{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "40732e7b",
   "metadata": {},
   "source": [
    "# Section 4.2 - Venus SmallSat Aerocapture - Tradespace Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e543e74f",
   "metadata": {},
   "source": [
    "We use aerocapture feasibility charts to explore the design trade space for aerocapture at Venus using drag modulation aerocapture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d0e0065d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle\n",
    "\n",
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.patches import Polygon\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6173d083",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a planet object\n",
    "planet=Planet(\"VENUS\")\n",
    "planet.h_skip = 150000.0\n",
    "\n",
    "# Load an nominal atmospheric profile with height, temp, pressure, density data\n",
    "planet.loadAtmosphereModel('../../../atmdata/Venus/venus-gram-avg.dat', 0 , 1 ,2, 3)\n",
    "\n",
    "vinf_kms_array  = np.linspace( 0.0,   10.0,   11)\n",
    "betaRatio_array = np.linspace( 1.0,   21.0 ,  11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abc66f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "beta1 = 20.0\n",
    "runID = 'venus-smallsat-dm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a4869ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "v0_kms_array    = np.zeros(len(vinf_kms_array))\n",
    "v0_kms_array[:] = np.sqrt(1.0*(vinf_kms_array[:]*1E3)**2.0 + 2*np.ones(len(vinf_kms_array))*planet.GM/(planet.RP+150.0*1.0E3))/1.0E3\n",
    "\n",
    "overShootLimit_array  = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "underShootLimit_array = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "exitflag_os_array     = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "exitflag_us_array     = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "TCW_array             = np.zeros((len(v0_kms_array),len(betaRatio_array)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1c42eb88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VINF: 0.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 3.0 TCW: 0.22008299338631332 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 5.0 TCW: 0.3220487156722811 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 7.0 TCW: 0.38787807517292094 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 9.0 TCW: 0.4360231767168443 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 11.0 TCW: 0.4738638612579962 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 13.0 TCW: 0.5050491497822804 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 15.0 TCW: 0.5315089544747025 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 17.0 TCW: 0.5544410763759515 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 19.0 TCW: 0.5747357478830963 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 21.0 TCW: 0.5929213868803345 deg.\n",
      "VINF: 1.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 1.0 km/s, BETA RATIO: 3.0 TCW: 0.22123349050889374 deg.\n",
      "VINF: 1.0 km/s, BETA RATIO: 5.0 TCW: 0.3235909743088996 deg.\n",
      "VINF: 1.0 km/s, BETA RATIO: 7.0 TCW: 0.3896626835994539 deg.\n",
      "VINF: 1.0 km/s, BETA RATIO: 9.0 TCW: 0.4379480112984311 deg.\n",
      "VINF: 1.0 km/s, BETA RATIO: 11.0 TCW: 0.47592598646951956 deg.\n",
      "VINF: 1.0 km/s, BETA RATIO: 13.0 TCW: 0.5072144496916735 deg.\n",
      "VINF: 1.0 km/s, BETA RATIO: 15.0 TCW: 0.533770236565033 deg.\n",
      "VINF: 1.0 km/s, BETA RATIO: 17.0 TCW: 0.5568076887211646 deg.\n",
      "VINF: 1.0 km/s, BETA RATIO: 19.0 TCW: 0.5771486694866326 deg.\n",
      "VINF: 1.0 km/s, BETA RATIO: 21.0 TCW: 0.5954161477638991 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 3.0 TCW: 0.22448952694321633 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 5.0 TCW: 0.3279588277655421 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 7.0 TCW: 0.3947015734629531 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 9.0 TCW: 0.44340843832469545 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 11.0 TCW: 0.481769912490563 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 13.0 TCW: 0.5133577891137975 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 15.0 TCW: 0.5401650801031792 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 17.0 TCW: 0.5634292532413383 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 19.0 TCW: 0.5840196740136889 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 21.0 TCW: 0.6025053701414436 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 3.0 TCW: 0.22936612537887413 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 5.0 TCW: 0.33448939238951425 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 7.0 TCW: 0.40216131739362027 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 9.0 TCW: 0.45154499992349884 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 11.0 TCW: 0.4904835976776667 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 13.0 TCW: 0.52252868594951 