MyAmSatInStem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AmSat to kindle STEM in India"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "We need a LEO Polar Sun Sync orbit AmSat with FM repeater and few sensors data monitor.\n",
    "Which inturn can be tracked and listened to by students at heart by using either a suitable FM receiver\n",
    "or better still a SDR reciever.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## a NOTE\n",
    "\n",
    "These are some initial thoughts in general, need to think/check\n",
    "\n",
    "Below are few simple minded calculations to get a very very rough initial idea of few things. It is more simple geometry and physics based and far removed from actual orbital characteristics. But still ...\n",
    "\n",
    "Also I have nevered looked into satelite orbits and its implications before, on top these are my initial thoughts, that too with simple minded modeling, so take these with a big pinch of salt ;-)\n",
    "\n",
    "Thus this is a simple stupid look at some aspects of a possible amsat. It uses basic geometry and physics to try and get some rough estimates wrt velocity, orbital period, eclipse time, time to cover equatorial circumference fully, rf path loss, doppler drift ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Earth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarthCircumference: 40212385.96594935\n"
     ]
    }
   ],
   "source": [
    "# Gravitational Constant (m^3 / (kg * s^2))\n",
    "iGravitationalConstant = (6.674 / 10**11)\n",
    "\n",
    "# Earth Radius (m)\n",
    "iEarthRadius = 6_400_000\n",
    "\n",
    "# Earth Mass (kg)\n",
    "iEarthMass = 5.972 * 10**24\n",
    "\n",
    "# Earth Circumference\n",
    "iEarthCircumference = 2 * math.pi * iEarthRadius\n",
    "print(\"EarthCircumference:\", iEarthCircumference)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orbit\n",
    "\n",
    "The Polar orbit should allow the satellite to cover most parts of the earth.\n",
    "\n",
    "If a orbit altitude is reached such that the velocity achieved can ensure that N (1 or more) full orbits can be finished in time T, such that T is 24 hours or its multiples, then the satellite will retrace the same path at the same relative time once every T hours i.e T/24 days. NOTE: Not sure if the LEO orbits is sufficiently stable enough over a long period of atleast few weeks, so that it can be used to have a simple tracking wrt the satellite for that period.\n",
    "\n",
    "If a sun synchronous orbit is achived or maintained, then depending on the angle wrt earth-sun orbital plane,\n",
    "\n",
    "* if it is 90 degrees, then the satellite could be placed always in day light, so that solar power is always available. However one needs to verify if the temperature of the satellite parts can be maintained well within reasonable operating range, in this case. Is the attitude maintaining rotation good enough or not or conductive heat distribution pipes/paths good enough or ...\n",
    "\n",
    "* if it is 0 degrees, then the satellite would be always at around roughly mid day or mid night wrt the places on earth, depending on which side of the earth wrt sun, that place is.\n",
    "\n",
