{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 03 - a - Venus - Feasibility Charts - Lift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle\n",
    "\n",
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.patches import Polygon\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a planet object\n",
    "planet=Planet(\"VENUS\")\n",
    "planet.h_skip = 150000.0\n",
    "\n",
    "# Load an nominal atmospheric profile with height, temp, pressure, density data\n",
    "planet.loadAtmosphereModel('../atmdata/Venus/venus-gram-avg.dat', 0 , 1 ,2, 3 )\n",
    "\n",
    "vinf_kms_array = np.linspace( 0.0,   30.0,  11)\n",
    "LD_array       = np.linspace( 0.0,    0.4 , 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('../data/jsr-paper/venus/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.makedirs('../data/jsr-paper/venus/')\n",
    "runID = 'venus-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+150.0*1.0E3))/1.0E3\n",
    "\n",
    "overShootLimit_array  = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "underShootLimit_array = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "exitflag_os_array     = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "exitflag_us_array     = np.zeros((len(v0_kms_array),len(LD_array)))\n",
    "TCW_array             = np.zeros((len(v0_kms_array),len(LD_array)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "Run #87 of 121: Arrival V_infty: 21.0 km/s, L/D:0.36 OSL: -6.8413654634932755 USL: -16.4922250835225, TCW: 9.650859620029223 EFOS: 1.0 EFUS: 1.0\n",
      "Run #88 of 121: Arrival V_infty: 21.0 km/s, L/D:0.4 OSL: -6.813757385320059 USL: -18.45940128907023, TCW: 11.64564390375017 EFOS: 1.0 EFUS: 1.0\n",
      "Run #89 of 121: Arrival V_infty: 24.0 km/s, L/D:0.0 OSL: -7.635204620051809 USL: -7.635204620051809, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #90 of 121: Arrival V_infty: 24.0 km/s, L/D:0.04 OSL: -7.42147440957342 USL: -7.983169202660065, TCW: 0.5616947930866445 EFOS: 1.0 EFUS: 1.0\n",
      "Run #91 of 121: Arrival V_infty: 24.0 km/s, L/D:0.08 OSL: -7.2876336052431725 USL: -8.509109574577451, TCW: 1.2214759693342785 EFOS: 1.0 EFUS: 1.0\n",
      "Run #92 of 121: Arrival V_infty: 24.0 km/s, L/D:0.12 OSL: -7.195545198301261 USL: -9.23215636000532, TCW: 2.0366111617040588 EFOS: 1.0 EFUS: 1.0\n",
      "Run #93 of 121: Arrival V_infty: 24.0 km/s, L/D:0.16 OSL: -7.1264770285743 USL: -10.156276278827136, TCW: 3.029799250252836 EFOS: 1.0 EFUS: 1.0\n",
      "Run #94 of 121: Arrival V_infty: 24.0 km/s, L/D:0.2 OSL: -7.07118127711874 USL: -11.281281417552236, TCW: 4.210100140433497 EFOS: 1.0 EFUS: 1.0\n",
      "Run #95 of 121: Arrival V_infty: 24.0 km/s, L/D:0.24 OSL: -7.025021565721545 USL: -12.610175601246738, TCW: 5.585154035525193 EFOS: 1.0 EFUS: 1.0\n",
      "Run #96 of 121: Arrival V_infty: 24.0 km/s, L/D:0.28 OSL: -6.985417478776071 USL: -14.149251704162452, TCW: 7.163834225386381 EFOS: 1.0 EFUS: 1.0\n",
      "Run #97 of 121: Arrival V_infty: 24.0 km/s, L/D:0.32 OSL: -6.950687272266805 USL: -15.907775679985207, TCW: 8.957088407718402 EFOS: 1.0 EFUS: 1.0\n",
      "Run #98 of 121: Arrival V_infty: 24.0 km/s, L/D:0.36 OSL: -6.919730005040037 USL: -17.89427540468023, TCW: 10.974545399640192 EFOS: 1.0 EFUS: 1.0\n",
      "Run #99 of 121: Arrival V_infty: 24.0 km/s, L/D:0.4 OSL: -6.891788325854577 USL: -20.112015082988364, TCW: 13.220226757133787 EFOS: 1.0 EFUS: 1.0\n",
      "Run #100 of 121: Arrival V_infty: 27.0 km/s, L/D:0.0 OSL: -7.71808632351167 USL: -7.71808632351167, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #101 of 121: Arrival V_infty: 27.0 km/s, L/D:0.04 OSL: -7.49009803701847 USL: -8.100727698638366, TCW: 0.6106296616198961 EFOS: 1.0 EFUS: 1.0\n",
      "Run #102 of 121: Arrival V_infty: 27.0 km/s, L/D:0.08 OSL: -7.35102653910144 USL: -8.689636547485861, TCW: 1.3386100083844212 EFOS: 1.0 EFUS: 1.0\n",
      "Run #103 of 121: Arrival V_infty: 27.0 km/s, L/D:0.12 OSL: -7.256783246040868 USL: -9.504313935143728, TCW: 2.2475306891028595 EFOS: 1.0 EFUS: 1.0\n",
      "Run #104 of 121: Arrival V_infty: 27.0 km/s, L/D:0.16 OSL: -7.186538503068732 USL: -10.54647930261126, TCW: 3.359940799542528 EFOS: 1.0 EFUS: 1.0\n",
      "Run #105 of 121: Arrival V_infty: 27.0 km/s, L/D:0.2 OSL: -7.130489625636983 USL: -11.813168006123306, TCW: 4.682678380486323 EFOS: 1.0 EFUS: 1.0\n",
      "Run #106 of 121: Arrival V_infty: 27.0 km/s, L/D:0.24 OSL: -7.083774343042023 USL: -13.30581801828157, TCW: 6.222043675239547 EFOS: 1.0 EFUS: 1.0\n",
      "Run #107 of 121: Arrival V_infty: 27.0 km/s, L/D:0.28 OSL: -7.043729467670346 USL: -15.029293540632352, TCW: 7.985564072962006 EFOS: 1.0 EFUS: 1.0\n",
