{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 07 - a - Saturn - Feasibility Charts - Lift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle\n",
    "\n",
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.patches import Polygon\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the planet and atmosphere model.\n",
    "planet=Planet(\"SATURN\")\n",
    "planet.h_skip = 1000e3\n",
    "planet.h_low = 50e3\n",
    "planet.loadAtmosphereModel('../atmdata/Saturn/saturn-nominal.dat', 0 , 1 , 2, 3, heightInKmFlag=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "vinf_kms_array = np.linspace( 0.0,   30.0,  11)\n",
    "LD_array       = np.linspace( 0.0,    1.0 , 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('../data/jsr-paper/saturn/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "runID = 'saturn-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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "Run #86 of 121: Arrival V_infty: 21.0 km/s, L/D:0.8 OSL: -6.590040895025595 USL: -9.939152044753428, TCW: 3.3491111497278325 EFOS: 1.0 EFUS: 1.0\n",
      "Run #87 of 121: Arrival V_infty: 21.0 km/s, L/D:0.9 OSL: -6.52354975646449 USL: -10.352805243492185, TCW: 3.8292554870276945 EFOS: 1.0 EFUS: 1.0\n",
      "Run #88 of 121: Arrival V_infty: 21.0 km/s, L/D:1.0 OSL: -6.463113549929403 USL: -10.787476536999748, TCW: 4.324362987070344 EFOS: 1.0 EFUS: 1.0\n",
      "Run #89 of 121: Arrival V_infty: 24.0 km/s, L/D:0.0 OSL: -7.641142910251801 USL: -7.641142910251801, 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: -7.435422099122661 USL: -7.879827789474803, TCW: 0.4444056903521414 EFOS: 1.0 EFUS: 1.0\n",
      "Run #91 of 121: Arrival V_infty: 24.0 km/s, L/D:0.2 OSL: -7.259350191139674 USL: -8.154355079401284, TCW: 0.8950048882616102 EFOS: 1.0 EFUS: 1.0\n",
      "Run #92 of 121: Arrival V_infty: 24.0 km/s, L/D:0.30000000000000004 OSL: -7.1090398359592655 USL: -8.465675500236102, TCW: 1.356635664276837 EFOS: 1.0 EFUS: 1.0\n",
      "Run #93 of 121: Arrival V_infty: 24.0 km/s, L/D:0.4 OSL: -6.980516621166316 USL: -8.814589021072607, TCW: 1.8340723999062902 EFOS: 1.0 EFUS: 1.0\n",
      "Run #94 of 121: Arrival V_infty: 24.0 km/s, L/D:0.5 OSL: -6.869854594333447 USL: -9.198650198908581, TCW: 2.3287956045751343 EFOS: 1.0 EFUS: 1.0\n",
      "Run #95 of 121: Arrival V_infty: 24.0 km/s, L/D:0.6000000000000001 OSL: -6.773614204328624 USL: -9.618880185560556, TCW: 2.8452659812319325 EFOS: 1.0 EFUS: 1.0\n",
      "Run #96 of 121: Arrival V_infty: 24.0 km/s, L/D:0.7000000000000001 OSL: -6.68897582313366 USL: -10.071645694653853, TCW: 3.382669871520193 EFOS: 1.0 EFUS: 1.0\n",
      "Run #97 of 121: Arrival V_infty: 24.0 km/s, L/D:0.8 OSL: -6.6140063279890455 USL: -10.554397015468567, TCW: 3.940390687479521 EFOS: 1.0 EFUS: 1.0\n",
      "Run #98 of 121: Arrival V_infty: 24.0 km/s, L/D:0.9 OSL: -6.546718397432414 USL: -11.064919615193503, TCW: 4.518201217761089 EFOS: 1.0 EFUS: 1.0\n",
      "Run #99 of 121: Arrival V_infty: 24.0 km/s, L/D:1.0 OSL: -6.485768122583977 USL: -11.599067563627614, TCW: 5.113299441043637 EFOS: 1.0 EFUS: 1.0\n",
      "Run #100 of 121: Arrival V_infty: 27.0 km/s, L/D:0.0 OSL: -7.730294278080692 USL: -7.730294278080692, 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: -7.499225069572276 USL: -8.004159702832112, TCW: 0.5049346332598361 EFOS: 1.0 EFUS: 1.0\n",
      "Run #102 of 121: Arrival V_infty: 27.0 km/s, L/D:0.2 OSL: -7.305992839952523 USL: -8.32482340689603, TCW: 1.0188305669435067 EFOS: 1.0 EFUS: 1.0\n",
      "Run #103 of 121: Arrival V_infty: 27.0 km/s, L/D:0.30000000000000004 OSL: -7.144565865266486 USL: -8.694508581902483, TCW: 1.5499427166359965 EFOS: 1.0 EFUS: 1.0\n",
      "Run #104 of 121: Arrival V_infty: 27.0 km/s, L/D:0.4 OSL: -7.009214873913152 USL: -9.1127034205565, TCW: 2.103488546643348 EFOS: 1.0 EFUS: 1.0\n",
      "Run #105 of 121: Arrival V_infty: 27.0 km/s, L/D:0.5 OSL: -6.894585487010772 USL: -9.577568627821165, TCW: 2.682983140810393 EFOS: 1.0 EFUS: 1.0\n",
      "Run #106 of 121: Arrival V_infty: 27.0 km/s, L/D:0.6000000000000001 OSL: -6.796064561451203 USL: -10.086342729176977, TCW: 3.2902781677257735 EFOS: 1.0 EFUS: 1.0\n",
      "Run #107 of 121: Arrival V_infty: 27.0 km/s, L/D:0.7000000000000001 OSL: -6.710092622364755 USL: -10.63457140736864, TCW: 3.9244787850038847 EFOS: 1.0 EFUS: 1.0\n",
      "Run #108 of 121: Arrival V_infty: 27.0 km/s, L/D:0.8 OSL: -6.634350020962302 USL: -11.21866971077543, TCW: 4.584319689813128 EFOS: 1.0 EFUS: 1.0\n",
      "Run #109 of 121: Arrival V_infty: 27.0 km/s, L/D:0.9 OSL: -6.566575348217157 USL: -11.834503764934198, TCW: 5.267928416717041 EFOS: 1.0 EFUS: 1.0\n",
      "Run #110 of 121: Arrival V_infty: 27.0 km/s, L/D:1.0 OSL: -6.505291097382724 USL: -12.479021048609866, TCW: 5.973729951227142 EFOS: 1.0 EFUS: 1.0\n",
      "Run #111 of 121: Arrival V_infty: 30.0 km/s, L/D:0.0 OSL: -7.8093243590628845 USL: -7.8093243590628845, 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: -7.553177327950834 USL: -8.120297062814643, TCW: 0.5671197348638088 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: -7.343681289399683 USL: -8.490964959040866, TCW: 1.1472836696411832 EFOS: 1.0 EFUS: 1.0\n",
