HBN_Reader_Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading a HBN and Writing to HDF5\n",
    "+ **Entire HBN is Read and Written to HDF5 File (our prefered database)**\n",
    "\n",
    "+ **You Can View the HDF5 file with HDFView or HDFCompass**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Version table includes imports by read_HDF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from readHBN import read_HBN\n",
    "\n",
    "# for timing tests only\n",
    "import datetime as dtime\n",
    "now = dtime.datetime.now\n",
    "\n",
    "from versioninfo import versions\n",
    "versions(['numpy', 'pandas'])\n",
    "\n",
    "import matplotlib.pyplot as m_plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## read_HBN() code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reference, this is a copy of the readHBN.py file"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "from struct import unpack\n",
    "from numpy import fromfile, reshape, datetime64, timedelta64, empty\n",
    "from pandas import DataFrame\n",
    "from datetime import datetime\n",
    "from collections import defaultdict, Counter\n",
    "\n",
    "tcodes = {1:'_Minutely', 2:'_Hourly', 3:'_Daily', 4:'_Monthly', 5:'_Yearly'}\n",
    "\n",
    "def read_HBN(hbnfile, hdffile):\n",
    "    ''' Extracts all data in hbnfile and saves to HDF5 file'''\n",
    "    data = fromfile(hbnfile, 'B')\n",
    "    if data[0] != 0xFD:\n",
    "        print('BAD HBN FILE - must start with magic number 0xFD')\n",
    "        return\n",
    "    \n",
    "    # Build layout maps of the file's contents\n",
    "    map = {}\n",
    "    mapf = defaultdict(list)\n",
    "    target = {'P', 'I', 'R'}    # (P)erland, (I)mplnd, and (R)chres\n",
    "    index = 1                   # already used first byte (magic number)\n",
    "    while index < len(data):\n",
    "        if chr(data[index+8]) not in target:\n",
    "            index -= 1   # works, but why???\n",
    "\n",
    "        reclen, rectype, optype, lue, section = unpack('2I8sI8s', data[index : index+28])\n",
    "        reclen  = reclen >> 2\n",
    "        optype  = optype.decode().strip()  # Python3 converts to bytearray not string\n",
    "        section = section.decode().strip()\n",
    "        \n",
    "        if rectype:\n",
    "            level = unpack('I', data[index+32 : index+36])[0]  # 28 + 4\n",
    "            mapf[(optype, lue, section, level)].append(index)            \n",
    "        else:\n",
    "            map[(optype, lue, section)] = (index, reclen)\n",
    "        index += reclen + 6\n",
    "    \n",
    "    # For each mapf key, use map to extract floating point values. Save to HDF5 file\n",
    "    for optype, lue, section, level in mapf.keys():\n",
    "        index, reclen = map[(optype, lue, section)]\n",
    "        \n",
    "        # field names are concatenation of length, string pairs.  Use trick to ignore lengths.\n",
    "        fields = ''.join([' ' if chr(y) < 'A' else chr(y) for y in data[index+28:index+reclen+4]]).split()  # 4 ??? why   \n",
    "        nfields = len(fields)\n",
    "        \n",
    "        dindex = mapf[(optype, lue, section, level)]\n",
    "        d = empty(len(dindex) * nfields)      # preallocate for speed, just like MATLAB\n",
    "        t = empty(len(dindex)).astype('datetime64[ns]')\n",
    "\n",
    "        findex = 0  \n",
    "        tindex = 0\n",
    "        for di in dindex:\n",
    "            yr, mo, dy, hr, mn = unpack('5I', data[di+36 : di+56])  # 28 + 8\n",
    "            t[tindex] = todatetime64(yr, mo, dy, hr)\n",
    "            tindex += 1\n",
    "            \n",
    "            d[findex:findex+nfields] = unpack(f'{nfields}f', data[di+56:di+56+nfields*4])\n",
    "            findex += nfields\n",
    "            \n",
    "        # fix duplicate names in DataFrame (from nexits for example)\n",
    "        cnts = Counter(fields)\n",
    "        copy = cnts.copy()\n",
    "        temp = []\n",
    "        for x in fields[::-1]:\n",
    "            count = cnts[x]\n",
    "            if copy[x] == 1:\n",
    "                temp.append(x)\n",
    "            else:\n",
    "                temp.append(x+str(count))\n",
    "                cnts[x] -= 1\n",
    "                   \n",
    "        d = reshape(d, (len(dindex), nfields))\n",
    "        df = DataFrame(d, index=t, columns=reversed(temp)).astype('float32')\n",
    "        df.to_hdf(hdffile, f'/RESULTS{tcodes[level]}/{optype}/{section}/{optype[0]}{lue:03d}')\n",
    "        del d,t\n",
    "    return\n",
    "\n",
    "\n",
    "def todatetime64(yr=1900, mo=1, dy=1, hr=0):\n",
    "    '''returns datatime64 with fix for midnight given yr,mo,dy,hr'''\n",
    "    if hr == 24:\n",
    "        return datetime64(datetime(yr,mo,dy,23), 'm') + timedelta64(1,'h')\n",
    "    else:\n",
    "        return datetime64(datetime(yr,mo,dy,hr), 'm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RCH.HBN TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running time 0:00:16.952512\n"
     ]
    }
   ],
   "source": [
    "hbnfile = 'rch.hbn'\n",
    "hdffile = 'rch.h5'\n",
    "\n",
    "start = now()\n",
    "read_HBN(hbnfile, hdffile)\n",
    "print('Running time', now() - start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FINAL CHECK\n",
    "Look at one DataFrame in the rch.h5 file to check headers, date/time index, values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VOL</th>\n",
       "      <th>DEP</th>\n",
       "      <th>STAGE</th>\n",
       "      <th>AVDEP</th>\n",
       "      <th>TWID</th>\n",
       "      <th>HRAD</th>\n",
       "      <th>SAREA</th>\n",
       "      <th>AVVEL</th>\n",
       "      <th>AVSECT</th>\n",
       "      <th>USTAR</th>\n",
       "      <th>TAU</th>\n",
       "      <th>RO</th>\n",
       "      <th>IVOL</th>\n",
       "      <th>PRSUPY</th>\n",
       "      <th>VOLEV</th>\n",
       "      <th>ROVOL</th>\n",
       "      <th>POTEV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>10.062680</td>\n",
       "      <td>0.980155</td>\n",
       "      <td>0.980155</td>\n",
       "      <td>0.148426</td>\n",
       "      <td>74.229263</td>\n",
       "      <td>0.147835</td>\n",
       "      <td>67.796059</td>\n",
       "      <td>1.290917</td>\n",
       "      <td>11.017533</td>\n",
       "      <td>0.122452</td>\n",
       "      <td>0.029058</td>\n",
       "      <td>14.222718</td>\n",
       "      <td>5.138270</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.165185</td>\n",
       "      <td>196.910339</td>\n",
       "      <td>0.014803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>2.846288</td>\n",
       "      <td>0.792569</td>\n",
       "      <td>0.792569</td>\n",
       "      <td>0.094557</td>\n",
       "      <td>32.957726</td>\n",
       "      <td>0.094017</td>\n",
       "      <td>30.101387</td>\n",
       "      <td>0.956217</td>\n",
       "      <td>3.116374</td>\n",
       "      <td>0.097652</td>\n",
       "      <td>0.018480</td>\n",
       "      <td>2.979930</td>\n",
       "      <td>4.909300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.023405</td>\n",
       "      <td>12.102283</td>\n",
       "      <td>0.007221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>2.472814</td>\n",
       "      <td>0.775849</td>\n",
       "      <td>0.775849</td>\n",
       "      <td>0.092471</td>\n",
       "      <td>29.279121</td>\n",
       "      <td>0.091890</td>\n",
       "      <td>26.741596</td>\n",
       "      <td>0.885728</td>\n",
       "      <td>2.707461</td>\n",
       "      <td>0.096542</td>\n",
       "      <td>0.018062</td>\n",
       "      <td>2.398075</td>\n",
       "      <td>4.774124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015362</td>\n",
       "      <td>5.132233</td>\n",
       "      <td>0.006705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>2.412162</td>\n",
       "      <td>0.772931</td>\n",
       "      <td>0.772931</td>\n",
       "      <td>0.092225</td>\n",
       "      <td>28.637156</td>\n",
       "      <td>0.091634</td>\n",
       "      <td>26.155268</td>\n",
       "      <td>0.872221</td>\n",
       "      <td>2.641053</td>\n",
       "      <td>0.096407</td>\n",
       "      <td>0.018011</td>\n",
       "      <td>2.303581</td>\n",
       "      <td>4.602622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>4.648273</td>\n",
       "      <td>0.006827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-06</th>\n",
       "      <td>2.366732</td>\n",
       "      <td>0.770702</td>\n",
       "      <td>0.770702</td>\n",
       "      <td>0.092064</td>\n",
       "      <td>28.146723</td>\n",
       "      <td>0.091466</td>\n",
       "      <td>25.707338</td>\n",
       "      <td>0.861650</td>\n",
       "      <td>2.591312</td>\n",
       "      <td>0.096318</td>\n",
       "      <td>0.017978</td>\n",
       "      <td>2.232804</td>\n",
       "      <td>4.469147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021270</td>\n",
       "      <td>4.493306</td>\n",
       "      <td>0.009861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  VOL       DEP     STAGE     AVDEP       TWID      HRAD  \\\n",
       "1990-01-02  10.062680  0.980155  0.980155  0.148426  74.229263  0.147835   \n",
       "1990-01-03   2.846288  0.792569  0.792569  0.094557  32.957726  0.094017   \n",
       "1990-01-04   2.472814  0.775849  0.775849  0.092471  29.279121  0.091890   \n",
       "1990-01-05   2.412162  0.772931  0.772931  0.092225  28.637156  0.091634   \n",
       "1990-01-06   2.366732  0.770702  0.770702  0.092064  28.146723  0.091466   \n",
       "\n",
       "                SAREA     AVVEL     AVSECT     USTAR       TAU         RO  \\\n",
