{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example - 50 - Earth SmallSat Aerocapture Demonstration - Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we will use Monte Carlo simulations to assess flight performance. Reference: M.S.Werner and R.D.Braun, Mission Design and Performance Analysis of a Smallsat Aerocapture Flight Test, Journal of Spacecraft and Rockets, DOI: 10.2514/1.A33997"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.patches import Polygon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BATCH :../data/werner2019/MCB1, RUN #: 1, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 107, EFPA: -4.83, SIGMA: 0.22, APO : 1915.51\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 2, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 14, EFPA: -4.79, SIGMA: -1.00, APO : 2874.42\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 3, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 138, EFPA: -4.84, SIGMA: 0.54, APO : 1901.08\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 4, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 126, EFPA: -4.83, SIGMA: 0.10, APO : 1877.14\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 5, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 138, EFPA: -4.85, SIGMA: 0.23, APO : 1676.20\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 6, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 18, EFPA: -4.99, SIGMA: -1.71, APO : 1533.84\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 7, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 22, EFPA: -4.93, SIGMA: 0.05, APO : 1855.16\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 8, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 38, EFPA: -4.89, SIGMA: -0.84, APO : 2005.10\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 9, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 187, EFPA: -4.79, SIGMA: -0.30, APO : 2394.91\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 10, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 123, EFPA: -4.77, SIGMA: 0.85, APO : 2404.43\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 11, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 143, EFPA: -4.84, SIGMA: -0.55, APO : 2553.98\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 12, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 82, EFPA: -4.82, SIGMA: -1.59, APO : 1813.22\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 13, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 82, EFPA: -4.87, SIGMA: 0.05, APO : 1944.94\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 14, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 77, EFPA: -4.82, SIGMA: 1.32, APO : 1789.71\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 15, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 32, EFPA: -4.88, SIGMA: 1.99, APO : 1788.18\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 16, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 186, EFPA: -4.89, SIGMA: 0.75, APO : 2094.33\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 17, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 160, EFPA: -4.80, SIGMA: 0.80, APO : 2229.93\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 18, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 196, EFPA: -4.91, SIGMA: -0.04, APO : 1757.71\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 19, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 134, EFPA: -4.87, SIGMA: -1.32, APO : 2048.48\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 20, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 75, EFPA: -4.83, SIGMA: 0.33, APO : 2973.29\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 21, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 132, EFPA: -4.88, SIGMA: -0.51, APO : 2467.30\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 22, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 177, EFPA: -4.75, SIGMA: -0.25, APO : 1815.59\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 23, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 107, EFPA: -4.90, SIGMA: 2.15, APO : 1641.88\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 24, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 77, EFPA: -4.87, SIGMA: 0.79, APO : 2346.24\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 25, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 56, EFPA: -4.79, SIGMA: 0.52, APO : 2748.76\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 26, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 163, EFPA: -4.81, SIGMA: 0.91, APO : 1921.23\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 27, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 198, EFPA: -4.85, SIGMA: -1.79, APO : 1978.05\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 28, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 180, EFPA: -4.89, SIGMA: -0.43, APO : 1856.87\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 29, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 171, EFPA: -4.83, SIGMA: 0.45, APO : 1940.96\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 30, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 38, EFPA: -4.80, SIGMA: 0.23, APO : 1808.58\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 31, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 1, EFPA: -4.85, SIGMA: -1.00, APO : 2002.24\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 32, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 49, EFPA: -4.84, SIGMA: 0.04, APO : 2074.28\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 33, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 105, EFPA: -4.85, SIGMA: -0.88, APO : 2267.95\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 34, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 54, EFPA: -4.79, SIGMA: 3.02, APO : 1817.77\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 35, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 142, EFPA: -4.89, SIGMA: -1.61, APO : 959.84\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 36, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 43, EFPA: -4.84, SIGMA: -1.32, APO : 1808.74\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 37, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 117, EFPA: -4.83, SIGMA: 0.46, APO : 1806.22\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 38, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 126, EFPA: -4.97, SIGMA: -0.78, APO : 1885.88\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 39, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 73, EFPA: -4.92, SIGMA: 1.39, APO : 2295.27\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 40, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 41, EFPA: -4.79, SIGMA: -0.06, APO : 1910.13\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 41, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 29, EFPA: -4.84, SIGMA: 0.12, APO : 1860.60\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 42, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 58, EFPA: -4.81, SIGMA: 0.82, APO : 1810.06\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 43, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 88, EFPA: -4.86, SIGMA: -0.66, APO : 5832.76\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 44, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 164, EFPA: -4.82, SIGMA: 0.47, APO : 3232.68\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 45, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 93, EFPA: -4.84, SIGMA: 0.51, APO : 1893.28\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 46, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 141, EFPA: -4.87, SIGMA: -0.69, APO : 2347.15\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 47, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 115, EFPA: -4.82, SIGMA: -2.88, APO : 2131.86\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 48, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 96, EFPA: -4.84, SIGMA: 0.66, APO : 2325.42\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 49, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 183, EFPA: -4.83, SIGMA: -0.20, APO : 1924.36\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 50, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 14, EFPA: -4.80, SIGMA: 0.36, APO : 1848.65\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 51, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 148, EFPA: -4.78, SIGMA: 0.50, APO : 2519.58\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 52, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 4, EFPA: -4.86, SIGMA: 0.24, APO : 1934.50\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 53, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 44, EFPA: -4.89, SIGMA: -1.57, APO : 2895.36\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 54, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 196, EFPA: -4.88, SIGMA: 0.86, APO : 1813.61\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 55, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 93, EFPA: -4.87, SIGMA: 0.60, APO : 1830.42\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 56, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 73, EFPA: -4.92, SIGMA: -0.96, APO : 1713.14\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 57, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 175, EFPA: -4.81, SIGMA: 0.65, APO : 1807.05\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 58, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 77, EFPA: -4.82, SIGMA: 0.28, APO : 1840.52\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 59, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 110, EFPA: -4.82, SIGMA: 0.26, APO : 1889.03\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 60, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 174, EFPA: -4.79, SIGMA: -0.74, APO : 2839.38\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 61, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 151, EFPA: -4.92, SIGMA: -1.81, APO : 1810.20\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 62, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 93, EFPA: -4.82, SIGMA: 1.75, APO : 1809.73\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BATCH :../data/werner2019/MCB1, RUN #: 63, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 52, EFPA: -4.87, SIGMA: 0.78, APO : 1810.62\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 64, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 67, EFPA: -4.86, SIGMA: -0.45, APO : 1700.97\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 65, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 12, EFPA: -4.82, SIGMA: 0.32, APO : 2646.75\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 66, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 183, EFPA: -4.90, SIGMA: -0.44, APO : 333.28\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 67, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 59, EFPA: -4.90, SIGMA: -0.17, APO : 1692.33\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 68, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 