{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 04 - b - Earth - Feasibility Charts - Drag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle\n",
    "\n",
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.patches import Polygon\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a planet object\n",
    "planet=Planet(\"EARTH\")\n",
    "planet.h_skip = 140000.0\n",
    "\n",
    "# Load an nominal atmospheric profile with height, temp, pressure, density data\n",
    "planet.loadAtmosphereModel('../atmdata/Earth/earth-gram-avg.dat', 0 , 1 ,2, 3)\n",
    "\n",
    "vinf_kms_array  = np.linspace( 0.0,   20.0,   11)\n",
    "betaRatio_array = np.linspace( 1.0,   41.0 ,  11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta1 = 20.0\n",
    "\n",
    "runID = 'earth-drag-'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "v0_kms_array    = np.zeros(len(vinf_kms_array))\n",
    "v0_kms_array[:] = np.sqrt(1.0*(vinf_kms_array[:]*1E3)**2.0 + 2*np.ones(len(vinf_kms_array))*planet.GM/(planet.RP+140.0*1.0E3))/1.0E3\n",
    "\n",
    "overShootLimit_array  = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "underShootLimit_array = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "exitflag_os_array     = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "exitflag_us_array     = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "TCW_array             = np.zeros((len(v0_kms_array),len(betaRatio_array)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VINF: 0.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 5.0 TCW: 0.5024158579508367 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 9.0 TCW: 0.6920214957062854 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 13.0 TCW: 0.812993623148941 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 17.0 TCW: 0.9017098706171964 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 21.0 TCW: 0.971948733622412 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 25.0 TCW: 1.030215350270737 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 29.0 TCW: 1.080004599833046 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 33.0 TCW: 1.1235908525741252 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 37.0 TCW: 1.1621811866898497 deg.\n",
      "VINF: 0.0 km/s, BETA RATIO: 41.0 TCW: 1.1967777707832283 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 5.0 TCW: 0.5084928039359511 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 9.0 TCW: 0.7009137212880887 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 13.0 TCW: 0.8234570946151507 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 17.0 TCW: 0.9133765700680669 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 21.0 TCW: 0.9846327129707788 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 25.0 TCW: 1.043606335799268 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 29.0 TCW: 1.094154423684813 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 33.0 TCW: 1.1382936643422 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 37.0 TCW: 1.1773440357974323 deg.\n",
      "VINF: 2.0 km/s, BETA RATIO: 41.0 TCW: 1.2122794354363577 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 5.0 TCW: 0.5256826096701843 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 9.0 TCW: 0.7254891864940873 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 13.0 TCW: 0.8520983545931813 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 17.0 TCW: 0.9449915318509738 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 21.0 TCW: 1.0186891513876617 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 25.0 TCW: 1.0797628225773224 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 29.0 TCW: 1.132038149276923 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 33.0 TCW: 1.1774446051640552 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 37.0 TCW: 1.2175659179629292 deg.\n",
      "VINF: 4.0 km/s, BETA RATIO: 41.0 TCW: 1.253379664678505 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 5.0 TCW: 0.5478086118637293 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 9.0 TCW: 0.7566204537251906 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 13.0 TCW: 0.8884976135632314 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 17.0 TCW: 0.9854981788230361 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 21.0 TCW: 1.0623931251902832 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 25.0 TCW: 1.126083068193111 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 29.0 TCW: 1.1801420656847768 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 33.0 TCW: 1.2269663571860292 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 37.0 TCW: 1.2684021467612183 deg.\n",
      "VINF: 6.0 km/s, BETA RATIO: 41.0 TCW: 1.3056064268203045 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 5.0 TCW: 0.571179966955242 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 9.0 TCW: 0.7886026306987333 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 13.0 TCW: 0.9259906900842907 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 17.0 TCW: 1.0270388619428559 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 21.0 TCW: 1.1070871752563107 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 25.0 TCW: 1.1728258233742963 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 29.0 TCW: 1.2285473830161209 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 33.0 TCW: 1.2770321856914961 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 37.0 TCW: 1.3199306304995844 deg.\n",
