673 lines
36 KiB
Text
673 lines
36 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 92,
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"metadata": {},
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"outputs": [],
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"source": [
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"from matplotlib import pyplot as plt\n",
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"import numpy as np\n",
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"from scipy import signal as sig\n",
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"import struct\n",
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"import random\n",
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"import ipywidgets\n",
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"import itertools\n",
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"from multiprocessing import Pool\n",
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"from collections import defaultdict\n",
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"\n",
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"import colorednoise\n",
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"\n",
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"np.set_printoptions(linewidth=240)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib widget"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"sampling_rate = 10 # sp/s"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {},
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"outputs": [],
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"source": [
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"# From https://github.com/mubeta06/python/blob/master/signal_processing/sp/gold.py\n",
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"preferred_pairs = {5:[[2],[1,2,3]], 6:[[5],[1,4,5]], 7:[[4],[4,5,6]],\n",
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" 8:[[1,2,3,6,7],[1,2,7]], 9:[[5],[3,5,6]], \n",
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" 10:[[2,5,9],[3,4,6,8,9]], 11:[[9],[3,6,9]]}\n",
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"\n",
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"def gen_gold(seq1, seq2):\n",
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" gold = [seq1, seq2]\n",
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" for shift in range(len(seq1)):\n",
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" gold.append(seq1 ^ np.roll(seq2, -shift))\n",
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" return gold\n",
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"\n",
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"def gold(n):\n",
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" n = int(n)\n",
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" if not n in preferred_pairs:\n",
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" raise KeyError('preferred pairs for %s bits unknown' % str(n))\n",
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" t0, t1 = preferred_pairs[n]\n",
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" (seq0, _st0), (seq1, _st1) = sig.max_len_seq(n, taps=t0), sig.max_len_seq(n, taps=t1)\n",
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" return gen_gold(seq0, seq1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def modulate(data, nbits=5):\n",
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" # 0, 1 -> -1, 1\n",
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" mask = np.array(gold(nbits))*2 - 1\n",
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" \n",
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" sel = mask[data>>1]\n",
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" data_lsb_centered = ((data&1)*2 - 1)\n",
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"\n",
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" return (np.multiply(sel, np.tile(data_lsb_centered, (2**nbits-1, 1)).T).flatten() + 1) // 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def correlate(sequence, nbits=5, decimation=1, mask_filter=lambda x: x):\n",
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" mask = np.tile(np.array(gold(nbits))[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((2**nbits + 1, (2**nbits-1) * decimation))\n",
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"\n",
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" sequence -= np.mean(sequence)\n",
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" \n",
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" return np.array([np.correlate(sequence, row, mode='full') for row in mask])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"mean: 49.98625\n"
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]
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}
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],