      "Run #108 of 121: Arrival V_infty: 27.0 km/s, L/D:0.32 OSL: -7.008652499534946 USL: -16.990454664577555, TCW: 9.981802165042609 EFOS: 1.0 EFUS: 1.0\n",
      "Run #109 of 121: Arrival V_infty: 27.0 km/s, L/D:0.36 OSL: -6.977399395811517 USL: -19.192519044849178, TCW: 12.21511964903766 EFOS: 1.0 EFUS: 1.0\n",
      "Run #110 of 121: Arrival V_infty: 27.0 km/s, L/D:0.4 OSL: -6.949196316301823 USL: -21.633044674374105, TCW: 14.683848358072282 EFOS: 1.0 EFUS: 1.0\n",
      "Run #111 of 121: Arrival V_infty: 30.0 km/s, L/D:0.0 OSL: -7.7831924593156145 USL: -7.7831924593156145, TCW: 0.0 EFOS: 1.0 EFUS: 1.0\n",
      "Run #112 of 121: Arrival V_infty: 30.0 km/s, L/D:0.04 OSL: -7.542288943506719 USL: -8.198596523714514, TCW: 0.6563075802077947 EFOS: 1.0 EFUS: 1.0\n",
      "Run #113 of 121: Arrival V_infty: 30.0 km/s, L/D:0.08 OSL: -7.3988514584852965 USL: -8.84801716486254, TCW: 1.4491657063772436 EFOS: 1.0 EFUS: 1.0\n",
      "Run #114 of 121: Arrival V_infty: 30.0 km/s, L/D:0.12 OSL: -7.302871587729896 USL: -9.751291815522563, TCW: 2.448420227792667 EFOS: 1.0 EFUS: 1.0\n",
      "Run #115 of 121: Arrival V_infty: 30.0 km/s, L/D:0.16 OSL: -7.231714739522431 USL: -10.906050322388182, TCW: 3.6743355828657513 EFOS: 1.0 EFUS: 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run #116 of 121: Arrival V_infty: 30.0 km/s, L/D:0.2 OSL: -7.17510891191705 USL: -12.307637810030428, TCW: 5.132528898113378 EFOS: 1.0 EFUS: 1.0\n",
      "Run #117 of 121: Arrival V_infty: 30.0 km/s, L/D:0.24 OSL: -7.127968327105918 USL: -13.954226075722545, TCW: 6.8262577486166265 EFOS: 1.0 EFUS: 1.0\n",
      "Run #118 of 121: Arrival V_infty: 30.0 km/s, L/D:0.28 OSL: -7.087581150732149 USL: -15.849918038686155, TCW: 8.762336887954007 EFOS: 1.0 EFUS: 1.0\n",
      "Run #119 of 121: Arrival V_infty: 30.0 km/s, L/D:0.32 OSL: -7.052223581191356 USL: -17.99699445166698, TCW: 10.944770870475622 EFOS: 1.0 EFUS: 1.0\n",
      "Run #120 of 121: Arrival V_infty: 30.0 km/s, L/D:0.36 OSL: -7.020741473261296 USL: -20.394194874043023, TCW: 13.373453400781727 EFOS: 1.0 EFUS: 1.0\n",
      "Run #121 of 121: Arrival V_infty: 30.0 km/s, L/D:0.4 OSL: -6.9923170916954405 USL: -23.03235112711627, TCW: 16.04003403542083 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(150.0,0.0,0.0,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, 0.1, -80.0, -4.0, 1E-10, 400.0)\n",
    "        underShootLimit_array[i,j], exitflag_us_array[i,j] =  vehicle.findUnderShootLimit(2400.0, 0.1, -80.0, -4.0, 1E-10, 400.0)\n",
    "\n",
    "        TCW_array[i,j] = overShootLimit_array[i,j] - underShootLimit_array[i,j]\n",
    "\n",
    "        print(\"Run #\"+str(count)+\" of \"+ str(num_total)+\": Arrival V_infty: \"+str(vinf_kms_array[i])+\" km/s\"+\", L/D:\"+str(LD_array[j]) + \" OSL: \"+str(overShootLimit_array[i,j])+\" USL: \"+str(underShootLimit_array[i,j])+\", TCW: \"+str(TCW_array[i,j])+\" EFOS: \"+str(exitflag_os_array[i,j])+ \" EFUS: \"+str(exitflag_us_array[i,j]))\n",
    "        count = count +1\n",
    "        \n",
    "np.savetxt('../data/jsr-paper/venus/'+runID+'vinf_kms_array.txt',vinf_kms_array)\n",
    "np.savetxt('../data/jsr-paper/venus/'+runID+'v0_kms_array.txt',v0_kms_array)\n",
    "np.savetxt('../data/jsr-paper/venus/'+runID+'LD_array.txt',LD_array)\n",
    "np.savetxt('../data/jsr-paper/venus/'+runID+'overShootLimit_array.txt',overShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/venus/'+runID+'exitflag_os_array.txt',exitflag_os_array)\n",
    "np.savetxt('../data/jsr-paper/venus/'+runID+'undershootLimit_array.txt',underShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/venus/'+runID+'exitflag_us_array.txt',exitflag_us_array)\n",
    "np.savetxt('../data/jsr-paper/venus/'+runID+'TCW_array.txt',TCW_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 0.0 km/s, L/D: 0.0 G_MAX: 3.9590117157825047 QDOT_MAX: 232.21499545297152 J_MAX: 22137.654809971034 STAG. PRES: 0.07669370752080713\n",
      "V_infty: 0.0 km/s, L/D: 0.04 G_MAX: 4.613710145976934 QDOT_MAX: 251.33097256229536 J_MAX: 23583.538545760937 STAG. PRES: 0.06607229914848128\n",
      "V_infty: 0.0 km/s, L/D: 0.08 G_MAX: 5.38390810812425 QDOT_MAX: 272.51459724599175 J_MAX: 25110.639305239856 STAG. PRES: 0.05733986923684111\n",
      "V_infty: 0.0 km/s, L/D: 0.12 G_MAX: 6.272964691869694 QDOT_MAX: 295.62634003724287 J_MAX: 26685.049938105167 STAG. PRES: 0.050351244671863624\n",
      "V_infty: 0.0 km/s, L/D: 0.16 G_MAX: 7.2798512959232715 QDOT_MAX: 320.49766228250974 J_MAX: 28269.308447507257 STAG. PRES: 0.04474012549042083\n",
      "V_infty: 0.0 km/s, L/D: 0.2 G_MAX: 8.396247639784274 QDOT_MAX: 346.9055250278189 J_MAX: 29837.14821106817 STAG. PRES: 0.040162815837984485\n",
      "V_infty: 0.0 km/s, L/D: 0.24 G_MAX: 9.620246872032547 QDOT_MAX: 374.4123161356479 J_MAX: 31370.100150104685 STAG. PRES: 0.0363971079100197\n",
