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[merge] feature/refactor into develop

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This commit is contained in:
Marcel Paffrath 2019-04-12 10:31:29 +02:00
commit 6b9231abd3
25 changed files with 1858 additions and 995 deletions
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2
PyLoT.py
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@ -62,7 +62,7 @@ from pylot.core.io.data import Data
from pylot.core.io.inputs import FilterOptions, PylotParameter
from autoPyLoT import autoPyLoT
from pylot.core.pick.compare import Comparison
from pylot.core.pick.utils import getQualityFromUncertainty
from pylot.core.pick.utils import symmetrize_error, getQualityFromUncertainty, getPickQuality, get_quality_class
from pylot.core.io.phases import picksdict_from_picks
import pylot.core.loc.nll as nll
from pylot.core.util.errors import DatastructureError, \

4
pylot/core/io/inputs.py
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@ -127,9 +127,7 @@ class PylotParameter(object):
:return:
:rtype: bool
"""
if parameter in self.__parameter.keys():
return True
return False
return parameter in self.__parameter.keys()
def get(self, *args):
"""

6
pylot/core/io/phases.py
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@ -944,7 +944,7 @@ def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
:rtype:
"""
from pylot.core.pick.utils import getQualityFromUncertainty
from pylot.core.pick.utils import get_quality_class
from pylot.core.util.utils import loopIdentifyPhase, identifyPhase
# read all onset weights
@ -992,7 +992,7 @@ def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
for Pick in arrivals_copy:
phase = identifyPhase(loopIdentifyPhase(Pick.phase_hint))
if phase == 'P':
Pqual = getQualityFromUncertainty(Pick.time_errors.uncertainty, ErrorsP)
Pqual = get_quality_class(Pick.time_errors.uncertainty, ErrorsP)
if Pqual == 0:
Pw0.append(Pick.time_errors.uncertainty)
elif Pqual == 1:
@ -1004,7 +1004,7 @@ def getQualitiesfromxml(xmlnames, ErrorsP, ErrorsS, plotflag=1):
elif Pqual == 4:
Pw4.append(Pick.time_errors.uncertainty)
elif phase == 'S':
Squal = getQualityFromUncertainty(Pick.time_errors.uncertainty, ErrorsS)
Squal = get_quality_class(Pick.time_errors.uncertainty, ErrorsS)
if Squal == 0:
Sw0.append(Pick.time_errors.uncertainty)
elif Squal == 1:

1925
pylot/core/pick/autopick.py
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16
pylot/core/pick/charfuns.py
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@ -243,6 +243,13 @@ class AICcf(CharacteristicFunction):
class HOScf(CharacteristicFunction):
def __init__(self, data, cut, pickparams):
"""
Call parent constructor while extracting the right parameters:
:param pickparams: PylotParameters instance
"""
super(HOScf, self).__init__(data, cut, pickparams["tlta"], pickparams["hosorder"])
def calcCF(self, data):
"""
Function to calculate skewness (statistics of order 3) or kurtosis
@ -299,6 +306,9 @@ class HOScf(CharacteristicFunction):
class ARZcf(CharacteristicFunction):
def __init__(self, data, cut, t1, t2, pickparams):
super(ARZcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Parorder"], fnoise=pickparams["addnoise"])
def calcCF(self, data):
"""
function used to calculate the AR prediction error from a single vertical trace. Can be used to pick
@ -431,6 +441,9 @@ class ARZcf(CharacteristicFunction):
class ARHcf(CharacteristicFunction):
def __init__(self, data, cut, t1, t2, pickparams):
super(ARHcf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"], fnoise=pickparams["addnoise"])
def calcCF(self, data):
"""
Function to calculate a characteristic function using autoregressive modelling of the waveform of
@ -580,6 +593,9 @@ class ARHcf(CharacteristicFunction):
class AR3Ccf(CharacteristicFunction):
def __init__(self, data, cut, t1, t2, pickparams):
super(AR3Ccf, self).__init__(data, cut, t1=t1, t2=t2, order=pickparams["Sarorder"], fnoise=pickparams["addnoise"])
def calcCF(self, data):
"""
Function to calculate a characteristic function using autoregressive modelling of the waveform of

