nlos.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from typing import List\n",
    "\n",
    "class LifiAccessPoint:\n",
    "    def __init__(self, x, y, Φ_1by2=60, Apd=100*1e-4, ref_index=1.5, FOV=90, gf=1,\n",
    "            room_x=5, room_y=5, room_z=5, h=0.8, Rpd=0.53, pw=0.8, Popt=3, k=3, Nlifi=1e-21, Blifi=20*1e6):\n",
    "        # attribute for Half-intensity radiation angle (Φ1/2)\n",
    "        self.Φ_1by2 = Φ_1by2 * np.pi / 180\n",
    "        self.m = -np.log(2) / np.log(np.cos(np.radians(Φ_1by2)))\n",
    "        # Refractive ref_index of the medium\n",
    "        self.ref_index = ref_index\n",
    "        # FoV semi-angle of PD, Ψmax\n",
    "        self.FOV = FOV * np.pi / 180\n",
    "        # physical area of the PD, Apd\n",
    "        self.Apd = Apd\n",
    "        # gain of the optical filter, gf\n",
    "        self.gf = gf\n",
    "        # length of the room in the x-direction (horizontal) in meters.\n",
    "        self.room_x = room_x\n",
    "        # length of the room in the y-direction (vertical) in meters.\n",
    "        self.room_y = room_y\n",
    "        # height of the room in meters\n",
    "        self.room_z = room_z\n",
    "        # height of the user above the receiver plane\n",
    "        self.h = h\n",
    "        # position of the lifi access point in ceiling\n",
    "        self.lifi_position = np.array([x, y, room_z])\n",
    "        # Detector responsivity, Rpd\n",
    "        self.Rpd = Rpd\n",
    "        # Transmitted optical power per LiFi AP, Popt\n",
    "        self.Popt = Popt\n",
    "        # optical to electrical conversion coefficient, k\n",
    "        self.k = k\n",
    "        # PSD of noise in LiFi AP, NLiFi\n",
    "        self.Nlifi = Nlifi\n",
    "        # Bandwidth of LiFi AP, BLiFi\n",
    "        self.Blifi = Blifi\n",
    "        # Wall reflectivity, pw\n",
    "        self.pw = pw\n",
    "        \n",
    "        # Number of points in the x and y directions\n",
    "        # self.Nx = int(room_x * 10)\n",
    "        # self.Ny = int(room_y * 10)\n",
    "        # self.x = np.linspace(0, room_x, self.Nx)\n",
    "        # self.y = np.linspace(0, room_y, self.Ny)\n",
    "        # self.XR, self.YR = np.meshgrid(self.x, self.y)\n",
    "        # print(self.XR)\n",
    "        # print(self.XR.shape)\n",
    "\n",
    "    def get_channel_gain(self, user_x, user_y):\n",
    "        # uncomment this line to include NLOS channel gain\n",
    "        return self.channel_gain_los(user_x, user_y) #+ self.channel_gain_nlos(user_x, user_y)\n",
    "\n",
    "    def channel_gain_los(self, user_x, user_y):\n",
    "        d = self.distance(user_x, user_y)        \n",
    "        # both angles are equal due to symmetry\n",
    "        incidence = self.angle_incidence(user_x, user_y)\n",
    "        irradiance = incidence           \n",
    "        gc = self.optical_gain(incidence)\n",
    "        channel_gain = ((self.m + 1) * self.Apd * (np.cos(irradiance)**self.m) * np.cos(incidence) * self.gf * gc) / (2 * np.pi * (d**2))\n",
    "        # print(incidence, irradiance, channel_gain, self.m, gc, self.Apd, self.gf)\n",
    "        return channel_gain\n",
    "    \n",
    "    def channel_gain_nlos(self, user_x, user_y):\n",
    "        boxes_per_meter = 10\n",
    "        dl, dh = 1/boxes_per_meter, 1/boxes_per_meter\n",
    "        height_coord = np.arange(self.h, self.room_z, dh)\n",
    "        length_coord = np.arange(self.h, self.room_x, dl)\n",
    "        user_position = np.array([user_x, user_y, self.h])\n",
    "        integration = 0\n",
    "        \n",
    "        for height in height_coord:\n",
    "            # print(f'{height:.3f}  ')\n",
    "            for length in length_coord:\n",
    "                locations = [np.array([length, 0, height]), np.array([length, self.room_y, height]),\n",
    "                            np.array([0, length, height]), np.array([self.room_x, length, height])]\n",
    "                for curr_location in locations:\n",
    "                    d_iw = np.linalg.norm(curr_location - self.lifi_position)\n",
    "                    d_wu = np.linalg.norm(user_position - curr_location)\n",
    "                    theta_iw = np.arccos((self.room_z - height) / d_iw)\n",
    "                    ϑ_iw = 90 - theta_iw\n",
    "                    # print(f'd_iw: {d_iw}, d_wu: {d_wu}, theta_iw: {theta_iw}, ϑ_iw: {ϑ_iw}')\n",
    "                    phi_wu = np.arccos((height - self.h) / d_wu)\n",
    "                    ϑ_wu = 90 - phi_wu\n",
    "                    numerator = (self.m + 1) * self.Apd * self.pw * (np.cos(theta_iw)**self.m) * \\\n",
    "                                self.gf * self.optical_gain(phi_wu) * np.cos(phi_wu) * np.cos(ϑ_iw) * np.cos(ϑ_wu)\n",
    "                    integration += (numerator * dl * dh) / (2 * (np.pi * d_iw * d_wu)**2)\n",
    "        return integration\n",
    "    \n",
    "    def signal_to_noise_ratio(self, user_x, user_y, otherLifiAPs:List):\n",
    "        summation_term = 0\n",
    "        # for lifi in otherLifiAPs:\n",
    "        #     # print(f'Lifi at {lifi.lifi_position} has gain', lifi.get_channel_gain(user_x, user_y))\n",
    "        #     summation_term += (lifi.Rpd * lifi.get_channel_gain(user_x, user_y) * lifi.Popt / lifi.k) ** 2\n",
    "        # print('self gain: ', self.get_channel_gain(user_x, user_y))\n",
    "        numerator = (self.Rpd * self.get_channel_gain(user_x, user_y) * self.Popt / self.k) ** 2\n",
    "        # uncomment this line to include noise from other LiFi APs\n",
    "        denominator = self.Nlifi * self.Blifi + summation_term \n",
    "        # print(f'numerator: {numerator}, denominator: {denominator}, sum: {summation_term}')   \n",
    "        return numerator / denominator\n",
    "    \n",
    "    def distance(self, user_x, user_y):\n",
    "        user_position = np.array([user_x, user_y, self.h])\n",
    "        distance =  np.linalg.norm(user_position - self.lifi_position)\n",
