t, array of time stamps
:type: numpy array
:param: t1, time from which relativ to it signal window is extracted
:type: float
:param: tsignal, length of time window [s] for signal level calculation
:type: float
'''
# get signal window
isignal, = np.where((t <= min([t1 + tsignal, len(t)])) \
& (t >= t1))
if np.size(isignal) < 1:
print ("getsignalwin: Empty array isignal, check signal window!")
return isignal
def getResolutionWindow(snr):
"""
Number -> Float
produce the half of the time resolution window width from given SNR
value
SNR >= 3 -> 2 sec HRW
3 > SNR >= 2 -> 5 sec MRW
2 > SNR >= 1.5 -> 10 sec LRW
1.5 > SNR -> 15 sec VLRW
see also Diehl et al. 2009
>>> getResolutionWindow(0.5)
7.5
>>> getResolutionWindow(1.8)
5.0
>>> getResolutionWindow(2.3)
2.5
>>> getResolutionWindow(4)
1.0
>>> getResolutionWindow(2)
2.5
"""
res_wins = {'HRW': 2., 'MRW': 5., 'LRW': 10., 'VLRW': 15.}
if snr < 1.5:
time_resolution = res_wins['VLRW']
elif snr < 2.:
time_resolution = res_wins['LRW']
elif snr < 3.:
time_resolution = res_wins['MRW']
else:
time_resolution = res_wins['HRW']
return time_resolution / 2
def select_for_phase(st, phase):
'''
takes a STream object and a phase name and returns that particular component
which presumably shows the chosen PHASE best
:param st: stream object containing one or more component[s]
:type st: `~obspy.core.stream.Stream`
:param phase: label of the phase for which the stream selection is carried
out; 'P' or 'S'
:type phase: str
:return:
'''
from pylot.core.util.defaults import COMPNAME_MAP
sel_st = Stream()
if phase.upper() == 'P':
comp = 'Z'
alter_comp = COMPNAME_MAP[comp]
sel_st += st.select(component=comp)
sel_st += st.select(component=alter_comp)
elif phase.upper() == 'S':
comps = 'NE'
for comp in comps:
alter_comp = COMPNAME_MAP[comp]
sel_st += st.select(component=comp)
sel_st += st.select(component=alter_comp)
else:
raise TypeError('Unknown phase label: {0}'.format(phase))
return sel_st
def wadaticheck(pickdic, dttolerance, iplot):
'''
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
from regression of S-P time the S pick is marked and down graded.
: param: pickdic, dictionary containing picks and quality parameters
: type: dictionary
: param: dttolerance, maximum adjusted deviation of S-P time from
S-P time regression
: type: float
: param: iplot, if iplot > 1, Wadati diagram is shown
: type: int
'''
checkedonsets = pickdic
# search for good quality picks and calculate S-P time
Ppicks = []
Spicks = []
SPtimes = []
for key in pickdic:
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']
# add S-P time to dictionary
pickdic[key]['SPt'] = spt
# add P onsets and corresponding S-P times to list
UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
UTCSpick = UTCDateTime(pickdic[key]['S']['mpp'])
Ppicks.append(UTCPpick.timestamp)
Spicks.append(UTCSpick.timestamp)
SPtimes.append(spt)
if len(SPtimes) >= 3:
# calculate slope
p1 = np.polyfit(Ppicks, SPtimes, 1)
wdfit = np.polyval(p1, Ppicks)
wfitflag = 0
# calculate vp/vs ratio before check
vpvsr = p1[0] + 1
print ("###############################################")
print ("wadaticheck: Average Vp/Vs ratio before check: %f" % vpvsr)
checkedPpicks = []
checkedSpicks = []
checkedSPtimes = []
# calculate deviations from Wadati regression
ii = 0
ibad = 0
for key in pickdic:
if pickdic[key].has_key('SPt'):
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
ibad += 1
else:
marker = 'goodWadatiCheck'
checkedPpick = UTCDateTime(pickdic[key]['P']['mpp'])
checkedPpicks.append(checkedPpick.timestamp)
checkedSpick = UTCDateTime(pickdic[key]['S']['mpp'])
checkedSpicks.append(checkedSpick.timestamp)
checkedSPtime = pickdic[key]['S']['mpp'] - pickdic[key]['P']['mpp']
checkedSPtimes.append(checkedSPtime)
pickdic[key]['S']['marked'] = marker
if len(checkedPpicks) >= 3:
# calculate new slope
p2 = np.polyfit(checkedPpicks, checkedSPtimes, 1)
wdfit2 = np.polyval(p2, checkedPpicks)
# 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)
else:
print ("###############################################")
print ("wadatacheck: Not enough checked S-P times available!")
print ("Skip Wadati check!")
checkedonsets = pickdic
else:
print ("wadaticheck: Not enough S-P times available for reliable regression!")
print ("Skip wadati check!")
wfitflag = 1
# plot results
if iplot > 1:
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')
else:
plt.title('Wadati-Diagram, %d S-P Times' % len(SPtimes))
plt.ylabel('S-P Times [s]')
plt.xlabel('P Times [s]')
plt.show()
raw_input()
plt.close(iplot)
return checkedonsets
def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot):
'''
Function to detect spuriously picked noise peaks.
Uses RMS trace of all 3 components (if available) to determine,
how many samples [per cent] after P onset are below certain
threshold, calculated from noise level times noise factor.
: param: X, time series (seismogram)
: type: `~obspy.core.stream.Stream`
: param: pick, initial (AIC) P onset time
: type: float
: param: TSNR, length of time windows around initial pick [s]
: type: tuple (T_noise, T_gap, T_signal)
: param: minsiglength, minium required signal length [s] to
declare pick as P onset
: type: float
: param: nfac, noise factor (nfac * noise level = threshold)
: type: float
: param: minpercent, minimum required percentage of samples
above calculated threshold
: type: float
: param: iplot, if iplot > 1, results are shown in figure
: type: int
'''
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print ("Checking signal length ...")
if len(X) > 1:
# all three components available
# make sure, all components have equal lengths
ilen = min([len(X[0].data), len(X[1].data), len(X[2].data)])
x1 = X[0][0:ilen]
x2 = X[1][0:ilen]
x3 = X[2][0:ilen]
# get RMS trace
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))
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])
# get signal window
isignal = getsignalwin(t, pick, minsiglength)
# calculate minimum adjusted signal level
minsiglevel = max(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.")
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)
returnflag = 0
if iplot == 2:
plt.figure(iplot)
p1, = plt.plot(t, rms, 'k')
p2, = plt.plot(t[inoise], rms[inoise], 'c')
p3, = plt.plot(t[isignal], rms[isignal], 'r')
p4, = plt.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]],
[minsiglevel, minsiglevel], 'g', linewidth=2)
p5, = plt.plot([pick, pick], [min(rms), max(rms)], 'b', linewidth=2)
plt.legend([p1, p2, p3, p4, p5], ['RMS Data', 'RMS Noise Window',
