{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d024c1a8-e2c8-4f33-979f-4d5fbd952fa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ase.io as io\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9908944d-2d4b-48ca-8505-1c136a50c730",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Atoms(symbols='CFH3F', pbc=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q=io.read('sn2_MEP_trj.xyz')\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "56001255-0b36-47cc-93c3-b3144a55ce00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.409264873554172\n",
      "1.4304559610589203\n",
      "68.30920842199383\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Coordinates': True,\n",
       " 'from': True,\n",
       " 'ORCA-job': True,\n",
       " 'sn2_MEP': True,\n",
       " 'E': True,\n",
       " '-239.266548387007': True}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(q.get_distance(0,1))\n",
    "print(q.get_distance(0,5))\n",
    "print(q.get_angle(1,0,2))\n",
    "q.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "21c34151-c34b-487c-86f4-44d996edd1f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   2.4082539 ,    1.430608  ,  111.1435216 , -239.26654832],\n",
       "       [   2.29468976,    1.44467201,  108.65768013, -239.26544145],\n",
       "       [   2.21493359,    1.46845439,  106.29084639, -239.26335754],\n",
       "       [   2.14730555,    1.5056288 ,  103.92488046, -239.26087325],\n",
       "       [   2.08248001,    1.55439828,  101.44377867, -239.25793583],\n",
       "       [   2.01924966,    1.60909157,   98.87749595, -239.25475583],\n",
       "       [   1.95777621,    1.66611848,   96.28005578, -239.25186516],\n",
       "       [   1.89391997,    1.72772135,   93.51263788, -239.24961257],\n",
       "       [   1.81804243,    1.80258516,   90.17233046, -239.24855154],\n",
       "       [   1.7750836 ,    1.8456316 ,   88.27641235, -239.24875324],\n",
       "       [   1.72208645,    1.89954644,   85.91389463, -239.24979741],\n",
       "       [   1.66536498,    1.9584755 ,   83.36888877, -239.25191261],\n",
       "       [   1.60848255,    2.01984924,   80.78706611, -239.25480606],\n",
       "       [   1.55372982,    2.08318761,   78.22885329, -239.25797266],\n",
       "       [   1.50507433,    2.14801115,   75.75117811, -239.26090194],\n",
       "       [   1.46809165,    2.21566827,   73.36700251, -239.26338035],\n",
       "       [   1.44448784,    2.29552448,   70.94674492, -239.26545614],\n",
       "       [   1.43045596,    2.40926487,   68.30920842, -239.26654839]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reaz=io.iread('sn2_MEP_trj.xyz')\n",
    "dati=[(r.get_distance(0,5), r.get_distance(0,1), r.get_angle(1,0,2),\n",
    "       float(list(r.info.keys())[5])) for r in reaz]\n",
    "dati=np.array(dati)\n",
