dissertation/where_fi/cli/__init__.py
2025-01-30 13:58:08 +02:00

66 lines
2.0 KiB
Python

import logging
import multiprocessing as mp
import numpy as np
import numpy.typing as npt
import torch
import typer
from .. import visualise
from ..config import config
from ..processing.aoa import AoA
from ..processing.preprocess import Preprocessor
from . import file, globals
app = typer.Typer(callback=globals.main)
logger = logging.getLogger(__name__)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@app.command()
def antennas() -> None:
"""Utility to help determine the order in which antennas are plugged in
Once the script is running, unplug and replug antennas from left to right, to get
the correct order. Every time an antenna is unplugged and replugged, the script will
print the antenna identifier. When you are done, press Ctrl+C to stop the script and
get the final order.
"""
from ..utils import antenna_order
if not globals.is_live:
raise ValueError("This command only works with live data")
antenna_order.main()
@app.command()
def heatmap() -> None:
preprocessor = Preprocessor()
aoa = AoA()
# Start webapp in background process
manager = mp.Manager()
webapp_queue: "mp.Queue[npt.NDArray[np.float32]]" = manager.Queue(
config.sample_rate
)
webapp = mp.Process(target=visualise.start, args=(webapp_queue,))
webapp.start()
def callback(antenna_data: npt.NDArray[np.complex64]) -> None:
logger.info(f"Got final CSI data with shape {antenna_data.shape}")
processed = preprocessor.preprocess(antenna_data)
logger.info(f"Processed CSI data with shape {processed.shape}")
processed_tensor = torch.tensor(processed, device=device)
aoa.update(processed_tensor)
if not webapp_queue.full():
heatmap = aoa.heatmap()
webapp_queue.put(heatmap)
globals.csi_producer(csi_callback=callback)
logger.info("Finished processing CSI data")
app.add_typer(file.app, name="file", help="Commands for working with CSI files")