{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 04 - a - Earth - Feasibility Charts - Lift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle\n",
    "\n",
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.patches import Polygon\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a planet object\n",
    "planet=Planet(\"EARTH\")\n",
    "planet.h_skip = 140000.0\n",
    "\n",
    "# Load an nominal atmospheric profile with height, temp, pressure, density data\n",
    "planet.loadAtmosphereModel('../atmdata/Earth/earth-gram-avg.dat', 0 , 1 ,2, 3 )\n",
    "\n",
    "vinf_kms_array = np.linspace( 0.0,   20.0,  11)\n",
    "LD_array       = np.linspace( 0.0,    0.4 , 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.makedirs('../data/jsr-paper/earth/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "runID = 'earth-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+140.0*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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "Run #87 of 121: Arrival V_infty: 14.0 km/s, L/D:0.36 OSL: -7.620822502012743 USL: -13.330716663243948, TCW: 5.709894161231205 EFOS: 1.0 EFUS: 1.0\n",
      "Run #88 of 121: Arrival V_infty: 14.0 km/s, L/D:0.4 OSL: -7.580909455282381 USL: -14.17892676486008, TCW: 6.598017309577699 EFOS: 1.0 EFUS: 1.0\n",
      "Run #89 of 121: Arrival V_infty: 16.0 km/s, L/D:0.0 OSL: -8.650760881697352 USL: -8.650760881697352, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #90 of 121: Arrival V_infty: 16.0 km/s, L/D:0.04 OSL: -8.442153188676457 USL: -8.92070073863215, TCW: 0.4785475499556924 EFOS: 1.0 EFUS: 1.0\n",
      "Run #91 of 121: Arrival V_infty: 16.0 km/s, L/D:0.08 OSL: -8.281785105016752 USL: -9.26363566512373, TCW: 0.9818505601069774 EFOS: 1.0 EFUS: 1.0\n",
      "Run #92 of 121: Arrival V_infty: 16.0 km/s, L/D:0.12 OSL: -8.156875138214673 USL: -9.690786391671281, TCW: 1.5339112534566084 EFOS: 1.0 EFUS: 1.0\n",
      "Run #93 of 121: Arrival V_infty: 16.0 km/s, L/D:0.16 OSL: -8.057274884380604 USL: -10.209691394953552, TCW: 2.152416510572948 EFOS: 1.0 EFUS: 1.0\n",
      "Run #94 of 121: Arrival V_infty: 16.0 km/s, L/D:0.2 OSL: -7.975609327870188 USL: -10.826399475332437, TCW: 2.8507901474622486 EFOS: 1.0 EFUS: 1.0\n",
      "Run #95 of 121: Arrival V_infty: 16.0 km/s, L/D:0.24 OSL: -7.906897581931844 USL: -11.549786252548074, TCW: 3.6428886706162302 EFOS: 1.0 EFUS: 1.0\n",
      "Run #96 of 121: Arrival V_infty: 16.0 km/s, L/D:0.28 OSL: -7.847870547047933 USL: -12.387684264525888, TCW: 4.539813717477955 EFOS: 1.0 EFUS: 1.0\n",
      "Run #97 of 121: Arrival V_infty: 16.0 km/s, L/D:0.32 OSL: -7.7962798802764155 USL: -13.243740370613523, TCW: 5.447460490337107 EFOS: 1.0 EFUS: 1.0\n",
      "Run #98 of 121: Arrival V_infty: 16.0 km/s, L/D:0.36 OSL: -7.750544747945241 USL: -14.139378646985278, TCW: 6.388833899040037 EFOS: 1.0 EFUS: 1.0\n",
      "Run #99 of 121: Arrival V_infty: 16.0 km/s, L/D:0.4 OSL: -7.7096054264548 USL: -15.0928672417067, TCW: 7.383261815251899 EFOS: 1.0 EFUS: 1.0\n",
      "Run #100 of 121: Arrival V_infty: 18.0 km/s, L/D:0.0 OSL: -8.798424121723656 USL: -8.798424121723656, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #101 of 121: Arrival V_infty: 18.0 km/s, L/D:0.04 OSL: -8.572566226521303 USL: -9.095263991846878, TCW: 0.5226977653255744 EFOS: 1.0 EFUS: 1.0\n",
      "Run #102 of 121: Arrival V_infty: 18.0 km/s, L/D:0.08 OSL: -8.401815629720659 USL: -9.478020334921894, TCW: 1.076204705201235 EFOS: 1.0 EFUS: 1.0\n",
      "Run #103 of 121: Arrival V_infty: 18.0 km/s, L/D:0.12 OSL: -8.270795504351554 USL: -9.959156363725924, TCW: 1.6883608593743702 EFOS: 1.0 EFUS: 1.0\n",
      "Run #104 of 121: Arrival V_infty: 18.0 km/s, L/D:0.16 OSL: -8.167395869182656 USL: -10.546975884401036, TCW: 2.37958001521838 EFOS: 1.0 EFUS: 1.0\n",
      "Run #105 of 121: Arrival V_infty: 18.0 km/s, L/D:0.2 OSL: -8.08319677063264 USL: -11.249256912655255, TCW: 3.1660601420226158 EFOS: 1.0 EFUS: 1.0\n",
      "Run #106 of 121: Arrival V_infty: 18.0 km/s, L/D:0.24 OSL: -8.012665864869632 USL: -12.075754800986033, TCW: 4.063088936116401 EFOS: 1.0 EFUS: 1.0\n",
      "Run #107 of 121: Arrival V_infty: 18.0 km/s, L/D:0.28 OSL: -7.952213297197886 USL: -12.982532037698547, TCW: 5.030318740500661 EFOS: 1.0 EFUS: 1.0\n",
      "Run #108 of 121: Arrival V_infty: 18.0 km/s, L/D:0.32 OSL: -7.8994770556601 USL: -13.9139684998554, TCW: 6.014491444195301 EFOS: 1.0 EFUS: 1.0\n",
      "Run #109 of 121: Arrival V_infty: 18.0 km/s, L/D:0.36 OSL: -7.852789261687576 USL: -14.909576674806885, TCW: 7.056787413119309 EFOS: 1.0 EFUS: 1.0\n",
