wsl-windows/lab: Initial commit of DSSS experiements
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jaseg
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408
lab-windows/dsss_experiments.ipynb
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408
lab-windows/dsss_experiments.ipynb
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{
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"cells": [
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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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"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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"\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": 2,
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"#colorednoise.powerlaw_psd_gaussian(1, int(1e4))"
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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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"# 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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" print(seq1.shape, seq2.shape)\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": 5,
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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": "7560730a2391425ab9dad7a1f22e5fb2",
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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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"(31,) (31,)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x7f0496c50b80>"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fig, ax = plt.subplots()\n",
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"ax.matshow(gold(5))"
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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, code=29):\n",
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" # 0, 1 -> -1, 1\n",
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" mask = gold(nbits)[code]*2 - 1\n",
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" # same here\n",
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" data_centered = (data*2 - 1)\n",
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" return (mask[:, np.newaxis] @ data_centered[np.newaxis, :] + 1).T.flatten() //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, code=29, decimation=1):\n",
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" # 0, 1 -> -1, 1\n",
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" mask = np.repeat(gold(nbits)[code]*2 -1, decimation)\n",
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" # center\n",
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" sequence -= np.mean(sequence)\n",
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" return np.correlate(sequence, mask, mode='full')"
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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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"(31,) (31,)\n"
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]
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},
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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": "eb7e7e5d7dfe4e00b18c4e5038c11182",
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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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"(31,) (31,)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7f0494537dc0>]"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"foo = modulate(np.array([0, 1, 0, 0, 1, 1, 1, 0])).astype(float)\n",
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"fig, ax = plt.subplots()\n",
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"ax.plot(correlate(foo))"
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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": 9,
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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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"(31,) (31,)\n"
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]
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},
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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": "c693349edfe843d6adab192d6a95c4dd",
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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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"(31,) (31,)\n",
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"(31,) (31,)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"(2.0, 1.0311014124075548)"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"decimation = 10\n",
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"signal_amplitude = 2.0\n",
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"nbits = 5\n",
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"\n",
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"foo = np.repeat(modulate(np.array([0, 1, 0, 0, 1, 1, 1, 0]), nbits) * 2.0 - 1, decimation) * signal_amplitude\n",
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"noise = colorednoise.powerlaw_psd_gaussian(1, len(foo))\n",
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"\n",
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"sosh = sig.butter(4, 0.01, btype='highpass', output='sos', fs=decimation)\n",
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"sosl = sig.butter(6, 1.0, btype='lowpass', output='sos', fs=decimation)\n",
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"filtered = sig.sosfilt(sosh, sig.sosfilt(sosl, foo + noise))\n",
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"#filtered = sig.sosfilt(sosh, foo + noise)\n",
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"\n",
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"fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(2, 2, figsize=(16, 9))\n",
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"fig.tight_layout()\n",
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"\n",
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"ax1.plot(foo + noise)\n",
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"ax1.plot(foo)\n",
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"ax1.set_title('raw')\n",
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"\n",
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"ax2.plot(filtered)\n",
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"ax2.plot(foo)\n",
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"ax2.set_title('filtered')\n",
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"\n",
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"ax3.plot(correlate(foo + noise, nbits=nbits, decimation=decimation))\n",
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"ax3.set_title('corr raw')\n",
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" \n",
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"ax3.grid()\n",
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"\n",
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"ax4.plot(correlate(filtered, nbits=nbits, decimation=decimation))\n",
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"ax4.set_title('corr filtered')\n",
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"ax4.grid()\n",
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"\n",
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"rms = lambda x: np.sqrt(np.mean(np.square(x)))\n",
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"rms(foo), rms(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": 19,
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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": 54,
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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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"(63,) (63,)\n",
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"(63,) (63,)\n",
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"(63,) (63,)\n"
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]
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},
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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-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",
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" fig, ((ax1, ax3, ax5), (ax2, ax4, ax6)) = plt.subplots(2, 3, figsize=(16, 9))\n"
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]
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},
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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": "f58125333c294cb1b426b735829c30c5",
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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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"data": {
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"text/plain": [
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"(0.002, 0.012591236)"
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]
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},
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"execution_count": 54,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"decimation = 10\n",
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"signal_amplitude = 2.0e-3\n",
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"nbits = 6\n",
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"\n",
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"foo = np.repeat(modulate(np.array([0, 1, 0, 0, 1, 1, 1, 0]), nbits) * 2.0 - 1, decimation) * signal_amplitude\n",
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"noise = np.resize(mains_noise, len(foo))\n",
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"\n",
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"sosh = sig.butter(12, 0.05, btype='highpass', output='sos', fs=decimation)\n",
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"sosl = sig.butter(6, 1.0, btype='lowpass', output='sos', fs=decimation)\n",
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"#filtered = sig.sosfilt(sosh, sig.sosfilt(sosl, foo + noise))\n",
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"filtered = sig.sosfilt(sosh, foo + noise)\n",
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"\n",
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"cor1 = correlate(foo + noise, nbits=nbits, decimation=decimation)\n",
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"cor2 = correlate(filtered, nbits=nbits, decimation=decimation)\n",
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"\n",
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"sosn = sig.butter(12, 4, btype='highpass', output='sos', fs=decimation)\n",
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"#cor1_flt = sig.sosfilt(sosn, cor1)\n",
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"#cor2_flt = sig.sosfilt(sosn, cor2)\n",
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"cor1_flt = cor1[1:] - cor1[:-1]\n",
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"cor2_flt = cor2[1:] - cor2[:-1]\n",
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"\n",
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"fig, ((ax1, ax3, ax5), (ax2, ax4, ax6)) = plt.subplots(2, 3, figsize=(16, 9))\n",
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"fig.tight_layout()\n",
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"\n",
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"ax1.plot(foo + noise)\n",
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"ax1.plot(foo)\n",
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"ax1.set_title('raw')\n",
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"\n",
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"ax2.plot(filtered)\n",
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"ax2.plot(foo)\n",
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"ax2.set_title('filtered')\n",
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"\n",
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"ax3.plot(cor1)\n",
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"ax3.set_title('corr raw')\n",
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"ax3.grid()\n",
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"\n",
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"ax4.plot(cor2)\n",
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"ax4.set_title('corr filtered')\n",
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"ax4.grid()\n",
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"\n",
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"ax5.plot(cor1_flt)\n",
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"ax5.set_title('corr raw (highpass)')\n",
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"ax5.grid()\n",
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"\n",
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"ax6.plot(cor2_flt)\n",
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"ax6.set_title('corr filtered (highpass)')\n",
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"ax6.grid()\n",
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"\n",
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"rms = lambda x: np.sqrt(np.mean(np.square(x)))\n",
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"rms(foo), rms(noise)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "labenv",
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"language": "python",
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"name": "labenv"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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