[refactor] major refactoring of Magnitude objects finished
now the changed usage of the Magnitude object has to be implemented into autoPyLoT and QtPyLoT (pending)
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@ -39,39 +39,52 @@ class Magnitude(object):
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self._stream = stream
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self._magnitudes = dict()
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def __str__(self):
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print('number of stations used: {0}\n'.format(len(self.magnitudes.values())))
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print('\tstation\tmagnitude')
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for s, m in self.magnitudes.items(): print('\t{0}\t{1}'.format(s, m))
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def __nonzero__(self):
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return bool(self.magnitudes)
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@property
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def plot_flag(self):
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return self._plot_flag
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@plot_flag.setter
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def plot_flag(self, value):
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self._plot_flag = value
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@property
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def stream(self):
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return self._stream
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@stream.setter
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def stream(self, value):
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self._stream = value
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@property
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def event(self):
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return self._event
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@property
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def arrivals(self):
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return self._event.origins[0].arrivals
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@property
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def magnitudes(self):
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return self._magnitudes
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@magnitudes.setter
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def magnitudes(self, value):
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"""
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@ -84,169 +97,22 @@ class Magnitude(object):
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station, magnitude = value
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self._magnitudes[station] = magnitude
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def get(self):
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return self.magnitudes
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class Magnitude(object):
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'''
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Superclass for calculating Wood-Anderson peak-to-peak
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amplitudes, local magnitudes, source spectra, seismic moments
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and moment magnitudes.
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'''
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def __init__(self, wfstream, t0, pwin, iplot, NLLocfile=None, \
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picks=None, rho=None, vp=None, Qp=None, invdir=None):
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'''
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:param: wfstream
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:type: `~obspy.core.stream.Stream
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:param: t0, onset time, P- or S phase
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:type: float
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:param: pwin, pick window [t0 t0+pwin] to get maximum
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peak-to-peak amplitude (WApp) or to calculate
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source spectrum (DCfc) around P onset
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:type: float
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:param: iplot, no. of figure window for plotting interims results
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:type: integer
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:param: NLLocfile, name and full path to NLLoc-location file
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needed when calling class MoMw
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:type: string
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:param: picks, dictionary containing picking results
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:type: dictionary
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:param: rho [kg/m³], rock density, parameter from autoPyLoT.in
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:type: integer
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:param: vp [m/s], P-velocity
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:param: integer
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:param: invdir, name and path to inventory or dataless-SEED file
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:type: string
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'''
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assert isinstance(wfstream, Stream), "%s is not a stream object" % str(wfstream)
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self._stream = wfstream
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self._invdir = invdir
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self._t0 = t0
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self._pwin = pwin
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self._iplot = iplot
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self.setNLLocfile(NLLocfile)
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self.setrho(rho)
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self.setpicks(picks)
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self.setvp(vp)
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self.setQp(Qp)
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self.calcwapp()
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self.calcsourcespec()
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self.run_calcMoMw()
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@property
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def stream(self):
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return self._stream
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@stream.setter
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def stream(self, wfstream):
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self._stream = wfstream
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@property
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def t0(self):
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return self._t0
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@t0.setter
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def t0(self, value):
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self._t0 = value
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@property
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def invdir(self):
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return self._invdir
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@invdir.setter
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def invdir(self, value):
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self._invdir = value
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@property
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def pwin(self):
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return self._pwin
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@pwin.setter
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def pwin(self, value):
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self._pwin = value
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@property
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def plot_flag(self):
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return self.iplot
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@plot_flag.setter
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def plot_flag(self, value):
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self._iplot = value
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def setNLLocfile(self, NLLocfile):
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self.NLLocfile = NLLocfile
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def getNLLocfile(self):
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return self.NLLocfile
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def setrho(self, rho):
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self.rho = rho
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def getrho(self):
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return self.rho
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def setvp(self, vp):
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self.vp = vp
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def getvp(self):
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return self.vp
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def setQp(self, Qp):
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self.Qp = Qp
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def getQp(self):
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return self.Qp
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def setpicks(self, picks):
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self.picks = picks
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def getpicks(self):
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return self.picks
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def getwapp(self):
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return self.wapp
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def getw0(self):
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return self.w0
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def getfc(self):
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return self.fc
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def get_metadata(self):
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return read_metadata(self.invdir)
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def plot(self):
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pass
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def getpicdic(self):
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return self.picdic
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def calcwapp(self):
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self.wapp = None
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def calcsourcespec(self):
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self.sourcespek = None
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def run_calcMoMw(self):
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self.pickdic = None
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def net_magnitude(self):
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if self:
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return np.median([M["mag"] for M in self.magnitudes.values()])
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return None
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class RichterMagnitude(Magnitude):
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'''
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"""
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Method to derive peak-to-peak amplitude as seen on a Wood-Anderson-
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seismograph. Has to be derived from instrument corrected traces!