    "        return distance\n",
    "    \n",
    "    def optical_gain(self, angle_incidence):\n",
    "        if 0 <= angle_incidence <= self.FOV:\n",
    "            return (self.ref_index**2) / (np.sin(self.FOV)**2)\n",
    "        else:\n",
    "            return 0\n",
    "    \n",
    "    def angle_incidence(self, user_x, user_y):\n",
    "        d = self.distance(user_x, user_y)\n",
    "        return np.arccos((self.room_z - self.h) / d)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if __name__ == \"__main__\":\n",
    "#     x, y = 1.25, 1.25\n",
    "#     lifi_ap1 = LifiAccessPoint(x=1.25, y=1.25)\n",
    "#     # lifi_ap2 = LifiAccessPoint(x=1.25, y=3.75)\n",
    "#     # lifi_ap3 = LifiAccessPoint(x=3.75, y=3.75)\n",
    "#     # lifi_ap4 = LifiAccessPoint(x=3.75, y=1.25)\n",
    "\n",
    "#     # ang_incidence = lifi_ap1.angle_incidence(x, y)\n",
    "#     # optical_gain = lifi_ap1.optical_gain(ang_incidence)\n",
    "#     # # print(ang_incidence)\n",
    "#     # # print(optical_gain)\n",
    "#     # # H = lifi_ap1.get_channel_gain(x, y)\n",
    "    \n",
    "#     # print('Location: ', 0, 0)\n",
    "#     # snr = lifi_ap1.signal_to_noise_ratio(0, 0, otherLifiAPs=[lifi_ap2, lifi_ap3, lifi_ap4])\n",
    "#     # print(snr)\n",
    "#     # print('Location: ', 1.25, 1.25)\n",
    "#     # snr = lifi_ap1.signal_to_noise_ratio(1.2, 1.2, otherLifiAPs=[lifi_ap2, lifi_ap3, lifi_ap4])\n",
    "#     # print(snr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = 1.25, 1.25\n",
    "lifi_ap1 = LifiAccessPoint(x=1.25, y=1.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of squares in x-direction: 51\n",
      "Number of squares in y-direction: 51\n",
      "Total number of squares: 2601\n",
      "\n",
      "Grid points:\n",
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4.1), (4.8, 4.2), (4.8, 4.3), (4.8, 4.4), (4.8, 4.5), (4.8, 4.6), (4.8, 4.7), (4.8, 4.8), (4.8, 4.9), (4.8, 5.0), (4.9, 0.0), (4.9, 0.1), (4.9, 0.2), (4.9, 0.3), (4.9, 0.4), (4.9, 0.5), (4.9, 0.6), (4.9, 0.7), (4.9, 0.8), (4.9, 0.9), (4.9, 1.0), (4.9, 1.1), (4.9, 1.2), (4.9, 1.3), (4.9, 1.4), (4.9, 1.5), (4.9, 1.6), (4.9, 1.7), (4.9, 1.8), (4.9, 1.9), (4.9, 2.0), (4.9, 2.1), (4.9, 2.2), (4.9, 2.3), (4.9, 2.4), (4.9, 2.5), (4.9, 2.6), (4.9, 2.7), (4.9, 2.8), (4.9, 2.9), (4.9, 3.0), (4.9, 3.1), (4.9, 3.2), (4.9, 3.3), (4.9, 3.4), (4.9, 3.5), (4.9, 3.6), (4.9, 3.7), (4.9, 3.8), (4.9, 3.9), (4.9, 4.0), (4.9, 4.1), (4.9, 4.2), (4.9, 4.3), (4.9, 4.4), (4.9, 4.5), (4.9, 4.6), (4.9, 4.7), (4.9, 4.8), (4.9, 4.9), (4.9, 5.0), (5.0, 0.0), (5.0, 0.1), (5.0, 0.2), (5.0, 0.3), (5.0, 0.4), (5.0, 0.5), (5.0, 0.6), (5.0, 0.7), (5.0, 0.8), (5.0, 0.9), (5.0, 1.0), (5.0, 1.1), (5.0, 1.2), (5.0, 1.3), (5.0, 1.4), (5.0, 1.5), (5.0, 1.6), (5.0, 1.7), (5.0, 1.8), (5.0, 1.9), (5.0, 2.0), (5.0, 2.1), (5.0, 2.2), (5.0, 2.3), (5.0, 2.4), (5.0, 2.5), (5.0, 2.6), (5.0, 2.7), (5.0, 2.8), (5.0, 2.9), (5.0, 3.0), (5.0, 3.1), (5.0, 3.2), (5.0, 3.3), (5.0, 3.4), (5.0, 3.5), (5.0, 3.6), (5.0, 3.7), (5.0, 3.8), (5.0, 3.9), (5.0, 4.0), (5.0, 4.1), (5.0, 4.2), (5.0, 4.3), (5.0, 4.4), (5.0, 4.5), (5.0, 4.6), (5.0, 4.7), (5.0, 4.8), (5.0, 4.9), (5.0, 5.0)]\n"
     ]
    }
   ],
   "source": [
    "room_width = 5.0\n",
    "room_height = 5.0\n",
    "user_height = 0.8\n",
    "\n",
    "# Divide the floor into 0.1x0.1m squares\n",
    "grid_size = 0.1\n",
    "# x_grid = np.arange(0, room_width, grid_size)\n",
    "# y_grid = np.arange(0, room_height, grid_size)\n",
    "x_grid = [round(i * grid_size, 1) for i in range(int(room_width / grid_size) + 1)]\n",
    "y_grid = [round(i * grid_size, 1) for i in range(int(room_height / grid_size) + 1)]\n",
    "\n",
    "print(f\"Number of squares in x-direction: {len(x_grid)}\")\n",
    "print(f\"Number of squares in y-direction: {len(y_grid)}\")\n",
    "print(f\"Total number of squares: {len(x_grid) * len(y_grid)}\")\n",
    "print()\n",
    "print(\"Grid points:\")\n",
    "# print(list(zip(x_grid, y_grid)))\n",
    "# make list of all ombination of x and y\n",
    "grid_points = [(x, y) for x in x_grid for y in y_grid]\n",
    "print(grid_points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cells in x-direction: 50\n",
      "Number of cells in y-direction: 50\n",
      "Total number of cells: 2500\n",
      "\n",
      "Midpoints of each cell:\n",
      "[(0.05, 0.05), (0.05, 0.15), (0.05, 0.25), (0.05, 0.35), (0.05, 0.45), (0.05, 0.55), (0.05, 0.65), (0.05, 0.75), (0.05, 0.85), (0.05, 0.95), (0.05, 1.05), (0.05, 1.15), (0.05, 1.25), (0.05, 1.35), (0.05, 1.45), (0.05, 1.55), (0.05, 1.65), (0.05, 1.75), (0.05, 1.85), (0.05, 1.95), (0.05, 2.05), (0.05, 2.15), (0.05, 2.25), (0.05, 2.35), (0.05, 2.45), (0.05, 2.55), (0.05, 2.65), (0.05, 2.75), (0.05, 2.85), (0.05, 2.95), (0.05, 3.05), (0.05, 3.15), (0.05, 3.25), (0.05, 3.35), (0.05, 3.45), (0.05, 3.55), (0.05, 3.65), (0.05, 3.75), (0.05, 3.85), (0.05, 3.95), (0.05, 4.05), (0.05, 4.15), (0.05, 4.25), (0.05, 4.35), (0.05, 4.45), (0.05, 4.55), (0.05, 4.65), (0.05, 4.75), (0.05, 4.85), (0.05, 4.95), (0.15, 0.05), (0.15, 0.15), (0.15, 0.25), (0.15, 0.35), (0.15, 0.45), (0.15, 0.55), (0.15, 0.65), (0.15, 0.75), (0.15, 0.85), (0.15, 0.95), (0.15, 1.05), (0.15, 1.15), (0.15, 1.25), (0.15, 1.35), (0.15, 1.45), (0.15, 1.55), (0.15, 1.65), (0.15, 1.75), (0.15, 1.85), (0.15, 1.95), (0.15, 2.05), (0.15, 2.15), (0.15, 2.25), (0.15, 2.35), (0.15, 2.45), (0.15, 2.55), (0.15, 2.65), (0.15, 2.75), (0.15, 2.85), (0.15, 2.95), (0.15, 3.05), (0.15, 3.15), (0.15, 3.25), (0.15, 3.35), (0.15, 3.45), (0.15, 3.55), (0.15, 3.65), (0.15, 3.75), (0.15, 3.85), (0.15, 3.95), (0.15, 4.05), (0.15, 4.15), (0.15, 4.25), (0.15, 4.35), (0.15, 4.45), (0.15, 4.55), (0.15, 4.65), (0.15, 4.75), (0.15, 4.85), (0.15, 4.95), (0.25, 0.05), (0.25, 0.15), (0.25, 0.25), (0.25, 0.35), (0.25, 0.45), (0.25, 0.55), (0.25, 0.65), (0.25, 0.75), (0.25, 0.85), (0.25, 0.95), (0.25, 1.05), (0.25, 1.15), (0.25, 1.25), (0.25, 1.35), (0.25, 1.45), (0.25, 1.55), (0.25, 1.65), (0.25, 1.75), (0.25, 1.85), (0.25, 1.95), (0.25, 2.05), (0.25, 2.15), (0.25, 2.25), (0.25, 2.35), (0.25, 2.45), (0.25, 2.55), (0.25, 2.65), (0.25, 2.75), (0.25, 2.85), (0.25, 2.95), (0.25, 3.05), (0.25, 3.15), (0.25, 3.25), (0.25, 3.35), (0.25, 3.45), (0.25, 3.55), (0.25, 3.65), (0.25, 3.75), (0.25, 3.85), (0.25, 3.95), (0.25, 4.05), (0.25, 4.15), 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     ]
