{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ExoMarsRSP SPICE Kernel Dataset Testcase "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Jupyter Notebook is used to illustrate some examples on how to use the testcase provided in the ExoMarsRSP SKD v0.1.0. This SKD features the following scenario:\n",
    "\n",
    "This is the first release of the ExoMarsRSP SPICE Kernel Dataset it is in draft state and it is aimed to provide the ROCC the capability to start testing SPICE in their systems. It includes a basic set of reference frames defined for the RM structures and PanCam and includes sample IKs for NavCam and PanCam, it also includes an example of an structures SPKs and several sample SPKs and CKs.\n",
    "\n",
    "The scenario that this release covers simulates a first travel of the rover and a first observation by moving the payload bench on top of the mast by a given pan and tilt set of angles. The travel of the rover can be depicted as follows (XY plane):\n",
    "\n",
    "            .----------------.\n",
    "          .                   .\n",
    "         .                     .                   +Ydisp\n",
    "         |                     |                     ^\n",
    "         |                     |                     |  Rover Disposal\n",
    "         |                      .                    |\n",
    "         |                       '.__________________o----->\n",
    "         |                                                 +Xdisp\n",
    "         |\n",
    "         |\n",
    "         |\n",
    "         X End Point\n",
    "\n",
    "\n",
    "The timeline of the scenario is as follows:\n",
    "\n",
    "         2020 NOV 01 00:00:00 TDB  -  Lander Disposal\n",
    "         2020 NOV 01 00:00:01 TDB  -  Drive 5 meters\n",
    "         2020 NOV 01 00:01:42 TDB  -  Turn Right\n",
    "         2020 NOV 01 00:02:43 TDB  -  Drive 5 meters\n",
    "         2020 NOV 01 00:04:06 TDB  -  Turn Left\n",
    "         2020 NOV 01 00:04:34 TDB  -  Drive 3.7 meters\n",
    "         2020 NOV 01 00:05:46 TDB  -  Stop at ( -1.3e-02 -2.e-03 0 ) [km]\n",
    "\n",
    "         2020 NOV 01 01:04:22 UTC  -  Start PTR Pan to 20 degrees\n",
    "         2020 NOV 01 01:04:40 UTC  -  PTR Pan at 20 degrees\n",
    "         2020 NOV 01 01:04:41 UTC  -  Start PTR Tilt to 10 degrees\n",
    "         2020 NOV 01 01:04:42 UTC  -  PTR Tilt at 10 degrees\n",
    "\n",
    "         2020 NOV 01 10:04:50 UTC  -  Start PTR Tilt to 0 degrees\n",
    "         2020 NOV 01 10:10:04 UTC  -  PTR Tilt at 0 degrees\n",
    "         2020 NOV 01 10:10:07 UTC  -  Start PTR Pan to 0 degrees\n",
    "         2020 NOV 02 10:10:19 UTC  -  PTR Pan at 0 degrees\n",
    "\n",
    "         2020 NOV 02 00:00:00 TDB  -  End of test scenario\n",
    "         \n",
    "First we load the basic Python Packages we need including SpiceyPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.figsize'] = (20,10)\n",
    "\n",
    "import spiceypy as cspice\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now load the EMRSP meta-kernel and define a series of Time Windows that we will use to plot different quantities. We use the CSPICE FURNSH and STR2ET APIs for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cspice.furnsh('/Users/mcosta/ExoMarsRSP/kernels/mk/emrsp_preops_local.tm')\n",
    "\n",
    "start_time = '2020-11-01 00:00:00 TDB'\n",
    "finish_time = '2020-11-01 23:00:00 TDB'\n",
    "\n",
    "start_et = cspice.str2et(start_time)\n",
    "finish_et = cspice.str2et(finish_time)\n",
    "\n",
    "interval_state = np.arange(start_et, finish_et, 5.0)\n",
    "\n",
    "start_time = '2020-11-01 00:00:00 TDB'\n",
    "finish_time = '2020-11-01 00:05:00 TDB'\n",
    "\n",
    "start_et = cspice.str2et(start_time)\n",
    "finish_et = cspice.str2et(finish_time)\n",
    "\n",
    "interval_rm = np.arange(start_et, finish_et, 5.0)\n",
    "\n",
    "start_time = '2020-11-01 00:03:00 TDB'\n",
    "finish_time = '2020-11-01 23:50:00 TDB'\n",
    "\n",
    "start_et = cspice.str2et(start_time)\n",
    "finish_et = cspice.str2et(finish_time)\n",
    "\n",
    "interval_htc = np.arange(start_et, finish_et, 5.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now loop through those time intervals to retrieve some quantities:\n",
    "\n",
    "1-Position of the Rover w.r.t the Disposal Site (Landing Site). We use the SPICE API SPKEZR for that.\n",
    "\n",
