{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 05 - a - Mars - Feasibility Charts - Lift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a planet object\n",
    "planet=Planet(\"MARS\")\n",
    "planet.h_skip = 120000.0\n",
    "\n",
    "# Load an nominal atmospheric profile with height, temp, pressure, density data\n",
    "planet.loadAtmosphereModel('../atmdata/Mars/mars-gram-avg.dat', 0 , 1 ,2, 3 )\n",
    "\n",
    "vinf_kms_array = np.linspace( 0.0,   20.0,  11)\n",
    "LD_array       = np.linspace( 0.0,    0.4 , 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('../data/jsr-paper/mars/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "runID = 'mars-lift-'\n",
    "\n",
    "num_total      = len(vinf_kms_array)*len(LD_array)\n",
    "count = 1\n",
    "\n",
    "v0_kms_array    = np.zeros(len(vinf_kms_array))\n",
    "v0_kms_array[:] = np.sqrt(1.0*(vinf_kms_array[:]*1E3)**2.0 +\\\n",
    "                          2*np.ones(len(vinf_kms_array))*\\\n",
    "                          planet.GM/(planet.RP+120.0*1.0E3))/1.0E3\n",
    "\n",
    "overShootLimit_array  = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "underShootLimit_array = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "exitflag_os_array     = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "exitflag_us_array     = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "TCW_array             = np.zeros((len(v0_kms_array),len(LD_array)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "Run #87 of 121: Arrival V_infty: 14.0 km/s, L/D:0.36 OSL: -10.877274520953506 USL: -21.387743348361255, TCW: 10.510468827407749 EFOS: 1.0 EFUS: 1.0\n",
      "Run #88 of 121: Arrival V_infty: 14.0 km/s, L/D:0.4 OSL: -10.806441295710101 USL: -22.911227225718903, TCW: 12.104785930008802 EFOS: 1.0 EFUS: 1.0\n",
      "Run #89 of 121: Arrival V_infty: 16.0 km/s, L/D:0.0 OSL: -12.526954812943586 USL: -12.526954812943586, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #90 of 121: Arrival V_infty: 16.0 km/s, L/D:0.04 OSL: -12.145394416929776 USL: -13.027689916492818, TCW: 0.8822954995630425 EFOS: 1.0 EFUS: 1.0\n",
      "Run #91 of 121: Arrival V_infty: 16.0 km/s, L/D:0.08 OSL: -11.856474038802844 USL: -13.66780687123537, TCW: 1.8113328324325266 EFOS: 1.0 EFUS: 1.0\n",
      "Run #92 of 121: Arrival V_infty: 16.0 km/s, L/D:0.12 OSL: -11.637940400629304 USL: -14.460136897272605, TCW: 2.8221964966433006 EFOS: 1.0 EFUS: 1.0\n",
      "Run #93 of 121: Arrival V_infty: 16.0 km/s, L/D:0.16 OSL: -11.465070767626457 USL: -15.413583043729886, TCW: 3.948512276103429 EFOS: 1.0 EFUS: 1.0\n",
      "Run #94 of 121: Arrival V_infty: 16.0 km/s, L/D:0.2 OSL: -11.323338300968317 USL: -16.530375583504792, TCW: 5.207037282536476 EFOS: 1.0 EFUS: 1.0\n",
      "Run #95 of 121: Arrival V_infty: 16.0 km/s, L/D:0.24 OSL: -11.203810609687935 USL: -17.808119564051594, TCW: 6.60430895436366 EFOS: 1.0 EFUS: 1.0\n",
      "Run #96 of 121: Arrival V_infty: 16.0 km/s, L/D:0.28 OSL: -11.10085298453123 USL: -19.249015872050222, TCW: 8.148162887518993 EFOS: 1.0 EFUS: 1.0\n",
      "Run #97 of 121: Arrival V_infty: 16.0 km/s, L/D:0.32 OSL: -11.010938749463094 USL: -20.84983719337106, TCW: 9.838898443907965 EFOS: 1.0 EFUS: 1.0\n",
      "Run #98 of 121: Arrival V_infty: 16.0 km/s, L/D:0.36 OSL: -10.931096833723132 USL: -22.484557334715646, TCW: 11.553460500992514 EFOS: 1.0 EFUS: 1.0\n",
      "Run #99 of 121: Arrival V_infty: 16.0 km/s, L/D:0.4 OSL: -10.85957802453413 USL: -24.14530194989129, TCW: 13.28572392535716 EFOS: 1.0 EFUS: 1.0\n",
      "Run #100 of 121: Arrival V_infty: 18.0 km/s, L/D:0.0 OSL: -12.617501919619826 USL: -12.617501919619826, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #101 of 121: Arrival V_infty: 18.0 km/s, L/D:0.04 OSL: -12.211702373817388 USL: -13.158136307236418, TCW: 0.9464339334190299 EFOS: 1.0 EFUS: 1.0\n",
      "Run #102 of 121: Arrival V_infty: 18.0 km/s, L/D:0.08 OSL: -11.909275331781828 USL: -13.856772256993281, TCW: 1.9474969252114533 EFOS: 1.0 EFUS: 1.0\n",
      "Run #103 of 121: Arrival V_infty: 18.0 km/s, L/D:0.12 OSL: -11.683114796720474 USL: -14.730515893876145, TCW: 3.047401097155671 EFOS: 1.0 EFUS: 1.0\n",
      "Run #104 of 121: Arrival V_infty: 18.0 km/s, L/D:0.16 OSL: -11.506918537335878 USL: -15.784431231582857, TCW: 4.27751269424698 EFOS: 1.0 EFUS: 1.0\n",
      "Run #105 of 121: Arrival V_infty: 18.0 km/s, L/D:0.2 OSL: -11.363197669386864 USL: -17.01836090283905, TCW: 5.6551632334521855 EFOS: 1.0 EFUS: 1.0\n",
      "Run #106 of 121: Arrival V_infty: 18.0 km/s, L/D:0.24 OSL: -11.242423350537138 USL: -18.43423199123572, TCW: 7.191808640698582 EFOS: 1.0 EFUS: 1.0\n",
      "Run #107 of 121: Arrival V_infty: 18.0 km/s, L/D:0.28 OSL: -11.138540935848141 USL: -20.025345347614348, TCW: 8.886804411766207 EFOS: 1.0 EFUS: 1.0\n",
      "Run #108 of 121: Arrival V_infty: 18.0 km/s, L/D:0.32 OSL: -11.047844724416791 USL: -21.753165516329318, TCW: 10.705320791912527 EFOS: 1.0 EFUS: 1.0\n",
