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 test_nbsta(self):
exp_nbsta = compute_nbsta(self.event, self.coeff, self.threshold)
self.assertEqual(exp_nbsta.all(), self.nbsta.all())
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()
