SPATZ/STAHR_antennas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# Handle all includes\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from spatz.utils.preprocess import preprocess_file\n",
    "from spatz.simulation import Simulation, UniformTimeSteps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do preprocessing\n",
    "PATH = \"data/simulations\"\n",
    "TMP = f\"{PATH}/temp\"\n",
    "FILE = \"19km\"\n",
    "\n",
    "if os.path.isdir(TMP):\n",
    "    shutil.rmtree(TMP)\n",
    "os.mkdir(TMP)\n",
    "\n",
    "df = preprocess_file(f\"{PATH}/{FILE}.txt\")\n",
    "df.to_csv(f\"{TMP}/{FILE}.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create simulation objects\n",
    "timesteps = UniformTimeSteps(0.1, mu=0, sigma=0, delay_only=True)\n",
    "simulation = Simulation(timesteps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed 65160 lines in 0.3s.\n",
      "Used 65160 lines due to step size\n"
     ]
    }
   ],
   "source": [
    "from spatz.sensors.antenna.pathloss import AntennaPathloss\n",
    "from spatz.sensors.antenna.tx_gain import AntennaTxGain\n",
    "from spatz.sensors.antenna.constants import STAHR_LORA_FREQ, STAHR_GS_OFFSET\n",
    "\n",
    "alt = simulation.add_observer(['altitude'])\n",
    "fspl_gs = simulation.add_sensor(AntennaPathloss,frequency=STAHR_LORA_FREQ,rx_antenna_offset=STAHR_GS_OFFSET)\n",
    "fspl_nope = simulation.add_sensor(AntennaPathloss,frequency=STAHR_LORA_FREQ,name)\n",
    "\n",
    "tx_gain = simulation.add_sensor(AntennaTxGain,gain_pattern_path=f\"data/gain_pattern/farfield_tx_aisler_v1.txt\",rx_antenna_offset = STAHR_GS_OFFSET)\n",
    "\n",
    "simulation.load(f\"{TMP}/{FILE}.csv\")\n",
    "logger = simulation.get_logger()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 579.9000000000633/580.0 [00:21<00:00, 27.40it/s] \n"
     ]
    }
   ],
   "source": [
    "# Run simulation\n",
    "for step, t, dt in simulation.run(verbose=True):\n",
    "    fspl_gs()\n",
    "    fspl_nope()\n",
    "    tx_gain()\n",
    "    alt()\n",
    "\n",
    "df = logger.get_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>antenna/pathloss/distance</th>\n",
       "      <th>antenna/pathloss/out</th>\n",
       "      <th>antenna/tx_gain/rocket_x</th>\n",
       "      <th>antenna/tx_gain/rocket_y</th>\n",
       "      <th>antenna/tx_gain/rocket_z</th>\n",
       "      <th>antenna/tx_gain/pos_x</th>\n",
       "      <th>antenna/tx_gain/pos_y</th>\n",
       "      <th>antenna/tx_gain/pos_z</th>\n",
       "      <th>antenna/tx_gain/theta</th>\n",
       "      <th>antenna/tx_gain/out</th>\n",
       "      <th>general/altitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>319.472878</td>\n",
       "      <td>90.319785</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.171335</td>\n",
       "      <td>0.985213</td>\n",
       "      <td>0.750379</td>\n",
       "      <td>0.6369</td>\n",
       "      <td>-0.176889</td>\n",
       "      <td>86.264551</td>\n",
       "      <td>4.977619</td>\n",
       "      <td>319.083996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>320.418701</td>\n",
       "      <td>90.345462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.171335</td>\n",
       "      <td>0.985213</td>\n",
       "      <td>0.750342</td>\n",
       "      <td>0.636834</td>\n",
       "      <td>-0.177281</td>\n",
       "      <td>86.241706</td>\n",
       "      <td>4.977825</td>\n",
       "      <td>319.251988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>321.364681</td>\n",
       "      <td>90.371068</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.171335</td>\n",
       "      <td>0.985213</td>\n",
       "      <td>0.750305</td>\n",
       "      <td>0.636768</td>\n",
       "      <td>-0.177674</td>\n",
       "      <td>86.218865</td>\n",
       "      <td>4.97803</td>\n",
       "      <td>319.419979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>322.433254</td>\n",
       "      <td>90.399901</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.171335</td>\n",
       "      <td>0.985213</td>\n",
       "      <td>0.750264</td>\n",
       "      <td>0.636694</td>\n",
       "      <td>-0.178116</td>\n",
       "      <td>86.193076</td>\n",
       "      <td>4.978262</td>\n",
       "      <td>319.692081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5</td>\n",
       "      <td>324.359314</td>\n",
       "      <td>90.451632</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.171335</td>\n",
       "      <td>0.985213</td>\n",
