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Modified for running updated CharFuns.py showing all kinds of CFs on all 3 components

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This commit is contained in:
Ludger Küperkoch 2014-11-21 14:52:19 +01:00
parent 8fa9ec74c0
commit 8fb9ca9dc2
1 changed files with 59 additions and 10 deletions
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69
pylot/core/pick/run_makeCF.py
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@ -97,19 +97,42 @@ def run_makeCF(project, database, event, iplot, station=None):
#calculate ARH-CF using subclass ARHcf of class CharcteristicFunction #calculate ARH-CF using subclass ARHcf of class CharcteristicFunction
arhcf = ARHcf(H_copy, cuttimes, tpredh, arhorder, tdeth, addnoise) #instance of ARHcf arhcf = ARHcf(H_copy, cuttimes, tpredh, arhorder, tdeth, addnoise) #instance of ARHcf
############################################################## ##############################################################
#create stream with 3 traces
#merge streams
AllC = read('%s' % wfefiles[i])
AllC += read('%s' % wfnfiles[i])
AllC += read('%s' % wfzfiles[i])
#filter and taper data
All1_filt = AllC[0].copy()
All2_filt = AllC[1].copy()
All3_filt = AllC[2].copy()
All1_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
All2_filt.filter('bandpass', freqmin=bph[0], freqmax=bph[1], zerophase=False)
All3_filt.filter('bandpass', freqmin=bpz[0], freqmax=bpz[1], zerophase=False)
All1_filt.taper(max_percentage=0.05, type='hann')
All2_filt.taper(max_percentage=0.05, type='hann')
All3_filt.taper(max_percentage=0.05, type='hann')
AllC[0].data = All1_filt.data
AllC[1].data = All2_filt.data
AllC[2].data = All3_filt.data
#calculate AR3C-CF using subclass AR3Ccf of class CharacteristicFunction
ar3ccf = AR3Ccf(AllC, cuttimes, tpredz, arhorder, tdetz, addnoise) #instance of AR3Ccf
##############################################################
if iplot: if iplot:
#plot vertical trace #plot vertical trace
plt.figure() plt.figure()
tr = st[0] tr = st[0]
#time vectors tstepz = tpredz / 16
tdata = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta) tdata = np.arange(0, tr.stats.npts / tr.stats.sampling_rate, tr.stats.delta)
tCF = np.arange(cuttimes[0], cuttimes[1], tr.stats.delta) thoscf = np.arange(0, len(hoscf.getCF()) / tr.stats.sampling_rate, tr.stats.delta) + cuttimes[0]
tARZCF = np.arange(cuttimes[0] + tdetz + tpredz, cuttimes[1], tr.stats.delta) taiccf = np.arange(0, len(aiccf.getCF()) / tr.stats.sampling_rate, tr.stats.delta) + cuttimes[0]
tarzcf = np.arange(0, len(arzcf.getCF()) * tstepz, tstepz) + cuttimes[0] + tdetz +tpredz
taraiccf = np.arange(0, len(araiccf.getCF()) * tstepz, tstepz) + cuttimes[0] +tdetz + tpredz
p1 = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k') p1 = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p2 = plt.plot(tCF, hoscf.getCF()/max(hoscf.getCF()), 'r') p2 = plt.plot(thoscf, hoscf.getCF()/max(hoscf.getCF()), 'r')
p3 = plt.plot(tCF, aiccf.getCF()/max(aiccf.getCF()), 'b') p3 = plt.plot(taiccf, aiccf.getCF()/max(aiccf.getCF()), 'b')
p4 = plt.plot(tARZCF, arzcf.getCF()/max(arzcf.getCF()), 'g') p4 = plt.plot(tarzcf, arzcf.getCF()/max(arzcf.getCF()), 'g')
p5 = plt.plot(tARZCF, araiccf.getCF()/max(araiccf.getCF()), 'y') p5 = plt.plot(taraiccf, araiccf.getCF()/max(araiccf.getCF()), 'y')
plt.yticks([]) plt.yticks([])
plt.xlabel('Time [s]') plt.xlabel('Time [s]')
plt.ylabel('Normalized Counts') plt.ylabel('Normalized Counts')
@ -119,11 +142,12 @@ def run_makeCF(project, database, event, iplot, station=None):
#plot horizontal traces #plot horizontal traces
plt.figure(2) plt.figure(2)
plt.subplot(211) plt.subplot(211)
tsteph = tpredh / 4
th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta) th1data = np.arange(0, trH1_filt.stats.npts / trH1_filt.stats.sampling_rate, trH1_filt.stats.delta)
th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta) th2data = np.arange(0, trH2_filt.stats.npts / trH2_filt.stats.sampling_rate, trH2_filt.stats.delta)
tARHCF = np.arange(cuttimes[0] + tdeth + tpredh, cuttimes[1], trH1_filt.stats.delta) tarhcf = np.arange(0, len(arhcf.getCF()) * tsteph, tsteph) + cuttimes[0] + tdeth +tpredh
p21 = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k') p21 = plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
p22 = plt.plot(tARHCF, arhcf.getCF()/max(arhcf.getCF()), 'r') p22 = plt.plot(tarhcf, arhcf.getCF()/max(arhcf.getCF()), 'r')
plt.yticks([]) plt.yticks([])
plt.ylabel('Normalized Counts') plt.ylabel('Normalized Counts')
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel]) plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
@ -131,11 +155,36 @@ def run_makeCF(project, database, event, iplot, station=None):
plt.legend([p21, p22], ['Data', 'ARH-CF']) plt.legend([p21, p22], ['Data', 'ARH-CF'])
plt.subplot(212) plt.subplot(212)
p23 = plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k') p23 = plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
p24 = plt.plot(tARHCF, arhcf.getCF()/max(arhcf.getCF()), 'r') p24 = plt.plot(tarhcf, arhcf.getCF()/max(arhcf.getCF()), 'r')
plt.title([trH2_filt.stats.station, trH2_filt.stats.channel]) plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
plt.yticks([]) plt.yticks([])
plt.xlabel('Time [s]') plt.xlabel('Time [s]')
plt.ylabel('Normalized Counts') plt.ylabel('Normalized Counts')
#plot 3-component window
plt.figure(3)
tar3ccf = np.arange(0, len(ar3ccf.getCF()) * tsteph, tsteph) + cuttimes[0] + tdetz +tpredz
plt.subplot(311)
p31 = plt.plot(tdata, tr_filt.data/max(tr_filt.data), 'k')
p32 = plt.plot(tar3ccf, ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.yticks([])
plt.xticks([])
plt.ylabel('Normalized Counts')
plt.title([tr.stats.station, tr.stats.channel])
plt.legend([p31, p32], ['Data', 'AR3C-CF'])
plt.subplot(312)
plt.plot(th1data, trH1_filt.data/max(trH1_filt.data), 'k')
plt.plot(tar3ccf, ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.yticks([])
plt.xticks([])
plt.ylabel('Normalized Counts')
plt.title([trH1_filt.stats.station, trH1_filt.stats.channel])
plt.subplot(313)
plt.plot(th2data, trH2_filt.data/max(trH2_filt.data), 'k')
plt.plot(tar3ccf, ar3ccf.getCF()/max(ar3ccf.getCF()), 'r')
plt.yticks([])
plt.ylabel('Normalized Counts')
plt.title([trH2_filt.stats.station, trH2_filt.stats.channel])
plt.xlabel('Time [s]')
plt.show() plt.show()
raw_input() raw_input()
plt.close() plt.close()