{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 06 - a - Jupiter - Feasibility Charts - Lift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle\n",
    "\n",
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.patches import Polygon\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the planet and atmosphere model.\n",
    "planet=Planet(\"JUPITER\")\n",
    "planet.h_skip = 1000e3\n",
    "planet.h_low = 50e3\n",
    "planet.loadAtmosphereModel('../atmdata/Jupiter/jupiter-galileo-asi.dat', 0 , 1 , 2, 3, heightInKmFlag=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "vinf_kms_array = np.linspace( 0.0,   30.0,  11)\n",
    "LD_array       = np.linspace( 0.0,    1.0 , 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('../data/jsr-paper/jupiter/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "runID = 'jupiter-lift-'\n",
    "\n",
    "num_total      = len(vinf_kms_array)*len(LD_array)\n",
    "count = 1\n",
    "\n",
    "v0_kms_array    = np.zeros(len(vinf_kms_array))\n",
    "v0_kms_array[:] = np.sqrt(1.0*(vinf_kms_array[:]*1E3)**2.0 +\\\n",
    "                          2*np.ones(len(vinf_kms_array))*\\\n",
    "                          planet.GM/(planet.RP+1000.0*1.0E3))/1.0E3\n",
    "\n",
    "overShootLimit_array  = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "underShootLimit_array = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "exitflag_os_array     = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "exitflag_us_array     = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "TCW_array             = np.zeros((len(v0_kms_array),len(LD_array)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "Run #86 of 121: Arrival V_infty: 21.0 km/s, L/D:0.8 OSL: -8.219902030061348 USL: -9.137729546579067, TCW: 0.9178275165177183 EFOS: 1.0 EFUS: 1.0\n",
      "Run #87 of 121: Arrival V_infty: 21.0 km/s, L/D:0.9 OSL: -8.204195070116839 USL: -9.267689783366222, TCW: 1.0634947132493835 EFOS: 1.0 EFUS: 1.0\n",
      "Run #88 of 121: Arrival V_infty: 21.0 km/s, L/D:1.0 OSL: -8.19002866257506 USL: -9.408053433078749, TCW: 1.218024770503689 EFOS: 1.0 EFUS: 1.0\n",
      "Run #89 of 121: Arrival V_infty: 24.0 km/s, L/D:0.0 OSL: -8.503221475351893 USL: -8.503221475351893, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #90 of 121: Arrival V_infty: 24.0 km/s, L/D:0.1 OSL: -8.451266806281637 USL: -8.564189181626716, TCW: 0.11292237534507876 EFOS: 1.0 EFUS: 1.0\n",
      "Run #91 of 121: Arrival V_infty: 24.0 km/s, L/D:0.2 OSL: -8.407241546548903 USL: -8.635650133743184, TCW: 0.2284085871942807 EFOS: 1.0 EFUS: 1.0\n",
      "Run #92 of 121: Arrival V_infty: 24.0 km/s, L/D:0.30000000000000004 OSL: -8.370022758972482 USL: -8.717896062567888, TCW: 0.34787330359540647 EFOS: 1.0 EFUS: 1.0\n",
      "Run #93 of 121: Arrival V_infty: 24.0 km/s, L/D:0.4 OSL: -8.338409002950357 USL: -8.812324618826096, TCW: 0.4739156158757396 EFOS: 1.0 EFUS: 1.0\n",
      "Run #94 of 121: Arrival V_infty: 24.0 km/s, L/D:0.5 OSL: -8.311677066063567 USL: -8.919488180072221, TCW: 0.6078111140086548 EFOS: 1.0 EFUS: 1.0\n",
      "Run #95 of 121: Arrival V_infty: 24.0 km/s, L/D:0.6000000000000001 OSL: -8.288698779782862 USL: -9.039820672944188, TCW: 0.7511218931613257 EFOS: 1.0 EFUS: 1.0\n",
      "Run #96 of 121: Arrival V_infty: 24.0 km/s, L/D:0.7000000000000001 OSL: -8.268579938958283 USL: -9.173316740008886, TCW: 0.9047368010506034 EFOS: 1.0 EFUS: 1.0\n",
      "Run #97 of 121: Arrival V_infty: 24.0 km/s, L/D:0.8 OSL: -8.251027622340189 USL: -9.319941298781487, TCW: 1.0689136764412979 EFOS: 1.0 EFUS: 1.0\n",
      "Run #98 of 121: Arrival V_infty: 24.0 km/s, L/D:0.9 OSL: -8.235136163239076 USL: -9.479642935591983, TCW: 1.2445067723529064 EFOS: 1.0 EFUS: 1.0\n",
      "Run #99 of 121: Arrival V_infty: 24.0 km/s, L/D:1.0 OSL: -8.220873843645677 USL: -9.65208055088442, TCW: 1.4312067072387435 EFOS: 1.0 EFUS: 1.0\n",
      "Run #100 of 121: Arrival V_infty: 27.0 km/s, L/D:0.0 OSL: -8.55135935574799 USL: -8.55135935574799, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #101 of 121: Arrival V_infty: 27.0 km/s, L/D:0.1 OSL: -8.4936682023108 USL: -8.620492698406451, TCW: 0.12682449609565083 EFOS: 1.0 EFUS: 1.0\n",
      "Run #102 of 121: Arrival V_infty: 27.0 km/s, L/D:0.2 OSL: -8.445743934898928 USL: -8.702612521163246, TCW: 0.25686858626431786 EFOS: 1.0 EFUS: 1.0\n",
      "Run #103 of 121: Arrival V_infty: 27.0 km/s, L/D:0.30000000000000004 OSL: -8.405889333458617 USL: -8.799347696221957, TCW: 0.3934583627633401 EFOS: 1.0 EFUS: 1.0\n",
      "Run #104 of 121: Arrival V_infty: 27.0 km/s, L/D:0.4 OSL: -8.372680775726622 USL: -8.91198932901898, TCW: 0.5393085532923578 EFOS: 1.0 EFUS: 1.0\n",
      "Run #105 of 121: Arrival V_infty: 27.0 km/s, L/D:0.5 OSL: -8.34495007237274 USL: -9.04085531198507, TCW: 0.6959052396123298 EFOS: 1.0 EFUS: 1.0\n",
      "Run #106 of 121: Arrival V_infty: 27.0 km/s, L/D:0.6000000000000001 OSL: -8.321395591134205 USL: -9.186529687431175, TCW: 0.8651340962969698 EFOS: 1.0 EFUS: 1.0\n",
      "Run #107 of 121: Arrival V_infty: 27.0 km/s, L/D:0.7000000000000001 OSL: -8.300960183049028 USL: -9.348987574601779, TCW: 1.0480273915527505 EFOS: 1.0 EFUS: 1.0\n",
      "Run #108 of 121: Arrival V_infty: 27.0 km/s, L/D:0.8 OSL: -8.28314246312948 USL: -9.528902320169436, TCW: 1.2457598570399568 EFOS: 1.0 EFUS: 1.0\n",