      "Run #109 of 121: Arrival V_infty: 18.0 km/s, L/D:0.36 OSL: -10.967463833298098 USL: -23.46782077904936, TCW: 12.500356945751264 EFOS: 1.0 EFUS: 1.0\n",
      "Run #110 of 121: Arrival V_infty: 18.0 km/s, L/D:0.4 OSL: -10.89552598631417 USL: -25.26331175258383, TCW: 14.367785766269662 EFOS: 1.0 EFUS: 1.0\n",
      "Run #111 of 121: Arrival V_infty: 20.0 km/s, L/D:0.0 OSL: -12.688963695265556 USL: -12.688963695265556, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #112 of 121: Arrival V_infty: 20.0 km/s, L/D:0.04 OSL: -12.261410841590987 USL: -13.266362298149033, TCW: 1.0049514565580466 EFOS: 1.0 EFUS: 1.0\n",
      "Run #113 of 121: Arrival V_infty: 20.0 km/s, L/D:0.08 OSL: -11.947363539795333 USL: -14.02023447439933, TCW: 2.0728709346039977 EFOS: 1.0 EFUS: 1.0\n",
      "Run #114 of 121: Arrival V_infty: 20.0 km/s, L/D:0.12 OSL: -11.715029493665497 USL: -14.969837235123123, TCW: 3.2548077414576255 EFOS: 1.0 EFUS: 1.0\n",
      "Run #115 of 121: Arrival V_infty: 20.0 km/s, L/D:0.16 OSL: -11.536238524466171 USL: -16.118028376404254, TCW: 4.581789851938083 EFOS: 1.0 EFUS: 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run #116 of 121: Arrival V_infty: 20.0 km/s, L/D:0.2 OSL: -11.391016460602259 USL: -17.464980788015964, TCW: 6.073964327413705 EFOS: 1.0 EFUS: 1.0\n",
      "Run #117 of 121: Arrival V_infty: 20.0 km/s, L/D:0.24 OSL: -11.269306836060423 USL: -19.006885519844218, TCW: 7.737578683783795 EFOS: 1.0 EFUS: 1.0\n",
      "Run #118 of 121: Arrival V_infty: 20.0 km/s, L/D:0.28 OSL: -11.164763968587067 USL: -20.73861427821612, TCW: 9.573850309629051 EFOS: 1.0 EFUS: 1.0\n",
      "Run #119 of 121: Arrival V_infty: 20.0 km/s, L/D:0.32 OSL: -11.073568312971474 USL: -22.526569638917863, TCW: 11.453001325946389 EFOS: 1.0 EFUS: 1.0\n",
      "Run #120 of 121: Arrival V_infty: 20.0 km/s, L/D:0.36 OSL: -10.992764173239266 USL: -24.365277909175347, TCW: 13.372513735936082 EFOS: 1.0 EFUS: 1.0\n",
      "Run #121 of 121: Arrival V_infty: 20.0 km/s, L/D:0.4 OSL: -10.92049562828106 USL: -26.28420725035903, TCW: 15.363711622077972 EFOS: 1.0 EFUS: 1.0\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,len(v0_kms_array)):\n",
    "    for j in range(0,len(LD_array)):\n",
    "        vehicle=Vehicle('Apollo', 1000.0, 200.0, LD_array[j], 3.1416, 0.0, 1.00, planet)\n",
    "        vehicle.setInitialState(110.0,0.0,0.0,v0_kms_array[i],0.0,-4.5,0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-5)\n",
    "        overShootLimit_array[i,j],  exitflag_os_array[i,j]  = vehicle.findOverShootLimit (2400.0, 1.0, -80.0, -4.0, 1E-10, 400.0)\n",
    "        underShootLimit_array[i,j], exitflag_us_array[i,j] =  vehicle.findUnderShootLimit(2400.0, 1.0, -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(\"Run #\"+str(count)+\" of \"+ str(num_total)+\": Arrival V_infty: \"+str(vinf_kms_array[i])+\" km/s\"+\", L/D:\"+str(LD_array[j]) + \" OSL: \"+str(overShootLimit_array[i,j])+\" USL: \"+str(underShootLimit_array[i,j])+\", TCW: \"+str(TCW_array[i,j])+\" EFOS: \"+str(exitflag_os_array[i,j])+ \" EFUS: \"+str(exitflag_us_array[i,j]))\n",
    "        count = count +1\n",
    "\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'vinf_kms_array.txt',vinf_kms_array)\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'v0_kms_array.txt',v0_kms_array)\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'LD_array.txt',LD_array)\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'overShootLimit_array.txt',overShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'exitflag_os_array.txt',exitflag_os_array)\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'undershootLimit_array.txt',underShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'exitflag_us_array.txt',exitflag_us_array)\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'TCW_array.txt',TCW_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 0.0 km/s, L/D: 0.0 G_MAX: 1.0118818318625384 QDOT_MAX: 24.48278520225487 J_MAX: 4596.452052086277 STAG. PRES: 0.019638627599913725\n",
      "V_infty: 0.0 km/s, L/D: 0.04 G_MAX: 1.0850166948004838 QDOT_MAX: 25.225121883260726 J_MAX: 4725.512095858182 STAG. PRES: 0.01835742143460218\n",
      "V_infty: 0.0 km/s, L/D: 0.08 G_MAX: 1.164756126696007 QDOT_MAX: 26.009616727842587 J_MAX: 4856.7379121319245 STAG. PRES: 0.0171756525163453\n",
      "V_infty: 0.0 km/s, L/D: 0.12 G_MAX: 1.2472585720682954 QDOT_MAX: 26.83694189509977 J_MAX: 4990.429982101152 STAG. PRES: 0.016084096366433365\n",
      "V_infty: 0.0 km/s, L/D: 0.16 G_MAX: 1.3367079765259307 QDOT_MAX: 27.698182225345093 J_MAX: 5125.668585057925 STAG. PRES: 0.015083755243593034\n",
      "V_infty: 0.0 km/s, L/D: 0.2 G_MAX: 1.4357745697852375 QDOT_MAX: 28.545209088450132 J_MAX: 5262.146618172648 STAG. PRES: 0.01417725720054207\n",