      "Run #110 of 121: Arrival V_infty: 18.0 km/s, L/D:0.4 OSL: -7.811023971255054 USL: -15.96272197808139, TCW: 8.151698006826336 EFOS: 1.0 EFUS: 1.0\n",
      "Run #111 of 121: Arrival V_infty: 20.0 km/s, L/D:0.0 OSL: -8.919610838409426 USL: -8.919610838409426, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #112 of 121: Arrival V_infty: 20.0 km/s, L/D:0.04 OSL: -8.677689588850626 USL: -9.242411942945182, TCW: 0.5647223540945561 EFOS: 1.0 EFUS: 1.0\n",
      "Run #113 of 121: Arrival V_infty: 20.0 km/s, L/D:0.08 OSL: -8.497580736424425 USL: -9.664761000043654, TCW: 1.1671802636192297 EFOS: 1.0 EFUS: 1.0\n",
      "Run #114 of 121: Arrival V_infty: 20.0 km/s, L/D:0.12 OSL: -8.361249250698165 USL: -10.199463703062065, TCW: 1.8382144523639 EFOS: 1.0 EFUS: 1.0\n",
      "Run #115 of 121: Arrival V_infty: 20.0 km/s, L/D:0.16 OSL: -8.254648856673157 USL: -10.855854135941627, TCW: 2.60120527926847 EFOS: 1.0 EFUS: 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run #116 of 121: Arrival V_infty: 20.0 km/s, L/D:0.2 OSL: -8.168350669100619 USL: -11.644615649474872, TCW: 3.4762649803742534 EFOS: 1.0 EFUS: 1.0\n",
      "Run #117 of 121: Arrival V_infty: 20.0 km/s, L/D:0.24 OSL: -8.096331028387795 USL: -12.573065239052085, TCW: 4.47673421066429 EFOS: 1.0 EFUS: 1.0\n",
      "Run #118 of 121: Arrival V_infty: 20.0 km/s, L/D:0.28 OSL: -8.03473959026087 USL: -13.525573590541171, TCW: 5.4908340002803016 EFOS: 1.0 EFUS: 1.0\n",
      "Run #119 of 121: Arrival V_infty: 20.0 km/s, L/D:0.32 OSL: -7.981077174339589 USL: -14.548643576490576, TCW: 6.567566402150987 EFOS: 1.0 EFUS: 1.0\n",
      "Run #120 of 121: Arrival V_infty: 20.0 km/s, L/D:0.36 OSL: -7.933630145493225 USL: -15.639545474081388, TCW: 7.705915328588162 EFOS: 1.0 EFUS: 1.0\n",
      "Run #121 of 121: Arrival V_infty: 20.0 km/s, L/D:0.4 OSL: -7.891160102451977 USL: -16.790053904609522, TCW: 8.898893802157545 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(140.0,0.0,0.0,v0_kms_array[i],0.0,-4.5,0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-5)\n",
    "        overShootLimit_array[i,j],  exitflag_os_array[i,j]  = vehicle.findOverShootLimit (2400.0, 1.0, -80.0, -4.0, 1E-10, 400.0)\n",
    "        underShootLimit_array[i,j], exitflag_us_array[i,j] =  vehicle.findUnderShootLimit(2400.0, 1.0, -80.0, -4.0, 1E-10, 400.0)\n",
    "\n",
    "        TCW_array[i,j] = overShootLimit_array[i,j] - underShootLimit_array[i,j]\n",
    "\n",
    "        print(\"Run #\"+str(count)+\" of \"+ str(num_total)+\": Arrival V_infty: \"+str(vinf_kms_array[i])+\" km/s\"+\", L/D:\"+str(LD_array[j]) + \" OSL: \"+str(overShootLimit_array[i,j])+\" USL: \"+str(underShootLimit_array[i,j])+\", TCW: \"+str(TCW_array[i,j])+\" EFOS: \"+str(exitflag_os_array[i,j])+ \" EFUS: \"+str(exitflag_us_array[i,j]))\n",
    "        count = count +1\n",
    "\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'vinf_kms_array.txt',vinf_kms_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'v0_kms_array.txt',v0_kms_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'LD_array.txt',LD_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'overShootLimit_array.txt',overShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'exitflag_os_array.txt',exitflag_os_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'undershootLimit_array.txt',underShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'exitflag_us_array.txt',exitflag_us_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+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: 3.8442174578118227 QDOT_MAX: 285.7546109416119 J_MAX: 27093.237121927777 STAG. PRES: 0.0745144124363312\n",
      "V_infty: 0.0 km/s, L/D: 0.04 G_MAX: 4.253276250941478 QDOT_MAX: 302.0251504864408 J_MAX: 28099.41150789806 STAG. PRES: 0.06746954114851204\n",
      "V_infty: 0.0 km/s, L/D: 0.08 G_MAX: 4.714241331112608 QDOT_MAX: 319.7045883342328 J_MAX: 29152.78231749199 STAG. PRES: 0.061203631473746856\n",
      "V_infty: 0.0 km/s, L/D: 0.12 G_MAX: 5.230618190057675 QDOT_MAX: 338.76985815198606 J_MAX: 30233.08389497681 STAG. PRES: 0.055689690721382294\n",
      "V_infty: 0.0 km/s, L/D: 0.16 G_MAX: 5.804742425292187 QDOT_MAX: 359.08429506049185 J_MAX: 31335.82799354548 STAG. PRES: 0.05086728341447316\n",
      "V_infty: 0.0 km/s, L/D: 0.2 G_MAX: 6.438848444405146 QDOT_MAX: 380.9396111443384 J_MAX: 32451.99599863324 STAG. PRES: 0.046681893111492796\n",
      "V_infty: 0.0 km/s, L/D: 0.24 G_MAX: 7.134016854684438 QDOT_MAX: 403.9593367196527 J_MAX: 33573.425103402966 STAG. PRES: 0.043017046021843176\n",
      "V_infty: 0.0 km/s, L/D: 0.28 G_MAX: 7.9057411797614074 QDOT_MAX: 428.62470089519456 J_MAX: 34682.46719533342 STAG. PRES: 0.03980894651600455\n",