    }
   ],
   "source": [
    "room_length = 5.0\n",
    "room_breadth = 5.0\n",
    "cell_dimension = 0.1\n",
    "\n",
    "# Calculate the number of cells in each dimension\n",
    "num_cells_x = int(room_length / cell_dimension)\n",
    "num_cells_y = int(room_breadth / cell_dimension)\n",
    "\n",
    "# Find midpoints of each cell\n",
    "midpoints = []\n",
    "\n",
    "for i in range(num_cells_x):\n",
    "    for j in range(num_cells_y):\n",
    "        x = i * cell_dimension + cell_dimension / 2\n",
    "        y = j * cell_dimension + cell_dimension / 2\n",
    "        midpoints.append((round(x, 2), round(y, 2)))\n",
    "\n",
    "print(\"Number of cells in x-direction:\", num_cells_x)\n",
    "print(\"Number of cells in y-direction:\", num_cells_y)\n",
    "print(\"Total number of cells:\", num_cells_x * num_cells_y)\n",
    "print(\"\\nMidpoints of each cell:\")\n",
    "print(midpoints)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {}\n",
    "for x, y in midpoints:\n",
    "    d[(x, y)] = lifi_ap1.channel_gain_nlos(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in d.items():\n",
    "    #if j=nan then assign 0\n",
    "    if np.isnan(j):\n",
    "        d[i]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0.05, 0.05): 4.519505902852275e-06,\n",
       " (0.05, 0.15): 3.5918014949313057e-06,\n",
       " (0.05, 0.25): 2.61002887025805e-06,\n",
       " (0.05, 0.35): 1.5440550644352549e-06,\n",
       " (0.05, 0.45): 3.5233518927686316e-07,\n",
       " (0.05, 0.55): -1.020509716765157e-06,\n",
       " (0.05, 0.65): -2.5827038009883965e-06,\n",
       " (0.05, 0.75): -2.7386122252887994e-06,\n",
       " (0.05, 0.85): -2.7503604515371304e-06,\n",
       " (0.05, 0.95): -3.988189733909279e-06,\n",
       " (0.05, 1.05): -4.968551798163456e-06,\n",
       " (0.05, 1.15): -5.701483662853236e-06,\n",
       " (0.05, 1.25): -6.2367172713414386e-06,\n",
       " (0.05, 1.35): -6.608254125284101e-06,\n",
       " (0.05, 1.45): -6.838436984396666e-06,\n",
       " (0.05, 1.55): -6.941812047511673e-06,\n",
       " (0.05, 1.65): -6.927559342385101e-06,\n",
       " (0.05, 1.75): -6.801180769530554e-06,\n",
       " (0.05, 1.85): -6.5658117030235955e-06,\n",
       " (0.05, 1.95): -6.2232983752130965e-06,\n",
       " (0.05, 2.05): -5.775085094385303e-06,\n",
       " (0.05, 2.15): -5.222920939256606e-06,\n",
       " (0.05, 2.25): -4.569389310353414e-06,\n",
       " (0.05, 2.35): -3.818267922497112e-06,\n",
       " (0.05, 2.45): -2.974733302859847e-06,\n",
       " (0.05, 2.55): -2.0454292795397793e-06,\n",
       " (0.05, 2.65): -1.0384223174184972e-06,\n",
       " (0.05, 2.75): 3.6932101742046494e-08,\n",
       " (0.05, 2.85): 1.1701881869975822e-06,\n",
       " (0.05, 2.95): 2.3500513396392197e-06,\n",
       " (0.05, 3.05): 3.5646425853060575e-06,\n",
       " (0.05, 3.15): 4.801769032507484e-06,\n",
       " (0.05, 3.25): 6.049186665597137e-06,\n",
       " (0.05, 3.35): 7.294845019707472e-06,\n",
       " (0.05, 3.45): 8.527106365079338e-06,\n",
       " (0.05, 3.55): 9.734934792231598e-06,\n",
       " (0.05, 3.65): 1.0908052896546045e-05,\n",
       " (0.05, 3.75): 1.2037065459299417e-05,\n",
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       " (1.85, 1.05): 1.1100528353928228e-05,\n",
       " (1.85, 1.15): 1.27230846355873e-05,\n",
       " (1.85, 1.25): 1.4227266378226854e-05,\n",
       " (1.85, 1.35): 1.5618190768220328e-05,\n",
       " (1.85, 1.45): 1.6902220973307982e-05,\n",
       " (1.85, 1.55): 1.808648925008496e-05,\n",
       " (1.85, 1.65): 1.917850593879195e-05,\n",
       " (1.85, 1.75): 2.018584699383017e-05,\n",
       " (1.85, 1.85): 2.1115911361934985e-05,\n",
       " (1.85, 1.95): 2.1975739077806722e-05,\n",
       " (1.85, 2.05): 2.2771881056505852e-05,\n",
       " (1.85, 2.15): 2.35103120123392e-05,\n",
       " (1.85, 2.25): 2.419637858622407e-05,\n",
       " (1.85, 2.35): 2.4834775521914278e-05,\n",
       " (1.85, 2.45): 2.542954353007596e-05,\n",
       " (1.85, 2.55): 2.598408327450861e-05,\n",
       " (1.85, 2.65): 2.6501180680711297e-05,\n",
       " (1.85, 2.75): 2.6983039490759372e-05,\n",
       " (1.85, 2.85): 2.7431317667564707e-05,\n",
       " (1.85, 2.95): 2.784716489151532e-05,\n",
       " (1.85, 3.05): 2.8231259005474824e-05,\n",
       " (1.85, 3.15): 2.8583839869464127e-05,\n",
       " (1.85, 3.25): 2.8904739712186174e-05,\n",
       " (1.85, 3.35): 2.9193409754111527e-05,\n",
       " (1.85, 3.45): 2.9448943688044768e-05,\n",
       " (1.85, 3.55): 2.9670099633992112e-05,\n",
       " (1.85, 3.65): 2.985532358943944e-05,\n",
       " (1.85, 3.75): 3.0002779426731217e-05,\n",
       " (1.85, 3.85): 3.0110393574076322e-05,\n",
       " (1.85, 3.95): 3.017592739844676e-05,\n",
       " (1.85, 4.05): 3.0197098303448822e-05,\n",
       " (1.85, 4.15): 3.017178401398891e-05,\n",
       " (1.85, 4.25): 3.0098367513968902e-05,\n",
       " (1.85, 4.35): 2.9976318560200746e-05,\n",
       " (1.85, 4.45): 2.980716237977717e-05,\n",
       " (1.85, 4.55): 2.959599031773535e-05,\n",
       " (1.85, 4.65): 2.9352977145838554e-05,\n",
       " (1.85, 4.75): 2.9086565410448854e-05,\n",
       " (1.85, 4.85): 2.8663578865353383e-05,\n",
       " (1.85, 4.95): 2.5281922768412545e-05,\n",
       " (1.95, 0.05): -6.223298375213096e-06,\n",
       " (1.95, 0.15): -7.303781923681826e-06,\n",
       " (1.95, 0.25): -5.131482226067917e-06,\n",
       " (1.95, 0.35): -2.7443690792865624e-06,\n",
       " (1.95, 0.45): -3.7024356435035625e-07,\n",
       " (1.95, 0.55): 1.9433606242250844e-06,\n",
       " (1.95, 0.65): 4.170698711115372e-06,\n",
