{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 09 - a - Uranus - 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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a planet object for Titan\n",
    "planet=Planet(\"URANUS\")\n",
    "planet.h_skip = 1000e3\n",
    "planet.h_trap = 50e3\n",
    "\n",
    "# Load an nominal atmospheric profile with height, temp, pressure, density data\n",
    "planet.loadAtmosphereModel('../atmdata/Uranus/uranus-ames.dat', 0 , 1 , 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('../data/jsr-paper/uranus/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "runID = 'uranus-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": 16,
   "metadata": {},
   "outputs": [
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      "Run #85 of 121: Arrival V_infty: 21.0 km/s, L/D:0.7000000000000001 OSL: -9.391733021144319 USL: -12.911759704849828, TCW: 3.5200266837055096 EFOS: 1.0 EFUS: 1.0\n",
      "Run #86 of 121: Arrival V_infty: 21.0 km/s, L/D:0.8 OSL: -9.351492226549453 USL: -13.533846618287498, TCW: 4.182354391738045 EFOS: 1.0 EFUS: 1.0\n",
      "Run #87 of 121: Arrival V_infty: 21.0 km/s, L/D:0.9 OSL: -9.315336941588612 USL: -14.196703940560838, TCW: 4.881366998972226 EFOS: 1.0 EFUS: 1.0\n",
      "Run #88 of 121: Arrival V_infty: 21.0 km/s, L/D:1.0 OSL: -9.284159555045335 USL: -14.893755529163172, TCW: 5.609595974117838 EFOS: 1.0 EFUS: 1.0\n",
      "Run #89 of 121: Arrival V_infty: 24.0 km/s, L/D:0.0 OSL: -10.520071507289686 USL: -10.520071507289686, 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: -10.309504139637284 USL: -10.800786836134648, TCW: 0.49128269649736467 EFOS: 1.0 EFUS: 1.0\n",
      "Run #91 of 121: Arrival V_infty: 24.0 km/s, L/D:0.2 OSL: -10.15379944725646 USL: -11.162181669347774, TCW: 1.0083822220913135 EFOS: 1.0 EFUS: 1.0\n",
      "Run #92 of 121: Arrival V_infty: 24.0 km/s, L/D:0.30000000000000004 OSL: -10.038259889592155 USL: -11.608741062620538, TCW: 1.5704811730283836 EFOS: 1.0 EFUS: 1.0\n",
      "Run #93 of 121: Arrival V_infty: 24.0 km/s, L/D:0.4 OSL: -9.950103010654857 USL: -12.137984981010959, TCW: 2.187881970356102 EFOS: 1.0 EFUS: 1.0\n",
      "Run #94 of 121: Arrival V_infty: 24.0 km/s, L/D:0.5 OSL: -9.879739208496176 USL: -12.745867249705043, TCW: 2.866128041208867 EFOS: 1.0 EFUS: 1.0\n",
      "Run #95 of 121: Arrival V_infty: 24.0 km/s, L/D:0.6000000000000001 OSL: -9.82234273384529 USL: -13.423567377998552, TCW: 3.6012246441532625 EFOS: 1.0 EFUS: 1.0\n",
      "Run #96 of 121: Arrival V_infty: 24.0 km/s, L/D:0.7000000000000001 OSL: -9.773826107724744 USL: -14.16367480667759, TCW: 4.3898486989528465 EFOS: 1.0 EFUS: 1.0\n",
      "Run #97 of 121: Arrival V_infty: 24.0 km/s, L/D:0.8 OSL: -9.731847962932079 USL: -14.957407834695914, TCW: 5.225559871763835 EFOS: 1.0 EFUS: 1.0\n",
      "Run #98 of 121: Arrival V_infty: 24.0 km/s, L/D:0.9 OSL: -9.69420696342786 USL: -15.79624481553401, TCW: 6.10203785210615 EFOS: 1.0 EFUS: 1.0\n",
      "Run #99 of 121: Arrival V_infty: 24.0 km/s, L/D:1.0 OSL: -9.660662254660565 USL: -16.675034603227687, TCW: 7.014372348567122 EFOS: 1.0 EFUS: 1.0\n",
      "Run #100 of 121: Arrival V_infty: 27.0 km/s, L/D:0.0 OSL: -10.916218152971851 USL: -10.916218152971851, 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: -10.673990373252309 USL: -11.249090849330969, TCW: 0.57510047607866 EFOS: 1.0 EFUS: 1.0\n",
      "Run #102 of 121: Arrival V_infty: 27.0 km/s, L/D:0.2 OSL: -10.501481864099333 USL: -11.687544530941523, TCW: 1.18606266684219 EFOS: 1.0 EFUS: 1.0\n",
      "Run #103 of 121: Arrival V_infty: 27.0 km/s, L/D:0.30000000000000004 OSL: -10.376474367898481 USL: -12.235432153491274, TCW: 1.8589577855927928 EFOS: 1.0 EFUS: 1.0\n",
      "Run #104 of 121: Arrival V_infty: 27.0 km/s, L/D:0.4 OSL: -10.281951485234458 USL: -12.888910943525843, TCW: 2.606959458291385 EFOS: 1.0 EFUS: 1.0\n",