       "      <td>0.750188</td>\n",
       "      <td>0.63656</td>\n",
       "      <td>-0.178914</td>\n",
       "      <td>86.146622</td>\n",
       "      <td>4.97868</td>\n",
       "      <td>320.692955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5794</th>\n",
       "      <td>579.5</td>\n",
       "      <td>23290.112901</td>\n",
       "      <td>127.574537</td>\n",
       "      <td>-0.017067</td>\n",
       "      <td>-0.743764</td>\n",
       "      <td>-0.668224</td>\n",
       "      <td>0.969129</td>\n",
       "      <td>0.022163</td>\n",
       "      <td>0.245555</td>\n",
       "      <td>78.631975</td>\n",
       "      <td>4.847807</td>\n",
       "      <td>-2747.465778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5795</th>\n",
       "      <td>579.6</td>\n",
       "      <td>23292.369533</td>\n",
       "      <td>127.575378</td>\n",
       "      <td>-0.017173</td>\n",
       "      <td>-0.745696</td>\n",
       "      <td>-0.666065</td>\n",
       "      <td>0.969119</td>\n",
       "      <td>0.022152</td>\n",
       "      <td>0.245595</td>\n",
       "      <td>78.653403</td>\n",
       "      <td>4.84875</td>\n",
       "      <td>-2748.066515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5796</th>\n",
       "      <td>579.7</td>\n",
       "      <td>23294.626207</td>\n",
       "      <td>127.57622</td>\n",
       "      <td>-0.017279</td>\n",
       "      <td>-0.747621</td>\n",
       "      <td>-0.663901</td>\n",
       "      <td>0.969109</td>\n",
       "      <td>0.022141</td>\n",
       "      <td>0.245635</td>\n",
       "      <td>78.674921</td>\n",
       "      <td>4.849697</td>\n",
       "      <td>-2748.667252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5797</th>\n",
       "      <td>579.8</td>\n",
       "      <td>23296.882923</td>\n",
       "      <td>127.577061</td>\n",
       "      <td>-0.017385</td>\n",
       "      <td>-0.749539</td>\n",
       "      <td>-0.661732</td>\n",
       "      <td>0.9691</td>\n",
       "      <td>0.02213</td>\n",
       "      <td>0.245675</td>\n",
       "      <td>78.696529</td>\n",
       "      <td>4.850647</td>\n",
       "      <td>-2749.267989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5798</th>\n",
       "      <td>579.9</td>\n",
       "      <td>23299.13968</td>\n",
       "      <td>127.577903</td>\n",
       "      <td>-0.017491</td>\n",
       "      <td>-0.751452</td>\n",
       "      <td>-0.659556</td>\n",
       "      <td>0.96909</td>\n",
       "      <td>0.022119</td>\n",
       "      <td>0.245715</td>\n",
       "      <td>78.718226</td>\n",
       "      <td>4.851602</td>\n",
       "      <td>-2749.868726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5799 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       time antenna/pathloss/distance antenna/pathloss/out  \\\n",
       "0       0.1                319.472878            90.319785   \n",
       "1       0.2                320.418701            90.345462   \n",
       "2       0.3                321.364681            90.371068   \n",
       "3       0.4                322.433254            90.399901   \n",
       "4       0.5                324.359314            90.451632   \n",
       "...     ...                       ...                  ...   \n",
       "5794  579.5              23290.112901           127.574537   \n",
       "5795  579.6              23292.369533           127.575378   \n",
       "5796  579.7              23294.626207            127.57622   \n",
       "5797  579.8              23296.882923           127.577061   \n",
       "5798  579.9               23299.13968           127.577903   \n",
       "\n",
       "     antenna/tx_gain/rocket_x antenna/tx_gain/rocket_y  \\\n",
       "0                         0.0                 0.171335   \n",
       "1                         0.0                 0.171335   \n",
       "2                         0.0                 0.171335   \n",
       "3                        -0.0                 0.171335   \n",
       "4                        -0.0                 0.171335   \n",
       "...                       ...                      ...   \n",
       "5794                -0.017067                -0.743764   \n",
       "5795                -0.017173                -0.745696   \n",
       "5796                -0.017279                -0.747621   \n",
       "5797                -0.017385                -0.749539   \n",
       "5798                -0.017491                -0.751452   \n",
