def test_catch_exceptions(self):
with pytest.raises(PyfkError) as execinfo:
_ = SeisModel(TestClassSeisModel.test_model_6column[:, :2],
flattening=False,
use_kappa=False)
assert str(execinfo.value
) == 'Must provide at least three columns for the model'
with pytest.raises(PyfkError) as execinfo:
_ = SeisModel(TestClassSeisModel.test_model_6column.tolist(),
flattening=False,
use_kappa=False)
assert str(execinfo.value) == 'Earth Model must be a 2D numpy array.'
with pytest.raises(PyfkError) as execinfo:
_ = SeisModel(TestClassSeisModel.test_model_6column.flatten(),
flattening=False,
use_kappa=False)
assert str(execinfo.value) == 'Earth Model must be a 2D numpy array.'
with pytest.raises(PyfkError) as execinfo:
_ = SeisModel(TestClassSeisModel.test_model_6column,
flattening=False,
use_kappa=False,
r_planet="test")
assert str(
execinfo.value) == 'r_planet should be a float or integer number'
with pytest.raises(PyfkError) as execinfo:
_ = SeisModel(TestClassSeisModel.test_model_6column,
flattening=False,
use_kappa=False,
r_planet=-6)
assert str(execinfo.value) == 'r_planet must be a positive value'
def test_free_surface(self):
test_model_data = np.array([
[10.5, 3.64, 6.301, 2.55, 700, 1300],
])
test_model = SeisModel(test_model_data,
flattening=False,
use_kappa=False)
test_source = SourceModel(sdep=-12, srcType="dc")
with pytest.raises(PyfkError) as execinfo:
_ = Config(model=test_model,
source=test_source,
receiver_distance=[10, 20, 30])
assert str(execinfo.value
) == "The source or receivers are located in the air."
test_model_data = np.array([[5.5, 3.18, 5.501, 2.53, 600, 1100],
[10.5, 3.64, 6.301, 2.55, 700, 1300],
[16.0, 3.87, 6.699, 2.59, 800, 1600],
[90.0, 4.50, 7.799, 2.6, 900, 1800]])
test_model = SeisModel(test_model_data,
flattening=False,
use_kappa=False)
test_source = SourceModel(sdep=-12, srcType="dc")
with pytest.raises(PyfkError) as execinfo:
_ = Config(model=test_model,
source=test_source,
receiver_distance=[10, 20, 30])
assert str(execinfo.value
) == "The source or receivers are located in the air."
def test_remove_topo(self):
to_test = TestClassSeisModel.test_model_6column.copy()
to_test[0, 0] = -to_test[0, 0]
test_model = SeisModel(to_test, flattening=False, use_kappa=False)
test_model.remove_topo()
prefered_output = TestClassSeisModel.test_model_6column.copy()
prefered_output[-1, 0] = 0
prefered_output[0, 0] = 0
assert np.allclose(test_model.model_values, prefered_output)
def test_add_layer(self):
test_model_6column_with_source = np.array(
[[5.5, 3.18, 5.501, 2.53, 600, 1100],
[3, 3.64, 6.301, 2.55, 700, 1300],
[7.5, 3.64, 6.301, 2.55, 700, 1300],
[16.0, 3.87, 6.699, 2.59, 800, 1600],
[0, 4.50, 7.799, 2.6, 900, 1800]])
test_model = SeisModel(TestClassSeisModel.test_model_6column,
flattening=False,
use_kappa=False)
test_model.add_layer(7.5, 1)
assert np.all(
test_model.model_values == test_model_6column_with_source)
def test_prem(self):
model_data = TestFunctionTaup.gen_test_model("prem")
