def test_DayNoise():
trN1, trN2, trNZ, trNP = test_utils.test_get_data()
args = test_args.test_get_dailyspec_arguments()
for tr1, tr2, trZ, trP in zip(trN1, trN2, trNZ, trNP):
daynoise = DayNoise(tr1,
tr2,
trZ,
trP,
args.window,
args.overlap,
key='7D.M08A')
daynoise.QC_daily_spectra(pd=args.pd,
tol=args.tol,
alpha=args.alpha,
smooth=args.smooth)
daynoise.average_daily_spectra(calc_rotation=True,
fig_average=True,
fig_coh_ph=True,
save='tmp',
form='png')
plot = plotting(daynoise.f,
daynoise.power,
daynoise.power,
daynoise.goodwins,
daynoise.ncomp,
key='7D.M08A')
daynoise.save('tmp')
def main(args=None):
if args is None:
# Run Input Parser
args = get_dailyspec_arguments()
# Load Database
# stdb>0.1.3
try:
db, stkeys = stdb.io.load_db(fname=args.indb, keys=args.stkeys)
# stdb=0.1.3
except:
db = stdb.io.load_db(fname=args.indb)
# Construct station key loop
allkeys = db.keys()
sorted(allkeys)
# Extract key subset
if len(args.stkeys) > 0:
stkeys = []
for skey in args.stkeys:
stkeys.extend([s for s in allkeys if skey in s])
else:
stkeys = db.keys()
sorted(stkeys)
# Loop over station keys
for stkey in list(stkeys):
# Extract station information from dictionary
sta = db[stkey]
# Path where data are located
datapath = Path('DATA') / stkey
if not datapath.is_dir():
print()
print("Path to "+str(datapath)+" doesn`t exist - continuing")
continue
# Path where spectra will be saved
specpath = Path('SPECTRA') / stkey
if not specpath.is_dir():
print()
print("Path to "+str(specpath)+" doesn`t exist - creating it")
specpath.mkdir(parents=True)
# Path where plots will be saved
if args.saveplot:
plotpath = specpath / 'PLOTS'
if not plotpath.is_dir():
plotpath.mkdir(parents=True)
else:
plotpath = False
# Get catalogue search start time
if args.startT is None:
tstart = sta.startdate
else:
tstart = args.startT
# Get catalogue search end time
if args.endT is None:
tend = sta.enddate
else:
tend = args.endT
if tstart > sta.enddate or tend < sta.startdate:
continue
# Temporary print locations
tlocs = sta.location
if len(tlocs) == 0:
tlocs = ['']
for il in range(0, len(tlocs)):
if len(tlocs[il]) == 0:
tlocs[il] = "--"
sta.location = tlocs
# Update Display
print()
print("|===============================================|")
print("|===============================================|")
print("| {0:>8s} |".format(
sta.station))
print("|===============================================|")
print("|===============================================|")
print("| Station: {0:>2s}.{1:5s} |".format(
sta.network, sta.station))
print("| Channel: {0:2s}; Locations: {1:15s} |".format(
sta.channel, ",".join(tlocs)))
print("| Lon: {0:7.2f}; Lat: {1:6.2f} |".format(
sta.longitude, sta.latitude))
print("| Start time: {0:19s} |".format(
sta.startdate.strftime("%Y-%m-%d %H:%M:%S")))
print("| End time: {0:19s} |".format(
sta.enddate.strftime("%Y-%m-%d %H:%M:%S")))
print("|-----------------------------------------------|")
# Get all components
trN1, trN2, trNZ, trNP = utils.get_data(datapath, tstart, tend)
# Window size
window = args.window
overlap = args.overlap
# minimum numer of windows
minwin = args.minwin
# Time axis
taxis = np.arange(0., window, trNZ[0].stats.delta)
# Cycle through available data
for tr1, tr2, trZ, trP in zip(trN1, trN2, trNZ, trNP):
year = str(trZ.stats.starttime.year).zfill(4)
jday = str(trZ.stats.starttime.julday).zfill(3)
print()
print("*"*60)
print("* Calculating noise spectra for key " +
stkey+" and day "+year+"."+jday)
tstamp = year+'.'+jday+'.'
