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Replace all usages of p_params with corresponding pickparams

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This commit is contained in:
Darius Arnold 2019-04-09 18:25:08 +02:00
parent 281e92e2a9
commit 076c498fab
1 changed files with 48 additions and 43 deletions
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91
pylot/core/pick/autopick.py
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@ -297,7 +297,7 @@ class AutopickStation(object):
""" """
# save given parameters # save given parameters
self.wfstream = wfstream self.wfstream = wfstream
self.pickparam = pickparam self.pickparams = pickparam
self.verbose = verbose self.verbose = verbose
self.iplot = correct_iplot(iplot) self.iplot = correct_iplot(iplot)
self.fig_dict = real_None(fig_dict) self.fig_dict = real_None(fig_dict)
@ -520,10 +520,11 @@ class AutopickStation(object):
"""If taupy failed to calculate theoretical starttimes, picking continues. """If taupy failed to calculate theoretical starttimes, picking continues.
For this a clean exit is required, since the P starttime is no longer relative to the theoretic onset but For this a clean exit is required, since the P starttime is no longer relative to the theoretic onset but
to the vertical trace starttime, eg. it can't be < 0.""" to the vertical trace starttime, eg. it can't be < 0."""
if self.p_params.pstart < 0: if self.pickparams["pstart"] < 0:
self.p_params.pstart = 0 # TODO here the pickparams is modified, instead of a copy
self.pickparams["pstart"] = 0
if self.p_params.use_taup is False or not self.origin or not self.metadata: if self.pickparams["use_taup"] is False or not self.origin or not self.metadata:
# correct user mistake where a relative cuttime is selected (pstart < 0) but use of taupy is disabled/ has # correct user mistake where a relative cuttime is selected (pstart < 0) but use of taupy is disabled/ has
# not the required parameters # not the required parameters
exit_taupy() exit_taupy()
@ -536,16 +537,16 @@ class AutopickStation(object):
if not self.origin: if not self.origin:
raise AttributeError('No source origins given!') raise AttributeError('No source origins given!')
arrivals = create_arrivals(self.metadata, self.origin, self.p_params.taup_model) arrivals = create_arrivals(self.metadata, self.origin, self.pickparams["taup_model"])
estFirstP, estFirstS = first_PS_onsets(arrivals) estFirstP, estFirstS = first_PS_onsets(arrivals)
# modifiy pstart and pstop relative to estimated first P arrival (relative to station time axis) # modifiy pstart and pstop relative to estimated first P arrival (relative to station time axis)
self.p_params.pstart += (self.origin[0].time + estFirstP) - self.ztrace.stats.starttime self.pickparams["pstart"] += (self.origin[0].time + estFirstP) - self.ztrace.stats.starttime
self.p_params.pstop += (self.origin[0].time + estFirstP) - self.ztrace.stats.starttime self.pickparams["pstop"] += (self.origin[0].time + estFirstP) - self.ztrace.stats.starttime
print('autopick: CF calculation times respectively:' print('autopick: CF calculation times respectively:'
' pstart: {} s, pstop: {} s'.format(self.p_params.pstart, self.p_params.pstop)) ' pstart: {} s, pstop: {} s'.format(self.pickparams["pstart"], self.pickparams["pstop"]))
# make sure pstart and pstop are inside the starttime/endtime of vertical trace # make sure pstart and pstop are inside the starttime/endtime of vertical trace
self.p_params.pstart = max(self.p_params.pstart, 0) self.pickparams["pstart"] = max(self.pickparams["pstart"], 0)
