RFI_Heat_MAP_clean_update.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85ad1fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "######## Importing the necessary libraries #########\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import healpy as hp\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f88468db",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude in degrees</th>\n",
       "      <th>Longitude in degrees</th>\n",
       "      <th>Frequency(MHz)</th>\n",
       "      <th>EIRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>50.947181</td>\n",
       "      <td>8.532986</td>\n",
       "      <td>87.6</td>\n",
       "      <td>164000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.509289</td>\n",
       "      <td>10.093932</td>\n",
       "      <td>87.6</td>\n",
       "      <td>131200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.832397</td>\n",
       "      <td>9.592726</td>\n",
       "      <td>87.6</td>\n",
       "      <td>41000.0</td>\n",
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       "      <td>49.629033</td>\n",
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       "      <td>87.6</td>\n",
       "      <td>820.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.681928</td>\n",
       "      <td>7.327415</td>\n",
       "      <td>87.6</td>\n",
       "      <td>328.0</td>\n",
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       "      <td>524.8</td>\n",
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       "      <td>102.1</td>\n",
       "      <td>328.0</td>\n",
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       "<p>13195 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Latitude in degrees  Longitude in degrees  Frequency(MHz)      EIRP\n",
       "0                50.947181              8.532986            87.6  164000.0\n",
       "1                53.509289             10.093932            87.6  131200.0\n",
       "2                49.832397              9.592726            87.6   41000.0\n",
       "3                49.629033              7.820645            87.6     820.0\n",
       "4                49.681928              7.327415            87.6     328.0\n",
       "...                    ...                   ...             ...       ...\n",
       "14102           -25.234722            130.991944            99.7     524.8\n",
       "14103           -25.234722            130.991944           100.5     524.8\n",
       "14104           -25.234722            130.991944           102.1     328.0\n",
       "14105           -25.234722            130.991944           105.3     656.0\n",
       "14106           -26.611389            149.383333            89.5      32.8\n",
       "\n",
       "[13195 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Fetching the cleaned FM transmitter data for the countries: CANADA, AUSTRALIA and GERMANY ####\n",
    "#####  EIRP in Watts #####\n",
    "##### Latitude ranges from 90(N.Pole) to 0 (EQUATOR) to -90(S.Pole) ########\n",
    "##### Longitude ranges between 0 and 360 degree eastwards ############\n",
    "\n",
    "\n",
    "#Reading the CSV file\n",
    "df=pd.read_csv(\"/home/ghoshsonia/rfi/Final_Revised.csv\")\n",
    "\n",
    "#Removing the Null/missing values in the CSV file\n",
    "df.dropna(subset = [\"Latitude in degrees\"], inplace=True)\n",
    "\n",
    "\n",
    "#df.set_index(\"Area Served\", inplace = True) # Set 'Area Served' column as index on a Dataframe\n",
    "#df=df.loc[[\"Perth\", \"Perth City\"]] # Using the operator .loc[] to select multiple rows from the Dataframe\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96de7962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13195, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ebc85229",
   "metadata": {},
   "outputs": [],
   "source": [
    "###### User defined frequency axis ######################\n",
    "fstart=87\n",
    "fstop=108\n",
    "fres=0.5\n",
    "freq=np.arange(fstart,fstop,fres)\n",
    "data_freq=df['Frequency(MHz)'].values\n",
    "diff=np.zeros((len(data_freq),len(freq)),dtype=object)\n",
    "freq_arr=np.zeros(len(data_freq))\n",
    "\n",
    "\n",
    "for i in range(len(data_freq)):\n",
    "    for j in range(len(freq)):\n",
    "        diff[i][j]=abs(data_freq[i]-freq[j])\n",
    "        freq_arr[i]=freq[np.argmin(diff[i])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc660640",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude in degrees</th>\n",
       "      <th>Longitude in degrees</th>\n",
       "      <th>Frequency(MHz)</th>\n",
       "      <th>EIRP</th>\n",
       "      <th>New Frequency</th>\n",
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       "      <td>164000.0</td>\n",