    "The satellite may pass over a given ground location between 0 to 2 times under normal conditions for a polar sun synchronous orbit. Ideally this would be twice if the satellite (and its RF system) covers all parts of the earth fully in a day.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LEO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LEO orbit altitude wrt Earth Center: 6900000\n",
      "LEO Velocity: 7600.260102337841\n",
      "LeoCircumference: 43353978.61953914\n",
      "LeoOrbitTimeTaken(mins): 95.0712607984812\n",
      "NumOfSiteings in a Day: 15.146533115326104\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "103.36593937566968"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## LowEarthOrbit\n",
    "\n",
    "iLeoAltitudeAboveGround = 500_000\n",
    "iLeoAltitudeFromEarthCenter = iEarthRadius + iLeoAltitudeAboveGround\n",
    "print(\"LEO orbit altitude wrt Earth Center:\", iLeoAltitudeFromEarthCenter)\n",
    "\n",
    "# LEO Velocity\n",
    "#F = (GMm)/(r^2) = ma = m(v^2/r) => GM = r(v^2)\n",
    "iLeoVelocity = math.sqrt((iGravitationalConstant * iEarthMass)/iLeoAltitudeFromEarthCenter)\n",
    "print(\"LEO Velocity:\", iLeoVelocity)\n",
    "\n",
    "# Circumference of the orbit\n",
    "iLeoCircumference = 2 * math.pi * iLeoAltitudeFromEarthCenter\n",
    "print(\"LeoCircumference:\", iLeoCircumference)\n",
    "\n",
    "# Time taken for a single orbit\n",
    "iLeoOrbitTimeTaken = iLeoCircumference/iLeoVelocity\n",
    "print(\"LeoOrbitTimeTaken(mins):\", iLeoOrbitTimeTaken/60)\n",
    "\n",
    "# Num of Siteings in a day\n",
    "iDayInSecs = 24*60*60\n",
    "iNumOfSiteingsInADay = iDayInSecs/iLeoOrbitTimeTaken\n",
    "print(\"NumOfSiteings in a Day:\", iNumOfSiteingsInADay)\n",
    "iDayInSecs/(iDayInSecs%iLeoOrbitTimeTaken)\n",
    "#0.146*103\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Orbital Velocities\n",
    "\n",
    "iAltitudesAboveGround = numpy.linspace(0,10_000_000, 20)\n",
    "iAltitudesAboveGround = numpy.arange(0,35_500_000, 500_000)\n",
    "def orbitvelocity_from_altitude(altitudeFromCenter, theMainMass=iEarthMass):\n",
    "    return numpy.sqrt((iGravitationalConstant*theMainMass)/altitudeFromCenter)\n",
    "\n",
    "def orbittime_from_altitude(altitudeFromCenter, theMainMass=iEarthMass):\n",
    "    orbVel = orbitvelocity_from_altitude(altitudeFromCenter, theMainMass)\n",
    "    orbCirumference = 2 * math.pi * altitudeFromCenter\n",
    "    return orbCirumference/orbVel\n",
    "\n",
    "iOrbitVelocities = orbitvelocity_from_altitude(iAltitudesAboveGround+iEarthRadius)\n",
    "iOrbitTimes = orbittime_from_altitude(iAltitudesAboveGround+iEarthRadius)\n",
    "plt.plot(iAltitudesAboveGround/1000, iOrbitVelocities, \"+-\")\n",
    "#plt.plot(iAltitudesAboveGround/1000, iOrbitTimes, \"+-r\")\n",
    "plt.xlabel(\"AltAboveGnd[Km]\")\n",
    "plt.ylabel(\"OrbitVelocities[m/s]\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Earth coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarthCoverageRadius[@60]:[  115470.05383793   288675.13459481   577350.26918963 20784609.69082653]\n",
      "EarthCircumferenceAtEquator CoveredPerOrbit: [  461880.2153517   1154700.53837925  2309401.0767585  83138438.76330611]\n",
      "Minimum number of orbits required to cover earths equatorial circumference fully: [87.06236948 34.82494779 17.4124739   0.48367983]\n",
      "EarthMovementPerSecAtEquatorDueToRotation: 465.4211338651545\n",
