Grav_Assist_Simulations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import csv\n",
    "import rebound\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display, clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching NASA Horizons for 'Sun'... Found: Sun (10).\n",
      "Searching NASA Horizons for 'Mercury'... Found: Mercury Barycenter (199).\n",
      "Searching NASA Horizons for 'Venus'... Found: Venus Barycenter (299).\n",
      "Searching NASA Horizons for 'Earth'... Found: Earth-Moon Barycenter (3).\n",
      "Searching NASA Horizons for 'Mars'... Found: Mars Barycenter (4).\n",
      "Searching NASA Horizons for 'Jupiter'... Found: Jupiter Barycenter (5).\n",
      "Searching NASA Horizons for 'Saturn'... Found: Saturn Barycenter (6).\n",
      "Searching NASA Horizons for 'Uranus'... Found: Uranus Barycenter (7).\n",
      "Searching NASA Horizons for 'Neptune'... Found: Neptune Barycenter (8).\n"
     ]
    }
   ],
   "source": [
    "sim = rebound.Simulation()\n",
    "choice_date = \"2011-08-11 13:00\"\n",
    "#\"2011-08-08 13:00\"\n",
    "\n",
    "\n",
    "sim.add([\"Sun\", \"Mercury\", \"Venus\", \"Earth\", \"Mars\", \"Jupiter\", \"Saturn\", \"Uranus\", \"Neptune\"],date=choice_date)\n",
    "sim.save(\"sim_bins/JUNO_ss.bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "choice_date2 = \"2012-08-08 13:00\"#\"2012-02-02 13:00\" #Feb 1 2012\n",
    "choice_date3 = \"2012-09-02 13:00\" #Aug 30 2012\n",
    "choice_date4 = \"2012-09-16 13:00\" #Sep 14 2012\n",
    "choice_date5_GRAVi = \"2013-10-07 13:00\" #Oct 8 2013\n",
    "choice_date5_GRAVf = \"2013-10-10 13:00\" #use this date for piecewise\n",
    "choice_date6 = \"2016-02-04 13:00\" #Feb 3 2016\n",
    "choice_date7 = \"2016-07-01 13:00\" #Jun 30 2016\n",
    "days1 = 365 #170 \n",
    "days2 = 207\n",
    "days3 = 10\n",
    "days4 = 382 #16 sep 2012 to 7 oct 2013\n",
    "days5 = 840\n",
    "days6 = 144"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching NASA Horizons for 'Sun'... Found: Sun (10).\n",
      "Searching NASA Horizons for 'Mercury'... Found: Mercury Barycenter (199).\n",
      "Searching NASA Horizons for 'Venus'... Found: Venus Barycenter (299).\n",
      "Searching NASA Horizons for 'Earth'... Found: Earth-Moon Barycenter (3).\n",
      "Searching NASA Horizons for 'Mars'... Found: Mars Barycenter (4).\n",
      "Searching NASA Horizons for 'Jupiter'... Found: Jupiter Barycenter (5).\n",
      "Searching NASA Horizons for 'Saturn'... Found: Saturn Barycenter (6).\n",
      "Searching NASA Horizons for 'Uranus'... Found: Uranus Barycenter (7).\n",
      "Searching NASA Horizons for 'Neptune'... Found: Neptune Barycenter (8).\n",
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n",
      "Searching NASA Horizons for 'Sun'... "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/rebound/horizons.py:137: RuntimeWarning: Warning: Mass cannot be retrieved from NASA HORIZONS. Set to 0.\n",
      "  warnings.warn(\"Warning: Mass cannot be retrieved from NASA HORIZONS. Set to 0.\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found: Sun (10).\n",
      "Searching NASA Horizons for 'Mercury'... Found: Mercury Barycenter (199).\n",
      "Searching NASA Horizons for 'Venus'... Found: Venus Barycenter (299).\n",
      "Searching NASA Horizons for 'Earth'... Found: Earth-Moon Barycenter (3).\n",
      "Searching NASA Horizons for 'Mars'... Found: Mars Barycenter (4).\n",
      "Searching NASA Horizons for 'Jupiter'... Found: Jupiter Barycenter (5).\n",
      "Searching NASA Horizons for 'Saturn'... Found: Saturn Barycenter (6).\n",
      "Searching NASA Horizons for 'Uranus'... Found: Uranus Barycenter (7).\n",
      "Searching NASA Horizons for 'Neptune'... Found: Neptune Barycenter (8).\n",
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n",
      "Searching NASA Horizons for 'Sun'... Found: Sun (10).\n",
      "Searching NASA Horizons for 'Mercury'... Found: Mercury Barycenter (199).\n",
      "Searching NASA Horizons for 'Venus'... Found: Venus Barycenter (299).\n",
      "Searching NASA Horizons for 'Earth'... Found: Earth-Moon Barycenter (3).\n",
      "Searching NASA Horizons for 'Mars'... Found: Mars Barycenter (4).\n",
      "Searching NASA Horizons for 'Jupiter'... Found: Jupiter Barycenter (5).\n",
      "Searching NASA Horizons for 'Saturn'... Found: Saturn Barycenter (6).\n",
      "Searching NASA Horizons for 'Uranus'... Found: Uranus Barycenter (7).\n",
      "Searching NASA Horizons for 'Neptune'... Found: Neptune Barycenter (8).\n",