      "VINF: 8.0 km/s, BETA RATIO: 41.0 TCW: 1.3580643990462704 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 5.0 TCW: 0.5928134542664338 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 9.0 TCW: 0.8178167006626609 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 13.0 TCW: 0.9601281899558671 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 17.0 TCW: 1.0648214961620397 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 21.0 TCW: 1.1470388543239096 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 25.0 TCW: 1.2144698030533618 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 29.0 TCW: 1.2719298508418433 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 33.0 TCW: 1.3216473308275454 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 37.0 TCW: 1.3650112365321547 deg.\n",
      "VINF: 10.0 km/s, BETA RATIO: 41.0 TCW: 1.4035480905768054 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 5.0 TCW: 0.6111976380707347 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 9.0 TCW: 0.8426389456617471 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 13.0 TCW: 0.9891743666012189 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 17.0 TCW: 1.0963743904831063 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 21.0 TCW: 1.1801824799076712 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 25.0 TCW: 1.2493653600358812 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 29.0 TCW: 1.3077243775369425 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 33.0 TCW: 1.3576669723952364 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 37.0 TCW: 1.4014748134941328 deg.\n",
      "VINF: 12.0 km/s, BETA RATIO: 41.0 TCW: 1.440504557267559 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 5.0 TCW: 0.6263489441480488 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 9.0 TCW: 0.863073983018694 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 13.0 TCW: 1.0129138316624449 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 17.0 TCW: 1.121699113966315 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 21.0 TCW: 1.2071531415895151 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 25.0 TCW: 1.277171142912266 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 29.0 TCW: 1.3356359909703315 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 33.0 TCW: 1.3859382518348866 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 37.0 TCW: 1.4302129275529296 deg.\n",
      "VINF: 14.0 km/s, BETA RATIO: 41.0 TCW: 1.4695520619461604 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 5.0 TCW: 0.6386032163682103 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 9.0 TCW: 0.879705040519184 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 13.0 TCW: 1.031826681290113 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 17.0 TCW: 1.141910403399379 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 21.0 TCW: 1.2284846077491238 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 25.0 TCW: 1.298522606088227 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 29.0 TCW: 1.3572335174940235 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 33.0 TCW: 1.4078978686666233 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 37.0 TCW: 1.4523462156466849 deg.\n",
      "VINF: 16.0 km/s, BETA RATIO: 41.0 TCW: 1.4917564620627672 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 5.0 TCW: 0.6485547510274046 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 9.0 TCW: 0.8931842920464987 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 13.0 TCW: 1.046759833869146 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 17.0 TCW: 1.158121125485195 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 21.0 TCW: 1.2449064757165615 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 25.0 TCW: 1.3149266510517919 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 29.0 TCW: 1.3739674147291225 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 33.0 TCW: 1.4247854124987498 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 37.0 TCW: 1.4692256475063914 deg.\n",
      "VINF: 18.0 km/s, BETA RATIO: 41.0 TCW: 1.5085103209930821 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 1.0 TCW: 0.0 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 5.0 TCW: 0.6568261779175373 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 9.0 TCW: 0.9042180979777186 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 13.0 TCW: 1.0587889082016773 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 17.0 TCW: 1.1710584895408829 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 21.0 TCW: 1.2576599730055023 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 25.0 TCW: 1.3278858745361504 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 29.0 TCW: 1.387020216898236 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 33.0 TCW: 1.4378129194119538 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 37.0 TCW: 1.4820925628991972 deg.\n",