      "EarthMovementPerOrbitAtEquatorDueToRotation: 2654890.439929136\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  17.39733619,   43.49334048,   86.98668096, 3131.52051448])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Earth Coverage and Altitude\n",
    "\n",
    "# Sin(Angle) = OppositeSide/Hypotinus\n",
    "# Cos(Angle) = AdjacentSide/Hypotinus\n",
    "# Tan(Angle) = Opposite/Adjacent\n",
    "# Opposite = Tan(Angle) * Adjacent\n",
    "# AdjacentSide = Altitude from surface\n",
    "# Angle = FieldOfView\n",
    "iSatAltitude = numpy.array([200_000, 500_000, 1_000_000, 36_000_000])\n",
    "iFieldOfView = 106 # Adjusted to sync NumOfSiteings/day with EarthEquatorialCircumferenceCoverage from 500KM\n",
    "iFieldOfView = 60\n",
    "iEarthCoverageRadius = numpy.tan(((iFieldOfView/2)/360)*math.pi*2)*iSatAltitude\n",
    "print(\"EarthCoverageRadius[@{}]:{}\".format(iFieldOfView, iEarthCoverageRadius))\n",
    "math.atan(iEarthRadius/(iEarthRadius+36_000_000)) * (360/(math.pi*2))\n",
    "math.atan(iEarthRadius/(iEarthRadius+500_000)) * (360/(math.pi*2))\n",
    "\n",
    "# Minimum number of orbits to cover the earths equatorial circumference\n",
    "iEarthCircumferenceAtEquatorCoveredPerOrbit = iEarthCoverageRadius*2*2\n",
    "print(\"EarthCircumferenceAtEquator CoveredPerOrbit:\", iEarthCircumferenceAtEquatorCoveredPerOrbit)\n",
    "iMinNumOfOrbits4Equator=iEarthCircumference/iEarthCircumferenceAtEquatorCoveredPerOrbit\n",
    "print(\"Minimum number of orbits required to cover earths equatorial circumference fully:\", iMinNumOfOrbits4Equator)\n",
    "\n",
    "# Movement of earth in a given time\n",
    "iEarthMovementPerSecAtEquatorDueToRotation = iEarthCircumference/iDayInSecs\n",
    "print(\"EarthMovementPerSecAtEquatorDueToRotation:\", iEarthMovementPerSecAtEquatorDueToRotation)\n",
    "iEarthMovementPerOrbitAtEquatorDueToRotation = iLeoOrbitTimeTaken * iEarthMovementPerSecAtEquatorDueToRotation\n",
    "print(\"EarthMovementPerOrbitAtEquatorDueToRotation:\", iEarthMovementPerOrbitAtEquatorDueToRotation)\n",
    "\n",
    "# Percent of required area covered per orbit\n",
    "(iEarthCircumferenceAtEquatorCoveredPerOrbit/iEarthMovementPerOrbitAtEquatorDueToRotation)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Return to Same point (roughly)\n",
    "\n",
    "### Earth Equatorial Circumference covered across orbits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EarthCircumferencePerDegree: 111701.07212763708\n",
      "EarthMovementPerOrbitAtEquatorInDegrees[500000]:23.7678\n",
      "Orbit: alt [400000] time [5580] FOVCvrgRadiusDegs [2.06748] cvrage% [72.3619] with [30] uniqOrbits\n",
      "Orbit: alt [450000] time [5642] FOVCvrgRadiusDegs [2.32592] cvrage% [120.172] with [45] uniqOrbits\n",
      "Orbit: alt [500000] time [5704] FOVCvrgRadiusDegs [2.58435] cvrage% [22.972] with [7] uniqOrbits\n",
      "Orbit: alt [550000] time [5766] FOVCvrgRadiusDegs [2.84279] cvrage% [25.2692] with [7] uniqOrbits\n",
      "Orbit: alt [600000] time [5828] FOVCvrgRadiusDegs [3.10123] cvrage% [27.5664] with [7] uniqOrbits\n",
      "Orbit: alt [650000] time [5891] FOVCvrgRadiusDegs [3.35966] cvrage% [166.117] with [43] uniqOrbits\n",
      "Orbit: alt [700000] time [5954] FOVCvrgRadiusDegs [3.6181] cvrage% [118.593] with [28] uniqOrbits\n",
      "Orbit: alt [750000] time [6017] FOVCvrgRadiusDegs [3.87653] cvrage% [94.7597] with [21] uniqOrbits\n",
