[update] add linecolor settings to autoPyLoT figs
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24
QtPyLoT.py
24
QtPyLoT.py
@ -1991,27 +1991,36 @@ class MainWindow(QMainWindow):
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'el_S1pick',
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'el_S2pick',
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'refSpick',
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'aicARHfig'
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'aicARHfig',
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'plot_style'
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]
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for key in self.fig_keys:
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fig = Figure()
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if key == 'plot_style':
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fig = self._style
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else:
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fig = Figure()
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self.fig_dict[key] = fig
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def init_canvas_dict(self):
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self.canvas_dict = {}
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for key in self.fig_keys:
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self.canvas_dict[key] = PylotCanvas(self.fig_dict[key], parent=self)
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if not key == 'plot_style':
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self.canvas_dict[key] = PylotCanvas(self.fig_dict[key], parent=self)
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def init_fig_dict_wadatijack(self, eventIDs):
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self.fig_dict_wadatijack = {}
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self.fig_keys_wadatijack = [
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'jackknife',
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'wadati'
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'wadati',
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'plot_style'
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]
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for eventID in eventIDs:
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self.fig_dict_wadatijack[eventID] = {}
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for key in self.fig_keys_wadatijack:
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fig = Figure()
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if key == 'plot_style':
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fig = self._style
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else:
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fig = Figure()
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self.fig_dict_wadatijack[eventID][key] = fig
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def init_canvas_dict_wadatijack(self):
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@ -2019,8 +2028,9 @@ class MainWindow(QMainWindow):
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for eventID in self.fig_dict_wadatijack.keys():
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self.canvas_dict_wadatijack[eventID] = {}
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for key in self.fig_keys_wadatijack:
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self.canvas_dict_wadatijack[eventID][key] = PylotCanvas(self.fig_dict_wadatijack[eventID][key],
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parent=self)
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if not key == 'plot_style':
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self.canvas_dict_wadatijack[eventID][key] = PylotCanvas(self.fig_dict_wadatijack[eventID][key],
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parent=self)
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def tune_autopicker(self):
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'''
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@ -322,9 +322,11 @@ def autopickstation(wfstream, pickparam, verbose=False,
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key = 'aicFig'
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if fig_dict:
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fig = fig_dict[key]
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, aictsmoothP, fig=fig)
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linecolor = 'k'
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aicpick = AICPicker(aiccf, tsnrz, pickwinP, iplot, None, aictsmoothP, fig=fig, linecolor=linecolor)
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# add pstart and pstop to aic plot
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if fig:
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for ax in fig.axes:
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@ -347,12 +349,14 @@ def autopickstation(wfstream, pickparam, verbose=False,
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key = 'slength'
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if fig_dict:
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fig = fig_dict[key]
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
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minsiglength / 2,
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nfacsl, minpercent, iplot,
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fig)
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fig, linecolor)
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else:
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# filter and taper horizontal traces
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trH1_filt = edat.copy()
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@ -369,12 +373,14 @@ def autopickstation(wfstream, pickparam, verbose=False,
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zne += trH2_filt
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if fig_dict:
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fig = fig_dict['slength']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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Pflag = checksignallength(zne, aicpick.getpick(), tsnrz,
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minsiglength,
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nfacsl, minpercent, iplot,
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fig)
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fig, linecolor)
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if Pflag == 1:
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# check for spuriously picked S onset
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@ -387,10 +393,12 @@ def autopickstation(wfstream, pickparam, verbose=False,
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if iplot > 1:
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if fig_dict:
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fig = fig_dict['checkZ4s']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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Pflag = checkZ4S(zne, aicpick.getpick(), zfac,
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tsnrz[2], iplot, fig)
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tsnrz[2], iplot, fig, linecolor)
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if Pflag == 0:
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Pmarker = 'SinsteadP'
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Pweight = 9
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@ -442,10 +450,12 @@ def autopickstation(wfstream, pickparam, verbose=False,
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algoP=algoP)
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if fig_dict:
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fig = fig_dict['refPpick']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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refPpick = PragPicker(cf2, tsnrz, pickwinP, iplot, ausP, tsmoothP,
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aicpick.getpick(), fig)
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aicpick.getpick(), fig, linecolor)
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mpickP = refPpick.getpick()
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#############################################################
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if mpickP is not None:
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@ -454,10 +464,13 @@ def autopickstation(wfstream, pickparam, verbose=False,
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if iplot:
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if fig_dict:
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fig = fig_dict['el_Ppick']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
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mpickP, iplot, fig=fig)
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mpickP, iplot, fig=fig,
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linecolor=linecolor)
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else:
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epickP, lpickP, Perror = earllatepicker(z_copy, nfacP, tsnrz,
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mpickP, iplot)
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@ -487,9 +500,10 @@ def autopickstation(wfstream, pickparam, verbose=False,
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if iplot:
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if fig_dict:
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fig = fig_dict['fm_picker']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot, fig)
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FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot, fig, linecolor)
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else:
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FM = fmpicker(zdat, z_copy, fmpickwin, mpickP, iplot)
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else:
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@ -624,10 +638,12 @@ def autopickstation(wfstream, pickparam, verbose=False,
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# of class AutoPicking
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if fig_dict:
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fig = fig_dict['aicARHfig']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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aicarhpick = AICPicker(haiccf, tsnrh, pickwinS, iplot, None,
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aictsmoothS, fig=fig)
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aictsmoothS, fig=fig, linecolor=linecolor)
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###############################################################
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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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@ -686,10 +702,12 @@ def autopickstation(wfstream, pickparam, verbose=False,
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# get refined onset time from CF2 using class Picker
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if fig_dict:
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fig = fig_dict['refSpick']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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refSpick = PragPicker(arhcf2, tsnrh, pickwinS, iplot, ausS,
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tsmoothS, aicarhpick.getpick(), fig)
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tsmoothS, aicarhpick.getpick(), fig, linecolor)
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mpickS = refSpick.getpick()
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#############################################################
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if mpickS is not None:
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@ -699,12 +717,15 @@ def autopickstation(wfstream, pickparam, verbose=False,
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if iplot:
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if fig_dict:
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fig = fig_dict['el_S1pick']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = 'k'
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epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
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tsnrh,
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mpickS, iplot,
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fig=fig)
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fig=fig,
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linecolor=linecolor)
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else:
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epickS1, lpickS1, Serror1 = earllatepicker(h_copy, nfacS,
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tsnrh,
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@ -714,12 +735,15 @@ def autopickstation(wfstream, pickparam, verbose=False,
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if iplot:
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if fig_dict:
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fig = fig_dict['el_S2pick']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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else:
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fig = None
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linecolor = ''
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epickS2, lpickS2, Serror2 = earllatepicker(h_copy, nfacS,
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tsnrh,
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mpickS, iplot,