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n",
      "Searching NASA Horizons for 'Sun'... Found: Sun (10).\n",
      "Searching NASA Horizons for 'Mercury'... Found: Mercury Barycenter (199).\n",
      "Searching NASA Horizons for 'Venus'... Found: Venus Barycenter (299).\n",
      "Searching NASA Horizons for 'Earth'... Found: Earth-Moon Barycenter (3).\n",
      "Searching NASA Horizons for 'Mars'... Found: Mars Barycenter (4).\n",
      "Searching NASA Horizons for 'Jupiter'... Found: Jupiter Barycenter (5).\n",
      "Searching NASA Horizons for 'Saturn'... Found: Saturn Barycenter (6).\n",
      "Searching NASA Horizons for 'Uranus'... Found: Uranus Barycenter (7).\n",
      "Searching NASA Horizons for 'Neptune'... Found: Neptune Barycenter (8).\n",
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n",
      "Searching NASA Horizons for 'Sun'... Found: Sun (10).\n",
      "Searching NASA Horizons for 'Mercury'... Found: Mercury Barycenter (199).\n",
      "Searching NASA Horizons for 'Venus'... Found: Venus Barycenter (299).\n",
      "Searching NASA Horizons for 'Earth'... Found: Earth-Moon Barycenter (3).\n",
      "Searching NASA Horizons for 'Mars'... Found: Mars Barycenter (4).\n",
      "Searching NASA Horizons for 'Jupiter'... Found: Jupiter Barycenter (5).\n",
      "Searching NASA Horizons for 'Saturn'... Found: Saturn Barycenter (6).\n",
      "Searching NASA Horizons for 'Uranus'... Found: Uranus Barycenter (7).\n",
      "Searching NASA Horizons for 'Neptune'... Found: Neptune Barycenter (8).\n",
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n",
      "Searching NASA Horizons for 'Sun'... Found: Sun (10).\n",
      "Searching NASA Horizons for 'Mercury'... Found: Mercury Barycenter (199).\n",
      "Searching NASA Horizons for 'Venus'... Found: Venus Barycenter (299).\n",
      "Searching NASA Horizons for 'Earth'... Found: Earth-Moon Barycenter (3).\n",
      "Searching NASA Horizons for 'Mars'... Found: Mars Barycenter (4).\n",
      "Searching NASA Horizons for 'Jupiter'... Found: Jupiter Barycenter (5).\n",
      "Searching NASA Horizons for 'Saturn'... Found: Saturn Barycenter (6).\n",
      "Searching NASA Horizons for 'Uranus'... Found: Uranus Barycenter (7).\n",
      "Searching NASA Horizons for 'Neptune'... Found: Neptune Barycenter (8).\n",
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n"
     ]
    }
   ],
   "source": [
    "sim2 = rebound.Simulation()\n",
    "sim2.add([\"Sun\", \"Mercury\", \"Venus\", \"Earth\", \"Mars\", \"Jupiter\", \"Saturn\", \"Uranus\", \"Neptune\"],date=choice_date2)\n",
    "sim2.add(\"Juno\",date=choice_date2)\n",
    "sim2.particles[-1].m = 1.823e-27\n",
    "sim2.save(\"sim_bins/JUNO_ss2.bin\")\n",
    "sim3 = rebound.Simulation()\n",
    "sim3.add([\"Sun\", \"Mercury\", \"Venus\", \"Earth\", \"Mars\", \"Jupiter\", \"Saturn\", \"Uranus\", \"Neptune\"],date=choice_date3)\n",
    "sim3.add(\"Juno\",date=choice_date3)\n",
    "sim3.particles[-1].m = 1.823e-27\n",
    "sim3.save(\"sim_bins/JUNO_ss3.bin\")\n",
    "sim4 = rebound.Simulation()\n",
    "sim4.add([\"Sun\", \"Mercury\", \"Venus\", \"Earth\", \"Mars\", \"Jupiter\", \"Saturn\", \"Uranus\", \"Neptune\"],date=choice_date4)\n",
    "sim4.add(\"Juno\",date=choice_date4)\n",
    "sim4.particles[-1].m = 1.823e-27\n",
    "sim4.save(\"sim_bins/JUNO_ss4.bin\")\n",
    "sim5 = rebound.Simulation()\n",
    "sim5.add([\"Sun\", \"Mercury\", \"Venus\", \"Earth\", \"Mars\", \"Jupiter\", \"Saturn\", \"Uranus\", \"Neptune\"],date=choice_date5_GRAVf)\n",
    "sim5.add(\"Juno\",date=choice_date5_GRAVf)\n",
    "sim5.particles[-1].m = 1.823e-27\n",
    "sim5.save(\"sim_bins/JUNO_ss5.bin\")\n",
    "sim6 = rebound.Simulation()\n",
    "sim6.add([\"Sun\", \"Mercury\", \"Venus\", \"Earth\", \"Mars\", \"Jupiter\", \"Saturn\", \"Uranus\", \"Neptune\"],date=choice_date6)\n",
    "sim6.add(\"Juno\",date=choice_date6)\n",
    "sim6.particles[-1].m = 1.823e-27\n",
    "sim6.save(\"sim_bins/JUNO_ss6.bin\")\n",
    "sim7 = rebound.Simulation()\n",
    "sim7.add([\"Sun\", \"Mercury\", \"Venus\", \"Earth\", \"Mars\", \"Jupiter\", \"Saturn\", \"Uranus\", \"Neptune\"],date=choice_date7)\n",
    "sim7.add(\"Juno\",date=choice_date7)\n",
    "sim7.particles[-1].m = 1.823e-27\n",
    "sim7.save(\"sim_bins/JUNO_ss7.bin\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = rebound.Simulation(\"sim_bins/juno.bin\")\n",
    "sim2 = rebound.Simulation(\"sim_bins/JUNO_ss2.bin\")\n",
    "sim3 = rebound.Simulation(\"sim_bins/JUNO_ss3.bin\")\n",
    "sim4 = rebound.Simulation(\"sim_bins/JUNO_ss4.bin\")\n",
