[merge] feature/refactor into develop
This commit is contained in:
78
tests/testPickingResults.py
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78
tests/testPickingResults.py
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@@ -0,0 +1,78 @@
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import unittest
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from pylot.core.pick.autopick import PickingResults
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class TestPickingResults(unittest.TestCase):
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def setUp(self):
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self.pr = PickingResults()
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def test_non_existing_key_dot_access(self):
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"""Accessing an attribute in the class that wasnt added to the dict should give a AttributeError"""
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with self.assertRaises(AttributeError):
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self.pr.doesntexist
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def test_non_existing_key_dict_access(self):
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"""Accessing a missing attribute in a dictionary throws a KeyError"""
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with self.assertRaises(KeyError):
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self.pr['keydoesnotexist']
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def test_dot_member_creation(self):
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self.pr.x = 0
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self.assertEqual(self.pr.x, 0)
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self.pr.x += 42
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self.assertEqual(self.pr.x, 42)
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def test_dot_builtin_member(self):
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self.assertEqual(self.pr.weight, 4)
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self.pr.weight = 99
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self.assertEqual(self.pr.weight, 99)
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def test_key_access(self):
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self.pr['y'] = 11
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self.assertEqual(self.pr['y'], 11)
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def test_builtin_fields(self):
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self.assertEqual(self.pr['weight'], 4)
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def test_in(self):
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self.assertFalse('keydoesnotexist' in self.pr)
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self.pr['k'] = 0
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self.assertTrue('k' in self.pr)
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def test_keys_function(self):
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a = 99
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self.pr.newkey = a
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self.assertIn(a, self.pr.values())
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self.assertIn('newkey', self.pr.keys())
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def test_len_and_clear(self):
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self.pr.clear()
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self.assertEqual(len(self.pr), 0)
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self.pr.a = 6
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self.pr['b'] = 9
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self.assertEqual(len(self.pr), 2)
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def test_get_default(self):
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self.assertEqual(self.pr.get('keynotexisting', 42), 42)
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weight = self.pr.get('weight', -1)
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self.assertEqual(weight, 4)
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self.assertNotEqual(weight, -1)
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def test_dunder_attributes(self):
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"""Storing Pythons special dunder method in a dictionary is valid and should not override the instances dunder
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methods"""
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prev_len = len(self.pr)
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try:
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self.pr['__len__'] = None
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except Exception:
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self.fail("test_dunder_attributes failed to add a dunder attribute to the dictionary keys")
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try:
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curr_len = len(self.pr)
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except Exception:
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self.fail("test_dunder_attributes overwrote an instance internal dunder method")
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self.assertEqual(prev_len+1, curr_len) # +1 for the added __len__ key/value-pair
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self.pr.__len__ = 42
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self.assertEqual(42, self.pr['__len__'])
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self.assertEqual(prev_len+1, curr_len, msg="__len__ was overwritten")
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35
tests/test_PickingParameters.py
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35
tests/test_PickingParameters.py
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@@ -0,0 +1,35 @@
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import unittest
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from pylot.core.pick.autopick import PickingParameters
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class TestPickingParameters(unittest.TestCase):
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def setUp(self):
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self.simple_dict = {'a': 3, 'b': 14}
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self.nested_dict = {'a': self.simple_dict, 'b': self.simple_dict}
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def assertParameterEquality(self, dic, instance):
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"""Test wether all parameters given in dic are found in instance"""
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for key, value in dic.items():
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self.assertEqual(value, getattr(instance, key))
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def test_add_params_from_dict_simple(self):
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pickparam = PickingParameters()
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pickparam.add_params_from_dict(self.simple_dict)
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self.assertParameterEquality(self.simple_dict, pickparam)
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def test_add_params_from_dict_nested(self):
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pickparam = PickingParameters()
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pickparam.add_params_from_dict(self.nested_dict)
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self.assertParameterEquality(self.nested_dict, pickparam)
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def test_init(self):
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pickparam = PickingParameters(self.simple_dict)
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self.assertParameterEquality(self.simple_dict, pickparam)
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def test_dot_access(self):
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pickparam = PickingParameters(self.simple_dict)
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self.assertEqual(pickparam.a, self.simple_dict['a'])
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if __name__ == '__main__':
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unittest.main()
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104
tests/test_autopickstation/autoPyLoT_global_taupy_false.in
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104
tests/test_autopickstation/autoPyLoT_global_taupy_false.in
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@@ -0,0 +1,104 @@
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%This is a parameter input file for PyLoT/autoPyLoT.
