def test_tfnoise_day_demo(tmp_path):
daynoise = test_daynoise_demo()
with pytest.raises(TypeError):
assert TFNoise([])
with pytest.raises(Exception):
TFNoise(objnoise=daynoise)
daynoise.average_daily_spectra()
tfnoise_day = TFNoise(daynoise)
tfnoise_day.transfer_func()
d = tmp_path / "tmp"
tfnoise_day.save(d)
return tfnoise_day
def main():
# Run Input Parser
args = arguments.get_transfer_arguments()
# Load Database
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]
if not args.skip_daily:
# Path where spectra are located
specpath = Path('SPECTRA') / stkey
if not specpath.is_dir():
raise (Exception("Path to " + str(specpath) +
" doesn't exist - aborting"))
if not args.skip_clean:
# Path where average spectra will be saved
avstpath = Path('AVG_STA') / stkey
if not avstpath.is_dir():
print("Path to " + str(avstpath) +
" doesn't exist - skipping cleaned station spectra")
args.skip_clean = True
if args.skip_daily and args.skip_clean:
print("skipping both daily and clean spectra")
continue
# Path where transfer functions will be located
tfpath = Path('TF_STA') / stkey
if not tfpath.is_dir():
print("Path to " + str(tfpath) + " doesn't exist - creating it")
tfpath.mkdir(parents=True)
# Path where plots will be saved
if args.saveplot:
plotpath = tfpath / '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("|===============================================|")
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("|-----------------------------------------------|")
# Filename for output transfer functions
dstart = str(tstart.year).zfill(4) + '.' + str(
tstart.julday).zfill(3) + '-'
dend = str(tend.year).zfill(4) + '.' + str(tend.julday).zfill(3) + '.'
fileavst = avstpath / (dstart + dend + 'avg_sta.pkl')
# Find all files in directories
p = specpath.glob('*spectra.pkl')
spectra_files = [x for x in p if x.is_file()]
if not args.skip_clean:
p = avstpath.glob('*avg_sta.pkl')
average_files = [x for x in p if x.is_file()]
if not args.skip_daily:
day_transfer_functions = []
# Cycle through available files
for filespec in spectra_files:
year = filespec.name.split('.')[0]
jday = filespec.name.split('.')[1]
print()
print("*********************************************" +
"***************")
print("* Calculating transfer functions for key " + stkey +
" and day " + year + "." + jday)
tstamp = year + '.' + jday + '.'
filename = tfpath / (tstamp + 'transfunc.pkl')
# Load file
file = open(filespec, 'rb')
daynoise = pickle.load(file)
file.close()
# Load spectra into TFNoise object
daytransfer = TFNoise(daynoise)
# Calculate the transfer functions
daytransfer.transfer_func()
# Store the frequency axis
f = daytransfer.f
# Append to list of transfer functions
day_transfer_functions.append(daytransfer.transfunc)
# Save daily transfer functions to file
daytransfer.save(filename)
if not args.skip_clean:
# Cycle through available files
for fileavst in average_files:
name = fileavst.name.split('avg_sta')
print()
print("*********************************************" +
"***************")
print("* Calculating transfer functions for key " + stkey +
" and range " + name[0])
filename = tfpath / (name[0] + 'transfunc.pkl')
# Load file
file = open(fileavst, 'rb')
stanoise = pickle.load(file)
file.close()
# Load spectra into TFNoise object - no Rotation object
# for station averages
rotation = Rotation(None, None, None)
statransfer = TFNoise(stanoise)
# Calculate the transfer functions
statransfer.transfer_func()
# Store the frequency axis
f = statransfer.f
# Extract the transfer functions
sta_transfer_functions = statransfer.transfunc
# Save average transfer functions to file
statransfer.save(filename)
if args.fig_TF:
fname = stkey + '.' + 'transfer_functions'
plot = plotting.fig_TF(f,
day_transfer_functions,
daynoise.tf_list,
sta_transfer_functions,
stanoise.tf_list,
skey=stkey)
if plotpath:
plot.savefig(plotpath / (fname + '.' + args.form),
dpi=300,
bbox_inches='tight',
format=args.form)
else:
plot.show()