    "sim5 = rebound.Simulation(\"sim_bins/JUNO_ss5.bin\")\n",
    "sim6 = rebound.Simulation(\"sim_bins/JUNO_ss6.bin\")\n",
    "sim7 = rebound.Simulation(\"sim_bins/JUNO_ss7.bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n"
     ]
    }
   ],
   "source": [
    "sim = rebound.Simulation(\"sim_bins/JUNO_ss.bin\")\n",
    "sim.add(\"Juno\",date=choice_date)\n",
    "sim.particles[-1].m = 1.823e-27\n",
    "sim.save(\"sim_bins/juno.bin\")\n",
    "#sim.add(\"Rosetta\",date=\"2011-08-08 13:00\")\n",
    "#sim.add(\"Cassini\",date=\"2011-08-08 13:00\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = rebound.Simulation(\"sim_bins/juno.bin\")\n",
    "sim.move_to_com()\n",
    "#sim.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "days1 = 170\n",
    "\n",
    "choice_date2 = \"2012-02-02 13:00\"#\"2012-02-02 13:00\" #Feb 1 2012\n",
    "choice_date3 = \"2012-09-02 13:00\" #Aug 30 2012\n",
    "choice_date4 = \"2012-09-15 13:00\" #Sep 14 2012\n",
    "choice_date5_GRAVi = \"2013-10-07 13:00\" #Oct 8 2013\n",
    "choice_date5_GRAVf = \"2013-10-10 13:00\" #use this date for piecewise\n",
    "choice_date6 = \"2016-02-04 13:00\" #Feb 3 2016\n",
    "choice_date7 = \"2016-07-01 13:00\" #Jun 30 2016\n",
    " \n",
    "days2 = 207\n",
    "days3 = 10\n",
    "days4 = 370 #382 #16 sep 2012 to 7 oct 2013\n",
    "days5 = 720 #840\n",
    "days6 = 60 #144"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_x = []\n",
    "sun_y = []\n",
    "mer_x = []\n",
    "mer_y = []\n",
    "ven_x = []\n",
    "ven_y = []\n",
    "ear_x = []\n",
    "ear_y = []\n",
    "mar_x = []\n",
    "mar_y = []\n",
    "jup_x = []\n",
    "jup_y = []\n",
    "sat_x = []\n",
    "sat_y = []\n",
    "ura_x = []\n",
    "ura_y = []\n",
    "nep_x = []\n",
    "nep_y = []\n",
    "jun_x = []\n",
    "jun_y = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "REBOUND version:     \t3.12.1\n",
      "REBOUND built on:    \tFeb 13 2020 23:13:03\n",
      "Number of particles: \t10\n",
      "Selected integrator: \tias15\n",
      "Simulation time:     \t3.2921369171679213e+00\n",
      "Current timestep:    \t0.016749\n",
      "---------------------------------\n",
      "<rebound.Particle object, m=1.0 x=-0.003022961296390099 y=-0.0015487982365477214 z=-5.26965846154002e-06 vx=0.0002710238040208727 vy=-0.0002450077455551605 vz=-5.611281010567239e-06>\n",
      "<rebound.Particle object, m=1.6601141530543488e-07 x=0.3418278869568142 y=0.03575083219102968 z=-0.028599229036219222 vx=-0.49154054052956464 vy=1.6990019435425054 vz=0.18395807619088764>\n",
      "<rebound.Particle object, m=2.4478382877847715e-06 x=0.10844835120125082 y=0.7099672983418682 z=0.00330981025894724 vx=-1.1653302608114253 vy=0.1758661148843616 vz=0.06967816400221115>\n",
      "<rebound.Particle object, m=3.040432648022642e-06 x=-0.8545181863129622 y=0.5002627474287693 z=-1.8171387553521535e-05 vx=-0.5237883345068156 vy=-0.86564386339696 vz=2.066323594521255e-05>\n",
      "<rebound.Particle object, m=3.2271560375549977e-07 x=-1.540347251632397 y=0.6382676762550629 z=0.051148488347821305 vx=-0.2817465202010985 vy=-0.6818708146934216 vz=-0.007362865801381571>\n",
      "<rebound.Particle object, m=0.0009547919152112404 x=3.495696944710834 y=3.5430601862406155 z=-0.09301955978023388 vx=-0.3175307245549332 vy=0.32909442628114655 vz=0.005738482425228279>\n",
      "<rebound.Particle object, m=0.0002858856727222417 x=-8.849939410382762 y=-3.971858833678697 z=0.4212584192752642 vx=0.1152766451371376 vy=-0.29660339650722606 vz=0.0005710944778038315>\n",
      "<rebound.Particle object, m=4.36624373583127e-05 x=20.0212713442771 y=1.3882110946749497 z=-0.25423127519695615 vx=-0.01748826494912862 vy=0.21743355134804895 vz=0.0010344991754684294>\n",
      "<rebound.Particle object, m=5.151383772628674e-05 x=26.089387601159174 y=-14.805205313608298 z=-0.29637109721308813 vx=0.08885403162390892 vy=0.15979138382399918 vz=-0.00533834874139847>\n",
      "<rebound.Particle object, m=1.823e-27 x=-0.09508363044320332 y=1.838872140241863 z=-0.0013809476423311672 vx=-0.6528473579346167 vy=0.21956019770568078 vz=0.0003733393454009819>\n",
      "---------------------------------\n",
      "0.0167488603315798\n",
