def number_good_kurtosis_for_location(kurt_files,data_files,loc,time_dict,snr_limit=10.0,snr_tr_limit=10.0,sn_time=10.0):
o_time=loc['o_time']
stack_x=loc['x_mean']
stack_y=loc['y_mean']
stack_z=loc['z_mean']
# TODO - Fix this to estimate K-time from o_time and propagation time to station
n_good_kurt=0
wf=Waveform()
for ifile in xrange(len(kurt_files)):
kfilename=kurt_files[ifile]
dfilename=data_files[ifile]
st=read(kfilename,headonly=True)
staname=st.traces[0].stats.station
if staname in time_dict.keys():
traveltime=time_dict[staname].value_at_point(stack_x,stack_y,stack_z)
start_time=o_time+traveltime-sn_time
end_time=o_time+traveltime+sn_time
try:
wf.read_from_file(kfilename,starttime=start_time,endtime=end_time)
snr=wf.get_snr(o_time+traveltime,start_time,end_time)
wf.read_from_file(dfilename,starttime=start_time,endtime=end_time)
snr_tr=wf.get_snr(o_time+traveltime,start_time,end_time)
if snr > snr_limit and snr_tr > snr_tr_limit:
n_good_kurt = n_good_kurt + 1
except UserWarning:
logging.info('No data around %s for file %s.'%(o_time.isoformat(),kfilename))
return n_good_kurt
def number_good_kurtosis_for_location(kurt_files,
data_files,
loc,
time_dict,
snr_limit=10.0,
snr_tr_limit=10.0,
sn_time=10.0):
o_time = loc['o_time']
stack_x = loc['x_mean']
stack_y = loc['y_mean']
stack_z = loc['z_mean']
# TODO - Fix this to estimate K-time from o_time and propagation time to station
n_good_kurt = 0
wf = Waveform()
for ifile in xrange(len(kurt_files)):
kfilename = kurt_files[ifile]
dfilename = data_files[ifile]
st = read(kfilename, headonly=True)
staname = st.traces[0].stats.station
if staname in time_dict.keys():
traveltime = time_dict[staname].value_at_point(
stack_x, stack_y, stack_z)
start_time = o_time + traveltime - sn_time
end_time = o_time + traveltime + sn_time
try:
wf.read_from_file(kfilename,
starttime=start_time,
endtime=end_time)
snr = wf.get_snr(o_time + traveltime, start_time, end_time)
wf.read_from_file(dfilename,
starttime=start_time,
endtime=end_time)
snr_tr = wf.get_snr(o_time + traveltime, start_time, end_time)
if snr > snr_limit and snr_tr > snr_tr_limit:
n_good_kurt = n_good_kurt + 1
except UserWarning:
logging.info('No data around %s for file %s.' %
(o_time.isoformat(), kfilename))
return n_good_kurt
# set start and end time of plot
start_time=stack_time-10.0
end_time=stack_time+20.0
start_time_migration=stack_time-60.0
end_time_migration=stack_time+60.0
# make dictionary of station names with snr ratios
snr_start_time=stack_time-options.sn_time
snr_end_time=stack_time+options.sn_time
snr_dict={}
wf=Waveform()
for filename in kurt_files:
wf.read_from_file(filename,starttime=snr_start_time,endtime=snr_end_time)
snr=wf.get_snr(stack_time,snr_start_time,snr_end_time)
station_name=wf.trace.stats.station
snr_dict[station_name]=snr
logging.debug("Signal to noise ratios on kurtosis")
logging.debug(snr_dict)
# set output filename
plot_filename=os.path.join(loc_path,"loc_%s.png"%stack_time.isoformat())
# select grad files for which the snr is > snr_limit
grad_files_selected=[]
for filename in grad_files:
st=read(filename,headonly=True)
station_name=st.traces[0].stats.station
logging.debug("Checking station %s : snr_value = %.2f"%(station_name,snr_dict[station_name]))
def number_good_kurtosis_for_location(kurt_files,
data_files,
loc,
time_dict,
snr_limit=10.0,
snr_tr_limit=10.0,
sn_time=10.0):
"""
Analyses the filtered data and the kurtosis time-series to determine the
number of stations whose traces have sufficiently high signal-to-noise
ratio (SNR) to be useful for the location. Both time-series need to satisfy
the conditions for a station to be counted as contributing to the location.