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fig=fig)
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fig=fig,
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linecolor=linecolor)
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else:
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epickS2, lpickS2, Serror2 = earllatepicker(h_copy, nfacS,
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tsnrh,
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@ -828,8 +852,10 @@ def autopickstation(wfstream, pickparam, verbose=False,
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if fig_dict == None or fig_dict == 'None':
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fig = plt.figure()
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plt_flag = 1
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linecolor = 'k'
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else:
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fig = fig_dict['mainFig']
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linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
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ax1 = fig.add_subplot(311)
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tdata = np.arange(0, zdat[0].stats.npts / tr_filt.stats.sampling_rate,
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tr_filt.stats.delta)
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@ -837,7 +863,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
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wfldiff = len(tr_filt.data) - len(tdata)
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if wfldiff < 0:
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tdata = tdata[0:len(tdata) - abs(wfldiff)]
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ax1.plot(tdata, tr_filt.data / max(tr_filt.data), 'k', label='Data')
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ax1.plot(tdata, tr_filt.data / max(tr_filt.data), color=linecolor, linewidth=0.7, label='Data')
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if Pweight < 4:
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ax1.plot(cf1.getTimeArray(), cf1.getCF() / max(cf1.getCF()),
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'b', label='CF1')
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@ -896,7 +922,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
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wfldiff = len(trH1_filt.data) - len(th1data)
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if wfldiff < 0:
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th1data = th1data[0:len(th1data) - abs(wfldiff)]
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ax2.plot(th1data, trH1_filt.data / max(trH1_filt.data), 'k', label='Data')
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ax2.plot(th1data, trH1_filt.data / max(trH1_filt.data), color=linecolor, linewidth=0.7, label='Data')
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if Pweight < 4:
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ax2.plot(arhcf1.getTimeArray(),
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arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
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@ -945,7 +971,7 @@ def autopickstation(wfstream, pickparam, verbose=False,
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wfldiff = len(trH2_filt.data) - len(th2data)
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if wfldiff < 0:
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th2data = th2data[0:len(th2data) - abs(wfldiff)]
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ax3.plot(th2data, trH2_filt.data / max(trH2_filt.data), 'k', label='Data')
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ax3.plot(th2data, trH2_filt.data / max(trH2_filt.data), color=linecolor, linewidth=0.7, label='Data')
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if Pweight < 4:
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p22, = ax3.plot(arhcf1.getTimeArray(),
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arhcf1.getCF() / max(arhcf1.getCF()), 'b', label='CF1')
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@ -35,7 +35,7 @@ class AutoPicker(object):
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warnings.simplefilter('ignore')
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def __init__(self, cf, TSNR, PickWindow, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None):
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def __init__(self, cf, TSNR, PickWindow, iplot=0, aus=None, Tsmooth=None, Pick1=None, fig=None, linecolor='k'):
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'''
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:param: cf, characteristic function, on which the picking algorithm is applied
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:type: `~pylot.core.pick.CharFuns.CharacteristicFunction` object
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@ -62,7 +62,8 @@ class AutoPicker(object):
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'''
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assert isinstance(cf, CharacteristicFunction), "%s is not a CharacteristicFunction object" % str(cf)
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self._linecolor = linecolor
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self._pickcolor_p = 'b'
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self.cf = cf.getCF()
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self.Tcf = cf.getTimeArray()
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self.Data = cf.getXCF()
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@ -264,13 +265,13 @@ class AICPicker(AutoPicker):
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print("Choose longer slope determination window!")
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if self.iplot > 1:
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if self.fig == None or self.fig == 'None':
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fig = plt.figure() # self.iplot) ### WHY? MP MP
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fig = plt.figure()
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plt_flag = 1
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else:
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fig = self.fig
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ax = fig.add_subplot(111)
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x = self.Data[0].data
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ax.plot(self.Tcf, x / max(x), 'k', label='(HOS-/AR-) Data')
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ax.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
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ax.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
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ax.legend(loc=1)
