Move test_autopickstation to subdirectory in pylot/tests
This commit is contained in:
Darius Arnold
104
tests/test_autopickstation/autoPyLoT_global_taupy_false.in
Normal file
104
tests/test_autopickstation/autoPyLoT_global_taupy_false.in
Normal file
@@ -0,0 +1,104 @@
|
||||
%This is a parameter input file for PyLoT/autoPyLoT.
|
||||
%All main and special settings regarding data handling
|
||||
%and picking are to be set here!
|
||||
%Parameters are optimized for %extent data sets!
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#main settings#
|
||||
/home/darius #rootpath# %project path
|
||||
alparray #datapath# %data path
|
||||
waveforms_used #database# %name of data base
|
||||
e0093.173.16 #eventID# %event ID for single event processing (* for all events found in database)
|
||||
/home/darius/alparray/metadata #invdir# %full path to inventory or dataless-seed file
|
||||
PILOT #datastructure# %choose data structure
|
||||
True #apverbose# %choose 'True' or 'False' for terminal output
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#NLLoc settings#
|
||||
/progs/bin #nllocbin# %path to NLLoc executable
|
||||
/home/darius/alparray/auto #nllocroot# %root of NLLoc-processing directory
|
||||
AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
|
||||
Insheim_min1d032016_auto.in #ctrfile# %name of autoPyLoT-output control file for NLLoc
|
||||
ttime #ttpatter# %pattern of NLLoc ttimes from grid
|
||||
AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#parameters for seismic moment estimation#
|
||||
3530.0 #vp# %average P-wave velocity
|
||||
2500.0 #rho# %average rock density [kg/m^3]
|
||||
300.0 0.8 #Qp# %quality factor for P waves (Qp*f^a); list(Qp, a)
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#settings local magnitude#
|
||||
1.0 1.0 1.0 #WAscaling# %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] If zeros are set, original Richter magnitude is calculated!
|
||||
1.0 1.0 #magscaling# %Scaling relation for derived local magnitude [a*Ml+b]. If zeros are set, no scaling of network magnitude is applied!
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#filter settings#
|
||||
0.01 0.01 #minfreq# %Lower filter frequency [P, S]
|
||||
0.5 0.5 #maxfreq# %Upper filter frequency [P, S]
|
||||
3 3 #filter_order# %filter order [P, S]
|
||||
bandpass bandpass #filter_type# %filter type (bandpass, bandstop, lowpass, highpass) [P, S]
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#common settings picker#
|
||||
global #extent# %extent of array ("local", "regional" or "global")
|
||||
-100.0 #pstart# %start time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
|
||||
350.0 #pstop# %end time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
|
||||
200.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
|
||||
875.0 #sstop# %end time [s] after P-onset for calculating CF for S-picking
|
||||
False #use_taup# %use estimated traveltimes from TauPy for calculating windows for CF
|
||||
IASP91 #taup_model# %define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
|
||||
0.01 0.1 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
|
||||
0.001 0.5 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
|
||||
0.01 0.5 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
|
||||
0.001 0.5 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz]
|
||||
#special settings for calculating CF#
|
||||
%!!Edit the following only if you know what you are doing!!%
|
||||
#Z-component#
|
||||
HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
|
||||
100.0 #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
|
||||
24.0 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
|
||||
20.0 #tpred1z# %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
|
||||
16.0 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
|
||||
8.0 #tpred2z# %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
|
||||
0.5 #addnoise# %add noise to seismogram for stable AR prediction
|
||||
30.0 5.0 20.0 10.0 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
|
||||
55.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
|
||||
20.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
|
||||
6.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
|
||||
4.0 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
|
||||
0.5 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
|
||||
1.1 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
|
||||
50.0 #checkwindowP# %time window before HOS/AR-maximum to check for smaller maxima [s]
|
||||
0.7 #minfactorP# %Second maximum must be at least minfactor * first maximum [-]
|
||||
#H-components#
|
||||
ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
|
||||
30.0 #tdet1h# %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
|
||||
18.0 #tpred1h# %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
|
||||
16.0 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
|
||||
8.0 #tpred2h# %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
|
||||
4 #Sarorder# %for AR-picker, order of AR process of H-components
|
||||
30.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
|
||||
