Plotting_dB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.projections import PolarAxes\n",
    "import numpy as np\n",
    "import scipy as sci\n",
    "import scipy.constants as scic\n",
    "\n",
    "import math as m\n",
    "import cmath\n",
    "\n",
    "def roomSections(array, px, py):\n",
    "    sect = 3\n",
    "    divx = (px+sect //2)//sect\n",
    "    divy = (py+sect//2)//sect\n",
    "    corner = array[4:divx, 4:divy]\n",
    "    center = array[divx+1:divx*2, divy+1:divy*2]\n",
    "    yside = array[4:divx, divy+1:divy*2]\n",
    "    xside = array[divx+1:divx*2, 4:divy]\n",
    "    print('Corner average: ', np.average(corner))\n",
    "    print('Center average: ', np.average(center))\n",
    "    print('Y-side average: ', np.average(yside))\n",
    "    print('X-side average: ', np.average(xside))\n",
    "\n",
    "def CDF_generation(array, num_bins = 50):\n",
    "    data = np.reshape(array, (1,-1))\n",
    "    data = data[data !=0]\n",
    "    #print('without zeros = ',np.shape(data))   \n",
    "    data = abs(data)\n",
    "    #sorted_data = data[np.searchsorted(data, 0) :]\n",
    "    counts, bin_edges =  np.histogram(data, bins = num_bins, normed = 'True')\n",
    "    cdf =np.cumsum(counts)\n",
    "    return cdf, bin_edges\n",
    "def sum_arrays(array, rows, start_rows = 0):\n",
    "    new_array = np.zeros((np.size(array, axis =1), np.size(array, axis =2)))\n",
    "    for i in range(rows):\n",
    "        new_array = new_array + array[i+start_rows,:,:]\n",
    "    return new_array\n",
    "\n",
    "#def processing(array):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load in all 2 antenna data\n",
    "corner2antenna = np.load('dBpaths\\\\2antennaCornerdB.npy')\n",
    "side2antenna = np.load('dBpaths\\\\2antennaSideddB.npy')\n",
    "ant2_10pt_corner = np.load('dBpaths\\\\2antennas10ptsCorner_dB.npy')\n",
    "ant2_10pt_side = np.load('dBpaths\\\\2antennas10ptsSide_dB.npy')\n",
    "ant2_20pt_corner = np.load('dBpaths\\\\2antennas20ptsCorner_dB.npy')\n",
    "ant2_20pt_side = np.load('dBpaths\\\\2antennas20ptsSide_dB.npy')\n",
    "ant2_30pt_corner = np.load('dBpaths\\\\2antennas30ptsCorner_dB.npy')\n",
    "ant2_30pt_side = np.load('dBpaths\\\\2antennas30ptsSide_dB.npy')\n",
    "\n",
    "ant2_10pt_side = side2antenna + ant2_10pt_side\n",
    "ant2_10s = ant2_10pt_side*np.conj(ant2_10pt_side)\n",
    "ant2_20pt_side = side2antenna + ant2_20pt_side\n",
    "ant2_20s = ant2_20pt_side*np.conj(ant2_20pt_side)\n",
    "ant2_30pt_side = side2antenna + ant2_30pt_side\n",
    "ant2_30s = ant2_30pt_side*np.conj(ant2_30pt_side)\n",
    "ant2_10pt_corner = corner2antenna + ant2_10pt_corner\n",
    "ant2_10c = ant2_10pt_corner*np.conj(ant2_10pt_corner)\n",
    "ant2_20pt_corner = corner2antenna + ant2_20pt_corner\n",
    "ant2_20c = ant2_20pt_corner*np.conj(ant2_20pt_corner)\n",
    "ant2_30pt_corner = corner2antenna + ant2_30pt_corner\n",
    "ant2_30c = ant2_30pt_corner*np.conj(ant2_30pt_corner)\n",
    "\n",
    "ant2_10s = sum_arrays(ant2_10s, 2*4)\n",
    "ant2_20s = sum_arrays(ant2_20s, 2*4)\n",
    "ant2_30s = sum_arrays(ant2_30s, 2*4)\n",
    "ant2_10c = sum_arrays(ant2_10c, 2*4)\n",
    "ant2_20c = sum_arrays(ant2_20c, 2*4)\n",
    "ant2_30c = sum_arrays(ant2_30c, 2*4)\n",
    "\n",
    "\n",
    "\n",
    "#Load in all 4antenna data\n",
    "corner4antenna = np.load('dBpaths\\\\4antennaCornerdB.npy')\n",
    "side4antenna = np.load('dBpaths\\\\4antennaSideddB.npy')\n",
    "ant4_10pt_corner = np.load('dBpaths\\\\4antennas10ptsCorner_dB.npy')\n",
    "ant4_10pt_side = np.load('dBpaths\\\\4antennas10ptsSide_dB.npy')\n",
    "ant4_20pt_corner = np.load('dBpaths\\\\4antennas20ptsCorner_dB.npy')\n",
    "ant4_20pt_side = np.load('dBpaths\\\\4antennas20ptsSide_dB.npy')\n",
    "ant4_30pt_corner = np.load('dBpaths\\\\4antennas30ptsCorner_dB.npy')\n",
    "ant4_30pt_side = np.load('dBpaths\\\\4antennas30ptsSide_dB.npy')\n",
    "\n",
    "\n",
    "ant4_10pt_side = side4antenna + ant4_10pt_side\n",
    "ant4_10s = ant4_10pt_side*np.conj(ant4_10pt_side)\n",
    "\n",
    "ant4_20pt_side = side4antenna + ant4_20pt_side\n",
    "ant4_20s = ant4_20pt_side*np.conj(ant4_20pt_side)\n",
    "\n",
    "ant4_30pt_side = side4antenna + ant4_30pt_side\n",
    "ant4_30s = ant4_30pt_side*np.conj(ant4_30pt_side)\n",
    "\n",
    "ant4_10pt_corner = corner4antenna + ant4_10pt_corner\n",
    "ant4_10c = ant4_10pt_corner*np.conj(ant4_10pt_corner)\n",
