{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 05 - a - Mars - Feasibility Charts - Drag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "betaRatio_array = np.linspace( 1.0,   41.0 ,  11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta1 = 20.0\n",
    "\n",
    "runID = 'mars-drag-'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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+120.0*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,
   "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: 5.0 TCW: 0.882454714319465 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 9.0 TCW: 1.1635984738932166 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 13.0 TCW: 1.340319584349345 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 17.0 TCW: 1.471829855319811 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 21.0 TCW: 1.5752168146937038 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 25.0 TCW: 1.6594261595528224 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 29.0 TCW: 1.730460369733919 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 33.0 TCW: 1.791919550181774 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 37.0 TCW: 1.845917571848986 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 41.0 TCW: 1.89534849194024 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 5.0 TCW: 0.8782551585427427 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 9.0 TCW: 1.1736965495874756 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 13.0 TCW: 1.3621433544321917 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 17.0 TCW: 1.4998228874974302 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 21.0 TCW: 1.606484231979266 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 25.0 TCW: 1.6935772852266382 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 29.0 TCW: 1.7670679606453632 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 33.0 TCW: 1.832578547859157 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 37.0 TCW: 1.8916595642913308 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 41.0 TCW: 1.945003867138439 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 5.0 TCW: 0.8918627480015857 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 9.0 TCW: 1.2148278364766156 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 13.0 TCW: 1.4169221074444067 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 17.0 TCW: 1.5614509170081874 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 21.0 TCW: 1.6755128271033755 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 25.0 TCW: 1.7727232120778353 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 29.0 TCW: 1.85603133987388 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 33.0 TCW: 1.9286842231012997 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 37.0 TCW: 1.9934097167060827 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 41.0 TCW: 2.051774145460513 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 5.0 TCW: 0.9233586405753158 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 9.0 TCW: 1.2646332009790058 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 13.0 TCW: 1.472626685983414 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 17.0 TCW: 1.6285877118680219 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 21.0 TCW: 1.753502510891849 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 25.0 TCW: 1.857130681968556 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 29.0 TCW: 1.9459386958915275 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 33.0 TCW: 2.023458634143026 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 37.0 TCW: 2.091992447956727 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 41.0 TCW: 2.1535377741674893 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 5.0 TCW: 0.949735077087098 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 9.0 TCW: 1.296785191676463 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 13.0 TCW: 1.5147500627172121 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 17.0 TCW: 1.6782344256462238 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 21.0 TCW: 1.8080999319645343 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 25.0 TCW: 1.9158909074867552 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 