Simulator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f134e817",
   "metadata": {
    "id": "f134e817"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "from typing import Tuple\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import csv\n",
    "random.seed(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f5dc853",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from pathlib import Path\n",
    "\n",
    "# Structure: (theta, phi) -> (theta_etilt, phi_scan)\n",
    "saved_tp = dict()\n",
    "saved_tp_file = Path(\"t0p0.pkl\")\n",
    "if saved_tp_file.is_file():\n",
    "    with open(saved_tp_file, \"rb\") as f:\n",
    "        saved_tp = pickle.load(f)\n",
    "        \n",
    "# Structure: (segment, polygon) -> (boolean True or False)\n",
    "saved_los = dict()\n",
    "\n",
    "saved_los_file = Path(\"los.pkl\")\n",
    "if saved_los_file.is_file():\n",
    "    with open(saved_los_file, \"rb\") as f:\n",
    "        saved_los = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b61743bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Geometry3D in c:\\users\\tniloy\\anaconda3\\lib\\site-packages (0.2.4)\n",
      "Requirement already satisfied: shapely in c:\\users\\tniloy\\anaconda3\\lib\\site-packages (1.8.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install Geometry3D shapely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4fc46799",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Geometry3D import *\n",
    "from shapely import geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e75eb39",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:\\\\Users\\\\tniloy\\\\Downloads\\\\opencellid dataset\\\\310.csv\", names=['radio', 'mcc', 'mnc', 'lac', 'cid', 'unit', 'longitude', 'latitude', 'range', 'samples', 'changeable', 'created', 'updated', 'averageSignal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b6f0dbb",
   "metadata": {},
   "outputs": [
    {
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       "      <th>radio</th>\n",
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       "  </thead>\n",
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      ],
      "text/plain": [
       "  radio  mcc  mnc    lac    cid  unit  longitude   latitude  range  samples  \\\n",
       "0   GSM  310  260  32192  22568     0 -73.858566  40.898438   1779       12   \n",
       "1   GSM  310  260  22629  61562     0 -95.876501  41.286475   3367       17   \n",
       "2   GSM  310  260  51052  44152     0 -71.084483  42.381728   1000       44   \n",
       "3   GSM  310  260  51051  40311     0 -71.085559  42.381356   1000       52   \n",
       "4   GSM  310  260   5973  60092     0 -95.570630  29.735371   1000       22   \n",
       "5   GSM  310  410  19007   7133     0 -87.258205  40.153886  19135        6   \n",
       "6   GSM  310  410  53005  50671     0 -95.563889  30.280380   1000        1   \n",
       "7   GSM  310  260  46136  15418     0 -76.999741  39.160080   1000        1   \n",
       "8   GSM  310  410  27672  59021     0 -81.741964  26.213691   1000       11   \n",
       "9   GSM  310  260   5973  50942     0 -95.560226  29.736557   1000        6   \n",
       "\n",
       "   changeable     created     updated  averageSignal  \n",
       "0           1  1459696254  1489125559              0  \n",
       "1           1  1459810860  1609205988              0  \n",
       "2           1  1459812328  1514115939              0  \n",
       "3           1  1459812328  1527695374              0  \n",
       "4           1  1459692332  1490851584              0  \n",
       "5           1  1459699529  1472415099              0  \n",
       "6           1  1459775023  1459775023              0  \n",
       "7           1  1459663271  1459663271              0  \n",
       "8           1  1459687298  1491031828              0  \n",
       "9           1  1459692332  1469493454              0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head (10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba20a2f7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ba20a2f7",
    "outputId": "c42edc1b-3983-4aba-dfc6-c575f164e1fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X, Y, Z = [843264.3231973607, -5003968.368478095, 3852122.3823358943]\n",
      "Total area of inclusion zone is = 78539816.33974482\n"
     ]
    }
   ],
   "source": [
    "# center_x = 0\n",
    "# center_y = 0\n",
    "# center_z = 0\n",
    "# FSS POsition:\n",
    "lat_FSS= 37.20250\n",
    "lon_FSS= -80.43444\n",
    "R  = 6.371e6  #Radius of the earth\n",
    "\n",
    "x_FSS = R * math.cos(math.radians(lat_FSS)) * math.cos(math.radians(lon_FSS))\n",
    "y_FSS = R * math.cos(math.radians(lat_FSS)) * math.sin(math.radians(lon_FSS))\n",
    "z_FSS = R * math.sin(math.radians(lat_FSS))\n",
    "\n",
    "print(\"X, Y, Z = \"+ str([x_FSS, y_FSS, z_FSS]))\n",
    "\n",
    "\n",
    "radius=5000 #radius of the inclusion zone\n",
    "latitude_range = radius / 110574\n",
    "longitude_range = radius/(111320*math.cos(math.radians(latitude_range)))\n",
    "print(\"Total area of inclusion zone is = \" + str(radius*radius*math.pi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2931c679",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04521858664785573\n",
      "0.044915572737550945\n"
     ]
    }
   ],
   "source": [
    "print(latitude_range)\n",
    "print(longitude_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8bf61e2d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>mcc</th>\n",
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       "      <td>310</td>\n",
       "      <td>260</td>\n",
       "      <td>32453</td>\n",
       "      <td>43603</td>\n",
       "      <td>0</td>\n",
       "      <td>-80.425415</td>\n",
       "      <td>37.222710</td>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1458834001</td>\n",
       "      <td>1474352285</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814368</th>\n",
       "      <td>GSM</td>\n",
       "      <td>310</td>\n",
       "      <td>260</td>\n",
       "      <td>32453</td>\n",
       "      <td>43621</td>\n",
       "      <td>0</td>\n",
       "      <td>-80.393486</td>\n",
       "      <td>37.224279</td>\n",
       "      <td>1033</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1458834001</td>\n",
       "      <td>1482422269</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        radio  mcc  mnc    lac    cid  unit  longitude   latitude  range  \\\n",
       "780410    GSM  310  260  37630  43582     0 -80.393175  37.196127   8733   \n",
       "1813932   GSM  310  260  32453  43561     0 -80.421524  37.189560   1000   \n",
       "1813934   GSM  310  260  32453  43602     0 -80.410309  37.233810   1000   \n",
       "1814053   GSM  310  260  32453  43822     0 -80.406704  37.227516   1000   \n",
       "1814075   GSM  310  260  32453  43583     0 -80.461073  37.203233   2207   \n",
       "1814213   GSM  310  260  32453  43581     0 -80.409897  37.217972   1000   \n",
       "1814220   GSM  310  260  32453  43601     0 -80.425186  37.236099   1000   \n",
       "1814361   GSM  310  260  32453  43562     0 -80.391645  37.165063   1000   \n",
       "1814366   GSM  310  260  32453  43603     0 -80.425415  37.222710   1000   \n",
       "1814368   GSM  310  260  32453  43621     0 -80.393486  37.224279   1033   \n",
       "\n",
       "         samples  changeable     created     updated  averageSignal  \n",
       "780410      3992           1  1413757287  1435049916              0  \n",
       "1813932        9           1  1458834001  1491931840              0  \n",
       "1813934        6           1  1458834001  1479555225              0  \n",
       "1814053        8           1  1458834001  1479576548              0  \n",
       "1814075       15           1  1458834001  1477155991              0  \n",
       "1814213        5           1  1458834001  1473235293              0  \n",
       "1814220        6           1  1458834001  1473191254              0  \n",
       "1814361       11           1  1458834001  1489446412              0  \n",
       "1814366        4           1  1458834001  1474352285              0  \n",
       "1814368       56           1  1458834001  1482422269              0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_within_zone = data[(data['radio'] == 'GSM') & (data['longitude'] <= (lon_FSS + longitude_range)) & (data['longitude'] >= (lon_FSS - longitude_range)) & (data['latitude'] <= (lat_FSS + latitude_range)) & (data['latitude'] >= (lat_FSS - latitude_range))]\n",
    "\n",
    "data_within_zone.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9dbffc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_within_zone)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a2a00d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>relation/548344</td>\n",
       "      <td>1406</td>\n",
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       "      <td>church</td>\n",
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       "      <td>Blacksburg First Wesleyan Church</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>relation/548347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>relation/548348</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>relation/548371</td>\n",
       "      <td>relation/548371</td>\n",
       "      <td>1416</td>\n",
       "      <td>Palmer Drive</td>\n",
       "      <td>detached</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Feature</td>\n",
       "      <td>relation/548372</td>\n",
       "      <td>relation/548372</td>\n",
       "      <td>900</td>\n",
       "      <td>Dickerson Lane</td>\n",
       "      <td>detached</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multipolygon</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Feature</td>\n",
       "      <td>relation/548373</td>\n",
       "      <td>relation/548373</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multipolygon</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Feature</td>\n",
       "      <td>relation/548386</td>\n",
       "      <td>relation/548386</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multipolygon</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      type               id   properties.@id properties.addr:housenumber  \\\n",
       "0  Feature  relation/308029  relation/308029                         630   \n",
       "1  Feature  relation/548333  relation/548333                         NaN   \n",
       "2  Feature  relation/548339  relation/548339                         NaN   \n",
       "3  Feature  relation/548344  relation/548344                        1406   \n",
       "4  Feature  relation/548347  relation/548347                         NaN   \n",
       "5  Feature  relation/548348  relation/548348                         NaN   \n",
       "6  Feature  relation/548371  relation/548371                        1416   \n",
       "7  Feature  relation/548372  relation/548372                         900   \n",
       "8  Feature  relation/548373  relation/548373                         NaN   \n",
       "9  Feature  relation/548386  relation/548386                         NaN   \n",
       "\n",
       "        properties.addr:street properties.building properties.height  \\\n",
       "0  Washington Street Southwest           dormitory              15.7   \n",
       "1                          NaN                 yes               NaN   \n",
       "2                          NaN                 yes               NaN   \n",
       "3                   Giles Road              church               NaN   \n",
       "4                          NaN                 yes               NaN   \n",
       "5                          NaN         residential               NaN   \n",
       "6                 Palmer Drive            detached               NaN   \n",
       "7               Dickerson Lane            detached               NaN   \n",
       "8                          NaN                 yes               NaN   \n",
       "9                          NaN                 yes               NaN   \n",
       "\n",
       "                    properties.name properties.operator properties.type  ...  \\\n",
       "0                    Pritchard Hall       Virginia Tech    multipolygon  ...   \n",
       "1                               NaN                 NaN    multipolygon  ...   \n",
       "2                               NaN                 NaN    multipolygon  ...   \n",
       "3  Blacksburg First Wesleyan Church                 NaN    multipolygon  ...   \n",
       "4                               NaN                 NaN    multipolygon  ...   \n",
       "5                               NaN                 NaN    multipolygon  ...   \n",
       "6                               NaN                 NaN    multipolygon  ...   \n",
       "7                               NaN                 NaN    multipolygon  ...   \n",
       "8                               NaN                 NaN    multipolygon  ...   \n",
       "9                               NaN                 NaN    multipolygon  ...   \n",
       "\n",
       "  properties.automatic_door properties.access properties.button_operated  \\\n",
       "0                       NaN               NaN                        NaN   \n",
       "1                       NaN               NaN                        NaN   \n",
       "2                       NaN               NaN                        NaN   \n",
       "3                       NaN               NaN                        NaN   \n",
