m-thesis-introduction/simulations/03_emitter_receiver_simulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Emitter/Receiver Simulation with Signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib import TravelSignal\n",
    "\n",
    "####\n",
    "from scipy.stats import norm\n",
    "\n",
    "sample_rate = 3e2 # Hz\n",
    "interp_sample_rate = sample_rate * 1/10 # Hz\n",
    "\n",
    "t_offset = 8\n",
    "periodic = False\n",
    "\n",
    "time   = t_offset + np.arange(0, 1, 1/sample_rate) #s\n",
    "time2  = t_offset + np.arange(-1.5, 1, 1/sample_rate) #s\n",
    "\n",
    "signal = norm.pdf(time, time[len(time)//2], (time[-1] - time[0])/10)\n",
    "\n",
    "if False:\n",
    "    mysignal = TravelSignal(signal, sample_rate, t_0 = t_offset, periodic=True)\n",
    "    mysignal2 = TravelSignal(signal, sample_rate, t_0 = t_offset, periodic=False)\n",
    "\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(16,4))\n",
    "    ax.set_title(\"Raw and TravelSignal\")\n",
    "    ax.set_ylabel(\"Amplitude\")\n",
    "    ax.set_xlabel(\"Time\")\n",
    "\n",
    "    ax.plot(time,   signal,                    label='Raw signal')\n",
    "    ax.plot(time2,  mysignal(time2)+0.5,     '.-',     label='TravelSignal(periodic)+0.5')\n",
    "    ax.plot(time2,  mysignal2(time2)-0.5,     '.-',     label='TravelSignal-0.5')\n",
    "\n",
    "    ax.legend()\n",
    "\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib.location import Receiver, Emitter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if True:\n",
    "    sample_rate = 3e2 # Hz\n",
    "    periodic = False\n",
    "    \n",
    "    t_offset = -0.5\n",
    "    t_start = 0\n",
    "    t_end = 1\n",
    "    time   = t_offset + np.arange(t_start, t_end, 1/sample_rate) #s\n",
    "\n",
    "if False:\n",
    "    if True:\n",
    "        periodic = True\n",
    "        freq = sample_rate/8\n",
    "        signal = np.cos(2*np.pi*freq*time)\n",
    "    else: \n",
    "        from scipy.stats import norm\n",
    "        signal = norm.pdf(time, time[len(time)//2], (time[-1] - time[0])/10)\n",
    "\n",
    "\n",
    "#####\n",
    "# Setup Signal, Emitter and Antennae\n",
    "\n",
    "rot = lambda phi=0.12345: np.cos(np.array([phi, phi-np.pi/2]))\n",
    "\n",
    "km=1e7\n",
    "\n",
    "mysignal = TravelSignal(signal, sample_rate, t_0 = t_offset, periodic=periodic)\n",
    "\n",
