"""Generate sparse synthetic observations following the paper-confirmed data contract."""

import argparse
from pathlib import Path

import numpy as np
import yaml


ROOT = Path(__file__).resolve().parents[1]
DAY_MS = 86_400_000
TARGET_ONLY_SLOTS = 3


def load_config():
    return yaml.safe_load((ROOT / "conf/config.yaml").read_text())


def standardized_signal(rng, count, frames, channels, size, source_phase):
    y, x = np.mgrid[-1:1:complex(size), -1:1:complex(size)].astype(np.float32)
    base = np.sin(3 * np.pi * x) * np.cos(2 * np.pi * y) + 0.4 * x + 0.2 * y
    values = np.empty((count, frames, channels, size, size), np.float32)
    for sample in range(count):
        phase = rng.uniform(0, 2 * np.pi) + source_phase
        for step in range(frames):
            seasonal = np.sin(2 * np.pi * step / max(frames, 1) + phase)
            for channel in range(channels):
                values[sample, step, channel] = base + 0.08 * channel + 0.25 * seasonal
        values[sample] += rng.normal(0, 0.04, values[sample].shape)
    values -= values.mean(axis=(0, 1, 3, 4), keepdims=True)
    values /= values.std(axis=(0, 1, 3, 4), keepdims=True).clip(1e-6)
    return np.clip(values, -6, 6).astype(np.float32)


def quality_mask(rng, count, frames, channels, size, sparse=False):
    probability = 0.08 if not sparse else 0.92
    mask = rng.random((count, frames, 1, size, size)) > probability
    if not sparse:
        mask[:, :, :, :2] = False
    return np.repeat(mask, channels, axis=2).astype(np.float32)


def make_split(path, count, config, seed):
    rng = np.random.default_rng(seed)
    data, size = config["data"], config["data"]["image_size"]
    start = np.datetime64("2020-01-01", "ms").astype(np.int64)
    payload = {"format_version": np.asarray(data["format_version"]), "data_source": np.asarray("synthetic")}

    for source_index, (name, spec) in enumerate(data["input_sources"].items()):
        frames, channels = spec["timesteps"], spec["channels"]
        payload[name] = standardized_signal(rng, count, frames, channels, size, source_index)
        days = np.linspace(0, 364, frames, dtype=np.int64)
        payload[f"timestamps_{name}"] = np.tile(start + days * DAY_MS + source_index, (count, 1))
        available = np.ones((count, frames), np.bool_)
        available[:, -max(1, frames // 10):] = False
        payload[f"frame_available_{name}"] = available
        channel_available = np.ones((count, frames, channels), np.bool_)
        if name == "sentinel1":
            channel_available[..., :4] = False
            for sample in range(count):
                for step in range(frames):
                    pair = (0, 1) if (sample + step) % 2 == 0 else (2, 3)
                    channel_available[sample, step, list(pair)] = True
        payload[f"channel_available_{name}"] = channel_available
        payload[f"pixel_valid_{name}"] = quality_mask(rng, count, frames, channels, size)
        geometry = np.zeros((count, frames, config["model"]["max_geometry_dim"]), np.float32)
        if name == "sentinel1":
            geometry[..., 0] = rng.uniform(-np.pi, np.pi, (count, frames))
            geometry[..., 1] = rng.uniform(1.6, 1.8, (count, frames))
        payload[f"geometry_{name}"] = geometry

    for target_index, (name, spec) in enumerate(data["target_sources"].items()):
        if name in data["input_sources"]:
            continue
        channels = 1 if spec["type"] == "categorical" else spec["channels"]
        values = standardized_signal(rng, count, TARGET_ONLY_SLOTS, channels, size, target_index + 3)
        if spec["type"] == "categorical":
            bins = np.linspace(values.min(), values.max(), spec["channels"] + 1)[1:-1]
            values = np.digitize(values[:, :, 0], bins).astype(np.int64)
        payload[f"target_sequence_{name}"] = values
        payload[f"target_timestamps_{name}"] = np.tile(
            start + np.asarray([30, 182, 330], np.int64) * DAY_MS + target_index, (count, 1)
        )
        mask_channels = 1 if spec["type"] == "categorical" else spec["channels"]
        payload[f"target_pixel_valid_{name}"] = quality_mask(
            rng, count, TARGET_ONLY_SLOTS, mask_channels, size, sparse=name == "gedi"
        )
        geometry = np.zeros((count, TARGET_ONLY_SLOTS, config["model"]["max_geometry_dim"]), np.float32)
        if name == "palsar2":
            geometry[..., 0] = rng.integers(0, 2, (count, TARGET_ONLY_SLOTS))
            geometry[..., 1] = rng.integers(0, 2, (count, TARGET_ONLY_SLOTS))
        payload[f"target_geometry_{name}"] = geometry
        payload[f"target_frame_available_{name}"] = np.ones((count, TARGET_ONLY_SLOTS), np.bool_)

    payload["support_period"] = np.tile(np.array([start, start + 365 * DAY_MS], np.int64), (count, 1))
    payload["valid_period"] = np.tile(np.array([start + 60 * DAY_MS, start + 300 * DAY_MS], np.int64), (count, 1))
    text = rng.normal(size=(count, config["model"]["embedding_dim"])).astype(np.float32)
    payload["text_target"] = text / np.linalg.norm(text, axis=1, keepdims=True)
    np.savez_compressed(path, **payload)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--force", action="store_true")
    args = parser.parse_args()
    config = load_config()
    data_dir = ROOT / config["data"]["root"]
    data_dir.mkdir(parents=True, exist_ok=True)
    for offset, (name, count) in enumerate((("train.npz", config["data"]["train_samples"]),
                                            ("test.npz", config["data"]["test_samples"]))):
        target = data_dir / name
        if args.force or not target.exists():
            make_split(target, count, config, config["seed"] + offset)
        print(f"generated={target.relative_to(ROOT)} samples={count} format={config['data']['format_version']}")


if __name__ == "__main__":
    main()