      "[0.7555840235517783, 0.7626227021830068, 0.7694598217412785, 0.7770625914261967, 0.7854476736014246, 0.7946924718844969, 0.8049933391642661, 0.8165089174401293, 0.8280286598151827, 0.8389675212626895, 0.8494264667433188, 0.8595367894128185, 0.8688592921483673, 0.8774684217885339, 0.8855240738042617, 0.8931485033621479, 0.9004634091530141, 0.9074210688587423, 0.9137901929975593, 0.9197233713701175, 0.9253371613489414, 0.9307328241435979, 0.9358468181432349, 0.9405294519187497, 0.9448997375647235, 0.9490473022480195, 0.9531383371070051, 0.9570038352159468, 0.9606354506474304, 0.9641045174835801, 0.9674741060285535, 0.9707520323741198, 0.9737691715924225, 0.9766047787593256, 0.9793133050940579, 0.9819435572620538, 0.9843522964134316, 0.9865666548036317, 0.9886294628060128, 0.9905700437977921, 0.9923652666979023, 0.9939423257713368, 0.9953376544782404, 0.996567355702488, 0.9976375600362789, 0.9985342839015366, 0.9992226886306528, 0.9997044565154098, 0.9999677733800975, 0.9999885579051639, 0.9997263545654977, 0.9991663824226666, 0.9982943689935456, 0.9970813242435633, 0.9954844329850007, 0.9934460503201606, 0.99088170193301, 0.9878321033877117, 0.9842896234343543, 0.9801975049946721, 0.9754907395705185, 0.9700926240235715, 0.9639092021418416, 0.9568234051580745, 0.9486913043254762, 0.9397231951307312, 0.9296693141620872, 0.918393178780859, 0.9057516503627663, 0.8916055166148927, 0.8758632881148405, 0.85856320030459, 0.8399838563846627, 0.8206345867266166, 0.8010323615601577, 0.7815144791043694, 0.7622379678285572, 0.7432513775835293, 0.7245444683460147, 0.7060771222308022, 0.6877725066080531, 0.6695139858980775, 0.65221312298664, 0.6354703228521504, 0.6190195973316, 0.6027579993181233, 0.5865765751440235, 0.5703325940711266, 0.5540575070730942, 0.5384798592429912, 0.5233872686865769, 0.5085833986488901, 0.4938932989118775, 0.4790963967907277, 0.4647424827647261, 0.45094089303851315, 0.43745525945195773, 0.42407353152931526, 0.41055773104249704, 0.3975134338420445, 0.38495634168660675, 0.3726463148506369, 0.3602741354273353, 0.3474915305411268, 0.33507486707456796, 0.3227626735872511, 0.3103235755834911, 0.2975041929056388, 0.2846935135195128, 0.2720919345537396, 0.2594387775902748, 0.24648177399486348, 0.23315936640256849, 0.22013088559444577, 0.20710724291600924, 0.19384522334553753, 0.18008753566541738, 0.1663637758868634, 0.15277264648228464, 0.139081914877061, 0.1250987572244036, 0.11052338988242932, 0.09574507239165511, 0.08077548650979327, 0.06541127246106566, 0.049458011415914285, 0.03268709324116641, 0.015145809290489472, -0.0024980087603982595, -0.020481490540850498, -0.03900459871447966, -0.05823695705388024, -0.07839140311150763, -0.09961338107499464, -0.12095243885882502, -0.14263331171655083, -0.16481713740167472, -0.18763597478393154, -0.2112120189378417, -0.23567670102909086, -0.26116661914614187, -0.287391416475467, -0.31410977637965604, -0.34163602709768576, -0.3700426172858434, -0.3993689216730023, -0.4295767810667033, -0.46047323280488467, -0.4915903303620894, -0.522167763528492, -0.551397507012827, -0.5787894028749164, -0.6042302768547853, -0.6278227279124464, -0.6497547880933776, -0.6702222155945255, -0.6894225775825708, -0.7075543592993018, -0.7244624605755207, -0.7398101124803426, -0.7541861991409955, -0.7677580983960084, -0.7806572518370731, -0.7930135216397721, -0.804970100687895, -0.8162279671541751, -0.8266351234448442, -0.8363866079406024, -0.8456358645677393, -0.8545181863129622]\n"
     ]
    }
   ],
   "source": [
    "#sim = rebound.Simulation(\"sim_bins/juno.bin\")\n",
    "#sim.move_to_com()\n",
    "sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "\n",
    "for i in range(days1):\n",
    "#    sim.dt = sim.particles[3].a/365.\n",
    "    sim.step()\n",
    "    sun_x.append(sim.particles[0].x)\n",
    "    sun_y.append(sim.particles[0].y)\n",
    "    mer_x.append(sim.particles[1].x)\n",
    "    mer_y.append(sim.particles[1].y)\n",
    "    ven_x.append(sim.particles[2].x)\n",
    "    ven_y.append(sim.particles[2].y)\n",
    "    ear_x.append(sim.particles[3].x)\n",
    "    ear_y.append(sim.particles[3].y)\n",
    "    mar_x.append(sim.particles[4].x)\n",
    "    mar_y.append(sim.particles[4].y)\n",
    "    jup_x.append(sim.particles[5].x)\n",
    "    jup_y.append(sim.particles[5].y)\n",
    "    sat_x.append(sim.particles[6].x)\n",
    "    sat_y.append(sim.particles[6].y)\n",
    "    ura_x.append(sim.particles[7].x)\n",
    "    ura_y.append(sim.particles[7].y)\n",
    "    nep_x.append(sim.particles[8].x)\n",
    "    nep_y.append(sim.particles[8].y)\n",
    "    jun_x.append(sim.particles[9].x)\n",
    "    jun_y.append(sim.particles[9].y)\n",
    "\n",
    "sim.status()\n",
    "print(sim.dt)\n",
    "print(ear_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/rebound/horizons.py:137: RuntimeWarning: Warning: Mass cannot be retrieved from NASA HORIZONS. Set to 0.\n",
