def read_kurtogram_frequencies(filename):
"""
Reads the binary file with kurtogram frequencies.
Plots the histograms of lower and upper frequencies
for each station.
Aims at determining the best filtering parameters.
:param filename: File to read
:type filename: string
"""
a = BinaryFile(filename)
freqs = a.read_binary_file()
for staname in sorted(freqs):
print "%s %.1f %.1f" % (staname, np.mean(freqs[staname][:, 0]),
np.mean(freqs[staname][:, 1]))
fig = plt.figure()
fig.set_facecolor('white')
plt.hist([freqs[staname][:, 0], freqs[staname][:, 1]], 35,
histtype='stepfilled', alpha=.2, color=('b', 'g'),
label=['f_low', 'f_up'])
plt.title(staname)
plt.xlabel('Frequency (Hz)')
plt.figtext(0.15, 0.85, "Lower f = %.1f Hz" %
np.mean(freqs[staname][:, 0]))
plt.figtext(0.15, 0.8, "Upper f = %.1f Hz" %
np.mean(freqs[staname][:, 1]))
plt.show()
def read_kurtogram_frequencies(filename):
"""
Reads the binary file with kurtogram frequencies.
Plots the histograms of lower and upper frequencies
for each station.
Aims at determining the best filtering parameters.
:param filename: File to read
:type filename: string
"""
a = BinaryFile(filename)
freqs = a.read_binary_file()
for staname in sorted(freqs):
print "%s %.1f %.1f" % (staname, np.mean(
freqs[staname][:, 0]), np.mean(freqs[staname][:, 1]))
fig = plt.figure()
fig.set_facecolor('white')
plt.hist([freqs[staname][:, 0], freqs[staname][:, 1]],
35,
histtype='stepfilled',
alpha=.2,
color=('b', 'g'),
label=['f_low', 'f_up'])
plt.title(staname)
plt.xlabel('Frequency (Hz)')
plt.figtext(0.15, 0.85,
"Lower f = %.1f Hz" % np.mean(freqs[staname][:, 0]))
plt.figtext(0.15, 0.8,
"Upper f = %.1f Hz" % np.mean(freqs[staname][:, 1]))
plt.show()
def plot_traces(CLUSTER, delay_file, coeff, locs, stations, datadir, data_files, threshold):
# Read the file containing the time delays
a=BinaryFile(delay_file)
delay=a.read_binary_file()
t_before=0.5
t_after=6.0
rg_x=[362,370]
rg_y=[7647,7653]
rg_z=[-4.0,2.5]
tr={}
for data_file in data_files:
wf=Waveform()
wf.read_from_file(data_file)
tr[wf.station]=wf.values
dt=wf.delta
tdeb=wf.starttime
list_name=sorted(tr)
for i in range(1,len(CLUSTER)+1): # cluster index
i=51
for j in range(len(CLUSTER[i])): # first event index
e1=CLUSTER[i][j]
for k in range(j+1,len(CLUSTER[i])): # second event index
e2=CLUSTER[i][k]
co=0
fig = plt.figure()
fig.set_facecolor('white')
for l in range(len(list_name)):
name=list_name[l]
if delay[name][e1-1][e2-1]!='NaN':
stack_time_1=locs[e1-1]['o_time']
i_start_1, i_end_1=waveval(stack_time_1,t_before,t_after,dt,tdeb)
val1=tr[name][i_start_1-1:i_end_1]
stack_time_2=locs[e2-1]['o_time']
i_start_2, i_end_2=waveval(stack_time_2-delay[name][e1-1][e2-1],t_before,t_after,dt,tdeb)
val2=tr[name][i_start_2-1:i_end_2]
t=np.linspace(0,t_after+t_before,(t_after+t_before)/dt+1)
ax=fig.add_subplot(len(list_name),1,l+1)
ax.set_axis_off()
ax.plot(t,val1/max(val1))
ax.plot(t,val2/max(val2),'r')
c='k'
if coeff[name][e1-1][e2-1]>=threshold:
co=co+1
c='r'
ax.text(0.5,0.5,"%s, %s, %s"%(name,str(coeff[name][e1-1][e2-1]),str(delay[name][e1-1][e2-1])),color=c)
fig.suptitle("Cluster : %s ; Event pair : (%s,%s) ; %d"%(str(i),str(e1),str(e2),co))
plt.show()
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_double_diff_setup_and_run(opdict):
"""
Do double difference (outer routine). Takes options from a
WavelocOptions.opdict dictionary.