       "      <td>87.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.509289</td>\n",
       "      <td>10.093932</td>\n",
       "      <td>87.6</td>\n",
       "      <td>131200.0</td>\n",
       "      <td>87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.832397</td>\n",
       "      <td>9.592726</td>\n",
       "      <td>87.6</td>\n",
       "      <td>41000.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>87.6</td>\n",
       "      <td>820.0</td>\n",
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       "      <td>130.991944</td>\n",
       "      <td>99.7</td>\n",
       "      <td>524.8</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14103</th>\n",
       "      <td>-25.234722</td>\n",
       "      <td>130.991944</td>\n",
       "      <td>100.5</td>\n",
       "      <td>524.8</td>\n",
       "      <td>100.5</td>\n",
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       "      <th>14104</th>\n",
       "      <td>-25.234722</td>\n",
       "      <td>130.991944</td>\n",
       "      <td>102.1</td>\n",
       "      <td>328.0</td>\n",
       "      <td>102.0</td>\n",
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       "      <td>130.991944</td>\n",
       "      <td>105.3</td>\n",
       "      <td>656.0</td>\n",
       "      <td>105.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14106</th>\n",
       "      <td>-26.611389</td>\n",
       "      <td>149.383333</td>\n",
       "      <td>89.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>89.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13195 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Latitude in degrees  Longitude in degrees  Frequency(MHz)      EIRP  \\\n",
       "0                50.947181              8.532986            87.6  164000.0   \n",
       "1                53.509289             10.093932            87.6  131200.0   \n",
       "2                49.832397              9.592726            87.6   41000.0   \n",
       "3                49.629033              7.820645            87.6     820.0   \n",
       "4                49.681928              7.327415            87.6     328.0   \n",
       "...                    ...                   ...             ...       ...   \n",
       "14102           -25.234722            130.991944            99.7     524.8   \n",
       "14103           -25.234722            130.991944           100.5     524.8   \n",
       "14104           -25.234722            130.991944           102.1     328.0   \n",
       "14105           -25.234722            130.991944           105.3     656.0   \n",
       "14106           -26.611389            149.383333            89.5      32.8   \n",
       "\n",
       "       New Frequency  \n",
       "0               87.5  \n",
       "1               87.5  \n",
       "2               87.5  \n",
       "3               87.5  \n",
       "4               87.5  \n",
       "...              ...  \n",
       "14102           99.5  \n",
       "14103          100.5  \n",
       "14104          102.0  \n",
       "14105          105.5  \n",
       "14106           89.5  \n",
       "\n",
       "[13195 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['New Frequency']=freq_arr\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c525f940",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------Resolution of the map---------\n",
      "The number of pixels for the given NSIDE: 12288\n",
      "Approximate resolution in degrees for given nside: 1.8322594196359498\n",
      "Pixel area: 3.36 square degrees\n"
     ]
    }
   ],
   "source": [
    "#Resolution of the map\n",
    "print('-----------Resolution of the map---------') \n",
    "nside = 32\n",
    "print(\"The number of pixels for the given NSIDE: \" + str(hp.nside2npix(nside)))\n",
    "print(\"Approximate resolution in degrees for given nside: \" + str(np.degrees(hp.nside2resol(nside))))\n",
    "print( \"Pixel area: %.2f square degrees\" % hp.nside2pixarea(nside, degrees=True))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c365ea14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1302 1106 1406 ... 8686 8686 8821]\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude in degrees</th>\n",
       "      <th>Longitude in degrees</th>\n",
       "      <th>Frequency(MHz)</th>\n",
       "      <th>EIRP</th>\n",
       "      <th>New Frequency</th>\n",
       "      <th>Pixel_number</th>\n",
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       "      <td>87.6</td>\n",
       "      <td>164000.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>1302</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.509289</td>\n",
       "      <td>10.093932</td>\n",
