Re-implemented second option for calculating fit to source spectrum, take median of both options; some bug fixes
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ludger
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570e47f2df
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@ -229,7 +229,7 @@ class LocalMagnitude(Magnitude):
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sqH = np.sqrt(power_sum)
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# get time array
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th = np.arange(0, st[0].stats.npts / st[0].stats.sampling_rate, st[0].stats.delta)
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th = np.arange(0, st[0].stats.npts / st[0].stats.sampling_rate, dt)
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# get maximum peak within pick window
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iwin = getsignalwin(th, t0 - stime, self.calc_win)
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ii = min([iwin[len(iwin) - 1], len(th)])
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@ -449,17 +449,18 @@ def calcMoMw(wfstream, w0, rho, vp, delta, verbosity=False):
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if verbosity:
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print(
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"calcMoMw: Calculating seismic moment Mo and moment magnitude Mw for station {0} ...".format(
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tr.stats.station))
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"calcMoMw: Calculating seismic moment Mo and moment magnitude Mw \
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for station {0} ...".format(tr.stats.station))
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# additional common parameters for calculating Mo
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rP = 2 / np.sqrt(
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15) # average radiation pattern of P waves (Aki & Richards, 1980)
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# average radiation pattern of P waves (Aki & Richards, 1980)
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rP = 2 / np.sqrt(15)
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freesurf = 2.0 # free surface correction, assuming vertical incidence
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Mo = w0 * 4 * np.pi * rho * np.power(vp, 3) * delta / (rP * freesurf)
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# Mw = np.log10(Mo * 1e07) * 2 / 3 - 10.7 # after Hanks & Kanamori (1979), defined for [dyn*cm]!
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# Mw = np.log10(Mo * 1e07) * 2 / 3 - 10.7 # after Hanks & Kanamori (1979),
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# defined for [dyn*cm]!
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Mw = np.log10(Mo) * 2 / 3 - 6.7 # for metric units
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if verbosity:
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@ -519,7 +520,7 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
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dist = delta * 1000 # hypocentral distance in [m]
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fc = None
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Fc = None
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w0 = None
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zdat = select_for_phase(wfstream, "P")
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@ -575,9 +576,7 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
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print("calcsourcespec: Something is wrong with the waveform, "
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"no zero crossings derived!\n")
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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]) * dt
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iwin = getsignalwin(t, rel_onset, calcwin)
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@ -585,9 +584,9 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
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# fft
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fny = freq / 2
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l = len(xdat) / freq
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#l = len(xdat) / freq
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# number of fft bins after Bath
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n = freq * l
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#n = freq * 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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@ -619,25 +618,23 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
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# use of implicit scipy otimization function
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fit = synthsourcespec(F, w0in, Fcin)
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[optspecfit, _] = curve_fit(synthsourcespec, F, YYcor, [w0in, Fcin])
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w0 = optspecfit[0]
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fc = optspecfit[1]
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# w01 = optspecfit[0]
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# fc1 = optspecfit[1]
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w01 = optspecfit[0]
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fc1 = optspecfit[1]
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if verbosity:
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print("calcsourcespec: Determined w0-value: %e m/Hz, \n"
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"calcsourcespec: Determined corner frequency: %f Hz" % (w0, fc))
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"calcsourcespec: Determined corner frequency: %f Hz" % (w01, fc1))
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# use of conventional fitting
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# [w02, fc2] = fitSourceModel(F, YYcor, Fcin, iplot, verbosity)
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[w02, fc2] = fitSourceModel(F, YYcor, Fcin, 2, verbosity)
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# get w0 and fc as median of both
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# source spectrum fits
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# w0 = np.median([w01, w02])
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# fc = np.median([fc1, fc2])
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# if verbosity:
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# print("calcsourcespec: Using w0-value = %e m/Hz and fc = %f Hz" % (
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# w0, fc))
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if iplot > 1:
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w0 = np.median([w01, w02])
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Fc = np.median([fc1, fc2])
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if verbosity:
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print("calcsourcespec: Using w0-value = %e m/Hz and fc = %f Hz" % (
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w0, Fc))
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if iplot >= 1:
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f1 = plt.figure()
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tLdat = np.arange(0, len(Ldat) * dt, dt)
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plt.subplot(2, 1, 1)
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@ -645,7 +642,7 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
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p1, = plt.plot(t, np.multiply(inttrz, 1000), 'k')
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p2, = plt.plot(tLdat, np.multiply(Ldat, 1000))
