kopia lustrzana https://github.com/bristol-seds/pico-tracker
534 wiersze
111 KiB
Plaintext
534 wiersze
111 KiB
Plaintext
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{
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"metadata": {
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"name": ""
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# Reflects an array of coefficients about the last element \n",
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"def sym_fir_coeff(coeff):\n",
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" for i in range(len(coeff)-2, -1, -1):\n",
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" coeff.append(coeff[i])\n",
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"\n",
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" return coeff\n",
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"\n",
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"# Normalises the si's 8-bit coefficients to 0 - 1 \n",
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"def si_normalise_coeff(coeff):\n",
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" coeff = [x-0x100 if x > 0x7f else x for x in coeff]\n",
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" return [x/0x300 for x in coeff] # 0x300 seems to be the value required to get 0 gain at dc.. Not sure about this: Test!\n",
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"\n",
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"def si_coeff(coeff):\n",
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" norm = [int(x*0x300) for x in coeff]\n",
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" return [x+0x100 if x < 0 else x for x in norm]"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 25
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"These are the default si coefficients"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# Default si coefficients \n",
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"si_default_fir_coeff = [0x1, 0x3, 0x8, 0x11, 0x21, 0x36, 0x4d, 0x60, 0x67]\n",
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"si_fir_coeff = si_normalise_coeff(sym_fir_coeff(si_default_fir_coeff))\n",
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"print si_fir_coeff"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[0.0013020833333333333, 0.00390625, 0.010416666666666666, 0.022135416666666668, 0.04296875, 0.0703125, 0.10026041666666667, 0.125, 0.13411458333333334, 0.125, 0.10026041666666667, 0.0703125, 0.04296875, 0.022135416666666668, 0.010416666666666666, 0.00390625, 0.0013020833333333333]\n"
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]
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}
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],
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"prompt_number": 26
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Setup graphs"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"%matplotlib inline\n",
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"from scipy import signal\n",
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"from pylab import *\n",
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"from __future__ import division\n",
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"import matplotlib.pyplot as plt"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 27
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"w,h=signal.freqz(si_fir_coeff,1) # Compute impulse response \n",
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"\n",