      "Run #114 of 121: Arrival V_infty: 30.0 km/s, L/D:0.30000000000000004 OSL: -7.172631128494686 USL: -8.924815455677162, TCW: 1.7521843271824764 EFOS: 1.0 EFUS: 1.0\n",
      "Run #115 of 121: Arrival V_infty: 30.0 km/s, L/D:0.4 OSL: -7.031803393409064 USL: -9.419318771200778, TCW: 2.387515377791715 EFOS: 1.0 EFUS: 1.0\n",
      "Run #116 of 121: Arrival V_infty: 30.0 km/s, L/D:0.5 OSL: -6.914241488178959 USL: -9.972124349522346, TCW: 3.057882861343387 EFOS: 1.0 EFUS: 1.0\n",
      "Run #117 of 121: Arrival V_infty: 30.0 km/s, L/D:0.6000000000000001 OSL: -6.814171822443313 USL: -10.57726046237076, TCW: 3.763088639927446 EFOS: 1.0 EFUS: 1.0\n",
      "Run #118 of 121: Arrival V_infty: 30.0 km/s, L/D:0.7000000000000001 OSL: -6.727330858047935 USL: -11.229219044609636, TCW: 4.5018881865617 EFOS: 1.0 EFUS: 1.0\n",
      "Run #119 of 121: Arrival V_infty: 30.0 km/s, L/D:0.8 OSL: -6.651091770057974 USL: -11.922576455130184, TCW: 5.27148468507221 EFOS: 1.0 EFUS: 1.0\n",
      "Run #120 of 121: Arrival V_infty: 30.0 km/s, L/D:0.9 OSL: -6.583000757440459 USL: -12.651127214914595, TCW: 6.068126457474136 EFOS: 1.0 EFUS: 1.0\n",
      "Run #121 of 121: Arrival V_infty: 30.0 km/s, L/D:1.0 OSL: -6.521492352228961 USL: -13.412382763555797, TCW: 6.890890411326836 EFOS: 1.0 EFUS: 1.0\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,len(v0_kms_array)):\n",
    "    for j in range(0,len(LD_array)):\n",
    "        vehicle=Vehicle('Apollo', 1000.0, 200.0, LD_array[j], 3.1416, 0.0, 1.00, planet)\n",
    "        vehicle.setInitialState(1000.0,0.0,0.0,v0_kms_array[i],0.0,-4.5,0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-5)\n",
    "        overShootLimit_array[i,j],  exitflag_os_array[i,j]  = vehicle.findOverShootLimit (2400.0, 1.0, -30.0, -4.0, 1E-10, 265.0e3)\n",
    "        underShootLimit_array[i,j], exitflag_us_array[i,j] =  vehicle.findUnderShootLimit(2400.0, 1.0, -30.0, -4.0, 1E-10, 265.0e3)\n",
    "\n",
    "        TCW_array[i,j] = overShootLimit_array[i,j] - underShootLimit_array[i,j]\n",
    "\n",
    "        print(\"Run #\"+str(count)+\" of \"+ str(num_total)+\": Arrival V_infty: \"+str(vinf_kms_array[i])+\" km/s\"+\", L/D:\"+str(LD_array[j]) + \" OSL: \"+str(overShootLimit_array[i,j])+\" USL: \"+str(underShootLimit_array[i,j])+\", TCW: \"+str(TCW_array[i,j])+\" EFOS: \"+str(exitflag_os_array[i,j])+ \" EFUS: \"+str(exitflag_us_array[i,j]))\n",
    "        count = count +1\n",
    "\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'vinf_kms_array.txt',vinf_kms_array)\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'v0_kms_array.txt',v0_kms_array)\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'LD_array.txt',LD_array)\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'overShootLimit_array.txt',overShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'exitflag_os_array.txt',exitflag_os_array)\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'undershootLimit_array.txt',underShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'exitflag_us_array.txt',exitflag_us_array)\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'TCW_array.txt',TCW_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 0.0 km/s, L/D: 0.0 G_MAX: 2.2380106468687773 QDOT_MAX: 717.9389837034478 J_MAX: 129976.61377323652 STAG. PRES: 0.043357923446794594\n",
      "V_infty: 0.0 km/s, L/D: 0.1 G_MAX: 2.3437467372537566 QDOT_MAX: 733.0925568470867 J_MAX: 132039.22993603474 STAG. PRES: 0.041591651952814446\n",
      "V_infty: 0.0 km/s, L/D: 0.2 G_MAX: 2.4769742301935027 QDOT_MAX: 748.3914702860064 J_MAX: 134138.746337036 STAG. PRES: 0.03990534181399003\n",
      "V_infty: 0.0 km/s, L/D: 0.30000000000000004 G_MAX: 2.6399780740061285 QDOT_MAX: 763.9159326941137 J_MAX: 136236.36277629616 STAG. PRES: 0.038263868326281425\n",
      "V_infty: 0.0 km/s, L/D: 0.4 G_MAX: 2.8364313168705975 QDOT_MAX: 779.9055271724576 J_MAX: 138419.1355258049 STAG. PRES: 0.036718853371113025\n",
      "V_infty: 0.0 km/s, L/D: 0.5 G_MAX: 3.0638142163346593 QDOT_MAX: 795.8858254350162 J_MAX: 140576.76574633943 STAG. PRES: 0.03521211825583597\n",
      "V_infty: 0.0 km/s, L/D: 0.6000000000000001 G_MAX: 3.3237378410004608 QDOT_MAX: 812.0432562444495 J_MAX: 142782.3308805871 STAG. PRES: 0.033776703393943966\n",
      "V_infty: 0.0 km/s, L/D: 0.7000000000000001 G_MAX: 3.6157991381384136 QDOT_MAX: 828.2862494265522 J_MAX: 145018.41080045086 STAG. PRES: 0.03239350993274063\n",
      "V_infty: 0.0 km/s, L/D: 0.8 G_MAX: 3.9449367686584487 QDOT_MAX: 845.0631424714177 J_MAX: 147257.38678269504 STAG. PRES: 0.03107984978209567\n",
      "V_infty: 0.0 km/s, L/D: 0.9 G_MAX: 4.303031468569035 QDOT_MAX: 861.5781446777557 J_MAX: 149449.6587919304 STAG. PRES: 0.029813369432466947\n",
      "V_infty: 0.0 km/s, L/D: 1.0 G_MAX: 4.694641089504148 QDOT_MAX: 878.3016363428877 J_MAX: 151736.5762479704 STAG. PRES: 0.02861147485735646\n",
      "V_infty: 3.0 km/s, L/D: 0.0 G_MAX: 2.3390657357879867 QDOT_MAX: 737.5343675300886 J_MAX: 133212.43968050648 STAG. PRES: 0.0453155875780144\n",
      "V_infty: 3.0 km/s, L/D: 0.1 G_MAX: 2.453443350457311 QDOT_MAX: 753.7159061453943 J_MAX: 135386.81646374456 STAG. PRES: 0.04339322456383322\n",