      "VINF: 20.0 km/s, BETA RATIO: 41.0 TCW: 1.521178109505854 deg.\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,len(v0_kms_array)):\n",
    "    for j in range(0,len(betaRatio_array)):\n",
    "        vehicle=Vehicle('DMVehicle', 150.0, beta1, 0.0, 3.1416, 0.0, 0.10, planet)\n",
    "        vehicle.setInitialState(140.0,0.0,0.0,v0_kms_array[i],0.0,-4.5,0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-6)\n",
    "        vehicle.setDragModulationVehicleParams(beta1,betaRatio_array[j])\n",
    "\n",
    "        underShootLimit_array[i,j], exitflag_us_array[i,j] = vehicle.findUnderShootLimitD(2400.0, 2.0, -80.0,-4.0,1E-10,400.0)\n",
    "        overShootLimit_array[i,j] , exitflag_os_array[i,j] = vehicle.findOverShootLimitD (2400.0, 2.0, -80.0,-4.0,1E-10,400.0)\n",
    "\n",
    "        TCW_array[i,j]    = overShootLimit_array[i,j] - underShootLimit_array[i,j]\n",
    "\n",
    "        print('VINF: '+str(vinf_kms_array[i])+' km/s, BETA RATIO: '+str(betaRatio_array[j])+' TCW: '+str(TCW_array[i,j])+' deg.')\n",
    "\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'vinf_kms_array.txt',vinf_kms_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'v0_kms_array.txt',v0_kms_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'betaRatio_array.txt',betaRatio_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'overShootLimit_array.txt',overShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'exitflag_os_array.txt',exitflag_os_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'underShootLimit_array.txt',underShootLimit_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'exitflag_us_array.txt',exitflag_us_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'TCW_array.txt',TCW_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_net_g_max_array       = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "stag_pres_atm_max_array   = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "q_stag_total_max_array    = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "heatload_max_array        = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "\n",
    "\n",
    "for i in range(0,len(v0_kms_array)):\n",
    "    for j in range(0,len(betaRatio_array)):\n",
    "        vehicle=Vehicle('DMVehicle', 150.0, beta1, 0.0, 3.1416, 0.0, 0.10, planet)\n",
    "        vehicle.setInitialState(140.0,0.0,0.0,v0_kms_array[i],0.0,overShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-6)\n",
    "\n",
    "        vehicle.propogateEntry (2400.0, 2.0, 0.0)\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",
    "\n",
    "        vehicle=Vehicle('DMVehicle', 150.0, beta1, 0.0, 3.1416, 0.0, 0.10, planet)\n",
    "        vehicle.setInitialState(140.0,0.0,0.0,v0_kms_array[i],0.0,underShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams( 1E-6)\n",
    "\n",
    "        vehicle.propogateEntry (2400.0, 2.0, 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_os))\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\"+\", BR: \"+str(betaRatio_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",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'acc_net_g_max_array.txt',acc_net_g_max_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'stag_pres_atm_max_array.txt',stag_pres_atm_max_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'q_stag_total_max_array.txt',q_stag_total_max_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'heatload_max_array.txt',heatload_max_array)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 0.0 km/s, BR: 1.0 G_MAX: 4.1238715408974125 QDOT_MAX: 228.76520634563138 J_MAX: 23556.850516412036 STAG. PRES: 0.00799217389229539\n",
      "V_infty: 0.0 km/s, BR: 5.0 G_MAX: 4.1238715408974125 QDOT_MAX: 310.13215828749435 J_MAX: 23556.850516412036 STAG. PRES: 0.00799217389229539\n",
      "V_infty: 0.0 km/s, BR: 9.0 G_MAX: 4.1238715408974125 QDOT_MAX: 332.95054702339985 J_MAX: 23556.850516412036 STAG. PRES: 0.00799217389229539\n",
      "V_infty: 0.0 km/s, BR: 13.0 G_MAX: 4.1238715408974125 QDOT_MAX: 346.06097940973405 J_MAX: 23556.850516412036 STAG. PRES: 0.00799217389229539\n",
      "V_infty: 0.0 km/s, BR: 17.0 G_MAX: 4.1238715408974125 QDOT_MAX: 354.619848457484 J_MAX: 23556.850516412036 STAG. PRES: 0.00799217389229539\n",
      "V_infty: 0.0 km/s, BR: 21.0 G_MAX: 4.1238715408974125 QDOT_MAX: 361.7390451897104 J_MAX: 23556.850516412036 STAG. PRES: 0.00799217389229539\n",
      "V_infty: 0.0 km/s, BR: 25.0 G_MAX: 4.1238715408974125 QDOT_MAX: 366.8353594528586 J_MAX: 23556.850516412036 STAG. PRES: 0.00799217389229539\n",
      "V_infty: 0.0 km/s, BR: 29.0 G_MAX: 4.1238715408974125 QDOT_MAX: 371.5261723901511 J_MAX: 23556.850516412036 STAG. PRES: 0.00799217389229539\n",
      "V_infty: 0.0 km/s, BR: 33.0 G_MAX: 4.1238715408974125 QDOT_MAX: 374.7576374866057 J_MAX: 23556.850516412036 STAG. PRES: 0.00799217389229539\n",
      "V_infty: 0.0 km/s, BR: 37.0 G_MAX: 4.1238715408974125 QDOT_MAX: 378.36458290571716 J_MAX: 23556.850516412036 STAG. PRES: 0.00799217389229539\n",
      "V_infty: 0.0 km/s, BR: 41.0 G_MAX: 4.1238715408974125 QDOT_MAX: 381.45591442030525 J_MAX: 23556.850516412036 STAG. PRES: 0.00799217389229539\n",