       " (1.95, 0.75): 6.2941857834467595e-06,\n",
       " (1.95, 0.85): 8.302131138193987e-06,\n",
       " (1.95, 0.95): 1.018774305942234e-05,\n",
       " (1.95, 1.05): 1.1948283654066046e-05,\n",
       " (1.95, 1.15): 1.3584270730516885e-05,\n",
       " (1.95, 1.25): 1.509874355527516e-05,\n",
       " (1.95, 1.35): 1.6496610341667393e-05,\n",
       " (1.95, 1.45): 1.7784084847631692e-05,\n",
       " (1.95, 1.55): 1.89682117372416e-05,\n",
       " (1.95, 1.65): 2.0056475833877077e-05,\n",
       " (1.95, 1.75): 2.1056487979239746e-05,\n",
       " (1.95, 1.85): 2.197573907780672e-05,\n",
       " (1.95, 1.95): 2.2821413547053485e-05,\n",
       " (1.95, 2.05): 2.3600253514594992e-05,\n",
       " (1.95, 2.15): 2.43184655238917e-05,\n",
       " (1.95, 2.25): 2.498166211257257e-05,\n",
       " (1.95, 2.35): 2.5594831330681438e-05,\n",
       " (1.95, 2.45): 2.6162328013529053e-05,\n",
       " (1.95, 2.55): 2.668788137645811e-05,\n",
       " (1.95, 2.65): 2.7174614231938743e-05,\n",
       " (1.95, 2.75): 2.76250698295461e-05,\n",
       " (1.95, 2.85): 2.8041242982215306e-05,\n",
       " (1.95, 2.95): 2.842461277118232e-05,\n",
       " (1.95, 3.05): 2.877617472762653e-05,\n",
       " (1.95, 3.15): 2.9096470989165912e-05,\n",
       " (1.95, 3.25): 2.9385617551277267e-05,\n",
       " (1.95, 3.35): 2.9643328418045747e-05,\n",
       " (1.95, 3.45): 2.9868937264844945e-05,\n",
       " (1.95, 3.55): 3.006141825283998e-05,\n",
       " (1.95, 3.65): 3.0219409033787204e-05,\n",
       " (1.95, 3.75): 3.0341241003558042e-05,\n",
       " (1.95, 3.85): 3.0424984927555772e-05,\n",
       " (1.95, 3.95): 3.0468524906595305e-05,\n",
       " (1.95, 4.05): 3.0469681585246687e-05,\n",
       " (1.95, 4.15): 3.042641886699438e-05,\n",
       " (1.95, 4.25): 3.0337191254267107e-05,\n",
       " (1.95, 4.35): 3.020152719356634e-05,\n",
       " (1.95, 4.45): 3.002099820596859e-05,\n",
       " (1.95, 4.55): 2.980072713949316e-05,\n",
       " (1.95, 4.65): 2.9550896315980434e-05,\n",
       " (1.95, 4.75): 2.927989405945855e-05,\n",
       " (1.95, 4.85): 2.8854115145300533e-05,\n",
       " (1.95, 4.95): 2.546605603426291e-05,\n",
       " ...}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('channel_gain_nlos_mids.json', 'w') as fp:\n",
    "    json.dump(d, fp, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the keys to string\n",
    "d = {str(k): v for k, v in d.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Original grid points\n",
    "grid_points = [(i / 10, j / 10) for i in range(51) for j in range(51)]\n",
    "\n",
    "# Convert grid points to a NumPy array\n",
    "matrix = np.array(grid_points)\n",
    "\n",
    "# Reshape the matrix to a 2D array\n",
    "matrix_2d = matrix.reshape((51, 51, 2))\n",
    "\n",
    "# Rotate the matrix to the right\n",
    "rotated_matrix_2d = np.rot90(matrix_2d, k=-1)\n",
    "\n",
    "# Reshape the rotated matrix back to a 1D array\n",
    "rotated_matrix = rotated_matrix_2d.reshape(-1, 2)\n",
    "\n",
    "# Print the older vs. new points mapping\n",
    "print(\"Original Points\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tRotated Points\")\n",
    "print(\"-----------------------------------------------------------------\")\n",
    "for original, rotated in zip(matrix, rotated_matrix):\n",
    "    print(f\"{original}\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t{rotated}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cells in x-direction: 50\n",
      "Number of cells in y-direction: 50\n",
      "Total number of cells: 2500\n",
      "\n",
      "Midpoints of each cell:\n",
      "[(0.05, 0.05), (0.05, 0.15), (0.05, 0.25), (0.05, 0.35), (0.05, 0.45), (0.05, 0.55), (0.05, 0.65), (0.05, 0.75), (0.05, 0.85), (0.05, 0.95), (0.05, 1.05), (0.05, 1.15), (0.05, 1.25), (0.05, 1.35), (0.05, 1.45), (0.05, 1.55), (0.05, 1.65), (0.05, 1.75), (0.05, 1.85), (0.05, 1.95), (0.05, 2.05), (0.05, 2.15), (0.05, 2.25), (0.05, 2.35), (0.05, 2.45), (0.05, 2.55), (0.05, 2.65), (0.05, 2.75), (0.05, 2.85), (0.05, 2.95), (0.05, 3.05), (0.05, 3.15), (0.05, 3.25), (0.05, 3.35), (0.05, 3.45), (0.05, 3.55), (0.05, 3.65), (0.05, 3.75), (0.05, 3.85), (0.05, 3.95), (0.05, 4.05), (0.05, 4.15), (0.05, 4.25), (0.05, 4.35), (0.05, 4.45), (0.05, 4.55), (0.05, 4.65), (0.05, 4.75), (0.05, 4.85), (0.05, 4.95), (0.15, 0.05), (0.15, 0.15), (0.15, 0.25), (0.15, 0.35), (0.15, 0.45), (0.15, 0.55), (0.15, 0.65), (0.15, 0.75), (0.15, 0.85), (0.15, 0.95), (0.15, 1.05), (0.15, 1.15), (0.15, 1.25), (0.15, 1.35), (0.15, 1.45), (0.15, 1.55), (0.15, 1.65), (0.15, 1.75), (0.15, 1.85), (0.15, 1.95), (0.15, 2.05), (0.15, 2.15), (0.15, 2.25), (0.15, 2.35), (0.15, 2.45), (0.15, 2.55), (0.15, 2.65), (0.15, 2.75), (0.15, 2.85), (0.15, 2.95), (0.15, 3.05), (0.15, 3.15), (0.15, 3.25), (0.15, 3.35), (0.15, 3.45), (0.15, 3.55), (0.15, 3.65), (0.15, 3.75), (0.15, 3.85), (0.15, 3.95), (0.15, 4.05), (0.15, 4.15), (0.15, 4.25), (0.15, 4.35), (0.15, 4.45), (0.15, 4.55), (0.15, 4.65), (0.15, 4.75), (0.15, 4.85), (0.15, 4.95), (0.25, 0.05), (0.25, 0.15), (0.25, 0.25), (0.25, 0.35), (0.25, 0.45), (0.25, 0.55), (0.25, 0.65), (0.25, 0.75), (0.25, 0.85), (0.25, 0.95), (0.25, 1.05), (0.25, 1.15), (0.25, 1.25), (0.25, 1.35), (0.25, 1.45), (0.25, 1.55), (0.25, 1.65), (0.25, 1.75), (0.25, 1.85), (0.25, 1.95), (0.25, 2.05), (0.25, 2.15), (0.25, 2.25), (0.25, 2.35), (0.25, 2.45), (0.25, 2.55), (0.25, 2.65), (0.25, 2.75), (0.25, 2.85), (0.25, 2.95), (0.25, 3.05), (0.25, 3.15), (0.25, 3.25), (0.25, 3.35), (0.25, 3.45), (0.25, 3.55), (0.25, 3.65), (0.25, 3.75), (0.25, 3.85), (0.25, 3.95), (0.25, 4.05), (0.25, 4.15), (0.25, 4.25), (0.25, 4.35), (0.25, 4.45), (0.25, 4.55), (0.25, 4.65), (0.25, 4.75), (0.25, 4.85), (0.25, 4.95), (0.35, 0.05), (0.35, 0.15), (0.35, 0.25), (0.35, 0.35), (0.35, 0.45), (0.35, 0.55), (0.35, 0.65), (0.35, 0.75), (0.35, 0.85), (0.35, 0.95), (0.35, 1.05), (0.35, 1.15), (0.35, 1.25), (0.35, 1.35), (0.35, 1.45), (0.35, 1.55), (0.35, 1.65), (0.35, 1.75), (0.35, 1.85), (0.35, 1.95), (0.35, 2.05), (0.35, 