SPATZ/conversion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/simulations/raw/40km.txt', sep='\\t')\n",
    "df = df.drop([0, 1], axis=0)\n",
    "df = df.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from spatz.dataset import T1, T2, T3\n",
    "from math import pi\n",
    "\n",
    "# Rename the columns\n",
    "df = df.rename({\n",
    "    'Time': 'time',\n",
    "    'Phase': 'phase',\n",
    "    'acceleration_without_gravity_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket': 'ax_B',\n",
    "    'acceleration_without_gravity_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket': 'ay_B',\n",
    "    'acceleration_without_gravity_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket': 'az_B',\n",
    "    'gravity_force_x~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket': 'gx_B',\n",
    "    'gravity_force_y~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket': 'gy_B',\n",
    "    'gravity_force_z~STAHR_Rocket#B~STAHR_Rocket@STAHR_Rocket': 'gz_B',\n",
    "    'acceleration_without_gravity_radial~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket': 'ax_L',\n",
    "    'acceleration_without_gravity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket': 'ay_L',\n",
    "    'acceleration_without_gravity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket': 'az_L',\n",
    "    'velocity_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket': 'vx_L',\n",
    "    'velocity_east~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket': 'vy_L',\n",
    "    'velocity_north~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket': 'vz_L',\n",
    "    'velocity_r~STAHR_Rocket#L~STAHR_Rocket:Earth@STAHR_Rocket': 'vx_L',\n",
    "    'latitude~STAHR_Rocket#PCPF~Earth@Earth': 'latitude',\n",
    "    'longitude~STAHR_Rocket#PCPF~Earth@Earth': 'longitude',\n",
    "    'declination~STAHR_Rocket#PCPF~Earth@Earth': 'declination',\n",
    "    'altitude~STAHR_Rocket@Earth': 'altitude',\n",
    "    'pitch~STAHR_Rocket#L~STAHR_Rocket:Earth': 'pitch_l',\n",
    "    'yaw~STAHR_Rocket#L~STAHR_Rocket:Earth': 'yaw_l',\n",
    "    'roll~STAHR_Rocket#L~STAHR_Rocket:Earth': 'roll_l',\n",
    "    'atmos_pressure~STAHR_Rocket': 'pressure',\n",
    "    'atmos_temperature~STAHR_Rocket': 'temperature',\n",
    "    'sonic_velocity~STAHR_Rocket': 'sonic_velocity',\n",
    "    'OMEGA_X~STAHR_Rocket': 'OMEGA_X',\n",
    "    'OMEGA_Y~STAHR_Rocket': 'OMEGA_Y',\n",
    "    'OMEGA_Z~STAHR_Rocket': 'OMEGA_Z',\n",
    "    'drag~STAHR_Rocket': 'drag',\n",
    "    'flightpath_speed~STAHR_Rocket': 'flightpath_speed',\n",
    "    'mass_total~STAHR_Rocket': 'mass_total',\n",
    "    'mach~STAHR_Rocket': 'mach',\n",
    "    'gravity~STAHR_Rocket': 'gravity'\n",
    "}, axis=1)\n",
    "\n",
    "df['gx_B'] /= (df['mass_total'] * 1000)\n",
    "df['gy_B'] /= (df['mass_total'] * 1000)\n",
    "df['gz_B'] /= (df['mass_total'] * 1000)\n",
    "\n",
    "df['vx_L'] *= 1000\n",
    "df['vy_L'] *= 1000\n",
    "df['vz_L'] *= 1000\n",
    "\n",
    "df['altitude'] *= 1000\n",
    "\n",
    "pos = np.array([df.at[2, 'altitude'], 0, 0])\n",
    "px, py, pz, acc_total = [0], [0], [0], [df.at[2, 'gravity']]\n",
    "\n",
    "for i in range(3, len(df)+2):\n",
    "    dt = df.at[i, 'time'] - df.at[i-1, 'time']\n",
    "\n",
    "    acc = np.array([df.at[i, 'ax_L'], df.at[i, 'ay_L'], df.at[i, 'az_L']])\n",
    "    acc += np.array([-df.at[i, 'gravity'], 0, 0])\n",
    "    vel = np.array([df.at[i, 'vx_L'], df.at[i, 'vy_L'], df.at[i, 'vz_L']])\n",
    "\n",
    "    pos += dt * vel + 1/2*dt**2*acc\n",
    "    px.append(pos[0])\n",
    "    py.append(pos[1])\n",
    "    pz.append(pos[2])\n",