      "V_infty: 2.0 km/s, BR: 1.0 G_MAX: 4.456212750229545 QDOT_MAX: 244.31631024231763 J_MAX: 24412.720934276764 STAG. PRES: 0.008635934270157066\n",
      "V_infty: 2.0 km/s, BR: 5.0 G_MAX: 4.456212750229545 QDOT_MAX: 329.2561263615547 J_MAX: 24412.720934276764 STAG. PRES: 0.008635934270157066\n",
      "V_infty: 2.0 km/s, BR: 9.0 G_MAX: 4.456212750229545 QDOT_MAX: 353.186327600604 J_MAX: 24412.720934276764 STAG. PRES: 0.008635934270157066\n",
      "V_infty: 2.0 km/s, BR: 13.0 G_MAX: 4.456212750229545 QDOT_MAX: 367.015538951275 J_MAX: 24412.720934276764 STAG. PRES: 0.008635934270157066\n",
      "V_infty: 2.0 km/s, BR: 17.0 G_MAX: 4.456212750229545 QDOT_MAX: 376.0440882335085 J_MAX: 24412.720934276764 STAG. PRES: 0.008635934270157066\n",
      "V_infty: 2.0 km/s, BR: 21.0 G_MAX: 4.456212750229545 QDOT_MAX: 383.170708803897 J_MAX: 24412.720934276764 STAG. PRES: 0.008635934270157066\n",
      "V_infty: 2.0 km/s, BR: 25.0 G_MAX: 4.456212750229545 QDOT_MAX: 388.75200526041056 J_MAX: 24412.720934276764 STAG. PRES: 0.008635934270157066\n",
      "V_infty: 2.0 km/s, BR: 29.0 G_MAX: 4.456212750229545 QDOT_MAX: 393.52286758609347 J_MAX: 24412.720934276764 STAG. PRES: 0.008635934270157066\n",
      "V_infty: 2.0 km/s, BR: 33.0 G_MAX: 4.456212750229545 QDOT_MAX: 396.80039374843517 J_MAX: 24412.720934276764 STAG. PRES: 0.008635934270157066\n",
      "V_infty: 2.0 km/s, BR: 37.0 G_MAX: 4.456212750229545 QDOT_MAX: 400.78501223092553 J_MAX: 24412.720934276764 STAG. PRES: 0.008635934270157066\n",
      "V_infty: 2.0 km/s, BR: 41.0 G_MAX: 4.456212750229545 QDOT_MAX: 403.9988233951479 J_MAX: 24412.720934276764 STAG. PRES: 0.008635934270157066\n",
      "V_infty: 4.0 km/s, BR: 1.0 G_MAX: 5.467418823009016 QDOT_MAX: 292.96791813323136 J_MAX: 26891.06243229321 STAG. PRES: 0.010595011946048278\n",
      "V_infty: 4.0 km/s, BR: 5.0 G_MAX: 5.467418823009016 QDOT_MAX: 390.2883506042765 J_MAX: 26891.06243229321 STAG. PRES: 0.010595011946048278\n",
      "V_infty: 4.0 km/s, BR: 9.0 G_MAX: 5.467418823009016 QDOT_MAX: 417.78444518187825 J_MAX: 26891.06243229321 STAG. PRES: 0.010595011946048278\n",
      "V_infty: 4.0 km/s, BR: 13.0 G_MAX: 5.467418823009016 QDOT_MAX: 433.40323241342196 J_MAX: 26891.06243229321 STAG. PRES: 0.010595011946048278\n",
      "V_infty: 4.0 km/s, BR: 17.0 G_MAX: 5.467418823009016 QDOT_MAX: 443.53344318667746 J_MAX: 26891.06243229321 STAG. PRES: 0.010595011946048278\n",
      "V_infty: 4.0 km/s, BR: 21.0 G_MAX: 5.467418823009016 QDOT_MAX: 451.9501628838298 J_MAX: 26891.06243229321 STAG. PRES: 0.010595011946048278\n",
      "V_infty: 4.0 km/s, BR: 25.0 G_MAX: 5.467418823009016 QDOT_MAX: 457.8896949316622 J_MAX: 26891.06243229321 STAG. PRES: 0.010595011946048278\n",
      "V_infty: 4.0 km/s, BR: 29.0 G_MAX: 5.467418823009016 QDOT_MAX: 463.8928315029548 J_MAX: 26891.06243229321 STAG. PRES: 0.010595011946048278\n",
      "V_infty: 4.0 km/s, BR: 33.0 G_MAX: 5.467418823009016 QDOT_MAX: 468.0348996243907 J_MAX: 26891.06243229321 STAG. PRES: 0.010595011946048278\n",
      "V_infty: 4.0 km/s, BR: 37.0 G_MAX: 5.467418823009016 QDOT_MAX: 471.3003611069189 J_MAX: 26891.06243229321 STAG. PRES: 0.010595011946048278\n",
      "V_infty: 4.0 km/s, BR: 41.0 G_MAX: 5.467418823009016 QDOT_MAX: 475.5749082498807 J_MAX: 26891.06243229321 STAG. PRES: 0.010595011946048278\n",
      "V_infty: 6.0 km/s, BR: 1.0 G_MAX: 7.238483146906695 QDOT_MAX: 380.55008895389506 J_MAX: 30881.64242640348 STAG. PRES: 0.014025522929466733\n",
      "V_infty: 6.0 km/s, BR: 5.0 G_MAX: 7.238483146906695 QDOT_MAX: 499.3274091563311 J_MAX: 30881.64242640348 STAG. PRES: 0.014025522929466733\n",
      "V_infty: 6.0 km/s, BR: 9.0 G_MAX: 7.238483146906695 QDOT_MAX: 532.5095236508031 J_MAX: 30881.64242640348 STAG. PRES: 0.014025522929466733\n",
      "V_infty: 6.0 km/s, BR: 13.0 G_MAX: 7.238483146906695 QDOT_MAX: 551.2267295371105 J_MAX: 30881.64242640348 STAG. PRES: 0.014025522929466733\n",
      "V_infty: 6.0 km/s, BR: 17.0 G_MAX: 7.238483146906695 QDOT_MAX: 564.9741339212634 J_MAX: 30881.64242640348 STAG. PRES: 0.014025522929466733\n",
      "V_infty: 6.0 km/s, BR: 21.0 G_MAX: 7.238483146906695 QDOT_MAX: 574.7655091974722 J_MAX: 30881.64242640348 STAG. PRES: 0.014025522929466733\n",
      "V_infty: 6.0 km/s, BR: 25.0 G_MAX: 7.238483146906695 QDOT_MAX: 583.3056447545197 J_MAX: 30881.64242640348 STAG. PRES: 0.014025522929466733\n",
      "V_infty: 6.0 km/s, BR: 29.0 G_MAX: 7.238483146906695 QDOT_MAX: 589.3534934704757 J_MAX: 30881.64242640348 STAG. PRES: 0.014025522929466733\n",
      "V_infty: 6.0 km/s, BR: 33.0 G_MAX: 7.238483146906695 QDOT_MAX: 594.6522647431033 J_MAX: 30881.64242640348 STAG. PRES: 0.014025522929466733\n",
      "V_infty: 6.0 km/s, BR: 37.0 G_MAX: 7.238483146906695 QDOT_MAX: 600.4216187211982 J_MAX: 30881.64242640348 STAG. PRES: 0.014025522929466733\n",
      "V_infty: 6.0 km/s, BR: 41.0 G_MAX: 7.238483146906695 QDOT_MAX: 604.8081843081897 J_MAX: 30881.64242640348 STAG. PRES: 0.014025522929466733\n",
      "V_infty: 8.0 km/s, BR: 1.0 G_MAX: 9.833761340424903 QDOT_MAX: 516.3044657298116 J_MAX: 36268.49196774403 STAG. PRES: 0.019051866516568348\n",
      "V_infty: 8.0 km/s, BR: 5.0 G_MAX: 9.833761340424903 QDOT_MAX: 669.0317097053579 J_MAX: 36268.49196774403 STAG. PRES: 0.019051866516568348\n",
