{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 09 - b - Uranus - Feasibility Charts - Drag"
   ]
  },
  {
   "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 for Titan\n",
    "planet=Planet(\"URANUS\")\n",
    "planet.h_skip = 1000e3\n",
    "planet.h_trap = 50e3\n",
    "\n",
    "# Load an nominal atmospheric profile with height, temp, pressure, density data\n",
    "planet.loadAtmosphereModel('../atmdata/Uranus/uranus-gram-avg.dat', 0 , 1 , 2, 3, heightInKmFlag=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta1 = 20.0\n",
    "\n",
    "runID = 'uranus-drag-'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "vinf_kms_array  = np.linspace( 0.0,   30.0,   11)\n",
    "betaRatio_array = np.linspace( 1.0,   41.0 ,  11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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))*\\\n",
    "                          planet.GM/(planet.RP+1000.0*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": 11,
   "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: 0.4300069945747964 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 9.0 TCW: 0.548236197559163 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 13.0 TCW: 0.6287807369953953 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 17.0 TCW: 0.6904995632939972 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 21.0 TCW: 0.7407522390130907 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 25.0 TCW: 0.7830369498115033 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 29.0 TCW: 0.8199181346572004 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 33.0 TCW: 0.8523810709593818 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 37.0 TCW: 0.8816194402170368 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 41.0 TCW: 0.9082424457883462 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 5.0 TCW: 0.4306469949078746 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 9.0 TCW: 0.5533987928647548 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 13.0 TCW: 0.6368987987516448 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 17.0 TCW: 0.7001530813286081 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 21.0 TCW: 0.7520165814785287 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 25.0 TCW: 0.7956012652721256 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 29.0 TCW: 0.8335674138506874 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 33.0 TCW: 0.8669485827558674 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 37.0 TCW: 0.8972552987979725 deg.\n",
      "VINF: 3.0 km/s, BETA RATIO: 41.0 TCW: 0.9245952178025618 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: 0.43009640561649576 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 9.0 TCW: 0.5665283860289492 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 13.0 TCW: 0.6570447093108669 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 17.0 TCW: 0.7259380482137203 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 21.0 TCW: 0.7817554083303548 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 25.0 TCW: 0.829021891055163 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 29.0 TCW: 0.8698718391242437 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 33.0 TCW: 0.9059941348386928 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 37.0 TCW: 0.9384503098553978 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 41.0 TCW: 0.9679333599633537 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 5.0 TCW: 0.4336301910225302 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 9.0 TCW: 0.5847309437813237 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 13.0 TCW: 0.6850503110326827 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 17.0 TCW: 0.7613813919597305 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 21.0 TCW: 0.8230569479637779 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 25.0 TCW: 0.8752881792606786 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 29.0 TCW: 0.9202921183314174 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 33.0 TCW: 0.9601127228233963 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 37.0 TCW: 0.9956420034868643 deg.\n",