:param kurt_files: Filenames for kurtosis files. Depending on the filenames
given in this list, the function will analyse kurtosis,
kurtosis-gradient or gaussian waveforms.
:param data_files: Filenames for filtered data.
:param loc: Location dictionary for the event to be analysed
:param time_dict: Dictionary of travel-times for the location to be
analysed
:param snr_limit: SNR limit for kurtosis-type data
:param snr_limit_tr: SNR limit for filtered data
:param snr_time: Length of time in seconds before the event for computation
of SNR.
:rtype: integer
:returns: Numer of stations that have contributed to the location.
"""
o_time = loc['o_time']
stack_x = loc['x_mean']
stack_y = loc['y_mean']
stack_z = loc['z_mean']
n_good_kurt = 0
wf = Waveform()
for ifile in xrange(len(kurt_files)):
kfilename = kurt_files[ifile]
dfilename = data_files[ifile]
st = read(kfilename, headonly=True)
staname = st.traces[0].stats.station
if staname in time_dict.keys():
traveltime = time_dict[staname].value_at_point(
stack_x, stack_y, stack_z)
start_time = o_time + traveltime - sn_time
end_time = o_time + traveltime + sn_time
try:
wf.read_from_file(kfilename,
starttime=start_time,
endtime=end_time)
snr = wf.get_snr(o_time + traveltime, start_time, end_time)
wf.read_from_file(dfilename,
starttime=start_time,
endtime=end_time)
snr_tr = wf.get_snr(o_time + traveltime, start_time, end_time)
if snr > snr_limit and snr_tr > snr_tr_limit:
n_good_kurt = n_good_kurt + 1
except UserWarning:
logging.info('No data around %s for file %s.' %
(o_time.isoformat(), kfilename))
return n_good_kurt
def number_good_kurtosis_for_location(kurt_files, data_files, loc, time_dict,
snr_limit=10.0, snr_tr_limit=10.0,
sn_time=10.0):
"""
Analyses the filtered data and the kurtosis time-series to determine the
number of stations whose traces have sufficiently high signal-to-noise
ratio (SNR) to be useful for the location. Both time-series need to satisfy
the conditions for a station to be counted as contributing to the location.
:param kurt_files: Filenames for kurtosis files. Depending on the filenames
given in this list, the function will analyse kurtosis,
kurtosis-gradient or gaussian waveforms.
:param data_files: Filenames for filtered data.
:param loc: Location dictionary for the event to be analysed
:param time_dict: Dictionary of travel-times for the location to be
analysed
:param snr_limit: SNR limit for kurtosis-type data
:param snr_limit_tr: SNR limit for filtered data
:param snr_time: Length of time in seconds before the event for computation
of SNR.
:rtype: integer
:returns: Numer of stations that have contributed to the location.
"""
o_time = loc['o_time']
stack_x = loc['x_mean']
stack_y = loc['y_mean']
stack_z = loc['z_mean']
n_good_kurt = 0
wf = Waveform()
for ifile in xrange(len(kurt_files)):
kfilename = kurt_files[ifile]
dfilename = data_files[ifile]
st = read(kfilename, headonly=True)
staname = st.traces[0].stats.station
if staname in time_dict.keys():
traveltime = time_dict[staname].value_at_point(stack_x, stack_y,
stack_z)
start_time = o_time+traveltime-sn_time
end_time = o_time+traveltime+sn_time
try:
wf.read_from_file(kfilename, starttime=start_time,
endtime=end_time)
snr = wf.get_snr(o_time+traveltime, start_time, end_time)
wf.read_from_file(dfilename, starttime=start_time,
endtime=end_time)
snr_tr = wf.get_snr(o_time+traveltime, start_time, end_time)
if snr > snr_limit and snr_tr > snr_tr_limit:
n_good_kurt = n_good_kurt + 1
except UserWarning:
logging.info('No data around %s for file %s.' %
(o_time.isoformat(), kfilename))
return n_good_kurt
