def test_Q_model_plotting(tmpdir):
"""
Tests the plotting of the Q Model.
"""
reset_matplotlib()
tmpdir = str(tmpdir)
Q_discrete.plot(WEIGHTS, RELAXATION_TIMES, f_min=1.0 / 100.0,
f_max=1.0 / 10.0)
images_are_identical("discrete_Q_model", tmpdir)
def test_Q_model_plotting(tmpdir):
"""
Tests the plotting of the Q Model.
"""
reset_matplotlib()
tmpdir = str(tmpdir)
Q_discrete.plot(WEIGHTS, RELAXATION_TIMES, f_min=1.0 / 100.0,
f_max=1.0 / 10.0)
images_are_identical("discrete_Q_model", tmpdir)
def test_Q_model_plotting(tmpdir):
"""
Tests the plotting of the Q Model.
"""
reset_matplotlib()
tmpdir = str(tmpdir)
weights = [2.50960201, 2.31899515, 0.19681762]
relaxation_times = [1.73160984, 14.41562154, 16.70330157]
Q_discrete.plot(weights, relaxation_times, f_min=1.0 / 100.0,
f_max=1.0 / 10.0)
images_are_identical("discrete_Q_model", tmpdir)
def _get_default_solver_settings(solver, min_period, max_period):
"""
Helper function returning etree representation of a solver's default
settings.
"""
known_solvers = ["ses3d_4_1", "ses3d_2_0", "specfem3d_cartesian"]
if solver.lower() == "ses3d_4_1":
from lasif.tools import Q_discrete
from lasif.utils import generate_ses3d_4_1_template
# Generate the relaxation weights for SES3D.
w_p, tau_p = Q_discrete.calculate_Q_model(
N=3,
# These are suitable for the default frequency range.
f_min=1.0 / max_period,
f_max=1.0 / min_period,
iterations=10000,
initial_temperature=0.1,
cooling_factor=0.9998)
return generate_ses3d_4_1_template(w_p, tau_p)
elif solver.lower() == "ses3d_2_0":
from lasif.utils import generate_ses3d_2_0_template
return generate_ses3d_2_0_template()
elif solver.lower() == "specfem3d_cartesian":
from lasif.utils import generate_specfem3d_cartesian_template
return generate_specfem3d_cartesian_template()
else:
msg = "Solver '%s' not known. Known solvers: %s" % (
solver, ",".join(known_solvers))
raise LASIFException(msg)
def test_weights_and_relaxation_times():
"""
Regression test for the weights and relaxation times.
"""
# Set the seed to get reproducible results.
np.random.seed(12345)
# These are the D_p and tau_p, respectively.
weights, relaxation_times, = Q_discrete.calculate_Q_model(
N=3,
f_min=1.0 / 100.0,
f_max=1.0 / 10.0,
iterations=10000,
initial_temperature=0.1,
cooling_factor=0.9998)
np.testing.assert_array_almost_equal(weights, WEIGHTS)
np.testing.assert_array_almost_equal(relaxation_times, RELAXATION_TIMES)
def test_weights_and_relaxation_times():
"""
Regression test for the weights and relaxation times.
"""
# Set the seed to get reproducible results.
np.random.seed(12345)
# These are the D_p and tau_p, respectively.
weights, relaxation_times, = Q_discrete.calculate_Q_model(
N=3,
f_min=1.0 / 100.0,
f_max=1.0 / 10.0,
iterations=10000,
initial_temperature=0.1,
cooling_factor=0.9998)
np.testing.assert_array_almost_equal(weights, WEIGHTS)
np.testing.assert_array_almost_equal(relaxation_times, RELAXATION_TIMES)
def test_weights_and_relaxation_times():
"""
Regression test for the weights and relaxation times.
"""
# Set the seed to get reproducible results.
random.seed(12345)
# These are the D_p and tau_p, respectively.
weights, relaxation_times, = Q_discrete.calculate_Q_model(
N=3,
f_min=1.0 / 100.0,
f_max=1.0 / 10.0,
iterations=10000,
initial_temperature=0.1,
cooling_factor=0.9998)
np.testing.assert_array_almost_equal(
weights, np.array([2.50960201, 2.31899515, 0.19681762]))
np.testing.assert_array_almost_equal(
relaxation_times, np.array([1.73160984, 14.41562154, 16.70330157]))
def lasif_calculate_constant_q_model(parser, args):
"""
Calculate a constant Q model useable by SES3D.
"""
from lasif.tools import Q_discrete
parser.add_argument("min_period", type=float,
help="minimum period for the constant frequency band")
parser.add_argument("max_period", type=float,
help="maximum period for the constant frequency band")
args = parser.parse_args(args)
weights, relaxation_times, = Q_discrete.calculate_Q_model(
N=3,
f_min=1.0 / args.max_period,
f_max=1.0 / args.min_period,
iterations=10000,
initial_temperature=0.1,
cooling_factor=0.9998)
print "Weights: %s" % ", ".join([str(i) for i in weights])
print "Relaxation Times: %s" % ", ".join([str(i) for i in weights])
def _get_default_solver_settings(solver, min_period, max_period, quiet=False):
"""
Helper function returning etree representation of a solver's default
settings.
:param quiet: Do not print anything if set to `True`.
"""
known_solvers = [
"ses3d_4_1", "ses3d_2_0", "specfem3d_cartesian", "specfem3d_globe_cem"
]
if solver.lower() == "ses3d_4_1":
from lasif.tools import Q_discrete
from lasif.utils import generate_ses3d_4_1_template
# Generate the relaxation weights for SES3D.
w_p, tau_p = Q_discrete.calculate_Q_model(
N=3,
# These are suitable for the default frequency range.
f_min=1.0 / max_period,
f_max=1.0 / min_period,
iterations=10000,
initial_temperature=0.1,
cooling_factor=0.9998,
quiet=quiet)
return generate_ses3d_4_1_template(w_p, tau_p)
elif solver.lower() == "ses3d_2_0":
from lasif.utils import generate_ses3d_2_0_template
return generate_ses3d_2_0_template()
elif solver.lower() == "specfem3d_cartesian":
from lasif.utils import generate_specfem3d_cartesian_template
return generate_specfem3d_cartesian_template()
elif solver.lower() == "specfem3d_globe_cem":
from lasif.utils import generate_specfem3d_globe_cem_template
return generate_specfem3d_globe_cem_template()
else:
msg = "Solver '%s' not known. Known solvers: %s" % (
solver, ",".join(known_solvers))
raise LASIFError(msg)
