def test_SNR(network,
station,
phase,
preproloc='ouput/waveforms/preprocessed'):
"""Test the automatic QC scripts for a certain station and writes ratings
in the rating file."""
noisematls = []
critls = []
loc = os.path.join(preproloc, phase, '/by_station/', network, station)
for file in os.listdir(loc):
try:
st = read(loc + '/' + file)
except IsADirectoryError:
continue
dt = st[0].stats.delta
sampling_rate = st[0].stats.sampling_rate
if phase == "S":
_, crit, _, noisemat = qcs(st, dt, sampling_rate)
elif phase == "P":
_, crit, _, noisemat = qcp(st, dt, sampling_rate)
noisematls.append(noisemat)
critls.append(crit)
return noisematls, critls
def automatic_rate(network, station, phase, preproloc):
"""
Checks the automatic QC criteria for SRF waveforms.
Parameters
----------
network : STRING
Network code (2 letters).
station : STRING
Station code (3 letters).
phase : STRING, optional
"P" or "S".
preproloc : string
Directory that contains the preprocessed files (not quality controlled)
Returns
-------
diff : INTEGER
Number of waveforms that were not rated 3 or 4.
ret : INTEGER
Number of automatically retained waveforms.
sts : LIST
List containing all retained + filtered streams.
crits : LIST
List containing bools (retained or not) corresponding to streams in
sts.
"""
inloc = os.path.join(preproloc, phase, '/by_station/', network, station)
diff = 0
ret = 0
sts = []
crits = []
for file in os.listdir(inloc):
if file[:4] == "info": # Skip the info files
continue
try:
st = read(inloc + file)
except IsADirectoryError as e:
print(e)
continue
starttime = str(st[0].stats.starttime)
if phase == "S":
st, crit, hf, noisemat = qcs(st, st[0].stats.delta,
st[0].stats.sampling_rate)
elif phase == "P":
st, crit, lf, noisemat = qcp(st, st[0].stats.delta,
st[0].stats.sampling_rate)
with shelve.open(
os.path.join(finddir(), 'ratings') + network + "." + station +
"rating") as f:
f[starttime + "_auto"] = crit
if starttime in f and int(f[starttime]) < 3 and crit:
diff = diff + 1
if crit:
ret = ret + 1
sts.append(st)
crits.append(crit)
return diff, ret, sts, crits
def rate(network,
phase,
preproloc,
station=None,
review=False,
retained=False,
decon_meth='it',
test_tt_calculation=False):
"""
Module to rate and review the quality of Sp or Ps waveforms from the given
station. Shows the automatic rating from qc. Tapers can be controlled with
the mouse. Rating is done with the keyboard. Makes only sense if the
files in preproloc have not been quality controlled (the script is a
reminant from an earlier alpha version).
Parameters
----------
network : STRING
Network code (two letters).
phase : STRING
Either "P" for Ps or "S" Sp. The default is "S".
preproloc : string
Directory that contains the preprocessed files (not quality controlled)
station : str or list, optional
Station code (three letters).
review : INTEGER, optional
If true, already rated waveforms are shown.
Can also be an integer between 1 and 4. Then, only waveforms rated
with the respected rating are shown. The default is False.
retained : Bool, optional
Show only waveforms retained by qcp or qcs. The default is False.
decon_meth : STRING, optional
Deconvolution method, "waterlevel", 'dampedf' for constant
damping level, 'it' for iterative time domain deconvoltuion, 'multit'
for multitaper or 'fqd' for frequency dependently damped spectral
division. The default is 'it'
Raises
------
Exception
For Typing mistakes.
Returns
-------
None.
