def do_clustering_setup_and_run(opdict):
base_path=opdict['base_path']
verbose=opdict['verbose']
# stations
stations_filename=os.path.join(base_path,'lib',opdict['stations'])
# output directory
output_dir=os.path.join(base_path,'out',opdict['outdir'])
# data
data_dir=os.path.join(base_path,'data',opdict['datadir'])
data_glob=opdict['dataglob']
data_files=glob.glob(os.path.join(data_dir,data_glob))
data_files.sort()
# location file
locdir=os.path.join(base_path,'out',opdict['outdir'],'loc')
loc_filename=os.path.join(locdir,'locations.dat')
# file containing correlation values
coeff_file=os.path.join(locdir,opdict['xcorr_corr'])
# Read correlation values
b=BinaryFile(coeff_file)
coeff=b.read_binary_file()
# file containing time delays
delay_file=os.path.join(locdir,opdict['xcorr_delay'])
# INPUT PARAMETERS
nbmin=int(opdict['nbsta'])
if nbmin > len(coeff.keys()):
raise Error('the minimum number of stations cannot be > to the number of stations !!')
event=len(coeff.values()[0])
tplot=float(opdict['clus']) # threshold for which we save and plot
cluster_file="%s/cluster-%s-%s"%(locdir,str(tplot),str(nbmin))
corr=[opdict['clus']]
#corr=np.arange(0,1.1,0.1)
for threshold in corr:
threshold=float(threshold)
nbsta=compute_nbsta(event,coeff,threshold)
CLUSTER = do_clustering(event,nbsta,nbmin)
if threshold == tplot:
print "----------------------------------------------"
print "THRESHOLD : ",threshold," # STATIONS : ",nbmin
print "# CLUSTERS : ",len(CLUSTER)
print CLUSTER
c=BinaryFile(cluster_file)
c.write_binary_file(CLUSTER)
print "Written in %s"%cluster_file
if verbose: # PLOT
# Read location file
locs=read_locs_from_file(loc_filename)
# Read station file
stations=read_stations_file(stations_filename)
# Look at the waveforms
plot_traces(CLUSTER, delay_file, coeff, locs, stations, data_dir, data_files, threshold)
def do_clustering_setup_and_run(opdict):
base_path = opdict['base_path']
verbose = opdict['verbose']
# stations
stations_filename = os.path.join(base_path, 'lib', opdict['stations'])
# output directory
output_dir = os.path.join(base_path, 'out', opdict['outdir'])
# data
data_dir = os.path.join(base_path, 'data', opdict['datadir'])
data_glob = opdict['dataglob']
data_files = glob.glob(os.path.join(data_dir, data_glob))
data_files.sort()
# location file
locdir = os.path.join(base_path, 'out', opdict['outdir'], 'loc')
loc_filename = os.path.join(locdir, 'locations.dat')
# file containing correlation values
coeff_file = os.path.join(locdir, opdict['xcorr_corr'])
# Read correlation values
b = BinaryFile(coeff_file)
coeff = b.read_binary_file()
# file containing time delays
delay_file = os.path.join(locdir, opdict['xcorr_delay'])
# INPUT PARAMETERS
nbmin = int(opdict['nbsta'])
if nbmin > len(coeff.keys()):
raise Error(
'the minimum number of stations cannot be > to the number of stations !!'
)
event = len(coeff.values()[0])
tplot = float(opdict['clus']) # threshold for which we save and plot
cluster_file = "%s/cluster-%s-%s" % (locdir, str(tplot), str(nbmin))
corr = [opdict['clus']]
#corr=np.arange(0,1.1,0.1)
for threshold in corr:
threshold = float(threshold)
nbsta = compute_nbsta(event, coeff, threshold)
CLUSTER = do_clustering(event, nbsta, nbmin)
if threshold == tplot:
print "----------------------------------------------"
print "THRESHOLD : ", threshold, " # STATIONS : ", nbmin
print "# CLUSTERS : ", len(CLUSTER)
print CLUSTER
c = BinaryFile(cluster_file)
c.write_binary_file(CLUSTER)
print "Written in %s" % cluster_file
if verbose: # PLOT
# Read location file
locs = read_locs_from_file(loc_filename)
# Read station file
stations = read_stations_file(stations_filename)
# Look at the waveforms
plot_traces(CLUSTER, delay_file, coeff, locs, stations,
data_dir, data_files, threshold)
def do_clustering_setup_and_run(opdict):
"""
Does clustering by applying the depth first search algorithm and saves the result
(= a dictionary containing the event indexes forming each cluster) in a binary file.
