def test_windows():
array1 = np.zeros(7, dtype=np.int)
array2 = np.zeros(8, dtype=np.int)
array1[3] = 1
array2[4] = 1
assert(my_centered(array1, 5))[2] == 1
assert(my_centered(array2, 5))[2] == 1
assert(my_centered(array1, 9))[4] == 1
assert(my_centered(array2, 9))[4] == 1
tr = Trace()
tr.stats.sac = {}
tr.stats.sac['dist'] = 3.0
tr.data = my_centered(array1, 15) + 1
params = {}
params['hw'] = 1
params['sep_noise'] = 0
params['win_overlap'] = True
params['wtype'] = 'hann'
params['causal_side'] = True
win = get_window(tr.stats, g_speed=1.0, params=params)
assert(len(win) == 3)
assert(pytest.approx(win[0][10]) == 1.0)
snr = snratio(tr, g_speed=1.0, window_params=params)
assert(int(snr) == 1)
def log_en_ratio_adj(corr_o, corr_s, g_speed, window_params):
success = False
window = wn.get_window(corr_o.stats, g_speed, window_params)
win = window[0]
wn.my_centered(corr_s.data, corr_o.stats.npts)
if window[2]:
sig_c = corr_s.data * win
sig_a = corr_s.data * win[::-1]
E_plus = np.trapz(np.power(sig_c, 2)) * corr_s.stats.delta
E_minus = np.trapz(np.power(sig_a, 2)) * corr_s.stats.delta
# to win**2
u_plus = sig_c * win
u_minus = sig_a * win[::-1]
adjt_src = 2. * (u_plus / E_plus - u_minus / E_minus)
success = True
else:
adjt_src = win - win + np.nan
return adjt_src, success
def get_correlation(trace1, trace2, wlen_samples, mlag_samples):
ix = 0
nw = 0
while ix < len(trace1) - wlen_samples:
win1 = trace1[ix:ix + wlen_samples]
win2 = trace2[ix:ix + wlen_samples]
wcorr = fftconvolve(win1[::-1], win2, mode="same")
if "correlation" not in locals():
correlation = np.zeros(len(wcorr))
correlation += wcorr
nw += 1
ix += wlen_samples
corr_len = 2 * mlag_samples + 1
return (my_centered(correlation, corr_len) / nw)
def compute_correlation(input_files,
all_conf,
nsrc,
all_ns,
taper,
insta=False):
"""
Compute noise cross-correlations from two .h5 'wavefield' files.
Noise source distribution and spectrum is given by starting_model.h5
It is assumed that noise sources are delta-correlated in space.
Metainformation: Include the reference station names for both stations
from wavefield files, if possible. Do not include geographic information
from .csv file as this might be error-prone. Just add the geographic
info later if needed.
"""
wf1, wf2 = input_files
ntime, n, n_corr, Fs = all_ns
ntraces = nsrc.src_loc[0].shape[0]
correlation = np.zeros(n_corr)
if insta:
# open database
dbpath = all_conf.config['wavefield_path']
# open
db = instaseis.open_db(dbpath)
# get receiver locations
station1 = wf1[0]
station2 = wf2[0]
lat1 = geograph_to_geocent(float(wf1[2]))
lon1 = float(wf1[3])
rec1 = instaseis.Receiver(latitude=lat1, longitude=lon1)
lat2 = geograph_to_geocent(float(wf2[2]))
lon2 = float(wf2[3])
rec2 = instaseis.Receiver(latitude=lat2, longitude=lon2)
else:
wf1 = WaveField(wf1)
taper1 = cosine_taper((wf1.stats["nt"]))
wf2 = WaveField(wf2)
taper2 = cosine_taper((wf2.stats["nt"]))
station1 = wf1.stats['reference_station']
station2 = wf2.stats['reference_station']
# Make sure all is consistent
if False in (wf1.sourcegrid[1, 0:10] == wf2.sourcegrid[1, 0:10]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[1, -10:] == wf2.sourcegrid[1, -10:]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[0, -10:] == nsrc.src_loc[0, -10:]):
raise ValueError("Wave field and source not consistent.")