'RMS Signal Window', 'Minimum Signal Level',
'Onset'], loc='best')
plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
plt.ylabel('Counts')
plt.title('Check for Signal Length, Station %s' % X[0].stats.station)
plt.yticks([])
plt.show()
raw_input()
plt.close(iplot)
return returnflag
def checkPonsets(pickdic, dttolerance, iplot):
'''
Function to check statistics of P-onset times: Control deviation from
median (maximum adjusted deviation = dttolerance) and apply pseudo-
bootstrapping jackknife.
: param: pickdic, dictionary containing picks and quality parameters
: type: dictionary
: param: dttolerance, maximum adjusted deviation of P-onset time from
median of all P onsets
: type: float
: param: iplot, if iplot > 1, Wadati diagram is shown
: type: int
'''
checkedonsets = pickdic
# search for good quality P picks
Ppicks = []
stations = []
for key in pickdic:
if pickdic[key]['P']['weight'] < 4:
# add P onsets to list
UTCPpick = UTCDateTime(pickdic[key]['P']['mpp'])
Ppicks.append(UTCPpick.timestamp)
stations.append(key)
# apply jackknife bootstrapping on variance of P onsets
print ("###############################################")
print ("checkPonsets: Apply jackknife bootstrapping on P-onset times ...")
[xjack, PHI_pseudo, PHI_sub] = jackknife(Ppicks, 'VAR', 1)
# get pseudo variances smaller than average variances
# (times safety factor), these picks passed jackknife test
ij = np.where(PHI_pseudo <= 2 * xjack)
# these picks did not pass jackknife test
badjk = np.where(PHI_pseudo > 2 * xjack)
badjkstations = np.array(stations)[badjk]
print ("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
# calculate median from these picks
pmedian = np.median(np.array(Ppicks)[ij])
# find picks that deviate less than dttolerance from median
ii = np.where(abs(np.array(Ppicks)[ij] - pmedian) <= dttolerance)
jj = np.where(abs(np.array(Ppicks)[ij] - pmedian) > dttolerance)
igood = ij[0][ii]
ibad = ij[0][jj]
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)))
goodmarker = 'goodPonsetcheck'
badmarker = 'badPonsetcheck'
badjkmarker = 'badjkcheck'
for i in range(0, len(goodstations)):
# 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
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 > 1:
p1, = plt.plot(np.arange(0, len(Ppicks)), Ppicks, 'r+', markersize=14)
p2, = plt.plot(igood, np.array(Ppicks)[igood], 'g*', 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] + 0.2, stations[i])
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('Check P Onsets')
plt.show()
raw_input()
return checkedonsets
def jackknife(X, phi, h):
'''
Function to calculate the Jackknife Estimator for a given quantity,
special type of boot strapping. Returns the jackknife estimator PHI_jack
the pseudo values PHI_pseudo and the subgroup parameters PHI_sub.
: param: X, given quantity
: type: list
: param: phi, chosen estimator, choose between:
"MED" for median
"MEA" for arithmetic mean
"VAR" for variance
: type: string
: param: h, size of subgroups, optinal, default = 1
: type: integer
'''
PHI_jack = None
PHI_pseudo = None
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!")
return PHI_jack, PHI_pseudo, PHI_sub
else:
# estimator of undisturbed spot check
if phi == 'MEA':
phi_sc = np.mean(X)
elif phi == 'VAR':
phi_sc = np.var(X)
elif phi == 'MED':
phi_sc = np.median(X)
# estimators of subgroups
PHI_pseudo = []
PHI_sub = []
for i in range(0, g - 1):
# subgroup i, remove i-th sample
xx = X[:]
del xx[i]
# calculate estimators of disturbed spot check
if phi == 'MEA':
phi_sub = np.mean(xx)
elif phi == 'VAR':
phi_sub = np.var(xx)
elif phi == 'MED':
phi_sub = np.median(xx)
PHI_sub.append(phi_sub)
# pseudo values
phi_pseudo = g * phi_sc - ((g - 1) * phi_sub)
PHI_pseudo.append(phi_pseudo)
# jackknife estimator
PHI_jack = np.mean(PHI_pseudo)
return PHI_jack, PHI_pseudo, PHI_sub
def checkZ4S(X, pick, zfac, checkwin, iplot):
'''
Function to compare energy content of vertical trace with
energy content of horizontal traces to detect spuriously
picked S onsets instead of P onsets. Usually, P coda shows
larger longitudal energy on vertical trace than on horizontal
traces, where the transversal energy is larger within S coda.
Be careful: there are special circumstances, where this is not
the case!
: param: X, fitered(!) time series, three traces
: type: `~obspy.core.stream.Stream`
: param: pick, initial (AIC) P onset time
: type: float
: param: zfac, factor for threshold determination,
vertical energy must exceed coda level times zfac
to declare a pick as P onset
: type: float
: param: checkwin, window length [s] for calculating P-coda
energy content
: type: float
: param: iplot, if iplot > 1, energy content and threshold
are shown
: type: int
'''
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
print ("Check for spuriously picked S onset instead of P onset ...")
returnflag = 0
# split components
zdat = X.select(component="Z")
if len(zdat) == 0: # check for other components
zdat = X.select(component="3")
edat = X.select(component="E")
if len(edat) == 0: # check for other components
edat = X.select(component="2")
ndat = X.select(component="N")
if len(ndat) == 0: # check for other components
ndat = X.select(component="1")
z = zdat[0].data
tz = np.arange(0, zdat[0].stats.npts / zdat[0].stats.sampling_rate,
zdat[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))
# get signal window
isignal = getsignalwin(tz, pick, checkwin)
# calculate energy levels
zcodalevel = max(absz[isignal])
encodalevel = max(absen[isignal])
# calculate threshold
minsiglevel = encodalevel * 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!")
else:
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)
plt.plot(tz, z / max(z), 'k')
plt.plot(tz[isignal], z[isignal] / max(z), 'r')
plt.plot(te, edat[0].data / max(edat[0].data) + 1, 'k')
plt.plot(te[isignal], edat[0].data[isignal] / max(edat[0].data) + 1, 'r')
plt.plot(tn, ndat[0].data / max(ndat[0].data) + 2, 'k')
plt.plot(tn[isignal], ndat[0].data[isignal] / max(ndat[0].data) + 2, 'r')
plt.plot([tz[isignal[0]], tz[isignal[len(isignal) - 1]]],
[minsiglevel / max(z), minsiglevel / max(z)], 'g',
linewidth=2)
plt.xlabel('Time [s] since %s' % zdat[0].stats.starttime)
plt.ylabel('Normalized Counts')
plt.yticks([0, 1, 2], [zdat[0].stats.channel, edat[0].stats.channel,
ndat[0].stats.channel])
plt.title('CheckZ4S, Station %s' % zdat[0].stats.station)
plt.show()
raw_input()
return returnflag
if __name__ == '__main__':
import doctest
doctest.testmod()