       "4                       NaN               NaN                        NaN   \n",
       "5                       NaN               NaN                        NaN   \n",
       "6                       NaN               NaN                        NaN   \n",
       "7                       NaN               NaN                        NaN   \n",
       "8                       NaN               NaN                        NaN   \n",
       "9                       NaN               NaN                        NaN   \n",
       "\n",
       "  properties.door properties.operator:short properties.operator:type  \\\n",
       "0             NaN                       NaN                      NaN   \n",
       "1             NaN                       NaN                      NaN   \n",
       "2             NaN                       NaN                      NaN   \n",
       "3             NaN                       NaN                      NaN   \n",
       "4             NaN                       NaN                      NaN   \n",
       "5             NaN                       NaN                      NaN   \n",
       "6             NaN                       NaN                      NaN   \n",
       "7             NaN                       NaN                      NaN   \n",
       "8             NaN                       NaN                      NaN   \n",
       "9             NaN                       NaN                      NaN   \n",
       "\n",
       "  properties.operator:wikidata properties.operator:wikipedia properties.foot  \\\n",
       "0                          NaN                           NaN             NaN   \n",
       "1                          NaN                           NaN             NaN   \n",
       "2                          NaN                           NaN             NaN   \n",
       "3                          NaN                           NaN             NaN   \n",
       "4                          NaN                           NaN             NaN   \n",
       "5                          NaN                           NaN             NaN   \n",
       "6                          NaN                           NaN             NaN   \n",
       "7                          NaN                           NaN             NaN   \n",
       "8                          NaN                           NaN             NaN   \n",
       "9                          NaN                           NaN             NaN   \n",
       "\n",
       "  properties.vehicle  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "5                NaN  \n",
       "6                NaN  \n",
       "7                NaN  \n",
       "8                NaN  \n",
       "9                NaN  \n",
       "\n",
       "[10 rows x 124 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "with open ('C:\\\\Users\\\\tniloy\\\\Downloads\\\\export (1).geojson') as f:\n",
    "    df=json.load(f)\n",
    "    data1=pd.json_normalize(df,record_path=['features'])\n",
    "data1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7cc63ef0",
   "metadata": {},
   "outputs": [
    {
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       "      <th>properties.operator:wikipedia</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Feature</td>\n",
       "      <td>relation/1360465</td>\n",
       "      <td>relation/1360465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>university</td>\n",
       "      <td>14.86</td>\n",
       "      <td>East Eggleston Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>multipolygon</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Feature</td>\n",
       "      <td>relation/1360466</td>\n",
       "      <td>relation/1360466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dormitory</td>\n",
       "      <td>14.86</td>\n",
       "      <td>West Eggleston Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>multipolygon</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Feature</td>\n",
       "      <td>relation/1360467</td>\n",
       "      <td>relation/1360467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dormitory</td>\n",
       "      <td>11.27</td>\n",
       "      <td>Slusher Wing</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>multipolygon</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Feature</td>\n",
       "      <td>relation/1360468</td>\n",
       "      <td>relation/1360468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dormitory</td>\n",
       "      <td>33.59</td>\n",
       "      <td>Slusher Tower</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>multipolygon</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Feature</td>\n",
       "      <td>relation/1360469</td>\n",
       "      <td>relation/1360469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dormitory</td>\n",
       "      <td>14.86</td>\n",
       "      <td>Main Eggleston Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>multipolygon</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/26210227</td>\n",
       "      <td>way/26210227</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>university</td>\n",
       "      <td>15.47</td>\n",
       "      <td>Cheatham Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/26210236</td>\n",
       "      <td>way/26210236</td>\n",
       "      <td>285</td>\n",
       "      <td>Ag Quad Lane</td>\n",
       "      <td>university</td>\n",
       "      <td>11.57</td>\n",
       "      <td>Dietrick Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/32709325</td>\n",
       "      <td>way/32709325</td>\n",
       "      <td>570</td>\n",
       "      <td>Washington Street Southwest</td>\n",
       "      <td>dormitory</td>\n",
       "      <td>25.82</td>\n",
       "      <td>Hoge Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/32953606</td>\n",
       "      <td>way/32953606</td>\n",
       "      <td>210</td>\n",
       "      <td>Drillfield Drive</td>\n",
       "      <td>university</td>\n",
       "      <td>11.61</td>\n",
       "      <td>Sandy Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/32954439</td>\n",
       "      <td>way/32954439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dormitory</td>\n",
       "      <td>14.56</td>\n",
       "      <td>Campbell Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/32954440</td>\n",
       "      <td>way/32954440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>18.78</td>\n",
       "      <td>War Memorial Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/43082481</td>\n",
       "      <td>way/43082481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>university</td>\n",
       "      <td>23.9</td>\n",
       "      <td>McBryde Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/43310773</td>\n",
       "      <td>way/43310773</td>\n",
       "      <td>155</td>\n",
       "      <td>Ag Quad Lane</td>\n",
       "      <td>university</td>\n",
       "      <td>19.91</td>\n",
       "      <td>Seitz Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/54784074</td>\n",
       "      <td>way/54784074</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>university</td>\n",
       "      <td>13.4</td>\n",
       "      <td>Hancock Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7626</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/55760105</td>\n",
       "      <td>way/55760105</td>\n",
       "      <td>185</td>\n",
       "      <td>Ag Quad Lane</td>\n",
       "      <td>university</td>\n",
       "      <td>20.41</td>\n",
       "      <td>Smyth Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7627</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/55861902</td>\n",
       "      <td>way/55861902</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>university</td>\n",
       "      <td>37.39</td>\n",
       "      <td>Whittemore Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7631</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/60193821</td>\n",
       "      <td>way/60193821</td>\n",
       "      <td>180</td>\n",
       "      <td>Kent Street</td>\n",
       "      <td>dormitory</td>\n",
       "      <td>12.42</td>\n",
       "      <td>Vawter Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7632</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/60193822</td>\n",
       "      <td>way/60193822</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dormitory</td>\n",
       "      <td>21.3</td>\n",
       "      <td>Ambler Johnston Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7636</th>\n",
       "      <td>Feature</td>\n",
       "      <td>way/60193832</td>\n",
       "      <td>way/60193832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>university</td>\n",
       "      <td>15.09</td>\n",
       "      <td>Owens Hall</td>\n",
       "      <td>Virginia Tech</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         type                id    properties.@id properties.addr:housenumber  \\\n",
       "0     Feature   relation/308029   relation/308029                         630   \n",
       "33    Feature  relation/1360465  relation/1360465                         NaN   \n",
       "34    Feature  relation/1360466  relation/1360466                         NaN   \n",
       "35    Feature  relation/1360467  relation/1360467                         NaN   \n",
       "36    Feature  relation/1360468  relation/1360468                         NaN   \n",
       "37    Feature  relation/1360469  relation/1360469                         NaN   \n",
       "66    Feature      way/26210227      way/26210227                         NaN   \n",
       "67    Feature      way/26210236      way/26210236                         285   \n",
       "69    Feature      way/32709325      way/32709325                         570   \n",
       "98    Feature      way/32953606      way/32953606                         210   \n",
       "100   Feature      way/32954439      way/32954439                         NaN   \n",
       "101   Feature      way/32954440      way/32954440                         NaN   \n",
       "113   Feature      way/43082481      way/43082481                         NaN   \n",
       "114   Feature      way/43310773      way/43310773                         155   \n",
       "1678  Feature      way/54784074      way/54784074                         NaN   \n",
       "7626  Feature      way/55760105      way/55760105                         185   \n",
       "7627  Feature      way/55861902      way/55861902                         NaN   \n",
       "7631  Feature      way/60193821      way/60193821                         180   \n",
       "7632  Feature      way/60193822      way/60193822                         NaN   \n",
       "7636  Feature      way/60193832      way/60193832                         NaN   \n",
       "\n",
       "           properties.addr:street properties.building properties.height  \\\n",
       "0     Washington Street Southwest           dormitory              15.7   \n",
       "33                            NaN          university             14.86   \n",
       "34                            NaN           dormitory             14.86   \n",
       "35                            NaN           dormitory             11.27   \n",
       "36                            NaN           dormitory             33.59   \n",
       "37                            NaN           dormitory             14.86   \n",
       "66                            NaN          university             15.47   \n",
       "67                   Ag Quad Lane          university             11.57   \n",
       "69    Washington Street Southwest           dormitory             25.82   \n",
       "98               Drillfield Drive          university             11.61   \n",
       "100                           NaN           dormitory             14.56   \n",
       "101                           NaN                 yes             18.78   \n",
       "113                           NaN          university              23.9   \n",
       "114                  Ag Quad Lane          university             19.91   \n",
       "1678                          NaN          university              13.4   \n",
       "7626                 Ag Quad Lane          university             20.41   \n",
       "7627                          NaN          university             37.39   \n",
       "7631                  Kent Street           dormitory             12.42   \n",
       "7632                          NaN           dormitory              21.3   \n",
       "7636                          NaN          university             15.09   \n",
       "\n",
       "           properties.name properties.operator properties.type  ...  \\\n",
       "0           Pritchard Hall       Virginia Tech    multipolygon  ...   \n",
       "33     East Eggleston Hall       Virginia Tech    multipolygon  ...   \n",
       "34     West Eggleston Hall       Virginia Tech    multipolygon  ...   \n",
       "35            Slusher Wing       Virginia Tech    multipolygon  ...   \n",
       "36           Slusher Tower       Virginia Tech    multipolygon  ...   \n",
       "37     Main Eggleston Hall       Virginia Tech    multipolygon  ...   \n",
       "66           Cheatham Hall       Virginia Tech             NaN  ...   \n",
       "67           Dietrick Hall       Virginia Tech             NaN  ...   \n",
       "69               Hoge Hall       Virginia Tech             NaN  ...   \n",
       "98              Sandy Hall       Virginia Tech             NaN  ...   \n",
       "100          Campbell Hall       Virginia Tech             NaN  ...   \n",