      "  warnings.warn(\"Warning: Mass cannot be retrieved from NASA HORIZONS. Set to 0.\", RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "sim.remove(index=9)\n",
    "sim.add(\"Juno\",date=choice_date2)\n",
    "sim.particles[-1].m = 1.823e-27\n",
    "#sim = sim2\n",
    "#sim.move_to_com()\n",
    "#sim.status()\n",
    "sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "for i in range(days2):\n",
    "#    sim.dt = sim.particles[3].a/365.\n",
    "    sim.step()\n",
    "    sun_x.append(sim.particles[0].x)\n",
    "    sun_y.append(sim.particles[0].y)\n",
    "    mer_x.append(sim.particles[1].x)\n",
    "    mer_y.append(sim.particles[1].y)\n",
    "    ven_x.append(sim.particles[2].x)\n",
    "    ven_y.append(sim.particles[2].y)\n",
    "    ear_x.append(sim.particles[3].x)\n",
    "    ear_y.append(sim.particles[3].y)\n",
    "    mar_x.append(sim.particles[4].x)\n",
    "    mar_y.append(sim.particles[4].y)\n",
    "    jup_x.append(sim.particles[5].x)\n",
    "    jup_y.append(sim.particles[5].y)\n",
    "    sat_x.append(sim.particles[6].x)\n",
    "    sat_y.append(sim.particles[6].y)\n",
    "    ura_x.append(sim.particles[7].x)\n",
    "    ura_y.append(sim.particles[7].y)\n",
    "    nep_x.append(sim.particles[8].x)\n",
    "    nep_y.append(sim.particles[8].y)\n",
    "    jun_x.append(sim.particles[9].x)\n",
    "    jun_y.append(sim.particles[9].y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n"
     ]
    }
   ],
   "source": [
    "sim.remove(index=9)\n",
    "sim.add(\"Juno\",date=choice_date3)\n",
    "sim.particles[-1].m = 1.823e-27\n",
    "#sim = sim3\n",
    "#sim.move_to_com()\n",
    "\n",
    "sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "for i in range(days3):\n",
    "#    sim.dt = sim.particles[3].a/365.\n",
    "    sim.step()\n",
    "    sun_x.append(sim.particles[0].x)\n",
    "    sun_y.append(sim.particles[0].y)\n",
    "    mer_x.append(sim.particles[1].x)\n",
    "    mer_y.append(sim.particles[1].y)\n",
    "    ven_x.append(sim.particles[2].x)\n",
    "    ven_y.append(sim.particles[2].y)\n",
    "    ear_x.append(sim.particles[3].x)\n",
    "    ear_y.append(sim.particles[3].y)\n",
    "    mar_x.append(sim.particles[4].x)\n",
    "    mar_y.append(sim.particles[4].y)\n",
    "    jup_x.append(sim.particles[5].x)\n",
    "    jup_y.append(sim.particles[5].y)\n",
    "    sat_x.append(sim.particles[6].x)\n",
    "    sat_y.append(sim.particles[6].y)\n",
    "    ura_x.append(sim.particles[7].x)\n",
    "    ura_y.append(sim.particles[7].y)\n",
    "    nep_x.append(sim.particles[8].x)\n",
    "    nep_y.append(sim.particles[8].y)\n",
    "    jun_x.append(sim.particles[9].x)\n",
    "    jun_y.append(sim.particles[9].y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n"
     ]
    }
   ],
   "source": [
    "sim.remove(index=9)\n",
    "sim.add(\"Juno\",date=choice_date4)\n",
    "sim.particles[-1].m = 1.823e-27\n",
    "#sim = sim4\n",
    "#sim.move_to_com()\n",
    "\n",
    "sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "\n",
    "for i in range(days4):\n",
    "#    sim.dt = sim.particles[3].a/365.\n",
    "    sim.step()\n",
    "    sun_x.append(sim.particles[0].x)\n",
    "    sun_y.append(sim.particles[0].y)\n",
    "    mer_x.append(sim.particles[1].x)\n",
    "    mer_y.append(sim.particles[1].y)\n",
    "    ven_x.append(sim.particles[2].x)\n",
    "    ven_y.append(sim.particles[2].y)\n",
    "    ear_x.append(sim.particles[3].x)\n",
    "    ear_y.append(sim.particles[3].y)\n",
    "    mar_x.append(sim.particles[4].x)\n",
    "    mar_y.append(sim.particles[4].y)\n",
    "    jup_x.append(sim.particles[5].x)\n",
    "    jup_y.append(sim.particles[5].y)\n",
    "    sat_x.append(sim.particles[6].x)\n",
    "    sat_y.append(sim.particles[6].y)\n",
    "    ura_x.append(sim.particles[7].x)\n",
    "    ura_y.append(sim.particles[7].y)\n",
    "    nep_x.append(sim.particles[8].x)\n",
    "    nep_y.append(sim.particles[8].y)\n",
    "    jun_x.append(sim.particles[9].x)\n",
    "    jun_y.append(sim.particles[9].y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n"
     ]
    }
   ],
   "source": [
    "sim.remove(index=9)\n",
    "sim.add(\"Juno\",date=choice_date5_GRAVf)\n",
    "sim.particles[-1].m = 1.823e-27\n",
    "#sim = sim2\n",
    "#sim.move_to_com()\n",
    "#sim.status()\n",
    "sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "for i in range(days5):\n",
    "#    sim.dt = sim.particles[3].a/365.\n",
    "    sim.step()\n",
    "    sun_x.append(sim.particles[0].x)\n",