      "Orbit: alt [800000] time [6080] FOVCvrgRadiusDegs [4.13497] cvrage% [165.399] with [35] uniqOrbits\n",
      "Orbit: alt [850000] time [6143] FOVCvrgRadiusDegs [4.3934] cvrage% [70.7826] with [13] uniqOrbits\n",
      "Orbit: alt [900000] time [6207] FOVCvrgRadiusDegs [4.65184] cvrage% [74.9463] with [13] uniqOrbits\n",
      "Orbit: alt [950000] time [6271] FOVCvrgRadiusDegs [4.91027] cvrage% [114.573] with [20] uniqOrbits\n",
      "Orbit: alt [1000000] time [6335] FOVCvrgRadiusDegs [5.16871] cvrage% [120.603] with [20] uniqOrbits\n",
      "Orbit: alt [1050000] time [6399] FOVCvrgRadiusDegs [5.42714] cvrage% [162.814] with [26] uniqOrbits\n",
      "Orbit: alt [1100000] time [6464] FOVCvrgRadiusDegs [5.68558] cvrage% [44.2212] with [6] uniqOrbits\n",
      "Orbit: alt [1150000] time [6529] FOVCvrgRadiusDegs [5.94401] cvrage% [46.2312] with [6] uniqOrbits\n",
      "Orbit: alt [1200000] time [6593] FOVCvrgRadiusDegs [6.20245] cvrage% [48.2413] with [6] uniqOrbits\n",
      "Orbit: alt [1250000] time [6659] FOVCvrgRadiusDegs [6.46089] cvrage% [50.2513] with [6] uniqOrbits\n",
      "Orbit: alt [1300000] time [6724] FOVCvrgRadiusDegs [6.71932] cvrage% [52.2614] with [6] uniqOrbits\n",
      "Orbit: alt [1350000] time [6790] FOVCvrgRadiusDegs [6.97776] cvrage% [54.2714] with [6] uniqOrbits\n",
      "Orbit: alt [1400000] time [6855] FOVCvrgRadiusDegs [7.23619] cvrage% [56.2815] with [6] uniqOrbits\n",
      "Orbit: alt [1450000] time [6921] FOVCvrgRadiusDegs [7.49463] cvrage% [58.2915] with [6] uniqOrbits\n",
      "Orbit: alt [1500000] time [6988] FOVCvrgRadiusDegs [7.75306] cvrage% [163.676] with [18] uniqOrbits\n",
      "Orbit: alt [1550000] time [7054] FOVCvrgRadiusDegs [8.0115] cvrage% [111.271] with [11] uniqOrbits\n",
      "Orbit: alt [1600000] time [7121] FOVCvrgRadiusDegs [8.26993] cvrage% [114.86] with [11] uniqOrbits\n",
      "Orbit: alt [1650000] time [7188] FOVCvrgRadiusDegs [8.52837] cvrage% [118.45] with [11] uniqOrbits\n",
      "Orbit: alt [1700000] time [7255] FOVCvrgRadiusDegs [8.7868] cvrage% [122.039] with [11] uniqOrbits\n",
      "Orbit: alt [1750000] time [7322] FOVCvrgRadiusDegs [9.04524] cvrage% [125.628] with [11] uniqOrbits\n",
      "Orbit: alt [1800000] time [7390] FOVCvrgRadiusDegs [9.30368] cvrage% [186.074] with [17] uniqOrbits\n",
      "Orbit: alt [1850000] time [7457] FOVCvrgRadiusDegs [9.56211] cvrage% [63.7474] with [5] uniqOrbits\n",
      "Orbit: alt [1900000] time [7525] FOVCvrgRadiusDegs [9.82055] cvrage% [65.4703] with [5] uniqOrbits\n",
      "Orbit: alt [1950000] time [7593] FOVCvrgRadiusDegs [10.079] cvrage% [67.1932] with [5] uniqOrbits\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 960x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'0b1000000'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Earth Equatorial Circumference covered across orbits\n",
    "\n",
    "\n",
    "bCoverageGraphics = False\n",
    "bDumpVistedEquatorPos = False\n",
    "\n",
    "\n",
    "def circular_pos(inPos):\n",
    "    inPos = (inPos % 360)\n",
    "    if inPos < 0:\n",
    "        inPos = 360 + inPos\n",
    "    return inPos\n",
    "\n",
    "\n",
    "def test_circular_pos():\n",
    "    for i in range(-520,520):\n",
    "        print(i, circular_pos(i))\n",
    "\n",
    "\n",
    "def dprint(msg, bDebug=False):\n",
    "    if bDebug:\n",
    "        print(msg)\n",
    "\n",
    "\n",
    "iEarthCircumferencePerDegree = iEarthCircumference/360\n",