29.0 TCW: 2.007521911727963 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 33.0 TCW: 2.0876208164518175 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 37.0 TCW: 2.1593845472380053 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 41.0 TCW: 2.2239236066925514 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 5.0 TCW: 0.9667894195590634 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 9.0 TCW: 1.3166931813466363 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 13.0 TCW: 1.5437213414807047 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 17.0 TCW: 1.7110876046972407 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 21.0 TCW: 1.8440058390588092 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 25.0 TCW: 1.953481780630682 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 29.0 TCW: 2.0477460082038306 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 33.0 TCW: 2.1303460477902263 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 37.0 TCW: 2.203646352238138 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 41.0 TCW: 2.269327391652041 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 5.0 TCW: 0.9774223067470302 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 9.0 TCW: 1.3328515178327507 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 13.0 TCW: 1.563499450287054 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 17.0 TCW: 1.7336686571070459 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 21.0 TCW: 1.867882706450473 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 25.0 TCW: 1.9795681334289839 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 29.0 TCW: 2.075488290589419 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 33.0 TCW: 2.1591556766434223 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 37.0 TCW: 2.2332187068132043 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 41.0 TCW: 2.3005774645316706 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 5.0 TCW: 0.9846942119074811 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 9.0 TCW: 1.3452062762662536 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 13.0 TCW: 1.5782401727628894 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 17.0 TCW: 1.749930027697701 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 21.0 TCW: 1.8852915834104351 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 25.0 TCW: 1.9986041700467467 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 29.0 TCW: 2.0954120229107502 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 33.0 TCW: 2.179554698675929 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 37.0 TCW: 2.255197461203352 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 41.0 TCW: 2.3234536612544616 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 5.0 TCW: 0.9891010514656955 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 9.0 TCW: 1.3537450307958352 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 13.0 TCW: 1.588552060220536 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 17.0 TCW: 1.7608844822425453 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 21.0 TCW: 1.8979285065033764 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 25.0 TCW: 2.0120143547901534 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 29.0 TCW: 2.109049541675631 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 33.0 TCW: 2.194532966506813 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 37.0 TCW: 2.2709211684123147 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 41.0 TCW: 2.339602104642836 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 5.0 TCW: 0.9912710469434387 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 9.0 TCW: 1.3595479120667733 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 13.0 TCW: 1.595786612277152 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 17.0 TCW: 1.76867715401022 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 21.0 TCW: 1.906875535420113 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 25.0 TCW: 2.021415647734102 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 29.0 TCW: 2.119098133771331 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 33.0 TCW: 2.205485085156397 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 37.0 TCW: 2.2822194797481643 