      "V_infty: 0.0 km/s, L/D: 0.32 G_MAX: 8.753578279249654 QDOT_MAX: 454.22124279949276 J_MAX: 35785.68432554481 STAG. PRES: 0.037000077611295756\n",
      "V_infty: 0.0 km/s, L/D: 0.36 G_MAX: 9.691902729300185 QDOT_MAX: 482.03831702554714 J_MAX: 36865.693068329616 STAG. PRES: 0.0345380491397446\n",
      "V_infty: 0.0 km/s, L/D: 0.4 G_MAX: 10.724971487993527 QDOT_MAX: 511.18121941389575 J_MAX: 37932.31170107399 STAG. PRES: 0.032376735965990666\n",
      "V_infty: 2.0 km/s, L/D: 0.0 G_MAX: 4.146233469952442 QDOT_MAX: 318.42193815726876 J_MAX: 28671.66205977635 STAG. PRES: 0.08036661459831329\n",
      "V_infty: 2.0 km/s, L/D: 0.04 G_MAX: 4.601416972516434 QDOT_MAX: 337.880305751566 J_MAX: 29740.96183818748 STAG. PRES: 0.07257814068039375\n",
      "V_infty: 2.0 km/s, L/D: 0.08 G_MAX: 5.114972717854862 QDOT_MAX: 359.2372202271179 J_MAX: 30852.329270229082 STAG. PRES: 0.06566504127105141\n",
      "V_infty: 2.0 km/s, L/D: 0.12 G_MAX: 5.689830610989343 QDOT_MAX: 382.0781353044461 J_MAX: 31999.044766424915 STAG. PRES: 0.05961713711046196\n",
      "V_infty: 2.0 km/s, L/D: 0.16 G_MAX: 6.328085143413631 QDOT_MAX: 406.91752614784326 J_MAX: 33166.283246303465 STAG. PRES: 0.054351617934427554\n",
      "V_infty: 2.0 km/s, L/D: 0.2 G_MAX: 7.033582500773306 QDOT_MAX: 433.3312435176637 J_MAX: 34348.24427588505 STAG. PRES: 0.049801645785322285\n",
      "V_infty: 2.0 km/s, L/D: 0.24 G_MAX: 7.815928058077634 QDOT_MAX: 461.9227949437593 J_MAX: 35537.79547882259 STAG. PRES: 0.04584557106010436\n",
      "V_infty: 2.0 km/s, L/D: 0.28 G_MAX: 8.671926901868336 QDOT_MAX: 491.5050859428412 J_MAX: 36714.967782318985 STAG. PRES: 0.04239613380453522\n",
      "V_infty: 2.0 km/s, L/D: 0.32 G_MAX: 9.624835373749253 QDOT_MAX: 523.7892619106874 J_MAX: 37884.1824903428 STAG. PRES: 0.03938245927368315\n",
      "V_infty: 2.0 km/s, L/D: 0.36 G_MAX: 10.673193593522187 QDOT_MAX: 557.7085248555467 J_MAX: 39024.39956981654 STAG. PRES: 0.03674450363439165\n",
      "V_infty: 2.0 km/s, L/D: 0.4 G_MAX: 11.832211931045062 QDOT_MAX: 593.3939936728347 J_MAX: 40150.672491617646 STAG. PRES: 0.03442784303205203\n",
      "V_infty: 4.0 km/s, L/D: 0.0 G_MAX: 5.07633389891968 QDOT_MAX: 428.02496942017183 J_MAX: 33944.83499930245 STAG. PRES: 0.09838775755191258\n",
      "V_infty: 4.0 km/s, L/D: 0.04 G_MAX: 5.677229034662468 QDOT_MAX: 457.67754635766613 J_MAX: 35174.95567672237 STAG. PRES: 0.08817686589314393\n",
      "V_infty: 4.0 km/s, L/D: 0.08 G_MAX: 6.35830017575202 QDOT_MAX: 491.41105573731124 J_MAX: 36461.566141934505 STAG. PRES: 0.07925781386414708\n",
      "V_infty: 4.0 km/s, L/D: 0.12 G_MAX: 7.122383275632911 QDOT_MAX: 529.133730382121 J_MAX: 37791.40009775309 STAG. PRES: 0.07152530413124601\n",
      "V_infty: 4.0 km/s, L/D: 0.16 G_MAX: 7.971067664098163 QDOT_MAX: 570.2207485318648 J_MAX: 39154.036957660835 STAG. PRES: 0.06488540251965053\n",
      "V_infty: 4.0 km/s, L/D: 0.2 G_MAX: 8.915018205350071 QDOT_MAX: 614.2322815566703 J_MAX: 40518.5421829906 STAG. PRES: 0.05920395547683286\n",
      "V_infty: 4.0 km/s, L/D: 0.24 G_MAX: 9.957534482169624 QDOT_MAX: 661.8611258051603 J_MAX: 41891.43108112133 STAG. PRES: 0.05434829574955348\n",
      "V_infty: 4.0 km/s, L/D: 0.28 G_MAX: 11.108635475561782 QDOT_MAX: 712.1012687910293 J_MAX: 43254.49789711327 STAG. PRES: 0.05016728792476996\n",
      "V_infty: 4.0 km/s, L/D: 0.32 G_MAX: 12.38315419440017 QDOT_MAX: 767.0725096148169 J_MAX: 44592.45511897913 STAG. PRES: 0.046539740645642765\n",
      "V_infty: 4.0 km/s, L/D: 0.36 G_MAX: 13.79368418569205 QDOT_MAX: 825.6014912177158 J_MAX: 45905.985166177896 STAG. PRES: 0.0433727180060021\n",
      "V_infty: 4.0 km/s, L/D: 0.4 G_MAX: 15.38406210609706 QDOT_MAX: 887.4487424887232 J_MAX: 47205.313448498324 STAG. PRES: 0.040597077714960475\n",
      "V_infty: 6.0 km/s, L/D: 0.0 G_MAX: 6.6929487718301335 QDOT_MAX: 699.7926426289932 J_MAX: 44436.44529469265 STAG. PRES: 0.1297072103244843\n",
      "V_infty: 6.0 km/s, L/D: 0.04 G_MAX: 7.5629769893604015 QDOT_MAX: 763.3413488476168 J_MAX: 45881.39223646087 STAG. PRES: 0.11504331194599542\n",
      "V_infty: 6.0 km/s, L/D: 0.08 G_MAX: 8.553297457998694 QDOT_MAX: 834.4188000049633 J_MAX: 47410.18288395857 STAG. PRES: 0.1024395844237603\n",
      "V_infty: 6.0 km/s, L/D: 0.12 G_MAX: 9.674898496547861 QDOT_MAX: 914.6718333813928 J_MAX: 48993.12175458939 STAG. PRES: 0.09170402188944447\n",
      "V_infty: 6.0 km/s, L/D: 0.16 G_MAX: 10.925561814142357 QDOT_MAX: 1001.7103236228058 J_MAX: 50620.786976811396 STAG. PRES: 0.08265115390398897\n",