      "Run #105 of 121: Arrival V_infty: 27.0 km/s, L/D:0.5 OSL: -10.208542882795882 USL: -13.637401683459757, TCW: 3.4288588006638747 EFOS: 1.0 EFUS: 1.0\n",
      "Run #106 of 121: Arrival V_infty: 27.0 km/s, L/D:0.6000000000000001 OSL: -10.14769344656088 USL: -14.469907089900516, TCW: 4.322213643339637 EFOS: 1.0 EFUS: 1.0\n",
      "Run #107 of 121: Arrival V_infty: 27.0 km/s, L/D:0.7000000000000001 OSL: -10.097106687284395 USL: -15.371188081189757, TCW: 5.274081393905362 EFOS: 1.0 EFUS: 1.0\n",
      "Run #108 of 121: Arrival V_infty: 27.0 km/s, L/D:0.8 OSL: -10.053139664814807 USL: -16.335977665432438, TCW: 6.28283800061763 EFOS: 1.0 EFUS: 1.0\n",
      "Run #109 of 121: Arrival V_infty: 27.0 km/s, L/D:0.9 OSL: -10.014251971108024 USL: -17.354609306752536, TCW: 7.340357335644512 EFOS: 1.0 EFUS: 1.0\n",
      "Run #110 of 121: Arrival V_infty: 27.0 km/s, L/D:1.0 OSL: -9.979242760316993 USL: -18.420471123390598, TCW: 8.441228363073606 EFOS: 1.0 EFUS: 1.0\n",
      "Run #111 of 121: Arrival V_infty: 30.0 km/s, L/D:0.0 OSL: -11.255171326738491 USL: -11.255171326738491, 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: -10.983217781827989 USL: -11.640436191217304, TCW: 0.6572184093893156 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: -10.794917572278791 USL: -12.15715901841395, TCW: 1.3622414461351582 EFOS: 1.0 EFUS: 1.0\n",
      "Run #114 of 121: Arrival V_infty: 30.0 km/s, L/D:0.30000000000000004 OSL: -10.661472898362263 USL: -12.80931716941268, TCW: 2.147844271050417 EFOS: 1.0 EFUS: 1.0\n",
      "Run #115 of 121: Arrival V_infty: 30.0 km/s, L/D:0.4 OSL: -10.562517329639377 USL: -13.5877179912859, TCW: 3.0252006616465223 EFOS: 1.0 EFUS: 1.0\n",
      "Run #116 of 121: Arrival V_infty: 30.0 km/s, L/D:0.5 OSL: -10.48561768811851 USL: -14.475570224651165, TCW: 3.9899525365326554 EFOS: 1.0 EFUS: 1.0\n",
      "Run #117 of 121: Arrival V_infty: 30.0 km/s, L/D:0.6000000000000001 OSL: -10.422583929295797 USL: -15.458632647136255, TCW: 5.036048717840458 EFOS: 1.0 EFUS: 1.0\n",
      "Run #118 of 121: Arrival V_infty: 30.0 km/s, L/D:0.7000000000000001 OSL: -10.369858040721738 USL: -16.527748416399845, TCW: 6.157890375678107 EFOS: 1.0 EFUS: 1.0\n",
      "Run #119 of 121: Arrival V_infty: 30.0 km/s, L/D:0.8 OSL: -10.324447614559176 USL: -17.66916951623716, TCW: 7.344721901677985 EFOS: 1.0 EFUS: 1.0\n",
      "Run #120 of 121: Arrival V_infty: 30.0 km/s, L/D:0.9 OSL: -10.284034929067275 USL: -18.872117528913805, TCW: 8.58808259984653 EFOS: 1.0 EFUS: 1.0\n",
      "Run #121 of 121: Arrival V_infty: 30.0 km/s, L/D:1.0 OSL: -10.248097073348617 USL: -20.126741409578244, TCW: 9.878644336229627 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-6)\n",
    "        overShootLimit_array[i,j],  exitflag_os_array[i,j]  = vehicle.findOverShootLimit (2400.0, 1.0, -80.0, -4.0, 1E-10, 903323.0)\n",
    "        underShootLimit_array[i,j], exitflag_us_array[i,j] =  vehicle.findUnderShootLimit(2400.0, 1.0, -80.0, -4.0, 1E-10, 903323.0)\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/uranus/'+runID+'vinf_kms_array.txt',vinf_kms_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'v0_kms_array.txt',v0_kms_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'LD_array.txt',LD_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'overShootLimit_array.txt',overShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'exitflag_os_array.txt',exitflag_os_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'undershootLimit_array.txt',underShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'exitflag_us_array.txt',exitflag_us_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'TCW_array.txt',TCW_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 0.0 km/s, L/D: 0.0 G_MAX: 0.1090402582343576 QDOT_MAX: 101.2430626156118 J_MAX: 45856.024178671505 STAG. PRES: 0.002116296073824336\n",
      "V_infty: 0.0 km/s, L/D: 0.1 G_MAX: 0.11037959489661121 QDOT_MAX: 101.57675104840278 J_MAX: 46030.99168536434 STAG. PRES: 0.002102274289794844\n",