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"plt.plot(w/pi,20*log10(abs(h)))\n",
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"plt.grid()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "display_data",
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"png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmUxqhEA6NmsW0KED8Ne/AhUV
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"text": [
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"<matplotlib.figure.Figure at 0x7f62308f7cd0>"
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]
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}
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],
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"prompt_number": 28
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Passband filter"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"\"\"\"\n",
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"sampling frequency: 16000 Hz\n",
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"\n",
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"* 0 Hz - 500 Hz\n",
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" gain = 0\n",
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" desired attenuation = -40 dB\n",
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" actual attenuation = -33.20090552393641 dB\n",
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"\n",
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"* 1200 Hz - 2500 Hz\n",
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" gain = 1\n",
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" desired ripple = 5 dB\n",
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" actual ripple = 7.628523622719886 dB\n",
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"\n",
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"* 3500 Hz - 8000 Hz\n",
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" gain = 0\n",
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" desired attenuation = -40 dB\n",
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" actual attenuation = -33.20090552393641 dB\n",
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"\"\"\"\n",
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"\n",
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"p_fir_coeff = [\n",
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"0.03315273058205705,\n",
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"0.04369271452900486,\n",
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"0.010060937885865288,\n",
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"-0.07363298137328711,\n",
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"-0.14853046126761885,\n",
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"-0.13375882936132458,\n",
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"-0.007516054328839414,\n",
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"0.15276142842227725,\n",
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"0.2256656942570718,\n",
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"0.15276142842227725,\n",
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"-0.007516054328839414,\n",
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"-0.13375882936132458,\n",
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"-0.14853046126761885,\n",
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"-0.07363298137328711,\n",
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"0.010060937885865288,\n",
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"0.04369271452900486,\n",
