ClassifNNerrors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YES\n"
     ]
    }
   ],
   "source": [
    "import os, sys\n",
    "LibPath = '../'\n",
    "if LibPath in sys.path:\n",
    "    print('YES')\n",
    "else:\n",
    "    print('NO, we\\'ll add it now')\n",
    "    sys.path.append(LibPath)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch.nn import Linear\n",
    "from torch_geometric.utils import from_networkx\n",
    "from torch_geometric.loader import DataLoader\n",
    "from torch_geometric.nn import GCNConv, global_mean_pool\n",
    "\n",
    "from random import shuffle\n",
    "from tqdm import tqdm\n",
    "from scipy.cluster.hierarchy import linkage, fcluster, leaders, ClusterNode\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "from Libraries.Util import Norm01, MovingAverage, RemTrend\n",
    "from Libraries.graph import AdjMatr, TransMatr, GraphEntropy\n",
    "from Libraries.features import  CEmbDim #CCorrent, CHurst,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"   # see issue #152\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_traject(row):\n",
    "    mindist=.03\n",
    "    demb=8  #CEmbDim(row)# В локальной версии можно рассчитывать #from Libraries.features import CCorrent, CHurst, CEmbDim\n",
    "    depth=demb#*2+1\n",
    "    traject=pd.DataFrame(columns=['step']+['t-'+str(j) for j in range(depth-1,-1,-1)])\n",
    "    traject['step']=np.arange(len(row)-depth)\n",
    "    for i in range(len(row)-depth):\n",
    "        traject.loc[i, traject.columns[1:]]=row[i:i+depth]\n",
    "    '''матрица связей для кластеризации'''\n",
    "    link = linkage(traject[traject.columns[1:]], 'ward', 'euclidean')\n",
    "    '''оптимальное расстояние для кластеризации'''\n",
    "    dist = link[:, 2]\n",
    "    dist_rev = dist[::-1]\n",
    "    i=np.where(np.abs(dist_rev[:-5]-dist_rev[5:])<(max(dist_rev)*mindist))[0][0]\n",
    "    d=dist_rev[i]\n",
    "    '''кластеризация'''\n",
    "    traject['cluster']=fcluster(link, d, criterion='distance')\n",
    "    traject['cluster']=[list(traject['cluster'].drop_duplicates().values).index(i)+1 for i in traject['cluster']]\n",
    "    traject['cluster']=traject['cluster'].astype(int)\n",
    "    '''делание графа''' \n",
    "    return traject\n",
    "def make_gr(traject):\n",
    "    nod=traject['cluster'].values\n",
    "    G=nx.DiGraph()\n",
    "    clu=traject['cluster'].unique() \n",
    "    nodes=[i for i in zip(clu, [{'points':len(traject[traject.cluster==c])/len(traject), #node capacity\n",
    "                                 'mean':np.mean(traject[traject.cluster==c]['t-0'].values)} for c in clu])] #mean value of the data in the node\n",
    "    #np.asarray(list(nx.degree_centrality(G).values()))\n",
    "    G.add_nodes_from(nodes)\n",
    "    r=[(nod[j], nod[j+1]) for j in range(len(traject)-1)]\n",
    "    c=[r.count(i) for i in r]\n",
    "    G.add_weighted_edges_from([(nod[j], nod[j+1],c[j]) for j in range(len(traject)-1)])\n",
    "    return G\n",
    "def show_gr(G):\n",
    "    fig=plt.figure(figsize=(7, 7))\n",
    "    lnd=G.number_of_nodes()+1\n",
    "    nnd=lnd//7\n",
    "    nlist=[range(i,i+lnd//nnd) for i in range(0,lnd-lnd//nnd,lnd//nnd)]+[range(lnd-lnd//nnd,lnd)]\n",
    "    nx.draw(G, pos=nx.shell_layout(G, nlist=nlist),\n",
    "                   with_labels=True,\n",
    "                   node_color='pink', font_weight='bold',font_size=10,\n",
    "                   edge_color='b',node_size=300, width=2 )\n",
    "    #nx.spectral_layout(G), random, circular\n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>t</th>\n",
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       "      <td>0.649513</td>\n",
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       "      <td>0.940617</td>\n",
       "      <td>0.009029</td>\n",
       "      <td>0.599111</td>\n",
       "      <td>0.168570</td>\n",
       "      <td>0.129886</td>\n",
       "      <td>0.734205</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>0.260337</td>\n",
       "      <td>0.260337</td>\n",
       "      <td>0.260337</td>\n",
       "      <td>0.260710</td>\n",
       "      <td>0.262667</td>\n",
       "      <td>0.260693</td>\n",
       "      <td>0.262096</td>\n",
       "      <td>0.265751</td>\n",
       "      <td>...</td>\n",
       "      <td>0.908652</td>\n",
       "      <td>0.640843</td>\n",
       "      <td>0.363166</td>\n",
       "      <td>0.750696</td>\n",
       "      <td>0.997029</td>\n",
       "      <td>0.001046</td>\n",
       "      <td>0.583546</td>\n",
       "      <td>0.116514</td>\n",
       "      <td>0.086301</td>\n",
       "      <td>0.724486</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.320063</td>\n",
       "      <td>0.320063</td>\n",
       "      <td>0.320063</td>\n",
       "      <td>0.320063</td>\n",
       "      <td>0.320280</td>\n",
       "      <td>0.322056</td>\n",
       "      <td>0.320117</td>\n",
       "      <td>0.321595</td>\n",
       "      <td>0.325106</td>\n",
       "      <td>...</td>\n",
       "      <td>0.934933</td>\n",
       "      <td>0.665507</td>\n",
       "      <td>0.356261</td>\n",
       "      <td>0.776824</td>\n",
       "      <td>0.923966</td>\n",
       "      <td>0.017329</td>\n",
       "      <td>0.606290</td>\n",
       "      <td>0.179704</td>\n",
       "      <td>0.103170</td>\n",
       "      <td>0.745012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.378571</td>\n",
       "      <td>0.378571</td>\n",
       "      <td>0.378571</td>\n",
       "      <td>0.378571</td>\n",
       "      <td>0.378456</td>\n",
       "      <td>0.380108</td>\n",
       "      <td>0.378335</td>\n",
       "      <td>0.380094</td>\n",
       "      <td>0.383261</td>\n",
       "      <td>...</td>\n",
       "      <td>0.941699</td>\n",
       "      <td>0.677883</td>\n",
       "      <td>0.407456</td>\n",
       "      <td>0.810320</td>\n",
       "      <td>0.861631</td>\n",
       "      <td>0.042103</td>\n",
       "      <td>0.604748</td>\n",
       "      <td>0.159807</td>\n",
       "      <td>0.075890</td>\n",
       "      <td>0.733275</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.435269</td>\n",
       "      <td>0.435269</td>\n",
       "      <td>0.435269</td>\n",
       "      <td>0.435269</td>\n",
       "      <td>0.435137</td>\n",
       "      <td>0.436532</td>\n",
       "      <td>0.434730</td>\n",
       "      <td>0.436384</td>\n",
