{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 17 - Comparative Studies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet1 = Planet('VENUS')\n",
    "planet2 = Planet('EARTH')\n",
    "planet3 = Planet('MARS')\n",
    "planet4 = Planet('JUPITER')\n",
    "planet5 = Planet('SATURN')\n",
    "planet6 = Planet('TITAN')\n",
    "planet7 = Planet('URANUS')\n",
    "planet8 = Planet('NEPTUNE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet1.loadAtmosphereModel('../atmdata/Venus/venus-gram-avg.dat', 0 , 1 ,2, 3)\n",
    "planet2.loadAtmosphereModel('../atmdata/Earth/earth-gram-avg.dat', 0 , 1 ,2, 3)\n",
    "planet3.loadAtmosphereModel('../atmdata/Mars/mars-gram-avg.dat', 0 , 1 , 2, 3)\n",
    "planet4.loadAtmosphereModel('../atmdata/Jupiter/jupiter-galileo-asi.dat', 0 , 1 , 2, 3, heightInKmFlag=True)\n",
    "planet5.loadAtmosphereModel('../atmdata/Saturn/saturn-nominal.dat', 0 , 1 , 2, 3, heightInKmFlag=True)\n",
    "planet6.loadAtmosphereModel('../atmdata/Titan/titan-gram-avg.dat', 0 , 1 , 2, 3)\n",
    "planet7.loadAtmosphereModel('../atmdata/Uranus/uranus-ames.dat', 0 , 1 , 2, 3)\n",
    "planet8.loadAtmosphereModel('../atmdata/Neptune/neptune-gram-avg.dat', 0 , 7 ,6, 5 , heightInKmFlag=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet1.h_skip = 150E3\n",
    "planet2.h_skip = 140E3\n",
    "planet3.h_skip = 120E3\n",
    "planet4.h_skip = 1000E3\n",
    "planet5.h_skip = 1000E3\n",
    "planet6.h_skip = 1000E3\n",
    "planet7.h_skip = 1000E3\n",
    "planet8.h_skip = 1000E3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1=Vehicle('Venus',  1000.0, 200.0, 0.30, 3.1416, 0.0, 1.00, planet1)\n",
    "vehicle2=Vehicle('Earth',  1000.0, 200.0, 0.30, 3.1416, 0.0, 1.00, planet2)\n",
    "vehicle3=Vehicle('Mars' ,  1000.0, 200.0, 0.30, 3.1416, 0.0, 1.00, planet3)\n",
    "vehicle4=Vehicle('Jupiter',1000.0, 200.0, 0.30, 3.1416, 0.0, 1.00, planet4)\n",
    "vehicle5=Vehicle('Saturn' ,1000.0, 200.0, 0.30, 3.1416, 0.0, 1.00, planet5)\n",
    "vehicle6=Vehicle('Titan'  ,1000.0, 200.0, 0.30, 3.1416, 0.0, 1.00, planet6)\n",
    "vehicle7=Vehicle('Uranus' ,1000.0, 200.0, 0.30, 3.1416, 0.0, 1.00, planet7)\n",
    "vehicle8=Vehicle('Neptune',1000.0, 200.0, 0.30, 3.1416, 0.0, 1.00, planet8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VENUS\n",
      "10.235384752669699\n",
      "11.864362647659727\n",
      "EARTH\n",
      "11.06521903710534\n",
      "12.58725833289833\n",
      "MARS\n",
      "4.940354888610406\n",
      "7.772200873974933\n",
      "JUPITER\n",
      "59.984279058078336\n",
      "60.606218609293016\n",
      "SATURN\n",
      "36.135394199294566\n",
      "37.15866943175457\n",
      "TITAN\n",
      "5.479293507620972\n",
      "10.248056271442763\n",
      "URANUS\n",
      "23.158303361242414\n",
      "28.918973262744508\n",
      "NEPTUNE\n",
      "25.172312110936126\n",
      "30.555609910626586\n"
     ]
    }
   ],
   "source": [
    "print(\"VENUS\")\n",
    "print(np.sqrt(0.0E3**2+2*planet1.GM/(planet1.h_skip+planet1.RP))/1E3)\n",
    "print(np.sqrt(6.0E3**2+2*planet1.GM/(planet1.h_skip+planet1.RP))/1E3)\n",
    "print(\"EARTH\")\n",
    "print(np.sqrt(0.0E3**2+2*planet2.GM/(planet2.h_skip+planet2.RP))/1E3)\n",
    "print(np.sqrt(6.0E3**2+2*planet2.GM/(planet2.h_skip+planet2.RP))/1E3)\n",
    "print(\"MARS\")\n",
    "print(np.sqrt(0.0E3**2+2*planet3.GM/(planet3.h_skip+planet3.RP))/1E3)\n",
    "print(np.sqrt(6.0E3**2+2*planet3.GM/(planet3.h_skip+planet3.RP))/1E3)\n",
    "print(\"JUPITER\")\n",
    "print(np.sqrt(5.0E3**2+2*planet4.GM/(planet4.h_skip+planet4.RP))/1E3)\n",
    "print(np.sqrt(10.0E3**2+2*planet4.GM/(planet4.h_skip+planet4.RP))/1E3)\n",
    "print(\"SATURN\")\n",
    "print(np.sqrt(5.0E3**2+2*planet5.GM/(planet5.h_skip+planet5.RP))/1E3)\n",
    "print(np.sqrt(10.0E3**2+2*planet5.GM/(planet5.h_skip+planet5.RP))/1E3)\n",
    "print(\"TITAN\")\n",
    "print(np.sqrt(5.0E3**2+2*planet6.GM/(planet6.h_skip+planet6.RP))/1E3)\n",
    "print(np.sqrt(10.0E3**2+2*planet6.GM/(planet6.h_skip+planet6.RP))/1E3)\n",
    "print(\"URANUS\")\n",
    "print(np.sqrt(10.0E3**2+2*planet7.GM/(planet7.h_skip+planet7.RP))/1E3)\n",
    "print(np.sqrt(20.0E3**2+2*planet7.GM/(planet7.h_skip+planet7.RP))/1E3)\n",
    "print(\"NEPTUNE\")\n",
    "print(np.sqrt(10.0E3**2+2*planet8.GM/(planet8.h_skip+planet8.RP))/1E3)\n",
    "print(np.sqrt(20.0E3**2+2*planet8.GM/(planet8.h_skip+planet8.RP))/1E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VENUS\n",
      "EARTH\n",
      "MARS\n",
      "JUPITER\n",
      "SATURN\n",
      "TITAN\n",
      "URANUS\n",
      "NEPTUNE\n"
     ]
    }
   ],
   "source": [
    "print(\"VENUS\")\n",
    "v1a = (np.sqrt(0.0E3**2+2*planet1.GM/(planet1.h_skip+planet1.RP))/1E3)\n",
    "v1b = (np.sqrt(6.0E3**2+2*planet1.GM/(planet1.h_skip+planet1.RP))/1E3)\n",
    "print(\"EARTH\")\n",
    "v2a = (np.sqrt(0.0E3**2+2*planet2.GM/(planet2.h_skip+planet2.RP))/1E3)\n",
    "v2b = (np.sqrt(6.0E3**2+2*planet2.GM/(planet2.h_skip+planet2.RP))/1E3)\n",
    "print(\"MARS\")\n",
    "v3a = (np.sqrt(0.0E3**2+2*planet3.GM/(planet3.h_skip+planet3.RP))/1E3)\n",
    "v3b = (np.sqrt(6.0E3**2+2*planet3.GM/(planet3.h_skip+planet3.RP))/1E3)\n",
    "print(\"JUPITER\")\n",
    "v4a = (np.sqrt(5.0E3**2+2*planet4.GM/(planet4.h_skip+planet4.RP))/1E3)\n",
    "v4b = (np.sqrt(10.0E3**2+2*planet4.GM/(planet4.h_skip+planet4.RP))/1E3)\n",
    "print(\"SATURN\")\n",
    "v5a = (np.sqrt(5.0E3**2+2*planet5.GM/(planet5.h_skip+planet5.RP))/1E3)\n",
    "v5b = (np.sqrt(10.0E3**2+2*planet5.GM/(planet5.h_skip+planet5.RP))/1E3)\n",
    "print(\"TITAN\")\n",
    "v6a = (np.sqrt(5.0E3**2+2*planet6.GM/(planet6.h_skip+planet6.RP))/1E3)\n",
    "v6b = (np.sqrt(10.0E3**2+2*planet6.GM/(planet6.h_skip+planet6.RP))/1E3)\n",
    "print(\"URANUS\")\n",
    "v7a = (np.sqrt(10.0E3**2+2*planet7.GM/(planet7.h_skip+planet7.RP))/1E3)\n",
    "v7b = (np.sqrt(20.0E3**2+2*planet7.GM/(planet7.h_skip+planet7.RP))/1E3)\n",
    "print(\"NEPTUNE\")\n",
    "v8a = (np.sqrt(10.0E3**2+2*planet8.GM/(planet8.h_skip+planet8.RP))/1E3)\n",
    "v8b = (np.sqrt(20.0E3**2+2*planet8.GM/(planet8.h_skip+planet8.RP))/1E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1.setInitialState (150.0,0.0,0.0,11.0,0.0,-4.5,0.0,0.0)\n",
    "vehicle2.setInitialState (140.0,0.0,0.0,11.5,0.0,-4.5,0.0,0.0)\n",
    "vehicle3.setInitialState (120.0,0.0,0.0, 7.0,0.0,-4.5,0.0,0.0)\n",
