Grav_Ros.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": [],
   "source": [
    "choice_date1 = \"2004-03-3 13:00\" # March 2 2004 -- launch\n",
    "choice_date2 = \"2004-04-05 13:00\" # April 4 2004 -- 33 days later\n",
    "choice_date3 = \"2004-04-26 13:00\" # April 25 2004 -- 21\n",
    "choice_date4 = \"2004-05-11 13:00\" # May 10 2004 -- 15\n",
    "choice_date5 = \"2004-05-17 13:00\" # May 16 2004 -- 6\n",
    "choice_date6 = \"2004-06-26 13:00\" # July 25 2004 -- 70\n",
    "choice_date7 = \"2005-03-26 13:00\" # March 25 2005 -- 243\n",
    "choice_date8 = \"2005-04-16 13:00\" # April 15 2005 -- 21\n",
    "choice_date9 = \"2005-08-09 13:00\" # August 8 2005 -- 115\n",
    "choice_date10 = \"2006-08-09 13:00\" # August 8 2006 -- 365\n",
    "choice_date1X = \"2007-02-26 13:00\" # Feb 26 2007 -- 201       186\n",
    "choice_date11 = \"2007-11-15 13:00\" # November 15 2007 -- 262\n",
    "choice_date12 = \"2008-09-06 13:00\" # September 5 2008 -- 295\n",
    "choice_date13 = \"2009-11-14 13:00\" # November 14 2009 -- 435\n",
    "choice_date14 = \"2010-08-09 13:00\" # July 10 2010 -- 365\n",
    "choice_date15 = \"2011-06-08 13:00\" # June 8 2011 -- 304\n",
    "\n",
    "choice_date16 = \"2014-01-21 13:00\" # January 20 2014 -- 957\n",
    "choice_date17 = \"2014-05-08 13:00\" # May 7 2014 -- 107\n",
    "choice_date18 = \"2014-05-22 13:00\" # May 21 -- 14\n",
    "choice_date19 = \"2014-06-05 13:00\" # June 4 -- 14\n",
    "choice_date20 = \"2014-06-19 13:00\" # June 18 -- 14\n",
    "choice_date21 = \"2014-07-03 13:00\" # July 2 -- 14\n",
    "choice_date22 = \"2014-07-17 13:00\" # July 16 -- 14\n",
    "choice_date23 = \"2014-07-24 13:00\" # July 23 -- 7\n",
    "choice_date24 = \"2014-08-04 13:00\" # August 3 -- 11\n",
    "choice_date25 = \"2014-08-07 13:00\" # August 6 -- 3\n",
    "\n",
    "\n",
    "\n",
    "days1 = 33 - 0\n",
    "days2 = 21 - 0\n",
    "days3 = 15 - 0\n",
    "days4 = 6 - 0\n",
    "days5 = 70 - 20\n",
    "days6 = 243 - 0\n",
    "days7 = 21 - 0\n",
    "days8 = 115 - 0\n",
    "days9 = 365 - 0\n",
    "days10 = 365 - 0\n",
    "days1X = 186\n",
    "days11 = 365 - 0\n",
    "days12 = 365 - 0\n",
    "days13 = 365 - 0\n",
    "days14 = 304 - 0\n",
    "\n",
    "days15 = 957 - 0\n",
    "days16 = 107 - 0\n",
    "days17 = 14 - 0\n",
    "days18 = 14 - 0\n",
    "days19 = 14 - 0\n",
    "days20 = 14 - 0\n",
    "days21 = 14 - 0\n",
    "days22 = 7 - 0\n",
    "days23 = 11 - 0\n",
    "days24 = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23 ['2004-04-05 13:00', '2004-04-26 13:00', '2004-05-11 13:00', '2004-05-17 13:00', '2004-06-26 13:00', '2005-03-26 13:00', '2005-04-16 13:00', '2005-08-09 13:00', '2006-08-09 13:00', '2007-08-09 13:00', '2008-08-09 13:00', '2009-08-09 13:00', '2010-08-09 13:00', '2011-06-08 13:00', '2014-01-21 13:00', '2014-05-08 13:00', '2014-05-22 13:00', '2014-06-05 13:00', '2014-06-19 13:00', '2014-07-03 13:00', '2014-07-17 13:00', '2014-07-24 13:00', '2014-08-04 13:00']\n",
      "23 [21, 15, 6, 50, 243, 21, 115, 365, 365, 365, 365, 365, 304, 957, 107, 14, 14, 14, 14, 14, 7, 11, 3]\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n"
     ]
    }
   ],
   "source": [
    "dates = [choice_date2, choice_date3, choice_date4, choice_date5, choice_date6,\n",
    "         choice_date7, choice_date8, choice_date9, choice_date10, choice_date11, choice_date12,\n",
    "         choice_date13, choice_date14, choice_date15, choice_date16, choice_date17, choice_date18,\n",
    "         choice_date19, choice_date20, choice_date21, choice_date22, choice_date23, choice_date24,]\n",
    "\n",
    "\n",
    "times = [days2, days3, days4, days5, days6,\n",
    "         days7, days8, days9, days10, days11, days12,\n",