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"0.03315273058205705,\n",
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"]\n",
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"\n",
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"w,h=signal.freqz([x/2 for x in p_fir_coeff],1) # Compute impulse response \n",
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"\n",
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"plt.plot(w/pi,20*log10(abs(h)))\n",
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"plt.grid()"
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],
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"language": "python",
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"metadata": {},
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|
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"outputs": [
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|
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{
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|
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"metadata": {},
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|
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"output_type": "display_data",
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|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXtYlGX6x78DjOKqieYhBW1UUCAV8IDH3LFExJIyW5NO\ny2q15ZY/t23NbEtbLxmtbCvLyt1VOsta29JBSU1GK0U84WlSURE5hIcUzROoPL8/Hl/eAWaG4T3P\nzP25rrn0Pd/c88z93O/3OZkYYwwEQRCEXxOktwEEQRCE+lCwJwiCCAAo2BMEQQQAFOwJgiACAAr2\nBEEQAQAFe4IgiABAtWCfk5OD6OhoREVFYeHChWo9hiAIgvACkxr97K9du4bevXtj3bp1CA8Px6BB\ng/Dpp58iJiZG6UcRBEEQXqBKZp+fn4/IyEhYLBaYzWZMnjwZ2dnZajyKIAiC8AJVgn1ZWRm6du1a\nux0REYGysjI1HkUQBEF4gSrB3mQyqXFbgiAIQiIhatw0PDwcJSUltdslJSWIiIiocw5VCARBENKQ\n0tSqSmY/cOBAFBYW4ujRo6iurkZWVhZSU1MbnMcYow9jmDNnju42GOVDviBfkC88f6SiSmYfEhKC\nt956C8nJybh27RqmTp1KPXE8cPToUb1NMAzkCxHyhQj5Qj6qBHsASElJQUpKilq3JwiCIJoAjaA1\nAOnp6XqbYBjIFyLkCxHyhXxUGVTl1YNNJln6E0EQRCAiNXZSZm8A7Ha73iYYBvKFCPlChHwhHwr2\nBEEQAQDJOARBED4EyTgEQRCEWyjYGwA5euTVq8DPPwO7dgGbNgEbNwJ2O7BtG3DgAHDmDOBLL1Ck\nzYqQL0TIF/JRrZ89oTwnTgA//ghs3gzs3Qv89BNQWgq0awd06gS0bAmYzYDJBFy4AJw7Bxw/zq+1\nWIC+fYEBA4DERP4xm3X9cwiC0BDS7A0MYzxDz84GvvwSOHYMGDaMf/r1A2JigO7dgRAPVTZjQGUl\ncOQIz/63b+dvAEVFgNUK3HUXMGECEBam2Z9FEIQMpMZOCvYGpKwMeP99IDOTZ+kTJgCpqcDgwUBw\nsDLPOHECWLcO+Owz4LvvgNtvB554Ahg9mj+TIAhjQg20PoygR+7aBTz0EJdbjh4FPvgA2L8fWLCA\nZ/NKBXoA6NgRuP9+4L//BYqLgbFjgb/8BYiNBZYuBaqrlXtWUyBtVoR8IUK+kA8FewNQVMTllJQU\n4JZbgMOHecAdMkSbLDssDHjsMV7ZvPMO8PnnQO/ewLJlvAGYIAjfh2QcHamoAJ57DvjmG2DWLGDa\nNCA0VG+rON9/D/ztb1zvf+st4NZb9baIIAiAZByf4to14O23uVzTsSNQWAg8/bRxAj3Ag7vdDsye\nzeWehx8GTp/W2yqCIKRCwV5jCguBESOA//yHB9OFC4GdO+16m+USkwm47z7exbNtW94DKCdH3WeS\nNitCvhAhX8iHgr1GMAa89x5vaH3gASA3l+vzvkCrVsAbb/AG48cfB6ZP168BlyAIaUjW7FeuXIm5\nc+di//792Lp1K/r37197zGazYdmyZQgODsabb76JMWPGNHxwAGn2v/4KTJnCG14/+QSIjtbbIulU\nVgK//z1w8iSwciUQHq63RQQRWGiu2fft2xdffPEFRo4cWWe/w+FAVlYWHA4HcnJyMG3aNNTU1Eh9\njM+zfz8frdq2LR/M5MuBHuA9d774Ahg/Hhg0CNiyRW+LCILwBsnBPjo6Gr169WqwPzs7G2lpaTCb\nzbBYLIiMjER+fr4sI32VdeuAkSOBZ57hXSndNcD6mh4ZFMR7Ef3rXzzoZ2crd29f84WakC9EyBfy\nUVyzLy8vR0RERO12REQEysrKlH6M4cnM5Nr8Z58BU6fqbY06jBsHrFrFR96++67e1hAE4QmPE6El\nJSWhoqKiwf6MjAyMHz/e64eY3IwMSk9Ph8ViAQCEhYUhPj4eVqsVgFiT++J2RgaweLEdr7wCjBzZ\n+PlWq9VQ9jd1+4cfgOHD7di3D1i8WH97/GlbwCj26LUt7DOKPVpu2+12ZGZmAkBtvJSC7EFVo0aN\nwqJFi2obaBcsWAAAmDVrFgBg7NixeOmllzB48OC6D/bDBlrGgBde4Jr2unVA5856W6QdxcXAqFHA\nU08Bf/6z3tYQhP+i66Aq5wenpqZixYoVqK6uRlFREQoLC5GYmKjEYwwNY8Bf/wp8/TXvP9+UQF8/\ni/NFbr6Z/91vvw0sWSL9Pv7gC6UgX4iQL+QjeT77L774AtOnT8epU6dwxx13ICEhAatXr0ZsbCwm\nTZqE2NhYhISEYMmSJW5lHH/ib38D1q/nn3bt9LZGH7p1A9au5aNvO3YE7r1Xb4sIghCguXEU4OWX\ngeXL+SpRHTrobY3+FBQAY8bwUcJOkitBEApAc+PoxD//yWWLtWsp0AvExwMrVgCTJvGlEQmC0B8K\n9jL49lveILt2LeDU27TJ+KMeedttwPz5wN138+URvcUffSEV8oUI+UI+FOwlsm8fX2hk5UogKkpv\na4zJo49yGefhh4EAHkRNEIaANHsJnDzJp0D4+995wCfcU13Ns/yxY3kjNkEQ8qA1aDXi2jXe+Dh4\nMJCRobc1vkF5OdC/P18Ccdgwva0hCN+GGmg14sUX+Tzv8+Ypd09/1yO7dOHTOz/4IHD2rOdz/d0X\nTYF8IUK+kA8F+ybw1VfAhx/yaYqVXPw7ELjrLiA5GfjTn/S2hCACE5JxvKS0FBgwgE+FQFKENC5e\n5D6cN48GXBGEVEizV5GaGiApiTc0Pv+83tb4Nj/+yPvf793L5/gnCKJpkGavIq+/DlRVAdfndlOc\nQNIjhw/nks7Mma6PB5IvGoN8IUK+kI/kuXEChT17AJsNyM8nnV4pbDagTx8+cRpNp0AQ2kAyjgeu\nXuVdLKdN898FSPTif//jb0p79gBms97WEITvQDKOCrzxBteVp0zR2xL/4667+CyZtMIVQWgDBXs3\nHDnC5Yb33uP96tUkEPVIkwlYtIj3zDlzRtwfiL5wB/lChHwhHwr2LmAMePxx3ojYs6fe1vgvffsC\nEyYoO0CNIAjXkGbvgqwsntVv2waEUBO2qhw/DtxyC7B5M00oRxDeoLlm/9e//hUxMTGIi4vDPffc\ng7NO4+BtNhuioqIQHR2NNWvWSH2ELly8yDP6N9+kQK8FnTrxNWvnztXbEoLwbyQH+zFjxmDfvn3Y\ntWsXevXqBZvNBgBwOBzIysqCw+FATk4Opk2bhhofmt/21VeBIUOAkSO1e2ag65HTp/M1AX76iXzh\nDPlChHwhH8nBPikpCUFB/PLBgwejtLQUAJCdnY20tDSYzWZYLBZERkYiPz9fGWtVpqSEZ/Qvv6y3\nJYFF69bA00/zKaMJglAHRRpoly1bhnHjxgEAysvLEeG0bFNERATKysqUeIzqzJrFJ+q6+WZtn2ul\nkUV48km+WHvHjla9TTEMVC5EyBfy8ahKJyUloaKiosH+jIwMjB8/HgAwf/58NGvWDPfff7/b+5jc\n9F1MT0+HxWIBAISFhSE+Pr72SxVe27Ta/te/7Fi9Gjh2TJ/nB/r2tm12jB8PvPKKFcuX628PbdO2\nUbbtdjsyMzMBoDZeSoLJYPny5WzYsGHs0qVLtftsNhuz2Wy128nJySwvL6/BtTIfrTjjxzP2+uv6\nPDs3N1efBxuMX35hrFWrXFZWprclxoDKhQj5QkRq7JQs4+Tk5OCVV15BdnY2QkNDa/enpqZixYoV\nqK6uRlFREQoLC5GYmCi9NtKAvDygoAD44x/1tiSwadeOzy66eLHelhCE/yG5n31UVBSqq6vRrl07\nAMDQoUOxZMkSAFzmWbZsGUJCQvDGG28gOTm54YMN1M9+9Ghg8mTgkUf0toQ4coSv71tUxBtuCYKo\nC81nL5HcXOCxxwCHgybkMgr33stnw3zySb0tIQjjQROhSSQjgy9IomegFxpjCO6LJ57gcxIZIBfQ\nFSoXIuQL+QR0sN++Hdi/H/DQ
|
||
|
|
"text": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f62308ab190>"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"prompt_number": 29
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"# Another filter design, low pass\n",
|
||
|
|
"rsid_fir_coeff = [x*5.2 for x in [\n",
|
||
|
|
"0.000306, \n",
|
||
|
|
"0.001117, \n",
|
||
|
|
"0.002461, \n",
|
||
|
|
"0.004306, \n",
|
||
|
|
"0.006491, \n",
|
||
|
|
"0.008734, \n",
|
||
|
|
"0.010690, \n",
|
||
|
|
"0.012026, \n",
|
||
|
|
"0.012500, \n",
|
||
|
|
"0.012026, \n",
|
||
|
|
"0.010690, \n",
|
||
|
|
"0.008734, \n",
|
||
|
|
"0.006491, \n",
|
||
|
|
"0.004306, \n",
|
||
|
|
"0.002461, \n",
|
||
|
|
"0.001117, \n",
|
||
|
|
"0.000306]]\n",
|
||
|
|
"\n",
|
||
|
|
"w,h=signal.freqz(rsid_fir_coeff,1) # Compute impulse response \n",
|
||
|
|
"\n",
|
||
|
|
"plt.plot(w/pi,20*log10(abs(h)))\n",
|
||
|
|
"plt.grid()"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data",
|
||
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzka7du2Qm5uL++67D1lZWTWJ
|
||
|
|
"text": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f623070ea50>"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"prompt_number": 70
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Convert to si hex"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"rsid_si_coeff_values = si_coeff(rsid_fir_coeff)[0:9]\n",
|
||
|
|
"\n",
|
||
|
|
"print rsid_si_coeff_values\n",
|
||
|
|
"print \", \".join(hex(n) for n in rsid_si_coeff_values)"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"stream": "stdout",
|
||
|
|
"text": [
|
||
|
|
"[1, 4, 9, 17, 25, 34, 42, 48, 49]\n",
|
||
|
|
"0x1, 0x4, 0x9, 0x11, 0x19, 0x22, 0x2a, 0x30, 0x31\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"prompt_number": 71
