70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
import logging
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from queue import Queue
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import numpy as np
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import numpy.typing as npt
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from scipy.signal import butter, correlate, sosfilt, sosfilt_zi
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from ..config import config
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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np.seterr(invalid="ignore")
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class Preprocessor:
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def __init__(self) -> None:
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self.prev_entries: Queue[npt.NDArray[np.complex64]] = Queue(maxsize=100)
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self.short_term_avg = np.zeros((1,), dtype=np.complex64)
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self.long_term_avg = np.zeros((1,), dtype=np.complex64)
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self.filter = butter(
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5,
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config.preprocessing.bandpass.bounds,
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fs=config.sample_rate,
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btype="band",
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output="sos",
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)
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def preprocess(self, h: npt.NDArray[np.complex64]) -> npt.NDArray[np.complex64]:
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# CSI data is not available for pilot subcarriers.
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h_hat = np.where(
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np.expand_dims(h[:, 0, 0] == 0, axis=(1, 2)),
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correlate(h, [[[1 / 2]], [[0]], [[1 / 2]]], mode="same"),
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h,
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)
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# Skip every other subcarrier
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# h_hat = h_hat[::2, :, :]
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# logger.info(f"CSI shape: {h_hat.shape}")
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h_hat = np.multiply(h_hat, h_hat.conj() / abs(h_hat.conj()))
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h_hat = np.nan_to_num(h_hat)
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# h_hat = correlate(h_hat, np.ones((3, 1, 1)) / 3)
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h_hat = correlate(h_hat, [[[1 / 4]], [[1 / 2]], [[1 / 4]]], mode="valid")
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# Assume that all csi matrices will have the same shape
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if self.long_term_avg.shape != h_hat.shape:
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self.long_term_avg = np.zeros(h_hat.shape, dtype=np.complex64)
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self.long_term_avg = (
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self.long_term_avg * (1 - config.preprocessing.moving_average_alpha)
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+ h_hat * config.preprocessing.moving_average_alpha
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)
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# Remove long term average, to remove static paths
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h_hat -= self.long_term_avg
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# Apply bandpass filter to remove low and high frequency noise
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if not hasattr(self, "filter_zi"):
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self.filter_zi = (
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np.expand_dims(sosfilt_zi(self.filter), axis=(-1, -2, -3)) * h_hat
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)
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h_hat_filt, self.filter_zi = sosfilt(
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self.filter, [h_hat], zi=self.filter_zi, axis=0
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)
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return h_hat_filt[0]
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