      "V_infty: 0.0 km/s, L/D: 0.24 G_MAX: 1.542891669340721 QDOT_MAX: 29.35704646858815 J_MAX: 5398.232493618921 STAG. PRES: 0.013356014073458499\n",
      "V_infty: 0.0 km/s, L/D: 0.28 G_MAX: 1.6610406789077252 QDOT_MAX: 30.207396913491742 J_MAX: 5537.113199159532 STAG. PRES: 0.012599664969592594\n",
      "V_infty: 0.0 km/s, L/D: 0.32 G_MAX: 1.7895287707496665 QDOT_MAX: 31.087745924291163 J_MAX: 5674.858783968777 STAG. PRES: 0.011886638154921426\n",
      "V_infty: 0.0 km/s, L/D: 0.36 G_MAX: 1.9296195762643726 QDOT_MAX: 31.994189528052974 J_MAX: 5811.172789326665 STAG. PRES: 0.011226511305048361\n",
      "V_infty: 0.0 km/s, L/D: 0.4 G_MAX: 2.0823171279822064 QDOT_MAX: 32.93808100003495 J_MAX: 5947.726892924672 STAG. PRES: 0.010633487335644547\n",
      "V_infty: 2.0 km/s, L/D: 0.0 G_MAX: 1.4238151302375774 QDOT_MAX: 32.992176993442364 J_MAX: 5401.236032923138 STAG. PRES: 0.02762652725899693\n",
      "V_infty: 2.0 km/s, L/D: 0.04 G_MAX: 1.5330483086556506 QDOT_MAX: 34.16287055751267 J_MAX: 5576.551572128484 STAG. PRES: 0.025681337723584673\n",
      "V_infty: 2.0 km/s, L/D: 0.08 G_MAX: 1.655298965896906 QDOT_MAX: 35.31750627558971 J_MAX: 5757.01156607507 STAG. PRES: 0.0238108621644407\n",
      "V_infty: 2.0 km/s, L/D: 0.12 G_MAX: 1.7903382162307309 QDOT_MAX: 36.4638654776298 J_MAX: 5940.761791575072 STAG. PRES: 0.022086143280625435\n",
      "V_infty: 2.0 km/s, L/D: 0.16 G_MAX: 1.9401772471110728 QDOT_MAX: 37.66608762272252 J_MAX: 6125.408180168015 STAG. PRES: 0.020549642230862537\n",
      "V_infty: 2.0 km/s, L/D: 0.2 G_MAX: 2.1031652801831604 QDOT_MAX: 38.91047094490188 J_MAX: 6311.989996524746 STAG. PRES: 0.019166527603218932\n",
      "V_infty: 2.0 km/s, L/D: 0.24 G_MAX: 2.2825659950198145 QDOT_MAX: 40.20834255974258 J_MAX: 6500.656927667227 STAG. PRES: 0.01792481547474438\n",
      "V_infty: 2.0 km/s, L/D: 0.28 G_MAX: 2.483907632453201 QDOT_MAX: 41.56107282633047 J_MAX: 6687.590393226854 STAG. PRES: 0.01681247075951515\n",
      "V_infty: 2.0 km/s, L/D: 0.32 G_MAX: 2.7037620883795523 QDOT_MAX: 42.92230915624275 J_MAX: 6873.959624121497 STAG. PRES: 0.01582604703542195\n",
      "V_infty: 2.0 km/s, L/D: 0.36 G_MAX: 2.9456640714597975 QDOT_MAX: 44.3321993434776 J_MAX: 7060.066599902308 STAG. PRES: 0.014943825137936732\n",
      "V_infty: 2.0 km/s, L/D: 0.4 G_MAX: 3.2069404047533876 QDOT_MAX: 45.808994836050985 J_MAX: 7242.645043744826 STAG. PRES: 0.01412410962375835\n",
      "V_infty: 4.0 km/s, L/D: 0.0 G_MAX: 2.749573322885367 QDOT_MAX: 62.392828166284644 J_MAX: 7612.484175515696 STAG. PRES: 0.05332603877077331\n",
      "V_infty: 4.0 km/s, L/D: 0.04 G_MAX: 3.0237229048875602 QDOT_MAX: 64.85971667152187 J_MAX: 7926.754167463476 STAG. PRES: 0.048580417094181885\n",
      "V_infty: 4.0 km/s, L/D: 0.08 G_MAX: 3.3401059502752415 QDOT_MAX: 67.47298590582825 J_MAX: 8251.139815481836 STAG. PRES: 0.044373605418484355\n",
      "V_infty: 4.0 km/s, L/D: 0.12 G_MAX: 3.6955946221807254 QDOT_MAX: 70.24019537481243 J_MAX: 8582.429917304526 STAG. PRES: 0.040666309022538476\n",
      "V_infty: 4.0 km/s, L/D: 0.16 G_MAX: 4.091301699787052 QDOT_MAX: 73.14330815441417 J_MAX: 8917.777020596008 STAG. PRES: 0.03747135748216829\n",
      "V_infty: 4.0 km/s, L/D: 0.2 G_MAX: 4.527228084915931 QDOT_MAX: 76.2681417505714 J_MAX: 9255.358655195447 STAG. PRES: 0.034591576057231385\n",
      "V_infty: 4.0 km/s, L/D: 0.24 G_MAX: 5.012205130597909 QDOT_MAX: 79.49430249278579 J_MAX: 9593.200562427932 STAG. PRES: 0.03196137598153363\n",
      "V_infty: 4.0 km/s, L/D: 0.28 G_MAX: 5.539593173770565 QDOT_MAX: 82.83422060925488 J_MAX: 9926.395392934155 STAG. PRES: 0.029697297499226453\n",
      "V_infty: 4.0 km/s, L/D: 0.32 G_MAX: 6.066207893229874 QDOT_MAX: 86.26974847022262 J_MAX: 10255.992415460247 STAG. PRES: 0.027732641970583234\n",
      "V_infty: 4.0 km/s, L/D: 0.36 G_MAX: 6.632245761211989 QDOT_MAX: 89.78674412190092 J_MAX: 10580.645352901138 STAG. PRES: 0.026005125109724577\n",
      "V_infty: 4.0 km/s, L/D: 0.4 G_MAX: 7.258066713236957 QDOT_MAX: 93.46893092924735 J_MAX: 10898.928219992802 STAG. PRES: 0.024476183159867815\n",
      "V_infty: 6.0 km/s, L/D: 0.0 G_MAX: 5.210090606910003 QDOT_MAX: 123.06149690312827 J_MAX: 10986.321112438918 STAG. PRES: 0.10099522609392249\n",
      "V_infty: 6.0 km/s, L/D: 0.04 G_MAX: 5.863494830554329 QDOT_MAX: 128.85204415183335 J_MAX: 11546.501328924614 STAG. PRES: 0.09001575274500022\n",
      "V_infty: 6.0 km/s, L/D: 0.08 G_MAX: 6.607823525783422 QDOT_MAX: 135.2186908331808 J_MAX: 12121.343092614936 STAG. PRES: 0.0806598476068775\n",
      "V_infty: 6.0 km/s, L/D: 0.12 G_MAX: 7.470227886615497 QDOT_MAX: 142.26470246624774 J_MAX: 12703.792655023317 STAG. PRES: 0.07278720244746748\n",
      "V_infty: 6.0 km/s, L/D: 0.16 G_MAX: 8.32979041725392 QDOT_MAX: 149.59796068098476 J_MAX: 13291.415924203624 STAG. PRES: 0.06609518793738088\n",
      "V_infty: 6.0 km/s, L/D: 0.2 G_MAX: 9.272543820268572 QDOT_MAX: 157.27520096075546 J_MAX: 13875.415953835052 STAG. PRES: 0.060428945399129394\n",
