kopia lustrzana https://github.com/kgoba/ft8_lib
273 wiersze
12 KiB
Python
273 wiersze
12 KiB
Python
import scipy.io.wavfile as wavfile
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from scipy import signal
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import numpy as np
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import sys
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import ldpc
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FT8_NUM_TONES = 8
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FT8_NUM_SYMBOLS = 79
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FT8_TONE_DEVIATION = 6.25
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FT8_SYMBOL_PERIOD = 0.160
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FT8_SYNC_SYMS = [3, 1, 4, 0, 6, 5, 2]
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FT8_SYNC_POS = [0, 36, 72]
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FT8_DATA_POS = [7, 43]
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FT8_LDPC_PAYLOAD_BITS = 91
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FT8_PAYLOAD_BITS = 77
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MIN_FREQ = 300
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MAX_FREQ = 3000
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def lin_to_db(x):
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return 20*np.log10(x + 1e-12)
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def db_to_lin(x):
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return 10**(x/20)
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def load_wav(path):
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rate, samples = wavfile.read(path)
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if samples.dtype == np.int16:
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samples = np.array(samples / 32768.0)
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return (rate, samples)
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def quantize(H, mag_db_step=0.5, phase_divs=256):
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mag_db = lin_to_db(np.abs(H))
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mag_db = mag_db_step * np.ceil(mag_db / mag_db_step)
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phase = np.angle(H)
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phase = np.ceil(0.5 + phase * phase_divs / (2*np.pi)) / phase_divs * (2*np.pi)
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return db_to_lin(mag_db) * np.exp(1j * phase)
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class Waterfall:
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def __init__(self):
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self.H = None
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self.freq_osr = self.time_osr = None
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pass
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def search_sync_coarse(H, bin_min, bin_max, freq_osr, time_osr, min_score=4.0, max_cand=30):
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Adb = lin_to_db(np.abs(H))
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freq_step = FT8_TONE_DEVIATION / freq_osr
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time_step = FT8_SYMBOL_PERIOD / time_osr
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print(f'Using bins {bin_min}..{bin_max} ({bin_max - bin_min})')
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score_map = dict()
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for freq_sub in range(freq_osr):
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for bin_first in range(bin_min + freq_sub, bin_max - FT8_NUM_TONES * freq_osr, freq_osr):
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for time_sub in range(time_osr):
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for time_start in range(-10 * time_osr + time_sub, 21 * time_osr + time_sub, time_osr):
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# calc sync score at (bin_first, time_start)
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score = []
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for sync_start in FT8_SYNC_POS:
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for sync_pos, sync_tone in enumerate(FT8_SYNC_SYMS):
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pos = time_start + (sync_start + sync_pos) * time_osr
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if pos >= 0 and pos < Adb.shape[1]:
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sym_db = Adb[bin_first + sync_tone * freq_osr, pos]
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if pos - 1 >= 0:
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sym_prev_db = Adb[bin_first + sync_tone * freq_osr, pos - 1]
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score.append(sym_db - sym_prev_db)
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if pos + 1 < H.shape[1]:
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sym_next_db = Adb[bin_first + sync_tone * freq_osr, pos + 1]
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score.append(sym_db - sym_next_db)
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if bin_first + (sync_tone - 1) * freq_osr >= bin_min:
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sym_down_db = Adb[bin_first + (sync_tone - 1) * freq_osr, pos]
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score.append(sym_db - sym_down_db)
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if bin_first + (sync_tone + 1) * freq_osr < bin_max:
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sym_up_db = Adb[bin_first + (sync_tone + 1) * freq_osr, pos]
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score.append(sym_db - sym_up_db)
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score_avg = np.mean(score)
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if score_avg > min_score:
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is_better = True
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# if (bin_first, time_start) in score_map:
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# if score_map[(bin_first, time_start)] >= score_avg:
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# is_better = False
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for delta_bin in [-2, -1, 0, 1, 2]:
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for delta_pos in [-2, -1, 0, 1, 2]:
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key = (bin_first + delta_bin, time_start + delta_pos)
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if key in score_map:
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if score_map[key] <= score_avg:
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del score_map[key]
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else:
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is_better = False
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if is_better:
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score_map[(bin_first, time_start)] = score_avg
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top_keys = sorted(score_map.keys(), key=lambda x: score_map[x], reverse=True)[:max_cand]
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for idx, (bin, pos) in enumerate(sorted(top_keys)):
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print(f'{idx+1}: {freq_step * bin:.2f}\t{time_step * pos:+.02f}\t{score_map[(bin, pos)]:.2f}')
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def downsample_fft(H, bin_f0, fs2=100, freq_osr=1, time_osr=1):
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sym_size2 = int(fs2 * FT8_SYMBOL_PERIOD)
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nfft2 = sym_size2 * freq_osr
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freq_step2 = fs2 / nfft2
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taper_width = 4
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pad_width = ((nfft2 - 2*taper_width - freq_osr*FT8_NUM_TONES) // 2)
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H2 = H[bin_f0 - taper_width - pad_width: bin_f0 + freq_osr*FT8_NUM_TONES + taper_width + pad_width, :]
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W_taper = np.linspace(0, 1, taper_width)
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W_pad = [0] * pad_width
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W = np.concatenate( (W_pad, W_taper, [1]*freq_osr*FT8_NUM_TONES, np.flipud(W_taper), W_pad) )
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H2 = np.multiply(H2, np.expand_dims(W, W.ndim))
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shift = taper_width + pad_width
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H2 = np.roll(H2, -shift, axis=0)
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_, sig2 = signal.istft(H2, window='hann', nperseg=nfft2, noverlap=nfft2 - (sym_size2//time_osr), input_onesided=False)
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f0_new = (taper_width + pad_width - shift) * freq_step2
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return sig2, f0_new
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def search_sync_fine(sig2, fs2, f0_new, pos_start):
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sym_size2 = int(fs2 * FT8_SYMBOL_PERIOD)
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n = np.arange(sym_size2)
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f_tones = np.arange(f0_new, f0_new + FT8_NUM_TONES*FT8_TONE_DEVIATION, FT8_TONE_DEVIATION)
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ctones_conj = np.exp(-1j * 2*np.pi * np.expand_dims(n, n.ndim) * np.expand_dims(f_tones/fs2, 0))
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ctweak_plus_tone = np.exp(-1j * 2*np.pi * n * FT8_TONE_DEVIATION/fs2)
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ctweak_minus_tone = np.exp(1j * 2*np.pi * n * FT8_TONE_DEVIATION/fs2)
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max_power, max_freq_offset, max_pos_offset = None, None, None
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all_powers = []
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win = signal.windows.kaiser(sym_size2, beta=2.0)
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for freq_offset in np.linspace(-2.5, 2.5, 21):
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power_time = []
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ctweak = np.exp(-1j * 2*np.pi * n * freq_offset/fs2)
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for pos_offset in range(-sym_size2//2, sym_size2//2 + 1):
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power = 0
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for sync_start in FT8_SYNC_POS:
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for sync_pos, sync_tone in enumerate(FT8_SYNC_SYMS):
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pos1 = pos_start + pos_offset + sym_size2 * (sync_start + sync_pos)
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if pos1 >= 0 and pos1 + sym_size2 < len(sig2):