    "vehicle4.setInitialState(1000.0,0.0,0.0,60.0,0.0,-4.5,0.0,0.0)\n",
    "vehicle5.setInitialState(1000.0,0.0,0.0,36.0,0.0,-4.5,0.0,0.0)\n",
    "vehicle6.setInitialState(1000.0,0.0,0.0, 7.5,0.0,-4.5,0.0,0.0)\n",
    "vehicle7.setInitialState(1000.0,0.0,0.0,25.0,0.0,-4.5,0.0,0.0)\n",
    "vehicle8.setInitialState(1000.0,0.0,0.0,27.5,0.0,-4.5,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1.setSolverParams(1E-6)\n",
    "vehicle2.setSolverParams(1E-6)\n",
    "vehicle3.setSolverParams(1E-6)\n",
    "vehicle4.setSolverParams(1E-6)\n",
    "vehicle5.setSolverParams(1E-6)\n",
    "vehicle6.setSolverParams(1E-6)\n",
    "vehicle7.setSolverParams(1E-6)\n",
    "vehicle8.setSolverParams(1E-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit1, exitflag_os1  = vehicle1.findOverShootLimit (2400.0,0.1,-80.0,-4.0,1E-10,400.0)\n",
    "underShootLimit1,exitflag_us1  = vehicle1.findUnderShootLimit(2400.0,0.1,-80.0,-4.0,1E-10,400.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.337756426539272 1.0\n",
      "-7.068433752585406 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit1, exitflag_os1)\n",
    "print(underShootLimit1,exitflag_us1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit2, exitflag_os2  = vehicle2.findOverShootLimit (2400.0,0.1,-80.0,-4.0,1E-10,400.0)\n",
    "underShootLimit2,exitflag_us2  = vehicle2.findUnderShootLimit(2400.0,0.1,-80.0,-4.0,1E-10,400.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-6.3867313949158415 1.0\n",
      "-8.141700465788745 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit2, exitflag_os2)\n",
    "print(underShootLimit2,exitflag_us2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AthulGirija\\anaconda3\\envs\\env1\\lib\\site-packages\\scipy\\integrate\\odepack.py:247: ODEintWarning: Excess work done on this call (perhaps wrong Dfun type). Run with full_output = 1 to get quantitative information.\n",
      "  warnings.warn(warning_msg, ODEintWarning)\n"
     ]
    }
   ],
   "source": [
    "overShootLimit3, exitflag_os3  = vehicle3.findOverShootLimit (2400.0,0.1,-30.0,-2.0,1E-10,400.0)\n",
    "underShootLimit3,exitflag_us3  = vehicle3.findUnderShootLimit(2400.0,0.1,-30.0,-2.0,1E-10,400.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-10.656794067006558 1.0\n",
      "-13.765197287466435 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit3, exitflag_os3)\n",
    "print(underShootLimit3,exitflag_us3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet4.h_trap = -130.0E3\n",
    "planet4.h_low  = 50.0E3\n",
    "\n",
    "overShootLimit4, exitflag_os4  = vehicle4.findOverShootLimit (2400.0,0.1,-80.0,-4.0,1E-10,430E3)\n",
    "underShootLimit4,exitflag_us4  = vehicle4.findUnderShootLimit(2400.0,0.1,-80.0,-4.0,1E-10,430E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8.190509113308508 1.0\n",
      "-8.361646710389323 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit4, exitflag_os4)\n",
    "print(underShootLimit4,exitflag_us4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet5.h_low  = 50.0E3\n",
    "\n",
    "overShootLimit5, exitflag_os5  = vehicle5.findOverShootLimit (2400.0,0.1,-80.0,-4.0,1E-10,263E3)\n",
    "underShootLimit5,exitflag_us5  = vehicle5.findUnderShootLimit(2400.0,0.1,-80.0,-4.0,1E-10,263E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-6.708508475014241 1.0\n",
      "-7.192161418864998 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit5, exitflag_os5)\n",
    "print(underShootLimit5,exitflag_us5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit6, exitflag_os6  = vehicle6.findOverShootLimit (6000.0,0.1,-80.0,-4.0,1E-10,1700.0)\n",
    "underShootLimit6,exitflag_us6  = vehicle6.findUnderShootLimit(6000.0,0.1,-80.0,-4.0,1E-10,1700.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-35.511385259556846 1.0\n",
      "-40.08044252597756 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit6, exitflag_os6)\n",
    "print(underShootLimit6,exitflag_us6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit7, exitflag_os7  = vehicle7.findOverShootLimit (2400.0,1.0,-50.0,-4.0,1E-10,900E3)\n",
    "underShootLimit7,exitflag_us7  = vehicle7.findUnderShootLimit(2400.0,1.0,-50.0,-4.0,1E-10,900E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8.378781035331485 1.0\n",
      "-9.03231991237044 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit7, exitflag_os7)\n",
    "print(underShootLimit7,exitflag_us7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit8, exitflag_os8  = vehicle8.findOverShootLimit (3000.0,0.1,-50.0,-4.0,1E-10,400E3)\n",
    "underShootLimit8,exitflag_us8  = vehicle8.findUnderShootLimit(3000.0,0.1,-50.0,-4.0,1E-10,400E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-12.738614854639309 1.0\n",
      "-13.40074354888202 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit8, exitflag_os8)\n",
    "print(underShootLimit8,exitflag_us8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('../data/jsr-paper/comparative-studies/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.figure(figsize=(6.25,6.25))\n",
    "ax = plt.gca()\n",
    "ax.add_patch(Rectangle((v1a,underShootLimit1), v1b-v1a, overShootLimit1-underShootLimit1, alpha=1, facecolor='xkcd:orange'))\n",
    "ax.add_patch(Rectangle((v2a,underShootLimit2), v2b-v2a, overShootLimit2-underShootLimit2, alpha=1, facecolor='xkcd:pastel blue'))\n",
    "ax.add_patch(Rectangle((v3a,underShootLimit3), v3b-v3a, overShootLimit3-underShootLimit3, alpha=1, facecolor='xkcd:light red'))\n",
    "ax.add_patch(Rectangle((v4a,underShootLimit4), v4b-v4a, overShootLimit4-underShootLimit4, alpha=1, facecolor='xkcd:amber'))\n",
    "ax.add_patch(Rectangle((v6a,underShootLimit6), v6b-v6a, overShootLimit6-underShootLimit6, alpha=1, facecolor='xkcd:goldenrod'))\n",
    "ax.add_patch(Rectangle((v7a,underShootLimit7), v7b-v7a, overShootLimit7-underShootLimit7, alpha=1, facecolor='xkcd:light turquoise'))\n",
    "ax.add_patch(Rectangle((v8a,underShootLimit8), v8b-v8a, overShootLimit8-underShootLimit8, alpha=1, facecolor='xkcd:cerulean blue'))\n",
    "\n",
    "plt.text( 6, -7, 'Venus' ,color='xkcd:orange', fontsize=12)\n",
    "plt.text( 13, -8, 'Earth' ,color='xkcd:pastel blue', fontsize=12)\n",
    "plt.text( 8, -13, 'Mars' ,color='xkcd:light red', fontsize=12)\n",
    "plt.text( 6.3, -35, 'Titan' ,color='xkcd:goldenrod', fontsize=12)\n",