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'''
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"""
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# poles, zeros and sensitivity of WA seismograph
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# (see Uhrhammer & Collins, 1990, BSSA, pp. 702-716)
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@ -257,29 +123,23 @@ class RichterMagnitude(Magnitude):
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'sensitivity': 1
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}
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def __init__(self, stream, event, t0, calc_win, verbosity=False):
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def __init__(self, stream, event, calc_win, verbosity=False):
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super(RichterMagnitude, self).__init__(stream, event, verbosity)
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self._t0 = t0
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self._calc_win = calc_win
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@property
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def t0(self):
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return self._t0
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@t0.setter
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def t0(self, value):
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self._t0 = value
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@property
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def calc_win(self):
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return self._calc_win
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@calc_win.setter
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def calc_win(self, value):
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self._calc_win = value
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def peak_to_peak(self, st):
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def peak_to_peak(self, st, t0):
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# simulate Wood-Anderson response
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st.simulate(paz_remove=None, paz_simulate=self._paz)
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@ -303,7 +163,7 @@ class RichterMagnitude(Magnitude):
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# get time array
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th = np.arange(0, len(sqH) * dt, dt)
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# get maximum peak within pick window
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iwin = getsignalwin(th, self.t0 - stime, self.calc_win)
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iwin = getsignalwin(th, t0 - stime, self.calc_win)
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wapp = np.max(sqH[iwin])
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if self._verbosity:
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print("Determined Wood-Anderson peak-to-peak amplitude: {0} "
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@ -315,7 +175,7 @@ class RichterMagnitude(Magnitude):
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f = plt.figure(2)
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plt.plot(th, sqH)
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plt.plot(th[iwin], sqH[iwin], 'g')
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plt.plot([self.t0, self.t0], [0, max(sqH)], 'r', linewidth=2)
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plt.plot([t0, t0], [0, max(sqH)], 'r', linewidth=2)
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plt.title('Station %s, RMS Horizontal Traces, WA-peak-to-peak=%4.1f mm' \
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% (st[0].stats.station, wapp))
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plt.xlabel('Time [s]')
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@ -326,6 +186,7 @@ class RichterMagnitude(Magnitude):
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return wapp
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def get(self):
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for a in self.arrivals:
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if a.phase not in 'sS':
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@ -334,18 +195,20 @@ class RichterMagnitude(Magnitude):
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station = pick.waveform_id.station_code
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wf = select_for_phase(self.stream.select(
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station=station), a.phase)
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if not wf:
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print('WARNING: no waveform data found for station {0}'.format(
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station))
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continue
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delta = degrees2kilometers(a.distance)
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wapp = self.peak_to_peak(wf)
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onset = pick.time
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wapp = self.peak_to_peak(wf, onset)
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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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# use of custom relation functions
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mag = np.log10(wapp) + richter_magnitude_scaling(delta)
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mag = dict(mag=np.log10(wapp) + richter_magnitude_scaling(delta))
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self.magnitudes = (station, mag)
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return self.magnitudes
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def net_magnitude(self):
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return np.median([M for M in self.magnitudes.values()])
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class MomentMagnitude(Magnitude):
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'''
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@ -357,6 +220,29 @@ class MomentMagnitude(Magnitude):
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corresponding moment magntiude Mw.