filename = specpath / (tstamp+'spectra.pkl')
if filename.exists():
if not args.ovr:
print("* -> file "+str(filename)+" exists - continuing")
continue
# Initialize instance of DayNoise
daynoise = DayNoise(tr1, tr2, trZ, trP, window, overlap, key=stkey)
# Quality control to identify outliers
daynoise.QC_daily_spectra(
pd=args.pd, tol=args.tol, alpha=args.alpha,
smooth=args.smooth, fig_QC=args.fig_QC,
save=plotpath, form=args.form, debug=args.debug)
# Check if we have enough good windows
nwin = np.sum(daynoise.goodwins)
if nwin < minwin:
print("* Too few good data segments to calculate " +
"average day spectra")
# continue
else:
print("* {0} good windows. Proceeding...".format(nwin))
# Average spectra for good windows
daynoise.average_daily_spectra(
calc_rotation=args.calc_rotation,
fig_average=args.fig_average,
fig_coh_ph=args.fig_coh_ph,
save=plotpath, form=args.form)
# Save to file
daynoise.save(filename)
def test_daynoise_demo():
return DayNoise('demo')
def transfer_func(self):
"""compute daily transfer function
"""
targetdt = 1. / self.sps
oyear = self.otime.year
omonth = self.otime.month
oday = self.otime.day
ohour = self.otime.hour
omin = self.otime.minute
osec = self.otime.second
label = '%d_%s_%d_%d_%d_%d' % (oyear, monthdict[omonth], oday, ohour,
omin, osec)
self.eventdir = self.datadir + '/' + label
self.outeventdir = self.outdir + '/' + label
self.label = label
chan_type = None
# load SAC data
for chtype in self.chan_rank:
fname1 = self.eventdir + '/%s_%sH1.SAC' % (self.staid, chtype)
fname2 = self.eventdir + '/%s_%sH2.SAC' % (self.staid, chtype)
fnamez = self.eventdir + '/%s_%sHZ.SAC' % (self.staid, chtype)
fnamep = self.eventdir + '/%s_%sDH.SAC' % (self.staid, chtype)
if os.path.isfile(fname1) and os.path.isfile(fname1) and \
os.path.isfile(fnamez) and os.path.isfile(fnamep):
chan_type = chtype
break
if chan_type is None:
return 0
self.chan_type = chan_type
fname1 = self.eventdir + '/%s_%sH1.SAC' % (self.staid, chan_type)
fname2 = self.eventdir + '/%s_%sH2.SAC' % (self.staid, chan_type)
fnamez = self.eventdir + '/%s_%sHZ.SAC' % (self.staid, chan_type)
fnamep = self.eventdir + '/%s_%sDH.SAC' % (self.staid, chan_type)
self.sth = obspy.read(fname1)
self.sth += obspy.read(fname2)
self.sth += obspy.read(fnamez)
self.stp = obspy.read(fnamep)
#
if abs(self.sth[0].stats.delta - targetdt) > 1e-3 or abs(self.sth[1].stats.delta - targetdt) > 1e-3 or \
abs(self.sth[2].stats.delta - targetdt) > 1e-3 or abs(self.stp[0].stats.delta - targetdt) > 1e-3:
raise ValueError('!!! CHECK fs :' + self.staid)
else:
self.sth[0].stats.delta = targetdt
self.sth[1].stats.delta = targetdt
self.sth[2].stats.delta = targetdt
self.stp[0].stats.delta = targetdt
stime_event = self.sth[-1].stats.starttime
etime_event = self.sth[-1].stats.endtime
self.sth.trim(starttime=stime_event,
endtime=etime_event,
pad=True,
nearest_sample=True,
fill_value=0.)
self.stp.trim(starttime=stime_event,
endtime=etime_event,
pad=True,
nearest_sample=True,
fill_value=0.)