      "Run #109 of 121: Arrival V_infty: 27.0 km/s, L/D:0.9 OSL: -8.267162528820336 USL: -9.723678112342895, TCW: 1.4565155835225596 EFOS: 1.0 EFUS: 1.0\n",
      "Run #110 of 121: Arrival V_infty: 27.0 km/s, L/D:1.0 OSL: -8.25284635207936 USL: -9.935327411280014, TCW: 1.6824810592006543 EFOS: 1.0 EFUS: 1.0\n",
      "Run #111 of 121: Arrival V_infty: 30.0 km/s, L/D:0.0 OSL: -8.599246204270457 USL: -8.599246204270457, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #112 of 121: Arrival V_infty: 30.0 km/s, L/D:0.1 OSL: -8.535567919658206 USL: -8.677285177436715, TCW: 0.14171725777850952 EFOS: 1.0 EFUS: 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run #113 of 121: Arrival V_infty: 30.0 km/s, L/D:0.2 OSL: -8.483794711784867 USL: -8.771834930390469, TCW: 0.2880402186056017 EFOS: 1.0 EFUS: 1.0\n",
      "Run #114 of 121: Arrival V_infty: 30.0 km/s, L/D:0.30000000000000004 OSL: -8.441580701910425 USL: -8.885201376499026, TCW: 0.4436206745886011 EFOS: 1.0 EFUS: 1.0\n",
      "Run #115 of 121: Arrival V_infty: 30.0 km/s, L/D:0.4 OSL: -8.407111298685777 USL: -9.019149668238242, TCW: 0.6120383695524652 EFOS: 1.0 EFUS: 1.0\n",
      "Run #116 of 121: Arrival V_infty: 30.0 km/s, L/D:0.5 OSL: -8.378377137611096 USL: -9.173707886635384, TCW: 0.7953307490242878 EFOS: 1.0 EFUS: 1.0\n",
      "Run #117 of 121: Arrival V_infty: 30.0 km/s, L/D:0.6000000000000001 OSL: -8.35435494016565 USL: -9.349420569364156, TCW: 0.9950656291985069 EFOS: 1.0 EFUS: 1.0\n",
      "Run #118 of 121: Arrival V_infty: 30.0 km/s, L/D:0.7000000000000001 OSL: -8.333632989808393 USL: -9.546438679790299, TCW: 1.2128056899819057 EFOS: 1.0 EFUS: 1.0\n",
      "Run #119 of 121: Arrival V_infty: 30.0 km/s, L/D:0.8 OSL: -8.315710944378225 USL: -9.763996230023622, TCW: 1.4482852856453974 EFOS: 1.0 EFUS: 1.0\n",
      "Run #120 of 121: Arrival V_infty: 30.0 km/s, L/D:0.9 OSL: -8.29962804094248 USL: -10.000122070501675, TCW: 1.700494029559195 EFOS: 1.0 EFUS: 1.0\n",
      "Run #121 of 121: Arrival V_infty: 30.0 km/s, L/D:1.0 OSL: -8.285239609322161 USL: -10.25484653532476, TCW: 1.969606926002598 EFOS: 1.0 EFUS: 1.0\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,len(v0_kms_array)):\n",
    "    for j in range(0,len(LD_array)):\n",
    "        vehicle=Vehicle('Apollo', 1000.0, 200.0, LD_array[j], 3.1416, 0.0, 1.00, planet)\n",
    "        vehicle.setInitialState(1000.0,0.0,0.0,v0_kms_array[i],0.0,-4.5,0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-5)\n",
    "        overShootLimit_array[i,j],  exitflag_os_array[i,j]  = vehicle.findOverShootLimit (2400.0, 1.0, -30.0, -4.0, 1E-10, 430.0e3)\n",
    "        underShootLimit_array[i,j], exitflag_us_array[i,j] =  vehicle.findUnderShootLimit(2400.0, 1.0, -30.0, -4.0, 1E-10, 430.0e3)\n",
    "\n",
    "        TCW_array[i,j] = overShootLimit_array[i,j] - underShootLimit_array[i,j]\n",
    "\n",
    "        print(\"Run #\"+str(count)+\" of \"+ str(num_total)+\": Arrival V_infty: \"+str(vinf_kms_array[i])+\" km/s\"+\", L/D:\"+str(LD_array[j]) + \" OSL: \"+str(overShootLimit_array[i,j])+\" USL: \"+str(underShootLimit_array[i,j])+\", TCW: \"+str(TCW_array[i,j])+\" EFOS: \"+str(exitflag_os_array[i,j])+ \" EFUS: \"+str(exitflag_us_array[i,j]))\n",
    "        count = count +1\n",
    "\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'vinf_kms_array.txt',vinf_kms_array)\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'v0_kms_array.txt',v0_kms_array)\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'LD_array.txt',LD_array)\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'overShootLimit_array.txt',overShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'exitflag_os_array.txt',exitflag_os_array)\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'undershootLimit_array.txt',underShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'exitflag_us_array.txt',exitflag_us_array)\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'TCW_array.txt',TCW_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 0.0 km/s, L/D: 0.0 G_MAX: 6.60833313158494 QDOT_MAX: 5595.037845296978 J_MAX: 391576.2268816193 STAG. PRES: 0.12796043643559096\n",
      "V_infty: 0.0 km/s, L/D: 0.1 G_MAX: 7.043231547625441 QDOT_MAX: 5921.453078299657 J_MAX: 394927.7180599654 STAG. PRES: 0.12047462209065912\n",
      "V_infty: 0.0 km/s, L/D: 0.2 G_MAX: 7.578275348593165 QDOT_MAX: 6272.25388971557 J_MAX: 398710.8480286293 STAG. PRES: 0.11347295297601283\n",
      "V_infty: 0.0 km/s, L/D: 0.30000000000000004 G_MAX: 8.219378503290944 QDOT_MAX: 6647.360125514389 J_MAX: 402725.0237254156 STAG. PRES: 0.10678090622853702\n",
      "V_infty: 0.0 km/s, L/D: 0.4 G_MAX: 8.95633261290585 QDOT_MAX: 7031.9872902046845 J_MAX: 406938.2819528091 STAG. PRES: 0.1004338567295424\n",
      "V_infty: 0.0 km/s, L/D: 0.5 G_MAX: 9.811249240097762 QDOT_MAX: 7439.535788502653 J_MAX: 411168.1565257269 STAG. PRES: 0.09444491745792809\n",
      "V_infty: 0.0 km/s, L/D: 0.6000000000000001 G_MAX: 10.807119069839324 QDOT_MAX: 7881.959030853774 J_MAX: 415825.99818081997 STAG. PRES: 0.08889829445663834\n",
      "V_infty: 0.0 km/s, L/D: 0.7000000000000001 G_MAX: 11.908834178578282 QDOT_MAX: 8335.586070840582 J_MAX: 420654.58704916167 STAG. PRES: 0.08374095904795664\n",
      "V_infty: 0.0 km/s, L/D: 0.8 G_MAX: 13.152306698784505 QDOT_MAX: 8824.048470491489 J_MAX: 425280.09796221356 STAG. PRES: 0.07889194398004747\n",