      "VINF: 9.0 km/s, BETA RATIO: 41.0 TCW: 1.0279079706524499 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: 0.43706142978044227 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 9.0 TCW: 0.6043902909150347 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 13.0 TCW: 0.7161780579481274 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 17.0 TCW: 0.8005300595541485 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 21.0 TCW: 0.8689311987836845 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 25.0 TCW: 0.9263564213179052 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 29.0 TCW: 0.9758633595192805 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 33.0 TCW: 1.0195248335949145 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 37.0 TCW: 1.0581424981355667 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 41.0 TCW: 1.0930620817816816 deg.\n",
      "VINF: 15.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 15.0 km/s, BETA RATIO: 5.0 TCW: 0.4454347515129484 deg.\n",
      "VINF: 15.0 km/s, BETA RATIO: 9.0 TCW: 0.6298921164707281 deg.\n",
      "VINF: 15.0 km/s, BETA RATIO: 13.0 TCW: 0.7525014239363372 deg.\n",
      "VINF: 15.0 km/s, BETA RATIO: 17.0 TCW: 0.8450111636775546 deg.\n",
      "VINF: 15.0 km/s, BETA RATIO: 21.0 TCW: 0.9195918379700743 deg.\n",
      "VINF: 15.0 km/s, BETA RATIO: 25.0 TCW: 0.982135801226832 deg.\n",
      "VINF: 15.0 km/s, BETA RATIO: 29.0 TCW: 1.035611552069895 deg.\n",
      "VINF: 15.0 km/s, BETA RATIO: 33.0 TCW: 1.0823744336958043 deg.\n",
      "VINF: 15.0 km/s, BETA RATIO: 37.0 TCW: 1.1237224540673196 deg.\n",
      "VINF: 15.0 km/s, BETA RATIO: 41.0 TCW: 1.1603765798499808 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: 0.47190874099032953 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 9.0 TCW: 0.6728750808397308 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 13.0 TCW: 0.8054167323280126 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 17.0 TCW: 0.9050693272729404 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 21.0 TCW: 0.9851783454068936 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 25.0 TCW: 1.0515629358706065 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 29.0 TCW: 1.1083436924382113 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 33.0 TCW: 1.1569818182615563 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 37.0 TCW: 1.1988971406244673 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 41.0 TCW: 1.2348786691436544 deg.\n",
      "VINF: 21.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 21.0 km/s, BETA RATIO: 5.0 TCW: 0.5037200312362984 deg.\n",
      "VINF: 21.0 km/s, BETA RATIO: 9.0 TCW: 0.7183635247056372 deg.\n",
      "VINF: 21.0 km/s, BETA RATIO: 13.0 TCW: 0.8598193866200745 deg.\n",
      "VINF: 21.0 km/s, BETA RATIO: 17.0 TCW: 0.9657282422995195 deg.\n",
      "VINF: 21.0 km/s, BETA RATIO: 21.0 TCW: 1.0499617995228618 deg.\n",
      "VINF: 21.0 km/s, BETA RATIO: 25.0 TCW: 1.1193449369748123 deg.\n",
      "VINF: 21.0 km/s, BETA RATIO: 29.0 TCW: 1.177060860558413 deg.\n",
      "VINF: 21.0 km/s, BETA RATIO: 33.0 TCW: 1.2252492255647667 deg.\n",
      "VINF: 21.0 km/s, BETA RATIO: 37.0 TCW: 1.2646268653916195 deg.\n",
      "VINF: 21.0 km/s, BETA RATIO: 41.0 TCW: 1.2969450384844095 deg.\n",
      "VINF: 24.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 24.0 km/s, BETA RATIO: 5.0 TCW: 0.5357726637739688 deg.\n",
      "VINF: 24.0 km/s, BETA RATIO: 9.0 TCW: 0.7629505083314143 deg.\n",
      "VINF: 24.0 km/s, BETA RATIO: 13.0 TCW: 0.9118634675396606 deg.\n",
      "VINF: 24.0 km/s, BETA RATIO: 17.0 TCW: 1.0225540153332986 deg.\n",
      "VINF: 24.0 km/s, BETA RATIO: 21.0 TCW: 1.1099410562310368 deg.\n",
      "VINF: 24.0 km/s, BETA RATIO: 25.0 TCW: 1.1800493606715463 deg.\n",
      "VINF: 24.0 km/s, BETA RATIO: 29.0 TCW: 1.236652796738781 deg.\n",
      "VINF: 24.0 km/s, BETA RATIO: 33.0 TCW: 1.2806346639990807 deg.\n",
      "VINF: 24.0 km/s, BETA RATIO: 37.0 TCW: 1.3166232906514779 deg.\n",
      "VINF: 24.0 km/s, BETA RATIO: 41.0 TCW: 1.34690492216032 deg.\n",
      "VINF: 27.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 27.0 km/s, BETA RATIO: 5.0 TCW: 0.5671427182387561 deg.\n",
      "VINF: 27.0 km/s, BETA RATIO: 9.0 TCW: 0.8048952496028505 deg.\n",
      "VINF: 27.0 km/s, BETA RATIO: 13.0 TCW: 0.9600149451871403 deg.\n",