      "V_infty: 3.0 km/s, L/D: 0.2 G_MAX: 2.5979715808489527 QDOT_MAX: 770.2347752568652 J_MAX: 137648.72282515842 STAG. PRES: 0.041567335373905\n",
      "V_infty: 3.0 km/s, L/D: 0.30000000000000004 G_MAX: 2.7746406266109687 QDOT_MAX: 787.0529083115665 J_MAX: 139920.38014289347 STAG. PRES: 0.03980475764114852\n",
      "V_infty: 3.0 km/s, L/D: 0.4 G_MAX: 2.9845232832782083 QDOT_MAX: 804.009939966568 J_MAX: 142197.61535090287 STAG. PRES: 0.038107582117878944\n",
      "V_infty: 3.0 km/s, L/D: 0.5 G_MAX: 3.2290127529916197 QDOT_MAX: 821.2027581096564 J_MAX: 144531.23686239225 STAG. PRES: 0.036489159173281854\n",
      "V_infty: 3.0 km/s, L/D: 0.6000000000000001 G_MAX: 3.507792689870363 QDOT_MAX: 838.5195437548717 J_MAX: 146849.9049566992 STAG. PRES: 0.034935188578214274\n",
      "V_infty: 3.0 km/s, L/D: 0.7000000000000001 G_MAX: 3.824066214314924 QDOT_MAX: 856.2211596795864 J_MAX: 149248.60220369734 STAG. PRES: 0.03345726003435176\n",
      "V_infty: 3.0 km/s, L/D: 0.8 G_MAX: 4.175580715739934 QDOT_MAX: 874.0067411753689 J_MAX: 151648.6313134472 STAG. PRES: 0.032053201863430233\n",
      "V_infty: 3.0 km/s, L/D: 0.9 G_MAX: 4.5621017580875955 QDOT_MAX: 891.9195317995683 J_MAX: 154003.4458779746 STAG. PRES: 0.03069761173127767\n",
      "V_infty: 3.0 km/s, L/D: 1.0 G_MAX: 4.986706005220775 QDOT_MAX: 910.0861309211908 J_MAX: 156450.89256849646 STAG. PRES: 0.029418527819852942\n",
      "V_infty: 6.0 km/s, L/D: 0.0 G_MAX: 2.64369307488232 QDOT_MAX: 795.655452872531 J_MAX: 142600.73256635584 STAG. PRES: 0.05121679582360347\n",
      "V_infty: 6.0 km/s, L/D: 0.1 G_MAX: 2.7855937200918803 QDOT_MAX: 815.0856934945565 J_MAX: 145242.03570967764 STAG. PRES: 0.048802419195258115\n",
      "V_infty: 6.0 km/s, L/D: 0.2 G_MAX: 2.966148191536393 QDOT_MAX: 835.3615823322965 J_MAX: 147933.02949790296 STAG. PRES: 0.046500350164903115\n",
      "V_infty: 6.0 km/s, L/D: 0.30000000000000004 G_MAX: 3.1834274263898217 QDOT_MAX: 855.7521215668482 J_MAX: 150635.13309366402 STAG. PRES: 0.044296671195366684\n",
      "V_infty: 6.0 km/s, L/D: 0.4 G_MAX: 3.440890875434656 QDOT_MAX: 876.499020078461 J_MAX: 153348.19961809425 STAG. PRES: 0.04218614311180908\n",
      "V_infty: 6.0 km/s, L/D: 0.5 G_MAX: 3.7394530324904727 QDOT_MAX: 897.4490303911418 J_MAX: 156145.13729395016 STAG. PRES: 0.04019313768614061\n",
      "V_infty: 6.0 km/s, L/D: 0.6000000000000001 G_MAX: 4.080649714580884 QDOT_MAX: 918.6197471480788 J_MAX: 158939.55039228144 STAG. PRES: 0.03829369736768658\n",
      "V_infty: 6.0 km/s, L/D: 0.7000000000000001 G_MAX: 4.467959655990097 QDOT_MAX: 940.281308514598 J_MAX: 161799.09420633945 STAG. PRES: 0.03650368100023747\n",
      "V_infty: 6.0 km/s, L/D: 0.8 G_MAX: 4.898960884760323 QDOT_MAX: 962.0069427321404 J_MAX: 164646.08028431318 STAG. PRES: 0.034802803039443976\n",
      "V_infty: 6.0 km/s, L/D: 0.9 G_MAX: 5.373557087945921 QDOT_MAX: 983.9413201590289 J_MAX: 167502.67860842386 STAG. PRES: 0.033188772956260854\n",
      "V_infty: 6.0 km/s, L/D: 1.0 G_MAX: 5.893690718864024 QDOT_MAX: 1005.9533589596206 J_MAX: 170372.80759272532 STAG. PRES: 0.031660836875315804\n",
      "V_infty: 9.0 km/s, L/D: 0.0 G_MAX: 3.1554165728879897 QDOT_MAX: 890.9604309254912 J_MAX: 157468.83947096675 STAG. PRES: 0.061129624407047334\n",
      "V_infty: 9.0 km/s, L/D: 0.1 G_MAX: 3.35569645728812 QDOT_MAX: 917.1257950391197 J_MAX: 160821.80352546347 STAG. PRES: 0.057742821041173266\n",
      "V_infty: 9.0 km/s, L/D: 0.2 G_MAX: 3.5980311891987395 QDOT_MAX: 943.5542815683299 J_MAX: 164293.17271041515 STAG. PRES: 0.05455898887954039\n",
      "V_infty: 9.0 km/s, L/D: 0.30000000000000004 G_MAX: 3.891309080288031 QDOT_MAX: 970.6856071490081 J_MAX: 167805.29717854114 STAG. PRES: 0.051543114568484025\n",
      "V_infty: 9.0 km/s, L/D: 0.4 G_MAX: 4.23838271713087 QDOT_MAX: 998.436911482786 J_MAX: 171357.97275805945 STAG. PRES: 0.04869331442556603\n",
      "V_infty: 9.0 km/s, L/D: 0.5 G_MAX: 4.639839772639175 QDOT_MAX: 1026.3920305400127 J_MAX: 174938.54598964445 STAG. PRES: 0.046008640201043456\n",
      "V_infty: 9.0 km/s, L/D: 0.6000000000000001 G_MAX: 5.098598611846284 QDOT_MAX: 1054.7751677509968 J_MAX: 178540.1230788366 STAG. PRES: 0.043481924538538136\n",
      "V_infty: 9.0 km/s, L/D: 0.7000000000000001 G_MAX: 5.6123406932573765 QDOT_MAX: 1083.0834395701856 J_MAX: 182207.44950477086 STAG. PRES: 0.04115374301626162\n",
      "V_infty: 9.0 km/s, L/D: 0.8 G_MAX: 6.1925752737759945 QDOT_MAX: 1112.2430434803475 J_MAX: 185863.89372175408 STAG. PRES: 0.03891888881497864\n",
      "V_infty: 9.0 km/s, L/D: 0.9 G_MAX: 6.828301158180394 QDOT_MAX: 1141.1610849703259 J_MAX: 189588.8330172631 STAG. PRES: 0.036866353776543265\n",
      "V_infty: 9.0 km/s, L/D: 1.0 G_MAX: 7.526731415885384 QDOT_MAX: 1170.3043189540776 J_MAX: 193287.93716487475 STAG. PRES: 0.03493716705021877\n",
      "V_infty: 12.0 km/s, L/D: 0.0 G_MAX: 3.8752715825882067 QDOT_MAX: 1022.8414699768633 J_MAX: 177018.98352793246 STAG. PRES: 0.075073946043497\n",
      "V_infty: 12.0 km/s, L/D: 0.1 G_MAX: 4.163694881831515 QDOT_MAX: 1058.8754088105813 J_MAX: 181558.19679459892 STAG. PRES: 0.07017676238053318\n",