      "V_infty: 6.0 km/s, L/D: 0.2 G_MAX: 12.310296671378355 QDOT_MAX: 1097.7699337755353 J_MAX: 52265.145635654786 STAG. PRES: 0.0750253988028211\n",
      "V_infty: 6.0 km/s, L/D: 0.24 G_MAX: 13.855004196383822 QDOT_MAX: 1201.374835181926 J_MAX: 53900.43262040894 STAG. PRES: 0.06860049985442446\n",
      "V_infty: 6.0 km/s, L/D: 0.28 G_MAX: 15.57059801932745 QDOT_MAX: 1313.8392083436356 J_MAX: 55517.95356418102 STAG. PRES: 0.06315099424319386\n",
      "V_infty: 6.0 km/s, L/D: 0.32 G_MAX: 17.488232655306863 QDOT_MAX: 1436.3739957439846 J_MAX: 57118.36618794767 STAG. PRES: 0.058547428453533\n",
      "V_infty: 6.0 km/s, L/D: 0.36 G_MAX: 19.66710342791676 QDOT_MAX: 1568.6333379237933 J_MAX: 58685.2523773643 STAG. PRES: 0.05448732732353624\n",
      "V_infty: 6.0 km/s, L/D: 0.4 G_MAX: 22.1276157318493 QDOT_MAX: 1712.1066504809514 J_MAX: 60214.899028499945 STAG. PRES: 0.050902780537105695\n",
      "V_infty: 8.0 km/s, L/D: 0.0 G_MAX: 9.062325564863443 QDOT_MAX: 1225.1920639049144 J_MAX: 62788.14518705143 STAG. PRES: 0.17560167791459852\n",
      "V_infty: 8.0 km/s, L/D: 0.04 G_MAX: 10.359296190097204 QDOT_MAX: 1362.6758880867135 J_MAX: 64400.72969847589 STAG. PRES: 0.15404274603695275\n",
      "V_infty: 8.0 km/s, L/D: 0.08 G_MAX: 11.853450317903558 QDOT_MAX: 1519.6960931460048 J_MAX: 66142.13224682926 STAG. PRES: 0.1357240593095901\n",
      "V_infty: 8.0 km/s, L/D: 0.12 G_MAX: 13.537653364923054 QDOT_MAX: 1695.7537551698385 J_MAX: 67976.07917365419 STAG. PRES: 0.12045432360562254\n",
      "V_infty: 8.0 km/s, L/D: 0.16 G_MAX: 15.430559511582718 QDOT_MAX: 1893.4056989893352 J_MAX: 69871.1789202708 STAG. PRES: 0.10782928666117488\n",
      "V_infty: 8.0 km/s, L/D: 0.2 G_MAX: 17.550022001115067 QDOT_MAX: 2106.8762334516573 J_MAX: 71799.46585411609 STAG. PRES: 0.09738906761120483\n",
      "V_infty: 8.0 km/s, L/D: 0.24 G_MAX: 19.926190453737007 QDOT_MAX: 2348.909882082769 J_MAX: 73714.36458585248 STAG. PRES: 0.08869816291980162\n",
      "V_infty: 8.0 km/s, L/D: 0.28 G_MAX: 22.64604315976905 QDOT_MAX: 2609.791121163746 J_MAX: 75618.13413753267 STAG. PRES: 0.08142126515638191\n",
      "V_infty: 8.0 km/s, L/D: 0.32 G_MAX: 25.686380398932393 QDOT_MAX: 2895.884293658847 J_MAX: 77485.92416827737 STAG. PRES: 0.0752450142889146\n",
      "V_infty: 8.0 km/s, L/D: 0.36 G_MAX: 29.095459448658474 QDOT_MAX: 3211.972392482647 J_MAX: 79326.88517110098 STAG. PRES: 0.06999016996374072\n",
      "V_infty: 8.0 km/s, L/D: 0.4 G_MAX: 32.90922147472895 QDOT_MAX: 3557.0220375331837 J_MAX: 81125.43401002735 STAG. PRES: 0.06540667547515787\n",
      "V_infty: 10.0 km/s, L/D: 0.0 G_MAX: 12.24013942801247 QDOT_MAX: 2175.063824004571 J_MAX: 92041.73531444957 STAG. PRES: 0.23715088771921716\n",
      "V_infty: 10.0 km/s, L/D: 0.04 G_MAX: 14.15905189578535 QDOT_MAX: 2464.4155233814618 J_MAX: 93643.79790406795 STAG. PRES: 0.2057612106504251\n",
      "V_infty: 10.0 km/s, L/D: 0.08 G_MAX: 16.377476763010293 QDOT_MAX: 2804.055004254466 J_MAX: 95449.94193073135 STAG. PRES: 0.17951243705416367\n",
      "V_infty: 10.0 km/s, L/D: 0.12 G_MAX: 18.913882152574622 QDOT_MAX: 3187.997554521816 J_MAX: 97417.70660212648 STAG. PRES: 0.15801865167368653\n",
      "V_infty: 10.0 km/s, L/D: 0.16 G_MAX: 21.786454804565842 QDOT_MAX: 3619.857663913786 J_MAX: 99474.3564036039 STAG. PRES: 0.14054992071933883\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 10.0 km/s, L/D: 0.2 G_MAX: 25.057284131553057 QDOT_MAX: 4104.487885360475 J_MAX: 101599.68125527508 STAG. PRES: 0.126367887907685\n",
      "V_infty: 10.0 km/s, L/D: 0.24 G_MAX: 28.793155978717525 QDOT_MAX: 4638.339142393573 J_MAX: 103746.24200452074 STAG. PRES: 0.1147127471659687\n",
      "V_infty: 10.0 km/s, L/D: 0.28 G_MAX: 32.909411092027554 QDOT_MAX: 5236.084845666794 J_MAX: 105871.39631779361 STAG. PRES: 0.10502079858654188\n",
      "V_infty: 10.0 km/s, L/D: 0.32 G_MAX: 37.57177760348084 QDOT_MAX: 5910.444928069601 J_MAX: 107974.51309127238 STAG. PRES: 0.09686512819301266\n",
      "V_infty: 10.0 km/s, L/D: 0.36 G_MAX: 43.00784492270303 QDOT_MAX: 6648.399948403473 J_MAX: 110056.69874657266 STAG. PRES: 0.08991550289045826\n",
      "V_infty: 10.0 km/s, L/D: 0.4 G_MAX: 49.31322972003822 QDOT_MAX: 7462.737550736608 J_MAX: 112096.63879803267 STAG. PRES: 0.08394255553755736\n",
      "V_infty: 12.0 km/s, L/D: 0.0 G_MAX: 16.28736477458129 QDOT_MAX: 3729.696335005211 J_MAX: 133611.51884510386 STAG. PRES: 0.31552298694250097\n",
      "V_infty: 12.0 km/s, L/D: 0.04 G_MAX: 19.048478262480256 QDOT_MAX: 4312.0624012924445 J_MAX: 134919.37857166436 STAG. PRES: 0.27073153702957425\n",
