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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPIAAACqCAYAAACXtRI+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAQuklEQVR4nO3dfbRVdZ3H8fcHEVAkynhIVCBdpqIktUgrZ1KzHNEpnFU2miU5FqmZzZpaiY3NOGOm0zRrKpXMykkzdFyrYaLGHkwzKx9hYkxERsYQCIGLSmIqBnznj9/vxu54z72Xyz133/Pj81rrrnP2w9n7ux8+e//2Pufco4jAzNrbkLoLMLOd5yCbFcBBNiuAg2xWAAfZrAAOslkBHOSdIOlgSb+UtEnSBS2e16ckfa2V88jzGS7pYUmv6mG810q6u9X1VOb3AUk/H6j5tZteBVnSaZLuk/Q7Sevz8/MkqdUF7ghJKyS9bQBn+UngzogYFRFf6qKeOyW9IOnZyt93+zKjiPhsRHwwT3eypJA0tDKv/trRZwN3RcTaHup5ENgo6R39MM9+IWlkXse37uDrjpW0ulV1DYQegyzp48AXgX8GXgWMB84BjgaGtbS6flbd8fvJJGBJD+OcHxF7Vf4GzY5fVVk3Hwa+2cuXfSuPP1i8G9gMnCBpn7qLGVAR0fQPGA38DnhXD+MNBz4PrATWAdcAe1SGfwhYDjwFLAAmVIYFcB7wKLAJuBQ4ELgHeAa4BRhWGf/PgcXARuBu4LW5/zeBbcDzwLOks+XkPP2zc213Af8FfLSh/geBU5os2ztJYd0I3AkcmvvfAWwFXsjze00Xr70T+GCT6R4LrM51rgeeAE4BTgL+N6+rT1XGvwS4MT9fmZfr2fz3plzH1ty9saftUpn/hcDavP4m5vU3tDLfk4CH87b5DfCJyrB98/jDmyzjWcDS/NrHgA93sfwfryz/WZXhr8z7yjPA/Xm/+HkP++EdwGXAf1frzMNWAJ/I2/q3wL8DI4CReRm2VdbnBNJJbg7wf8CTpP1w7zytzv1qVl63G4C/bdhWtwA35GVfAkyvDO+c7qa8bv+ioda/yuvtaeCHwKTuljsiegzyicCW6oZtMt4X8krfGxgFfBe4PA97a17Q1+cd60pS060a5AXAy4DDSEfU24EDSAeSh4FZedzX541+FLBbXpErOnek/PxtlWl3rvAb8gbbA3gPcF9lnCPyhhrWxXK9hnQgezuwOyl0yzvHpZug9jLIW4C/y9P+ENABzMvr8DBSOA/oIsidy1UN3Ado2NF72C6d8/+nvF32AE4GljRM4wngT/PzVwCvbxj+DPlg2sUynkw6KAs4Bniu8/WV+f9jXv6T8vBX5OE3k8IwEjicdBBpGmTSQWgbMIV0cHiwiyDfTwrp3qSgnFM9qDSM/9fAvcB+ef18BbipYf1/Na+3I0j7bedB/pK87U4i7aeXA/dWpn0q2w8Wf0nax/bJw04h7WOHAkOBi4G7dzbI7wPWNvS7m3R2eh54S95IvwMOrIzzJuDX+fnXgc9Vhu0F/B6YXAny0ZXhi4ALK93/AnwhP/8ycGlDPcuAY3oI8gGVfsNJZ7uDcvfngblNlv/TwC2V7iF5hzp2B4L8XF5fnX+XVnae54HdcveoXOtRDevilL4EuRfb5VjgRWBEZfgZ1R0u91tJaj6/rMky/gZ4S087Wh73P4GPNSx/dRnWA28k7fy/Bw6pDPss3Qf5YmBxfj6B1Dp5XUOQ31fp/hxwTTdBXgocX+neJ9c0tLL+96sMvx84rbKtflwZNgV4vpvaFwMz8/PvA2c37HPP0cNZuadr5CeBMdVry4h4c0S8PA8bAowF9gQWSdooaSPwg9y/c6U+Xnn9s/m1+1bms67y/PkuuvfKzycBH++cT57X/nke3VlVmf9m0pH+fZKGAKfT/JqwsfZteVr7Nhm/KxdExMsrf5+uDHsyIrbm58/nx2bLvqN62i4AHRHxQqX7adIBpepdpDPL45J+KulNDcNHkQ5QLyFphqR7JT2V538SMKYyypMRsaXS/RxpeceSArOqMuxxuncm6ZqdiFgD/JTUYquq3sDrnFczk4D5lXW3lHRwGN/L6TUOG9GZI0lnSlpcmfbhbF8vk4AvVoY9RTood7vP9RTke0hNhpndjLOBtMMdVtlZR0dE50KtycWRF2Ik6frnNz3MuyurgMsagrFnRNyUh0eT1zX2v5509jkeeC4i7mnyusbaRTpw9KX2/tTVcjb262m7dPWaB4EDGg7cD0TETGAc6Yx6S+cwSRNINzyXNRYjaTjwbVKLZ3w++N9K2il70kFqdu9f6Tex2ciS3gwcBFwkaa2ktaTLr9N7eYOzq/W5CpjRsK+NiIid2vaSJpGa5OcDr8zr5SG2r5dVpHsJ1fnuERHdvtXXbZAjYiPwD8BcSe+WtJekIZKmka5dOs9SXwX+VdK4XOy+kv4sT2YecJakaXnjfpZ0jbpih9dCms85ko5SMlLSyZI6zyLrSNfW3crB3UZqtnd3h/YW4GRJx0vanXTttZl0eVGnDlL91WVdB+wnaRj0aru8RESsJt10PDKPP0zSGZJGR8TvSdfDWysvORa4I7dyGg0jXcZ0AFskzQBO6M3C5VbKfwCXSNpT0hReenatmgXcRmrCTst/h5NaJDN6Mct1wCslja70uwa4LAcPSWMldXdC662RpANHR57uWbnW6nwvknRYHj5a0qk9TbTHt58i4nPA37D97uo60oX/hWzfoS8kXaDfK+kZ4MfAwfn1t5OuNb9NunFyIHBaT/NtUstC0k2hq0jNwOWka8NOlwMX52bJJ3qY3A3AVODGbua3jHSf4ErSGe4dwDsi4sUdKPuqhveRF+3Aa5vV9Rzp7uwv8rK+kXTHdgmwVtKGPGrT7dKNrwDvr3S/H1iRX38OaX10OoO043VV4ybgAtLB8GngvaQbb711Pqmpuhb4BvBvXY0kaQTpBuaVEbG28vdr0kG6uwNAZ62PADcBj+X1OYH0lusC4EeSNpFufB21A/U3m9fDpBPIPaQsTQV+URk+n3QD8ua8zh+iFwcj5QvqXY6kM4HZEfEnddcymORW0y9JN3qe6Ga8qcC1EdF4zWw12CWDLGlP0hlsbkTcUHc9Zjtrl/usdb5G7CA1a+bVXI5Zv9glz8hmpdnlzshmJXKQzQrQ398GaktjxoyJyZMn112G9dGiRYs2RMTYnscsV1sHWdJ1pG9DrY+Iw3O/vUnfbJlM+nzteyLi6e6mM3nyZBYuXNjaYq1lJPX08c3itXvT+hukb2hVzQFuj4iDSN+imjPQRVnrzF08t+4SBqW2DnJE3EX6UHnVTNJnqcmPpwxoUdZSX/6fL9ddwqDU1kFuYnznJ5Ly47ia6zFruba+Rt4ZkmaT/j8VEyc2/WKNDQJzF8/9ozPx1OunAnDuEedy3rTz6iprUGn7D4RImgx8r3Kzaxnpi/9P5P/bdGdEdPtFgenTp4dvdrWHqddP5VezfvVH/SQtiojpNZU0KJTYtF7A9m+8zAK+U2MtZgOirYMs6SbS18EOlrRa0tnAFcDbJT1K+l9bV9RZo/Wvc484t+4SBqW2vkaOiNObDDp+QAuxAeNr4q619RnZzBIH2awADrJZARxkswI4yGYFcJDNCuAgmxXAQTYrgINsVgAH2awADrJZARxkswI4yGYFcJDNCuAgmxXAQTYrgINsVgAH2awADrJZARxkswI4yGYFcJDNCuAgmxXAQTYrgINsVgAH2awADrJZARxkswI4yGYFaOtfY+yOpBXAJmArsGVX/yFsK1uxQc6Oi4gNdRdh1mpuWpsVoOQgB/AjSYskza67GLNWKrlpfXRErJE0DrhN0iMRcVfnwBzu2QATJ06sq0azflHsGTki1uTH9cB84MiG4ddGxPSImD527Ng6SjTrN0UGWdJISaM6nwMnAA/VW5VZ65TatB4PzJcEaRnnRcQP6i3JrHWKDHJEPAYcUXcdZgOlyKa12a7GQTYrgINsVgAH2awAfQqypEslfazSfZmkC/qvLDPbEX09I38dmAUgaQh
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if True:\n",
    "    sample_rate = 3e2 # Hz\n",
    "    periodic = False\n",
    "    \n",
    "    t_offset = 8\n",
    "    t_start = 0\n",
    "    t_end = 1\n",
    "    time   = t_offset + np.arange(t_start, t_end, 1/sample_rate) #s\n",
    "    \n",
    "    t_longstart = 0\n",
    "    t_longend = 30*t_end\n",
    "    longtime = np.arange(t_longstart, t_longend, 1/sample_rate) #s\n",
    "\n",
    "if False:\n",
    "    if 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",
    "mysignal = TravelSignal(signal, sample_rate, t_0 = t_offset, periodic=periodic)\n",
    "\n",
    "source = Emitter([1,1])\n",
    "emitted = source.emit(mysignal)\n",
    "\n",
    "antennae = [\n",
    "    Receiver([2,3]),\n",
    "    Receiver([10,10]),\n",
    "    Receiver([-2,-3]),\n",
    "]\n",
    "    \n",
    "#####\n",
    "# Follow traces, and show geometry\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",
    "\n",
    "### Geometry Plot\n",
    "i = 0\n",
    "axs[i].set_title(\"Geometry of Emitter(s) and Antennae\")\n",
    "axs[i].set_ylabel(\"y\", **ylabel_kw)\n",
    "axs[i].set_xlabel(\"x\")\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",
    "### Plot Traces\n",
    "fig, axs = plt.subplots(1+len(antennae),1, sharex=True, figsize=(12,6))\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].plot(time, emitted(time))\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",
    "    "
   ]
  }
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