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demod = win * sig2[pos1:pos1 + sym_size2] * ctones_conj[:, sync_tone] * ctweak
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mag2_sym = np.abs(np.sum(demod))**2
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# power += mag2_sym
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mag2_minus = np.abs(np.sum(demod * ctweak_minus_tone))**2
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mag2_plus = np.abs(np.sum(demod * ctweak_plus_tone))**2
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power += 2*mag2_sym - mag2_minus - mag2_plus
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# demod_prev = win * sig2[pos1 - sym_size2:pos1] * ctones_conj[:, sync_tone] * ctweak
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# demod_next = win * sig2[pos1 + sym_size2:pos1 + 2*sym_size2] * ctones_conj[:, sync_tone] * ctweak
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# mag2_prev = np.abs(np.sum(demod_prev))**2
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# mag2_next = np.abs(np.sum(demod_next))**2
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# power += 2*mag2_sym - mag2_prev - mag2_next
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power_time.append(power)
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if max_power is None or power > max_power:
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max_power = power
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max_freq_offset = freq_offset
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max_pos_offset = pos_offset
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print(f'{freq_offset:.1f}, {(np.argmax(power_time) - sym_size2//2)/fs2:.3f}, {np.max(power_time)}')
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all_powers.append(power_time)
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return max_freq_offset, max_pos_offset
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def extract_logl_db(A2db):
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# FT8 bits -> channel symbols 0, 1, 3, 2, 5, 6, 4, 7
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A2db_bit0 = np.max(A2db[[5, 6, 4, 7], :], axis=0) - np.max(A2db[[0, 1, 3, 2], :], axis=0) # 4/5/6/7 - 0/1/2/3
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A2db_bit1 = np.max(A2db[[3, 2, 4, 7], :], axis=0) - np.max(A2db[[0, 1, 5, 6], :], axis=0) # 2/3/6/7 - 0/1/4/5
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A2db_bit2 = np.max(A2db[[1, 2, 6, 7], :], axis=0) - np.max(A2db[[0, 3, 5, 4], :], axis=0) # 1/3/5/7 - 0/2/4/6
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A2db_bits = np.stack((A2db_bit0, A2db_bit1, A2db_bit2)).transpose()
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# a = [
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# A2db[7, :] - A2db[0, :],
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# A2db[3, :] - A2db[0, :],
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# A2db[6, :] - A2db[3, :],
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# A2db[6, :] - A2db[2, :],
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# A2db[7, :] - A2db[4, :],
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# A2db[4, :] - A2db[1, :],
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# A2db[5, :] - A2db[1, :],
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# A2db[5, :] - A2db[2, :]
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# ]
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# W = np.array([[ 48., 6., 36., 30., 6., 36., 30., 24.],
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# [ 42., 35., -28., -29., 1., 40., 5., -30.],
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# [ 42., 1., 40., 5., 35., -28., -29., -30.]])/34/6
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# A2db_bits = np.matmul(W, a).transpose()
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bits_logl = np.concatenate((A2db_bits[7:36], A2db_bits[43:72])).flatten() * 0.6
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return bits_logl, A2db_bits
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fs, sig = load_wav(sys.argv[1])
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print(f'Sample rate {fs} Hz')
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freq_osr = 2
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time_osr = 2
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sym_size = int(fs * FT8_SYMBOL_PERIOD)
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nfft = sym_size * freq_osr
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freq_step = fs / nfft
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_, _, H = signal.stft(sig, window='hann', nperseg=nfft, noverlap=nfft - (sym_size//time_osr), boundary=None, padded=None)
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H = quantize(H)
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Adb = lin_to_db(np.abs(H))
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print(f'Max magnitude {Adb.max(axis=(0, 1)):.1f} dB')
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print(f'Waterfall shape {Adb.shape}')
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bin_min = int(MIN_FREQ / freq_step)
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bin_max = int(MAX_FREQ / freq_step) + 1
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search_sync_coarse(H, bin_min, bin_max, freq_osr, time_osr)
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use_downsample = True
