# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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
# 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.
from collections import OrderedDict
from collections.abc import Generator, Iterator
import numpy as np
import torch
from earth2studio.models.batch import batch_coords, batch_func
from earth2studio.models.px.utils import PrognosticMixin
from earth2studio.utils import handshake_coords, handshake_dim
from earth2studio.utils.type import CoordSystem
[docs]
class Persistence(torch.nn.Module, PrognosticMixin):
"""Persistence model that generates a forecast by applying the identity operator on
the initial condition and indexing the lead time by 6 hours. Primarily used in
testing.
Parameters
----------
variable : Union[str, List[str]]
The variable or list of variables predicted by the model.
domain_coords : CoordSystem
The coordinates representing the domain for this model to operate on.
"""
def __init__(
self,
variable: str | list[str],
domain_coords: CoordSystem,
history: int = 1, # TODO
dt: np.timedelta64 = np.timedelta64(6, "h"),
):
super().__init__()
if isinstance(variable, str):
variable = [variable]
self._input_coords = OrderedDict(
{
"batch": np.empty(0),
"lead_time": np.array([np.timedelta64(0, "h")]),
"variable": np.array(variable),
}
)
self._output_coords = OrderedDict(
{
"batch": np.empty(0),
"lead_time": np.array([np.timedelta64(6, "h")]),
"variable": np.array(variable),
}
)
for key, value in domain_coords.items():
self._input_coords[key] = value
self._output_coords[key] = value
self._history = history
self._dt = dt
def __str__(
self,
) -> str:
return "persistence"
def input_coords(self) -> CoordSystem:
"""Input coordinate system of the prognostic model
Returns
-------
CoordSystem
Coordinate system dictionary
"""
return self._input_coords.copy()
@batch_coords()
def output_coords(self, input_coords: CoordSystem) -> CoordSystem:
"""Output coordinate system of the prognostic model
Parameters
----------
input_coords : CoordSystem
Input coordinate system to transform into output_coords
Returns
-------
CoordSystem
Coordinate system dictionary
"""
output_coords = self._output_coords.copy()
if input_coords is None:
return output_coords
test_coords = input_coords.copy()
test_coords["lead_time"] = (
test_coords["lead_time"] - input_coords["lead_time"][-1]
)
target_input_coords = self.input_coords()
for i, key in enumerate(target_input_coords):
if key != "batch":
handshake_dim(test_coords, key, i)
handshake_coords(test_coords, target_input_coords, key)
output_coords["batch"] = input_coords["batch"]
output_coords["lead_time"] = (
output_coords["lead_time"] + input_coords["lead_time"]
)
return output_coords
@torch.inference_mode()
def _forward(
self,
x: torch.Tensor,
coords: CoordSystem,
) -> tuple[torch.Tensor, CoordSystem]:
# Model is identity operator
# Update coordinates
output_coords = self.output_coords(coords)
return x, output_coords
[docs]
@batch_func()
def __call__(
self,
x: torch.Tensor,
coords: CoordSystem,
) -> tuple[torch.Tensor, CoordSystem]:
"""Runs prognostic model 1 step.
Parameters
----------
x : torch.Tensor
Input tensor
coords : CoordSystem
Coordinate system, should have dimensions ``[time, variable, *domain_dims]``
Returns
------
x : torch.Tensor
coords : CoordSystem
"""
return self._forward(x, coords)
@batch_func()
def _default_generator(
self, x: torch.Tensor, coords: CoordSystem
) -> Generator[tuple[torch.Tensor, CoordSystem], None, None]:
coords = coords.copy()
self.output_coords(coords)
yield x, coords
while True:
# Front hook
x, coords = self.front_hook(x, coords)
# Forward is identity operator
x, coords = self._forward(x, coords)
# Rear hook
x, coords = self.rear_hook(x, coords)
yield x, coords
[docs]
def create_iterator(
self, x: torch.Tensor, coords: CoordSystem
) -> Iterator[tuple[torch.Tensor, CoordSystem]]:
"""Creates a iterator which can be used to perform time-integration of the
prognostic model. Will return the initial condition first (0th step).
Parameters
----------
x : torch.Tensor
Input tensor
coords : CoordSystem
Input coordinate system
Yields
------
Iterator[tuple[torch.Tensor, CoordSystem]]
Iterator that generates time-steps of the prognostic model container the
output data tensor and coordinate system dictionary.
"""
yield from self._default_generator(x, coords)
