remove all usage of checkwindowP/S and minfactorP/S
Those changes have their own branch now, the parameters were not used during picking on this branch.
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@ -239,9 +239,9 @@ class AutopickStation(object):
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:rtype: (PickingParameters, PickingParameters, PickingParameters, PickingParameters)
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:rtype: (PickingParameters, PickingParameters, PickingParameters, PickingParameters)
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"""
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"""
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# Define names of all parameters in different groups
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# Define names of all parameters in different groups
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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(
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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'.split(
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' ')
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' ')
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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(
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s_parameter_names = 'algoS sstart sstop bph1 bph2 tsnrh pickwinS tpred1h tdet1h tpred2h tdet2h Sarorder aictsmoothS tsmoothS ausS minAICSslope minAICSSNR Srecalcwin nfacS timeerrorsS zfac'.split(
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' ')
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' ')
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first_motion_names = 'minFMSNR fmpickwin minfmweight'.split(' ')
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first_motion_names = 'minFMSNR fmpickwin minfmweight'.split(' ')
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signal_length_names = 'minsiglength minpercent noisefactor'.split(' ')
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signal_length_names = 'minsiglength minpercent noisefactor'.split(' ')
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@ -436,8 +436,7 @@ class AutopickStation(object):
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# get preliminary onset time from AIC-CF
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# get preliminary onset time from AIC-CF
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fig, linecolor = get_fig_from_figdict(self.fig_dict, 'aicFig')
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fig, linecolor = get_fig_from_figdict(self.fig_dict, 'aicFig')
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aicpick = AICPicker(aiccf, self.p_params.tsnrz, self.p_params.pickwinP, self.iplot,
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aicpick = AICPicker(aiccf, self.p_params.tsnrz, self.p_params.pickwinP, self.iplot,
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Tsmooth=self.p_params.aictsmooth, fig=fig, linecolor=linecolor,
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Tsmooth=self.p_params.aictsmooth, fig=fig, linecolor=linecolor)
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checkwindow=self.p_params.checkwindowP, minfactor=self.p_params.minfactorP)
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# add pstart and pstop to aic plot
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# add pstart and pstop to aic plot
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if fig:
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if fig:
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for ax in fig.axes:
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for ax in fig.axes:
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@ -508,8 +507,7 @@ class AutopickStation(object):
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'corrupted'.format(algoP=self.p_params.algoP)
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'corrupted'.format(algoP=self.p_params.algoP)
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fig, linecolor = get_fig_from_figdict(self.fig_dict, 'refPpick')
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fig, linecolor = get_fig_from_figdict(self.fig_dict, 'refPpick')
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refPpick = PragPicker(cf2, self.p_params.tsnrz, self.p_params.pickwinP, self.iplot, self.p_params.ausP,
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refPpick = PragPicker(cf2, self.p_params.tsnrz, self.p_params.pickwinP, self.iplot, self.p_params.ausP,
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self.p_params.tsmoothP, aicpick.getpick(), fig, linecolor,
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self.p_params.tsmoothP, aicpick.getpick(), fig, linecolor)
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checkwindow=self.p_params.checkWindowP, minfactor=self.p_params.minfactorP)
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mpickP = refPpick.getpick()
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mpickP = refPpick.getpick()
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if mpickP is not None:
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if mpickP is not None:
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# quality assessment, get earliest/latest pick and symmetrized uncertainty
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# quality assessment, get earliest/latest pick and symmetrized uncertainty
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@ -789,7 +787,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
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fig = None
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fig = None
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linecolor = 'k'
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linecolor = 'k'
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aicpick = AICPicker(aiccf, p_params['tsnrz'], p_params['pickwinP'], iplot, Tsmooth=p_params['aictsmooth'],
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aicpick = AICPicker(aiccf, p_params['tsnrz'], p_params['pickwinP'], iplot, Tsmooth=p_params['aictsmooth'],
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fig=fig, linecolor=linecolor, checkwindow=p_params['checkwindowP'], minfactor=p_params['minfactorP'])
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fig=fig, linecolor=linecolor)
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# add pstart and pstop to aic plot
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# add pstart and pstop to aic plot
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if fig:
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if fig:
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for ax in fig.axes:
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for ax in fig.axes:
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@ -1075,7 +1073,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
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fig = None
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fig = None
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linecolor = 'k'
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linecolor = 'k'
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aicarhpick = AICPicker(haiccf, s_params['tsnrh'], s_params['pickwinS'], iplot, None,
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aicarhpick = AICPicker(haiccf, s_params['tsnrh'], s_params['pickwinS'], iplot, None,
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s_params['aictsmoothS'], fig=fig, linecolor=linecolor, checkwindow=s_params['checkwindowS'], minfactor=s_params['minfactorS'])
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s_params['aictsmoothS'], fig=fig, linecolor=linecolor)
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###############################################################
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###############################################################
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# go on with processing if AIC onset passes quality control
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# go on with processing if AIC onset passes quality control
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slope = aicarhpick.getSlope()
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slope = aicarhpick.getSlope()
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