      "V_infty: 0.0 km/s, L/D: 0.28 G_MAX: 10.97633540150012 QDOT_MAX: 402.8090091543174 J_MAX: 32861.15032603297 STAG. PRES: 0.033283873474471384\n",
      "V_infty: 0.0 km/s, L/D: 0.32 G_MAX: 12.48592225884596 QDOT_MAX: 432.536866013435 J_MAX: 34297.968591671175 STAG. PRES: 0.0306728310780846\n",
      "V_infty: 0.0 km/s, L/D: 0.36 G_MAX: 14.14692484713174 QDOT_MAX: 464.00713304270846 J_MAX: 35688.38543245546 STAG. PRES: 0.028451625418091698\n",
      "V_infty: 0.0 km/s, L/D: 0.4 G_MAX: 15.957001236337302 QDOT_MAX: 496.9810519693244 J_MAX: 37033.64155038105 STAG. PRES: 0.02653515299317931\n",
      "V_infty: 3.0 km/s, L/D: 0.0 G_MAX: 4.9010901532044935 QDOT_MAX: 282.7381857454536 J_MAX: 24524.494872850846 STAG. PRES: 0.09493890195267343\n",
      "V_infty: 3.0 km/s, L/D: 0.04 G_MAX: 5.770570224001972 QDOT_MAX: 307.94470793566165 J_MAX: 26192.46819148666 STAG. PRES: 0.08105974505121456\n",
      "V_infty: 3.0 km/s, L/D: 0.08 G_MAX: 6.803906143389467 QDOT_MAX: 336.29105338851974 J_MAX: 27960.158989568186 STAG. PRES: 0.0697916119224303\n",
      "V_infty: 3.0 km/s, L/D: 0.12 G_MAX: 7.99912054052088 QDOT_MAX: 367.6067076832172 J_MAX: 29775.42372156062 STAG. PRES: 0.06081641331230056\n",
      "V_infty: 3.0 km/s, L/D: 0.16 G_MAX: 9.337128412723368 QDOT_MAX: 401.39712122695386 J_MAX: 31595.225520612265 STAG. PRES: 0.05375595609870488\n",
      "V_infty: 3.0 km/s, L/D: 0.2 G_MAX: 10.822147898481568 QDOT_MAX: 436.8604143341804 J_MAX: 33387.8170921613 STAG. PRES: 0.04811605453597531\n",
      "V_infty: 3.0 km/s, L/D: 0.24 G_MAX: 12.48413721498724 QDOT_MAX: 473.878045618567 J_MAX: 35132.57988386619 STAG. PRES: 0.04350488199648159\n",
      "V_infty: 3.0 km/s, L/D: 0.28 G_MAX: 14.32198192169329 QDOT_MAX: 513.0821898272609 J_MAX: 36818.041379185706 STAG. PRES: 0.03968734365074313\n",
      "V_infty: 3.0 km/s, L/D: 0.32 G_MAX: 16.320370922471177 QDOT_MAX: 554.4809469684816 J_MAX: 38446.0508714752 STAG. PRES: 0.03650169826878264\n",
      "V_infty: 3.0 km/s, L/D: 0.36 G_MAX: 18.515320119126486 QDOT_MAX: 597.4859390159986 J_MAX: 40015.51334730043 STAG. PRES: 0.03380620482654511\n",
      "V_infty: 3.0 km/s, L/D: 0.4 G_MAX: 20.984306704411868 QDOT_MAX: 642.1683040468455 J_MAX: 41531.33724118467 STAG. PRES: 0.03149500231312327\n",
      "V_infty: 6.0 km/s, L/D: 0.0 G_MAX: 7.937890884745086 QDOT_MAX: 551.4411846331212 J_MAX: 33806.776312330236 STAG. PRES: 0.15374742042093642\n",
      "V_infty: 6.0 km/s, L/D: 0.04 G_MAX: 9.581554844563765 QDOT_MAX: 614.701617300426 J_MAX: 36064.23118799789 STAG. PRES: 0.12827120610120626\n",
      "V_infty: 6.0 km/s, L/D: 0.08 G_MAX: 11.535060339899056 QDOT_MAX: 689.4382715321832 J_MAX: 38467.756924896144 STAG. PRES: 0.10847919857824559\n",
      "V_infty: 6.0 km/s, L/D: 0.12 G_MAX: 13.806004150304112 QDOT_MAX: 774.958114292056 J_MAX: 40929.419328652 STAG. PRES: 0.09327371864552295\n",
      "V_infty: 6.0 km/s, L/D: 0.16 G_MAX: 16.397555928438585 QDOT_MAX: 869.12438174959 J_MAX: 43375.6391359006 STAG. PRES: 0.0815083499995657\n",
      "V_infty: 6.0 km/s, L/D: 0.2 G_MAX: 19.23881003763215 QDOT_MAX: 970.7710555940766 J_MAX: 45755.70857743549 STAG. PRES: 0.07233575422767977\n",
      "V_infty: 6.0 km/s, L/D: 0.24 G_MAX: 22.378347966362657 QDOT_MAX: 1081.2684269943409 J_MAX: 48053.03804768852 STAG. PRES: 0.06507097655794743\n",
      "V_infty: 6.0 km/s, L/D: 0.28 G_MAX: 25.909891049689353 QDOT_MAX: 1201.0415293034612 J_MAX: 50262.346907077634 STAG. PRES: 0.05916367455665722\n",
      "V_infty: 6.0 km/s, L/D: 0.32 G_MAX: 29.800879851838157 QDOT_MAX: 1328.7581136658 J_MAX: 52381.70217988749 STAG. PRES: 0.05423065191860836\n",
      "V_infty: 6.0 km/s, L/D: 0.36 G_MAX: 34.04846881872402 QDOT_MAX: 1464.2473425637893 J_MAX: 54421.34605709919 STAG. PRES: 0.050067625975129065\n",
      "V_infty: 6.0 km/s, L/D: 0.4 G_MAX: 38.81553172148583 QDOT_MAX: 1610.3375955190263 J_MAX: 56388.90741278654 STAG. PRES: 0.04651331039587377\n",
      "V_infty: 9.0 km/s, L/D: 0.0 G_MAX: 13.4703278224725 QDOT_MAX: 2026.7934423901725 J_MAX: 69087.26511671564 STAG. PRES: 0.2608713650396997\n",
      "V_infty: 9.0 km/s, L/D: 0.04 G_MAX: 16.67838849552176 QDOT_MAX: 2382.1911609343265 J_MAX: 71668.98929592184 STAG. PRES: 0.21210320487634413\n",
      "V_infty: 9.0 km/s, L/D: 0.08 G_MAX: 20.638636761210886 QDOT_MAX: 2827.453961180893 J_MAX: 74554.55950923395 STAG. PRES: 0.1756055278361127\n",
      "V_infty: 9.0 km/s, L/D: 0.12 G_MAX: 25.167449294524296 QDOT_MAX: 3354.69322312229 J_MAX: 77563.68322912359 STAG. PRES: 0.14885425596215682\n",
      "V_infty: 9.0 km/s, L/D: 0.16 G_MAX: 30.343475545998004 QDOT_MAX: 3955.1724693020224 J_MAX: 80562.95241182478 STAG. PRES: 0.12891675535854447\n",