    "    sun_y.append(sim.particles[0].y)\n",
    "    mer_x.append(sim.particles[1].x)\n",
    "    mer_y.append(sim.particles[1].y)\n",
    "    ven_x.append(sim.particles[2].x)\n",
    "    ven_y.append(sim.particles[2].y)\n",
    "    ear_x.append(sim.particles[3].x)\n",
    "    ear_y.append(sim.particles[3].y)\n",
    "    mar_x.append(sim.particles[4].x)\n",
    "    mar_y.append(sim.particles[4].y)\n",
    "    jup_x.append(sim.particles[5].x)\n",
    "    jup_y.append(sim.particles[5].y)\n",
    "    sat_x.append(sim.particles[6].x)\n",
    "    sat_y.append(sim.particles[6].y)\n",
    "    ura_x.append(sim.particles[7].x)\n",
    "    ura_y.append(sim.particles[7].y)\n",
    "    nep_x.append(sim.particles[8].x)\n",
    "    nep_y.append(sim.particles[8].y)\n",
    "    jun_x.append(sim.particles[9].x)\n",
    "    jun_y.append(sim.particles[9].y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n"
     ]
    }
   ],
   "source": [
    "sim.remove(index=9)\n",
    "sim.add(\"Juno\",date=choice_date6)\n",
    "sim.particles[-1].m = 1.823e-27\n",
    "#sim = sim5\n",
    "#sim.move_to_com()\n",
    "\n",
    "sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "for i in range(days6):\n",
    "#    sim.dt = sim.particles[3].a/365.\n",
    "    sim.step()\n",
    "    sun_x.append(sim.particles[0].x)\n",
    "    sun_y.append(sim.particles[0].y)\n",
    "    mer_x.append(sim.particles[1].x)\n",
    "    mer_y.append(sim.particles[1].y)\n",
    "    ven_x.append(sim.particles[2].x)\n",
    "    ven_y.append(sim.particles[2].y)\n",
    "    ear_x.append(sim.particles[3].x)\n",
    "    ear_y.append(sim.particles[3].y)\n",
    "    mar_x.append(sim.particles[4].x)\n",
    "    mar_y.append(sim.particles[4].y)\n",
    "    jup_x.append(sim.particles[5].x)\n",
    "    jup_y.append(sim.particles[5].y)\n",
    "    sat_x.append(sim.particles[6].x)\n",
    "    sat_y.append(sim.particles[6].y)\n",
    "    ura_x.append(sim.particles[7].x)\n",
    "    ura_y.append(sim.particles[7].y)\n",
    "    nep_x.append(sim.particles[8].x)\n",
    "    nep_y.append(sim.particles[8].y)\n",
    "    jun_x.append(sim.particles[9].x)\n",
    "    jun_y.append(sim.particles[9].y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Searching NASA Horizons for 'Juno'... Found: Juno (spacecraft) (-61).\n"
     ]
    }
   ],
   "source": [
    "sim.remove(index=9)\n",
    "sim.add(\"Juno\",date=choice_date7)\n",
    "sim.particles[-1].m = 1.823e-27\n",
    "#sim = sim5\n",
    "#sim.move_to_com()\n",
    "remaining_days = 100\n",
    "\n",
    "\n",
    "sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "for i in range(remaining_days):\n",
    "#    sim.dt = sim.particles[3].a/365.\n",
    "    sim.step()\n",
    "    sun_x.append(sim.particles[0].x)\n",
    "    sun_y.append(sim.particles[0].y)\n",
    "    mer_x.append(sim.particles[1].x)\n",
    "    mer_y.append(sim.particles[1].y)\n",
    "    ven_x.append(sim.particles[2].x)\n",
    "    ven_y.append(sim.particles[2].y)\n",
    "    ear_x.append(sim.particles[3].x)\n",
    "    ear_y.append(sim.particles[3].y)\n",
    "    mar_x.append(sim.particles[4].x)\n",
    "    mar_y.append(sim.particles[4].y)\n",
    "    jup_x.append(sim.particles[5].x)\n",
    "    jup_y.append(sim.particles[5].y)\n",
    "    sat_x.append(sim.particles[6].x)\n",
    "    sat_y.append(sim.particles[6].y)\n",
    "    ura_x.append(sim.particles[7].x)\n",
    "    ura_y.append(sim.particles[7].y)\n",
    "    nep_x.append(sim.particles[8].x)\n",
    "    nep_y.append(sim.particles[8].y)\n",
    "    jun_x.append(sim.particles[9].x)\n",
    "    jun_y.append(sim.particles[9].y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18, 16))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "#plt.style.use('light_background')\n",
    "plt.scatter(sun_x, sun_y)\n",
    "plt.scatter(mer_x, mer_y)\n",
    "plt.scatter(ven_x, ven_y)\n",
    "plt.scatter(ear_x, ear_y)\n",
    "plt.scatter(mar_x, mar_y)\n",
    "plt.scatter(jup_x, jup_y)\n",
    "plt.scatter(sat_x, sat_y)\n",
    "#plt.scatter(ura_x, ura_y)\n",
    "#plt.scatter(nep_x, nep_y)\n",
    "plt.scatter(jun_x, jun_y)\n",
    "plt.legend(['Sun','Mercury','Venus', 'Earth', 'Mars', 'Jupiter', 'Saturn', 'Juno'])\n",
    "#fig, ax = plt.subplots()\n",
    "#ax.plot(x_list, y_list)\n",
    "#ax.set_title(\"Stitched Trajectory of Juno's Orbit From Launch to Final\")\n",
    "ax.set_xlim([10.,-10.])\n",
    "ax.set_ylim([10.,-10.])\n",
    "ax.set_xlabel(\"AU\")\n",
    "ax.set_ylabel(\"AU\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#o_xdata = []\n",