       "      <td>87.6</td>\n",
       "      <td>131200.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>1106</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49.832397</td>\n",
       "      <td>9.592726</td>\n",
       "      <td>87.6</td>\n",
       "      <td>41000.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>1406</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.629033</td>\n",
       "      <td>7.820645</td>\n",
       "      <td>87.6</td>\n",
       "      <td>820.0</td>\n",
       "      <td>87.5</td>\n",
       "      <td>1406</td>\n",
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       "    <tr>\n",
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       "      <td>7.327415</td>\n",
       "      <td>87.6</td>\n",
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       "      <td>130.991944</td>\n",
       "      <td>99.7</td>\n",
       "      <td>524.8</td>\n",
       "      <td>99.5</td>\n",
       "      <td>8686</td>\n",
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       "    <tr>\n",
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       "      <td>-25.234722</td>\n",
       "      <td>130.991944</td>\n",
       "      <td>100.5</td>\n",
       "      <td>524.8</td>\n",
       "      <td>100.5</td>\n",
       "      <td>8686</td>\n",
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       "    <tr>\n",
       "      <th>14104</th>\n",
       "      <td>-25.234722</td>\n",
       "      <td>130.991944</td>\n",
       "      <td>102.1</td>\n",
       "      <td>328.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>8686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14105</th>\n",
       "      <td>-25.234722</td>\n",
       "      <td>130.991944</td>\n",
       "      <td>105.3</td>\n",
       "      <td>656.0</td>\n",
       "      <td>105.5</td>\n",
       "      <td>8686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14106</th>\n",
       "      <td>-26.611389</td>\n",
       "      <td>149.383333</td>\n",
       "      <td>89.5</td>\n",
       "      <td>32.8</td>\n",
       "      <td>89.5</td>\n",
       "      <td>8821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13195 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Latitude in degrees  Longitude in degrees  Frequency(MHz)      EIRP  \\\n",
       "0                50.947181              8.532986            87.6  164000.0   \n",
       "1                53.509289             10.093932            87.6  131200.0   \n",
       "2                49.832397              9.592726            87.6   41000.0   \n",
       "3                49.629033              7.820645            87.6     820.0   \n",
       "4                49.681928              7.327415            87.6     328.0   \n",
       "...                    ...                   ...             ...       ...   \n",
       "14102           -25.234722            130.991944            99.7     524.8   \n",
       "14103           -25.234722            130.991944           100.5     524.8   \n",
       "14104           -25.234722            130.991944           102.1     328.0   \n",
       "14105           -25.234722            130.991944           105.3     656.0   \n",
       "14106           -26.611389            149.383333            89.5      32.8   \n",
       "\n",
       "       New Frequency  Pixel_number  \n",
       "0               87.5          1302  \n",
       "1               87.5          1106  \n",
       "2               87.5          1406  \n",
       "3               87.5          1406  \n",
       "4               87.5          1406  \n",
       "...              ...           ...  \n",
       "14102           99.5          8686  \n",
       "14103          100.5          8686  \n",
       "14104          102.0          8686  \n",
       "14105          105.5          8686  \n",
       "14106           89.5          8821  \n",
       "\n",
       "[13195 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "######-------------------Allocating Pixel number to the Latitude and Longitude of each Tx in the CSV---------#######\n",
    "\n",
    "\n",
    "\n",
    "# Healpy pixel number when input angles are assumed to be longitude and latitude in degrees\n",
    "pixel_indices = hp.ang2pix(nside, df['Longitude in degrees'].to_numpy() ,df['Latitude in degrees'].to_numpy(),lonlat=True)\n",
    "df['Pixel_number']=pixel_indices \n",
    "print(pixel_indices)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f7f1195",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######---------------Conversion of the pixel numbers w.r.t the given NSIDE to corresponding angular coordinates--------#######\n",
    "\n",
    "\n",