      "V_infty: 0.0 km/s, L/D: 0.2 G_MAX: 0.11282185446968376 QDOT_MAX: 101.91208897281038 J_MAX: 46213.35626917764 STAG. PRES: 0.002089245210272968\n",
      "V_infty: 0.0 km/s, L/D: 0.30000000000000004 G_MAX: 0.11628680398778453 QDOT_MAX: 102.22426425475935 J_MAX: 46368.1151206564 STAG. PRES: 0.002074410311675287\n",
      "V_infty: 0.0 km/s, L/D: 0.4 G_MAX: 0.12083903079089854 QDOT_MAX: 102.56063123661768 J_MAX: 46551.74772576325 STAG. PRES: 0.002061748382729338\n",
      "V_infty: 0.0 km/s, L/D: 0.5 G_MAX: 0.12640411167607513 QDOT_MAX: 102.91399807344816 J_MAX: 46736.10276047755 STAG. PRES: 0.0020491619565642777\n",
      "V_infty: 0.0 km/s, L/D: 0.6000000000000001 G_MAX: 0.1327864775022335 QDOT_MAX: 103.24808243871645 J_MAX: 46946.38157097875 STAG. PRES: 0.0020374806766573113\n",
      "V_infty: 0.0 km/s, L/D: 0.7000000000000001 G_MAX: 0.14024111874591824 QDOT_MAX: 103.66986783162454 J_MAX: 47131.50100518118 STAG. PRES: 0.0020244766901428932\n",
      "V_infty: 0.0 km/s, L/D: 0.8 G_MAX: 0.14857555347756635 QDOT_MAX: 104.13357299978908 J_MAX: 47303.656824478865 STAG. PRES: 0.002010430061604033\n",
      "V_infty: 0.0 km/s, L/D: 0.9 G_MAX: 0.15729868655769863 QDOT_MAX: 104.49900500036229 J_MAX: 47503.458246293674 STAG. PRES: 0.0019978451531759067\n",
      "V_infty: 0.0 km/s, L/D: 1.0 G_MAX: 0.16667412073287652 QDOT_MAX: 104.87674785647509 J_MAX: 47699.61602586981 STAG. PRES: 0.001983723551238657\n",
      "V_infty: 3.0 km/s, L/D: 0.0 G_MAX: 0.18587443989872293 QDOT_MAX: 130.4592872231255 J_MAX: 57434.9849013763 STAG. PRES: 0.003606884323984941\n",
      "V_infty: 3.0 km/s, L/D: 0.1 G_MAX: 0.18995158037456394 QDOT_MAX: 131.45342078681517 J_MAX: 57811.287795617485 STAG. PRES: 0.003545962634981126\n",
      "V_infty: 3.0 km/s, L/D: 0.2 G_MAX: 0.19618381439870577 QDOT_MAX: 132.5086103387869 J_MAX: 58156.06647140268 STAG. PRES: 0.003480808572402402\n",
      "V_infty: 3.0 km/s, L/D: 0.30000000000000004 G_MAX: 0.20433924552232813 QDOT_MAX: 133.5503570161175 J_MAX: 58630.192360045105 STAG. PRES: 0.003435728099717701\n",
      "V_infty: 3.0 km/s, L/D: 0.4 G_MAX: 0.21442421168823153 QDOT_MAX: 134.5896924881814 J_MAX: 59005.430128350796 STAG. PRES: 0.003377256744583855\n",
      "V_infty: 3.0 km/s, L/D: 0.5 G_MAX: 0.22738174947425474 QDOT_MAX: 135.8979100545251 J_MAX: 59366.304788892616 STAG. PRES: 0.003318493421665904\n",
      "V_infty: 3.0 km/s, L/D: 0.6000000000000001 G_MAX: 0.24074335568211674 QDOT_MAX: 136.82862932858527 J_MAX: 59824.26880728836 STAG. PRES: 0.0032719694514162525\n",
      "V_infty: 3.0 km/s, L/D: 0.7000000000000001 G_MAX: 0.2561573490547589 QDOT_MAX: 137.85445180722581 J_MAX: 60172.336893814536 STAG. PRES: 0.0032139811631137108\n",
      "V_infty: 3.0 km/s, L/D: 0.8 G_MAX: 0.2735810619140423 QDOT_MAX: 138.98238678501355 J_MAX: 60561.75213655221 STAG. PRES: 0.003162799648138839\n",
      "V_infty: 3.0 km/s, L/D: 0.9 G_MAX: 0.2916810038069253 QDOT_MAX: 139.91955438977817 J_MAX: 60953.86514617467 STAG. PRES: 0.003111167119972836\n",
      "V_infty: 3.0 km/s, L/D: 1.0 G_MAX: 0.31160332413997577 QDOT_MAX: 140.9562514723526 J_MAX: 61361.51657870328 STAG. PRES: 0.0030619053376226986\n",
      "V_infty: 6.0 km/s, L/D: 0.0 G_MAX: 0.5069413447827317 QDOT_MAX: 212.87759482562535 J_MAX: 80236.00260975654 STAG. PRES: 0.009834019967000285\n",
      "V_infty: 6.0 km/s, L/D: 0.1 G_MAX: 0.5332541636094262 QDOT_MAX: 217.3245926032154 J_MAX: 81557.26053990066 STAG. PRES: 0.009384033952244424\n",
      "V_infty: 6.0 km/s, L/D: 0.2 G_MAX: 0.5667171556421338 QDOT_MAX: 221.92561591072422 J_MAX: 82876.12251149694 STAG. PRES: 0.008945521129622611\n",
      "V_infty: 6.0 km/s, L/D: 0.30000000000000004 G_MAX: 0.6072257891549349 QDOT_MAX: 226.5481755254416 J_MAX: 84242.20007612747 STAG. PRES: 0.008535759256724269\n",