       "1990-01-02  67.796059  1.290917  11.017533  0.122452  0.029058  14.222718   \n",
       "1990-01-03  30.101387  0.956217   3.116374  0.097652  0.018480   2.979930   \n",
       "1990-01-04  26.741596  0.885728   2.707461  0.096542  0.018062   2.398075   \n",
       "1990-01-05  26.155268  0.872221   2.641053  0.096407  0.018011   2.303581   \n",
       "1990-01-06  25.707338  0.861650   2.591312  0.096318  0.017978   2.232804   \n",
       "\n",
       "                IVOL  PRSUPY     VOLEV       ROVOL     POTEV  \n",
       "1990-01-02  5.138270     0.0  0.165185  196.910339  0.014803  \n",
       "1990-01-03  4.909300     0.0  0.023405   12.102283  0.007221  \n",
       "1990-01-04  4.774124     0.0  0.015362    5.132233  0.006705  \n",
       "1990-01-05  4.602622     0.0  0.015000    4.648273  0.006827  \n",
       "1990-01-06  4.469147     0.0  0.021270    4.493306  0.009861  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import read_hdf\n",
    "df = read_hdf('rch.h5', 'RESULTS_Daily/RCHRES/HYDR/R001')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VOL</th>\n",
       "      <th>DEP</th>\n",
       "      <th>STAGE</th>\n",
       "      <th>AVDEP</th>\n",
       "      <th>TWID</th>\n",
       "      <th>HRAD</th>\n",
       "      <th>SAREA</th>\n",
       "      <th>AVVEL</th>\n",
       "      <th>AVSECT</th>\n",
       "      <th>USTAR</th>\n",
       "      <th>TAU</th>\n",
       "      <th>RO</th>\n",
       "      <th>IVOL</th>\n",
       "      <th>PRSUPY</th>\n",
       "      <th>VOLEV</th>\n",
       "      <th>ROVOL</th>\n",
       "      <th>POTEV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-02-01</th>\n",
       "      <td>1.584953</td>\n",
       "      <td>0.723182</td>\n",
       "      <td>0.723182</td>\n",
       "      <td>0.098089</td>\n",
       "      <td>17.691545</td>\n",
       "      <td>0.097013</td>\n",
       "      <td>16.158278</td>\n",
       "      <td>0.584799</td>\n",
       "      <td>1.735350</td>\n",
       "      <td>0.099196</td>\n",
       "      <td>0.019069</td>\n",
       "      <td>1.014830</td>\n",
       "      <td>958.037109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.777567</td>\n",
       "      <td>1155.674561</td>\n",
       "      <td>1.215866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-01</th>\n",
       "      <td>1.594507</td>\n",
       "      <td>0.723931</td>\n",
       "      <td>0.723931</td>\n",
       "      <td>0.097770</td>\n",
       "      <td>17.856308</td>\n",
       "      <td>0.096711</td>\n",
       "      <td>16.308760</td>\n",
       "      <td>0.589821</td>\n",
       "      <td>1.745811</td>\n",
       "      <td>0.099042</td>\n",
       "      <td>0.019009</td>\n",
       "      <td>1.029715</td>\n",
       "      <td>101.393509</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.401270</td>\n",
       "      <td>98.982651</td>\n",
       "      <td>1.512444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-04-01</th>\n",
       "      <td>2.378655</td>\n",
       "      <td>0.771291</td>\n",
       "      <td>0.771291</td>\n",
       "      <td>0.092104</td>\n",
       "      <td>28.276264</td>\n",
       "      <td>0.091508</td>\n",
       "      <td>25.825653</td>\n",
       "      <td>0.864464</td>\n",
       "      <td>2.604367</td>\n",
       "      <td>0.096341</td>\n",
       "      <td>0.017987</td>\n",
       "      <td>2.251380</td>\n",
       "      <td>428.855072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.090396</td>\n",
       "      <td>418.980560</td>\n",
       "      <td>2.881078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-05-01</th>\n",
       "      <td>16.004911</td>\n",
       "      <td>1.077606</td>\n",
       "      <td>1.077606</td>\n",
       "      <td>0.183168</td>\n",
       "      <td>95.669693</td>\n",
       "      <td>0.182469</td>\n",
       "      <td>87.378319</td>\n",
       "      <td>1.339930</td>\n",
       "      <td>17.523628</td>\n",
       "      <td>0.136042</td>\n",
       "      <td>0.035866</td>\n",
       "      <td>23.480427</td>\n",
       "      <td>349.889648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.863873</td>\n",
       "      <td>325.399414</td>\n",
       "      <td>5.357083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-06-01</th>\n",
       "      <td>2.412306</td>\n",
       "      <td>0.772938</td>\n",
       "      <td>0.772938</td>\n",
       "      <td>0.092225</td>\n",
       "      <td>28.638695</td>\n",
       "      <td>0.091635</td>\n",
       "      <td>26.156673</td>\n",
       "      <td>0.872254</td>\n",
       "      <td>2.641211</td>\n",
       "      <td>0.096407</td>\n",
       "      <td>0.018011</td>\n",
       "      <td>2.303806</td>\n",
       "      <td>405.437988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.544554</td>\n",
       "      <td>402.485962</td>\n",
       "      <td>6.360683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  VOL       DEP     STAGE     AVDEP       TWID      HRAD  \\\n",
       "1990-02-01   1.584953  0.723182  0.723182  0.098089  17.691545  0.097013   \n",
       "1990-03-01   1.594507  0.723931  0.723931  0.097770  17.856308  0.096711   \n",
       "1990-04-01   2.378655  0.771291  0.771291  0.092104  28.276264  0.091508   \n",
       "1990-05-01  16.004911  1.077606  1.077606  0.183168  95.669693  0.182469   \n",
       "1990-06-01   2.412306  0.772938  0.772938  0.092225  28.638695  0.091635   \n",
       "\n",
       "                SAREA     AVVEL     AVSECT     USTAR       TAU         RO  \\\n",
       "1990-02-01  16.158278  0.584799   1.735350  0.099196  0.019069   1.014830   \n",
       "1990-03-01  16.308760  0.589821   1.745811  0.099042  0.019009   1.029715   \n",
       "1990-04-01  25.825653  0.864464   2.604367  0.096341  0.017987   2.251380   \n",
       "1990-05-01  87.378319  1.339930  17.523628  0.136042  0.035866  23.480427   \n",
       "1990-06-01  26.156673  0.872254   2.641211  0.096407  0.018011   2.303806   \n",
       "\n",
       "                  IVOL  PRSUPY      VOLEV        ROVOL     POTEV  \n",
       "1990-02-01  958.037109     0.0   2.777567  1155.674561  1.215866  \n",
       "1990-03-01  101.393509     0.0   2.401270    98.982651  1.512444  \n",
       "1990-04-01  428.855072     0.0   9.090396   418.980560  2.881078  \n",
       "1990-05-01  349.889648     0.0  10.863873   325.399414  5.357083  \n",
       "1990-06-01  405.437988     0.0  16.544554   402.485962  6.360683  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import read_hdf\n",
    "df = read_hdf('rch.h5', 'RESULTS_Monthly/RCHRES/HYDR/R001')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>VOL</th>\n",
       "      <th>DEP</th>\n",
       "      <th>STAGE</th>\n",
       "      <th>AVDEP</th>\n",
       "      <th>TWID</th>\n",
       "      <th>HRAD</th>\n",
       "      <th>SAREA</th>\n",
       "      <th>AVVEL</th>\n",
       "      <th>AVSECT</th>\n",
       "      <th>USTAR</th>\n",
       "      <th>TAU</th>\n",
       "      <th>RO</th>\n",
       "      <th>IVOL</th>\n",
       "      <th>PRSUPY</th>\n",
       "      <th>VOLEV</th>\n",
       "      <th>ROVOL</th>\n",
       "      <th>POTEV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>0.535055</td>\n",
       "      <td>0.601703</td>\n",
       "      <td>0.601703</td>\n",
       "      <td>0.088113</td>\n",
       "      <td>6.648618</td>\n",
       "      <td>0.085837</td>\n",
       "      <td>6.072404</td>\n",
       "      <td>0.294970</td>\n",
       "      <td>0.585827</td>\n",
       "      <td>0.093308</td>\n",
       "      <td>0.016872</td>\n",
       "      <td>0.172801</td>\n",
       "      <td>4604.963867</td>\n",
       "      <td>0.0</td>\n",
       "      <td>126.468033</td>\n",
       "      <td>4679.960449</td>\n",
       "      <td>54.152374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>0.837818</td>\n",
       "      <td>0.646531</td>\n",
       "      <td>0.646531</td>\n",
       "      <td>0.098017</td>\n",
       "      <td>9.358748</td>\n",
       "      <td>0.096006</td>\n",
       "      <td>8.547656</td>\n",
       "      <td>0.374302</td>\n",
       "      <td>0.917319</td>\n",
       "      <td>0.098680</td>\n",
       "      <td>0.018871</td>\n",
       "      <td>0.343354</td>\n",
       "      <td>7451.998047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>194.187927</td>\n",
       "      <td>7257.505371</td>\n",
       "      <td>48.608078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-01</th>\n",
       "      <td>0.728723</td>\n",
       "      <td>0.632039</td>\n",
       "      <td>0.632039</td>\n",
       "      <td>0.094060</td>\n",
       "      <td>8.482585</td>\n",
       "      <td>0.092019</td>\n",
       "      <td>7.747427</td>\n",
       "      <td>0.353313</td>\n",
       "      <td>0.797872</td>\n",
       "      <td>0.096609</td>\n",
       "      <td>0.018087</td>\n",
       "      <td>0.281898</td>\n",
       "      <td>3331.141357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.566780</td>\n",
       "      <td>3235.682861</td>\n",
       "      <td>47.139690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-01</th>\n",
       "      <td>0.750747</td>\n",
       "      <td>0.635083</td>\n",
       "      <td>0.635083</td>\n",
       "      <td>0.094845</td>\n",
       "      <td>8.666605</td>\n",
       "      <td>0.092814</td>\n",
       "      <td>7.915499</td>\n",
       "      <td>0.358042</td>\n",
       "      <td>0.821986</td>\n",
       "      <td>0.097025</td>\n",
       "      <td>0.018243</td>\n",
       "      <td>0.294305</td>\n",
       "      <td>7700.260742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>176.521866</td>\n",
       "      <td>7523.715820</td>\n",