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%All main and special settings regarding data handling
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%and picking are to be set here!
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%Parameters are optimized for %extent data sets!
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#main settings#
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/home/darius #rootpath# %project path
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alparray #datapath# %data path
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waveforms_used #database# %name of data base
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e0093.173.16 #eventID# %event ID for single event processing (* for all events found in database)
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/home/darius/alparray/metadata #invdir# %full path to inventory or dataless-seed file
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PILOT #datastructure# %choose data structure
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True #apverbose# %choose 'True' or 'False' for terminal output
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#NLLoc settings#
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None #nllocbin# %path to NLLoc executable
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/home/darius/alparray/auto #nllocroot# %root of NLLoc-processing directory
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AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
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Insheim_min1d032016_auto.in #ctrfile# %name of autoPyLoT-output control file for NLLoc
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ttime #ttpatter# %pattern of NLLoc ttimes from grid
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AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#parameters for seismic moment estimation#
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3530.0 #vp# %average P-wave velocity
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2500.0 #rho# %average rock density [kg/m^3]
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300.0 0.8 #Qp# %quality factor for P waves (Qp*f^a); list(Qp, a)
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#settings local magnitude#
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1.0 1.0 1.0 #WAscaling# %Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] If zeros are set, original Richter magnitude is calculated!
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1.0 1.0 #magscaling# %Scaling relation for derived local magnitude [a*Ml+b]. If zeros are set, no scaling of network magnitude is applied!
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#filter settings#
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0.01 0.01 #minfreq# %Lower filter frequency [P, S]
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0.5 0.5 #maxfreq# %Upper filter frequency [P, S]
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3 3 #filter_order# %filter order [P, S]
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bandpass bandpass #filter_type# %filter type (bandpass, bandstop, lowpass, highpass) [P, S]
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#common settings picker#
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global #extent# %extent of array ("local", "regional" or "global")
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-100.0 #pstart# %start time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
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350.0 #pstop# %end time [s] for calculating CF for P-picking (if TauPy: seconds relative to estimated onset)
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200.0 #sstart# %start time [s] relative to P-onset for calculating CF for S-picking
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875.0 #sstop# %end time [s] after P-onset for calculating CF for S-picking
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False #use_taup# %use estimated traveltimes from TauPy for calculating windows for CF
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IASP91 #taup_model# %define TauPy model for traveltime estimation. Possible values: 1066a, 1066b, ak135, ak135f, herrin, iasp91, jb, prem, pwdk, sp6
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0.01 0.1 #bpz1# %lower/upper corner freq. of first band pass filter Z-comp. [Hz]
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0.001 0.5 #bpz2# %lower/upper corner freq. of second band pass filter Z-comp. [Hz]
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0.01 0.5 #bph1# %lower/upper corner freq. of first band pass filter H-comp. [Hz]
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0.001 0.5 #bph2# %lower/upper corner freq. of second band pass filter z-comp. [Hz]
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#special settings for calculating CF#
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%!!Edit the following only if you know what you are doing!!%
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#Z-component#
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HOS #algoP# %choose algorithm for P-onset determination (HOS, ARZ, or AR3)
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100.0 #tlta# %for HOS-/AR-AIC-picker, length of LTA window [s]
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4 #hosorder# %for HOS-picker, order of Higher Order Statistics
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2 #Parorder# %for AR-picker, order of AR process of Z-component
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24.0 #tdet1z# %for AR-picker, length of AR determination window [s] for Z-component, 1st pick
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20.0 #tpred1z# %for AR-picker, length of AR prediction window [s] for Z-component, 1st pick
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16.0 #tdet2z# %for AR-picker, length of AR determination window [s] for Z-component, 2nd pick
