def test_forward_model():
class args(object):
def __init__(self):
self.source_model = os.path.join('test', 'testdata_v1',
'testsource_v1')
self.step = 0
self.steplengthrun = False,
self.ignore_network = True
args = args()
all_config = config_params(args, comm, size, rank)
assert all_config.auto_corr
ns = get_ns(all_config)
assert (ns[0] == 3600)
assert (ns[1] == 7200)
p = define_correlationpairs(all_config.source_config['project_path'],
all_config.auto_corr)
assert len(p) == 3
assert p[0][0].split()[-1] == 'STA1'
input_files = add_input_files(p[1], all_config)[0]
assert os.path.basename(input_files[0]) == 'NET.STA1..MXZ.h5'
nsrc = os.path.join('test', 'testdata_v1', 'testsource_v1', 'iteration_0',
'starting_model.h5')
# use a one-sided taper: The seismogram probably has a non-zero end,
# being cut off wherever the solver stopped running.
taper = cosine_taper(ns[0], p=0.01)
taper[0:ns[0] // 2] = 1.0
correlation, sta1, sta2 = compute_correlation(input_files, all_config,
NoiseSource(nsrc), ns, taper)
corr_saved = np.load(
os.path.join('test', 'testdata_v1', 'testdata',
'NET.STA1..MXZ--NET.STA2..MXZ.npy'))
assert np.allclose(correlation, corr_saved)
grad_dq_1 = np.dot(grad[1, :, 0], d_q_1)
grad_dq = grad_dq_0 + grad_dq_1
dcheck = []
# loop:
for step in steps:
# add perturbation to archived model --> current model
os.system('cp {} temp.h5'.format(source_file))
n = h5py.File('temp.h5', 'r+')
n['model'][:, 0] += 10.**step * d_q_0
n['model'][:, 1] += 10.**step * d_q_1
n.flush()
n.close()
with NoiseSource('temp.h5') as nsrc:
correlation = compute_correlation(input_files, all_config, nsrc,
all_ns, taper)
# evaluate misfit and add to list.
syntest = Trace(data=correlation[0])
syntest.stats.sac = {}
syntest.stats.sac['dist'] = obs.stats.sac['dist']
syntest.write('temp.sac', format='SAC')
syntest = read('temp.sac')[0]
msr_sh = m_func(syntest, **m_a_options)
# plt.plot(correlation[0])
# plt.plot(syn.data, '--')
# plt.title(str(step))
# plt.show()
if mtype in ['ln_energy_ratio']:
jh = 0.5 * (msr_sh - msr_o)**2