{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5a7f7844",
   "metadata": {},
   "source": [
    "# Example - 73 - Uranus Aerocapture - Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9feb5489",
   "metadata": {},
   "source": [
    "In this example we illustrate the selection of an aerocapture entry corridor accounting for uncertainties in the atmopsheric profile of Uranus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "49b1618e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b94432d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e0e62d2",
   "metadata": {},
   "source": [
    "We first create a Planet object for Uranus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "955149dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "planet = Planet('URANUS')\n",
    "planet.loadAtmosphereModel('../atmdata/Uranus/uranus-gram-avg.dat', 0 , 1 ,2, 3, heightInKmFlag=True)\n",
    "planet.h_skip = 1000.0E3\n",
    "planet.h_low  = 120e3\n",
    "planet.h_trap = 100e3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cbbd740",
   "metadata": {},
   "source": [
    "Load a data file containing Uranus mean atmospheric profile density variations (1-sigma),\n",
    "and compute density interpolation functions for the following density profiles:\n",
    "\n",
    "- low : avg - 3-sigma\n",
    "- avg : avg\n",
    "- hig : avg + 3-sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bde1a8db",
   "metadata": {},
   "outputs": [],
   "source": [
    "ATM_height, ATM_density_low, ATM_density_avg, ATM_density_high, ATM_density_pert = planet.loadMonteCarloDensityFile2('../atmdata/Uranus/uranus-gram-mean-density-variations.txt', 0, 1, 2, 3, 4, heightInKmFlag=True)\n",
    "density_int_low = planet.loadAtmosphereModel5(ATM_height, ATM_density_low, ATM_density_avg, ATM_density_high, ATM_density_pert, -3.0, 2201, 1)\n",
    "density_int_avg = planet.loadAtmosphereModel5(ATM_height, ATM_density_low, ATM_density_avg, ATM_density_high, ATM_density_pert,  0.0, 2201, 1)\n",
    "density_int_hig = planet.loadAtmosphereModel5(ATM_height, ATM_density_low, ATM_density_avg, ATM_density_high, ATM_density_pert, +3.0, 2201, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9c4d9eb",
   "metadata": {},
   "source": [
    "Create three Planet objects and set their density interpolation functions manually from above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f1c9baeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "planet1 = Planet('URANUS')\n",
    "planet2 = Planet('URANUS')\n",
    "planet3 = Planet('URANUS')\n",
    "\n",
    "planet1.density_int = density_int_low\n",
    "planet2.density_int = density_int_avg\n",
    "planet3.density_int = density_int_hig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a49cd309",
   "metadata": {},
   "source": [
    "Compute the density for np.linspace(0, 1000e3, 1001) with the three functions/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ee0db2e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "h_array = np.linspace(0, 1000e3, 1001)\n",
    "\n",
    "d_min_arr = planet1.densityvectorized(h_array)\n",
    "d_avg_arr = planet2.densityvectorized(h_array)\n",
    "d_max_arr = planet3.densityvectorized(h_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f483f1c9",
   "metadata": {},
   "source": [
    "Plot the three different mean density profiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "93d6f91f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 625x425 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.25, 4.25])\n",
    "plt.plot(d_min_arr, h_array*1E-3, 'b-', linewidth=1.0, label=\"Minimum\")\n",
    "plt.plot(d_avg_arr, h_array*1E-3, 'g-', linewidth=1.0, label=\"Average\")\n",
    "plt.plot(d_max_arr, h_array*1E-3, 'r-', linewidth=1.0, label=\"Maximum\")\n",
    "plt.xlabel(\"Density, kg/m3\",fontsize=10)\n",
    "plt.ylabel(\"Altitude, km\",fontsize=10)\n",
    "plt.xscale('log')\n",
    "plt.yticks(fontsize=10)\n",
    "plt.xticks(np.logspace(-8, 0, 9), fontsize=10)\n",
    "plt.grid('on',linestyle='-', linewidth=0.2)\n",
    "\n",
    "ax=plt.gca()\n",
    "ax.xaxis.set_tick_params(direction='in', which='both')\n",
    "ax.yaxis.set_tick_params(direction='in', which='both')\n",
    "ax.xaxis.set_tick_params(width=2, length=8)\n",
    "ax.yaxis.set_tick_params(width=2, length=8)\n",
    "ax.xaxis.set_tick_params(width=1, length=6, which='minor')\n",
    "ax.yaxis.set_tick_params(width=1, length=6, which='minor')\n",