      "V_infty: 0.0 km/s, L/D: 0.9 G_MAX: 14.533535932549265 QDOT_MAX: 9342.878756691578 J_MAX: 430771.09777054586 STAG. PRES: 0.074435425476903\n",
      "V_infty: 0.0 km/s, L/D: 1.0 G_MAX: 16.069382323517342 QDOT_MAX: 9906.907243449761 J_MAX: 435974.71516678395 STAG. PRES: 0.07029918706170094\n",
      "V_infty: 3.0 km/s, L/D: 0.0 G_MAX: 6.741513745749217 QDOT_MAX: 5721.07433937279 J_MAX: 398327.8681168823 STAG. PRES: 0.13053926048007589\n",
      "V_infty: 3.0 km/s, L/D: 0.1 G_MAX: 7.19619866605896 QDOT_MAX: 6066.107604793628 J_MAX: 401642.8242077628 STAG. PRES: 0.12278480751485214\n",
      "V_infty: 3.0 km/s, L/D: 0.2 G_MAX: 7.7493521954624205 QDOT_MAX: 6433.265975979659 J_MAX: 405401.3138240142 STAG. PRES: 0.11549524904812276\n",
      "V_infty: 3.0 km/s, L/D: 0.30000000000000004 G_MAX: 8.397967249049868 QDOT_MAX: 6816.521078890267 J_MAX: 409462.91370352905 STAG. PRES: 0.1085782120150021\n",
      "V_infty: 3.0 km/s, L/D: 0.4 G_MAX: 9.167954858985544 QDOT_MAX: 7228.351499141474 J_MAX: 413760.47055943916 STAG. PRES: 0.10202518703983281\n",
      "V_infty: 3.0 km/s, L/D: 0.5 G_MAX: 10.061981333650326 QDOT_MAX: 7667.46962202776 J_MAX: 418118.0870858114 STAG. PRES: 0.09585556748014748\n",
      "V_infty: 3.0 km/s, L/D: 0.6000000000000001 G_MAX: 11.0815169642066 QDOT_MAX: 8132.238131956101 J_MAX: 422870.69211383833 STAG. PRES: 0.09011412619268094\n",
      "V_infty: 3.0 km/s, L/D: 0.7000000000000001 G_MAX: 12.223126202181252 QDOT_MAX: 8617.835529426575 J_MAX: 427830.11219992983 STAG. PRES: 0.0848201516211781\n",
      "V_infty: 3.0 km/s, L/D: 0.8 G_MAX: 13.50833954966684 QDOT_MAX: 9133.435858818717 J_MAX: 432806.6385414854 STAG. PRES: 0.0798424112723799\n",
      "V_infty: 3.0 km/s, L/D: 0.9 G_MAX: 14.951899793917983 QDOT_MAX: 9694.809774924253 J_MAX: 438010.11330520717 STAG. PRES: 0.07523881023646335\n",
      "V_infty: 3.0 km/s, L/D: 1.0 G_MAX: 16.53194604175592 QDOT_MAX: 10283.78436499519 J_MAX: 443399.36070847564 STAG. PRES: 0.07099891906880047\n",
      "V_infty: 6.0 km/s, L/D: 0.0 G_MAX: 7.142811321812331 QDOT_MAX: 6106.435977242654 J_MAX: 418989.3879584433 STAG. PRES: 0.1383096228836386\n",
      "V_infty: 6.0 km/s, L/D: 0.1 G_MAX: 7.64403562664276 QDOT_MAX: 6502.684897072459 J_MAX: 422293.2115303967 STAG. PRES: 0.12971723539546734\n",
      "V_infty: 6.0 km/s, L/D: 0.2 G_MAX: 8.251375692362085 QDOT_MAX: 6922.289212833742 J_MAX: 425672.57025501376 STAG. PRES: 0.12147843895602446\n",
      "V_infty: 6.0 km/s, L/D: 0.30000000000000004 G_MAX: 8.978276129437239 QDOT_MAX: 7379.354422255166 J_MAX: 430081.31681995466 STAG. PRES: 0.11394831934530163\n",
      "V_infty: 6.0 km/s, L/D: 0.4 G_MAX: 9.829560240053915 QDOT_MAX: 7861.258376626405 J_MAX: 434392.45464064885 STAG. PRES: 0.10672891717497791\n",
      "V_infty: 6.0 km/s, L/D: 0.5 G_MAX: 10.815306096398533 QDOT_MAX: 8373.568668811507 J_MAX: 439107.04076178506 STAG. PRES: 0.09998938481399763\n",
      "V_infty: 6.0 km/s, L/D: 0.6000000000000001 G_MAX: 11.932812928065575 QDOT_MAX: 8913.775362477789 J_MAX: 443860.75400873675 STAG. PRES: 0.09368570746946057\n",
      "V_infty: 6.0 km/s, L/D: 0.7000000000000001 G_MAX: 13.211189795373002 QDOT_MAX: 9502.518362998993 J_MAX: 448759.92307135504 STAG. PRES: 0.08779324664302626\n",
      "V_infty: 6.0 km/s, L/D: 0.8 G_MAX: 14.641698218417222 QDOT_MAX: 10129.317397983283 J_MAX: 454147.95802676294 STAG. PRES: 0.08241132980678899\n",
      "V_infty: 6.0 km/s, L/D: 0.9 G_MAX: 16.28930932272188 QDOT_MAX: 10831.660134992992 J_MAX: 459906.65074794384 STAG. PRES: 0.07753967015074643\n",
      "V_infty: 6.0 km/s, L/D: 1.0 G_MAX: 18.044978952906085 QDOT_MAX: 11535.447392075732 J_MAX: 465446.9552340496 STAG. PRES: 0.07296145313905651\n",
      "V_infty: 9.0 km/s, L/D: 0.0 G_MAX: 7.809318222122969 QDOT_MAX: 6786.555044380366 J_MAX: 454618.29702849843 STAG. PRES: 0.1512153147748921\n",
      "V_infty: 9.0 km/s, L/D: 0.1 G_MAX: 8.399198583887674 QDOT_MAX: 7280.538463140425 J_MAX: 457756.9371935236 STAG. PRES: 0.14113972386473475\n",
      "V_infty: 9.0 km/s, L/D: 0.2 G_MAX: 9.109209776699899 QDOT_MAX: 7812.150164152389 J_MAX: 460933.2883619692 STAG. PRES: 0.13148977774111506\n",
      "V_infty: 9.0 km/s, L/D: 0.30000000000000004 G_MAX: 9.955299296481284 QDOT_MAX: 8388.824914165863 J_MAX: 465192.8055147269 STAG. PRES: 0.12261481206294485\n",
      "V_infty: 9.0 km/s, L/D: 0.4 G_MAX: 10.944329302632932 QDOT_MAX: 9000.937464291106 J_MAX: 469730.7810096646 STAG. PRES: 0.11428392011931891\n",
      "V_infty: 9.0 km/s, L/D: 0.5 G_MAX: 12.112181548446996 QDOT_MAX: 9681.190021193544 J_MAX: 474414.51996758895 STAG. PRES: 0.10649875176774604\n",
      "V_infty: 9.0 km/s, L/D: 0.6000000000000001 G_MAX: 13.432478412585484 QDOT_MAX: 10395.143443082194 J_MAX: 479626.8014447119 STAG. PRES: 0.09933593947779623\n",
      "V_infty: 9.0 km/s, L/D: 0.7000000000000001 G_MAX: 14.945876345057927 QDOT_MAX: 11176.662269977965 J_MAX: 484518.5894666541 STAG. PRES: 0.09256425039554539\n",
      "V_infty: 9.0 km/s, L/D: 0.8 G_MAX: 16.663443741769328 QDOT_MAX: 12039.973864936557 J_MAX: 490326.976069227 STAG. PRES: 0.08651512048308478\n",
      "V_infty: 9.0 km/s, L/D: 0.9 G_MAX: 18.575716282137954 QDOT_MAX: 12941.793568179033 J_MAX: 496577.5377307467 STAG. PRES: 0.08102327291372527\n",
      "V_infty: 9.0 km/s, L/D: 1.0 G_MAX: 20.68629104269936 QDOT_MAX: 13910.805871953493 J_MAX: 502924.9830746388 STAG. PRES: 0.07599187677176975\n",
