profile_read_python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "08856a5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import re\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "arquivo = '/Volumes/GoogleDrive/My Drive/Estudos/IC/Henrique/Programs/RMlicelPython/RM1120200.012'\n",
    "#arquivo = '/Volumes/GoogleDrive/My Drive/Estudos/IC/Henrique/Programs/7/27/RM1172718.461'\n",
    "\n",
    "arquivo = '/Volumes/GoogleDrive/My Drive/Estudos/IC/Henrique/Programs/RMlicelPython/RM1183023.324' #30 agosto 2011"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac43d18",
   "metadata": {},
   "source": [
    "Links úteis:\n",
    "\n",
    "[1] https://stackoverflow.com/questions/628556/returning-only-part-of-match-from-regular-expression\n",
    "\n",
    "[2] https://stackoverflow.com/questions/8006551/how-to-split-long-regular-expression-rules-to-multiple-lines-in-python\n",
    "\n",
    "[3] https://stackoverflow.com/questions/1327369/extract-part-of-a-regex-match\n",
    "\n",
    "[4] https://stackoverflow.com/a/54692662\n",
    "\n",
    "[5] https://stackoverflow.com/questions/5832856/how-to-read-file-n-lines-at-a-time\n",
    "\n",
    "[6] https://numpy.org/doc/stable/user/basics.types.html?highlight=array  (data types do Python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a40bc927",
   "metadata": {},
   "outputs": [],
   "source": [
    "def profile_read(fname, dbin=0, dtime=0, ach=0, maxz=0):\n",
    "    \n",
    "    head = {}\n",
    "    \n",
    "    # Abrindo o arquivo\n",
    "    \n",
    "    with open(fname, 'r', encoding='utf8', errors='ignore') as fp:\n",
    "        \n",
    "        ## Linha 1\n",
    "        regexp = re.compile('([\\w]{9}.[\\d]{3})')             # filename\n",
    "        \n",
    "        line = regexp.search(fp.readline())\n",
    "        \n",
    "        head['file'] = line.group(1)\n",
    "        \n",
    "        ## Linha 2\n",
    "        regexp = re.compile(' ([\\w]*) '                      # site\n",
    "                             '([\\d]{2}/[\\d]{2}/[\\d]{4}) '    # datei\n",
    "                             '([\\d]{2}:[\\d]{2}:[\\d]{2}) '    # houri\n",
    "                             '([\\d]{2}/[\\d]{2}/[\\d]{4}) '    # datef\n",
    "                             '([\\d]{2}:[\\d]{2}:[\\d]{2}) '    # hourf\n",
    "                             '([\\d]{4}) '                    # alt\n",
    "                             '(-?[\\d]{3}\\.\\d) '              # lon\n",
    "                             '(-?[\\d]{3}\\.\\d) '              # lat\n",
    "                             '(-?[\\d]{1,2}) '                # zen\n",
    "                             '[\\d]{2} '                      # ---- empty\n",
    "                             '([\\d]{2}\\.\\d) '                # T0\n",
    "                             '([\\d]{4}\\.\\d)')                # P0 \n",
    "        \n",
    "        line = regexp.search(fp.readline())\n",
    "    \n",
    "        head['site']  = line.group(1)\n",
    "        head['datei'] = line.group(2)\n",
    "        head['houri'] = line.group(3)\n",
    "        head['datef'] = line.group(4)\n",
    "        head['hourf'] = line.group(5)\n",
    "        \n",
    "        def datenum(d):    # Conversão da datenum do matlab pra python (Link [4])\n",
    "            return 366 + d.toordinal() + (d - datetime.fromordinal(d.toordinal())).total_seconds()/(24*60*60)\n",
    "\n",
    "        jdi = head['datei'] + ' ' + head['houri']\n",
    "        jdi_strip = datetime.strptime(jdi, '%d/%m/%Y %H:%M:%S')\n",
    "        \n",
    "        jdf = head['datef'] + ' ' + head['hourf']\n",
    "        jdf_strip = datetime.strptime(jdf, '%d/%m/%Y %H:%M:%S')\n",
    "        \n",
    "        head['jdi'] = datenum(jdi_strip)\n",
    "        head['jdf'] = datenum(jdf_strip)\n",
    "        \n",
    "        head['alt'] = int(line.group(6))\n",
    "        head['lon'] = float(line.group(7))\n",
    "        head['lat'] = float(line.group(8))\n",
    "        head['zen'] = float(line.group(9))\n",
    "        head['T0']  = float(line.group(10)) \n",
    "        head['P0']  = float(line.group(11))\n",
    "        \n",
    "        ## Linha 3\n",
    "        regexp = re.compile('([\\d]{7}) '      # nshoots    \n",
    "                            '([\\d]{4}) '      # nhz\n",
    "                            '([\\d]{7}) '      # nshoots2\n",
    "                            '([\\d]{4}) '      # nhz2\n",
    "                            '([\\d]{2}) ')     # nch\n",
    "        \n",
    "        line = regexp.search(fp.readline())\n",
    "        \n",
    "        head['nshoots']  = int(line.group(1))\n",
    "        head['nhz']      = int(line.group(2))\n",
    "        head['nshoots2'] = int(line.group(3))\n",
    "        head['nhz2']     = int(line.group(4))\n",
    "        head['nch']      = int(line.group(5))\n",
    "        \n",
    "        ## Canais\n",
    "        \n",
    "        head['ch'] = {}\n",
    "        nch = head['nch']            # Número de canais\n",
    "        \n",
    "        regexp = re.compile('(\\d) '                # active\n",
    "                            '(\\d) '                # photons\n",
    "                            '(\\d) '                # elastic\n",
    "                            '([\\d]{5}) '           # ndata\n",
    "                            '\\d '                  # ----\n",
    "                            '([\\d]{4}) '           # pmtv\n",
    "                            '(\\d\\.[\\d]{2}) '       # binw\n",
    "                            '([\\d]{5})\\.'          # wlen\n",
    "                            '([osl]) '             # pol\n",
    "                            '[0 ]{10} '            # ----\n",
    "                            '([\\d]{2}) '           # bits\n",
    "                            '([\\d]{6}) '           # nshoots\n",
    "                            '(\\d\\.[\\d]{3,4}) '     # discr\n",
    "                            '([\\w]{3})')           # tr\n",
    "\n",
    "                            \n",
    "        channels = ''.join([next(fp) for x in range(nch)])  # Aqui eu imprimo todos os canais\n",
    "                                                            \n",
    "        lines = np.array(regexp.findall(channels))\n",
    "        \n",
    "        head['ch']['active']  = lines[:, 0].astype(int)\n",
    "        head['ch']['photons'] = lines[:, 1].astype(int)\n",
    "        head['ch']['elastic'] = lines[:, 2].astype(int)\n",
    "        head['ch']['ndata']   = lines[:, 3].astype(int)\n",
    "        head['ch']['pmtv']    = lines[:, 4].astype(int)\n",
    "        head['ch']['binw']    = lines[:, 5].astype(float)\n",
    "        head['ch']['wlen']    = lines[:, 6].astype(int)\n",
    "        head['ch']['pol']     = lines[:, 7]\n",
    "        head['ch']['bits']    = lines[:, 8].astype(int)\n",
    "        head['ch']['nshoots'] = lines[:, 9].astype(int)\n",
    "        head['ch']['discr']   = lines[:, 10].astype(float)\n",
    "        head['ch']['tr']      = lines[:, 11]\n",
    "        \n",
    "        # Criei os arrays phy e raw antes, pois no matlab elas são criadas enquanto declaradas\n",
    "        \n",