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8.0 #tpred2z# %for AR-picker, length of AR prediction window [s] for Z-component, 2nd pick
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0.5 #addnoise# %add noise to seismogram for stable AR prediction
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30.0 5.0 20.0 10.0 #tsnrz# %for HOS/AR, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
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55.0 #pickwinP# %for initial AIC pick, length of P-pick window [s]
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20.0 #Precalcwin# %for HOS/AR, window length [s] for recalculation of CF (relative to 1st pick)
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6.0 #aictsmooth# %for HOS/AR, take average of samples for smoothing of AIC-function [s]
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4.0 #tsmoothP# %for HOS/AR, take average of samples for smoothing CF [s]
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0.5 #ausP# %for HOS/AR, artificial uplift of samples (aus) of CF (P)
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1.1 #nfacP# %for HOS/AR, noise factor for noise level determination (P)
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50.0 #checkwindowP# %time window before HOS/AR-maximum to check for smaller maxima [s]
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0.7 #minfactorP# %Second maximum must be at least minfactor * first maximum [-]
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#H-components#
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ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
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30.0 #tdet1h# %for HOS/AR, length of AR-determination window [s], H-components, 1st pick
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18.0 #tpred1h# %for HOS/AR, length of AR-prediction window [s], H-components, 1st pick
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16.0 #tdet2h# %for HOS/AR, length of AR-determinaton window [s], H-components, 2nd pick
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8.0 #tpred2h# %for HOS/AR, length of AR-prediction window [s], H-components, 2nd pick
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4 #Sarorder# %for AR-picker, order of AR process of H-components
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30.0 #Srecalcwin# %for AR-picker, window length [s] for recalculation of CF (2nd pick) (H)
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195.0 #pickwinS# %for initial AIC pick, length of S-pick window [s]
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30.0 10.0 15.0 10.0 #tsnrh# %for ARH/AR3, window lengths for SNR-and slope estimation [tnoise, tsafetey, tsignal, tslope] [s]
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22.0 #aictsmoothS# %for AIC-picker, take average of samples for smoothing of AIC-function [s]
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10.0 #tsmoothS# %for AR-picker, take average of samples for smoothing CF [s] (S)
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0.001 #ausS# %for HOS/AR, artificial uplift of samples (aus) of CF (S)
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1.2 #nfacS# %for AR-picker, noise factor for noise level determination (S)
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250.0 #checkwindowS# %time window before AR-maximum to check for smaller maxima [s]
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0.4 #minfactorS# %Second maximum must be at least minfactor * first maximum [-]
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#first-motion picker#
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1 #minfmweight# %minimum required P weight for first-motion determination
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3.0 #minFMSNR# %miniumum required SNR for first-motion determination
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10.0 #fmpickwin# %pick window around P onset for calculating zero crossings
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#quality assessment#
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4.0 8.0 12.0 16.0 #timeerrorsP# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for P
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4.0 8.0 12.0 16.0 #timeerrorsS# %discrete time errors [s] corresponding to picking weights [0 1 2 3] for S
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0.01 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
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1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
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0.01 #minAICSslope# %below this slope [counts/s] the initial S pick is rejected
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1.1 #minAICSSNR# %below this SNR the initial S pick is rejected
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12.0 #minsiglength# %length of signal part for which amplitudes must exceed noiselevel [s]
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1.1 #noisefactor# %noiselevel*noisefactor=threshold
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20.0 #minpercent# %required percentage of amplitudes exceeding threshold
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1.25 #zfac# %P-amplitude must exceed at least zfac times RMS-S amplitude
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60.0 #mdttolerance# %maximum allowed deviation of P picks from median [s]
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60.0 #wdttolerance# %maximum allowed deviation from Wadati-diagram
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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
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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 @@
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%This is a parameter input file for PyLoT/autoPyLoT.
|
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%All main and special settings regarding data handling
|
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%and picking are to be set here!
|
||||
%Parameters are optimized for %extent data sets!