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 15.0 TCW: 0.5497326720760611 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 17.0 TCW: 0.5733744004428445 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 19.0 TCW: 0.594342006228544 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 21.0 TCW: 0.613180774023931 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 3.0 TCW: 0.23510779525895487 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 5.0 TCW: 0.34221262529172236 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 7.0 TCW: 0.4109283669422439 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 9.0 TCW: 0.46119430841645226 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 11.0 TCW: 0.5008089444927464 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 13.0 TCW: 0.533429117542255 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 15.0 TCW: 0.5611319533381902 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 17.0 TCW: 0.5852801491964783 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 19.0 TCW: 0.6067331068625208 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 21.0 TCW: 0.6259738363769429 deg.\n",
      "VINF: 5.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 5.0 km/s, BETA RATIO: 3.0 TCW: 0.24106473999927402 deg.\n",
      "VINF: 5.0 km/s, BETA RATIO: 5.0 TCW: 0.3502693158479815 deg.\n",
      "VINF: 5.0 km/s, BETA RATIO: 7.0 TCW: 0.4200981509893609 deg.\n",
      "VINF: 5.0 km/s, BETA RATIO: 9.0 TCW: 0.4713302899108385 deg.\n",
      "VINF: 5.0 km/s, BETA RATIO: 11.0 TCW: 0.5116798001981806 deg.\n",
      "VINF: 5.0 km/s, BETA RATIO: 13.0 TCW: 0.5449089188296057 deg.\n",
      "VINF: 5.0 km/s, BETA RATIO: 15.0 TCW: 0.5732341981347417 deg.\n",
      "VINF: 5.0 km/s, BETA RATIO: 17.0 TCW: 0.597998304405337 deg.\n",
      "VINF: 5.0 km/s, BETA RATIO: 19.0 TCW: 0.619935902828729 deg.\n",
      "VINF: 5.0 km/s, BETA RATIO: 21.0 TCW: 0.6395651521925174 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 3.0 TCW: 0.24676724357777857 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 5.0 TCW: 0.3580002305097878 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 7.0 TCW: 0.4290605300848256 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 9.0 TCW: 0.4812206557580794 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 11.0 TCW: 0.5222973023010127 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 13.0 TCW: 0.556252373757161 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 15.0 TCW: 0.5852871360075369 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 17.0 TCW: 0.6105959658088977 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 19.0 TCW: 0.6329810365095909 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 21.0 TCW: 0.6530580049839045 deg.\n",
      "VINF: 7.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 7.0 km/s, BETA RATIO: 3.0 TCW: 0.2521190142870182 deg.\n",
      "VINF: 7.0 km/s, BETA RATIO: 5.0 TCW: 0.3651661640360544 deg.\n",
      "VINF: 7.0 km/s, BETA RATIO: 7.0 TCW: 0.437451293477352 deg.\n",
      "VINF: 7.0 km/s, BETA RATIO: 9.0 TCW: 0.49050274864566745 deg.\n",
      "VINF: 7.0 km/s, BETA RATIO: 11.0 TCW: 0.5323522949183825 deg.\n",
      "VINF: 7.0 km/s, BETA RATIO: 13.0 TCW: 0.5670791782686138 deg.\n",
      "VINF: 7.0 km/s, BETA RATIO: 15.0 TCW: 0.5967333060434612 deg.\n",
      "VINF: 7.0 km/s, BETA RATIO: 17.0 TCW: 0.622537690695026 deg.\n",
      "VINF: 7.0 km/s, BETA RATIO: 19.0 TCW: 0.6454142326801957 deg.\n",
      "VINF: 7.0 km/s, BETA RATIO: 21.0 TCW: 0.6660513811730198 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 3.0 TCW: 0.25699539153356454 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 5.0 TCW: 0.37161893962911563 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 7.0 TCW: 0.44504548665645416 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 9.0 TCW: 0.4989177809766261 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 11.0 TCW: 0.5415804510485032 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 13.0 TCW: 0.5770209499860357 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 15.0 TCW: 0.6071955141742365 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 17.0 TCW: 0.6335191545731504 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 19.0 TCW: 0.6569753832445713 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 21.0 TCW: 0.678146709316934 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 3.0 TCW: 0.2612995168128691 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 5.0 TCW: 0.3772896297159605 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 7.0 TCW: 0.4517058561796148 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 9.0 TCW: 0.5063778333278606 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 11.0 TCW: 0.5498656327908975 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 13.0 TCW: 0.5858658559591277 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 15.0 TCW: 0.6165621917825774 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 17.0 TCW: 