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"and back again (to simulate precision loss)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"rsid_si_fir_coeff = si_normalise_coeff(sym_fir_coeff(rsid_si_coeff_values))\n",
|
||
|
|
"print rsid_si_fir_coeff"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"stream": "stdout",
|
||
|
|
"text": [
|
||
|
|
"[0.0013020833333333333, 0.005208333333333333, 0.01171875, 0.022135416666666668, 0.032552083333333336, 0.044270833333333336, 0.0546875, 0.0625, 0.06380208333333333, 0.0625, 0.0546875, 0.044270833333333336, 0.032552083333333336, 0.022135416666666668, 0.01171875, 0.005208333333333333, 0.0013020833333333333]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"prompt_number": 72
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"w,h=signal.freqz(rsid_si_fir_coeff,1) # Compute impulse response \n",
|
||
|
|
"\n",
|
||
|
|
"plt.plot(w/pi,20*log10(abs(h)))\n",
|
||
|
|
"plt.grid()"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data",
|
||
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns2bNKo/Hjx+P3bt3Y8eOHRg4
|
||
|
|
"text": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f6230b704d0>"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"prompt_number": 73
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Test RSID data"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"rsid_symbol_rate = 11025.0/1024\n",
|
||
|
|
"sqw_frequency = 2000 # approx 2kHz\n",
|
||
|
|
"\n",
|
||
|
|
"# The ratio between the rsid frequency step and the channel frequency step\n",
|
||
|
|
"# In our case the channel frequency step is bigger than the rsid frequency step so we try to \"interpolate\"\n",
|
||
|
|
"ratio = rsid_symbol_rate/7.805\n",
|
||
|
|
"\n",
|
||
|
|
"rsid_data = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 5, 2, 12, 0, 11, 4, 6, 2, 10, 11,1 ,8, 12, 13, 7, 4, 15, 12]\n",
|
||
|
|
"\n",
|
||
|
|
"# The number of sqw periods per symbol period\n",
|
||
|
|
"sqw_periods = int(round(sqw_frequency/rsid_symbol_rate))"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 74
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"tones = [(round(t), t-round(t)) for t in [x*ratio for x in rsid_data]]\n",
|
||
|
|
"print tones"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"stream": "stdout",
|
||
|
|
"text": [
|
||
|
|
"[(0.0, 0.0), (1.0, 0.3794492713004485), (3.0, -0.24110145739910305), (4.0, 0.13834781390134587), (6.0, -0.4822029147982061), (7.0, -0.10275364349775806), (8.0, 0.27669562780269175), (10.0, -0.3438551008968602), (11.0, 0.03559417040358781), (12.0, 0.41504344170403584), (14.0, -0.20550728699551613), (15.0, 0.17394198430493368), (17.0, -0.4466087443946165), (18.0, -0.06715947309416848), (19.0, 0.31228979820627956), (21.0, -0.3082609304932724), (7.0, -0.10275364349775806), (3.0, -0.24110145739910305), (17.0, -0.4466087443946165), (0.0, 0.0), (15.0, 0.17394198430493368), (6.0, -0.4822029147982061), (8.0, 0.27669562780269175), (3.0, -0.24110145739910305), (14.0, -0.20550728699551613), (15.0, 0.17394198430493368), (1.0, 0.3794492713004485), (11.0, 0.03559417040358781), (17.0, -0.4466087443946165), (18.0, -0.06715947309416848), (10.0, -0.3438551008968602), (6.0, -0.4822029147982061), (21.0, -0.3082609304932724), (17.0, -0.4466087443946165)]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"prompt_number": 75
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"def get_frac_signal(sr=16000):\n",
|
||
|
|
" signal = []\n",
|
||
|
|
" sqw_width=int(round(sr/sqw_frequency))\n",
|
||
|
|
" \n",
|
||
|
|
" #print \"Width: \"+str(sqw_width)\n",
|
||
|
|
" \n",
|
||
|
|
" for t in tones:\n",
|
||
|
|
" (inte, frac) = t\n",
|
||
|
|