    "        max_linhas = max(head['ch']['ndata'])       # A solucao que encontrei aqui foi achar o max de\n",
    "                                                    # linhas possivel que phy e raw podem ter para declarar antes        \n",
    "        \n",
    "        if ach == 0:\n",
    "            phy = np.zeros((max_linhas, nch))\n",
    "            raw = np.zeros((max_linhas, nch))\n",
    "        else:\n",
    "            phy = np.zeros((max_linhas, 1))\n",
    "            raw = np.zeros((max_linhas, 1))            \n",
    " \n",
    "        # conversion factor from raw to physical units\n",
    "        for ch in range(nch):\n",
    "            nz = head['ch']['ndata'][ch]\n",
    "            trash=np.fromfile(fp, np.byte, 2)\n",
    "            tmpraw = np.fromfile(fp, np.int32, nz)\n",
    "            if ch == ach or ach == 0:\n",
    "                if head['ch']['photons'][ch] == 0:\n",
    "                    dScale = head['ch']['nshoots'][ch]*(2**head['ch']['bits'][ch])/(head['ch']['discr'][ch]*1e3)\n",
    "                else:\n",
    "                    dScale = head['ch']['nshoots'][ch]/20.\n",
    "            \n",
    "            tmpphy=tmpraw/dScale\n",
    "            \n",
    "            if ch == 1 or ch == 3:\n",
    "                # displace by dbin's\n",
    "                tmpphy[:nz-dbin] = tmpphy[dbin:nz]\n",
    "                \n",
    "                # repeat the last dbin values to keep size of vectors\n",
    "                tmpphy[nz-dbin:nz] = tmpphy[nz-dbin:nz]\n",
    "            else:\n",
    "                # correct for dead-time\n",
    "                tmpphy[:nz] = tmpphy[:nz]/(1-tmpphy[:nz]*dtime)\n",
    "            \n",
    "            # copy to final destination\n",
    "            \n",
    "            if maxz == 0:\n",
    "                maxz = nz\n",
    "            else:\n",
    "                maxz = min(nz, maxz)\n",
    "                head['ch']['ndata'][ch] = maxz\n",
    "            \n",
    "            if ach == 0:\n",
    "                phy[:maxz, ch] = tmpphy[:maxz]\n",
    "                raw[:maxz, ch] = tmpraw[:maxz]\n",
    "            else:\n",
    "                phy[:maxz] = tmpphy[:maxz]\n",
    "                raw[:maxz] = tmpraw[:maxz]    \n",
    "        \n",
    "        \n",
    "    return head, phy, raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "377ca665",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'file': 'RM1183023.324',\n",
       "  'site': 'Embrapa_',\n",
       "  'datei': '30/08/2011',\n",
       "  'houri': '23:31:40',\n",
       "  'datef': '30/08/2011',\n",
       "  'hourf': '23:32:40',\n",
       "  'jdi': 734745.980324074,\n",
       "  'jdf': 734745.9810185186,\n",
       "  'alt': 100,\n",
       "  'lon': -60.0,\n",
       "  'lat': -2.9,\n",
       "  'zen': -90.0,\n",
       "  'T0': 30.0,\n",
       "  'P0': 1013.0,\n",
       "  'nshoots': 600,\n",
       "  'nhz': 10,\n",
       "  'nshoots2': 600,\n",
       "  'nhz2': 10,\n",
       "  'nch': 5,\n",
       "  'ch': {'active': array([1, 1, 1, 1, 1]),\n",
       "   'photons': array([0, 1, 0, 1, 1]),\n",
       "   'elastic': array([1, 1, 2, 2, 2]),\n",
       "   'ndata': array([4000, 4000, 4000, 4000, 4000]),\n",
       "   'pmtv': array([950, 950, 990, 990, 990]),\n",
       "   'binw': array([7.5, 7.5, 7.5, 7.5, 7.5]),\n",
       "   'wlen': array([355, 355, 387, 387, 408]),\n",
       "   'pol': array(['o', 'o', 'o', 'o', 'o'], dtype='<U6'),\n",
       "   'bits': array([12,  0, 12,  0,  0]),\n",
       "   'nshoots': array([600, 600, 600, 600, 600]),\n",
       "   'discr': array([0.5   , 3.1746, 0.02  , 3.1746, 3.1746]),\n",
       "   'tr': array(['BT0', 'BC0', 'BT1', 'BC1', 'BC2'], dtype='<U6')}},\n",
       " array([[  1.61051432, 135.06666667,   2.00600586,  78.7       ,\n",
       "           2.83333333],\n",
       "        [  1.61010742, 132.96666667,   2.00691732,  66.46666667,\n",
       "           2.23333333],\n",
       "        [  1.60725911, 131.23333333,   2.00645345,  62.56666667,\n",
       "           1.43333333],\n",
       "        ...,\n",
       "        [  1.61458333,   0.        ,   2.00984701,   0.        ,\n",
       "           0.        ],\n",
       "        [  1.61376953,   0.        ,   2.01035156,   0.        ,\n",
       "           0.        ],\n",
       "        [  1.61437988,   0.        ,   2.01047363,   0.        ,\n",
       "           0.        ]]),\n",
       " array([[7.91600e+03, 4.05200e+03, 2.46498e+05, 2.36100e+03, 8.50000e+01],\n",
       "        [7.91400e+03, 3.98900e+03, 2.46610e+05, 1.99400e+03, 6.70000e+01],\n",
       "        [7.90000e+03, 3.93700e+03, 2.46553e+05, 1.87700e+03, 4.30000e+01],\n",
       "        ...,\n",
       "        [7.93600e+03, 0.00000e+00, 2.46970e+05, 0.00000e+00, 0.00000e+00],\n",
       "        [7.93200e+03, 0.00000e+00, 2.47032e+05, 0.00000e+00, 0.00000e+00],\n",
       "        [7.93500e+03, 0.00000e+00, 2.47047e+05, 0.00000e+00, 0.00000e+00]]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read = profile_read(arquivo)\n",
    "read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9145e62c",
   "metadata": {},
   "outputs": [],
   "source": [
    "phy = read[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3a65d8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(phy)\n",
    "\n",
    "# Aqui é um jeito provisório de fazer o header\n",
    "\n",
    "lista = []\n",
    "for i in range(len(read[0]['ch']['active'])):\n",
    "    wlen = str(read[0]['ch']['wlen'][i])\n",
    "    pol = str(read[0]['ch']['pol'][i])\n",
    "    photon = read[0]['ch']['photons'][i]\n",
    "    an = 'analog' if photon == 0 else 'photon'\n",
    "    tr = read[0]['ch']['tr'][i]\n",
    "    lista.append(wlen + ' ' + pol + ' ' + an + ' ' + tr[-1])\n",
    "\n",
    "###############################################\n",
    "\n",
    "\n",
    "df.columns = lista\n",
    "alt = 7.5*(np.array(df.index) + 1)\n",
    "\n",
    "df['alt'] = alt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "914c2745",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>355 o analog 0</th>\n",
       "      <th>355 o photon 0</th>\n",
       "      <th>387 o analog 1</th>\n",
       "      <th>387 o photon 1</th>\n",
       "      <th>408 o photon 2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7.5</th>\n",
       "      <td>1.610514</td>\n",
       "      <td>135.066667</td>\n",
       "      <td>2.006006</td>\n",
       "      <td>78.700000</td>\n",
       "      <td>2.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15.0</th>\n",
       "      <td>1.610107</td>\n",
       "      <td>132.966667</td>\n",
       "      <td>2.006917</td>\n",
       "      <td>66.466667</td>\n",
       "      <td>2.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22.5</th>\n",
       "      <td>1.607259</td>\n",
       "      <td>131.233333</td>\n",
       "      <td>2.006453</td>\n",
       "      <td>62.566667</td>\n",
       "      <td>1.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30.0</th>\n",
       "      <td>1.609294</td>\n",
       "      <td>130.533333</td>\n",
       "      <td>2.006340</td>\n",
       "      <td>62.700000</td>\n",