    "\n",
    "ant4_20pt_corner = corner4antenna + ant4_20pt_corner\n",
    "ant4_20c = ant4_20pt_corner*np.conj(ant4_20pt_corner)\n",
    "\n",
    "ant4_30pt_corner = corner4antenna + ant4_30pt_corner\n",
    "ant4_30c = ant4_30pt_corner*np.conj(ant4_30pt_corner)\n",
    "\n",
    "ant4_10s = sum_arrays(ant4_10s, 4*4)\n",
    "ant4_20s = sum_arrays(ant4_20s, 4*4)\n",
    "ant4_30s = sum_arrays(ant4_30s, 4*4)\n",
    "ant4_10c = sum_arrays(ant4_10c, 4*4)\n",
    "ant4_20c = sum_arrays(ant4_20c, 4*4)\n",
    "ant4_30c = sum_arrays(ant4_30c, 4*4)\n",
    "\n",
    "#Load in all 8 antenna data\n",
    "corner8antenna = np.load('dBpaths\\\\8antennaCornerdB.npy')\n",
    "side8antenna = np.load('dBpaths\\\\8antennaSideddB.npy')\n",
    "ant8_10pt_corner = np.load('dBpaths\\\\8antennas10ptsCorner_dB.npy')\n",
    "ant8_10pt_side = np.load('dBpaths\\\\8antennas10ptsSide_dB.npy')\n",
    "ant8_20pt_corner = np.load('dBpaths\\\\8antennas20ptsCorner_dB.npy')\n",
    "ant8_20pt_side = np.load('dBpaths\\\\8antennas20ptsSide_dB.npy')\n",
    "ant8_30pt_corner = np.load('dBpaths\\\\8antennas30ptsCorner_dB.npy')\n",
    "ant8_30pt_side = np.load('dBpaths\\\\8antennas30ptsSide_dB.npy')\n",
    "\n",
    "\n",
    "ant8_10pt_side = side8antenna + ant8_10pt_side\n",
    "ant8_10s = ant8_10pt_side*np.conj(ant8_10pt_side)\n",
    "\n",
    "\n",
    "ant8_20pt_side = side8antenna + ant8_20pt_side\n",
    "ant8_20s = ant8_20pt_side*np.conj(ant8_20pt_side)\n",
    "\n",
    "ant8_30pt_side = side8antenna + ant8_30pt_side\n",
    "ant8_30s = ant8_30pt_side*np.conj(ant8_30pt_side)\n",
    "\n",
    "ant8_10pt_corner = corner8antenna + ant8_10pt_corner\n",
    "ant8_10c = ant8_10pt_corner*np.conj(ant8_10pt_corner)\n",
    "\n",
    "ant8_20pt_corner = corner8antenna + ant8_20pt_corner\n",
    "ant8_20c = ant8_20pt_corner*np.conj(ant8_20pt_corner)\n",
    "\n",
    "ant8_30pt_corner = corner8antenna + ant8_30pt_corner\n",
    "ant8_30c = ant8_30pt_corner*np.conj(ant8_30pt_corner)\n",
    "\n",
    "ant8_10s = sum_arrays(ant8_10s, 8*4)\n",
    "ant8_20s = sum_arrays(ant8_20s, 8*4)\n",
    "ant8_30s = sum_arrays(ant8_30s, 8*4)\n",
    "ant8_10c = sum_arrays(ant8_10c, 8*4)\n",
    "ant8_20c = sum_arrays(ant8_20c, 8*4)\n",
    "ant8_30c = sum_arrays(ant8_30c, 8*4)\n",
    "\n",
    "#load in all 16 antenna data\n",
    "corner16antenna = np.load('dBpaths\\\\16antennaCornerdB.npy')\n",
    "side16antenna = np.load('dBpaths\\\\16antennaSideddB.npy')\n",
    "ant16_10pt_corner = np.load('dBpaths\\\\16antennas10ptsCorner_dB.npy')\n",
    "ant16_10pt_side = np.load('dBpaths\\\\16antennas10ptsSide_dB.npy')\n",
    "ant16_20pt_corner = np.load('dBpaths\\\\16antennas20ptsCorner_dB.npy')\n",
    "ant16_20pt_side = np.load('dBpaths\\\\16antennas20ptsSide_dB.npy')\n",
    "ant16_30pt_corner = np.load('dBpaths\\\\16antennas30ptsCorner_dB.npy')\n",
    "ant16_30pt_side = np.load('dBpaths\\\\16antennas30ptsSide_dB.npy')\n",
    "\n",
    "\n",
    "ant16_10pt_side = side16antenna + ant16_10pt_side\n",
    "ant16_10s = ant16_10pt_side*np.conj(ant16_10pt_side)\n",
    "\n",
    "ant16_20pt_side = side16antenna + ant16_20pt_side\n",
    "ant16_20s = ant16_20pt_side*np.conj(ant16_20pt_side)\n",
    "\n",
    "ant16_30pt_side = side16antenna + ant16_30pt_side\n",
    "ant16_30s = ant16_30pt_side*np.conj(ant16_30pt_side)\n",
    "\n",
    "ant16_10pt_corner = corner16antenna + ant16_10pt_corner\n",
    "ant16_10c = ant16_10pt_corner*np.conj(ant16_10pt_corner)\n",
    "\n",
    "ant16_20pt_corner = corner16antenna + ant16_20pt_corner\n",
    "ant16_20c = ant16_20pt_corner*np.conj(ant16_20pt_corner)\n",
    "\n",
    "ant16_30pt_corner = corner16antenna + ant16_30pt_corner\n",
    "ant16_30c = ant16_30pt_corner*np.conj(ant16_30pt_corner)\n",
    "\n",
    "ant16_10s = sum_arrays(ant16_10s, 16*4)\n",
    "ant16_20s = sum_arrays(ant16_20s, 16*4)\n",
    "ant16_30s = sum_arrays(ant16_30s, 16*4)\n",
    "ant16_10c = sum_arrays(ant16_10c, 16*4)\n",
    "ant16_20c = sum_arrays(ant16_20c, 16*4)\n",
    "ant16_30c = sum_arrays(ant16_30c, 16*4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cdf2_10, bins2_10 = CDF_generation(10*np.log10(abs(ant2_10s)))\n",
    "cdf2_10c, bins2_10c = CDF_generation(10*np.log10(abs(ant2_10c)))\n",
    "cdf2_20, bins2_20 = CDF_generation(10*np.log10(abs(ant2_20s)))\n",
    "cdf2_20c, bins2_20c = CDF_generation(10*np.log10(abs(ant2_20c)))\n",
    "cdf2_30, bins2_30 = CDF_generation(10*np.log10(abs(ant2_30s)))\n",