       "101      War Memorial Hall       Virginia Tech             NaN  ...   \n",
       "113           McBryde Hall       Virginia Tech             NaN  ...   \n",
       "114             Seitz Hall       Virginia Tech             NaN  ...   \n",
       "1678          Hancock Hall       Virginia Tech             NaN  ...   \n",
       "7626            Smyth Hall       Virginia Tech             NaN  ...   \n",
       "7627       Whittemore Hall       Virginia Tech             NaN  ...   \n",
       "7631           Vawter Hall       Virginia Tech             NaN  ...   \n",
       "7632  Ambler Johnston Hall       Virginia Tech             NaN  ...   \n",
       "7636            Owens Hall       Virginia Tech             NaN  ...   \n",
       "\n",
       "     properties.automatic_door properties.access properties.button_operated  \\\n",
       "0                          NaN               NaN                        NaN   \n",
       "33                         NaN               NaN                        NaN   \n",
       "34                         NaN               NaN                        NaN   \n",
       "35                         NaN               NaN                        NaN   \n",
       "36                         NaN               NaN                        NaN   \n",
       "37                         NaN               NaN                        NaN   \n",
       "66                         NaN               NaN                        NaN   \n",
       "67                         NaN               NaN                        NaN   \n",
       "69                         NaN               NaN                        NaN   \n",
       "98                         NaN               NaN                        NaN   \n",
       "100                        NaN               NaN                        NaN   \n",
       "101                        NaN               NaN                        NaN   \n",
       "113                        NaN               NaN                        NaN   \n",
       "114                        NaN               NaN                        NaN   \n",
       "1678                       NaN               NaN                        NaN   \n",
       "7626                       NaN               NaN                        NaN   \n",
       "7627                       NaN               NaN                        NaN   \n",
       "7631                       NaN               NaN                        NaN   \n",
       "7632                       NaN               NaN                        NaN   \n",
       "7636                       NaN               NaN                        NaN   \n",
       "\n",
       "     properties.door properties.operator:short properties.operator:type  \\\n",
       "0                NaN                       NaN                      NaN   \n",
       "33               NaN                       NaN                      NaN   \n",
       "34               NaN                       NaN                      NaN   \n",
       "35               NaN                       NaN                      NaN   \n",
       "36               NaN                       NaN                      NaN   \n",
       "37               NaN                       NaN                      NaN   \n",
       "66               NaN                       NaN                      NaN   \n",
       "67               NaN                       NaN                      NaN   \n",
       "69               NaN                       NaN                      NaN   \n",
       "98               NaN                       NaN                      NaN   \n",
       "100              NaN                       NaN                      NaN   \n",
       "101              NaN                       NaN                      NaN   \n",
       "113              NaN                       NaN                      NaN   \n",
       "114              NaN                       NaN                      NaN   \n",
       "1678             NaN                       NaN                      NaN   \n",
       "7626             NaN                       NaN                      NaN   \n",
       "7627             NaN                       NaN                      NaN   \n",
       "7631             NaN                       NaN                      NaN   \n",
       "7632             NaN                       NaN                      NaN   \n",
       "7636             NaN                       NaN                      NaN   \n",
       "\n",
       "     properties.operator:wikidata properties.operator:wikipedia  \\\n",
       "0                             NaN                           NaN   \n",
       "33                            NaN                           NaN   \n",
       "34                            NaN                           NaN   \n",
       "35                            NaN                           NaN   \n",
       "36                            NaN                           NaN   \n",
       "37                            NaN                           NaN   \n",
       "66                            NaN                           NaN   \n",
       "67                            NaN                           NaN   \n",
       "69                            NaN                           NaN   \n",
       "98                            NaN                           NaN   \n",
       "100                           NaN                           NaN   \n",
       "101                           NaN                           NaN   \n",
       "113                           NaN                           NaN   \n",
       "114                           NaN                           NaN   \n",
       "1678                          NaN                           NaN   \n",
       "7626                          NaN                           NaN   \n",
       "7627                          NaN                           NaN   \n",
       "7631                          NaN                           NaN   \n",
       "7632                          NaN                           NaN   \n",
       "7636                          NaN                           NaN   \n",
       "\n",
       "     properties.foot properties.vehicle  \n",
       "0                NaN                NaN  \n",
       "33               NaN                NaN  \n",
       "34               NaN                NaN  \n",
       "35               NaN                NaN  \n",
       "36               NaN                NaN  \n",
       "37               NaN                NaN  \n",
       "66               NaN                NaN  \n",
       "67               NaN                NaN  \n",
       "69               NaN                NaN  \n",
       "98               NaN                NaN  \n",
       "100              NaN                NaN  \n",
       "101              NaN                NaN  \n",
       "113              NaN                NaN  \n",
       "114              NaN                NaN  \n",
       "1678             NaN                NaN  \n",
       "7626             NaN                NaN  \n",
       "7627             NaN                NaN  \n",
       "7631             NaN                NaN  \n",
       "7632             NaN                NaN  \n",
       "7636             NaN                NaN  \n",
       "\n",
       "[20 rows x 124 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[data1[\"properties.height\"].notnull()].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4507ec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [[[-80.4194702, 37.2238774], [-80.4194505, 37....\n",
       "1    [[[-80.4070717, 37.2320238], [-80.4069865, 37....\n",
       "2    [[[-80.4068521, 37.2319488], [-80.4069373, 37....\n",
       "3    [[[-80.4104274, 37.2506844], [-80.4104412, 37....\n",
       "4    [[[-80.4355296, 37.2138316], [-80.4355103, 37....\n",
       "5    [[[-80.4416012, 37.2238365], [-80.4417281, 37....\n",
       "6    [[[-80.3924701, 37.2185727], [-80.3923812, 37....\n",
       "7    [[[-80.4121293, 37.2397986], [-80.4121648, 37....\n",
       "8    [[[-80.4075916, 37.2253453], [-80.4076676, 37....\n",
       "9    [[[-80.4071473, 37.2322051], [-80.4072218, 37....\n",
       "Name: geometry.coordinates, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1[\"geometry.coordinates\"].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "79b66b2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Building:\n",
    "    def __init__(self, coordinates=None, height=None):\n",
    "        self.coordinates = []\n",
    "        if coordinates is not None:\n",
    "            self.coordinates = coordinates\n",
    "        \n",
    "        if height is None or not str(height).isdigit():\n",
    "            self.height = random.uniform(10, 40)\n",
    "        else:\n",
    "            self.height = height\n",
    "        \n",
    "        self.x_coord, self.y_coord, self.z_coord = self.latlon_to_XYZ()\n",
    "        \n",
    "        self.points = []\n",
    "        for i in range(len(self.x_coord)):\n",
    "            x, y, z = self.x_coord[i], self.y_coord[i], self.z_coord[i]\n",
    "            self.points.append(Point(x,y,z))\n",
    "        \n",
    "        self.xy_points = np.array([(p.x, p.y) for p in self.points])\n",
    "        self.xy_polygon = geometry.Polygon(self.xy_points)\n",
    "        \n",
    "        self.wall_polygons = self.get_wall_polygons()\n",
    "    \n",
    "    def latlon_to_XYZ(self):\n",
    "        x_coord = np.array([])\n",
    "        y_coord = np.array([])\n",
    "        z_coord = np.array([])\n",
    "        \n",
    "        # bottom points of building at each coordinate\n",
    "        for lon, lat in self.coordinates:\n",
    "            x = R * math.cos(math.radians(lat)) * math.cos(math.radians(lon))\n",
    "            y = R * math.cos(math.radians(lat)) * math.sin(math.radians(lon))\n",
    "            x_coord = np.append(x_coord, x)\n",
    "            y_coord = np.append(y_coord, y)\n",
    "            z_coord = np.append(z_coord, 0)\n",
    "            # z_coord = np.append(z_coord, self.height)\n",
    "            \n",
    "        # make coordinates to relative to FSS position\n",
    "        x_coord -= x_FSS\n",
    "        y_coord -= y_FSS\n",
    "        \n",
    "        return x_coord, y_coord, z_coord\n",
    "\n",
    "    def get_wall_polygons(self):\n",
    "        polygons = []\n",
    "\n",
    "        # points = [Point(x,y,0) for x,y in p.boundary.coords]\n",
    "        # polygons.append(ConvexPolygon(points))\n",
    "        points = [Point(x,y,self.height) for x,y in self.xy_polygon.boundary.coords]\n",
    "        # polygons.append(ConvexPolygon(points))\n",
    "\n",
    "        for i in range(len(points)-1):\n",
    "            high_point_1 = points[i]\n",
    "            high_point_2 = points[i + 1]\n",
    "            low_point_1 = Point(high_point_1.x, high_point_1.y, 0)\n",
    "            low_point_2 = Point(high_point_2.x, high_point_2.y, 0)\n",
    "\n",
    "            poly_points = [low_point_1, low_point_2, high_point_1, high_point_2]\n",
    "            #print(poly_points)\n",
    "            polygons.append(ConvexPolygon(poly_points))\n",
    "            \n",
    "        return polygons\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8197d035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20b1659b580>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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esmHfcV76eBOjBrTnpiF2Pkh57B1rKu2f/9nBio1ZPHxtP3q2tS/CjHcUFPqZNDuZZg2jmHHTQDsf5AxsSMdUSmbWiZKLPf80DC72bILLH5dvJn3vcd746VBaN4l2OxzPqnQPX0QiRWStiCxx1seKSJqI+EVkWKn94kQkV0SSnNtrtRG4qTuFPj+TEpOpHxVhX4QZz/luxxFeXbGFsUNj+eGA9m6H42lV6eH/BkgHioucrwNuBl4vZ98MVU04t9CMV7y6IoPknUf5021DaN+8gdvhGFMip6CQKYnJdGjekMevD88a91VRqR6+iMQC1wFvFm9T1XRV3VhbgRlvSN55lD8s38wNCR25Pj58LvZsgsNzSzew9eBJfj82nqYN6rkdjudVdkhnFjAN8Fdy/27O8M/nInJJeTuIyN0islpEVmdlZVXysKYu5Rb4mJSYRNum0Tw9ZqDb4RgTYOWmLP7+9XbuurgbI3uEb437qqgw4YvIaOCAqq6p5DH3Al1UdQgwGXhHRE671p2qvqGqw1R1WExMTJWCNnXjuaXpZGYV9Z6aN7Lek/GOYzmnmDY3hZ5tmzD1mvCucV8VlenhXwSMEZFtwLvAFSLy9pl2VtV8VT3kLK8BMoDeNRCrqUMrN2Xx1tfb+flFcVzUs43b4RgT4MnFaWSdyGfmuPiwr3FfFRUmfFWdrqqxqhoHTAA+VdWJZ9pfRGJEJNJZ7g70AjJrKF5TB47mFDB1bjI92zbhwVF93Q7HmAAfpO5lwdrd/PqKngyObeF2OEGl2ideichNIrILGAm8LyIfOXddCqSISDIwF7hXVQ+fe6imLqgqj7y3jkMnCpg1PsF6T8ZTDmTn8ciCVAbHNue+y3u6HU7QqdKJV6q6AljhLC8AFpSzzzxgXg3EZlywKHkP76fsZeo1fewKQcZTVJXp81LJKSiqcW+lParOfmOmxJ6juTz63jrO69KCey7t7nY4xgRIXL2T5RsO8OCovvRs29TtcIKSJXwDgN+vPDAnGZ9fmTkuwa4QZDxl5+Ecnl68npHdW3PHhXFuhxO07F1tAPjbV9v4KuMQj43uT1ybxm6HY0wJv1+ZMicZEeHFsYOttMc5sIRv2Lw/m+c+3MCVfdsyYXhnt8MxJsBfvtzKN1sP88T1/Ylt2cjtcIKaJfwwV1Do5/7ZSTSJjuK5WwZbWVnjKZv2Z/PCRxu5un87bh0a63Y4Qc/KI4e5PyzfRNqe47z+06HENLWyssY7imrcJ9E0Oopnbx5knZEaYAk/jK3Zfpj/WZHB2KGxXGNlZY3H/PnTzaTtOc5rE4fSxmrc1wgb0glTJ/MLmTQ7mY4trKys8Z6knUd5ZUUGt5wXy6iB1hmpKdbDD1PPvL+enUdymH33SCsrazwlt8DH5MQk2jWN5okx1hmpSZbww9An6/fzr292cu9lPRjRrZXb4RgT4PkPN5CZdZJ//uJ8mllnpEbZkE6YOXgin4fmp9C3fVMmXd3L7XCMCfDlloP87att3HGhVWmtDdbDDyOqyvT5qRzPLeTtX5xPdJQVRjPecSz3FA/MSaZ7TGOr0lpLrIcfRuas2cWy9fuZek0f+rY/7Zo0xrjqqcVpHMjOZ+a4BBrWt85IbbCEHyZ2Hs7hqUVpXNC9FXdd3M3tcIwJ8OG6vcz/bjf3Xd6ThM4t3A4nZFnCDwM+vzI5MYkIEX4/Nt5qkRhPycrO5+EF6xjYqRm/vsJq3NcmG8MPA2+szOTbbUd4aWy81SIxnqKqPLwglRP5hcwcl2A17muZ/XZDXNqeY8xctpEfDWzPzed1cjscYwLMdb5XmnZNH3q3sxr3tc0SfgjLO+Vj8uxkWjSqz4ybrBaJ8ZZdR3J4avF6zu/Wijsvsu+V6oIN6YSwlz7eyMb92fz158Np1bi+2+EYU6L4gjuqat8r1SHr4YeorzIO8ua/tzLxgi5c3qet2+EYE+CvX21jVeZhnrh+AJ1b2fdKdaXSCV9EIkVkrYgscdbHikiaiPhFZFiZfaeLyBYR2Sgi19R00Obsjued4oHEZOJaN+bha/u5HY4xAbYcyOZ554I7Y4dZjfu6VJUe/m+A9FLr64CbgZWldxKR/sAEYAAwCnhVROwsijr05MI09mfnM3NcPI3q26id8Y5TPj+TE5NpXD+SZ2+x75XqWqUSvojEAtcBbxZvU9V0Vd1Yzu43AO+qar6qbgW2ACNqIlhTsQ9S9zJ/bdEJLEO6tHQ7HGMCvPLZFlJ2HeN3Nw2ibdMGbocTdirbw58FTAP8ldi3E7Cz1PouZ1sAEblbRFaLyOqsrKxKhmHO5sDxPB5ekMrg2OZ2AovxnJRdR/nTp1u4aUgnfjSog9vhhKUKE76IjAYOqOqaSh6zvM9oetoG1TdUdZiqDouJiankoc2ZqCpT56aQd8rHy+PtBBbjLXmnfEyanURMk2ieHDPA7XDCVmWywkXAGBHZBrwLXCEib59l/11A51LrscCeakdoKuXt/+zg801ZTP9RP3rENHE7HGMCvPDhRjKyTvLi2ME0b2g17t1SYcJX1emqGquqcRR9Gfupqk48y0MWARNEJFpEugG9gG9qJFpTrsysE8x4fz2X9GrDTy/o6nY4xgT4KuMgf/lyKz8b2ZVLetmneTdV+3O/iNwkIruAkcD7IvIRgKqmAYnAeuBD4D5V9dVEsOZ0hT4/kxKTiY6K5MVb7QQW4y3ZeaeYOieFbm0a89CPrMa926o0Z09VVwArnOUFwIIz7DcDmHGOsZlKeOWzDJJ3HuXPPx5C++Y268F4y9OL17P3WC5zf3WhTRH2APtmL4gl7zzKHz/dzI0JHRk9uKPb4RgT4OO0fcxZs4v/+kFPzrMpwp5gCT9I5RYUzXpo2zSap24Y6HY4xgQ4eCKf6fNT6d+hGf/vSrt2slfYZ6wg9ezSdDIPnuSfvzjfZj0YT1FVHlmQSnZeIe/8MoH6Udav9Ar7SwShzzdl8fevt3PnRd24qGcbt8MxJsD873bzUdp+HrimN33aW417L7GEH2SOnCxg6pxkerVtwrRRfdwOx5gAe47m8uSiNEbEteKui7u7HY4pw4Z0goiq8uh76ziSU8Bf7hhOg3pWk854h9+vTJ2bjM+pcR9pU4Q9x3r4QWRh0h7eT93L/Vf1ZmCn5m6HY0yAv3+9jS+3HOKx0f3p0tpq3HuRJfwgsedoLo8tXMfQri2551L7qGy8ZcuBEzy7dAOX94lhwvDOFT/AuMISfhAovhycz6/MHBdPlBVGMx5S6PMzJTGJhvUjef6WwVbj3sMscwSBv361ja8yDvH46P50bd3Y7XCMCfDqigySdx1jxo2DaNvMzvb2Mkv4Hrdpf9Hl4K7q15bx9lHZeEzqrmP8cflmxsR35LrBVuPe6yzhe1hBoZ/7302iaXQUz95sH5WNt+Sd8jE5MYnWTerz9A1W4z4Y2LRMD5v1ySbW7z3O6z8dSkzTaLfDMSbASx9vZPOBE7x15whaNKrvdjimEqyH71Grtx3mtc8zGDcslmsGtHc7HGMCrMo8xJv/3srEC7pwWW+rcR8sLOF70In8QiYnJtOpZUMev94+Khtvyc47xQNzkunSqhEPX9vP7XBMFdiQjgc9s2Q9O4/kkHjPSJpE25/IeMszS9LZczSXOfeOtBr3QcZ6+B6zbP1+3v12J/dc2oPhca3cDseYAJ+s38/s1Tu557IeDO1qr89gYwnfQw6eyOeheSn069CMSVdbDXHjLYdPFvDQ/FT6tm/K/VfZ6zMY2ecxj1BVps//voZ4dJQVRjPeUVzj/lhuAf+4a4S9PoOU9fA9Ys7qXSxbv59po/pYDXHjOQuT9rB03T4mX92Hfh2auR2OqaZKJ3wRiRSRtSKyxFlvJSLLRGSz87Olsz1ORHJFJMm5vVZbwYeKHYdyeGpxGiO7t+bOi7q5HY4xAfYe+75w391WuC+oVaWH/xsgvdT6Q8ByVe0FLHfWi2WoaoJzu7cG4gxZPr8yOTGJCBF+Py6eCKshbjzE71emzkkpKdxnNe6DW6USvojEAtcBb5bafAPwlrP8FnBjjUYWJl5fmcHq7Ud46oYBdGrR0O1wjAnw9n+28+8tB3n42n5WuC8EVLaHPwuYBvhLbWunqnsBnJ9tS93XzRn++VxELinvgCJyt4isFpHVWVlZ1Qg9+KXtOcbLyzZx7aD23DSkk9vhGBNg68GT/O6DdC7tHcNPzu/idjimBlSY8EVkNHBAVddU8ph7gS6qOgSYDLwjIqd9y6Oqb6jqMFUdFhMTfqdm553yMWl2Ei0b1WfGjYOsMJrxlEKfn8mJSURHRfKC1bgPGZWZlnkRMEZErgUaAM1E5G1gv4h0UNW9ItIBOACgqvlAvrO8RkQygN7A6lppQZD6/Ucb2bT/BH/7+XBaNrbCU8ZbXl+ZydodR/njbUNo39xq3IeKCnv4qjpdVWNVNQ6YAHyqqhOBRcDtzm63AwsBRCRGRCKd5e5ALyCzFmIPWl9tOVhSeOoHfdpW/ABj6tC63UVDjaMHd2BMfEe3wzE16FxOvHoOSBSRu4AdwFhn+6XA0yJSCPiAe1X18LmFGTqO5RYVnurWprEVnjKek3fKx5TEZFo1rs9vbxjodjimhlUp4avqCmCFs3wIuLKcfeYB82ogtpD01KI09mfnM9cKTxkPennZJjbuz+avd9hQYyiyM23r0Pspe5m/djf/fXlPhnRp6XY4xgT4dtth3vgik9tGdOHyvjbUGIos4deR/cfzeOS9VOJjm/PfV/R0OxxjAhRdgyGJzi0b8eh1NtQYqmxMoQ6oKlPnppB3ysfM8QnUi7T/s8ZbZryfzq4juSTeM5LGdg2GkGWZpw68vWo7Kzdl8fC1/egR08TtcIwJ8NmGA/zrmx3cfWl3uwZDiLOEX8sys04wwzlb8acXdHU7HGMCHDlZwLR5KfRp15TJV/d2OxxTy+yzWy065fMzaXbR2Yov3mpnKxpvUVUefW8dR3MK+NvPh1uN+zBgPfxa9MpnW0jedYzf3TSIds3sbEXjLYuS9/B+6l7uv6o3Azo2dzscUwcs4deSpJ1H+dOnW7gxoSPXDe7gdjjGBNh3LI/HF6YxpEsL7rEa92HDEn4tyC3wMXl2Em2bRvOUna1oPEZVmTYvhYJCPzPHJRBls8bChv2la8GzS9PJPHiSl8bG07xhPbfDMSbAP/+zw5k11pdubazGfTixhF/DVmw8wN+/3s5dF3fjwp5t3A7HmADbDp5kxvvpXNKrDRNt1ljYsYRfg46cLGDa3BR6tW3C1Gv6uB2OMQGKL6dZL1J4wWaNhSWblllDVJVH3kvlSE4Bf7ljOA3q2RQ34y2vr8zgux1HmTU+gQ7N7XKa4ch6+DXkvaTdfJC6j/uv6s3ATjbFzXhL+t7jJZfTvCHBatyHK0v4NWD30VweX5jG0K4tufeyHm6HY0yA/MKiy2k2b1ifZ+xymmHNhnTOkd+vPJCYjN+vvDwugcgIezMZb5n1yWY27Mvm/24fRiurcR/WrId/jv7y5Va+zjzE49f3p0vrRm6HY0yA1dsO8/rnGUwY3pkr+7VzOxzjMkv452Djvmxe+GgjV/Vry7hhnd0Ox5gAJ/MLmTInmY4tGvLo6P5uh2M8wIZ0qqmg0M/9s5NoGh3FszfbFDfjPb/7IJ0dh3N495cX0MRq3Buq0MMXkUgRWSsiS5z1ViKyTEQ2Oz9bltp3uohsEZGNInJNbQTutlmfbCJ973GevXkQMU2j3Q7HmACfb8rin//ZwS8u7sb53Vu7HY7xiKoM6fwGSC+1/hCwXFV7AcuddUSkPzABGACMAl4VkZCalP7ttsO89nkG44d15ocD2rsdjjEBjuYUMG1uMr3aNmHKD+0EQPO9SiV8EYkFrgPeLLX5BuAtZ/kt4MZS299V1XxV3QpsAUbUSLQeUHztz04tG/LY9TYuarzn8YVpHDpRwMvjE+wEQBOgsj38WcA0wF9qWztV3Qvg/Cy+zH0nYGep/XY520LCbxevZ9eRXGaOS7BxUeM5i5P3sCh5D7+5spedAGhOU2HCF5HRwAFVXVPJY5b37aWWc9y7RWS1iKzOysqq5KHd9XHaPmav3sm9l/Wwa38az9l/PI/HFq4jvnMLfvUDOwHQnK4yPfyLgDEisg14F7hCRN4G9otIBwDn5wFn/11A6TmKscCesgdV1TdUdZiqDouJiTmHJtSNgyfymT4/lX4dmjHpKrv2p/EWVeXBeSnknfIxc1y81bg35arwVaGq01U1VlXjKPoy9lNVnQgsAm53drsdWOgsLwImiEi0iHQDegHf1HjkdUhVeWheKtn5hcwan0D9KHszGW9599udrNiYxUOj+tIjponb4RiPOpdB6OeARBG5C9gBjAVQ1TQRSQTWA4XAfarqO+dIXZS4eiefpO/n0ev60ad9U7fDMSbAjkM5/HbJei7q2ZqfjYxzOxzjYVVK+Kq6AljhLB8CrjzDfjOAGecYmydsP3SSpxavZ2T31tx5UTe3wzEmgM+vTJmTRGSE8OKt8URYLSdzFjbN5Cx8fmVKYjKRIvx+nL2ZjPe8+UUm3247wsxx8XRsYTXuzdlZwj+L11dmsHr7EV4eH08nezMZj9mw7zgvfbyJUQPac9OQkJn5bGqRfft4But2H+PlZZu4blAHbkywN5PxloJCP5NmJ9OsYRQzbhpotZxMpVgPvxx5p4ouGNGyUX2eudHeTMZ7/rC8qJbT//5sGK2bWC0nUzmW8Mvx4kcb2XzgBH/7+XBa2gUjjMd8t+MI/7Mig7FDY7m6v9W4N5VnQzplfLXlIP/376389IKu/KBP24ofYEwdyikoZEpiMh2aN+Rxq+Vkqsh6+KUcyz3FA3OS6d6mMdOv7et2OMac5rmlG9h68CT/+uUFNG1Qz+1wTJCxhF/Kk4vS2J+dz7xfXUij+varMd6yclMWf/96O3dd3I2RPazGvak6G9JxLEnZw4K1u/n1FT1J6NzC7XCMCXAs5xTT5qbQs20Tpl5jNe5N9VjCB/Ydy+ORBeuIj23OfZf3dDscY07zxKJ1ZJ3IZ+a4eKtxb6ot7BO+qjJtXgr5hT5mjk+gnlUZNB7zQepe3kvaw6+v6Mng2BZuh2OCWNhnt7dXbWflpiweubafVRk0nnMgO49HFqQy2D59mhoQ1gk/I+sEMz5I59LeMUy8oKvb4RgTQFWZPi+VnIKiGvf26dOcq7B9BZ3y+Zk8O4kG9SJ58dbBdjat8ZzE1TtZvuEAD47qS8+2VpbbnLuwnXv450+3kLzrGK/8+DzaNWvgdjjGBNh5OIennbLcd1wY53Y4JkSEZQ8/aedR/vzZFm4a0onrBndwOxxjAvj9ypQ5yYgIL44dbGW5TY0Ju4SfU1DIpNlJtGsazZNjBrgdjjGn+cuXW/lm62GeuL4/sS0buR2OCSFhN6Tz7AdFp6a/88vzad7QTk033rJpfzYvfLSRq/u349ahsW6HY0JMWPXwP9t4gH+s2s4vLu7GhT3auB2OMQGKatwn0TQ6imdvHmQTCUyNC5se/pGTBUybm0Lvdk14wE5NNx705083k7bnOK9NHEobq3FvakGFPXwRaSAi34hIsoikichTzvZ4EflaRFJFZLGINHO2x4lIrogkObfXarsRFVFVHnkvlaM5Bcwcl2CnphvPWbvjCK+syOCW82IZNbC92+GYEFWZHn4+cIWqnhCResC/RWQp8CfgAVX9XETuBKYCjzmPyVDVhFqJuBreS9rNB6n7mDaqDwM7NXc7HGMC5Bb4mJKYTLum0Twxxmrcm9pTYQ9fi5xwVus5NwX6ACud7cuAW2olwnO0+2guj7+XxrCuLbnn0h5uh2PMaZ7/cAOZB0/y4th4mlmNe1OLKvWlrYhEikgScABYpqr/AdYBY5xdxgKdSz2km4isFZHPReSSMxzzbhFZLSKrs7Kyqt+Cs/D7lSmJSfhVmTkugUibz2w85sstB/nbV9u448I4LuppEwlM7apUwldVnzNEEwuMEJGBwJ3AfSKyBmgKFDi77wW6qOoQYDLwTvH4fpljvqGqw1R1WExMTA005XR/+XIrqzIP8/j1/enS2uYzG28pucJaTGMeHGVXWDO1r0rTMlX1KLACGKWqG1T1h6o6FPgXkOHsk6+qh5zlNc723jUZdGVs3JfNCx9u5Kp+7Rg3rHPFDzCmjj21OI0D2fnMHJdAw/o2kcDUvsrM0okRkRbOckPgKmCDiLR1tkUAjwKvldo/0lnuDvQCMmsl+jPIL/Rx/+wkmjaI4rlbbD6z8Z4P1+1l/ne7ue9yu8KaqTuV6eF3AD4TkRTgW4rG8JcAt4nIJmADsAf4q7P/pUCKiCQDc4F7VfVwzYd+ZrM+2Uz63uM8d8tgm89sPCcrO5+HF6xjYKdm/PoKq3Fv6k6F0zJVNQUYUs72PwB/KGf7PGBejURXDd9uO8xrn2cwYXhnru7fzq0wjCmXqjJ9fion8guZOc6usGbqVki92rLzTjFpdhKxLRvy6Gibz2y8Z+6aXXySvp9p1/ShdzurcW/qVkiVVvjtkvXsOZpL4j0jaRIdUk0zIWDXkRyeWrye87u14s6LurkdjglDIdPD/zhtH4mrd3HvZT0YFtfK7XCMCeD3Kw/MSUZV+f3YeKtxb1wREgk/Kzuf6fNT6d+hGfdfVeczQI2p0F+/2saqzMM8cf0AOreyc0KMO4J+3KPoS7AUsvML+deEBOpHhcT/sJClqvgVfH7Fr0U3n1/x+ylaVsXvd35qUc/Y56yrKj7/948NPEbRdnWOUXKfn8ods3ifMsf0l37sGY9BybLfH/jY4u3L1u/nqn5tGTvMatwb9wR9wp/97U4+ST/Ao9f1q9aXYN+/ib9PDkUJSEslIMq8mYuTgvNGL7PuP1PCKZugShLO2Y7xfYwlCe20JFZ8fAKT6BmPUU6CKjcRfr+97DFVy/6uysTkL/X7KfU8fq2FF0EdiowQIkUQ+X45IkKIcNYjREp+RkRQcv/QLi35ndW4Ny4L6oR/+GQBTy9ZD8Dfv97OW19vK0qwZXqPpyWoUok1mBUnGZGixBNZJvFERHy/vbwEVZycvj9G6aQVQXRU8THKeZ7i5yr1PBERBCY8ESIj+P7+gJgoOl6pmCIF57hyemIte8yAxFr+85/5GGdOziXtLHtM53mMCWZBnfAb1Ivg+sEdOVlQeFpCOD0RlnqjlySgUsko4uzJpHh7yX3lHKP8xFrOMUsSbcXHPGPvUbDeojGmSoI64TeqH8Xztw52OwxjjAkK9g2nMcaECUv4xhgTJizhG2NMmLCEb4wxYcISvjHGhAlL+MYYEyYs4RtjTJiwhG+MMWFCVN0vLyAiWcB2t+OopjbAQbeDqCXWtuAVyu2ztn2vq6rGVHZnTyT8YCYiq1V1mNtx1AZrW/AK5fZZ26rPhnSMMSZMWMI3xpgwYQn/3L3hdgC1yNoWvEK5fda2arIxfGOMCRPWwzfGmDBhCd8YY8KEJfyzEJGxIpImIn4RGVZq+wgRSXJuySJyk7O9aantSSJyUERmOfdNFpH1IpIiIstFpKtLzSpuQ022LVpEZovIFhH5j4jEudOqkjZUqW3OffVF5A0R2SQiG0TkFmd7FxH5TETWOn+7a91oU2k12T7nvnHOazNNRN6p6/aUVtNtc+6/VUS09PHcUMOvy+rlE3UuWG23029AP6APsAIYVmp7IyDKWe4AHCheL/P4NcClzvLlQCNn+VfA7BBq238BrznLE4KxbcBTwDPOcgTQxll+A/iVs9wf2BaMr8uztK8XsBZo6ay3DZW2OetNgZXAqtLHC/a2VTefBPUlDmubqqbD6deOVdWcUqsNgNO++RaRXkBb4AvnMZ+VunsVMLGGw62SmmwbcAPwpLM8F/iziIg6r8a6Vs223Qn0dfbz8/3Zjgo0c5abA3tqPuKqqeH2/RJ4RVWPOPcdqJ2oK6eG2wbwW+AF4IFaCLdKarJt1c0nNqRTTSJyvoikAanAvapaWGaX2yj6r1te0rsLWFrbMVZXNdrWCdgJ4Ox7DGhdV/FWRXltE5EWzt2/FZHvRGSOiLRztj0JTBSRXcAHwK/rPOgqqEb7egO9ReRLEVklIqPciLsyqto2ERkCdFbVJS6FXGnV+LuVVvl84uZHHC/cgE+AdeXcbii1zwrO8HGQoo9p3wANymxfDwwtZ/+JFP1Hjg6VtgFpQGyp9QygdbC0jaL6JQrc4tw3GfhHqeUpzvJIp+0RwfS3q6B9S4AFQD2gG7ALaBHsbaOoM7sCiKvoeMHWtjL7Vymf1GrjQ+VW0YsF+IzAMbl4YFM5+10FpOPyOGlNtw34CBjpLEdR9LFTgqVtgAAnixM50BlIc5bTKOolFj8m0yt/vxpq32vAHaUesxwYHuxto2j47SCwzbnlUTQc5+o4fk393Zz1KucTG9KpBhHpJiJRznJXir6I2VZql9uAf5V5zBDgdWCMujxOejbVaRuwCLjdWb4V+FSdV6SXnKltTqyLgR84u15JUU8eYIezjoj0o6jnlVWHYVdaNdv3HkVfACIibSga4smsu6grp6ptU9VjqtpGVeNUNY6iXvAYVV1d99GfXXX+btXOJ27/t/PyDbiJoo+4+cB+4CNn+08p6kUkAd8BN5Z5XCbQt8y2T5xjJDm3RSHUtgbAHGALRR9Huwdb24CuFM3mSKGol9vF2d4f+BJIdh73w2B8XZ6lfQLMpCiRpAITQqVtZY67Avdn6dTk361a+cRKKxhjTJiwIR1jjAkTlvCNMSZMWMI3xpgwYQnfGGPChCV8Y4wJE5bwjTEmTFjCN8aYMPH/ATriH5PYzXsKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from shapely import geometry\n",
    "\n",
    "b = Building(data1.iloc[500][\"geometry.coordinates\"][0], data1.iloc[500][\"properties.height\"])\n",
    "x, y = b.xy_polygon.boundary.xy\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e40e671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = Renderer(backend='matplotlib')\n",
    "for p in b.wall_polygons:\n",
    "    r.add((p,'b',1),normal_length=0)\n",
    "r.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a00e8dec",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|▎                                                                               | 36/8644 [00:00<01:49, 78.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping building 26\n",
      "Skipping building 26\n",
      "Skipping building 27\n",
      "Skipping building 27\n",
      "Skipping building 27\n",
      "Skipping building 28\n",
      "Skipping building 28\n",
      "Skipping building 29\n",
      "Skipping building 29\n",
      "Skipping building 29\n",
      "Skipping building 29\n",
      "Skipping building 29\n",
      "Skipping building 29\n",
      "Skipping building 29\n",
      "Skipping building 29\n",
      "Skipping building 29\n",
      "Skipping building 29\n",
      "Skipping building 29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|▋                                                                               | 69/8644 [00:00<01:49, 78.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping building 53\n",
      "Skipping building 53\n",
      "Skipping building 53\n",
      "Skipping building 53\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|█████████████████████████████████████████████████████████████▍               | 6897/8644 [00:37<00:08, 198.51it/s]"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "buildings = []\n",
    "\n",
    "for i in tqdm(range(len(data1))):\n",
    "    for coords in data1.iloc[i][\"geometry.coordinates\"]:\n",
    "        try:\n",
    "            buildings.append(Building(coords, data1.iloc[i][\"properties.height\"]))\n",
    "        except:\n",
    "            print(f\"Skipping building {i}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dlC9qcOpl-Yw",
   "metadata": {
    "id": "dlC9qcOpl-Yw"
   },
   "outputs": [],
   "source": [
    "class FSS_Channels:\n",
    "  # channels start at 0, so a channel plan with 8 channels is from 0 to 7\n",
    "\n",
    "  band_width = 240e6 # 240MHz\n",
    "\n",
    "  # 10.7-12.7 GHz frequency range\n",
    "  range_start = 10.7e9 # 10.7GHz\n",
    "  range_end = 12.7e9 # 12.7GHz\n",
    "\n",
    "  channel_count = 8\n",
    "\n",
    "  range_size = range_end - range_start\n",
    "  unused_space = range_size - band_width * channel_count\n",
    "\n",
    "  # FSS channel spacing should be 10MHz\n",
    "  space_between_channels = unused_space / channel_count\n",
    "\n",
    "  @staticmethod\n",
    "  def getChannelRange(channel) -> Tuple[int, int]:\n",
    "    channel_start = int((0.5 + channel) * FSS_Channels.space_between_channels + channel * FSS_Channels.band_width)\n",
    "    channel_end = channel_start + FSS_Channels.band_width\n",
    "    return channel_start, channel_end\n",
    "\n",
    "  def __init__(channel):\n",
    "    self.channel = channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "YFrFecDrp-kH",
   "metadata": {
    "id": "YFrFecDrp-kH"
   },
   "outputs": [],
   "source": [
    "class BS_Channels:\n",
    "  # channels start at 1, so a channel plan with 5 channels is from 1 to 5\n",
    "\n",
    "  band_width = 100e6 # 100MHz\n",
    "\n",
    "  # 12.2-12.7 GHz frequency range\n",
    "  range_start = 12.2e9 # 12.2GHz\n",
    "  range_end = 12.7e9 # 12.7GHz\n",
    "\n",
    "  channel_count = 5\n",
    "\n",
    "  range_size = range_end - range_start\n",
    "  unused_space = range_size - band_width * channel_count\n",
    "\n",
    "  # BS channel spacing should be 0MHz\n",
    "  space_between_channels = unused_space / channel_count\n",
    "\n",
    "  @staticmethod\n",
    "  def getChannelRange(channel) -> Tuple[int, int]:\n",
    "    channel -= 1\n",
    "    \n",
    "    channel_start = int((0.5 + channel) * BS_Channels.space_between_channels + channel * BS_Channels.band_width)\n",
    "    channel_end = channel_start + BS_Channels.band_width\n",
    "    return channel_start, channel_end\n",
    "\n",
    "  def __init__(channel):\n",
    "    self.channel = channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "105b844b",
   "metadata": {
    "id": "105b844b"
   },
   "outputs": [],
   "source": [
    "class BS:\n",
    "  def __init__(self, radius, max_height, carr_freq, interference_type):\n",
    "    self.radius = radius\n",
    "    self.max_height = max_height\n",
    "    self.carr_freq = carr_freq\n",
    "    self.interference_type = interference_type\n",
    "    self.base_heights = {\n",
    "        'UMi': 10,\n",
    "        'UMa': 25,\n",
    "        'RMa': 35,\n",
    "        None: random.randint(10, 35)\n",
    "    }\n",
    "    self.BS_x = random.randint(-4000,4000) + 1000*random.random()\n",