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 41.0 TCW: 2.3512368811134365 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 5.0 TCW: 0.9924433124615462 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 9.0 TCW: 1.3639687009163026 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 13.0 TCW: 1.6011606473985012 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 17.0 TCW: 1.774852570346411 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 21.0 TCW: 1.9136563684733119 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 25.0 TCW: 2.028332237539871 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 29.0 TCW: 2.1270575631897373 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 33.0 TCW: 2.2138109439911204 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 37.0 TCW: 2.2908371192315826 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 41.0 TCW: 2.3598452530495706 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', 150.0, beta1, 0.0, 3.1416, 0.0, 0.10, planet)\n",
    "        vehicle.setInitialState(120.0,0.0,0.0,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/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+'betaRatio_array.txt',betaRatio_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, BR: 1.0 G_MAX: 0.8802169883242571 QDOT_MAX: 23.010132507255943 J_MAX: 4588.414832443239 STAG. PRES: 0.0017087535593077485\n",
      "V_infty: 0.0 km/s, BR: 5.0 G_MAX: 0.8802169883242571 QDOT_MAX: 32.28708458343495 J_MAX: 4588.414832443239 STAG. PRES: 0.0017087535593077485\n",
      "V_infty: 0.0 km/s, BR: 9.0 G_MAX: 0.8802169883242571 QDOT_MAX: 34.989850900860404 J_MAX: 4588.414832443239 STAG. PRES: 0.0017087535593077485\n",
      "V_infty: 0.0 km/s, BR: 13.0 G_MAX: 0.8802169883242571 QDOT_MAX: 36.52275945791371 J_MAX: 4588.414832443239 STAG. PRES: 0.0017087535593077485\n",
      "V_infty: 0.0 km/s, BR: 17.0 G_MAX: 0.8802169883242571 QDOT_MAX: 37.55989597765508 J_MAX: 4588.414832443239 STAG. PRES: 0.0017087535593077485\n",
      "V_infty: 0.0 km/s, BR: 21.0 G_MAX: 0.8802169883242571 QDOT_MAX: 38.3380786142878 J_MAX: 4588.414832443239 STAG. PRES: 0.0017087535593077485\n",
      "V_infty: 0.0 km/s, BR: 25.0 G_MAX: 0.8802169883242571 QDOT_MAX: 38.934910917159684 J_MAX: 4588.414832443239 STAG. PRES: 0.0017087535593077485\n",
      "V_infty: 0.0 km/s, BR: 29.0 G_MAX: 0.8802169883242571 QDOT_MAX: 39.41189455692323 J_MAX: 4588.414832443239 STAG. PRES: 0.0017087535593077485\n",
      "V_infty: 0.0 km/s, BR: 33.0 G_MAX: 0.8802169883242571 QDOT_MAX: 39.84754118467324 J_MAX: 4588.414832443239 STAG. PRES: 0.0017087535593077485\n",
      "V_infty: 0.0 km/s, BR: 37.0 G_MAX: 0.8802169883242571 QDOT_MAX: 40.18641781057541 J_MAX: 4588.414832443239 STAG. PRES: 0.0017087535593077485\n",
      "V_infty: 0.0 km/s, BR: 41.0 G_MAX: 0.8802169883242571 QDOT_MAX: 40.53137053268188 J_MAX: 4588.414832443239 STAG. PRES: 0.0017087535593077485\n",
      "V_infty: 2.0 km/s, BR: 1.0 G_MAX: 1.2922586475989402 QDOT_MAX: 30.780991513427722 J_MAX: 5426.4217636394405 STAG. PRES: 0.0025077505408398247\n",
      "V_infty: 2.0 km/s, BR: 5.0 G_MAX: 1.2922586475989402 QDOT_MAX: 42.58534881807495 J_MAX: 5426.4217636394405 STAG. PRES: 0.0025077505408398247\n",
      "V_infty: 2.0 km/s, BR: 9.0 G_MAX: 1.2922586475989402 QDOT_MAX: 45.90511285179835 J_MAX: 5426.4217636394405 STAG. PRES: 0.0025077505408398247\n",
      "V_infty: 2.0 km/s, BR: 13.0 G_MAX: 1.2922586475989402 QDOT_MAX: 47.77616535543122 J_MAX: 5426.4217636394405 STAG. PRES: 0.0025077505408398247\n",
      "V_infty: 2.0 km/s, BR: 17.0 G_MAX: 1.2922586475989402 QDOT_MAX: 49.0630598246032 J_MAX: 5426.4217636394405 STAG. PRES: 0.0025077505408398247\n",
      "V_infty: 2.0 km/s, BR: 21.0 G_MAX: 1.2922586475989402 QDOT_MAX: 49.98017597665684 J_MAX: 5426.4217636394405 STAG. PRES: 0.0025077505408398247\n",
      "V_infty: 2.0 km/s, BR: 25.0 G_MAX: 1.2922586475989402 QDOT_MAX: 50.71077560278259 J_MAX: 5426.4217636394405 STAG. PRES: 0.0025077505408398247\n",
      "V_infty: 2.0 km/s, BR: 29.0 G_MAX: 1.2922586475989402 QDOT_MAX: 51.335131270568695 J_MAX: 5426.4217636394405 STAG. PRES: 0.0025077505408398247\n",
      "V_infty: 2.0 km/s, BR: 33.0 G_MAX: 1.2922586475989402 QDOT_MAX: 51.846801553295165 J_MAX: 5426.4217636394405 STAG. PRES: 0.0025077505408398247\n",