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f0 = float(sys.argv[2])
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time_start = float(sys.argv[3])
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bin_f0 = int(0.5 + f0 / freq_step)
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f0_real = bin_f0 * freq_step
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print(f'Frequency {f0:.2f} Hz (bin {bin_f0}), coarse {f0_real:.2f} Hz')
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if use_downsample:
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fs2 = 100
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env_alpha = 0.06
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sig2, f0_new = downsample_fft(H[:, ::time_osr], bin_f0, fs2=fs2, freq_osr=freq_osr, time_osr=1)
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print(f'Downsampled signal to {fs2} Hz sample rate, freq shift {f0_real} Hz -> {f0_new} Hz')
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pos_start = int(0.5 + time_start * fs2)
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max_freq_offset, max_pos_offset = search_sync_fine(sig2, fs2, f0_new, pos_start)
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print(f'Max power at {f0_real:.2f} + {max_freq_offset:.2f} = {f0_real + max_freq_offset:.2f} Hz, {max_pos_offset/fs2:.3f} s')
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env = signal.filtfilt(env_alpha, [1, -(1-env_alpha)], np.abs(sig2))
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# max_freq_offset = f0 - f0_real
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# max_pos_offset = 0
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sym_size2 = int(fs2 * FT8_SYMBOL_PERIOD)
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ctweak = np.exp(-1j * 2*np.pi * np.arange(len(sig2)) * (f0_new + max_freq_offset)/fs2)
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sig3 = (sig2*ctweak)[pos_start + max_pos_offset:pos_start + max_pos_offset + int(FT8_NUM_SYMBOLS*FT8_SYMBOL_PERIOD*fs2)]
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_, _, H2 = signal.stft(sig3, window='boxcar', nperseg=sym_size2, noverlap=0, return_onesided=False, boundary=None, padded=False)
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A2db = lin_to_db(np.abs(H2))
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A2db = A2db[0:FT8_NUM_TONES, :]
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else:
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pos_start = int(0.5 + (time_start + FT8_SYMBOL_PERIOD/2) * fs / sym_size * time_osr)
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print(f'Start time {time_start:.3f} s (pos {pos_start}), coarse {pos_start / time_osr * sym_size / fs - FT8_SYMBOL_PERIOD/2:.3f} s')
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A2db = Adb[bin_f0:bin_f0+freq_osr*FT8_NUM_TONES:freq_osr, pos_start:pos_start+FT8_NUM_SYMBOLS*time_osr:time_osr]
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A2db -= np.max(A2db, axis=0)
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bits_logl, A2db_bits = extract_logl_db(A2db)
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(num_errors, bits) = ldpc.bp_solve(bits_logl, max_iters=30, max_no_improvement=15)
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print(f'LDPC decode: {num_errors} errors')
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if num_errors == 0:
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print(f'Payload bits: {"".join([str(x) for x in bits[:FT8_PAYLOAD_BITS]])}')
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print(f'CRC bits : {"".join([str(x) for x in bits[FT8_PAYLOAD_BITS:FT8_LDPC_PAYLOAD_BITS]])}')
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print(f'Parity bits : {"".join([str(x) for x in bits[FT8_LDPC_PAYLOAD_BITS:]])}')
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import matplotlib.pyplot as plt
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import matplotlib.ticker as plticker
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import matplotlib.colors as pltcolors
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fig, ax = plt.subplots(4)
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plt.colorbar(ax[0].imshow(A2db, cmap='inferno', norm=pltcolors.Normalize(-30, 0, clip=True)), orientation='horizontal', ax=ax[0])
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plt.colorbar(ax[1].imshow(A2db_bits.transpose(), cmap='bwr', norm=pltcolors.Normalize(-10, 10, clip=True)), orientation='horizontal', ax=ax[1])
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# ax[2].imshow(A2db_bits2, cmap='bwr', norm=pltcolors.Normalize(-10, 10, clip=True))
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ax[2].hist(bits_logl, bins=25)
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# ax[3].plot(np.arange(len(sig3))/sym_size2, np.real(sig3))
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# ax[3].plot(np.arange(len(sig3))/sym_size2, np.abs(sig3))
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ax[3].margins(0, 0)
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# loc = plticker.MultipleLocator(base=32.0) # this locator puts ticks at regular intervals
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# ax[1].xaxis.set_major_locator(loc)
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# ax[0].plot(np.array(all_powers).transpose())
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plt.grid()
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plt.show()
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