    "    self.BS_y = random.randint(-4000,4000) + 1000*random.random()\n",
    "    self.BS_z = self.base_heights[self.interference_type]\n",
    "\n",
    "  def BS_random_co_ordinates(self):\n",
    "    return self.BS_x, self.BS_y, self.BS_z\n",
    " \n",
    "  def UE_random_co_ordinates(self):\n",
    "    UE_x = random.randint(-4000,4000) + 1000*random.random()\n",
    "    UE_y = random.randint(-4000,4000) + 1000*random.random()\n",
    "    UE_z = random.uniform(0,1.5)\n",
    "    \n",
    "    return UE_x, UE_y, UE_z\n",
    "\n",
    "# Creating the FSS class\n",
    "  def FSS_random_co_ordinates(self):\n",
    "\n",
    "    FSS_x = random.randint(-4000,4000) + 1000*random.random()\n",
    "    FSS_y = random.randint(-4000,4000) + 1000*random.random()\n",
    "    FSS_z = random.choice([1.5,4.5])\n",
    "    \n",
    "    return FSS_x, FSS_y, FSS_z\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "588f6adb",
   "metadata": {
    "id": "588f6adb"
   },
   "outputs": [],
   "source": [
    "def path_loss_UMi(BS_X,BS_Y,BS_Z,FSS_X,FSS_Y,FSS_Z):\n",
    "##UMi\n",
    "##LOS,SF=4:\n",
    "\n",
    "##(10m<=d_2D)<=D_BP:\n",
    "    h=5\n",
    "    d_2D=math.sqrt(((FSS_X-BS_X)**2)+((FSS_Y-BS_Y)**2)+((FSS_Z-BS_Z)**2))\n",
    "    hBs=10\n",
    "    fc=12e3\n",
    "    d_3D=math.sqrt(hBs**2+d_2D**2)\n",
    "    PL1umi=32.4+21*math.log10(d_3D)+20*math.log10(fc)\n",
    "    \n",
    "##(D_BP<=d_2D)<=5000m:\n",
    "    hBs=10\n",
    "    hUT=1.5\n",
    "    hE=1\n",
    "    hBs1=hBs-hE\n",
    "    hUT1=hUT-hE\n",
    "    c=3*10**8\n",
    "    D_BP= (4*hBs1*hUT1*fc)/c\n",
    "    PL2umi=32.4+40*math.log10(d_3D)+20*math.log10(fc)-9.5*math.log10((D_BP)**2+(hBs-hUT)**2)\n",
    "    \n",
    "    if 10 <=d_2D and d_2D<=D_BP:\n",
    "        PLUMiLOS=PL1umi\n",
    "    elif D_BP<=d_2D and  d_2D<=5000:\n",
    "        PLUMiLOS= PL2umi  \n",
    "    else:\n",
    "        PLUMiLOS=1\n",
    "\n",
    "##NLOS,SF=7.82:\n",
    "    PL1umiNLOS= 35.3*math.log10(d_3D)+22.4+21.3*math.log10(fc)-0.3*(hUT-1.5)\n",
    "    PLUMiNLOS= max(PL2umi,PL1umiNLOS)\n",
    "    \n",
    "    line_of_sight = True\n",
    "    \n",
    "##realistic pathloss:\n",
    "    bs_to_fss_segment = Segment(Point(BS_X, BS_Y, BS_Z), Point(FSS_X, FSS_Y, FSS_Z))\n",
    "    for i in tqdm(range(len(buildings))):\n",
    "#     for i in range(len(buildings)):\n",
    "        for polygon in buildings[i].wall_polygons:\n",
    "            if (hash(bs_to_fss_segment), hash(polygon)) in saved_los:\n",
    "                if saved_los.get((hash(bs_to_fss_segment), hash(polygon))):\n",
    "                    path_loss_UMi = PLUMiNLOS\n",
    "                    line_of_sight = False\n",
    "                else:\n",
    "                    path_loss_UMi = PLUMiLOS\n",
    "#                     line_of_sight = True\n",
    "    \n",
    " # intersection(line, polygon): [Intersection], None\n",
    "            elif intersection(bs_to_fss_segment, polygon) is not None:\n",
    "                path_loss_UMi = PLUMiNLOS\n",
    "                line_of_sight = False\n",
    "                saved_los[(hash(bs_to_fss_segment), hash(polygon))] = True\n",
    "            else:\n",
    "                path_loss_UMi = PLUMiLOS\n",
    "#                 line_of_sight = True\n",
    "                saved_los[(hash(bs_to_fss_segment), hash(polygon))] = False\n",
    "    \n",
    "# #Loss Probability:\n",
    "#     if d_2D>18:\n",
    "#         PrLosUmi=(18/d_2D)+math.exp((-(d_2D/36))*(1-(18/d_2D)))     \n",
    "#     else:\n",
    "#         PrLosUmi=1 ##d_2d<=18     \n",
    "#     if PrLosUmi > random.random():\n",
    "#         path_loss_UMi = PLUMiLOS   \n",
    "#     else:\n",
    "#         path_loss_UMi = PLUMiNLOS   \n",
    "##path_loss \n",
    "#     path_loss_UMi= PL2umi*PrLosUmi+PL1umiNLOS*(1-PrLosUmi)\n",
    "    \n",
    "    return path_loss_UMi, d_2D, line_of_sight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b511d323",
   "metadata": {
    "id": "b511d323"
   },
   "outputs": [],
   "source": [
    "def path_loss_UMa(BS_X,BS_Y,BS_Z,FSS_X,FSS_Y,FSS_Z):\n",
    "##LOS,SF=4:\n",
    "##(10m<=d_2D)<=D_BP:\n",
    "    fc=12e3\n",
    "    h=5\n",
    "    d_2D=math.sqrt(((FSS_X-BS_X)**2)+(FSS_Y- BS_Y)**2)+((FSS_Z-BS_Z)**2)\n",
    "    hBs=25\n",
    "    d_3D=math.sqrt(hBs**2+d_2D**2)\n",
    "    PL3uma=28.0+22*math.log10(d_3D)+20*math.log10(fc)\n",
    "    \n",
    "##(D_BP<=d_2D) <=5000m:\n",
    "    hUT=1.5\n",
    "    hE=1\n",
    "    hBs1=hBs-hE\n",
    "    hUT1=hUT-hE\n",
    "    c=3*10**8\n",
    "    D_BP= (4*hBs1*hUT1*fc)/c\n",
    "    PL4uma=28.0+40*math.log10(d_3D)+20*math.log10(fc)-9*math.log10((D_BP)**2+(hBs-hUT)**2)\n",
    "    \n",
    "    if 10 <=d_2D and d_2D<=D_BP:\n",
    "        PLUMALOS=PL3uma\n",
    "    elif D_BP<=d_2D and d_2D<=5000:\n",
    "         PLUMALOS= PL4uma  \n",
    "    else:\n",
    "        PLUMALOS=1\n",
    "        \n",
    "##NLOS,SF=6:\n",
    "    PL1NLOSuma=13.54+39.08*math.log10(d_3D)+20*math.log10(fc)-0.6*(hUT-1.5)\n",
    "    PLUMANLOS=max(PL4uma,PL1NLOSuma)\n",
    "    \n",
    "    line_of_sight = True\n",
    "\n",
    "##NLOS,SF=7.8 (optional)\n",
    "##PL_Optional=32.4+20*math.log(fc)+30*math.log(d_3D)\n",
    "\n",
    "\n",
    "##realistic pathloss:\n",
    "    bs_to_fss_segment = Segment(Point(BS_X, BS_Y, BS_Z), Point(FSS_X, FSS_Y, FSS_Z))\n",
    "    for i in tqdm(range(len(buildings))):\n",
    "#     for i in range(len(buildings)):\n",
    "        for polygon in buildings[i].wall_polygons:\n",
    "            if (hash(bs_to_fss_segment), hash(polygon)) in saved_los:\n",
    "                if saved_los.get((hash(bs_to_fss_segment), hash(polygon))):\n",
    "                    path_loss_UMa = PLUMANLOS\n",
    "                    line_of_sight = False\n",
    "                else:\n",
    "                    path_loss_UMa = PLUMALOS\n",
    "#                     line_of_sight = True\n",
    "                \n",
    "            elif intersection(bs_to_fss_segment, polygon) is not None:\n",
    "                path_loss_UMa = PLUMANLOS\n",
    "                line_of_sight = False\n",
    "                saved_los[(hash(bs_to_fss_segment), hash(polygon))] = True\n",
    "            else:\n",
    "                path_loss_UMa = PLUMALOS\n",
    "#                 line_of_sight = True\n",
    "                saved_los[(hash(bs_to_fss_segment), hash(polygon))] = False\n",
    "\n",
    "\n",
    "##Loss Probability:\n",
    "#     if d_2D >18:\n",
    "#         if hUT<=13:\n",
    "#             ChUT=0\n",
    "#         elif 13<hUT and hUT<=23:\n",
    "#             ChUT=((hUT-13)/10)**1.5  \n",
    "#         PrLOSUma=((18/d_2D)+math.exp((-(d_2D/63))*(1-(18/d_2D))))*(1+ChUT*(5/4)*((d_2D/100)**3)*math.exp(-(d_2D/150)))\n",
    "#     else:##18m<d_2D:\n",
    "#         PrLOSUma=1\n",
    "        \n",
    "#     if PrLOSUma > random.random():\n",
    "#         path_loss_UMa = PLUMALOS\n",
    "    \n",
    "#     else:\n",
    "#         path_loss_UMa = PLUMANLOS\n",
    "    \n",
    "##Path Loss\n",
    "#     path_loss_UMa= PL4uma*PrLOSUma+PL1NLOSuma*(1-PrLOSUma)\n",
    "    return path_loss_UMa, d_2D, line_of_sight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a3d8b7b",
   "metadata": {
    "id": "4a3d8b7b"
   },
   "outputs": [],
   "source": [
    "def path_loss_RMa(BS_X,BS_Y,BS_Z,FSS_X,FSS_Y,FSS_Z):\n",
    "##LOS,SF=4(PL1),SF=6(PL2)\n",
    "##10m<=d_2D<=d_BP: \n",
    "    fc=12e3\n",
    "    h=5\n",
    "    d_2D=math.sqrt(((FSS_X-BS_X)**2)+((FSS_Y- BS_Y)**2)+((FSS_Z-BS_Z)**2))\n",
    "    hBs=35\n",
    "    d_3D= math.sqrt(hBs**2+d_2D**2)\n",
    "    PL1rma=20*math.log10((40*math.pi*d_3D*fc)/3)+ min(0.03*h**1.72,10)*math.log10(d_3D)-min(0.044*h**1.72,14.77)+0.002*math.log10(h)*d_3D\n",
    "    \n",
    "##d_BP<=d_2D<=10km:\n",
    "    hUT=1.5\n",
    "    c=3*10**8\n",
    "    d_BP= (2*math.pi*hBs*hUT*fc)/c\n",
    "    PL2rma= PL1rma*(d_BP)+40*math.log10(d_3D/d_BP)\n",
    "\n",
    "    if 10<=d_2D and d_2D <=d_BP:\n",
    "        PLRMALOS=PL1rma\n",
    "    elif d_BP<=d_2D and d_2D <=10000:\n",
    "        PLRMALOS= PL2rma  \n",
    "    else:\n",
    "        PLRMALOS=1\n",
    "\n",
    "##NLOS,SF=8:\n",
    "    W=20\n",
    "    h=5\n",
    "    PL1NLOSrma= 161.04-7.11*math.log10(W)+7.5*math.log10(h)-(24.37-3.7*(h/hBs)**2)*math.log10(hBs)+(43.42-3.1*math.log10(hBs))*(math.log10(d_3D)-3)+20*math.log10(fc)-(3.2*(math.log10(11.75*hUT))**2-4.97)\n",
    "    PLRMANLOS=max(PL2rma,PL1NLOSrma)\n",
    "    \n",
    "    line_of_sight = True\n",
    "    \n",
    "##realistic pathloss:\n",
    "    bs_to_fss_segment = Segment(Point(BS_X, BS_Y, BS_Z), Point(FSS_X, FSS_Y, FSS_Z))\n",
    "    for i in tqdm(range(len(buildings))):\n",
    "#     for i in range(len(buildings)):\n",
    "        for polygon in buildings[i].wall_polygons:\n",
    "            if (hash(bs_to_fss_segment), hash(polygon)) in saved_los:\n",
    "                if saved_los.get((hash(bs_to_fss_segment), hash(polygon))):\n",
    "                    path_loss_RMa = PLRMANLOS\n",
    "                    line_of_sight = False\n",
    "                else:\n",
    "                    path_loss_RMa = PLRMALOS\n",
    "                    # line_of_sight = True\n",
    "   \n",
    "            elif intersection(bs_to_fss_segment, polygon) is not None:\n",
    "                path_loss_RMa = PLRMANLOS\n",
    "                line_of_sight = False\n",
    "                saved_los[(hash(bs_to_fss_segment), hash(polygon))] = True\n",
    "            else:\n",
    "                path_loss_RMa = PLRMALOS\n",
    "                # line_of_sight = True\n",
    "                saved_los[(hash(bs_to_fss_segment), hash(polygon))] = False\n",
    "                \n",
    "    \n",
    "\n",
    "# ##Loss Probability:\n",
    "#     if d_2D>10:\n",
    "#         PrLOSrma=math.exp(-((d_2D-10)/1000))\n",
    "        \n",
    "# ## 10m<d_2D:\n",
    "#     else:\n",
    "#         PrLOSrma=1\n",
    "        \n",
    "#     if PrLOSrma > random.random():\n",
    "#         path_loss_RMa= PLRMALOS\n",
    "    \n",
    "#     else:\n",
    "#         path_loss_RMa = PLRMANLOS\n",
    "\n",
    "##PathLoss:\n",
    "#     path_loss_RMa=PL2rma*PrLOSrma+PL1NLOSrma*(1-PrLOSrma)\n",
    "    return path_loss_RMa, d_2D,  line_of_sight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af179d64",
   "metadata": {
    "id": "af179d64"
   },
   "outputs": [],
   "source": [
    "def Interface_UMi_1(BS_X,BS_Y,BS_Z,FSS_X,FSS_Y,FSS_Z,FSS_phi,pathloss_UMi,theta_tilt,phi_scan,output=False):\n",
    "        LBodyLoss=4\n",
    "#         LSpectralOverlap=10*math.log(10)\n",
    "        # theta_tilt, phi_scan = max_gain_5g_parameters(theta, phi)\n",
    "        x, y, z = BS_X-FSS_X, BS_Y-FSS_Y, BS_Z-FSS_Z\n",
    "\n",
    "        theta_bs_es = math.degrees(math.atan(y/x)) % 360\n",
    "        phi_bs_es = math.degrees(math.sqrt(x**2 + y**2) / z) % 360\n",
    "        fss_phi_difference = abs(FSS_phi - phi_bs_es)\n",
    "        if output: print(\"theta_bs_es:\", theta_bs_es, \"phi_bs_es:\", phi_bs_es)\n",
    "\n",
    "        G_5G_R=gain_5g(theta_bs_es, phi_bs_es, theta_tilt, phi_scan)\n",
    "        G_Rx_5G=gain_fss_wbes_b(fss_phi_difference)\n",
    "\n",
    "        TXPower=41.3\n",
    "#         LBuildingLoss=1\n",
    "        interface1=TXPower+ G_5G_R-pathloss_UMi- LBodyLoss + G_Rx_5G\n",
    "        return interface1,pathloss_UMi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ece46a3",
   "metadata": {
    "id": "5ece46a3"
   },
   "outputs": [],
   "source": [
    "def Interface_UMa_1(BS_X,BS_Y,BS_Z,FSS_X,FSS_Y,FSS_Z,FSS_phi,pathloss_UMa,theta_tilt,phi_scan,output=False):\n",
    "        LBodyLoss=4\n",
    "#         LSpectralOverlap=10*math.log(10)\n",
    "        x, y, z = BS_X-FSS_X, BS_Y-FSS_Y, BS_Z-FSS_Z\n",
    "\n",
    "        theta_bs_es = math.degrees(math.atan(y/x)) % 360\n",
    "        phi_bs_es = math.degrees(math.sqrt(x**2 + y**2) / z) % 360\n",
    "        fss_phi_difference = abs(FSS_phi - phi_bs_es)\n",
    "        # if output: print(\"theta_bs_es:\", theta_bs_es, \"phi_bs_es:\", phi_bs_es)\n",
    "\n",
    "        G_5G_R=gain_5g(theta_bs_es, phi_bs_es, theta_tilt, phi_scan)\n",
    "        G_Rx_5G=gain_fss_wbes_b(fss_phi_difference)\n",
    "        \n",
    "        TXPower=41.3\n",
    "#         LBuildingLoss=1\n",
    "        interface2=TXPower+ G_5G_R-pathloss_UMa- LBodyLoss + G_Rx_5G\n",
    "        return interface2,pathloss_UMa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3edfcd7",
   "metadata": {
    "id": "c3edfcd7"
   },
   "outputs": [],
   "source": [
    "def Interface_RMa_1(BS_X,BS_Y,BS_Z,FSS_X,FSS_Y,FSS_Z,FSS_phi,pathloss_RMa,theta_tilt,phi_scan,output=False):\n",
    "        LBodyLoss=4\n",
    "#         LSpectralOverlap=10*math.log(10)\n",
    "        x, y, z = BS_X-FSS_X, BS_Y-FSS_Y, BS_Z-FSS_Z\n",
    "\n",
    "        theta_bs_es = math.degrees(math.atan(y/x)) % 360\n",
    "        phi_bs_es = math.degrees(math.sqrt(x**2 + y**2) / z) % 360\n",
    "        # if output: print(\"theta_bs_es:\", theta_bs_es, \"phi_bs_es:\", phi_bs_es)\n",
    "        fss_phi_difference = abs(FSS_phi - phi_bs_es)\n",
    "        if output: print(\"fss_phi_difference:\", fss_phi_difference)\n",
    "\n",
    "        G_5G_R=gain_5g(theta_bs_es, phi_bs_es, theta_tilt, phi_scan)\n",
    "        G_Rx_5G=gain_fss_wbes_b(fss_phi_difference)\n",
    "        \n",
    "        TXPower=41.3\n",
    "#         LBuildingLoss=1\n",
    "        interface3=TXPower+ G_5G_R-pathloss_RMa-LBodyLoss + G_Rx_5G\n",