model_prem = SeisModel(model=model_data)
source_prem = SourceModel(sdep=16.5)
# * note, use larger distance will integrate more, the waveform of only calculating 10km and calculating to 100km will be grealy different
config_prem = Config(model=model_prem,
source=source_prem,
npt=512,
dt=0.1,
receiver_distance=np.arange(10, 20, 10))
gf = calculate_gf(config_prem)
# # * calculate sync_prem_gcmt for the event
event = obspy.read_events(
join(dirname(__file__), "../data/sync_prem_gcmt/test_gcmt"))[0]
source_prem.update_source_mechanism(event)
# # * generate a source time function
source_time_function = generate_source_time_function(
4, 0.5, gf[0][0].stats.delta)
sync_result = calculate_sync(gf, config_prem, 30, source_time_function)
# * test if the cc value is large enough
for index, component in enumerate(["z", "r", "t"]):
sac_path = join(dirname(__file__),
f"../data/sync_prem_gcmt/prem.{component}")
sac_wave = obspy.read(sac_path)[0].data
coef = np.corrcoef(
sac_wave,
sync_result[0][index].data,
)[0, 1]
assert coef > 0.9999
def test_earth_models(self):
for earth_model_name in ["prem", "ak135f_no_mud", "1066a"]:
earthmodel = TauPyModel(model=earth_model_name)
for source_depth in [12, 50, 200]:
model_data = self.gen_test_model(earth_model_name)
test_model = SeisModel(model_data,
flattening=True,
use_kappa=False)
test_source = SourceModel(sdep=source_depth, srcType="dc")
receiver_distance = [1, 10, 50]
test_config = Config(model=test_model,
source=test_source,
receiver_distance=receiver_distance,
degrees=True)
t0_list, _, _, _ = taup(
test_config.src_layer, test_config.rcv_layer,
test_config.model.th.astype(np.float64),
test_config.model.vp.astype(np.float64),
test_config.receiver_distance.astype(np.float64))
for index, each_distance in enumerate(receiver_distance):
arrivals = earthmodel.get_travel_times(
source_depth_in_km=source_depth,
distance_in_degree=each_distance,
phase_list=["p", "P"])
assert np.allclose(arrivals[0].time,
t0_list[index],
rtol=0.01)
def test_topo(self):
test_model_data = np.array([
[-5.5, 3.18, 5.501, 2.53, 600, 1100],
[10.5, 3.64, 6.301, 2.55, 700, 1300],
[16.0, 3.87, 6.699, 2.59, 800, 1600],
[90.0, 4.50, 7.799, 2.6, 900, 1800]
])
test_model = SeisModel(
test_model_data, flattening=False, use_kappa=False)
test_source = SourceModel(sdep=12, srcType="dc")
test_config = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30])
newmodel = np.array([
[0., 3.18, 5.501, 2.53, 600, 1100],
[5.5, 3.64, 6.301, 2.55, 700, 1300],
[5, 3.64, 6.301, 2.55, 700, 1300],
[7.0, 3.87, 6.699, 2.59, 800, 1600],
[9.0, 3.87, 6.699, 2.59, 800, 1600],
[0., 4.50, 7.799, 2.6, 900, 1800]
])
assert np.all(test_config.model.model_values == newmodel)
def test_init_noflattening_kappa_6column(self):
to_test = TestClassSeisModel.test_model_6column.copy()
to_test[:, 2] = to_test[:, 2] / to_test[:, 1]
test_model = SeisModel(to_test, flattening=False, use_kappa=True)
prefered_output = TestClassSeisModel.test_model_6column.copy()
prefered_output[-1, 0] = 0