      "V_infty: 6.0 km/s, L/D: 0.24 G_MAX: 10.31589700944977 QDOT_MAX: 165.258107486178 J_MAX: 14454.114930752758 STAG. PRES: 0.055681512203017494\n",
      "V_infty: 6.0 km/s, L/D: 0.28 G_MAX: 11.485116432493973 QDOT_MAX: 173.18069462248164 J_MAX: 15021.073237268472 STAG. PRES: 0.05155384385732163\n",
      "V_infty: 6.0 km/s, L/D: 0.32 G_MAX: 12.75148382150923 QDOT_MAX: 180.6328567837455 J_MAX: 15577.50106971032 STAG. PRES: 0.04780429726280509\n",
      "V_infty: 6.0 km/s, L/D: 0.36 G_MAX: 14.137570173924104 QDOT_MAX: 187.94506560802668 J_MAX: 16121.901974693898 STAG. PRES: 0.04461195536810546\n",
      "V_infty: 6.0 km/s, L/D: 0.4 G_MAX: 15.635419634531067 QDOT_MAX: 195.49088317124 J_MAX: 16653.146698885303 STAG. PRES: 0.0418640537635484\n",
      "V_infty: 8.0 km/s, L/D: 0.0 G_MAX: 8.92258476839041 QDOT_MAX: 228.76419217255753 J_MAX: 15447.619387140705 STAG. PRES: 0.17289551496859637\n",
      "V_infty: 8.0 km/s, L/D: 0.04 G_MAX: 10.261254871597 QDOT_MAX: 241.7683976390394 J_MAX: 16362.042302366246 STAG. PRES: 0.151684885479443\n",
      "V_infty: 8.0 km/s, L/D: 0.08 G_MAX: 11.653931736049637 QDOT_MAX: 256.0879211333671 J_MAX: 17297.19969410557 STAG. PRES: 0.13370087834689287\n",
      "V_infty: 8.0 km/s, L/D: 0.12 G_MAX: 13.197216750417423 QDOT_MAX: 271.29917244914526 J_MAX: 18241.724106173464 STAG. PRES: 0.11889295630752048\n",
      "V_infty: 8.0 km/s, L/D: 0.16 G_MAX: 14.946801635052596 QDOT_MAX: 287.4926422359574 J_MAX: 19182.775502966 STAG. PRES: 0.10700373269890827\n",
      "V_infty: 8.0 km/s, L/D: 0.2 G_MAX: 16.903340864138354 QDOT_MAX: 303.90726502872343 J_MAX: 20112.304897646318 STAG. PRES: 0.0970647225260243\n",
      "V_infty: 8.0 km/s, L/D: 0.24 G_MAX: 19.042359660251673 QDOT_MAX: 318.99809504647806 J_MAX: 21021.204064920992 STAG. PRES: 0.08883529662207605\n",
      "V_infty: 8.0 km/s, L/D: 0.28 G_MAX: 21.369205359405317 QDOT_MAX: 334.20049037784963 J_MAX: 21909.330348781838 STAG. PRES: 0.08198477427252036\n",
      "V_infty: 8.0 km/s, L/D: 0.32 G_MAX: 23.94247964588416 QDOT_MAX: 349.52462040177596 J_MAX: 22773.506947846094 STAG. PRES: 0.07614608496485746\n",
      "V_infty: 8.0 km/s, L/D: 0.36 G_MAX: 26.75430040725472 QDOT_MAX: 365.92188922172187 J_MAX: 23611.50673257246 STAG. PRES: 0.07079920697379885\n",
      "V_infty: 8.0 km/s, L/D: 0.4 G_MAX: 29.895264754608455 QDOT_MAX: 382.53423000986805 J_MAX: 24431.30395428462 STAG. PRES: 0.06620185724727648\n",
      "V_infty: 10.0 km/s, L/D: 0.0 G_MAX: 14.040569323137767 QDOT_MAX: 395.44465377843795 J_MAX: 20999.36106447281 STAG. PRES: 0.2719925099697535\n",
      "V_infty: 10.0 km/s, L/D: 0.04 G_MAX: 16.15016596903038 QDOT_MAX: 420.8328866677455 J_MAX: 22364.966218596932 STAG. PRES: 0.23379055841231464\n",
      "V_infty: 10.0 km/s, L/D: 0.08 G_MAX: 18.55601253691971 QDOT_MAX: 448.5448988610446 J_MAX: 23776.07616578217 STAG. PRES: 0.20369007763202815\n",
      "V_infty: 10.0 km/s, L/D: 0.12 G_MAX: 21.33412415738584 QDOT_MAX: 478.58275587108824 J_MAX: 25191.831861169885 STAG. PRES: 0.1792617350128622\n",
      "V_infty: 10.0 km/s, L/D: 0.16 G_MAX: 24.49076717808283 QDOT_MAX: 508.39632261083983 J_MAX: 26592.040433513666 STAG. PRES: 0.15989512893132654\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 10.0 km/s, L/D: 0.2 G_MAX: 27.904455418228963 QDOT_MAX: 536.6970307254231 J_MAX: 27962.10247843834 STAG. PRES: 0.14443120198021298\n",
      "V_infty: 10.0 km/s, L/D: 0.24 G_MAX: 31.695255486663942 QDOT_MAX: 565.7511864956583 J_MAX: 29294.95979767439 STAG. PRES: 0.13164041325056422\n",
      "V_infty: 10.0 km/s, L/D: 0.28 G_MAX: 35.91905801890433 QDOT_MAX: 595.163929249458 J_MAX: 30586.976041764163 STAG. PRES: 0.1210492314778109\n",
      "V_infty: 10.0 km/s, L/D: 0.32 G_MAX: 40.5921045037678 QDOT_MAX: 625.0499146962788 J_MAX: 31839.630579168374 STAG. PRES: 0.11228427181583904\n",
      "V_infty: 10.0 km/s, L/D: 0.36 G_MAX: 45.46119677763393 QDOT_MAX: 656.2051040384956 J_MAX: 33051.12516873689 STAG. PRES: 0.10456891812500439\n",
      "V_infty: 10.0 km/s, L/D: 0.4 G_MAX: 50.877799341224 QDOT_MAX: 686.0946408521463 J_MAX: 34222.97131138374 STAG. PRES: 0.09754131268387775\n",
      "V_infty: 12.0 km/s, L/D: 0.0 G_MAX: 20.417781080912135 QDOT_MAX: 638.5748294799772 J_MAX: 27625.245753281295 STAG. PRES: 0.3954469936544291\n",
      "V_infty: 12.0 km/s, L/D: 0.04 G_MAX: 23.67425076036325 QDOT_MAX: 683.5408203999916 J_MAX: 29563.223757376418 STAG. PRES: 0.33776753467167486\n",
      "V_infty: 12.0 km/s, L/D: 0.08 G_MAX: 27.604977357980218 QDOT_MAX: 732.7690719860452 J_MAX: 31562.03052246913 STAG. PRES: 0.29053513065451003\n",
      "V_infty: 12.0 km/s, L/D: 0.12 G_MAX: 32.10853368148876 QDOT_MAX: 784.4089250923084 J_MAX: 33561.205918403255 STAG. PRES: 0.2537339997665505\n",
      "V_infty: 12.0 km/s, L/D: 0.16 G_MAX: 37.09874845855183 QDOT_MAX: 833.7376373826675 J_MAX: 35526.80245779065 STAG. PRES: 0.22480014799729486\n",
