{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re \n",
    "\n",
    "def sorted_nicely( l ): \n",
    "    \"\"\" Sort the given iterable in the way that humans expect.\"\"\" \n",
    "    convert = lambda text: int(text) if text.isdigit() else text \n",
    "    alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] \n",
    "    return sorted(l, key = alphanum_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import h5py\n",
    "import logging\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "from scipy import stats, sparse\n",
    "import bottleneck\n",
    "#%matplotlib notebook\n",
    "%matplotlib inline\n",
    "#import matplotlib\n",
    "#matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as plticker\n",
    "\n",
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "from scipy import sparse\n",
    "#import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import sparse\n",
    "from scipy.sparse import csr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_egad(go, nw, **kwargs):\n",
    "    \"\"\"EGAD running function\n",
    "    \n",
    "    Wrapper to lower level functions for EGAD\n",
    "\n",
    "    EGAD measures modularity of gene lists in co-expression networks. \n",
    "\n",
    "    This was translated from the MATLAB version, which does tiled Cross Validation\n",
    "    \n",
    "    The useful kwargs are:\n",
    "    int - nFold : Number of CV folds to do, default is 3, \n",
    "    int - {min,max}_count : limits for number of terms in each gene list, these are exclusive values\n",
    "\n",
    "\n",
    "    Arguments:\n",
    "        go {pd.DataFrame} -- dataframe of genes x terms of values [0,1], where 1 is included in gene lists\n",
    "        nw {pd.DataFrame} -- dataframe of co-expression network, genes x genes\n",
    "        **kwargs \n",
    "    \n",
    "    Returns:\n",
    "        pd.DataFrame -- dataframe of terms x metrics where the metrics are \n",
    "        ['AUC', 'AVG_NODE_DEGREE', 'DEGREE_NULL_AUC', 'P_Value']\n",
    "    \"\"\"\n",
    "    assert nw.shape[0] == nw.shape[1] , 'Network is not square'\n",
    "    #print(nw.index)\n",
    "    #nw.columns = nw.columns.astype(int)\n",
    "    #print(nw.columns.astype(int))\n",
    "    assert np.all(nw.index == nw.columns) , 'Network index and columns are not in the same order'\n",
    "\n",
    "    #nw_mask = nw.isna().sum(axis=1) != nw.shape[1]\n",
    "    #nw = nw.loc[nw_mask, nw_mask].astype('float')\n",
    "    #np.fill_diagonal(nw.values, 1)\n",
    "    return _runNV(go, nw, **kwargs)\n",
    "\n",
    "def _runNV(go, nw, nFold=3, min_count=1, max_count=1807):\n",
    "\n",
    "    #Make sure genes are same in go and nw\n",
    "    #go.index = go.index.map(str) \n",
    "    #nw.index = nw.index.map(str)\n",
    "    #nw.index = nw.index.str.replace('_', '')\n",
    "    #go.index = go.index.str.replace('_', '')\n",
    "    print (nw)\n",
    "    genes_intersect = go.index.intersection(nw.index)\n",
    "\n",
    "\n",
    "    print (genes_intersect)\n",
    "    go = go.loc[genes_intersect, :]\n",
    "    nw = nw.loc[genes_intersect, genes_intersect]\n",
    "    print (go)\n",
    "    print (nw.shape)\n",
    "    print (go.shape)\n",
    "    sparsity = 1.0 - np.count_nonzero(go) / go.size\n",
    "    print (sparsity)\n",
    "    sparsity = 1.0 - np.count_nonzero(nw) / nw.size\n",
    "    print (sparsity)\n",
    "    #print(nw\n",
    "    #print(go\n",
    "    nw_mask = nw.isna().sum(axis=1) != nw.shape[1]\n",
    "    nw = nw.loc[nw_mask, nw_mask].astype('float')\n",
    "    np.fill_diagonal(nw.values, 1)\n",
    "    #Make sure there aren't duplicates\n",
    "    duplicates = nw.index.duplicated(keep='first')\n",
    "    nw = nw.loc[~duplicates, ~duplicates]\n",
    "\n",
    "    go = go.loc[:, (go.sum(axis=0) > min_count) & (go.sum(axis=0) < max_count)]\n",
    "    go = go.loc[~go.index.duplicated(keep='first'), :]\n",
    "    print(go.sum(axis=0))\n",
    "\n",
    "    roc = _new_egad(go.values, nw.values, nFold)\n",
    "\n",
    "    col_names = ['AUC', 'AVG_NODE_DEGREE', 'DEGREE_NULL_AUC', 'P_Value']\n",
    "    #Put output in dataframe\n",
    "    return pd.DataFrame(dict(zip(col_names, roc)), index=go.columns)\n",
    "\n",
    "def _new_egad(go, nw, nFold):\n",
    "\n",
    "    #Build Cross validated Positive\n",
    "    x, y = np.where(go)\n",
    "    #print(x, y)\n",
    "    cvgo = {}\n",
    "    for i in np.arange(nFold):\n",
    "        a = x[i::nFold]\n",
    "        #print(a)\n",
    "        b = y[i::nFold]\n",
    "        dat = np.ones_like(a)\n",
    "        mask = sparse.coo_matrix((dat, (a, b)), shape=go.shape)\n",
    "        cvgo[i] = go - mask.toarray()\n",
    "\n",
    "    CVgo = np.concatenate(list(cvgo.values()), axis=1)\n",
    "    #print(CVgo)\n",
    "\n",
    "    sumin = np.matmul(nw.T, CVgo)\n",
    "\n",
    "    degree = np.sum(nw, axis=0)\n",
    "    #print(degree)\n",
    "    #print(degree[:, None])\n",
    "\n",
    "    predicts = sumin / degree[:, None]\n",
    "    #print(predicts)\n",
    "\n",
    "    np.place(predicts, CVgo > 0, np.nan)\n",
    "\n",
    "    #print(predicts)\n",
    "\n",
    "    #Calculate ranks of positives\n",
    "    rank_abs = lambda x: stats.rankdata(np.abs(x))\n",
    "    predicts2 = np.apply_along_axis(rank_abs, 0, predicts)\n",
    "    #print(predicts2)\n",
    "\n",
    "    #Masking Nans that were ranked (how tiedrank works in matlab)\n",
    "    predicts2[np.isnan(predicts)] = np.nan\n",
    "    #print(predicts2)\n",
    "\n",
    "    filtering = np.tile(go, nFold)\n",
    "    #print(filtering)\n",
    "\n",
    "    #negatives :filtering == 0\n",
    "    #Sets Ranks of negatives to 0\n",
    "    np.place(predicts2, filtering == 0, 0)\n",
    "\n",
    "    #Sum of ranks for each prediction\n",
    "    p = bottleneck.nansum(predicts2, axis=0)\n",
    "    n_p = np.sum(filtering, axis=0) - np.sum(CVgo, axis=0)\n",
    "\n",
    "    #Number of negatives\n",
    "    #Number of GO terms - number of postiive\n",
    "    n_n = filtering.shape[0] - np.sum(filtering, axis=0)\n",
    "\n",
    "    roc = (p / n_p - (n_p + 1) / 2) / n_n\n",
    "    U = roc * n_p * n_n\n",
    "    Z = (np.abs(U - (n_p * n_n / 2))) / np.sqrt(n_p * n_n *\n",
    "                                                (n_p + n_n + 1) / 12)\n",
    "    roc = roc.reshape(nFold, go.shape[1])\n",
    "    Z = Z.reshape(nFold, go.shape[1])\n",
    "    #Stouffer Z method\n",
    "    Z = bottleneck.nansum(Z, axis=0) / np.sqrt(nFold)\n",
    "    #Calc ROC of Neighbor Voting\n",
    "    roc = bottleneck.nanmean(roc, axis=0)\n",
    "    P = stats.norm.sf(Z)\n",
    "\n",
    "    #Average degree for nodes in each go term\n",
    "    avg_degree = degree.dot(go) / np.sum(go, axis=0)\n",
    "\n",
    "    #Calc null auc for degree\n",
    "    ranks = np.tile(stats.rankdata(degree), (go.shape[1], 1)).T\n",
    "\n",
    "    np.place(ranks, go == 0, 0)\n",
    "\n",
    "    n_p = bottleneck.nansum(go, axis=0)\n",
    "    nn = go.shape[0] - n_p\n",
    "    p = bottleneck.nansum(ranks, axis=0)\n",
    "\n",
    "    roc_null = (p / n_p - ((n_p + 1) / 2)) / nn\n",
    "    #print(roc)\n",
    "    return roc, avg_degree, roc_null, P\n",
    "\n",
    "\n",
    "#reads the go_prop file\n",
    "#pickle_in = open('gotermindex.pickle','rb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import coo_matrix\n",
    "#Data handling\n",
    "import h5py\n",
    "\n",
    "import logging\n",
    "#Mathematical libraries\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import bottleneck\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "#df_go_id_enseml = pd.read_csv('/data/johlee/geneIDConversions/9606_gene_info.tab', sep='\\t') #this file has a lot of mapping, EntrezID, uniprot, Ensembl ID, genename\n",
    "#df_go_id_enseml.dropna(subset=['EnsemblID'])\n",
    "#mydict_EID_UID = dict(zip(df_go_id_enseml.EnsemblID, df_go_id_enseml.UniProtID))\n",
    "def parse_go_hd5(filename):\n",
    "    \"\"\"\n",
    "    Loads data in file to dataframe.\n",
    "    \"\"\"\n",
    "    with h5py.File(filename, 'r') as f:\n",
    "        col_uid = []\n",
    "        logging.debug(\"reading matrix...\")\n",
    "        row = [ s.decode() for s in  f['GO'][:] ]\n",
    "        columns = [ s.decode() for s in  f['genes'][:] ]\n",
    "        matrix = f['ind'][:] - 1 #the indices in this matrix are stored from 1 \n",
    "        coo = coo_matrix(( np.ones(1901323) , (matrix[0,:],matrix[1,:] )), shape=(len(columns), len(row)) )\n",
    "        logging.debug(\"reading columns. converting to unicode\")\n",
    "        for x in columns:\n",
    "            try:\n",
    "                col_uid.append(x)\n",
    "            except KeyError:\n",
    "                print (x)\n",
    "                col_uid.append('del')\n",
    "        #col_uid = [mydict_EID_UID[x] for x in columns]\n",
    "        logging.debug(\"making dataframe...\")\n",
    "\n",
    "        print (coo.toarray().shape)\n",
    "        df = pd.DataFrame(coo.toarray(),  index=col_uid, columns = row )\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19016, 22517)\n"
     ]
    }
   ],
   "source": [
    "go_df = parse_go_hd5(\"/data/johlee/CoCoCoNet/gene2go/human_gene2go.hdf5\")\n",
    "duplicates = go_df.index.duplicated(keep='first')\n",
    "go_df = go_df.loc[~duplicates, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lohia_utilities.load_lee_matrix import parse_expression_hd5_lee\n",
    "exp_file = parse_expression_hd5_lee(\"/data/johlee/CoCoCoNet/networks/human_prioAggNet.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "  </tbody>\n",
       "</table>\n",
       "<p>24631 rows × 24631 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ENSG00000278267  ENSG00000233750  ENSG00000268903  \\\n",
       "ENSG00000278267         1.000000         0.343890         0.487717   \n",
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       "ENSG00000241860         0.479969         0.673940         0.811533   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000276345         0.085152         0.055478         0.092469   \n",
       "ENSG00000277856         0.025113         0.045883         0.077274   \n",
       "ENSG00000275063         0.005367         0.013963         0.031017   \n",
       "ENSG00000278817         0.458116         0.154940         0.374283   \n",
       "ENSG00000277196         0.389884         0.049407         0.242737   \n",
       "\n",
       "                 ENSG00000269981  ENSG00000241860  ENSG00000279928  \\\n",
       "ENSG00000278267         0.504516         0.479969         0.343116   \n",
       "ENSG00000233750         0.809451         0.673940         0.028341   \n",
       "ENSG00000268903         0.946427         0.811533         0.065573   \n",
       "ENSG00000269981         1.000000         0.833857         0.110389   \n",
       "ENSG00000241860         0.833857         1.000000         0.083856   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000276345         0.101842         0.018642         0.001793   \n",
       "ENSG00000277856         0.079891         0.034978         0.067405   \n",
       "ENSG00000275063         0.014742         0.019394         0.019134   \n",
       "ENSG00000278817         0.169647         0.278851         0.255696   \n",
       "ENSG00000277196         0.230788         0.139266         0.257408   \n",
       "\n",
       "                 ENSG00000279457  ENSG00000228463  ENSG00000237094  \\\n",
       "ENSG00000278267         0.896050         0.229694         0.550274   \n",
       "ENSG00000233750         0.463029         0.399338         0.764022   \n",
       "ENSG00000268903         0.767021         0.441015         0.856639   \n",
       "ENSG00000269981         0.656542         0.527122         0.903084   \n",
       "ENSG00000241860         0.508489         0.340035         0.944649   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000276345         0.261986         0.020327         0.091009   \n",
       "ENSG00000277856         0.140386         0.012266         0.094181   \n",
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       "\n",
       "                 ENSG00000225972  ...  ENSG00000275594  ENSG00000276497  \\\n",
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       "ENSG00000269981         0.067739  ...         0.069903         0.070774   \n",
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       "...                          ...  ...              ...              ...   \n",
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       "ENSG00000277196         0.250084  ...         0.012124         0.167913   \n",
       "\n",
       "                 ENSG00000273850  ENSG00000271254  ENSG00000275405  \\\n",
       "ENSG00000278267         0.708864         0.491642         0.013446   \n",
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       "ENSG00000269981         0.478497         0.264099         0.021557   \n",
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       "...                          ...              ...              ...   \n",
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       "ENSG00000277856         0.095768         0.078241         0.056294   \n",
       "ENSG00000275063         0.062007         0.091597         0.009911   \n",
       "ENSG00000278817         0.651330         0.456410         0.063993   \n",
       "ENSG00000277196         0.475094         0.344548         0.027745   \n",
       "\n",
       "                 ENSG00000276345  ENSG00000277856  ENSG00000275063  \\\n",
       "ENSG00000278267         0.085152         0.025113         0.005367   \n",
       "ENSG00000233750         0.055478         0.045883         0.013963   \n",
       "ENSG00000268903         0.092469         0.077274         0.031017   \n",
       "ENSG00000269981         0.101842         0.079891         0.014742   \n",
       "ENSG00000241860         0.018642         0.034978         0.019394   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000276345         1.000000         0.011710         0.004271   \n",
       "ENSG00000277856         0.011710         1.000000         0.738243   \n",
       "ENSG00000275063         0.004271         0.738243         1.000000   \n",
       "ENSG00000278817         0.095571         0.057777         0.005105   \n",
       "ENSG00000277196         0.024167         0.063847         0.007100   \n",
       "\n",
       "                 ENSG00000278817  ENSG00000277196  \n",
       "ENSG00000278267         0.458116         0.389884  \n",
       "ENSG00000233750         0.154940         0.049407  \n",
       "ENSG00000268903         0.374283         0.242737  \n",
       "ENSG00000269981         0.169647         0.230788  \n",
       "ENSG00000241860         0.278851         0.139266  \n",
       "...                          ...              ...  \n",
       "ENSG00000276345         0.095571         0.024167  \n",
       "ENSG00000277856         0.057777         0.063847  \n",
       "ENSG00000275063         0.005105         0.007100  \n",
       "ENSG00000278817         1.000000         0.851643  \n",
       "ENSG00000277196         0.851643         1.000000  \n",
       "\n",
       "[24631 rows x 24631 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = pd.read_csv('/data/lohia/gene_distance_expresseion/gene_contact_map_rao/processed_hi_c_files_one_buffer/chr1_tss_tss_100.csv')\n",
    "df_2_or.set_index('Gene stable ID', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>192</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>74</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>4900</td>\n",
       "      <td>4900</td>\n",
       "      <td>4900</td>\n",
       "      <td>4900</td>\n",
       "      <td>5000</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000185220</th>\n",
       "      <td>1815</td>\n",
       "      <td>1815</td>\n",
       "      <td>1815</td>\n",
       "      <td>1815</td>\n",
       "      <td>0</td>\n",
       "      <td>291</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>...</td>\n",
       "      <td>3769</td>\n",
       "      <td>3769</td>\n",
       "      <td>3769</td>\n",
       "      <td>3769</td>\n",
       "      <td>3869</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2426 rows × 2426 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ENSG00000227232  ENSG00000223972  ENSG00000237613  \\\n",
       "Gene stable ID                                                       \n",
       "ENSG00000227232                0               26               26   \n",
       "ENSG00000223972               26                0               26   \n",
       "ENSG00000237613               26               26                0   \n",
       "ENSG00000186092               26               26               26   \n",
       "ENSG00000235249                0                0                0   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000175137              207              207              207   \n",
       "ENSG00000264500              207              207              207   \n",
       "ENSG00000171161              207              207              207   \n",
       "ENSG00000171163              207              207              207   \n",
       "ENSG00000185220             1815             1815             1815   \n",
       "\n",
       "                 ENSG00000186092  ENSG00000235249  ENSG00000185097  \\\n",
       "Gene stable ID                                                       \n",
       "ENSG00000227232               26                0             1011   \n",
       "ENSG00000223972               26                0             1011   \n",
       "ENSG00000237613               26                0             1011   \n",
       "ENSG00000186092                0                0             1011   \n",
       "ENSG00000235249                0                0                0   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000175137              207                0              192   \n",
       "ENSG00000264500              207                0              192   \n",
       "ENSG00000171161              207                0              192   \n",
       "ENSG00000171163              207                0              192   \n",
       "ENSG00000185220             1815                0              291   \n",
       "\n",
       "                 ENSG00000228327  ENSG00000228794  ENSG00000230368  \\\n",
       "Gene stable ID                                                       \n",
       "ENSG00000227232              379              379              208   \n",
       "ENSG00000223972              379              379              208   \n",
       "ENSG00000237613              379              379              208   \n",
       "ENSG00000186092              379              379              208   \n",
       "ENSG00000235249                0                0                0   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000175137              136              136               74   \n",
       "ENSG00000264500              136              136               74   \n",
       "ENSG00000171161              136              136               74   \n",
       "ENSG00000171163              136              136               74   \n",
       "ENSG00000185220              161              161               96   \n",
       "\n",
       "                 ENSG00000230699  ...  ENSG00000177151  ENSG00000187701  \\\n",
       "Gene stable ID                    ...                                     \n",
       "ENSG00000227232              208  ...               29               29   \n",
       "ENSG00000223972              208  ...               29               29   \n",
       "ENSG00000237613              208  ...               29               29   \n",
       "ENSG00000186092              208  ...               29               29   \n",
       "ENSG00000235249                0  ...                0                0   \n",
       "...                          ...  ...              ...              ...   \n",
       "ENSG00000175137               74  ...             4900             4900   \n",
       "ENSG00000264500               74  ...             4900             4900   \n",
       "ENSG00000171161               74  ...             4900             4900   \n",
       "ENSG00000171163               74  ...             4900             4900   \n",
       "ENSG00000185220               96  ...             3769             3769   \n",
       "\n",
       "                 ENSG00000189181  ENSG00000232694  ENSG00000266949  \\\n",
       "Gene stable ID                                                       \n",
       "ENSG00000227232               29               29               47   \n",
       "ENSG00000223972               29               29               47   \n",
       "ENSG00000237613               29               29               47   \n",
       "ENSG00000186092               29               29               47   \n",
       "ENSG00000235249                0                0                0   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000175137             4900             4900             5000   \n",
       "ENSG00000264500             4900             4900             5000   \n",
       "ENSG00000171161             4900             4900             5000   \n",
       "ENSG00000171163             4900             4900             5000   \n",
       "ENSG00000185220             3769             3769             3869   \n",
       "\n",
       "                 ENSG00000175137  ENSG00000264500  ENSG00000171161  \\\n",
       "Gene stable ID                                                       \n",
       "ENSG00000227232              207              207              207   \n",
       "ENSG00000223972              207              207              207   \n",
       "ENSG00000237613              207              207              207   \n",
       "ENSG00000186092              207              207              207   \n",
       "ENSG00000235249                0                0                0   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000175137                0               18               18   \n",
       "ENSG00000264500               18                0               18   \n",
       "ENSG00000171161               18               18                0   \n",
       "ENSG00000171163               18               18               18   \n",
       "ENSG00000185220               18               18               18   \n",
       "\n",
       "                 ENSG00000171163  ENSG00000185220  \n",
       "Gene stable ID                                     \n",
       "ENSG00000227232              207             1815  \n",
       "ENSG00000223972              207             1815  \n",
       "ENSG00000237613              207             1815  \n",
       "ENSG00000186092              207             1815  \n",
       "ENSG00000235249                0                0  \n",
       "...                          ...              ...  \n",
       "ENSG00000175137               18               18  \n",
       "ENSG00000264500               18               18  \n",
       "ENSG00000171161               18               18  \n",
       "ENSG00000171163                0               18  \n",
       "ENSG00000185220               18                0  \n",
       "\n",
       "[2426 rows x 2426 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2_or"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = np.where(df_2_or >=3, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = np.where(df_2_or >=3, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe=pd.DataFrame(array, columns=df_2_or.columns , index=df_2_or.index ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ENSG00000227232</th>\n",
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       "    <tr>\n",
       "      <th>ENSG00000171163</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2426 rows × 2426 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ENSG00000227232  ENSG00000223972  ENSG00000237613  \\\n",
       "Gene stable ID                                                       \n",
       "ENSG00000227232                0                1                1   \n",
       "ENSG00000223972                1                0                1   \n",
       "ENSG00000237613                1                1                0   \n",
       "ENSG00000186092                1                1                1   \n",
       "ENSG00000235249                0                0                0   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000175137                1                1                1   \n",
       "ENSG00000264500                1                1                1   \n",
       "ENSG00000171161                1                1                1   \n",
       "ENSG00000171163                1                1                1   \n",
       "ENSG00000185220                1                1                1   \n",
       "\n",
       "                 ENSG00000186092  ENSG00000235249  ENSG00000185097  \\\n",
       "Gene stable ID                                                       \n",
       "ENSG00000227232                1                0                1   \n",
       "ENSG00000223972                1                0                1   \n",
       "ENSG00000237613                1                0                1   \n",
       "ENSG00000186092                0                0                1   \n",
       "ENSG00000235249                0                0                0   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000175137                1                0                1   \n",
       "ENSG00000264500                1                0                1   \n",
       "ENSG00000171161                1                0                1   \n",
       "ENSG00000171163                1                0                1   \n",
       "ENSG00000185220                1                0                1   \n",
       "\n",
       "                 ENSG00000228327  ENSG00000228794  ENSG00000230368  \\\n",
       "Gene stable ID                                                       \n",
       "ENSG00000227232                1                1                1   \n",
       "ENSG00000223972                1                1                1   \n",
       "ENSG00000237613                1                1                1   \n",
       "ENSG00000186092                1                1                1   \n",
       "ENSG00000235249                0                0                0   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000175137                1                1                1   \n",
       "ENSG00000264500                1                1                1   \n",
       "ENSG00000171161                1                1                1   \n",
       "ENSG00000171163                1                1                1   \n",
       "ENSG00000185220                1                1                1   \n",
       "\n",
       "                 ENSG00000230699  ...  ENSG00000177151  ENSG00000187701  \\\n",
       "Gene stable ID                    ...                                     \n",
       "ENSG00000227232                1  ...                1                1   \n",
       "ENSG00000223972                1  ...                1                1   \n",
       "ENSG00000237613                1  ...                1                1   \n",
       "ENSG00000186092                1  ...                1                1   \n",
       "ENSG00000235249                0  ...                0                0   \n",
       "...                          ...  ...              ...              ...   \n",
       "ENSG00000175137                1  ...                1                1   \n",
       "ENSG00000264500                1  ...                1                1   \n",
       "ENSG00000171161                1  ...                1                1   \n",
       "ENSG00000171163                1  ...                1                1   \n",
       "ENSG00000185220                1  ...                1                1   \n",
       "\n",
       "                 ENSG00000189181  ENSG00000232694  ENSG00000266949  \\\n",
       "Gene stable ID                                                       \n",
       "ENSG00000227232                1                1                1   \n",
       "ENSG00000223972                1                1                1   \n",
       "ENSG00000237613                1                1                1   \n",
       "ENSG00000186092                1                1                1   \n",
       "ENSG00000235249                0                0                0   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000175137                1                1                1   \n",
       "ENSG00000264500                1                1                1   \n",
       "ENSG00000171161                1                1                1   \n",
       "ENSG00000171163                1                1                1   \n",
       "ENSG00000185220                1                1                1   \n",
       "\n",
       "                 ENSG00000175137  ENSG00000264500  ENSG00000171161  \\\n",
       "Gene stable ID                                                       \n",
       "ENSG00000227232                1                1                1   \n",
       "ENSG00000223972                1                1                1   \n",
       "ENSG00000237613                1                1                1   \n",
       "ENSG00000186092                1                1                1   \n",
       "ENSG00000235249                0                0                0   \n",
       "...                          ...              ...              ...   \n",
       "ENSG00000175137                0                1                1   \n",
       "ENSG00000264500                1                0                1   \n",
       "ENSG00000171161                1                1                0   \n",
       "ENSG00000171163                1                1                1   \n",
       "ENSG00000185220                1                1                1   \n",
       "\n",
       "                 ENSG00000171163  ENSG00000185220  \n",
       "Gene stable ID                                     \n",
       "ENSG00000227232                1                1  \n",
       "ENSG00000223972                1                1  \n",
       "ENSG00000237613                1                1  \n",
       "ENSG00000186092                1                1  \n",
       "ENSG00000235249                0                0  \n",
       "...                          ...              ...  \n",
       "ENSG00000175137                1                1  \n",
       "ENSG00000264500                1                1  \n",
       "ENSG00000171161                1                1  \n",
       "ENSG00000171163                0                1  \n",
       "ENSG00000185220                1                0  \n",
       "\n",
       "[2426 rows x 2426 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 ENSG00000278267  ENSG00000233750  ENSG00000268903  \\\n",
      "ENSG00000278267         1.000000         0.343890         0.487717   \n",
      "ENSG00000233750         0.343890         1.000000         0.860658   \n",
      "ENSG00000268903         0.487717         0.860658         1.000000   \n",
      "ENSG00000269981         0.504516         0.809451         0.946427   \n",
      "ENSG00000241860         0.479969         0.673940         0.811533   \n",
      "...                          ...              ...              ...   \n",
      "ENSG00000276345         0.085152         0.055478         0.092469   \n",
      "ENSG00000277856         0.025113         0.045883         0.077274   \n",
      "ENSG00000275063         0.005367         0.013963         0.031017   \n",
      "ENSG00000278817         0.458116         0.154940         0.374283   \n",
      "ENSG00000277196         0.389884         0.049407         0.242737   \n",
      "\n",
      "                 ENSG00000269981  ENSG00000241860  ENSG00000279928  \\\n",
      "ENSG00000278267         0.504516         0.479969         0.343116   \n",
      "ENSG00000233750         0.809451         0.673940         0.028341   \n",
      "ENSG00000268903         0.946427         0.811533         0.065573   \n",
      "ENSG00000269981         1.000000         0.833857         0.110389   \n",
      "ENSG00000241860         0.833857         1.000000         0.083856   \n",
      "...                          ...              ...              ...   \n",
      "ENSG00000276345         0.101842         0.018642         0.001793   \n",
      "ENSG00000277856         0.079891         0.034978         0.067405   \n",
      "ENSG00000275063         0.014742         0.019394         0.019134   \n",
      "ENSG00000278817         0.169647         0.278851         0.255696   \n",
      "ENSG00000277196         0.230788         0.139266         0.257408   \n",
      "\n",
      "                 ENSG00000279457  ENSG00000228463  ENSG00000237094  \\\n",
      "ENSG00000278267         0.896050         0.229694         0.550274   \n",
      "ENSG00000233750         0.463029         0.399338         0.764022   \n",
      "ENSG00000268903         0.767021         0.441015         0.856639   \n",
      "ENSG00000269981         0.656542         0.527122         0.903084   \n",
      "ENSG00000241860         0.508489         0.340035         0.944649   \n",
      "...                          ...              ...              ...   \n",
      "ENSG00000276345         0.261986         0.020327         0.091009   \n",
      "ENSG00000277856         0.140386         0.012266         0.094181   \n",
      "ENSG00000275063         0.067660         0.011989         0.042390   \n",
      "ENSG00000278817         0.762126         0.355218         0.176952   \n",
      "ENSG00000277196         0.619342         0.117430         0.171613   \n",
      "\n",
      "                 ENSG00000225972  ...  ENSG00000275594  ENSG00000276497  \\\n",
      "ENSG00000278267         0.208983  ...         0.089306         0.275199   \n",
      "ENSG00000233750         0.018238  ...         0.042306         0.004487   \n",
      "ENSG00000268903         0.194718  ...         0.019009         0.025281   \n",
      "ENSG00000269981         0.067739  ...         0.069903         0.070774   \n",
      "ENSG00000241860         0.002976  ...         0.208856         0.125405   \n",
      "...                          ...  ...              ...              ...   \n",
      "ENSG00000276345         0.017107  ...         0.007437         0.026895   \n",
      "ENSG00000277856         0.032696  ...         0.003437         0.000490   \n",
      "ENSG00000275063         0.051837  ...         0.005523         0.000559   \n",
      "ENSG00000278817         0.365041  ...         0.063697         0.229018   \n",
      "ENSG00000277196         0.250084  ...         0.012124         0.167913   \n",
      "\n",
      "                 ENSG00000273850  ENSG00000271254  ENSG00000275405  \\\n",
      "ENSG00000278267         0.708864         0.491642         0.013446   \n",
      "ENSG00000233750         0.202850         0.146670         0.028417   \n",
      "ENSG00000268903         0.510202         0.078068         0.015855   \n",
      "ENSG00000269981         0.478497         0.264099         0.021557   \n",
      "ENSG00000241860         0.327995         0.624400         0.257520   \n",
      "...                          ...              ...              ...   \n",
      "ENSG00000276345         0.174295         0.115015         0.018660   \n",
      "ENSG00000277856         0.095768         0.078241         0.056294   \n",
      "ENSG00000275063         0.062007         0.091597         0.009911   \n",
      "ENSG00000278817         0.651330         0.456410         0.063993   \n",
      "ENSG00000277196         0.475094         0.344548         0.027745   \n",
      "\n",
      "                 ENSG00000276345  ENSG00000277856  ENSG00000275063  \\\n",
      "ENSG00000278267         0.085152         0.025113         0.005367   \n",
      "ENSG00000233750         0.055478         0.045883         0.013963   \n",
      "ENSG00000268903         0.092469         0.077274         0.031017   \n",
      "ENSG00000269981         0.101842         0.079891         0.014742   \n",
      "ENSG00000241860         0.018642         0.034978         0.019394   \n",
      "...                          ...              ...              ...   \n",
      "ENSG00000276345         1.000000         0.011710         0.004271   \n",
      "ENSG00000277856         0.011710         1.000000         0.738243   \n",
      "ENSG00000275063         0.004271         0.738243         1.000000   \n",
      "ENSG00000278817         0.095571         0.057777         0.005105   \n",
      "ENSG00000277196         0.024167         0.063847         0.007100   \n",
      "\n",
      "                 ENSG00000278817  ENSG00000277196  \n",
      "ENSG00000278267         0.458116         0.389884  \n",
      "ENSG00000233750         0.154940         0.049407  \n",
      "ENSG00000268903         0.374283         0.242737  \n",
      "ENSG00000269981         0.169647         0.230788  \n",
      "ENSG00000241860         0.278851         0.139266  \n",
      "...                          ...              ...  \n",
      "ENSG00000276345         0.095571         0.024167  \n",
      "ENSG00000277856         0.057777         0.063847  \n",
      "ENSG00000275063         0.005105         0.007100  \n",
      "ENSG00000278817         1.000000         0.851643  \n",
      "ENSG00000277196         0.851643         1.000000  \n",
      "\n",
      "[24631 rows x 24631 columns]\n",
      "Index(['ENSG00000228327', 'ENSG00000228794', 'ENSG00000230699',\n",
      "       'ENSG00000188976', 'ENSG00000187634', 'ENSG00000187961',\n",
      "       'ENSG00000187583', 'ENSG00000187642', 'ENSG00000188290',\n",
      "       'ENSG00000187608',\n",
      "       ...\n",
      "       'ENSG00000177535', 'ENSG00000169224', 'ENSG00000162722',\n",
      "       'ENSG00000238243', 'ENSG00000203663', 'ENSG00000196071',\n",
      "       'ENSG00000175137', 'ENSG00000171161', 'ENSG00000171163',\n",
      "       'ENSG00000185220'],\n",
      "      dtype='object', length=1808)\n",
      "                 ENSG00000227232  ENSG00000223972  ENSG00000237613  \\\n",
      "ENSG00000228327                1                1                1   \n",
      "ENSG00000228794                1                1                1   \n",
      "ENSG00000230699                1                1                1   \n",
      "ENSG00000188976                1                1                1   \n",
      "ENSG00000187634                1                1                1   \n",
      "...                          ...              ...              ...   \n",
      "ENSG00000196071                1                1                1   \n",
      "ENSG00000175137                1                1                1   \n",
      "ENSG00000171161                1                1                1   \n",
      "ENSG00000171163                1                1                1   \n",
      "ENSG00000185220                1                1                1   \n",
      "\n",
      "                 ENSG00000186092  ENSG00000235249  ENSG00000185097  \\\n",
      "ENSG00000228327                1                0                1   \n",
      "ENSG00000228794                1                0                1   \n",
      "ENSG00000230699                1                0                1   \n",
      "ENSG00000188976                1                0                1   \n",
      "ENSG00000187634                1                0                1   \n",
      "...                          ...              ...              ...   \n",
      "ENSG00000196071                1                0                1   \n",
      "ENSG00000175137                1                0                1   \n",
      "ENSG00000171161                1                0                1   \n",
      "ENSG00000171163                1                0                1   \n",
      "ENSG00000185220                1                0                1   \n",
      "\n",
      "                 ENSG00000228327  ENSG00000228794  ENSG00000230368  \\\n",
      "ENSG00000228327                0                1                1   \n",
      "ENSG00000228794                1                0                1   \n",
      "ENSG00000230699                1                1                1   \n",
      "ENSG00000188976                1                1                1   \n",
      "ENSG00000187634                1                1                1   \n",
      "...                          ...              ...              ...   \n",
      "ENSG00000196071                1                1                1   \n",
      "ENSG00000175137                1                1                1   \n",
      "ENSG00000171161                1                1                1   \n",
      "ENSG00000171163                1                1                1   \n",
      "ENSG00000185220                1                1                1   \n",
      "\n",
      "                 ENSG00000230699  ...  ENSG00000177151  ENSG00000187701  \\\n",
      "ENSG00000228327                1  ...                1                1   \n",
      "ENSG00000228794                1  ...                1                1   \n",
      "ENSG00000230699                0  ...                1                1   \n",
      "ENSG00000188976                1  ...                1                1   \n",
      "ENSG00000187634                1  ...                1                1   \n",
      "...                          ...  ...              ...              ...   \n",
      "ENSG00000196071                1  ...                1                1   \n",