    "plt.text( 23.5, -10.5, 'Uranus' ,color='xkcd:light turquoise', fontsize=12)\n",
    "plt.text( 24.5, -14.9, 'Neptune' ,color='xkcd:cerulean blue', fontsize=12)\n",
    "\n",
    "plt.xlim([2.0, 30.0])\n",
    "plt.ylim([-40.0, -2.0])\n",
    "\n",
    "plt.xlabel('Entry speed '+r'$V_e$, km/s', fontsize=16)\n",
    "plt.ylabel('Entry flight path angle '+r'$\\gamma_e$, deg', fontsize=16)\n",
    "\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=16)\n",
    "ax.tick_params(axis='y',labelsize=16)\n",
    "\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/v-gamma-lift-modulation.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/v-gamma-lift-modulation.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/v-gamma-lift-modulation.eps', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D=Vehicle('Venus',  200.0, 20.0, 0.00, 3.1416, 0.0, 0.10, planet1)\n",
    "vehicle2D=Vehicle('Earth',  200.0, 20.0, 0.00, 3.1416, 0.0, 0.10, planet2)\n",
    "vehicle3D=Vehicle('Mars' ,  200.0, 20.0, 0.00, 3.1416, 0.0, 0.10, planet3)\n",
    "vehicle4D=Vehicle('Jupiter',200.0, 20.0, 0.00, 3.1416, 0.0, 0.10, planet4)\n",
    "vehicle5D=Vehicle('Saturn' ,200.0, 20.0, 0.00, 3.1416, 0.0, 0.10, planet5)\n",
    "vehicle6D=Vehicle('Titan'  ,200.0, 20.0, 0.00, 3.1416, 0.0, 0.10, planet6)\n",
    "vehicle7D=Vehicle('Uranus' ,200.0, 20.0, 0.00, 3.1416, 0.0, 0.10, planet7)\n",
    "vehicle8D=Vehicle('Neptune',200.0, 20.0, 0.00, 3.1416, 0.0, 0.10, planet8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D.setInitialState (150.0,0.0,0.0,11.0,0.0,-4.5,0.0,0.0)\n",
    "vehicle2D.setInitialState (140.0,0.0,0.0,11.5,0.0,-4.5,0.0,0.0)\n",
    "vehicle3D.setInitialState (120.0,0.0,0.0, 7.0,0.0,-4.5,0.0,0.0)\n",
    "vehicle4D.setInitialState(1000.0,0.0,0.0,60.0,0.0,-4.5,0.0,0.0)\n",
    "vehicle5D.setInitialState(1000.0,0.0,0.0,36.0,0.0,-4.5,0.0,0.0)\n",
    "vehicle6D.setInitialState(1000.0,0.0,0.0, 7.5,0.0,-4.5,0.0,0.0)\n",
    "vehicle7D.setInitialState(1000.0,0.0,0.0,25.0,0.0,-4.5,0.0,0.0)\n",
    "vehicle8D.setInitialState(1000.0,0.0,0.0,27.5,0.0,-4.5,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D.setSolverParams(1E-6)\n",
    "vehicle2D.setSolverParams(1E-6)\n",
    "vehicle3D.setSolverParams(1E-6)\n",
    "vehicle4D.setSolverParams(1E-6)\n",
    "vehicle5D.setSolverParams(1E-6)\n",
    "vehicle6D.setSolverParams(1E-6)\n",
    "vehicle7D.setSolverParams(1E-6)\n",
    "vehicle8D.setSolverParams(1E-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D.setDragModulationVehicleParams(20.0,7.0)\n",
    "vehicle2D.setDragModulationVehicleParams(20.0,7.0)\n",
    "vehicle3D.setDragModulationVehicleParams(20.0,7.0)\n",
    "vehicle4D.setDragModulationVehicleParams(20.0,7.0)\n",
    "vehicle5D.setDragModulationVehicleParams(20.0,7.0)\n",
    "vehicle6D.setDragModulationVehicleParams(20.0,7.0)\n",
    "vehicle7D.setDragModulationVehicleParams(20.0,7.0)\n",
    "vehicle8D.setDragModulationVehicleParams(20.0,7.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit1D, exitflag_os1D  = vehicle1D.findOverShootLimitD(2400.0,0.1,-80.0,-4.0,1E-10,400.0)\n",
    "underShootLimit1D,exitflag_us1D  = vehicle1D.findUnderShootLimitD(2400.0,0.1,-80.0,-4.0,1E-10,400.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.203492604807252 1.0\n",
      "-5.614676727258484 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit1D, exitflag_os1D)\n",
    "print(underShootLimit1D,exitflag_us1D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit2D, exitflag_os2D  = vehicle2D.findOverShootLimitD(2400.0,0.1,-80.0,-4.0,1E-10,400.0)\n",
    "underShootLimit2D,exitflag_us2D  = vehicle2D.findUnderShootLimitD(2400.0,0.1,-80.0,-4.0,1E-10,400.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-6.103771843398135 1.0\n",
      "-6.732959025812306 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit2D, exitflag_os2D)\n",
    "print(underShootLimit2D,exitflag_us2D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit3D, exitflag_os3D  = vehicle3D.findOverShootLimitD(2400.0,0.1,-80.0,-4.0,1E-10,400.0)\n",
    "underShootLimit3D,exitflag_us3D  = vehicle3D.findUnderShootLimitD(2400.0,0.1,-80.0,-4.0,1E-10,400.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-10.193927379001252 1.0\n",
      "-11.291493911885482 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit3D, exitflag_os3D)\n",
    "print(underShootLimit3D,exitflag_us3D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit4D, exitflag_os4D  = vehicle4D.findOverShootLimitD(2400.0,0.1,-30.0,-4.0,1E-10,4E3)\n",
    "underShootLimit4D,exitflag_us4D  = vehicle4D.findUnderShootLimitD(2400.0,0.1,-30.0,-4.0,1E-10,4E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8.176375376337091 1.0\n",
      "-8.432869144795404 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit4D, exitflag_os4D)\n",
    "print(underShootLimit4D,exitflag_us4D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit5D, exitflag_os5D  = vehicle5D.findOverShootLimitD(2400.0,0.1,-20.0,-4.0,1E-10,4E3)\n",
    "underShootLimit5D,exitflag_us5D  = vehicle5D.findUnderShootLimitD(2400.0,0.1,-20.0,-4.0,1E-10,4E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-6.428392354224343 1.0\n",
      "-7.476114542689174 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit5D, exitflag_os5D)\n",
    "print(underShootLimit5D,exitflag_us5D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit6D, exitflag_os6D  = vehicle6D.findOverShootLimitD(6000.0,0.1,-80.0,-4.0,1E-10,1700.0)\n",
    "underShootLimit6D,exitflag_us6D  = vehicle6D.findUnderShootLimitD(6000.0,0.1,-80.0,-4.0,1E-10,1700.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-34.00011686890866 1.0\n",
      "-36.610938443158375 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit6D, exitflag_os6D)\n",
    "print(underShootLimit6D,exitflag_us6D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit7D, exitflag_os7D  = vehicle7D.findOverShootLimitD(3000.0,0.1,-20.0,-4.0,1E-10,4E3)\n",
    "underShootLimit7D,exitflag_us7D  = vehicle7D.findUnderShootLimitD(3000.0,0.1,-20.0,-4.0,1E-10,4E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-8.112847047974356 1.0\n",
      "-8.650135981675703 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit7D, exitflag_os7D)\n",
    "print(underShootLimit7D,exitflag_us7D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "overShootLimit8D, exitflag_os8D  = vehicle8D.findOverShootLimitD(3000.0,0.1,-20.0,-4.0,1E-10,4E3)\n",