    "2-Angular Offset of the Rover +X axis (driving direction) w.r.t to the -X axis of the Mars Local Geodetic frame of the Disposal Site. We use the SPICE APIs PXFORM, MXV and VSEP for that.\n",
    "\n",
    "3-We retrieve the SPICE variables for the PanCam HRC stored in SPICE, we plot them and then we calculate the Euler Angles of the rotation of the HRC boresight w.r.t. the Rover Mast. We use the SPICE APIs BODN2C, GETFOV, PXFORM and M2EUL for that purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RM_PANCAM_HRC shape:   RECTANGLE\n",
      "RM_PANCAM_HRC frame:   RM_PANCAM_HRC\n",
      "RM_PANCAM_HRC bsight:  [0. 0. 1.]\n",
      "RM_PANCAM_HRC vectors: 4\n",
      "[[ 0.04253463  0.04253463  0.99818917]\n",
      " [-0.04253463  0.04253463  0.99818917]\n",
      " [-0.04253463 -0.04253463  0.99818917]\n",
      " [ 0.04253463 -0.04253463  0.99818917]]\n"
     ]
    }
   ],
   "source": [
    "rm_state = []\n",
    "for et in interval:\n",
    "\n",
    "    rm_state.append(cspice.spkezr('EXOMARS ROVER',et, 'RM_MLG_DISPOSAL',\n",
    "                                  'NONE', 'RM_SITE_DISPOSAL')[0])\n",
    "\n",
    "rm_xoffset = []\n",
    "for et in interval_rm:\n",
    "\n",
    "\n",
    "    mat = cspice.pxform('RM_MLG_DISPOSAL', 'RM_ROVER', et)\n",
    "    mlg_x  = cspice.mxv(mat, [1,0,0])\n",
    "    ang = cspice.vsep(mlg_x, [1,0,0])*cspice.dpr()\n",
    "    rm_xoffset.append(ang)\n",
    "\n",
    "\n",
    "pancam_hrc_euler_1 = []\n",
    "pancam_hrc_euler_2 = []\n",
    "pancam_hrc_euler_3 = []\n",
    "\n",
    "sensor_name = 'RM_PANCAM_HRC'\n",
    "\n",
    "sensor_id = cspice.bodn2c(sensor_name)\n",
    "(shape, frame, bsight, vectors, bounds) = cspice.getfov(sensor_id, 100)\n",
    "\n",
    "print('{} shape:   {}'.format(sensor_name, shape))\n",
    "print('{} frame:   {}'.format(sensor_name, frame))\n",
    "print('{} bsight:  {}'.format(sensor_name, bsight))\n",
    "print('{} vectors: {}'.format(sensor_name, vectors))\n",
    "print(bounds)\n",
    "\n",
    "for et in interval_htc:\n",
    "\n",
    "    mat = cspice.pxform(frame, 'RM_PTR_ZERO', et)\n",
    "    eul = cspice.m2eul(mat,3,2,1)\n",
    "    pancam_hrc_euler_1.append(cspice.dpr()*eul[0])\n",
    "    pancam_hrc_euler_2.append(cspice.dpr()*eul[1])\n",
    "    pancam_hrc_euler_3.append(cspice.dpr()*eul[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a test we check that the final velocity of the Rover is aligned with the driving direction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0., 0., 0.]), 0.0)\n",
      "(array([6.123234e-17, 0.000000e+00, 1.000000e+00]), 1.0)\n"
     ]
    }
   ],
   "source": [
    "print(cspice.unorm(rm_state[-1][3:6]))\n",
    "\n",
    "mat = cspice.invert(mat)\n",
    "vec  = cspice.mxv(mat, [1,0,0])\n",
    "\n",
    "print(cspice.unorm(vec))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we plot the quantities that we computed before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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fdc7VSNpkZvmSjnbOvSVphyQ55yrM7ANJOf4xZ0k61T/WA5JekXR9V50c0JvU1jdqV1Xtx7edVTVhX9dqt/9nVU19YBmdC+ylFeBLywV54gEK9vsd3ItH6+dcCvazHh8bo+RQnFJC8UoOxSk5FK+UPv6fH2+LU0qfeA1IStDwtCR5P64AAACgo4IsrnIkbQ27v03SMQfaxzlXb2ZlktL87W83e25O+BP9aYXTJL3jb8p0zu3wvy6UlNlSKDO7StJVknTYYYcdyvkAPd7uqlrlbdujFVv3KG/rHm0qrdKuylpVHKCQMpMGJCVoYF/vlpUSUrC/qwX34kGed5BvebDnHaXf7yh9z/0Agairb1RFdb0Ky6u1vrhe5dV1qqiuV0Njy2VaZkqiThuXodPGZerE3EHqkxDbzYkBAAB6jyCLqy5jZv0kPSHpu8658uaPO+ecmbX406Zz7h5J90jS9OnTg/4PZqDL1NQ3aM32cuVt/aSo2rxzryTvF+PRGf00KSdVg/olflxMpTX92S9BA/smKrVPvGJjGFUAIPo457SvrkHl++pVUV2n8uo6lVfXq6isWq9+WKKFedv1yLtblRgXo+NHpem08Zk6bVyGcvr3CTo6AABAjxJkcVUgaWjY/SH+tpb22WZmcZJSJe1s7blmFi+vtPqnc+7JsH2KzCzbObfDzLIlFXfmyQCRzDmnzTv3Km/rbuVt2aO8bWX6YHu5ahsaJUkZyYmaOrS/PnfUUE0d2l+TclKVHIoPODUARC4zU1JCnJIS4pSVGtrvsYuPPky19Y16d9Muvbi2SC9+UKyX172vn0gal5WsM8Zn6rTxGZoypD/lPwAAwEFYUGtG+EXUh5JOl1c6LZH0Befc6rB9rpY0yTn3DTO7WNIFzrnPmdlESf+St07WYHmLrY+W1Chv/apdzrnvNnu930na6Zz7tX8Fw4HOuR+2lnH69Olu6dKlnXTGQPeqrW/Uqx+WaEFegV7PL9WevXWSpKSEWE3KSdXUof2922H9/Sl+/PIEAF3BOacNJVV68YMivbi2WMs+2q2GRqe0vgk6dWyGvnDMUB05bGDQMQEAALqNmS1zzk1v075BLnZqZudK+qOkWEn3O+duNbObJS11zi00s5Ckh+StVbVL0sVhi7n/r6SvyLuS4Hedc8+Y2YmSXpO0Sl6JJUk/cs4tNrM0SXMlHSbpI0mfc87tai0fxRV6msZGpyWbd2l+3nYtXrVDZfvqNLBvgk4fl6Ejhg3Q1KH9NTqjn+JiA7ugKABEvT17a/XqhyV6aW2xXl5brPLqes2cMlg3njtO2alMJQQAAL1fjymuIh3FFXoC55w+2FGhBSsK9FTedm0vq1af+FidNTFTs6bm6MTRgxRPUQUAEWlvbb3++soG/e2/GxVrpm+eOkpXnTxSoXgWdAcAAL0XxVUnobhCJNu6a68WrtiuBXkF+rCoUnExppPHpGvW1ME6c0KmkhJ65bUXAKBX2rprr371zAdavKpQOf376Efnjte5k7KYxg0AAHoliqtOQnGFSFPf0KiFK7brn+9s0bKPdkuSpg8boFlTB+vcSdlK65cYcEIAQEe8tWGnfv7Uaq0trNAxIwbqp+dP1ITBKUHHAgAA6FQUV52E4gqRora+UU++t013vbJBW3bt1aj0vrrgiCGaOWWwhg5MCjoeAKAT1Tc06tElW/X759apbF+dLj76MF135hj+cwIAAPQaFFedhOIKQauua9C/l27V3a9uVMGefZo8JFXfOm20zhifwfQRAOjlyvbW6Y8vfqgH3/pIfRNi9d0zxuiy44axbiEAAOjxKK46CcUVgrKvtkGPvLtFf/vvBhWV1+iIw/rr26eP1ilj0imsACDKrC+q0M2L1ui19aUald5XN886XCfkDgo6FgAAQLtRXHUSiit0t6qaej389ke697WNKq2s1TEjBurbp4/W8aPSKKwAIIo55/TS2mL9YtEabdm1V7+9cIouPHJI0LEAAADa5VCKKy47BkSA8uo6PfjmZt33+ibt3lunk0YP0rdOG62jRwwMOhoAIAKYmU4fn6njRqXpqgeX6fv/XqF9dQ267NhhQUcDAADoUhRXQIDqGhp13+ubdNfL+Sqvrtdp4zJ0zWm5OuKwAUFHAwBEoKSEOP3fl6br6n++p5/Mf181dQ366kkjg44FAADQZSiugICs2LpHNzy5Sh/sKNdp4zL0vTPGaNKQ1KBjAQAiXCg+Vn+99Eh977E83fL0B9pX26BrTstlSjkAAOiVKK6AblZZU6/fP7dOD7y5WenJibr70iN19uFZQccCAPQgCXEx+tPFU5UYF6PfP/+h9tU16AdnjaW8AgAAvQ7FFdCNXlpbpB/Pe187yqt16THD9IOzxyolFB90LABADxQXG6PbLpqixPhY3fXKBu2tbdBPz59AeQUAAHoViiugGxRXVOvnT63R0yt3aExmPz3+heN05DAWXgcAdExMjOmXcw5Xn/hY3f/GJtXUN+iW2ZMUG0N5BQAAegeKK6ALOec0d+lW3fr0B6qua9R1Z47R108ZpYS4mKCjAQB6CTPTT2aMV1JCrO58OV/VdY363YWTFRfLvzUAAKDno7gCusiGkkr96MlVemfTLh09YqB+dcEkjUrvF3QsAEAvZGb6/lljFYqP0W3Pfajqugb96eJp/EcJAADo8SiugE5WW9+ov726QXe8nK9QXIx+fcEkfW76UMUwbQMA0MWuOW20+iTE6ReL1qjm4WW664tHKBQfG3QsAACAdqO4AjpRYVm1vv7QUq3YVqYZk7N10/kTlJEcCjoWACCKXHniCIXiY/Tj+e/rygeW6N7LpyspgR/5AABAz8RPMUAnWfbRbn3j4WXaW1Ovuy89Qmcfnh10JABAlPriMcPUJz5W3//3Cn3p/nd1/xVHKZmr2AIAgB6IhQ+ATjB3yVZdcs/bSkqI1byrT6C0AgAE7oIjhuiOS47Q8i179MX/e0d79tYGHQkAAOCQUVwBHVDX0KifLVytHz6xUseMHKgFV5+gMZnJQccCAECSdN7kbN196ZFau6NCF9/zNuUVAADocSiugHbaVVWry+97V/94c7O+euII/f2Ko9Q/KSHoWAAA7OeMCZm674rpyi+u1M8Wrg46DgAAwCGhuALa4YMd5Zp55+tatmW3fn/RFP14xgTFxfLXCQAQmU4ana5rTsvV/Lzten5NUdBxAAAA2ozftIFD9MyqHbrgrjdV19CouV8/Tv/vyCFBRwIA4KD+59Rcjc9O0Y/mrWLKIAAA6DEoroA2amx0+sNz6/TNf76ncdnJeuqaEzV1aP+gYwEA0CYJcTG67aLJ2l1Vq5sXrQk6DgAAQJtQXAFtUFFdp6seWqY/v5Svi44cokevOlYZKaGgYwEAcEgmDk7V/5w6Sk++V6AXP2DKIAAAiHwUV8BBbC6t0gV3vamX1xXrZ+dP0G8vnKzEuNigYwEA0C7XnDZa47KS9aN5q1S2ry7oOAAAAK2iuAJa8d8PSzTzztdVUlmjh75ytK44YYTMLOhYAAC0W0JcjH534RSVVtbqFqYMAgCACEdxBRzAQ29t1hV/f1eD+/fRwqtP1PG5g4KOBABAp5g0JFXfPGWU/r1sm15eVxx0HAAAgAOiuAJa8OBbm/WTBat12rgMPfHN43VYWlLQkQAA6FTfOj1XYzL76cYnVqm8mimDAAAgMlFcAc08/PZHumnBap05IVN3ffFI9U2MCzoSAACdLjEuVr+7cIqKK6p166IPgo4DAADQIoorIMwj727Rj+e/r9PHZegvXzhCCXH8FQEA9F5ThvbX108ZpceWbtWrH5YEHQcAAOBT+K0c8M1dslU3PrlKp45N112XUloBAKLDd04frdyMfrrxiZWqYMogAACIMPxmDkh6fNk2Xf/kSp08Jl13X3qkEuNig44EAEC3CMXH6ncXTlZhebV+uXht0HEAAAD2Q3GFqDdv+Tb94PEVOmHUIN1z2ZEKxVNaAQCiy7TDBuhrJ43UI+9u0evrS4OOAwAA8DGKK0S1BXkFum7uCh03Mk33Xj6d0goAELW+d+YYjUzvq+ufWKnKmvqg4wAAAEiiuEIUW7Ryu773WJ6OGj5Q//el6eqTQGkFAIheTVMGt5ft06+f4SqDAAAgMlBcISo9s2qHvvNonqYPG6j7rzhKSQlxQUcCACBwRw4bqCtPGKGH396iN/OZMggAAIJHcYWo8+zqQn3rkeWaNrS/7v/yUeqbSGkFAECT6z47ViMG9dUPn1ipKqYMAgCAgFFcIaq8sKZI1/zrPU0akqq/f/ko9aO0AgBgP30SYvXbCyerYM8+3fFSftBxAABAlKO4QtR4eV2xvvnPZZowOFUPfOVoJYfig44EAEBEOmr4QH12QqYeX7ZN9Q2NQccBAABRjOIKUSG/uELf+tdyjclM1oNfOVoplFYAALRqzrQclVbW6I0NO4OOAgAAohjFFXq9iuo6XfXQMoXiY3Tv5dOV2ofSCgCAgzl1bIZSQnGav7wg6CgAACCKUVyhV2tsdLp27gp9tHOv7vzCERrcv0/QkQAA6BFC8bE6b3K2nl1dqL21LNIOAACCQXGFXu3Ol/P1/Joi/fi88Tp2ZFrQcQAA6FFmTc3R3toGPb+mKOgoAAAgSlFcodd6aW2Rbn/hQ82ZlqMrjh8edBwAAHqco4cP1ODUkOYxXRAAAASE4gq90qbSKn3n0TxNyE7RL+dMkpkFHQkAgB4nJsY0a1qOXltfqtLKmqDjAACAKERxhV6nqqZeX39oqeJiTHdfeqT6JMQGHQkAgB5r9tQcNTQ6LVqxPegoAAAgClFcoVdxzukHj69QfnGl7rjkCA0dmBR0JAAAerSxWckan52ieXkUVwAAoPtRXKFXufvVjVq8qlA3nDNOJ44eFHQcAAB6hTnTBmvF1j3aVFoVdBQAABBlKK7Qa/z3wxL97tm1mjE5W187aWTQcQAA6DVmTsmRmTSfRdoBAEA3o7hCr7B1115965HlGpOZrN9eOJnF2AEA6ERZqSEdNzJN8/MK5JwLOg4AAIgiFFfo8fbVNuiqh5bJOae/XXakkhLigo4EAECvM3tajj7auVfLt+4JOgoAAIgiFFfo0ZxzuuHJlVpbWK4/XTJNw9L6Bh0JAIBe6ezDs5QYF6MFTBcEAADdiOIKPdr9b2zWgrztuu7MMfrM2Iyg4wAA0GulhOJ1xvhMPbVyh+oaGoOOAwAAokSgxZWZnW1m68ws38xuaOHxRDN7zH/8HTMbHvbYjf72dWZ2Vtj2+82s2Mzeb3asn5lZgZnl+bdzu/Lc0PXe3FCqXy7+QGdNzNT/nJobdBwAAHq92dNytKuqVq+tLwk6CgAAiBKBFVdmFivpL5LOkTRB0iVmNqHZbldK2u2cy5V0u6Tf+M+dIOliSRMlnS3pLv94kvQPf1tLbnfOTfVvizvzfNC9Cvbs07f+tVzD05J020VTFBPDYuwAAHS1U8akq39SvOYt3x50FAAAECWCHHF1tKR859xG51ytpEclzWq2zyxJD/hfPy7pdPMuFzdL0qPOuRrn3CZJ+f7x5Jz7r6Rd3XECCEZ1XYO+8dAy1dQ36p7Lpys5FB90JAAAokJCXIzOm5St59cUqrKmPug4AAAgCgRZXOVI2hp2f5u/rcV9nHP1ksokpbXxuS25xsxW+tMJB7S0g5ldZWZLzWxpSQnD4CPRLxat0aqCMt3++akald4v6DgAAESVOdNyVF3XqGffLww6CgAAiALRtDj7XyWNkjRV0g5Jv29pJ+fcPc656c656enp6d2ZD23wzsad+uc7W/TVE0fozAmZQccBACDqHDlsgIYM6KP5eVxdEAAAdL0gi6sCSUPD7g/xt7W4j5nFSUqVtLONz92Pc67IOdfgnGuUdK/8qYXoOWrqG3TjvFUaOrCPrv3smKDjAAAQlcxMc6bl6I38UhWXVwcdBwAA9HJBFldLJI02sxFmliBvsfWFzfZZKOlL/tcXSnrJOef87Rf7Vx0cIWm0pHdbezEzyw67O0fS+wfaF5Hprpc3aGNJlW6ZPUlJCXFBxwEAIGrNmpqjRictXMEi7QAAoGsFVlz5a1ZdI+lZSR9ImuucW21mN5vZTH+3+ySlmVm+pGsl3eA/d7WkuZLWSPqjJgvYAAAgAElEQVSPpKudcw2SZGaPSHpL0lgz22ZmV/rH+q2ZrTKzlZI+I+l73XKi6BT5xRW665V8zZ46WKeMYQonAABBys3op0k5qUwXBAAAXc68AUxoyfTp093SpUuDjhH1GhudPn/PW1pfXKkXrj1Fg/olBh0JAICod9/rm/SLRWv0wrUnKzcjOeg4AACgBzGzZc656W3ZN5oWZ0cP9ciSLVqyebf+99zxlFYAAESI86dkK8ak+cuZLggAALoOxRUiWlF5tX69eK2OG5mmC48cEnQcAADgy0gO6YTcQZqfV6DGRkbwAwCArkFxhYj286dWq6ahUb+8YJLMLOg4AAAgzJxpOdq2e5+WbdkddBQAANBLUVwhYj2/pkiLVxXqO6eP1ohBfYOOAwAAmjlrYpb6xMdq/nIWaQcAAF2D4goRqbKmXjcteF9jM5N11ckjg44DAABa0DcxTmdOyNSilTtUW98YdBwAANALUVwhIt327DoVllfrV/9vkuJj+ZgCABCp5kzLUdm+Or2yrjjoKAAAoBeiEUDEydu6Rw+8tVmXHztMRxw2IOg4AACgFSeOHqS0vgman8d0QQAA0PkorhBR6hoadcMTK5WZHNL3zxobdBwAAHAQ8bExmjE5Wy98UKzy6rqg4wAAgF6G4goR5f9e26S1hRW6edZEJYfig44DAADaYPa0HNXWN+o/qwqDjgIAAHoZiitEjM2lVfrjCx/q7IlZ+uzErKDjAACANpo6tL+GpyVpHlcXBAAAnYziChHBOaf/nb9KCbEx+vmsiUHHAQAAh8DMNHtajt7etFM7yvYFHQcAAPQiFFeICE++V6A38nfq+nPGKTMlFHQcAABwiGZPzZFz0sK87UFHAQAAvQjFFQK3s7JGtzy9RkcOG6AvHH1Y0HEAAEA7DB/UV1OH9me6IAAA6FQUVwjcb/6zVpU19frVBZMUE2NBxwEAAO00Z1qO1hZWaG1hedBRAABAL0FxhUBtLq3SE+8V6PLjhmtMZnLQcQAAQAecNzlbsTGm+cuZLggAADoHxRUC9eeX1is+1vT1U0YGHQUAAHTQoH6JOnn0IC3MY7ogAADoHBRXCMym0irNX16gS48ZpoxkFmQHAKA3OGZkmraXVauiui7oKAAAoBeguEJg7nhpvRLiYnQVo60AAOg1svyrAxeV1wScBAAA9AYUVwgEo60AAOidMj8urqoDTgIAAHoDiisE4o4XvdFWXz9lVNBRAABAJ8pMSZQkFZZRXAEAgI6juEK321hSqfl5Bbrs2GFKT04MOg4AAOhEWan+iKsKiisAANBxFFfodne+lO+tbXUyo60AAOhtkhLilByKUxEjrgAAQCeguEK3YrQVAAC9X2ZKiMXZAQBAp6C4QrdqGm3F2lYAAPReWSkhFbI4OwAA6AQUV+g2G/zRVpcfN1yD+jHaCgCA3iojJVHFFFcAAKATUFyh23yyttXIoKMAAIAulJUSUnFFjRobXdBRAABAD0dxhW6xoaRSCxhtBQBAVMhKDam+0am0inWuAABAx1BcoVvc+VK+EuNiGW0FAEAUyEgOSZKKWaAdAAB0EMUVutwno62GMdoKAIAokJXqFVeFZaxzBQAAOobiCl3ujhfXKzEuVl9jtBUAAFEhM8X7j6qiCoorAADQMRRX6FL5xZVauGI7o60AAIgi6f0SZSYVMeIKAAB0EMUVutSdLzHaCgCAaBMXG6NB/RJVWE5xBQAAOobiCl3m49FWxzPaCgCAaJOVElIRi7MDAIAOorhCl7nDH2111UmMtgIAINpkpoRUxIgrAADQQRRX6BLho63SGG0FAEDUyUxJpLgCAAAdRnGFLnHHS+vVJ57RVgAARKuslJB2761TdV1D0FEAAEAPRnGFTvfJlQSHM9oKAIAolZkSkiQVs84VAADoAIordLq/vrJBfeJj9bWTRgQdBQAABCQz1SuuiiqYLggAANqP4gqdqqqmXotX7dCsqTmMtgIAIIpl+SOuCssorgAAQPtRXKFTPb+mSPvqGjRnWk7QUQAAQIAyU7z/wGKBdgAA0BEUV+hU85YXKKd/H00fNiDoKAAAIECpfeKVGBdDcQUAADqE4gqdprSyRq/nl2rW1MGKibGg4wAAgACZmTJTQipicXYAANABFFfoNItWbFdDo9NspgkCAAB561wVMuIKAAB0AMUVOs28vO0an52iMZnJQUcBAAARIDM1xFRBAADQIRRX6BSbSqu0YusezZk2OOgoAAAgQmQmJ6qovFrOuaCjAACAHoriCp1i/vICmUkzpzBNEAAAeLJSQ6qua1T5vvqgowAAgB6K4god5pzTgrwCHTcyTVmpoaDjAACACJGR4v1cUFTBdEEAANA+FFfosBXbyrR5517NnspoKwAA8Iksv7gqLKO4AgAA7UNxhQ6bv7xACXExOntSVtBRAABABPm4uGKBdgAA0E4UV+iQuoZGPbViu84Yn6GUUHzQcQAAQATJSEmUJBVTXAEAgHaiuEKHvJ5fqp1VtZrFNEEAANBMKD5W/ZPiGXEFAADajeIKHbJgeYFS+8Tr1LHpQUcBAAARKDM5pKLymqBjAACAHirQ4srMzjazdWaWb2Y3tPB4opk95j/+jpkND3vsRn/7OjM7K2z7/WZWbGbvNzvWQDN73szW+38O6MpziwZVNfV6dnWRzp2UrcS42KDjAACACJSZGlIRI64AAEA7BVZcmVmspL9IOkfSBEmXmNmEZrtdKWm3cy5X0u2SfuM/d4KkiyVNlHS2pLv840nSP/xtzd0g6UXn3GhJL/r30QHPrynSvroGzZnGNEEAANCyzORErioIAADaLcgRV0dLynfObXTO1Up6VNKsZvvMkvSA//Xjkk43M/O3P+qcq3HObZKU7x9Pzrn/StrVwuuFH+sBSbM782Si0fy8AuX076Ppwxi8BgAAWpaVGlJpZY3qGxqDjgIAAHqgIIurHElbw+5v87e1uI9zrl5SmaS0Nj63uUzn3A7/60JJmS3tZGZXmdlSM1taUlLSlvOISqWVNXptfalmTh2smBgLOg4AAIhQmSkhNTqptLI26CgAAKAHisrF2Z1zTpI7wGP3OOemO+emp6ez4PiBLFqxXQ2NjmmCAACgVZkpIUlinSsAANAuQRZXBZKGht0f4m9rcR8zi5OUKmlnG5/bXJGZZfvHypZU3O7k0Py87RqfnaIxmclBRwEAABEsyy+uCimuAABAOwRZXC2RNNrMRphZgrzF1hc222ehpC/5X18o6SV/tNRCSRf7Vx0cIWm0pHcP8nrhx/qSpAWdcA5RaVNplfK27tGcaYODjgIAACJcZkqiJKmY4goAALRDYMWVv2bVNZKelfSBpLnOudVmdrOZzfR3u09SmpnlS7pW/pUAnXOrJc2VtEbSfyRd7ZxrkCQze0TSW5LGmtk2M7vSP9avJZ1pZuslneHfRzssyCuQmTRzCtMEAQBA69L6JSo2xhhxBQAA2iUuyBd3zi2WtLjZtpvCvq6WdNEBnnurpFtb2H7JAfbfKen0juSF5JzTgrztOnZEmrJSQ0HHAQAAES42xpSRnKjCspqgowAAgB4oKhdnR/ut2FamTaVVLMoOAADaLCMlpOIKRlwBAIBDR3GFQzJ/eYES4mJ09qSsoKMAAIAeIislUYVlFFcAAODQUVyhzeobGrVo5XadMT5DKaH4oOMAAIAeIjMlpCLWuAIAAO1AcYU2ez2/VKWVtZo1lWmCAACg7TJTQiqvrte+2oagowAAgB6G4gpttiBvu1L7xOvUselBRwEAAD1IVop3QReuLAgAAA4VxRXaZG9tvZ5dXahzJ2UrMS426DgAAKAHyfSLK6YLAgCAQ0VxhTZ5fk2R9tY2aPbUwUFHAQAAPUxWaqIkiisAAHDoKK7QJvOWFyinfx8dNXxg0FEAAEAPk8GIKwAA0E4UVzio0soavba+VDOnDlZMjAUdBwAA9DDJiXFKSohVYVlN0FEAAEAPQ3GFg3p65Q41NDrNmcbVBAEAwKEzM2WmhBhxBQAADhnFFQ5q3vICjc9O0ZjM5KCjAACAHiozJZHiCgAAHDKKK7Rqc2mV8rbuYVF2AADQIVkpIRVSXAEAgENEcYVWzc8rkJk0k+IKAAB0QGZKSMXlNXLOBR0FAAD0IBRXOCDnnBbkbdexI9KUndon6DgAAKAHy0wJqbahUbv31gUdBQAA9CAUVzigldvKtKm0ikXZAQBAh2WmhCSJda4AAMAhobjCAc3PK1BCXIzOnpQVdBQAANDDZaUmShLrXAEAgENCcYUDem/LHk0fNkApofigowAAgB7u4xFXZRRXAACg7Siu0CLnnDYUV2p0Rr+gowAAgF4gI7lpqmBNwEkAAEBPQnGFFhWWV6uypl65mclBRwEAAL1AQlyM0vomMFUQAAAcEoortGh9UaUkKTedEVcAAKBzZKSEVExxBQAADgHFFVqUX+wVV6MzKa4AAEDnyEpJZMQVAAA4JBRXaFF+SaX6J8UrrW9C0FEAAEAvkZUaUhHFFQAAOAQUV2hRflGlctP