[minor] corrected time axes to correct endtime (was increased by one sample using npts*dt instead of npts-1*dt)
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@ -589,7 +589,7 @@ class AutopickStation(object):
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plt_flag = 0
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plt_flag = 0
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fig._tight = True
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fig._tight = True
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ax1 = fig.add_subplot(311)
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ax1 = fig.add_subplot(311)
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tdata = np.linspace(start=0, stop=self.ztrace.stats.npts*self.ztrace.stats.delta, num=self.ztrace.stats.npts)
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tdata = np.linspace(start=0, stop=self.ztrace.stats.endtime-self.ztrace.stats.starttime, num=self.ztrace.stats.npts)
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# plot tapered trace filtered with bpz2 filter settings
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# plot tapered trace filtered with bpz2 filter settings
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ax1.plot(tdata, self.tr_filt_z_bpz2.data/max(self.tr_filt_z_bpz2.data), color=linecolor, linewidth=0.7, label='Data')
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ax1.plot(tdata, self.tr_filt_z_bpz2.data/max(self.tr_filt_z_bpz2.data), color=linecolor, linewidth=0.7, label='Data')
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if self.p_results.weight < 4:
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if self.p_results.weight < 4:
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@ -632,7 +632,7 @@ class AutopickStation(object):
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if self.horizontal_traces_exist() and self.s_data.Sflag == 1:
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if self.horizontal_traces_exist() and self.s_data.Sflag == 1:
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# plot E trace
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# plot E trace
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ax2 = fig.add_subplot(3, 1, 2, sharex=ax1)
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ax2 = fig.add_subplot(3, 1, 2, sharex=ax1)
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th1data = np.linspace(0, self.etrace.stats.npts*self.etrace.stats.delta, self.etrace.stats.npts)
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th1data = np.linspace(0, self.etrace.stats.endtime-self.etrace.stats.starttime, self.etrace.stats.npts)
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# plot filtered and tapered waveform
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# plot filtered and tapered waveform
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ax2.plot(th1data, self.etrace.data / max(self.etrace.data), color=linecolor, linewidth=0.7, label='Data')
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ax2.plot(th1data, self.etrace.data / max(self.etrace.data), color=linecolor, linewidth=0.7, label='Data')
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if self.p_results.weight < 4:
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if self.p_results.weight < 4:
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@ -668,7 +668,7 @@ class AutopickStation(object):
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# plot N trace
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# plot N trace
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ax3 = fig.add_subplot(3, 1, 3, sharex=ax1)
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ax3 = fig.add_subplot(3, 1, 3, sharex=ax1)
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th2data= np.linspace(0, self.ntrace.stats.npts*self.ntrace.stats.delta, self.ntrace.stats.npts)
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th2data= np.linspace(0, self.ntrace.stats.endtime-self.ntrace.stats.starttime, self.ntrace.stats.npts)
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# plot trace
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# plot trace
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ax3.plot(th2data, self.ntrace.data / max(self.ntrace.data), color=linecolor, linewidth=0.7, label='Data')
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ax3.plot(th2data, self.ntrace.data / max(self.ntrace.data), color=linecolor, linewidth=0.7, label='Data')
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if self.p_results.weight < 4:
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if self.p_results.weight < 4:
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@ -72,7 +72,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=0, verbosity=1, fig=None, linecol
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' relative to most likely pick ...')
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' relative to most likely pick ...')
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x = X[0].data
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x = X[0].data
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t = np.linspace(0, X[0].stats.npts / X[0].stats.sampling_rate,
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t = np.linspace(0, X[0].stats.endtime - X[0].stats.starttime,
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X[0].stats.npts)
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X[0].stats.npts)
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inoise = getnoisewin(t, Pick1, TSNR[0], TSNR[1])
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inoise = getnoisewin(t, Pick1, TSNR[0], TSNR[1])
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@ -218,7 +218,7 @@ def fmpicker(Xraw, Xfilt, pickwin, Pick, iplot=0, fig=None, linecolor='k'):
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xraw = Xraw[0].data
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xraw = Xraw[0].data
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xfilt = Xfilt[0].data
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xfilt = Xfilt[0].data
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t = np.linspace(0, Xraw[0].stats.npts / Xraw[0].stats.sampling_rate,
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t = np.linspace(0, Xraw[0].stats.endtime - Xraw[0].stats.starttime,
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Xraw[0].stats.npts)
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Xraw[0].stats.npts)
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# get pick window
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# get pick window
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ipick = np.where((t <= min([Pick + pickwin, len(Xraw[0])])) & (t >= Pick))
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ipick = np.where((t <= min([Pick + pickwin, len(Xraw[0])])) & (t >= Pick))
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@ -824,7 +824,7 @@ def checksignallength(X, pick, minsiglength, pickparams, iplot=0, fig=None, line
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ilen = len(x1)
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ilen = len(x1)
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rms = abs(x1)
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rms = abs(x1)
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t = np.linspace(0, X[0].stats.delta * ilen, ilen)
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t = np.linspace(0, X[0].stats.endtime - X[0].stats.starttime, ilen)
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if pick >= t[np.size(t)-1]: # it might happen, that for individual stations cut times
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if pick >= t[np.size(t)-1]: # it might happen, that for individual stations cut times
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# are set to zero because of too small time series
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# are set to zero because of too small time series
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# => pick time has to be reduced for pstart
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# => pick time has to be reduced for pstart
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@ -1196,7 +1196,7 @@ def checkZ4S(X, pick, pickparams, iplot, fig=None, linecolor='k'):
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for i, key in enumerate(['Z', 'N', 'E']):
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for i, key in enumerate(['Z', 'N', 'E']):
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rms = rms_dict[key]
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rms = rms_dict[key]
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trace = traces_dict[key]
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trace = traces_dict[key]
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t = np.linspace(diff_dict[key], trace.stats.npts / trace.stats.sampling_rate + diff_dict[key],
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t = np.linspace(diff_dict[key], trace.stats.endtime - trace.stats.starttime + diff_dict[key],
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trace.stats.npts)
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trace.stats.npts)
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if i == 0:
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if i == 0:
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if real_None(fig) is None:
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if real_None(fig) is None:
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