    "NPIX = hp.nside2npix(nside) # Storing the number of pixels of the map corresponding to the given NSIDE\n",
    "arr=np.arange(NPIX) #Create an an array of pixel numbers with respect to the NSIDE\n",
    "\n",
    "theta, phi = (hp.pix2ang(nside, ipix=arr,lonlat=True)) # Array of the angular coordinates co-latitude(theta) \n",
    "                                                        #and longitude(phi) in degrees \n",
    "                                                       # With respect to the given NSIDE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2401509f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter number of data points : 3\n",
      "[  400.         3794.7331922 36000.       ]\n"
     ]
    }
   ],
   "source": [
    "#######------------------Altitudes of the satellite orbit--------####\n",
    "\n",
    "\n",
    "#Creating user defined data points between 400 km to 36000 km in log scale\n",
    "data_point=int(input(\"Enter number of data points : \"))\n",
    "altitude= np.logspace(np.log10(400),np.log10(36000),data_point) \n",
    "print(altitude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "350782ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The Field of view of the satellite at a height of 400.0000000000001 km is 2.4507006996152447 radians\n",
      " The Field of view of the satellite at a height of 3794.733192202054 km is 1.3546564445594882 radians\n",
      " The Field of view of the satellite at a height of 35999.99999999996 km is 0.30186940950576446 radians\n"
     ]
    }
   ],
   "source": [
    "###########----------------- Calculation of Field of view of the satellite---------#######\n",
    "###########-----------------Considering Nadir-pointing Field of View Geometry-------######\n",
    "##########-----------------Considering the FOV of the satellite to be tangent to the surface of the Earth------######\n",
    "\n",
    "\n",
    "FOV=np.zeros(len(altitude))\n",
    "for i in range(0,len(altitude)):\n",
    "# Consider a case of full coverage under elevation of 0 º\n",
    "    Rad= 6371 # Mean radius of Earth in km\n",
    "    FOV[i]= 2*np.arcsin(Rad/(Rad+ altitude[i]))  # Field of view for maximal coverage in radians when elevation is 0 º \n",
    "    print(\" The Field of view of the satellite at a height of\",altitude[i],\"km is\",FOV[i], \"radians\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "15ffcaa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The Radius of the Field of View for a height of 400.0000000000001 km in radians is 0.3454459769872743\n",
      " The Radius of the Field of View for a height of 3794.733192202054 km in radians is 0.8934681045151526\n",
      " The Radius of the Field of View for a height of 35999.99999999996 km in radians is 1.4198616220420144\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###########----------------- Calculation of the radius of the FOV of the satellite---------#######\n",
    "\n",
    "\n",
    "\n",
    "# The surface of the coverage area of the Earth depends on the central angle\n",
    "Central_angle=np.zeros(len(altitude))\n",
    "for i in range(0,len(altitude)):\n",
    "    Central_angle[i]=np.arccos(Rad/(Rad+altitude[i])) # Central angle in radians\n",
    "    \n",
    "    Dia_of_FOV=2*Central_angle*Rad  # Diameter of the FOV (disc on the Earth's surface)in km\n",
    "    Rad_of_FOV= Dia_of_FOV/2 # Radius of the FOV (disc on the Earth's surface)in km\n",
    "    Rad_of_FOV=Rad_of_FOV/Rad  # Radius of the FOV in Radians\n",
    "    print(\" The Radius of the Field of View for a height of\",altitude[i],\"km in radians is\",Rad_of_FOV[i])\n",
    "len(Rad_of_FOV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aca6c7e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "###############-----Storing indices of the pixel number that are inside the circle/disc(FOV) wrt the altitude-----------######\n",
    "\n",
    "\n",
    "vec1 = hp.ang2vec(theta,phi,lonlat=True) #Using ang2vec convert angles that is co-latitude and longitude in radians\n",
    "                                        #to 3D position vector\n",
    "    \n",
    "disc=np.zeros((len(arr),len(Rad_of_FOV)),dtype=object)#Array of indices of the pixel number that are inside the \n",
    "                                                      #circle/disc specified by vec and radius\n",
    "def pixel_disc(a,b):\n",
    "    for i in range(len(a)):\n",
    "        for j in range(len(b)):\n",
    "            disc[j][i]=hp.query_disc(nside, vec1[j], radius=a[i])\n",
    "pixel_disc(Rad_of_FOV,arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88aba10d",
   "metadata": {},
   "outputs": [