    "    acc_total.append(np.linalg.norm(acc))\n",
    "\n",
    "df.insert(0, 'px_L', px)\n",
    "df.insert(0, 'py_L', py)\n",
    "df.insert(0, 'pz_L', pz)\n",
    "df.insert(0, 'acc_total', acc_total)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HWoaq+vaICIidjlvjnXycz54cIiJqI4acIHA4gIeqvMakeULOvjNWNLvcCldDRESBgCEnCHjn4wxOCpz9cb6tf3wEIo061DtdKKqoUbocIiIKAAw5QeDwWU8oGJIUeCurvDQaCaNaDuvcU2JVtBYiIgoMDDkqV+doxqnzdQACuycHAEanRgEA9nJeDhERtQFDjsodsdRACCDBZERshFHpcjpkdMt+OXtKGHKIiOjaGHJU7uJ8nMCddOw1JtUTck6dr0d1nVPhaoiIyN8x5KicvLJKBSHHHKZH3x7hAIC97M0hIqJrYMhROe8eOWroyQEuLiXnkBUREV0LQ46Kud0CRRbPyirVhBzvvJzTVmULISIiv8eQo2JnLjSgockFg1aDXrFhSpfTKbgpIBERtRVDjoodbdk0r0+PcOi06vhRc1NAIiJqK3V88lGrjlZ6QsCAhMDeH+dS3BSQiIjaiiFHxY5V1AIABiQE5snjV8JNAYmIqC0YclTMO1zVX0U9OQA3BSQiorZhyFEpl1vgeKW3J0ddIefSTQHP1zoUroaIiPwVQ45KlVbXw9HshlGnQVqMOlZWefluCmhVthgiIvJbDDkq5V151C8+AlqNpHA1nY+bAhIR0bUw5KjUsQr1ray61BjOyyEiomtgyFGpoy0rq/qrbGWVl7cnZ/8ZGzcFJCKiVjHkqJR3ZdWAeHX25HBTQCIiuhaGHBVqdrlxsqoOgHqHq7gpIBERXQtDjgqdrq6H0+VGqF6LlOhQpcvpMtwUkIiIroYhR4WOXbKySqPClVVe3BSQiIiupl0hZ9myZRgxYgRMJhNMJhOysrLw+eefy9cbGxuRm5uL2NhYREREYMqUKaioqPB5jZKSEkyaNAlhYWGIj4/H008/jebmZp82W7ZswZgxY2A0GtGvXz8sX778slqWLl2KXr16ISQkBJmZmdi5c2d7bkXVvMc59I9X56RjL24KSEREV9OukJOSkoLnn38e+fn52L17N77zne/g+9//Pg4ePAgAmDdvHj777DOsXLkSW7duRXl5Oe677z75+10uFyZNmgSn04nt27fj7bffxvLly7Fo0SK5TXFxMSZNmoTbb78dBQUFmDt3Lh599FGsX79ebvP+++9j/vz5ePbZZ7Fnzx6MHDkSOTk5qKys7OjfhyqcPOeZj9NX5SHn0k0BOS+HiIguIzooOjpavPnmm8JqtQq9Xi9WrlwpXzt8+LAAIPLy8oQQQqxdu1ZoNBphsVjkNsuWLRMmk0k4HA4hhBDPPPOMGDp0qM97TJ06VeTk5Mhfjxs3TuTm5spfu1wukZycLBYvXtyu2m02mwAgbDZbu77P393zypcifcFq8XlhudKldLmnVxaI9AWrxfOfH1a6FCIi6iZt/fy+7jk5LpcLK1asQF1dHbKyspCfn4+mpiZkZ2fLbQYNGoS0tDTk5eUBAPLy8jB8+HAkJCTIbXJycmC32+XeoLy8PJ/X8LbxvobT6UR+fr5PG41Gg+zsbLnNlTgcDtjtdp+H2ggh5JVVfXqouycHADJa5uXkc/IxERF9S7tDTmFhISIiImA0GjFz5kysWrUKQ4YMgcVigcFgQFRUlE/7hIQEWCwWAIDFYvEJON7r3mtXa2O329HQ0IBz587B5XK12sb7GleyePFimM1m+ZGamtre2/d7VTUO1DqaoZGA9Fh1nVnVmoz0GADAvlIrnM3cFJCIiC5qd8gZOHAgCgoKsGPHDsyaNQvTpk3DoUOHuqK2Trdw4ULYbDb5UVpaqnRJne54lWfScWpMGIw6rcLVdL0+ceGICtPD0ezGobPq65kjIqLr1+6QYzAY0K9fP2RkZGDx4sUYOXIklixZgsTERDidTlitVp/2FRUVSExMBAAkJiZettrK+/W12phMJoSGhiIuLg5arbbVNt7XuBKj0SivDPM+1EYeqooLV7iS7qHRSPIRDxyyIiKiS3V4nxy32w2Hw4GMjAzo9Xps3LhRvlZUVISSkhJkZWUBALKyslBYWOizCmrDhg0wmUwYMmSI3ObS1/C28b6GwWBARkaGTxu3242NGzfKbYKZN+T0DYL5OF7eeTl7GHKIiOgSuvY0XrhwIe6++26kpaWhpqYG//73v7FlyxasX78eZrMZM2bMwPz58xETEwOTyYQnn3wSWVlZGD9+PABg4sSJGDJkCB5++GG88MILsFgs+M1vfoPc3FwYjUYAwMyZM/Hqq6/imWeewSOPPIJNmzbhgw8+wJo1a+Q65s+fj2nTpmHs2LEYN24cXnrpJdTV1WH69Omd+FcTmE60DFcFw6RjL2/I2X26GkIISJJ6N0AkIqK2a1fIqaysxE9/+lOcPXsWZrMZI0aMwPr163HnnXcCAP7yl79Ao9FgypQpcDgcyMnJwWuvvSZ/v1arxerVqzFr1ixkZWUhPDwc06ZNw+9+9zu5Te/evbFmzRrMmzcPS5YsQUpKCt58803k5OTIbaZOnYqqqiosWrQIFosFo0aNwrp16y6bjByMTp7zhpzgGK4CgJEpUdBqJFTYHSizNiAlWv0TromI6NokIYRQugil2O12mM1m2Gw2VczPaWxyYfCidRAC2PVf2egRaVS6pG5z76tfYf8ZG5b8eBS+P6qn0uUQEVEXauvnN8+uUpFT5+sgBBAZokNchEHpcrqVd/Ix5+UQEZEXQ46KXLoJYLDNS7k4L4chh4iIPBhyVORky6TjvkE0H8drbC9PyDl81o46R/M1WhMRUTBgyFGRE0G4fNwryRyKZHMI3MKz+zERERFDjop4e3KCZSPAbxvDc6yIiOgSDDkqEWwHc7aG83KIiOhSDDkqUV3nRI2jGVKQHMzZmrEth3XuKbkAtztod0YgIqIWDDkqcep8PQAgyRSCEL36D+ZszaCkSITqtahpbJYPKiUiouDFkKMSp897hqrSY4NzPg4A6LUajEw1AwB2n+KQFRFRsGPIUQlvT06vuOAcqvK6oZdnyGpn8XmFKyEiIqUx5KgEe3I8MnvHAgB2FHsO6yQiouDFkKMSck9OkE469hqTHgWdRsJZWyPOXGhQuhwiIlIQQ45KlLAnBwAQZtBheIpnXs6O4mqFqyEiIiUx5KiArb4JF+qbAATv8vFLjevNeTlERMSQowqnqz29OPGRRoQZdApXo7zxl8zLISKi4MWQowIX5+ME91CVV0avaGgk4PT5elhsjUqXQ0RECmHIUYHT57zzcThUBQCmED2GJJsAADs4ZEVEFLQYclTg4h457MnxGtfLM2S1k0NWRERBiyFHBS7ukcOeHK/MPp7Jx5yXQ0QUvBhyVIBzci43rmXn4+OVtThX61C4GiIiUgJDToCrdTTLH+Jp7MmRRYcbMDAhEgCwi705RERBiSEnwHmHqmLCDTCF6BWuxr94h6y+OcnJx0REwYghJ8Cdbhmq4nycy93Y1zP5+OsTDDlERMGIISfAneZ8nCvK6hMHSfLMy+F+OUREwYchJ8BxZdWVmcP0GNHTc47V18fPKVwNERF1N4acAMfhqqub0C8OAPD1CYYcIqJgw5AT4EoveEJOajRDTmvkkHP8HIQQCldDRETdiSEngDW73DjbMtckhSGnVRnp0TDqNKiwO3CiqlbpcoiIqBsx5ASws7ZGuNwCBp0G8ZFGpcvxSyF6LW5o2Rjwq2McsiIiCiYMOQHMO1SVEhUKjUZSuBr/5R2y+uo4l5ITEQUThpwAdqa6AQCQEsOhqqu5qSXk7Dh5Hs0ut8LVEBFRd2HICWAXJx2HKlyJfxuSbEJUmB41jmbsLbUqXQ4REXUThpwAVlrdEnLYk3NVWo2EW/r3AABsKapUuBoiIuouDDkBrPRCy3AVe3Ku6baB3pBTpXAlRETUXRhyApjck8Pl49d0y4AekCTgYLkdFXYe8UBEFAwYcgJUY5MLlTUOAByuaou4CCNGpEQBALayN4eIKCgw5ASoMy1DVeEGLaLD9ApXExhubxmy2sx5OUREQYEhJ0CduXBx0rEkcY+ctrh9YDwAz6aATVxKTkSkegw5AeripGMOVbXV8J5mxIYbUONoRv7pC0qXQ0REXYwhJ0CdaZl0zJVVbafRSLh1QMuQ1REOWRERqR1DToAqvcA9cq7HbYM8Q1ZfHK5QuBIiIupqDDkBqrTlSAfudtw+tw3sAb1WwomqOhyv5KnkRERqxpAToNiTc31MIXrc2NdzltV/DlkUroaIiLoSQ04AqmlsgrW+CQBDzvWYODQBALD+IIesiIjUjCEnAHmHqqLD9Igw6hSuJvDcOSQBkgTsK7XCYuPux0REasWQE4C8Q1VcPn594iNDMCYtGgCHrIiI1IwhJwCV8WDODsuRh6wYcoiI1IohJwCVWz0hp2cUQ871mjgkEQDwzclqWOudCldDRERdgSEnAJXbPCEnmSHnuvWKC8fAhEi43AL/OcQJyEREasSQE4DKrJ7Jsgw5HTNpRBIA4LN95QpXQkREXYEhJwB5h6s4J6dj7h2ZDADYfuI8ztU6FK6GiIg6G0NOgHE0u1BV4/lAZk9Ox/SKC8eIFDNcboHPC88qXQ4REXUyhpwA493XJUSvQXSYXuFqAt89Izy9OZ/tY8ghIlIbhpwA410+nhwVCkmSFK4m8H1vpGdezs5T1fIwIBERqQNDToAp4/LxTpVkDsW4XjEAgDX72ZtDRKQmDDkBpty7ssrMkNNZ7hnlGbL6ZF+ZwpUQEVFnYsgJMN4hFU467jyThidBr5VwoMyOIkuN0uUQEVEnYcgJMBc3AgxRuBL1iAk34DuD4gEAH+aXKlwNERF1FoacACPPyeEeOZ3q/oxUAMCqvWVocrkVroaIiDoDQ04AEULw3KoucuvAHoiLMOBcrRNbiqqULoeIiDoBQ04AuVDfhMYmTy9DopnDVZ1Jr9XgB6N7AgBW7uaQFRGRGjDkBBBvL06PSCOMOq3C1ajP/WM9Q1abjlTymAciIhVgyAkgZVxZ1aUGJERiZIoZzW6BVXu4nJyIKNAx5AQQ727HPbmyqsv86AZPb86/d5bA7RYKV0NERB3BkBNA5D1yuBFgl5k8qicijDoUn6vD9hPnlS6HiIg6gCEngHj3yOHy8a4TbtThvjGeCcjvfHNa4WqIiKgjGHICSJn3SAfOyelSPxmfDgDYcLhCPvWdiIgCD0NOAOEeOd1jQEIkxvWKgcstsGJXidLlEBHRdWLICRCOZheqajzLmtmT0/UeGp8GAHhvZwl3QCYiClAMOQGiwuYJOEadBtFheoWrUb+7hiUiLsKICrsDawvPKl0OERFdh3aFnMWLF+OGG25AZGQk4uPjMXnyZBQVFfm0aWxsRG5uLmJjYxEREYEpU6agoqLCp01JSQkmTZqEsLAwxMfH4+mnn0Zzc7NPmy1btmDMmDEwGo3o168fli9fflk9S5cuRa9evRASEoLMzEzs3LmzPbcTUM62TDpOModAkiSFq1E/o06Ln2Z55ua8+WUxhOByciKiQNOukLN161bk5ubim2++wYYNG9DU1ISJEyeirq5ObjNv3jx89tlnWLlyJbZu3Yry8nLcd9998nWXy4VJkybB6XRi+/btePvtt7F8+XIsWrRIblNcXIxJkybh9ttvR0FBAebOnYtHH30U69evl9u8//77mD9/Pp599lns2bMHI0eORE5ODiorKzvy9+G3LHbPBFge59B9fjI+HUadBoVlNuwsrla6HCIiai/RAZWVlQKA2Lp1qxBCCKvVKvR6vVi5cqXc5vDhwwKAyMvLE0IIsXbtWqHRaITFYpHbLFu2TJhMJuFwOIQQQjzzzDNi6NChPu81depUkZOTI389btw4kZubK3/tcrlEcnKyWLx4cZvrt9lsAoCw2WztuGtlvL7luEhfsFrMeW+P0qUElYUf7RfpC1aLGct3KV0KERG1aOvnd4fm5NhsNgBATEwMACA/Px9NTU3Izs6W2wwaNAhpaWnIy8sDAOTl5WH48OFISEiQ2+Tk5MBut+PgwYNym0tfw9vG+xpOpxP5+fk+bTQaDbKzs+U2rXE4HLDb7T6PQHHW5u3J4aTj7jTjpt4AgI1HKnCyqlbhaoiIqD2uO+S43W7MnTsXEyZMwLBhwwAAFosFBoMBUVFRPm0TEhJgsVjkNpcGHO9177WrtbHb7WhoaMC5c+fgcrlabeN9jdYsXrwYZrNZfqSmprb/xhVS0TJclcThqm7Vt0cEsgfHQwjgH18XK10OERG1w3WHnNzcXBw4cAArVqzozHq61MKFC2Gz2eRHaWmp0iW1mbcnJ8HEkNPdZtzUBwDwYf4ZVNc5Fa6GiIja6rpCzuzZs7F69Wps3rwZKSkp8vOJiYlwOp2wWq0+7SsqKpCYmCi3+fZqK+/X12pjMpkQGhqKuLg4aLXaVtt4X6M1RqMRJpPJ5xEovDvvsien+43vE4MRKWY0Nrnx969OKl0OERG1UbtCjhACs2fPxqpVq7Bp0yb07t3b53pGRgb0ej02btwoP1dUVISSkhJkZWUBALKyslBYWOizCmrDhg0wmUwYMmSI3ObS1/C28b6GwWBARkaGTxu3242NGzfKbdSk2eVGVa1nnxyurup+kiQh9/Z+AIC3t5+Grb5J4YqIiKgt2hVycnNz8c477+Df//43IiMjYbFYYLFY0NDg2cPFbDZjxowZmD9/PjZv3oz8/HxMnz4dWVlZGD9+PABg4sSJGDJkCB5++GHs27cP69evx29+8xvk5ubCaDQCAGbOnImTJ0/imWeewZEjR/Daa6/hgw8+wLx58+Ra5s+fjzfeeANvv/02Dh8+jFmzZqGurg7Tp0/vrL8bv3Gu1gmXW0CrkRAXYVS6nKB05+AEDEyIRK2jGcu3n1K6HCIiaov2LNkC0Orjrbfekts0NDSIJ554QkRHR4uwsDDxgx/8QJw9e9bndU6dOiXuvvtuERoaKuLi4sRTTz0lmpqafNps3rxZjBo1ShgMBtGnTx+f9/B65ZVXRFpamjAYDGLcuHHim2++ac/tBMwS8r0lF0T6gtUi63+/ULqUoPZpQZlIX7BajHhuvbA3OJUuh4goaLX181sSIni3crXb7TCbzbDZbH49P2fdgbOY+c4ejE6LwqonJihdTtByuQXu/MtWnKyqwzN3DcQTt/VTuiQioqDU1s9vnl0VADjp2D9oNRJmt8zNefPLYtQ7m6/xHUREpCSGnABw