      "V_infty: 12.0 km/s, L/D: 0.0 G_MAX: 8.729922233711266 QDOT_MAX: 7810.42942788344 J_MAX: 506820.4041955367 STAG. PRES: 0.16904100065281583\n",
      "V_infty: 12.0 km/s, L/D: 0.1 G_MAX: 9.45857647470843 QDOT_MAX: 8474.3235588643 J_MAX: 509436.77232541103 STAG. PRES: 0.1567241836237047\n",
      "V_infty: 12.0 km/s, L/D: 0.2 G_MAX: 10.33314709206336 QDOT_MAX: 9200.086537936473 J_MAX: 512341.94905023655 STAG. PRES: 0.14508434809527693\n",
      "V_infty: 12.0 km/s, L/D: 0.30000000000000004 G_MAX: 11.383479870934858 QDOT_MAX: 10004.91115013964 J_MAX: 515933.7767308879 STAG. PRES: 0.13417916518743983\n",
      "V_infty: 12.0 km/s, L/D: 0.4 G_MAX: 12.607021770954404 QDOT_MAX: 10872.330224493937 J_MAX: 520519.7156151095 STAG. PRES: 0.12425226451040867\n",
      "V_infty: 12.0 km/s, L/D: 0.5 G_MAX: 14.051991677189848 QDOT_MAX: 11839.787579372778 J_MAX: 525391.4624649727 STAG. PRES: 0.11498764470455787\n",
      "V_infty: 12.0 km/s, L/D: 0.6000000000000001 G_MAX: 15.729224334728237 QDOT_MAX: 12916.439971451247 J_MAX: 530385.1751253281 STAG. PRES: 0.10648902576544737\n",
      "V_infty: 12.0 km/s, L/D: 0.7000000000000001 G_MAX: 17.61228036801265 QDOT_MAX: 14053.70412818262 J_MAX: 536552.6169411003 STAG. PRES: 0.09881256081561172\n",
      "V_infty: 12.0 km/s, L/D: 0.8 G_MAX: 19.74263936127538 QDOT_MAX: 15303.304794546713 J_MAX: 542560.3533120464 STAG. PRES: 0.09172341709726903\n",
      "V_infty: 12.0 km/s, L/D: 0.9 G_MAX: 22.10643054673961 QDOT_MAX: 16643.598023758084 J_MAX: 549246.3173217674 STAG. PRES: 0.08542661953928099\n",
      "V_infty: 12.0 km/s, L/D: 1.0 G_MAX: 24.75291700575155 QDOT_MAX: 18092.197943470757 J_MAX: 555917.0416682918 STAG. PRES: 0.07972806733346724\n",
      "V_infty: 15.0 km/s, L/D: 0.0 G_MAX: 9.922968737823012 QDOT_MAX: 9284.688961040163 J_MAX: 579599.5002984622 STAG. PRES: 0.19214199775265406\n",
      "V_infty: 15.0 km/s, L/D: 0.1 G_MAX: 10.84968916938849 QDOT_MAX: 10227.616573509986 J_MAX: 580607.7514140778 STAG. PRES: 0.17649216011696361\n",
      "V_infty: 15.0 km/s, L/D: 0.2 G_MAX: 11.977307289028255 QDOT_MAX: 11292.181692950668 J_MAX: 582213.9033304038 STAG. PRES: 0.16187830859203373\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 15.0 km/s, L/D: 0.30000000000000004 G_MAX: 13.312499396514253 QDOT_MAX: 12474.851444384292 J_MAX: 585081.4726919101 STAG. PRES: 0.14847905119402144\n",
      "V_infty: 15.0 km/s, L/D: 0.4 G_MAX: 14.913270489644626 QDOT_MAX: 13813.460513429694 J_MAX: 588872.7807658188 STAG. PRES: 0.13620804526461947\n",
      "V_infty: 15.0 km/s, L/D: 0.5 G_MAX: 16.760931623711546 QDOT_MAX: 15295.982362551946 J_MAX: 593870.3940665117 STAG. PRES: 0.12503753406866705\n",
      "V_infty: 15.0 km/s, L/D: 0.6000000000000001 G_MAX: 18.90877066788297 QDOT_MAX: 16940.281314541975 J_MAX: 598757.6579068056 STAG. PRES: 0.11481382225824917\n",
      "V_infty: 15.0 km/s, L/D: 0.7000000000000001 G_MAX: 21.35591591474936 QDOT_MAX: 18743.612846914642 J_MAX: 604575.1364022498 STAG. PRES: 0.10578611190748091\n",
      "V_infty: 15.0 km/s, L/D: 0.8 G_MAX: 24.091236302654853 QDOT_MAX: 20699.544704793578 J_MAX: 611067.0009203001 STAG. PRES: 0.09753792234499294\n",
      "V_infty: 15.0 km/s, L/D: 0.9 G_MAX: 27.169296558487467 QDOT_MAX: 22863.94952819333 J_MAX: 618253.6014553575 STAG. PRES: 0.09025326648610126\n",
      "V_infty: 15.0 km/s, L/D: 1.0 G_MAX: 30.512331409129903 QDOT_MAX: 25134.720997898898 J_MAX: 625552.4942372511 STAG. PRES: 0.08369948320597263\n",
      "V_infty: 18.0 km/s, L/D: 0.0 G_MAX: 11.394044188395783 QDOT_MAX: 11365.255815725257 J_MAX: 677378.8541218569 STAG. PRES: 0.2206263462292148\n",
      "V_infty: 18.0 km/s, L/D: 0.1 G_MAX: 12.612991261161136 QDOT_MAX: 12779.079728160428 J_MAX: 675084.2458966136 STAG. PRES: 0.2001660557028763\n",
      "V_infty: 18.0 km/s, L/D: 0.2 G_MAX: 14.10215285691332 QDOT_MAX: 14417.186396913447 J_MAX: 674513.0702554319 STAG. PRES: 0.1816501208839501\n",
      "V_infty: 18.0 km/s, L/D: 0.30000000000000004 G_MAX: 15.86391368504883 QDOT_MAX: 16275.344429398208 J_MAX: 675334.2626718375 STAG. PRES: 0.1648405404213626\n",
      "V_infty: 18.0 km/s, L/D: 0.4 G_MAX: 17.954393428835107 QDOT_MAX: 18408.220815469012 J_MAX: 677595.792358849 STAG. PRES: 0.1496273863744141\n",
      "V_infty: 18.0 km/s, L/D: 0.5 G_MAX: 20.405790721767964 QDOT_MAX: 20797.224489695007 J_MAX: 681451.7518794012 STAG. PRES: 0.13602997224631946\n",
      "V_infty: 18.0 km/s, L/D: 0.6000000000000001 G_MAX: 23.223096624279417 QDOT_MAX: 23476.487815561595 J_MAX: 686060.132361615 STAG. PRES: 0.12382936098510414\n",
      "V_infty: 18.0 km/s, L/D: 0.7000000000000001 G_MAX: 26.391892401520774 QDOT_MAX: 26409.556219503633 J_MAX: 692020.1043919593 STAG. PRES: 0.11313178460024151\n",
      "V_infty: 18.0 km/s, L/D: 0.8 G_MAX: 29.980653467711676 QDOT_MAX: 29607.61002095788 J_MAX: 698541.1029262873 STAG. PRES: 0.10360245648180631\n",
      "V_infty: 18.0 km/s, L/D: 0.9 G_MAX: 33.95560445951548 QDOT_MAX: 33045.51405597463 J_MAX: 705887.7059898053 STAG. PRES: 0.09527822262887109\n",
      "V_infty: 18.0 km/s, L/D: 1.0 G_MAX: 38.33675000620985 QDOT_MAX: 36776.70922180161 J_MAX: 713915.8089937068 STAG. PRES: 0.08787958987536587\n",
      "V_infty: 21.0 km/s, L/D: 0.0 G_MAX: 13.167372942447102 QDOT_MAX: 14291.616849926315 J_MAX: 807564.3222795672 STAG. PRES: 0.2549625894437373\n",