2
pylot/core/pick/picker.py
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@ -451,7 +451,7 @@ class PragPicker(AutoPicker):
ipick1 = np.argmin(abs(self.Tcf - self.getpick1()))
cfpick1 = 2 * self.cf[ipick1]
# check trend of CF, i.e. differences of CF and adjust aus ("artificial uplift
# check trend of CF, i.e. differences of CF and adjust aus ("artificial uplift
# of picks") regarding this trend
# prominent trend: decrease aus
# flat: use given aus

255
pylot/core/pick/utils.py
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@ -10,7 +10,7 @@
import matplotlib.pyplot as plt
import numpy as np
import warnings
from scipy.signal import argrelmax
from obspy.core import Stream, UTCDateTime
from pylot.core.util.utils import real_Bool, real_None, SetChannelComponents
@ -417,9 +417,9 @@ def getSNR(X, TSNR, t1, tracenum=0):
assert isinstance(X, Stream), "%s is not a stream object" % str(X)
SNR = None
SNRdB = None
noiselevel = None
SNR = -1
SNRdB = -1
noiselevel = -1
x = X[tracenum].data
npts = X[tracenum].stats.npts
@ -489,13 +489,13 @@ def getsignalwin(t, t1, tsignal):
Function to extract data out of time series for signal level calculation.
Returns an array of indices.
:param t: array of time stamps
:type t: `numpy.ndarray`
:type t: `~numpy.ndarray`
:param t1: time from which relative to it signal window is extracted
:type t1: float
:param tsignal: length of time window [s] for signal level calculation
:type tsignal: float
:return: indices of signal window i t
:rtype: `numpy.ndarray`
:return: indices of signal window in t
:rtype: `~numpy.ndarray`
"""
# get signal window
@ -507,6 +507,26 @@ def getsignalwin(t, t1, tsignal):
return isignal
def getslopewin(Tcf, Pick, tslope):
"""
Function to extract slope window out of time series
>>> (np.arange(15., 85.), 30.0, 10.0)
array([15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25])
:param Tcf:
:type Tcf:
:param Pick:
:type Pick:
:param tslope:a
:type tslope:
:return:
:rtype: `numpy.ndarray`
"""
# TODO: fill out docstring
slope = np.where( (Tcf <= min(Pick + tslope, Tcf[-1])) & (Tcf >= Pick) )
return slope[0]
def getResolutionWindow(snr, extent):
"""
Produce the half of the time resolution window width from given SNR value
@ -736,7 +756,7 @@ def RMS(X):
return np.sqrt(np.sum(np.power(X, 2)) / len(X))
def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fig=None, linecolor='k'):
def checksignallength(X, pick, minsiglength, pickparams, iplot=0, fig=None, linecolor='k'):
"""
Function to detect spuriously picked noise peaks.
@ -747,14 +767,10 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
:type X: `~obspy.core.stream.Stream`
:param pick: initial (AIC) P onset time
:type pick: float
:param TSNR: length of time windows around initial pick [s]
:type TSNR: (T_noise, T_gap, T_signal)
:param minsiglength: minium required signal length [s] to declare pick as P onset
:type minsiglength: float
:param nfac: noise factor (nfac * noise level = threshold)
:type nfac: float
:param minpercent: minimum required percentage of samples above calculated threshold
:type minpercent: float
:param pickparams: PylotParameter instance that holds the current picker settings loaded from a .in file
:type pickparams: PylotParameter
:param iplot: iplot, if iplot > 1, results are shown in figure
:type iplot: int
:param fig: Matplotlib figure to plot results in
@ -766,6 +782,19 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
:rtype: int
"""
"""
Extract additional parameters from pickparams
:param TSNR: length of time windows around initial pick [s]
:type TSNR: (T_noise, T_gap, T_signal)
:param nfac: noise factor (nfac * noise level = threshold)
:type nfac: float
:param minpercent: minimum required percentage of samples above calculated threshold
:type minpercent: float
"""
TSNR = pickparams["tsnrz"]
nfac = pickparams["noisefactor"]
minpercent = pickparams["minpercent"]
plt_flag = 0
try:
iplot = int(iplot)
@ -1034,7 +1063,7 @@ def jackknife(X, phi, h=1):
return PHI_jack, PHI_pseudo, PHI_sub
def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
def checkZ4S(X, pick, pickparams, iplot, fig=None, linecolor='k'):
"""
Function to compare energy content of vertical trace with
energy content of horizontal traces to detect spuriously
@ -1051,11 +1080,8 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
:type X: `~obspy.core.stream.Stream`
:param pick: initial (AIC) P onset time
:type pick: float
:param zfac: factor for threshold determination, vertical energy must
exceed coda level times zfac to declare a pick as P onset
:type zfac: float
:param checkwin: window length [s] for calculating P-coda engergy content
:type checkwin: float
:param pickparams: PylotParameter instance that holds the current picker settings loaded from a .in file
:type pickparams: PylotParameter
:param iplot: if iplot > 1, energy content and threshold are shown
:type iplot: int
:param fig: Matplotlib figure to plot results in
@ -1066,6 +1092,17 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
:rtype: int
"""
"""
Extract required parameters from pickparams
:param zfac: factor for threshold determination, vertical energy must
exceed coda level times zfac to declare a pick as P onset
:type zfac: float
:param checkwin: window length [s] for calculating P-coda engergy content
:type checkwin: float
"""
zfac = pickparams["zfac"]
checkwin = pickparams["tsnrz"][2]
plt_flag = 0
try:
iplot = int(iplot)
@ -1250,7 +1287,7 @@ def getQualityFromSNR(snrdb):
return quality_modifier
def getQualityFromUncertainty(uncertainty, Errors):
def get_quality_class(uncertainty, weight_classes):
"""
Script to transform uncertainty into quality classes 0-4 regarding adjusted time errors
:param uncertainty: symmetric picking error of picks
@ -1260,7 +1297,182 @@ def getQualityFromUncertainty(uncertainty, Errors):
:return: quality of pick (0-4)
:rtype: int
"""
try:
# create generator expression containing all indices of values in weight classes that are >= than uncertainty.
# call next on it once to receive first value
quality = next(i for i, v in enumerate(weight_classes) if v >= uncertainty)
except StopIteration:
# raised when uncertainty is larger than all values in weight_classes
# set quality to max possible value
quality = len(weight_classes)