1s7l4/7i3pHJSIsJQ3WdE29vP610OUREdBUMOQGAPTn+Q6fVYM4d/QEAr289AVsDV1oREfkrhpwAYOGRDn5l8uieGJAQAVtDE97Yxn1ziIj8FUNOAJBPIOdwlV/QaiQ8NXEgAM9RD1U1DoUrIiKi1jDk+DkhBIer/NDEIQkYmRqFeqcLSzcfV7ocIiJqBUOOn7PWN8HR7AYAxJu4EaC/kCQJz+R4enP+vaMEpdX1CldERETfxpDj57xDVbHhBhh1WoWroUtN6BeHCf1i4XS58eL6IqXLISKib2HI8XMXJx1zqMofLbx7MCQJ+HRfOfaUXFC6HCIiugRDjp87a+OkY382rKcZPxyTAgD4/epDCOINxImI/A5Djp+TV1axJ8dvPZ0zEGEGLfaUWPHZ/rNKl0NERC0YcvycxeY54Z09Of4r3hSCWbf2BQD88fMjaGxyKVwREREBDDl+z2L37MGSwJ4cv/bozX2QZA5BmbUBf/+qWOlyiIgIDDl+r5LnVgWEUIMWC+4aBABYuvm4PGGciIiUw5Dj5ypbdtNN4B45fu/ekckYk+bZIPB/1hxSuhwioqDHkOPHnM1uVNc5AQDxkezJ8XcajYT/mTwMGglYs/8sth2tUrokIqKgxpDjx87Venpx9FoJ0WF6hauhthiabMa0G3sBAJ799CAczZyETESkFIYcP1bRMh8nPjIEkiQpXA211bw7B6BHpBHF5+rwt608pZyISCkMOX7MOx+nRyTn4wQSU4gev5k0GADw6ubjPNeKiEghDDl+zBty4hlyAs69I5OR1ScWjmY3/uvjA9wJmYhIAQw5fsy7fJynjwceSZLw+x8Mg0GnwbajVfhoT5nSJRERBR2GHD9W6d0IkCurAlLfHhGYc0d/AMDvVh9CVUvPHBERdQ+GHD9WWcOenED3+C19MDTZBFtDE5799IDS5RARBRWGHD92cU4Oe3IClV6rwR+njIBWI2FtoQXrDvAATyKi7sKQ48cqWoar2JMT2Ib1NGPmrX0AAL/95CBs9U0KV0REFBwYcvxUs8uN83XsyVGLJ7/TH317hKOqxoH/Xn1Q6XKIiIICQ46fOl/nhBCAViMhNtygdDnUQSF6LV744QhIEvDRnjIOWxERdQOGHD/l3e24R4QRGg13O1aDjPQYzLy1LwBg4UeF8sRyIiLqGgw5fqqS83FUaV72AAxOMuFCfRMWfLifmwQSEXUhhhw/xd2O1cmg02DJj0fBoNNgc1EV3t1RonRJRESqxZDjp7xDGT046Vh1BiREYsFdgwAAf1hzGCerahWuiIhInRhy/JR3+XgCh6tUafqNvXBj31g0NLkw7/0COJvdSpdERKQ6DDl+qsq72zF7clRJo5Hwf/ePhDlUj31nbHhx/RGlSyIiUh2GHD/FOTnqlxwVihd/OAIA8MaXxdh4uELhioiI1IUhx095l5AnmNiTo2YThyZi+oReAICnVu7DWVuDsgUREakIQ44fcrkFztU6AXAJeTD41d2DMLynGdb6Jvzivb1odnF+DhFRZ2DI8UPVdU643AKSBO52HASMOi1efXA0Iow67Dp1AS99cUzpkoiIVIEhxw95l4/Hhhuh0/JHFAzSY8Px/JThAIClW45j29EqhSsiIgp8/AT1Q5VcPh6UvjciGQ9mpkEI4Bcr9qK0ul7pkoiIAhpDjh+qlJePM+QEm0XfG4IRKZ75OTPfyUdjk0vpkoiIAhZDjh+Sz63iHjlBJ0SvxbKfZCAm3ICD5Xb8+qNCnm9FRHSdGHL8UEWNd/k4e3KCUc+oULz64GhoNRI+2luGf+adVrokIqKAxJDjh7w9OT24R07QurFvHBbe7Tnf6n9WH8LO4mqFKyIiCjwMOX6Iux0TAMy4qTfuGZmMZrfAE+/uQbmVGwUSEbUHQ44fqmLIIQCSJOGPU4ZjUGIkztU68Ojbu1HnaFa6LCKigMGQ42eEEPLqKh7pQGEGHd746VjEhhtw6Kwd894vgNvNichERG3BkONnLtQ3ocnl+RCLi2BPDgGpMWH4208zYNBq8J9DFXhhfZHSJRERBQSGHD/j7cWJCTfAoOOPhzwy0mPwQsuJ5a9vPYGVu0sVroiIyP/xU9TPVNg5H4daN3l0T8y+vR8A4NerCrHj5HmFKyIi8m8MOX6m0t6y2zHn41Ar5t85AN8dnogml8DP38nH8cpapUsiIvJbDDl+hsvH6Wo0Ggl/un8URqZGwVrfhGn/2CkHYyIi8sWQ42e4fJyuJdSgxT+mjUWv2DCUWRvws7d2oaaxSemyiIj8DkOOn6mwc/k4XVtshBH/fCQTcRGepeWz3tkDZ7Nb6bKIiPwKQ46f4XAVtVVabBje+tk4hBm0+Or4OTzz4T7uoUNEdAmGHD/jXUIez8M5qQ2Gp5jx2kNjoNNI+LigHH9cd0TpkoiI/AZDjh8RQlyyhJzDVdQ2tw2Mx/NTPHvo/HXbSby25bjCFRER+QeGHD9ib2iW51X04HAVtcMPM1LkU8tfWFeEf+WdUrYgIiI/wJDjR7xDVeZQPUL0WoWroUDz81v7ypsF/vaTg/hozxmFKyIiUhZDjh/hpGPqqKcmDsDPbuwFAHj6w/1Yd8CibEFERApiyPEjXD5OHSVJEhZ9bwh+mJECl1vgF+/txbajVUqXRUSkCIYcP8KeHOoMGo2E5+8bjruHJcLpcuPxf+1G3gmec0VEwYchx49Utqys6sHl49RBOq0GL/14FG4b2AONTW48snwXgw4RBR2GHD9S0TLxOIHLx6kTGHVavP6TDNwyoAcamlwMOkQUdBhy/EiVd48c9uRQJwnRa/G3hxl0iCg4MeT4EXm3Y/bkUCdi0CGiYMWQ4yeEEJx4TF2GQYeIghFDjp+odTSj3ukCwOEq6hrfDjrTl+/ElqJKpcsiIuoyDDl+wtuLE2nUIcygU7gaUitv0Lm9ZdXVY//cjc8LzypdFhFRl2DI8RNcPk7dJUSvxV8fHotJw5PQ5BLI/fcefJjPIyCISH0YcvxEJZePUzcy6DR4+YHRuD8jBW4B/HLlPvyTh3oSkcq0O+Rs27YN99xzD5KTkyFJEj7++GOf60IILFq0CElJSQgNDUV2djaOHTvm06a6uhoPPfQQTCYToqKiMGPGDNTW1vq02b9/P26++WaEhIQgNTUVL7zwwmW1rFy5EoMGDUJISAiGDx+OtWvXtvd2/EYll49TN9NqJPxxygj5rKtFnxzE0s3HlS2KiKgTtTvk1NXVYeTIkVi6dGmr11944QW8/PLLeP3117Fjxw6Eh4cjJycHjY2NcpuHHnoIBw8exIYNG7B69Wps27YNjz/+uHzdbrdj4sSJSE9PR35+Pl588UU899xz+Nvf/ia32b59Ox544AHMmDEDe/fuxeTJkzF58mQcOHCgvbfkFy4uH2fIoe6j0Uh49p4h+MV3PKeXv7i+CH9cdwRCCIUrIyLqBKIDAIhVq1bJX7vdbpGYmChefPFF+Tmr1SqMRqN47733hBBCHDp0SAAQu3btktt8/vnnQpIkUVZWJoQQ4rXXXhPR0dHC4XDIbRYsWCAGDhwof/2jH/1ITJo0yaeezMxM8fOf/7zN9dtsNgFA2Gy2Nn9PV/nFe3tE+oLV4m9bTyhdCgWp17ccF+kLVov0BavF0ysLRFOzS+mSiIha1dbP706dk1NcXAyLxYLs7Gz5ObPZjMzMTOTl5QEA8vLyEBUVhbFjx8ptsrOzodFosGPHDrnNLbfcAoPBILfJyclBUVERLly4ILe59H28bbzv0xqHwwG73e7z8BfeE8g5XEVK+fmtfbH4vuHQSMAHu8/g5//KR0PLtgZERIGoU0OOxWIBACQkJPg8n5CQIF+zWCyIj4/3ua7T6RATE+PTprXXuPQ9rtTGe701ixcvhtlslh+pqantvcUuc3EjQE48JuU8MC4Nr/8kA0adBhuPVOLBN79BdZ1T6bKIiK5LUK2uWrhwIWw2m/woLS1VuiQZz60ifzFxaCLefTQT5lA99pZY8cPXt6O0ul7psoiI2q1TQ05iYiIAoKKiwuf5iooK+VpiYiIqK313WW1ubkZ1dbVPm9Ze49L3uFIb7/XWGI1GmEwmn4c/qHc2o8bRDABIMLEnh5Q3tlcMPpyZhWRzCE5W1WHKsu04fNZ/hneJiNqiU0NO7969kZiYiI0bN8rP2e127NixA1lZWQCArKwsWK1W5Ofny202bdoEt9uNzMxMuc22bdvQ1NQkt9mwYQMGDhyI6Ohouc2l7+Nt432fQOJdPh5m0CLCyN2OyT/0T4jE/3viRgxMiERljQM/ej0P20+cU7osIqI2a3fIqa2tRUFBAQoKCgB4JhsXFBSgpKQEkiRh7ty5+P3vf49PP/0UhYWF+OlPf4rk5GRMnjwZADB48GDcddddeOyxx7Bz5058/fXXmD17Nn784x8jOTkZAPDggw/CYDBgxowZOHjwIN5//30sWbIE8+fPl+uYM2cO1q1bhz/96U84cuQInnvuOezevRuzZ8/u+N9KN+PBnOSvksyh+ODnWRjXKwY1jmZM+8dO/D/ujkxEgaK9y7Y2b94sAFz2mDZtmhDCs4z8t7/9rUhISBBGo1HccccdoqioyOc1zp8/Lx544AEREREhTCaTmD59uqipqfFps2/fPnHTTTcJo9EoevbsKZ5//vnLavnggw/EgAEDhMFgEEOHDhVr1qxp1734yxLyz/aVifQFq8X9y7YrWgfRlTQ4m8UT7+TLS8z/tP6IcLvdSpdFREGqrZ/fkhDBu+uX3W6H2WyGzWZTdH7O378qxv+sPoTvjUjCqw+OUawOoqtxuwX+7z9FeG3LCQDA90cl449TRiBEr1W4MiIKNm39/A6q1VX+6uJux5x0TP5Lo5HwzF2D8MKUEdBpJHxSUI6fvLmDS8yJyG8x5PgBLh+nQPKjG1Lx9iPjEBmiw+7TF/CD177Giaraa38jEVE3Y8jxAxU8t4oCzIR+cfho1o1IiQ7F6fP1uO+17cg7cV7psoiIfDDk+AGLzRNyErlHDgWQ/gmR+Dh3AkanRcHW0ISf/mMH3ttZonRZREQyhhw/4N0nJ8HMkEOBJS7CiPceG4/vjUhCk0tg4UeFWPTJATS53EqXRkTEkKO0Ogd3O6bAFqLX4pUHRuPpnIEAgH/mncbDf+eEZCJSHkOOwrynj0cYddztmAKWJEnIvb0f3vjpWIQbtPjmZDXuffUrHLHwKAgiUg5DjsIsLSGHK6tIDe4ckoBVuROQHhuGMxcacN9r27HuwFmlyyKiIMWQozDvfBxOOia1GJAQiU9yJ2BCv1jUO12Y+c4evPTFUbjdQbvvKBEphCFHYd6eHM7HITWJCjPg7enj8LMbewEAXvriGGa9m4+axqarfyMRUSdiyFFYBUMOqZROq8Fz9w7FC1NGwKDVYP3BCnz/1a9xtKJG6dKIKEgw5CjsYsjhnBxSpx/dkIr3fz4eSeYQnDxXh8lLv8Zn+8qVLouIggBDjsIqOCeHgsDotGisfvIm3NjXM0/nyff24nefHeJ+OkTUpRhyFObd7TieIYdULjbCiH8+Mg6zbusLAPjH18V48I1vUNnSm0lE1NkYchQkhJBPIE/kbscUBHRaDRbcNQh/fTgDkUYddp26gEmvfIVdp6qVLo2IVIghR0HVdU40uTzLantEcE4OBY+coYn4ZPYEDEiIQFWNAz/+2zd4Y9tJCMFl5kTUeRhyFOSdjxMXYYBBxx8FBZc+PSLwce4E3DsyGS63wB/WHsZj/9wNaz2PgyCizsFPVgV5V1bFR3KoioJTmEGHJT8ehf+ZPAwGnQZfHK7Ed5d8ifzTHL4ioo5jyFEQl48Tec69enh8OlY9cSN6x4Wj3NaIH/31G7y+9QR3SSaiDmHIUdBZGycdE3kNTTbjsydvkoevnv/8CB55exdPMyei68aQo6CztgYAQM+oUIUrIfIPEUbP8NXi+4bDqNNgS1EVvrvkS+ws5vAVEbUfQ46Cyq2enpxkhhwimSRJeGBcGj7OnYA+PcJhsTfigTe+waubjsHF4SsiageGHAWVWz09OQw5RJcbnGTCZ7Nvwn2je8LlFvi//xzFA298I//vhojoWhhyFCKEQJmVw1VEVxNu1OFPPxqJ/7t/JMINWuwsrsZdL23D6v08+4qIro0hRyHVdU44mt2QJJ5ATnQ1kiThhxkpWPOLmzEyNQr2xmbM/vde/HLlPtQ6mpUuj4j8GEOOQrzzceIjjdwIkKgNesWF48OZWZh9ez9IEvBh/hlMevlLFJRalS6NiPwUP10VUsb5OETtptdq8MucgVjx2Hj0jArF6fP1mLJsOyclE1GrGHIUwknHRNcvs08s1s65Gd8bkeQzKbmMk5KJ6BIMOQop56Rjog4xh+rxygOjfScl/2UbVu4u5UGfRASAIUcx5S0bASZzt2Oi6+adlLx2zs0YkxaFGkcznv5wPx77Zz6qahxKl0dECmPIUUgZNwIk6jTpseFYOfNGPHPXQOi1Er44XIGcl7bh88KzSpdGRApiyFEI5+QQdS6tRsITt/XDp7NvwuAkE6rrnJj17h7MXbEXtvompcsjIgUw5CjA0eySu9I5J4eocw1OMuGT3AnIvb0vNBLwcUE5cl7ahq1Hq5QujYi6GUOOArx75ITqtYgK0ytcDZH6GHQaPJ0zCB/OuhG94zznX037x07816pC1HEDQaKgwZCjgJLqegBAWkwYJElSuBoi9RqTFo21v7gZP7uxFwDg3R0luHvJl9h+4pyyhRFRt2DIUYA35KTGhClcCZH6hRq0eO7eoXj30Uwkm0NQUl2PB9/YgV+vKkRNI+fqEKkZQ44CSs7XAfD05BBR95jQLw7r592Cn4xPAwD8e0cJJv5lGzYXVSpcGRF1FYYcBXh7ctJjGXKIulNkiB6/nzwc7z02HmkxYThra8T0t3Zh/gcFsNY7lS6PiDoZQ44CSqo9y8fZk0OkjKy+sVg392bMuKk3JAn4aE8Zsv+8DesOWJQujYg6EUNONxNCyMNVnJNDpJwwgw6//d4QfDjzRvTtEY5ztQ7MfCcfue/uwbla7pZMpAYMOd2sus6JOqcLkgSkRHOPHCKlZaRHY80vbkbu7X2h1UhYU3gW2X/eig/zz/AMLKIAx5DTzbzzcRJNIQjRaxWuhogAIESvxdM5g/BJ7gQMTjLBWt+EX67ch4fe3IHic3VKl0dE14khp5tx+TiR/xrW04xPZ0/AgrsGwajTYPuJ88h5aRte2XgMzma30uURUTsx5HSzkvMtK6sYcoj8kl6rwazb+uI/827Bzf3j4Gx2408bjmLSy19i16lqpcsjonZgyOlmp9mTQxQQ0mPD8c9HxmHJj0chNtyAY5W1uP/1PCz8qJAHfhIFCIacbnayqhYA0KdHuMKVENG1SJKE74/qiY1P3YqpY1MBAO/