7ycyCjgIAAHqJjOSQSitrVdfQGHQUAADQQ1BcoUX5JZVMEwQAAJ0qK9W7smBxBVcWBAAAbUNxhU/ZWVmjXVW1GsXC7AAAoBNlpiRKEtMFAQBAm1Fc4VM+WZg9OeAkAACgN8lM8UZcFZVRXAEAgLahuMKn5Jd4xVVuBiOuAABA52kqrriyIAAAaCuKK3zK+qJKJSXEarC/DgUAAEBnGJiUoPhYU1E5a1wBAIC2objCp2woqVRuBlcUBAAAnSsmxpSRHGKNKwAA0GYUV/iU9UWVymVhdgAA0AUyUxIprgAAQJtRXGE/FdV1KiyvVm4mxRUAAOh8Wakh1rgCAABtRnGF/TRdUZARVwAAoCtkJIdUzBpXAACgjSiusJ+m4mp0ZnLASQAAQG+UlRpSZU29Kmvqg44CAAB6AIor7Ce/pFIJsTEaOqBP0FEAAEAvlJXiXbW4sGxfwEkAAEBPQHGF/eQXVWrEoL6Ki+WjAQAAOt/47BRJ0lsbdgacBAAA9AS0E9hPfkklC7MDAIAuMyazn3Iz+umplTuCjgIAAHoAiit8rLquQVt27WVhdgAA0GXMTDMmZ2vJ5l0q4uqCAADgICiu8LGNJVVyThrNiCsAANCFZkweLOekpxl1BQAADoLiCh/LL/GuKJibQXEFAAC6Tm5GP43LStailduDjgIAACIcxRU+ll9UoRiTRgzqG3QUAADQy50/ZbDe27JHBXu4uiAAADgwiit8LL+kUsPS+ioxLjboKAAAoJebMTlbkvQ0o64AAEArKK7wsfVFlRrFwuwAAKAbDEvrq0k5qVrEOlcAAKAVFFeQJNU1NGrzzioWZgcAAN1mxuRsrdxWpi079wYdBQAARCiKK0iSPtq5V3UNTrmMuAIAAN3k3EnedMFFq5guCAAAWkZxBUlSfjFXFAQAAN1r6MAkTR3aX4tWMF0QAAC0jOIKkqQNJV5xNYriCgAAdKMZk7O1Zke5Nvo/iwAAAISjuIIkaX1RhQanhtQvMS7oKAAAIIqc519dkEXaAQBASyiuIEnKL6lUbmZy0DEAAECUyU7to6OGD9CilaxzBQAAPo3iCmpsdMovrmRhdgAAEIgZkwfrw6JKfVhUEXQUAAAQYSiuoII9+1Rd16jRmRRXAACg+50zKUsxJi1awagrAACwP4orKL+EKwoCAIDgZCSHdMyINC1auUPOuaDjAACACEJxBeUX+cUVUwUBAEBAZkzJ1sbSKq3ZUR50FAAAEEEorqD84koN6pegAX0Tgo4CAACi1DmHZys2xri6IAAA2E+gxZWZnW1m68ws38xuaOHxRDN7zH/8HTMbHvbYjf72dWZ21sGOaWb/MLNNZpbn36Z29fn1FOuLKzSK0VYAACBAA/sm6PhRaVq0cjvTBQEAwMcCK67MLFbSXySdI2mCpEvMbEKz3a6UtNs5lyvpdkm/8Z87QdLFkiZKOlvSXWYW24Zj/sA5N9W/5XXh6fUYznlXFGRhdgAAELTzJw/W1l37tHJbWdBRAABAhAhyxNXRkvKdcxudc7WSHpU0q9k+syQ94H/9uKTTzcz87Y8652qcc5sk5fvHa8sxEaakokbl1fWsbwUAAAJ31sQsxceaFq3k6oIAAMATZHGVI2lr2P1t/rYW93HO1Usqk5TWynMPdsxbzWylmd1uZokthTKzq8xsqZktLSkpOfSz6mHyi5uuKJgccBIAABDtUpPiddLodD3N1QUBAIAvmhZnv1HSOElHSRoo6fqWdnLO3eOcm+6cm56ent6d+QKRX+IVV0wVBAAAkWDG5GxtL6vWe1v2BB0FAABEgCCLqwJJQ8PuD/G3tbiPmcVJSpW0s5XnHvCYzrkdzlMj6e/yphVGvfVFlUpOjFNGcosD0AAAALrVmRMylRAXw3RBAAAgKdjiaomk0WY2wswS5C22vrDZPgslfcn/+kJJLzlv3PhCSRf7Vx0cIWm0pHdbO6aZZft/mqTZkt7v0rPrIfKLK5Wb2U/e2wIAABCs5FC8Th2TrsWrdqixkemCAABEu8CKK3/NqmskPSvpA0lznXOrzexmM5vp73afpDQzy5d0raQb/OeuljRX0hpJ/5F0tXOu4UDH9I/1TzNbJWmVpEGSbumO84x064srWZgdAABElBlTBquovEZLNu8KOgoAAAhYXJAv7pxbLGlxs203hX1dLemiAzz3Vkm3tuWY/vbTOpq3tynbW6fSyhrlZlBcAQCAyHH6uAyF4mO0aOUOHTMyLeg4AAAgQNG0ODuayS+pkMTC7AAAILL0TYzT6eMy9cz7O1Tf0Bh0HAAAECCKqyi2vsi7omBuenLASQAAAPY3Y3K2Sitr9c4mpgsCABDNKK6iWH5xpULxMcoZ0CfoKAAAAPv5zLgM9U2I5eqCAABEOYqrKLa+uFIjB/VTbAxXFAQAAJElFB+rMyZk6pn3C1XHdEEAAKIWxVUUyy+uZH0rAAAQsWZMHqw9e+v0Rn5p0FEAAEBAKK6iVFVNvQr27FNuOsUVAACITCePGaTkUJwWrdwRdBQAABAQiqsotbGkSpKUm0FxBQAAIlNiXKw+OyFLz64uVE19Q9BxAABAACiuolR+SYUkMVUQAABEtBlTslVRXa/XPmS6IAAA0YjiKkqtL6pUXIxpWFrfoKMAAAAc0Im5gzSoX6LufDlfjY0u6DgAAKCbUVxFqfziSg0f1FfxsXwEAABA5IqPjdH/njdOeVv36JElW4KOAwAAuhmtRZTKL65kYXYAANAjzJ6ao+NGpuk3z6xVSUVN0HEAAEA3oriKQrX1jfpo114WZgcAAD2CmemWOYeruq5Rtz69Jug4AACgG1FcRaHNO6vU0OhYmB0AAPQYo9L76RunjNT8vO16I5+F2gEAiBYUV1FofVGlJO8HQAAAgJ7ifz6Tq2FpSfrx/PdVXdcQdBwAANANKK6iUH5xpcworgAAQM8Sio/VL2Ydrk2lVfrbqxuDjgMAALpBXGsPmtkFbThGtXNucSflQTdYX1yhIQP6qE9CbNBRAAAADsnJY9J1/pTB+ssr+Zo5dbBGDOobdCQAANCFWi2uJN0raYEka2WfkyVRXPUgXFEQAAD0ZD85b7xeWVusmxa8rwe/crTMWvtRFQAA9GQHK66ecc59pbUdzOzhTsyDLtbQ6LSxtEonj0kPOgoAAEC7ZKSE9IOzx+qmBav11ModmjllcNCRAABAF2l1jSvn3KUHO0Bb9kHk2Lprr2rrGxlxBQAAerQvHjNMk4ek6heL1qhsX13QcQAAQBdp0+LsZhZrZjPN7Ntmdm3TravDofPlF3tXFMzNpLgCAAA9V2yM6dbZk7Szska/f25d0HEAAEAXaetVBZ+SdIWkNEnJYTf0MOubiqsMiisAANCzTRqSqsuPG66H3v5IeVv3BB0HAAB0gYOtcdVkiHNucpcmQbfIL65UZkqiUkLxQUcBAADosOs+O0aLV+3Q/85bpQVXn6C42Lb+vywAAOgJ2vov+zNm9tkuTYJukV9cwWgrAADQaySH4vXT8ydq9fZyPfjWR0HHAQAAnaytxdXbkuaZ2T4zKzezCjMr78pg6HzOOW0oqWJhdgAA0KucOylLp4xJ1++fW6fCsuqg4wAAgE7U1uLqD5KOk5TknEtxziU751K6MBe6QGF5tSpr6pWbyfJkAACg9zAz/WLW4apvdLp50eqg4wAAgE7U1uJqq6T3nXOuK8Oga60v8hdmZ8QVAADoZQ5LS9K3Tx+txasK9fLa4qDjAACATtLWxdk3SnrFzJ6RVNO00Tn3hy5JhS6R719RcHQmxRUAAOh9vnbSSM1bXqCbFr6v50aeoj4JsUFHAgAAHdTWEVebJL0oKUFSsn+j/ehh1hdXqn9SvNL6JgQdBQAAoNMlxMXoltmHa+uuffrTi+uDjgMAADpBW0dcPeic2xS+wcyO6oI86EI7yvZp6IAkmVnQUQAAALrEsSPT9PnpQ3X3qxuUM6CPLjt2WNCRAABAB7R1xNXjZpbTdMfMTpZ0f9dEQlfZW9OgJIbMAwCAXu4Xsw/XGeMz9JP57+uRd7cEHQcAAHRAW4urb0iab2ZZZnaupDskndt1sdAV9tVRXAEAgN4vIS5Gf/niETp1bLp+NG+V/r10a9CRAABAO7WpuHLOLZH0bUnPSfqZpDOcc/wE0MPsra1XUkJbZ4cCAAD0XIlxsbr70iN1Yu4g/fCJlZq3fFvQkQAAQDu02mKY2VOSXNimJEllku4zMznnZnZlOHSufbUNXF0HAABEjVB8rO65bLq+8o8lum7uCsXGxGjmlMFBxwIAAIfgYMNvbuuWFOgWe5kqCAAAokyfhFjdd8V0XfH3JfreY3mKNdN5k7ODjgUAANqo1eLKOfdqdwVB19vLiCsAABCFkhLidP8VR+mK+9/Vdx5drrhY+//t3XuYZmV5J+rfQ3NWUQ4KBEQwoiNgNiMdIJ4VxDYxoklMnOxs2hOOpxCTzDi62RFDxmubmEv3GKMOQ0jA6KDGUUkMIhA1k4koLaKIhgE8QQcQASXahIbm2X/U6lhWqroLur7+VlH3fV3rqm