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# -*- coding: utf-8 -*-
from pylot.core.util.version import get_git_version as _getVersionString

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

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import glob
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, \
remove_underscores
def time_from_header(header):
"""
Function takes in the second line from a .gse file and takes out the date and time from that line.
:param header: second line from .gse file
:type header: string
:return: a list of integers of form [year, month, day, hour, minute, second, microsecond]
"""
timeline = header.split(' ')
time = timeline[1].split('/') + timeline[2].split(':')
time = time[:-1] + time[-1].split('.')
return [int(t) for t in time]
def check_time(datetime):
"""
Function takes in date and time as list and validates it's values by trying to make an UTCDateTime object from it
:param datetime: list of integers [year, month, day, hour, minute, second, microsecond]
:type datetime: list
:return: returns True if Values are in supposed range, returns False otherwise
>>> check_time([1999, 01, 01, 23, 59, 59, 999000])
True
>>> check_time([1999, 01, 01, 23, 59, 60, 999000])
False
>>> check_time([1999, 01, 01, 23, 59, 59, 1000000])
False
>>> check_time([1999, 01, 01, 23, 60, 59, 999000])
False
>>> check_time([1999, 01, 01, 23, 60, 59, 999000])
False
>>> check_time([1999, 01, 01, 24, 59, 59, 999000])
False
>>> check_time([1999, 01, 31, 23, 59, 59, 999000])
True
>>> check_time([1999, 02, 30, 23, 59, 59, 999000])
False
>>> check_time([1999, 02, 29, 23, 59, 59, 999000])
False
>>> check_time([2000, 02, 29, 23, 59, 59, 999000])
True
>>> check_time([2000, 13, 29, 23, 59, 59, 999000])
False
"""
try:
UTCDateTime(*datetime)
return True
except ValueError:
return False
def get_file_list(root_dir):
"""
Function uses a directorie to get all the *.gse files from it.
:param root_dir: a directorie leading to the .gse files
:type root_dir: string
:return: returns a list of filenames (without path to them)
"""
file_list = glob.glob1(root_dir, '*.gse')
return file_list
def checks_station_second(datetime, file):
"""
Function uses the given list to check if the parameter 'second' is set to 60 by mistake
and sets the time correctly if so. Can only correct time if no date change would be necessary.
:param datetime: [year, month, day, hour, minute, second, microsecond]
:return: returns the input with the correct value for second
"""
if datetime[5] == 60:
if datetime[4] == 59:
if datetime[3] == 23:
err_msg = 'Date should be next day. ' \
'File not changed: {0}'.format(file)
raise ValueError(err_msg)
else:
datetime[3] += 1
datetime[4] = 0
datetime[5] = 0
else:
datetime[4] += 1
datetime[5] = 0
return datetime
def make_time_line(line, datetime):
"""
Function takes in the original line from a .gse file and a list of date and
time values to make a new line with corrected date and time.
:param line: second line from .gse file.
:type line: string
:param datetime: list of integers [year, month, day, hour, minute, second, microsecond]
:type datetime: list
:return: returns a string to write it into a file.
"""
ins_form = '{0:02d}:{1:02d}:{2:02d}.{3:03d}'
insertion = ins_form.format(int(datetime[3]),
int(datetime[4]),
int(datetime[5]),
int(datetime[6] * 1e-3))
newline = line[:16] + insertion + line[28:]
return newline
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.
:type root_dir: string
:param out_dir: a directory to store the new files somwhere els.
:return: returns nothing
"""
if not out_dir:
print('WARNING files are going to be overwritten!')
inp = str(raw_input('Continue? [y/N]'))
if not inp == 'y':
sys.exit()
filelist = get_file_list(root_dir)
nfiles = 0
for file in filelist:
infile = open(os.path.join(root_dir, file), 'r')
lines = infile.readlines()
infile.close()
datetime = time_from_header(lines[1])
if check_time(datetime):
continue
else:
nfiles += 1
datetime = checks_station_second(datetime, file)
print('writing ' + file)
# write File
lines[1] = make_time_line(lines[1], datetime)
if not out_dir:
out = open(os.path.join(root_dir, file), 'w')
out.writelines(lines)
out.close()
else:
out = open(os.path.join(out_dir, file), 'w')
out.writelines(lines)
out.close()
print(nfiles)
def read_metadata(path_to_inventory):
"""
take path_to_inventory and return either the corresponding list of files
found or the Parser object for a network dataless seed volume to prevent
read overhead for large dataless seed volumes
:param path_to_inventory:
:return: tuple containing a either list of files or `obspy.io.xseed.Parser`
object and the inventory type found
:rtype: tuple
"""
dlfile = list()
invfile = list()
respfile = list()
inv = dict(dless=dlfile, xml=invfile, resp=respfile)
if os.path.isfile(path_to_inventory):
ext = os.path.splitext(path_to_inventory)[1].split('.')[1]
inv[ext] += [path_to_inventory]
else:
for ext in inv.keys():
inv[ext] += glob.glob1(path_to_inventory, '*.{0}'.format(ext))
invtype = key_for_set_value(inv)
if invtype is None:
raise IOError("Neither dataless-SEED file, inventory-xml file nor "
"RESP-file found!")
elif invtype == 'dless': # prevent multiple read of large dlsv
print("Reading metadata information from dataless-SEED file ...")
if len(inv[invtype]) == 1:
fullpath_inv = os.path.join(path_to_inventory, inv[invtype][0])
robj = Parser(fullpath_inv)
else:
robj = inv[invtype]
else:
print("Reading metadata information from inventory-xml file ...")
robj = inv[invtype]
return invtype, robj
def restitute_data(data, invtype, inobj, unit='VEL', force=False):
"""
takes a data stream and a path_to_inventory and returns the corrected
waveform data stream
:param data: seismic data stream
:param invtype: type of found metadata
:param inobj: either list of metadata files or `obspy.io.xseed.Parser`
object
:param unit: unit to correct for (default: 'VEL')
:param force: force restitution for already corrected traces (default:
False)
:return: corrected data stream
"""
restflag = list()
data = remove_underscores(data)
# loop over traces
for tr in data:
seed_id = tr.get_id()
# check, whether this trace has already been corrected
if 'processing' in tr.stats.keys() \
and np.any(['remove' in p for p in tr.stats.processing]) \
and not force:
print("Trace {0} has already been corrected!".format(seed_id))
continue
stime = tr.stats.starttime
prefilt = get_prefilt(tr)
if invtype == 'resp':
fresp = find_in_list(inobj, seed_id)
if not fresp:
raise IOError('no response file found '
'for trace {0}'.format(seed_id))
fname = fresp
seedresp = dict(filename=fname,
date=stime,
units=unit)
kwargs = dict(paz_remove=None, pre_filt=prefilt, seedresp=seedresp)
elif invtype == 'dless':
if type(inobj) is list:
fname = Parser(find_in_list(inobj, seed_id))
else:
fname = inobj
seedresp = dict(filename=fname,
date=stime,
units=unit)
kwargs = dict(pre_filt=prefilt, seedresp=seedresp)
elif invtype == 'xml':
invlist = inobj
if len(invlist) > 1:
finv = find_in_list(invlist, seed_id)
else:
finv = invlist[0]
inventory = read_inventory(finv, format='STATIONXML')
else:
data.remove(tr)
continue
# apply restitution to data
try:
if invtype in ['resp', 'dless']:
tr.simulate(**kwargs)
else:
tr.attach_response(inventory)
tr.remove_response(output=unit,
pre_filt=prefilt)
except ValueError as e:
msg0 = 'Response for {0} not found in Parser'.format(seed_id)
msg1 = 'evalresp failed to calculate response'
if msg0 not in e.message or msg1 not in e.message:
raise
else:
# restitution done to copies of data thus deleting traces
# that failed should not be a problem
data.remove(tr)
continue
restflag.append(True)
# 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)
if len(restflag) > 0:
restflag = bool(np.all(restflag))
else:
restflag = False
return data, restflag
def get_prefilt(trace, tlow=(0.5, 0.9), thi=(5., 2.), verbosity=0):
"""
takes a `obspy.core.stream.Trace` object, taper parameters tlow and thi and
returns the pre-filtering corner frequencies for the cosine taper for
further processing
:param trace: seismic data trace
:type trace: `obspy.core.stream.Trace`
:param tlow: tuple or list containing the desired lower corner
frequenices for a cosine taper
:type tlow: tuple or list
:param thi: tuple or list containing the percentage values of the
Nyquist frequency for the desired upper corner frequencies of the cosine
taper
:type thi: tuple or list
:param verbosity: verbosity level
:type verbosity: int
:return: pre-filt cosine taper corner frequencies
:rtype: tuple
..example::
>>> st = read()
>>> get_prefilt(st[0])
(0.5, 0.9, 47.5, 49.0)
"""
if verbosity:
print("Calculating pre-filter values for %s, %s ..." % (
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.)
return (tlow[0], tlow[1], fc21, fc22)
if __name__ == "__main__":
import doctest
doctest.testmod()