    "cdf2_30c, bins2_30c = CDF_generation(10*np.log10(abs(ant2_30c)))\n",
    "\n",
    "cdf4_10, bins4_10 = CDF_generation(10*np.log10(abs(ant4_10s)))\n",
    "cdf4_10c, bins4_10c = CDF_generation(10*np.log10(abs(ant4_10c)))\n",
    "cdf4_20, bins4_20 = CDF_generation(10*np.log10(abs(ant4_20s)))\n",
    "cdf4_20c, bins4_20c = CDF_generation(10*np.log10(abs(ant4_20c)))\n",
    "cdf4_30, bins4_30 = CDF_generation(10*np.log10(abs(ant4_30s)))\n",
    "cdf4_30c, bins4_30c = CDF_generation(10*np.log10(abs(ant4_30c)))\n",
    "\n",
    "cdf8_10, bins8_10 = CDF_generation(10*np.log10(abs(ant8_10s)))\n",
    "cdf8_10c, bins8_10c = CDF_generation(10*np.log10(abs(ant8_10c)))\n",
    "cdf8_20, bins8_20 = CDF_generation(10*np.log10(abs(ant8_20s)))\n",
    "cdf8_20c, bins8_20c = CDF_generation(10*np.log10(abs(ant8_20c)))\n",
    "cdf8_30, bins8_30 = CDF_generation(10*np.log10(abs(ant8_30s)))\n",
    "cdf8_30c, bins8_30c = CDF_generation(10*np.log10(abs(ant8_30c)))\n",
    "\n",
    "cdf16_10, bins16_10 = CDF_generation(10*np.log10(abs(ant16_10s)))\n",
    "cdf16_10c, bins16_10c = CDF_generation(10*np.log10(abs(ant16_10c)))\n",
    "cdf16_20, bins16_20 = CDF_generation(10*np.log10(abs(ant16_20s)))\n",
    "cdf16_20c, bins16_20c = CDF_generation(10*np.log10(abs(ant16_20c)))\n",
    "cdf16_30, bins16_30 = CDF_generation(10*np.log10(abs(ant16_30s)))\n",
    "cdf16_30c, bins16_30c = CDF_generation(10*np.log10(abs(ant16_30c)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib qt\n",
    "fig, ax = plt.subplots(figsize = (8,6))\n",
    "ax.semilogy(bins2_10[1:], cdf2_10/cdf2_10[-1], label = '10 pts, side', linestyle = ':',color = 'b')\n",
    "ax.plot(bins2_10c[1:], cdf2_10c/cdf2_10c[-1], label = '10 pts, corner', color = 'b')\n",
    "ax.plot(bins2_20[1:], cdf2_20/cdf2_20[-1], label = '20 pts, side',linestyle = ':', color = 'g')\n",
    "ax.plot(bins2_20c[1:], cdf2_20c/cdf2_20c[-1], label = '20 pts, corner', color = 'g')\n",
    "ax.plot(bins2_30[1:], cdf2_30/cdf2_30[-1], label = '30 pts, side',linestyle = ':', color = 'r')\n",
    "ax.plot(bins2_30c[1:], cdf2_30c/cdf2_30c[-1], label = '30 pts, corner', color = 'r')\n",
    "\n",
    "ax.set_title('Comparison of Scattering Environments for 2 Antenna Basestation')\n",
    "ax.set_xlabel('FSPL, dB scale')\n",
    "ax.grid()\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "fig4, ax4 = plt.subplots(figsize = (8,6))\n",
    "ax4.semilogy(bins4_10[1:], cdf4_10/cdf4_10[-1], label = '10 pts, side', linestyle = ':',color = 'b')\n",
    "ax4.plot(bins4_10c[1:], cdf4_10c/cdf4_10c[-1], label = '10 pts, corner', color = 'b')\n",
    "ax4.plot(bins4_20[1:], cdf4_20/cdf4_20[-1], label = '20 pts, side',linestyle = ':', color = 'g')\n",
    "ax4.plot(bins4_20c[1:], cdf4_20c/cdf4_20c[-1], label = '20 pts, corner', color = 'g')\n",
    "ax4.plot(bins4_30[1:], cdf4_30/cdf4_30[-1], label = '30 pts, side',linestyle = ':', color = 'r')\n",
    "ax4.plot(bins4_30c[1:], cdf4_30c/cdf4_30c[-1], label = '30 pts, corner', color = 'r')\n",
    "\n",
    "ax4.set_title('Comparison of Scattering Environments for 4 Antenna Basestation')\n",
    "ax4.set_xlabel('FSPL, dB scale')\n",
    "ax4.grid()\n",
    "ax4.legend()\n",
    "\n",
    "fig8, ax8 = plt.subplots(figsize = (8,6))\n",
    "ax8.semilogy(bins8_10[1:], cdf8_10/cdf8_10[-1], label = '10 pts, side', linestyle = ':',color = 'b')\n",
    "ax8.plot(bins8_10c[1:], cdf8_10c/cdf8_10c[-1], label = '10 pts, corner', color = 'b')\n",
    "ax8.plot(bins8_20[1:], cdf8_20/cdf8_20[-1], label = '20 pts, side',linestyle = ':', color = 'g')\n",
    "ax8.plot(bins8_20c[1:], cdf8_20c/cdf8_20c[-1], label = '20 pts, corner', color = 'g')\n",
    "ax8.plot(bins8_30[1:], cdf8_30/cdf8_30[-1], label = '30 pts, side',linestyle = ':', color = 'r')\n",
    "ax8.plot(bins8_30c[1:], cdf8_30c/cdf8_30c[-1], label = '30 pts, corner', color = 'r')\n",
    "\n",
    "ax8.set_title('Comparison of Scattering Environments for 8 Antenna Basestation')\n",
    "ax8.set_xlabel('FSPL, dB scale')\n",
    "ax8.grid()\n",
    "ax8.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ant8_30c\n",
    "\n",
    "ant8_30pt_side1 = np.load('dBpaths\\\\8antennas30pts1Side_dB32.npy')\n",
    "ant8_30pt_corner1 = np.load('dBpaths\\\\8antennas30pts1Corner_dB32.npy')\n",
    "ant8_30pt_side2 = np.load('dBpaths\\\\8antennas30pts2Side_dB32.npy')\n",
    "ant8_30pt_corner2 = np.load('dBpaths\\\\8antennas30pts2Corner_dB32.npy')\n",
    "ant8_30pt_side3 = np.load('dBpaths\\\\8antennas30pts3Side_dB32.npy')\n",