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"source": [
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"with open('/mnt/c/Users/jaseg/shared/raw_freq.bin', 'rb') as f:\n",
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" mains_noise = np.copy(np.frombuffer(f.read(), dtype='float32'))\n",
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" print('mean:', np.mean(mains_noise))\n",
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" mains_noise -= np.mean(mains_noise)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 72,
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"metadata": {},
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"outputs": [],
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"source": [
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"def generate_test_signal(duration, nbits=6, signal_amplitude=2.0e-3, decimation=10, seed=0):\n",
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" test_data = np.random.RandomState(seed=seed).randint(0, 2 * (2**nbits), duration)\n",
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" \n",
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" signal = np.repeat(modulate(test_data, nbits) * 2.0 - 1, decimation) * signal_amplitude\n",
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" noise = np.resize(mains_noise, len(signal))\n",
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" \n",
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" return test_data, signal + noise"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"nonlinear_distance = lambda x: 100**(2*np.abs(0.5-x%1)) / (np.abs(x)+3)**2\n",
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"\n",
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"def plot_distance_func():\n",
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" fig, ax = plt.subplots()\n",
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" x = np.linspace(-1.5, 5.5, 10000)\n",
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" ax.plot(x, nonlinear_distance(x))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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"noprint = lambda *args, **kwargs: None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 100,
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"metadata": {},
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"outputs": [],
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"source": [
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"def run_ser_test(sample_duration=128, nbits=6, signal_amplitude=2.0e-3, decimation=10, threshold_factor=4.0, power_avg_width=2.5, max_lookahead=6.5, seed=0, ax=None, print=print, ser_maxshift=3):\n",
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"\n",
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" test_data, signal = generate_test_signal(sample_duration, nbits, signal_amplitude, decimation, seed)\n",
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" cor_an = correlate(signal, nbits=nbits, decimation=decimation)\n",
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"\n",
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" power_avg_width = int(power_avg_width * (2**nbits - 1) * decimation)\n",
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"\n",
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" bit_period = (2**nbits) * decimation\n",
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" peak_group_threshold = 0.1 * bit_period\n",
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" \n",
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" cwt_res = np.array([ sig.cwt(row, sig.ricker, [0.73 * decimation]).flatten() for row in cor_an ])\n",
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" if ax:\n",
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" ax.grid()\n",
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" ax.plot(cwt_res.T)\n",
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" \n",
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" th = np.array([ np.convolve(np.abs(row), np.ones((power_avg_width,))/power_avg_width, mode='same') for row in cwt_res ])\n",
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"\n",
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" def compare_th(elem):\n",
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" idx, (th, val) = elem\n",
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" #print('compare_th:', th.shape, val.shape)\n",
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" return np.any(np.abs(val) > th*threshold_factor)\n",
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"\n",
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" peaks = [ list(group) for val, group in itertools.groupby(enumerate(zip(th.T, cwt_res.T)), compare_th) if val ]\n",
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" peak_group = []\n",
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" for group in peaks:\n",