      "ENSG00000175137                1  ...                1                1   \n",
      "ENSG00000171161                1  ...                1                1   \n",
      "ENSG00000171163                1  ...                1                1   \n",
      "ENSG00000185220                1  ...                1                1   \n",
      "\n",
      "                 ENSG00000189181  ENSG00000232694  ENSG00000266949  \\\n",
      "ENSG00000228327                1                1                1   \n",
      "ENSG00000228794                1                1                1   \n",
      "ENSG00000230699                1                1                1   \n",
      "ENSG00000188976                1                1                1   \n",
      "ENSG00000187634                1                1                1   \n",
      "...                          ...              ...              ...   \n",
      "ENSG00000196071                1                1                1   \n",
      "ENSG00000175137                1                1                1   \n",
      "ENSG00000171161                1                1                1   \n",
      "ENSG00000171163                1                1                1   \n",
      "ENSG00000185220                1                1                1   \n",
      "\n",
      "                 ENSG00000175137  ENSG00000264500  ENSG00000171161  \\\n",
      "ENSG00000228327                1                1                1   \n",
      "ENSG00000228794                1                1                1   \n",
      "ENSG00000230699                1                1                1   \n",
      "ENSG00000188976                1                1                1   \n",
      "ENSG00000187634                1                1                1   \n",
      "...                          ...              ...              ...   \n",
      "ENSG00000196071                1                1                1   \n",
      "ENSG00000175137                0                1                1   \n",
      "ENSG00000171161                1                1                0   \n",
      "ENSG00000171163                1                1                1   \n",
      "ENSG00000185220                1                1                1   \n",
      "\n",
      "                 ENSG00000171163  ENSG00000185220  \n",
      "ENSG00000228327                1                1  \n",
      "ENSG00000228794                1                1  \n",
      "ENSG00000230699                1                1  \n",
      "ENSG00000188976                1                1  \n",
      "ENSG00000187634                1                1  \n",
      "...                          ...              ...  \n",
      "ENSG00000196071                1                1  \n",
      "ENSG00000175137                1                1  \n",
      "ENSG00000171161                1                1  \n",
      "ENSG00000171163                0                1  \n",
      "ENSG00000185220                1                0  \n",
      "\n",
      "[1808 rows x 2426 columns]\n",
      "(1808, 1808)\n",
      "(1808, 2426)\n",
      "0.008360296638919129\n",
      "0.0\n",
      "ENSG00000227232    1560\n",
      "ENSG00000223972    1560\n",
      "ENSG00000237613    1560\n",
      "ENSG00000186092    1560\n",
      "ENSG00000185097    1803\n",
      "                   ... \n",
      "ENSG00000187701    1803\n",
      "ENSG00000189181    1803\n",
      "ENSG00000232694    1803\n",
      "ENSG00000266949    1789\n",
      "ENSG00000185220    1801\n",
      "Length: 1594, dtype: int64\n",
      "                      AUC  AVG_NODE_DEGREE  DEGREE_NULL_AUC       P_Value\n",
      "ENSG00000227232  0.666411      1083.048606         0.535959  1.520367e-38\n",
      "ENSG00000223972  0.666411      1083.048606         0.535959  1.520367e-38\n",
      "ENSG00000237613  0.666411      1083.048606         0.535959  1.520367e-38\n",
      "ENSG00000186092  0.666411      1083.048606         0.535959  1.520367e-38\n",
      "ENSG00000185097  0.643981      1078.037408         0.799445  2.729862e-02\n",
      "...                   ...              ...              ...           ...\n",
      "ENSG00000187701  0.554903      1076.933426         0.505935  1.001797e-01\n",
      "ENSG00000189181  0.554903      1076.933426         0.505935  1.001797e-01\n",
      "ENSG00000232694  0.554903      1076.933426         0.505935  1.001797e-01\n",
      "ENSG00000266949  0.493790      1076.515436         0.480245  3.766669e-01\n",
      "ENSG00000185220  0.483874      1076.648238         0.360435  2.053939e-01\n",
      "\n",
      "[1594 rows x 4 columns]\n",
      "0.599582305201838\n",
      "ENSG00000025800\n"
     ]
    },
    {
     "data": {
      "image/png": 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k1gQRB/S0rPRwqesVQ5+8ZCd2HKoO2m6AV2DF+5yzdh9qHMECmmEWUP6nIQCAzoVlstfg8HYs0/rc1DqzRRojVqoDb2pmlu7Fsk+Owc05jIxhUq+OmJffPd7LihiPx4M//vGPeOmll/DII4/ghRdeAGPy3etaGyTWBBEn9LKsInWpK9U1SwlH8JWEGgACJ00rVXNJt4v31e5nd/odY2QsrBcbtXsQLXKlEIC43WZ3wnixXtyqMaEup21sM5YDPRhuzn1/N0fB9ng8ePDBB7FkyRI89thjKC4uJqGWQL4FgmjmRNIrWnTj2kLEmMOxPJWEWoroXldyS9dKtov3Zb6uF8zX9fJtd3Ou2E9cyszSvbh2xnrF+8swCz5Blj4LcY0zS/f6tovXFe8hlITktE3GJ08NDnFUdCz75FhY2xMZt9uN3/zmN1iyZAmefPJJEmoZyLImiGZOJC51LW5ccYpV36ItuiZBqV1X+nIgXuf3L5fBwzmE9lf6nUPNRR8qbi4YGWaN6gZAOQQgupflUIqdN+Xca6W1hWpCk2g0NjbinnvuwTvvvIM5c+bg6aefJqGWgcSaIFoA4brU1VzDYoJXOHOg9UDOG5Cfa8VdH7wIALjs7iK/fYH3IHVZhyItOcl3D0rPIpTohRs71xujQhtXYzMSOpfLhcmTJ2PFihVYsGABZsyYEe8lJSwk1gTRClGKc1stJuwoHAAA6Fu0Jayks77XZmpyhUuRDubo3zULxRsOYnpJhZ/4pVyss5a7B5Fws7Ol7nalZ6EkhiLSZxUPJvXqKOs9mNSrYxxWEz719fWYOHEi3n//fSxatAiPPvpovJeU0FDMmiCaiFjOOA4XLXHucLPMl953C/peG95MYVEMHQ2NKPn0mOyc646ZJhgCrMXAtYabnS0VeqVnMalXx6DtStePB/Pyu2NK704+S9rIGKb07tQskssuXLiAMWPG4P3338eLL75IQq0BsqwJogmIdy/wQLTEubVmmQdmUi+e2DNoFORjyytV479yZVyiFd+hTQoAIF3F5RxO1nqg0Ko9i7yrMmWzwROlgcm8/O7NQpylOBwO5OfnY9OmTfj73/+O+++/P95LahYwHqdkhLy8PL579+64XJsgYoVS3Wvfoi0h3c6JhpxrOTCJambp3qDe3nKJVpE2EWEArtpVDADYtm2bYomV0vMNJBEFtzVx/vx5jBo1Ch999BFef/11TJ06Nd5LalIYY3s453mRfJYsa4LQCbW61+Y4ZSmU9a2UcS0X146kJSkAGBhD/7seRN/rOqC03IaClZVwuS+VUBWsrASg3p9cnGfdlMlyRDBnz57F8OHDsXPnTrz11luYPHlyvJfUrCCxJgidUKt7ba5TlpSyzEOVRoV6CVHq8R2Im3O8a7OgR68bMGftPp9Qi7jcHHPW7vN1PVNyWSuVZ81esy+uYh3PASxNid1ux+233449e/bg3Xffxfjx4+O9pGYHiTVB6IRa3WsiTlmKVChKy21YqiLUgHxcO9Je2rXHv8bsN47CbpZfmxjvVitfm1ZSIbvd7nShtNwWM4FUe8aJlscQK86cOYMhQ4Zg7969WLlyJe644454L6lZQmJNEDqhVveaaFOWIhGKcOqY+3fN8vs7ml7a1ZtfRTWC66zDQa0MK1YzxEM945Y001yJqqoqDBo0CAcPHkRpaSmGDx8e7yU1W0isCUInQtW9JtKUpXCFIlzLeOuBKr+/o43NJxmVq0wtJiHk59XqpbW8fERCqGfcHPMYwuGHH37AwIEDcfjwYaxduxaDB8e2/WpLh+qsCUInmlPda7hCEa5lHCiA0cTmDYypBrhnj+6muE+sbVcjVh2/Qj1jpWeS6HkMWrDZbLjtttvw7bffYv369STUOkCWNUHoSHOpe1VKeEs3CX69wPt3zcLWA1VhW5+GAP1Ty9YGlBPOjIzhmqw0fHPqvOK11Nz2WrwBevXSDoxPp5sE2YElohgnYh6DHhw9ehQDBgzAqVOnsGHDBvz85z+P95JaBGRZE0QrRK5rl2BgqGto9Osi9vauoxG5iT0cuFrSqS0/14qFY7rDajGBweu6zjALYPDWmk/u3Um2i9iiCT3QoU0KUpLkO4lZVaxQrd4AI2NRd5OTm9xV19AIwaDceS3wmVgtpiYdBBILjhw5gltvvRWnT5/Ghx9+SEKtI2RZE0QrRC7hzdHQKNtJTAmTYERKkkFx3CXgFa1pJRW+bGyrxYTnAzqcAV6xW1f5vU9cM8wCZo3qhvxcKw5MnY4THx9Gg8z5r26vLNZaXzLEkZtA6OQ6peRAuRcDscxMrRFLIuUxRMvXX3+NAQMGoK6uDps3b8bNN98c7yW1KKiDGUEQAIDOhWWam5WIwgMol0WFgjHAlGSAw+VRHDcJIKQrWykv4NoZ68NycYvd5AKFOXD6mHR9otCGenZNPT6zqdm/fz8GDhwIl8uFTZs2oUePHvFeUkISTQczcoMTBAFAe2KTKGqiVaglG1sOzgGHy+P93wH7xKzp4g0HUXPkS1w4vl/xPErNaMKNRZ+wO2Xd2W/vOqqY1S0S6tkFHt+S+PLLL9GvXz94PB5s27aNhDpGkFgTRCtEbgKYXBw7EGnMVTyH3elCLPKpbXYnbHYn7Nv/Cfv2fyoe5+ZcNuasFs+W4wqLKaysd2m2t5Zn11JKsqSUl5ejX79+SEpKwkcffYRu3ZQz84nooJg1QbRwQrl1xWYdC8d0x8Ix3YOO3XqgKihWG5hpzaG9hWgsEGPOwKU4fLpJgGBkQS1K5RBfQqaH4dKXWtPSHAClWHlLKMmS8tlnn2HIkCFo27YttmzZguuuuy7eS2rRkFgTRAtGrotW4JQs4JKbNrBsKO+qTNl4sJwFyuG1Zvt3zVLtGx4LnC435qzdhwsuj29ddqcLgoHBwLzZ6UpYTAJmj+7m6yqmJTFNrsRKDAsoTSuLtiQrkfqI79y5E7fffjsyMzOxdetWXH311XFZR2uC3OAE0YJRElU5RAtbGq+dsXqvrItZreHHvPzuWDyxZ8Sx7EipcbiCM7I9XFWoAaDW6cKctfvQubAMdfWNEIyhnfpqyWKxKMkSJ45Jv5uClZVRl5xFwvbt2zFkyBBkZ2dj+/btJNRNBFnWBNGCCSdOamRMcwtSpaYqHEDfoi0oGNoFFbO8k7C0zpqOFxyXhoGI1niGWVAsY5vSu1NI4Y22JCvQirY7GhQnjjWldb1582aMHj0anTp1wubNm3HFFVc02bVbOyTWBNGCUeqiJVcqpZRYZbM7/bqaFQzt4v0/yWzpwOOldcvRCnXmwPuj+ny4uDwc5uQklP9pCGaW7vULG6QlG5F3VaZu15JzbQMICl0oEU5dfLRs2LAB+fn5uO6667Bp0ybk5OQ02bUJcoMTRIultNyGuobGoO2CgWFy705Bblql7GkGBLnGASAtWfldX5wVHaovtxZyOndB9tVN24JT9EjkXZWJVEmWd12DWzE0EC5yZWIzVu/F7DX7Ip5QFivWrVuH0aNHo2vXrti6dSsJdRwgy5ogWijFGw7KWr5tUpMU+5cHJkbJZXg7XW5MX16BUGXMdqdLtbuZFvpem4l7O9cBAH67KapThYXF7I236znGMtCKdjQ0yp47HKFuiryA9957DxMnTkSPHj2wYcMGZGZmJlSyW2uBxJogWihK8Wq7gutUrgWpkgu2qRof7jhUjXULn0LHTBMwaGbTXBTeZ1RablN8hnKhATWxksvKjxbBwFQnjulBSUkJJk+ejJ/+9Kf44IMPkJ6eHtEsdCJ6yA1OEC0ULSMYA5ujAMCOwgE4UjQCOwoHhN1YJBbUN7pxuKoOZqHpfq44vF4G0cIORC40oOYaD3fEaGBGukkwYkpA6KJ4fI+YiuNbb72Fu+++G3369MHGjRuRnp4OQN3bQMQOsqwJooXSv2tWUE11YAeyUBZSwdAumnt/SwdWhDsUJBQezpu84Yrokg4MBSiFBh5bXglA3roMt3tZWnIS0lKS4uZmfv311/Hb3/4W/fv3x5o1a5CWlubbF+4sdEIfNIk1Y+x2AH8BYATwGue8KGB/JwD/BGC5eEwh53y9zmslCEIjgVnMgFdkxt5s9XN3h4rH5udaMWftvpDCK/YLFyktt2F6SYWuAuu82EdcigFA8FZ9kXZns6qEBgKnd0njuoaLLzJaqXW6fKVveqE1zvzKK6/goYcewpAhQ1BaWgqTyd+7ohQeaWkd2hKNkH4lxpgRwF8BDAPwYwCTGGM/DjhsJoDlnPNcAHcB+JveCyUIQhul5TbZLmUcwNYDVb6/tVpIs0Z1U+17bRKM6N81K8id3hSWcLpZaJIkK1GoQ4UGxJedwEzvcIeKGHSYsS1FKfM88BovvPACHnroIYwYMQLvv/9+kFAD8n3Q5f4NxKNhS0tGi2X9MwDfcM4PAwBj7F0AdwD4SnIMB9Du4v9OB3BCz0USBKGd4g0HFYVSKsRKNdjpJgGl5TbMXrPPtz8t2QiLSUCt04V0kwDGvElYSr3GC1ZU6nY/7Yc+orjP7nDhSNEIzCzdG/MWp7aLU7nq6oPL4aScsDvDjlEHomXGdjho8aIUFxfjiSeewJ133ol3330XycnJsueSS0RU6jev1/oJbWJtBSCdQXccQK+AY2YD2MgY+z2ANACDdFkdQRBhoxY7lLoqmUJXzYZGNx5dXuHXprOuwQ3B6MHzE3sG/fj2Ldoi2+ZTL4T2VyruE+9HLEWLtWBrid9fYTHpEr+NtEQskNJym6LrXlzn/PnzMXPmTEycOBFvvfUWBEHdWxHYoU3u34Be6ye8aEmvlPtPOvC/xEkA3uScXwlgOIC3GGNB52aM3c8Y280Y211VVRW4myAIHVCKHTLAb5iEUgmXw+WR7aftcnPZjN9YJxY5vvkEjm8+CdoemCwndfHHC3FNesVvo322ovtbicvTUzFr1izMnDkTv/zlL/H222+HFGo5KOks9mgR6+MAOkr+vhLBbu7fAFgOAJzznQBSAXQIPBHn/FXOeR7nPC8rKyuyFRMEoYrSbGUO+OKpQGQJQXI/vrFOLDr76Xs4++l7ftukwzFKy20oWFEZ1/7jgQM7tMy31kK0z1bNHZ+aZMBl/1uNuXPn4te//jXeeOMNJCVFViCkpUyQiA4tYv0ZgOsZY50ZY8nwJpCtCTjmKICBAMAYuwFesY7/ay5BtEKkU58Af9eYNLFIKVFIDbkfX72ESQsmwYDFE3sCAKaXVKBv0RbMWP2FJrd7hlmAYAg9UStcrBaTry5dmkW/cEx3ZCjUaQciGFnQ2vQYq6lk2XLO8aMjq/Hem3/Dgw8+iCVLlsBojPw7VPq3FO36iUuEFGvOeSOARwBsALAf3qzvfYyxuYyx0RcPewzAfYyxSgDLAEzlvKl6HBEEEUh+rhU7CgfAYhJUa4LlRjmqZVcXDO0i20hl4Rj59qV6Y2DMZ0WLWc1yJV1y1DhcAAP07K0iGBgcDY2yGdD5uVaYVfqnizkDVosJxeN6oHh8D13HagLyL1ece1D94StY+85rGDHp1/jb3/4GgyG6hxKLsaCEPyxempqXl8d3794dl2sTRGugtNymmhAlGBnSkpNQ63T51d2KbuVAa3VK704AIFsWJtcoRC9+eKcQAHDZ3UUhjkwMTIIBC8fciPxcKzoXlik+l2+LRsR8LYH17px7UP3BSzj/xUa0+9kYXD74tygaeyOJahPBGNvDOc+L5LPUbpQgWiih2j+63Bx2pyuo7jY/1xpk5S2e2BN5V2XKCjXQNDXVzQWny4OCFZUoLbcpxmybqo1rfq71klB73Diz/i9eob5lIiz97sWFRg+1CW0mULtRgmihhJuJKy21CSzNAbzlOfEQ5Q4jH4vDVaPD5eGYVlIBs2CAYGBBXoq6+kbfi1GssVpMOF59HqfXPQfH/o+Q/vPJsPSd5Nsfz8Q8QjtkWRNECyWSTFy1H+54leEktctCUrvmWT3icHng5jxoCInd6cL0kgrMLFUuq9KLaQM64/T7f4Zj/0ew3DbVT6gBb093IvEhsSaIFkqkWdpKApLeBG095ajbvx11+7fH5dp64OFAfWOwT4LDG/+PZVvO+vp6vDXvj3D877/IGPBbpPceF3RMuK1QifhAbnCCaKEEtoW0mDCvjPkAACAASURBVAVw7h0SYTELisM5lu46iryrMv1ctKXlNtQ1qLfZjBXnyr0zgdJuuDUu19cDJUHkAOas3RdyFra09WuGWcCsUd1CutCdTifGjBmDDz74AJ1H/wGeG+QHgyTCGFQiNCTWBNGCCYw9SycvKSE2TxE/V1puw2PLK8kCiwIDg2xXOMBbUqYUv5bLzK9xuFCwUnkcJwA4HA7ccccd2Lx5M5YsWYIONw/DYysq4ZZZRP+uzTPE0NogsSaIVkLg/Go1bHYnOheWwWIWcP5CIwl1tIR4fEo9tIs3HJRt+CK2fpX7zPnz5zFy5Ej85z//wRtvvIF77rkHAPyscymJ0KaVCA3FrAmilRDuJCgOrxWn51CO1ooH3vprJZQ8HWoJf+ILlbQZS21tLYYOHYqPP/4Yb7/9tk+oAW/4I5xrE4kFWdYEkWBIXdXSZiXRQj/K8eWCywOLwlhSDmDykp1Yet8tvu9fS0mVtEb+/Fk7Xnzi1/j8889RUlKCsWPH+h17hcUke07q3908ILEmiBgRiegGuqr1nAus9GOd6GTlz/D971h2Sos1HN6SLaV72HGoGoOf24bjNRfCnoV9/mwNHrz792io+g6rVq3C6NGjg44pGNolKAxC/bubDyTWBBEDIhVdOVd1pHOBA18W+nfNUuxAlsgYzelISzYiOcmgmMHenFB7/l+fqgv7fO46O06WzISr2ob1a9/HsGHDZI8LrA7Q02tDxB4Sa4KIAZGKrl5zgeVeFlbtsSHFyHDB3bzk+vzeTTgPoE33QfFeSsLReL4ap959Co21p/DjqfN9Qq3k1ZHrTEc0D0isCSIGRCq6esUVlV4WmiPn924CQGIdSOPZ0zj57pNwn69G9vjZuHPk7ehbtAU2u9PP1a5nKIWIH5QNThAxQElcQ4muXnOBKZmsZdNYewonlxXCXVeD7AlzkXFtD6zaY/O96MmNRaWBHc0bEmuCiAGRiq5ec4GjyfClTtGxxyQYcX12WkSfddl/wA/v/B/cznPImTgPqVf+GKmCMaTnhF7gmjfkBieIGBBNMo8ecUWlzN92qUacPNeg+DlrM80Yb05YZf4tqM29luKqtuHksifBGxuQc9d8pFx2HTLMAuwaEu+oRKt5Q2JNEDEinsk8ai8Lg5/bJpt1zOBtPbmu8nvZWmAiejLMAnYUDgjarqWsruH0UZx69ylw7kHOpAVIzu4MAJg1qlvIumwq0Wr+MB6nNoJ5eXl89+7dcbk2QbR2xESkQBgAc7IRdQ2Jk4zmcV0AABiE1DivJHoYgCNFI4K2y7WCFYwM4N7Z2A1V3+Lku0+BMQOy75qP5A6dAAAWk4CKWUNkPy8mmclZ8kR8YIzt4ZznRfJZsqwJopUgLedRekXnQEIJNdAyRFpEyRWt5AkBgIdfWIWTJU+DGQXkTFoAIfOS6M4e3U318yTQLQcSa4JIEPRoM6p0jnCGeDQ1RsZUB4Wc+7wMAND2pmCLtDkhhhmkhPrOP/30U5xc9iRYihk5dy2AkHG54vmphrplQ2JNEAmAHm1G1c4RzhAPpf7VsYABWDShB6aXVCha+3UH/gOg+Ys1B7Bqj803K1zu+ypYWYnZa/ah1ulCm9rDOPTWkzCktkHOpIVISs8OOifVT7ceqHSLIBIAtY5nepxDa9mOSTBiZI/Lm6x8K90koHjDwWbXAjVSpN+p3PflcnPYnS44j+7Fvn88gcbkdsi5+8+yQh14PqJlQ5Y1QcSRUBOW5ERWyXWq1jVNS7YxA3BTp3Ss2mOTFc9YDNGwO12tLvNc/J6Uvi/ntxWoWvUMktKzkX3XfCS1yVQ9X+D3OnnJTuw4VO37u++1mVh63y1RrpqIN2RZE0ScKC23oWBFpaqIBiYkia5T28UkMdHVXVpuU+2aJtekJRAOYNfhGll3uZExPD+xJzLMQsj7ItSxXHyGct+X8/AeVK2ai6SMy72u7xBCLdJzzkaUltuChBrwTvOavGRn9Asn4gqJNUHEidlr9sHlUbZV5Wpj1Vzdal3TxM5ooVBK9BK3n7/QGPIcLZHAsEA0YQLxEQd+X45vPsGp1c8gKfNK5Nw1H8Y0i+Zz2p0uzFi9N0ioRZS2E80HEmuCiBNq7l+lNqNqru5QrUrzc60hLWMjU5ahaSUVqi8XseKyu4tw2d1FTX5dKWK9svhc+1yrzeKVw+50oW/RFgDAwjHdYRYMqDu4A1XveRud5ExaAKM5PezzJmKmP6EfFLMmiBgQbRmWXJcrIPRUrlDlO7NGdUPBykq4ZMZkMihb1oQ35GC9OBd81R5b1OeasXovbuqUjqrKrTi9bhFSruiC7PGzYUjx9gzPMAs4f6ExLi9IROJBljVB6IxaXFmKkpWrZv1GO5UrP9eK4nE9YL0o7qIlHYvksWjIMAuY0rsTTIIRtZ+sRu0nq+O9JADe73LprqO6WLFOlxsb3l/hFeorf4zs8XN8Qm1ggDk5CS4P931HVosJU3p3Uj1nSpL8T3rfKDwBRGJAYk0QOqO1DGvWqG7elpISBCPDrFHdFM+tx1Su/FwrdhQOwOKJPXFZurc7WCIJtVnw/iy9fVEUnYc+hfPQp3Fe1SX0elbnKjfiTNlipHbqjuxxs2FIMfv2efilLG83574Xsnn53RVf5hiAP4+9MUiYKRu8ZUBucILQGbW4spRIW0Squbq1ut8TuaOZw+WBw+WJ9zJiyrnPy1D94ctI7Xwzsu58EgYhRfV4p8uNaSUVeGx5JdycB3lCGIDJvTtRF7MWDIk1QehMqLiyFD1/XMPpghZORzMimEjCBoKRweXmOPvZ+6jZsgRtru+FEdP+jF1Hz2s+h5hTwEGDOlob5AYnCJ2JNq4cKeF0QdPa0YwIxiQYMTlE7FiO4nE9gEqvUGf+5Bd4/e1luOuW62CIsA6M49LITRLqlg9Z1gShM3pOQAonq1yr+x3QNj9ZDgOAdLOAGocLBuaNrcYalqTuIm5qxByBrQeqND9DI2O49w//B/vHS/HzoXdg67qVSEpKQt+iLVE9wxqHC6XlNhLrVgCJNUHEAD3c2+EO9wjlfpcKv8UsQDAwzWVBDIjbFK+cCXNifg2tZJgF37MvGNoFBSsqQz5DzjnObH8LtTtLkNatP6puvg+z1+0PS+zVKN5wkMS6FUBiTRAJippbO/DHubTcBkdDcHcx0f0eKK41DhcEI4PFJKDW6YLF7J20JVdmbbWYfHXfpeU29C3aoovINEdqHC7kzt3oy9hXqksXx35yzmHf9gbOfroabW4cgsyhv8MFN7B011HdssoppNE6ILEmiARFq1tbycq1mATMHt0N+blW5M7dKDvhKS0lCRWzhiieRxprj1cGuX3HMgCApe+kJr2uEjUOFwpWVsLt4YoubDfnAOeo2bwE5/asQZvc4cgc/CAY86YJ6Rk9UOoJT7QsKMGMIBIUtcEcUpQyu9NSknwu6xqHfGtTm92JzoVlfu0vlWq445VBfuG7Slz4rrLJr6uGy60s1ADAuQdnPnwZ5/asQdu8O5A5+CGfUKsRbq6ZSTCif9cs9C3a4vseA5vvEC0DsqwJIkEpGNpF1dIVCWWBh5p3LO2ytnBM96BWp6HGeBL+cI8b1Rv+ivNfbES7XmNhuW0qmKTnulLZl9ViwtXtTX5DNwQDoFZyPvZmK1btsWnOayCaL2RZE0SCotatTIwddy4sg0Fh+IZogWuNacqVeUlbpyYaDMrtNeMF97hxZv1inP9iI9L73BUk1EbGMPliG1UpJsEYJNSAV6hNgvw9Wi0mbD1Qpblcj2jekGVNEAmMXFZ5YOxYLsmJwWtl9S3agnSToDrhS0qgKCdy8xQOoL4xcTqdcXcjTq9bBMeB/yD9F1Ng6XOX336TYPS9bOVdlRlUkvfYcnlXf32jBybBKOthmV5SIfsZSjpreZBYE0QzQ0lAxQxkqZvVZndCMDLNZVqBIzIT4UffaGoX7yWEhLtdOL2mGI7//ReWfvcivdfYoGPG3nzpxUvuJWyagvB6uDeXQK7eXik8QUlnLQ8Sa4JoZigJqIdzWGVqrV1uDrNggId7rXBR1OUI3B5p8xQ9ybrzybhc18gAmUmiQfBGF6reXwjnN5/ipol/RPXVg2Vj0iWfHUPeVZmKsWSl78XImGLdvlxeg2BgcDQ0onNhWVQNeYjEIrECPgRBhEQtS1xJyB0uj08IROtbDmvAuft3zYp4nc2dZA3xcI+rHqdWPwPnN58ic8jDcFx/Oyb37iTbQtTl5pizdp/iuSb16hjWdiA4r8FiEgDmLS9TG89KND9IrAkiAZEmkAWW46j1Htfq/lQyGAPFeeuBqrDWHQtqPnoTNR+92eTXdYaY/OVpuICqVXNw4Ug5Mm//A9rmDofT5cayT44plnXVOFyKJVbz8rtjSu9OvlCEkTFM6d0J8/K7q65DHHl6pGgE0lKS4ApwB1DCWcuA3OAEkWCEajMaqvd4NI1LRHFOpHKtetuBsI7PMAvgHKh1umBQcflHipExNF6ow6mVc1Bv24/2I6ajzU8ulbuFup7U4gX8S6zm5XdXFGctfeLD6Q9PNC9IrAkiwVBqM/rY8kpML6nw/VAH1kMD8kNEHA2Nik1RAjlhdyb0rGst1DhcvslYb+86GtW5AmuiTYIRMwdfhb8W/gZHT+xHh1GPI+2GWyM6t1LrWDm09okPZzwr0bwgsSaIBEPJChIttlCNLwKTkUrLbZheUqGpxaWBMcWs5OaE0+XG0iiFGrg0L1p88Xnolhz85fF7UF5ejrGPP4s97EdRtQ4VX45CWcxa+8RrbaRDND9IrAkiwdCSgR3KKistt2HO2n2aLWoRvV3G8USPO5EOMTl9+jQGDx6Mr776Ck8UL8HqM5eBR+l9MCcbNVnMWt3beo5nJRKLuIn1wYMH0a9fP79tEyZMwMMPPwyHw4Hhw4cHfWbq1KmYOnUqTp8+jXHjxgXtf+ihhzBx4kQcO3YMv/zlL4P2P/bYYxg1ahQOHjyIBx54IGj/zJkzMWjQIFRUVGDatGlB+xcsWIA+ffrgv//9L558MricZPHixejZsyc2bdqEefPmBe3/+9//ji5dumDt2rVYtGhR0P633noLHTt2RElJCV5++eWg/StXrkSHDh3w5ptv4s033wzav379epjNZvztb3/D8uXLg/Zv27YNAPDss89i3bp1fvtMJhP+/e9/AwCeeeYZbN682W9/+/btsWrVKgDAjBkzsHPnTr/9V155Jd5++20AwLRp01BR4W+d/ehHP8Krr74KALj//vvxv//9z29/z549sXjxYgDAlClTcPz4cb/9t9xyCxYuXAgAGDt2LM6cOeO3f+DAgXj66acBAMOGDYPT6f8jNnLkSDz++OMAEPTvDkisf3u1K5/Cqao6eCTCabn1HqReeQMuHN8P+/Z/AgB+ANDvg/YA/P/tTSt8Goeq6sAln28/9BEI7a+E45tPcPbT94Ku32HkY0hql4W6/dtxrnx90P6s/BkwmtNxfu8mnN+7KWh/9vjZMAipOPd5GeoO/Cdo/2V3FwEAaj9ZDeehT/32saQU3xhM+45lQX3AG+0/ILXjTwB4k80CY9hJbTugwyjvd1u96VU0nDrst1/ItKL97b8HAJz54EW4qv0Tu9pecR0eKJyLVXtsOLb6z2g8dxqA18vQJisNM2r7Ydq0aRg0aBC++uordOvWDS+9sBj1jV6BTb2qh2/IyMnls8Ab6/3Ob7r2Z0jvNQaCkeHYW/8X9GzSuv4CbW8aAY/rAk6tmA0AuGepEbmdLAC8//ausHTC0RM/oKp0od9nU5KMKMk9H/RvTwBw1cVjjN0eA3Kt9LuXAL970UDZ4ASRYHRok4JrstLAQox1SEkyym4/Vu30E+rmTsrlP/KJcSyob/Sg7IvvMfZmK0zJ3meakmTENVlp6NAmBefPn0e/fv1w6NAh9OnTB5mZmT6h1oqRMaQla7eNAs9fMLQLUgMqAAyMoWMmxaJbCyxe/1Hn5eXx3bt3x+XaBNEc6FxYpujKlbauDOdzzQGLSUBdQ2NQCVKskXumx48fx4ABA3DixAmsX78et97qTSaLZKa30gAPOaTudxEtsW0isWGM7eGc50XyWYpZE0SCohS7NjKmKNRqnwMulTVp7RXeVGSYBcwa1c13T9KkuOpNXjdi5qDo3IihCMwD+PbbbzFgwACcOXMGGzduRJ8+fXzHyiVyqSE2m5H7XuQyzuUSwpS6mBGtA3KDE0SCotT8ZNGEHqqJZXX1jbL7DAw4f6ExoYTaJBiweGJPlP9piN895edafQLWcOpwUBxaC+HOhgYuDT95ec0O3HbbbaipqcGmTZv8hFpcn9g5DLjUU13umqL4Kn2fk3t3UpwhThAiZFkTRIISbmavWn20xSQAkLeoTYIhZLeuWNHQyGMmTMlJhoimcn176Gv8YcFTMBk9+GjLFuTm5soep2TphnJXkyubiAQSa4JIYAIFW2wbKfcDrzSNS4x/di4sk73GBZcHfa/NDJql3BS4OUdpuU32fixhjPaUQ0moBQODYGRwyLygNJw+ipPvPglw4JpfFyM3N1dRfAPL4ywmAbNHd1N1V5Mrm4gUEmuCSGC0dK4K1RpUrMVV62717Zn4taOcVlKBJ1ZWwuXmMU+Ms0rEtuecjX4vAw2njuBkyUwwZkDOpAWwp16OmaV7sWqPLej57/6uGiWfHfNLgrM7XShY4S07I0Em9IZi1gSRwKh1rgIuiblaZrLYalJtAEi8e0c3qAi1kGmFkBm9+DEAOwoH+IS0ViLU9T98g5PLngQzJCHn7iIIHbyTrpbuOir7/Jd9ckw2W93l4Zi9RnmyFkFECok1QSQwoTpXKbm+RaSZxYHjFKXJTLHoHS0YGDLMQtTnaX/7731NTaIh8B7Fv+tPHMTJd58CSzYhZ/Kf/V4MlF4g1Dq92Z0uGklJ6A65wQkigQk1mEHNIrbKJDApxUzDLUUKhcUkIKttMr4+VafL+bSgVscsVw5VMLQLHly0FKdWzIbRnI6cuxYgKT1bl7VoHdBBEFohy5ogEhg11zWgPE1JTCrTKhiBVrdYihQpdqdLN6E+88GLOPPBi6rHZJgFPD+xZ9CzEvfJlUNZar9G1YpZMLbJRM7dRboJNUAjKQn9IcuaIBIYaTa4ze6EkTG/mHW0U5akmc7pJgGiRifSQI/AXt5y1DhcmFZSAbNggMUkoNbpUi2N2rRpE0aPHg3rlZ2QMnoWXCnpuq7ZooP7nyCkkGVNEDGktNyGvkVb0LmwDH2LtkQUy8zPtfosbLkxmUpxaC1rE5PTOLzWcI3D1axblTpcHtTVN+L5iT0VPQtPv/gvDBk2HI1tcpAzaQEm3nbp+WWYBVhMgu9ZRkoCvesQLQSyrAkiRmgpu9KKWlZ4OO7uUOdsCbg8XDFmPOP511H0+ANIzroK2ROfQVVjKlbtsSm+4FytUJseitoE6hJHtAxIrAkiRqgJbLjiqnWesR7nbAmI9yZ18ycf+xT/e3c+knOuRc6EuTCktgEQ+XeiRiyy64nWDYk1QcQIpdrncKc1AcpZ4QbGFDuAyRHYdas5kJx9Tdif4QBy527E+QuNcHk46r7ahtPrnkPKFV2QPX4ODClmv+OVXlwyzILss2JM3dXdv2tW2GsmCDUoZk0QMUIpozqSTGu5rHDAmwg2Y/VeTbHw0nIbClZWJpRQa3kSmYPuj2jiVo3DBZeH4/zezTi9dhFSrvwxsifMDRJqwN8SluYZcA4YDf6rFIwMk3t1Ur321gNVYa+XINQgsSaIGKGUUR1JprVYWiUn9NLscDWKNxxUnRFtMQlISw5+IYjlj4R04lS05WJynKv4AGfWL0bqVT2QPX42DMnB7mlp9rxc0p0BXgtbTDorHtcD8/K7qzZ8ackhBiI+kFgTRIxQyiaONMs4P9cKj4LQ2+zOkBnnagLCAFTMGoJ9c2/H4ok9fQJqMQkwGvUXUZGtB6pQMLQLjhSNwKIJPWS9B6fXPovTa58N+9znPl+H6g0vIfWam5A97k8wCKkAvPeklD0vl2fg8nCYk5NwpGiEXzLfrFHdFD0DFLMm9IZi1gQRI6KtgZZDKXYNeOO0ahnnap+Viou0y1nfoi0xnX8tXa+X4JeRxnOnwz7v2c9KUbPlNZiu742s0f8HluS1gk2C0TcZSw6lFxrxZUhau52fa8Xu76qxdNdRv1UzUMya0B+yrAkiRqj14o4Updi1lMBBH2L8taauXvZ4wcAUXyCawp3rdLkxZ+0+FKyo1GWudu2uFajZ8hrMXfoi645Cn1BbTPKdzKSoWcTSlyHRezEvvzsm9+7kZ2FzAKv22Kg/OKErZFkTRAzRe35x4HxrpQj0CbszqM5bbn6zYACKx/dQXKOaNa4neiS9cc5R+993UfvxUphvuA0dRj4KZrj0YqM031qktNwGR0NjyOsElnptPVAV9D3EohyMaN2QWBNEM0N8ASgtt2F6SYWsYF9hMWlqehJCv3Qf8BErOOew/+ctnN25HGk/GYj2w/7gJ9SAV0AfX1GJaSUVvm3XZ6fhw0f7Bb3YhELqcYhFDTxBBEJiTRDNlOINB2WFmsErstMloqQEx6UJUdIGImJsFgBSBUNMxZr5riH/5pBi7ar6ec457NvewNlPV6NNj6HIHPo7MCYf4Wv0+D+xr0/VYfBz2+Bo8Mjeo5Ex2ex9qbs81GQ0gtADEmuCaKYoWW4cwPSSChgUhEbuPHKtUQtWVAIMquVeesDhFVEDADm5zrhtKgBvzDkw2Y1zjprNr+LcnrVoe9MIZAx6QFGolfj6VJ1iVrfS85MmkMUikZAgAqEEM4JopoRKhtJaz21gDLPX7JMtWZITammPEL2KulxujvSLQzSUCBZqD6o3/tUr1Hl3IGPQg2ELtUi4VrC06UksEgkJIhASa4JIMLRO6tKSGQ54XbkMgFmQ/8/dzXlY5VmcA4svzo7W0+aucbgwe3S3oOYoVe8tQNV7C/zX4HHjzL9fwPmKD9Cu9zhkDPgtWBRNVbQ+S5FAr0Z+rhU7CgcE1WIThF6QG5wgEgi1SV0AgmLKC8d0D5kZ7uEcR4pG+M7/2PLKqOZVcyDqcygxY/XeoPO6nWf9r+9x40zZ86j7ahvS+05Cet+7oxLqJAPzies0DXF+gOLRRNNDljVBJBBKk7pmr9nn1wZTKuI7Cgfg+Yk9NXXTUuuCFohgYBAUupfFQqgBhExk4+5GnF77LOq+2gbLrb+C5eeTfUItGCIT7MCks1BQPJqIB5rEmjF2O2PsIGPsG8ZYocIxExhjXzHG9jHG3tF3mQTROlBKGrM7XYrjNgHlzHDAK+xXF5Yhd+5GlJbbNFmFFpOANqlJcLl5WD27Y9HfW4S7Xaha82c4DvwHln6/RvotE/yuWzy+R0R9xsVjQ/VXp3g0EU9CusEZY0YAfwUwGMBxAJ8xxtZwzr+SHHM9gBkA+nLOaxhj2bFaMEG0ZMJtQiKKu5aa3hqHCwUrKzHxpx2xao9N1Yqtb7xUyhSOFT2pV8eg9pu6wDmq3lsA56HPkDHwfrTLGx10XWkDms6FZZpPLd6f2jMU67EJIl5osax/BuAbzvlhznkDgHcB3BFwzH0A/so5rwEAzvkpfZdJEK0DuUQnk2BUnPAkWslaY6guN8fWA1WKE7wAr6UZaV31sk+OwSwzuUsOOTe73IpSOnaD+3w1nIc+Q+bQ3/kJtZExTOndCfPyu/t9JpyYsjXEMzQAJNRE3NEi1lYAxyR/H7+4TcqPAPyIMbaDMbaLMXa7XgskiNaEUhnQrFHdZEVcjJ2Gk81ssztRvOGgrMVsEoxRxaPdnKOuQV7oU5IMsJgkoybH98DEn3b0vTQYGUOfazP97sPTcAH1x/ej0f4D2g/7I9r2HObbZzEJOLRweJBQA97noRRvD8TuaPDO+lZ4Ubq7dydN2fkEEUu0iLXcv/jA/5qTAFwPoB+ASQBeY4xZgk7E2P2Msd2Msd1VVTScnSDkUCoDSkm69J9rWrIRKUkGTC+pQN+iLQDgJ/JqMEDW1c4AjL3ZGvEIz1AYGMPs0d189wV4B16ILwduzvH50VrfGjz1DpxaMQv1x75E+5GPos2Ng33nEgzec6mWuWl856hrcKNgZSUABL0ojb3ZilV7bEGJfSTYRFPDeIi3aMbYLQBmc86HXvx7BgBwzhdKjnkFwC7O+ZsX/94MoJBz/pnSefPy8vju3bujvgGCaOlo6VttEox+iU+5czdGNBzDerEkTK4jF8CjnopltZiwo3CAagmZ1WJC2YM3oeONfXD++EEIWVfBmJaJnAlzQp5ffA7FGw6GPYBEXJuUvkVbZM8jdyxBhIIxtodznhfJZ7VY1p8BuJ4x1pkxlgzgLgBrAo4pBdD/4mI6wOsWPxzJggiC8EfLQA5pZjgAzBrVTbMbWMoJuzPIFW8xCaq9u8PBdrG1acFK5TrtY9+fwqBBg3Dh+29w2Z0zYEhJA2+UH+8ZiNPlxmPLKyOaFCaXYKY235qsa6IpCSnWnPNGAI8A2ABgP4DlnPN9jLG5jDEx02MDgDOMsa8AbAVQwDk/E6tFE0RrQuv0JrHHd9+iLZheUoE2KUl+MeLFE3uGdHGLSVaiK35y706odbp0GWEJeOPSc9buU+w37nbU4syKmdi7dy+eeHYJzF36hH2NSGPucglmaolq5A4nmhJNddac8/Wc8x9xzq/lnM+/uO1PnPM1F/8355w/yjn/Mee8O+f83VgumiBaE1ozm9NNgl/jlBqHC/WNHjw/sacv9i0dQBFIYLOP0nKb5jIs0Ya3WkxIU8kGd3OuKPzuuhqcXDYDzqpjyLzzaSz9vgPcYTYsUcMkGDGldyfZ5imCkck2OlFL3Av0ZhBELKEOZgSR4GjJ9BaMDIwFdwALFBTpAAopRsb8Yt4zS/dimsKsbDk4LsVxHQrZ4Go0njuDH96Zgcbak8gaNwspV+eGfQ41xKz6efndUTy+h9/AkAyzCX95CQAAIABJREFUgOJxPWQbnYghASVoZjXRVFBvcIJIcEQRUUuaSktOUrRYpZ9REhcP535C/fauo2GvUzx3uI1dGs9W4eS7T8JdZ0f2+DlI7fgTv/2ma38W9lqkBCaDSZunaCE/16r47KlHONFUkGVNEM0AMYaslDJW63SpNjkRURIX6fZlnxyTPSYU4jnUXO2BuOw/4Id3CuGuq0XOhLlBQg0A6b3GIL3XmIjWJHXta51mJodSDTb1CCeaChJrgkgQtIiJktiqza+WbtciOpEmaBUM7YLSchtW7dEmgq6aEzj5zgzw+jrk3DUfKdYbQn7GLFxqrBIKaR9vsfwt0nppmllNxBtygxNEAqA2GlMqCHI10KGQZoBLXerSUZvSaxgZi0iwd39Xrdl97jpzDCfffQrc3YicuxYgOecaxWNPLZuBa7LS8OXunb5toVz1jMHvvtSmmak9Cynhus8JQk9IrAkiAVASk+INB/0EIty5y3Ku2lCiM6lXx4hi1lo/01D1LU6WzAQ4kDNpAZKzrlY81sgYstqmoEObFN82LTF1zuH3sqM2zczu9Mb6lV6QYkVpuU3ziwJBkBucIBIAJTGR2777u2rVc4XjqpVzvc/L744pvTuFtX6tNJw6jKqSp8CYAZfdvTBIqBmDn5vezTmqztXj9PlLTVG0xtSlmfBaE8GaqhwrWrc80fogy5ogEgClDOpAkRFrn9XQ2gZTzfUeDoKBwaWhHrr++69xesWfwJNSkHPXfAiZwS8RnAeXn3k4x7HqS88mHBe9+LITTvigKcqxtHpSCEKExJogEgClftyBLuziDQdVa59z2iYr7gt0u9bVN8oKxv+t+gL1jeqtRcW4tthLPJRbvt52AKdW/Akd2rfHlZMX4owhaM6P+ucbL60znJi6tCMbcClWbzELsDtdkDtNU5RjheNJIQiA3OAEkRBozTZW+zE3MOCTpwbL7pNzu4qx2kBCCbXVYsKiCT2weGJPAMD0EEJ94fg+nFr+NLKzsvDZzo/x9KR+il3EpM1KRNK6/gJX5F7yFkzq1VH1eiKBLzvSFqp2h7xQN1U5lpYSOoKQEnLqVqygqVsEET5KU6AYgMm9O2HrgSrZhCWlz0WKYGQAR0j394XvvsCpVXOQasnGi2+txuvlZ32W7QWX2zccJMMsYNaobtj9XXVQi9PAiWIAcHVhmep1jYxh0YTgrmSl5TZMV+jMpvSZWCA3SU3uPomWRTRTt8gNThDNCDl3OQPQ59pMrNpj84s/TyupwJy1+zBrVDfd3atKgzikOI98jqrV85CUfhnaT5iPoo9O+cS9xuGCSTBi8cSePnESa7SlZ2YARv8kE0O6ZPid26rSJc0kGDH2Zm/XseklFX4vLmphBGkXt1ghDUVYzAJSkgyodbooG5wICYk1QTQjlOqklcZo1jhcmF5SAXOyEXUyPbszzILvOD1xHPoMVe/Nh9C+I3ImzoPRnB5khQcmVMndAwfwyoz7UVZsQvq4+b577t81y+/lRHo/I268HCWfHvNdz2Z3omBFJQD1MEKsXdCB1rT4wvK85IWFIJQgsSaIZoZcnbRa3JgDqGtwQzCyIItY6orWC8f/dqLq/T8jOftqZE94BkZTW8VjpeKpJKT1jW4crqpD9sX9NrsTq/bYMPZmK7YeqILN7vQlnZmTk7Bqz/GgFwOXh2P2mn2KWfcMiHmsmjLAiWigBDOCaAFosQqTDCyoTafeQl134GNUvV+E5JxrvRa1ilAD/utWugcGBg8Ptsq3HqjytU8Vs8NtdqfiPdmdLtl2q2K8P9aCSRngRDSQWBNEC0DLGE2ny6N55GUknN+3FafX/D+kXNEFOROfgSG1jerxgTOklfqWc4VVn7A7Fd3/SuTnWjH2ZqtvuImRMUzu3Qnz8pXHYOoFZYAT0UBucIJoAYTbhjQUgpHB7ebQanef/+JDnPn3C0jp1B3ZY5+GITm0AKUlJ8m2Ug2Mx9+z1OhXZy1yhcUUllWaYRZ8SWyiJe7mHCWfHcO6yu/DSvSaWboXyz455lfvbQ3xWa219AQhB5VuEUQLQq20y2IWNCWSiaIDADNWf6HqKjcyBnv5v1G94SWkXp2LrDFPwSCkal4vA0IK5B/mPI8Ve44j5ceXaq3FMie1Gd9SBCND8bgemo4Xj1Vaj1pv8lDlV9QPvHUTTekWiTVBJBjR/KDL1e+KMdm8qzI1tdz8tmiE4rkCObdnLao3/R2ma/KQdeeTYEnBHdQYENL9HqnIKdUri8lngcd3LizTFArIMAso/9MQ2X3Xzliv2kHNajFpbvlKtC6ozpogWghaR2UqoWUEppp1KcZyxePUhPrsp6tRs/V1/Ohn/XHhF38ASwruPgYA5mQjGho9qg1UxHGVcvd4+vRp/LxjCvJlBFDL/UpRygYPRM0DEarVKSWMEbGALGuCSCCU3Nh6W2tqHcBEy1rNCq3duRz27f9Cn8Ejsa1sNeaUHQiK4UoRjAxpyUmodbpULdvFMjXH/fr1AwBs27ZN5ZPa0OItkOP67DR8+Kh3HdFY1uQGb91EY1lTNjhBJBB6lvfIjb8UsSpkIFtDlFJxzmH/eCns2/+FW4fdiY/WvwdBEDAvvzsOLRyOb4tGyJ7b5eZIS0nCEYX9IrEeTxnYg10rX5+qw+DntgFQ702uljBGYzGJaCA3OEEkEFpHZYZiZulevx7bgeMva+rqgz4TKDT9u2b5JVJxzmHf/i+c3bUCU6dOxWuvvQaj0VtqJbUYlWxOm92JzoVlSJcZ1iE9Rmr19702U8Pdhoe0qUyoHuNSvj5VBwC+Mq9ws8FDNUUhq5tQg8SaIBIIPcp7xJnXgaLpdLlVS7vE/WI/8XWV3/v2cc5Rs/UfOPdZKdr2vB2jHpnrJ9RaXcscUJz2JceOQ9Vwfn8WN1zeznctPQVNrce4GvPyu4ddm63mNYk2V4Fo+ZBYE0ScCRQgpUxmrYSaeR2KGofLT9Q596Bm06s49/k6tL1pJDIGPYCnSvfh82N2X7vPSNCSJQ4AtU4Xyo/aMbN0b9CwkmgFTe7lKFaoeU2oFSkRChJrgogjchbVqj22qEYl6pmNzLkH1Rv+ivOVG9Dup3fC0v/XYIzB6XIr1hprPrfG49rmDgcARW9BNIIml03e6Hbj5LmGoGOvz06L6Boial4Tpd7ulFlOiJBYE0Qc0dOiEi10veo7uMeNM/9+EXVfbkK7WybA8otfgrFw0rLUERPNQlnmaTfc6l2Pwv5oBU1uMMrg57b5YtSAfzZ4NNcB5MvMlMrpqBUpIUJiTRBxJJLsb7m4LQBd3bnc48bpsufg+OojpPe9G+l9J4Ul1KFc3NI4fKh1N56tAgAktcuS3R8LQdMqzIHfRf+uWbIhDGl7Url+5NSKlAgFiTVBxBGlOKaBMZSW24IsPqVEpJQkg35C7W7E6bXPwnHwY1hu/RXSb5kQ/jlU9hkZC3Lzi4KXZAACu5ueXrcIAHDZ3UVB54qnoMl9F9LQgPjdrNh9FDsOVfu2uzn3HScKdrjNXYjWB4k1QcQRpQQnN+eyyVNKbnPdhLrRhao1f4bz613I6P8btPvZnbqcV4qH86ABHtK/S8ttKFhRqdrxTEtP8VijZeKX0+X2E2opSz856mddy7njCUKExJog4oj44/zY8sqgrlhysetYJhzxxgZUvbcAzsO7kTHoAbS7eZTmz2rN7AZCu62LNxxUFWojYzi0cHjI65SW2zBn7T5f61CLScDs0d10E8Rov4s4NY8kmikk1gQRZ/JzrZqzgZXc5oxF9uMviqzHdQFVq+fjwrcVyBz6CNr2vF31c4IBaJMqwO5w+WK1WrLDtbitQ4mgUqtPafzYYhZQ63RBqvl2p7ckTVqWFk3imNY+42rEohFKrF9SiPhA7UYJIgFQsjYDtxcM7QKTYPTbZhKMmoQ6MD3MJBiRbhLgaXDi1Mo5uPBtBdoP/2NIobZaTPh6wQiU/2kIjhSNQMHQLli1R1vLTC0laVoSxgLbpwa28qxx+Au1EtI2ouEi912EQ7KR6d5+tLTchoKVlX6DSOxOFwpWVFJb02YOiTVBJABKIhxohQb2trZaTL6/Q8EvHi/9XI29FqeWz0L9sX1oP/JRtOk+KOR56uob/X74tcRuAa+FpybUYi/zQGu13c/uDIqdBwqb1jXIIS3R0opoETtdbt+kMqvFhCm9O2n6LgwMMCcnKZbtRUrxhoNwuYPfUlweHvO+60RsITc4QcQBOffnwjHdNblElRKRQpVAMcDvnHa7HdWrZqH++/+hw+gnkNb1575jjYwpupvtTpdfn3EtrmDBwDB7dDfF/WotS83X9ZL9jNPlxmPLKwE0bfOQwLW6Ofe9WInPVmmuuPjCpBY2iOZe1D5LDVaaNyTWBNHEKJVfLRzTPeIxmNLSHyXx5Lg01erpkp3435uFaKj6Dln5M2C+vrffsaFmNjtdbjy6vEKTqxkAisf3ULWq1Sxj15njAACh/ZVB+8SseYtZUJ1BrQWt8WMtjWzUSrHE71+JaOrG1eLo1GCleUNiTRBNTKz6QGuZJmWzO/HoP7fj+DtPwlVtQ/aYp2C69qcRXU+rUAciJ4pqVt+ZDS8BkK+zBrzPLiXJAJNg9HuugpFBMDA4LhZuK2WsX5+dFtYgDa2NbJQ8IGovJtHWjRcM7YKClZVBrnDBwKjBSjOHYtYE0cToObNaCaW4Ka+rwfG3C9FYcwLZY5+OWKjDRbTolWY6K43NtFpM6H1N+5Dnr3W6gmL5xeN64KtnhuHbohH4tmgEjhSNCOrvLWaDq71ABaI1GVAJte85mp7wgPcFoXhcD2SYLz1Pi0kI6dkgEh+yrAmiidFrZrWInKUq12wlyVmD794phPvcaWSPm43Uq26M+B7CRRQoJVFMFYItY9HKXPwBkGRUtyvMyUZfrbqRMfTvmiUrTkplWuG8QPXvmhU0VERqEYdypyt9/1aLSRdBpeYqLROyrAmiidGa+a0FsduX1FItWOFNuhp7s9WXqczPVuHkskK4z1cje8LcJhVq4NKLiJIo2h3BlrGflanicjcaGOoa3L44u9jOc2apclxYaX1K28VM9asLy4KEmsH7rMUe4NNLKlTLsZRKvhwNjVReRShCljVBNDFa+0BrSXiavWZfULcvl4djxuovAHgzul32H3By2ZPw1NchZ+I8pFwRm9ilxSRgZI/L/WZOA/4vImpeBbm2o32LtmDP4TOq1/UoBM+XfXLMr52neE65Z6o2SCMwnh14NQ5g64EqlJbbNI3xFP//7DX7YHdeSoqrcbgU4+QEQWJNEHEglKtSa8KT9MdeivNiUpWr2oaT7z4F7rqAnLvmI+Wy64KONQsGuNxctcVnKDLMAsr/NAQAkHdVpuJLhtbpUtL7T+9zl+J1rSrZz4EZ7Vqeqdy6+xZtCVnDbbM7VceTSj0K0glcgeiRaEi0TEisCSLGRNJSUo+McdfpYzhZ8hS4uxE5kxYgOfuaoGMYgK+eGeZbo83u9NVYi/XAgZZyICbBiFmjLtVQy72IBLYCTUkywO50wciYXyKXVDTFa5qu7ql43YKhXWT7qgPwhQBEQj1TpRcoLYl/DOr15qI7fWbp3pBtWakempCDxJogYkg4JUFStCY8ZSjUFzee/hY/LJsJMCBn0kIkZ10le74rLKagWcuTenX0cx/nXZXp57I1CwYw5o0TA0BKknrqS+AzqHG4fGVVojUf+Fyk99lw8jAAIDnn0stGSpLBF9MOHEEpMqlXR7+/I83C19IDnEO5kYzYjAbwuuZDQfXQhBwk1gQRQyK1kENljIuWqpxQe6oOo3rFn2AwGpE9cb5sMxHAKyJXtzf5WXpys5YBoL7x0pBpR8DA6cCOZkGZ6SsqgmZUy7XElD4X6f1Xb34VgH+ddX2jByt2e9f5+dHaoHP1vTYzKF4daRb+1e21DewQO5kFft+pgsHvGDXiOZ+bSGwoG5wgYkik1pxaxri0VllEdPi2O/cdqlc8jQ6WtnjhrVJkXdlZ8RqpggE7D8vPWpZagFrnNs9Zuy+ohnpaSbBQqyE+Fy1DMnYcqlZc27dn5Euu5FDaLrLrcI3qfhExg11a4wx48wfEjPBA17yUDLMQdZ010XIhsSaIGBJpAw2lgR35uVZZgeIA2tUexndvz0BW+wxs374dD93xc6SlKDvPnC6PYhcyqQWoNYZa43BFPExDRHwu0vtXI5yXoa0HqmSPVdouEsoaBi7FrIs3HJSdgOZ0uTGtpAIelXOZk5NIqAlFyA1OEDFEa/azHOEkPF049iWOrpyDaztZsWXLFuw5/f/bu/PoqKpsDeDfSahAgkqwQRsCOKCAKDbYtCi8J1BJGMIUQDpBgsCjBRGVoRmbsSEtkbQKPoiATMoYIBhIGAJkwhZB0KCgNjxlDtoiErohATKc90dSsVKpW3Urqcqp4futxVpJ5datzRWz7zl3n338MGRz5R2s9DIfATpj32ZrzJ9ZA5Wvi+nvXydB+xxasfkJgeSc3ArXr6qzHLY2NQEqtjHV82xbCwvLyBaOrIlcyNYIuaosR+UF547jp61zUKdeQ2RnZ+Pzn/0qTZM7yrw4S8+UdKDBH8EaLUO1xA/6na7rotWKtFPzezVjK5ay0h7OWrMZEpX3xzZnWahmYtoOs+oL3ipiYRnZwpE1kYs5u/2j+Wi94MznuPLR3xBQvzESNn6Exo0bI/5D++uCzQUa/HCnSGpWgwOl1demc9YPMqDXk42Q+c8rFQrJAPvbdJp0an6v7uvy0ZolmLvzJE5bvH/DS88CAI6d/8XqcqjCEom5O7+2ucbbxFaVvulaWKuYf0hjwxRHsbCM7GGyJvIwpmQyfdFanN8+H3XvexAJ67fjRWNpUnFkOjXQ4I8FA0rfZ1pnvenIRaw/fEFznfWtwhK0f6BytbWJqRrc1ojTlGgtWV2T3rEj9nXsqHl80ufaLTpNy81M5y0oLNac1rZVpR8b2cbq39fWIwKtXb6sqV3LD8fO/+LwenzyHULqKJ5whfbt28tjx44p+WwiT/fRRx/hj3+MQu37H8a9A+eiaaP7KnTc0jsFviiqtOGI1ohTK+GEBAfa3XvbVhzWRvCW67GB0puJYc1v47kWDdHRImEn5+RqNkQxtyiqrdVtI60RAM7G9bJ7nK2YzQUa/Mq7yTlKABjyTDPNmyLyPEKIz6WU7avyXj6zJnIDpj7YD03bZfP5KQAkJibi+UGDUOv+5rh30Dz4Bd5dPo07M/kE8u8U6fpM0y5PtpZmaaW33LwCu5tO2HrWbW2zDa016XNnz4QxejSaT99dfrwpSeqp1P5ryte6EjXg+HNjU02ClluFJYh5pplD5zSRADYcvsDNPQgAkzWRclp7PFv7Jb1u3Tq88MILuKtpazQcNA9+tX/dn7mgsBjrD1+o1CglyOAHg1/F9b3mz0irWoWsFaOJnuVX5uu57cVhnuD1rP02sdY4xpqqPjeObBei+XdsHByI2Ej7S9C0SPy6Fzj5NiZrIsVsdTkzt3r1agwbNgxdunRBcP858KsdpOv89evWtll5XdUqZGsxWopsF2Jzutx8ZKw3jo1HLui+wQg02P4VFxxocEqVvr1tT/VU1Gvhki4CmKyJlNOz/nfZsmUYOXIkwsPDkZqaiib31dd9/ty8AkxIPA4AeCeqLT6ZZqyQlGoikWh17jJ/XW8cJRKw0468XB07S8rq1q6Fs3G9Kl0Ta2w9qrC2RG/g70sfMTw0bRfi005h4O9Dyn8eHGiAwV+7m5k5CVR4BEC+idXgRIrZ61n97rvvYty4cejVqxe2bduGOnXq2FyGZI359DpQeXlSHcOvS7NMRWV6qpn1joYHd2hqdXlVsZToFJdRofJZTzW53pqtvPxCvBPVFuPLblYsWd5saO2QprUhy7Hzv1RawqZ1fNLnuRVG79Z2OtOi1bOdfAdH1kSK2ZpCjY+Px7hx49C/f39s374dderUAaC/Hacly6lrU1Ixf65rGo3qKcmy11fbJDayDWKeaWZ1hJ2bV4DJ274s7zj2yTQjzsb1Ki/Mujd0FO4NHaXrcyw1Liuis+zXbf5zE1u1A1qPKjYcvuDQ8ebXPrJdSPl/e/NEbWu8rWfXLvJOTNZEiml1OTu5aw2mTJmCqKgoJCYmIiAgoNL7PplmxKKotg5NY5uPJrWSiml9sj32+mqbi41sg+8XRKBuQOVYC4sl/prydaXjAw1+CLj/4QrbY5rTugEAKj4zntPncZvPlAFg+vavNBOs1nS/5Q2NveMtZ1C0+rxr0VP9Tt6JyZrIDZiPKP8xtSu++Gg5Zs2ahZiYGKxfvx4Gg/ZzV2vJflFUW5sVysk5uWg3b1+1e347WvyUnJNbvg+2JWtV2wsGPImCc8dRcK7yNLa/EOU3AOfiepX/na0VjNlr+zoz+YTmemjTFLdeto4XQIVn3Y5eP1u7dpF3Y7ImciNSSkyfPh3z58/HiBEjsHbtWtSqZb+0xDSl2jg4EJfLdn/q2qqh1dFk11YNMXnbl7qXNNniaCW5o8uQItuFADlJuH5oc6WfWfbstnYNLIvATDdElgVltqaXTc+i9c5emI63llYtl2JpXT9rsw+Adp9y8n5M1kRuQkqJiRMn4s0338TLL7+MlStXwt9fX4Kw9rw16fPcChXIptFk5j+v6G4SYs6yetngJ5B/p0hXIxcTWyNJrartxxrdg/vvqVM+qvQXAjFWOns5sl7dkq3pZVPRmPnI3BbT8VpnNL8GWvUKf+tf8Rm/1t+ZfAerwYncQElJCV577TUkJCTg9ddfx6JFiyAcmPLUevac+c8rldY5T9CojLYlpGy0aKpeFqJ0owzT6Dw3rwDjE4/jrylfY06fxytVPJumhusFGjSfh8/t+7jm5z/UoC6yFkTYjNFWUZetZVm2krmfQIWpdNPXWq1UgwMN5ceE2KnyN53TFLtlRXlkuxAmZyrHZE2kWElJCUaPHo2VK1di8uTJePPNNx1K1IBjezU7uj+1qRCrfAMRG0vGruUXli8Pszw2N68ABn9RaR9rUw/s6m5aUZX9qk2jcS0vdGhW4VhTUq1Xtk7afIYi0OBf4YZD717mzt6VjbwTkzWRQsXFxRg5ciQ++OADzJgxA/Pnz3c4UQPaCTjYypKlyd1b6t7YIiQ4sNIaaHtru82XKFkeW1gsYd75NDjQgLl9H3dKsrK3Xt0aW38f82lny3XTeQWFMPgJ1A8yIC+/0OouWbZGzUSOYrImUqSoqAgvvvgiNm3ahHnz5mHWrFlVPlfXVg2tNh25cauofP2yienrv6Z8bbPIzNrOWnqrl20dZzaoxu0i+91Nli9frusz9Y5k9cQpULH5iLWkXlgiERRQCzmzu2men6NmchYWmBEpcOfOHURHR2PTpk2Ii4urVqK2tadzYYm0WoEd2S4EObO74VxZ8xHLsbxWktNb/d04OFDXsdb6i1u29fw2/y60bGl/gw17y7O04tTzuq1103qL64iqg/tZE9Ww27dvY9CgQUhJScHbb7+NCRMmVOt89vav1rNHs1abTWvH2WtzGmjwL982Um9LVIHSBNm1VUMkfZ5b4T1FZ49i2LMP4m/jh9s9j6O09tC2TPL2rrG19xBZqs5+1kzWRDWooKAAAwYMwN69e7FkyRKMHTu22ud8aNoum12vrE1nV4dlYu/aqqHV/tiWx/rZ6X8NWO9H/uPGaahdyx/XzzhexV6Vv4+1G5XknFxM3vplhcI4S86+zuR9mKyJPEB+fj769euH9PR0LF++HC+99JJTzmtr1OfqEZ95ogsOMkBK4HqB9YIrPaNya37cOA0AcOuC2l2n2s3bZ/MZv54ZDPJt1UnWLDAjqgE3btxA79698fHHH2PNmjUYNmyYU86bnJOLm7eLrP6sfpChwppnZ7NMvuaJzNoOX47sqmWpdi3Ht/DUO7WvV56djm9V3RecSA8mayIXu379OiIiInDkyBGsX78egwcPdsp5tUaq5knaVKzliqVD9pZxWWtIoqexiOVUuJ8QaHqvY4lQa0tLUwxVYWt9ur2qc6LqYjU4kQtdu3YN3bp1w2effYbExESnJWpAO1leL+sQVp32m3roWcZlq1paq9XmkGeaVajofrhhXTS4q7ZDsenZotJRWv3BgwMNLC4jl+PImshFrl69ivDwcJw8eRJJSUno27evU8+vNcorkcDkbV+ibkCtKrXf1EtvJzStUa3epiEXh2xxODatuKqzyxibnJBKTNZELvDTTz8hLCwMp0+fxo4dO9CzZ0+nnn9msu1iq8JiqdmD29FtGbVYa0KiResmQU/TkKZNHd9pyl+j8ry6W0yyyQmpwmRN5GQ//PADQkNDce7cOaSmpiIsLMzpn2FrS0d7nFkIVbuWn+7q7qreJCQmJgIAoqKidL9Ha4mYvaVjRO6KyZrIiS5dugSj0YjLly9jz5496Ny5s0s+R0/SqR9kwK3CEofab+pVlWVYVb1JeO+99wA4lqy1drwKYcU2eSgWmBE5yfnz59G5c2f8+OOPSEtLc1miBuxP5xr8BXo92Qh1DL/+L663EMqy3ae14jA9G3qYq+lqaa3iNVZsk6fSlayFED2EEKeEEN8JIabZOO55IYQUQlRp0TeRpzpz5gyee+45XL16Ffv370enTp1c+nnPPFxf82f1gwyI+kNTJH2eW2Hts55NM/RWkOuZ0jbdTujp0W36bHs3CXpVpU84kTuzOw0uhPAHsBRAOIBLAI4KIXZKKb+xOO5uAK8DOOKKQInc1enTp2E0GlFQUICMjAw89dRTTju3tcYeAPDFheuVju3U/F5seOnZ0q/jMnRXgttrCWrtfVqV4P5CoERKBNTyK785yM0rwNZjF2wmSlvroquKxWDkTfSMrJ8G8J2U8oyU8g6AzQD6WTluPoCFAG45MT4it/bNN9+gc+fOuHPnDjIzM52eqK2Ncufu/NrqFPS5q78mT62Rr+Xrlp+h9Szc8n1a08xpYWI3AAAgAElEQVRv/fF36Nj83kqj+E++/wVD3v9U66/qknXRRN5ET4FZCADz0tNLADqYHyCEaAegqZQyVQgxyYnxEbmtEydOIDQ0FP7+/sjKykLr1q2den6tBKb1rNg8oWqNfP2EwEPTdpWP0v+aYj3xW7IsDrO15nh8ovUNNz75/hfN89u6uTi6bZvd+Ii8nZ5kba2Spfz2WwjhB+AdAMPtnkiIUQBGAUCzZs30RUjkhnJychAeHo46deogIyMDLVq0cPpnOLrUyTyhaq2BNo2cc/MKMHnblygstl9VrlWY5cxpZq2bi8bBgWjQoIFTPoPIk+mZBr8EwLwrQRMAl82+vxvAEwCyhBDnADwDYKe1IjMp5QopZXspZfuGDRtWPWoihT777DMYjUbUrVsX2dnZLknUgPZSpyCDX6U7aMuEallgZa163Fai9heiRguzbFVvr127FmvXrnXp5xO5Oz3J+iiAR4UQDwkhAgBEA9hp+qGU8rqUsoGU8kEp5YMADgPoK6Xk/pfkdQ4dOoSwsDDUr18fBw8eRPPmzV32WdYSmMFPoLBEVtjoQgAY+PvKo9zIdiH4ZJoRZ+N6ocTBZiDFUla5nWan5vc69LopVq3qbSZrIh3T4FLKIiHEqwDSAPgDWC2l/FoIMQ/AMSnlTttnIPIO2dnZ6NWrFxo3boyMjAw0adLEpZ9n7blw/p2iSnsqSwCZ/7xi81x6+3ibq+pOVRteehZD3v+0wjNq80p1LazeJtImpKL2e+3bt5fHjnHwTZ4hPT0dffr0wYMPPoj09HQ0atSoxmNIzsnVLN4SAM7G9bL5Xkc7jpmEBAfik2lGh99nMjP5BDYduYhiKeEvBAZ3aIrYyDa639+lSxcAQFZWVpVjIHIHQojPpZRV6kPCdqNEduzduxf9+/fHo48+igMHDuC+++6r8RhMyVZLvUCDzX2rzUfpuXkFlfaMtqU6G3/MTD6B9YcvlH9fLGX5944kbCJfx3ajRDakpKSgX79+aNWqFTIyMpQkasB2e0+Dn8DNO0V2u46ZnmEHBxqsJmqtBqbV2fhDa8OR6mxEQuSLOLIm0pCUlITo6Gi0a9cOaWlpqF9fu8Wnq9ka3d5Vp1al59jmDUXMn3l3bdVQc+tMiV8L2Eyq20/bGbtf7d69u8qfT+QtOLImsmLTpk2IiorC008/jf379ytN1ID26DYkOBB5+daTr2mEbT7i3mA2JW3NXXVqObWfttaGI47sKx0UFISgoKAqx0DkDZisiSx8+OGHiImJQadOnbB3717Uq1dPdUg21yFrJXJ/ISpNndsbz+blF5Yv9/pkmrHa1dmDOzR16HVrEhISkJCQUK04iDwdkzWRmVWrVmH48OHo0qULdu/ejbvvvlt1SABsr0PWSuSOTDWbVOf5tDWxkW0Q80yz8pG0vxCIeaaZQ8VlW7ZswZYtW5waF5Gn4TNrojIJCQkYO3YsevToge3btyMw0LmJq7q01iFr9ek2VX47whX7PcdGtmHlN1E1MVkTAVi0aBEmTJiAPn36YOvWrahdu7bqkByilcgt11YHGvwhIJFfWHlv6+BAA5uSELkpJmvyeQsXLsTUqVMxcOBAbNy4EQEBAapDcgqtETdgPYnP7fu4kjiJyD4ma/Jp8+fPx+zZsxEdHY1169ahVi3P/l8iOSe3UnLW6j5mbXtLInJPbDdKPklKidmzZyM2NhZDhw7FmjVr4O/vb/+NbsxaS9FAg3+N7JpFRPZVp90oq8HJ50gpMXXqVMTGxmLkyJFekagB613OzJujEJHn8uw5PyIHSSkxYcIELF68GGPGjMGSJUvg5+eZ96yWU95ald/V6e3tDv7+978DACZNmqQ4EiJ1PPO3FFEVlJSUYOzYsVi8eDHGjRuHpUuXenSituxOptUTTALoFJdRqVe4ns/oFJeBh6btqtL7nSU1NRWpqalKPpvIXXBkTT6huLgYo0ePxqpVqzBlyhTExcVBONDy0t1Ym/KWgOZuWo7uTW35/Luqe1sTkXN45rCCyAFFRUUYMWIEVq1ahVmzZnl8oga0p7YlSrubWePI82s+/yZyL0zW5NUKCwsRExODdevWYf78+Zg3b57HJ2rA9sYeWku1AOjuaKZ1M+Dpz7+JPBWnwclr3blzB4MHD8b27duxcOFCTJ48WXVI1TIz+QQ2HbmIYinhJ0rvtM37kJlvZ+kvhNXe4Hp3u9IqWHN273A93K3tK5EKTNbklW7fvo1BgwYhJSUFixYtwrhx41SHVC0zk09gvdn2lqYtpwMNfrhVWFKpsUl195Ge3L2l1TXbrugdbs+ePXtq/DOJ3A2TNXmdgoIC9O/fH2lpaUhISMCYMWNUh2SXtc5j5oVcm45ctPq+O0USZ+N6VXo9RGNk7C8EHpq2y27XMq1WpSwuI1KDyZq8ys2bN9G3b19kZmZi5cqVGDlypOqQ7NJTee3oSNnayNj8+Ny8AkxIPI5j53/R3BFLa3OQmjZ//nwAwKxZsxRHQqQOC8zIa/znP/9Bz549kZWVhQ8++MAjEjWgr/Ja61mzn4DVtdCW+19be78EsOHwBWXrp/VKT09Henq66jCIlGKyJq9w/fp1dO/eHYcOHcKGDRswdOhQ1SHppqfyenCHplaPKZGo0Bhl+vYTFRL2J9OMOBvXCyUaI3AJcDkWkQdgsiaPd+3aNYSHh+Po0aNITExEdHS06pAcolVhbf56bGQbxDzTrHyE7KdR1K21FtpWFTeXYxG5PyZr8mg///wzjEYjvvzyS2zfvh0DBw5UHZLDJndviUBDxY1ErFVex0a2wfcLInAurhca1XMs+U7u3lKzHamK5VhE5BgWmJHH+te//oWwsDB899132LFjB3r06KE6pArsVXibVKXy2tZo2E8IJOfkVnh/ZLsQHDv/CzYcvlCpHWle/h20/es+XC8odMuq79/85jeqQyBSjsmaPNIPP/wAo9GI8+fPIzU1FaGhoapDqsDR3tqOVl7b2mWrWEqrn9X+gXuR+uUPyCsorHD8zTvFANy3B3hSUpLqEIiU4zQ4eZxLly6hc+fOuHjxIvbu3et2iRpwfW9ta1Pntj7LdPNgmaj1vJeI1OPImjzKuXPnYDQacfXqVezbtw8dO3ZUHZJVru6tbT51rmcfa2s3D7a4U9HZ9OnTAQALFixQHAmROkzW5DG+//57GI1G/Pvf/8aBAwfwhz/8QXVImmqit7Zp6rxTXIbVzwoOMqBTXAYuly3tcoQ7FZ19+umnqkMgUo7T4OQRTp06heeeew43b95ERkaGWydqQH+Ft6s+y+AvcONWUfkabEeo6gFORNo4sia3980338BoNEJKiczMTLRpY709pjupyd7a1j7r5u0iu8+nBUqbotQPMkBKuG01OBExWZOb++qrrxAWFgZ/f39kZmbiscceUx2Sbq7srW1tWZj5PtYPTdul+V4BMCkTeRhOg5Pb+uKLL9C1a1cEBAQgOzvboxK1K5kqu7XajALaz5zrBxnwTlRbAMCExOMV+om7qyZNmqBJkyaqwyBSSkid+9s6W/v27eWxY8eUfDa5vyNHjqB79+4IDg5GRkYGHn74YdUhuQ2tgrKQ4MDy0XVyTi4mb/sShcUV///2E6WbehSW/Pp6oMEfCwa04SibyMWEEJ9LKdtX5b0cWZPb+cc//oHw8HD85je/QXZ2NhO1BT3LwiLbhaBuQOWnXCUSFRI1wHXVRJ6AyZrcSlZWFnr06IFGjRrh4MGDeOCBB1SH5Ha0priDgwwVvr+uowGKiTutq7Y0fvx4jB8/XnUYREoxWZPbOHDgACIiIvDAAw8gKysLISGclrVmcveWMPhX3pbjxq0iXc+trXGnddWWjh8/juPHj6sOg0gpJmtyC7t370bv3r3xyCOPIDMzE40aNVIdktvSmuIuLJEVprPttSQ14bpqIvfHZE3K7dixA5GRkXj88ceRmZmJ++67T3VIbk9ritvyufWCAW0QUjZqtrZFZnCggcVlRB6AyZqU2rZtG55//nm0a9cO6enp3A5RJ61pa8vXI9uF4JNpRoQEB1rtZFa3di0maiIPwGRNymzcuBHR0dF4+umnsX//fgQHB6sOyWM42s7U1RuLuFKLFi3QokUL1WEQKcUOZqTEBx98gBEjRuC5555Damoq7rrrLtUheRRH25nWxMYirrJixQrVIRApx2RNNe7999/H6NGjERoaih07diAoKEh1SB7JXjtT85akwUEGGPwqN0NhYRmRZ+A0ONWopUuXYtSoUejRowdSUlKYqF3EsiXptfxCQJQWlAmUdjvzlMKyUaNGYdSoUarDIFKKI2uqMe+88w4mTpyIvn37YsuWLahdu7bqkLxWfNopFBQWV3itsFiibu1aOD6nm6Koqub06dOqQyBSjiNrqhFxcXGYOHEiBg4ciK1btzJRu5gnF5QRUWVM1uRSUkrMmzcP06dPx+DBg7F582YEBASoDsvr6V3aRUSegcmaXEZKiZkzZ2LOnDkYNmwY1q1bh1q1+OSlJji6tIuI3Bt/c5JLSCkxZcoU/P3vf8dLL72EZcuWwc+P94Y1xdGlXe6sbdu2qkMgUo77WZPTSSkxfvx4vPvuuxg7dizeffddJmoi8nnV2c+aI2tyqpKSErzyyitYvnw5JkyYgLfeegtCWOtKTUREenG4Q05TXFyMP/3pT1i+fDmmTZvGRE1OERMTg5iYGNVhECnFkTU5RVFREYYPH44NGzZgzpw5mDNnDhM1OcWlS5dUh0CkHJM1VVthYSFiYmKwZcsWxMbGYsaMGapDIiLyKkzWVC137txBdHQ0PvroI8THx2PSpEmqQyIi8jpM1lRlt27dwvPPP49du3Zh8eLFeP3111WHRETklZisqUry8/PRv39/7Nu3D8uWLcPo0aNVh0Re6tlnn1UdApFyTNbksJs3b6JPnz7IysrC6tWrMWLECNUhkRdbsGCB6hCIlGOyJof85z//QUREBA4dOoQPP/yQS2qIiGoAkzXplpeXh549e+Lo0aPYuHEjoqKiVIdEPmDgwIEAgKSkJMWREKnDZE26/PLLL+jWrRu++uorbN26Ff3791cdEvmIq1evqg6BSDkma7LrypUrCA8Px7fffovt27ejd+/eqkMiIvIpTNZk07/+9S+Ehobi+++/R0pKCrp166Y6JCIin8NkTZouX76M0NBQXLhwAbt27YLRaFQdEhGRT2KyJqsuXrwIo9GIH3/8EXv37sV///d/qw6JfFRoaKjqEIiUY7KmSs6ePQuj0YhffvkF+/btY1MKUmrWrFmqQyBSjsmaKvjuu+9gNBpx48YNpKeno337Ku2TTkRETsRkTeX++c9/wmg04s6dO8jIyEDbtm1Vh0SEnj17AgD27NmjOBIidZisCQBw8uRJhIWFQUqJrKwsPPHEE6pDIiuSc3IRn3YKl/MK0Dg4EJO7t0RkuxDVYblUQUGB6hCIlGOyJnz55ZcICwuDwWBARkYGWrVqpToksiI5JxfTt59AQWExACA3rwDTt58AAK9P2ES+zk91AKTWsWPH0LVrV9SpUwfZ2dlM1G4sPu1UeaI2KSgsRnzaKUUREVFNYbL2YYcPH0ZoaCjq1auHgwcP4tFHH1UdEtlwOc/6dLDW60TkPTgN7qP+8Y9/oGfPnrj//vuRkZGBZs2aqQ6J7GgcHIhcK4m5cXCggmhqDtvbEjFZ+6TMzEz07t0bTZs2RXp6OkJC+LzTE0zu3rLCM2sACDT4Y3L3lgqjcr1JkyapDoFIOSZrH7Nv3z7069cPDz/8MNLT0/Hb3/5WdUikk6mIzNeqwYmIydqn7Nq1CwMGDMBjjz2G/fv3o2HDhqpDIgdFtgvxueTcpUsXAEBWVpbSOIhUYoGZj0hOTkb//v3Rpk0bZGRkMFETEXkQXclaCNFDCHFKCPGdEGKalZ9PFEJ8I4T4SgiRLoR4wPmhUlVt3boVgwYNwlNPPYUDBw7g3nvvVR0SERE5wG6yFkL4A1gKoCeA1gAGCyFaWxyWA6C9lPJJANsALHR2oFQ1GzZsQHR0NJ555hns27cPwcHBqkMiIiIH6RlZPw3gOynlGSnlHQCbAfQzP0BKmSmlzC/79jCAJs4Nk6pizZo1GDp0KDp37ow9e/bgnnvuUR0SERFVgZ4CsxAAF82+vwSgg43jRwJgx33FVqxYgdGjRyM8PBzJyckICgpSHRJRlfzxj39UHQKRcnqStbDymrR6oBAxANoD6Kzx81EARgFgEw4XWrJkCV577TVEREQgKSkJderUUR0SUZW98sorqkMgUk7PNPglAE3Nvm8C4LLlQUKIMAAzAPSVUt62diIp5QopZXspZXtWI7vGW2+9hddeew39+vXD9u3bmajJ4+Xn5yM/P9/+gUReTE+yPgrgUSHEQ0KIAADRAHaaHyCEaAdgOUoT9U/OD5P0eOONNzBp0iQMGjQIW7duRe3atVWHRFRtERERiIiIUB0GkVJ2k7WUsgjAqwDSAHwLYIuU8mshxDwhRN+yw+IB3AVgqxDiuBBip8bpyAWklJg7dy5mzJiBIUOGYOPGjTAYDKrDIiIiJ9HVwUxKuRvAbovXZpt9HebkuEgnKSVmzJiBBQsWYPjw4Vi5ciX8/f1Vh0VERE7EdqMeTEqJSZMm4e2338aoUaPw3nvvwc+PTemIiLwNf7N7qJKSErz++ut4++238eqrr2LZsmVM1EREXoojaw9UUlKCl19+Ge+//z7+/Oc/Iz4+HkJYW2FH5PmGDx+uOgQi5ZisPUxxcTFGjhyJDz74AH/5y18QGxvLRE1ejcmaiMnaoxQVFWHYsGHYuHEj5s6di9mzZzNRk9f7+eefAQANGjRQHAmROkzWHqKwsBBDhgzB1q1b8cYbb2D69OmqQ6IalpyTi/i0U7icV4DGwYGY3L2lT+xt/fzzzwPgftbk25isPcDt27cRFRWFHTt24K233sLEiRNVh0Q1LDknF9O3n0BBYTEAIDevANO3nwAAn0jYRL6O5cNu7tatWxgwYAB27NiB//3f/2Wi9lHxaafKE7VJQWEx4tNOKYqIiGoSR9ZuLD8/H5GRkdi/fz+WL1+OUaNGqQ6JFLmcV+DQ60TkXTiydlM3btxAr169cODAAaxevZqJ2sc1Dg506HUi8i4cWbuhf//734iIiMCnn36KdevWYciQIapDIsUmd29Z4Zk1AAQa/DG5e0uFUdWMMWPGqA6BSDkmazeTl5eHHj164PPPP8fmzZsxaNAg1SGRGzAVkfliNXhUVJTqEIiUY7J2I1evXkW3bt1w4sQJbNu2Df369VMdErmRyHYhPpGcLV28eBEA0LRpU8WREKnDZO0mrly5grCwMJw6dQrJycncv5eozNChQwFwnTX5NiZrN/Djjz8iNDQUZ86cwc6dO9GtWzfVIRERkRthslYsNzcXRqMRly5dwu7du9G1a1fVIRERkZthslbowoULMBqN+Omnn5CWlob/+q//Uh0SERG5ISZrRc6ePYuuXbsiLy8P+/fvR4cOHVSHREREborJWoH/+7//g9FoxM2bN5Geno7f//73qkMiclt//vOfVYdApByTdQ379ttvERoaisLCQmRmZuJ3v/ud6pCI3FqfPn1Uh0CkHJN1DTp58iRCQ0MhhEBWVhYef/xx1SERub1Tp0o3K2nZ0vu7tRFpYbKuITk5OQgPD0ft2rWRkZHBXzxEOo0ePRoA11mTb+NGHjXg6NGjMBqNCAoKQnZ2NhM1ERE5hMnaxT799FOEhYUhODgYBw8exCOPPKI6JCIi8jBM1i508OBBdOvWDffddx8OHjyIBx98UHVIRETkgZisXSQ9PR09e/ZEkyZNkJ2dzU0IiIioylhg5gJpaWmIjIzEI488ggMHDuD+++9XHRKRx5o5c6bqEIiUY7J2stTUVAwcOBCtW7fG/v370aBBA9UhEXm0sLAw1SEQKcdpcCf66KOPMGDAADz55JNIT09noiZyguPHj+P48eOqwyBSiiNrJ0lMTMSQIUPwhz/8AXv37kW9evVUh0TkFcaPHw+A66zJt3Fk7QTr1q3DCy+8gI4dO2Lfvn1M1ERE5FRM1tW0evVqDBs2DJ07d8aePXtw9913qw6JiIi8DJN1NSxbtgwjR45EeHg4UlNTUbduXdUhERGRF2KyrqJ3330XY8aMQa9evbBjxw4EBQWpDomIiLwUC8yqID4+HlOmTEH//v2xefNmBAQEqA6JyGu98cYbqkMgUo7J2kGxsbGYNWsWoqKisG7dOhgMBtUhEXm1jh07qg6BSDlOg+skpcTs2bMxa9YsxMTEYP369UzURDXg0KFDOHTokOowiJTiyFoHKSWmT5+ON998EyNGjMD7778Pf39/1WER+YS//OUvALjOmnwbk7UdUkpMnDgRixYtwssvv4ylS5fCz48TEkREVHOYdWwoKSnBq6++ikWLFuH1119HQkICEzUREdU4Zh4NJSUlGD16NBISEjBp0iQsWrQIQgjVYRERkQ9israiuLgY//M//4OVK1dixowZWLhwIRM1EREpw2fWFoqKivDiiy9i06ZNmDdvHmbNmqU6JCKftmjRItUhECnHZG3mzp07eOGFF5CUlIS4uDhMnTpVdUhEPq9t27aqQyBSjsm6zO3btzFo0CCkpKTg7bffxoQJE1SHREQADhw4AAAICwtTHAmROkzWAAoKCjBgwADs3bsXS5YswdixY1WHRERlYmNjATBZk2/z+WSdn5+Pfv36IT09HStWrMBLL72kOiQiIqIKfDpZ37hxA71798bHH3+MNWvWYNiwYapDIiIiqsRnk/X169cRERGBI0eOYP369Rg8eLDqkIiIiKzyyWR97do19OjRA1988QUSExMxcOBA1SERERFp8rlkffXqVYSHh+PkyZNISkpC3759VYdERDYsX75cdQhEyvlUsv7pp58QFhaG06dPY8eOHejZs6fqkIjIjpYtW6oOgUg5n0nWP/zwA0JDQ3Hu3DmkpqZyGQiRh0hJSQEA9OnTR3EkROr4RLK+dOkSjEYjLl++jD179qBz586qQyIind566y0ATNbk27w+WZ8/fx5GoxFXrlxBWloaOnXqpDokIiIih3h1sj5z5gy6du2K69evY//+/ejQoYPqkIiIiBzmtcn69OnTMBqNKCgoQEZGBp566inVIREREVWJVybrb775BqGhoSguLkZmZiaefPJJ1SERERFVmdcl6xMnTiA0NBT+/v7IyspC69atVYdERNWwbt061SEQKedVyTonJwfh4eGoU6cOMjIy0KJFC9UhEVE1NW3aVHUIRMp5TbL+7LPP0L17d9xzzz3IyMhA8+bNVYdE5DTJObmITzuFy3kFaBwciMndWyKyXYjqsGpEYmIiACAqKkpxJETqeEWyPnToEHr06IEGDRogMzMTDzzwgOqQiJwmOScX07efQEFhMQAgN68A07efAACfSNjvvfceACZr8m1+qgOoruzsbHTr1g2//e1vcfDgQSZq8jrxaafKE7VJQWEx4tNOKYqIiGqaRyfr9PR09OzZE02bNkV2djaaNGmiOiQip7ucV+DQ60TkfTw2We/duxe9e/dG8+bNkZWVhUaNGqkOicglGgcHOvQ6EXkfj0zWKSkp6NevH1q1aoXMzEzcf//9qkMicpnJ3Vsi0OBf4bVAgz8md+duVES+wuMKzJKSkhAdHY127dohLS0N9evXVx0SkUuZish8tRp827ZtqkMgUs6jkvWmTZswdOhQdOjQAbt370a9evVUh0RUIyLbhfhMcrbUoEED1SEQKecx0+AffvghYmJi0KlTJ+zdu5eJmshHrF27FmvXrlUdBpFSHpGsV61aheHDh6NLly7YvXs37r77btUhEVENYbIm8oBknZCQgD/96U/o3r07UlNTUbduXdUhERER1Si3TtaLFi3C2LFj0adPHyQnJyMwkEtViIjI97htsl64cCEmTJiAAQMGYNu2bahdu7bqkIiIiJRwy2Q9f/58TJ06FdHR0di8eTMCAgJUh0RERKSMWy3dklJi9uzZiI2NxdChQ7FmzRr4+/vbfyMRea3du3erDoFIObdJ1lJKTJ06FfHx8Rg5ciSWL1/ORE1ECAoKUh0CkXJukayllJgwYQIWL16MMWPGYMmSJfDzc8sZeiKqYQkJCQCAV155RXEkROooz4glJSUYO3YsFi9ejHHjxmHp0qVM1ERUbsuWLdiyZYvqMIiU0pUVhRA9hBCnhBDfCSGmWfl5bSFEYtnPjwghHtRz3uLiYowaNQrvvfcepkyZgnfeeQdCCMf+BkRERF7ObrIWQvgDWAqgJ4DWAAYLIVpbHDYSwDUp5SMA3gHwpr3zSikxYsQIrFq1CrNmzUJcXBwTNRERkRV6nlk/DeA7KeUZABBCbAbQD8A3Zsf0AzC37OttAJYIIYSUUmqd9OzZs/jiiy8wf/58zJw5s0rBExER+QI90+AhAC6afX+p7DWrx0gpiwBcB/AbWye9du0a3nzzTSZqIiIiO/SMrK3NTVuOmPUcAyHEKACjyr69PXXq1JNTp07VEQJVUQMAP6sOwgfwOrteAyEEr7Fr8d+x67Ws6hv1JOtLAJqafd8EwGWNYy4JIWoBqAfgF8sTSSlXAFgBAEKIY1LK9lUJmvThNa4ZvM6ux2vserzGrieEOFbV9+qZBj8K4FEhxENCiAAA0QB2WhyzE8Cwsq+fB5Bh63k1ERER6Wd3ZC2lLBJCvAogDYA/gNVSyq+FEPMAHJNS7gSwCsA6IcR3KB1RR7syaCIiIl+iq4OZlHI3gN0Wr802+/oWgEEOfvYKB48nx/Ea1wxeZ9fjNXY9XmPXq/I1FpytJiIicm/s60lEROTmXJ6sXdWqlH6l4xpPFEJ8I4T4SgiRLoR4QEWcnszeNTY77nkhhBRCsKq2CvRcZyHEH8v+PX8thNhY0zF6Oh2/L5oJITKFEDllvzMiVMTpyYQQq4UQPwkhTmr8XAgh3i37bxVSsEUAAAMASURBVPCVEOIpuyeVUrrsD0oL0r4H8DCAAABfAmhtccwrAJaVfR0NINGVMXnbH53XuCuAoLKvx/AaO/8alx13N4CDAA4DaK86bk/7o/Pf8qMAcgDUL/v+PtVxe9Ifndd4BYAxZV+3BnBOddye9gfAcwCeAnBS4+cRAPagtEfJMwCO2Dunq0fW5a1KpZR3AJhalZrrB+CDsq+3AQgVbBLuCLvXWEqZKaXML/v2MErXypN+ev4dA8B8AAsB3KrJ4LyInuv8EoClUsprACCl/KmGY/R0eq6xBHBP2df1ULmvBtkhpTwIK71GzPQD8KEsdRhAsBCika1zujpZu6RVKVWg5xqbG4nSOzrSz+41FkK0A9BUSplak4F5GT3/llsAaCGE+EQIcVgI0aPGovMOeq7xXAAxQohLKF0F9FrNhOZTHP29rW/pVjU4rVUpadJ9/YQQMQDaA+js0oi8j81rLITwQ+luc8NrKiAvpeffci2UToV3QekM0cdCiCeklHkujs1b6LnGgwGslVK+JYR4FqU9NJ6QUpa4Pjyf4XDec/XI2pFWpbDVqpQ06bnGEEKEAZgBoK+U8nYNxeYt7F3juwE8ASBLCHEOpc+gdrLIzGF6f1/skFIWSinPAjiF0uRN+ui5xiMBbAEAKeWnAOqgtG84OY+u39vmXJ2s2arU9exe47Ip2uUoTdR8xuc4m9dYSnldStlASvmglPJBlNYF9JVSVrkPsI/S8/siGaUFkxBCNEDptPiZGo3Ss+m5xhcAhAKAEOIxlCbrKzUapffbCeDFsqrwZwBcl1L+YOsNLp0Gl2xV6nI6r3E8gLsAbC2r3bsgpeyrLGgPo/MaUzXpvM5pALoJIb4BUAxgspTyqrqoPYvOa/xnAO8LISagdGp2OAdQjhFCbELpo5oGZc/+5wAwAICUchlKawEiAHwHIB/ACLvn5H8DIiIi98YOZkRERG6OyZqIiMjNMVkTERG5OSZrIiIiN8dkTURE5OaYrImIiNwckzUREZGbY7ImIiJyc/8PDxBaNe56iC8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = run_egad(dataframe, exp_file)\n",