self.p_params.pstop = min(self.p_params.pstop, len(self.ztrace) * self.ztrace.stats.delta) self.pickparams["pstop"] = min(self.pickparams["pstop"], len(self.ztrace) * self.ztrace.stats.delta)
def autopickstation(self): def autopickstation(self):
""" """
@ -589,8 +590,8 @@ class AutopickStation(object):
else: else:
# dummy values (start of seismic trace) in order to derive # dummy values (start of seismic trace) in order to derive
# theoretical onset times for iterative picking # theoretical onset times for iterative picking
self.p_results.lpp = self.ztrace.stats.starttime + self.p_params.timeerrorsP[3] self.p_results.lpp = self.ztrace.stats.starttime + self.pickparams["timeerrorsP"][3]
self.p_results.epp = self.ztrace.stats.starttime - self.p_params.timeerrorsP[3] self.p_results.epp = self.ztrace.stats.starttime - self.pickparams["timeerrorsP"][3]
self.p_results.mpp = self.ztrace.stats.starttime self.p_results.mpp = self.ztrace.stats.starttime
self.p_results.channel = self.ztrace.stats.channel self.p_results.channel = self.ztrace.stats.channel
@ -766,7 +767,7 @@ class AutopickStation(object):
if aicpick.getpick() is None: if aicpick.getpick() is None:
msg = "Bad initial (AIC) P-pick, skipping this onset!\nAIC-SNR={0}, AIC-Slope={1}counts/s\n " \ msg = "Bad initial (AIC) P-pick, skipping this onset!\nAIC-SNR={0}, AIC-Slope={1}counts/s\n " \
"(min. AIC-SNR={2}, min. AIC-Slope={3}counts/s)" "(min. AIC-SNR={2}, min. AIC-Slope={3}counts/s)"
msg = msg.format(aicpick.getSNR(), aicpick.getSlope(), self.p_params.minAICPSNR, self.p_params.minAICPslope) msg = msg.format(aicpick.getSNR(), aicpick.getSlope(), self.pickparams["minAICPSNR"], self.pickparams["minAICPslope"])
self.vprint(msg) self.vprint(msg)
return 0 return 0
# Quality check initial pick with minimum signal length # Quality check initial pick with minimum signal length
@ -820,7 +821,7 @@ class AutopickStation(object):
'trace ...\n{data}'.format(station=self.station_name, data=str(self.zstream)) 'trace ...\n{data}'.format(station=self.station_name, data=str(self.zstream))
self.vprint(msg) self.vprint(msg)
tr_filt, z_copy = self.prepare_wfstream(self.zstream, self.p_params.bpz1[0], self.p_params.bpz1[1]) tr_filt, z_copy = self.prepare_wfstream(self.zstream, self.pickparams["bpz1"][0], self.pickparams["bpz1"][1])
# save filtered trace in instance for later plotting # save filtered trace in instance for later plotting
self.tr_filt_z_bpz2 = tr_filt self.tr_filt_z_bpz2 = tr_filt
try: try:
@ -833,29 +834,29 @@ class AutopickStation(object):
cuttimes = self._calculate_cuttimes('P', 1) cuttimes = self._calculate_cuttimes('P', 1)
# calculate first CF # calculate first CF
if self.p_params.algoP == 'HOS': if self.pickparams["algoP"] == 'HOS':
self.cf1 = HOScf(z_copy, cuttimes, self.pickparams) self.cf1 = HOScf(z_copy, cuttimes, self.pickparams)
elif self.p_params.algoP == 'ARZ': elif self.pickparams["algoP"] == 'ARZ':
self.cf1 = ARZcf(z_copy, cuttimes, self.pickparams["tdet1z"], self.pickparams["tpred1z"], self.pickparams) self.cf1 = ARZcf(z_copy, cuttimes, self.pickparams["tdet1z"], self.pickparams["tpred1z"], self.pickparams)
else: else:
self.cf1 = None self.cf1 = None
assert isinstance(self.cf1, CharacteristicFunction), 'cf1 is not set correctly: maybe the algorithm name ({})' \ assert isinstance(self.cf1, CharacteristicFunction), 'cf1 is not set correctly: maybe the algorithm name ({})' \