    "dati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d18215cd-b10a-472d-8800-7860c7d38d97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm4AAADZCAYAAACZ+Lz3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA7HUlEQVR4nO3de1wU9f4/8NeC3FRYAUUgCcX7lVDJ+y0Fj3ez0mPej9mxn3nB0rxlaqmVCurXjKMZlOWlAlKzTETBTOmoQVkaKGpaQR6NQDFBls/vDw9zXNmFWdjLzO7r+Xjs48HMfnZ4z8z789nPzuUzGiGEABEREREpnpOtAyAiIiIiedhxIyIiIlIJdtyIiIiIVIIdNyIiIiKVYMeNiIiISCXYcSMiIiJSCXbciIiIiFSCHTciIiIilahl6wCUqKysDL/99hs8PT2h0WhsHQ45GCEEbt68icDAQDg52e63FesBWQtznkh+PWDHzYDffvsNQUFBtg6DHNzVq1fRqFEjm/1/1gOyNuY8UdX1gB03Azw9PQHc23heXl42joYcTWFhIYKCgqQ8tBXWA7IW5jyR/HrAjpsB5YfIvby8WHnJZmx9qob1gKyNOU9UdT3gzQkyxSRnY2PKeYPvbUw5j5jkbCtHRGRdrANERKYzd9vJjptMzk4aRBvY+BtTziM6ORvOTryQlewb6wARkenM3XbyVKlMs/o3BwBE/7dnPKt/c2mjz41oIb1PZK9YB4iITGfutpMdNxPcv/E3Hb6AEl0Zv7DIobAOEBGZzpxtJ0+VmmhW/+ZwdXZCia4Mrs5O/MIih8M6QERkOnO1ney4mWhjynlpo5foyoxecEhkr1gHiIhMZ662k6dKTfDgOenyaQA86kAOgXWAiMh05mw72XGTydCFhIYuOCSyV6wDRESmM3fbyY6bTLoyYfBCwvJpXZmwRVhEVsM6QERkOnO3nRohBFvbBxQWFkKr1aKgoICjZ5PVKSX/lBIH2T+l5JpS4iDHJDf/eHMCERERkUqw40ZERESkEuy4EREREakEO25EREREKsGOGxEREZFKsONGREREpBLsuJlJTHK20cdXbEw5j5j/DrJHpHbMdSKie2zRHrLjZibOThpEG9iB5SMmOztpbBQZkXkx14mI7rFFe8gnJ5iJocdXGHrMBZHaMdeJiO6xRXvIjpsZ3b8DNx2+gBJdGb/IyC4x14mI7rF2e8hTpWY2q39zuDo7oURXBldnJ36Rkd1irhMR3WPN9pAdNzPbmHJe2nElujKjFy0SqR1znYjoHmu2hzbtuK1evRrh4eHw9PSEn58fRo4ciaysrEo/k5iYiIiICDRo0ABeXl7o1q0bvvzyS70y8fHx0Gg0FV537tyx5OrondfOXjkIcyNaGLxokUjtmOtERPdYuz206TVuaWlpmDFjBsLDw1FaWorFixcjMjISZ8+eRZ06dQx+5ujRo4iIiMCqVatQr149xMXFYdiwYfjmm28QFhYmlfPy8qrQCXR3d7fYuhi6GNHQRYtEasdcJyK6xxbtoU07bgcOHNCbjouLg5+fH06fPo3evXsb/Mz69ev1pletWoU9e/Zg3759eh03jUYDf39/s8dsjK5MGLwYsXxaVyasFguRJTHXiYjusUV7qKi7SgsKCgAAPj4+sj9TVlaGmzdvVvjMrVu3EBwcDJ1Oh0ceeQSvvvqqXsfufsXFxSguLpamCwsLTY49KqKF0fd49IHUQG49YK6TvTBH20+OzRbtoWJuThBCYO7cuejZsyfatWsn+3Pr1q1DUVERRo8eLc1r1aoV4uPjsXfvXuzcuRPu7u7o0aMHzp83fL559erV0Gq10isoKKjG60OkNqwH5GiY86RGGiGEIs5rzJgxA/v378exY8fQqFEjWZ/ZuXMnnnnmGezZswcDBgwwWq6srAwdO3ZE7969sXHjxgrvG/rVFRQUhIKCAnh5eZm+MtUU899Rlg310jemnIeuTFTauyf7UFhYCK1Wa/X8q249YN5STakt58lxWLN9k1sPFHHEbebMmdi7dy+OHDkiu9O2e/duTJ06FR999FGlnTYAcHJyQnh4uNEjbm5ubvDy8tJ72QIfJUS2VN16wLwltVJK20/KpcT2zabXuAkhMHPmTCQlJSE1NRVNmjSR9bmdO3fiH//4B3bu3IkhQ4bI+j+ZmZlo3759TUO2KD5KiNSIeUtE9kqJ7ZtNO24zZszAjh07sGfPHnh6eiIvLw8AoNVq4eHhAQBYuHAhfv31V7z//vsA7nXaJk6ciA0bNqBr167SZzw8PKDVagEAy5cvR9euXdG8eXMUFhZi48aNyMzMxFtvvWWDtTQNHyVEasS8JSJ7pbT2zaanSt9++20UFBSgb9++CAgIkF67d++WyuTm5uLKlSvS9L/+9S+UlpZixowZep+ZPXu2VObPP//Es88+i9atWyMyMhK//vorjh49ikcffdSq61ddfJQQqRHzlojslZLaN5ufKq1KfHy83nRqamqVn4mJiUFMTEw1o7I9Q4/O4JcgKR3zlojslZLaN9lH3B5++GHcuHFDmt60aRPHvLEAPkqI1Ih5S0T2Smntm+wjbr/88gt0Op00vWjRIgwePJh34ZgRHyVEasS8JSJ7pcT2rdqnShUy/Jtd4aOESI2Yt0Rkr5TYvinqkVeOjo8SIjVi3hKRvVJi+2ZSx+2dd95B3bp1AQClpaWIj49H/fr19crMmjXLfNFRjXBEe7IV5h4RqYEa2yrZHbeHH34YW7dulab9/f2xfft2vTIajYYdNwUpH/EZ0P9lcP85eyJLYO4RkRqosa2S3XG7fPmyBcMgS1DiiM/kGJh7RKQGamyranSN2y+//ILAwEA4OSnikadkgNJGfCbHwdwjIjVQW1tVox5XmzZteCROBZQ04jM5FuYeEamBmtqqGnXcOCSIOhga8ZnIGph7RKQGamqrOByInXvwXH35NMChGsiymHtEpAZqa6tq1HFbtGgRfHx8zBULmZkSR3wmx8DcIyI1UGNbZVLHrbCwEHXr1pVuRli4cCEAQKfToaioiI+/UhgljvhMjoG5R0RqoMa2SnbHLSkpCS+99BIyMzNRu3ZtvfeKi4sRHh6OtWvXYtiwYWYPkqrH0iM+q3HgQrKOyva7rkzA2Ulj8D3mDRGZU1XfU4Dx70OlHWkrJ/vmhLfffhvz58+v0GkDgNq1a+Oll17Cpk2bzBocKVv5wIUPXsRZfujZ2JczOTbmDRFZiz22N7KPuP3www/YvHmz0fd79+6NJUuWmCUoUgc1DlxItse8ISJrscf2RnbHLT8/H6WlpUbfv3v3LvLz880SFKmH2gYuJGVg3hCRtdhbeyP7VGnjxo1x6tQpo++fOnUKwcHBZgmK1EVNAxeScjBviMha7Km9kd1xGzVqFBYvXozff/+9wnt5eXlYsmQJnnjiCbMGR+qgpoELSTmYN0RkLfbU3sg+VbpgwQLs2bMHzZs3x/jx49GyZUtoNBqcO3cOH374IYKCgrBgwQJLxkoKpLaBC0kZmDdEZC321t7I7rh5enri66+/xsKFC7F7927pejZvb2+MHz8eq1atgqenp8UCJeVR48CFZHvMGyKyFntsb0wagFer1WLz5s146623cP36dQgh0KBBA2g06rudlmpOjQMXku0xb4jIWuyxvdEIPim+gsLCQmi1WhQUFPBpEArgaAP9KiX/bBWHo+1vYs5T9dhbWyE3/2TfnEBkK/Y4gCIZx/1NRHI4altRo4fME1mDPQ6gSMZxfxORHI7aVtj0iNvq1asRHh4OT09P+Pn5YeTIkcjKyqryc2lpaejUqRPc3d0REhKC2NjYCmUSEhLQpk0buLm5oU2bNkhKSrLEKpCVzOrfHHMjWiA6ORstFn9h9xXT0XF/E5EcjthW2LTjlpaWhhkzZiA9PR3JyckoLS1FZGQkioqKjH7m0qVLGDx4MHr16oWMjAwsWrQIs2bNQkJCglTmxIkTGDNmDCZMmIDvvvsOEyZMwOjRo/HNN99YY7XIQuxpAEWqGvc3EcnhaG1FtU6VFhUVIS0tDVeuXEFJSYnee7NmzZK9nAMHDuhNx8XFwc/PD6dPn0bv3r0NfiY2NhYPP/ww1q9fDwBo3bo1Tp06hbVr10oDAK9fvx4RERFYuHAhAGDhwoVIS0vD+vXrsXPnTtnxkbIYGkDR3iuoI+P+JiI5HK2tMLnjlpGRgcGDB+P27dsoKiqCj48Prl+/jtq1a8PPz8+kjtuDCgoKAAA+Pj5Gy5w4cQKRkZF68wYOHIht27bh7t27cHFxwYkTJxAVFVWhTHln70HFxcUoLi6WpgsLC6u5BmQp9jaAohIpqR5wf5M1KCnnqXocsa0w+VRpVFQUhg0bhj/++AMeHh5IT0/Hzz//jE6dOmHt2rXVDkQIgblz56Jnz55o166d0XJ5eXlo2LCh3ryGDRuitLQU169fr7RMXl6ewWWuXr0aWq1WegUFBVV7Pcj8jA2gWH5dg5ofXaIkSqkH3N9kLUrJeaoeR20rTO64ZWZm4oUXXoCzszOcnZ1RXFyMoKAgvPnmm1i0aFG1A3n++efx/fffyzqV+eCAv+VD0d0/31AZYwMFL1y4EAUFBdLr6tWrpoZPFlTZAIpzI1qocgBFJVJKPeD+JmtRSs5T9ThqW2HyqVIXFxepA9SwYUNcuXIFrVu3hlarxZUrV6oVxMyZM7F3714cPXoUjRo1qrSsv79/hSNn165dQ61ateDr61tpmQePwpVzc3ODm5tbtWIny6tsAMWaHgq3twEca0Ip9aCq/R3z31/S3GdUU0rJeaqopm2zvZ4mBapxxC0sLAynTp0CAPTr1w9Lly7Fhx9+iDlz5qB9+/YmLUsIgeeffx6JiYk4fPgwmjRpUuVnunXrhuTkZL15Bw8eROfOneHi4lJpme7du5sUH9k/Rx3AUc24z4jsH+t5JYSJTp48KQ4fPiyEEOLatWti0KBBwtPTU4SFhYmMjAyTlvXcc88JrVYrUlNTRW5urvS6ffu2VGbBggViwoQJ0vTFixdF7dq1RVRUlDh79qzYtm2bcHFxEZ988olU5uuvvxbOzs7i9ddfF+fOnROvv/66qFWrlkhPT5cVV0FBgQAgCgoKTFofUqcNh7JF8EufiQ2Hsg1OW5tS8k8pcRiitH1GNaOUXFNKHHSPo9Vzufln02eVGrvmLC4uDpMnTwYATJ48GZcvX0Zqaqr0flpaGqKiovDjjz8iMDAQL730EqZPn663jE8++QRLlizBxYsX0bRpU6xcuRKjRo2SFRefV+d4yn/Fld9ObssBHJWSf0qJwxgl7TOqGaXkmlLioP9xpHouN/9M7ritWLECPXv2xGOPPaY3v6ioCOvWrcPSpUurF7GCsPI6phaLv5DGAspeOchmcSgl/5QSR2WUss+oZpSSa0qJg/Q5Sj232EPmly1bhkGDBiE6Olpv/q1bt7B8+XLTIyVSAEMDOJKycZ8R2T/W84qq9cir999/H6tXr8bkyZMrPDmBSG3uHwsoe+Ugux8DyB5wnxHZP9Zzw6r1yKt+/fohPT0dw4YNQ9++ffkAd1ItYwM4ArD70bfVivuMyP6xnhtncset/IaCpk2bIj09HaNHj0bnzp0RGxtr9uCILK2yARzL3ydl4T4jsn+s58aZfHOCk5MT8vLy4OfnBwAoKyvDnDlz8Pbbb6OsrAw6nc4igVoTL1AlU5lzIF+l5J9S4qgODqysLkrJNaXEYe9YPw2z2M0JcXFx0Gq1/1uAkxM2btyILVu2YOLEidWLlkjlOFiksnB/ECkX62fNmHyqdNKkSQbnT5kyBVOmTKlxQERqZOjaC0PXaJB1cH8QKRfrZ81U6+aElJQUpKSk4Nq1aygrK5PmazQabNu2zWzBEanJ/Y3RpsMX7H6wSKXj/iBSLtbP6jP5VOny5csRGRmJlJQUXL9+Hfn5+dLrjz/+sESMRKoxq39zabwhV2cnNkI2xv1BpFysn9Vj8hG32NhYxMfHY8KECZaIh0jVDA0WycbIdrg/iJSL9bN6TO64lZSUoHv37paIhUjVHrxGo3wacNzxhmyJ+4NIuVg/q8/kjtszzzyDHTt24OWXX7ZEPESqxMEilYX7g0i5WD9rxuSO2507d7BlyxYcOnQIHTp0gIuLi977Dz7DlMgRcLBIZeH+IFIu1s+aMXkA3n79+hlfmEaDw4cP1zgoW+MgjGRLSsk/pcRB9k8puaaUOMgxyc0/k4+4HTlypEaBEREREVH1mDwcCBFVT4yBkcLLbUw5j5j/XttB1sX9QlRzrEfWU60BeE+ePImPP/4YV65cQUlJid57iYmJZgmMyN6UP+YF0L/w9v4Ldcn6uF+Iao71yHpM7rjt2rULEydORGRkJJKTkxEZGYnz588jLy8Pjz/+uCViJLILfMyLMnG/ENUc65H1mNxxW7VqFWJiYjBjxgx4enpiw4YNaNKkCf75z38iICDAEjES2Q0+5kWZuF+Iao71yDpMvsYtJycHQ4YMAQC4ubmhqKgIGo0GUVFR2LJli9kDJLI3fMyLMnG/ENUc65Hlmdxx8/Hxwc2bNwEADz30EH744QcAwJ9//onbt2+bNzoiO2ToMS9ke9wvRDXHemR5Jp8q7dWrF5KTk9G+fXuMHj0as2fPxuHDh5GcnIz+/ftbIkYiVYtJzoazk8bgNR9Pb03nSOEKYOzxO+kXb2DHtK4Gy+vKBKJ4wTU5kPvbsgdtTDmP4znXkX7xDz7GysJM7rht2rQJd+7cAQAsXLgQLi4uOHbsGEaNGsXHYBEZUH63VfrFGziec0OvUTuecwPdm/qycbMhY4/fKd9fT29N1+u88S45clRV3TkKgI+xsgKTO24+Pj7S305OTpg/fz7mz59v1qCI7Mn9jVf3pr4Gj7yVH8Eh6zP2+J0d07ri6a3pOJ5zAxtTzvMuOXJ4ld052jXEB92b1udjrKzA5EdeOTs7Izc3F35+fnrzb9y4AT8/P+h0OrMGaAt87AlZQnkDV37th7Evf6Xkn1LisDW5+42qTym5ppQ4lI51wjLk5p/JNycY6+cVFxfD1dXVpGUdPXoUw4YNQ2BgIDQaDT799NNKy0+ePBkajabCq23btlKZ+Ph4g2XKT+8S2QrvtlIn7jcifawTtiX7VOnGjRsB3HuQ/DvvvIO6detK7+l0Ohw9ehStWrUy6Z8XFRUhNDQUU6ZMwRNPPFFl+Q0bNuD111+XpktLSxEaGoqnnnpKr5yXlxeysrL05rm7u5sUG5G5Gbrbig2e8nG/EeljnbAt2R23mJgYAPeOuMXGxsLZ2Vl6z9XVFY0bN0ZsbKxJ/3zQoEEYNGiQ7PJarRZarVaa/vTTT5Gfn48pU6boldNoNPD39zcpFiJLMnbXIsALdpWM+41IH+uE7cnuuF26dAkA0K9fPyQmJsLb29tiQcm1bds2DBgwAMHBwXrzb926heDgYOh0OjzyyCN49dVXERYWZnQ5xcXFKC4ulqYLCwstFjM5HmN3LQLKutuK9UCfWvYbVR9z3jSsE8pg8l2lR44c0ZvW6XQ4c+YMgoODrdqZy83NxRdffIEdO3bozW/VqhXi4+PRvn17FBYWYsOGDejRowe+++47NG9uOKFWr16N5cuXWyNsskNVjW309YXrBi/eVdrdVqwH+ozdbVo+/fWF63rT9+M4b+rAnNdnL22Z3RMmmj17tnjnnXeEEEKUlpaK7t27C41GI+rUqSOOHDli6uIkAERSUpLs8qtWrRK+vr6iuLi40nI6nU6EhoaKmTNnGi1z584dUVBQIL2uXr0qAIiCggLZ8ZDj2nAoWwS/9JnYcChb1vyqFBQU2CT/WA9MY+797siY88rAnLYtufXA5CNuH3/8McaPHw8A2LdvHy5fvoyffvoJ77//PhYvXoyvv/7afL1KI4QQePfddzFhwoQq72R1cnJCeHg4zp83/tgNNzc3uLm5mTtMchCVjW2kptvkWQ9MYy/73ZEx5/Uxp9XB5I7bjRs3pAv/P//8czz11FNo0aIFpk6dKt15amlpaWm4cOECpk6dWmVZIQQyMzPRvn17K0RGjur+Bm/T4Qsc28hBcL+TvWFOK5/J47g1bNgQZ8+ehU6nw4EDBzBgwAAAwO3bt/XuNJXj1q1byMzMRGZmJoB7N0BkZmbiypUrAO49UmvixIkVPrdt2zZ06dIF7dq1q/De8uXL8eWXX+LixYvIzMzE1KlTkZmZienTp5u4pkSm4dhGjon7newNc1rZTO64TZkyBaNHj0a7du2g0WgQEREBAPjmm29MHsft1KlTCAsLk+74nDt3LsLCwrB06VIA925AKO/ElSsoKEBCQoLRo21//vknnn32WbRu3RqRkZH49ddfcfToUTz66KOmriqRSQyNbUT2j/ud7A1zWtlMPlW6bNkytGvXDlevXsVTTz0lXR/g7OyMBQsWmLSsvn37Gn0SA3DvKQgP0mq1uH37ttHPxMTESGPOEVkLxzZyTNzvZG+Y08pncscNAJ588skK8yZNmlTjYIjUiGMbOSbud7I3zGl1kNVx27hxI5599lm4u7tXeQPCrFmzzBIYkaVVNWaR3HG4qhrvi2Mb2SeO80ZqIqe9A8C2TAVkddxiYmIwbtw4uLu7V3oaUqPRsONGquHspDH4K/L+X51yVPbly1+n9kvOfjdHfhGZg5z2rrL2im2ZcsjquJU/7urBv4nUjGMWkSUxv0hJmI/2o1rXuBHZC45ZRJbE/CIlYT7aB1kdt7lz58peYHR0dLWDIbKFWf2bS40Yxywic2N+kZIwH9VPVsctIyNDb/r06dPQ6XRo2bIlACA7OxvOzs7o1KmT+SMksjBDYxaxMSNzYX6RkjAf1U9Wx+3IkSPS39HR0fD09MR7770Hb29vAEB+fj6mTJmCXr16WSZKIgvhmEVkScwvUhLmo30w+Rq3devW4eDBg1KnDQC8vb3x2muvITIyEi+88IJZAySyFI5ZRJbE/CIlYT7aD5M7boWFhfj999/Rtm1bvfnXrl3DzZs3zRYYEWC+sdYM4fhrZEly8suS+U2Opapc+vrCdbZ3dsLkjtvjjz+OKVOmYN26dejatSsAID09HfPmzcOoUaPMHiA5NnONtWYIx18jS5KTX8ZOVXGsNzJVTcZpY3unLiZ33GJjY/Hiiy9i/PjxuHv37r2F1KqFqVOnYs2aNWYPkBwbxx4ie8b8JnNhLjkOjajsKe+VKCoqQk5ODoQQaNasGerUqWPu2GymsLAQWq0WBQUF8PLysnU4hP/9aiy/E8qeGyKl5J9S4nAEjpTfhigl15QSR004ei6pmdz8c6ruP6hTpw46dOiA0NBQu+q0kTLN6t9caog49hDZG+Y3mQtzyf5Vu+NGZE2Gxh4ishfMbzIX5pL94yOvSPE49hDZM+Y3mQtzyTGw40aKxrGHyJ4xv8lcmEuOgx03qjZrjEHFsdbInnGsN5JDTg4AYFvpINhxo2qz5Bhr5TjWGtkzjvVGctRkjLYHP0Pqx44bVRvHDSKyPNYzYg7Q/dhxoxq5v0HZdPgCxw0isgDWM2IOUDkOB0I1xnGDiCyP9YyYAwSw40ZmwHGDiCyP9YyYAwTwVCnVEMcNIrI81jNiDlA5dtzsmKWHEeC4QUSWJ6ee6coEhwxRsara6uM515F+8Q+2tQTAxqdKjx49imHDhiEwMBAajQaffvpppeVTU1Oh0WgqvH766Se9cgkJCWjTpg3c3NzQpk0bJCUlWXAtlKv8FvIHD6eXfxE4O2lqtPzKxqCaG9GC4wYRmYGcembpuk6WVdX+A4yP0ca21vHY9IhbUVERQkNDMWXKFDzxxBOyP5eVlQUvLy9pukGDBtLfJ06cwJgxY/Dqq6/i8ccfR1JSEkaPHo1jx46hS5cuZo1f6Sx9CznHWCOyPFPqGYeLUKeatNXct45HI4RQRFddo9EgKSkJI0eONFomNTUV/fr1Q35+PurVq2ewzJgxY1BYWIgvvvhCmve3v/0N3t7e2Llzp6xYCgsLodVqUVBQoNdBVKvyBqD8glY25MqmlPxTShwkn1rrulJyzdZxqHX/kXnIzT9V3lUaFhaGgIAA9O/fH0eOHNF778SJE4iMjNSbN3DgQBw/ftzo8oqLi1FYWKj3sie8hZzksPd64AhY102jtJzn/iM5VNVxCwgIwJYtW5CQkIDExES0bNkS/fv3x9GjR6UyeXl5aNiwod7nGjZsiLy8PKPLXb16NbRarfQKCgqy2DpUJsbANQ7lNqacR8x/D6ObireQkxxKqQdUfVXVdUu1MWplrZyXu93ZVpMcquq4tWzZEtOmTUPHjh3RrVs3bN68GUOGDMHatWv1ymk0+hfiCiEqzLvfwoULUVBQIL2uXr1qkfirYokLjO+/TiJ75SDMjWhh8H8QKaUeUPXIqeu8iUGftXJeznZnW01yqX44kK5du+KDDz6Qpv39/SscXbt27VqFo3D3c3Nzg5ubm8VilMvcNxNwuA4yhVLqAZlObl3nMy/1WSvnq9ru5e+xrSY5VN9xy8jIQEBAgDTdrVs3JCcnIyoqSpp38OBBdO/e3Rbhmcycz6OrbBiB8veJSP1Mqet85qVtVLbdY4x0nNlWkyE27bjdunULFy5ckKYvXbqEzMxM+Pj44OGHH8bChQvx66+/4v333wcArF+/Ho0bN0bbtm1RUlKCDz74AAkJCUhISJCWMXv2bPTu3RtvvPEGRowYgT179uDQoUM4duyYRdfFnIPdzurfXKrYNblAlcN1EDkGU+u6nDbG0gN42wtTtpOx7c62mkxh02vcTp06hbCwMISFhQEA5s6di7CwMCxduhQAkJubiytXrkjlS0pK8OKLL6JDhw7o1asXjh07hv3792PUqFFSme7du2PXrl2Ii4tDhw4dEB8fj927d1t8DDdzXjvCC1SJyJLktDG8Hk4eU7YT23YyB5secevbty8qG0YuPj5eb3r+/PmYP39+lct98skn8eSTT9Y0PD1yflWVX0wKVP/aET6PjogsSW4bY47r4dR+1M6U+KvaTmzbyVxUf42btZT/qgL0K5mhClrda0d4MwERWZKpbUxN2zQ57aaSyY2/qu3Etp3MiR03meT++qzJ9Wm8mYCILKk6bUxN2jS138VqSvyVbSe27WROinnklZJU9tiJqh5JwkeWUE3Z+rE7SouDbMscbVpVy1BKrhmLQ842YNtPNWXXj7yypcoeScIBFInInpirTVP7o5yqip9tP1kTT5WayNBdQZWdNgV4DQMRqY852zRj7aZaVBY/236yNnbcTFDZXUG8hoGI7Im52jS1301ZVfxs+8na2HGTqapfVZVdz6CGxomI6H7mGBRW7Uej5MTPwXPJ2thxk4m/qoiITKP2dlPt8ZN94l2lBijlDidyTErJP6XEQfZPKbmmlDjIMcnNPx5xM6C8L1tYWGjjSMgRleedrX9TsR6QtTDnieTXA3bcDLh58yYAICgoyMaRkCO7efMmtFqtTf8/wHpA1sOcJ6q6HvBUqQFlZWX47bff4OnpCY2m4oOUCwsLERQUhKtXrzrM4XSus/XWWQiBmzdvIjAwEE5Othtqsap6IIcj5o0puH3usaectxXmkj41bg+59YBH3AxwcnJCo0aNqizn5eWlmoQwF66zddjyqEM5ufVADkfMG1Nw+9hfztsKc0mf2raHnHrAJycQERERqQQ7bkREREQqwY5bNbi5ueGVV16Bm5ubrUOxGq4zVQe3YeW4fchcmEv67Hl78OYEIiIiIpXgETciIiIilWDHjYiIiEgl2HEjIiIiUgl23IiIiIhUgh03E23evBlNmjSBu7s7OnXqhK+++srWIZnN6tWrER4eDk9PT/j5+WHkyJHIysrSKyOEwLJlyxAYGAgPDw/07dsXP/74o40iNr/Vq1dDo9Fgzpw50jx7X+eaOHr0KIYNG4bAwEBoNBp8+umnlZZPTU2FRqOp8Prpp5+sE7AVyalPhqSlpaFTp05wd3dHSEgIYmNjrRAtqUFpaSmWLFmCJk2awMPDAyEhIVixYgXKysqkMvbcXlXV3shZ9+LiYsycORP169dHnTp1MHz4cPzyyy9WXIuaY8fNBLt378acOXOwePFiZGRkoFevXhg0aBCuXLli69DMIi0tDTNmzEB6ejqSk5NRWlqKyMhIFBUVSWXefPNNREdHY9OmTTh58iT8/f0REREhPeNPzU6ePIktW7agQ4cOevPteZ1rqqioCKGhodi0aZNJn8vKykJubq70at68uYUitB059elBly5dwuDBg9GrVy9kZGRg0aJFmDVrFhISEqwYOSnVG2+8gdjYWGzatAnnzp3Dm2++iTVr1uD//u//pDL23F5V1d7IWfc5c+YgKSkJu3btwrFjx3Dr1i0MHToUOp3OWqtRc4Jke/TRR8X06dP15rVq1UosWLDARhFZ1rVr1wQAkZaWJoQQoqysTPj7+4vXX39dKnPnzh2h1WpFbGysrcI0i5s3b4rmzZuL5ORk0adPHzF79mwhhH2vs7kBEElJSZWWOXLkiAAg8vPzrRKTkjxYnwyZP3++aNWqld68f/7zn6Jr166WDo9UYMiQIeIf//iH3rxRo0aJ8ePHCyEcq716sL2Rs+5//vmncHFxEbt27ZLK/Prrr8LJyUkcOHDAarHXFI+4yVRSUoLTp08jMjJSb35kZCSOHz9uo6gsq6CgAADg4+MD4N7RgLy8PL1t4Obmhj59+qh+G8yYMQNDhgzBgAED9Obb8zrbUlhYGAICAtC/f38cOXLE1uFYxYP1yZATJ05UaGMGDhyIU6dO4e7duxaNj5SvZ8+eSElJQXZ2NgDgu+++w7FjxzB48GAAjt1eyVn306dP4+7du3plAgMD0a5dO1VtHz5kXqbr169Dp9OhYcOGevMbNmyIvLw8G0VlOUIIzJ07Fz179kS7du0AQFpPQ9vg559/tnqM5rJr1y58++23OHnyZIX37HWdbSUgIABbtmxBp06dUFxcjO3bt6N///5ITU1F7969bR2exRiqT4bk5eUZzLXS0lJcv34dAQEBlg6VFOyll15CQUEBWrVqBWdnZ+h0OqxcuRJjx44F4NjtlZx1z8vLg6urK7y9vSuUUdP3ODtuJtJoNHrTQogK8+zB888/j++//x7Hjh2r8J49bYOrV69i9uzZOHjwINzd3Y2Ws6d1tqWWLVuiZcuW0nS3bt1w9epVrF271q47bpXVpwcZyjVD88nx7N69Gx988AF27NiBtm3bIjMzE3PmzEFgYCAmTZoklXPk9qo666627cNTpTLVr18fzs7OFXrl165dq9DDV7uZM2di7969OHLkCBo1aiTN9/f3BwC72ganT5/GtWvX0KlTJ9SqVQu1atVCWloaNm7ciFq1aknrZU/rrDRdu3bF+fPnbR2GxRirT4b4+/sbzLVatWrB19fXkmGSCsybNw8LFizA3//+d7Rv3x4TJkxAVFQUVq9eDcA+22i55Ky7v78/SkpKkJ+fb7SMGrDjJpOrqys6deqE5ORkvfnJycno3r27jaIyLyEEnn/+eSQmJuLw4cNo0qSJ3vtNmjSBv7+/3jYoKSlBWlqaardB//79cebMGWRmZkqvzp07Y9y4ccjMzERISIjdrbPSZGRk2OUpwKrqkyHdunWr0MYcPHgQnTt3houLi6VCJZW4ffs2nJz0v7adnZ2l4UDssY2WS866d+rUCS4uLnplcnNz8cMPP6hr+9jopghV2rVrl3