:param opdict: Dictionary of parameters and options
"""
base_path = opdict['base_path']
verbose = opdict['verbose']
dd_loc = opdict['dd_loc']
# Station
stations_filename = os.path.join(base_path, 'lib', opdict['stations'])
stations = read_stations_file(stations_filename)
# Location file
locdir = os.path.join(base_path, 'out', opdict['outdir'], 'loc')
loc_filename = os.path.join(locdir, 'locations.dat')
locs = read_locs_from_file(loc_filename)
opdict = read_header_from_file(loc_filename, opdict)
# ------------------------------------------------------------------------
# search grid
search_grid_filename = os.path.join(base_path, 'lib',
opdict['search_grid'])
# traveltimes grid
grid_info = read_hdr_file(search_grid_filename)
time_grids = get_interpolated_time_grids(opdict)
# Extract the UTM coordinates of the area of study
xstart = grid_info['x_orig']
xend = xstart+grid_info['nx']*grid_info['dx']
ystart = grid_info['y_orig']
yend = ystart+grid_info['ny']*grid_info['dy']
zend = -grid_info['z_orig']
zstart = -(-zend+grid_info['nz']*grid_info['dz'])
area = [xstart, xend, ystart, yend, zstart, zend]
# ------------------------------------------------------------------------
nbmin = int(opdict['nbsta'])
threshold = float(opdict['clus'])
# Correlation, time delay and cluster files
corr_file = os.path.join(locdir, opdict['xcorr_corr'])
cfile = BinaryFile(corr_file)
coeff = cfile.read_binary_file()
delay_file = os.path.join(locdir, opdict['xcorr_delay'])
dfile = BinaryFile(delay_file)
delay = dfile.read_binary_file()
cluster_file = os.path.join(locdir, 'cluster-%s-%s' % (str(threshold),
str(nbmin)))
clfile = BinaryFile(cluster_file)
cluster = clfile.read_binary_file()
# ------------------------------------------------------------------------
# Input parameters
len_cluster_min = 2
if dd_loc:
new_loc_filename = os.path.join(locdir, 'relocations.dat')
new_loc_file = open(new_loc_filename, 'w')
write_header_options(new_loc_file, opdict)
# ------------------------------------------------------------------------
# Iterate over clusters
for i in cluster.keys():
print "CLUSTER %d:" % i, cluster[i], len(cluster[i])
N = len(cluster[i])
# Hypocentral parameters to be changed
x, y, z, z_ph, to = coord_cluster(cluster[i], locs)
# Replace bad locations by the centroid coordinates
centroid_x = np.mean(x)
centroid_y = np.mean(y)
centroid_z = np.mean(z)
for ii in range(len(cluster[i])):
if np.abs(x[ii]-centroid_x) > .75:
x[ii] = centroid_x
if np.abs(y[ii]-centroid_y) > .75:
y[ii] = centroid_y
if np.abs(z[ii]-centroid_z) > .75:
z[ii] = centroid_z
if N > len_cluster_min:
# Theroretical traveltimes and arrival times
t_th, arr_times = traveltimes(x, y, z, to, stations, time_grids)
# do double difference location
x, y, z, to = do_double_diff(x, y, z, to, stations, coeff, delay,
cluster[i], threshold, t_th,
arr_times)
if verbose:
from clustering import compute_nbsta
nbsta = compute_nbsta(len(locs), coeff, threshold)
plot_events(cluster, locs, stations, x, y, z, i, threshold, nbmin,
area, nbsta)
if dd_loc:
ind = 0
for j in cluster[i]:
locs[j-1]['x_mean'] = x[ind]
locs[j-1]['y_mean'] = y[ind]
locs[j-1]['z_mean'] = z[ind]
locs[j-1]['o_time'] = to[ind]
locs[j-1]['x_sigma'] = 0
locs[j-1]['y_sigma'] = 0
locs[j-1]['z_sigma'] = 0
locs[j-1]['o_err_right'] = 0
locs[j-1]['o_err_left'] = 0
ind += 1
new_loc_file.write("Max = %.2f, %s - %.2f s + %.2f s, x= %.4f pm\
%.4f km, y= %.4f pm %.4f km, z= %.4f pm %.4f km\n" %
(locs[j-1]['max_trig'], locs[j-1]['o_time'].isoformat(),
locs[j-1]['o_err_left'], locs[j-1]['o_err_right'],
locs[j-1]['x_mean'], locs[j-1]['x_sigma'],
locs[j-1]['y_mean'], locs[j-1]['y_sigma'],
locs[j-1]['z_mean'], locs[j-1]['z_sigma']))
if dd_loc:
new_loc_file.close()
def do_double_diff_setup_and_run(opdict):
"""
Do double difference (outer routine). Takes options from a
WavelocOptions.opdict dictionary.