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ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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@ -307,7 +308,7 @@ class AICPicker(AutoPicker):
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x = self.Data[0].data
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if len(self.Tcf) > len(self.Data[0].data): # why? LK
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self.Tcf = self.Tcf[0:len(self.Tcf)-1]
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ax1.plot(self.Tcf, x / max(x), 'k', label='(HOS-/AR-) Data')
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ax1.plot(self.Tcf, x / max(x), color=self._linecolor, linewidth=0.7, label='(HOS-/AR-) Data')
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ax1.plot(self.Tcf, aicsmooth / max(aicsmooth), 'r', label='Smoothed AIC-CF')
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if self.Pick is not None:
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ax1.plot([self.Pick, self.Pick], [-0.1, 0.5], 'b', linewidth=2, label='AIC-Pick')
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@ -317,7 +318,7 @@ class AICPicker(AutoPicker):
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if self.Pick is not None:
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ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
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ax2.plot(self.Tcf, x, 'k', label='Data')
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ax2.plot(self.Tcf, x, color=self._linecolor, linewidth=0.7, label='Data')
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ax1.axvspan(self.Tcf[inoise[0]], self.Tcf[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
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ax1.axvspan(self.Tcf[isignal[0]], self.Tcf[isignal[-1]], color='b', alpha=0.2, lw=0,
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label='Signal Window')
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@ -473,10 +474,10 @@ class PragPicker(AutoPicker):
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else:
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fig = self.fig
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ax = fig.add_subplot(111)
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ax.plot(Tcfpick, cfipick, 'k', label='CF')
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ax.plot(Tcfpick, cfipick, color=self._linecolor, linewidth=0.7, label='CF')
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ax.plot(Tcfpick, cfsmoothipick, 'r', label='Smoothed CF')
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if pickflag > 0:
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ax.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], 'b', linewidth=2, label='Pick')
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ax.plot([self.Pick, self.Pick], [min(cfipick), max(cfipick)], self._pickcolor_p, linewidth=2, label='Pick')
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ax.set_xlabel('Time [s] since %s' % self.Data[0].stats.starttime)
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ax.set_yticks([])
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ax.set_title(self.Data[0].stats.station)
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@ -15,7 +15,7 @@ import numpy as np
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from obspy.core import Stream, UTCDateTime
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def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
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def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecolor='k'):
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'''
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Function to derive earliest and latest possible pick after Diehl & Kissling (2009)
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as reasonable uncertainties. Latest possible pick is based on noise level,
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@ -131,16 +131,16 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
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fig = plt.figure() # iplot)
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plt_flag = 1
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ax = fig.add_subplot(111)
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ax.plot(t, x, 'k', label='Data')
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ax.plot(t, x, color=linecolor, linewidth=0.7, label='Data')
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ax.axvspan(t[inoise[0]], t[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
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ax.axvspan(t[isignal[0]], t[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
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ax.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k', label='Noise Level')
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ax.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], color=linecolor, linewidth=0.7, linestyle='dashed', label='Noise Level')
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ax.plot(t[pis[zc]], np.zeros(len(zc)), '*g',
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markersize=14, label='Zero Crossings')
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ax.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], '--k')
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ax.plot([t[0], t[int(len(t)) - 1]], [-nlevel, -nlevel], color=linecolor, linewidth=0.7, linestyle='dashed')
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ax.plot([Pick1, Pick1], [max(x), -max(x)], 'b', linewidth=2, label='mpp')
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ax.plot([LPick, LPick], [max(x) / 2, -max(x) / 2], '--k', label='lpp')
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ax.plot([EPick, EPick], [max(x) / 2, -max(x) / 2], '--k', label='epp')
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ax.plot([LPick, LPick], [max(x) / 2, -max(x) / 2], color=linecolor, linewidth=0.7, linestyle='dashed', label='lpp')
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ax.plot([EPick, EPick], [max(x) / 2, -max(x) / 2], color=linecolor, linewidth=0.7, linestyle='dashed', label='epp')
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ax.plot([Pick1 + PickError, Pick1 + PickError],
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[max(x) / 2, -max(x) / 2], 'r--', label='spe')
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ax.plot([Pick1 - PickError, Pick1 - PickError],
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@ -160,7 +160,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None):
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return EPick, LPick, PickError
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def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
|
||||
def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
|
||||
'''
|
||||
Function to derive first motion (polarity) of given phase onset Pick.