      "V_infty: 21.0 km/s, L/D: 0.1 G_MAX: 14.800808548216441 QDOT_MAX: 16484.25051455717 J_MAX: 799546.0305656473 STAG. PRES: 0.22798698564548747\n",
      "V_infty: 21.0 km/s, L/D: 0.2 G_MAX: 16.768488692282453 QDOT_MAX: 19074.23085011018 J_MAX: 793982.912860765 STAG. PRES: 0.2040304079084438\n",
      "V_infty: 21.0 km/s, L/D: 0.30000000000000004 G_MAX: 19.124255722567053 QDOT_MAX: 22086.958848160626 J_MAX: 791056.4959697423 STAG. PRES: 0.182924161464999\n",
      "V_infty: 21.0 km/s, L/D: 0.4 G_MAX: 21.87135641896589 QDOT_MAX: 25528.411490918825 J_MAX: 790293.9541363913 STAG. PRES: 0.16411743760967543\n",
      "V_infty: 21.0 km/s, L/D: 0.5 G_MAX: 25.11223129221971 QDOT_MAX: 29522.976619797686 J_MAX: 792023.8626171054 STAG. PRES: 0.14763161059486746\n",
      "V_infty: 21.0 km/s, L/D: 0.6000000000000001 G_MAX: 28.7862341595809 QDOT_MAX: 33891.82374544265 J_MAX: 795476.4054955812 STAG. PRES: 0.1331437200948225\n",
      "V_infty: 21.0 km/s, L/D: 0.7000000000000001 G_MAX: 32.917105149768354 QDOT_MAX: 38736.446369240075 J_MAX: 800195.4704518921 STAG. PRES: 0.120656908969168\n",
      "V_infty: 21.0 km/s, L/D: 0.8 G_MAX: 37.55183754326253 QDOT_MAX: 43942.51013373867 J_MAX: 806882.4528465143 STAG. PRES: 0.10977576409222162\n",
      "V_infty: 21.0 km/s, L/D: 0.9 G_MAX: 42.62751283564169 QDOT_MAX: 49683.46647034674 J_MAX: 814282.30705008 STAG. PRES: 0.10036555619411287\n",
      "V_infty: 21.0 km/s, L/D: 1.0 G_MAX: 48.265186391275286 QDOT_MAX: 55668.29365492902 J_MAX: 822189.5365679235 STAG. PRES: 0.09216141219759302\n",
      "V_infty: 24.0 km/s, L/D: 0.0 G_MAX: 15.251781156663094 QDOT_MAX: 18412.245975104688 J_MAX: 980438.0429009466 STAG. PRES: 0.29532165199671373\n",
      "V_infty: 24.0 km/s, L/D: 0.1 G_MAX: 17.401057356583756 QDOT_MAX: 21828.825303689126 J_MAX: 961988.8690550114 STAG. PRES: 0.25987045064102493\n",
      "V_infty: 24.0 km/s, L/D: 0.2 G_MAX: 20.028352090803605 QDOT_MAX: 26006.779739634723 J_MAX: 948084.348448731 STAG. PRES: 0.22894335365804636\n",
      "V_infty: 24.0 km/s, L/D: 0.30000000000000004 G_MAX: 23.14778597666597 QDOT_MAX: 30940.886679492167 J_MAX: 938312.3126862595 STAG. PRES: 0.2022197208698362\n",
      "V_infty: 24.0 km/s, L/D: 0.4 G_MAX: 26.790912412149716 QDOT_MAX: 36692.12192339968 J_MAX: 932196.0120018887 STAG. PRES: 0.17926994195814458\n",
      "V_infty: 24.0 km/s, L/D: 0.5 G_MAX: 30.99189429294562 QDOT_MAX: 43174.9230431518 J_MAX: 930056.4027121287 STAG. PRES: 0.15951754589169131\n",
      "V_infty: 24.0 km/s, L/D: 0.6000000000000001 G_MAX: 35.73760988183896 QDOT_MAX: 50441.16362249387 J_MAX: 930797.5636828381 STAG. PRES: 0.14252696702251108\n",
      "V_infty: 24.0 km/s, L/D: 0.7000000000000001 G_MAX: 41.06552288625742 QDOT_MAX: 58343.41910695099 J_MAX: 933764.6139832869 STAG. PRES: 0.12814170679193598\n",
      "V_infty: 24.0 km/s, L/D: 0.8 G_MAX: 46.93100197536054 QDOT_MAX: 66719.20007044362 J_MAX: 939099.1273045485 STAG. PRES: 0.11590794401419029\n",
      "V_infty: 24.0 km/s, L/D: 0.9 G_MAX: 53.359829986236065 QDOT_MAX: 76081.86555662741 J_MAX: 945782.0488581558 STAG. PRES: 0.10545825292458441\n",
      "V_infty: 24.0 km/s, L/D: 1.0 G_MAX: 60.53100657309034 QDOT_MAX: 85620.88665448123 J_MAX: 953942.6890182712 STAG. PRES: 0.09658859944231314\n",
      "V_infty: 27.0 km/s, L/D: 0.0 G_MAX: 17.654381756435384 QDOT_MAX: 24195.040144460243 J_MAX: 1210386.474972252 STAG. PRES: 0.3418405122634713\n",
      "V_infty: 27.0 km/s, L/D: 0.1 G_MAX: 20.494906692220546 QDOT_MAX: 29623.795931329478 J_MAX: 1173804.94682999 STAG. PRES: 0.2955603246297943\n",
      "V_infty: 27.0 km/s, L/D: 0.2 G_MAX: 23.94013438058497 QDOT_MAX: 36355.92782259728 J_MAX: 1145755.3040934622 STAG. PRES: 0.2560651080819194\n",
      "V_infty: 27.0 km/s, L/D: 0.30000000000000004 G_MAX: 27.993114033756473 QDOT_MAX: 44405.48242354252 J_MAX: 1124445.9977078028 STAG. PRES: 0.2226703313187498\n",
      "V_infty: 27.0 km/s, L/D: 0.4 G_MAX: 32.72496904412938 QDOT_MAX: 53865.61515645089 J_MAX: 1109819.5143488147 STAG. PRES: 0.19480803918513254\n",
      "V_infty: 27.0 km/s, L/D: 0.5 G_MAX: 38.047133719398886 QDOT_MAX: 64356.25111319066 J_MAX: 1101050.131472198 STAG. PRES: 0.1714712683364802\n",
      "V_infty: 27.0 km/s, L/D: 0.6000000000000001 G_MAX: 44.10780357197926 QDOT_MAX: 76172.13002806276 J_MAX: 1096761.3612597892 STAG. PRES: 0.15191647788465334\n",
      "V_infty: 27.0 km/s, L/D: 0.7000000000000001 G_MAX: 50.75211902161297 QDOT_MAX: 88810.57600108808 J_MAX: 1096457.341680765 STAG. PRES: 0.1356635111477062\n",
      "V_infty: 27.0 km/s, L/D: 0.8 G_MAX: 58.265797289951415 QDOT_MAX: 102726.95863356604 J_MAX: 1099199.8334942344 STAG. PRES: 0.12203398093008667\n",
      "V_infty: 27.0 km/s, L/D: 0.9 G_MAX: 66.2874511412182 QDOT_MAX: 117725.04789905922 J_MAX: 1104911.3160334679 STAG. PRES: 0.11076430672442693\n",
      "V_infty: 27.0 km/s, L/D: 1.0 G_MAX: 75.30440900759369 QDOT_MAX: 133261.5939829139 J_MAX: 1112477.5661834301 STAG. PRES: 0.10126879364498595\n",
      "V_infty: 30.0 km/s, L/D: 0.0 G_MAX: 20.38872663835972 QDOT_MAX: 32327.436688775026 J_MAX: 1517253.5728198946 STAG. PRES: 0.3947824754103541\n",
      "V_infty: 30.0 km/s, L/D: 0.1 G_MAX: 24.081106081450553 QDOT_MAX: 40936.85481724495 J_MAX: 1451560.5489578221 STAG. PRES: 0.3349984361196657\n",
      "V_infty: 30.0 km/s, L/D: 0.2 G_MAX: 28.528823550791998 QDOT_MAX: 51757.830055492086 J_MAX: 1399961.2084257929 STAG. PRES: 0.285084468610133\n",