       "      <td>0.439805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.894942</td>\n",
       "      <td>0.643097</td>\n",
       "      <td>0.435345</td>\n",
       "      <td>0.828033</td>\n",
       "      <td>0.891476</td>\n",
       "      <td>0.081389</td>\n",
       "      <td>0.638462</td>\n",
       "      <td>0.167049</td>\n",
       "      <td>0.113257</td>\n",
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       "      <td>0.434618</td>\n",
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       "      <td>0.523453</td>\n",
       "      <td>0.155611</td>\n",
       "      <td>0.793299</td>\n",
       "      <td>0.090298</td>\n",
       "      <td>0.551612</td>\n",
       "      <td>0.559820</td>\n",
       "      <td>0.726068</td>\n",
       "      <td>0.517497</td>\n",
       "      <td>0.267537</td>\n",
       "      <td>0.716003</td>\n",
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       "    <tr>\n",
       "      <th>496</th>\n",
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       "      <td>0.378571</td>\n",
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       "      <td>0.378571</td>\n",
       "      <td>0.378571</td>\n",
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       "      <td>0.377828</td>\n",
       "      <td>0.378640</td>\n",
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       "      <td>0.378712</td>\n",
       "      <td>...</td>\n",
       "      <td>0.568935</td>\n",
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       "      <td>0.775095</td>\n",
       "      <td>0.106035</td>\n",
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       "      <th>497</th>\n",
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       "      <td>0.320063</td>\n",
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       "      <td>0.321480</td>\n",
       "      <td>0.320921</td>\n",
       "      <td>...</td>\n",
       "      <td>0.581972</td>\n",
       "      <td>0.170990</td>\n",
       "      <td>0.754697</td>\n",
       "      <td>0.057452</td>\n",
       "      <td>0.513601</td>\n",
       "      <td>0.486238</td>\n",
       "      <td>0.722791</td>\n",
       "      <td>0.503879</td>\n",
       "      <td>0.347248</td>\n",
       "      <td>0.704792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>498</td>\n",
       "      <td>0.260337</td>\n",
       "      <td>0.260337</td>\n",
       "      <td>0.260337</td>\n",
       "      <td>0.260337</td>\n",
       "      <td>0.259482</td>\n",
       "      <td>0.260102</td>\n",
       "      <td>0.260787</td>\n",
       "      <td>0.261976</td>\n",
       "      <td>0.261640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.613724</td>\n",
       "      <td>0.173882</td>\n",
       "      <td>0.705824</td>\n",
       "      <td>0.046649</td>\n",
       "      <td>0.440846</td>\n",
       "      <td>0.511512</td>\n",
       "      <td>0.731909</td>\n",
       "      <td>0.458787</td>\n",
       "      <td>0.348476</td>\n",
       "      <td>0.666617</td>\n",
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       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>499</td>\n",
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       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.199197</td>\n",
       "      <td>0.200025</td>\n",
       "      <td>0.200688</td>\n",
       "      <td>0.201853</td>\n",
       "      <td>0.201495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.591918</td>\n",
       "      <td>0.177947</td>\n",
       "      <td>0.707898</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>0.480486</td>\n",
       "      <td>0.539054</td>\n",
       "      <td>0.750931</td>\n",
       "      <td>0.522272</td>\n",
       "      <td>0.347483</td>\n",
       "      <td>0.677406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 1001 columns</p>\n",
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      ],
      "text/plain": [
       "       t       000       001       002       003       004       005  \\\n",
       "0      0  0.200000  0.200000  0.200000  0.200000  0.200482  0.202545   \n",
       "1      1  0.260337  0.260337  0.260337  0.260337  0.260710  0.262667   \n",
       "2      2  0.320063  0.320063  0.320063  0.320063  0.320280  0.322056   \n",
       "3      3  0.378571  0.378571  0.378571  0.378571  0.378456  0.380108   \n",
       "4      4  0.435269  0.435269  0.435269  0.435269  0.435137  0.436532   \n",
       "..   ...       ...       ...       ...       ...       ...       ...   \n",
       "495  495  0.435269  0.435269  0.435269  0.435269  0.433664  0.434468   \n",
       "496  496  0.378571  0.378571  0.378571  0.378571  0.377338  0.377828   \n",
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       "498  498  0.260337  0.260337  0.260337  0.260337  0.259482  0.260102   \n",
       "499  499  0.200000  0.200000  0.200000  0.200000  0.199197  0.200025   \n",
       "\n",
       "          006       007       008  ...       990       991       992  \\\n",
       "0    0.200543  0.202054  0.205929  ...  0.866122  0.649513  0.333150   \n",
       "1    0.260693  0.262096  0.265751  ...  0.908652  0.640843  0.363166   \n",
       "2    0.320117  0.321595  0.325106  ...  0.934933  0.665507  0.356261   \n",
       "3    0.378335  0.380094  0.383261  ...  0.941699  0.677883  0.407456   \n",
       "4    0.434730  0.436384  0.439805  ...  0.894942  0.643097  0.435345   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "495  0.434958  0.436222  0.434618  ...  0.523453  0.155611  0.793299   \n",
       "496  0.378640  0.379535  0.378712  ...  0.568935  0.178591  0.775095   \n",
       "497  0.320297  0.321480  0.320921  ...  0.581972  0.170990  0.754697   \n",
       "498  0.260787  0.261976  0.261640  ...  0.613724  0.173882  0.705824   \n",
       "499  0.200688  0.201853  0.201495  ...  0.591918  0.177947  0.707898   \n",
       "\n",
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       "1    0.750696  0.997029  0.001046  0.583546  0.116514  0.086301  0.724486  \n",
       "2    0.776824  0.923966  0.017329  0.606290  0.179704  0.103170  0.745012  \n",
       "3    0.810320  0.861631  0.042103  0.604748  0.159807  0.075890  0.733275  \n",
       "4    0.828033  0.891476  0.081389  0.638462  0.167049  0.113257  0.774902  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "495  0.090298  0.551612  0.559820  0.726068  0.517497  0.267537  0.716003  \n",
       "496  0.106035  0.581839  0.537680  0.747879  0.461279  0.305500  0.707708  \n",