       "      <td>1.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37.5</th>\n",
       "      <td>1.608073</td>\n",
       "      <td>125.733333</td>\n",
       "      <td>2.005892</td>\n",
       "      <td>55.833333</td>\n",
       "      <td>1.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29970.0</th>\n",
       "      <td>1.615804</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.010620</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29977.5</th>\n",
       "      <td>1.618856</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.010539</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29985.0</th>\n",
       "      <td>1.614583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.009847</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29992.5</th>\n",
       "      <td>1.613770</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.010352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30000.0</th>\n",
       "      <td>1.614380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.010474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         355 o analog 0  355 o photon 0  387 o analog 1  387 o photon 1  \\\n",
       "alt                                                                       \n",
       "7.5            1.610514      135.066667        2.006006       78.700000   \n",
       "15.0           1.610107      132.966667        2.006917       66.466667   \n",
       "22.5           1.607259      131.233333        2.006453       62.566667   \n",
       "30.0           1.609294      130.533333        2.006340       62.700000   \n",
       "37.5           1.608073      125.733333        2.005892       55.833333   \n",
       "...                 ...             ...             ...             ...   \n",
       "29970.0        1.615804        0.000000        2.010620        0.000000   \n",
       "29977.5        1.618856        0.000000        2.010539        0.033333   \n",
       "29985.0        1.614583        0.000000        2.009847        0.000000   \n",
       "29992.5        1.613770        0.000000        2.010352        0.000000   \n",
       "30000.0        1.614380        0.000000        2.010474        0.000000   \n",
       "\n",
       "         408 o photon 2  \n",
       "alt                      \n",
       "7.5            2.833333  \n",
       "15.0           2.233333  \n",
       "22.5           1.433333  \n",
       "30.0           1.566667  \n",
       "37.5           1.233333  \n",
       "...                 ...  \n",
       "29970.0        0.000000  \n",
       "29977.5        0.000000  \n",
       "29985.0        0.000000  \n",
       "29992.5        0.000000  \n",
       "30000.0        0.000000  \n",
       "\n",
       "[4000 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('alt')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "97317b2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X07dvXwByco6rigULFpCSklJ3jsul/U/v37+fvXv3cmL+h759+5KVlcW+ffsA+PDDDxk/fnyj9svLy4mNjcXhcDB37tzT9re0tJSoqCiMRiPLli3j4MHGEXbHjRvH/Pnzcblc5Ofns3z5ctLS0hgzZgyff/45breb3Nzck9rLJ02axJw5cwCYM2cOV1xxxWn71Rw6n4ZpBnZnrbeMdyCl5JH/bmFPbjnv3ZpGYkTnTzJgMRm4c1wPAA54gpbty6tg7pqDfOt0888d6QzzxLtJTQzFqFcjeoWmwKdNm4bL5cLtdnPdddfVjdAfffRRMjIyEEKQmJjIv//9bwCWL1/OU089hcFgQK/X8+abbxIWFtagXrPZzHvvvceUKVNwOp0MHz68SZfJ5557jhEjRtCtWzcGDBhw2snlm266icsuu4zU1FQGDx5cdyOqz1VXXcWqVasYNEjzeps5cyYxMTFcc801LFmyhJSUFJKSkhgxYgTBwcGNzv+///s/br/9dt555x26du3KZ5991uzv81QIb7CZpqamyrZM1vHq/3bz96X76BtjZdGMcW3QwzNjdnomLy3axeOX9OXu8T1PWs4XJu8OF1Uya8EKygyhrDlQRLndib9Rz9BuIaQlhjO8eyhDuoTi79c5Yv74wjWpJT09nejoaPr169fRXTljOktsmYqKCiwWC4WFhaSlpbFy5UpiYmIalGmuLDt37mx07YQQG6SUqU2V99GRu2YWqHZ2vHlg9f5C/vbDLiYPjOW3npGuL9MlLIDLe/oxYcJwbNVOftqTz9oDRaw9UMRrS/YgJRj1giFdQ7n1nERG9QwnJMCvo7utULQLkydPpqSkhJqaGv70pz81UuztiW8qd4/NvarG1aH9KLbVMGNeBt3CA3npmoGdYgK1LQk0GZg0IJZJAzQHqtIqBxsPFrPmQBHfbjnKPXM3AjCyRxjXpXZhdK8IIi0mdLpf1/ek8F06co2Ebyt3R8cp98oaJ7/9aAOFtmoWTBtNYCecQG1rgv2NTOwbxcS+Ufz+giQ2HtI8b77KOMLvP90MaLb8lPggBiWEMLFvFGmJYUrZtzFSyl/dQKOz0xrzuU9qnFpXSHsHKfeKaic3vb2aLUdKef2GIaTEN55E+bXjZ9Axskc4I3uEM+P83vzimZDdl1fBluwS3luZxb+X7yfSaqJHRCADE4KJspo5t18UPSPbJqbHrxGz2UxhYaEK+9uJqI3nbjabW3Sejyp3TalXO9243fKsjvycLjcz5mWw7WgZb/5mGBclnz0bW2dFCMHoXhGM7hVRt6+qxsUP24/x0558sgptvLcyC6db8peFOzEbdVzQP4a+MVa6RwQyskc4YYHKbt8cEhISyM7OJj8///SFvRi73d5iZeetNEeW2kxMLcE3lXu9iVS700WA39kR0+WW/P7TzSzemcuzVyQrxX4G+PvpuXJIPFcO0RaqOF1uCm01fLruMJn5FSzemcc3m4/WlR/ZI4y+MUH0iAykR4SFfrFWwlVawUYYjcYWZfPxVtLT01u0FN+baS9ZfFO51zPH2B1uzoYzRkW1kxnzMli8M5dHL+7DLaMS27/RXxEGvY7oIDMPnKfF4HC5JVUOF+sOFLHxUDFLd+Xx2frD2OpNoocH+jGkayj944LoG2NlWLdQooN8Y7SnUJwOn1Tu1fWUe0llTbs/smcXV3LHnPXsyS3nz5cnM+2cxHZtTwF6ncBiMtRN0D50YR+klOSXV5OZb2PjoWKyCmxsOFjM0l25dTH+A/30+PsZiAk2ERPkT2ywmbTuYfSOthAX4k+QWeWXVfgGPqnc7Q43QmjhB659cxU/PTIBazv90244WMxvP1xPtdPN+7emMS4psl3aUZweIQRRQWaigsyM6hlet9/ucLH7WDlrDxRxtLSKqhoXx8rsZBdXsmZ/IR+uPr6k3GoyEBtiJi7En9hgf6qLa7CF5RATbMLfaCAp2oJBrbZVdAJ8U7k7XVw6IJbRvSJ4YsFWxs1cxvRzunPvxJ5ttgy+xunmreWZ/H3JPmJDzMy7K5VeUd6/Yu7XiNmoZ1CXEAZ1CWl0zOWWbMkuIbu4ipzSKo6W2DlSom1vyS6lyObgi70b68pbTQbCLX5EWc3EhpgJDzSRFG0hwmIiJMCI1WxErxP0jAxU3iiKDsUnlXthRQ0RFhNT07qiF4IvNmUza/EecsvtPDW5P2bjmS1933SomD98sZVdx8q5dEAsz1+ZQqjy1uiU6HXaatkhXZtOGPbDkmXE9hlCcaWDksoa1h4oorTKQW6ZnU2HSsgrtzcZhdRqMhAVZMJiMhDkb2RIlxCMeh3+fnp6RAYSEuBHfIg/ZoMeg14Q4KdXNwNFm+Jzyr2i2klFtZOYYG3i7LrhXbhueBee/3YH/1lxgNWZhfzpsv6M6hHeYiW/J7ecD1ZlMXfNIaKtZt6+JZUL+kef/kRFp8WkFwxMCKn7fMXghmmCXW7J0ZIqiitrKKl0UG53UmZ3sPtYOXnldsqqnBwosPHz3oJTtqMTEOhnqLP9B/sbCfY3EuR5D/Y3EmSu3Wcg2P/4U4JC0RQ+p9yPlWqxlmNO8Ir446X9GNM7gj98sZVb31uHUS/oF6uthBzUJYSkaAvhFhPhgX6YjXqklBwqqiSrsJIdR8v4YfsxMg6XYNAJfjOiG49e3Kfd7PiKzoNeJ+gSFkCXsIBTlpNS4nBJqmpcbD9aSpXDxZGSKmqcblxuSUW1k3K7k21HStl+tIzSKgdlVQ6c7lOvTAwJMKIXAp1OkBCqPQmEBBgJ8DMgkfSKsmAxGdDrBJnZDmxbcgg06al2uokL9ickwIgQYDUbsZoMajWwD+Fzyj23TFPuJ7q8CSGY0CeK9Ecm8NPufDYdLmHz4RIWbDrSYEINwGTQYdCJBm51/WKDePLSflw1JF75TytajBACP4PAz6DjnHqLtU6FlJLKGhdldodH2TvrlH6p51Voq0ZKzYkgt8xOtdPF3rwKqmpcuNySLzYeaVDnu9s2nqQ17enBZNBj0AkMekG4xURciD9SyroFgZFWE7HB/hgNoi4IXKTFhNGgq/NIig0y43BpTg3+fgb8jXoC/PT4++nrts1GPS7PCcok1T74nHKvG7kHN+3PbDLouTA5hgs9C4zcbklmfgVZhZUU2aopqKihrMpBtdNNrygLSdFWuoYFnLQ+haK9EEIQaDIQaDIQG+zfqjrK7Q5qnG6cbsnPK3+h36Bh2B0u/PR6jpRUUVblAKi7gdgdLpxuidMlySu3k1NqR68T+Ol1GPSC3bnl/LQnH5dbIoTmWHCah4vTYjLoCPY3UlXjwmTUYTLo697NRh0mgw6zUY/J4Dlm0HH4qJ1v8zdj1OsI8jdg1OlwSYlbStxuiZ9BR0ywP/YaF0H+Bvz9DNTePurfRwQCnQCL2YDTJTEZdbjd4HC7sXq+e7fn5hbib6SyxoVeJ/A36nG63ZgMeqxmA0IIDDrt5m3QCa+4WbWbchdCXAy8DuiB/0gpX2yvtupzrKxps8zJ0OkEvaOt9I5Wni4K36O+6TDCX0dy3PE4RwMSzjzmkZSSgooa3FJT9m435JRWYTJo81lVDidVNW4qa5xUOVxU1biorHFhd7rQexRgQUU1JZUOAk0Galxuqh1u7E4X1Q431Z73YlsNds9nu8ONdLo5VFlAjUtSWlWDW2pPHjoh0AlBjctd92RwttEJLXaSUacDAQLtRi3qb1N7kxGkhLpoj5QB7aLchRB64J/ABUA2sE4I8bWUckdbtnO4qJJ3t1XzTd5mnG5tdLLjaBlBZkOnSQahUHRmhBBEWhuaKc/GU+7pkqi43NqCNn8/PeV2R5OpN2W9shXVTgw6QZXDhVGvQycElTVOKuxOJGA26upuQE6XpNrpwqDXUVXjxFbtQqKFyKhxuqlxueuegGrbk1IiPe1LpOdd+2yyHWv7L4j2G7mnAfuklPsBhBDzgCuANlXuZXYHW/JdBFYUYtB7Hov0OqakdmnLZhQKRSdDrxN1N5lgf+92fEhPL2yXettLuccDh+t9zgZGtHUjyXHBvDYxwGfSoCkUCkVb0V7KvanZhAYGMCHEXcBdno8VQojdrWwrAji1E3HnwVdk8RU5QMnirShZNLqd7EB7KfdsoL5tJAE4Wr+AlPIt4K0zbUgIsf5kCWI7G74ii6/IAUoWb0XJcnraKwLSOqC3EKK7EMIPuAH4up3aUigUCsUJtMvIXUrpFELcD/yA5gr5rpRye3u0pVAoFIrGtJufu5RyIbCwveqvxxmbdrwIX5HFV+QAJYu3omQ5DaI1WbUVCoVC4d2c1uYuhHhXCJEnhNhWb98zQogjQogMz2tSvWN/EELsE0LsFkJc1F4dVygUCsXJOe3IXQgxDqgAPpBSpnj2PQNUSClfPqFsf+ATtEVMccBiIElK6eIUREREyMTExFYJYLPZCAwMbNW53oavyOIrcoCSxVtRsmhs2LChQErZZPq309rcpZTLhRCJzWzrCmCelLIaOCCE2Iem6Fed6qTExETWr1/fzCYacrplyGcdKcHtBL1RC7Shq/dwVJwF+xZDtzEQFKeVMweDsxps+WxdMo8BXUIg5VpY9jxE9Yduo7Xzuo4Ev0BwVMHhtRDoiSwYN0Q7Lt0Q3hNshWAv0bZPh9sNRzdBzACtv6WHwRIDhjNLPOJ11+QMULJ4J0oWDSHEwZMea47N3aPcvz1h5D4dKAPWAw9JKYuFEG8Aq6WUH3nKvQN8L6X8bxN11i1iio6OHjZv3ryWSeWhoqICi8XSqnPbksCKA5jt+SRkf0NA5REKIkYQc2wxdnMMZUFJBNoOYi3PROBGIpBCj046W9SGWxhx6/wwuGwASAQufUDd50r/WMz2PHTSRUVgN6QwYKouREgHLn0AOreDyoAE9K5KavxCMdvzCKw8TI0xCLfOiLm6kBpjCNkJk4koWEtBxHCKwobhV1NMaXB/DE4bpuoCQkq24jCGABKnwUJIyVachgAKIkZR4xdCcPZSHGFJlIQMwL8qh8qABPxqiqjxC2sYkq8T4C2/r7ZAyeKdnIksEydO3HAyH/nWKvdotBVVEngOiJVS3iaE+Cew6gTlvlBK+fmp6k9NTZWdbuSetwtKs+Hwalj3H6gqblwmsp82Mi/cByFdwBQE4x+DfT9qo3WdHiqLoOQQdDuHrYV6BoTYYe//4KK/aGWy10OXEdo5OVtAb4DBN0HZEe08oYPYQeDwnBfdHwIi4OBKbZQv3WAwQ2UhRPSGIxvAP0w7ZjBBwnDY9BH0nAiJYzRZCveBNRbKc47LYjCDq0ar70QMZu0pxO3Utp32pr+z4C6Qcg1Ul2ltud3Q+3zw96S4s5fBgZ+g94Wa7H6Whk8+HYAaIXonShYNIcRJlXurXCGllLn1Kn8b+Nbz8bQrUzs9NZWaGeT9SccVeq8LNOXYZQSEdNVMM0WZmuLUNxG0KHF0k1UXpqfDhAlw/tPHdyZ55qR7n3/6vo28+/j2mBnNEMbDJS8dH1EPvB4K9kKXNNifrskRkqjdXPwsEBgJcYPBEgUI2P099L1Uu1H89zYIjGRdwESGV6XD9gUQGAW2PK3u0sOw8jVte/27x9sP6wnBCdqNp6YCrHFgywe3A8J7wZCbIToFElLBP6T5cikUv2JapdyFELFSytph3VVArSfN18DHQohX0SZUewNrz7iX3sKOr+CLu7SRqSkYLvordBul2b1PxNqJcqvWN5UEhEFXT4y3nhO1F5z85jLq3uPbt2rLGmzp6XDxW9oovfeFkL9LG7W7nVBdDlUlsHshxA6EH5/W5hKqirQbiyUastdpNxPQniIW17vZBURAaDeI7AuOSq3eEXdrTzFuJ8SkaNdGCO0lZaczBSkUbcFplbsQ4hNgAhAhhMgGngYmCCEGo5llsoDfAkgptwshPkUL7esE7judp8yZElyyHeT49v0Hzt2hKaP0F7WJyqSLIPU2CE1svzY7OwY/6HeZth076Ph+S5T2njBMe+9/RdPnu5yamSkiCSoLNDPNwoeh+CAYA2DPIs3UBPDL34+fFxillY3qB9HJsOs7uP5DyPgYhk3Tbh5BcW0rq0LhhTTHW2ZqE7vfOUX5vwB/OZNONZvMZQzJeAIMmXDJTM0e3dZs+gi+fkCzNXcZCTfOO24jVrQfegP0rV0+0Ut7u2Mx2EuPm76k1OYW1r8L3c7RRvKZy8Dor92MD6/WbgTvXKCdv+E97T0hDZKv1MpJt/YUEBgFkUlnW0qFot3o3DlUu4/nUJer6br+HcjZDJP+BvFD267+g7/A1w9Cjwlw5Zudy9Tii5iDtRccN7t0H6u9ahn9O03pL39Zm1wOioctn0JAKGStgK6jtLmAH55oXH9QvOZ+6hcIg24Aayw6V/XZkU2haGM6t3LX6djfcxpdh5wL3z8G3zwIv/25bUw0+bvhs1s1++6U948rFYX3IwSMf+T457jB2vs4z77xjx33BHK7ICdD837K3QZHNmoTuTu+BGCUwQoHUzSbfmh3baI3fihMnuXxEnJByUHt3JOZmBSKDqBzK/daht4MSM18sucH6HPxmdVXdAD+PU7zAPnN50qx+xpCNLS7h3Q5Pj8A4HLA6n+BMYDqn/6B8dg2bZ5lm2e5RsFu2PpfzaRj9Nc8fAAueFZbEBYQrk30BoSdPZkUihPwDeUOmqfFL2/At/+nreY8E5e5n17SHu3vXqHZdxW/LvRGzbwDbKjozvgxozRTzfjHtBG83ghr/q2ZAhNStRtF5lL48amG9fQ6X1ttLHTHVxL3Ok9b36BQtDO+o9wNJrjqTfjP+bDocW27NayeDZs/gdEzlGJXIHUGTbGDNuFaO+naY3zDgm4XHF6jvVcVw4HlsO5tLdxEfXqeq/22YgbAts817yFlzlG0A76j3EGzhY59CJbPhJ3fwJQ5zVv8U8uWz7QbQ9/JcN5Tpy+vUNSi02seO7X0u0xzvdSboCxbc6ONToFNH2qj/DoEDJiiefqYgjRF32O8dl7tpLFC0Qp8S7mDNml2eI22jH3Bb+H+dc2zfRYdgC/v0YJ6XfOOenRWnBlCaKNz0Eb7Pc/VtkfeC0c3ahO3fS7WBhR7f9AmagE2f6zdBGwF2sAk7S7tc9kRbeVue7j7KnwSr/2lOBwOsrOzsdtPEqfEQ3BwMDt37my4M+0lGFYD5bmwc2fzlLu9BC74GIJiYd+B1nf8DGhSlk6IxWLB4XBgNDYReuHXTkQv7TXwOu1zjwna/I7LoXl75e+GvB3aKuhNH2kvoQfp0lw1LVGaT/5FL2j1KBQnoTkrVN8FJgN59QKHhQHzgUS0FarXSSmLPcf+ANwOuIAHpZQ/tKZj2dnZWK1WEhMTEad4NC0vL8dqtTZ9sDRbGxH5GbR/DL+AxmWk1AJWlTjAL6p5oXLbiVPK0kmQUpKdnU12djbdu3fv6O50DoTQVvTWzhNVl2uvVf/U7P01Ni2mT+ZSyN2uhWne+4Pms3/e01B+VLP1F+yB+GFaPJ6wnh0edE3RsTRn5P4+8AbwQb19jwNLpJQvCiEe93x+zJOs4wYgGU+yDiHEaZN1NIXdbj+tYj8tQfGaZ0NFnhajxBKpxR0xmLQIh24XlB0Fh02zcQbHt74tBQBCCIKDgzly5EhHd6XzYrJqr4tOWOhdGwyuLEebjE3/K7x3ErdfSwyYg7SgdqGJkHZne/ZY4YW0NlnHFWjxZgDmAOnAY7QyWcfJOCPFrlWgxRIxh2rKvfyY9kKghcURmm291idZqJFOW3DG101xaoJi4Zz7oc8l2ig+vCfs+Z+2MKuyUBu9H92kRexc/U/tnFVv0DV0HHy/SIvOOe5hCOuh1nD4MK21uUfXRoWUUuYIITzRoIgHVtcrl+3Z17EY/LQAVDU2LZZ4jQ2cVaAzaHFFmpikstvtjBs3jurqapxOJ9deey1//vOfAXjmmWd4++23iYzUslu98MILTJo0iaysLPr160efPn0AGDlyJG++2UqXzJPwzDPPYLFYePjhh5tVvqSkhI8//ph777339IWbQVFREddffz1ZWVkkJiby6aefEhqqYu10COE9j5sRm4pMWpYDGXO1Ac2h1fQ48CHUTift+haMgdpKXmMgdB+n1SXlGWfiUngHbT2h2tSQrclsICdkYiI9Pb3B8eDgYMrLy0/boMvlalY5DR3oQ7QFTtKtda2yqsmSUkq++uqrusnBCy+8kHHjxpGWlkZ1dTX33nsvDz74YF358vJyKioq6N69Oz///HOD/c2lObJUV1djNBqbXW92djZvvPEGN998c7P7cSqeffZZxowZwxdffMGrr77Ks88+y7PPPtugjMvlwm63N7qmnZGKiopOLkcqBAL9JuMXso4wSikKG0K3g58Rf/R7WPxMg9IunR9H4idzsNsULBVZOIwW7OYY3HrvUvid/7ocp91kkVKe9oU2cbqt3ufdaNmXAGKB3Z7tPwB/qFfuB2DU6eofNmyYPJEdO3Y02tcUZWVlzSp3JthsNjlkyBC5evVqKaWUTz/9tPzb3/7WqNyBAwdkcnLyaetbvHixHDx4sExJSZG33nqrtNvtUsqGsowfP17+7ne/k6NGjZLJyclyzZo1dW3feuutcvz48bJ79+7y9ddfrzvnlVdekcnJyTI5OVnOmjVLSinl9ddfL81msxw0aJB8+OGHpdvtlg8//LBMTk6WKSkpct68eVJKKZctWybHjx8vr7nmGtmnTx954403Srfb3ajvSUlJ8ujRo1JKKY8ePSqTkpIalSkrK2v29fN2li1b1tFdaDMayXJwtZTbv5Ky+JCUb50r5Us9pPx4qpRPBzV8/SVeykVPSFmYKeWS56Rc+oKULleHyFCLT1+XFgCslyfRq60duX8NTANe9Lx/VW9/2yfr+P5xOLa1yUP+LmfrfH9jBsAlL56yiMvlYtiwYezbt4/77ruPESNG1B174403+OCDD0hNTeWVV16pM00cOHCAIUOGEBQUxPPPP8/YsWMb1Gm325k+fTpLliwhKSmJW265hdmzZzNjxoxG7dtsNn755ReWL1/ObbfdxrZtWk6UXbt2sWzZMsrLy+nTpw/33HMPW7Zs4b333mPNmjVIKRkxYgTjx4/nxRdfZNu2bWRkZADw+eefk5GRwebNmykoKGD48OGMGzcOgE2bNrF9+3bi4uIYPXo0K1euZMyYMQ36lJubS2xsLACxsbHk5eU1/ztXeBddj/+eue0HLdmJ0ayldlwxS3NIiB+qrbJdPRtWvXG8/MY5motm7wu0TFzdx2vOC8e2aH75ap1Ih3PaGURPso5VQB8hRLYQ4nY0pX6BEGIvcIHnM1LK7UBtso5FnIVkHe2JXq8nIyOD7Oxs1q5dW6dc77nnHjIzM8nIyCA2NpaHHnoI0JTdoUOH2LRpE6+++io33ngjZWVlDercvXs33bt3JylJW8Y+bdo0li9f3mT7U6dqofTHjRtHWVkZJSUlAFx66aWYTCYiIiKIiooiNzeXFStWcNVVVxEYGIjFYuHqq69uYB6qZcWKFUydOhW9Xk90dDTjx49n3bp1AKSlpZGQkIBOp2Pw4MFkZWWd8Xeo6CToDZpiBy1ezg1zYdJMLfTxNf+BBzfBhX+B6z/Swl/HDNASrmycA3Ovhecj4YU4LeDe/N/Ahvc1d85aXA44tFqz6SvOCq1N1gFw3knKt32yjlOMsKvOgm94SEgIEyZMYNGiRaSkpBAdfTyu+5133snkyZMBMJlMmEwmAIYNG0bPnj3Zs2cPqanH89fKFvy4T/Q6qf1c2wZoNyCn09nsek9Vrql6TyQ6OpqcnBxiY2PJyckhKiqqURmFDxLaTfPQqWWwRy0UZ8Hm+bBlvuZxlr1OS5SyeyH870/QZ5LmkZO9TluZe/FL2qpb5YPf7qhv+CTk5+fXjZSrqqpYvHgxffv2BSAnJ6eu3IIFC0hJSak7x+XSHlT279/P3r176dGjR4N6+/btS1ZWFvv27QPgww8/ZPz4E4JQeZg/fz6gjbaDg4MJDj6529q4ceP48ssvqaysxGazsWDBAsaOHYvVam0w+Tpu3Djmz5+Py+UiPz+f5cuXk5aW1uzv5fLLL2fOnDkAzJkzhyuuUEGvftWEJsKEx+DBjVqmrAczYPid8JsvtDDJuxfC2re0BVYAix6DWcmw6AltJJ+7XYu0eXCVGtW3MV4bfqCjycnJYdq0abhcLtxuN9ddd13dCP3RRx8lIyMDIQSJiYn8+9//BmD58uU89dRTGAwG9Ho9b775JmFhDUMfmM1m3nvvPaZMmYLT6WT48OHcfffdTfYhNDSUc845h7KyMt59991T9nfo0KFMnz69TlHfcccdDBmiuceNHj2alJQULrnkEmbOnMmqVasYNGgQQghmzpxJTEwMu3btatb38vjjj3Pdddfxzjvv0LVrVz777LNmnaf4lRDWHS59Wdvu5Xm4txVobscVebDmTS0u/up/HvfBr6XrKC1wWurtWnyookxIvlpbjKVoMaIlZoL2IjU1Va5fv77Bvp07d9KvX7/TnusLS/ZrqS/LhAkTePnllxuYdDoL5eXlZGdnN+v6eTvp6elMmDCho7vRJniNLC6HFjtn8Z/h0CotuUl4T9ifDkX7IShBi6QJ2krbHuO19IddRmiB1cwhrKvpwfBJt/jExO2ZXBchxAYpZZNKQo3cFQrF2UVv1F61I/z6bHgflr2ghd0eNBVWvg57Fmk2/XoMB6j+Ba78l/ZUIIRm1tn5NSSOhcJMLS5PdP+zIZFXopS7l+IrCzQUihYxbLr2qqXfZHDWaCGPd3+vefJ8Nl37vPVTLbS3vUxLlRjZV1PugVFg87joGvy1yJsDp2g5cA+uhFH3/yri5CvlrlAovBuDn2bLH+UJofHgJtJXrGJCeAH870mISYHqCk2xRyRpo/ZaXNWw53vtpTdpn9f8WzPxmIO1eofdqmXPKjuq+f7nbIacLTDkN536JuDVyl1KqYJQdUK8YR5H4cMYPC67A66FlGs0Bex2aYHTrHFalFeDv/bZHKy5YM6/RTPRHFqlBU6rLNSSm1cWagu0yjxRTNN+C2s1Bwn2p8PQm7UFWrVtSLdmUuoEeK1yN5vNFBYWEh4erhR8J0JKSWlpKWazuaO7ovg1UKsbdHoITtC2TR4Hi5Au2nuPCfB/W8HPqsXBr8jTUiACZK3Q/PENZs07Z+2/IT5Vm+Td9a3m2RPcVQs0aCvQ9o+8R8uzPOb/tNG9l+K1yj0hIYHs7Gzy8/NPWc5ut/uMIvEVWWw2G4MGDerobigUx6kNbdznkob7E8fAXcu0bWe1ZpoJ6aYtsnLYNe+czKWaUg+MgnX/gaXPQUAEfHUfHNmgjeZjBmpZsoqzIPkq7Qli5Wuaf3/KNeBngW6jzqbEZ6bchRBZQDla1iWnlDL1VFmaWoLRaGxWJp/09PQ6f+7Ojq/Ikp6erlLsKTofBpNmg6/FaIbU27RXLf0mQ+kR6HU+fDsD1r/rseW/f7zM8pe1p4baeFibP9HeB0zRRv/nP+OJpd++/vttMXKfKKUsqPe5ySxNbdCOQqFQdCyxg7QXaG6YFz6v2e5zd0BlAVhj4fPbNcU+7lFNgS/7q3aj2OpZ8PeW50lB6EC66R85Btph/UF7mGVOlqVJoVAofIsAzwr0hGHH992Vrpl3Inprn0feq6X13P4lVBzTXDrtZZpJKCCc/Hw37RGh6YxWqAohDgDFaAk5/i2lfEsIUSKlDKlXplhK2ShVzwnJOobNmzevVX2oqKjAYrG06lxvw1dk8RU5QMnirShZNCZOnHjSFapnqtzjpJRHPWn2fgQeAL5ujnI/oZ584GAruxEBFJy2VOfAV2TxFTlAyeKtKFk0ukkpI5s6cEZmGSnlUc97nhBiAVoy7FwhRKzUcqvGAqfN5nCyzjUHIcT6k925Ohu+IouvyAFKFm9FyXJ6Wh3yVwgRKISw1m4DFwLbOJ6lCRpmaVIoFArFWeJMRu7RwALPAiMD8LGUcpEQYh3wqSdj0yFgypl3U6FQKBQtodXKXUq5H2i0UkVKWchJsjS1E2+dxbbaG1+RxVfkACWLt6JkOQ1eEc9doVAoFG2LSrOnUCgUPohXxJaJiIiQiYmJrTrXZrMRGBjYth3qIHxFFl+RA5Qs3oqSRWPDhg0FJ/U2lFI2+wX0ATLqvcqAGSeUmQCU1ivz1OnqHTZsmGwty5Yta7TP6XLKl9a+JLPLs1tdb0fQlCydEV+RQ0oli7eiZNEA1suT6NUWjdyllLuBwQBCCD1wBFjQRNGfpZSTW1J3W7KraBcf7viQLflb+GjSRx3VDYVCoegwzsTmfh6QKaVs7crS9sMT4rnaVd2x/WhDHC4HORU5dZ+rnFUUVPnKAj2FQtHWnInN/Qbgk5McGyWE2AwcBR6WUm4/g3ZajF5oGdFd0nU2m21zNudv5pvMb+gW1I2v9n3F7uLd9A7tjb/Bn6zSLMpqyugZ3JOJXSdy54A7CTAGdHSXFQqFl9AqV0ghhB+a4k6WUuaecCwIcEspK4QQk4DXpZS9m6ij3QKHHao+xN+O/Y0YYwx/jPtjq+rtCGpl+ansJ5aXLyfPmYdAIJEE64NJ8U+hwFmAW7oxCAO9zL3YY9/DHvseJJJQfSiXh15Osn8y/jr/DpfDF/A1WaxWK4GBgej1+o7uzhkhfSgFZ3Nkcblc2Gy2RiksTxU4rLUj90uAjScqdk9Hy+ptLxRC/EsIESEbxnxHSvkWHuf91NRUOaGV8YzT09M58dyMvAz4Hsz+5kbHvJn09HSc3Z38N/2/JAYlEuWM4vWJrxPhH0G4fzhGXdMJMNbmrGXFkRWsylnFnII5GHVGbux7Iw8MfQCT3nSWpWj6mnRWfE2Wrl27YrVaO336yvLycqxWa0d3o004nSxSSgoLCykvL29WAqNaWqvcp3ISk4wQIgbIlVJKIUQaml2/sJXttIoaVw0ATrfzbDZ7Rkgp2Vm1kw9WfkBKeApzLpmDn96vWeemxaaRFptGeU053x/4ns35m5mzYw6f7/2cewffi91pJzowmkndJ2HQeYX3q6KDsNvtJCYmdmrF/mtDCEF4ePhpU46eSIv/04UQAcAFwG/r7bsbQEr5JnAtcI8QwglUATfI1th+zgCH2wF0Hpu73WnnkZ8eIT0vnXhLPC9PeLnZir0+Vj8r1/W5juv6XMeVva7knxn/ZOa6mXXHfzz4I7el3MagyEHohFq/9mtFKfbOR2uuWYuVu5SyEgg/Yd+b9bbfAN5ocU/akNqRe2dQ7jWuGmakz+CXI79wRcgVPDX5qVYp9hMZHjOcdy96l51FOwn2C2bJoSW8vvF10g+nkxSaxMQuExkZO5LUGJ+ImqpQKE7AJ4dvNW6Pcnd7t3KvcdXwyE+PsPLISp4e9TTnB5/fJoq9Fp3QkRyeTII1gWnJ0/jp+p94bvRzCARvb32bW3+4lT+t/BM2h63N2lQoToXdbictLY1BgwaRnJzM008/XXcsIyODkSNHMnjwYFJTU1m7di0Ac+fOZfDgwXUvnU7Hli1bOkqEOtpqon3BggUkJyej0+lYv359m9QJXhJ+oK3pDCN3l9vFvYvvZc2xNTye9jjXJF1D+tH0dm3T6mflyl5XcmWvK6l0VPKfrf/h7a1v8+3+bxkWPYxu1m5cnXQ1yeHJ7doPxa8Xk8nE0qVLsVgsOBwOxowZwyWXXMLIkSN59NFHefrpp7nkkktYuHAhjz76KOnp6dx0003cdNNNAGzdupUrrriCgQMHdrAkbUf//v354osv+O1vf3v6wi3AJ0futROp3qzc5+yYw5pja3hq1FPc1O+ms95+gDGAB4c+yIeXfMjN/W4m15bLp3s+5dZFt/LL0V/Oen8Uvw6EEHUjXofDgcPhqLMnCyEoK9Oc7UpLS4mLi2t0/ieffMLUqVObrHvJkiUMGTKEAQMGcNttt1Fd3XgR49tvv83w4cMZNGgQ11xzDZWVlQBMnz6dBx98kHPOOYcePXrw3//+F9DcR8877zyGDh3KgAED+OqrxrmHpJQ88sgjpKSkMGDAAObPnw+A2+3m3nvvJTk5mcmTJzNp0qS6euvTp08f+vTpc9rvrqX49MjdLd0d3JOm+WTXJ8zaMIuJXSZybe9rO7Qvg6MGMzhqML9P/T35lfnc9eNd/PbH3zImfgwpESnEBsZyVa+r1CScD/LS2pfYVbSrTevsG9aXx9IeO2UZl8vFsGHD2LdvH/fddx8jRowA4LXXXuOiiy7i4Ycfxu1288svjQcZ8+fPb1LB2u12pk+fzpIlS0hKSuKWW25h9uzZzJgxo0G5q6++mjvvvBOAJ598knfeeYcHHngAgJycHFasWMGuXbu4/PLLufbaazGbzSxYsICgoCAKCgoYOXIkl19+eYP/hy+++IKMjAw2b95MQUEBw4cPZ9y4caxcuZKsrCy2bt1KXl4e/fr147bbbmvR93km+OTI3Ztt7vtL9/PK+lcYEz+GVye86lVKMzIgkg8v+ZB7Bt3DqqOreHPzmzz9y9M8ufJJZq6bSWZJZkd3UeED6PV6MjIyyM7OZu3atWzbtg2A2bNnM2vWLA4fPsysWbO4/fbbG5y3Zs0aAgICSElJaVTn7t276d69O0lJSQBMmzaN5cuXNyq3bds2xo4dy4ABA5g7dy7btx9fPH/llVei0+no378/ubnaEh4pJU888QQDBw7k/PPP58iRI3XHalmxYgVTp05Fr9cTHR3N+PHjWbduHStWrGDKlCnodDpiYmKYOHHimX1xLcQnR+61rpBO6V1+7k63kz/+/EfMBjPPnvOsV/qcW/ws3Dv4Xq7vcz1mg5kX177IV/u+QiL5dPenjI4bzSXdL+Hi7hd3dFcVZ8jpRtjtTUhICBMmTGDRokWkpKQwZ84cXn/9dQCmTJnCHXfc0aD8vHnzTmqSaa639fTp0/nyyy8ZNGgQ77//Punp6XXHTKbjC/5q65s7dy75+fls2LABo9FIYmIidru9WW2fZQ/wRrR45C6EyBJCbBVCZAghGk3tCo2/CyH2CSG2CCGGtk1Xm483mmWklPx909/ZVriNP438E5EBTYdg9hbC/cMJNAby3OjnWH3jan689kcu6HYBm/I28cjyR7jiyyuYu3Mu2wu2U2Iv6ejuKjoJ+fn5lJSUAFBVVcXixYvp27cvAHFxcfz0008ALF26lN69j0ctcbvdfPbZZ9xwww1N1tu3b1+ysrLYt28fAB9++CHjx49vVK68vJzY2FgcDgdz5849bX9LS0uJiorCaDSybNkyDh5sHCdx3LhxzJ8/H5fLRX5+PsuXLyctLY0xY8bw+eef43a7yc3NbXAjORu0dug48cRwAvW4BOjteY0AZnvezxr1lbvT7ezwEbLdaeeZVc/w3f7vuKb3NVyUeFGH9qelBBgDCDAG8Nexf8XpdvLF3i/4JvMbXlz7IgBmvZlLe1zK9X2up194vw7urcKbycnJYdq0abhcLtxuN9dddx2TJ2vRwd9++21+97vf4XQ6MZvNvPXW8dSiy5cvJyEhgR49ejRZr9ls5r333mPKlCk4nU6GDx/O3Xff3ajcc889x4gRI+jWrRsDBgygvLz8lP296aabuOyyy0hNTWXw4MF1N6L6XHXVVaxatYpBgwYhhGDmzJnExMRwzTXXsGTJElJSUkhKSmLEiBEEBwc3Ov+bb77h0UcfJT8/n0svvZTBgwfzww8/nLJfzaHFgcOEEFlA6smUuxDi30C6lPITz+fdwAQpZU5T5UGLLdNa/86mYn+8sv4V3t/+PgCrpq7C4tdxgZ9ybbn8btnv2F64nQeGPMCdA+48qZ29M8UxcbldvLbxNQKNgRyzHWPhgYXYnXa6BnVF2AV3DL+DoVFDSbAmeNW8QkvpTNfkdKSnpxMdHU2/fp3/BtxZYsvUBp4rLCwkLS2NlStXEhMT06BMc2XZuXNno2snhGjTwGES+J8QQgL/9gQAq088cLje52zPvgbK/YSokK1+ZKmoqGh07oGiA3XbS39eSpA+qFV1nyk2l42Xj71MuaucOyPvJKkoqe6xsymaksWbGcYwAPrSlxGxI1hevpzsmmyOuo7y5MonARAIrHorYy1jGWMdg0XfuSIsdrZrcioqKioIDg4+7Wi1M+ByuTqFHJMmTaK0tJSamhoeeeQRAgMDG/W7ubLY7fYW/RZbo9xHSymPCiGigB+FELuklPWnpZsapjV6PGjPqJDLVy0Hz3c1ZPgQugR1aVXdZ0JBVQF3/3g3pe5S3r34XQZHDT7tOZ19lHgJlwCwdNlSLH0trDiygtU5q7H6Wfnu2HcsrljM5B6TuaznZfQL64dO6DAbzB3c61PT2a9JfdLT0zGbzZ1ixHs6OsvI/eeffz5tmebKYjabGTJkSLPbbk1smaOe9zwhxAIgDaiv3LOB+to0AS32+1mj1uYOUOYoO0XJ9sHhcvDg0gc5VH6If5z7j2Ypdl9CJ3R1kSpr2V20m3e2vsN3+7/j872fA9qofkjUEK5NupbU6FRiAmM6tQmns+BLsdB/LbTG86ZFyl0IEQjopJTlnu0LgWdPKPY1cL8QYh7aRGrpqezt7UGtKyRAUVXR2WyaKmcVr214ja0FW3l1wquMjh99Vtv3VvqE9WHm+JlUOir5757/4pRObA4b3x/4nidWPAFAiCmEvmF96Rfej8SgRCL8I0gOTybcP/w0tSuai9lsprCwsNPHc/81URvP3Wxu2VNuS0fu0cACz4/CAHwspVx0QsjfhcAkYB9QCdzawjbOGIfbgUlvotpVTZH97Cl3m8PGnf+7k60FW7m699Vc0O2Cs9Z2ZyHAGMAtybfUfb5v8H1sK9jGzsKd7CzayY7CHXy046O6G7RBZ+CKnldQUl2iRbGMTiUxOLHDPaA6KwkJCWRnZ7c4Nri3YbfbW6zsvJXmyGI2m0lISGhRvS36D5FS7gcGNbG/fshfCdzXol60MTWuGmICYzhYdpBC+9nJE+KWbp74+Ql2FO7g5fEvc2G3C89Ku50dndAxMHIgAyOPB4JyuBzkV+WTW5nLvF3z+P7A9+iEjiWHlgCa62VKRApX9b4KP50fXYK60D+svxqJNgOj0diibD7eSnp6eovsz95Me8nik8OfGlcNwX7B+Bv8z4pZRkrJvzL+xdLDS3l0+KOdzo/d2zDqjcRZ4oizxDEkSvvRSynJLMlkZ5E2wv/+wPf8ccXx/Lg9gntg0BlwuV2MTRjLDX1voKKmgp4hPdUoX/GrxCd/9Q63A6PeSJg5rN3NMsdsx3hyxZOsObaGy3tezm/6/aZd2/u1IoSgV2gveoX24rKel/Hbgb9lb/Fegk3BZORn8GPWj+h0OgSCD3Z8ULfOIdgUTLegbiSFJmHSm4i3xNMtqBs9gnvgki7CzGH4G/zVDUDhc/jkL7rGXUOgIZAwcxjf7P+G8V3Gt8to+nDZYaYvmo7NaeOpUU9xTe9rlGngLBFsCq7LItU7tDdTkqbUHdteuJ3NeZsx6o1sK9jGobJDLD64mBpXDZXOykZ1GXVGru59NXGWOAzCQO/Q3oyMHYlLuihznX1vK4WiLfBJ5e5wOTCajLhqtKiQD//0MIlBifQJa7uYydsLt/NQ+kPUuGv44JIPSApNarO6FWdGcnhyXcKR+kpfSklxdTEHSg+QWZKJ2WCm2F7MnuI9fL738wYJ1aMCoqhyVlFeU87sz2cT5h+Gn86P4THDCTYFoxd64i3x9ArphdlgJtQcetblVChOhW8qd7cDP50fVc6qun1TvpnCC2NfYFL3SWeUHLrSUcnrG19n3u55RPhHMPv82UqxdxKEEISZwwgzhzEseliDY39I+0NdcpdFBxaRkZ+B2WAmLycPQ4gBm8OGzWHjzc1vIhuvySPUFEpEQARh5jB06DAZTAQaAxkbPxazwUxZdRmxllh6BPfA7rQTYg4hyK9jVk4rfh201M+9C/ABEAO4gbeklK+fUGYC8BVQGwPgCynlib7w7UqNqwaj3sjL419mT/EeksOTeeinh/jDz3/gs92fMfv82QQYA1pUp1u6+WrfV7y77V0Olh3k+j7Xc/+Q+wk2NQ4EpOh81I8/dH3f67m+7/VA4xWqpdWldW6a2wq2kVeZR1lNGZklmRwqO0SVowoEFNmLKLQX8t3+707aZhdrF/qG9cWg024eFTUVBJuCibfEk2BNIN4Sj17oMeqN2s3DPwK3dFNWU0aIKQSd0KknBsVJaenI3Qk8JKXcKISwAhuEED9KKXecUO5nKeXktuliy5BSUmwvJsgviKTQpLpR9TsXvsPcnXN5e+vb3PDdDVzZ60qiAqIINYUSExhDj+AeDezlTreTIxVH+OXoL+ws3Mn63PUcLj9M37C+vHn+m5wTf05HiKfoYOrfzCd0mXDKsi63ix2FO3BJFxH+EWRXZLO/ZD9+ej9KqkvYUbiDPcV7cEs3FqOFAGMAh8sPszpndYOnzlORYEkg3hpPkF8QeZV5ON1OIgMi8dP5YdKbiAyIJCogigBDALsrdlOyrwSBQAiB1WhleMxw9Do9OqHDT+fX4H+gylmFlLLFAyGFd9BSP/ccPAHAPKtUd6IFBTtRuXcYhfZCyh3ldA9u6Msbag7l/iH3MzR6KH9b9zdmbZjV4LhJbyLEFEKAMYDS6lJKqkvq4sGHmcPoG9aXB4c+yEXdLlKTpopmodfpGRA5oO5zgjWBkbEjT3uelJIiexE5thxc0oXD5aC4uphjtmNUu6qJC4yjuLoYh8vBloIt5FXmkVeZR5BfECGmEI5WHKXGVUONq4a8qrwGcwmsPKGPQt8g17BJb8JPr90YCqsKkUjCzeFEBkQSYAjA3+Df4OXGTZR/FEa9kWpXNdWuagSCPqF9qHRWIhB0DepKSXUJcZY4bT5Mb6SLtQvlNeVUu6oJMATU/f+p/622o8Uhf+tOFCIRLaZMipSyrN7+CcDnaDFmjgIPSym3N3F