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Finish DSSS demodulation stage 1

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jaseg 2020-03-06 12:39:21 +01:00
parent e4693349cf
commit 55ebbcbdbc
5 changed files with 167 additions and 58 deletions
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103
lab-windows/scratch.ipynb
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@ -2,11 +2,12 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import csv\n",
"\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
@ -17,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@ -170,13 +171,105 @@
"source": [
"sig.butter(8, 20e-3, output='sos', fs=10.0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "655ce5c77d2a4047905245df39a095b0",
"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": [
"[<matplotlib.lines.Line2D at 0x7f4609ce6a90>]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"ax.plot([0-0.00012937261, 0-0.00022784119 , 0-0.00039295876 , 0-0.00066361829 , 00-0.0010971602 , 00-0.0017754816 ,\n",
" 00-0.0028116399 , 00-0.0043560231 , 00-0.0066005666 , 00-0.0097788338 , 000-0.014159188 , 000-0.020027947 ,\n",
" 000-0.027659611 , 000-0.037272236 , 000-0.048968014 , 0000-0.06266222 , 0000-0.07800759 , 000-0.094325546 ,\n",
" 0000-0.11055938 , 0000-0.12526666 , 0000-0.13666715 , 0000-0.14275811 , 0000-0.14149973 , 00000-0.1310612 ,\n",
" 0000-0.11010384 , 000-0.078063987 , 000-0.035389599 , 00000.016317957 , 00000.074297836 , 000000.13478363 ,\n",
" 000000.19331697 , 000000.24519242 , 000000.28597909 , 000000.31204596 , 000000.32101141 , 000000.31204596 ,\n",
" 000000.28597909 , 000000.24519242 , 000000.19331697 , 000000.13478363 , 00000.074297836 , 00000.016317957 ,\n",
" 000-0.035389599 , 000-0.078063987 , 0000-0.11010384 , 00000-0.1310612 , 0000-0.14149973 , 0000-0.14275811 ,\n",
" 0000-0.13666715 , 0000-0.12526666 , 0000-0.11055938 , 000-0.094325546 , 0000-0.07800759 , 0000-0.06266222 ,\n",
" 000-0.048968014 , 000-0.037272236 , 000-0.027659611 , 000-0.020027947 , 000-0.014159188 , 00-0.0097788338 ,\n",
" 00-0.0066005666 , 00-0.0043560231 , 00-0.0028116399 , 00-0.0017754816 , 00-0.0010971602 , 0-0.00066361829 ,\n",
" 0-0.00039295876 , 0-0.00022784119 , 0-0.00012937261 ])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"data = np.genfromtxt('/tmp/foo.csv', delimiter=',')[1000:]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7c771472882e4ceeb8aeddfa5c08ca17",
"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"
}
],
"source": [
"fig, axs = plt.subplots(2, figsize=(15, 9), sharex=True)\n",
"axs = axs.flatten()\n",
"axs[0].set_title('corr')\n",
"axs[1].set_title('cwt')\n",
"#axs[2].set_title('iir')\n",
"\n",
"axs[0].plot(data[:,0], label='corr')\n",
"axs[1].plot(data[:,1], label='cwt')\n",
"axs[0].plot(data[:,2], label='avg')\n",
"axs[1].plot(data[:,2], label='avg')\n",
"\n",
"for ax in axs:\n",
" ax.legend()\n",
" ax.grid()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "winlabenv",
"display_name": "labenv",
"language": "python",
"name": "winlabenv"
"name": "labenv"
},
"language_info": {
"codemirror_mode": {
@ -188,7 +281,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
"version": "3.8.1"
}
},
"nbformat": 4,