      "V_infty: 12.0 km/s, L/D: 0.2 G_MAX: 4.516143526191402 QDOT_MAX: 1096.212975047085 J_MAX: 186160.91593004158 STAG. PRES: 0.06555595655660079\n",
      "V_infty: 12.0 km/s, L/D: 0.30000000000000004 G_MAX: 4.930367009237206 QDOT_MAX: 1133.89072893818 J_MAX: 190879.50385147266 STAG. PRES: 0.06123851491499311\n",
      "V_infty: 12.0 km/s, L/D: 0.4 G_MAX: 5.419942429922247 QDOT_MAX: 1172.5338940052252 J_MAX: 195648.04786372712 STAG. PRES: 0.057217531797665415\n",
      "V_infty: 12.0 km/s, L/D: 0.5 G_MAX: 5.985175208226825 QDOT_MAX: 1211.7297296976349 J_MAX: 200464.3396463482 STAG. PRES: 0.053490985628345186\n",
      "V_infty: 12.0 km/s, L/D: 0.6000000000000001 G_MAX: 6.628930680715582 QDOT_MAX: 1251.2382201505923 J_MAX: 205326.45941745685 STAG. PRES: 0.05003379580484292\n",
      "V_infty: 12.0 km/s, L/D: 0.7000000000000001 G_MAX: 7.361780566444383 QDOT_MAX: 1291.6296950728286 J_MAX: 210234.75404759374 STAG. PRES: 0.046864233413422794\n",
      "V_infty: 12.0 km/s, L/D: 0.8 G_MAX: 8.181669085022166 QDOT_MAX: 1332.5506944864856 J_MAX: 215121.51371327095 STAG. PRES: 0.04392415884402488\n",
      "V_infty: 12.0 km/s, L/D: 0.9 G_MAX: 9.084211586664269 QDOT_MAX: 1373.2051000883603 J_MAX: 220041.99927133016 STAG. PRES: 0.04123657652846711\n",
      "V_infty: 12.0 km/s, L/D: 1.0 G_MAX: 10.086349053916264 QDOT_MAX: 1415.0252289858713 J_MAX: 224924.34921792572 STAG. PRES: 0.03877416694144854\n",
      "V_infty: 15.0 km/s, L/D: 0.0 G_MAX: 4.805506772869295 QDOT_MAX: 1193.3433354763156 J_MAX: 200786.11807232778 STAG. PRES: 0.09309268424972543\n",
      "V_infty: 15.0 km/s, L/D: 0.1 G_MAX: 5.2310071767240816 QDOT_MAX: 1244.6117045630363 J_MAX: 206857.50341018647 STAG. PRES: 0.0858815321187772\n",
      "V_infty: 15.0 km/s, L/D: 0.2 G_MAX: 5.743654209776959 QDOT_MAX: 1297.9523723819339 J_MAX: 213090.74177930885 STAG. PRES: 0.07917046926248787\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 15.0 km/s, L/D: 0.30000000000000004 G_MAX: 6.339693900389184 QDOT_MAX: 1351.493957843136 J_MAX: 219483.6983346919 STAG. PRES: 0.07298922788991853\n",
      "V_infty: 15.0 km/s, L/D: 0.4 G_MAX: 7.046852388123195 QDOT_MAX: 1407.2424220741573 J_MAX: 225939.37112661262 STAG. PRES: 0.06731728557362891\n",
      "V_infty: 15.0 km/s, L/D: 0.5 G_MAX: 7.871322649364641 QDOT_MAX: 1464.6555902840953 J_MAX: 232440.24250564 STAG. PRES: 0.062138474824016264\n",
      "V_infty: 15.0 km/s, L/D: 0.6000000000000001 G_MAX: 8.810930078681583 QDOT_MAX: 1522.9213950124815 J_MAX: 239027.268359933 STAG. PRES: 0.057442398760789486\n",
      "V_infty: 15.0 km/s, L/D: 0.7000000000000001 G_MAX: 9.871373534686887 QDOT_MAX: 1581.9786847462046 J_MAX: 245629.04691965418 STAG. PRES: 0.05319669786648506\n",
      "V_infty: 15.0 km/s, L/D: 0.8 G_MAX: 11.075534139705193 QDOT_MAX: 1642.801648860263 J_MAX: 252194.2153759143 STAG. PRES: 0.049368148905046545\n",
      "V_infty: 15.0 km/s, L/D: 0.9 G_MAX: 12.407040853555426 QDOT_MAX: 1704.4275083696023 J_MAX: 258718.84072073625 STAG. PRES: 0.04594123579163838\n",
      "V_infty: 15.0 km/s, L/D: 1.0 G_MAX: 13.863975728248608 QDOT_MAX: 1765.6303858426252 J_MAX: 265259.9277600195 STAG. PRES: 0.04284009037917659\n",
      "V_infty: 18.0 km/s, L/D: 0.0 G_MAX: 5.94596032712364 QDOT_MAX: 1408.0539348334441 J_MAX: 228586.66252562916 STAG. PRES: 0.11518254497772357\n",
      "V_infty: 18.0 km/s, L/D: 0.1 G_MAX: 6.565891292461084 QDOT_MAX: 1481.4760022829216 J_MAX: 236652.77860541065 STAG. PRES: 0.10470860909751634\n",
      "V_infty: 18.0 km/s, L/D: 0.2 G_MAX: 7.309438657267204 QDOT_MAX: 1558.2873756330264 J_MAX: 244969.82631045685 STAG. PRES: 0.09510220362364995\n",
      "V_infty: 18.0 km/s, L/D: 0.30000000000000004 G_MAX: 8.191048404149443 QDOT_MAX: 1638.4290084801123 J_MAX: 253483.0895973696 STAG. PRES: 0.08640959916369796\n",
      "V_infty: 18.0 km/s, L/D: 0.4 G_MAX: 9.21912064455282 QDOT_MAX: 1720.9663231232007 J_MAX: 262171.39796171966 STAG. PRES: 0.07854946903225458\n",
      "V_infty: 18.0 km/s, L/D: 0.5 G_MAX: 10.421914006453944 QDOT_MAX: 1807.3536000883112 J_MAX: 270931.5392310815 STAG. PRES: 0.07155138755857506\n",
      "V_infty: 18.0 km/s, L/D: 0.6000000000000001 G_MAX: 11.797226541130815 QDOT_MAX: 1895.976775621908 J_MAX: 279716.03329412267 STAG. PRES: 0.06532533833928034\n",
      "V_infty: 18.0 km/s, L/D: 0.7000000000000001 G_MAX: 13.354415039476864 QDOT_MAX: 1986.436025063528 J_MAX: 288475.43548211874 STAG. PRES: 0.05983997000645634\n",
      "V_infty: 18.0 km/s, L/D: 0.8 G_MAX: 15.102788683232122 QDOT_MAX: 2079.228974441553 J_MAX: 297200.20053732465 STAG. PRES: 0.05498935395055377\n",
      "V_infty: 18.0 km/s, L/D: 0.9 G_MAX: 17.040682268707563 QDOT_MAX: 2173.1600590533885 J_MAX: 305844.1955366729 STAG. PRES: 0.05072810651684567\n",
      "V_infty: 18.0 km/s, L/D: 1.0 G_MAX: 19.17373149273583 QDOT_MAX: 2267.959776146953 J_MAX: 314468.7506632991 STAG. PRES: 0.04699342084311495\n",
      "V_infty: 21.0 km/s, L/D: 0.0 G_MAX: 7.303490858290266 QDOT_MAX: 1677.3656437238883 J_MAX: 260631.23662659444 STAG. PRES: 0.1414756804136292\n",
      "V_infty: 21.0 km/s, L/D: 0.1 G_MAX: 8.200985597406891 QDOT_MAX: 1784.28395796178 J_MAX: 271132.2596938639 STAG. PRES: 0.1265004135234987\n",