      "V_infty: 6.0 km/s, L/D: 0.4 G_MAX: 0.6556770551445077 QDOT_MAX: 231.274232320342 J_MAX: 85597.93935573881 STAG. PRES: 0.008136701021142627\n",
      "V_infty: 6.0 km/s, L/D: 0.5 G_MAX: 0.7124783134088902 QDOT_MAX: 236.10564168631433 J_MAX: 86942.9511220152 STAG. PRES: 0.007750108881381531\n",
      "V_infty: 6.0 km/s, L/D: 0.6000000000000001 G_MAX: 0.7780707307732329 QDOT_MAX: 241.0541021540451 J_MAX: 88283.7377491679 STAG. PRES: 0.007376481855244986\n",
      "V_infty: 6.0 km/s, L/D: 0.7000000000000001 G_MAX: 0.8536099599696245 QDOT_MAX: 246.2381739164809 J_MAX: 89649.30895536207 STAG. PRES: 0.007019529797007445\n",
      "V_infty: 6.0 km/s, L/D: 0.8 G_MAX: 0.9378814588068985 QDOT_MAX: 251.43265078420794 J_MAX: 90981.75398486512 STAG. PRES: 0.006671216451149694\n",
      "V_infty: 6.0 km/s, L/D: 0.9 G_MAX: 1.0319575400342385 QDOT_MAX: 256.74305996384754 J_MAX: 92299.0715143207 STAG. PRES: 0.006338586774477649\n",
      "V_infty: 6.0 km/s, L/D: 1.0 G_MAX: 1.1367337713225862 QDOT_MAX: 262.25924449761703 J_MAX: 93626.95384713092 STAG. PRES: 0.0060263693100626485\n",
      "V_infty: 9.0 km/s, L/D: 0.0 G_MAX: 1.051564315503073 QDOT_MAX: 318.37901108309325 J_MAX: 104507.19199544757 STAG. PRES: 0.020393049019572132\n",
      "V_infty: 9.0 km/s, L/D: 0.1 G_MAX: 1.122069814394027 QDOT_MAX: 325.42472290240266 J_MAX: 107587.37222601783 STAG. PRES: 0.019391074804698826\n",
      "V_infty: 9.0 km/s, L/D: 0.2 G_MAX: 1.2167459139316832 QDOT_MAX: 334.57341256228983 J_MAX: 110816.92932079472 STAG. PRES: 0.018340626197631077\n",
      "V_infty: 9.0 km/s, L/D: 0.30000000000000004 G_MAX: 1.3297636732955997 QDOT_MAX: 344.5222701098924 J_MAX: 114185.00357278757 STAG. PRES: 0.017054040626339084\n",
      "V_infty: 9.0 km/s, L/D: 0.4 G_MAX: 1.4595852520350954 QDOT_MAX: 354.2935195283254 J_MAX: 117593.39932314683 STAG. PRES: 0.015671813616105743\n",
      "V_infty: 9.0 km/s, L/D: 0.5 G_MAX: 1.6108630803831958 QDOT_MAX: 364.04191191593725 J_MAX: 121000.32391041373 STAG. PRES: 0.014413651277572208\n",
      "V_infty: 9.0 km/s, L/D: 0.6000000000000001 G_MAX: 1.7861789119653506 QDOT_MAX: 374.0251650657326 J_MAX: 124396.1017510657 STAG. PRES: 0.013306966439595879\n",
      "V_infty: 9.0 km/s, L/D: 0.7000000000000001 G_MAX: 1.9829562469851343 QDOT_MAX: 384.1772401417596 J_MAX: 127775.09135013523 STAG. PRES: 0.012329663391603487\n",
      "V_infty: 9.0 km/s, L/D: 0.8 G_MAX: 2.1948940871484437 QDOT_MAX: 393.9358679446918 J_MAX: 131140.36202649036 STAG. PRES: 0.011461356613801967\n",
      "V_infty: 9.0 km/s, L/D: 0.9 G_MAX: 2.4199050562936972 QDOT_MAX: 403.02237017952217 J_MAX: 134460.87245965912 STAG. PRES: 0.010682273525003175\n",
      "V_infty: 9.0 km/s, L/D: 1.0 G_MAX: 2.6637254627867772 QDOT_MAX: 411.4283073177354 J_MAX: 137767.66015358682 STAG. PRES: 0.009979813458472835\n",
      "V_infty: 12.0 km/s, L/D: 0.0 G_MAX: 1.7862325258295018 QDOT_MAX: 445.0433897253314 J_MAX: 133338.00663531997 STAG. PRES: 0.03463331437564545\n",
      "V_infty: 12.0 km/s, L/D: 0.1 G_MAX: 1.966238091634569 QDOT_MAX: 464.81875910745623 J_MAX: 139173.82821378228 STAG. PRES: 0.03125791776047696\n",
      "V_infty: 12.0 km/s, L/D: 0.2 G_MAX: 2.1858226188410756 QDOT_MAX: 482.8713918971306 J_MAX: 145211.9769120538 STAG. PRES: 0.028196709057054826\n",
      "V_infty: 12.0 km/s, L/D: 0.30000000000000004 G_MAX: 2.466537923276898 QDOT_MAX: 501.4472046323427 J_MAX: 151415.09624566283 STAG. PRES: 0.025465852066824862\n",
      "V_infty: 12.0 km/s, L/D: 0.4 G_MAX: 2.769089467818695 QDOT_MAX: 522.6200249224803 J_MAX: 157684.8450310947 STAG. PRES: 0.022994674924121078\n",
      "V_infty: 12.0 km/s, L/D: 0.5 G_MAX: 3.0972196316652787 QDOT_MAX: 541.8717096215073 J_MAX: 164043.96888998913 STAG. PRES: 0.021209793399798553\n",
      "V_infty: 12.0 km/s, L/D: 0.6000000000000001 G_MAX: 3.4921014661046206 QDOT_MAX: 559.1259614953467 J_MAX: 170545.64497046845 STAG. PRES: 0.019544324558191824\n",
