{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example - 52 - Mars 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-Mars, RUN #: 1, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 473, EFPA: -12.02, SIGMA: 0.05, APO : 470.71\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 2, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 948, EFPA: -12.11, SIGMA: -0.68, APO : 249.09\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 3, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 982, EFPA: -12.11, SIGMA: -0.00, APO : 183.77\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 4, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 909, EFPA: -12.14, SIGMA: 0.97, APO : 165.56\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 5, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 795, EFPA: -11.96, SIGMA: 0.19, APO : 467.54\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 6, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 493, EFPA: -12.00, SIGMA: 0.10, APO : 442.98\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 7, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 255, EFPA: -12.00, SIGMA: 0.47, APO : 474.19\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 8, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 772, EFPA: -12.01, SIGMA: -0.41, APO : 470.02\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 9, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 819, EFPA: -12.08, SIGMA: -0.51, APO : 383.96\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 10, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 707, EFPA: -12.11, SIGMA: -0.56, APO : 396.13\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 11, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 348, EFPA: -12.15, SIGMA: -0.59, APO : 30.81\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 12, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 183, EFPA: -12.07, SIGMA: 1.09, APO : 314.32\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 13, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 236, EFPA: -12.05, SIGMA: 1.01, APO : 307.66\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 14, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 517, EFPA: -12.05, SIGMA: 1.79, APO : 262.04\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 15, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 643, EFPA: -12.15, SIGMA: -0.94, APO : 626.13\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 16, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 95, EFPA: -11.93, SIGMA: -0.67, APO : 477.67\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 17, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 718, EFPA: -12.02, SIGMA: 0.07, APO : 410.59\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 18, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 18, EFPA: -12.07, SIGMA: -1.54, APO : 444.56\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/athul/anaconda3/lib/python3.7/site-packages/AMAT-2.1.2-py3.7.egg/AMAT/vehicle.py:504: RuntimeWarning: invalid value encountered in sqrt\n",
      "  ans[:]  = 1.8980E-8 * (rho_vec[:]/self.RN)**0.5 * v[:]**3.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 19, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 42, EFPA: -12.08, SIGMA: -0.68, APO : 462.32\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 20, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 291, EFPA: -11.98, SIGMA: 0.15, APO : 428.46\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 21, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 54, EFPA: -12.16, SIGMA: 1.26, APO : 30.55\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 22, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 471, EFPA: -12.06, SIGMA: -0.18, APO : 429.90\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 23, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 404, EFPA: -11.98, SIGMA: -0.57, APO : 445.32\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 24, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 206, EFPA: -11.97, SIGMA: -0.55, APO : 473.24\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 25, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 913, EFPA: -12.10, SIGMA: 0.73, APO : 346.17\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 26, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 24, EFPA: -12.07, SIGMA: 0.01, APO : 537.43\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 27, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 398, EFPA: -12.02, SIGMA: 0.96, APO : 434.48\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 28, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 318, EFPA: -12.05, SIGMA: -0.05, APO : 445.43\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 29, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 200, EFPA: -12.04, SIGMA: 0.59, APO : 418.05\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 30, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 237, EFPA: -12.08, SIGMA: 0.45, APO : 397.03\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 31, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 708, EFPA: -12.00, SIGMA: -1.92, APO : 484.91\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 32, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 