195.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
|
||||
30.0 10.0 15.0 10.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
|
||||
22.0 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
|
||||
10.0 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
|
||||
0.001 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
|
||||
1.2 #nfacS# %for AR-picker, noise factor for noise level determination (S)
|
||||
250.0 #checkwindowS# %time window before AR-maximum to check for smaller maxima [s]
|
||||
0.4 #minfactorS# %Second maximum must be at least minfactor * first maximum [-]
|
||||
#first-motion picker#
|
||||
1 #minfmweight# %minimum required P weight for first-motion determination
|
||||
3.0 #minFMSNR# %miniumum required SNR for first-motion determination
|
||||
10.0 #fmpickwin# %pick window around P onset for calculating zero crossings
|
||||
#quality assessment#
|
||||
4.0 8.0 12.0 16.0 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
|
||||
4.0 8.0 12.0 16.0 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
|
||||
1.0 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
|
||||
1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
|
||||
1.0 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
|
||||
1.1 #minAICSSNR# %below this SNR the initial S pick is rejected
|
||||
12.0 #minsiglength# %length of signal part for which amplitudes must exceed noiselevel [s]
|
||||
1.1 #noisefactor# %noiselevel*noisefactor=threshold
|
||||
20.0 #minpercent# %required percentage of amplitudes exceeding threshold
|
||||
1.25 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
|
||||
60.0 #mdttolerance# %maximum allowed deviation of P picks from median [s]
|
||||
60.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
|
||||
5.0 #jackfactor# %pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor
|
||||
104
tests/test_autopickstation/autoPyLoT_global_taupy_true.in
Normal file
104
tests/test_autopickstation/autoPyLoT_global_taupy_true.in
Normal file
@@ -0,0 +1,104 @@
|
||||
%This is a parameter input file for PyLoT/autoPyLoT.
|
||||
%All main and special settings regarding data handling
|
||||
%and picking are to be set here!
|
||||
%Parameters are optimized for %extent data sets!
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#main settings#
|
||||
/home/darius #rootpath# %project path
|
||||
alparray #datapath# %data path
|
||||
waveforms_used #database# %name of data base
|
||||
e0093.173.16 #eventID# %event ID for single event processing (* for all events found in database)
|
||||
/home/darius/alparray/metadata #invdir# %full path to inventory or dataless-seed file
|
||||
PILOT #datastructure# %choose data structure
|
||||
True #apverbose# %choose 'True' or 'False' for terminal output
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#NLLoc settings#
|
||||
/progs/bin #nllocbin# %path to NLLoc executable
|
||||
/home/darius/alparray/auto #nllocroot# %root of NLLoc-processing directory
|
||||
AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
|
||||
Insheim_min1d032016_auto.in #ctrfile# %name of autoPyLoT-output control file for NLLoc
|
||||
ttime #ttpatter# %pattern of NLLoc ttimes from grid
|
||||
AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#parameters for seismic moment estimation#
|
||||
3530.0 #vp# %average P-wave velocity
|
||||
2500.0 #rho# %average rock density [kg/m^3]
|
||||
300.0 0.8 #Qp# %quality factor for P waves (Qp*f^a); list(Qp, a)
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#settings local magnitude#
|
||||
1.0 1.0 1.0 #WAscaling# %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] If zeros are set, original Richter magnitude is calculated!
|
||||
1.0 1.0 #magscaling# %Scaling relation for derived local magnitude [a*Ml+b]. If zeros are set, no scaling of network magnitude is applied!
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#filter settings#
|
||||
0.01 0.01 #minfreq# %Lower filter frequency [P, S]
|
||||
0.5 0.5 #maxfreq# %Upper filter frequency [P, S]
|
||||
3 3 #filter_order# %filter order [P, S]
|
||||
bandpass bandpass #filter_type# %filter type (bandpass, bandstop, lowpass, highpass) [P, S]
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#common settings picker#
|
||||
global #extent# %extent of array ("local", "regional" or "global")
|
||||
-100.0 #pstart# %start time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
|
||||
350.0 #pstop# %end time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
|
||||
200.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
|
||||
875.0 #sstop# %end time [s] after P-onset for calculating CF for S-picking
|
||||
True #use_taup# %use estimated traveltimes from TauPy for calculating windows for CF
|
||||
IASP91 #taup_model# %define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
|
||||
0.01 0.1 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
|
||||
0.001 0.5 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
|
||||
0.01 0.5 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
|
||||
0.001 0.5 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz]
|
||||
#special settings for calculating CF#
|
||||