      "V_infty: 12.0 km/s, L/D: 0.7000000000000001 G_MAX: 3.9437425771911503 QDOT_MAX: 577.3842824526511 J_MAX: 177085.70014128057 STAG. PRES: 0.017730436333392333\n",
      "V_infty: 12.0 km/s, L/D: 0.8 G_MAX: 4.430989022768288 QDOT_MAX: 596.3573866952223 J_MAX: 183525.90822601135 STAG. PRES: 0.016105430693438234\n",
      "V_infty: 12.0 km/s, L/D: 0.9 G_MAX: 4.961573507683341 QDOT_MAX: 614.2929409338717 J_MAX: 189842.9126056078 STAG. PRES: 0.014749657565313684\n",
      "V_infty: 12.0 km/s, L/D: 1.0 G_MAX: 5.544983741733933 QDOT_MAX: 631.2707379628412 J_MAX: 195972.46121911035 STAG. PRES: 0.013605045987161281\n",
      "V_infty: 15.0 km/s, L/D: 0.0 G_MAX: 2.774340652184764 QDOT_MAX: 638.1022644902157 J_MAX: 169773.35476333296 STAG. PRES: 0.05378343082878653\n",
      "V_infty: 15.0 km/s, L/D: 0.1 G_MAX: 3.1357366159561617 QDOT_MAX: 674.4419354138704 J_MAX: 179535.78191287236 STAG. PRES: 0.04704422003044997\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 15.0 km/s, L/D: 0.2 G_MAX: 3.601053254757068 QDOT_MAX: 708.4504259583899 J_MAX: 189696.93624279182 STAG. PRES: 0.04132099521013143\n",
      "V_infty: 15.0 km/s, L/D: 0.30000000000000004 G_MAX: 4.120472552805178 QDOT_MAX: 741.2634497661662 J_MAX: 200211.8701109276 STAG. PRES: 0.03644621539745654\n",
      "V_infty: 15.0 km/s, L/D: 0.4 G_MAX: 4.721465505413278 QDOT_MAX: 777.3424027023278 J_MAX: 210917.89798037635 STAG. PRES: 0.03206891494618197\n",
      "V_infty: 15.0 km/s, L/D: 0.5 G_MAX: 5.425352218356713 QDOT_MAX: 810.9222286664758 J_MAX: 221619.20084743973 STAG. PRES: 0.0284783907072941\n",
      "V_infty: 15.0 km/s, L/D: 0.6000000000000001 G_MAX: 6.204793465289903 QDOT_MAX: 843.678958611088 J_MAX: 232258.54264758254 STAG. PRES: 0.02548657242010007\n",
      "V_infty: 15.0 km/s, L/D: 0.7000000000000001 G_MAX: 7.056565860997439 QDOT_MAX: 877.1765575725931 J_MAX: 242642.76866308512 STAG. PRES: 0.023441764773400067\n",
      "V_infty: 15.0 km/s, L/D: 0.8 G_MAX: 8.039612524666202 QDOT_MAX: 909.7226953330754 J_MAX: 253144.9234617641 STAG. PRES: 0.0214763286381165\n",
      "V_infty: 15.0 km/s, L/D: 0.9 G_MAX: 9.11851151244071 QDOT_MAX: 939.8310253453101 J_MAX: 263314.85718752706 STAG. PRES: 0.019599971973632252\n",
      "V_infty: 15.0 km/s, L/D: 1.0 G_MAX: 10.26922808929866 QDOT_MAX: 969.0311644986805 J_MAX: 273130.2112327014 STAG. PRES: 0.017999001556916288\n",
      "V_infty: 18.0 km/s, L/D: 0.0 G_MAX: 4.030948835673565 QDOT_MAX: 1002.9874351544522 J_MAX: 224635.30026158673 STAG. PRES: 0.0781344140154704\n",
      "V_infty: 18.0 km/s, L/D: 0.1 G_MAX: 4.700310515723279 QDOT_MAX: 1065.0928395011492 J_MAX: 240149.28858244888 STAG. PRES: 0.06657160822704203\n",
      "V_infty: 18.0 km/s, L/D: 0.2 G_MAX: 5.520627461952985 QDOT_MAX: 1122.3696503383728 J_MAX: 256441.56092274946 STAG. PRES: 0.057374718679203775\n",
      "V_infty: 18.0 km/s, L/D: 0.30000000000000004 G_MAX: 6.444786067476501 QDOT_MAX: 1187.4728214518361 J_MAX: 273263.2271097039 STAG. PRES: 0.04897862196925591\n",
      "V_infty: 18.0 km/s, L/D: 0.4 G_MAX: 7.537924015762504 QDOT_MAX: 1248.78036733191 J_MAX: 290116.5837078394 STAG. PRES: 0.042784939371298236\n",
      "V_infty: 18.0 km/s, L/D: 0.5 G_MAX: 8.766176850970039 QDOT_MAX: 1310.6762908380745 J_MAX: 306896.307887205 STAG. PRES: 0.037470600312051607\n",
      "V_infty: 18.0 km/s, L/D: 0.6000000000000001 G_MAX: 10.156240404397126 QDOT_MAX: 1372.5711837571066 J_MAX: 323339.56101985264 STAG. PRES: 0.03331716439664907\n",
      "V_infty: 18.0 km/s, L/D: 0.7000000000000001 G_MAX: 11.71151981567194 QDOT_MAX: 1430.1360934559236 J_MAX: 339360.4106028781 STAG. PRES: 0.02990090892618242\n",
      "V_infty: 18.0 km/s, L/D: 0.8 G_MAX: 13.412808892600873 QDOT_MAX: 1486.4716287674603 J_MAX: 354931.80998719815 STAG. PRES: 0.027504068618418746\n",
      "V_infty: 18.0 km/s, L/D: 0.9 G_MAX: 15.346934597459015 QDOT_MAX: 1543.2807607165428 J_MAX: 370065.37584435486 STAG. PRES: 0.025443063264039348\n",