      "V_infty: 9.0 km/s, L/D: 0.2 G_MAX: 36.13020796123395 QDOT_MAX: 4639.839817697857 J_MAX: 83490.43965928926 STAG. PRES: 0.11360620658583945\n",
      "V_infty: 9.0 km/s, L/D: 0.24 G_MAX: 42.395781072822736 QDOT_MAX: 5399.404882183654 J_MAX: 86314.49249277964 STAG. PRES: 0.10157337467632196\n",
      "V_infty: 9.0 km/s, L/D: 0.28 G_MAX: 49.413913861739495 QDOT_MAX: 6221.6969763527295 J_MAX: 89036.22869733511 STAG. PRES: 0.09194318893789245\n",
      "V_infty: 9.0 km/s, L/D: 0.32 G_MAX: 57.33259883925675 QDOT_MAX: 7126.6025620865075 J_MAX: 91654.56254455089 STAG. PRES: 0.08406248234103005\n",
      "V_infty: 9.0 km/s, L/D: 0.36 G_MAX: 66.10573653643132 QDOT_MAX: 8143.301290775558 J_MAX: 94157.32486943169 STAG. PRES: 0.07745978943151396\n",
      "V_infty: 9.0 km/s, L/D: 0.4 G_MAX: 76.0418947013196 QDOT_MAX: 9266.270113975828 J_MAX: 96403.1221568101 STAG. PRES: 0.07182261621538175\n",
      "V_infty: 12.0 km/s, L/D: 0.0 G_MAX: 21.79189989710367 QDOT_MAX: 12557.036120207737 J_MAX: 254217.07749206503 STAG. PRES: 0.42198554831521395\n",
      "V_infty: 12.0 km/s, L/D: 0.04 G_MAX: 27.78264034910383 QDOT_MAX: 15435.384066486708 J_MAX: 252669.74606984793 STAG. PRES: 0.33535297249591867\n",
      "V_infty: 12.0 km/s, L/D: 0.08 G_MAX: 35.05675363179061 QDOT_MAX: 19144.243665800685 J_MAX: 252328.27651466444 STAG. PRES: 0.27261686174599514\n",
      "V_infty: 12.0 km/s, L/D: 0.12 G_MAX: 43.706638449591615 QDOT_MAX: 23617.046193796228 J_MAX: 252801.61270297738 STAG. PRES: 0.22821966985506886\n",
      "V_infty: 12.0 km/s, L/D: 0.16 G_MAX: 53.24941633498074 QDOT_MAX: 28929.23026474505 J_MAX: 253760.1632073617 STAG. PRES: 0.19613771010335412\n",
      "V_infty: 12.0 km/s, L/D: 0.2 G_MAX: 63.97144238283071 QDOT_MAX: 34924.406593071675 J_MAX: 254986.07633568562 STAG. PRES: 0.17204262449813582\n",
      "V_infty: 12.0 km/s, L/D: 0.24 G_MAX: 75.95998700799457 QDOT_MAX: 41537.77844170234 J_MAX: 256394.4873018328 STAG. PRES: 0.1532733426332668\n",
      "V_infty: 12.0 km/s, L/D: 0.28 G_MAX: 89.14281150867787 QDOT_MAX: 49030.389539367716 J_MAX: 257896.27242834642 STAG. PRES: 0.13827807088155228\n",
      "V_infty: 12.0 km/s, L/D: 0.32 G_MAX: 104.19597663537321 QDOT_MAX: 57412.810035811446 J_MAX: 250776.46380667508 STAG. PRES: 0.12608653994698424\n",
      "V_infty: 12.0 km/s, L/D: 0.36 G_MAX: 121.23104644174862 QDOT_MAX: 66596.69819320485 J_MAX: 260956.11889855276 STAG. PRES: 0.11597740611278727\n",
      "V_infty: 12.0 km/s, L/D: 0.4 G_MAX: 140.11327078321906 QDOT_MAX: 76753.880730803 J_MAX: 261525.72032834275 STAG. PRES: 0.10742554063848696\n",
      "V_infty: 15.0 km/s, L/D: 0.0 G_MAX: 33.21861632124411 QDOT_MAX: 85929.58888100758 J_MAX: 1270928.3676866305 STAG. PRES: 0.643202945792697\n",
      "V_infty: 15.0 km/s, L/D: 0.04 G_MAX: 43.233912582127985 QDOT_MAX: 108711.83732102803 J_MAX: 1236211.6882785484 STAG. PRES: 0.4988686781003947\n",
      "V_infty: 15.0 km/s, L/D: 0.08 G_MAX: 55.93693247201538 QDOT_MAX: 138413.04043596153 J_MAX: 1208744.5475983985 STAG. PRES: 0.3999841366622385\n",
      "V_infty: 15.0 km/s, L/D: 0.12 G_MAX: 70.44352228551416 QDOT_MAX: 175459.80245216668 J_MAX: 1187158.5043968388 STAG. PRES: 0.33157334915054815\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 15.0 km/s, L/D: 0.16 G_MAX: 87.0711262820914 QDOT_MAX: 218902.07635646375 J_MAX: 1169616.8826942716 STAG. PRES: 0.2832830285297486\n",
      "V_infty: 15.0 km/s, L/D: 0.2 G_MAX: 105.46307410646645 QDOT_MAX: 267611.5728063924 J_MAX: 1155130.7754748978 STAG. PRES: 0.24758237114834314\n",
      "V_infty: 15.0 km/s, L/D: 0.24 G_MAX: 126.0327222027572 QDOT_MAX: 323499.8592901027 J_MAX: 1142707.7436133313 STAG. PRES: 0.22006811265454826\n",
      "V_infty: 15.0 km/s, L/D: 0.28 G_MAX: 149.33526403833085 QDOT_MAX: 386027.9814141959 J_MAX: 1131970.0496762795 STAG. PRES: 0.19815687493978554\n",
      "V_infty: 15.0 km/s, L/D: 0.32 G_MAX: 174.85743604563925 QDOT_MAX: 454271.4316466637 J_MAX: 1122214.4202489683 STAG. PRES: 0.18035643992966016\n",
      "V_infty: 15.0 km/s, L/D: 0.36 G_MAX: 203.86574902584823 QDOT_MAX: 530738.4463998055 J_MAX: 1113622.4191915924 STAG. PRES: 0.16564320172830146\n",
      "V_infty: 15.0 km/s, L/D: 0.4 G_MAX: 237.3140941216704 QDOT_MAX: 617843.2322503973 J_MAX: 1103056.7733876007 STAG. PRES: 0.1532806991135227\n",
      "V_infty: 18.0 km/s, L/D: 0.0 G_MAX: 47.816398134676184 QDOT_MAX: 528922.2484563233 J_MAX: 6308021.836173096 STAG. PRES: 0.9257971406089985\n",
      "V_infty: 18.0 km/s, L/D: 0.04 G_MAX: 63.67096306341289 QDOT_MAX: 683002.7585607963 J_MAX: 6088254.560254838 STAG. PRES: 0.7038840270640357\n",
      "V_infty: 18.0 km/s, L/D: 0.08 G_MAX: 83.61591747947959 QDOT_MAX: 889047.0581183094 J_MAX: 5912040.689909392 STAG. PRES: 0.557602538105873\n",