       "      <td>40.988445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>1.636711</td>\n",
       "      <td>0.727159</td>\n",
       "      <td>0.727159</td>\n",
       "      <td>0.096518</td>\n",
       "      <td>18.566629</td>\n",
       "      <td>0.095525</td>\n",
       "      <td>16.957521</td>\n",
       "      <td>0.611303</td>\n",
       "      <td>1.792020</td>\n",
       "      <td>0.098432</td>\n",
       "      <td>0.018776</td>\n",
       "      <td>1.095468</td>\n",
       "      <td>6487.256836</td>\n",
       "      <td>0.0</td>\n",
       "      <td>163.681351</td>\n",
       "      <td>6322.687500</td>\n",
       "      <td>50.294876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 VOL       DEP     STAGE     AVDEP       TWID      HRAD  \\\n",
       "1991-01-01  0.535055  0.601703  0.601703  0.088113   6.648618  0.085837   \n",
       "1992-01-01  0.837818  0.646531  0.646531  0.098017   9.358748  0.096006   \n",
       "1993-01-01  0.728723  0.632039  0.632039  0.094060   8.482585  0.092019   \n",
       "1994-01-01  0.750747  0.635083  0.635083  0.094845   8.666605  0.092814   \n",
       "1995-01-01  1.636711  0.727159  0.727159  0.096518  18.566629  0.095525   \n",
       "\n",
       "                SAREA     AVVEL    AVSECT     USTAR       TAU        RO  \\\n",
       "1991-01-01   6.072404  0.294970  0.585827  0.093308  0.016872  0.172801   \n",
       "1992-01-01   8.547656  0.374302  0.917319  0.098680  0.018871  0.343354   \n",
       "1993-01-01   7.747427  0.353313  0.797872  0.096609  0.018087  0.281898   \n",
       "1994-01-01   7.915499  0.358042  0.821986  0.097025  0.018243  0.294305   \n",
       "1995-01-01  16.957521  0.611303  1.792020  0.098432  0.018776  1.095468   \n",
       "\n",
       "                   IVOL  PRSUPY       VOLEV        ROVOL      POTEV  \n",
       "1991-01-01  4604.963867     0.0  126.468033  4679.960449  54.152374  \n",
       "1992-01-01  7451.998047     0.0  194.187927  7257.505371  48.608078  \n",
       "1993-01-01  3331.141357     0.0   95.566780  3235.682861  47.139690  \n",
       "1994-01-01  7700.260742     0.0  176.521866  7523.715820  40.988445  \n",
       "1995-01-01  6487.256836     0.0  163.681351  6322.687500  50.294876  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import read_hdf\n",
    "df = read_hdf('rch.h5', 'RESULTS_Yearly/RCHRES/HYDR/R001')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9862.000000\n",
       "mean       10.056992\n",
       "std        22.658529\n",
       "min         0.044570\n",
       "25%         0.815545\n",
       "50%         2.526409\n",
       "75%         9.183600\n",
       "max       568.209412\n",
       "Name: ROVOL, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read RCHRES 1 ROVOL (ac-ft / day) and convert to cfs\n",
    "df1 = read_hdf('rch.h5', 'RESULTS_Daily/RCHRES/HYDR/R001')['ROVOL'] * 0.50416968504062\n",
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'flow (cfs)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NAAAApuNmrKQlxCRGqoAAFpXhmuwbvqfAo4QYAAAA+xP3ZWApIa4GAlhUhnsqFnhGnmfke4YSYgBA7X3kvrN6112PzPsYQC25+0umEFcHASwqwz0h8720fLjhGTKwAIDae8dff1E/+0ef1bVONO+jALUTJ70SYjKw1UAAi8pwwWqQDXBq+h69CgCA2gvjRFthrA/fd3beRwFqp5uBDRjiVBUEsKiM/j2wkhQ0PC40AIDac1mh933m8TmfBKifOLHyPaMm95WVQQCLynD9rv0lxG6wEwAAdeUC2A9//qw2tsM5nwaolzBJ+gJYqyShuq/sCGBRGS5YdSXEge+pE3GRAQDUWye2Onm4pU6U6I7PPTnv4wC1EsdWgWe695chyZHSI4BFZbgMbCPLwAY+Q5wAAPXXiWK96ObrdObYst77mcfmfRygViJXQpwFsAxyKj8CWFRGtwfW62VgKSEGANRdJ07Uavh65W2n9BdfOKdL1zrzPhJQG1GSKPA9NRtZBpYBoaVHAIvKiLJsqxvi1KCEGACwAMLIqul7etXzTitKrD7w2SfmfSSgNqI4zcB2S4ip7is9AlhURphlYIMsgG36DHECANRfJ04UNIyee/qIbjmxovfeTRkxMCthbHdkYCkhLj8CWFSGWzTt95UQ85QMAFB3YZSo6fsyxuiVt53SXz9wXmc3tud9LKAW4mwKsUuQdLi3LD0CWFRGuGuIU8M39CkAAGqvHSfd7NCrnndaiZX+9B7KiIFZCBOrhs8QpyohgEVluCnE/Wt0yMACAOrMWqtOlKiZZYf+3g2H9cwbDut9lBEDMxHHVo1sD6xED2wVEMCiMlwJsRviFPheN6gFAKCO3AR+d3MtSa963in97Rcv6rFLW/M6FlAbUZKo4XkMcaoQAlhUxu4SYvbAAgDqzpUzuptrSXrlbaclSe+/+/G5nAmokygrIXbfY21KiEuPABaVEXUzsF73vzTaAwDqzAWw/RnYm0+s6LabjjKNGJiBaE8JMdV9ZUcAi8pwZVQuA9ukhBgAUHOu0qg/gJWkV952Snc/cllfOn91HscCasOVEDPEqToIYFEZ0e4pxB4lxACAemsPKCGWpO/MyojfRxkxsC9RnE0hZohTZRDAojKi2O2BzXpgGx5lHgCAWnOtMq1dGdgzx5Z1+9Ou03s/QxkxsB9RYnfsgSWALT8CWFRGlFgFvpExvRJiLjIAgDrrlhD7e2/Z/v5X3qj7ntjQ2SvbB30soDaiJFHgewxxqhACWFSGe0LmNDzTzcoCAFBHg6YQOycPtyRJm+3oQM8E1Ikb4tSihLgyCGBRGWGcKPB6X7KUEAMA6m7YECepV1ZMxgiY3u41OgxxKj8CWFRGnF1gnMAz6sSJrCWIBQDUU3vAGh2nFfiSpO0wPtAzAXUSJzadQkwGtjIIYFEZYWzl92dgsydlcUIACwCop1ElxGRggf0L40QNr5eBpbqv/AoNYI0xx4wx7zLG3GeMudcY83XGmOPGmDuMMV/I/ntdkWdAfURx0p0QJ0kNLjQAgJpzr3G7pxBL0hIZWGDf4m4JcXqPyQOh8is6A/urkj5grX2WpOdJulfST0v6kLX2Vkkfyn4NjLWnhNiNO0+40AAA6okMLFAsV+FnjGHDRUUUFsAaY45I+iZJb5Mka23HWntJ0qslvSP7sHdIek1RZ0C9hFmPgtPtVeCFGwBQU504za4O6oElAwvsX7pGJ02KBL7hvrICiszAPl3SuqS3G2M+ZYz5LWPMiqQbrLWPS1L23+sLPANqJMp6FBwXzEb0wAIAaiqM0tc4phADxYjj3prGZsNThwxs6RUZwDYkvVDS/2GtfYGkq5qgXNgY8wZjzJ3GmDvX19eLOiMqJB1z3j/EKb3YMO4cAFBX7diVEJs973MZ2DYZWGBqYZJ0S/QDSogrocgA9hFJj1hrP579+l1KA9onjTGnJCn779lBv9la+1Zr7e3W2ttPnjxZ4DFRFbszsL1pcVxoAAD15MoZW76/5329EmJeB4FpxUkvAxv4HhUNFVBYAGutfULSl40xz8ze9DJJn5P0x5Jel73tdZLeU9QZUC/RniFOlBADAOrNlTOOLiEmAwtMw1qrMLYKsgC21fDYblEBjYL//B+V9E5jTFPSg5JerzRo/n1jzA9LeljSf1XwGVATUWwV9A1xalBCDACoud4U4r0lxIHvyfcMGVhgSi4H4nt9JcTcV5ZeoQGstfbTkm4f8K6XFfl5UU9RkuycQkwGFgBQc2GcyDPaMQOiX6vhkYEFpuTa0FxShCFO1VD0HlhgZsJ4cAkxPbAAgLrqRMnA8mFnKfDJwAJTirMkSMPrW6PDfWXpEcCiMuLE7ljk7oJZLjQAgLpqR8mO177dyMAC04uyftdG3xRihjiVHwEsKiOMk+6UOKk/A0sJMQCgnsI46Q5rGoQMLDC9KMlKiPv2wJIYKT8CWFRGlNgdQyzcz2m2BwDUVYcMLFAYN0el2wPLHthKIIBFZcSJ3THEqbdGhwsNAKCeOvHoHtgWGVhgai6AdVsumg2P7RYVQACLygjjpFviIfUysB1KiAEANRXGSXfq/iBkYIHpRVm21e8OcWIPbBUQwKIyoiFTiCNKPQAANTWuhJgeWGB6u0uIA58MbBUQwKIyosTu2IPHGh0AQN11Yju6hLjhaTskAwtMozuFuL+EmPvK0iOARWVEyc4S4t4aHUo9AAD11InisXtgyRgB0+lOIe4OcWIPbBUQwKIyonjnEKcmGVgAQM11otE9sEtkYIGp9TKwlBBXCQEsKiNKkh1rdBoEsACAmgvHlRAHntrccANT6fXA9kqIua8sPwJYVEYU2+6UOKlvDywlxACAmhqfgfXJwAJTcoNAG7umECcJ95ZlRgCLSrDW7h3i5JGBBQDUWydOFJCBBQrRzcBmAayrdggTvqfKjAAWlRB3F033MrCeZ+R7ptu/AABA3eTJwEaJZaUcMIXda3R681W4tywzAlhUgrvA+H09sFJaRkwGFgBQV504UbNhhr6/FaS3cmRhgcn1SojT7yPXnsYgp3IjgEUluCA18HZ+yQaex1MyAEBthfGYDGzgSxJ9sMAUugmSbglx+v1EcqTcCGBRCfGuEg8nYFocAKDGOlEyegpxgwwsMC3Xhhb4ZGCrhAAWlRDu2tPlNDzTXUINAEDddKKke3M9CBlYYHruHtLfNcSpQ3Kk1AhgUQnuAtPY9SKeLpymhBgAUD9Jkk7gz5OB3Q654QYm1cvA7h7ixPdTmRHAohKiIRlYhjgBAOrKZYFGBrBZBrYdkYEFJhXv6oF11Q6UEJcbASwqYfeYcyfwPUqIAQC11A1gR5QQk4EFpuf2vbrAtbsHluRIqRHAohJ2jzl3KCEGANRVGI3PwC6RgQWm1h0SuicDy71lmRHAohJcBjYYsAeWDCwAoI5cBnbkEKeGG+LEayEwqd6Q0J0ZWIY4lRsBLCrB9cD6AzKwlHkAAOrI9eGNLCEO3BodMrDApOLukNBdQ5zogS01AlhUQrjrAuM0fNN9egYAQJ2EOYY4dUuIycACEwvjXUOcGtkeWJIjpUYAi0pwPQoBGVgAwIJoR+NLiN0QJzKwwOS695duiBNrdCqBABaV4C4k/p41OgSwAIB6ctmhVo4MLD2wwOTckFB3e8kanWoggEUl7F407QS+6b4PAIA66eSYQkwGFphelFgFvpEx6f1liyFOlUAAi0rojjn3B6zR4SIDAKihTo4S4sD35HuGDCwwhSixO6r7AoY4VQIBLCoh7O6B3VtCTAYWAFBHeYY4SWnWaDskAwtMKortjvkqARnYSiCARSVE3Qzs3hJiemABAHXUzrFGR0r7YNtkjICJRUkiv+/esjfEieRImRHAohK6AeyuKcQN3+MiAwCopU43A2tGfhwZWGA6YWx33Fu6WSsMcSo3AlhUQjSkhLjJFGIAQE2F3QysP/LjyMAC04mTZMe9pTFGgW8oIS45AlhUgutz3V1C3PAoIQYA1JO7iQ7IwAKFiGK7596y6XsMcSo5AlhUQrRr0bQTNBjiBACop07OHtgWGVhgKlFi9w4IbbDhouwIYFEJUZJeSPzdFxkvLfOwliAWAFAveacQL5GBBaYSJcmeFY20p5UfASwqwQ1qCry9e2Cl3p5YAADqop1jD6xEBhaYVhQPyMD6njoR95VlRgCLSoizDOyeNToNxp0DAOqpm4Edt0aHDCwwlSgZ0ANLCXHpEcCiElyAuruE2D01CxMuNACAeulEiQLfyPPGDHEKfNZ+AFNIe2AHlBDz/VRqBLCoBDeoaXcZlesL4kIDAKibNIAdf6tGBhaYThQnA4Y4sUan7AhgUQlxksiYQRlYSogBAPUUxsnYAU6S1Ao8bfMgF5jYwBJihjiVHgEsKiFM7J4BTpIUZBcdLjQAgLrpxMnY/ldJWmr4apOBBSaWZmD3DgilJL/cCGBRCVGc7Mm+Sr2SYgJYAEDdtHOWEJOBBaYTM8SpkghgUQmDSjykXgAbsUYHAFAzYWzVylFCvNTwFSdWETfdwETCAWt0KCEuPwJYVEIU24FPoV0JMaUeAIC66URx7gysJLKwwITiAVOIA99TyB7YUiOARSVEyegSYjKwAIC6CWOba4jTUuBLEn2wwITCJJFPCXHlEMCiEqLYKqAHFgCwQDpRzinEDTKwwDTiZO/9JUOcyo8AFpWQ9sDu/XJtMIUYAFBT6R7YvQ9vdyMDC0wniq38XSXETfbAlh4BLCohHLBoWurPwFJCDACol06cqNnwx35cK/uY7ZCbbmASYbz3IRFDnMqPABaVMGjMudS3B5ZSDwBAzXSifHtg3RCndkQGFphEnNg9M1bSIU7cV5YZASwqIR1zPmgKsRvixIUGAFAvaQY2RwkxGVhgKmkGdncJMUOcyo4AFpUQJcnIPbAdSogBADUTxpNlYLfJwAITGZqBja2s5d6yrAhgUQnpnq7hJcQsbwcA1E3eKcQuA9smAwtMJBzQoua+58jClhcBLCohjJOBU4hZowMAqKt0CjE9sEBRBiVImgwILT0CWFRCFA/OwPbW6HCRAQDUS9oDmyMDG5CBBSZlrc0C2J3fYwwILT8CWFTCsD2wTTKwAICayj2FuEEPLDCpKEmTH3vW6GQl+ZQQlxcBLCohShIFAzOwBLAAgHoKycAChYmy6j1/SAa2Qwa2tAhgUQlRvHdKnNRX5kEJMQCgRqI4UWI1WQY2JAML5OVWMO7NwDLEqewIYFEJUWIHDrIIPDKwAID6cTfPQY4MbOB78j2jNhkjILdeBnbYECe+n8qKABaVEMWD98B6npHvme5FCACAOgij9HUtTwZWSrOwZGCB/FwP7O4ZK90NFxH3lmVFAItKCIeUEEtp6QdPyQAAddKO02A0Tw+slPbBkoEF8nMlxLu3XATdEmIeCJUVASwqIU5st1x4t8Dz6IEFANSKGyBDBhYohqveG7YHtkMGtrQIYFEJUTK4hFhKn5SRgQUA1Il7MDtJBnabDCyQW6+EePcQp2wKMfeWpUUAi0oIY7vnCZnT8CghBgDUi8vADhpgOEir4alNBhbILe6WEO/8Hmv66VqqkAdCpTVRAGuMWTHG+EUdBhgmTuyeJnsn8CkhBgDUS7eEOGcGtkUGFphIOKSEOGi4FY18P5XVyKuiMcYzxnyfMeb9xpizku6T9Lgx5u+MMb9kjLn1YI6JRRcOmUIspS/uXGQAAHXiyhdzlxCTgQUmEo+ZQkwJcXmNuyp+RNIzJP2MpButtU+x1l4v6Rsl/Y2knzfGfH/BZwQUJaNLiN0kOQAA6qBXQjz4tW83MrDAZFzyY/gQJ76fyqox5v0vt9aGu99orb0g6d2S3m2MCQo5GZCx1qYlxMOmEPsek+IAALXibq5bE2Rgz5KBBXIbPsSJDGzZjbwquuDVGPMMY0wr+/lLjTFvNMYc6/8YoCjuAjPsKXTgk4EFANRLb41OvtEjLfbAAhNxa3T8IRlYhjiVV94hTu+WFBtjvkLS2yTdIun/KexUQJ/eBWbUECcuMgCA+nDZHzdQZhx6YIHJuOTH7knfQZaBZUBoeeUNYBNrbSTptZJ+xVr745JOFXcsoKd3gRnSA+sbhZQQAwBqxD2YbeZdoxN49MACE3AVfrszsO6l+rmsAAAgAElEQVR+kxLi8sobwIbGmO+V9DpJ78veRu8rDkQ0ZMy5E/ieQkqIAQA10p5wjc5SwycDC0zA3V8Ge/bAMsSp7PIGsK+X9HWS/mdr7UPGmFsk/U5xxwJ6XHDqD3kK3aSEGABQM70eWDKwQBHi7P5y9xAnY4wC33BvWWIjpxAbYz5krX2ZpNdZa9/o3m6tfUjSzxd9OEDqf0I2vIQ4ok8BAFAj4cR7YH3FiVUYJ3t6+gDsFY6o8Gv6HhnYEhu3RueUMeabJX2XMeb/lbTj/7C19pOFnQzIDFs07QS+R58CAKBWentg82dgpbT0mAAWGG/U/WXQoLqvzMYFsP9a0k9LuknSm3e9z0r6liIOBfQbtmjaCXyPDCwAoFYmzsAG6bqd7TDWamvc7R2AUfeXJEfKbeQVzlr7LknvMsb8rLX25w7oTMAOwxZNO/QpAADqphMlMmb4w9vdlhppAMsuWCCfeMT9ZVpCTHKkrPLWmHzWGHPU/cIYc8wY85qCzgTs0JtCPPjLtcEQJwBAzbSzXlZj8gWwroR4m0nEQC7hkDU6Ulr5wL1leeUNYN9krb3sfmGtvSTpTcUcCdjJ7YEd9hQ6nULMUzIAQH2EkVVrgl7WlsvAhtx0A3nEWYC6e42OxBCnsst7ZRz0cTRY4EB0p8RRQgwAWBCdOM7d/yr1ZWAjMrBAHq5FzR9wfxk0uLcss7xXxjuNMW82xjzDGPN0Y8xbJN1V5MEAx/UoDJuq2GCIEwCgZjoTThNeIgMLTMQFsIMysAxxKre8V8YfldSR9HuSfl/SlqQfKepQQL8ou4AM6lGQehcZawliAQD1EMaWDCxQoFH3l5QQl1uuMmBr7VWl63SAAxd2M7BDAtjswhMndmiZMQAAVdKJkokCWDKwwGS6Wy6GDHG62o4O+kjIaeSV0RjzVmPMVw1534ox5oeMMf+omKMBqbg7xGnwl2uQvcAzyAkAUBftCUuIXQa2TQYWyCWKrTwjeeyBrZxxGdjfkPSzWRD7WUnrkpYk3SrpiKT/IOmdhZ4QC88FpsNKiN2TszBJtCz/wM4FAEBRwnjCDGyQvv6xRgfIJ0wSNYY8JGr6nkL2wJbWyADWWvtpSf/AGLMq6XZJp5T2v95rrf38AZwP6A5oGvYk2r3Ah/QqAABqohMlak7QFtNquAwsr4VAHnFsh65oDBpkYMssbw/spqSPFnsUYLDuHtghL+SutJgSYgBAXYRx0i0LzoMMLDCZKBkewDLEqdzyXxmnZIzxjTGfMsa8L/v1LcaYjxtjvmCM+T1jTLPoM6DaXAZ26FOyLLBlXxcAoC46caLmJD2wLgPLECcgl2hUCTF7YEut8ABW0o9Jurfv178g6S3W2lslXZT0wwdwBlRYLwM7poSYCw0AoCYm3QMb+J58z7BGB8gpGlVCzBCnUst1ZTTGPH2aP9wYc5Ok75T0W9mvjaRvkfSu7EPeIek10/zZWBy9RdOjS4jdxwEAUHWdCYc4SdJSwyMDC+Q0roSY2SrllasHVtJvG2POSPpbSX8u6S+stffk+H2/IulfSjqc/XpN0iVrrVus9IikMxOcFwsoGjOF2JUQ06sAAKiLSffASlIr8MnAYgdr03uoNIeEflE8vISYIU7lluvKaK39JknPlvTrkq6T9H5jzIVRv8cY80pJZ621d/W/edAfP+T3v8EYc6cx5s719fU8x0RNudLgoRcZnwwsAKBe0inEZGAxPWutvvEXP6Lf/cSX532UUooSO3RAaNP3FMa2+wAA5ZIrA2uM+QZJ35j9OCbpfZL+Ysxve4mk7zLGfIfS3bFHlGZkjxljGlkW9iZJjw36zdbat0p6qyTdfvvtfPUssNiVEA+5yLgAlh5YAEBdTLoHVnIZWF4LkerEiR65uKXPfPmSvu9rnjrv45TOqB7Y3nwVq2aD7HXZ5L0yfkxpr+pbJb3UWvvfWmt/d9RvsNb+jLX2JmvtzZK+R9KHrbX/SNJHJH139mGvk/SeqU6OheEyq8NKiN3TM3oVAAB1MU0GttXw1GaNDjJbnfRr4bHLW3M+STmlPbDDqvuy9jSSI6WU98q4JunfSfo6SR8wxvxHY8zPTfk5f0rSTxhj7s/+3LdN+edgQbjMajD0IpM9JaOEGABQE504UUAGFvuwlT3MePQSAewg6Rqd4SXEEsmRsspVQmytvWSMeVDSU5SW/X69pCDvJ7HWflTSR7OfPyjpxZMeFIsrTqw8I3kjJsVJXGQAAPVgrU1LF6fogd0mA4tMNwN7aUvWWgY57RKPmEIcsKKx1PKu0XlA0v8q6bik35T0TGvtNxd5MMAJ4+ElHlKvhNjtiwUAoMpc2eI0PbBtHuYi4zKw22Gii9fCOZ+mfMI4GXp/6R4e8f1UTnnX6NxqreX/IOYiHXM+/KmhKyHuxJQQAwCqL8xez6bJwJ4lA4tMfzb+sUtbOr7SnONpyidObPcecrcmGdhSy3tlPG2M+UNjzFljzJPGmHcbY24q9GRAZtSiaanXaB9xkQEA1IDba04GFvux1el9LdAHu1cY26EDQnvJEb6fyijvlfHtkv5Y0mlJZyS9N3sbULi0yX74lyprdAAAdeIC2GHZoWHogUW/a52o+/PHCGD3GJmB7c5XobqvjPJeGU9aa99urY2yH78t6WSB5wK6Ru3pkno9sJQQAwDqIJy6B9YjA4uurV0lxNgpjJPhGdgGGdgyy3tlPGeM+X5jjJ/9+H5J54s8GOBEI56QSb2nZJQQAwDqoD1lCfFSwycDiy73tbAUeHrs0vacT1M+aQZ29IaLDg+ESinvlfGHJP0DSU9IelzSd2dvAwoXjXhCJlFCDACol24P7IgBhoMs0QOLPm6NztNPrNIDO0CUWPnDphA30u897i3LKe8e2IclfVfBZwEGChM7cgqxe19ICTEAoAamLiFueIoTqzBOJu6fRf1shenX0TOuX9XHH6RwcrcwThSMG+LEA6FSGhnAGmN+XdLQqMBa+8aZnwjYJY6tghF7YN37eEoGAKgD13c38RCnwJeUliATwML1wN5yYkXv/cxjakexWg1/zqcqjzgZPoWYNTrlNi4De+eBnAIYIZ1CPDwD63lGvmcUkYEFANRA2C0hnnyIk5T2Pq62chXZoca2w1hLgaebrluWJD15ua2nrh2a86nKI4zt0C0XrNEpt3FXt5dba3/AGPNj1tpfPZATAbuEY6YQS+kuWJ6SAQDqoD1lCfFSo5eBBa51Ih1qNnTmWBrAPnZ5iwC2T5wkQ+8vGeJUbuOujF9tjHmapB8yxlxnjDne/+MgDgjEyfAnZE7geTwlAwDUwrR7YPszsMBWJ9Fy4Ou0C2AZ5LRDFA+fsdIrIaa6r4zGZWB/U9IHJD1d0l2S+v8v2+ztQKHCePgTMidoeJQQAwBqwVUUtSYe4pRmYAlgIfVKiE8dXZJEALvbqDWNvSFOfC+V0cgro7X216y1z5b0H6y1T7fW3tL3g+AVByIaM4VYooQYAFAfnSn3wLoMLCXEkNIhTstNX0uBrxOrTT3KLtgdomT4mkYysOWW68porf1nRR8EGCZKrBojphBLUsPzuMgAAGph2hLiJTKw6LPVibWcTaY+fWyZDOwuUWJHrNFJ3057WjkxYx2lF8VJ90IyTLPhkYEFANTC1HtgycCiz1YYd1crnT5KANsvTqyslfwhCRK3opEhTuVEAIvSi+Lhe7qchmcUJVxkAADV156yhLg7hZgMLJRmYA8106+JU8eW9NilLVlLtZqk7j3jsBY1zzO0p5UYASxKL90DO2YKse+pE3FRBgBUnytbnHYPLBlYSFkPbJaBPXNsWVc7sa5sRXM+VTm4wZ+jhoSm95Z8L5URASxKb1SPghP4ZGABAPUQZg9kJ+6BDeiBRY8b4iSpu0rnUcqIJaX3lpJGJkhoTysvAliUXlpCPD4Dy0UGAFAHnTiW75mx7TO7ubU7ZGAhSdudvh5YdsHuEGX3jGMzsAwILSUCWJRelIwf4hT4XveJNQAAVRbGduLyYYkMLHbqLyE+fSzbBXuZAFZKhzhJw3tgpbSEnxLiciKARelF8fg9sA3fKKSEGABQA50omXiAkyQtuQxsyOvhogvjRFFiuwHsiZWWmr5HCXEmTMb3wFJCXF4EsCi9ME7G7oFtUkIMAKiJdpRM3P8qpf18vme0HZGBXXTXOunXgOuB9TyTTSLenuexSiPuDnEa/n0W+IYMbEkRwKL04sSOfEImpRnYiD4FAEANhHHS7Wed1FLD0zYZ2IXnyshdACuxC7ZfOGaNjkQGtswIYFF6YWLzrdHhIgMAqIFpS4glqRX4apOBXXhbLgMb9AWwxwhgnShXBpZ7y7IigEXpRXEyNgMb+B4ZWABALXSi8cMLhyEDCykd4CTtDGDPHFvSk1e2ySpK3dWLoyZ9swe2vAhgUWpJYpXY0SUeUtqnwAUZAFAHYbzfDCyvh4vOBbBLzZ0Z2MRKT16hD9YlPUY9KGpRQlxaBLAoNbdoetwwC/bAAgDqohMnU63RkdKbbtboYHtICbEkBjmpd385LgMbUt1XSgSwKLU8JR4SFxkAQH1MO4VYIgOLlJtCfKi5N4B9nF2wirKkx6jvM/bAlhcBLEotyrGnS6KEGABQH/spIV4iAwsN7oE9fWxJktgFq3TDhTT6/jKghLi0CGBRar0ehdFfqg2GOAEAaqIT7aOEmAws1NcD2xfAHmo2dN2hoLSTiP/6gfN67W/81YFM0Q5dADuiBzbwDd9LJUUAi1JzJR55Sog7cSJrCWIBANW23wxsmwzswhu0B1aSTh1dLm0P7CcfvqhPPXxJT15uF/65YrcHdsQaHYY4lRcBLEqtN8RpTACbBbiuJAQAgKra/x5YbroX3aA9sFK5d8FubEeSpHNXiw9g3dyU8UOc+F4qIwJYlFqeRdNS2qcgiUFOAIDK6+xjiBM9sJAGlxBL6S7YsvbAbmyHkqTzm53CP1ecY8sFQ5zKiwAWpRa6Eo+xe2DTL+UOT8oAABXXie30JcSBTwALbYWxWg1vT4bx9LFlbWxHupIFi2VyJcvAnt88iAzs+Ba1dIgTiZEyIoBFqfWmxI3bA5tegCICWABAxXWieF97YCkhxlYn3tP/KvWt0ilhH2w3A3v1IDOwo0uIma9STgSwKDX3hCxvBpYnZQCAquvsZ4hTloHlpnuxbXXiPf2vUi+ALWMfbLcH9gAysFGOHtgW7WmlRQCLUuv1wI4OYN37abYHAFRdGNt9ZWAT2xuCiMW0FQ4OYM9kAWwZ+2APsgc2ytED67Kz3FuWDwEsSi3q7uka/aXa7D4l4yIDAKiuOLGKk/31wEqiD3bBbYfxngFOknTycEsNz5Q6A3v+AKYQR0mOHlg3X4WS/NIhgEWpuZ7WYGwGNv1S5okzAKDK3IPYaacQt4L099EHu9i2wsE9sL5ndOPRpVIGsFe2DjADm1X4BSNmrJAcKS8CWJSaC0hHPSGTemUePCUDAFSZCzynzsA2yMAi7YE9NCCAldwu2HINcYoTq6vZ7tpzB1JCnGVgxwxxkngYVEYEsCi1vCXEvSFOXGQAANXlHsQ2xwwvHIYMLCTpWmdwCbGU9sGWrQd2MysfXmn6unC1raTgirru/WWuIU58L5UNASxKrVtCnHMKMSXEAIAqczfL02ZgW2RgofT//6AhTpJ0+tiSnriy3V0lUwZuL+3NJ1aUWOnSVrF7avMMCWXDRXkRwKLUwhxjzqW+SXE8cQYAVFhnnyXEZGAhDZ9CLKUlxHFidXajPGXEboDTzSdWJEnnC16l4xIkDHGqJgJYlFqcY8y51CsxDkv0NBEAgEl19jnEiR5YSNke2BE9sFK5dsG6DOwta2kAW3QfbJRYNTwjY4YHsO4hUocS4tIhgEWpuSb7cXtg3b48MrAAgCrr9cDuMwMb8nq4yLbDZGQPrCQ9WqJBTnsysAWv0okSq8bY9jQGhJYVASxKLez2KIzLwKYXGRfwAgBQRZ199sC6DGw7IgO7qKI4USdOhk4hPnV0SZL0eIkysBsuA3vikKTiV+lEsR25QkdiiFOZEcCi1GKXgc05xKlDoz0AoML2m4FdyjKw22RgF9ZWVj4+rAf28FKgw0uNUk0idhnYpxw/JM8cQA9skoxcoSOx4aLMCGBRat0MbM4yD0qIcRDe/lcP6Zf/7PPzPgaAGtr3FOKADOyicwHs0pAMrCSdXG3pwtXi963m5TKwx5abOr7S1LmCz5b2wOZb0UgJcfkQwKLU3JS4vBcZSohxEP70s0/ovXc/Nu9jAKih/U4hXmqQgV102530//2wDKwkHV4OdCXLepbBle1IrYanZsPT2krrQKYQj52vwhCn0iKARal1F01TQowSOb/Z1oWC+3MALKZwn1OIycBiXAmxJB1ZauhywbtWJ7GxHerwUiBJWlttFt8Dm2OIU5M9sKVFAItScwHsuEZ7V0Ic8ZQMB+DcZkcb7YgbRAAz1yYDi33qBrDN4V9DR5cDbZQogL2yHenIUkOStLba0vmiS4hjmz8DSwlx6RDAotTyLJqWaLTHwelESfep9cWr5XnxB1AP+x3i1PA9+Z7hAdsC2+q4DGxj6MccWQ66u1fLYGM70mEXwK40da7gEuI4sWqM+R7j3rK8CGBRat0M7JgyD1cGQpkHinbxWu+pcNF76gAsHvc6Nm0GVkqzsGRgF9dWmPa2Lo8Y4nRkKdDlrVDWluO+aWM71JHltIT4xGpTG9vFVjmFOXpg2QNbXgSwKLUotvI9I2PGXGQ8npLhYKxv9ILWMk1wBFAPneymfdoMrJT2wZKBXVxbOYY4HV0OFMa2NA86dmRgV1uSin2NjfP0wDLEqbQIYFFqYZKMLR+WJM8z8j2jiAwsCtbfl0MAC2DW3M1yQAYWU8o1xGk5DRbLUkZ8ZSvU4VY2xGmlKUmFDnIKEyt/3HwVkiOlRQCLUotjqyBHACulpR5cZFC0c30Z2KKnJAJYPN0S4n1mYLdDMrCLqrcHdvjX0JFs4m9ZJhEPysAW2QcbJ8nY+0vPMwp8QwlxCRHAotSiHE32TuB7lHmgcP19r/39sAAwC24K8bjZD6O0Gl73z8Hi2e6Mz8AezfpNr5QggA3jRFth3F2jc2L1ADKwWYvaOIHvkRwpoeHjyYASyNNk7wS+RwkxCnd+s6Nmw9PhVqPwMf8AFk8YJ2r63tjZD6OQgV1s+UqIswC2BCXEm9vp0ClX1uwysEUOSowTO/Lfxwl8jwxsCRHAotTyNNk7lBDjIKxvtnVytaVDTV8XKCEGMGOdKNnXBGIp7YElA7u4rnViNX1vZAWb27lahhLijSyAdRnYlaavVsMrNAMbxYn81vgwqNnw1CE5UjoEsCi1MLZqjGmydxqexxodFO78Zkdrq00tBz5DnADMXCdK9lU+LElLga9LtDgsrO0w1lIw+t6pV0IcHcSRRnJZYNcDa4zRidWW1gvsgQ1jm+v7rEkJcSnRA4tSi5Ikdwa22eAig+Kdv9rW2kpTa6tN9sACmLkw3n8Glh7YxbbViUfugJV62c4y9MDuDmAlpa+xBWZg4yRfD2yzQQlxGRHAotSixObugW14RlHCRQbFOrfR0YnVlo6vNMnAApi5mZQQ0wO70LbCeGx/Z7PhaTnwS1VC7CYjS+kqnSIfEodJkmtIKO1p5UQAi1KL4kTBJFOII0qIURxrbZqBXW3p+KGmLm2FihO+5gDMTnuC171hyMAutq0w1lKOAUVHl4NSDHEaGMCutgrPwOZZ08gQp3IigEWpRTnHnEvp0neekqFIV7YjhbHVidWmjq80ZS2rdADMVhgl+9oBK5GBXXTb4fgSYimd+luGHtiNESXE1hbzkDi9vxz/fZYOceLesmwIYFFqE+2BpYQYBXNL1U+stnQ8G/NPGTGAWerEiVr0wGIftjqxDuUJYJeCUpQQuyB6tS+APbHSUidOtNEuJsCOknzD0tgDW04EsCi1KElylXhI2UWGEmIUyJUzra02tbaSLlongAUwS+kU4tlkYIvKXqHcrnXG98BKZSohDrUc+Du+7tdW09fYosqI81b4NSkhLiUCWJRaOEEJccM3CsnAokDnswzs2ko6xEkigAUwW7OaQpxYsVpuQW3n7IE9UpoANtpRPiylPbBS73V31qLE5npQlG644PuobNgDi1KLE5vrKaLEri4Ur1tCfLgpZa9n5wlgAcxQJ0q00trf7ZkLXtpRvO9gGNWTZwqxJB1ZaujytRIEsO1QR5aDHW9zVU7nCsvAJrkSJIFvyMCWEFc1lFoU598D2/CNIp6SoUDuhfT4oaaucxnYAqckAlg8nThfZmiUVpD+/u2QG+9FtJVziNPR5UAb7UjJnKfpD8rAnnAZ2IJW6aQzVvLsgfVJjpQQASxKLYzz74ENfCbFoVjnNtu67lCghu8p8D0dWWroQoF76gAsns4MsqZLjV4GFotnK2cP7JHlQNZKm535TiK+shXq8NLODKxr0ymsBzbJd38Z+IZ7yxIigEWpxYlVI8eYc4kSYhTv/Gan+1RYyvbUUUIMYIY68f7X6JCBXVxJYtWOknxrdLKgcd5lxIMysM1G+pC4iB5Ya23u+0uGOJUTASxKLUwoIUZ5nL/a7k5GlKTrDgUMcQIwU2Fk9x/AkoFdWFvZ/t+8GVhJcx/kdGU70pGlvX3fJ1ZbOlfAa2yUlUznycCurTZ14Wpn7v9G2IkAFqUWTdALxK4uFO3crgzs8ZUWASywD2/+4Od19yOX5n2MUunMYAqxy75tdQhgF003gM2TgV1Og0a3h3VeNrb3lhBLaQBbRAY2dgFsjvvL/+KZ1ytKrD72+fWZnwPTI4BFqcVJ/jU6aQBLBhbFObfZ3llCvNKkhBiY0tV2pF/78P16yx3/ed5HKZVZ7IE9fog1X4vKPbTItUbHlRBvzS+72IkStaNkYAZ2bbVZSA+sS3bkycC+4KnX6cRqUx/83JMzPwemRwCLUgvjREHOEuLAN2RgUZh2FGtjO+qO9pek46tNXbzakbU8OMH+vP7tn9BvfuyBeR/jQLm1VH/+hXNa32AYmjOLDKxrdShqBQnKa3uCEuKjJSgh3sg+96AM7NpqMQ+JexnY8feXvmf0smfdoI/ed5Ze2BIhgEWpRRMMcWr4Hj2wKIx7Cnzi8M4MbJRYXdmeb/kVqs1aq7958II+/uD5eR/lQLmgNU6s3nf3Y3M+TTlYa9WJZhnA8mBg0UzVAzvHDKx7/dw9xEmS1lZaunito2jGyQlXrZd3y8UrnnODNtqR/mbBrtFlRgCLUsu7aFrqrdEhG4YiuAB2RwZ2hTI97N/VTqytMNbjl7fnfZQD5YKrw62G/vBTj875NOXgbqybOSuPhmk1/MImuKLcXAnxoRw9sIdbDRkz3wB2VAb2xGpT1koXZzwleZIeWEn6hltPaDnwdQdlxKVBAItSixKbu4TYveBHc17IjXo6l+17XdsxxMkFsNwkYnouE/nYpa05n+Rgub/3933NU3X3I5d1/9nNOZ9o/lwbzH4zsFI2wZUS4oVzLcvALuUIYD3P6HCrMdcqoo1RGdjs9fb8jF9j3fdZ3gTJUuDrm/7eCd3xuSdJkpQEASxKLYpt7idk7uMoI0YRzmU32yd3DHHKXly5ScQ+uEDuynakzfbilKOvb3ZkjPSDL7lZnpH+iCxst8duv2t0pDSAXScDu3C2O/lLiKW0jLgMGdgjg3pgs4fEs36NdYmOvAkSSXrFc27UE1e2dc+jl2d6FkyHABalFiVJ7h4FN7WxwyAnFMANkujfA3t8lRLig3Jus60PfPbxeR+jEP19io8vUBZ2faOt44eaOnV0WS/5ihP6o08/qmTBK2hcZiiYQQY2neBKALtoJumBldLAcZ5TiEf2wGYPjGfdyx0nLgOb//vsZc+6Xp4RZcQlQQCL0koSq8Qq9xAnV0LMlDgU4fxmW0uBt6OvyK2qYJVO8X7/zi/rn/7OJ/XIxWvzPsrM9U/gfXSBAthzm22dzIai/ZcvPKNHLm7procvzvlU89WecQaWEuLFM8keWCmdRDzPKcQu+zsoA3titZgMrOs1D3ImSCTpupWmXnTzcX3w7whgy4AAFqUVZk/I8ow5l6Qz1y1Lkh46d7WwM2Fxndvs6MRqS8b0vh6Xm76WA58M7AG4mP0b/+0XL8z5JLPXH8Au0iCn9Y3eXuVvfc6NWg58/cEnF7uMuDPjHtjLWyEPdRfMJHtgJenIckNXtubfA7s6IAN7ZClQwzMz74GddIiT84rn3KDPP7mhh8/X70Fq1RDAorS6F5icT8huu+mYJOnuRy4VdiYsrnOb7R0DnJzjK00C2ANwKZtC+YmH6hnArq005ZnFKiHuz8CutBr6tufeoPff/ZjaUTznk81Pd4jTDDKwa7Q4LCS3BzbPFGJp/iXEG9uRVpr+wIFKnmd0fKVZQAY2S5BMkIGV0gdtkvTBzz0x0/NgcgSwKK3unq6cL+QnVls6c2xZn3mEBnvM3rnNjk70rdBxilq0jp3cDdbHaxjAntts68ajS7rhyJIevbQYGVhrbZaB7X1PvfaFN+nKdqSP3Hd2jiebr+4QpxllYCV2wS6aa51YDc9054KMM+8S4o3tsLuPdpC1AkrhexnYyQLYp64d0rNuPKwP