    "print(\"EarthCircumferencePerDegree:\", iEarthCircumferencePerDegree)\n",
    "iEarthMovementPerOrbitAtEquatorInDegrees = iEarthMovementPerOrbitAtEquatorDueToRotation/iEarthCircumferencePerDegree\n",
    "print(\"EarthMovementPerOrbitAtEquatorInDegrees[%d]:%g\"%(iLeoAltitudeAboveGround, iEarthMovementPerOrbitAtEquatorInDegrees))\n",
    "\n",
    "\n",
    "'''\n",
    "def in_coverageradius(inPos, coverageRadiusInDegs):\n",
    "    startPos = sane_pos(inPos-coverageRadiusInDegs)\n",
    "    endPos = sane_pos(inPos+coverageRadiusInDegs)\n",
    "    ...\n",
    "'''\n",
    "def circular_diff(pos1, pos2):\n",
    "    '''\n",
    "    pos1 and pos2 need to be +ve and within circular range\n",
    "    '''\n",
    "    delta = pos1 - pos2\n",
    "    if delta > 180:\n",
    "        delta = delta - 360\n",
    "    if delta < -180:\n",
    "        delta = 360 + delta\n",
    "    return delta\n",
    "\n",
    "\n",
    "def circular_absdiff(pos1, pos2):\n",
    "    '''\n",
    "    pos1 and pos2 need to be +ve and within circular range\n",
    "    '''\n",
    "    delta = pos1 - pos2\n",
    "    if abs(delta) > 180:\n",
    "        delta = 360-abs(delta)\n",
    "    return abs(delta)\n",
    "\n",
    "\n",
    "def test_circular_diffs():\n",
    "    t1=circular_absdiff(270,89)\n",
    "    t2=circular_absdiff(89,270)\n",
    "    t3=circular_absdiff(359,0)\n",
    "    t4=circular_absdiff(0,359)\n",
    "    print(t1,t2,t3,t4)\n",
    "\n",
    "\n",
    "def earthcoverage_given_orbittime(orbitTime, coverageRadiusInDegs=0):\n",
    "    iEarthMovementPerOrbitAtEquator = orbitTime * iEarthMovementPerSecAtEquatorDueToRotation\n",
    "    iEarthMovementPerOrbitAtEquatorInDegrees = iEarthMovementPerOrbitAtEquator / iEarthCircumferencePerDegree\n",
    "    iMaxNumOrbits = 4096\n",
    "    iStartPos = 0\n",
    "    iCurPos = iStartPos\n",
    "    iVisited = set()\n",
    "    iVisitedL = []\n",
    "    for i in range(iMaxNumOrbits*2):\n",
    "        #print(iCurPos)\n",
    "        iVisited.add(round(iCurPos))\n",
    "        iVisitedL.append(round(iCurPos))\n",
    "        # Move to opposite during orbit\n",
    "        iCurPos = iCurPos + 180 - (iEarthMovementPerOrbitAtEquatorInDegrees/2)\n",
    "        iCurPos = circular_pos(iCurPos)\n",
    "        # Doesnt bother if the point being revisited is on sun facing or eclipsed side\n",
    "        if circular_absdiff(iStartPos, iCurPos) <= coverageRadiusInDegs:\n",
    "            iVisited.add(round(iCurPos))\n",
    "            iVisitedL.append(round(iCurPos))\n",
    "            dprint(\"Reached back to starting pos on %d th orbit\"%(i/2))\n",
    "            break\n",
    "    coverageMultiplier = 2*coverageRadiusInDegs\n",
    "    if coverageMultiplier == 0:\n",
    "        coverageMultiplier = 1\n",
    "    iPercentageCovered = (len(iVisited)/360)*coverageMultiplier*100\n",
    "    dprint(iVisited, False)\n",
    "    dprint(iVisitedL, False)\n",
    "    dprint(\"%d[/%d] unique orbits, covered %d %% of earth with FOVCvrageRadiusDegs of %g\"%(i/2, iMaxNumOrbits, iPercentageCovered, coverageRadiusInDegs), False)\n",
    "    return iPercentageCovered, i/2, iVisitedL\n",
    "\n",
    "\n",
    "lAlts = numpy.array([])\n",
    "lCvrd = numpy.array([])\n",
    "lUniqOrbits = numpy.array([])\n",
    "iFieldOfView=60\n",
