Lab043: add hardware diagnostics and HackRF comparison tools
This commit is contained in:
LittleSam129
402
experiments/lab043_hackrf_gain_ladder.py
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402
experiments/lab043_hackrf_gain_ladder.py
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"""Безопасный однотоновый поиск рабочего RX-усиления HackRF для Lab043.
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Разрешён один непрерывный циклический тон Pluto+ и заранее фиксированная
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лестница усилений HackRF. RF amp и antenna bias всегда выключены. Здесь нет
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BPSK, PRBS, пакетов, счётчика или JPEG.
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"""
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from __future__ import annotations
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import argparse
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import json
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import math
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import threading
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import time
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from datetime import datetime, timezone
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from pathlib import Path
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import numpy as np
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from experiments import lab043_hackrf_continuity as continuity
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from experiments import lab043_hackrf_hardware as hackrf
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from experiments import lab043_pluto_to_rtlsdr as lab043
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from experiments import lab043_prbs11_hardware as pluto_hardware
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GAIN_LADDER = ((0, 0), (8, 8), (16, 8), (16, 16), (24, 16), (24, 24))
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SETTLE_SECONDS = 0.25
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MEASUREMENT_SECONDS = 0.75
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FIRST_CAPTURE_SECONDS = SETTLE_SECONDS + MEASUREMENT_SECONDS
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FFT_SAMPLES = 262_144
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SEARCH_LOW_HZ = 35_000.0
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SEARCH_HIGH_HZ = 65_000.0
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NOISE_EXCLUSION_HZ = 2_000.0
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DESIRABLE_TONE_OVER_NOISE_DB = 35.0
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NEAR_LIMIT_COMPONENT = 0.90
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PREFERRED_MAX_COMPONENT = 0.75
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CLIPPED_COMPONENT = 127.0 / 128.0
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NEAR_LIMIT_FRACTION_STOP = 1e-4
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NONLINEAR_HARMONIC_LIMIT_DBC = -30.0
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HARMONIC_DETECTION_MARGIN_DB = 10.0
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HARMONIC_SEARCH_HALF_WIDTH_HZ = 2_000.0
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HARMONIC_NOISE_OUTER_HALF_WIDTH_HZ = 10_000.0
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OUTPUT_DIRECTORY = Path("data/processed/lab043_hackrf_gain_ladder")
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def _db(power: float) -> float:
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return 10.0 * math.log10(max(float(power), 1e-300))
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def alias_frequency_to_nyquist(frequency_hz: float, sample_rate_hz: float) -> float:
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"""Свернуть частоту в полуоткрытую полосу [-Fs/2, Fs/2)."""
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if not np.isfinite(sample_rate_hz) or sample_rate_hz <= 0.0:
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raise ValueError("Частота дискретизации должна быть положительной")
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return float(
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(float(frequency_hz) + sample_rate_hz / 2.0) % sample_rate_hz
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- sample_rate_hz / 2.0
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)
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def _cyclic_frequency_distance(
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frequencies_hz: np.ndarray,
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target_hz: float,
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sample_rate_hz: float,
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) -> np.ndarray:
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return np.abs(
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(frequencies_hz - target_hz + sample_rate_hz / 2.0) % sample_rate_hz
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- sample_rate_hz / 2.0
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)
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def analyze_harmonic(
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powers: np.ndarray,
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frequencies: np.ndarray,
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fundamental_frequency_hz: float,
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fundamental_power: float,
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order: int,
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sample_rate_hz: float,
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) -> dict:
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"""Оценить гармонику отдельно относительно тона и локального фона."""