    "ax.xaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)\n",
    "ax.xaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)\n",
    "ax.yaxis.grid(which='major', color='k', linestyle='dotted', linewidth=0.5)\n",
    "ax.yaxis.grid(which='minor', color='k', linestyle='dotted', linewidth=0.0)\n",
    "\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    \n",
    "plt.legend(loc='upper right', fontsize=10, framealpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad97c4fe",
   "metadata": {},
   "source": [
    "The variation of the entry corridor in response to mean density variations must be accounted for when selecting the aerocapture entry corridor. Here we illustrate the calculation of the aerocapture entry corridor for the three selected atmospheric profiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b98430e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overshoot  limit : -12.0304 deg\n",
      "Undershoot limit : -13.6659 deg\n",
      "TCW: 1.6355 deg\n"
     ]
    }
   ],
   "source": [
    "planet.density_int = density_int_low\n",
    "\n",
    "vehicle=Vehicle('Titania', 3000.0, 200 , 0.36, np.pi*4.5**2.0, 0.0, 1.125, planet)\n",
    "vehicle.setInitialState(1000.0,-80.95,25.22,27.5946,132.066,-11.0 ,0.0,0.0)\n",
    "vehicle.setSolverParams(1E-6)\n",
    "\n",
    "# Compute the corridor bounds and TCW for low density atnosphere\n",
    "overShootLimit, exitflag_os  = vehicle.findOverShootLimit2(2400.0,0.1,-25,-4.0,1E-10,500e3)\n",
    "underShootLimit, exitflag_us  = vehicle.findUnderShootLimit2(2400.0,0.1,-25 ,-4.0,1E-10,500e3)\n",
    "\n",
    "# print the overshoot and undershoot limits we just computed.\n",
    "print(\"Overshoot  limit : \"+str('{:.4f}'.format(overShootLimit))+ \" deg\")\n",
    "print(\"Undershoot limit : \"+str('{:.4f}'.format(underShootLimit))+ \" deg\")\n",
    "print(\"TCW: \"+ str('{:.4f}'.format(overShootLimit-underShootLimit))+ \" deg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8938d8b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overshoot  limit : -11.8266 deg\n",
      "Undershoot limit : -13.4984 deg\n",
      "TCW: 1.6718 deg\n"
     ]
    }
   ],
   "source": [
    "planet.density_int = density_int_avg\n",
    "\n",
    "vehicle=Vehicle('Titania', 3000.0, 200 , 0.36, np.pi*4.5**2.0, 0.0, 1.125, planet)\n",
    "vehicle.setInitialState(1000.0,-80.95,25.22,27.5946,132.066,-11.0 ,0.0,0.0)\n",
    "vehicle.setSolverParams(1E-6)\n",
    "\n",
    "# Compute the corridor bounds and TCW for low density atnosphere\n",
    "overShootLimit, exitflag_os  = vehicle.findOverShootLimit2(2400.0,0.1,-25,-4.0,1E-10,500e3)\n",
    "underShootLimit, exitflag_us  = vehicle.findUnderShootLimit2(2400.0,0.1,-25 ,-4.0,1E-10,500e3)\n",
    "\n",
    "# print the overshoot and undershoot limits we just computed.\n",
    "print(\"Overshoot  limit : \"+str('{:.4f}'.format(overShootLimit))+ \" deg\")\n",
    "print(\"Undershoot limit : \"+str('{:.4f}'.format(underShootLimit))+ \" deg\")\n",
    "print(\"TCW: \"+ str('{:.4f}'.format(overShootLimit-underShootLimit))+ \" deg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c0136e11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overshoot  limit : -11.6798 deg\n",
      "Undershoot limit : -13.3650 deg\n",
      "TCW: 1.6852 deg\n"
     ]
    }
   ],
   "source": [
    "planet.density_int = density_int_hig\n",
    "\n",
    "vehicle=Vehicle('Titania', 3000.0, 200 , 0.36, np.pi*4.5**2.0, 0.0, 1.125, planet)\n",
    "vehicle.setInitialState(1000.0,-80.95,25.22,27.5946,132.066,-11.0 ,0.0,0.0)\n",
    "vehicle.setSolverParams(1E-6)\n",
    "\n",
    "# Compute the corridor bounds and TCW for low density atnosphere\n",
    "overShootLimit, exitflag_os  = vehicle.findOverShootLimit2(2400.0,0.1,-25,-4.0,1E-10,500e3)\n",
    "underShootLimit, exitflag_us  = vehicle.findUnderShootLimit2(2400.0,0.1,-25 ,-4.0,1E-10,500e3)\n",
    "\n",
    "# print the overshoot and undershoot limits we just computed.\n",
    "print(\"Overshoot  limit : \"+str('{:.4f}'.format(overShootLimit))+ \" deg\")\n",
    "print(\"Undershoot limit : \"+str('{:.4f}'.format(underShootLimit))+ \" deg\")\n",
    "print(\"TCW: \"+ str('{:.4f}'.format(overShootLimit-underShootLimit))+ \" deg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d35375c",
   "metadata": {},
   "source": [