    "for alt in range(400_000, 2_000_000, 50_000):\n",
    "    lAlts = numpy.append(lAlts, alt) # easy to infer\n",
    "    orbTime = orbittime_from_altitude(alt+iEarthRadius)\n",
    "    iFOVCoverageRadius = numpy.tan(((iFieldOfView/2)/360)*math.pi*2)*alt\n",
    "    iFOVCoverageRadiusInDegs = iFOVCoverageRadius / iEarthCircumferencePerDegree\n",
    "    cvrageP, uniqOrbits, lVisited = earthcoverage_given_orbittime(orbTime, iFOVCoverageRadiusInDegs)\n",
    "    lCvrd = numpy.append(lCvrd, cvrageP)\n",
    "    lUniqOrbits = numpy.append(lUniqOrbits, uniqOrbits)\n",
    "    print(\"Orbit: alt [%d] time [%d] FOVCvrgRadiusDegs [%g] cvrage%% [%g] with [%d] uniqOrbits\"%(alt, orbTime, iFOVCoverageRadiusInDegs, cvrageP, uniqOrbits))\n",
    "    if bDumpVistedEquatorPos:\n",
    "        print(\"DBUG:\", lVisited)\n",
    "    if bCoverageGraphics:\n",
    "        y = numpy.zeros(len(lVisited))\n",
    "        plt.plot(lVisited, y, \"+\")\n",
    "        plt.show()\n",
    "\n",
    "savedFigSize = plt.rcParams['figure.figsize']\n",
    "savedFigDpi = plt.rcParams['figure.dpi']\n",
    "plt.rcParams['figure.figsize'] = [8,4]\n",
    "plt.rcParams['figure.dpi'] = 120\n",
    "#plt.figure(figsize=(8,4), dpi=100)\n",
    "plt.subplots(1,2)\n",
    "plt.subplot(121)\n",
    "tCvrd = numpy.clip(lCvrd, 0, 100)\n",
    "plt.plot(lAlts/1000, tCvrd, \"+-\")\n",
    "plt.ylabel(\"Coverage%\")\n",
    "plt.xlabel(\"OrbitAlt[Km]\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(lAlts/1000, lUniqOrbits, \"+-\")\n",
    "plt.ylabel(\"UniqOrbitsB4Repeat\")\n",
    "plt.xlabel(\"OrbitAlt[Km]\")\n",
    "plt.show()\n",
    "plt.rcParams['figure.figsize'] = [4,3]\n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "\n",
    "bin(2**512-1)\n",
    "bin(1 << 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Find Orbit Radius given a Orbit Time\n",
    "\n",
    "iOrbitTime = iOrbitCircumference/iOrbitVelocity\n",
    "\n",
    "iOrbitCircumference = iOrbitTime * iOrbitVelocity\n",
    "\n",
    "2 * Pi * iOrbitRadius = iOrbitTime * sqrt((iGravitationalConstant * iEarthMass)/iOrbitRadius)\n",
    "\n",
    "4 * Pi^2 * iOrbitRadius^2 = iOrbitTime^2 * (iGravitationalConstant * iEarthMass) / iOrbitRadius\n",
    "\n",
    "iOrbitRadius^3 = (iOrbitTime^2 * iGravitationalConst * iEarthMass) / (4 * Pi^2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1658800.7683976134"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def orbitradius_giventime(orbitTime, theMainMass=iEarthMass, gravitationalConstant=iGravitationalConstant):\n",
    "    return numpy.cbrt((((orbitTime)**2)*gravitationalConstant*theMainMass)/(4*(numpy.pi**2)))\n",
    "\n",
    "orbitradius_giventime(iLeoOrbitTimeTaken)-iEarthRadius\n",
    "orbitradius_giventime(2*60*60)-iEarthRadius\n",
    "#orbitradius_giventime(1.41545*60*60)-iEarthRadius\n",
    "#1.41545*60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eclipse due to earth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Satellite could be Eclipsed by Earth for a minimum of around 28.06921479801882 mins per Orbit\n",
      "Satellite could be Eclipsed by Earth for around 35.94430921257815 mins per Orbit\n"
     ]
    }
   ],
   "source": [
    "## Show Earth and Satellite Orbit\n",
    "rads=numpy.linspace(0,numpy.pi*2,128)\n",
    "xE = numpy.sin(rads)*iEarthRadius/1000\n",
    "yE = numpy.cos(rads)*iEarthRadius/1000\n",