0.6434872302379517 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 19.0 TCW: 0.6674812458804809 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 21.0 TCW: 0.6890528016920143 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 3.0 TCW: 0.2649485937181453 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 5.0 TCW: 0.3821906918747118 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 7.0 TCW: 0.45745376532067894 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 9.0 TCW: 0.5129644375265343 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 11.0 TCW: 0.5571190046539414 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 13.0 TCW: 0.5936377712023386 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 15.0 TCW: 0.624924942956568 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 17.0 TCW: 0.6524087362122373 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 19.0 TCW: 0.6768076225562254 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 21.0 TCW: 0.6986981209010992 deg.\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,len(v0_kms_array)):\n",
    "    for j in range(0,len(betaRatio_array)):\n",
    "        vehicle=Vehicle('DMVehicle', 100.0, beta1, 0.0, 1.767, 0.0, 0.35, planet)\n",
    "        vehicle.setInitialState(150.0,0.0,0.0,v0_kms_array[i],0.0,-4.5,0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-6)\n",
    "        vehicle.setDragModulationVehicleParams(beta1,betaRatio_array[j])\n",
    "\n",
    "        underShootLimit_array[i,j], exitflag_us_array[i,j] = vehicle.findUnderShootLimitD(2400.0, 2.0, -80.0,-4.0,1E-10, 400.0)\n",
    "        overShootLimit_array[i,j] , exitflag_os_array[i,j] = vehicle.findOverShootLimitD (2400.0, 2.0, -80.0,-4.0,1E-10, 400.0)\n",
    "\n",
    "        TCW_array[i,j]    = overShootLimit_array[i,j] - underShootLimit_array[i,j]\n",
    "\n",
    "        print('VINF: '+str(vinf_kms_array[i])+' km/s, BETA RATIO: '+str(betaRatio_array[j])+' TCW: '+str(TCW_array[i,j])+' deg.')\n",
    "\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'vinf_kms_array.txt',vinf_kms_array)\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'v0_kms_array.txt',v0_kms_array)\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'betaRatio_array.txt',betaRatio_array)\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'overShootLimit_array.txt',overShootLimit_array)\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'exitflag_os_array.txt',exitflag_os_array)\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'underShootLimit_array.txt',underShootLimit_array)\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'exitflag_us_array.txt',exitflag_us_array)\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'TCW_array.txt',TCW_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f3832ee0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 0.0 km/s, BR: 1.0 G_MAX: 4.079933711421796 QDOT_MAX: 113.05000994278133 J_MAX: 10817.79164972663 STAG. PRES: 0.007903249111091654\n",
      "V_infty: 0.0 km/s, BR: 3.0 G_MAX: 4.079933711421796 QDOT_MAX: 144.09189070737747 J_MAX: 10817.79164972663 STAG. PRES: 0.007903249111091654\n",
      "V_infty: 0.0 km/s, BR: 5.0 G_MAX: 4.079933711421796 QDOT_MAX: 155.67921157028482 J_MAX: 10817.79164972663 STAG. PRES: 0.007903249111091654\n",
      "V_infty: 0.0 km/s, BR: 7.0 G_MAX: 4.079933711421796 QDOT_MAX: 162.37217328901724 J_MAX: 10817.79164972663 STAG. PRES: 0.007903249111091654\n",
      "V_infty: 0.0 km/s, BR: 9.0 G_MAX: 4.079933711421796 QDOT_MAX: 167.02177853435035 J_MAX: 10817.79164972663 STAG. PRES: 0.007903249111091654\n",
      "V_infty: 0.0 km/s, BR: 11.0 G_MAX: 4.079933711421796 QDOT_MAX: 170.24635176423186 J_MAX: 10817.79164972663 STAG. PRES: 0.007903249111091654\n",
      "V_infty: 0.0 km/s, BR: 13.0 G_MAX: 4.079933711421796 QDOT_MAX: 173.2497530224659 J_MAX: 10817.79164972663 STAG. PRES: 0.007903249111091654\n",
      "V_infty: 0.0 km/s, BR: 15.0 G_MAX: 4.079933711421796 QDOT_MAX: 175.36565257217123 J_MAX: 10817.79164972663 STAG. PRES: 0.007903249111091654\n",
      "V_infty: 0.0 km/s, BR: 17.0 G_MAX: 4.079933711421796 QDOT_MAX: 177.31047678878255 J_MAX: 10817.79164972663 STAG. PRES: 0.007903249111091654\n",
      "V_infty: 0.0 km/s, BR: 19.0 G_MAX: 4.079933711421796 QDOT_MAX: 179.10538406312406 J_MAX: 10817.79164972663 STAG. PRES: 0.007903249111091654\n",
      "V_infty: 0.0 km/s, BR: 21.0 G_MAX: 4.079933711421796 QDOT_MAX: 180.51057722900234 J_MAX: 10817.79164972663 STAG. PRES: 0.007903249111091654\n",
      "V_infty: 1.0 km/s, BR: 1.0 G_MAX: 4.185371270625705 QDOT_MAX: 115.38471879871071 J_MAX: 10924.477953928468 STAG. PRES: 0.0081074007731149\n",
      "V_infty: 1.0 km/s, BR: 3.0 G_MAX: 4.185371270625705 QDOT_MAX: 146.9185750954554 J_MAX: 10924.477953928468 STAG. PRES: 0.0081074007731149\n",
      "V_infty: 1.0 km/s, BR: 5.0 G_MAX: 4.185371270625705 QDOT_MAX: 158.45496273385552 J_MAX: 10924.477953928468 STAG. PRES: 0.0081074007731149\n",
      "V_infty: 1.0 km/s, BR: 7.0 G_MAX: 4.185371270625705 QDOT_MAX: 165.25884198936 J_MAX: 10924.477953928468 STAG. PRES: 0.0081074007731149\n",