+/aiQw+bNm9eqftSG1Kxlr30vf8/9O/dG3Us//5OHNi1zlVHprqTCVUGBs4BjjmPYXDbs0k6gLhCr3kq4IZyufl2J82ucqLc9OFGWzoqvyAGdXxa3dGNz26iRNVTaKvEP8AdAIilxlbCrahdGYUQndDikA6d04pAOHNJBkD4IP+FHniOPCncF1e5qamQNNbKmbhugwl1R155RGJFS4qTlWdCEJ+Zg7ZyGWZjx0/lhwECALgAhBA7pwCAMGNwGXDoXfsIPozBSLatxSRc6dBiFkWBDMEgI1AfilE5c0oVJZ8JP+OGUTvyEH1a9FZ3Q3Gdr/2pkDSH6EEIMITikgxpZg0VnwY0bl3RpbQgdZmFGL/R1/RZCO9+is6ATOnToKHGVIJF1ffQTfnXt6dHX3cjO5Dc2ceLENg35ixDCgqbAZ9RX7B42At2klBVCiEnAl2iJOxrQXlEh8/fkQy5cPvZy4i3xraqzo/CVCIS+IgcoWZpDlbMKgcBPrykvh9tBZkkmAYYAJJIjFUcINgVzuPwwgYZA7C47R8qPEGQKwqQ3YXfasTlslFSX1ClJIQRl1WV1TwPlNVqYVz+9H5XOSvIL84kOj8bmsOGSLkIMIeiFHrd0Y3fZOWo7CgJK7aWY9ZoirqqpospZhV6nx+F2dGimNoHApDdhMphIMabw5uQ3T39SC2mxchdCGNEU+1wp5RcnHq+v7KWUC4UQ/xJCRJwic1OrOVx+mA8KPuCHn3/ALd1IJHuL92LSm4gNjG3r5hQKRRP4G/wbfDbqjPQNO56xqFtQN4A699S24ExuVFJKbQ1DTZmmN6TELd24pRs/vR/HKo9RUFmAv8G/bn5EL/QYdAZtFbR0YXPY6s6RyLo68iu1mD1O6SQ6IBq90FPtqqbKWUW1S3u6cEs3Na4aql3V2J12RH77mKJa6i0jgHeAnVLKV09SJgbIlVJKIUQaWp7WdgnwUumoJNOeSU5eDkKIukeeK3tdeUbujgqFwncRQmAQBsLMYU0eD/cPh7MYiLS9ksG0dOQ+GrgZ2CqEyPDsewLoCnUBxK4F7hFCOIEq4AbZTmnA+4T14c8Jf/aZx2aFQqFoK1o9odqmnRAiH2icVrx5RABtbvLpIHxFFl+RA5Qs3oqSRaOblDKyqQNeodzPBCHE+pPNFnc2fEUWX5EDlCzeipLl9CjDtEKhUPggSrkrFAqFD+ILyv2tju5AG+IrsviKHKBk8VaULKeh09vcFQqFQtEYXxi5KxQKheIEvCKee0REhExMTGzVuTabjcDAwLbtUAfhK7L4ihygZPFWlCwaGzZsKDiZK6RXKPfExETWr1/fqnM7c+wPKSU1+/ejCwjAmZvLzlmv0XvaNIRBj7FLF4refRcAfUQEpp69sIwbiz4oCOl04iwowBgT08ESNE1nviYnomTxTpQsGkKIk64P8grl/muicuMmiufOpXr/ftwVFTgOH6475g9kr1lT91kYjUi3G1yekKw6Heb+/XGVleE4fBhTUhKGiAgCRowgYOgQ/AcPRhgMuGtqtABMRuNZlk6hUHgLSrm3M9Llouy77zj2lxfwS+yGffMW9CEhmFNSEPFxhE2bBm4X0u1md0UFcT+vIHDcWITBSNAlFyNMJqTDiTP3GLZfVlG5di36ICt+CQm4bBU4CwrIf9UT5kevxxgTg6u8HENUJFG//z3uqipwuRB+fljPO08pfIXiV4JS7u2Eq6SE/H/+i7KFC3EVFmpK1S0JmzaNyN89iC6gcXabmvR0Eu+/v8n6/BLiCRg2rMljzuJiKteswb5rF44jR3GVlFCVkUH2vfc1KBc4biyWseOoOXCA4KuuQudvxtilC0KnU0pfofAxlHJvB5zFxRy69TaqMzOxnnsulgkTCLrwAnTtNAFkCA0l6OKLCbr44rp9rrIybKtXYwgLA52eynXryH/jDWzLfwag+OOP68oKkwlz//5Yzp1I+PTpSKcTnb9/o3YUnRshBAcOHMBut3d0V86Y4OBgdu7c2dHdaBOaI4vZbCYhIQFjCwZhSrm3Mc78fA7ddjs1hw7RZfZsLGNGd0g/9EFBBF14Yd3ngKFDCLvlZtw2G/YdOyiY/aZmsw8Pp2zRIqo2baJq0ybyX3kVXUAAYdOn4T9kKIFjRuPMy0NvtTb5tKHoPAQGBmK1WklMTOz06ezKy8uxWq0d3Y024XSySCkpLCwkOzub7t27n7TciSjl3ka4Kmwce/bPVCxegnS76fLmbAJHjerobjVA5++Pzt8fy7hxWMaNq9sffsftuIqLqdyUQdGcObjLyij41+yG51qthFx9FaFTp6KzWtGHhCD0+rMtguIM0Ov1hIeHd3rF/mtDCEF4eDj5+fktOu+0yl0I8S4wGciTUqZ49oUB84FEIAu4TkpZ7Dn2B+B2wAU8KKX8oUU96oTYd+0i968vUrlmDcFXXkn4nXdg6tmzo7vVbHQBAegCAgiOjyd48qUAuG02Dt9/P5WrVhM27RacBYUUzf2YojkfAGCIiSHh9dcwJiQg9HoceXmYk5I6UgxFM1CKvXPSmuvWnJH7+8AbwAf19j0OLJFSviiEeNzz+TEhRH/gBiAZiAMWCyGSpKyXXt2HcFdVcez55yn7+huE2UzMs38m9LrrOrpbbYIuMJCEf/yD6j17CBg6FICoxx6l7OuvcdfUUPD3f5B1/Q1gMKC3WHCVlODXrRshN9wA8WcnobhCoTg5pw0/IKVcDhSdsPsKYI5new5wZb3986SU1VLKA8A+IK1tuup9FH/8CaWff4H1kovp+b8ffEax16K3WOoUO4AxKorwO+4g8t57iX9tFqE33UTYLbfg1707QZMuwVVaSt5LLxH56GMcuusuDt58C5mXTibvlVeoyc7uQEkU3oDdbictLY1BgwaRnJzM008/XXcsIyODkSNHMnjwYFJTU1m7di0Ac+fOZfDgwXUvnU5HRkZGm/brmWee4eWXX252+ZKSEv71r3+1WfsLFiwgOTkZnU7X6sWcTdGswGFCiETg23pmmRIpZUi948VSylAhxBvAainlR5797wDfSyn/20SddwF3AURHRw+bN29eqwSoqKjAYrG06twzwX/5cqyf/Zeanj0pmfG7Nqmzo2RpM9xu/HbswP/rbzCWlOAOCQYJRs9Crcrx43F2ScAZF4cjMRGkBC+323f6a1KPoKAgevfu3WHtSymx2WxYLBYcDgcXXnghL730EmlpaVxxxRXcd999XHjhhfzwww+8/vrrLFy4sMH527dvZ+rUqWzZsgWXy4W+jX47L7zwAhaLhQcffLBZ5Q8ePMh1113HmnoLDs+EHTt2YDQa+d3vfsfzzz/P0HoDqvrs27eP0tLSBvsmTpy44WSJPtp6QrUpw1CTdw8p5Vt4Ql2mpqbK1i6/7YhlyGU//siR+Z8SOCKN2BdeaLMwAD6xpPrcc0lPSWkgR9mPP3LkgQcJ+Omnun3CZEK63VjGjCH6j09giIjAtmqV9qQgBPqgoA7ofGN84pp42LRpU4d7mAR5rmtlZSVutxuLxYLVasVoNOJ0OrFarTgcDrp06dKor19//TU33ngjVqu1kYfJkiVLePjhh3E6nQwfPpzZs2djMpkanD9hwgQGDx7M2rVrKSsr49133yUtLQ2TyURmZiaXXXYZhw4dYsaMGXWK/tVXX+VdTxiQO+64gxkzZvD8889z4MABxo4dywUXXMDMmTN59NFH+f777xFC8OSTT3L99deTnp7OM888Q0REBNu2bWPYsGF89NFHjezn/fv3x2q1otfr6zyamsJsNjNkyJBmf9etVe65QohYKWWOECIWyPPszwa61CuXABxtZRteSel333H0oYcx9e1L/Ouvo/cRd6z2JOiCCzB98zWG2DhcBflUbsrAvnULws9Eyeefs/+yy7WwCeXl2gkGA4nz5+HXLRG9xTeCQ3kbx154geqdu9q0TlO/vsQ88cQpy7hcLoYNG8a+ffu47777GDFiBACvvfYaF110EQ8//DBut5tffvml0bnz58/nq6++arTfbrczffp0lixZQlJSErfccguzZ89mxowZjcrabDZ++eUXli9fzm233ca2bdsA2LVrF8uWLaO8vJw+ffpwzz33sGXLFt577z3WrFmDlJIRI0Ywfvx4XnzxRbZt21ZnHvr888/JyMhg8+bNFBQUMHz4cMZ5vNE2bdrE9u3biYuLY/To0axcuZIxY8a05GttNa0N+fs1MM2zPQ34qt7+G4QQJiFEd6A3sPbMuug9OI4c4dgzf8Z/0CC6fzpfKfYWYOrdG70lEL/EREKuupKYp54i+vHHSPx4LpYJ47Gefz7BV12lFXY6ybrmWvaNH8+Rhx7myO9/T8XKlYD2aK/ovOj1ejIyMsjOzmbt2rV1ynX27NnMmjWLw4cPM2vWLG6//fYG561Zs4aAgABSUlIa1bl79266d+9Oksdba9q0aSxfvrzJ9qdOnQrAuHHjKCsro6SkBIBLL70Uk8lEREQEUVFR5ObmsmLFCq666ioCAwOxWCxcffXV/Pzzz43qXLFiBVOnTkWv1xMdHc348eNZt24dAGlpaSQkJKDT6Rg8eDBZWVmt+t5aQ3NcIT8BJgARQohs4GngReBTIcTtwCFgCoCUcrsQ4lNgB+AE7vMVTxnpcnH0scfB5SLu5b8h/Pw6uks+gal3bxJmzQJAOhxYL7qQ6l27sO/eTfWu3ZR99x0A5YuXYIyLw11TjWX8eMJuvhljXBxCr1ehE1rB6UbY7U1ISAgTJkxg0aJFpKSkMGfOHF5//XUApkyZwh133NGg/Lx58+oU84m05IZ/okmk9nN9E45er8fpdDa73lOVa6res0VzvGWmSiljpZRGKWWClPIdKWWhlPI8KWVvz3tRvfJ/kVL2lFL2kVJ+356dl1Kiz8/XgmO1M4X/eYfK9euJfupP+HXpcvoTFC1GGI1YJ0wg4u67SZg1i25zPyL+tdfovXIFAampOAsLASj5ZB4Hrp3CnpGj2H/FlZQs+BLpcGDfvRu3zdbBUihORn5+ft1IuaqqisWLF9O3b18A4uLi+MkzJ7N06dIGE79ut5vPPvuMG264ocl6+/btS1ZWFvv27QPgww8/ZPz48U2WnT9/PqCNtoODgwkODj5pf8eNG8eXX35JZWUlNpuNBQsWMHbs2Dqbf/1y8+fPx+VykZ+fz/Lly0lL63gnwU69QrVy3Toi/vQUlTExWMaObbd2qrZuJf8f/yBo0iUEX3FFu7WjaIghLIygiy8CoOu779Ttrzl8mNyXXqImcz+Oo0fJ+cMfyPnDH7RzYmMJnjyZwHNG4dejBzUHsggYNlSN7r2AnJwcpk2bhsvlwu12c9111zF58mQA3n77bX73u9/hdDoxm8289dbxtKLLly8nISGBHj16NFmv2WzmvffeY8qUKXUTqnfffXeTZUNDQznnnHPqJlRPxdChQ5k+fXqdor7jjjvqJjRHjx5NSkoKl1xyCTNnzmTVqlUMGjQIIQQzZ84kJiaGXbuaN6fxzTff8Oijj5Kfn8+ll17K4MGD+eGHM1/76RU5VFNTU2Vr/DtdFTZ2Dx9O5H33EXn/fac/oRW4bTYOXH0N7poaeny5AP0p7vRniq94ZpxNOaTLReHbb1P+vx+xnHculavXULlpE9R7/NVHRiCMRgJHjMTUswdht9zSbLOar1wT0Cb3WuJt4c20JrbMhAkTePnll0lNbdJzsMNoriw7d+6kX79+DfYJIc6aK+RZRW8JxBkbS9WWze3WRu6LL1Jz6BBd57zfropd0TqEXk/E3XcTUTtSu+8+3DYblevXY9+xA2NCF8r/9wPlPy6mdMECAPJefoXQG6cScsMN2LdswdSvH7K6usGCLYWis9OplTuAs3si9s1bkG43Qte2+b5Lv/mWks/+S/hddxHoBTY0RfPQBQZiGT8ei8fuGnzZZJz5+ZR9vwjh50fp119T/PEnFH/8SYPz/BITsUyYgPW8cxEBAZprpssn/AEUaE9hvyY6vXKvTk7Gf+UvZN93P1GPPIKpR/NDYp6KynXrOPrYY/gPG9ZuJh/F2cMQGUnYLTcDEHL9ddh37MC+fTuu4hIqfvqJmsxMpMNB0UcfUfT++3XnhcfFkrtxI8GXX4EhKhJhMHRqF1gppQoe1glpjfm88yv3IUMIuvRSyr77Dlld3WDi7UwomP0m+vAwur71b+X26GMIIfBPTsY/ORmAiN/eVXfMcewYVVu3gstF/j/egMxMit55l6J3tMk34e9P4KhRGOPjcR7LwdS7NxEPPIC020FKr45573K5KCwsVGF/Oxm18dzNZnOLzjsj5S6EyALK0cL7OqWUqacKB9wuCEH8Ky9jTEig8K23qMk+gl9C/BlVWfTxx9h++YXIh37fbtmTFN6JMSamLpxE4DnnsPattxh+660UfzIPXUAAtpUrqd69G9vq1cjKSsp/XEz50mU4Dh9Gut2Yk/tjGTsOQ0Q41gsv9KpRvs1mo7y8vMVxwb0Ru93eYmXnrTRHltpMTC2hLUbuE6WUBfU+NxkOuA3aOSWh119H0fvvc+T3v6fbhx+gOyGuRHOp3LCB3L+8gGWilnJO8etFHxREdWoqhvDwOtNc+G231h2XUlL0/hwqli7FfPFFIKF88WKq1m8A4Nhzz2NOSUYfFIwzNxe/bl2RDgfmAQMJnnwpxrizGxpZStmiTD7eTHp6us94/rSXLO1hlrkCbUUraOGA0zkLyt0YF0fc32Zy5MHfkf/3vxP9yCMtrqM6M5Pse+/DGB9P3MyXlG+04pQIIQi/dTrht06v2+f43YPYVqzA1LMnpd9+R9XmzVQsXQpoKRiFyUT5j4vJf/VV/IcNw9y/P4awUKoz96MPCSHivnuxrViJ9YLztTZMJmVCUbSKM/JzF0IcAIrRIj/+W0r51snCATdxbruE/A364EPMa9ZQ8JfncYeEnPzEE9AVFxP6j38gyisofvQRXJGRrerPmeAr4WV9RQ5oG1mMmZm4g4K035SU+G3fTuAP/8O4bx8YDAiHo9E50s8PHA5kYCAVky7BFROLKzIC44ED6MrKqR40EFdEBLRA8avr4p2ciSynCvl7pso9Tkp5VAgRBfwIPAB83RzlXp/WLmKCxotMag4fJvOiiwkcM5rYZ55B+PtjCD1581JKSj79jLy//Q3pctHlX//ssNynvrJgxlfkgPaTRbrduMvK0IeEIJ1OkJK8l1+mbOH3BF16KdLlQhcYgH3LVmxNREgEbVSvDw3FnJJMxF13UblxI6ZevXHm5WGMjSEgLa1Bnlt1XbyTM5Gl3RYxSSmPet7zhBAL0LIunSwc8FnBr0sXoh56iPzXX2ffuecBYE5JIfLBBxokhdZikewh7+WXqVy9moARI4h9/jkVN0ZxVhA6HXrPk6UwaP+G0X/4A9GeMAq1SIeDo489Rs3hbAzh4YTffhv60FBK/vs50uXCVVxMRXo6WYuXNGpDHxGBuV8/Yp95moqff0av11O5aRP64GCE0UjNoUO4ikvwHzRQy4WrzD8+RauVuxAiENBJKcs92xcCz3I8HPCLNAwHfNYIv/02AkaMoGD2bCp++gn7tm0cvvc+TEm9McbFYerZi7Lvv8dx6BC6wEBi/vxnQq6bon7cCq9DGI3Ev/pqo/3Rjx+fxqret4/yxYsJGDGC0q+/xnr++bgrbJQt+p7y7xex7zzNfh8BHDxJO+aBA7UJXrcbQ0QElgnjsYwbh6usDHdVFcbo6LqyrrIydP7+ak7KyzmTkXs0sMCjEA3Ax1LKRUKIdTQRDvhs45+STJd/voGrvBy3zUbBv/9N9a7dVK5eQ8WydEw9uhP91J+wnndegx+uQtHZMPXqhalXLwAC6nldBF10IRXXXEvZN9/gl9iNwz+voMf11+HyRGaUTid+3bpRc+gwxR9/TM2BA+jDQnH9UkTxxx+jj4zAVVAIUmIeOBBHzlFMid3rRv/ht07HvmMHgWPGYkpKwnH0CAGpqdh37sR/4ECvcgP9NdJq5S6l3A8MamJ/IXDemXSqLdFbreitVmI9yXil0wk6XZuHKlAovBHLmNFYxowGYFu/fgSfxLYb5vH4EUIgHQ4K3n4bx5EjGOPjwemkfMlSzEl9cObnE3zZZVSuXUvey68AULawcWRvfXAwgaNHU/HzzwSOHIEuOBhT9x7oLBZ0ZhPOgkIsEyfgLi/Hr1s3dFZrg/kBxZnT6VeotpRa+6ZCoThOfZOkMBqJvPfeBscjT0geXXP4MBXL0gm+6kotoYpOhy4wkLwXXyJw3FgqFi+hbOFCTP36UbVtO66iImR1dYM68v72t+MfjEYChg1DOhy4bTbtadpoQOcfgCEsFGdhEfrgYPQhITiOHsVaVIgtIAB9cDCyqgp9aCiGmBjt/1sIkPJXf7NQmk6hULQYvy5d6mL1hNZLohE0aRJCCJwzZiClxBgVBWihmav37EEXEID0BGOz/bIKQ2QkVRs34rJVUL1nLzp/fwzRUTjy88DlxllUiKukFGNMDK7SUs3DKDgYf5uNQz+dkErPaMQQFoa025FOJ6Z+fTEn9UEfEY7OXwsE5662g4TQqTeA0GmmpKFDcdvtuG2VOA4fwpiQ0CDpvZQS3O5Od7NQyl2hULQZtU8AhhPWiQi9HvMJschNnuQbQRddeNL6pNuNdDjqVpxLlwuEYMVnn9G/ogJdYCA6f3+cRcXUHDiAIzsbQ3Q0rpISqvfsodizWtjTOZASDAaK6iXq0FksuKurod56A0N0tBYryGrFVVwMLhfGLl3QBwVhvfAC/Lr3wPbLL9i3bkUfGoo+JAR9cDCmpN5Url1L6M03466sxBAWhjE2FndVFa7SUgxhYaDX4ywoxBAR3q43DKXcFQqF1yJ0OkS9UCK1ytAVHU349def9nxncTGyuhrpcGAID9cUtcFI6YIvcBYU4j9kMLZffsEQFo4+JBh9aBjOvFyqM/cjTH64iktwR0ait1pxV9up3rmrwboDY1wcHD6Mq7hYS/HoWTdUP5y0IToaV1kZsqoKdDrQ68HhQBiNGOJi8T/nHGgHn32l3BUKhc9y4gLG2qidEfXS8AVfemmz63NXVeE4epTqzEz8unRp8DTiyM2j5sB+dFYrlatXY4iOwVVcTFVGBrqAAMzJyTjzcpEOJ4aoKBzHcrBv3oK+nQK5KeWuUCgUzUTn74+pZ09MPXs2OmaMjsIYrc0x1IaTBuDm35yyzgOe2ENtjVfkUBVC5HPy9RWnIwIoOG2pzoGvyOIrcoCSxVtRsmh0k1I2GQjLK5T7mSCEWH+y2AqdDV+RxVfkACWLt6JkOT1qJY9CoVD4IEq5KxQKhQ/iC8r9rY7uQBviK7L4ihygZPFWlCynodPb3BUKhULRGF8YuSsUCoXiBLzCzz0iIkImJia26lybzUZgYGDbdqiD8BVZfEUOULJ4K0oWjQ0bNhSczBUSKWWHv4YNGyZby7Jly1p9rrdRK8u6hQfk7jU5HduZM8AXr4kvoGTxTs5EFmC9PIle9YqRu+I4+zbksear/QgBSWkxpz9BoVAomkDZ3DuYssIqfvl8Hy6XG4A9a48BYAo8nsKspsqJ260mvhUKRfNRI/cO5pfPM8ncmMeBLQUEdJOUHSoHwF7hoMbuRKcXvP1/y/G3GjFb/Lj20WH4+avLplAoTo3SEh1AUY6N0JgAhBA4qp0AlORWUpILUE109yByD5RRVmCnpkqLMV1V7qCq3MG67w6QlBZDRIIFoVMJvRXNRwjBgQMHsNvtHd2VMyY4OJidO3d2dDfahObIYjabSUhIwNiCpORKuZ8lygqqyN5dTEiUPwte2cTwyd1Jm9yd4mOVjcr2GhZF7oEycg+UsvOXnAbHMhYfJmPxYSbfP4huKeFnq/sKHyAwMBCr1UpiYmKDtHqdkfLycqw+koD7dLJIKSksLCQ7O5vu3bs3u952U+5CiCygHHABTukjQX5ay6K3tpF/qJzEAZpCXvftAfZn5FNeaCdlXDxdU8JZ9PYWuvaLoO+oWFb+dx/pc3cD4G81ktA3jFFX9SR7VxFLP9hFSW6lUu6KFqHX6wkPD+/0iv3XhhCC8PBw8lsY9729R+4TpZS+EpbzjKip0swvWVsL6dIvlOjuwWxeehhLqInBF3QhODKAvlcJJp47EICYHsEc21+KKdDAFTOGEB5vAaDvqFh++mQPh3cWERIdgJ9ZT2Q3K7aSar54eSPnXN2LPiOOe9k4HVq+SoOxc+V/VLQPSrF3Tlpz3ZRZpp2QUpKXVU5EVwt6vQ6zxUhpfhUAQy7sRpd+YaRemogAdHrNaam+DT2hbyjH9pdyztW96hQ7aBfZEmLi4LZCDm4rbNTuqgWZBAT7YQ4wEtnVygdP/ILeqGPaC6PbV2CFQuFVtFtsGSHEAaAYkMC/pZRvnXD8LuAugOjo6GHz5s1rVTsVFRVYLJbTFzzLlGVLDq+QGAOg58WCzEUSRyUIHfSbIpq8E9eXxe2UlGRBaA8aTZxmLXVjy2vcZkAkVNZ7cut/nWDHp9r1Tb7h7Hm9eus1aQ2+JEtQUBC9e/fu6G7gcrkYP348sbGxfPbZZwAUFRVx6623cvDgQbp168b7779PaGgoDoeD+++/n82bN+N0Opk6dSoPPfQQLpcLfRsll37hhRewWCw8+OCDzSpfUlLCZ599xp133tkm7T/xxBP88MMP+Pn50b17d/71r38REhLSqNy+ffsoLS1tsG/ixIkbTmbybs+R+2gp5VEhRBTwoxBil5Ryee1Bj7J/CyA1NVVOaGWC2PT0dFp7bnuyfP4eDpONoxLy1wbiqKxg0HldSJvc/aSujM2V5estGdjyihh9bS8SB0Sw8X8HGXtdEgY/HR8/s4aSXG2SNkLXG9gDwLix47CV1mANM7eViCfFW69Ja/AlWTZt2uQVk5CvvvoqycnJlJWV1fXnueee48ILL+Txxx/nxRdf5J///CcvvfQSH3/8MW63m+3bt1NZWUn//v2ZPn064eHhbSaLyWTCZDI1u77CwkLeffddfv/737dJ++eddx6zZs3CYDDw2GOP8cYbb/DSSy81Kmc2mxkyZEiz62234ZyU8qjnPQ9YAKS1V1veSM6+EuL7hHLhHckUZlcAEBYb2CY+6tU2zT0yPN5CSHQA597cD6NJjxCCax4dxuUzBqM36lg+b0/dOUs+2MkHT/xStyCqILucqvKaM+6LQtESsrOz+e6777jjjjsa7P/qq6+YNm0aANOmTePLL78ENDOkzWbD6XRSVVWFn58fQUFBjepdsmQJQ4YMYcCAAdx2221UV1c3KjNhwgRmzJjBOeecQ0pKCmvXrq07tmPHDiZMmECPHj34+9//Xrf/1VdfJSUlhZSUFF577TUAHn/8cTIzMxk8eDCPPPIIUkoeeeQRUlJSGDBgAPPnzweODwyuvfZa+vbty0033URTlpLzzjsPg0HTCyNHjiQ7O7sF3+jJaZeRuxAiENBJKcs92xcCz7ZHW97Ipv8doiC7gtRJifROjSZnbwlbfzqCf5Bfm9TfZ2QMeQfLiUhobC4wBxrp0jeMbsnh7M84bqPZsyYXgJK8SvIPlZM+dzdBEWZ+89yoRiaissIqdDqBJbT9R/mKjuHnT/dQcLiiTeuM6GJh7HVJpywzY8YMZs6cSXl5eYP9ubm5xMbGAhAbG0tenmZ3vPbaa/nqq6+IjY2lsrKSWbNmERYW1uB8u93O9OnTWbJkCUlJSdxyyy3Mnj2bGTNmNGrfZrPxyy+/sHz5cm677Ta2bdsGwK5du1i2bBnl5eX06dOHe+65hy1btvDee++xZs0apJSMGDGC8ePH8+KLL7Jt2zYyMjIA+Pzzz8nIyGDz5s0UFBQwfPhwxo0bB2hPS9u3bycuLo7Ro0ezcuVKxowZc9Lv59133+X6668/9RfdTNpr5B4NrBBCbAbWAt9JKRe1U1teRVlBFasW7KNrvzAGndsFgNFTenP+9H50Sw5rkzYGTEjgt/8Yj7/15DeL5HFxAAw6r0uD/RmLD7N5yWFPX+11Jpz6fPjHVcz5wy9t0leFopZvv/2WqKgohg0b1uxz1q5di16v5+jRoxw4cIBXXnmF/fv3Nyize/duunfvTlKSdmOZNm0ay5cvb6o6pk6dCsC4ceMoKyujpKQEgEsvvRSTyURERARRUVHk5uayYsUKrrrqKgIDA7FYLFx99dX8/PPPjepcsWIFU6dORa/XEx0dzfjx41m3bh0AaWlpJCQkoNPpGDx4MFlZWSeV9S9/+QsGg4Gbbrqp2d/PqWiXkbuUcj8wqD3q9nYyfjyE0Akm3twPsyc+jN6go8/I2DZrQwhxWtfGrv3Duf2VsdhKquuUOcDeddoIPr5PCEd2l/D5zA1c/2RanS2+/mOjlFK5zvkopxthtwcrV67k66+/ZuHChdjtdsrKyvjNb37DRx99RHR0NDk5OcTGxpKTk0NUVBQAH3/8MRdffDFGo5GoqChGjx7N+vXrueSSS+rqbYlTyIm/59rPJpOpbp9er8fpdDa73lOVa6reppgzZw7ffvstS5YsabP/ORU47AzJ3l3M/L+spbzIztd/z2DrT0foMzIGS6jp9Ce3M+ZAIwHBTY/uUyd1JyoxiOpKJ5v+d4hlH+5kyQc7KSs4vjR9+bw91NiPBy07ureYzE1NuOkoFM3gr3/9K9nZ2WRlZTFv3jzOPfdcPvroIwAuv/xy5syZA2iK7oorrgCga9euLF26FCklNpuN1atX07dv3wb19u3bl6ysLPbt2wfAhx9+yPjx45vsQ609fMWKFQQHBxMcHHzS/o4bN44vv/ySyspKbDYbCxYsYOzYsVit1gZmoXHjxjF//nxcLhf5+fksX76ctLTmTzH++OOPvPTSS3z99dcEBAQ0+7zTofzcz5B13x6g4HAFHzxx3Iwx5IKuHdijhpgDtKeHuN4hHN1XAhLu+ddEdDrBtY8N44e3t7M1/fgETl5WWd32tp+OICVk7ywiMMTE0b0lANzywjlnxetG8evh8ccf57rrruOdd96ha9eudS6S9913H7feeispKSlIKbn11lsZOHBgA+VqNpt57733mDJlCk6nk+HDh3P33Xc32U5oaCjnnHMOZWVlvPvuu6fs09ChQ5k+fXqdor7jjjvqvFVGjx5NSkoKl1xyCTNnzmTVqlUMGjQIIQQzZ84kJiaGXbt2NUv2hx9+GIfDwQUXXABok6pvvvlms849FV6RQzU1NVWuX7++Ved2lKtablYZxzJLWfHZ3rp9fUbGcO7NfesWJbWU9pKlvMhOgNWP8iI7jmoXkV2Pu3xl7y7mq1mbAOidGsXe9acfmQ+5oCvnXNOLnz7ZTVC4PwPPTaAkt5KQ6AD0Bh1Llyyjanc4/cfE0X1gRJvLczbxNVfIlrjSeTOtiS0zYcIEXn75ZVJTvSsSSnNl2blzJ/369WuwTwjRIX7uPs2qBZkc2V0MQNpl3emaHE5kVys6L4zUWDvKDolu/MiX0CeUoRd341hmKRfcnowEju4pYdB5XYhIsLDxf4cwBxroPiiSbsnhLHp7G4d3FZF3sIxtPx0BYNvPRyjLryK2ZzBXPjSUwt2Qt6UAW0l1p1fuCkVnpVMr9xq7k/IjErvNUTd5ebYoL9SUWdKIGPqMjMHo13ljt4y6smfd9oW3JeOodtX543dNbhicLD4phLXfHODbNzajMwikS1LmCauQk1nK169nkLf7+NOgo8aF0U9PWUEVu1YfY/ikxLoVt0f3FmO2+BEW6xu5MBXeTXp6ekd34azSqSdUi47aOPSzrBtBny3cbklFUTWxvUNIGRffqRX7iQidOOVCq7jeIYAWX/7yBwYz6Z6B+Jn13PjMCIKj/DmyuxhziOaumX+onLdnLKei2M53/9rCum8PkOux6ecdLGPBK5v45h8Z7S+Uog5vMMMqWk5rrlunHrlHdrMi9HBkbwk9h0a1e3u2kmrWLcwiPikEt1sSFP7rm1SM6xXC+Bv70C0lvM7cc8escQgh6DE4kk3/O0RQF0FYrGYCkm7Jik/3UnTUBsCBzQXsWn2M7cs1k05FUTXOGhcGH7pBeisul4vCwkIV9reTURvP3Wxumb7p1Mpdr9cREKHZiM8GS+bs4PDOYg5t16IxBoX7n5V2vQmhE6SMi2+4z6Mo+o6M5eC2QoK7VRLbKwQAs8VI5qbjK2U3/nCwbrv38Gj2rsvl6N4SorsHIXSCXaty6DMiBlOAkcM7i9j0v4Ncet8g9IZO/ZDpFdhsNsrLy1scF9wbsdvtLVZ23kpzZKnNxNQSOrVyBwiMEuRtq6C60oEpoP3s7m6Xm5xMLSJbeaHmC26N8I0fV1sRFhfI1KdGkJ6eTni8hbv/MYGsbQUs+vc2TAEGUiclsvK/mi/yhXck0y0lnAMZ+Xzzj80IAcFRAZTkVrJlWTbjb+jD13/PAGB/Rj6L39/BpHsG0i1ZJShpLVLKFmXy8WbS09N9xvOnvWTp9MMhcyggofBI28bJOJGinEqcNW5MAdr90GDUKV/v06A36kjoG4bQCRL6htWN+HunRtE7NRo/s4GunmxSUkJpfhWJAyOQkjrFDtpaArdTsvKzvXXJR