2.15), (0.35, 2.25), (0.35, 2.35), (0.35, 2.45), (0.35, 2.55), (0.35, 2.65), (0.35, 2.75), (0.35, 2.85), (0.35, 2.95), (0.35, 3.05), (0.35, 3.15), (0.35, 3.25), (0.35, 3.35), (0.35, 3.45), (0.35, 3.55), (0.35, 3.65), (0.35, 3.75), (0.35, 3.85), (0.35, 3.95), (0.35, 4.05), (0.35, 4.15), (0.35, 4.25), (0.35, 4.35), (0.35, 4.45), (0.35, 4.55), (0.35, 4.65), (0.35, 4.75), (0.35, 4.85), (0.35, 4.95), (0.45, 0.05), (0.45, 0.15), (0.45, 0.25), (0.45, 0.35), (0.45, 0.45), (0.45, 0.55), (0.45, 0.65), (0.45, 0.75), (0.45, 0.85), (0.45, 0.95), (0.45, 1.05), (0.45, 1.15), (0.45, 1.25), (0.45, 1.35), (0.45, 1.45), (0.45, 1.55), (0.45, 1.65), (0.45, 1.75), (0.45, 1.85), (0.45, 1.95), (0.45, 2.05), (0.45, 2.15), (0.45, 2.25), (0.45, 2.35), (0.45, 2.45), (0.45, 2.55), (0.45, 2.65), (0.45, 2.75), (0.45, 2.85), (0.45, 2.95), (0.45, 3.05), (0.45, 3.15), (0.45, 3.25), (0.45, 3.35), (0.45, 3.45), (0.45, 3.55), (0.45, 3.65), (0.45, 3.75), (0.45, 3.85), (0.45, 3.95), (0.45, 4.05), (0.45, 4.15), (0.45, 4.25), (0.45, 4.35), (0.45, 4.45), (0.45, 4.55), (0.45, 4.65), (0.45, 4.75), (0.45, 4.85), (0.45, 4.95), (0.55, 0.05), (0.55, 0.15), (0.55, 0.25), (0.55, 0.35), (0.55, 0.45), (0.55, 0.55), (0.55, 0.65), (0.55, 0.75), (0.55, 0.85), (0.55, 0.95), (0.55, 1.05), (0.55, 1.15), (0.55, 1.25), (0.55, 1.35), (0.55, 1.45), (0.55, 1.55), (0.55, 1.65), (0.55, 1.75), (0.55, 1.85), (0.55, 1.95), (0.55, 2.05), (0.55, 2.15), (0.55, 2.25), (0.55, 2.35), (0.55, 2.45), (0.55, 2.55), (0.55, 2.65), (0.55, 2.75), (0.55, 2.85), (0.55, 2.95), (0.55, 3.05), (0.55, 3.15), (0.55, 3.25), (0.55, 3.35), (0.55, 3.45), (0.55, 3.55), (0.55, 3.65), (0.55, 3.75), (0.55, 3.85), (0.55, 3.95), (0.55, 4.05), (0.55, 4.15), (0.55, 4.25), (0.55, 4.35), (0.55, 4.45), (0.55, 4.55), (0.55, 4.65), (0.55, 4.75), (0.55, 4.85), (0.55, 4.95), (0.65, 0.05), (0.65, 0.15), (0.65, 0.25), (0.65, 0.35), (0.65, 0.45), (0.65, 0.55), (0.65, 0.65), (0.65, 0.75), (0.65, 0.85), (0.65, 0.95), (0.65, 1.05), (0.65, 1.15), (0.65, 1.25), (0.65, 1.35), (0.65, 1.45), (0.65, 1.55), (0.65, 1.65), (0.65, 1.75), (0.65, 1.85), (0.65, 1.95), (0.65, 2.05), (0.65, 2.15), (0.65, 2.25), (0.65, 2.35), (0.65, 2.45), (0.65, 2.55), (0.65, 2.65), (0.65, 2.75), (0.65, 2.85), (0.65, 2.95), (0.65, 3.05), (0.65, 3.15), (0.65, 3.25), (0.65, 3.35), (0.65, 3.45), (0.65, 3.55), (0.65, 3.65), (0.65, 3.75), (0.65, 3.85), (0.65, 3.95), (0.65, 4.05), (0.65, 4.15), (0.65, 4.25), (0.65, 4.35), (0.65, 4.45), (0.65, 4.55), (0.65, 4.65), (0.65, 4.75), (0.65, 4.85), (0.65, 4.95), (0.75, 0.05), (0.75, 0.15), (0.75, 0.25), (0.75, 0.35), (0.75, 0.45), (0.75, 0.55), (0.75, 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     ]
    }
   ],
   "source": [
    "room_length = 5.0\n",
    "room_breadth = 5.0\n",
    "cell_dimension = 0.1\n",
    "\n",
    "# Calculate the number of cells in each dimension\n",
    "num_cells_x = int(room_length / cell_dimension)\n",
    "num_cells_y = int(room_breadth / cell_dimension)\n",
    "\n",
    "# Find midpoints of each cell\n",
    "midpoints = []\n",
    "\n",
    "for i in range(num_cells_x):\n",
    "    for j in range(num_cells_y):\n",
    "        x = i * cell_dimension + cell_dimension / 2\n",
    "        y = j * cell_dimension + cell_dimension / 2\n",
    "        midpoints.append((round(x, 2), round(y, 2)))\n",
    "\n",
    "print(\"Number of cells in x-direction:\", num_cells_x)\n",
    "print(\"Number of cells in y-direction:\", num_cells_y)\n",
    "print(\"Total number of cells:\", num_cells_x * num_cells_y)\n",
    "print(\"\\nMidpoints of each cell:\")\n",
    "print(midpoints)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "midpoints_mat = np.array(midpoints).reshape((50, 50, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 50, 2)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "midpoints_mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rotate the matrix by 90 degree\n",
    "rotated_midpoints_mat = np.rot90(midpoints_mat, k=-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(array([0.05, 0.05]), array([0.05, 4.95])), (array([0.05, 0.15]), array([0.15, 4.95])), (array([0.05, 0.25]), array([0.25, 4.95])), (array([0.05, 0.35]), array([0.35, 4.95])), (array([0.05, 0.45]), array([0.45, 4.95])), (array([0.05, 0.55]), array([0.55, 4.95])), (array([0.05, 0.65]), array([0.65, 4.95])), (array([0.05, 0.75]), array([0.75, 4.95])), (array([0.05, 0.85]), array([0.85, 4.95])), (array([0.05, 0.95]), array([0.95, 4.95])), (array([0.05, 1.05]), array([1.05, 4.95])), (array([0.05, 1.15]), array([1.15, 4.95])), (array([0.05, 1.25]), array([1.25, 4.95])), (array([0.05, 1.35]), array([1.35, 4.95])), (array([0.05, 1.45]), array([1.45, 4.95])), (array([0.05, 1.55]), array([1.55, 4.95])), (array([0.05, 1.65]), array([1.65, 4.95])), (array([0.05, 1.75]), array([1.75, 4.95])), (array([0.05, 1.85]), array([1.85, 4.95])), (array([0.05, 1.95]), array([1.95, 4.95])), (array([0.05, 2.05]), array([2.05, 4.95])), (array([0.05, 2.15]), array([2.15, 4.95])), (array([0.05, 2.25]), array([2.25, 4.95])), (array([0.05, 2.35]), array([2.35, 4.95])), (array([0.05, 2.45]), array([2.45, 4.95])), (array([0.05, 2.55]), array([2.55, 4.95])), (array([0.05, 2.65]), array([2.65, 4.95])), (array([0.05, 2.75]), array([2.75, 4.95])), (array([0.05, 2.85]), array([2.85, 4.95])), (array([0.05, 2.95]), array([2.95, 4.95])), (array([0.05, 3.05]), array([3.05, 4.95])), (array([0.05, 3.15]), array([3.15, 4.95])), (array([0.05, 3.25]), array([3.25, 4.95])), (array([0.05, 3.35]), array([3.35, 4.95])), (array([0.05, 3.45]), array([3.45, 4.95])), (array([0.05, 3.55]), array([3.55, 4.95])), (array([0.05, 3.65]), array([3.65, 4.95])), (array([0.05, 3.75]), array([3.75, 4.95])), (array([0.05, 3.85]), array([3.85, 4.95])), (array([0.05, 3.95]), array([3.95, 4.95])), (array([0.05, 4.05]), array([4.05, 4.95])), (array([0.05, 4.15]), array([4.15, 4.95])), (array([0.05, 4.25]), array([4.25, 4.95])), (array([0.05, 4.35]), array([4.35, 4.95])), (array([0.05, 4.45]), array([4.45, 4.95])), (array([0.05, 4.55]), array([4.55, 4.95])), (array([0.05, 4.65]), array([4.65, 4.95])), (array([0.05, 4.75]), array([4.75, 4.95])), (array([0.05, 4.85]), array([4.85, 4.95])), (array([0.05, 4.95]), array([4.95, 4.95]))]\n"