      "V_infty: 21.0 km/s, L/D: 0.2 G_MAX: 9.275361952346165 QDOT_MAX: 1898.3484782891637 J_MAX: 282021.5847395874 STAG. PRES: 0.11306273338255833\n",
      "V_infty: 21.0 km/s, L/D: 0.30000000000000004 G_MAX: 10.550251374276765 QDOT_MAX: 2019.4367009435869 J_MAX: 293241.6258641103 STAG. PRES: 0.10113440063258898\n",
      "V_infty: 21.0 km/s, L/D: 0.4 G_MAX: 12.04092128596415 QDOT_MAX: 2146.5785036352245 J_MAX: 304632.8481206539 STAG. PRES: 0.09058785655739165\n",
      "V_infty: 21.0 km/s, L/D: 0.5 G_MAX: 13.76580731769798 QDOT_MAX: 2279.1463384583685 J_MAX: 316116.9640437141 STAG. PRES: 0.08141161264806698\n",
      "V_infty: 21.0 km/s, L/D: 0.6000000000000001 G_MAX: 15.741872585682232 QDOT_MAX: 2416.7298307585934 J_MAX: 327622.94910926046 STAG. PRES: 0.07345567383591473\n",
      "V_infty: 21.0 km/s, L/D: 0.7000000000000001 G_MAX: 17.96822137449178 QDOT_MAX: 2558.6669926303666 J_MAX: 339045.2819536526 STAG. PRES: 0.06660517166991094\n",
      "V_infty: 21.0 km/s, L/D: 0.8 G_MAX: 20.478311695325974 QDOT_MAX: 2704.7077101402256 J_MAX: 350394.14995651017 STAG. PRES: 0.06073812177249558\n",
      "V_infty: 21.0 km/s, L/D: 0.9 G_MAX: 23.249726772864257 QDOT_MAX: 2853.910306148151 J_MAX: 361606.9406312084 STAG. PRES: 0.055680031488109155\n",
      "V_infty: 21.0 km/s, L/D: 1.0 G_MAX: 26.3056819134661 QDOT_MAX: 3005.2755588167847 J_MAX: 372595.94047322654 STAG. PRES: 0.051323137390678314\n",
      "V_infty: 24.0 km/s, L/D: 0.0 G_MAX: 8.89006666032084 QDOT_MAX: 2019.5919783182462 J_MAX: 297511.89922911755 STAG. PRES: 0.17220319875800533\n",
      "V_infty: 24.0 km/s, L/D: 0.1 G_MAX: 10.159046325405777 QDOT_MAX: 2178.0373045765373 J_MAX: 310837.7789956283 STAG. PRES: 0.15117790499680522\n",
      "V_infty: 24.0 km/s, L/D: 0.2 G_MAX: 11.684552133688607 QDOT_MAX: 2351.701812815474 J_MAX: 324787.3869244705 STAG. PRES: 0.13279595925622262\n",
      "V_infty: 24.0 km/s, L/D: 0.30000000000000004 G_MAX: 13.488640372615366 QDOT_MAX: 2538.6591805028243 J_MAX: 339215.2798701408 STAG. PRES: 0.11691114617185083\n",
      "V_infty: 24.0 km/s, L/D: 0.4 G_MAX: 15.596621528748905 QDOT_MAX: 2738.259296328205 J_MAX: 353805.8721980371 STAG. PRES: 0.10320838582101215\n",
      "V_infty: 24.0 km/s, L/D: 0.5 G_MAX: 18.01796573434827 QDOT_MAX: 2948.248052996014 J_MAX: 368520.1645753261 STAG. PRES: 0.09159620039853693\n",
      "V_infty: 24.0 km/s, L/D: 0.6000000000000001 G_MAX: 20.784644459127332 QDOT_MAX: 3169.026108962693 J_MAX: 383239.14873999683 STAG. PRES: 0.0818816185388928\n",
      "V_infty: 24.0 km/s, L/D: 0.7000000000000001 G_MAX: 23.92097174128626 QDOT_MAX: 3397.9120741054903 J_MAX: 397756.74084269546 STAG. PRES: 0.0736084836601442\n",
      "V_infty: 24.0 km/s, L/D: 0.8 G_MAX: 27.43733241938811 QDOT_MAX: 3636.809356980768 J_MAX: 412130.86500763544 STAG. PRES: 0.06669514858530369\n",
      "V_infty: 24.0 km/s, L/D: 0.9 G_MAX: 31.366936528312834 QDOT_MAX: 3884.115341348367 J_MAX: 426188.48394637613 STAG. PRES: 0.06089313165389397\n",
      "V_infty: 24.0 km/s, L/D: 1.0 G_MAX: 35.69298254652038 QDOT_MAX: 4142.579513562536 J_MAX: 440101.1172493722 STAG. PRES: 0.05597521278312054\n",
      "V_infty: 27.0 km/s, L/D: 0.0 G_MAX: 10.710309237571366 QDOT_MAX: 2463.885341961962 J_MAX: 340519.81763164065 STAG. PRES: 0.20745445223828435\n",
      "V_infty: 27.0 km/s, L/D: 0.1 G_MAX: 12.459596311829685 QDOT_MAX: 2703.3521923343533 J_MAX: 356887.3015427984 STAG. PRES: 0.178738441474163\n",
      "V_infty: 27.0 km/s, L/D: 0.2 G_MAX: 14.565637798982964 QDOT_MAX: 2972.4283598994457 J_MAX: 374264.28054389247 STAG. PRES: 0.15421781099159756\n",
      "V_infty: 27.0 km/s, L/D: 0.30000000000000004 G_MAX: 17.05466788341054 QDOT_MAX: 3268.8940952888597 J_MAX: 392215.9801215858 STAG. PRES: 0.1335445067370609\n",
      "V_infty: 27.0 km/s, L/D: 0.4 G_MAX: 19.95137123814681 QDOT_MAX: 3591.6184352029695 J_MAX: 410517.2750147493 STAG. PRES: 0.11632453099773235\n",
      "V_infty: 27.0 km/s, L/D: 0.5 G_MAX: 23.297674302760985 QDOT_MAX: 3937.467093360018 J_MAX: 428904.63713225385 STAG. PRES: 0.10210902988384275\n",
      "V_infty: 27.0 km/s, L/D: 0.6000000000000001 G_MAX: 27.125332250625597 QDOT_MAX: 4304.13640728328 J_MAX: 447183.489305108 STAG. PRES: 0.09048742863532645\n",
      "V_infty: 27.0 km/s, L/D: 0.7000000000000001 G_MAX: 31.469334942733404 QDOT_MAX: 4695.845049508369 J_MAX: 465222.53917055007 STAG. PRES: 0.08089490946725371\n",
      "V_infty: 27.0 km/s, L/D: 0.8 G_MAX: 36.34217112855154 QDOT_MAX: 5109.349986780449 J_MAX: 482990.1266596074 STAG. PRES: 0.07303724026049654\n",
      "V_infty: 27.0 km/s, L/D: 0.9 G_MAX: 41.78258893261535 QDOT_MAX: 5548.221689256438 J_MAX: 500260.1275128956 STAG. PRES: 0.06653602557296222\n",
      "V_infty: 27.0 km/s, L/D: 1.0 G_MAX: 47.788089386640415 QDOT_MAX: 6009.705990371816 J_MAX: 517222.457198984 STAG. PRES: 0.061072813541482154\n",
      "V_infty: 30.0 km/s, L/D: 0.0 G_MAX: 12.764520706447254 QDOT_MAX: 3055.912745589956 J_MAX: 391641.2525780376 STAG. PRES: 0.24723484452834535\n",
      "V_infty: 30.0 km/s, L/D: 0.1 G_MAX: 15.126923880851113 QDOT_MAX: 3427.1584884549025 J_MAX: 411176.8581179773 STAG. PRES: 0.20916480024376638\n",