      "VINF: 27.0 km/s, BETA RATIO: 17.0 TCW: 1.0744621621561237 deg.\n",
      "VINF: 27.0 km/s, BETA RATIO: 21.0 TCW: 1.1627207996789366 deg.\n",
      "VINF: 27.0 km/s, BETA RATIO: 25.0 TCW: 1.2315627416828647 deg.\n",
      "VINF: 27.0 km/s, BETA RATIO: 29.0 TCW: 1.283228982181754 deg.\n",
      "VINF: 27.0 km/s, BETA RATIO: 33.0 TCW: 1.3243737865122966 deg.\n",
      "VINF: 27.0 km/s, BETA RATIO: 37.0 TCW: 1.358186729310546 deg.\n",
      "VINF: 27.0 km/s, BETA RATIO: 41.0 TCW: 1.3864998817443848 deg.\n",
      "VINF: 30.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 30.0 km/s, BETA RATIO: 5.0 TCW: 0.5939707551151514 deg.\n",
      "VINF: 30.0 km/s, BETA RATIO: 9.0 TCW: 0.8407557697501034 deg.\n",
      "VINF: 30.0 km/s, BETA RATIO: 13.0 TCW: 1.0008656184654683 deg.\n",
      "VINF: 30.0 km/s, BETA RATIO: 17.0 TCW: 1.1174983243690804 deg.\n",
      "VINF: 30.0 km/s, BETA RATIO: 21.0 TCW: 1.2049253375153057 deg.\n",
      "VINF: 30.0 km/s, BETA RATIO: 25.0 TCW: 1.2690666004200466 deg.\n",
      "VINF: 30.0 km/s, BETA RATIO: 29.0 TCW: 1.3177535697468556 deg.\n",
      "VINF: 30.0 km/s, BETA RATIO: 33.0 TCW: 1.3566493302350864 deg.\n",
      "VINF: 30.0 km/s, BETA RATIO: 37.0 TCW: 1.388413718319498 deg.\n",
      "VINF: 30.0 km/s, BETA RATIO: 41.0 TCW: 1.4149620372336358 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', 300.0, beta1, 0.0, 3.1416, 0.0, 0.10, planet)\n",
    "        vehicle.setInitialState(1000.0,0.0,0.0,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, 1.0, -20.0,-4.0,1E-10,4000)\n",
    "        overShootLimit_array[i,j] , exitflag_os_array[i,j] = vehicle.findOverShootLimitD (2400.0, 1.0, -20.0,-4.0,1E-10,4000)\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/uranus/'+runID+'vinf_kms_array.txt',vinf_kms_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'v0_kms_array.txt',v0_kms_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'betaRatio_array.txt',betaRatio_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'overShootLimit_array.txt',overShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'exitflag_os_array.txt',exitflag_os_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'underShootLimit_array.txt',underShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'exitflag_us_array.txt',exitflag_us_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'TCW_array.txt',TCW_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 0.0 km/s, BR: 1.0 G_MAX: 1.8278605124542677 QDOT_MAX: 271.63896100022583 J_MAX: 251148.25410870728 STAG. PRES: 0.003553624041011706\n",
      "V_infty: 0.0 km/s, BR: 5.0 G_MAX: 1.8278605124542677 QDOT_MAX: 435.71346317409103 J_MAX: 251148.25410870728 STAG. PRES: 0.003553624041011706\n",
      "V_infty: 0.0 km/s, BR: 9.0 G_MAX: 1.8278605124542677 QDOT_MAX: 479.64582633787853 J_MAX: 251148.25410870728 STAG. PRES: 0.003553624041011706\n",
      "V_infty: 0.0 km/s, BR: 13.0 G_MAX: 1.8278605124542677 QDOT_MAX: 504.58188910669907 J_MAX: 251148.25410870728 STAG. PRES: 0.003553624041011706\n",
      "V_infty: 0.0 km/s, BR: 17.0 G_MAX: 1.8278605124542677 QDOT_MAX: 520.1699448478237 J_MAX: 251148.25410870728 STAG. PRES: 0.003553624041011706\n",
      "V_infty: 0.0 km/s, BR: 21.0 G_MAX: 1.8278605124542677 QDOT_MAX: 531.6403839661771 J_MAX: 251148.25410870728 STAG. PRES: 0.003553624041011706\n",
      "V_infty: 0.0 km/s, BR: 25.0 G_MAX: 1.8278605124542677 QDOT_MAX: 540.1466279922666 J_MAX: 251148.25410870728 STAG. PRES: 0.003553624041011706\n",
      "V_infty: 0.0 km/s, BR: 29.0 G_MAX: 1.8278605124542677 QDOT_MAX: 546.7376408682949 J_MAX: 251148.25410870728 STAG. PRES: 0.003553624041011706\n",
      "V_infty: 0.0 km/s, BR: 33.0 G_MAX: 1.8278605124542677 QDOT_MAX: 552.6315994156959 J_MAX: 251148.25410870728 STAG. PRES: 0.003553624041011706\n",
      "V_infty: 0.0 km/s, BR: 37.0 G_MAX: 1.8278605124542677 QDOT_MAX: 557.110194331954 J_MAX: 251148.25410870728 STAG. PRES: 0.003553624041011706\n",
      "V_infty: 0.0 km/s, BR: 41.0 G_MAX: 1.8278605124542677 QDOT_MAX: 561.3700996211214 J_MAX: 251148.25410870728 STAG. PRES: 0.003553624041011706\n",
      "V_infty: 3.0 km/s, BR: 1.0 G_MAX: 1.813509125837342 QDOT_MAX: 285.27881234617496 J_MAX: 254310.6625966577 STAG. PRES: 0.0035257034148754485\n",
      "V_infty: 3.0 km/s, BR: 5.0 G_MAX: 1.813509125837342 QDOT_MAX: 453.72407122074287 J_MAX: 254310.6625966577 STAG. PRES: 0.0035257034148754485\n",