    "ant8_30pt_corner3 = np.load('dBpaths\\\\8antennas30pts3Corner_dB32.npy')\n",
    "ant8_30pt_side4 = np.load('dBpaths\\\\8antennas30pts4Side_dB32.npy')\n",
    "ant8_30pt_corner4 = np.load('dBpaths\\\\8antennas30pts4Corner_dB32.npy')\n",
    "ant8_30pt_side5 = np.load('dBpaths\\\\8antennas30pts5Side_dB32.npy')\n",
    "ant8_30pt_corner5 = np.load('dBpaths\\\\8antennas30pts5Corner_dB32.npy')\n",
    "ant8_30pt_side6 = np.load('dBpaths\\\\8antennas30pts6Side_dB32.npy')\n",
    "ant8_30pt_corner6 = np.load('dBpaths\\\\8antennas30pts6Corner_dB32.npy')\n",
    "ant8_30pt_side7 = np.load('dBpaths\\\\8antennas30pts7Side_dB32.npy')\n",
    "ant8_30pt_corner7 = np.load('dBpaths\\\\8antennas30pts7Corner_dB32.npy')\n",
    "ant8_30pt_side8 = np.load('dBpaths\\\\8antennas30pts8Side_dB32.npy')\n",
    "ant8_30pt_corner8 = np.load('dBpaths\\\\8antennas30pts8Corner_dB32.npy')\n",
    "ant8_30pt_side9 = np.load('dBpaths\\\\8antennas30pts9Side_dB32.npy')\n",
    "ant8_30pt_corner9 = np.load('dBpaths\\\\8antennas30pts9Corner_dB32.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ant8_30pt_side1 = side8antenna + ant8_30pt_side1\n",
    "ant8_30s1 = ant8_30pt_side1*np.conj(ant8_30pt_side1)\n",
    "ant8_30pt_corner1 = corner8antenna + ant8_30pt_corner1\n",
    "ant8_30c1 = ant8_30pt_corner1*np.conj(ant8_30pt_corner1)\n",
    "\n",
    "ant8_30pt_side2 = side8antenna + ant8_30pt_side2\n",
    "ant8_30s2 = ant8_30pt_side2*np.conj(ant8_30pt_side2)\n",
    "ant8_30pt_corner2 = corner8antenna + ant8_30pt_corner2\n",
    "ant8_30c2 = ant8_30pt_corner2*np.conj(ant8_30pt_corner2)\n",
    "\n",
    "ant8_30pt_side3 = side8antenna + ant8_30pt_side3\n",
    "ant8_30s3 = ant8_30pt_side3*np.conj(ant8_30pt_side3)\n",
    "ant8_30pt_corner3 = corner8antenna + ant8_30pt_corner3\n",
    "ant8_30c3 = ant8_30pt_corner3*np.conj(ant8_30pt_corner3)\n",
    "\n",
    "ant8_30pt_side4 = side8antenna + ant8_30pt_side4\n",
    "ant8_30s4 = ant8_30pt_side4*np.conj(ant8_30pt_side4)\n",
    "ant8_30pt_corner4 = corner8antenna + ant8_30pt_corner4\n",
    "ant8_30c4 = ant8_30pt_corner4*np.conj(ant8_30pt_corner4)\n",
    "\n",
    "ant8_30pt_side5 = side8antenna + ant8_30pt_side5\n",
    "ant8_30s5 = ant8_30pt_side5*np.conj(ant8_30pt_side5)\n",
    "ant8_30pt_corner5 = corner8antenna + ant8_30pt_corner5\n",
    "ant8_30c5 = ant8_30pt_corner5*np.conj(ant8_30pt_corner5)\n",
    "\n",
    "ant8_30pt_side6 = side8antenna + ant8_30pt_side6\n",
    "ant8_30s6 = ant8_30pt_side6*np.conj(ant8_30pt_side6)\n",
    "ant8_30pt_corner6 = corner8antenna + ant8_30pt_corner6\n",
    "ant8_30c6 = ant8_30pt_corner6*np.conj(ant8_30pt_corner6)\n",
    "\n",
    "ant8_30pt_side7 = side8antenna + ant8_30pt_side7\n",
    "ant8_30s7 = ant8_30pt_side7*np.conj(ant8_30pt_side7)\n",
    "ant8_30pt_corner7 = corner8antenna + ant8_30pt_corner7\n",
    "ant8_30c7 = ant8_30pt_corner7*np.conj(ant8_30pt_corner7)\n",
    "\n",
    "ant8_30pt_side8 = side8antenna + ant8_30pt_side8\n",
    "ant8_30s8 = ant8_30pt_side8*np.conj(ant8_30pt_side8)\n",
    "ant8_30pt_corner8 = corner8antenna + ant8_30pt_corner8\n",
    "ant8_30c8 = ant8_30pt_corner8*np.conj(ant8_30pt_corner8)\n",
    "\n",
    "ant8_30pt_side8 = side8antenna + ant8_30pt_side8\n",
    "ant8_30s8 = ant8_30pt_side8*np.conj(ant8_30pt_side8)\n",
    "ant8_30pt_corner8 = corner8antenna + ant8_30pt_corner8\n",
    "ant8_30c8 = ant8_30pt_corner8*np.conj(ant8_30pt_corner8)\n",
    "\n",
    "ant8_30pt_side9 = side8antenna + ant8_30pt_side9\n",
    "ant8_30s9 = ant8_30pt_side9*np.conj(ant8_30pt_side9)\n",
    "ant8_30pt_corner9 = corner8antenna + ant8_30pt_corner9\n",
    "ant8_30c9 = ant8_30pt_corner9*np.conj(ant8_30pt_corner9)\n",
    "\n",
    "ant8_30c1 = sum_arrays(ant8_30c1, 8*4)\n",
    "ant8_30s1 = sum_arrays(ant8_30s1, 8*4)\n",
    "ant8_30c2 = sum_arrays(ant8_30c2, 8*4)\n",
    "ant8_30s2 = sum_arrays(ant8_30s2, 8*4)\n",
    "ant8_30c3 = sum_arrays(ant8_30c3, 8*4)\n",
    "ant8_30s3 = sum_arrays(ant8_30s3, 8*4)\n",
    "ant8_30c4 = sum_arrays(ant8_30c4, 8*4)\n",
    "ant8_30s4 = sum_arrays(ant8_30s4, 8*4)\n",
    "ant8_30c5 = sum_arrays(ant8_30c5, 8*4)\n",
    "ant8_30s5 = sum_arrays(ant8_30s5, 8*4)\n",
    "ant8_30c6 = sum_arrays(ant8_30c6, 8*4)\n",
    "ant8_30s6 = sum_arrays(ant8_30s6, 8*4)\n",
    "ant8_30c7 = sum_arrays(ant8_30c7, 8*4)\n",
    "ant8_30s7 = sum_arrays(ant8_30s7, 8*4)\n",
    "ant8_30c8 = sum_arrays(ant8_30c8, 8*4)\n",
    "ant8_30s8 = sum_arrays(ant8_30s8, 8*4)\n",