18, EFPA: -4.84, SIGMA: 0.51, APO : 1928.06\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 69, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 25, EFPA: -4.85, SIGMA: 0.94, APO : 1940.93\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 70, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 151, EFPA: -4.88, SIGMA: 1.75, APO : 1873.31\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 71, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 130, EFPA: -4.84, SIGMA: -0.57, APO : 2509.63\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 72, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 33, EFPA: -4.90, SIGMA: 0.97, APO : 1824.60\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 73, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 154, EFPA: -4.88, SIGMA: 0.57, APO : 1485.52\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 74, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 21, EFPA: -4.82, SIGMA: -1.09, APO : 1833.94\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 75, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 27, EFPA: -4.85, SIGMA: -0.38, APO : 2849.74\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 76, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 11, EFPA: -4.90, SIGMA: 0.87, APO : 2129.86\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 77, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 149, EFPA: -4.87, SIGMA: 0.24, APO : 1729.69\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 78, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 20, EFPA: -4.86, SIGMA: 0.11, APO : 1778.55\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 79, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 61, EFPA: -4.85, SIGMA: -0.36, APO : 1967.42\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 80, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 151, EFPA: -4.80, SIGMA: -0.53, APO : 1867.09\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 81, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 9, EFPA: -4.87, SIGMA: 0.24, APO : 1780.05\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 82, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 78, EFPA: -4.78, SIGMA: -0.15, APO : 4029.84\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 83, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 14, EFPA: -4.88, SIGMA: -0.21, APO : 124.35\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 84, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 152, EFPA: -4.80, SIGMA: 0.27, APO : 1906.28\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 85, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 141, EFPA: -4.88, SIGMA: -0.44, APO : 1893.22\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 86, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 171, EFPA: -4.85, SIGMA: 0.90, APO : 706.47\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 87, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 87, EFPA: -4.82, SIGMA: 0.13, APO : 1825.07\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 88, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 70, EFPA: -4.83, SIGMA: -1.14, APO : 1918.20\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 89, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 166, EFPA: -4.83, SIGMA: -0.06, APO : 1752.07\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 90, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 40, EFPA: -4.84, SIGMA: -0.77, APO : 1837.95\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 91, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 189, EFPA: -4.87, SIGMA: -0.62, APO : 1843.50\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 92, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 127, EFPA: -4.87, SIGMA: -0.79, APO : 2685.77\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 93, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 131, EFPA: -4.85, SIGMA: 0.13, APO : 1829.54\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 94, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 97, EFPA: -4.86, SIGMA: 0.20, APO : 1769.45\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 95, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 37, EFPA: -4.84, SIGMA: 0.08, APO : 2547.89\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 96, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 14, EFPA: -4.88, SIGMA: -1.27, APO : 2019.88\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 97, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 53, EFPA: -4.91, SIGMA: 0.98, APO : 1816.88\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 98, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 197, EFPA: -4.82, SIGMA: 0.22, APO : 1804.41\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 99, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 195, EFPA: -4.79, SIGMA: 0.75, APO : 1966.88\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 100, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 107, EFPA: -4.89, SIGMA: -1.37, APO : 2458.49\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 101, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 1, EFPA: -4.88, SIGMA: -2.25, APO : 5353.50\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 102, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 40, EFPA: -4.89, SIGMA: -0.66, APO : 2664.62\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 103, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 114, EFPA: -4.91, SIGMA: 0.64, APO : 1700.72\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 104, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 13, EFPA: -4.92, SIGMA: -1.19, APO : 22.63\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 105, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 132, EFPA: -4.78, SIGMA: 0.83, APO : 1815.90\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 106, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 25, EFPA: -4.88, SIGMA: 0.37, APO : 2613.84\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 107, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 184, EFPA: -4.86, SIGMA: -0.02, APO : 1823.35\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 108, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 52, EFPA: -4.83, SIGMA: 0.86, APO : 2309.78\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 109, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 48, EFPA: -4.87, SIGMA: -0.43, APO : 1908.16\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 110, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 89, EFPA: -4.84, SIGMA: -1.45, APO : 2984.50\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 111, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 96, EFPA: -4.83, SIGMA: 0.13, APO : 1895.90\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 112, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 176, EFPA: -4.90, SIGMA: 1.10, APO : 22.69\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 113, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 67, EFPA: -4.85, SIGMA: 0.49, APO : 1760.06\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 114, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 72, EFPA: -4.83, SIGMA: 1.05, APO : 1822.80\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 115, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 84, EFPA: -4.84, SIGMA: -0.73, APO : 2175.26\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 116, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 171, EFPA: -4.83, SIGMA: 1.08, APO : 1878.67\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 117, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 68, EFPA: -4.86, SIGMA: -1.16, APO : 1808.78\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 118, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 200, EFPA: -4.84, SIGMA: 1.32, APO : 1766.54\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 119, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 51, EFPA: -4.80, SIGMA: 0.01, APO : 1793.98\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 120, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 14, EFPA: -4.81, SIGMA: -0.63, APO : 2147.00\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 121, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 136, EFPA: -4.87, SIGMA: -0.15, APO : 3850.89\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 122, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 10, EFPA: -4.80, SIGMA: -0.83, APO : 3750.97\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 123, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 193, EFPA: -4.91, SIGMA: 0.53, APO : 22.77\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 124, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 112, EFPA: -4.84, SIGMA: -0.21, APO : 1965.62\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BATCH :../data/werner2019/MCB1, RUN #: 125, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 135, EFPA: -4.89, SIGMA: 0.06, APO : 22.54\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 126, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 164, EFPA: -4.84, SIGMA: 0.21, APO : 1941.02\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 127, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 17, EFPA: -4.87, SIGMA: -1.32, APO : 2672.25\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 128, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 124, EFPA: -4.80, SIGMA: 1.49, APO : 1803.72\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 129, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 195, EFPA: -4.80, SIGMA: 0.22, APO : 1835.42\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 130, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 102, EFPA: -4.80, SIGMA: 1.93, APO : 1835.56\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 131, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 18, EFPA: -4.84, SIGMA: -0.79, APO : 1811.93\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 132, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 4, EFPA: -4.84, SIGMA: -0.69, APO : 2030.44\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 133, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 37, EFPA: -4.86, SIGMA: 0.16, APO : 1812.47\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 134, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 181, EFPA: -4.84, SIGMA: -3.70, APO : 1818.59\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 135, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 4, EFPA: -4.79, SIGMA: -0.71, APO : 1868.37\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 136, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 184, EFPA: -4.85, SIGMA: -1.87, APO : 1771.29\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 137, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 102, EFPA: -4.85, SIGMA: -0.48, APO : 2188.19\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 138, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 99, EFPA: -4.75, SIGMA: -0.85, APO : 9823.91\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 139, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 138, EFPA: -4.86, SIGMA: 0.28, APO : 1777.30\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 140, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 166, EFPA: -4.83, SIGMA: -0.17, APO : 1900.76\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 141, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 160, EFPA: -4.89, SIGMA: 0.06, APO : 1798.07\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 142, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 196, EFPA: -4.88, SIGMA: 0.33, APO : 1885.40\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 143, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 19, EFPA: -4.89, SIGMA: 2.38, APO : 1298.20\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 144, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 185, EFPA: -4.90, SIGMA: 0.52, APO : 1942.11\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 145, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 95, EFPA: -4.83, SIGMA: 2.00, APO : 2038.34\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 146, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 11, EFPA: -4.85, SIGMA: 0.68, APO : 1814.08\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 147, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 187, EFPA: -4.90, SIGMA: -0.51, APO : 1868.46\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 148, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 81, EFPA: -4.85, SIGMA: 0.87, APO : 1835.14\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 149, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 67, EFPA: -4.78, SIGMA: 1.77, APO : 2056.58\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 150, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 47, EFPA: -4.84, SIGMA: 0.08, APO : 2000.82\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 151, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 28, EFPA: -4.80, SIGMA: 0.33, APO : 4423.67\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 152, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 14, EFPA: -4.89, SIGMA: -0.18, APO : 1826.45\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 153, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 35, EFPA: -4.83, SIGMA: 0.58, APO : 1913.70\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 154, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 49, EFPA: -4.83, SIGMA: 0.04, APO : 1839.21\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 155, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 99, EFPA: -4.85, SIGMA: -1.20, APO : 1911.89\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 156, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 93, EFPA: -4.91, SIGMA: -2.07, APO : 2050.29\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 157, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 150, EFPA: -4.82, SIGMA: 0.80, APO : 2043.39\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 158, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 93, EFPA: -4.85, SIGMA: -0.89, APO : 2905.72\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 159, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 119, EFPA: -4.87, SIGMA: 1.05, APO : 1762.34\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 160, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 88, EFPA: -4.86, SIGMA: 0.97, APO : 1768.09\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 161, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 137, EFPA: -4.82, SIGMA: 0.27, APO : 1874.13\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 162, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 113, EFPA: -4.85, SIGMA: -0.48, APO : 1808.50\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 163, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 90, EFPA: -4.82, SIGMA: -0.51, APO : 4621.58\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 164, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 119, EFPA: -4.93, SIGMA: -0.25, APO : 22.55\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 165, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 188, EFPA: -4.80, SIGMA: 0.77, APO : 2672.75\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 166, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 77, EFPA: -4.93, SIGMA: -0.62, APO : 1391.23\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 167, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 55, EFPA: -4.88, SIGMA: -0.00, APO : 1905.84\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 168, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 162, EFPA: -4.86, SIGMA: -0.02, APO : 1887.71\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 169, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 6, EFPA: -4.88, SIGMA: 0.29, APO : 2017.17\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 170, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 159, EFPA: -4.84, SIGMA: 0.94, APO : 1814.33\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 171, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 200, EFPA: -4.87, SIGMA: -0.82, APO : 3258.49\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 172, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 57, EFPA: -4.93, SIGMA: -1.65, APO : 2096.31\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 173, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 92, EFPA: -4.90, SIGMA: -0.13, APO : 1450.62\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 174, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 