def test_spec_accels(params, expected):
m = DBC14(Scenario(**params))
np.testing.assert_allclose(
m.interp_spec_accels(expected['periods']),
# Need to convert from m/sec to g
np.array(expected['spec_accels']) / m.GRAVITY,
rtol=RTOL,
err_msg='Spectral accelerations')
def test_im_values(params, expected, key):
m = DBC14(Scenario(**params))
# PGA needs to be converted from m/sec² to g, and PGV need to be
# converted from m/sec into cm/sec
scale = m.GRAVITY if key == "pga" else 0.01
np.testing.assert_allclose(
getattr(m, key),
expected[key] / scale,
rtol=RTOL,
)
def test_model(event, prediction, indices, attr):
m = HKR13(Scenario(**dict(zip(names, event))))
prediction = np.asarray(prediction)
if 'ln_' in attr:
# Convert from variance to standard deviation
prediction = np.sqrt(prediction[indices])
else:
# Convert from log to natural space.
prediction = np.exp(prediction[indices])
np.testing.assert_allclose(
getattr(m, attr),
prediction,
rtol=RTOL,
)
def model():
"""Instance of the DBC13 model."""
return DBC14(Scenario(dist_jb=10, mag=6, v_s30=600, depth_hyp=10, mechanism="SS"))
def test_scenario():
s = Scenario(mag=6, dist_jb=20)
assert_allclose(s.mag, s["mag"])
assert_allclose(s.dist_jb, s["dist_jb"])
def model():
return C03(Scenario(mag=6.5, dist_rup=20))
def test_scenario():
s = Scenario(mag=6, dist_jb=20)
assert_allclose(s.mag, s['mag'])
assert_allclose(s.dist_jb, s['dist_jb'])
def create_model(params):
s = Scenario(**params)
m = Model(s)
return m
def create_model(params, dist):
params = dict(params)
params[dist] = params.pop("dist")
s = Scenario(**params)
m = ASB14(s)
return m