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 26 12:31:25 2014
@author: sebastianw
"""
import os
from pylot.core.loc import nll
from pylot.core.loc import hsat
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
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
FILTERDEFAULTS = readFilterInformation(os.path.join(os.path.expanduser('~'),
'.pylot',
'filter.in'))
AUTOMATIC_DEFAULTS = os.path.join(os.path.expanduser('~'),
'.pylot',
'autoPyLoT.in')
TIMEERROR_DEFAULTS = os.path.join(os.path.expanduser('~'),
'.pylot',
'PILOT_TimeErrors.in')
OUTPUTFORMATS = {'.xml': 'QUAKEML',
'.cnv': 'CNV',
'.obs': 'NLLOC_OBS'}
LOCTOOLS = dict(nll=nll, hsat=hsat, velest=velest)
COMPPOSITION_MAP = dict(Z=2, N=1, E=0)
COMPPOSITION_MAP['1'] = 1
COMPPOSITION_MAP['2'] = 0
COMPPOSITION_MAP['3'] = 2
COMPNAME_MAP = dict(Z='3', N='1', E='2')

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# -*- coding: utf-8 -*-
"""
Created on Thu Mar 20 09:47:04 2014
@author: sebastianw
"""
class OptionsError(Exception):
pass
class FormatError(Exception):
pass
class DatastructureError(Exception):
pass
class OverwriteError(IOError):
pass
class ParameterError(Exception):
pass
class ProcessingError(RuntimeError):
pass