self.window = self.sth[-1].stats.npts / self.sps
# trim data
# load daily noise data
daystr = '%d.%s.%d.%s' % (self.otime.year, monthdict[self.otime.month],
self.otime.day, self.staid)
dfname1 = self.daydir + '/ft_%s.%sH1.SAC' % (daystr, chan_type)
dfname2 = self.daydir + '/ft_%s.%sH2.SAC' % (daystr, chan_type)
dfnamez = self.daydir + '/ft_%s.%sHZ.SAC' % (daystr, chan_type)
dfnamep = self.daydir + '/ft_%s.%sDH.SAC' % (daystr, chan_type)
if not( os.path.isfile(dfname1) and os.path.isfile(dfname2) and \
os.path.isfile(dfnamez) and os.path.isfile(dfnamep)):
return 0
tr1 = obspy.read(dfname1)[0]
tr2 = obspy.read(dfname2)[0]
trZ = obspy.read(dfnamez)[0]
trP = obspy.read(dfnamep)[0]
if abs(tr1.stats.delta - targetdt) > 1e-3 or abs(tr2.stats.delta - targetdt) > 1e-3 or \
abs(trZ.stats.delta - targetdt) > 1e-3 or abs(trP.stats.delta - targetdt) > 1e-3:
raise ValueError('!!! CHECK fs :' + self.staid)
else:
tr1.stats.delta = targetdt
tr2.stats.delta = targetdt
trP.stats.delta = targetdt
trZ.stats.delta = targetdt
# trim data
slidind_wlength = self.window - int(
self.overlap * self.window) * tr1.stats.delta
stime_noise = tr1.stats.starttime
newtime = np.floor((tr1.stats.endtime - stime_noise) /
slidind_wlength) * slidind_wlength
tr1.trim(starttime=stime_noise, endtime=stime_noise + newtime)
tr2.trim(starttime=stime_noise, endtime=stime_noise + newtime)
trZ.trim(starttime=stime_noise, endtime=stime_noise + newtime)
trP.trim(starttime=stime_noise, endtime=stime_noise + newtime)
if np.all(trP.data == 0.) and not (np.all(tr1.data == 0.)
or np.all(tr2.data == 0.)):
self.daynoise = DayNoise(tr1=tr1,
tr2=tr2,
trZ=trZ,
trP=obspy.Trace(),
overlap=self.overlap,
window=self.window)
self.out_dtype = 'Z2-1'
elif (np.all(tr1.data == 0.)
or np.all(tr2.data == 0.)) and (not np.all(trP.data == 0.)):
self.daynoise = DayNoise(tr1=obspy.Trace(),
tr2=obspy.Trace(),
trZ=trZ,
trP=trP,
overlap=self.overlap,
window=self.window)
self.out_dtype = 'ZP'
elif (not (np.all(tr1.data == 0.) or np.all(tr2.data == 0.))) and (
not np.all(trP.data == 0.)):
self.daynoise = DayNoise(tr1=tr1,
tr2=tr2,
trZ=trZ,
trP=trP,
overlap=self.overlap,
window=self.window)
self.out_dtype = 'ZP-21'
else:
return 0
# self.daynoise.QC_daily_spectra()
# self.daynoise.average_daily_spectra()
# self.tfnoise = TFNoise(self.daynoise)
# self.tfnoise.transfer_func()
try:
self.daynoise.QC_daily_spectra()
self.daynoise.average_daily_spectra()
self.tfnoise = TFNoise(self.daynoise)
self.tfnoise.transfer_func()
except:
return -1
return 1
class atacr_event_sta(object):
def __init__(self,
inv,
datadir,
outdir,
noisedir,
otime,
overlap=0.3,
chan_rank=['L', 'H', 'B'],
sps=1.):
network = inv.networks[0]
station = network[0]
channel = station[0]
self.stdb_inv = stdb.StDbElement(network = network.code, station = station.code, channel = channel.code[:2],\
