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b02763e | 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 | """Read one extracted NumPy RF episode; returned RSS values are float32 dBm."""
import argparse
import json
from pathlib import Path
import numpy as np
def load_rss(directory, frames=slice(None)):
directory = Path(directory)
metadata = json.loads((directory / "metadata.json").read_text())
stored = np.load(directory / metadata["training_file"], mmap_mode="r", allow_pickle=False)
selected = stored[frames]
quantization_file = metadata.get("quantization_file")
if quantization_file:
encoding = json.loads((directory / quantization_file).read_text())
if encoding["schema"] != "uint16_centi_dbm_v1" or stored.dtype != np.dtype("uint16"):
raise ValueError("Unsupported RSS encoding")
values = (selected.astype(np.int32) - encoding["integer_offset"]).astype(np.float32)
values *= np.float32(encoding["step_db"])
values[selected == encoding["invalid_code"]] = np.nan
else:
if stored.dtype != np.dtype("float32"):
raise ValueError("Expected direct float32 RSS")
values = selected
return np.ascontiguousarray(values)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("directory", type=Path)
parser.add_argument("--frame", type=int, default=0)
args = parser.parse_args()
rss = load_rss(args.directory, args.frame)
valid = rss[np.isfinite(rss)]
print(json.dumps(dict(unit="dBm", shape=list(rss.shape), dtype=str(rss.dtype),
valid_voxels=int(valid.size),
min_dbm=float(valid.min()) if valid.size else None,
max_dbm=float(valid.max()) if valid.size else None)))
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