BxcRHbtm0TZ8+eFXPmzBF16tQRly9ftnVoZvHcc88JrVYrUlNTRW5urvS6ffu2VOb1118XWq1WJCYmijNnzoixY8eKgIAAUVhYaMPIzev+u0qFcIx1rq6bN2+KjIwMkZGRIQCI6OhokZGRIX7++WchhBALFiwQEyZMkMrHxMSIpKQkkZ2dLX744QexYMECAUAkJCTYahUsRk59enD7XLx4UdSuXVtERUWJs2fPim3btgkXFxfxySef2GIVSGEmTZokHnroIfHZZ5+JS5cuicTERFG/fn0xf/58qYw9t1dVtTdy1n369OmiUaNG4tChQ+Lbb78Vjz32mAgNDRWlpaW2Wi2TseNmorfeeksEBwcLV1dX0bFjx0pv7VcbAAZfcXFxUpmysjLxyiuvCH9/f+Hm5iZ69+4tzpw5Y7ugLeDBjpsjrHN1lQ/v8eBr0qRJQoh7XzR9+vSRyr/xxhuiadOmwt3dXXh7e4uePXuK/fv32yZ4C5NTnx7cPkIIkZqaKsLCwoSrq6to3LixePvtt60bOClWYWGhmD17tnj44YeFu7u7CAkJEYsXLxbFxcVSGXtur6pqb+Ss+19//SWef/554ePjIzw8PMTQoUPFlStXbLA21acR4r9XvhIRERGRovEaNyIiIiKVYMeNiIiISCXYcSMiIiJSCXbciIiIiFSCHTciIiIilWDHjYiIiEgl2HEjIiIiUgl23EiWyZMnY+TIkbLLX758GRqNBhqNBo888ki1l2NNjRs3xvr16/Wmy9fhzz//tFlcVLm+fftizpw50vSD+5GIzEOj0eDTTz+1dRgOjx03kmXDhg2Ij483+XOHDh1CSkqK7PLx8fGoV6+ewfldu3Y1+f/XxMmTJ5GQkGDV/0k1d/LkSTz77LOyyqqtk1dYWIjFixejVatWcHd3h7+/PwYMGIDExERUNpb6/T9Cyl9VPfCeyNzu/0F//2v8+PGVfk4IgS1btqBLly6oW7cu6tWrh86dO2P9+vW4ffu20c8ZO1CQmpqq6h/ktWwdANnW3bt3ZT28WqvVVmv5vr6+8PX1rdZn77d3716MGDGixssxRYMGDeDj42PV/0k116BBA1uHYBF//vknevbsiYKCArz22msIDw9HrVq1kJaWhvnz5+Oxxx4z+KOn3IoVKzBt2jRp2tnZ2QpRE1V06NAhtG3bVpr28PCotPyECROQmJiIJUuWYNOmTWjQoAG+++47rF+/Ho0bN1bsWRxL4RE3BRFC4M0330RISAg8PDwQGhqKTz75BMD/fiGkpKSgc+fOqF27Nrp3746srCy9Zezbtw+dOnWCu7s7QkJCsHz5cpSWlkrvazQaxMbGYsSIEahTpw5ee+01AMBrr70GPz8/eHp64plnnsGCBQsqPcV54MAB9OzZE/Xq1YOvry+GDh2KnJwck9f59OnT8PPzw8qVK42WuXPnDg4ePIjhw4cDuHf04LXXXsPEiRNRt25dBAcHY8+ePfjPf/6DESNGoG7dumjfvj1OnTqlt5yEhAS0bdsWbm5uaNy4MdatW2dyvGRbRUVF0n4PCAgwuA8fPIq2bNkyPPzww3Bzc0NgYCBmzZoF4N4p1p9//hlRUVHSL38AuHHjBsaOHYtGjRqhdu3aaN++PXbu3Kn3P/r27YtZs2Zh/vz58PHxgb+/P5YtWya9Hx8fb/DIQnmZkydPIiIiAvXr14dWq0WfPn3w7bffVrruixYtwuXLl/HNN99g0qRJaNOmDVq0aIFp06YhMzMTdevWrfTznp6e8Pf3l1722sG1N1W1teVHsRITE9GvXz/Url0boaGhOHHihN5ytm7diqCgINSuXRuPP/44oqOjK3T03377bTRt2hSurq5o2bIltm/fXmlsZ86cwWOPPQYPDw/4+vri2Wefxa1bt6pcJ19fX71crOzAwEcffYQPP/wQO3fuxKJFixAeHo7GjRtjxIgROHz4MPr161fl/7M7Nn1SKulZtGiRaNWqlThw4IDIyckRcXFxws3NTaSmpkoP1+3SpYtITU0VP/74o+jVq5fo3r279PkDBw4ILy8vER8fL3JycsTBgwdF48aNxbJly6QyAISfn5/Ytm2byMnJEZcvXxYffPCBcHd3F++++67IysoSy5cvF15eXiI0NFT63KRJk8SIESOk6U8++UQkJCSI7OxskZGRIYYNGybat28vdDqdEEKIS5cuCQAiIyNDbx3vX86RI0eEVqsVmzdvlt6Pi4sTWq1W7zOfffaZaNq0qTQdHBwsfHx8RGxsrMjOzhbPPfec8PT0FH/729/ERx99JLKyssTIkSNF69atRVlZmRBCiFOnTgknJyexYsUKkZWVJeLi4oSHh4feA7+Dg4NFTEyM3v8u3+75+flV7D2yhueee040atRIHDx4UHz//fdi6NChom7dumL27NlSmfv348cffyy8vLzE559/Ln7++WfxzTffiC1btgghhLhx44Zo1KiRWLFihcjNzRW5ublCCCF++eUXsWbNGpGRkSFycnLExo0bhbOzs0hPT5f+R58+fYSXl5dYtmyZyM7OFu+9957QaDTi4MGDQgghbt++LS0zNzdX7Ny5U9SqVUt6PyUlRWzfvl2cPXtWnD17VkydOlU0bNhQFBYWGlxvnU4nvL29xbPPPlut7WYot0kd5La1rVq1Ep999pnIysoSTz75pAgODhZ3794VQghx7Ngx4eTkJNasWSOysrLEW2+9JXx8fPTa2sTEROHi4iLeeustkZWVJdatWyecnZ3F4cOHpTIARFJSkhBCiKKiIhEYGChGjRolzpw5I1JSUkSTJk2kB74bYux7oTLDhw8XLVu2lF3+fg9+b5VTe7vOjptC3Lp1S7i7u4vjx4/rzZ86daoYO3aslGiHDh2S3tu/f78AIP766y8hhBC9evUSq1at0vv89u3bRUBAgDQNQMyZM0evTJcuXcSMGTP05vXo0aPSjtuDrl27JgCIM2fOCCGq7rh9+umnwtPTU+zYsUPvfUMdt2nTpom5c+dK08HBwWL8+PHSdG5urgAgXn75ZWneiRMnBADpy/jpp58WEREResudN2+eaNOmjd5y2XFTrps3bwpXV1exa9cuad6NGzeEh4eH0Y7bunXrRIsWLURJSYnBZcrt0AwePFi88MIL0nSfPn1Ez5499cqEh4eLl156qcJnL1y4IHx9fcWbb75pdPmlpaXC09NT7Nu3z+D7v//+uwAgoqOjq4zVkODgYOHq6irq1KkjvTZs2FCtZZFtGWtr33nnHanMjz/+KACIc+fOCSGEGDNmjBgyZIjecsaNG6fX1nbv3l1MmzZNr8xTTz0lBg8