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" pos = np.mean([idx for idx, _val in group])\n",
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" pol = np.mean([max(val.min(), val.max(), key=abs) for _idx, (_th, val) in group])\n",
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" pol_idx = np.argmax(np.bincount([ np.argmax(np.abs(val)) for _idx, (_th, val) in group ]))\n",
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" #print(f'group', pos, pol, pol_idx)\n",
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" #for pol, (_idx, (_th, val)) in zip([max(val.min(), val.max(), key=abs) for _idx, (_th, val) in group], group):\n",
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" # print(' ', pol, val)\n",
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" if ax:\n",
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" ax.axvline(pos, color='cyan', alpha=0.3)\n",
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"\n",
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" if not peak_group or pos - peak_group[-1][1] > peak_group_threshold:\n",
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" if peak_group:\n",
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" peak_pos = peak_group[-1][3]\n",
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" if ax:\n",
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" ax.axvline(peak_pos, color='red', alpha=0.6)\n",
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" #ax3.text(peak_pos-20, 2.0, f'{0 if pol < 0 else 1}', horizontalalignment='right', verticalalignment='center', color='black')\n",
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"\n",
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" peak_group.append((pos, pos, pol, pos, pol_idx))\n",
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" #ax3.axvline(pos, color='cyan', alpha=0.5)\n",
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"\n",
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" else:\n",
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" group_start, last_pos, last_pol, peak_pos, last_pol_idx = peak_group[-1]\n",
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"\n",
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" if abs(pol) > abs(last_pol):\n",
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" #ax3.axvline(pos, color='magenta', alpha=0.5)\n",
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" peak_group[-1] = (group_start, pos, pol, pos, pol_idx)\n",
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" else:\n",
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" #ax3.axvline(pos, color='blue', alpha=0.5)\n",
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" peak_group[-1] = (group_start, pos, last_pol, peak_pos, last_pol_idx)\n",
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"\n",
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" avg_peak = np.mean(np.abs(np.array([last_pol for _1, _2, last_pol, _3, _4 in peak_group])))\n",
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" print('avg_peak', avg_peak)\n",
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"\n",
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" noprint = lambda *args, **kwargs: None\n",
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" def mle_decode(peak_groups, print=print):\n",
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" peak_groups = [ (pos, pol, idx) for _1, _2, pol, pos, idx in peak_groups ]\n",
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" candidates = [ (0, [(pos, pol, idx)]) for pos, pol, idx in peak_groups if pos < bit_period*2.5 ]\n",
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"\n",
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" while candidates:\n",
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" chain_candidates = []\n",
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" for chain_score, chain in candidates:\n",
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" pos, ampl, _idx = chain[-1]\n",
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" score_fun = lambda pos, npos, npol: abs(npol)/avg_peak + nonlinear_distance((npos-pos)/bit_period)\n",
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" next_candidates = sorted([ (score_fun(pos, npos, npol), npos, npol, nidx) for npos, npol, nidx in peak_groups if pos < npos < pos + bit_period*max_lookahead ], reverse=True)\n",
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"\n",
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" print(f' candidates for {pos}, {ampl}:')\n",
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" for score, npos, npol, nidx in next_candidates:\n",
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" print(f' {score:.4f} {npos:.2f} {npol:.2f} {nidx:.2f}')\n",
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"\n",
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" nch, cor_len = cor_an.shape\n",