    "ant8_30c9 = sum_arrays(ant8_30c9, 8*4)\n",
    "ant8_30s9 = sum_arrays(ant8_30s9, 8*4)\n",
    "\n",
    "cdf8_30s1, bins8_30s1 = CDF_generation(10*np.log10(abs(ant8_30s1)))\n",
    "cdf8_30c1, bins8_30c1 = CDF_generation(10*np.log10(abs(ant8_30c1)))\n",
    "cdf8_30s2, bins8_30s2 = CDF_generation(10*np.log10(abs(ant8_30s2)))\n",
    "cdf8_30c2, bins8_30c2 = CDF_generation(10*np.log10(abs(ant8_30c2)))\n",
    "cdf8_30s3, bins8_30s3 = CDF_generation(10*np.log10(abs(ant8_30s3)))\n",
    "cdf8_30c3, bins8_30c3 = CDF_generation(10*np.log10(abs(ant8_30c3)))\n",
    "cdf8_30s4, bins8_30s4 = CDF_generation(10*np.log10(abs(ant8_30s4)))\n",
    "cdf8_30c4, bins8_30c4 = CDF_generation(10*np.log10(abs(ant8_30c4)))\n",
    "cdf8_30s5, bins8_30s5 = CDF_generation(10*np.log10(abs(ant8_30s5)))\n",
    "cdf8_30c5, bins8_30c5 = CDF_generation(10*np.log10(abs(ant8_30c5)))\n",
    "cdf8_30s6, bins8_30s6 = CDF_generation(10*np.log10(abs(ant8_30s6)))\n",
    "cdf8_30c6, bins8_30c6 = CDF_generation(10*np.log10(abs(ant8_30c6)))\n",
    "cdf8_30s7, bins8_30s7 = CDF_generation(10*np.log10(abs(ant8_30s7)))\n",
    "cdf8_30c7, bins8_30c7 = CDF_generation(10*np.log10(abs(ant8_30c7)))\n",
    "cdf8_30s8, bins8_30s8 = CDF_generation(10*np.log10(abs(ant8_30s8)))\n",
    "cdf8_30c8, bins8_30c8 = CDF_generation(10*np.log10(abs(ant8_30c8)))\n",
    "cdf8_30s9, bins8_30s9 = CDF_generation(10*np.log10(abs(ant8_30s9)))\n",
    "cdf8_30c9, bins8_30c9 = CDF_generation(10*np.log10(abs(ant8_30c9)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x2a381696108>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "fig8, ax8 = plt.subplots(figsize = (8,6))\n",
    "ax8.semilogy(bins8_30[1:], cdf8_30/cdf8_30[-1], label = 'set0, side', linestyle = ':', color = 'tab:blue')\n",
    "ax8.plot(bins8_30c[1:], cdf8_30c/cdf8_30c[-1], label = 'set0, corner',color = 'tab:blue')\n",
    "ax8.plot(bins8_30s1[1:], cdf8_30s1/cdf8_30s1[-1], label = 'set1, side', linestyle = ':',color = 'tab:red')\n",
    "ax8.plot(bins8_30c1[1:], cdf8_30c1/cdf8_30c1[-1], label = 'set1, corner',color = 'tab:red')\n",
    "ax8.plot(bins8_30s2[1:], cdf8_30s2/cdf8_30s2[-1], label = 'set2, side',linestyle = ':', color = 'tab:green')\n",
    "ax8.plot(bins8_30c2[1:], cdf8_30c2/cdf8_30c2[-1], label = 'set2, corner', color = 'tab:green')\n",
    "ax8.plot(bins8_30s3[1:], cdf8_30s3/cdf8_30s3[-1], label = 'set3, side',linestyle = ':', color = 'tab:purple')\n",
    "ax8.plot(bins8_30c3[1:], cdf8_30c3/cdf8_30c3[-1], label = 'set3, corner', color = 'tab:purple')\n",
    "ax8.plot(bins8_30s4[1:], cdf8_30s4/cdf8_30s4[-1], label = 'set4, side',linestyle = ':', color = 'tab:grey')\n",
    "ax8.plot(bins8_30c4[1:], cdf8_30c4/cdf8_30c4[-1], label = 'set4, corner', color = 'tab:grey')\n",
    "ax8.plot(bins8_30s5[1:], cdf8_30s5/cdf8_30s5[-1], label = 'set5, side',linestyle = ':', color = 'tab:pink')\n",
    "ax8.plot(bins8_30c5[1:], cdf8_30c5/cdf8_30c5[-1], label = 'set5, corner', color = 'tab:pink')\n",
    "ax8.plot(bins8_30s6[1:], cdf8_30s6/cdf8_30s6[-1], label = 'set6, side',linestyle = ':', color = 'tab:orange')\n",
    "ax8.plot(bins8_30c6[1:], cdf8_30c6/cdf8_30c6[-1], label = 'set6, corner', color = 'tab:orange')\n",
    "ax8.plot(bins8_30s7[1:], cdf8_30s7/cdf8_30s7[-1], label = 'set7, side',linestyle = ':', color = 'tab:olive')\n",
    "ax8.plot(bins8_30c7[1:], cdf8_30c7/cdf8_30c7[-1], label = 'set7, corner', color = 'tab:olive')\n",
    "\n",
    "ax8.plot(bins8_30s8[1:], cdf8_30s8/cdf8_30s8[-1], label = 'set8, side',linestyle = ':', color = 'tab:brown')\n",
    "ax8.plot(bins8_30c8[1:], cdf8_30c8/cdf8_30c8[-1], label = 'set8, corner', color = 'tab:brown')\n",
    "\n",
    "ax8.plot(bins8_30s9[1:], cdf8_30s9/cdf8_30s9[-1], label = 'set9, side',linestyle = ':', color = 'tab:cyan')\n",
    "ax8.plot(bins8_30c9[1:], cdf8_30c9/cdf8_30c9[-1], label = 'set9, corner', color = 'tab:cyan')\n",
    "\n",
    "ax8.set_title('Comparing Different 30 Scattering Point Layouts with 8 BS Elements')\n",
    "ax8.set_xlabel('FSPL, dB scale')\n",
    "ax8.grid()\n",
    "ax8.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "ax8.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ant8_20pt_side1 = np.load('dBpaths\\\\8antennas20pts1Side_dB32.npy')\n",
    "ant8_20pt_corner1 = np.load('dBpaths\\\\8antennas20pts1Corner_dB32.npy')\n",
    "ant8_20pt_side2 = np.load('dBpaths\\\\8antennas20pts2Side_dB32.npy')\n",
    "ant8_20pt_corner2 = np.load('dBpaths\\\\8antennas20pts2Corner_dB32.npy')\n",