      "V_infty: 12.0 km/s, L/D: 0.08 G_MAX: 22.279997740927513 QDOT_MAX: 4996.296619233672 J_MAX: 136517.96227501123 STAG. PRES: 0.23403941851646487\n",
      "V_infty: 12.0 km/s, L/D: 0.12 G_MAX: 26.022148738644994 QDOT_MAX: 5776.636287659811 J_MAX: 138398.0987520817 STAG. PRES: 0.20449240212058944\n",
      "V_infty: 12.0 km/s, L/D: 0.16 G_MAX: 30.36265545462354 QDOT_MAX: 6679.534590390547 J_MAX: 140432.7187933433 STAG. PRES: 0.18085664843822957\n",
      "V_infty: 12.0 km/s, L/D: 0.2 G_MAX: 35.28555845520267 QDOT_MAX: 7697.5143788967935 J_MAX: 142609.8178640361 STAG. PRES: 0.16197494005412247\n",
      "V_infty: 12.0 km/s, L/D: 0.24 G_MAX: 40.669434022484616 QDOT_MAX: 8856.499419811484 J_MAX: 144835.01150251567 STAG. PRES: 0.14662004558409822\n",
      "V_infty: 12.0 km/s, L/D: 0.28 G_MAX: 46.91627212827243 QDOT_MAX: 10158.554465933461 J_MAX: 147086.32599558082 STAG. PRES: 0.13396450869064888\n",
      "V_infty: 12.0 km/s, L/D: 0.32 G_MAX: 54.225834565984336 QDOT_MAX: 11561.386749813813 J_MAX: 149340.90860860824 STAG. PRES: 0.12335538938686344\n",
      "V_infty: 12.0 km/s, L/D: 0.36 G_MAX: 62.54780188066532 QDOT_MAX: 13187.17510960092 J_MAX: 151586.6346398426 STAG. PRES: 0.11436089607142799\n",
      "V_infty: 12.0 km/s, L/D: 0.4 G_MAX: 70.17289001666009 QDOT_MAX: 14696.358360043909 J_MAX: 153804.52627451066 STAG. PRES: 0.10662579116502623\n",
      "V_infty: 14.0 km/s, L/D: 0.0 G_MAX: 21.23777748226146 QDOT_MAX: 5940.493937775674 J_MAX: 180027.58856890368 STAG. PRES: 0.4113797149327234\n",
      "V_infty: 14.0 km/s, L/D: 0.04 G_MAX: 25.101773721267325 QDOT_MAX: 7006.31007388641 J_MAX: 180748.75670290267 STAG. PRES: 0.34924629228354676\n",
      "V_infty: 14.0 km/s, L/D: 0.08 G_MAX: 29.715836451878467 QDOT_MAX: 8272.898938391605 J_MAX: 181985.8238651393 STAG. PRES: 0.2993152600792713\n",
      "V_infty: 14.0 km/s, L/D: 0.12 G_MAX: 35.15288649734649 QDOT_MAX: 9734.472678770942 J_MAX: 183631.27465745038 STAG. PRES: 0.2599270041257556\n",
      "V_infty: 14.0 km/s, L/D: 0.16 G_MAX: 41.35716241656784 QDOT_MAX: 11469.523669908282 J_MAX: 185525.29925040482 STAG. PRES: 0.2288058504138458\n",
      "V_infty: 14.0 km/s, L/D: 0.2 G_MAX: 48.337718274935405 QDOT_MAX: 13476.040905487433 J_MAX: 187657.98932764915 STAG. PRES: 0.2041855050206782\n",
      "V_infty: 14.0 km/s, L/D: 0.24 G_MAX: 56.4448080289978 QDOT_MAX: 15696.33420500719 J_MAX: 189918.78380080697 STAG. PRES: 0.18442820974276075\n",
      "V_infty: 14.0 km/s, L/D: 0.28 G_MAX: 65.86864019974556 QDOT_MAX: 18080.868822119555 J_MAX: 192253.29584024058 STAG. PRES: 0.16821666312518374\n",
      "V_infty: 14.0 km/s, L/D: 0.32 G_MAX: 76.60025784261545 QDOT_MAX: 21175.66113279114 J_MAX: 194639.77264191996 STAG. PRES: 0.15470272928212056\n",
      "V_infty: 14.0 km/s, L/D: 0.36 G_MAX: 86.34021648322363 QDOT_MAX: 23957.62703586032 J_MAX: 197019.9113772927 STAG. PRES: 0.14326238500057584\n",
      "V_infty: 14.0 km/s, L/D: 0.4 G_MAX: 97.0164940883635 QDOT_MAX: 26850.982242770177 J_MAX: 199417.68957825756 STAG. PRES: 0.13345092626200417\n",
      "V_infty: 16.0 km/s, L/D: 0.0 G_MAX: 27.111331311150696 QDOT_MAX: 8617.007533933393 J_MAX: 225986.87401152437 STAG. PRES: 0.5251018529339532\n",
      "V_infty: 16.0 km/s, L/D: 0.04 G_MAX: 32.407485066249684 QDOT_MAX: 10420.358398099652 J_MAX: 226077.97783670432 STAG. PRES: 0.441672711845808\n",
      "V_infty: 16.0 km/s, L/D: 0.08 G_MAX: 38.799548270039615 QDOT_MAX: 12491.498754532437 J_MAX: 226914.64682873475 STAG. PRES: 0.3755651999740838\n",
      "V_infty: 16.0 km/s, L/D: 0.12 G_MAX: 46.35652540151979 QDOT_MAX: 15085.443482557897 J_MAX: 228326.27071296523 STAG. PRES: 0.32422224049242926\n",
      "V_infty: 16.0 km/s, L/D: 0.16 G_MAX: 54.96154473871162 QDOT_MAX: 17962.344068142265 J_MAX: 230107.751173984 STAG. PRES: 0.2842985253349713\n",
      "V_infty: 16.0 km/s, L/D: 0.2 G_MAX: 64.96284627059505 QDOT_MAX: 21528.294134681404 J_MAX: 232259.8227413579 STAG. PRES: 0.25304515270492284\n",
      "V_infty: 16.0 km/s, L/D: 0.24 G_MAX: 76.66534099187241 QDOT_MAX: 25088.47674762319 J_MAX: 234594.81945149615 STAG. PRES: 0.22810667498270018\n",
      "V_infty: 16.0 km/s, L/D: 0.28 G_MAX: 89.87476541332835 QDOT_MAX: 29961.775631892215 J_MAX: 237079.13094695442 STAG. PRES: 0.20776871011351725\n",
      "V_infty: 16.0 km/s, L/D: 0.32 G_MAX: 102.79401962620264 QDOT_MAX: 34108.86409013842 J_MAX: 239647.95710364918 STAG. PRES: 0.19086638523741253\n",
      "V_infty: 16.0 km/s, L/D: 0.36 G_MAX: 116.44884611042573 QDOT_MAX: 38893.74075223884 J_MAX: 242272.21113975652 STAG. PRES: 0.1765962003550625\n",
      "V_infty: 16.0 km/s, L/D: 0.4 G_MAX: 131.41918033501182 QDOT_MAX: 44255.848596595286 J_MAX: 244928.82938172886 STAG. PRES: 0.16438692786560113\n",