" for i in range(sqw_periods):\n",
|
||
|
|
" high = int(round(sqw_width*(0.5+frac))) #(1+frac)/2)\n",
|
||
|
|
" #print \"High: \"+str(high)\n",
|
||
|
|
" for j in range(high):\n",
|
||
|
|
" signal.append(1)\n",
|
||
|
|
" for j in range(sqw_width-high):\n",
|
||
|
|
" signal.append(-1)\n",
|
||
|
|
" return signal\n",
|
||
|
|
"\n",
|
||
|
|
"def get_inte_signal(sr=16000):\n",
|
||
|
|
" signal = []\n",
|
||
|
|
" \n",
|
||
|
|
" for t in tones:\n",
|
||
|
|
" (inte, frac) = t\n",
|
||
|
|
" for i in range(int(round(sr/rsid_symbol_rate))):\n",
|
||
|
|
" signal.append(inte)\n",
|
||
|
|
" \n",
|
||
|
|
" return signal\n",
|
||
|
|
"\n",
|
||
|
|
"def get_perfect_signal(sr=16000):\n",
|
||
|
|
" signal = []\n",
|
||
|
|
" \n",
|
||
|
|
" for t in rsid_data:\n",
|
||
|
|
" for i in range(int(round(sr/rsid_symbol_rate))):\n",
|
||
|
|
" signal.append(t*ratio)\n",
|
||
|
|
" \n",
|
||
|
|
" return signal"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 76
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"# Returns rsid-like signal as a list of modem output channels over time\n",
|
||
|
|
"def get_rsid_channel_series(sr=16000):\n",
|
||
|
|
" inte = get_inte_signal(sr)\n",
|
||
|
|
" frac = get_frac_signal(sr)\n",
|
||
|
|
" return [x + y for x, y in zip(inte,frac)]\n",
|
||
|
|
" \n",
|
||
|
|
"\n",
|
||
|
|
"channel_data = get_rsid_channel_series(16000)\n",
|
||
|
|
"plt.plot(channel_data)"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "pyout",
|
||
|
|
"prompt_number": 77,
|
||
|
|
"text": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7f6230755150>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data",
|
||
|
|
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|
||
|
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"text": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f6230a39390>"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"prompt_number": 77
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## What happens when we add a filter"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"# Which fir filter to use\n",
|
||
|
|
"fir = rsid_si_fir_coeff"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 78
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"# Returns rsid-like signal as a list of modem output channels over time\n",
|
||
|
|
"def get_rsid_channel_series_filtered_frac(sr=16000):\n",
|
||
|
|
" inte = get_inte_signal(sr)\n",
|
||
|
|
" frac = get_frac_signal(sr)\n",
|
||
|
|
" filtered_frac = convolve(frac, fir, mode='valid')\n",
|
||
|
|
" return [x+y for x, y in zip(inte,filtered_frac)]"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 79
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"sr = 16000\n",
|
||
|
|
"\n",
|
||
|
|
"filtered_channel_data = get_rsid_channel_series_filtered_frac(sr)\n",
|
||
|
|
"plt.plot(filtered_channel_data)\n",
|
||
|
|
"\n",
|
||
|
|
"perfect = get_perfect_signal(sr)\n",
|
||
|
|
"plt.plot(perfect)\n",
|
||
|
|
"\n",
|
||
|
|
"inte = get_inte_signal(sr)\n",
|
||
|
|
"#plt.plot(inte)"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data",
|
||
|
|
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|
||
|
|
"text": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f6230509090>"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"prompt_number": 80
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 40
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 18
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 18
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 18
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|