From e9f7c87d38b214183b87fa7846339c320282b36c Mon Sep 17 00:00:00 2001
From: jaseg <git-bigdata-wsl-arch@jaseg.de>
Date: Sun, 16 Feb 2020 17:05:14 +0000
Subject: wsl-windows/lab: Initial commit of DSSS experiements
---
lab-windows/dsss_experiments.ipynb | 408 +++++++++++++++++++++++++++++++++++++
1 file changed, 408 insertions(+)
create mode 100644 lab-windows/dsss_experiments.ipynb
(limited to 'lab-windows')
diff --git a/lab-windows/dsss_experiments.ipynb b/lab-windows/dsss_experiments.ipynb
new file mode 100644
index 0000000..bb56362
--- /dev/null
+++ b/lab-windows/dsss_experiments.ipynb
@@ -0,0 +1,408 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "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",
+ "\n",
+ "import colorednoise\n",
+ "\n",
+ "np.set_printoptions(linewidth=240)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib widget"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#colorednoise.powerlaw_psd_gaussian(1, int(1e4))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "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",
+ " print(seq1.shape, seq2.shape)\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": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "7560730a2391425ab9dad7a1f22e5fb2",
+ "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": [
+ "(31,) (31,)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x7f0496c50b80>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "ax.matshow(gold(5))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def modulate(data, nbits=5, code=29):\n",
+ " # 0, 1 -> -1, 1\n",
+ " mask = gold(nbits)[code]*2 - 1\n",
+ " # same here\n",
+ " data_centered = (data*2 - 1)\n",
+ " return (mask[:, np.newaxis] @ data_centered[np.newaxis, :] + 1).T.flatten() //2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def correlate(sequence, nbits=5, code=29, decimation=1):\n",
+ " # 0, 1 -> -1, 1\n",
+ " mask = np.repeat(gold(nbits)[code]*2 -1, decimation)\n",
+ " # center\n",
+ " sequence -= np.mean(sequence)\n",
+ " return np.correlate(sequence, mask, mode='full')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(31,) (31,)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "eb7e7e5d7dfe4e00b18c4e5038c11182",
+ "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": [
+ "(31,) (31,)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7f0494537dc0>]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "foo = modulate(np.array([0, 1, 0, 0, 1, 1, 1, 0])).astype(float)\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.plot(correlate(foo))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(31,) (31,)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "c693349edfe843d6adab192d6a95c4dd",
+ "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": [
+ "(31,) (31,)\n",
+ "(31,) (31,)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(2.0, 1.0311014124075548)"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "decimation = 10\n",
+ "signal_amplitude = 2.0\n",
+ "nbits = 5\n",
+ "\n",
+ "foo = np.repeat(modulate(np.array([0, 1, 0, 0, 1, 1, 1, 0]), nbits) * 2.0 - 1, decimation) * signal_amplitude\n",
+ "noise = colorednoise.powerlaw_psd_gaussian(1, len(foo))\n",
+ "\n",
+ "sosh = sig.butter(4, 0.01, 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, foo + noise))\n",
+ "#filtered = sig.sosfilt(sosh, foo + noise)\n",
+ "\n",
+ "fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(2, 2, figsize=(16, 9))\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax1.plot(foo + noise)\n",
+ "ax1.plot(foo)\n",
+ "ax1.set_title('raw')\n",
+ "\n",
+ "ax2.plot(filtered)\n",
+ "ax2.plot(foo)\n",
+ "ax2.set_title('filtered')\n",
+ "\n",
+ "ax3.plot(correlate(foo + noise, nbits=nbits, decimation=decimation))\n",
+ "ax3.set_title('corr raw')\n",
+ " \n",
+ "ax3.grid()\n",
+ "\n",
+ "ax4.plot(correlate(filtered, nbits=nbits, decimation=decimation))\n",
+ "ax4.set_title('corr filtered')\n",
+ "ax4.grid()\n",
+ "\n",
+ "rms = lambda x: np.sqrt(np.mean(np.square(x)))\n",
+ "rms(foo), rms(noise)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "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": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(63,) (63,)\n",
+ "(63,) (63,)\n",
+ "(63,) (63,)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "<ipython-input-54-34e6ee3f3fc5>:22: 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, ax5), (ax2, ax4, ax6)) = plt.subplots(2, 3, figsize=(16, 9))\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "f58125333c294cb1b426b735829c30c5",
+ "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"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(0.002, 0.012591236)"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "decimation = 10\n",
+ "signal_amplitude = 2.0e-3\n",
+ "nbits = 6\n",
+ "\n",
+ "foo = np.repeat(modulate(np.array([0, 1, 0, 0, 1, 1, 1, 0]), nbits) * 2.0 - 1, decimation) * signal_amplitude\n",
+ "noise = np.resize(mains_noise, len(foo))\n",
+ "\n",
+ "sosh = sig.butter(12, 0.05, 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, foo + noise))\n",
+ "filtered = sig.sosfilt(sosh, foo + noise)\n",
+ "\n",
+ "cor1 = correlate(foo + noise, nbits=nbits, decimation=decimation)\n",
+ "cor2 = correlate(filtered, nbits=nbits, decimation=decimation)\n",
+ "\n",
+ "sosn = sig.butter(12, 4, btype='highpass', output='sos', fs=decimation)\n",
+ "#cor1_flt = sig.sosfilt(sosn, cor1)\n",
+ "#cor2_flt = sig.sosfilt(sosn, cor2)\n",
+ "cor1_flt = cor1[1:] - cor1[:-1]\n",
+ "cor2_flt = cor2[1:] - cor2[:-1]\n",
+ "\n",
+ "fig, ((ax1, ax3, ax5), (ax2, ax4, ax6)) = plt.subplots(2, 3, figsize=(16, 9))\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax1.plot(foo + noise)\n",
+ "ax1.plot(foo)\n",
+ "ax1.set_title('raw')\n",
+ "\n",
+ "ax2.plot(filtered)\n",
+ "ax2.plot(foo)\n",
+ "ax2.set_title('filtered')\n",
+ "\n",
+ "ax3.plot(cor1)\n",
+ "ax3.set_title('corr raw')\n",
+ "ax3.grid()\n",
+ "\n",
+ "ax4.plot(cor2)\n",
+ "ax4.set_title('corr filtered')\n",
+ "ax4.grid()\n",
+ "\n",
+ "ax5.plot(cor1_flt)\n",
+ "ax5.set_title('corr raw (highpass)')\n",
+ "ax5.grid()\n",
+ "\n",
+ "ax6.plot(cor2_flt)\n",
+ "ax6.set_title('corr filtered (highpass)')\n",
+ "ax6.grid()\n",
+ "\n",
+ "rms = lambda x: np.sqrt(np.mean(np.square(x)))\n",
+ "rms(foo), rms(noise)"
+ ]
+ }
+ ],
+ "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
+}
--
cgit