      "V_infty: 18.0 km/s, L/D: 0.0 G_MAX: 33.938677811772166 QDOT_MAX: 10844.286401565074 J_MAX: 270531.72148298123 STAG. PRES: 0.6572840558511268\n",
      "V_infty: 18.0 km/s, L/D: 0.04 G_MAX: 41.007077893147176 QDOT_MAX: 13527.444897700792 J_MAX: 270112.2157507051 STAG. PRES: 0.5480652772901177\n",
      "V_infty: 18.0 km/s, L/D: 0.08 G_MAX: 49.66920206163626 QDOT_MAX: 16889.192034339612 J_MAX: 270580.03864614316 STAG. PRES: 0.46282300281906696\n",
      "V_infty: 18.0 km/s, L/D: 0.12 G_MAX: 59.821016768083425 QDOT_MAX: 20542.51345082004 J_MAX: 271850.1597177515 STAG. PRES: 0.39755836412858253\n",
      "V_infty: 18.0 km/s, L/D: 0.16 G_MAX: 71.54402829211585 QDOT_MAX: 25297.632734349158 J_MAX: 273624.21316408156 STAG. PRES: 0.34743831933751357\n",
      "V_infty: 18.0 km/s, L/D: 0.2 G_MAX: 85.44079797527232 QDOT_MAX: 30170.025430199283 J_MAX: 275875.0245619509 STAG. PRES: 0.30844894231922226\n",
      "V_infty: 18.0 km/s, L/D: 0.24 G_MAX: 101.33833559466582 QDOT_MAX: 36498.67269852983 J_MAX: 278416.5747449846 STAG. PRES: 0.2775825455170207\n",
      "V_infty: 18.0 km/s, L/D: 0.28 G_MAX: 117.45159733468203 QDOT_MAX: 43379.11626056986 J_MAX: 281192.1601656358 STAG. PRES: 0.2525549035118022\n",
      "V_infty: 18.0 km/s, L/D: 0.32 G_MAX: 134.24526077724886 QDOT_MAX: 50343.16432472699 J_MAX: 284084.72731584875 STAG. PRES: 0.23183432883163532\n",
      "V_infty: 18.0 km/s, L/D: 0.36 G_MAX: 152.90512687250566 QDOT_MAX: 54507.37306306826 J_MAX: 287057.5971248712 STAG. PRES: 0.214362210962367\n",
      "V_infty: 18.0 km/s, L/D: 0.4 G_MAX: 173.66316041824285 QDOT_MAX: 64496.68967054921 J_MAX: 290115.3017384292 STAG. PRES: 0.1994111375656075\n",
      "V_infty: 20.0 km/s, L/D: 0.0 G_MAX: 41.76613762875947 QDOT_MAX: 12951.620670821592 J_MAX: 313520.6523081259 STAG. PRES: 0.8088267550711212\n",
      "V_infty: 20.0 km/s, L/D: 0.04 G_MAX: 50.98053460686898 QDOT_MAX: 16450.02646855446 J_MAX: 312621.8123405105 STAG. PRES: 0.6688501871197748\n",
      "V_infty: 20.0 km/s, L/D: 0.08 G_MAX: 62.356478553643385 QDOT_MAX: 21105.156296813984 J_MAX: 312869.9376289518 STAG. PRES: 0.5609053332726694\n",
      "V_infty: 20.0 km/s, L/D: 0.12 G_MAX: 75.56012302291784 QDOT_MAX: 26584.50401491139 J_MAX: 314111.64525661105 STAG. PRES: 0.47966218328382354\n",
      "V_infty: 20.0 km/s, L/D: 0.16 G_MAX: 91.44513419601654 QDOT_MAX: 32269.088083331277 J_MAX: 316064.3035379183 STAG. PRES: 0.41806474152165973\n",
      "V_infty: 20.0 km/s, L/D: 0.2 G_MAX: 109.78232105281104 QDOT_MAX: 40404.25133362119 J_MAX: 318589.8498253024 STAG. PRES: 0.37036636861281924\n",
      "V_infty: 20.0 km/s, L/D: 0.24 G_MAX: 130.55356866444072 QDOT_MAX: 48281.64551720745 J_MAX: 321489.65215279744 STAG. PRES: 0.33288429569849787\n",
      "V_infty: 20.0 km/s, L/D: 0.28 G_MAX: 150.54011979396972 QDOT_MAX: 59025.59991080439 J_MAX: 324704.29115396953 STAG. PRES: 0.302614009708048\n",
      "V_infty: 20.0 km/s, L/D: 0.32 G_MAX: 173.51346135767471 QDOT_MAX: 68181.19253103792 J_MAX: 328071.6642654272 STAG. PRES: 0.2775974115794935\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 20.0 km/s, L/D: 0.36 G_MAX: 198.57144005917877 QDOT_MAX: 73079.39873906251 J_MAX: 331571.1589542983 STAG. PRES: 0.2565339262344884\n",
      "V_infty: 20.0 km/s, L/D: 0.4 G_MAX: 221.76249991942987 QDOT_MAX: 88109.54857906738 J_MAX: 334959.8159744388 STAG. PRES: 0.2385245359861931\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(140.0,0.0,0.0,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(140.0,0.0,0.0,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/earth/'+runID+'acc_net_g_max_array.txt',acc_net_g_max_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'stag_pres_atm_max_array.txt',stag_pres_atm_max_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'q_stag_total_max_array.txt',q_stag_total_max_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'heatload_max_array.txt',heatload_max_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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/earth/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/earth/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'stag_pres_atm_max_array.txt')\n",
    "\n",
    "\n",
    "f1 = interpolate.interp2d(x, y, np.transpose(Z1), kind='cubic')\n",
    "g1 = interpolate.interp2d(x, y, np.transpose(G1), kind='cubic')\n",
    "q1 = interpolate.interp2d(x, y, np.transpose(Q1), kind='cubic')\n",
    "h1 = interpolate.interp2d(x, y, np.transpose(H1), kind='cubic')\n",
    "#s1 = interpolate.interp2d(x, y, transpose(S1), kind='cubic')\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   20,  210)\n",