      "V_infty: 3.0 km/s, BR: 9.0 G_MAX: 1.813509125837342 QDOT_MAX: 500.0958186744359 J_MAX: 254310.6625966577 STAG. PRES: 0.0035257034148754485\n",
      "V_infty: 3.0 km/s, BR: 13.0 G_MAX: 1.813509125837342 QDOT_MAX: 525.7400282935042 J_MAX: 254310.6625966577 STAG. PRES: 0.0035257034148754485\n",
      "V_infty: 3.0 km/s, BR: 17.0 G_MAX: 1.813509125837342 QDOT_MAX: 541.6476177013972 J_MAX: 254310.6625966577 STAG. PRES: 0.0035257034148754485\n",
      "V_infty: 3.0 km/s, BR: 21.0 G_MAX: 1.813509125837342 QDOT_MAX: 553.0037740228105 J_MAX: 254310.6625966577 STAG. PRES: 0.0035257034148754485\n",
      "V_infty: 3.0 km/s, BR: 25.0 G_MAX: 1.813509125837342 QDOT_MAX: 561.5656671054957 J_MAX: 254310.6625966577 STAG. PRES: 0.0035257034148754485\n",
      "V_infty: 3.0 km/s, BR: 29.0 G_MAX: 1.813509125837342 QDOT_MAX: 568.412971091998 J_MAX: 254310.6625966577 STAG. PRES: 0.0035257034148754485\n",
      "V_infty: 3.0 km/s, BR: 33.0 G_MAX: 1.813509125837342 QDOT_MAX: 574.2790466667215 J_MAX: 254310.6625966577 STAG. PRES: 0.0035257034148754485\n",
      "V_infty: 3.0 km/s, BR: 37.0 G_MAX: 1.813509125837342 QDOT_MAX: 578.989663885992 J_MAX: 254310.6625966577 STAG. PRES: 0.0035257034148754485\n",
      "V_infty: 3.0 km/s, BR: 41.0 G_MAX: 1.813509125837342 QDOT_MAX: 583.235087172835 J_MAX: 254310.6625966577 STAG. PRES: 0.0035257034148754485\n",
      "V_infty: 6.0 km/s, BR: 1.0 G_MAX: 1.9239973748466965 QDOT_MAX: 327.2763452091848 J_MAX: 264002.34462654754 STAG. PRES: 0.003740432746181789\n",
      "V_infty: 6.0 km/s, BR: 5.0 G_MAX: 1.9239973748466965 QDOT_MAX: 509.3556477521291 J_MAX: 264002.34462654754 STAG. PRES: 0.003740432746181789\n",
      "V_infty: 6.0 km/s, BR: 9.0 G_MAX: 1.9239973748466965 QDOT_MAX: 562.4487767763521 J_MAX: 264002.34462654754 STAG. PRES: 0.003740432746181789\n",
      "V_infty: 6.0 km/s, BR: 13.0 G_MAX: 1.9239973748466965 QDOT_MAX: 589.3801338713673 J_MAX: 264002.34462654754 STAG. PRES: 0.003740432746181789\n",
      "V_infty: 6.0 km/s, BR: 17.0 G_MAX: 1.9239973748466965 QDOT_MAX: 605.8638333199464 J_MAX: 264002.34462654754 STAG. PRES: 0.003740432746181789\n",
      "V_infty: 6.0 km/s, BR: 21.0 G_MAX: 1.9239973748466965 QDOT_MAX: 617.6453709454064 J_MAX: 264002.34462654754 STAG. PRES: 0.003740432746181789\n",
      "V_infty: 6.0 km/s, BR: 25.0 G_MAX: 1.9239973748466965 QDOT_MAX: 626.4498205844337 J_MAX: 264002.34462654754 STAG. PRES: 0.003740432746181789\n",
      "V_infty: 6.0 km/s, BR: 29.0 G_MAX: 1.9239973748466965 QDOT_MAX: 633.2704677685279 J_MAX: 264002.34462654754 STAG. PRES: 0.003740432746181789\n",
      "V_infty: 6.0 km/s, BR: 33.0 G_MAX: 1.9239973748466965 QDOT_MAX: 639.2913984541158 J_MAX: 264002.34462654754 STAG. PRES: 0.003740432746181789\n",
      "V_infty: 6.0 km/s, BR: 37.0 G_MAX: 1.9239973748466965 QDOT_MAX: 644.1799308185472 J_MAX: 264002.34462654754 STAG. PRES: 0.003740432746181789\n",
      "V_infty: 6.0 km/s, BR: 41.0 G_MAX: 1.9239973748466965 QDOT_MAX: 648.0977508314228 J_MAX: 264002.34462654754 STAG. PRES: 0.003740432746181789\n",
      "V_infty: 9.0 km/s, BR: 1.0 G_MAX: 2.0258130250579387 QDOT_MAX: 400.25377250048905 J_MAX: 278489.1582496572 STAG. PRES: 0.003938281787666398\n",
      "V_infty: 9.0 km/s, BR: 5.0 G_MAX: 2.0258130250579387 QDOT_MAX: 609.3306408965296 J_MAX: 278489.1582496572 STAG. PRES: 0.003938281787666398\n",
      "V_infty: 9.0 km/s, BR: 9.0 G_MAX: 2.0258130250579387 QDOT_MAX: 668.5018256552937 J_MAX: 278489.1582496572 STAG. PRES: 0.003938281787666398\n",
      "V_infty: 9.0 km/s, BR: 13.0 G_MAX: 2.0258130250579387 QDOT_MAX: 696.3639900479758 J_MAX: 278489.1582496572 STAG. PRES: 0.003938281787666398\n",
      "V_infty: 9.0 km/s, BR: 17.0 G_MAX: 2.0258130250579387 QDOT_MAX: 713.3785806026733 J_MAX: 278489.1582496572 STAG. PRES: 0.003938281787666398\n",
      "V_infty: 9.0 km/s, BR: 21.0 G_MAX: 2.0258130250579387 QDOT_MAX: 724.9975795128324 J_MAX: 278489.1582496572 STAG. PRES: 0.003938281787666398\n",
      "V_infty: 9.0 km/s, BR: 25.0 G_MAX: 2.0258130250579387 QDOT_MAX: 734.2923884518979 J_MAX: 278489.1582496572 STAG. PRES: 0.003938281787666398\n",
      "V_infty: 9.0 km/s, BR: 29.0 G_MAX: 2.0258130250579387 QDOT_MAX: 740.974411219486 J_MAX: 278489.1582496572 STAG. PRES: 0.003938281787666398\n",