      "V_infty: 2.0 km/s, BR: 37.0 G_MAX: 1.2922586475989402 QDOT_MAX: 52.335512745788805 J_MAX: 5426.4217636394405 STAG. PRES: 0.0025077505408398247\n",
      "V_infty: 2.0 km/s, BR: 41.0 G_MAX: 1.2922586475989402 QDOT_MAX: 52.756821194936826 J_MAX: 5426.4217636394405 STAG. PRES: 0.0025077505408398247\n",
      "V_infty: 4.0 km/s, BR: 1.0 G_MAX: 2.6556218073586857 QDOT_MAX: 59.35647933469471 J_MAX: 7664.301898881714 STAG. PRES: 0.0051501345539506915\n",
      "V_infty: 4.0 km/s, BR: 5.0 G_MAX: 2.6556218073586857 QDOT_MAX: 78.37809273807032 J_MAX: 7664.301898881714 STAG. PRES: 0.0051501345539506915\n",
      "V_infty: 4.0 km/s, BR: 9.0 G_MAX: 2.6556218073586857 QDOT_MAX: 83.62977095017672 J_MAX: 7664.301898881714 STAG. PRES: 0.0051501345539506915\n",
      "V_infty: 4.0 km/s, BR: 13.0 G_MAX: 2.6556218073586857 QDOT_MAX: 86.62811635572457 J_MAX: 7664.301898881714 STAG. PRES: 0.0051501345539506915\n",
      "V_infty: 4.0 km/s, BR: 17.0 G_MAX: 2.6556218073586857 QDOT_MAX: 88.60005071383527 J_MAX: 7664.301898881714 STAG. PRES: 0.0051501345539506915\n",
      "V_infty: 4.0 km/s, BR: 21.0 G_MAX: 2.6556218073586857 QDOT_MAX: 90.16363849616087 J_MAX: 7664.301898881714 STAG. PRES: 0.0051501345539506915\n",
      "V_infty: 4.0 km/s, BR: 25.0 G_MAX: 2.6556218073586857 QDOT_MAX: 91.44279186919564 J_MAX: 7664.301898881714 STAG. PRES: 0.0051501345539506915\n",
      "V_infty: 4.0 km/s, BR: 29.0 G_MAX: 2.6556218073586857 QDOT_MAX: 92.51144072637459 J_MAX: 7664.301898881714 STAG. PRES: 0.0051501345539506915\n",
      "V_infty: 4.0 km/s, BR: 33.0 G_MAX: 2.6556218073586857 QDOT_MAX: 93.33812320622258 J_MAX: 7664.301898881714 STAG. PRES: 0.0051501345539506915\n",
      "V_infty: 4.0 km/s, BR: 37.0 G_MAX: 2.6556218073586857 QDOT_MAX: 94.23627916189568 J_MAX: 7664.301898881714 STAG. PRES: 0.0051501345539506915\n",
      "V_infty: 4.0 km/s, BR: 41.0 G_MAX: 2.6556218073586857 QDOT_MAX: 94.92419865279852 J_MAX: 7664.301898881714 STAG. PRES: 0.0051501345539506915\n",
      "V_infty: 6.0 km/s, BR: 1.0 G_MAX: 5.227929047435482 QDOT_MAX: 118.73798111103807 J_MAX: 11027.478268924884 STAG. PRES: 0.010132860210917341\n",
      "V_infty: 6.0 km/s, BR: 5.0 G_MAX: 5.227929047435482 QDOT_MAX: 151.725650601039 J_MAX: 11027.478268924884 STAG. PRES: 0.010132860210917341\n",
      "V_infty: 6.0 km/s, BR: 9.0 G_MAX: 5.227929047435482 QDOT_MAX: 160.88964013106397 J_MAX: 11027.478268924884 STAG. PRES: 0.010132860210917341\n",
      "V_infty: 6.0 km/s, BR: 13.0 G_MAX: 5.227929047435482 QDOT_MAX: 165.9515661775459 J_MAX: 11027.478268924884 STAG. PRES: 0.010132860210917341\n",
      "V_infty: 6.0 km/s, BR: 17.0 G_MAX: 5.227929047435482 QDOT_MAX: 169.6333526303966 J_MAX: 11027.478268924884 STAG. PRES: 0.010132860210917341\n",
      "V_infty: 6.0 km/s, BR: 21.0 G_MAX: 5.227929047435482 QDOT_MAX: 172.54061321429495 J_MAX: 11027.478268924884 STAG. PRES: 0.010132860210917341\n",
      "V_infty: 6.0 km/s, BR: 25.0 G_MAX: 5.227929047435482 QDOT_MAX: 174.8507079691975 J_MAX: 11027.478268924884 STAG. PRES: 0.010132860210917341\n",
      "V_infty: 6.0 km/s, BR: 29.0 G_MAX: 5.227929047435482 QDOT_MAX: 176.34899592064525 J_MAX: 11027.478268924884 STAG. PRES: 0.010132860210917341\n",
      "V_infty: 6.0 km/s, BR: 33.0 G_MAX: 5.227929047435482 QDOT_MAX: 178.33867312114853 J_MAX: 11027.478268924884 STAG. PRES: 0.010132860210917341\n",
      "V_infty: 6.0 km/s, BR: 37.0 G_MAX: 5.227929047435482 QDOT_MAX: 179.8741342449452 J_MAX: 11027.478268924884 STAG. PRES: 0.010132860210917341\n",
      "V_infty: 6.0 km/s, BR: 41.0 G_MAX: 5.227929047435482 QDOT_MAX: 181.0662533266709 J_MAX: 11027.478268924884 STAG. PRES: 0.010132860210917341\n",
      "V_infty: 8.0 km/s, BR: 1.0 G_MAX: 9.104871040859468 QDOT_MAX: 223.52470297611444 J_MAX: 15471.775196159788 STAG. PRES: 0.017640083281891306\n",
      "V_infty: 8.0 km/s, BR: 5.0 G_MAX: 9.104871040859468 QDOT_MAX: 278.3250974393591 J_MAX: 15471.775196159788 STAG. PRES: 0.017640083281891306\n",
      "V_infty: 8.0 km/s, BR: 9.0 G_MAX: 9.104871040859468 QDOT_MAX: 293.77146179051095 J_MAX: 15471.775196159788 STAG. PRES: 0.017640083281891306\n",
      "V_infty: 8.0 km/s, BR: 13.0 G_MAX: 9.104871040859468 QDOT_MAX: 303.07626937119113 J_MAX: 15471.775196159788 STAG. PRES: 0.017640083281891306\n",
      "V_infty: 8.0 km/s, BR: 17.0 G_MAX: 9.104871040859468 QDOT_MAX: 309.655184580125 J_MAX: 15471.775196159788 STAG. PRES: 0.017640083281891306\n",
