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Delete unused methods in sim_test_case. #559

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Nov 21, 2024
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Delete unused methods in sim_test_case. #559

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99 changes: 0 additions & 99 deletions torax/tests/test_lib/sim_test_case.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,6 @@
# limitations under the License.

"""TestCase base class for running tests with sim.py."""

import functools
import importlib
import os
from typing import Optional, Sequence
Expand All @@ -24,21 +22,14 @@
import jax.numpy as jnp
import numpy as np
import torax
from torax import geometry
from torax import output
from torax import sim as sim_lib
from torax import simulation_app
from torax.config import build_sim
from torax.config import runtime_params as general_runtime_params
from torax.config import runtime_params_slice
from torax.sources import source_models as source_models_lib
from torax.stepper import nonlinear_theta_method
from torax.stepper import stepper as stepper_lib
from torax.tests import test_lib
from torax.tests.test_lib import paths
from torax.time_step_calculator import array_time_step_calculator
from torax.transport_model import runtime_params as transport_params_lib
from torax.transport_model import transport_model as transport_model_lib

PYTHON_MODULE_PREFIX = '.tests.test_data.'
PYTHON_CONFIG_PACKAGE = 'torax'
Expand Down Expand Up @@ -298,93 +289,3 @@ def _test_torax_sim(
ds=ds,
write_output=write_output,
)


def make_frozen_optimizer_stepper(
transport_model: transport_model_lib.TransportModel,
source_models_builder: source_models_lib.SourceModelsBuilder,
source_models: source_models_lib.SourceModels,
runtime_params: general_runtime_params.GeneralRuntimeParams,
transport_params: transport_params_lib.RuntimeParams,
geo: geometry.Geometry,
) -> stepper_lib.Stepper:
"""Makes an optimizer stepper with frozen coefficients.

Under these conditions, we can test that the optimizer behaves the same as
the linear solver.

Args:
transport_model: Transport model.
source_models_builder: Holds the runtime_params for source_models
source_models: TORAX sources/sinks used to compute profile terms in the
state evolution equations.
runtime_params: General TORAX runtime input parameters.
transport_params: Runtime params for the transport model.
geo: The geometry of the simulation.

Returns:
Stepper: the stepper.
"""
# Get the dynamic runtime params for the start of the simulation.
dynamic_runtime_params_slice = (
runtime_params_slice.DynamicRuntimeParamsSliceProvider(
runtime_params=runtime_params,
transport=transport_params,
sources=source_models_builder.runtime_params,
torax_mesh=geo.torax_mesh,
)(
t=runtime_params.numerics.t_initial,
)
)
callback_builder = functools.partial(
sim_lib.FrozenCoeffsCallback,
dynamic_runtime_params_slice=dynamic_runtime_params_slice,
)
return nonlinear_theta_method.OptimizerThetaMethod(
transport_model,
source_models=source_models,
callback_class=callback_builder, # pytype: disable=wrong-arg-types
)


def make_frozen_newton_raphson_stepper(
transport_model: transport_model_lib.TransportModel,
source_models: source_models_lib.SourceModels,
runtime_params: general_runtime_params.GeneralRuntimeParams,
geo: geometry.Geometry,
) -> stepper_lib.Stepper:
"""Makes a Newton Raphson stepper with frozen coefficients.

Under these conditions, we can test that the nonlinear stepper behaves the
same as
the linear solver.

Args:
transport_model: Transport model.
source_models: TORAX sources/sinks used to compute profile terms in the
state evolution equations.
runtime_params: General TORAX runtime input parameters.
geo: The geometry of the simulation.

Returns:
Stepper: the stepper.
"""
# Get the dynamic runtime params for the start of the simulation.
dynamic_runtime_params_slice = (
runtime_params_slice.DynamicRuntimeParamsSliceProvider(
runtime_params,
torax_mesh=geo.torax_mesh,
)(
t=runtime_params.numerics.t_initial,
)
)
callback_builder = functools.partial(
sim_lib.FrozenCoeffsCallback,
dynamic_runtime_params_slice=dynamic_runtime_params_slice,
)
functools.partial(sim_lib.FrozenCoeffsCallback, runtime_params=runtime_params)
return nonlinear_theta_method.NewtonRaphsonThetaMethod(
transport_model,
source_models=source_models,
callback_class=callback_builder, # pytype: disable=wrong-arg-types
)