:param opdict: Dictionary of parameters and options
"""
base_path = opdict['base_path']
verbose = opdict['verbose']
dd_loc = opdict['dd_loc']
# Station
stations_filename = os.path.join(base_path, 'lib', opdict['stations'])
stations = read_stations_file(stations_filename)
# Location file
locdir = os.path.join(base_path, 'out', opdict['outdir'], 'loc')
loc_filename = os.path.join(locdir, 'locations.dat')
locs = read_locs_from_file(loc_filename)
opdict = read_header_from_file(loc_filename, opdict)
# ------------------------------------------------------------------------
# search grid
search_grid_filename = os.path.join(base_path, 'lib',
opdict['search_grid'])
# traveltimes grid
grid_info = read_hdr_file(search_grid_filename)
time_grids = get_interpolated_time_grids(opdict)
# Extract the UTM coordinates of the area of study
xstart = grid_info['x_orig']
xend = xstart + grid_info['nx'] * grid_info['dx']
ystart = grid_info['y_orig']
yend = ystart + grid_info['ny'] * grid_info['dy']
zend = -grid_info['z_orig']
zstart = -(-zend + grid_info['nz'] * grid_info['dz'])
area = [xstart, xend, ystart, yend, zstart, zend]
# ------------------------------------------------------------------------
nbmin = int(opdict['nbsta'])
threshold = float(opdict['clus'])
# Correlation, time delay and cluster files
corr_file = os.path.join(locdir, opdict['xcorr_corr'])
cfile = BinaryFile(corr_file)
coeff = cfile.read_binary_file()
delay_file = os.path.join(locdir, opdict['xcorr_delay'])
dfile = BinaryFile(delay_file)
delay = dfile.read_binary_file()
cluster_file = os.path.join(locdir,
'cluster-%s-%s' % (str(threshold), str(nbmin)))
clfile = BinaryFile(cluster_file)
cluster = clfile.read_binary_file()
# ------------------------------------------------------------------------
# Input parameters
len_cluster_min = 2
if dd_loc:
new_loc_filename = os.path.join(locdir, 'relocations.dat')
new_loc_file = open(new_loc_filename, 'w')
write_header_options(new_loc_file, opdict)
# ------------------------------------------------------------------------
# Iterate over clusters
for i in cluster.keys():
print "CLUSTER %d:" % i, cluster[i], len(cluster[i])
N = len(cluster[i])
# Hypocentral parameters to be changed
x, y, z, z_ph, to = coord_cluster(cluster[i], locs)
# Replace bad locations by the centroid coordinates
centroid_x = np.mean(x)
centroid_y = np.mean(y)
centroid_z = np.mean(z)
for ii in range(len(cluster[i])):
if np.abs(x[ii] - centroid_x) > .75:
x[ii] = centroid_x
if np.abs(y[ii] - centroid_y) > .75:
y[ii] = centroid_y
if np.abs(z[ii] - centroid_z) > .75:
z[ii] = centroid_z
if N > len_cluster_min:
# Theroretical traveltimes and arrival times
t_th, arr_times = traveltimes(x, y, z, to, stations, time_grids)
# do double difference location
x, y, z, to = do_double_diff(x, y, z, to, stations, coeff, delay,
cluster[i], threshold, t_th,
arr_times)
if verbose:
from clustering import compute_nbsta
nbsta = compute_nbsta(len(locs), coeff, threshold)
plot_events(cluster, locs, stations, x, y, z, i, threshold, nbmin,
area, nbsta)
if dd_loc:
ind = 0
for j in cluster[i]:
locs[j - 1]['x_mean'] = x[ind]
locs[j - 1]['y_mean'] = y[ind]
locs[j - 1]['z_mean'] = z[ind]
locs[j - 1]['o_time'] = to[ind]
locs[j - 1]['x_sigma'] = 0
locs[j - 1]['y_sigma'] = 0
locs[j - 1]['z_sigma'] = 0
locs[j - 1]['o_err_right'] = 0
locs[j - 1]['o_err_left'] = 0
ind += 1
new_loc_file.write(
"Max = %.2f, %s - %.2f s + %.2f s, x= %.4f pm\
%.4f km, y= %.4f pm %.4f km, z= %.4f pm %.4f km\n" %
(locs[j - 1]['max_trig'], locs[j - 1]['o_time'].isoformat(),
locs[j - 1]['o_err_left'], locs[j - 1]['o_err_right'],
locs[j - 1]['x_mean'], locs[j - 1]['x_sigma'],
locs[j - 1]['y_mean'], locs[j - 1]['y_sigma'],
locs[j - 1]['z_mean'], locs[j - 1]['z_sigma']))
if dd_loc:
new_loc_file.close()
def plot_traces(CLUSTER, delay_file, coeff, locs, stations, datadir,
data_files, threshold):
# Read the file containing the time delays
a = BinaryFile(delay_file)