    "         days13, days14, days15, days16, days17, days18,\n",
    "         days19, days20, days21, days22, days23, days24]\n",
    "\n",
    "print(len(dates), dates)\n",
    "print(len(times), times)\n",
    "for i in range(len(dates)):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 'Rosetta'... Found: Rosetta (spacecraft) (-226).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/rebound/horizons.py:140: 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 = rebound.Simulation()\n",
    "#choice_date = \"2004-03-3 13:00\" #launch\n",
    "\n",
    "sim.add([\"Sun\", \"Mercury\", \"Venus\", \"Earth\", \"Mars\", \"Jupiter\", \"Saturn\", \"Uranus\", \"Neptune\", \"Rosetta\"],date=choice_date1)\n",
    "sim.particles[-1].m = 1.509e-27\n",
    "sim.save(\"sim_bins/rosetta_ss.bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
    "ros_x = []\n",
    "ros_y = []\n",
    "#sim.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = rebound.Simulation(\"sim_bins/rosetta_ss.bin\")\n",
    "sim.move_to_com()\n",
    "sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "total_days = np.sum(times)\n",
    "\n",
    "for i in range(total_days):\n",
    "\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",
    "#    ros_x.append(sim.particles[9].x)\n",
    "#    ros_y.append(sim.particles[9].y)\n",
    "\n",
    "#sim.status()\n",
    "#print(sim.dt)\n",
    "#print(ear_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ros_x = []\n",
    "ros_y = []\n",
    "\n",
    "sim = rebound.Simulation(\"sim_bins/rosetta_ss.bin\")\n",
    "for i in range(days1):\n",
    "    sim.step()\n",
    "    ros_x.append(sim.particles[9].x)\n",
    "    ros_y.append(sim.particles[9].y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "index1 = 4\n",
    "index2 = 4\n",
    "index3 = 4\n",
    "index4 = 4\n",
    "index5 = 4\n",
    "index6 = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2004-04-05 13:00 21\n",
      "Searching NASA Horizons for 'Rosetta'... Found: Rosetta (spacecraft) (-226).\n",
      "2004-04-26 13:00 15\n",
      "Searching NASA Horizons for 'Rosetta'... "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/rebound/horizons.py:140: 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: Rosetta (spacecraft) (-226).\n",
      "2004-05-11 13:00 6\n",
      "Searching NASA Horizons for 'Rosetta'... Found: Rosetta (spacecraft) (-226).\n",
      "2004-05-17 13:00 50\n",
      "Searching NASA Horizons for 'Rosetta'... Found: Rosetta (spacecraft) (-226).\n"
     ]
    }
   ],
   "source": [
    "for j in range(index1):\n",
    "    chosen_date = dates[j]\n",
    "    chosen_time = times[j] \n",
    "    \n",
    "    print(chosen_date, chosen_time)\n",
    "\n",
    "    sim.remove(index=9)\n",
    "    sim.add(\"Rosetta\",date=chosen_date)\n",
    "    sim.particles[-1].m = 1.509e-27\n",
    "    sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "    for i in range(chosen_time):\n",
    "        sim.step()\n",
    "        ros_x.append(sim.particles[9].x)\n",
    "        ros_y.append(sim.particles[9].y)\n",
    "        \n",
    "sim.save(\"RosJ.bin\")\n",
    "x_j = ros_x\n",
    "y_j = ros_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sim = rebound.Simulation(\"RosJ.bin\")\n",
    "\n",
    "#ros_x = x_j\n",
    "#ros_y = y_j\n",
    "\n",
    "#index2 = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2004-06-26 13:00 243\n",
      "Searching NASA Horizons for 'Rosetta'... Found: Rosetta (spacecraft) (-226).\n"
     ]
    }
   ],
   "source": [
    "index2 = 1\n",
    "for k in range(index2):\n",
    "    chosen_date = dates[k + index1]\n",
    "    chosen_time = times[k + index1] \n",
    "    \n",
    "    print(chosen_date, chosen_time)\n",
    "\n",
    "\n",
    "    sim.remove(index=9)\n",