727, EFPA: -12.03, SIGMA: -1.57, APO : 470.89\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 33, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 526, EFPA: -11.99, SIGMA: -0.34, APO : 470.23\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 34, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 479, EFPA: -12.07, SIGMA: 0.45, APO : 477.59\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 35, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 454, EFPA: -12.13, SIGMA: 1.18, APO : 219.58\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 36, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 573, EFPA: -11.91, SIGMA: -0.10, APO : 528.42\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 37, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 207, EFPA: -12.01, SIGMA: 0.82, APO : 436.65\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 38, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 597, EFPA: -12.08, SIGMA: 0.11, APO : 375.03\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 39, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 379, EFPA: -12.10, SIGMA: 1.37, APO : 231.86\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 40, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 848, EFPA: -12.12, SIGMA: -2.18, APO : 370.00\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 41, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 257, EFPA: -12.01, SIGMA: -0.02, APO : 439.20\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 42, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 738, EFPA: -12.01, SIGMA: -1.37, APO : 435.96\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 43, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 847, EFPA: -12.06, SIGMA: 0.42, APO : 287.91\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 44, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 892, EFPA: -12.12, SIGMA: -0.27, APO : 485.94\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 45, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 229, EFPA: -12.05, SIGMA: -1.43, APO : 457.72\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 46, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 471, EFPA: -11.96, SIGMA: -0.19, APO : 469.02\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 47, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 107, EFPA: -12.00, SIGMA: 0.39, APO : 423.73\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 48, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 128, EFPA: -12.03, SIGMA: 1.40, APO : 317.12\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 49, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 427, EFPA: -12.10, SIGMA: -0.70, APO : 29.04\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 50, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 497, EFPA: -12.07, SIGMA: -0.69, APO : 446.25\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 51, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 206, EFPA: -11.99, SIGMA: 0.40, APO : 431.06\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 52, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 579, EFPA: -12.04, SIGMA: -0.30, APO : 404.20\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 53, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 738, EFPA: -11.97, SIGMA: 0.37, APO : 455.28\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 54, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 781, EFPA: -12.11, SIGMA: -1.31, APO : 307.05\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 55, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 608, EFPA: -12.09, SIGMA: -0.49, APO : 280.04\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 56, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 586, EFPA: -11.89, SIGMA: -0.31, APO : 3114.30\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 57, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 170, EFPA: -12.08, SIGMA: 1.99, APO : 359.22\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 58, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 555, EFPA: -12.21, SIGMA: 0.20, APO : 30.35\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 59, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 870, EFPA: -11.96, SIGMA: -1.26, APO : 2180.48\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 60, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 218, EFPA: -12.07, SIGMA: 0.67, APO : 392.25\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 61, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 136, EFPA: -12.05, SIGMA: 0.93, APO : 243.16\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 62, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 819, EFPA: -12.03, SIGMA: 0.07, APO : 475.63\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 63, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 398, EFPA: -11.97, SIGMA: 0.79, APO : 422.55\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 64, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 899, EFPA: -12.07, SIGMA: 1.07, APO : 384.39\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 65, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 665, EFPA: -12.12, SIGMA: -0.33, APO : 628.14\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 66, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 727, EFPA: -12.08, SIGMA: -1.20, APO : 562.68\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 67, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 767, EFPA: -11.99, SIGMA: -0.34, APO : 466.43\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 68, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 21, EFPA: -11.97, SIGMA: -1.06, APO : 486.86\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 69, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 104, EFPA: -12.02, SIGMA: 0.82, APO : 439.20\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 70, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 850, EFPA: -12.08, SIGMA: 0.15, APO : 333.87\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 71, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 63, EFPA: -12.14, SIGMA: 0.36, APO : 329.06\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 72, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 898, EFPA: -12.12, SIGMA: -1.67, APO : 285.72\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 73, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 861, EFPA: -12.00, SIGMA: -0.27, APO : 478.64\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 74, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 883, EFPA: -11.98, SIGMA: 0.52, APO : 470.65\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 75, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 204, EFPA: -12.07, SIGMA: -0.67, APO : 402.21\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 76, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 431, EFPA: -12.09, SIGMA: -1.27, APO : 443.38\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 77, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 51, EFPA: -11.99, SIGMA: 1.09, APO : 467.80\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 78, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 835, EFPA: -12.16, SIGMA: -0.06, APO : 180.61\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 79, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 424, EFPA: -12.20, SIGMA: -1.29, APO : 652.95\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 80, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 558, EFPA: -11.91, SIGMA: 0.10, APO : 616.51\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 81, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 184, EFPA: -11.96, SIGMA: 0.77, APO : 475.03\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 82, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 993, EFPA: -11.97, SIGMA: 0.64, APO : 453.54\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 83, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 501, EFPA: -11.99, SIGMA: -0.83, APO : 468.49\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 84, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 493, EFPA: -11.99, SIGMA: -0.11, APO : 508.56\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 85, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 638, EFPA: -12.14, SIGMA: -0.23, APO : 29.83\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 86, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 340, EFPA: -12.22, SIGMA: 1.23, APO : 29.68\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 87, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 218, EFPA: -12.07, SIGMA: 0.48, APO : 385.04\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 88, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 318, EFPA: -11.93, SIGMA: 1.64, APO : 483.52\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 89, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 817, EFPA: -12.13, SIGMA: 0.94, APO : 384.50\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 90, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 894, EFPA: -12.07, SIGMA: 0.56, APO : 264.38\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 91, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 541, EFPA: -12.09, SIGMA: -0.64, APO : 389.50\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 92, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 160, EFPA: -12.12, SIGMA: -1.07, APO : 328.40\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 93, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 723, EFPA: -12.07, SIGMA: 1.14, APO : 262.13\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 94, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 473, EFPA: -12.03, SIGMA: -1.37, APO : 467.98\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 95, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 803, EFPA: -12.08, SIGMA: -0.36, APO : 488.40\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 96, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 571, EFPA: -12.01, SIGMA: 0.73, APO : 336.25\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 97, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 941, EFPA: -12.13, SIGMA: 0.54, APO : 393.71\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 98, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 337, EFPA: -12.08, SIGMA: -0.36, APO : 300.38\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 99, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 870, EFPA: -12.04, SIGMA: -0.65, APO : 485.49\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 100, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 740, EFPA: -12.15, SIGMA: -1.16, APO : 672.33\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 101, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 261, EFPA: -12.10, SIGMA: 1.70, APO : 28.73\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 102, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 139, EFPA: -12.03, SIGMA: -0.76, APO : 477.85\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 103, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 828, EFPA: -12.14, SIGMA: 1.64, APO : 29.30\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 104, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 523, EFPA: -11.99, SIGMA: 1.19, APO : 299.59\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 105, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 86, EFPA: -12.08, SIGMA: -0.91, APO : 391.42\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 106, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 650, EFPA: -12.08, SIGMA: 1.97, APO : 302.54\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 107, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 819, EFPA: -12.04, SIGMA: 0.84, APO : 351.63\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 108, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 567, EFPA: -12.02, SIGMA: 1.34, APO : 565.22\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 109, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 826, EFPA: -12.02, SIGMA: 0.21, APO : 449.97\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 110, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 814, EFPA: -11.95, SIGMA: -2.35, APO : 470.46\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 111, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 383, EFPA: -12.07, SIGMA: 0.18, APO : 438.04\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 112, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 966, EFPA: -12.04, SIGMA: 0.45, APO : 346.67\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 113, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 121, EFPA: -12.02, SIGMA: 1.17, APO : 311.76\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 114, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 470, EFPA: -11.98, SIGMA: -0.82, APO : 491.70\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 115, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 43, EFPA: -12.00, SIGMA: -0.37, APO : 445.74\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 116, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 363, EFPA: -12.13, SIGMA: -1.66, APO : 401.13\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 117, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 592, EFPA: -12.06, SIGMA: -1.24, APO : 404.40\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 118, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 27, EFPA: -12.09, SIGMA: -1.55, APO : 332.11\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 119, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 825, EFPA: -12.12, SIGMA: 0.23, APO : 308.05\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 120, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 483, EFPA: -11.98, SIGMA: 1.25, APO : 429.94\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 121, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 8, EFPA: -12.05, SIGMA: 0.07, APO : 345.33\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 122, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 343, EFPA: -11.95, SIGMA: 0.18, APO : 484.28\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 123, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 311, EFPA: -12.15, SIGMA: 1.17, APO : 30.46\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 124, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 426, EFPA: -12.06, SIGMA: -1.71, APO : 422.83\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 125, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 369, EFPA: -12.08, SIGMA: 1.58, APO : 270.59\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 126, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 291, EFPA: -11.98, SIGMA: -0.21, APO : 440.37\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 127, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 328, EFPA: -12.13, SIGMA: -0.21, APO : 252.96\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 128, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 107, EFPA: -12.08, SIGMA: 1.50, APO : 481.17\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 129, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 398, EFPA: -12.00, SIGMA: 1.43, APO : 381.01\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 130, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 472, EFPA: -12.01, SIGMA: -0.72, APO : 469.38\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 131, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 860, EFPA: -12.11, SIGMA: 0.83, APO : 252.62\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 132, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 64, EFPA: -12.02, SIGMA: 1.19, APO : 448.25\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 133, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 113, EFPA: -12.03, SIGMA: 1.44, APO : 379.96\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 134, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 996, EFPA: -11.97, SIGMA: -0.72, APO : 458.85\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 135, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 496, EFPA: -12.09, SIGMA: -1.28, APO : 404.90\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 136, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 975, EFPA: -12.14, SIGMA: -0.21, APO : 845.32\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 137, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 196, EFPA: -12.04, SIGMA: 0.75, APO : 290.71\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 138, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 398, EFPA: -12.15, SIGMA: -0.36, APO : 356.07\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 139, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 798, EFPA: -12.11, SIGMA: -0.45, APO : 407.26\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 140, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 850, EFPA: -11.99, SIGMA: -0.63, APO : 429.75\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 141, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 364, EFPA: -12.01, SIGMA: -2.51, APO : 470.25\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 142, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 497, EFPA: -12.12, SIGMA: -0.11, APO : 197.46\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 143, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 942, EFPA: -12.06, SIGMA: -0.13, APO : 392.91\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 144, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 292, EFPA: -11.99, SIGMA: -0.16, APO : 462.44\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 145, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 457, EFPA: -11.99, SIGMA: -1.14, APO : 474.83\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 146, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 202, EFPA: -12.07, SIGMA: 0.95, APO : 352.26\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 147, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 411, EFPA: -12.05, SIGMA: -1.34, APO : 439.96\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 148, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 926, EFPA: -12.08, SIGMA: 0.69, APO : 339.36\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 149, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 341, EFPA: -12.15, SIGMA: 1.01, APO : 177.54\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 150, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 897, EFPA: -12.03, SIGMA: 1.11, APO : 248.00\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 151, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 759, EFPA: -12.04, SIGMA: -0.16, APO : 424.57\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 152, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 736, EFPA: -12.01, SIGMA: -1.08, APO : 476.70\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 153, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 367, EFPA: -12.05, SIGMA: -2.00, APO : 439.34\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 154, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 715, EFPA: -12.10, SIGMA: -1.63, APO : 444.77\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 155, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 196, EFPA: -12.03, SIGMA: 1.02, APO : 334.06\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 156, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 822, EFPA: -12.12, SIGMA: -0.38, APO : 251.38\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 157, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 77, EFPA: -12.05, SIGMA: 0.36, APO : 343.02\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 158, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 582, EFPA: -11.99, SIGMA: 0.97, APO : 371.69\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 159, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 483, EFPA: -12.10, SIGMA: 1.34, APO : 294.31\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 160, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 230, EFPA: -12.08, SIGMA: -0.16, APO : 398.86\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 161, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 171, EFPA: -12.08, SIGMA: -0.31, APO : 434.57\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 162, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 431, EFPA: -12.06, SIGMA: -1.51, APO : 437.77\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 163, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 744, EFPA: -12.02, SIGMA: 0.56, APO : 425.09\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 164, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 808, EFPA: -12.10, SIGMA: -0.18, APO : 318.38\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 165, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 678, EFPA: -11.99, SIGMA: 0.96, APO : 425.24\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 166, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 815, EFPA: -12.06, SIGMA: -0.91, APO : 407.80\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 167, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 929, EFPA: -12.06, SIGMA: -2.28, APO : 436.81\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 168, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 902, EFPA: -12.07, SIGMA: -0.27, APO : 389.17\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 169, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 739, EFPA: -12.04, SIGMA: -0.56, APO : 475.41\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 170, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 116, EFPA: -12.10, SIGMA: -1.21, APO : 345.25\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 171, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 521, EFPA: -12.03, SIGMA: 0.19, APO : 436.42\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 172, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 706, EFPA: -12.18, SIGMA: -0.71, APO : 166.46\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 173, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 918, EFPA: -12.04, SIGMA: -0.41, APO : 416.91\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 174, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 730, EFPA: -12.09, SIGMA: 0.29, APO : 265.49\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 175, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 773, EFPA: -12.00, SIGMA: -1.76, APO : 463.55\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 176, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 976, EFPA: -12.09, SIGMA: -1.97, APO : 418.32\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 177, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 522, EFPA: -11.99, SIGMA: 0.59, APO : 396.80\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 178, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 284, EFPA: -12.15, SIGMA: 1.21, APO : 30.75\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 179, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 773, EFPA: -11.97, SIGMA: 0.05, APO : 448.87\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 180, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 928, EFPA: -11.99, SIGMA: -1.09, APO : 445.25\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 181, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 519, EFPA: -12.07, SIGMA: 0.02, APO : 417.03\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 182, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 330, EFPA: -12.13, SIGMA: 0.62, APO : 389.94\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 183, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 241, EFPA: -12.10, SIGMA: -1.98, APO : 405.54\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 184, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 855, EFPA: -11.98, SIGMA: -0.27, APO : 478.47\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 185, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 381, EFPA: -12.08, SIGMA: 0.99, APO : 244.64\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 186, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 182, EFPA: -11.95, SIGMA: 2.14, APO : 474.76\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 187, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 52, EFPA: -12.08, SIGMA: -0.18, APO : 486.57\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 188, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 296, EFPA: -12.03, SIGMA: -0.24, APO : 481.16\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 189, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 737, EFPA: -12.13, SIGMA: 0.89, APO : 326.65\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 190, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 985, EFPA: -11.97, SIGMA: -0.81, APO : 489.30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 191, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 707, EFPA: -12.19, SIGMA: -1.24, APO : 396.14\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 192, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 385, EFPA: -12.05, SIGMA: -0.86, APO : 425.49\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 193, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 594, EFPA: -12.02, SIGMA: -1.46, APO : 488.71\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 194, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 576, EFPA: -12.05, SIGMA: -1.01, APO : 457.67\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 195, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 189, EFPA: -12.06, SIGMA: 0.96, APO : 401.33\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 196, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 220, EFPA: -12.04, SIGMA: 1.90, APO : 239.64\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 197, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 714, EFPA: -12.09, SIGMA: -0.08, APO : 430.14\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 198, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 992, EFPA: -12.13, SIGMA: 0.62, APO : 618.85\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 199, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 446, EFPA: -12.12, SIGMA: 0.34, APO : 590.72\n",