    "#df_0 = run_egad(go, nw_0)\n",
    "print(df)\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8), sharey=True)\n",
    "#ax = df.plot.scatter(x='AUC',y='DEGREE_NULL_AUC')\n",
    "ax.scatter(x=df['AUC'].values,y=df['DEGREE_NULL_AUC'].values)\n",
    "#ax = df.plot('AUC', 'DEGREE_NULL_AUC', kind='scatter', ax=ax)\n",
    "#df['go_an'] = df.index\n",
    "#df[['AUC','DEGREE_NULL_AUC','go_an']].apply(lambda row: ax.text(*row),axis=1)\n",
    "#print df.mean()\n",
    "ax.set_xlim([0,1])\n",
    "ax.set_ylim([0,1])\n",
    "ax.plot([0, 1], [0, 1], transform=ax.transAxes, c='black')\n",
    "plt.axvline(x=df['AUC'].mean(),c='black',ls='--')\n",
    "plt.axhline(y=df['DEGREE_NULL_AUC'].mean(), c='black', ls='--')\n",
    "print(df['AUC'].mean())\n",
    "#print(df_0['AUC'].mean())\n",
    "print (df['AUC'].idxmax())\n",
    "#plt.show()\n",
    "plt.savefig('%s.pdf' %0, bbox_inches='tight', dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/X_dist_with_georg_hic_sub_median_hic_500.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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>tss_tss</th>\n",
       "      <th>exp</th>\n",
       "      <th>exp_georg</th>\n",
       "      <th>hi-c-rao</th>\n",
       "      <th>strand_x</th>\n",
       "      <th>gene_order_tss_x</th>\n",
       "      <th>gene_order_tes_x</th>\n",
       "      <th>Gene stable ID_x</th>\n",
       "      <th>Gene type_x</th>\n",
       "      <th>Uniprot_dc_x</th>\n",
       "      <th>...</th>\n",
       "      <th>chrom_x</th>\n",
       "      <th>strand_y</th>\n",
       "      <th>gene_order_tss_y</th>\n",
       "      <th>gene_order_tes_y</th>\n",
       "      <th>Gene stable ID_y</th>\n",
       "      <th>Gene type_y</th>\n",
       "      <th>Uniprot_dc_y</th>\n",
       "      <th>dc_y</th>\n",
       "      <th>seq_length_y</th>\n",
       "      <th>chrom_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>+</td>\n",
       "      <td>1013</td>\n",
       "      <td>1015</td>\n",
       "      <td>ENSG00000089289</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P78318</td>\n",
       "      <td>...</td>\n",
       "      <td>chrX</td>\n",
       "      <td>+</td>\n",
       "      <td>1013</td>\n",
       "      <td>1015</td>\n",
       "      <td>ENSG00000089289</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P78318</td>\n",
       "      <td>0.218289</td>\n",
       "      <td>339.0</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48500772</td>\n",
       "      <td>0.194329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>1693</td>\n",
       "      <td>1692</td>\n",
       "      <td>ENSG00000230399</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>chrX</td>\n",
       "      <td>+</td>\n",
       "      <td>1013</td>\n",
       "      <td>1015</td>\n",
       "      <td>ENSG00000089289</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P78318</td>\n",
       "      <td>0.218289</td>\n",
       "      <td>339.0</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14644244</td>\n",
       "      <td>0.912021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>-</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>ENSG00000247746</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q70EK9</td>\n",
       "      <td>...</td>\n",
       "      <td>chrX</td>\n",
       "      <td>+</td>\n",
       "      <td>1013</td>\n",
       "      <td>1015</td>\n",
       "      <td>ENSG00000089289</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P78318</td>\n",
       "      <td>0.218289</td>\n",
       "      <td>339.0</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22650500</td>\n",
       "      <td>0.964389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>-</td>\n",
       "      <td>598</td>\n",
       "      <td>597</td>\n",
       "      <td>ENSG00000147124</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P51814</td>\n",
       "      <td>...</td>\n",
       "      <td>chrX</td>\n",
       "      <td>+</td>\n",
       "      <td>1013</td>\n",
       "      <td>1015</td>\n",
       "      <td>ENSG00000089289</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P78318</td>\n",
       "      <td>0.218289</td>\n",
       "      <td>339.0</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13404206</td>\n",
       "      <td>0.989904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2345.0</td>\n",
       "      <td>+</td>\n",
       "      <td>905</td>\n",
       "      <td>907</td>\n",
       "      <td>ENSG00000204272</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>A0A0U1RRE5</td>\n",
       "      <td>...</td>\n",
       "      <td>chrX</td>\n",
       "      <td>+</td>\n",
       "      <td>1013</td>\n",
       "      <td>1015</td>\n",
       "      <td>ENSG00000089289</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P78318</td>\n",
       "      <td>0.218289</td>\n",
       "      <td>339.0</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597524</th>\n",
       "      <td>30681475</td>\n",
       "      <td>0.903413</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>+</td>\n",
       "      <td>2358</td>\n",
       "      <td>2359</td>\n",
       "      <td>ENSG00000269335</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q9Y6K9</td>\n",
       "      <td>...</td>\n",
       "      <td>chrX</td>\n",
       "      <td>+</td>\n",
       "      <td>1809</td>\n",
       "      <td>1812</td>\n",
       "      <td>ENSG00000101966</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P98170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>497.0</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597525</th>\n",
       "      <td>5020185</td>\n",
       "      <td>0.513123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4116.0</td>\n",
       "      <td>+</td>\n",
       "      <td>1698</td>\n",
       "      <td>1699</td>\n",
       "      <td>ENSG00000203650</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>chrX</td>\n",
       "      <td>+</td>\n",
       "      <td>1809</td>\n",
       "      <td>1812</td>\n",
       "      <td>ENSG00000101966</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P98170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>497.0</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597526</th>\n",
       "      <td>103592204</td>\n",
       "      <td>0.985573</td>\n",
       "      <td>0.701583</td>\n",
       "      <td>270.0</td>\n",
       "      <td>-</td>\n",
       "      <td>254</td>\n",
       "      <td>254</td>\n",
       "      <td>ENSG00000177189</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P51812</td>\n",
       "      <td>...</td>\n",
       "      <td>chrX</td>\n",
       "      <td>+</td>\n",
       "      <td>1809</td>\n",
       "      <td>1812</td>\n",
       "      <td>ENSG00000101966</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P98170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>497.0</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597527</th>\n",
       "      <td>8359757</td>\n",
       "      <td>0.973737</td>\n",
       "      <td>0.705286</td>\n",
       "      <td>3663.0</td>\n",
       "      <td>-</td>\n",
       "      <td>1913</td>\n",
       "      <td>1912</td>\n",
       "      <td>ENSG00000123728</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>Q9Y3L5</td>\n",
       "      <td>...</td>\n",
       "      <td>chrX</td>\n",
       "      <td>+</td>\n",
       "      <td>1809</td>\n",
       "      <td>1812</td>\n",
       "      <td>ENSG00000101966</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P98170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>497.0</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597528</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.851248</td>\n",
       "      <td>0.0</td>\n",
       "      <td>+</td>\n",
       "      <td>1809</td>\n",
       "      <td>1812</td>\n",
       "      <td>ENSG00000101966</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P98170</td>\n",
       "      <td>...</td>\n",
       "      <td>chrX</td>\n",
       "      <td>+</td>\n",
       "      <td>1809</td>\n",
       "      <td>1812</td>\n",
       "      <td>ENSG00000101966</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>P98170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>497.0</td>\n",
       "      <td>chrX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>597529 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          tss_tss       exp  exp_georg  hi-c-rao strand_x  gene_order_tss_x  \\\n",
       "0               0  1.000000   0.851248       0.0        +              1013   \n",
       "1        48500772  0.194329        NaN       NaN        -              1693   \n",
       "2        14644244  0.912021        NaN    2119.0        -               891   \n",
       "3        22650500  0.964389        NaN    1966.0        -               598   \n",
       "4        13404206  0.989904        NaN    2345.0        +               905   \n",
       "...           ...       ...        ...       ...      ...               ...   \n",
       "597524   30681475  0.903413        NaN       NaN        +              2358   \n",
       "597525    5020185  0.513123        NaN    4116.0        +              1698   \n",
       "597526  103592204  0.985573   0.701583     270.0        -               254   \n",
       "597527    8359757  0.973737   0.705286    3663.0        -              1913   \n",
       "597528          0  1.000000   0.851248       0.0        +              1809   \n",
       "\n",
       "        gene_order_tes_x Gene stable ID_x     Gene type_x Uniprot_dc_x  ...  \\\n",
       "0                   1015  ENSG00000089289  protein_coding       P78318  ...   \n",
       "1                   1692  ENSG00000230399             NaN          NaN  ...   \n",
       "2                    891  ENSG00000247746  protein_coding       Q70EK9  ...   \n",
       "3                    597  ENSG00000147124  protein_coding       P51814  ...   \n",
       "4                    907  ENSG00000204272  protein_coding   A0A0U1RRE5  ...   \n",
       "...                  ...              ...             ...          ...  ...   \n",
       "597524              2359  ENSG00000269335  protein_coding       Q9Y6K9  ...   \n",
       "597525              1699  ENSG00000203650             NaN          NaN  ...   \n",
       "597526               254  ENSG00000177189  protein_coding       P51812  ...   \n",
       "597527              1912  ENSG00000123728  protein_coding       Q9Y3L5  ...   \n",
       "597528              1812  ENSG00000101966  protein_coding       P98170  ...   \n",
       "\n",
       "        chrom_x  strand_y gene_order_tss_y gene_order_tes_y  Gene stable ID_y  \\\n",
       "0          chrX         +             1013             1015   ENSG00000089289   \n",
       "1          chrX         +             1013             1015   ENSG00000089289   \n",
       "2          chrX         +             1013             1015   ENSG00000089289   \n",
       "3          chrX         +             1013             1015   ENSG00000089289   \n",
       "4          chrX         +             1013             1015   ENSG00000089289   \n",
       "...         ...       ...              ...              ...               ...   \n",
       "597524     chrX         +             1809             1812   ENSG00000101966   \n",
       "597525     chrX         +             1809             1812   ENSG00000101966   \n",
       "597526     chrX         +             1809             1812   ENSG00000101966   \n",
       "597527     chrX         +             1809             1812   ENSG00000101966   \n",
       "597528     chrX         +             1809             1812   ENSG00000101966   \n",
       "\n",
       "           Gene type_y Uniprot_dc_y      dc_y seq_length_y  chrom_y  \n",
       "0       protein_coding       P78318  0.218289        339.0     chrX  \n",
       "1       protein_coding       P78318  0.218289        339.0     chrX  \n",
       "2       protein_coding       P78318  0.218289        339.0     chrX  \n",
       "3       protein_coding       P78318  0.218289        339.0     chrX  \n",
       "4       protein_coding       P78318  0.218289        339.0     chrX  \n",
       "...                ...          ...       ...          ...      ...  \n",
       "597524  protein_coding       P98170  0.000000        497.0     chrX  \n",
       "597525  protein_coding       P98170  0.000000        497.0     chrX  \n",
       "597526  protein_coding       P98170  0.000000        497.0     chrX  \n",
       "597527  protein_coding       P98170  0.000000        497.0     chrX  \n",
       "597528  protein_coding       P98170  0.000000        497.0     chrX  \n",
       "\n",
       "[597529 rows x 22 columns]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2_or"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = df_2_or[df_2_or['exp_georg'] >= 0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "gene_order_tss_y\n",
      "35      35735.0\n",
      "36      35735.0\n",
      "39      35735.0\n",
      "40      35735.0\n",
      "42      10093.0\n",
      "         ...   \n",
      "2393    16489.0\n",
      "2395    16489.0\n",
      "2399    39399.0\n",
      "2408    39399.0\n",
      "2410    39399.0\n",
      "Length: 597, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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nf+VXce/3cHbX5gHmQTYMwzAMYzk9yLEnGETgNUZw4zzIsQDV6LDui1OXTU4GcaziUH20cQRYj8UiPE5R0JhjWI/r2FWkx/v1HPXwlkqhbSYjgvjUKRG8K1aEfjUNW0eHRIE1ml6tSoLhOEc0BGE9NiaKsr9fttetk3f1SoMcr4drfa4jEYgGyLUBjz0GV1whO8bHJbrd1weZDCs4MZPXbXgY7rpLbnvfPmmuWfG2boVLL5VhfvGLs6cu5t575z+u+BtfLRcrdbZ2whJhAtkwDMMwLm6Wz20ZC9z5xGjcXvfHdgtIitzY6hD3o1Hp+JzGnF0LWSwaxxJfN14YGNPYZmpKwpTt7cmCHooWBKlUpF25LO8qxiF4EjKZUDgknQ7XilfARePRwHlMTw8yJlW6k5PSqFBI/jAZHeXEiUup1WSIuj5Rp0k1vaZpXkrcR/5i6TudBxPIhmEYhnFxszwRZC2k0VgYpJHG1VLNhHS8vzGyrJ/nS2DbaI2Yb4VWMxtFM9QGMjkpEdxaLWkJKZWS6Rr0s3OiMOPoulof1OyrmStiIT49nSwmot5okLZ1r3J68iT5fDJrh6tMJ+9DBXJbm5x76tTMPOZycjvOBTdHXDOlvz8ZGD8T/Gt+Fj5z61lN6dYMW6RnGIZhGMbyCOR4cR4EX3BjiebGhW+xlSEWwrFYjG0U2ofuK5eTQlXTx8U0nh8T79MqcxCEeYz+CND70bGfOlW3DZRCfWaN3nZ1SXhWvcdxSjwllZLzY2+xzl28iDEe58mTM5/bGyvplSsyDrVl1Gohwq3eaT0fsVLkcrMz2DW6QM6YSgVe85olqY63GMyDbBiGYRjG8izSixfMqS2hUSDHHl89PlfUVoV0Y27k+Pl/nANZxWVjruO472ZqLx4PhOiuCu24HHY2G146pmxWfMOal1hFqeZe1uixzouek0qFjBU9PRKuzeXCPNZqcORISA0R+yjUDw2SHmJ0dPZ9TU8no87t7TLOOMd0RwdXXBEsFHoptVrkcvDgg3Lpa66RfTodR46IBl+zJlTFVspl6WPPnuQ6Q2q1cy6OFRPIhmEYhnFxszwPk9UMG0eAi8Ww2K4xbZoKxdg6kc8H4axCNZWCVatgeDike9NsE5lMMvQZFxdRYp9vo5dZ+9f32Aedy80+lsnIWLq6JBI8OSn50Q4eDFFsTdmg5+bzsHq1HBsbC2nr1q6F739f2gwPw3XXSduBAVlMd9ddojJVXepiPZAxaDqJEydmV+gYGJDUFJqOoq9PvBJf/KIc09R4117Lq14Fr32t7HbjJwD4zl7xVHzhC5Kt4lnPgl/9VelKL/ue90hmi0OH4Kqr5HKq04eH5X3vXrjzznAL7nP/j1WrZIqLRdi2Dd79brj88jB0XauofPCDye0775T3np6QUa8VTCAbhmEYxsXN8glkCJFcFYxxGrVYpWQyyWp6GinW6KmKXI225nIh9Zr6gVMpEZulUrA4xNeE5CK7eH88Dn1vHG8ccdbIcWzX0Cp6l1wiloXIGzzjQdaUbWNj4djKlXI8rjJYLMr26Chs2iSCeMeOULJOs1roWFRVZjKzI8iDg1RXrSGtP07WrZO+i0VRsuvXw+HDHM2s5WMfk6D3li3Q3y/C+J//WU47ckTyGB88KEIZQlD6wAG4/Xb5fMsts6d1LjQ6DRKd/ta3SFTqa1xwuHlzcltve+NGeOMbW7umWSwMwzAMw1g+D7JGdFX4aslkCIJYUfEWR51jH3MsHrXfxiwMKpxjy0Nsw9A286mjOHdznE4uFu86Ns2BrOJZy0v39UlUeXIyKEDNW7xiRSglrX0PD0v/cf7oYlFsEOWylIlev16E7apV0ib2MFQqyXnVKh51nhrvIDUKq7duBeBkqY1CAdyWLZzouYQjT8LQ0GbyNUnd/M//LHr9+uvlfLVceC/5jQ8ehHvumXsKF8K/8RdxH/3IrP2Dg1IxXG8RQi5mJb5tCE6WxmriC2EC2TAMwzAubpYvgqxRVY0i9/TMFpwqkjViq4/7IYQPU6lQKU4jn1pEo/Ga2kbFcry/2RgbabRgxJaQeIGcCm/1Put1x8dFzHZ2ilDdtEmOaT+dneIbOHIkzMX+/XKe5kpOpcQqkcmEfGtveAPHsmtRU0h3NMzO1AmqGUknd999MDbWkbilTAauvRbuvkci1kND0FXwPM4llI7I1GrQfefOUK/kc59LTs2P/qh4j7/61ebTOR/+tT838324j/4lv/7r8N73JtuMjcHnPw/f+EbY95KXJNts25bcfuEL5b1ROM+HZbEwDMMwDMOkgGE0YBFkwzAMw7i4OfcCObYixNFVtUfMjCyTPB6fF5edjqvhqW2imbc5zpKh/TemZ5vrczPU06xtNTLdmJlDF+LpwsNSSZ75axYKPaaMjEgbDXvGWSh0Wyv06UK+Wi1hL17bXw3thydJ1/vq7nYcPZq8jf5+aC+doKdHPMWXDHkYHubkyTV0dEhwO5uFtskTjIzMneS4sxMuu2z+KWuGf/0boErCUNxonXjGMySofvx4shR1o5Wjs6Hg3rXXyntsyV4I8yAbhmEYhmERZMNowASyYRiGYVzcLE8eZI0Gq7lVo6LxIri5Is2NeZPjdGsaEdYqdvE5uj+uthcv4oPWI8h6fmMquvhd76PxWDqdTE0HoeR0R0eIcuvqN13QqNfKZkPlPM108eSTdG+8NHSp2SxA/Mt1j3OhsIKVK5O3smmTtNesFB6HQ6zStRp05qoz+ZqbJfZQGi3f8yFR43qUuwrulk8ljus/A6WzU3IoHz8e1ipCshghzM5gp/8sGqt/z8dSRZCdcxuAW4ABoAbc7L3/oHPuncAvABrL/23v/Zfq5/wW8PNITP0t3vuv1Pe/FPggkAb+ynv/R2c+QsMwDMMw5uKcCGTn3E3ATQBDFp4zznOW6J9oBfg17/0Dzrku4H7n3Nfqx97vvU8sQ3TOXQncCDwTWAfc7pzbUj/858CPAQeBbzvnbvPeP7wkozQMwzAMYxbnRCB7728GbgbYkcn4BZobxrKxVFksvPeHgcP1zyedc48A6+c55RXArd77KWCfc24PcHX92B7v/V4Zn7u13tYEsmEYhmGcJZbHg6y2hzh/seYn1u3G/MTx50wmmeat0coQ50OG2SWm40WBsZUiXvDX7Ll8Y9v4unFVvMZ28blaNlor/UFI1Bt7BNrbJSeyWjJi30E2K/vTaVF0l1ySXMvX1R7aRnNZKs0urlEswop0eqaCXX8/UC4zWi9sOF1L09bZCaUSJ0/OnhJlfHzhdY1QT+lWZcYf4f76E7PaaEY7JZ7eeIoaLRVTU8ntuK7LYmgxgrzKOXdftH1z/YfgLJxzG4FnA/8B/DDwZufca4H7kCjzcUQ8x8sODxIE9RMN+5/X0ggNwzAMwzgtbJGeYUQswoM84r3fsXB/rgD8PfBW7/24c+4jwB8Avv7+p8AbANfkdA80G409hTEMwzCMs8i8UsA5l3bO/cm5GoxhnA/EDzjmerWCc64NEcd/473/BwDv/VPe+6r3vgZ8jGCjOAhsiE4fBA7Ns98wDMMwjLPEvBFk733VOfcc55zzvvGB9hmgloc4R/HkZDKvsOYzhlBxr1hMZp9Qi4P6A1Ip8QVoHuHJSWk3Ph6q52m2jGJREgfP5QtopoL0evqupaL1HhS1jGhpa+2vu1tSMkxMSEoGPV89BUND8q7jm5gQ+0WtJtXzQM7P5eQ+czm4/HLYuxcGNnBJ/6n6tdrCWNat4xQdM8NsqDTNwYNQ7h+cGf7+/QAb2LRJKuc9/DBcdZVjbKyD1atDJT0t+azWh6kpaTsf/o2/CLk+3AfeP2+7D31IEnrUk2dw773yT+Paa5P2i8Z8yTfckNyuV8+ms3Ji/oFFLGEWCwd8HHjEe/++aP/auj8Z4L8CD9Y/3wb8rXPufcgivc3AvUhkebNz7lLgSWQh38/Mcc15s10454aATwE99Ta/qRk0DMM499jfrGGcv7RisfgO8E/Ouc8DE7pTI2KLxvsgkOPUa41lnCFZVlpNtqpeNNVbY0GRUimUpoZkEQ/tNy4m0liEQ5XifIZaTTWnuc0ai5DE+zT3WjYrQr1UkhRncblrTdemfeictLVJOrexsaAMC4Vgti2VpILGtm3UajCVEiHcXo7MwiMjdNSF9+RkOpEBDmQIPT0ilEEuu2qVaO5SSY7pb4zduxPdJti3DzZvnnvK/OvfACWP++hH5m5U59JL4ctfTu4rl+Gb30zu+7d/S25v3Jjcvuqq+odWzNERS1Rq+oeBnwW+65zbWd/328CrnXPbEZvEfuB/AXjvH3LOfQ5ZfFcB3uS9rwI4594MfAX5H+QnvPcPNV7MOZdm4WwX/wf4nPf+I/WsGV8CNi7J3RqGsSjsb9Ywzm9akQJ9wDHg+mifB05PIBsXJ3/91/C61y33KBZkqSLI3vtv0txXPGf0x3v/LuBdTfZ/ab7z6lzNwtkuPFAv0cgKzKphGMuJ/c0axnnMggLZe//6Jb9qHHFVO4UW/YDZFSnioiFxRLhZv3HWiDgi21gKujG7ROPnZv3Hkep6ieeZ8+KMGJrhIp8PtZrb28Nrakoi6Y2hylRKFFpbm/Q/NSUR41wuXLu7W6LKPT2yf906aGtLRHRTqa6Zz33ruhg5Ip8PNflP6+SkTImWqc5kQnT4yJFQEXvnztnnxuzfD3fd1fyYf/tvwNhNuJv/cv5OCNdtJA70K2r3UNSho8z8M3JtLIYLNFX3ehbOdvFO4KvOuV8GOoEXz9VZIne5Wn8Mw1hKluxv1v5eDWPpWVAg14sVfARY473f5px7FvBy7/0fntYVnQvKRdWXimS1HOi+OE+Xfo73N1bDU9tEJiPCNO4bkh7icjnZVyPNVJKOTz3C8bmxwFchXirJOCqVUC0vmw0iWK0SemzlSlGsk5OSBq29XewVU1PBhnH0qPiSy2Xpu1iU8+ZwEbTXTpHNdswMcS6LRZw1b80aufShQ9J9pQKXXda8/xjXEC/1b/xF+TDd0bI4Brl+Ix0d4p+ObRzPeU6yjfqilZnfO40l9+ZhqSLIy8BcWTBiXg180nv/p8655wOfds5tqy8YTJ4Y5y7fscOyZhjG0rNkf7P292oYS08rFouPAW8D/hLAe7/LOfe3wOkJZJitQFKpecJ/BK9wnMNYBW6tFs7NZiX8GedU1oV7jQv/NLqrq8wgGUFuVlc5jkhXKsEXHJeO1hLauiBQFyKCXGt8HHp7ReTqirk4x/H0tLRra5OxTk2Jql1fT4mr914sBr91LkdlHIaHpcm2bdGYSyVWd6fqQ2uflQe5sxOes22KvXtFpF91lZSfvuMOmbrt26Gvb7awbsZzngOPPAL+NT9bv7bMdSu+45iXvxw++tGw/axnSYR63z55KU88kTyvtze5vaOehC3T07Go61+gArmVbBc/D7wUwHv/7865HLAKGD4nIzQMI8b+Zg3jPKYVKZD33t/bsK9JFQ3DaOCTn1zuEZwWS5Xm7RzzberZLpxzWSTbxW0NbQ4ALwJwzj0DyAFHz+koDcNQ7G/WMM5jWokgjzjnLqP+6Mc591PUS+ieMWqPaPQDa6Q2jgLH7bVtnI1C0UwUk5PBJ6xRV80OEVfSa1Q7Gu1tZr3Qa2v6uMZxabq3xnGmUvKYf2IiRJhXrAi2idgyUq3K53RafMrVqrzH9pMTJ2T/+Lhcs1gEemdFhxNzSXNht2YNUCzS0SER5L4+cCNHKZdXc9llEiTPZMT6vBBxMB6A17ymeUXCBejvT24PDUl0XLP5KY88ktzesye5re1XdzeU2JuHpSo1fa7x3leaZbtwzv0+cJ/3/jbg14CPOed+Bfl7ft2Spm80DKNl7G/WMM5vWpECb0K8TVudc08C+4DXnNVRGcYycQF7kJtmu/DevyP6/DCSfs4wjPMA+5s1jPOXVrJY7AVe7JzrBFLe+5MLnbMgqkCGh0PUtVgMeYU1j7GG8vS5dpw/WX3G5XJI4guyUmt4WHzJGr3UUGJ3dzK3crNFevNFPLVtd7dcd9cu2S4Uktk1tCjIwIBEikGizs98Jjz2mESD//3fg29Zo8MrV8Lq1fJ5927ps6sLtmyB+++X/V1dcM01kr1i5Up42cvgk5/kkk2eMUIAACAASURBVHXruET7uz9a5dbZObMC78c7qzAwQYKeTUzkNvMTPfUkw+MboL+fl03+PXAFFC6DyUm+tnMlr361dLVxYxjy//2/oasvfEHe3Wc+PecU9vTAi6N12OWy3MrDD0sWjExGvMNf/apMz8mT8PnPw/veB6/+iZPJVByxdxuoDl6S2NaI8tHx9jnH04wLVSAbhmEYhrE0zCmQnXO/Osd+AOLqYItGhWnjwrzGLBaqVOIUao0V6+LzVEQXCvJSoa2iVUW1FvmIr9HYdzMaF+6poNesGLFVpFaT3Gm5nFyvs1NWmF16qaSIuOSScL04O8boqNgn1q2Tfo8ckR8AKpz7+0U5jo7KNW+5RRRnTw8TGRHjicpxk5MziwmPlzqaZrHIlWH0kmtnprAPKL/4v8uawv2QSsl5n/3s/NMD4HG4WQuxA2Nj8Hd/N/f5lQq8+tXw7nfL9okTMl133w3FYhdbt16RGHvMkV3J7UsvlXf93dAqJpANwzAM4+JmvgiyJtO9AnguYfHATwJzZLxtAc0Fpj5gjdg2RnIbt+MIbTYbMkTotvZZLCZzFGuGDC1VraJW3+OIcaNgbiT2IEPIYtF4Dxr91ki3loUeHxcf8ooVzX8gTE2JONY8yB0dYb/mN5ueFsWoaeJGRmBwkKlKmn2PSZNtV+RDn2NjM+bgTG62QL78cugae4JifTH1+lVTkEqxc2cbW7fKMMfGmif1aMrb3w7vabHtHKxeLVkrNPvdNdfI9fftI+Gzbgz2ay5n5fHH5X2+Cn+NXMgWC8MwDMMwloY5BbL3/vcAnHNfBa5Sa4Vz7p3A58/sqpkQcW0sC63HGxfvxdHgWPw2LtBrjDbrgj3Ni6xtJieDvSM+Px7DXOh1Va3pYr844q3CvVAQYVwoyHacxk1Xtan6zOfDgrxMRsRyLicL9vRa2WwQ0dmsHBsepn1ykm3P3CRtDjRkCqqHWrs4yaZNXYlDXblpGBigr54qeop2SpMwOChTNDIiw7/zzuZT4d/+G2GjVsO950+attu4UfosFsV5oov+pqflNTEhgnh0VG7rsstk2NWqiNZ8XtK9xaK4Me9x4yK+LVvCtC4GE8iGYRiGcXHTihQYAuI6ZmWsFrzRyHvOMGx8nqBZLBZ6GYZhGIbx9KWV/9V/GrjXOfePSJqZ/wp86oyu2hilVbuBRkljL7KileOapXRTq0IqJQv0cjl5qc+4UJBjY2MhnFgoSBT21KnkuOZTP1qRb3JSxrp2rWxrVFij16VS8DqPjYV0buWy2DL6+mSxnS7gi73UahHp6Qnh0pMn4Yq697ZUCp6Hclne+/s5UUyzt14Oetu2sFitrXh85p4f3ZOeWVeoXHNNGxsGpnn4YdkeHJTmt94qEdjubnjyydmFQvxbf6V+73XLzNvfjnvPH885dfv3y2s+1q6VSPK73w2/9EthreP27TIln/50sgz1hg3J87dvT2739cn75ZfPf91GLIJsGIZhGBc3rWSxeJdz7l+AF9R3vd57/52zOyzDWB7Mg2wYhmEYxrwC2TmXAnZ577cBD5ybIRnG8mIC2TAMwzAubuYVyN77mnPuP51zQ977A0tyRQ3RpVLJSnf5fFi0lskkc3hpdoo4VZtWxoPmad60f+ckX3CpJK9cTo6tXi02h3hl19RUSJ3QrCydZq+o1eTa+oz/iSdkXPFCQs2yUY7s2+WyXL9QkDRvAwOz+81kxLIBYv+Ynpa+0mnZ19kpPoNcbqZaYJU0u3fDgw9Kk6GhcMmVKfApOXfv3tk2h4EB2LAuxfe+J9uFgmSSe/BBWQSnjo6dO8M5/s2/DLUUfOhD8Na3ytc6j72iVVavFtfJi66dIpdrn0lVfdVVkif5u99Njv+ee5LnN1b7e+Ur5X1tzylaxSLIhmEYhmG04kFeCzzknLsXmKky4b1/+VkblXFh8Ja3LPcIzgq2CM8wDMMwLm5akQK/d9ZHYRjnCRZBNgzDMAyjlUV6/+qcW4MUCwG413s/fEZXVQWi+X81ZKfbaoPQ7A9tbcmKeGrJUEtCnBVD++jrC7aHiQlJqNvdLX21tYl1IbY/6HWVZiWnte/u7pDTGMSqAbKtbcrlkHlDcyD39ootY3BQEgNr0RRN6Kt1l3X8IJaKajXYMPQ+OjvltXIl4+NiN1BXyF1RGZft23sZr5tj7rgDDjQYZaTYX5ovfUm2i0Up0Xz0qNT8uPZamUK1WPgPfBDe+mF485sBcB/64Ox5Ok127ZKpOlFqJ5WSqtw9PfCf/wnf+Y4UFIyju+pQUTTvsdLfX/+wSMVrAtkwDMMwLm4WFMjOuVcBfwLcCTjgz5xzb/Pez1MwuAXUx6sCd3Q0KZRj/64K0nI5WUBE/chKXFUvnRYhnE6L6lOBHV97cnK2SFbR2uw5ux5ra5M+4hRx7e3yin3R6jlWurpEDKsgrlaT/ff1iXA+ckSUYXe3jDGTCVX1nBMVuXGjCPK+Pvbvh0OHgj9XC/wB7N4twhLg/vvFLh0zOSma/NFHQ/ebN4sgHRmBL3whtPUf/Ut441vhAx/AvfV/z56fJaBYFK90qSQ1U6anZcxaMTtO2dZYIU+naBYtlwG0CLJhGIZhGK1ZLH4HeK5GjZ1zq4HbgTMTyMaFyZ/92Wxh/zTDBLJhGIZhXNy0IpBTDZaKY7RWga853st7uSwhSi33PD4e0hDUaqHwBkgaBS0PrXaKbDZEmrV6RColYcaxsVBmWguEZDJyrFQK0Wgdx8ydplpTR1rEQ0O2cSQ7lZJI8hVXSJueHokil0pw5ZVyXw8+KNuaQUPvc+NGmYOtW2VuSiV43vMkC4emmXjuc2HlSqb61s6UgL66Ty61Y4c0+da3wlDvvTfURunqkowQMTt3whe/GCKzn/3s3Lft3vi/FpyarVvFmgGhuMh73iMZJTQ6vXVruGWd8rExeZVK8NGPyriKRan7cv/98MxnwrO3JrNRVLPJkHF65KnkYNQlc6TGYjCBbBiGYRgXN60I5C87574CqHT6H8CXTvuK+gw7lxOrgaZkiwWy2hMUFbaaOi2TCX1AMiVbLideXzWoqn+3rU2OnTol/amn9+TJcK56iqG5xULH198v49P209NJv/TKlfI6dUq2s1k5d+NGUaIjI3L+oUPh/kCUY7ks59bTuVX715IePTpzP6cGLqVSgZ33yu1873tw333w3vcGi8Gp1rOazbBnz/zHPQ6Hn9nWqnX63tMjU3b11XDddbIv1v8vfKFMQ18frOk+NcvaMpFZMVMY8Q1vgPZd36YdWJlK8Ywf6Rel/HcPJ85J1xqEb6NvXI83tpsHLTVtGIZhGMbFSyuL9N7mnPtvwLWIB/lm7/0/nvYVVYFoBFgX3UEydKce4ng7bl+thghxo6LRczUvsoYy29tDdLpSkZcPoi9hHWhmI1BBfOxYUoXqeCAI+bExiWyvXy/HV64Un7VGs4vFoCBV4Le1wfHjQVBXKqR37QrnAB1rnoSeHl6wdQ2kUgy8biW33w4vfWkYTqwTe3pEjwM88EAy7XOr+C/9C+6GME8/9VPwIz8in1evlveBAfEya9VwCNbfa66R3wYzv2kqBNGaz0MmQ61utc5koP2efw2LFPN5+TFTLMrnWFg35qpu9BrrDyjNr90C5kE2DMMwDKOlWJn3/h+Af1jSK8d2Bg0dzowqE17atrNThKlux6g4VRFdq80IrxlxVK2G/lR4T03Nji624q/VLBiqRFMpEdralx73Xo45J9vt7UE9xmPTcXV0yJj0x4OOOZMJwjDut7ubTDn8BtAIcnwLw8PBCXJa4vj2r8Op2XOi11JNql9nuRxuS6dnfFzGp/p2ZV8u3HMmw1TZzbSdnITVuZyIW311dARLynzR4EbBrHO2CIGs92IYhmEYxsXL8jxMjsVxLhcUne5zTvbp/ra2IKhigaTtNQpcrYaosWacUMGskWatSFcuJ0Wtnj8fep1sdrYYix/n12rJMcfj7e8XtahWkfg+1G5Sqcg1isWQqk5F3sSEhIXr91MqSTB7//4gVuOh9fQkXSSLwd/9TXjBi5nJAVenUgk6X90h3d3y0mg1hCHv3i3tTp2CtWuhu9vRVr9nj5sJ5pdK9SnRbCTOQTbLiWqBFYXCbOvNQhYLnYjGTCULYALZMAzDMC5uzr1A9j6Zwk2FogpBbZNKBTGbTks7jcxqFLVaTQpRXcgHyahyPi/vU1NJkdsosFRcz4VeR4WXPsJXUazXVYWl7XQsp05Ju3RaFKb6Ey65RN77617bUilEwHWxnvqfe3pCme2JCfr7Cxw9KptTU2H6lHJZXBuLxd/yaXjBa+Huu3EvuDZx7JvfhJfX6yhqVHrLljC0p+pr5b7/fXnv6grW6kpF12bKdxu7XdRazuio1Muu71hRPibfl6jrMJDG2tKjo8nto0flfZEeZBPIhmEYhnFx05JAds51AEPe+0eX5Koa0VVhWa0mo7JdXaLyVESnUkH4glgVisUgmLVQB4hIUqWWy0n/PT1BWIMIVH18H4dbNc8yJP3PigpkLQKilSgmJ6UfLW6STss9abtcTjy1hw5JpY6VK+V+OzvDfICoxAMHQp5kVWqTkyHNxJo1Ei6uVOD4cToL0xw50kupFCK2MwUySOZEXhS5HNxyS0IcZzIytGc/W7JnAGzbJu9XXQVtY0cpFFaza5fs0yjzwICct25dsF7rb4t8XgS1pojO5YB/fEw6PHVKGo+MiPpfsyb5Xcc5puMLKvH8LQITyIZhGIZxcbOgFHDO/SSwE/hyfXu7c+62sz0ww1gOdA3pQi/DMAzDMJ6+tPK/+ncCVyOV9PDe73TObVzMRZxzNwE3AQyl06F6nobqNFKokVOtfqeo3WByUs5RC4JGofXRuuZBLhbFvlCpSCR3eDhEjMtl2VcsSlSyvT1cJ44yT0zMvpGVK+VdrzFcTw+tpbHVFqKL8YaG5KW2i40bZX9bW4iMQrjXQkFsA1qaOpuFZz1L7lWvlcnAxo2cLKylXIa77pTgdLEYHAfDUdbq4UUWBfd3fAOuvx73Kj/r2FVXSbaMHTvghhuSQwc4mVpNNgMv2yHWBr9KLCR33CEZ7MbG5CteXXoCymF13+rVNejv5+iII5WCjhe8QEr7eS9zu2mTzNcDDySjwc0sMjHqSV6GLBbOuQ3ALcAAUEOyv3zQOdcH/D9gI7AfeJX3/rhzzgEfBG4AJoHXee8fqPf1c8D/qXf9h977T535CA3DMAzDmItWBHLFe3/C6aK308B7fzNwM8CO9nZPrRbsEyqYZwyohHxgcdnmbFYEbbyQrpkdQhfjtbeHcKCOXdVPOi2f29qSC7ti8676n2M0dJjLiYDWVA6NbaempO34uIj3XE6EXS4n4lrFvQq33l55X7MmpKQrFOTzyEiyJPbYGBQKM4fXrQu/D04nS0VT7rgDrm9+6LHHxDKxe7dsq4Wjrw/ameLJkXbyeRHGo3vl2Oio3EYqJQ6STZs2JPp0eB4/4GYcLh2jo3KRVCpUOdEFjrHPuNFz3LgaMS5KswiWyGJRAX7Ne/+Ac64LuN859zXgdcDXvfd/5Jz7TeA3gd8AXgZsrr+eB3wEeF5dUP8usAPw9X5u896fhrPcMAzDMIxWaEUgP+ic+xkg7ZzbDLwF+NYC5yyMiuBcLuQnjnOGZbPBY5rJiJDN5STCG2cz0BRwIIK3Wg2CWlOlrVgRRHJ3t+wvFER4xhHJajWI3WYqSYVsW5uMWQWyLjyM7w3kniqVEF0eHxcRnE6LotTz168H4KmJAmvWrBGBreHgkREJA6tQzOVgdBTX00MvsH37ytPOUhHjv/BP8qGSn1Mhlstw992y6E+nTa3R118P7WPDkNow409WwZ7Py210dsoU794dvtpKBcbH3UwEvLsbePJJCVc3puXYsiXpGT9wIDlAraioqIBuzDiyAEshkL33h4HD9c8nnXOPAOuBVwDX1Zt9Cnky8xv1/bd47z1wj3Ouxzm3tt72a977UYC6yH4poXCPYRiGYRhLTCsC+ZeB3wGmkP8pfwX4g9O+okZ149zHjZYLFbPx4rU4i4Ue0xzDSpwWLp2eXTpahbRer5FmUeMY7V+LlMQp47TfePyVinwul4PYm5gQcRyPqz6WSgWxm+jiQgj3oAvQ9HN3N9RqjI6eXpaKWdTH517yY3M26e2V2+npCQsB160Lw6K7m9p4COirps9m5XNnp0S9G4sgxtnuMhmkg3I5+X00S9nWGBluVLbxE4kWWYTFYpVz7r5o++b6k5ImfbqNwLOB/wDW1MUz3vvDzjldUrkeeCI67WB931z7DcMwDMM4S7RSSW8SEci/c/aHYywbN9wwK9/xxcgiSk2PeO93LNyfKwB/D7zVez8+j1Wp2QE/z37DMAzDMM4SC0oB59wO4LeRRUUz7b33zzqtK3ovIUXNhTwxIdHSOPeXFgpRtPCGFtgolyUkmc1KRDlehLVxozxqL5XkWLksj9rTabFanDgh1zl2TNrE5Ymr1RChboZGdQcH5f3JJ+U9tltkMjLWgQEJta5ZI1HhQkEWm2Wzch8HDoQSd/Wyyuuf+1w5LyqPPTV4Ge19fczkTuvuxm+5ggcflCb/+q/h0qeLv/fbuKu9LA+bh+3b4dJLpXT0c6+qJucE8PkVFGrwE8+vVzysR72/dlc7g4PyNW3cCL0TB6FUPz+fh54sU1tWMDxcj6JffbUs0uvslP63bZP3e+5JftdxuW8IiaAV/W4bS1AvwFKleXPOtSHi+G/q1SgBnnLOra1Hj9cCuozyIBCbsweBQ/X91zXsv3NpRmgYhmEYRjNaiZX9DfA24LvIavwzQyNoah3IZkXExqqkvT3pDc7lklXwtMqa5h2OPaYqhjQ7RewNzmaTj+0b8+jGRT6aqaTYM9DdHa7b0ZHMhhELMrV1gJzT1yeCva8PDh9OjmPlSnyuQ9wbtWkolSiVoL27e1aVONWlx47Ntt4uBn/gCWjx/M7OMAUz34+GW6tVyDTJHU34qmduIZ1Oer3rB4rF+m8FvUh9geVEpZ3OeBHnzOAbAqlzVdZbpOJdoiwWDvg48Ij3/n3RoduAnwP+qP7+T9H+NzvnbkUW6Z2oi+ivAO92ztUN8LwE+K0zH6FhGIZhGHPRikA+6r1furzH8TPsQkE+a5YH3a9V7lTwaKoEzeagYqkxMqjFRtSjq17nsbGwIFCrUWh5t1h4xhkvmvmR65FeCgVZdaYG3EIh+I0hiOViMaSea2+HkRG+3/McMpkV9GyCFfVIdGM6tGwWRkbayGbb5NbHxsKivVWrcGPHyed7qVQkmvuBD7Q+/f6ObyR3DF2Pa/GJ/Z49sHOnvJdukIIdGzfKsQ0D07iJIqVSgW8flHR4WlGvWoW99YwWw8MwOLh2xlI9Oiq3p+vpCgVg94PwQz8EmQw+00ZlHFkN2NMDBw+GATX+MmhM56YDWMhbHrGElfR+GPhZ4LvOuZ31fb+NCOPPOed+HjgA/HT92JeQGP4eJM3b6wG896POuT8Avl1v9/u6YM8wDMMwjLNDKwL5d51zfwV8HVmoB0D0yNi4kLnnHrhmuQdxfrFEWSy+SXP/MMCLmrT3wJvm6OsTwCfOfFSGYRiGYbRCKwL59cBWoI1gsfDA6Qlk75OWCI26ZrPJ1VFxJgeNBOfzoRDH1JRElNvaQnQ1kwmlptvaxPowORki0OovzmRkX+K5fwPNVFKcSUILf4CMU+8pznQxOSnlpfN58RYfPEjf1RJNPXAAtmyRyPGRur34kUfE55vJiHWivb1+iYcPiX9Zrz0xQV9fL93di8t97L/8Fag1fOWLKMN86FC4nXvukX0axO1+SRsrhvdTymzm4YeT5/X3y/T09Um0uDHN24kT8nXN2Df27IHrrmOqkmZyDHrbikxXChQzq+m9sjt03FhaujGirP9+lqFQiGEYhmEYFy6tCOQf9N7/wFkfiWGcJ1gpacMwDMO4uGklVnaPc+7Ksz4S49zx4hcv9wjOWzSCvNDLMAzDMIynL638r/5aYKdz7lHn3C7n3Hedc7vO9sCMs8jtty/3CM5rLlSB7Jx7af3vdE+9jHWzNq9yzj3snHvIOfe353qMhmEE7G/WMM5fWnmY/NIlvaJzIbVbtSrmU81trH5k9fNq9gEt2QziYZ6aCim+qtVk2elsVny1Wv65XJbtdFo8yZWKnDMxEfpTMplk/uVG1Dir2Te0xnM6PbuqWz5fz/HbE0rJpVKMj4c00Gqdji2yBw5Iog3v5XbGxmBlLhcapVIwMMD4QfEf79698JT7b/27fCjnZpVddi/98YU7qPOsZ8mtDg1JamII1ugVqZOQStHTI3ZrSGZd6+lJJhHRqazV5OsZHyec29kJo6O053JkurugnKatdJLeNgeHjiYnK6bRkK33OpfPvAkXqgfZOZcG/hz4MSR38redc7d57x+O2mxGUsT9sPf+eFTFzzCMc4z9zRrG+c2CUsB7/zjQA/xk/dVT32dcaFx33XKP4ILgAo0gXw3s8d7v9d6XgVuBVzS0+QXgz733xwG898MYhrFc2N+sYZzHtFJJ738jf6SateIzzrmbvfd/dkZXrtWSUdxyOUT8GjNdtLVJ6PLEiZCJIpWSfalUyMRQKoVoc7EobaenJbKYyUglvWP1Km8TExItjnMpZzLhus1WamnYU/MwH61HMzs7k7l2VUUVCpLCQSsAFgr098slcznoGJc8vZcNSpaN7dvbGRiQY93dcvrYGKzctCkkEs5m8Zm2mS7jtMDN8Ld/HcoZEcff+hbuh54//wkN9PbChg1SyG/LFsmysWOHRJMBXK0ecZ8ETp2iN3eKH78uqSAffKydZ2ypMl1LMzQErngyhJfrYfRqTZ4epGvTsHq1ZLLo6iLd2SnzXatJ7um4et7ISHKwjVH8+Yq+zMEiSk2fb6wHnoi2DyIFR2K2ADjn/g1IA+/03n+5WWfOuZuAmwCGhoaWfLCGYSzd36z9vRrG0tOKFPh54Hne+wkA59wfA/8OnJlANs4td9653CO4YDhPI8QL0SzncmMFmAywGSldPQjc7Zzb5r0fm3Wi9zcDNwPs2LGjtUoyhmEshiX7m7W/V8NYeloRyA6IjblV5i6AsDDqDZ7prRqivuoHnppKlp8ulSTs6n0yz7BW3FN/bqUi0Uf1HVer8qpUpN3UVDh3enq2N3Wh0KGOr1KRsWm0OZeTKHdMe7u0aW8PEdC+PjqyVSqFNF3ZKThSL4hWv8/161dTKslmd7e8j4/DsbE0K6OyyaOjEgA/dgyefLLJMO+7P2xMZmciq+66H5n//iK2b5fodTYrQXCQnMxaZduNHU+eMD4uczA2Fuax/t7b2w4HDtCmuak1/A0zF0nn8xIyLxbFr10sSpvpaTEnj43JNWLDdmNp6bm+v8aS4vNwoXqQkejThmh7EDjUpM093vtpYJ9z7lHkf77fxjCMc439zRrGeUwrUuCvgf9wzr3TOfd7wD3Ax8/usIwl44UvXO4RXHBcoB7kbwObnXOXOueywI1AY4n4LwA/CuCcW4U8vt17TkdpGIZif7OGcR6zYATZe/8+59ydSLo3B7zee/+d075irDKy2eAjjivpZTISrdXtfF6ihblc0jOsfmStqFYuSxSzs1PeNYLb1SXb3svnXE6uG6eS0LG5eYLjWuqtUpHXypWyvWJF0jOt1f40wlkoSPqG6WkmSmnGxmC43E5P/zMAOHxYmh0/DoODchujoyGIns1G1+7rozQuwfBKRS6j+Du+UR9fPuy87z7cjufMfU9zcOiQXD+blawVAI8/LtebnITuF/YCYfra1Oedz3MquyLRV2UcuX+dC62ECPK5o4OJUhpK0JnJSJRYKx2uWiXfiX7H8fc1NssZkGR0dNH3DeetAJ4X733FOfdm4CuIV/ET3vuHnHO/D9znvb+tfuwlzrmHkSdBb/PeH1u+URvGxYv9zRrG+U0ri/QuAx7y3j/gnLsOeIFzbl8z36JhXOhcwBYLvPdfAr7UsO8d0WcP/Gr9ZRjGMmN/s4Zx/tKKFPh7oOqcuxz4K+BSwJKVG09LNIvFQi/DMAzDMJ6+tCKQa977CvDfgA96738FWHt2h2WcEddcs9wjuKC5QD3IhmEYhmEsEa3Ewqadc68GXosUCgFom6f9/MSV6+rV5ahUxBesyqNQSGYeyOVC5ohaTTy+mkO5rU38qiD7jx+X7XQa+vrEp1wqSR9TU2Kq7eyUvtTbrGSz86sf9cGOjYlB9xniIaZUSmbS0NzMxWIYTy4HBw9Sq8HDD0sXaqdV+/JDD8H//J8SodyzR7rYvh26Ksc52XcJAF3ZKfr65Nz+fnjkkfq0fvkrUKmP/Y47ZnJDu+t/tJVvJUF3NwwPSw5k/Vo6OuD734cjR+Cqq4L9d/t2eX/29iF46CEmMit44D7Zp1/hunUwle2ivXaKoyOOyckC2azMZa0Ek4dlavN5oJCH/fvhla8MFz9wQD5v3pzMg6zfh9JQJXDmu23Ml7wAJoANwzAM4+KmFSnweuD5wLu89/ucc5cCnzm7wzKM5UE9yBZBNgzDMIyLl1ayWDzsnPt1YItzbhvwqPf+j87sqplk5FZzFceVz2q1sB2vnCqXZVuzWkCopKd5djVinE5LBPnECWnf2RkyX5TL8mrMpavElfEUzXWsGTc0S0JjP9mshEN7eiTMW6vJa3CQ3btlGNksbNokzffsCaceOCCB0WxWhnrkCBzf2EvvpFTdq+bXMDYsKYH37oWvfz26puY7Po2occz4uIwhnZYup6clcDs4KNOay8G2bdJW74G9e8F7OisnGBpa0bTfaraDbFamRX28xaJca+brL5clc0WxKN9jW5uEtMtlqVx45EhyoDGNEWRtq995i5gANgzDMIyLm1ayWFwHfArYj6R52+Cc+znv/V1nd2iGce65kLNYGIZhGIaxNLTiQf5T4CXe+0cBnHNbgM8Ci0+uC6GSXjYbIr3ViyWW/gAAIABJREFUqnzWSDAErzHAyZPyXi6HKGE+HyK6et7EhFTSGx+XtupTPnYsnFcqhUp7erwZ1ersfVq2rr9fIpwPPyzbfX3JCHI+L8bbYlHGMjUl1xsc5Dl9++Sex8dhrxRNuqRfcgS/6He2yZimp3nO9m4mSmnx/6amYFhMv+nOTjKZAqdOya3ceSf4T38Gd/1r5pv1edmyJdQTSaUkwn3ppeHr6emBDRvg5S+HgQHo4iR873tywp66mtQo+f79XKKR24EBec/l4MH9AKwYGoLdu2eu3ZXNyr8FzZOs3u276r+/urvDv5WDB5O5jxsjxisaItcdHWFsi8CyVBiGYRjGxU0rUqBNxTGA9363c+70F+np4rhiUV5qTygWg2UimxUBqaG86Wk5pgvharVgc6hUgtUhk4GtW6XKxdiYCOvpaTk+OSlKb3RUxNjISHi+r0Q2hXkLhgwMyPlPPRXaamGLTAbWrIEf+AEZ89atIuQOHOAfDz6XG7ZD++hhGZ8Kbr3m4CDffKiXqSnR15kMvP/9cOpUO3/9ARGbU20FPvHn8I53REP9WRHHz3++bKt2BfkNoTaIwUHYsSN5K3v3yoK8G2+U7cOHZcpe+Uo5dvCgnPcv/wKbK4/AA8MyvwcOhDkH+ZGwbp3M765dYR/AtdfCY4+JgN2/f3YBj2pVxKyK4euvh7/4i1AlZetWOW/37tmiOCYuQw0yHphdUnweLIJsGIZhGEYrAvk+59zHgU/Xt/8ncP9pX7FRgdRqoTKd7i+VZFsjfxpldk7UmwoebR8LoM5OaTs9LepQ21cqsp3LhX4blVC83dbkN4B6WSsVGYtGmdWsGwtsEIGYzUr7UolyGdrLJ0WUZzKhPxV25TLptPh9NXFHtVoPmtbHVqmILtfLeBwOz3XXwYteJPt+8AfDEKamYP16+Tw4KNHimI0bRbteeaVsFwqif3s5ztatvTMZM57xDORHwdiYvFTkagYRfQIQZ/RQ8XzokNxUe7v88Dl5MmQzyeXknOlp6atUSs5jrSb9njyZfMLQClp1Ma6+2AImkA3DMAzj4qYVgfyLwJuAtyAe5LuAvzjtK6ZSIoryeREuKpAymaBM2tpku1Hg6MI7EPHZ2SmWBI0qqvhesUKsFvo4Xn0CmtYtnw/XzEdlmXU80Pw5u9oAMhm5ho6rry953tq1ojzTaf5zdwfj4x309r6A+26DjRu7WLUKLtu2LeR5WytppR95osDHPia3f/iwrDG7+WZp8pHPdAHwD/8At98u+/zNH8Pd5LnrLmn7Qz8k+++7L3lLMwvpkGHFPPigBFrX5k/IVG1ZIe2PHKFzQxtDQwXSp4pcfXVB5vfIEbGsqO0lvlCtJvOqi+f0R8bwsIhr3X/s2OxFmuvXi5Du6RHryokT8l23tYXvUX9MxdeMUbGuLHJxHlgE2TAMwzCM1rJYTAHvq7+WBrUi1GrByhBntkink3mQ02l5Vathfyol4qmzM3hNvZd+entDGoZSST7HuZU1y0R3d/I6hcL8j/D7+5PnDw7K9urVEh3NZKS/TZtg5UqYmmL3bnFkfP/7cvr4uDgQDlzeznXXbQbExgDwhS/ILX3ve/K6555w6V/6peRQ/If+DJCxa2BV+4l1YyqVTPYwPJzsR50q1YL4dzMapK3fayoFTE3R3V2AbIf8GBgbk/uDYKNQsV+phHlqpFwOP46USiX8GFHLzMGD0NUl/x7a2kI0WdNdKI0/YhoFc+OvgRYxgWwYhmEYFzetZLH4CeAPgEvq7R1SIr77jK6sYlgftcdp3SApnBojgdWqtJ+eDnYKCP7mUkmOnzol6rFucQDk8X9Xl+ybnEyKNY2Ayo3PHnPjY38Va1NTYcxxBDWVoqNDTlu5Umy7V18t+lHtsRCGsGGD+H5rNdGC/f1B9M7COdxNvwBIu4GBEECNp6u9PdistbhITKkkr/RhuVDX6tVM59rhqVNQLuOmp6Fclq9mfFzEcTMbhUb944WUakGZnJSbqlbDAsk4hV+1GsQxiNAdHZV9OoETEzLP8c3FP27i70fRRZiLiCRrqWnDMAzDMC5eWpECH0DKTH/X+7gM3mmiIlSzHqiojMWpZi1QVqwIFRo040WpJNFjFV6Keo0h5ETW/SpqvRcxpZFpJbZwNIsk6zEV4opGs6N79J2iSnfulGFkMhIVHh4WobxyZRCuWujt2DGxPNxzj3iF5xLH/r1/ivvlXwPgp35KchJPTsKKdLF+G2HhYTYLvT31r210FPYnF8htXrUK39ML99YXDE5N0ZbNis1BF83VamzfvhZuPQD79kk+YhXI+uOgVBILyvBwyD+sAvbJJ8OCRF2gqWI4k5FXqSTzOzkpan9iIjwxiH3K8fey0OK7xh9hLWIRZMMwDMO4uGlFID8BPLgk4hiCKFaRmU6HQhAqOrPZ8MgeQunoclnOa28P/tR0OqT3ck4EloZKDx0Su8XatXJOZ6eILV08p1YJJY5GN0YnQfqAoGzXrJH3fD54qFOpmbG7WpUf+IE0+bzYKqpVWWD35JPwne8E3XZIsr3x5S9LsgZIZEJL4N/5e7hf/92Z7c2boS1VJZdLQ0l+KKzpmUqeVKqFeW1MeVar4Yongz2hWJT5zmZDpL5WkwWG+mOkpyf8KNGbqFRE4TdbSJdOhzLetVoy0qtFYnR81aqsGoyfDhw/HjKexOFdtdYojdkx9ClAs6Ivc2AeZMMwDMMwWhHIbwe+5Jz7V2BGeXnvz8yTrAvi1Gd6+eVBXGl6r9gzoIvqnAuRX40mqhe4WJTwrK5K6+kRW8Bll0mbRx+Va2WzsH170noBEsrVSHCc/i0eM4QsG897nmzv2RMsId3d0N2NO/A4PPEE27ZdO7PG7GMfk8V3J0/KUO+4Q/Y/8IC8T06Kro+Lxc3H9u3wO78DVdKiG1XUxwJ0eDiIylRqtseiVhOFriJVv4PNm0WtP/UUdHaG/ru65AZ0jtXGUKmEhXu6mDHOWR17zHWxZDwGHXcuJ78eYj+xfi+N3vS58h4rce7rRWAC2TAMwzAubloRyO8CisiKsMXly6rjnLsJuAlgSBddaVqwVCrYHxRN63b0qGx3ds7uNPa5NkZFR0dFSHd2itCt1WRftSqiqlQS0dXbW89fRuhL/cPNikvoGA8dSgroDRskyqniM5MRQZ5KsftBud3hYckucfSoRIofeyxovX374rlqPof+HRI1du/8vZl9AwPQWTvJdK2LttGnOJmXiHbXaKSw4zLYzSLIe/cmPcFjY8GKcvToTKGTjiP75Jj3s20pIPM7MSGveMEeiNDWFHC5XPAq6w23tQVP+dRUqLMNci21zehTgLkmq1Ew69OBRRQKsQiyYRiGYRitCOQ+7/1LzuQi3vubgZsBdnR0LI1VwzDOEiaQDcMwDOPiphUpcLtz7owEsmFcSLhadcFXS/049wnn3LBz7sFo3zudc08653bWXzdEx37LObfHOfeoc+7Ho/0vre/b45z7zSW9WcMwDMMwZtFKBPlNwNudc2VA82Wdfpo3TZGmj9zzeXmEPjKSLPSgC/JAfK+a/SCVksf8PT3Bw6y2iFJJLBOHD0saiGPHZP+JE3Lepk1i8L3sMjk2PZ20C+h1oHmGBB3f9dfLo/4775Tt7dvl3Dp371rBu38JjhzZwM6di5uexqWQ/gMfhLGxhLVC+fKX4WWv6uJfPl/k7t1rZtL+7jxwyUybfD44PwoF6M2dSvRxePC5VCqwYbJeTXzjRrFIPPCAeDgGBmBykmPdl7JyfFy+g6NHxVICwfcbZ/HQjBU6t5s2yXfT1SUWjDgxM4Sqh/m8eByGhiT9h6Z56+0NnnM1dOsNxTSat3Vsi1ikh/eLsmQswCeBDwO3NOx/v/f+vfEO59yVwI3AM4F1yA9TrXv458CPAQeBbzvnbvPeP7xUgzQMwzAMI0krhUK6FmqzuCtmQsGHri4RRpr5Qb2rWnJYF5dpVgX1r05MhMIf8UK7XE5EUU9PSA6cSonPtlyW7BXpdMjC0N8viwOVuBBFs/LEOp7xcRFSl9SF6Pr1TFdcYk3Y9u3Sxc6dsytQt4r/i49AJYV75+82PZ7NwmtfC6fSBSqVkMRhaCi00SkDGcNUPrmQbW1/3Xc9Vh+glpLWCnb1Mt0rx/fJfOmCSRWdsTCu50yeVdFubCxkoMjn5aXp4bQfLbIyOSk/Xvr6RKhmMtKmUJBrxSn9Gr+jxkV6mmVksRX1lkgge+/vcs5tbLH5K4Bb64V59jnn9gBX14/t8d7vBXDO3VpvawLZMAzDMM4SLZVEcM69HHhhffNO7/0Xz+iqmg5NM0pASLumn2u1ZOU7LURRqyUX32nmCBAx55wIpUIhZE8YGQltOzrk/OlpybYQpZOrZtpJU4+wNqkWMV0TMdemFfx6ewE4WZTFYprOWeZMhOo110iSC8113Cr+tv8PRvOzSzpHbNwo+n54WDSoBmbj0tLxrVQqIaWcUiikWZ2qJbNDdHfLjaTTIlbb2oLSXr1axqTCVs+bmJA51R8pOiEgPyY02q9CWNto9FgrJWoO7DjDid5AZ2fyl8ZCkWFdVLkYwdt6BHmVcy4q6s3Nda99K7zZOfda4D7g17z3x4H1QFQ7kYP1fSCpFuP9z2vxOoZhGIZhnAatVNL7I+C5wN/Ud/1v59y13vvT90JqzuB8XoSsiistW7xqVSjkASESqBXZtFR1pTI7WplKSZm6rVvlfO+DUNLSdOvWyTWGhphOtYdT436aWCzGxmU8hw4V6O6GVaskvVysp1TL9faKQL7uOrnFr3+99XoV/o5vSN4QwP3ym5u22boVnv/8MGUQ9GisIbPZcPtanC6mVoPV3bXgw9AwtEZ6s9mQwk4zg2zYEOwrevO5nAjnXC7ki46jzBMTMrDe3uTTgfZ26df7UAhmYiIUh9ES3tpPvIKuWYXFmEYrR6u0JpBHvPc7TqP3jyCVKX39/U+BNyAVKhvxNF8nYAtdDcMwDOMs0koE+QZgu/e+BuCc+xTwHeDMBbK+r1wpwkqFMIgYUl+vWh/ifLZqy8hkgkpUf3NnZygkks2KGFYxncnIe7ks50c6OJ2KxHRcQKROrLcqlVDpTrWlarfRURnSqlVyuXXrxO1x+HCL86PiPFa/EbmcXHPlypAyei7iqtCl0uzMaDOB4zglmnq9IXS+alWI+moqtpg4TZ8K2vhiOtB0WkSvWizUohEXkKlWQ/o3PTeTSdo55rrZmNO1SiydB3kW3vun9LNz7mOAPo05CGyImg4CGu+fa79hGIZhGGeBliwWQA+gZcpWzNdwQbSqWlx5TsVY/Ngdwv72djlHq6mBKDsVadpORW+tFh6Vj4+L8Go8r1SCiQnapqKqc3EJ5CYV4S7buA6AR/ekyeVCfZLG4nD5vJSV7u6WdWOHDklAtGWB/JKXwFe/invJjzU9XCrJ9fbsgYcegp+4rsjQUIF1MrxE8Duut1IsJgsUQn3c2WxY4KYiU0Wy5qyemJA5GRuTbc1RraJVi3xoGfCY7u4QhY4VO0i/qVRQ6pWKfNZKevpvBWQi4xB443fUmJ/t/2/v3cPjusp7/88aaUajsW6W5bujOI6dOIljQhJCwg9IwiUNFEjooeVS2tIfEKDQcGkfoD9amob2nEB7uOTAgaaclEsPpIGnTVMamhCSEKDkBoRcjOM4juO7ZcmRZVkejaRZvz/e/Wqt2RpJI0eW5Oj9PM88M3vvtddee4+2/d3vfNf7HkMlPbyfvIT1c8A5t9x7r38JbwQ0w8WtwLecc59FJumtAx5AIsvrnHOnALuRiXxvO24DNAzDMAyjJoH8P4BfOufuRv6zfjnwZ8d8RPUZaNnhfF4Ek04OA/G5xoZeLRbR0lIpilTIaDW32MOgk7oGB0X8jYzIsXbskMis+mjj4hLLlk04m66nVy0WIjpVkGpCDtXrhYJsU7ttd3dt1fH8fffDnj243/KQSqynWu/ss6X/Cy+UcSxaJNenv796wbjh4XAZu7vHBsZLJSCfCTvrA0Vfn3hEymX5Xnp65ASXLZPCH3qt9fvo7w8T6vQgKkxjS4X6meMTq6uTCLV6j0dGgi85m5WTbGwMVg1lMFVSOy2EVbxPpUr6NGaxcM59G7gE8SvvAv4SuMQ5dw5ik9gOvEcO6x93zt2MTL4bBt7vvR9J+vkAcDtQB9zovX98WgZoGIZhGEZVJhTIzjkH/AS4EPEhO+Bj3vsaiyFPQD4vXtZ8XtRmoQDLl8u2hQsrRc3q1SKe9u0TkVYoiDDTiWNqzWhuFvHW2cne4kJ6doP3TZx9ThMUixyilda1OfYOtJJb3kpXF/TtrhzW0FBD8j52yI8lsb5DhyRT3L33yvKaNTKklhbRcXv2yKlcdJHYMPr7JaI8Hv6uu+VDMRNUd7pNcjna2uCCC0S7XnYZXLRmP0/tWcqPfxzaXnxx+LxqFTz1lHx++GH46U8r+z3jDFh5+iF2F9YBsHJ5v5SXXrNGrm1Xl/g5cjlZ19YmB9fZfvpQs2VL8HfrLMFYmWsUf8mSykp6EDzL+jCzd6+cZHOznPjRo/I3ceaZwSMN8rATk35C0Ej1VCPC05fF4q1VVv+fCdr/DVK5Mr3+NuC2aRmUYRiGYRiTMqFA9t5759wt3vvzkJ+ADeP5zfTmQTYMwzAM4wSkFovFfc65F3nvH5y2o5ZKEgHWjAVqp9BcaOVypUhZskQih3GE8PBhWdfSUpnJYudOyGRYvvEAy85aTG8vHC030JiH1q6d0NVFx8bz2LpVPLzbt4ddDx0KWdXGmR8HSPR4zRq48UZZftvbwoS8xkaJJjc0yOl0d8OmTdXtD/7v/qd82JXM8lu1Cnfh2AxebW0hUNvWJnVKNm6Ele1H4bafcOprX8tFFzWOTiJMss8BcErTAU55kRiPly9vqLD/gvTDLQ+y8uWJ//vhX0sRkM5OscFs2RImP6qNIpcLfl89MfUnt7eHbfod7toVCn0sWSK2iqNRwRL1JusFGxiQLyKXk7+R3bvlop59djB+gxSDiUmH6Z95Rt5nKQ+yYRiGYRgnJrUI5EuB9zjnngGOIDYL773feFxHZhizwXGepGcYhmEYxtynFoH8mmk/ahx9rKsLJZ81GnnokCzrZK6+Pokq6kQvjT5r5TYVNMPD4mNNysq5+nqamhbKZo2AAtl6TybjeOwxePLJMKyGhhCETBdlAzjrLHm/4AIJaGrE9siRYB3WdL6Dg8E2Xc1/7G/+DhxMSiV3dQHgfv/3ql6uF74wRLrPPVeCtCuXjUB/SQbR08PatavYlNRWUyu39N09GhFdsWLpmNTArvuAROI1xcaBAzLwgwdDWWjNQNHXJxH7eCKeXvtSKWS80MlzGrnVCZFHjki/cSU9kAs/MBCOBWPz0w0Oyr5aTxvGpuRIF1VRv3KVjCQTYhFkwzAMw5jXVCtCUIH3/hkkR+sQMvNeX8Z08N73zvYIjBj1IE/2MgzDMAzjeUstlfT+GElPtR9QZeABs1hMB1/5CmOMwcbsYgLYMAzDMOY1tVgsPgic7r3vmZYjakU1rWiXz8tP6729YXZZa6v81N6UWBAaG+WlRSUglJzO5YKgyeVk4lZTE4OrT6e3F0r7JHUv5bLUZ+7q4pkdjvp6yRoWT57r7Q32hDhVr6IpeO+6Sw6zfr0s798vgce2Nvk1f/NmsUG0tsI994yteOxv/XcoZUZnArr3vmfCS/brX4c8yLmcZDcrl+vo7Gxl5caN0NbGtgfDcXRCH8CiNWt4dkDSqG15ZGya5/zGxbSuXh1O/OhR6aBQCHmNQcaq19z7kNj50CF51zLUmUy4UPq9tLXJPtms2Cnigh5aMhzEMjE0JCe5YIFsq6+XPMjqadHyhVCZ8g3GXuhqtbdrwQSyYRiGYcxrahHIO4FDx3sghjEnsDRvhmEYhjHvGVcgO+c+knzcBtzjnPsPYHRmlff+s8d8VC03rX7O+nqJRC5J0p1lMjIJT6OTnZ2hwlqxKFFGjUbqepC+klLTvb1h3li2ey+MjLC/y7E0l+PwASnWt359ZTDz4MEQOY6jsMqvfiXv3d2MRqBj+vslkFoqwRNPhCxpY3jDG+DWW3FveH1Nl2vfPnj5y8Pyf/0XnHeejGPlJadBXR3ZbMh2pxX9AA6XGkYDqRoUjunthVatmAcStS+X5Ro7F4qy5HIhstzdHWYNqj1kaCgU/Ni/X971YtbVyUVpaJDosnOVqde0quLwsHyvhYK01+qHra0yhn37YNu2sN9kpaZ1+1SyUlgWC8MwDMOY90wUQW5O3nckr1zyem44F4SU/vSt2Q9UyeXz8lO/Kr2RkZBFQbNZFAqy3bmQvWBwUBIU9/XR1yeCsaUF2NMLw8OU2lZBPkNzMyxtG6StraFCU+3ZExIjVAsiqmi+805pd8klsqxZKjIZ0YKbNokNolr6XX/nDyFzF+4Vl07psr361fK+bJlcJk0j/OxwMwt7d7JgwUmjWTbS1bjVqQJjEz90dyPXUe0KKg57eiqtDeof2bFDLoRmvdCHk97eyvQdEHwhCxaEC5quia37NzQE9a4ZTfRBKRbecaaKtHVivHzHWnK6Vk7QCLJz7nLgC0hJ6q96768bp92bgO8AL/LePzSDQzQMI8LuWcOYu4wrkL33f3Xcjupc8A+3tIg4a24OIdBCoTLcqf5YVXoqjltapJ9Fi2R9LjcagdSMYSMj0HrqaiiV6N8DrGhizybo7W2gXK60sZbLQbv1VHFcxwHJffvg0UdleWQk2HP7+2VbuTy+LpuqOI5ZtUqeH5Ytk8u1sO+Z0TGprtNAPEA2M8LgcN3o+aWDrvX1QDGKmKqAzWREmMYdayelUvAmqyjNZuWz90EYxxdA/cQqiGNhDLLPyIj0rX02Nso6jWCn+1Svs6J9KnGaualwAgpk51wd8CXg1UjWmQedc7d67zel2jUDVwP3z/woDcNQ7J41jLlNLVksfgD8tve+N1leCNzkvf+NYzpiJiM/me/dGyZc9fdLZFInWXV0VCrUrVtFEfb3i2AbGBB/hEad1ccwPAzveAfcfjvn/VYH7OgSUXb7wzA8zBlvehN8+2dcdNFF8Nhj0NbGyCUXjx5m166g0dO/1kPIg/yqV0nA9PVrfw3AE5kzRi0d5bLkVv7yl+GBByr39zjcq44tQ966dfK+Zg188Ytw/vlyGbacczJv7vsHXrRxI9yTXM+lS8OO2SwNyVPAyv7+sRPZVq0S8flQEpTI5eS1fbs8tDQ2BuvKI4/I/ocPS7U9ba/v+XyohBdva2+X9Q0NIlpjC4OK0UJBjuW9nOwvfynr6+vFDwNy3DhqnEv9oNHRUbms+1X7MsfjxPUgXwBs9d5vA3DO3QRcAWxKtfsU8BngT2d2eIZhpLB71jDmMLVM0lus4hjAe/+sc27JRDtMiPciajUKXC6LsFqxolKdLlgQooX5fLBWZLMipIaHg9jT4hGlkqjT006TfdaskfV6nIEBOOMMhjpPJdvRwf5iK5t/EoYW66K0jtRuQDRluQyPjZwBwO4d4fClEvz851VOG4ebJH10Ph+iw4cOBRfDxo3wi1/I5127pNT0aaeJ7nxx6cfQkgxIyzDHFob9+4NAzOfHir++PnnpyWn0eMUKCa9rFHbbNvl++vpEOGvkN2ZwUCLB6uPQiG5sgWloCOH2NFo4ZvPmyuwkPT3Bzxx7