    "y_new =  np.linspace( 0.0,   0.4 ,110)\n",
    "z_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "z1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "g1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "q1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "h1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "#s1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "for i in range(0,len(x_new)):\n",
    "    for j in range(0,len(y_new)):\n",
    "\n",
    "        z1_new[i,j] = f1(x_new[i],y_new[j])\n",
    "        g1_new[i,j] = g1(x_new[i],y_new[j])\n",
    "        q1_new[i,j] = q1(x_new[i],y_new[j])\n",
    "        h1_new[i,j] = h1(x_new[i],y_new[j])\n",
    "        #s1_new[i,j] = s1(x_new[i],y_new[j])\n",
    "\n",
    "\n",
    "Z1 = z1_new\n",
    "G1 = g1_new\n",
    "Q1 = q1_new\n",
    "#S1 = s1_new\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "\n",
    "\n",
    "Zlevels = np.array([0.5,1.0])\n",
    "\n",
    "Glevels = np.array([15.0, 30.0])\n",
    "Qlevels = np.array([1000, 5000.0])\n",
    "Hlevels = np.array([65.0])\n",
    "#Slevels = np.array([0.8])\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "#plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "#plt.xlim([0.0,30.0])\n",
    "#plt.ylim([0.0,0.4])\n",
    "#plt.tight_layout()\n",
    "#plt.contourf(X, Y, Z, levels=levels)\n",
    "\n",
    "\n",
    "#plt.axvline(x=25.0,linewidth=3, linestyle='dotted' ,color='red',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(LV$'+r' '+r'$C3$'+r' '+r'$limit)$')\n",
    "#plt.axvline(x=13.1,linewidth=1, linestyle='dotted' ,color='cyan',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(Chem. OI)$')\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.25,6.25])\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "\n",
    "ZCS1 = plt.contour(X, Y, np.transpose(Z1), levels=Zlevels, colors='black')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.clabel(ZCS1, inline=1, fontsize=12, colors='black',fmt='%.1f',inline_spacing=1)\n",
    "ZCS1.collections[0].set_linewidths(1.5)\n",
    "ZCS1.collections[1].set_linewidths(1.5)\n",
    "ZCS1.collections[0].set_label(r'$TCW, deg$')\n",
    "\n",
    "\n",
    "GCS1 = plt.contour(X, Y, np.transpose(G1), levels=Glevels, colors='blue',linestyles='dashed')\n",
    "\n",
    "plt.clabel(GCS1, inline=1, fontsize=12, colors='blue',fmt='%d',inline_spacing=0)\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[1].set_linewidths(1.5)\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted')\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=12, colors='red',fmt='%d',inline_spacing=0)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "QCS1.collections[1].set_linewidths(1.5)\n",
    "\n",
    "QCS1.collections[0].set_label(r'$\\dot{q}$'+', '+r'$W/cm^2$')\n",
    "\n",
    "\n",
    "HCS1 = plt.contour(X, Y, np.transpose(H1), levels=Hlevels, colors='magenta',linestyles='dashdot')\n",
    "\n",
    "plt.clabel(HCS1, inline=1, fontsize=12, colors='magenta',fmt='%d',inline_spacing=0)\n",
    "HCS1.collections[0].set_linewidths(1.5)\n",
    "\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "\n",
    "\n",
    "#SCS1 = plt.contour(X, Y, transpose(S1), levels=Slevels, colors='cyan')\n",
    "\n",
    "#plt.clabel(SCS1, inline=1, fontsize=12, colors='cyan',fmt='%.1f',inline_spacing=1)\n",
    "#SCS1.collections[0].set_linewidths(3.0)\n",
    "#SCS1.collections[0].set_label(r'$Peak$'+r' '+r'$stag. pressure,atm$')\n",
    "\n",
    "#plt.axhline(y=0.36,linewidth=1, linestyle='dotted' ,color='white',label=r'$Apollo$'+' '+r'$CM$'+' '+r'$L/D$')\n",
    "\n",
    "\n",
    "\n",
    "#matplotlib.rcParams['text.usetex'] = True\n",
    "#plt.rc('text', usetex=True)\n",
    "\n",
    "\n",
    "# circles for b=50 plot\n",
    "#plt.plot(7.5,0.20,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.30,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "#plt.plot(7.5,0.211,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.315,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "\n",
    "#plt.grid(True,linestyle='dotted', linewidth=0.1)\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(\"L/D\",fontsize=16)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=16)\n",
    "plt.xticks(np.array([ 0, 4, 8, 12, 16, 20]), fontsize=16)\n",
    "plt.yticks(np.array([ 0.0, 0.1, 0.2, 0.3, 0.4]),fontsize=16)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "plt.legend(loc='upper right', fontsize=16)\n",
    "\n",
    "dat0 = ZCS1.allsegs[1][0]\n",
    "x1,y1=dat0[:,0],dat0[:,1]\n",