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" if cor_len - pos < 1.5*bit_period or not next_candidates:\n",
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" score = sum(score_fun(opos, npos, npol) for (opos, _opol, _oidx), (npos, npol, _nidx) in zip(chain[:-1], chain[1:])) / len(chain)\n",
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" yield score, chain\n",
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"\n",
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" else:\n",
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" print('extending')\n",
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" for score, npos, npol, nidx in next_candidates[:3]:\n",
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" if score > 0.5:\n",
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" new_chain_score = chain_score * 0.9 + score * 0.1\n",
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" chain_candidates.append((new_chain_score, chain + [(npos, npol, nidx)]))\n",
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" print('chain candidates:')\n",
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" for score, chain in sorted(chain_candidates, reverse=True):\n",
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" print(' ', [(score, [(f'{pos:.2f}', f'{pol:.2f}') for pos, pol, _idx in chain])])\n",
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" candidates = [ (chain_score, chain) for chain_score, chain in sorted(chain_candidates, reverse=True)[:10] ]\n",
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"\n",
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" res = sorted(mle_decode(peak_group, print=noprint), reverse=True)\n",
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" #for i, (score, chain) in enumerate(res):\n",
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" # print(f'Chain {i}@{score:.4f}: {chain}')\n",
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" (_score, chain), *_ = res\n",
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"\n",
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" def viz(chain):\n",
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" last_pos = None\n",
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" for pos, pol, nidx in chain:\n",
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" if last_pos:\n",
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" delta = int(round((pos - last_pos) / bit_period))\n",
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" if delta > 1:\n",
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" print(f'skipped {delta} symbols at {pos}')\n",
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" for i in range(delta-1):\n",
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" yield None\n",
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" decoded = nidx*2 + (0 if pol < 0 else 1)\n",
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" yield decoded\n",
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" if ax:\n",
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" ax.axvline(pos, color='blue', alpha=0.5)\n",
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" ax.text(pos-20, 0.0, f'{decoded}', horizontalalignment='right', verticalalignment='center', color='black')\n",
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"\n",
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" last_pos = pos\n",
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"\n",
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" decoded = list(viz(chain))\n",
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" print('decoding [ref|dec]:')\n",
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" failures = defaultdict(lambda: 0)\n",
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" for shift in range(-ser_maxshift, ser_maxshift):\n",
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" print(f'=== shift = {shift} ===')\n",
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" a = test_data if shift > 0 else test_data[-shift:]\n",
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" b = decoded if shift < 0 else decoded[shift:]\n",
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" for i, (ref, found) in enumerate(itertools.zip_longest(a, b)):\n",
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" print(f'{ref or -1:>3d}|{found or -1:>3d} {\"✔\" if ref==found else \"✘\" if found else \" \"}', end=' ')\n",
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" if ref != found:\n",
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" failures[shift] += 1\n",
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" if i%8 == 7:\n",
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" print()\n",
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" if i%8 != 7:\n",
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" print()\n",
