mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-12-22 11:33:32 +01:00
245 lines
62 KiB
Text
245 lines
62 KiB
Text
{
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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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"# Emitter/Receiver Simulation with Signals"
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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 numpy as np\n",
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"\n",
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"import matplotlib.pyplot as plt"
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]
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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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"#### Signal"
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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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"from lib import TravelSignal\n",
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"\n",
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"####\n",
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"from scipy.stats import norm\n",
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"\n",
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"sample_rate = 3e2 # Hz\n",
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"interp_sample_rate = sample_rate * 1/10 # Hz\n",
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"\n",
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"t_offset = 8\n",
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"periodic = False\n",
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"\n",
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"time = t_offset + np.arange(0, 1, 1/sample_rate) #s\n",
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"time2 = t_offset + np.arange(-1.5, 1, 1/sample_rate) #s\n",
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"\n",
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"signal = norm.pdf(time, time[len(time)//2], (time[-1] - time[0])/10)\n",
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"\n",
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"if False:\n",
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" mysignal = TravelSignal(signal, sample_rate, t_0 = t_offset, periodic=True)\n",
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" mysignal2 = TravelSignal(signal, sample_rate, t_0 = t_offset, periodic=False)\n",
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"\n",
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" fig, ax = plt.subplots(1, 1, figsize=(16,4))\n",
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" ax.set_title(\"Raw and TravelSignal\")\n",
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" ax.set_ylabel(\"Amplitude\")\n",
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" ax.set_xlabel(\"Time\")\n",
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"\n",
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" ax.plot(time, signal, label='Raw signal')\n",
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" ax.plot(time2, mysignal(time2)+0.5, '.-', label='TravelSignal(periodic)+0.5')\n",
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" ax.plot(time2, mysignal2(time2)-0.5, '.-', label='TravelSignal-0.5')\n",
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"\n",
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" ax.legend()\n",
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"\n",
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" plt.show();"
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]
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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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"## New code"
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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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"from lib.location import Receiver, Emitter"
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]
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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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"## Testing"
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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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"if True:\n",
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" sample_rate = 3e2 # Hz\n",
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" periodic = False\n",
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" \n",
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" t_offset = -0.5\n",
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" t_start = 0\n",
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" t_end = 1\n",
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" time = t_offset + np.arange(t_start, t_end, 1/sample_rate) #s\n",
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"\n",
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"if False:\n",
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" if True:\n",
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" periodic = True\n",
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" freq = sample_rate/8\n",
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" signal = np.cos(2*np.pi*freq*time)\n",
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" else: \n",
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" from scipy.stats import norm\n",
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" signal = norm.pdf(time, time[len(time)//2], (time[-1] - time[0])/10)\n",
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"\n",
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"\n",
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"#####\n",
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"# Setup Signal, Emitter and Antennae\n",
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"\n",
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"rot = lambda phi=0.12345: np.cos(np.array([phi, phi-np.pi/2]))\n",
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"\n",
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"km=1e7\n",
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"\n",
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"mysignal = TravelSignal(signal, sample_rate, t_0 = t_offset, periodic=periodic)\n",
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"\n",
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"source = Emitter([0,0])*km\n",
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"emitted = source.emit(mysignal)\n",
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"\n",
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"antennae = [\n",
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" Receiver(2*km*rot(0)),\n",
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" Receiver(3*km*rot(2)),\n",
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" Receiver(10*km*rot(1)),\n",
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"]"
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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": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
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"text/plain": [
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"<Figure size 144x144 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"########\n",
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"### Geometry Plot\n",
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"########\n",
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"ylabel_kw = {\"rotation\": \"horizontal\", \"va\":\"center\", \"ha\":\"center\", \"labelpad\": 30}\n",
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"\n",
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"fig, axs = plt.subplots(1,1, figsize=(2,2))\n",
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"axs = [ axs ]\n",
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"i = 0\n",
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"axs[i].grid()\n",
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"axs[i].set_title(\"Geometry of Emitter(s) and Antennae\")\n",
|
|
"axs[i].set_ylabel(\"y (m)\", **ylabel_kw)\n",
|
|
"axs[i].set_xlabel(\"x (m)\")\n",
|
|
"axs[i].plot(*source.x, '*', label=\"Emitter\")\n",
|
|
"\n",
|
|
"for j, ant in enumerate(antennae):\n",
|
|
" axs[i].plot(*ant.x, '+', label=\"Antenna {}\".format(j))\n",
|
|
"\n",
|
|
"# make it a square plot\n",
|
|
"axs[i].set_aspect('equal', 'datalim')\n",
|
|
"axs[i].margins(0.1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 864x432 with 4 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"t_longoffset = t_offset\n",
|
|
"t_longstart = t_start\n",
|
|
"t_longend = 2*t_end\n",
|
|
"longtime = t_longoffset + np.arange(t_longstart, t_longend, 1/sample_rate) #s\n",
|
|
"\n",
|
|
"########\n",
|
|
"### Plot Traces\n",
|
|
"########\n",
|
|
"\n",
|
|
"fig, axs = plt.subplots(1+len(antennae),1, sharex=True, figsize=(12,6), gridspec_kw={\"hspace\":0})\n",
|
|
"axs[0].set_title(\"Traces of Emitter and Antennae\")\n",
|
|
"\n",
|
|
"# Emitter\n",
|
|
"i = 0\n",
|
|
"axs[i].set_ylabel(\"Emitter\\n at ({},{})\".format(*source.x), **ylabel_kw)\n",
|
|
"axs[i].set_xlabel(\"Time (s)\")\n",
|
|
"axs[i].plot(time, emitted(time))\n",
|
|
"axs[i].plot(longtime, emitted(longtime))\n",
|
|
"\n",
|
|
"# Antenna\n",
|
|
"for j, ant in enumerate(antennae):\n",
|
|
" i +=1\n",
|
|
" axs[i].set_ylabel(\"Antenna {}\\n at ({},{})\".format(j, *ant.x), **ylabel_kw)\n",
|
|
" axs[i].plot(longtime, ant.recv(emitted)(longtime), label=\"Antenna {}\".format(j))\n",
|
|
" axs[i].set_xlabel(\"Time (s)\")\n",
|
|
" "
|
|
]
|
|
}
|
|
],
|
|
"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
|
|
}
|