      "V_infty: 12.0 km/s, L/D: 0.2 G_MAX: 42.68165201188502 QDOT_MAX: 882.0067228371955 J_MAX: 37435.183423379145 STAG. PRES: 0.20238940605320682\n",
      "V_infty: 12.0 km/s, L/D: 0.24 G_MAX: 48.971513905938785 QDOT_MAX: 934.3575249107901 J_MAX: 39280.62083985077 STAG. PRES: 0.18397011066970048\n",
      "V_infty: 12.0 km/s, L/D: 0.28 G_MAX: 55.746142153086524 QDOT_MAX: 987.8172625412557 J_MAX: 41064.30480188976 STAG. PRES: 0.1688429017209025\n",
      "V_infty: 12.0 km/s, L/D: 0.32 G_MAX: 62.884201349392924 QDOT_MAX: 1040.3557457295365 J_MAX: 42785.954268040354 STAG. PRES: 0.15629166974952605\n",
      "V_infty: 12.0 km/s, L/D: 0.36 G_MAX: 70.72127491484646 QDOT_MAX: 1089.0414123071666 J_MAX: 44447.17048527111 STAG. PRES: 0.14562524817266806\n",
      "V_infty: 12.0 km/s, L/D: 0.4 G_MAX: 79.58834048380453 QDOT_MAX: 1146.7032053277655 J_MAX: 46054.11456730226 STAG. PRES: 0.1358871857143302\n",
      "V_infty: 14.0 km/s, L/D: 0.0 G_MAX: 28.0771954101466 QDOT_MAX: 973.7152852491578 J_MAX: 35316.91616981044 STAG. PRES: 0.5437279891440973\n",
      "V_infty: 14.0 km/s, L/D: 0.04 G_MAX: 32.940375449039834 QDOT_MAX: 1047.7003167646715 J_MAX: 37955.54142281736 STAG. PRES: 0.46413054158151995\n",
      "V_infty: 14.0 km/s, L/D: 0.08 G_MAX: 38.880347142178024 QDOT_MAX: 1129.7070152232811 J_MAX: 40660.44564888429 STAG. PRES: 0.3940803505431392\n",
      "V_infty: 14.0 km/s, L/D: 0.12 G_MAX: 45.62421659969929 QDOT_MAX: 1211.2227740279072 J_MAX: 43362.23543289942 STAG. PRES: 0.34207529738618553\n",
      "V_infty: 14.0 km/s, L/D: 0.16 G_MAX: 53.15874615106762 QDOT_MAX: 1287.4047627783477 J_MAX: 45996.20290326343 STAG. PRES: 0.30173056583159785\n",
      "V_infty: 14.0 km/s, L/D: 0.2 G_MAX: 61.68582782403283 QDOT_MAX: 1367.8935301425072 J_MAX: 48543.15324368007 STAG. PRES: 0.27084358828428856\n",
      "V_infty: 14.0 km/s, L/D: 0.24 G_MAX: 71.0298736785218 QDOT_MAX: 1450.765092999506 J_MAX: 50996.01726095032 STAG. PRES: 0.245816008252646\n",
      "V_infty: 14.0 km/s, L/D: 0.28 G_MAX: 80.86304913659862 QDOT_MAX: 1535.808153894724 J_MAX: 53353.11425694081 STAG. PRES: 0.2252799456077287\n",
      "V_infty: 14.0 km/s, L/D: 0.32 G_MAX: 91.75344489039414 QDOT_MAX: 1626.936391937521 J_MAX: 55625.79034875427 STAG. PRES: 0.20823412618224957\n",
      "V_infty: 14.0 km/s, L/D: 0.36 G_MAX: 103.90010021675664 QDOT_MAX: 1704.6910185212848 J_MAX: 57814.01041326089 STAG. PRES: 0.1939563832577951\n",
      "V_infty: 14.0 km/s, L/D: 0.4 G_MAX: 113.93838882371159 QDOT_MAX: 1787.832735946424 J_MAX: 59932.54575312464 STAG. PRES: 0.18111908685469244\n",
      "V_infty: 16.0 km/s, L/D: 0.0 G_MAX: 37.13724714048259 QDOT_MAX: 1418.9502750972147 J_MAX: 44079.59892442144 STAG. PRES: 0.719124354241293\n",
      "V_infty: 16.0 km/s, L/D: 0.04 G_MAX: 44.0805613365712 QDOT_MAX: 1531.522804305717 J_MAX: 47537.51141533095 STAG. PRES: 0.6113035166388155\n",
      "V_infty: 16.0 km/s, L/D: 0.08 G_MAX: 52.47773362532673 QDOT_MAX: 1655.3334852974874 J_MAX: 51080.40877117323 STAG. PRES: 0.5147431922232637\n",
      "V_infty: 16.0 km/s, L/D: 0.12 G_MAX: 61.960067561728465 QDOT_MAX: 1777.048949353583 J_MAX: 54598.766289387146 STAG. PRES: 0.4443317910269551\n",
      "V_infty: 16.0 km/s, L/D: 0.16 G_MAX: 72.91364023460693 QDOT_MAX: 1898.541587734582 J_MAX: 58009.31248466138 STAG. PRES: 0.39062831142650106\n",
      "V_infty: 16.0 km/s, L/D: 0.2 G_MAX: 85.08072941054964 QDOT_MAX: 2027.2126261778767 J_MAX: 61293.051583862034 STAG. PRES: 0.3496613925732268\n",
      "V_infty: 16.0 km/s, L/D: 0.24 G_MAX: 97.95262382154179 QDOT_MAX: 2159.774651980551 J_MAX: 64443.5310168103 STAG. PRES: 0.31706768063402774\n",
      "V_infty: 16.0 km/s, L/D: 0.28 G_MAX: 112.14967686389716 QDOT_MAX: 2286.1679088430446 J_MAX: 67465.69213297947 STAG. PRES: 0.29023307248703073\n",
      "V_infty: 16.0 km/s, L/D: 0.32 G_MAX: 127.48318210525169 QDOT_MAX: 2413.3476192752255 J_MAX: 70372.40614955689 STAG. PRES: 0.2681181073984638\n",
      "V_infty: 16.0 km/s, L/D: 0.36 G_MAX: 142.36984223017754 QDOT_MAX: 2508.701160362361 J_MAX: 73166.74940265644 STAG. PRES: 0.2496066101959306\n",
      "V_infty: 16.0 km/s, L/D: 0.4 G_MAX: 156.7717141896747 QDOT_MAX: 2621.289028733081 J_MAX: 75862.50706187145 STAG. PRES: 0.23319341961937254\n",
      "V_infty: 18.0 km/s, L/D: 0.0 G_MAX: 47.5892886723512 QDOT_MAX: 1987.3809166529813 J_MAX: 53898.78195965362 STAG. PRES: 0.9214630061859076\n",
      "V_infty: 18.0 km/s, L/D: 0.04 G_MAX: 57.10323195587598 QDOT_MAX: 2154.1674123184916 J_MAX: 58318.7135415652 STAG. PRES: 0.7786244889655722\n",
      "V_infty: 18.0 km/s, L/D: 0.08 G_MAX: 68.40806626322421 QDOT_MAX: 2336.9012653200166 J_MAX: 62824.74763324576 STAG. PRES: 0.6529672059780488\n",
      "V_infty: 18.0 km/s, L/D: 0.12 G_MAX: 81.43407711731886 QDOT_MAX: 2505.9767716891506 J_MAX: 67272.23181191801 STAG. PRES: 0.5601660936495645\n",