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" ser = min(failures.values())/len(test_data)\n",
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" print(f'Symbol error rate e={ser}')\n",
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" br = sampling_rate / decimation / (2**nbits) * nbits * (1 - ser) * 3600\n",
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" print(f'maximum bitrate r={br} b/h')\n",
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" return ser, br"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9b7546a233fb4b6cb6e8f809250ba768",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(63,) (63,)\n",
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"(63,) (63,)\n",
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"avg_peak 1.6845488102985742\n",
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"skipped 2 symbols at 10079.0\n",
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"skipped 2 symbols at 30870.0\n",
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"skipped 2 symbols at 42209.5\n",
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"decoding [ref|dec]:\n",
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" 44| 44 ✔ 47| 47 ✔ 117|117 ✔ 64| 64 ✔ 67| 67 ✔ 123|123 ✔ 67| 67 ✔ 103|103 ✔ \n",
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" 9| 9 ✔ 83| 83 ✔ 21| 21 ✔ 114|114 ✔ 36| 36 ✔ 87| 87 ✔ 70| -1 88| 88 ✔ \n",
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" 88| 88 ✔ 12| 12 ✔ 58| 58 ✔ 65| 65 ✔ 102|102 ✔ 39| 39 ✔ 87| 87 ✔ 46| 46 ✔ \n",
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" 88| 88 ✔ 81| 81 ✔ 37| 37 ✔ 25| 25 ✔ 77| 77 ✔ 72| 72 ✔ 9| 9 ✔ 20| 20 ✔ \n",
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"115|115 ✔ 80| 80 ✔ 115|115 ✔ 69| 69 ✔ 126|126 ✔ 79| 79 ✔ 47| 47 ✔ 64| 64 ✔ \n",
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" 82| 82 ✔ 99| 99 ✔ 88| 88 ✔ 49| 49 ✔ 115|115 ✔ 29| 29 ✔ 19| 19 ✔ 19| -1 \n",
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" 14| 14 ✔ 39| 39 ✔ 32| 32 ✔ 65| 65 ✔ 9| 9 ✔ 57| 57 ✔ 127|127 ✔ 32| 32 ✔ \n",
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" 31| 31 ✔ 74| 74 ✔ 116|116 ✔ 23| 23 ✔ 35| 35 ✔ 126|126 ✔ 75| 75 ✔ 114|114 ✔ \n",
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" 55| 55 ✔ 28| -1 34| 34 ✔ -1| -1 ✔ -1| -1 ✔ 36| 36 ✔ 53| 53 ✔ 5| 5 ✔ \n",
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" 38| 38 ✔ 104|104 ✔ 116|116 ✔ 17| 17 ✔ 79| 79 ✔ 4| 4 ✔ 105|105 ✔ 42| 42 ✔ \n",
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" 58| 58 ✔ 31| 31 ✔ 120|120 ✔ 1| 1 ✔ 65| 65 ✔ 103|103 ✔ 41| 41 ✔ 57| 57 ✔ \n",
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" 35| 35 ✔ 102|103 ✘ 119|119 ✔ 11| 11 ✔ 46| 46 ✔ 82| 82 ✔ 91| 91 ✔ -1| -1 ✔ \n",
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" 14| 14 ✔ 99| 99 ✔ 53| 53 ✔ 12| 12 ✔ 121|121 ✔ 42| 42 ✔ 84| 84 ✔ 75| 75 ✔ \n",
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" 68| 68 ✔ 6| 6 ✔ 68| 68 ✔ 47| 47 ✔ 127|127 ✔ 116|116 ✔ 3| 3 ✔ 76| 76 ✔ \n",
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"100|100 ✔ 52| 52 ✔ 104|104 ✔ 78| 78 ✔ 15| 15 ✔ 20| 20 ✔ 99| 99 ✔ 58| 58 ✔ \n",
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" 23| 23 ✔ 79| 79 ✔ 13| 13 ✔ 117|117 ✔ 85| 85 ✔ 48| 48 ✔ 49| 49 ✔ 69| 69 ✔ \n",
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"Symbol error rate e=0.03125\n",
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"maximum bitrate r=326.953125 b/h\n"
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]
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}
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],
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"source": [
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"fig, ax = plt.subplots(figsize=(12,5))\n",
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"run_ser_test(ax=ax)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 111,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<ipython-input-111-2589572e37aa>:13: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
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" fig, ax = plt.subplots(figsize=(12, 9))\n"
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]
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},
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{
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"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "794478ea08254c9da3608685434de9d6",
|
|
"version_major": 2,
|
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"version_minor": 0
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},
|
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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|
]
|
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},
|
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"metadata": {},
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"output_type": "display_data"
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},