' is corrupted'.format(self.p_params.algoP) ' is corrupted'.format(self.pickparams["algoP"])
# calculate AIC cf from first cf (either HOS or ARZ) # calculate AIC cf from first cf (either HOS or ARZ)
z_copy[0].data = self.cf1.getCF() z_copy[0].data = self.cf1.getCF()
aiccf = AICcf(z_copy, cuttimes) aiccf = AICcf(z_copy, cuttimes)
# get preliminary onset time from AIC-CF # get preliminary onset time from AIC-CF
self.set_current_figure('aicFig') self.set_current_figure('aicFig')
aicpick = AICPicker(aiccf, self.p_params.tsnrz, self.p_params.pickwinP, self.iplot, aicpick = AICPicker(aiccf, self.pickparams["tsnrz"], self.pickparams["pickwinP"], self.iplot,
Tsmooth=self.p_params.aictsmooth, fig=self.current_figure, linecolor=self.current_linecolor) Tsmooth=self.pickparams["aictsmooth"], fig=self.current_figure, linecolor=self.current_linecolor)
# save aicpick for plotting later # save aicpick for plotting later
self.p_data.aicpick = aicpick self.p_data.aicpick = aicpick
# add pstart and pstop to aic plot # add pstart and pstop to aic plot
if self.current_figure: if self.current_figure:
# TODO remove plotting from picking, make own plot function # TODO remove plotting from picking, make own plot function
for ax in self.current_figure.axes: for ax in self.current_figure.axes:
ax.vlines(self.p_params.pstart, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P start') ax.vlines(self.pickparams["pstart"], ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P start')
ax.vlines(self.p_params.pstop, ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P stop') ax.vlines(self.pickparams["pstop"], ax.get_ylim()[0], ax.get_ylim()[1], color='c', linestyles='dashed', label='P stop')
ax.legend(loc=1) ax.legend(loc=1)
Pflag = self._pick_p_quality_control(aicpick, z_copy, tr_filt) Pflag = self._pick_p_quality_control(aicpick, z_copy, tr_filt)
@ -865,11 +866,11 @@ class AutopickStation(object):
# todo why did picking fail was saved in the pick dictionary, should this be reimplemented? # todo why did picking fail was saved in the pick dictionary, should this be reimplemented?
if Pflag != 1: if Pflag != 1:
raise PickingFailedException('AIC P onset quality control failed') raise PickingFailedException('AIC P onset quality control failed')
if slope <= self.p_params.minAICPslope: if slope <= self.pickparams["minAICPslope"]:
error_msg = 'AIC P onset slope to small: got {}, min {}'.format(slope, self.p_params.minAICPslope) error_msg = 'AIC P onset slope to small: got {}, min {}'.format(slope, self.pickparams["minAICPslope"])
raise PickingFailedException(error_msg) raise PickingFailedException(error_msg)
if aicpick.getSNR() < self.p_params.minAICPSNR: if aicpick.getSNR() < self.pickparams["minAICPSNR"]:
error_msg = 'AIC P onset SNR to small: got {}, min {}'.format(aicpick.getSNR(), self.p_params.minAICPSNR) error_msg = 'AIC P onset SNR to small: got {}, min {}'.format(aicpick.getSNR(), self.pickparams["minAICPSNR"])
raise PickingFailedException(error_msg) raise PickingFailedException(error_msg)
self.p_data.p_aic_plot_flag = 1 self.p_data.p_aic_plot_flag = 1
@ -877,44 +878,44 @@ class AutopickStation(object):
'autopickstation: re-filtering vertical trace...'.format(aicpick.getSlope(), aicpick.getSNR()) 'autopickstation: re-filtering vertical trace...'.format(aicpick.getSlope(), aicpick.getSNR())
self.vprint(msg) self.vprint(msg)
# refilter waveform with larger bandpass # refilter waveform with larger bandpass
tr_filt, z_copy = self.prepare_wfstream(self.zstream, freqmin=self.p_params.bpz2[0], freqmax=self.p_params.bpz2[1]) tr_filt, z_copy = self.prepare_wfstream(self.zstream, freqmin=self.pickparams["bpz2"][0], freqmax=self.pickparams["bpz2"][1])