      "V_infty: 18.0 km/s, L/D: 0.12 G_MAX: 106.95183154142933 QDOT_MAX: 1148090.9565321465 J_MAX: 5770837.625288787 STAG. PRES: 0.45887941463769916\n",
      "V_infty: 18.0 km/s, L/D: 0.16 G_MAX: 133.21462200940314 QDOT_MAX: 1447282.52205411 J_MAX: 5653589.048697188 STAG. PRES: 0.3903103984838007\n",
      "V_infty: 18.0 km/s, L/D: 0.2 G_MAX: 162.73448970008198 QDOT_MAX: 1792826.987127725 J_MAX: 5554514.987124934 STAG. PRES: 0.34021100249407804\n",
      "V_infty: 18.0 km/s, L/D: 0.24 G_MAX: 195.73856870575517 QDOT_MAX: 2184649.9547400456 J_MAX: 5467083.11888542 STAG. PRES: 0.30189468479937076\n",
      "V_infty: 18.0 km/s, L/D: 0.28 G_MAX: 231.83375256195075 QDOT_MAX: 2612226.3466775054 J_MAX: 5390108.845223657 STAG. PRES: 0.27152640854982446\n",
      "V_infty: 18.0 km/s, L/D: 0.32 G_MAX: 273.2511109660912 QDOT_MAX: 3095224.6439155582 J_MAX: 5319941.648366391 STAG. PRES: 0.24685155989376845\n",
      "V_infty: 18.0 km/s, L/D: 0.36 G_MAX: 319.6997558976642 QDOT_MAX: 3644393.5844074585 J_MAX: 5254174.1505609695 STAG. PRES: 0.22648487358824598\n",
      "V_infty: 18.0 km/s, L/D: 0.4 G_MAX: 371.3772941167138 QDOT_MAX: 4250077.279452785 J_MAX: 5193311.58768972 STAG. PRES: 0.20940640294498683\n",
      "V_infty: 21.0 km/s, L/D: 0.0 G_MAX: 65.63399353682075 QDOT_MAX: 2802518.5900281672 J_MAX: 28119988.622378718 STAG. PRES: 1.2707139391182507\n",
      "V_infty: 21.0 km/s, L/D: 0.04 G_MAX: 89.35678341097416 QDOT_MAX: 3684336.8800618188 J_MAX: 27046298.14294503 STAG. PRES: 0.9513166355042214\n",
      "V_infty: 21.0 km/s, L/D: 0.08 G_MAX: 118.91003539925883 QDOT_MAX: 4892919.655525487 J_MAX: 26199616.934593383 STAG. PRES: 0.7452044444948112\n",
      "V_infty: 21.0 km/s, L/D: 0.12 G_MAX: 153.96565449259876 QDOT_MAX: 6398582.179950749 J_MAX: 25522772.840207204 STAG. PRES: 0.6100476252191264\n",
      "V_infty: 21.0 km/s, L/D: 0.16 G_MAX: 193.25766265899784 QDOT_MAX: 8152984.2934386 J_MAX: 24966538.500375737 STAG. PRES: 0.5171404513495854\n",
      "V_infty: 21.0 km/s, L/D: 0.2 G_MAX: 237.67663252344613 QDOT_MAX: 10199737.967270328 J_MAX: 24490712.994823325 STAG. PRES: 0.4498641086824712\n",
      "V_infty: 21.0 km/s, L/D: 0.24 G_MAX: 285.8518972269457 QDOT_MAX: 12450684.046833275 J_MAX: 24076460.85940194 STAG. PRES: 0.3987101207771301\n",
      "V_infty: 21.0 km/s, L/D: 0.28 G_MAX: 340.87615163620126 QDOT_MAX: 14973766.716695668 J_MAX: 23708284.155848857 STAG. PRES: 0.35832155252092307\n",
      "V_infty: 21.0 km/s, L/D: 0.32 G_MAX: 401.9371583282262 QDOT_MAX: 17851913.682837665 J_MAX: 23375021.340923782 STAG. PRES: 0.32555283803658197\n",
      "V_infty: 21.0 km/s, L/D: 0.36 G_MAX: 469.9604725579138 QDOT_MAX: 21008990.33325123 J_MAX: 23068007.462695267 STAG. PRES: 0.29847422274431157\n",
      "V_infty: 21.0 km/s, L/D: 0.4 G_MAX: 548.3646580892022 QDOT_MAX: 24507305.16128587 J_MAX: 22788486.92632269 STAG. PRES: 0.27579137248694374\n",
      "V_infty: 24.0 km/s, L/D: 0.0 G_MAX: 86.83078099602477 QDOT_MAX: 12835445.004059833 J_MAX: 111071470.75709051 STAG. PRES: 1.681038256801247\n",
      "V_infty: 24.0 km/s, L/D: 0.04 G_MAX: 120.32148750818115 QDOT_MAX: 17151565.32149598 J_MAX: 106601013.42392544 STAG. PRES: 1.2412114137483983\n",
      "V_infty: 24.0 km/s, L/D: 0.08 G_MAX: 162.63593132817945 QDOT_MAX: 23190959.876288094 J_MAX: 103130566.54747233 STAG. PRES: 0.9626679542180737\n",
      "V_infty: 24.0 km/s, L/D: 0.12 G_MAX: 212.07987644311496 QDOT_MAX: 30629323.303380266 J_MAX: 100392925.23797636 STAG. PRES: 0.7850130738174549\n",
      "V_infty: 24.0 km/s, L/D: 0.16 G_MAX: 268.69370058206437 QDOT_MAX: 39473249.3313202 J_MAX: 98152410.85306574 STAG. PRES: 0.663728246768183\n",
      "V_infty: 24.0 km/s, L/D: 0.2 G_MAX: 330.47884485301387 QDOT_MAX: 49598698.71692301 J_MAX: 96247180.40141 STAG. PRES: 0.5765561904949994\n",
      "V_infty: 24.0 km/s, L/D: 0.24 G_MAX: 399.97602828667254 QDOT_MAX: 60755925.964060925 J_MAX: 94577580.01938099 STAG. PRES: 0.5105060873935819\n",
      "V_infty: 24.0 km/s, L/D: 0.28 G_MAX: 477.6704451043603 QDOT_MAX: 73495796.48916762 J_MAX: 93103636.96654771 STAG. PRES: 0.45852686088614514\n",
      "V_infty: 24.0 km/s, L/D: 0.32 G_MAX: 563.108896163149 QDOT_MAX: 87575960.57090226 J_MAX: 91768691.26933944 STAG. PRES: 0.41639215293110005\n",
      "V_infty: 24.0 km/s, L/D: 0.36 G_MAX: 661.12248294585 QDOT_MAX: 103404560.52598642 J_MAX: 90553628.38418765 STAG. PRES: 0.3815720118004039\n",
      "V_infty: 24.0 km/s, L/D: 0.4 G_MAX: 768.2410362366784 QDOT_MAX: 120899616.52187343 J_MAX: 89438099.96779191 STAG. PRES: 0.3524345884117909\n",
      "V_infty: 27.0 km/s, L/D: 0.0 G_MAX: 111.5442386465644 QDOT_MAX: 51590692.588848256 J_MAX: 391866401.0683289 STAG. PRES: 2.1594301953043176\n",
      "V_infty: 27.0 km/s, L/D: 0.04 G_MAX: 156.79218968811193 QDOT_MAX: 70004907.98897798 J_MAX: 375447482.90135854 STAG. PRES: 1.5731946723044288\n",