      "V_infty: 8.0 km/s, BR: 9.0 G_MAX: 9.833761340424903 QDOT_MAX: 712.3122969665475 J_MAX: 36268.49196774403 STAG. PRES: 0.019051866516568348\n",
      "V_infty: 8.0 km/s, BR: 13.0 G_MAX: 9.833761340424903 QDOT_MAX: 737.7989874242227 J_MAX: 36268.49196774403 STAG. PRES: 0.019051866516568348\n",
      "V_infty: 8.0 km/s, BR: 17.0 G_MAX: 9.833761340424903 QDOT_MAX: 753.7281349987688 J_MAX: 36268.49196774403 STAG. PRES: 0.019051866516568348\n",
      "V_infty: 8.0 km/s, BR: 21.0 G_MAX: 9.833761340424903 QDOT_MAX: 768.5585368080998 J_MAX: 36268.49196774403 STAG. PRES: 0.019051866516568348\n",
      "V_infty: 8.0 km/s, BR: 25.0 G_MAX: 9.833761340424903 QDOT_MAX: 778.3641284108481 J_MAX: 36268.49196774403 STAG. PRES: 0.019051866516568348\n",
      "V_infty: 8.0 km/s, BR: 29.0 G_MAX: 9.833761340424903 QDOT_MAX: 785.8216114549768 J_MAX: 36268.49196774403 STAG. PRES: 0.019051866516568348\n",
      "V_infty: 8.0 km/s, BR: 33.0 G_MAX: 9.833761340424903 QDOT_MAX: 794.6301763648714 J_MAX: 36268.49196774403 STAG. PRES: 0.019051866516568348\n",
      "V_infty: 8.0 km/s, BR: 37.0 G_MAX: 9.833761340424903 QDOT_MAX: 802.0082416143698 J_MAX: 36268.49196774403 STAG. PRES: 0.019051866516568348\n",
      "V_infty: 8.0 km/s, BR: 41.0 G_MAX: 9.833761340424903 QDOT_MAX: 807.6372504392759 J_MAX: 36268.49196774403 STAG. PRES: 0.019051866516568348\n",
      "V_infty: 10.0 km/s, BR: 1.0 G_MAX: 13.326852936034982 QDOT_MAX: 711.8232789580408 J_MAX: 43005.73076907522 STAG. PRES: 0.025816654612041624\n",
      "V_infty: 10.0 km/s, BR: 5.0 G_MAX: 13.326852936034982 QDOT_MAX: 913.523058369278 J_MAX: 43005.73076907522 STAG. PRES: 0.025816654612041624\n",
      "V_infty: 10.0 km/s, BR: 9.0 G_MAX: 13.326852936034982 QDOT_MAX: 969.9076982809572 J_MAX: 43005.73076907522 STAG. PRES: 0.025816654612041624\n",
      "V_infty: 10.0 km/s, BR: 13.0 G_MAX: 13.326852936034982 QDOT_MAX: 1006.5248839520195 J_MAX: 43005.73076907522 STAG. PRES: 0.025816654612041624\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 10.0 km/s, BR: 17.0 G_MAX: 13.326852936034982 QDOT_MAX: 1028.096389570821 J_MAX: 43005.73076907522 STAG. PRES: 0.025816654612041624\n",
      "V_infty: 10.0 km/s, BR: 21.0 G_MAX: 13.326852936034982 QDOT_MAX: 1048.552723222141 J_MAX: 43005.73076907522 STAG. PRES: 0.025816654612041624\n",
      "V_infty: 10.0 km/s, BR: 25.0 G_MAX: 13.326852936034982 QDOT_MAX: 1062.1911660315056 J_MAX: 43005.73076907522 STAG. PRES: 0.025816654612041624\n",
      "V_infty: 10.0 km/s, BR: 29.0 G_MAX: 13.326852936034982 QDOT_MAX: 1070.4174948845573 J_MAX: 43005.73076907522 STAG. PRES: 0.025816654612041624\n",
      "V_infty: 10.0 km/s, BR: 33.0 G_MAX: 13.326852936034982 QDOT_MAX: 1082.2339899260355 J_MAX: 43005.73076907522 STAG. PRES: 0.025816654612041624\n",
      "V_infty: 10.0 km/s, BR: 37.0 G_MAX: 13.326852936034982 QDOT_MAX: 1093.0021567150889 J_MAX: 43005.73076907522 STAG. PRES: 0.025816654612041624\n",
      "V_infty: 10.0 km/s, BR: 41.0 G_MAX: 13.326852936034982 QDOT_MAX: 1101.3367432917912 J_MAX: 43005.73076907522 STAG. PRES: 0.025816654612041624\n",
      "V_infty: 12.0 km/s, BR: 1.0 G_MAX: 17.768325412866375 QDOT_MAX: 982.2457002731644 J_MAX: 51078.41466149958 STAG. PRES: 0.03441668939295336\n",
      "V_infty: 12.0 km/s, BR: 5.0 G_MAX: 17.768325412866375 QDOT_MAX: 1252.8508901367202 J_MAX: 51078.41466149958 STAG. PRES: 0.03441668939295336\n",
      "V_infty: 12.0 km/s, BR: 9.0 G_MAX: 17.768325412866375 QDOT_MAX: 1331.984284650074 J_MAX: 51078.41466149958 STAG. PRES: 0.03441668939295336\n",
      "V_infty: 12.0 km/s, BR: 13.0 G_MAX: 17.768325412866375 QDOT_MAX: 1376.9824347625915 J_MAX: 51078.41466149958 STAG. PRES: 0.03441668939295336\n",
      "V_infty: 12.0 km/s, BR: 17.0 G_MAX: 17.768325412866375 QDOT_MAX: 1410.827869189508 J_MAX: 51078.41466149958 STAG. PRES: 0.03441668939295336\n",
      "V_infty: 12.0 km/s, BR: 21.0 G_MAX: 17.768325412866375 QDOT_MAX: 1428.300094790352 J_MAX: 51078.41466149958 STAG. PRES: 0.03441668939295336\n",
      "V_infty: 12.0 km/s, BR: 25.0 G_MAX: 17.768325412866375 QDOT_MAX: 1451.4831040715926 J_MAX: 51078.41466149958 STAG. PRES: 0.03441668939295336\n",
      "V_infty: 12.0 km/s, BR: 29.0 G_MAX: 17.768325412866375 QDOT_MAX: 1471.6856834822872 J_MAX: 51078.41466149958 STAG. PRES: 0.03441668939295336\n",
      "V_infty: 12.0 km/s, BR: 33.0 G_MAX: 17.768325412866375 QDOT_MAX: 1485.6588430505526 J_MAX: 51078.41466149958 STAG. PRES: 0.03441668939295336\n",
      "V_infty: 12.0 km/s, BR: 37.0 G_MAX: 17.768325412866375 QDOT_MAX: 1495.1904544904191 J_MAX: 51078.41466149958 STAG. PRES: 0.03441668939295336\n",
      "V_infty: 12.0 km/s, BR: 41.0 G_MAX: 17.768325412866375 QDOT_MAX: 1501.9762188821849 J_MAX: 51078.41466149958 STAG. PRES: 0.03441668939295336\n",
      "V_infty: 14.0 km/s, BR: 1.0 G_MAX: 23.210798390249305 QDOT_MAX: 1332.6648097501122 J_MAX: 60331.612522290234 STAG. PRES: 0.04495470923827458\n",
      "V_infty: 14.0 km/s, BR: 5.0 G_MAX: 23.210798390249305 QDOT_MAX: 1689.5404599542887 J_MAX: 60331.612522290234 STAG. PRES: 0.04495470923827458\n",
      "V_infty: 14.0 km/s, BR: 9.0 G_MAX: 23.210798390249305 QDOT_MAX: 1799.4447912147089 J_MAX: 60331.612522290234 STAG. PRES: 0.04495470923827458\n",