      "BATCH :../data/werner2019/MCB1-Mars, RUN #: 200, PROF: ../atmdata/Mars/LAT00N-N1000.txt, SAMPLE #: 574, EFPA: -12.07, SIGMA: 0.05, APO : 389.68\n"
     ]
    }
   ],
   "source": [
    "# Set up the planet and atmosphere model.\n",
    "planet=Planet(\"MARS\")\n",
    "planet.loadAtmosphereModel('../atmdata/Mars/mars-gram-avg.dat', 0 , 1 ,2, 3)\n",
    "planet.h_skip = 150000.0\n",
    "planet.h_low=10.0E3\n",
    "\n",
    "# Set up the drag modulation vehicle.\n",
    "vehicle=Vehicle('MarsSmallSat1', 25.97, 66.4, 0.0, np.pi*0.25**2, 0.0, 0.0563, planet)\n",
    "\n",
    "vehicle.setInitialState(150.0,0.0,0.0,5.74,0.0,-12.12,0.0,0.0)\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(2.0, 15.0, 101, -200.0)\n",
    "\n",
    "# Set the target orbit parameters.\n",
    "vehicle.setTargetOrbitParams(450.0, 450.0, 20.0)\n",
    "\n",
    "# Define the path to atmospheric files to be used for the Monte Carlo simulations.\n",
    "atmfiles = ['../atmdata/Mars/LAT00N-N1000.txt']\n",
    "\n",
    "# Set up the Monte Carlo simulation for drag modulation.\n",
    "# NPOS = 156, NMONTE = 1000\n",
    "# Target EFPA = -12.05 deg\n",
    "# EFPA 1-sigma error = +/- 0.067 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(156, 1000, atmfiles, 0 , 1, 2, 3, 4, True,\n",
    "                                   -12.05,  0.0667, 66.4, 0.0,\n",
    "                                    1.0, 1.0, 0.1, 2400.0)\n",
    "# Run 200 trajectories\n",
    "vehicle.runMonteCarloD(200, '../data/werner2019/MCB1-Mars')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the terminal apoapsis, deceleration and heat rate data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "apoap = np.loadtxt('../data/werner2019/MCB1-Mars/terminal_apoapsis_arr.txt')\n",
    "peria = np.loadtxt('../data/werner2019/MCB1-Mars/terminal_periapsis_arr.txt')\n",
    "decel = np.loadtxt('../data/werner2019/MCB1-Mars/acc_net_g_max_arr.txt')\n",
    "heatr = np.loadtxt('../data/werner2019/MCB1-Mars/q_stag_max_arr.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove cases which resulted in apoapsis < 100 km or > 800 km."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 10,  20,  48,  57,  84,  85, 100, 102, 122, 177]),)\n"
     ]
    }
   ],
   "source": [
    "del_index_low = np.where(apoap < 100) \n",
    "print(del_index_low)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seven cases resulted in apoapis less than 100 km, and are considered to crash."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove these entries from the data before plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 > 800 km."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 52,  54, 126]),)\n"
     ]
    }
   ],
   "source": [
    "del_index_high = np.where(apoap_clean > 800) \n",
    "print(del_index_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4gwTAgcC9q+qa3sYkj13UiiRJIzHIOYB38Kvf/Df4+iLVIkkaoUH2AH4JnJnkKz1todkzeORiFiVJGr5BAuDBwHbAfWiHhaQJgHssdlGSpOEbJADeCFxWVbduaGjHCH7QolclSRq6QQLgB8Ddm23+RnenOQR02SLWJEkagUEC4EqaQz+9CVDAtcApi1iTJGkEBrkK6KVV1d8NxHOAw4ZUmyRpiOYdAFX19lmaPwq8d/HKkSSNyiC9gT67r2lrms7h1ixqRZKkkRjkHMCbaW762nAJ6HrgeuDQQT4wySHAicDTqurKnvYTgePbl5dV1d6DLFeSNJg5AyDJY6rqgvblEbMNEjOIJLsCOwIP62vfgWagmUPapqu25HMkSZu3uT2Atye5iHZM4CTPmGWe71fVvMYFrqrrk5w3y6QjgcuBS6vq5/NZliRpy2w2AIBTq+pX+gBKciDwfuA7DDgofFWt772XoL2Z7OnAQ4HXJXlhVX1gkGVKkga3uauAzpll43888GngQ8Ajq+r7W1JANfajOQR0BnBuEi8tlaQhm3MPoKpmNjxP8ps03/p/H3hWVX1oMQupqpuAE9o9gqOAT/bPMzMzw8TExMbXk5OTTE5OLmYZkjQWpqammJqa2vBy1Wzz5NdHeJxlpuQRwAeBm4EnVdU3t6SwJEUztsCVs0zbFbioqu7fP21iYqKmp6e35KM15q791a5KtABr5rFN0MqSZF1VTfS3b/ZGsCTHAp8DvgA8tH/jn2TPRauysR748iIvU5LUZ3OXgV5A09XDecDfAQ/u6wxuW+DZNF1CzEvuWEDa12toOpT7IM09Bkdzx/0AkqQh2dxVQPvSjPf7M+Des0zfmgHGA0iyE/DM9uVzk7wN2AU4meZqokuB02Y7NCRJWlybC4DXVNWZc82Q5Kvz/bCquhk4s31sMAPsPt9lSJIWx+bOAbxvcwuoqnWLVIskaYTmDICq+vGoCpEkjdYgncGp4xb7EksvN5SW1iADwkiSxogBIEkdZQBIUkcZAJLUUQaAJHWUASBJHWUASFJHGQCS1FEGgCR1lAEgSR1lAEhSRxkAktRRBoAkdZQBIEkdZQBIUkc5HsAyYn/7kkbJPQBJ6igDQJI6ygCQpI4yACSpowwASeooA0CSOsoAkKSO8j4ALZnFvu9B0mDcA5CkjjIAJKmjDABJ6qiRB0CSQ5L8W5K1PW3bJTkjyd8kOTPJzqOuS5K6ZqQBkGRXYEfgYX2T3g5cWlXHAecBZ4+yLknqopEGQFVdT7OB3yjJGuAI4BNt04XA43v3ECRJi2/kh4Cqan1f04HADVX1s3b6bcAVwAEjLk2SOmU5nATeDbixr+1mYM0S1CJJnbEcbgQr4Ja+tjsDv+yfcWZmhomJiY2vJycnmZycHG51krQCTU1NMTU1teHlqtnmWQ4BcA2wS1/bTsC1/TOuXr2a6enpkRQlSStZ7xfkJDfMNs9yOAT0eWBNku0AkmwL7A5cvKRVSdKYW4r7ADZ0ABOAqvo+cD5wcNv+KOBjVXXNqGuTpC4Z9X0AOwEvbF8+N8nq9vmLgCckOR44HPDAviQN2UjPAVTVzcCZ7aO3/SbgyFHWIkldtxzOAUiSloABIEkdZQBIUkcZAJLUUQaAJHWUASBJHWUASFJHGQCS1FEGgCR1lAEgSR21HLqD1pBcu7HfPUn6de4BSFJHGQCS1FEGgCR1lAEgSR1lAEhSRxkAktRRBoAkdZQBIEkdZQBIUkcZAJLUUQaAJHWUASBJHWUASFJHGQCS1FEGgCR1VGfGA1jsvvHXVC3q8iRp1NwDkKSOMgAkqaMMAEnqqGUVAElOTFLt46tLXY8kjbNlcxI4yQ7AKuCQtumqJSxHksbesgkA4EjgcuDSqvr5UhcjSeNuWRwCShLg6cCbgeuTPGOJS5KksbcsAqAa+9EcAjoDODfJYf3zzczMMDExsfExNTU18lolaSWYmprauK2k2bb+mtQyvKEpyRuBiap6dG/7xMRETU9PL2iZK+FGsMWuUVoIb3IcP0nWVdVEf/uy2AOYxVuB3Ze6CEkaZ8s1ANYDX17qIiRpnC2LAEiyJskRSbZqTwgfDRy/1HVJ0jhbFgEA7AKcDFwGnAm8p6quXNKKJGnMLYv7AKrq63jMX5JGarnsAUiSRswAkKSOWhaHgCRpKa2E+4SGwT0ASeooA0CSOsoAkKSOMgAkqaMMAEnqKANAkjrKAJCkjvI+AElD1dVr7FcC9wAkqaMMAEnqKANAkjrKAJCkjjIAJKmjDABJ6igDQJI6yvsAFmixr22Wlgv/trfcSrn3wT0ASeooA0CSOsoAkKSOMgAkqaMMAEnqKANAkjrKAJCkjvI+AEkrivcpLB73ACSpowwASeooA0CSOmrZnANIsh1wCnAzsAtwbFX9eGmrkqTxtZz2AN4OXFpVxwHnAWcvcT2b9Q9LXcCIub7jzfXtnmURAEnWAEcAn2ibLgQen2TtUtU0H+cudQEj5vqON9e3e5ZFAAAHAjdU1c8Aquo24ArggKUsSpLG2XI5B7AbcGNf283Amt6GdevW/SRJb2jNADcMuba5rNptaT9/1Fzf8eb6LlcLu/dhFbC6fb7nbDMslwAo4Ja+tjsDv/yVmap2GllFkjTmlsshoGtorvzptRNw7RLUIkmdkBrSUGMDFZHcHfgvYHVV3ZJkW+AnwH2q6pqlrU6SxtOy2AOoqu8D5wMHt02PAj7mxl+ShmdZ7AEAJNmF5kawK4G7A6+qqp8sUS1PAt4M7ExzufDRVXVbklXAG4H/AbaluVntl+17NjltJUhyADAF/Bbw3qo6qm3f5A16K/3mvSR3Av4d+Iuqumic1xUgyYnA8e3Ly6pq7w6s837AI2iOMFwK/JQxXt+BVZWPngewO/D3wEOAZ9P8wRzTTvs8sG/7/IXAST3v2+S05f4A7gocR/NH/xiak++Paqe9G3ha+/xQ4AM979vktJXwoNkY/gg4sAPrugNwFs3e9aOAPTqwzn8KnNjXNrbru6Cf0VIXsNwewP7ANj2vTwEuAPYDru1p343mPMUOc01b6vWZ5zpvR7s32L5eBzyS5jLcXwDbt+3btIG4dq5pS70+81znhwPPo9njPHCc17Wt+aj2cZeetrFdZ5p7iC7s+7se2/Vd6GNZnANYTqrqkmpuRNvgGuBq4CCam9M2zLfh/MTEZqYte1V1S7V/9Ul2AL4JXMTcN+jNNW1Za9fxKVV1Tk/zgYzhugIkCfB0msOa1yd5RjvpQMZ0nYG30Pwdn5HkU0n+gPFe3wUxADbvoTS7znPdrDavG9mWuyT7A5+kOYexPeO7zscCJ/e1jeu6Uo39aG4MOgM4N8lhjOk6J9kT2Ad4R1W9FPgs8CngtxnD9d0SBsAcktwXmKmqrzL3zWrzupFtBfgv4D00V2O9iTFc5ySPBqar6gd9k8ZuXftV1U1VdQJwEs3hoHFd5wcCP6yqy9rXbwPC+K7vgi2XO4GXnSRbAy8AXtk2XQPcr2d6aI7/X7uZaStGVV0HnJNkPfBy4BI2fYNezTFtOTsG2Cd33Fq/C03vs29h/NZ1U95Kc4hvrhswV/I6b0PPtq2ae4u+DWzNeK7vwi31SYjl+qA5TLBrz+sJ4Kqe1/em2UW801zTlno9FrjuewGfprkc9yfAdm37tjQnynaba9pS17+ZdVsN3LPn8T3gqTRXf43Vum7mZ3DuOP5+21p/l2aD3vv/99+B54zj+m7Jw0NAs0hyHPAVYLsk90nyPJrr+69Lslc726HAGVV1a1VNb2rayItfgCTbJ9mnp+lw4PSa4wa9uaaNqu6FqKqZqvrvDQ/gdprDfFczZuu6QZI1SY5IslW7d3o0cPw4/n4BqupymnNZTwFI8hs0X9TOZQzXd0ssmxvBloskJwBv6Gu+vKrun+RewKtpLh3ckeY/0W3t+zY5bblL8jCaS10vB74IfKmqPtJO2+QNesvp5r2FSnIl8NxqbgQby3VN8kCaDeKPaW6GOq3dSI7t77e9MfN0YJpm7+4dVfWNcV3fhTIAJKmjPAQkSR1lAEhSRxkAktRRBoAkdZQBIEkdZQBIUkcZAOq8JG9P8opFWM7aJO9IcvYc85yf5Olb+Dlr2po3+TnzWMYzk3wzydotqUUrm30BaeiSPI7mFvsPL3Utm/B+mju9t9SGDubuPMc8bwe+toWf80vgN4AtudP8yzRdJqjD3APQKLwQmFzqIjalqi6tqi3dKFNNX/K/0nlYksf1fsuuqn9uu53YMP1lC/icGZqeW7fE5Vv4fo0BA0BD1XaRsTNwUJLfWep6RmDjrfVJ7knzjX9WSQ4BBg6A1voFvg+Aqtqi92s8GAAatiPbx7eBP+udkOSpSb7Y/ntJkh+2HfFtmL4myVuTnJjkc0kO75n24iQnJXlnknPbjs4emuRjSV6TZCrJzUk+mOQu7XsOTHJse/z8mrbtIUk+nuSv29c7Jnl9klcl+V6SQ/tXqJ3nXUlekeTCnhG2+h1B0+PosUmemOTgdv7nJPlN4FnArklOTrJ3kvOSXNR+xkSSy5O8tudzn9n+PE4FHt9X04uT/HX783zpPH83G957lyTT7bL3TvKiJN9K8ogkFyS5KclTkhya5GtJrk3ykEE+Q8vUUndH6mN8HzRd6p7bPn858H1g257p29OMwvQ2mr7YJ2m+Qe/XTv+/wJ7t8wma7nnX0uxR3ErzBWZrmk7OHkJzTut84PPAHm3bT4FXtsv4LLB1+/yN7b93Bv4FeG37+iXAH7fP9wcOnWW9Xgqc0z5/KvD/eqa9FnhPz+uiHVeWZuzlb9J0PgfNMIRX9sz7p8BFPa/f21PXg4FLeqadv+FzgGcAz2yf349m7+BB8/j9VPvz/CPghT3ta9tpk+3P9Fia4REPb6efAbx/qf++fGz5wz0ADdMTaQZbgWZjdjd6vrlWc8z8ZuCj1YxWNUWzp/DY9hvmHlX1rXbeaZqN53Oq6sfAo2k2UocAtwE7VtP76g3AxVX17apaR3OC97HtR24FfLg9Jn96u9xf0ATTBj8BTmlPXF9Kc7K038eBv217j3wwTe+vm1VVt3DHieJZZ5nj9dE0PXpuMN3z/LnA/ZMcRdOV978A95hPTTQ/m72q6qyetqvafz/d/kynaTqO/Oe2/Ss0vWVqhTMANEx/AhyQ5DTgOOBqmsNBc/kGzRUue9DsQfT6Ds0hFWiG+HsDTde9P25fz+br7fKgGRDkN4D/pPkWv0HvhvZ9NGH1EZoR0baeZZk/oAm3Pwa+MMdnz2ah3e/+L5qwnM3uwAVVdVr7OKyqPj3P5T4GODTNCHhNgVX9Nd7e93o9bjvGgr9EDUWSPYCvV9VLquqoqjqK5oTnIZu59vxONBvoq4Gdk+zWu1jgW2kG/T4L+Otq+7Wfx/IAtqqqg2mOvR+T5KBZ5t+9ql4DPIDm8NDfzjLPq4Htq+psmj2GxbKe2QMHmpC7/yamXUc7+AlAkm0GOEZ/DPBbNAGtjjEANCwvA97V1/YvNIdbXtzXfg+AJNvRHMM+l+b4/1dpTxy331DvB/wDzZCVO9IExMOAu7bP79m7vNaB3HElzjEAVfUhmsMpG765p+f505Lcs6q+C7ye2b/d7wOsTrJtu/ztktxnlmVBc83+Lkl+d5bpt7Z1b5PkfsD1wJ5J7pZk33Y9VyfZhmaP5Fm5Y9S5e/dM+wDw50lel+QAmsNb30uyKsm7k9y7fwWSjYMi/4wmEI9P8vC+ab3r0f9zGGSvR8uUAaBFl+Q5NCcQ/7D30ALw+zR/cy9L0ntfwGOT/AXwJuDPqurG9jDEk4EDk7yNZtD2F1czdN9naL71fg14IM2x+hfRnFAGeGCSo5O8AfhUVV3Y8znnJTkG+FpVfTbNaFn7AY9s90y2Ay5ur0Y6CDhhllU8C3g6cCHNCGpp13V3miEF90vye+2876PZeN8tyX5tvYcn2ZUm4K6mOWl9U7u8L9Nco//w9vnOwO/QnCh/N/C5JB9ql307TRi9Czi5/Rm8C/inqvpB+77DaU5s9/5+tuKOy0+fQbOH9B3g40meDDy/nfYnSdbQnOi+e3sl0L1oDn89YENgaOVyRDAtqfQMybhIy3sPzZU1r12M5a107V7KS6rqtKWuRcuPXTRGmeAAAABnSURBVEFoqfUfMlluy1uxktyZ5sT3+5a6Fi1PHgLSkknyLJrLCZ+WZPUiLG9/msM5Byd5wJYubwzsTHOvwI2bnVOd5CEgSeoo9wAkqaMMAEnqKANAkjrKAJCkjjIAJKmjDABJ6qj/D2yOCAEcoA7SAAAAAElFTkSuQmCC\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=15, 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-mars.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-apoa-hist-mars.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-apoa-hist-mars.eps', dpi=300,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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-mars.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-decel-hist-mars.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-decel-hist-mars.eps', dpi=300,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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-mars.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-heat-hist-mars.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-heat-hist-mars.eps', dpi=300,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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=450, 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-mars.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-apoa-peri-mars.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/werner-smallsat-apoa-peri-mars.eps', dpi=300,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the percentage of cases which achieved apoapsis between 100 and 800 km."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93.5"
      ]
     },
     "execution_count": 13,
     "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
}