|
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{
|
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
|
"nbits=5\n",
|
|
"signal_amplitude=0.00029: ser=0.99141 ±0.01434, br=4.83398\n",
|
|
"signal_amplitude=0.00020: ser=0.99344 ±0.01365, br=3.69141\n",
|
|
"signal_amplitude=0.00052: ser=0.98953 ±0.01621, br=5.88867\n",
|
|
"signal_amplitude=0.00024: ser=0.98984 ±0.01426, br=5.71289\n",
|
|
"signal_amplitude=0.00043: ser=0.98906 ±0.01531, br=6.15234\n",
|
|
"signal_amplitude=0.00036: ser=0.98859 ±0.01451, br=6.41602\n",
|
|
"signal_amplitude=0.00032: ser=0.98531 ±0.01621, br=8.26172\n",
|
|
"signal_amplitude=0.00022: ser=0.99125 ±0.01377, br=4.92188\n",
|
|
"signal_amplitude=0.00027: ser=0.99172 ±0.01339, br=4.65820\n",
|
|
"signal_amplitude=0.00047: ser=0.98938 ±0.01821, br=5.97656\n",
|
|
"signal_amplitude=0.00057: ser=0.98453 ±0.02223, br=8.70117\n",
|
|
"signal_amplitude=0.00039: ser=0.99328 ±0.01499, br=3.77930\n",
|
|
"signal_amplitude=0.00063: ser=0.97531 ±0.02277, br=13.88672\n",
|
|
"signal_amplitude=0.00077: ser=0.95766 ±0.03962, br=23.81836\n",
|
|
"signal_amplitude=0.00093: ser=0.89844 ±0.07690, br=57.12891\n",
|
|
"signal_amplitude=0.00112: ser=0.69250 ±0.15978, br=172.96875\n",
|
|
"signal_amplitude=0.00136: ser=0.34797 ±0.13550, br=366.76758\n",
|
|
"signal_amplitude=0.00165: ser=0.15875 ±0.07678, br=473.20312\n",
|
|
"signal_amplitude=0.00070: ser=0.97562 ±0.02444, br=13.71094\n",
|
|
"signal_amplitude=0.00084: ser=0.93437 ±0.05440, br=36.91406\n",
|
|
"signal_amplitude=0.00102: ser=0.83734 ±0.10122, br=91.49414\n",
|
|
"signal_amplitude=0.00124: ser=0.56047 ±0.18288, br=247.23633\n",
|
|
"signal_amplitude=0.00150: ser=0.24266 ±0.09904, br=426.00586\n",
|
|
"signal_amplitude=0.00182: ser=0.10359 ±0.03178, br=504.22852\n",
|
|
"signal_amplitude=0.00200: ser=0.06453 ±0.03562, br=526.20117\n",
|
|
"nbits=6\n",
|
|
"signal_amplitude=0.00024: ser=1.00156 ±0.01855, br=-0.52734\n",
|
|
"signal_amplitude=0.00020: ser=1.00172 ±0.01365, br=-0.58008\n",
|
|
"signal_amplitude=0.00052: ser=0.95688 ±0.04998, br=14.55469\n",
|
|
"signal_amplitude=0.00029: ser=1.00078 ±0.01391, br=-0.26367\n",
|
|
"signal_amplitude=0.00043: ser=0.98406 ±0.02215, br=5.37891\n",
|
|
"signal_amplitude=0.00036: ser=0.99844 ±0.01733, br=0.52734\n",
|
|
"signal_amplitude=0.00027: ser=0.99469 ±0.01339, br=1.79297\n",
|
|
"signal_amplitude=0.00032: ser=0.99766 ±0.01541, br=0.79102\n",
|
|
"signal_amplitude=0.00047: ser=0.97813 ±0.03233, br=7.38281\n",
|
|
"signal_amplitude=0.00022: ser=1.00016 ±0.01612, br=-0.05273\n",
|
|
"signal_amplitude=0.00057: ser=0.93609 ±0.07689, br=21.56836\n",
|
|
"signal_amplitude=0.00039: ser=0.99625 ±0.01883, br=1.26562\n",
|
|
"signal_amplitude=0.00063: ser=0.86875 ±0.08992, br=44.29688\n",
|
|
"signal_amplitude=0.00077: ser=0.58109 ±0.17566, br=141.38086\n",
|
|
"signal_amplitude=0.00093: ser=0.28594 ±0.13348, br=240.99609\n",
|
|
"signal_amplitude=0.00112: ser=0.12547 ±0.06352, br=295.15430\n",
|
|
"signal_amplitude=0.00165: ser=0.06500 ±0.01642, br=315.56250\n",
|
|
"signal_amplitude=0.00136: ser=0.08516 ±0.06513, br=308.75977\n",
|
|
"signal_amplitude=0.00070: ser=0.75609 ±0.15173, br=82.31836\n",
|
|
"signal_amplitude=0.00084: ser=0.46125 ±0.18383, br=181.82812\n",
|
|
"signal_amplitude=0.00102: ser=0.19703 ±0.10488, br=271.00195\n",
|
|
"signal_amplitude=0.00124: ser=0.10562 ±0.06833, br=301.85156\n",
|
|
"signal_amplitude=0.00182: ser=0.06563 ±0.02007, br=315.35156\n",
|
|
"signal_amplitude=0.00150: ser=0.06984 ±0.01818, br=313.92773\n",
|
|
"signal_amplitude=0.00200: ser=0.06500 ±0.02196, br=315.56250\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.legend.Legend at 0x7fe3073ed0a0>"
|
|
]
|
|
},
|
|
"execution_count": 111,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"sample_duration=128\n",
|
|
"sample_reps = 50\n",
|
|
"sweep_points = 25\n",
|
|
"\n",
|
|
"default_params = dict(\n",
|
|
" nbits=6,\n",
|
|
" signal_amplitude=2.0e-3,\n",
|
|
" decimation=10,\n",
|
|
" threshold_factor=4.0,\n",
|
|
" power_avg_width=2.5,\n",
|
|
" max_lookahead=6.5)\n",
|
|
"\n",
|
|
"fig, ax = plt.subplots(figsize=(12, 9))\n",
|
|
"\n",
|
|
"for nbits in [5, 6]: # FIXME make sim stable for higher bit counts\n",
|
|
" print(f'nbits={nbits}')\n",
|
|
" \n",
|
|
" def calculate_ser(v):\n",
|
|
" params = dict(default_params)\n",
|
|
" params['signal_amplitude'] = v\n",
|
|
" params['nbits'] = nbits\n",
|
|
" sers, brs = [], []\n",
|
|
" for i in range(sample_reps):\n",
|
|
" seed = np.random.randint(0xffffffff)\n",
|
|
" ser, br = run_ser_test(**params, sample_duration=sample_duration, print=noprint, seed=seed)\n",
|
|
" sers.append(ser)\n",
|
|
" brs.append(br)\n",
|
|
" #print(f'nbits={nbits} ampl={v:>.5f} seed={seed:08x} > ser={ser:.5f}')\n",
|
|
" sers, brs = np.array(sers), np.array(brs)\n",
|
|
" ser, std = np.mean(sers), np.std(sers)\n",
|
|
" print(f'signal_amplitude={v:<.5f}: ser={ser:<.5f} ±{std:<.5f}, br={np.mean(brs):<.5f}')\n",