tLMEdf96Kz/aVc2IykZ9jyOlGQgicqPJMYuzbI0LhaoioraLCDPjjD0fg/cfHo0/LcvMn39uL6ct34fR5Tkwm8lcMOd2ous4JW0MTJAnoHceeHKJAk9knFp/PuRlzs/vDoNVgS1EV7vzLNvxlw1E0NrmULo+IvoUhpxt5e3F6RoVy+ThRgDLqtJibPQCfz70ZN/XzTExesvGY5xysIzwHi8ifMOR0oxMt83E4VEUU+Pr2iMC/ZozDqw+ORoLJiJLqekxfvguP/3M3zlyoV7o8IgJDTrc6UclJx0RqIkkSvjciGRufug2P39IHOo2E/xyqQPaft2Lp5uNwNHMIi0hJDDnd6OQ5TjomUqMIow6//u5grJ1zMzJ7x6CxyY0X1xfh7pe+xJfHqpQujyhoMeR0Iw5XEanbgIRIrHh8PF6aOgpxEUacPFeHh/++E7nv7kGZtUHp8oiCDkNON2lwulDashFg33gOVxGplSRJmDy6Jzb98lZMn9ALGglYU3gWd/xpC/6y4SganBzCIuouDDndpKiiBm4BxEUYEB8ZonQ5RNTFTCF6PHvPUKx+8maMaxnCWrLxGO740xZ8UlDGjQSJugFDTjc5ctYOABiUaFK4EiLqTkOSTXj/8fF47aEx6BkVinJbI+asKMD9r+dh/xmr0uURqRpDTjc53BJyBidFKlwJEXU3SZLw3eFJ2PjUrXjqzgEI1Wux+/QFfH/p1/jlyn2cr0PURXRKFxAsDp+tAQAMTmJPDlGwCtFr8eQd/XH/2FS8sO4IPtpbhg/zz+DD/DNIiwnDqNQojEgxY0RKFIYmmxBu5K9ooo7g/4K6gRAChy0criIij0RzCP48dRQezkrH4s+PYNepapRU16Okuh6f7isHAEgS0K9HBIanmDEyJQrDU8wYkmTibulE7cCQ0w3OXGhATWMz9FoJ/eK5fJyIPEanReODn2fB3tiEvSVWFJ6xYv8ZGwrLbDhra8Sxylocq6zFR3vKAABajYQBCZEYmWLG8BQzRvSMwsDESBh0nHlA1BqGnG6wp+QCAGBIkom/jIjoMqYQPW4d0AO3DughP1dZ04jCMzY59Ow/Y8W5WicOn7Xj8Fk7VuwqBQAYtBoMSor0DHP19PT49I+PgE7L3zVEDDndYG+JFYDn/7UREbVFfGQI7hgcgjsGJwDwDHuftTW2hJ6LPT7W+ibsbwlDQAkAIESvwdBkM4b3NLfM8TGjd1wEtBpJwTsi6n4MOd3A25MzJp0hh4iujyRJSI4KRXJUKO4algjAE3xKqxuwv8wq9/ocKLOhxtGM/NMXkH/6gvz9YQYthiSZMKynueVhQr8e7PEhdWPI6WKNTS4cKvdMOh6dGqVsMUSkKpIkIS02DGmxYfjeiGQAgNstUHy+7pKhLisOlNlR73Rh9+kL2H1J8DHqNBicZMKwniYMS/aEnwEJnOND6sGQ08V2napGs1sgwWRESnSo0uUQkcppNBL69ohA3x4RmDy6JwDA5RY4WVWLA+U2HCiz40CZDQfL7ah1NKOg1IqCUqv8/XqthIGJkXLoGZxkQnykEVFhekQYdZAkDnlR4GDI6WJfHjsHALi5fw/+ciAiRWg1EvonRKJ/QiR+MNrznNstcLq6HgfKPENc3gBka2hqCUJ2oGVys5deKyEqzIDoML3835hww7eeMyAm/OKfzaF6zgUixTDkdLFtR6sAALdcsmqCiEhpGo2E3nHh6B0XjntGeoa6hBA4c6HBJ/Qcq6hBdb0TjU1uNLkEqmocqKpxtPl9JAkwh+oRHWZAVNjF/8aEGRAdfvG56DADosMvXjfquB8QdRxDThc6UVWLI5YaaDUSbuoXp3Q5RERXJUkSUmPCkBoThruHJ/lca3C6cKHeiQv1Tljrm1r+3IQLdd96rq7l+XonahqbIQRgrW+Ctb6pXbWEGbTfCj6e3qJo739bepBivOEp3IBwg5Y95uSDIacLrWrZwOuW/nGICTcoXA0R0fULNWgRavCs7mqrJpcbtoYmn+BjrXeiuq4J1pbA9O2gZG1ogsstUO90od7Z0K5zvbzDaTGX9BpFtwydffs5b++RicNpqsaQ00VqHc14Z8dpAMCUjBSFqyEi6n56rQZxEUbERRjb/D1ut0CNo/myHqLquot/bu05R3PHh9Oiw77VaxRukJ+PaglHMS3XuQItMAR8yFm6dClefPFFWCwWjBw5Eq+88grGjRunaE1nbQ34zaoDsNY3oXdcOO4elnTtbyIiImg0EsyhephD9eiF8DZ/X4PTheqW4bKLw2lOXKi72IN04ZLnrXVNqHH4DqcVt6POcINWDj7ynKKWMBTzrblGHE5TTkCHnPfffx/z58/H66+/jszMTLz00kvIyclBUVER4uPjFatr5r/yse+MDQDw/H3D2RVKRNTFQg1a9DSEomc7htOczW5YG1pCUcuQmrXeiep63+cunYtkrXfCLYA6pwt17RxOM2g1MIfpPWEo1BN+oi7pPYryXrtkaM0cquehrB0gCSGE0kVcr8zMTNxwww149dVXAQButxupqal48skn8atf/eqa32+322E2m2Gz2WAydd7p4BOe34QyawMWfW8IHrmpd6e9LhERKcvtFqhpbPb0Gnl7iOou6TnyBqU63wnazmb3db9nqF6L6DA9zJcMqXnDkjcIeXuMvENt5lC9qnezbuvnd8D25DidTuTn52PhwoXycxqNBtnZ2cjLy2v1exwOBxyOi2O1dru9S2prdnv+MY/rHdMlr09ERMrQaCSYw/Qwh+nRu43DaUIINDS55AB06dwiqxyMLk7GtjY0+fQaNTS50GBzodzW2K5aI0N0l4WfqFDfniKfa2EGRBp10Kho9CFgQ865c+fgcrmQkJDg83xCQgKOHDnS6vcsXrwY//3f/93ltblaAjuHqYiISJIkhBl0CDPo2jWc5p2EfaVgZGu42FNkq/ddug8ANY3NqGlsRkl122vVSPCEoNCLQ2nmS5bum+WwdHGuUVSoHmF+Ot8oYEPO9Vi4cCHmz58vf22325Gamtrp7/N/949AY5MLPXmMAxERXadLJ2Gnx7b9+5pblu57eoQ8w2nyn+Wg5BuarA1NqHe64BZAdZ1nuK09DFqNPMfIG5KiwwyICtdj5i19Ea3QNioBG3Li4uKg1WpRUVHh83xFRQUSExNb/R6j0Qijse1LGa/XbQOVm/RMRETBTafVIDbCiNh2LN0HPAdK2xouDUDeHqQmzwRt70q1hkuH2Zxocgk4XW5U1jhQ2cry/RkKzk0N2JBjMBiQkZGBjRs3YvLkyQA8E483btyI2bNnK1scERFRgAnRaxGi1yLBFNLm7xHCs3GjtWXTR2tLILpQ3wRrnScQRYUqtxluwIYcAJg/fz6mTZuGsWPHYty4cXjppZdQV1eH6dOnK10aERGR6kmShHCjDuHG9s036i4BHXKmTp2KqqoqLFq0CBaLBaNGjcK6desum4xMREREwSeg98npqK7aJ4eIiIi6Tls/v9W7UxAREREFNYYcIiIiUiWGHCIiIlIlhhwiIiJSJYYcIiIiUiWGHCIiIlIlhhwiIiJSJYYcIiIiUiWGHCIiIlIlhhwiIiJSJYYcIiIiUqWAPqCzo7zHdtntdoUrISIiorbyfm5f6/jNoA45NTU1AIDU1FSFKyEiIqL2qqmpgdlsvuL1oD6F3O12o7y8HJGRkZAkqdNe1263IzU1FaWlpUFxujnvV92C6X6D6V4B3q/aqfl+hRCoqalBcnIyNJorz7wJ6p4cjUaDlJSULnt9k8mkun9YV8P7Vbdgut9guleA96t2ar3fq/XgeHHiMREREakSQw4RERGpEkNOFzAajXj22WdhNBqVLqVb8H7VLZjuN5juFeD9ql2w3W9rgnriMREREakXe3KIiIhIlRhyiIiISJUYcoiIiEiVGHKIiIhIlRhyusDSpUvRq1cvhISEIDMzEzt37lS6pHbbtm0b7rnnHiQnJ0OSJHz88cc+14UQWLRoEZKSkhAaGors7GwcO3bMp011dTUeeughmEwmREVFYcaMGaitre3Gu2i7xYsX44YbbkBkZCTi4+MxefJkFBUV+bRpbGxEbm4uYmNjERERgSlTpqCiosKnTUlJCSZNmoSwsDDEx8fj6aefRnNzc3feyjUtW7YMI0aMkDcIy8rKwueffy5fV8t9Xsnzzz8PSZIwd+5c+Tk13fNzzz0HSZJ8HoMGDZKvq+levcrKyvCTn/wEsbGxCA0NxfDhw7F79275upp+X/Xq1euyn68kScjNzQWgzp9vhwjqVCtWrBAGg0H84x//EAcPHhSPPfaYiIqKEhUVFUqX1i5r164V//Vf/yU++ugjAUCsWrXK5/rzzz8vzGaz+Pjjj8W+ffvEvffeK3r37i0aGhrkNnfddZcYOXKk+Oabb8SXX34p+vXrJx544IFuvpO2ycnJEW+99ZY4cOCAKCgoEN/97ndFWlqaqK2tldvMnDlTpKamio0bN4rdu3eL8ePHixtvvFG+3tzcLIYNGyays7PF3r17xdq1a0VcXJxYuHChErd0RZ9++qlYs2aNOHr0qCgqKhK//vWvhV6vFwcOHBBCqOc+W7Nz507Rq1cvMWLECDFnzhz5eTXd87PPPiuGDh0qzp49Kz+qqqrk62q6VyGEqK6uFunp6eJnP/uZ2LFjhzh58qRYv369OH78uNxGTb+vKisrfX62GzZsEADE5s2bhRDq+/l2FENOJxs3bpzIzc2Vv3a5XCI5OVksXrxYwao65tshx+12i8TERPHiiy/Kz1mtVmE0GsV7770nhBDi0KFDAoDYtWuX3Obzzz8XkiSJsrKybqv9elVWVgoAYuvWrUIIz/3p9XqxcuVKuc3hw4cFAJGXlyeE8ARDjUYjLBaL3GbZsmXCZDIJh8PRvTfQTtHR0eLNN99U9X3W1NSI/v37iw0bNohbb71VDjlqu+dnn31WjBw5stVrartXIYRYsGCBuOmmm654Xe2/r+bMmSP69u0r3G63Kn++HcXhqk7kdDqRn5+P7Oxs+TmNRoPs7Gzk5eUpWFnnKi4uhsVi8blPs9mMzMxM+T7z8vIQFRWFsWPHym2ys7Oh0WiwY8eObq+5vWw2GwAgJiYGAJCfn4+mpiafex40aBDS0tJ87nn48OFISEiQ2+Tk5MBut+PgwYPdWH3buVwurFixAnV1dcjKylLtfQJAbm4uJk2a5HNvgDp/tseOHUNycjL69OmDhx56CCUlJQDUea+ffvopxo4di/vvvx/x8fEYPXo03njjDfm6mn9fOZ1OvPPOO3jkkUcgSZIqf74dxZDTic6dOweXy+XzjwcAEhISYLFYFKqq83nv5Wr3abFYEB8f73Ndp9MhJibG7/8u3G435s6diwkTJmDYsGEAPPdjMBgQFRXl0/bb99za34n3mj8pLCxEREQEjEYjZs6ciVWrVmHIkCGqu0+vFStWYM+ePVi8ePFl19R2z5mZmVi+fDnWrVuHZcuWobi4GDfffDNqampUd68AcPLkSSxbtgz9+/fH+vXrMWvWLPziF7/A22+/DUDdv68+/vhjWK1W/OxnPwOgvn/LnSGoTyEnak1ubi4OHDiAr776SulSuszAgQNRUFAAm82GDz/8ENOmTcPWrVuVLqtLlJaWYs6cOdiwYQNCQkKULqfL3X333fKfR4wYgczMTKSnp+ODDz5AaGiogpV1DbfbjbFjx+J///d/AQCjR4/GgQMH8Prrr2PatGkKV9e1/v73v+Puu+9GcnKy0qX4LfbkdKK4uDhotdrLZrJXVFQgMTFRoao6n/dernafiYmJqKys9Lne3NyM6upqv/67mD17NlavXo3NmzcjJSVFfj4xMRFOpxNWq9Wn/bfvubW/E+81f2IwGNCvXz9kZGRg8eLFGDlyJJYsWaK6+wQ8QzSVlZUYM2YMdDoddDodtm7dipdffhk6nQ4JCQmqu+dLRUVFYcCAATh+/Lgqf75JSUkYMmSIz3ODBw+Wh+jU+vvq9OnT+OKLL/Doo4/Kz6nx59tRDDmdyGAwICMjAxs3bpSfc7vd2LhxI7KyshSsrHP17t0biYmJPvdpt9uxY8cO+T6zsrJgtVqRn58vt9m0aRPcbjcyMzO7veZrEUJg9uzZWLVqFTZt2oTevXv7XM/IyIBer/e556KiIpSUlPjcc2Fhoc8vyw0bNsBkMl32S9jfuN1uOBwOVd7nHXfcgcLCQhQUFMiPsWPH4qGHHpL/rLZ7vlRtbS1OnDiBpKQkVf58J0yYcNl2D0ePHkV6ejoAdf6+AoC33noL8fHxmDRpkvycGn++Hab0zGe1WbFihTAajWL58uXi0KFD4vHHHxdRUVE+M9kDQU1Njdi7d6/Yu3evACD+/Oc/i71794rTp08LITxLMqOiosQnn3wi9u/fL77//e+3uiRz9OjRYseOHeKrr74S/fv398slmUIIMWvWLGE2m8WWLVt8lmfW19fLbWbOnCnS0tLEpk2bxO7du0VWVpbIysqSr3uXZk6cOFEUFBSIdevWiR49evjd0sxf/epXYuvWraK4uFjs379f/OpXvxKSJIn//Oc/Qgj13OfVXLq6Sgh13fNTTz0ltmzZIoqLi8XXX38tsrOzRVxcnKisrBRCqOtehfBsC6DT6cQf/vAHcezYMfHuu++KsLAw8c4778ht1Pb7yuVyibS0NLFgwYLLrqnt59tRDDld4JVXXhFpaWnCYDCIcePGiW+++Ubpktpt8+bNAsBlj2nTpgkhPMsyf/vb34qEhARhNBrFHXfcIYqKinxe4/z58+KBBx4QERERwmQyienTp4uamhoF7ubaWrtXAOKtt96S2zQ0NIgnnnhCREdHi7CwMPGDH/xAnD171ud1Tp06Je6++24RGhoq4uLixFNPPSWampq6+W6u7pFHHhHp6enCYDCIHj16iDvuuEMOOEKo5z6v5tshR033PHXqVJGUlCQMBoPo2bOnmDp1qs+eMWq6V6/PPvtMDBs2TBiNRjFo0CDxt7/9zee62n5frV+/XgC47B6EUOfPtyMkIYRQpAuJiIiIqAtxTg4RERGpEkMOERERqRJDDhEREakSQw4RERGpEkMOERERqRJDDhEREakSQw4RERGpEkMOERERqRJDDhEREakSQw4RERGpEkMOERERqRJDDhEREanS/wcRbA4M9yhalwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['time'], df['px_L'], label='alt')\n",
    "plt.show()\n",
    "plt.plot(df['time'], df['vx_L'], label='speed')\n",
    "plt.show()\n",