delay = a.read_binary_file()
t_before = 0.5
t_after = 6.0
rg_x = [362, 370]
rg_y = [7647, 7653]
rg_z = [-4.0, 2.5]
tr = {}
for data_file in data_files:
wf = Waveform()
wf.read_from_file(data_file)
tr[wf.station] = wf.values
dt = wf.delta
tdeb = wf.starttime
list_name = sorted(tr)
for i in range(1, len(CLUSTER) + 1): # cluster index
i = 51
for j in range(len(CLUSTER[i])): # first event index
e1 = CLUSTER[i][j]
for k in range(j + 1, len(CLUSTER[i])): # second event index
e2 = CLUSTER[i][k]
co = 0
fig = plt.figure()
fig.set_facecolor('white')
for l in range(len(list_name)):
name = list_name[l]
if delay[name][e1 - 1][e2 - 1] != 'NaN':
stack_time_1 = locs[e1 - 1]['o_time']
i_start_1, i_end_1 = waveval(stack_time_1, t_before,
t_after, dt, tdeb)
val1 = tr[name][i_start_1 - 1:i_end_1]
stack_time_2 = locs[e2 - 1]['o_time']
i_start_2, i_end_2 = waveval(
stack_time_2 - delay[name][e1 - 1][e2 - 1],
t_before, t_after, dt, tdeb)
val2 = tr[name][i_start_2 - 1:i_end_2]
t = np.linspace(0, t_after + t_before,
(t_after + t_before) / dt + 1)
ax = fig.add_subplot(len(list_name), 1, l + 1)
ax.set_axis_off()
ax.plot(t, val1 / max(val1))
ax.plot(t, val2 / max(val2), 'r')
c = 'k'
if coeff[name][e1 - 1][e2 - 1] >= threshold:
co = co + 1
c = 'r'
ax.text(0.5,
0.5,
"%s, %s, %s" %
(name, str(coeff[name][e1 - 1][e2 - 1]),
str(delay[name][e1 - 1][e2 - 1])),
color=c)
fig.suptitle("Cluster : %s ; Event pair : (%s,%s) ; %d" %
(str(i), str(e1), str(e2), co))
plt.show()
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 plot_traces(CLUSTER, delay_file, coeff, locs, datadir, data_files,
threshold):
"""
Plots the waveforms of all possible event pairs within a cluster.
On the same figure, displays the superimposed waveforms of the event pair for all stations.
Also displays the correlation value.
:param CLUSTER: dictionary containing the event indexes belonging to each cluster
:param delay_file: file name of the file containing the time delays
:param coeff: cross-correlation values of all possible event pairs for all stations
:param locs: list of the whole Waveloc locations (each element of the list is a dictionary)
:param datadir: data directory path
:param data_files: list of paths of data files
:param threshold: correlation coefficient threshold
:type CLUSTER: dictionary
:type delay_file: string
:type coeff: dictionary
:type locs: list
:type datadir: string
:type data_files: list
:type threshold: float
"""
# Read the file containing the time delays
a = BinaryFile(delay_file)
delay = a.read_binary_file()
t_before = 0.5
t_after = 6.0
tr = {}
for data_file in data_files:
wf = Waveform()
wf.read_from_file(data_file)
tr[wf.station] = wf.values
dt = wf.delta
tdeb = wf.starttime
list_name = sorted(tr)
for i in range(1, len(CLUSTER) + 1): # cluster index
for j in range(len(CLUSTER[i])): # first event index
e1 = CLUSTER[i][j]
for k in range(j + 1, len(CLUSTER[i])): # second event index
e2 = CLUSTER[i][k]
co = 0
fig = plt.figure()
fig.set_facecolor('white')
for l in range(len(list_name)):
name = list_name[l]
if delay[name][e1 - 1][e2 - 1] != 'NaN':
stack_time_1 = locs[e1 - 1]['o_time']
i_start_1, i_end_1 = waveval(stack_time_1, t_before,
t_after, dt, tdeb)
val1 = tr[name][i_start_1 - 1:i_end_1]
stack_time_2 = locs[e2 - 1]['o_time']
i_start_2, i_end_2 = \
waveval(stack_time_2-delay[name][e1-1][e2-1],
t_before, t_after, dt, tdeb)
val2 = tr[name][i_start_2 - 1:i_end_2]
t = np.linspace(0, t_after + t_before,
(t_after + t_before) / dt + 1)
ax = fig.add_subplot(len(list_name), 1, l + 1)
ax.set_axis_off()
ax.plot(t, val1 / max(val1), 'k')
ax.plot(t, val2 / max(val2), 'y--')
c = 'k'
if coeff[name][e1 - 1][e2 - 1] >= threshold:
co = co + 1
c = 'r'
ax.text(0.2,
0.5,
"%s, %s, %s" %
(name, str(coeff[name][e1 - 1][e2 - 1]),
str(delay[name][e1 - 1][e2 - 1])),
color=c)
fig.suptitle("Cluster : %s ; Event pair : (%s,%s) ; %d" %
(str(i), str(e1), str(e2), co))
plt.show()