    "    sim.add(\"Rosetta\",date=chosen_date)\n",
    "    sim.particles[-1].m = 1.509e-27\n",
    "    sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "    for i in range(chosen_time):\n",
    "        sim.step()\n",
    "        ros_x.append(sim.particles[9].x)\n",
    "        ros_y.append(sim.particles[9].y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2005-03-26 13:00 21\n",
      "Searching NASA Horizons for 'Rosetta'... Found: Rosetta (spacecraft) (-226).\n",
      "2005-04-16 13:00 115\n",
      "Searching NASA Horizons for 'Rosetta'... Found: Rosetta (spacecraft) (-226).\n"
     ]
    }
   ],
   "source": [
    "index3 = 2\n",
    "for l in range(index3):\n",
    "    chosen_date = dates[l + index1 + index2]\n",
    "    chosen_time = times[l + index1 + index2] \n",
    "    \n",
    "    print(chosen_date, chosen_time)\n",
    "\n",
    "    sim.remove(index=9)\n",
    "    sim.add(\"Rosetta\",date=chosen_date)\n",
    "    sim.particles[-1].m = 1.509e-27\n",
    "    sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "    for i in range(chosen_time):\n",
    "        sim.step()\n",
    "        ros_x.append(sim.particles[9].x)\n",
    "        ros_y.append(sim.particles[9].y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2005-08-09 13:00 365\n",
      "Searching NASA Horizons for 'Rosetta'... Found: Rosetta (spacecraft) (-226).\n",
      "2006-08-09 13:00 365\n",
      "Searching NASA Horizons for 'Rosetta'... Found: Rosetta (spacecraft) (-226).\n"
     ]
    }
   ],
   "source": [
    "index4 = 2\n",
    "for l in range(index4):\n",
    "    chosen_date = dates[l + index1 + index2 + index3]\n",
    "    chosen_time = times[l + index1 + index2 + index3] \n",
    "    \n",
    "    print(chosen_date, chosen_time)\n",
    "\n",
    "    sim.remove(index=9)\n",
    "    sim.add(\"Rosetta\",date=chosen_date)\n",
    "    sim.particles[-1].m = 1.509e-27\n",
    "    sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "    for i in range(chosen_time):\n",
    "        sim.step()\n",
    "        ros_x.append(sim.particles[9].x)\n",
    "        ros_y.append(sim.particles[9].y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2007-08-09 13:00 365\n",
      "Searching NASA Horizons for 'Rosetta'... Found: Rosetta (spacecraft) (-226).\n",
      "2008-08-09 13:00 365\n",
      "Searching NASA Horizons for 'Rosetta'... Found: Rosetta (spacecraft) (-226).\n"
     ]
    }
   ],
   "source": [
    "index5 = 2\n",
    "for l in range(index5):\n",
    "    chosen_date = dates[l + index1 + index2 + index3 + index4]\n",
    "    chosen_time = times[l + index1 + index2 + index3 + index4] \n",
    "    \n",
    "    print(chosen_date, chosen_time)\n",
    "\n",
    "    sim.remove(index=9)\n",
    "    sim.add(\"Rosetta\",date=chosen_date)\n",
    "    sim.particles[-1].m = 1.509e-27\n",
    "    sim.dt = sim.particles[3].a/365.\n",
    "\n",
    "    for i in range(chosen_time):\n",
    "        sim.step()\n",
    "        ros_x.append(sim.particles[9].x)\n",
    "        ros_y.append(sim.particles[9].y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "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",
    "#plt.style.use('light_background')\n",
    "plt.plot(sun_x, sun_y)\n",
    "plt.plot(mer_x, mer_y)\n",
    "plt.plot(ven_x, ven_y)\n",
    "plt.plot(ear_x, ear_y)\n",
    "plt.plot(mar_x, mar_y)\n",
    "plt.plot(jup_x, jup_y)\n",
    "plt.plot(sat_x, sat_y)\n",
    "#plt.scatter(ura_x, ura_y)\n",
    "#plt.scatter(nep_x, nep_y)\n",
    "plt.plot(ros_x, ros_y, color='black')\n",
    "plt.legend(['Sun','Mercury','Venus', 'Earth', 'Mars', 'Jupiter', 'Saturn', 'Rosetta'])\n",
    "\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": 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
}