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       "[12288 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(disc)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9366dc5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "######-----Storing indices of the pixel number that are common between the FOV disc and the satellite pixel no------####\n",
    "\n",
    "\n",
    "Comm_pix=np.zeros((len(disc[:,i]),len(Rad_of_FOV)),dtype=object)\n",
    "\n",
    "for i in range(len(Rad_of_FOV)):\n",
    "    for k in range(len(disc[:,i])):\n",
    "     \n",
    "\n",
    "         Comm_pix[k][i]=np.intersect1d(pixel_indices,disc[k][i])#Array of indices of the pixel number that are\n",
    "                                                                #common between the FOV disc and the satellite pixel no\n",
    "         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "90858cd8",
   "metadata": {},
   "outputs": [
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       "12287                                                 []   \n",
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       "12285  [7141, 7154, 7155, 7279, 7283, 7394, 7406, 740...  \n",
       "12286  [7141, 7154, 7155, 7279, 7283, 7394, 7406, 740...  \n",
       "12287  [7279, 7283, 7394, 7406, 7407, 7408, 7410, 753...  \n",
       "\n",
       "[12288 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(Comm_pix)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a8c88507",
   "metadata": {},
   "outputs": [],
   "source": [
    "######-----Storing indices of the pixel number that are common between the FOV disc and the satellite pixel no ------#\n",
    "\n",
    "\n",
    "Comm_TX=np.zeros((len(Comm_pix[:,j]),len(Rad_of_FOV)),dtype=object)\n",
    "found_common=np.zeros((len(Comm_pix[:,j]),len(Rad_of_FOV)),dtype=object)\n",
    "\n",
    "for j in range(len(Rad_of_FOV)):\n",
    "    for i in range(len(Comm_pix[:,j])):\n",
    "        Comm_TX[i][j]=set(Comm_pix[i][j])\n",
    "        found_common[i][j] = [l for l in pixel_indices if l in Comm_TX[i][j]]#Array of indices of the pixel number that \n",
    "                                                    #are common between the FOV disc and the satellite pixel no with\n",
    "                                                    # Tx having same pixel number\n",
    "                                                                       \n",
    "         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e8172a1",
   "metadata": {},
   "outputs": [
    {
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       "      <td>[9971, 9971, 9713, 9713, 9713, 9713, 9713, 971...</td>\n",
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       "                                                       0  \\\n",
       "0                                        [253, 301, 209]   \n",
       "1                                        [253, 301, 209]   \n",
       "2      [401, 400, 399, 253, 403, 403, 403, 407, 301, ...   \n",
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       "4                                        [253, 301, 209]   \n",
       "...                                                  ...   \n",
       "12283                                                 []   \n",
       "12284                                                 []   \n",
       "12285                                                 []   \n",
       "12286                                                 []   \n",
       "12287                                                 []   \n",
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       "12287  [10228, 10228, 10228, 10354, 10354, 10354, 103...   \n",
       "\n",
       "                                                       2  \n",
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       "12283  [9971, 6947, 9971, 9713, 9713, 9713, 9713, 971...  \n",
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       "12287  [9971, 9971, 9713, 9713, 9713, 9713, 9713, 971...  \n",
       "\n",
       "[12288 rows x 3 columns]"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame(found_common)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a292035",
   "metadata": {},
   "outputs": [],
   "source": [
    "#######-------Store the values of the received power in Watt,dBm and Kelvin wrt altitude----#####\n",
    "######------- Calculation of the received power using Friis Transmission Equation--------#######\n",
    "######-------Considering isotropic transmitter and receiver with gain =1 -------------#########\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "res=fres*1e6 #Bandwidth\n",
    "Rx_Power= np.zeros((len(df),len(altitude)))\n",
    "Rx_Power_in_Kelvin=np.zeros((len(df),len(altitude)))\n",
    "for i in range(0,len(altitude)):\n",
    "    for j in range(0,len(df)):\n",