      "V_infty: 14.0 km/s, BR: 13.0 G_MAX: 23.210798390249305 QDOT_MAX: 1865.8713942380969 J_MAX: 60331.612522290234 STAG. PRES: 0.04495470923827458\n",
      "V_infty: 14.0 km/s, BR: 17.0 G_MAX: 23.210798390249305 QDOT_MAX: 1908.9967369534634 J_MAX: 60331.612522290234 STAG. PRES: 0.04495470923827458\n",
      "V_infty: 14.0 km/s, BR: 21.0 G_MAX: 23.210798390249305 QDOT_MAX: 1927.2314937665715 J_MAX: 60331.612522290234 STAG. PRES: 0.04495470923827458\n",
      "V_infty: 14.0 km/s, BR: 25.0 G_MAX: 23.210798390249305 QDOT_MAX: 1963.5053889269684 J_MAX: 60331.612522290234 STAG. PRES: 0.04495470923827458\n",
      "V_infty: 14.0 km/s, BR: 29.0 G_MAX: 23.210798390249305 QDOT_MAX: 1991.4924633551254 J_MAX: 60331.612522290234 STAG. PRES: 0.04495470923827458\n",
      "V_infty: 14.0 km/s, BR: 33.0 G_MAX: 23.210798390249305 QDOT_MAX: 2011.2591171135425 J_MAX: 60331.612522290234 STAG. PRES: 0.04495470923827458\n",
      "V_infty: 14.0 km/s, BR: 37.0 G_MAX: 23.210798390249305 QDOT_MAX: 2024.8877418409124 J_MAX: 60331.612522290234 STAG. PRES: 0.04495470923827458\n",
      "V_infty: 14.0 km/s, BR: 41.0 G_MAX: 23.210798390249305 QDOT_MAX: 2033.846619886884 J_MAX: 60331.612522290234 STAG. PRES: 0.04495470923827458\n",
      "V_infty: 16.0 km/s, BR: 1.0 G_MAX: 29.60502598247962 QDOT_MAX: 1757.472972495462 J_MAX: 70672.49186749666 STAG. PRES: 0.05733377883112919\n",
      "V_infty: 16.0 km/s, BR: 5.0 G_MAX: 29.60502598247962 QDOT_MAX: 2221.6377504781794 J_MAX: 70672.49186749666 STAG. PRES: 0.05733377883112919\n",
      "V_infty: 16.0 km/s, BR: 9.0 G_MAX: 29.60502598247962 QDOT_MAX: 2364.9680612603406 J_MAX: 70672.49186749666 STAG. PRES: 0.05733377883112919\n",
      "V_infty: 16.0 km/s, BR: 13.0 G_MAX: 29.60502598247962 QDOT_MAX: 2438.6648940501764 J_MAX: 70672.49186749666 STAG. PRES: 0.05733377883112919\n",
      "V_infty: 16.0 km/s, BR: 17.0 G_MAX: 29.60502598247962 QDOT_MAX: 2497.4796552015623 J_MAX: 70672.49186749666 STAG. PRES: 0.05733377883112919\n",
      "V_infty: 16.0 km/s, BR: 21.0 G_MAX: 29.60502598247962 QDOT_MAX: 2548.570794914163 J_MAX: 70672.49186749666 STAG. PRES: 0.05733377883112919\n",
      "V_infty: 16.0 km/s, BR: 25.0 G_MAX: 29.60502598247962 QDOT_MAX: 2581.240507387901 J_MAX: 70672.49186749666 STAG. PRES: 0.05733377883112919\n",
      "V_infty: 16.0 km/s, BR: 29.0 G_MAX: 29.60502598247962 QDOT_MAX: 2600.3885217563643 J_MAX: 70672.49186749666 STAG. PRES: 0.05733377883112919\n",
      "V_infty: 16.0 km/s, BR: 33.0 G_MAX: 29.60502598247962 QDOT_MAX: 2599.39036241835 J_MAX: 70672.49186749666 STAG. PRES: 0.05733377883112919\n",
      "V_infty: 16.0 km/s, BR: 37.0 G_MAX: 29.60502598247962 QDOT_MAX: 2630.5388045029094 J_MAX: 70672.49186749666 STAG. PRES: 0.05733377883112919\n",
      "V_infty: 16.0 km/s, BR: 41.0 G_MAX: 29.60502598247962 QDOT_MAX: 2656.961104404154 J_MAX: 70672.49186749666 STAG. PRES: 0.05733377883112919\n",
      "V_infty: 18.0 km/s, BR: 1.0 G_MAX: 37.1477169513897 QDOT_MAX: 2279.8994452267116 J_MAX: 82084.37074667947 STAG. PRES: 0.07193700078634853\n",
      "V_infty: 18.0 km/s, BR: 5.0 G_MAX: 37.1477169513897 QDOT_MAX: 2841.8308175754414 J_MAX: 82084.37074667947 STAG. PRES: 0.07193700078634853\n",
      "V_infty: 18.0 km/s, BR: 9.0 G_MAX: 37.1477169513897 QDOT_MAX: 3001.5294486972507 J_MAX: 82084.37074667947 STAG. PRES: 0.07193700078634853\n",
      "V_infty: 18.0 km/s, BR: 13.0 G_MAX: 37.1477169513897 QDOT_MAX: 3111.983871998538 J_MAX: 82084.37074667947 STAG. PRES: 0.07193700078634853\n",
      "V_infty: 18.0 km/s, BR: 17.0 G_MAX: 37.1477169513897 QDOT_MAX: 3161.779978015933 J_MAX: 82084.37074667947 STAG. PRES: 0.07193700078634853\n",
      "V_infty: 18.0 km/s, BR: 21.0 G_MAX: 37.1477169513897 QDOT_MAX: 3205.8421096717284 J_MAX: 82084.37074667947 STAG. PRES: 0.07193700078634853\n",
      "V_infty: 18.0 km/s, BR: 25.0 G_MAX: 37.1477169513897 QDOT_MAX: 3262.4722807231865 J_MAX: 82084.37074667947 STAG. PRES: 0.07193700078634853\n",
      "V_infty: 18.0 km/s, BR: 29.0 G_MAX: 37.1477169513897 QDOT_MAX: 3303.556830687734 J_MAX: 82084.37074667947 STAG. PRES: 0.07193700078634853\n",
      "V_infty: 18.0 km/s, BR: 33.0 G_MAX: 37.1477169513897 QDOT_MAX: 3332.879097636536 J_MAX: 82084.37074667947 STAG. PRES: 0.07193700078634853\n",
      "V_infty: 18.0 km/s, BR: 37.0 G_MAX: 37.1477169513897 QDOT_MAX: 3354.7263451104704 J_MAX: 82084.37074667947 STAG. PRES: 0.07193700078634853\n",
      "V_infty: 18.0 km/s, BR: 41.0 G_MAX: 37.1477169513897 QDOT_MAX: 3371.205243865491 J_MAX: 82084.37074667947 STAG. PRES: 0.07193700078634853\n",
      "V_infty: 20.0 km/s, BR: 1.0 G_MAX: 45.645657568928826 QDOT_MAX: 2909.281062795391 J_MAX: 94537.85677363485 STAG. PRES: 0.08838803962171238\n",
      "V_infty: 20.0 km/s, BR: 5.0 G_MAX: 45.645657568928826 QDOT_MAX: 3567.6586443150572 J_MAX: 94537.85677363485 STAG. PRES: 0.08838803962171238\n",
      "V_infty: 20.0 km/s, BR: 9.0 G_MAX: 45.645657568928826 QDOT_MAX: 3799.8670798856842 J_MAX: 94537.85677363485 STAG. PRES: 0.08838803962171238\n",
      "V_infty: 20.0 km/s, BR: 13.0 G_MAX: 45.645657568928826 QDOT_MAX: 3872.212470096929 J_MAX: 94537.85677363485 STAG. PRES: 0.08838803962171238\n",