assert np.allclose(test_model.model_values, prefered_output)
def test_init_noflattening_nokappa_6column(self):
test_model = SeisModel(TestClassSeisModel.test_model_6column,
flattening=False,
use_kappa=False)
prefered_output = TestClassSeisModel.test_model_6column.copy()
prefered_output[-1, 0] = 0
assert np.allclose(test_model.model_values, prefered_output)
def test_hk():
# * perl fk.pl -Mhk/15/k -N512/0.1 10 20 30
model_path = join(dirname(__file__), f"../data/hk")
model_data = np.loadtxt(model_path)
model_hk = SeisModel(model=model_data, use_kappa=True)
source_hk = SourceModel(sdep=15)
config_hk = Config(model=model_hk,
source=source_hk,
npt=512,
dt=0.1,
receiver_distance=[10, 20, 30])
result = calculate_gf(config_hk)
# * for all the gf in data/hk_gf, test if the results are close (in FK, it uses float but we are using double)
for irec, each_rec in enumerate([10, 20, 30]):
for icomn in range(9):
hk_gf_data = obspy.read(
join(dirname(__file__),
f"../data/hk_gf/{each_rec}.grn.{icomn}"))[0]
coef = np.corrcoef(
hk_gf_data.data,
result[irec][icomn].data,
)[0, 1]
if np.isnan(coef):
coef = 1.0
assert coef > 0.99
def test_init_noflattening_nokappa_4column_normal(self):
test_model_4column = TestClassSeisModel.test_model_6column[:, :-2]
test_model = SeisModel(
test_model_4column, flattening=False, use_kappa=False)
prefered_output = TestClassSeisModel.test_model_6column.copy()
prefered_output[-1, 0] = 0
prefered_output[:, -1] = 1000.
prefered_output[:, -2] = 500.
assert np.allclose(test_model.model_values, prefered_output)
def test_copy(self):
from copy import copy
test_model = SeisModel(TestClassSeisModel.test_model_6column,
flattening=False,
use_kappa=False)
copied_model = copy(test_model)
assert np.all(copied_model.model_values == test_model.model_values)
copied_model.model_values[:, :] += 1
assert np.all(copied_model.model_values != test_model.model_values)
def test_get_attribute(self):
test_model = SeisModel(TestClassSeisModel.test_model_6column,
flattening=False,
use_kappa=False)
assert np.all(test_model.th[:-1] == TestClassSeisModel.
test_model_6column[:-1, 0]) and test_model.th[-1] == 0
assert np.all(
test_model.vs == TestClassSeisModel.test_model_6column[:, 1])
assert np.all(
test_model.vp == TestClassSeisModel.test_model_6column[:, 2])
assert np.all(
test_model.rh == TestClassSeisModel.test_model_6column[:, 3])
assert np.all(
test_model.qs == TestClassSeisModel.test_model_6column[:, 4])
assert np.all(
test_model.qp == TestClassSeisModel.test_model_6column[:, 5])
test_model.flattening = True
assert test_model.flattening is True
test_model.flattening = False
def test_init_noflattening_nokappa_3column(self):
# have rho in the column
test_model_3column = TestClassSeisModel.test_model_6column[:, :-3]
test_model = SeisModel(
test_model_3column, flattening=False, use_kappa=False)
prefered_output = TestClassSeisModel.test_model_6column.copy()
prefered_output[-1, 0] = 0
prefered_output[:, -1] = 1000.
prefered_output[:, -2] = 500.