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'''
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def __init__(self, stream, event, vp, Qp, density, verbosity=False):
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super(MomentMagnitude, self).__init__(stream, event)
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self._vp = vp
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self._Qp = Qp
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self._density = density
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@property
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def p_velocity(self):
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return self._vp
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@property
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def p_attenuation(self):
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return self._Qp
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@property
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def rock_density(self):
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return self._density
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def run_calcMoMw(self):
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picks = self.getpicks()
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@ -405,6 +291,32 @@ class MomentMagnitude(Magnitude):
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self.picdic = picks
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def get(self):
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for a in self.arrivals:
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if a.phase not in 'pP':
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continue
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pick = a.pick_id.get_referred_object()
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station = pick.waveform_id.station_code
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wf = select_for_phase(self.stream.select(
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station=station), a.phase)
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if not wf:
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continue
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onset = pick.time
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distance = degrees2kilometers(a.distance)
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azimuth = a.azimuth
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incidence = a.takeoff_angle
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w0, fc = calcsourcespec(wf, onset, self.p_velocity, distance, azimuth,
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incidence, self.p_attenuation)
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if w0 is None or fc is None:
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print("WARNING: insufficient frequency information")
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continue
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wf = select_for_phase(wf, "P")
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M0, Mw = calcMoMw(wf, w0, self.rock_density, self.p_velocity, distance)
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mag = dict(w0=w0, fc=fc, M0=M0, mag=Mw)
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self.magnitudes = (station, mag)
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return self.magnitudes
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def calc_woodanderson_pp(st, metadata, T0, win=10., verbosity=False):
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if verbosity:
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print ("Getting Wood-Anderson peak-to-peak amplitude ...")
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@ -491,8 +403,8 @@ def calcMoMw(wfstream, w0, rho, vp, delta):
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return Mo, Mw
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def calcsourcespec(wfstream, onset, metadata, vp, delta, azimuth, incidence,
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qp, iplot):
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def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
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qp, iplot=0):
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'''
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Subfunction to calculate the source spectrum and to derive from that the plateau
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(usually called omega0) and the corner frequency assuming Aki's omega-square
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@ -500,16 +412,12 @@ def calcsourcespec(wfstream, onset, metadata, vp, delta, azimuth, incidence,
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thus restitution and integration necessary! Integrated traces are rotated
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into ray-coordinate system ZNE => LQT using Obspy's rotate modul!
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:param: wfstream
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:param: wfstream (corrected for instrument)
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:type: `~obspy.core.stream.Stream`
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:param: onset, P-phase onset time
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:type: float
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:param: metadata, tuple or list containing type of inventory and either
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list of files or inventory object
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:type: tuple or list
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:param: vp, Vp-wave velocity
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:type: float
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@ -533,163 +441,151 @@ def calcsourcespec(wfstream, onset, metadata, vp, delta, azimuth, incidence,
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# get Q value
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Q, A = qp
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delta = delta * 1000 # hypocentral distance in [m]
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dist = delta * 1000 # hypocentral distance in [m]
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fc = None
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w0 = None
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wf_copy = wfstream.copy()
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invtype, inventory = metadata
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zdat = select_for_phase(wfstream, "P")
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[cordat, restflag] = restitute_data(wf_copy, invtype, inventory)
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if restflag is True:
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zdat = cordat.select(component="Z")
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if len(zdat) == 0:
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zdat = cordat.select(component="3")
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cordat_copy = cordat.copy()
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# get equal time stamps and lengths of traces
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# necessary for rotation of traces
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trstart, trend = common_range(cordat_copy)
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cordat_copy.trim(trstart, trend)
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try:
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# rotate into LQT (ray-coordindate-) system using Obspy's rotate
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# L: P-wave direction
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# Q: SV-wave direction
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# T: SH-wave direction
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LQT = cordat_copy.rotate('ZNE->LQT', azimuth, incidence)
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ldat = LQT.select(component="L")
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if len(ldat) == 0:
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# if horizontal channels are 2 and 3
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# no azimuth information is available and thus no
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# rotation is possible!
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print("calcsourcespec: Azimuth information is missing, "
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"no rotation of components possible!")
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ldat = LQT.select(component="Z")
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dt = zdat[0].stats.delta
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# integrate to displacement
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# unrotated vertical component (for copmarison)
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inttrz = signal.detrend(integrate.cumtrapz(zdat[0].data, None,
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zdat[0].stats.delta))
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# rotated component Z => L
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Ldat = signal.detrend(integrate.cumtrapz(ldat[0].data, None,
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ldat[0].stats.delta))
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freq = zdat[0].stats.sampling_rate
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# get window after P pulse for
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# calculating source spectrum
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tstart = UTCDateTime(zdat[0].stats.starttime)
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tonset = onset.timestamp - tstart.timestamp
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impickP = tonset * zdat[0].stats.sampling_rate
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wfzc = Ldat[impickP: len(Ldat) - 1]
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# get time array
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t = np.arange(0, len(inttrz) * zdat[0].stats.delta, \
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zdat[0].stats.delta)
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# calculate spectrum using only first cycles of
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# waveform after P onset!