      "V_infty: 9.0 km/s, BR: 33.0 G_MAX: 2.0258130250579387 QDOT_MAX: 746.8216917769777 J_MAX: 278489.1582496572 STAG. PRES: 0.003938281787666398\n",
      "V_infty: 9.0 km/s, BR: 37.0 G_MAX: 2.0258130250579387 QDOT_MAX: 752.1778747450783 J_MAX: 278489.1582496572 STAG. PRES: 0.003938281787666398\n",
      "V_infty: 9.0 km/s, BR: 41.0 G_MAX: 2.0258130250579387 QDOT_MAX: 756.2828367440743 J_MAX: 278489.1582496572 STAG. PRES: 0.003938281787666398\n",
      "V_infty: 12.0 km/s, BR: 1.0 G_MAX: 2.5189076325782014 QDOT_MAX: 506.5215997712668 J_MAX: 296835.90644155553 STAG. PRES: 0.004886858063109952\n",
      "V_infty: 12.0 km/s, BR: 5.0 G_MAX: 2.5189076325782014 QDOT_MAX: 756.9382577972025 J_MAX: 296835.90644155553 STAG. PRES: 0.004886858063109952\n",
      "V_infty: 12.0 km/s, BR: 9.0 G_MAX: 2.5189076325782014 QDOT_MAX: 819.0144172542351 J_MAX: 296835.90644155553 STAG. PRES: 0.004886858063109952\n",
      "V_infty: 12.0 km/s, BR: 13.0 G_MAX: 2.5189076325782014 QDOT_MAX: 847.0458882792574 J_MAX: 296835.90644155553 STAG. PRES: 0.004886858063109952\n",
      "V_infty: 12.0 km/s, BR: 17.0 G_MAX: 2.5189076325782014 QDOT_MAX: 864.3742549780326 J_MAX: 296835.90644155553 STAG. PRES: 0.004886858063109952\n",
      "V_infty: 12.0 km/s, BR: 21.0 G_MAX: 2.5189076325782014 QDOT_MAX: 876.6277585268331 J_MAX: 296835.90644155553 STAG. PRES: 0.004886858063109952\n",
      "V_infty: 12.0 km/s, BR: 25.0 G_MAX: 2.5189076325782014 QDOT_MAX: 886.3051663733083 J_MAX: 296835.90644155553 STAG. PRES: 0.004886858063109952\n",
      "V_infty: 12.0 km/s, BR: 29.0 G_MAX: 2.5189076325782014 QDOT_MAX: 893.9000127633334 J_MAX: 296835.90644155553 STAG. PRES: 0.004886858063109952\n",
      "V_infty: 12.0 km/s, BR: 33.0 G_MAX: 2.5189076325782014 QDOT_MAX: 900.1261414641633 J_MAX: 296835.90644155553 STAG. PRES: 0.004886858063109952\n",
      "V_infty: 12.0 km/s, BR: 37.0 G_MAX: 2.5189076325782014 QDOT_MAX: 905.897535259384 J_MAX: 296835.90644155553 STAG. PRES: 0.004886858063109952\n",
      "V_infty: 12.0 km/s, BR: 41.0 G_MAX: 2.5189076325782014 QDOT_MAX: 912.1255446132999 J_MAX: 296835.90644155553 STAG. PRES: 0.004886858063109952\n",
      "V_infty: 15.0 km/s, BR: 1.0 G_MAX: 3.6836563647030673 QDOT_MAX: 645.2381706851423 J_MAX: 317919.7825151407 STAG. PRES: 0.007145096565839003\n",
      "V_infty: 15.0 km/s, BR: 5.0 G_MAX: 3.6836563647030673 QDOT_MAX: 953.6745584519241 J_MAX: 317919.7825151407 STAG. PRES: 0.007145096565839003\n",
      "V_infty: 15.0 km/s, BR: 9.0 G_MAX: 3.6836563647030673 QDOT_MAX: 1017.1843569444363 J_MAX: 317919.7825151407 STAG. PRES: 0.007145096565839003\n",
      "V_infty: 15.0 km/s, BR: 13.0 G_MAX: 3.6836563647030673 QDOT_MAX: 1046.1243324464817 J_MAX: 317919.7825151407 STAG. PRES: 0.007145096565839003\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 15.0 km/s, BR: 17.0 G_MAX: 3.6836563647030673 QDOT_MAX: 1064.9488358779624 J_MAX: 317919.7825151407 STAG. PRES: 0.007145096565839003\n",
      "V_infty: 15.0 km/s, BR: 21.0 G_MAX: 3.6836563647030673 QDOT_MAX: 1077.9082122746656 J_MAX: 317919.7825151407 STAG. PRES: 0.007145096565839003\n",
      "V_infty: 15.0 km/s, BR: 25.0 G_MAX: 3.6836563647030673 QDOT_MAX: 1089.8843744095432 J_MAX: 317919.7825151407 STAG. PRES: 0.007145096565839003\n",
      "V_infty: 15.0 km/s, BR: 29.0 G_MAX: 3.6836563647030673 QDOT_MAX: 1101.1681341272536 J_MAX: 317919.7825151407 STAG. PRES: 0.007145096565839003\n",
      "V_infty: 15.0 km/s, BR: 33.0 G_MAX: 3.6836563647030673 QDOT_MAX: 1110.858375608081 J_MAX: 317919.7825151407 STAG. PRES: 0.007145096565839003\n",
      "V_infty: 15.0 km/s, BR: 37.0 G_MAX: 3.6836563647030673 QDOT_MAX: 1119.3099028026904 J_MAX: 317919.7825151407 STAG. PRES: 0.007145096565839003\n",
      "V_infty: 15.0 km/s, BR: 41.0 G_MAX: 3.6836563647030673 QDOT_MAX: 1126.680541361289 J_MAX: 317919.7825151407 STAG. PRES: 0.007145096565839003\n",
      "V_infty: 18.0 km/s, BR: 1.0 G_MAX: 5.426536661833298 QDOT_MAX: 846.7541409867274 J_MAX: 341452.1706464557 STAG. PRES: 0.010522977092040508\n",
      "V_infty: 18.0 km/s, BR: 5.0 G_MAX: 5.426536661833298 QDOT_MAX: 1202.7713958257084 J_MAX: 341452.1706464557 STAG. PRES: 0.010522977092040508\n",
      "V_infty: 18.0 km/s, BR: 9.0 G_MAX: 5.426536661833298 QDOT_MAX: 1269.3041890068848 J_MAX: 341452.1706464557 STAG. PRES: 0.010522977092040508\n",