    "F1 = interpolate.interp1d(x1, y1, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat1 = GCS1.allsegs[0][0]\n",
    "x2,y2=dat1[:,0],dat1[:,1]\n",
    "F2 = interpolate.interp1d(x2, y2, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat2 = QCS1.allsegs[0][0]\n",
    "x3,y3= dat2[:,0],dat2[:,1]\n",
    "F3 = interpolate.interp1d(x3, y3, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat0a = ZCS1.allsegs[0][0]\n",
    "x1a,y1a=dat0a[:,0],dat0a[:,1]\n",
    "F1a = interpolate.interp1d(x1a, y1a, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "\n",
    "x4 = np.linspace(0,30,101)\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([0.0,20.0])\n",
    "plt.ylim([0.0,0.4])\n",
    "\n",
    "plt.savefig('../data/jsr-paper/earth/earth-lift-small.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/earth/earth-lift-small.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/earth/earth-lift-small.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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/earth/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/earth/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'stag_pres_atm_max_array.txt')\n",
    "\n",
    "\n",
    "f1 = interpolate.interp2d(x, y, np.transpose(Z1), kind='cubic')\n",
    "g1 = interpolate.interp2d(x, y, np.transpose(G1), kind='cubic')\n",
    "q1 = interpolate.interp2d(x, y, np.transpose(Q1), kind='cubic')\n",
    "h1 = interpolate.interp2d(x, y, np.transpose(H1), kind='cubic')\n",
    "#s1 = interpolate.interp2d(x, y, transpose(S1), kind='cubic')\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   20,  210)\n",
    "y_new =  np.linspace( 0.0,   0.4 ,110)\n",
    "z_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "z1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "g1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "q1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "h1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "#s1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "for i in range(0,len(x_new)):\n",
    "    for j in range(0,len(y_new)):\n",
    "\n",
    "        z1_new[i,j] = f1(x_new[i],y_new[j])\n",
    "        g1_new[i,j] = g1(x_new[i],y_new[j])\n",
    "        q1_new[i,j] = q1(x_new[i],y_new[j])\n",
    "        h1_new[i,j] = h1(x_new[i],y_new[j])\n",
    "    #s1_new[i,j] = s1(x_new[i],y_new[j])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Z1 = z1_new\n",
    "\n",
    "G1 = g1_new\n",
    "\n",
    "Q1 = q1_new\n",
    "\n",
    "#S1 = s1_new\n",
    "\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "#X, Y = meshgrid(x, y)\n",
    "\n",
    "\n",
    "Zlevels = np.array([0.5,1.0,1.5,2.0,2.5])\n",
    "\n",
    "Glevels = np.array([5.0, 10.0, 20.0, 30.0])\n",
    "Qlevels = np.array([400.0, 800.0, 2000.0, 5000.0 ])\n",
    "Hlevels = np.array([50.0, 100.0, 150.0])\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",
    "\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",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted',zorder=13)\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=10, colors='red',fmt='%d',inline_spacing=0,zorder=13)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "QCS1.collections[1].set_linewidths(1.5)\n",
    "QCS1.collections[2].set_linewidths(1.5)\n",
    "QCS1.collections[3].set_linewidths(1.5)\n",
    "\n",
    "\n",
    "QCS1.collections[0].set_label(r'$\\dot{q}$'+', '+r'$W/cm^2$')\n",
    "\n",
    "\n",
    "HCS1 = plt.contour(X, Y, np.transpose(H1), levels=Hlevels, colors='xkcd:brown',linestyles='dashdot',zorder=14)\n",
    "\n",
    "labelsH = 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",
    "\n",
    "\n",
    "\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "for l in labelsH:\n",
    "    l.set_rotation(-90)\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",
    "\n",
    "#plt.grid(True,linestyle='dotted', linewidth=0.1)\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(\"L/D\",fontsize=12)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=12)\n",
    "plt.xticks(np.array([ 0.0, 5, 10, 15, 20]),fontsize=12)\n",
    "plt.yticks(np.array([ 0.0, 0.1, 0.2, 0.3, 0.4]),fontsize=12)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "plt.legend(loc='upper right', fontsize=10)\n",
    "\n",
    "\n",
    "plt.savefig('../data/jsr-paper/earth/earth-lift-large.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/earth/earth-lift-large.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/earth/earth-lift-large.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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