%!!Edit the following only if you know what you are doing!!%
|
||||
#Z-component#
|
||||
HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
|
||||
100.0 #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
|
||||
24.0 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
|
||||
20.0 #tpred1z# %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
|
||||
16.0 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
|
||||
8.0 #tpred2z# %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
|
||||
0.5 #addnoise# %add noise to seismogram for stable AR prediction
|
||||
30.0 5.0 20.0 10.0 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
|
||||
55.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
|
||||
20.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
|
||||
6.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
|
||||
4.0 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
|
||||
0.5 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
|
||||
1.1 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
|
||||
50.0 #checkwindowP# %time window before HOS/AR-maximum to check for smaller maxima [s]
|
||||
0.7 #minfactorP# %Second maximum must be at least minfactor * first maximum [-]
|
||||
#H-components#
|
||||
ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
|
||||
30.0 #tdet1h# %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
|
||||
18.0 #tpred1h# %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
|
||||
16.0 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
|
||||
8.0 #tpred2h# %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
|
||||
4 #Sarorder# %for AR-picker, order of AR process of H-components
|
||||
30.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
|
||||
195.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
|
||||
30.0 10.0 15.0 10.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
|
||||
22.0 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
|
||||
10.0 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
|
||||
0.001 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
|
||||
1.2 #nfacS# %for AR-picker, noise factor for noise level determination (S)
|
||||
250.0 #checkwindowS# %time window before AR-maximum to check for smaller maxima [s]
|
||||
0.4 #minfactorS# %Second maximum must be at least minfactor * first maximum [-]
|
||||
#first-motion picker#
|
||||
1 #minfmweight# %minimum required P weight for first-motion determination
|
||||
3.0 #minFMSNR# %miniumum required SNR for first-motion determination
|
||||
10.0 #fmpickwin# %pick window around P onset for calculating zero crossings
|
||||
#quality assessment#
|
||||
4.0 8.0 12.0 16.0 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
|
||||
4.0 8.0 12.0 16.0 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
|
||||
1.0 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
|
||||
1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
|
||||
1.0 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
|
||||
1.1 #minAICSSNR# %below this SNR the initial S pick is rejected
|
||||
12.0 #minsiglength# %length of signal part for which amplitudes must exceed noiselevel [s]
|
||||
1.1 #noisefactor# %noiselevel*noisefactor=threshold
|
||||
20.0 #minpercent# %required percentage of amplitudes exceeding threshold
|
||||
1.25 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
|
||||
60.0 #mdttolerance# %maximum allowed deviation of P picks from median [s]
|
||||
60.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
|
||||
5.0 #jackfactor# %pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,21 @@
|
||||
<?xml version='1.0' encoding='utf-8'?>
|
||||
<q:quakeml xmlns:q="http://quakeml.org/xmlns/quakeml/1.2" xmlns="http://quakeml.org/xmlns/bed/1.2">
|
||||
<eventParameters publicID="smi:local/53a38563-739a-48b2-9f34-bf40ee7b656a">
|
||||
<event publicID="smi:local/e0001.024.16">
|
||||
<origin publicID="smi:local/e0001.024.16">
|
||||
<time>
|
||||
<value>2016-01-24T10:30:30.000000Z</value>
|
||||
</time>
|
||||
<latitude>
|
||||
<value>59.66</value>
|
||||
</latitude>
|
||||
<longitude>
|
||||
<value>-153.45</value>
|
||||
</longitude>
|
||||
<depth>
|
||||
<value>128.0</value>
|
||||
</depth>
|
||||
</origin>
|
||||
</event>
|
||||
</eventParameters>
|
||||
</q:quakeml>
|
||||
1
tests/test_autopickstation/e0001.024.16/notes.txt
Normal file
1
tests/test_autopickstation/e0001.024.16/notes.txt
Normal file
@@ -0,0 +1 @@
|
||||
/data/AlpArray/mini_SEED_LH/2016-01-24T10:30:30
|
||||
163
tests/test_autopickstation/test_autopickstation.py
Normal file
163
tests/test_autopickstation/test_autopickstation.py
Normal file
@@ -0,0 +1,163 @@
|
||||
import unittest
|
||||
from unittest import skip
|
||||
import obspy
|
||||
from obspy import UTCDateTime
|
||||
import os
|
||||
import sys
|
||||
from pylot.core.pick.autopick import autopickstation
|
||||
from pylot.core.io.inputs import PylotParameter
|
||||
from pylot.core.io.data import Data
|
||||
from pylot.core.util.utils import trim_station_components
|
||||
|
||||
|
||||
class HidePrints:
|
||||
"""
|
||||
Used to hide all standard output the Function to be tested have, since it clutters the test results.