      "V_infty: 1.0 km/s, BR: 9.0 G_MAX: 4.185371270625705 QDOT_MAX: 169.93190376121265 J_MAX: 10924.477953928468 STAG. PRES: 0.0081074007731149\n",
      "V_infty: 1.0 km/s, BR: 11.0 G_MAX: 4.185371270625705 QDOT_MAX: 173.56456722064638 J_MAX: 10924.477953928468 STAG. PRES: 0.0081074007731149\n",
      "V_infty: 1.0 km/s, BR: 13.0 G_MAX: 4.185371270625705 QDOT_MAX: 176.1989410764226 J_MAX: 10924.477953928468 STAG. PRES: 0.0081074007731149\n",
      "V_infty: 1.0 km/s, BR: 15.0 G_MAX: 4.185371270625705 QDOT_MAX: 178.5919100925727 J_MAX: 10924.477953928468 STAG. PRES: 0.0081074007731149\n",
      "V_infty: 1.0 km/s, BR: 17.0 G_MAX: 4.185371270625705 QDOT_MAX: 180.614842235483 J_MAX: 10924.477953928468 STAG. PRES: 0.0081074007731149\n",
      "V_infty: 1.0 km/s, BR: 19.0 G_MAX: 4.185371270625705 QDOT_MAX: 182.13003816021427 J_MAX: 10924.477953928468 STAG. PRES: 0.0081074007731149\n",
      "V_infty: 1.0 km/s, BR: 21.0 G_MAX: 4.185371270625705 QDOT_MAX: 183.545793748858 J_MAX: 10924.477953928468 STAG. PRES: 0.0081074007731149\n",
      "V_infty: 2.0 km/s, BR: 1.0 G_MAX: 4.499625254497456 QDOT_MAX: 122.53197096799147 J_MAX: 11242.423821849141 STAG. PRES: 0.00871607098252073\n",
      "V_infty: 2.0 km/s, BR: 3.0 G_MAX: 4.499625254497456 QDOT_MAX: 155.18888167725282 J_MAX: 11242.423821849141 STAG. PRES: 0.00871607098252073\n",
      "V_infty: 2.0 km/s, BR: 5.0 G_MAX: 4.499625254497456 QDOT_MAX: 167.2880969153415 J_MAX: 11242.423821849141 STAG. PRES: 0.00871607098252073\n",
      "V_infty: 2.0 km/s, BR: 7.0 G_MAX: 4.499625254497456 QDOT_MAX: 174.3564938838953 J_MAX: 11242.423821849141 STAG. PRES: 0.00871607098252073\n",
      "V_infty: 2.0 km/s, BR: 9.0 G_MAX: 4.499625254497456 QDOT_MAX: 178.9909753353484 J_MAX: 11242.423821849141 STAG. PRES: 0.00871607098252073\n",
      "V_infty: 2.0 km/s, BR: 11.0 G_MAX: 4.499625254497456 QDOT_MAX: 182.90398753130393 J_MAX: 11242.423821849141 STAG. PRES: 0.00871607098252073\n",
      "V_infty: 2.0 km/s, BR: 13.0 G_MAX: 4.499625254497456 QDOT_MAX: 185.47783785944654 J_MAX: 11242.423821849141 STAG. PRES: 0.00871607098252073\n",
      "V_infty: 2.0 km/s, BR: 15.0 G_MAX: 4.499625254497456 QDOT_MAX: 188.18642810352839 J_MAX: 11242.423821849141 STAG. PRES: 0.00871607098252073\n",
      "V_infty: 2.0 km/s, BR: 17.0 G_MAX: 4.499625254497456 QDOT_MAX: 190.19575483893533 J_MAX: 11242.423821849141 STAG. PRES: 0.00871607098252073\n",
      "V_infty: 2.0 km/s, BR: 19.0 G_MAX: 4.499625254497456 QDOT_MAX: 191.69153517270712 J_MAX: 11242.423821849141 STAG. PRES: 0.00871607098252073\n",
      "V_infty: 2.0 km/s, BR: 21.0 G_MAX: 4.499625254497456 QDOT_MAX: 193.37816128273002 J_MAX: 11242.423821849141 STAG. PRES: 0.00871607098252073\n",
      "V_infty: 3.0 km/s, BR: 1.0 G_MAX: 5.025610446074039 QDOT_MAX: 134.76303143702 J_MAX: 11766.481654399877 STAG. PRES: 0.009734754932408227\n",
      "V_infty: 3.0 km/s, BR: 3.0 G_MAX: 5.025610446074039 QDOT_MAX: 169.31586417830457 J_MAX: 11766.481654399877 STAG. PRES: 0.009734754932408227\n",
      "V_infty: 3.0 km/s, BR: 5.0 G_MAX: 5.025610446074039 QDOT_MAX: 182.06708256512798 J_MAX: 11766.481654399877 STAG. PRES: 0.009734754932408227\n",
      "V_infty: 3.0 km/s, BR: 7.0 G_MAX: 5.025610446074039 QDOT_MAX: 189.52196554256182 J_MAX: 11766.481654399877 STAG. PRES: 0.009734754932408227\n",
      "V_infty: 3.0 km/s, BR: 9.0 G_MAX: 5.025610446074039 QDOT_MAX: 194.80378654097836 J_MAX: 11766.481654399877 STAG. PRES: 0.009734754932408227\n",
      "V_infty: 3.0 km/s, BR: 11.0 G_MAX: 5.025610446074039 QDOT_MAX: 198.71729942757838 J_MAX: 11766.481654399877 STAG. PRES: 0.009734754932408227\n",
      "V_infty: 3.0 km/s, BR: 13.0 G_MAX: 5.025610446074039 QDOT_MAX: 201.84202106304377 J_MAX: 11766.481654399877 STAG. PRES: 0.009734754932408227\n",
      "V_infty: 3.0 km/s, BR: 15.0 G_MAX: 5.025610446074039 QDOT_MAX: 204.05549715623746 J_MAX: 11766.481654399877 STAG. PRES: 0.009734754932408227\n",
      "V_infty: 3.0 km/s, BR: 17.0 G_MAX: 5.025610446074039 QDOT_MAX: 206.56034208741548 J_MAX: 11766.481654399877 STAG. PRES: 0.009734754932408227\n",
      "V_infty: 3.0 km/s, BR: 19.0 G_MAX: 5.025610446074039 QDOT_MAX: 208.501297757804 J_MAX: 11766.481654399877 STAG. PRES: 0.009734754932408227\n",
      "V_infty: 3.0 km/s, BR: 21.0 G_MAX: 5.025610446074039 QDOT_MAX: 210.00588608225814 J_MAX: 11766.481654399877 STAG. PRES: 0.009734754932408227\n",
      "V_infty: 4.0 km/s, BR: 1.0 G_MAX: 5.772153427461481 QDOT_MAX: 152.62426191016726 J_MAX: 12492.724552981714 STAG. PRES: 0.011180451157413517\n",
      "V_infty: 4.0 km/s, BR: 3.0 G_MAX: 5.772153427461481 QDOT_MAX: 189.53338611998737 J_MAX: 12492.724552981714 STAG. PRES: 0.011180451157413517\n",
      "V_infty: 4.0 km/s, BR: 5.0 G_MAX: 5.772153427461481 QDOT_MAX: 203.63674558443122 J_MAX: 12492.724552981714 STAG. PRES: 0.011180451157413517\n",
      "V_infty: 4.0 km/s, BR: 7.0 G_MAX: 5.772153427461481 QDOT_MAX: 211.72327134722445 J_MAX: 12492.724552981714 STAG. PRES: 0.011180451157413517\n",