      "V_infty: 30.0 km/s, L/D: 0.30000000000000004 G_MAX: 33.72840480394705 QDOT_MAX: 64793.66083553554 J_MAX: 1360609.9569604837 STAG. PRES: 0.2439379973274961\n",
      "V_infty: 30.0 km/s, L/D: 0.4 G_MAX: 39.71117287076062 QDOT_MAX: 80030.78321837848 J_MAX: 1332343.507256619 STAG. PRES: 0.21055271702878356\n",
      "V_infty: 30.0 km/s, L/D: 0.5 G_MAX: 46.43666954933903 QDOT_MAX: 97276.35549688038 J_MAX: 1312912.2294077817 STAG. PRES: 0.1834525325506392\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 30.0 km/s, L/D: 0.6000000000000001 G_MAX: 53.923105629675106 QDOT_MAX: 116088.67473614821 J_MAX: 1301031.5011579597 STAG. PRES: 0.16134710724631943\n",
      "V_infty: 30.0 km/s, L/D: 0.7000000000000001 G_MAX: 62.4195922697935 QDOT_MAX: 136899.75126370176 J_MAX: 1294811.7928845256 STAG. PRES: 0.14330095194744186\n",
      "V_infty: 30.0 km/s, L/D: 0.8 G_MAX: 71.58645158748398 QDOT_MAX: 158943.21511057884 J_MAX: 1293790.9850990435 STAG. PRES: 0.12848236628213894\n",
      "V_infty: 30.0 km/s, L/D: 0.9 G_MAX: 81.70267041343187 QDOT_MAX: 182943.0505166007 J_MAX: 1296564.7582887742 STAG. PRES: 0.11638151338614418\n",
      "V_infty: 30.0 km/s, L/D: 1.0 G_MAX: 93.05605683401274 QDOT_MAX: 208201.03367199216 J_MAX: 1302315.7329420766 STAG. PRES: 0.10628996228470153\n"
     ]
    }
   ],
   "source": [
    "acc_net_g_max_array       = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "stag_pres_atm_max_array   = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "q_stag_total_max_array    = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "heatload_max_array        = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "\n",
    "\n",
    "for i in range(0,len(v0_kms_array)):\n",
    "    for j in range(0,len(LD_array)):\n",
    "        vehicle=Vehicle('Apollo', 1000.0, 200.0, LD_array[j], 3.1416, 0.0, 1.00, planet)\n",
    "        vehicle.setInitialState(1000.0,0.0,0.0,v0_kms_array[i],0.0,overShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-5)\n",
    "        vehicle.propogateEntry (2400.0, 1.0, 180.0)\n",
    "\n",
    "        # Extract and save variables to plot\n",
    "        t_min_os         = vehicle.t_minc\n",
    "        h_km_os          = vehicle.h_kmc\n",
    "        acc_net_g_os     = vehicle.acc_net_g\n",
    "        q_stag_con_os    = vehicle.q_stag_con\n",
    "        q_stag_rad_os    = vehicle.q_stag_rad\n",
    "        rc_os            = vehicle.rc\n",
    "        vc_os            = vehicle.vc\n",
    "        stag_pres_atm_os = vehicle.computeStagPres(rc_os,vc_os)/(1.01325E5)\n",
    "        heatload_os      = vehicle.heatload\n",
    "\n",
    "        vehicle=Vehicle('Apollo', 1000.0, 200.0, LD_array[j], 3.1416, 0.0, 1.00, planet)\n",
    "        vehicle.setInitialState(1000.0,0.0,0.0,v0_kms_array[i],0.0,underShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-5)\n",
    "        vehicle.propogateEntry (2400.0, 1.0, 0.0)\n",
    "\n",
    "        # Extract and save variable to plot\n",
    "        t_min_us         = vehicle.t_minc\n",
    "        h_km_us          = vehicle.h_kmc\n",
    "        acc_net_g_us     = vehicle.acc_net_g\n",
    "        q_stag_con_us    = vehicle.q_stag_con\n",
    "        q_stag_rad_us    = vehicle.q_stag_rad\n",
    "        rc_us            = vehicle.rc\n",
    "        vc_us            = vehicle.vc\n",
    "        stag_pres_atm_us = vehicle.computeStagPres(rc_us,vc_us)/(1.01325E5)\n",
    "        heatload_us      = vehicle.heatload\n",
    "\n",
    "        q_stag_total_os  = q_stag_con_os + q_stag_rad_os\n",
    "        q_stag_total_us  = q_stag_con_us + q_stag_rad_us\n",
    "\n",
    "        acc_net_g_max_array[i,j]      = max(max(acc_net_g_os),max(acc_net_g_us))\n",
    "        stag_pres_atm_max_array[i,j]  = max(max(stag_pres_atm_os),max(stag_pres_atm_os))\n",
    "        q_stag_total_max_array[i,j]   = max(max(q_stag_total_os),max(q_stag_total_us))\n",
    "        heatload_max_array[i,j]       = max(max(heatload_os),max(heatload_os))\n",
    "\n",
    "        print(\"V_infty: \"+str(vinf_kms_array[i])+\" km/s\"+\", L/D: \"+str(LD_array[j])+\" G_MAX: \"+str(acc_net_g_max_array[i,j])+\" QDOT_MAX: \"+str(q_stag_total_max_array[i,j])+\" J_MAX: \"+str(heatload_max_array[i,j])+\" STAG. PRES: \"+str(stag_pres_atm_max_array[i,j]))\n",
    "\n",
    "\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'acc_net_g_max_array.txt',acc_net_g_max_array)\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'stag_pres_atm_max_array.txt',stag_pres_atm_max_array)\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'q_stag_total_max_array.txt',q_stag_total_max_array)\n",
    "np.savetxt('../data/jsr-paper/jupiter/'+runID+'heatload_max_array.txt',heatload_max_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'stag_pres_atm_max_array.txt')\n",
    "\n",
    "\n",
    "f1 = interpolate.interp2d(x, y, np.transpose(Z1), kind='cubic')\n",
    "g1 = interpolate.interp2d(x, y, np.transpose(G1), kind='cubic')\n",
    "q1 = interpolate.interp2d(x, y, np.transpose(Q1), kind='cubic')\n",
    "h1 = interpolate.interp2d(x, y, np.transpose(H1), kind='cubic')\n",
    "#s1 = interpolate.interp2d(x, y, transpose(S1), kind='cubic')\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   30,  210)\n",
    "y_new =  np.linspace( 0.0,   1 ,110)\n",