67, EFPA: -4.85, SIGMA: -1.60, APO : 2194.75\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 175, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 20, EFPA: -4.82, SIGMA: -1.65, APO : 1855.57\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 176, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 111, EFPA: -4.85, SIGMA: 0.13, APO : 2629.28\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 177, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 86, EFPA: -4.90, SIGMA: -0.57, APO : 1953.92\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 178, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 200, EFPA: -4.82, SIGMA: 0.17, APO : 3527.15\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 179, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 73, EFPA: -4.80, SIGMA: 0.96, APO : 1870.57\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 180, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 70, EFPA: -4.85, SIGMA: 0.25, APO : 1813.48\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 181, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 181, EFPA: -4.91, SIGMA: 0.02, APO : 1800.06\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 182, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 135, EFPA: -4.86, SIGMA: 1.21, APO : 2210.22\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 183, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 200, EFPA: -4.83, SIGMA: 0.42, APO : 1796.40\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 184, PROF: ../atmdata/Earth/LAT40N.txt, SAMPLE #: 34, EFPA: -4.84, SIGMA: 1.11, APO : 1853.91\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 185, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 136, EFPA: -4.93, SIGMA: 0.08, APO : 1870.74\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 186, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 144, EFPA: -4.90, SIGMA: 0.93, APO : 1764.16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BATCH :../data/werner2019/MCB1, RUN #: 187, PROF: ../atmdata/Earth/LAT80N.txt, SAMPLE #: 67, EFPA: -4.88, SIGMA: -2.00, APO : 2258.70\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 188, PROF: ../atmdata/Earth/LAT80S.txt, SAMPLE #: 81, EFPA: -4.92, SIGMA: -0.95, APO : 22.62\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 189, PROF: ../atmdata/Earth/LAT20N.txt, SAMPLE #: 164, EFPA: -4.84, SIGMA: -1.46, APO : 1904.96\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 190, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 112, EFPA: -4.87, SIGMA: -1.30, APO : 1737.33\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 191, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 111, EFPA: -4.88, SIGMA: -1.19, APO : 1818.10\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 192, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 35, EFPA: -4.83, SIGMA: -0.38, APO : 3184.55\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 193, PROF: ../atmdata/Earth/LAT60S.txt, SAMPLE #: 184, EFPA: -4.91, SIGMA: -0.57, APO : 1939.20\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 194, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 96, EFPA: -4.85, SIGMA: 1.09, APO : 1742.70\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 195, PROF: ../atmdata/Earth/LAT00N.txt, SAMPLE #: 22, EFPA: -4.86, SIGMA: -0.01, APO : 1843.61\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 196, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 73, EFPA: -4.82, SIGMA: -1.92, APO : 1873.99\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 197, PROF: ../atmdata/Earth/LAT20S.txt, SAMPLE #: 26, EFPA: -4.84, SIGMA: 0.28, APO : 1885.78\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 198, PROF: ../atmdata/Earth/LAT40S.txt, SAMPLE #: 122, EFPA: -4.85, SIGMA: -1.06, APO : 1897.63\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 199, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 114, EFPA: -4.84, SIGMA: -0.12, APO : 3436.07\n",
      "BATCH :../data/werner2019/MCB1, RUN #: 200, PROF: ../atmdata/Earth/LAT60N.txt, SAMPLE #: 4, EFPA: -4.86, SIGMA: -0.87, APO : 3910.03\n"
     ]
    }
   ],
   "source": [
    "# Set up the planet and atmosphere model.\n",
    "planet=Planet(\"EARTH\")\n",
    "planet.loadAtmosphereModel('../atmdata/Earth/earth-gram-avg.dat', 0 , 1 ,2, 3)\n",
    "planet.h_skip = 125000.0\n",
    "planet.h_low=10.0E3\n",
    "\n",
    "# Set up the drag modulation vehicle.\n",
    "vehicle=Vehicle('EarthSmallSat', 2.82, 66.4, 0.0, np.pi*0.25**2, 0.0, 0.0563, planet)\n",
    "vehicle.setInitialState(125.0,0.0,0.0,9.8,0.0,-5.00,0.0,0.0)\n",
    "vehicle.setSolverParams(1E-6)\n",
    "vehicle.setDragModulationVehicleParams(66.4,4.72)\n",
    "\n",
    "# Set up the drag modulation entry phase guidance parameters.\n",
    "vehicle.setDragEntryPhaseParams(5.0, 50.0, 101, -200.0)\n",
    "\n",
    "# Set the target orbit parameters.\n",
    "vehicle.setTargetOrbitParams(180.0, 1760.0, 50.0)\n",
    "\n",
    "# Define the path to atmospheric files to be used for the Monte Carlo simulations.\n",
    "#atmfiles = ['../atmdata/Earth/LAT00N.txt']\n",
    "\n",
    "atmfiles = ['../atmdata/Earth/LAT00N.txt',\n",
    "            '../atmdata/Earth/LAT20N.txt',\n",
    "            '../atmdata/Earth/LAT20S.txt',\n",
    "            '../atmdata/Earth/LAT40N.txt',\n",
    "            '../atmdata/Earth/LAT40S.txt',\n",
    "            '../atmdata/Earth/LAT60N.txt',\n",
    "            '../atmdata/Earth/LAT60S.txt',\n",
    "            '../atmdata/Earth/LAT80N.txt',\n",