Needs to define the correlation value threshold and the minimum number of stations
where this threshold should be reached to form a cluster (should be done in the options
dictionary)
:param opdict: Dictionary of waveloc options
"""
base_path = opdict['base_path']
verbose = opdict['verbose']
# stations
stations_filename = os.path.join(base_path, 'lib', opdict['stations'])
# data
data_dir = os.path.join(base_path, 'data', opdict['datadir'])
data_glob = opdict['dataglob']
data_files = glob.glob(os.path.join(data_dir, data_glob))
data_files.sort()
# location file
locdir = os.path.join(base_path, 'out', opdict['outdir'], 'loc')
loc_filename = os.path.join(locdir, 'locations.dat')
# file containing correlation values
coeff_file = os.path.join(locdir, opdict['xcorr_corr'])
# Read correlation values
b = BinaryFile(coeff_file)
coeff = b.read_binary_file()
# INPUT PARAMETERS
nbmin = int(opdict['nbsta'])
if nbmin > len(coeff.keys()):
raise Exception('the minimum number of stations cannot be > to the\
number of stations !!')
event = len(coeff.values()[0])
tplot = float(opdict['clus']) # threshold for which we save and plot
cluster_file = "%s/cluster-%s-%s" % (locdir, str(tplot), str(nbmin))
corr = [opdict['clus']]
#corr = np.arange(0, 1.1, 0.1)
for threshold in corr:
threshold = float(threshold)
nbsta = compute_nbsta(event, coeff, threshold)
CLUSTER = do_clustering(event, nbsta, nbmin)
if threshold == tplot:
print "----------------------------------------------"
print "THRESHOLD : ", threshold, " # STATIONS : ", nbmin
print "# CLUSTERS : ", len(CLUSTER)
print CLUSTER
c = BinaryFile(cluster_file)
c.write_binary_file(CLUSTER)
print "Written in %s" % cluster_file
if verbose: # PLOT
# Read location file
locs = read_locs_from_file(loc_filename)
# Read station file
stations = read_stations_file(stations_filename)
# Look at the waveforms
#plot_traces(CLUSTER, delay_file, coeff, locs,
# data_dir, data_files, threshold)
# Plot graphs
plot_graphs(locs, stations, nbsta, CLUSTER, nbmin, threshold)
def do_kurtogram_setup_and_run(opdict):
"""
Run the kurtogram analysis using the parameters contained in the
WavelocOptions.opdict.
:param opdict: Dictionary containing the waveloc parameters and options.
"""
base_path = opdict['base_path']
# data
data_dir = os.path.join(base_path, 'data', opdict['datadir'])
data_glob = opdict['dataglob']
data_files = glob.glob(os.path.join(data_dir, data_glob))
data_files.sort()
kurt_glob = opdict['kurtglob']
kurt_files = glob.glob(os.path.join(data_dir, kurt_glob))
kurt_files.sort()
# output directory
out_dir = os.path.join(base_path, 'out', opdict['outdir'])
# location file
locdir = os.path.join(out_dir, 'loc')
locfile = os.path.join(locdir, 'locations.dat')
# Read locations
locs = read_locs_from_file(locfile)
# create a file containing the best filtering parameters for each event and
# each station
kurto_file = os.path.join(out_dir, 'kurto')
tdeb = utcdatetime.UTCDateTime(opdict['starttime'])
tfin = utcdatetime.UTCDateTime(opdict['endtime'])
# write filenames in a dictionary
kurtdata = {}
for filename in kurt_files:
try:
wf = Waveform()
wf.read_from_file(filename)
sta = wf.station
kurtdata[sta] = filename
except UserWarning:
logging.info('No data around %s for file %s.' %
(tdeb.isoformat(), filename))
data = {}
for filename in data_files:
try:
wf = Waveform()
wf.read_from_file(filename)
sta = wf.station
data[sta] = filename
except UserWarning:
logging.info('No data around %s for file %s.' %
(tdeb.isoformat(), filename))
# ------------------------------------------------------------------------
# Create an empty dictionnary that will contain the filtering parameters
param = {}
for station in sorted(data):
wf1 = Waveform()
wf1.read_from_file(data[station], starttime=tdeb, endtime=tfin)
wf2 = Waveform()
wf2.read_from_file(kurtdata[station], starttime=tdeb, endtime=tfin)
info = {}
info['data_file'] = data[station]
info['station'] = station
info['tdeb_data'] = wf1.starttime
info['tdeb_kurt'] = wf2.starttime
info['kurt_file'] = kurtdata[station]
info['data_ini'] = wf1.values
info['kurt_ini'] = wf2.values
info['dt'] = wf1.dt
info['filter'] = []
logging.info('Processing station %s' % info['station'])
if opdict['new_kurtfile']:
new_filename = 'filt_kurtogram'
new_kurt_filename = \
os.path.join("%s%s" % (data[station].split(data_glob[1:])[0],
new_filename))
info['new_kurt_file'] = new_kurt_filename
trace_kurt_fin = Waveform()
trace_kurt_fin.read_from_file(new_kurt_filename)
info['new_kurt'] = trace_kurt_fin.values
for loc in locs:
origin_time = loc['o_time']
if opdict['verbose']:
print "******************************************************"
print logging.info(origin_time)
if origin_time > tdeb and origin_time < tfin:
info = kurto(origin_time, info, opdict)
else:
continue
info['filter'] = np.matrix(info['filter'])
sta = info['station']
param[sta] = info['filter']
if 'new_kurt_file' in info:
trace_kurt_fin.values[:] = info['new_kurt']
trace_kurt_fin.write_to_file_filled(info['new_kurt_file'],
format='MSEED', fill_value=0)
# Write the dictionnary 'param' in a binary file
if os.path.isfile(kurto_file):
ans = raw_input('%s file already exists. Do you really want to replace\
it ? (y or n):\n' % kurto_file)
if ans != 'y':
kurto_file = "%s_1" % kurto_file
a = BinaryFile(kurto_file)
a.write_binary_file(param)
# read and plot the file you have just written
read_kurtogram_frequencies(kurto_file)
def do_kurtogram_setup_and_run(opdict):
"""
Run the kurtogram analysis using the parameters contained in the
WavelocOptions.opdict.
