diff options
Diffstat (limited to 'lab-windows/dsss_experiments-ber.ipynb')
| -rw-r--r-- | lab-windows/dsss_experiments-ber.ipynb | 225 |
1 files changed, 199 insertions, 26 deletions
diff --git a/lab-windows/dsss_experiments-ber.ipynb b/lab-windows/dsss_experiments-ber.ipynb index f490f36..d4a2992 100644 --- a/lab-windows/dsss_experiments-ber.ipynb +++ b/lab-windows/dsss_experiments-ber.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -124,12 +124,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 77, "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", + "def generate_test_signal(duration, nbits=6, signal_amplitude=2.0e-3, decimation=10, seed=0, data=None):\n", + " test_data = np.random.RandomState(seed=seed).randint(0, 2 * (2**nbits), duration) if data is None else data\n", " \n", " signal = np.repeat(modulate(test_data, nbits) * 2.0 - 1, decimation) * signal_amplitude\n", " noise = np.resize(mains_noise, len(signal))\n", @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -153,13 +153,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 43, "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-42-882fdfbdc9fa>:4: 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()\n" + ] + }, + { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac41374377284feaa0d8dc20bcb4a341", + "model_id": "cddadc4204f54789905875fcfb8e4126", "version_major": 2, "version_minor": 0 }, @@ -177,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -186,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -366,13 +374,21 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 217, "metadata": {}, "outputs": [ { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-217-c761a61291a7>: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": "b4952032e6ed4d62b23ddf7889a0efb9", + "model_id": "42da8a07c0e241f2b1d0f3358e68935b", "version_major": 2, "version_minor": 0 }, @@ -382,19 +398,57 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[<matplotlib.lines.Line2D at 0x7efd98cd17f0>]" + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "fig, ax = plt.subplots(figsize=(12, 9))\n", "\n", - "params = dict(\n", - " nbits=8,\n", - " signal_amplitude=0.00015,\n", - " decimation=10,\n", - " threshold_factor=6.0,\n", - " power_avg_width=2.5,\n", - " max_lookahead=6.5)\n", + "decimation = 100\n", + "extra_dec = 10\n", + "nbits = 5\n", + "signal_amplitude=3e-3,\n", + "\n", + "seed = 42\n", + "\n", + "test_data, signal = generate_test_signal(duration, nbits, signal_amplitude, decimation, seed, data=np.array([4,5,6,7] * 8))\n", + "\n", + "#sosh = sig.butter(6, 1/(2**nbits), btype='highpass', output='sos', fs=decimation)\n", + "#sosl = sig.butter(6, 1.0, btype='lowpass', output='sos', fs=decimation)\n", + "#filtered = sig.sosfilt(sosh, sig.sosfilt(sosl, signal))\n", + "#filtered = sig.sosfilt(sosh, signal)\n", + "\n", + "cor_an1 = correlate(signal, nbits=nbits, decimation=decimation)\n", + "#cor_an2 = correlate(filtered, nbits=nbits, decimation=decimation)\n", + "#cor_an2 = correlate(sig.decimate(signal, 9), nbits=nbits, decimation=decimation//9)\n", + "cor_an2 = correlate(sig.decimate(signal, extra_dec), nbits=nbits, decimation=int(round(decimation/extra_dec)))\n", + "#ax.plot(cor_an[2])\n", + "#ax.matshow(sig.cwt(cor_an[2], sig.ricker, np.arange(1, 64)), aspect='auto')\n", + "cwt_ed1 = sig.cwt(cor_an1[2], sig.ricker, np.arange(1, 130))\n", + "cwt_ed2 = sig.cwt(cor_an2[2], sig.ricker, np.arange(1, 130))\n", + "#for f in [0.73, 1.0]:\n", + "# ax.plot(cwt_ed[int(round(f * decimation))], label=f'{f}')\n", + "\n", + "#ax.plot(signal)\n", + "#ax.twiny().plot(sig.decimate(signal, 9), color='orange')\n", + "#ax.twiny().plot(sig.decimate(signal, 10), color='orange')\n", + "\n", + "#ax.matshow(cwt_ed2, aspect='auto')\n", + "ax.plot(cwt_ed1[int(round(0.73 * decimation))], label=f'unfiltered')\n", + "ax.twinx().twiny().plot(cwt_ed2[int(round(0.73 * decimation / extra_dec))], label=f'filtered', color='orange')\n", "\n", + "#ax.legend()\n", + "#ax.matshow(cor_an[2:4], aspect='auto')\n", + "#ser, br = run_ser_test(**params, sample_duration=100, print=print, seed=seed, ax=ax) #, debug_range=(16100, 16700))\n", "#seed = 0xcbb3b8cf\n", "#for seed in range(10):\n", "# ser, br = run_ser_test(**params, sample_duration=32, print=print, seed=seed) #, debug_range=(16100, 16700))\n", @@ -612,21 +666,21 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "<ipython-input-103-3e101b35c97a>: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", + "<ipython-input-36-8e813d331cd8>: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, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05]})\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84e1acbb31914f5f8b3a031654fd7733", + "model_id": "c05f590a7c0040628c5c5d4032bb7c8e", "version_major": 2, "version_minor": 0 }, @@ -805,7 +859,7 @@ " std = data[:,1]\n", " \n", " ax.set_xlim([min(x), max(x)])\n", - " l = ax.plot(x, y, label='SER @ a=0.5', color='orange')\n", + " l = ax.plot(x, y, label='Amplitude at SER=0.5', color='orange')\n", " \n", " x, y, std = zip(*[ (le_x, le_y, le_std) for le_x, le_y, le_std in zip(x, y, std) if le_y is not None ])\n", " y, std = np.array(y), np.array(std)\n", @@ -815,6 +869,7 @@ " ax.fill_between([-1, min(ser_valid)], 0, 1, facecolor='red', alpha=0.2, transform=trans, zorder=1)\n", " ax.fill_between([max(ser_valid), max(ser_valid)*10], 0, 1, facecolor='red', alpha=0.2, transform=trans)\n", " ax.set_ylim([min(y)*0.9, max(y)*1.1])\n", + " ax.grid()\n", " return l\n", "\n", "l1 = plot_intercepts(intercept_ax)\n", @@ -835,6 +890,124 @@ "source": [ "#fig.savefig('dsss_prototype_symbol_error_rate_5-8_bit.svg')" ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-35-cafaa6062c72>: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, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05]})\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fa93fb31355840148f941dab8b60f51c", + "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": "stderr", + "output_type": "stream", + "text": [ + "<ipython-input-35-cafaa6062c72>:16: RuntimeWarning: divide by zero encountered in log10\n", + " cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n" + ] + } + ], + "source": [ + "fig, ((ax, cbar_ax), (intercept_ax, empty)) = plt.subplots(2, 2, figsize=(12, 9), gridspec_kw={'width_ratios': [1, 0.05]})\n", + "empty.axis('off')\n", + "#fig.tight_layout()\n", + "\n", + "results = []\n", + "for fn in [\n", + " '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-14-10-13.json',\n", + " '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-13-21-57.json',\n", + " '/mnt/c/Users/jaseg/shared/dsss_experiments_res-2020-02-20-13-23-47.json',\n", + "]:\n", + " with open(fn, 'r') as f:\n", + " results += json.load(f)\n", + "\n", + "decimations = [decimation for (_nbits, thf, _reps, _points, _duration, decimation), series in results]\n", + "cmap = matplotlib.cm.viridis\n", + "cm_func = lambda x: cmap(np.log10(x - min(decimations)) / (np.log10(max(decimations)) - np.log10(min(decimations))))\n", + "\n", + "decimation_sers = {}\n", + "for (nbits, thf, reps, points, duration, decimation), series in results:\n", + " data = [ [ mean for mean, _std, _msg in reps if mean is not None ] for _amp, reps in series ]\n", + " amps = [ amp for amp, _reps in series ]\n", + " sers = np.array([ np.mean(values) for values in data ])\n", + " stds = np.array([ np.std(values) for values in data ])\n", + " decimation_sers[decimation] = list(zip(amps, sers, stds))\n", + " \n", + " l, = ax.plot(amps, np.clip(sers, 0, 1), label=f'decimation={decimation}', color=cm_func(decimation))\n", + " ax.fill_between(amps, np.clip(sers + stds, 0, 1), np.clip(sers - stds, 0, 1), facecolor=l.get_color(), alpha=0.2)\n", + " ax.axhline(0.5, color='gray', ls=(0, (3, 4)), lw=0.8)\n", + "ax.grid()\n", + "ax.set_xlabel('Amplitude in mHz')\n", + "ax.set_ylabel('Symbol error rate')\n", + "\n", + "norm = matplotlib.colors.Normalize(vmin=np.log10(min(decimations)), vmax=np.log10(max(decimations)))\n", + "cb1 = matplotlib.colorbar.ColorbarBase(cbar_ax, cmap=cmap, norm=norm, orientation='vertical', label=\"Decimation\", ticks=[np.log10(d) for d in decimations])\n", + "cb1.ax.set_yticklabels([f'{d:.1f}' for d in decimations])\n", + "\n", + "def plot_intercepts(ax, SER_TH = 0.5):\n", + " intercepts = {}\n", + " for dec, sers in decimation_sers.items():\n", + " last_ser, last_amp, last_std = 0, 0, 0\n", + " for amp, ser, std in sorted(sers):\n", + " if last_ser > SER_TH and ser < SER_TH:\n", + " icp = last_amp + (SER_TH - last_ser) / (ser - last_ser) * (amp - last_amp)\n", + " ic_std = abs(last_amp - amp) / 2# np.sqrt(np.mean(last_std**2 + std**2))\n", + " intercepts[dec] = (icp, ic_std)\n", + " break\n", + " last_amp, last_ser = amp, ser\n", + " else:\n", + " intercepts[dec] = None, None\n", + " \n", + " ser_valid = [dec for dec, (ser, _std) in intercepts.items() if ser is not None]\n", + " #ax.axvline(min(ser_valid), color='red')\n", + " #ax.axvline(max(ser_valid), color='red')\n", + " \n", + " x = sorted(intercepts.keys())\n", + " data = np.array([ intercepts[dec] for dec in x ])\n", + " y = data[:,0]\n", + " std = data[:,1]\n", + " \n", + " ax.set_xlim([min(x), max(x)])\n", + " l = ax.plot(x, y, label='Amplitude at SER=0.5', color='orange')\n", + " ax.legend(loc=3)\n", + " ax.grid()\n", + " \n", + " x, y, std = zip(*[ (le_x, le_y, le_std) for le_x, le_y, le_std in zip(x, y, std) if le_y is not None ])\n", + " y, std = np.array(y), np.array(std)\n", + " ax.fill_between(x, y-std, y+std, color=l[0].get_color(), alpha=0.3)\n", + " \n", + " trans = matplotlib.transforms.blended_transform_factory(ax.transData, ax.transAxes)\n", + " ax.fill_between([-1, min(ser_valid)], 0, 1, facecolor='red', alpha=0.2, transform=trans, zorder=1)\n", + " ax.fill_between([max(ser_valid), max(ser_valid)*10], 0, 1, facecolor='red', alpha=0.2, transform=trans)\n", + " ax.set_ylim([min(y)*0.9, max(y)*1.1])\n", + " ax.set_xscale('log')\n", + " ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, _: '{:g}'.format(x)))\n", + " ax.set_xticks([1, 2, 5, 10, 20, 50])\n", + " ax.set_xlim([1, 60])\n", + " return l\n", + "\n", + "l1 = plot_intercepts(intercept_ax)\n" + ] } ], "metadata": { |