"""
if phase == 'P':
onset = 30
elif phase[-1] == 'S':
onset = 120
inloc = os.path.join(preproloc, phase, 'by_station', network)
infiles = [] # List of all files in folder
pattern = [] # List of input constraints
streams = [] # List of files filtered for input criteria
for root, dirs, files in os.walk(inloc):
for name in files:
infiles.append(os.path.join(root, name))
# Set filter patterns
if station:
if type(station) == str:
pattern.append('*%s*%s*.mseed' % (network, station))
elif type(station) == list:
for stat in station:
pattern.append('*%s*%s*.mseed' % (network, stat))
else:
pattern.append('*%s*.mseed' % (network))
# Do filtering
for pat in pattern:
streams.extend(fnmatch.filter(infiles, pat))
# clear memory
del pattern, infiles
# For TauPy lookup
model = TauPyModel()
if test_tt_calculation:
client = Client()
for f in streams:
# if file[:4] == "info": # Skip the info files
# continue
# try:
# st = read(inloc + file)
# except IsADirectoryError as e:
# print(e)
# continue
st = read(f)
# List for taper coordinates
if "taper" in rating:
del rating["taper"]
rating["taper"] = []
st.normalize()
# Additional filter "test"
if phase == "S":
st.filter("lowpass", freq=1.0, zerophase=True, corners=2)
elif phase == "P":
st.filter("lowpass", freq=1.0, zerophase=True, corners=2)
y = []
ch = []
starttime = str(st[0].stats.starttime)
dt = st[0].stats.delta
sampling_f = st[0].stats.sampling_rate
old = __r_file(network, st[0].stats.station, starttime) # old
# read info file
# location info file
infof = os.path.join(inloc, st[0].stats.station, 'info')
with shelve.open(infof) as info:
ii = info["starttime"].index(st[0].stats.starttime)
rdelta = info["rdelta"][ii] # epicentral distance
mag = info["magnitude"][ii]
statlat = info["statlat"]
statlon = info["statlon"]
rayp = info["rayp_s_deg"][ii] / 111319.9
evt_depth = info["evt_depth"][ii] / 1000
ot = info["ot_ret"][ii]
evtlat = info['evtlat'][ii]
evtlon = info['evtlon'][ii]
if old and not review: # skip already rated files
continue
if type(review) == int:
if review > 4:
raise Exception("review has to be between 0 and 4")
if review != int(old):
continue
# check automatic rating
if phase == "S":
st_f, crit, hf, noisemat = qcs(st, dt, sampling_f, onset=onset)
elif phase == "P":
st_f, crit, hf, noisemat = qcp(st, dt, sampling_f, onset=onset)
# skip files that have not been retained
if retained and not crit:
continue
# create RF
if not test_tt_calculation:
_, _, PSV = rotate_PSV(statlat, statlon, rayp, st_f)
else:
PSV = st_f # to see correlation at theoretical arrival
try:
RF = createRF(PSV, phase, method=decon_meth, shift=onset)
except ValueError as e: # There were some problematic events
print(e)
continue
# TauPy lookup
ph_name = []
ph_time = []
if not test_tt_calculation:
arrivals = model.get_travel_times(evt_depth,
distance_in_degree=rdelta)
primary_time = model.get_travel_times(evt_depth,
distance_in_degree=rdelta,
phase_list=phase)[0].time
for arr in arrivals:
if arr.time < primary_time - onset or arr.time > \
primary_time + 120 or arr.name == phase:
continue
ph_name.append(arr.name)
ph_time.append(arr.time - primary_time)
else:
# Caluclate travel times with different methods
ph_time.append(
model.get_travel_times_geo(evt_depth,
evtlat,
evtlon,
statlat,
statlon,
phase_list=phase)[0].time)
d = []
d.append(
client.distaz(statlat, statlon, evtlat, evtlon)['distance'])
d.append(
kilometer2degrees(
gps2dist_azimuth(statlat, statlon, evtlat, evtlon)[0] /
1000))
for dis in d:
ph_time.append(
model.get_travel_times(evt_depth, dis,
phase_list=phase)[0].time)
ph_name = ['taup', 'iris', 'geodetics']
ph_time = np.array(ph_time) - (st[0].stats.starttime + onset -
UTCDateTime(ot))
# waveform data
st.sort()
for tr in st:
y.append(tr.data)
ch.append(tr.stats.channel[2])
# create time vector
t = np.linspace(0 - onset, tr.stats.npts * tr.stats.delta - onset,
len(y[0]))
# plot
fig, ax = __draw_plot(starttime, t, y, ph_time, ph_name, ch, noisemat,
RF, old, rdelta, mag, crit, ot, evt_depth, phase,
test_tt_calculation)
while not plt.waitforbuttonpress(30):
# Taper when input is there
if len(rating["taper"]) == 2:
if rating["taper"][0] < rating["taper"][1]:
trim = [rating["taper"][0], rating["taper"][1]]
else:
trim = [rating["taper"][1], rating["taper"][0]]
trim[0] = trim[0] - t[0]
trim[1] = t[-1] - trim[1]
RF = createRF(PSV,
phase,
method=decon_meth,
shift=onset,
trim=trim)
__draw_plot(starttime, t, y, ph_time, ph_name, ch, noisemat,
RF, old, rdelta, mag, crit, ot, evt_depth, phase,
test_tt_calculation)
rating["taper"].clear()
# fig.canvas.mpl_connect('key_press_event', ontype)
if rating["k"] == "q":
break
elif "k" in rating:
__w_file(network, st[0].stats.station, starttime)