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#main settings#
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/home/darius #rootpath# %project path
|
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alparray #datapath# %data path
|
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waveforms_used #database# %name of data base
|
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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
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PILOT #datastructure# %choose data structure
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True #apverbose# %choose 'True' or 'False' for terminal output
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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#NLLoc settings#
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None #nllocbin# %path to NLLoc executable
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/home/darius/alparray/auto #nllocroot# %root of NLLoc-processing directory
|
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AUTOPHASES.obs #phasefile# %name of autoPyLoT-output phase file for NLLoc
|
||||
Insheim_min1d032016_auto.in #ctrfile# %name of autoPyLoT-output control file for NLLoc
|
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ttime #ttpatter# %pattern of NLLoc ttimes from grid
|
||||
AUTOLOC_nlloc #outpatter# %pattern of NLLoc-output file
|
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
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#parameters for seismic moment estimation#
|
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3530.0 #vp# %average P-wave velocity
|
||||
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]
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||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
#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]
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||||
0.7 #minfactorP# %Second maximum must be at least minfactor * first maximum [-]
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#H-components#
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ARH #algoS# %choose algorithm for S-onset determination (ARH or AR3)
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||||
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 [-]
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||||
#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
|
||||
0.01 #minAICPslope# %below this slope [counts/s] the initial P pick is rejected
|
||||
1.1 #minAICPSNR# %below this SNR the initial P pick is rejected
|
||||
0.01 #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
|
||||
211
tests/test_autopickstation/test_autopickstation.py
Normal file
211
tests/test_autopickstation/test_autopickstation.py
Normal file
@@ -0,0 +1,211 @@
|
||||
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 __init__(self, hide_prints=True):
|
||||
"""Create object with hide_prints=False to disable print hiding"""
|
||||
self.hide = hide_prints
|
||||
|
||||
def __enter__(self):
|
||||
if self.hide:
|
||||
self._original_stdout = sys.stdout
|
||||
devnull = open(os.devnull, "w")
|
||||
sys.stdout = devnull
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
if self.hide:
|
||||
sys.stdout = self._original_stdout
|
||||
|
||||
|
||||
class MockMetadata:
|
||||
"""Mock metadata object used for taupy to avoid reading large dless file from disk.
|
||||
get_coordinates must take the same arguments as pylot.core.utils.dataprocssing.py/class Metadata."""
|
||||
|
||||
def __init__(self):
|
||||
self.station_names = ['GR.GRA1', 'GR.GRA2', 'G.ECH', 'CH.FIESA', 'Z3.A106A']
|
||||
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}
|
||||
fiesa = {'azimuth': 0.0, 'dip': -90.0, 'elevation': 2340.5, 'latitude': 46.43521, 'local_depth': 0.0,
|
||||
'longitude': 8.11051}
|
||||
a106 = {'azimuth': 90.0, 'dip': 0.0, 'elevation': 468.0, 'latitude': 48.753388, 'local_depth': 0.0,
|
||||
'longitude': 9.721937}
|
||||
|
||||
self.coordinates = [gra1, gra2, ech, fiesa, a106]
|
||||
|
||||
def get_coordinates(self, station_id, time=None):
|
||||
"""
|
||||
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
|
||||
|
||||
>>>m = MockMetadata(); m.get_coordinates('GR.GRA2..LHZ')
|
||||
{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}
|
||||
"""
|
||||
|
||||
for index, name in enumerate(self.station_names):
|
||||
if station_id.startswith(name):
|
||||
return self.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')
|
||||
self.a106 = self.wfstream.select(station='A106A')
|
||||
self.a005a = self.wfstream.select(station='A005A')
|
||||
# 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 = MockMetadata()
|
||||
|
||||
# show complete diff when difference in results dictionaries are found
|
||||
self.maxDiff = None
|
||||
|
||||
#@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'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('GRA1', station)
|
||||
|
||||
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'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.gra1, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('GRA1', station)
|
||||
|
||||
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'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('GRA2', station)
|
||||
|
||||