    "To accomplish aerocapture in both minimum and maximum density atmospheres, the shallow limit should be steeper than the overshoot limit for the minumum density atmosphere and the steep limit should be shallower than the undershoot limit for the maximum density atmosphere. This is graphically shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cbbacc18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l04p7LZ1OV0Nf7bZLQT4GuDkDmfkScq8oBxY93QSaNjKiVAecu6TsV5KA1s0qOku5pEKZTlLEA7yN8HAVyLkiIStf2a/WRSCwsRF6Q0VO1bUKMECtAlKzVCguU/bbxMuIRlqB/GIJ6bnKfl2cBEL8Krb5iQum/TZvYoDGEUjLVeFKsbJfHw8jfDwECkuBC9W2oaMD0MK/Yl1PpatR/dZVwb4GuGqAS3kSLhcqc/JyE/BvZERpOXAuU9mvSgXcEVDR2dlLKpRX2YYpKamAgzsAwFjkDWORj6KtpCmE2vMihMEBhhzTP0DUvqcgSQKGy0EQOhfl+7pfgsolH8YSTxivNFH261gCdaPzEEKCIauVab+Nz0JS62HID4Ao0yr7dcuGyi0XxjItjPnKiwkkh3Kovc8BAPRZrQCh3P7qRn9DciyD4UoTiBJPZb+ul6HSZkGUu8CQV+2LVGWAg8+Zin5zmgMGR2W/XhcgORXDWNQYxqLGypw0V6D2TIfQO8GQG2qyrg5+JwEAhsvBEDpn5dt6pEPlfAXGEi8YrygPvUtOxVB7XYAwqmDIbmnSr7rxGUhqA4xlgYB7M5yuEvMvuwTf8mzkOXjgvItyXZ0NpWhVXLGux7RtICTlNmxVdBrOxjJccG6Gy45eiphveTb8yy6hUO2GFFflujoa9QgvSgYAnHBrDZ1KuRtqXnwOWkMRMjRNkOWk/Bw20uUhsDQNpSoNTrkp11USAu0KKy6SOeXaAqVq5TYMKjkPL30Bspx8kKFRfg7d9VcQWpIKneSAE9rWqK7NlSSoYUSKSygKHdwUsYDSdDTW5SLPwRPnXQIVMVdDCVoUnwUAHHVva9Jvq6JTcDaWI9U5EPmOys+hX1kmmpRn4Ypai3OuIYqYk7EcrYtOAQCStOHQSxX/1yt/t8VXj/RcyNIjM0/5BeLjoUZwEwcUlxpx4rxyX6WSgI4tK/ZViX+Xo7RcuWcIa+oAL60aGbl6XMxR9uvlpkJYgCPKdQLHzpnuqzq2cIJKJeHkBR0KS5T7qmA/B/h4qpGdb0BqpnJfpXVR4Y5ARxiNAsdTAeDafrew0PJBhnotdqyRmpqK3NxcpKamwmAwICEhAQDQsmVLaLVaHDt2DP3798egQYMwc+ZM+ZwbtVotF1R79+7F+PHjsXXrVjRr1gwREREYPHgwpkyZgg8//BA6nQ7Tp0/HY489dsMrsfpFZkByKJKnxwxww9poX1zIMuDuaekmy+t/DwUATHojG3uSyhSxNbN8MHagFl/sKMKMd3MVsfu6OOOn1/1RVCrM9pv+ZRB8vdSIWpmL7+NLFLGlTzdC5L88sfVgKR5bmKWIdWrphH0fVqzjvTPSUV6tPjv8SQDahjoh+sML+L8/AODaf0TjuQ8gziwFvLpB3WWjop0oTYdxVw8AgOre3ZCcmyrihgOPAXl7ILV4EarQZxQxY9omiBOzALdWUN/zq7JfYxmMceEV/d71HSSPdsp+j/4HyPwRUtBkqO5QjuiJrN9hPDIFcPSGuvcBVGfY3h4wFELVcQ2kxr2VOSXPgbiwDpL/cKjavq3sN/8gjPtHAADUA1JM+93dFyj5G1Kbt6FqOlzZ79l3IFKWA969oe60Rtlv8TkY4/tVrGuv/ZCclDtNw75HgYJDkFq9DFXwk8p+L6yDSJ4DuLeF+u7vlf3qr8C4o2NFv91+gaS9o0q0I+DVDUACypIeREn804q2ji23wf2BVyBKGqFg02qTdW30TF9ArUNR3H+hT+usiLn2XwLntt+h/GxvFG+bpYg5NDsIj0efBYwOZvv1emI4JG0WindPg+50f0XMpfuHcOm6Dvq0jij84XVFTO2dAs+x4wAABV+9D+iUOyiPUU/Awe8kSg+MRdnREYqYpuNGuPV6F/qcMFz5cpUiJjlfRqMpDwEACn9YAmO+cuemfTgSTiF7UXpsGEr3TlbEnFr/Au39C2As9DW7rt7P3lvR7+8vw5Ch/Hy73TcfmvBfUX6qP4p3RCliDsF74DFsJqB3Nr8Nn3wQkksedBdfgPruPniuSuyVk69iyvkY/OndE8+0f1/Rrl3BMfy4bygAYPhdm1GuUu5YfvvrfrQuOoUVoc9iY7NRitgz5z7ArDNLcdS9HUZV+47wL03H3qvfEeM7rkZGte+ITQceQ/e8PYgNHI8Pqn1HPJa2CW+cmIW/XYLxYLXPt5OxDKevfkc81/YdHKv2HfHB0f/gocwfsaXJMCys9h0xMOt3fHpkCgocPEz6BYDj29vD3VCIV1rPx85q3xELk+dgwoV12ObTD89X+47onH8QW65+R5jrd+fuvggt+RvLwqLwdbXviOfPvoOZKctx0LMzHq/2HRFSfA5/XP2OGN1pPXKrfEeoAZzOKEBPAG9/WYDlXynPa5n2sDvendEYJ87rTPYp7q4SLn9bUVg9tiDL5A/3rxf4YWgPV8T+XIjZn+YpYiN6u2LTHD9k5pnfBxb9GAKNE/D0W9nYeUS5D/xoZmNMHuKOb3YV46m3chSx3h002PZWU+j0MOlX6K+YvM/1SKLyNsi3uYkTJ2LNmjUm8+Pi4tC3b1/MmzdPvjy9qpCQEJw7dw4AsH37dvTr1w8pKSkIDQ0FUHFTwenTp+O7776DSqXCiBEjsGLFCmi1WpO+gIphM09PT+w4mAmt9tphrIqRHUe7G9n5YWsCHnpkHKSwKEjOV//yE/qKF1SAyqlaSwEYr36QVRoAyr8GYSwHYAQkh4qXoqkBELqKNiozFbux1IJ+1YCk/GsbwgiIq9tOpfxrT9mvE0xOYxO6irygBlSO1Rtefd/r9VsGQFTkI1Ub+bnNtqExfz9w+jV4va6B2t+fIzt2OLJT9Ks3itcJvAegcimO7FSwp5Gd6QD++GUtenbvbHcjOwlnlP0WFhagT2c/5OfnK04rMafBFDu3i8pix5KNaw8OHjyILl26mB1NIfthTN8CkRgJr2U+cGxRvfgie1CyoxiFb+fhZwDt6zsZqhNHAQxGxU12O3fufKPFGzxr9sdWX41FRERE1JDYtNhp3rw5Jk+ejIsXL9qyWyIiIqKbZtNiZ8KECTAYDLj33ntt2S0RERHRTbPp1Vjz5s2zZXdEREREtXbTIzvl5eVITk6GXq+/8cJERERE9cTqYqe4uBiTJ0+Gq6sr2rZti9TUVADAs88+iyVLltg8QSIiIqLasLrYiY6OxuHDh7F9+3Y4O1+7j8HAgQOxadMmmyZHREREVFtWn7OzZcsWbNq0Cffccw+kKjeZatu2Lc6cOWPT5IiIiIhqy+qRnaysLPj5+ZnMLyoqUhQ/RERERLcDq4udrl274ocffpCnKwucTz75BN27d7ddZkREREQ2YPVhrNdeew0PPPAAEhMTodfrsXz5ciQmJmL37t3YsWNHXeRIREREdNOsHtnp2bMnEhISoNfr0b59e/z666/w8/NDfHw8unTpUhc5EhEREd20m7qpYIsWLfDxxx/bOhciIiIim7Oo2CkoKLC4w3/Ck8CJiIio4bCo2PHy8rrhlVZCCEiSBIPBYJPEiIiIiGzBomInLi6urvMgIiIiqhMWFTt9+vSp6zyIiIiI6oTVJygfOXLE7HxJkuDs7Izg4GBoNJpaJ0ZERERkC1YXOx07dqzx/B1HR0eMGjUKH330keLZWURERET1wer77Hz99ddo1aoVVq1ahYSEBCQkJGDVqlVo3bo1NmzYgJiYGGzbtg2zZ8+ui3yJiIiIrGL1yM6iRYuwfPlyDBo0SJ7Xvn17BAYG4pVXXsHevXvh5uaGqKgovPnmmzZNloiIiMhaVo/sHD16FCEhISbzQ0JCcPToUQAVh7rS09Nrnx0RERFRLVld7ISHh2PJkiUoLy+X5+l0OixZsgTh4eEAgLS0NDRp0sR2WRIRERHdJKsPY73//vt4+OGHERgYiA4dOgCoGO0xGAz4/vvvAQBnz57FM888Y9tMiYiIiG6C1cVOjx49kJKSgs8++wwnT54EAIwcORJjxoyBu7s7AODxxx+3bZZEREREN+mmHgTq7u6Op59+2ta5EBEREdncTRU7p06dQlxcHDIzM2E0GhWxOXPm2CQxIiIiIluwutj5+OOPMW3aNPj4+MDf319xg0FJkljsEBER0W3F6mLn1VdfxaJFi/DSSy/VRT5ERERENmX1peeXL1/GyJEj6yIXIiIiIpuzutgZOXIkfv3117rIhYiIiMjmrD6M1bJlS7zyyiv466+/0L59ezg6OiriM2bMsFlyRERERLVldbGzatUqaLVa