mock.py

# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 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,
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# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Any, Literal

import numpy as np

from .dataset import StormCastDataset


class _MockDataset(StormCastDataset):
    """A minimal mock dataset implementation for testing without real data.

    Args:
        num_state_channels: Number of channels in the state data
        num_background_channels: Number of channels in the background data
        image_size: Tuple of (height, width) for the images
        num_samples: Number of samples in the dataset (default: 100)
    """

    def __init__(
        self,
        num_state_channels: int = 3,
        num_background_channels: int = 4,
        num_invariant_channels: int = 2,
        num_scalar_cond_channels: int = 2,
        image_size: tuple[int, int] = (256, 128),
        num_samples: int = 100,
        train: bool = True,
        model_type: Literal[
            "hybrid", "nowcasting", "downscaling", "unconditional"
        ] = "hybrid",
    ):
        self._num_state_channels = num_state_channels
        self._num_background_channels = num_background_channels
        self._num_invariant_channels = num_invariant_channels
        self._num_scalar_cond_channels = num_scalar_cond_channels
        self._image_size = image_size
        self._num_samples = num_samples
        self._model_type = model_type

    def __len__(self) -> int:
        return self._num_samples

    def __getitem__(self, idx: int) -> dict[str, Any]:
        """Return a sample with random data."""
        rng = np.random.default_rng(seed=idx)  # Use idx as seed for reproducibility

        height, width = self._image_size

        # Generate random background data
        background = rng.normal(
            size=(self._num_background_channels, height, width)
        ).astype(np.float32)

        # Generate random state data (input and target)
        state_input = rng.normal(size=(self._num_state_channels, height, width)).astype(
            np.float32
        )
        state_target = rng.normal(
            size=(self._num_state_channels, height, width)
        ).astype(np.float32)

        if self._model_type == "hybrid":
            item = {
                "background": background,
                "state": [state_input, state_target],
            }
        elif self._model_type == "nowcasting":
            item = {"state": [state_input, state_target]}
        elif self._model_type == "downscaling":
            item = {
                "background": background,
                "state": state_target,
            }
        elif self._model_type == "unconditional":
            item = {"state": state_target}

        # Generate scalar conditions
        if self._num_scalar_cond_channels:
            item["scalar_conditions"] = rng.normal(
                size=(self._num_scalar_cond_channels,)
            ).astype(np.float32)

        return item

    def background_channels(self) -> list[str]:
        """Return metadata for background channels."""
        return [f"background_{i}" for i in range(self._num_background_channels)]

    def state_channels(self) -> list[str]:
        """Return metadata for state channels."""
        return [f"state_{i}" for i in range(self._num_state_channels)]

    def scalar_condition_channels(self) -> list[str]:
        """Return metadata for state channels."""
        return [f"scalar_cond_{i}" for i in range(self._num_scalar_cond_channels)]

    def image_shape(self) -> tuple[int, int]:
        """Return the (height, width) of the data."""
        return self._image_size

    def get_invariants(self) -> np.ndarray | None:
        """Return invariants used for training."""
        if self._num_invariant_channels > 0:
            rng = np.random.default_rng(seed=42)
            return rng.normal(
                size=(
                    self._num_invariant_channels,
                    self._image_size[0],
                    self._image_size[1],
                )
            ).astype(np.float32)
        else:
            return None


class MockDataset(_MockDataset):
    def __init__(self, params, train):
        super().__init__(train=train, **params)