prefered_output[:, -3] = 0.77 + 0.32 * prefered_output[:, 2]
assert np.allclose(test_model.model_values, prefered_output)
def test_init_noflattening_nokappa_4column_abnormal(self):
# have rho in the column
test_model_4column = TestClassSeisModel.test_model_6column[:, :-2].copy(
)
test_model_4column[:, 3] = TestClassSeisModel.test_model_6column[:, 4]
test_model = SeisModel(
test_model_4column, flattening=False, use_kappa=False)
prefered_output = TestClassSeisModel.test_model_6column.copy()
prefered_output[-1, 0] = 0
prefered_output[:, 3] = 0.77 + 0.32 * prefered_output[:, 2]
prefered_output[:, 5] = prefered_output[:, 4] * 2
assert np.allclose(test_model.model_values, prefered_output)
def test__flattening(self):
test_model = SeisModel(TestClassSeisModel.test_model_6column,
flattening=True,
use_kappa=False)
test_source = SourceModel(sdep=12, srcType="dc")
test_config = Config(model=test_model,
source=test_source,
receiver_distance=[10, 20, 30],
rdep=22)
assert test_config.source.sdep == R_EARTH * \
np.log(R_EARTH / (R_EARTH - 12))
assert test_config.rdep == R_EARTH * \
np.log(R_EARTH / (R_EARTH - 22))
def test_catch_exceptions(self):
with pytest.raises(PyfkError) as execinfo:
_ = SeisModel(
TestClassSeisModel.test_model_6column[:, :2], flattening=False, use_kappa=False)
assert str(
execinfo.value) == 'Must provide at least three columns for the model'
with pytest.raises(PyfkError) as execinfo:
_ = SeisModel(
TestClassSeisModel.test_model_6column.tolist(),
flattening=False,
use_kappa=False)
assert str(
execinfo.value) == 'Earth Model must be a 2D numpy array.'
with pytest.raises(PyfkError) as execinfo:
_ = SeisModel(
TestClassSeisModel.test_model_6column.flatten(),
flattening=False,
use_kappa=False)
assert str(
execinfo.value) == 'Earth Model must be a 2D numpy array.'
def test_big_array(self):
# model_data = TestFunctionTaup.gen_test_model("prem")
# there is a possibility that we write x=f(x) where x is a memoryview in the code
# this might cause segmentation fault
model_data = np.loadtxt(join(dirname(__file__), f"../data/hk"))
model_hk = SeisModel(model=model_data)
source_hk = SourceModel(sdep=16.5)
config_hk = Config(model=model_hk,
source=source_hk,
npt=512,
dt=0.1,
receiver_distance=np.arange(10, 40, 10))
_ = calculate_gf(config_hk)
def test_catch_warning(self):
test_model = SeisModel(TestClassSeisModel.test_model_6column,
flattening=False,
use_kappa=False)
test_source = SourceModel(sdep=12, srcType="dc")
# dk
with pytest.warns(PyfkWarning) as execinfo:
_ = Config(model=test_model,
source=test_source,
receiver_distance=[10, 20, 30],
dk=0.05)
assert len(execinfo[0].message.args) == 1
assert execinfo[0].message.args[
0] == "dk is recommended to be within (0.1,0.4)"
def test_init_flattening_kappa_6column(self):
to_test = TestClassSeisModel.test_model_6column.copy()
to_test[:, 2] = to_test[:, 2] / to_test[:, 1]
test_model = SeisModel(to_test, flattening=True, use_kappa=True)
r = R_EARTH
fl = np.ones(TestClassSeisModel.test_model_6column.shape[0],
dtype=float)
for irow in range(TestClassSeisModel.test_model_6column.shape[0]):
r = r - TestClassSeisModel.test_model_6column[irow, 0]
fl[irow] = R_EARTH / \
(r + 0.5 * TestClassSeisModel.test_model_6column[irow, 0])
prefered_output = TestClassSeisModel.test_model_6column.copy()
prefered_output[-1, 0] = 0
prefered_output[:, 0] *= fl
prefered_output[:, 1] *= fl
prefered_output[:, 2] *= fl
assert np.allclose(test_model.model_values, prefered_output)
def test_static_source(self):
model_data = TestFunctionTaup.gen_test_model("prem")
model_prem = SeisModel(model=model_data)
source_prem = SourceModel(sdep=16.5, srcType="dc")
config_prem = Config(model=model_prem,
source=source_prem,
npt=1,
dt=1,
receiver_distance=[50])
gf = calculate_gf(config_prem)
ref_gf = [
-0.242E-06, -0.103E-05, 0.000E+00, 0.236E-06, 0.118E-05,
-0.548E-07, -0.942E-07, -0.156E-05, 0.285E-06
]
coef = np.corrcoef(
gf,
ref_gf,
)[0, 1]
assert coef > 0.99999
def test_prem_sf(self):
model_data = TestFunctionTaup.gen_test_model("prem")
model_prem = SeisModel(model=model_data)
source_prem = SourceModel(sdep=16.5, srcType="sf")
config_prem = Config(model=model_prem,
source=source_prem,
npt=512,
dt=1,
receiver_distance=[50])
gf = calculate_gf(config_prem)
for index, comnname in enumerate(range(6)):
gf_data = obspy.read(
join(dirname(__file__),
f"../data/sync_prem_sf/50.grn.{comnname}"))[0]
coef = np.corrcoef(
gf_data.data,
gf[0][index].data,
)[0, 1]
if np.isnan(coef):
coef = 1.