      "V_infty: 18.0 km/s, L/D: 0.16 G_MAX: 96.51623265717367 QDOT_MAX: 2689.504611587057 J_MAX: 71576.50712715703 STAG. PRES: 0.49141364276569827\n",
      "V_infty: 18.0 km/s, L/D: 0.2 G_MAX: 112.60643004407797 QDOT_MAX: 2884.1249623521658 J_MAX: 75695.54524218343 STAG. PRES: 0.43888538286309975\n",
      "V_infty: 18.0 km/s, L/D: 0.24 G_MAX: 130.20777894297046 QDOT_MAX: 3072.1655585243348 J_MAX: 79630.34075215185 STAG. PRES: 0.3976473216359171\n",
      "V_infty: 18.0 km/s, L/D: 0.28 G_MAX: 149.6917728143659 QDOT_MAX: 3248.9839866326647 J_MAX: 83402.76594851851 STAG. PRES: 0.3638865817081939\n",
      "V_infty: 18.0 km/s, L/D: 0.32 G_MAX: 167.69601922704672 QDOT_MAX: 3418.4099707742603 J_MAX: 87024.13966478067 STAG. PRES: 0.3358638276075754\n",
      "V_infty: 18.0 km/s, L/D: 0.36 G_MAX: 189.36380420046737 QDOT_MAX: 3588.2579734536066 J_MAX: 90509.5692233002 STAG. PRES: 0.3125872188262122\n",
      "V_infty: 18.0 km/s, L/D: 0.4 G_MAX: 209.69113806557823 QDOT_MAX: 3724.01373377566 J_MAX: 93869.91920299239 STAG. PRES: 0.2920928542445839\n",
      "V_infty: 20.0 km/s, L/D: 0.0 G_MAX: 59.50109941173818 QDOT_MAX: 2696.90130693979 J_MAX: 64787.914269141496 STAG. PRES: 1.1520377746065327\n",
      "V_infty: 20.0 km/s, L/D: 0.04 G_MAX: 71.98136720469469 QDOT_MAX: 2930.577550972126 J_MAX: 70282.66884496858 STAG. PRES: 0.9654580734053128\n",
      "V_infty: 20.0 km/s, L/D: 0.08 G_MAX: 86.87402591472797 QDOT_MAX: 3191.5653203871607 J_MAX: 75888.22850671031 STAG. PRES: 0.8079699818769791\n",
      "V_infty: 20.0 km/s, L/D: 0.12 G_MAX: 104.04691748095365 QDOT_MAX: 3435.9484222777082 J_MAX: 81400.36406676231 STAG. PRES: 0.6898864109203796\n",
      "V_infty: 20.0 km/s, L/D: 0.16 G_MAX: 123.71073167013358 QDOT_MAX: 3689.406564238731 J_MAX: 86690.01626479563 STAG. PRES: 0.6039919637033849\n",
      "V_infty: 20.0 km/s, L/D: 0.2 G_MAX: 144.92147347185417 QDOT_MAX: 3937.0798684075326 J_MAX: 91750.71017792942 STAG. PRES: 0.5385278194017546\n",
      "V_infty: 20.0 km/s, L/D: 0.24 G_MAX: 167.2983473172622 QDOT_MAX: 4232.67093204938 J_MAX: 96575.2763112492 STAG. PRES: 0.4877731623528622\n",
      "V_infty: 20.0 km/s, L/D: 0.28 G_MAX: 193.28996208986106 QDOT_MAX: 4474.751715158452 J_MAX: 101180.91451354093 STAG. PRES: 0.44607353217204165\n",
      "V_infty: 20.0 km/s, L/D: 0.32 G_MAX: 216.31434049932875 QDOT_MAX: 4683.669164807718 J_MAX: 105597.90131295692 STAG. PRES: 0.411432869526391\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 20.0 km/s, L/D: 0.36 G_MAX: 242.38612022297815 QDOT_MAX: 4912.621064125741 J_MAX: 109844.39688230143 STAG. PRES: 0.3828733009492963\n",
      "V_infty: 20.0 km/s, L/D: 0.4 G_MAX: 266.87948521769374 QDOT_MAX: 5178.319336521834 J_MAX: 113931.03597469686 STAG. PRES: 0.35780026090431105\n"
     ]
    }
   ],
   "source": [
    "acc_net_g_max_array       = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "stag_pres_atm_max_array   = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "q_stag_total_max_array    = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "heatload_max_array        = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "\n",
    "\n",
    "for i in range(0,len(v0_kms_array)):\n",
    "    for j in range(0,len(LD_array)):\n",
    "        vehicle=Vehicle('Apollo', 1000.0, 200.0, LD_array[j], 3.1416, 0.0, 1.00, planet)\n",
    "        vehicle.setInitialState(110.0,0.0,0.0,v0_kms_array[i],0.0,overShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-5)\n",
    "        vehicle.propogateEntry (2400.0, 1.0, 180.0)\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",
    "        vehicle=Vehicle('Apollo', 1000.0, 200.0, LD_array[j], 3.1416, 0.0, 1.00, planet)\n",
    "        vehicle.setInitialState(110.0,0.0,0.0,v0_kms_array[i],0.0,underShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-5)\n",
    "        vehicle.propogateEntry (2400.0, 1.0, 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_us))\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]   = max(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\"+\", L/D: \"+str(LD_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",
    "\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'acc_net_g_max_array.txt',acc_net_g_max_array)\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'stag_pres_atm_max_array.txt',stag_pres_atm_max_array)\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'q_stag_total_max_array.txt',q_stag_total_max_array)\n",
    "np.savetxt('../data/jsr-paper/mars/'+runID+'heatload_max_array.txt',heatload_max_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "x = np.loadtxt('../data/jsr-paper/mars/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/mars/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/mars/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/mars/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/mars/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/mars/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/mars/'+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, transpose(S1), kind='cubic')\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   20,  210)\n",