|
||||
"""
|
||||
def __enter__(self):
|
||||
self._original_stdout = sys.stdout
|
||||
devnull = open(os.devnull, "w")
|
||||
sys.stdout = devnull
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
sys.stdout = self._original_stdout
|
||||
|
||||
|
||||
class MockParser:
|
||||
|
||||
@staticmethod
|
||||
def get_coordinates(station_id):
|
||||
"""
|
||||
Mocks the method get_coordinates from obspy.io.xseed.parser.Parser object
|
||||
to avoid building a parser for the unit tests
|
||||
:param station_id: 'GR.GRA1..LHZ' or similar
|
||||
:type station_id: str
|
||||
:return: dictionary containing azimuth, dip, elevation, latitude, longitude,
|
||||
local depth as keys
|
||||
:rtype: dict
|
||||
|
||||
>>>MockParser.get_coordinates('GR.GRA2')
|
||||
{u'azimuth': 0.0, u'dip': -90.0, u'elevation': 512.0, u'latitude': 49.655208, u'local_depth': 0.0, u'longitude': 11.359444}
|
||||
"""
|
||||
station_names = ['GR.GRA1', 'GR.GRA2', 'G.ECH']
|
||||
gra1 = {u'azimuth': 0.0, u'dip': -90.0, u'elevation': 499.5, u'latitude': 49.691888, u'local_depth': 0.0, u'longitude': 11.22172}
|
||||
gra2 = {u'azimuth': 0.0, u'dip': -90.0, u'elevation': 512.0, u'latitude': 49.655208, u'local_depth': 0.0, u'longitude': 11.359444}
|
||||
ech = {u'azimuth': 90.0, u'dip': 0.0, u'elevation': 580.0, u'latitude': 48.216313, u'local_depth': 250.0, u'longitude': 7.158961}
|
||||
coordinates = [gra1, gra2, ech]
|
||||
|
||||
for index, name in enumerate(station_names):
|
||||
if station_id.startswith(name):
|
||||
return coordinates[index]
|
||||
|
||||
|
||||
class TestAutopickStation(unittest.TestCase):
|
||||
"""
|
||||
Test the autopickstation function as if it were called from GUI.
|
||||
Three stations (GR.GRA1, GR.GRA2, G.ECH) are tested with and without TauPy respectively
|
||||
"""
|
||||
|
||||
def setUp(self):
|
||||
self.event_id = 'e0001.024.16'
|
||||
# Create wfstream for picking
|
||||
mseed_relative_path = os.path.join(os.path.dirname(__file__), self.event_id, '*.mseed')
|
||||
self.wfstream = obspy.read(mseed_relative_path)
|
||||
# trim waveform to get the same results as the GUI call
|
||||
with HidePrints():
|
||||
self.wfstream = trim_station_components(self.wfstream, trim_start=True, trim_end=False)
|
||||
self.gra1 = self.wfstream.select(station='GRA1')
|
||||
self.gra2 = self.wfstream.select(station='GRA2')
|
||||
self.ech = self.wfstream.select(station='ECH')
|
||||
self.fiesa = self.wfstream.select(station='FIESA')
|
||||
# Create input parameter container
|
||||
self.inputfile_taupy_enabled = os.path.join(os.path.dirname(__file__), 'autoPyLoT_global_taupy_true.in')
|
||||
self.inputfile_taupy_disabled = os.path.join(os.path.dirname(__file__), 'autoPyLoT_global_taupy_false.in')
|
||||
self.pickparam_taupy_enabled = PylotParameter(fnin=self.inputfile_taupy_enabled)
|
||||
self.pickparam_taupy_disabled = PylotParameter(fnin=self.inputfile_taupy_disabled)
|
||||
self.xml_file = os.path.join(os.path.dirname(__file__),self.event_id, 'PyLoT_'+self.event_id+'.xml')
|
||||
self.data = Data(evtdata=self.xml_file)
|
||||
# create origin for taupy testing
|
||||
self.origin = [obspy.core.event.origin.Origin(magnitude=7.1, latitude=59.66, longitude=-153.45, depth=128.0, time=UTCDateTime("2016-01-24T10:30:30.0"))]
|
||||
# mocking metadata since reading it takes a long time to read from file
|
||||
self.metadata = ('dless', MockParser())
|
||||
|
||||
@staticmethod
|
||||
def construct_error_message(station_id, taupy_status, expected, got, phase=''):
|
||||
error_message = """Difference in auto {phase} picks for station {station_id} with taupy {status}.\n
|
||||
Expeceted: {exp}\n
|
||||
Got: {got}\n""".format(station_id=station_id, status='enabled' if taupy_status else 'disabled', phase=phase, exp=expected, got=got)
|
||||
return error_message
|
||||
|
||||
#@skip("Works")
|
||||
def test_autopickstation_taupy_disabled_gra1(self):
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 34.718816470730317, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000), 'w0': None, 'spe': 0.93333333333333235, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'fm': 'D', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665, 'channel': u'LHE'}, 'station': u'GRA1'}
|
||||
with HidePrints():
|
||||
result = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
|
||||
error_message = self.construct_error_message('GR.GRA1', False, expected, result)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual(expected['station'], result['station'])
|
||||
|
||||
#@skip("Works")
|
||||
def test_autopickstation_taupy_enabled_gra1(self):
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 15.599905299126778, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 36.307013769185403, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 27, 690000), 'w0': None, 'spe': 0.93333333333333235, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 24, 890000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 28, 690000), 'fm': 'U', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.669661906545489, 'network': u'GR', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 30, 690000), 'snr': 11.667187857573905, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 690000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 29, 690000), 'fm': None, 'spe': 2.6666666666666665, 'channel': u'LHE'}, 'station': u'GRA1'}
|
||||
with HidePrints():
|
||||
result = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