    "ant8_20pt_side3 = np.load('dBpaths\\\\8antennas20pts3Side_dB32.npy')\n",
    "ant8_20pt_corner3 = np.load('dBpaths\\\\8antennas20pts3Corner_dB32.npy')\n",
    "ant8_20pt_side4 = np.load('dBpaths\\\\8antennas20pts4Side_dB32.npy')\n",
    "ant8_20pt_corner4 = np.load('dBpaths\\\\8antennas20pts4Corner_dB32.npy')\n",
    "ant8_20pt_side5 = np.load('dBpaths\\\\8antennas20pts5Side_dB32.npy')\n",
    "ant8_20pt_corner5 = np.load('dBpaths\\\\8antennas20pts5Corner_dB32.npy')\n",
    "ant8_20pt_side6 = np.load('dBpaths\\\\8antennas20pts6Side_dB32.npy')\n",
    "ant8_20pt_corner6 = np.load('dBpaths\\\\8antennas20pts6Corner_dB32.npy')\n",
    "ant8_20pt_side7 = np.load('dBpaths\\\\8antennas20pts7Side_dB32.npy')\n",
    "ant8_20pt_corner7 = np.load('dBpaths\\\\8antennas20pts7Corner_dB32.npy')\n",
    "ant8_20pt_side8 = np.load('dBpaths\\\\8antennas20pts8Side_dB32.npy')\n",
    "ant8_20pt_corner8 = np.load('dBpaths\\\\8antennas20pts8Corner_dB32.npy')\n",
    "ant8_20pt_side9 = np.load('dBpaths\\\\8antennas20pts9Side_dB32.npy')\n",
    "ant8_20pt_corner9 = np.load('dBpaths\\\\8antennas20pts9Corner_dB32.npy')\n",
    "\n",
    "ant8_20pt_side1 = side8antenna + ant8_20pt_side1\n",
    "ant8_20s1 = ant8_20pt_side1*np.conj(ant8_20pt_side1)\n",
    "ant8_20pt_corner1 = corner8antenna + ant8_20pt_corner1\n",
    "ant8_20c1 = ant8_20pt_corner1*np.conj(ant8_20pt_corner1)\n",
    "\n",
    "ant8_20pt_side2 = side8antenna + ant8_20pt_side2\n",
    "ant8_20s2 = ant8_20pt_side2*np.conj(ant8_20pt_side2)\n",
    "ant8_20pt_corner2 = corner8antenna + ant8_20pt_corner2\n",
    "ant8_20c2 = ant8_20pt_corner2*np.conj(ant8_20pt_corner2)\n",
    "\n",
    "ant8_20pt_side3 = side8antenna + ant8_20pt_side3\n",
    "ant8_20s3 = ant8_20pt_side3*np.conj(ant8_20pt_side3)\n",
    "ant8_20pt_corner3 = corner8antenna + ant8_20pt_corner3\n",
    "ant8_20c3 = ant8_20pt_corner3*np.conj(ant8_20pt_corner3)\n",
    "\n",
    "ant8_20pt_side4 = side8antenna + ant8_20pt_side4\n",
    "ant8_20s4 = ant8_20pt_side4*np.conj(ant8_20pt_side4)\n",
    "ant8_20pt_corner4 = corner8antenna + ant8_20pt_corner4\n",
    "ant8_20c4 = ant8_20pt_corner4*np.conj(ant8_20pt_corner4)\n",
    "\n",
    "ant8_20pt_side5 = side8antenna + ant8_20pt_side5\n",
    "ant8_20s5 = ant8_20pt_side5*np.conj(ant8_20pt_side5)\n",
    "ant8_20pt_corner5 = corner8antenna + ant8_20pt_corner5\n",
    "ant8_20c5 = ant8_20pt_corner5*np.conj(ant8_20pt_corner5)\n",
    "\n",
    "ant8_20pt_side6 = side8antenna + ant8_20pt_side6\n",
    "ant8_20s6 = ant8_20pt_side6*np.conj(ant8_20pt_side6)\n",
    "ant8_20pt_corner6 = corner8antenna + ant8_20pt_corner6\n",
    "ant8_20c6 = ant8_20pt_corner6*np.conj(ant8_20pt_corner6)\n",
    "\n",
    "ant8_20pt_side7 = side8antenna + ant8_20pt_side7\n",
    "ant8_20s7 = ant8_20pt_side7*np.conj(ant8_20pt_side7)\n",
    "ant8_20pt_corner7 = corner8antenna + ant8_20pt_corner7\n",
    "ant8_20c7 = ant8_20pt_corner7*np.conj(ant8_20pt_corner7)\n",
    "\n",
    "ant8_20pt_side8 = side8antenna + ant8_20pt_side8\n",
    "ant8_20s8 = ant8_20pt_side8*np.conj(ant8_20pt_side8)\n",
    "ant8_20pt_corner8 = corner8antenna + ant8_20pt_corner8\n",
    "ant8_20c8 = ant8_20pt_corner8*np.conj(ant8_20pt_corner8)\n",
    "\n",
    "ant8_20pt_side8 = side8antenna + ant8_20pt_side8\n",
    "ant8_20s8 = ant8_20pt_side8*np.conj(ant8_20pt_side8)\n",
    "ant8_20pt_corner8 = corner8antenna + ant8_20pt_corner8\n",
    "ant8_20c8 = ant8_20pt_corner8*np.conj(ant8_20pt_corner8)\n",
    "\n",
    "ant8_20pt_side9 = side8antenna + ant8_20pt_side9\n",
    "ant8_20s9 = ant8_20pt_side9*np.conj(ant8_20pt_side9)\n",
    "ant8_20pt_corner9 = corner8antenna + ant8_20pt_corner9\n",
    "ant8_20c9 = ant8_20pt_corner9*np.conj(ant8_20pt_corner9)\n",
    "\n",
    "ant8_20c1 = sum_arrays(ant8_20c1, 8*4)\n",
    "ant8_20s1 = sum_arrays(ant8_20s1, 8*4)\n",
    "ant8_20c2 = sum_arrays(ant8_20c2, 8*4)\n",
    "ant8_20s2 = sum_arrays(ant8_20s2, 8*4)\n",
    "ant8_20c3 = sum_arrays(ant8_20c3, 8*4)\n",
    "ant8_20s3 = sum_arrays(ant8_20s3, 8*4)\n",
    "ant8_20c4 = sum_arrays(ant8_20c4, 8*4)\n",
    "ant8_20s4 = sum_arrays(ant8_20s4, 8*4)\n",
    "ant8_20c5 = sum_arrays(ant8_20c5, 8*4)\n",
    "ant8_20s5 = sum_arrays(ant8_20s5, 8*4)\n",
    "ant8_20c6 = sum_arrays(ant8_20c6, 8*4)\n",
    "ant8_20s6 = sum_arrays(ant8_20s6, 8*4)\n",
    "ant8_20c7 = sum_arrays(ant8_20c7, 8*4)\n",
    "ant8_20s7 = sum_arrays(ant8_20s7, 8*4)\n",
    "ant8_20c8 = sum_arrays(ant8_20c8, 8*4)\n",