    "underShootLimit8D,exitflag_us8D  = vehicle8D.findUnderShootLimitD(3000.0,0.1,-20.0,-4.0,1E-10,4E3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-12.40664541971637 1.0\n",
      "-13.19187773426529 1.0\n"
     ]
    }
   ],
   "source": [
    "print(overShootLimit8D, exitflag_os8D)\n",
    "print(underShootLimit8D,exitflag_us8D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.figure(figsize=(6.25,6.25))\n",
    "ax = plt.gca()\n",
    "ax.add_patch(Rectangle((v1a,underShootLimit1), v1b-v1a, overShootLimit1-underShootLimit1, alpha=1, facecolor='xkcd:orange', hatch='|||||'))\n",
    "ax.add_patch(Rectangle((v2a,underShootLimit2), v2b-v2a, overShootLimit2-underShootLimit2, alpha=1, facecolor='xkcd:pastel blue', hatch='|||||'))\n",
    "ax.add_patch(Rectangle((v3a,underShootLimit3), v3b-v3a, overShootLimit3-underShootLimit3, alpha=1, facecolor='xkcd:light red', hatch='|||||'))\n",
    "ax.add_patch(Rectangle((v4a,underShootLimit4), v4b-v4a, overShootLimit4-underShootLimit4, alpha=1, facecolor='xkcd:amber', hatch='|||||'))\n",
    "ax.add_patch(Rectangle((v6a,underShootLimit6), v6b-v6a, overShootLimit6-underShootLimit6, alpha=1, facecolor='xkcd:goldenrod', hatch='|||||'))\n",
    "ax.add_patch(Rectangle((v7a,underShootLimit7), v7b-v7a, overShootLimit7-underShootLimit7, alpha=1, facecolor='xkcd:light turquoise', hatch='|||||'))\n",
    "ax.add_patch(Rectangle((v8a,underShootLimit8), v8b-v8a, overShootLimit8-underShootLimit8, alpha=1, facecolor='xkcd:cerulean blue', hatch='|||||'))\n",
    "\n",
    "ax.add_patch(Rectangle((v1a,underShootLimit1D), v1b-v1a, overShootLimit1D-underShootLimit1D, alpha=1, edgecolor='xkcd:black', facecolor='None', linewidth=0.2, hatch='xxxxxxx'))\n",
    "ax.add_patch(Rectangle((v2a,underShootLimit2D), v2b-v2a, overShootLimit2D-underShootLimit2D, alpha=1, edgecolor='xkcd:black', facecolor='None', linewidth=0.2, hatch='xxxxxxx'))\n",
    "ax.add_patch(Rectangle((v3a,underShootLimit3D), v3b-v3a, overShootLimit3D-underShootLimit3D, alpha=1, edgecolor='xkcd:black', facecolor='None', linewidth=0.2, hatch='xxxx'))\n",
    "ax.add_patch(Rectangle((v6a,underShootLimit6D), v6b-v6a, overShootLimit6D-underShootLimit6D, alpha=1, edgecolor='xkcd:black', facecolor='None', linewidth=0.2, hatch='xxxx'))\n",
    "ax.add_patch(Rectangle((v7a,underShootLimit7D), v7b-v7a, overShootLimit7D-underShootLimit7D, alpha=1, edgecolor='xkcd:black', facecolor='None', linewidth=0.2, hatch='xxxxxx'))\n",
    "ax.add_patch(Rectangle((v8a,underShootLimit8D), v8b-v8a, overShootLimit8D-underShootLimit8D, alpha=1, edgecolor='xkcd:black', facecolor='None', linewidth=0.2, hatch='xxxxxx'))\n",
    "\n",
    "ax.add_patch(Rectangle((15.5, -30-5), 3.0, 2.0, alpha=1, edgecolor='xkcd:black', facecolor='None', linewidth=0.2, hatch='|||||'))\n",
    "ax.add_patch(Rectangle((15.5, -33-5), 3.0, 2.0, alpha=1, edgecolor='xkcd:black', facecolor='None', linewidth=0.2, hatch='xxxxx'))\n",
    "\n",
    "\n",
    "plt.text( 6, -7, 'Venus' ,color='xkcd:orange', fontsize=16)\n",
    "plt.text( 13, -8, 'Earth' ,color='xkcd:pastel blue', fontsize=16)\n",
    "plt.text( 8, -13, 'Mars' ,color='xkcd:light red', fontsize=16)\n",
    "plt.text( 6.2, -33, 'Titan' ,color='xkcd:goldenrod', fontsize=16)\n",
    "plt.text( 23.5, -10.5, 'Uranus' ,color='xkcd:light turquoise', fontsize=16)\n",
    "plt.text( 24.5, -14.9, 'Neptune' ,color='xkcd:cerulean blue', fontsize=16)\n",
    "\n",
    "plt.text( 19, -29.6-5, 'Lift modulation' ,color='xkcd:black', fontsize=16)\n",
    "plt.text( 19, -32.6-5, 'Drag modulation' ,color='xkcd:black', fontsize=16)\n",
    "\n",
    "\n",
    "plt.xlim([2.0, 30.0])\n",
    "plt.ylim([-40.0, -2.0])\n",
    "\n",
    "plt.xlabel('Entry speed '+r'$V_e$, km/s', fontsize=16)\n",
    "plt.ylabel('Entry flight path angle '+r'$\\gamma_e$, deg', fontsize=16)\n",
    "\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=16)\n",
    "ax.tick_params(axis='y',labelsize=16)\n",
    "\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/v-gamma-lift-drag-modulation.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/v-gamma-lift-drag-modulation.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/v-gamma-lift-drag-modulation.eps', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "TCW1 = overShootLimit1-underShootLimit1\n",
    "TCW2 = overShootLimit2-underShootLimit2\n",
    "TCW3 = overShootLimit3-underShootLimit3\n",
    "TCW4 = overShootLimit4-underShootLimit4\n",
    "TCW5 = overShootLimit5-underShootLimit5\n",
    "TCW6 = overShootLimit6-underShootLimit6\n",
    "TCW7 = overShootLimit7-underShootLimit7\n",
    "TCW8 = overShootLimit8-underShootLimit8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7306773260461341 1.754969070872903 3.108403220459877 0.17113759708081488 0.48365294385075686 4.569057266420714 0.6535388770389545 0.6621286942427105\n"
     ]
    }
   ],
   "source": [
    "print(TCW1,TCW2,TCW3,TCW4,TCW5,TCW6, TCW7, TCW8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "TCW1D = overShootLimit1D-underShootLimit1D\n",
    "TCW2D = overShootLimit2D-underShootLimit2D\n",
    "TCW3D = overShootLimit3D-underShootLimit3D\n",
    "TCW4D = overShootLimit4D-underShootLimit4D\n",
    "TCW5D = overShootLimit5D-underShootLimit5D\n",
    "TCW6D = overShootLimit6D-underShootLimit6D\n",
    "TCW7D = overShootLimit7D-underShootLimit7D\n",
    "TCW8D = overShootLimit8D-underShootLimit8D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4111841224512318 0.6291871824141708 1.0975665328842297 0.25649376845831284 1.0477221884648316 2.6108215742497123 0.5372889337013476 0.7852323145489208\n"
     ]
    }
   ],
   "source": [
    "print(TCW1D,TCW2D,TCW3D,TCW4D,TCW5D,TCW6D, TCW7D, TCW8D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 882x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.figure(figsize=(12.25,6.25))\n",
    "\n",
    "labels = [ 'Venus', 'Earth', 'Mars', 'Jupiter', 'Saturn', 'Titan', 'Uranus', 'Neptune']\n",
    "TCW    = [TCW1,TCW2,TCW3,TCW4,TCW5,TCW6, TCW7, TCW8]\n",
    "TCWD   = [TCW1D,TCW2D,TCW3D,TCW4D,TCW5D,TCW6D, TCW7D, TCW8D]\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.25  # the width of the bars\n",
    "\n",
    "ax = plt.gca()\n",
    "rects1 = ax.bar(x - width/2, TCW, width, label='Lift Modulation', color='black')\n",