    "#o_ydata = []\n",
    "#7,12,28\n",
    "\n",
    "#for i in range(7):\n",
    "#    for j in range(12):\n",
    "#        for k in range(28):\n",
    "#            chosen_date = \"\" + str(2011+i+1) + \"-\" + str(j+1) + \"-\" + str(k+1) + \" 13:00\"\n",
    "#            sim.add(\"Juno\",date=chosen_date)\n",
    "#            o_xdata.append(sim.particles[-1].x)\n",
    "#            o_ydata.append(sim.particles[-1].y)\n",
    "#            sim.remove(index=9)\n",
    "\n",
    "#print(o_xdata)\n",
    "#print(o_ydata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mmMAmqKoLstoieWF3P7P0PJDkd0neVlUnV9VRSS5O8qOFZ4LU6lOBm5M82N1fXXoeeFF3X9PdO9avMy9O8hsxgU2xfs/zaFWdtr7q/CQPbHXMId2hsB+7kuyqqr8keS7JpT5tA9jSN5K8Pskv1zto7uruzyw7Eoez7n6+qj6X5OdJjkiyq7vvX3gsSFafAn8iyX1V9af1ddd2908WnAng1eCKJLesPyh4JMmntrpzeQ8PAAAATC15ygMAAADwKiUoAAAAAGOCAgAAADAmKAAAAABjggIAAAAwJigAAAAAY4ICAAAAMCYoAAAAAGP/Bx7FSC0GaPmZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print(o_xdata)\n",
    "#print(o_ydata)\n",
    "#fig = plt.figure(figsize=(18, 16))\n",
    "#ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "#ax.plot(o_xdata, o_ydata, label=\"satellite\")\n",
    "\n",
    "#ax.legend(loc=\"upper right\")\n",
    "#ax.set_title(\"JUNO \") #+ str(plotted_steps) + \" days\")\n",
    "#ax.set_xlim([-6.,6.])\n",
    "#ax.set_ylim([-6.,6.])\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "REBOUND version:     \t3.12.1\n",
      "REBOUND built on:    \tFeb 13 2020 23:13:03\n",
      "Number of particles: \t10\n",
      "Selected integrator: \tias15\n",
      "Simulation time:     \t0.0000000000000000e+00\n",
      "Current timestep:    \t0.001000\n",
      "---------------------------------\n",
      "<rebound.Particle object, m=1.0 x=-0.003667104704982513 y=-0.0007615549591466391 z=7.280353891223119e-06 vx=0.00018878060089616528 vy=-0.00031039968446616327 vz=-3.5953675228674207e-06>\n",
      "<rebound.Particle object, m=1.6601141530543488e-07 x=0.021853570351438957 y=0.30535011312807486 z=0.022676655563299453 vx=-1.9577072741508919 vy=0.19554421462689628 vz=0.19564706058792522>\n",
      "<rebound.Particle object, m=2.4478382877847715e-06 x=-0.722197701858332 y=-0.027876844014487714 z=0.04110435215145321 vx=0.03754987030268725 vy=-1.180448539337741 vz=-0.018328357509010285>\n",
      "<rebound.Particle object, m=3.040432648022642e-06 x=0.9751641053922188 y=-0.23847864641818367 z=1.1614979450331914e-05 vx=0.21991675903485006 vy=0.9678077735575227 vz=-3.1336384305743085e-05>\n",
      "<rebound.Particle object, m=3.2271560375549977e-07 x=0.1857763553350519 y=1.5399156214846605 z=0.02763769101930186 vx=-0.7763574031098537 vy=0.16809905118608667 vz=0.022592703040200345>\n",
      "<rebound.Particle object, m=0.0009547919152112404 x=4.262811429991912 y=2.5216352990788566 z=-0.1059426572704465 vx=-0.22859655303491572 vy=0.3985343873290193 vz=0.003459887298021993>\n",
      "<rebound.Particle object, m=0.0002858856727222417 x=-9.133564814494996 y=-3.127532216551976 z=0.41786024120967735 vx=0.08758872868260337 vy=-0.30753198916904734 vz=0.0018633655706996775>\n",
      "<rebound.Particle object, m=4.36624373583127e-05 x=20.060445130490024 y=0.7802880139898736 z=-0.2569977533825345 vx=-0.01055912041927156 vy=0.21780759530005284 vz=0.0009461554138158441>\n",
      "<rebound.Particle object, m=5.151383772628674e-05 x=25.837432145653867 y=-15.249382974218635 z=-0.2814174926812701 vx=0.09154281129494357 vy=0.1582346999873374 vz=-0.005368260492009422>\n",
      "<rebound.Particle object, m=1.823e-27 x=1.0296496387575573 y=-0.14667692938642074 z=-0.000753875720163049 vx=0.31046393981650583 vy=1.0996692325691033 vz=-0.0011343568833522132>\n",
      "---------------------------------\n"
     ]
    }
   ],
   "source": [
    "#sim = rebound.Simulation(\"sim_bins/juno.bin\")\n",
    "#sim.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sim.dt = sim.particles[3].P/365.\n",
    "#sim.integrate(6*365.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "REBOUND version:     \t3.12.1\n",
      "REBOUND built on:    \tFeb 13 2020 23:13:03\n",
      "Number of particles: \t10\n",
      "Selected integrator: \tias15\n",
      "Simulation time:     \t2.1900000000000000e+03\n",
      "Current timestep:    \t0.016879\n",
      "---------------------------------\n",
      "<rebound.Particle object, m=1.0 x=0.004896728428712737 y=-0.003914956345084534 z=-0.00010654842336667425 vx=0.0001772912429007925 vy=0.0004412013052038976 vz=-7.76783707080242e-06>\n",
