# Copyright 2024 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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"""Conversions utilities for fvm objects."""
import dataclasses
import jax
from jax import numpy as jnp
from torax import state
from torax.fvm import cell_variable
[docs]
def cell_variable_tuple_to_vec(
x_tuple: tuple[cell_variable.CellVariable, ...],
) -> jax.Array:
"""Converts a tuple of CellVariables to a flat array.
Args:
x_tuple: A tuple of CellVariables.
Returns:
A flat array of evolving state variables.
"""
x_vec = jnp.concatenate([x.value for x in x_tuple])
return x_vec
[docs]
def vec_to_cell_variable_tuple(
x_vec: jax.Array,
core_profiles: state.CoreProfiles,
evolving_names: tuple[str, ...],
) -> tuple[cell_variable.CellVariable, ...]:
"""Converts a flat array of core profile state vars to CellVariable tuple.
Args:
x_vec: A flat array of evolving core profile state variables. The order of
the variables in the array must match the order of the evolving_names.
core_profiles: CoreProfiles containing all CellVariables with appropriate
boundary conditions.
evolving_names: The names of the evolving cell variables.
Returns:
A tuple of updated CellVariables.
"""
x_split = jnp.split(x_vec, len(evolving_names))
x_out = [
dataclasses.replace(core_profiles[name], value=value)
for name, value in zip(evolving_names, x_split)
]
return tuple(x_out)