    "rects2 = ax.bar(x + width/2, TCWD, width, label='Drag Modulation', facecolor='None', edgecolor='black', linewidth=1.0)\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Theoretical Corridor Width, deg', fontsize=16)\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels, fontsize=16)\n",
    "ax.legend(loc='upper left', fontsize=16)\n",
    "\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=16)\n",
    "ax.tick_params(axis='y',labelsize=16)\n",
    "\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/tcw-histogram.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/tcw-histogram.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/tcw-histogram.eps', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1.setInitialState(150.0,0.0,0.0,11.0,0.0, underShootLimit1,0.0,0.0)\n",
    "vehicle2.setInitialState (140.0,0.0,0.0,11.5,0.0,underShootLimit2,0.0,0.0)\n",
    "vehicle3.setInitialState (120.0,0.0,0.0, 7.0,0.0,underShootLimit3,0.0,0.0)\n",
    "vehicle4.setInitialState(1000.0,0.0,0.0,60.0,0.0,underShootLimit4,0.0,0.0)\n",
    "vehicle5.setInitialState(1000.0,0.0,0.0,36.0,0.0,underShootLimit5,0.0,0.0)\n",
    "vehicle6.setInitialState(1000.0,0.0,0.0, 7.5,0.0,underShootLimit6,0.0,0.0)\n",
    "vehicle7.setInitialState(1000.0,0.0,0.0,25.0,0.0,underShootLimit7,0.0,0.0)\n",
    "vehicle8.setInitialState(1000.0,0.0,0.0,27.5,0.0,underShootLimit8,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle2.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle3.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle4.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle5.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle6.propogateEntry(6000.0, 0.1, 0.0)\n",
    "vehicle7.propogateEntry(3000.0, 0.1, 0.0)\n",
    "vehicle8.propogateEntry(3000.0, 0.1, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1a = max(vehicle1.acc_net_g)\n",
    "g2a = max(vehicle2.acc_net_g)\n",
    "g3a = max(vehicle3.acc_net_g)\n",
    "g4a = max(vehicle4.acc_net_g)\n",
    "g5a = max(vehicle5.acc_net_g)\n",
    "g6a = max(vehicle6.acc_net_g)\n",
    "g7a = max(vehicle7.acc_net_g)\n",
    "g8a = max(vehicle8.acc_net_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.374017950196038 10.374594885516943 8.360047065222073 8.789301073476533 2.8857312714267533 5.505619347483617 3.343822672976156 5.796824037992481\n"
     ]
    }
   ],
   "source": [
    "print(g1a, g2a, g3a, g4a, g5a, g6a, g7a, g8a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1.setInitialState(150.0,0.0,0.0,11.0,0.0, overShootLimit1,0.0,0.0)\n",
    "vehicle2.setInitialState (140.0,0.0,0.0,11.5,0.0,overShootLimit2,0.0,0.0)\n",
    "vehicle3.setInitialState (120.0,0.0,0.0, 7.0,0.0,overShootLimit3,0.0,0.0)\n",
    "vehicle4.setInitialState(1000.0,0.0,0.0,60.0,0.0,overShootLimit4,0.0,0.0)\n",
    "vehicle5.setInitialState(1000.0,0.0,0.0,36.0,0.0,overShootLimit5,0.0,0.0)\n",
    "vehicle6.setInitialState(1000.0,0.0,0.0, 7.5,0.0,overShootLimit6,0.0,0.0)\n",
    "vehicle7.setInitialState(1000.0,0.0,0.0,25.0,0.0,overShootLimit7,0.0,0.0)\n",
    "vehicle8.setInitialState(1000.0,0.0,0.0,27.5,0.0,overShootLimit8,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1.propogateEntry(2400.0, 0.1, 180.0)\n",
    "vehicle2.propogateEntry(2400.0, 0.1, 180.0)\n",
    "vehicle3.propogateEntry(2400.0, 0.1, 180.0)\n",
    "vehicle4.propogateEntry(2400.0, 0.1, 180.0)\n",
    "vehicle5.propogateEntry(2400.0, 0.1, 180.0)\n",
    "vehicle6.propogateEntry(6000.0, 0.1, 180.0)\n",
    "vehicle7.propogateEntry(3000.0, 0.1, 180.0)\n",
    "vehicle8.propogateEntry(3000.0, 0.1, 180.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1b = max(vehicle1.acc_net_g)\n",
    "g2b = max(vehicle2.acc_net_g)\n",
    "g3b = max(vehicle3.acc_net_g)\n",
    "g4b = max(vehicle4.acc_net_g)\n",
    "g5b = max(vehicle5.acc_net_g)\n",
    "g6b = max(vehicle6.acc_net_g)\n",
    "g7b = max(vehicle7.acc_net_g)\n",
    "g8b = max(vehicle8.acc_net_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.316415423595727 2.3951724238463665 2.030574858793238 6.047715599187108 2.2109983102117203 2.1239918744654416 1.697074847865149 3.4556776816180337\n"
     ]
    }
   ],
   "source": [
    "print(g1b, g2b, g3b, g4b, g5b, g6b, g7b, g8b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D.setInitialState(150.0,0.0,0.0,11.0,0.0, underShootLimit1D,0.0,0.0)\n",
    "vehicle2D.setInitialState (140.0,0.0,0.0,11.5,0.0,underShootLimit2D,0.0,0.0)\n",
    "vehicle3D.setInitialState (120.0,0.0,0.0, 7.0,0.0,underShootLimit3D,0.0,0.0)\n",
    "vehicle4D.setInitialState(1000.0,0.0,0.0,60.0,0.0,underShootLimit4D,0.0,0.0)\n",
    "vehicle5D.setInitialState(1000.0,0.0,0.0,36.0,0.0,underShootLimit5D,0.0,0.0)\n",
    "vehicle6D.setInitialState(1000.0,0.0,0.0, 7.5,0.0,underShootLimit6D,0.0,0.0)\n",
    "vehicle7D.setInitialState(1000.0,0.0,0.0,25.0,0.0,underShootLimit7D,0.0,0.0)\n",
    "vehicle8D.setInitialState(1000.0,0.0,0.0,27.5,0.0,underShootLimit8D,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle2D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle3D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle4D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle5D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle6D.propogateEntry(6000.0, 0.1, 0.0)\n",
    "vehicle7D.propogateEntry(3000.0, 0.1, 0.0)\n",
    "vehicle8D.propogateEntry(3000.0, 0.1, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1aD = max(vehicle1D.acc_net_g)\n",
    "g2aD = max(vehicle2D.acc_net_g)\n",
    "g3aD = max(vehicle3D.acc_net_g)\n",
    "g4aD = max(vehicle4D.acc_net_g)\n",
    "g5aD = max(vehicle5D.acc_net_g)\n",
    "g6aD = max(vehicle6D.acc_net_g)\n",
    "g7aD = max(vehicle7D.acc_net_g)\n",
    "g8aD = max(vehicle8D.acc_net_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.657672791677737 4.666411475961085 3.820623202398641 29.092908983227836 7.901839420794543 3.148209939839383 3.3776314306001205 8.246800209301407\n"
     ]
    }
   ],
   "source": [
    "print(g1aD, g2aD, g3aD, g4aD, g5aD, g6aD, g7aD, g8aD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D.setInitialState(150.0,0.0,0.0,11.0,0.0, overShootLimit1D,0.0,0.0)\n",
    "vehicle2D.setInitialState (140.0,0.0,0.0,11.5,0.0,overShootLimit2D,0.0,0.0)\n",
    "vehicle3D.setInitialState (120.0,0.0,0.0, 7.0,0.0,overShootLimit3D,0.0,0.0)\n",
    "vehicle4D.setInitialState(1000.0,0.0,0.0,60.0,0.0,overShootLimit4D,0.0,0.0)\n",