RNIh+nTqES1mEhclqYUTUyCvRCbUKruAirrlzrkXAid577/nnJvwP1vn3FXAVQCdnZ3TPFTDMJjGe9buV8OYfmoRyCPOuU7v/Q4A59zJPJdCIfrzuxp2h4ZESGmkUrfl80EAxQUastnw834s/EDE1+BgEFFtbSKklyyR4wwNje7TM9zK5s2wM/rnqbc3dF0tNbF2294uWlvni+lhhodFwKYn+PnPfg7qr5cfySagWJTAeD4f5ieCrNuRiPAlS+DUU0W/dnYC9x6UAWUyYYBxlLVUqhSO+hCi9PeLENb1WpmwUJBrfeSIiFWtptfeLgPVY6nNwXsZtE6chCBMtephsSg2mbhqIgQhqwL56FE5lgpp/e4Lhcpz0f2UdPnDY5lsd+JGkF2VdaP3qXMuA3wOeEctnXnvbwBuADj//POtMJBhTD/Tds/a/WoY008tAvkTwE+ccz9Kll9O8qR6bEesl5/Ct28XJTkyIu87diQJi5F8yM8+GzzI7e2yTQXTgQOiGtUjG6eK+L3fg9tvF7H0sFgrRmswX3YZdHWRbWpi0cMPc/GqVey8+KLRXe+8M2juJEVxBarHDh6UoOYfXi4h3tsfWc6BA6IBGxsro8/+uk9DCdxHPlzR1/nnSyQYwmm3tcF994lGfNvbJGr8ta/BzTfLeoAXnz/CX/11HUND4nW+4OVXsPBLfw2nn57MuEsNvlAIOYu7usLsPmX5chm0hqg1jP3MM2KFcU5EY6EgqTl6eyUirDYK9YXncnICXV3hAujFLBREiGcy8n1olFrp7pblpiYR6KecIilDymXpd8kS+TvRfNnpL0TRCLqi3vR0u8k4MbNY7AJOipZXAXui5WZgA5KRBmAZcKtz7g026ccwZgW7Zw1jDjOpQPbe/6dz7lzgQuSJ98Pe++5jPqKm0dIIMoSIsf7Mr2ngVEQtWJCMtl7W19XJK5ORdaow1Q7Q3i5h1s2bpY1GWBPh6Neuw3V3w+rVbIncXnEGsfR8r5jzzkuynCVRV03soG4F1aMAfPzjVa0VsdbToKieXrEoOlMzqUGYTPjU9rrR9pkMLMwfDek6NCqr10tRb265PDa83dIiglejr3V1sq61NXwfel2bm0Vgex++G22jEWg9CR2gvut3pTaPOEqr4lhtFRpBBlmvlpx0ZDftQU5bLnR7OrfdRJy4EeQHgXXOuVOA3cBbgLfpRu/9IWDUpO2cuwf4U/uP1jBmDbtnDWMOU5NySATx96bliLmceIZ37ZJJVGqZGB4OXuIFCyp9pytWwNpWLgi9AAAgAElEQVS1EmnM58VbsHatiOCdO8Nkv95eyYd29ChHOs9gQUeHHO8Vr4Dubr7z0Cm85CXwjetgzZqL+Lf/Bd/+9rGfyl/8ReO42/wN/wADA7gPVYrjT35SAp3r1weXgp5mRwfce69cirVrJcq8dSu87GXwmg2JF+SZZ/jYR14sonFgAHb1weOPy/VS8RvndVOLAkgUN53GoqtL2j/9tCxrvuMdO8LEu+Fh+Q62bpVtxWKwcaj43LdPPsfblI4OOa6m+UhvL5Xkuy0Ugldlzx5pr7YKPU4sdtM+5u7Uc5s+YVQrizgRJ6BA9t4PO+c+ANyOpIy60Xv/uHPuWuAh7/2tsztCwzBi7J41jLnNFEJr08TISIggNzfL+0kniXDSKGR7u0TyNKq5cqX8zK77gfx83tws+2q4t72dQ8UGWk87jU2bYMmSxWQSu2z9kmYeeUS6ue8+ieA+/vhxPM9cbqwYRYLamuxBf8mPLbjd3eFSqGY880yCmtbMDEpfnwjcOMIaC2QIXmBNrRdTKFT6l1UUt7XJg0s2G7zfDQ1jI7lp4RpProu36fr6+rHCVn8t0Aiz7hO/4mi0Mll2Cv27GC8/cjVO3Agy3vvbgNtS6z45TttLZmJMhmGMj92zhjF3mXmBrGgWi/Z2iRa2tQXvbFtbZSLhk04S4aQTxJyTNm1tIpJOPlnaFYtSGe7UU3niFgl2lsuSzKK/H556SqK3W7aIZopzIB+3c0yxZ4/o/fb2cHoqlA8ckPe+PgngdnVJkPykkwiiN59nJJOlrpCRyGt/v4jbtrYgfuPjDg4GIdnQMNaGoDaXODtELJBBLlZ9vUT29QEnbgtBVOpkOqgUtWlhnkaFMISJhblcWP9cRGs6Yj0ZJ6hANgzDMAxjeqhJICcJzZfG7TWrxZQZGYHduyW6qiqxvV3WawR50aJKD/KiRSLOhodFODU1iU+1r0+EnYrH4WE6O+BAdxObNoVf2LdsEQfB9u2iETdvDtXvjgceh3tHsFaccYYEu887T1K3tbaKONeCHhrgHRgQMb9smXzu7IQNG+D1l/bDjgOj51h3790iQnt7Ja2d5oZW4aqlnmFsSWcVvUqpJIPRaGuxKP1o1gmdGNnTIxkrdOKcHiudMUOzXUCl0NRt+bx8Tkd/dZ2m46uvD33pNp3kF59PTDpir8tTzYN8Yk7SMwzDMAxjmqilUMgfA38J7AdU8Xhg4zEdUXMca7YETft1+HAQbxq91Khja6sIJc2nFuc11jzKSd+uNEgu10A2G5ItLF8OL3yh5CfWQKkmZ5gu/LWfGv3sPhk6vvpqeMlLQvKOri4RxIsWhaQLLkr2c9JJwZ0xMJAEYx9/PPiL+/vFe6EiVfPUxZkh0jMM4/R5aQYHK0WupsMbGQlp11Qg6+TImLRQVt9wtW0aDY5tGGqbiZfjKLT2qQVMYtKR4WrWDT3uVLAIsmEYhmHMa2pRDh8ETvfeVym+bBjPM05gD7JhGIZhGNNDLQJ5J3Bo0lZTQQtHpAt9xP7YUqkyYqgRxDhKWC5LOw0LJ9tiO2vcXRxwnNbo8Sf/EsoZuOYauPbaim3x3LdMRgLBw8MhtXBMtXS9Y4KfGknWbA8wNqPERKQjwNpXvK9GcTV6nMkEq0Z6Ul41MZmeUKd2Ce0r3Ua3xxX24rbpSX/xOGPS2/Xvqto5T4QJZMMwDMOY19QikLchicr/Axit2eu9/+wxHdF7EcgjIyKAnJP3o0fH/kSuyy0tInb6+4MoijMfKLkcPtdAPlmlc8tyOdGQhw5VFnCbdq65ZsyqlpZQNTuXE0eD96Jz1YOs+nZ4uPK5YDSpw8CAeDO0EUhHdXXB23us6PcRk85TrJRKody3VtDTY6vA1YcWqN37Gx8rnuxXjbjPtOViPAE91etjAtkwDMMw5jW1COQdySuXvJ47Gh3Uoh/eB08yiAArFMKkPU0t1tQUIqdxtFk/5/O40iDF4YbReWZq2S0U5DCqM6eVOPLpxlYP1eHF9TsGBkIWjThDGlRqwIbMkPSv/eqxhobk2mnqNAjXLy7lp+2UalHmkZHKNmlhmxaMDQ1BVFcTk+m8w/EkOz2+ilZtG0fEdZzpCHImI8dWJhPIOsap/FxgFgvDMAzDmPfUUknvr47rCEZGxk7KiquxxdTXS0h2cDCozlJpVHmOlB11fX2U6hePdj04KOnTGhunPldrSiSiyl3zlxWrVeNDGPLAgAj3nsTVrcJZnxEqxlkui3DUjg4fDpPaNGq7YEGl+IwFapzZoVAYexE0S0Ra1GrYXVGhrd+Lpt7Q/TT7RZzLuFrGCX0w0lB+2kqhy9Um3DU0VI5zsvzG8d9IrVgWC8MwDMOY94wrGZ1zn/fef8g59+8wtlay9/4NtR7EOXcVcBVAZ1OTqEMIuW6zWTjnnOCvzeWklFwiko4UFrOg+5mwz/Llkt4sn4dCgR/cKdHVYhFe37KJzbmL6euDX/9aosdxtbzHHqt11JPjr/4gXH897q/Hj1C+++1HebbYyO7dcHr512w+6Qw2b5Y8x3fcIW3Up/y618FrXyun3dUFZ6w4BI9skTx169dLo7Vr4c47RfiWSlLBbv36UOADxEui9PaG9VoUJKa/X8SuCt/Dh+VCamGWYjHYKtST3NgYBLtm0Rgelsh1OvuEvmcyY9PApbdpqrq+Pulf8x9r2reGBkn/MR5x5BxCiN7yIBuGYRiGMQUmiql+M3n/u+d6EO/9DcANAOcvWTKN0+PmAFdfDdfP9iCMacUEsmEYhmHMa8adReW9/3ny/qNqr5kbomHMIOpBnuxVA865G51zXc65x6J17c65HzjnnkzeFybrnXPueufcVufcI865c6N9/iBp/6Rz7g+m/ZwNwzAMw6hg5ktN19VJhQz96V7LRd92G1x4obRpa5OazMnP9aWmk9hTOpl1a0akFN3QEGzYwNO7shQH4LTTZLdMBu7eejHdu+Dznw+HXLZMnAjTicfhrp88GP7X/7ORs8+WU/r3vjO47TZYsQJe9Sr40z+VNmoT3rYNHnlECorU18Pu/laWnfsi6splePhhaVQqic1Cq+ctWgTPPivXqrtb2sQT7lasCIJucDBkn1ByOSnzF9sT8nnxeGhqvaEhsVj094d8eWpfUL9uXGFPL3ac5m1gQJb7++WVLl4Sp3JTG04+HwqPlMviS9kRFXBMC9Xx0r7NXqGQrwFfBL4Rrfs48EPv/XXOuY8nyx8DXgOsS14vBr4MvNg5144U6jkfsTr93Dl3q/f+2ekapGEYhmEYlcy8QNZJUJqpoqUl+FJVmLS3hypuhOJ59PbKumwWBgc5ZUWZo+WQ1aC/HxYvFjEaV8pbu1b2j7XVMQ39mmS+4jXX4Mbasqtyxhki0NX++4MfwG/8hpyTVtJTPfnUU6FtsSj6sC6T5IRT37ZuLJfFk9vaKgK2rS14beNcdnFSaC3zHKNp2eL0atq/+po195xWQcxkQnlvPZb6hHUSnh5PSU++Gy+3sT44NTVVjlsn8MXiNZ2zbzxhO5VJd9M4Sc97f69zbnVq9RXAJcnnrwP3IAL5CuAb3nsP3Oeca3POLU/a/sB7fxDAOfcD4HLg2xiGYRiGcVyYkkB2zmWAJu9936SNx0Mneqno6+mRNBPd3bBpk7Q5elQinYmYWtzWBozA3qQcdX+/RJhLJRpXrZIoKdBYX2Yxuzm6fgPveIcEm1WLb9w4NYHsP/yRsSuLiRi/5hq4prZ+fv5zOZ1Fi2Qu3fvfL0K4p0eC5iARYxCtu3EjLGob4antdaIvSyURwMk5juap0zxxu3fD0qVyXXTi3P79YQB9fSFFXHPzWEHa3w+7dslDibbXB5ZiUbY7J591Ap4KWAiCd3g4RH21nQpWjRhrhovx8iPrJL44j3ImI+fa0iLn19gY2qezWKQF87Ekva49zVuHc+6haPmGxGs/GUu993vlUH6vcy7Jhs1KpCiPsitZN956wzAMwzCOE5MKZOfct4D3AiPAz4FW59xnvfd/e7wHZxizQm0Cudt7f/40HnVsAm2xVIy33jAMwzCM40Qtpc7OTCLGVwK3AZ3A7x3XUc1VrrtutkdgzATTNElvHPYn1gmSdy1dsws4KWq3CtgzwXrDMAzDMI4TtVgsss65LCKQv+i9H3LOPbcI1sBA+Pn+4EH5SV2tEyC2gaEhsQSA5NodGZGfv3UCV3e3/Bx/8GCwFvT2wlNP0bhgAW9+8ykcOCC73nGHpFmuBX/1B+XDSJWNH/4wDA7irvsfUzrdw4flVDo64CUvkVO4/37Yvl22X3yxvG/YAItyh2FPL6eu6BAv794Dcu5qWyiV5Np1d8t1PHJErkFjoxiwobI4iE7cA9lPr6ly8KBcN7U9HDwox2trk4GrP7mpKVgdIIwn9uuqn1m9x2qViGtpq80i9jxrHmQtNgKVfmUtcd3SAp2d4XjqTVGeeKJyWfNqz61KercCfwBcl7z/W7T+A865m5BJeocSC8btwH/XbBfAZcCfHc8BGoZhGMZ8pxaB/PfAduBXwL3OuZOBY/cgG8ZcZ5oEsnPu28gkuw7n3C4kG8V1wM3OuXciJdx/O2l+G/BaYCswAPwhgPf+oHPuU8CDSbtrdcKeYRiGYRjHh1pKTV9PZSmMZ5xzlx6/Ic1Brr9eCoIYz3+mN4vFW8fZ9MoqbT3w/nH6uRG4cVoGZRiGYRjGpEzqQXbOLXXO/R/n3PeT5TORn4bnByaO5x/H14NsGIZhGMYcp5ZJel8DbgeSPGNsAT50vAY05zBxPL+Yxkp6hmEYhmGcmNQikDu89zcDZQDv/TDVp7AZxvMDE8iGYRiGMa+pRSAfcc4tIsm96py7EDh0XEc1F7j++snbGM8/LIJsGIZhGPOeWrJYfARJQXWqc+6nwGLgTc/pqMPDoaKepvLyPlR50/ek1PRom0wmlE4er+RwsQhHjlBfL5WYMxkpLLdt2+TD8h/+CPjkWFWsFe5zn53CSYZT0AxlmtGsrU2GefSotDmY5CTIZpOdSqVQunlwsLLynKbCI2o7NCSp2aqRnnCWFne6X7y+rq6ySl0mE/pxTlLFaeq0vr6xx9HP6bLWUP37i/8OdCzp5XJZLuLoRSKknFOWLq1c1jFOtaKeCWDDMAzDmNfUksXiF865i4HTkapeT3jvhybZzTBOTKYxi4VhGIZhGCcmtUSQAS4AViftz3XO4b3/xjEftbFRIoR1dVJwIpORohRaYCKXq2xfKISCFfX1EhHM50OEUqOq+by0bW2l70mpG7FzJ+zbJ/U0quE/+rGwMIRESAF3/ReO+fTGo7dXhl4sSk2P1lZZr7U7slnk3Ovrob2dQ32O1o4OOS+9JnqN8nm5bvl8iKpq5DMujKHtIUTkY7yvjMrW18s1iKO6euxyOfQ9OCjvcWEPpVrkON4+mQDVCLMWFlH6+qRgibJlS+V+6civnvtUBa9FkA3DMAxjXlNLmrdvAn8HvBR4UfI6/ziPa3b43OdmewTGbGMeZMMwDMOY99QSQT4fODMpZDA9FIsSUWxoECNuS4tEBrVkdEMDLFgQIoAdHaEccrks4Vf1lebzsH69fB4chM2bYdUquv8TnnwSNm2CZ5+VVxr/vj+CUkNY8dGP4j7z6Wk7TYBLLgmW2lWrxG/c3i6nunOntNFIMiDR16YmhoadnP6Bw2KiXr06XIunnw7luotFWLgw7AuVAm5gIESAq0VS1Q+uqEe4vl7604i2RpVBRGRDct10XX19iDpnUs9d6h1XcRl7kIeHx7ZPl6+O3zVyDbBiBRUcOFC9n2oR7YkwAWwYhmEY85paBPJjwDJg73Eei2HMDUwgG4ZhGMa8phaB3AFscs49AIyG77z3bzhuo5ppPv95+ND8qX1iTIBN0jMMwzCMeU8tAvma4z2IWcfEsaGoB9kwDMMwjHlLLWnefuScOxlY572/0zlXAKqkQ6gRzbvb0SFm3EwGliyBtWthxw7ZlsnAmjWjPtehhiayw8Mhs0F7u2S9SLIcPPaEZGEYGsrywlKJu+9xbNkCt94q1uWq5/Wud4Ovr/C0us9P3yS9VavgZS+DV565Fzo62H8wS91PfkRh2cWcdpqcilqs1V7c0gIcOgRtbWTrPWzdKq8nnggd5/Owe7fkKfZePMaa0UK9vJqbGCr9t85VZoWA4DVubw/tBwfFE64+7/p6yQ5SLIonOc6qcehQOKb6iTU/sUZiNaez7p/LVUZp075l9ahD8C0Xi/Kd6zhhbO5nTSit6HU4fJgpYQLZMAzDMOY1kwpk59y7gauAduBUYCXwFeCVx3RETU125EiomrFkSaimoW0yGQazktKrvw8W1ddLOrdiMaSGSyaTNTbKbnV1QEsL27fDAw9MII6v/iAMZUVgVkt9Ng3k83DyycggmptpasqOHqu+Xk5Z5ySq1mxrA4a9iN9SSc61r6+yqoimyGtoECGXy4VziAWt4lzlOabFnwpeFaRxAZdsVkSo9jc0JK/+/pAaTguKlEphv4EBWZe2Kmg/mrJP28TiWPuIi4VooZKjR4Mgh3AcJV1ERf8A0usnwwSyYRiGYcxrarFYvB/Jg3w/gPf+Sefckud0VI1AxhkT2ttDNNd7aGujlOiagQFY1JSHnp6QBUGjkOmcyUgg8d57JxnDl78M73oX7itffk6nksY5Gf66dXDOOYhIq69nwZIl0NNDy6lB96l+VD2WywE9h2RFf39l4mQVv01Nsj3OMBFHW9PU+gCQvo51dfLKZisF97EmM4mjxPpLgH6OM2FoxDidA1mjyHGminQ0PC2YNYI8FcFrFgvDMAzDmPfUIpAHvfclpwU0nKsHjj3lm/5cr8JYCz88/HAoFdzRkQjCxtHdesoLWcRuEU7FooRbE4HUnnTR1ASsfilL908yhkIBrr76uBQDUf349NNS3tr/zkW44lGG6hvJbtjAjh2Swa65GU5dI42HhuXadncDJ50UBHChIFH1FStCFHR4GBYtqjxoOnVaXPijuTmI5Hx+rPgrFKQ/FakLFkjUeGhI+tG+tG63Wl3UzqCitL4+eEaqlXbWMeo5xAVG9LMWfykUxo4xk5F+Y8tIOn1bOl3cZOvH4wQVyM65y4EvIBaor3rvr0tt/wjwLmAYOAD8v977Z2Z8oIZhAHbPGsZcphaB/CPn3P8HNDrnXg38EfDvx3zETEYE2eHDEuEbGQniSSPIPT3SdOEqQDTYSU3Pimgrl8PP7IOD0NDAwiQPsK9vgoN9rFzZOOawir/qPbjr/v6Yh1+NCy4Qy4RWxNu3T6LHZ58NbvcuOHCA7OLFsG0bZ154Ks2uXywmD4nnOpsIuOWrV0PXQRHF/f1SKa6rS176IKHXbGhIEiir5QGCJzf25sZ+5LRnV9cVi2HbkSPhc7ksAtknto+BgSA2VRirSB0YCFFf3aZR6Wp5iNMWC10uFoMIj/M6x3aLdB/jkY4w18IJmsXCOVcHfAl4NbALeNA5d6v3flPU7JfA+d77Aefc+4DPAG+e+dEahmH3rGHMbWpREB8H3gk8CrwHuA346jEf0XsRYKVSiI6Wy/CTn8D5SYG+NWugWCQXWVYf272QDStK8hN7a6vss2gR5HL4BU2j7XpZOn5Z6Q9/BFhwzEMfj4EBeOopGVYuJ1py377ENtzTIyK1vh7WrOGZZ2Dx4iaWLlkQorCJ+D1Uv4jWtnKYzNbWNlo4ZFToqvVBLSZxnWoVtrGIXLgwFPVIfgWoIJsdG8VVkZjNyr7Dw9K3Ctm4mpzupxHs2PKh6K8FOrZ4/1yuUuiqt1ztM+WyRJBVNMdt00I2LZh1eX5EkC8AtnrvtwE4524CrgBG/7P13t8dtb8PePuMjtAwjBi7Zw1jDjOpcvDel733/+C9/23v/ZuSz9NXVW8msVLSxmScuKWmVwI7o+VdybrxeCfw/fE2Oueucs495Jx76EC6QqFhGNPBtN2zdr8axvQzbgTZOXcFsMp7/6Vk+X5gcbL5Y9777xzTEQcHxZw7OCjWgO5uiZAuWTLmJ/lsRiKiK1bUSRCwK8lKkMvBrl2yXybDQF48uQMDsLRwmJ6e5jGH9e96N+6rHibQyGvXwuteJ5/V0RBz333yfued8v6qV8n7K14hcwyLRZlX98wzcPHFSV/37AmZOx55hA2vXirnvq80ZpZea3E/7NkT0pzl89JhX5/kjYPRfhgZkdeBA5Iuo7c3eIDjTA9DQyF6u3Dh2El2R45I/xpd1kixfj50SCLJmqVCBaL6hLVtf3+IROv3qMdVa0ZspYjtDxp91vNWq4ZGjTUCr5MSlXRkOC1c9XpMVdDOTQE8GVV+Hqg+V8A593akhPzF43Xmvb8BuAHg/PPPPzEfiA1jbjNt96zdr4Yx/Uxksfgo8JZouQF4EeJR+Efg2ASyMjQkFotyGTo6eGzodE7qkE0tObjrLtHAIDro994yBKU2yOV4pm8hJzf1ije3UOCL/yLtymX4szW3sXlzpUXLv/334EgV/22KpibYlPy4lZ4HByFf8RveIO1UKF9xheh7gM5Oeb3lLdDwwI/hllvEMjIwAD/+MWzcKN6L1avxy5ZX9O+O9EuO4+XLRQw+9ph4NTZvhmXLpFGpJLmRNdPDwIBMbhwclAl2em2Vnp6wXlO6xfT2itWjoyMsl8syWbCnJ4hb3a9QkKcBFcGau7qvT0RyU1PIVaxCs79f2g8MhAma8cOB5kjWyX0DA8F3XF8vYyoUZAw6Th1LTDqLhVpYqk0aHI8TN4vFLuCkaHkVsCfdyDn3KuATwMXe+8H0dsMwZgy7Zw1jDjORQM557+Off37ive8Bepxzx27kzWbFN9vbK6Ivk4FcjrPOAjcsws6TZcOGIDq3b4cnt2dZtWrp6Bwu2ttHI4tnnintymWgnBvVkjHu29+adGjbtsmxADZsqD50EK9xnFr3wAFJT5zLSSA2kxHduPalL8Nt2xaE3RlniOe6WGSooYli0ofORVvUlIXFi0VADw/LPgcPijLXaGlvrxxMo60DA/LZuUo/sKLJlmH0WlcQ50DWNjDWzxxPmOvvD4I5zq6RyUi7dP463a6RYhW+ul7J5WR/9Syrz1rTAhYKlQI5LWTTAjlOHzcVTkyB/CCwzjl3CrAbebh9W9zAOfdC4O+By733XTM/RMMwIuyeNYw5zETKYWG84L3/QLS4mGMllxO7QFMT+/MnS9a2Lji1ybO7SxTo5s2ibbqSfw7uv1+ywL30paKfnnwSTj11MRs2LKZchq13Sbt9+2DR69/Ip9+fjPnP/wKGh3HXfXPc4axdGz6fc07lcpqf/ETev/c90W0qotUVAaKtVqwQXeu69otA1YIonZ08uauRfL6RXFS8TjXio1saOLuzUzrTwin79olA1KhwJiOWCk13l89LyDoWufFkvLTFIk4BBzIuHTSI+B4aku9p2TLJNpLNhghvc7OI5yTTyCgazY1TyakY1+p5mYxEmLV6X4zaJ0olefApFMI+8SS/PVGAJR0ZTgvbakJ9Mk7QLBbe+2Hn3AeA25GUUTd67x93zl0LPOS9vxX4W6AJ+E6StnGH9/4NszZow5jH2D1rGHObiQTy/c65d3vv/yFe6Zx7D/DAMR+xrk6UaH8/d9whGqexEVascGzeLE1+8YvKXZ5+GvbuFV2dzcJPfyoabvt2EdEPPyzt1Hmwdy/4a/4KypnqmRsS2trgJS8Jy+vWwemny+fBKj9k6XFUP2kwM5sNaYMzmWDrragIl0RcYx2b7X82GYc8i+zeTchKkckEP3ChEGwS6gEuleSgsejUqG86kqrR3tbW6sU14ihzPi/fUX29XLtFi8KxVPCWy8HLHGen0Ehv2hucFrnaB4T28RNGHF2Os1r091f2kRbZ4wnh+eFBxnt/G5JlJl73yejzq2Z8UIZhjIvds4Yxd5lIIH8YuMU59zZAJet5iBf5yuM9MMOYFU5cD7JhGIZhGNPEuAI58Tu9xDn3CuCsZPV/eO/vmupBnHNXAVcBdK5cKZPWurt5+Rr5xXzNGmi889955WWXAbBiRQOdnSFSe8klEjzcuFGCqS97Gdxzj0R0GxvhT/5E2vX0wJWJdHfX/OXo8T/0IQlUfvObsn3pUnl/wZrDYSIXyAG7u8c9j9ff9GIAvvtdePxx+OynxfYwRJbubgmClsviP87n4UhhMQuamsQ3vH07rFzJqTvuDv6RxC7gEnvChjPOgEfrJPx88GCY+FYoiEEaJMK8aFFY772EzvXgaeIKewcOjI26qgd5925ZrquTSPTRoxJB1jzImYzYPTTbRLXiHzoRT69pHO3VTBTxtYZKP7Iu53KV30s+L8dMR8arnWv63I4FE8iGYRiGMa+ZdPZSIoinLIpTfVSkoHk2s4i20xbx6H/Ar38tOm8p8KvNUtBi06ZK3aTJHLq6pJbI1q3w9a9L2rWlS8UmC7IewL/3fbivfHn0+J/7rOeZHY7168VW29eXeI23bqsUQyr+oGqet9YWsRV0dDiWL2c0zUb96lMq/MRaMXlB6Vk5WLEowlvFan9/ZT5dFX47d4o4rasTUd3fL69SKQjSurowMU/ToA0NyfZqwq6vL1geGqtUGNRtR4/Ku3MiugcGgqVCx6Te6LiwSJy2TVPB6Vhjq4RaTXTfdKlptWDoeWgfcX/az3ikJyBOtUCIYgLZMAzDMOY1x1CL9zkyMkI+LwHVz35WxOSWLXDyya+ndbs0+eUv4VtR0olSKVSnvv9+qffR2Smabvt2+OIXU8dIGYi/cL2jWISzzgpVmr/yFTj33Bdw6SVRyshdu0LVuQVjE3UcLYqfubNTxvSzfacAsL5N5tOpZs3lYGVrP3z/TlH3bW1iWL7rLplpqI3U+6tCcsmSkEYjnxdxu2ePhMZPPlnWaw5oFfI7d0pKNu+r50GOqaurnhqtvz+I58OHZXx6QupzLhZlXxWd+gDRH6XiUIGsTwvatq8vVMXTyXjxRLi06O7vD5MbdX0mI2OaSNA6G/YAACAASURBVPSOt20qk+5O0El6hmEYhmFMHzMukI+W6vj858U2sW+fCNazz4ZPfALe+U5pc999sk4Dq3fcMbYfTb8b43//D2DdOtxf3MiSJXDhhfL64Mt/CW1t7C+cQkeH6Cj32KMSkr4nElWajxfGRiOBxkQ4ZbPrWL4cLmp6VDY0rcdnsizIjyTit8yhUhOtF1wQIsbOiTjWWtSjM/kIwvbgwcrCGyBp35qb4VmZ0EexKGFwtVS0tEi0OU7zFmeqiG0GhUJljmRdF0dpCwX5fOSI9JPLVaZ80zRu1TJIpCfcpdOsaZR4NFcflRFkFcRNTZVFQXSbRt6VtJBNR/31+FW+y3ExD7JhGIZhzHtmPoKM6JimJtFJDQ0SNL322qDDDh+Gm2+uvT//xt+SD6WGUb/F6tXy8ckn4YcXvJDVLdCWaLNcDro7zqbcDiuLT4WOBgaq5xJWkrQVCwckev2z/rMBuCADdXhZmUQgW0t98NBDovY7OyUies89cPnloX9N9Ky0twebRy4n6Th27xbvcJzcedMmuVgNDfKU0dlZOd5YvGoqOKWurvKYxaIIdW2jwlsj6YqKRhWhcREQEOGs+ZGrtdHxVbNBpLNYDAyEaLHaLVpaGH26UdICOb0c526eCtMkkJ1z24HDwAgw7L0/3znXDvwzsBrYDvyO9/5ZJzmcvgC8FhgA3uG9/0W1fg3DMAzDOL4co0nz2Mnl4NJL5b23V3TTgQNinVD27Tv2/t1nPj36WUvSHzwox4oDjL1JIT6WLg2vfD68NNoZv6JzWLZMbMWjc/qKRRGtGh3dsSOU1N6zR05Ky0ar31fFYXOzvOrrZVBavW5kJKRYq6sL4laPMzAg2+vq5DjOyauvL7zSE9vS56SCXD3R6mdWIXvkiKxTW4hGdvWl10sr++kF0qp9KrxjC4VGmuMIc5w/Wd/jNvGyvvQ4cZ7laq+poBHkyV61c6n3/hzv/fnJ8seBH3rv1wE/TJYBXgOsS15XAV8e05NhGIZhGDPCjEeQy2URritWiHugrU38xUeOhMQF2ewUbaBqKYi8x6tXi85btqyyMBuEyscjI1RGSovFoKKrZUBIxFZTk+hObVKX8ZURUBWHbW1hbHEhjMFBEbUq3mLPtHNBLGo+4ljkNTXB/v1hf7U7aCJmPZYST97TyHRMtSirfh4eFsGoof04U4aOqdryeJX0dFssllWk67a4nfYbF0WJx58Wv+mov/YxVU/x8bVYXAFcknz+OnAP8LFk/Te89x64zznX5pxb7r3fezwHYxiGYRjGWGZFIG/ZItpxaEjEajYL73433HijtFm8WAKu1TKJVSURTe7mfx5dVSxKWrj2dnjd6yA7fJSRciMN9SOU83Wc2tYDTcOwOareWSwGy0A1gdzZKc3KMr5XvyIRjkeOVo6lqUmqmqxeLVFjFXKrV8t7Y2PIRAGVlgRNq6Zp3Do6Kie1FYvyRBGXbtaiHensEdp3NfEajzefDyK4oUFesRc7Ljet4lGPFWeYKBTkXaPW1UR03F7fY6+wFhyJo81xWeuJIsJpgZz2QtdKbQK5wzn3ULR8Q5KtJcYDdzjnPPD3yfalKnq993udc+qzWQnEpd13JetMIBuGYRjGDDPjAjmTEZ155IgkinjmGVi/Xira3XKLtNm5M2i+ifBXvQcAd8PYUtIrVshrwwbI/uJ+qK+nbs0a2L2bxtZWUbjFYmW+3VgATiCS2s5ZREsLovT1pFSsqp92ZCREPp0TUdzSIuJRfbZaS1sFYEdHyDFcKoW2UCmQs1l5NTSIjWJ4WI535MjYwcYT9kqlsZPrtKSzTt7T48WlARX1BMciOEaj57otLjVdKgWhr9ku4n7Ty9qfppuLo8tKWuyPlwf5+JSa7o5sE+Px/3jv9yQi+AfOuc0TtK1W8rGGu8AwDMMwjOlmVibpZTISWD1yJAhhrYMBtYnjWujqSrROU07EXiyUVHTFac9iAVYt6pgIrlGNqfsWi0E81tfLes1QoaJZ27W1jc2UEQu7gYHgCVHPsQpgHZf6ZAcHRWxqBFjFcJypIj4+VApmJZ3ZYjz0HCfbVm2CYzyGuHhJepuOuVq/1UpYT0T64aIWpjGLhfd+T/Le5Zz7V+ACYL9aJ5xzywH9CWMXcFK0+ypgz7QMxDAMwzCMKTErEeS+PrE+PP20rHvoIbjpJvjGN2rrw1/5Rmhrw93wj1W3d3ZKsLalJQkQr18WCm+ohaGaeNJcvTrQNEnEt/GkkzhjbQM8lkSfCwXpX+0DXV0SMdaUaIVCZeT18OEwGQ6C+FXhqJHe4WG5WJq8GcLEPPXjxsVCdMyxCD58OCyncyCDHCeOmmofQ0Oyvr5ejq9t1Eudvn6xdUIFctqaEUeK0+/p7CHpSXw6cTD+XtJR7LS/uppQr4VpEMjOuQVAxnt/OPl8GXAtcCvwB8B1yfu/JbvcCnzAOXcT8GLgkPmPDcMwDGN2mJUI8rTwta8B1QWyYTwnpieCvBT4V8neRj3wLe/9fzrnHgRuds69E9gB/HbS/jYkxdtWJM3bH07HIAzDMAzDmDozLpDrMp7f+i14+OGwbutWuP76KXRSLsOVV8It1Tfv2CEB3QcekLlyTU3LufDC5bT27ZSwcrksM/gyGal0p3R1hShrtRmCLS3JSdRJNHr9elnesqUyStrfL7mKN2+WV1OTGKK7usRsrVXvNBoaR3b37Anr9+2TEPjBgyEPciYjaUA0i0VfH5xySmVKtdHcc9H1AomwpqPIBw9K9Far+vX2hmWNTtfXi52jUJBzca6yPYSy2HGFQD2Prq4wtqamEBFW8vnKSYsDA3K+cQYL7TcWr+mJlOnvrOZZnhHTZLHw3m8DXlBlfQ/wyirrPfD+53xgwzAMwzCeMzMukD2uQlsqCxeGYnET7v+m34bhDO6Wfx23zWmnwWWXiX6qq5N6HK3DPSJ8+vpEoG3fLnYLF82NamkJP9NXE0kq/OKSyCAHUh8yyOe6Opl0t3BhGEhLS6VNoZrNoFAI1gHNTdfcHI55+LCMe3CwMo8xBKtGbDXQdhDyGcfElo743I4cqSwqopMOta90+3hyowrTajYVTemWLvihfQ8PVz486ATItL0iPqaStliogJ5bad4MwzAMw5jjzLhALpUkSKraxTmxyLa1TS6Q/ZVvhBE3qbf0N39TmrzoRaIt16wBdiZ2Tu9DRgWoFIFNTdVz+CoqCvv7gy8XKoWb+o7Vv9vcPDbNmeY5jnMnp/uBcJGy2Uoh71zI36wJnbWwSDX0nLwfm+lCxWl6LLGvOc5brA8YE1WxS2cCiYtrqBd7ogl08fF0WYV3erJfLUzFi1x7FgvDMAzDMJ6nzFoWCw2+qsbs6Zl4H/87b4abb4Err8R99zsTts3nQ/a2urpEZzY0SAS3qyuIy7TQ6+sbm+c3Ji7E0dYGu3aFZc3dq5YEjSqXSiJoVTBrWrV0ujXtR3MJx9HaeAKeok8W2aycZFzwJG4bR8mr5XYeGQnj0uWRkRDF1Sp+KlB1ImO6wEi16nfp4iPp7BXxWKvtE/dXLcdzrUzVamERZMMwDMOY18y4QNYqzGrnBdEvp51WaQceQyYDv/M7FcVAxuPwYfjxj6G1NSkU8poRaG/nmf5FnLyiXlbu2BFy8sZMJKaamwHYObCITBFWqrguFIJnV6Oc9fVisdi2LQjMpiY50X37Kp8SVJy2twdhfORIsBto9T0IJac1atzQECruKXG0ubGxMhKdFtq5XGUUvbk5RMFzOfmiSqXKTBvxdUtXxIPq1gcV3ONV4FPinNI6bk17pw8OymTfXdpyUQvTmObNMAzDMIwTk1mJIOt8rpiJxLF/y1uhvh73T2MLglRjxw7RfM8+K8f5wV11nHvuIvr64KnhReQGoK39ZJoLIzJDUImjttVIhGQ5seOOVsbr7hYxd+BACIkXixJhHhgIRT3Ue6uFObSCnnqbW1rCpLfGxhAGT+cpVr/z4KCIYRWVShxNjgWliutq6D5a7KO/X85JhemCBTLehoYQ5YbKinoqgFWYxt5qHeNkUWTNH51OCaf7VttvPGoo+jLhfoZhGIZhzEtmRSDv2iUT56bEFMoFF4ui55S+vqDjjh4V/dbbC/l8Hdl0KHu8n/ojCoUkyFsUsbkgkxEhrOJ4aCh4jOMCHv39sm6iEsjlcogAa3W8+ISyWVmn+x49KlFftXGkic+pWgR5eFjGqwJZ7QwqcjVyrYJVs2dodDdttYDqEx3jnMaxJ1m3xevGiwzHExt1OSYdMY7LWdeKRZANwzAMY94zK4VC1q+vrPBcDX/pK0SEff/7uJtqL6137bWS3u2ii2S5szMUr1u/XsSy6qjsYL9ks1Ceeip8rjbhLfEcLz7jDNne3i7rt24Vxa3Cqrtbosl79sDevRJ5LRalzaZNsH+/+D80jK6ir6ND9lXB2Nsr5myNKgMsXiyidXAw2DTSKUDi8HyxWOllTkeQ1SetmTMGBoK9olyW86yrkwtXLMq6BQuqR4EHBoL3Oj6v9OQ6qCxMEhdIUXtHbOHQ6xFHp2HsGNLCeqIJlxNhk/QMwzAMY15Te1h2tnjNa2Z7BMZ8QiPIk70MwzAMw3jeMvOFQpJ0wPv2jd/Gv+a1MJIdv0GNqI5ZskSCs/X1odZEuQz0D47dSSPH1Sbr6WS6xkaxNsRCKfYvV8sW4X2wMTQ0VOYe1n7jHMPxJLY4aqqRXi0zPVlWh2qT4NLLsd83n6+0cGjas3i/2BNdTSymJ+BVy32cbl8taptOE6eRZOV4RXpNABuGYRjGvGbGBXJ9kkRi0mxdd9wBl1465f513lt7u9g48nn5nM+DKx6lDqiDIGLjdGuDg0F0pSfGQRBOajdIslrQ11cpXAcGZH8Vupp9YXBQUmwMDlafLKf5iNXaoJaDWKxrxT7tU9tC5aS4uE8V2LEfOn1ecV5i9TJr/ypM477S16SW6Op4Qle3VSs6ouOYggf9OWEeZMMwDMOY98y4QC6XxbLb1TVBo2wWXvMa3Pdvq6nPxkZ5HxqCu+4Sm67adnt7JYKcLQ+GBMmFgviJ9+2rLJwxWVownSi3YoX0s2ePLDc1ja06VyrBokUhi0WhIMfPZmXA1YStc6HssvchawRU9p/LhQIhcZU5FXZxBDsuKlKNfL7Sm6xV/rQQSl2dvOsx9DjpPM4a6U4X+UgznjiOI+X69KTXRf3Q6UjzZBFk7cfyIBuGYRiGMQVmJYuFYcxpTCAbhmEYxrxmVgRyPi/B1TT+st8AwN16e039/P7vy7tmxGhvh8svl0QQZ54p69auhSxDEjHWbAoLFkhktVwOacxAws0aGa0WndRIZGNjpW93z55gjRgakuhrb69Ep/v65HgNDaF/tWBo9DqOJB84EIqO7Nsn4+vvr/QI9/XJcqEgY9I8zNXy/nofztG56pkeYluFnocW6lCbhUaNS6XKPMhqLSkWx1ok0uWntY/YiqGp2+K8yprFolqVvtibk7ZdpKPax5IH2UpNG4ZhGMa8Z25lsbjjjtkegTHfsSwWhmEYhjHvmRUP8uLF8M1UUTx/7nm4X3iYQCO3tEjkee9euOoq+PM/Fz/zpz4l2w8cgLee+Sv++t9fwCtfPsTR4Sz19TBYztLQ1ga33QYXXCB5iM85R/y1cQm/vXtDJDUu16yosVmjpa99rSzfe6+EsdUMXSxKX7t2SXS3UJB+9+yR442MSB5kjYZqFHbXLti5M1TWO3hQjtnfH2pzFwqyLpOR7cPDkuy5v78yC4ZSKoX16l2O0RzI2r/mQW5pkf414pzJSL/ah/aj2wcGQnRYZ0qqkKwWlY/71Ui1Rqj1+qoY1XPQyn5KOlvIeBHlqUaETQAbhmEYxrzGPMiGkcYEsmEYhmHMa2ZcIHd1wac/Df/0T7LsL/sNuOMOiR5PQrEIu3eLffe220ImDE0u0dMDX7jnBTzzDHz7u1lGRuCss+CFaw6Jb/elL5VIa1ubVNDbs6cy6rh0aYjmVkMjli95iQxm505Z7uyUl3pse3slOq15iuvrJara2SkZMDSH8aFDsr+mXmtvl+MXCtJXa2vwAsfR2La20K96ghsaQiaKOLKazs9cLb9eb2+IWvf2yj6NjTLmkZGQ8m54OIwtHZ3V7BfV0rHFpam1ap620+wVGiHWCHY+H7JXxB7lOBrc11d5nHSkOC5vXSuW5s0wDMMw5j0zLpAPHIjE8aWvgDvuhssum9BaoZRK4nzwXqwVPT3yecMG2d7bCzfdJJWXe3pEM+7eDS8s7JONy5aJiBoYEKHc1VUpqsab6KYsXizvKvJUOKowzWSCqMznZcCaLk3FqZaKzmTC5DkVyM6FstRa0lkLkqjIiy0M2WzYFovAuEx2JhPsIs6NLaHtvbTR9dlsWPZe+h0aGuvBjUtF6zVJT9KL07SpUE9PEkynbtOJeHGqN73ek5WWHi9X8lRzKJtANgzDMIx5zax4kEfxHi6/HPef3695fx8FmhcsEG2lgcSREQnKqhUYYOVKRAwPDMiruzuI0FyuMtpaLAaxmhaSEPIJq082zqGs61U46+e6usq+BgdDNo1YlOoxBwbGFgKpljs49hYPDgZhmx679qPHGa/y3dGj4bNGiOMHBhWjWrSjWrRWRXC1Qh+6n3qX4ywW6WwXaUEbi/H4QWCyvNXHInQti4VhGIZhzHtm34P8HMRIU1OlBoo14uLFou86OoA9idDct29skYtYIGtkFiYuoayRZ40Aq8iLJ5UNDYkdQe0FhYIoeZ1gV61frdAXr0uPo9r18n788s9x+2KxegW/gYEgNgcGQgq73t7KiYTxuNPjm6yUdPw5Xo6FfnoffTCIy2HHx6hW0rvacacqlC2CbBiGYRjzmlkTyB6HuyeEg//wD+EVr5DPPT1S/S7WQsuWiUW3qUk01a5dIbXwunWh3Y4dkvv4lN5fysY7t8N//VfIHJHPB59tsSgRZSUWb9UiyCrI2tpEkPb3V+5bVycqvVgUQdzfL5kgymVZjv3Nmu83plAYW55aI9Eq3rWNCtj+fhG3GpVOE4u9tMCM28T2CLVE6Gd9qNAocTWhXK2v+FxVBMdWER1TjIpiPW99wNAHh4nsEmnBPJmAroZ5kA3DMAxj3jMrAtlvfAHukSCOr7oKrr0WNm2S5ZUrYc2aUABk3z7JOtbSIvqwrQ0eeST84q7aackSybh2ySVAuUVUdGyFWLlSdujokE6LxSTEnBBHMmPxqxQK8q7+Yu23ubmyXT4vij6TkZRyBw7Iuvb2kJotlxs7yQxECNfXSzsVtHo8RSfU5XLyec+eIGRhrICdqDyzTrpLe4p132qR4fhBIp5sp0zkBR4v8hzbL3I5eZhQL3J9vXz5+vAwHunjtreH/rZvH3+/NCeoQHbOXQ58AagDvuq9vy61vQH4BnAe0AO82Xu/fabHaRiGYPesYcxdZlwgn9f0BDRtrFhXKMDmzUGrasE31YQjI6IXW1okaFoqwerVIamCBmafekqWu7th+cBAEJKnny7m5KVLJcqrloJYHOtAJspioQJaB6cRSp056Jz0f+SIvB89KhHl5mbZ3tsrx6ivF3tG2kOrArK+XsZeKomI1gi0jkEju5qbWDM+qEAcz9Nbzcus56P9p7NS6PHUj6xj1j5j4a59a7Rbt8WTDKuJ53R1PP1i05P10vunz0UFsaJjXb587DHH4wSNIDvn6oAvAa8GdgEPOudu9d5vipq9E3jWe7/WOfcW4NPAm2d+tIZh2D1rGHObmY8gqyUgoalJFlevDhpNg6aqVRcvrrSkFgpio0jbYvN5eOIJWF44JMfR0LIeT/23Gr3VghiKcyFnXDV0hqCKY/Uga7/19dJHNivrVq6UVHDFolgyli0Tpe+c7K/9pW0QmnVCL0RTU7gYafEYC8hY3Crp8svpCPJ4AjRt2ahmr6g2drVHpLfp9ngSn7YplYLA1y84LiQSe5EnmpiX9jLrd6uTK2vlxJykdwGw1Xu/DcA5dxNwBRD/Z3sFcE3y+bvAF51zzns/eY5FwzCmG7tnDWMOMyMC2Tl3FXBVsjjo7rnnMRAR2N8Pn/mMvKaLd71r+vqqkQ6ge9JWJxbPt3M6uZZGP4fbnZz7ZMy1a7MS2Bkt7wJePF4b7/2wc+4QsIgq5zLmnnXusXSbOcJc/judy2ODuT2+uTy206epn2m7Z+1+nTbm8vhsbMfOMd2zMyKQvfc3ADcAOOce8t6fPxPHnSnsnJ4/eO8vn+0xHCNVaqOTjjLV0kZWniD3rI3t2JnL45vrY5uurqqsO6Z71u7X6WEuj8/Gduwc6z07xQoKhmHMUXYBJ0XLq4A947VxztUDrcDBGRmdYRhp7J41jDmMCWTDeH7wILDOOXeKcy4HvAW4NdXmVuAPks9vAu4yL6NhzBp2zxrGHGY20rzdMAvHPN7YORmzSuJP/ABwO5Iy6kbv/ePOuWuBh7z3twL/B/imc24rEoV6S43dz+W/BRvbsTOXx/e8H9txvGef99fuODKXx2djO3aOaXzOHkYNwzAMwzAMI2AWC8MwDMMwDMOIMIFsGIZhGIZhGBEmkA3DwDl3uXPuCefcVufcx6tsb3DO/XOy/X7n3Oo5Nr6POOc2Oececc790DlXU97rmRhb1O5NzjnvnJuxdEi1jM059zvJtXvcOfetmRpbLeNzznU65+52zv0y+W5fO4Nju9E51zVeTmEnXJ+M/RHn3LkzNbbk+HP2nrX79fiOb7bu2Xl3v3rv7WUve83jFzJB6ClgDZADfgWcmWrzR8BXks9vAf55jo3vUqCQfH7fTI2vlrEl7ZqBe4H7gPPnytiAdcAvgYXJ8pI59r3eALwv+XwmsH0Gx/dy4FzgsXG2vxb4PpKr+ELg/jl27WblnrX79bhfu1m5Z+fj/WoRZMMwRkveeu9LgJa8jbkC+Hry+bvAK51z1YoYzMr4vPd3e++Teuzch+SUnRNjS/gU8BmgWGXbbI7t3cCXvPfPAnjvu+bY+DyQ1IynlbF5go8b3vt7mTjn8BXAN7xwH9DmnFs+M6Ob0/es3a/Hzly+Z+fd/WoC2TCMaiVvV47Xxns/DGjJ25mglvHFvBOJFMwEk47NOfdC4CTv/fdmaExKLdftNOA059xPnXP3OedmspJkLeO7Bni7c24XcBvwxzMztJqY6t/lTB97tu5Zu1+Pnbl8z867+3VOCmTn3FLn3Lecc9uccz93zv3MOffGWRjH+c6566ehn2ucc386HWOaD9j1mnGmtUz1caDmYzvn3g6cD/ztcR1RdMgq60bH5pzLAJ8D/mSGxhNTy3WrR36yvQR4K/BV51zbcR6XUsv43gp8zXu/CvmJ9JvJNZ0LzPV7YrbGZ/frsTOX79l5d7/OlYGPkvwEdAtwr/d+jff+PMQ/NVM/wYzivX/Ie3/1TBwrMZDPue/jeOOcq5uGPmaj4M3zible8raW8eGcexXwCeAN3vvBOTK2ZmADcI9zbjvifbt1hib+1Pq9/pv3fsh7/zTwBPKf70xQy/jeCdwM4L3/GZAHOmZkdJNT09/lLB57tu5Zu1+P3/i0zWzcs/Pvfp0pA/UUjNavBH40wfY65GnzQeAR4D3J+kuAexCv1Wbg/xIKoZwH/Aj4OVK1aHmy/mpgU9LPTVWOdQnwveTzNcCNyTG2AVePM77LgV8gBvYfTrQvsBr4NfC/EdP9ycgT2KPAY8Cno377gU8n53An4gfS/t6QtMkD/5js/0vg0mT9WcADwMPJua5L1n8kOc5jwIeiMW0Gvpqs/7/Aq4CfAk8CFyTtFiTn9GByrCuqXAuXfFePJWN6c3Rd7wa+BWxK1n0CudHvBL4N/Gmy/lTgP5Pz/jGwPln/NeCzST//c7b/bk/kFxKR2AacQph8cVaqzfupnPBz8xwb3wuRCSTr5tq1S7W/h5mbpFfLdbsc+HryuQP5CXLRHBrf94F3JJ/PQP5DczP4/a5m/Ek/v0nlpJ8H5tLf3Wzds3a/HvdrNyv37Hy8X2fsD3MKJ3g18LkJtl8F/HnyuQF4KPnCLkE8VquQyPjPgJcCWeC/gMXJPm9GSnqSfHkNyee2Kse6hEqB/F/JMTuAHiCbar84+WM9JVlun2jf5MssAxcm7VYAO5J+6oG7gCuTbR54TfL5X4E7kj5eADycrP8T4B+Tz+uTvvLA/wJ+N1mfAxqRh4ZHEaHbBDyO/KO1GhgGzk6u488RIewQk/stST//HXi7XjtgC7AgdT3+G/AD5KFmaTKe5cl1PRJdJx1LATH4byUI5B8SBP2LgbuSz18DvgfUzfbf7PPhhfwctgX5T+sTybprqXz4+k7y3TwArJlj47sT2I88BD4M3DpXxpZqew8z+x/uZNfNIQ+am5J78C1z7Hs9E3k4/1XyvV42g2P7NrAXGEKiT+8E3gu8N7p2X0rG/uhMfq81XrtZu2ftfj2u127W7tn5dr/OuVLTzrmrEeH04WT5S4jQLXnvX+Sc+y6wEdAZsK3Ae4AS8oW9Otnvy8gX9TAiTrcl7euAvd77y5xz/4lEZm9BhF9/aiyXIELtdc65a4Ah7/3fJNt+Dbzae78rav965I/1d1P9VN0XEcF3e+9PSdZfAfw37/3vJ8vvRJ7QPuKcGwTy3nvvnLsWGPTe/01iyzjovW9zzv0r8L+893cl+/8YiSJsQCK03wD+xXv/pHPug8hT5yeTtp8CDgC3Aj/w3q9L1n8DuN17/3+dc2uS/c9xzj2E/AM8nJxmO/Ab3vtfR+f9OeBR7/2NyfI3kX+w+4C/9N5fmqz/EPIwoWP5LPLw8pVkTE9El7PBe3+Gc+5rybX7OoZhGIZhGNPIXPRuPo5EHgHw3r/fOdeBRIpBngL+2Ht/e7xTImZjH9MIcn4OeNx7f1GVY/0mkjvvDcBfOOfO8jLbdzyq9V8xDMY3fY+375HU/uMx5MPTTFn7896XIw9u1f29999yzt2PnO/tzrl3TXKseKzlaLkcjdshQiT8LQAAAfFJREFUYj4Wr2kmOsaR1HK165YBer3359TYh2EYhmEYxnNmLk4KuwvIO+feF60rRJ9vB97nnMsCOOdOc84tmKC/J4DFzrmLkvZZ59xZSeT1JO/93cBHEZtA03Mc+8+Ai51zGhFun+L+9yf7dyST196KeKdr5V7gd5NjnwZ0Ak8kkd9t3vvrkQjxxqTtlc65QnL93oh4fGvlduCPNa9mkhqn2nje7Jyrc84tRh5GHhin3Rudc43OuWbg9QDe+z7gaefcbyfHcM65F0xhjIZhGIZhGFNmzkWQEwvBlcDnnHMfRX5iPwJ8LGnyVcQn+4tEnB0Arpygv5Jz7k3A9c65VuScP4/4aP4pWecQ33Pvcxz7AefcVcC/JAK8C7FS1Lr/XufcnyETzxxwm/f+36YwhP8NfMU59yhifXiH937QOfdmJDfhELAPuNZ7fzCxKahg/ar3/peu9nKkn0Ku4yPJ97AdeF2qzb8CFyF+JA981Hu/zzm3PnXev3DO/TNih3mGSqH+u8CXnXN/jniub0r6MwzDMAzDOC7MOQ+yYRiGYRiGYcwmc9FiYRiGYRiGYRizhglkwzAMwzAMw4gwgWwYhmEYhmEYESaQDcMwDMMwDCPCBLJhGIZhGIZhRJhANgzDMAzDMIwIE8iGYRiGYRiGEfH/A/7az1SX44BOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots_with_1_level_3d(df_chr, 'hi-c-rao')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "gene_order_tss_y\n",
      "35      0.999161\n",
      "36      0.999161\n",
      "39      0.999161\n",
      "40      0.999161\n",
      "42      0.999161\n",
      "          ...   \n",
      "2393    0.999161\n",
      "2395    0.999161\n",
      "2399    0.999161\n",
      "2408    0.999161\n",
      "2410    0.999161\n",
      "Length: 597, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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2+PsD06bRo4cGRPv0sb7mzstjzRqNAteuWUH9+nCjfpCah5OSwNaW0lJIW/YVWa+v1mNiYmjRAjp7HueJ9A8oK4OCM9fp1AnqvP0SrFkDQ4ZQUaFR5awsjYJ29D1LTAxMmgTbtmm09tIlGDcOPlzviIeHely9vdVi8Mor6nHNyoJT2Xa0amWNbhYU8Omn+rrfxgb+8Ae1I7BmDX5+agVYulR91xMmaDeefhreegu++AJqrF9K97AKDtxqSatWmnDCw0OD0+++CzRvzkTfz3n7bRg/Hq30z3+G7duZOxdGjtRhXrdOj3ktpiVs2MDgwfBIRDvYsIHjxyrYvRto1IjDNm3g0iU9B2Vl6inu0wcKCpgyBe2svz906sSLwytg0CAuXxIWLYKGHte5FbMVxo5l+Zg0Fs69TViYnh5OnaK01OoBz8nhlZE36BV0RqPUsbEsGniIwEB9M+DoCOTns2KF9byHh7PtD3NpM/px/vxnPWW3829QWKjjT9OmxMbCv/4FCQkaeL5yBV57DQYM0HF94gn48ks9PzRv/pMu0YfoQTYYDAaDwfAr5Jf5p75+ffUgBwTAnj1UVsLFVxeSn6/2A0vZbfLygJAQunbVt/7rP6mCuzu4WHRWV0XrdhAfj78/vLi+I61WvICHB1CrFjExsPdSE9ZXG8WePWDnXp358+HMpKUweDAkJ3PqFNSsqfUVF8Pwv9Rnzhx9Pf/aazBvnoq1v/1N7QTHjqnws7PTV/mffWYV1h2hoV8Fhw9D7ezDULMm69apbaJmTbUHnzgB14a/QlXbCtzctMy+fWHxYs0ml5Cg9U2fDgwdyvGTVQiV/Xz2GTRwuIi7u9pAPv0UmDGD6136sns3fPIJ4OurM/d8fFi1Ck6eBPbsYeJEFZbvzLgBgYFkZMCOmd/A5Mm8OLoKCxYAqam0KvkKatZUcb9+PY0aAW+8AZMnc+gQquBzcqCwkLT0KuDuTvs/WPjLX+DlmdWpN7YXrFhBx3FNWbS8Khs2WH3QFy9y5Ag8+STg58cn21z04aikBPr14xBtSE/X7G89egBHjjBiBLRuDRw+TM+bn3Jo5VFWr4ZZs6BxiAsVFWqB4cYN+vSBgQOhnd9l5s7Vc5WVpedv6FDdz8kJunQB6v1wnsuPYwSywWAwGAy/bx7+P/WVlRAdTadOaA7isDAKC6Huzo9ISYHERPjyQFWdBFVQwDvvaFQ58o/XKCyEzV86Q0kJVaJWg7c36enQs6d6ah0ctMyhQ6Fz+RdE9inGxwfeflvFaIO8Q7B+PbRvj7e36r5GjTRC+847an318FDRWlKiEeQ//lGFl6en+oXPnNEUvB066Ge+vkBcHOHhsL+0FVRW8skn6lX29dWtogJqbPkYjh+noECjqk8+Cc89p9kWbt/WoG2tWsDu3TTxu82O4ieYPx/IyMDXV/20XboAUVHk5Wmk+5VXtG6cnCA1FT8/Fe9UqcLcuSq4+z3vAs7OtG8P3e2/hL//nWbNrCK2UyeuBHaEnBxcuAFvv03LOrkwZAgcOKBZNaxlk5ZGU7dzsHUr69ZpewIDoVcv+KrPItasgYn9ztK6NTxaeQrq1qV9ex1jMjL0fD/2mA5yWRlXrui45OZqJJ7Vq3nzTX1Y4Pp1vqg5gBbDmuHtDeHh8NJL2rfwcO1zQICeg22H61BYqOft8GG1OG/frlp88mR9GLnSOPS+L0+LxQhkg8FgMBh+7/wyk/RiY/Wt95UrnM6tyr59QHg4c+ZA58DLdO0imubN1ZWiIhW+Zwpr4OMDT1/4uyret96C+fPx8NAMFy8/uk1Tx+Xn88gjQLt2bN3rTFmZHr9mjbXuY8fA1ZWBA3Vy1+DBaqeIjNRJbx4e0K+fRos7ddJ0bYMHwwsv6KS9WbPgkYI0QkJ0vZCKCqC0lPfegyd8z0FBAXXq6H537mgZffrAzYjn2XEuiP37Vdj26aNRzqtXtYy1a3VSIQB5eXTPX6eT95KSaNtWM1a4ugLJyTS8fID4eGsWCxsbjcS7uVFYaHUTPPIIzZrpg8Wmd06BrS0dS3bA/PmQlsaf/wwrVgDR0dRO+UKVZkEBLF7MPw95q4/Bx0dTrM2bpwNcpQq33OvBuXMUFGjkvU8feO896DjhceLiYPba+pw8CXd8G3KuYyRHj+qDBg4OrFwJ36ZYVOWvX8/ly/rw0d39MEeOAAkJjBqFWj9sbXmy6J8sWaLujm7ddELgK96f6oPTwYM4OOjDR716+qDi5KTD4OWl53PoUE3t9847UHvGqPu+PO9msfixzWAwGAwGw/8uD/+f+urVYd8+kpKeZNTTDTl50rqoWl4egwfXpX37OkREaGC0Y7NmHD8Oq1froadPw9y5YyhoPoaKCvWgum/XV/SvrunJgQPQt0kTnn0WvvjCmV49KhgwuApZWSqgBg9uxfaAVozJeoPGjdWn6uYG//ynitSpU1VvHjig6eHGjFEL7rZtmsO3dWsVyyNGqAc2N1c9yn9J7oWbG3wcV4/nKytZu1aF7ynVpnh4wBi/XeTmPkXXrpq2DtQOcfu2Cunhw3Xf0EVtGDvHm8LC5/jXv+DCW6+ypoe2z80Nuu7twKGUqixZovU/N2WKFubkRGGh5mNe80FvCpL0oWBCakMOzb9As/EdWb++O03z9zJpEiQnw1eTJrHo2JNMLJnFNxfqEzThDWa0hmd2ekBCAnEHn+TFrGWQkcGJyobELoHXdu9mobpUGDtW+/La/v1k/VXbdzfV27s99nLtWmfWrIExXl5UVlqTlzRvDj4+DB4Mf/0rjLVrRWEh0Ls5G5ZYfdwzZ7K5uCsLX7+X9KRhQ0hrOIDERHhp8WIcF0GdOhrdHzNGNX7v3poOrrxcH2yKijTd2+rVH8DK+1/kzkSIDQaDwWD4ffPwpcCdO9xs/yQHDgAi3ws/CW5GaKhOMrOx0Sgup0/Tpo1aD3x8dLKenx+0O/YhrVrBnDk60W7nTo00tmsHPPYYEREQ2uo2O3ZXYehQaNtWrRT1d3/IGNsPoWpVtm7VKHFOji7IkZen7di0ScXo5s3ajrw8tUHY2qrg9fdXe4Gnp0YbN29WkXzlinUCWXk5/furm6BLF41++voCJSU0aqST8558Uq0Abm6am9nOTifcBQUBt2/Tq5c+FPTvryK0VSu1MrRqBWRk0CakAmdna0q1NWtUCZaVkZeni6ewZw9OThARARs3AgkJTJ0KTRM+hBUr6N1b7Qds3KhlpKcTEAAu+Wc0dVx2Njg56QS7tm1h+nQax8wmIgKYNInmzfWc7NunwpR//EPtH6i3OiIC8PKioECjuuTmEhBgTUF34ACkpJCdrX1/5hm1T9ytz8kJyMzk6U7X6dBBI/BNm6pAbtrUOukvP5/CQl245fx5teXY299bvdrLS8vu0UPHr+7GpT/pEjUWC4PBYDAYft88/AjytWtUm/4yM2e+D/7+9Nz9Pv/0fplt2zS42NXvDOLbQCfpJSWRlaVv+Z+x3Qy9m/PVwfp0jIuj6pkzcP48oxYv5p9xNXgm6AQVIxpDl1RmRQFTp7KbRQwdqsK1Xz8gcpUq11u3aPnMcdbNdeH517157jkVeydPqrf373/XLBB3s074+WnK5oYNVRg/E3ic3IFN8PTU1/i1erRhypRDdA28CE5O9OwJ336rAr6kRK0Ql9v3pWPyDrp27c7Nmyoig4M1nbOTk9ohysuBnBy6d/GGjAw+XBEEo0cze/aHeHqqMEs71oym+Zf54vkvoGtXWH5BJ+kVFzN9utZLaipV6n2fcph6J04waHIFRMZBVhaenprhgZwcGvhUQFkZNZbNhsBA/lJzC2w+C/368UF/YMApLf/gQR6NjAQPD4YM0fZu2aL+7zvDRxFwScWsnU0FREVRMfQFRo+Gd94oho021Kun6ZqZfhBsbO7O1SMkRG3px87XoWlTq6AesYSKEaOoWlUtFC1b6sPRd99ZfdhpadSrp9H3c+f0XD3zjJ6/5s1ViNvYqHW6Sxegc+f7vjzvepANBoPBYDD8jnmQ3HD/ydbSx0dk5kyRrCxNELxunUhysiatLSkRKSrS36dO1WS969ZJVpZovt6DB0Vmzbp3fHm5yMqVIkOGiKSmimRni/j6SlqaiIwfL9K+vSY2rqwUSU8XmTJFZMUKLTcxUaSwUIqKNH+vzJkjEhOj+5WUiKxZI7Gxmp9Y8vO1zJwczbWclCTbt2tKX0lM1Pbl5IjMny9iby/r1mku4CVLdNuyRUQGDtS+lZWJFBVJVpbIqlWapzchQbQ/ly5pGXFxIitWSGqqfjRsmIhs3ap5nDt3Fhk9WqSkRPMaz5ypiXl9fSU311pWaKjIxo2Sk2Mdt7AwzS+cliYSFyeZmdacyN26iaSkiISEaGcCAiQ6WjSBc2CgSEaGSIcO+t3dcTl3TqSsTPLyRGTyZM1JPWGCSGSklrVmjZw8KSLLl2udZWUiPXvq+fD317qs+8+Zo6d/927RfZKTRfr2FSks1PJiY0VmzZKSEhEpL5dLl/RUSkyMlJbqeZDsbJF166RnT+3fxo2aFjomRrSta9aIBATcdx5EO7uW4u0tP7rdb3n/C5vJq2ow3OPXfu+b+9Vg+Hce9J79ZWJlvr4Mmt5A3/PfvKmfbd7MmEmOkJvLtX/G8YHvXCgv59WjkaxdCyQk8A3tNOS4YYOaYN9+m9NdXsQu+mMa9mvKNdf60LevRlHHjuXIkq9h/Hjq+1o4cLWJhoHz8jQs7O/P0ezquLpqZoqOO6dhM+BZpq1tQm0fR97MHsYHH2iEmMxMpkyB4W/W49w5oE4d5s2DqlXB0roVDQqO8O6Gehqqbt6cyE65vPSSVufpqVFili2DDRuYu8CO2n4u/OEP6sEF9c5edW2AX7s6msS3Zk1ISiIxEerELGXMGDjXrCfLlgGff8669n/n8DFHdu4EFiyAadMAtRRMmAA89RQv73tGU765usKMGfj7w53GQfDZZ0yeDCNGoGHz+Hi4cIFb7y3ndspxBg0UbdScOczd1Fj9JVa/hzg4wqBBjBpnR0gIMH8+Ln26QlYW7wavY3N2Mw4FDlOrQ1AQISHwWawdbN/OS5Xvc3HfKfVCxMbySY91pKRA5GPf6sTCGTPA25tFT2xS03ZREZSW8r7rG4SFQR2vKtRxvUVpKTB+PI6OYAlpCe+8wwc3I3nqKbAJDmLSJJg5U4tbvcubI8HDftKlaSbpGQwGg8FgePgC2cEBPD1xdkbVY1gYm3Me53CGiy5ysXYtNTZ+yKiIq9CtG/36qT/3fMTLtHM/pekTXF017cOKFTzqW8GCBfp6vUblVcjO5rmIW3yR05jUVKhwrcHbb6OZEoYO1UwOPj5w8iTl5TBsmL769/PTyXojRqilY9Mm1YaNGsGHqW1o3FhtCU2aADdvMn++ZqEIDgZefVXFaLduKsDv3OGVV+CVyMs8G36D8eNhb2otbkW+yIoVOhlw9Wr1FE+dql2qlXuUN96AChs7Ff9OTrzQ7xpERdEqfwfXr8PsiKMQGclzDQ4QFAQvdDilHo4dO6C0lK5dNTcwR47w9tvWBTvWroU5c2jwr2XYzXoDdu0iJkbtEbi6cjdNiKPNbap2+YN2vqKCG12eZlr/0ypWralALMfSwMaGHj1g9Gh4bapF577VqkVenu7WJqSC2uMHQdu2/OEP8GyXq2Bvz9LJZ9VfXFkJNWvi4aG/kp9PbXeBTz/lTHFtTQfXpQu0asX18AF4empmiw0bgMxM/ennh6en+tUrli5nzhy1Wqxefc9HfueOTlhs6XZavR8/AeNBNhgMBoPh980vMkmP5csZNw44ehQKCni6xTla9ajN4z5X1SOclASZmdC8Oe2+fJuyMnjE6ZqmR0hOVmU5aZKK7ZUreTnsOJbzuXDrFlRU8FWiIyEhMGxdl+9TuXXoAAduNNMZcvv2QWAggYH6Z69e8NGK2/TsqaItNBTSduQyapRml3ixz5Xvhbq7O5CaSpuap3B2Vr1N/frMmoV6dUNCYM0aQkLgMnWgpIRWQbfovHYYjinf8Mwzmg2jRQuN+A4frtvtgGZERECVxe+paG3fnus2NdTk6+BAkG+xCvudO+HOHRxt7+g4OTmp6dbenkGDdMIg9va4LHiTJzxOwNmzOumuToljQv8AACAASURBVB0V8A4O2NiA3YZ1WvH69Xr8tm36ANGvHzzxBC4L3tTy8/LuzYC7cAFKSrhwQZv1Tut/6sTEq1cZOBCe7XlL9w8Ohn79eOopVLzb28OqVVTf9JHOWHR3p2v+pzrBz95e6//oIxoUfsvjK0ZpGLekhOolFxkQchrHosua4zohgWFP5oK7O88+C8uXQ5W5b5OeruK8Tx9NHRcYCG3a6CRHoqOtq4vcHyYPssFgMBgMBovaMx4eIZ6ektSsmf5RrZoKZtAQcIcOGspdtUpFYkGBfhYcrIps1SrNbdali4q5uDhN6HvpErRvrz8LClThpqZqDrDAQBWWBw6oiho5UnMoT5miK0p4eGgYt1s3Vb9OTtqGzEwVkCkpqrz274e9e/XzCRNUCBcV6fv8WbO0Ha6umlLhtdegbl3dNyJCV79YuVLLLS3VfjdurAI/PZ3vV7pwd9djAgO1rY0aaRYJLy9V+f366eofnTrp566u2r6YGJ2w5+CgZYnoWAwfruO1ZAm3S4WqfXtoKoiyMh2vxYt1bDIzNRJdXq45pu+G5Lt319D78uWaz61nT90vJUUF8+7dagcpK7uXB+/kSQ1jR0frOTp/XtsTG6vl+/lpO21sVMF6e+sDy91E0EuWaBR76lRdgxv0KSY+XtX/oUNYXhqHtG2nwn/JknvnYOhQbbuLi6YV2bpVU5IUFGA5duyIiIT82PXp4BAi9esn/eh1fPKk5b7K+18gJCREkpJ+fEwMht8DFsuv+94396vB8O886D378N2UJSUq/GbO1BRl7u4a3fzLX+4tADJzpgqwfv3A15fb89+n6ojnNHHxH/+oYnbbNvVGLFumImvjRhVd8+dreDYhQdMf9OihEecuXTStRGwshIVpWoRVq9iR3wrbSnhywwaOBw/A11c1ro8P/OMfUK8fPN/jCp86DSPvcWiwA3q6uXGt9/Ns2gROy19i0MrOyLHvsKxfp/W++CKH8xvQqsNRLSgwUOsNDOQdz0UMGaLdTk3VJaQdHKzRTqB67Mcqmhs2VJPzpk3cmvUujh073lsxw9ZWhbCnp/q427eHvXvZu/0Wqanwcv4bnBoym4bb39eHCx8fqqYc0uTMy5ZxLfcmNTIPq5iNiFCB/fzz6lOorNTz4+nJjQ+icWndRIWxs7MK4eRkmDqViwMnUtdnkY51To4K8rtiNycHJk2iYvxEqsR/qe3evl0TGo8dq8J+1iy21XmBnu2u6ucTJui5Hz9ec+nt28d5z5Y8kvON5r/r1487TtWx8/ZGPmrJ0sFHcHeHbp3g8mfP8t134LrxWWxsVDcfSoM173Xioufj1B39tL59uA9MFguDwWAwGAwPXwp4e6vIysrSSOQ//6lJa1NSNEr42GMQFaUR3spKGDyYqhfOaITR0VGji0OGaISxb1+NzCYmqpj+6itwdeXO8FEacT1yBOrU4Vi1NhrtbdtWj9m5U/OBxcfj4qI6k507aZK/H8f33iY0FBqkfM7zz6u24/Jl3NzgpZJ3uH0baN2a7GytIjwcmDQJy6SJakGw2j48PQFPTw6drMH50loqGnv35jXfT6nrfocTJ1Qkv9TzDOHhUH3cc7oyXmamRpm3btWGtWhBYiKahDkgQCPgwcEqGouKVDAfPAgdOtDZ9wwvd/gWoqNp+Fo/zvZ5WcW0jw9fFrdR8b5pE9u2wRn3Vvei9I0aqT2lsBCKi3V8cnNxWfgX/b5mTV0Xe948CA/ny+CJmobPy4tbrTvqPkFBGgX399f9Vq7Ulfi++UbP+ZQpOnPOyQmaNYPAQKpXh60Ha2lEfMECFc8jR6poDw/nkcEdYft2Tl1yYVdCdRYsgG+y60JYGAEBMKj1aWrPGEVQw9v4+kJ33+Pk5sKoDsfp2xcVx/Neti45eP8Yi4XBYDAYDL9vHn4EubISJkzg1Y1teDc6mmuFFmbOhFHf3uH0aXj60R1cjN7Lxo3w0raneG3Pk3QD9n4Ic2YPVcVaVAQffQQZGZz/cDsHD8KzXnf45NlNDJrTlPHjoX3717g0+TXsL0DUHPD3f4k1a7T66tev8+43oQQEhGJ7Q10AyZ0+wq0Anm5VQnIyRO/rS/F2dQUMHRpEUBCMiX2Nwe3hvF8vcpPhQ11zhEUtrzO75iLeCNwKtrZ81mIu+dvA3r4OoLZc1/RzFBbC2xuAONW+GRlQWNgAGxvYMWcSOSehnbu7zhQ8d46XPwvl/R67aN4cuhd/xpBNMGjAAA6XP86xHMi91JU3Atao8M/N5eXFDXB1hdkjRnBx6DTid8PzYWGQna2LkGzZAgsWkNfnKU6ehAY+PhoVbteOT55cwyCnzRppP30aOndmR/mTdN+zR6OvFRWcq9mMeiUlLF6s+YmffXYADinQbs4cmnleZvJkSMzuynR7qBsRwcSJ0LfvDHo5fMxnM9O4fRsi44bDtWt8lNGO7Gz4S8hWvsrrRccuXTgy4u9cvAg932wJ0dGcD3ySceOg6y59plrz1KdszR8ABw+yMAUcpj/KE8OHs+zDqly7BgmuTdi5E8LDm1BYqC8PEhPfx27cOH0IuA/uZrEwGAwGg8Hw++Xhx8KcnCA3l9hYuFNuISdHXQhBN75h2jRYmtmdnTutyxJXVnLypFp669WDQZHWSHNSEmnDFsLmzRw4oC4Dp+p25OQAbm7fz8nq31+F1YkTGgQtKIA9e4D4eFJTNbDYv79acatUUV2a5vUUPXvCogUVnD+vzoCOTod56SXdd+5cDdgOGKD21o8/hq3VI6mshFvdekFAADY2WuaGDWqNHTkSXM4eo6RE7cexsTB7tmZb6NVLyzvh9DhDhwKdOrEroz5Ur05GBmwufYqpU9XhMHIk8PTTTJ8O776rFmCcnVX1e3uzZo0G2unRg9hYreeDLXVh5EgiIuDDvKfhk0/4618hMhKNdoeGwq1bJCfDx4VP8+l2F/Ue79jBwoV6DmjbFoYPZ/p0oF8//vUvfTjo3El0ot3QoTg4qEX79Gmd90ePHnTpok4YbGxITNR0enTvDq1bs3OnlrHXuRfbtwPdupGbq7Zy7O1h714eKT/LpUsafP7oIzjVYoCeWF9fdu2yrtj3xRc8+6wukLJ+vb6YeMT1BvPnqw180yashd4/JoJsMBgMBsPvm4c/Sa96dUlasUL9xzk5qn6CgvT1e3m5qtDJkzXEWlqq3tjz5zWyeemSeo6ffFKtBU89pRO3iovvrSe9YQO8+qqu3zxvnvoY+vdXdezkBBcvwiefaBaM9HS1NMybpxPL7mZrKC5WRd2tm6qh8HC1PHz2GSxcqJ7devWgdm2dJNanzz2Pa2KiRmrj41X529vr59u2qVpes0YzORw9qn5qJyfdPzNTQ5cBAZo9ws5O7SB3I+bR0Vrf7Nk6Ia9jR7U0xMVpn0eMUKXdsqWqxcpKrWvJErVMfPkljBqlYx0SohPq+vfXOu/mnktIUMU/d66OvY2NhmGTklR5Hzum7czL0/5PnKjllZWp7SM/X+0UvXvrOC1ZoipYRNV8fr72z8lJLS7Nm+u+iYmq2J94Qs91VJTaLnr00Pp694Zr1/Th6Pnn9akFtO/9+qmZe+ZMncAYFqbn/MwZnaiXnAwlJViys+/LpF+tWogEBPz4BJfk5F/3RJ2fEzPpx2C4h5mkZzD8tnjge/ZBVhf5T7aWLVqILF4sjz8uItu3S36+daW5oiJp21Zk8WJdSK2sTEQ8PWXYMJFOnXQBvNmzdUE3WbFCUlJ0tbQlS0SGDhUJChJZuFBERoyQvn11AbWTJ0Xi40Xs7UWmTZN7K8h5eMjMmSLbt4s4O+v3mZkiM2boYnBhYbpAX40aIgEBWk+7drqIn7u7LvJnb6/fBQSIbN0q0ru3yNKlIuLgIFOm6GJ9bduK2NqK+Pjoon/Ll2s7mjfXem1tRVq31pXZKiu1LCkpkXXrtP9hYSIbNujnEyZoH2XyZElIEHn8cS1HQkNF+vQRGTdOPDy0LJk4UU6e1HFbtUpEpkwRT09dtU/OnZPmzUUaNBCR7t0lI0NEgoIkJUVEli6VqVNFBzoqSpYsEW38li0iGRny1lsikpAgHTpo+xMSrG1KSZFBg3QxvKVLdTFA2bNH2rbVhQylRQtJS9NVAWXyZJGFC2X+fJG6dXVRwAkTRGT+fCks1PGXhQtFEhMlJ0ekf39tgpubltupk4hER4utrdZXXKzXgIODjq3FoufH2VkXCty4UUTi4u57JR0np5YSEiI/ut1vef8Lm1mZy2C4x6/93jf3q8Hw7zzoPfvw3Za3bkHLliT3vwgrE3Ht1h1/f6B9e+KT0khPh+or3tEosqsra6ad4i/RDRk4ENLSoMnu98HZmWYFewno3fl7v2hKinXhiUs1aewOw7x2wZQVuK74nPR0dRM84nxdo6QLFtA7UI9JTtbA46Ox7zFhwiuABmtrxa6m6PUX8PPTIK6PjwZbg4Kgr99RAgObkZSkSTMuXdIgdBP/O/BSKV26aGKJ+Hj485/VVVDF5xF6J54nIwNeflkTSGzfDrdvazKOnBwN9JKaSmT1PMj0Z9y4Jvj6avKNviHn+LxDPdh6lTaBN/jb31y070PyNb3ZP/7BvHkaoCW7Fbm5GhQODQUmFLBpE7RLXw1TvuTbpHV8k1gF3qqgcc0r4OVFs/eHQ2EhIX8aB/m1ISODl0afheN/1Ej0hg3MmAFM30RUVBsSEqDN+DakzZoKuzOJ7pHGtT9GUsO1gr+vrAKZmUyY0BUvLyDZm6BaFyF2i0ally3j1d298fd/lKAgDVBT6k5qqjXg7u4O+/ZRb2wQq1Y5kpSk4/+oxw2Cglwg4xJ792quY7tli3gpK4stoUt59VUNQm/dqi8Jelbby9XgzhA5/yddosZCYTAYDAbD75uHLwVu34YPP+R8ZV3o04fUVHU0EB5ORIR6hD/2fI3VUVX0NXvNmly+rOIxOxu+CHxZ06bl5VFcrG/jq8x6k5Ypq3VVt7VrqVYNTeM2ciTz5sGj9ufUn7x4sdoLgoN54w11ZDRcPpHnnwe2baOW7XVqDX+aWge3Qu/ebNgAfXvcZsMGaONxhsxMTfSw40IzmjfX36tGffD9wn4fR9sB2qYLF9RJ8d13Vt9zixaUl1tX3gOe8D3H9Okq5pyddUW/c+eA0aO5GNKL9i804ehRzYgWGAj9Xq5Hdjacfn0NR7NcSE+HXbtQe8PgwXDxIr6+mrKY4mLi4u6llSY9XYXnjBnw+ecQH68PJY89xlfptSE1lUVN17B/4iaCg+F0t1GwYQPvxtTXRo0dCydP6jLPW7bQoPQ47dujto7vvuP2+FfhT3+iBteYNqOKZvbIzaWyUlNXs20bV2zrMi17lM5MDAwEd3d8fKCh80V69AA6dODtt2HzZiAmhncrX8Hi5MjChXp9PGp7luO5LrRtCxw+TOidvdht+pTz/Sdy9a2llJVBjbwTdPc6ypEj6sgAfUg48vaO+748zVLTBoPBYDAYHr5ALi0FBwcGDwY2bMDX1xr1nDWL7Gy1xvr6qo7iwAGGTa7F4cNqMQZ4suYRVSgREVy8qAKyV/JfqD/rBXx8gMcf5x//gC8vB3GxeXd694a6reuxbh0wdCg37GtBZCT162sU13HFIkaOhCML9tKgeXU+7LkZpwG9OHCiNnl5UL9RVV7sfZnBrzcgLk6ttWVlanU9eBAaLhhFRgZcv67RZTw88PXVyPKaNSoQ58wBpkyh/o1jLFmiy1Xb+dWjTh19Bhg7Fp59Fl58Edizh4QEOLi7mM8+U/vxwoWq9zdtgkddr5CXB//6l1VMennBe+9BUBC9e2saYmxtcXLSzGmrVwPTprF4MZw5eBF27qT9G13p2BH49lt8fQEHBzp1gif8LxIdDY8uGANDh/LNN3oOWL8e0tPV+jtyJO2GN6FRIzjW7kWqvzuDqps+4aMoC28srIGtrVqCad6cvDyd/IirK6tWaapjLl0CPz8ul1YnIgI+O1CXNWuAxERWrtTIO8XFvDryOlJwjYQEnZzYJLw+u3erjZt69ajepzMNpg7gkdxDZGfrm4DGvRvj1b0ZbdvqmI6J6YyLC7RM/vC+L0+zkp7BYDAYDIaH74+qUUNkyBCRnTtFrH5kyc8X2bNHiopEzbjnzonExYm0aCEZGfe8w5WVoqbd2FiRFSvU07pggUjfviJLlujxDg4SGysiO3dKYaFIVpbIpk0iqakikpSkJtdGjWT/fpHSUlED7L59In5+IhERIgMHisyZI1Jerj7oLVvUQLx0qciYMfp7crIkJVk9tSNGSHm5+qZl9mwRGxstLyFBjcRFRdqBxYu13W+9pd+tWCEFBbpLXp6IJCeL7N8vEhwskpYmsnu3rF0rWlZ8vGRlWffr3VvE319KSrQ7MnGiGoHDw7U927eLdOggEhMjmZkikpgoMm6cjmlUlEjnzvpZQoKIl5eadO+alSMitJLQUJEpU3TAW7TQfq1dq2Vs2SKSl6djl5YmMm+eSPv2OtBpadqmjAyRkBC5dMnaZl9fNY+vWqXlBQXpGEdHi+zerW2ZOVO3/Hw1kAcHiyxeLHl5Vh9xaanIwYN6HidPFtmzRyQyUsd2yhT1Lg8bJsnJoh7m6GitPDpaZM+e+/YgOTu3lA4d5Ee3+y3vf2EznkaD4R6/9nvf3K8Gw7/zoPfsw4+FVVRoLq7WrTXkevYst5xqcSW4KwUFcLXAornCWrWCkyc5eBAe9blDURFYMo5rBoSMDMjKorwctUy8/jo0b46DA+DggL29pmtLTtYI83ffaeCazEyNYBYWIgJVP1uvGSg8PDR0OW6cZkZo3hyWLcPudrGGmXfs0OWLxZrxIy4OLy+oc+UYtG9Pleh1VE/8QjNT2Nhw3u8JaN2aK4V2HM91UQNtz57q6fD2ZvOlNtwaMorMTLCL/UztD1lZnK4bqvt+9x34+VFWBhL6BAQGcv68BnOxsYHRo8nN1eQP3LypbcrPp46HaHTdywtcXTXFma8vZGdzrNBbw/D9+kHdumzNa6ODc3e56T59NItIfr5mqDhwgGuFFs3+8cUXEBTEdRdv2LKFM8W1NcXcxYtqsi4q4qprA6haVX3gRUVgb0+dwhPat4AA9ZxcvKjt9/PjSNg0bvYepJ4Td3fNDBIQcC8TRnEx+Ppy+bI1+UhBVVi+HFdX9LoJCtL90tMhNBR3d+Dpp7X+8nIICuJUYW39vbj4J12iJoJsMBgMBsPvm18mzdszz6gXtrRU07b94x+aRDcyUkVkbKyKmpwcFXsdO6qZ9tQpNSOHhWnKs1691H+QmPi9X5kVK2DQIDUIf/mlppHbuFHTt4EuZTxlyr2lro8d00TL772n4rJmzXsrzIWHq4/iiSfuJTAeOlTFcna2+ge2bNGUcXFxus+uXbpk8rFj+n27dioAR45UH8XWrdoWe3vtW0WF+jMaNVKxV1Cgyzd7eak5ubhYV6eLjdW/DxzQsfDwUEGZmamz3Pz9dTzvLkVtY6PCt1kzePNNrf/QIbhzR1PoBQTouFWpounUPv5Yyxo9WvucmKgm5tRULW/5cq3bxUVnwPn56SzDmjX1YebwYe2n1ePNwoX6s1o1XdEwP1+9KU88oe0IC9P+VFRArVq6IuILL+g527Pn+9X02LNHlyI/e1br9PbW9kRF6bLlHTvq7M3ISC3jblq8wkL96eEB5eVYDhy4rzQvrq4h0qrVj6dIiov7dad6+jkxaaMMhnuYNG8Gw2+LB71nH/50o9q1ddu3T8VPeTl3dsVhN2SQimVnZ83j26oVlJdTMf0Nqtwq5uxVZ+onf645eHv00C0/XyPR/v7cnreIykpwjIvjm5eiCQyE6rEfc6v/82QPno2NjWpQS/YZKC5mh/eLdF/SXEVpv36a4zgsTMVXVpYKuaZN74n3xx9X0Tprlq7S8corXPdvSXXnMZqjOSxMI9t792p+4PPnwd2dCrdaVKFChfE772gWj/maVeF4cT2aeF7TPn3xhZY7Z47mObax4ZBXXwIDwcX2FtdHvEJ1pzsa9Q0LgylTuOVcG8fo1RolHTlSBXF6um5Xr3J66oc8+tZz0K8fd2bNxcYGqsT+U/NHX72qot7VVUXzm2/CmDHInLlYBg7QB4l69VQAJyeroO7WDdav52j0dzRrdEv3ef55eOYZLqZeoW7pGY4UNKBlC9EcyBs36rGJiXr88uUqeq9eheHDNbrt66vif8UKnbw3caIK9exsTTMSFaXC28FB+9izpy5PfumSmrbv3IF+/bgyaS61jx3jzLxPaTBvFEydyu22Hak6ZrjmzT5w4L4vURMhNhgMBoPhd86D+DL+k61lkyYiUVHi7i4iGzfKvHmafzgpScTRUXPXNm+ueYRlzhxp3VoE1BLr4yMiN26IxMdLbq7aesPDNUdxkyaao1h695YGDdQyu2qV5sr18FAb7N3cuhIfL506qc21bl1r/uHkZHFzU6txgwZqX3V3F/H3F5HSUomJ0Zy9gwapVdbeXst0d9d21K2rFl+xt5fevUU8PUVcXTUvsbOziOzcKXv2qM3X2fle/uXmzbV/s2ZpLl/ZuVMmT9b2Oztrbt9hw7QPFotagAcN0n3d3ET915cuiQwZIqC5lSUlRdzc1G7ct6+IFBRIeLhIQYGILFggAwda8yV36qR5pfv3lxEj1Go8ebKozzoiQsvv3VuTQp87JyNGiMhbb33fZiktlXbtRGTmTOndW3MWOzpqXmrJyZElS6x9d3cXX19rDuN580Q6d5bJk/XvDRvURp6Rocc7O2t7ysr0nDs6al2urnqNLF+u3/v7W3Mw37ghFy7oOfT2vnd+QkJ0Xx8fEamsvG8PkqtrSwkPlx/d7re8/4XNeBoNhnv82u99c78aDP/Og96zD99iUbWqJLVura/iMzN1GzJEo7gnT6r1wNZWkwgvXKipGAIDNV9X8+ZqXSgq0sinNf8vTZuqVaBBA7VODBqkKR7mzdPopZeX2jViY9WKcfCgRjBLStTgam+vkeGYGPXKHj2qdojyco1iFhRAbq5GVGvV0naGhOi+Dg4aKR0yRF/rz5wJ+/frSniJiVrHY4/B8eMaxQwO1mNtbLTvZWX6fb16Wl94uNocbt7UPh05As89p/aSzp057v0kTRa+qNaExx7TaHRgoNa1bZt6cyMitC8jR2o/7yZEXr9e6+3fXz3H8+ZppLd1a41c79+vZW7ZouO1YYNGib28NFrfq5dGb/v00TqWLNF2HzmiUfDWrXU8kpL0vE2ZokmmIyPvpdhr21bPb8eO6unu0AHCwvjkZEsG5b2v7Z8wQSPX48drVDw3VyPRrVvrinybNula3126aNrA4mJNgm1np7aZP/1Jj5szR+0vixZh2bv3vl6xVK8eIqGhP/56cvv2X/dr1p8T88rWYLiHsVgYDL8tHvSeffgCOSREkhYv5ul3Q9m87Bybk+sRF6epfOfMUQttURE4OkJ0nYl0ObqI48dVyy1YAGlrv1Wv6Zw5fJVag6go1U6vvgp/+xt8c6kBb0SeoU4duHxZtWxhoWrN7euucqWyFrWXvMGbNrM5dUr1blGRpgWbPBn2f5LLhzu8CQ1V7enoCMuf2sxe16eZN081maurrqDctKnq1pdfVk138CA08K/Ce/Mr+Pprtc4WFKgL4L33VNsdOKD1lZXdW/362DHVdfPnw/7+S/nE/SVsbFRTzpunNuAlS1QHHjyoGjwlRfX0cftmOgDLl9NwyUs4OEBa7GnaRT5Kkybav3d7fsWkzR1ZWPddKC1lru0bxMbCodxH1MowdizPlX5Inz6qi0eMAJfhz9I04zPSurysVpidO3lpfj2WZnZnUpMdbN0Kp+LO8cbKesy2n82AY28A6jIJDoZ1q26z/rOqTJsG50pr077hFapWhb2jP4V58+ju+S0hITB73GVenlOHGTNUv8+aBdf6Dofhw3nzi1BNAYc6b47HpPFlXlO6jmtCG9fjmvpu7gl2ZDUmOlpdLaNGaZdKSu5ZvqdFnsPi43NfN4ibW4g88cSP/+Oybduv+x/JnxPzD67BcA8jkA2G3xYPes8+fLfltWtw4oRmYDh4kNBQtRuXlOgid1lZmou2alXg9m2++06/O3ZMg4oUFsLrr/PJzhps26a+4sce02BjeTkQGsrixfD11zqnq1YtFaanT8M3J2uxcCGQn8+6dVrHnj1qxW1T+Q3+/nD4ojdvv61zwU6csK5uFxZGXJwGdtesUXGakqLHNW6slt8mTazJEmxt2b1bA+Aff6zW5r17oYHXbTp00GBsQoIem5ioFtyKCm37d98BY8eye7cGeg8d0qDw2rVadmGhivOgIA2qZmWhkffoaNixg9xcteaSnExYmM6by8sDiopYtw52BL0KQ4awZYu2nx49OJNrBxkZVFSosN25E1wO7oKbNykrQ1Xrtm1cc66nZU+eTFSUitDL9vV0cZYLFzh5Uk9vaalahjlwgKNHVdji70+9evoCgM2bYfBgBg/Wh4V3oupQVga1C07Qvj107oz+59o1wsP1YSI3V7d3tjdl2TLg9de5dEmt7Dg5fW9jbtpUxX1Zmc4ffP11bSeenj/pEjVZLAwGg8Fg+H3z8P+pd3KCNm10tbXHHmP3bnU0+Pnp23kXF7hxQ5MWUL06np4qcmvW1M/x9IS9exnUq5hmzXSht9q1dW7X1avAzZs0bAg1aqhLoWpVTfDg5aUCMCBAm+HioqJz7Fid74ezM46O0Kp0P5MmgUv6IapWhT/+EaioYPZrxeTkqLsgIEC30FDdSkt17l16OuDqyoUL2qZHH1UHgq+v1pmUpPv7+WlbWrfWz6tW1b4/9hgQHU3z5iqqq1ZVgRkertFmW1sV1aWlGtm+cwdVusHB0LEj3t5W8da9O3FxOtSnTgHXr9O+PXS3/xJiYvDxsT5snD5NZiYwciQXL6pwDw5GbSjnzqlADguDr7+mstJa9u7dPPusnoY6h7cxdqyeEx8frc8i+wAAIABJREFU7WdRkT5YEBxMly5WgVpcTHq6Cnwefxzs7cnN1X516qTnBnd3cnNV1N/NoDFlio6Pq6uO2+TJ6mRh1y769dNoPjVq0MTjKkFBmjBjyhQds8hIfXZISYETWXb3fXmahUIMBoPBYDA8lEkDwEggCUjycXfXmXAJCTqDbPJkXSzCx0fS03XdB+nfXxcTsbUV2bpVF6XYt08XnZg2TWTqVJGsLNm9W3Qm3qxZukjInj0ioaE68WzpUl0IY+VKXfAjJkYkPl4X46hZUxfwyMuTnBxdV0KcnERWrBBZsEByc0Wkc2fZtEl0wtq6dZKWJiJxcTJhgogUFMi6dbpOhqSlafkFBbqoh42NJCWJrFsncvCgyPjx1oUu/P0lOVkXBpGyMklM1CYVFFgX0xg2TPuwf7/2aedOyczUBUhGjhSRCxdkyxbRWYHR0bJihXWhkJEjte2zZklJiR4unTuLzJkjMTEiJSUi0q2blpGXJ5KfL3FxIlu3ikjbtjpmAwfqLMHevSUhQXTWpIODFhYYKDJ/vkh8vNY3Y4bs369DKX366CBMmKCLe1RWisTG6mIeI0bo/jk5ut+MGbqoSdu2OjsvOVnWrtW1Pi5cEJ1NN3u2zqxbu1Zn2yUliURHS3y8/H/snXl4jfe6/j8riIgkEkMkERFBhAYhMc9qLkpMYauiiqqirbbYKGq3iipqm6rmpoQdiqqZoikxRRBTELNGJBFBEpHv74872Odc5xyprefXnv3e17UuWWu96zu967Xu7/3ez/OYs2c11KQkrdHVq0aTuHzZmC5dzKhRxpjwcBMVpe/Q+PG5azllijEtW+bZpO/qGmxCQ80zH3lt7//Cwwr6sWDhKf7o1751vVqw8B/xvNfs/0qaN2PMAmABQEiVKgbg4021mfD229yr8zLbt8Or8+aRmJh7G37FCjZuKUC706eZeb4d+RLg9u2GtMwPJYKDFTB26BAtQmBj5hucT4dyP0HJki9T82p/kpLgs7tDGHWmDwdOOrFwMDRv3pXudTLlS3B0ZEuUMzExzvz6q9TkFfNVcKNXtxS2r4cO/9hJ5FC4di0fXbv24vBmWHSsKUWLwplEN06e1G38woUDmTULFq50462iCtq7fFnz3rNHquaOHdB55UpcXRWzlj9/AS5ckKIaG6v3o95/hRkzYGyvUvrwlSts2qT1aN4cvo/2JDIS2tvZkdK6B8f+KjtF8Llzkm537WJQ/Fjq1YMGwcFc6z2KXX+DriEX4cMPcd8Dl+6XoEzqMTZsKEbp0ig4MTYWkpJYHTCWrmkfSB1t1gyuXmUfDWhQvLjyOl+/TuohICODRYukYjf+9FO+2l6Jd2JieM/2Je13Q2LGq9jHQ5VmzYiIgCPlS/NmbKwCGbOyFDCYkMDU7dWZM0cvh4fD+/b2nOk6BldXKDn9I61Bairf0YPd4VLQV/f6nsXrX6Uv53hnegNCQ9vRK2YDOwat5tFWmHC2B4fCpSKnp8PsmAa8PqgBzqdO5fm7arNJqbdgwYIFCxYs/Pvi/w8VeOkltv0VJrxZkU2bdEu+5ftt6B2g2+JJSQVISoJ2YWFs3y4O17mz0gR/8UVnyno8AAcH1q6zERn51K97+jQcad2aXr0U8PaPLU7s3y+fcXw8dPfYzzHXxlQLD2fyOPFKFxdZCyYMT6FKIzfq1nVj927ZOrZvFxcPCpK9I7cI3xMP8cOH8k+fOKHnDm270zenD9On67gCBWTzsLcHasYS4xoMyJvs4SHyvHCh6oT0+6Eze/fC2CZXmR/XkJwcmDsXJkzQONav19J9PelXVnRUAo2sLPjY6VflF54wgSNHYPlyGHhmAKGhShhRPbQsR1dmcfs2lOESTJ/OzYdLWbcO3i2ZxZUu71J6xQoWL4bkV6fimwwUdYKrVxk6FI6UdFRU4PXrxMQU4eXsbFJTVZsDV1euXgUmTuT0Z1qPPXvkyuj0XhMcTiu19ZtA9ZB8QCGONreHl14iJUUJLE6cEJm9uPUcA/po3Y41uk/m4nCqV5dl3clJm5jZV14lMxPIugkoAK/rsvaUPKf18faWfWXxYrh1S+e2USOIjl6kF/MIy0JhwYIFCxYs/Hvjfz+LRcmS5tDw4UpdNnaszLTvvKMouooVxaiuXNH7H36oFBHGyEgcFaXUEJmZYpapqTB0KHcGj6LIgqmqmjZypFIhXL7Mo/GfkG/6VKWDW7JExSgcHBRF1r+/0sLVrQsxMZxyDKaS/yMxz6tXSQmoS0KCDre3V7lrMjMl9w4aBLNnY0I7k5gIJYd21xzefltpIN5/X2blRYs0r8GDxYbPncP06Yst/a6izlJTuVO57pOscbaMB9oJ3LihNGX+/gqSCwmBDh1kcv71V+jb9ykjnD5dZbKjosROk5Pl4R0wQPJsQoJSeVy7BkWK6JidO5+m16tcWSnSevVSm/HxigL09VW/kyapjR49FDz3uGhI587ccfamSNSPWtuxY2UgzsjQTqV8eTIHvEPBZV+rhPeUKZrz1q1al8em86FDoUwZ7ZK6dFHfbdvqXM6dq52PvT106gSrVunvMWM4v/MSDg5Q6r3u3Jq9ihJHtkBICHfti+Fsn8nB2IKULw9uO/8BERHYIiLyFMVarFiIad362RHg4eF/7Ej2FwkrKt6ChaewslhYsPDnwu+S5s1ms+UDJhtjPvhXBvfPCKlQwRyKjMSzZRVuLNjA2Oj2BAUpaOyVV6BCBXHMgAD4pfdc/Ka+xZUr4rMrV0LKorVQvDi3Ahry2Wc8yXKRlSUemNKmJ2V+DmfkSGWHOHBAiq+zMxz54Qar93nS9cd+VDu8iKwscdDChSG87Qrahvdi8WLxudWTzlCvb0V8fSH83YPc8q3J5s3w44/ijl98IYXWx0ecMCZG1bMDqxdgYL+HbNqk/u/eVd/XfzjKOafqxMSIcyYmit937KjEDsuXa44pb43mo0efUrSohNu335aKGh6uOS5bpjF8953sADkdQ1VRbvp07HZso0QJ+HXlLjrNasr9+1LCw8eeom6/Svyy7Bxs2cKwc0NYtw4u5S/H4YjzBM/uS5VDi58EzPXsCWVaVsTu3BlyunYXeW7alDZdnfix7GBqHpxDbCxkXr9N29eKsel2bdq7H8DBQRy7dWv4vusKvsnsxaBB8NDJjYruKdjZwak+n0NMDBMCvqNYMQUyurpqHZctU6q/u+99DPnzMzZrLDNmSGH29dUab9oEPbb1Y2CBReTLB3O67gIXF+q9E0xQkM5lQIC+E5UrKzXg+vVgb5+3C6RYsRDTtu2zf1xWrPhj/0i+SFg/uBYsPIVFkC1Y+HPhdyk1bYx5ZLPZgm02m828KKnZ0ZGHAVWUHqx1ayreUbqvL8fc5tdfi5GVBcUObSGlVivY7cHBg6qxce9eboXm8ERYvpwSFTYyvaIf5ouBpKaC2/4f2evUBqbcZeNGCZWtWin92sCBElBTHDzp6ncYSpZk61aJoW0apkPhwhyL7cXcuVDy8/dY/WEPSM7mq6+UlYGiRSlxei/u7g0JX/oQoqL4298a8+23sl8EnlrNxte7ErjlC8jJoVs3mD/lDgdOF8HLS4LqbZ/qpF+WQD15MrRp+Yjl4fmIj5dQ2jn2Y2ocmQBfZfB5taVgZ0eTPa9R2+4guLgQGloRLy8IHt2K1zZu5M03CyidWuhxqchhYfw8UZYQYmJYO86VEwWqE/jgIKzbzs6dlWDARLhwgZlzGjJ8eDVokk1wxEfQsSORo6HCqkk8GD6GQklXwN2dzBPA7v4qtrFoEQsW1ITPYMyY3MwccXFsmuIGrybRbYgE+WHDJPKTVA6ny7kVnqe35MxKw7FYG9ytD7t307q1yHjp9FNsu1qJUkM60XrcWipXBi64wp49jA5XW2+/rXNVJKwNPSZNguU36fkRNHY/BddzyAwM5m9/U9YOd3eJzPfvy8by41/3wbDwPH89H2exeBGw2WytgZlAPmChMWbyf3rfB1gKuOYeM9IYs+nP0p8FCxb+NVjXrAULf1zkxYN8FPjeZrOtBu49ftEYE/lcPaamUmDNd8xd3oP2LlHUrduYIkVgb1wxPv4YPvoIYmNb8WkYpCyChg1Fxnx9RZJGOT8UYcvMhEAFyO3bB8WLt2HTJrjklUStWsqBu3q1XAk//6w0bAkJcM0jmFIjfAnLvZufnu7EzZtKIzZgAGzZ+DmjxhXglVfg79OUbu7bb8tRNsidm5FwO60ADiGNGdRbpP3iRUhp0pUdkdCuX0uYPJkbN2DCjCKsWaPic8ZAi/vfU8zPD7smVejfH2bMyMetW1C/vtRjp6kTiBgJqxxT+MX/dQIDobUPpFwO4PBZZ8aMEfFbuHALi0ZoXidPwnU3e8mqoaGMj3iDhw+h/dggOk2sjpsb1K9fkze84oiOhsZjxkC/fvyUWo2F02B5erqSB0dGMm3Tq3z66RgS4iB43QIYP55evWDVhdEy87q6Ku1bRgZz5qj/K3u8eX18WZY6Oj4pmDdzptwmnze8TeXKUuN/uhCFY2Ebw4dDNfMDxMaydatSvJU+sZvKHSpBr164uChd36vBUdCoERkZ4v5ffy1HxtqePTlRMJjAn39m/nxYUbgSXyeO5HTxl1mwQCr02bOyZP/6q75LZco04MsJFbULySNeRJBe7t2XvwMtgKvAQZvNtt4YE/dPh40BIowxc202W2VgE+D7Z+jPggUL/xqsa9aChT828qKVFQVuA82A9rmPds/dozGQkUG9enrq7a2kCXXqSC1MT5cN1tcXcHUlIECW5J49cwPD9u6VDzg9nX3nSgKyHlSuLAUROzv8/aUa+/rKulGlivIiF4nexunTwOXLT2pHeHpKafTzy80rjP6uW1dW2NKloezZLbB/P+7uss0mJSlw7HH+YgeH3LzNycmQlkZmpvp2dZW9onx55MOoXJm0NBHzOnWUK9nfX59v1Cg3N3FmpoLS7DNxdYXbWc54eUkRffRIuZSbN9exxiBfb1oaODqSkZGbCzonBwcHBRCWLw9cvy5VdOlSSEx82pednTwOsbG4u2sDcuIEWsyNG5VDuUYNTXTnTk6d0jxq1NAhfPstHToAFy4QFCSCWuBxymFj8PbWuSEtjQcPdB7JzobkZAIDoWFgCpQujYMDpDTrjJ+f7NccOQK7d5Odre+Eg0PuPCIiVEDGzg5fX72Ojw/V/O7i769qid26gafTXerX13fA1ZWnEY55wAvMg1wLiDfGXDDGZAErgVf/0zEGcMn9uwhwPc8D/f/fnwULFv41WNesBQt/YPzvB+lVqWIO9eolw6m3N7RrJ2I5b578EP36qW60l5cCs1avljn5ccm6PXueVudwcJCZd+dOyZEAkZFqw85OxKhsWVXgSE0Va3VyUhBfz576/P79MvtOnKi2PDw0Hnd3eOstGYQbNNB4fXzkqUhK0t83bsi7ERYm/0fr1kqJNmOGSF5QkAjmxo1PgwRXrNA4y5dXOw4OSj2XkSED7cWLeq1pU425bt2nqTYCAjTW8uUl16alaTexcqUCEePjNf6kJB0/b54Y76xZ6nfdOrHIsmXlW0lIELPv3l1yeGqq2vH21lzc3bWDSE2FFi302qxZqnCSkKBgu/Ll1Uf79tpN3L2rdevdW2O/eVO3ASIi1G+BAirX16+fAvNefVWkecgQne8rVxSsN2mS5njsmJh+VpbuGly8KNn9wgVtlkA7oxEjNLagIEnATk7ytty7B6dOYbt5M08epBIlQkxo6LP9ewsW2C4BSf/8Um46QwBsNlsXoLUxpn/u89eA2saYIf90jCewFXADCgPNjTGHn9n5f4EX3Z/NZhuA8pfj4+MTfOnSpecZlgUL/+fwojzIL/Kata5XCxb+ezz3NfusRMmAP7ADOJH7vCow5nmSLhtjCA4MNGbECFO+vDEmKsqsWWPMypWq9+DqqrohHToYU7WqMaZXL9O0qTFubqod0aWL6nuY/ftNdrYxcXHG9OljTPnyxpQta0ytWsaYTp1M9eqqtbF+veqNeHurVoW5e9ekphpjxowxrVurvUKFjPH3Vx0Lm82YdeuMcXc35sgR1eTw9zfG3LxpOnVSW926qe3ixY2pW1f1NObMUV2L7duNMT4+ZuhQY3x9NaaAAGM8PIwqdmRnm6FDVf/E3t4YJyfVSrHZVFDEZjMmPf1pkZHy5VV7ZMQIYwIDjfH0VF2OOnWMad1a7Zt69YyJiTGmd2/j5JTb186dplIlrWNYmDEmI8OEheUWNlm50vj7G+PiYoxp1MjcvGmMadnSjBmjNV6yxBgTG2vMxInG09OoqIe/vzFnz+q9oUNN9eoa//79Wj+ze7fp1Uv1W7y9jfnwQ53b8HCtg3FwMGFhxkyebPTmuHGmSxeN//JlY8aNM8bEx5uJE1XjxLRpY0x0tImIUP0QDw/1ExeneiSmaVPj5JQ7h0OHzOTJ+r7Y2xsTFKQiIf7+WrdevYwxmzfnOVF4iRLBZtAg88zHs9oDuiJP4ePnrwFf/adj3gPez/27LhAH2D3PdfV79mcVHrBg4Sny+n/Jsx6/1zVrXa8WLPxHPO81m5eL+Cd0K+joP7124nk6M8YQXKCAMV5eqo7n7y8GFRBgzMqVJiLCGHP3rsrLpaYaA+b06dzqcw0aGLNpkxhThw7GLFigynAREWbPHpHsnBxjTNGiquDWqZNY78KFZsoUo9JvHh5izQ4O5uxZY0xU1BNCbo4cMSNGqArbmjXGmORks3NnbrW6PXvMsmVqY906Y8yCBU8q5G3daow5fVpjadpULC0y0mzYICK9ZImK1Jnhw8XoxowxI0aIZI0fr35GjNC/CxcaY/r3N+bmTWOaNzcLFhhjNm0yMTEaQ0yMCvuZTz4xJjra7NxptKsIDDTGzs7MmyeubHx9tb7Z2RrbuHGqqHf2rDHz5pmVK3Mr6QUFacL584v99+6tzzg4GNO3r8rWgdZ81iyV/ps3z5isLDNjhuY9bZrRAkZHG7NggUlIyF2PkSON2bxZ58TOTuS8ZUutQZ06xixYoMqAkZEi5Nu3a1FSU40JCVG1w06djImLMwsW6HSbhARVWixf3kRGaklN8+bGHDlipk/XED/5xBiTmGiWL9d0zaFDxvj6/iaCPHiweeYjDwS5LrDln56PAkb9p2NOAqX/6fkFwP25LuTfsT/rB9eChad4gQT5d7lmrevVgoX/iOe9ZvPipnQ0xkT/p9ey8/C5/xoVKsCWLcpIMWUKZzZf5Iv+pyAxkexsdAvezk7RVkOGULSosj6MbriXnxzbwOnTHB3//ZO8yNfqdaVhwC26l9on90KrVuzeDVdmRsKIERwLeYOUFHh3XWPYtk32gyZNOHECPlpXl9at5To4nFOdqUMuUS77DCVLQugbbkyZojv/ry1oyJ490HhcYxITgbAwKlSQE+TyZcDFheotS8iOkZPDWjpx797TYC9XV6B3b3atu0Pbw58QGiqnxIED4FjKjdhY2BXjJl927948Kl4S1q8nPh5o2ZLr1+G9tSoekq/f6zB8OJ/vrElyMrJD1KkDgYFcvy6HCQEBAGzZnk+2jZs35S0+cgS2bKFLFzk4sLOT6bttW34ZtBTu3+dWcj7lUW7alGsZxRTx9tJLEBTEr426QuHCrF5XgKwsuGhfkfe7XIJSpZiwqSY/er/Jpk0oz3RODrbWrWT/LV6cUqWQxaN1a+jWjVsd32TWLLgY1IlVcVXgyBFODZ3Llv1FZEvZvp3vukayNLoSVasqXbbxKaPvTVgYs2ZpDX9dsY0J66sza5b8x8uXQ89hJdixQ0OnZElF+v0GvCAP8kGggs1mK2uz2eyBMOA/m6EvAy8D2Gy2SoADcOs3Dfb/X38WLFj412BdsxYs/IGRl5/6JJvNVg4FCzz2Td147h4fPICjR0lPB3x8OHdOuYAJCiIpCUZPKMjt7CL0nVMTnJyIjVVg2qej0zlyBIiIoPqUHlCiBJmtX2XXLug0oASf7W2glGLHjrFvnyysxMWRmKhsD7/+ChsuBirCzcODEydko12xQhzSyQl6jirDioMV+fvfIfKLizg4yPa6fOhBWraU9TUzE5avcyYuTjmGV6yA73Z7Urw4XEp2huLFycpS9bvPP5fN9rvvgMBAiheXHXfePBg+XHl9W7VS6remATeIigKcnMjIAK5eZfVq+H5jPnx9ZVEeNw4IC+PWAyfWrYO//Q1F73l5gZ8fGzbkBhr+5S+sWAG7dyMPbr58+tvHB0qXZvp0lbymZEkVGElKIiIC+PJLPvoIeaYvXWLaNOS9zi3vvWcPsHQpa9bICl525zesji4DW7Zw545S6+3eDaO/KMatEZ8zfXouSU1LY+FCWL2xkBZ7yRJccsNO3N1lsSYjg0r+j1i5Eu1Yxo+nR8cHbN6suiw//QS26V/QsyeQmEhysjKVlPz8PT4OPY67u+zfwcGKO/z1V1mlN8Z4yyudRzwuNf2sx7NgjMkGhgBbgFMoEv2kzWabaLPZOuQe9j7wps1mOwZ8B/TJ3e3+Zvxv92fBgoV/DdY1a8HCHxvPDNKz2Wx+wAKgHpACXAR6GWMSnqfDkMKFzaF+/RSAtXChAqzee0+ssX59OHpUAVmPayVHRSkI7dEjBZaBgu22blWQWuXKamfTJr3er58C5rp0UXWNvXtV1SM8XDWrk5MVwPbxx1CtGpm1GlLwwikpqRkZ6rtgQaWYePx8yhQdX66c2nnwQCkkevXCuLphe6Wtgv62bROhnD5dqSqSktRuTo6C55YskcQbEwPe3hy/WYLKlZWIosy7ocqPtnKlAtBiYnTs3LkKUiteXMxs5Eix25s3NffBgzXOnBwFK0ZGam38/RUAOW2a0nI0aaJAuYwMsfr8+RV86O6uILyQkCeq/ZPgxgIF9JnkZJ0jkAo8erQU/HXrFNRXtaqC5w4c0HrdvKkdR5s2Yszvvqtqf1WrwoIFOmbDBjJrNqDg1EmqpDdypJTjgQMlzSckaNyTJumzaWnqd+RIaN4ck5yCLfm2Ki5+/70WMSlJivr+/ZoPKDHzli3Y7t/Pk0nfwyPE/OUvzw7Smz79j10s4EXCKjxgwcJTWIVCLFj4c+F5r9lnKshGKWiaAyWAAGNMg+clx4CI2c6dImIrV4osrVwp0rZ6td6fMkW53+zsVMI4IwOqVxdhjo0VoWvQQDKtq6uOPXFCcmVWlt4fOVKS640bIrX79ok05c8vwnftGrz3HgVbNpYK27KlSPX48fCXvyjH24YNSo7s56d7976+kjs3bBCx//ZbbB4lRVLnzhWpA9lIfv1Vf4eEKDtG//7w8ssilIsWwWefUeXSRvK9N4wyg9qoTPPEiSKC3t7qa/16rcWbb4pYJiaKCE6frjHfvKlxZmeLXIaHawz+/rJTODlpXlFRyrTRurXGc+KE1s7LS4/mzSVjx8frs97e8O23IseP2+7YUWS6ZUut3/LlyhrRsaPI+6uv6hzOnq1zMm0abNnyNAvI2bMat6urcszNmUPBcR9xd9gYrenNmxr7wIGS1+3sRPrHjNF6zZ2rdhIS4K23sA14Mzf3G9ooJCVpk7JypVTzL7/UJuyXX6Sc/wa8IIuFBQsWLFiwYOFPiv9WQbbZbO/9Tx80xvw2Y2cuQipWNIdee41Oh8cQGSlRsF49caFevWRBOHdO+XzPnZMIevq0uPHNm3D0jdnwxhuQlMQXEaWJjRXnu3BB3OyhX0Ucr5zhgw8kgC5fLg546JBsAevXw8fFZlPogyEEBop3RkfDwJ536TfMmUXDjnHcrhpVfO/Svb8zQUEwavgDOH2a7y9Xx96eJ7aPa9dUjOLQIbUbHQ0FvUsw9cNbODnB1Kmy73p4wNdTUjiT6EZCgjh48eLyLz/O8jZ7tnjgltTafBl2gLg4CeOLFomvtmwpH+6qVSo1vXq1OKFp0FCdN29O9ZzDqh7XeibvxA8jNVXCe/gn5/lyfTnebXAQIiM51+8zJk6E5VtLanEnT6ZVzOc0aaI1/9vfwHPyMNyWzSTFLxjatuVUz0+IioI39rxOp7SlnDgB5/bcYOwcTz5ZU4ky90/Rtq1uBLRsCVteW8Hcu734618h+VERinCHWrVgW/9VMH063w0/QPHi0MLpF84UrUtFh0tsO1uGdu0gc/QEKFaMUdeGMG2a9iL168Mm38GcGTaHii3L8OXwS9SrB7U3fQzjxlGjZj66d9d6hoVpn7Fmjc7VX/4C7dvnbQfp4RFiXn/92erLlCl/bBXpRcJSpCxYeApLQbZg4c+F30NBds59hABvAaVyH4OAys8zSPVoB2XKcOEC2E4cJztbYuf9+xL+EhJEjgsXhmLbV+HkpPcqVZIoa94eAjExXMopze3bKh5y6JBI5uMiFY+LdxQpoupz2dkSgf38cuPXSpTA1VVDiYiQ5ZakJM6dg4eVqzFrFpCezu7dIuUkJvKoanViYiR+JySIDN+7J5fBiRMSYFev1udOnJDTIiNDx23fDly7xoULclwAnD8vEpcvn9rZt0+uCtq1Iy1NYnH+/CLR+/fr74wMCbE3b/I0CNDRkXNJbuDuTny81oJGjfjhBxUpOXhQax4Xl7swHh5MmwaHDwMvvcS+k26QlkZiosgkaC6sW0dqKlKh69XDy0tjAgnIiYlqLyEB+PBDqlbV+EDni9KlsbNTWmTu36dYsdwy2Ln5q+PiYNcuSAmoS0wMnLhbhrVrc4t/XL3KndeGsH275piVBSkpwCefaB4vvcTZs3KSqFKJXC9xcbp5sHKlHn5+Ojeennn/er7AQiEWLFiwYMGChT8p/tufemPMBGPMBKA4UMMY874x5n0gGPB+7h4LFQJ7e86eBdzc6NVLKnFqKlSrpsC2zp31Gj4+nDwp9djBQQ4B2+TP4NAhyjjd5tM3zvPBByJDBw9C165AcDD168sOvGuX7r7//LNIXVyc2uLAAXx95RyIjJTDYOqasqSni7RevQrfR3vSsWPFeyfGAAAgAElEQVQu6XV3Z98+cbtmzeDtt6VKd+2q4LGJE6V893rpKLi4MHKk3AjDhmkuderA0YeBtAm5xYULuSWvr2lMbduKyE+Zor6pXp127TSumjVl/W3aVK4EX194f/ADUlJkhy5dGrh5kwoHVoC7O46OIoVs3sybb2rzEB4OJCRQrRpq4P59XFxyNxOHD9Pg4JeQk4O9vVwRf/87BKf/BNOm6RykpcGgQRRJPKfqe2lpXL8ukngpzY1164CRI/nhB5F/NzfVCCEuDn9/zQ8PD4oVk0OCy5ehcGH275fV223P99SqBYEHF9OpU26FvrNnKdKyNlu3irAWL55b4W/RIgX0XbzIypW51aMnT+a7iHyEhYmMr1oFw14+Qf78mvuQIRDsdOY3fUUtgmzBggULFiz8eyMvP/U+QNY/Pc/iX6kFf+8eIKvrI09vfH1lkfXykrp7/77eu3AByMigcGGRzdu3RUIJDFQA2aJFEBND5cq6pf/ZZ7mljHftIjVVhNvZWWQ0Xz4pnqdP63X8/PD0lPV10iTFr6WliZQX2/0PBg+WmuviojbInx93d9lAGjTQFFq2VHaMmjU13mvX4LFEnJoq0p2ergKAtWpJCb2YXgJyp/DeeyLohw8rdq0QD+jfH/jHP3B01HhTU8GZu5QqJVIWHAzs20dgoIhkmTJIHk9Nhf376dxZheRwcqJ5c/WTmQls20aFCvosERFUrqwxkZEh38mFC7i6Qqmzu5Q67vRpuH9fFt/UVE38+nW1nZrKvXsa8/37ssWQloYxskI4O2s9WL+ePXtyCW9SEh4euWtvs4ExBAVps8HNmypHfeECLSpdldrr6wuFC+Pmanj4UBuMypWBqCi8vIDr13FwUOwf8fHs3KmpNGyo2EqMoXJlbRBeeum3fT1fVBYLCxYsWLBgwcKfF3n5qV8ORNtstrUo1VsnYOlz92hnB6mpZGVBvvgzhO+sSK1aIqiRkU9vkz98COzZw717TVm37mnMXd+t5WHHDi51+4DPP4eUNWpy6VIRIlo3J3a9EiGUKCGV9nECiL6hd/hqWRGqXb7Ezz8rfg9g7VoIrz6Vtrs+gMH1WPw2RI46SOMRNalRA4iKolLVqjRv7saIESLsU6bIO+3vr8rKaWnwi1dn6mb3ZM4cEd9z5+RQcHODd5sf51RGFTw9xT8fk/XQUMWy/RRdiB9+gK86F2fJEinGp0/DsDHOJCfLXrJmDazt2YICBXKVbYBX/SUl58/P4sVS2ueHODBrlmLu4uNh27B6jBsHrSIqQ9OmxMTk2kqKFuXWgrWUaFSJ+8Vh9Lam3F4J82f1gXr1mHn0db4sg0zPZcowezDU9vWlwAXZYio5XSE1tTR06EDdK3DrlgTiOXOgcWgozQJyNy/Fi3PyZK59IqgQpKcTEKAq0u4tBnLuHPiO+YRly6T8078otGvH3Hk2QPP29ITMPWuJ2wiVOnaknb3OLWvW8HXiL9QeXhcfH5HpLderkJys5CUffgjfflvxN39FLViwYMGCBQv/vnhmmjcAm81WA2iY+3SPMebo83YYUrKkOeTlJb/E/ftcHD6TsrHfw6pV7HgjHCcnqB1wR+bd6dOhfHkO9v6KmvbHlHh4xw4xo3v3uPPOGIrcvyGWOXEid+u0wNnLmStxdyk9+yPIyODepzMpvHWtsif8/LNSqXXowJUVP1HaKQW6dcNs3YbNvoBY9Y8/qr0TJ/i+5ypejXxdz9etg2bNuNH8NTw8wJZwUdkRBg2C6dN55OjMqVMQWC0fa9c8olOLdE4kOHHjhgTRCt2qy8OQlCSZ98QJsLPj13qdKOnygH2HC9Gg4EFl0ti6lZ9uVqRxI4PBRlqaxOmiRaFM8lFwcuK8XQXy54cyXWpKfr95E7P/AD//DA1+GAVDh0JUFHMTO/PWRE/5Rjw8xFLDw0kJbIjbyIGSsuvUkVG6YkVSHjrhtn01DB7MjdhbeHasLZl9+3adsxEjlJEjLEz+36JFJdN6ekJgIClBTXHb+Q/44Qfo0YMzPi2o2MQTZs3iYceuFOjVXUr25MnaOZQpw6PKVcgXtZcz7g3x8gLn10Nh4EAmHWzFK68oSLHEnn9o3ZKTtVNq3VpZM7ZuhbAw1g7aQqeYj8VuBw2SfN+/vwIYjx3D1rBhnkz6pUqFmEGDnh3gMm7cHztQ50XCCvqxYOEprCA9Cxb+XPhd0rzZbDY7m812whhzxBgzM/fx3OQYkPG0Vi0a7vsMgoIo65oij8P8+fTsKVV1/soiDF7bAi5cYGDWV/TrB9jb8/WP3tCoEWRlkfnBGLKyYO1+T+rZfqFM/xYsXAi0bavb/hMnkjllJjt3QpnhnZh7uqki/e7ehZwc5syBpevdsNuxDQ8P2LLxIYW+/JSPWh6l0Kdjmd9sFUOHQoWopdCsGW1/Xczr21+jbVtxLueqZalYOR9F//E1Jy45M2kSBPqmg4cHLi5QupITr7yiDHA1avAkn/DUy92xq1QRu66dce7diZkzwa5wIY4eBbeWNWHrVrZdrkjjosepXUfkePhw9RkWBg8CqrN4XwWWLJGqjqMjlCoFN2/i6qqsdNy8ydg5nkw61VlBiXv2MHBwPh5cvgWLFtF4TEONaetWkeacHI7lD+aucVJA4eLF0LMnffogj8p774GjI6uTmsKYMdRd9haFvNxYMXAvBTd/D/Xq8fn9d/gxoynvvw/X6nSG0FCWXm+hYiOpqbwb1VUBjxkZ0KABX2e8hteAduxNraIiJfnzk5WlVNGcPw8ZGYzpc5UpU6TSF32zM0v3lePjjTXB2xvnMcNwiz8IJ0/yXZ8tjBsHjlMn4Lf0Y+avK0mJOuV4zXsXY6c453pv8g7Lg2zBggULFiz8eyMvhUK+RfXhL7+IDkOKFDGHvL1ZNe4k3U+MhdRUHk7/ishIeVJLTPngqToYEcGV0/conbBXqRVOn5Ycu2KFFMITJ2QcHjlSuYl9feV3iIuDBQvoe/UTFlf9kl1B79LU4Repz1FRUonPnoXUVM4XrUl2NlR0uCSVOSaGh/Ua0727mvL2hkpZx7hStBrXr8sL67xoJufbDePCBdktqvV8ibv7T+LcvLa8Bx07krliNQUjv1Obfn5ccaxI6cHtOTNtA0WL6uXHKZ0dHaHuzbVcqtFJinCvXvKb9O6tPHi9e8srMHSoVNeUFHUcFiYPQ26hkHOnH1HB4YrWYfJkGaZ37FCxlIYNRTzXrZOSXbCgogjbtdN6DhigscfFqb0hQ8RMJ06UIbpoUZ2X06dh/XouZnji5wcm7S706sWpyd9TKeuYxjVgANy7x8VvdlJ25zfaJYwdC40bS3UODIQVKzieVoYqi9/TpiU7W/NxcNDnixeHPn043+xNyhW8qk1ARAQpLbvj1uAltkw/SauWBrZu5a11rZg0CYqlXWTYjLLMnHiHA6eLEBMDA29Ngrt3sU2ZkqcdpLd3iHn77WerL6NH/7FVpBcJS5GyYOEpLAXZgoU/F573ms0LQd4J1ASigXuPXzfGdPhvP/Q/IKRsWXNo/HgqTHqdcx+vILNrrye8t25dZbIoVEipzDbdrk3NnAPExCht2pIlsGWyLAZT11XAxUXOgehoeV9dXOBAnWGUXDmTOXNUR2LDBuUj9veHg7N+4bx7XcptnEnXfcOws1P2C2PgYswdqjUqwrhxEkwvxaQQ+oYbp07Bqa1XmLS0NBUqwIQJ8Mknqknh4aGsEQ0aaBxDhkBwsyL063yH3btFfo0RGb44fil07szsJU7MnKmgw3v31NeMGaofsn07nFt7gk/WBtKliwLpPvxQQm92tspWHz6sGh7Ll8ttcKvjmyLU48fjfGgXHh5wLuoWnQYoILBSJfi04Y9U+bANa9ZAxQs/0mpGG2Ji4FePamRGH6Ngm2Z80nQngweLD8/0ngoXLlB281wuUlYTDgrinfmBfNXzF6oMqMvZs+q/QQM42uFjBt+aQMGC4tgBATDf7i0+953LvHlw0fElQiue5JVX4I0fQqF0ac4Mnsmnn6rOyL170OuVFBavcyMyEja0nQsODhyr0Ze+fRUwWaqU8kcfOgRzw36i8luNefQIziw7AAEBeAYUoVIlpXtr3VrjaNFCPueve+/F1qhRngnysGHP/nH58MM/9o/ki4T1g2vBwlNYBNmChT8XnveazUuQ3oTnGM9/DycnOHKEIUNehyJFyMhQNoQiO9dSqlQnXF0lmCYlAZ/aMewdFWPz9hYpveFRHc/bJ+jTR2m8mjSR1XTAgNysCFuOU6uWnBgl0i9yukJZOnSAbt2AwEBO7IRymzbhXXkYfn5SbzMzgXr1GDTkJJ0df2RjszZw6BC3b7dQyjJ7e6pVE9EtVkxk2M9P5M7VVeS7eXP5hINdXChVCkqWVEG9y5dzszrExXG73etUrKgxnz2rXM/16ysAr1YtibcYQ716Emr79NFygWzQkZFQds9SqlV7ne+/z83s8LjyiLc37Txy1y02lrCwlzl2LDdw8cFWvLzaKANEZAz+/m1kE6jVkaQkKHXjBh07SpSvWhVo1BGGDZO1ZV6W2GlCAgUKBMK+fVSoUBc7OygcdxB//5qY8RNo+4M2NoUKaaOD9wDCiubmRl7vwKhRuet8owYcP87Dhwrq27ZN68T27aSnd6VlSxTx2Lw50bu13gEBSmuXkyPBnNhYWrduTKtWaBFycggMlMC+Z4/WctMmCdbXr5Ob2y9veJwH2YIFCxYsWLDw74u8lJr+CTjN08Ihp3Jfey7kFCgIyclERgK+vjg4wFdfAQEB+PmJMN6+LRcElSqxbZvIZFycinR4bvwabt9m507o108q5pgxIlvJyUC5cpw8qSwTl+zKcuSIsiDMng1LI52VRzcxkexsEdozZ5QE4l70STZsgFshbfjpJ8Dfn8KFpdRy+TJubiJqrVpJOc7OVoaIdetEApOSpNaSnU2zZopZS04WYbxyRfNbuBAOHJAieuGCYgZB+Zr7ht55UnwjMBA61bjE+fMij82ba/7p6UBMDD/+KNU1KUnHU7w4FC/+ZC54eeHurtRzQ4YAQ4fi6qrMeDRsyI4dcOcOkJpKqRWfQ+/eLFumWD9XV8TO8+VT+rWiRWH3bqhZU5lFsrNZuzaXeHp7Ex0Ntj6vM2CAXBSnTuWm6MvKIjo6d8wODlKGN6ABFi7Mt98qU0n37uqOzEyCgnLbjYmBTZt4s8Eprl3T3YQffoA3w+7qHB85wuLF2jAwZgygJdi3T2vaprWhWDFtnMqUQcm1fwMsD7IFCxYsWLDw7428WCy6AVOB3YANZbP4wBiz5nk6DPHyMocKFBATS0tTZgJXV907HzpUpe/Gj5dPdft2ybTdusln4O6u/GSDB+u4rCyxPicnSYyenqodvWaNPuPjo3/d3OR3mDtXfQwdqmOWLRPLXLNGKqOfn9iYi4vqV/v5SS4ODdXgW7YUS58yRbYGHx+9tn692LqXl8a8bp3K3z329bq7w5dfio2npqr/69fFfKOjtQZpaWo7KEgy6OXLqtuclaX5jRyp91askMybmQnt28vIvGKF2HvHjuo3JETrGRqqfjZu1JgiIsTusrM11sWLoXZtMcukJLHZI0f0eQ8PrUNs7NOE0Tk52Hr2wHz6mca1caPy5zk5qc1ChZRbr0MHeafHjhWT37JFRH7VKt0K8PbWbYMBA+SNPnBAO5iAAO06atbUse7ukt/799fY9uyRfyIhQedl/3796+CgHURYmB5JSXD0qL5Xv/wCV69ii4/P0y0WH58Q8/77z749OXz4H/s264uEdcvWgoWnsCwWFiz8ufB7epCPAS2MMYm5z0sA240x1Z5noCGVKplDc+YwdmdTPhn9gG37CrFrl6wOO3eKp168qNvpq4bspWzvhnh4yHZx+TJ85jtft/wPHmS+1wQKF1YhkB6hmfhVKsgFWzlalT//hFNnZIjLxcfDuV1XOZ7iTZWP2tLq0SYaNlSc2ubN0LXjQ+YvKsDAtKmcD/2Ab76RnzUwEN6odRySklh+tSnFi4tjhodLAX39dYmsAweK01UILMg7AzIpWVL2howMZUErV068sGhRZa+rUUPcLSVF3G/aNHHYLxM68UmNtTg6yg+ckCDO2q6dxhIW9jROMSoKfvWpqcH078+b/j8RHQ3H6gzkuybzOX1a8XVz225geWp7Xts3EE6f5sHmn3j/fZjzTUFuX8+k2PDX6JS+nGnTxHmH+f8IM2ZQ7eYWjqWVlWchPJzFy/LRN/kLlhZ/n/BwKbiJiVC2QxWalThOlSrac3z6KbzV7TYboooxaRIcuFqKml7XyM6Go76dID6eL/sdp3dvlROfn9odV1ftFypUgORRU6FQId46OYSNG8XV+/eHgUVXsyqnK91Hl2P+h+fp0AE87W/z3dZirFyptcmfX3y+Vb27TJiuOwayw+TtAvHxCTEffPDsH5ehQ//YP5IvEtYPrgULT2ERZAsW/lz4PQnycWNMlX96bgcc++fXfgtCvL3NoYIFpVLmyydmuWePVONhwxRZNXWq1NLDh6WsTpkitdDBQawsJER/V64sBjp6tJTibt2k6C5cqFrK77wjM+qCBSpD5+wsZhoWJnV2+nQx0DZtYOZMMdj4eOUiXr8e/vpXZYHIypJfomjRJ/mFqVFD3olvv4VZs/Te9u0aS3i4bAKvvKJ5vPuuIgz79IFHj+QHcXaW3cDZWfWq+/dXzezNm6FBA+5GbsP50C4tWnS0PhsRISW3dWup6B4ePDFcR0XJr1C2rNpxdFSd50aNtOvYv19rk5Oj9Bz37qmvJk3EzMeP11r7+monMnq01Plx49RXbKzW9733NNarV7WGzZtLgW7SROOpVk0m5Hr14P33pQYPHizFuksXKcVpaYqgrFtXmTWqVtU5W7dOu6Tjx1WCcPx4+TXWrFF/+/ZpnXNyxIJ375ZKnJ6u95o1k2ru7685bN2qMfv7Y+vdO88E+aOPnv3jMmTIH/tH8kXC+sG1YOEpLIJswcKfC79nkN5mm822Bfgu93l3YNNv7egJ7O1F6mrUgI0bueHfGE9HR0mpU6dy8XI+yva4KmIXHg6DB3PLriQlmjWTdDt9uiTKhATONehLhfvHZC1o3pyLfi9T1seHlI59cXM1unVfpYoI9v37Iq0ZGXD5MneDGnJ/XkNK9m8vIn30qIj35cuSbB0dReDWrlXqigkT1L+7u8het24i06tXc96vBeXmfSASByKi9evzawFv3AtDxqyvKRQfL6I4a5Y+26CBxuLgIJtDcrLSWbRrB6++inPMXg46NSU7G+oOqgFJSfwS8g51g3/UZ4KCOHyiIMHvvKPPVqwoi4nNJstDt25cbPYGZb0yYcUK7voH47xypdbBw4Pb5WtTLOFVydIDBoh49u7NjYi9eMbt0Fo4OopsXryIbcVyshZBgfv3RYbPnRMRB/U3YgRcv86poB6kpUHtRo2gTRtuBTSkhOMI9XH6NLz6qjY+06aJ2Pr788i3HPkiIjS2oUP1b506isp0cFDbZ8+KWE+dCm+/rbUrX/6JDeaMQzUqVqumTZOrqzYMoaGywVy6lOev5+NS0xYsWLBgwYKFf188kwoYYz6w2WyhQAPkQV5gjFn73D0+eCCvaWIiNGqE5/61Ug19fWHnTsrWqiXVMD5eRKlHD0r85S8ia5cvi5yGh8OJE1QoX17qYvHikJBAWZ8D4OOD2+SPpAynpioCcNMmqal79ypgKysL55mTcDZG7SUkiLyVLy8l0tVVaujly/rctm3y2NasKTW5VCl5Xvftg5YtKeedKdKflSV1Mz4eoqIoWa4c1K9PodhYEevERP179arUZgcHzfv+famh+/ZpLHfvwv791AzKEInML99u3aJn4KOP5C8+coTgKlVgQG7EnIODogRr11Zus8REykZ+8SRPtLOrqxRcV1f47DOKnT79NCfboEHyc4SE4OmcLuU1Lk5zzvUmm7AecGioNhszZoi85uSIqNrZ6RxWqEClm7uekutZsygxJbe4i729NgiPqwkOHy4m2rIl+WrVkiq9ebPW8fXXpebv26f1X7FCnwfo3FlkuUED3W04eBCSk6mYuFdznzdP/Xl7S3EPDc2tq503WFksLFiwYMGCBQt5KjX9IhESEmIOzZhB5QENiDuayeLwgkRHK8atWTMJmiCeeGP6d1Qc34OzZyVA7t8vHunmmMmOfQVZs0bHFS8uLrdmDTxs3Z4iezYwdKi476JF4qP378OdVMMX02283+0K1dqVxsVFToDdu+HA1jtUrFWEM7GZDPuw4JMKzJUqwRdfKHZv714Jp4GBcgRkZEhMrVpVHKxtW/h4ehFqB9zhyhXxcwcHxa2dmbeL+WebEh8vvufmps8XKyZu3qCBOOml2Rv4YE97fv5Z8/3gA7WzbJnmOHq0xhIZqdcftmjLqS82UWneMDwjZpKUJF7fpIm47/HjcGHsYhov6ctPfz8Bn3/Oayxn9264EjqMLW1n0uqn0TTc+yndu0vcnTcPCtYLpsTlw9wKaQMrVnAtoxiTJ8NXjh9RZdPnXLsGyf/YxSf7mjJ28G3qtS9GsWIac506sGHGeVb8Uo6+feGhTzm8HpynWDE43u9LOHuWL/3nkp6ucS5cCF27wt//Lp6dOf4zNgSOol8/icYg3jxunOb+2tXPaLV7FB4esHRGCpfS3KheXfsXf/+nQv7jmMbISKhaNW+3WHx9Q8zYsc++Pdm//x/7NuuLhHXL1oKFp7AsFhYs/Lnwu3mQXzRCvLzMoR9/1G3zpCQx46tXxTDT06Wonj4tBdfDQ97XLl3EDj08pGBWrizmlJamW+wgVbFhQzE8e3sdm5ys9zIydNyRI2Ks6eliT7VqyZ9cp476vXJF3tyzZ3V8drYU33v3lL+sTh2ptxkZsiS0bStWm5ws1XTPHj0fM0ZqaFaW2ihfXm126iTbxujRGoO3t7JhnDmjZM9lysgOkJj4NNqsfn0x4SJFnjK/7GzNIyNDf589+zTTRny8xmZvrza2blU/Pj5a48fr5e2tdc7J0ZrOmqXnQUE6J+PGQd262NZGYuzyyWvcrp080O7umltamuZub69dSq1a6r9aNXmrAwLEaJcte5rtw99fLLpJE71Xtary+rm66nm5ck8UeFq21DkrWlRrceLE07kVLapdT/782hH4+em1x1k84uO1bi4ucPMmtqysPBPkjz9+9o9Lv35/7B/JFwnrB9eChaewCLIFC38u/J4e5BeLfPnAzo5697axI0rcqcNw8Jw4Eb9KBWnsBz9dgcI5cPy1FYw62YvZs3VXf8UK2LW5mchPnTrM3V2JDRtUlKNcG/j6a7iU5Um9UpcY/ZGqKS9YIC6ang57V27jgEsLah+cTcNVQ6hwG0oNnYqTE3z0ygnajwpk2TrZYJcP2Evolw0pUABWNZ/JvQ072bkTEtuJd/3wA2Qc0F19Dw+YPx+++QaaNs/H7JKfkNLhE7Zv13Rd80HkkgOcca1NTAzsb96e06fFhescEJd+993pLFsGxzu8y1SvL0lPl916QXvd8h8wQFz2u++0B5gxQ5ne7jduo0UcPBjP07twc4O47Zm0aFeQoUPh2Eswpu4OPj/0Mh9VGQ0HD/L9kG18/z0sulxWEnP9+rTxOkZAE/HUj/s8hIkTRY47hcJbmyEtjS8vd+bdqusZW2cba9bAqXFz+STpLcbGduVd79XY28OCrfBeRxjb6QTX3AKpXBnupE+ixsH5PCw+n+OlekJQEL+M38LVq9DV7zAPqwaTlCQnzKRJcLFsM9i8mamHmhJ+VY6bWrVg28mrbIzxpl14T0aVCcfFBUb11Iaix/SauLhAw7/onBw5Iq4/apSy++Fhy9PX07JYWLBgwYIFCxbyRAVsNlshm81W8YX0aAxkZVGmDBSaPfVJieUfdxYkLEyC4sCBudXVihVj1Srdft+xA15+GambuWpjz55KyFCokBJDeHkB6enExIisRkRIYDx2TJx6h10LPDyACxe4dk1JJFq1EiE6+jCQ3r0lTvbvDxe9G9Kvn6rd0asXheMO0r75Axo1gveHPsTXVwJp794Sn+vUgab594KLC61aSfz09ZVt5M034ZJHbSoWvkr3ZrfIyJCA++CBUsDVqQMfdLukynX+/jRrBhPGGzIyJIZ27ChxNDAQ2pU7xcdDU6hTRyIy6elSa+3tadNGVaEZP55BgxTLOOaDTDh1ilq1wPztU6hXj4AA2T64fl256fr0oWpV+LLnQYKD0Q4hOppevRCB/vFHMtt1VuW+1q2JjVU8IF26qPDKmjVs2SJBOycn9zyHhz8RivH1pVQpbXCoXBl8fEhKUpwmp05RIOMunlPf443Kv6jq4CuvQLt2fOC+lPz5dV5Llwa++YZr14DUVO7cUTIPhg6FwECcnGDOHFlqKlzeQfd6V+jbVwlEStqn/KavqFUoxIIFCxYsWPj3xjN/6m02W3sgBtic+zzIZrOtf+4enZ3h1ClVTOvYkZUrxZmuXhUXs7PTnf6TJ4HsbKpUUUW4t95SSWZCQhR4Fx//xC2xebPi2uztgcqVadhQiqOXl8hzqVJKtODikksq69enRQv5iQ8fFsmtHmSYPh0Km3Ti4qBsxikePJCzgQ8+gPBwNu4oRIWM4xw4UoAiRaBHDw2lSROp2FStCjk5ZGTI9eDuLrdBaiqUyT4PBQowe1UJrl6Va6NJEyWfuHYNbtiXYedOYP9+3N2Ba9do1kxzKBi9FwcHKcbn8lfinXFu2GxyI3D/vhYmLY0rV5QpjebN2bhRS/XT/oLg60utWmD7+2xYsgQ7u1z3SU7Ok6p1WVmwLbUmxYqRy2pz/b8VKsDq1RQ88oueT5pEqVJab6Ki1I67O0WKaG0dHTVv2rals9cv3L8P2Nk9qQNDvnxw5Ahr14r08+ABmfbOMGwYW9LqEh2NbCy+vjzq9TpJSdpMpKQAK1cy0H0t7NiBMTB5stq7lFiI119XJpWczzQAACAASURBVLzPPweuXuVUemmWLs1NYLFoUZ6/no+zWDzrYcGCBQsWLFj4v4u85EE+DDQDdhtjque+FmuMqZrnTmy2AcAAAB83t+BLb74peTV/fq60fpPS+W9IDly/nvPZZSh3eZdU4uho2LSJuyu+xznnjtjXwIFP/bctWkjG3bxZEvLUqWJdERHy8X73HbeWbqLEwFBVsouNlRd3xAjYvJnbqfko9k5PMc3wcGjcWFVKvL11j37nTt3z9/PT/f8BA0RIH5d3dnSUx7dRI7H8ZctU8e7sWfUVEKC2oqPFDhctEjO9fl0eXn9/Scz796v9Ro3kt377bUnb5crxsKQ3BRLOkVK8AhcuQPCQujBrFsfsa1LN9ZJSxhUurGi/yZPV/pYtYt6urppvSIjGVLmydgk9e+q1ceMkS9vba54ODpCQgK1ObUz+AprrkiWKfvzwQ25VfZkSIWWUy9jBQR7s/v0lDU+bBi4u3GjSAzs7KDnpHRgwgPOOVSjXrIzGFhiodV65UmsbEaGx372rczhpkqIVBw8GNzfOv/Ep5bweyKN88aIybVSooF1NDaW+o2VLScfr1umxe7dk9CtXdL4WLoTkZGw9e+bJg1SuXIj59NNn+/fCwv7YPsQXCcvTaMHCU1geZAsW/lz4PT3I2caYOzZb3jyc/xWMMQuABQAhBQoYjh+XQXTZMkr/8ouk43r1IDubcom/SEK+f1/kZuJEnDNuiRjXqCGydPeuUoEFBiqNRZUq8gzs2wc5OTxo1IpCrzSDhg0pMX+SiNXZs7JnLF4s1XTfPort2aMiIT17igQOHJibGuKhxpORIcIaFydD8Jo1Sol2+DC3g16mmOMDBea5uvKk5Ju9vZ7Xq4dxL0lODuRzdZWsOmiQAvjq1ZNn4MEDEcchQzhsX5fgrF8U0NepE+zbx8N6jSmw7BtSQt/A7cgOgmvXhtWr4a23qDZkCI8CW5EvJ0dzj4rioU857HzLkW/PHm1APv1UwXfu7iLllSsr0K1JE8nq/v4iuUOGaJ5XrnDxlSGY8hUgvTi/Jheg5Pjxkthnz6ZE2wuSaXftgs8+k0zv76+NQEICFC6M59WpWreMDBg2jHLr1+tcNmggsj1rltaoWzcVhilTRmvu5aXzArqdEBVFOTc3qFSJe03bUfjoUZ27gwdVbCU0VD6PN95QqhFvb32nXFxk4/Hzg9de0wbhNyjIYFkoLFiwYMGChX97GGP+xwfwDdATiAUqAF8B8571uf/uERwYaMymTcbX1xizaZNJSDBm82ZjsrONqV7dmK++MqZLF2OaNzfG1KplWrc2xmYzJiJC75v4eGPWrDGXLxuzdasxI0ca4+lpjJubMW3aGGOaNzdlyxoTFWXMhg3GDB9ujJOTMVWrGmMOHTJxccaYbt3Mhx8a06uXMfnzG1O+vDHm/n3j7m5MeLgxrq7GpKaqP1dXY0xkpHnrLWMWLjSmUSNjJk82plAhY7y99f5XX6n9vXuNMXZ2ZtkyYypV0pjc3IxxdzfGxMSY48eNmTFD47XZ9Hj3Xf2blKRxmrNnzfTpxuTkGOPjY0xkpObVqJExDg7GJCcb8+GHatPHx2ixpk0zpk8fA8bY2xtjtm41AwYYM2KEMUuWqO9GjYy5edMYs2CBadDAaP0DA01SkjEmKMh88okx27cb07u3MWbePGN69TJly+ocmFmzjNm+3bz7rjFmzhzj42MMGGPS0rQ+nTqZsDBjmjTRo39/Y8z+/WbDBq2D8fU11asbM2CAMWbcOGNatzZffWVM3brGHDqk4WdlGbN/vzHt2hlj6tXTIt+/bwICdI5sNn1PJk40xrRpYxwcjHFxMcasWWPM3bvG19eYwEANd+tWYxo0MGbMGJ1/ExFhgEN5+X76+QWbNWvMMx95be//wiM4ONhYsGBB+KNf+9b1asHCf8TzXrN50creAV4CMlE1vTRg+HMz8ocPISGBSpUAJycWLZKPOD5ed9GXLpUjgf/H3ruHVVWu3f+fxUlEQERQJERARTRExBOa5ygPG495wFLzlKlbS808lJlpmZnbTN1WZmbqNjOzUreZGRKaeSBDJCIyQkRFRERURESe7x9joXvv3/v+JHd9v/t9m+O65gWsNddzmmvqeO457nsADB2Kj48Cs1lZ0u1y7RrUqkVdWw4pKUrAW78eXnpJwUVSUqhSRW0UFkqD2quXgpwEBipI6efHzp3c0gpPmwYMH87ixTAk+xW2boXqS1+gZk09+cfbm5V15hMUJBVEhdFbdLT6OXRIet3CQg07Pl7BzHvvlYY5KAg4cYLLlxWs7tRJhnJ9+sDZsxrb1q2q40vPnhQUyA8jL09a7EmT7HND/VVUuMvNta/T2LGQkkLjxlJakJLCjRsKHK9cCSQnExYGtTe+Blu2MG+evd60pydZWUB0NB9+qDrEQUFwLPpx2LSJU6dQOPXBB7nZ+X71tWYNcXEK2nL0KMuXA/XrExAg1caBA9xKpKtdW/MjJwcvLwXiK4pWOzqq2l1UlILYDg5SpSQl6TrdmDqDNp2rcuaMPjJpEnRz+ILYWMDZmZQUuJR3XVHxDz8kIEBzfecdrc2CBbcr6H0TMLDSX8+KKhZWkp4FCxYsWLDwx0VlnPSKgWftx7+PatUgJoamWUBQEE2a6Gl4cbGUENHRKpfr5AQEBxOSL4JUUeqMv/5V8oCSEp6aXAd3d0eSk1WmNy4O2Ay9e0tJEBMDzZuLBDs6ws9FviLKuwrp2VMqh2DvSzweB3Sch3s6sGcPHafNgO9rMm6c2mlTnAI1auDlJYVEcbHGHBKixLkBA/SEv1UrwMGB9u31d0WJ5KIioFs3Qq5KARAaqjllZ4tU1qgBjw26RG5uddhbl/nPXONQSlVKSkSmW7bUlH18oFvXG+zc6Uzz5vZEuYMHtavo1o2+SIpNVBTLx0l2PXs2sHEXXfs+Cjd8wdv7lqKBEwW02PEC3HMPKSkqh+zvD009T0J5OTEPArklsHgxjs88g69vMFy5Qnm5CDybNtFmaicYuZSUrq9y771Sb9x7rwZS5qm1ISGI++6TAoY9eyA7m6gobSJs2z7Fs2UfHN9YQesOEwkOBtLScO7dg7i4z3j1VUnNXVyAl1+m+Wu14MQJ0tIgIKAKVadOhWefZdEibm2m6teHqjk/sTenIU89BW0DTlX662lZTVuwYMGCBQsWKpOk1xJ4BgjiHwi1+RVJev+IlnXqmKSRI1XN4uBBvdi5s7S4f/oTBAdLq+rpqRBoQYGYsbe3ErAqTCdiYnTOiy8q4W2gPUqYnKxCuJ6eClGOHKnQtJ+fdLB//7s0tM2by1jkwAEx0EWLxGqLisTcEhKgSxeFVI8cUbjWxUVa5IoQcUaGkvQqiu1euSJGvXAhfPONQsMtWui1Zct0/tChmperq9jooUOKqufliU1u2iTdbHm5mF6/ftI0r14t7fK4cdL+PvOMdMWbNin0Onas2jFGVS0++gj+9jd44QUx8cREEel9+7QLadcOpk/Xex99BF9/reS7yZMVCl+1SgmELi7aEURFaUfx1ltaq2+/heee0/j27lUtOj8/jalrV42ne3eN3ctL/Ts5qZ3Dh7Xmp08roe7oUfXburXWf8QIJRA2aaKdkbe3PldR0NrHR1r0d9/VNU1IUGLfli3afaSm3rYmdHCAvDxsBQWVEuk3bNjSLF165wSX2Nj/7ESd3xJW0o8FC7dhJelZsPA/C79nkt7fgKeB40D5Hc69My5elCbCyUlkKiBAWofQUD0P9/C4XemhwlFu507VaTt9Ws5xzs4KR3burPdLSuCLL/T74cNyCKlwkvv730U0s7LUpr+/CGDTpiKBFUTVxUXvu7qKuDs4KET7zTdqp6xMVRO++koVEry9lUyYk6M5paSIqJeXi8RdvKgjOVnakCZNpBOIjxfZKyuTA0ZysqphFBbK47m8/HY7oaF6fds29VWh4UhOlvajZk2tQUKCiHJZmY7AQJHar7/Wz+JikfzLl2+v4yefqK3ISM0nPl6fDQoS0awoAVdeLg1KebnG26uX9CAXLmjNvvxS63/woDYUKSkiyIGBIqpRUSLH2dmaZ36+1nPzZrW5ZYs+W1Kia+fqqs1NcbHm7Oqqtf7qK+k1QkP12gcfaPyHD6udwkJ9XwID9X5qqjYit3QolYclobBgwYIFCxb+2KgMQT5vjLn7usf/iqpVVamhY0dFM599VmStqIjUp9+jTh2oOXu2CM64cTB9OntDRsuEo3lzEd2VK6FZM37pOprg3r0V+WzZknOte1E7pBqnV3zMPWN6iDw+84zIXJ06Im6JiXDgACenvk69l16CDz9kb9BIuuzeDXPnKjoZHg5LlvBZ37foceYdRawzM+HyZfZWi6WL2yH2lbbBZzg0XjWFG+Mm4TRxErbDhyA6mv0PvIDvwwoYV8gxWn0crAjsJ58oupmXx1W/+uTn33a09jj8JcycyaU9R3B1VZeN/S/xkcNAHlpyQ8QyPl7EddAgfiqpS8O+98qO+ocfOLbrLI6OED6+gwhnWhpH+r9Mq8S/cKrTUOquWSNSPXAgN3v3w7FvL2w7tmMKG0kc3b8/x1qOptnu3SKlP/2kiH5mpgj+008rEt6gAddXvE2VsY/eJqFXrshG+6WXRFRjYmDcOD7PbUa3XR7a0KSl6fN5eRAVxY3de3HOPaVrFB8PXbtyodSDml2bwUcf8ZVHLJ2anOdUiS91k7dz/cFelJVBtTFDVDbPy0uLu2ULr0W+x5SGDaFDB464dSIyEpwfHihJx44dqppRCVhOehYsWLBgwYKFyhDk520222rgS5SoB4AxZutd9ejpKZHwgQOKrJaXi7impBB+fi/HTRdqrlt0O9J48CBdSkv16L2gQI/3Cwvh0CGCo6Jgc7yYaGYmtYuLAbjnL1MV9dyxQyW+HBxEpp55RllgGRnUWzlDMoOVK+kytEh9ffWVSs65u0NpKT36fqYI5ZUritC2bk2Xns6Qkk6Hot0aT3Exzls/EInMzgYXF9rXPQlpaTQ6+bOkHzduiKBNm6Z6wfaIa7WgIKqFhytCXFio48wZqk9XSbvGBQUQG8tDMYGQlKbo+ooVEBsL+fk0dCsWcU1MhBYtaLZwiKQNubkqcfbcc7TK/AD27KGuva4yV66Ary+OSYcgKEgl3UaMUgm83r1p1r+/CPz+/ZJgBAWJkPfurY3GJ5/AsmVUmTtL0V8vL50zYgRcuqSo/KhRisq/9RbdGjbUGOfMgeefVwQ4Lw+GD8d5wQvaxAQESLqSkEDNrl31+YED6bRpE+wqou7evVCvHlXef58q589rDi4uWq89eyAggCkNtkNGVRg7llYzZ0K6k95fvVqR+V8BiyBbsGDBggULf2xUhiCPBMIAZ25LLAxwdwT5xg1YtoyLR3+hxtLn+aXBA7i7g+8XXzD4zS64uEC3bq9iDAy7/jBXl77NsmWqdLBxI7yyyW4Qcfo0P3u34kRkK7qFfQr16/PK38OZUWsm7zVbwqN574O7O68HvcaTD5/nbJkvdTa9hhk1Gpu7O28XDabsDIwfmsXxrk/SdM4crr2zkapPPKbo9mefMWxjD9Y/jIj05cvcnDWbHTvArUk3CgsVZL2fL/kp8H5yaoF3T2i2ahUfHq5HcXE9Spzh/NtSc/TZulUa4/79+aVWG4K7dwdXV1b81UbDmA7Ex0PPodDpRHNOz3mLl15SIpxTATgUwtGjbcnKgtc2buSnnKokJqqgx8TUVJHTzZt5f8EvZCTC8w88wJPOK3k95DpdJjRmb62tfHM5nLbJySKXNWvyfnJjhrTO4IcJy2nctxGnhs7i8PRDdPUB93ENcZ49m/VtVzJsb2OR2/R0fgjqQWMvL06GPkB6+QN0c3LS04Du3SULyc9n+f7mRGdBK1dXePJJhr3YiPXJMzmbdZ09e2CY13si4NHRfBc5Ej8/qON0Htas4dWt9XHPgfHHRkFxMW3b2Rg0CK6GDOPZZ8FWaJesLF+uL0RFOZGkJJ5P6sULT1+BgABWnB9MUBDEts9kwg+TeGn/JPCuXB1vK4JswYIFCxYsWKgMFWhmjGlpjHnUGDPSfoy66x7r1IGOHXn1VaB/f4IDbkhaO3gwoIDi0IeuERAAfPsts2crQOzmJi6Enx8kJXGkdixubuLbj+3oQ9TwcBo0AKKjiY+Hd0uGQNeueHuDbxNfUlKAuDi2bgUmT8bdXcHM4KVPEhsLH6y6RFQU/DjtbfpsGcapVzby/ffQaWEPcHen16HZvPGGLK9PnJC3xksvQdTT95OdraBts6BL4OREerry9DZuhNdfl9kcGzZA48a8k9qG1q2hhn9V3KrZuHBBQd9hw+THcX73d5SXw8rnz/GXv0gWPX26gtrp6cDq1axapT3CF1+gRMDjx2HQIJYvV2Aef3+aNIFR46vQuzcwdChxcbB8+BG+WfMDNdo1ZvRo4PBhGodch8BAvvoKHup2hQULwHnhfJgwQXbgV65ILxwXpyoUK1YwcqRstq/Pnk+9OY/CmTM8v7ouG75vjqsrvP8+0LUr7x9tpHJyQUGcOAHDel6Q9MLZmb/sbEzHjgpID5vqC3PnEhlpL8fn6gqbN7N5s+a/Zo0C0h/F1+DFr7vAo4/iP2UwndaOhD172O41jA8/hJ6D3Gk0ZzBnzsBTT8FzZc/TsCHUmPn4r/qKWlbTFixYsGDBwh8blSHIB202W5PfrEcjl7PatVFyWHo6JSVAdjaenipkwM8/07EjUFREu3ZSYDQMuIaXF2KnTk54eekJet26MmPz9ZW7MqmpNGlirz2cnc2w6J9o1Urk+seiOri5Afn5XLgA9zc5i82mPLKuXaX+aNBAwdC6hz+ibl2pAzh7loYNRQrbtBHh9fMTqa1SReS9c2cwntUhIAA/P/HKsDCRKXd3jeuns+54eyvy7OSkyHL//pLRhnvl4OgIvmVnqet5CdLSaNNGRLhWrduFLThxAnd35QZWq8Zt5z9/f8LC7OfUr09cnHIZu3YFwsMJDJSyoa3PTxQWar24elUJkH5+auvmTc23uBgOHBBZdXGRlCQ7W/lue/aQm6u1rrLhHV2TrVsJClJ/nTvL6I7MTAYNsq/flSu0bw/Hcmqqvw4daNTodnm/0lIgJ4cHWl6kc2eUUPjpp9TN+JKICNWSvn5d0vDHHwcSEvDy0joC9GqeQ7du+r1jR1gw/+at97p2RReokrDqIFuwYMGCBQsWKvNffXsg2Waz/Wiz2VJsNttxm82Wctc95uTAihU8OeKSqjysWEHTDTPgwAECAkQs5VABlJbSt6+krWzZotq9ycnQqhWZmao01sz7FAMHiox6eAB165KfD10SX5BuOCiIIUPE8xpN7iHC5u+vWr15eQwbJgmsb89WREeD4w+pNG8O/P3vTJhgr7388cf07w815z2JzSZSV60aRETA0qUiTHX2f6gkvTNnbklyHR2VV9ivHzB7Ng1fm0C/gneIjlbN5uhoaLr/DWJj4fjFANVR3r+fcyXVISwMFxdxuwqZdmgoEBnJ88/cYNAgkXWcnKTPXreOXr2kXsDH59Zn4uOBRYsIC1NlNZAcumNHVP4uMhJ276Zfw1QoLRXhzcoCV1f8/FDb/ftDRgY9ewLdu/P889pIsHmzCGi7doxs9yODW/+Ci4vkyixdiuOqNwgMBEJDiY+HZmnvi6UvW0Zs9X088YT4/TPPoMFlZ2Mru6ExVasGycm0bCliPGyYpNq+ThchLIzly3Ua33wDr76Ki4vOiYsD4+DImDEwf9Bxke8KC+tK4rciyDabrbv9vjlhs9lm/jfnDLLZbGk2m+17m8228VcN9P9xfxYsWPj3YN2zFiz8B+NOVntAvf/quBvbPmMMLZo2NWbZMvPEE8aYTz4xprzcJCcbY44fNw0aGDNypDHbthmzcqUxJjDQxMXJPnjrVmPGjZMltUlONlu2yEZ54UJjnnhCts9TphhjxowxERHGbNpkjCkpMWvXyg565kxjTEqKWbDAGDNggJk2TZbEwcHG+PkZYw4fNh07GnPmjGydTWGhiY42Ji7OGFNWZuLijMnLk/30kiWyP+7XT9bIb74pi+rMTGOMm5sZNMgYHx+16+Ki38369aa4WOOqU0e20lWr6u/QUNli+/ior23b1FZYmDFDhmiMTzxht6w+eNDMni0r5bAwY8zcucYMGmTMrFkmMlJtmX37zJo1sl4eMEB9e3lp3AUFWo+qVY0x7dppzL17m40bZUU9a5bRYPr2NRMn6hqY1atNSYnR36dOmcBAuy12Xp7srtPTzbRpxixapGs0dqwxZs8ec/y4xm9atzYzZxqzc6fRhRg71sydq3kNGWLM+PHGmI0bTVqabKfN8uXGrF9v0tONefBBWUq7u6vd6dONMfHxJjzcmC5djDFr1pjsbM27b1/ZV69cqes0Zcrt9qik1WRYWAtz4IC543Gn9gBH4GcgBHABjgFN/uWchsB3QA3737Xu9r76PfuzrGstWLiNyv5bcqfj97pnrfvVgoV/xt3es5W9kZsBE+1Hs7vpqOJo4eNjjIODMbt2GdOkiTENGoiV+fuLva1cKaazYIExrq5ibadOiQ3Hx4stz50rcp2bK3KYkGAyMoxe9/c38fFGLKZ7d2NSUkTM9uxRG0uWGOPgYEpKjDHTp5usLGOKi40xcXGmoMAYM22aKS01+mxBgVjx3Ll6LSzM5OcbY+bNMydOGGMyMowpKhKhjI01Zv58jXn1apOdrY+mpOin8fISE4yLM8nJxhw9akxiotoyRUXGjBwpUj9vnjFz5hgTHW3S040pLdVUzKxZmqOTkzFJSaa01N5u585iwj4+Gk9ysjEtWxozcaLZt88Ys2+fMd27m/h4+9UuLjazZ2v5TevWYpkREcasX29M795aJy8vXZtZs/Sh1q21m0hIMGbRIrN0qU4zI0aYrVu1LmbnTrVx+LAxx49rd7Bpk9bE21ttde6scyMjjcnP13yKizVGBwftOAYN0vtubmojOdkcP27MqlXGmLQ0XYcePUxxsdbGeHsbs2iRSUkxxmRn61pHR5vt2+3Xdfp0Y7y8fhVBPnjQ3PGoBEFuC3z+D3/PAmb9yzmLgDH/zv30f6M/6z9cCxZu4zckyL/LPWvdrxYs/DN+N4IMPAmkAvPsx3Fg0t10ZowhKqqFMdOnm0WLjDHp6ebKFWNycowxu3aZHj0UpSwstJO/iAizerW45+rVihab5GRjSkrMzp3GDB1qzLp1IqEjRogfmzFjzIgR9gjy7t0mM1Oca906Y0xpqVm2zBgTEGCmTTMmLc2YadOMiY42xvj4iPRt2WKWLlU7w4fb20lL0+czMkxAgLh7gwbiXrt2KSh6+LAOAyYuTm1GRiry2bKlMWbyZLN5s8jumDF6Pzxc3DouzpjycmOGDzfGbN9utm3TNDt2NGbyZEVHx4xRn2blSrNtm9oMDjZalLAwY+bONUOHimOa7dtNZqYxMTHGzJ5tjJkyxYD2F+byZdO+vb2tBQtMdrYxZto0zbOw0HTubBRG9/TUesfGGlNaaoqK7NHgrKxbHNbMn2/mzDHGrFljdu3SNVqyRB8xa9eaw4ft0eQBA0xiopo1y5YZM2WKOX5c8x0wwB6Znj7dXLliH+OcOcZs2mSSkrSnCApSxHzdOmPWrDGmpETXOjPT6Iuybp2JiND6L1igpw29e+utrVuNMcePV/oGady4hUlKMnc8gCwg6R+Osf9y3wwAVv/D38OAFf9yzif2/wC/Bg4C3e/2vvqt+wPGVswtMDDw//MPjgULf1T8hgT5N7tnrfvVgoX/Hnd7z1ZGTTkaaGOMmWOMmQNEA49V4nP/JWzlN6FXLz7+GKhVi6QkldP9KaQbx49L/jp2LPTpA9x/P6tXyyguO1s/cXODxET8/aVN/ewzJc0dOiSDN7y82LNHJY2/4AEWLtTrb7wBv+Q4U1wMTJhAYqI8K7780p7YtmwZkyfDTxEPsWIFMHEip07JO4QrV3Bzg2+LGlZ4fJCVJdO4F15QWeOXX5a3Ca6uFR4dZGVJr3riBNC/P35+Ki+8ebPeP3ECfv5ZeWuHD8u1mrZtSU3V+xUGcyEh0t9mZUFqh/GsWqW+z55FNs1z5wJyjN63Dy7eF8vo0Up++/57YPBg6tSxJwsmJFBaqraIj6esDDh9mk2b4POD1alVC53YoAGLFyMNclYWHlfOqtrIpk0kJNgrajRtKkO+I0d4/XWVJF6+XKWR6d0bf3/Yvh1ISmLqVJU3xssLDh3io4/kWdKsmT1J77nn2LBB2m3Ky6FaNbKz5SadlaW1DQ9XgYsqc2awYYPK4JGTo8xAlMD3xRc65/JlVRFZswYu1wv/Vd/RSmqQ842qu1Qcq/71q/5fNP2vvu5O6BFqZ2AIsNpms3n9qsH+Tv0ZY1ZVzM3X1/cuh2TBgoX/H/xm96x1v1qw8DvgTgwaRYxd/+FvV+D43bBxYwwt3NwUVu3RQ4/HXVz0u5ubwo3z5un3gABjQGHQpUsVLh4zRue3bm3Mli16fD92rD5/4IDCsrVqSXjasaPkCDExCj1GR0tPO3262q2QdcyZI4lDUJAE0O3b66erq8TFkycbExVlzObNkjEkJyvseeaMws/t29+SdhhPT41vxAjJOVavNiYrS59xdVUo29NT4968WXOIiNDfQUGaZ/fuCq36+OicRYsknejdW9HXirEvXqxwa0WfXl5aiwEDJI8ICZEOYuRIY9zdpd3OzFRY9fJlY7Kz9ZnQUJ3furWuy9KlGquDg9bQwUHXomVL9duli8Kyu3bpOi1ZonB0VJTCu337Kuzu7a1w9rx5ardWLbXVoIHaHzpU81+wQD/hdsg9JkbXztVV/SUkSIYxZIg+t3y52h03Tue5uemc7t2lfxg6VGOcOFHX/VdILJo0aWGSk80djzu1R+Uen74JjPiHv78EWt3NffV79mc9srVg4TYq+2/JnY7fMAxmFgAAIABJREFU65617lcLFv4Zd3vPVuYmnoqSB+YCLwDJwOS76cwYQwtfX2OCgqT3XbPGpKUZ6Xq3bTORkZIbbN4s3moiI83mzeJLCxeKR+bnG2kwCgvN4sXiVyYx0WRliT+b0FDz4INGmti1a82UKeKRW7YYY8aNM9u2GWNWrjQDBth1rfHxkiEEBelxfPv2Egdv3ChpxLJleqafkmJMRsYtiXDHjhqn2bTJLFumPnbvNsY4OJiYGMku2rcX/5082Uh/m55uzM6dZvlySSvy8jSnRYvEnRcu1Jy3bBHH7ttXHH3iRM2tXz/tA5YvF1eNjTUigytXGhMYaLZuFR82y5ebJ54QTwVjTO/emuO+fcYMH26Sk+3Jh7GxkoUEBJht2yQhXr/eaJxeXmb+fCOi+8knxpSUmIQEY8zs2WbXLntC5Pz5xuTmmvR0NW1KSsyQIXY5y6xZxpw6JcmHn59JSBB/rtCfpKcrsXHLFrvG+sQJM3u29hxm40azbZs4dZcuWps337SPLS9PGYtbtkgjc/y4McePm9atxY1NUZGZPt2u705ONkOHan/xawhySoq541EJguwEZALB3E7AufdfzukOvGf/3Qc4BdS8qxv5d+zP+g/XgoXb+A0J8u9yz1r3qwUL/4y7vWfvKLEwxixBbnoF9mOkMWbpnT7338LHB9LS+NOfgIYNKSqSBOIbn16Ul8Of/qTH6SUlgIsLq1fL4fjBB+H0aaiZslcahYMH6dlTzTWd0IHx4/VonaAgysthxc89eDXvUfr3h3HjYO1aYOpUesVcgwsXiIhQCbRG47qwYwe8M/sXZs6EFYP30XhsB1ZcGEJhIdRZMIlr5VV4Pb4pXR9vSH4+NGwoZ+nFi6HutMFERsKECapXDBrHW2+pVvKNG5rf1R17OV7aiG5Le/DsszB6tMrLrV8PM2bIeGTNGuDAAWrXlqN0QYEMMkpLVaksPV3t37wJX38tdQFpaaqN17Ejjzwi4w3uuQdHR1i50v73pk3k5cGNNu2hZ09GjJAygxMnNObwcHbvhvvvV2k6cnPhzTclzxg1ChYsgM2bJXHx9mbpUvjb3+DSpNm07VubRnMG88or8PDIKpSUSDpy/fkFPP16gGoiFxfz+uuShRAcDJGRbN2qy1jhQ0JgIKGhmhfr1tGr4D1WrFAZuOPH4ZVX5JT9wkpfSEzEd9xD1OveGBwdWb43HBcXldPrNsCDvDyt6fg3m9G6NTTctfxXfUV/izJvxpgylNT6OfADsNkY873NZptns9l620/7HLhgs9nSgL3A08aYC79qsP+P+rNgwcK/B+uetWDhPxt3/K/eZrPVB743xixDO9wO/4ZOUnV7Fy6UfrZmTQICoHFjSVMdHVUa+cYN6W+ZPp2MDH1s/nz75wsKICaGCy274eMjQxA3N5X0/flntfn119LilpfL+e6nn6Rf/uxEQz7YVhWqVeONN24bcISFqelq1WDi6GuEhqrGbrt2Mo4oLIRz5+Cvf5WZSMOGGm+FsYiXl2r/Nis9ArVqMWMG9Ool+a6vr+oRVys+T1OOM2aMCPSNGyJaQ4fK6MRm09T45BMuXlTd5qNHpbsNCZFhSGEhtC36nAMHRJpzc4HatXVSWRn16sF99wG5uXh7Syt9+TISPiPSzb33EhFh30xcuUK9wx9C8+bcvAl1vK4RFYU68vSUY9zWreDkxI24YXqvQQOOHtUpJSV2op2Wxu7dWodduyRhrvLCM9SqpTUiKIhTp+yaYQ8PKC/nzBmZfzza95JqTY8aRVycpkNwMOzaxcDQY5w6pe/EqVPQtm6OHBbd3Lh5014X+qmnaNBAm4Y+faTlfnf6D7Rpow1QeTnw8MOV/nr+lkYhxpidxphQY0x9Y8xL9tfmGGO22X83xpipxpgmxpimxphNlR7of0B/FixY+Pdg3bMWLPznojKmuR8BLW02WwNgNbAd2Aj0vKsez52Dhg1lDpGczD1+uaw72IV162DOHJlYZGcjU4rRzzBgwEPExsoAw/HaFfg+AEaPpqavL7RsyQPt25M8oC1PR37BDwEPQPvP+egjEed27cTvvvhCvKxH9ltilTk5nPnlOqk/VaFKFRG1Q4dg1iwgO5vNmxvRvr2SBR94AOo4nWdBtzRe3dGJ+Y/nwM2brFxZj7w8RZKbbpxFUNDLYmQFBXh4wKJFUFam/YCrKzw2zZe3x2ZRqxbMns2tZL+cHPVTr56SytiTTK8Fg2DtWtatG03wqlnMCAvD5YlHiYuDL1K78cFzqVwNDue774CHvlUj+flMn84tV8I2XaFtW7shyKh1vDXGBd58ExYl8N7GjRz36QIxpfDxx/Dppzx5VFHiRsnJyhpcsYLJk4GjrpCSgnPPB+g1ezas2UJ2di82boQpU+ykvqMLTzyha9atG/Rx/RxK29LaE2JjgY5nmL4SfvwR2i9bBjExdH4Qlr94CWbNovHjj8PatZQU27ns1ot6jODnx4gR8ilxcwO+/prWrQfDnCyef97ulhj4EguegNde01juvRcICWHuXG0qJk4E4o9W+utps1lW0hYsWLBgwcIfHZWhAuXGmDKbzdYfeN0Ys9xms333b/W6cyeP/fS6mOH+/cxqXcqsNRPBZSGsyCE4J4fg4cOhfn1ezR0GNWfAmm8UVt2/X/7ONWuKeX77LU9f/xIOGhp7pYO3Nz0cPofln3Ix5jy4T6dL0xNiU0vjpbdYsAAOHyY8KUllFjp1IvzQIbHheWuo0qABRwpToerzUKcOvHkAdu/m6QYN4C9ecOkSHR54QOHKmjWhXTvGH5gFMTFQWkqz7O2wO13hUz8/yMvj7eRVsKs3nVzi6XTyJJQ30vjT0lSGo8SVuqk7xPKmTYPISPr5fSN2uHkzU/rshyu1qTO5LyQmUi33Z9pXK1IbO3aAszMjW6XCkSPYFr7MldlQ7bmp8EMIHDgAgwdL++DlBbm5NHXapxDsqVPQpg2NzuzVLqFVK81p6FBaZGcrRO7jo7Bpejr4+1Ml8wdGdnVj5JgQuBoLmZm8OmI5ZLiIobdvDy+9RKdrKzE7PwMnJwZ6fwlleQqHHzzIQ13fhZ6rVbZiyxaYMwePUaMYGBUOizPV57ZtvNo7DFzc9FXdsoWmV9+D0FCe9HwXjp+GFz9ln4cHZI/X+PbsgYsxVD98mPWtW0PeGO28fgUsK2kLFixYsGDhj43KEOQbNpttCDAc6GV/zfmue3R3F6nr3RuWLJHP8PnzYiWhoZxt9xB1+rZRpNfDQ37KxojYlZdLKzFqlIiVm5uiol9/DS++qDbmzoUmTaRhmDRJWo3mzRWujYtTWPfgQa637kCVY8egUSNuzn4eR+/qULcudOig8SUkSHORliYi6eensHBGhrQKgwZJozFnDvz5z7B6tc53c+NSx15Ub94cqlYl9WxNqoZD/RUrRDYXLVJ9udBQnb9+PRw7JnL6+OPqNyxM/bZrx2W/hhQ+0Yq6ed/q8yUlWrOpUzkf0BzfOXPUXlQUHDkCnp6YaU/D5EJYtUq149q1IzUolvCHf5LwecAAaTTq1tW6NmsmkfO336pe3datulYvvCCS7+3NjVXvUlYGVZO/kXbEw0NrEiuCTHGxjvvu43JoCzwuXoTevbGd+An69pX2Ij4ennhC1ygnB155hcvN2uPhdE3jCgrS9dqwQbqU775TOLd7dzhzRvMvL5cWp6RENf4CAuDwYS53H4jHN1MlDM/K0mblww8Vlvf0/FVfUYsgW7BgwYIFC39sVIYgjwTGAS8ZY36x2WzBwIa77vHSJWXbvfKK/o6IUBT3oYd472hTuneHc9sOUVYG93y8Ai5e5I39TRkfcobL7brh4eUF8+bdFv/++c8itKmpfOz5KP3c3bnsVRePv47V8/UJE0SSYmLgqaekC4iOprwc9t47kS61PyQzExp6enJp0VtUH/8wPPkkeHjwpWc/7m//nUhrYiLEx3M+34bPxEmkp0N22f102xDCZacaHJjwKd1u7ICSEqq73eCnvACKL4iP16yJyJ+DA6xfz83G4TiWXee7tCo4tBtPdjZEzRxKcTE0TEwUgYyMZN9+Gx3Kf8TD25sPTrQgqAzaLF0K5eUcL21EQRp0mjtXJHL3bj7t+y5ubvAAB5Wht3Yt+xqMpEPiWMJXX4dWz4hgBgZyI6oNzlevwrBhIrnHjnFj4hRyc6FuVBRcucI79Rcwen1deOABnAf3x3nqVG0IZszgfEgbfAcNUlQ6Kkr6irw8vqvWnualFyTu9vbm7YSGPLZ2LdcLr1FQAHUe6SoinZICI0bgkbRXZHnRIujfn6suNajWsjEsXsyXPoNxd4dqFyC8LFubirQ0Rb0HDxZBd3CA6GgSEqDXX/4CO3ZwtedAnJygysaNnBv6FLWLi+1Fpu+MCg2yBQsWLFiwYOGPi8pUsUgDpgHHbTZbOJBjjFl41z3WqQPTp/NjgS+MGcP7ac14OG02PP447u5K/qpVS0FKGjWCLl1wcpKRSFoakhM0aaLo4e7dklsMHw65uTLqKCjAw+0m89t9BoMG8UW8I8TG8t7uOrzdZaOis+HhpKbac9ccHGhYNYfzR09pfI89JplDSAj5+XCotLnIXmAgHyfVxffo59jKbxIWJl73Y2kwHidT+eoroHp18PLiWpkzTk7QLPwmkZF2g47Vq3k9dzAbksM1t6IiPD1VuaKwEO7xvIy/P9ClC2RkcC7PJt1taSkkJJCbq4AziYlw//3k5tr1xvn5MHMmjBlz2wAkLk5vxsXR4cwHEvamp4tgurvzg2cbnLmhSGx8PCxdyqufheOcfhwXFxSZDw2V/8aAAfD3v8PIkVxo3B6+/ppjft3w9TFw8iRER8POnbx9sCmfl93Prl3wTUZNAL4JfZTGjQFvb1JT7RrrV18FNzfOrfhQMopFizjZcRjk5fFDbg2WLUOvHzxIlSrwwQcQXvcSZwPbKHLerBl88AHHaKYNUnw8hIRgs+naY7NRzeUGTk5wdeFyahf/ogj0r8BvlaRnwYIFCxYsWPificpUsegM/AT8FVgJZNhsto532+FNpyoQHs6RI3DZvQ45OVJCsHAhRUVSMKSkiAfi5ARt2rB3rzhqfDyKHPv5ichOn843Sc5MmlOD+aUzRCjDw/lgiyPR0cBXX+HgANv318DRUQlr+495wLlz5OeLX71T+BDnqwTg+8EKxo8HUlJYfqIHbNmCi4u9tNqSJdCkCRERwIMP8l2KIxMmQOvWKlH2g2M4PXrADz4doKCA9HTYtAmem+vIhg12CeyKFbRoAdeuSQL92DO+rFol7rp3L1BYyMCBaFAxMdQu/JETJ+BISVOOBA3EwcHeTng4y991Z/NmSaAJCIB166BJE/bvt5P+0lJST3pwKKUqqfcOhiZNeC+5mWQLzZpx8CA8NsEZrl69lUlYXAyptqYqSeffEIqKWLcOaYabNIHCQlXBCA9n5UoYOcoGp07xRm4/GDiQkBBtGNzcpKZg9mza5m9XImFgYEUeoU4oKyMxEfYlVeWnZZ+RlKR+MjLscyopAZuN9m1vUqUKTH2hOn/+M9Jm37wJPXuyaxe8k9lFJUUOHyYoCJ6cV1OR+rQ0Nm+Gah9v4KRDMOqgcvgtq1hYsGDBggULFv6H4k6FkoFvgUb/8Hco8O3dFF02xu6kFxIiI4mYGDmi7dypv3NzZcqxa5d+du8uh4jjx2UOkpAgd4ywMDm7LV1qzpwxxjz4oDHh4TKHALncLVlizL59ZudOY8zcuaakxMhUY/JkY2JiZDiyfbuc6KZPl+Nav34yxvDxkRNIWpoc5hYs0Otz5siJY+5cY8aPl1NdaKiMOsrL9bqDg1wzunc35vBhuXVMniy3i1mz1Ef79nL/W7pU527apLmPGCFnuAEDTGamujdr1hjTpYvJzjaa17p1mkdmpsnKMsaEhGguTZrovV27jOnRwxQWGmMWLjRXrhi50w0YoLadnOSMN3as3P9iYuREl5mpvlav1pq7u5vcXCM3u8WLZc7Rvbtc+6KiZNTh7q5rGRoq148FCzTmgwfVx+zZMiLx8pKzYEGBrhvIBaXCva9dO63J5csmL88YEx5uzLx5ZssWY0pLja7/oEG6LrGxumZ9+8qVb8gQHXl5Gtf48XJe6dhRR2KiMcnJlS4UHhHRwuTmmjselW3vf8NhGQ9YsHAb/+n3vnW/WrDwz7jbe7YyGmRnY8yP/0CoM2w2290n6YFkAz/8oJBjZqaqLKSmKmksJEQJd4sXK5wYFiZtwUcfSXuRnAwtW0pP3KwZdV5+RnqM2bPp8Nkz0jMcPKhw84sv0qN3b+jalSqD+ig0vXs3hIZSc+d6JdZ17gzTpytKPHWqkuWiolTqbO1ajXXNGjmVLFqkRLjjx6VtnjhR4cSAADl6BATYC++itpyc+KX1YILvvVeJgjNnKqFw0SKFxAsKVKi5VSv49FPJKw4cgIgIgpdPhQce4FL/kRR0Hknwxleko166VAmC339PvWZO0KkTNf2cwdtbn710CW7epPqAB2DZMqqVXQIfH35Z9CHB3d+R5tvPT+uUkqKosq+vEvqOHYPnn1elDycnai+cckvfzOTJen3LFs3VxUVr07Kl6vItWQIBATQunKF5XrkCW7bQyt9fUfGVK2HhQq3PyJHqMzBQ65yRIQ3z6NH4nj+vaiLLlvFQuyTI6qhQ/aRJmv+BA2pn7VqNY+9e9TVhgur6lZbq8UP9+tKbbN6syhaVhKVBtmDBggULFixUhiAn2Wy2d4D19r8fQVHlu0NpqRhIcrIqE1SgpETE5tQpiXJLS/X60aMiOk5OqkRRXAw1asiq7upVVTooLFRim7e3iNvWrfqMj4/I0aVLer7v43PbNOK770TQ09NFDsvKRJiLizU2JyeJZl1dNd6EBJ3z1lvg7KyxnjihfipcMvLz1fbGjSKbrVsTHJSo311c9JkrV0Qm77lHJLGoSAWCi4tFvLOyJAlISQGg+r59VK9TR8lply5pzVatUnWPb7/F9u4aTK2/S7oQH6/xFhaq3QULNJ4rVwje/Ipd+2BHrVrSg2/YIAK6YYM+t2mTxlqhLwkLk1b522/tlndo4+Lnp3MOHlTFkQYNpO/Iz9dmwsFBa5iYqPcOH9b5bm7a7Dg5aUMREKA+y8t1rZycZIP3zjvaNJ05ozaTkrR2xcW6Djk5Gq+Tk/r64gtdh5AQrWFioq7JsWO/+itqEWQLFixYsGDhj43KUIHxwPfAE8CTQBqqanF38PQU6fL3lwNccPDtJKoaNeQ0UWHTFhQkQtS5s863V0bg0CHVUH75ZREkb29FMsPDRRw9PNRPVpb0s6dPi1z7+6stJye137q1SFRUlPoPDBTxKym5HR11d1dbgYEiZj17qr2yMvVVXi7nj+xsjQMUCY6KEglt3x4GDlR7XbveJtz164vMVZDR/HxFQAMCFFkODdU6jBypMRcUyE2kvFyJavXqQYsWmHHjtUZeXurP3R1atNAc/f3VjpOTkum6dtWcOnbURuHqVa3P9esqnefnp5JsUVFar+vXNa6gIPlQh4ZC1aoq0Vdaqn5LSkR6i4p0npOTNhqurlqjJk10Pby9b0favb11rVq0kJVgz55ag5gYvZaRoTWqOLdaNUXZu3TRurVvr3l6eWkNS0q0LqWlt0lzkya6Fv7+t6P6lYClQbZgwYIFCxYs2CTP+L+HlhERJqlTJ77su5z7U19nQ80neeQRsP1tA/0+Gsq5c+KEV67AlAMD+WzUhzz7rEoEJyfDG0/8wIVajcnOvl3Q4qFGqbz2RTgpKfCu02N0ynibv/4Vwr9U+6dOicM92e4I76S0YnTCMAaWrCckBF5ZaBg12saa9c58tecGnVKWc3bAJOq0C2Zw61/4YJNRZPTSJbh2jQl7B9KsmZ7wt28Prw49xvg3m9G/v/hpsyhHunS8SY0aGm9hoXj0Z2UP8MuqL/DyUjEGT09x3uhoBVQfe0zqgVf9X+PLiCmkpCgQ3a6drLLr1lWw9ti2k+w4Xo/581Xq+WjnqaojffUqk+7ZSmEhrO/7EcvPPERJiQLDx8Ysp82GSRxKvA7x8az4uQcffghfRc/gndBXGJ3yJO9Hv05AALz/viy1bU6O9Im9yaeug7m8+gPy8qRseHvOKZ5eVpeMDPi05ijMO2uw7djOEb9elJerep+vL7zVawd060anGGe+Km7Fh9OPcO4cTDz2mAjtkiV8vN+XEye0Di+3/pgVp/vx44+wvPNHEBrKKzubcuwYfP+99lAPPii+3DzzIy3qkSOK5k+aRNfgX2656+3apX1Ai1qnmLGiLnPngpub7VtjTMs7fT+bN29pEhLunNTn5VW59v43oGXLlibpVyQ6WrDwvxk223/2vW/drxYs/DPu9p6tTBWLWJvN9p3NZiuw2WxFNpvtss1mK7q7YSLmGxGhkm01a1Ktmp6iL7849JZnhbe3goiEh7Nxo4KXLi4KPDNnDjXfX0HzvM8JDZVqYkNyOFevKmjJwYOEhIjk7YsSOc7O1hP6m1GtGD4ccHfH2VlB0gsFNurWBXbsUAB4wACNc/RowsLgfL6Ny5EdYN8+CAhgyhSRsKAgBWJPeTcjIkIKiGZ+58DHh4gIlelt00YB2c6dgc2bCQhQULVdOwVV3dxEshs2VIC6ZUvg1ClcXBTEdXZWkHT0aEUtIyKA4mJiI3NuBUipXVt65xs3cHVVQJbsbPz8pIiYMwcICmLYMMTWgYceEjEnOVnnt28PQAf/nwkOBtvhQ7BwIZGRQLVqeOz5mJQU+2nHjuHtrcAt996rsnILFvD551I9XLigQDFZWVwucVZpupISzpxRcJiICMjL47KrLwEB8HTfnxg0CCgtpVUrqTVo0ABefpkBA7QJCApS8Pr+A/NxdgYKCjjrGMDVB/tpkVavpk8f6BR6lpQUePzhy7RwOsZF97p07gxVs29J6O+ICqvpOx0WLFiwYMGChf+9qMzD4qXAo0BNY4ynMcbDGPPrrMn+EfbH4u+/Dzz0EA0aKDL5yy8iymFhelKemQnk5JCYKCKbnq4ILdOnK2JaXMzg/jcoKRF3PXdOJJrISI4dExdctkwqizVrFM09elRRWMrK8PKSymD2bBHVDwq78fjjwJYtrFoFb9eeTVaWKos5OMDJCa/wflZbkpNv59elpYmAxsXZn+LbH/O7ukqqXF6u6Gd8PLy1uQZ5eYroTp8O772nOW3dKulsVJTdO8XDA09P6BR2jurV5eHh5HTb6O6kW2PaDQqguFgGg+zfLy10UBDZ2RovISEUF+tzX38N5Odz7Rqcd6gNWVmMGGGvSZySQpXN6+Gzz9i6FU651Nf+4OhRSEsjLw9pwl98kX5dL0kWnpXFqlXKrfuq5VM8+yzQsiULFqhccnm5+C0jRrB/vzYJFBayf7+k12RkQGQk/ftrLUyDhmzbBkRE4OpqJ5+zZkH16nh6cuv6z5wJjBzJ2bPA1q307g3PPQcUFfHy4fs5eRIGT66jUnS5ubyT1Ix774Wff0bfl18BS2JhwYIFCxYs/LFRmf/qTwGp5rfSYty4Ad7evPAC8M47NA29zsMPw/z5ijAWFipSOWwYkJrKqFEivq+M+0UE+MUXpXf18WHDB850e9DQvbuImI8PcOEC/fvLbG/RIgWEH35YDsetHL4VyUtJoU8fcfWgIP0cXPyuIsndu9O6NTwW/g0DBki2W23u09QLuElMjALgjf0u4u8P994rKa2Li2S5FBVBcTEeHtC0qaYbGSny+3jgZ5SW6vw6dRQtDglRNLltW7CVXqdPH+DMGZo5pkJKCrVqiUQnJ0P//pLffvKJHKDr11fbuLoqac/Hh9BQ+2spKUREKErcrx/QpAkXL4Jv5iFIS6NdO7tEt7RU1TW8vJg9G+rmfau6z716QW6uou3t2skAJT9fOYiFhYwbp346Jc6nRw/AyYnXXoNHHtF6FRYC16/TI+C4alO7uhIermtARAQUF9O7tyLwG+yejN+VNKaZ/3mZqvTpA4Bv+TkGDVL0eOBA4J57FMU+c4bhwyVfp6CAWd2/wxhJohctgh/LGxIUJKl427bASy9V+utpaZAtWLBgwYIFC3fUINtstlbAfOAr4HrF68aYJXfTYctGjUzSqlUKDR4+DEuXcry4Pk1dfoSQEK6XO1Ml4XOxkPh4hWh37uRm34dwLDgvBubmJlY6bZqStZKTISWFi0vfo4Z/VRHVXbu4FtOLqg7XITMTE9YY24Txai82lmv7v6XqgS9VumzMGBH3pk3VZlmZktZat5bQuVYtjSU6Wglj1arp9wsXZPX2xBNi5xMmSAB79KhCx9evi7n7+sLKlZwc9QL1lk7RTuCnn+QAGBgoDciiRep/xgwx+qgo/UxJgQMHON5zBk39ziti3KAB5OZyMvQB6kXVFNPMzVWY1c9PFT1GjbJrNtDv48apdB5IhxEVBSNGSPB95gw8/bTYt6en1m/7dkk3PvxQpd127MB07oIt/F5Vx3B0VMj7yhU4cYJreZepuvYNfuw6Hm9v8J03SesXGwuDBnFz1xc45pzUGLduVZ8DB6qaR2wsfPUVBAdzcczT1Ijrpqj42rWaQ1KSQvZPPCEWvny51qai7F+7diq5t3KlQtje3qqI8eijajc1FduuXZXSILVo0dIcPHhn/Z6Ly3+2DvG3hKVptGDhNiwNsgUL/7Pwu2mQgZeAYsAV8PiH49cMbqzNZkuy2WxJ50+cELmZNk360VWraPrJfEWGt26lyuTxIqt2ksucORATg+OG90RG8/P1XpMmqpaQny9SFBREjaNfitx+8gkUF1P1qQl2azm7BMLJSQQrM5Oqq17Xs/sVKyQUPnRIxG3CBGkBLl4UAXvxRZGz4GARzXvukWg4NZXTzWNFaJcs4bRLsKK5QUEiqKdOicQ7OorkZmRQb/YwCap//hkiIjjrVh+yszEh9UWqMzPVZ1wcODlxw6269BhZWTQNN0o5ObSdAAAgAElEQVRuGzECxozhRucHqMdJvebjA6mpXOzYR+Q5PV3zfvttCaCzsvTalStar5MnNa+cHGkw3ngD/vY3bQLKyyVe/v57fXbDBhHOoUOx/XmCxhgdzdUusQq9T58OV65QNeUQ5weMp5HTz/jOGKUNkLu72klIwHHRy3D5sq5NWZnWuU8fbZI2blSZttRUaqx6RToYJyd47TWd6+l5uxJF8+YQFMSNFW9pLjdvSmMDkJzMtcmzVNqtf39dp/x8e1i9st9VK4JswYIFCxYs/OFxJycRfmPXoBbe3sYsWWICA40xy5cbc+CAMUVFJjdXJmimqMgsXiyTNTN/vgkPlwncxo0ynDOzZhmze7cxW7cas2yZyc42JiJCx8yZxpiAABMebszChTLp69tXZnB+fkaOc2VlxkycaKZNk/FcnTrGjBun86OjjcnJUVu7d+v1wEBjTGmpWbNGBm179hiTni5jt9atZfpmCgrMiBHGmG3bjKlVy5w4Ycz8+ep72jRjhg83cr3bssUsWaI+a9SQIdyaNTKny8szJjLSGLN5s0lK0jwDAowxyckmN9eYzp3t7+/ZY7Zvl0lc+/ZGDnqffGJMkybG01N9mbFjzeHDWl7zySfGbN5sDh60z3/WLLN4sX2tatWSy5+/v3nzTWNMQoLZt8/IMW/tWjNmjDEmKEgdL1okV7uFC03jxlrTtDQZ4ZlatUxoqEwGW7c2t1z8li+XYZ/x9DRDhsiBzoSHG7NokVm1SmtoEhJMZqaRm+G+fWbzZiNHvJYtjRk+3HTurCF4eWltFi82xgQGGnd3rYEZO9YkJ8ugsHt3ratJSjLjxmnYmzYZYzZurLSTTosWLUx5ubnj8VvfF//Jh+XMZcHCbfyn3/vW/WrBwj/jbu/ZysTC9thstgd/M0ZevTpER6u6Qd++7C1py3tbPUhOVqBzyhwPUlIUgOWRRwgKUsDVy8uuV712TVKBK1dg5EgWL1beV61aShzDwQE3N5VMc3XVUaeO1A5pafBzliP0738rea9zZz3FLy1VYNXJSU/s27WTltbFBVixgpG9L3Dxoio02D08iIxUgPKj+Bry4HjwQfDy4uxZqS9KShTgrCgPfDr6IY4eVSJiw4bS1u7bBzVrKtjq5ASkpXHtGlx9dgFOTvBdeTNKSqQyOHkSjnjez9/+pkBuRgaSVvTuDa6utxQHTJzIjBkKpB7x7wPXr5OWBjUTP4bt23F311pQUmKvTdeMN9+ETws78fPPYHZ+Bvn57N6NNMOlpRAayrJlwIIFVK+uoHXj/W+rhHRhIadOSTWSkiK1A/37c999CgxTVsZnn9mrYAwaBJs28cMP9oobBw9q3oMGcb5Re73m7g5+flxc+h6HDimqW6cOsHIlT024Bjk5BAXZEzkTEnB3l2LkzBmVAzzr34KgIAX809KQhOVXwFZ+846HBQsWLFiwYOF/LypDkP8M7LLZbCX2Em//Xpm30lJITqakBPjiC7q0v0G7dtAt6jzGiDQGBSkBjqlT8fVVslVoqEgy3buLacXFweXLBAaKtGZkSPKKjw8lJZIVR0ToCf2NG1I2dPD5gfpuZ+H0afz99bR+1y5VkahRQ6qKo0dvm8mVldlJa1ERU1+qSe3aInwREVIGZGWJeEdF2b1OcnMhPx8PD5G3c+fg7FmR9XvKT3FP4vv4+amoQkaGyOTgweKpe/ZIjcCIEVy9CtUyvqNOHflcJCZKBVLhaVLh3RESguQPmZlQuza5uXavkkuXKCyUvBmAevXIy4PLMf1gxgySkuyFHUJDlcVYUsKECVo/FxewLX0NTp+WlKC0VPKGtDSVq4uLu+Ufcjb2MQ4fBu67j0ce0f6gZUu79LlqVZycoFEjwMGBjh0ls6a8HHJzMcZegi4mRomTpaUcOKA2cHWFpk2pUXSSzp21Jzp9Gli1ih1fVoXISLKzdX0IC+PaNV23y5elzkhKklqkRg27IWOFw2FlYIzGeKfDggULFixYsPC/Fnes6GqM+VV64zuiVi1wcuLyZWDECJ6ebsPZGRo29CU+XtHCU6fsnGb3PL6MleR41SoRyuudu1HFywuKili+pQ5ffini6O0tGe3b3etR+K1IaUGBosAODnIt/vJMYzgD958/z8GDyrULClLO38RHLvLCCzXoEXmW8PA6lJWJmDZpAkyYwKve4pKBgbedoc+cURtLlthLuUXX43E3N158Ua7R165pLjdvwi9ldfn65hAKC5V/VmEEN2uWxpibq1w+du4k22k8k3Y058gReP11Bd03bdI8Nm5U9HbLFrXLPSXaIXh43Io0c+ECfn4i8UlJ8OlMJ5KTwSP/F/j8cy5cHcovvwAt/dSYlxcrVki27OkJ0VOnEDy1n6pRlJXB5MnwyCOsmATvpqfg7Cwi7eenTQZhYaR8KxJbIR9+8uEqpKbaJeAODiQnKz+v358uQceOFBbKGO9cQAtWL4XZs9sS/4zWnMgSuP9+Hp5Vj127oEoVXSe8vDSm2rWJtBscsmgRF/PsBBpdn0uXtLYHDiiifvHvr95OUKwMLAJswYIFCxYs/KFRKcsDm83WG+ho/zPBGLPjrns8exb27OH8Kh/48xfM+8tKqhadg1dewe+TJeTlwaP9L3O6yAPiJnByzxpe39mQ/v0VmazStwc88wzX3WtSXKwiC9U+3sAPLYaKPC24wdy5MLLzLyJ22zZxjapULTwLGRl859kJpk9nxYEplJdDG5fv+DirOfj4cD49HRauYN+L/cGnJ+dOnJBWYuxzOEZF8XHxfja0+pymTeXPMWMGOM+YyqQbSzi57ZhYc04OYWHw5JNyyCsr07iDh95H8OefM3TnWBad2Uhmph7/D2ueypFr4bTy/pkBA+rDMyn0f1F5jK6uii4bI4J88CAMS3yMYQO607fvQyLDDWaLxffpw65d9qhyfD47n/qS/VXu15q8+Sbl5W2pcBJ5wV+EnmQf7Truu4+/ToX2tX9iyJyGBOcfgd27+WQnMC4HBg3i2x/dVcpu/oesvqix2zw9OLBqFWw8xaGx70CbNnz6c7jkMf37M2TxYhxWt4FNXTmxGZwP7Ye9NeDTT3m35yROPrmclSuVd1ht8Qu8Pnk4U6cGQ9Y8WLOGjfODmDGjPgkJKnSBkxNDO+dAw+f5Kv097VCSQ+gwNJyOk2/w6oBD2u2EhTG0UTJs2MDFv79Ojb++WPnvZ0UE2YIFCxYsWLDwh8UdCbLNZlsItAL+Zn/pSZvN1t4YM/OuenR3h9BQ/pLZ7/+w9+5xPd/////11Uml0oGEIiUqIclxIY3QnOX4dl5yeOeUw7BmhsXYbGhmWAxvM9taw5xnDi0hCSE5JUknSSWV9Pj9cX+Z9+fz/bw/a957fy7v92/P2+Xyuuj1ej1fz8fh+Xxtt8ftdXvc7sweZktCAty+XZc3//pXJviJKvj115Y4OcHnI0cyYpEbCQmiAF+5AkFHDpCRARWp4iwIC4Mt0X8h86h4Tu8VnSS6ECorG9Px/R8Y2gbeeAPy8+uxZelzkg5B63376NlTyju3D69Pwi4Y+NZbXChxo3X37jz06ord/v30n1yPqKh65C/6Hm9vUXoPTRel95dfIDQUtiyZRWUk9J7fipEjYbSpKQcPSn+zskTFzs2FscHBnLpgge8XO4ndJRwsLg727/fCygreveeKuzusBj76SBLOQFLiHB1h2jRRq/ts3kRVFbz3V1GdV70Rx8Ozt7AbHcTfCsUHfGp3ED3G1MPNDX7+Gfp0MaWsENi2DZKSuDm8J8ePw4wmtTlU1pWeP77H6l9m82SSG02bwl37tjSysmLMGLhra8ojbKhdWwj/wORNHK/9HidPwrCwMB4GjsDOy4u521owoZOoxBMmAD178kVKeyIiYFjJURYuhFu3/Ihx2AktW5K3aB2pSWJNSU+HZtOn03eMDenpcHlxPqdCviTQS5Tj/HxRq4062JCUZMPAS19wa9S7uJrc47GVE7XONuW7wbBoUXvi4yFqIYwZ0xb7QW2JHA7u7hHAO9W/RzWCrEGDBg0aNPypUZ0c5EuAt1KqSv/cELiglGr5Kg362tioRHt7vou8zuAxNcVyYWoqbG/DBjloyRIx3+bng4UFJ5adouvQui/Nvy4uEqW2c6cw0KNHwcSEiWPK2bTVWCK+EhJEfm3eHM6dY+/OYvqOtJT2MjIkT9nAQEzI/v5w/Lioj2Vl0gcjIznm4EHxbxQUyOvu7sJ8mzblswnnmDJVJ8bgykpRkG/elMzhXbtE9nzyRJjtyZOyq+/oURlvaamMsWdPMTb7+Ei/zp4FBwe+j7rPwIOTpK2SEmnXxESk20uXZC7CwkR+fdFnd3fpd1KS9D0jQ8rNjR0rKnNKirxfVSX+49xcOU9KisxFmzYSSxcXJ6uBsDAh1f7+cv74+Jexa6WlL729Vlay8KldW3Yc1qwp+dENG0p2cWSkvG9qKtFyFRUvbwgHB5nfoCB5z9MTzp2DCxckCi4xUeY0OVmOq19fPDfp6fJ5T08xdIeFSelEAwOR7JOT5W8HBygoQFdYWK0cRF8fH5V44sRv3sc6K6t/6yzUPxJarqoGDS+h5SBr0PCfhVf9zlbLYgFYA3qGSK3f28j/AxcXiaYNDZVaz0+fwqNHvBfTAm9vcP16GE+fQtuQVtyIPsXXH4PPzRyysqDZJ1NkJ5etLZcjvsbFBWoW3ONulRN+x4Hd5ixw+ZqIaKh5J4Wvr3iRNwjqlALR0ZT3G0INv7Z8PeEQPj7C4XbsgDdzm/M4/gq1zh7hmX8PjONP8FNlV5puBKeSa0Im7e05lOGBg4NEMruZIq/b2vLRJ4bMnAmG1pbseO0zrN/4jPR0UbnbtoUR9d8SEpefz5mK1lRUCId3dobyTrKZrbwc/Ga3R3f2DOk+EH7qcyZ9KHz1yhVJcpi76CGkpPB1dleS4uGDLl2EBO7fz5pxF0hPh4/9ZvFg3scYGMhnBwd8yd7pR+ib+K4Q9dhYjpytRY/5bXgc+zO1PBsICc3N5av4RoxosRC2bGFVw3XMrdoqfo+8PPI2/0CdkP7kbPyBTZsgwnINz6bOwNj/Na5t/oW0NBnvzJmgW7iA58uWExUFMyoWc3nfXe7ehT6rugrJ37hRSHFWlvT/0iVwcWHfmTr0YTk0b86DUXPZaQCVPuAdKZw/ORna7pjBrelrcC25KAuKykqmXp7C+vxFXMu2waPpc+5lGeLgIMN93f6yLASqA6X0u/80aNCgQYMGDX9WVCfFYjlwQafTbdXpdF8C54HIV26xUSP4y18kPm3pUk5dteP6E0f4+GPatBGR9PlzfVzZ4sW4Wefh6Sli4K5diFp56RIcOEBurvy576ITpaX6lAtnZ8aNE0Gazz/HykqSIlxcEHJcUQz9+tGggYjDly4JTyMgQNr8/nuMq8pldxx6sXPfPkhKYsd5Dxo0EKG2c2eJgvs+rg4HDhtiZKTfIFdRQa1asmmsZk3xKnt5AXFxHEl14gJCjgsKwMNDNvzl5UHbhjkYGwOmpqjLKZibS2G9ggIplNegAbKxbsUKbjl2JS5OhFTi4sDJCdzdqazUx9I5O5OaKoKvry+Qn4+9PaK05uaScq8WJSWAuTm1oj+GJk34br8Zd6oaUVkJp3pHQv36kmLRo4dUqGvVSmL2Ll3CyEifMrJkiXDJggJMTKQ/JSUioKPTYfggU8TtXr2Ij5dUD8LDobKS++0G8qCgBpiacj3DDLZu5W5pHX76CbnQlZUSYydTgr09GI8cQtvCIxAbS0kJ3DBvJUp2WhodO8KNfBs8uEZ5pSGlpWC8fo2MOz6++venlmKhQYMGDRo0/OnxvxJknU6nA+KADkCM/tFRKbXrlVs0NIScHCFu167RoYOQKj74gHfeETJ06hTs2QPs3En/kDp89BHUvBBHeTmiHltZwezZv+YXT5ki3tfPPwdatmTFCiFUd8LXUVYmBDg8HGqkXhSLwdWr3L8vaQddu8KKFbC93Tr8/eHEsPW4eNTg1qoYtm2Tasg8eMCqylncuSPF6WqUPGTFChgxQtLmenvdw9wc2ltfB2trKiqEU27dKu6F4GB4cvgXenQsYfVqIbwhIeI+MDSEhQvhRGpdundHyGE9SdE4ckTI+MmTslfw1Cl4FrmKFSvEsVBSglgOrl8HW1u++EI2LWJiwrp10vaxY8DevZJHvGEDhIYyZYq4F8jIEAnbzo7ataXYnrMzdE77AkxM+OUXZNWRmgoxMZKyMWECEyaI+E9iogizwcFERQk5f/RIv+BYsIAHho5cvw4cPMiJE/rIvJgYaN2ajRuFSBdb1BO/dVQU6el610ZZGZSV0bXlI5KTpdjh4MFIo7Vrg6kp3bvrRWFTU3Lmf0xmprhGln3nQXKyOE++dpghKRoxMb/vHtUIsgYNGjRo0PCnxv9qsVBKKZ1OF6uUagPs+UNaLCsDb28iIgB3d4yryklLq8HTp3UYN05UyDDXA4Tt6QW+t/EbLlbefYV+QlYdPEU13LaNWtOn09Uli+PHnXB1fk7caUM45ExQS332cAPhUy1a6BXP0lL5ST4nByMjaNVKni5dKrxbZdyD5GROneorxBKx7+6wW43pI1Fj9+4FbG3p109cCatWATY2XLgABJpCfj65ueKmKCsTB0aTJqJU5+ZaUKeOnOPhQ7EjxMcLQe7qmsnZs47i/S0podLBjmbN9CQYsdq2bg3GC+eyaU4I2882E8KZYiT+5V27mD1bbNTEHidmy0h6Dq3Foe9KIHI9hpaRkg3n7MzBg6Kes9Gd8qCB1Dh5ki5dQDduLGemfimy94QJbBoEzAWGD0et/pgGxwE7X+Z3l7Fjbc2Nq88gLJcOATLXAwfqC4LklzF4sCXLlwNXPNn5cY6sZuJNwcmJ9ybc5a31jbh7Vzg+MTEYOAwR4j4gEnJz2RJrg6+vKPUWFvA8oAcrV8KCtWsJSxC+fCZjEue+kcWDyn9Ixz52XLkiFcUNDGDKuKdgMUrCrqsDLcVCgwYNGjRo+NOjOpv0PgW2KqXO/REN+hoaqsQ+fSS+4EVFDhcXkWIrKsQy8EIOXLRI/K/Dh8u/TZrIMVlZwsYqKuR1c3MJHPb0hLlzxQS7YQPMmydeC2trsRfs2yft2dqKVOrtLedu2hS2bkWFTZMx/xJHcSs/LI2eCpONjQUzM2lr82bYvRsiIlCBPdHl5ohEvGePyLK7dgnj7ttXpN/SUmHnX+pjyRYsENm4rAx8fckxcfq14l+NWVPh++9hzBjxVjx/LubjyZOFAWZnc2PNftzGdJTxOzvLODt0EENzaakw6pYtpT8jR0LjxuL3MDcXD4iREYwfL3M1axZs2QKjR8vcenuLH2TFCrk+U6cK08/MFLbp7i79srUVifxFVt3JkxJg3aqVjOvSJTEMu7hI2+++K+O/dUvyiCsqpCpJaKjMpZsb21uvZrT5d0LOhwyRMb3+ulhyLl6Udnr3lmvl7w95edx/bEGD/Ivg4sIzU0uM8x9ARgbPfNpjnHpZ2u3fHz78EF1KSvU26bVqpRIPHvzN+1hXv/6/9UadPxLaph8NGl5C26SnQcN/Fl75O/tbtaiBq0AlcAu4BFwGLr1KXWv1ok78qVPK3V0plZ2tQkOVmjNHqcJCpYyMlGraVB5+fkqpzZtVaKhSoFSfPkrZ2yt17JhSublKZWQoNWCAUv7+Su3fr1REhFJWVkqpLl2UjY1SK1cqtXu3Ul5eSrVsqVTjxkqp4mIVG6uU2rNHWVvLazExSnl7K6VWr1aRkUrl5ysVGalUYqJSFhZKBQYqdfCgUgkJSo0ZI8cOH66UjY1SJiZKLVqk1MCB8vesWUopAwPVrZu0W7u2UmZmSnl4KKU2blQrVyo1YoRSHTvKeKysZMwmJkoFB+v7sW6dGjFCqQ4d5L05c5Tq0kUpnU6enzwp/TMzU8rUVCnVu7dSFRVK+fsrExM5RiUmqu7dlQoNVcrRUSkVHa1at1ZKJScrFRqqtm2T9tT06WrpUqXU8OFqwAClPvxQ2o2JUUr5+yszM6XUqFFKFRWp0lIZv4qMVIGB0n5VlZL5PHlSBQbKXBkZyRhVQoLy89P3p3591a6dUk2aKLko8+apbdtk7rZtU2r9eqXOnlVq6FA5r9q2TR08qJS1tYzRzEzmbM8efR9mzVKTJysVHy+vFRRIO0uXKjVunFLdusk1WrdOPrdtW/Vrsbdp0UJurt94VPd8/394tGnTRmnQoEHw7/7d176vGjT8V7zqd7Y6KRa9fzfr/l9QUQFYW8vmrYIC3N3rUlQk1gpLS7E6ODvrK9it/RafAW9Sr54Ijk+eiID5wn87apSks6WmitAbEABYu+BUCEOHvqzAN3u2CMU8fy6+1XnbGD68LwUFkkB29SowaBDZq0XAjo0V8bakRCwS+/aJpaCgQMRUX18pX21oKO336ycCtqmpjDE7Wx41ashxd+4AHTtSHiuBHU+fisiKdInnz+Wc9+8D3t546qvMWVmJkPrdd2ITMTWV56mpksVsaopYAkJDwdERExN94ltKCsHBbUhMFNEXHx+ePUOU5aFD2bxE7yLo1ZIGBsAPN2nTX6wM9+9LWWu229C+PTLpsbGYBQZSs2Zd8PTENEHsK1FR0t79kZ3p00cE6okTpeTzi2vcogWQZ8Do0SKgAxAYSO0KEacHDRJRe0rAdUxMmjFsGJCZSYW1iNpGRtJGcbGk+lVWAsHd2DdV3hswQK4PyCbAhAQZ27lz4kN3c4PRQ8sZM+Z33KSaxUKDBg0aNGj4U+M3UyyUUneBTOAZoP7u8UowMYFyNy88POBJQw+KisR261R5BycnIUUNG+o3a82cybhx8qt7UZH8gt+9uxzfOOk7BgcW4+Ulm93Wf6qkapyJCba2QsZsbcXxMHCgOBQeVdWisXkO9OtHero4EiIixIHxXWIjnJ2FlE6bJhvkTE1lI196ujgV7t0Tf62Xl7gSysqEyHfqJA4ODw8Zo5OTbCrr2FH627GjvG5oKH1v1058x1ZWwm9BzvXmm8D58zRrJkTQwEDOHR4ufR06FIwz79C0qRBlHx+E3b/+OiQk4OwsiwySkqiqksVG165AYaF4gZctg/ffJypKzsnWrbKosLDAzU3cC40agQ4FEyfSuDGS07x5M5SWMmsWkJjIxIlCTD09Ye6UEhoc3sKxY5LWdv68LBgwMeHKFX357Px85HcA+Tz79mFrK3x98WK53piaYmqq9zbn5tK4scyVtbU4PSZNku5bWgKbN+PsLE6Lqipxejx/LucxM5O5cnaW69a2LVxOq1H9G1RpKRYaNGjQoEHDnx2/SZB1Ot00IAc4Avyof7x6qemnT6kxbwYjRkBNo3JCQoTMcPgwnToJGRoS+FiU3rQ0amTcYOpUUYdXrhTSWu/SIWFS69czqdddDIseQXY2vXoBKSkMGCAKboOsczg4CBFr1Uof/ZadDdHRjBypr/iGKJNBQWKZ7VZ2AG9v2RjYsaMQzLVrxRbbvLnYg/v0EStxixZC+DvumsHChTC65UWpEviREM1p03jZTl4eLVoIiSwqEmW3e3chyT17CuGsqpL+DfG+QVCQWI/rrQpn2DAhgwEBwDpJ5ggPh7ff1o/ngw/AxITwcIm0Y/9+pvhdxs9PiDyHDtHHv0QaePjwRf0VsLamUdRcqKqiaVOw27+dGeMeizR8/rwk3X3yiRQeWbwYN+dncPAg9++LOtupE2z5xgIyMigrE0Lu7g5DfO9AbCydO4saj5ERXbrI4gd3d0hOJjNTSH4jg3sMHAhER7NihTRNUhJedXIIDpb5GjBAiLLr7SO88468b2EhC40e9VIoK5P5sbIS6/eiRWLRtrKS8bewz/l996hGkDVo0KBBg4Y/NaqTgzwDaKaUaq6UaqF/vFIVPUDIxZgxUphu7VpSUmBGwGUoLSU/X7je3cJakud76RLlDd24dk2I0q5dUGPbJjAx4ULgW0TVfIsLBY24U2jDexvrCQHu1IkzZ2DgAAX79jFunCiJvr7gcXMvKYatoHFjLl0ShfnqVXj/fTBzb0R+Phwx6s2XX8Lcqg+wtJTiciUlYqWIiRGSXlkp6Rb16wtx/tJnDQ4OcL92K0hNZdEiKUW9YgUsXy579oiNpc/rTxk3Ts734Ydw964sCMrKpB/HjgGenuy77oatrRQjOdBjNaGh0tbSpcDYsTRpIqLu++8jLLpvX+jfnz17JNqNLVs4lNWC8+f1EcB9+zIy1EJk7dWrOXJE2mfYMI4ErgJvb0xN4UH30UxdUEty6Nat4913kY17UVGwcCEz5hjrpWcpYW02f4ZYHuzsOLA1BwsLIfGfH24MISEsWSKLC3x8SEyENwc9AmNjMDNjcLt73LoF01Y6SQSdszOXLumzpMeNg4wM/P2F+C5aJHPzTWEPsaEsX84bb4hyjIcHy9NHkJcnpL1lS/n1wdQUoqP1c3rud+4v1QiyBg0aNGjQ8KdGdQjyPeDxH9bikydgbo6hIWBhQdOmiNQ3YQKLFkFE/8ts2KD312ZnU1EhVoLWjnlCqq9ehbAwWpecIixrIZ6e4uft0kXUXQ4eFCvA229zY9R71Ms8h6enqJskJuL1/CL8+CORkaJAuriIb5bVq/HyEiLt6AiEh+PvD7VqQYuS07w3p5j8fDh9pIRmTZ7/ajNIT4ex7a5RXAwNLh0AIOaLR8TGSg7yrFn6whmLFsG2bbRsKeEatrYiptavL7aAERZ7xTKxYwd9uhZjuWsTQUHQ2/o032/Mw9gYXnsNqKjAykr62aGDjBel4KuvyM4WxZrjx2nXDvrU/JkRlduhpISdM85IesXkycyapfftLlhAD9NTcPgwDg5iaVg/47qo82VlYlkpLJTS3zt3sno1sHMnnTrpc4mnTpU+fPQRPHv2a6XpSWOewu3btG9eIoU6kpLw9obvjtkIq8/JAQsLNm2CdfWXM6vLeXBxoZvjDYmpW7sWpk6l53lBS8gAACAASURBVLN92NuLlaNZM1GmW7UCPvsMDw+xrLByJYSGsnGjKNrW1rAg8Dz798t94e6O5ERXF5rFQoMGDRo0aPjT4x9u0tPpdOH6P28Dx3U63Y9A+Yv3lVKrX6lFS0tYtIjPU78hbLE9BgZwPr8RJkXiXY2IaEGvXuLR/crJia5dpfiEm1sd+Xy/fnrG3Jpb9Tvz4JxstDMwEA/tl3366AtZRNIyC2IS2hIZKUrw55+/h4EBuHbqRJ8+IrzOni0qpdM7g+nSUTbUffIJtGtnzO7dokaOPubIux9aUlYGqakWhIXJ51q1EsWzbZwHhYWQ49Obut7ejA+34cQJ2aBnbCwcMzTUjpoDBrB4kWwcNDGRdcGgQcJxvxjcl+XLYe7Dg5w5q8O530Qiu0H7HzqyI0pcDmlpMHu6N5HzhMM+fAgzXhilPTzIvCy2iynDs4iKgtOnuxEUBGHBT5k43YywzedoFf8Zfn5w4wZU+buJQty4MZ98In1Zc7wZMyzi4M03qaWAc5kizTZpQkQELPfzY8IE6Uu/m81o3whKP5pHj/GO+PhI4tuzZ2YMdDPk9GULzp6F8dbWREXpFzCtW0OrVkyab0NCAqxcuYCDO+DjLt9zoLQr587BCFNTmDePnLZ9mO4nCntFBUyd2piEBOhRqxYDBsgiKm93B3bc78amTdJ2hw7w4K9tWDREhta6NbzjaFv9+1NppaY1aNCgQYOGPzv+YQ6yTqd793/7oFLqvVdp0LdBA5VYUiJ+hfBwkW/PnJE83BfMcetWOTgtTeIcVq2S38otLGSnVmSk/O3iIlJmhw7wzjsS9/DZZ3JMdLR4MkJChIkaGkoUgrm5qI4bNogkm5MjO++aNJGyyv37C9tesULaycgQwuTsDLGx3Ahbg9vqKTz58DNqVj6G2FjKh4+lxrJ35LjVq8XU3L69yM8lJbJrzNNTilXk50t/ateWiIUzZ2SsZ8+KLJySImy4slL67uXFU6u6mPXrIX3q0EF8BPv3i3VgyhTZkWhhIWw9Lk7G90Kq3r8fdu1CpVxBd+wnmeeQEJGtZ88WT8T+/bLD7epVyuPPU2PRW3INVqyQTGlHRzlXr17CQhs2FNY/YYLM0YoVMsbhw2Xuxo0TdpqeLqX9Pv1UViH798viJitLSmofP0F+PtRJPSXsfPRoifjIyYETJ8TsbWAg98WZMy+zrvfvl4XW8+fCnFes4P6lhzRYMknaNjGRdgICJOqipARdQkL1cpCbN1eJu367UKSuZcvfPJ9Op+sFrAEMgc1KqRX/4Lhg4BugrVLqlQNM/1XtabmqGjS8xB+Zg/yv+M5q31cNGv4r/mU5yH/0o42rq1LFxWrzZqXU1q1K3b6tpkxRau9eJWHIx44ptXWrSk1VSvn7q/h4pTIzlbp8WUlI8fz5EuLr5SVhwpcvq2+/VUpVVKj4eKVU/fpq+3bJxx0/XvKMt21TaudOpYqKlFIzZyq1ZIlSFRUqJkapq1eVOnxYMowzMyXbV12+rFavVmrBAslTPnhQKZWerqZMUaqsTCm1d6/Kz1cqLExOpZYuVevWKembgYFSaWkqP1+p9HSlVHa2KiiQrObUVDlnVpZSaWmSAzxihLShVq9WpaVKqX79lFqxQqnp09XNm0opX1+ljh9Xp05Jv9Tu3UqtX68yMpQ6flxJmPK2bUr5+KjoaH1fAwNVZqaMWS1aJAHDe/cqNX26hAvfvClzaW0tczhihFJJSRI+ffu29CE1VV5bulTGtHq1NNihg0pPl9zitDQlg/H0VBs3StuxsUplZyuljh1TGzYoVVmplHJxUTdv6v+eNUupDh3U7t1Krtu8efL65MlK3bsnfZ4+XalevZS6elXt3SvXpLBQ5nfzZqWUvb2KjNSPPzVVRUbKHJaVSXezsn6NfFbq1Cml5s+vfg6yp6d8+Dcev3U+5H94twAXwAS4CHj+D8dZAieBBMD3Vb9X/8r2tFxVDRpeorr/Lfmtx7/qO6t9XzVo+K941e9sdVIsjuh0Ouu/e26j0+kO/W4mrkelhTX89JNs2CooAGNjDA2hz9NviLtci3tNukFFBc0ufQP16tHR/pZsCDODCxl2UrHN2RmmTuVu1F549IjBBt/Dhg0UFQEODnTuLLFs0WNP0Mb8GqO7ZdKunf6X806dYMcOrt00pk4d8Mg4hIMDrK6xgAa3T6FzbwaPHjGr5U80ayYRwz3NT0FCAutXlnD4MDzu3IfkZIldCw8H5s7FyQkezVsO5uZ8neTG1asiPn8eW5crV6D3rSjc3UWkrmeQg5vRHfz9JQnDzw8oK8PAAHB1FSNxWBhFRXBt2zn2FXfl2jXYdNpL5iwkhB07JA8Zb29Ry997j379IC8P8POjQekN8RCPHAktWvDN0z7i+fDz40aVK7cK7URltrSEN98kp35rCAnhUFpj8bf06sV189aiDAcEgL8/HyV2haIidu2SuXQL7yt+YQuLX+P33N1hxw4gI4OmTfWb5Ly8cHV+zvHjSGSHiQlDfO8wOOARTxZ9ID8YuLtTXscRBwdEze/QgbvmHjRoIMJ4djZgasqbAXfA25s+faBr2ibIzmaBwxasraFG0mkiFjzH1lYE9c9XPJJ7JT+/+jfoH+dBbgfcVErdVkpVALuA/v/DcUuBlUBZ9Tv5b9GeBg0a/jlo31kNGv6NUZ1NenWUUoUvniilHgH2r9qg0b07UFbGtWwbIWhHjjBhAoz8fgh+T4/gdFdfarp7d8jL48htV0Y924Jr1ilax74rlSCiouCTT2jkbQPbtnHBeSDcvElP97uQmkqjg5/TuDH4zOoKz59zPscRCwuwST0NFy5Abi4el77GLzkKXFxocfsHvnRfLhaHgAC4eZPP0l5nrGsctSryxDKRlwdK0ff0Qmrt/4rXbS/QuWkOaWnAuHEEBoKNT2MoK2NYzX10Tt9O54RVTLL6Cr/iA+imhaHWReHsDE8s6kJFBd28H7GgbwodOyg4coQat6+JDSQqCpYsobVnOR7hvenzbhvGjYOJDQ/9Guu2wGELk0KVsMeyMnj/fewMHjHK6YSUmd6zhz59kB2CP/7IkPhZ8NVXcPYsbie/wNX6oczl6dMwYQJ1a5bAoUP0/HyQWCdMTGhWeEZKXyclQXg4s602QbNmvGW/hY5N8ri3fi+88QYkJ1NSAsOCimlm/4jZzfaBtTXdrC/Qo+BrYcnffsvr3g9lVZGVJdaHX36hpmcj3nQ8BAYG1PByo5tbpthvliyhUVhfKiuhbeVpmj1LESvHlStw+za1ayO2jK1bOeAwXubG05NHRYbUiPoI1/3rOJCg3xT4oipLdVE9glxbp9Ml/t0j9L+dpQGywfUFMvWv/QqdTtcacFJKvXps4r+oPZ1OF/pibHl5eX9A9zRo0PDf8Id9Z7XvqwYN/wL8lsQMnAca/t3zRkDSq8jVSinatG6t1IcfSunj3FwVEyOOArVnj/L2FgfEvHlij1DduqmVK6UU89GjUlV5/36l1NatKj5e7BNTpoiDIDhY/lYDB6p+/aQksyosVFOmSKnpceOUeCzy85XaulVFRMj5mjbVl2yuqFAtW0q5ZQcHpTZskPLFDRuKIyEw8GXZ6gULxJ3g4CBlkI8elbLVK1cqpUxNVViYvO/rK593dFRKffKJ2r9fqdat5djGjaWUdePGUkZ66VKlnJ2lj4cPK7VrlzgdYmOl/PP48dJXlZiolix5Wa5aNW0q9olx45ROpy+3nZ6uAgNlzLNmydz6+ckY1caNavhwGYuaPFklJyulfHzU5ctSRXnpUiXeifnz1cCBcoyaOVOpvXvlOs2ZowYMkHGXlOhLVhcXq8WLxRkREqLEBpGQoMaPlzLZyt5eLVggpZ/Vtm1K9emjFi2SufnwQ/21SUtToaH6MtVhYerSJSmz3bq1lPVu2FDmfcwYpdSKFcrMTMZQVCTX6sVcenuLO8PUVK7HmDEyH1TXYuHuLv6R33j81vmAIYin8MXz0cC6v3tuABwHnPXPj/PPWSz+Ze1pP9lq0PAS1f1vyW89/lXfWe37qkHDf8WrfmeroyC/DcTpdLrtOp1uO+KFWlBN/v3/Ii8Pzp6VuK4zZzAwkLi050F9MTWV/V/p6frqc7Vq0a6duA58faX4Ru+9U+H6dXx9pQx0ixYiLIaHi7jI5cs4O8serVv5tejcWURUBwfg8GEuZtrB6tVYWIjd4dtvYcQIoF07Pv0UZg/LZPdumGT7DcHBIkC6HtvEoVHb2bxZkhjc3KQ/gYEiwGZlSfW8kBCgrAxvbymj7OwsJZmdnAArK3o7pXDkCMydKznKnTqJ4+D11yFiykMpmOHnR3m57D27fVuKikyeDLduSYRZSo02zJwp6RgZGdIeZmZw9ixt2ojjgkWLWLhQ5tXDA/j4Y7FxTJ0KGzbw1Y7nBAcDjx9LwZCsLL7/XvYJmpnBk/pusHatWDSOHZMNdrduSbnqo0cJCoLOnaHmwe9YuRKYMgV/fym2kpoqJbbJziY8XB/XV1r66947EhPh8GHem/yA3bvlum3cCMUObowaJTYN7t+nhcl1atWSfY6urjKfkSNTiI4Gbt7k0CERhi0Tf2aSww906CC50Rs2SNDJnj0wd0yOjNv+d/7gUVn524/fRibg9HfPHYGsv3tuCXghCTHpQAdgj06ne9XNP//X7WnQoOGfg/ad1aDh3xj/MObtBZRSB3U6nQ/y5dQBs5RSv8PU+d9gbw+DBlGwE2jWDPt8iRxrZn6Pu3edOHxYwhJKSwETE2JjhQNu2CC85MGS9QDsXCvFOFxdhShFRurJWJ8+7N4l5LdPi7scLWrEypUSsHC56WA2rIfPjIyIjZUS1W+9JUUvojvacPw4+PV/RGSkIwemW3D8uPya/tqKiURHQxcHifwdPFgKh3h5wd69QvxsbaV4x1wTk1+LgNSoIYTP1BQwNeXzX7ywt5dQjvv3oWZNCWlISYG98Xb89BOwaxfHNklAQ2YmHDggZa8tLSXWzcvuAcuj6nHpEpSXA8vDJfNt6FAy1guxJnwQ+fmwbZssNibOnUDUFBgZv4kWDnnUczSksBDejRlNYiK4urtTUfFyDLNDiyUJAsQKERSEMjLmTD8gIoLP3pfzMtOc2bMh5u2ZJMfJa9bW8MMPMGpJU86fleAJWrZ8yVEfPoO//pUTafWYPl2CSExM4OtxB7id25u2bQHPYdy3aMaTJ7L4yMiQxdDt215YWcEHY8bw8cfiNhni5w6lpcTFyTU5dkxCNzZtgqRhdbl+HYYPN6v+/fnCg/zP4xzgptPpGgP3geHAyJfNqMdA7RfPdTrdcWCOevUUi//r9jRo0PDPQfvOatDwb4zqKMgopfKVUvuUUnv/KXIMIommpTFuHJCURFycvtS0vT3W1rIHz8BAX6LY2RkXFyGHtrbC1eod3U69sz8we9xDjI2FDHl5wfz5QrTZtw8jI6hbF3B0pLRUFNicHGjhXEzz5kBQEFVVcOqUJKL17g08foyrK1BSIirl+fN07CgpZ40rb7A08BTu7pKnbGEhCnLTpvDmm7IHzMREXwra1BQbG1GGHz8WRdzfHygtpXlzIYSdO8t5W7SQc3l7S8GQ8HDg7bdxcZF9amVlkk4XECDqubs73K2ox8yZ8repKcJKTU3hxAl69ZK54OBBCgslTc7WFnj6lO7doUXmAVi9mm3bRF1m0SIh1BUV1K0rx9+9K2PA11fm0EBukaQkvRJ+7RoTJujHlJbG3/4G7NqFq6tYytPShK+TloaPjxB9srJIS5PxYGcHP/5I107PCAkRS3JAAODlhZ2d2I+pqKBB0TWMjGReHR2lzkpEhMw78fGsWyeK9d2KerB1K87Och9Nnixz+u678Nbkx9jZgWXurd93j/4Bm/SUUpVAGHAIuAbsVkpd0el0S3Q6Xb/f16Hfxv91exo0aPjnoH1nNWj498ZvKsh/OJ48gfx8BvYogZ9MmT4damxYA6uSmD37S5yd4fUuz+jVyxiiDZhWspza8xcwgq+4PnMExFWIdFhaytq1dhgZiWpqYQHr1gFzKomIgLbbpkFEGjMPHkJX9lQk6Z3f0rfvJAg9S3i4ENw6dcTywNK6YjeorBTS+M03TFwRQUefcjh2E27exCkxka1bZ73gjzg6inUjOBgGN7koDLOoiO3bpeCfh4cQWyMjIDwcv/fFZ9Cr11JMTETVbm11i2+SXKmo0NtATp8mrP+X3PIbi5WVkM6yMolNTk6GRrbFUFbJpk02UkF511VZVVy5wsQlenvDoQYEBEhp5wMJNhBzlsjIiRB9DFJScJ4gbbPZgdF+d2DebcJGP+ZAfC0pZ711K6Sn028CkGoE775LG0tLar+1AD7IYtgkfVW/oVGYhM2Apk3pY3kC6llQ56M2KAWkXKXFwYMsWfIZhOQzezZ41boHI4/LgBYuZNyiVRQVQQOVCUoWQhERwNoNMGoUISEe+PiIgty9OzSwKmZI9lZISKDBmAfcyKqH28dT4fRpNmxYipUVtK46T/8VXtzPrwEffMCgQZHiHaku/jgFGaXUfmD/f3tt0T841v8/rT0NGjT8c9C+sxo0/Pvi/54g16sHtraMDLVg5wgDNm0C16Yz6D25nHB9WeHDXYypqICPryYx1fUQ36+FhKEjuLwJVq16Ey8vqYexebOknk2YIH8XFsJgDw8WLYImO9fxuvdDFi6E+HgzgoLMCAmZxMFvYZK/P8uWiQfYy0tCI663sebnn6Hv+75Er4D3oqIYMwgiImrQoUNvcp1FzZwXIMplVJSQ5PnzxcrwU4NW2NlJHo+/vwjlFRVig7C0hDeHD+f7BmFYecDSabJOsLMDNzdX4uOFAJ44AbcjI/msdCzn3hfrR2CgVNpzcRF/r/dJS+LiYPFiaWP8GHdo2xbS0xkzRrzJ17f1YPFiKC624ZdfIGdZO/z9IT5+Fa4ZPzNypHiaC2b48lFMY2b37s34mbV+9e4OXhQAkZFs3w4jWrYkb1YkJiYwJwS+mT6CSZPg2jW4dvgw0dEQFDSRffvEBnH2rNRG6TvGgZ/aLWDOOBjWpAlffAG2tk68M2wY3L1LXP9VLA2WNUX9+o6smf+Azz8XQXzY+8s4ZdSNGQMl1CM7W0pjOzlZ0qLFNGbNb8eXh+thYQH1V62n5p6vCA6WdLofn7ThxHyZ8/DwSJYuhhUrRvB3v1z+NrRS0ho0aNCgQcOfGtUiyDqdzhCo+/fHK6UyXqnF0lIIDOSjUGB/Lo0aCSkqrqiBv79wvb599d7VmyZ06iTe2y5dZONdm6dxcMOa1z3tON6kHk2bwrChCh8fHV9/DXx9j6AgPcfJymL6dDtq15af3+0SDzFoUE/YUMnQoZKV/Je/iM+Xp0+ZNQvYuJExY2bA2UxatIB27UQpbu9ZzKF4S157TSwRvXqJqhsYKPaD2rVFxcbAgMzMl2lm+fnSDn37UiHiOe3bS3/u3hUvcqdOkoU8ciSwLxnLtmL7KC4W64W3N78qzm72j2k4shapqaIo8+yZrAzs7Jg0CQ4dAnJzadlSPMzFxUDDhgQEgOvxL+DKFUJDu7F7N3DiBO2XAA8d8HOFgX557NmjL+ldWUlQEJDTkDonv4OAALy8bKCggGnTxHvNvHlMDAmBY7m0bDma/HxxZ5iYAO7uFGXrNw0WmOPlpXdrpN2Ho0fxW/KU7t3NePRI0ua4ehVPz3pitykqwtMPmjUTi0pxsWxkFJIP/PADXoPb06ZuJpjUhaws3N3FlvKi0N7UqeDn8ZBOnexozYXq359KKzWtQYMGDRo0/NlRnUIh04Ac4Ajwo/7x6rmtNWtCVhYzZwIuLvTqJepoUZEopDk5euIHcPYsX34paQ4uLqKkYmwMDx5wp6wejo6iVvq00REYKFyR4GAKCsTWUOzcgqwsWLJEzoGtrfy7dSsZGeKPtbXVe5fnzycoCK4FzqBvX2DoUExNxdPaIP8ig8ZaUrOmbAwsKpKq1qam4gWurJTz++r3FgcHS1+NjYUkX7gAdO6Mt7eoyvv2yabC77+XysgJCUKoBw2Ch4vWUF4upDk/X4h+drb0MyYGKChgxw6pMp2RATx6JLIvomrfuwfk52NkJCq7jw/Qrh3x8fCT85vwl78QESEb2igvx9oaePqUqirIqaojm+mOHgUfH65fR859/jyUlEhhEgsL5s2TTXDs3k2j0J5QVsbOnTIuExP9Br7CQhwc9B5o5Nra2iKT1rEjM+abERMjPyiYmgLe3rz2mv4afvstdjvXER0Nly/D3/4Go0ZJjHN6OmBnR58+0Og1R7h0iUcTZvP8ufiUR4+WCtpLl8Jnu+2kzWPHft89+scUCtGgQYMGDRo0/IeiOpv0ZgDNlFLNlVIt9I+Wr9xiZSXExXHyJODgQFKSiMoNqu7x7JlYD1JSRAWkZUssLYUo/vKLvpqaPv8sNVXIn4WFWBWys+UnfsrKuHdPyGNVlcS4VVWJJeDnkrakpACdOpGby68pFU2aAM2bU1IipLmwELh0ibw8vep57x5t20pCnYuLfKZGDbFPlJWJd7hpUz1vsrcnIECU3xeb7MrL5Ry1a4s3+flzIYLl5dLvggK4eFFvuzj5PRYW8pqZmZBHOzshvrm5QFIS334Ld+6I9ZibN+VEubk8fKgnm7a2WFlJHJ2JCZCbi4ODfpFgbo6JiaRNUKMGXqTA2bPcvi1z7uWF7JorLeX8eWQyzMygsFC81CdPkpqqT8soLJRrkp3NzZvSVkyM3nOdno6trf64jAxq1dLz1A4doHFjcnNlcdO4sajDHD1KRobMAZmZUFJCZqZYQSoqZC67dtVvDkxPB2ReOHoUm8o8ataU9p8+FXXf0FDmrKQE/S7QauKPq6SnQYMGDRo0aPgPRXUsFveAx39oq5mZon7O2UDTxWto3/ABdO/Fu+9eYewYxZNSHTXz78LQImImfMWOQSMY1eoyYVM8IdkcCgvpbfQFD7zfxMICRtf/iaedXpdS0+128tHW5XQr+A6m72F5RATL364njHTjRroZGMDt2yxaKz//b1lXwunLFlCvHndOn4boaK5dWw2bj9GrV2uWhj+C2/VYYHkC2rTB0dGCggLZx+bpCVOmSFoFiJqsyMbaWiowGxlJ7K+3N9C3LwZJd1i2TBIs8vKEZCckiH/aw0OEWhZuJHjfQG7flk2HVlaiLufm6jcT7olj9+7BHD0qBJi++cJ2TUzYskVPoi0s8G8J4wc84kiiDYSEEBV1iraHlkH3z8jIvI8uNweGPhdGmpbGzBioW/VAzhUVCyUlvB8JhFnIKiQykiWRX0FoPKdP65XqXr0oT9wMKU044Pkdd30H4+AANW6kwBVbmtUvZvhwS4g3IKLzCX4o7Cq78NLS+Op9M55sDuPOHT3RTcyl+yBZaDCvEjp3Jj5eFjjZ2WJBMSt7JOQ5MZHbt8Es9iswaQLR0Ywa9RZj6x8Ba2vyStuycSO0vvQld/3HwvGS33d/agRYgwYNGjRo+FOjOgT5NhJU/iNQ/uJFpdTqV2nwmTKifMc3tPOFi/vmkJ4Oi7fWY97hK8zxhqwsHV5ecPduI8KMjJhwZATJyfB0SgvOroVN61tCZSXnjdrjUAW7d8OJE69z/74Q1u+7dGHhQpgwYTAeEwfzNB3GdJFYs7Vr35LCICb72b0bTp6EyEgLHj6Ev/2tmIV9YePG1YS1g/XrZ/NRX9i82YY9e9owaZYkNxw9KraIxYt/5aWEh0v279WrgK8569cLMW7YUFRbAwO4efMOmz+RTWxnzogwa2go3uX8fPExv/EGZF3bxbffijrep49sQNy2TZTh9HTIu72EqCipfl1SAneCg6Wh0lJCQkSgnxadS1wW/PUrGzp3hh7HjzPSHdavj6DH2LGYm4OpaV0ezQyg3Ls9NVav5oMPYPz4eiyZDt+sDIYLF4iOhm6BgcL0o6KYPRFiNm6kdxfp89WrZ8SWEpXLiM2vY3VYlO+KCi+2bvVi4TwZf397e5ad6ioRexERkJ7OgvthrK8Pc+bA4cNwamZ9zp4VQbyjmxs4O2P9UN5PTpYFxaef2nDjhg2rZ87E1ha8vEaQkCDK9PRgWGzbg9JSieI7fRp8fcfSPRveamjy+25SjSBr0KBBgwYNf2pUx2KRgfiPTZDKPi8erwRjY6hRmCOqan4+RUXC7xqk/oSXl/yc7uenr2539SqvvfayoIaFBTwqMUa1a4+BgfycX14uvtM2bfTqY0YGjo6y4c2v0T18feV8/v5gvO97RgQ/A3t7KUSC/OJfqxaM8L3B4sXCjXx8pJ6Jq6ucs6iIX60eBQXiQLC2Ft4YECCiZs2a+gFWVmJtLYqwiYkQ4Pr1wSnpB3x85PP+/vKoUwdsbOSYzEzJR86rqIW3t9hOWraUcT98KIVPPD2BXbvw85P2/f0R2TkpCRISqF8fWreWOSgrk9ziDh3Q+1Ve5jR36aKfq5wc9u0Drl1j7lxoYX1PLCX164OdncTOlZdDZibPre3ElpGZib+/XLPGDk+lIp+BAStXSltduogX28bgMTk5ModUVDB+vFgfxMgsiwF7eyHQnp5AWhrp6XJeBg2CGjUoK5MNmzVr6j+LEGUaNMDbW5T1pCSZp6oqed68udhPjIz4tRAJjo7Vv0E1i4UGDRo0aNDwp8dvEmSl1Hv/0+OVW6yqgpQUnj2DBw6t2bdPyOBz/9e5eVPUwH799HaC4cNJTRUOGBcnG9/S00F3P/NXcnXjhpy2bl39Jj5DQx4+FAX3cqETkyeL+nroENC2LWvWG0PNmty6JbzR3V0exMaSkgI93e/SsiW0ML3Bkycvi3gMGCCEzs4O1q8X3ujkJESspETSKA4eBCoqKCqShUBhoSi69vaAiQnFxULmrK3FU2tsLHysrEwKo+TmQp1jXxMTA6tXi3KamytZyt99px/rhAkkJUlBjrg4xNA8YADk55OX9/KYa9fEVTJ1KuDggLk5GN68mOxIAgAAIABJREFUDpGRODiIdYMnT6hdGzh9mk8+gQMpTlhZwcXUGpCfz65dyA7DUaMwrCzn0SMgLY3sbL23uLRUCKi3N8uXy/i3bBGyz86ddOwI33wDFBWxb58kfuDsDHFxpKeL37llS/3iYs4ckpJk3Dx6xP2KOkRFwZo1/DouR8eXxVG8vYXku7jIfFpYSJ+GDZMy5QEB4i3/+We4bt+5+vfnixSLf77UtAYNGjRo0KDhPxT/0GKh0+k+UUrN1Ol0ewH1399XSlW70o9OpwsFQgEa1q8PmZn8/DPUM8hh1YraXL9pSFWVWArWrhXil5gIxCRj20+q0m2Zeo6vAtvSun4OBL5Bg5Ur+dgijiOhSwkKEqK0ezewzI5ZY0S9TU2VtIigIAgNBSwtmXFzGly4wP7E5yxbbsg770h82sgLc/nmG/D1bURkJBQUuPH8uZS4XtPpa1YkD+PSJejfX851/booz5s3g0q7waC33OjeHXB25vhxUbRDQsRGceoUXHDozcAu8N57QpyfPxdiPHas+Jk/X/GIWs420KsXCyryYMMG9ga/Q1+rE9wK7Upysr664Nq1ZGbNYvJk/ebC0LPi+7C1RemLbfD551haRuDmBtE9v4byBqxciTD9nBzS7ohqzf79dLWdBZmZjF4nam9vz7vSoQEDCHYArlqJJOzuzqKtF6HXQkpc3qSkBC5m2jF5Mozf+yYPjWOoWVMq2HXqBFhNwHaHVCokLJeiIhgzBtixA5KTOWUrlpTv12Vy4pYjbN3KJ59M5LPPgKlTadBpB6dGDWLqxUncuSO2lL5Nr+OzuBl4TWND4Sg2bAD1yRrC785g5kxxbzg5SZ50q1aymOnZE5qF/A6CDJpCrEGDBg0aNPzJ8b95kLfr//3wn21EKbUR2Ajg6+Gh6NCBoUMRRhsYyO7dzZg3T+LRjHlGt+PL6ObrCykp+CwWpfHLq20ZNQqeVtTFbOpUYYcNGtChkRCtrCy9bcDUlLt3RWn08BCF19tbFOaHlbWwCw4GAwPuZRkye7YIhteuwc5PH7FmjQ1lZRIXVlQkpNvbGz4rGEb9+qLIXr0q5+zRA44cgRUrgGPHCAhwEwtCURHz5wuRLi0VVbN5c0m6iI8XjhocLAkUCQkSazd0KDysspEYNwMDntvWwTA4GG4Cfn7sWSvHNG0KjNnGqu092HLOS0juqFEiq9vaMmKEjAdraxZOFML4eOQwagW2J3nQGXr6m0CjRoQP1KvPAQsleDo1FScnMBvUm/LYA9QwMICsLD78EBhlC/PmQW4uiYnQetky+uXLxjkLC/2ixHYWM41E7e5vsBdSLKBhQ6KiXDl6FFhcm7kma8DeWW6IceNYPBUGD4YvfxL7Q9eUFK66Q0TXU1DnQwgM5LukxrRpI4pwYCBcpxlffgqRSUm0Gy6FQeLsZuBhDhs3it2ie3fpboMGspEwIABgjn7A1YRGkDVo0KBBg4Y/Nf6hxUIpdV7/74n/6fHKLT55AvPmsWZInLDakhJGjoQavi24dAnizhiDlRWbsvuCuTk9/cs5cOCl39TEBHjtNfKsXDlwz4uKCnBzKMbZWV9EIjmZK1egR+o6vD4az7ffikf52TOw27OFa/ZdYdw4UlLkl/J27eRneNLT+fprsR4EBMDSRc8wM5NNcu7u4j9+6y0hvNnZYu+oUUPsshc7TOKNN/S8qqyMrCxRhw8fls/Vqwf1JvSmXTvJVba1FZW1Rw8phmFtDUOG6CPlcnMpK4MbRh7ExcFPxw1JTZVjDh5Eoi3c3OjVS5+DbG0tnUxPJyhILAuMHcuqVTK21asBV1fKypAG/vIXiboDuHyZxy38ICuLWtEfw7hxTJ6MDKSwUJwEN2+KsXjyZCaGKJ6Nm0jnznKI67Lx9O+UB02b0rHkCP2DnlEe2JcfirpBcjKffCJiNLa23Ok3QxiriwskJlKv5AZt2shL8fFAr148fgzF3p0hO5uLRY158ECKqhQUiF/7yBFR5klKYtAgUeebNYOJftd4+FA2QIaHy23l4wN7vy0nOhq4dKn696fmQdagQYMGDRr+9KjOJr0/FjY2EBSEx0Q/aN6cfQ/aEBcHd/ddZuVK+PRTeLdotmTptmlD18AabN8ucV/h4WBY+BCePCErS4jV4sUwLMQSPz+xZ+DtzYkTcNp3GveWbKFuXRg/Xk/CgoOFHIaGsn07zJghiRSlpUBhITEx4u0dNAgeFhlz44YQTF9fsS7Ex4tYu2OHPAoLxSKxbJmozgkJQFkZZWUQHS1EetcueZ/ISBITxfbx8cdCiJcskYi4TZuExA8aBFRW8v774nGOjZU0hmPHRHVetgyetvHjq5gaeHtLm5SUiNHX3JzBg/WWhn37MDYW+8fx48C0aezYAbcyjKFBA2JjJRmDa9eEO5qb86XtLC40HSYLED8/SE2ld29ESi8qAnd3gt7QYXxwL+PHi2J7K2ILnl3rQGws78b1YOoMY0aNkjFTWflrkQ+srFi5Ej54Eia78ExNWbPfjaNHZfwmJnLdbtyQhRD169Mq/Qe6dpV5mjNHbC0PH0oeNiUlfP45rFoFdW6fodjRA6XEabJzpyw8rl6FLTtrcPcuv2+THmgEWYMGDRo0aPiTo1qlpv9QPHwIBw9y8uQk2FNOt27Cv+plnWfcuDa0ayd2hMJC4P59Tuy+ztzNzfDz039+40YoLaVVPwNaLfbh+k1DmjV5zrU0Q27eBEIOMyJMHBh1jn9DgwZDiI7Wb0rLziYw0BIiipg0SYhZ9BeKzzboICSEI2m3MIz5hoSEIdgd2EHnzqN47z2wPPsTq18rRA0ajIuLJCpMny4pEt7eQpqLivQbyKqqCA6GsDAhzJmZ+sIZqan4BrX+tax0aamQV2dnUZXHD3+Kn58ZjJvAsPW/4OVcQmCgBWPHinqamiqWEbMvovDtGcaECfpCIWVmUlklM5P582XuOHKEkFUjGDVKpou332bChGO47lwK335LYuJFsT6stqCzyRlITaVfP5kjLy9E9s7O5o1+wIoCeWPtWvb/OA+G72DZsr7cuAGu+9Zw9dAgWJ9O0ABR+IuK9CWzY7IJm1DM7duW8EUy87fpPdTFTyElhRkrb+Dp6UaHDvprvXkzs+bMoW1bM1j/M/TvT2amLAyKimTOhgQ+5kFpLYhM5KefxtLYOBP2JmGZkcGAAUP4eGkJKekW1K0r3uO2Jhf5wbYV5P8OQquVmtagQYMGDRr+9PhdBFmn0xkAFkqpoldu8fFj0Omoc/sMJCRQs7KSmqWlcO4c4Wt2UufST5BVKP6F1FT48EPmRW6izp4vaFxUBC1awBdf/Bpp0CwzE47cwsPfH4/SK2BuTlgY2HTygNq16TYdsRYkZ0LYZmq5uUFREV2L94nvIrcTRUV1oWtXDC9d4LTjEDpW3uPRG6OwPCcOhi0Zr9OrFxjlS6KCupfJrXJHDhwQsty44DwPW7YRiwQwrN9TnlSZYW4uloesLMB/PrVSUyEigh7O6VC7Nv0DTfkq1gwXF3hSZYZbyQVo2ZJWVRfgq0TWhXeHmD34+s1gUsU6YeTW/XF7ayTLR44Uj8H0vcLSExIICNDHmv1/7L1/fM/1/v9/fc3MzMzMMMMwv9fyY0ZoCfndEkJ0EELlLOFIeEva6ex0JJGOpMKhs5C01JFfLb8SmrU2hzWsZWvNzJphZmb37x+3Sb0v73Ms73p/+5yet8vleWmv1+v5fPx6Pp+6P2/P2/12X/E5tUpPw4IYJs1cAhtPMzsyBUZthLw8Km99n/6enlBQwHctbqNmaSk1OzWHqCjGT3gcorZC06b8YexZ2BwiOrygQOxvcDD3l6zl8hOj4UA7aSgGDOC2eX25rdzjmI15EBrKFc/qPPUU8Gopjd5/mceLL8qWonFjGDWKBqsPUt3bqH4oXtVT3NyIuLRD+pScHFp1gv7uO+QfFx/PwdR7ZQNXty5NKmdh9RvguuceMCM4B/j2W2rWbE5cnHTLHEvj3qFtYPjWil+f1yQWDhw4cODAgYPfLszs325ALOADVANSgW+BJ2503L/aOoSGmm3caDVrmllcnGVnmy1fbpaYaBYebpadbbZxo9nixWbWtavNmGHWvr1ZWprZSy+ZXbhgZosWacfERNu0ScdFRpq9/rqZjRlj0dFmJ06YLVhglppq1qqVWUyMmeXkmM2ebdajh+3aZTZ/vpnLZebjYzZjhlmdOmbTppnVrGnWr5+Zu7uZt7fa7d5d37doYRYXZxYUZFa1qo4vKzNr08bs6FEzCwqyRYvMAgLMBg7U2GvWNCsqMktPNwsNNfP313fe3vobzLZuVZuWm2tZWfrvtGlmVlRkkyZpPcaONbP0dEtLMxs+XJsFBmqAgwebu7uZh4eZtWlj0dGaX0KCme3cabNnqy2bN8969ND8rGtXKy01s2bNLCXFzE6dsj17zOy558xiY23qVFMndeqYjRplW7eaWVSUjRql8U+dqrW1+HibMcMsOtps7VqdK8vNtW7dtJ7m52fz55slJZnZ4MFms2fb4sVav507zVasMLOjR23xYrPVq80sLMxKSjStESPMIiK0buvWlc85OtpcLjNfX7NDh8y6dPnxmvbqpfPSvXv5dRQba0BCha7PRo3U8Q22irb3n7B16NDBHDhwIPza733nfnXg4Me42Xu2IgxyiJkVulyu3wFbgCeBw8DzNxWRnz8PISEMGgT4+pKRIZ/gNm1ki1bP/Qz9+tVWQtqddzJ+tH4/fVp2v9UunIbPPhOr2bAhoUu2MHy4dKq7dgHLjjB8jlQCERFKZJs+XY4L5zzrUqNjR8jMpFMnJdpNnCgfXi8v6ZF79pTeOClJZHWVKipkMXCg2v/rX/V3djasXQuXLoHr5aVUq/YYrRtegJISQkIkm+jUSfKKwkKoWnqeJrVdlJR4M3685nT4sNps0kTyjPnzgc2bqd+1KyQn89hj98Mf/sCrLyxg6SpvHnoISEsjoGsTIiKUO0d4Lpw8CV9+SUREucwjIAxvb42zadlxeGkNMXODIGwglJXx9v5n2LMHiMqg0ocfQFYWly+r7dCwhpATDFlZcoBYWSJ7iowM+oafheFrWHRiKSEhWquuXYHXXsOtYQ8CA7V2zd1OwqkCoDYtWwKFhXTqpHWgfXuIj6e4n4jhu0q3ERjRF44cISSkNXl5gIcHld9chbf3OOrUkT2cpyf0KHgXr1GDYcpK2rZ9ivbtoWPYVQICKlG7tiQzfn5KpOzeXec2JARIKPpp16jDIDtw4MCBAwe/aVQkSa+yy+WqDAwC3jOzK/wPvsgVRuXKcOQIZ88CrVrRJfQ8p05BpfwzxMbC51m1KSwsL9t86RKrVytxrnLl8mS6atWgf3/YvBmeeAIPD3j3XVmGJScDQUGMGqUAtrRU5aQXLpQS4MQJFIn37MmUKQq64uIUpJ46pQSv3btVQKSoSKqC8iJ1rF6tgHvHDiXfxcVdd7Ng0CBatoSvz3qDtzfHj6uNDRuUDLdhA3xXWp3z5k2rVort33hDrhSVKkmWvWeP+jhy20N8nNOay4Pul4Xc0qUsecObU6dUzpqyMlJTFdDHxSHfuHr1wMeH/PxyOceQIYDmnenZHAYM4NX45pzZcwzGj2f0aJlhEBAg/7s2bSTNCAwkLQ3JJIYOlTY4KUl93Horu4/UgkWLGDpUDwf+/vDnPwMdOxIfr4eJAwfgW6+m4O1NUJACVtzdOXy4PO7MywN3d6pVUzef1+lLUhJQXExBgdYUT0/o3RsPD0lali+Hhx6C46GDdQ59fPjiC533q1Ti4kWdp8OHtd7FxUrmmz5d1RYZP77i16c5LhYOHDhw4MDBbx0VCZBfBTKQxGKPy+VqBNy8BtndHVq1wssL8PPjskd1VWXz92fiRGjf4Az10naLmfTwoLhYLhJbtkBL39Oikrt1U0SZmEgjz9M8+KACNV9fIDeX6tWVt3b0qJjjoiJZhRUVlfdfUkL16gqgBw6UJDYxUYl3EyfKmSIvT7Gjh4eCzoAAVcurWxfq8S09e4oMb9cOOHGC/Hxo5HsOioqoVk1sd+PGGmpYmILB/HxtYWEqtezrK1a1aVP5944fD6GNzhMaClXSjylA3bKFDh00jwceADZvJjxczKivLwomGzcGHx/uvrs8Nt64kaFDlajYMPsgJCYSGVnuFnHlCgMGaI7k5Eif3aoVublATIwC1EOHoGZNBg5EuuOiIrjjDu70/QJCQqhfX4U4EhNh8GDkZ4eKp/j7Qz3fS+Dhwc6dSizE35/eveHOoK8UXHbrxq23im1u3/g7PSx9/DEtWmgd6NkTCgoIC1MxlkGDlHTX3Oe0KlW3aUPlypItV3Kz7y+radPk/uHtLc/oy5dh1iwUMf8UOAGyAwcOHDhw8JvGDSUWZvYS8NIPvvra5XL1uOkei4pg927emgM8F0eVfv14Ykob+NPzdBw2VzTwunXcFZIIiYksGbMWwsN583BrUa7BwVzpfAeV3dykU8jI4OETC8GtBS3HjoXlZUyfDpEX1yuqbdeOyZNrUFICtUb0lhZj506eWfkw1UvOMvrcWyy5GsWmv52Xb9rFh4i6soPzw6eRmSn2sl07sbHz55fP4ehRJk+uR3a2quxR6sfGjUDyCcjPp08fxa2enlIneHpCvehH4YEH2PtBG0XFFy6wYEFrBg6UfduJE/Cg9zvw+D+o/cQTXGrcmmXLgBN18K0GT08/r0jd0xPX9Gl0ue8+ugxqCMuQVcWRI8TEXSIzryqsbkbDhHf5w9AwCGgHMTHULzwGr2+A7GyixmaJbu3WTWt45Agtty+F5GTunf65dBPvvEONggJR3D16KJpdtAgmTyZ22md6aklOhuxdMH8+n4Vdkb/b0KGikT/5hF695hJaIxPy87mtYBvMW6T1a9eOO5OW8Oyzj0NhIVG3nYCDV8jNhd7F7+vJpV49tm+CV2achPx8xoXCOc+O9OsHFPQk5yWomXYQ8oJ5+eXarFkDj134M2UvzsbLS8OoVg2qns3SQ1VFYY6LhQMHDhw4cPCbx41EykBd4A3gw/LPIcBDNyN4NjM6tGxplp1tjRublZSYlZaabdhgZmlpNm2aPh86pIQ8mz/fYmLMhg41S05WcpwlJZlt2qTMuPnzLSPDrFkzs86dzZYtM7OpU61/f7P339cxL72kJLxu3cwsM1PJfYsW2fLlStzz9FSC3aRJZo0bK7GvTh2zkSOV/AXqsksX9TFpktmpU2qvTp3ypLjcXGvd2szy880CAmz/fiWWRUUpqWzUKO1TUKDvmzXTse7uZk2aKLEsPV19pKWpfYuLsz59zCwlxTZsUDtjx5rZmjW2f7/ZhAlmgwaZGlu50mzKFPPxUbu2c6e99FJ5gtpLL5ktXWrr1mkMNmWKPfKI5mfh4Uo6i4hQguP27ZaYaBr46tVKiIuMNAsLMwsLs+3bzWzmTGvdWklxR48qadFCQqxFC+XfxcSoG0tOts6dzZYu1e/Dh+uc2NixZv36WUKC1sIKCiw52cwWLrSNG5VYaIGBytwsLrbISLN27crnlZlpy5eb2cCBBjp3Fh9vUVE6h61aKfFvxgwlSXbpYrZrl5mtW1fxJL2GDbVwN9gq2t5/wuYk/ThwcB2/9nvfuV8dOPgxbvaevfEO8CEwHPii/LM7kHIznZkZHXx8FBFGRyvyadFCdg8eHopsZs0yc3PTBopuZ85UkBYaqqCuXz9FZ4MGmYWEmM2cKeeEOnXU1qRJOg5kDREba+bnp/6mTNH33bubeXpaerrZ3r2mQDA11WzvXisrM7ldpKfLNWPcOAWtgwbZ5s0mm4T0dEXgSUn63KaNolhfX42rtFSWDmVlZvv3K5obONBsyxazlBSzvDy1PXeurBzGjjXLyNDTQv/+GmtkpFl0tBUUmCK+6GitS0yMIvQePRQVgn7fuFFbcLDWddQoRaEgB4uCArMJE+Qmcf68xjxihNps1Uprsny5nhTCwjQvX19Fw97eekKZO1dPNtdsQjIzZWdRp47a8vU169NHEXhMjNoNCND3YWE6L56eZo0b26FDZlZcLFeSxo01Xz8/nVM/P30XFGS2Z4/O77U+p0zR2pWUqP3nntM6p6ZaTo6ZZWfrYWntWrXh5VXxALlBA43nBtuv/X+SP+fm/A/XgYPr+LXf+8796sDBj3Gz92xFXCz8zWyDy+WaXc44l7pcrqs3TVlfvqwKFy+9pKoUHh6yHSgrU1WNoiLVdnZz06vu9HQJkN3cpLXdtUu/jx+vff384OWXaR4Xp2SyrVtlIQFqu04dmDlTfVyzlHB3lzXChQs0CatJE39/aRySkyE3F1d4OCQkQNWqVNu5EypVomFIdQDuSW8rs+FrFSwKCjSPWrWkpygs1Bg6ddJ4jh9XlmFxsdofOFBjbtGCavn5EjXHxmqsU6ZorgEB0v5mZEBaGjViYyXE3b9fa7B1q0S2CQnaJyhIfS9cWF5rGomd4+KgY0dl9IWHS6JRUEDb3FyNJShIyY5ubteFuzNm6Ji9e2HuXPXh66vzsHGjMg6vXoX339d3K1boeH9/2LdP865WTSUAP/9cY8nN1Tlr1Eje1qWlkJdHx1EtNba0NMk9MjK0ZqmpGsvAgdJJz5pF85CQ69mUnp5U27xZc/T3l/blxAmIjaVucDAUFfFogwbq181NbZ44UfFr1NEYO3DgwIEDB79pVCRJ76LL5apFuXOFy+XqDJy76R69vCA6mg9WnZGVQosW7J3zIatWXMFi39Lvzz0nPbCnJ/s+uqyksV27VBwkPV3ZZ4WFCp5GjSLlwEUFxUuXgr8/mTOWwOrV3NvvMme3fsYLUzN5783zykxLTVWw1qwZzJrFRxu/Y/2zx3lypvHZxq/5Nv0SBxfuxVV8iScKn+KB+rv5c+94Zked596e5zm/7wu47z7Obj/MxO7HeWLsGUhOZk7neIiMVEC2ciXndx1WEN65M7z2Gq8sM3j9ddauvMKncachPp53n/0nd16NZ879J+HQIb5a/J4yz958U8des9P4/e/5Zu4rcPvtKrTRvbsCyDlzFMzdfjv4+LDv+U/5dOyrGsPRozp+4kS47Tbpijt3VnA+ZowC4/37YdQo7f/gg1rnfv2k2c3PV7JcSIgCV19fuHgRJk+Ghx/ms5UpDOl7kTP7j0OLFjwz/J9qb+NGZTNu3MiQ1sf0cOLuztXkf2LvbNLDSXAwXx85zzsxX0rgvXMn/Nd/QXw83+1J0QNIt24wfTof3LcKli2TGPzll3lj2WXw8WFir694euo5OHAAv6qXePPO1/hs10X6N0hh98qTPN19N4NbHZPAe/nyil+f5rhYOHDgwIEDB791VIRBng5sBpq6XK5PgNrA0JvusXZt8PXljTcgcsQ/YeVKCrbCOP/3CbnlHv70p7fo2kkOCf2L53H8ODz1VFVWrKjKnvBXeMj7El/FvIWfn+KxwkK48gUsSp5G8AV4KjycAQNg587BvFd7H29tj+Af/xApeu/8h/hmcBT1Z43mwdfvoE0b2YIlJytGW7hQDhNxcYrPly4VMdyokSzWbr1VBO2sWaOICVN83q8f7N50ltQl8MH4PxDpE0PvlSMpXAyZmcO4ckUk57Hot9nrMYwPP1SMfulSZbKzFZseOgSXLlWnuBjmzXuejRth/Pg7iYyEOSsziY6G7qchN3cur9Z7A6ZP59FZNchPgPVDNolV9vdn9GglBB574AHe2FybgoLaBAbCyL+2Y2R0a+YtgtYlX/DAX9ry3Xfw4cSJfPn4MloePapA+tZbeeDVO4kd/wLExfHHjNE8lZOj3yZO5O0T7RnWJYuRTzQgNRVef10x+mvPPktEHry6pSGFhdD95YN09D3Jt9/C40njWBIYzfbtcqio3KcPNG5MUJDWMmnKKk4uhdh73mLtVxGEhkLNyZM5PuBxvjwoEnrJkraUlEDPnn3ZvRseGj+erSvhjjvgeEFtXn0Vhg+X+9++fTByJHz3narpvVE0kodCvv1p16gTADtw4MCBAwe/adyQQTazROBOoCvwMHCLmSXfdI9m0Lmz3ngXFvJtXmWZBvTpQ61aIh9fflmGCCD1wdGj0LzBJQ4fBg4fpsnqp6nhdYXOnUWwTpsm44T8fKBOHfz8ZN+7+2oEBQX6LS0N2LeP+vkpEBlJSIic4lJTFV8ePSpSNSFBFnFeXorlT56EP//uCNWr6419mzYioAsLpd54/32pNzIztT+FhYSGXv8cGloeb/XsyR0tTlNQINVFdrZs4urUkeokOFhBeFGRiPJqOSfJy9P3oaFal/374dzQh1i1qQZFRWqH5GSxw76+ZGeXGzb06sVLLynwHjnCIDCQdu2gdfUs2LSJ8+c1bz75BG9voKCAHXuq8E2zO2nfHhkz+/qycydi2lNS4B//kE3e3//O119LsdC4sdaQv/yFjz7SmhcVSQVBfj49epSvu5sb8+fDq68i+ciePSxfLoXHwIF6eOGee8jLE+HM8uU03/YyfftqDgcO6MVBnz7QsCEQH8833+gaae57hrIyjeXTT/WAcOedUoy0a1fujb1hw0+7PktLb7w5cODAgQMHDv5jURGJBUAnoC0QBox0uVxjbrrHSpUgLk6BWb9+xMaWB5AeHiQkwPr1UlMUFADDh+PpqWB04pSqpKTAp5Ui2N3zGY58WZk1axSgPfusAqSNG4GTJ0lMLPc8RgF23bpQtSrQujWv7r8VCgt55hkxwGPHShL92PDThIaKER0yRCqGnBy45x54/sNQhgxREbiCAhWfKC1VYBwdrX4DAyE+HnBz44sv1Hdu7g8C9507Oetel4kTFXxXqwb166uPSpWuS3ub/nU6yclw/5ym15QKfP21WO2MDK1NfLzaPXkSUd6XL0NhIdWqqQ1cLnx9JWuu4euCggK2bEFWbd7eNG5cvj6BgRw6BPj4sGGD2Fd3dySjeP11MjLQJGfOJPPBuSpCEhxMQIDmt2+f1B2Wg5bDAAAgAElEQVSsXcuxY5IfL16sBw3i4zl7Fq3FhQvUqaM4m7AwmDwZb289CBw9qvWxat7s3Sv/aQYN4mT/KEJDFSBTPvT9+8u9n0NDGTYMnnoK8Pamfn09cAweLEXIuXMKji9e1Hq9Vefxn3aNOhILBw4cOHDg4DeNGwbILpdrLbAQiAA6lm/hN91jSQns20fNmkBCAlOmwH1D7HvmddkySWTPnAFycmjeXPllr004SPfu0KXOSe5cP5nQs7t5fNDXjB+vXLKsLIiKAmrV4p57lNPWrJnKV9esqQCaM2d4OPwwlJbyyitiJFeuVBD8wLS6JCSI/dy+XdKH8orPPJE9ja+/VjW/fv1kB9yxI/zud/DMM/Dq4ksMGgSz7zkCffrwt7+pv9atJa0NCAAGDqTW0b3s36+5XbyoIHv+fAWlUVFaGsaPZ+pUWP/sce66C56ccJYXX1Q/4eEac0mJculGjEA648JCaNiQxo3FyvLZZ4CCxNWrtezz5sF3ZTXgu+84dEhBOmlpDM59FS5cYMAA8PEpLzo3bx74+Cjfr2pVGD6chiueYuhQ4PXXKS6WlPne7ud4+21g6lRCQ3X84MFihQkP59U5X3PPPUBeHhcvqhIh69bBwoVkZ8OFC/Bg03306QOu9JM89pjYeRYvpumKJ/ny0Dn69NF3Z87AH1p+wJKhe2HjRt5/H/74R+CRRzh/XtLtVas0hqeeEuPeuDEMGAAjt4+r+PXpaJAdOHDgwIGD3zxccsD4Nzu4XMeAELvRjhVEeLVqltCrl6K9uDjRh0FBokTHjtU79Oef/57FZO5cBSTBwdonIUFRqpubNAbFxapx/PLLOmbnTq6mnaTSIxNFL8+ZAx06SMy6ciXcdZciqw8++D75670DdenXD6q8/Sa0bCma2MeHc61uw8tLFeIyM6H/1Q/4pn0k9Z99FKZM4fPi1rTP/0jv9jt2hJgYUZ5jx0pwGxqqZLpDhxR05ubCggVw+TKXe0dSUgLVva5qDT74QPqAkyel23B350zcJ9Re+RdFw926KZqOjFQUGx4uCnbUKFHFbm4SVCcminbu00cR8qJF0ifs2iX9xeef65jVq0V/9+8P//iHxpmerjWaM0fRrJ+f3EZGjdLfBw4ocS88XFF/ZKTo9FGj9B0o2m/ZEj75hEubPqTqnm0waZK0KeUV95g0CZKS2P3sXu4s/UhPN5Mn64kkIUHJhMnJcP/9WtvYWJ3nwEAyC2vQcOcqLj8wjirJn8Err0CPHqxlNKOPzWFt6xgeeECHt2sHrt9Phg8/xJWRcdjMbvhgFx4YaAkTJ97wOnZFR1eovf8EhIeHW0JCwv/fw3Dg4FcBl8v1q773nfvVgYMf46bv2Rv5wAFvA/VuxkPuf9o6tGpltmuXCkzs3WszZqj2wvvvq8hDUJCsbSMizGzNGmvdWt+PHVtelCItzay42PLyZLU7eLCsk9u3Ly8cMXas+frK7vill2QF3KCBLINTU83mzTOzQYPM21u2v4MHq1iHrV5tY8aoSMeoUWYWH/998QvLy7OUFB3bpo1sfz08NJ6ZM2XLHBBgtmKFmXl4WJcuZvXqyTrY5ZIFsBUUWFycipk0aSI7YA8P1fmoWlVtBgWZ2bJlNmGCCoGArJP79FFREZdLlsrR0fobTN7Dhw6Z9etnLpfasqNHv7dA7tHDzDZvtnr1TN7My5fb4MEar40ZYzt3mtmkSRYRoZoh4eGyS7Y2bczHx9R5cbEVFpYXJpk92yIi1E9JSfl3Y8bYyJEas8tVfu5SUmzRovJCKs2aWbdumovNnm0WE2MbNqgAzKJFZtOmmVlZmcXElK/BypVmmZk2ZozOKcgWessWs0cfNbOpU23ECLOFCzUlO3TIfHx0HQwfrrV85BHNp00bWWZTUR/kevXk9XyDraLt/Sdsjq+qAwfX8Wu/95371YGDH+Nm79kK+SADR10u1yHg8g8C64E/ORoHCX7LyujcGahVi6goEZc9/FNo2vRWBg2CP865xL7DVeHRBaxYMZrZs+EPfxAJeT6gOdVTv+DTzLaMHCny0dtbxOOcOYB7Czp3hiXPnoddu/is8z0UFIgQbZn1ERMm3AVRZURFiZT9r/+Cr76C80MexDdRRG5JCazP7UFGhkjdJW/WYskSyTZOnRLZ6umpv0+dktShrEyEMUgnW7eutMWlpZoyu3YRHHwvu3dD8+Zw6ZJkDoWFOqZVK2l4L455lM4e0tIePiy5wksviexNSYEu7p8RNL4j6enl1r5t2kjIW1pKQIBcN9iwgaFDn+a996RzJj+fRo3gUkklqg4ciG0tH6uPD25uwIED7N1zDoqK2LWrHjUWPgUDBjDHF4gH1q2jep8+REbWg0F/oOtVkezp6SJ7GTqPJy9oGNfslsnMZO/eUMktUj2JioJh/S/AxK+gcWP69JH8pXnzcjnNunUEB49UYuCbGZyp0oBhw8QCx8XpnGzfrsRG7ovk6HSRyn+YcoWPL3Tk/HkR8YcO6XrIy5Mu/IknoPX2JRW/Ps0pNe3AgQMHDhz81lGRJL35wCAgBnjhB9vNwd0dunWTdtXLi7Q0KQ9AmtFOnWDf4apKQIuKYscO/f7xxwpGqxefgaAgKleGe7qd49ZblbR2LUgmNZVz5+Cd7dVJaXwPa9bIsWHJEthWehcNS05Cu3bk50tN8O230q0+/LACvKNHtf/69VIupKQoGKxUSW/727VTgFy/vgK0bduU1PfZZ/oeDw8efFDqh9BQ6NFDNnH06cOJEwr2zp+XQiEnR8HhlStqr7AQquVnEhgo1Um1alIv3H23JAOensCqVeTmXq+Rwptvlp9JNwoKNCbS08nN1fF/nHIGWrTgd7+Dqu5XYNEigoLK9cCJifRo9x0EBLA3uQakpkpnHBEBkyYpUbJpU/Wxaxft2gEffsiOHdfrnmzbBixYwJQpOg/790Pv3kBhIR9/XK51LiggPR0yv/PWxMvKSEnRmj78sHTCXLhAYeH16dROP0itWlJ1uLkp4H3kkfI12LyZ5GQ9HJ0vrszWrVLZFBcrkdHPTw8sq1ZBvXqUR/EVhDkaZAcOHDhw4OC3jhtqkAFcLlcjoLmZ7XS5XF5AJTM7fzMdhoeEWELTpvowdCi0a8fFZm2pNuBOeP11rjRuTuX7h8gjLTtb9gWPPioPMR8f0bMpKdCxI+c696XGsj+LOgwIEGW4cKF0tyUlsrcYO1bHZGWJ6t20SVTze+8pi+5a5bpRo6QPnj8fZs2CnTs5v/Jtqj/7pKLd3FxFtMuXS+fr6yutb4sW8mrbuFGa3D59NO46dSAri69pRGAgVO7bk6s74qk06wlpipOSFIEHBMA770CHDpyP6E/18cOkt46IgNJSzvUcjLs7VFvxoujk7t0VZfv4KKIeMULRso+PbDaeeQamT4eGDfni4WW0bXUZQkJIiTvJrW/NUcT5zjt8mVuTljPu0QNLu3bS/778soq0uLnp3NxxB/zzn+pzyBAutWhL1bH3czV2PZU2v6t1/P3vdT569ZI+OSGBr1r0pcmo27X+778PH36odXriCdixQ1H0m29Cbi7f+N1KnTpQObytMu0CAzXG1FRVFvTy0niu+dpVr671DwzU00zXroqKZ87U8Z0763y1b6++srJg82ZcO3dWTIMcEGAJo0ff+J5YuPBXrUP8OeFoGh04uA5Hg+zAwf9buNl79oYSC5fLNRGYBPgBTYH6wHLgrp/aGSAPLi8vnmy8nr94vQ3BweTmgt/m3cycqVioxR2buHABnsocwGO1Yjn8JCxYcAdJSRDl9bbey3fvTuxK6DV0Njt3il0+sgveL4mh4ag75UixsDcHDsD+7YoD1/r8jW8iH6Z+Tg7DlkQQHAwBAaM58AGsX7aMv5y4jydjW/DigS5MW9CHBQvg4pW/sCjyK854N8HLC6ImwJgxHfmvx+HKldksWybmNKvsDpb2vAoeHrwSV4+kJNi1qxG+viJhYx9+mOxsCIh5nilToKCgL19/DePGwQefzWXq7fDJEpjbvDlrW8fQvokKXaxZI3b622+nUZYE8/dk0iT3IONimrNrF3zV/lYYNgzGj6d/42P4zIX1ISG80eIvlOyH+PgqTJswgVWrYNGD90PTpqzfXpPERPjLkSMc3/4Vzfs15XDk04Ss683kybAq5Hnw8WHYmWW8nd1cgWp6Olu3wuAFC1i8GBISBrMmeTAvvwzTPB/n1fxh+O6EuLi+hIXBE23bktmsB/cf6MH+kvd4osV73AtEnInVdfDBB7zt8xDBwVI0NHr9dU76dWThQnjFJx5atOB0cBfGjlWsPGIE9Op1G/n5MMz/FE9lPUxoV7i/2gdwyy20DnHx7LNL2LVL7iVBeVC58mD+WQRPfjCpnHquIByG2IEDBw4cOPhNoyISi98DtwOFAGZ2HKhz0z1WqwZXrsjPtl8/PtxXndxcqHFoB9u3iwzOzS3X1z76KLm5ehUfGwuffAKEhfGhz/1w5Qp+fiJTBw5UAHzNM7ewUARtixYiL3Ny5A/8VbcHWbgQKC6WZzIwbfJlgoOBrCzZseXmyhIuK4ucHBkynPdvQmmpgveAAP23cWNtHcOukp2tPg4nVQIfH/LyROg2bqx9a9RQXxcuKBhMTxchWrmyCO0zZ+C118p9gr/9Fg8PGW24ucmuLDBQhPeePdDE5yyrjt7GwYPl/sqNG0vAHByMv3+5pCEykqIiBdb79wPBwVSqBJdbtYWsLM6cEflKv354eAADBrBhA2zdqmCe6dOhRg1yclDnmzZxvlVH6aWTk8nI0HNO5YzjOo+VKrFtm2QicXHl8eXVqyQkyM4ONzeysqSpxtcXAgL4dsBDAHQIMxXzSE1lzx6RwwQGwrBhjBsnD2iXS0TwXc2+pk4dIDubbt3g/j7fyYaucmXOnVNg7OcnHbabm8w58vLgtTVVKn59OhILBw4cOHDg4DePiti8HTSz21wu1+dm1t7lcrkDiWbW5mY6DK9SxRL69ZMEYedORXTXIscpUyQkDQsTLbtsmV6h//GPihC9vCQc9vdXFH3vvQq4Cwqui1RHjFDVjVmzFCnPmwfHjmnfqVOhSRNl0I0ZI0lFfr76W7kS7rtPr+tvu03U7f1iXFmxQkFRYaEEwQcPKhutWzf461/FaD/2mGze3npLNapPn1a0lpurOS1bJoH1tSohly5JluDhofnk5MhL7u9/lwTDz08R/rvv6u/mzZW5N2+e2hswQDKMiAhJSqZMuV7KbuFCfZ41S+t69KjE0Hv3ag6DBmne+/dLUjF3ruQRnp6q2bx4MaSmciXrNJV9q6k03ZdfSuidliYZQ/fu6sfHR2MfMECBur+/ShEeOiQh9ebN2ufllyU9WbRIY/T3VyBcUKD9atXS00hgoKQZrVrpyaeoSL+vXy95hZubsvUmTLguXo+NlSxjyhStUcOGsss7dEiWctnZuBYsqJjEom5dSxgx4obXseull27Ynsvl6gcsASoBr5vZc//t9+nABKAUOAOMN7Ovb9j5/3F/zitbBw6u4+eUWPwS96xzvzpw8GP8kjZvC4A5QCrQG3gX+NPNWGaYGR1CQ81WrLDWrc1s717bvNls+3azoiJZj02adN2aLTdXNmpVq5rFxMi6y7ZutaIis5075Qa2eLH+XrfOZHvWtav5+Jht2GAWF2fWrZss4lwutbd1q5mtW2eNG8tmLCdHbduePbZggWzDUlLMCgvN+veXK9muXWaWkmInTqivuDhZiblcGvu6ddrXSkvN3N1t+XKNq0kTubA9+qiZJSZaUpJs2saMMVu+3KxLF7PWrWVRN2tWud1Z//4WH2/WqZOs3VauVP+enmovPV12dd26ySrOevUyy8w0Cw62Pn3KrdRWrrSZM83WrtX62fz5tmGDvregILPiYouNNfnTlfvaxcSY7d1rNmNGuc1bVJS1amVq8NAhy8szS0oys3nzbMKE8vkmJ1tCgtrp00e2eu7u5VZ6jz5q0dHldnJ16lj37rLes8WLNfjMTLtwwcySkiw7W+d/9Wqzxo3NbMECs127bNQoWf517qzfLC7ODhzQnNetMysu1vVgSUnWpo3Znj1mzz1ntmaN2YED6urRR3+izVvt2maTJ99wu1F76H94J4FgwAP4AvmJ/3CfHoBX+d+PAutv9r76JftzbKMcOLiOiv5bcqPtl7pnnfvVgYMf42bv2YowyG7AQ0AfwAVsQ0+6N1U4JNzb2xJuv13FOzIy9F5+wwaxjOHhYnO3bJF0YOfO6yxrtWra/+23pafo1EnfVa4shrVzZ7GN0dFKpEtIkGYgKkrsb3y8WNiaNcV8Ll8udjMtTYl8J06IzTx1Su/zi4vFcjdooHbd3PR7YKDY4enTxdi+/bYS/Nat07F5eWKWi4rEymZni5lNS9NvdetC27aa/+XLqtfs76/xNmsmn7mICLU9fboY34ICsdZ9+4rx9fPTuIYNkz1Hfr5Y+FmzNI68PP0+dqzmnZQkdnXRIvUVFqa5rVundQ4J0Zr7+Ghdi4vVZlmZrCry88XafvKJEt9CQmQvt2KFrDo2bIAHHlA/kyZJRjFqlFjgnj2VkNelixjs3r3ho4+UzHiNbfb21jjDw8Wau7uL0ffz0/633aZrYMECFYmJjdUcfH11Dvv0Edu8fbveArzxhsbcs6dY5BMncOXmVoxBrlPHEu6774bXsWv58n/bnsvl6gLMN7O+5Z9nA5jZn//F/u2Bl83s9ht2/n/cn8NIOXBwHT8Xg/xL3bPO/erAwY/xizHIP/fWoU0bs5EjLSfHzLZssdxcsaI2darZqVNiBWfPFru4a5dZSopduFDOahYVqQrFiBFmS5fqmClTROnm5+uYwEA7cEBFJGzzZjt0SKzvgQPlDHN2tllgoKWniz22lBTtsHatWWKiGNmVK9V+WZnY2V69zHr00Bg2b1axksJCFakoKDDLyNB+zz1n5uYmVjM/37KzzZKTTe3s32/5+SqukZYmVnPTJrP8fDM7f179FRWp+smGDWYJCbZ3r4n2njHD0tI0dPP2NktLsz17yhnd8HBR0suWWUmJifoePtxs7FgVAXnkEbM6ddTx6tVmdepYaqqIYwsJEQ3do4edOGGi1DMzNcewMNHjfn5mkZFa49xcs5AQW71a3dgf/6jz5eNjK1eaZWVpCffvN1UQSUnRVt7WtWOsVy8NoKxM887JMfPyUgWUo0dF+9apYzZ/vi1erPXJyDDtExdn1qCBWP2pU1VpZcMG/b51q66lqCiz4mI7etREZ8+YUXEG2d9frzFusAEZQMIPtkk/bAcYih4kr30ejf7n9q/YpJeBuTd7X/3c/aHE3AQgISgoyBw4cCBU9N+SG20/5z3r3K8OHPxr3Ow9+y+T9Fwu170ul+v3P/h80OVypZdvw35yJH4NFy/C00+zdSvg5kbtguOcOAH3pr/IW/sacuAAfNw7htRU4PXXeezVUI4cEQF72a2qWMa0NGVvTZ3K6TlLmH3gXvD0ZNMmtdmggUjJe1+/h6pVRZQmJoJlZkmTm59Pbi7U9b0sk+KSErjrLr5r3P777LmT/aPgm284V70B3Hor1K4tI4QGDSA4mL1J1al0NIVj2TUgP58PkhqIuS0r46sWffk0tSYXLogYPpnuosecLtQ89QWV3a5y5IiI4eJiqMl37PjUG86eZf3mqnD2LOf6DINLl9i7F7GpAwcyY4akzVfyz3OpQXPy8lQtmsJCMbrx8axbB299UF2s8HPPcewYqjLStStfplfWAZ6eBAXJlY78fOmy27bl00+RoXOtWhq0vz9rc3qL7a5cGXJy+CK7NgQH06mTSNzzj88lL09rnpQksnbrVhHhLFgAhw/zzpehkJrKR269WbwYsdCpqWw72lBZh2fOgKcnZzIuQlERf45rLXPq4mKYMoXdu+GN9d5kZqIMye7dwdNTRP6sF+G550hpNYxGQcbl7n1xd0cs9OrV8oSeM+d6GeyKomJJenlmFv6DbcV/a8X1P7T8P751cblco4Bw4PmfNtBfrj8zW3FtbrVr1/5fDMuBAwf/Aj/bPevcrw4c/AL4V5Ez8AnQ8Aefk4BaQBDw0c1E42ZGh5YtzTIzpZ/Nz7cFC8zmzxfDW7WqyMVWraTNtY0brXNnaX0feUTlm+38eSsuNouP13GDBkljPGiQ9K82dqy5u0tXPHeutLJBQWY1a6oi8/vvm1lsrNWrp+9HjRIJafn51quXyN+RI0Ve16ypsVxjoR99VCTrpEkai8slie68eSJiV640Mzc3mzBBRHfNmiJ8g4LMLDXVNmyQzjg8XN95eEhb7HKpjWbNzGzRIlu0SPN1uaRVvva3yyVyOTpaa+VymRasrMwsIuL7ctR24oRFRIhgHTjQzLZssaFDTYs2cKAtXlxeDnrqVEtNNbORI23wYBHMjzxiYuInTFDZ6hEjJHouK7NZs8xsxgxr3bq8fHZpqU2damb9+lm7dtrV3V3rZ6Wl1r17+TkJCLCgIJV9tilTzCZNsvff1/6nTqlEd26u1rdqVTObNs0uXFCVax8ffde+vQj2uXPNbNIk69FDZLRlZFhWltYyKkp9DB+u62HwYGm5s7Iq/gTZoVYts/Hjb7jdqD2gC7DtB59nA7P/h/16AceAOjd7T/3S/TmaRgcOrqOi/5bcaPul7lnnfnXg4Me42Xv23/kge5hZ5g8+7zOzs8BZl8tV7aaicYDvvoPPPycsrAG8+SatWz/GlSuSxN5yiyStvXqV27z9/e8MGXIfGRkiSU+f1vFVzmXQtWsoubmS916rGZGfD3h7ExwsWaqbm36rXl0GER2PrJJu9yMvfvc7SWYbNhTrSWIiQ4bcRVKSiMqCAjl+NW4sQrVOHX3289NY9u+XEUWtWvp8TZZLWRkNGojYPXlSxgzu7sCyZfSav4Q335QUulkzyYovXlQlvbAwkdcUFuJZR8Tuvn367fJlyXBdLslqu3WTVLlWLeB8sXS4J07g719eOW7zZgYNmoaXV/k+hYVidQMCwNeX7t3Lq/B9eJrUVGi5dy/jlqndU6egWtl5aNCAvn1R50eOQEAAPXvWgI8r06+f1ux4eiWVkvbtTK8LsrM7cqS8cuCHH5KTEyny9mgRXbvrnHCoBHJzqVdP527fPtUpqe1zmXr1qnD33cAtt1Ct8Fs6d67HlSsik9u10/nKywNq1aJVJcm5M90akZer8xMaKiu5jAzVWhkyRJZ69XMO/7Rr9OexcfsMaO5yuZoA3wAjgAd+uEO5pvBVoJ+Z5f4/1p8DBw7+d3DuWQcOfs34V5EzcOLf/HbyZqJxs3KXgHbtpHN97jlpUBMSzNq1k541P1/036OPSiMbHy+N6nPPyWZh6FAJjE+d0r7l9hbZ2SYbg8BAMaDbt5uFhl7X9q5cKeFserqZu7v2GTfOrKhIx44cKReKuXPN8vLMOnWSxnjLFu2XnW02d660unv32qFDJtozLU1jHDlS+lhf3+/1r6WlmuaaNeXt79xptn+/FRSIqU5MNLNNm6yszMySk6VHHjVKNPfSpWapqZaRoS6uuWCYr68o13L3B+vXTzTrrFnS+xYXq6/Fi8UOb99uNniwdMHJyWYrVtjKlRqyDR9u1r27WWCgxj5vnjqbMUPWEYmJorUbNND6FxaazZ9v8fFaIgsLE3s9cKDZ66+L3t68WZrs6GjNYc8ejXHuXFHU7dqpzalTbf9+DffUKdMYYmM19qFD9Xn2bLO0NMvIKNdbL1qkNidMkFZ65UqzHj3MxoyRBvnUKa3lsmWWk6P1t9hYs8jIijPIfn46BzfYKtIeMABIQ5nq/1X+XTQwsPzvncBp9HYmCdh8s/fVL9mfw0g5cHAdFf23pCLbL3HPOverAwc/xs3es/+OQT7ocrkmmtlrP/zS5XI9DBz6KUH4j+DhIaeBrCz44gu46y4xoEFBVOneRT7Fe/ZINzpihHSzPj6iB3//e9GD16pA1KolGjIignpxr4he9POj2p4PRduWlclX2NdX9PLf/iZXCS8vqr34rCqQTJlCPU9POHsW7rlHmtjcXDhyhEoP3C9P3i+/1Hh8fGiakAChoXQM+hxWHYPz50Uf5+TI67igAGJjaf3NN1C9Ove0agU5yaKLb70VkpKo0fUQfbt2hQvFsGgRrs8+gxdfpOaCBdJX//3vco+oW5dG+5erKsf999M241WJq/ftAx8f6uXna/6dO2te27ZJ41ulCixbRks3N42/pIQuw95SVZJ9+xj3+9NQUE3nICfnuuPE3Lkq8X3ihPqPjRXFXqWKytmdOAGxsfQIDYXtJdpn/PjrXseLFqm/rCxR7FlZ8lUG6YLT0kQHnzoFBw7QZVd7aNCAhsHBOqcxMaKKr1zRuLZtg65dabR/FY0CA+UBPWCAqOS//lV9HDkCBw/SKDcXsrJw3XorfPEFdVNSRCd7eJTTzj8BP1MhEDPbAmz5b9/N+8HfvX6Wjv5/6s+BAwf/Ozj3rAMHv178uwB5GhDncrkeABLLv+sAVAEG3XSPLhfEx/P2o/EMa5kLISG85fUQIxu8xkfBE0lPh25PQ0v/szBjBl/O+RtJSXD/7Vm87fcwwxbfDr16cWnURNzcoMqB3RASwl9W1qZzN7jzyBGe2t+fyEi47YMPIDCQ97ZWoUFruYrt3w8jS5fwdsu5NLtb+Weh1b5SkHv0KPj7c/BodW47coQjT62n4DREVKmi9/Xt2vF1r4do5PEtO47Uw7czdAw6zVX/uuSNn03dxA+lx3jgAT7ap+ptiYnQs2dfOhQVcT5qNtUTPubLwB5s3aq4e9mGDRwrqEfrJk14y3siI1scgtdfh5ISXv5bdW4fMww/PyXAZRXBtLAtMHQokz+8h0aN4MkDD6j4SKtWvOH7Bxo0gL6FhdIzDB8uy7hJk/giZCQtBo2k6oh7eavpXABGhqVrvDk5fFHckrabNvGNV3Pqt24Ne/bwZtvnGbW4Mjz9NDRvzlo6lEUAACAASURBVDvp7bnP05N719xHZCRMHLWddweuYvDWrRyeuhaPHCh6YAlhYVB5+XJ4801Oerel6Zrm2KjRfPIJRCQlQf/+XPzjIqoVnYH8fI5caUkoSD/SqZPs6ho0gJkzeeN0JN96RXLlDDwz4yxno56m1qyJHB//Z5qPPy4rwIULeWfCh9xXdx+Hq0bQIes9znS9l/374d5O3yqgHjCgYtenmVMpz4EDBw4cOPiNoyI+yD2BW8o//tPM4n9yJy7XJGRDQ1CNGh2+bthQ0Wrr1tCxo9jJZcvkcdurlwS27u5ifbOy4JlnxNRmZ6vCXadOYnxbthS7umOHAqoRI8RC9usnEfCBA/I83r5dnslduqg08ejRsoTYtk0B1jVRrbe32EYPD7ldjBkjdtTLS4ymm5v6jovTtmaNguo1a1T9rm9fePFFmD1bXr+33KIqdAcPqq/hwzXPFi0kAr5wQf01bSrnhmtV7zp3liDYz0/s9549EjgHBMiVoVcvjcPNTSz8J5/o7wYNtF6hoYqo775bW79+8MQT8MILmmNoqNj3lBSdoDvukGj3yhWJo93d5R/ds6fOTUiI+vPz01q3baugc8OG657U/v5q199fzPy0aRrP5MmqZPjFFxAZCcePq/53s2ZaZx8fPTTt3i2GOC9PY9y8WfP099fn0lKtc0YGTJwIa9de95h2c9McPvlEa1QinfP3tbd9fXEdOFAxH2Q/P0vo3fvG1/SGDT9bNa1fOxxfVQcOruPnrKT3S8C5Xx04+DFu9p79dwwyAOUB8U8Oiv9bGyuAFQDhISHGggWcCe9P7Zhp/M1tHKHDoUOfPrydfxcffwzLNo1n38HKRGyZw/nZMaxYoZjSJwTuPHVKQdCCBcxe0YSZMVAz9VPOhXRh9mxY5recJV3XExkJTfes4vN242gx4iGOHpVKIisLGnZ/gyVXoygIj+LpJ/exjwgi0m5ROeOYGJW3fuEFPp20ii5RhyUl2LAB5s1jx4UueKfBgcKHaP7EQ0RW+5gXEnvQK7Y/bfkC3niD2RbDhFjVLPFJgJ69H2RwtSdYGvhnHkudwhe59ahUSbFe1aqK7yIilKRXI7QhxMdz/FQVFixQjJjVbiJlZXD5a5j75Dm49VYe23gnWVnwrs+DCp5DQ/m405OUlkLv1KVcXL6Wd9+Fyxnw0JAhPF8zhieGZ0Lt2lx9fhErV8LEGuNUnjs/n6t9+lMp+XNGLmjPW6svw9Sp7Jj0Nr1jx8FDD/FNkwji42F08wz23f4k34XAPUlJKgIyfz5s2sTuPS62bZOKYtH4FHjkEZ79R3vmfnk7l/YdpqAA6h14V9mL06dzdej9ZGXp+WViw4dh7FhWbanLuN5ZUFDAxXl/YeNGtRc5BOqnp+uBZ8sW9RsfrzLU8+fzzG1beHrpSQXNoaHwzTfwj3+QMuBJbj30hh6WKgqHQXbgwIEDBw5+27gZ4fL/Zuvg5WW2fLmtW2fyIZs2zaygwDp1Uo0IW7tWyWBlZWYtWtiKFabKE6dOKSlr4ECzFi3kn9asmVl0tBK4oqNVKMPDQ8U7ioutUyfl1q1YodwumzVLCV5BQfrhWu3ojRstNbU88eyll8xWrLAxY0zJfcXFStSbPVtFP0aNUhvlhS4uXDCziAgVrrhWH3rZMo119mx1vGuXylmPGGG2d68KeBQUWEaGmtm718y2bFGCWUCAktzGjFESX9euZt26qTT0zp3qb/Nms82blTDo7a1kOz8/KysrL+ARHGy2d68KmyxfLl+58HB5uwUHyx8vLc0MZBPn4aG+o6JUBtrTU+tQUKCkQFAy3LRpSszbssUSE8vLP+fnm3l42Ny5qkWycqVpv0mTdN6Sksy8vbW2BQVKKGzRQv2tWKGCINHR38+poMB0fry9zebOVcGXEyeUcRcYqJMElp2tS8R69bL587V+lp2thMbu3c2io2Xpt3ixWXR0xZP0fH2VJHqDraLt/SdsTtKPAwfX8Wu/95371YGDH+Nm79kbSix+boS3bm0Jzz9P15hI9r93hvujatO5s97m9+kjVcLnnytn7NOp61maez8zZ6rC8vz58F3S15CYyNlug9m0SWqM+vWlJd6+HY6VtaR30JfMmaN8ua1bxdCeOwexKy5wJMOb0L8+yr3Zr5CRIXu1qlVh2W1/4/4tDzJ1qt7kvxJ9htbdajN+PDzR7SDccgtPP+/NlStSJqSlSRnh6an/vvmmyOdx411Mm2qkpysX7dw5KQ6Obz7GwcLW31d+PnVKZKePj9qKjYU//QkSO0zkjc6vkZ2tGh/Tp4tZXrwYrl6Fjz+WrnnqVKlG8jsPgD/+EWbMwG33xzRsCF8vfJvJHw+jbl318WrMWSbOqsVrIS/CgQO80Gk9hw7B+jM9+fyFeNrP6c+4gA8pK5OKYvx4uHVoS7wyv6To/nGSRvTty7jHvFl1bgjjfTexaxekbzvOq/HNeTh1Gs/4vkhOjvIg77sP1i47z7s7qzN8OFzxqEbP2y7yzTfw5dRXYNkyXhyfQkGB8gL37JHC46WXVMAkc+g0GD6cB5d3Yft2reHtt8OO179m/YFG3L/lQZ4M+Bvt2sHIAecgPZ3+c9oTFKQ8yytXxM537Xq9qri/f8VesYT7+lrCnXfe8Dp2bd78q37N+nPCeWXrwMF1OBILBw7+38IvJrH42XH+PGRmctddQE4O69/05VxRZWp4X2XMmEo8H3OFpcsry3jgyhW+/VbBYFmZ5MIsWQKzZ1NrwZNM7NOHiavDWPtBTRo0UEDMvlI8PSVbLSiQGmPrVklRqVKFS5eAo0cJ6So5b1qa5KrHuz5I1wLoUvwxqZ168MrG2owYIXXFkf63Ue2MJBqNG8tKec8eveG/+254aNBZkpNrMa7Vp+DvT3T0dWnwiRMK9mnQgNsufIvvkHocP662kpLk0xwYKMlxejrg2YZ27ZQTuHIlzB6dBfXrs26dS4mHV/bh0TmCSZPK57srRU4OJSU8+KDkCHz5JcuePs1Z97rU8r4Mi1awYMFsONUTduygWzfNg6hjtM94F5KT8Q+F5/vs4KtmvWmydDp4e/PQQ8CaTTpvpaWsWno3/L46NWqooB2+vly4AGzYwN+qvMjvfqcHnNWrgVf+xp6TUURHAzFu3H57eZ/PSjfSr5/WpvLmdwgOu48mWXt5cUoDOnRoAn/aCufOsXhxF+66S9Ly06eBxETq128EiYnsqKyHKuLj2Vd7MOfOyYzj5Zdh/bPH2eHdnHnzFCDXcvvup12jjsTCgQMHDhw4+E3jX5aa/sVQuTLs30+VKsDRo7y2ujK7dsHl0krs3AkTJ1dm505VKqZqVT7/XDFxZqby4Bg8WNHv6NE8s+8uXlxdk/37VU56717A05OdO5Xr1aCBAlkvLwWjXL6sAHLBAhISlBPm6alAs3n+Qe1z6hSenvCo91oOHBALGVr1JE0+eZNu3SR/XbtWjmmVKinPbs4LtahVCy616wJFRcydqxy9efPU79atwJEjfJ5Tj+RkOcmVlGjr1El5efv2iVWmrIxvv9XnjAx4/PkGvPa6i8JCsbOZjSI4cUIud7t2oYS+Tp2gsJAvvxTbSocOTHuuLrNmwYDBVcDLizlzEJXeqhV5eXKSw8uLK5GDoVMnzp6FVVm9mTkTzsxeBO7ueiDp1Amiojjd/X6eft4bRozg4EEF//z5zyqC0qABzZtfd4cbPRpo3JhRo0Ru4+7ORx+VP+B06wZZWeTlKfePIUP45z8Bl4svS5owbx7SVPfsya5dkhKvXq3LhrIyfH2B3r15/HHl+FFYSMR371OjhvL6Bg2Cb7yaExQEM2eK1f9JAa9ZRUtNO3DgwIEDBw7+Q/F/zyCfOwdt2tCyMXD6DBO7HAZ/fz7a14jly+G2Cx8B0LvoLnBzo2ZNGR8UFkLrD56HZYlyJ/jnP3naDPr1Y7bvk9+7mjEqn169ZHqwb59Yxk8/lWEGlSpxR87bXBk0jHvvVSCXnw/DIi8xetJttGwJdO9Odw9YunE0ixdLOkFgINx5J9u3w5OeSyDnIGxayo7EWly+DI/nPYX9/Y+4fvcAtGrFkrlnoKyMDh3qElntYwgL42BqF26b3oOS5z6mdm0l5Y0YIVbVx0dBfGQkkAqRX74AVaowa1YUf07sC6ngFrONyEiom3+Mc61bk/73Tznm2wXCt0ibUFLCtm2aN4v2M2hQf0pKoHfrLPhrDq/sbwvBo2D9evq9sEiV9LpmULnXnXDhArMXQPMDaxnXJgM6r4bYWLbUAQblQng4dSdN4pk77oDYbTzzTH+ys4H7oil4AcjJIb9MBh0BAfDioN2wZS8h8yO5eBFwL2TcOJjY5QhMTIOsLEJCZNBBixZEPvss7E6g5fAqzJ7dEZ5LgLQ0Bj9Xm/fv7sv06bI0JiaG0CV14cUXSZyySNfTc8/x/PhjLFsm6Ul2NtTPT4FmzZg/vypvvXkVNu/5adeoEwA7cODAgQMHv2n832uQ69a1hJ49FclVry4btIAAFZgYP57LD4yjSmhzBcGlpWKLx46V3djly/DOO3Je8PCACRNg6FBRr/36aZs6VdTh6tWyZhs/XpZsw4eLsi0tFa24cqXamDxZVGxEhOjkNm1EhV64oGN27ZKH7jWKd8UKCYJnzBD1PGmShNArVoi2jv//2jvzuKqq9f+/FyCgHhFRERURcUJCwzElMhxTr/NskVqa01VTb9nkVVOvaZaalpmVQ5aZkfPX1Mw5R0RUQkQkVCRCREJEJlm/P56NWr/ujayQcn1er/PynH32XvtZa+8lz/6cz/o8O0XE/MUXwnaXLCmi5fnzJZFdtEj28/ISejolRbQVw4fDnDmSKZcsKYLkI0fQAwehIk+JN3KDBhJ7crKIk5s2lRimT5eCK1lZ4hDh4SF2HTNnCrUeGgrr1klM2dmyT6dO0s/4eBmvceMgIYGbiT9g/9EyofBbtJA269WTa5CRIeOamio2e7t23ba+i4sTAfM//iF2d506wbPPShZ89apQ+RERoi05cEC2t2wJj8tDBT17Qteucr7+/eW71q3l6cHOTs5ZpYow5tevi01fcrJc/82b5f7w9pY4S5YUKzmQ6xoTg9q+vXAa5LJldVjz5r96Hxe2vb8DjKbRwOA2jAbZwOCvhbues3ezsu/3vBp7emo9bpyuUUNrPXeuTkkRg4L8fDGISEmR6sDNmmmtn3tOt2ol5gvTp4sphF64UBwLLl7UOiJCr1wppgc1akiFYt22ra5cWUwRDhzQevBgMWKoU0eMK5KTtdYrV+revcXUolYtrTt3lirGHh7SdPPmWuv8fN2jh9a+vlrrixf1/PlSjXnlSvmua1cxfAgKul0Ju6DUdFKSVKdu3lwMHcaN02LRMWuWnjZNqi27u0tcmzbJvxEREotOTdVxcVrr5ct1vXoSb2qqxNqkidZ640a9dKnWI0ZYY9SokZTqDg7WHh5SqVmPG6fHj5e+68RErS9e1Js2aSk9vWCBnjxZ4tMBARLz0KHipJGYKCW0R47Ueu5cPXSo1nriROnI3r16/Xppu0mT20YglStrrZs31/37i6tFs2Zav/661nrNGh0aqnX79lprV1fdqpUWR5CXXtL6qaf0rFkydnrrVik1HRKi8/LEYEPXqiXn/OgjPXas1vXqyfn0pk1640Y5X61a1nh07Kjz86Xfbdta1ysvT0+cKGYWK1dKnymsi0WZMlq3bv2rr8K293d4mVXxBga3UdznvpmvBgY/xd3O2aJnkO3tdZiLi7B9kZHC2jo7i4bCz0+Ewzst22VnZ2ENXVyE6czKkleDBsLABgQIK1vAOu7eLQxxlSrCoCYnC6tZwELGxgojnZws50lPF3uEAgsMd3dhKn18ZPVe8+a3Px85IsctXSrWCy1biq3EqVMS96FDwkjHx0s73t5yjqpV4ehRYU0fe0zEu76+YuyckSF9dXOTl5+fMK2pqcKgu7mJETLIefv3l3/t7IR5zckRf+bMTGGN/f1l35gYGYe2bUUAnZwsLPPKldJmXp7oOZYvl3Hq3Vv6l5QkjP7x4yIz8PCQMatTR7QLbdoIa1yrlrSzcaOsjFu79nbBjqws+T46Wsa94JWVJdvc3G4XNHF2vlXCm1Wr5HhHx9sCbZtNCpF4esr26tWFubazk2uXlCTfJybKqsHwcGGz9++XsSwoQZiRgcrJKRyDXKaMDmvy6w+aavfuYs0i/ZEwjJSBwW0YBtnA4K+Fvw6D7Oen9d69umFDrXVEhN67V6xqo6K09vcXFrl9e3mv9+7VAQFiy/vSSxazu3691klJOiNDLH4nTxZb4v79tS5XTmsdFKQdHYURnjlTGE0XF2FpU1Mtz+EdO7SXl1jaTpwox2dmWuxraqoeMULr8HD5vkYNrfW1a1onJ+s1a2T/Vau09vYWonPyZK3XrNHa01PrU6e01o6OeuJEsWt2d5f9evTQWoeH6y1bJOaWLaUvzs7Cerq4aL12rdXnpUv1yJHCxjo4aB0aKgy0o6Ps/8EHYlHs4qK1Ulp8jVNStG7fXru7a12ypNb60CHdtq0w6jNnaq0nThR2/dQprQcO1IsXa92ihda6QQOdmKi17thRt2ih9c6dMv46Jkbr3r21zaa17tBBvKLj4/WkSVrrwYO1r691nn37dP/+WusqVXSNGtKmg4PYMuu4OD11qrDy2s9POztbjHdIiNadO+s1a7Q+dEjGfckSYeG3b5c+6r59tV6+XL/xhvSzZEmLbU5J0QsWaK2Dg3XbtlriOXVKx8fLtVi4UPYLDZV7auBAiSkurvBPkI1tNrlAv/IqbHt/h5dhpAwMbqO4z30zXw0Mfoq7nbNFzyD7+OiwqlW5vHYfFV9/XthMHx9YsoTzm09RoQKUDl0hrGh4OHTtymeZXeiX/r7oibt3h7ZtUeOeRV9MkNLHUVFsajmHBg2g+uRBHByxghYLBgibu2gRlz0b4ugIZVe+LR5tPj7iGxYRAbVrs/loJTr3KCE2C0lJon0NDubHA99S1kXLSr0qVWDcOPa8fYpHSx3lhGNTYmOhl22bMKyHDknMQ4dCYiKX8iuTkyPkaqNG0GpEXdH57twJy5bxw3UbUVHQKvULbnTqRcmk7zibV4PaL/eBsWOhQQO+Sy1Lfr7Ie9t5nOJYTn0a960JYWFgs3E0ogRNFzwp49S2LddnvgVA6edHQbduwrA7O4OPD19+fIWOORsgPp6znZ4lLQ2ahr4gY7F+vWiaMzKk7+PHw4YNwsR27y5eaSkpohPeuJHPHJ/EwwMefbGFMONDhohOOTgYPDzQntVQbuWk8y1bCkM9eLDEefGiXJd//AP69eNqaU/KxR+HV16Bfv1EU33+vFiSPPccNG/OVzHV8fCA+nnHRaPt4CDXY8sWeOcduH6d/W8eJujYW9wc/Sz2Rw5K3yMjZbzXrkUdOVJ4Bjkg4FfvY7V/f7Fmkf5IGEbKwOA2DINsYPDXwl/HB9nRESZNom9f2LVkGNhsbAqrjHptDF9MlWIghzwHkZMDHb/qSet5XXB1hbTHnsFnJ7QbNw7q1EF3OouuWpudXk8RlgwJ2yUXmpeczIgRMG7cp1y/DuWjYcjD8NBDsHXraFJSoGpQEBMm2/DxCWLVRMkhS2zOZcV78NprMPppcPb7lhIjYetWxaefPknoakjzPYVHKNj1bcrLEySXfrPiYxzYdo1WSwawfDlUt43jwy2VWb1acriYGMnT/PefITUVFseMZPcjkoumpUG1ar1IGgN79tTg5Zfh81Gj+Pj8I4QE3qR7Szix9BifHm7M3G/q4+sLjSMj+XxzSbZvl7b35MRKIrlpE+3aiYXcWb9LbOMxYtaIyqLerFliIVfLBidOsDpdFA5NQz8QecLbb/NZajv6Rb/KtlI9eOzKFVixgn+vqsd0Z2dZsDdwIEdjytI0I4Md4fI8MHHiQXYMhhX20OjT52kaK+qHf/4T/JOS6DfQieBoGLlzJ6Mqr6NFi248ubY+PP00m2qN591hsGkT/HttQ6avXAkuLsx7uwTjL/WDTp043aAfT/Sw3C6A2bMb8sFceC+mFWXCdtG5cy8+/b9ODB9Xkq1PQPfuz7K6Crz4Ygu+mg7R0U1ZsaIpjwwcKBejMNDauFgYGBgYGBjc5yj6BFkpSE8Xb+BDieDoSKlSlWnTMpfGjUuQlwdtqpwW3WlyN94PEmI2NdXyzV0dJRnTxx+jnn4ab+/6tPE8A9HRrEjrBksPMONjkaJGRgr7euKEkKFODjepGrMXSpemdm0hR0c7vS963KgoHuuTDt2nsal9exgxglzPGoSFQQvfqzyWuF7Y1dRUSE9n3+4gLiXZU9XxMtmOFWnSBKonHoSsLDp1giEDc7mULP2pnnkaErJwrtWQecNOwwQb/Pgjx7L9iYqCJ/tmw+XLfD7sNCz+gJCpHny1sx5Dh8J138ZMyj/Ks5ebio3d11/Tx7sSzl2bSjGVQw1Ej3zpEgeevcaJuDJwqBOPPZyBj4+N2l7ZMHQ5w3cPhm5vwvnz/HvQHmG993uIA0RQEB4ewNoogl8EtvrA+PFMDO8M39YUttfVlabel+G1r5i/ciQODuD03Bie7N0ehv4f/SaIuciaNVCtGvDcc/z73ws5exZwcWHRvGwOhjtBxYqwYAFdFtamy0gNb0QxfWBPmPY212a8Ra1aAM0hLo7ISBgzRq77Q1UT4PnnaTp1Kjxn48gRqHdkBcyK472Qtixr/ghPJc/m5ZMv4OYG45vsI7vZI+KVvD/2t92jJkE2MDAwMDC4r1H0CfL58zB2LE4FlmFpabQJCICPoPKSJfKz+aFDsqhr8WJqRgWBYx54O0PoFlnE5uUlCXRqKjVf6ATly0PbtgyyfQEODvj5QaWlr1EpPZ02gYF8b+vCU8HfwdNT4ZlnYN06+r4P5d9+VRbiVaggdm1Tp8riOh8fCA2lxIABtHC+DOlusgAuJIQfQvdRKfVrsvPsqepxE0ZPxqlDB+ZUiYNVcZCfT+ULhyHLnarXr3Ojpj/ZdvVwGvEUZVJShGJdvRocHGjslk3jmLVAT1nwFxAglQY3b6Zd/kYaDX2B0jHHoVYt3nr6hLDvTz8PQUF0aR/PpfZ9YGuaLIpzd4c2bXjQzk7kEeHh1H75ZfjqlCxoc3CAl14SOzpPT/mcny8L7aKieLTBuxAVhdPR/SIp8fKizIaPpSJJ1aqyYC40FAYNonTkYW42eUhY2YwM6NCBF2p9AQuieKZ9e/joCGzciH/nzvhXcYX69WHSJFocOiTykGHD4LPPhN4eOFAkFxs3UiYpiS6ZmbK4LiyMPumD5Ho9EADxaSI9qVoVgoOpl3lMnoCuXIGwMHoPfQRmplGhAtj36QnDh+OU9oOMdUpK4e9PrS0zaQMDAwMDA4P7FUWvQfbz02H/+hdTLgzhVa8P2eQ+hJwc6OV1lEefa4qX1+0qeO+fa82/H97JJ5/A7NliS6znvyX0cLNmvPa6/S17XA8PMXj4KusRnvTexwsvQM2akvPabEL8zpt5gyuZJSk/og8DHD7nzBmYN0+kseNT/82/mc70cVfYdKA8XbaMpFviuzz9NHRLWwG+vlys8hAbNkDZspJnenuL3e+5c/Dtt0hRCgcHVizXREWJaYO7u1SD29J0Cse7v8qlS7J/fLyct1EjOHxYqgXOnAnr8rtx5vUNRERIn//xD+nf+vVSpe77xRvYQDcmTZKcOqriozB3Lowdy4MZ31CnDnzeeQXP7B8EiPHDpI7HmL2jMS802wWHDrGr+Uts2QJz9rcQDfc//8mbDVbg4iIWwiH9cqFOHVp5f8eu4FeFwp81i3cXK0YmTWFQ/KvEx8Oe1q9ypv8U6r7Uk2Vd1uLgIKH4+sKnHVawx3sQAwZA4o1yPN7xKk5OsMw2BvbvZ8PU48THCymflwcNYz9nWUYfli6FfX7DITCQj+0H8dlnYkTRujV8OmofZ9wfoe7kfnw97DNKlYIWEe/CV1/Rk7UMGCDPMSkp0mar5jd4aVpJXnv6LKpOncJpkEuX1mG+vr96H6vw8GKtQ/wjYTSNBga3YTTIBgZ/Lfx1NMg3b0JMDK/W+RDy8ujifUqy4YDe7I4/T1ISVB7SCagKN28y3fVNRuz7F25uoNN+hIXXJBsOD6dnz6bU3f0epKZyrfNLDPA7ATM8GDYM/OM3Q7dnmb1hA+fL+FPd+Qe0cyXKp/8A4eEsjxLy09VVklUiLjB98Q2Y9gZdJkwA557M8hR98iXHQcTFwSMqgRIlPHkybxneM5/C2xuqlcsg18lGid1fwZfZgBDQzZqJvDcnx5IcPLOdhiNG0LCWjR+aliEvTxLovDypVVLb/UfWhdrg3XbUvbyfuhcO0nzd86SmCom7bdE5zubXhMChdNvqSdeTjVE52TCgvCyEmzSJExNvcjbOHiZt4f3OdnysniSkxTn4zztQd6kw1DExZGVJwsmlGlJQw9ubf9XdDHPm8OmIPTBlCjRpwq6gtyAuVQJNS6N163JQaihvOFvXsutW6vbvDw0b8lTgGXBxISRE9NfMmsvF5weRuD0S1k1gVYejMG0aeDcBR0e6ue5hpZtUJ/TygoY7d/LU1JY8Vfss7K4GQAgfEzK/BZddahIdDdcCHqGuww3w9cXVFRpn7JH7qX596gN9uuey71AJlBJ9OCkpvOa9BV7YVvj702iQDQwMDAwM7nvY3ZOz5uTwhesQoYTLlWPFxnJcizzPO+/IwrPz727h+Kj3oUMH1vn8i5Mnpdjb+bSyUL482rsGZGXJuquuXWHCBPLy4LUtD8L+/UyaBCuudOb8znNM+dyfAQPgqRcr8ckn8HVkJUhLo2NHMWDIzITGjRGqNy1NfrZ3dUW3bUdSkmhqq1bI5pE6P8A33xAQAFe7P0V0tGXXfPUqAMfc2onUw8UFZ2eIioKvvxYb4nPnIHfvwVu+vGvWCBscHi7nj4oCoqPZtddeVtk9/DBnuz9P2Y+LjQAAIABJREFU796SQA8ZAi1CahIailQLdHVl1iyY8JKTeDi3bg0xMRw/aU9kJJKhJydz4wYcT68J585JaelSpWDPHi5ckKKAXLwoFfbi43lhX2eYNYs6dZBKddHRfO3/rMgt2reHuDgSEoBVq5g4URht1q9nV2JdWL2aR4bW5WB8ZXr1EladrCyuXIETN/1h/XpOODZl38RNIqF5+GHw9b1VHryP/2kZhO3bOVEmSOj4/fvJ7hNC62dqsnq1KDPKfDCPS6klITmZzZuRJ5BLl2DUKNLTZQznzYOg3TNo1gxaD6pG9uDhHHtl7W+7P/Pzf/1lYGBgYGBg8PfF3XjD/Z5XgwaNtb54UTyL09P1gQPi85uaKlXQJk2SKm9eXlrrI0e0r6/4/YaEiCWvDgvT+sgRvWOHvJ08Wd/y4K1QQWvdsaN2d5c258+XYnEODuIjvG+f1nv3aq0XLNCtWonvceXK4ru8apWc88IFrW02qXDXsKHl93vtmp4+Xetp08QH+Y035DhHR/Hp3bpV/I4jIrTWbm56yRKJx8NDqsDVqiUV8ebOFW9iFxc53sFB+qSUWA1Xrqy1TkvTS5bI+R0ctN69W85ZsqR8Tk0Vz+eSJaUd3by5nHjuXO3gYG1LTtZjx4r/8qFDWuu9e/XAgVonJGit16zRXbtKvLpZMxnEqVN127byduRILb7KY8dqT08tJfumTdM6LEyPHau1njhROzpqDVrr2FiJuXt37eIizdlsWj/3nNZpaVLJr0YNrXVgoK5c2fKD/ugjrVu10mPHis90Sopcq/x86bO7u5YgFi3SsbHSHwcH6zrk5OjJk7XWHTpopay216/XoaHiId2smdwDq1bJsDRrZlUTPHKk8D7Izs7S2K+8Ctve3+FlfFUNDG6juM99M18NDH6Ku52zRS6xKJF0EebMISDgLdi8mRZ+fsQEPMi0adCqlWhyvbyEGGTKFKKjt9CxozgZZGYCn3wCtWvTxjMJAtuSmlqS6GhRXVStCly4QmCg/MR+4IAQrlFRQhAHuUaKcPj17VSsOIbGjUX+0KWL2MAdPQrV9n/KjBkDcHUVDXBYGLBuHZPG9eD0RRvR0dDD7wwbN9ZlzBjpk58fjB4ND0Z9CqVKUbKktFetmuiHnZ3Fcnl813O0bVuTc+fEzCE5WZjY/v1FkhEQAOzdS3BwF9ysdYEuLrI2LSJC+lSqFMyYIeTphQvIwr20NFi1it69x1OuHDBhAqWrrSQ6WvZ5yC2Hli2hqsMPsH8/ISF9mDkTobAjIuDAAQIDoXHOQRr/xxcGPw2NGhESAixfL6LiYcNo0ADYcYH+/eWwi441hX0PD8fdXaQiixcLoV120hhef30hU6YAXybSvLms1WPaNKhdm5wcsYUuf+RLAgI6orZvo379x+QcN27A669Ts29fPDzKk5oKQUFAaCje3gNgbxaDBsnaSsqXJ/M7GD5cCPHSpWU8AwNh/nwhv/H0/G03qVmkZ2BgYGBgcF+jyCUW+VWrwTPPUKYM0KED7x54kAsXJG/atUskCampkjxSvz79+8OxY5IMZWUh+tjHHwdvb97/uCS7dsEjj0jF4/37gYwMkpKkZsXy5aIzfvhhSwfs7c2Gr23g58e1a5JwP/KI1JrIzEQsyapVIz8fateWRXOZmUDXrkTG25gzR/TKb22ti53d7crNW7eKucZB7wGQmMjMmWLWcfSoVL8+dEgS/zlra3LggCRxLi5Qo4YYOYSHS9JbvjwQEcHhw/D887LYLCxMFgSePSv9Dw8X2YerqxhRkJMD9vYQHExmpuiVmTiR69ehVy+xMMbOjpMnkYIfyckkJkLlykCXLhwLGALu7iQnw2cXWtBvRDmufbwB1qzhwAHEu83LCypUkDYCA8nNlby8WvgGxo0Dli/H31/GASQ+hg5l2zZrW6lSODtb4ztrFly+jI+PjM0pz45s3gyRVR9j8WIxpaBrV65HfkefEeXJzJT8tnJloHt3bDbA1RWbTQxIqFiRJ/vn8uabUvV7wgSpSfLBB5KAr1qFPB0VFlobiYWBgYGBgcF9jiJnkO3sgBUr6NNnDqSnM3hwOZKTocyKt9mxYzRVk49Dfj4vjKoDYR341P8yZ1IrUrd0Ape7e4pFWGQkPPooz2RlcWXOUt5+WxLJCxcAzxR2hknCWu9mJKNG+RMTI4no5l02uuV8Dlu28On+2aSlSfL66dzv+XRLZZo1A44do0mTICodWMekST0kKRw/Hv8nnqBduzYM8NgFfn707VsJm01cytYNXEfPpT0on3oWHByI+uI0+6/UI6jaefDw4FqOE2XWr+T5zk04X6oeHh5StK9SJUnQY2LgmYx5zHYbDz9cJaTc24T4pJEdOwmn5e9BxVwazRtNTg60GPcQYSGHGTvWGsuukSJ2dnGhd29xRWPPHt56PJMTzg8xPW0MLLzEWwtqw9lOsHgx/v5WMj5mF433lIcqVegwA7q57KLfhFIwLRR692b5YKDTalmk5+/PWxP6QqfF9JkxRhY2bt5MYstuMLYnQZOu4uoq8udGjYDZnxHyjAvdTtSAWil8+KEw+oTFQmrqLYa8/pSefP7pp9C9O1PXf8kTTwD9JlH68cf5/O23+bzvELy94aOPAB8f+sycCVu3ktHfeogKCCA77QbfxWm+3qlwdYUVge+xz284SUmwcvDX4Dvit92kJgE2MDAwMDC4r1H0i/Ti4iA5WRKs9HS2b7eY4eHDef55eHVjQ9q92BjlUgZSUngspCKtWsF7/+fJyy8jv7WHhMjv9QsWsHkznDkjLGOdOtJmQIBIEPZc8adNG2GSfXxEwnG+WR+u7j3F2LEioShdGvafq0xgoBg6ULMmsbFAXh47dlgsbf36kJwsRSeaNOHL8Eps3SoF7AID4aBHD958E6FVnZ3ZfK4eV65AlRbVeWuxkyyu69wZIiPZu1eseUuUEBnJhQuwYAEscx1Pbi6Qm8vRh0az79FJuLjA6ZbDITiYN96AZ5+F44sPk5cnx0yeDFSpwvXnpsDcuaxbJ7kyjz5K65ceYssWmOK2EAYN4rKTpzwNxMQQHGyxuQXexg4ORETA+7GtmHfgIWHot26la1erTyNHQteuHE+tDj4+fPWVrLUjL0/UCBUqsH27sNq9eondHp06QUqKFHdJSSEwEL74AtFmZGbi6Yn8ivDgg2TjBPPnk5cHAwYgvs4ffcSHDGHiRJGvuLkheolatcBmIzDQekCYMQOnywmMHKWYP19Y9+PNhuPiIqF3md9GnkYKC8MgGxgYGBgY3Pco+gTZwwNatyY8HLLr1KdmTfkZfs78Ely5clv+2aEDUFL0xQ89JDlOo0bIz/2NGkGZMpy+aMNmk8S3XDmoVw9wc8PVVZLQ2bPl2M2bRbsbHS0JVLlp4zl6VM7Tpo1IHap7ZPPkk8CBA7RvD7Pj+gBW0jxiBPj4kJwMl7PK4Okp3rwuLpKYu7nBvn1IRpaTw9mzwgqPGiVV/EqUgNNJ5cju2gd/f5FtaC2xbNwoGuamTS3ddXQ0sbGiN/bwEGnJ9+X9OXJE5NMN/XOpUEFqfyQlAS4ulP74PWjQgGrV4McfgQ0beP55kRykp0ufduxAgnV2JiJCxoLMTGHjAwJYvx6eaXmGli2RAevd+1Zyy7lz4OEhDzVZWVy6JA81B4ct4/p1ICmJ7dvhm28gN1fi5J13eGltU7mONhs1aliGHw4O0KwZR4+K5zLe3tJueDhJSZKz4+UFGRk4OcmDTsG1w96eEy6PgIcHe/fKMwfR0byw0JOSJeHpp2HFCummo6Not9u3h9OZ1X/bPWoSZAMDAwMDg/saRV8oxMlJh+XlSRZlZyfULki2FxgoC8JcXEQjsX69ZDiuriJM9vS8bRNWsaJ83rhRjn36aRGcXrgAL78s73NyJPutVEky2tRUyaZXrBBaee5cScaCg0W8GhQkbbVtK7/pjx0rouPoaNmeliZ0ZnKyCGC3bhVtxxtvWB6/3rJt5Uqp/lGrlsR/4YJkcw4O0ue+fYXuzsmRPnboAGvXCuu6d6/Em5srq81SU8UTztkZGjSAF1+U4wMC5HwpKUJlu7lBv34ifM7Lk7YffFCy7xdekIopBw+Ck5Nkj0FBsH27ZO/Hjkl/7OzkPAWL97p3l7jGjZMk+YEH5Fq5uMg5li6V9qOiJBMvVUoo4K++kl8KWrYUD70VK+ShxtlZxislRcaic2epHKi1JOVdush5qlQRQXnTptKHhx+W6oCWHzMhISIyLligmJIi2uYlS2DoULnWmZkyBlWqQGwsav/+whUKcXLSYR4ev3ofqwsXinWxgD8SpvCAgcFtmEIhBgZ/Ldz1nL0b64vf82rs46P1uHG6ZUutdUyMnjVL6zVrtNbp6Vqp23Zd7u5a6yVL9EsviZ3a4sWy/dQpLR5iqal67lythw2T4wus0HSTJtrbW+uTJ7XWSUm6QQOxHWvWTGu9datevlxrffKkrlNH67ZttV6wQGzT9MqVumFDrXVOjh49WmudmakdHcWaLT5eLNBef13rsWO1Hj1aLOk8PWXb/PliFbd+vdbaZtO9e4u9XI0aWgcHi82b7ttXJyVpvWiRbG/fXuvOncVuzdlZ/q1TR2vdv79esEDrJk3Esmz5cmnf1VVs4xYtEsu7oCCxkNN16oh/XZ062tvbGrf167Wvr9YrV1q2bzExeuFCrXVoqNbt2+ulS+V4PW2a3r5dax0YqEeOlL5Nnap1XJzWulkzaat7d61Hj9b5+TJWetgw/dxzlk1cXp6eNUvGZ9w4rdeuFZu36dO11suX6y1brGvi6Kg7drTGOTRU67Zt9ZIlcl3CwuS4Cxfkevj6aq2nTtVr18r4urhIvzt2lGsaF6e1DgjQM2fevm+SkiSejh213r5dLN46dtQ6PFyuw6ZNhbd5aVyihNZVqvzqq7Dt/R1exjbKwOA2ivvcN/PVwOCnuNs5W/QSC2dncHMTd4iUFLy9hSA+FV8GrUWXWqqUSB+IiyMtTXTCVavKoZUrAzk5nE8vR2KiHLtzp1VsA245JiQlwbaISvzjH0LKlioF+PmJbGDJEnJyhKz+8ktRGXD1Kvb2cD6xBBkZoJ1L4uws1mFZWUKIurjIT/42mxCXNptsz8yUfdLSgPx8cnIsqQOijy5VChg6lLNnpX9aC6n87bfSps0mZHBaGuDlRWqqLGAr6MeWLaCUnKNJE/nulsSiUSPRUuTni5YbIC+PHj1E3uHjA2Rmima4dWvw8+PAAcst4vRpGU8nJ+rVk6bs7aGG3Xnw8qJ0aYRN9vJC5d8kJwdo0oT0dHEHuZxqz6FDUP1aJElJt+UxpUvLdUhLk7LcODpStqylTDh5EgIC8PSUeiQ3bki/qlW5ib+/XE/c3OgR8B0BAbKYMDtbiPyMDMuppEkTjhyxCrVkZpKXd/vcGzcKIX/5snzv73+b+C4UtNEgGxgYGBgY3O8o+gT58mXIz7+VuPVz+AI/P7FFGzAADmzPYPFiUVCwZQsHD8KcOdC58fds2QLl174PL7xA9RnPMMf3Q2bP0kRGSiK4bx8QG0v//vLr++bN0mZcnCRKRETQdO+bcOECo0aJA8XZs/JL//DIMURHSyJ2+DD8859Swe7zz6Fu6kGOH5dkt39/eK3rQZo3l1/zd+6El2wL6dkTBjl+Ct7eDB0qCoh+/WR9m68vrEhsR9D1bXzzjSgCfvxR1A2zZkmCOHOmtRBt1CimvJzLsoC36NQJXmi2izVrRAYdEiKKkKpVRdUxfDiSwFapAsHB+Ppa+t+TJylbVvY9vuMKusGDfPYZIjlQips3Lc/ogwfx3/QaODvTpAkM8t7DpC4nJJFu2ZKnn0YG9sUXYcwY0ScfOICrqyT9hw7J6RkzhsREUYAMHQrj+yTwY/s+BAbC++8D+flkZMCkSchFOXCAkyclSQ/6bqUktzt2MG/UWYYNQ+Qqw4axacIu2rWThLd0aWhx7G3xNQ4NZc8ey0Lu1VeZNEkqLe7aJQn04MGyrvKLL0R5U+OTGb/tHjUJsoGBgYGBwX2Nok+Q3dxg8mROnQISE4ms24u9e4W4y82Ftz60sXSpSFMpXx4PD3jzTfhsb2Wefx7R0VavDs8+S+7AISx8WxEWJsnSkiVAv34cOiQyVH9/kbw6OAhbS8uWot9t3RoHB1i2TJLU8+fhvTczqFZNkleAefNkUVlODpCURNOmctqcHDhICxISpG0XFzjYZAw7d4LuPwBiYoiKkvgPHZLkPD0dBtU5yGmvxwDJZ52chK2NiJDztWh2UxaolSrFsZMlwNWVixfhoHMrNm0Sz+CCxYb79onMevduRMtsnSQ8XJ4/aNeO2FgZg2uO5VFhR1m9mlta6uvXRepLQoIMTm4uc+bARZ9H2Zf+IPTsCW+8IY4Y9eqJztjVVYqmuLqydask9V0Cr+DrK+3k5opUOTwc3v/Sk7KON7h2zXLacHYmOVkk39SqBYi0umpV+L7tk3h4AK1bs/lMbXHACAoSnXdqKkeOSD8vXQK6dCEmBmjSBCcny/2kfHmWjTtBWpo8lPTqBdW9NDVqyAPEoUPcpvMLC5MgGxgYGBgY3Ncoch9k7O1h507WrWsDFTpRLktcxT76SCyOExKEKU1JAWZWo39rOczNzZJdOPUU3cOuXZTIzmbMqABGjbLH/shBPk9oAXMPMmyiuD9ERNyuPOfgAN+llsXVtSzltm4lcOqzeHvDopcusiu2GleybUR9q2HtWnrtacm+IxXx9ZV1c3TqRI+406yjHg0aSJujR0tczZtLHYomTUAlfQ92dri4CHNZrZrIJkqWhOPOLVi6SNjkoUPlmFWrRCYxwyI433gD+PhjGvfpw7U6g3jGRfLfWrVEQuLnB2Vee5nXX5/JmjUiwWBmrDRavz7L+1sJ/fXrPPusFA3JyQEOH6ZTj6bwzjawt2fSS5ac44PHRevRvz//bgTVDnxGqbb9oNYEyMtj1URgfhnJRHv0IG0HkJbGAw9IIZNcl/Jsm3UcZgYwoKXII1xdxVGEjAy2bSvJq68Cb/izcwu8/TaQW58CLUi7dlDZ+SpzvixHO684oC59+wJ5LaFzZ4569aJlS2GmU1NlgHK6LwRnZxYtgh7ex/nRZzrR0SJT2btXFBzHIxTBwXJMhw5A2lBrcAuBAomFgYGBgYGBwX2LomeQlQI/P0nccnLw9JSk+MlaB0lNlcR2927L1us//2FQwAkSE0X64OiIOCRcuCCsqZ8fZ+PsxQ/X15czZ4C0NPz8JKka4/gederAkMcSKF9ekqicHCA/n9RUSx4QHY2PD5Tf/QW5eUpEsVFRPLLkSZo1EzKVkydh+3Y8PKDmjUhcXEQb6+MDdWvdJDZWEt6rzpUBMdfo2lVY2vLlxfKtYcQyFr6QQGCg9DEnR8wZXF0tP18secTixVBS9M8JCfKgUDHzPGXLSmiUKEFi4s+IzIQE+OYbYVcBjhyhenUhy5OTgcuXRdv7+eewezf1OSXF5SIjxamiQgViYuC7Zv2IjISLeZVh/342buSWNdz3nk3p3VvGd9kyMZcoMWGMdC4tjYEDoWFDKQZy9SoQG0vnzpZOOC4OBwdJ8KldG8LCePllq79xccyeDaxeTW6upSW+fh2Sk7GzE6lNhQpWyeiYGNqEzQagR9BlOHKEsqve5aHV4/H1lWMbJ26ioe8NmjeHbnlfiI91ZORvu0cNg2xgYGBgYHBfo+gT5Bs3IC2Njz6CUymVWbRIJBS6eQtWrRL28cIFK7HbvZsZmx7k7FnJUVNT4fKkt7g84TXJLB0diYoS+USurRzHjgFZWbz4oiRmJ5oPZ/58eOkdT2bPhoopp6lU4Sb4+rJ0qWhg9zm3Y/Zs2FOhF0uXwvUeIRx2fpRHL6zklVfEeezZj5sy49qzjBgBC3f5k5gIp08L4z3jNXtcXMTxLDUVsLNj+3ZhS+PjJdmPiQFq1+b8TU9CQ+W7iAhxP5s6VeQcr86wF6/inj05kVCe0FBJNus6fse+C9X54ANZIEjv3nh5CQN84QKSHFeuDFFRnDljyRgSEzlyRL4PDQXatpVEMSQEqlThFPWlql1+vmT5oaHEx0MNr5vY2UG1hIMwZYrklQsWQEoKlXPOSyJfrhyrVgnjf2rYQk749oOuXQkKEo325s0WgxwTQ9++ltdxSgqvvmo9kEREQGAgO3bIgjrc3Fi8GKhZk5gYYYG5dg2ee4433xQW/OJFcaRjwQI+rPAChIUxYGxFtnkP58fHRzIyax5jx4rz2zMbu9CpV0mGD4dOH/Zi92446tWr8Pen1txa9fe/XgYGBgYGBgZ/WxR9guziAmlpfP21JI7VqonqQn0biaur5Ea1aslubNjAAw8IE7t/v5DGzs5WMYmAAIiJIT8ffvhBEmibDbDZyM8XGURioiRthw/Lr/qnqcepKHtITMTDQ3L12FjR7e7bJ7li6e8iSU4WSceNG6J9LbBhrllT2omIgO++k/dHj8q+OTniskB+PjNnyvltNmnbwQGoVKmAbMXLSxLspCTpp729LP6Ljgaio3F0FEYaAG9vMjIkhjNngPXr2bhRHCry8pDGqlcHN7dbsZKRga+v5L/+/sCBA+Ji0bIluLuTmChFSEhPlwQ7KkqcNmJjxRnDzQ3sLWbe2VkuSGqqfHZyYutWeShJTbUWRp4+zZUrco1sNotBPnCApCTLxcJmo1IlS1mhlAi/sfTBNpswy1eu4O5uFYNJTwc7O/z8pI8XL4odM3Z2Ig2pV48Cm8+y0YdxdISbN6FZM4mrSxfRs1etKqF7ef3Ge9QwyAYGBgYGBvc1ij5BzsmBvDyOH4deXkepXl00ucdz/W/Vm4iIsEohV6vGwoXiYjZ1qhxeJnQZJee/xuXAbnDzJmlpUidj8GDrl/TatXF0lMT1wAF44gk5/tw5yfvqu14EZKGcgwO88ooks926CZN79IY/c+eKg4WHh8Tzau9TtGolyaeDg9QV2bVL5A9HjkDFkhmUK2dJCPLyyMiQ75OT5bxbt8LpvNq0a3IVZ2dZTLhokSSUDz8s+9nZCQNKQACOjpJoZmcD+fmkp4s93KlTwNixVKggtT3WrkU85tLTwdmZnTutfaKjSUwU1jU4WMY8NtYa++vXiY6W85OTc6sMc0ICnHeuK1KGnTshLEyqDXp4SEK7Y4ckpVbie+iQJPjvvANcukTFipI3enhYCWlwMKNHSyKPzca1ayKV4MoVqFaNhAQrwXd2xtNT+l0gKyE7G/r04amn5KFj40ZLk56dTaVKQIkSpKVJrReysqhShVslxa9elT4nJMiwXLwIlT5+s/D3p7F5MzAwMDAwuO9xbxJkZ2dhNrOySE4WKUJ6unyVmirs6Y8/Arm51K0rjg+enpb1m6sr5OVR8aM3oXJl8vKEGJ040Vp4duUKFSpA3bry8vKShXaurlBp6wrJcJs3x9lZ8r5RoySpc3AQFjIvT2S5165JUta8OeDuTsuWknw1by6sc4MGkvC1aAGULs2VK1Ai4TtAErUCX+PAQCnM5+sL2aXKEREhsYwZI3GfPSva6shIsaQjKws3NytRBPj+exo1Eia6aVM4HleWhATRRjdpgmTXDg4QE0NQkMXY+vuzZIns83//B3h7M3gwUi3w6FF695Zzk54usovERLy9ZezT05GGt28XNrp+fbH5yM4Wdjcnh+efh0cfhX7ds5k4EQgMxNdXxuTHHy15zLlzt/yiSUggPNzKxe3sIDQUb28red+9m1Kl4GqDR0lPF89nbt6Enj1ZskTa8/e3FkVeusTDDwP799O9u/WLQfnyBAdz6z6aNk1sAVu3FinLuXPccs4oNP6gBFkp1UEpdUYpFauUevEXvndSSn1mfX9YKeX92wK9t+czMDD4fTBz1sCg+KLoS02XKKHDGjWSbKZCBUmYYmIk6XB3F2o3PNzSWCC/y5cqxa3VfAXahZYthYKNjuaWMW9eHrf8vi5cEIrTZpPzZGQIzXjihJzP21v2d3eXzDA+Xijm5s2FFk5KkowvP18SUEdH0XE0aCAZcqlS0m56umSLK1dKIhYdLeWss7MlhoK4EhKk/YwMaS8gQDLwhATpq7u7sMHx8bK/o+NtWwiQGB0dZX8vL/lsZyfZ4/79IoUoKAFts0nGWBBfevptyjwzU7L1xMRbUgacnWV7gf4jL09ic3WV8c/KknGJipL9XV3l+LS0231ycZH9CsY0Pl7OmZkp3yUkWKsskfOlp8t41awp16SgAktmpnjDzZghxxWMv6urvLfZpP2CWJKTpb0CHYy7u2yrVUv6YFUwUVC4UtNK6TCHXzd3UXl5/7M9pZQ9EAO0AxKAo8AArXXUHfuMAhporUcopfoDPbTW/X715EV8PlO61sDgNv6oUtN/1pw189XA4Ke42zlb5DZvx/IehCeflKQyOloSzqwsMdFduFC0CZ6et5OpKlXg9ddFgJudDevXi9A0OFgsIhYvlqQpIkKsIzp0gLFjJUH64ANhQ4ODJXHKyhJ9w+uvi8bAw0OSXS8v+R3f31/ieOUV+OYbmD9fKN5vvxW9xogRoqPYu1eYVzs7SQ5PnxZ6t29fSUpnz5a2oqLk38uXYeJErixcRfmUMxKHzQZHjnCz7wDsUy/DwIFc/uhLKk4eKaLqRo1ERFuhgoyDpyc6oCHKvSI69hwq/Ji0ERQkyfb48VJNw9dXqnNcvcr1t5dR+sUxEB5O9s5vcBo7XBLG1aslqWzWTMbaw0MW4y1YIP07d05Moh9/XPq3ZQsMGkRuvxBKzHyV689NoXTqRTEaHjJExnPmTH5s0oay4bs4XKoVD73RB6ZMkUT5/fdFB9OwoYzF5MnyEJSYKKsYP/xQ+rtzp/S3WTPo00d0L6VLi8a6Vi2J78cfRWC8di18/LF45XXtKhqb0FCrbCFCIR86JJVYCjzgCos/ZhFeMyBWax0HoJRaDXQDou7Ypxsw1XofCrytlFL67p5ai/p8BgYEzzU+AAAKlElEQVQGvw9mzhoYFGMUCYOslBoGDLM++gO/0Xer2KMCkHKvg/iD8XfrU3WtdcVf20kptRXp+6/BGci64/MSrfWSO9rpDXTQWg+1Pj8JPKS1Hn3HPpHWPgnW53PWPr953P/o8/2F5mxxvk+Lc2xQvOMrzrHV1VqX+b2N/JFz1szXPwzFOT4T293jruZskTDIVuKwBEApFfZH/DxVnGD69PeB1rrDH9SU+qXm72Kfe3K+v8qcNbHdPYpzfMU9tj+qqV/Ydldz1szXPwbFOT4T293jbuds0S/SMzC4P5AAVLvjsyeQ+N/2UUo5AGWB1L/I+QwMDH4fzJw1MCjGMAmygcGfg6NAbaVUDaWUI9Af2PizfTYCg6z3vYGdv0NbWNTnMzAw+H0wc9bAoBijyBfpYf0M9DeD6ZPBT6C1zlNKjQa2AfbAUq31t0qpaUCY1noj8CGwUikVi7BC/Yvp+YrzvWBiu3sU5/j+9rH9iXP2bz92fyKKc3wmtrvHXcVX5DZvBgYGBgYGBgYGBsUZRmJhYGBgYGBgYGBgcAdMgmxgYGBgYGBgYGBwB0yCbGBgUOxL3hYivglKqSil1Eml1NdKqerFJbY79uutlNJKqSKzQypMbEqpvtbYfauUWlVUsRUmPqWUl1Jql1LquHVtOxVhbEuVUsmWF/Evfa+UUgus2E8qpRoVVWzW+YvtnDXz9c+N717N2ftuvmqtzcu8zOs+fiELhM4BPoAjcALw+9k+o4DF1vv+wGfFLL5WQCnr/ciiiq8wsVn7lQH2AoeAJsUlNqA2cBwoZ312L2bXdQkw0nrvB8QXYXwtgUZA5H/5vhPwJeJV3Bw4XMzG7p7MWTNf//Sxuydz9n6cr4ZBNjAwuFXyVmudAxSUvL0T3YAV1vtQoI1S6peKGNyT+LTWu7TWmdbHQ4inbLGIzcJ04HV+Wn2xOMT2DPCO1voqgNY6uZjFpwEX631Z/n+f4D8NWuu9/G/P4W7AR1pwCHBVSlUumuiK9Zw18/XuUZzn7H03X02CbGBgUBW4eMfnBGvbL+6jtc4DfgTKF0l0hYvvTgxBmIKiwK/GppRqCFTTWm8uopgKUJhxqwPUUUp9o5Q6pJT6oypJFgaFiW8qEKKUSgC2AGOKJrRC4bfel0V97ns1Z818vXsU5zl7383XYpkgK6UqKaVWKaXilFLHlFIHlVI97kEcTZRSC/6AdqYqpZ77I2K6H2DGq8hR1GWxfysKfW6lVAjQBJjzp0Z0xyl/Ydut2JRSdsA84F9FFM+dKMy4OSA/2QYDA4APlFKuf3JcBShMfAOA5VprT+Qn0pXWmBYHFPc5ca/iM/P17lGc5+x9N1+LS+C3YP0EtB7Yq7X20Vo3RvRTRfUTzC1orcO01mOL4lyWgLzYXY8/G0op+z+gjXtR8ObvhOJe8rYw8aGUagu8AnTVWmcXk9jKAP7AbqVUPKJ921hEC38Ke103aK1ztdbfAWeQP75FgcLENwRYA6C1Pgg4AxWKJLpfR6Huy3t47ns1Z818/fPiK9jnXszZ+2++FpWA+jcIrdsAe/7H9/bI0+ZR4CQw3NoeDOxGtFbRwCfcLoTSGNgDHEOqFlW2to8Foqx2Vv/CuYKBzdb7qcBS6xxxwNj/El8HIBwRsH/9v44FvIHTwCJEdF8deQI7BUQCs+9oNwOYbfVhB6IHKmivq7WPM7DMOv440Mra/gBwBIiw+lrb2j7BOk8kMO6OmKKBD6ztnwBtgW+As0Aza7/SVp+OWufq9gtjoaxrFWnF1O+Ocd0FrAKirG2vIBN9B/Ap8Jy1vSaw1er3PsDX2r4cmGu18+a9vm//yi+EkYgDanB78cUDP9vnn/x0wc+aYhZfQ2QBSe3iNnY/2383RbdIrzDj1gFYYb2vgPwEWb4YxfclMNh6Xw/5g6aK8Pp6898X/fyDny76OVKc7rt7NWfNfP3Tx+6ezNn7cb4W2Y35Gzo4Fpj3P74fBkyy3jsBYdYFC0Y0Vp4IM34QCAJKAAeAitYx/ZCSnlgXz8l67/oL5wrmpwnyAeucFYArQImf7V/RullrWJ/d/tex1sXMB5pb+1UBLljtOAA7ge7WdxroaL1fB2y32ngQiLC2/wtYZr33tdpyBhYCT1jbHYGSyEPDKSTRtQHfIv9peQN5QH1rHI8hibBCRO7rrXZmAiEFYwfEAKV/Nh69gK+Qh5pKVjyVrXG9fsc4FcRSChH4x3I7Qf6a2wn9Q8BO6/1yYDNgf6/v2b/DC/k5LAb5o/WKtW0aP334+ty6NkcAn2IW3w7gB+QhMALYWFxi+9m+uynaP7i/Nm4KedCMsuZg/2J2Xf2Qh/MT1nVtX4SxfQp8D+Qi7NMQYAQw4o6xe8eK/VRRXtdCjt09m7Nmvv6pY3fP5uz9Nl+LXalppdRYJHEab31+B0l0c7TWTZVSoUADoGAFbFlgOJCDXLB21nHvIhcqAklO46z97YHvtdbtlVJbEWZ2PZL4ZfwslmAkUeuslJoK5Gqt/2N9dxpop7VOuGP/LsjN+sTP2vnFY5EkeJfWuoa1vRvQS2s90Po8BHlCm6CUygactdZaKTUNyNZa/8eSZaRqrV2VUuuAhVrrndbx+xAWwR9haD8C1mqtzyqlnkWeOidb+04HLgMbga+01rWt7R8B27TWnyilfKzjA5RSYch/wHlWN92Ax7TWp+/o9zzglNZ6qfV5JfIfdjowRWvdyto+DnmYKIhlLvLwstiK6cwdw+mkta6nlFpujd0KDAwMDAwMDAz+QBRH7ea3CPMIgNb6n0qpCghTDPIUMEZrve3Og6xk9k4d002kfwr4Vmvd4hfO9Q/EO68r8G+l1ANaVvv+N/xS+z8Jg/8u+v5vx17/2fH/Dbn69tNMfkF7Wuv8OzS4v3i81nqVUuow0t9tSqmhv3KuO2PNv+Nz/h1xKySZvzN5/Tn+1zmu/+zzL42bHZCmtQ4oZBsGBgYGBgYGBr8bxXFR2E7AWSk18o5tpe54vw0YqZQqAaCUqqOUKv0/2jsDVFRKtbD2L6GUesBiXqtprXcBExGZgO13xn4QeFQpVcAIu/3G4w9bx1ewFq8NQLTThcVe4Anr3HUAL+CMxfzGaa0XIAxxA2vf7kqpUtb49UA0voXFNmBMga+mZY3zS/H0U0rZK6UqIg8jR/7Lfj2UUiWVUmWALgBa63TgO6VUH+scSin14G+I0cDAwMDAwMDgN6PYMciWhKA7ME8pNRH5if068IK1yweITjbcSs4uA93/R3s5SqnewAKlVFmkz/MRHc3H1jaF6J7Tfmfsl5VSw4C1VgKejEgpCnv890qpl5CFZwrYorXe8BtCWAQsVkqdQqQPg7XW2Uqpfog3YS6QBEzTWqdaMoWChPUDrfVxVfhypNORcTxpXYd4oPPP9lkHtED0SBqYqLVOUkr5/qzf4UqpzxA5zHl+mqg/AbyrlJqEaK5XW+0ZGBgYGBgYGPwpKHYaZAMDAwMDAwMDA4N7ieIosTAwMDAwMDAwMDC4ZzAJsoGBgYGBgYGBgcEdMAmygYGBgYGBgYGBwR0wCbKBgYGBgYGBgYHBHTAJsoGBgYGBgYGBgcEdMAmygYGBgYGBgYGBwR0wCbKBgYGBgYGBgYHBHfh/M4VqSNiDVBcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lohia_utilities.create_corr_network import rank\n",
    "df_chr = df_chr[df_chr['hi-c-rao'] >= 0] \n",
    "ranked_matirx = rank(df_chr['exp'])\n",
    "df_chr['exp'] = ranked_matirx\n",
    "plots_with_1_level_3d(df_chr,'exp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "gene_order_tss_y\n",
      "35      0.402076\n",
      "36      0.402076\n",
      "39      0.454589\n",
      "40      0.454589\n",
      "42      0.454589\n",
      "          ...   \n",
      "2393    0.994606\n",
      "2395    0.994606\n",
      "2399    0.994606\n",
      "2408    0.994606\n",
      "2410    0.994606\n",
      "Length: 597, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lohia_utilities.create_corr_network import rank\n",
    "df_chr = df_chr[df_chr['hi-c-rao'] >= 0] \n",
    "ranked_matirx = rank(df_chr['exp_mean'])\n",
    "df_chr['exp_mean'] = ranked_matirx\n",
    "plots_with_1_level_3d(df_chr,'exp_mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "gene_order_tss_y\n",
      "6       0.999501\n",
      "9       0.999501\n",
      "11      0.999501\n",
      "12      0.999501\n",
      "14      0.999501\n",
      "          ...   \n",
      "3345    0.999501\n",
      "3347    0.999501\n",
      "3348    0.999501\n",
      "3349    0.999501\n",
      "3355    0.999501\n",
      "Length: 1004, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lohia_utilities.create_corr_network import rank\n",
    "df_2_or_2 = df_2_or_2[df_2_or_2['hi-c-rao'] >= 0] \n",
    "ranked_matirx = rank(df_2_or_2['exp'])\n",
    "df_2_or_2['exp'] = ranked_matirx\n",
    "plots_with_1_level_3d(df_2_or_2,'exp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "gene_order_tss_y\n",
      "6       0.883556\n",
      "9       0.883556\n",
      "11      0.883556\n",
      "12      0.883556\n",
      "14      0.883556\n",
      "          ...   \n",
      "3345    0.943445\n",
      "3347    0.943445\n",
      "3348    0.943445\n",
      "3349    0.943445\n",
      "3355    0.943445\n",
      "Length: 1293, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ranked_matirx = rank(df_2_or['exp_median'])\n",
    "df_2_or['exp_median'] = ranked_matirx\n",
    "plots_with_1_level_3d(df_2_or,'exp_median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "resoultion=500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/11_dist_with_georg_hic_sub_median_hic_%s.h5' %resoultion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2_or = df_2_or[['tss_tss', 'exp', 'hi-c-rao', 'Gene stable ID_x', 'Gene stable ID_y', 'chrom_x', 'gene_order_tss_x', 'gene_order_tss_y']]\n",
    "df_2_or = df_2_or[df_2_or['hi-c-rao'] >= 0] \n",
    "df_2_or['hi-c-rao'].isnull().astype(int).sum() / df_2_or.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "597\n"
     ]
    }
   ],
   "source": [
    "change_group_level_1 = df_2_or.groupby(['chrom_x'])\n",
    "for chrm in change_group_level_1.groups.keys():\n",
    "    df_chr = change_group_level_1.get_group(chrm)\n",
    "    df_chr['pairs'] = [x+\"_\"+y for x,y in zip(df_chr['Gene stable ID_y'], df_chr['Gene stable ID_x'])]\n",
    "    df_chr.set_index('pairs', inplace=True)\n",
    "    unique_genes = list(set(df_chr['Gene stable ID_x'].unique()))\n",
    "    print (len(unique_genes))\n",
    "    gene_resolution_pairs= {}\n",
    "    for each_gene in  unique_genes:\n",
    "        gene_resolution_pairs[each_gene] = df_chr[(df_chr['Gene stable ID_x'] == each_gene) & (df_chr['tss_tss'] <=resoultion * 1000)]['Gene stable ID_y'].to_list()\n",
    "    counter = 0\n",
    "    for pairs in itertools.combinations(unique_genes, 2):\n",
    "        index_list = [r[0]+ '_' + r[1] for r in itertools.product(gene_resolution_pairs[pairs[0]], gene_resolution_pairs[pairs[1]])]\n",
    "        df_chr.at[index_list, 'exp_median'] = df_chr.loc[index_list,:]['exp'].median()\n",
    "        df_chr.at[index_list, 'exp_mean'] = df_chr.loc[index_list,:]['exp'].mean()\n",
    "        index_list = [r[0]+ '_' + r[1] for r in itertools.product(gene_resolution_pairs[pairs[1]], gene_resolution_pairs[pairs[0]])] \n",
    "        df_chr.at[index_list, 'exp_mean'] = df_chr.loc[index_list,:]['exp'].mean()\n",
    "        df_chr.at[index_list, 'exp_median'] = df_chr.loc[index_list,:]['exp'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tss_tss</th>\n",
       "      <th>exp</th>\n",
       "      <th>hi-c-rao</th>\n",
       "      <th>Gene stable ID_x</th>\n",
       "      <th>Gene stable ID_y</th>\n",
       "      <th>chrom_x</th>\n",
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       "      <td>ENSG00000101868</td>\n",
       "      <td>ENSG00000101868</td>\n",
       "      <td>chrX</td>\n",
       "      <td>308</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15885463</td>\n",
       "      <td>0.794246</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>ENSG00000182220</td>\n",
       "      <td>ENSG00000101868</td>\n",
       "      <td>chrX</td>\n",
       "      <td>476</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>129717615</td>\n",
       "      <td>0.745656</td>\n",
       "      <td>282.0</td>\n",
       "      <td>ENSG00000102125</td>\n",
       "      <td>ENSG00000101868</td>\n",
       "      <td>chrX</td>\n",
       "      <td>2344</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>40340805</td>\n",
       "      <td>0.928513</td>\n",
       "      <td>987.0</td>\n",
       "      <td>ENSG00000126970</td>\n",
       "      <td>ENSG00000101868</td>\n",
       "      <td>chrX</td>\n",
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       "      <td>308</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68980104</td>\n",
       "      <td>0.263513</td>\n",
       "      <td>694.0</td>\n",
       "      <td>ENSG00000179083</td>\n",
       "      <td>ENSG00000101868</td>\n",
       "      <td>chrX</td>\n",
       "      <td>1331</td>\n",
       "      <td>308</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>597524</th>\n",
       "      <td>37279597</td>\n",
       "      <td>0.398370</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>ENSG00000158427</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>chrX</td>\n",
       "      <td>1507</td>\n",
       "      <td>976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597525</th>\n",
       "      <td>8728478</td>\n",
       "      <td>0.641730</td>\n",
       "      <td>3344.0</td>\n",
       "      <td>ENSG00000198205</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>chrX</td>\n",
       "      <td>920</td>\n",
       "      <td>976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597526</th>\n",
       "      <td>19056891</td>\n",
       "      <td>0.424817</td>\n",
       "      <td>1580.0</td>\n",
       "      <td>ENSG00000102265</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>chrX</td>\n",
       "      <td>602</td>\n",
       "      <td>976</td>\n",
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       "    <tr>\n",
       "      <th>597527</th>\n",
       "      <td>5271519</td>\n",
       "      <td>0.441382</td>\n",
       "      <td>2781.0</td>\n",
       "      <td>ENSG00000204131</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>chrX</td>\n",
       "      <td>1079</td>\n",
       "      <td>976</td>\n",
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       "    <tr>\n",
       "      <th>597528</th>\n",
       "      <td>0</td>\n",
       "      <td>0.999161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>chrX</td>\n",
       "      <td>976</td>\n",
       "      <td>976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>356409 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          tss_tss       exp  hi-c-rao Gene stable ID_x Gene stable ID_y  \\\n",
       "0               0  0.999161       0.0  ENSG00000101868  ENSG00000101868   \n",
       "4        15885463  0.794246    1278.0  ENSG00000182220  ENSG00000101868   \n",
       "5       129717615  0.745656     282.0  ENSG00000102125  ENSG00000101868   \n",
       "6        40340805  0.928513     987.0  ENSG00000126970  ENSG00000101868   \n",
       "7        68980104  0.263513     694.0  ENSG00000179083  ENSG00000101868   \n",
       "...           ...       ...       ...              ...              ...   \n",
       "597524   37279597  0.398370    1065.0  ENSG00000158427  ENSG00000131080   \n",
       "597525    8728478  0.641730    3344.0  ENSG00000198205  ENSG00000131080   \n",
       "597526   19056891  0.424817    1580.0  ENSG00000102265  ENSG00000131080   \n",
       "597527    5271519  0.441382    2781.0  ENSG00000204131  ENSG00000131080   \n",
       "597528          0  0.999161       0.0  ENSG00000131080  ENSG00000131080   \n",
       "\n",
       "       chrom_x  gene_order_tss_x  gene_order_tss_y  \n",
       "0         chrX               308               308  \n",
       "4         chrX               476               308  \n",
       "5         chrX              2344               308  \n",
       "6         chrX               950               308  \n",
       "7         chrX              1331               308  \n",
       "...        ...               ...               ...  \n",
       "597524    chrX              1507               976  \n",
       "597525    chrX               920               976  \n",
       "597526    chrX               602               976  \n",
       "597527    chrX              1079               976  \n",
       "597528    chrX               976               976  \n",
       "\n",
       "[356409 rows x 8 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2_or"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_chr['exp_mean'].fillna(1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ENSG00000101868_ENSG00000182220</th>\n",
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       "      <th>ENSG00000101868_ENSG00000102125</th>\n",
       "      <td>129717615</td>\n",
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       "      <td>282.0</td>\n",
       "      <td>ENSG00000102125</td>\n",
       "      <td>ENSG00000101868</td>\n",
       "      <td>chrX</td>\n",
       "      <td>0.625642</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000101868_ENSG00000126970</th>\n",
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       "      <td>0.702912</td>\n",
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       "      <td>37279597</td>\n",
       "      <td>0.574872</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>ENSG00000158427</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>chrX</td>\n",
       "      <td>0.566538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000131080_ENSG00000198205</th>\n",
       "      <td>8728478</td>\n",
       "      <td>0.794596</td>\n",
       "      <td>3344.0</td>\n",
       "      <td>ENSG00000198205</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>chrX</td>\n",
       "      <td>0.567519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000131080_ENSG00000102265</th>\n",
       "      <td>19056891</td>\n",
       "      <td>0.601445</td>\n",
       "      <td>1580.0</td>\n",
       "      <td>ENSG00000102265</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>chrX</td>\n",
       "      <td>0.581551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000131080_ENSG00000204131</th>\n",
       "      <td>5271519</td>\n",
       "      <td>0.617975</td>\n",
       "      <td>2781.0</td>\n",
       "      <td>ENSG00000204131</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>chrX</td>\n",
       "      <td>0.583395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000131080_ENSG00000131080</th>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>ENSG00000131080</td>\n",
       "      <td>chrX</td>\n",
       "      <td>0.767078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>356409 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   tss_tss       exp  hi-c-rao  \\\n",
       "pairs                                                            \n",
       "ENSG00000101868_ENSG00000101868          0  1.000000       0.0   \n",
       "ENSG00000101868_ENSG00000182220   15885463  0.893384    1278.0   \n",
       "ENSG00000101868_ENSG00000102125  129717615  0.864865     282.0   \n",
       "ENSG00000101868_ENSG00000126970   40340805  0.963447     987.0   \n",
       "ENSG00000101868_ENSG00000179083   68980104  0.423822     694.0   \n",
       "...                                    ...       ...       ...   \n",
       "ENSG00000131080_ENSG00000158427   37279597  0.574872    1065.0   \n",
       "ENSG00000131080_ENSG00000198205    8728478  0.794596    3344.0   \n",
       "ENSG00000131080_ENSG00000102265   19056891  0.601445    1580.0   \n",
       "ENSG00000131080_ENSG00000204131    5271519  0.617975    2781.0   \n",
       "ENSG00000131080_ENSG00000131080          0  1.000000       0.0   \n",
       "\n",
       "                                Gene stable ID_x Gene stable ID_y chrom_x  \\\n",
       "pairs                                                                       \n",
       "ENSG00000101868_ENSG00000101868  ENSG00000101868  ENSG00000101868    chrX   \n",
       "ENSG00000101868_ENSG00000182220  ENSG00000182220  ENSG00000101868    chrX   \n",
       "ENSG00000101868_ENSG00000102125  ENSG00000102125  ENSG00000101868    chrX   \n",
       "ENSG00000101868_ENSG00000126970  ENSG00000126970  ENSG00000101868    chrX   \n",
       "ENSG00000101868_ENSG00000179083  ENSG00000179083  ENSG00000101868    chrX   \n",
       "...                                          ...              ...     ...   \n",
       "ENSG00000131080_ENSG00000158427  ENSG00000158427  ENSG00000131080    chrX   \n",
       "ENSG00000131080_ENSG00000198205  ENSG00000198205  ENSG00000131080    chrX   \n",
       "ENSG00000131080_ENSG00000102265  ENSG00000102265  ENSG00000131080    chrX   \n",
       "ENSG00000131080_ENSG00000204131  ENSG00000204131  ENSG00000131080    chrX   \n",
       "ENSG00000131080_ENSG00000131080  ENSG00000131080  ENSG00000131080    chrX   \n",
       "\n",
       "                                 exp_mean  \n",
       "pairs                                      \n",
       "ENSG00000101868_ENSG00000101868  0.605398  \n",
       "ENSG00000101868_ENSG00000182220  0.704656  \n",
       "ENSG00000101868_ENSG00000102125  0.625642  \n",
       "ENSG00000101868_ENSG00000126970  0.702912  \n",
       "ENSG00000101868_ENSG00000179083  0.425180  \n",
       "...                                   ...  \n",
       "ENSG00000131080_ENSG00000158427  0.566538  \n",
       "ENSG00000131080_ENSG00000198205  0.567519  \n",
       "ENSG00000131080_ENSG00000102265  0.581551  \n",
       "ENSG00000131080_ENSG00000204131  0.583395  \n",
       "ENSG00000131080_ENSG00000131080  0.767078  \n",
       "\n",
       "[356409 rows x 7 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =  df.join(long_form[[col_name]], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2_or_u = df_2_or[df_2_or['Gene stable ID_x'] != df_2_or['Gene stable ID_y']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 500)"
      ]
     },
     "execution_count": 205,
     "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": [
    "import seaborn as sns\n",
    "ax = sns.distplot(df_2_or_u['hi-c-rao'], bins=10000, hist=True, kde=False, hist_kws={\"range\":(1, 10000)})\n",
    "#ax.set_ylim(0, 500)\n",
    "ax.set_xlim(0, 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import seaborn as sns\n",
    "ax = sns.distplot(df_2_or_u['hi-c-rao'], bins=10000, hist=True, kde=False, hist_kws={\"range\":(1, 10000)})\n",
    "#ax.set_ylim(0, 1500)\n",
    "#ax.set_xlim(0, 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import seaborn as sns\n",