       "497  0.057452  0.513601  0.486238  0.722791  0.503879  0.347248  0.704792  \n",
       "498  0.046649  0.440846  0.511512  0.731909  0.458787  0.348476  0.666617  \n",
       "499  0.000803  0.480486  0.539054  0.750931  0.522272  0.347483  0.677406  \n",
       "\n",
       "[500 rows x 1001 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds=pd.read_csv('artdata_1000.csv')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 1000/1000 [10:01<00:00,  1.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done! 1000\n"
     ]
    }
   ],
   "source": [
    "'''делание графов'''\n",
    "nG=[]\n",
    "for c in tqdm(ds.columns[1:]):\n",
    "#     if c<'165': l=0\n",
    "#     elif c<'330': l=1\n",
    "#     else: l=2\n",
    "    if c<'300': l=0\n",
    "    elif c<'700': l=1\n",
    "    else: l=2\n",
    "    row=ds[c].values\n",
    "    #row=RemTrend(row)[0]\n",
    "    traject=make_traject(row)\n",
    "    G=make_gr(traject)\n",
    "    tG=from_networkx(G, group_node_attrs=['points','mean'], group_edge_attrs=all) \n",
    "    cent=np.asarray(list(nx.degree_centrality(G).values()))# degree_centrality to node features\n",
    "    cent=torch.tensor(cent.reshape(len(cent),1), dtype=float)\n",
    "    tG.x=torch.cat((tG.x, cent),1) #node centrality to feture list\n",
    "    tG.x=tG.x.float()\n",
    "    tG.y=torch.tensor([l])\n",
    "    nG.append(tG)\n",
    "print('Done!', len(nG))\n",
    "#trmat=TransMatr(G0, paint=True) #from Libraries.graph import AdjMatr, TransMatr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================\n",
      "Number of graphs: 1000\n",
      "\n",
      "Data(edge_index=[2, 26], x=[13, 3], edge_attr=[26, 1], y=[1])\n",
      "=============================================================\n",
      "Number of nodes: 13\n",
      "Number of edges: 26\n",
      "Average node degree: 2.00\n",
      "Has isolated nodes: False\n",
      "Has self-loops: True\n",
      "Is undirected: False\n"
     ]
    }
   ],
   "source": [
    "batch=25\n",
    "loader = DataLoader(nG.copy(), batch_size=batch, shuffle=True)\n",
    "dataset=loader.dataset\n",
    "print()\n",
    "#print(f'Dataset: {dataset}:')\n",
    "print('====================')\n",
    "print(f'Number of graphs: {len(dataset)}')\n",
    "\n",
    "d = dataset[0]  # Get the first graph object.\n",
    "\n",
    "print()\n",
    "print(d)\n",
    "print('=============================================================')\n",
    "\n",
    "# Gather some statistics about the first graph.\n",
    "print(f'Number of nodes: {d.num_nodes}')\n",
    "print(f'Number of edges: {d.num_edges}')\n",
    "print(f'Average node degree: {d.num_edges / d.num_nodes:.2f}')\n",
    "print(f'Has isolated nodes: {d.has_isolated_nodes()}')\n",
    "print(f'Has self-loops: {d.has_self_loops()}')\n",
    "print(f'Is undirected: {d.is_undirected()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training graphs: 750\n",
      "Number of test graphs: 250\n",
      "Step 1:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 945], x=[384, 3], edge_attr=[945, 1], y=[35], batch=[384], ptr=[36])\n",
      "\n",
      "Step 2:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 911], x=[356, 3], edge_attr=[911, 1], y=[35], batch=[356], ptr=[36])\n",
      "\n",
      "Step 3:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 942], x=[377, 3], edge_attr=[942, 1], y=[35], batch=[377], ptr=[36])\n",
      "\n",
      "Step 4:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 940], x=[362, 3], edge_attr=[940, 1], y=[35], batch=[362], ptr=[36])\n",
      "\n",
      "Step 5:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 974], x=[374, 3], edge_attr=[974, 1], y=[35], batch=[374], ptr=[36])\n",
      "\n",
      "Step 6:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 901], x=[354, 3], edge_attr=[901, 1], y=[35], batch=[354], ptr=[36])\n",
      "\n",
      "Step 7:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 906], x=[365, 3], edge_attr=[906, 1], y=[35], batch=[365], ptr=[36])\n",
      "\n",
      "Step 8:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 936], x=[361, 3], edge_attr=[936, 1], y=[35], batch=[361], ptr=[36])\n",
      "\n",
      "Step 9:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 936], x=[380, 3], edge_attr=[936, 1], y=[35], batch=[380], ptr=[36])\n",
      "\n",
      "Step 10:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 906], x=[354, 3], edge_attr=[906, 1], y=[35], batch=[354], ptr=[36])\n",
      "\n",
      "Step 11:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 953], x=[367, 3], edge_attr=[953, 1], y=[35], batch=[367], ptr=[36])\n",
      "\n",
      "Step 12:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 975], x=[367, 3], edge_attr=[975, 1], y=[35], batch=[367], ptr=[36])\n",
      "\n",
      "Step 13:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 902], x=[353, 3], edge_attr=[902, 1], y=[35], batch=[353], ptr=[36])\n",
      "\n",
      "Step 14:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 932], x=[371, 3], edge_attr=[932, 1], y=[35], batch=[371], ptr=[36])\n",
      "\n",
      "Step 15:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 905], x=[357, 3], edge_attr=[905, 1], y=[35], batch=[357], ptr=[36])\n",
      "\n",
      "Step 16:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 968], x=[366, 3], edge_attr=[968, 1], y=[35], batch=[366], ptr=[36])\n",
      "\n",
      "Step 17:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 932], x=[365, 3], edge_attr=[932, 1], y=[35], batch=[365], ptr=[36])\n",
      "\n",
      "Step 18:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 884], x=[353, 3], edge_attr=[884, 1], y=[35], batch=[353], ptr=[36])\n",
      "\n",
      "Step 19:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 877], x=[359, 3], edge_attr=[877, 1], y=[35], batch=[359], ptr=[36])\n",
      "\n",
      "Step 20:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 903], x=[360, 3], edge_attr=[903, 1], y=[35], batch=[360], ptr=[36])\n",
      "\n",
      "Step 21:\n",
      "=======\n",
      "Number of graphs in the current batch: 35\n",
      "DataBatch(edge_index=[2, 907], x=[347, 3], edge_attr=[907, 1], y=[35], batch=[347], ptr=[36])\n",
      "\n",
      "Step 22:\n",
      "=======\n",
      "Number of graphs in the current batch: 15\n",
      "DataBatch(edge_index=[2, 433], x=[157, 3], edge_attr=[433, 1], y=[15], batch=[157], ptr=[16])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "shuffle(dataset)\n",