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'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.gra2, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin = self.origin)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('GRA2', station)
|
||||
|
||||
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'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_disabled)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('ECH', station)
|
||||
|
||||
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'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.ech, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('ECH', station)
|
||||
|
||||
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': None, 'weight': 9, 'Mo': None, 'marked': 'SinsteadP', 'Mw': None, 'fc': None, 'snr': None, 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58), 'w0': None, 'spe': None, 'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 35, 42), 'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'fm': 'N', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'CH', 'weight': 4, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 36, 14), 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 35, 42), 'mpp': UTCDateTime(2016, 1, 24, 10, 35, 58), 'fm': None, 'spe': None, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_disabled)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('FIESA', station)
|
||||
|
||||
def test_autopickstation_taupy_enabled_fiesa(self):
|
||||
# this station has a long time of before the first onset, so taupy will help during picking
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 13.921049277904373, 'weight': 0, 'Mo': None, 'marked': [], 'Mw': None, 'fc': None, 'snr': 24.666352170589487, 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 47), 'w0': None, 'spe': 1.2222222222222285, 'network': u'CH', 'epp': UTCDateTime(2016, 1, 24, 10, 41, 43, 333333), 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 48), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.893086316477728, 'network': u'CH', 'weight': 0, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 51, 5), 'snr': 12.283118216397849, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 59, 333333), 'mpp': UTCDateTime(2016, 1, 24, 10, 51, 2), 'fm': None, 'spe': 2.8888888888888764, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.fiesa, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
self.assertDictContainsSubset(expected=expected['P'], actual=result['P'])
|
||||
self.assertDictContainsSubset(expected=expected['S'], actual=result['S'])
|
||||
self.assertEqual('FIESA', station)
|
||||
|
||||
def test_autopickstation_gra1_z_comp_missing(self):
|
||||
"""Picking on a stream without a vertical trace should return None"""
|
||||
wfstream = self.gra1.copy()
|
||||
wfstream = wfstream.select(channel='*E') + wfstream.select(channel='*N')
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
|
||||
self.assertIsNone(result)
|
||||
self.assertEqual('GRA1', station)
|
||||
|
||||
def test_autopickstation_gra1_horizontal_comps_missing(self):
|
||||
"""Picking on a stream without horizontal traces should still pick the P phase on the vertical component"""
|
||||
wfstream = self.gra1.copy()
|
||||
wfstream = wfstream.select(channel='*Z')
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 15.405649120980094, 'network': u'GR', 'weight': 0, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 32, 690000), 'Mw': None, 'fc': None, 'snr': 34.718816470730317, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 28, 890000), 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 31, 690000), 'w0': None, 'spe': 0.9333333333333323, 'fm': 'D', 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': None, 'weight': 4, 'Mo': None, 'Ao': None, 'lpp': None, 'Mw': None, 'fc': None, 'snr': None, 'marked': [], 'mpp': None, 'w0': None, 'spe': None, 'epp': None, 'fm': 'N', 'channel': None}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=wfstream, pickparam=self.pickparam_taupy_disabled, metadata=(None, None))
|
||||
self.assertEqual(expected, result)
|
||||
self.assertEqual('GRA1', station)
|
||||
|
||||
def test_autopickstation_a106_taupy_enabled(self):
|
||||
"""This station has invalid values recorded on both N and E component, but a pick can still be found on Z"""
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 12.862128789922826, 'network': u'Z3', 'weight': 0, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 34), 'Mw': None, 'fc': None, 'snr': 19.329155459132608, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 30), 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 33), 'w0': None, 'spe': 1.6666666666666667, 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': None, 'network': u'Z3', 'weight': 4, 'Ao': None, 'Mo': None, 'marked': [], 'lpp': UTCDateTime(2016, 1, 24, 10, 28, 56), 'Mw': None, 'fc': None, 'snr': None, 'epp': UTCDateTime(2016, 1, 24, 10, 28, 24), 'mpp': UTCDateTime(2016, 1, 24, 10, 28, 40), 'w0': None, 'spe': None, 'fm': None, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream=self.a106, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
self.assertEqual(expected, result)
|
||||
|
||||
def test_autopickstation_station_missing_in_metadata(self):
|
||||
"""This station is not in the metadata, but Taupy is enabled. Taupy should exit cleanly and modify the starttime
|
||||
relative to the theoretical onset to one relative to the traces starttime, eg never negative.