       "\n",
       "     antenna/tx_gain/rocket_z antenna/tx_gain/pos_x antenna/tx_gain/pos_y  \\\n",
       "0                    0.985213              0.750379                0.6369   \n",
       "1                    0.985213              0.750342              0.636834   \n",
       "2                    0.985213              0.750305              0.636768   \n",
       "3                    0.985213              0.750264              0.636694   \n",
       "4                    0.985213              0.750188               0.63656   \n",
       "...                       ...                   ...                   ...   \n",
       "5794                -0.668224              0.969129              0.022163   \n",
       "5795                -0.666065              0.969119              0.022152   \n",
       "5796                -0.663901              0.969109              0.022141   \n",
       "5797                -0.661732                0.9691               0.02213   \n",
       "5798                -0.659556               0.96909              0.022119   \n",
       "\n",
       "     antenna/tx_gain/pos_z antenna/tx_gain/theta antenna/tx_gain/out  \\\n",
       "0                -0.176889             86.264551            4.977619   \n",
       "1                -0.177281             86.241706            4.977825   \n",
       "2                -0.177674             86.218865             4.97803   \n",
       "3                -0.178116             86.193076            4.978262   \n",
       "4                -0.178914             86.146622             4.97868   \n",
       "...                    ...                   ...                 ...   \n",
       "5794              0.245555             78.631975            4.847807   \n",
       "5795              0.245595             78.653403             4.84875   \n",
       "5796              0.245635             78.674921            4.849697   \n",
       "5797              0.245675             78.696529            4.850647   \n",
       "5798              0.245715             78.718226            4.851602   \n",
       "\n",
       "     general/altitude  \n",
       "0          319.083996  \n",
       "1          319.251988  \n",
       "2          319.419979  \n",
       "3          319.692081  \n",
       "4          320.692955  \n",
       "...               ...  \n",
       "5794     -2747.465778  \n",
       "5795     -2748.066515  \n",
       "5796     -2748.667252  \n",
       "5797     -2749.267989  \n",
       "5798     -2749.868726  \n",
       "\n",
       "[5799 rows x 12 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128.19324355144363\n"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid()\n",
    "plt.plot(df['time'][1:], df['antenna/pathloss/out'][1:], label='FSPL')\n",
    "plt.ylabel(\"Frespace pathloss [dB]\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "\n",
    "print(np.max(df['antenna/pathloss/out'][1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23299.139680366974\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid()\n",
    "plt.plot(df['time'][1:], df['antenna/pathloss/distance'][1:], label='Dist to GS')\n",
    "plt.plot(df['time'][1:], df['antenna/pathloss/distance'][1:], label='Dist to Pad')\n",
    "plt.ylabel(\"Frespace pathloss [dB]\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "\n",
    "print(np.max(df['antenna/pathloss/distance'][1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9391183839865815\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid()\n",
    "plt.plot(df['time'][1:], df['antenna/tx_gain/out'][1:], label='FSPL')\n",
    "plt.ylabel(\"TX Antenna Gain [dBi]\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "\n",
    "print(np.min(df['antenna/tx_gain/out'][1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-94.78810957233904\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate recieved power\n",
    "# Assumptions:  3 dB polarization loss\n",
    "#               25 dBm output power per channel\n",
    "#               16 dBi antenna rx gain\n",
    "#               -6 dB on reciever side\n",
    "\n",
    "\n",
    "df[\"antenna/power_rx\"] = 25+df[\"antenna/tx_gain/out\"] - df[\"antenna/pathloss/out\"] - 3 + 16 - 6 \n",
    "plt.grid()\n",
    "plt.plot(df['time'][1:], df['antenna/power_rx'][1:], label='FSPL')\n",
    "plt.ylabel(\"Recieved Power [dBm]\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "\n",
    "print(np.min(df['antenna/power_rx'][1:]))"
   ]
  }
 ],
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