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plt.legend([p1, p2], ['Displacement', 'Rotated Displacement'])
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if plotflag == 1:
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if iplot == 1:
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plt.plot(t[iwin], np.multiply(xdat, 1000), 'g')
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plt.title('Seismogram and P Pulse, Station %s-%s' \
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% (zdat[0].stats.station, zdat[0].stats.channel))
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@ -655,19 +652,19 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
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plt.xlabel('Time since %s' % zdat[0].stats.starttime)
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plt.ylabel('Displacement [mm]')
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if plotflag == 1:
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if iplot >= 1:
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plt.subplot(2, 1, 2)
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p1, = plt.loglog(f, Y.real, 'k')
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p2, = plt.loglog(F, YY.real)
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p3, = plt.loglog(F, YYcor, 'r')
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p4, = plt.loglog(F, fit, 'g')
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plt.loglog([fc, fc], [w0 / 100, w0], 'g')
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plt.loglog([Fc, Fc], [w0 / 100, w0], 'g')
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plt.legend([p1, p2, p3, p4], ['Raw Spectrum',
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'Used Raw Spectrum',
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'Q-Corrected Spectrum',
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'Fit to Spectrum'])
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plt.title('Source Spectrum from P Pulse, w0=%e m/Hz, fc=%6.2f Hz' \
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% (w0, fc))
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% (w0, Fc))
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plt.xlabel('Frequency [Hz]')
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plt.ylabel('Amplitude [m/Hz]')
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plt.grid()
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@ -678,7 +675,7 @@ def calcsourcespec(wfstream, onset, vp, delta, azimuth, incidence,
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pass
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plt.close(f1)
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return w0, fc
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return w0, Fc
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def synthsourcespec(f, omega0, fcorner):
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@ -776,42 +773,43 @@ def fitSourceModel(f, S, fc0, iplot, verbosity=False):
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# get best found w0 anf fc from minimum
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if len(STD) > 0:
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fc = fc[np.argmin(STD)]
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Fc = fc[np.argmin(STD)]
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w0 = w0[np.argmin(STD)]
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elif len(STD) == 0:
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fc = fc0
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Fc = fc0
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w0 = max(S)
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if verbosity:
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print(
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"fitSourceModel: best fc: {0} Hz, best w0: {1} m/Hz".format(fc, w0))
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if iplot > 1:
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if iplot > 0:
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plt.figure() # iplot)
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plt.loglog(f, S, 'k')
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plt.loglog([f[0], fc], [w0, w0], 'g')
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plt.loglog([fc, fc], [w0 / 100, w0], 'g')
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plt.loglog([f[0], Fc], [w0, w0], 'g')
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plt.loglog([Fc, Fc], [w0 / 100, w0], 'g')
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plt.title('Calculated Source Spectrum, Omega0=%e m/Hz, fc=%6.2f Hz' \
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% (w0, fc))
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% (w0, Fc))
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plt.xlabel('Frequency [Hz]')
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plt.ylabel('Amplitude [m/Hz]')
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plt.grid()
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plt.figure() # iplot + 1)
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plt.subplot(311)
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plt.plot(f[il:ir], STD, '*')
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plt.title('Common Standard Deviations')
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plt.xticks([])
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plt.subplot(312)
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plt.plot(f[il:ir], stdw0, '*')
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plt.title('Standard Deviations of w0-Values')
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plt.xticks([])
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plt.subplot(313)
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plt.plot(f[il:ir], stdfc, '*')
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plt.title('Standard Deviations of Corner Frequencies')
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plt.xlabel('Corner Frequencies [Hz]')
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plt.show()
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if iplot == 2:
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plt.figure() # iplot + 1)
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plt.subplot(311)
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plt.plot(fc, STD, '*')
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plt.title('Common Standard Deviations')
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plt.xticks([])
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plt.subplot(312)
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plt.plot(fc, stdw0, '*')
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plt.title('Standard Deviations of w0-Values')
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plt.xticks([])
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plt.subplot(313)
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plt.plot(fc, stdfc, '*')
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plt.title('Standard Deviations of Corner Frequencies')
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plt.xlabel('Corner Frequencies [Hz]')
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plt.show()
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try:
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input()
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except SyntaxError:
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pass
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plt.close()
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return w0, fc
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return w0, Fc
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0
pylot/core/util/generate_array_maps.py
Normal file → Executable file
0
pylot/core/util/generate_array_maps.py
Normal file → Executable file
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