    "y_new =  np.linspace( 0.0,   0.4 ,110)\n",
    "z_new =  np.zeros((len(x_new),len(y_new)))\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",
    "\n",
    "Zlevels = np.array([0.5,1.0])\n",
    "\n",
    "Glevels = np.array([15.0, 30.0])\n",
    "Qlevels = np.array([300, 800.0])\n",
    "Hlevels = np.array([30.0])\n",
    "#Slevels = np.array([0.8])\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "#plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "#plt.xlim([0.0,30.0])\n",
    "#plt.ylim([0.0,0.4])\n",
    "#plt.tight_layout()\n",
    "#plt.contourf(X, Y, Z, levels=levels)\n",
    "\n",
    "\n",
    "#plt.axvline(x=25.0,linewidth=3, linestyle='dotted' ,color='red',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(LV$'+r' '+r'$C3$'+r' '+r'$limit)$')\n",
    "#plt.axvline(x=13.1,linewidth=1, linestyle='dotted' ,color='cyan',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(Chem. OI)$')\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.25,6.25])\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\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='%.1f',inline_spacing=1)\n",
    "ZCS1.collections[0].set_linewidths(1.5)\n",
    "ZCS1.collections[1].set_linewidths(1.5)\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",
    "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[1].set_linewidths(1.5)\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",
    "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",
    "\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='magenta',linestyles='dashdot')\n",
    "\n",
    "plt.clabel(HCS1, inline=1, fontsize=12, colors='magenta',fmt='%d',inline_spacing=0)\n",
    "HCS1.collections[0].set_linewidths(1.5)\n",
    "\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "\n",
    "\n",
    "#SCS1 = plt.contour(X, Y, transpose(S1), levels=Slevels, colors='cyan')\n",
    "\n",
    "#plt.clabel(SCS1, inline=1, fontsize=12, colors='cyan',fmt='%.1f',inline_spacing=1)\n",
    "#SCS1.collections[0].set_linewidths(3.0)\n",
    "#SCS1.collections[0].set_label(r'$Peak$'+r' '+r'$stag. pressure,atm$')\n",
    "\n",
    "#plt.axhline(y=0.36,linewidth=1, linestyle='dotted' ,color='white',label=r'$Apollo$'+' '+r'$CM$'+' '+r'$L/D$')\n",
    "\n",
    "\n",
    "\n",
    "#matplotlib.rcParams['text.usetex'] = True\n",
    "#plt.rc('text', usetex=True)\n",
    "\n",
    "\n",
    "# circles for b=50 plot\n",
    "#plt.plot(7.5,0.20,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.30,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "#plt.plot(7.5,0.211,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.315,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "\n",
    "#plt.grid(True,linestyle='dotted', linewidth=0.1)\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(\"L/D\",fontsize=16)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=16)\n",
    "plt.xticks(np.array([ 0, 4, 8, 12, 16, 20]), fontsize=16)\n",
    "plt.yticks(np.array([ 0.0, 0.1, 0.2, 0.3, 0.4]),fontsize=16)\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 right', fontsize=16)\n",
    "\n",
    "dat0 = ZCS1.allsegs[1][0]\n",
    "x1,y1=dat0[:,0],dat0[:,1]\n",
    "F1 = interpolate.interp1d(x1, y1, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat1 = GCS1.allsegs[0][0]\n",
    "x2,y2=dat1[:,0],dat1[:,1]\n",
    "F2 = interpolate.interp1d(x2, y2, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat2 = QCS1.allsegs[0][0]\n",
    "x3,y3= dat2[:,0],dat2[:,1]\n",
    "F3 = interpolate.interp1d(x3, y3, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat0a = ZCS1.allsegs[0][0]\n",
    "x1a,y1a=dat0a[:,0],dat0a[:,1]\n",
    "F1a = interpolate.interp1d(x1a, y1a, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "\n",
    "x4 = np.linspace(0,20,101)\n",
    "y4 = F1(x4)\n",
    "y4a =F1a(x4)\n",
    "y5 = F2(x4)\n",
    "y6 = F3(x4)\n",
    "\n",
    "y7 = np.minimum(y5,y6)\n",
    "y8 = np.minimum(y4,y6)\n",
    "\n",
    "plt.fill_between(x4, y4, y7, where=y4<=y7,color='xkcd:neon green')\n",
    "\n",
    "plt.fill_between(x4, y4a, y8, where=y4a<=y8,color='xkcd:bright yellow')\n",
    "\n",
    "\n",
    "plt.xlim([0.0,20.0])\n",
    "plt.ylim([0.0,0.4])\n",
    "\n",
    "plt.savefig('../data/jsr-paper/mars/mars-lift-small.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/mars/mars-lift-small.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/mars/mars-lift-small.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 468x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.loadtxt('../data/jsr-paper/mars/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/mars/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/mars/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/mars/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/mars/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/mars/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/mars/'+runID+'stag_pres_atm_max_array.txt')\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, transpose(S1), kind='cubic')\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   20,  310)\n",