location = channel.location_code, latitude = station.latitude, longitude = station.longitude,\
elevation = station.elevation, polarity=1., azcorr=0., startdate = station.start_date,\
enddate = station.end_date, restricted_status = station.restricted_status)
self.datadir = datadir
self.outdir = outdir
self.noisedir = noisedir
self.otime = otime
self.overlap = overlap
self.staid = network.code + '.' + station.code
self.chan_rank = chan_rank
self.stlo = station.longitude
self.stla = station.latitude
self.monthdir = self.noisedir + '/%04d.%s' % (
self.otime.year, monthdict[self.otime.month])
self.daydir = self.monthdir + '/%d.%s.%d' % (
self.otime.year, monthdict[self.otime.month], self.otime.day)
self.sps = sps
return
def transfer_func(self):
"""compute daily transfer function
"""
targetdt = 1. / self.sps
oyear = self.otime.year
omonth = self.otime.month
oday = self.otime.day
ohour = self.otime.hour
omin = self.otime.minute
osec = self.otime.second
label = '%d_%s_%d_%d_%d_%d' % (oyear, monthdict[omonth], oday, ohour,
omin, osec)
self.eventdir = self.datadir + '/' + label
self.outeventdir = self.outdir + '/' + label
self.label = label
chan_type = None
# load SAC data
for chtype in self.chan_rank:
fname1 = self.eventdir + '/%s_%sH1.SAC' % (self.staid, chtype)
fname2 = self.eventdir + '/%s_%sH2.SAC' % (self.staid, chtype)
fnamez = self.eventdir + '/%s_%sHZ.SAC' % (self.staid, chtype)
fnamep = self.eventdir + '/%s_%sDH.SAC' % (self.staid, chtype)
if os.path.isfile(fname1) and os.path.isfile(fname1) and \
os.path.isfile(fnamez) and os.path.isfile(fnamep):
chan_type = chtype
break
if chan_type is None:
return 0
self.chan_type = chan_type
fname1 = self.eventdir + '/%s_%sH1.SAC' % (self.staid, chan_type)
fname2 = self.eventdir + '/%s_%sH2.SAC' % (self.staid, chan_type)
fnamez = self.eventdir + '/%s_%sHZ.SAC' % (self.staid, chan_type)
fnamep = self.eventdir + '/%s_%sDH.SAC' % (self.staid, chan_type)
self.sth = obspy.read(fname1)
self.sth += obspy.read(fname2)
self.sth += obspy.read(fnamez)
self.stp = obspy.read(fnamep)
#
if abs(self.sth[0].stats.delta - targetdt) > 1e-3 or abs(self.sth[1].stats.delta - targetdt) > 1e-3 or \
abs(self.sth[2].stats.delta - targetdt) > 1e-3 or abs(self.stp[0].stats.delta - targetdt) > 1e-3:
raise ValueError('!!! CHECK fs :' + self.staid)
else:
self.sth[0].stats.delta = targetdt
self.sth[1].stats.delta = targetdt
self.sth[2].stats.delta = targetdt
self.stp[0].stats.delta = targetdt
stime_event = self.sth[-1].stats.starttime
etime_event = self.sth[-1].stats.endtime
self.sth.trim(starttime=stime_event,
endtime=etime_event,
pad=True,
nearest_sample=True,
fill_value=0.)
self.stp.trim(starttime=stime_event,
endtime=etime_event,
pad=True,
nearest_sample=True,
fill_value=0.)