      "V_infty: 20.0 km/s, BR: 17.0 G_MAX: 45.645657568928826 QDOT_MAX: 3988.383274983685 J_MAX: 94537.85677363485 STAG. PRES: 0.08838803962171238\n",
      "V_infty: 20.0 km/s, BR: 21.0 G_MAX: 45.645657568928826 QDOT_MAX: 4062.3310460124085 J_MAX: 94537.85677363485 STAG. PRES: 0.08838803962171238\n",
      "V_infty: 20.0 km/s, BR: 25.0 G_MAX: 45.645657568928826 QDOT_MAX: 4108.2883651506245 J_MAX: 94537.85677363485 STAG. PRES: 0.08838803962171238\n",
      "V_infty: 20.0 km/s, BR: 29.0 G_MAX: 45.645657568928826 QDOT_MAX: 4136.9902323893375 J_MAX: 94537.85677363485 STAG. PRES: 0.08838803962171238\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V_infty: 20.0 km/s, BR: 33.0 G_MAX: 45.645657568928826 QDOT_MAX: 4155.376578161884 J_MAX: 94537.85677363485 STAG. PRES: 0.08838803962171238\n",
      "V_infty: 20.0 km/s, BR: 37.0 G_MAX: 45.645657568928826 QDOT_MAX: 4167.040932811504 J_MAX: 94537.85677363485 STAG. PRES: 0.08838803962171238\n",
      "V_infty: 20.0 km/s, BR: 41.0 G_MAX: 45.645657568928826 QDOT_MAX: 4174.138730849896 J_MAX: 94537.85677363485 STAG. PRES: 0.08838803962171238\n"
     ]
    }
   ],
   "source": [
    "acc_net_g_max_array       = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "stag_pres_atm_max_array   = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "q_stag_total_max_array    = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "heatload_max_array        = np.zeros((len(v0_kms_array),len(betaRatio_array)))\n",
    "\n",
    "\n",
    "for i in range(0,len(v0_kms_array)):\n",
    "    for j in range(0,len(betaRatio_array)):\n",
    "        vehicle=Vehicle('DMVehicle', 150.0, beta1, 0.0, 3.1416, 0.0, 0.10, planet)\n",
    "        vehicle.setInitialState(140.0,0.0,0.0,v0_kms_array[i],0.0,overShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams(1E-6)\n",
    "\n",
    "        vehicle.propogateEntry (2400.0, 2.0, 0.0)\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",
    "\n",
    "        vehicle=Vehicle('DMVehicle', 150.0, beta1, 0.0, 3.1416, 0.0, 0.10, planet)\n",
    "        vehicle.setInitialState(140.0,0.0,0.0,v0_kms_array[i],0.0,underShootLimit_array[i,j],0.0,0.0)\n",
    "        vehicle.setSolverParams( 1E-6)\n",
    "\n",
    "        vehicle.propogateEntry (2400.0, 2.0, 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_os))\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\"+\", BR: \"+str(betaRatio_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",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'acc_net_g_max_array.txt',acc_net_g_max_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'stag_pres_atm_max_array.txt',stag_pres_atm_max_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'q_stag_total_max_array.txt',q_stag_total_max_array)\n",
    "np.savetxt('../data/jsr-paper/earth/'+runID+'heatload_max_array.txt',heatload_max_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\AthulGirija\\anaconda3\\envs\\env1\\lib\\site-packages\\scipy\\interpolate\\interpolate.py:630: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  slope = (y_hi - y_lo) / (x_hi - x_lo)[:, None]\n",
      "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 450x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.loadtxt('../data/jsr-paper/earth/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/earth/'+runID+'betaRatio_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'stag_pres_atm_max_array.txt')\n",
    "\n",
    "\n",
    "f1 = interpolate.interp2d(x, y, np.transpose(Z1), kind='cubic')\n",
    "g1 = interpolate.interp2d(x, y, np.transpose(G1), kind='cubic')\n",
    "q1 = interpolate.interp2d(x, y, np.transpose(Q1), kind='cubic')\n",
    "h1 = interpolate.interp2d(x, y, np.transpose(H1), kind='cubic')\n",
    "s1 = interpolate.interp2d(x, y, np.transpose(S1), kind='cubic')\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   20,  110)\n",
    "y_new =  np.linspace( 0.0,   41 ,110)\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",
    "Zlevels = np.array([0.3,0.6])\n",
    "\n",
    "Glevels = np.array([10, 15])\n",
    "Qlevels = np.array([400.0, 600.0])\n",
    "Hlevels = np.array([30, 40.0])\n",
    "#Slevels = np.array([0.8])\n",
    "\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",
    "plt.xlim([0.0,20.0])\n",
    "plt.ylim([1.0,41.0])\n",
    "\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='%.2f',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",
    "Glabels=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[0].set_linewidths(1.5)\n",
    "\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",
    "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')\n",
    "\n",
    "Hlabels=plt.clabel(HCS1, inline=1, fontsize=12, colors='xkcd:brown',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",
    "#GCS1.collections[0].set_label(r'$Peak$'+r' '+r'$g-load$')\n",
    "plt.ylim(1,40)\n",
    "#plt.grid(True,linestyle='dotted', linewidth=0.3)\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(r'$\\beta_2$'+' / '+r'$ \\beta_1 $' ,fontsize=16)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=16)\n",
    "plt.xticks(np.array([ 0, 4, 8, 12, 16, 20]), fontsize=16)\n",
    "plt.yticks(np.array([ 1, 10, 20, 30, 40,]),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",