# Loop over source locations
print_each_n = max(5, round(max(ntraces // 5, 1), -1))
for i in range(ntraces):
# noise source spectrum at this location
S = nsrc.get_spect(i)
if S.sum() == 0.:
# If amplitude is 0, continue. (Spectrum has 0 phase anyway.)
continue
if insta:
# get source locations
lat_src = geograph_to_geocent(nsrc.src_loc[1, i])
lon_src = nsrc.src_loc[0, i]
fsrc = instaseis.ForceSource(latitude=lat_src,
longitude=lon_src,
f_r=1.e12)
Fs = all_conf.config['wavefield_sampling_rate']
s1 = db.get_seismograms(source=fsrc, receiver=rec1,
dt=1. / Fs)[0].data * taper1
s2 = db.get_seismograms(source=fsrc, receiver=rec2,
dt=1. / Fs)[0].data * taper2
s1 = np.ascontiguousarray(s1)
s2 = np.ascontiguousarray(s2)
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
else:
if not wf1.fdomain:
# read Green's functions
s1 = np.ascontiguousarray(wf1.data[i, :] * taper1)
s2 = np.ascontiguousarray(wf2.data[i, :] * taper2)
# Fourier transform for greater ease of convolution
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
else:
spec1 = np.ascontiguousarray(wf1.data[i, :])
spec2 = np.ascontiguousarray(wf2.data[i, :])
# convolve G1G2
g1g2_tr = np.multiply(np.conjugate(spec1), spec2)
# convolve noise source
c = np.multiply(g1g2_tr, S[:len(g1g2_tr)])
# transform back
correlation += my_centered(np.fft.fftshift(np.fft.irfft(c, n)),
n_corr) * nsrc.surf_area[i]
# occasional info
if i % print_each_n == 0 and all_conf.config['verbose']:
print("Finished {} of {} source locations.".format(i, ntraces))
# end of loop over all source locations #######################################
return (correlation, station1, station2)
def g1g2_kern(wf1str, wf2str, kernel, adjt, src, source_conf, insta=False):
measr_conf = yaml.safe_load(
open(os.path.join(source_conf['source_path'], 'measr_config.yml')))
bandpass = measr_conf['bandpass']
conf = yaml.safe_load(
open(os.path.join(source_conf['project_path'], 'config.yml')))
if bandpass is None:
filtcnt = 1
elif type(bandpass) == list:
if type(bandpass[0]) != list:
filtcnt = 1
else:
filtcnt = len(bandpass)
ntime, n, n_corr, Fs = get_ns(wf1str, source_conf, insta)
# use a one-sided taper: The seismogram probably has a non-zero end,
# being cut off whereever the solver stopped running.
taper = cosine_taper(ntime, p=0.01)
taper[0:ntime // 2] = 1.0
########################################################################
# Prepare filenames and adjoint sources
########################################################################
filenames = []
adjt_srcs = []
for ix_f in range(filtcnt):
filename = kernel + '.{}.npy'.format(ix_f)
filenames.append(filename)
f = Stream()
for a in adjt:
adjtfile = a + '*.{}.sac'.format(ix_f)
adjtfile = glob(adjtfile)
try:
f += read(adjtfile[0])[0]
f[-1].data = my_centered(f[-1].data, n_corr)
except IndexError:
warn('No adjoint source found: {}\n'.format(a))
if len(f) > 0:
adjt_srcs.append(f)
else:
return ()
########################################################################
# Compute the kernels
########################################################################
with NoiseSource(src) as nsrc:
# Uniform spatial weights.
nsrc.distr_basis = np.ones(nsrc.distr_basis.shape)
ntraces = nsrc.src_loc[0].shape[0]
if insta:
# open database
dbpath = conf['wavefield_path']
# open and determine Fs, nt
db = instaseis.open_db(dbpath)
# get receiver locations
lat1 = geograph_to_geocent(float(wf1[2]))
lon1 = float(wf1[3])
rec1 = instaseis.Receiver(latitude=lat1, longitude=lon1)
lat2 = geograph_to_geocent(float(wf2[2]))
lon2 = float(wf2[3])
rec2 = instaseis.Receiver(latitude=lat2, longitude=lon2)
else:
wf1 = WaveField(wf1str)
wf2 = WaveField(wf2str)
# Make sure all is consistent
if False in (wf1.sourcegrid[1, 0:10] == wf2.sourcegrid[1, 0:10]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[1, -10:] == wf2.sourcegrid[1, -10:]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[0, -10:] == nsrc.src_loc[0, -10:]):
raise ValueError("Wave field and source not consistent.")