      "V_infty: 4.0 km/s, BR: 9.0 G_MAX: 5.772153427461481 QDOT_MAX: 217.25958641699856 J_MAX: 12492.724552981714 STAG. PRES: 0.011180451157413517\n",
      "V_infty: 4.0 km/s, BR: 11.0 G_MAX: 5.772153427461481 QDOT_MAX: 221.66690761767333 J_MAX: 12492.724552981714 STAG. PRES: 0.011180451157413517\n",
      "V_infty: 4.0 km/s, BR: 13.0 G_MAX: 5.772153427461481 QDOT_MAX: 225.00067131942575 J_MAX: 12492.724552981714 STAG. PRES: 0.011180451157413517\n",
      "V_infty: 4.0 km/s, BR: 15.0 G_MAX: 5.772153427461481 QDOT_MAX: 227.39205306578089 J_MAX: 12492.724552981714 STAG. PRES: 0.011180451157413517\n",
      "V_infty: 4.0 km/s, BR: 17.0 G_MAX: 5.772153427461481 QDOT_MAX: 230.17475544018822 J_MAX: 12492.724552981714 STAG. PRES: 0.011180451157413517\n",
      "V_infty: 4.0 km/s, BR: 19.0 G_MAX: 5.772153427461481 QDOT_MAX: 232.32538652184485 J_MAX: 12492.724552981714 STAG. PRES: 0.011180451157413517\n",
      "V_infty: 4.0 km/s, BR: 21.0 G_MAX: 5.772153427461481 QDOT_MAX: 234.02023114911597 J_MAX: 12492.724552981714 STAG. PRES: 0.011180451157413517\n",
      "V_infty: 5.0 km/s, BR: 1.0 G_MAX: 6.744827193800617 QDOT_MAX: 177.11773499221331 J_MAX: 13420.023386313193 STAG. PRES: 0.013064013033291657\n",
      "V_infty: 5.0 km/s, BR: 3.0 G_MAX: 6.744827193800617 QDOT_MAX: 217.56780969525204 J_MAX: 13420.023386313193 STAG. PRES: 0.013064013033291657\n",
      "V_infty: 5.0 km/s, BR: 5.0 G_MAX: 6.744827193800617 QDOT_MAX: 232.35553246600068 J_MAX: 13420.023386313193 STAG. PRES: 0.013064013033291657\n",
      "V_infty: 5.0 km/s, BR: 7.0 G_MAX: 6.744827193800617 QDOT_MAX: 241.4591524684363 J_MAX: 13420.023386313193 STAG. PRES: 0.013064013033291657\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 5.0 km/s, BR: 9.0 G_MAX: 6.744827193800617 QDOT_MAX: 247.73933480785607 J_MAX: 13420.023386313193 STAG. PRES: 0.013064013033291657\n",
      "V_infty: 5.0 km/s, BR: 11.0 G_MAX: 6.744827193800617 QDOT_MAX: 252.36648638461264 J_MAX: 13420.023386313193 STAG. PRES: 0.013064013033291657\n",
      "V_infty: 5.0 km/s, BR: 13.0 G_MAX: 6.744827193800617 QDOT_MAX: 255.7528597021401 J_MAX: 13420.023386313193 STAG. PRES: 0.013064013033291657\n",
      "V_infty: 5.0 km/s, BR: 15.0 G_MAX: 6.744827193800617 QDOT_MAX: 259.1477843365355 J_MAX: 13420.023386313193 STAG. PRES: 0.013064013033291657\n",
      "V_infty: 5.0 km/s, BR: 17.0 G_MAX: 6.744827193800617 QDOT_MAX: 261.85755271638715 J_MAX: 13420.023386313193 STAG. PRES: 0.013064013033291657\n",
      "V_infty: 5.0 km/s, BR: 19.0 G_MAX: 6.744827193800617 QDOT_MAX: 264.1390694442427 J_MAX: 13420.023386313193 STAG. PRES: 0.013064013033291657\n",
      "V_infty: 5.0 km/s, BR: 21.0 G_MAX: 6.744827193800617 QDOT_MAX: 265.9345806708452 J_MAX: 13420.023386313193 STAG. PRES: 0.013064013033291657\n",
      "V_infty: 6.0 km/s, BR: 1.0 G_MAX: 7.973734857778368 QDOT_MAX: 209.27400020134556 J_MAX: 14557.188027182849 STAG. PRES: 0.015443962875124029\n",
      "V_infty: 6.0 km/s, BR: 3.0 G_MAX: 7.973734857778368 QDOT_MAX: 254.9735537050905 J_MAX: 14557.188027182849 STAG. PRES: 0.015443962875124029\n",
      "V_infty: 6.0 km/s, BR: 5.0 G_MAX: 7.973734857778368 QDOT_MAX: 272.34775862140435 J_MAX: 14557.188027182849 STAG. PRES: 0.015443962875124029\n",
      "V_infty: 6.0 km/s, BR: 7.0 G_MAX: 7.973734857778368 QDOT_MAX: 282.14754009216193 J_MAX: 14557.188027182849 STAG. PRES: 0.015443962875124029\n",
      "V_infty: 6.0 km/s, BR: 9.0 G_MAX: 7.973734857778368 QDOT_MAX: 289.35977284682525 J_MAX: 14557.188027182849 STAG. PRES: 0.015443962875124029\n",
      "V_infty: 6.0 km/s, BR: 11.0 G_MAX: 7.973734857778368 QDOT_MAX: 294.175545038905 J_MAX: 14557.188027182849 STAG. PRES: 0.015443962875124029\n",
      "V_infty: 6.0 km/s, BR: 13.0 G_MAX: 7.973734857778368 QDOT_MAX: 299.0129480628764 J_MAX: 14557.188027182849 STAG. PRES: 0.015443962875124029\n",
      "V_infty: 6.0 km/s, BR: 15.0 G_MAX: 7.973734857778368 QDOT_MAX: 302.4372052854686 J_MAX: 14557.188027182849 STAG. PRES: 0.015443962875124029\n",
      "V_infty: 6.0 km/s, BR: 17.0 G_MAX: 7.973734857778368 QDOT_MAX: 304.90105256977273 J_MAX: 14557.188027182849 STAG. PRES: 0.015443962875124029\n",
      "V_infty: 6.0 km/s, BR: 19.0 G_MAX: 7.973734857778368 QDOT_MAX: 307.502526015813 J_MAX: 14557.188027182849 STAG. PRES: 0.015443962875124029\n",
      "V_infty: 6.0 km/s, BR: 21.0 G_MAX: 7.973734857778368 QDOT_MAX: 310.3008269274457 J_MAX: 14557.188027182849 STAG. PRES: 0.015443962875124029\n",
      "V_infty: 7.0 km/s, BR: 1.0 G_MAX: 9.490721690883763 QDOT_MAX: 251.2779030517506 J_MAX: 15930.422352801837 STAG. PRES: 0.018381361664702955\n",
      "V_infty: 7.0 km/s, BR: 3.0 G_MAX: 9.490721690883763 QDOT_MAX: 304.56291535573257 J_MAX: 15930.422352801837 STAG. PRES: 0.018381361664702955\n",
      "V_infty: 7.0 km/s, BR: 5.0 G_MAX: 9.490721690883763 QDOT_MAX: 324.4606371027999 J_MAX: 15930.422352801837 STAG. PRES: 0.018381361664702955\n",
      "V_infty: 7.0 km/s, BR: 7.0 G_MAX: 9.490721690883763 QDOT_MAX: 336.7032508558425 J_MAX: 15930.422352801837 STAG. PRES: 0.018381361664702955\n",