    "source = Emitter([0,0])*km\n",
    "emitted = source.emit(mysignal)\n",
    "\n",
    "antennae = [\n",
    "    Receiver(2*km*rot(0)),\n",
    "    Receiver(3*km*rot(2)),\n",
    "    Receiver(10*km*rot(1)),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAACqCAYAAACEYlLKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAVI0lEQVR4nO2debhWVb3HP18gBkEpBXEGNSeUUCOHtDpomVD36n3SeyFSU8occLjpc62u+limZreupeZAZlczJW5JUdd5QC0RAUURcUA9CiKTisggivzuH2u9sN28531fDu909v59nud9zt57/fZav7X2/u417H3WkpnhOE626dRoBxzHqT0udMfJAS50x8kBLnTHyQEudMfJAS50x8kBLvQaImkPSU9KelfSmTVO6weSbqhlGjGdbpKelbRNGbtPSXq01v4k0vumpL/XK72ORlWELmmEpCmSVkhaFLdPk6RqxF8tJLVK+mIdk/wPYJKZbW5mVxbxZ5Kk9yQtT/z+2p6EzOxSM/tWjHeAJJPUJZFWtYRwMvCwmS0o48/TwFJJ/1SFNKuCpJ6xjO/YyPNaJM2rlV/1YJOFLukc4JfAfwHbAP2AU4BDgK6bGn89SQqjSvQHZpWxGWNmvRK/phFGkkTZfAf4XYWn/T7aNwvHAKuBIyRt22hn6oqZtfsH9AZWAF8rY9cN+BnwGrAQuA7okQj/NjAHeAuYCGyXCDPgNOBF4F3gYmBXYDKwDBgPdE3YfxWYASwFHgU+FY//DlgLrAKWE2rbATH+0dG3h4H/A85I+f80cHQbeftngpiXApOAveLxB4APgfdiersXOXcS8K024m0B5kU/FwFvAEcDw4EXYln9IGF/EXBL3H4t5mt5/B0c/fgw7i8td10S6Z8HLIjlt1Msvy6JdIcDz8Zr8zpwbiJs+2jfrY08ngjMjue+DHynSP7PSeT/xET4VvFeWQY8Hu+Lv5e5Dx8ALgGeSPoZw1qBc+O1fgf4A9Ad6BnzsDZRntsRKsnvAS8BbxLuwy1jXIX76oRYtkuA/0xdq/HAzTHvs4AhifBCvO/Gsv2XlK8nxXJ7G7gb6F9Wq5so9COBNckL34bdL+JF2RLYHPgrcFkMOywWxP7xxruK0DRMCn0isAWwN+GJfD+wC+FB8yxwQrTdP94UBwKdY0G3Fm60uP3FRNyFC3JzvKA9gH8FpiRsBscL2bVIvnYnPOi+BHyMIMo5BVtKCLlCoa8BLoxxfxtYDNway3Bvgnh3KSL0Qr6SgvwmKSGUuS6F9C+P16UH8BVgViqON4DPxe1PAPunwpcRH7ZF8vgVwkNbwBeAlYXzE+n/KOZ/eAz/RAwfRxBLT2AfwkOmTaETHlJrgYGEh8fTRYT+OEHEWxKEdEryoZOyPxt4DNghls/1wG2p8v91LLfBhPu2UAlcFK/dcMJ9ehnwWCLuY1n/MPk3wj22bQw7mnCP7QV0Ac4HHq210L8BLEgde5RQu60CPh8v4gpg14TNwcArcfs3wE8TYb2AD4ABCaEfkgifDpyX2P858Iu4fS1wccqf54EvlBH6Lolj3Qi15W5x/2fANXH7RsKD5Jm4fwEwPnFup3jDtQBXEJ7+rxFq4KVtCH1lLK/C7+LEzbUK6Bz3N4++Hpgqi6PbI/QKrksL8D7QPRE+isQNGY+9Rmieb9HGPfI68PkK76c/A2el8p/MwyLgIII4PgD2TIRdSmmhnw/MiNvbEVo3+6WE/o3E/k+B60oIfTZweGJ/2+hTl0T575AIfxwYkbhW9yXCBgKrSvg+Azgqbt8JjE7dcyspU6tvah/9TaBPsm9rZp81s4/HsE5AX2AzYLqkpZKWAnfF44VCfzVx/vJ47vaJdBYmtlcV2e8Vt/sD5xTSiWntGNMoxdxE+qsJNcU3JHUCRrK+T/o/hFZMgbTva2Nc25vZvwPTCDXSVcDtbaR9ppl9PPG7IBH2ppl9mMgnJfK+sZS7LgCLzey9xP7bhAdOkq8RaqZXJT0k6eBU+OaEB9gGSBom6TFJb8X0hwN9EiZvmtmaxP5KQn77EgQ1NxH2KqU5njBmgJnNBx4itPiSJAcYC2m1RX9gQqLsZhMeHv0qjC8d1r2gI0nHS5qRiHsf1pdLf+CXibC3CA/tpF42YFOFPpnQJDmqhM0Swg25d+Jm7m1mhUzPj84DYWSU0P96vR3+zAUuSQlnMzO7LYZbG+elj99EqL0OB1aa2WQAM3uYULAF5gMDJd0labqkR4