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zc = crossings_nonzero_all(wfzc)
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if np.size(zc) == 0 or len(zc) <= 3:
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print ("calcsourcespec: Something is wrong with the waveform, "
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"no zero crossings derived!")
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print ("No calculation of source spectrum possible!")
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plotflag = 0
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else:
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plotflag = 1
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index = min([3, len(zc) - 1])
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calcwin = (zc[index] - zc[0]) * zdat[0].stats.delta
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iwin = getsignalwin(t, tonset, calcwin)
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xdat = Ldat[iwin]
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# trim traces to common range (for rotation)
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trstart, trend = common_range(wfstream)
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wfstream.trim(trstart, trend)
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# fft
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fny = zdat[0].stats.sampling_rate / 2
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l = len(xdat) / zdat[0].stats.sampling_rate
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# number of fft bins after Bath
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n = zdat[0].stats.sampling_rate * l
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# find next power of 2 of data length
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m = pow(2, np.ceil(np.log(len(xdat)) / np.log(2)))
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N = int(np.power(m, 2))
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y = zdat[0].stats.delta * np.fft.fft(xdat, N)
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Y = abs(y[: N / 2])
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L = (N - 1) / zdat[0].stats.sampling_rate
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f = np.arange(0, fny, 1 / L)
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# rotate into LQT (ray-coordindate-) system using Obspy's rotate
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# L: P-wave direction
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# Q: SV-wave direction
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# T: SH-wave direction
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LQT = wfstream.rotate('ZNE->LQT', azimuth, incidence)
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ldat = LQT.select(component="L")
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if len(ldat) == 0:
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# if horizontal channels are 2 and 3
|
||||
# no azimuth information is available and thus no
|
||||
# rotation is possible!
|
||||
print("calcsourcespec: Azimuth information is missing, "
|
||||
"no rotation of components possible!")
|
||||
ldat = LQT.select(component="Z")
|
||||
|
||||
# remove zero-frequency and frequencies above
|
||||
# corner frequency of seismometer (assumed
|
||||
# to be 100 Hz)
|
||||
fi = np.where((f >= 1) & (f < 100))
|
||||
F = f[fi]
|
||||
YY = Y[fi]
|
||||
# integrate to displacement
|
||||
# unrotated vertical component (for comparison)
|
||||
inttrz = signal.detrend(integrate.cumtrapz(zdat[0].data, None, dt))
|
||||
|
||||
# correction for attenuation
|
||||
wa = 2 * np.pi * F # angular frequency
|
||||
D = np.exp((wa * delta) / (2 * vp * Q * F ** A))
|
||||
YYcor = YY.real * D
|
||||
# rotated component Z => L
|
||||
Ldat = signal.detrend(integrate.cumtrapz(ldat[0].data, None, dt))
|
||||
|
||||
# get plateau (DC value) and corner frequency
|
||||
# initial guess of plateau
|
||||
w0in = np.mean(YYcor[0:100])
|
||||
# initial guess of corner frequency
|
||||
# where spectral level reached 50% of flat level
|
||||
iin = np.where(YYcor >= 0.5 * w0in)
|
||||
Fcin = F[iin[0][np.size(iin) - 1]]
|
||||
# get window after P pulse for
|
||||
# calculating source spectrum
|
||||
rel_onset = onset - trstart
|
||||
impickP = int(rel_onset * freq)
|
||||
wfzc = Ldat[impickP: len(Ldat) - 1]
|
||||
# get time array
|
||||
t = np.arange(0, len(inttrz) * dt, dt)
|
||||
# calculate spectrum using only first cycles of
|
||||
# waveform after P onset!
|
||||
zc = crossings_nonzero_all(wfzc)
|
||||
if np.size(zc) == 0 or len(zc) <= 3:
|
||||
print ("calcsourcespec: Something is wrong with the waveform, "
|
||||
"no zero crossings derived!")
|
||||
print ("No calculation of source spectrum possible!")