# save filtered trace in instance for later plotting # save filtered trace in instance for later plotting
self.tr_filt_z_bpz2 = tr_filt self.tr_filt_z_bpz2 = tr_filt
# determine new times around initial onset # determine new times around initial onset
cuttimes2 = self._calculate_cuttimes('P', 2) cuttimes2 = self._calculate_cuttimes('P', 2)
if self.p_params.algoP == 'HOS': if self.pickparams["algoP"] == 'HOS':
self.cf2 = HOScf(z_copy, cuttimes2, self.pickparams) self.cf2 = HOScf(z_copy, cuttimes2, self.pickparams)
elif self.p_params.algoP == 'ARZ': elif self.pickparams["algoP"] == 'ARZ':
self.cf2 = ARZcf(z_copy, cuttimes2, self.pickparams["tdet2z"], self.pickparams["tpred2z"], self.pickparams) self.cf2 = ARZcf(z_copy, cuttimes2, self.pickparams["tdet2z"], self.pickparams["tpred2z"], self.pickparams)
else: else:
self.cf2 = None self.cf2 = None
assert isinstance(self.cf2, CharacteristicFunction), 'cf2 is not set correctly: maybe the algorithm name () is ' \ assert isinstance(self.cf2, CharacteristicFunction), 'cf2 is not set correctly: maybe the algorithm name () is ' \
'corrupted'.format(self.p_params.algoP) 'corrupted'.format(self.pickparams["algoP"])
self.set_current_figure('refPpick') self.set_current_figure('refPpick')
# get refined onset time from CF2 # get refined onset time from CF2
refPpick = PragPicker(self.cf2, self.p_params.tsnrz, self.p_params.pickwinP, self.iplot, self.p_params.ausP, refPpick = PragPicker(self.cf2, self.pickparams["tsnrz"], self.pickparams["pickwinP"], self.iplot, self.pickparams["ausP"],
self.p_params.tsmoothP, aicpick.getpick(), self.current_figure, self.current_linecolor) self.pickparams["tsmoothP"], aicpick.getpick(), self.current_figure, self.current_linecolor)
# save PragPicker result for plotting # save PragPicker result for plotting
self.p_data.refPpick = refPpick self.p_data.refPpick = refPpick
self.p_results.mpp = refPpick.getpick() self.p_results.mpp = refPpick.getpick()
if self.p_results.mpp is None: if self.p_results.mpp is None:
msg = 'Bad initial (AIC) P-pick, skipping this onset!\n AIC-SNR={}, AIC-Slope={}counts/s\n' \ msg = 'Bad initial (AIC) P-pick, skipping this onset!\n AIC-SNR={}, AIC-Slope={}counts/s\n' \
'(min. AIC-SNR={}, min. AIC-Slope={}counts/s)' '(min. AIC-SNR={}, min. AIC-Slope={}counts/s)'
msg.format(aicpick.getSNR(), aicpick.getSlope(), self.p_params.minAICPSNR, self.p_params.minAICPslope) msg.format(aicpick.getSNR(), aicpick.getSlope(), self.pickparams["minAICPSNR"], self.pickparams["minAICPslope"])
self.vprint(msg) self.vprint(msg)
self.s_data.Sflag = 0 self.s_data.Sflag = 0
raise PickingFailedException(msg) raise PickingFailedException(msg)
# quality assessment, get earliest/latest pick and symmetrized uncertainty # quality assessment, get earliest/latest pick and symmetrized uncertainty
#todo quality assessment in own function #todo quality assessment in own function
self.set_current_figure('el_Ppick') self.set_current_figure('el_Ppick')
elpicker_results = earllatepicker(z_copy, self.p_params.nfacP, self.p_params.tsnrz, self.p_results.mpp, elpicker_results = earllatepicker(z_copy, self.pickparams["nfacP"], self.pickparams["tsnrz"], self.p_results.mpp,
self.iplot, fig=self.current_figure, linecolor=self.current_linecolor) self.iplot, fig=self.current_figure, linecolor=self.current_linecolor)