return quality
def set_NaNs_to(data, nan_value):
"""
Replace all NaNs in data with nan_value
:param data: array holding data
:type data: `~numpy.ndarray`
:param nan_value: value which all NaNs are set to
:type nan_value: float, int
:return: data array with all NaNs replaced with nan_value
:rtype: `~numpy.ndarray`
"""
nn = np.isnan(data)
if np.any(nn):
data[nn] = nan_value
return data
def taper_cf(cf):
"""
Taper cf data to get rid off of side maximas
:param cf: characteristic function data
:type cf: `~numpy.ndarray`
:return: tapered cf
:rtype: `~numpy.ndarray`
"""
tap = np.hanning(len(cf))
return tap * cf
def cf_positive(cf):
"""
Shifts cf so that all values are positive
:param cf:
:type cf: `~numpy.ndarray`
:return:
:rtype: `~numpy.ndarray`
"""
return cf + max(abs(cf))
def smooth_cf(cf, t_smooth, delta):
"""
Smooth cf by taking samples over t_smooth length
:param cf: characteristic function data
:type cf: `~numpy.ndarray`
:param t_smooth: Time from which samples for smoothing will be taken (s)
:type t_smooth: float
:param delta: Sample rate of cf
:type delta: float
:return: smoothed cf data
:rtype: `~numpy.ndarray`
"""
ismooth = int(round(t_smooth / delta)) # smooth values this many indexes apart
cf_smooth = np.zeros(len(cf))
if len(cf) < ismooth:
raise ValueError
for i in range(1, len(cf)):
if i > ismooth:
ii1 = i - ismooth
cf_smooth[i] = cf_smooth[i - 1] + (cf[i] - cf[ii1]) / ismooth
else:
cf_smooth[i] = np.mean(cf[1: i])
offset = abs(min(cf) - min(cf_smooth))
cf_smooth -= offset # remove offset from smoothed function
return cf_smooth
def check_counts_ms(data):
"""
check if data is in counts or m/s
:param data: data array
:type data: `~numpy.ndarray`
:return:
:rtype: `~numpy.ndarray`
"""
# this is quick and dirty, better solution?
if max(data < 1e-3) and max(data >= 1e-6):
data = data * 1000000.
elif max(data < 1e-6):
data = data * 1e13
return data
def calcSlope(Data, datasmooth, Tcf, Pick, TSNR):
"""
Calculate Slope for Data around a given time Pick.
:param Data: trace containing data for which a slope will be calculated
:type Data: `~obspy.core.trace.Trace`
:param datasmooth: smoothed data array
:type datasmooth: ~numpy.ndarray`
:param Tcf: array of time indices for Data array
:type Tcf: ~numpy.ndarray`
:param Pick: onset time around which the slope should be calculated
:type Pick: float
:param TSNR: tuple containing (tnoise, tsafety, tsignal, tslope). Slope will be calculated in time
window tslope around the onset
:type TSNR: (float, float, float, float)
:return: tuple containing (slope of onset, slope index array, data fit information)
:rtype: (float, `~numpy.ndarray`, `~numpy.ndarray`
"""
islope = getslopewin(Tcf, Pick, TSNR[3])
try:
dataslope = Data[0].data[islope]
except IndexError as e:
print("Slope Calculation: empty array islope, check signal window")
raise e
if len(dataslope) <= 1:
print('Slope window outside data. No or not enough data in slope window found!')
raise ValueError
# find maximum within slope determination window
# 'cause slope should be calculated up to first local minimum only!
imaxs, = argrelmax(dataslope)
if imaxs.size:
imax = imaxs[0]
else:
imax = np.argmax(dataslope)
iislope = islope[0:imax + 1] # cut index so it contains only the first maximum
if len(iislope) < 2:
# calculate slope from initial onset to maximum of AIC function
print("AICPicker: Not enough data samples left for slope calculation!")
print("Calculating slope from initial onset to maximum of AIC function ...")
imax = np.argmax(datasmooth[islope])
if imax == 0:
print("AICPicker: Maximum for slope determination right at the beginning of the window!")
print("Choose longer slope determination window!")
raise IndexError
iislope = islope[0][0:imax + 1] # cut index so it contains only the first maximum
dataslope = Data[0].data[iislope] # slope will only be calculated to the first maximum
# calculate slope as polynomal fit of order 1
xslope = np.arange(0, len(dataslope))
P = np.polyfit(xslope, dataslope, 1)
datafit = np.polyval(P, xslope)
if datafit[0] >= datafit[-1]:
print('AICPicker: Negative slope, bad onset skipped!')
raise ValueError
slope = 1 / (len(dataslope) * Data[0].stats.delta) * (datafit[-1] - datafit[0])
return slope, iislope, datafit
def get_pickparams(pickparam):
"""
Get parameter names out of pickparam into dictionaries and return them
:return: dictionaries containing 1. p pick parameters, 2. s pick parameters, 3. first motion determinatiion
parameters, 4. signal length parameters
:rtype: (dict, dict, dict, dict)
"""
# Define names of all parameters in different groups
p_parameter_names = 'algoP pstart pstop use_taup taup_model tlta tsnrz hosorder bpz1 bpz2 pickwinP aictsmooth tsmoothP ausP nfacP tpred1z tdet1z Parorder addnoise Precalcwin minAICPslope minAICPSNR timeerrorsP checkwindowP minfactorP'.split(' ')
s_parameter_names = 'algoS sstart sstop bph1 bph2 tsnrh pickwinS tpred1h tdet1h tpred2h tdet2h Sarorder aictsmoothS tsmoothS ausS minAICSslope minAICSSNR Srecalcwin nfacS timeerrorsS zfac checkwindowS minfactorS'.split(' ')
first_motion_names = 'minFMSNR fmpickwin minfmweight'.split(' ')
signal_length_names = 'minsiglength minpercent noisefactor'.split(' ')
# Get list of values from pickparam by name
p_parameter_values = map(pickparam.get, p_parameter_names)
s_parameter_values = map(pickparam.get, s_parameter_names)
fm_parameter_values = map(pickparam.get, first_motion_names)
sl_parameter_values = map(pickparam.get, signal_length_names)
# construct dicts from names and values
p_params = dict(zip(p_parameter_names, p_parameter_values))
s_params = dict(zip(s_parameter_names, s_parameter_values))
first_motion_params = dict(zip(first_motion_names, fm_parameter_values))
signal_length_params = dict(zip(signal_length_names, sl_parameter_values))
p_params['use_taup'] = real_Bool(p_params['use_taup'])
return p_params, s_params, first_motion_params, signal_length_params
def getQualityFromUncertainty(uncertainty, Errors):
# set initial quality to 4 (worst) and change only if one condition is hit
quality = 4
@ -1283,7 +1495,6 @@ def getQualityFromUncertainty(uncertainty, Errors):
return quality
if __name__ == '__main__':
import doctest