    "for l in Hlabels:\n",
    "    l.set_rotation(-90)\n",
    "for l in Glabels:\n",
    "    l.set_rotation(-90)\n",
    "\n",
    "dat0 = ZCS1.allsegs[1][0]\n",
    "\n",
    "x1,y1=dat0[:,0],dat0[:,1]\n",
    "F1 = interpolate.interp1d(x1, y1, kind='linear',fill_value='extrapolate', bounds_error=False)\n",
    "\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(0,20,301)\n",
    "y4 = F1(x4)\n",
    "y4a =F1a(x4)\n",
    "\n",
    "\n",
    "y6 = F3(x4)\n",
    "\n",
    "y7 = y6\n",
    "y8 = np.minimum(y4,y6)\n",
    "\n",
    "\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",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('../data/jsr-paper/earth/earth-drag-small.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/earth/earth-drag-small.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/earth/earth-drag-small.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 468x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.loadtxt('../data/jsr-paper/earth/'+runID+'vinf_kms_array.txt')\n",
    "y = np.loadtxt('../data/jsr-paper/earth/'+runID+'betaRatio_array.txt')\n",
    "\n",
    "Z1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'TCW_array.txt')\n",
    "G1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'acc_net_g_max_array.txt')\n",
    "Q1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'q_stag_total_max_array.txt')\n",
    "H1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'heatload_max_array.txt')\n",
    "S1 = np.loadtxt('../data/jsr-paper/earth/'+runID+'stag_pres_atm_max_array.txt')\n",
    "\n",
    "\n",
    "\n",
    "x_new =  np.linspace( 0.0,   20,  210)\n",
    "y_new =  np.linspace( 1.0,   40  ,110)\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",
    "\n",
    "Z1 = z1_new\n",
    "G1 = g1_new\n",
    "Q1 = q1_new\n",
    "H1 = h1_new/1000.0\n",
    "\n",
    "X, Y = np.meshgrid(x_new, y_new)\n",
    "\n",
    "Zlevels = np.array([0.20, 0.40, 0.60, 0.80, 1.00, 1.20])\n",
    "\n",
    "Glevels = np.array([5.0, 10.0, 20.0])\n",
    "Qlevels = np.array([400.0, 600.0, 900.0, 1500.0])\n",
    "Hlevels = np.array([30.0, 45.0, 60.0])\n",
    "#Slevels = np.array([0.8])\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",
    "\n",
    "ZCS1 = plt.contour(X, Y, np.transpose(Z1), levels=Zlevels, colors='black')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.clabel(ZCS1, inline=1, fontsize=10, colors='black',fmt='%.1f',inline_spacing=1)\n",
    "ZCS1.collections[0].set_linewidths(1.5)\n",
    "ZCS1.collections[1].set_linewidths(1.5)\n",
    "ZCS1.collections[2].set_linewidths(1.5)\n",
    "ZCS1.collections[3].set_linewidths(1.5)\n",
    "ZCS1.collections[4].set_linewidths(1.5)\n",
    "ZCS1.collections[5].set_linewidths(1.5)\n",
    "ZCS1.collections[0].set_label(r'$TCW, deg$')\n",
    "\n",
    "\n",
    "\n",
    "GCS1 = plt.contour(X, Y, np.transpose(G1), levels=Glevels, colors='blue',linestyles='dashed')\n",
    "\n",
    "Glabels=plt.clabel(GCS1, inline=1, fontsize=10, colors='blue',fmt='%d',inline_spacing=0)\n",
    "GCS1.collections[0].set_linewidths(1.5)\n",
    "GCS1.collections[1].set_linewidths(1.5)\n",
    "GCS1.collections[2].set_linewidths(1.5)\n",
    "\n",
    "\n",
    "GCS1.collections[0].set_label(r'$g$'+r'-load')\n",
    "\n",
    "for l in Glabels:\n",
    "    l.set_rotation(-90)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "QCS1 = plt.contour(X, Y, np.transpose(Q1), levels=Qlevels, colors='red',linestyles='dotted')\n",
    "\n",
    "plt.clabel(QCS1, inline=1, fontsize=10, colors='red',fmt='%d',inline_spacing=0)\n",
    "QCS1.collections[0].set_linewidths(1.5)\n",
    "QCS1.collections[1].set_linewidths(1.5)\n",
    "QCS1.collections[2].set_linewidths(1.5)\n",
    "QCS1.collections[3].set_linewidths(1.5)\n",
    "\n",
    "QCS1.collections[0].set_label(r'$\\dot{q}$'+', '+r'$W/cm^2$')\n",
    "\n",
    "HCS1 = plt.contour(X, Y, np.transpose(H1), levels=Hlevels, colors='xkcd:brown',linestyles='dashdot')\n",
    "\n",
    "Hlabels=plt.clabel(HCS1, inline=1, fontsize=10, colors='xkcd:brown',fmt='%d',inline_spacing=0)\n",
    "HCS1.collections[0].set_linewidths(1.75)\n",
    "HCS1.collections[1].set_linewidths(1.75)\n",
    "HCS1.collections[2].set_linewidths(1.75)\n",
    "HCS1.collections[0].set_label(r'$Q$'+', '+r'$kJ/cm^2$')\n",
    "\n",
    "for l in Hlabels:\n",
    "    l.set_rotation(-90)\n",
    "    \n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "plt.ylabel(r'$\\beta_2$'+' / '+r'$ \\beta_1 $' ,fontsize=12)\n",
    "plt.xlabel(\"Arrival \"+r'$V_\\infty$'+r', km/s' ,fontsize=12)\n",
    "plt.xticks(np.array([ 0, 5, 10, 15, 20,]),fontsize=12)\n",
    "plt.yticks(np.array([ 1, 10, 20, 30, 40,]),fontsize=12)\n",
    "ax = plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "plt.legend(loc='upper right', fontsize=10)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('../data/jsr-paper/earth/earth-drag-large.png', dpi= 300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/earth/earth-drag-large.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../data/jsr-paper/earth/earth-drag-large.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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