assert coef > 0.99
def test_exceptions(self):
model_data = TestFunctionTaup.gen_test_model("prem")
model_prem = SeisModel(model=model_data)
source_prem = SourceModel(sdep=16.5)
# * note, use larger distance will integrate more, the waveform of only calculating 10km and calculating to 100km will be grealy different
# ! note receiver_distance can not be 0
config_prem = Config(model=model_prem,
source=source_prem,
npt=512,
dt=0.1,
receiver_distance=np.arange(1, 10))
gf = calculate_gf(config_prem)
event = obspy.read_events(
join(dirname(__file__), "../data/sync_prem_gcmt/test_gcmt"))[0]
source_prem.update_source_mechanism(event)
source_time_function = generate_source_time_function(
4, 0.5, gf[0][0].stats.delta)
# * the main tests
with pytest.raises(PyfkError) as execinfo:
_ = calculate_sync(gf, config_prem, [30], source_time_function)
assert str(execinfo.value) == "az must be a number"
with pytest.raises(PyfkError) as execinfo:
_ = calculate_sync(gf, config_prem, 30, None)
assert str(execinfo.value) == "must provide a source time function"
with pytest.raises(PyfkError) as execinfo:
source_time_function_abnormal = generate_source_time_function(
4, 0.5, 1.2)
_ = calculate_sync(None, config_prem, 30,
source_time_function_abnormal)
assert str(execinfo.value) == "check input Green's function"
with pytest.raises(PyfkError) as execinfo:
source_time_function_abnormal = generate_source_time_function(
4, 0.5, 1.2)
_ = calculate_sync(gf, config_prem, 30,
source_time_function_abnormal)
assert str(
execinfo.value
) == "delta for the source time function and the Green's function should be the same"
def test_catch_exceptions(self):
test_model = SeisModel(
TestClassSeisModel.test_model_6column,
flattening=False,
use_kappa=False)
test_source = SourceModel(sdep=12, srcType="dc")
# receiver_distance
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model, source=test_source)
assert str(
execinfo.value) == "Must provide a list of receiver distance"
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model, source=test_source, receiver_distance=np.arange(10))
assert str(
execinfo.value) == "Can't set receiver distance as 0, please consider to use a small value instead"
# taper
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
taper=-0.2)
assert str(
execinfo.value) == "Taper must be with (0,1)"
# npt
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
npt=-4)
assert str(
execinfo.value) == "npt should be positive."
# dt
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
dt=-0.4)
assert str(
execinfo.value) == "dt should be positive."
# dk
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
dk=0.7)
assert str(
execinfo.value) == "dk should be within (0,0.5)"
# smth
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
smth=0)
assert str(
execinfo.value) == "smth should be positive."