     ]
    }
   ],
   "source": [
    "print(list(zip(midpoints_mat[0], rotated_midpoints_mat[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_125_375 = {}\n",
    "for i in range(len(midpoints_mat)):\n",
    "    for j in range(len(midpoints_mat)):\n",
    "        d_125_375[(rotated_midpoints_mat[i][j][0], rotated_midpoints_mat[i][j][1])] = d[str((midpoints_mat[i][j][0], midpoints_mat[i][j][1]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the keys to string\n",
    "d_125_375 = {str(k): v for k, v in d_125_375.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'(0.05, 4.95)': 4.519505902852275e-06,\n",
       " '(0.15, 4.95)': 3.5918014949313057e-06,\n",
       " '(0.25, 4.95)': 2.61002887025805e-06,\n",
       " '(0.35, 4.95)': 1.5440550644352549e-06,\n",
       " '(0.45, 4.95)': 3.5233518927686316e-07,\n",
       " '(0.55, 4.95)': -1.020509716765157e-06,\n",
       " '(0.65, 4.95)': -2.5827038009883965e-06,\n",
       " '(0.75, 4.95)': -2.7386122252887994e-06,\n",
       " '(0.85, 4.95)': -2.7503604515371304e-06,\n",
       " '(0.95, 4.95)': -3.988189733909279e-06,\n",
       " '(1.05, 4.95)': -4.968551798163456e-06,\n",
       " '(1.15, 4.95)': -5.701483662853236e-06,\n",
       " '(1.25, 4.95)': -6.2367172713414386e-06,\n",
       " '(1.35, 4.95)': -6.608254125284101e-06,\n",
       " '(1.45, 4.95)': -6.838436984396666e-06,\n",
       " '(1.55, 4.95)': -6.941812047511673e-06,\n",
       " '(1.65, 4.95)': -6.927559342385101e-06,\n",
       " '(1.75, 4.95)': -6.801180769530554e-06,\n",
       " '(1.85, 4.95)': -6.5658117030235955e-06,\n",
       " '(1.95, 4.95)': -6.2232983752130965e-06,\n",
       " '(2.05, 4.95)': -5.775085094385303e-06,\n",
       " '(2.15, 4.95)': -5.222920939256606e-06,\n",
       " '(2.25, 4.95)': -4.569389310353414e-06,\n",
       " '(2.35, 4.95)': -3.818267922497112e-06,\n",
       " '(2.45, 4.95)': -2.974733302859847e-06,\n",
       " '(2.55, 4.95)': -2.0454292795397793e-06,\n",
       " '(2.65, 4.95)': -1.0384223174184972e-06,\n",
       " '(2.75, 4.95)': 3.6932101742046494e-08,\n",
       " '(2.85, 4.95)': 1.1701881869975822e-06,\n",
       " '(2.95, 4.95)': 2.3500513396392197e-06,\n",
       " '(3.05, 4.95)': 3.5646425853060575e-06,\n",
       " '(3.15, 4.95)': 4.801769032507484e-06,\n",
       " '(3.25, 4.95)': 6.049186665597137e-06,\n",
       " '(3.35, 4.95)': 7.294845019707472e-06,\n",
       " '(3.45, 4.95)': 8.527106365079338e-06,\n",
       " '(3.55, 4.95)': 9.734934792231598e-06,\n",
       " '(3.65, 4.95)': 1.0908052896546045e-05,\n",
       " '(3.75, 4.95)': 1.2037065459299417e-05,\n",
       " '(3.85, 4.95)': 1.3113550401084586e-05,\n",
       " '(3.95, 4.95)': 1.413011698132633e-05,\n",
       " '(4.05, 4.95)': 1.5080429040161854e-05,\n",
       " '(4.15, 4.95)': 1.5959185625371378e-05,\n",
       " '(4.25, 4.95)': 1.6762039720735893e-05,\n",
       " '(4.35, 4.95)': 1.7485412011699483e-05,\n",
       " '(4.45, 4.95)': 1.8126109456489813e-05,\n",
       " '(4.55, 4.95)': 1.8680579528657407e-05,\n",
       " '(4.65, 4.95)': 1.914366837321013e-05,\n",
       " '(4.75, 4.95)': 1.950936480217106e-05,\n",
       " '(4.85, 4.95)': 1.983042009218229e-05,\n",
       " '(4.95, 4.95)': 2.15299187332939e-05,\n",
       " '(0.05, 4.85)': 3.591801494931305e-06,\n",
       " '(0.15, 4.85)': 2.7130756581206814e-06,\n",
       " '(0.25, 4.85)': 1.8082184156524696e-06,\n",
       " '(0.35, 4.85)': 8.471124551474908e-07,\n",
       " '(0.45, 4.85)': -2.0773311968670275e-07,\n",
       " '(0.55, 4.85)': -1.397628205812971e-06,\n",
       " '(0.65, 4.85)': -2.745584084660468e-06,\n",
       " '(0.75, 4.85)': -4.1651484353277355e-06,\n",
       " '(0.85, 4.85)': -5.5315057086837464e-06,\n",
       " '(0.95, 4.85)': -6.721954486619913e-06,\n",
       " '(1.05, 4.85)': -7.648845514248873e-06,\n",
       " '(1.15, 4.85)': -8.326294896949599e-06,\n",
       " '(1.25, 4.85)': -8.783430892795373e-06,\n",
       " '(1.35, 4.85)': -9.044642418021869e-06,\n",
       " '(1.45, 4.85)': -9.128643086000799e-06,\n",
       " '(1.55, 4.85)': -9.049797394668435e-06,\n",
       " '(1.65, 4.85)': -8.819362520049808e-06,\n",
       " '(1.75, 4.85)': -8.446468998168335e-06,\n",
       " '(1.85, 4.85)': -7.938908472363252e-06,\n",
       " '(1.95, 4.85)': -7.303781923681831e-06,\n",
       " '(2.05, 4.85)': -6.548031560119819e-06,\n",
       " '(2.15, 4.85)': -5.678862833279881e-06,\n",
       " '(2.25, 4.85)': -4.704057636758282e-06,\n",
       " '(2.35, 4.85)': -3.6321806823515468e-06,\n",
       " '(2.45, 4.85)': -2.472684715323612e-06,\n",
       " '(2.55, 4.85)': -1.2359244013674713e-06,\n",
       " '(2.65, 4.85)': 6.690779607483097e-08,\n",
       " '(2.75, 4.85)': 1.423908151240958e-06,\n",
       " '(2.85, 4.85)': 2.822634071115418e-06,\n",
       " '(2.95, 4.85)': 4.250306046779672e-06,\n",
       " '(3.05, 4.85)': 5.694024083299154e-06,\n",
       " '(3.15, 4.85)': 7.1409854603935325e-06,\n",
       " '(3.25, 4.85)': 8.578693041025448e-06,\n",
       " '(3.35, 4.85)': 9.995145900820165e-06,\n",
       " '(3.45, 4.85)': 1.1379006589060343e-05,\n",
       " '(3.55, 4.85)': 1.2719741677159441e-05,\n",
       " '(3.65, 4.85)': 1.4007734251024414e-05,\n",
       " '(3.75, 4.85)': 1.5234368506061361e-05,\n",
       " '(3.85, 4.85)': 1.6392087407822986e-05,\n",
       " '(3.95, 4.85)': 1.747442415907223e-05,\n",
       " '(4.05, 4.85)': 1.8476006355828364e-05,\n",
       " '(4.15, 4.85)': 1.9392527044573247e-05,\n",
       " '(4.25, 4.85)': 2.0220667157087414e-05,\n",
       " '(4.35, 4.85)': 2.095793494530783e-05,\n",
       " '(4.45, 4.85)': 2.1602354489653375e-05,\n",
       " '(4.55, 4.85)': 2.2151891336151895e-05,\n",
       " '(4.65, 4.85)': 2.2603557781709764e-05,\n",
       " '(4.75, 4.85)': 2.295320259537489e-05,\n",
       " '(4.85, 4.85)': 2.3204874909570617e-05,\n",
       " '(4.95, 4.85)': 2.340645910971325e-05,\n",