    "            '../atmdata/Earth/LAT80S.txt']\n",
    "\n",
    "# Set up the Monte Carlo simulation for drag modulation.\n",
    "# NPOS = 141, NMONTE = 200\n",
    "# Target EFPA = -4.85 deg\n",
    "# EFPA 1-sigma error = +/- 0.04 deg\n",
    "# Nominal beta_1 = 66.4 kg/m2\n",
    "# beta_1 1-sigma = 0.0\n",
    "# guidance time step for entry = 1.0s (Freq. = 1 Hz)\n",
    "# guidance time step after jettison = 1.0 s\n",
    "# max. solver time step = 0.1 s\n",
    "# max. time used by solver = 2400 s\n",
    "\n",
    "vehicle.setupMonteCarloSimulationD_Earth(141, 200, atmfiles,1, 4, 14, 9, True,\n",
    "                                   -4.85,  0.04, 66.4, 0.0,\n",
    "                                    1.0, 1.0, 0.1, 2400.0)\n",
    "# Run 200 trajectories\n",
    "vehicle.runMonteCarloD_Earth(200, '../data/werner2019/MCB1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the terminal apoapsis, deceleration and heat rate data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "apoap = np.loadtxt('../data/werner2019/MCB1/terminal_apoapsis_arr.txt')\n",
    "peria = np.loadtxt('../data/werner2019/MCB1/terminal_periapsis_arr.txt')\n",
    "decel = np.loadtxt('../data/werner2019/MCB1/acc_net_g_max_arr.txt')\n",
    "heatr = np.loadtxt('../data/werner2019/MCB1/q_stag_max_arr.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove cases which resulted in apoapsis < 1000 km or < 6000 km."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 34,  65,  82,  85, 103, 111, 122, 124, 163, 187]),)\n"
     ]
    }
   ],
   "source": [
    "del_index_low = np.where(apoap < 1000) \n",
    "print(del_index_low)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seven cases resulted in apoapis less than 1000 km, and are considered to crash."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove these entries from the data before plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "apoap_clean = np.delete(apoap, del_index_low)\n",
    "peria_clean = np.delete(peria, del_index_low)\n",
    "decel_clean = np.delete(decel, del_index_low)\n",
    "heatr_clean = np.delete(heatr, del_index_low)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also remove cases with apoapsis > 3000 km."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 41,  42,  79,  96, 114, 115, 129, 142, 154, 161, 168, 181, 188,\n",
      "       189]),)\n"
     ]
    }
   ],
   "source": [
    "del_index_high = np.where(apoap_clean > 3000) \n",
    "print(del_index_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "apoap_clean2 = np.delete(apoap_clean, del_index_high)\n",
    "peria_clean2 = np.delete(peria_clean, del_index_high)\n",
    "decel_clean2 = np.delete(decel_clean, del_index_high)\n",
    "heatr_clean2 = np.delete(heatr_clean, del_index_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "plt.hist(apoap_clean2, bins=25, color='xkcd:red')\n",
    "plt.xlabel('Apoapsis altitude, km',fontsize=14)\n",
    "plt.ylabel('Number of cases',fontsize=14)\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",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "plt.savefig('../plots/werner-smallsat-apoa-hist.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-apoa-hist.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-apoa-hist.eps', dpi=300,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "plt.hist(decel_clean2, bins=20, color='xkcd:green')\n",
    "plt.xlabel('Deceleration, g',fontsize=14)\n",
    "plt.ylabel('Number of cases',fontsize=14)\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",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "plt.savefig('../plots/werner-smallsat-decel-hist.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-decel-hist.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-decel-hist.eps', dpi=300,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "plt.hist(heatr_clean2, bins=20, color='xkcd:blue')\n",
    "plt.xlabel('Heat rate, '+r'$W/cm^2$',fontsize=14)\n",
    "plt.ylabel('Number of cases',fontsize=14)\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",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "plt.savefig('../plots/werner-smallsat-heat-hist.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-heat-hist.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-heat-hist.eps', dpi=300,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }\n",
    "plt.rcParams.update(params)\n",
    "plt.scatter(peria_clean2, apoap_clean2, s=30, facecolors='none', edgecolors='b', lw=2)\n",
    "plt.axhline(y=1760, lw=2.0, ls='dashed', color='k')\n",
    "plt.xlabel('Periapsis altitude, km',fontsize=14)\n",
    "plt.ylabel('Apoapsis altitude, km',fontsize=14)\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",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "plt.savefig('../plots/werner-smallsat-apoa-peri.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-apoa-peri.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-apoa-peri.eps', dpi=300,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the percentage of cases which achieved apoapsis between 1000 and 3000 km."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.size(apoap) - np.size(del_index_low) - np.size(del_index_high))*100/np.size(apoap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These results are different from those in the paper because of the different guidance schemes used here. This exercide did not attempt to optimize the guidance parameters. Further study can improve the apoapsis targeting."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}