      "V_infty: 18.0 km/s, L/D: 1.0 G_MAX: 17.456112496908872 QDOT_MAX: 1596.0452128373495 J_MAX: 384769.4123134736 STAG. PRES: 0.023423991194367284\n",
      "V_infty: 21.0 km/s, L/D: 0.0 G_MAX: 5.5877034850697544 QDOT_MAX: 1865.4244016327175 J_MAX: 326274.2832087736 STAG. PRES: 0.10829744321647228\n",
      "V_infty: 21.0 km/s, L/D: 0.1 G_MAX: 6.700303042977563 QDOT_MAX: 1993.4730466736726 J_MAX: 351384.227609173 STAG. PRES: 0.09053665796578333\n",
      "V_infty: 21.0 km/s, L/D: 0.2 G_MAX: 8.029206475369351 QDOT_MAX: 2110.840825657647 J_MAX: 377741.096827726 STAG. PRES: 0.07551238950189793\n",
      "V_infty: 21.0 km/s, L/D: 0.30000000000000004 G_MAX: 9.560985618617227 QDOT_MAX: 2245.699640162727 J_MAX: 404312.763643828 STAG. PRES: 0.06422728878083293\n",
      "V_infty: 21.0 km/s, L/D: 0.4 G_MAX: 11.34924889301785 QDOT_MAX: 2372.3853504259696 J_MAX: 431124.3563849051 STAG. PRES: 0.05490018371700919\n",
      "V_infty: 21.0 km/s, L/D: 0.5 G_MAX: 13.355559936797121 QDOT_MAX: 2500.790642037115 J_MAX: 457092.1961475746 STAG. PRES: 0.04812057557537198\n",
      "V_infty: 21.0 km/s, L/D: 0.6000000000000001 G_MAX: 15.684577683457697 QDOT_MAX: 2628.4357604034076 J_MAX: 482353.1222954918 STAG. PRES: 0.042543045841315914\n",
      "V_infty: 21.0 km/s, L/D: 0.7000000000000001 G_MAX: 18.250321146378198 QDOT_MAX: 2745.719316499919 J_MAX: 506695.6770856811 STAG. PRES: 0.03818220684841469\n",
      "V_infty: 21.0 km/s, L/D: 0.8 G_MAX: 21.140000649351546 QDOT_MAX: 2862.816414887849 J_MAX: 530195.9863662831 STAG. PRES: 0.034573660562169144\n",
      "V_infty: 21.0 km/s, L/D: 0.9 G_MAX: 24.45289115659391 QDOT_MAX: 2976.7216837246524 J_MAX: 552845.6100773612 STAG. PRES: 0.03196232389268016\n",
      "V_infty: 21.0 km/s, L/D: 1.0 G_MAX: 28.210212892021328 QDOT_MAX: 3084.1649315913637 J_MAX: 574765.7472003683 STAG. PRES: 0.029762817508431964\n",
      "V_infty: 24.0 km/s, L/D: 0.0 G_MAX: 7.484015977308703 QDOT_MAX: 4200.124944135723 J_MAX: 547877.010287714 STAG. PRES: 0.14503360755488784\n",
      "V_infty: 24.0 km/s, L/D: 0.1 G_MAX: 9.17990588445119 QDOT_MAX: 4511.228338846646 J_MAX: 591967.9910059756 STAG. PRES: 0.11820009812964409\n",
      "V_infty: 24.0 km/s, L/D: 0.2 G_MAX: 11.204304482602792 QDOT_MAX: 4804.350358629743 J_MAX: 637443.5739494626 STAG. PRES: 0.09732209369753095\n",
      "V_infty: 24.0 km/s, L/D: 0.30000000000000004 G_MAX: 13.561008550618354 QDOT_MAX: 5134.6126230657355 J_MAX: 682835.7006000404 STAG. PRES: 0.08129684663051652\n",
      "V_infty: 24.0 km/s, L/D: 0.4 G_MAX: 16.36064455027732 QDOT_MAX: 5450.1916709069055 J_MAX: 727105.3521312928 STAG. PRES: 0.06915062399900582\n",
      "V_infty: 24.0 km/s, L/D: 0.5 G_MAX: 19.507416261765545 QDOT_MAX: 5768.227710569047 J_MAX: 769720.5760595631 STAG. PRES: 0.060304620075694745\n",
      "V_infty: 24.0 km/s, L/D: 0.6000000000000001 G_MAX: 23.219128557440897 QDOT_MAX: 6079.305473744261 J_MAX: 810671.609317392 STAG. PRES: 0.05332272039012773\n",
      "V_infty: 24.0 km/s, L/D: 0.7000000000000001 G_MAX: 27.57623581470543 QDOT_MAX: 6364.847392342401 J_MAX: 849884.8582017538 STAG. PRES: 0.04774489875146841\n",
      "V_infty: 24.0 km/s, L/D: 0.8 G_MAX: 32.66279380770087 QDOT_MAX: 6654.847492143761 J_MAX: 887719.2260964646 STAG. PRES: 0.0432699262945637\n",
      "V_infty: 24.0 km/s, L/D: 0.9 G_MAX: 39.30398091035945 QDOT_MAX: 6932.752892930583 J_MAX: 923859.1305217042 STAG. PRES: 0.03951849561580796\n",
      "V_infty: 24.0 km/s, L/D: 1.0 G_MAX: 47.427064993377236 QDOT_MAX: 7190.4133382731325 J_MAX: 958884.2365495492 STAG. PRES: 0.03678468911950146\n",
      "V_infty: 27.0 km/s, L/D: 0.0 G_MAX: 9.711811247809925 QDOT_MAX: 10709.835677929828 J_MAX: 1072186.417484603 STAG. PRES: 0.18818783745332243\n",
      "V_infty: 27.0 km/s, L/D: 0.1 G_MAX: 12.14877276158875 QDOT_MAX: 11561.063084290563 J_MAX: 1158919.8245666064 STAG. PRES: 0.1507252396033831\n",
      "V_infty: 27.0 km/s, L/D: 0.2 G_MAX: 15.100234218884209 QDOT_MAX: 12376.045955025322 J_MAX: 1246593.3772686913 STAG. PRES: 0.12178289079265027\n",