      "<rebound.Particle object, m=1.6601141530543488e-07 x=0.34964368927530076 y=0.03576037759714499 z=-0.02816875077924688 vx=-0.5036226662152766 vy=1.6954910613684904 vz=0.18502623223325876>\n",
      "<rebound.Particle object, m=2.4478382877847715e-06 x=0.6747835320153974 y=-0.2848264693131236 z=-0.042685432434112144 vx=0.44850247607898525 vy=1.0797930656747878 vz=-0.009924307899>\n",
      "<rebound.Particle object, m=3.040432648022642e-06 x=-0.8987980543892465 y=0.3994681865945086 z=-0.0003579364746653254 vx=-0.42352855281551094 vy=-0.9168483290298091 vz=0.0007752498699708292>\n",
      "<rebound.Particle object, m=3.2271560375549977e-07 x=-1.6236460986039376 y=0.3327104971833549 z=0.045731729092264486 vx=-0.13586890197336177 vy=-0.7257106471800117 vz=-0.012065695558003034>\n",
      "<rebound.Particle object, m=0.0009547919152112404 x=-5.284320346898342 y=1.200168184204989 z=0.11198522138760006 vx=-0.10292494564945202 vy=-0.4069096093362032 vz=0.004056752504027698>\n",
      "<rebound.Particle object, m=0.0002858856727222417 x=-6.614611239902909 y=6.30065965377685 z=0.16016614885724426 vx=-0.24110182956540777 vy=-0.23634665438498226 vz=0.013696251000784782>\n",
      "<rebound.Particle object, m=4.36624373583127e-05 x=12.12160294582705 y=15.424124378937494 z=-0.10012679747163815 vx=-0.18135436284401582 vy=0.13035453869833954 vz=0.0028144904522342555>\n",
      "<rebound.Particle object, m=5.151383772628674e-05 x=29.286196420496093 y=5.695749205008442 z=-0.7927460866293091 vx=-0.03606071333866658 vy=0.18026561392337612 vz=-0.002881816793620332>\n",
      "<rebound.Particle object, m=1.823e-27 x=0.7794549429037708 y=-0.6452087929436535 z=-0.00972153844188986 vx=0.7340280444166327 vy=0.8880421593506408 vz=0.07475686724636907>\n",
      "---------------------------------\n"
     ]
    }
   ],
   "source": [
    "#sim.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 576x576 with 3 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x109522cd0>,\n",
       " <matplotlib.axes._axes.Axes at 0x109579dd0>,\n",
       " <matplotlib.axes._axes.Axes at 0x1095b5250>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rebound.OrbitPlot(sim, slices=0.3, unitlabel=\"[AU]\", color=True, periastron=True, xlim=[-5.,5.], ylim=[-5.,5.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sim.integrator = \"mercurius\"\n",
    "#sim.dt = sim.particles[3].P/365."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_x = 0\n",
    "sun_y = 0\n",
    "\n",
    "x_1 = []\n",
    "x_2 = []\n",
    "x_3 = []\n",
    "x_4 = []\n",
    "x_5 = []\n",
    "x_6 = []\n",
    "\n",
    "\n",
    "y_1 = []\n",
    "y_2 = []\n",
    "y_3 = []\n",
    "y_4 = []\n",
    "y_5 = []\n",
    "y_6 = []\n",
    "\n",
    "plotted_steps = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/rebound/simulation.py:675: RuntimeWarning: At least 10 predictor corrector loops in IAS15 did not converge. This is typically an indication of the timestep being too large.\n",
      "  warnings.warn(msg[1:], RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "times = 4*365.\n",
    "num_steps = np.linspace(0,times,1000)\n",
    "\n",
    "#sim.particles[-1].m = 0.4\n",
    "\n",
    "for step in num_steps:\n",
    "    sim.dt = sim.particles[3].P/365.\n",
    "    sim.integrate(step)\n",
    "#    sim.step()\n",
    "    x_1.append(sim.particles[1].x) #mercury\n",
    "    y_1.append(sim.particles[1].y) \n",
    "    x_2.append(sim.particles[2].x) #venus\n",
    "    y_2.append(sim.particles[2].y)\n",
    "    x_3.append(sim.particles[3].x) #earth\n",
    "    y_3.append(sim.particles[3].y)\n",
    "    x_4.append(sim.particles[4].x) #mars\n",
    "    y_4.append(sim.particles[4].y)\n",
    "    x_5.append(sim.particles[5].x) #Jupiter\n",
    "    y_5.append(sim.particles[5].y)\n",
    "    x_6.append(sim.particles[-1].x) #satellite\n",
    "    y_6.append(sim.particles[-1].y)\n",
    "\n",
    "\n",
    "#plotted_steps += num_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18, 16))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.plot(sun_x, sun_y, color='gold', marker='o', label=\"sun\")\n",
    "ax.plot(x_1, y_1, label=\"mercury\")\n",
    "ax.plot(x_2, y_2, label=\"venus\")\n",
    "ax.plot(x_3, y_3, label=\"earth\")\n",
    "ax.plot(x_4, y_4, label=\"mars\")\n",
    "ax.plot(x_5, y_5, label=\"jupiter\")\n",
    "ax.plot(x_6, y_6, label=\"satellite\")\n",
    "\n",
    "ax.legend(loc=\"upper right\")\n",
    "ax.set_title(\"JUNO \") #+ str(plotted_steps) + \" days\")\n",
    "ax.set_xlim([-6.,6.])\n",
    "ax.set_ylim([-6.,6.])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}