self.p_results.epp, self.p_results.lpp, self.p_results.spe = elpicker_results self.p_results.epp, self.p_results.lpp, self.p_results.spe = elpicker_results
snr_results = getSNR(z_copy, self.p_params.tsnrz, self.p_results.mpp) snr_results = getSNR(z_copy, self.pickparams["tsnrz"], self.p_results.mpp)
self.p_results.snr, self.p_results.snrdb, _ = snr_results self.p_results.snr, self.p_results.snrdb, _ = snr_results
# weight P-onset using symmetric error # weight P-onset using symmetric error
self.p_results.weight = get_quality_class(self.p_results.spe, self.p_params.timeerrorsP) self.p_results.weight = get_quality_class(self.p_results.spe, self.pickparams["timeerrorsP"])
if self.p_results.weight <= self.first_motion_params.minfmweight and self.p_results.snr >= self.first_motion_params.minFMSNR: if self.p_results.weight <= self.first_motion_params.minfmweight and self.p_results.snr >= self.first_motion_params.minFMSNR:
# if SNR is low enough, try to determine first motion of onset # if SNR is low enough, try to determine first motion of onset
self.set_current_figure('fm_picker') self.set_current_figure('fm_picker')
@ -939,20 +940,24 @@ class AutopickStation(object):
:return: tuple of (starttime, endtime) in seconds :return: tuple of (starttime, endtime) in seconds
:rtype: (int, int) :rtype: (int, int)
""" """
# extract parameters
Precalcwin = self.pickparams["Precalcwin"]
Srecalcwin = self.pickparams["Srecalcwin"]
if type.upper() == 'P': if type.upper() == 'P':
if iteration == 1: if iteration == 1:
return [self.p_params.pstart, self.p_params.pstop] return [self.pickparams["pstart"], self.pickparams["pstop"]]
if iteration == 2: if iteration == 2:
starttime2 = round(max(self.p_data.aicpick.getpick() - self.p_params.Precalcwin, 0)) starttime2 = round(max(self.p_data.aicpick.getpick() - Precalcwin, 0))
endtime2 = round( endtime2 = round(
min(len(self.ztrace.data) * self.ztrace.stats.delta, self.p_data.aicpick.getpick() + self.p_params.Precalcwin)) min(len(self.ztrace.data) * self.ztrace.stats.delta, self.p_data.aicpick.getpick() + Precalcwin))
return [starttime2, endtime2] return [starttime2, endtime2]
elif type.upper() == 'S': elif type.upper() == 'S':
if iteration == 1: if iteration == 1:
# Calculate start times for preliminary S onset # Calculate start times for preliminary S onset
start = round(max(self.p_results.mpp + self.s_params.sstart, 0)) # limit start time to >0 seconds start = round(max(self.p_results.mpp + self.pickparams["sstart"], 0)) # limit start time to >0 seconds
stop = round(min([ stop = round(min([
self.p_results.mpp + self.s_params.sstop, self.p_results.mpp + self.pickparams["sstop"],
self.etrace.stats.endtime - self.etrace.stats.starttime, self.etrace.stats.endtime - self.etrace.stats.starttime,
self.ntrace.stats.endtime - self.ntrace.stats.starttime self.ntrace.stats.endtime - self.ntrace.stats.starttime
])) ]))
@ -962,8 +967,8 @@ class AutopickStation(object):
return cuttimesh return cuttimesh
if iteration == 2: if iteration == 2:
# recalculate cf from refiltered trace in vicinity of initial onset # recalculate cf from refiltered trace in vicinity of initial onset
start = round(self.aicarhpick.getpick() - self.s_params.Srecalcwin) start = round(self.aicarhpick.getpick() - Srecalcwin)
stop = round(self.aicarhpick.getpick() + self.s_params.Srecalcwin) stop = round(self.aicarhpick.getpick() + Srecalcwin)
return (start, stop) return (start, stop)
else: else:
raise ValueError('Wrong type given, can only be P or S') raise ValueError('Wrong type given, can only be P or S')