23
pylot/core/util/utils.py
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@ -139,7 +139,7 @@ def excludeQualityClasses(picks, qClasses, timeerrorsP, timeerrorsS):
:return: dictionary containing only picks above the excluded quality class(es)
:rtype: dict
"""
from pylot.core.pick.utils import getQualityFromUncertainty
from pylot.core.pick.utils import get_quality_class
if type(qClasses) in [int, float]:
qClasses = [qClasses]
@ -154,7 +154,7 @@ def excludeQualityClasses(picks, qClasses, timeerrorsP, timeerrorsS):
if not type(pick) in [AttribDict, dict]:
continue
pickerror = phaseError[identifyPhaseID(phase)]
quality = getQualityFromUncertainty(pick['spe'], pickerror)
quality = get_quality_class(pick['spe'], pickerror)
if not quality in qClasses:
if not station in picksdict_new:
picksdict_new[station] = {}
@ -1219,6 +1219,25 @@ def check_event_folder(path):
return ev_type
def correct_iplot(iplot):
"""
iplot should be in range 0...2, but it can be given as True or 'True' as well, which should be converted
to an integer. Both will be converted to 2.
:type iplot: Bool or int
:return: iplot as an integer
:rtype: int
"""
# TODO this is a hack, there should never be the ability to pass anything else but an int
try:
iplot = int(iplot)
except ValueError:
if real_Bool(iplot):
iplot = 2
else:
iplot = 0
return iplot
def station_id_remove_channel(station_id):
"""
Remove the channel from a SEED station id and return Network.Station.Location.