    "train_dataset = dataset[:750]\n",
    "test_dataset = dataset[750:]\n",
    "\n",
    "print(f'Number of training graphs: {len(train_dataset)}')\n",
    "print(f'Number of test graphs: {len(test_dataset)}')\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "train_loader = DataLoader(train_dataset, batch_size=35, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=35, shuffle=False)\n",
    "\n",
    "for step, data in enumerate(train_loader):\n",
    "    print(f'Step {step + 1}:')\n",
    "    print('=======')\n",
    "    print(f'Number of graphs in the current batch: {data.num_graphs}')\n",
    "    print(data)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (conv1): GCNConv(3, 64)\n",
      "  (conv2): GCNConv(64, 64)\n",
      "  (conv3): GCNConv(64, 64)\n",
      "  (lin): Linear(in_features=64, out_features=3, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "from torch.nn import Linear\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GCNConv\n",
    "from torch_geometric.nn import global_mean_pool\n",
    "\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, hidden_channels):\n",
    "        super(GCN, self).__init__()\n",
    "        torch.manual_seed(12345)\n",
    "        self.conv1 = GCNConv(3, hidden_channels)\n",
    "        self.conv2 = GCNConv(hidden_channels, hidden_channels)\n",
    "        self.conv3 = GCNConv(hidden_channels, hidden_channels)\n",
    "        self.lin = Linear(hidden_channels, 3)\n",
    "\n",
    "    def forward(self, x, edge_index, batch):\n",
    "        # 1. Obtain node embeddings \n",
    "        x = self.conv1(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv2(x, edge_index)\n",
    "        x = x.relu()\n",
    "        x = self.conv3(x, edge_index)\n",
    "\n",
    "        # 2. Readout layer\n",
    "        x = global_mean_pool(x, batch)  # [batch_size, hidden_channels]\n",
    "\n",
    "        # 3. Apply a final classifier\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = self.lin(x)\n",
    "        \n",
    "        return x\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model = GCN(hidden_channels=64)#.to(device)\n",
    "#data = dataset[0].to(device)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   0, Train Acc: 0.6747, Test Acc: 0.6160\n",
      "Epoch:  10, Train Acc: 0.7800, Test Acc: 0.7560\n",
      "Epoch:  20, Train Acc: 0.8027, Test Acc: 0.7520\n",
      "Epoch:  30, Train Acc: 0.8187, Test Acc: 0.7880\n",
      "Epoch:  40, Train Acc: 0.8267, Test Acc: 0.8360\n",
      "Epoch:  50, Train Acc: 0.8107, Test Acc: 0.8000\n",
      "Epoch:  60, Train Acc: 0.8547, Test Acc: 0.8600\n",
      "Epoch:  70, Train Acc: 0.8467, Test Acc: 0.8560\n",
      "Epoch:  80, Train Acc: 0.8320, Test Acc: 0.8560\n",
      "Epoch:  90, Train Acc: 0.8600, Test Acc: 0.9040\n",
      "Epoch: 100, Train Acc: 0.8747, Test Acc: 0.8880\n"
     ]
    }
   ],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "def train(train_loader):\n",
    "    model.train()\n",
    "    for data in train_loader:  # Iterate in batches over the training dataset.\n",
    "        out = model(data.x, data.edge_index, data.batch)  # Perform a single forward pass.\n",
    "        loss = criterion(out, data.y)  # Compute the loss.\n",
    "        loss.backward()  # Derive gradients.\n",
    "        optimizer.step()  # Update parameters based on gradients.\n",
    "        optimizer.zero_grad()  # Clear gradients.\n",
    "\n",
    "def test(loader):\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    for data in loader:  # Iterate in batches over the training/test dataset.\n",
    "        out = model(data.x, data.edge_index, data.batch)  \n",
    "        pred = out.argmax(dim=1)  # Use the class with highest probability.\n",
    "        correct += int((pred == data.y).sum())  # Check against ground-truth labels.\n",
    "    return correct / len(loader.dataset)  # Derive ratio of correct predictions.\n",
    "\n",
    "def predict(data):\n",
    "    model.eval()\n",
    "    out = model(data.x, data.edge_index, torch.tensor([0]))  \n",
    "    pred = out.argmax(dim=1)[0:1]\n",
    "    return pred\n",
    "\n",
    "acc=[]\n",
    "for epoch in range(101):\n",
    "    train(train_loader)\n",
    "    train_acc = test(train_loader)\n",
    "    test_acc = test(test_loader)\n",
    "    acc.append((train_acc, test_acc))\n",
    "    if epoch%10==0:\n",
    "        print(f'Epoch: {epoch:3d}, Train Acc: {train_acc:.4f}, Test Acc: {test_acc:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6), facecolor='w') \n",
    "plt.plot(acc, label=['train','test'], lw=3)\n",
    "plt.legend(loc=4, fontsize=16)\n",
    "plt.xticks(size=14)\n",
    "plt.yticks(size=14)\n",
    "plt.xlabel('Epoch', size=16)\n",
    "plt.ylabel('Accuracy', size=16)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "files=os.listdir('Weekly_data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DGS30.csv Index(['DATE', 'DGS30'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "image/png": 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1Y6qKXuix6dQm1elRbVFUVoR1x9dh/4X9UpmWXYa6RXWDDupTmx68eNDJ0VQq15dj06lNSM1N1eTziYhIW0wIiJxgb9peCJh/w989qjtahLeweA21hcSsUVhWKPX/9/LwQu8mva06z9CtRj6w2HCd9SfXo+eSnuj8cWfsPLezWnFVZc62ORiyYgi6Le5mssqyLcb/MB63fn0rHl3/qFSmZZehcP9wjIweqXqsut/jmnphywsYsmIIOn7UUdGSQkRE7oEJAZETWOoKMqHTBOh0OsUAXjWns05jx7kduPmrmzHqm1FIykyy6rPPZJ+RtpuFNkOz0GZWnWcY/KyYevT/uwyN+36cVHbfT/dZdT1bvfbHawAq+/7HLYzDtZ9di0d/fdSmcQxCCPyY9KNJeXyjeDtFWT3TW03Hra1vNSnPLs5GVlGW0+N58683pc83172MiIjqLiYERE5grivIC/1ewDtD3gGg7JoDAItuXqTYP511GlN+noLfTv6GNUlr8MLWF6z6bPlb57jwOHRt1NWq8+LC4yrjUmkhkM9YdC73nFXXs4UQwmQKzh3nduD9Pe/j6/++tvo6V4quqJZPv2Z6jeKzh09HfIqnr30aM3vPVCyIplUrgUF2cbamn09ERM7HhIDICeQLfsnNuWEOGgY1BABEBylbCAbEDsAnt34i7R+7fAxHMo5I+/9d+s+qzzZOCCwNapVrHNy4Mi6VFgI5RyyoJV8PwZj8a2BJhb4CMfNjTMpn9ZmFIJ+g6oZmNw0CG+DNwW/inZvewY1xN0rlzk4IjGeY0gu9Uz+fiIi056V1AETuIKvYtBtIk5Amin3jFoLIgEjpLT0AbEvZpjheVFZk1Wefy7n6Br9ZaDN0j+4u7dfzr4eNEzfCQ+eBcd+Pw4krJ6RjhoW71AYVy5VUlGD0t6PRp0kfTO0xtdoP23+k/IE/zv4Bbw9vfPHvF2brWTvT0faU7SgqN/0a9Y3pW634HKl5WHNpOyUnxamfbTxY3VzySkREdRcTAiInkPcLf6rvU9iWtA3vjnxXUadhYEPFfj3/eoqEwHhhMLUkQ83losvSdkRABOr518Pykcux4uAKzOozS0oQujTqokgIDNS6DBn74egP+OHoD8grycPLN7xsVVxyWUVZuGnFTaoP8MbUkhI18nUHDB675jGMbDvS1vAcTtEKY+X92Yvx56Xl1+4F6IiIyHbsMkTkBPKH9+eufw5vdnrT5E21v7e/Yt/b01vqtqMmtyTXqu468n709QPqAwDu7nI3Nk7aiJta3iQde+WGV+DlUfmOYMFNC6TyqroMyb3797sWj5uzO3W3VckAYP2KycZ94TtEdsCCoQvgoXO9X3uKFaFteCA/eeUkOn7YEdd9dl21Zwcy/no6OyEhIiLtud5fRqI6RAiB7SnbpUG4HjoPBPsGq9ZtVa+VSZmvly+CfdTrA8BPx36qcsEyeQtBPf96Zuu1jWiLfQn7sH7CejzS6xGpvEFgA2ne/MzCTIuz/AR4B1iMxRydTn1efjXWdhnacGqDYl9tVh9XUVW3LDV7Uveg00edcDjjMP469xcW7lpYrc827qJ0OOMwCssKq3UtIiKqnZgQEDnQykMr0f/z/tJ+mF+Y2TfUXaO6IqFbAiIDIrFy1EqpPDIw0uz1R387Gh/s+cBiDIoWAv/6Fut2atgJQ1sOlcYPAJVjCeSz4Fga8Fvd+f1tebudXZxd5fiJjac2msxG9ETfJ6oVmzPY0goDAJtObUKvpb1QXF4slX175Ntqffa+9H2K/dySXKw+srpa1yIiotqJCQGRA01cM1GxH+4XbrH+J8M/wcUnLuKuTndJZYb1AMwxLLi149wO3P7N7SbzyF8utK6FwBJ5QrDqv1Vm62UWZlbr+mr9/S2pauDrHd/eodj/6JaPqvw6asmacRpy7+16z6Tsv0v/VWuGIrU1MowHsBMRUd3GhIDIicL9LScEgGn3GWsfZAd9MQg/Jv2ISWsmSQ/mQghFC4E9EgJjz173rLSdXZyN0opSm2eqySkxbSGY0GkCXh/4Oo5OO6r4DKByXQdzXaWyirKQV5qnKIsMMN/K4grC/MKkr3F+aT7yS/NxqeCSyVoMAFBcXowd53aoXmfSmknIKsrCpYJL0As9MgoyLH5uSXmJ6gJ3lhbSIyKiuocJAZEThfmF2XyONQ+zQghF95FTV04BqHy4LNOXAQD8vfxNBi5by1xC8MoNr+D1Qa8rYmy9qDUazmuIt/962+rrq7UQPNH3CTx7/bNoG9EWrw96HYPjBkvHRqwaYXaFZLU37L5evlbHogWdTqdI1ubtmIdG8xqh44cdFd2p0vPS0XBeQ7MzTO04twP15tZDw3kN4TnHEw3nNcRHez4y+7lXiq5I6w74el79Gv136T/FzxMREdVtTAiInKg6b6qNWwiM++n7evqarMibUZiBt/96G18e/FIqM8wwVB1qCYGnzhPP93veJEbDINWnNj9l9fXVxhDEhsUq9o2/dsv/Xa46+FWt21KnBp2sjkUr8u/ry9tehoDAscvH8FLiS1L5W3+9ZVP3KgGBh3992KS8uLwY7+9+XzH+JDYsVprmtlxfjuOXj1fjLoiIqDZiQkDkRJ0bdrb5HOOE4L2h72F0u9HSfklFCc7lnlPUeXzD43hq81OY9us0qay63YUA9YSgd5PeZmM0uJh/0arrGz/kfjbiM5PWFLXPOJN9xqQso1DZTWb+TfPRLKyZVXFoKdQ3VLV8wa4FUvco4648o9uNxrrx66q8tnH3qsV7F+PR9Y/itT9ek8rC/cMVM10lZyVbHTsREdVuTAhcRLm+HLd/cztiF8Qi8Uyi1uGQg3SP6l51JSPyrjLhfuEY2XYkvh/7veKN+aGLhxTnnLxy0uQ6Vc0wZIlaQiCfKvPGuBtVzxv61VCrri8fQ/DNHd/gvq6m3YHUEgK1QbTyFoIHuj6AGb1nWBWD1izN0LTz/E4AptO6xobFYlirYVVeO7MwE5tPb0bz95pj4g8T8dhvj5nUCfcLVyyEdzrrNF7Z9goav9sYH+y2PJMVERHVbkwIXMSGkxvwY9KPSMlJwQ3Lb5Dmrafay9A3W65rVFebr9Mjugf+mfIPPrrlI+x7cJ/05lz+gHzw4sEqr1OTFgK1PvjRQVenynzmumcw45oZJnUOXDiADSc3mJQbk7cQmHtTrjb+4t2d70p93YUQSDyTiD/O/iEdd+WZhYyF+qnfNwCsOboGAHA+97yi/NqYawFU3SVqX/o+DPtqGM5kn8FXh75SrRPur0wI5myfgxcTX0RaXhoeWf8IyirKrLoPIiKqfZgQuAjjaf76LeunUSRkL3klypluXuj3QrUfULtHd8fUHlMV/erl1zpw8UCV13BkC4G3p7fZN/FDvxqKY5nHLF5f3kJg7k252niBrWe24u41dwMAPtzzIW5YfgNWHrJuDQdXE+JjvoVgb/peCCEULSL9m/XHiDYjAAAf3/qxxVWth341VBpcbo5xC4HxSs9HMo5YPJ+IiGovJgQu4mjmUcX+/gv7q1x8iVyb/CG3cXBjzLlhjl2v3zjk6gPg5tObq6xv7zEE8sW0AKBJSBOz569JWmPx+vJBxebelI9uN1paMVlu/cn1AIC5O+aaHKsrLQT70vchPT9dag0J8wtD4r2J0gJyfWP64tzj5yBmC7SNaFutzzdOCIzFfxKPv8//Xa1rExGRa2NC4CIOXDhgUna56LJpRao15G9YqzPdaFW6NrKt+5G9ZxmSL6YFQLG6sbGquptY02WoVf1WWD12NWb3n415N86TyvNL85FXkoezOWdNzqlNCYGlMQQ5JTnYmrxV2m8R3sKkjmH9iiCfoGp9fqBPIJqHNbdYx7AIHhER1S1MCFxAUVmRSd9gAFUuKkSuzdEJga0DlB3dQgAAbw56EwDQNLSporygrMDi9a3pMgQAt7e7HS8NeAmz+s5SPLym5qUq5tE3aF2/tcXPdSXmEiEDw8BiABbf5Fd3/YC0vDSLrRQA8E/aP9W6NhERuTYmBBo6nnccr21/DXO2qXcl+eLfL5wcEdmTNd1gaiK+UbxN9Y1nqLGFn6flMQQGT1/3NFJmpODU9FN4Z8g7UnlWkfpCWkDlDFuG8QE66Kx+wy3//Hd3vouSihKTOmpv0mur3am7pW1Lb/LVxlpY47Y2t1lVz3hsDBER1X5eWgfgrorLi/HMoWeQVWb+QWnBrgWY2HkiukfbPlUlac/RLQT1/OshwDug2g+AtlCbZSjcL1y1rqF1QN6CYG5lXUDZXSjEN0Tq+lIV+fWX7FuiWsfaa7mCqgb97knbI21baiEoKLXcGiMX7BOMG5rfgFb1WmFg84FWnZOen45g32CrP4OIiFyfpi0Eer0e8+fPR9u2beHn54eYmBjMmjULBQXW/UHLz8/H66+/jk6dOiE4OBgRERHo27cvPv/8c5OFeFxNUmaSxWTA4MlNTzohGnIERULgG2b36+t0OpN+/LP6zELfmL4mdcP9wnFLq1uq/VnGXYbaR7av8mFbnjB8d+Q7NHm3CT7d96lJvenrp0vbtrSkWGp1AIBFNy+y+lquYGLnifD28FaUmes+ZeklQVXds+RubX0rfrrzJ8wbMk/6fn5+2+eKOq8PfF3xM5WWl1bldfNK8tD/8/7QvayD7mUdeizugdTcVKvjIiIi59I0IXj88ccxc+ZMtG/fHosWLcKYMWOwcOFCDB8+HHq96Rzucnq9HjfffDNeeOEF9OzZE++88w6ef/55VFRU4L777sMzzzzjpLuonsuF1g0YPpxx2MGRkKMkpiRK247oMgSYdtuJCIjAsJZXF6pqGtoUa8atwf4H99fora5x//zEexKrPCfcX9mCkJqXiue2PKcoM54X39L4AWNDW6ovenZTi5vw6/hf8VCPh6y+liuIDo7GoYcO4Ze7fsHuB3Zj48SNeKrvUyb1OjXoZHH8iLzrT/vI9opj8Y3iFV/j+7veb3L+pC6TsGnSJnwx8gv8fNfPeKLvE4rEMz0vvcp7WXloJbanbJf296bvxeS1k6s8j4iItKFZl6HDhw9j0aJFGDVqFFavXi2VN2/eHNOnT8eqVaswfvx4s+fv2rULf/75J2bMmIH58+dL5Q8//DDatm2LTz75BG+99ZZD76Em0vPV/6gOaj4Ivyf/Lu3b0vxPrmPz6c344egP0r4jugwBpgN7IwMiMaHzBGw6vQmns07j2zu+xTVNrqnx5xh3Z7Fmfn+1LkWXCi4pxlbsS9+nOK42rag5EzpNwId7PkRydrKifHS70bi51c1WX8eVtIlogzYRbaT9M9lnTOokdE+w2Drz1uC38O/Ff6EXevw6/lfEvhcrHYsNi8XDPR7GE5uewIg2I1S7CXnoPBSrYwPKnzNrWgiOXTZdd2LDqQ2cSpmIyEVplhB8/fXXEEJgxowZivIpU6bgmWeewYoVKywmBLm5lf2Oo6OVD0Q+Pj6IiIhASYnpAENXovaW7cvbv0RsWKwyISgrgF7o4aHj+O/a5MFfHlTsOyohMO4yFBEQAT8vPyTemwghhN360Fdn5WzjFgKD5f8uxzf/fYOH6j1ksmCZLVPtRgVH4dT0U+j8cWf8d+k/qdxS//raRm3a1Ls63mXxnMYhjXFwauXK1Wrf/yndp+CBbg/Y9LPRLLSZtJ2UmWSxbnJWMub/PV/12MkrJ63+TCIich7NEoI9e/bAw8MDvXr1UpT7+fkhPj4ee/bsMXNmpV69eiEsLAxz585FbGwsrrnmGhQWFmL58uXYu3cvPv74Y0eGXyNZRVnYeHqjSXmL8Bbo3aQ3lt22DPf9dJ9Unl+ab1NXCndQoa/AlaIrLrsSrXxFWaDqKSWry3h1WvnXw54DaquTEJhLgh777TEAwD9r/0HLei0Vx6ztSmeg0+nQPaq72yQEPp4+Vk0fa+573z6ivcXj5nSNurrmxd70vRbrztlufgG+szlnoS+z3B2UiIicT7OEIC0tDREREfD1NZ29pHHjxtixYwdKS0vh4+Ojen54eDjWrl2LBx54AGPHjpXKg4ODsXr1aowcOdLi5y9evBiLFy8GAJw/fx6JiYnVvhdbbby40WRl2bjAOBSeLMS2U9sQi1jU86mHK6VXKusnbkSEb+1ZYMla+fn51fq6F1UUYdr+aThTcAbTW07HyMYj7R5bTfl4+KBUXyrtpxxPQWJmorRf3Xs31qSoCfw8/FCsL0azgGYoPFGIxFM1v66x06nKBMfa2Ac3GIzNl9RXUS6tKMWRjCOKspKKEpu/Lt113fElvoQeenQN64ozB84gRZdi0zWczdrv/9lC5WJrgR6B2LZtm02f9UK7F/Dq0VcR6h2Ka/TXVOvnrrD86kxWhy4ewsYtG+Hjof67+fMDn5u9zq1f3wpPeOKlnJdwXcR1NsdRF9jr335t5M73DvD+3fn+a8O9a5YQFBYWqiYDQGUrgaGOuYQAAIKCgtCxY0eMGDECffv2xZUrV/DBBx9g/Pjx+Omnn3DjjTeaPTchIQEJCQkAgB49emDAgAHVvxkbnfv3HCBrdf9g2AdI6J4AL4+r3456h+rhypXKhCCkZQj+Sv0Lw1oNU7ypq+0SExOr9XV/dfurSC6o7Df+ZeqXWDBhgX0Ds4OAXQEoLb6aEPTr1Q+9m/SW9qt772puGXgLkjKT0C2qm+JnyJ4eingI21ZXPoj2b9bf6tj79++P/y79hy8Pfom3d7xdZf1ZfWbZ/HUZgAEYN3gczueeR3yj+FrRvc7a739mYSYgayxtGNqwWl+fqXlTEe4XDn9vf9sClWn6X1OczTmLclGO2C6x5hd9qyJfqUAFXjj8AsRs154JzlHs+W+/tnHnewd4/+58/7Xh3jVLCAICAnDp0iXVY8XFxVIdcw4dOoS+ffti/vz5mDp1qlR+1113oWPHjpgyZQpOnToFT09P+wZuB2F+YejYoCMKCwoxofsEPNzzYZM6wT5XZ4QZuWokisqLsGj3IpyZcUZ11Vh3suLgCmn7StEVu/aVtwchBEorShVljnpQBypn5unVuFfVFWtgTIcx2JW6C2eyz2D+Ter9w9XodDp0atgJj/Z6FO/sfAd6Yb67yF0d78Iz11VvdrAGgQ3QILBBtc51ZcYDs61dtM2Y2qrStqrnXw9ncypbLMwtTmY8FezQlkPx28nfavzZRETkWJq9SouOjkZmZqbq4N/U1FRERERYbB2YP38+iouLMWbMGEV5QEAAbrnlFqSkpODMmTP2DtsuhrcZjkMPHcKnPT7FnBvU+9vKp4gsKq+cmeNiwUUcunjIKTG6KiEEUvOU85lnFmZqFI26vNI8xWJhTUKaoEvDLhpGVHMeOg+8e9O7+GHcD2gW1qzqE4zEhMZYXPhq+73bsXL0StVBtO7M00P5QsNSQuVo8pcU5saUyMfOdGzQEesnrMfKUStV67b7oB2OXz5u3yCJiKhaNEsIevbsCb1ej927dyvKi4uLceDAAfTo0cPi+amplQ+FFRUVJsfKy8sV/6+N5H985cxNV2qLi/kX8dXBr7A/fb/ZOudyzmHbmW2aPoCouVx02eRhxHgAr1ZOZ53Gz8d+Nll8a/cDu+Ht6W3mLPcxpdsUs8fiG8U7L5BarEJv+vvOWeStE3mlV1sIMgszsf7Eepy4fAIf/3N1MofmYc0BwGwCmZSZpJg8gYiItKNZQjBu3DjodDosWLBAUb5kyRIUFhZiwoQJUtmpU6eQlKSc6q59+8rZMj7//HNFeXZ2Nn766SeEh4ejZUvlDCa1ibmuATV9+M0vzUfnjztj4pqJ6L64OxLPJJrUuZh/EW0/aIsBywdg3o55Nfo8e1O7/1NZpzSIROlM9hm0/6A9RqwagZkbZ0rlg5oPMlk8zF2NbjcaYzuMRbhfOP7X9n94od8LCPENwez+s2u0aJo7Kddr95JD/j0ydBkqrShFt0+6YdjKYWj9fmss3b9UqtMkpAkAoE+TPmavuePcDlzIv+CgiImIyFqaJQSdOnXCtGnT8MMPP2DUqFFYunQpZs2ahZkzZ6J///6KNQgGDRqEdu3aKc6fMWMG6tWrh2eeeQaTJk3Cxx9/jNdffx1du3ZFeno6Xn31VZccP2Atcy0Ej294HBtObqj2dXed34VLBZVjNwQEblh+A57f8ryiJWDhroVSl5enNz9d7c9yBLWEwJqFkhztlW2voKTCtPub4S0pVXZ/+eaOb3D5qcsY3HAw5twwB9lPZ+OlAS9pHVqtoWlCoNJl6FjmMZzLPada37BGhk6nwx/3/WH2un+d/cuOURIRUXVoOh3HggULMG/ePBw+fBjTpk3DqlWr8Oijj+KXX36Bh4fl0Jo1a4bdu3dj0qRJ2Lp1Kx599FG8+eabiImJwerVq/Hww6YDdWsTS29M7/7x7mp35VF7oH7tj9ewbP8yaf9klvaLBxWVFan2U07OSjYp02oMgRBC+uyDlw6q1qlLc+Lbi3wAuCsNBndV8jVI2ke21ywOeUJg6DJk6d+evGXMUpewqtY1ICIix9M0IfD09MSsWbNw7NgxlJSUIDU1Fe+++y6CgpTdZc6cOQMhTKeoa9GiBZYvX47z58+jrKwMubm52L59O0aNGuWsW3AYcy0EAHCp4BKuFF2p1nXNdTl64OcHAAB/n/8b3x7+tlrXtpejGUfRYF4DRL0ThYMXlQ/aavFrkRAIITDwi4GIfDsSb/75ptnvBxMCqqm1d66Ft4c3gnyC8O5N72oWh2IMQUnVCYF8ZqMgnyDc0+Ue1XpMCIiItOf6E3a7qapW4K1uN5nT2ebHIOQU5+CJjU+YlJubYtBREn5JQH5pPvJL8/HUpqcUx9TizyjMcFZoku0p26XxF8/+/qzZRKtFvRZOjIrqov6x/ZE6MxVpM9MQGxarWRzyVktD652lf3uGLkMGy25bhpOPnsTG6zdi7Z1rpfJjmcfsHCkREdmKCYGLGt56uGJfBx1a1Wsl7afnVW+2IbUuNwYHLhzAzvM7Tcqd3Uf/z7N/StubTm9SHHOVFgJrBzJ3iOzg4EjIHUQGRmo+8LomXYaAyu5hLeq1gLeHN/o16yeVu9q0wURE7ogJgYtqFtYMYzuMlfbv7HinYvGp6k4/aum8vel7VVcf/d+W/1Xrs6rDeGyEfGGmjac24kz2GZNztHiguFx42ap6NVkZlsiVKGYZKs1DdnE2ZifOlsreHfIu+jfrD6ByDQJLC8WF+IbA26NyKt6CsgLoXtZh4PKBZtc3ICIix2JC4MI+HfEpvrz9S3x5+5dYMnyJogm+Om/t5YNg1Ry6dAiNgxublK8+utpp85+fvKIc0Hy56DLKKsoAVA5+VnP88nFFIpGWl4ZNpzZJ51VHXkkeNpzcgKKyItXjm5M3V3mNzg07V/vziVyNfAxBfmk+fkz6UXE8MjASP4z7Ad/c8Q223L0FHjrzf150Op3JInRbz2zF0n1LzZxBRESOxITAhQX5BGFi54mY2HkiAn0CFU3wqbmpFs5UV1hWiOLyYgCAp84TN8bdqDielpeGgrIC1XPP5563+fOqY2+a6QDDqb9MBaBsCZjeazp0uDpDjWG9hLySPHT4sAOGrBiiOh7CGkJUTsc69KuhGPv9WJPjhy8dxsZTG1XPXXvnWsQ3ikdsWCxWjV5Vrc8nckXyLkM5xTnYnapcVDLUNxT1/OthbIexVY6BAtRnUvs9+feaB0pERDZjQlCLyMcQfPjPh4q+9uuOr8Pt39yO307+ZvZ8+QDAqOAobJy0EXsTrj6Ap+WlKZrs/b2udndx5GrAQgi8lPgSPOd4YvwP402ObzhVue5CVlGWVPbktU8qHijWnVgHANiduhvZxdkAgIW7F1rdtUcuozBDmvnkl+O/mLSOGI9rkIsLj8O+hH04Pf002kW2M1uPqLaJCY2Rtv8+/7dJC0CXRl1sul5BqenLh6YhTasXHBER1QgTglpkSIshCPMLk/YfXle51sKlgku49etb8WPSj7jvp/tUp2gFgIyCqwmBoblePjVgel66YkYh+dzhp7NOQy/01XrArsqapDV4edvLZtdWSM1LxcX8i8gqvpoQhPuF44uRX0j7hsHSxi0ca4+tha1yinMU+4aF3Mwdlwv3D4dOp+P8+lTntK7fGt2iugEASipK8OGeD6Vj/Zr1Q9NQ2x7mDQOT5bSYMYyIiJgQ1Cq+Xr6Ycc0Maf9M9hlcKbqChvMaSmUX8i+orpgrhECvpVcHJRsSgsiASHjqKld0vlx0WdEtp0vDq2/8kjKT0GtJL0S+HYkPdn9gt3sCgHd2vmNSFhsWi04NOkn7f537S+ru5O3hjQDvANzc6map29D53PMoKS9Bbkmu4jrHLx+3OR554gGYDsQ2/gw5+SBooromoVuCtC1w9cXDxE4Tbb6W2r+j7458h5+P/Vy94IiIqNqYENQyL/R/QdrOK83Dx/98bFJHbaaOIxlHFPvl+nIAgKeHJxoGXU0o5G/YDW8DAWDeznnYm74XAgKPrH+k+jeg4tDFQ4r9hG4JSJqWhGtjrpXKlh24upKy4S28j6eP1I1BQCAlJ8Xk7b2ldRfMkXdNAkwHcOeUqLcQ+Hr6clYhqtPu6nQX/Lz8TMrlA46t9fz1z6uWj/xmpM3XIiKimmFCUMt46DwUg/vm/z3fpI7aQmLGq4H6evpK28YLCAGVg45Htx+tqOcIeqFXdB14qMdD+PCWD+Hr5asYRP3L8V+kbflbePlKwCevnDR561idsQ8mLQRGaz6YSwjC/dk6QHVbiG+I6tTE1VkjYVbfWRjfabzJ9cx1HSQiIsdhQlALhfqFSttq04iq9c01nr3ngW4PSNsDmw80qR/sG4x6/vVUjwHA63+8jmYLmtW4+5B8YGGgdyA+vOVDeHpUdmEynpbQoLCsUNpuW7+ttH3w4kGTh/VqJQRVtBCY6zLE7kLkDuRJuIH8JYW1wvzC8NWor/DLXb+YHPN91RfNFjTDd4e/q1aMRERkGyYEtVCIb4jF41W1EMy4ZgZGtxst7b868FWT+oY/8E1Cmqh+xv+2/A9nc87ikfWP1GiNAnnyYtztIDJAferCc7nnpG15t6a96XtNHtavFF0x6UJVUFqAjac2ml0EybiFwLh1xdyg4voB9VXLieqSuDCVhKAGqyirtayVVpTibM5ZPL356Wpfl4iIrMeEoBaqKiEwftCt0Fdg/4X90v5T1z6lmAXHx9MHd3e5W3GO4eFcPguROal5tq+JYCBPXowfKsy1EMh1j+4ube9J3aPanccwDanB2O/H4qYVN6H/5/1VZ2QybiH49cSvinUfzLUQ9Gvar8p4iWq7FvVamJRVZwyBgXzmNGPJ2ckOmdmMiIiUmBDUQj6ePiZl8in/jLsMHbt8TOpmExUUpeibb9A9qrtiv0FgA6l+VarTLeeLf7/Ai4dfVMzpb9ztwFxC8OagN6Xtjg06ItA7EACQkpOiumCY/AE+uzgbv574FQCwL30frhRdMalv3EJQISoUg5rlSYfh6+7r6YuHej6kGi9RXSJfD8WgOl2GDLw8vNAhsoPZ48YtdEREZH9MCGoheR96A/kUocZdhv698K+0Le9iI2ecEHRt1BWAdS0EtiYEJy6fwL0/3os/Mv/Ao+sflcqNWwjMrXY6rdc0advH0wfjOoyT9tXGVMgf+ven71ccU3vbb9yiAADfH/keuSW5KKsoU4wpWDNuDZ7o8wTWjV9ntnsVUV3SNaqrSVlNugwBwNejvzZ7TP77i4iIHIMJQS2kusKnhRYC+Tz6LcJNm/sB5SJkAKRVduv516syHlsTgg/2fKCYw9zAuNtBfX/TPvnPXPuMSb072t9h8fOuX3a99OBv/LZRLSFQK/v34r+IeicKPq8qW2fiG8Xj7SFvY1DcIIsxENUVEQERJslvgHdAja7ZqWEnxUKDcmpJPhER2RcTglrIeDXe5mHNFU32xmMI5H9QzXXDCfQJRNuIqzP23BB7AwCgVX3T7gHGbEkISspL8NWhr1SPGXc78Pb0RqOgRooyD53pj6x8HIE57+yoXPzsaMZRRbnamANzg42NW2ZCfUNV4yGq6/rG9JW2m4Y2tcu/g0ldJuHsjLM4//h5/O/6/0nl5qb5JSIi++HTTC1kPKj469FfK96aG3cZsiYhAIClw5diYPOBeGfIO1Ii0CCwAT659RMMaj4Irw98XfU8WxKCDac2mH3jp9YPecnwJYp9tZaFBoENquyuY2gZSMs3WmRMZcYgtWlb1bw1+C2r6hHVNa/e8CqGthyK3k1644Nh9lu5PCY0Bo1DGiteTlhaGZyIiOyDCUEtJP8DvHrsalzT5BpFH943/3oT3Rd3R0p2CgAgozBDOmauXz4AXNv0Wvx+9++Y2WemojyhewI2371ZsXaB3K7UXYh7Lw4zfpth9tol5SW4/Zvbcduq28zWUeuHfGvrW6VBxB46D7MxdG7Y2ex1AaC4vBiA6SJjhoeN7w5/h7j34vDkxidVp201FhkQiYTuCVXWI6qLWtVvhfUT1mPn5J24tfWtdr++/KUHEwIiIsfz0joAst31Ta/Hjvt3oLi8GANiBwAwnbpvX/o+fLr/U8y5YY7VLQRViQiIgJ+Xn/RwLZecnYz3dr2HqT2mKt7uGXyw5wP8mPSjxeubm7pwZp+ZaB/ZHs3CmqkuigSoz40utyV5C4QQJouMGbojjP1+LABg3s55iuOB3oEmXbSAym5K8qlbich+Qn2vLr7ILkNERI7HFoJaSKfToU9MH9zQ/AbpoXRA7ADooHxA/fq/ypk77JUQ6HQ6vDX4Lfh7+ZtdNCw5K1m1/LU/XjMpa+DbQLFvbupCb09vDG8z3GIrgHGiEBMSo9gXEFj+73JFawlQ+fbxYv5Fs9dtHNJYtXxE6xFmzyGimpGvxs4WAiIix2NCUEc0DW2K4W2GK8ra1G8DAMgokHUZMvMgb63p10xHzjM5SJ+VbrKYGQDc8+M9KNeX4/jl45j4w0Qs2rUIZRVlqlN5dgzpqNi31J2pKs3Dmyv21VopFu5aaFL27O/PKqY+lfPUeUrrMRib0HlCNaIkImvIuwyZWxmciIjshwlBHbL41sXo1biXtH+l6ApKK0oV8/BbM41oVbw9veHp4ak6qDajMAOLdi3CXavvwleHvsL036Zj3Yl10Au9Sd32Ie0V+4a1D6rDeFDxdU2vM6kjX61Z7rsj36mWB/sGK7ouGGyYuKHK1aKJqPrk/+7YQkBE5HhMCOqQhkENsXzkcmk/ozAD/136T5qZJy48Dt6e3nb7vEZBjbD7gd0m5TM3zsS+9H3S/od7PjSpM7HzRPh7+ivK2ke2N6lnrW5R3dAjugcAYFafWXjsmsfMvt23VrBPsKLrAgDooMPguME1ui4RWWY8qFgI09nFiIjIfpgQ1DHyMQKZhZn4J+0fad94NWJ76Nm4J94Z8o7FOvJ++/2b9cf+B/fji5FfKFoImoQ0qVGy4qHzwM7JO3Hy0ZN4+8a3EeoXijOPncG5x8+pthZYI8gnCCE+ypaAYN9grj1A5GDent7w96p8YVAhKnA+97zGERER1W18sqljwv3CpcHF2cXZ2JO6RzrWLaqbQz6zqjUAUnNTpe0bYm9AfKN46HQ6xAbG4tUbXsXguMFYM25NjePw8vBCi3otpIHW/t7+aBLSBGPbj1XUe7D7g/DUeVZ5vWBf0xYC48XJiMgx5P/2Ht/wuIaREBHVfUwI6hhPD0/UD6gv7R+4eEDaVhtoaw83tbhJta+9gbyFwHjl4f/1+x82TdokdfdxhImdJ0rdh4a1GoaPb/0Y341RHzcgF+wTLE3ralCuL3dEiERkRN7l71TWKQ0jISKq+5gQ1EHyP6SHLh6Stms6w5A5oX6h2JuwF4tvXVxlXbXFxxwt3D8cOyfvxIrbV2DlqJUAgOjg6CrPC/YNxk0tbsLw1ldnb+rTpI/D4iSiqwz/VgHlTGlERGR/TAjqoBvjbpS2SypKpO2arEFQlRb1WmBK9ynYfu92i/XMrTXgaHHhcZjQeYLUDSEqOKrKc8L8wqDT6fD92O9xS6tbEO4XjqevfdrRoRIRgNiwWGk7szCTA4uJiByICUEdNKXbFNXymszzb63rm12PX8f/ava4Fi0EaqKClAlB4+DG8PH0UZSF+4UDAHw8ffDL+F9w+anLuK3tbU6LkcidBXgHwM/LD0Dliw21FcOJiMg+mBDUQR0adEDfmL6KMg+dB8L8wpzy+UNbDsW1MdeqHgvyCXJKDFUxntEoLS9N8UYSuJoQGBgGKxOR4+l0OkU3R3YbIiJyHCYEdVRCtwTFfn3/+k6bLlOn02Fmn5mqx7TqMqQmwDtA2hYQaB6mXO043D/c+BQiciLjaZSJiMgxmBDUUWM6jFHM/OOM7kJy5loCXKXLEADM7j9b2n7smscQFx6nOG7cQkBEziX/vcWEgIjIcZgQ1FEB3gGY2HmitO/IAcVqzLUEuFILweO9H8cDXR/AyLYj8dS1T5kmBGwhINKU/PcWFycjInIcJgR12LSe06RBedc3vd6pn