    "        return interface3,pathloss_RMa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54017c74",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "54017c74",
    "outputId": "7bf744bb-353f-4262-d386-40287c84faaf"
   },
   "outputs": [],
   "source": [
    "k = 1.38064852*10**(-23)\n",
    "T=200\n",
    "B=240e6\n",
    "Noise = 10*math.log10(k*T*B)\n",
    "Noise_W = 10 ** (Noise / 10)\n",
    "print('Noise:', Noise)\n",
    "print('Noise in Watts:', Noise_W)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f88ba92f",
   "metadata": {
    "id": "f88ba92f"
   },
   "outputs": [],
   "source": [
    "bs_ue_max_radius = 1000\n",
    "bs_ue_min_radius= 1\n",
    "base_station_count=33\n",
    "\n",
    "fss1 = BS(radius, max_height=1.5, carr_freq=12e3, interference_type=None)\n",
    "x,y,z = 0,0,0\n",
    "\n",
    "FSS_phi = {\n",
    "    'UMi': 15,\n",
    "    'UMa': 48,\n",
    "    'RMa': 5\n",
    "}\n",
    "        \n",
    "def simulate(output=True):\n",
    "    FSS_X=np.array([])\n",
    "    FSS_Y=np.array([])\n",
    "    FSS_Z=np.array([])\n",
    "    FSS_CHANNELS = []\n",
    "    for i in range (1):\n",
    "        FSS_X = np.append(FSS_X, x)\n",
    "        FSS_Y = np.append(FSS_Y, y)\n",
    "        FSS_Z = np.append(FSS_Z, z)\n",
    "        # 0 means not in use, 1 means in use\n",
    "        channel_status = [random.randint(0, 1) for i in range(FSS_Channels.channel_count)]\n",
    "        channels_used = np.array([i for i in range(FSS_Channels.channel_count) if channel_status[i] == 1])\n",
    "        FSS_CHANNELS.append(channels_used)\n",
    "        if output: print(\"FSS Co-ordinates=\" +str (x)+ \",\" + str(y)+ \",\" + str(z) + \", channel: \" + str(channels_used))\n",
    "    if output: print(FSS_X, FSS_Y, FSS_Z, FSS_CHANNELS)\n",
    "\n",
    "    # Create base stations\n",
    "    BS_X=np.array([])\n",
    "    BS_Y=np.array([])\n",
    "    BS_Z=np.array([])\n",
    "    \n",
    "    # Randomly select base stations \n",
    "    # base_station_indexes = random.sample(range(len(data_within_zone)), base_station_count)\n",
    "\n",
    "    # for i in base_station_indexes:\n",
    "    for i in range(base_station_count):\n",
    "        lat_BS, lon_BS = data_within_zone.iloc[i]['latitude'], data_within_zone.iloc[i]['longitude']\n",
    "        bs1 = BS(radius, max_height=35, carr_freq=12e3, interference_type=None)\n",
    "        x_BS = R * math.cos(math.radians(lat_BS)) * math.cos(math.radians(lon_BS))\n",
    "        y_BS = R * math.cos(math.radians(lat_BS)) * math.sin(math.radians(lon_BS))\n",
    "#         z_BS = R * math.sin(math.radians(lat_BS))\n",
    "\n",
    "        BS_X = np.append(BS_X, x_BS - x_FSS)\n",
    "        BS_Y = np.append(BS_Y, y_BS - y_FSS)\n",
    "        BS_Z = np.append(BS_Z, 0)\n",
    "        if output: print(\"Bs Co-ordinates=\" +str (x_BS)+ \",\" + str(y_BS)+ \",\" + str(z_BS))\n",
    "    \n",
    "    if output: print(BS_X,BS_Y,BS_Z)\n",
    "\n",
    "    # Create user equipment\n",
    "    UE_X=np.array([])\n",
    "    UE_Y=np.array([])\n",
    "    UE_Z=np.array([])\n",
    "    UE_CHANNEL=np.array([])\n",
    "    for p in range (len(BS_X)):\n",
    "        for i in range(3):\n",
    "    # number of split regions\n",
    "    # i is the sector number\n",
    "            for j in range(10):\n",
    "      # number of UEs per region\n",
    "      # j is the number of the UE in one sector\n",
    "                bs_x, bs_y, bs_z = BS_X[p], BS_Y[p],BS_Z[p]\n",
    "                theta_bs_ue = random.uniform(120 * i, 120 * (i+1))\n",
    "                # 0-120, 120-240, 240-360\n",
    "                radius_bs_ue = random.uniform(bs_ue_min_radius, bs_ue_max_radius)\n",
    "\n",
    "                x1 = bs_x + radius_bs_ue * math.cos(math.radians(theta_bs_ue))\n",
    "                y1 = bs_y + radius_bs_ue * math.sin(math.radians(theta_bs_ue))\n",
    "\n",
    "                UE_X = np.append(UE_X, x1)\n",
    "                UE_Y = np.append(UE_Y, y1)\n",
    "                UE_Z = np.append(UE_Z, 1.5)\n",
    "\n",
    "                maximum_UEs_per_channel = 4\n",
    "\n",
    "                if j > maximum_UEs_per_channel * BS_Channels.channel_count:\n",
    "                    raise Exception(f\"BS cannot support {j} UEs\")\n",
    "\n",
    "                count = {i: 0 for i in range(1, BS_Channels.channel_count + 1)}\n",
    "\n",
    "                channel = random.randint(1, BS_Channels.channel_count)\n",
    "\n",
    "                while count[channel] >= maximum_UEs_per_channel:\n",
    "                    channel = random.randint(1, BS_Channels.channel_count)\n",
    "\n",
    "                count[channel] += 1\n",
    "\n",
    "                UE_CHANNEL = np.append(UE_CHANNEL, channel)\n",
    "\n",
    "                if output: print(\"UE Co-ordinates=\" +str (x1)+ \",\" + str(y1)+ \",\" + str(z1) + \", channel: \" + str(channel))\n",
    "            if output: print(UE_X,UE_Y,UE_Z)\n",
    "        \n",
    "    pathloss_UMa=np.empty([0])\n",
    "    pathloss_UMi=np.empty([0])\n",
    "    pathloss_RMa=np.empty([0])\n",
    "    distance_UMa=np.empty([0])\n",
    "    distance_UMi=np.empty([0])\n",
    "    distance_RMa=np.empty([0])\n",
    "    line_of_sight=np.empty([0])\n",
    "    for i in range(len(BS_X)):\n",
    "        for j in range (len(FSS_X)):\n",
    "            pathlossumi,distance,los_single = path_loss_UMi(BS_X[i],BS_Y[i],10,FSS_X[j],FSS_Y[j],FSS_Z[j])\n",
    "            pathlossuma,distance,los_single = path_loss_UMa(BS_X[i],BS_Y[i],25,FSS_X[j],FSS_Y[j],FSS_Z[j])\n",
    "            pathlossrma,distance,los_single = path_loss_RMa(BS_X[i],BS_Y[i],35,FSS_X[j],FSS_Y[j],FSS_Z[j])\n",
    "            if output: print(\"pathloss umi:\", pathlossumi, \"uma:\", pathlossuma, \"rma:\", pathlossrma, \"for distance\", distance)\n",
    "            \n",
    "            pathloss_UMa=np.append(pathloss_UMa,pathlossuma)\n",
    "            distance_UMa=np.append(distance_UMa,distance)\n",
    "            pathloss_UMi=np.append(pathloss_UMi,pathlossumi)\n",
    "            distance_UMi=np.append(distance_UMi,distance)\n",
    "            pathloss_RMa=np.append(pathloss_RMa,pathlossrma)\n",
    "            distance_RMa=np.append(distance_RMa,distance)\n",
    "            \n",
    "            line_of_sight=np.append(line_of_sight,los_single)\n",
    "            \n",
    "\n",
    "    if output: print(pathloss_UMi,distance_UMi)\n",
    "    if output: print(pathloss_UMa,distance_UMa)\n",
    "    if output: print(pathloss_RMa,distance_RMa)\n",
    "\n",
    "    interface_UMi_W=np.empty([0])\n",
    "    interface_UMa_W=np.empty([0])\n",
    "    interface_RMa_W=np.empty([0])\n",
    "    for i in range(len(BS_X)):\n",
    "        interface_UMi_BS=np.empty([0])\n",
    "        interface_UMa_BS=np.empty([0])\n",
    "        interface_RMa_BS=np.empty([0])\n",
    "        for j in range(len(FSS_X)):\n",
    "            if output: print(f\"BS {i}, FSS {j}, pathloss {i*len(FSS_X)+j}\")\n",
    "            for k in range(len(UE_X)):\n",
    "                # channel check\n",
    "                # if UE is using channel 1, the start is 12.2GHz and the end is 12.3GHz\n",
    "                bs_channel_start, bs_channel_end = BS_Channels.getChannelRange(UE_CHANNEL[k])\n",
    "                bs_channel_range = range(bs_channel_start, int(bs_channel_end + (5e6)), int(5e6))\n",
    "\n",
    "                interference_found = False\n",
    "\n",
    "                for fss_channel in FSS_CHANNELS[j]:\n",
    "                    # if fss_channel >= 6:\n",
    "                    fss_channel_start, fss_channel_end = FSS_Channels.getChannelRange(fss_channel)\n",
    "                    fss_channel_range = range(bs_channel_start, int(bs_channel_end + (5e6)), int(5e6))\n",
    "                    \n",
    "                    bs_set = set(bs_channel_range)\n",
    "                    if len(bs_set.intersection(fss_channel_range)):\n",
    "                        interference_found = True\n",
    "\n",
    "                if not interference_found:\n",
    "#                     interface_UMi_W=np.append(interface_UMi_W,0)\n",
    "#                     interface_UMa_W=np.append(interface_UMa_W,0)\n",
    "#                     interface_RMa_W=np.append(interface_RMa_W,0)\n",
    "                    continue\n",
    "\n",
    "                for interference_type in [\"UMi\", \"UMa\", \"RMa\"]:\n",
    "                    if interference_type == \"UMi\":\n",
    "                        BS_Z = np.array([10 for i in range(len(BS_X))])\n",
    "                    elif interference_type == \"UMa\":\n",
    "                        BS_Z = np.array([25 for i in range(len(BS_X))])\n",
    "                    elif interference_type == \"RMa\":\n",
    "                        BS_Z = np.array([35 for i in range(len(BS_X))])\n",
    "                    # bs_ue_x, bs_ue_y, bs_ue_z = BS_X-UE_X, BS_Y-UE_Y, BS_Z-UE_Z\n",
    "                    bs_ue_x, bs_ue_y, bs_ue_z = UE_X[k]-BS_X[i], UE_Y[k]-BS_Y[i], UE_Z[k]-BS_Z[i]\n",
    "\n",
    "                    theta_bs_ue = np.arctan(bs_ue_y/bs_ue_x)\n",
    "                    phi_bs_ue = np.sqrt(bs_ue_x**2 + bs_ue_y**2) / bs_ue_z\n",
    "\n",
    "                    theta_bs_ue = np.degrees(theta_bs_ue) % 360\n",
    "                    phi_bs_ue = np.degrees(phi_bs_ue) % 360\n",
    "                    if output: print(\"theta_bs_ue:\", theta_bs_ue, \"phi_bs_ue:\", phi_bs_ue)\n",
    "\n",
    "                    theta_tilt, phi_scan = max_gain_5g_parameters(theta_bs_ue, phi_bs_ue)\n",
    "                    \n",
    "\n",
    "                    if interference_type == \"UMi\":\n",
    "                        interfaceumi,pathloss_UMi_x= Interface_UMi_1(BS_X[i],BS_Y[i],BS_Z[i],FSS_X[j],FSS_Y[j],FSS_Z[j],FSS_phi['UMi'],pathloss_UMi[i*len(FSS_X)+j],theta_tilt,phi_scan)\n",
    "                        if output: print(\"interference umi:\", interfaceumi, \"pathloss:\", pathlossumi)\n",
    "                        interface_UMi_BS=np.append(interface_UMi_BS,interfaceumi)\n",
    "                    elif interference_type == \"UMa\":\n",
    "                        interfaceuma,pathloss_UMa_x= Interface_UMa_1(BS_X[i],BS_Y[i],BS_Z[i],FSS_X[j],FSS_Y[j],FSS_Z[j],FSS_phi['UMa'],pathloss_UMa[i*len(FSS_X)+j],theta_tilt,phi_scan)\n",
    "                        if output: print(\"interference uma:\", interfaceuma, \"pathloss:\", pathlossuma)\n",
    "                        interface_UMa_BS=np.append(interface_UMa_BS,interfaceuma)\n",
    "                    elif interference_type == \"RMa\":\n",
    "                        interfacerma,pathloss_RMa_x= Interface_RMa_1(BS_X[i],BS_Y[i],BS_Z[i],FSS_X[j],FSS_Y[j],FSS_Z[j],FSS_phi['RMa'],pathloss_RMa[i*len(FSS_X)+j],theta_tilt,phi_scan)\n",
    "                        if output: print(\"interference rma:\", interfacerma, \"pathloss:\", pathlossrma)\n",
    "                        interface_RMa_BS=np.append(interface_RMa_BS,interfacerma)\n",
    "        interface_UMi_W = np.append(interface_UMi_W, np.sum(10 ** (interface_UMi_BS / 10)))\n",
    "        interface_UMa_W = np.append(interface_UMa_W, np.sum(10 ** (interface_UMa_BS / 10)))\n",
    "        interface_RMa_W = np.append(interface_RMa_W, np.sum(10 ** (interface_RMa_BS / 10)))\n",
    "              \n",
    "    if output: print(interface_UMi_W,pathloss_UMi)\n",
    "    if output: print(interface_UMa_W,pathloss_UMa)\n",
    "    if output: print(interface_RMa_W,pathloss_RMa)\n",
    "\n",
    "    # I_N_UMi = np.array([interfaceumi-Noise for interfaceumi in interface_UMi])\n",
    "    I_N_UMi = interface_UMi_W / Noise_W\n",
    "    if output: print('I/N UMi:', I_N_UMi)\n",
    "    I_N_UMa = interface_UMa_W / Noise_W\n",
    "    if output: print('I/N UMa:', I_N_UMa)      \n",
    "    I_N_RMa = interface_RMa_W / Noise_W\n",
    "    if output: print('I/N RMa:', I_N_RMa)  \n",
    "\n",
    "    return distance_RMa, I_N_RMa, distance_UMa, I_N_UMa, distance_UMi, I_N_UMi, line_of_sight\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "tofMR_8qCKOX",
   "metadata": {
    "id": "tofMR_8qCKOX"
   },
   "outputs": [],
   "source": [
    "# Prototype functions to calculate antenna gain of 5G base station and FSS earth station\n",
    "\n",
    "import math, cmath\n",
    "from scipy import optimize\n",
    "\n",
    "# gain is in dBi\n",
    "\n",
    "# 3GPP TR 38.901\n",
    "# https://www.etsi.org/deliver/etsi_tr/138900_138999/138901/14.00.00_60/tr_138901v140000p.pdf\n",
    "antenna_gain_max = 8\n",
    "\n",
    "# values recommended by Dr. Zoheb\n",
    "rows = 16  # Nv\n",
    "cols = 16  # Nh\n",
    "hspace = 0.5  # dh/λ\n",
    "vspace = 0.5  # dv/λ\n",
    "\n",
    "# constant values based on ECC Rep 281\n",
    "phi_3db = 80  # degrees\n",
    "theta_3db = 65  # degrees\n",
    "front_to_back_ratio = 30  # dB\n",
    "side_lobe_level_limit = 30  # dB\n",
    "\n",
    "# antenna horizontal pattern\n",
    "def gain_antenna_element_horizontal(phi) -> float:\n",
    "    return -min(\n",
    "                12 * (phi / phi_3db)**2, \n",
    "                front_to_back_ratio\n",
    "            )\n",
    "\n",
    "# antenna vertical pattern\n",
    "def gain_antenna_element_vertical(theta) -> float:\n",
    "    return -min(\n",
    "                12 * ((theta - 90) / theta_3db)**2,\n",
    "                side_lobe_level_limit\n",
    "            )\n",
    "\n",
    "# antenna element gain of elevation and azimuth plane\n",
    "def gain_antenna_element(theta, phi) -> float:\n",
    "    return antenna_gain_max - \\\n",
    "            min(\n",