      "V_infty: 7.0 km/s, BR: 9.0 G_MAX: 9.490721690883763 QDOT_MAX: 343.8879152586743 J_MAX: 15930.422352801837 STAG. PRES: 0.018381361664702955\n",
      "V_infty: 7.0 km/s, BR: 11.0 G_MAX: 9.490721690883763 QDOT_MAX: 351.0691879899309 J_MAX: 15930.422352801837 STAG. PRES: 0.018381361664702955\n",
      "V_infty: 7.0 km/s, BR: 13.0 G_MAX: 9.490721690883763 QDOT_MAX: 356.1960852262272 J_MAX: 15930.422352801837 STAG. PRES: 0.018381361664702955\n",
      "V_infty: 7.0 km/s, BR: 15.0 G_MAX: 9.490721690883763 QDOT_MAX: 359.70479240385765 J_MAX: 15930.422352801837 STAG. PRES: 0.018381361664702955\n",
      "V_infty: 7.0 km/s, BR: 17.0 G_MAX: 9.490721690883763 QDOT_MAX: 362.876483125122 J_MAX: 15930.422352801837 STAG. PRES: 0.018381361664702955\n",
      "V_infty: 7.0 km/s, BR: 19.0 G_MAX: 9.490721690883763 QDOT_MAX: 366.7836302362091 J_MAX: 15930.422352801837 STAG. PRES: 0.018381361664702955\n",
      "V_infty: 7.0 km/s, BR: 21.0 G_MAX: 9.490721690883763 QDOT_MAX: 369.92457701043656 J_MAX: 15930.422352801837 STAG. PRES: 0.018381361664702955\n",
      "V_infty: 8.0 km/s, BR: 1.0 G_MAX: 11.27512063925072 QDOT_MAX: 307.48958350705897 J_MAX: 17599.78813386462 STAG. PRES: 0.02183629924529477\n",
      "V_infty: 8.0 km/s, BR: 3.0 G_MAX: 11.27512063925072 QDOT_MAX: 371.0070043672199 J_MAX: 17599.78813386462 STAG. PRES: 0.02183629924529477\n",
      "V_infty: 8.0 km/s, BR: 5.0 G_MAX: 11.27512063925072 QDOT_MAX: 395.0682293228483 J_MAX: 17599.78813386462 STAG. PRES: 0.02183629924529477\n",
      "V_infty: 8.0 km/s, BR: 7.0 G_MAX: 11.27512063925072 QDOT_MAX: 410.2557080149199 J_MAX: 17599.78813386462 STAG. PRES: 0.02183629924529477\n",
      "V_infty: 8.0 km/s, BR: 9.0 G_MAX: 11.27512063925072 QDOT_MAX: 419.52270431392014 J_MAX: 17599.78813386462 STAG. PRES: 0.02183629924529477\n",
      "V_infty: 8.0 km/s, BR: 11.0 G_MAX: 11.27512063925072 QDOT_MAX: 428.3225573548883 J_MAX: 17599.78813386462 STAG. PRES: 0.02183629924529477\n",
      "V_infty: 8.0 km/s, BR: 13.0 G_MAX: 11.27512063925072 QDOT_MAX: 434.1583735824523 J_MAX: 17599.78813386462 STAG. PRES: 0.02183629924529477\n",
      "V_infty: 8.0 km/s, BR: 15.0 G_MAX: 11.27512063925072 QDOT_MAX: 437.9917294972842 J_MAX: 17599.78813386462 STAG. PRES: 0.02183629924529477\n",
      "V_infty: 8.0 km/s, BR: 17.0 G_MAX: 11.27512063925072 QDOT_MAX: 442.84973545349084 J_MAX: 17599.78813386462 STAG. PRES: 0.02183629924529477\n",
      "V_infty: 8.0 km/s, BR: 19.0 G_MAX: 11.27512063925072 QDOT_MAX: 447.6915140264788 J_MAX: 17599.78813386462 STAG. PRES: 0.02183629924529477\n",
      "V_infty: 8.0 km/s, BR: 21.0 G_MAX: 11.27512063925072 QDOT_MAX: 451.570817174868 J_MAX: 17599.78813386462 STAG. PRES: 0.02183629924529477\n",
      "V_infty: 9.0 km/s, BR: 1.0 G_MAX: 13.386654133434867 QDOT_MAX: 385.60786928336074 J_MAX: 19684.254558276338 STAG. PRES: 0.025924909831565646\n",
      "V_infty: 9.0 km/s, BR: 3.0 G_MAX: 13.386654133434867 QDOT_MAX: 466.16144427706377 J_MAX: 19684.254558276338 STAG. PRES: 0.025924909831565646\n",
      "V_infty: 9.0 km/s, BR: 5.0 G_MAX: 13.386654133434867 QDOT_MAX: 496.48777992935163 J_MAX: 19684.254558276338 STAG. PRES: 0.025924909831565646\n",
      "V_infty: 9.0 km/s, BR: 7.0 G_MAX: 13.386654133434867 QDOT_MAX: 515.0763935669046 J_MAX: 19684.254558276338 STAG. PRES: 0.025924909831565646\n",
      "V_infty: 9.0 km/s, BR: 9.0 G_MAX: 13.386654133434867 QDOT_MAX: 527.2433823460432 J_MAX: 19684.254558276338 STAG. PRES: 0.025924909831565646\n",
      "V_infty: 9.0 km/s, BR: 11.0 G_MAX: 13.386654133434867 QDOT_MAX: 538.9139356167964 J_MAX: 19684.254558276338 STAG. PRES: 0.025924909831565646\n",
      "V_infty: 9.0 km/s, BR: 13.0 G_MAX: 13.386654133434867 QDOT_MAX: 546.5709725338615 J_MAX: 19684.254558276338 STAG. PRES: 0.025924909831565646\n",
      "V_infty: 9.0 km/s, BR: 15.0 G_MAX: 13.386654133434867 QDOT_MAX: 551.5785787626592 J_MAX: 19684.254558276338 STAG. PRES: 0.025924909831565646\n",
      "V_infty: 9.0 km/s, BR: 17.0 G_MAX: 13.386654133434867 QDOT_MAX: 556.3715694877183 J_MAX: 19684.254558276338 STAG. PRES: 0.025924909831565646\n",
      "V_infty: 9.0 km/s, BR: 19.0 G_MAX: 13.386654133434867 QDOT_MAX: 563.351986931205 J_MAX: 19684.254558276338 STAG. PRES: 0.025924909831565646\n",
      "V_infty: 9.0 km/s, BR: 21.0 G_MAX: 13.386654133434867 QDOT_MAX: 568.97751095439 J_MAX: 19684.254558276338 STAG. PRES: 0.025924909831565646\n",
      "V_infty: 10.0 km/s, BR: 1.0 G_MAX: 15.795629284230742 QDOT_MAX: 501.43806962512593 J_MAX: 22406.99315223976 STAG. PRES: 0.030589213513601086\n",
      "V_infty: 10.0 km/s, BR: 3.0 G_MAX: 15.795629284230742 QDOT_MAX: 608.3479616761747 J_MAX: 22406.99315223976 STAG. PRES: 0.030589213513601086\n",
      "V_infty: 10.0 km/s, BR: 5.0 G_MAX: 15.795629284230742 QDOT_MAX: 648.3542864021587 J_MAX: 22406.99315223976 STAG. PRES: 0.030589213513601086\n",
      "V_infty: 10.0 km/s, BR: 7.0 G_MAX: 15.795629284230742 QDOT_MAX: 675.522980832742 J_MAX: 22406.99315223976 STAG. PRES: 0.030589213513601086\n",
      "V_infty: 10.0 km/s, BR: 9.0 G_MAX: 15.795629284230742 QDOT_MAX: 688.633778624763 J_MAX: 22406.99315223976 STAG. PRES: 0.030589213513601086\n",