kern = np.zeros((filtcnt, ntraces, len(adjt)))
# Loop over locations
print_each_n = max(5, round(max(ntraces // 5, 1), -1))
for i in range(ntraces):
# noise source spectrum at this location
# For the kernel, this contains only the basis functions of the
# spectrum without weights; might still be location-dependent,
# for example when constraining sensivity to ocean
S = nsrc.get_spect(i)
if S.sum() == 0.:
# The spectrum has 0 phase so only checking
# absolute value here
continue
if insta:
# get source locations
lat_src = geograph_to_geocent(nsrc.src_loc[1, i])
lon_src = nsrc.src_loc[0, i]
fsrc = instaseis.ForceSource(latitude=lat_src,
longitude=lon_src,
f_r=1.e12)
dt = 1. / source_conf['sampling_rate']
s1 = db.get_seismograms(source=fsrc, receiver=rec1,
dt=dt)[0].data * taper
s1 = np.ascontiguousarray(s1)
s2 = db.get_seismograms(source=fsrc, receiver=rec2,
dt=dt)[0].data * taper
s2 = np.ascontiguousarray(s2)
else:
s1 = np.ascontiguousarray(wf1.data[i, :] * taper)
s2 = np.ascontiguousarray(wf2.data[i, :] * taper)
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
g1g2_tr = np.multiply(np.conjugate(spec1), spec2)
c = np.multiply(g1g2_tr, S)
#######################################################################
# Get Kernel at that location
#######################################################################
corr_temp = my_centered(np.fft.fftshift(np.fft.irfft(c, n)),
n_corr)
#######################################################################
# Apply the 'adjoint source'
#######################################################################
for ix_f in range(filtcnt):
f = adjt_srcs[ix_f]
if f is None:
continue
for j in range(len(f)):
delta = f[j].stats.delta
kern[ix_f, i, j] = np.dot(corr_temp, f[j].data) * delta
if i % print_each_n == 0 and conf['verbose']:
print("Finished {} of {} source locations.".format(i, ntraces))
if not insta:
wf1.file.close()
wf2.file.close()
for ix_f in range(filtcnt):
filename = filenames[ix_f]
if kern[ix_f, :, :].sum() != 0:
np.save(filename, kern[ix_f, :, :])
return ()
def measurement(source_config, mtype, step, ignore_net, ix_bandpass, bandpass,
step_test, taper_perc, **options):
"""
Get measurements on noise correlation data and synthetics.
options: g_speed,window_params (only needed if
mtype is ln_energy_ratio or enery_diff)
"""
verbose = yaml.safe_load(
open(os.path.join(source_config['project_path'],
'config.yml')))['verbose']
step_n = 'iteration_{}'.format(int(step))
step_dir = os.path.join(source_config['source_path'], step_n)
if step_test:
corr_dir = os.path.join(step_dir, 'obs_slt')
else:
corr_dir = os.path.join(source_config['source_path'],
'observed_correlations')
files = [f for f in os.listdir(corr_dir)]
files = [os.path.join(corr_dir, f) for f in files]
synth_dir = os.path.join(step_dir, 'corr')
columns = [
'sta1', 'sta2', 'lat1', 'lon1', 'lat2', 'lon2', 'dist', 'az', 'baz',
'syn', 'syn_a', 'obs', 'obs_a', 'l2_norm', 'snr', 'snr_a', 'nstack'
]
measurements = pd.DataFrame(columns=columns)
options['window_params']['causal_side'] = True # relic for signal to noise
_options_ac = copy.deepcopy(options)
_options_ac['window_params']['causal_side'] = (
not (options['window_params']['causal_side']))
if files == []:
msg = 'No input found!'