|
||||
plotflag = 0
|
||||
else:
|
||||
plotflag = 1
|
||||
index = min([3, len(zc) - 1])
|
||||
calcwin = (zc[index] - zc[0]) * dt
|
||||
iwin = getsignalwin(t, rel_onset, calcwin)
|
||||
xdat = Ldat[iwin]
|
||||
|
||||
# use of implicit scipy otimization function
|
||||
fit = synthsourcespec(F, w0in, Fcin)
|
||||
[optspecfit, _] = curve_fit(synthsourcespec, F, YYcor, [w0in,
|
||||
Fcin])
|
||||
w01 = optspecfit[0]
|
||||
fc1 = optspecfit[1]
|
||||
print ("calcsourcespec: Determined w0-value: %e m/Hz, \n"
|
||||
"Determined corner frequency: %f Hz" % (w01, fc1))
|
||||
# fft
|
||||
fny = freq / 2
|
||||
l = len(xdat) / freq
|
||||
# number of fft bins after Bath
|
||||
n = freq * l
|
||||
# find next power of 2 of data length
|
||||
m = pow(2, np.ceil(np.log(len(xdat)) / np.log(2)))
|
||||
N = int(np.power(m, 2))
|
||||
y = dt * np.fft.fft(xdat, N)
|
||||
Y = abs(y[: N / 2])
|
||||
L = (N - 1) / freq
|
||||
f = np.arange(0, fny, 1 / L)
|
||||
|
||||
# use of conventional fitting
|
||||
[w02, fc2] = fitSourceModel(F, YYcor, Fcin, iplot)
|
||||
# remove zero-frequency and frequencies above
|
||||
# corner frequency of seismometer (assumed
|
||||
# to be 100 Hz)
|
||||
fi = np.where((f >= 1) & (f < 100))
|
||||
F = f[fi]
|
||||
YY = Y[fi]
|
||||
|
||||
# get w0 and fc as median of both
|
||||
# source spectrum fits
|
||||
w0 = np.median([w01, w02])
|
||||
fc = np.median([fc1, fc2])
|
||||
print("calcsourcespec: Using w0-value = %e m/Hz and fc = %f Hz" % (w0, fc))
|
||||
# correction for attenuation
|
||||
wa = 2 * np.pi * F # angular frequency
|
||||
D = np.exp((wa * dist) / (2 * vp * Q * F ** A))
|
||||
YYcor = YY.real * D
|
||||
|
||||
except TypeError as er:
|
||||
raise TypeError('''{0}'''.format(er))
|
||||
# get plateau (DC value) and corner frequency
|
||||
# initial guess of plateau
|
||||
w0in = np.mean(YYcor[0:100])
|
||||
# initial guess of corner frequency
|
||||
# where spectral level reached 50% of flat level
|
||||
iin = np.where(YYcor >= 0.5 * w0in)
|
||||
Fcin = F[iin[0][np.size(iin) - 1]]
|
||||
|
||||
if iplot > 1:
|
||||
f1 = plt.figure()
|
||||
tLdat = np.arange(0, len(Ldat) * zdat[0].stats.delta, \
|
||||
zdat[0].stats.delta)
|
||||
plt.subplot(2, 1, 1)
|
||||
# show displacement in mm
|
||||
p1, = plt.plot(t, np.multiply(inttrz, 1000), 'k')
|
||||
p2, = plt.plot(tLdat, np.multiply(Ldat, 1000))
|
||||
plt.legend([p1, p2], ['Displacement', 'Rotated Displacement'])
|
||||
if plotflag == 1:
|
||||
plt.plot(t[iwin], np.multiply(xdat, 1000), 'g')
|
||||
plt.title('Seismogram and P Pulse, Station %s-%s' \
|
||||
% (zdat[0].stats.station, zdat[0].stats.channel))
|
||||
else:
|
||||
plt.title('Seismogram, Station %s-%s' \
|
||||
% (zdat[0].stats.station, zdat[0].stats.channel))
|
||||
plt.xlabel('Time since %s' % zdat[0].stats.starttime)
|
||||
plt.ylabel('Displacement [mm]')
|
||||
# use of implicit scipy otimization function
|
||||
fit = synthsourcespec(F, w0in, Fcin)
|
||||
[optspecfit, _] = curve_fit(synthsourcespec, F, YYcor, [w0in, Fcin])
|
||||
w01 = optspecfit[0]
|
||||
fc1 = optspecfit[1]
|
||||
print ("calcsourcespec: Determined w0-value: %e m/Hz, \n"
|
||||
"Determined corner frequency: %f Hz" % (w01, fc1))
|
||||
|
||||
if plotflag == 1:
|
||||
plt.subplot(2, 1, 2)