self.window = self.sth[-1].stats.npts / self.sps
# trim data
# load daily noise data
daystr = '%d.%s.%d.%s' % (self.otime.year, monthdict[self.otime.month],
self.otime.day, self.staid)
dfname1 = self.daydir + '/ft_%s.%sH1.SAC' % (daystr, chan_type)
dfname2 = self.daydir + '/ft_%s.%sH2.SAC' % (daystr, chan_type)
dfnamez = self.daydir + '/ft_%s.%sHZ.SAC' % (daystr, chan_type)
dfnamep = self.daydir + '/ft_%s.%sDH.SAC' % (daystr, chan_type)
if not( os.path.isfile(dfname1) and os.path.isfile(dfname2) and \
os.path.isfile(dfnamez) and os.path.isfile(dfnamep)):
return 0
tr1 = obspy.read(dfname1)[0]
tr2 = obspy.read(dfname2)[0]
trZ = obspy.read(dfnamez)[0]
trP = obspy.read(dfnamep)[0]
if abs(tr1.stats.delta - targetdt) > 1e-3 or abs(tr2.stats.delta - targetdt) > 1e-3 or \
abs(trZ.stats.delta - targetdt) > 1e-3 or abs(trP.stats.delta - targetdt) > 1e-3:
raise ValueError('!!! CHECK fs :' + self.staid)
else:
tr1.stats.delta = targetdt
tr2.stats.delta = targetdt
trP.stats.delta = targetdt
trZ.stats.delta = targetdt
# trim data
slidind_wlength = self.window - int(
self.overlap * self.window) * tr1.stats.delta
stime_noise = tr1.stats.starttime
newtime = np.floor((tr1.stats.endtime - stime_noise) /
slidind_wlength) * slidind_wlength
tr1.trim(starttime=stime_noise, endtime=stime_noise + newtime)
tr2.trim(starttime=stime_noise, endtime=stime_noise + newtime)
trZ.trim(starttime=stime_noise, endtime=stime_noise + newtime)
trP.trim(starttime=stime_noise, endtime=stime_noise + newtime)
if np.all(trP.data == 0.) and not (np.all(tr1.data == 0.)
or np.all(tr2.data == 0.)):
self.daynoise = DayNoise(tr1=tr1,
tr2=tr2,
trZ=trZ,
trP=obspy.Trace(),
overlap=self.overlap,
window=self.window)
self.out_dtype = 'Z2-1'
elif (np.all(tr1.data == 0.)
or np.all(tr2.data == 0.)) and (not np.all(trP.data == 0.)):
self.daynoise = DayNoise(tr1=obspy.Trace(),
tr2=obspy.Trace(),
trZ=trZ,
trP=trP,
overlap=self.overlap,
window=self.window)
self.out_dtype = 'ZP'
elif (not (np.all(tr1.data == 0.) or np.all(tr2.data == 0.))) and (
not np.all(trP.data == 0.)):
self.daynoise = DayNoise(tr1=tr1,
tr2=tr2,
trZ=trZ,
trP=trP,
overlap=self.overlap,
window=self.window)
self.out_dtype = 'ZP-21'
else:
return 0
# self.daynoise.QC_daily_spectra()
# self.daynoise.average_daily_spectra()
# self.tfnoise = TFNoise(self.daynoise)
# self.tfnoise.transfer_func()
try:
self.daynoise.QC_daily_spectra()
self.daynoise.average_daily_spectra()
self.tfnoise = TFNoise(self.daynoise)
self.tfnoise.transfer_func()
except:
return -1
return 1
def correct(self):
"""compute monthly transfer function
"""
tmptime = self.sth[-1].stats.starttime
tstamp = str(tmptime.year).zfill(4)+'.' + \
str(tmptime.julday).zfill(3)+'.'
tstamp = tstamp + str(tmptime.hour).zfill(2) + \
'.'+str(tmptime.minute).zfill(2)
eventstream = EventStream( sta = self.stdb_inv, sth = self.sth, stp = self.stp,\
tstamp = tstamp, lat = self.stla, lon = self.stlo, time = tmptime,\
window = self.window, sampling_rate = 1., ncomp = 4)
eventstream.correct_data(self.tfnoise)
# save data
if not os.path.isdir(self.outeventdir):
os.makedirs(self.outeventdir)
outTrZ = (self.sth.select(channel='??Z')[0]).copy()
outTrZ.data = eventstream.correct[self.out_dtype].copy()
outfnameZ = self.outeventdir + '/%s_%sHZ.SAC' % (self.staid,
self.chan_type)
if os.path.isfile(outfnameZ):
shutil.copyfile(src=outfnameZ, dst=outfnameZ + '_old')
os.remove(outfnameZ)
outTrZ.write(outfnameZ, format='SAC')
def transfer_func(self):
"""compute monthly transfer function
"""
targetdt = 1./self.sps
stime = obspy.UTCDateTime('%04d%02d01' %(self.year, self.month))
monthdir = self.monthdir
if not os.path.isdir(monthdir):
return False
chan_type = None
Nday = 0
while( (stime.year == self.year) and (stime.month == self.month)):
daydir = monthdir+'/%d.%s.%d' %(self.year, monthdict[self.month], stime.day)
if not os.path.isdir(daydir):
stime += 86400.