      "V_infty: 10.0 km/s, BR: 11.0 G_MAX: 15.795629284230742 QDOT_MAX: 706.4537756005727 J_MAX: 22406.99315223976 STAG. PRES: 0.030589213513601086\n",
      "V_infty: 10.0 km/s, BR: 13.0 G_MAX: 15.795629284230742 QDOT_MAX: 719.2849628779097 J_MAX: 22406.99315223976 STAG. PRES: 0.030589213513601086\n",
      "V_infty: 10.0 km/s, BR: 15.0 G_MAX: 15.795629284230742 QDOT_MAX: 728.2666236416914 J_MAX: 22406.99315223976 STAG. PRES: 0.030589213513601086\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 10.0 km/s, BR: 17.0 G_MAX: 15.795629284230742 QDOT_MAX: 734.3618177914249 J_MAX: 22406.99315223976 STAG. PRES: 0.030589213513601086\n",
      "V_infty: 10.0 km/s, BR: 19.0 G_MAX: 15.795629284230742 QDOT_MAX: 738.4864017047046 J_MAX: 22406.99315223976 STAG. PRES: 0.030589213513601086\n",
      "V_infty: 10.0 km/s, BR: 21.0 G_MAX: 15.795629284230742 QDOT_MAX: 744.5620616687944 J_MAX: 22406.99315223976 STAG. PRES: 0.030589213513601086\n"
     ]
    }
   ],
   "source": [
    "acc_net_g_max_array       = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "stag_pres_atm_max_array   = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "q_stag_total_max_array    = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "heatload_max_array        = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "\n",
    "\n",
    "for i in range(0,len(v0_kms_array)):\n",
    "    for j in range(0,len(betaRatio_array)):\n",
    "        vehicle=Vehicle('DMVehicle', 50.0, beta1, 0.0, 1.767, 0.0, 0.35, planet)\n",
    "        vehicle.setInitialState(150.0,0.0,0.0,v0_kms_array[i],0.0,overShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-6)\n",
    "\n",
    "        vehicle.propogateEntry (2400.0, 2.0, 0.0)\n",
    "\n",
    "        # Extract and save variables to plot\n",
    "        t_min_os         = vehicle.t_minc\n",
    "        h_km_os          = vehicle.h_kmc\n",
    "        acc_net_g_os     = vehicle.acc_net_g\n",
    "        q_stag_con_os    = vehicle.q_stag_con\n",
    "        q_stag_rad_os    = vehicle.q_stag_rad\n",
    "        rc_os            = vehicle.rc\n",
    "        vc_os            = vehicle.vc\n",
    "        stag_pres_atm_os = vehicle.computeStagPres(rc_os,vc_os)/(1.01325E5)\n",
    "        heatload_os      = vehicle.heatload\n",
    "\n",
    "\n",
    "        vehicle=Vehicle('DMVehicle', 50.0, beta1, 0.0, 1.767, 0.0, 0.35, planet)\n",
    "        vehicle.setInitialState(150.0,0.0,0.0,v0_kms_array[i],0.0,underShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams( 1E-6)\n",
    "\n",
    "        vehicle.propogateEntry (2400.0, 2.0, 0.0)\n",
    "\n",
    "        # Extract and save variable to plot\n",
    "        t_min_us         = vehicle.t_minc\n",
    "        h_km_us          = vehicle.h_kmc\n",
    "        acc_net_g_us     = vehicle.acc_net_g\n",
    "        q_stag_con_us    = vehicle.q_stag_con\n",
    "        q_stag_rad_us    = vehicle.q_stag_rad\n",
    "        rc_us            = vehicle.rc\n",
    "        vc_us            = vehicle.vc\n",
    "        stag_pres_atm_us = vehicle.computeStagPres(rc_us,vc_us)/(1.01325E5)\n",
    "        heatload_us      = vehicle.heatload\n",
    "\n",
    "        q_stag_total_os  = q_stag_con_os + q_stag_rad_os\n",
    "        q_stag_total_us  = q_stag_con_us + q_stag_rad_us\n",
    "\n",
    "        acc_net_g_max_array[i,j]      = max(max(acc_net_g_os),max(acc_net_g_os))\n",
    "        stag_pres_atm_max_array[i,j]  = max(max(stag_pres_atm_os),max(stag_pres_atm_os))\n",
    "        q_stag_total_max_array[i,j]   = min(max(q_stag_total_os),max(q_stag_total_us))\n",
    "        heatload_max_array[i,j]       = max(max(heatload_os),max(heatload_os))\n",
    "\n",
    "        print(\"V_infty: \"+str(vinf_kms_array[i])+\" km/s\"+\", BR: \"+str(betaRatio_array[j])+\" G_MAX: \"+str(acc_net_g_max_array[i,j])+\" QDOT_MAX: \"+str(q_stag_total_max_array[i,j])+\" J_MAX: \"+str(heatload_max_array[i,j])+\" STAG. PRES: \"+str(stag_pres_atm_max_array[i,j]))\n",
    "\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'acc_net_g_max_array.txt',acc_net_g_max_array)\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'stag_pres_atm_max_array.txt',stag_pres_atm_max_array)\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'q_stag_total_max_array.txt',q_stag_total_max_array)\n",
    "np.savetxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'heatload_max_array.txt',heatload_max_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "004dd32d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.loadtxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'betaRatio_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/'+runID+'stag_pres_atm_max_array.txt')\n",
    "\n",
    "\n",
    "f1 = interpolate.interp2d(x, y, np.transpose(Z1), kind='cubic')\n",
    "g1 = interpolate.interp2d(x, y, np.transpose(G1), kind='cubic')\n",
    "q1 = interpolate.interp2d(x, y, np.transpose(Q1), kind='cubic')\n",
    "h1 = interpolate.interp2d(x, y, np.transpose(H1), kind='cubic')\n",