    "y_new =  np.linspace( 0.0,   0.4 ,110)\n",
    "z_new =  np.zeros((len(x_new),len(y_new)))\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",
    "\n",
    "\n",
    "\n",
    "Z1 = z1_new\n",
    "\n",
    "G1 = g1_new\n",
    "\n",
    "Q1 = q1_new\n",
    "\n",
    "#S1 = s1_new\n",
    "\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "#X, Y = meshgrid(x, y)\n",
    "\n",
    "\n",
    "Zlevels = np.array([0.5,1.0,1.5,2.0])\n",
    "\n",
    "Glevels = np.array([5.0, 10.0, 20.0, 30.0, 50.0])\n",
    "Qlevels = np.array([50.0, 200.0, 400.0, 800.0])\n",
    "Hlevels = np.array([12, 20.0, 30.0, 50.0])\n",
    "#Slevels = np.array([0.8])\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.5,6.5])\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "#plt.xlim([0.0,30.0])\n",
    "#plt.ylim([0.0,0.4])\n",
    "#plt.tight_layout()\n",
    "#plt.contourf(X, Y, Z, levels=levels)\n",
    "\n",
    "\n",
    "#plt.axvline(x=25.0,linewidth=3, linestyle='dotted' ,color='red',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(LV$'+r' '+r'$C3$'+r' '+r'$limit)$')\n",
    "#plt.axvline(x=13.1,linewidth=1, linestyle='dotted' ,color='cyan',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(Chem. OI)$')\n",
    "\n",
    "\n",
    "ZCS1 = plt.contour(X, Y, np.transpose(Z1), levels=Zlevels, colors='black',zorder=0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.clabel(ZCS1, inline=1, fontsize=10, colors='black',fmt='%.1f',inline_spacing=1,zorder=0)\n",
    "ZCS1.collections[0].set_linewidths(1.5)\n",
    "ZCS1.collections[1].set_linewidths(1.5)\n",
    "ZCS1.collections[2].set_linewidths(1.5)\n",
    "ZCS1.collections[3].set_linewidths(1.5)\n",
    "\n",
    "\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',zorder=1)\n",
    "\n",
    "plt.clabel(GCS1, inline=1, fontsize=10, colors='blue',fmt='%d',inline_spacing=0,zorder=1)\n",
    "\n",
    "\n",
    "\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[1].set_linewidths(1.5)\n",
    "GCS1.collections[2].set_linewidths(1.5)\n",
    "GCS1.collections[3].set_linewidths(1.5)\n",
    "GCS1.collections[4].set_linewidths(1.5)\n",
    "\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted',zorder=13)\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=10, colors='red',fmt='%d',inline_spacing=0,zorder=13)\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",
    "\n",
    "\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',zorder=14)\n",
    "\n",
    "plt.clabel(HCS1, inline=1, fontsize=10, colors='xkcd:brown',fmt='%d',inline_spacing=0,zorder=14)\n",
    "HCS1.collections[0].set_linewidths(1.75)\n",
    "HCS1.collections[1].set_linewidths(1.75)\n",
    "HCS1.collections[2].set_linewidths(1.75)\n",
    "HCS1.collections[3].set_linewidths(1.75)\n",
    "\n",
    "\n",
    "\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "\n",
    "\n",
    "#SCS1 = plt.contour(X, Y, transpose(S1), levels=Slevels, colors='cyan')\n",
    "\n",
    "#plt.clabel(SCS1, inline=1, fontsize=12, colors='cyan',fmt='%.1f',inline_spacing=1)\n",
    "#SCS1.collections[0].set_linewidths(3.0)\n",
    "#SCS1.collections[0].set_label(r'$Peak$'+r' '+r'$stag. pressure,atm$')\n",
    "\n",
    "#plt.axhline(y=0.36,linewidth=1, linestyle='dotted' ,color='white',label=r'$Apollo$'+' '+r'$CM$'+' '+r'$L/D$')\n",
    "\n",
    "\n",
    "\n",
    "#matplotlib.rcParams['text.usetex'] = True\n",
    "#plt.rc('text', usetex=True)\n",
    "\n",
    "\n",
    "# circles for b=50 plot\n",
    "#plt.plot(7.5,0.20,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.30,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "#plt.plot(7.5,0.211,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.315,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "\n",
    "#plt.grid(True,linestyle='dotted', linewidth=0.1)\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(\"L/D\",fontsize=12)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=12)\n",
    "plt.xticks(np.array([ 0.0, 5, 10, 15, 20]),fontsize=12)\n",
    "plt.yticks(np.array([ 0.0, 0.1, 0.2, 0.3, 0.4]),fontsize=12)\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 right', fontsize=12)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('../data/jsr-paper/mars/mars-lift-large.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/mars/mars-lift-large.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/mars/mars-lift-large.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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