    "vehicle5D.setInitialState(1000.0,0.0,0.0,36.0,0.0,overShootLimit5D,0.0,0.0)\n",
    "vehicle6D.setInitialState(1000.0,0.0,0.0, 7.5,0.0,overShootLimit6D,0.0,0.0)\n",
    "vehicle7D.setInitialState(1000.0,0.0,0.0,25.0,0.0,overShootLimit7D,0.0,0.0)\n",
    "vehicle8D.setInitialState(1000.0,0.0,0.0,27.5,0.0,overShootLimit8D,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle2D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle3D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle4D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle5D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle6D.propogateEntry(6000.0, 0.1, 0.0)\n",
    "vehicle7D.propogateEntry(3000.0, 0.1, 0.0)\n",
    "vehicle8D.propogateEntry(3000.0, 0.1, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1bD = max(vehicle1D.acc_net_g)\n",
    "g2bD = max(vehicle2D.acc_net_g)\n",
    "g3bD = max(vehicle3D.acc_net_g)\n",
    "g4bD = max(vehicle4D.acc_net_g)\n",
    "g5bD = max(vehicle5D.acc_net_g)\n",
    "g6bD = max(vehicle6D.acc_net_g)\n",
    "g7bD = max(vehicle7D.acc_net_g)\n",
    "g8bD = max(vehicle8D.acc_net_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9687154975603938 0.8319068440187664 0.749533855592529 5.1808606325354125 1.4342199854535276 0.8411918340738396 0.47340569464643756 1.4942989124917714\n"
     ]
    }
   ],
   "source": [
    "print(g1bD, g2bD, g3bD, g4bD, g5bD, g6bD, g7bD, g8bD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 882x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.figure(figsize=(12.25,6.25))\n",
    "\n",
    "labels = [ 'Venus', 'Earth', 'Mars', 'Jupiter', 'Saturn', 'Titan', 'Uranus', 'Neptune']\n",
    "\n",
    "\n",
    "g1     = 0.5*(g1a+g1b) \n",
    "g2     = 0.5*(g2a+g2b) \n",
    "g3     = 0.5*(g3a+g3b) \n",
    "g4     = 0.5*(g4a+g4b) \n",
    "g5     = 0.5*(g5a+g5b) \n",
    "g6     = 0.5*(g6a+g6b) \n",
    "g7     = 0.5*(g7a+g7b) \n",
    "g8     = 0.5*(g8a+g8b) \n",
    "\n",
    "\n",
    "g1D     = 0.5*(g1aD+g1bD) \n",
    "g2D     = 0.5*(g2aD+g2bD) \n",
    "g3D     = 0.5*(g3aD+g3bD) \n",
    "g4D     = 0.5*(g4aD+g4bD) \n",
    "g5D     = 0.5*(g5aD+g5bD) \n",
    "g6D     = 0.5*(g6aD+g6bD) \n",
    "g7D     = 0.5*(g7aD+g7bD) \n",
    "g8D     = 0.5*(g8aD+g8bD) \n",
    "\n",
    "\n",
    "\n",
    "g    = [g1,g2,g3,g4,g5a,g6a, g7a,g8a]\n",
    "gD   = [g1D,g2D,g3D,g4D,g5D,g6D, g7D, g8D]\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.25  # the width of the bars\n",
    "\n",
    "ax = plt.gca()\n",
    "rects1 = ax.bar(x - width/2, g, width, label='Lift Modulation', color='black')\n",
    "rects2 = ax.bar(x + width/2, gD, width, label='Drag Modulation', facecolor='None', edgecolor='black', linewidth=1.0)\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Peak deceleration, Earth g', fontsize=16)\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels, fontsize=16)\n",
    "ax.legend(loc='upper right', fontsize=16)\n",
    "\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=16)\n",
    "ax.tick_params(axis='y',labelsize=16)\n",
    "\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/g-load-histogram.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/g-load-histogram.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/g-load-histogram.eps', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1.setInitialState(150.0,0.0,0.0,11.0,0.0, underShootLimit1,0.0,0.0)\n",
    "vehicle2.setInitialState (140.0,0.0,0.0,11.5,0.0,underShootLimit2,0.0,0.0)\n",
    "vehicle3.setInitialState (120.0,0.0,0.0, 7.0,0.0,underShootLimit3,0.0,0.0)\n",
    "vehicle4.setInitialState(1000.0,0.0,0.0,60.0,0.0,underShootLimit4,0.0,0.0)\n",
    "vehicle5.setInitialState(1000.0,0.0,0.0,36.0,0.0,underShootLimit5,0.0,0.0)\n",
    "vehicle6.setInitialState(1000.0,0.0,0.0, 7.5,0.0,underShootLimit6,0.0,0.0)\n",
    "vehicle7.setInitialState(1000.0,0.0,0.0,25.0,0.0,underShootLimit7,0.0,0.0)\n",
    "vehicle8.setInitialState(1000.0,0.0,0.0,27.5,0.0,underShootLimit8,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle2.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle3.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle4.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle5.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle6.propogateEntry(6000.0, 0.1, 0.0)\n",
    "vehicle7.propogateEntry(3000.0, 0.1, 0.0)\n",
    "vehicle8.propogateEntry(3000.0, 0.1, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "q1a = max(vehicle1.q_stag_total)\n",
    "q2a = max(vehicle2.q_stag_total)\n",
    "q3a = max(vehicle3.q_stag_total)\n",
    "q4a = max(vehicle4.q_stag_total)\n",
    "q5a = max(vehicle5.q_stag_total)\n",
    "q6a = max(vehicle6.q_stag_total)\n",
    "q7a = max(vehicle7.q_stag_total)\n",
    "q8a = max(vehicle8.q_stag_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "642.1276341392222 614.2559824945225 121.65317456170806 7191.927633087862 805.1978133056165 94.69176577591485 627.2842478635683 1182.2291718253605\n"
     ]
    }
   ],
   "source": [
    "print(q1a, q2a, q3a, q4a, q5a, q6a, q7a, q8a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1a = max(vehicle1.heatload)\n",
    "Q2a = max(vehicle2.heatload)\n",
    "Q3a = max(vehicle3.heatload)\n",
    "Q4a = max(vehicle4.heatload)\n",
    "Q5a = max(vehicle5.heatload)\n",
    "Q6a = max(vehicle6.heatload)\n",
    "Q7a = max(vehicle7.heatload)\n",
    "Q8a = max(vehicle8.heatload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18044.889764786225 24664.83198000201 6584.470202019118 404156.7218334246 129041.91277755701 10503.10537602897 132108.93936047758 157527.4898229573\n"
     ]
    }
   ],
   "source": [
    "print(Q1a, Q2a, Q3a, Q4a, Q5a, Q6a, Q7a, Q8a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1adfba28820>]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(vehicle1.stag_pres_atm, vehicle1.q_stag_total)\n",
    "plt.plot(vehicle2.stag_pres_atm, vehicle2.q_stag_total)\n",
    "plt.plot(vehicle3.stag_pres_atm, vehicle3.q_stag_total)\n",
    "#plt.plot(vehicle4.stag_pres_atm, vehicle4.q_stag_total)\n",
    "#plt.plot(vehicle5.stag_pres_atm, vehicle5.q_stag_total)\n",
    "plt.plot(vehicle6.stag_pres_atm, vehicle6.q_stag_total)\n",
    "plt.plot(vehicle7.stag_pres_atm, vehicle7.q_stag_total)\n",
    "plt.plot(vehicle8.stag_pres_atm, vehicle8.q_stag_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.figure(figsize=(6.25,6.25))\n",