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#!/usr/bin/env python
# -*- 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.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
to represent the pick as a probability density funtion (PDF) with two
Gauss branches
:param te:
:param tm:
:param tl:
:param eta:
:return:
'''
sig1 = (tm - te) / np.sqrt(2 * np.log(1 / eta))
sig2 = (tl - tm) / np.sqrt(2 * np.log(1 / eta))
a1 = 2 / (1 + sig2 / sig1)
a2 = 2 / (1 + sig1 / sig2)
return tm, sig1, sig2, a1, a2
def exp_parameter(te, tm, tl, eta):
'''
takes three onset times te, tm and tl and returns the parameters sig1,
sig2 and a to represent the pick as a probability density function (PDF)
with two exponential decay branches
:param te:
:param tm:
:param tl:
:param eta:
:return:
'''
sig1 = np.log(eta) / (te - tm)
sig2 = np.log(eta) / (tm - tl)
a = 1 / (1 / sig1 + 1 / sig2)
return tm, sig1, sig2, a
def gauss_branches(k, (mu, sig1, sig2, a1, a2)):
'''
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
list containing the values of a probability density function (PDF)
consisting of gauss branches
:param x:
:type x:
:param mu:
:type mu:
:param sig1:
:type sig1:
:param sig2:
:type sig2:
:param a1:
:type a1:
:param a2:
:returns fun_vals: list with function values along axes x
'''
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)
else:
rval = a2 * 1 / (np.sqrt(2 * np.pi) * sig2) * np.exp(-((k - mu) /
sig2) ** 2 / 2)
return rval
try:
return [_func(x, mu, sig1, sig2, a1, a2) for x in iter(k)]
except TypeError:
return _func(k, mu, sig1, sig2, a1, a2)
def exp_branches(k, (mu, sig1, sig2, a)):
'''
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
list containing the values of a probability density function (PDF)
consisting of exponential decay branches
:param x:
:param mu:
:param sig1:
:param sig2:
:param a:
:returns fun_vals: list with function values along axes x:
'''
def _func(k, mu, sig1, sig2, a):
mu = float(mu)
if k < mu:
rval = a * np.exp(sig1 * (k - mu))
else:
rval = a * np.exp(-sig2 * (k - mu))
return rval
try:
return [_func(x, mu, sig1, sig2, a) for x in iter(k)]
except TypeError:
return _func(k, mu, sig1, sig2, a)
# define container dictionaries for different types of pdfs
parameter = dict(gauss=gauss_parameter, exp=exp_parameter)
branches = dict(gauss=gauss_branches, exp=exp_branches)
class ProbabilityDensityFunction(object):
'''
A probability density function toolkit.
'''
version = __version__
def __init__(self, x0, incr, npts, pdf, mu, params, eta=0.01):
self.x0 = x0
self.incr = incr
self.npts = npts
self.axis = create_axis(x0, incr, npts)
self.mu = mu
self.eta = eta
self._pdf = pdf
self.params = params
def __add__(self, other):
assert self.eta == other.eta, 'decline factors differ please use equally defined pdfs for comparison'
eta = self.eta
x0, incr, npts = self.commonparameter(other)
axis = create_axis(x0, incr, npts)
pdf_self = np.array(self.data(axis))
pdf_other = np.array(other.data(axis))
pdf = np.convolve(pdf_self, pdf_other, 'full') * incr
# shift axis values for correct plotting
npts = pdf.size
x0 *= 2
axis = create_axis(x0, incr, npts)
mu = axis[np.where(pdf == max(pdf))][0]
func, params = fit_curve(axis, pdf)
return ProbabilityDensityFunction(x0, incr, npts, func, mu,
params, eta)
def __sub__(self, other):
assert self.eta == other.eta, 'decline factors differ please use equally defined pdfs for comparison'
eta = self.eta
x0, incr, npts = self.commonparameter(other)
axis = create_axis(x0, incr, npts)
pdf_self = np.array(self.data(axis))
pdf_other = np.array(other.data(axis))
pdf = np.correlate(pdf_self, pdf_other, 'full') * incr
# shift axis values for correct plotting
npts = len(pdf)
midpoint = npts / 2
x0 = -incr * midpoint
axis = create_axis(x0, incr, npts)
mu = axis[np.where(pdf == max(pdf))][0]
func, params = fit_curve(axis, pdf)
return ProbabilityDensityFunction(x0, incr, npts, func, mu,
params, eta)
def __nonzero__(self):
prec = self.precision(self.incr)
data = np.array(self.data())
gtzero = np.all(data >= 0)
probone = bool(np.round(self.prob_gt_val(self.axis[0]), prec) == 1.)
return bool(gtzero and probone)
def __str__(self):
return str([self.data()])
@staticmethod
def precision(incr):
prec = int(np.ceil(np.abs(np.log10(incr)))) - 2
return prec if prec >= 0 else 0
def data(self, value=None):
if value is None:
return self._pdf(self.axis, self.params)
return self._pdf(value, self.params)
@property
def eta(self):
return self._eta
@eta.setter
def eta(self, value):
self._eta = value
@property
def mu(self):
return self._mu
@mu.setter
def mu(self, mu):
self._mu = mu
@property
def axis(self):
return self._x
@axis.setter
def axis(self, x):
self._x = np.array(x)
@classmethod
def from_pick(self, lbound, barycentre, rbound, incr=0.001, decfact=0.01,
type='gauss'):
'''
Initialize a new ProbabilityDensityFunction object.
Takes incr, lbound, barycentre and rbound to derive x0 and the number
of points npts for the axis vector.
Maximum density
is given at the barycentre and on the boundaries the function has
declined to decfact times the maximum value. Integration of the
function over a particular interval gives the probability for the
variable value to be in that interval.
'''
# derive adequate window of definition
margin = 2. * np.max([barycentre - lbound, rbound - barycentre])
# find midpoint accounting also for `~obspy.UTCDateTime` object usage
try:
midpoint = (rbound + lbound) / 2
except TypeError:
try:
midpoint = (rbound + float(lbound)) / 2
except TypeError:
midpoint = float(rbound + float(lbound)) / 2
# find x0 on a grid point and sufficient npts
was_datetime = None
if isinstance(barycentre, UTCDateTime):
barycentre = float(barycentre)
was_datetime = True
n = int(np.ceil((barycentre - midpoint) / incr))
m = int(np.ceil((margin / incr)))
midpoint = barycentre - n * incr
margin = m * incr
x0 = midpoint - margin
npts = 2 * m
if was_datetime:
barycentre = UTCDateTime(barycentre)
# calculate parameter for pdf representing function
params = parameter[type](lbound, barycentre, rbound, decfact)
# select pdf type
pdf = branches[type]
# return the object
return ProbabilityDensityFunction(x0, incr, npts, pdf, barycentre,
params, decfact)
def broadcast(self, pdf, si, ei, data):
try:
pdf[si:ei] = data
except ValueError as e:
warnings.warn(str(e), Warning)
return self.broadcast(pdf, si, ei, data[:-1])
return pdf
def expectation(self):
'''
returns the expectation value of the actual pdf object
..formula::
mu_{\Delta t} = \int\limits_{-\infty}^\infty x \cdot f(x)dx
:return float: rval
'''
rval = 0
for x in self.axis:
rval += x * self.data(x)
return rval * self.incr
def standard_deviation(self):
mu = self.mu
rval = 0
for x in self.axis:
rval += (x - float(mu)) ** 2 * self.data(x)
return rval * self.incr
def prob_lt_val(self, value):
if value <= self.axis[0] or value > self.axis[-1]:
raise ValueError('value out of bounds: {0}'.format(value))
return self.prob_limits((self.axis[0], value))
def prob_gt_val(self, value):
if value < self.axis[0] or value >= self.axis[-1]:
raise ValueError('value out of bounds: {0}'.format(value))
return self.prob_limits((value, self.axis[-1]))
def prob_limits(self, limits, oversampling=1.):
sampling = self.incr / oversampling
lim = np.arange(limits[0], limits[1], sampling)
data = self.data(lim)
min_est, max_est = 0., 0.
for n in range(len(data) - 1):
min_est += min(data[n], data[n + 1])
max_est += max(data[n], data[n + 1])
return (min_est + max_est) / 2. * sampling
def prob_val(self, value):
if not (self.axis[0] <= value <= self.axis[-1]):
Warning('{0} not on axis'.format(value))
return None
return self.data(value) * self.incr
def quantile(self, prob_value, eps=0.01):
'''
:param prob_value:
:param eps:
:return:
'''
l = self.axis[0]
r = self.axis[-1]
m = (r + l) / 2
diff = prob_value - self.prob_lt_val(m)
while abs(diff) > eps and ((r - l) > self.incr):
if diff > 0:
l = m
else:
r = m
m = (r + l) / 2
diff = prob_value - self.prob_lt_val(m)
return m
def quantile_distance(self, prob_value):
"""
takes a probability value and and returns the distance
between two complementary quantiles
.. math::
QA_\alpha = Q(1 - \alpha) - Q(\alpha)
:param value: probability value :math:\alpha
:type value: float
:return: quantile distance
"""
if 0 >= prob_value or prob_value >= 0.5:
raise ValueError('Value out of range.')
ql = self.quantile(prob_value)
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
corresponding quantile distances (
:func:`pylot.core.util.pdf.ProbabilityDensityFunction
#quantile_distance`)
.. math::
Q\Theta_\alpha = \frac{QA(0.5 - \alpha)}{QA(\alpha)}
:param value: probability value :math:\alpha
:return: quantile distance fraction
"""
if x <= 0 or x >= 0.25:
raise ValueError('Value out of range.')
return self.quantile_distance(0.5-x)/self.quantile_distance(x)
def plot(self, label=None):
import matplotlib.pyplot as plt
plt.plot(self.axis, self.data())
plt.xlabel('x')
plt.ylabel('f(x)')
plt.autoscale(axis='x', tight=True)
if self:
title_str = 'Probability density function '
if label:
title_str += label
title_str.strip()
else:
title_str = 'Function not suitable as probability density function'
plt.title(title_str)
plt.show()
def limits(self):
l1 = self.x0
r1 = l1 + self.incr * self.npts
return l1, r1
def cincr(self, other):
if not self.incr == other.incr:
raise NotImplementedError(
'Upsampling of the lower sampled PDF not implemented yet!')
else:
return self.incr
def commonlimits(self, incr, other, max_npts=1e5):
'''
Takes an increment incr and two left and two right limits and returns
the left most limit and the minimum number of points needed to cover
the whole given interval.
:param incr:
:param l1:
:param l2:
:param r1:
:param r2:
:param max_npts:
:return:
'''
x0, r = clims(self.limits(), other.limits())
# calculate index for rounding
ri = self.precision(incr)
npts = int(round(r - x0, ri) // incr)
if npts > max_npts:
raise ValueError('Maximum number of points exceeded:\n'
'max_npts - %d\n'
'npts - %d\n' % (max_npts, npts))
npts = np.max([npts, self.npts, other.npts])
if npts < self.npts or npts < other.npts:
raise ValueError('new npts is to small')
return x0, npts
def commonparameter(self, other):
assert isinstance(other, ProbabilityDensityFunction), \
'both operands must be of type ProbabilityDensityFunction'
incr = self.cincr(other)
x0, npts = self.commonlimits(incr, other)
return x0, incr, npts