# pmin
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
pmin=1.2)
assert str(
execinfo.value) == "pmin should be within [0,1]"
# pmax
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
pmax=1.2)
assert str(
execinfo.value) == "pmax should be within [0,1]"
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
pmax=0.3,
pmin=0.8)
assert str(
execinfo.value) == "pmin should be smaller than pmax"
# kmax
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
kmax=1.2)
assert str(
execinfo.value) == "kmax should be larger or equal to 10"
# updn
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
updn="bbq")
assert str(
execinfo.value) == "the selection of phases should be either 'up', 'down' or 'all'"
# samples_before_first_arrival
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
samples_before_first_arrival=-
12)
assert str(
execinfo.value) == "samples_before_first_arrival should be positive"
# source and receiver
with pytest.raises(PyfkError) as execinfo:
_ = Config(
source=test_source, receiver_distance=[10, 20, 30])
assert str(
execinfo.value) == "Must provide a seisModel"
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=1, source=test_source, receiver_distance=[10, 20, 30])
assert str(
execinfo.value) == "Must provide a seisModel"
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model, receiver_distance=[10, 20, 30])
assert str(
execinfo.value) == "Must provide a source"
with pytest.raises(PyfkError) as execinfo:
_ = Config(
source=1, model=test_model, receiver_distance=[10, 20, 30])
assert str(
execinfo.value) == "Must provide a source"
# source located at real interface
test_source_interface = SourceModel(sdep=16, srcType="dc")
with pytest.raises(PyfkError) as execinfo:
_ = Config(
model=test_model,
source=test_source_interface,
receiver_distance=[
10,
20,
30])
assert str(
execinfo.value) == "The source is located at a real interface."
def test_init(self):
test_model = SeisModel(
TestClassSeisModel.test_model_6column,
flattening=False,
use_kappa=False)
test_source = SourceModel(sdep=12, srcType="dc")
test_config = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30])
newmodel = np.array([
[5.5, 3.18, 5.501, 2.53, 600, 1100],
[6.5, 3.64, 6.301, 2.55, 700, 1300],
[4, 3.64, 6.301, 2.55, 700, 1300],
[16.0, 3.87, 6.699, 2.59, 800, 1600],
[0, 4.50, 7.799, 2.6, 900, 1800]
])
assert np.all(test_config.model.model_values == newmodel)
test_config = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
rdep=16,
degrees=True)
receiver_distance_km = [degrees2kilometers(
10), degrees2kilometers(20), degrees2kilometers(30)]
assert np.allclose(test_config.receiver_distance, receiver_distance_km)
test_config = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
rdep=16)
assert np.all(test_config.model.model_values == newmodel)
test_config = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
rdep=30)
newmodel = np.array([
[5.5, 3.18, 5.501, 2.53, 600, 1100],
[6.5, 3.64, 6.301, 2.55, 700, 1300],
[4, 3.64, 6.301, 2.55, 700, 1300],
[14.0, 3.87, 6.699, 2.59, 800, 1600],
[2.0, 3.87, 6.699, 2.59, 800, 1600],
[0, 4.50, 7.799, 2.6, 900, 1800]
])
assert np.all(test_config.model.model_values == newmodel)
test_config = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
rdep=13)
newmodel = np.array([
[5.5, 3.18, 5.501, 2.53, 600, 1100],
[6.5, 3.64, 6.301, 2.55, 700, 1300],
[1, 3.64, 6.301, 2.55, 700, 1300],
[3, 3.64, 6.301, 2.55, 700, 1300],
[16.0, 3.87, 6.699, 2.59, 800, 1600],
[0, 4.50, 7.799, 2.6, 900, 1800]
])
assert np.all(test_config.model.model_values == newmodel)
test_config = Config(
model=test_model,
source=test_source,
receiver_distance=[
10,
20,
30],
rdep=7)
newmodel = np.array([
[5.5, 3.18, 5.501, 2.53, 600, 1100],
[1.5, 3.64, 6.301, 2.55, 700, 1300],
[5, 3.64, 6.301, 2.55, 700, 1300],
[4, 3.64, 6.301, 2.55, 700, 1300],
[16.0, 3.87, 6.699, 2.59, 800, 1600],
[0, 4.50, 7.799, 2.6, 900, 1800]
])
assert np.all(test_config.model.model_values == newmodel)