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if order not in (2, 3):
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raise ValueError("Поддерживаются только вторая и третья гармоники")
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expected_hz = alias_frequency_to_nyquist(
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order * fundamental_frequency_hz,
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sample_rate_hz,
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)
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distances = _cyclic_frequency_distance(frequencies, expected_hz, sample_rate_hz)
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peak_mask = distances <= HARMONIC_SEARCH_HALF_WIDTH_HZ
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noise_mask = (
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(distances > HARMONIC_SEARCH_HALF_WIDTH_HZ)
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& (distances <= HARMONIC_NOISE_OUTER_HALF_WIDTH_HZ)
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)
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if not np.any(peak_mask) or not np.any(noise_mask):
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raise ValueError("Недостаточно спектральных отсчётов около гармоники")
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local_indexes = np.flatnonzero(peak_mask)
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peak_index = int(local_indexes[np.argmax(powers[local_indexes])])
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peak_power = float(powers[peak_index])
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local_noise_power = float(np.median(powers[noise_mask]))
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above_local_noise_db = _db(peak_power) - _db(local_noise_power)
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relative_to_fundamental_db = _db(peak_power) - _db(fundamental_power)
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credible = bool(above_local_noise_db >= HARMONIC_DETECTION_MARGIN_DB)
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nonlinear = bool(
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credible and relative_to_fundamental_db >= NONLINEAR_HARMONIC_LIMIT_DBC
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)
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return {
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"order": order,
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"expected_frequency_hz": expected_hz,
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"peak_frequency_hz": float(frequencies[peak_index]),
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"peak_power_dbfs": _db(peak_power),
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"local_noise_floor_dbfs_per_fft_bin": _db(local_noise_power),
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"above_local_noise_db": float(above_local_noise_db),
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"relative_to_fundamental_db": float(relative_to_fundamental_db),
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"credible": credible,
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"nonlinear": nonlinear,
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}
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def measure_tone_segment(
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samples: np.ndarray,
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sample_rate_hz: float,
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fft_samples: int = FFT_SAMPLES,
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) -> dict:
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"""Измерить тон и перегрузку по неизменяемой спектральной методике."""
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received = np.asarray(samples, dtype=np.complex64)
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if received.ndim != 1 or len(received) < fft_samples:
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raise ValueError("Участок короче одного фиксированного БПФ-окна")
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if not np.all(np.isfinite(received)):
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raise ValueError("Участок содержит нечисловые отсчёты")
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segment_count = len(received) // fft_samples
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window = np.hanning(fft_samples).astype(np.float64)
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coherent_scale = float(np.sum(window))
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powers = np.zeros(fft_samples, dtype=np.float64)
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for index in range(segment_count):
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segment = received[index * fft_samples : (index + 1) * fft_samples]
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spectrum = np.fft.fft(segment * window)
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powers += np.abs(spectrum / coherent_scale) ** 2
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powers /= segment_count
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frequencies = np.fft.fftfreq(fft_samples, d=1.0 / sample_rate_hz)
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search = (frequencies >= SEARCH_LOW_HZ) & (frequencies <= SEARCH_HIGH_HZ)
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candidates = np.flatnonzero(search)
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peak_index = int(candidates[np.argmax(powers[candidates])])
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peak_bin_hz = float(frequencies[peak_index])
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resolution_hz = float(sample_rate_hz / fft_samples)
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left = (peak_index - 1) % fft_samples
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right = (peak_index + 1) % fft_samples
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y0, y1, y2 = np.log(np.maximum(powers[[left, peak_index, right]], 1e-300))
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denominator = y0 - 2.0 * y1 + y2
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bin_offset = 0.0 if abs(denominator) < 1e-18 else 0.5 * (y0 - y2) / denominator
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tone_frequency_hz = peak_bin_hz + float(np.clip(bin_offset, -0.5, 0.5)) * resolution_hz
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noise_mask = search & (np.abs(frequencies - tone_frequency_hz) >= NOISE_EXCLUSION_HZ)
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noise_power = float(np.median(powers[noise_mask]))
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tone_power = float(powers[peak_index])
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tone_over_noise_db = _db(tone_power) - _db(noise_power)
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second = analyze_harmonic(
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powers,
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frequencies,
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tone_frequency_hz,
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tone_power,
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2,
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sample_rate_hz,
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)
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third = analyze_harmonic(
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powers,
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frequencies,
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tone_frequency_hz,
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tone_power,
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3,
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sample_rate_hz,
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)
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component_peak = np.maximum(np.abs(received.real), np.abs(received.imag))
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near_fraction = float(np.mean(component_peak >= NEAR_LIMIT_COMPONENT))
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clipped_fraction = float(np.mean(component_peak >= CLIPPED_COMPONENT))
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max_component = float(np.max(component_peak))
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harmonic_nonlinearity = bool(second["nonlinear"] or third["nonlinear"])
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stop = bool(
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clipped_fraction > 0.0
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or near_fraction >= NEAR_LIMIT_FRACTION_STOP
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or max_component >= NEAR_LIMIT_COMPONENT
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or harmonic_nonlinearity
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)
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return {
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"sample_count": int(len(received)),
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"fft_samples": int(fft_samples),
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"fft_segment_count": int(segment_count),
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"fft_resolution_hz": resolution_hz,
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"tone_frequency_hz": float(tone_frequency_hz),
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"tone_peak_bin_hz": peak_bin_hz,
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"tone_power_dbfs": _db(tone_power),
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"noise_floor_dbfs_per_fft_bin": _db(noise_power),
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"tone_over_noise_db": float(tone_over_noise_db),
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"mean_amplitude": float(np.mean(np.abs(received))),
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"rms_amplitude": float(np.sqrt(np.mean(np.abs(received) ** 2))),
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"maximum_amplitude": float(np.max(np.abs(received))),
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"maximum_component": max_component,
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"near_limit_component_fraction": near_fraction,
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"clipped_component_fraction": clipped_fraction,
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"dc_mean_i": float(np.mean(received.real)),
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"dc_mean_q": float(np.mean(received.imag)),
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"second_harmonic": second,
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"third_harmonic": third,
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"second_harmonic_dbc": second["relative_to_fundamental_db"],
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"third_harmonic_dbc": third["relative_to_fundamental_db"],
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"harmonic_nonlinearity_detected": harmonic_nonlinearity,
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"stop_gain_escalation": stop,
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"stop_reason": (
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"перегрузка/близость к пределу/гармоническая нелинейность"
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if stop
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else ""
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),
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}
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def choose_preferred_gain(rows: list[dict]) -> dict | None:
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"""Выбрать минимальное безопасное усиление без погони за лишними децибелами."""