:param opdict: Dictionary containing the waveloc parameters and options.
"""
base_path = opdict['base_path']
# data
data_dir = os.path.join(base_path, 'data', opdict['datadir'])
data_glob = opdict['dataglob']
data_files = glob.glob(os.path.join(data_dir, data_glob))
data_files.sort()
kurt_glob = opdict['kurtglob']
kurt_files = glob.glob(os.path.join(data_dir, kurt_glob))
kurt_files.sort()
# output directory
out_dir = os.path.join(base_path, 'out', opdict['outdir'])
# location file
locdir = os.path.join(out_dir, 'loc')
locfile = os.path.join(locdir, 'locations.dat')
# Read locations
locs = read_locs_from_file(locfile)
# create a file containing the best filtering parameters for each event and
# each station
kurto_file = os.path.join(out_dir, 'kurto')
tdeb = utcdatetime.UTCDateTime(opdict['starttime'])
tfin = utcdatetime.UTCDateTime(opdict['endtime'])
# write filenames in a dictionary
kurtdata = {}
for filename in kurt_files:
try:
wf = Waveform()
wf.read_from_file(filename)
sta = wf.station
kurtdata[sta] = filename
except UserWarning:
logging.info('No data around %s for file %s.' %
(tdeb.isoformat(), filename))
data = {}
for filename in data_files:
try:
wf = Waveform()
wf.read_from_file(filename)
sta = wf.station
data[sta] = filename
except UserWarning:
logging.info('No data around %s for file %s.' %
(tdeb.isoformat(), filename))
# ------------------------------------------------------------------------
# Create an empty dictionnary that will contain the filtering parameters
param = {}
for station in sorted(data):
wf1 = Waveform()
wf1.read_from_file(data[station], starttime=tdeb, endtime=tfin)
wf2 = Waveform()
wf2.read_from_file(kurtdata[station], starttime=tdeb, endtime=tfin)
info = {}
info['data_file'] = data[station]
info['station'] = station
info['tdeb_data'] = wf1.starttime
info['tdeb_kurt'] = wf2.starttime
info['kurt_file'] = kurtdata[station]
info['data_ini'] = wf1.values
info['kurt_ini'] = wf2.values
info['dt'] = wf1.dt
info['filter'] = []
logging.info('Processing station %s' % info['station'])
if opdict['new_kurtfile']:
new_filename = 'filt_kurtogram'
new_kurt_filename = \
os.path.join("%s%s" % (data[station].split(data_glob[1:])[0],
new_filename))
info['new_kurt_file'] = new_kurt_filename
trace_kurt_fin = Waveform()
trace_kurt_fin.read_from_file(new_kurt_filename)
info['new_kurt'] = trace_kurt_fin.values
for loc in locs:
origin_time = loc['o_time']
if opdict['verbose']:
print "******************************************************"
print logging.info(origin_time)
if origin_time > tdeb and origin_time < tfin:
info = kurto(origin_time, info, opdict)
else:
continue
info['filter'] = np.matrix(info['filter'])
sta = info['station']
param[sta] = info['filter']
if 'new_kurt_file' in info:
trace_kurt_fin.values[:] = info['new_kurt']
trace_kurt_fin.write_to_file_filled(info['new_kurt_file'],
format='MSEED',
fill_value=0)
# Write the dictionnary 'param' in a binary file
if os.path.isfile(kurto_file):
ans = raw_input('%s file already exists. Do you really want to replace\
it ? (y or n):\n' % kurto_file)
if ans != 'y':
kurto_file = "%s_1" % kurto_file
a = BinaryFile(kurto_file)
a.write_binary_file(param)
# read and plot the file you have just written
read_kurtogram_frequencies(kurto_file)