       " '(0.05, 4.75)': 2.61002887025805e-06,\n",
       " '(0.15, 4.75)': 1.8082184156524685e-06,\n",
       " '(0.25, 4.75)': 1.019148757564547e-06,\n",
       " '(0.35, 4.75)': 2.1199244601314205e-07,\n",
       " '(0.45, 4.75)': -6.465207643734674e-07,\n",
       " '(0.55, 4.75)': -1.5837989471487981e-06,\n",
       " '(0.65, 4.75)': -2.6063523703626563e-06,\n",
       " '(0.75, 4.75)': -3.6747221525568535e-06,\n",
       " '(0.85, 4.75)': -4.6981382900629216e-06,\n",
       " '(0.95, 4.75)': -5.583062225368297e-06,\n",
       " '(1.05, 4.75)': -6.281627877473829e-06,\n",
       " '(1.15, 4.75)': -6.78651406028645e-06,\n",
       " '(1.25, 4.75)': -7.106887720584235e-06,\n",
       " '(1.35, 4.75)': -7.2555781156313554e-06,\n",
       " '(1.45, 4.75)': -7.2448685235213015e-06,\n",
       " '(1.55, 4.75)': -7.085511024942206e-06,\n",
       " '(1.65, 4.75)': -6.786755432919207e-06,\n",
       " '(1.75, 4.75)': -6.356680911126673e-06,\n",
       " '(1.85, 4.75)': -5.8026039852784296e-06,\n",
       " '(1.95, 4.75)': -5.131482226067917e-06,\n",
       " '(2.05, 4.75)': -4.350277322531852e-06,\n",
       " '(2.15, 4.75)': -3.4662571069274094e-06,\n",
       " '(2.25, 4.75)': -2.487224471702092e-06,\n",
       " '(2.35, 4.75)': -1.4216677059609863e-06,\n",
       " '(2.45, 4.75)': -2.788326588299281e-07,\n",
       " '(2.55, 4.75)': 9.312778185914149e-07,\n",
       " '(2.65, 4.75)': 2.1979677867892062e-06,\n",
       " '(2.75, 4.75)': 3.5099635032523634e-06,\n",
       " '(2.85, 4.75)': 4.855566772262677e-06,\n",
       " '(2.95, 4.75)': 6.222832887765022e-06,\n",
       " '(3.05, 4.75)': 7.59976048155352e-06,\n",
       " '(3.15, 4.75)': 8.974482074300657e-06,\n",
       " '(3.25, 4.75)': 1.0335446068844551e-05,\n",
       " '(3.35, 4.75)': 1.1671583099792779e-05,\n",
       " '(3.45, 4.75)': 1.2972451868498045e-05,\n",
       " '(3.55, 4.75)': 1.4228361687850397e-05,\n",
       " '(3.65, 4.75)': 1.5430470801507937e-05,\n",
       " '(3.75, 4.75)': 1.6570860997185185e-05,\n",
       " '(3.85, 4.75)': 1.7642589944516813e-05,\n",
       " '(3.95, 4.75)': 1.863972280598842e-05,\n",
       " '(4.05, 4.75)': 1.9557343551218106e-05,\n",
       " '(4.15, 4.75)': 2.0391543246215297e-05,\n",
       " '(4.25, 4.75)': 2.1139376025138045e-05,\n",
       " '(4.35, 4.75)': 2.179876163531169e-05,\n",
       " '(4.45, 4.75)': 2.23682960108567e-05,\n",
       " '(4.55, 4.75)': 2.284692007504465e-05,\n",
       " '(4.65, 4.75)': 2.3233459384934592e-05,\n",
       " '(4.75, 4.75)': 2.352642408972257e-05,\n",
       " '(4.85, 4.75)': 2.37253971828009e-05,\n",
       " '(4.95, 4.75)': 2.3834004835437842e-05,\n",
       " '(0.05, 4.65)': 1.5440550644352547e-06,\n",
       " '(0.15, 4.65)': 8.471124551474908e-07,\n",
       " '(0.25, 4.65)': 2.1199244601314337e-07,\n",
       " '(0.35, 4.65)': -3.985390073902462e-07,\n",
       " '(0.45, 4.65)': -1.0196409911594964e-06,\n",
       " '(0.55, 4.65)': -1.6746499967308566e-06,\n",
       " '(0.65, 4.65)': -2.3663107570743475e-06,\n",
       " '(0.75, 4.65)': -3.0695027210048514e-06,\n",
       " '(0.85, 4.65)': -3.734882680654187e-06,\n",
       " '(0.95, 4.65)': -4.309496789847892e-06,\n",
       " '(1.05, 4.65)': -4.757291408207394e-06,\n",
       " '(1.15, 4.65)': -5.062545886580802e-06,\n",
       " '(1.25, 4.65)': -5.222746425675258e-06,\n",
       " '(1.35, 4.65)': -5.241652885057553e-06,\n",
       " '(1.45, 4.65)': -5.125373462663838e-06,\n",
       " '(1.55, 4.65)': -4.880582480566126e-06,\n",
       " '(1.65, 4.65)': -4.513846920306574e-06,\n",
       " '(1.75, 4.65)': -4.031475156576361e-06,\n",
       " '(1.85, 4.65)': -3.4396018133843693e-06,\n",
       " '(1.95, 4.65)': -2.7443690792865607e-06,\n",
       " '(2.05, 4.65)': -1.9521317560760876e-06,\n",
       " '(2.15, 4.65)': -1.06964535028392e-06,\n",
       " '(2.25, 4.65)': -1.0421388737253807e-07,\n",
       " '(2.35, 4.65)': 9.362148364450394e-07,\n",
       " '(2.45, 4.65)': 2.0430109460859826e-06,\n",
       " '(2.55, 4.65)': 3.2068795882974363e-06,\n",
       " '(2.65, 4.65)': 4.417927588342901e-06,\n",
       " '(2.75, 4.65)': 5.66577092260051e-06,\n",
       " '(2.85, 4.65)': 6.939672666674003e-06,\n",
       " '(2.95, 4.65)': 8.228700589005544e-06,\n",
       " '(3.05, 4.65)': 9.52189397233563e-06,\n",
       " '(3.15, 4.65)': 1.0808430336170961e-05,\n",
       " '(3.25, 4.65)': 1.2077784283949169e-05,\n",
       " '(3.35, 4.65)': 1.3319872509925782e-05,\n",
       " '(3.45, 4.65)': 1.4525180902753425e-05,\n",
       " '(3.55, 4.65)': 1.5684871536269004e-05,\n",
       " '(3.65, 4.65)': 1.6790869031328985e-05,\n",
       " '(3.75, 4.65)': 1.7835927209660916e-05,\n",
       " '(3.85, 4.65)': 1.8813678040115164e-05,\n",
       " '(3.95, 4.65)': 1.9718665455713696e-05,\n",
       " '(4.05, 4.65)': 2.054636645081127e-05,\n",
       " '(4.15, 4.65)': 2.129320051837961e-05,\n",
       " '(4.25, 4.65)': 2.1956525265641795e-05,\n",
       " '(4.35, 4.65)': 2.2534610173470067e-05,\n",
       " '(4.45, 4.65)': 2.302657240193292e-05,\n",
       " '(4.55, 4.65)': 2.3432255237263933e-05,\n",
       " '(4.65, 4.65)': 2.3752058654708632e-05,\n",
       " '(4.75, 4.65)': 2.398685136687406e-05,\n",
       " '(4.85, 4.65)': 2.413828209325909e-05,\n",
       " '(4.95, 4.65)': 2.4209574843500405e-05,\n",
       " '(0.05, 4.55)': 3.5233518927686284e-07,\n",
       " '(0.15, 4.55)': -2.077331196867051e-07,\n",
       " '(0.25, 4.55)': -6.465207643734655e-07,\n",
       " '(0.35, 4.55)': -1.0196409911594975e-06,\n",
       " '(0.45, 4.55)': -1.372318491803224e-06,\n",
       " '(0.55, 4.55)': -1.7313196792201085e-06,\n",
       " '(0.65, 4.55)': -2.103147259121742e-06,\n",
       " '(0.75, 4.55)': -2.4747139680730283e-06,\n",
       " '(0.85, 4.55)': -2.818540462802102e-06,\n",
       " '(0.95, 4.55)': -3.103007015483424e-06,\n",
       " '(1.05, 4.55)': -3.3022357772113046e-06,\n",
       " '(1.15, 4.55)': -3.4001262103248374e-06,\n",
       " '(1.25, 4.55)': -3.3891564925293196e-06,\n",
       " '(1.35, 4.55)': -3.267478814915137e-06,\n",
       " '(1.45, 4.55)': -3.0364592378170525e-06,\n",