    "plt.plot(df['time'], df['mach'], label='mach')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dario\\AppData\\Local\\Temp\\ipykernel_19092\\1329883484.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['pitch_l'] *= np.pi / 180\n",
      "C:\\Users\\Dario\\AppData\\Local\\Temp\\ipykernel_19092\\1329883484.py:37: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['yaw_l'] *= np.pi / 180\n",
      "C:\\Users\\Dario\\AppData\\Local\\Temp\\ipykernel_19092\\1329883484.py:38: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['roll_l'] *= np.pi / 180\n",
      "C:\\Users\\Dario\\AppData\\Local\\Temp\\ipykernel_19092\\1329883484.py:40: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['declination'] *= np.pi / 180\n",
      "C:\\Users\\Dario\\AppData\\Local\\Temp\\ipykernel_19092\\1329883484.py:41: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['longitude'] *= np.pi / 180\n",
      "C:\\Users\\Dario\\AppData\\Local\\Temp\\ipykernel_19092\\1329883484.py:42: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['latitude'] *= np.pi / 180\n"
     ]
    }
   ],
   "source": [
    "df_new = df[[\n",
    "    'time',\n",
    "    'phase',\n",
    "    'declination',\n",
    "    'longitude',\n",
    "    'latitude',\n",
    "    'altitude',\n",
    "    'ax_B',\n",
    "    'ay_B',\n",
    "    'az_B',\n",
    "    'gx_B',\n",
    "    'gy_B',\n",
    "    'gz_B',\n",
    "    'ax_L',\n",
    "    'ay_L',\n",
    "    'az_L',\n",
    "    'vx_L',\n",
    "    'vy_L',\n",
    "    'vz_L',\n",
    "    'pitch_l',\n",
    "    'yaw_l',\n",
    "    'roll_l',\n",
    "    'pressure',\n",
    "    'temperature',\n",
    "    'sonic_velocity',\n",
    "    'mach',\n",
    "    'OMEGA_X',\n",
    "    'OMEGA_Y',\n",
    "    'OMEGA_Z',\n",
    "    'mass_total',\n",
    "    'flightpath_speed', \n",
    "    'acc_total', \n",
    "    'drag'\n",
    "]]\n",
    "\n",
    "df_new['pitch_l'] *= np.pi / 180\n",
    "df_new['yaw_l'] *= np.pi / 180\n",
    "df_new['roll_l'] *= np.pi / 180\n",
    "\n",
    "df_new['declination'] *= np.pi / 180\n",
    "df_new['longitude'] *= np.pi / 180\n",
    "df_new['latitude'] *= np.pi / 180\n",
    "\n",
    "descriptions = pd.DataFrame.from_dict({\n",
    "    'time': ['Second'],\n",
    "    'phase': [None],\n",
    "    'declination': ['Radians'],\n",
    "    'longitude': ['Radians'],\n",
    "    'latitude': ['Radians'],\n",
    "    'altitude': ['Meter'],\n",
    "    'ax_B': ['m/s2'],\n",
    "    'ay_B': ['m/s2'],\n",
    "    'az_B': ['m/s2'],\n",
    "    'gx_B': ['m/s2'],\n",
    "    'gy_B': ['m/s2'],\n",
    "    'gz_B': ['m/s2'],\n",
    "    'ax_L': ['m/s2'],\n",
    "    'ay_L': ['m/s2'],\n",
    "    'az_L': ['m/s2'],\n",
    "    'vx_L': ['m/s'],\n",
    "    'vy_L': ['m/s'],\n",
    "    'vz_L': ['m/s'],\n",
    "    'px_L': ['m'],\n",
    "    'py_L': ['m'],\n",
    "    'pz_L': ['m'],\n",
    "    'acc_total': ['m/s2'],\n",
    "    'pitch_l': ['Radians'],\n",
    "    'yaw_l': ['Radians'],\n",
    "    'roll_l': ['Radians'],\n",
    "    'atmos_pressure': ['Pascal'],\n",
    "    'atmos_temperature': ['Kelvin'],\n",
    "    'sonic_velocity': ['Meter / Second'],\n",
    "    'mach': [None],\n",
    "    'OMEGA_X': ['Radian / Second'],\n",
    "    'OMEGA_Y': ['Radian / Second'],\n",
    "    'OMEGA_Z': ['Radian / Second'],\n",
    "    'mass_total': ['Mega-Gram'],\n",
    "    'flightpath_speed': ['Kilo-Meter / Second'], \n",
    "    'acc_total': ['Meter/Second**2'], \n",
    "    'drag': ['Kilo-Newton'],\n",
    "    'gravity': ['m/s2']\n",
    "}, dtype=str)\n",
    "\n",
    "df_new.to_csv('data/simulations/40km.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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