Cdi/g+EriN+lEsn+lj5a5LsXOeBnZJPdinmtlRwNaEGnl8IUzSdoQB2efTzkjqBvyJ0GLqFyuHOwg3bTkWE5r1OyaO7dSWsaTPArsB35e0QNICQvduZIUDsMXKcy4wLHWvdTez9ty3SV/7E5r8Y4CtYrk8w/pymUsYy0im28PMSr7K3CShm9lS4IfANZKOkdRLUidJ+xL6ToVa7tfAFZK2jpnZXtKXYzS3AidK2jde/EsJfeTWdrj0a+AUSQcq0FPSVyQVaqGFhL59uXxNJvTnfk7pEebxhBr+VkKT8knCWEKy0LckiP+Bjc3MJrCY4H8yrwuBHSR1hYquywaY2TzCoOgB0b6rpFGSepvZB4T++IeJU1qAB2IrKU1XQjdpMbBG0jDgiEoyF1s5twMXSdpM0kA2rJ2TnADcS2gi7xt/+xBaNMMqSHIhsJWk3olj1wGXRGEiqa+kUhVepfQkPFgWx3hPjL4m0/2+pL1jeG9Jx5aLdJNfr5nZT4Hvsn50eCFhYOI81t/w5xEGEB6TtAy4D9gjnn8/oa/7J8LAzq7AiHb6Mo0waHU1oZk5h9A3LXAZcH5s9pxbJrqbgUHALSVsXifULGMJgysnAfPN7P2EzQHAHxNN8DRXp96jTy/jV1nMbCVhdPkfMa8HER40s4AFkpZE0zavSwmuB45L7B8HtMbzTyGM2xQYRbgxi/n4LnAm4WH5NvB1wsBgpYwhNIUXELpUvy1mJKk7YYD1KjNbkPi9QniIl3pAFHx9jtAiezmW53aEV8oTgXskvUsYmDtwI/xvK61nCRXMZIKWBgH/SIRPIAyQjotl/gwVPKwUO/ROCknHAyeb2aGp4wOAv5nZPpK2AJ43szbfyUp6Eji9XNOqoxBbXU8SBqLeKGE3CBhrZuk+u9MA/BPYIkjajPDufmwpOzNbBrxSaDrF7sLgRDx7EF45Ta6hu3XFzFab2cBSIo92M13kzYMLPUXsoy4mNJtuTYXdRhDtHpLmSRpNaJ6OlvQUoWmc7KeNBMaZN5ucBuNNd8fJAV6jO04OcKE7Tg6o9n9rZZI+ffrYgAEDGu0GK1asoGfPno12o+rUOl/Tp09fYmZ9y1tmFxd6BQwYMIBp06Y12g0mTZpES0tLo92oOrXOl6Ryn8dmHm+6O04OcKE7Tg5woTtODnChO04OcKE7Tcfiq65utAuZw4XuNB1LfvWrRruQOVzojpMDXOhOU7D4qquZvedezN5zL4B1296Mrw7+wYzTFPQ9Ywx9zxgDBJHv9dzsBnuULbxGd5wc4EJ3mo4+p5/eaBcyhwvdaToKTXinerjQHScHZEroCssUz0j8lkk6O2XTIumdhM2FjfLXcepFpkbdzex5wpzdSOpMmI55QhHTR8zsq/X0zXEaSaZq9BSHAy+ZWe7/F9lxsiz0EbS9DNLBkp6SdGdhxQvHyTKZnAU2Ljs0n7Cu2MJU2BbAWjNbLmk48Esz261IHCcDJwP069fv0+PGjauD56VZvnw5vXq1d03F5qXW+Ro6dOh0MxtSswQ6AFkV+lGE1VHKruUlqZWwCP2StmyGDBliPpVU7ajDVFK5F3pWm+5trl4qaRtJitsHEMrgzTr65jh1J1Oj7rBuOaUvAd9JHDsFwMyuA44BTpW0hrBs8AhfScXJOpkTelxJdKvUsesS21cTVlt1nNyQ1aa74zgJXOiOkwNc6I6TA1zojpMDXOiOkwNc6I6TA1zojpMDXOiOkwNc6I6TA1zojpMDXOiOkwNc6I6TA1zojpMDXOiOkwNc6I6TA1zojpMDXOiOkwNc6I6TAzIndEmtkmbG5ZY2mLp
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "########\n",
    "### Geometry Plot\n",
    "########\n",
    "ylabel_kw = {\"rotation\": \"horizontal\", \"va\":\"center\", \"ha\":\"center\", \"labelpad\": 30}\n",
    "\n",
    "fig, axs = plt.subplots(1,1, figsize=(2,2))\n",
    "axs = [ axs ]\n",
    "i = 0\n",
    "axs[i].grid()\n",
    "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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
      "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",
    "    "
   ]
  }
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