      "V_infty: 30.0 km/s, L/D: 0.2 G_MAX: 17.959623334481165 QDOT_MAX: 3855.516711357892 J_MAX: 431999.0126849971 STAG. PRES: 0.17715948015357552\n",
      "V_infty: 30.0 km/s, L/D: 0.30000000000000004 G_MAX: 21.318708318269543 QDOT_MAX: 4341.5174543913445 J_MAX: 453774.3418546412 STAG. PRES: 0.15104451241587175\n",
      "V_infty: 30.0 km/s, L/D: 0.4 G_MAX: 25.235352001721278 QDOT_MAX: 4879.030297122354 J_MAX: 475995.3169801266 STAG. PRES: 0.12994632013728238\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 30.0 km/s, L/D: 0.5 G_MAX: 29.77777504931889 QDOT_MAX: 5468.109350675368 J_MAX: 498317.7703051502 STAG. PRES: 0.11303993104275512\n",
      "V_infty: 30.0 km/s, L/D: 0.6000000000000001 G_MAX: 34.98406606865331 QDOT_MAX: 6110.276532711647 J_MAX: 520517.91931661783 STAG. PRES: 0.09953216669797063\n",
      "V_infty: 30.0 km/s, L/D: 0.7000000000000001 G_MAX: 40.889360507643886 QDOT_MAX: 6808.979452877515 J_MAX: 542284.4496847636 STAG. PRES: 0.0886699985502327\n",
      "V_infty: 30.0 km/s, L/D: 0.8 G_MAX: 47.48695008277542 QDOT_MAX: 7560.886155844075 J_MAX: 563659.74166031 STAG. PRES: 0.07988659765012956\n",
      "V_infty: 30.0 km/s, L/D: 0.9 G_MAX: 54.81055239929137 QDOT_MAX: 8356.645918455428 J_MAX: 584494.1626790692 STAG. PRES: 0.07269124852505386\n",
      "V_infty: 30.0 km/s, L/D: 1.0 G_MAX: 62.91253549874263 QDOT_MAX: 9205.997841369557 J_MAX: 604834.9423468049 STAG. PRES: 0.06667842486042036\n"
     ]
    }
   ],
   "source": [
    "acc_net_g_max_array       = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "stag_pres_atm_max_array   = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "q_stag_total_max_array    = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "heatload_max_array        = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "\n",
    "\n",
    "for i in range(0,len(v0_kms_array)):\n",
    "    for j in range(0,len(LD_array)):\n",
    "        vehicle=Vehicle('Apollo', 1000.0, 200.0, LD_array[j], 3.1416, 0.0, 1.00, planet)\n",
    "        vehicle.setInitialState(1000.0,0.0,0.0,v0_kms_array[i],0.0,overShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-5)\n",
    "        vehicle.propogateEntry (2400.0, 1.0, 180.0)\n",
    "\n",
    "        # Extract and save variables to plot\n",
    "        t_min_os         = vehicle.t_minc\n",
    "        h_km_os          = vehicle.h_kmc\n",
    "        acc_net_g_os     = vehicle.acc_net_g\n",
    "        q_stag_con_os    = vehicle.q_stag_con\n",
    "        q_stag_rad_os    = vehicle.q_stag_rad\n",
    "        rc_os            = vehicle.rc\n",
    "        vc_os            = vehicle.vc\n",
    "        stag_pres_atm_os = vehicle.computeStagPres(rc_os,vc_os)/(1.01325E5)\n",
    "        heatload_os      = vehicle.heatload\n",
    "\n",
    "        vehicle=Vehicle('Apollo', 1000.0, 200.0, LD_array[j], 3.1416, 0.0, 1.00, planet)\n",
    "        vehicle.setInitialState(1000.0,0.0,0.0,v0_kms_array[i],0.0,underShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-5)\n",
    "        vehicle.propogateEntry (2400.0, 1.0, 0.0)\n",
    "\n",
    "        # Extract and save variable to plot\n",
    "        t_min_us         = vehicle.t_minc\n",
    "        h_km_us          = vehicle.h_kmc\n",
    "        acc_net_g_us     = vehicle.acc_net_g\n",
    "        q_stag_con_us    = vehicle.q_stag_con\n",
    "        q_stag_rad_us    = vehicle.q_stag_rad\n",
    "        rc_us            = vehicle.rc\n",
    "        vc_us            = vehicle.vc\n",
    "        stag_pres_atm_us = vehicle.computeStagPres(rc_us,vc_us)/(1.01325E5)\n",
    "        heatload_us      = vehicle.heatload\n",
    "\n",
    "        q_stag_total_os  = q_stag_con_os + q_stag_rad_os\n",
    "        q_stag_total_us  = q_stag_con_us + q_stag_rad_us\n",
    "\n",
    "        acc_net_g_max_array[i,j]      = max(max(acc_net_g_os),max(acc_net_g_us))\n",
    "        stag_pres_atm_max_array[i,j]  = max(max(stag_pres_atm_os),max(stag_pres_atm_os))\n",
    "        q_stag_total_max_array[i,j]   = max(max(q_stag_total_os),max(q_stag_total_us))\n",
    "        heatload_max_array[i,j]       = max(max(heatload_os),max(heatload_os))\n",
    "\n",
    "        print(\"V_infty: \"+str(vinf_kms_array[i])+\" km/s\"+\", L/D: \"+str(LD_array[j])+\" G_MAX: \"+str(acc_net_g_max_array[i,j])+\" QDOT_MAX: \"+str(q_stag_total_max_array[i,j])+\" J_MAX: \"+str(heatload_max_array[i,j])+\" STAG. PRES: \"+str(stag_pres_atm_max_array[i,j]))\n",
    "\n",
    "\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'acc_net_g_max_array.txt',acc_net_g_max_array)\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'stag_pres_atm_max_array.txt',stag_pres_atm_max_array)\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'q_stag_total_max_array.txt',q_stag_total_max_array)\n",
    "np.savetxt('../data/jsr-paper/saturn/'+runID+'heatload_max_array.txt',heatload_max_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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/saturn/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/saturn/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/saturn/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/saturn/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/saturn/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/saturn/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/saturn/'+runID+'stag_pres_atm_max_array.txt')\n",