raise ValueError(msg)
for i, f in enumerate(files):
# Read data
try:
tr_o = read(f)[0]
except IOError:
if verbose:
print('\nCould not read data: ' + os.path.basename(f))
continue
# Read synthetics
synth_filename = get_synthetics_filename(os.path.basename(f),
synth_dir,
ignore_network=ignore_net)
if synth_filename is None:
continue
try:
tr_s = read(synth_filename)[0]
except IOError:
if verbose:
print('\nCould not read synthetics: ' + synth_filename)
continue
# Assigning stats to synthetics, cutting them to right length
tr_s.stats.sac = tr_o.stats.sac.copy()
tr_s.data = my_centered(tr_s.data, tr_o.stats.npts)
# Get all the necessary information
info = get_station_info(tr_o.stats)
# Collect the adjoint source
adjoint_source = Stream()
adjoint_source += Trace()
adjoint_source[0].stats.sampling_rate = tr_s.stats.sampling_rate
adjoint_source[0].stats.sac = tr_s.stats.sac.copy()
# Filter
if bandpass is not None:
tr_o.taper(taper_perc / 100.)
tr_o.filter('bandpass',
freqmin=bandpass[0],
freqmax=bandpass[1],
corners=bandpass[2],
zerophase=True)
tr_s.taper(taper_perc / 100.)
tr_s.filter('bandpass',
freqmin=bandpass[0],
freqmax=bandpass[1],
corners=bandpass[2],
zerophase=True)
# Weight observed stack by nstack
tr_o.data /= tr_o.stats.sac.user0
# Take the measurement
func = rm.get_measure_func(mtype)
msr_o = func(tr_o, **options)
msr_s = func(tr_s, **options)
# Get the adjoint source
adjt_func = am.get_adj_func(mtype)
adjt, success = adjt_func(tr_o, tr_s, **options)
if not success:
continue
# timeseries-like measurements:
if mtype in ['square_envelope', 'full_waveform', 'windowed_waveform']:
l2_so = 0.5 * np.sum(np.power((msr_s - msr_o), 2))
snr = snratio(tr_o, **options)
snr_a = snratio(tr_o, **_options_ac)
info.extend([
np.nan, np.nan, np.nan, np.nan, l2_so, snr, snr_a,
tr_o.stats.sac.user0
])
adjoint_source[0].data = adjt
# single value measurements:
else:
if mtype == 'energy_diff':
l2_so = 0.5 * (msr_s - msr_o)**2
msr = msr_o[0]
msr_a = msr_o[1]
snr = snratio(tr_o, **options)
snr_a = snratio(tr_o, **_options_ac)
l2 = l2_so.sum()
info.extend([
msr_s[0], msr_s[1], msr, msr_a, l2, snr, snr_a,
tr_o.stats.sac.user0
])
adjoint_source += adjoint_source[0].copy()
for ix_branch in range(2):
adjoint_source[ix_branch].data = adjt[ix_branch]
adjoint_source[ix_branch].data *= (msr_s[ix_branch] -
msr_o[ix_branch])
elif mtype == 'ln_energy_ratio':
l2_so = 0.5 * (msr_s - msr_o)**2
msr = msr_o
snr = snratio(tr_o, **options)
snr_a = snratio(tr_o, **_options_ac)
info.extend([
msr_s, np.nan, msr, np.nan, l2_so, snr, snr_a,
tr_o.stats.sac.user0
])
adjt *= (msr_s - msr_o)
adjoint_source[0].data = adjt
measurements.loc[i] = info
# save the adjoint source
if len(adjoint_source) == 1:
adjt_filename = os.path.basename(synth_filename).rstrip('sac') +\
'{}.sac'.format(ix_bandpass)
adjoint_source[0].write(os.path.join(step_dir, 'adjt',
adjt_filename),
format='SAC')
elif len(adjoint_source) == 2:
for ix_branch, branch in enumerate(['c', 'a']):
adjt_filename = os.path.basename(synth_filename).\
rstrip('sac') + '{}.{}.sac'.format(branch, ix_bandpass)
adjoint_source[ix_branch].write(os.path.join(
step_dir, 'adjt', adjt_filename),
format='SAC')
else:
raise ValueError("Some problem with adjoint sources.")
return measurements
def g1g2_corr(wf1, wf2, corr_file, src, source_conf, insta=False):
"""
Compute noise cross-correlations from two .h5 'wavefield' files.
Noise source distribution and spectrum is given by starting_model.h5
It is assumed that noise sources are delta-correlated in space.
Metainformation: Include the reference station names for both stations
from wavefield files, if possible. Do not include geographic information
from .csv file as this might be error-prone. Just add the geographic
info later if needed.