      "V_infty: 27.0 km/s, L/D: 0.08 G_MAX: 214.94653880489463 QDOT_MAX: 96089650.6187349 J_MAX: 362908806.31273574 STAG. PRES: 1.2099499615433325\n",
      "V_infty: 27.0 km/s, L/D: 0.12 G_MAX: 282.598881873152 QDOT_MAX: 128171024.28421503 J_MAX: 353145288.902695 STAG. PRES: 0.9836753227318676\n",
      "V_infty: 27.0 km/s, L/D: 0.16 G_MAX: 359.62962236601464 QDOT_MAX: 166531898.24553177 J_MAX: 345180570.10417414 STAG. PRES: 0.8300344836068337\n",
      "V_infty: 27.0 km/s, L/D: 0.2 G_MAX: 443.6254445290409 QDOT_MAX: 209761641.97218788 J_MAX: 338437149.10517526 STAG. PRES: 0.7202151451924369\n",
      "V_infty: 27.0 km/s, L/D: 0.24 G_MAX: 538.6461111760674 QDOT_MAX: 257929759.10057172 J_MAX: 332529206.2485152 STAG. PRES: 0.6372601049258582\n",
      "V_infty: 27.0 km/s, L/D: 0.28 G_MAX: 642.7742499078059 QDOT_MAX: 313432614.7084922 J_MAX: 327308272.2662373 STAG. PRES: 0.5721346333802094\n",
      "V_infty: 27.0 km/s, L/D: 0.32 G_MAX: 760.3212782673115 QDOT_MAX: 374654859.15271825 J_MAX: 322593942.6605898 STAG. PRES: 0.5193700593708664\n",
      "V_infty: 27.0 km/s, L/D: 0.36 G_MAX: 892.2414429582639 QDOT_MAX: 442071436.8226371 J_MAX: 318313606.7130965 STAG. PRES: 0.47581216234291135\n",
      "V_infty: 27.0 km/s, L/D: 0.4 G_MAX: 1032.3819903429658 QDOT_MAX: 515978713.81727225 J_MAX: 314390206.1651225 STAG. PRES: 0.43933326200785106\n",
      "V_infty: 30.0 km/s, L/D: 0.0 G_MAX: 139.84700789418326 QDOT_MAX: 184999290.86062068 J_MAX: 1250256011.4716566 STAG. PRES: 2.707299825329253\n",
      "V_infty: 30.0 km/s, L/D: 0.04 G_MAX: 199.11405909214392 QDOT_MAX: 254580276.84675002 J_MAX: 1195960745.8091698 STAG. PRES: 1.9470265612220308\n",
      "V_infty: 30.0 km/s, L/D: 0.08 G_MAX: 276.03534202147694 QDOT_MAX: 353942532.59429413 J_MAX: 1155247378.1735034 STAG. PRES: 1.4870609969628599\n",
      "V_infty: 30.0 km/s, L/D: 0.12 G_MAX: 366.1509830250218 QDOT_MAX: 476470411.1650716 J_MAX: 1123878195.4282231 STAG. PRES: 1.20597782003327\n",
      "V_infty: 30.0 km/s, L/D: 0.16 G_MAX: 466.50842644571844 QDOT_MAX: 623483778.9451517 J_MAX: 1098397754.363262 STAG. PRES: 1.0160248880936757\n",
      "V_infty: 30.0 km/s, L/D: 0.2 G_MAX: 578.594566691388 QDOT_MAX: 787722485.5894513 J_MAX: 1076826839.08915 STAG. PRES: 0.8808288706883097\n",
      "V_infty: 30.0 km/s, L/D: 0.24 G_MAX: 702.7382883391813 QDOT_MAX: 973281110.7605577 J_MAX: 1058039986.2382952 STAG. PRES: 0.7789679920200574\n",
      "V_infty: 30.0 km/s, L/D: 0.28 G_MAX: 840.2847745010763 QDOT_MAX: 1182237991.0956407 J_MAX: 1041420732.0153769 STAG. PRES: 0.6991240367053264\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 30.0 km/s, L/D: 0.32 G_MAX: 996.0520134918464 QDOT_MAX: 1414735391.4837267 J_MAX: 1026425806.4939837 STAG. PRES: 0.6345026451496921\n",
      "V_infty: 30.0 km/s, L/D: 0.36 G_MAX: 1162.788727396109 QDOT_MAX: 1672306513.015172 J_MAX: 1012787090.1055467 STAG. PRES: 0.5811520813707975\n",
      "V_infty: 30.0 km/s, L/D: 0.4 G_MAX: 1343.5011781249593 QDOT_MAX: 1937802706.271445 J_MAX: 1000241391.800571 STAG. PRES: 0.5364496712478201\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(150.0,0.0,0.0,v0_kms_array[i],0.0,overShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-6)\n",
    "        vehicle.propogateEntry (2400.0, 0.1, 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(150.0,0.0,0.0,v0_kms_array[i],0.0,underShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-6)\n",
    "        vehicle.propogateEntry (2400.0, 0.1, 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/venus/'+runID+'acc_net_g_max_array.txt',acc_net_g_max_array)\n",
    "np.savetxt('../data/jsr-paper/venus/'+runID+'stag_pres_atm_max_array.txt',stag_pres_atm_max_array)\n",
    "np.savetxt('../data/jsr-paper/venus/'+runID+'q_stag_total_max_array.txt',q_stag_total_max_array)\n",
    "np.savetxt('../data/jsr-paper/venus/'+runID+'heatload_max_array.txt',heatload_max_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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/venus/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/venus/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/venus/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/venus/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/venus/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/venus/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/venus/'+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,   0.4 ,110)\n",
    "z_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "z1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "g1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "q1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "h1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "#s1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "for i in range(0,len(x_new)):\n",