      "V_infty: 18.0 km/s, BR: 13.0 G_MAX: 5.426536661833298 QDOT_MAX: 1300.5053341718078 J_MAX: 341452.1706464557 STAG. PRES: 0.010522977092040508\n",
      "V_infty: 18.0 km/s, BR: 17.0 G_MAX: 5.426536661833298 QDOT_MAX: 1322.5482784091985 J_MAX: 341452.1706464557 STAG. PRES: 0.010522977092040508\n",
      "V_infty: 18.0 km/s, BR: 21.0 G_MAX: 5.426536661833298 QDOT_MAX: 1343.0843053141464 J_MAX: 341452.1706464557 STAG. PRES: 0.010522977092040508\n",
      "V_infty: 18.0 km/s, BR: 25.0 G_MAX: 5.426536661833298 QDOT_MAX: 1359.6247359709578 J_MAX: 341452.1706464557 STAG. PRES: 0.010522977092040508\n",
      "V_infty: 18.0 km/s, BR: 29.0 G_MAX: 5.426536661833298 QDOT_MAX: 1373.619805964247 J_MAX: 341452.1706464557 STAG. PRES: 0.010522977092040508\n",
      "V_infty: 18.0 km/s, BR: 33.0 G_MAX: 5.426536661833298 QDOT_MAX: 1385.2337491766125 J_MAX: 341452.1706464557 STAG. PRES: 0.010522977092040508\n",
      "V_infty: 18.0 km/s, BR: 37.0 G_MAX: 5.426536661833298 QDOT_MAX: 1395.200072332318 J_MAX: 341452.1706464557 STAG. PRES: 0.010522977092040508\n",
      "V_infty: 18.0 km/s, BR: 41.0 G_MAX: 5.426536661833298 QDOT_MAX: 1403.3071515720953 J_MAX: 341452.1706464557 STAG. PRES: 0.010522977092040508\n",
      "V_infty: 21.0 km/s, BR: 1.0 G_MAX: 7.573289638134486 QDOT_MAX: 1109.2358111938918 J_MAX: 368407.0119218065 STAG. PRES: 0.014682577162996762\n",
      "V_infty: 21.0 km/s, BR: 5.0 G_MAX: 7.573289638134486 QDOT_MAX: 1516.6279209877707 J_MAX: 368407.0119218065 STAG. PRES: 0.014682577162996762\n",
      "V_infty: 21.0 km/s, BR: 9.0 G_MAX: 7.573289638134486 QDOT_MAX: 1589.5153466287386 J_MAX: 368407.0119218065 STAG. PRES: 0.014682577162996762\n",
      "V_infty: 21.0 km/s, BR: 13.0 G_MAX: 7.573289638134486 QDOT_MAX: 1627.0660644783588 J_MAX: 368407.0119218065 STAG. PRES: 0.014682577162996762\n",
      "V_infty: 21.0 km/s, BR: 17.0 G_MAX: 7.573289638134486 QDOT_MAX: 1658.4778851537048 J_MAX: 368407.0119218065 STAG. PRES: 0.014682577162996762\n",
      "V_infty: 21.0 km/s, BR: 21.0 G_MAX: 7.573289638134486 QDOT_MAX: 1683.5658170619477 J_MAX: 368407.0119218065 STAG. PRES: 0.014682577162996762\n",
      "V_infty: 21.0 km/s, BR: 25.0 G_MAX: 7.573289638134486 QDOT_MAX: 1703.7330065643607 J_MAX: 368407.0119218065 STAG. PRES: 0.014682577162996762\n",
      "V_infty: 21.0 km/s, BR: 29.0 G_MAX: 7.573289638134486 QDOT_MAX: 1720.0423825436449 J_MAX: 368407.0119218065 STAG. PRES: 0.014682577162996762\n",
      "V_infty: 21.0 km/s, BR: 33.0 G_MAX: 7.573289638134486 QDOT_MAX: 1733.48294170645 J_MAX: 368407.0119218065 STAG. PRES: 0.014682577162996762\n",
      "V_infty: 21.0 km/s, BR: 37.0 G_MAX: 7.573289638134486 QDOT_MAX: 1743.9092978600181 J_MAX: 368407.0119218065 STAG. PRES: 0.014682577162996762\n",
      "V_infty: 21.0 km/s, BR: 41.0 G_MAX: 7.573289638134486 QDOT_MAX: 1752.617310778698 J_MAX: 368407.0119218065 STAG. PRES: 0.014682577162996762\n",
      "V_infty: 24.0 km/s, BR: 1.0 G_MAX: 10.164674157032687 QDOT_MAX: 1445.2283791763298 J_MAX: 401761.6388641296 STAG. PRES: 0.019702790245935957\n",
      "V_infty: 24.0 km/s, BR: 5.0 G_MAX: 10.164674157032687 QDOT_MAX: 1923.6465719753949 J_MAX: 401761.6388641296 STAG. PRES: 0.019702790245935957\n",
      "V_infty: 24.0 km/s, BR: 9.0 G_MAX: 10.164674157032687 QDOT_MAX: 2012.320255331466 J_MAX: 401761.6388641296 STAG. PRES: 0.019702790245935957\n",
      "V_infty: 24.0 km/s, BR: 13.0 G_MAX: 10.164674157032687 QDOT_MAX: 2057.3052051223267 J_MAX: 401761.6388641296 STAG. PRES: 0.019702790245935957\n",
      "V_infty: 24.0 km/s, BR: 17.0 G_MAX: 10.164674157032687 QDOT_MAX: 2093.4961750458774 J_MAX: 401761.6388641296 STAG. PRES: 0.019702790245935957\n",
      "V_infty: 24.0 km/s, BR: 21.0 G_MAX: 10.164674157032687 QDOT_MAX: 2123.3810490858928 J_MAX: 401761.6388641296 STAG. PRES: 0.019702790245935957\n",
      "V_infty: 24.0 km/s, BR: 25.0 G_MAX: 10.164674157032687 QDOT_MAX: 2147.1521790826096 J_MAX: 401761.6388641296 STAG. PRES: 0.019702790245935957\n",
      "V_infty: 24.0 km/s, BR: 29.0 G_MAX: 10.164674157032687 QDOT_MAX: 2165.6502942315856 J_MAX: 401761.6388641296 STAG. PRES: 0.019702790245935957\n",
      "V_infty: 24.0 km/s, BR: 33.0 G_MAX: 10.164674157032687 QDOT_MAX: 2179.7459047020434 J_MAX: 401761.6388641296 STAG. PRES: 0.019702790245935957\n",
