elif _is_quakeml(ev_catalog):
print("reading QUAKEML catalog")
else:
print("warning error in reading ZMAP or QUAKEML")
cat = read_events(ev_catalog)
ncat = len(cat)
print(cat.__str__(print_all=True))
for iev in range(start_itemp, stop_itemp):
inplist = tempinp_dir + str(
iev) + ".??" + "." + "*" + "..???" + "." + "mseed"
print("inplist == ...", inplist)
st.clear()
for filename in glob.glob(inplist):
csize = os.path.getsize(filename)
if csize > 0:
print("filename ..", filename)
st += read(filename)
nst = len(st)
# print nst
print(st.__str__(extended=True))
refT = min([tr.stats.starttime for tr in st])
Tshift = np.empty(nst)
arrP = np.empty(nst)
arrS = np.empty(nst)
prepick = 0.15, length = 4.6, swin = 'P',
parallel = True)
if len(template[0]) < 3:
print('Skipping template -- %i picks & %i WF in template' % (n_picks[i], len(template[0])))
else:
# stats = trace.Stats()
template[0].sort(['starttime'])
timestamp = template[0][0].stats.starttime
end_template = template[0][0].stats.endtime
# make plots
stplot = std.copy()
stplot = stplot.trim(starttime = timestamp - 5,
endtime= end_template + 5)
# stplot = stplot.detrend()
# stplot = stplot.resample(25)
stplot = stplot.filter('bandpass', freqmin= 3, freqmax = 10, corners = 4, zerophase = True)
pretty_template_plot(template[0], background = stplot,
picks = new_catalog[i].picks,
save = True, savefile=os.path.join(os.getcwd() + "/Template_plots", "template_" + str(timestamp) + '-' + str(events_clust[i]) + ".png"))
template[0].write(os.getcwd() + '/Templates_MSEED/template_' + str(timestamp) + '-'+ str(events_clust[i]) +'.ms', format='MSEED')
template_names.append('template_' + str(timestamp) + '.ms')
st.clear()
std.clear()
st_filter.clear()
stplot.clear()
print('Script took ', time.time() - init_time, ' s to complete.')
maxon_of=max(max_on_of,max_on_of1,max_on_of2)
minon=int(minon_of/100)
maxon=int(maxon_of/100)
print minon_of,maxon_of
if args.save_mseed:
mseed_dir = os.path.join(args.output, "mseed")
output_mseed = os.path.join(mseed_dir,mseed_files)
threechannels.slice(t+minon, t+maxon).write(output_mseed, format="mseed")
if args.plot:
viz_dir = os.path.join(args.output, "viz")
if not os.path.exists(viz_dir):
os.makedirs(viz_dir)
threechannels.slice(t+minon, t+maxon).plot(outfile=os.path.join(viz_dir,
mseed_files.split(".mseed")[0]+'.png'))
threechannels.clear()
st1.clear()
<<<<<<< HEAD
=======
st2.clear()
st3.clear()
>>>>>>> cc97cd94a4e720f5b877dfefb44fa8ccb2fd3458
df = pd.DataFrame.from_dict(times_csv)
print (df.shape[0])
output_catalog = os.path.join(args.output,'detection.csv')
df.to_csv(output_catalog)
#
# writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
# writer.writeheader()
# writer.writerow({k: str(v) for k, v in mdict.items()} )