      "V_infty: 8.0 km/s, BR: 21.0 G_MAX: 9.104871040859468 QDOT_MAX: 314.6957528686912 J_MAX: 15471.775196159788 STAG. PRES: 0.017640083281891306\n",
      "V_infty: 8.0 km/s, BR: 25.0 G_MAX: 9.104871040859468 QDOT_MAX: 317.8980887035052 J_MAX: 15471.775196159788 STAG. PRES: 0.017640083281891306\n",
      "V_infty: 8.0 km/s, BR: 29.0 G_MAX: 9.104871040859468 QDOT_MAX: 321.5415330271017 J_MAX: 15471.775196159788 STAG. PRES: 0.017640083281891306\n",
      "V_infty: 8.0 km/s, BR: 33.0 G_MAX: 9.104871040859468 QDOT_MAX: 324.8850688212515 J_MAX: 15471.775196159788 STAG. PRES: 0.017640083281891306\n",
      "V_infty: 8.0 km/s, BR: 37.0 G_MAX: 9.104871040859468 QDOT_MAX: 327.5400137030362 J_MAX: 15471.775196159788 STAG. PRES: 0.017640083281891306\n",
      "V_infty: 8.0 km/s, BR: 41.0 G_MAX: 9.104871040859468 QDOT_MAX: 329.62026103621764 J_MAX: 15471.775196159788 STAG. PRES: 0.017640083281891306\n",
      "V_infty: 10.0 km/s, BR: 1.0 G_MAX: 14.304598311624453 QDOT_MAX: 388.6262749395543 J_MAX: 20984.95787944843 STAG. PRES: 0.027708308813992465\n",
      "V_infty: 10.0 km/s, BR: 5.0 G_MAX: 14.304598311624453 QDOT_MAX: 476.4742673502999 J_MAX: 20984.95787944843 STAG. PRES: 0.027708308813992465\n",
      "V_infty: 10.0 km/s, BR: 9.0 G_MAX: 14.304598311624453 QDOT_MAX: 501.7534025759173 J_MAX: 20984.95787944843 STAG. PRES: 0.027708308813992465\n",
      "V_infty: 10.0 km/s, BR: 13.0 G_MAX: 14.304598311624453 QDOT_MAX: 516.6983260123902 J_MAX: 20984.95787944843 STAG. PRES: 0.027708308813992465\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 10.0 km/s, BR: 17.0 G_MAX: 14.304598311624453 QDOT_MAX: 527.4827313314122 J_MAX: 20984.95787944843 STAG. PRES: 0.027708308813992465\n",
      "V_infty: 10.0 km/s, BR: 21.0 G_MAX: 14.304598311624453 QDOT_MAX: 533.4324216219031 J_MAX: 20984.95787944843 STAG. PRES: 0.027708308813992465\n",
      "V_infty: 10.0 km/s, BR: 25.0 G_MAX: 14.304598311624453 QDOT_MAX: 541.2271415414065 J_MAX: 20984.95787944843 STAG. PRES: 0.027708308813992465\n",
      "V_infty: 10.0 km/s, BR: 29.0 G_MAX: 14.304598311624453 QDOT_MAX: 547.8216296278498 J_MAX: 20984.95787944843 STAG. PRES: 0.027708308813992465\n",
      "V_infty: 10.0 km/s, BR: 33.0 G_MAX: 14.304598311624453 QDOT_MAX: 552.9552638664154 J_MAX: 20984.95787944843 STAG. PRES: 0.027708308813992465\n",
      "V_infty: 10.0 km/s, BR: 37.0 G_MAX: 14.304598311624453 QDOT_MAX: 556.582314249187 J_MAX: 20984.95787944843 STAG. PRES: 0.027708308813992465\n",
      "V_infty: 10.0 km/s, BR: 41.0 G_MAX: 14.304598311624453 QDOT_MAX: 558.4628397750381 J_MAX: 20984.95787944843 STAG. PRES: 0.027708308813992465\n",
      "V_infty: 12.0 km/s, BR: 1.0 G_MAX: 20.946618740536405 QDOT_MAX: 629.303319092016 J_MAX: 27569.851474373587 STAG. PRES: 0.040566845549945406\n",
      "V_infty: 12.0 km/s, BR: 5.0 G_MAX: 20.946618740536405 QDOT_MAX: 762.6656281622896 J_MAX: 27569.851474373587 STAG. PRES: 0.040566845549945406\n",
      "V_infty: 12.0 km/s, BR: 9.0 G_MAX: 20.946618740536405 QDOT_MAX: 797.5643025221304 J_MAX: 27569.851474373587 STAG. PRES: 0.040566845549945406\n",
      "V_infty: 12.0 km/s, BR: 13.0 G_MAX: 20.946618740536405 QDOT_MAX: 826.6937299071749 J_MAX: 27569.851474373587 STAG. PRES: 0.040566845549945406\n",
      "V_infty: 12.0 km/s, BR: 17.0 G_MAX: 20.946618740536405 QDOT_MAX: 841.1980519163874 J_MAX: 27569.851474373587 STAG. PRES: 0.040566845549945406\n",
      "V_infty: 12.0 km/s, BR: 21.0 G_MAX: 20.946618740536405 QDOT_MAX: 849.0471402572424 J_MAX: 27569.851474373587 STAG. PRES: 0.040566845549945406\n",
      "V_infty: 12.0 km/s, BR: 25.0 G_MAX: 20.946618740536405 QDOT_MAX: 863.5684144354888 J_MAX: 27569.851474373587 STAG. PRES: 0.040566845549945406\n",
      "V_infty: 12.0 km/s, BR: 29.0 G_MAX: 20.946618740536405 QDOT_MAX: 874.2982984138607 J_MAX: 27569.851474373587 STAG. PRES: 0.040566845549945406\n",
      "V_infty: 12.0 km/s, BR: 33.0 G_MAX: 20.946618740536405 QDOT_MAX: 882.5680790079577 J_MAX: 27569.851474373587 STAG. PRES: 0.040566845549945406\n",
      "V_infty: 12.0 km/s, BR: 37.0 G_MAX: 20.946618740536405 QDOT_MAX: 888.0086139355695 J_MAX: 27569.851474373587 STAG. PRES: 0.040566845549945406\n",
      "V_infty: 12.0 km/s, BR: 41.0 G_MAX: 20.946618740536405 QDOT_MAX: 890.6421797348206 J_MAX: 27569.851474373587 STAG. PRES: 0.040566845549945406\n",