    "\n",
    "\n",
    "f1 = interpolate.interp2d(x, y, np.transpose(Z1), kind='cubic')\n",
    "g1 = interpolate.interp2d(x, y, np.transpose(G1), kind='cubic')\n",
    "q1 = interpolate.interp2d(x, y, np.transpose(Q1), kind='cubic')\n",
    "h1 = interpolate.interp2d(x, y, np.transpose(H1), kind='cubic')\n",
    "#s1 = interpolate.interp2d(x, y, transpose(S1), kind='cubic')\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   30,  210)\n",
    "y_new =  np.linspace( 0.0,   1 ,110)\n",
    "z_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "z1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "g1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "q1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "h1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "#s1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "for i in range(0,len(x_new)):\n",
    "    for j in range(0,len(y_new)):\n",
    "\n",
    "        z1_new[i,j] = f1(x_new[i],y_new[j])\n",
    "        g1_new[i,j] = g1(x_new[i],y_new[j])\n",
    "        q1_new[i,j] = q1(x_new[i],y_new[j])\n",
    "        h1_new[i,j] = h1(x_new[i],y_new[j])\n",
    "        #s1_new[i,j] = s1(x_new[i],y_new[j])\n",
    "\n",
    "\n",
    "Z1 = z1_new\n",
    "G1 = g1_new\n",
    "Q1 = q1_new\n",
    "#S1 = s1_new\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "\n",
    "\n",
    "Zlevels = np.array([0.5,1.0])\n",
    "\n",
    "Glevels = np.array([10.0, 20.0])\n",
    "Qlevels = np.array([1600, 3200.0])\n",
    "Hlevels = np.array([300, 400])\n",
    "#Slevels = np.array([0.8])\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "#plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "#plt.xlim([0.0,30.0])\n",
    "#plt.ylim([0.0,0.4])\n",
    "#plt.tight_layout()\n",
    "#plt.contourf(X, Y, Z, levels=levels)\n",
    "\n",
    "\n",
    "#plt.axvline(x=25.0,linewidth=3, linestyle='dotted' ,color='red',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(LV$'+r' '+r'$C3$'+r' '+r'$limit)$')\n",
    "#plt.axvline(x=13.1,linewidth=1, linestyle='dotted' ,color='cyan',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(Chem. OI)$')\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.25,6.25])\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "\n",
    "ZCS1 = plt.contour(X, Y, np.transpose(Z1), levels=Zlevels, colors='black')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.clabel(ZCS1, inline=1, fontsize=12, colors='black',fmt='%.1f',inline_spacing=1)\n",
    "ZCS1.collections[0].set_linewidths(1.5)\n",
    "ZCS1.collections[1].set_linewidths(1.5)\n",
    "ZCS1.collections[0].set_label(r'$TCW, deg$')\n",
    "\n",
    "\n",
    "GCS1 = plt.contour(X, Y, np.transpose(G1), levels=Glevels, colors='blue',linestyles='dashed')\n",
    "\n",
    "plt.clabel(GCS1, inline=1, fontsize=12, colors='blue',fmt='%d',inline_spacing=0)\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[1].set_linewidths(1.5)\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted')\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=12, colors='red',fmt='%d',inline_spacing=0)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "QCS1.collections[1].set_linewidths(1.5)\n",
    "\n",
    "QCS1.collections[0].set_label(r'$\\dot{q}$'+', '+r'$W/cm^2$')\n",
    "\n",
    "\n",
    "HCS1 = plt.contour(X, Y, np.transpose(H1), levels=Hlevels, colors='magenta',linestyles='dashdot')\n",
    "\n",
    "plt.clabel(HCS1, inline=1, fontsize=12, colors='magenta',fmt='%d',inline_spacing=0)\n",
    "HCS1.collections[0].set_linewidths(1.5)\n",
    "HCS1.collections[1].set_linewidths(1.5)\n",
    "\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "\n",
    "\n",
    "#SCS1 = plt.contour(X, Y, transpose(S1), levels=Slevels, colors='cyan')\n",
    "\n",
    "#plt.clabel(SCS1, inline=1, fontsize=12, colors='cyan',fmt='%.1f',inline_spacing=1)\n",
    "#SCS1.collections[0].set_linewidths(3.0)\n",
    "#SCS1.collections[0].set_label(r'$Peak$'+r' '+r'$stag. pressure,atm$')\n",
    "\n",
    "#plt.axhline(y=0.36,linewidth=1, linestyle='dotted' ,color='white',label=r'$Apollo$'+' '+r'$CM$'+' '+r'$L/D$')\n",
    "\n",
    "\n",
    "\n",
    "#matplotlib.rcParams['text.usetex'] = True\n",
    "#plt.rc('text', usetex=True)\n",
    "\n",
    "\n",
    "# circles for b=50 plot\n",
    "#plt.plot(7.5,0.20,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.30,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "#plt.plot(7.5,0.211,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.315,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "\n",
    "#plt.grid(True,linestyle='dotted', linewidth=0.1)\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(\"L/D\",fontsize=16)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=16)\n",
    "plt.xticks( fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "plt.legend(loc='upper right', fontsize=16)\n",
    "\n",
    "dat0 = ZCS1.allsegs[1][0]\n",
    "x1,y1=dat0[:,0],dat0[:,1]\n",