|
||||
p1, = plt.loglog(f, Y.real, 'k')
|
||||
p2, = plt.loglog(F, YY.real)
|
||||
p3, = plt.loglog(F, YYcor, 'r')
|
||||
p4, = plt.loglog(F, fit, 'g')
|
||||
plt.loglog([fc, fc], [w0 / 100, w0], 'g')
|
||||
plt.legend([p1, p2, p3, p4], ['Raw Spectrum', \
|
||||
'Used Raw Spectrum', \
|
||||
'Q-Corrected Spectrum', \
|
||||
'Fit to Spectrum'])
|
||||
plt.title('Source Spectrum from P Pulse, w0=%e m/Hz, fc=%6.2f Hz' \
|
||||
% (w0, fc))
|
||||
plt.xlabel('Frequency [Hz]')
|
||||
plt.ylabel('Amplitude [m/Hz]')
|
||||
plt.grid()
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(f1)
|
||||
# use of conventional fitting
|
||||
[w02, fc2] = fitSourceModel(F, YYcor, Fcin, iplot)
|
||||
|
||||
# get w0 and fc as median of both
|
||||
# source spectrum fits
|
||||
w0 = np.median([w01, w02])
|
||||
fc = np.median([fc1, fc2])
|
||||
print("calcsourcespec: Using w0-value = %e m/Hz and fc = %f Hz" % (w0, fc))
|
||||
|
||||
if iplot > 1:
|
||||
f1 = plt.figure()
|
||||
tLdat = np.arange(0, len(Ldat) * dt, dt)
|
||||
plt.subplot(2, 1, 1)
|
||||
# show displacement in mm
|
||||
p1, = plt.plot(t, np.multiply(inttrz, 1000), 'k')
|
||||
p2, = plt.plot(tLdat, np.multiply(Ldat, 1000))
|
||||
plt.legend([p1, p2], ['Displacement', 'Rotated Displacement'])
|
||||
if plotflag == 1:
|
||||
plt.plot(t[iwin], np.multiply(xdat, 1000), 'g')
|
||||
plt.title('Seismogram and P Pulse, Station %s-%s' \
|
||||
% (zdat[0].stats.station, zdat[0].stats.channel))
|
||||
else:
|
||||
plt.title('Seismogram, Station %s-%s' \
|
||||
% (zdat[0].stats.station, zdat[0].stats.channel))
|
||||
plt.xlabel('Time since %s' % zdat[0].stats.starttime)
|
||||
plt.ylabel('Displacement [mm]')
|
||||
|
||||
if plotflag == 1:
|
||||
plt.subplot(2, 1, 2)
|
||||
p1, = plt.loglog(f, Y.real, 'k')
|
||||
p2, = plt.loglog(F, YY.real)
|
||||
p3, = plt.loglog(F, YYcor, 'r')
|
||||
p4, = plt.loglog(F, fit, 'g')
|
||||
plt.loglog([fc, fc], [w0 / 100, w0], 'g')
|
||||
plt.legend([p1, p2, p3, p4], ['Raw Spectrum', \
|
||||
'Used Raw Spectrum', \
|
||||
'Q-Corrected Spectrum', \
|
||||
'Fit to Spectrum'])
|
||||
plt.title('Source Spectrum from P Pulse, w0=%e m/Hz, fc=%6.2f Hz' \
|
||||
% (w0, fc))
|
||||
plt.xlabel('Frequency [Hz]')
|
||||
plt.ylabel('Amplitude [m/Hz]')
|
||||
plt.grid()
|
||||
plt.show()
|
||||
raw_input()
|
||||
plt.close(f1)
|
||||
|
||||
return w0, fc
|
||||
|
||||
@ -847,9 +743,17 @@ def calc_moment_magnitude(e, wf, metadata, vp, Qp, rho):
|
||||
continue
|
||||
onset = pick.time
|
||||
dist = degrees2kilometers(a.distance)
|
||||
w0, fc = calcsourcespec(wf, onset, metadata, vp, dist, a.azimuth, a.takeoff_angle, Qp, 0)
|
||||
invtype, inventory = metadata
|
||||
[corr_wf, rest_flag] = restitute_data(wf, invtype, inventory)
|
||||
if not rest_flag:
|
||||
print("WARNING: data for {0} could not be corrected".format(
|
||||
station))
|
||||
continue
|
||||
w0, fc = calcsourcespec(corr_wf, onset, vp, dist, a.azimuth,
|
||||
a.takeoff_angle, Qp, 0)
|
||||
if w0 is None or fc is None:
|
||||
continue
|
||||
wf = select_for_phase(corr_wf, "P")
|
||||
station_mag = calcMoMw(wf, w0, rho, vp, dist)
|
||||
mags[station] = station_mag
|
||||
mag = np.median([M[1] for M in mags.values()])
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user