    "z_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "z1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "g1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "q1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "h1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "#s1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "for i in range(0,len(x_new)):\n",
    "    for j in range(0,len(y_new)):\n",
    "\n",
    "        z1_new[i,j] = f1(x_new[i],y_new[j])\n",
    "        g1_new[i,j] = g1(x_new[i],y_new[j])\n",
    "        q1_new[i,j] = q1(x_new[i],y_new[j])\n",
    "        h1_new[i,j] = h1(x_new[i],y_new[j])\n",
    "        #s1_new[i,j] = s1(x_new[i],y_new[j])\n",
    "\n",
    "\n",
    "Z1 = z1_new\n",
    "G1 = g1_new\n",
    "Q1 = q1_new\n",
    "#S1 = s1_new\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "\n",
    "\n",
    "Zlevels = np.array([0.2,0.5])\n",
    "\n",
    "Glevels = np.array([25.0, 30.0])\n",
    "Qlevels = np.array([12000, 16000.0])\n",
    "Hlevels = np.array([600, 800])\n",
    "#Slevels = np.array([0.8])\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "#plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "#plt.xlim([0.0,30.0])\n",
    "#plt.ylim([0.0,0.4])\n",
    "#plt.tight_layout()\n",
    "#plt.contourf(X, Y, Z, levels=levels)\n",
    "\n",
    "\n",
    "#plt.axvline(x=25.0,linewidth=3, linestyle='dotted' ,color='red',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(LV$'+r' '+r'$C3$'+r' '+r'$limit)$')\n",
    "#plt.axvline(x=13.1,linewidth=1, linestyle='dotted' ,color='cyan',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(Chem. OI)$')\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.25,6.25])\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "\n",
    "ZCS1 = plt.contour(X, Y, np.transpose(Z1), levels=Zlevels, colors='black')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.clabel(ZCS1, inline=1, fontsize=12, colors='black',fmt='%.1f',inline_spacing=1)\n",
    "ZCS1.collections[0].set_linewidths(1.5)\n",
    "ZCS1.collections[1].set_linewidths(1.5)\n",
    "ZCS1.collections[0].set_label(r'$TCW, deg$')\n",
    "\n",
    "\n",
    "GCS1 = plt.contour(X, Y, np.transpose(G1), levels=Glevels, colors='blue',linestyles='dashed')\n",
    "\n",
    "plt.clabel(GCS1, inline=1, fontsize=12, colors='blue',fmt='%d',inline_spacing=0)\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[1].set_linewidths(1.5)\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted')\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=12, colors='red',fmt='%d',inline_spacing=0)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "QCS1.collections[1].set_linewidths(1.5)\n",
    "\n",
    "QCS1.collections[0].set_label(r'$\\dot{q}$'+', '+r'$W/cm^2$')\n",
    "\n",
    "\n",
    "HCS1 = plt.contour(X, Y, np.transpose(H1), levels=Hlevels, colors='magenta',linestyles='dashdot')\n",
    "\n",
    "plt.clabel(HCS1, inline=1, fontsize=12, colors='magenta',fmt='%d',inline_spacing=0)\n",
    "HCS1.collections[0].set_linewidths(1.5)\n",
    "HCS1.collections[1].set_linewidths(1.5)\n",
    "\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "\n",
    "\n",
    "#SCS1 = plt.contour(X, Y, transpose(S1), levels=Slevels, colors='cyan')\n",
    "\n",
    "#plt.clabel(SCS1, inline=1, fontsize=12, colors='cyan',fmt='%.1f',inline_spacing=1)\n",
    "#SCS1.collections[0].set_linewidths(3.0)\n",
    "#SCS1.collections[0].set_label(r'$Peak$'+r' '+r'$stag. pressure,atm$')\n",
    "\n",
    "#plt.axhline(y=0.36,linewidth=1, linestyle='dotted' ,color='white',label=r'$Apollo$'+' '+r'$CM$'+' '+r'$L/D$')\n",
    "\n",
    "\n",
    "\n",
    "#matplotlib.rcParams['text.usetex'] = True\n",
    "#plt.rc('text', usetex=True)\n",
    "\n",
    "\n",
    "# circles for b=50 plot\n",
    "#plt.plot(7.5,0.20,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.30,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "#plt.plot(7.5,0.211,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.315,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "\n",
    "#plt.grid(True,linestyle='dotted', linewidth=0.1)\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(\"L/D\",fontsize=16)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=16)\n",
    "plt.xticks( fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "plt.legend(loc='upper right', fontsize=16)\n",
    "\n",
    "dat0 = ZCS1.allsegs[1][0]\n",
    "x1,y1=dat0[:,0],dat0[:,1]\n",
    "F1 = interpolate.interp1d(x1, y1, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat1 = GCS1.allsegs[0][0]\n",
    "x2,y2=dat1[:,0],dat1[:,1]\n",
    "F2 = interpolate.interp1d(x2, y2, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat2 = QCS1.allsegs[0][0]\n",
    "x3,y3= dat2[:,0],dat2[:,1]\n",
    "F3 = interpolate.interp1d(x3, y3, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat0a = ZCS1.allsegs[0][0]\n",
    "x1a,y1a=dat0a[:,0],dat0a[:,1]\n",
    "F1a = interpolate.interp1d(x1a, y1a, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "\n",
    "x4 = np.linspace(0,30,101)\n",
    "y4 = F1(x4)\n",
    "y4a =F1a(x4)\n",
    "y5 = F2(x4)\n",
    "y6 = F3(x4)\n",
    "\n",
    "y7 = np.minimum(y5,y6)\n",
    "y8 = np.minimum(y4,y6)\n",
    "\n",
    "plt.fill_between(x4, y4, y7, where=y4<=y7,color='xkcd:neon green')\n",
    "\n",
    "plt.fill_between(x4, y4a, y8, where=y4a<=y8,color='xkcd:bright yellow')\n",
    "\n",
    "\n",
    "plt.xlim([0.0,30.0])\n",
