def test_smc_vs_pt(self):
problem_smc = load_model(smc_res_dir, 'geometry', build=True)
problem_pt = load_model(pt_res_dir, 'geometry', build=True)
print('Loaded models')
step_smc = problem_smc.init_sampler(False)
print('compiled smc')
step_pt = problem_pt.init_sampler(False)
print('compiled pt')
maxpoint = {
'depth':
array([1.]),
'dip':
array([42.]),
'duration':
array([8.]),
'east_shift':
array([-4.]),
'h_any_P_0_Z':
array([
3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.,
3., 3., 3.
]),
'magnitude':
array([6.2]),
'north_shift':
array([-4.]),
'rake':
array([-110.]),
'strike':
array([120.]),
'time':
array([-7.4])
}
point = problem_smc.get_random_point()
point_smc, _ = step_smc.step(point)
point_pt, _ = step_pt.step(point)
print(point_pt, point_smc)
def test_linear_velmod_covariance(self):
print('Warning!: Needs specific project_directory!')
project_dir = '/home/vasyurhm/BEATS/LaquilaJointPonlyUPDATE_wide_cov'
problem = load_model(project_dir, mode='ffi', build=False)
gc = problem.composites['geodetic']
point = problem.get_random_point()
gc.update_weights(point)
fig, axs = plt.subplots(2, 2)
for i, ds in enumerate(gc.datasets):
im1 = axs[i, 1].matshow(ds.covariance.data)
im2 = axs[i, 0].matshow(ds.covariance.pred_v)
print('predv mena', ds.covariance.pred_v.mean())
print('data mena', ds.covariance.data.mean())
plt.colorbar(im1)
plt.show()
def load_problem(dirname, mode):
beat_dir = get_run_directory()
project_dir = os.path.join(beat_dir, 'data/examples', dirname)
return models.load_model(project_dir, mode=mode)
def setUp(self):
self.mode = 'geometry'
self.problem = models.load_model(project_dir, self.mode)
util.setup_logging('test_ffi_stacking', 'info')
# set random seed for reproducible station locations
num.random.seed(10)
nuc_dip = 5.
nuc_strike = 2.
time_shift = -10. # from previous inversion
# general
project_dir = '/home/vasyurhm/BEATS/LaquilaJointPonlyUPDATE_wide_kin3'
store_superdirs = ['/home/vasyurhm/GF/Laquila']
white_noise_perc_max = 0.025 # White noise to disturb the synthetic data, in percent to the maximum amplitude [Hallo et al. 2016 use 0.01]
problem = models.load_model(project_dir, mode='ffi', build=False)
event = problem.config.event
components = ['uparr'] #, 'uperp']
starttime_sampling = 0.5
arrival_taper = heart.ArrivalTaper(
a=-15.,
b=-10.,
c=50.,
d=55.)
sc = problem.composites['seismic']
fault = sc.load_fault_geometry()