|
|
" return ser, std\n",
|
|
" \n",
|
|
" vs = 0.2e-3 * 10 ** np.linspace(0, 1.0, sweep_points)\n",
|
|
" with Pool(6) as p:\n",
|
|
" data = np.array(p.map(calculate_ser, vs))\n",
|
|
" sers, stds = data[:,0], data[:,1]\n",
|
|
" \n",
|
|
" l, = ax.plot(vs, np.clip(sers, 0, 1), label=f'{nbits} bit')\n",
|
|
" ax.fill_between(vs, np.clip(sers + stds, 0, 1), np.clip(sers - stds, 0, 1), facecolor=l.get_color(), alpha=0.3)\n",
|
|
"ax.grid()\n",
|
|
"ax.set_xlabel('Amplitude in mHz')\n",
|
|
"ax.set_ylabel('Symbol error rate')\n",
|
|
"ax.legend()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 101,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<ipython-input-101-d477d71c27f9>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
|
" fig, ax = plt.subplots(figsize=(12, 9))\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "cfaf2edf2f214ca6a3e97994a14a80f5",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"avg_peak 1.6752232386509318\n",
|
|
"skipped 2 symbols at 15749.0\n",
|
|
"skipped 3 symbols at 42209.0\n",
|
|
"skipped 2 symbols at 57959.5\n",
|
|
"skipped 2 symbols at 61108.5\n",
|
|
"skipped 2 symbols at 77489.5\n",
|
|
"decoding [ref|dec]:\n",
|
|
"=== shift = -3 ===\n",
|
|
"102| 69 ✘ 2|124 ✘ 3|102 ✘ 78| 2 ✘ 29| 3 ✘ 122| 78 ✘ 73| 29 ✘ 98|123 ✘ \n",
|
|
" 34| 73 ✘ -1| 98 ✘ 97| 34 ✘ 7| -1 97| 97 ✔ 86| 7 ✘ 120| 97 ✘ 95| 86 ✘ \n",
|
|
" 90|120 ✘ 49| 95 ✘ 89| 90 ✘ 83| 49 ✘ 19| 89 ✘ 84| 83 ✘ 117| -1 92| 84 ✘ \n",
|
|
"119|117 ✘ 16| 92 ✘ 45|119 ✘ 23| 16 ✘ 16| 45 ✘ 111| 23 ✘ 9| 16 ✘ 89|111 ✘ \n",
|
|
" 18| 9 ✘ 36| 89 ✘ 2| 18 ✘ 115| 36 ✘ 40| 2 ✘ 100|115 ✘ 105| 40 ✘ 93|100 ✘ \n",
|
|
" 85|105 ✘ 107| 93 ✘ 90| 85 ✘ 62|107 ✘ 116| 90 ✘ 42| 62 ✘ 123|116 ✘ 40| 42 ✘ \n",
|
|
" -1|123 ✘ 77| 40 ✘ 40| -1 57| 77 ✘ 110| 40 ✘ 29| 57 ✘ 94|110 ✘ 1| 29 ✘ \n",
|
|
" 29| 94 ✘ 71| 1 ✘ 119| 29 ✘ 15| 71 ✘ 115|119 ✘ 120| 15 ✘ 70|115 ✘ 50| -1 \n",
|
|
" 71| -1 50| 50 ✔ 61| 71 ✘ 38| 50 ✘ 4| 61 ✘ 3| 38 ✘ 124| 4 ✘ 95| 3 ✘ \n",
|
|
" 27|124 ✘ 48| 95 ✘ 116| 27 ✘ 3| 64 ✘ 63|116 ✘ 19| 3 ✘ 79| 63 ✘ 2| 19 ✘ \n",
|
|
" 43| 79 ✘ 92| 2 ✘ 8| 43 ✘ 65| 92 ✘ 35| 8 ✘ 30| 65 ✘ 73| 35 ✘ 73| 30 ✘ \n",
|
|
" 38| 73 ✘ 58| -1 49| 38 ✘ 45| 58 ✘ 58| 49 ✘ 46| 45 ✘ 116| -1 101| 46 ✘ \n",
|
|
" 5|116 ✘ 78|101 ✘ 126| 5 ✘ 105| 78 ✘ 108|126 ✘ 59| 76 ✘ 46|108 ✘ 27| 59 ✘ \n",
|
|
" 14| 46 ✘ 57| 27 ✘ 81| 14 ✘ 3| 57 ✘ 9| 81 ✘ 126| 3 ✘ 18| 9 ✘ 76|126 ✘ \n",
|
|
"101| 55 ✘ 124| 76 ✘ 4|101 ✘ 3|124 ✘ 102| 4 ✘ 79| 3 ✘ 121|102 ✘ 103| 79 ✘ \n",
|
|
" 92| -1 30|103 ✘ 4| 92 ✘ 103| 30 ✘ 59| 4 ✘ -1|103 ✘ -1| 48 ✘ \n",
|
|
"=== shift = -2 ===\n",
|
|
"124| 69 ✘ 102|124 ✘ 2|102 ✘ 3| 2 ✘ 78| 3 ✘ 29| 78 ✘ 122| 29 ✘ 73|123 ✘ \n",
|
|
" 98| 73 ✘ 34| 98 ✘ -1| 34 ✘ 97| -1 7| 97 ✘ 97| 7 ✘ 86| 97 ✘ 120| 86 ✘ \n",
|
|
" 95|120 ✘ 90| 95 ✘ 49| 90 ✘ 89| 49 ✘ 83| 89 ✘ 19| 83 ✘ 84| -1 117| 84 ✘ \n",
|
|
" 92|117 ✘ 119| 92 ✘ 16|119 ✘ 45| 16 ✘ 23| 45 ✘ 16| 23 ✘ 111| 16 ✘ 9|111 ✘ \n",
|
|
" 89| 9 ✘ 18| 89 ✘ 36| 18 ✘ 2| 36 ✘ 115| 2 ✘ 40|115 ✘ 100| 40 ✘ 105|100 ✘ \n",
|
|
" 93|105 ✘ 85| 93 ✘ 107| 85 ✘ 90|107 ✘ 62| 90 ✘ 116| 62 ✘ 42|116 ✘ 123| 42 ✘ \n",
|
|
" 40|123 ✘ -1| 40 ✘ 77| -1 40| 77 ✘ 57| 40 ✘ 110| 57 ✘ 29|110 ✘ 94| 29 ✘ \n",
|
|
" 1| 94 ✘ 29| 1 ✘ 71| 29 ✘ 119| 71 ✘ 15|119 ✘ 115| 15 ✘ 120|115 ✘ 70| -1 \n",
|
|
" 50| -1 71| 50 ✘ 50| 71 ✘ 61| 50 ✘ 38| 61 ✘ 4| 38 ✘ 3| 4 ✘ 124| 3 ✘ \n",
|
|
" 95|124 ✘ 27| 95 ✘ 48| 27 ✘ 116| 64 ✘ 3|116 ✘ 63| 3 ✘ 19| 63 ✘ 79| 19 ✘ \n",
|
|
" 2| 79 ✘ 43| 2 ✘ 92| 43 ✘ 8| 92 ✘ 65| 8 ✘ 35| 65 ✘ 30| 35 ✘ 73| 30 ✘ \n",
|
|
" 73| 73 ✔ 38| -1 58| 38 ✘ 49| 58 ✘ 45| 49 ✘ 58| 45 ✘ 46| -1 116| 46 ✘ \n",
|
|
"101|116 ✘ 5|101 ✘ 78| 5 ✘ 126| 78 ✘ 105|126 ✘ 108| 76 ✘ 59|108 ✘ 46| 59 ✘ \n",
|
|
" 27| 46 ✘ 14| 27 ✘ 57| 14 ✘ 81| 57 ✘ 3| 81 ✘ 9| 3 ✘ 126| 9 ✘ 18|126 ✘ \n",
|
|
" 76| 55 ✘ 101| 76 ✘ 124|101 ✘ 4|124 ✘ 3| 4 ✘ 102| 3 ✘ 79|102 ✘ 121| 79 ✘ \n",
|
|
"103| -1 92|103 ✘ 30| 92 ✘ 4| 30 ✘ 103| 4 ✘ 59|103 ✘ -1| 48 ✘ \n",
|
|
"=== shift = -1 ===\n",
|
|
" 69| 69 ✔ 124|124 ✔ 102|102 ✔ 2| 2 ✔ 3| 3 ✔ 78| 78 ✔ 29| 29 ✔ 122|123 ✘ \n",
|
|
" 73| 73 ✔ 98| 98 ✔ 34| 34 ✔ -1| -1 ✔ 97| 97 ✔ 7| 7 ✔ 97| 97 ✔ 86| 86 ✔ \n",
|
|
"120|120 ✔ 95| 95 ✔ 90| 90 ✔ 49| 49 ✔ 89| 89 ✔ 83| 83 ✔ 19| -1 84| 84 ✔ \n",
|
|
"117|117 ✔ 92| 92 ✔ 119|119 ✔ 16| 16 ✔ 45| 45 ✔ 23| 23 ✔ 16| 16 ✔ 111|111 ✔ \n",
|
|
" 9| 9 ✔ 89| 89 ✔ 18| 18 ✔ 36| 36 ✔ 2| 2 ✔ 115|115 ✔ 40| 40 ✔ 100|100 ✔ \n",
|
|
"105|105 ✔ 93| 93 ✔ 85| 85 ✔ 107|107 ✔ 90| 90 ✔ 62| 62 ✔ 116|116 ✔ 42| 42 ✔ \n",
|
|
"123|123 ✔ 40| 40 ✔ -1| -1 ✔ 77| 77 ✔ 40| 40 ✔ 57| 57 ✔ 110|110 ✔ 29| 29 ✔ \n",
|
|
" 94| 94 ✔ 1| 1 ✔ 29| 29 ✔ 71| 71 ✔ 119|119 ✔ 15| 15 ✔ 115|115 ✔ 120| -1 \n",
|
|
" 70| -1 50| 50 ✔ 71| 71 ✔ 50| 50 ✔ 61| 61 ✔ 38| 38 ✔ 4| 4 ✔ 3| 3 ✔ \n",
|
|
"124|124 ✔ 95| 95 ✔ 27| 27 ✔ 48| 64 ✘ 116|116 ✔ 3| 3 ✔ 63| 63 ✔ 19| 19 ✔ \n",
|
|
" 79| 79 ✔ 2| 2 ✔ 43| 43 ✔ 92| 92 ✔ 8| 8 ✔ 65| 65 ✔ 35| 35 ✔ 30| 30 ✔ \n",
|
|
" 73| 73 ✔ 73| -1 38| 38 ✔ 58| 58 ✔ 49| 49 ✔ 45| 45 ✔ 58| -1 46| 46 ✔ \n",
|
|
"116|116 ✔ 101|101 ✔ 5| 5 ✔ 78| 78 ✔ 126|126 ✔ 105| 76 ✘ 108|108 ✔ 59| 59 ✔ \n",