    "ax = plt.gca()\n",
    "\n",
    "plt.plot(vehicle1.stag_pres_atm, vehicle1.q_stag_total, linewidth=1.5, color='xkcd:orange')\n",
    "plt.plot(vehicle2.stag_pres_atm, vehicle2.q_stag_total, linewidth=1.5, color='xkcd:pastel blue')\n",
    "plt.plot(vehicle3.stag_pres_atm, vehicle3.q_stag_total, linewidth=1.5, color='xkcd:light red')\n",
    "#plt.plot(vehicle4.stag_pres_atm, vehicle4.q_stag_total, linewidth=2.0, color='xkcd:brown')\n",
    "#plt.plot(vehicle5.stag_pres_atm, vehicle5.q_stag_total, linewidth=2.0, color='xkcd:magenta')\n",
    "plt.plot(vehicle6.stag_pres_atm, vehicle6.q_stag_total, linewidth=1.5, color='xkcd:goldenrod')\n",
    "plt.plot(vehicle7.stag_pres_atm, vehicle7.q_stag_total*1.6, linewidth=1.5, color='xkcd:light turquoise')\n",
    "plt.plot(vehicle8.stag_pres_atm, vehicle8.q_stag_total, linewidth=1.5, color='xkcd:cerulean blue')\n",
    "\n",
    "\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=16)\n",
    "ax.tick_params(axis='y',labelsize=16)\n",
    "\n",
    "plt.xlabel('Stagnation pressure, '+r'$atm$', fontsize=16)\n",
    "plt.ylabel('Stagnation-point heat rate '+r'$\\dot{q}, W/cm^2$', fontsize=16)\n",
    "\n",
    "plt.xlim([0.0,0.35])\n",
    "plt.ylim([-80.0,1300])\n",
    "\n",
    "plt.text( 0.25, 700, 'Venus' ,color='xkcd:orange', fontsize=16)\n",
    "plt.text( 0.13, 650, 'Earth' ,color='xkcd:pastel blue', fontsize=16)\n",
    "plt.text( 0.17, 100, 'Mars' ,color='xkcd:light red', fontsize=16)\n",
    "plt.text( 0.05, -45, 'Titan' ,color='xkcd:goldenrod', fontsize=16)\n",
    "#plt.text( 170, 4000, 'Saturn' ,color='xkcd:magenta', fontsize=12)\n",
    "plt.text( 0.06, 800 ,  'Uranus' ,color='xkcd:light turquoise', fontsize=16)\n",
    "plt.text( 0.12 ,980,  'Neptune' ,color='xkcd:cerulean blue', fontsize=16)\n",
    "#plt.text( 640 ,16000,  'Jupiter' ,color='xkcd:brown', fontsize=8)\n",
    "\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/heat-rate-stag-pres.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/heat-rate-stag-pres.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/heat-rate-stag-pres.eps', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1.setInitialState(150.0,0.0,0.0,11.0,0.0, overShootLimit1,0.0,0.0)\n",
    "vehicle2.setInitialState (140.0,0.0,0.0,11.5,0.0,overShootLimit2,0.0,0.0)\n",
    "vehicle3.setInitialState (120.0,0.0,0.0, 7.0,0.0,overShootLimit3,0.0,0.0)\n",
    "vehicle4.setInitialState(1000.0,0.0,0.0,60.0,0.0,overShootLimit4,0.0,0.0)\n",
    "vehicle5.setInitialState(1000.0,0.0,0.0,36.0,0.0,overShootLimit5,0.0,0.0)\n",
    "vehicle6.setInitialState(1000.0,0.0,0.0, 7.5,0.0,overShootLimit6,0.0,0.0)\n",
    "vehicle7.setInitialState(1000.0,0.0,0.0,25.0,0.0,overShootLimit7,0.0,0.0)\n",
    "vehicle8.setInitialState(1000.0,0.0,0.0,27.5,0.0,overShootLimit8,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1.propogateEntry(2400.0, 0.1, 180.0)\n",
    "vehicle2.propogateEntry(2400.0, 0.1, 180.0)\n",
    "vehicle3.propogateEntry(2400.0, 0.1, 180.0)\n",
    "vehicle4.propogateEntry(2400.0, 0.1, 180.0)\n",
    "vehicle5.propogateEntry(2400.0, 0.1, 180.0)\n",
    "vehicle6.propogateEntry(6000.0, 0.1, 180.0)\n",
    "vehicle7.propogateEntry(3000.0, 0.1, 180.0)\n",
    "vehicle8.propogateEntry(3000.0, 0.1, 180.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "q1b = max(vehicle1.q_stag_total)\n",
    "q2b = max(vehicle2.q_stag_total)\n",
    "q3b = max(vehicle3.q_stag_total)\n",
    "q4b = max(vehicle4.q_stag_total)\n",
    "q5b = max(vehicle5.q_stag_total)\n",
    "q6b = max(vehicle6.q_stag_total)\n",
    "q7b = max(vehicle7.q_stag_total)\n",
    "q8b = max(vehicle8.q_stag_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "213.70689411138312 249.14696923482092 66.72466001880417 4997.522281418908 703.3820311809341 65.20473125440216 472.8205177325455 959.4442316298216\n"
     ]
    }
   ],
   "source": [
    "print(q1b, q2b, q3b, q4b, q5b, q6b, q7b, q8b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1b = max(vehicle1.heatload)\n",
    "Q2b = max(vehicle2.heatload)\n",
    "Q3b = max(vehicle3.heatload)\n",
    "Q4b = max(vehicle4.heatload)\n",
    "Q5b = max(vehicle5.heatload)\n",
    "Q6b = max(vehicle6.heatload)\n",
    "Q7b = max(vehicle7.heatload)\n",
    "Q8b = max(vehicle8.heatload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41014.212051477145 40440.18449134592 12383.296999224456 423193.2157923033 142840.38811816543 15666.053682169444 177518.4451903292 192638.13137657772\n"
     ]
    }
   ],
   "source": [
    "print(Q1b, Q2b, Q3b, Q4b, Q5b, Q6b, Q7b, Q8b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.figure(figsize=(6.25,6.25))\n",
    "ax = plt.gca()\n",
    "\n",
    "#arbitrary scaling factors to make figure nice\n",
    "ax.add_patch(Rectangle((Q1a/1E3,q1a), (Q1b-Q1a)/1E3, q1a-q1b,  alpha=1, facecolor='xkcd:orange'))\n",
    "ax.add_patch(Rectangle((Q2a/1E3,q2a), (Q2b-Q2a)*3/1E3, q2a-q2b,  alpha=1, facecolor='xkcd:pastel blue'))\n",
    "ax.add_patch(Rectangle((Q3a/1E3,q3a), (Q3b-Q3a)*4/1E3, q3a-q3b,  alpha=1, facecolor='xkcd:light red'))\n",
    "ax.add_patch(Rectangle((Q4a/1E3,q4a), (Q4b-Q4a)*100/1E3, (q4a-q4b)*3,  alpha=1, facecolor='xkcd:brown'))\n",
    "ax.add_patch(Rectangle((Q5a/1E3,q5a*2), (Q5b-Q5a)*30/1E3, (q5a-q5b)*20,  alpha=1, facecolor='xkcd:magenta'))\n",
    "ax.add_patch(Rectangle((Q6a/1E3,q6a), (Q6b-Q6a)*7/1E3, q6a-q6b,  alpha=1, facecolor='xkcd:goldenrod'))\n",
    "ax.add_patch(Rectangle((Q7a/1E3,q7a), (Q7b-Q7a)*7/1E3, (q7a-q7b)*8,  alpha=1, facecolor='xkcd:light turquoise'))\n",
    "ax.add_patch(Rectangle((Q8a/1E3,q8a), (Q8b-Q8a)*12/1E3, (q8a-q8b)*8,  alpha=1, facecolor='xkcd:cerulean blue'))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.text( 10, 1220, 'Venus' ,color='xkcd:orange', fontsize=16)\n",
    "plt.text( 35, 450, 'Earth' ,color='xkcd:pastel blue', fontsize=16)\n",
    "plt.text( 10, 200, 'Mars' ,color='xkcd:light red', fontsize=16)\n",
    "plt.text( 35, 70, 'Titan' ,color='xkcd:goldenrod', fontsize=16)\n",
    "plt.text( 170, 4000, 'Saturn' ,color='xkcd:magenta', fontsize=16)\n",
    "plt.text( 500 ,600 ,  'Uranus' ,color='xkcd:light turquoise', fontsize=16)\n",
    "plt.text( 650 ,980,  'Neptune' ,color='xkcd:cerulean blue', fontsize=16)\n",
    "plt.text( 640 ,16000,  'Jupiter' ,color='xkcd:brown', fontsize=16)\n",
    "\n",
