244 lines
20 KiB
Plaintext
244 lines
20 KiB
Plaintext
{
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"cells": [
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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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"### Recover timing errors from a list of antennas\n",
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"\n",
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"timing t_i, initial time y, \\\n",
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"distance d(i) to receiver i from emit, \\\n",
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"error s_i\n",
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"\n",
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"\n",
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"t_a = y + d(a)/v + s_a \\\n",
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"t_b = y + d(b)/v + s_b\n",
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"\n",
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"-----\n",
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"\n",
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"y = t_a - d(a)/v + s_a\n",
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"\n",
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"t_b = t_a - d(a)/v + s_a + d(b)/v + s_b \\\n",
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" = t_a - d(a)/v + d(b)/v + s_b + s_a\n",
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"\n",
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"s_b - s_a = t_b - t_a + (d(b) - d(a))/v"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"rng = np.random.default_rng(12345)"
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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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"N = 50\n",
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"D = 2\n",
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"emit_location = rng.random((1, D))\n",
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"velocity = 1\n",
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"\n",
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"\n",
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"def distance(x, y = emit_location):\n",
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" return np.sqrt( np.sum((x - y)**2, axis=-1) )\n",
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"\n",
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"actual_err_timings = 2 * rng.random(N) - 1\n",
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"if not True:\n",
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" # undo timing errors\n",
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" actual_err_timings = np.zeros_like(actual_err_timings)\n",
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"\n",
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"actual_locations = rng.random((N, D))\n",
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"\n",
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"actual_timings = distance(actual_locations)/velocity"
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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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"# Create data to work with\n",
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"timings = actual_timings + actual_err_timings\n",
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"\n",
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"## use the first number as reference\n",
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"ref_timing = timings[0]\n",
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"timings = timings\n",
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"\n",
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"ref_err_timing = actual_err_timings[0]\n",
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"err_timings = actual_err_timings\n",
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"\n",
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"ref_location = actual_locations[0]\n",
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"locations = actual_locations"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" \t Timing \t Error \t Location\n",
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"0 \t 1.2114015836686018 \t 0.7973654573327341 \t [0.61682898 0.17632632]\n",
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"1 \t 0.8223537613300713 \t 0.6762546707509746 \t [0.30438839 0.44088681]\n",
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"2 \t 0.5164764930422616 \t 0.391109550601909 \t [0.15020234 0.21792886]\n",
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"3 \t 0.626893985846481 \t 0.33281392786638453 \t [0.47433312 0.47636886]\n",
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"4 \t 0.632169930455275 \t 0.5983087535871898 \t [0.25523235 0.29756527]\n",