7NixAzt27FDEJElisUNERES3FauLnZSUlLrIg4iIiKhOWH3ODhEREVFDclM3Fbxw4QK+/fZbpKamKh4ICgBvvfWWTRIjIiIisgWrR3a2bt2K1q1bY+XKlVi2bBni4uKwevVqfPrpp0hISKiDFCssWrQIPXr0gKurK7y8vEzihw8fxujRoxEUFAQXFxdERERg+fLlN+w3NDQUkiQpXkuWLKmDNSAiIqL6YPXITnR0NF544QXMnz8f7u7u+Oqrr+Dn54exY8di8ODBdZEjAKC8vBwjR45E9+7dERMTYxI/cOAA/Pz8sH79egQFBWH37t2YOnUq1Go1pk+fXmPfCxYswJQpU+TpygeaEhERUcNndbGTlJSEzz//vKKxgwNKSkqg1WqxYMECDBs2DNOmTbN5kgAwf/58AEBsbKzZ+KRJkxTTYWFhiI+Px+bNm29Y7Li7u8Pf398meRIREdHtxerDWG5ubvJ5Ok2bNsWZM2fkWHZ2tu0ys4H8/Hx4e3vfcLklS5agcePG6NSpE5YuXQq9Xn/DNgUFBYpXWVmZLVImIiIiM8rKykz2vZayemTnnnvuwZ9//omIiAgMGTIEUVFROHr0KDZv3ox77rnH2u7qzO7du7Fp0yb88MMPNS43Y8YMdO7cGd7e3ti9ezeio6ORnp5+wxOtg4KCFNNz587FvHnzaps2ERERmbF48WL5KI+1rC523nrrLRQWFgKoOLRUWFiITZs2oVWrVlZfiTVr1iy8/vrrNS6TlJSE8PBwq/o9duwYhg0bhrlz5+L++++vcdmZM2fKP3fo0AFOTk546qmnsHjxYmg0muu2O3/+PDw8POTpmpYlIiKi2omOjlbsswsKCkwGHq7H6mInLCxM/tnNzQ0ffvihtV3IoqKiMHHiRIvfzxKJiYkYMGAApk6ditmzZ1udU7du3aDX63Hu3Dm0bt36ust5eHgoih0iIiKqOxqN5qYHFm7qPju24uvrC19fX5v1d/z4cfTv3x8TJkzAokWLbqqPhIQEqFQq+Pn52SwvIiIiqj/1WuxYIzU1Fbm5uUhNTYXBYJDv6dOyZUtotVocO3YM/fv3x6BBgzBz5kxkZGQAANRqtVxQ7d27F+PHj8fWrVvRrFkzxMfHY8+ePejXrx/c3d0RHx+PyMhIjBs3Do0aNaqvVSUiIiIbajDFzpw5c7BmzRp5ulOnTgCAuLg49O3bF19++SWysrKwfv16rF+/Xl4uJCQE586dAwAUFxcjOTkZOp0OQMWQ2MaNGzFv3jyUlZWhefPmiIyMVBwTJCIiooatwRQ7sbGx173HDgDMmzfvhldD9e3bF0IIebpz587466+/bJQhERER3Y74IFAiIiKya1aP7BgMBsTGxmLr1q3IzMyE0WhUxLdt22az5IiIiIhqy+pi57nnnkNsbCwefPBBtGvXDpIk1UVeRERERDZhdbGzceNG/N///R+GDBlSF/kQERER2ZTV5+w4OTmhZcuWdZELERERkc1ZXexERUVh+fLliquaiIiIiG5XFh3GevTRRxXT27Ztw08//YS2bdvC0dFREdu8ebPtsiMiIiKqJYuKHU9PT8X0I488UifJEBEREdmaRcXO6tWr6zoPIiIiojph9Tk7/fv3R15ensn8goIC9O/f3xY5EREREdmM1cXO9u3bUV5ebjK/tLQUf/zxh02SIiIiIrIVi4udI0eO4MiRIwCAxMREefrIkSM4dOgQYmJi0KxZszpL9HYTHh6OwMBAbN++XTE/JSUFgYGBCAwMxLPPPmvS7uGHH5bj1cXGxsqx6id6X7lyRY6NHTvWpO0TTzwhx3NychSx77//Xo6tWrXKpG1YWBgCAwMxePBgk9g777wDADAmTIQoOqOIifyDMPzZHYY/u8N47gOTtoa9D1fE9z5sEjOe+0BuK/IPKvstOnOt31OvmfZ7aEJFfFdv037TNlzrN3ursl/d5Wv9Jr5o2vbY89fa6guVbTN/vtb24pemOV2NGQ4/adpv8txr/ZacV/Z7+a9r/aZ+YtrvnsEV8f0jTfs9u/xavwVHlf0WnrjW75mlpv0eHFMRjx9gEiv5oQg5kzOQMzkD5YdKle1yDXLsygd5Jm3z38iV40KnvGKzdEexHCvdUazMVyfkWP4buSb9XvkgT44bcg2KWPmhUjlW8kORSdvc/2QiZ3IG8l7JNokVrS+Q2+rP6RQx3elyOVa08YpJ28vR2ciZnIHc5zJNYsXfFF7bhkfLFDHDJf21bbgq36Rt/qIcOV5d6dZr27AsvkQRM5YY5VjBW5dN2/5Ssc3HAai+hX8D0OXqaz1Mdb8aM/3mARZWaXu6Wmx/ldi7Zto+cDX2gJnYu1Xa7q8WO10lttBM27FXY93NxNZXaftbtVhulVikmbbTq8QLq8V+rBLbZKZtZWyimdjsKvHUarHdVWIfmWk78GpsuJnY/Pnz5e/+AwcOKGJHjx6VYy+//LJJ2/79+yMwMBDh4eEmsffee09u++uvvypi6enpcuypp54yaTty5Eg5Xlam/L+xYcMGObZhwwZFrKysTI6NHGn6fXgjFt9UsGPHjpAkCZIkmT1c5eLignffNfdxtk/p6ekAYPLLMhgMSEtLAwBcvmz6hZOVlSXHqysqKpJjxcXVdgZCyLHsbNMv7ZycHDle/REeJSUlcqywsPp/USAtLQ3l5eXw8/MziRUUFFT8oMsBhHInA2M5UHb1C1lv2i/Ks67Fq9MXXosZq40UCkOVfk13BtDlVMQlJ9OYofhaW4NyRw1hlGNCZ/q7EbrcKvkqd9TCUFKlX+XvBsC1mKapaUyffy1usg3Lat6GZVmALhdQacz0e6VKv8odNYz6azFdgWnb8uyKuFprEhIlRhhzjGa7hRFyzFhoRHUi/1pbk1i5uNZvuemtK+RYvml7Y2GVfquFha5K2xIzbXMNECUCkquZWFGVdTVUy0lfpd9iM20vG2DMMUKqvo2uLi/nq6/Wb5VtKIrM9FtQwzYsq7INy6r1K6r8bgrM/G5KK+Zlo/qnGygFUPnJNy0XK2LlAHzMxPKrtK326UZ5lZiZTzcyq8SrK6wSq34swVAlZuYbAtlX42a+IVBUpW21bwiIKjHTb4iKYsj8NwRQUiVWAlOVsQAzsdpsw0tX83I2129+vvzdX/2IjE6nk2PmTk25dOkS0tLS4O7ubhK7cuWK3La0tNofRFX2gbm5pn+41LQPLC4uvu4+EIAcy8rKMtu+JhYXOykpKRBCICwsDHv37oWvr68cc3Jygp+fH9RqtdUJNFRNmzaFSqWCRqPcCanVanmEq1GjRibtfH19rzsC5ubmJsdcXV0VMUmS5JiPj+lXTuPGjeW4SqUcsHNxcZFjWq3pzq1Zs2bXLXY8PDwqfnBsDEjVfr8qJ0DjX/Gzg2m/cPJV/luVg/ZaW1W1ryRJXaVf5ZWAci4af0ByNI2pXa+1VVf77y+p5JjkaPq7kRy9ISrbQvkYFEntci2mVv5uAFx7Tydv05iD57W4yTbU1LwNNb4V28fsNnSv0m+1baFyuBZz9DBt6+RTUSyZWRfJRQVVY5XZbqGCHFNpTQeGJc9rbU1iTtK1fp1MHzMjxzxN26u0VfqtFpYcq7R1MdPWWw1RaoTKy0zMrcq6qqvl5FClX1czbRupAZ2AZG47uFbJ16Fav1W2oeRmpl+PGrahpso21FTrV6ryu/Ewk5NzxTwfVP90V+woKz/5bmbe1x/XL3Y8q7StvgdwqhIz8+mGX7V/q9JWaVu9aFFXiZn5hoDP1bi5YsetStvqBYJUJWb6DQF4V4lX34YuVWIuZtpWxhqbidVmGza5uoyZbwh4enrK3/1OTsqt4ejoKMe8vLxM+23SBPn5+Wb3Ge7u7nJbZ2flVqy6D/T2Nv0+rGkf6Orqet19IAA5VrX+sJQkeHdAqxQUFMDT0xP5+fnXCgE7dvDgQXTp0gWqu76D5NGuvtOhOmJM3wKRGAmvZT5wbGFuF0ENXcmOYhS+nYefAbSv72SoThwFMBjAgQMH0Llz5/pOp85Zsz+2+tlYlRITE5GammoyNPbww6bnZxARERHVF6uLnbNnz+KRRx7B0aNHIUmS/NiIyqefGwzVjzgSERER1R+rLz1/7rnn0Lx5c2RmZsLV1RXHjx/Hzp070bVrV5Mrk4iIiIjqm9UjO/Hx8di2bRt8fHygUqmgUqnQs2dPLF68GDNmzMChQ4fqIk8iIiKim2L1yI7BYJAvRfPx8cHFixcBACEhIUhOTrZtdkRERES1ZPXITrt27XD48GE0b94c3bp1wxtvvAEnJyesWrUKYWFhdZEjERER0U2zutiZPXs2iooqbju1YMECPPTQQ+jVqxcaN26MTZvM3TeSiIiIqP5YXewMGjRI/rlly5Y4ceIEcnNz0ahRI/mKLCIiIqLbxU3fZwcAzp+veM5PUFCQTZIhIiIisjWrT1DW6/V45ZVX4OnpidDQUISGhsLT0xOzZ8+GTmfmITFERERE9cjqkZ1nn30WmzdvxhtvvIHu3SueKRsfH4958+YhJycHK1eutHmSRERERDfL6mJnw4YN2LhxIx544AF5XocOHRAUFITRo0ez2CEiIqLbitWHsTQaDUJDQ03mN2/e3OSpqkRERET1zepiZ/r06Vi4cCHKysrkeWVlZVi0aBGmT59u0+SIiIiIasvqw1iHDh3C1q1bERgYiDvvvBMAcPjwYZSXl2PAgAF49NFH5WU3b95su0yJiIiIboLVxY6XlxdGjBihmMdLz4mIiOh2ZXWxs3r16rrIg4iIiKhOWH3OTlVLlixBXl6ejVIhIiIisr1aFTuvvfYacnNzbZULERERkc3VqtgRQtgqDyIiIqI6Uatih4iIiOh2V6tiJzExESEhIbbKpUaLFi1Cjx494OrqCi8vL5N4Tk4OBg8ejICAAGg0GgQFBWH69OkoKCiosd/c3FyMHTsWHh4e8PLywuTJk1FYWFhHa0FERES3mtXFTlhYGHJycgBUXHKuVqsBAHl5eQgLC7NtdlWUl5dj5MiRmDZtmtm4SqXCsGHD8O233+LkyZOIjY3F77//jqeffrrGfseOHYvjx4/jt99+w/fff4+dO3di6tSpdbEKREREVA+svvT83LlzMBgMJvPLysqQlpZmk6TMmT9/PgAgNjbWbLxRo0aKQigkJATPPPMMli5det0+k5KS8PPPP2Pfvn3o2rUrAODdd9/FkCFD8OabbyIgIMB2K0BERET1wuJi59tvv5V//uWXX+Dp6SlPGwwGbN261ewzs+rLxYsXsXnzZvTp0+e6y8THx8PLy0sudABg4MCBUKlU2LNnDx555JHrtq1+eEyj0UCj0dQ+cSIiIjJRVlameFTVjU5TqcriYmf48OEAAEmSMGHCBEXM0dERoaGhWLZsmcVvXFdGjx6Nb775BiUlJRg6dCg++eST6y6bkZEBPz8/xTwHBwd4e3sjIyOjxvepftfouXPnYt68eTedNxEREV3f4sWL5aM81rL4nB2j0Qij0Yjg4GBkZmbK00ajEWVlZUhOTsZDDz1k1ZvPmjULkiTV+Dpx4oRVfb799ts4ePAgvvnmG5w5cwYzZ860qr2lzp8/j/z8fPkVHR1dJ+9DREREQHR0tGK/e/78eYvbWn3OTkpKirVNrisqKgoTJ06scRlrT3r29/eHv78/wsPD4e3tjV69euGVV15B06ZNzS6bmZmpmKfX65Gbmwt/f/8a38fDwwMeHh5W5UZEREQ3pzani1hd7ADA1q1bsXXrVnmEp6pPP/3U4n58fX3h6+t7MylYpDK3qsf4qurevTvy8vJw4MABdOnSBQCwbds2GI1GdOvWrc7yIiIiolvH6mJn/vz5WLBgAbp27YqmTZtCkqS6yMtEamoqcnNzkZqaCoPBgISEBABAy5YtodVq8eOPP+LSpUu46667oNVqcfz4cbz44ou499575ROn9+7di/Hjx2Pr1q1o1qwZIiIiMHjwYEyZMgUffvghdDodpk+fjscee4xXYhEREdkJq4udDz/8ELGxsXj88cfrIp/rmjNnDtasWSNPd+rUCQAQFxeHvn37wsXFBR9//DEiIyNRVlaGoKAgPProo5g1a5bcpri4GMnJydDpdPK8zz77DNOnT8eAAQOgUqkwYsQIrFix4tatGBEREdUpq4ud8vJy9OjRoy5yqVFsbOx177EDAP369cPu3btr7KNv374mz/Py9vbGhg0bbJEiERER3YasvoPyk08+yeKAiIiIGgyLRnaqXr5tNBqxatUq/P777+jQoQMcHR0Vy7711lu2zZCIiIioFiwqdg4dOqSY7tixIwDg2LFjivm36mRlIiIiIktZVOzExcXVdR5EREREdcLqc3aIiIiIGhKrr8Z65JFHzB6ukiQJzs7OaNmyJcaMGYPWrVvbJEEiIiKi2rB6ZMfT0xPbtm3DwYMH5edXHTp0CNu2bYNer8emTZtw5513YteuXXWRLxEREZFVrB7Z8ff3x5gxY/Dee+9BpaqolYxGI5577jm4u7tj48aNePrpp/HSSy/hzz//tHnCRERERNawemQnJiYGzz//vFzoAIBKpcKzzz6LVatWQZIkTJ8+3eRKLSIiIqL6YHWxo9frceLECZP5J06cgMFgAAA4OzvzMnQiIiK6LVh9GOvxxx/H5MmT8b///Q933XUXAGDfvn147bXXMH78eADAjh070LZtW9tmSkRERHQTrC523n77bTRp0gRvvPEGLl26BABo0qQJIiMj8dJLLwEA7r//fgwePNi2mRIRERHdBKuLHbVajZdffhkvv/wyCgoKAAAeHh6KZYKDg22THREREVEtWV3sVFW9yCEiIiK63VhU7HTu3Blbt25Fo0aN0KlTpxpPPj548KDNkiMiIiKqLYuKnWHDhkGj0QAAhg8fXpf5EBEREdmURcXO3Llzzf5MREREdLvjg0CJiIjIrlk0stOoUSOLbxKYm5tbq4SIiIiIbMmiYuedd96p4zSIiIiI6oZFxc7hw4excOFCuLm5YefOnejRowccHGp11ToRERHRLWHROTvvvvsuCgsLAQD9+vXjoSoiIiJqMCwangkNDcWKFStw//33QwiB+Ph4NGrUyOyyvXv3tmmCRERERLVhUbGzdOlSPP3001i8eDEkScIjjzxidjlJkuQnnxMRERHdDiwqdoYPH47hw4ejsLAQHh4eSE5Ohp+fX13nRkRERFRrVp1lrNVqERcXh+bNm/MEZSIiImoQrK5Y+vTpUxd5NDjlCQko12rlaVWjRnBo3hyitBS6xEST5Z06dwYA6JOTYSwqUsQcQkOh8vaGISsLhvPnFTGVuzscWrWCMBigO3zYpF/H9u0hOTpCf+YMjPn5ipi6WTOomzSB8fJl6FNSFDHJxQWOEREAAN2hQxBCKPuNiIDk4gJVejraAVAVnUblnZaynHxwydkfbvpCNC8+p2inUzkgWRsOAGhdeAKORr0inuIaiiIHLZqUZsC3PFsRu+zohTSXQGgMpWhVdFoRE5KE4+5tAQAti07D2VCqiJ93CUK+oyd8yrLgX3ZJEbvi4I6/XUPgYNQhvDAZ1SW6R8AoqRFanAKtXvm7uejcFLlOjeGpy0NQyQVFrFjtgrNuLQAA7QqOmfR7yq0VytQaBJWch6dO+bvJ1PghU+MHrf4KQov/VsTKVU44qb0DABB+JQkOQnlo+KxrcxQ7uMG/NB0+5TmKWK5TI1x0bgZnQwlaFp1RxIySConubQAArQpPQWMsuxYrOY/KLBpfNsA3V/meV9xUSPN3gINOoGWqzmRdk5s7QqgkBKfp4VpqVMQyfNXI81DDq8AA/yxlv8XOKqQ2c4BkFGidYtrv6WBH6B0lNMvQw71I2W+Wtxo5jdRwLzSi2SXl56zcScLZIEcAQOuzOkjVPt/nAh1QqlHBP0sPrwJlv7meamT6qOFSYkTIRWW/BrWEU6EV/bZI1cFRp+z3fFMHFLmq4JNrgM9l5boWaFW42MQBTuUCYedN1/VECycAQEiaHi7VtuFFPwcUuKvQKN+AJtnVtqGLCqkBDlAZBO44Z2YbhjhC7yAhKM8ANYAsAKeuxvRXXyoATtXaCQCVnxBnk14rYgKAIwB1tZil/WoAVL+DWzkAIyp2TtV3UAYAuqttNGZyqvxWsLZf49U4YH5dK/t1gulVPbqrealRsS2s6dcW27Bqv1kA2gGQiosBAIYLF2DIzFS0Vfn4wCE4GMbiYuhPnFDEJJUKjh07VqxXYiJEqfJ71iEsDCovLxgyMmC4eFHZr5cXHMLCIMrLoTtm+n3o2LEjJJUK+pMnYbx6wVMldXAw1D4+MGRnw5CaquxXq4XDHXdAGI3QJSQoYuXV+qmRIKvk5+cLAOIEINKqvHLHjhVCCKE7dUoxv/JVKfOee0xiRevWCSGEKHzvPZNY9v33CyGEMOTnm+1Xn5kphBAie+hQk9iVZcuEEEIU/9//mcQyO3WSc0pzcjKJlx87JoQQ4tSwYSax/4VME+oBKeLezp+bxPZr/IV6QIpQD0gR+zX+JvF7O38u1ANSxP9CppnE3gz4t1APSBER3X4xiZ2VnOR+f3ZvaxJ/uN17Qj0gRTzV6mWTWKzPAKEekCJ8e+03uw09+xwR6gEpYoN3L5PYE3fMF+oBKeJfbd4yiX3r0UnOyVy/Yd3jhHpAinjX33QbvtD8OaEekCL6dYw1ie1yCZH7PeLobRK/q+tXQj0gRcwLmmwSe63ZOKEekCLuvOs7k9gJtVbuN86tlUn8PkB4LfMRL41zN92GPZyF75YA0faTJmbXNeCLpsJ3S4D4pq3pZ2nqfzyF75YAMfU/niaxb9o6Cd8tASLgi6Zm+237SRPhuyVAxPZwNom9NM5d+G4JEP/6n+k22hnkIHy3BAjfLQEi2UUyifda5iN8twSItx5wNYktedhN+G4JEAOW+JjEjnqo5H7j/dWmn8O53sJ3S4B4ZZTWJLaqj4vw3RIgOq/0M7uulf3+2NrRJDbheS/huyVATJ9qug2/6KgRvlsCROgG0/9