2pxOlzsWp2Dy+VusL8kt5Iae9OrARWKXwOdfuRuDtXeC7IriOsd2m7t5OwrATRf9i1LswlPsChTQTMw+Ru4+K4UwmIDMfjpuWPWOAz1vre0y7pTVV7Dhben1K3qLSuoYt5zawkMMeGscdXlmS0+Vsknz67lglv7s2VZNmOv682BzQUs+2gXpXlVDL80keJjlVjDzcx9ejWxvYK5+uHmxzFRKHyJTq/cDWYwmPT8PH8vwVEBbfrYXl5k58ieYg5sLiBrawHh8YH0HBrFlqXZJI+Na7N2fJ0egyPrtk0neOKEx1kaKeCgCH9+89woTP4GNi0+xJqvjgeKKsuv4vOZGwBwOd1IT2iE9QuzWL8wC6AuT23OPi3BeN9RMRzdW8LW9GwGTEggPqn9BgEKhbfQ6ZW7EIKQKH8KDlew6M2t/Ob5UQQGty6uy/6MfL5/cys3/XkkS+bs5NiBUpCg0wv6j44j7fLu+Fv8+M1zowiO/PVNpp5NAjzhkfuMiGHNV/sxBRi449VxrP1mP8f2l+LnbyBzozYx2Ht4NDn7SrBXOHA63GRt1Sa8A4L9WPrBTnatyuFYZilut+TQjiIuujOFmion1jAzuQfKiEiwEJcUUjcxXJBdriUlrpfe0FZaTUCQXwMvExVYTeHNdHrlDnDRnSns+Pkom348xPuPrWT81CRSxrdsZhlgwyLNk2PZR7s4tr+UoAgzKeMTiE8KIarb8QQBSrGfPaxhZibc1KcuZELaZT0Aba3BwtlbqLY5GXF5D0+yEig8amPzksMMu7gbUYlBLJ2zk91rjhEaG8jwSxP533+28+0/NjdqRwiI6RlMj8GRrPzvPowmPdc8Ngzpliyfv4ety7IZe30SAycm1E2ur1+YhZSSi+8agDnQSHWVE4NRp8x1Cq/AJ5R7SFQAo67qSXCUP7tW5fDz/L0k9A1rcrn9ybDbHHWTskf3lmAONHLTn0e2Ok6Mou1IHhvfaJ9OJ5h8X+Oo0gl9/Ejoc9zscv6t/Rl8QRcsIWb8/I/70k/5Qyq2kmqCIwP49K/rcDnc5OwrJWdfKaZAA26n5PvZW5EmSZknM87677PIP1jGrtXHGrT5/mMrMVuM2G0OTP4Grvi/IZQX2ik+ZmPLsmyufSy17knkZFSW1eB2uU+ZIEVKicvpPm24Z4UCfES5g+Z/nTw2nsSBEXz01GoWvrmVfufEkrkxD3Ogkd7Dozm8s4gxU3pjr3BgNOvJ2lJAcU4lZquRDYsO4nK4SRwYwZE9xfQdFaMUu48QkXA8KNONz4zAz9+gme66afuufngoWVsKcNa42fTjIYZc0JW4XiF88fJGQAuU1mNIJJ/P3FCn2P2tRpLHxVN01Mb+TfnYSrS0bpWOGn58d3uDLEfvPbqCPiNjmHhTX47tL8VsNXIsU5sP6JocjsPuJGOxFnP/zlnjyDtUjhAQGGwiOMoft1sigEVvbSP/UDmTHxjEmq/2M+Gmvo1uGi6XG7363SrwIeVeS2CwiYm/6cOP7+zgl8/31e0/uE2zw+4+YdSl0wncbkl8n1BGXNad2F4hypbqw4TGNI5jE9UtiKhuQThrXAQE+5E8Nh6jSc9VDw3hp683kXZZdwx+ei757QDyD5eTMi6egGDN/p57oIxD2woJiQkgIMiPrsnhrPh0b6M2dq8+Vvfb0xkEbqc2EZx7oKxBubf/r2EGodRJiWRuzKvzGAKY96yW+9PPvI8u/UIpyasiJDqAQzsKydyYz3nT+tFzSCRCJxr9jguPVKDTC4qO2tians2IK3pyeEdhXerG6MQggiKU2dEXaDflLoS4GHgd0AP/kVK+2F5tnUjS8BjieoXgcroxmgxsXHSQzUsPE909qO6fKbKrlQk39SEsLrDu8bxe389WVxVehMFPz+Dzj8fij+sdSuwwXV1ohB5DIukxJLLBOdHdg7jr7w0TQ5QVVOGsdpF2uTY/YCupZuOig2Ruyic4yp/SvKoG5RMHhNdNAgOMn5rEnnW55OwrrfMAAujSPwx/q7EuH+7uNcfYvabhYAVg6Ye7+Onj3RiMOoROYDQbcLvcVJS62V69tkHZL/6meR6t+05rx8+sp985cSC0tQK20mrOv7U/P328m5CoACpKqvEzG6gsraZL/zCKcmz4W/xInZSIv/X4IkJHtYtv39jM8End6dI/jJoqJ1uWHaaq3IEEjCY9AVY/BkxMQAhOOlEtpSQvqxw/fz0h0QFnNKEtpaSiWAt30dRN/mTn1LZRXmTHEmpqVpsn9q0jBoztlSBbD/wTuADIBtYJIb6WUu5oj/aaor7tctRVPYnuEUSPwZEs+2gXfdJi6NL/eD7T+opdoWgpJ/7TnpjCLjDYxLipfYjsZmXguV2wFVdjtznQG3UIIQiNCaC80M7hnUVEJQYRnRhEyvgECo9UsPqr/SSP0ZRtbK8Q/Mx6hl7UjYLDFWxZepjUS7vjZ9JzdF8Jg87tQnFuJQtnb6Gm0klYXCDWcDM1lU50Bh1OUUV1iR5ntbYQrNuAcIIi/DEYdWxNzyZ5XDxbl2azdXk2Or0Oa6iJ4mOVdU8KOftKG8iVk1lKQLAfVWU17Nuo5QFw1Wh1G016bKU1fP33DLomhyHdksM7Gwf427U6h7L8KvqMiMHpdFOaV0XhkQp6DI7k4LZC9AYd5UXHs4P5W43E9Q7h2BE3+77+mdHX9qYox0b+wTKS0mIwmvUgYcOiLJwONxEJFkz+BsoK7RzdV1L3xDRgQoLm/aSD/RkFRHW1EhzljzXMzM5fcqgsq8HldFNWUMXg87vidrnZ+MMhLGEmYnsEU15kJ7q7lsWpNK+SqgoH0d2D6JoczrHMUvasPcbk+wfV5ShetSATS6iJbilaCOz9GflEdrUS2dXabsHc2iVZhxBiFPCMlPIiz+c/AEgp/9pU+c6YrKM98BVZfEUO6JyyuFxu3C7ZKFpprSw1dieOalcDl2Gnw4XBqKe60oGf2VAXlnn+X9ZScLiCCTf1oXdqNEaTno3/O0hgiInSvCqGXtyNnL0lrPl6PwFBfgSEmCjOsZGzrxSDnw5nzfGVw6Ou6knKuHgqy2owBRjYtvwIW386QlVZDQCmAAPVlU5tcrrCQUh0AJZQE/FJoWz84SCO6uOrkw3+YAnSwlUIncDfaqSytKbuuH+QH0HhZuwVDuw2B5YwM4XZzVvF7m81Eh5vQafXoTcIDmwuADTPLbPFSP4hTxYoAXjUZ23fm4sl1ERFsTZPE9QVbn7i3GafW59TJetoL+V+LXCxlPIOz+ebgRFSyvvrlbkLuAsgOjp62Lx581rVVkVFBRaL5fQFOwG+IouvyAFKlpoKib0EghJaZlKwF0v0ZnBUgtuh7bPENF2HlBJXNehNHH+vAYPpeHmXQ9NTNeVgCgJbpY3AwECqCsBo0RYzOmwg3eC0Q0Cktj6lPo4qSWU+WGJA6LSQF9IFOiNUFYCfBcqOgjUO/AKPn1uRI5FusMZr++wlEoMZhB6QYC8F/1Cw5YEtV/u+IvoKKo5JzKECZzXoDBCUoMknXWAK0bbLj4CDKqJ6ts56MHHixLOeiampq9jgLiKlfAt4C7SRe2tHR51xZHUyfEUWX5EDlCzeipLl9LSXcs8GutT7nAAcPVnhDRs2FAghDp7s+GmIAApaea634Suy+IocoGTxVpQsGt1OdqC9zDIGYA9wHnAEWAfcKKXc3g5trT/ZY0lnw1dk8RU5QMnirShZTk+7jNyllE4hxP3AD2iukO+2h2JXKBQKRdO0m5+7lHIhsLC96lcoFArFyfGFdcpvdXQH2hBfkcVX5AAli7eiZDkN7WJzVygUCkXH4gsjd4VCoVCcQKdV7kKIi4UQu4UQ+4QQj3d0f5qDECJLCLFVCJEhhFjv2RcmhPhRCLHX8x5ar/wfPPLtFkJc1HE9ByHEu0KIPCHEtnr7Wtx3IcQwz3ewTwjxd9EBgXxOIsszQogjnmuTIYSY5O2yCCG6CCGWCSF2CiG2CyF+59nf6a7LKWTpjNfFLIRYK4TY7JHlz579Z/e6SCk73QvNAycT6AH4AZuB/h3dr2b0OwuIOGHfTOBxz/bjwEue7f4euUxAd4+8+g7s+zhgKLDtTPoOrAVGoS10+x64xEtkeQZ4uImyXisLEAsM9Wxb0dyP+3fG63IKWTrjdRGAxbNtBNYAI8/2demsI/c0YJ+Ucr+UsgaYB1zRwX1qLVcAczzbc4Ar6+2fJ6WsllIeAPahyd0hSCmXA0Un7G5R34UQsUCQlHKV1H65H9Q756xxEllOhtfKIqXMkVJu9GyXAzuBeDrhdTmFLCfDm2WRUsraQDZGz0tylq9LZ1Xu8cDhep+zOfUPwVuQwP+EEBuEFlsHIFpKmQPaDxyI8uzvDDK2tO/xnu0T93sL9wshtnjMNrWPzJ1CFiFEIjAEbZTYqa/LCbJAJ7wuQgi9ECIDyAN+lFKe9evSWZX7aWPXeCmjpZRDgUuA+4QQ405RtrPKCCfvuzfLNBvoCQwGcoBXPPu9XhYhhAX4HJghpSw7VdEm9nm7LJ3yukgpXVLKwWihV9KEECmnKN4usnRW5d6i2DXegpTyqOc9D1iAZmbJ9Tx+4XnP8xTvDDK2tO/Znu0T93c4Uspczz+kG3ib4yYwr5ZFCGFEU4ZzpZRfeHZ3yuvSlCyd9brUIqUsAdKBiznL16WzKvd1QG8hRHchhB9wA/B1B/fplAghAoUQ1tpt4EJgG1q/p3mKTQO+8mx/DdwghDAJIboDvdEmV7yJFvXd8yhaLoQY6Zn1v6XeOR1K7T+dh6vQrg14sSyedt8BdkopX613qNNdl5PJ0kmvS6QQIsSz7Q+cD+zibF+XszmL3JYvYBLajHom8MeO7k8z+tsDbUZ8M7C9ts9AOLAE2Ot5D6t3zh898u2mA7xKTuj/J2iPxQ60EcXtrek7kIr2D5oJvIFnIZ0XyPIhsBXY4vlni/V2WYAxaI/pW4AMz2tSZ7wup5ClM16XgcAmT5+3AU959p/V66JWqCoUCoUP0lnNMgqFQqE4BUq5KxQKhQ+ilLtCoVD4IEq5KxQKhQ+ilLtCoVD4IEq5KxSnQWjRPCOEECFCiHs7uj8KRXNQyl2haD4hgFLuik6BUu4KRT2EEF96ArttrxfcrZYXgZ6euOJ/64j+KRTNRS1iUijqIYQIk1IWeZaNrwPGAxvQVgpagG+llKcKAqVQeAWGju6AQuFlPCiEuMqz3QUtzodC0elQyl2h8CCEmIAW5GmUlLJSCJEOmDuyTwpFa1E2d4XiOMFAsUex90VLjVafcrQUcAqF16OUu0JxnEWAQQixBXgOWF3/oJSyEFgphNimJlQV3o6aUFUoFAofRI3cFQqFwgdRyl2hUCh8EKXcFQqFwgdRyl2hUCh8EKXcFQqFwgdRyl2hUCh8EKXcFQqFwgdRyl2hUCh8kP8H197OgkpWQqQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = df[:3000].plot(grid=True, subplots=True, title='First test plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0d9f930",
   "metadata": {},
   "source": [
    "# Vou tentar fazer a inversão aqui"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "06d987a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run fernald_new1.ipynb\n",
    "\n",
    "from scipy import integrate \n",
    "from scipy import optimize\n",
    "\n",
    "zref = np.array([6*1e3, 12*1e3])\n",
    "\n",
    "LR = 28.\n",
    "\n",
    "z = alt[:, None]\n",
    "pr = (df['355 o analog 0']).values[:, None]\n",
    "ref = zref[:, None]\n",
    "lambd = 355*1e-9\n",
    "LidarRatio = LR\n",
    "Pair = read[0]['P0']*np.exp(-(z-100)/8e3)    # H aproximadamente 8km\n",
    "Tair = read[0]['T0'] - (9.8*1e-3)*z    # 6.5 lapse rate\n",
    "\n",
    "beta_par, alpha_par, LR_par, model_mol, ab_fit = fernald_new1(z,pr,ref,lambd,LidarRatio,Pair,Tair)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "91d1742e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'beta [1/Mm sr]')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot((beta_par['aer']*1e6), alt*1e-3)\n",
    "plt.plot(beta_par['mol']*1e6, alt*1e-3)\n",
    "plt.plot(beta_par['tot']*1e6, alt*1e-3)\n",
    "plt.grid(True)\n",
    "plt.legend(['aer', 'mol', 'tot'])\n",
    "plt.xlim(-10, 100)\n",
    "plt.ylim(0, 10)                        # Aqui eu limito a altura ate 3km, mas sei que ta errado pq o\n",
    "                                       # lapse rate eh maior antes da camada limite e as estimativas\n",
    "                                       # de pressao e temp. devem estar bem ruins\n",
    "        \n",
    "\n",
    "plt.xlabel('beta [1/Mm sr]')"
   ]
  }
 ],
 "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}