    "ant8_20s8 = sum_arrays(ant8_20s8, 8*4)\n",
    "ant8_20c9 = sum_arrays(ant8_20c9, 8*4)\n",
    "ant8_20s9 = sum_arrays(ant8_20s9, 8*4)\n",
    "\n",
    "cdf8_20s1, bins8_20s1 = CDF_generation(10*np.log10(abs(ant8_20s1)))\n",
    "cdf8_20c1, bins8_20c1 = CDF_generation(10*np.log10(abs(ant8_20c1)))\n",
    "cdf8_20s2, bins8_20s2 = CDF_generation(10*np.log10(abs(ant8_20s2)))\n",
    "cdf8_20c2, bins8_20c2 = CDF_generation(10*np.log10(abs(ant8_20c2)))\n",
    "cdf8_20s3, bins8_20s3 = CDF_generation(10*np.log10(abs(ant8_20s3)))\n",
    "cdf8_20c3, bins8_20c3 = CDF_generation(10*np.log10(abs(ant8_20c3)))\n",
    "cdf8_20s4, bins8_20s4 = CDF_generation(10*np.log10(abs(ant8_20s4)))\n",
    "cdf8_20c4, bins8_20c4 = CDF_generation(10*np.log10(abs(ant8_20c4)))\n",
    "cdf8_20s5, bins8_20s5 = CDF_generation(10*np.log10(abs(ant8_20s5)))\n",
    "cdf8_20c5, bins8_20c5 = CDF_generation(10*np.log10(abs(ant8_20c5)))\n",
    "cdf8_20s6, bins8_20s6 = CDF_generation(10*np.log10(abs(ant8_20s6)))\n",
    "cdf8_20c6, bins8_20c6 = CDF_generation(10*np.log10(abs(ant8_20c6)))\n",
    "cdf8_20s7, bins8_20s7 = CDF_generation(10*np.log10(abs(ant8_20s7)))\n",
    "cdf8_20c7, bins8_20c7 = CDF_generation(10*np.log10(abs(ant8_20c7)))\n",
    "cdf8_20s8, bins8_20s8 = CDF_generation(10*np.log10(abs(ant8_20s8)))\n",
    "cdf8_20c8, bins8_20c8 = CDF_generation(10*np.log10(abs(ant8_20c8)))\n",
    "cdf8_20s9, bins8_20s9 = CDF_generation(10*np.log10(abs(ant8_20s9)))\n",
    "cdf8_20c9, bins8_20c9 = CDF_generation(10*np.log10(abs(ant8_20c9)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x2a391956fc8>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "fig8, ax8 = plt.subplots(figsize = (8,6))\n",
    "ax8.semilogy(bins8_20[1:], cdf8_20/cdf8_20[-1], label = 'set0, side', linestyle = ':', color = 'tab:blue')\n",
    "ax8.plot(bins8_20c[1:], cdf8_20c/cdf8_20c[-1], label = 'set0, corner',color = 'tab:blue')\n",
    "ax8.plot(bins8_20s1[1:], cdf8_20s1/cdf8_20s1[-1], label = 'set1, side', linestyle = ':',color = 'tab:red')\n",
    "ax8.plot(bins8_20c1[1:], cdf8_20c1/cdf8_20c1[-1], label = 'set1, corner',color = 'tab:red')\n",
    "ax8.plot(bins8_20s2[1:], cdf8_20s2/cdf8_20s2[-1], label = 'set2, side',linestyle = ':', color = 'tab:green')\n",
    "ax8.plot(bins8_20c2[1:], cdf8_20c2/cdf8_20c2[-1], label = 'set2, corner', color = 'tab:green')\n",
    "ax8.plot(bins8_20s3[1:], cdf8_20s3/cdf8_20s3[-1], label = 'set3, side',linestyle = ':', color = 'tab:purple')\n",
    "ax8.plot(bins8_20c3[1:], cdf8_20c3/cdf8_20c3[-1], label = 'set3, corner', color = 'tab:purple')\n",
    "ax8.plot(bins8_20s4[1:], cdf8_20s4/cdf8_20s4[-1], label = 'set4, side',linestyle = ':', color = 'tab:grey')\n",
    "ax8.plot(bins8_20c4[1:], cdf8_20c4/cdf8_20c4[-1], label = 'set4, corner', color = 'tab:grey')\n",
    "ax8.plot(bins8_20s5[1:], cdf8_20s5/cdf8_20s5[-1], label = 'set5, side',linestyle = ':', color = 'tab:pink')\n",
    "ax8.plot(bins8_20c5[1:], cdf8_20c5/cdf8_20c5[-1], label = 'set5, corner', color = 'tab:pink')\n",
    "ax8.plot(bins8_20s6[1:], cdf8_20s6/cdf8_20s6[-1], label = 'set6, side',linestyle = ':', color = 'tab:orange')\n",
    "ax8.plot(bins8_20c6[1:], cdf8_20c6/cdf8_20c6[-1], label = 'set6, corner', color = 'tab:orange')\n",
    "ax8.plot(bins8_20s7[1:], cdf8_20s7/cdf8_20s7[-1], label = 'set7, side',linestyle = ':', color = 'tab:olive')\n",
    "ax8.plot(bins8_20c7[1:], cdf8_20c7/cdf8_20c7[-1], label = 'set7, corner', color = 'tab:olive')\n",
    "\n",
    "ax8.plot(bins8_20s8[1:], cdf8_20s8/cdf8_20s8[-1], label = 'set8, side',linestyle = ':', color = 'tab:brown')\n",
    "ax8.plot(bins8_20c8[1:], cdf8_20c8/cdf8_20c8[-1], label = 'set8, corner', color = 'tab:brown')\n",
    "\n",
    "ax8.plot(bins8_20s9[1:], cdf8_20s9/cdf8_20s9[-1], label = 'set9, side',linestyle = ':', color = 'tab:cyan')\n",
    "ax8.plot(bins8_20c9[1:], cdf8_20c9/cdf8_20c9[-1], label = 'set9, corner', color = 'tab:cyan')\n",
    "\n",
    "ax8.set_title('Comparing Different 20 Scattering Point Layouts with 8 BS Elements')\n",
    "ax8.set_xlabel('FSPL, dB scale')\n",
    "ax8.grid()\n",
    "ax8.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "ax8.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "def roomSections(array):\n",
    "    px = np.size(array, axis = 0)\n",
    "    py = np.size(array, axis = 1)\n",
    "    sect = 3\n",
    "    divx = (px+sect //2)//sect\n",
    "    divy = (py+sect//2)//sect\n",
    "    corner = array[4:divx, 4:divy]\n",
