Marginal changes.
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@ -89,7 +89,7 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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p2, = plt.plot(t[inoise], x[inoise])
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p2, = plt.plot(t[inoise], x[inoise])
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p3, = plt.plot(t[isignal], x[isignal], 'r')
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p3, = plt.plot(t[isignal], x[isignal], 'r')
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p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
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p4, = plt.plot([t[0], t[int(len(t)) - 1]], [nlevel, nlevel], '--k')
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p5, = plt.plot(t[isignal[0][zc]], np.zeros(len(zc)), '*g',
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p5, = plt.plot(t[isignal[zc]], np.zeros(len(zc)), '*g',
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markersize=14)
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markersize=14)
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plt.legend([p1, p2, p3, p4, p5],
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plt.legend([p1, p2, p3, p4, p5],
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['Data', 'Noise Window', 'Signal Window', 'Noise Level',
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['Data', 'Noise Window', 'Signal Window', 'Noise Level',
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@ -105,8 +105,6 @@ def earllatepicker(X, nfac, TSNR, Pick1, iplot=None):
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[max(x) / 2, -max(x) / 2], 'r--')
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[max(x) / 2, -max(x) / 2], 'r--')
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plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
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plt.xlabel('Time [s] since %s' % X[0].stats.starttime)
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plt.yticks([])
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plt.yticks([])
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ax = plt.gca()
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ax.set_xlim([t[inoise[0][0]] - 2, t[isignal[0][len(isignal) - 1]] + 3])
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plt.title(
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plt.title(
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'Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' %
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'Earliest-/Latest Possible/Most Likely Pick & Symmetric Pick Error, %s' %
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X[0].stats.station)
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X[0].stats.station)
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