|
||||
Calculation is based on zero crossings determined within time window pickwin
|
||||
@ -324,7 +324,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
|
||||
fig = plt.figure() # iplot)
|
||||
plt_flag = 1
|
||||
ax1 = fig.add_subplot(211)
|
||||
ax1.plot(t, xraw, 'k')
|
||||
ax1.plot(t, xraw, color=linecolor, linewidth=0.7)
|
||||
ax1.plot([Pick, Pick], [max(xraw), -max(xraw)], 'b', linewidth=2, label='Pick')
|
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if P1 is not None:
|
||||
ax1.plot(t[islope1], xraw[islope1], label='Slope Window')
|
||||
@ -338,7 +338,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None):
|
||||
|
||||
ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
|
||||
ax2.set_title('First-Motion Determination, Filtered Data')
|
||||
ax2.plot(t, xfilt, 'k')
|
||||
ax2.plot(t, xfilt, color=linecolor, linewidth=0.7)
|
||||
ax2.plot([Pick, Pick], [max(xfilt), -max(xfilt)], 'b',
|
||||
linewidth=2)
|
||||
if P2 is not None:
|
||||
@ -668,15 +668,18 @@ def wadaticheck(pickdic, dttolerance, iplot=0, fig_dict=None):
|
||||
if iplot > 0:
|
||||
if fig_dict:
|
||||
fig = fig_dict['wadati']
|
||||
linecolor = fig_dict['plot_style']['linecolor']['rgba_mpl']
|
||||
plt_flag = 0
|
||||
else:
|
||||
fig = plt.figure()
|
||||
linecolor = 'k'
|
||||
plt_flag = 1
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(Ppicks, SPtimes, 'ro', label='Skipped S-Picks')
|
||||
if wfitflag == 0:
|
||||
ax.plot(Ppicks, wdfit, 'k', label='Wadati 1')
|
||||
ax.plot(checkedPpicks, checkedSPtimes, 'ko', label='Reliable S-Picks')
|
||||
ax.plot(Ppicks, wdfit, color=linecolor, linewidth=0.7, label='Wadati 1')
|
||||
ax.plot(checkedPpicks, checkedSPtimes, color=linecolor,
|
||||
linewidth=0, marker='o', label='Reliable S-Picks')
|
||||
ax.plot(checkedPpicks, wdfit2, 'g', label='Wadati 2')
|
||||
ax.set_title('Wadati-Diagram, %d S-P Times, Vp/Vs(raw)=%5.2f,' \
|
||||
'Vp/Vs(checked)=%5.2f' % (len(SPtimes), vpvsr, cvpvsr))
|
||||
@ -699,7 +702,7 @@ 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):
|
||||
def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fig=None, linecolor='k'):
|
||||
'''
|
||||
Function to detect spuriously picked noise peaks.
|
||||
Uses RMS trace of all 3 components (if available) to determine,
|
||||
@ -785,7 +788,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
|
||||
fig = plt.figure() # iplot)
|
||||
plt_flag = 1
|
||||
ax = fig.add_subplot(111)
|
||||
ax.plot(t, rms, 'k', label='RMS Data')
|
||||
ax.plot(t, rms, color=linecolor, linewidth=0.7, label='RMS Data')
|
||||
ax.axvspan(t[inoise[0]], t[inoise[-1]], color='y', alpha=0.2, lw=0, label='Noise Window')
|
||||
ax.axvspan(t[isignal[0]], t[isignal[-1]], color='b', alpha=0.2, lw=0, label='Signal Window')
|
||||
ax.plot([t[isignal[0]], t[isignal[len(isignal) - 1]]],
|
||||
@ -975,7 +978,7 @@ def jackknife(X, phi, h):
|
||||
return PHI_jack, PHI_pseudo, PHI_sub
|
||||
|
||||
|
||||
def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
|
||||
def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None, linecolor='k'):
|
||||
'''
|
||||
Function to compare energy content of vertical trace with
|
||||
energy content of horizontal traces to detect spuriously
|
||||
@ -1103,7 +1106,7 @@ def checkZ4S(X, pick, zfac, checkwin, iplot, fig=None):
|
||||
plt_flag = 1
|
||||
ax = fig.add_subplot(3, 1, i + 1, sharex=ax1)
|
||||
ax.plot(t, abs(trace.data), color='b', label='abs')
|
||||
ax.plot(t, trace.data, color='k')
|
||||
ax.plot(t, trace.data, color=linecolor, linewidth=0.7)
|
||||
name = str(trace.stats.channel) + ': {}'.format(rms)
|
||||
ax.plot([pick, pick + checkwin], [rms, rms], 'r', label='RMS {}'.format(name))
|
||||
ax.plot([pick, pick], ax.get_ylim(), 'm', label='Pick')
|
||||
|
||||
@ -2766,7 +2766,8 @@ class TuneAutopicker(QWidget):
|
||||
'locflag': 0,
|
||||
'savexml': False}
|
||||
for key in self.fig_dict.keys():
|
||||
self.fig_dict[key].clear()
|
||||
if not key == 'plot_style':
|
||||
self.fig_dict[key].clear()
|
||||
self.ap_thread = Thread(self, autoPyLoT, arg=args,
|
||||
progressText='Picking trace...',
|
||||
pb_widget=self.pb_widget,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user