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
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
histogram plotting
:param l_boundary: Any number.
:type l_boundary: float
:param u_boundary: Any number that is greater than l_boundary.
:type u_boundary: float
:param nbins: Any positive integer.
:type nbins: int
:return: A list of equidistant bins.
"""
if u_boundary <= l_boundary:
raise ValueError('Upper boundary must be greather than lower!')
elif nbins <= 0:
raise ValueError('Number of bins is not valid.')
binlist = []
for i in range(nbins):
binlist.append(l_boundary + i * (u_boundary - l_boundary) / nbins)
return binlist
def histplot(array, binlist, xlab='Values',
ylab='Frequency', title=None, fnout=None):
"""
function to quickly show some distribution of data. Takes array like data,
and a list of bins. Editing detail and inserting a legend is not possible.
:param array: List of values.
:type array: Array like
:param binlist: List of bins.
:type binlist: list
:param xlab: A label for the x-axes.
:type xlab: str
:param ylab: A label for the y-axes.
:type ylab: str
:param title: A title for the Plot.
:type title: str
:param fnout: A path to save the plot instead of showing.
Has to contain filename and type. Like: 'path/to/file.png'
:type fnout. str
:return: -
"""
plt.hist(array, bins=binlist)
plt.xlabel(xlab)
plt.ylabel(ylab)
if title:
plt.title(title)
if fnout:
plt.savefig(fnout)
else:
plt.show()

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pylot/core/util/structure.py Normal file
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 26 17:47:25 2015
@author: sebastianw
"""
from pylot.core.io.data import SeiscompDataStructure, PilotDataStructure
DATASTRUCTURE = {'PILOT': PilotDataStructure, 'SeisComP': SeiscompDataStructure,
None: None}

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pylot/core/util/thread.py Normal file
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# -*- coding: utf-8 -*-
import sys
from PySide.QtCore import QThread, Signal
class AutoPickThread(QThread):
message = Signal(str)
finished = Signal()
def __init__(self, parent, func, data, param):
super(AutoPickThread, self).__init__()
self.setParent(parent)
self.func = func
self.data = data
self.param = param
def run(self):
sys.stdout = self
picks = self.func(self.data, self.param)
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)