    "        wavelength= (3*1e8)/(df.iloc[j]['Frequency(MHz)']*1e6)\n",
    "        Rx_Power[j][i]= ((df.iloc[j]['EIRP'])*(wavelength)**2)/(4*np.pi*altitude[i])**2 #the Friis Transmission Equation\n",
    "        \n",
    "        Rx_Power_in_Kelvin[j][i]=Rx_Power[j][i]/(1.38*1e-23*res)#in Kelvin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "21586820",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>Pixel_number</th>\n",
       "      <th>New Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.103289e+16</td>\n",
       "      <td>1.225877e+14</td>\n",
       "      <td>1.362085e+12</td>\n",
       "      <td>1302</td>\n",
       "      <td>87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.826313e+15</td>\n",
       "      <td>9.807015e+13</td>\n",
       "      <td>1.089668e+12</td>\n",
       "      <td>1106</td>\n",
       "      <td>87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.758223e+15</td>\n",
       "      <td>3.064692e+13</td>\n",
       "      <td>3.405213e+11</td>\n",
       "      <td>1406</td>\n",
       "      <td>87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.516446e+13</td>\n",
       "      <td>6.129384e+11</td>\n",
       "      <td>6.810427e+09</td>\n",
       "      <td>1406</td>\n",
       "      <td>87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.206578e+13</td>\n",
       "      <td>2.451754e+11</td>\n",
       "      <td>2.724171e+09</td>\n",
       "      <td>1406</td>\n",
       "      <td>87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13190</th>\n",
       "      <td>2.725569e+13</td>\n",
       "      <td>3.028410e+11</td>\n",
       "      <td>3.364900e+09</td>\n",
       "      <td>8686</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13191</th>\n",
       "      <td>2.682350e+13</td>\n",
       "      <td>2.980389e+11</td>\n",
       "      <td>3.311543e+09</td>\n",
       "      <td>8686</td>\n",
       "      <td>100.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13192</th>\n",
       "      <td>1.624337e+13</td>\n",
       "      <td>1.804819e+11</td>\n",
       "      <td>2.005354e+09</td>\n",
       "      <td>8686</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13193</th>\n",
       "      <td>3.054223e+13</td>\n",
       "      <td>3.393582e+11</td>\n",
       "      <td>3.770646e+09</td>\n",
       "      <td>8686</td>\n",
       "      <td>105.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13194</th>\n",
       "      <td>2.113886e+12</td>\n",
       "      <td>2.348762e+10</td>\n",
       "      <td>2.609735e+08</td>\n",
       "      <td>8821</td>\n",
       "      <td>89.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13195 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0             1             2  Pixel_number  New Frequency\n",
       "0      1.103289e+16  1.225877e+14  1.362085e+12          1302           87.5\n",
       "1      8.826313e+15  9.807015e+13  1.089668e+12          1106           87.5\n",
       "2      2.758223e+15  3.064692e+13  3.405213e+11          1406           87.5\n",
       "3      5.516446e+13  6.129384e+11  6.810427e+09          1406           87.5\n",
       "4      2.206578e+13  2.451754e+11  2.724171e+09          1406           87.5\n",
       "...             ...           ...           ...           ...            ...\n",
       "13190  2.725569e+13  3.028410e+11  3.364900e+09          8686           99.5\n",
       "13191  2.682350e+13  2.980389e+11  3.311543e+09          8686          100.5\n",
       "13192  1.624337e+13  1.804819e+11  2.005354e+09          8686          102.0\n",
       "13193  3.054223e+13  3.393582e+11  3.770646e+09          8686          105.5\n",
       "13194  2.113886e+12  2.348762e+10  2.609735e+08          8821           89.5\n",
       "\n",
       "[13195 rows x 5 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9=pd.DataFrame(Rx_Power_in_Kelvin)\n",
    "df9['Pixel_number']=pixel_indices\n",
    "df9['New Frequency']=freq_arr\n",
    "#df9=df9.groupby(['New Frequency']).sum()\n",
    "\n",
    "df9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4253afa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df8=pd.DataFrame(Rx_Power_in_Kelvin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00b041ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "power_output=np.zeros((len(Rad_of_FOV),nside,len(freq_arr)),dtype=object)\n",
    "\n",
    "g=np.zeros(len(Rad_of_FOV),len(found_common))\n",
    "for j in range(len(Rad_of_FOV)):\n",
    "    for k in range(nside):\n",
    "        for l in range(len(freq_arr)):\n",
    "             if df9['Pixel_number'].isin(found_common[k][j]):\n",
    "                    df11=df9"
   ]
  }
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