    "plt.plot(xE,yE,\"b\", label=\"Earth\")\n",
    "xS = numpy.sin(rads)*iLeoAltitudeFromEarthCenter/1000\n",
    "yS = numpy.cos(rads)*iLeoAltitudeFromEarthCenter/1000\n",
    "plt.plot(xS,yS,\"r.\", label=\"SatOrbit\")\n",
    "plt.plot([0,xS[24]],[0,yS[24]],\"g\")\n",
    "plt.plot([0,xS[-1-24]],[0,yS[-1-24]],\"g\")\n",
    "plt.plot([xS[24],xS[-1-24]], [yS[24],yS[-1-24]])\n",
    "#plt.plot([xS[-1-24],xS[-1-39]], [yS[-1-24],yS[-1-39]])\n",
    "plt.plot([-iEarthRadius/1000, -iEarthRadius/1000],[-iEarthRadius/1000,iEarthRadius/1000])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "'''\n",
    "\n",
    "## EvenMoreCrudeMath: rough amount of time for which satellite will be eclipsed by earth in a orbit\n",
    "iHalfOfEarthToLeoCircumference = (0.5*iEarthCircumference)/iLeoCircumference\n",
    "iMaxEclipseTime = iHalfOfEarthToLeoCircumference * iLeoOrbitTimeTaken\n",
    "print(\"Satellite could be Eclipsed by Earth for a max of {} mins per Orbit\".format(iMaxEclipseTime/60))\n",
    "# Actual Eclipse time will be smaller than this, as light bends over (among others...)\n",
    "\n",
    "'''\n",
    "\n",
    "## Blind, based on simple direct EarthDia\n",
    "iMinEclipseTime = (((iEarthRadius*2)/iLeoCircumference)*iLeoOrbitTimeTaken)\n",
    "#print(\"EarthDia:\", iEarthRadius*2)\n",
    "#print(\"LeoCircumference:\", iLeoCircumference)\n",
    "print(\"Satellite could be Eclipsed by Earth for a minimum of around {} mins per Orbit\".format(iMinEclipseTime/60))\n",
    "\n",
    "## Bit better based on circle segment\n",
    "halfAngle = math.asin(iEarthRadius/iLeoAltitudeFromEarthCenter)\n",
    "#iEclipsedCircumference = (2*halfAngle) * iLeoAltitudeFromEarthCenter\n",
    "iEclipsedCircumference = ((2*halfAngle)/(2*numpy.pi)) * iLeoCircumference\n",
    "iEclipseTime = ((iEclipsedCircumference/iLeoCircumference)*iLeoOrbitTimeTaken)\n",
    "print(\"Satellite could be Eclipsed by Earth for around {} mins per Orbit\".format(iEclipseTime/60))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Util functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dB wrt milliwatt normally\n",
    "def to_dBm(inWatts):\n",
    "    return 10 * math.log10(inWatts/0.001)\n",
    "\n",
    "# dB wrt isotropic antenna\n",
    "def to_dBi(inPower):\n",
    "    return 10 * math.log10(inPower/1)\n",
    "\n",
    "iLightSpeed = 300_000_000 # meters / sec\n",
    "\n",
    "def wavelen2freq(waveLen):\n",
    "    return iLightSpeed/waveLen\n",
    "\n",
    "def freq2wavelen(freq):\n",
    "    return iLightSpeed/freq\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RF Comm\n",
    "\n",
    "### RF Frequencies\n",
    "\n",
    "AmSats normally use the following bands\n",
    "\n",
    "* In V band = 145.8 - 146 MHz\n",
    "* In U band = 435 - 438 MHz\n",
    "\n",
    "The V band will be more crowded compared to U band.\n",
    "\n",
    "At same time V band has\n",
    "\n",
    "* less doppler effect (and so the related freq drift)\n",
    "* lesser rf path losses\n",
    "\n",
    "compared to the higher frequency U band.\n",
    "\n",
    "\n",
    "### RF Losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TransmitPower(dBm): 26.989700043360187\n",
      "Frequencies: [ 145800000  435000000  900000000 2400000000]\n",
      "PowerAtReciever_Rough(dBm):\n",
      " [[-102.70662271 -112.20125737 -118.51632242 -127.03569706]\n",