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pylot/core/util/utils.py Normal file
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import hashlib
import numpy as np
from scipy.interpolate import splrep, splev
import os
import pwd
import re
import subprocess
from obspy import UTCDateTime, read
from pylot.core.io.inputs import AutoPickParameter
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 fit_curve(x, y):
return splev, splrep(x, y)
def getindexbounds(f, eta):
mi = f.argmax()
m = max(f)
b = m * eta
l = find_nearest(f[:mi], b)
u = find_nearest(f[mi:], b) + mi
return mi, l, u
def worker(func, input, cores='max', async=False):
import multiprocessing
if cores == 'max':
cores = multiprocessing.cpu_count()
pool = multiprocessing.Pool(cores)
if async == True:
result = pool.map_async(func, input)
else:
result = pool.map(func, input)
pool.close()
return result
def clims(lim1, lim2):
"""
takes two pairs of limits and returns one pair of common limts
:param lim1:
:param lim2:
:return:
>>> clims([0, 4], [1, 3])
[0, 4]
>>> clims([1, 4], [0, 3])
[0, 4]
>>> clims([1, 3], [0, 4])
[0, 4]
>>> clims([0, 3], [1, 4])
[0, 4]
>>> clims([0, 3], [0, 4])
[0, 4]
>>> clims([1, 4], [0, 4])
[0, 4]
>>> clims([0, 4], [0, 4])
[0, 4]
>>> clims([0, 4], [1, 4])
[0, 4]
>>> clims([0, 4], [0, 3])
[0, 4]
"""
lim = [None, None]
if lim1[0] < lim2[0]:
lim[0] = lim1[0]
else:
lim[0] = lim2[0]
if lim1[1] > lim2[1]:
lim[1] = lim1[1]
else:
lim[1] = lim2[1]
return lim
def demeanTrace(trace, window):
"""
takes a trace object and returns the same trace object but with data
demeaned within a certain time window
:param trace: waveform trace object
:type trace: `~obspy.core.stream.Trace`
:param window:
:type window: tuple
:return: trace
:rtype: `~obspy.core.stream.Trace`
"""
trace.data -= trace.data[window].mean()
return trace
def findComboBoxIndex(combo_box, val):
"""
Function findComboBoxIndex takes a QComboBox object and a string and
returns either 0 or the index throughout all QComboBox items.
:param combo_box: Combo box object.
:type combo_box: `~QComboBox`
:param val: Name of a combo box to search for.
:type val: basestring
:return: index value of item with name val or 0
"""
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
matching the string pattern
:param list: list to search in
:param str: pattern to search for
:return: first list item containing pattern
.. example::
>>> l = ['/dir/e1234.123.12', '/dir/e2345.123.12', 'abc123', 'def456']
>>> find_in_list(l, 'dir')
'/dir/e1234.123.12'
>>> find_in_list(l, 'e1234')
'/dir/e1234.123.12'
>>> find_in_list(l, 'e2')
'/dir/e2345.123.12'
>>> find_in_list(l, 'ABC')
'abc123'
>>> find_in_list(l, 'f456')
'def456'
>>> find_in_list(l, 'gurke')
"""
rlist = [s for s in list if str.lower() in s.lower()]
if rlist:
return rlist[0]
return None
def find_nearest(array, value):
'''
function find_nearest takes an array and a value and returns the
index of the nearest value found in the array
:param array: array containing values
:type array: `~numpy.ndarray`
:param value: number searched for
:return: index of the array item being nearest to the value
>>> a = np.array([ 1.80339578, -0.72546654, 0.95769195, -0.98320759, 0.85922623])
>>> find_nearest(a, 1.3)
2
>>> find_nearest(a, 0)
1
>>> find_nearest(a, 2)
0
>>> find_nearest(a, -1)
3
>>> a = np.array([ 1.1, -0.7, 0.9, -0.9, 0.8])
>>> find_nearest(a, 0.849)
4
'''
return (np.abs(array - value)).argmin()
def fnConstructor(s):
'''
takes a string and returns a valid filename (especially on windows machines)
:param s: desired filename
:type s: str
:return: valid filename
'''
if type(s) is str:
s = s.split(':')[-1]
else:
s = getHash(UTCDateTime())
badchars = re.compile(r'[^A-Za-z0-9_. ]+|^\.|\.$|^ | $|^$')
badsuffix = re.compile(r'(aux|com[1-9]|con|lpt[1-9]|prn)(\.|$)')
fn = badchars.sub('_', s)
if badsuffix.match(fn):
fn = '_' + fn
return fn
def four_digits(year):
"""
takes a two digit year integer and returns the correct four digit equivalent
from the last 100 years
:param year: two digit year
:type year: int
:return: four digit year correspondant
>>> four_digits(20)
1920
>>> four_digits(16)
2016
>>> four_digits(00)
2000
"""
if year + 2000 <= UTCDateTime.utcnow().year:
year += 2000
else:
year += 1900
return year
def common_range(stream):
'''
takes a stream object and returns the earliest end and the latest start
time of all contained trace objects
:param stream: seismological data stream
:type stream: `~obspy.core.stream.Stream`
:return: maximum start time and minimum end time
'''
max_start = None
min_end = None
for trace in stream:
if max_start is None or trace.stats.starttime > max_start:
max_start = trace.stats.starttime
if min_end is None or trace.stats.endtime < min_end:
min_end = trace.stats.endtime
return max_start, min_end
def full_range(stream):
'''
takes a stream object and returns the latest end and the earliest start
time of all contained trace objects
:param stream: seismological data stream
:type stream: `~obspy.core.stream.Stream`
:return: minimum start time and maximum end time
'''
min_start = UTCDateTime()
max_end = None
for trace in stream:
if trace.stats.starttime < min_start:
min_start = trace.stats.starttime
if max_end is None or trace.stats.endtime > max_end:
max_end = trace.stats.endtime
return min_start, max_end
def getHash(time):
'''
takes a time object and returns the corresponding SHA1 hash of the
formatted date string
:param time: time object for which a hash should be calculated
:type time: :class: `~obspy.core.utcdatetime.UTCDateTime` object
:return: str
'''
hg = hashlib.sha1()
hg.update(time.strftime('%Y-%m-%d %H:%M:%S.%f'))
return hg.hexdigest()
def getLogin():
'''
returns the actual user's login ID
:return: login ID
'''
return pwd.getpwuid(os.getuid())[0]
def getOwner(fn):
'''
takes a filename and return the login ID of the actual owner of the file
:param fn: filename of the file tested
:type fn: str
:return: login ID of the file's owner
'''
return pwd.getpwuid(os.stat(fn).st_uid).pw_name
def getPatternLine(fn, pattern):
"""
takes a file name and a pattern string to search for in the file and
returns the first line which contains the pattern string otherwise 'None'
:param fn: file name
:type fn: str
:param pattern: pattern string to search for
:type pattern: str
:return: the complete line containing the pattern string or None
>>> getPatternLine('utils.py', 'python')
'#!/usr/bin/env python\\n'
>>> print(getPatternLine('version.py', 'palindrome'))
None
"""
fobj = open(fn, 'r')
for line in fobj.readlines():
if pattern in line:
fobj.close()
return line
return None
def is_executable(fn):
"""
takes a filename and returns True if the file is executable on the system
and False otherwise
:param fn: path to the file to be tested
:return: True or False
"""
return os.path.isfile(fn) and os.access(fn, os.X_OK)
def isSorted(iterable):
'''
takes an iterable and returns 'True' if the items are in order otherwise
'False'
:param iterable: an iterable object
:type iterable:
:return: Boolean
>>> isSorted(1)
Traceback (most recent call last):
...
AssertionError: object is not iterable; object: 1
>>> isSorted([1,2,3,4])
True
>>> isSorted('abcd')
True
>>> isSorted('bcad')
False
>>> isSorted([2,3,1,4])
False
'''
assert isIterable(iterable), 'object is not iterable; object: {' \
'0}'.format(iterable)
if type(iterable) is str:
iterable = [s for s in iterable]
return sorted(iterable) == iterable
def isIterable(obj):
"""
takes a python object and returns 'True' is the object is iterable and
'False' otherwise
:param obj: a python object
:return: True of False
"""
try:
iterator = iter(obj)
except TypeError as te:
return False
return True
def key_for_set_value(d):
"""
takes a dictionary and returns the first key for which's value the
boolean is True
:param d: dictionary containing values
:type d: dict
:return: key to the first non-False value found; None if no value's
boolean equals True
"""
r = None
for k, v in d.items():
if v:
return k
return r
def prepTimeAxis(stime, trace):
'''
takes a starttime and a trace object and returns a valid time axis for
plotting
:param stime: start time of the actual seismogram as UTCDateTime
:param trace: seismic trace object
:return: valid numpy array with time stamps for plotting
'''
nsamp = trace.stats.npts
srate = trace.stats.sampling_rate
tincr = trace.stats.delta
etime = stime + nsamp / srate
time_ax = np.arange(stime, etime, tincr)
if len(time_ax) < nsamp:
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')
time_ax = np.arange(stime, etime - tincr, tincr)
if len(time_ax) != nsamp:
raise ValueError('{0} samples of data \n '
'{1} length of time vector \n'
'delta: {2}'.format(nsamp, len(time_ax), tincr))
return time_ax
def find_horizontals(data):
"""
takes `obspy.core.stream.Stream` object and returns a list containing the component labels of the horizontal components available
:param data: waveform data
:type data: `obspy.core.stream.Stream`
:return: components list
:rtype: list
..example::
>>> st = read()
>>> find_horizontals(st)
[u'N', u'E']
"""
rval = []
for tr in data:
if tr.stats.channel[-1].upper() in ['Z', '3']:
continue
else:
rval.append(tr.stats.channel[-1].upper())
return rval
def remove_underscores(data):
"""
takes a `obspy.core.stream.Stream` object and removes all underscores
from stationnames
:param data: stream of seismic data
:type data: `obspy.core.stream.Stream`
:return: data stream
"""
for tr in data:
# remove underscores
tr.stats.station = tr.stats.station.strip('_')
return data
def scaleWFData(data, factor=None, components='all'):
"""
produce scaled waveforms from given waveform data and a scaling factor,
waveform may be selected by their components name
:param data: waveform data to be scaled
:type data: `~obspy.core.stream.Stream` object
:param factor: scaling factor
:type factor: float
:param components: components labels for the traces in data to be scaled by
the scaling factor (optional, default: 'all')
:type components: tuple
:return: scaled waveform data
:rtype: `~obspy.core.stream.Stream` object
"""
if components is not 'all':
for comp in components:
if factor is None:
max_val = np.max(np.abs(data.select(component=comp)[0].data))
data.select(component=comp)[0].data /= 2 * max_val
else:
data.select(component=comp)[0].data /= 2 * factor
else:
for tr in data:
if factor is None:
max_val = float(np.max(np.abs(tr.data)))
tr.data /= 2 * max_val
else:
tr.data /= 2 * factor
return data
def runProgram(cmd, parameter=None):
"""
run an external program specified by cmd with parameters input returning the
stdout output
:param cmd: name of the command to run
:type cmd: str
:param parameter: filename of parameter file or parameter string
:type parameter: str
:return: stdout output
:rtype: str
"""
if parameter:
cmd.strip()
cmd += ' %s 2>&1' % parameter
subprocess.check_output('{} | tee /dev/stderr'.format(cmd), shell=True)
def which(program):
"""
takes a program name and returns the full path to the executable or None
modified after: http://stackoverflow.com/questions/377017/test-if-executable-exists-in-python
:param program: name of the desired external program
:return: full path of the executable file
"""
try:
from PySide.QtCore import QSettings
settings = QSettings()
for key in settings.allKeys():
if 'binPath' in key:
os.environ['PATH'] += ':{0}'.format(settings.value(key))
bpath = os.path.join(os.path.expanduser('~'), '.pylot', 'autoPyLoT.in')
if os.path.exists(bpath):
nllocpath = ":" + AutoPickParameter(bpath).get('nllocbin')
os.environ['PATH'] += nllocpath
except ImportError as e:
print(e.message)
def is_exe(fpath):
return os.path.exists(fpath) and os.access(fpath, os.X_OK)
def ext_candidates(fpath):
yield fpath
for ext in os.environ.get("PATHEXT", "").split(os.pathsep):
yield fpath + ext
fpath, fname = os.path.split(program)
if fpath:
if is_exe(program):
return program
else:
for path in os.environ["PATH"].split(os.pathsep):
exe_file = os.path.join(path, program)
for candidate in ext_candidates(exe_file):
if is_exe(candidate):
return candidate
return None
if __name__ == "__main__":
import doctest
doctest.testmod()