0gc7dwSwKC23uHqSKXFfdeYoGVgU4vxmr9yxX5qB5QaxaJeyAPbB9as7Sm7rYD372jp1dEmPX16MDOxmO1I7SroZWEl6yTPWdGK1tdA7YV0Amzf4GMU9HCCAXSxbYZx7gJOUTiG+1om7WcmDtrEdDRzg5JxYbRawRicNYPOu0en3iufcoDu/eIHKhjkjgEVpuSdkk1xgbnvKUX3p/DVdnvHSayy2JLG6cLWzYwKxc3ylqYtX+Xor2pWtUDccSYOduvXBrm+kpbSnji0vTA+sewjR/1Co4Xt69fNP68P3ndWla4t5czjrHlhJDHJaMNthnGsHrHMkCx7ntUrnynaowwMGOMtwZQkAACAASURBVDlrBZQQx901OpN/n33rc25UYqUP3UsWdp4KC2CNMU8xxnzEGHOvMebvjDE/lr39uDHmDmPMF7L/XlfUGVBtobvATDDm/HmuD/ZRsrCYnctboaLEDszArq3M/ukw9rq8Ferrn3FCy4Ffqz5Ya223F/TMsWU9dmlL1tZ/lYwLqvozsJL02hecURhbvf+eeq5MGmemJcSH3Z5qrk+j/Ps/uVdvvuM/z/sYM7PVmSwDe/RQGjy6dTYHbVwGdm21NfOv4bC7RmfyDOxXnjmiU0eXWKczZ0VmYCNJ/8Ja+2xJXyvpR4wxz5H005I+ZK29VdKHsl8De7gS4kl6FL7yzFFJ0t30wWKGXIA6OAPb0naY6FpnflMcF8Gla6FOrDb1wqcdq1Uf7OWtUGFsdTIrIW5HyUKUpg3KwErSc08f0Vdcv6o/XNBpxK60cRZDnFaavloNjxLiMd77mcf04fvqE4xMXEKcZT/nlYHdGJeBXW3qaifuTleehTiePEHiGGP08mffoD//wvpMz4TJFBbAWmsft9Z+Mvv5hqR7JZ2R9GpJ78g+7B2SXlPUGVBt0RQlxEeXA91yYoU+WMyUyxYNy8BKs99Th552FGsrjHV0OdCLb17TfU9cqU2bQDeQO9zSqaPpKrBFKCN2QdXuDKwxRq99wRnd+aWLC7mqYpYZWGMMu2DHuNqO9PjlbZ29Up8gfytMcu+AldQdoDSvScQb21G3jHmQEytZJcEMK52ifWRgJelbn3uDtsNEf3n/uZmdCZM5kB5YY8zNkl4g6eOSbrDWPi6lQa6k6w/iDKieKJ6uR+G2m46SgcVMuZvtYUOcJFZVFMndWB1dDvTiW47LWunOL9UjC+sC2JPZFHVJenQBVumsb7TlGem6Q3urGl79/NOSpPd8evGysJ04zejMIgMrpQ9GyMAO5/bGn9tsd/siq2570hLiZVdCfPABrLU2KyEenYGVZvuQOOr2wE4XwH7NLWs63GroDtbpzE3hAawxZlXSuyX9c2vtlQl+3xuMMXcaY+5cX18v7oAorWn3dH3VmaPpE9WN+mcxcDDcC+fAEmJ2LRbOlbYdPdTUC556TIFv9ImaDHJa78tEnjq2JGkxdsG6vcqDMiA3XXdIL7r5uoVcVRFG2Y31DDKwknRipUkGdoQH1jclSYmtT6/wtTCaLAPbLSE++DaYdpSoEydje2ClGWdg9zGFWEorJF76rOv1oXvP1ubBR9UUGsAaYwKlwes7rbV/kL35SWPMqez9pyQNXPhmrX2rtfZ2a+3tJ0+eLPKYKKlp93Q97ynZIKcvk4XFbJzfHJ4tOp69jV2wxenPwC4Fvp5307HaDHLqZmAPt7S20lSz4S1ECXG6A3ZvRYNz6w2H9cjFxSshbrspxLPKwBYwAKdOHji72f352Zqs55p0iNOR5TR4nEcJscv6jiohdm06s3wQE+1jCrHz9597o85f7egf/p9/rQ/+3RNKCGQPVJFTiI2kt0m611r75r53/bGk12U/f52k9xR1BlSb61FoTNhk/9zTR+QZ6e5HCWAxG+ubHR1faQ58WtvLwNbj5qeMLmX9rseyUrcX33Jc9zxyuRaDs9Y322r6no4sNWSM0emjSwtRQuwmLw9z6siSLl4LtR0u1pCUbg/sjALYtdWmzl/tcHM9xANZCbEkPXmlHg+OtsNESxMEsMuBr8A3cykh3sgmHx94CXG8vx5YSfqOr7pR/+ZVz9Hjl7f1hv/7Lr38LR/T737i4YW7Zs1LkRnYl0j6AUnfYoz5dPbjOyT9vKRXGGO+IOkV2a+BPdw0xkkzsIeaDd16/WEGOWFmzm+2tbYy+Gb7cKuhwDdkYAvUn4GV0gA2Sqw+9XD1v8fdDtj0ma906uhi7IJNM7B7KxqcG46m5dR1Gq6TRzjDPbBSmoGNE6tLcxrQU3YPnN3Us248LKlGGdgw1nIz/9ePMUZHloK5TCF2AazLAg9yqNnQoaY/00qCaMoKv37GGP3gS27Rx37ypfrV73m+lgNfP/MH9+gbfuEj+t8/cj+lxQUrcgrxX1prjbX2Nmvt87Mff2KtPW+tfZm19tbsv/WoA8PMdUuIpxhz7gY5LcI+RRTv/NXOwP5XKX0RO77S1EUC2MJ0M7DZvsKvftp18oxqsU5nfaPd3dcpSaeOLdW+BzbdfdsZmYG98UjWD3y53v8Wu81yCrHELthRksTqoXNX9bVPX5NUnwzsVifWoebwgHCQI8vBXEqIN7Ks76gMrNSrJJiV7prGKe4vd2v4nl79/DN6349+g975X3+Nnn3qsH7pzz6vD3yWAU9FOpApxMA0win2wDq3PeWYLlztLEQpHop3bnN0v97xlRZDnArkbqzcTc7hpUDPPX1Un3jo/DyPNRPnNjs62fe1debYsp64st29waqjK9uROnGy4++926ksA/tETYKKvDpRIs/sr7Sx34msf3CdAHaPRy9tqR0leuaNh3V8pVmLDKy1VlthPFEJsZQGsFe2D74lo1dCPDrgXluZ7TTtWWRgdzPG6CVfcUL/1z++XcZI9/f1V2P2CGBRWtE+Fk3fduaoJLFOBzNxfnN4BlZKh0xQQlycy1uhDi81dtzUv+jm4/rUw5fUjqrdb5SWEPe+tk4dXVZi61POOEj/4KphXAnxEwtQTt0vjJOZZV+l/gws16fd3ATiZ5xc1fWHW7UoV98O0wdfkwxxktIhSvMoIb6ylS8De2K1WcganUm3XOSxFPi68ciSvnTh6vgPxtQIYFFa7gIzzZPoZ506rMA3+gx9sNin7TDWZjsak4FtkoEt0OWtsFs+7Lz4luNqR4nuqfBDqjixunC1vSMT2V2lU+PS2VF7lZ3DrYZWmv7CZWDbUTKzAU5S/wTX6gdns/bgehpgPP3kiq4/slSL1Xtb2QCh5WCyr6Ejy/Ptgc2TgZ3tGp3ZlRAP8pTjh/TlC4s3Rf0gEcCitNwU4mkWTbcavp596kilb25RDr2b7eEZ2OMrTV0gw1GYy1thd4CT86Kbr5NU7T7YC1c7SuzOTOSZY8uSpEcvVf9mepg8GVhjjG44ukQGdp+uO5ROTyeA3euB9U0dXQ60ttKsTQa2G8BOsAdWSgfkzWcKcShjpNUxPbtrWQZ2VnNNiszAStLTjh/Sl84TwBaJABalFXWnEE/3ZfpVZ47qnkcusz4A++LKloZNIU7f19RGO6p8OWtZXbrW0bHlnQ8Q1lZbuvX61Urvgx0UyLnezzoPcsqTgZXSf4tFy8B2ZpyB9bx0yBwlxHs9sL6pZ5xcSR+WHGlpfbNd+fuFrU76GjRxD+xSoCtb0YEPvryyHWm11ZA3JpBcW20pSqyubM2mTzeKrTyjsZ93Wk9bO6SzG+3u/w/MHgEsSmu/T8ied9MxbbQjPXSePgRMr3uzPSJb5HbBXrzKqooiDMrASmkZ8V1fuljZdQXrAwK5w0uBDrcatV6ls77RVsMz3b2+w9xwZPEysJ04UTDDDKyUPmAjA7vXg+tX9fSTq5Kk6w8vKU5s5WcZuB2kk08hbqgTJ90e2oOysR3pyJj+V6lXATWrYWRRYqdOjuTx1LUVSdLDlBEXhgAWpRXtYwqxJN32FDfIiT5YTK+XgR1RQnwoW7Q+wx4d9FzeCnVkSAC72Y507+NX5nCq/RtWSnv62HKtJ6if22xrbbU5Nvtx6uiSzm60K/uAYhphPNsMrJR+fZ0jA7vDle1QZzfaekY3gE2/B6veB9vrgZ28hFjSgZcRX9kOx/a/SurOCVif0XC7KE4KKx+WpKf+/+ydd3wcd533P7O9N0mrXfXmIvdux05CKikEAgkJJORIArk7IJQ74Hjx3MFzcJc7cjwPx4WQo14CpEESeAghMSF2GnFsucgtlmzL6rLarlar7W1mnj9mZ1W8bXZnZov2/Vde9lrarFaz8/19Pt/Px6IBUBlghaQywFYoWqJ59MACQEeNDiq5pJJEXCEvnP7MdkdLfLitBDnxD03TSUOcAGaABUp3DzaVldZuUpV1iBOTvJzePgwwXbAkRS+rDtNIjN8dWKCiwCaDDXBqr2GUMmu8d7jU92ADEXYHlmOIU1wFFTvIyZvlAGs18HvAEKNoQQfY5vgAO1xxAApGZYCtULSQiZS43C4yMqkE6+qMlQG2Ql44vRFoFdK0oRhsxU5lgOWfYJRElKSTWojtRjWaLJqS7YN1eMPQKKTQKhffwNmNakyUcYiT0xfJuP8KADYjE2hVznbqpYRjFOQ8K7DVOqZDU+z9xmJmIF6h01ZuCmyuO7Dx6+uc6ANsLGOFDsD/AUOMogS1EJs0cuhVsooCKyCVAbZC0cJH0fSGBhPOjM8l7MgVKnBlxh9GVYabbUs84KkywPKPO8DcUKXal9zRasHhQVdJ3pynUiLrTSrM+COJfbZyw+FdXB2UClv8pnU5BTnxnUIMMPv7oSiVUOcqMAFOMgmB5ipGKUsofCWuwIZKzELM7MBmVmD1ShnUcimmeLoWkAIrsARBoMmiqQywAlIZYCsULdF4CnE+p9EbGowIRSn0Tfv4eloVlhkzvkhCYU2FSS2HhKgMsELAKgLJFFiAGWBnA1FcKMHf8VSDnL2MlUeKojHjD6cNRWOpNTKPWU5BTpEYBaUAFmKg0gW7kAGHH01VmsT9hVImhUkjx1SpK7A51uiwQyRfKb/ZwliIMyuwBEHAalBimqcd2Cgp7AALMEnEI5UqHcGoDLAVihZWNZXmcZHZ0FAJcqqQH05fOKPdUSIhYNYoSj7BshhhFdhUA+zWZqYP9sRo6f2OO33JFVi7qXyrdOaCUURJOisLcbVWCZmEWFYKbIQUwEIcf49Vgpzm6Xf40FatW/Rn5dAFy1qINXKuKcTiW4hpmoYnFMtqBxYAavUqfhVYAS3EANBk0WJ0NrCsQujEpDLAViha+CiabqnSQq+S4WRlD7ZCjjD7eukVWIAJcnJVbhB5J6HAJglxAoBGswYEAYyWoFXLkeJwpN7EKLDlmETMqoDZhDhJJMSyq9KZ9Udh1mS+3nChWssOsKU9nPEFSdEYcgbQbtUu+vNag4o3ha9QsAqsqgRCnIJREiRFZ6XAAuBZgRU2hRhgkoijJF3WgXyFpDLAVihaYhRzgSGI3C8yEgmB9fXGigJbIScoiobLH0aVNvPNtkWrqFiIBcCTwUKskElQZ1RjdLa0bhLCMRLuQDTpIGczxhXYMhzc2BqMbA6FAOa1WC4DLE3TWSc0c6FaX7EQL2RsNoAISaF9iQJbo1diWiC1X6wd/WCEhIQA5yomhUwCtVwq6g6sN8TYlbNVYK16FW8/H0aBFd5CDFSqdISiMsBWKFpiJJ2XfZhlQ4MJ5ya9ZRuIUkE4ZgMRUHR2N9tVOkWlB1YA3EHmUMCURpVqMKtL7iaB7RdONqwoZVJU6xRleXLviA9R1iyHNJtBtWwsxJ5gDBGS4n2AZQ/gZioOEQCMfRjAJQqsVa+CwxcGJYDl84u/PoEvPnuc96+7lGCUhFouzeng36CWiWoh9saH5WQd38moNSjhj5DwhfPf042SNKQ5VjRmS6ILtrIHKwiVAbZC0RKjaF52gTY2GBElafROeHh4VhWWE+xOa6YUYqCiwArFXDAKqYSANk0oSaNFU3IW4oSVNsV7q86kxsUyrNKZV2CzHGDjCmwppkxzxeFjft58D7AKmQQGlWxZKLDT3hDe93/ewPGR2ZSPYTtgl+7A1hqUiJI0ZgP8XscHnX68dHIcb513CP4+DkZJzgFOLEa1XNQQJw9XBTaRFJ3/dZGkKMgFVmDtRhVkEgLDJfbZVCpUBtgKRUuMpHixeGxuYkJeukcqNuIK3GBv+DKlEAOARaOAOxitBDbwjDsQhVEtT6soNFk0mPaGS8plkRjkUgwrdqOqLEOcnL4I5FIipSV8KTaDCsEoKXo6aiGY5miv5kK1XrksFNgTI24MzwTw878MpnxMv8MHi1YBs3bx62zVx7tGed6D/dXBIQDMYZzQawGhCMm5A5bFoJKLaiFm10OyqdEBmBAnAJjiIWgrRvHj8EuHTCph3EEVBVYQKgNsGTLlCeHBp7tF7/PimyhPPV02owr1JjW6h1OfyFaokAw2tTObzkqLVgGaBu+n98uduWA0ZQcsS6OFCT0aK6E9WHaATaW22Y1qjLuDZac8OrxMcFW2Fkd2H3g52IjZ90S29mouVGuVCft2OTPgZNTVV89MplTq+qf9aK/RXvLnCYWPxwHWH47hhaNjaKtmvp/QTrBglIQmRwXWoJaLbCFmFdhsQ5zYA4b8rwUxkoZcYAsxADRVaUtuvaVUqAywZchf+px4+fQETpZgrcRCSJKGjKcLzOYmE7rTWIoqVEjGTEKBzWKAjT+mYiPml7lgNOOOVKOZ2TUqJRtxpjCjepMa/giZsNmVC6mqg1KxHAfYGp2K969drVcsCwvxoMMPlVyCGEXjuaOjSR8z4PShvUZ3yZ/PK3z8vdd+1z0GbziGb9+6FgDQMy78AKvOUYE1qsVVYDmHOCUsxHwosJTgCiwANFs0GJ7x5/zvgxGyJCvixKAywJYh7GlPqSdYRil+LMQA0xU5MRfCeBla8ioIh9MXhlRCZFQAAaAqbkerDLD8MheMwpSiQoelMR6WMTpbOgOs0xeGUS2HUpb8ZjPRBVtmQU6sApsttrjqMllmr0MyHL4wFFIJDGpuHZ7ZUK1bHhbiAacPG+pN2N1ehWcPj16y0uEOROD0RdCWRoF18KTA0jSNXx4cxvp6Iy7vqEZzlQa9k8IOsIG8LMQyUa36iRCnLBVYvVIGtVzKywFDlBQ+hRhg1ls8oRjcOTqznjk8gtv++wBv78lyojLAliGsCjFV4gNsjOTHQgwwAyyAigpbgRMzvggsWgUkWbwPLZUBVhDmgtGM+5I1OiWUMklpKbC+cNpdR7uRsUVPlFmQk9MXzsqSz8IOFZNz5X8Dx1bo5FMdl4oqrRJzwSgiMYr3r11MDDj8aKvR4p5dzbjoDuKt89OL/r4/HuCUTIFVyaUwqGS8VbUcuDCDC9M+3Lu7BQRBoNNmQO+El5evnYpQHiFOhrgCK0QKczK8oRikEiJryzNBELAalJjiYZgjeQoJzURTnlU6fVNeUDTQUwkhvYTKAFuGsHaFiRK3XDE9Xfy8RTvtBqjkEhyr7MFW4IDTF0koq5lgHzdTGWB5xR3IvAMrkRAlV6WTqe+z3sQMsBfLyDVCUTRm/JFEL2k2KGVSVGkVmPSUz+uQCoc3nDLUK1/Y17ycD9jmAlHM+Bl19fo1tajRK/HUoZFFjxmIV+i0JRlgAWbPko+QIAD4xbtDsGgVuGWDHQBzHzI044efhxqYVAQj+VmIaRrwRcRRYb2hKHRKGacDm1qeumCjpEgW4vgAO5xjkNNgfKf7bGWAvYTKAFuGjLiYD/pSL3+PkhRvCqxcKsGGBlMlibgCJ5y+7O2ObKKlaxnY9MSComh4QpkVWICt0imdIYcZYFPvOtbolZBJiLKyEM8GIiApmpMCC8xX6ZQ7Di83dZoLbBdsOe/BDjiZ4bS1Wge5VIKPbWvEG+emMbZgtaDf4YdcSqDRrE76Nax6JS8hQaOuAPafncJdOxoTlt41dQbQNHB2UjgVNp8dWNbK6xEpyMkTimW9/8piNSh5CdliFFjhB1g2nyHXw9WhuCBVqYG8lMoAW2YEIrHEB1Sp78DGKH53FLY0mXHm4lxJVW1UKBw0TaN/2oeWak1Wj5dLma5Fl798bxD5gqJo/GB/H94+70j7OG8oBprOrui+yaIpsR3YSNphRSohUGtQlZWFmE3B5aoy2gyqkv88ywanL8J7ByxLTVyBLeck4kS/a3y/9a6dTSAA/Prw6ILH+NBSpU3p7qrlSYF96tAwJASBe3Y1J/6s064HIOwwkp+FmBkmxUoi9oaiWScQs1j1Kl52YJkaHeFHIK1ShmqdMqcgp0AklngvCm09L0UqA2yZwSoQRrW85EMvYhR/KcQAswcbo2icvjjH29esUL6MzQbhDcfQaTdk/W+qdMqKhTgDNE3joZd78Z+vncfTXcNpH8veSJk0mS2njWYNvKEY5gLFXx8WiMTgC8cyWmnrTKqyshA7vdnXUi3EZuTnprWYISkaLj+3hGYusE6Scg5yGnT6IZUQaIqHutWb1Lh6lRW/PjKa2P3td/iSBjixWPVKOLzhvOqrghESvz4yihvW1iZ22dnnY1DJBN1nzMdCzB4UihXklIsCW2tQIhAh4cvThh2jKMhFsBADjI04FwV2yMn8m9ZqLfodPoRjFfFlIZUBtsxgf0m2t1gwG4iWtNoYIyleLR6bm0wAUNmDrZAV7E0GlwHWolWU9Y4ZHzz6+gU8fmAQCpkkY28rO8BmZyFmbhRLYQ8220HOblSXlfLo8DH/L7kosKX+eZaJGX8YFJ26Fzhf2CqwcrcQN1k0i8J5PrGrCU5fGK/1TCFKUhieCSQNcGKp0SsRIam8VMgXT1zEXDCKey9rWfTnBEGg024QTIGlaZqxEOeqwLIWYpGqdLyhWNYJxCy1Bn6qjmIkLcoOLMC4g0Zy2IFl7cM3rbMhRtG4MO3j+6mVNJUBtsxgb952tVkA8NtnJjZ8X2CqdUq0VGnQXRlg0T0ym3Os+3Khd8IDggBW2/RZ/xuzpjLApuOX7w7hP187j9u21OOOrQ0ZB1h3kHktM9XoAECDuXSqdFgbZ6ZhxW5idj/FSgUVmsTgznWANfLfz1lszHfACjPAahVSqOSSRLd1OTLg8KOterG6+r6VVtSb1Hi6axijrgBiFJ12gJ0fkHJ7nWiaxi/eHcJqmx47Wi2X/H2n3YBzk95L6n34IByjQNHIuUaHPSgU00Js4LoDq+enCzbGY0hoJposGkx4QpwVVDbA6cZ1NgDA2YqNeBGVAbbMGHUFoFfKsNrGqEalfHofoyjeY863NJnRPTKblz2o1Bl1BXDbf7+LK/7jDTyyry/RxVZhMT3jHrRWaaFRZP8BW6VVVCzEKfj98Yv45z+cwXWdtfju7RvQZNFgLhhN+/7josCydQWlUKWTGFYyDHL1JjUiJAVnmexVO3xhKGQS6JXcblrZAbaUP88yke17IlcIgkCVVglnmVqIKYrGoNN/iT1YKiFw984mvNs/g329UwCQ0UIMIOcgp8ODLpyd9OK+eHXOUtbYDQhEyJx2IjPBOhTytxCLFOIUjOYQ4sRcC/IN2orxGBKaieYqDWgaGQ9slzLk9MOqV2KN3QClTFIJclpCZYAtM0ZcATRaNLCb2PL30v3AZ3Zg+b3AbGk2w+mLlFRaKd+8F98BXm3X4/v7zuOK776BH73Zj4BI0fmlQu+kh5N9GAAsOgVm/ZFlfUCSjH09U/jK8ydxWVsVfnj3ZsikEtSbM9fEuAPZD7AGlRxGtbwkLMQJBTYLCzFQPl2wznjKLteeU/syUmCtAg2wAGPdLlcL8fhcEOEYhdbqS9XVO7c1QiYh8OjrFwCkrtABFgxIOSp8vzw4BKNajls31Sf9+zV1zGeKEKE8QXaAzdFCrFfKQBDMbqrQ0DQNXzjGPcQp3gudt4WY55DQdLA72VxtxEMzfrRUM4Fjq2x69E5WBtiFVAbYMmPEFUCTRQObofRPrKMk/ylxW5rMAIBjIy5ev24p0TPhgVRC4MlP78QfPr8HmxpN+I8/ncWV330Dj78zWPZF99ngCUUx6gombjaypUqrQIyiRbkBKBUODczgwWe6sbbOgJ/duy1hb2Mtv2NpDpO4KLAAswc7yvGUuxA4vGEQBLMznQ57Qnks/v+nbHD4cus5ZW2dfB/IuvyRojm4SyQ0C2QhBoAanaJsFdilCcQLqdErccM6G7yhGGr0yrTXE/YAYSoHhc/lj+DVM1P42PbGlENkh1UHqYQQRE0LRpgBVpPjACuRENApZaIosP4ICYoGZwVWr5RBLZfmbyEm+RdIUtGU6ILlproPOgNorWLez6ttevROeCuH4wuoDLBlBEXRzABbpYFWKYNeJSvpJOJwjIRSxu9bdJVND61Ciu7h5dsHe2bcgxVWHVRyKTY0mPCL+3fgt5+9DCtr9fiXP/bg4b1nC/0UCw67a8LWHmQLa/8rZ6WIC396bxIP/PIoGsxq/OL+HdAtsI42xBXYsTQ7q55gFEqZJOudriaLBmMloMA6fWFUaRUZd7DqTaxKXR7vp1x7TvUqObQKKa8HsjRN47b/PoB//WMPb18zHxzeMHNznuPwkQ2Mhbg8FVh2XzCVPfgTO5uYv69ObR8GmNoTnVKW04D0xtlpkBSNWzbYUz5GJZeivUYrzAAbV2Bz3YEFmMNCMQZYdnUkm4q0hRAEgVqDElN5dsGSIu7A1uiU0CikGObw2eQNReH0hdESf7922g1w+SMJp0aFygBbVkx7w4jEKDTG7Qp2Y2l35zk8/FcKSCUENjWZlnUScc+4B2uWWGO3NlvwzF/vwu1bGvB01/Cyv0j25pBADCDxu1cKe5hC4g/H8PXfnsJnnjqG1motnnpg5yVqY5VWAZVcktFCnE2AE0ujWYOx2WDRhx45vOGslDaTRg6VXIKJMqnSYXpOM1ciJYPvKp3jo24MzQRwbrI4glEcXuEqdFiq9UzIXLH/fuTCgMMHnVKW8oDksrYqbG8x4/KO6oxfy2pQ5rRj+frZaVj1SqyrM6Z93Bq7QZAqnXx3YAFmFUOMFGJv3KXEVYEF+OmCjVLi7cASBFPtxOW+YHiGrdBh7inYexEhK5hKjcoAW0awu1+s395mVJesEuQPx+ANxxLWMT7Z2mTG2UkP/Hn2iJUiM74wJj2hlNbYB69uR5Sk8PN3BkR+ZsVF74QHZo08YcXPlsSuyzIeYE+OuvGBH/wFvzk6is9d1Y7ffnb3oi5EFoIgUG9Spw22mAtGs7YPA0CDRYMISeVk/xOTbIcVgiBQVyZVOome0xwtsjaeD2RfOTUBgHuwilA4vLnZq7lQpVWCpGi4RQrpEZOBeIBTqv1qTzXFgAAAIABJREFUgiDw/Gd24wvXrsj4tax6JWcFNhKj8NZ5B65ZbYUkw2DUaTdgYi7EexNAIJLfDiwAGNQyUVKIWZWX6w4swBww5HPITlE0aBqQ8byilo5GiyYxlGYD6yhIKLDxYNazRXLgVgxUBtgyYukAazeUrgLLDt42I/8f6JubzaBo5kZ7ucEGRyxVYFnaanS4ZUMdnjo4jNllnKbbM8EEOHENm6nSKqBRSJflAEtSNB574wJu/9G7iMQoPPvXu/C1G1dDkWYNoCGumKbCHYxwGmCbEgp4cQwlqeBipa0zqdOq1KWCyx8BRXPvgGWxGfg7kKVpGnvfmwTAOJeKoV/W4RNDgS3fLthkFTq5YtWrMM1xQDoy5IIvHMO1nbUZHyuUmsbuwOajwDIWYuEP9wupwEYpJudDrBAnAGi2aDDiCmTtfhiKD7DNFuY9bdTIUWdUVZKIF1AZYMuIEVcABDG/N2UzquDwhRElSy+UZzJ+cRJCgd3SGA9yWoY24p4JJoE4nTX2was74I+QeOLdIZGeVXERIymcm/Rytg8DC61CpT9wcMEXjuGunx7C/3n1HG5cZ8PeL12JXW1VGf9dg1mddgd2LhiDUZ295bQxvldbzBZumqbh5DCs2I0qjLgCJXkdX0i+Pac2oxLT3jAv/Zknx+Zw0R1M2EmL4YAg1/1gLlTrmN+lchtgQ1ES43PBpAnEuVBrUGLKE+IUmLOvdwoKmQR7OjJf9xID7DjPAywPO7BiWYjZ78G1BxZgfj6BCAlfji469hoiloUYYKp0wjEqEdaWicEZP2wG1SI1fbXdUOmCXUBlgC0jRl0B1BnVCcXDblSBpsH5JLEYYO07QgywRo0cHVYdukeW4QA77kGdUQVzmvTTVTY9blhbiycODIryQVZsDM34EY5RKVXqTDRy3HUpB17rmcThIRf+7SPr8Ohdm2HMcm+13qzGbCCa0s7v4WghrjerQRDFbeH2hmMIx6is02bfv9YGlz+CZ7pGBH5mwsIOTTkrsEY1SIrmZfh65fQE5FIC9+9pAVB4G3EoSiYScoWEfc9xTSKOxKiiduQMOv2g6fT9rlyw6lUIx6is0+Rpmsb+3mnsaa/Kqje8Rq9EjV7Je5UO6yTINYUYYEKVxLAQswqsIQcLMXtfmKsKGyWZAVYq4gDL5mNkayMecvrREt9/Zem069Hv8CEcK7xjpBioDLBlBFuhw1JrZKsHCn+6zBVWgeW6g5gtW5vM6B5xl2WYRTp6JjxZVcN84ZoV8IZiePLgsAjPqrjoSSQQ5zbANsWtQssp7r5vygeZhMCd2xo52a7ZKp1UCpg7EOEU4qSUSVGrV2E0japbaBJKZJbDynWdVuxur8L3953HXKB0D5TyVmB5qoajaRovn5rA5R3ViWthOheAGHB9T+RKYoDleKj96Ot92Pmd/Xjl9IQQTytvMiUQc4XtGnVkuUvf7/BhxBXANVnYh1k67Qbe7aB8WYgDEVJwx8e8hTiHHdg80/5j8f83uUgpxADQHK/DybZKZ2gmgNYllvhOuwExikbflI/351eKVAbYMmLpADvfIVh6e7CTcyHolTJoldztJdmwpdmEuWAUA05uvVylTChKot/hz0pZXFdvxNWravDzvwwUTU+iWPSMeyCXEuiw5mZHazSrEYySZdu3mIy+aR9aqrWcbwjSVelESQr+CMlJgQXYKp3iPbTjOqwQBIFvfGAN5oJRPLK/T8inJij5KrB2Iz9dsKfi9uGb19th1asglxIFV2BZW6HQA6xJLYdUQmDGz22APdg/g0iMwoPPdOMXBwYFena5M+BgbuiX3vDnilXPKnzZvU77e6cBANeutmb9PdbYDeib9vLaux6MMl8rrxCnuKXXK3CXuScUhUxCQCXnPoZY44dZuQY5JSzEIu7A1pvUkGTpDpoLRuHyR9BStfj9vLoS5LSIygBbJgQjJBzecKIwGQDsBubmkO/ydzGY9oYSp6BCsLWZ2YPtXkZ7sOcmvSApOisFFgA+f80KzAaiJW9d5ErvhAcdVn3a8KF0sL+DxWxj5ZsL0z6syGHgb2B7TpMMEKyNjesA22BRF7UC68xhWFlTZ8DHtzfiVweHEjfrpYbDG4ZaLoU2x5tr1jaYr6PoldMTkEkIvH+NDVIJgboMSdhikK86nS0SCQGLVgGnN/vDNZKicWbcg7t2NOH6zlp866UefOeV3qJyLw04/LAbVVnZd7OBvffItkpnf+80Ou0G1JkuTVtPRaddjyhJo5/H3+dg/LBZmeNnFzDfyyq0jdgbikKvknEOSgSYHVggDwtxAXZgFTIJ7EZ1VvcFQ0sSiFlaq7VQyiSVIKc4lQG2TGBv2BoXKLAGtQxqubQkB9jJuRBsRmHswwDQVq2DQSVbVnuwbOLhGnv6jjqWrc1m7G6vwk/eHiiKlE6x6J3woNOuz/nfNy2zLthwjMTwjD+nAbZap4RCJkk6QLA3UFwsxADTBTvpCRXtnhA7rGS7A8vy5etXQSWX4t9f6RXiaQmO0xdGtV6R0w0rwCR8y6UEJjnWmyyEpmm8fHoCezqqE3vaDWZ1wX9X2feEVWAFFmDed1wU2H6HD8Eoie0tZvzonq34q13N+MnbA/jycyd4VQ/zga3Q4Yt5i2rm18kdiODosAvXdWavvgLzTQB8DiPBKAm1XJrz7xgwf2DoEXyAjeVkHwYAnZK5t81WIV8KSbIDrLgjUHNVdlU6Q3Gb8VJHgVRCYJVNXxlg41QG2DKB/aVYaCEmCAJ2owoTJdgFO+UJCxLgxCKRENjSbF5WScQ94x7olTI0WrI/Jf78NR1weMN47uiogM+seHD6wpj2hnMOcALm9zoLfVMsFoNOPyga6KjlPvRLJAQaUihg7ABryMFCTNPJVd1iwOENQyYhYOL4/1WjV+LBqzuwr3ca7/Q5BXp2wuHw5ZeyK5EQeddnvHfRg7HZID6w3p74s8YMVU5i4PCGQRCAJU24Hl9U6xRwcFhvODXGJNdvaDBCKiHwL7euxT/csAq/PzGO+39xGN4CB/3RNI0Bh483+zDADEgahTSrLtg3zzlA0cA1HOzDwLyaxmcScTBK5mUfBuavt0IHOHpDMRjUuSnmBEGg1qDMOaC0EDU6ADPAZqPADjr9IIjF9/MsnTZmd3o5ZWykojLAlglLO2BZbEZVySmwFEVj2hsSdIAFgC1NZvRN+0RJ3CsGeiY86Kzj1m16WVsVtjab8eM3+4vmtF1IehMqde4DrEouRa1BuWwsxGygRC4KLMAkByfbgWUDi7gOeqwLZbSIB9hqnRKSHOxr9+9pQYNZjYde7uGlTkZMnN4IZ9V5KTajChN5WIhfZu3Da+fDdhrMajh9he2CdfjCqNIqIBMhVKZap+QU4nR6zA2tQpqoqCEIAg9e3YHv3bERXQMu3PmTQwUdYmf8EXhCMbTxVKEDMP+PVr0yKwvx/rPTqNYpsLHBxOl7yKQSRk2b5HGAjVB5BTgB86nAQt4XuQMRHBl0ob0m959ZPodZ8zU64o5ATRYtXP5Ixt+XIacfdUZ10jqkTrses4FoSbaL8E1lgC0TRl0B6JQymJfY7WyG0htgXYEIoiQtWAIxC7sHe3wZ2IgpikbvhIfzYEYQBD5/TQfG50L4XfeYQM+ueGAH2FwTiFnYJOLlQN+0DxIi9wCVBrM6aQpxrjuwrMOgWF9/Lh2wS1HJpfjHmztxdtKL3xwpLVeEI4//bxabUZWzbZCmabxyegK7O6ph0swrnaxjopAqLHuoIQZVWgVm/OGsFZxTF+ewtt54SeXI7Vsb8IO7NqN3woO3zxfOEcB3AjGL1aDKqMBGSQpvnpvG1ausOR1IMWqalzc1LcSDAjtvIRYuxOmnbw/AF4nhc1d15Pw1rAYlpnOu0WEO48Ws0QHmBaZMn02DM4FLKnRYVgtgPS9VKgNsmTDiCqDRorlEXWM+8ENFFbiQCXbgFlqB3dhogoQAjo+4Bf0+xcCwK4BAhMw6wGkhV62swYYGI7754nv46vMncX6qfBPwesY9sGfoyc2G5dQFe2Hai+YqbdLT4mxoMGvg9EUSFRAs7gBjc1w4bGRDrV4FhVSCsSJ9/R2+MKp1ub+/blpnw44WC77353Ml09McJSnMBnhQYA2MApvLDf+ZcQ9GXAF8YL1t0Z+nS8IWC4c3/+E+W6r1SoSiTMJ3JqIkhZ5xDzbUJ89NuHqVFQTBhLgVCjbUjE8FFkBWCuzRoVl4QzFcy6E+ZyFr6gxw+SM5H8oshd2BzQfW1pvs2kJRdOK6nCtOXxhPHBjCBzfUYZUt96yJWoMK097sD2IWwiqw8gJYiAFgJMMe7JDTf0kCMUunjR1gy/c+LFsqA2yZwFToXLrbaDeqEKNoODnG5hcS1hZSK2AKMcDsubRUaxPhRuUMu2eTizWWIAj87JPbcNeOJvzx1Dje//23cf8Th3Gwf6bs9jB6J7x5q68As1c3UcRBQnzSN+XLuXIImB8gLroXf6jPBdmie257UhIJwQTzFGkScb7DCkEQ+OYta+AKRPDYGxd4fGbCwdxo5l8TYzeqEIpSOalDL5+egFRC4Po1SwfY4lBghU4gZmEPEWZ8me8J+qZ8CMcorG9IPsCqFVI0mNXomy7czfSAww+FTIJ6c/bZDtlg1WcekPb3TkEhleCKFdU5fY9OntW0QCSW9wCrlkshkxCXWIijJIXPPn0M2/9tX16ZGD9+sx/hGIkvXbcir+dp1SsRiJDwhblfC6LxECfRFdj4ADuc5nB11h/BXDCa0tFk1MhRb1JXFFhUBtiygKJojC7pgGWxGUuvSoc9jRQyhZhljd3Aa4hCsdIzMQeZhMCK2twGjVqDCv9y6zoc/Pq1+PL1K3FqbA53/ewQbn3sAF7rmeL52RYGpifXl1cCMUuxBwnxRZSkMOj05zXA1ptYBWzxa+UORqBTynLaC2ywaDBahF2wkRgFpy+S9yC3vsGI2zY34Il3hkpC6WfXNNanUPKyhXXlTHi4/WwT9uH2qkuCkqx6JRRSScEOPGia5sVenS1VcfXfmcUA+95FNsAp9X7nCqu+sAqs04+WKg3vw0itIfOA9PrZaexqr8q5r351/LOGr0P0YJSCKk8LMUEQMKrli1KIoySFLz57HK+emUJrtRZfe+EUvrOXe5XSlCeEJw8N4yObG/LafwXmrwW57ILOK7DijkAGlRwmjTxRk5OMwXgCcSoFFgBW2/Q4y+PudKlSGWDLAIcvjHCMQlOSNzxb/j5RQgPspCcEghC+Ew9gTkAvuoNlH+R0ZtyDDqsOSll+H25mrQJfvHYFDnz9Gvz7R9bDG4rhb548mthDKmUuTPsQo+isa4bSwZ60FmuQEF8Mz/gRo+icA5yA1ArYXDDKef+VpdGcXd+e2PRMeJguZh7eY1+9YSUomsYTB4byf2ICc3RoFmq5NKcVhoWwn2dcD2TPjHswPBPAzQvSh1kkEiIeJFaY31VPKIZIjBJtgGU/Vx1ZdMGeuuiGXiVDc5LDcZYVVh0GnH7EyMKE/PGdQMwy3wWbfEAacPgw4PRzrs9ZiEElR6OFPzUtFCGhlud/W29Qy+EJMYN7jKTwd78+gb3vTeKbt6zBy1+8AvfsasJP3hrA3z51DH4OCuhjb1wASdH40rX5qa/Awqoj7ve2sQLtwALA9hYL9vVOpQzFTNUBu5BOuwH9Dv+yqjdMRmWALQNSJRAD8ypmSSmwcyFU65SiJDKyN1TlbsfoGffkffO4EJVcirt3NuFnn9wKmgaODLl4+9qFoicR4MSPAgsUb5AQX7DKywpr7q+ZVa+EXEpcMkB48hhgmywazAWjRbcjytZ2bWnmlliaDLtRjQ9ssOO5o6MFrzLJxOFBF7Y0m/JWPFjVhevn2Stx+/ANa21J/76hgAMs2wErtgKbTRfs6bE5rKszpg0o6rDqEIlRBTmsi5EURlwBtOWp5iXDqmfea6kGpNfPTgPgXp+zlE4bfy6wYJSERpGbGrwQg0qGuWAUMZLC3z93Ei+fnsA/3dyJT1/eCrlUgn+9dR2+9cE12N87hY/++CDGk4TwLeWiO4hfHx7FHdsaEwe8+WBlFdgc9odjBdqBBYC7dzTB6YukdK4NOf2QpKjQYVlt14Ok6II6H4qBygBbBowk6YBlsWiY8vdSUmCnvCHBE4hZhCgTLzYc3vy7TVPRVq2DQSUriyTn3gkP1HIpmtNYd7KlRqeEUiYpCXtnPrAVOu3W3F8ziYRAnenSKh13IAqTJkcF1lKcXbzdI7OoN6lhN/Kzr3f/nlb4wjE8f7R4E8I9oSjOTnqwrdmS99dKDLAcVZe9701iV5slZc9qg1mNiwWyECcGWNFSiFkFNv2NfyRGoXfCiw0p9l9Z2PWBvgKE+43NBhElabQJoMCyGRypXqd9vVNYbdMnHCS5srHRhAGnP1Eblg/BKJlzmN5CDGo53IEIvvL8Sbx0chxfv2k1/vrKtsTfEwSB+/a04vH7tmPUFcCtjx3AidH0YZg/fL0PAPCFa3JPHl5IbUIhz0GBpVgFVvwR6MqVNag3qfHs4ZGkfz84E0C9WQ2FLPVz43t3ulSpDLBlwLArAIKY3yVbiERCoNaQX/m72EzOCd8By2LVK1GlVZT1Hix7kVtbl79tcSkSCYHNTeaEslTK9E54sNqu58VWJJEQaLRoMqYNljp90z40mNV5n/onq9LJz0JcpAPs8Cw2N+WvvrJsajRha7MZT7w7WLS9sMdH3KBoxjqXLwqZBNU6BafPs4m5IAadflyzOnVSbKokbDFw+MRVYBUyCTqsOrzbP5P2ceenvIiQqQOcWNgB9oJDfDVowBlPIBZAga3Rp1b4+h0+HBmazVt9BYDNjcz14Pho/p+hjIWYnwH21NgcXjwxjn+4YRU+8772pI+7apUVv/vcbihlEnzsJwfx/dfOJ13HGp7x4/mjY7h7ZxPqktyn5oJOKYNaLs0pwTlGsj2w4iuwUgmBj21vxDsXnEl3YdMlELO0VGmhkkuWfRJxZYAtA0ZdAdQZU5/Y2PMsfxebKU9I8ARiFoIg0Gk38FomXmyw1lghFFiA6dPtm/YVnV2TCzRNo2fcw0sCMUux7mHySd+0L6/9V5YGkyZJiFMeA2w8kb2YgpzG3UFMzIUS/dN88enLWzHqCmJfb3GGqR0ZdEEqIXgb3GsNKk6OIvZwbVua172QVTpiW4gB4NaNdTg86Erav8xyaiwe4FSf/uemV8lhN6pwYaoAA6wj3gErgAJrUMmglEkuOSw5OuTC7T96Fya1HHdua8z7+2zgqc6PpmkEoiTUivxv603x6+5Xrl+JB69Or5iurNXjxQf34JrVVjyyvw+X/8fr+K995xfdDzyyvw9SCYHPXZV8EM4FgiBQa1DmtgMbP+yTFcBCDAAf294IqYTAs0cWq7A0TWPI6c+40y2VEFhVWwlyqgywZQDTAZv6VMtmVJfMDmw4RmI2EBXNQgwwe7DnJ32Jcutyo2fcg3qTGsYc7ZiZ2NJkBk0DJ0q4T3d8LgRPKMbrANsU74Itt6ohFpKi0e/wYUVt/jvDDWY1HN7wolCKuWA05/esUS2HXiUrqiqd7rjNfksTvwPs+9fUot6kxv+8M8jr1+WLI0MurK0z5JzUuhS7UcXp8+zY8CxUcknaDIBCVuk4vGHIpUTOhzW5cOumegDAiycupnzM6YtuGNXytPcWLB1WHfoKsI834PTDrJHn3dudDGZAUi0Kcdp7egJ3/7wLZo0Cv/vc7rRBO9miU8qwslaP4xksuJmIkjRIiuZFgf305a344d2b8YUsw5aqdEr86J6tePmLl+Oytir8174+XP7w63hkXx9OjLrx++MX8cnLmhN7q3xhXfLzyZbEAFsACzHAHMJdu9qKF46OLQpzmvFH4A3HMiqwAGMj7p3wlO39RTZUBtgyYCRFhQ4Lo8CGSuKNztp1akWo0GHptOsRIanEaW650TPBb4DTUjY2GkEQ8zfopUhvHj25qWi0aOANx8o24XrUFUAkRuVVocPCdjiyYSChKIlIjMr5pp4gCDSaNUVlIe4edmccpHJBJpXgvt0tODzoStSeFAuRGIUTo25e9l9Z6k1qjLoCWafeHhuexcaG9AFSjQVWYGt0ShCEeGpQU5UGW5vN+P3xiynvC05fnMOGBmNWz6vDqsOFaR/nWpV8GXD4BLEPs1j1ysSO5c//MoDPPdON9fVG/Pazu3nJSmDZ3GTG8ZHZvF6/YPzwj48d2LYaHW7ZUMf5362tM+Knn9yGP37hcuxsq8L3953Hhx87AJVcmtKGnA9WvRLTeaQQF8JCzHLXzibM+CP4c89k4s9YS3E2qdqrbXrMBqI5WajLhcoAW+IEIyQc3nDaAdZmUCEco+DmISRAaNhwDrF2YAEkKi3KcSE+GCEx4PAJZh8GGAvZqlo9uktYge2Z8IAgmA8Fvij3JGJWceFjgF2qgLHXKpM6d2Wl0VJcFu5jI7PYUJ9/Em8y7tzeCI1CiscPFJcK+974HMIxCjta+VOdt7ZY4I+QeC+L3IJAJIYz456Mtu1qnRIKmaQwCqyIHbAL+fCmOpyf8iXdowtFSZyb9Gbd27vCqkcwSmJc5FWlAUdmu2U+1BoYtf/bL53BQy/34sa1Njz9wM6UYWC5sqXJBG8oltjpzQXWvcJHCnG+rKs34mfxQfaWDXb80wc6USVASBmT7xLmLM4U2kIMAFeuYMKcnumatxEPZlGhw1IJcqoMsCUPa5FrTDfAllAXLLvPIKaFuK1GC4VUwluZeDFxdtIDioagCizAzwlyIemd8KDZouHN5gjMd8EW0xDFJ33TzI0vPwMsq4AxN8Csap2PrbLJwuzVFoPzJBQl0TM+hy0877+yGNVy3LG1AS+dHM9JkRCKI4NMvdZWHhXY3e1VAIADF5wZH3tydA4kRWNbS/rXXSIh0GAqTJWOw1uYAfYDG+ogkxBJbcTnJr2IknTGBGKWFbXxJGIRbcS+cAzT3jDaaoQbYGv0SgzNBPDEgSF8+vJWPHb3Fl4UzqVsjq8V5HMIzAaQ8bEDyxfr6o344d1b8ImdzYJ8fateiWCUhI9DFy0wH+IkxGFitkglBO7a0Yh3+2cSg+vQjB9SCZH4PEzHmjoDszudp/W8lCmed3qFnEhXocPCDrClkETM7jaJOcDKpRKstOnK8iRL6AAnlq3NZnhDsYIkUfJBrwA2azYJt1wH2AtTPtgMKhhU+e/u1RpUkEmIhIXTHYgAyG+AbbRoEI5RGetCxOD0xTlESRpbeEwgXsp9e1oRo2g8dWhYsO/BlSNDs2it1vI6oFXrlFht0+Pd/swDLJe943qzuiA704UaYC1aBd63sgYvnhi/JMH6VNyKvi5LBbYjbuMVM8hpMBHgJJyFuLlKA4IAvnnLGnzzljVp+3Dzoa1am3cdHWsh5mMHtlRgnXpcbbTzNTqFU2AB4I5tTJjTr+OVOkPOABrN6qwGa71KjrV1RnQNpE8TL2cqA2yJw94cp9vHsJeYAquUSWBQi2uDYcvEM6k1gUispBKde8Y90KtkWZ3o5QN7Y95dgnU6vnAMQzMBdNr4HWC1ShmqdYqi2sPkkwsOX0J5yRephIDdpEqkorIKbK49sEBxHSCwSbhCKbAAszd17WornuoaWRSGVSgoisaxYRe2Z1A/c2F3ezWODs1m/P88OuRCh1UHkyaz5bPBfGkSttCQFA2XPyxaB+xSPry5HpOeELoGF98Enx5zw6JVJK3mS4ZZq0C1ToELIiqw7IEzX9egZHxiZzPe/OpV+PTlrYJ9D2C+ji6fJGI+d2BLBWuOXbAJBbZAIU4stQYVruu04vljYwjHSAw6/ZyCwXa2WnB81F0U1/tCUBlgS5wRVwA6pQzmNDd6NTolJAQwWQKD15QnDJtRJWqgBcDYMWb8kYxqzb+93Itrv/dWyfR79kx4sMZuEPz1bK3WwqyRl2SQE3vqvaGRf3WswawpigGKbyiKxoVpHy/2YZaFVTp8WIgTVTpFkETcPTyLlioNqgUeVD51eStc/kjadFmx6Hf4MBuIYhsP/a9L2dNRhXCMSntgRlE0ukfc2Jpl6nOjRQ2XPwI/RztiPsz4w6BocSt0FnJdZy20Cil+f3zx++XU2BzW12cX4MTCJBGL10t5bHgWJo1ckAodFoVMwmtYUzo2N5lwbsrL2Q7LkrAQL6cBNk1XbzoSCmwBd2BZ7t7ZDJc/glfPTGFoJnMH7EJ2tFoQiVGJyqvlRmWALXFGXQE0WjRpP2hkUgmsem7deZn4XfcYtj20j/fi90lPSNQAJxZ2If5MGhsxRdF49cwUAhESX33hZNHve5IUjbMTXqyty84Glg8EwZwgl2KQU9cA01OZricyV5gqneI/OOLK+FwQgQiJFVb+Qq8azOqEhTgxwOahwDaYNVBIJTg9VtjVAJqm0T0yy3t9TjIua6vCapsej78zVPDd3yNDzHC5Q4ABdkerBVIJgQNpbMT9Dh/mglFszVIBZoPE0nWj8k0hOmAXolZIceM6O/aenkyoOKEoib5pX9b7ryxslY5Y77ujwy5sbTKLftgtFJvjdXQnc9xp9IaYwbcYQpzEojauwHJdj5uv0Sn8e+eKjmo0mNX44et9CERITqFkO1qZa+tytRFXBtgSZ9gVQFMWPW02oyqR8MsHe9+bhNMXxplxfk9+pgo8wKbbgz055obTF8bVq2pweNCFJ94dEunZ5cbQjB/BKCl4gBPLliYTLkz7MFcCadcLOTQwg/X1Rl4DnFiaLBpcdAezrvwoFdiwFj7te/VmNaa9YYRjJOaCUUgIQJfHzZhKLsXlK6rx6pnJgg5zo64gnL6IoPZhFoIg8KnLW3FuyotfvFvYIfbokAvVOiWaq1LnM+SKXiXHxgYjDlxIfePG2rYzJRCzNBSgSsfpY3a9CzXAAsCHN9fBG47h9bPTABjXDknRWScQs6yw6uENxXLq5eTKrD+CfodflN8psdgUHifUAAAgAElEQVQUdwDlugd7dpJJ0m8VMNSq2NApZVDLpZzfc6yFuBgGWImEwF07mnA+vj/OxUJs0iiw2qZHVzwsb7lRGWBLGIqiMZqhA5bFZuBPgaUoGofjvzAneExAo2kaU54QbAbxP8yNajnqTWr0pKlm2Nc7BamEwPc/tgnXrrbiu386i/4iDi16rWcKgPABTiyswtQ9mvoDmKZp/MtLPXg3iwRRMQhGSJwcc2NnG/8qEcAMsCRFl8T+ORfYsJYOHjsYG8wa0DQw4Q7BHYjCoJbnHZpy4zobLrqDOF3AftRjI8y1UgwFFgA+vKkeV62qwbdf6sHXf3u6YPtRh4eY/VehFLI9HdU4NeaGJ5T8wOzY8CzMHCymS5OwxSChwOrEP7Rl2d1ejRq9MmEjPh23I25o4LZSsSK+TiDGHuzx+GeMEK6ZQmFUy9Fh1eXsYjo1NoeOGh10AhzEFisEQaDWoMxZgS10iBPLHdsaEsN0K0fL+s5WC44NzyJaZofk2VAZYEsYhy+McIzKboA1qjDF00302UlvwuLHp/feE4whFKUKosACzB5sOgV2f+80treYYdIo8J3b1kMll+Krz5+8JMGx0AQiMfzD8yfx8N6z2NlqwUoBQy4WsrHRxMS6p9lLe/OcA48fGCwa9fr4yCyiJI1drVWCfP3GMu2C7Zv2olqnhJnHPsSFA8RcMApTHvuvLNd31kIqIfDK6cnMDxaI7mE3tAopVvHYMZwOhUyC/7l3Oz5/dQd+c3QUH/vJQYyLaIsFgIm5IMZmg9gugH2YZXd7NSiaWQFIxrHhWWxtzn6ArtEpoZRJRA1dYwfYaj2/vaJckEoIfGhjHd44Nw13IIJTY3Oo0SsT9sxs6WCrdKaE34M9NjwLmYTgPGQXO5sbTTg+MsvZOUHTNE6Nucvu9cgGq0HFfQeWpCCTEEVjP7fqVbh+TS2UMgnqTNzuf3e2VSEYJQt6SFsoKgNsCcPeFKfrgGWxG1XwhmPwpjit5sKhuN9+Q4MRJ8f4U2BZi3OhBthOuwGDTn/Svd5RVwBnJ724rrMWAHPR/Jdb1+L4iBs/fXtA7KeakjPjc7jl0XfwQvcYvnBNB55+YCdkInWdaZUyrLYZUp4g0zSN/9rfBwA4MuQqih3iQ4MuSAhk7InMlXLtgu2b9qHDyq9VjU08HZsNwB2M5hXgxGLWKrC7vQp/em+iYHbaY8Oz2NRkEvW0Xyoh8NUbVuEnf7UV/Q4/PvjoOzjYL96e1NH4/quQA+yWZhNUcknSPliXP4IBp59T/yxBEPE9bHEVWJ1SVvC9xY9srkeUpPHy6QmcvujGBo4BTgBzAGBQyUTpgj06NIu1dQaoFeUVWLSl2YzZQBTDHEMix+dCcPoi2NgofN5FsWHVKzmnEJMUDVkRBDgt5Nu3rsWvPrWD8/0ae409vAxtxJUBtoTJpgOWhc8u2K7BGTRa1LhpnR3DMwHM+iN5f01g/rmxz1Vs1tgNoGjgXJIT5H29jB2XHWAB4EMb63DTOhu+/9p5nJsUL30xGTRN44kDg/jIY+/CH47h6Qd24ivvXyXa8MqypdmEE6PupKr0231OnBx1Y2erBe5ANOnrLDZdAzNYV2+Enocu02TYDCrIpURZDbA0TePClI/XACeAOWSTSghcdDMKrDGL6pNsuHGdDUMzzAFUrrxwbAzf/P17eI/jKbc/HMPZSU/WSbh8c8NaG37/4B6YNHLc8z9d+PlfBkQZ5I8MuaBVSNFpF051Vsqk2N5iSdoHy3X/lUXsKh2HrzAdsEtZW2dAe40Wvz48igvTvqz7XxdCEARW1OoFH2CjJIWTY25OhxOlwma2jo7jHuyp+CrXclRgaw0qTHnCnK5rUZKGrMAVOkux6lXY2cbdCVajV6K9Rrssg5yK6ydYISORGIVRVwBHhlx4u88BgmDCTzJhNzKPyXcXj6JodA26sLO1ChvjKYWneLIusAqsrVAW4viuaLI92P290+iw6hYt2BMEgYc+vA56lQxffu5EwXYQ5gJR/PWvjuLbL/XgihXV2PulK7G7vbogz2VLkxm+cOySOgWapvHIvvOoN6nxH7dvAFD45LxQlMTx+EAtFFIJgXqTuqwG2ClPGN5wjPf+RZlUAptBhbHZIDw8KbAA8P41NhAEEzyXCyRF4+G9vXjy0DBuefQd3PrDd/DckdGsEthPjrpB0cL2v2aiw6rD7x/cg+s6rXjo5V58/7Xzgn/PI0Oz2NJsFvwAbXd7Nc5P+S5RYI4Nz0IuJTgn6S5MwhYDhzdUsA7YhRAEgY9srsfpi3OgaHB+3VhWWHXoF3iA7Z3wIBSlOB9OlAIrrHrolDLOfbAnx+YglxKCHhgVK7UGJYJREl4O9UMkRRWdApsPO1qrcHRotujW2YSmMsAWKdPeEN48N40fvdmPLzx7HB989B1se2gfVn5jL6747hu448cH8eKJcay2GaCUZbbR2OOqZr4D7PlpL9yBKHa1VWFdgxEEkXvs+1LYHd1CnUg3mNXQK2WX7MF6QlEcGphZpL6yVOmU+LePrMOZcQ8ee+OCWE91EQ+93IO3zjvwv29Zg5/fuw0WHvcSuZIIchpe/J44cGEG3SNufPaqdrRUa1FvUhc8Oe/EqBuRGIWdAu2/sjRaNBgrowGWDWnhswOWpT4+QLgDEV52YAHmerK9xYI/vTeR07/vGpyB0xfBw7etx7c+uAaBCImv/fYUdvz7PnzrD2dwIU33JaukbG4s7M22XiXHj+/ZivevqcUvDw4jHBMu2MkTiuLspAfbRFDI9nQwv7tL7dHHhl1YW2eEimMnZoNZg9lANOcuTq44vMWhwALArZvqE//NNYGYpcOqw4w/ghmfcEnErD19S3P5qY1SCYGNjcZESFW2nBx1o9Oe3b1guZFLF2yUoosigZgvdrVZ4A3H0ma4lCPLJ66syKFpGo8fGMJb5x3oGffAueADoN6kRrtVh7V1BtiNatiNKtiMKtiNqqz2XwHAGg9kmMxzgD0Uv1HY2WqBQSVHe42OvwHWG4JZI+d808EXEgmB1XY9epZcBN4650CMonFdpzXpv7txnR0f3lSHH75+ATevt2NlrXinoE5fGC+eGMfHtzfhU5e3ivZ9U9FcpYFFq8Cx4VncvbMJQFx93X8edqMKd2xrAADsbLPgzXMO0DRdsCCFrgEXCALYLqACCzAW/1dO5zY8FSOsus63hRhgDpHevTDDWIh5GmAB4KZ1Nnz7pR70O3xo55ic/PKpCajlUty6qR5qhRT37m7BkaFZPN01jGe6RvDUoWH8z33b8b6VNZf822PDs1hh1eXVZ8sXBEHgnl3N+HPPFPb3TuPm9XZBvs+x4VnQNLBdoL3yhaytM8KgkuHABWdiAIvEKJwcm8MndzVz/noLq3RW24RPb3d4w7hiRXEMsI0WDbY1mzHuDsKaowuqY0EScZVAyvKxkVnUm9QJV1m5sbnRjB+91Y9AJJbVbjRF0Xjv4hxu3VwnwrMrPth722lPKOtDVbIILcT5wPbBHoqvRC0XyucnWOK81jOFf/1jDybcQVy1qgb/+5Y1+PXf7MLJ//1+HPj6NfjVp3bg4ds34EvXrcCd2xtx5coarKjVZz3sKWVSVGkVeXfBdg26UG9SJwZnJshpjpe9qsm5cMECnFjW2A04O+FZFDC0v3cKFq0Cm9Pssf3zB9dCJZfikXhIkVg80zWCCEnh3t0ton7fVBAEgS1N5kVddgcHZnBkaBafvao9cUK8q7UKLn9ElMCPVHQNzqDTZuB1UEpGk4VRdVLVfZQafdM+mDRyVOv4V/obzBpMekKgaPD6c7lxnQ0A8CeONmKSovHqmUlc02lNBMYQBIEdrRY88vHNePd/XYMVtXo8+HT3JXvwFEWje8QtWn1ONuzpqIbNoMILx8YE+x5Hh1yQSQhsahJeIZNKCOxur8aBCzOJz6D3xucQieVmMU0MsC7h92BDURKeUKxoFFgA+M87N+Gnn9yW879fET+8vSBgvVx3PF26XNncZAJJ0Yk6o0wMOP3whmPLcv8VWKDAcuiCjVJU0VTo8IHdqEaTRbPsgpwqA2wREIlR+M7es+iw6rD3S1fg/96xEZ+6vBW72qp4Pbm3GVV5KbA0Hd9/XdCZuanRBKcvjHEeKnqmPKGCD7CddgP8ERKj8T2oKEnh9bPTuHqVNe0Fz6xV4L7dLXjl9IRogU6RGIUnDw3jypU1gtg5c2VLswkDTn8i3OuRfX2oNShx57bGxGN2xcMKCrUHG46R6B6ZTTwPIWFD1lLVc0RipdXfxgQ46QRRzhsW7PPzee2zG9XY1GjCXo42YtY+/IEUamW1TonH79sGrVKKT/3iCKYXHBAOOP2YC0aL6mZbKiFw25Z6vHXesei58smRoVmsrTeKlqy7p6MKF93BxJ55d44BTsB8or8Ye7Csy6oYdmBZmqo0eSk4dUYVtAop+qaEGWAvuoOYmAsV1e8U37AH5cezdLadijdBbFymAyxb93SRQ1UYSdGQl9EOLMC4Ig8XSbuDWFQG2CLgqUPDGHT68U83dwoaemE3qvLage2b9sHljyy66WdP/fiwEU95QgULcGJZU7c4yOno0Cw8oRiuX5PcPryQT1/eCo1cih+8Lo4K+8rpCTi8Ydy/p0WU75ctWxIfwLM4NDCDrkEXPvO+9kVugUYLY4U/VKATw1NjcwhFqUWHMULRmGaAHZsNYPfDr+PhvWcFfx5cePu8Ay8cG7skFIKmaZyf9qJDAPswADSYFgywPCvjN62z4b2LHk49n6x9+OpVqX//7UY1/ufe7ZgNRPDAr44iEGH2J9lBqth29W7f2gCSovH7Exdz/hozvjCu/r9v4uM/PYgfv9WP3gkPaJpGOEbi5Kgb20UcMHZ3MIF1By4wh2FHh2bRaFHnZIOt0iqgkktESSJmO2CLSYHNF4Ig0G7VJfbk+SbXdOlSwqJVoKVKk7h+ZOLU2Bw0CmlRHWKLiV4lR6fdgJdOjmftBIyRdFkpsADTB+sORHE+TSZDuVEZYAuMOxDBI/v7cMWKaly16tIdKj5hFNjcP5jZ/tddC0JvOu16yKVE3n2wMZKC0xdGbYEqdFhW1uohIZBYht/fOwWFVIIrVmT+2Zi1Cty3h1FhzwtcEcPW5rRVa/G+LJ6bmGxoMEIqIdA97MYP9vehRq/EXTuaFj2GIAjsbLWga8BVkH5OVvndIWBPJQs7wC5NIo6SFL747HE4fWH87C8D6CuCWiEAePeCE5/+5RF89fmTuOXRdxK/9wAw44/AHYhihUA3Sw3m+Z1+vkKcWG5ax6io2aqwCfvwamvGvsl19UY8etdmvHdxDn//mxMgKRrdI7MwquVoqy6uG8v2Gh22NJnwwrGxnH/3Xjg2hkGnH7P+KB7eexY3PfIX7Pz3/fjcU90IxyjB98oX0lathc2gwoF+J2iaxrGR2ZwDpJguWHGqdMpxgAWYPdilKfR80T08C41CitW28k7b3dxkxvFRd1a/nyfH3FhXZyy7gYwL9+xqwtlJb9b1QzGKglzkikGhYdsUlpONuLx+giXID/ZfgDcUxT99oFPwMBu7UY3ZQBShaG4JlF0DLtQZVWi0zKskSpkUa+yGvBVYhy8Mip63gxQKlVyK9hodeuKKwmu9U7isvQpaZXZ2uAcub2NUWIF3YY+PunFybA737WmBpMg+uDQKGTrtejx/bBTv9s/gb69sS7qrvbOtCk5fGP0Ov+jPsWvQhdU2PcwiJDYb1XIY1XKMLtmr+8/XzqN7xI1vf2gttAop/vXl3oIM8ws5O+nB3z55DK3VWvznnRvhCUbx8Z8ewoNPd2NsNpCwBgp12m8zqsC+nfkOPmqq0mBtnSHrOp2EfXhDdmFH13bW4pu3rMGrZ6bw8N5eHBuexeYmU9H9fgLAR7c24vyUD6dzqECjaRrPHh7BjhYLXv37K9H1j9fiux/dgO2tFhwZckEtZ/pZxYIgCOzuqMLB/hmMuAJweMN51RY1mNWJFRIhcfjKc4BdYdVjyhMWZOf/6LALmxpNovebi82WJhMc3nBGW2yUpNAz7sm59qhc+PCmeuiUMjx1aCSrx8dIuqxqdADmulVnVKFroDLAVsgTmqbhiu8ApmLA4cOvDg7hY9sbRUk8ZPdLc9mDZfZfZ7CrreqSQXtDgwmnx+by6qCaikegF9pCDDB7sL0TXvQ7fBieCeC6NZfW56SCVWFfFliFfeLAEPQqGW7f0iDY98iHLU1mTHnCqNYp8ImdydNAE3uwg+LuwUZJCseGZwXtf11Kk0WzSIF9+7wDP3qzHx/f3oh7d7fgS9etxNvnHXjj3LRoz2kpE3NB3P/EEWiUUjxx/w7ctqUB+7/yPvz9dSux/+wUrv3eW4kOUb47YFkUMkniOiVEuNZN62w4PuLGRBZOlFdOZ7YPL+X+Pa24b3cLfvaXQfRN+7C1iAKcFvKBDXYoZZKcwpwODsxgaCaAu3YyO+21BhXu3NaIx+7egu5vXo9D/3it6FVee9qr4fJH8HQXcwO7Lc8BVkwFtpC1Z0KwMImYT/zhGHonvGVtH2Zh92C7M/TBnpv0IhyjsKGxuNYUxEarlOG2LfV4+dRExvtugKnRkZZRCjEwHy7YNThT8INwsSivn2AR8cSBIWx76DV878/nECWTh7Q8vPcslDIJvnz9KlGeE9sFm0sScb/DB6cvknRncGOjCf4IiYE8kgfZobrQIU4Aswd70R3EC8eYHbFU9TmpEFqFnZwLYe/pCXxsW2PWyrDYsDcZf3NlW0r7ZUuVBla9UvQTw9MX5xCIkKIEOLE0WTSJ3ctpbwhffu4EVtbq8M8fXAsA+ORlzWiv0eJf/9hbkFAnbyiK+584Ak8wisfv2476+C6qSi7Fl65bgf1fuQrXr6nF4SEX9EqZoAdNbJCTSc3/jf2NcRvxqxlUWJKi8af3srMPL+Wbt6zBtauZa0ax3mwb1XLcsNaGF0+Mc+6EffbwKIxqecKSvRCZVCJ4qncy9sT3YJ88OAy9UpZXlVmDWYO5oPCp4Q5vGBatouysjOx6wQWeg5xOjrlBUnRe6nqpsMqmh0ouWZTmn4xT8aTiTcs0wGkh9+xqRoSk8NzR0YyPJSkK8iJ0xuQL42qLYMApvqutEJTXlbOIeO7oKDQKGR59/QI++uODGFryhjrYP4M/90zhc1d3iGYhshlzV2APxoeMZDf9mxoZ+8qJPGzEU57iGWA77Ywa/uTBoUT3LhfMWgXu3c2osELsNT55aAgkTRdNdU4yblhrw7c+uAafvKwl5WMIgsDOtirRTwzZnc4dIiqwjRZmry5KUvj735yALxzDD+/ekhiO5FIJvnHLGgw6/fjlu0OiPS+ASUH+7FPduDDtw4/u2Yq1dZfa0epNavzw7i347Wcvw0/+aqug6w4NZg0UUglUcv4/njqsOqyw6jLaiFn7cC5dqVIJgUfv3oxHPr5J1EMSrnx0awPmglHs781e9Z/xhfHqe5O4bUt9wfq6k2EzqtBWo0UwSmJTkymvfcDG+B72RYFVWIc3XFQJxHzRaNFAIZPwXqVzbCgeilakrgY+kUsl2NBgyqjAnhx1w6yRL1rrWq6srNVjR6sFT3cNZ0zijZZhiBMwvwe7XGzElQFWAM5NenF20ot/uGEV/vsTWzDk9OPmH/wFzx0dBU3ToCgaD73cg3qTGp++vFW058WqJk93DeOJA4PoGpjJ+pS5a2AGNoMqUQmykLZqHXRKWV5BTlOeEORSAlVFYKfqtDOn9/4Iies6s7cPL+SBK+Iq7OsX+HxqCEVJPNM1gus6axPhQMWISi7FfXtaM97k7my1YMoTxvCM8DtnLF0DLqyw6lAl4s1jk0WDCEnhn/9wBgcuzODbH1p7iUp09Sorrl5Vgx/s70tUbAgNTdP4+u9O4Z0LTnzntvW4cmX6QLCtzZZE6qtQfGRzPR64olWwIfmmdTYcGXKlfY1fOT0BlVyCq1fnFpCmUchw66b6otx/ZcmlE/Z33RcRIalLQtmKgT3tzPsyX9U70QUr9ADrC5fd/ivAHOC01+h4P7w9NjKLlbW6gij8hWBzkwk943NpM0tOjrmxvsEkeH5KqXDPrmaMuoJ4q8+R9nFMjU75jT+t1VpU65Q4LPJaVqEov59gEfCHkxchlRC4eb0dN6+3409/dwU2NpjwtRdO4XNPd+PxA4M4M+7B125cJeoptlYpw53bGjDg8OPbL/XgYz89hA3f+jOu/O4b+MyTx/BOnzPpv6NpGocGXNjVZkl6oZRICKyvNybsLLkw6QnBqlcVxQ2fVa9CdXy4yXWAtcRV2D+eGuf1g/zFExcxG4gWXXVOruyKW9IPidQHGyMpHB1yiVKfsxD24OeZrhF8aGPdok7chXzjljUIRkl878/nRHle39/Xh991X8TfXbcCd6R4TmJz5coafO3G1YJ9/RvX2UHRwJ/PTCX9e9Y+fO3qWtG6TAsB105YNrxpW7M5L4uuULCHLztb81O95wdYYQ/VHN7yHGABNomYPwWWomh0D88WrSVfCHa2WhAlafy/48nrroIREn3TPmxc5gFOC7lxrQ3VOgWePjSc9nExkipLBZZxtVnQNViYdgexqQywPEPTNF48MY49HdWJDye7UY2nHtiJr9+0Gvt6p/DQy73Y1GjChzbWif78vvvRjTj6jetw+B+vxRP3b8c/3LAK6xuMODHqxn1PHMbe05dWTAw4/XD6wtiZxg63sdGE3glPzgnHU54QrAVOIF7I+noD7EYV1tXnHq71wBVtUPOowjLVOUNYbdPjsiK2JnKhvUaHap0CXSJFv58Z98AfIfO+yeUKa/FqrtLg3z6yLuWJeXuNDvfubsGvj4zizHjuB0LZcGHahx/s78PtWxrwpWtXCPq9iolOux4tVRr895sXsK9n6pIP+nzsw6UGl07YrkEXBpz+olRfASar4Lef3Z04FMsVi1YBtVx6SWo4n9A0XdYD7AqrDmOzwUQncr70O3zwhGLYmmM9Uily1UordrZa8O+v9CZWrBZyZpwJztxQ2X9NoJBJ8LHtjXj97HTaA6gYRUNeZinELLtaLZiYCwl6/SoWKgMsz3SPuDE2G8StS4ZTqYTAZ97Xjv/3uT24rrMWD3049U2s0BAEAatBhatXWfHg1R147O4teO3LV2JTowmff/Y4XlxyM5Pof003wDYYESXpRH8qVybnQkWRQMzy0EfW41ef2pHXz2ihCnuwP3+F8dCAC2cnvbh/T0vZWIaYPtgqdA2IswfLvpfFVmAbzRo8eHU7fvbJbdCr0lvgvnjtCpg1Cnz7pR5BX5PHDwxCIZPgf928umzeT9lAEAS+c9sGyCQEHvjVUdzx44M4OjR/gJKvfbiU4NIJ+0zXCAwqWda1QmJDEAS2Npvzfi8zXbBqQRVYTyiGcIwqyx1YYD7IaYCnirSjw8z+63JSYCUSAg/fvgGRGIVv/P69S34/T8YdbxUFdjHsAduzh1NX6sTKdAcWAHbED+cL2WggFpUBlmf+cOIilDIJ3r82ufV0Xb0RP793G9bVF9dFR6+S45ef2oHtLWb83W9O4PkFSW5dAy5Y9Uq0VKXeudwYj3HP1UY87QkXRYATS71JjRU82OT+5oq2/9/efYdXVaV7HP+udBJSSCAkQAKEFjrSQhDBMijoDDjiIA4IjjKWEdRBuWO7jzqOM45lrjA4louFESuKFQURdaSGJoROgBAI1RAIJaSv+0dOuBGBFA6n/j7Pk4fkZJ/N2vCeffZ79rveRXJsOKOnL+PZeWfvSF2TwpIypizYSqPwYIb3aH7e4/IkaSmx7C0ocsnSFRnZ+aQ0iSA+0rWxFhBgmHxVaq1KL6MbBHPfle1Znp3PF+tqt2ZpXR0+UcLs1bn8ukfzU+Xy/iS9TRzzJw3iL9d2ISe/kOtfWsr4GSvYuPfoqe7Dvlw+XF1t1oTNP1HC3PX7ua5nC49q3nShVDVdu1BmOkocO59HhY8n65BQeZ6buazmhjq1sSrnMHERIee8BvFFrRtHMGlwe+ZvPPCz94LM3CMkRIUR70HXTZ6gRaNwLk+N570Vu8/a0b+sosJn1xJuF9+Q7kkxPPnFJpZsP/O0QF/hm/+DblJWXsHnmfu4omN8jXdZPFFEaBCv39yXAW0bM/mDTN7O2OWY/3rm9V+rS4wOo0lkKGvr0Yn4RHEZx4rLPCqBdZZGESF8PnEAI3q2YNq32xjx4hK217E749YDxxg+bTEZ2fncf5Vr5027QlU5r7Pmwc5clsPlz37Hja8s44EPM3nh2218nrmXtbuPsGJnvsvLh+tjVJ9kUhMiefjjdSysoSFFfby9fBdFpRXc4sImcp4mODCAMf1a8p/JlzL5qg5kZOdz9dSF5B0v4Zqurp/e4S5Va8K+uij7rMnG7NW5lJRXMKqvZ8yTvtCSGjVgR95x1p1HX4ez2Zl3gqkLshjaJYH+bS5sQzR3SWnSkNsHpvDuit386cPM81ojHmB1zmF6OuHuuje6dUBrujaP5tFP13O42hqnmbkFdNPd1zMa3a8lecdLmLvhzB8Al1VYgnz0DmxAgOH1m/vQMjac389YWeNSTN5MCawTLd5+iEMnShjW3XvvkDUICeR/x/bm8tR4HvpoHX+Zs4mDx4prLLk0xtC9RTRr6tGJuGpd2oRo37wTFBkWzDO/6c5LY3qyO7+Qa6Yu5M2lO2tVHjpr5W6GTVvE4cIS3rwljdFpLS/8gF2sXXxDYiNCWOaE1u8ZOw7x6KcbCAsOpLisnK83HeCZeVuY8PYPDH9hMceKys57jpwrBAYYXr6pF00jwxj32nL+9d02p5UTl5RVMGPJTga0bXzqTok/Cw8J4q7L2vL95Mu4bWAK6SlxflE+XCW6QTBj01vyyZq9jHk142fLrFlreXv5Lnomx5Ca4Jt3DE9388WtiYsI5TcvLzljX4j6stbyyMfrCQkM4LFhnZ22X0/0wNBU7rmiHbNW5XLve9uOKEIAABEkSURBVGvqXX106HgxO/JO+FX5cHVBgQH8fUQ3jhSW8sScjQAUnCwlO+/Eqco3+alB7ZqQFNvgVKXD6crKLUEBvpv+xEaEMHN8Go0jQxn32vJ6T+3zdL77P+gGn6zZQ2RYEJd28O6Ln7DgQF4a04srOzXl1UXZwLnnv1bp3iKGHT+eqPMC8J60BuyFNKRLIvPuHUjf1nH89ycb+N0bK8jMPcKRwpKfJSeFJWVMen8Nkz/I5KKkRnxx9yUMaOebn9YHBBj6tool4zxbv+cdL2biOz+QHBvOe7f3Y/YfLmblI4PZ8PhVfHnPJbw0phdPXdeVoV08cw7f6VrGRfDRXf25plsznp67hTtmruJYHV9bZzJn3V4OHit26RJe3qBRRAgPXd2Rd27r5zflw1UeurojT4/oxprdRxgy5XvmVlsnd3l2Pjt+9NzmTRdC68YRfHzXxXRKjOLOt1Yz7Zssp3yA9PGaPSzalsd/Deng8+93xhj+OLg9Dw5N5bO1e/nDW6spLqt7k8eqtVD9NYEF6NQsijsvbcPs1Xv4bsvBU5UB3dXA6YwCAgyj01qyPDufrWdYBaKsosJn78BWaRoVxsxb0wgPCeKmVzPY4eR1mT2BElgnKSot56sNBxjaJcEnSjxDggJ4YXRPruvZnO4toklpHFHjc6o+Daxr2ZW/JLAA8VFhzPhdHx4f1pml2w8xbNpievx5Pl0f+4ohz3/P+BkrePST9QybtpiPfqhc3mTm+DSfn+eSlhJL7uGT7DlSv3ln5RWWe99dQ8HJUl74bc+flPBHhAbRMTGKIV0SGNU3mZAg7znthYcEMXVUDx65piNfbzrI8BcWs+1g/Zdlstby6qJsUppEMKiGNV/FfxhjGNkniTl3X0JybDh3zFzFg7MzKSwp453lu4gMC+KX3fynrBqgSWQob/++H8N7NOPZr7Yy6f219e6yD5Xzzp/4fBMXJcf4ZCXN2dw+qA1/Ht6Z+RsPMH7GSk6WnPvfsKi0nPV7Cpi1cjdPfL6Rp+duJjiwcqk+fzbh8ra0aRLBwx+tPzW3satKiM9qZO8kQoICmL5wx88+fCqvsAT5aBfi6pJiw5k5Pg1rYcz0jAu+NJir+dfHzBfQN5sPcry4zKca7AQHBvCPkT2w1tZq7knVfIw1u49wcdva3y3cX1AM4FFdiC8kYwzj+rdicKemZOYWkHu4kNzDJ08lcBk78mkYFsRbt6bRvw7/jt6sal5qxo5DXNezRZ2f/89vsli0LY+/j+hKp2a+VeZojGH8JSl0bhbNxHdWM3zaYp78dVd+1b1ZnTspLs/OZ/2eo/zl2i4eseayeJbWjSP44I7+/M/XW3npP9vJ2JFP7pGTjOqTRIMQ7/9gtq7CggN5/oYetG3SkOfmb2VXfiEv39SrXo3P/vrFJo6eLOVv13X1u9fe2PRWhAUH8sCHmYx7fTmPD+tM3vFi9hUUcaCgiH1Hi9hfUETOoRNk552gaspsaFAAHRIimTTY93o/1FVoUCBPX9+N619aysvf76B14wiiG3hfrxVXiY0I4cY+ScxYmkNZheXJa7ueOoeVlvvuHNjTtY1vyJu3pjHqlaWMnp7BrNvTfeaGiBJYJ/lkzR6aRIbWqtTW29S2cUJMeGWXwMw6zoM9cLSIyNAgIkL9KxybxTSgWUyDM/6uth8a+IrUhEhiwoOZ9P5a7p+1lqCAAAICICgggMAAQ3xkKCN6teD6Xi1+dvG4KCuPKQuyuK5nc0b29t0mM+lt4vhs4gDunLmae99bw9NzNzOyTxIjeyedNY5O9+qibGLCgxlRjw8JxD+EBAXwpyGpXNKuMZPeW0tJWQWj+vhP+fDpjDFMvKIdKU0act+sNQz75yIevLojv+yWWOtz9NLth5i1Kpc7L23jN/OITzeydxJhwYH88b01DJ2y8Ce/i40IISEqjJQmDbm6ayKpCVGkJkbSKi7CZ5c7qY9eLWMZl96KN5bsVAOnWnj0V52JjQjl+QVb2bTvGC+P6UVyXLjjDqz3VGKdr07Nonjjlr6MmZ7Bza+v4LOJA3zideVfGcMFUnCylG83/8iYfi19IijOR/ekGDLq2IznwNEi4qN8s4FTfflT8gqVc1amjLqI1TmHqbCWsgpLebWvjXuP8tSXm3nuqy1c2TmB3/ZNJj0ljh+PF3PPuz/QLr6hW9dWdpXE6AbMuiOdrzce4O3lu5iyIIupC7K4tEM8o/okcXlq/FnfmHMOnWD+pgPcOaiNX95Nk7rp36Yx8+4dyLYfj/lcVUN9XNMtkaTYBvzXB5lMfOcHpi/cwUNXdySthg+ti8vKefijdSTHhnPPFe1cNFrPNKx7M1rHRbAj7zgJUWEkRjcgPirU7++u1sXkqzqQdfCY35X010dAgOGeX7SjW1I09767hl/+cyFTRl1Eabnvz4E9Xc/kRkwf15sTxeU+k6cogXWCeev3U1JewfAeOqF0axHDJ2v2cuBoUa3ntO4/WkRCtG+UNEj9DWrf5JzzMrcdPMY7y3fz4epc5mTuo1VcOGHBgZwsLedfo3v6TeOd4MAAhnZNZGjXRHbnF/Leit28v3I3t20+SNOoUMamt2J0WjIx4SE/ed7ri3cSaAxj01u5Z+DidaLDg+nV0vO7drtKtxYxzLn7EmavzuW5r7ZywyvL+EXHpjwwNJW28Q1PbWetZW9BEVv2H+WztfvYkXeCN2/tq0SNynmbmrtZfxGhQbw1vp+7h+FVLusQz2cTBnDHzFXcMmMF1uIXc2BP52vLdhlnLc1Qp7/UmCHAFCAQmG6tfepc2/fu3duuXLnSJWOrj9HTl7Hn8Em+vf9Sn78DVJNVOfmMeHEp3VpEc2mHePq3ieOi5BhCg87+xn3xU9+QlhLLP0b2cOFIxVsVlZYzd/1+3s7YxYqcfJ6/oYdPzT2vj7LyCr7ZfJA3l+WwMCuPBsGB3NAniVsubk1yXDhHi0pJ/+sCBndqyvOjLnL3cEW83smScl5bnM2L323nZGk5I3o2JyQogC37j7F5/zGOFZWd2nZsekv+PLyLG0crIkWl5Tz80Xo+XJ3LfYPbM9HPKyI8lTFmlbW2d43buTqBNcYEAluBwUAusAK40Vq78WzP8eQE9uDRItL+toCJl7Vl0pUd3D0ctyuvsExdkFXZ6n1PARUWwoID6NMqlvQ2cXRuFk1idBgJ0WFEhgZhLbR/5Et+PzCFPw1JdffwxcsUlpT5zZ3X2tq07yjTF2bz6do9lFdYhnRJIC4ilDeX5fDZhAG6+yHiRIeOFzN1QRZvZeyiQXAgqYmRdEiIpENCFB0TImmfEElUmJrtiHgCay0Ls/Lo0jya2IiQmp8gLufJCWw68Ji19irHzw8CWGv/drbneHIC++7yXTwwex1fTxpI2/hIdw/HoxScLGV5dj5LtuexZNshtpy2Hld4SCDxkaHsPFTI48M6M65/K/cMVMQHHThaxBtLdjJzWQ7Hisro2yqW9+9Id/ewRHxSUWk5oUEBfl+FJSJyPjw5gb0eGGKtHe/4+SYgzVo74bTtbgNuA0hOTu6Vk5Pj0nHWlrWWrIPHad9UyWtN8o4XszPvBPsKKlvm73e0zi84Wcpjwzr/ZA6RiDjH8eIy5mTupXerWNo00WtMREREPFNtE1h31N6d6ePJn2XR1tpXgFeg8g7shR5UfRljlLzWUuOGofVaP09E6q9haBA3+PEyKCIiIuJb3LEQUi5QfbHGFsBeN4xDREREREREvIg7EtgVQDtjTGtjTAgwCvjUDeMQERERERERL+LyEmJrbZkxZgIwj8pldF6z1m5w9ThERERERETEu7hl/Qlr7RfAF+74u0VERERERMQ7uaOEWERERERERKTOlMCKiIiIiIiIV1ACKyIiIiIiIl5BCayIiIiIiIh4BSWwIiIiIiIi4hWUwIqIiIiIiIhXUAIrIiIiIiIiXkEJrIiIiIiIiHgFJbAiIiIiIiLiFZTAioiIiIiIiFdQAisiIiIiIiJeQQmsiIiIiIiIeAUlsCIiIiIiIuIVlMCKiIiIiIiIVzDWWnePoUbGmB+BHHePA2gM5Ll7EOJTFFPibIopcTbFlDiT4kmcTTHlO1paa5vUtJFXJLCewhiz0lrb293jEN+hmBJnU0yJsymmxJkUT+Jsiin/oxJiERERERER8QpKYEVERERERMQrKIGtm1fcPQDxOYopcTbFlDibYkqcSfEkzqaY8jOaAysiIiIiIiJeQXdgRURERERExCv4dQJrjEkyxnxrjNlkjNlgjLnH8XisMWa+MSbL8Wcjx+PGGDPVGLPNGJNpjOlZbV/jHNtnGWPGueuYxL2cFVPGmB7GmKWOfWQaY25w53GJ+zjzPOX4fZQxZo8xZpo7jkfcz8nvfcnGmK8c+9pojGnlnqMSd3FyPD3t2McmxzbGXccl7lOPmEp1XDMVG2PuP21fQ4wxWxzx9oA7jkecz68TWKAMuM9a2xHoB9xljOkEPAAssNa2AxY4fgYYCrRzfN0GvAiVLyjgUSAN6As8WvWiEr/jlJgCCoGx1trOwBDgeWNMjOsOQzyIs2KqyhPAf1wxcPFYzoypfwPPOPbVFzjomkMQD+Ksa6n+wMVAN6AL0AcY5MLjEM9R15jKB+4Gnq2+E2NMIPAClTHXCbjRsR/xcn6dwFpr91lrVzu+PwZsApoDw4EZjs1mANc6vh8O/NtWWgbEGGMSgauA+dbafGvtYWA+lUmH+BlnxZS1dqu1Nsuxn71UXhTWuLCz+B4nnqcwxvQCmgJfufAQxMM4K6YcF4JB1tr5jn0dt9YWuvJYxP2ceI6yQBgQAoQCwcABlx2IeIy6xpS19qC1dgVQetqu+gLbrLU7rLUlwLuOfYiX8+sEtjpH2dNFQAbQ1Fq7DypfREC8Y7PmwO5qT8t1PHa2x8WPnWdMVd9PXyrf0Ldf2BGLpzufmDLGBADPAZNdNV7xfOd5nmoPHDHGzDbG/GCMecZxx0P81PnEk7V2KfAtsM/xNc9au8k1IxdPVcuYOhtdn/soJbCAMaYh8CFwr7X26Lk2PcNj9hyPi59yQkxV7ScReBP4nbW2wrmjFG/ihJj6A/CFtXb3GX4vfsgJMRUEXALcT2W5Zwpws5OHKV7ifOPJGNMW6Ai0oDLJuNwYM9D5IxVvUYeYOusuzvCYrs99gN8nsMaYYCpfHG9Za2c7Hj5QreQukf+f05MLJFV7egtg7zkeFz/kpJjCGBMFzAEecZRZiZ9yUkylAxOMMTupnCc01hjzlAuGLx7Iie99PzjK88qAj4GfNA0T/+CkePo1sMxRin4c+JLK+Y/ih+oYU2ej63Mf5dcJrKO73avAJmvtP6r96lOgqpPwOOCTao+PdXTQ6wcUOEoY5gFXGmMaOZo3Xel4TPyMs2LKGBMCfETlPKFZLhq+eCBnxZS1drS1Ntla24rKO2b/ttaqI6MfcuJ73wqgkTGman7+5cDGC34A4lGcGE+7gEHGmCBH8jKIyrmP4mfqEVNnswJoZ4xp7biuGuXYh3g5Y63/3kk3xgwAFgLrgKryzIeorLN/H0im8oT6G2ttvuMFNY3KBk2FVJZ1rnTs6xbHcwGetNa+7rIDEY/hrJgyxowBXgc2VNv9zdbaNa45EvEUzjxPVdvnzUBva+0ElxyEeBQnv/cNpnJutQFWAbc5mqWIn3Di+14g8C9gIJVlnnOttZNcejDiEeoRUwnASiDKsf1xoJO19qgx5mrgeSAQeM1a+6RLD0YuCL9OYEVERERERMR7+HUJsYiIiIiIiHgPJbAiIiIiIiLiFZTAioiIiIiIiFdQAisiIiIiIiJeQQmsiIiIiIiIeAUlsCIiIiIiIuIVlMCKiIiIiIiIV1ACKyIiIiIiIl7h/wCWy+y7ijYUZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Plot of Mean Monthly Flow from 2000 to 2010\n",
    "m_plt.figure(figsize=(16,8))\n",
    "m_plt.plot(df1['2000':'2010'].resample('M').mean())\n",
    "m_plt.title('Flow at RCHRES ###')\n",
    "m_plt.ylabel('flow (cfs)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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