       " '(1.55, 4.55)': -2.6991275500101817e-06,\n",
       " '(1.65, 4.55)': -2.259347491957275e-06,\n",
       " '(1.75, 4.55)': -1.7214404161922591e-06,\n",
       " '(1.85, 4.55)': -1.0900669813266819e-06,\n",
       " '(1.95, 4.55)': -3.702435643503554e-07,\n",
       " '(2.05, 4.55)': 4.325820008040566e-07,\n",
       " '(2.15, 4.55)': 1.312441542700604e-06,\n",
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       " '(2.95, 3.15)': 2.784716489151532e-05,\n",
       " '(3.05, 3.15)': 2.8231259005474824e-05,\n",
       " '(3.15, 3.15)': 2.8583839869464127e-05,\n",
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       " '(3.35, 3.15)': 2.9193409754111527e-05,\n",
       " '(3.45, 3.15)': 2.9448943688044768e-05,\n",
       " '(3.55, 3.15)': 2.9670099633992112e-05,\n",
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       " '(3.75, 3.15)': 3.0002779426731217e-05,\n",
       " '(3.85, 3.15)': 3.0110393574076322e-05,\n",
       " '(3.95, 3.15)': 3.017592739844676e-05,\n",
       " '(4.05, 3.15)': 3.0197098303448822e-05,\n",
       " '(4.15, 3.15)': 3.017178401398891e-05,\n",
       " '(4.25, 3.15)': 3.0098367513968902e-05,\n",
       " '(4.35, 3.15)': 2.9976318560200746e-05,\n",
       " '(4.45, 3.15)': 2.980716237977717e-05,\n",
       " '(4.55, 3.15)': 2.959599031773535e-05,\n",
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       " '(4.95, 3.15)': 2.5281922768412545e-05,\n",
       " '(0.05, 3.05)': -6.223298375213096e-06,\n",
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       " '(0.35, 3.05)': -2.7443690792865624e-06,\n",
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       " '(0.55, 3.05)': 1.9433606242250844e-06,\n",
       " '(0.65, 3.05)': 4.170698711115372e-06,\n",
       " '(0.75, 3.05)': 6.2941857834467595e-06,\n",
       " '(0.85, 3.05)': 8.302131138193987e-06,\n",
       " '(0.95, 3.05)': 1.018774305942234e-05,\n",
       " '(1.05, 3.05)': 1.1948283654066046e-05,\n",
       " '(1.15, 3.05)': 1.3584270730516885e-05,\n",
       " '(1.25, 3.05)': 1.509874355527516e-05,\n",
       " '(1.35, 3.05)': 1.6496610341667393e-05,\n",
       " '(1.45, 3.05)': 1.7784084847631692e-05,\n",
       " '(1.55, 3.05)': 1.89682117372416e-05,\n",
       " '(1.65, 3.05)': 2.0056475833877077e-05,\n",
       " '(1.75, 3.05)': 2.1056487979239746e-05,\n",
       " '(1.85, 3.05)': 2.197573907780672e-05,\n",
       " '(1.95, 3.05)': 2.2821413547053485e-05,\n",
       " '(2.05, 3.05)': 2.3600253514594992e-05,\n",
       " '(2.15, 3.05)': 2.43184655238917e-05,\n",
       " '(2.25, 3.05)': 2.498166211257257e-05,\n",
       " '(2.35, 3.05)': 2.5594831330681438e-05,\n",
       " '(2.45, 3.05)': 2.6162328013529053e-05,\n",
       " '(2.55, 3.05)': 2.668788137645811e-05,\n",
       " '(2.65, 3.05)': 2.7174614231938743e-05,\n",
       " '(2.75, 3.05)': 2.76250698295461e-05,\n",
       " '(2.85, 3.05)': 2.8041242982215306e-05,\n",
       " '(2.95, 3.05)': 2.842461277118232e-05,\n",
       " '(3.05, 3.05)': 2.877617472762653e-05,\n",
       " '(3.15, 3.05)': 2.9096470989165912e-05,\n",
       " '(3.25, 3.05)': 2.9385617551277267e-05,\n",
       " '(3.35, 3.05)': 2.9643328418045747e-05,\n",
       " '(3.45, 3.05)': 2.9868937264844945e-05,\n",
       " '(3.55, 3.05)': 3.006141825283998e-05,\n",
       " '(3.65, 3.05)': 3.0219409033787204e-05,\n",
       " '(3.75, 3.05)': 3.0341241003558042e-05,\n",
       " '(3.85, 3.05)': 3.0424984927555772e-05,\n",
       " '(3.95, 3.05)': 3.0468524906595305e-05,\n",
       " '(4.05, 3.05)': 3.0469681585246687e-05,\n",
       " '(4.15, 3.05)': 3.042641886699438e-05,\n",
       " '(4.25, 3.05)': 3.0337191254267107e-05,\n",
       " '(4.35, 3.05)': 3.020152719356634e-05,\n",
       " '(4.45, 3.05)': 3.002099820596859e-05,\n",
       " '(4.55, 3.05)': 2.980072713949316e-05,\n",
       " '(4.65, 3.05)': 2.9550896315980434e-05,\n",
       " '(4.75, 3.05)': 2.927989405945855e-05,\n",
       " '(4.85, 3.05)': 2.8854115145300533e-05,\n",
       " '(4.95, 3.05)': 2.546605603426291e-05,\n",
       " ...}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_125_375"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('channel_gain_nlos_mids_125_375.json', 'w') as fp:\n",
    "    json.dump(d_125_375, fp, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = [1,2,4,6,8,10]\n",
    "y1 = [219.57424387505597,235.35126491845972,231.1728382890945,268.37015041603314,233.12358048811774,231.0953126475178]\n",
    "y2 = [884.2071576906355,766.2872658073504,813.8466174876554,710.9811791069699,713.4887047138029,655.5018099589936]\n",
    "plt.plot(x1, y1, label =  \"Conventional\")\n",
    "plt.plot(x1, y2, label = \"Proposed\")\n",
    "plt.legend()\n",
    "plt.xlabel('Mean occurance rate light path blockage')\n",
    "plt.ylabel('Average Throughput (Mbps)')\n",
    "plt.title('Average Throughput vs Mean occurance rate light path blockage')\n",
    "plt.savefig('mean_occurance_rate_light_path_blockage_vs_throughput_plot.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.imsave('channel_gain_nlos_mids_125_375.png', d_125_375)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "step_sizes = [0.5, 1, 1.5, 2, 2.5, 3]\n",
    "all_avg_throughputs_proposed =  [776.9442873327106, 729.4602048366222, 704.0375951056822, 704.1381197264243, 700.9983421527124, 690.5962989524083]\n",
    "all_avg_throughputs_conventional =  [236.1354245457723, 232.38281178853396, 237.95518610300732, 239.42972259288567, 218.69977038543396, 217.7388815680769]\n",
    "\n",
    "# Plot steps vs throughputs for both methods\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.plot(step_sizes, all_avg_throughputs_conventional, label='Conventional')\n",
    "plt.plot(step_sizes, all_avg_throughputs_proposed, label='Proposed')\n",
    "plt.title('Average Throughput vs Step Size')\n",
    "plt.xlabel('Step Size')\n",
    "plt.ylabel('Average Throughput (Mbps)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.savefig('step_size_vs_throughput_plot.png')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}