10
pylot/core/util/widgets.py
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@ -44,7 +44,7 @@ from obspy.taup.utils import get_phase_names
from pylot.core.io.data import Data
from pylot.core.io.inputs import FilterOptions, PylotParameter
from pylot.core.pick.utils import getSNR, earllatepicker, getnoisewin, \
getResolutionWindow, getQualityFromUncertainty
getResolutionWindow, get_quality_class
from pylot.core.pick.compare import Comparison
from pylot.core.util.defaults import OUTPUTFORMATS, FILTERDEFAULTS
from pylot.core.util.utils import prepTimeAxis, full_range, demeanTrace, isSorted, findComboBoxIndex, clims, \
@ -2604,10 +2604,10 @@ class PickDlg(QDialog):
# get quality classes
if self.getPhaseID(phase) == 'P':
quality = getQualityFromUncertainty(picks['spe'], self.parameter['timeerrorsP'])
quality = get_quality_class(picks['spe'], self.parameter['timeerrorsP'])
phaseID = 'P'
elif self.getPhaseID(phase) == 'S':
quality = getQualityFromUncertainty(picks['spe'], self.parameter['timeerrorsS'])
quality = get_quality_class(picks['spe'], self.parameter['timeerrorsS'])
phaseID = 'S'
mpp = picks['mpp'] - self.getStartTime()
@ -3619,8 +3619,8 @@ class TuneAutopicker(QWidget):
('refSpick', 0),
('el_S1pick', 0),
('el_S2pick', 0)]
qualityPpick = getQualityFromUncertainty(picks['P']['spe'], self.parameter['timeerrorsP'])
qualitySpick = getQualityFromUncertainty(picks['S']['spe'], self.parameter['timeerrorsS'])
qualityPpick = get_quality_class(picks['P']['spe'], self.parameter['timeerrorsP'])
qualitySpick = get_quality_class(picks['S']['spe'], self.parameter['timeerrorsS'])
for p_ax in p_axes:
axes = self.parent().fig_dict[p_ax[0]].axes
if not axes:

78
tests/testPickingResults.py Normal file
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@ -0,0 +1,78 @@
import unittest
from pylot.core.pick.autopick import PickingResults
class TestPickingResults(unittest.TestCase):
def setUp(self):
self.pr = PickingResults()
def test_non_existing_key_dot_access(self):
"""Accessing an attribute in the class that wasnt added to the dict should give a AttributeError"""
with self.assertRaises(AttributeError):
self.pr.doesntexist
def test_non_existing_key_dict_access(self):
"""Accessing a missing attribute in a dictionary throws a KeyError"""
with self.assertRaises(KeyError):
self.pr['keydoesnotexist']
def test_dot_member_creation(self):
self.pr.x = 0
self.assertEqual(self.pr.x, 0)
self.pr.x += 42
self.assertEqual(self.pr.x, 42)
def test_dot_builtin_member(self):
self.assertEqual(self.pr.weight, 4)
self.pr.weight = 99
self.assertEqual(self.pr.weight, 99)
def test_key_access(self):
self.pr['y'] = 11
self.assertEqual(self.pr['y'], 11)
def test_builtin_fields(self):
self.assertEqual(self.pr['weight'], 4)
def test_in(self):
self.assertFalse('keydoesnotexist' in self.pr)
self.pr['k'] = 0
self.assertTrue('k' in self.pr)
def test_keys_function(self):
a = 99
self.pr.newkey = a
self.assertIn(a, self.pr.values())
self.assertIn('newkey', self.pr.keys())
def test_len_and_clear(self):
self.pr.clear()
self.assertEqual(len(self.pr), 0)
self.pr.a = 6
self.pr['b'] = 9
self.assertEqual(len(self.pr), 2)
def test_get_default(self):
self.assertEqual(self.pr.get('keynotexisting', 42), 42)
weight = self.pr.get('weight', -1)
self.assertEqual(weight, 4)
self.assertNotEqual(weight, -1)
def test_dunder_attributes(self):
"""Storing Pythons special dunder method in a dictionary is valid and should not override the instances dunder
methods"""
prev_len = len(self.pr)
try:
self.pr['__len__'] = None
except Exception:
self.fail("test_dunder_attributes failed to add a dunder attribute to the dictionary keys")
try:
curr_len = len(self.pr)
except Exception:
self.fail("test_dunder_attributes overwrote an instance internal dunder method")
self.assertEqual(prev_len+1, curr_len) # +1 for the added __len__ key/value-pair
self.pr.__len__ = 42
self.assertEqual(42, self.pr['__len__'])
self.assertEqual(prev_len+1, curr_len, msg="__len__ was overwritten")

35
tests/test_PickingParameters.py Normal file
View File

@ -0,0 +1,35 @@
import unittest
from pylot.core.pick.autopick import PickingParameters
class TestPickingParameters(unittest.TestCase):
def setUp(self):
self.simple_dict = {'a': 3, 'b': 14}
self.nested_dict = {'a': self.simple_dict, 'b': self.simple_dict}
def assertParameterEquality(self, dic, instance):
"""Test wether all parameters given in dic are found in instance"""
for key, value in dic.items():
self.assertEqual(value, getattr(instance, key))
def test_add_params_from_dict_simple(self):
pickparam = PickingParameters()
pickparam.add_params_from_dict(self.simple_dict)
self.assertParameterEquality(self.simple_dict, pickparam)
def test_add_params_from_dict_nested(self):
pickparam = PickingParameters()
pickparam.add_params_from_dict(self.nested_dict)
self.assertParameterEquality(self.nested_dict, pickparam)
def test_init(self):
pickparam = PickingParameters(self.simple_dict)
self.assertParameterEquality(self.simple_dict, pickparam)
def test_dot_access(self):
pickparam = PickingParameters(self.simple_dict)
self.assertEqual(pickparam.a, self.simple_dict['a'])
if __name__ == '__main__':
unittest.main()

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%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#
None #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
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

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%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#
None #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
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

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<?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>

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/data/AlpArray/mini_SEED_LH/2016-01-24T10:30:30

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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
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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()