eLE3f33HbsmWL8Pb2Frdu3ZLe379/v3BychJ5eXkGYy+P1cPDQy8Xv/32W6Pr27p1azF8+PBKtohxkyZNEs7Oznr/q06dOsLd3V3V7TpPlSrE2bNncefOHURERKBu3brS6/3339c7LN6hQwfp74CAAADAtWvXANw77bhixQq9z0+bNg25ubl6F3B27txZ739nZWXh0Ucf1Zv34PSDcnJy8PTTTyMkJAReXl5o0qQJAODKlStVrus333yDJ554Au+99x7Gjh1baVkhBPbt2yedJi13/3Zo2LAhAKB9+/YV5pVvm3PnzqFHjx56y+jRowfOnz8PnU5XZcxkezk5OSgpKUG3bt2keT4+PmjZsqXRzzz11FP466+/EBISgmnTpiEpKUnv0gFDdDodVq5ciQ4dOsDX1xd169bFwYMHK+T2/TkI3KuP5flWrqCgAEOHDsWgQYMwb948af61a9cwffp0tGjRAlqtFlqtFrdu3TJaf8R/bzwoP51rzKpVq/Tq//3LmzdvHjIzM6XXxIkTK10WKYPctray7wY5bbyxNvLcuXMG4zp37hxCQ0NRp04dvfJlZWUVLuF50O7du/VysU2bNgCAtm3bSrk7aNAgAPdyv6q8/+qrr/Ty/sMPP5Te69evn97/yszMxDvvvFPp8pSONycoRFlZGQBg//79eOihh/Tec3Nzkzpv999IUJ7M5Z8tKyvD8uXLMWrUqArLd3d3l/6+v6I9uKxyopI71ABg2LBhCAoKwtatWxEYGIiysjK0a9cOJSUllX4OAJo2bQpfX1+8++67GDJkCFxdXY2W/fe//42SkhL07NlTb76h7VDZtjFU+ataR1KW6uyvoKAgZGVlITk5GYcOHcL/+3//D2vWrEFaWprRm3LWrVuHmJgYrF+/Hu3bt0edOnUwZ86cCrn94Oc1Go2Ub8C9DuCYMWPg5eWFrVu36pWdPHky/vOf/2D9+vUIDg6Gm5sbunXrZrT+NGjQAN7e3ka/RMtNnz4do0ePlqYDAwOlv+vXr49mzZpV+nlSHrltrTnaP0NljHWaKnuvqo5WUFCQwVz8/PPPcffuXQD/u2GhRYsWVeZ9586dkZmZKU2X/3AH7n3fPfi/fvnll0qXp3Q84qYQbdq0gZubG65cuYJmzZrpvYKCgmQto2PHjsjKyqrw+WbNmsHJyfiubtmyJf7973/rzXvwwv773bhxA+fOncOSJUvQv39/tG7dGvn5+fJWFPe+QA4fPoycnByMGTNGqqiG7NmzB0OGDKnxHXBt2rTBsWPH9OYdP34cLVq04N11KtGsWTO4uLggPT1dmpefn4/s7OxKP+fh4YHhw4dj48aNSE1NxYkTJ3DmzBkAgKura4Ujrl999RVGjBiB8ePHIzQ0FCEhITh//rzJ8UZFReHMmTNISkrS++FU/j9mzZqFwYMHSzfMXL9+3eiynJycMGbMGHz44Yf47bffKrxfVFSE0tJS+Pj46NX7WrX421zNatrWlmvVqlWVbXzr1q0NtpGtW7c2uMw2bdogMzMTRUVF0ryvv/4aTk5OaNGihckxAkBwcLCUu+UHMJ5++mlkZ2djz549FcoLIVBQUAAPDw+9vPf09KzW/1cL1mqF8PT0xIsvvoioqCiUlZWhZ8+eKCwsxPHjx6U7J6uydOlSDB06FEFBQXjqqafg5OSE77//HmfOnJHuHjVk5syZmDZtGjp37ozu3btj9+7d+P777xESEmKwvLe3N3x9fbFlyxYEBATgypUrWLBggUnr6+fnJ90RNHbsWOzatcvgl8zevXuxfPlyk5ZtyAsvvIDw8HC8+uqrGDNmDE6cOIFNmzZh8+bNNV42WUfdunUxdepUzJs3D76+vmjYsCEWL15c6Y+S+Ph46HQ6dOnSBbVr18b27dvh4eEh1afGjRvj6NGj+Pvf/w43NzfpqFRCQgKOHz8Ob29vREdHIy8vz+gXmCFxcXHYvHkzkpKS4OTkhLy8PGkd6tati2bNmmH79u3o3LkzCgsLMW/evCqHRFi1ahVSU1PRpUsXrFy5Ep07d4aLiwu++uorrF69GidPnqx0OBBSH3O0tcC9Nr53796Ijo7GsGHDcPjwYXzxxRd6R8bmzZuH0aNHo2PHjujfvz/27duHxMREHDp0yOAyx40bh1deeQWTJk3CsmXL8J///AczZ87EhAkT9I541dTo0aORlJSEsWPH4uWXX0ZERAQaNGiAM2fOICYmBjNnzuRwIGQ7r776KpYuXYrVq1ejdevWGDhwIPbt2ydd01CVgQMH4rPPPkNycjLCw8PRtWtXREdHV9npGzduHBYuXIgXX3wRHTt2xKVLlzB58uQKRwnKOTk5YdeuXTh9+jTatWuHqKgorFmzxuT19ff3x+HDh3HmzBmMGzcOOp0OZWVlUgcuJycHFy5cwMCBA01e9oM6duyIjz76CLt27UK7du2wdOlSrFixApMnT67xssl61qxZg969e2P48OEYMGAAevbsiU6dOhktX69ePWzduhU9evRAhw4dkJKSgn379kljC65YsQKXL19G06ZNpeExXn75ZXTs2BEDBw5E37594e/vb/IXQ1paGnQ6HYYPH46AgADptXbtWgDAu+++i/z8fISFhWHChAmYNWsW/Pz8Kl2mt7c30tPTMX78eLz22msICwtDr169sHPnTqxZs6baYy2Scpmrre3RowdiY2MRHR2N0NBQHDhwAFFRUXpt/MiRI7FhwwasWbMGbdu2xb/+9S/ExcWhb9++BpdZu3ZtfPnll/jjjz8QHh6OJ598Ev3798emTZuqu7oGaTQa7NixA9HR0UhKSkKfPn3QoUMHLFu2DCNGjDDL94PaaAQv9CEDIiIi4O/vX+U4PsZcvnwZTZo0QUZGht54cFV5/fXX8cEHH+CHH35AdHQ0Dh06hM8//7xaMZhDamoq+vXrh/z8fB7NICK7MW3aNPz000/46quvbB0KmYinSgm3b99GbGwsBg4cCGdnZ+zcuROHDh1CcnJyjZfdvXt3PPLIIzh+/HiVMfz000+Ii4uT7iZq1KgRFi5cWOMYqqtt27a4ePGizf4/EZG5rF27FhEREahTpw6++OILvPfee7xURKV4xI3w119/YdiwYfj2229RXFyMli1bYsmSJQbvTpWrtLQUly9fBnDvrtiqbrBYv349VqxYgQEDBiA2NlYRj5r6+eefpRsnQkJCKr2WiohIyUaPHo3U1FTcvHkTISEhmDlzJqZPn27rsKga2HEjIiIiUgkeQiAiIiJSCXbciIiIiFSCHTciIiIilWDHjYiIiEgl2HEjIiIiUgl23IiIiIhUgh03IiIiIpVgx42IiIhIJdhxIyIiIlKJ/w9nASF7W+bY5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 700x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w,ax=plt.subplots(ncols=3, sharey=True, figsize=(7,2))\n",