      "V_infty: 27.0 km/s, L/D: 0.30000000000000004 G_MAX: 18.648966607398496 QDOT_MAX: 13291.920361062472 J_MAX: 1331738.689585758 STAG. PRES: 0.1004732497734868\n",
      "V_infty: 27.0 km/s, L/D: 0.4 G_MAX: 22.78760395946223 QDOT_MAX: 14158.085274268704 J_MAX: 1413226.7315820495 STAG. PRES: 0.08573448270661627\n",
      "V_infty: 27.0 km/s, L/D: 0.5 G_MAX: 27.856597234177222 QDOT_MAX: 15045.435863099921 J_MAX: 1490329.8604392065 STAG. PRES: 0.0739667414684513\n",
      "V_infty: 27.0 km/s, L/D: 0.6000000000000001 G_MAX: 34.062921080662456 QDOT_MAX: 15878.769478914648 J_MAX: 1564057.394880169 STAG. PRES: 0.06565698993378866\n",
      "V_infty: 27.0 km/s, L/D: 0.7000000000000001 G_MAX: 42.804024338728226 QDOT_MAX: 16673.642713977428 J_MAX: 1530803.151521349 STAG. PRES: 0.058636653972262164\n",
      "V_infty: 27.0 km/s, L/D: 0.8 G_MAX: 53.27108238259486 QDOT_MAX: 17466.85631634403 J_MAX: 1701316.67412192 STAG. PRES: 0.053155140847146225\n",
      "V_infty: 27.0 km/s, L/D: 0.9 G_MAX: 65.60539826569887 QDOT_MAX: 18201.413045171907 J_MAX: 1765766.8390743341 STAG. PRES: 0.04854475361537273\n",
      "V_infty: 27.0 km/s, L/D: 1.0 G_MAX: 80.17488920893584 QDOT_MAX: 18917.30980356591 J_MAX: 1827463.6144230089 STAG. PRES: 0.044709209901353124\n",
      "V_infty: 30.0 km/s, L/D: 0.0 G_MAX: 12.29254085330182 QDOT_MAX: 28680.379021332876 J_MAX: 2337729.3655774514 STAG. PRES: 0.23817150892191583\n",
      "V_infty: 30.0 km/s, L/D: 0.1 G_MAX: 15.655361353018858 QDOT_MAX: 31065.649018855165 J_MAX: 2527584.8037244673 STAG. PRES: 0.18714410270664697\n",
      "V_infty: 30.0 km/s, L/D: 0.2 G_MAX: 19.8755577836531 QDOT_MAX: 33461.41957606978 J_MAX: 2714146.3443973074 STAG. PRES: 0.14920473849121885\n",
      "V_infty: 30.0 km/s, L/D: 0.30000000000000004 G_MAX: 24.933447738897215 QDOT_MAX: 36035.1047279274 J_MAX: 2891157.9946602727 STAG. PRES: 0.12263818978803208\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 30.0 km/s, L/D: 0.4 G_MAX: 31.302413281196674 QDOT_MAX: 38555.536810910686 J_MAX: 3057156.7853474068 STAG. PRES: 0.10404412495565281\n",
      "V_infty: 30.0 km/s, L/D: 0.5 G_MAX: 40.11046148648928 QDOT_MAX: 41066.52806320823 J_MAX: 3212805.222622822 STAG. PRES: 0.08962819989160854\n",
      "V_infty: 30.0 km/s, L/D: 0.6000000000000001 G_MAX: 52.12785731443632 QDOT_MAX: 43369.25473110754 J_MAX: 3360056.4338141275 STAG. PRES: 0.07939632197290038\n",
      "V_infty: 30.0 km/s, L/D: 0.7000000000000001 G_MAX: 66.59299773606264 QDOT_MAX: 45643.88365281003 J_MAX: 3500571.8189659617 STAG. PRES: 0.07105037966020795\n",
      "V_infty: 30.0 km/s, L/D: 0.8 G_MAX: 84.19447970694604 QDOT_MAX: 47842.984418806845 J_MAX: 3633967.6277569197 STAG. PRES: 0.06424111129690452\n",
      "V_infty: 30.0 km/s, L/D: 0.9 G_MAX: 105.65051299199446 QDOT_MAX: 49934.279316912325 J_MAX: 3761866.0082948273 STAG. PRES: 0.058718001901563226\n",
      "V_infty: 30.0 km/s, L/D: 1.0 G_MAX: 129.6986617206044 QDOT_MAX: 51933.54834703978 J_MAX: 3881108.9365113587 STAG. PRES: 0.053989833746519555\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-6)\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-6)\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/uranus/'+runID+'acc_net_g_max_array.txt',acc_net_g_max_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'stag_pres_atm_max_array.txt',stag_pres_atm_max_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'q_stag_total_max_array.txt',q_stag_total_max_array)\n",
    "np.savetxt('../data/jsr-paper/uranus/'+runID+'heatload_max_array.txt',heatload_max_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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/uranus/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/uranus/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'stag_pres_atm_max_array.txt')\n",
    "\n",
    "\n",
