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#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Implementation of perturbation modules."""
import abc
from typing import Any, Callable
from dinosaur import coordinate_systems
from dinosaur import pytree_utils
from dinosaur import spherical_harmonic
from dinosaur import typing
import gin
import jax
import jax.numpy as jnp
from model.legacy import transforms
Pytree = typing.Pytree
PerturbationFn = Callable[..., Pytree]
PerturbationModule = Callable[..., PerturbationFn]
_ALLOWED_PERTURBATION_BASIS = (
# Converts vorticity/divergence to u/v then perturbs.
'uv',
# Perturbs in whatever the state is in (typically vorticity/divergence).
'generic',
)
# We ♥ λ's
# pylint: disable=g-long-lambda
@gin.register
class NoPerturbation:
"""No-op perturbation that introduces no perturbation to `inputs`."""
def __init__(
self,
coords: coordinate_systems.CoordinateSystem,
dt: float,
physics_specs: Any,
aux_features: Any,
):
"""Initializes a random field."""
del coords, dt, physics_specs, aux_features # unused.
def __call__(
self,
inputs: typing.Pytree,
state: typing.Pytree,
randomness: typing.Pytree,
) -> typing.Pytree:
"""Updates the state of a random field."""
del state, randomness # unused.
return inputs
class BasePerturbation(abc.ABC):
"""Base class for perturbations."""
def __init__(
self,
coords: coordinate_systems.CoordinateSystem,
dt: float,
physics_specs: Any,
aux_features: Any,
randomness_transform_module: transforms.TransformModule = (
transforms.IdentityTransform
),
return_modal: bool = True,
perturbation_basis: str = 'generic',
):
"""Initializes module to perturb random fields.
Args:
coords: Model coordinate system.
dt: Time step.
physics_specs:
aux_features:
randomness_transform_module: Module that transforms jax.Array of random
variables before converting to nodal.
return_modal: Whether results should be returned in modal space.
perturbation_basis: Whether to perturb wind in "uv" or "generic" basis.
"""
self.coords = coords
self.randomness_transform_fn = randomness_transform_module(
coords, dt, physics_specs, aux_features
)
self.return_modal = return_modal
self.to_modal = coords.horizontal.to_modal
self.to_nodal = coords.horizontal.to_nodal
self.maybe_to_modal = lambda tr: coordinate_systems.maybe_to_modal(
tr, coords
)
self.maybe_to_nodal = lambda tr: coordinate_systems.maybe_to_nodal(
tr, coords
)
if perturbation_basis not in _ALLOWED_PERTURBATION_BASIS:
raise ValueError(
f'{perturbation_basis=} which was not in '
f'{_ALLOWED_PERTURBATION_BASIS=}'
)
self.perturbation_basis = perturbation_basis
def __call__(
self,
inputs: typing.Pytree,
state: typing.Pytree,
randomness: typing.Pytree,
) -> typing.Pytree:
"""Updates the state of a random field."""
del state # unused.
# TODO(dkochkov) allow pytree randomness in addition to broadcasting option.
if self.perturbation_basis == 'generic':
return self._perturb_in_generic_coordinates(inputs, randomness)
elif self.perturbation_basis == 'uv':
return self._perturb_in_uv_coordinates(inputs, randomness)
@abc.abstractmethod
def _perturb_core(
self,
inputs: typing.Pytree,
randomness: typing.Pytree,
) -> typing.Pytree:
"""Perturbs inputs using randomness."""
def _perturb_in_generic_coordinates(
self,
inputs: typing.Pytree,
randomness: typing.Pytree,
) -> typing.Pytree:
"""Perturb `inputs` in (vorticity, divergence) coordinate system."""
nodal_inputs = self.maybe_to_nodal(inputs)
nodal_randomness = self.maybe_to_nodal(randomness)
nodal_randomness = self.randomness_transform_fn(
pytree_utils.tree_map_over_nonscalars(
# Broadcast randomness so that self.randomness_transform_fn can use
# the shape of x to determine what to do.
lambda x: jnp.broadcast_to(nodal_randomness, x.shape),
nodal_inputs,
scalar_fn=jnp.zeros_like,
)
)
perturbed_nodal_inputs = self._perturb_core(nodal_inputs, nodal_randomness)
if self.return_modal:
return self.to_modal(perturbed_nodal_inputs)
else:
return perturbed_nodal_inputs
def _perturb_in_uv_coordinates(
self,
inputs: typing.Pytree,
randomness: typing.Pytree,
) -> typing.Pytree:
"""Perturb `inputs` in (u, v) coordinate system."""
inputs, from_dict_fn = pytree_utils.as_dict(inputs)
# Remove vorticity/divergence from inputs and replace with u/v.
vordiv = self.maybe_to_modal({
'vorticity': inputs.pop('vorticity'),
'divergence': inputs.pop('divergence'),
})
u_nodal, v_nodal = spherical_harmonic.vor_div_to_uv_nodal(
grid=self.coords.horizontal,
vorticity=vordiv['vorticity'],
divergence=vordiv['divergence'],
clip=True,
)
nodal_inputs = self.maybe_to_nodal(inputs) # Recall we popped vor/div.
nodal_inputs['u'] = u_nodal
nodal_inputs['v'] = v_nodal
# Perturb in u/v space
nodal_randomness = self.maybe_to_nodal(randomness)
nodal_randomness = self.randomness_transform_fn(
pytree_utils.tree_map_over_nonscalars(
# Broadcast randomness so that self.randomness_transform_fn can use
# the shape of x to determine what to do.
lambda x: jnp.broadcast_to(nodal_randomness, x.shape),
nodal_inputs,
scalar_fn=jnp.zeros_like,
)
)
perturbed_nodal_inputs = self._perturb_core(nodal_inputs, nodal_randomness)
# Transform perturbed u/v to vor/div (modal).
vorticity, divergence = spherical_harmonic.uv_nodal_to_vor_div_modal(
grid=self.coords.horizontal,
u_nodal=perturbed_nodal_inputs.pop('u'),
v_nodal=perturbed_nodal_inputs.pop('v'),
clip=True,
)
# Insert vorticity/divergence into perturbed_inputs in the right space.
if self.return_modal:
perturbed_inputs = self.to_modal(perturbed_nodal_inputs)
perturbed_inputs['vorticity'] = vorticity
perturbed_inputs['divergence'] = divergence
else:
perturbed_inputs = perturbed_nodal_inputs.copy()
perturbed_inputs['vorticity'] = self.to_nodal(vorticity)
perturbed_inputs['divergence'] = self.to_nodal(divergence)
return from_dict_fn(perturbed_inputs)
@gin.register
class ProportionalPerturbation(BasePerturbation):
"""Perturbation that scales inputs by 1 + randomness."""
def _perturb_core(
self,
inputs: typing.Pytree,
randomness: typing.Pytree,
) -> typing.Pytree:
"""Multiplies inputs by (1 + randomness)."""
return jax.tree_util.tree_map(lambda x, y: x * (1 + y), inputs, randomness)
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