vaYC4Y03FNlzXyrTfqYAAIB4y0+7nqzEA4qy5fq/GlpqJzboa624mtr9Kv/vNxLtfjc0yE1t6NXaHmdjZKv3+bCb+0NXYVDOx1Vdj3mZiaYDQXo1/ZiY24WrsUTOxb6vkZK7f0KuxFWZiM6/G+piJ/Vml3yNm4n9v3iyEECIvMtIkdvmZZ4QQQpQdOGASu+juLu8XLrVpYxIv+eYbIYQQBa+9ZhLL+de/hBBC6M+fN7uuxtJSIYQQWX36mMQKP/64Yh/48ccmsaw+fYQQQhhLS01iJ65ug/z8/BvuuyUhqv3JQzUqKCiAp6cnsnbsgMc/YGQn4Ycf8PhDD0HV5m1Ibi0BcGSnkl2N7GTH4e+Ut6Ba5gN/bzVHduxwZMd/TwnE4st4deFCNG/eHABg9PODsXFjSAUFUKelKdoJZ2cYri7ncOIEUG0b6ps3B5ydoUpPhyovTxEzNm4Mo58fpKIiqKv9pQ4HB+hbtar48dQpQF9tGwcHQ7i5QZWZCVWO8vNt9PKCsWlToLQUDtW+0yBJ0IdXfPeoU1IgVRuVMDRrBuHhAVVODlTVRjuEVgtDUBCg08HhtPK7BwD0d9wBqNVQp6ZCqvb9bfD3h2jUCFJ+PtTVRjuEiwsMoaEV65qUZNpvWBig0UCdlgapoEC5rj4+MPr6QioshLrafgFOTtC3qPjuqb4Nvby8ENinD1Rard2P7BQUFsK3Tx/k5+fDw8PD5D0V68ZixzqVxY4lG9ceHDx4EF26dIHqru8gebSr73SojhjTt0AkRsJrmQ8cW1QfNCd7oDtTjryobBw4cACdr/7xRdSQWbM/tvpBoKtXr0bx1eOBRERERLc7q4udWbNmwd/fH5MnT8bu3bvrIiciIiIim7G62ElLS8OaNWuQnZ2Nvn37Ijw8HK+//joyMjLqIj8iIiKiWrG62HFwcMAjjzyCb775BufPn8eUKVPw2WefITg4GA8//DC++eYbGI3GG3dEREREdAtYXexU1aRJE/Ts2RPdu3eHSqXC0aNHMWHCBLRo0QLbt2+3UYpEREREN++mip1Lly7hzTffRNu2bdG3b18UFBTg+++/R0pKCtLS0vDvf/8bEyZMsHWuRERERFazutgZOnQogoKCEBsbiylTpiAtLQ2ff/45Bg4cCABwc3NDVFQUzle/L0AtLVq0CD169ICrqyu8vLxM4jk5ORg8eDACAgKg0WgQFBSE6dOno6DavQuqCw0NhSRJiteSJUtsmjsRERHVH6sfF+Hn54cdO3age/fu113G19cXKdVv+FRL5eXlGDlyJLp3746YmBiTuEqlwrBhw/Dqq6/C19cXp0+fxn/+8x/k5uZiw4YNNfa9YMECTJkyRZ52d3e3ae5ERERUf6wqdnQ6Hc6dOwcfH58al5MkCSEhIbVKrLr58+cDAGJjY83GGzVqhGnTpsnTISEheOaZZ7B06dIb9u3u7g5/f3+b5ElERES3F6uKHUdHRxw5cqSucrGpixcvYvPmzRY9uHTJkiVYuHAhgoODMWbMGERGRt7wqe7VD49pNBpoNOYeTUfUcBgu6G+8EDVI/N1SQ1dWVoaysmuPubnRaSpVWX0Ya9y4cYiJibltz2sZPXo0vvnmG5SUlGDo0KH45JNPalx+xowZ6Ny5M7y9vbF7925ER0cjPT0db731Vo3tgoKCFNNz587FvHnzaps+Uf1w8AAk4MrbefWdCdUhZ1fnG47ME92uFi9eLB/lsZbVz8Z69tlnsXbtWrRq1QpdunSBm5ubIn6jIqGqWbNm4fXXX69xmaSkJIRffbgbUHEY6/nnn0detQfPVcrIyEBeXh5OnjyJ6Oho9OnTBx988IHFOX366ad46qmnUFhYaHakpvJZHOfPn1c8i8NeR3b4bKx/BlFwDMZ9Q7F+/XpEXH1ALNkfHx8fBAcH13caRDfF3MhOUFCQRc/Gsnpk59ixY/JD5E6ePGltc4WoqChMnDixxmXCwsKs6tPf3x/+/v4IDw+Ht7c3evXqhVdeeQVNmza1qH23bt2g1+tx7tw5tG7d+rrLeXh4/CMeBEr/LBEREXxIJBHdlmozqGB1sRMXF3dTb2SOr68vfH19bdZfdZV3cq5aCd5IQkICVCoV/Pz86iotIiIiuoWsvs/OpEmTcOXKFZP5RUVFmDRpkk2SMic1NRUJCQlITU2FwWBAQkICEhISUFhYCAD48ccfsXr1ahw7dgznzp3DDz/8gKeffhr33nsvQkNDAQB79+5FeHg40tLSAADx8fF45513cPjwYZw9exafffYZIiMjMW7cODRq1KjO1oWIiIhuHauLnTVr1qCkpMRkfklJCdauXWuTpMyZM2cOOnXqhLlz56KwsBCdOnVCp06dsH//fgCAi4sLPv74Y/Ts2RMRERGIjIzEww8/jO+//17uo7i4GMnJydDpdAAqhsQ2btyIPn36oG3btli0aBEiIyOxatWqOlsPIiIiurUsPoxVUFAAIQSEELhy5QqcnZ3lmMFgwI8//linh35iY2Ove48dAOjXrx92795dYx99+/ZF1fOxO3fujL/++stWKRIREdFtyOJix8vLS36cwh133GESlyTppi8JIyIiIqorFhc7cXFxEEKgf//++Oqrr+Dt7S3HnJycEBISgoCAgDpJkoiIiOhmWVzsVN6JOCUlBUFBQVCpbuqB6URERES3lNWXnoeEhCAvLw979+5FZmamfHl3pfHjx9ssOSIiIqLasrrY+e677zB27FgUFhbCw8MDkiTJMUmSWOwQERHRbcXqY1FRUVGYNGkSCgsLkZeXh8uXL8uv3NzcusiRiIiI6KZZXeykpaVhxowZcHV1rYt8iIiIiGzK6sNYgwYNwv79+61+ZhU1bKL4dH2nQHWIv18ismdWFzsPPvggXnzxRSQmJqJ9+/ZwdHRUxB9++GGbJUf1z8fHB87Orig9Hglx48WpAXN2doWPj099p0FEZHOSqHpLYQvUdMm5JEkwGAy1Tup2VlBQAE9PT4seKW8vUlNTkZ2dXd9pUB3z8fFBcHBwfadBRGQRa/bHVo/sVL/UnOxfcHAwd4JERNRg8c6AREREZNcsLnaGDBmC/Px8eXrJkiXIy8uTp3NyctCmTRubJkdERERUWxYXO7/88gvKysrk6ddee01xXx29Xo/k5GTbZkdERERUSxYXO9XPY7byvGYiIiKiesFzdoiIiMiuWVzsSJKkeA5W5TwiIiKi25nFl54LITBx4kRoNBoAQGlpKZ5++mm4ubkBgOJ8HiIiIqLbhcXFzoQJExTT48aNM1mGTzwnIiKi243Fxc7q1avrMg8iIiKiOsETlImIiMiusdghIiIiu8Zih4iIiOwaix0iIiKyayx2iIiIyK6x2CEiIiK7xmKHiIiI7BqLHSIiIrJrLHaIiIjIrrHYISIiIrvGYoeIiIjsGosdIiIismssdoiIiMiusdghIiIiu8Zih4iIiOxagyl2Fi1ahB49esDV1RVeXl41LpuTk4PAwEBIkoS8vLwal83NzcXYsWPh4eEBLy8vTJ48GYWFhbZLnIiIiOpVgyl2ysvLMXLkSEybNu2Gy06ePBkdOnSwqN+xY8fi+PHj+O233/D9999j586dmDp1am3TJSIiottEgyl25s+fj8jISLRv377G5VauXIm8vDy88MILN+wzKSkJP//8Mz755BN069YNPXv2xLvvvouNGzfi4sWLtkqdiIiI6lGDKXYskZiYiAULFmDt2rVQqW68avHx8fDy8kLXrl3leQMHDoRKpcKePXvqMlUiIiK6RRzqOwFbKSsrw+jRo7F06VIEBwfj7NmzN2yTkZEBPz8/xTwHBwd4e3sjIyOjxrYFBQWKaY1GA41GY33iREREdENlZWUoKyuTp6vvh2tSryM7s2bNgiRJNb5OnDhhUV/R0dGIiIjAuHHj6jjrCkFBQfD09JRfixcvviXvS0RE9E+0ePFixX43KCjI4rb1OrITFRWFiRMn1rhMWFiYRX1t27YNR48exZdffgkAEEIAAHx8fPDyyy9j/vz5Jm38/f2RmZmpmKfX65Gbmwt/f/8a3+/8+fPw8PCQpzmqQ0REVHeio6Mxc+ZMebqgoMDigqdeix1fX1/4+vrapK+vvvoKJSUl8vS+ffswadIk/PHHH2jRooXZNt27d0deXh4OHDiALl26AKgomoxGI7p161bj+3l4eCiKHSIiIqo7tTldpMGcs5Oamorc3FykpqbCYDAgISEBANCyZUtotVqTgiY7OxsAEBERId+XZ+/evRg/fjy2bt2KZs2aISIiAoMHD8aUKVPw4YcfQqfTYfr06XjssccQEBBwK1ePiIiI6kiDKXbmzJmDNWvWyNOdOnUCAMTFxaFv374W9VFcXIzk5GTodDp53meffYbp06djwIABUKlUGDFiBFasWGHT3ImIiKj+SKLy5BaySEFBATw9PZGfn8/DWERERPXEmv2xXd1nh4iIiKg6FjtERERk11jsEBERkV1jsUNERER2jcUOERER2TUWO0RERGTXWOwQERGRXWOxQ0RERHaNxQ4RERHZNRY7REREZNdY7BAREZFdY7FDREREdo3FDhEREdk1FjtERERk11jsEBERkV1jsUNERER2jcUOERER2TUWO0RERGTXWOwQERGRXWOxQ0RERHaNxQ4RERHZNRY7REREZNdY7BAREZFdY7FDREREdo3FDhEREdk1FjtERERk11jsEBERkV1jsUNERER2jcUOERER2TUWO0RERGTXWOwQERGRXWOxQ0RERHaNxQ4RERHZNRY7REREZNdY7BAREZFdY7FDREREdq3BFDuLFi1Cjx494OrqCi8vrxqXzcnJQWBgICRJQl5eXo3LhoaGQpIkxWvJkiW2S5yIiIjqVYMpdsrLyzFy5EhMmzbthstOnjwZHTp0sLjvBQsWID09XX49++yztUmViIiIbiMO9Z2ApebPnw8AiI2NrXG5lStXIi8vD3PmzMFPP/1kUd/u7u7w9/evbYpERER0G2owxY4lEhMTsWDBAuzZswdnz561uN2SJUuwcOFCBAcHY8yYMYiMjISDg/lNI4QAAKSlpaGgoECer9FooNFoarcCREREZFZZWRnKysrk6StXrgC4tl+uid0UO2VlZRg9ejSWLl2K4OBgi4udGTNmoHPnzvD29sbu3bsRHR2N9PR0vPXWW2aXr9y4bdq0sVnuREREdHOuXLkCT0/PGpep12Jn1qxZeP3112tcJikpCeHh4TfsKzo6GhERERg3bpxVOcycOVP+uUOHDnBycsJTTz2FxYsXmx2pCQgIwJkzZ+Do6AhJkuT5HNkhIiKqO9VHdoQQ0Ol0CAgIuGFbSVgy/lNHsrKykJOTU+MyYWFhcHJykqdjY2Px/PPPm1xl1bFjRxw9elQuQIQQMBqNUKvVePnll+Vzfm7k+PHjaNeuHU6cOIHWrVtbt0JERER026nXkR1fX1/4+vrapK+vvvoKJSUl8vS+ffswadIk/PHHH2jRooXF/SQkJEClUsHPz88meREREVH9ajDn7KSmpiI3NxepqakwGAxISEgAALRs2RJardakoMnOzgYAREREyPfl2bt3L8aPH4+tW7eiWbNmiI+Px549e9CvXz+4u7sjPj4ekZGRGDduHBo1anQrV4+IiIjqSIMpdubMmYM1a9bI0506dQIAxMXFoW/fvhb1UVxcjOTkZOh0OgAV59ls3LgR8+bNQ1lZGZo3b47IyEjFeTxERETUsDWYmwrGxsZCCGHyul6h07dvXwghFHdbrpwXGhoKAOjcuTP++usv5OXloaSkBImJiYiOjuaJxtTgSJKELVu2NLi+a2PevHno2LGjVW1utC7nzp2DJEnyyPH27dsVd2KPjY294R3cb4XbJQ+ihqLBFDtEthYfHw+1Wo0HH3zQJHYzO1J7lZ6ejgceeKC+0zDxwgsvYOvWrTbtMygoCOnp6WjXrp3Z+KhRo3Dy5El52lafk8qiqlGjRigtLVXE9u3bJz/K5np52NrixYuhVquxdOnSOnuPmli7PYhuhMUO/WPFxMTg2Wefxc6dO3Hx4sX6Tue2U15eDgDw9/e/rUY7hRDQ6/XQarVo3LixTftWq9Xw9/e/7k1FXVxc6vTiBXd3d3z99deKeTExMQgODr6leXz66af473//i08//bTO3sMSlm4PohthsUP/SIWFhdi0aROmTZuGBx98UPEYktjYWMyfPx+HDx+W/4KsjEuShI8++ggPPfQQXF1dERERgfj4eJw+fRp9+/aFm5sbevTogTNnzijeb+XKlWjRogWcnJzQunVrrFu3To4JITBv3jwEBwdDo9EgICAAM2bMkOOhoaFYuHAhRo8eDTc3NzRr1gzvv/++yTplZ2fjkUcegaurK1q1aoVvv/1WET927BgeeOABaLVaNGnSBI8//rh8Ij9QcZh3+vTpeP755+Hj44NBgwbJ61z10M+FCxcwevRoeHt7w83NDV27dsWePXvMbucePXrgpZdeUszLysqCo6Mjdu7cCQBYt24dunbtKj+2ZcyYMcjMzJSXr/wr/6effkKXLl2g0Wjw559/moyq7Nu3D/fddx98fHzg6emJPn364ODBgyY5VY5Uubi4ICwsDF9++aUcq34Yq7qqh4+u9zmZNGkSHnroIUU7nU4HPz8/xMTEmO230oQJExQFRklJCTZu3IgJEyZcNw/g2gjTunXrEBoaCk9PTzz22GPyTVCtsWPHDpSUlGDBggUoKCjA7t27AQBGoxGBgYFYuXKlYvlDhw5BpVLh77//BgCcOHECPXv2hLOzM9q0aYPff//9pg+FWro9cnJyMHr0aDRr1gyurq5o3749Pv/8czmelZUFf39/vPbaa/K83bt3w8nJyeajg3SbEkT/QDExMaJr165CCCG+++470aJFC2E0GoUQQhQXF4uoqCjRtm1bkZ6eLtLT00VxcbEQQggAolmzZmLTpk0iOTlZDB8+XISGhor+/fuLn3/+WSQmJop77rlHDB48WH6vzZs3C0dHR/H++++L5ORksWzZMqFWq8W2bduEEEJ88cUXwsPDQ/z444/i77//Fnv27BGrVq2S24eEhAh3d3exePFikZycLFasWCHUarX49ddf5WUAiMDAQLFhwwZx6tQpMWPGDKHVakVOTo4QQojLly8LX19fER0dLZKSksTBgwfFfffdJ/r16yf30adPH6HVasWLL74oTpw4IU6cOCH3/fXXXwshhLhy5YoICwsTvXr1En/88Yc4deqU2LRpk9i9e7fZ7fzee++J4OBgedsKIcS7776rmBcTEyN+/PFHcebMGREfHy+6d+8uHnjgAXn5uLg4AUB06NBB/Prrr+L06dMiJydHzJ07V9x5553yclu3bhXr1q0TSUlJIjExUUyePFk0adJEFBQUKLZT48aNxccffyySk5PF7NmzhVqtFomJiUIIIVJSUgQAcejQIcV7X758WQghxOrVq4Wnp2eNn5Ndu3YJtVotLl68qPgMuLm5iStXrpjdTpXvk5ycLDQajfj777+FEEKsW7dO3HnnneLrr78WVb+uq+YhhBBz584VWq1WPProo+Lo0aNi586dwt/fX/zvf/8z+341efzxx8ULL7wghBAiKipKTJo0SY698MILomfPnorlo6Ki5Hl6vV60bt1a3HfffSIhIUH88ccf4u6771Z8hixh7fa4cOGCWLp0qTh06JA4c+aM/H9kz5498jI//PCDcHR0FPv27RMFBQUiLCxMREZGWr19qGFisUP/SD169BDvvPOOEEIInU4nfHx8RFxcnByvviOtBEDMnj1bno6PjxcARExMjDzv888/F87Ozor3mjJliqKfkSNHiiFDhgghhFi2bJm44447RHl5udlcQ0JCFMWTEEKMGjVKURBUz6uwsFAAED/99JMQQoiFCxeK+++/X9HH+fPn5R2KEBXFTqdOncyuc+WO6qOPPhLu7u5yEXUjmZmZwsHBQezcuVOe1717d/HSSy9dt82+ffsEALkwqNzxbdmyRbHc9X5HlQwGg3B3dxffffedYl2efvppxXLdunUT06ZNE0JYV+zUlEObNm3E66+/Lk8PHTpUTJw48bq5Vn2f4cOHi/nz5wshhOjXr59Yvny5RcWOq6urorB78cUXRbdu3a77nubk5+cLFxcXkZCQIIQQ4tChQ0Kr1cq/i0OHDglJkuTiw2AwiGbNmomVK1cKIYT46aefhIODg0hPT5f7/O2332662LF0e5jz4IMPiqioKMW8Z555Rtxxxx1izJgxon379qK0tNTinKhh42Es+sdJTk7G3r17MXr0aACAg4MDRo0adcNDDJU6dOgg/9ykSRMAQPv27RXzSktL5QfFJiUl4d5771X0ce+99yIpKQkAMHLkSJSUlCAsLAxTpkzB119/Db1er1i+e/fuJtOV7c3l5ebmBg8PD/lw0OHDhxEXFwetViu/Kh/DUvWQW5cuXWpc94SEBHTq1Ane3t41LlfJ19cX999/Pz777DMAQEpKCuLj4zF27Fh5mQMHDmDo0KEIDg6Gu7s7+vTpA6Di3lpVde3atcb3unTpEqZMmYJWrVrB09MTHh4eKCwsNOnHkm1ZW08++SRWr14t5/XTTz9h0qRJFrWdNGkSYmNjcfbsWZNtVZPQ0FC4u7vL002bNlUcDrTE559/jhYtWuDOO+8EUHFn+pCQEGzatEmejoiIwIYNGwBUHPLKzMzEyJEjAVT83woKCoK/v7/c5913321VDtVZsj0MBgMWLlyI9u3bw9vbG1qtFr/88ovJ7/7NN9+EXq/HF198gc8+++y2OheN6haLHfrHiYmJgV6vR0BAABwcHODg4ICVK1fiq6++Qn5+/g3bOzo6yj9XXhFibp7RaLQon6CgICQnJ+ODDz6Ai4sLnnnmGfTu3Vu+H5SlquZQmUdlDoWFhRg6dCgSEhIUr1OnTqF3795yGzc3txrfw8XFxaqcAGDs2LH48ssvodPpsGHDBrRv314uDouKijBo0CB4eHjgs88+w759++