|
||||
"""
|
||||
self.pickparam_taupy_enabled.setParamKV('pstart', -100) # modify starttime to be relative to theoretical onset
|
||||
expected = {'P': {'picker': 'auto', 'snrdb': 14.464757855513506, 'network': u'Z3', 'weight': 0, 'Mo': None, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 41, 39, 605000), 'Mw': None, 'fc': None, 'snr': 27.956048519707181, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 41, 38, 605000), 'w0': None, 'spe': 1.6666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 41, 35, 605000), 'fm': None, 'channel': u'LHZ'}, 'S': {'picker': 'auto', 'snrdb': 10.112844176301248, 'network': u'Z3', 'weight': 1, 'Mo': None, 'Ao': None, 'lpp': UTCDateTime(2016, 1, 24, 10, 50, 51, 605000), 'Mw': None, 'fc': None, 'snr': 10.263238413785425, 'marked': [], 'mpp': UTCDateTime(2016, 1, 24, 10, 50, 48, 605000), 'w0': None, 'spe': 4.666666666666667, 'epp': UTCDateTime(2016, 1, 24, 10, 50, 40, 605000), 'fm': None, 'channel': u'LHE'}}
|
||||
with HidePrints():
|
||||
result, station = autopickstation(wfstream = self.a005a, pickparam=self.pickparam_taupy_enabled, metadata=self.metadata, origin=self.origin)
|
||||
self.assertEqual(expected, result)
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
56
tests/test_get_quality_class.py
Normal file
56
tests/test_get_quality_class.py
Normal file
@@ -0,0 +1,56 @@
|
||||
import unittest
|
||||
from pylot.core.pick.utils import get_quality_class
|
||||
|
||||
|
||||
class TestQualityClassFromUncertainty(unittest.TestCase):
|
||||
"""
|
||||
Test function that assigns a quality value [0...4] to a pick uncertainty.
|
||||
The pick uncertainty is compared to the error classes.
|
||||
A pick uncertainty that is below the first error class is assigned the best quality, quality 0.
|
||||
A pick uncertainty that is above the first error class but below the second is assigned quality 1 and so on.
|
||||
A pick uncertainty that is larger than the biggest error class is assigned quality 4.
|
||||
The upper border of a quality class is inclusive, the lower border exclusive. Meaning if a value is exactly on the
|
||||
border between two classes, it is assigned into the higher quality class (represented by the lower number).
|
||||
"""
|
||||
|
||||
def setUp(self):
|
||||
# entries hold upper/lower bound of error classes
|
||||
self.error_classes = [float(x) for x in range(1, 9, 2)]
|
||||
# [1.0, 3.0, 5.0, 7.0]
|
||||
|
||||
def test_out_of_lower_bound(self):
|
||||
# Error out of lower bound of classes
|
||||
self.assertEqual(0, get_quality_class(0.5, self.error_classes))
|
||||
|
||||
def test_out_of_upper_bound(self):
|
||||
# Error out of upper bound of error classes
|
||||
self.assertEqual(4, get_quality_class(14.7, self.error_classes))
|
||||
|
||||
def test_on_lower_border(self):
|
||||
# Error exactly on lower bound
|
||||
self.assertEqual(0, get_quality_class(1., self.error_classes))
|
||||
|
||||
def test_on_upper_border(self):
|
||||
# Error exactly on upper bound
|
||||
self.assertEqual(3, get_quality_class(7., self.error_classes))
|
||||
|
||||
def test_on_middle_border_inclusive(self):
|
||||
# Error exactly between two classes, since lower bound is exclusive and upper bound is inclusive it should
|
||||
# fall into the class with better quality
|
||||
self.assertEqual(1, get_quality_class(3., self.error_classes))
|
||||
self.assertNotEqual(2, get_quality_class(3., self.error_classes))
|
||||
|
||||
def test_in_class1(self):
|
||||
# Error exactly in class 1
|
||||
self.assertEqual(1, get_quality_class(1.5, self.error_classes))
|
||||
|
||||
def test_in_class2(self):
|
||||
# Error exactly in class 2
|
||||
self.assertEqual(2, get_quality_class(3.5, self.error_classes))
|
||||
|
||||
def test_in_class3(self):
|
||||
# Error exactly in class 3
|
||||
self.assertEqual(3, get_quality_class(5.6, self.error_classes))
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user