error_message = self.construct_error_message('GR.GRA1', True, expected, result)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual(expected['station'], result['station'])
|
||||
|
||||
#@skip("works")
|
||||
def test_autopickstation_taupy_disabled_gra2(self):
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'shortsignallength', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'w0': None, 'spe': None, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000), 'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'GR', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 37, 15, 150000), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 36, 43, 150000), 'mpp': UTCDateTime(2016, 1, 24, 10, 36, 59, 150000), 'fm': None, 'spe': None, 'channel': u'LHE'}, 'station': u'GRA2'}
|
||||
with HidePrints():
|
||||
result = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
|
||||
error_message = self.construct_error_message('GR.GRA2', False, expected, result)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual(expected['station'], result['station'])
|
||||
|
||||
#skip("")
|
||||
def test_autopickstation_taupy_enabled_gra2(self):
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 13.957959025719253, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 24.876879503607871, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 29, 150000), 'w0': None, 'spe': 1.0, 'network': u'GR', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 26, 150000), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 30, 150000), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.573236990555648, 'network': u'GR', 'weight': 1, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 34, 150000), 'snr': 11.410999834108294, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 21, 150000), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 33, 150000), 'fm': None, 'spe': 4.666666666666667, 'channel': u'LHE'}, 'station': u'GRA2'}
|
||||
with HidePrints():
|
||||
result = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin = self.origin)
|
||||
error_message = self.construct_error_message('GR.GRA2', True, expected, result)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual(expected['station'], result['station'])
|
||||
|
||||
#@skip("Works")
|
||||
def test_autopickstation_taupy_disabled_ech(self):
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57), 'w0': None, 'spe': None, 'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 26, 41), 'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'G', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 27, 13), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 26, 41), 'mpp': UTCDateTime(2016, 1, 24, 10, 26, 57), 'fm': None, 'spe': None, 'channel': u'LHE'}, 'station': u'ECH'}
|
||||
with HidePrints():
|
||||
result = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_disabled)
|
||||
error_message = self.construct_error_message('G.ECH', False, expected, result)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual(expected['station'], result['station'])
|
||||
|
||||
#@skip("Works")
|
||||
def test_autopickstation_taupy_enabled_ech(self):
|
||||
# this station has a long time of before the first onset, so taupy will help during picking
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 9.9753586609166316, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 9.9434218804137107, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'w0': None, 'spe': 1.6666666666666667, 'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 29), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 35), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 12.698999454169567, 'network': u'G', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 44), 'snr': 18.616581906366577, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 33), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 43), 'fm': None, 'spe': 3.3333333333333335, 'channel': u'LHE'}, 'station': u'ECH'}
|
||||
with HidePrints():
|
||||
result = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
error_message = self.construct_error_message('G.ECH', True, expected, result)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual(expected['station'], result['station'])
|
||||
|
||||
@skip("Results are just a copy of ech")
|
||||
def test_autopickstation_taupy_disabled_fiesa(self):
|
||||
# this station has a long time of before the first onset, so taupy will help during picking
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 9.9753586609166316, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 9.9434218804137107, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'w0': None, 'spe': 1.6666666666666667, 'network': u'G', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 29), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 35), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 12.698999454169567, 'network': u'G', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 44), 'snr': 18.616581906366577, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 33), 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 43), 'fm': None, 'spe': 3.3333333333333335, 'channel': u'LHE'}, 'station': u'ECH'}
|
||||
with HidePrints():
|
||||
result = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_disabled)
|
||||
error_message = self.construct_error_message('CH.FIESE', True, expected, result)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual(expected['station'], result['station'])
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user