    "plt.ylim([0.0,1])\n",
    "\n",
    "plt.savefig('../data/jsr-paper/jupiter/jupiter-lift-small.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/jupiter/jupiter-lift-small.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/jupiter/jupiter-lift-small.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "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/jupiter/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/jupiter/'+runID+'stag_pres_atm_max_array.txt')\n",
    "\n",
    "\n",
    "f1 = interpolate.interp2d(x, y, np.transpose(Z1), kind='cubic')\n",
    "g1 = interpolate.interp2d(x, y, np.transpose(G1), kind='cubic')\n",
    "q1 = interpolate.interp2d(x, y, np.transpose(Q1), kind='cubic')\n",
    "h1 = interpolate.interp2d(x, y, np.transpose(H1), kind='cubic')\n",
    "#s1 = interpolate.interp2d(x, y, transpose(S1), kind='cubic')\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   30,  310)\n",
    "y_new =  np.linspace( 0.0,   1.0 ,110)\n",
    "z_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "z1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "g1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "q1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "h1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "#s1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "for i in range(0,len(x_new)):\n",
    "    for j in range(0,len(y_new)):\n",
    "\n",
    "        z1_new[i,j] = f1(x_new[i],y_new[j])\n",
    "        g1_new[i,j] = g1(x_new[i],y_new[j])\n",
    "        q1_new[i,j] = q1(x_new[i],y_new[j])\n",
    "        h1_new[i,j] = h1(x_new[i],y_new[j])\n",
    "    #s1_new[i,j] = s1(x_new[i],y_new[j])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Z1 = z1_new\n",
    "\n",
    "G1 = g1_new\n",
    "\n",
    "Q1 = q1_new\n",
    "\n",
    "#S1 = s1_new\n",
    "\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "#X, Y = meshgrid(x, y)\n",
    "\n",
    "\n",
    "Zlevels = np.array([0.1, 0.2,0.4,0.6,0.8])\n",
    "\n",
    "Glevels = np.array([10.0, 15.0, 20.0, 30.0])\n",
    "Qlevels = np.array([7000.0, 8000.0, 12000.0, 16000.0, 26000.0])\n",
    "Hlevels = np.array([350.0, 400.0, 500.0, 600.0, 800.0])\n",
    "#Slevels = np.array([0.8])\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.5,6.5])\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "#plt.xlim([0.0,30.0])\n",
    "#plt.ylim([0.0,0.4])\n",
    "#plt.tight_layout()\n",
    "#plt.contourf(X, Y, Z, levels=levels)\n",
    "\n",
    "\n",
    "#plt.axvline(x=25.0,linewidth=3, linestyle='dotted' ,color='red',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(LV$'+r' '+r'$C3$'+r' '+r'$limit)$')\n",
    "#plt.axvline(x=13.1,linewidth=1, linestyle='dotted' ,color='cyan',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(Chem. OI)$')\n",
    "\n",
    "\n",
    "ZCS1 = plt.contour(X, Y, np.transpose(Z1), levels=Zlevels, colors='black',zorder=0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.clabel(ZCS1, inline=1, fontsize=10, colors='black',fmt='%.1f',inline_spacing=1,zorder=0)\n",
    "ZCS1.collections[0].set_linewidths(1.5)\n",
    "ZCS1.collections[1].set_linewidths(1.5)\n",
    "ZCS1.collections[2].set_linewidths(1.5)\n",
    "ZCS1.collections[3].set_linewidths(1.5)\n",
    "ZCS1.collections[4].set_linewidths(1.5)\n",
    "\n",
    "\n",
    "\n",
    "ZCS1.collections[0].set_label(r'$TCW, deg$')\n",
    "\n",
    "\n",
    "GCS1 = plt.contour(X, Y, np.transpose(G1), levels=Glevels, colors='blue',linestyles='dashed',zorder=1)\n",
    "\n",
    "plt.clabel(GCS1, inline=1, fontsize=10, colors='blue',fmt='%d',inline_spacing=0,zorder=1)\n",
    "\n",
    "\n",
    "\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[1].set_linewidths(1.5)\n",
    "GCS1.collections[2].set_linewidths(1.5)\n",
    "GCS1.collections[3].set_linewidths(1.5)\n",
    "\n",
    "\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted',zorder=13)\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=10, colors='red',fmt='%d',inline_spacing=0,zorder=13)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "QCS1.collections[1].set_linewidths(1.5)\n",
    "QCS1.collections[2].set_linewidths(1.5)\n",
    "QCS1.collections[3].set_linewidths(1.5)\n",
    "QCS1.collections[4].set_linewidths(1.5)\n",
    "\n",
    "\n",
    "QCS1.collections[0].set_label(r'$\\dot{q}$'+', '+r'$W/cm^2$')\n",
    "\n",
    "\n",
    "HCS1 = plt.contour(X, Y, np.transpose(H1), levels=Hlevels, colors='xkcd:brown',linestyles='dashdot',zorder=14)\n",
    "\n",
    "plt.clabel(HCS1, inline=1, fontsize=10, colors='xkcd:brown',fmt='%d',inline_spacing=0,zorder=14)\n",
    "HCS1.collections[0].set_linewidths(1.75)\n",
    "HCS1.collections[1].set_linewidths(1.75)\n",
    "HCS1.collections[2].set_linewidths(1.75)\n",
    "HCS1.collections[3].set_linewidths(1.75)\n",
    "HCS1.collections[4].set_linewidths(1.75)\n",
    "\n",
    "\n",
    "\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(\"L/D\",fontsize=12)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(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/jupiter/jupiter-lift-large.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/jupiter/jupiter-lift-large.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/jupiter/jupiter-lift-large.eps', dpi=300,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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