c452e44e1a
One of the issues I've been facing was that the range of the eigenvalues was quite large, and I couldn't easily see a natural threshold to set to separate the signal and noise subspaces. This visualisation provides a histogram for this scenario, to assist in choosing the parameter (which is different in different configurations).
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
import logging
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import multiprocessing as mp
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from typing import Any
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import numpy as np
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import numpy.typing as npt
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import torch
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import typer
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from ..config import config
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from ..processing.aoa import AoA
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from ..processing.preprocess import Preprocessor
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from ..visualise import server as visualise
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from . import file, globals
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app = typer.Typer(callback=globals.main)
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logger = logging.getLogger(__name__)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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@app.command()
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def antennas() -> None:
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"""Utility to help determine the order in which antennas are plugged in
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Once the script is running, unplug and replug antennas from left to right, to get
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the correct order. Every time an antenna is unplugged and replugged, the script will
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print the antenna identifier. When you are done, press Ctrl+C to stop the script and
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get the final order.
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"""
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from ..utils import antenna_order
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if not globals.is_live:
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raise ValueError("This command only works with live data")
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antenna_order.main()
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@app.command()
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def heatmap() -> None:
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preprocessor = Preprocessor()
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aoa = AoA()
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# Start webapp in background process
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webapp_queue: "mp.Queue[visualise.VisualiserData]" = mp.Queue(config.sample_rate)
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webapp = mp.Process(target=visualise.start, args=(webapp_queue,))
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webapp.start()
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def visualise_data(data: npt.NDArray[Any], dtype: visualise.DataType) -> None:
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if not webapp_queue.full():
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webapp_queue.put(visualise.VisualiserData(data, dtype))
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def callback(antenna_data: npt.NDArray[np.complex64]) -> None:
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logger.info(f"Got final CSI data with shape {antenna_data.shape}")
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visualise_data(antenna_data, visualise.DataType.RAW_CSI)
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processed = preprocessor.preprocess(antenna_data, visualiser=visualise_data)
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visualise_data(processed, visualise.DataType.PROCESSED_CSI)
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logger.info(f"Processed CSI data with shape {processed.shape}")
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processed_tensor = torch.tensor(processed, device=device)
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aoa.update(processed_tensor)
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aoa.heatmap(visualiser=visualise_data)
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globals.csi_producer(csi_callback=callback)
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logger.info("Finished processing CSI data")
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app.add_typer(file.app, name="file", help="Commands for working with CSI files")
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