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# -*- coding: utf-8 -*-
# Author: Douglas Creager <dcreager@dcreager.net>
# This file is placed into the public domain.
# Calculates the current version number. If possible, this is the
# output of “git describe”, modified to conform to the versioning
# scheme that setuptools uses. If “git describe” returns an error
# (most likely because we're in an unpacked copy of a release tarball,
# rather than in a git working copy), then we fall back on reading the
# contents of the RELEASE-VERSION file.
#
# To use this script, simply import it your setup.py file, and use the
# results of get_git_version() as your package version:
#
# from version import *
#
# setup(
# version=get_git_version(),
# .
# .
# .
# )
#
# This will automatically update the RELEASE-VERSION file, if
# necessary. Note that the RELEASE-VERSION file should *not* be
# checked into git; please add it to your top-level .gitignore file.
#
# You'll probably want to distribute the RELEASE-VERSION file in your
# sdist tarballs; to do this, just create a MANIFEST.in file that
# contains the following line:
#
# include RELEASE-VERSION
from __future__ import print_function
__all__ = "get_git_version"
# NO IMPORTS FROM PYLOT IN THIS FILE! (file gets used at installation time)
import os
import inspect
from subprocess import Popen, PIPE
# NO IMPORTS FROM PYLOT IN THIS FILE! (file gets used at installation time)
script_dir = os.path.abspath(os.path.dirname(inspect.getfile(
inspect.currentframe())))
PYLOT_ROOT = os.path.abspath(os.path.join(script_dir, os.pardir,
os.pardir, os.pardir))
VERSION_FILE = os.path.join(PYLOT_ROOT, "pylot", "RELEASE-VERSION")
def call_git_describe(abbrev=4):
try:
p = Popen(['git', 'rev-parse', '--show-toplevel'],
cwd=PYLOT_ROOT, stdout=PIPE, stderr=PIPE)
p.stderr.close()
path = p.stdout.readlines()[0].strip()
except:
return None
if os.path.normpath(path) != PYLOT_ROOT:
return None
try:
p = Popen(['git', 'describe', '--dirty', '--abbrev=%d' % abbrev,
'--always'],
cwd=PYLOT_ROOT, stdout=PIPE, stderr=PIPE)
p.stderr.close()
line = p.stdout.readlines()[0]
# (this line prevents official releases)
if "-" not in line and "." not in line:
line = "0.0.0-g%s" % line
return line.strip()
except:
return None
def read_release_version():
try:
version = open(VERSION_FILE, "r").readlines()[0]
return version.strip()
except:
return None
def write_release_version(version):
open(VERSION_FILE, "w").write("%s\n" % version)
def get_git_version(abbrev=4):
# Read in the version that's currently in RELEASE-VERSION.
release_version = read_release_version()
# First try to get the current version using “git describe”.
version = call_git_describe(abbrev)
# If that doesn't work, fall back on the value that's in
# RELEASE-VERSION.
if version is None:
version = release_version
# If we still don't have anything, that's an error.
if version is None:
return '0.0.0-tar/zipball'
# If the current version is different from what's in the
# RELEASE-VERSION file, update the file to be current.
if version != release_version:
write_release_version(version)
# Finally, return the current version.
return version
if __name__ == "__main__":
print(get_git_version())

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pylot/core/util/widgets.py Normal file
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pylot/testing/__init__.py Normal file
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pylot/testing/testHelpForm.py Executable file
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#!/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_()

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pylot/testing/testPickDlg.py Executable file
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#!/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_()

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pylot/testing/testPropDlg.py Executable file
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#!/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_()

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pylot/testing/testUIcomponents.py Executable file
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#!/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()

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# -*- 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()

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pylot/testing/testWidgets.py Normal file
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# -*- 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()

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#!/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)

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

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

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

24
scripts/pylot-pick-firstmotion.py Executable file
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#!/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)

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scripts/pylot-reasses-pilot-db.py Executable file
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#!/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)

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scripts/pylot-reasses-pilot-event.py Executable file
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#!/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)

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scripts/pylot-signalwindow.py Executable file
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#!/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)

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scripts/pylot-snr.py Executable file
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#!/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)

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scripts/run_makeCF.py Executable file
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#!/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)

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from distutils.core import setup
setup(
name='PyLoT',
version='0.1a1',
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'],
url='dummy',
license='LGPLv3',
author='Sebastian Wehling-Benatelli',
author_email='sebastian.wehling@rub.de',
description='Comprehensive Python picking and Location Toolbox for seismological data.'
)

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