    "                -(gain_antenna_element_horizontal(phi) + gain_antenna_element_vertical(theta)), \n",
    "                front_to_back_ratio\n",
    "            )\n",
    "\n",
    "def superposition(n, m, theta, phi, hspace, vspace) -> complex:\n",
    "    return cmath.exp(complex(0, 2 * math.pi * \\\n",
    "                (\n",
    "                    (n-1) * vspace * math.cos(math.radians(theta)) + \\\n",
    "                    (m-1) * hspace * math.sin(math.radians(theta)) * math.sin(math.radians(phi))\n",
    "                )\n",
    "            ))\n",
    "\n",
    "def weighting(n, m, theta_tilt, phi_scan, hspace, vspace) -> complex:\n",
    "    return cmath.exp(complex(0, -2 * math.pi * \\\n",
    "                (\n",
    "                    (n-1) * vspace * math.sin(math.radians(theta_tilt)) + \\\n",
    "                    (m-1) * hspace * math.cos(math.radians(theta_tilt)) * math.sin(math.radians(phi_scan))\n",
    "                )\n",
    "            )) / cmath.sqrt(rows * cols)\n",
    "\n",
    "# returns theta_tilt and phi_scan which yield maximum antenna gain given theta and phi\n",
    "def max_gain_5g_parameters(theta, phi, coarse=True, rounding_precision=0) -> tuple:\n",
    "    if coarse:\n",
    "        theta = round(theta, rounding_precision)\n",
    "        phi = round(phi, rounding_precision)\n",
    "    if (theta, phi) in saved_tp:\n",
    "        # print(f'match found for ({theta}, {phi}), using that')\n",
    "        return saved_tp.get((theta, phi))\n",
    "    \n",
    "    \n",
    "    \n",
    "    # return max_parameters\n",
    "    # scipy's optimization can only find the minimum, so we pass a function which returns the negative of the weighting function\n",
    "    result = optimize.brute(\n",
    "        lambda x: -beam_pattern_5g(theta, phi, x[0], x[1]),  # x[0] = theta_tilt, x[1] = phi_scan\n",
    "        # theta_tilt is between -90 and 90 degrees, phi_scan is between -180 and 180 degrees\n",
    "        ranges = [(-90, 90), (-180, 180)]\n",
    "        \n",
    "    )\n",
    "    saved_tp[(theta, phi)] = tuple(x for x in result)\n",
    "    \n",
    "    return saved_tp[(theta, phi)]\n",
    "\n",
    "\n",
    "# a_A, the directional pattern from beam forming with an array of elements\n",
    "def beam_pattern_5g(theta, phi, theta_tilt, phi_scan) -> float:\n",
    "    summation = 0\n",
    "    # weighting multiplied by the superposition vector\n",
    "    for n in range(1, rows+1):\n",
    "        for m in range(1, cols+1):\n",
    "            # print(f\"{theta_tilt}, {phi_scan}, {abs(weighting(n, m, theta_tilt, phi_scan, hspace, vspace))}\")\n",
    "            summation += weighting(n, m, theta_tilt, phi_scan, hspace, vspace) * superposition(n, m, theta, phi, hspace, vspace)\n",
    "\n",
    "    return abs(summation)**2\n",
    "\n",
    "# antenna gain of 5G base station (represented by a single beam i)\n",
    "def gain_5g(theta, phi, theta_tilt, phi_scan) -> float:\n",
    "    beam_pattern = beam_pattern_5g(theta, phi, theta_tilt, phi_scan)\n",
    "\n",
    "    return gain_antenna_element(theta, phi) + \\\n",
    "           10 * math.log10(beam_pattern)\n",
    "\n",
    "# space refers to D/λ\n",
    "def get_phi_min(space) -> float:\n",
    "    # Rec. ITU-R S.465-6\n",
    "    if space >= 50:\n",
    "        return max(1.0, 100 * (1 / space))\n",
    "    else:\n",
    "        return max(2.0, 114 * (space ** -1.09))\n",
    "\n",
    "# receiving antenna gain of FSS earth station\n",
    "# phi is an angle between base station antenna direction and FSS ES antenna's main axis (elevation angle)\n",
    "def gain_fss_s1428(phi, phi_min) -> float:\n",
    "\n",
    "    if 0 < phi < phi_min:\n",
    "        return 32 - 25 * math.log10(phi_min)\n",
    "    elif phi_min <= phi < 48:\n",
    "        return 32 - 25 * math.log10(phi)\n",
    "    elif 48 <= phi <= 180:\n",
    "        return -10\n",
    "\n",
    "    # This function is only defined where phi is within (0, 180]\n",
    "    raise ValueError(f'Angle phi must be within the interval (0, 180] degrees, was {phi} degrees instead')\n",
    "\n",
    "# co-polarized components only\n",
    "# https://www.etsi.org/deliver/etsi_en/303900_303999/303981/01.02.01_60/en_303981v010201p.pdf\n",
    "def gain_fss_wbes_b(phi) -> float:\n",
    "    if 0 <= phi < 6:\n",
    "        return 0\n",
    "    elif 6 <= phi < 48:\n",
    "        return 40 - 25 * math.log10(phi)\n",
    "    elif 48 <= phi <= 180:\n",
    "        return -2\n",
    "    elif 180 <= phi <= 360:\n",
    "        return gain_fss_wbes_b(360 - phi)\n",
    "\n",
    "    # This function is only defined where phi is within [6, 180]\n",
    "    raise ValueError(f'Angle phi must be within the interval [6, 180] degrees, was {phi} degrees instead')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4ea83a9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e4ea83a9",
    "outputId": "bc9dc30b-e5a3-4cb7-9368-794b2d6c0ab2",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "distance_RMa, I_N_RMa_W, I_N_RMa_noAverage, distance_UMa, I_N_UMa_W, I_N_UMa_noAverage, distance_UMi, I_N_UMi_W, I_N_UMi_noAverage, line_of_sight = [np.empty([0]) for x in range(10)]\n",
    "simulation_count = 10\n",
    "for i in tqdm(range(simulation_count)):\n",
    "   distance_RMa_single, I_N_RMa_single_W, distance_UMa_single, I_N_UMa_single_W, distance_UMi_single, I_N_UMi_single_W, line_of_sight_single = simulate(output=False)\n",
    "\n",
    "   distance_RMa = np.append(distance_RMa, distance_RMa_single)\n",
    "   # print(I_N_RMa_single_W)\n",
    "   I_N_RMa_W = np.append(I_N_RMa_W, I_N_RMa_single_W)\n",
    "   I_N_RMa_noAverage = np.append(I_N_RMa_noAverage, 10 * np.log10(I_N_RMa_single_W))\n",
    "   \n",
    "   distance_UMa = np.append(distance_UMa, distance_UMa_single)\n",
    "   I_N_UMa_W = np.append(I_N_UMa_W, I_N_UMa_single_W)\n",
    "   I_N_UMa_noAverage = np.append(I_N_UMa_noAverage, 10 * np.log10(I_N_UMa_single_W))\n",
    "\n",
    "   distance_UMi = np.append(distance_UMi, distance_UMi_single)\n",
    "   I_N_UMi_W = np.append(I_N_UMi_W, I_N_UMi_single_W)\n",
    "   I_N_UMi_noAverage = np.append(I_N_UMi_noAverage, 10 * np.log10(I_N_UMi_single_W))\n",
    "    \n",
    "   line_of_sight = np.append(line_of_sight, line_of_sight_single)\n",
    "    \n",
    "for arr in (I_N_RMa_noAverage,I_N_UMa_noAverage,I_N_UMi_noAverage):\n",
    "    arr[arr == -np.inf]=0\n",
    "    \n",
    "I_N_RMa = 10 * np.log10(np.average(I_N_RMa_W))\n",
    "I_N_UMa = 10 * np.log10(np.average(I_N_UMa_W))\n",
    "I_N_UMi = 10 * np.log10(np.average(I_N_UMi_W))\n",
    "\n",
    "pairs = {\n",
    "  'RMa': (np.average(distance_RMa), I_N_RMa),\n",
    "  'UMa': (np.average(distance_UMa), I_N_UMa),\n",
    "  'UMi': (np.average(distance_UMi), I_N_UMi)\n",
    "}\n",
    "\n",
    "pairs_noAverage = {\n",
    "  'RMa': (distance_RMa, I_N_RMa_noAverage),\n",
    "  'UMa': (distance_UMa, I_N_UMa_noAverage),\n",
    "  'UMi': (distance_UMi, I_N_UMi_noAverage)\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4acd0ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(saved_tp_file, \"wb\") as f:\n",
    "    pickle.dump(saved_tp, f)\n",
    "with open(saved_los_file, \"wb\") as f:\n",
    "    pickle.dump(saved_los, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2eeee6af",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(pairs_noAverage['RMa'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dac5a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(pairs_noAverage['RMa'][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f27af976",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "f27af976",
    "outputId": "b797d632-7ed4-4cfd-bd6e-9ed511b6eceb"
   },
   "outputs": [],
   "source": [
    "for key in pairs:\n",
    "  plt.scatter(pairs[key][0], pairs[key][1], label=key)\n",
    "\n",
    "plt.title('Average I/N Vs Distance graph')\n",
    "plt.xlabel('Distance (meter)')\n",
    "plt.ylabel('I/N (db)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbc9f9bb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "fbc9f9bb",
    "outputId": "97cd4311-d0d6-463a-b414-83b8696d3f07"
   },
   "outputs": [],
   "source": [
    "for key in pairs_noAverage:\n",
    "  plt.scatter(pairs_noAverage[key][0], pairs_noAverage[key][1], label=key)\n",
    "\n",
    "plt.title('I/N Vs Distance graph')\n",
    "plt.xlabel('Distance (meter)')\n",
    "plt.ylabel('I/N (db)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "116a182d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "116a182d",
    "outputId": "9b1cb3aa-f96c-4f55-ea9f-60bc908d2d16"
   },
   "outputs": [],
   "source": [
    "distance, interface_Noise = pairs_noAverage['RMa']\n",
    "distance_sorted,interface_Noise_sorted = zip(*sorted(zip(distance, interface_Noise)))\n",
    "plt.scatter(np.array(distance_sorted), np.array(interface_Noise_sorted))\n",
    "\n",
    "plt.title('I/N Vs Distance graph (RMa)')\n",
    "plt.xlabel('Distance (meter)')\n",
    "plt.ylabel('I/N (db)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7957592",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "e7957592",
    "outputId": "37fc7a3d-9698-4e7c-fcd7-2489aad45d79"
   },
   "outputs": [],
   "source": [
    "distance, interface_Noise = pairs_noAverage['UMa']\n",
    "distance_sorted,interface_Noise_sorted = zip(*sorted(zip(distance, interface_Noise)))\n",
    "plt.scatter(np.array(distance_sorted), np.array(interface_Noise_sorted))\n",
    "\n",
    "plt.title('I/N Vs Distance graph (UMa)')\n",
    "plt.xlabel('Distance (meter)')\n",
    "plt.ylabel('I/N (db)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4ede59d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "f4ede59d",
    "outputId": "5eb3c983-a8bf-434d-a36d-4629e0d96d15"
   },
   "outputs": [],
   "source": [
    "distance, interface_Noise = pairs_noAverage['UMi']\n",
    "distance_sorted,interface_Noise_sorted = zip(*sorted(zip(distance, interface_Noise)))\n",
    "plt.scatter(np.array(distance_sorted), np.array(interface_Noise_sorted))\n",
    "\n",
    "plt.title('I/N Vs Distance graph (UMi)')\n",
    "plt.xlabel('Distance (meter)')\n",
    "plt.ylabel('I/N (db)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43a6075a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "43a6075a",
    "outputId": "36b65c35-5fd5-4f04-aaf9-309cd206bb5f"
   },
   "outputs": [],
   "source": [
    "for key in pairs_noAverage:\n",
    "  distance, interface_Noise = pairs_noAverage[key]\n",
    "  distance_1D = np.array([])\n",
    "  for arr in distance:\n",
    "    distance_1D = np.append(distance_1D, arr)\n",
    "  interface_Noise_1D = np.array([])\n",
    "  for arr in interface_Noise:\n",
    "    interface_Noise_1D = np.append(interface_Noise_1D, arr)\n",
    "  distance_sorted,interface_Noise_sorted = zip(*sorted(zip(distance_1D, interface_Noise_1D)))\n",
    "  coefficients = np.polyfit(distance_sorted, interface_Noise_sorted,1)\n",
    "  # y = ax + b, where y is the average of interface_Noise_sorted\n",
    "  plt.plot(np.array(distance_sorted), np.polyval(coefficients, distance_sorted), label=key)\n",
    "\n",
    "plt.title('I/N Vs Distance graph')\n",
    "plt.xlabel('Distance (meter)')\n",
    "plt.ylabel('I/N (db)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54d9d8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "output= False\n",
    "bs_ue_max_radius = 1000\n",
    "bs_ue_min_radius= 1\n",
    "base_station_count=33\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "\n",
    "fss1 = BS(radius, max_height=1.5, carr_freq=12e3, interference_type=None)\n",
    "x,y,z = 0,0,0\n",
    "        \n",
    "FSS_X=np.array([])\n",
    "FSS_Y=np.array([])\n",
    "FSS_Z=np.array([])\n",
    "FSS_CHANNELS = []\n",
    "for i in range (1):\n",
    "    FSS_X = np.append(FSS_X, x)\n",
    "    FSS_Y = np.append(FSS_Y, y)\n",
    "    FSS_Z = np.append(FSS_Z, z)\n",
    "    # 0 means not in use, 1 means in use\n",
    "    channel_status = [random.randint(0, 1) for i in range(FSS_Channels.channel_count)]\n",
    "    channels_used = np.array([i for i in range(FSS_Channels.channel_count) if channel_status[i] == 1])\n",
    "    FSS_CHANNELS.append(channels_used)\n",
    "    if output: print(\"FSS Co-ordinates=\" +str (x)+ \",\" + str(y)+ \",\" + str(z) + \", channel: \" + str(channels_used))\n",
    "if output: print(FSS_X, FSS_Y, FSS_Z, FSS_CHANNELS)\n",
    "\n",
    "\n",
    "BS_X=np.array([])\n",
    "BS_Y=np.array([])\n",
    "BS_Z=np.array([])\n",
    "\n",
    "# Randomly select base stations \n",
    "base_station_indexes = random.sample(range(len(data_within_zone)), base_station_count)\n",
    "\n",
    "for i in base_station_indexes:\n",
    "\n",
    "    lat_BS, lon_BS = data_within_zone.iloc[i]['latitude'], data_within_zone.iloc[i]['longitude']\n",
    "    bs1 = BS(radius, max_height=35, carr_freq=12e3, interference_type=None)\n",
    "    x_BS = R * math.cos(math.radians(lat_BS)) * math.cos(math.radians(lon_BS))\n",
    "    y_BS = R * math.cos(math.radians(lat_BS)) * math.sin(math.radians(lon_BS))\n",
    "#         z_BS = R * math.sin(math.radians(lat_BS))\n",
    "\n",
    "    BS_X = np.append(BS_X, x_BS - x_FSS)\n",
    "    BS_Y = np.append(BS_Y, y_BS - y_FSS)\n",
    "    BS_Z = np.append(BS_Z, 0)\n",
    "    if output: print(\"Bs Co-ordinates=\" +str (x_BS)+ \",\" + str(y_BS)+ \",\" + str(z_BS))\n",
    "\n",
    "if output: print(BS_X,BS_Y,BS_Z)\n",
    "\n",
    "# Create user equipment\n",
    "UE_X=np.array([])\n",
    "UE_Y=np.array([])\n",
    "UE_Z=np.array([])\n",
    "UE_CHANNEL=np.array([])\n",
    "for p in range (len(BS_X)):\n",
    "    UE_X=np.array([])\n",