    "s1 = interpolate.interp2d(x, y, np.transpose(S1), kind='cubic')\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   10,  110)\n",
    "y_new =  np.linspace( 0.0,   21 , 110)\n",
    "\n",
    "z1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "g1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "q1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "h1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "s1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "for i in range(0,len(x_new)):\n",
    "    for j in range(0,len(y_new)):\n",
    "\n",
    "        z1_new[i,j] = f1(x_new[i],y_new[j])\n",
    "        g1_new[i,j] = g1(x_new[i],y_new[j])\n",
    "        q1_new[i,j] = q1(x_new[i],y_new[j])\n",
    "        h1_new[i,j] = h1(x_new[i],y_new[j])\n",
    "        s1_new[i,j] = s1(x_new[i],y_new[j])\n",
    "\n",
    "\n",
    "Z1 = z1_new\n",
    "G1 = g1_new\n",
    "Q1 = q1_new\n",
    "S1 = s1_new\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "\n",
    "Zlevels = np.array([0.20, 0.30, 0.40, 0.50, 0.55, 0.58])\n",
    "\n",
    "Glevels = np.array([6, 10])\n",
    "Qlevels = np.array([180, 200, 240, 300])\n",
    "Hlevels = np.array([14, 20])\n",
    "#Slevels = np.array([0.8])\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([5,5])\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.xlim([0.0,10.0])\n",
    "plt.ylim([1.0,21.0])\n",
    "\n",
    "\n",
    "ZCS1 = plt.contour(X, Y, np.transpose(Z1), levels=Zlevels, colors='black')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.clabel(ZCS1, inline=1, fontsize=12, colors='black',fmt='%.2f',inline_spacing=1)\n",
    "ZCS1.collections[0].set_linewidths(1.75)\n",
    "ZCS1.collections[1].set_linewidths(1.75)\n",
    "ZCS1.collections[2].set_linewidths(1.75)\n",
    "ZCS1.collections[3].set_linewidths(1.75)\n",
    "ZCS1.collections[4].set_linewidths(1.75)\n",
    "ZCS1.collections[5].set_linewidths(1.75)\n",
    "\n",
    "ZCS1.collections[0].set_label(r'$TCW, deg$')\n",
    "\n",
    "\n",
    "GCS1 = plt.contour(X, Y, np.transpose(G1), levels=Glevels, colors='blue',linestyles='dashed')\n",
    "\n",
    "Glabels=plt.clabel(GCS1, inline=1, fontsize=12, colors='blue',fmt='%d',inline_spacing=0)\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted')\n",
    "\n",
    "Qlabels = plt.clabel(QCS1, inline=1, fontsize=12, colors='red',fmt='%d',inline_spacing=0)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "QCS1.collections[1].set_linewidths(1.5)\n",
    "QCS1.collections[2].set_linewidths(1.5)\n",
    "QCS1.collections[3].set_linewidths(1.5)\n",
    "QCS1.collections[0].set_label(r'$\\dot{q}$'+', '+r'$W/cm^2$')\n",
    "\n",
    "\n",
    "HCS1 = plt.contour(X, Y, np.transpose(H1), levels=Hlevels, colors='xkcd:brown',linestyles='dashdot')\n",
    "\n",
    "Hlabels=plt.clabel(HCS1, inline=1, fontsize=12, colors='xkcd:brown',fmt='%d',inline_spacing=0)\n",
    "HCS1.collections[0].set_linewidths(1.75)\n",
    "#HCS1.collections[1].set_linewidths(1.75)\n",
    "\n",
    "\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "\n",
    "#GCS1.collections[0].set_label(r'$Peak$'+r' '+r'$g-load$')\n",
    "#plt.grid(True,linestyle='dotted', linewidth=0.3)\n",
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(r'$\\beta_2$'+' / '+r'$ \\beta_1 $' ,fontsize=14)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=14)\n",
    "plt.yticks(np.array([1, 5, 10, 15, 20]),fontsize=14)\n",
    "plt.xticks(fontsize=14)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "#plt.legend(loc='upper left', fontsize=16)\n",
    "from matplotlib.lines import Line2D\n",
    "colors = ['black', 'red', 'green']\n",
    "lines = [Line2D([0], [0], color='black', linewidth=1.75, linestyle='solid'),\n",
    "         Line2D([0], [0], color='blue',  linewidth=1.75, linestyle='dashed'),\n",
    "         Line2D([0], [0], color='red',  linewidth=1.75, linestyle='dotted'),\n",
    "         Line2D([0], [0], color='xkcd:brown',  linewidth=2.00, linestyle='dashdot')]\n",
    "labels = [r'$TCW, deg$', r'$g$'+r'-load', r'$\\dot{q}$'+', '+r'$W/cm^2$', r'$Q$'+', '+r'$kJ/cm^2$']\n",
    "plt.legend(lines, labels, loc='upper right',fontsize=12, framealpha=1)\n",
    "\n",
    "for l in Hlabels:\n",
    "    l.set_rotation(-90)\n",
    "for l in Glabels:\n",
    "    l.set_rotation(-90)\n",
    "\n",
    "#plt.axhline(y=7.5, color='k', linestyle='dotted')\n",
    "#plt.text(4, 8.0, r'$\\beta_2$'+' / '+r'$ \\beta_1 $', fontsize=12)\n",
    "plt.scatter(3.51, 7.5, marker=\"*\", s=300, color='xkcd:black', zorder=100)\n",
    "\n",
    "plt.savefig('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/venus-smallsat-ac-tradespace.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/venus-smallsat-ac-tradespace.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../../../data/mdpi-aerospace/smallsat-mission-concepts/venus/venus-smallsat-ac-tradespace.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
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