+937ve71n6Ul31q7XelWcdvt+0ywIAFmGLa1xV1V9tbYDF9GH6Nt3T2Xj3Pdl9J2tcAQArzwN32TF/+uKfzuMOfHBe8/7LcvHXbpp2SQDAImwtxXhSVZ23hfcryWFLWA8TsmHj3UniVkEAYMV60K475eyXHJ1fO/NzeeWfX5b/etJRefpjHjbtsgCALdhacHXiIsbYuBSFMFl3bNyUJG4VBABWtD123Snvfckx+dUzL8m/f+8XcuZJq/OURz902mUBAAuwxtUKsWEIrlxxBQCsdA/efaf8+UuPyb/7b5fk5HPW5U9f9NN5wqP2mXZZAMA8trjGFfcfgisAgB/Z8wE7530vOyaP2Hv3vPTsdfnc12+ZdkkAwDwEVyvEHXfNrHG1284WZwcASJK9H7hL3veyY/MTD9k1L/6zS7Pum7dOuyQAYI5FB1dVtVtVPWaSxTA5rrgCAPjXHvqgXfLfTz42++2xa9ae9flc9u3bpl0SADDLooKrqvr5JJcn+cSwf+RWnjbIyGwOrnbbSXAFADDbw/bYNe8/+djs86BdsvZPPp+PXb4+3T3tsgCALP6KqzclOTrJ95Kkuy9PcsiEamIC7nDFFQDAgvZ78K757ycfm0c+7IH5jXMvz0lnfT7fvmXDtMsCgBVvscHVXd39/Tlt/gy1jPzoVkFrXAEAzOcnHrJb/scrn5Dffe7huexbt+WE/+8zec9nrs1dm+6ZdmkAsGItNri6sqp+Ncmqqjq0qv4oyd9PsC6W2IaNmxdnd8UVAMBCVu1QWfuEg3PRbz81Tzn0oXnL+f+Qn/+jv8vl131v2qUBwIq02ODq15McnuTOJO9P8v0kr51UUSw9twoCACze/g/eLWectDrv+bWjctuGjXn+u/5X3nTelfnBnXdPuzQAWFEWdd9Yd29IcuqwsQxtuGtTdlpV2WnVoh8kCQCw4q05Yr888VF7560XXJWzP/vNXHDljTn9xCPyzMP2nXZpALAiLPapghdW1UNm7e9ZVRdMriyW2h0bN3miIADAffCgXXfK6ScekQ+/8gnZY9edcvI56/KK934hN37/n6ddGgDc7y328pt9uvtfbuzv7tuSPGwyJTEJGzbebX0rAIBt8PiD9sxfnfKkvG7NY/Kpq76T49/2mbz3s9/Mpns8swgAJmWxwdU9VXXQ5p2qekQ8VXBZ2bBxkycKAgBso51W7ZBXPe1R+eRvPiVHPvwh+Z2PXZknvOXivPFjX8nfX/vd3O0JhACwpBabZJya5O+q6jNJKsmTk7x8YlWx5NwqCACwdB6x9wPy3pcenQuuvCkf/eL6fHDddTnns9/KXg/YOc987L5Z87j98sSf3Cc772h9UQDYFotdnP0TVfX4JMcOTa/t7u9OriyW2swVV4IrAIClUlVZc8R+WXPEftmw8e585qqbc/5XbszHr7ghH1h3XR606445/rH7Zs0R++Wpj35odvVHRAC41+7NvWO7JLl1OOawqkp3/+22fHhVrUnyX5KsSnJmd79lzvu7JDknyVFJbknyK939zeG9NyR5aZJNSU7p7gu2NGZVHZLk3CR7J/lCkv+ruzduS/3LyR13bcqDdnWrIADAJOy+84559uP2z7Mft3/++a5N+V/XfDfnf+XGXPjVm/KRL67P7juvytMf87CsOWK/PP3fPCwP3MXPZQCwGIv6F7Oqfj/JryS5MsnmG/c7yX0OrqpqVZI/TvLMJNcnubSqzuvur87q9tIkt3X3o6rqhUl+P8mvVNVhSV6Y5PAkP5Hkoqp69HDMQmP+fpK3d/e5VfWeYex339f6l5s7Nm7KvnvsMu0yAADu93bdaVWOe+y+Oe6x++auTffkc1+/Ned/5YZccOVN+fgVN2TnHXfIUw7dJ0961D556H9AI7YAAB8wSURBVIN2zd4P3Dl7P2Dn7PWAnfOQ3XfOqh1q2qcAAKOx2D/1PC/JY7r7ziX87KOTXNPdX0+Sqjo3yYlJZgdXJyZ50/D6L5K8s6pqaD93qOcbVXXNMF7mG7OqvpbkGUl+dehz9jDuigmuNtx1t8XZAQC2s51W7ZAnHbpPnnToPjn9xCPyhW/dNhNifeXGXPS17/yr/jtUsufuO2fvB+6ch+y2c0qGBcAspxx3aJ74qH2mXcZ2tdgk4+tJdkqylMHVAUmum7V/fZJjFurT3XdX1fczc6vfAUkumXPsAcPr+cbcO8n3uvvuefr/mKp6eYaF5w866KD5uixLd2zclN2scQUAMDWrdqgcfcheOfqQvfLG5xyWm//pztzyw4259Ycbc8sPN+aWH9z5Y6+/f8ddac/xBmCFW2xwtSHJ5VV1cWaFV919ykSqmqLuPiPJGUmyevXq+82PChs2bsruFgQFABiFqsrD9tg1D9tj12mXAgCjttjg6rxhW0rrkzx81v6BQ9t8fa6vqh2TPDgzi7Rv6dj52m9J8pCq2nG46mq+z7rf6u7ccZenCgIAAADLy6KCq+4+ewKffWmSQ4en/a3PzGLrvzqnz3lJ1ib5bJJfSvI33d1VdV6S91fV2zKzOPuhST6fpOYbczjmU8MY5w5jfmwC5zRK/3zXPelOdrPGFQAAALCMLPapgocm+X+THJbkX65n7u5H3tcPHtasek2SC5KsSnJWd19ZVacnWdfd5yX5kyTvHRZfvzUzQVSGfh/MzELudyd5dXdvGmr9V2MOH/mfkpxbVf85yReHsVeEDRtnlvZyxRUAAACwnCz2Epw/TXJakrcneXqSFyfZYVs/vLv/Oslfz2l746zX/5zkBQsc++Ykb17MmEP71/OjJw+uKBs2bkoSi7MDAAAAy8piw6fduvviJNXd3+ruNyX5ucmVxVK6466Z4MoVVwAAAMBystgrru6sqh2SXD3circ+yQMnVxZLafMVV4IrAAAAYDlZ7BVXv5Fk9ySnJDkqya8lOWlSRbG0Nq9xtdtOFmcHAAAAlo/FBlcHd/cPuvv67n5xd/9ikoMmWRhL5w5XXAEAAADL0GKDqzcsso0RcqsgAAAAsBxt8d6xqnp2kp9NckBVvWPWW3skuXuShbF07vBUQQAAAGAZ2tqiR/+YZF2S5yb5wqz2f0rym5MqiqW1eY2r3Xe2xhUAAACwfGwxyejuLyX5UlW9v7vvSpKq2jPJw7v7tu1RINtuw11uFQQAAACWn8WucXVhVe1RVXsluSzJf6uqt0+wLpbQHRs3pSrZZcfF/t8NAAAAMH2LTTIe3N23J/mFJOd09zFJjptcWSylDRs3ZbedVqWqpl0KAAAAwKItNrjasar2T/LLSf5qgvUwARs2bnKbIAAAALDsLDa4Oj3JBUmu6e5Lq+qRSa6eXFkspTs23u2JggAAAMCys6jHzHX3h5J8aNb+15P84qSKYmlt2Lgpu+/kiYIAAADA8rLFNKOqXtfdf1BVf5Sk577f3adMrDKWzKZ7OjvtaH0rAAAAYHnZ2mU4Xxu+rpt0IQAAAAAw2xaDq+7+y+Hr2dunHAAAAACYsdXF2atqbVVdVlU/HLZ1VXXS9igOAAAAgJVra2tcrU3y2iS/leSyJJXk8UneWlXd3e+dfIkAAAAArERbu+LqlUme392f6u7vd/f3uvtvMvNEwVdPvjwAAAAAVqqtBVd7dPc35zYObXtMoiAAAAAASLYeXN1xH98DAAAAgG2yxTWukjy2qr48T3sleeQE6gEAAACAJIsIrrZLFQAAAAAwxxaDq+7+1vYqBAAAAABm29oaVwAAAAAwFYIrAAAAAEbpXgdXVfX4SRQCAAAAALPdlyuuzlzyKgAAAABgjvsSXNWSVwEAAAAAc9yX4Op3l7wKAAAAAJjjXgdX3f3RSRQCAAAAALN5qiAAAAAAo7TF4Kqq/rqqDt4+pQAAAADAj2ztiqs/TfLJqjq1qnbaHgUBAAAAQJLsuKU3u/tDVXV+kt9Jsq6q3pvknlnvv23C9QEAAACwQm0xuBpsTPLDJLskeVBmBVcAAAAAMClbDK6qak2StyU5L8nju3vDdqkKAAAAgBVva1dcnZrkBd195fYoBgAAAAA229oaV0/eXoUAAAAAwGxbe6ogAAAAAEyF4AoAAACAURJcAQAAADBKgisAAAAARklwBQAAAMAoCa4AAAAAGCXBFQAAAACjJLgCAAAAYJQEVwAAAACMkuAKAAAAgFESXAEAAAAwSoIrAAAAAEZJcAUAAADAKAmuAAAAABilqQRXVbVXVV1YVVcPX/dcoN/aoc/VVbV2VvtRVXVFVV1TVe+oqtrSuFX1tKr6flVdPmxv3D5nCgAAAMB9Na0rrl6f5OLuPjTJxcP+j6mqvZKcluSYJEcnOW1WwPXuJCcnOXTY1ixi3P/Z3UcO2+kTOCcAAAAAltC0gqsTk5w9vD47yfPm6fOsJBd2963dfVuSC5Osqar9k+zR3Zd0dyc5Z9bxixkXAAAAgGVgWsHVvt19w/D6xiT7ztPngCTXzdq/fmg7YHg9t31r4/5MVX2pqs6vqsMXKqyqXl5V66pq3c0337z4MwIAAABgSe04qYGr6qIk+83z1qmzd7q7q6qX+vPnjHtZkkd09w+q6meTfDQztxjOd9wZSc5IktWrVy95XQAAAAAszsSCq+4+fqH3quqmqtq/u28Ybv37zjzd1id52qz9A5N8emg/cE77+uH1vON29+2z6vrrqnpXVe3T3d+9D6cGAAAAwHYwrVsFz0uy+SmBa5N8bJ4+FyQ5oar2HBZlPyHJBcOtgLdX1bHD0wRPmnX8vONW1X6znjx4dGbO+5alPy0AAAAAlsrErrjairck+WBVvTTJt5L8cpJU1eokr+jul3X3rVX1e0kuHY45vbtvHV6/KsmfJdktyfnDtuC4SX4pySur6u4kdyR54bCwOwAAAAAjNZXgqrtvSXLcPO3rkrxs1v5ZSc5aoN8R92LcdyZ557ZVDQAAAMD2NK1bBQEAAABgiwRXAAAAAIyS4AoAAACAURJcAQAAADBKgisAAAAARklwBQAAAMAoCa4AAAAAGCXBFQAAAACjJLgCAAAAYJQEVwAAAACMkuAKAAAAgFESXAEAAAAwSoIrAAAAAEZJcAUAAADAKAmuAAAAABglwRUAAAAAoyS4AgAAAGCUBFcAAAAAjJLgCgAAAIBRElwBAAAAMEqCKwAAAABGSXAFAAAAwCgJrgAAAAAYJcEVAAAAAKMkuAIAAABglARXAAAAAIyS4AoAAACAURJcAQAAADBKgisAAAAARklwBQAAAMAoCa4AAAAAGCXBFQAAAACjJLgCAAAAYJQEVwAAAACMkuAKAAAAgFESXAEAAAAwSoIrAAAAAEZJcAUAAADAKAmuAAAAABglwRUAAAAAoyS4AgAAAGCUBFcAAAAAjJLgCgAAAIBRElwBAAAAMEqCKwAAAABGSXAFAAAAwCgJrgAAAAAYJcEVAAAAAKMkuAIAAABglARXAAAAAIyS4AoAAACAURJcAQAAADBKgisAAAAARmkqwVVV7VVVF1bV1cPXPRfot3boc3VVrZ3VflRVXVFV11TVO6qqhvYXVNWVVXVPVa2eM9Ybhv5XVdWzJnuGAAAAAGyraV1x9fokF3f3oUkuHvZ/TFXtleS0JMckOTrJabMCrncnOTnJocO2Zmj/SpJfSPK3c8Y6LMkLkxw+9H1XVa1a4nMCAAAAYAlNK7g6McnZw+uzkzxvnj7PSnJhd9/a3bcluTDJmqraP8ke3X1Jd3eSczYf391f6+6rFvi8c7v7zu7+RpJrMhOGAQAAADBS0wqu9u3uG4bXNybZd54+ByS5btb+9UPbAcPrue1bstBY/0pVvbyq1lXVuptvvnkrwwIAAAAwKTtOauCquijJfvO8dersne7uqupJ1XFvdfcZSc5IktWrV4+mLgAAAICVZmLBVXcfv9B7VXVTVe3f3TcMt/59Z55u65M8bdb+gUk+PbQfOKd9/VbKWZ/k4ffyGAAAAACmaFq3Cp6XZPNTAtcm+dg8fS5IckJV7Tksyn5CkguGWwxvr6pjh6cJnrTA8XM/74VVtUtVHZKZBd0/vxQnAgAAAMBkTCu4ekuSZ1bV1UmOH/ZTVaur6swk6e5bk/xekkuH7fShLUleleTMzCyyfm2S84fjn19V1yf5mSQfr6oLhrGuTPLBJF9N8okkr+7uTdvjRAEAAAC4byZ2q+CWdPctSY6bp31dkpfN2j8ryVkL9DtinvaPJPnIAp/55iRvvu9VAwAAALA9TeuKKwAAAADYIsEVAAAAAKMkuAIAAABglARXAAAAAIyS4AoAAACAURJcAQAAADBKgisAAAAARklwBQAAAMAoCa4AAAAAGCXBFQAAAACjJLgCAAAAYJQEVwAAAACMkuAKAAAAgFESXAEAAAAwSoIrAAAAAEZJcAUAAADAKAmuAAAAABglwRUAAAAAoyS4AgAAAGCUBFcAAAAAjJLgCgAAAIBRElwBAAAAMEqCKwAAAABGSXAFAAAAwCgJrgAAAAAYJcEVAAAAAKMkuAIAAABglARXAAAAAIyS4AoAAACAURJcAQAAADBKgisAAAAARklwBQAAAMAoCa4AAAAAGCXBFQAAAACjJLgCAAAAYJQEVwAAAACMkuAKAAAAgFESXAEAAAAwSoIrAAAAAEZJcAUAAADAKAmuAAAAABglwRUAAAAAoyS4AgAAAGCUBFcAAAAAjJLgCgAAAIBRElwBAAAAMEqCKwAAAABGSXAFAAAAwCgJrgAAAAAYJcEVAAAAAKMkuAIAAABglARXAAAAAIzSVIKrqtqrqi6sqquHr3su0G/t0Ofqqlo7q/2oqrqiqq6pqndUVQ3tL6iqK6vqnqpaPav/wVV1R1VdPmzvmfxZAgAAALAtpnXF1euTXNzdhya5eNj/MVW1V5LTkhyT5Ogkp80KuN6d5OQkhw7bmqH9K0l+IcnfzvOZ13b3kcP2iqU8GQAAAACW3rSCqxOTnD28PjvJ8+bp86wkF3b3rd19W5ILk6ypqv2T7NHdl3R3Jzln8/Hd/bXuvmry5QMAAAAwadMKrvbt7huG1zcm2XeePgckuW7W/vVD2wHD67ntW3NIVX2xqj5TVU9eqFNVvbyq1lXVuptvvnkRwwIAAAAwCTtOauCquijJfvO8dersne7uqupJ1TG4IclB3X1LVR2V5KNVdXh33z63Y3efkeSMJFm9evWk6wIAAABgARMLrrr7+IXeq6qbqmr/7r5huPXvO/N0W5/kabP2D0zy6aH9wDnt67dSy51J7hxef6Gqrk3y6CTrtn4mAAAAAEzDtG4VPC/J5qcErk3ysXn6XJDkhKrac1iU/YQkFwy3GN5eVccOTxM8aYHj/0VVPbSqVg2vH5mZBd2/vjSnAgAAAMAkTCu4ekuSZ1bV1UmOH/ZTVaur6swk6e5bk/xekkuH7fShLUleleTMJNckuTbJ+cPxz6+q65P8TJKPV9UFQ/+nJPlyVV2e5C+SvGLWWAAAAACM0MRuFdyS7r4lyXHztK9L8rJZ+2clOWuBfkfM0/6RJB+Zp/3DST68bVUDAAAAsD1N64orAAAAANgiwRUAAAAAoyS4AgAAAGCUBFcAAAAAjJLgCgAAAIBRElwBAAAAMEqCKwAAAABGSXAFAAAAwCgJrgAAAAAYJcEVAAAAAKMkuAIAAABglARXAAAAAIyS4AoAAACAURJcAQAAADBKgisAAAAARklwBQAAAMAoCa4AAAAAGCXBFQAAAACjJLgCAAAAYJQEVwAAAACMkuAKAAAAgFESXAEAAAAwSoIrAAAAAEZJcAUAAADAKAmuAAAAABglwRUAAAAAoyS4AgAAAGCUBFcAAAAAjJLgCgAAAIBRElwBAAAAMEqCKwAAAABGSXAFAAAAwCgJrgAAAAAYJcEVAAAAAKMkuAIAAABglARXAAAAAIyS4AoAAACAURJcAQAAADBKgisAAAAARklwBQAAAMAoCa4AAAAAGCXBFQAAAACjJLgCAAAAYJQEVwAAAACMkuAKAAAAgFESXAEAAAAwSoIrAAAAAEZJcAUAAADAKAmuAAAAABglwRUAAAAAoyS4AgAAAGCUBFcAAAAAjJLgCgAAAIBRmkpwVVV7VdWFVXX18HXPBfqtHfpcXVVrZ7UfVVVXVNU1VfWOqqqh/a1V9Q9V9eWq+khVPWTWMW8Y+l9VVc+a/FkCAAAAsC2mdcXV65Nc3N2HJrl42P8xVbVXktOSHJPk6CSnzQq43p3k5CSHDtuaof3CJEd0908l+d9J3jCMdViSFyY5fOj7rqpaNZlTAwAAAGApTCu4OjHJ2cPrs5M8b54+z0pyYXff2t23ZSaUWlNV+yfZo7sv6e5Ocs7m47v7k91993D8JUkOnPV553b3nd39jSTXZCYMWzG+sv72aZcAAAAAcK/sOKXP3be7bxhe35hk33n6HJDkuln71w9tBwyv57bP9ZIkH5g11iWLOCZV9fIkL0+Sgw46aIsnsVw85dEPzY6ratplAAAAANwrEwuuquqiJPvN89aps3e6u6uql/izT01yd5L33dtju/uMJGckyerVq5e0rmlZ+4SDs/YJB0+7DAAAAIB7ZWLBVXcfv9B7VXVTVe3f3TcMt/59Z55u65M8bdb+gUk+PbQfOKd9/ayxX5TkOUmOG24l3DzWwxc6BgAAAIDxmdYaV+cl2fyUwLVJPjZPnwuSnFBVew6Lsp+Q5ILhFsPbq+rY4WmCJ20+vqrWJHldkud294Y5n/fCqtqlqg7JzILun5/EiQEAAACwNKYVXL0lyTOr6uokxw/7qarVVXVmknT3rUl+L8mlw3b60JYkr0pyZmYWWb82yflD+zuTPCjJhVV1eVW9ZxjryiQfTPLVJJ9I8uru3jTxswQAAADgPqsf3U3HXKtXr+5169ZNuwwAAACA+42q+kJ3r15M32ldcQUAAAAAWyS4AgAAAGCUBFcAAAAAjJLgCgAAAIBRElwBAAAAMEqCKwAAAABGSXAFAAAAwCgJrgAAAAAYJcEVAAAAAKMkuAIAAABglARXAAAAAIyS4AoAAACAURJcAQAAADBKgisAAAAARklwBQAAAMAoCa4AAAAAGCXBFQAAAACjJLgCAAAAYJQEVwAAAACMUnX3tGsYraq6Ocm3pl0HU7VPku9OuwhWDPON7cl8Y3sy39iezDe2N3OO7en+Mt8e0d0PXUxHwRVsQVWt6+7V066DlcF8Y3sy39iezDe2J/ON7c2cY3taifPNrYIAAAAAjJLgCgAAAIBRElzBlp0x7QJYUcw3tifzje3JfGN7Mt/Y3sw5tqcVN9+scQUAAADAKLniCgAAAIBRElwBAAAAMEqCK1akqtqrqi6sqquHr3su0G/t0Ofqqlo7q/3NVXVdVf1gTv/fqqqvVtWXq+riqnrEpM+F8ZvgfNulqj5QVddU1eeq6uDJngnLwRLMt6Oq6ophXr2jqmpoP7KqLqmqy6tqXVUdvb3OifGa1Hwb3vv1qvqHqrqyqv5ge5wP4zbJ+Ta8/9tV1VW1z6TPhfGb4L+nbx2+t325qj5SVQ/ZXufE+FTVmqq6apgnr5/n/QV/3q+qNwztV1XVsxY75nIkuGKlen2Si7v70CQXD/s/pqr2SnJakmOSHJ3ktFn/YP3l0DbXF5Os7u6fSvIXSfygTTK5+fbSJLd196OSvD3J70+gdpafbZ1v705ycpJDh23N0P4HSX63u49M8sb4/saMicy3qnp6khOT/B/dfXiSP5zwebA8TOr7W6rq4UlOSPLtSZ4Ay8qk5tuFSY4Yfl/430neMMmTYLyqalWSP07y7CSHJfl3VXXYnG7z/rw/9HthksMzM7feVVWrFjnmsiO4YqU6McnZw+uzkzxvnj7PSnJhd9/a3bdl5h+ZNUnS3Zd09w1zD+juT3X3hmH3kiQHLnnlLEcTmW9zxv2LJMfN/esxK9J9nm9VtX+SPYY510nOmXV8J9ljeP3gJP84qRNgWZnUfHtlkrd0951J0t3fmeRJsGxMar4lM78Qvi4z3+sgmdB86+5Pdvfdw/F+X1jZjk5yTXd/vbs3Jjk3M/NutoV+3j8xybndfWd3fyPJNcN4ixlz2RFcsVLtOysIuDHJvvP0OSDJdbP2rx/aFuulSc6/b+VxPzOp+fYvxww/AH0/yd7bVir3A9sy3w4YXs9tT5LXJnlrVV2Xmatf/IWYZHLz7dFJnjzcFvGZqvrppS2bZWoi862qTkyyvru/tOQVs5xN6vvbbC+J3xdWssX8/L/Qz/tbmnvb8jvsKO047QJgUqrqoiT7zfPWqbN3ururakn/ulZVv5ZkdZKnLuW4jNc05xsrz5Tm2yuT/GZ3f7iqfjnJnyQ5fonGZsSmNN92TLJXkmOT/HSSD1bVI4crF7gf297zrap2T/J/Z+Y2QVaYKf++cGqSu5O8bynHhfsjwRX3W9294C9UVXVTVe3f3TcMl/LOdwvC+iRPm7V/YJJPb+1zq+r4zPxj99TNtzhw/zel+bY+ycOTXF9VO2bm9q1b7k3dLE8TnG/r8+O3LBw4tCXJ2iS/Mbz+UJIz71PxLDtTmm/XJ/kfQ1D1+aq6J8k+SW6+r+fB8jCF+faTSQ5J8qXhbvsDk1xWVUd3943bcCosA1P6/paqelGS5yQ5TiC/om3+WX6zH5snc/rM/Xl/S8dubcxlx62CrFTnZeaXsAxfPzZPnwuSnFBVew6LLJ4wtC2oqv5tkv+a5LnW42CWicy3OeP+UpK/8cMP2Yb5NtwScXtVHTusn3DSrOP/MT+6ivQZSa6e1AmwrExqvn00ydOTpKoenWTnJN+d3GmwTCz5fOvuK7r7Yd19cHcfnJnQ9PFCKzKh729VtSYz66k9d9bauKxMlyY5tKoOqaqdM7PY+nlz+iz08/55SV44PHXwkMw8AODzixxz+elum23FbZm5L/jizPzidVGSvYb21UnOnNXvJZlZ6O6aJC+e1f4HmfnB5p7h65uG9ouS3JTk8mE7b9rnapv+NsH5tmtmrny5JjP/UD1y2udqm/62BPNtdZKvJLk2yTuT1ND+pCRfSPKlJJ9LctS0z9U2/W2C823nJH8+vHdZkmdM+1xt098mNd/mfMY3k+wz7XO1TX+b4Pe3azKzBtHm3xfeM+1ztU11nv1sZp4ueW2SU4e20zMTbG7x5/3M3OVzbZKrkjx7S2Mu923zfzwAAAAAMCpuFQQAAABglARXAAAAAIyS4AoAAACAURJcAQAAADBKgisAAAAARklwBQAAAMAoCa4AABZQVQ+vqm9U1V7D/p7D/sH3YoynVdX3q+qLVXVVVf1tVT1n1vuvqKqTtnL8E7blPO6rqnpRVd1cVWfO2n/nNoz3qar6QVWtXroqAYD7sx2nXQAAwFh193VV9e4kb0ny8uHrGd39zXs51P/s7uckSVUdmeSjVXVHd1/c3e/ZyrFPS/KDJH9/Lz9zqXygu1+zFAN199Or6tNLMRYAsDK44goAYMvenuTYqnptkicl+cNtGay7L09yepLXJElVvamq/sPw+pSq+mpVfbmqzh2u7HpFkt+sqsur6slV9fNV9bnhCq6LqmrfWeOcVVWfrqqvV9Upmz+zqk4axvxSVb13aHtoVX24qi4dtifem/Ooqp+rqs9W1T5V9WdV9e6qumT47KcNtXytqv5sW/73AgBWNldcAQBsQXffVVX/McknkpzQ3XctwbCXJfmP87S/Pskh3X1nVT2ku79XVe9J8oPu/sNk5nbFJMd2d1fVy5K8LslvD8f/myRPT/KgJFcNV4s9Osn/k+QJ3f3dzbc9JvkvSd7e3X9XVQcluSDJYxdTfFU9P8lvJfnZ7r6tqpJkzyQ/k+S5Sc5L8sQkL0tyaVUdOQR2AAD3iuAKAGDrnp3khiRHJLlwCcarBdq/nOR9VfXRJB9doM+BST5QVfsn2TnJN2a99/HuvjPJnVX1nST7JnlGkg9193eTpLtvHfoen+SwIXRKkj2q6oHd/YOt1P6MJKszE+LdPqv9L4cw7YokN3X3FUlSVVcmOTiJ4AoAuNfcKggAsAXDmlTPTHJsZm7Z23+ePq8ebuW7vKp+YhHD/tskX5un/eeS/HGSx2fmSqX5/sj4R0ne2d2PS/Lvk+w66707Z73elC3/kXKHzFy5deSwHbCI0CpJrs3MFV2PntO++bPvmVPHPVupAwBgQYIrAIAF1MzlSO9O8tru/naSt2aeNa66+49nBUD/uJUxfyrJ72QmoJrdvkOSh3f3p5L8pyQPTvLAJP+UmaBoswcnWT+8XruI0/ibJC+oqr2Hz9l8q+Ank/z6rM8/chFjJcm3kvxiknOq6vBFHgMAcJ8IrgAAFnZykm939+bbA9+V5LFV9dR7Oc6Th8XUr8pMYHVKd188p8+qJH8+3Gr3xSTv6O7vJfnLJM/fvDh7kjcl+VBVfSHJd7f2wd19ZZI3J/lMVX0pyduGt05JsnpYtP2rmVkEflG6+x+S/J9DHT+52OMAAO6t6u5p1wAAwAhV1YuSrO7u1yzhmJ9O8h+6e91SjQkA3H+54goAgIXckeTZVXXmUgxWVZ9K8sgkS/FkRgBgBXDFFQAAAACj5IorAAAAAEZJcAUAAADAKAmuAAAAABglwRUAAAAAo/T/AzmXzVzXZBLfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = [], []\n",
    "\n",
    "for element in rm_state:\n",
    "    x.append(element[0])\n",
    "    y.append(element[1])\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.title('ExoMars RSP Rover Module Testcase Travel')\n",
    "plt.ylabel('Y - Distance [km]')\n",
    "plt.xlabel('X - Distance [km]')\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure()\n",
    "\n",
    "plt.plot(interval_rm,rm_xoffset)\n",
    "plt.title('ExoMars RSP Rover Module Testcase X-Offset')\n",
    "plt.ylabel('+X offset [deg]')\n",
    "plt.xlabel('Ephemeris Time [s]')\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.plot(interval_htc,pancam_hrc_euler_1, label='Euler 1')\n",
    "ax1.plot(interval_htc,pancam_hrc_euler_2, label='Euler 2')\n",
    "plt.title('ExoMars RSP Rover Module PanCam HTC Euler Angles w.r.t Mast')\n",
    "plt.legend(loc='upper right');\n",
    "plt.ylabel('Angle [deg]')\n",
    "plt.xlabel('Ephemeris Time [s]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}