"""
with open(os.path.join(source_conf['project_path'], 'config.yml')) as fh:
conf = yaml.safe_load(fh)
with NoiseSource(src) as nsrc:
ntime, n, n_corr, Fs = get_ns(wf1, source_conf, insta)
# use a one-sided taper: The seismogram probably has a non-zero end,
# being cut off wherever the solver stopped running.
taper = cosine_taper(ntime, p=0.01)
taper[0:ntime // 2] = 1.0
ntraces = nsrc.src_loc[0].shape[0]
correlation = np.zeros(n_corr)
if insta:
# open database
dbpath = conf['wavefield_path']
# open
db = instaseis.open_db(dbpath)
# get receiver locations
lat1 = geograph_to_geocent(float(wf1[2]))
lon1 = float(wf1[3])
rec1 = instaseis.Receiver(latitude=lat1, longitude=lon1)
lat2 = geograph_to_geocent(float(wf2[2]))
lon2 = float(wf2[3])
rec2 = instaseis.Receiver(latitude=lat2, longitude=lon2)
else:
wf1 = WaveField(wf1)
wf2 = WaveField(wf2)
# Make sure all is consistent
if False in (wf1.sourcegrid[1, 0:10] == wf2.sourcegrid[1, 0:10]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[1, -10:] == wf2.sourcegrid[1, -10:]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[0, -10:] == nsrc.src_loc[0, -10:]):
raise ValueError("Wave field and source not consistent.")
# Loop over source locations
print_each_n = max(5, round(max(ntraces // 5, 1), -1))
for i in range(ntraces):
# noise source spectrum at this location
S = nsrc.get_spect(i)
if S.sum() == 0.:
# If amplitude is 0, continue. (Spectrum has 0 phase anyway.)
continue
if insta:
# get source locations
lat_src = geograph_to_geocent(nsrc.src_loc[1, i])
lon_src = nsrc.src_loc[0, i]
fsrc = instaseis.ForceSource(latitude=lat_src,
longitude=lon_src,
f_r=1.e12)
Fs = conf['wavefield_sampling_rate']
s1 = db.get_seismograms(source=fsrc, receiver=rec1,
dt=1. / Fs)[0].data * taper
s2 = db.get_seismograms(source=fsrc, receiver=rec2,
dt=1. / Fs)[0].data * taper
s1 = np.ascontiguousarray(s1)
s2 = np.ascontiguousarray(s2)
else:
if not wf1.fdomain:
# read Green's functions
s1 = np.ascontiguousarray(wf1.data[i, :] * taper)
s2 = np.ascontiguousarray(wf2.data[i, :] * taper)
# Fourier transform for greater ease of convolution
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
else:
pass
# convolve G1G2
g1g2_tr = np.multiply(np.conjugate(spec1), spec2)
# convolve noise source
c = np.multiply(g1g2_tr, S)
# transform back
correlation += my_centered(np.fft.ifftshift(np.fft.irfft(c, n)),
n_corr) * nsrc.surf_area[i]
# occasional info
if i % print_each_n == 0 and conf['verbose']:
print("Finished {} of {} source locations.".format(i, ntraces))
# end of loop over all source locations #######################################
if not insta:
wf1.file.close()
wf2.file.close()
# save output
trace = Trace()
trace.stats.sampling_rate = Fs
trace.data = correlation
# try to add some meta data
try:
sta1 = wf1.stats['reference_station']
sta2 = wf2.stats['reference_station']
trace.stats.station = sta1.split('.')[1]
trace.stats.network = sta1.split('.')[0]
trace.stats.location = sta1.split('.')[2]
trace.stats.channel = sta1.split('.')[3]
trace.stats.sac = {}
trace.stats.sac['kuser0'] = sta2.split('.')[1]
trace.stats.sac['kuser1'] = sta2.split('.')[0]
trace.stats.sac['kuser2'] = sta2.split('.')[2]
trace.stats.sac['kevnm'] = sta2.split('.')[3]
except (KeyError, IndexError):
pass
trace.write(filename=corr_file, format='SAC')
def compute_kernel(input_files, all_conf, nsrc, all_ns, taper, insta=False):
ntime, n, n_corr, Fs = all_ns
wf1, wf2, adjt = input_files
########################################################################
# Prepare filenames and adjoint sources
########################################################################