      "V_infty: 14.0 km/s, BR: 1.0 G_MAX: 28.98298054801421 QDOT_MAX: 961.3177109363633 J_MAX: 35207.27635651625 STAG. PRES: 0.05612549232784251\n",
      "V_infty: 14.0 km/s, BR: 5.0 G_MAX: 28.98298054801421 QDOT_MAX: 1156.5354329858735 J_MAX: 35207.27635651625 STAG. PRES: 0.05612549232784251\n",
      "V_infty: 14.0 km/s, BR: 9.0 G_MAX: 28.98298054801421 QDOT_MAX: 1216.3408427113184 J_MAX: 35207.27635651625 STAG. PRES: 0.05612549232784251\n",
      "V_infty: 14.0 km/s, BR: 13.0 G_MAX: 28.98298054801421 QDOT_MAX: 1246.3548183417868 J_MAX: 35207.27635651625 STAG. PRES: 0.05612549232784251\n",
      "V_infty: 14.0 km/s, BR: 17.0 G_MAX: 28.98298054801421 QDOT_MAX: 1265.9280612463822 J_MAX: 35207.27635651625 STAG. PRES: 0.05612549232784251\n",
      "V_infty: 14.0 km/s, BR: 21.0 G_MAX: 28.98298054801421 QDOT_MAX: 1291.771432174556 J_MAX: 35207.27635651625 STAG. PRES: 0.05612549232784251\n",
      "V_infty: 14.0 km/s, BR: 25.0 G_MAX: 28.98298054801421 QDOT_MAX: 1309.8960237334825 J_MAX: 35207.27635651625 STAG. PRES: 0.05612549232784251\n",
      "V_infty: 14.0 km/s, BR: 29.0 G_MAX: 28.98298054801421 QDOT_MAX: 1322.389809184962 J_MAX: 35207.27635651625 STAG. PRES: 0.05612549232784251\n",
      "V_infty: 14.0 km/s, BR: 33.0 G_MAX: 28.98298054801421 QDOT_MAX: 1328.2556942114131 J_MAX: 35207.27635651625 STAG. PRES: 0.05612549232784251\n",
      "V_infty: 14.0 km/s, BR: 37.0 G_MAX: 28.98298054801421 QDOT_MAX: 1329.8014614899982 J_MAX: 35207.27635651625 STAG. PRES: 0.05612549232784251\n",
      "V_infty: 14.0 km/s, BR: 41.0 G_MAX: 28.98298054801421 QDOT_MAX: 1329.4133043725735 J_MAX: 35207.27635651625 STAG. PRES: 0.05612549232784251\n",
      "V_infty: 16.0 km/s, BR: 1.0 G_MAX: 38.61855704085347 QDOT_MAX: 1401.4187156106377 J_MAX: 43900.946372184364 STAG. PRES: 0.07477760357911035\n",
      "V_infty: 16.0 km/s, BR: 5.0 G_MAX: 38.61855704085347 QDOT_MAX: 1676.3815507380045 J_MAX: 43900.946372184364 STAG. PRES: 0.07477760357911035\n",
      "V_infty: 16.0 km/s, BR: 9.0 G_MAX: 38.61855704085347 QDOT_MAX: 1756.4807543215459 J_MAX: 43900.946372184364 STAG. PRES: 0.07477760357911035\n",
      "V_infty: 16.0 km/s, BR: 13.0 G_MAX: 38.61855704085347 QDOT_MAX: 1809.216758840956 J_MAX: 43900.946372184364 STAG. PRES: 0.07477760357911035\n",
      "V_infty: 16.0 km/s, BR: 17.0 G_MAX: 38.61855704085347 QDOT_MAX: 1830.0699549993928 J_MAX: 43900.946372184364 STAG. PRES: 0.07477760357911035\n",
      "V_infty: 16.0 km/s, BR: 21.0 G_MAX: 38.61855704085347 QDOT_MAX: 1846.6902397737188 J_MAX: 43900.946372184364 STAG. PRES: 0.07477760357911035\n",
      "V_infty: 16.0 km/s, BR: 25.0 G_MAX: 38.61855704085347 QDOT_MAX: 1881.1507036219673 J_MAX: 43900.946372184364 STAG. PRES: 0.07477760357911035\n",
      "V_infty: 16.0 km/s, BR: 29.0 G_MAX: 38.61855704085347 QDOT_MAX: 1906.7822628153392 J_MAX: 43900.946372184364 STAG. PRES: 0.07477760357911035\n",
      "V_infty: 16.0 km/s, BR: 33.0 G_MAX: 38.61855704085347 QDOT_MAX: 1927.3242955818043 J_MAX: 43900.946372184364 STAG. PRES: 0.07477760357911035\n",
      "V_infty: 16.0 km/s, BR: 37.0 G_MAX: 38.61855704085347 QDOT_MAX: 1942.3711252968378 J_MAX: 43900.946372184364 STAG. PRES: 0.07477760357911035\n",
      "V_infty: 16.0 km/s, BR: 41.0 G_MAX: 38.61855704085347 QDOT_MAX: 1951.317571517066 J_MAX: 43900.946372184364 STAG. PRES: 0.07477760357911035\n",
      "V_infty: 18.0 km/s, BR: 1.0 G_MAX: 49.71335600379753 QDOT_MAX: 1965.2824510037092 J_MAX: 53690.36228266996 STAG. PRES: 0.09625524457288546\n",
      "V_infty: 18.0 km/s, BR: 5.0 G_MAX: 49.71335600379753 QDOT_MAX: 2331.3083101567504 J_MAX: 53690.36228266996 STAG. PRES: 0.09625524457288546\n",
      "V_infty: 18.0 km/s, BR: 9.0 G_MAX: 49.71335600379753 QDOT_MAX: 2447.6829015521294 J_MAX: 53690.36228266996 STAG. PRES: 0.09625524457288546\n",
      "V_infty: 18.0 km/s, BR: 13.0 G_MAX: 49.71335600379753 QDOT_MAX: 2485.081990536427 J_MAX: 53690.36228266996 STAG. PRES: 0.09625524457288546\n",
      "V_infty: 18.0 km/s, BR: 17.0 G_MAX: 49.71335600379753 QDOT_MAX: 2559.272075976625 J_MAX: 53690.36228266996 STAG. PRES: 0.09625524457288546\n",
      "V_infty: 18.0 km/s, BR: 21.0 G_MAX: 49.71335600379753 QDOT_MAX: 2607.089307630684 J_MAX: 53690.36228266996 STAG. PRES: 0.09625524457288546\n",
      "V_infty: 18.0 km/s, BR: 25.0 G_MAX: 49.71335600379753 QDOT_MAX: 2639.9283413622597 J_MAX: 53690.36228266996 STAG. PRES: 0.09625524457288546\n",