    "f1 = interpolate.interp2d(x, y, np.transpose(Z1), kind='cubic')\n",
    "g1 = interpolate.interp2d(x, y, np.transpose(G1), kind='cubic')\n",
    "q1 = interpolate.interp2d(x, y, np.transpose(Q1), kind='cubic')\n",
    "h1 = interpolate.interp2d(x, y, np.transpose(H1), kind='cubic')\n",
    "#s1 = interpolate.interp2d(x, y, 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",
    "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, 2.0])\n",
    "\n",
    "Glevels = np.array([15, 30])\n",
    "Qlevels = np.array([2000, 7000])\n",
    "Hlevels = np.array([600])\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[2].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",
    "\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( 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='lower left', fontsize=16)\n",
    "\n",
    "dat0 = ZCS1.allsegs[2][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[1][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/uranus/uranus-lift-small.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/uranus/uranus-lift-small.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/uranus/uranus-lift-small.eps', dpi=300,bbox_inches='tight')\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/uranus/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/uranus/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/uranus/'+runID+'stag_pres_atm_max_array.txt')\n",
    "\n",
    "\n",
    "\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([0.5,1.0,2.0,3.0])\n",
    "\n",
    "Glevels = np.array([10.0, 20.0, 30.0, 50.0])\n",
    "Qlevels = np.array([500.0, 1000.0, 2000.0, 4000.0, 8000.0])\n",
    "Hlevels = np.array([120.0, 200.0, 400.0, 700.0, 1200.0])\n",
    "#Slevels = np.array([0.8])\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.5,6.5])\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "#plt.xlim([0.0,30.0])\n",
    "#plt.ylim([0.0,0.4])\n",
    "#plt.tight_layout()\n",
    "#plt.contourf(X, Y, Z, levels=levels)\n",
    "\n",
    "\n",
    "#plt.axvline(x=25.0,linewidth=3, linestyle='dotted' ,color='red',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(LV$'+r' '+r'$C3$'+r' '+r'$limit)$')\n",
    "#plt.axvline(x=13.1,linewidth=1, linestyle='dotted' ,color='cyan',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(Chem. OI)$')\n",
    "\n",
    "\n",
    "ZCS1 = plt.contour(X, Y, np.transpose(Z1), levels=Zlevels, colors='black',zorder=0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.clabel(ZCS1, inline=1, fontsize=10, colors='black',fmt='%.1f',inline_spacing=1,zorder=0)\n",
    "ZCS1.collections[0].set_linewidths(1.5)\n",
    "ZCS1.collections[1].set_linewidths(1.5)\n",
    "ZCS1.collections[2].set_linewidths(1.5)\n",
    "ZCS1.collections[3].set_linewidths(1.5)\n",
    "\n",
    "\n",
    "\n",
    "ZCS1.collections[0].set_label(r'$TCW, deg$')\n",
    "\n",
    "\n",
    "GCS1 = plt.contour(X, Y, np.transpose(G1), levels=Glevels, colors='blue',linestyles='dashed',zorder=1)\n",
    "\n",
    "plt.clabel(GCS1, inline=1, fontsize=10, colors='blue',fmt='%d',inline_spacing=0,zorder=1)\n",
    "\n",
    "\n",
    "\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[1].set_linewidths(1.5)\n",
    "GCS1.collections[2].set_linewidths(1.5)\n",
    "GCS1.collections[3].set_linewidths(1.5)\n",
    "\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(np.array([ 0.0, 5, 10, 15, 20, 25, 30]),fontsize=12)\n",
    "plt.yticks(np.array([ 0.0, 0.2, 0.4, 0.6, 0.8, 1.0]),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='lower left', fontsize=12)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('../data/jsr-paper/uranus/uranus-lift-large.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/uranus/uranus-lift-large.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/uranus/uranus-lift-large.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  }
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