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safe = [
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row
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for row in rows
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if not row["metrics"]["stop_gain_escalation"]
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and row["metrics"]["maximum_component"] <= PREFERRED_MAX_COMPONENT
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]
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if not safe:
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return None
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desirable = [
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row
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for row in safe
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if row["metrics"]["tone_over_noise_db"] >= DESIRABLE_TONE_OVER_NOISE_DB
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]
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if desirable:
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candidates = desirable
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else:
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best_margin = max(row["metrics"]["tone_over_noise_db"] for row in safe)
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candidates = [
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row
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for row in safe
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if row["metrics"]["tone_over_noise_db"] >= best_margin - 2.0
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]
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return min(
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candidates,
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key=lambda row: (row["lna_gain_db"] + row["vga_gain_db"], row["step_index"]),
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)
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def _capture(receiver: hackrf.HackrfRxDevice, seconds: float) -> hackrf.HackrfCapture:
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count = math.ceil(seconds * hackrf.HACKRF_SAMPLE_RATE_HZ)
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return receiver.capture(count)
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def run_gain_ladder(output_directory: Path = OUTPUT_DIRECTORY) -> dict:
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"""Выполнить один аппаратный сеанс с одним непрерывным тоном."""
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for lna, vga in GAIN_LADDER:
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hackrf.validate_hackrf_rx_gains(lna, vga)
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pluto_preflight = pluto_hardware.preflight_pluto()
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hackrf_preflight = hackrf.preflight_hackrf()
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pluto, tx_parameters = pluto_hardware.configure_pluto()
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actual_tx_rate = float(tx_parameters["actual_sample_rate_hz"])
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waveform, actual_tone_hz = continuity.build_periodic_tone(actual_tx_rate)
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device_waveform = lab043.to_pluto_tx_samples(waveform)
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rows: list[dict] = []
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tx_started = False
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stop_reason = "лестница завершена"
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started = datetime.now(timezone.utc)
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try:
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with hackrf.HackrfRxDevice() as receiver:
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rx_parameters = receiver.configure_rx()
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first_holder: list[hackrf.HackrfCapture] = []
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first_errors: list[str] = []
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def first_receive() -> None:
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try:
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first_holder.append(_capture(receiver, FIRST_CAPTURE_SECONDS))
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except Exception as error: # pragma: no cover - аппаратный путь
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first_errors.append(f"{type(error).__name__}: {error}")
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first_gain = receiver.set_rx_gains(*GAIN_LADDER[0])
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rx_thread = threading.Thread(target=first_receive, daemon=True)
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rx_thread.start()
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deadline = time.perf_counter() + 5.0
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while time.perf_counter() < deadline:
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if receiver.library.hackrf_is_streaming(receiver.device) == 1:
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break
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if first_errors:
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break
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time.sleep(0.01)
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else:
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raise RuntimeError("HackRF RX не подтвердил поток; TX не включён")
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time.sleep(SETTLE_SECONDS)
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pluto.tx_cyclic_buffer = True
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pluto.tx_destroy_buffer()
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pluto.tx(device_waveform)
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tx_started = True
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rx_thread.join(timeout=hackrf.HACKRF_STREAM_TIMEOUT_SECONDS + 5.0)
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if rx_thread.is_alive():
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raise RuntimeError("Первый короткий RX-сеанс не завершился")
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if first_errors:
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raise RuntimeError("; ".join(first_errors))
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if len(first_holder) != 1:
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raise RuntimeError("Первый RX-сеанс не вернул единственный массив")
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first_capture = first_holder[0]
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measurement_count = math.floor(MEASUREMENT_SECONDS * hackrf.HACKRF_SAMPLE_RATE_HZ)
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metrics = measure_tone_segment(first_capture.samples[-measurement_count:], hackrf.HACKRF_SAMPLE_RATE_HZ)
