[new] added Wood-Anderson amplitude output for further analysis
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@ -126,6 +126,8 @@ class RichterMagnitude(Magnitude):
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'sensitivity': 1
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'sensitivity': 1
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}
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}
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_amplitudes = dict()
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def __init__(self, stream, event, calc_win, verbosity=False, iplot=0):
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def __init__(self, stream, event, calc_win, verbosity=False, iplot=0):
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super(RichterMagnitude, self).__init__(stream, event, verbosity, iplot)
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super(RichterMagnitude, self).__init__(stream, event, verbosity, iplot)
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@ -141,6 +143,15 @@ class RichterMagnitude(Magnitude):
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def calc_win(self, value):
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def calc_win(self, value):
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self._calc_win = value
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self._calc_win = value
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@property
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def amplitudes(self):
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return self._amplitudes
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@amplitudes.setter
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def amplitudes(self, value):
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station, a0 = value
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self._amplitudes[station] = a0
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def peak_to_peak(self, st, t0):
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def peak_to_peak(self, st, t0):
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# simulate Wood-Anderson response
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# simulate Wood-Anderson response
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@ -203,11 +214,12 @@ class RichterMagnitude(Magnitude):
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continue
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continue
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delta = degrees2kilometers(a.distance)
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delta = degrees2kilometers(a.distance)
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onset = pick.time
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onset = pick.time
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wapp = self.peak_to_peak(wf, onset)
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a0 = self.peak_to_peak(wf, onset)
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self.amplitudes = (station, a0)
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# using standard Gutenberg-Richter relation
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# using standard Gutenberg-Richter relation
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# TODO make the ML calculation more flexible by allowing
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# TODO make the ML calculation more flexible by allowing
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# use of custom relation functions
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# use of custom relation functions
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mag = dict(mag=np.log10(wapp) + richter_magnitude_scaling(delta))
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mag = dict(mag=np.log10(a0) + richter_magnitude_scaling(delta))
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self.magnitudes = (station, mag)
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self.magnitudes = (station, mag)
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