    "ax = sns.distplot(df_2_or_u['hi-c-rao'], hist=True, kde=False)\n",
    "#ax.set_ylim(0, 500)\n",
    "#ax.set_xlim(0, 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 10000)"
      ]
     },
     "execution_count": 204,
     "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": [
    "import seaborn as sns\n",
    "ax = sns.distplot(df_2_or_u['hi-c-rao'], hist=True, kde=False)\n",
    "#ax.set_ylim(0, 500)\n",
    "ax.set_xlim(0, 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lohia/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py:2446: PerformanceWarning: \n",
      "your performance may suffer as PyTables will pickle object types that it cannot\n",
      "map directly to c-types [inferred_type->mixed,key->block2_values] [items->Index(['chrm', 'plot', 'pr_curve', 'Gene stable ID'], dtype='object')]\n",
      "\n",
      "  encoding=encoding,\n"
     ]
    }
   ],
   "source": [
    "# import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "from lohia_utilities.calculate_auc import *\n",
    "from pandas.core.common import SettingWithCopyWarning\n",
    "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n",
    "from lohia_utilities.create_corr_network import rank\n",
    "\n",
    "#df_2_or = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/11_dist_with_georg_hic_sub_median_hic_500.h5')\n",
    "\n",
    "#df_2_or = df_2_or[df_2_or['exp_georg'] >= 0] # liming the matrix to only chosen values for rank standerization\n",
    "#df_2_or = df_2_or[df_2_or['hi-c-rao'] >= 0] # liming the matrix to only chosen values for rank standerization\n",
    "df_2_or_u = df_2_or[df_2_or['Gene stable ID_x'] != df_2_or['Gene stable ID_y']]\n",
    "#ranked_matirx = rank(df_2_or['exp_georg'])\n",
    "#df_2_or['exp_georg'] = ranked_matirx\n",
    "#df_2_or.rename(columns={\"exp_georg\": \"exp (GK)\"}, inplace=True)\n",
    "\n",
    "ranked_matirx = rank(df_2_or['exp'])\n",
    "df_2_or['exp'] = ranked_matirx\n",
    "\n",
    "#ranked_matirx = rank(df_2_or['hi-c-rao'])\n",
    "#df_2_or['hi-c-rao'] = ranked_matirx\n",
    "m_l = []\n",
    "change_group_level_1 = df_2_or.groupby(['chrom_x'])\n",
    "for chrm in change_group_level_1.groups.keys():\n",
    "    df = change_group_level_1.get_group(chrm)\n",
    "    num_pairs = df['Gene stable ID_x'].nunique()\n",
    "\n",
    "    prot_list_sp = np.array_split(df, num_pairs, axis=0)\n",
    "    for i in range(0,num_pairs):\n",
    "\n",
    "        long_form_top = prot_list_sp[int(i)]\n",
    "        long_form_top['dist'] = long_form_top['hi-c-rao']\n",
    "        long_form_top = long_form_top[long_form_top['tss_tss'] >= 10000000] # liming the matrix to only chosen values for rank standerization\n",
    "\n",
    "        long_form_top = long_form_top[long_form_top['Gene stable ID_x'] != long_form_top['Gene stable ID_y']] # remove all the self pairs from each set\n",
    "        long_form_top['hi-c-rao'].fillna(-1, inplace=True)\n",
    "        mp =  long_form_top['Gene stable ID_y'].values[0]\n",
    "        #print (long_form_top.shape)\n",
    "        \n",
    "        exp_median = long_form_top['exp'].median()\n",
    "        exp_mean = long_form_top['exp'].mean()\n",
    "        exp_var = long_form_top['exp'].var()\n",
    "\n",
    "        long_form_top = long_form_top.reset_index()\n",
    "        if long_form_top['exp'].values[0] >=0:\n",
    "    \n",
    "       \n",
    "\n",
    "            for dist_thresh in [1,5,10,100,int(num_pairs/2),700]:\n",
    "            #for dist_thresh in [100000,1000000,10000000,100000000]:\n",
    "            #for dist_thresh in [4000]:\n",
    "            #for dist_thresh in [df_2_or_u[\"hi-c-rao\"].min(), df_2_or[\"hi-c-rao\"].max()-1, df_2_or[\"hi-c-rao\"].mean(), df_2_or[\"hi-c-rao\"].median()]:\n",
    "                #long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]  \n",
    "                \n",
    "                long_form_top = long_form_top.sort_values(by=['dist'], ascending=False) \n",
    "                long_form_top[\"True_sim\"] = [0 if score > dist_thresh else 0 for score in long_form_top[\"dist\"]]  \n",
    "                for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                    long_form_top.at[ind_val, 'True_sim'] = 1\n",
    "                    \n",
    "                #long_form_top = long_form_top.sort_values(by=['dist'], ascending=True) \n",
    "                #long_form_top[\"True_sim\"] = [1 if score > dist_thresh else 1 for score in long_form_top[\"dist\"]]  \n",
    "                #for ind_val in long_form_top.index.values[0:dist_thresh]:\n",
    "                #    long_form_top.at[ind_val, 'True_sim'] = 0\n",
    "                #\n",
    "                #long_form_top[\"True_sim\"] = [1 if score <= dist_thresh else 0 for score in long_form_top[\"dist\"]] \n",
    "                #long_form_top[\"True_sim\"] = [1 if score >= dist_thresh else 1 if score2 <= 1000 else 0 for score, score2 in zip(long_form_top[\"dist\"],long_form_top[\"tss_tss\"])]    \n",
    "                long_form_top[\"true_pos\"] = [score for score in long_form_top[\"True_sim\"]]\n",
    "                long_form_top[\"true_neg\"] = [1 if score==0 else 0 for score in long_form_top[\"True_sim\"]]\n",
    "                long_form_top[\"predicted_sim_from_exp\"] = [score for score in long_form_top[\"exp_median\"]]\n",
    "                ca = calc_auroc (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "                m_curve = calc_auc_curve (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "                pr_curve = prec_recall (long_form_top,predicted_score='predicted_sim_from_exp')\n",
    "\n",
    "                tpd = pd.DataFrame(m_curve)\n",
    "                if m_curve:\n",
    "                    tpd[0] =  tpd[0].astype(float).round(2)\n",
    "                    tpd = tpd.groupby([0]).mean()\n",
    "                    m_curve  = dict(zip(tpd.index, tpd[1]))\n",
    "                else:\n",
    "                    m_curve  = {}\n",
    "                tpd = pd.DataFrame(pr_curve)\n",
    "                if pr_curve:\n",
    "                    tpd[0] =  tpd[0].astype(float).round(2)\n",
    "                    tpd = tpd.groupby([0]).mean()\n",
    "                    pr_curve  = dict(zip(tpd.index, tpd[1]))\n",
    "                else:\n",
    "                    pr_curve  = {}\n",
    "                m_l.append((chrm, num_pairs,dist_thresh, ca, m_curve, pr_curve, long_form_top[\"true_pos\"].sum(), long_form_top[\"true_neg\"].sum(), exp_median, exp_mean, exp_var, mp))\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "df_scores = pd.DataFrame(m_l, columns =['chrm', 'num_pairs','dist_thresh',  'auc', 'plot', 'pr_curve', 'true_pos', 'true_neg', 'exp_median', 'exp_mean', 'exp_var', 'Gene stable ID'])\n",
    "df_scores.to_hdf('/data/lohia/gene_distance_expresseion/dist_files/chr11_100kb.h5', key='df', mode='w')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores = pd.read_hdf('/data/lohia/gene_distance_expresseion/dist_files/chr11_100kb.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
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       "      <th>chrm</th>\n",
       "      <th>num_pairs</th>\n",
       "      <th>dist_thresh</th>\n",
       "      <th>auc</th>\n",
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       "      <th>exp_var</th>\n",
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       "      <td>{0.0: 0.0, 0.08: 0.021899579772188874, 0.17: 0...</td>\n",
       "      <td>12</td>\n",
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       "      <td>0.650224</td>\n",
       "      <td>0.608971</td>\n",
       "      <td>0.084299</td>\n",
       "      <td>ENSG00000168496</td>\n",
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       "      <td>0.649545</td>\n",
       "      <td>0.071352</td>\n",
       "      <td>ENSG00000239306</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1004 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chrm  num_pairs  dist_thresh       auc  \\\n",
       "0     chr11       1004         4000       NaN   \n",
       "1     chr11       1004         4000       NaN   \n",
       "2     chr11       1004         4000       NaN   \n",
       "3     chr11       1004         4000       NaN   \n",
       "4     chr11       1004         4000  0.561063   \n",
       "...     ...        ...          ...       ...   \n",
       "999   chr11       1004         4000       NaN   \n",
       "1000  chr11       1004         4000  0.820235   \n",
       "1001  chr11       1004         4000       NaN   \n",
       "1002  chr11       1004         4000  0.427163   \n",
       "1003  chr11       1004         4000       NaN   \n",
       "\n",
       "                                                   plot  \\\n",
       "0     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "1     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "2     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "3     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "4     {0.0: 0.0, 0.01: 0.0, 0.02: 0.0104166666666666...   \n",
       "...                                                 ...   \n",
       "999   {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "1000  {0.0: 0.0, 0.01: 0.0, 0.02: 0.0, 0.03: 0.0, 0....   \n",
       "1001  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "1002  {0.0: 0.0, 0.01: 0.0, 0.02: 0.0, 0.03: 0.01739...   \n",
       "1003  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....   \n",
       "\n",
       "                                               pr_curve  true_pos  true_neg  \\\n",
       "0                                                    {}         0       650   \n",
       "1                                                    {}         0       932   \n",
       "2                                                    {}         0       647   \n",
       "3                                                    {}         0       828   \n",
       "4     {0.0: 0.0, 0.08: 0.021899579772188874, 0.17: 0...        12       696   \n",
       "...                                                 ...       ...       ...   \n",
       "999                                                  {}         0       896   \n",
       "1000  {0.0: 0.0, 0.33: 0.011573465587966434, 0.67: 0...         3       738   \n",
       "1001                                                 {}         0       646   \n",
       "1002  {0.0: 0.0, 0.04: 0.03137291143578475, 0.09: 0....        23       913   \n",
       "1003                                                 {}         0       647   \n",
       "\n",
       "      exp_median  exp_mean   exp_var   Gene stable ID  \n",
       "0       0.592583  0.547375  0.063044  ENSG00000174669  \n",
       "1       0.732026  0.656574  0.070272  ENSG00000183340  \n",
       "2       0.392391  0.379631  0.036577  ENSG00000182791  \n",
       "3       0.822697  0.728352  0.067169  ENSG00000132275  \n",
       "4       0.650224  0.608971  0.084299  ENSG00000168496  \n",
       "...          ...       ...       ...              ...  \n",
       "999     0.710174  0.617268  0.067416  ENSG00000151702  \n",
       "1000    0.580503  0.522850  0.076284  ENSG00000156587  \n",
       "1001    0.653405  0.605870  0.066594  ENSG00000173715  \n",
       "1002    0.657461  0.592015  0.051981  ENSG00000166086  \n",
       "1003    0.728689  0.649545  0.071352  ENSG00000239306  \n",
       "\n",
       "[1004 rows x 12 columns]"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "803"
      ]
     },
     "execution_count": 595,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores[df_scores['dist_thresh'] == 4000]['auc'].isnull().astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>428</td>\n",
       "      <td>0.425241</td>\n",
       "      <td>0.405120</td>\n",
       "      <td>0.049482</td>\n",
       "      <td>ENSG00000260807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3849</th>\n",
       "      <td>chr16</td>\n",
       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>432</td>\n",
       "      <td>0.490119</td>\n",
       "      <td>0.454270</td>\n",
       "      <td>0.064232</td>\n",
       "      <td>ENSG00000169627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3854</th>\n",
       "      <td>chr16</td>\n",
       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>442</td>\n",
       "      <td>0.474922</td>\n",
       "      <td>0.465165</td>\n",
       "      <td>0.052825</td>\n",
       "      <td>ENSG00000248124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3859</th>\n",
       "      <td>chr16</td>\n",
       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>494</td>\n",
       "      <td>0.360949</td>\n",
       "      <td>0.368468</td>\n",
       "      <td>0.032043</td>\n",
       "      <td>ENSG00000102935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3864</th>\n",
       "      <td>chr16</td>\n",
       "      <td>773</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....</td>\n",
       "      <td>{}</td>\n",
       "      <td>0</td>\n",
       "      <td>495</td>\n",
       "      <td>0.572479</td>\n",
       "      <td>0.551079</td>\n",
       "      <td>0.061528</td>\n",
       "      <td>ENSG00000177200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>773 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chrm  num_pairs  dist_thresh  auc  \\\n",
       "4     chr16        773         2000  NaN   \n",
       "9     chr16        773         2000  NaN   \n",
       "14    chr16        773         2000  NaN   \n",
       "19    chr16        773         2000  NaN   \n",
       "24    chr16        773         2000  NaN   \n",
       "...     ...        ...          ...  ...   \n",
       "3844  chr16        773         2000  NaN   \n",
       "3849  chr16        773         2000  NaN   \n",
       "3854  chr16        773         2000  NaN   \n",
       "3859  chr16        773         2000  NaN   \n",
       "3864  chr16        773         2000  NaN   \n",
       "\n",
       "                                                   plot pr_curve  true_pos  \\\n",
       "4     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "9     {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "14    {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "19    {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "24    {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "...                                                 ...      ...       ...   \n",
       "3844  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "3849  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "3854  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "3859  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "3864  {0.0: nan, 0.01: nan, 0.02: nan, 0.03: nan, 0....       {}         0   \n",
       "\n",
       "      true_neg  exp_median  exp_mean   exp_var   Gene stable ID  \n",
       "4          495    0.345003  0.322651  0.030117  ENSG00000245694  \n",
       "9          496    0.619299  0.585025  0.087750  ENSG00000245694  \n",
       "14         437    0.486055  0.477094  0.069154  ENSG00000198931  \n",
       "19         420    0.554352  0.556762  0.077769  ENSG00000243716  \n",
       "24         428    0.828832  0.726001  0.064853  ENSG00000168434  \n",
       "...        ...         ...       ...       ...              ...  \n",
       "3844       428    0.425241  0.405120  0.049482  ENSG00000260807  \n",
       "3849       432    0.490119  0.454270  0.064232  ENSG00000169627  \n",
       "3854       442    0.474922  0.465165  0.052825  ENSG00000248124  \n",
       "3859       494    0.360949  0.368468  0.032043  ENSG00000102935  \n",
       "3864       495    0.572479  0.551079  0.061528  ENSG00000177200  \n",
       "\n",
       "[773 rows x 12 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores[df_scores['dist_thresh'] == 2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\"\n",
    "                 ,hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W5PLLL88JJ5yQf/3Xf82RRx6ZW265JZdccknuuuuuUcv8EuURAAAAwG42btiY3/uN947a+T573xdG/LnLly/PypUrkyQXXnhh5s2bl8svvzxvectbdsw57bTTsmbNmv2NuUfKIwBgXOjv78/g4GDTMfbZ0NBQkqSrq6vhJPuuu7s7fX19TccAgINaKSVnnnlmSin5vd/7vVxyySVZt25dZs6cmSSZOXNmHnvssV/6vGXLluXss89uSSblEQBACzz33HNNRwAAxqE77rgjxxxzTB577LHMnz8/r33ta1/2c26//fYsW7Ys3/zmN1uSSXkEAIwL421FTE9PT5Jk6dKlDScBAMaTY445Jkkyffr0nHvuubn77rszY8aMrF27NjNnzszatWszffr0HfPvv//+9PT05JZbbsnRRx/dkkyetgYAAAAwBjz77LN5+umnd3z8ta99LbNnz87ChQtzzTXXJEmuueaaLFq0KEny05/+NOedd17+9m//Nr/2a7/WslxWHgEAAACMAevWrcu5556bJHnhhRfynve8J2eddVbe+MY35l3veleWLVuW4447Ll/5yleSJJ/4xCfy+OOP5/d///eTJJMmTcqqVatGPZfyCAAAAGA3R3UetV9PSNvT+V7Oq1/96tx3332/dPzoo4/Obbfd9kvHly5dekC2yCuPAAAAAHZzxWc+1XSEMcM9jwAAAACopTwCAAAAoJbyCAAAADgoVVXVdIQDbiR/ZuURAAAAcNCZPHlyHn/88YOqQKqqKo8//ngmT548rM9zw2wAAADgoDNr1qysWbMm69evbzrKATV58uTMmjVrWJ+jPAIAAAAOOh0dHTnhhBOajjEu2LYGAAAAQC3lEQAAAAC1lEcAAAAA1FIeAQAAAFBLeQQAAABALeURAAAAALWURwAAAADUmtR0AA4uQ0ND6XhmU466/4amo8CwdDyzIUNDLzQdAwAA4ICz8ggAAACAWlYecUB1dXVl6OeTsvGk85qOAsNy1P03pKtrZtMxAAAADjgrjwAAAACopTwCAAAAoJbyCAAAAIBayiMAAAAAaimPAAAAAKilPAIAAACglvIIAAAAgFrKIwAAAABqKY8AAAAAqKU8AgAAAKCW8ggAAACAWsojAAAAAGopjwAAAACopTwCAAAAoJbyCAAAAIBayiMAAAAAaimPAAAAAKilPAIAAACglvIIAAAAgFrKIwAAAABqKY8AAAAAqKU8AgAAAKCW8ggAAACAWsojAAAAAGopjwAAAACopTwCAAAAoJbyCAAAAIBak5oOADAe9ff3Z3BwsOkYbeulr21PT0/DSdpTd3d3+vr6mo4BAMA4oTwCGIHBwcH84P5VOfZXtjYdpS1N3Lp9YeyTP72r4STt55FNE5uOAADAOKM8AhihY39laz5yxjNNx4Bh+evbDm86AgAA44x7HgEAAABQS3kEAAAAQC3lEQAAAAC1lEcAAAAA1GppeVRKOauUMlhKebCUsngPrx9XSrm9lHJvKeX+UsqCVuYBAAAAYHhaVh6VUiYmuTLJ2Ulel+T8Usrrdpv2p0n+rqqqU5K8O8n/26o8AAAAAAxfK1ce/WaSB6uq+lFVVb9I8uUki3abUyU54sWPpyZ5tIV5AAAAABimSS0897FJhnYar0nypt3mXJbka6WUP0xyWJK3tTAPAAAAAMPUyvKo7OFYtdv4/CRXV1X1f5dS3pzkb0sps6uq2rbLiUq5JMklSTJjxoysXLmyFXk5AE4//fSc8ptbsvWQqU1HgWGZOPvcHP7Kjh1//5x++unZ+otTs2nK1maDwTCd/d8nZuIrDvPf0gNg06ZNSeJrDQCMe60sj9Yk6dppPCu/vC3t4iRnJUlVVXeWUiYn6Uzy2M6Tqqq6KslVSTJnzpxq3rx5LYpMq/X09ORbD67NxpPOazoKDMtR9381b3nNzFx44YVJtl/LT/70rnzkjGcaTgbDc81th2fqcW/acS3TOtdee22SxM8tAMB418p7Ht2T5MRSygmllFdk+w2xb9ptzk+TnJEkpZRfTzI5yfoWZgIAAABgGFpWHlVV9UKSS5P8U5IfZvtT1b5fSvlEKWXhi9P+9yS9pZT7knwpyUVVVe2+tQ0AAACAhrRy21qqqlqRZMVuxz6+08c/SPJfWpkBAAAAgJFr5bY1AAAAAMY55REAAAAAtZRHAAAAANRSHgEAAABQS3kEAAAAQC3lEQAAAAC1lEcAAAAA1FIeAQAAAFBLeQQAAABALeURAAAAALUmNR0AAAAOJv39/RkcHGw6xj4bGhpKknR1dTWcZN90d3enr6+v6RgAbcXKIwAAoNZzzz2X5557rukYsF/Wr1+fiy++OBs2bGg6CoxLVh4BAMABNN5WxfT09CRJli5d2nASGLmBgYHce++9GRgYyB//8R83HQfGHSuPAAAAaFvr16/PTTfdlKqqsnz5cquPYASsPAKAg9h4u/fKePLS1/WlVRuMPve2AfbFwMBAtm3bliTZtm2b1UcwAsojADiIDQ4O5p77V2fLkYc1HaXtTNz6iyTJt4YeaDhJe+p44tmmIwDjxIoVK7Jly5YkyZYtW3LzzTcrj2CYlEcAcJDbcuRh2TB/dtMxYFg6b/1e0xGAcWLBggW5/vrrs23btkyYMCHnnHNO05Fg3HHPIwAAANrWeeedt8u2tXe84x0NJ4LxR3kEAABA27rhhhtSSkmSlFJy/fXXN5wIxh/lEQAAAG1rxYoVqaoqSVJVVW6++eaGE8H4ozwCAACgbS1YsCAdHR1Jko6ODvc8ghFQHgEAANC2ent7M2HC9l99J0yYkN7e3oYTwfijPAIAAKBtTZs2LQsXLkwpJYsWLUpnZ2fTkWDcmdR0AAAAAGil3t7ePPTQQ1YdwQgpjwAAAGhr06ZNy7Jly5qOAeOW8ggAAIBh6e/vz+DgYNMx9tnQ0FCSpKurq+Ek+667uzt9fX1Nx4AkyiMAAADa3HPPPdd0BBjXlEcAAAAMy3hbEdPT05MkWbp0acNJYHzytDUAAAAAaimPAAAAAKilPAIAAACglvIIAAAAgFrKIwAAAABqKY8AAAAAqDWp6QAA49HQ0FCe2jQxf33b4U1HgWFZs2linipDTccAAGAcsfIIAAAAgFpWHgGMQFdXV56sHs1Hznim6SgwLH992+GZ2tXVdAwAAMYRK48AAAAAqKU8AgAAAKCW8ggAAACAWsojAAAAAGopjwAAAACopTwCAAAAoJbyCAAAAIBayiMAAAAAaimPAAAAAKilPAIAAACglvIIAAAAgFrKIwAAAABqKY8AAAAAqKU8AgAAAKCW8ggAAACAWsojAAAAAGopjwAAAACopTwCAAAAoNakpgMAAM0ZGhpKxxPPpvPW7zUdBYal44lnM5ShpmMAwEHByiMAAAAAall5BAAHsa6urgxlczbMn910FBiWzlu/l66urqZjAMBBwcojAAAAAGopjwAAAACoZdsaAADjWn9/fwYHB5uO0bZe+tr29PQ0nKQ9dXd3p6+vr+kYAHulPAIAYFwbHBzM91ffk2MOeaHpKG1pws8nJkmeGLyz4STt59Hn/ToGjA/+tgIAYNw75pAX8sETn2w6BgzLZx6Y2nQEgH3inkcAAAAA1FIeAQAAAFDLtjUOuI5nNuSo+29oOkZbmvj89uX6Ww+xBHq0dTyzIcnMpmMAAAAccMojDqju7u6mI7S1wcGnkiTdr1FyjL6Zrl8AAOCgpDzigPIY0tZ66RG6S5cubTgJAAAA7cI9jwAAAACopTwCAAAAoJbyCAAAAIBayiMAAAAAaimPAAAAAKilPAIAAACglvIIAAAAgFrKIwAAAABqKY8AAAAAqKU8AgAAAKCW8ggAAACAWsojAAAAAGopjwAAAACopTwCAAAAoJbyCAAAAIBayiMAAAAAaimPAAAAAKilPAIAAACglvIIAAAAgFrKIwAAAABqKY8AAAAAqKU8AgAAAKBWS8ujUspZpZTBUsqDpZTFNXPeVUr5QSnl+6WUL7YyDwAAAADDM6lVJy6lTExyZZL5SdYkuaeUclNVVT/Yac6JSf44yX+pquqJUsr0VuUBAKA9DQ0N5annJuUzD0xtOgoMy6PPTcozQ0NNxwB4Wa1cefSbSR6squpHVVX9IsmXkyzabU5vkiurqnoiSaqqeqyFeQAAAAAYppatPEpybJKda/Q1Sd6025xfS5JSyh1JJia5rKqqf2xhJgAA2kxXV1eeeG5NPnjik01HgWH5zANTc2RXV9MxAF5WK8ujsodj1R7e/8Qk85LMSvKNUsrsqqo27XKiUi5JckmSzJgxIytXrhz1sNAONm3a/q3je6T1Tj/99Gz9xanZNGVr01FgWM7+7xMz8RWH7fh74vTTT88pv3hjXjjikGaDwTBNeudJOfwVk7Ny5cqcfvrpeeGNp2TzZH8nM74seNPETJp8uJ/dDgA/J8P+aWV5tCbJzjX6rCSP7mHOt6uq2pLkx6WUwWwvk+7ZeVJVVVcluSpJ5syZU82bN69VmWFcu/baa5Mkvkdar6enJ0/+9K585Ixnmo4Cw3LNbYdn6nFvyoUXXphk+7X8raEHsmH+7IaTwfB03vq9vKXrxFx44YXp6enJE4N3WnnEuLPigak5svvNO/5O7u/vz+DgYMOp2tO6deuS/OfPy4y+7u7u9PX1NR2DFmlleXRPkhNLKSckeSTJu5O8Z7c5NyY5P8nVpZTObN/G9qMWZgIAABiTBgcH84P7v59jp85sOkrbmfjC9tv9PvmTjQ0naU+PPLm26Qi0WMvKo6qqXiilXJrkn7L9fkZ/U1XV90spn0iyqqqqm1587cxSyg+SbE3SV1XV463KBAAAMJYdO3VmPjy3p+kYMCxXfH1p0xFosVauPEpVVSuSrNjt2Md3+rhK8j9f/AcAAACAMWZC0wEAAAAAGLuURwAAAADUUh4BAAAAUEt5BAAAAEAt5REAAAAAtZRHAAAAANRSHgEAAABQS3kEAAAAQC3lEQAAAAC1lEcAAAAA1FIeAQAAAFCrtjwqpby9lPLOPRx/byllfmtjAQAAADAW7G3l0Z8l+dc9HL8tySdaEwcAAACAsWRv5dGhVVWt3/1gVVU/S3JY6yIBAAAAMFZM2strk0spk6qqemHng6WUjiSHtDYWwNj3yKaJ+evbDm86Rlta/8z2/7cx7fBtDSdpP49smpipx+16rOOJZ9N56/eaCdTGJj69OUmydcrkhpO0p44nnk26mk4BAAeHvZVHNyQZKKVcWlXVs0lSSjksyRUvvgZw0Oru7m46Qlv72eBgkmTqcb7Oo23qcbtev67l1hl88Tru7jqx4SRtqsv1CwAHyt7Koz9N8r+S/KSU8pMkJdv//86yJP/HAcgGMGb19fU1HaGt9fT0JEmWLl3acJL251puHdcxANAuasujF7erLS6l/FmS17x4+MGqqp4/IMkAAAAAaFxteVRKOW+3Q1WSXymlrK6q6unWxgIAAABgLNjbtrX/todjRyU5qZRycVVV/9KiTAAAAACMEXvbtvb+PR0vpbwqyd8leVOrQgEAAAAwNkwY7idUVfWTJB0tyAIAAADAGLO3bWt7VEp5bZKftyALAACMyKPPT8pnHpjadIy2tOHnE5Mkna/c2nCS9vPo85NyZNMhAPbB3m6Y/ffZfpPsnR2VZGaS97UyFAAA7Kvu7u6mI7S1xwYHkyRH+jqPuiPj+gXGh72tPPrkbuMqycZsL5Del+TOVoUCAIB91dfX13SEttbT05MkWbp0acNJ2t/Q0FCe2vRUrvi6rzXjy5pNa/PUhGebjkEL7e2G2f/60sellJOTvCfJu5L8OMn1rY8GAAAAQNP2tm3t15K8O8n5SR5P8v8lKVVVvfUAZQMAADhodHV15cltG/PhuT1NR4FhueLrSzO166imY9BCe9u29u9JvpHkv1VV9WCSlFL+xwFJBQAAAMCYMGEvr70jyc+S3F5KGSilnJGkHJhYAAAAAIwFteVRVVVfrarqd5K8NsnKJP8jyYxSyqdLKWceoHwAAAAANGhvK4+SJFVVPVtV1ReqqvrtJLOSrE6yuOXJAAAAAGjcy5ZHO6uqamNVVZ+tquq3WhUIAAAAgLFjWOURAAAAAAcX5REAAAAAtZRHAAAAAMj6gdMAABeHSURBVNRSHgEAAABQS3kEAAAAQC3lEQAAAAC1lEcAAAAA1FIeAQAAAFBLeQQAAABALeURAAAAALWURwAAAADUUh4BAAAAUGtS0wEAAADY7pEn1+aKry9tOkbbWf/M40mSaYcf3XCS9vTIk2szNUc1HYMWUh4BAACMAd3d3U1HaFs/G1yfJJn6KgVHK0zNUa7fNqc8AgAAGAP6+vqajtC2enp6kiRLl1rVBSPhnkcAAAAA1FIeAQAAAFBLeQQAAABALeURAAAAALWURwAAAADUUh4BAAAAUEt5BAAAAEAt5REAAAAAtZRHAAAAANRSHgEAAABQS3kEAAAAQC3lEQAAAAC1lEcAAAAA1FIeAQAAAFBLeQQAAABALeURAAAAALWURwAAAADUUh4BAAAAUEt5BAAAAEAt5REAAAAAtZRHAAAAANRSHgEAAABQS3kEAAAAQC3lEQAAAAC1lEcAAAAA1FIeAQAAAFBLeQQAAABALeURAAAAALWURwAAAADUUh4BAAAAUEt5BAAAAEAt5REAAAAAtZRHAAAAANRSHgEAAABQS3kEAAAAQC3lEQAAAAC1lEcAAAAA1FIeAQAAAFBLeQQAAABALeURAAAAALWURwAAAADUUh4BAAAAUKul5VEp5axSymAp5cFSyuK9zHtnKaUqpcxpZR4AAAAAhqdl5VEpZWKSK5OcneR1Sc4vpbxuD/OmJPlwkrtalQUAAACAkWnlyqPfTPJgVVU/qqrqF0m+nGTRHub9X0n+IsnmFmYBAAAAYARaWR4dm2Rop/GaF4/tUEo5JUlXVVX/0MIcAAAAAIzQpBaeu+zhWLXjxVImJPnLJBe97IlKuSTJJUkyY8aMrFy5cnQSQpvZtGlTkvgeYdxzLdMOXMe0C9cy7cB1DPunleXRmiRdO41nJXl0p/GUJLOTrCylJMn/luSmUsrCqqpW7XyiqqquSnJVksyZM6eaN29eC2PD+HXttdcmSXyPMN65lmkHrmPahWuZPenv78/g4GDTMfbZunXrkvzn9TwedHd3p6+vr+kYkKS15dE9SU4spZyQ5JEk707ynpderKrqySSdL41LKSuT/NHuxREAAADsj0MPPbTpCDCutaw8qqrqhVLKpUn+KcnEJH9TVdX3SymfSLKqqqqbWvXeAAAAtI4VMXBwaeXKo1RVtSLJit2Ofbxm7rxWZgEAAABg+Fr5tDUAAAAAxjnlEQAAAAC1lEcAAAAA1FIeAQAAAFBLeQQAAABALeURAAAAALWURwAAAADUUh4BAAAAUEt5BAAAAEAt5REAAAAAtZRHAAAAANRSHgEAAABQS3kEAAAAQC3lEQAAAAC1JjUdAAAADib9/f0ZHBxsOsY+eylrT09Pw0n2TXd3d/r6+pqOAdBWlEcAAECtQw89tOkIADRMeQQAAAeQVTEAjDfueQQAAABALeURAAAAALWURwAAALS19evX5+KLL86GDRuajgLjkvIIAACAtjYwMJB77703AwMDTUeBcUl5BAAAQNtav359brrpplRVleXLl1t9BCOgPAIAAGrZ7sN4NzAwkG3btiVJtm3bZvURjIDyCAAAqGW7D+PdihUrsmXLliTJli1bcvPNNzecCMYf5REAALBHtvvQDhYsWJCOjo4kSUdHR84555yGE8H4ozwCAAD2yHYf2kFvb28mTNj+q++ECRPS29vbcCIYf5RHAADAHtnuQzuYNm1aFi5cmFJKFi1alM7OzqYjwbijPAIAAPbIdh/aRW9vb0455RSrjmCElEcAAMAe2e5Du5g2bVqWLVtm1RGMkPIIAADYI9t9AEiURwAAwF6cd955Oeyww/KOd7yj6SgANER5BAAA1Lrhhhvy7LPP5vrrr286CgANUR4BAAB7tH79+tx0002pqirLly/Phg0bmo4EQAOURwAAwB4NDAxk27ZtSZJt27ZlYGCg4UQANEF5BAAA7NGKFSuyZcuWJMmWLVty8803N5wIgCYojwAAgD1asGBBOjo6kiQdHR0555xzGk4EQBOURwAAwB719vZmwoTtvzJMmDAhvb29DScCoAnKIwAAYI+mTZuWhQsXppSSRYsWpbOzs+lIADRgUtMBAACAsau3tzcPPfSQVUcABzHlEQAAUGvatGlZtmxZ0zEAaJBtawAAAADUUh4BAAAAUEt5BAAAAEAt5REAAAAAtZRHAAAAANRSHgEAAABQS3kEAAAAQC3lEQAAAAC1lEcAAAAA1FIeAQAAAFBLeQQAAABALeURAAAAALWURwAAAADUUh4BAAAAUEt5BAAAAEAt5REAAAAAtZRHAAAAANRSHgEAAABQS3kEAAAAQC3lEQAAAAC1JjUdAABgX/T392dwcLDpGPvshz/8YTZv3pwLL7wwHR0dTcfZJ93d3enr62s6BgAwxlh5BADQAlu3bs22bdvyyCOPNB0FAGC/WHkEAIwL42lFzPr163PWWWclSTZt2pQlS5aks7Oz4VQAACNj5REAwCi74oorsm3btiTbVyBdccUVDScCABg55REAwCj7x3/8x13Gt9xyS0NJAAD2n/IIAAAAgFrKIwCAUfbS/Y5ecvbZZzeUBABg/ymPAABG2Xvf+95dxu973/saSgIAsP+URwAAo+yGG27YZXz99dc3lAQAYP8pjwAARtmKFSt2Gd98880NJQEA2H/KIwCAUbZgwYJ0dHQkSTo6OnLOOec0nAgAYOSURwAAo6y3tzcTJmz/MWvChAnp7e1tOBEAwMgpjwAARtm0adOycOHClFKyaNGidHZ2Nh0JAGDEJjUdAACgHfX29uahhx6y6ggAGPeURwAALTBt2rQsW7as6RgAAPvNtjUAAAAAaimPAAAAAKilPAIAAACglvIIAAAAgFrKIwAAAABqKY8AAAAAqKU8AgAAAKCW8ggAAACAWsojAAAAAGopjwAAAACopTwCAAAAoNakpgPAWNbf35/BwcGmY+yzl7L29PQ0nGTfdXd3p6+vr+kYAAAA1FAeQRs59NBDm44AAABAm1EewV5YEQMAAMDBzj2PoI3ceeedecMb3pC777676SgAAAC0iZaWR6WUs0opg6WUB0spi/fw+v8spfyglHJ/KeW2UsqrWpkH2t1HP/rRbNu2zYopAAAARk3LyqNSysQkVyY5O8nrkpxfSnndbtPuTTKnqqqTklyX5C9alQfa3Z133pmnn346SfLUU09ZfQQAAMCoaOXKo99M8mBVVT+qquoXSb6cZNHOE6qqur2qqudeHH47yawW5oG29tGPfnSXsdVHAAAAjIZWlkfHJhnaabzmxWN1Lk5ySwvzQFt7adXRS5566qmGkgCQJOvXr8/FF1+cDRs2NB0FAGC/tPJpa2UPx6o9TizlfUnmJPmvNa9fkuSSJJkxY0ZWrlw5ShGhfbzyla/Mz3/+813GvlcYrzZt2pQkrmHGteuuuy733ntvLrvssrzzne9sOg4AwIi1sjxak6Rrp/GsJI/uPqmU8rYkH0vyX6uq+vnurydJVVVXJbkqSebMmVPNmzdv1MPCeHfkkUfmZz/72Y7xUUcdFd8rjFfXXnttkriGGbfWr1+fxYsXp6qqrFq1Kpdddlk6OzubjgUAMCKt3LZ2T5ITSyknlFJekeTdSW7aeUIp5ZQkn02ysKqqx1qYBdrezsVRkqxdu7ahJAAMDAxk27ZtSZJt27ZlYGCg4UQAACPXsvKoqqoXklya5J+S/DDJ31VV9f1SyidKKQtfnNaf5PAkXymlrC6l3FRzOuBlTJkyZZfxEUcc0VASAFasWJEtW7YkSbZs2ZKbb7654UQAACPXym1rqapqRZIVux37+E4fv62V7w8Hk5d+SXnJL37xi4aSALBgwYLceOON2bJlSzo6OnLOOec0HQkAYMRauW0NOIDe9rZdu9j58+c3lASA3t7eTJiw/cesCRMmpLe3t+FEAAAjpzwCABhl06ZNy8KFC1NKyaJFi9wsGwAY15RH0CZuv/32Xcb/8i//0lASAJLtq49OOeUUq44AgHGvpfc8Ag6ct771rfmHf/iHHePf+q3fajANY01/f38GBwebjrHPXsra09PTcJJ9093dnb6+vqZjMMZMmzYty5YtazoGAMB+Ux5Bm9i8efNexzCeHHrooU1HAAAAXqQ8gjZh2xp7Y1UMAAAwUu55BG1i69atex0DAADASCiPAAAAAKilPAIAAACglvIIAAAAgFrKI2gTEydO3OsYAAAARkJ5BG3irW996y7jM844o6EkAAAAtBPlEbSJn//857uMN2/e3FASAAAA2onyCNrEN7/5zV3G3/jGNxpKAgAAQDtRHkGbqKpqr2MAAAAYCeURtIlXvepVex0DAADASCiPoE0sWbJkl/Ff/MVfNJQEAACAdqI8AgAAAKCW8gjaxMc+9rFdxn/yJ3/SUBIAAADaifII2sSPfvSjXcYPPfRQQ0kAAABoJ8ojaBNumE07Wb9+fS6++OJs2LCh6SgAAHDQUx5BmzjuuON2GSuPGM8GBgZy7733ZmBgoOkoAABw0FMeQZv41re+tcv4jjvuaCgJ7J/169fnpptuSlVVWb58udVHAADQMOURtImtW7fudQzjxcDAQLZt25Yk2bZtm9VHAADQMOURAGPKihUrsmXLliTJli1bcvPNNzecCAAADm7KIwDGlAULFqSjoyNJ0tHRkXPOOafhRAAAcHBTHkGbOOyww/Y6hvGit7c3EyZs/8/ThAkT0tvb23AiAAA4uCmPoE3Mnj17l/HrX//6hpLA/pk2bVoWLlyYUkoWLVqUzs7OpiMBAMBBbVLTAYDR8Z3vfGeX8apVqxpKAvuvt7c3Dz30kFVHAAAwBiiPABhzpk2blmXLljUdAwAAiG1r0DaOPfbYvY4BAABgJJRH0CbWrVu31zEAAACMhPII2sSRRx651zEAAACMhPII2sTatWv3OgYAAICRUB4BAAAAUEt5BG1i8uTJex0DAADASCiPoE2UUnYZT5jg2xsAAID957dLaBPPP//8LuPnnnuuoSQAAAC0E+URtIkpU6bsMj7iiCMaSgIAAEA7UR5Bm7j88st3Gff//+3dT4jed50H8PcnM42ULWxrG2RJZGuaZUW97CKB4iJt9OC2ialJ1z8XFwnTXjyWIuxN8GClCgN7qGOl0tbdhbZIkpnUQwymrMuqVGob12KTHja04EyztF0PSub5eshE8sQ8STrJ05/9+nqdfp/f853h/Rzm8p7P73m+9rWBkgAAANAT5RF0Ytu2bWPz1q1bB0oCAABAT5RH0ImFhYXMzMwkSWZmZrKwsDBwIgAAAHqgPIJOLC0tZXV1NUmyurqaxcXFgRMBAADQA+URdOKOO+7Ihg1n/qQ3bNiQO++8c+BEAAAA9EB5BJ3Ys2dPRqNRkmQ0GmXv3r0DJwIAAKAHyiPoxFNPPTU2P/nkkwMlAQAAoCfKI+jEwYMHx+YDBw4MlAQAAICeKI+gE7Ozs2PzNddcM1ASAAAAeqI8gk68+eabY/Mbb7wxUBIAAAB6ojyCTmzdunVsvuWWWwZKAgAAQE+UR9CJubm5sfnee+8dKAkAAAA9UR5BJxYWFsbmhx56aKAkAAAA9ER5BJ04ceLE2Hz8+PGBkgAAANAT5RF0wmceAQAAMA3KI+jEfffdNzbff//9AyUBAACgJ8oj6MSRI0fG5sOHDw+UBAAAgJ4oj6ATBw8eHJsPHDgwUBIAAAB6ojyCTlx//fVj8w033DBQEgAAAHqiPIJOvPrqq2PzK6+8MlASAAAAeqI8AgAAAGAi5RF0YnZ29qIzAAAArIfyCDoxMzNz0RkAAADWQ3kEndi8efPYvGXLloGSAAAA0BPlEXTCB2YDAAAwDcoj6MTOnTvH5l27dg2UBAAAgJ4oj6ATe/bsGZv37t07UBIAAAB6ojyCTjz++ONj82OPPTZQEgAAAHqiPIJOHDp0aGxeWloaKAkAAAA9UR5BJ1ZXVy86AwAAwHoojwAAAACYSHkEAAAAwETKI+jEddddd9EZAAAA1kN5BJ144IEHxuYHH3xwoCQAAAD0RHkEnbj11lszOzubJJmdnc327dsHTgQAAEAPlEfQieXl5T9cV1VWVlYGTAMAAEAvlEfQiYWFhT9ct9bGZgAAAFgv5RF0YmlpKadPn06SnD59OouLiwMnAgAAoAfKI+jE7bffPjbv2LFjoCQAAAD0RHkEnXj99dcvOgMAAMB6KI+gE88888zYfPTo0YGSAAAA0BPlEQAAAAATKY8AAAAAmEh5BJ3YvHnz2Lxly5aBkgAAANAT5RF04tSpU2Pza6+9NlASAAAAeqI8gk7s3LlzbN61a9dASQAAAOiJ8gg6MTc3l40bNyZJNm7cmLm5uYETAQAA0APlEXRi06ZN2b17d6oqd911V2666aahIwEAANCB2aEDAFfP3Nxcjh8/busIAACAq0Z5BB3ZtGlTHn744aFjAAAA0BGPrUFHlpeXs2/fvqysrAwdBQAAgE5MtTyqqk9U1YtV9VJVfekCr7+rqv5j7fX/rqqbp5kHejc/P59nn3028/PzQ0cBAACgE1Mrj6pqJsm/JvnHJB9I8rmq+sB5x/Yl+b/W2rYk30jy1Wnlgd4tLy9naWkpSbK4uGj7CAAAgKtimptH25O81Fo70Vr7XZJ/T7L7vDO7k3xn7fqJJB+rqppiJujW/Px8RqNRkmQ0Gtk+AgAA4KqYZnm0Ocn/njOfXLt3wTOttdNJXk9y4xQzQbeefvrpsfnQoUMDJQEAAKAn0/y2tQttELV1nElV3ZPknrXx/6vqxSvMBt259tpr/z7jf1Otqp4dKg8ASZKbkniOGAB4J/jrSS9Mszw6meS958xbkrwy4czJqppN8pdJTp3/i1pr30zyzSnlBACYiqr6aWvtw0PnAAC4EtN8bO0nSf6mqt5XVRuTfDbJ/vPO7E/yz2vXdyf5QWvtjzaPAAAAABjG1DaPWmunq+qLSb6fZCbJt1trx6rqy0l+2lrbn+ThJI9W1Us5s3H02WnlAQAAAOCtK4s+AADTUVX3rD1+DwDwjqU8AgAAAGCiaX7mEQAAAADvcMojAIB1qKpHquruyzz77ar6dVW9cN79f6qqY1U1qirfygYA/ElSHgEATElVzaxdPpLkExc48kKSPUmOvl2ZAADeKuURAMBlqKrPV9XPq+q5qnp07fZHq+pHVXXi7BZSVd1WVUeq6rtJnk+S1trRnPlm2TGttf9prb34tr0JAIB1mB06AADAn7qq+mCSf0nykdbaSlW9O8nXk/xVkn9I8v4k+5M8sfYj25N8qLX28hB5AQCuJptHAACXtiPJE621lSRprZ3dIvpea23UWvtFkvecc/7HiiMAoBfKIwCAS6sk7QL3f3vembN+M904AABvH+URAMClHU7y6aq6MUnWHlsDAPizoDwCALiE1tqxJF9J8sOqei5nPu/oslXVvyX5ryR/W1Unq2rf2v1PVdXJJLcmWayq71/l6AAAV6xau9AGNgAAAADYPAIAAADgIpRHAAAAAEykPAIAAABgIuURAAAAABMpjwAAAACYSHkEAHAFquqRqrp76BwAANOiPAIAmLKqmhk6AwDAeimPAADegqr6fFX9vKqeq6pH125/tKp+VFUnzm4hVdVtVXWkqr6b5PmqurmqfllV36qqF6rq8ar6eFX9Z1X9qqq2D/euAAAmq9ba0BkAAN4RquqDSZ5K8pHW2kpVvTvJ15P8RZLPJHl/kv2ttW1VdVuSxSQfaq29XFU3J3kpyd8lOZbkJ0meS7IvySeTfKG1dtfb+44AAC7N5hEAwOXbkeSJ1tpKkrTWTq3d/15rbdRa+0WS95xz/settZfPmV9urT3fWhvlTIF0uJ35T97zSW6efnwAgLdOeQQAcPkqyYXWtn973pmzfnORc6Nz5lGS2StOBwAwBcojAIDLdzjJp6vqxiRZe2wNAKBr/sMFAHCZWmvHquorSX5YVatJfjZ0JgCAafOB2QAAAABM5LE1AAAAACZSHgEAAAAwkfIIAAAAgImURwAAAABMpDwCAAAAYCLlEQAAAAATKY8AAAAAmEh5BAAAAMBEvwcApgfeN1ItzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\"\n",
    "                 ,hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\"\n",
    "                 ,hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\"\n",
    "                 ,hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\"\n",
    "                 ,hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_scores_plot = pd.concat([df_scores.drop(['plot'], axis=1), df_scores['plot'].apply(pd.Series)], axis=1)\n",
    "df_plot = df_scores_plot.groupby(['chrm']).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       {0.0: 0.15891472868217105, 0.1: 0.358914728682...\n",
       "1       {0.0: 0.19172932330827042, 0.1: 0.479591836734...\n",
       "2       {0.0: 0.027435610302351626, 0.1: 0.14389697648...\n",
       "3       {0.0: 0.03850267379679142, 0.1: 0.107486631016...\n",
       "4       {0.0: 0.3575498575498566, 0.1: 0.7236467236467...\n",
       "                              ...                        \n",
       "999     {0.0: 0.0858757062146893, 0.1: 0.3598870056497...\n",
       "1000    {0.0: 0.09021739130434761, 0.1: 0.192391304347...\n",
       "1001    {0.0: 0.03621346886912323, 0.1: 0.156289707750...\n",
       "1002    {0.0: 0.09125188536953226, 0.1: 0.208898944193...\n",
       "1003    {0.0: 0.06352459016393448, 0.1: 0.174863387978...\n",
       "Name: plot, Length: 1004, dtype: object"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores['plot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "for thresh in df_plot.index.tolist():\n",
    "    axes = df_plot.T.reset_index()[11::].plot.scatter(x='index', y=thresh)\n",
    "    #axes = zt.reset_index().plot.scatter(x='x_p', y=0, s=1)\n",
    "    #axes.plot([0, 1], [0, 1], 'red', linewidth=1)\n",
    "    #axes = df_plot.T.reset_index().plot(x='index', y=0.3)\n",
    "    axes.plot([0, 1], [0, 1], 'red', linewidth=1)\n",
    "    #bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "    #axes.set_title('Predicting structure similarity from expression')\n",
    "    #axes.yaxis.grid(True)\n",
    "    #axes.set_xlabel('Co-expression')\n",
    "    #axes.set_ylabel('AUC')\n",
    "    #axes.set_ylim([0.0,1.101])\n",
    "    #plt.setp(bp['fliers'], markersize=3.0)\n",
    "    #0.5\n",
    "    #fig, axes = plt.subplots()\n",
    "    #axes.scatter(x, y)\n",
    "    #axes.plot(x, y)\n",
    "    axes.set_ylim([0,1])\n",
    "    axes.set_xlim([0,1])\n",
    "\n",
    "    #plt.show()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "for thresh in df_plot.index.tolist():\n",
    "    axes = df_plot.T.reset_index()[11::].plot.scatter(x='index', y=thresh)\n",
    "    #axes = zt.reset_index().plot.scatter(x='x_p', y=0, s=1)\n",
    "    #axes.plot([0, 1], [0, 1], 'red', linewidth=1)\n",
    "    #axes = df_plot.T.reset_index().plot(x='index', y=0.3)\n",
    "    axes.plot([0, 1], [0, 1], 'red', linewidth=1)\n",
    "    #bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "    #axes.set_title('Predicting structure similarity from expression')\n",
    "    #axes.yaxis.grid(True)\n",
    "    #axes.set_xlabel('Co-expression')\n",
    "    #axes.set_ylabel('AUC')\n",
    "    #axes.set_ylim([0.0,1.101])\n",
    "    #plt.setp(bp['fliers'], markersize=3.0)\n",
    "    #0.5\n",
    "    #fig, axes = plt.subplots()\n",
    "    #axes.scatter(x, y)\n",
    "    #axes.plot(x, y)\n",
    "    axes.set_ylim([0,1])\n",
    "    axes.set_xlim([0,1])\n",
    "\n",
    "    #plt.show()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>chrm</th>\n",
       "      <th>num_pairs</th>\n",
       "      <th>dist_thresh</th>\n",
       "      <th>auc</th>\n",
       "      <th>pr_curve</th>\n",
       "      <th>true_pos</th>\n",
       "      <th>true_neg</th>\n",
       "      <th>exp_median</th>\n",
       "      <th>exp_mean</th>\n",
       "      <th>exp_var</th>\n",
       "      <th>...</th>\n",
       "      <th>0.82</th>\n",
       "      <th>0.83</th>\n",
       "      <th>0.85</th>\n",
       "      <th>0.86</th>\n",
       "      <th>0.88</th>\n",
       "      <th>0.89</th>\n",
       "      <th>0.9</th>\n",
       "      <th>0.91</th>\n",
       "      <th>0.92</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.603357</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>566</td>\n",
       "      <td>2</td>\n",
       "      <td>0.818331</td>\n",
       "      <td>0.709948</td>\n",
       "      <td>0.077252</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.863074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>480</td>\n",
       "      <td>1</td>\n",
       "      <td>0.711796</td>\n",
       "      <td>0.638367</td>\n",
       "      <td>0.072380</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.990826</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>436</td>\n",
       "      <td>1</td>\n",
       "      <td>0.680995</td>\n",
       "      <td>0.613632</td>\n",
       "      <td>0.075320</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.995413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.964930</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>499</td>\n",
       "      <td>2</td>\n",
       "      <td>0.690213</td>\n",
       "      <td>0.630627</td>\n",
       "      <td>0.070869</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.994990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.937400</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>623</td>\n",
       "      <td>2</td>\n",
       "      <td>0.712888</td>\n",
       "      <td>0.625489</td>\n",
       "      <td>0.079132</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.981541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.074297</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>498</td>\n",
       "      <td>1</td>\n",
       "      <td>0.061497</td>\n",
       "      <td>0.109356</td>\n",
       "      <td>0.028743</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.537149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.426445</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>571</td>\n",
       "      <td>2</td>\n",
       "      <td>0.264574</td>\n",
       "      <td>0.274264</td>\n",
       "      <td>0.040111</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.834501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.650485</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>618</td>\n",
       "      <td>1</td>\n",
       "      <td>0.480949</td>\n",
       "      <td>0.495839</td>\n",
       "      <td>0.064536</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.825243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.793478</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>552</td>\n",
       "      <td>3</td>\n",
       "      <td>0.684659</td>\n",
       "      <td>0.632675</td>\n",
       "      <td>0.075314</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.987319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>chr11</td>\n",
       "      <td>1004</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.855932</td>\n",
       "      <td>{0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....</td>\n",
       "      <td>472</td>\n",
       "      <td>1</td>\n",
       "      <td>0.745523</td>\n",
       "      <td>0.660855</td>\n",
       "      <td>0.078882</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.927966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1004 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       chrm  num_pairs  dist_thresh       auc  \\\n",
       "0     chr11       1004          2.0  0.603357   \n",
       "1     chr11       1004          1.0  0.950000   \n",
       "2     chr11       1004          1.0  0.990826   \n",
       "3     chr11       1004          2.0  0.964930   \n",
       "4     chr11       1004          5.0  0.937400   \n",
       "...     ...        ...          ...       ...   \n",
       "999   chr11       1004          2.0  0.074297   \n",
       "1000  chr11       1004          6.0  0.426445   \n",
       "1001  chr11       1004          6.0  0.650485   \n",
       "1002  chr11       1004          6.0  0.793478   \n",
       "1003  chr11       1004          6.0  0.855932   \n",
       "\n",
       "                                               pr_curve  true_pos  true_neg  \\\n",
       "0     {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       566         2   \n",
       "1     {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       480         1   \n",
       "2     {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       436         1   \n",
       "3     {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       499         2   \n",
       "4     {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       623         2   \n",
       "...                                                 ...       ...       ...   \n",
       "999   {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       498         1   \n",
       "1000  {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       571         2   \n",
       "1001  {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       618         1   \n",
       "1002  {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       552         3   \n",
       "1003  {0.0: 1.0, 0.01: 1.0, 0.02: 1.0, 0.03: 1.0, 0....       472         1   \n",
       "\n",
       "      exp_median  exp_mean   exp_var  ... 0.82  0.83  0.85  0.86  0.88  0.89  \\\n",
       "0       0.818331  0.709948  0.077252  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1       0.711796  0.638367  0.072380  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2       0.680995  0.613632  0.075320  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "3       0.690213  0.630627  0.070869  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "4       0.712888  0.625489  0.079132  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "...          ...       ...       ...  ...  ...   ...   ...   ...   ...   ...   \n",
       "999     0.061497  0.109356  0.028743  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1000    0.264574  0.274264  0.040111  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1001    0.480949  0.495839  0.064536  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1002    0.684659  0.632675  0.075314  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1003    0.745523  0.660855  0.078882  ...  NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "      0.9  0.91  0.92       1.0  \n",
       "0     NaN   NaN   NaN  0.863074  \n",
       "1     NaN   NaN   NaN  0.975000  \n",
       "2     NaN   NaN   NaN  0.995413  \n",
       "3     NaN   NaN   NaN  0.994990  \n",
       "4     NaN   NaN   NaN  0.981541  \n",
       "...   ...   ...   ...       ...  \n",
       "999   NaN   NaN   NaN  0.537149  \n",
       "1000  NaN   NaN   NaN  0.834501  \n",
       "1001  NaN   NaN   NaN  0.825243  \n",
       "1002  NaN   NaN   NaN  0.987319  \n",
       "1003  NaN   NaN   NaN  0.927966  \n",
       "\n",
       "[1004 rows x 66 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_scores_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(figsize=(20,10))\n",
    "#grouped = df_scores.groupby(['threshold'])\n",
    "\n",
    "#bp = grouped.boxplot(subplots=False, sym='k+', figsize=(8,10))\n",
    "#bp = df_scores.boxplot(column=['auc'], by=['chrm', 'dist_thresh'], ax=axes,rot=40, fontsize=8,layout=(2, 1))\n",
    "sns.boxplot(y='auc', x='chrm', \n",
    "                 data=df_scores, \n",
    "                 palette=\"colorblind\",\n",
    "                 hue='dist_thresh'\n",
    "           )\n",
    "#bp = axes.boxplot([[x if x>=0 else -1 for x in top_500_score_auroc_0_9], [x if x>=0 else -1 for x in top_500_score_auroc_0_7], [x if x>=0 else -1 for x in top_500_score_auroc_0_5], [x if x>=0 else -1 for x in top_500_score_auroc_0_4]] , sym='k+')\n",
    "#axes.set_title('Predicting structure similarity from expression')\n",
    "axes.yaxis.grid(True)\n",
    "#axes.set_xlabel('Co-expression')\n",
    "axes.set_ylabel('AUC')\n",
    "axes.set_ylim([0.0,1.101])\n",
    "#plt.setp(bp['fliers'], markersize=3.0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}