    "F1 = interpolate.interp1d(x1, y1, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat1 = GCS1.allsegs[0][0]\n",
    "x2,y2=dat1[:,0],dat1[:,1]\n",
    "F2 = interpolate.interp1d(x2, y2, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat2 = QCS1.allsegs[0][0]\n",
    "x3,y3= dat2[:,0],dat2[:,1]\n",
    "F3 = interpolate.interp1d(x3, y3, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat0a = ZCS1.allsegs[0][0]\n",
    "x1a,y1a=dat0a[:,0],dat0a[:,1]\n",
    "F1a = interpolate.interp1d(x1a, y1a, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "\n",
    "x4 = np.linspace(0,30,101)\n",
    "y4 = F1(x4)\n",
    "y4a =F1a(x4)\n",
    "y5 = F2(x4)\n",
    "y6 = F3(x4)\n",
    "\n",
    "y7 = np.minimum(y5,y6)\n",
    "y8 = np.minimum(y4,y6)\n",
    "\n",
    "plt.fill_between(x4, y4, y7, where=y4<=y7,color='xkcd:neon green')\n",
    "\n",
    "plt.fill_between(x4, y4a, y8, where=y4a<=y8,color='xkcd:bright yellow')\n",
    "\n",
    "\n",
    "plt.xlim([0.0,30.0])\n",
    "plt.ylim([0.0,1])\n",
    "\n",
    "plt.savefig('../data/jsr-paper/saturn/saturn-lift-small.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/saturn/saturn-lift-small.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/saturn/saturn-lift-small.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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/saturn/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/saturn/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/saturn/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/saturn/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/saturn/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/saturn/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/saturn/'+runID+'stag_pres_atm_max_array.txt')\n",
    "\n",
    "\n",
    "f1 = interpolate.interp2d(x, y, np.transpose(Z1), kind='cubic')\n",
    "g1 = interpolate.interp2d(x, y, np.transpose(G1), kind='cubic')\n",
    "q1 = interpolate.interp2d(x, y, np.transpose(Q1), kind='cubic')\n",
    "h1 = interpolate.interp2d(x, y, np.transpose(H1), kind='cubic')\n",
    "#s1 = interpolate.interp2d(x, y, transpose(S1), kind='cubic')\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   30,  310)\n",
    "y_new =  np.linspace( 0.0,   1.0 ,110)\n",
    "z_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "z1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "g1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "q1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "h1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "#s1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "for i in range(0,len(x_new)):\n",
    "    for j in range(0,len(y_new)):\n",
    "\n",
    "        z1_new[i,j] = f1(x_new[i],y_new[j])\n",
    "        g1_new[i,j] = g1(x_new[i],y_new[j])\n",
    "        q1_new[i,j] = q1(x_new[i],y_new[j])\n",
    "        h1_new[i,j] = h1(x_new[i],y_new[j])\n",
    "        #s1_new[i,j] = s1(x_new[i],y_new[j])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Z1 = z1_new\n",
    "\n",
    "G1 = g1_new\n",
    "\n",
    "Q1 = q1_new\n",
    "\n",
    "#S1 = s1_new\n",
    "\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "#X, Y = meshgrid(x, y)\n",
    "\n",
    "\n",
    "Zlevels = np.array([0.3, 0.6, 0.9, 1.2, 1.5, 2.0])\n",
    "\n",
    "Glevels = np.array([5.0, 10.0, 15.0])\n",
    "Qlevels = np.array([800.0, 1000.0, 1500.0, 2000.0, 2500.0])\n",
    "Hlevels = np.array([150.0, 200.0, 220.0, 270.0, 320.0])\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",
    "ZCS1.collections[5].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",
    "\n",
    "\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted',zorder=13)\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=10, colors='red',fmt='%d',inline_spacing=0,zorder=13)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "QCS1.collections[1].set_linewidths(1.5)\n",
    "QCS1.collections[2].set_linewidths(1.5)\n",
    "QCS1.collections[3].set_linewidths(1.5)\n",
    "QCS1.collections[4].set_linewidths(1.5)\n",
    "\n",
    "\n",
    "QCS1.collections[0].set_label(r'$\\dot{q}$'+', '+r'$W/cm^2$')\n",
    "\n",
    "\n",
    "HCS1 = plt.contour(X, Y, np.transpose(H1), levels=Hlevels, colors='xkcd:brown',linestyles='dashdot',zorder=14)\n",
    "\n",
    "plt.clabel(HCS1, inline=1, fontsize=10, colors='xkcd:brown',fmt='%d',inline_spacing=0,zorder=14)\n",
    "HCS1.collections[0].set_linewidths(1.75)\n",
    "HCS1.collections[1].set_linewidths(1.75)\n",
    "HCS1.collections[2].set_linewidths(1.75)\n",
    "HCS1.collections[3].set_linewidths(1.75)\n",
    "HCS1.collections[4].set_linewidths(1.75)\n",
    "\n",
    "\n",
    "\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(\"L/D\",fontsize=12)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "plt.legend(loc='upper right', fontsize=12)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('../data/jsr-paper/saturn/saturn-lift-large.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/saturn/saturn-lift-large.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/saturn/saturn-lift-large.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
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