      "V_infty: 24.0 km/s, BR: 37.0 G_MAX: 10.164674157032687 QDOT_MAX: 2190.964736799414 J_MAX: 401761.6388641296 STAG. PRES: 0.019702790245935957\n",
      "V_infty: 24.0 km/s, BR: 41.0 G_MAX: 10.164674157032687 QDOT_MAX: 2200.379990292354 J_MAX: 401761.6388641296 STAG. PRES: 0.019702790245935957\n",
      "V_infty: 27.0 km/s, BR: 1.0 G_MAX: 13.231949270350407 QDOT_MAX: 1905.7084344396844 J_MAX: 447065.25571461295 STAG. PRES: 0.025643897044314443\n",
      "V_infty: 27.0 km/s, BR: 5.0 G_MAX: 13.231949270350407 QDOT_MAX: 2494.8637498622566 J_MAX: 447065.25571461295 STAG. PRES: 0.025643897044314443\n",
      "V_infty: 27.0 km/s, BR: 9.0 G_MAX: 13.231949270350407 QDOT_MAX: 2622.9345131712353 J_MAX: 447065.25571461295 STAG. PRES: 0.025643897044314443\n",
      "V_infty: 27.0 km/s, BR: 13.0 G_MAX: 13.231949270350407 QDOT_MAX: 2685.7417146694183 J_MAX: 447065.25571461295 STAG. PRES: 0.025643897044314443\n",
      "V_infty: 27.0 km/s, BR: 17.0 G_MAX: 13.231949270350407 QDOT_MAX: 2724.0702343817607 J_MAX: 447065.25571461295 STAG. PRES: 0.025643897044314443\n",
      "V_infty: 27.0 km/s, BR: 21.0 G_MAX: 13.231949270350407 QDOT_MAX: 2756.6469392507915 J_MAX: 447065.25571461295 STAG. PRES: 0.025643897044314443\n",
      "V_infty: 27.0 km/s, BR: 25.0 G_MAX: 13.231949270350407 QDOT_MAX: 2775.92697999241 J_MAX: 447065.25571461295 STAG. PRES: 0.025643897044314443\n",
      "V_infty: 27.0 km/s, BR: 29.0 G_MAX: 13.231949270350407 QDOT_MAX: 2792.0420524520086 J_MAX: 447065.25571461295 STAG. PRES: 0.025643897044314443\n",
      "V_infty: 27.0 km/s, BR: 33.0 G_MAX: 13.231949270350407 QDOT_MAX: 2803.6854909597773 J_MAX: 447065.25571461295 STAG. PRES: 0.025643897044314443\n",
      "V_infty: 27.0 km/s, BR: 37.0 G_MAX: 13.231949270350407 QDOT_MAX: 2815.738320993501 J_MAX: 447065.25571461295 STAG. PRES: 0.025643897044314443\n",
      "V_infty: 27.0 km/s, BR: 41.0 G_MAX: 13.231949270350407 QDOT_MAX: 2825.663474147248 J_MAX: 447065.25571461295 STAG. PRES: 0.025643897044314443\n",
      "V_infty: 30.0 km/s, BR: 1.0 G_MAX: 16.90302063923796 QDOT_MAX: 2655.1890245179093 J_MAX: 519761.8784462273 STAG. PRES: 0.03275324229726046\n",
      "V_infty: 30.0 km/s, BR: 5.0 G_MAX: 16.90302063923796 QDOT_MAX: 3436.725170579263 J_MAX: 519761.8784462273 STAG. PRES: 0.03275324229726046\n",
      "V_infty: 30.0 km/s, BR: 9.0 G_MAX: 16.90302063923796 QDOT_MAX: 3636.966863267076 J_MAX: 519761.8784462273 STAG. PRES: 0.03275324229726046\n",
      "V_infty: 30.0 km/s, BR: 13.0 G_MAX: 16.90302063923796 QDOT_MAX: 3756.883255846687 J_MAX: 519761.8784462273 STAG. PRES: 0.03275324229726046\n",
      "V_infty: 30.0 km/s, BR: 17.0 G_MAX: 16.90302063923796 QDOT_MAX: 3828.3357624603673 J_MAX: 519761.8784462273 STAG. PRES: 0.03275324229726046\n",
      "V_infty: 30.0 km/s, BR: 21.0 G_MAX: 16.90302063923796 QDOT_MAX: 3875.2080696198727 J_MAX: 519761.8784462273 STAG. PRES: 0.03275324229726046\n",
      "V_infty: 30.0 km/s, BR: 25.0 G_MAX: 16.90302063923796 QDOT_MAX: 3891.8761523320354 J_MAX: 519761.8784462273 STAG. PRES: 0.03275324229726046\n",
      "V_infty: 30.0 km/s, BR: 29.0 G_MAX: 16.90302063923796 QDOT_MAX: 3919.5713578059385 J_MAX: 519761.8784462273 STAG. PRES: 0.03275324229726046\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 30.0 km/s, BR: 33.0 G_MAX: 16.90302063923796 QDOT_MAX: 3939.793314640587 J_MAX: 519761.8784462273 STAG. PRES: 0.03275324229726046\n",
      "V_infty: 30.0 km/s, BR: 37.0 G_MAX: 16.90302063923796 QDOT_MAX: 3967.916036764199 J_MAX: 519761.8784462273 STAG. PRES: 0.03275324229726046\n",
      "V_infty: 30.0 km/s, BR: 41.0 G_MAX: 16.90302063923796 QDOT_MAX: 3967.6619222539325 J_MAX: 519761.8784462273 STAG. PRES: 0.03275324229726046\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', 300, beta1, 0.0, 3.1416, 0.0, 0.10, planet)\n",
    "        vehicle.setInitialState(1000.0,0.0,0.0,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, 1.0, 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', 300.0, beta1, 0.0, 3.1416, 0.0, 0.10, planet)\n",