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"5 \t 0.2631030324439694 \t 0.18673418560371335 \t [0.27906712 0.26057921]\n",
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"6 \t 0.9488374167867456 \t 0.6727560440146213 \t [0.48276159 0.21197904]\n",
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"7 \t 1.2192047533568204 \t 0.9418028652699372 \t [0.4956306 0.24626133]\n",
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"8 \t 0.8744783753275451 \t 0.248245714629571 \t [0.83848265 0.18013059]\n",
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"9 \t 1.5986254396525794 \t 0.9488811518333182 \t [0.86215629 0.17829944]\n",
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"10 \t 1.267555999078216 \t 0.6672374531003724 \t [0.75053133 0.6111204 ]\n",
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"---\n",
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"Ref: \t 1.2114015836686018 \t 0.7973654573327341 \t [0.61682898 0.17632632]\n"
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]
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},
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{
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"data": {
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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# show data\n",
|
|
"N_clip = min(N, 10)\n",
|
|
"print(\" \", \"\\t\", \"Timing \", \"\\t\", \"Error \", \"\\t\", \"Location\")\n",
|
|
"for i, _ in enumerate(timings):\n",
|
|
" print(i, \"\\t\", timings[i], \"\\t\", err_timings[i], \"\\t\", locations[i])\n",
|
|
" if i > N_clip - 1:\n",
|
|
" break\n",
|
|
"print('---')\n",
|
|
"print(\"Ref:\", \"\\t\", ref_timing, \"\\t\", ref_err_timing, \"\\t\", ref_location)\n",
|
|
"\n",
|
|
"# plot\n",
|
|
"if True:\n",
|
|
" fig, ax = plt.subplots()\n",
|
|
" ax.plot(distance(locations, 0), actual_timings, 'o', label='actual')\n",
|
|
" ax.plot(distance(locations, 0), timings, 'o', label='with errors' )\n",
|
|
" ax.set_xlabel(\"Distance\")\n",
|
|
" ax.set_ylabel(\"Timing\")\n",
|
|
" ax.legend()\n",
|
|
" plt.show();"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Clipped to [:10]:\n",
|
|
"[ 0. -0.26793704 -0.28866918 -0.11995607 -0.38017495 -0.33766728\n",
|
|
" -0.13795475 -0.13663424 0.21219653 0.23570816]\n",
|
|
"[ 0. -0.38904782 -0.69492509 -0.5845076 -0.57923165 -0.94829855\n",
|
|
" -0.26256417 0.00780317 -0.33692321 0.38722386]\n",
|
|
"---- + \n",
|
|
"Calc [ 0. -0.12111079 -0.40625591 -0.46455153 -0.1990567 -0.61063127\n",
|
|
" -0.12460941 0.14443741 -0.54911974 0.15151569]\n",
|
|
"---------------\n",
|
|
"Correct result: True\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# s_b - s_a = t_b - t_a - (d(b) - d(a))/v\n",
|
|
"\n",
|
|
"calc_err_timings = timings - ref_timing - (distance(locations) - distance(ref_location))/velocity\n",
|
|
"\n",
|
|
"N_clip = min(N, 10)\n",
|
|
"\n",
|
|
"print(\"Clipped to [:{}]:\".format(N_clip))\n",
|
|
"print((distance(locations) - distance(ref_location))[:N_clip])\n",
|
|
"print((timings - ref_timing)[:N_clip])\n",
|
|
"print('---- + ')\n",
|
|
"\n",
|
|
"print(\"Calc\", calc_err_timings[:N_clip])\n",
|
|
"\n",
|
|
"print('---'*5)\n",
|
|
"print(\"Correct result:\", np.allclose(calc_err_timings + ref_err_timing, actual_err_timings))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"---------------\n",
|
|
"Actual error: [0.79736546 0.67625467 0.39110955 0.33281393 0.59830875 0.18673419\n",
|
|
" 0.67275604 0.94180287 0.24824571 0.94888115 0.66723745 0.09589794\n",
|
|
" 0.44183967 0.88647992 0.6974535 0.32647286 0.73392816 0.22013496\n",
|
|
" 0.08159457 0.1598956 0.34010018 0.46519315 0.26642103 0.8157764\n",
|
|
" 0.19329439 0.12946908 0.09166475 0.59856801 0.8547419 0.60162124\n",
|
|
" 0.93198836 0.72478136 0.86055132 0.9293378 0.54618601 0.93767296\n",
|
|
" 0.49498794 0.27377318 0.45177871 0.66503892 0.33089093 0.90345401\n",
|
|
" 0.25707418 0.33982834 0.2588534 0.35544648 0.00502233 0.62860454\n",
|
|
" 0.28238271 0.06808769]\n",
|
|
"Calc+ref error: [0.79736546 0.67625467 0.39110955 0.33281393 0.59830875 0.18673419\n",
|
|
" 0.67275604 0.94180287 0.24824571 0.94888115 0.66723745 0.09589794\n",
|
|
" 0.44183967 0.88647992 0.6974535 0.32647286 0.73392816 0.22013496\n",
|
|
" 0.08159457 0.1598956 0.34010018 0.46519315 0.26642103 0.8157764\n",
|
|
" 0.19329439 0.12946908 0.09166475 0.59856801 0.8547419 0.60162124\n",
|
|
" 0.93198836 0.72478136 0.86055132 0.9293378 0.54618601 0.93767296\n",
|
|
" 0.49498794 0.27377318 0.45177871 0.66503892 0.33089093 0.90345401\n",
|
|
" 0.25707418 0.33982834 0.2588534 0.35544648 0.00502233 0.62860454\n",
|
|
" 0.28238271 0.06808769]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"if True:\n",
|
|
" print('---'*5)\n",
|
|
" print(\"Actual error:\", actual_err_timings)\n",
|
|
" print(\"Calc+ref error:\", calc_err_timings + ref_err_timing)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|