      "V_infty: 18.0 km/s, BR: 29.0 G_MAX: 49.71335600379753 QDOT_MAX: 2656.2721190242987 J_MAX: 53690.36228266996 STAG. PRES: 0.09625524457288546\n",
      "V_infty: 18.0 km/s, BR: 33.0 G_MAX: 49.71335600379753 QDOT_MAX: 2660.998625065829 J_MAX: 53690.36228266996 STAG. PRES: 0.09625524457288546\n",
      "V_infty: 18.0 km/s, BR: 37.0 G_MAX: 49.71335600379753 QDOT_MAX: 2660.881014785145 J_MAX: 53690.36228266996 STAG. PRES: 0.09625524457288546\n",
      "V_infty: 18.0 km/s, BR: 41.0 G_MAX: 49.71335600379753 QDOT_MAX: 2658.1734700731786 J_MAX: 53690.36228266996 STAG. PRES: 0.09625524457288546\n",
      "V_infty: 20.0 km/s, BR: 1.0 G_MAX: 62.29900887002885 QDOT_MAX: 2658.493424780865 J_MAX: 64482.36382731208 STAG. PRES: 0.12061687042753541\n",
      "V_infty: 20.0 km/s, BR: 5.0 G_MAX: 62.29900887002885 QDOT_MAX: 3168.0735449158537 J_MAX: 64482.36382731208 STAG. PRES: 0.12061687042753541\n",
      "V_infty: 20.0 km/s, BR: 9.0 G_MAX: 62.29900887002885 QDOT_MAX: 3276.5311368704943 J_MAX: 64482.36382731208 STAG. PRES: 0.12061687042753541\n",
      "V_infty: 20.0 km/s, BR: 13.0 G_MAX: 62.29900887002885 QDOT_MAX: 3380.841016969562 J_MAX: 64482.36382731208 STAG. PRES: 0.12061687042753541\n",
      "V_infty: 20.0 km/s, BR: 17.0 G_MAX: 62.29900887002885 QDOT_MAX: 3469.715014588073 J_MAX: 64482.36382731208 STAG. PRES: 0.12061687042753541\n",
      "V_infty: 20.0 km/s, BR: 21.0 G_MAX: 62.29900887002885 QDOT_MAX: 3526.8813497828896 J_MAX: 64482.36382731208 STAG. PRES: 0.12061687042753541\n",
      "V_infty: 20.0 km/s, BR: 25.0 G_MAX: 62.29900887002885 QDOT_MAX: 3556.75163477355 J_MAX: 64482.36382731208 STAG. PRES: 0.12061687042753541\n",
      "V_infty: 20.0 km/s, BR: 29.0 G_MAX: 62.29900887002885 QDOT_MAX: 3564.3278480043373 J_MAX: 64482.36382731208 STAG. PRES: 0.12061687042753541\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 20.0 km/s, BR: 33.0 G_MAX: 62.29900887002885 QDOT_MAX: 3563.3599262859993 J_MAX: 64482.36382731208 STAG. PRES: 0.12061687042753541\n",
      "V_infty: 20.0 km/s, BR: 37.0 G_MAX: 62.29900887002885 QDOT_MAX: 3557.9667430608934 J_MAX: 64482.36382731208 STAG. PRES: 0.12061687042753541\n",
      "V_infty: 20.0 km/s, BR: 41.0 G_MAX: 62.29900887002885 QDOT_MAX: 3548.679895009678 J_MAX: 64482.36382731208 STAG. PRES: 0.12061687042753541\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', 150.0, beta1, 0.0, 3.1416, 0.0, 0.10, planet)\n",
    "        vehicle.setInitialState(120.0,0.0,0.0,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', 150.0, beta1, 0.0, 3.1416, 0.0, 0.10, planet)\n",
    "        vehicle.setInitialState(120.0,0.0,0.0,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]   = 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\"+\", 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/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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AthulGirija\\anaconda3\\envs\\env1\\lib\\site-packages\\scipy\\interpolate\\interpolate.py:630: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n",
      "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 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+'betaRatio_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, np.transpose(S1), kind='cubic')\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   20,  110)\n",
    "y_new =  np.linspace( 0.0,   41 ,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([0.5,1.0])\n",
    "\n",
    "Glevels = np.array([10, 20])\n",
    "Qlevels = np.array([200.0, 600.0])\n",
    "Hlevels = np.array([20])\n",
    "#Slevels = np.array([0.8])\n",
    "\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",
    "plt.xlim([0.0,20.0])\n",
    "plt.ylim([1.0,41.0])\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(1.5)\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",
    "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",
    "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[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(1.5)\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.ylim(1,40)\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 $' ,fontsize=16)\n",
    "plt.xlabel(\"Arrival \"+r'$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([ 1, 10, 20, 30, 40,]),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",