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rows.append({
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"step_index": 0,
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"lna_gain_db": GAIN_LADDER[0][0],
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"vga_gain_db": GAIN_LADDER[0][1],
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"gain_configuration": first_gain,
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"capture_diagnostics": first_capture.diagnostics,
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"metrics": metrics,
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})
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if metrics["stop_gain_escalation"]:
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stop_reason = metrics["stop_reason"]
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else:
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for step_index, (lna_gain, vga_gain) in enumerate(GAIN_LADDER[1:], start=1):
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gain_configuration = receiver.set_rx_gains(lna_gain, vga_gain)
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time.sleep(SETTLE_SECONDS)
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capture = _capture(receiver, MEASUREMENT_SECONDS)
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metrics = measure_tone_segment(capture.samples, hackrf.HACKRF_SAMPLE_RATE_HZ)
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rows.append({
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"step_index": step_index,
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"lna_gain_db": lna_gain,
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"vga_gain_db": vga_gain,
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"gain_configuration": gain_configuration,
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"capture_diagnostics": capture.diagnostics,
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"metrics": metrics,
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})
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if metrics["stop_gain_escalation"]:
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stop_reason = metrics["stop_reason"]
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break
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finally:
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if tx_started:
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pluto.tx_destroy_buffer()
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preferred = choose_preferred_gain(rows)
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preferred_summary = None if preferred is None else {
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"step_index": preferred["step_index"],
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"lna_gain_db": preferred["lna_gain_db"],
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"vga_gain_db": preferred["vga_gain_db"],
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"tone_over_noise_db": preferred["metrics"]["tone_over_noise_db"],
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"desirable_margin_reached": bool(
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preferred["metrics"]["tone_over_noise_db"] >= DESIRABLE_TONE_OVER_NOISE_DB
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),
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}
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result = {
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"experiment": "Lab043 HackRF RX gain ladder with one continuous tone",
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"started_utc": started.isoformat().replace("+00:00", "Z"),
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"physical_scheme": "Pluto+ TX -> SMA -> AT30S 30 dB -> HackRF RX; antennas removed",
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"method": "one continuous cyclic Pluto tone; separate short HackRF RX sessions per gain point",
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"fixed_gain_ladder": [list(point) for point in GAIN_LADDER],
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"fixed_thresholds": {
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"desirable_tone_over_noise_db": DESIRABLE_TONE_OVER_NOISE_DB,
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"near_limit_component": NEAR_LIMIT_COMPONENT,
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"preferred_max_component": PREFERRED_MAX_COMPONENT,
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"clipped_component": CLIPPED_COMPONENT,
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"near_limit_fraction_stop": NEAR_LIMIT_FRACTION_STOP,
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"nonlinear_harmonic_limit_dbc": NONLINEAR_HARMONIC_LIMIT_DBC,
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"harmonic_detection_margin_db": HARMONIC_DETECTION_MARGIN_DB,
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"harmonic_search_half_width_hz": HARMONIC_SEARCH_HALF_WIDTH_HZ,
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"harmonic_noise_outer_half_width_hz": HARMONIC_NOISE_OUTER_HALF_WIDTH_HZ,
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},
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"pluto_preflight": pluto_preflight,
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"hackrf_preflight": hackrf_preflight,
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"tx_parameters": tx_parameters,
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"rx_parameters": rx_parameters,
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"actual_transmitted_tone_offset_hz": actual_tone_hz,
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"rf_amp_enabled": False,
|
||||
"antenna_bias_enabled": False,
|
||||
"completed_step_count": len(rows),
|
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"stop_reason": stop_reason,
|
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"rows": rows,
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||||
"preferred_gain": preferred_summary,
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||||
"digital_experiment_authorized": False,
|
||||
}
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output_directory.mkdir(parents=True, exist_ok=True)
|
||||
stamp = started.strftime("%Y%m%d_%H%M%S")
|
||||
output_path = output_directory / f"lab043_hackrf_gain_ladder_{stamp}.json"
|
||||
output_path.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
|
||||
result["result_path"] = str(output_path)
|
||||
return result
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--output-directory", type=Path, default=OUTPUT_DIRECTORY)
|
||||
arguments = parser.parse_args()
|
||||
result = run_gain_ladder(arguments.output_directory)
|
||||
print(json.dumps(result, ensure_ascii=False, indent=2))
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
Reference in New Issue
Block a user