    "    for j in range(0,len(y_new)):\n",
    "\n",
    "        z1_new[i,j] = f1(x_new[i],y_new[j])\n",
    "        g1_new[i,j] = g1(x_new[i],y_new[j])\n",
    "        q1_new[i,j] = q1(x_new[i],y_new[j])\n",
    "        h1_new[i,j] = h1(x_new[i],y_new[j])\n",
    "        #s1_new[i,j] = s1(x_new[i],y_new[j])\n",
    "\n",
    "\n",
    "Z1 = z1_new\n",
    "G1 = g1_new\n",
    "Q1 = q1_new\n",
    "#S1 = s1_new\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "\n",
    "\n",
    "Zlevels = np.array([0.5,1.0])\n",
    "\n",
    "Glevels = np.array([25.0, 40.0])\n",
    "Qlevels = np.array([5000.0])\n",
    "Hlevels = np.array([50.0])\n",
    "#Slevels = np.array([0.8])\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "#plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "#plt.xlim([0.0,30.0])\n",
    "#plt.ylim([0.0,0.4])\n",
    "#plt.tight_layout()\n",
    "#plt.contourf(X, Y, Z, levels=levels)\n",
    "\n",
    "\n",
    "#plt.axvline(x=25.0,linewidth=3, linestyle='dotted' ,color='red',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(LV$'+r' '+r'$C3$'+r' '+r'$limit)$')\n",
    "#plt.axvline(x=13.1,linewidth=1, linestyle='dotted' ,color='cyan',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(Chem. OI)$')\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.25,6.25])\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "\n",
    "ZCS1 = plt.contour(X, Y, np.transpose(Z1), levels=Zlevels, colors='black')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.clabel(ZCS1, inline=1, fontsize=12, colors='black',fmt='%.1f',inline_spacing=1)\n",
    "ZCS1.collections[0].set_linewidths(1.5)\n",
    "ZCS1.collections[1].set_linewidths(1.5)\n",
    "ZCS1.collections[0].set_label(r'$TCW, deg$')\n",
    "\n",
    "\n",
    "GCS1 = plt.contour(X, Y, np.transpose(G1), levels=Glevels, colors='blue',linestyles='dashed')\n",
    "\n",
    "plt.clabel(GCS1, inline=1, fontsize=12, colors='blue',fmt='%d',inline_spacing=0)\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[1].set_linewidths(1.5)\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted')\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=12, colors='red',fmt='%d',inline_spacing=0)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "\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(np.array([ 0.0, 0.1, 0.2, 0.3, 0.4]),fontsize=16)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "plt.legend(loc='upper right', fontsize=16)\n",
    "\n",
    "dat0 = ZCS1.allsegs[1][0]\n",
    "x1,y1=dat0[:,0],dat0[:,1]\n",
    "F1 = interpolate.interp1d(x1, y1, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat1 = GCS1.allsegs[0][0]\n",
    "x2,y2=dat1[:,0],dat1[:,1]\n",
    "F2 = interpolate.interp1d(x2, y2, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\n",
    "dat2 = HCS1.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([0.0,0.4])\n",
    "\n",
    "plt.savefig('../data/jsr-paper/venus/venus-lift-small.png', bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/venus/venus-lift-small.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/venus/venus-lift-small.eps', dpi=300,bbox_inches='tight')\n",
    "\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/venus/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/venus/'+runID+'LD_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/venus/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/venus/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/venus/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/venus/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/venus/'+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='linear')\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,  110)\n",
    "y_new =  np.linspace( 0.0,   0.4 ,110)\n",
    "z_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "z1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "g1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "q1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "h1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "#s1_new =  np.zeros((len(x_new),len(y_new)))\n",
    "\n",
    "for i in range(0,len(x_new)):\n",
    "    for j in range(0,len(y_new)):\n",
    "\n",
    "        z1_new[i,j] = f1(x_new[i],y_new[j])\n",
    "        g1_new[i,j] = g1(x_new[i],y_new[j])\n",
    "        q1_new[i,j] = q1(x_new[i],y_new[j])\n",
    "        h1_new[i,j] = h1(x_new[i],y_new[j])\n",
    "        #s1_new[i,j] = s1(x_new[i],y_new[j])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Z1 = z1_new\n",
    "\n",
    "G1 = g1_new\n",
    "\n",
    "Q1 = q1_new\n",
    "\n",
    "#S1 = s1_new\n",