    "for l in Hlabels:\n",
    "    l.set_rotation(-90)\n",
    "for l in Glabels:\n",
    "    l.set_rotation(-90)\n",
    "\n",
    "dat0 = ZCS1.allsegs[1][0]\n",
    "\n",
    "x1,y1=dat0[:,0],dat0[:,1]\n",
    "F1 = interpolate.interp1d(x1, y1, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\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(0,20,301)\n",
    "y4 = F1(x4)\n",
    "y4a =F1a(x4)\n",
    "\n",
    "\n",
    "y6 = F3(x4)\n",
    "\n",
    "y7 = y6\n",
    "y8 = np.minimum(y4,y6)\n",
    "\n",
    "\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",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('../data/jsr-paper/mars/mars-drag-small.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/mars/mars-drag-small.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/mars/mars-drag-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+'betaRatio_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",
    "\n",
    "x_new =  np.linspace( 0.0,   20,  210)\n",
    "y_new =  np.linspace( 1.0,   41 , 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",
    "\n",
    "Z1 = z1_new\n",
    "G1 = g1_new\n",
    "Q1 = q1_new\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "\n",
    "Zlevels = np.array([0.4, 0.8, 1.2, 1.6, 1.8, 2.0])\n",
    "\n",
    "Glevels = np.array([5.0, 10.0, 15.0, 25.0])\n",
    "Qlevels = np.array([50.0, 100.0, 200.0,  400.0, 800.0 ])\n",
    "Hlevels = np.array([5.0, 10.0, 20.0, 30.0])\n",
    "#Slevels = np.array([0.8])\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",
    "\n",
    "ZCS1 = plt.contour(X, Y, np.transpose(Z1), levels=Zlevels, colors='black')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.clabel(ZCS1, inline=1, fontsize=10, 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[2].set_linewidths(1.5)\n",
    "ZCS1.collections[3].set_linewidths(1.5)\n",
    "ZCS1.collections[4].set_linewidths(1.5)\n",
    "ZCS1.collections[5].set_linewidths(1.5)\n",
    "\n",
    "ZCS1.collections[0].set_label(r'$TCW, deg$')\n",
    "\n",
    "\n",
    "GCS1 = plt.contour(X, Y, np.transpose(G1), levels=Glevels, colors='blue',linestyles='dashed')\n",
    "\n",
    "Glabels=plt.clabel(GCS1, inline=1, fontsize=10, colors='blue',fmt='%d',inline_spacing=0)\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[1].set_linewidths(1.5)\n",
    "GCS1.collections[2].set_linewidths(1.5)\n",
    "GCS1.collections[3].set_linewidths(1.5)\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "for l in Glabels:\n",
    "    l.set_rotation(-90)\n",
    "\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted')\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=10, colors='red',fmt='%d',inline_spacing=0)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "QCS1.collections[1].set_linewidths(1.5)\n",
    "QCS1.collections[2].set_linewidths(1.5)\n",
    "QCS1.collections[3].set_linewidths(1.5)\n",
    "QCS1.collections[4].set_linewidths(1.5)\n",
    "\n",
    "QCS1.collections[0].set_label(r'$\\dot{q}$'+', '+r'$W/cm^2$')\n",
    "\n",
    "\n",
    "HCS1 = plt.contour(X, Y, np.transpose(H1), levels=Hlevels, colors='xkcd:brown',linestyles='dashdot')\n",
    "\n",
    "Hlabels=plt.clabel(HCS1, inline=1, fontsize=10, colors='xkcd:brown',fmt='%d',inline_spacing=0)\n",
    "HCS1.collections[0].set_linewidths(1.75)\n",
    "HCS1.collections[1].set_linewidths(1.75)\n",
    "HCS1.collections[2].set_linewidths(1.75)\n",
    "HCS1.collections[3].set_linewidths(1.75)\n",
    "\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "for l in Hlabels:\n",
    "    l.set_rotation(-90)\n",
    "    \n",
    "#GCS1.collections[0].set_label(r'$Peak$'+r' '+r'$g-load$')\n",
    "plt.ylim(1,40)\n",
    "#plt.grid(True,linestyle='dotted', linewidth=0.3)\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(r'$\\beta_2$'+' / '+r'$ \\beta_1 $' ,fontsize=12)\n",
    "plt.xlabel(\"Arrival \"+r'$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([ 1, 10, 20, 30, 40]),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",
    "plt.savefig('../data/jsr-paper/mars/mars-drag-large.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/mars/mars-drag-large.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/mars/mars-drag-large.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}