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| -rw-r--r-- | lab-windows/dsss_experiments-ber.ipynb | 505 | ||||
| -rw-r--r-- | lab-windows/dsss_experiments.ipynb | 86 |
2 files changed, 556 insertions, 35 deletions
diff --git a/lab-windows/dsss_experiments-ber.ipynb b/lab-windows/dsss_experiments-ber.ipynb new file mode 100644 index 0000000..eda21d7 --- /dev/null +++ b/lab-windows/dsss_experiments-ber.ipynb @@ -0,0 +1,505 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "from scipy import signal as sig\n", + "import struct\n", + "import random\n", + "import ipywidgets\n", + "import itertools\n", + "from multiprocessing import Pool\n", + "\n", + "import colorednoise\n", + "\n", + "np.set_printoptions(linewidth=240)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib widget" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "sampling_rate = 10 # sp/s" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# From https://github.com/mubeta06/python/blob/master/signal_processing/sp/gold.py\n", + "preferred_pairs = {5:[[2],[1,2,3]], 6:[[5],[1,4,5]], 7:[[4],[4,5,6]],\n", + " 8:[[1,2,3,6,7],[1,2,7]], 9:[[5],[3,5,6]], \n", + " 10:[[2,5,9],[3,4,6,8,9]], 11:[[9],[3,6,9]]}\n", + "\n", + "def gen_gold(seq1, seq2):\n", + " gold = [seq1, seq2]\n", + " for shift in range(len(seq1)):\n", + " gold.append(seq1 ^ np.roll(seq2, -shift))\n", + " return gold\n", + "\n", + "def gold(n):\n", + " n = int(n)\n", + " if not n in preferred_pairs:\n", + " raise KeyError('preferred pairs for %s bits unknown' % str(n))\n", + " t0, t1 = preferred_pairs[n]\n", + " (seq0, _st0), (seq1, _st1) = sig.max_len_seq(n, taps=t0), sig.max_len_seq(n, taps=t1)\n", + " return gen_gold(seq0, seq1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def modulate(data, nbits=5):\n", + " # 0, 1 -> -1, 1\n", + " mask = np.array(gold(nbits))*2 - 1\n", + " \n", + " sel = mask[data>>1]\n", + " data_lsb_centered = ((data&1)*2 - 1)\n", + "\n", + " return (np.multiply(sel, np.tile(data_lsb_centered, (2**nbits-1, 1)).T).flatten() + 1) // 2" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def correlate(sequence, nbits=5, decimation=1, mask_filter=lambda x: x):\n", + " mask = np.tile(np.array(gold(nbits))[:,:,np.newaxis]*2 - 1, (1, 1, decimation)).reshape((2**nbits + 1, (2**nbits-1) * decimation))\n", + "\n", + " sequence -= np.mean(sequence)\n", + " \n", + " return np.array([np.correlate(sequence, row, mode='full') for row in mask])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean: 49.98625\n" + ] + } + ], + "source": [ + "with open('/mnt/c/Users/jaseg/shared/raw_freq.bin', 'rb') as f:\n", + " mains_noise = np.copy(np.frombuffer(f.read(), dtype='float32'))\n", + " print('mean:', np.mean(mains_noise))\n", + " mains_noise -= np.mean(mains_noise)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_test_signal(duration, nbits=6, signal_amplitude=2.0e-3, decimation=10, seed=0):\n", + " test_data = np.random.RandomState(seed=seed).randint(0, 2 * (2**nbits), duration)\n", + " \n", + " signal = np.repeat(modulate(test_data, nbits) * 2.0 - 1, decimation) * signal_amplitude\n", + " noise = np.resize(mains_noise, len(signal))\n", + " \n", + " return test_data, signal + noise" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "nonlinear_distance = lambda x: 100**(2*np.abs(0.5-x%1)) / (np.abs(x)+3)**2\n", + "\n", + "def plot_distance_func():\n", + " fig, ax = plt.subplots()\n", + " x = np.linspace(-1.5, 5.5, 10000)\n", + " ax.plot(x, nonlinear_distance(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "noprint = lambda *args, **kwargs: None" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "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):\n", + "\n", + " test_data, signal = generate_test_signal(sample_duration, nbits, signal_amplitude, decimation, seed)\n", + " cor_an = correlate(signal, nbits=nbits, decimation=decimation)\n", + "\n", + " power_avg_width = int(power_avg_width * (2**nbits - 1) * decimation)\n", + "\n", + " bit_period = (2**nbits) * decimation\n", + " peak_group_threshold = 0.1 * bit_period\n", + " \n", + " cwt_res = np.array([ sig.cwt(row, sig.ricker, [0.73 * decimation]).flatten() for row in cor_an ])\n", + " if ax:\n", + " ax.grid()\n", + " ax.plot(cwt_res.T)\n", + " \n", + " th = np.array([ np.convolve(np.abs(row), np.ones((power_avg_width,))/power_avg_width, mode='same') for row in cwt_res ])\n", + "\n", + " def compare_th(elem):\n", + " idx, (th, val) = elem\n", + " #print('compare_th:', th.shape, val.shape)\n", + " return np.any(np.abs(val) > th*threshold_factor)\n", + "\n", + " peaks = [ list(group) for val, group in itertools.groupby(enumerate(zip(th.T, cwt_res.T)), compare_th) if val ]\n", + " peak_group = []\n", + " for group in peaks:\n", + " pos = np.mean([idx for idx, _val in group])\n", + " pol = np.mean([max(val.min(), val.max(), key=abs) for _idx, (_th, val) in group])\n", + " pol_idx = np.argmax(np.bincount([ np.argmax(np.abs(val)) for _idx, (_th, val) in group ]))\n", + " #print(f'group', pos, pol, pol_idx)\n", + " #for pol, (_idx, (_th, val)) in zip([max(val.min(), val.max(), key=abs) for _idx, (_th, val) in group], group):\n", + " # print(' ', pol, val)\n", + " if ax:\n", + " ax.axvline(pos, color='cyan', alpha=0.3)\n", + "\n", + " if not peak_group or pos - peak_group[-1][1] > peak_group_threshold:\n", + " if peak_group:\n", + " peak_pos = peak_group[-1][3]\n", + " if ax:\n", + " ax.axvline(peak_pos, color='red', alpha=0.6)\n", + " #ax3.text(peak_pos-20, 2.0, f'{0 if pol < 0 else 