    "en=dati[:,3]\n",
    "en=(en-min(en))*2625.5\n",
    "ax[0].plot(en, dati[:,0], 'x')\n",
    "ax[0].set_ylabel('distanza C-F')\n",
    "ax[0].set_xlabel('energia[kJ/mol]')\n",
    "ax[1].plot(dati[:,1], dati[:,0], 'x')\n",
    "ax[1].set_xlabel('distanza C-F')\n",
    "ax[1].set_aspect(1)\n",
    "ax[2].plot(dati[:,2], dati[:,0], 'x')\n",
    "ax[2].set_xlabel('angolo F-C-H')\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "96c0fb1a-1170-425d-aad8-5a8dff801ef8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7dfe73c-9e05-4a94-87e9-3b3ba1427051",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4790a96-7e69-448a-a74f-46b72acf5732",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "afa7a591-3776-48ba-8784-bb9a1fc73309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfilename\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpathlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPurePath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIO\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mformat\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mparallel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdo_not_split_by_at_sign\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matoms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAtoms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matoms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAtoms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Read Atoms object(s) from file.\n",
       "\n",
       "filename: str or file\n",
       "    Name of the file to read from or a file descriptor.\n",
       "index: int, slice or str\n",
       "    The last configuration will be returned by default.  Examples:\n",
       "\n",
       "        * ``index=0``: first configuration\n",
       "        * ``index=-2``: second to last\n",
       "        * ``index=':'`` or ``index=slice(None)``: all\n",
       "        * ``index='-3:'`` or ``index=slice(-3, None)``: three last\n",
       "        * ``index='::2'`` or ``index=slice(0, None, 2)``: even\n",
       "        * ``index='1::2'`` or ``index=slice(1, None, 2)``: odd\n",
       "format: str\n",
       "    Used to specify the file-format.  If not given, the\n",
       "    file-format will be guessed by the *filetype* function.\n",
       "parallel: bool\n",
       "    Default is to read on master and broadcast to slaves.  Use\n",
       "    parallel=False to read on all slaves.\n",
       "do_not_split_by_at_sign: bool\n",
       "    If False (default) ``filename`` is splited by at sign ``@``\n",
       "\n",
       "Many formats allow on open file-like object to be passed instead\n",
       "of ``filename``. In this case the format cannot be auto-decected,\n",
       "so the ``format`` argument should be explicitly given.\n",
       "\u001b[0;31mFile:\u001b[0m      /usr/lib/python3/dist-packages/ase/io/formats.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "io.read?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfddb82b-0ebc-4322-9643-661d5fe7b1c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}