    "#plt.text( 19, -29.6-5, 'Lift modulation' ,color='xkcd:black', fontsize=8)\n",
    "#plt.text( 19, -32.6-5, 'Drag modulation' ,color='xkcd:black', fontsize=8)\n",
    "\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=16)\n",
    "ax.tick_params(axis='y',labelsize=16)\n",
    "\n",
    "\n",
    "ax.xaxis.set_tick_params(direction='in', which='both')\n",
    "ax.yaxis.set_tick_params(direction='in', which='both')\n",
    "\n",
    "#ax.xaxis.set_tick_params(width=2, length=8)\n",
    "#ax.yaxis.set_tick_params(width=2, length=8)\n",
    "\n",
    "#ax.xaxis.set_tick_params(width=1, length=4, which='minor')\n",
    "#ax.yaxis.set_tick_params(width=1, length=4, which='minor')\n",
    "\n",
    "\n",
    "plt.xlim([5,3000])\n",
    "plt.ylim([50,30000])\n",
    "\n",
    "plt.xlabel('Stagnation-point heat load '+r'$Q, kJ/cm^2$', fontsize=16)\n",
    "plt.ylabel('Stagnation-point heat rate '+r'$\\dot{q}, W/cm^2$', fontsize=16)\n",
    "\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=16)\n",
    "ax.tick_params(axis='y',labelsize=16)\n",
    "\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/heating-planets.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/heating-planets.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/comparative-studies/heating-planets.eps', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D.setInitialState(150.0,0.0,0.0,11.0,0.0, underShootLimit1D,0.0,0.0)\n",
    "vehicle2D.setInitialState (140.0,0.0,0.0,11.5,0.0,underShootLimit2D,0.0,0.0)\n",
    "vehicle3D.setInitialState (120.0,0.0,0.0, 7.0,0.0,underShootLimit3D,0.0,0.0)\n",
    "vehicle4D.setInitialState(1000.0,0.0,0.0,60.0,0.0,underShootLimit4D,0.0,0.0)\n",
    "vehicle5D.setInitialState(1000.0,0.0,0.0,36.0,0.0,underShootLimit5D,0.0,0.0)\n",
    "vehicle6D.setInitialState(1000.0,0.0,0.0, 7.5,0.0,underShootLimit6D,0.0,0.0)\n",
    "vehicle7D.setInitialState(1000.0,0.0,0.0,25.0,0.0,underShootLimit7D,0.0,0.0)\n",
    "vehicle8D.setInitialState(1000.0,0.0,0.0,27.5,0.0,underShootLimit8D,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle2D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle3D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle4D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle5D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle6D.propogateEntry(6000.0, 0.1, 0.0)\n",
    "vehicle7D.propogateEntry(3000.0, 0.1, 0.0)\n",
    "vehicle8D.propogateEntry(3000.0, 0.1, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "q1aD = max(vehicle1D.q_stag_total)\n",
    "q2aD = max(vehicle2D.q_stag_total)\n",
    "q3aD = max(vehicle3D.q_stag_total)\n",
    "q4aD = max(vehicle4D.q_stag_total)\n",
    "q5aD = max(vehicle5D.q_stag_total)\n",
    "q6aD = max(vehicle6D.q_stag_total)\n",
    "q7aD = max(vehicle7D.q_stag_total)\n",
    "q8aD = max(vehicle8D.q_stag_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "745.7953451113492 705.0900492653783 232.24889779550733 28919.33825256155 3094.717901751404 202.9699291503758 1481.0197840024714 2494.010736865881\n"
     ]
    }
   ],
   "source": [
    "print(q1aD, q2aD, q3aD, q4aD, q5aD, q6aD, q7aD, q8aD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1aD = max(vehicle1D.heatload)\n",
    "Q2aD = max(vehicle2D.heatload)\n",
    "Q3aD = max(vehicle3D.heatload)\n",
    "Q4aD = max(vehicle4D.heatload)\n",
    "Q5aD = max(vehicle5D.heatload)\n",
    "Q6aD = max(vehicle6D.heatload)\n",
    "Q7aD = max(vehicle7D.heatload)\n",
    "Q8aD = max(vehicle8D.heatload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63225.013843509114 70037.36642182757 24048.699415528376 2022034.680126669 589062.4330898894 34263.17986232094 703556.0767908201 456933.58759085485\n"
     ]
    }
   ],
   "source": [
    "print(Q1aD, Q2aD, Q3aD, Q4aD, Q5aD, Q6aD, Q7aD, Q8aD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D.setInitialState(150.0,0.0,0.0,11.0,0.0, overShootLimit1D,0.0,0.0)\n",
    "vehicle2D.setInitialState (140.0,0.0,0.0,11.5,0.0,overShootLimit2D,0.0,0.0)\n",
    "vehicle3D.setInitialState (120.0,0.0,0.0, 7.0,0.0,overShootLimit3D,0.0,0.0)\n",
    "vehicle4D.setInitialState(1000.0,0.0,0.0,60.0,0.0,overShootLimit4D,0.0,0.0)\n",
    "vehicle5D.setInitialState(1000.0,0.0,0.0,36.0,0.0,overShootLimit5D,0.0,0.0)\n",
    "vehicle6D.setInitialState(1000.0,0.0,0.0, 7.5,0.0,overShootLimit6D,0.0,0.0)\n",
    "vehicle7D.setInitialState(1000.0,0.0,0.0,25.0,0.0,overShootLimit7D,0.0,0.0)\n",
    "vehicle8D.setInitialState(1000.0,0.0,0.0,27.5,0.0,overShootLimit8D,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "vehicle1D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle2D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle3D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle4D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle5D.propogateEntry(2400.0, 0.1, 0.0)\n",
    "vehicle6D.propogateEntry(6000.0, 0.1, 0.0)\n",
    "vehicle7D.propogateEntry(3000.0, 0.1, 0.0)\n",
    "vehicle8D.propogateEntry(3000.0, 0.1, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "q1bD = max(vehicle1D.q_stag_total)\n",
    "q2bD = max(vehicle2D.q_stag_total)\n",
    "q3bD = max(vehicle3D.q_stag_total)\n",
    "q4bD = max(vehicle4D.q_stag_total)\n",
    "q5bD = max(vehicle5D.q_stag_total)\n",
    "q6bD = max(vehicle6D.q_stag_total)\n",
    "q7bD = max(vehicle7D.q_stag_total)\n",
    "q8bD = max(vehicle8D.q_stag_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "365.4707051163394 345.9663810607608 125.67563352755842 9711.796590326772 1568.7988952148437 132.3824890675988 703.7262586043155 1393.3043964733772\n"
     ]
    }
   ],
   "source": [
    "print(q1bD, q2bD, q3bD, q4bD, q5bD, q6bD, q7bD, q8bD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1bD = max(vehicle1D.heatload)\n",
    "Q2bD = max(vehicle2D.heatload)\n",
    "Q3bD = max(vehicle3D.heatload)\n",
    "Q4bD = max(vehicle4D.heatload)\n",
    "Q5bD = max(vehicle5D.heatload)\n",
    "Q6bD = max(vehicle6D.heatload)\n",
    "Q7bD = max(vehicle7D.heatload)\n",
    "Q8bD = max(vehicle8D.heatload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23557.77862944383 27401.63760097738 12385.99159334015 771752.6244654971 276172.44603547413 26293.128049837567 204465.24369679764 234753.4118006923\n"
     ]
    }
   ],
   "source": [
    "print(Q1bD, Q2bD, Q3bD, Q4bD, Q5bD, Q6bD, Q7bD, Q8bD)"
   ]
  }
 ],
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