sts = sts.split()
sts.sort(keys=['starttime', 'endtime', 'channel'])
print(sts)
for j, tr in enumerate(sts):
print("Working on trace {}".format(j))
print(tr)
length = tr.stats['endtime'] - tr.stats['starttime']
cumlen = cumlen + length
nevents_tr = nevents*length/secyear
ppsd = PPSD(tr.stats, metadata=inv, ppsd_length=200.0)
ppsd.add(Stream(tr))
psdmean = 0
for period in psdperiodrange:
psds = ppsd.extract_psd_values(period)[0]
psdmean = psdmean + math.pow(10.0, 0.05*np.mean(psds))
psdamp = psdmean/len(psdperiodrange)
threshold = psdamp*snr
print("{} Threshold: {}".format(j,threshold))
nev_tr = np.zeros_like(nevents)
for k, mag in enumerate(magarray):
idx = next((x for x, v in enumerate(amp_mag_dist[k][::-1])
if v>threshold), None)
if idx is not None:
idx = len(distarray)-idx-1
nev_tr[:, :, k] = afrac[idx]*nevents_tr[:, :, k]
# We run into problems if cum_nev > ~0.1, so if this happens, we
# divide the number of events evenly over smaller chunks
nev_tr_chk = np.zeros((len(avals), len(bvals)))