continue
fpattern= daydir+'/ft_%d.%s.%d.%s' %(self.year, monthdict[self.month], stime.day, self.staid)
if chan_type is None:
for chtype in self.chan_rank:
fname1 = fpattern+'.%sH1.SAC' %chtype
fname2 = fpattern+'.%sH2.SAC' %chtype
fnamez = fpattern+'.%sHZ.SAC' %chtype
fnamep = fpattern+'.%sDH.SAC' %chtype
if os.path.isfile(fname1) and os.path.isfile(fname1) and \
os.path.isfile(fnamez) and os.path.isfile(fnamep):
chan_type = chtype
break
if chan_type is None:
stime += 86400.
continue
fname1 = fpattern+'.%sH1.SAC' %chan_type
fname2 = fpattern+'.%sH2.SAC' %chan_type
fnamez = fpattern+'.%sHZ.SAC' %chan_type
fnamep = fpattern+'.%sDH.SAC' %chan_type
if not (os.path.isfile(fname1) and os.path.isfile(fname1) and \
os.path.isfile(fnamez) and os.path.isfile(fnamep)):
stime += 86400.
continue
tr1 = obspy.read(fname1)[0]
stimetr = tr1.stats.starttime
tr2 = obspy.read(fname2)[0]
trZ = obspy.read(fnamez)[0]
trP = obspy.read(fnamep)[0]
if abs(tr1.stats.delta - targetdt) > 1e-3 or abs(tr2.stats.delta - targetdt) > 1e-3 or \
abs(trZ.stats.delta - targetdt) > 1e-3 or abs(trP.stats.delta - targetdt) > 1e-3:
raise ValueError('!!! CHECK fs :'+ self.staid)
else:
tr1.stats.delta = targetdt
tr2.stats.delta = targetdt
trP.stats.delta = targetdt
trZ.stats.delta = targetdt
# # # if np.all(tr1.data == 0.) or np.all(tr2.data == 0.) or np.all(trZ.data == 0.) or np.all(trP.data == 0.):
# # # stime += 86400.
# # # continue
tr1.trim(starttime = stimetr, endtime = stimetr + 8400.*10-1)
tr2.trim(starttime = stimetr, endtime = stimetr + 8400.*10-1)
trZ.trim(starttime = stimetr, endtime = stimetr + 8400.*10-1)
trP.trim(starttime = stimetr, endtime = stimetr + 8400.*10-1)
# # # print (self.staid)
if np.all(trP.data == 0.) and not (np.all(tr1.data == 0.) or np.all(tr2.data == 0.)):
self.stanoise += DayNoise(tr1=tr1, tr2=tr2, trZ=trZ, trP=obspy.Trace(), overlap=self.overlap, window = self.window)
self.out_dtype = 'Z2-1'
elif (np.all(tr1.data == 0.) or np.all(tr2.data == 0.)) and (not np.all(trP.data == 0.)):
self.stanoise += DayNoise(tr1=obspy.Trace(), tr2=obspy.Trace(), trZ=trZ, trP=trP, overlap=self.overlap, window = self.window)
self.out_dtype = 'ZP'
elif (not (np.all(tr1.data == 0.) or np.all(tr2.data == 0.))) and (not np.all(trP.data == 0.)):
self.stanoise += DayNoise(tr1=tr1, tr2=tr2, trZ=trZ, trP=trP, overlap=self.overlap, window = self.window)
self.out_dtype = 'ZP-21'
else:
stime += 86400.
continue
stime += 86400.
Nday += 1
if Nday <= 1:
return False
self.stanoise.QC_sta_spectra()
self.stanoise.average_sta_spectra()
self.tfnoise= TFNoise(self.stanoise)
self.tfnoise.transfer_func()
return True