File size: 5,849 Bytes
3549cf5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | """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()
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