|
|
" 46| 46 ✔ 27| 27 ✔ 14| 14 ✔ 57| 57 ✔ 81| 81 ✔ 3| 3 ✔ 9| 9 ✔ 126|126 ✔ \n",
|
|
" 18| 55 ✘ 76| 76 ✔ 101|101 ✔ 124|124 ✔ 4| 4 ✔ 3| 3 ✔ 102|102 ✔ 79| 79 ✔ \n",
|
|
"121| -1 103|103 ✔ 92| 92 ✔ 30| 30 ✔ 4| 4 ✔ 103|103 ✔ 59| 48 ✘ \n",
|
|
"=== shift = 0 ===\n",
|
|
" 10| 69 ✘ 69|124 ✘ 124|102 ✘ 102| 2 ✘ 2| 3 ✘ 3| 78 ✘ 78| 29 ✘ 29|123 ✘ \n",
|
|
"122| 73 ✘ 73| 98 ✘ 98| 34 ✘ 34| -1 -1| 97 ✘ 97| 7 ✘ 7| 97 ✘ 97| 86 ✘ \n",
|
|
" 86|120 ✘ 120| 95 ✘ 95| 90 ✘ 90| 49 ✘ 49| 89 ✘ 89| 83 ✘ 83| -1 19| 84 ✘ \n",
|
|
" 84|117 ✘ 117| 92 ✘ 92|119 ✘ 119| 16 ✘ 16| 45 ✘ 45| 23 ✘ 23| 16 ✘ 16|111 ✘ \n",
|
|
"111| 9 ✘ 9| 89 ✘ 89| 18 ✘ 18| 36 ✘ 36| 2 ✘ 2|115 ✘ 115| 40 ✘ 40|100 ✘ \n",
|
|
"100|105 ✘ 105| 93 ✘ 93| 85 ✘ 85|107 ✘ 107| 90 ✘ 90| 62 ✘ 62|116 ✘ 116| 42 ✘ \n",
|
|
" 42|123 ✘ 123| 40 ✘ 40| -1 -1| 77 ✘ 77| 40 ✘ 40| 57 ✘ 57|110 ✘ 110| 29 ✘ \n",
|
|
" 29| 94 ✘ 94| 1 ✘ 1| 29 ✘ 29| 71 ✘ 71|119 ✘ 119| 15 ✘ 15|115 ✘ 115| -1 \n",
|
|
"120| -1 70| 50 ✘ 50| 71 ✘ 71| 50 ✘ 50| 61 ✘ 61| 38 ✘ 38| 4 ✘ 4| 3 ✘ \n",
|
|
" 3|124 ✘ 124| 95 ✘ 95| 27 ✘ 27| 64 ✘ 48|116 ✘ 116| 3 ✘ 3| 63 ✘ 63| 19 ✘ \n",
|
|
" 19| 79 ✘ 79| 2 ✘ 2| 43 ✘ 43| 92 ✘ 92| 8 ✘ 8| 65 ✘ 65| 35 ✘ 35| 30 ✘ \n",
|
|
" 30| 73 ✘ 73| -1 73| 38 ✘ 38| 58 ✘ 58| 49 ✘ 49| 45 ✘ 45| -1 58| 46 ✘ \n",
|
|
" 46|116 ✘ 116|101 ✘ 101| 5 ✘ 5| 78 ✘ 78|126 ✘ 126| 76 ✘ 105|108 ✘ 108| 59 ✘ \n",
|
|
" 59| 46 ✘ 46| 27 ✘ 27| 14 ✘ 14| 57 ✘ 57| 81 ✘ 81| 3 ✘ 3| 9 ✘ 9|126 ✘ \n",
|
|
"126| 55 ✘ 18| 76 ✘ 76|101 ✘ 101|124 ✘ 124| 4 ✘ 4| 3 ✘ 3|102 ✘ 102| 79 ✘ \n",
|
|
" 79| -1 121|103 ✘ 103| 92 ✘ 92| 30 ✘ 30| 4 ✘ 4|103 ✘ 103| 48 ✘ 59| -1 \n",
|
|
"=== shift = 1 ===\n",
|
|
" 10|124 ✘ 69|102 ✘ 124| 2 ✘ 102| 3 ✘ 2| 78 ✘ 3| 29 ✘ 78|123 ✘ 29| 73 ✘ \n",
|
|
"122| 98 ✘ 73| 34 ✘ 98| -1 34| 97 ✘ -1| 7 ✘ 97| 97 ✔ 7| 86 ✘ 97|120 ✘ \n",
|
|
" 86| 95 ✘ 120| 90 ✘ 95| 49 ✘ 90| 89 ✘ 49| 83 ✘ 89| -1 83| 84 ✘ 19|117 ✘ \n",
|
|
" 84| 92 ✘ 117|119 ✘ 92| 16 ✘ 119| 45 ✘ 16| 23 ✘ 45| 16 ✘ 23|111 ✘ 16| 9 ✘ \n",
|
|
"111| 89 ✘ 9| 18 ✘ 89| 36 ✘ 18| 2 ✘ 36|115 ✘ 2| 40 ✘ 115|100 ✘ 40|105 ✘ \n",
|
|
"100| 93 ✘ 105| 85 ✘ 93|107 ✘ 85| 90 ✘ 107| 62 ✘ 90|116 ✘ 62| 42 ✘ 116|123 ✘ \n",
|
|
" 42| 40 ✘ 123| -1 40| 77 ✘ -1| 40 ✘ 77| 57 ✘ 40|110 ✘ 57| 29 ✘ 110| 94 ✘ \n",
|
|
" 29| 1 ✘ 94| 29 ✘ 1| 71 ✘ 29|119 ✘ 71| 15 ✘ 119|115 ✘ 15| -1 115| -1 \n",
|
|
"120| 50 ✘ 70| 71 ✘ 50| 50 ✔ 71| 61 ✘ 50| 38 ✘ 61| 4 ✘ 38| 3 ✘ 4|124 ✘ \n",
|
|
" 3| 95 ✘ 124| 27 ✘ 95| 64 ✘ 27|116 ✘ 48| 3 ✘ 116| 63 ✘ 3| 19 ✘ 63| 79 ✘ \n",
|
|
" 19| 2 ✘ 79| 43 ✘ 2| 92 ✘ 43| 8 ✘ 92| 65 ✘ 8| 35 ✘ 65| 30 ✘ 35| 73 ✘ \n",
|
|
" 30| -1 73| 38 ✘ 73| 58 ✘ 38| 49 ✘ 58| 45 ✘ 49| -1 45| 46 ✘ 58|116 ✘ \n",
|
|
" 46|101 ✘ 116| 5 ✘ 101| 78 ✘ 5|126 ✘ 78| 76 ✘ 126|108 ✘ 105| 59 ✘ 108| 46 ✘ \n",
|
|
" 59| 27 ✘ 46| 14 ✘ 27| 57 ✘ 14| 81 ✘ 57| 3 ✘ 81| 9 ✘ 3|126 ✘ 9| 55 ✘ \n",
|
|
"126| 76 ✘ 18|101 ✘ 76|124 ✘ 101| 4 ✘ 124| 3 ✘ 4|102 ✘ 3| 79 ✘ 102| -1 \n",
|
|
" 79|103 ✘ 121| 92 ✘ 103| 30 ✘ 92| 4 ✘ 30|103 ✘ 4| 48 ✘ 103| -1 59| -1 \n",
|
|
"=== shift = 2 ===\n",
|
|
" 10|102 ✘ 69| 2 ✘ 124| 3 ✘ 102| 78 ✘ 2| 29 ✘ 3|123 ✘ 78| 73 ✘ 29| 98 ✘ \n",
|
|
"122| 34 ✘ 73| -1 98| 97 ✘ 34| 7 ✘ -1| 97 ✘ 97| 86 ✘ 7|120 ✘ 97| 95 ✘ \n",
|
|
" 86| 90 ✘ 120| 49 ✘ 95| 89 ✘ 90| 83 ✘ 49| -1 89| 84 ✘ 83|117 ✘ 19| 92 ✘ \n",
|
|
" 84|119 ✘ 117| 16 ✘ 92| 45 ✘ 119| 23 ✘ 16| 16 ✔ 45|111 ✘ 23| 9 ✘ 16| 89 ✘ \n",
|
|
"111| 18 ✘ 9| 36 ✘ 89| 2 ✘ 18|115 ✘ 36| 40 ✘ 2|100 ✘ 115|105 ✘ 40| 93 ✘ \n",
|
|
"100| 85 ✘ 105|107 ✘ 93| 90 ✘ 85| 62 ✘ 107|116 ✘ 90| 42 ✘ 62|123 ✘ 116| 40 ✘ \n",
|
|
" 42| -1 123| 77 ✘ 40| 40 ✔ -1| 57 ✘ 77|110 ✘ 40| 29 ✘ 57| 94 ✘ 110| 1 ✘ \n",
|
|
" 29| 29 ✔ 94| 71 ✘ 1|119 ✘ 29| 15 ✘ 71|115 ✘ 119| -1 15| -1 115| 50 ✘ \n",
|
|
"120| 71 ✘ 70| 50 ✘ 50| 61 ✘ 71| 38 ✘ 50| 4 ✘ 61| 3 ✘ 38|124 ✘ 4| 95 ✘ \n",
|
|
" 3| 27 ✘ 124| 64 ✘ 95|116 ✘ 27| 3 ✘ 48| 63 ✘ 116| 19 ✘ 3| 79 ✘ 63| 2 ✘ \n",
|
|
" 19| 43 ✘ 79| 92 ✘ 2| 8 ✘ 43| 65 ✘ 92| 35 ✘ 8| 30 ✘ 65| 73 ✘ 35| -1 \n",
|
|
" 30| 38 ✘ 73| 58 ✘ 73| 49 ✘ 38| 45 ✘ 58| -1 49| 46 ✘ 45|116 ✘ 58|101 ✘ \n",
|
|
" 46| 5 ✘ 116| 78 ✘ 101|126 ✘ 5| 76 ✘ 78|108 ✘ 126| 59 ✘ 105| 46 ✘ 108| 27 ✘ \n",
|
|
" 59| 14 ✘ 46| 57 ✘ 27| 81 ✘ 14| 3 ✘ 57| 9 ✘ 81|126 ✘ 3| 55 ✘ 9| 76 ✘ \n",
|
|
"126|101 ✘ 18|124 ✘ 76| 4 ✘ 101| 3 ✘ 124|102 ✘ 4| 79 ✘ 3| -1 102|103 ✘ \n",
|
|
" 79| 92 ✘ 121| 30 ✘ 103| 4 ✘ 92|103 ✘ 30| 48 ✘ 4| -1 103| -1 59| -1 \n",
|
|
"Symbol error rate e=0.0859375\n",
|
|
"maximum bitrate r=308.49609375 b/h\n",
|
|
"nbits=6 ampl=0.00387 seed=cbb3b8cf > ser=0.08594\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, ax = plt.subplots(figsize=(12, 9))\n",
|
|
"\n",
|
|
"params = dict(default_params)\n",
|
|
"params['signal_amplitude'] = 2.0e-3\n",
|
|
"params['nbits'] = 6\n",
|
|
"seed = 0xcbb3b8cf\n",
|
|
"ser, br = run_ser_test(**params, sample_duration=sample_duration, print=print, seed=seed, ax=ax)\n",
|
|
"print(f'nbits={nbits} ampl={v:>.5f} seed={seed:08x} > ser={ser:.5f}')"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "labenv",
|
|
"language": "python",
|
|
"name": "labenv"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|