    "\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "#X, Y = meshgrid(x, y)\n",
    "\n",
    "\n",
    "Zlevels = np.array([0.5,1.0,1.5,2.0,3.0])\n",
    "\n",
    "Glevels = np.array([5.0, 10.0, 20.0, 40.0, 80.0])\n",
    "Qlevels = np.array([400.0, 1200.0, 2400.0, 4800.0, 12000.0 ])\n",
    "Hlevels = np.array([40.0, 80.0, 200.0, 600.0])\n",
    "#Slevels = np.array([0.8])\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.5,6.5])\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "#plt.xlim([0.0,30.0])\n",
    "#plt.ylim([0.0,0.4])\n",
    "#plt.tight_layout()\n",
    "#plt.contourf(X, Y, Z, levels=levels)\n",
    "\n",
    "\n",
    "#plt.axvline(x=25.0,linewidth=3, linestyle='dotted' ,color='red',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(LV$'+r' '+r'$C3$'+r' '+r'$limit)$')\n",
    "#plt.axvline(x=13.1,linewidth=1, linestyle='dotted' ,color='cyan',label=r'$Max.$'+' '+r'$arrival$'+' '+r'$V_{\\infty}$'+ r' ' +r'$(Chem. OI)$')\n",
    "\n",
    "\n",
    "ZCS1 = plt.contour(X, Y, np.transpose(Z1), levels=Zlevels, colors='black',zorder=0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.clabel(ZCS1, inline=1, fontsize=10, colors='black',fmt='%.1f',inline_spacing=1,zorder=0)\n",
    "ZCS1.collections[0].set_linewidths(1.5)\n",
    "ZCS1.collections[1].set_linewidths(1.5)\n",
    "ZCS1.collections[2].set_linewidths(1.5)\n",
    "ZCS1.collections[3].set_linewidths(1.5)\n",
    "ZCS1.collections[4].set_linewidths(1.5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ZCS1.collections[0].set_label(r'$TCW, deg$')\n",
    "\n",
    "\n",
    "GCS1 = plt.contour(X, Y, np.transpose(G1), levels=Glevels, colors='blue',linestyles='dashed',zorder=1)\n",
    "\n",
    "plt.clabel(GCS1, inline=1, fontsize=10, colors='blue',fmt='%d',inline_spacing=0,zorder=1)\n",
    "\n",
    "\n",
    "\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[1].set_linewidths(1.5)\n",
    "GCS1.collections[2].set_linewidths(1.5)\n",
    "GCS1.collections[3].set_linewidths(1.5)\n",
    "GCS1.collections[4].set_linewidths(1.5)\n",
    "\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted',zorder=13)\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=10, colors='red',fmt='%d',inline_spacing=0,zorder=13)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "QCS1.collections[1].set_linewidths(1.5)\n",
    "QCS1.collections[2].set_linewidths(1.5)\n",
    "QCS1.collections[3].set_linewidths(1.5)\n",
    "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",
    "labelsH = plt.clabel(HCS1, inline=1, fontsize=10, colors='xkcd:brown',fmt='%d',inline_spacing=0,zorder=14)\n",
    "HCS1.collections[0].set_linewidths(1.75)\n",
    "HCS1.collections[1].set_linewidths(1.75)\n",
    "HCS1.collections[2].set_linewidths(1.75)\n",
    "HCS1.collections[3].set_linewidths(1.75)\n",
    "\n",
    "\n",
    "\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "for l in labelsH:\n",
    "    l.set_rotation(-90)\n",
    "\n",
    "\n",
    "\n",
    "#SCS1 = plt.contour(X, Y, transpose(S1), levels=Slevels, colors='cyan')\n",
    "\n",
    "#plt.clabel(SCS1, inline=1, fontsize=12, colors='cyan',fmt='%.1f',inline_spacing=1)\n",
    "#SCS1.collections[0].set_linewidths(3.0)\n",
    "#SCS1.collections[0].set_label(r'$Peak$'+r' '+r'$stag. pressure,atm$')\n",
    "\n",
    "#plt.axhline(y=0.36,linewidth=1, linestyle='dotted' ,color='white',label=r'$Apollo$'+' '+r'$CM$'+' '+r'$L/D$')\n",
    "\n",
    "\n",
    "\n",
    "#matplotlib.rcParams['text.usetex'] = True\n",
    "#plt.rc('text', usetex=True)\n",
    "\n",
    "\n",
    "# circles for b=50 plot\n",
    "#plt.plot(7.5,0.20,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.30,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "#plt.plot(7.5,0.211,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "#plt.plot(4.95,0.315,marker='o',mfc='none',mec='k',markersize=16,markeredgewidth=3.0)\n",
    "\n",
    "\n",
    "\n",
    "#plt.grid(True,linestyle='dotted', linewidth=0.1)\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(\"L/D\",fontsize=12)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=12)\n",
    "plt.xticks(np.array([ 0.0, 5, 10, 15, 20, 25, 30]),fontsize=12)\n",
    "plt.yticks(np.array([ 0.0, 0.1, 0.2, 0.3, 0.4]),fontsize=12)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "plt.legend(loc='upper right', fontsize=10)\n",
    "\n",
    "plt.savefig('../data/jsr-paper/venus/venus-lift-large.png', dpi=300, bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/venus/venus-lift-large.pdf', dpi=300, bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/venus/venus-lift-large.eps', dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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