    "    UE_Y=np.array([])\n",
    "    UE_Z=np.array([])\n",
    "    UE_CHANNEL=np.array([])\n",
    "    for i in range(33):\n",
    "# number of split regions\n",
    "# i is the sector number\n",
    "        for j in range(10):\n",
    "  # number of UEs per region\n",
    "  # j is the number of the UE in one sector\n",
    "            bs_x, bs_y, bs_z = BS_X[p], BS_Y[p],BS_Z[p]\n",
    "            theta_bs_ue = random.uniform(120 * i, 120 * (i+1))\n",
    "            # 0-120, 120-240, 240-360\n",
    "            radius_bs_ue = random.uniform(bs_ue_min_radius, bs_ue_max_radius)\n",
    "\n",
    "            x1 = bs_x + radius_bs_ue * math.cos(math.radians(theta_bs_ue))\n",
    "            y1 = bs_y + radius_bs_ue * math.sin(math.radians(theta_bs_ue))\n",
    "\n",
    "            UE_X = np.append(UE_X, x1)\n",
    "            UE_Y = np.append(UE_Y, y1)\n",
    "            UE_Z = np.append(UE_Z, 1.5)\n",
    "\n",
    "            maximum_UEs_per_channel = 4\n",
    "\n",
    "            if j > maximum_UEs_per_channel * BS_Channels.channel_count:\n",
    "                raise Exception(f\"BS cannot support {j} UEs\")\n",
    "\n",
    "            count = {i: 0 for i in range(1, BS_Channels.channel_count + 1)}\n",
    "\n",
    "            channel = random.randint(1, BS_Channels.channel_count)\n",
    "\n",
    "            while count[channel] >= maximum_UEs_per_channel:\n",
    "                channel = random.randint(1, BS_Channels.channel_count)\n",
    "\n",
    "            count[channel] += 1\n",
    "\n",
    "            UE_CHANNEL = np.append(UE_CHANNEL, channel)\n",
    "\n",
    "            if output: print(\"UE Co-ordinates=\" +str (x1)+ \",\" + str(y1)+ \",\" + str(z1) + \", channel: \" + str(channel))\n",
    "        if output: print(UE_X,UE_Y,UE_Z)\n",
    "    ax.scatter(UE_X,UE_Y,alpha=0.8, s=30,label= 'UE Location', color='green',)\n",
    "            \n",
    "\n",
    "ax.scatter(BS_X,BS_Y,alpha=0.8, s=30,label= 'BS Location', color='red')\n",
    "ax.scatter(FSS_X,FSS_Y,alpha=0.8, s=30,label= 'FSS Location',color='yellow')\n",
    "legend_handles = [Line2D([0], [0], marker='o', color='w', label='UE Location', markerfacecolor='g'),\n",
    "          Line2D([0], [0], marker='o', color='w', label='BS Location', markerfacecolor='r'),\n",
    "          Line2D([0], [0], marker='o', color='w', label='FSS Location', markerfacecolor='y')\n",
    "]\n",
    "          \n",
    "plt.legend(handles=legend_handles)\n",
    "\n",
    "# plt.legend(loc=2)\n",
    "plt.title('FSS, BS and UE Location plot')\n",
    "ax.set_xlabel('X Label')\n",
    "ax.set_ylabel('Y Label')\n",
    "\n",
    "# fig.savefig('graphcoex.pdf', dpi=100)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3acdb40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    " \n",
    "# Creating dataset\n",
    "box_dict_UMi = dict()\n",
    "distance, interface_Noise = pairs_noAverage['UMi']\n",
    "for i in range(len(interface_Noise)):\n",
    "    if distance[i] not in box_dict_UMi:\n",
    "        box_dict_UMi[distance[i]] = []\n",
    "    box_dict_UMi[distance[i]].append(interface_Noise[i])\n",
    "        \n",
    "fig = plt.figure(figsize =(10, 7))\n",
    " \n",
    "# Creating plot\n",
    "keys = sorted([key for key in box_dict_UMi])\n",
    "plt.boxplot([box_dict_UMi[key] for key in keys])\n",
    "# ax.set_xticklabels(keys)\n",
    "\n",
    "plt.title('I/N Vs Distance graph (UMi)')\n",
    "plt.xlabel('No. Of BS')\n",
    "plt.ylabel('I/N (db)')\n",
    "plt.legend()\n",
    " \n",
    " \n",
    "#show plot\n",
    "plt.show()\n",
    "# plt.save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9128840",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dataset\n",
    "box_dict_UMa = dict()\n",
    "distance, interface_Noise = pairs_noAverage['UMa']\n",
    "for i in range(len(interface_Noise)):\n",
    "    if distance[i] not in box_dict_UMa:\n",
    "        box_dict_UMa[distance[i]] = []\n",
    "    box_dict_UMa[distance[i]].append(interface_Noise[i])\n",
    "        \n",
    "fig = plt.figure(figsize =(10, 7))\n",
    " \n",
    "# Creating plot\n",
    "keys = sorted([key for key in box_dict_UMa])\n",
    "plt.boxplot([box_dict_UMa[key] for key in keys])\n",
    "# ax.set_xticklabels(keys)\n",
    "\n",
    "plt.title('I/N Vs Distance graph (UMa)')\n",
    "plt.xlabel('No. Of BS')\n",
    "plt.ylabel('I/N (db)')\n",
    "plt.legend()\n",
    " \n",
    " \n",
    "# show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4baf0366",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dataset\n",
    "box_dict_RMa = dict()\n",
    "distance, interface_Noise = pairs_noAverage['RMa']\n",
    "for i in range(len(interface_Noise)):\n",
    "    if distance[i] not in box_dict_RMa:\n",
    "        box_dict_RMa[distance[i]] = []\n",
    "    box_dict_RMa[distance[i]].append(interface_Noise[i])\n",
    "        \n",
    "fig = plt.figure(figsize =(10, 7))\n",
    " \n",
    "# Creating plot\n",
    "keys = sorted([key for key in box_dict_RMa])\n",
    "plt.boxplot([box_dict_RMa[key] for key in keys])\n",
    "# ax.set_xticklabels(keys)\n",
    "\n",
    "plt.title('I/N Vs Distance graph (RMa)')\n",
    "plt.xlabel('No. of BS')\n",
    "plt.ylabel('I/N (db)')\n",
    "plt.legend()\n",
    " \n",
    "# show plot\n",
    "# plt.show()\n",
    "\n",
    " \n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e31dc80",
   "metadata": {},
   "outputs": [],
   "source": [
    "box_dict_UMi\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6e36a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    " \n",
    "# Creating dataset\n",
    "box_dict_UMi = dict()\n",
    "distance, interface_Noise = pairs_noAverage['UMi']\n",
    "for i in range(len(interface_Noise)):\n",
    "    if distance[i] not in box_dict_UMi:\n",
    "        box_dict_UMi[distance[i]] = []\n",
    "    box_dict_UMi[distance[i]].append(interface_Noise[i])\n",
    "        \n",
    "fig = plt.figure(figsize =(10, 7))\n",
    " \n",
    "# Creating plot\n",
    "keys = sorted([key for key in box_dict_UMi])\n",
    "plt.boxplot([box_dict_UMi[key] for key in keys][0])\n",
    "# ax.set_xticklabels([keys[0]])\n",
    " \n",
    "# show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c1e7c20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Creating dataset\n",
    "# box_dict_UMi = dict()\n",
    "# distance, interface_Noise = pairs_noAverage['UMi']\n",
    "# for i in range(len(interface_Noise)):\n",
    "#     if distance[i] not in box_dict_UMi:\n",
    "#         box_dict_UMi[distance[i]] = []\n",
    "#     box_dict_UMi[distance[i]].append(interface_Noise[i])\n",
    "\n",
    "# elevation_dict_UMi = dict()\n",
    "\n",
    "# for p in range (len(BS_X)):\n",
    "#     x, y, z = BS_X[p]-FSS_X[0], BS_Y[p]-FSS_Y[0], 10-FSS_Z[0]\n",
    "\n",
    "#     distance_2D = math.sqrt(((FSS_X[0]-BS_X[p])**2)+((FSS_Y[0]- BS_Y[p])**2)+((FSS_Z[0]-10)**2))\n",
    "\n",
    "#     theta_bs_es = math.degrees(math.atan(y/x)) % 360\n",
    "#     phi_bs_es = math.degrees(math.sqrt(x**2 + y**2) / z) % 360\n",
    "#     fss_phi_difference = abs(15 - phi_bs_es)\n",
    "\n",
    "    \n",
    "#     if distance_2D in box_dict_UMi:\n",
    "#         elevation_dict_UMi[fss_phi_difference] = box_dict_UMi[distance_2D]\n",
    "\n",
    "# plt.title('I/N Vs Elevation graph (UMi)')\n",
    "# plt.xlabel('Elevation (degrees)')\n",
    "# plt.ylabel('I/N (db)')\n",
    "# plt.legend()\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c797a14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# elevation_dict_UMi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cf91918",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Elevation angles (FSS towards to sky )\n",
    "fig, ax=plt.subplots()\n",
    "\n",
    "FSS_phi = {\n",
    "    'UMi': 15,\n",
    "    'UMa': 48,\n",
    "    'RMa': 5\n",
    "}\n",
    "\n",
    "contexts = sorted([context for context in FSS_phi], key=lambda x:FSS_phi[x])\n",
    "boxplots = []\n",
    "\n",
    "boxplots=ax.boxplot([pairs_noAverage[context][1] for context in contexts])\n",
    "# for context in contexts:\n",
    "#     distance, interface_Noise = pairs_noAverage[context]\n",
    "#     boxplots.append(plt.boxplot(interface_Noise))\n",
    "\n",
    "ax.set_xticklabels([FSS_phi[context] for context in contexts])\n",
    "\n",
    "ax.set_title('I/N Vs Elevation graph')\n",
    "ax.set_xlabel('Elevation (degrees)')\n",
    "ax.set_ylabel('I/N (db)')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "866c10e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "line_of_sight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60b379fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dataset\n",
    "box_dict_UMi = dict()\n",
    "distance, interface_Noise = pairs_noAverage['UMi']\n",
    "for i in range(len(interface_Noise)):\n",
    "    if distance[i] not in box_dict_UMi:\n",
    "        box_dict_UMi[distance[i]] = []\n",
    "    box_dict_UMi[distance[i]].append([interface_Noise[i],line_of_sight[i]])\n",
    "\n",
    "# print(box_dict_UMi)\n",
    "\n",
    "fig, ax= plt.subplots()\n",
    " \n",
    "# Creating plot\n",
    "keys = sorted([key for key in box_dict_UMi])\n",
    "for i, key in enumerate(keys):\n",
    "    # key is distance\n",
    "    # print(key)\n",
    "    # print(box_dict_UMi[key])\n",
    "    line_of_sight1 = box_dict_UMi[key][0][1]\n",
    "    ax.boxplot([I_N for I_N, los in box_dict_UMi[key]],\n",
    "               patch_artist=True,\n",
    "               positions=[i],\n",
    "               boxprops=dict(facecolor='white', color='blue' if line_of_sight1 else 'red'))\n",
    "                \n",
    "# keys = sorted([key for key in box_dict_UMi])\n",
    "# ax.boxplot([box_dict_UMi[key] for key in keys])\n",
    "ax.set_xticklabels([int(i) for i in keys])\n",
    "\n",
    "ax.set_title('I/N Vs Distance graph (UMi)')\n",
    "ax.set_xlabel('Distnace of Each BS to FSS (meters)')\n",
    "ax.set_ylabel('I/N (db)')\n",
    "ax.legend()\n",
    " \n",
    "# show plot\n",
    "# plt.show()\n",
    "fig.set_size_inches(18, 8.5)\n",
    "# fig.savefig('graphUmi.pdf', dpi=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b8876cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dataset\n",
    "box_dict_UMa = dict()\n",
    "distance, interface_Noise = pairs_noAverage['UMa']\n",
    "for i in range(len(interface_Noise)):\n",
    "    if distance[i] not in box_dict_UMa:\n",
    "        box_dict_UMa[distance[i]] = []\n",
    "    box_dict_UMa[distance[i]].append([interface_Noise[i],line_of_sight[i]])\n",
    "\n",
    "# print(box_dict_UMi)\n",
    "\n",
    "fig, ax= plt.subplots()\n",
    " \n",
    "# Creating plot\n",
    "keys = sorted([key for key in box_dict_UMa])\n",
    "for i, key in enumerate(keys):\n",
    "    # key is distance\n",
    "    # print(key)\n",
    "    # print(box_dict_UMi[key])\n",
    "    line_of_sight1 = box_dict_UMa[key][0][1]\n",
    "    ax.boxplot([I_N for I_N, los in box_dict_UMa[key]],\n",
    "               patch_artist=True,\n",
    "               positions=[i],\n",
    "               boxprops=dict(facecolor='white', color='blue' if line_of_sight1 else 'red'))\n",
    "                \n",
    "# keys = sorted([key for key in box_dict_UMi])\n",
    "# ax.boxplot([box_dict_UMi[key] for key in keys])\n",
    "ax.set_xticklabels([int(i) for i in keys])\n",
    "\n",
    "ax.set_title('I/N Vs Distance graph (UMa)')\n",
    "ax.set_xlabel('Distnace of Each BS to FSS (meters)')\n",
    "ax.set_ylabel('I/N (db)')\n",
    "ax.legend()\n",
    " \n",
    "# show plot\n",
    "# plt.show()\n",
    "fig.set_size_inches(18, 8.5)\n",
    "# fig.savefig('graphUMa.pdf', dpi=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57efd155",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dataset\n",
    "box_dict_RMa = dict()\n",
    "distance, interface_Noise = pairs_noAverage['RMa']\n",
    "for i in range(len(interface_Noise)):\n",
    "    if distance[i] not in box_dict_RMa:\n",
    "        box_dict_RMa[distance[i]] = []\n",
    "    box_dict_RMa[distance[i]].append([interface_Noise[i],line_of_sight[i]])\n",
    "\n",
    "# print(box_dict_UMi)\n",
    "\n",
    "fig, ax= plt.subplots()\n",
    " \n",
    "# Creating plot\n",
    "keys = sorted([key for key in box_dict_RMa])\n",
    "for i, key in enumerate(keys):\n",
    "    # key is distance\n",
    "    # print(key)\n",
    "    # print(box_dict_UMi[key])\n",
    "    line_of_sight1 = box_dict_RMa[key][0][1]\n",
    "    ax.boxplot([I_N for I_N, los in box_dict_RMa[key]],\n",
    "               patch_artist=True,\n",
    "               positions=[i],\n",
    "               boxprops=dict(facecolor='white', color='blue' if line_of_sight1 else 'red'))\n",
    "                \n",
    "# keys = sorted([key for key in box_dict_UMi])\n",
    "# ax.boxplot([box_dict_UMi[key] for key in keys])\n",
    "ax.set_xticklabels([int(i) for i in keys])\n",
    "\n",
    "ax.set_title('I/N Vs Distance graph (RMa)')\n",
    "ax.set_xlabel('Distnace of Each BS to FSS (meters)')\n",
    "ax.set_ylabel('I/N (db)')\n",
    "ax.legend()\n",
    " \n",
    "# show plot\n",
    "# plt.show()\n",
    "fig.set_size_inches(18, 8.5)\n",
    "# fig.savefig('graphRma.pdf', dpi=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "954e8d58",
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   "outputs": [],
   "source": []
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47b7916e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a135e52e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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