    "    center = array[divx+1:divx*2, divy+1:divy*2]\n",
    "    yside = array[4:divx, divy+1:divy*2]\n",
    "    xside = array[divx+1:divx*2, 4:divy]\n",
    "    return corner, center, yside, xside\n",
    "corner_ant8c, center_ant8c, yside_ant8c, xside_ant8c = roomSections(10*np.log10(corner8antenna))\n",
    "corner_counts_c, corner_bins_c = CDF_generation(corner_ant8c)\n",
    "center_counts_c, center_bins_c = CDF_generation(center_ant8c)\n",
    "yside_counts_c, yside_bins_c = CDF_generation(yside_ant8c)\n",
    "xside_counts_c, xside_bins_c = CDF_generation(xside_ant8c)\n",
    "\n",
    "corner_ant8s, center_ant8s, yside_ant8s, xside_ant8s = roomSections(10*np.log10(side8antenna))\n",
    "corner_counts_s, corner_bins_s = CDF_generation(corner_ant8s)\n",
    "center_counts_s, center_bins_s = CDF_generation(center_ant8s)\n",
    "yside_counts_s, yside_bins_s = CDF_generation(yside_ant8s)\n",
    "xside_counts_s, xside_bins_s = CDF_generation(xside_ant8s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x2a3919db408>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,6))\n",
    "ax.semilogy(corner_bins_c[1:], corner_counts_c/corner_counts_c[-1], label = 'Corner, Corner', color = 'blue')\n",
    "ax.plot(center_bins_c[1:], center_counts_c/center_counts_c[-1], label = 'Center, Corner', color = 'green')\n",
    "ax.plot(yside_bins_c[1:], yside_counts_c/yside_counts_c[-1], label = 'Y-Side, Corner', color = 'red')\n",
    "ax.plot(xside_bins_c[1:], xside_counts_c/xside_counts_c[-1], label = 'X-side, Corner', color = 'orange')\n",
    "ax.semilogy(corner_bins_s[1:], corner_counts_s/corner_counts_s[-1], label = 'Corner, Sidewall', color = 'blue', linestyle = ':')\n",
    "ax.plot(center_bins_s[1:], center_counts_s/center_counts_s[-1], label = 'Center, Sidewall', color = 'green', linestyle = ':')\n",
    "ax.plot(yside_bins_s[1:], yside_counts_s/yside_counts_s[-1], label = 'Y-Side, Sidewall', color = 'red', linestyle = ':')\n",
    "ax.plot(xside_bins_s[1:], xside_counts_s/xside_counts_s[-1], label = 'X-side, Sidewall', color = 'orange', linestyle = ':')\n",
    "ax.set_title('8 Antenna Element Base Station with LOS and Wall Reflection Room Sections')\n",
    "ax.set_xlabel('FSPL, dB scale')\n",
    "ax.grid()\n",
    "ax.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "TypeError",
     "evalue": "Illegal arguments to pcolormesh; see help(pcolormesh)",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-6757238ccd61>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfigp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0maxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpcolormesh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1597\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1598\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1599\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1600\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1601\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mpcolormesh\u001b[1;34m(self, alpha, norm, cmap, vmin, vmax, shading, antialiased, *args, **kwargs)\u001b[0m\n\u001b[0;32m   6163\u001b[0m         \u001b[0mallmatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mshading\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'gouraud'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6165\u001b[1;33m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mC\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pcolorargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'pcolormesh'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallmatch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mallmatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6166\u001b[0m         \u001b[0mNy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6167\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36m_pcolorargs\u001b[1;34m(funcname, allmatch, *args)\u001b[0m\n\u001b[0;32m   5735\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5736\u001b[0m             raise TypeError(\n\u001b[1;32m-> 5737\u001b[1;33m                 'Illegal arguments to %s; see help(%s)' % (funcname, funcname))\n\u001b[0m\u001b[0;32m   5738\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5739\u001b[0m         \u001b[0mNx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Illegal arguments to pcolormesh; see help(pcolormesh)"
     ]
    }
   ],
   "source": []
  }
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