1}', horizontalalignment='right', verticalalignment='center', color='black')\n", + "\n", + " peak_group.append((pos, pos, pol, pos, pol_idx))\n", + " #ax3.axvline(pos, color='cyan', alpha=0.5)\n", + "\n", + " else:\n", + " group_start, last_pos, last_pol, peak_pos, last_pol_idx = peak_group[-1]\n", + "\n", + " if abs(pol) > abs(last_pol):\n", + " #ax3.axvline(pos, color='magenta', alpha=0.5)\n", + " peak_group[-1] = (group_start, pos, pol, pos, pol_idx)\n", + " else:\n", + " #ax3.axvline(pos, color='blue', alpha=0.5)\n", + " peak_group[-1] = (group_start, pos, last_pol, peak_pos, last_pol_idx)\n", + "\n", + " avg_peak = np.mean(np.abs(np.array([last_pol for _1, _2, last_pol, _3, _4 in peak_group])))\n", + " print('avg_peak', avg_peak)\n", + "\n", + " noprint = lambda *args, **kwargs: None\n", + " def mle_decode(peak_groups, print=print):\n", + " peak_groups = [ (pos, pol, idx) for _1, _2, pol, pos, idx in peak_groups ]\n", + " candidates = [ (0, [(pos, pol, idx)]) for pos, pol, idx in peak_groups if pos < bit_period*2.5 ]\n", + "\n", + " while candidates:\n", + " chain_candidates = []\n", + " for chain_score, chain in candidates:\n", + " pos, ampl, _idx = chain[-1]\n", + " score_fun = lambda pos, npos, npol: abs(npol)/avg_peak + nonlinear_distance((npos-pos)/bit_period)\n", + " 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", + "\n", + " print(f' candidates for {pos}, {ampl}:')\n", + " for score, npos, npol, nidx in next_candidates:\n", + " print(f' {score:.4f} {npos:.2f} {npol:.2f} {nidx:.2f}')\n", + "\n", + " nch, cor_len = cor_an.shape\n", + " if cor_len - pos < 1.5*bit_period or not next_candidates:\n", + " score = sum(score_fun(opos, npos, npol) for (opos, _opol, _oidx), (npos, npol, _nidx) in zip(chain[:-1], chain[1:])) / len(chain)\n", + " yield score, chain\n", + "\n", + " else:\n", + " print('extending')\n", + " for score, npos, npol, nidx in next_candidates[:3]:\n", + " if score > 0.5:\n", + " new_chain_score = chain_score * 0.9 + score * 0.1\n", + " chain_candidates.append((new_chain_score, chain + [(npos, npol, nidx)]))\n", + " print('chain candidates:')\n", + " for score, chain in sorted(chain_candidates, reverse=True):\n", + " print(' ', [(score, [(f'{pos:.2f}', f'{pol:.2f}') for pos, pol, _idx in chain])])\n", + " candidates = [ (chain_score, chain) for chain_score, chain in sorted(chain_candidates, reverse=True)[:10] ]\n", + "\n", + " res = sorted(mle_decode(peak_group, print=noprint), reverse=True)\n", + " #for i, (score, chain) in enumerate(res):\n", + " # print(f'Chain {i}@{score:.4f}: {chain}')\n", + " (_score, chain), *_ = res\n", + "\n", + " def viz(chain):\n", + " last_pos = None\n", + " for pos, pol, nidx in chain:\n", + " if last_pos:\n", + " delta = int(round((pos - last_pos) / bit_period))\n", + " if delta > 1:\n", + " print(f'skipped {delta} symbols at {pos}')\n", + " for i in range(delta-1):\n", + " yield None\n", + " decoded = nidx*2 + (0 if pol < 0 else 1)\n", + " yield decoded\n", + " if ax:\n", + " ax.axvline(pos, color='blue', alpha=0.5)\n", + " ax.text(pos-20, 0.0, f'{decoded}', horizontalalignment='right', verticalalignment='center', color='black')\n", + "\n", + " last_pos = pos\n", + "\n", + " decoded = list(viz(chain))\n", + " print('decoding [ref|dec]:')\n", + " failures = 0\n", + " for i, (ref, found) in enumerate(itertools.zip_longest(test_data, decoded)):\n", + " print(f'{ref or -1:>3d}|{found or -1:>3d} {\"✔\" if ref==found else \"✘\" if found else \" \"}', end=' ')\n", + " if ref != found:\n", + " failures += 1\n", + " if i%8 == 7:\n", + " print()\n", + " ser = failures/len(test_data)\n", + " print(f'Symbol error rate e={ser}')\n", + " br = sampling_rate / decimation / (2**nbits) * nbits * (1 - ser) * 3600\n", + " print(f'maximum bitrate r={br} b/h')\n", + " return ser, br" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9b7546a233fb4b6cb6e8f809250ba768", + "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": [ + "(63,) (63,)\n", + "(63,) (63,)\n", + "avg_peak 1.6845488102985742\n", + "skipped 2 symbols at 10079.0\n", + "skipped 2 symbols at 30870.0\n", + "skipped 2 symbols at 42209.5\n", + "decoding [ref|dec]:\n", + " 44| 44 ✔ 47| 47 ✔ 117|117 ✔ 64| 64 ✔ 67| 67 ✔ 123|123 ✔ 67| 67 ✔ 103|103 ✔ \n", + " 9| 9 ✔ 83| 83 ✔ 21| 21 ✔ 114|114 ✔ 36| 36 ✔ 87| 87 ✔ 70| -1 88| 88 ✔ \n", + " 88| 88 ✔ 12| 12 ✔ 58| 58 ✔ 65| 65 ✔ 102|102 ✔ 39| 39 ✔ 87| 87 ✔ 46| 46 ✔ \n", + " 88| 88 ✔ 81| 81 ✔ 37| 37 ✔ 25| 25 ✔ 77| 77 ✔ 72| 72 ✔ 9| 9 ✔ 20| 20 ✔ \n", + "115|115 ✔ 80| 80 ✔ 115|115 ✔ 69| 69 ✔ 126|126 ✔ 79| 79 ✔ 47| 47 ✔ 64| 64 ✔ \n", + " 82| 82 ✔ 99| 99 ✔ 88| 88 ✔ 49| 49 ✔ 115|115 ✔ 29| 29 ✔ 19| 19 ✔ 19| -1 \n", + " 14| 14 ✔ 39| 39 ✔ 32| 32 ✔ 65| 65 ✔ 9| 9 ✔ 57| 57 ✔ 127|127 ✔ 32| 32 ✔ \n", + " 31| 31 ✔ 74| 74 ✔ 116|116 ✔ 23| 23 ✔ 35| 35 ✔ 126|126 ✔ 75| 75 ✔ 114|114 ✔ \n", + " 55| 55 ✔ 28| -1 34| 34 ✔ -1| -1 ✔ -1| -1 ✔ 36| 36 ✔ 53| 53 ✔ 5| 5 ✔ \n", + " 38| 38 ✔ 104|104 ✔ 116|116 ✔ 17| 17 ✔ 79| 79 ✔ 4| 4 ✔ 105|105 ✔ 42| 42 ✔ \n", + " 58| 58 ✔ 31| 31 ✔ 120|120 ✔ 1| 1 ✔ 65| 65 ✔ 103|103 ✔ 41| 41 ✔ 57| 57 ✔ \n", + " 35| 35 ✔ 102|103 ✘ 119|119 ✔ 11| 11 ✔ 46| 46 ✔ 82| 82 ✔ 91| 91 ✔ -1| -1 ✔ \n", + " 14| 14 ✔ 99| 99 ✔ 53| 53 ✔ 12| 12 ✔ 121|121 ✔ 42| 42 ✔ 84| 84 ✔ 75| 75 ✔ \n", + " 68| 68 ✔ 6| 6 ✔ 68| 68 ✔ 47| 47 ✔ 127|127 ✔ 116|116 ✔ 3| 3 ✔ 76| 76 ✔ \n", + "100|100 ✔ 52| 52 ✔ 104|104 ✔ 78| 78 ✔ 15| 15 ✔ 20| 20 ✔ 99| 99 ✔ 58| 58 ✔ \n", + " 23| 23 ✔ 79| 79 ✔ 13| 13 ✔ 117|117 ✔ 85| 85 ✔ 48| 48 ✔ 49| 49 ✔ 69| 69 ✔ \n", + "Symbol error rate e=0.03125\n", + "maximum bitrate r=326.953125 b/h\n" + ] + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12,5))\n", + "run_ser_test(ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-84-f36c7b0ffb61>: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", + " fig, ax = plt.subplots(figsize=(12, 9))\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b5eb2cd4f4224f75bc3dc73b6143d849", + "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": [ + "nbits=5\n", + "signal_amplitude=0.00029: ser=1.01000 ±0.012207515615390381, br=-5.62500\n", + "signal_amplitude=0.00020: ser=1.01469 ±0.013678792892649557, br=-8.26172\n", + "signal_amplitude=0.00052: ser=1.00406 ±0.018895270572288715, br=-2.28516\n", + "signal_amplitude=0.00043: ser=1.01000 ±0.018817586521655747, br=-5.62500\n", + "signal_amplitude=0.00024: ser=1.01156 ±0.014510233457804875, br=-6.50391\n", + "signal_amplitude=0.00036: ser=1.00687 ±0.014875787794264881, br=-3.86719\n", + "signal_amplitude=0.00032: ser=1.01156 ±0.01837117307087384, br=-6.50391\n", + "signal_amplitude=0.00022: ser=1.01000 ±0.013535254892317322, br=-5.62500\n", + "signal_amplitude=0.00057: ser=1.00281 ±0.012476540486048206, br=-1.58203\n", + "signal_amplitude=0.00039: ser=1.00812 ±0.014402148277253642, br=-4.57031\n", + "signal_amplitude=0.00047: ser=1.00438 ±0.012899854650343935, br=-2.46094\n", + "signal_amplitude=0.00027: ser=1.00937 ±0.014657549249448218, br=-5.27344\n", + "signal_amplitude=0.00063: ser=0.99938 ±0.019253652250936705, br=0.35156\n", + "signal_amplitude=0.00077: ser=0.99156 ±0.03231920868461974, br=4.74609\n", + "signal_amplitude=0.00093: ser=0.95156 ±0.06625442202223185, br=27.24609\n", + "signal_amplitude=0.00112: ser=0.76000 ±0.2099632594348354, br=135.00000\n", + "signal_amplitude=0.00136: ser=0.51375 ±0.30673813139223494, br=273.51562\n", + "signal_amplitude=0.00165: ser=0.39844 ±0.38814210912370745, br=338.37891\n", + "signal_amplitude=0.00070: ser=0.99281 ±0.023688242072809035, br=4.04297\n", + "signal_amplitude=0.00084: ser=0.96375 ±0.050769469787461836, br=20.39062\n", + "signal_amplitude=0.00102: ser=0.91063 ±0.10310321739645179, br=50.27344\n", + "signal_amplitude=0.00124: ser=0.72500 ±0.23567348639059932, br=154.68750\n", + "signal_amplitude=0.00150: ser=0.40969 ±0.3064419041596629, br=332.05078\n", + "signal_amplitude=0.00182: ser=0.32531 ±0.38085840544748384, br=379.51172\n", + "signal_amplitude=0.00200: ser=0.29000 ±0.3885339029608613, br=399.37500\n", + "nbits=6\n", + "signal_amplitude=0.00052: ser=1.00375 ±0.027432445434193427, br=-1.26562\n", + "signal_amplitude=0.00029: ser=1.01531 ±0.013528038013695853, br=-5.16797\n", + "signal_amplitude=0.00020: ser=1.02000 ±0.01698459780212649, br=-6.75000\n", + "signal_amplitude=0.00024: ser=1.01844 ±0.0197494066366562, br=-6.22266\n", + "signal_amplitude=0.00043: ser=1.01000 ±0.013535254892317322, br=-3.37500\n", + "signal_amplitude=0.00036: ser=1.01500 ±0.01860884366369926, br=-5.06250\n", + "signal_amplitude=0.00032: ser=1.00906 ±0.01443601182806387, br=-3.05859\n", + "signal_amplitude=0.00022: ser=1.01656 ±0.015200483133769137, br=-5.58984\n", + "signal_amplitude=0.00057: ser=0.98281 ±0.04926213365760764, br=5.80078\n", + "signal_amplitude=0.00027: ser=1.02000 ±0.015946688527716343, br=-6.75000\n", + "signal_amplitude=0.00047: ser=1.00687 ±0.02815276407388802, br=-2.32031\n", + "signal_amplitude=0.00039: ser=1.00906 ±0.016189792308735775, br=-3.05859\n", + "signal_amplitude=0.00077: ser=0.76906 ±0.23454244018940368, br=77.94141\n", + "signal_amplitude=0.00063: ser=0.94031 ±0.08557822627572974, br=20.14453\n", + "signal_amplitude=0.00112: ser=0.29750 ±0.347296478171029, br=237.09375\n", + "signal_amplitude=0.00093: ser=0.50125 ±0.3293776683952632, br=168.32812\n", + "signal_amplitude=0.00136: ser=0.37250 ±0.42536588111001566, br=211.78125\n", + "signal_amplitude=0.00165: ser=0.51000 ±0.46215950303980546, br=165.37500\n", + "signal_amplitude=0.00070: ser=0.90063 ±0.1848975645458858, br=33.53906\n", + "signal_amplitude=0.00084: ser=0.64687 ±0.26652421325275494, br=119.17969\n", + "signal_amplitude=0.00124: ser=0.38500 ±0.39889079606767064, br=207.56250\n", + "signal_amplitude=0.00102: ser=0.40875 ±0.3467111099315971, br=199.54688\n", + "signal_amplitude=0.00150: ser=0.40375 ±0.4435118198819508, br=201.23438\n", + "signal_amplitude=0.00182: ser=0.58531 ±0.46179168734397985, br=139.95703\n", + "signal_amplitude=0.00200: ser=0.61594 ±0.4584529436730666, br=129.62109\n" + ] + }, + { + "data": { + "text/plain": [ + "<matplotlib.legend.Legend at 0x7fe2bdd64430>" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_duration=128\n", + "sample_reps = 25\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", + " ser, br = run_ser_test(**params, sample_duration=sample_duration, print=noprint, seed=np.random.randint(0xffffffff))\n", + " sers.append(ser)\n", + " brs.append(br)\n", + " sers, brs = np.array(sers), np.array(brs)\n", + " ser = np.mean(sers)\n", + " print(f'signal_amplitude={v:<.5f}: ser={ser:<.5f} ±{np.std(sers):<.5f}, br={np.mean(brs):<.5f}')\n", + " return ser\n", + " \n", + " vs = 0.2e-3 * 10 ** np.linspace(0, 1.0, sweep_points)\n", + " with Pool(6) as p:\n", + " data = p.map(calculate_ser, vs)\n", + " \n", + " ax.plot(vs, data, label=f'{nbits} bit')\n", + "ax.grid()\n", + "ax.set_xlabel('Amplitude in mHz')\n", + "ax.set_ylabel('Symbol error rate')\n", + "ax.legend()" + ] + } + ], + "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 +} diff --git a/lab-windows/dsss_experiments.ipynb b/lab-windows/dsss_experiments.ipynb index be41b59..f2bbc0b 100644 --- a/lab-windows/dsss_experiments.ipynb +++ b/lab-windows/dsss_experiments.ipynb @@ -30,6 +30,15 @@ }, { "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [], + "source": [ + "sampling_rate = 10 # sp/s" + ] + }, + { + "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], @@ -410,29 +419,29 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 145, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(31,) (31,)\n", - "(31,) (31,)\n" + "(63,) (63,)\n", + "(63,) (63,)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "<ipython-input-96-b3aae757ccad>:33: 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", + "<ipython-input-145-babcf8a4e867>:33: 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, ((ax1, ax3), (ax2, ax4)) = plt.subplots(2, 2, figsize=(16, 9))\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "246c19a3c1424e7eb0b675ff060ea5b3", + "model_id": "10aa67d294304f2ba26c8e6d5555d5e6", "version_major": 2, "version_minor": 0 }, @@ -446,10 +455,10 @@ { "data": { "text/plain": [ - "(0.002, 0.013899708)" + "(0.002, 0.014074279)" ] }, - "execution_count": 96, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -457,15 +466,15 @@ "source": [ "decimation = 10\n", "signal_amplitude = 2.0e-3\n", - "nbits = 5\n", + "nbits = 6\n", "\n", "#test_data = np.random.randint(0, 2, 100)\n", "#test_data = np.array([0, 1, 0, 0, 1, 1, 1, 0])\n", - "#test_data = np.random.RandomState(seed=0).randint(0, 2 * (2**nbits), 64)\n", + "test_data = np.random.RandomState(seed=0).randint(0, 2 * (2**nbits), 64)\n", "#test_data = np.random.RandomState(seed=0).randint(0, 8, 64)\n", "#test_data = np.array(list(range(8)) * 8)\n", "#test_data = np.array([0, 1] * 32)\n", - "test_data = np.array(list(range(64)))\n", + "#test_data = np.array(list(range(64)))\n", "\n", "foo = np.repeat(modulate(test_data, nbits) * 2.0 - 1, decimation) * signal_amplitude\n", "noise = np.resize(mains_noise, len(foo))\n", @@ -493,12 +502,20 @@ "ax1.plot(foo + noise)\n", "ax1.plot(foo)\n", "ax1.set_title('raw')\n", + "ax1.grid(axis='y')\n", "\n", "ax2.plot(filtered)\n", "ax2.plot(foo)\n", "ax2.set_title('filtered')\n", + "ax2.grid(axis='y')\n", + "\n", + "for i in range(0, len(foo) + 1, decimation*(2**nbits - 1)):\n", + " ax1.axvline(i, color='gray', alpha=0.5, lw=1)\n", + " ax2.axvline(i, color='gray', alpha=0.5, lw=1)\n", "\n", - "ax3.plot(cor1.T)\n", + "for i, (color, trace) in enumerate(zip(plt.cm.winter(np.linspace(0, 1, cor1.shape[0])), cor1.T)):\n", + " if i%3 == 0:\n", + " ax3.plot(trace + 0.5 * i, alpha=1.0, color=color)\n", "ax3.set_title('corr raw')\n", "ax3.grid()\n", "\n", @@ -678,21 +695,21 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 146, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "<ipython-input-97-2d2c2f814215>:11: 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", + "<ipython-input-146-badd40342f73>:11: 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, (ax1, ax3) = plt.subplots(2, figsize=(12, 5))\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b083c661b5b441d6b7fc45201faa0576", + "model_id": "509bf67d93b74741b48bca58529c4b9d", "version_major": 2, "version_minor": 0 }, @@ -707,26 +724,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "cor_an (33, 20149)\n", - "cwt_res (33, 20149)\n", - "th (33, 20149)\n", - "[((33,), (33,)), ((33,), (33,)), ((33,), (33,)), ((33,), (33,)), ((33,), (33,))]\n", - "peaks: 180\n", - "avg_peak 1.058897833824206\n", - "skipped 3 symbols at 5889.0\n", - "skipped 2 symbols at 8369.0\n", - "skipped 2 symbols at 14568.5\n", - "skipped 2 symbols at 16739.0\n", + "cor_an (65, 40949)\n", + "cwt_res (65, 40949)\n", + "th (65, 40949)\n", + "[((65,), (65,)), ((65,), (65,)), ((65,), (65,)), ((65,), (65,)), ((65,), (65,))]\n", + "peaks: 982\n", + "avg_peak 1.6673786030736735\n", + "skipped 2 symbols at 30238.5\n", "decoding [ref|dec]:\n", - " -1| -1 ✔ 1| 1 ✔ 2| 2 ✔ 3| 3 ✔ 4| 4 ✔ 5| 5 ✔ 6| 6 ✔ 7| 7 ✔ \n", - " 8| 8 ✔ 9| 9 ✔ 10| 10 ✔ 11| 11 ✔ 12| 12 ✔ 13| 13 ✔ 14| 14 ✔ 15| 15 ✔ \n", - " 16| -1 ✘ 17| -1 ✘ 18| 18 ✔ 19| 19 ✔ 20| 20 ✔ 21| 21 ✔ 22| 22 ✔ 23| 23 ✔ \n", - " 24| 24 ✔ 25| -1 ✘ 26| 26 ✔ 27| 27 ✔ 28| 28 ✔ 29| 29 ✔ 30| 30 ✔ 31| 31 ✔ \n", - " 32| 32 ✔ 33| 33 ✔ 34| 34 ✔ 35| 35 ✔ 36| 36 ✔ 37| 37 ✔ 38| 38 ✔ 39| 39 ✔ \n", - " 40| 40 ✔ 41| 41 ✔ 42| 42 ✔ 43| 43 ✔ 44| 44 ✔ 45| -1 ✘ 46| 46 ✔ 47| 47 ✔ \n", - " 48| 48 ✔ 49| 49 ✔ 50| 50 ✔ 51| 51 ✔ 52| -1 ✘ 53| 53 ✔ 54| 54 ✔ 55| 55 ✔ \n", - " 56| 56 ✔ 57| 57 ✔ 58| 58 ✔ 59| 59 ✔ 60| 60 ✔ 61| 61 ✔ 62| 62 ✔ 63| 56 ✘ \n", - "Symbol error rate r=0.09375\n" + " 44| 44 ✔ 47| 47 ✔ 117|117 ✔ 64| 64 ✔ 67| 67 ✔ 123|123 ✔ 67| 67 ✔ 103|103 ✔ \n", + " 9| 9 ✔ 83| 83 ✔ 21| 21 ✔ 114|114 ✔ 36| 36 ✔ 87| 87 ✔ 70| 70 ✔ 88| 88 ✔ \n", + " 88| 88 ✔ 12| 12 ✔ 58| 58 ✔ 65| 65 ✔ 102|102 ✔ 39| 39 ✔ 87| 87 ✔ 46| 46 ✔ \n", + " 88| 88 ✔ 81| 81 ✔ 37| 37 ✔ 25| 25 ✔ 77| 77 ✔ 72| 72 ✔ 9| 9 ✔ 20| 20 ✔ \n", + "115|115 ✔ 80| 80 ✔ 115|115 ✔ 69| 69 ✔ 126|126 ✔ 79| 79 ✔ 47| 47 ✔ 64| 64 ✔ \n", + " 82| 82 ✔ 99| 99 ✔ 88| 88 ✔ 49| 49 ✔ 115|115 ✔ 29| 29 ✔ 19| -1 19| 19 ✔ \n", + " 14| 14 ✔ 39| 39 ✔ 32| 32 ✔ 65| 64 ✘ 9| 9 ✔ 57| 57 ✔ 127|127 ✔ 32| 32 ✔ \n", + " 31| 31 ✔ 74| 74 ✔ 116|116 ✔ 23| 23 ✔ 35| 35 ✔ 126|126 ✔ 75| 75 ✔ 114| 26 ✘ \n", + "Symbol error rate e=0.046875\n", + "maximum bitrate r=321.6796875 b/h\n" ] } ], @@ -869,12 +884,13 @@ "print('decoding [ref|dec]:')\n", "failures = 0\n", "for i, (ref, found) in enumerate(itertools.zip_longest(test_data, decoded)):\n", - " print(f'{ref or -1:>3d}|{found or -1:>3d} {\"✔\" if ref==found else \"✘\"}', end=' ')\n", + " print(f'{ref or -1:>3d}|{found or -1:>3d} {\"✔\" if ref==found else \"✘\" if found else \" \"}', end=' ')\n", " if ref != found:\n", " failures += 1\n", " if i%8 == 7:\n", " print()\n", - "print(f'Symbol error rate r={failures/len(test_data)}')\n", + "print(f'Symbol error rate e={failures/len(test_data)}')\n", + "print(f'maximum bitrate r={sampling_rate / decimation / (2**nbits) * nbits * (1 - failures/len(test_data)) * 3600} b/h')\n", "#ax3.plot(th)" ] }, |