QTUitPkLY0twkTJiAhIQHLly/H7t27kZCQgMaNG5v0cyuMHz9e3jmvX78ezZs3R69evSxq+8ADD6CkpASTJ0/G0KFDLT4Ju6bfv6ViYmJw/Phx+f+Fg4MDEhMTFefNjB07Vi52NmzYgMGDB9v8RPGqLNkeS5cuxfLly/HSSy8hLi4OCQkJGDRokMnv/syZM7h48SKMRiPOnTtXZznT7YfFDv2j6PV6rF27FsuWLVPs9A8fPoyAgAD5pEYnJycYDAabvGdERAR27dqlmLdr1y60adNGnnZxccHQoUOxYsUKbN++HfHx8Th69Kgc/+uvvxTt//rrL0RERFicQ+fOnXH8+HGEhoaiZcuWiteNioiqOnTogISEBOTm5lrcZtiwYSgtLcXPP/+MDRs2KP4yP3HiBHJycrBkyRL06tUL4eHhVo9GVNq1axdmzJiBIUOGoG3bttBoNIoTsCvVdltWdb3PSePGjTF8+HCsXr0asbGxeOKJJyzu08HBAePHj8f27dstHg2yhaNHj2L//v3Yvn274v9G5efxxIkTAIAxY8bg2LFjOHDgAL788kvF77N169Y4f/48Ll26JM/bt29frfKyZHvs2rULw4YNw7hx43DnnXciLCzM5NL88vJyjBs3DqNGjcLChQvx5JNP3vRnjRoeFjv0j/L999/j8uXLmDx5Mtq1a6d4jRgxQj6UFRoaipSUFCQkJCA7O1vxpF1rvfjii4iNjcXKlStx6tQpvPXWW9i8eTNeeOEFABVX1sTExODYsWM4e/Ys1q9fDxcXF4SEhMh97Nq1C2+88QZOnjyJ999/H1988QWee+45i3P4z3/+g9zcXIwePRr79u3DmTNn8Msvv+CJJ56wqqgbPXo0/P39MXz4cOzatQtnz57FV199hfj4+Ou2cXNzw/Dhw/HKK68gKSlJPnwIAMHBwXBycsK7776Ls2fP4ttvv8XChQstzqeqVq1aYd26dUhKSsKePXswduxYsyNRX3zxBT799FOcPHkSc+fOxd69ezF9+vSbes+aPidPPvkk1qxZg6SkJJOrh25k4cKFyMrKkq+IuxViYmJw9913o3fv3or/F71798Zdd92l+L/Ro0cPTJ48GQaDAQ8//LDcx3333YcWLVpgwoQJOHLkCHbt2oXZs2cDQK3ui3Oj7dGqVSv89ttv2L17N5KSkvDUU08pCi4AePnll5Gfn48VK1bgpZdewh133HFLi0mqXyx26B8lJiYGAwcOhKenp0lsxIgR2L9/P44cOYIRI0Zg8ODB6NevH3x9fRWXsVpr+PDhWL58Od588020bdsWH330EVavXi3f/dvLywsff/wx7r33XnTo0AG///47vvvuO8VwfVRUFPbv349OnTrh1VdfxVtvvWXVjjAgIAC7du2CwWDA/fffj/bt2+P555+Hl5cXVCrLvwacnJzw66+/ws/PD0OGDEH79u2xZMkSqNXqGtuNHTsWhw8fRq9evRT3SPH19UVsbCy++OILtGnTBkuWLMGbb75pcT5VxcTE4PLly+jcuTMef/xxzJgxw+y9aObPn4+NGzeiQ4cOWLt2LT7//HPFKJs1avqcDBw4EE2bNsWgQYMQEBBgVb9OTk7w8fGx6Y3zYmNjr9tfeXk51q9fjxEjRpiNjxgxAmvXrpUPrVb+Ph955BFFQalWq7FlyxYUFhbirrvuwpNPPomXX34ZAODs7Cwv17dvX0ycONHi3G+0PWbPno3OnTtj0KBB6Nu3r1yQV9q+fTveeecdrFu3Dh4eHlCpVFi3bh3++OMPk0vpyT5JQghR30kQ0fWFhobi+eefx/PPP1/fqZAVCgsL0axZM6xevRqPPvpofaeDuXPnYseOHdi+ffstfd9du3ahZ8+eOH36tPzA5pCQEMyfP9+qgoeoNhrMg0CJiBoCo9GI7OxsLFu2DF5eXorDPPXpp59+wnvvvVfn7/P1119Dq9WiVatWOH36NJ577jnce++9cqFz/PhxeHp6Yvz48XWeC1ElFjtERDaUmpqK5s2bIzAwELGxsdd99MSttnfv3lvyPleuXMFLL72E1NRU+Pj4YODAgVi2bJkcb9u2LY4cOXJLciGqxMNYREREZNd4gjIRERHZNRY7REREZNdY7BAREZFdY7FDREREdo3FDhEREdk1FjtERERk11jsEBERkV1jsUNERER2jcUOERER2bX/B6mzls1pYyytAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 625x425 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Polygon\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.set_size_inches([6.25,4.25])\n",
    "     \n",
    "ax = plt.gca()\n",
    "\n",
    "\n",
    "x1 = [1.0, 1.0, 2.0, 2.0]\n",
    "y1 = [-13.6659, -12.0304, -12.0304, -13.6659]\n",
    "\n",
    "x2 = [2.0, 2.0, 3.0, 3.0]\n",
    "y2 = [-13.4984, -11.8266, -11.8266, -13.4984]\n",
    "\n",
    "x3 = [3.0, 3.0, 4.0, 4.0]\n",
    "y3 = [-13.3650, -11.6798, -11.6798, -13.3650]\n",
    "\n",
    "\n",
    "\n",
    "poly1 = Polygon( list(zip(x1,y1)), facecolor='xkcd:blue', edgecolor='k')\n",
    "ax.add_patch(poly1)\n",
    "\n",
    "poly2 = Polygon( list(zip(x2,y2)), facecolor='xkcd:green', edgecolor='k')\n",
    "ax.add_patch(poly2)\n",
    "\n",
    "poly3 = Polygon( list(zip(x3,y3)), facecolor='xkcd:red', edgecolor='k')\n",
    "ax.add_patch(poly3)\n",
    "\n",
    "\n",
    "plt.ylabel(\"Entry flight-path angle, deg\",fontsize=10)\n",
    "plt.xlabel(\"Atmospheric variability Min, Avg, Max\",fontsize=10)\n",
    "\n",
    "plt.tick_params(axis='x',          # changes apply to the x-axis\n",
    "                which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False) \n",
    "\n",
    "plt.axhline(y=-12.0304, linewidth=1.0, linestyle='dashed' ,color='xkcd:blue')\n",
    "plt.axhline(y=-13.3650, linewidth=1.0, linestyle='dashed' ,color='xkcd:red')\n",
    "plt.axhline(y=0.5*(-13.3650+-12.0304), linewidth=2.0, linestyle='dotted' ,color='xkcd:black')\n",
    "\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "\n",
    "\n",
    "ax.set_xlim([0.5, 4.5])\n",
    "ax.set_ylim([-14.5, -11])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb2f8495",
   "metadata": {},
   "source": [
    "As a preliminary estimate, the target EFPA should be chosen at the middle of the dashed blue and red lines, indicated by the dotted black line. Additional considerations are required because of sensitivity of aerocapture trajectories near the shallow limit, and is typically biased towards the steep end to avoid escape scenarios. This will be the target EFPA (planet-relative) at atmospheric entry targeted during the approach phase."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}