def test_get_data():
datapath = exmpl_path / 'data'
tstart = UTCDateTime('2012-04-01')
tend = UTCDateTime('2012-04-04')
trN1, trN2, trNZ, trNP = utils.get_data(datapath, tstart, tend)
assert trN1 is not None
assert trN2 is not None
assert trNZ is not None
assert trNP is not None
return trN1, trN2, trNZ, trNP
def test_get_data():
datapath = Path('DATA') / '7D.M08A'
tstart = UTCDateTime('2012-03-01')
tend = UTCDateTime('2012-03-10')
trN1, trN2, trNZ, trNP = utils.get_data(datapath, tstart, tend)
assert trN1 is not None
assert trN2 is not None
assert trNZ is not None
assert trNP is not None
return trN1, trN2, trNZ, trNP
def test_get_P_data_sr2(tmp_path):
datapath = Path('DATA') / '7D.M08A'
for filename in glob.glob(os.path.join(datapath, '*H.SAC')):
shutil.copy(filename, tmp_path)
for filename in glob.glob(os.path.join(tmp_path, '*H.SAC')):
stP = read(filename)
stP[0].resample(10.)
stP[0].write(filename, format='SAC')
tstart = UTCDateTime('2012-03-01')
tend = UTCDateTime('2012-03-10')
trN1, trN2, trNZ, trNP = utils.get_data(tmp_path, tstart, tend)
def test_get_P_data(tmp_path):
datapath = Path('DATA') / '7D.M08A'
# Test only P data
for filename in glob.glob(os.path.join(datapath, '*H.SAC')):
shutil.copy(filename, tmp_path)
for filename in glob.glob(os.path.join(datapath, '*Z.SAC')):
shutil.copy(filename, tmp_path)
tstart = UTCDateTime('2012-03-01')
tend = UTCDateTime('2012-03-10')
trN1, trN2, trNZ, trNP = utils.get_data(tmp_path, tstart, tend)
assert [len(tr.data) == 0 for tr in trN1]
assert [len(tr.data) == 0 for tr in trN2]
assert len(trNZ) > 0
assert len(trNP) > 0
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)