      " [-108.72722262 -118.22185728 -124.53692233 -133.05629698]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using Friis transmission formula\n",
    "# Pr = Pt * Dt * Dr * (Lambda/4*Pi*D)^2\n",
    "# Pr_dBm = Pt_dBm + Dt_dBi + Dr_dBi + 10*log10((Lambda/4*Pi*D)^2)\n",
    "# Pr_dBm = Pt_dBm + Dt_dBi + Dr_dBi + 20*log10(Lambda/4*Pi*D)\n",
    "# Pr_dBm = Pt_dBm + Dt_dBi + Dr_dBi + 20*log10(Lambda) - 20*log10(D) - 20*log10(1/4*Pi)\n",
    "#\n",
    "powerTransmitter = 0.5 # Watts; this is what is normal for LEO AmSats\n",
    "powerTransmitterDBm = to_dBm(powerTransmitter)\n",
    "print(\"TransmitPower(dBm):\", powerTransmitterDBm)\n",
    "directivityTransmitter = 1\n",
    "directivityTransmitterDBi = to_dBi(directivityTransmitter)\n",
    "directivityReciever = 1 # Change based on antenne system used\n",
    "directivityRecieverDBi = to_dBi(directivityReciever)\n",
    "frequencies = numpy.array([145_800_000, 435_000_000, 900_000_000, 2_400_000_000 ])\n",
    "print(\"Frequencies:\", frequencies)\n",
    "distances = numpy.array([[500_000],[1_000_000]])\n",
    "\n",
    "\n",
    "# This doesnt consider any losses through_the/due_to medium of propagation, nor multipath, nor ...\n",
    "# However as the path loss is the major factor given the distances involved, its used to get a rough feel\n",
    "def freespace_pathloss(Pt_dBm, Dt_dBi, Dr_dBi, frequencies, distances):\n",
    "    Pr_dBm = Pt_dBm + Dt_dBi + Dr_dBi + 20*numpy.log10(iLightSpeed/(4*numpy.pi*distances*frequencies))\n",
    "    return Pr_dBm\n",
    "\n",
    "\n",
    "powerRecieverDBm = freespace_pathloss(powerTransmitterDBm, directivityTransmitterDBi, directivityRecieverDBi,\n",
    "                                       frequencies, distances)\n",
    "print(\"PowerAtReciever_Rough(dBm):\\n\",powerRecieverDBm)\n",
    "plt.plot(frequencies, numpy.atleast_2d(powerRecieverDBm).T,\"+-\")\n",
    "plt.legend([\"500Km\", \"1000Km\"])\n",
    "plt.xlabel(\"Freqs\")\n",
    "plt.ylabel(\"RecieverDBm\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Doppler effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DopplerShifts[in Hz] for [145800000 436000000] is [ 3693.72640974 11045.71134873]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "commFreqs = numpy.array([145_800_000, 436_000_000])\n",
    "dopShifts = commFreqs*(iLeoVelocity/iLightSpeed)\n",
    "print(\"DopplerShifts[in Hz] for {} is {}\".format(commFreqs, dopShifts))\n",
    "dopShifts = frequencies*(iLeoVelocity/iLightSpeed)\n",
    "plt.plot(frequencies, dopShifts, \"+-\")\n",
    "plt.xlabel(\"Freqs\")\n",
    "plt.ylabel(\"Drift(Hz)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data comm\n",
    "\n",
    "In turn use a simple mechanism to transmit the sensor data like\n",
    "\n",
    "a) Simple Analog frequency modulation: Different frequencies for different values\n",
    "\n",
    "b) Analog frequency modulated tones for different decimal digits from 0 to 9, maybe each 200 Hz appart.\n",
    "\n",
    "c) Binary Digital Keying: Presence or absence of Frequency or Toggle btw 2 different sets of frequencies indicate between 0 and 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Misc\n",
    "\n",
    "#### FootNotes\n",
    "\n",
    "Will reside somewhere at https://github.com/hanishkvc"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
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}