    "        vehicle.setInitialState(1000.0,0.0,0.0,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, 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_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/uranus/'+runID+'acc_net_g_max_array.txt',acc_net_g_max_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'stag_pres_atm_max_array.txt',stag_pres_atm_max_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'q_stag_total_max_array.txt',q_stag_total_max_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'heatload_max_array.txt',heatload_max_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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 234x234 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.loadtxt('../data/jsr-paper/uranus/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/uranus/'+runID+'betaRatio_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/uranus/'+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,   30,  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([8])\n",
    "Qlevels = np.array([1200, 1600])\n",
    "Hlevels = np.array([400])\n",
    "#Slevels = np.array([0.8])\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([3.25,3.25])\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "\n",
    "plt.xlim([0.0,30.0])\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=10, 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=10, 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=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[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(1.5)\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.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 $' ,fontsize=10)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=10)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\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='lower right', fontsize=8)\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(0,30,301)\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/uranus/uranus-drag-small.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/uranus/uranus-drag-small.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/uranus/uranus-drag-small.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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/uranus/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/uranus/'+runID+'betaRatio_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/uranus/'+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,   30,  310)\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, 0.6 ,0.7, 0.8,0.9, 1.0, 1.1])\n",
    "\n",
    "Glevels = np.array([2.0,  4.0, 5.0 , 8.0])\n",
    "Qlevels = np.array([700, 900.0, 1000.0 , 1200.0, 1500.0 ])\n",
    "Hlevels = np.array([300, 350, 400, 450])\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",
    "ZCS1.collections[6].set_linewidths(1.5)\n",
    "\n",
    "\n",
    "ZCS1.collections[0].set_label(r'$TCW, deg$')\n",
    "\n",
    "\n",
    "GCS1 = plt.contour(X, Y, np.transpose(G1), levels=Glevels, colors='blue',linestyles='dashed')\n",
    "\n",
    "Glabels=plt.clabel(GCS1, inline=1, fontsize=10, colors='blue',fmt='%d',inline_spacing=0)\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[1].set_linewidths(1.5)\n",
    "GCS1.collections[2].set_linewidths(1.5)\n",
    "\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "for l in Glabels:\n",
    "    l.set_rotation(-90)\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted')\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=10, colors='red',fmt='%d',inline_spacing=0)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "QCS1.collections[1].set_linewidths(1.5)\n",
    "QCS1.collections[2].set_linewidths(1.5)\n",
    "QCS1.collections[3].set_linewidths(1.5)\n",
    "QCS1.collections[4].set_linewidths(1.5)\n",
    "\n",
    "QCS1.collections[0].set_label(r'$\\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$')\n",
    "\n",
    "for l in Hlabels:\n",
    "    l.set_rotation(-90)\n",
    "    \n",
    "plt.ylim(1,11)\n",
    "#plt.grid(True,linestyle='dotted', linewidth=0.3)\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(r'$\\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, 25, 30]),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",
    "plt.savefig('../data/jsr-paper/uranus/uranus-drag-large.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/uranus/uranus-drag-large.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/uranus/uranus-drag-large.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  }
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