adjt_srcs = []
for ix_f in range(all_conf.filtcnt):
f = Stream()
for a in adjt:
adjtfile = a + '*.{}.sac'.format(ix_f)
adjtfile = glob(adjtfile)
try:
f += read(adjtfile[0])[0]
f[-1].data = my_centered(f[-1].data, n_corr)
except IndexError:
if all_conf.config['verbose']:
print('No adjoint source found: {}\n'.format(a))
else:
pass
if len(f) > 0:
adjt_srcs.append(f)
else:
return None
# Uniform spatial weights. (current model is in the adjoint source)
nsrc.distr_basis = np.ones(nsrc.distr_basis.shape)
ntraces = nsrc.src_loc[0].shape[0]
if insta:
# open database
dbpath = all_conf.config['wavefield_path']
# open and determine Fs, nt
db = instaseis.open_db(dbpath)
# get receiver locations
lat1 = geograph_to_geocent(float(wf1[2]))
lon1 = float(wf1[3])
rec1 = instaseis.Receiver(latitude=lat1, longitude=lon1)
lat2 = geograph_to_geocent(float(wf2[2]))
lon2 = float(wf2[3])
rec2 = instaseis.Receiver(latitude=lat2, longitude=lon2)
else:
wf1 = WaveField(wf1)
wf2 = WaveField(wf2)
# Make sure all is consistent
if False in (wf1.sourcegrid[1, 0:10] == wf2.sourcegrid[1, 0:10]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[1, -10:] == wf2.sourcegrid[1, -10:]):
raise ValueError("Wave fields not consistent.")
if False in (wf1.sourcegrid[0, -10:] == nsrc.src_loc[0, -10:]):
raise ValueError("Wave field and source not consistent.")
kern = np.zeros(
(nsrc.spect_basis.shape[0], all_conf.filtcnt, ntraces, len(adjt)))
# Loop over locations
print_each_n = max(5, round(max(ntraces // 5, 1), -1))
for i in range(ntraces):
# noise source spectrum at this location
# For the kernel, this contains only the basis functions of the
# spectrum without weights; might still be location-dependent,
# for example when constraining sensivity to ocean
S = nsrc.get_spect(i)
if S.sum() == 0.:
# The spectrum has 0 phase so only checking
# absolute value here
continue
if insta:
# get source locations
lat_src = geograph_to_geocent(nsrc.src_loc[1, i])
lon_src = nsrc.src_loc[0, i]
fsrc = instaseis.ForceSource(latitude=lat_src,
longitude=lon_src,
f_r=1.e12)
dt = 1. / all_conf.source_config['sampling_rate']
s1 = db.get_seismograms(source=fsrc, receiver=rec1,
dt=dt)[0].data * taper
s1 = np.ascontiguousarray(s1)
s2 = db.get_seismograms(source=fsrc, receiver=rec2,
dt=dt)[0].data * taper
s2 = np.ascontiguousarray(s2)
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
else:
if not wf1.fdomain:
s1 = np.ascontiguousarray(wf1.data[i, :] * taper)
s2 = np.ascontiguousarray(wf2.data[i, :] * taper)
spec1 = np.fft.rfft(s1, n)
spec2 = np.fft.rfft(s2, n)
else:
spec1 = wf1.data[i, :]
spec2 = wf2.data[i, :]
g1g2_tr = np.multiply(np.conjugate(spec1), spec2)
# spectrum
for ix_spec in range(nsrc.spect_basis.shape[0]):
c = np.multiply(g1g2_tr, nsrc.spect_basis[ix_spec, :])
###################################################################
# Get Kernel at that location
###################################################################
corr_temp = my_centered(np.fft.fftshift(np.fft.irfft(c, n)),
n_corr)
###################################################################
# Apply the 'adjoint source'
###################################################################
for ix_f in range(all_conf.filtcnt):
f = adjt_srcs[ix_f]
if f is None:
continue
for j in range(len(f)):
delta = f[j].stats.delta
kern[ix_spec, ix_f, i,
j] = np.dot(corr_temp, f[j].data) * delta
if i % print_each_n == 0 and all_conf.config['verbose']:
print("Finished {} of {} source locations.".format(i, ntraces))
if not insta:
wf1.file.close()
wf2.file.close()
return kern