+3v7a9aHugT6NQ4LPH29MaSEUuwZtwaRAdHs4WAyMV0iOwgbX9z+BsNIyEiqtuYENRh7SLb4e/Jf+P7Md/jpQEvOfWz1QYwB3oHOm0cQ3UYJwTOGoRNROomdZ4krby+JXkLVwonInIQ1306I7vo0qgLRrcfDS8PL6d+bmxYrGJBL8C1xg+oMR5UzISASFsxoTFoEtIEQOXA//S8dI0jIiKqm5gQkMOsumOVYt9Vphw1J9QvFC3CWwCoXKfA2QOxiciUfFXx9HwmBEREjuDc18bkVvy9lOMIvD28zdR0HWvGrcHyf5djTPsxTm9VISJT8lXF0/LSNIyEiKju4hMPOYzxQl4t6rXQKBLrdWrYCfOGzNM6DCL6f/JVxdlliIjIMdhliByqV+Ne0vaTfZ/UMBIiqo3s2WUoLS8Nb/35Fvam7a1pWEREdQpbCMih5g6ei5e2vYRbWt2Cfs36aR0OEdUy8haCmnYZuufHe7D59GaE/hmKE4+e4DghIqL/x4SAHKp/bH9sjd2qdRhEVEvJH9qvFF2p0bU2n94MAMgpycG8HfPw1o1v1eh6RER1BbsMERGRywrxDZG2s4qzsDV5K1JzU2t83bk75nJdAyKi/8eEgIiIXFaob6i0vePcDgz8YiC6Le6GSwWXbLpOWUWZSdmNX95Y4/iIiOoCJgREROSy5C0EBpcKLqHhvIbo/3l/PLXpKVToK6q8Tn5pvknZjnM7kFeSZ5c4iYhqM44hICIilxXqF2r22PaU7diesh1x4XGY2mOqxevklao/+GcVZ6FCVCDUN9RkqmQiInfBFgIiInJZai0Exj7Z+0mVddRaCADgzT/fRMTcCPT5tI9VLQ1ERHUREwIiInJZPp4+8PPys1jnwIUDuJh/0WIdc12DPvrnI1SICuxK3YVx34+DEKLasRIR1VZMCIiIyKXJBxabsy99n8Xj5roMya0+uhrLDiyzOi4iorqCCQEREbk0a7oN7U3fi6c2PYU277fB2mNrTY5bO3h48trJ+P307zbHSERUm3FQMRERuTRLA4sN3tv1HjILMwEAt626DWK2suuPuTEEaqb9Og1Hpx3lIGMichtsISAiIpdmqYXAy6PyvZYhGTDHmi5DBscuH8PO8zutrk9EVNsxISAiIpcW7heuWu7t4Y2bWtykemzUN6Nw4vIJaV/eZcjH06fKz9yfvt/GKImIai8mBERE5NIiAiJUy7s06oKODTqqHluTtAZT111dmyC3JFfabhra1KT+8NbDcVfHu6T9qlociIjqEiYERETk0tQSAh9PH3w64lPEhceZPW9L8hZp+2zuWWn7tja3Ker1iO6BtXetRa/GvaQyJgRE5E6YEBARkUszTgjWjV+H1Jmp6Nyws8WEAAA+3PMhCkoLkJyVLJXd3PJmXN/0emn/8d6PAwAiAyKlsozCDHuETkRUK3CWISIicmnyB3UAaFmvpZQkVJUQTPt1Gi4VXMLprNNSWVx4HD665SM8sv4RdGrQCXd2vBOAMvFgCwERuRMmBERE5NKMWwjkC5U1DW2KcL9wZBVnmT3/5W0vS9ueOk/EhMbAy8MLW+/ZavZzmBAQkTthlyEiInJpxgmBfBpSLw8v3N3lbquv1SysmTRVqbHIQHYZIiL3xISAiIhcmvE6BH5efor9Kd2mWH2tG+NuNHvMuIVACGG2LhFRXcKEgIiIXFrT0KZSEtAuop3JCsIdGnRAv2b9rLrWtJ7TzB4L8A6Ar6cvAKC0ohRF5UXVjJiIqHZhQkBERC7N18sXv9/9Ox7v/Ti+HfOtap2Vo1ZiZu+ZeGPQG2avMzhuMDo17GTxswJ9AqXtgtKC6gVMRFTLcFAxERG5vL4xfdE3pq/Z441DGuOdm94BAHRu2BnDvx4OvdAr6twXf1+VnxPkE4QrRVcAAAVlBYhEZBVnEBHVfmwhICKiOmVYq2FImZGChoENFeXy2YnMCfS+2kKQX5pv99iIiFyRpgmBXq/H/Pnz0bZtW/j5+SEmJgazZs1CQYH1zbRXrlzBE088gZYtW8LPzw+RkZG44YYb8McffzgwciIicmVNQpqYrEgc6mdFQsAuQ0TkhjTtMvT4449j4cKFuP322zFr1iwcPXoUCxcuxP79+7F582Z4eFjOV1JSUjBgwADk5+dj8uTJaN26NXJycnDw4EGkpqY66S6IiMgVRQVHKfaNZytSE+QTJG0XlDEhICL3oFlCcPjwYSxatAijRo3C6tWrpfLmzZtj+vTpWLVqFcaPH2/xGhMnTkR5eTkOHjyIqKgoi3WJiMi9RAdHK/Zt7TLEFgIicheadRn6+uuvIYTAjBkzFOVTpkxBQEAAVqxYYfH87du3488//8RTTz2FqKgolJWVobCw0IERExFRbRIVZHsLgbzLEMcQEJG70Cwh2LNnDzw8PNCrVy9FuZ+fH+Lj47Fnzx6L5//6668AgKZNm2L48OHw9/dHYGAgWrduXWUyQUREdV+joEaKfau6DHmzyxARuR/NugylpaUhIiICvr6+JscaN26MHTt2oLS0FD4+PqrnHzt2DEBli0KrVq2wfPlylJaW4p133sGkSZNQVlaG++4zP8Xc4sWLsXjxYgDA+fPnkZiYWPObslF+fr4mn+sq3Pn+3fneAd6/O9+/M+89vShdsf/H9qonm8jKyJK2Dxw5gMTcRLvGxO99otZhaIb37773XxvuXbOEoLCwUDUZACpbCQx1zCUEeXl5AIDg4GBs3bpVqjdy5EjExcXhueeewz333GN2YHJCQgISEhIAAD169MCAAQNqcjvVkpiYqMnnugp3vn93vneA9+/O9+/MexdCYGnGUmxJ3oIHuj5g1eduKN8A/P+cFNHNojHg+qrPsQW/9wO0DkMzvH/3vf/acO+adRkKCAhASUmJ6rHi4mKpjjn+/v4AgLvuukuRNISHh2PEiBG4cOGC1IpARETuR6fTYePEjTg67SgWD19s1TnyWYY4hoCI3IVmCUF0dDQyMzNVk4LU1FRERESYbR0AgCZNmgAAGjVqZHLMMONQVlaWyTEiInIfnh6eaBvRFjqdzqr6XIeAiNyRZglBz549odfrsXv3bkV5cXExDhw4gB49elg83zAY+fz58ybHDGUNGjSwU7REROQOFNOOclAxEbkJzRKCcePGQafTYcGCBYryJUuWoLCwEBMmTJDKTp06haSkJEW9kSNHIjg4GCtWrEB+/tVm3fT0dPz4449o3bo1WrZs6dB7ICKiukXeZSivNE/DSKx34vIJ/HfpP63DIKJaTLNBxZ06dcK0adPw/vvvY9SoURg2bJi0UnH//v0Vi5INGjQIKSkpEEJIZeHh4Zg3bx4efPBB9O7dG/fffz9KS0vx0UcfobS0FIsWLdLitoiIqBarH1Bf2r5ceFnDSKyzN20veiypbFH/dfyvuLnVzRpHRES1kWYJAQAsWLAAsbGxWLx4MdatW4eIiAg8+uijmDNnjtnZgeQSEhIQERGBuXPn4oUXXoCHhwf69OmDlStX4tprr3XCHRARUV0SERAhbWcWZmoYiXWm/DxF2r73p3tx8YmLGkZDRLWVpgmBp6cnZs2ahVmzZlmsd+bMGbPHRo0ahVGjRtk5MiIickfyhCCjMEPDSKwj7yp0qeCShpEQUW2m2RgCIiIiV2PcQiDvquqKyvRlWodARHUAEwIiIqL/F+AdgADvyjVwSitKuRYBEbkFJgREREQytaXbkHGy4qHzgF7oNYqGiGozJgREREQykQGR0rYrDyy+UnRFsa8XervNjLT+xHp0+LADntz4pF2uR0SujQkBERGRjHzqUVdOCArLCk3KzueaLtZZHcNWDsORjCOYt3Me/r3wL4rKirDtzDaUVpTa5fpE5FqYEBAREcmE+IZI2648hkAtITiTfcbun3Ps8jEMWzkMA5YPwIivR9j9+kSkPSYEREREMoHegdJ2QWlBja714Z4PMfmnyUjOSq5pWCbUEoLTWadrfF3jcQi5JblIPJMIANhwagPHKRDVQZquQ0BERORqFAlBWfUTgv3p+zHt12kAgCOZR7Bz8s4axyanlhCcyjpV4+tmF2cr9g9fOqzYLyorQqBPIIio7mALARERkUyQT5C0XZMWgp+O/SRt/33+7xrFpEYtNnu0EBiPm9h3YZ9iXy0RIaLajS0EREREMvK33zUZQ2BYz8BR1B7Mz+actfk6qbmpWP7vcpzNOYsGgQ3Qsl5LxfH96fur/Fwiqt1sSggqKirw1VdfYePGjbh48SLmzp2Lrl27IisrCz///DMGDRqExo0bOypWIiIih7NXlyEtEoLqzIo0cc1EaYyAmrzSvCo/l4hqN6u7DBUWFqJ///6499578dNPP2HLli3IysoCAISEhOCZZ57BRx995LBAiYiInMFeXYaMEwIhRLWvBQCPrX8Mbd9vi19P/IrSilIk/JJgUudy0WWbBv3qhd5iMqCmJkkSEbkmqxOCl156Cf/88w/WrFmD06dPK36xeXp6YtSoUdiwYYNDgiQiInIWRZehsup3GTJ+MDd+026LE5dPYOHuhTh2+RhuWXkLPtqj/gJOL/Qmg4Itqc5CZmwhoNrsr7N/YcXBFfgx6UekZKdoHY7LsDoh+O6775CQkIDbbrsNHh6mp7Vs2RJnzpyxZ2xEREROZ69pR0vKSxT7WUVZ1b5WSo7ywWXGhhlm62YUZFh93fT8dJtjYUJAtdln+z/DpDWTcPs3t2PjqY1ah+MyrE4I0tLS0KVLF7PHAwICkJdX/bcfRERErkDRZagG3WNKKowSguLqJwS2vMm3ZhxBSXkJnt38LO5afZfNsTAhoNpM/m+a0+deZfWg4vr16yM1NdXs8cOHDyM6OtouQREREWnFXrMMlVaUKvazirKgg65a17JlsHBGYdUtBEv3LcWbf72pKJvQaQIu5F/A78m/WzyXCQHVZvJ/0/Lk391Z3UIwaNAgLFu2DIWFpr8IkpOT8dlnn2Ho0KF2DY6IiMjZHNZlqAYtBNY85Bvc/s3tVQ4snrN9jklZVFAU1k9Yj25R3SyeW9PVm4m0pGgh8GYLgYHVCcHs2bORlZWFnj174qOPPoJOp8Nvv/2GZ599Ft26dYOvry+effZZR8ZKRETkcI7qMlSdKUGtPdf4TWdVMwd5e3iblDUNbQpvT2/8ed+fFldV/u3UbxavTeTK5AktuwxdZXVC0LJlS/z+++/w8vLCiy++CCEE5s2bh7feegsxMTH4/fffERMT48hYiYiIHM5eXYaMWwjS82wfwGtQVQvB5K6TFfvL/11usb6Pp49iXwcdRrQZAQDw9/bHNY2vgZeHeq9izs5CtRm7DKmzaWGy7t27499//8V///2Ho0ePQgiBVq1aoWvXro6Kj4iIyKlCfUOl7ayiLAghoNPZ3vffeAxBen46UM3nj6paCJ657hnc3vZ2DFg+AACQlJmkWu/DPR9i4a6FSM5OVpT3bNwTzcKaSfs6nQ5hfmFmPzf2vVh0bNAR82+aj8Fxg62/ESKNscuQOpsSAoOOHTuiY8eO9o6FiIhIc/7e/gj0DkRBWQHK9GXIK81DiG+Izdcx7jKUlpdmc0JwPvc8LuZfxIX8C2brfHvHt2gU1Egx2DctL01RJykzCV4eXpj26zTVazQNbWpSZikhAID/Lv2H21bdhoLnOKaAag92GVJXrYSAiIioLosIiEBBTuWDQ2Zhpl0SAlvn/E/OSkbbD9qatDQYu77Z9QAqBwUbXMi/AL3Qw0PngQ0nN2DoV5Yn/YgOMp0lUN5SYg5nHKLaRt5CwC5DV1k9hsDDwwOenp4W//PyYn5BRES1X0RAhLRty0JfcjUdQ/DuznerTAaAqw/u/t7+CPMLAwCU68ult/vDVg6r8hqNghqZlPl6+doQLZHrq9BXoLi8GEDluBl/L3+NI3IdVj/B33333SZ9KMvLy3Hq1Cns2rULnTt3Rnx8vL3jIyIicrrIwEhp+0L+BRSXF8PPy8+maxg/zF/Iv4ByfbnV56flp1VZx8fTRxFXdHA0souzAVQmIA0CG1Q5BSkABPsGm5QZDxx+pOcjeH/P+yb1KvQV8PTwrPIziLQmbx0I8A6o1tigusrqhODzzz83e2zHjh0YMWIEPvroI3vEREREpCl5C8HIb0aiQWAD/Hnfn2hVv5XV1zDuMlQhKnCm8IzV51foK6qsE+YXpnioiQqKwpGMIwAqxxF0adTFqs9Se1OamqdcjPTNwW+qJgS5JbkI9w+36nOItCQfP8DuQkpWdxmypG/fvrjvvvvw9NNP2+NyREREmorwj1DsXyq4hK8OfWXTNYy7DAHAsbxjVp9vPBOQGuN+/vKuPxcLLlr9Wdc0ucak7Ik+T0jbT/V9CoE+gapjKWqy4BqRM8mnHOWAYiW7dfpv1aoVWwiIiKhOkHcZMjh22fqHecC0hQAATuSfsOrcsooynLpyqsp6hjEDBvX860nbWUXWPag/2fdJdGxgOnPgrL6zcOzyMQT6BOKlAS8BABoENkBuSa6iXlZRFsAGAqoFOOWoeXZLCBITE+Hvz8EZRERU+8ln7DFoEtzEpmuoDQg+X3jeqnPXHltrskpy35i+yCvJw6FLh6Qy44Qg3O/qk/nMjTMxrFXVA4pfHfiqanmjoEZYe9daRZnaGAi2EFBtwS5D5lmdEHzxxReq5VeuXMHmzZuxfv16TJ48WbUOERFRbRIXHmdSVlReZNM11LoMpRdXPdNQcXkx7lp9l7Q/vtN43BB7A25rcxtuW3Wbom6on7LLkHFf/vE/jK/y84xXLa4qNmPWtkQQaS2nJEfaZkKgZHVCcO+990Kn00EIYXoRLy9MnjwZ8+fPt2twREREWlBLCNQehi1R6zJ0obhypiEvD/N/fu/49g6U6csAVE6N+PrA16VVhI37PYf5hin25S0EALAvfZ9NMVelrKLMpOxy0WW7fgaRo5zJPiNtx4TEaBeIC7I6Idi6datJmU6nQ7169dC8eXMEBrIvFhER1Q3RwaYLddnSQlBQWoCkzCSTcj30OJdzDs3Dm6uel5SZhHUn1kn7t7S+RUoGANN+zyZdhmyc7ad3k9421V908yKTVoffTv6GqT2m2nQdIi0kZ10dqK+W9LszqxOC/v37OzIOIiIil6E2r74tLQSf7P1Esd88rLk0a1BydrLZhOC7w98pr3Or8jrG3RxMugz5VZ0QdG3UFfOGzMOPST/iwe4PVllfbkyHMTibcxaHLh2SZl365fgvyC/NZxcMcnmns09L20wIlOwy7SgREVFd8/KAlxX7RWXWtxDsTd+r2O8e3V3avphvfjpQ+dz/79/8vklLhT1aCFrUa4GBzQdi4c0L0aFBhyrry3l5eOHp657GilErpAeqClGB87nWDZYm0tLpLCYE5phtIZgzZ47NF9PpdHjhhRdqFBAREZEreKHfC6jnXw+Prn8UgG0tBPIHj02TNuGHoz9I+5mFmWbPk8/YI59C1MBkDIGFWYbMMV5jobqig6Ol+0zLS0PbiLZ2uS6RI1ToK3D88nFpnwmBktmE4KWXXrL5YkwIiIiortDpdOjaqKu0b8sYAnlC0Lp+a0QGXF3XwGJCIJuxx/hhHzDtMmQ8Pao1LQRqayxUh7z1Ij2v6tmTiLSUlJmEwrJCAJU/u/b6d1BXmE0IkpOrXiGRiIioLvP3vrq+jrUtBPml+bhUcAkA4O3hjcbBjRERcPWtfEZhhtlz5S0Eag/3xl2GjLsU+Xv5o3tUd5MuS3LyWGpCnoyk5aXZ5ZpEjiKfcat7VHcLNd2T2YSgWbNm5g4RERG5BT8vP2nb2jEE53LOSdtNQ5vC08NT8RB+Ous0dp7biV6Ne5kMXpa3EKh1/zHuMhQVrGwh0Ol0WDd+HX47+RsW7l6oOu2ovRICRQtBPlsIyLX9d+k/aVve8keVOKiYiIjIDH8v21sIcktypW3DOAB594QNpzag72d98cTGJ0zOraqFQC/0iv1Q31CTOg2DGuKe+HvQILCBanz1/etXcQfWkbcQMCEgVydvmYsJ5RoExqyedtTgn3/+wa5du5CVlQW9XvmLiWMIiIioLlG0EFg5hiC/NF/aNvT5V3srv2DXAswfenVBT73QI7s4W9pXayGQJxtA5d9dcyZ0moDfTv5mUm7cylBd8hYCdhkiV3el6Iq0rTZg391ZnRAUFRVh1KhR2LhxI4QQilWLDdtMCIiIqC6p7hgCA0sJAQCUlJfA18sXAJBXkie1AAR6B8Lb09ukfohviHWBAxjXYRxSslNQUlGCV7a/IpXb62FI3l2Jg4rJ1clX1LZXK1ldYnWXoTlz5mDjxo343//+h61bt0IIgeXLl2P9+vW4/vrr0bNnTxw5csSRsRIRETlVdcYQqCUE0cHRaBzc2KRuSk6KtF1VdyEAuL/r/WgY2BAA8MGwDyzG4e3pjf/1+x/m3DBHWoCsb0xftItoZ9V9VIUtBFSbsIXAMqsTgu+//x5jxozBnDlz0LFjRwBA48aNcdNNN2Hz5s0oLS3F559/7qg4iYiInM7X01faLtOXmXTZUaOWEHjoPDCp8ySTuvLpSeXdhdSmHDVc79T0Uzj+yHE83PPhKmMx+OiWj3DooUPYdu82i92MbBHqGyolTAVlBcgrybPLdYkc4XKhrIUggC0ExqxOCM6dO4f+/fsDADw9K2dFKC0tBQB4eXnhrrvuwqpVqxwQIhERkTaMH54f++2xKs9RSwgAoGW9liZ1T105pXpesE+w2esH+gSiVf1WVcYhp9Pp0LFBR3h52Dx00OI12UpAtYEQgi0EVbA6IQgODkZ5ebm07eHhgbS0q//4Q0NDceHCBftHSERE5CI+P/B5lXXMJQRq/f8PXjwobcvfsAf7mk8IXInaTENnc86i37J+uGXlLYqvBZFW8kvzUaYvAwAEeAcougJSJasTghYtWuD48colnz09PdGhQwd8//33ACozrx9++AExMZzGiYiI3Ju5hCDUz3SKUPkCYnmlsoTAQguBK5EPLL6QX/lS8Nnfn8UfZ//Aryd+xYtbX9QqNCIJWweqZjEhKCkpkbYHDx6M1atXo6KiAgDw4IMP4rfffkOLFi3QqlUrbN68GZMnT3ZstERERE72QNcHpG1rBuSaTQhU1gw4dOmQNHtRbWwhCPG52uphiH/loZVS2cf/fOz0mIiMcYahqllMCKKiojBt2jTs3bsXzz77rDS7EAA8/PDDmDdvHkJDQxEeHo7XX38dTz31lFOCJiIicpanr3ta2i6pKLFQs1J+mfVdhkorSvFT0k+V51k5hsCVyO+voKzA5Li1azcQORJbCKpmMSEIDQ3FRx99hF69euHaa6/Fxo0bkZt7dYaFmTNnYt++fdizZw+efvppu81cQERE5Crk/Y3laxEUlBZIL8nkbOkyBACL9y0GoOwyJD/PlckXOSsorUwIzM2QRKQVzjBUNYsJQXJyMjZv3ozx48fjxIkTeOyxx9C4cWPceeed2Lhxo7NiJCIi0oy/19XFyQxrEXy2/zPUm1sPA78YaJIU2DKoGAC2JG/BySsnlV2GakkLQaC3LCH4/xYC4zewhsXWiLSiaCHwYwuBmioHFQ8cOBBffvklLly4gI8//hjx8fH49ttvcfPNN6NZs2aYPXs2zpw544RQiYiInE+thWDy2skorShF4plE/J78u6K+uYRA/vBsbP2J9cpBxbVkDIG8heCDPR+gpLzEJEHKKMhwdlhECooxBGwhUGXTtKMJCQnYuXMnjh49ilmzZqGsrAyvvPIKWrZsiUGDBmHlypVVX4iIiKgWUaxWXF5k8sAr744AmE8IjLvV9ojuIW3vTd9b68cQ5Jbk4sM9HyoWWAOUKzATaYFjCKpmdUIg16ZNG8ydOxfnz5/Hzz//jCFDhmDr1q24++677R0fERGRpjw9POHt4S3ty9/kA5XzmsuZSwiMxTeMl7b3pu+tnWMIjFo9Zm6ciZySHEXZgM8HoPfS3kgtSnVmaEQSzjJUtWolBAa7d+/G2rVrsWPHDgCAj4+PXYIiIiJyJf7eV8cRrD6yWnHMeCYdaxOC4W2GS9tHMo4gq+jqm/Ta2GXIwHjMwMWCi9iVugtvJL3hrLCIJEVlRfj52M/SPlsI1NmcEFy8eBFvv/022rdvj2uvvRZLlixBXFwcFi1apFi5mIiIqK6Qdxu6f+39imOG2XUMLCUEz7V9DkE+QbitzW0Y3nq49HCiF3qk5KRI9Wpjl6GqHM497MBIiEwJIdD3s76KbmscQ6DOy5pK5eXlWLt2LZYtW4YNGzagvLwcYWFheOihhzB58mR07drV0XESERFpRp4QGJMnAOX6cmngsQ46xQxFAHBjwxvx8piX4eVR+ec3IiBC6t98JvuMVK+2TN1paaC0moLSAtVWBSJHOJtzFgcuHFCUNQpqpE0wLs5iQnDw4EEsW7YMX331FS5frux/dcMNN2Dy5MkYNWoUfH19nRIkERGRlowf7OXkC3LJWwuCfIJU1+cxJAMAEBkQieOXj5vUiQqOqm6oTmXrw/3BiwfRJ6aPg6IhUpIn6wZx4XEaROL6LCYE8fHxAICYmBg8//zzuO+++xAbG+uEsIiIiFyHpRYCeRIw+tvR0rY1D8sRARGqnxXqq76ImauxtYXg5JWTTAjIaYwHuAOAh65Gw2frLIsJwR133IHJkydjyJAhXIWYiIjclnxQsTHDW8gL+RcUaxJY079eLSGICoqqNX9zbZ0N6XTWaQdFQmQqtyRXsT+t5zSNInF9FhOCb7/91llxEBERuSyLLQT/32WosKxQUa5D1Q/1kQGRJmW1pbsQYHuXodPZTAjIeXKKlS0EL/R7QaNIXB/bTYiIiKpgaQzBkn1LcKXoislsQ8brFahRayGoTW/R/b38Ed8o3ur6teneqPaTtxDcH38/GgY11DAa18aEgIiIqAqWWggA4MM9HyoGFwNAXknVCUFkoGkLwai2o2wLTkM6nQ6/jv8VkzpPsqp+claygyMiuko+hiDUr3aMy9EKEwIiIqIqWBpDAABfHfrKpIXAOEFQ0zS0qUnZ1B5TbQtOY1HBUfj41o+tqnsh/wIq9BUOjoiokryFoLYM1NcKEwIiIqIqVDWbTqt6rVSnOKyK8RSIU7tPRaeGnWy+jtYCvANMulU1CGxgUq9CVCCzMNNZYZGbk48hCPEN0TAS18eEgIiIqArGff09dZ6K/RDfEJMWAWsWQGoc3Fixb1i5uDYy7v70zpB3MKvPLCy6eRE6RHaQytPy0pwdGrmp3FJZCwG7DFnEhICIiKgKxrMBDY4brNjPLck16TL01aivqryup4cysSipKKlmhNozTppahLfAvCHz8EivRxQzJ3Vb3A1CCGeHR26ILQTW0zQh0Ov1mD9/Ptq2bQs/Pz/ExMRg1qxZKCiout+lscLCQsTFxUGn0+GRRx5xQLREROSujB92IwIisG78Omk/pyRH0WVoZNuRGNh8oFXXblO/jbRtnGjUJsZfo/oB9aXtYJ9gxbH/Lv3nlJjIvXEMgfU0TQgef/xxzJw5E+3bt8eiRYswZswYLFy4EMOHD4der7fpWi+++CIyMjIcFCkREbkz4+4wsWGxaBh4dQrD3JJcRZeh9hHtrb72N3d8g+ubXo9Hez2Km1rcVPNgNaIXyr/b8vERXh7KZY/O5553Skzk3uSzDLGFwDKLC5M50uHDh7Fo0SKMGjUKq1evlsqbN2+O6dOnY9WqVRg/frxV19q3bx8WLFiAuXPnYtasWY4KmYiI3JTx2++48DhFn+Sc4hxFlyFbFuzq0qgLtt+3veZBaiwlO0WxL08C7ulyD7478p20n56f7rS4yH3JuwxxDIFlmrUQfP311xBCYMaMGYryKVOmICAgACtWrLDqOhUVFZgyZQqGDh2KUaNqz9zNRERUexiPIYgLj1O8ccwtyVV0GapqVqK6SN5FqltUN8WxYa2GITYgVtpPz2NCQI4n7zLEFgLLNEsI9uzZAw8PD/Tq1UtR7ufnh/j4eOzZs8eq68yfPx9JSUl4//33HREmERGRSQtB87Dmij7JOSU5ii5DQT5BTovNVTx3/XOIDYtFo6BGWDV6leKYTqfDLVG3SPucaYicQbEwGccQWKRZQpCWloaIiAj4+vqaHGvcuDEyMzNRWlpq8RrJycmYPXs2XnzxRcTGxjooUiIicncB3gFoHtYcQOUA2SYhTeDr5QsfTx8AQLm+HMsOLJPq29JlqK5oGtoUJx89idSZqWhVv5XJ8fo+VwcZs8sQOco/af/gnh/vweojq1FcXgygcprgAO8AjSNzbZqNISgsLFRNBoDKVgJDHR8fH7PXmDp1KuLi4jBz5kybP3/x4sVYvHgxAOD8+fNITEy0+Ro1lZ+fr8nnugp3vn93vneA9+/O91+b7/2J5k9gw4UNGNxwMP7Y/gcAIMAjAKUVpi+vTiedRmJmokl5bb7/mgqouPpAlpSa5HZfB3f+3gPOu/8H/nkApwpO4Yt/v5DKAjwDsG3bNod/tjm14XuvWUIQEBCAS5cuqR4rLi6W6pizYsUKbNq0Cdu3b4e3t7fNn5+QkICEhAQAQI8ePTBgwACbr1FTiYmJmnyuq3Dn+3fnewd4/+58/7X53gdgAB7Gw4qyZknNkH0x26TuiH4j0LFBR5Py2nz/NZWy7uqg4wqfCrf7Orjz9x5wzv3rhR6ntp0yKa8fVF/Tr31t+N5r1mUoOjoamZmZKCkxXYQlNTUVERERZlsHSkpKMHPmTAwbNgyNGjXCyZMncfLkSaSkVP6yycnJwcmTJ5Gdne3IWyAiIjcnn1pTrm1EWydH4voCva52o5LP/kJkL5mFmarlHFBcNc0Sgp49e0Kv12P37t2K8uLiYhw4cAA9evQwe25RUREyMjKwbt06tGrVSvrPkH2tWLECrVq1wtKlSx15C0RE5OZiQmJMyjx0Hibz7hMQ5HV1oHV2cbZ2gVCdZW72Kg4orppmv7HGjRuH119/HQsWLMD1118vlS9ZsgSFhYWYMGGCVHbq1CmUlZWhbdvKNy6BgYH47rvvTK6ZkZGBhx9+GEOHDsXkyZPRuXNnx98IERG5LW9P0y6rcwbM0SAS1+fr4QtvD2+U6ctQUlGC4vJi+Hn5aR0W1SHmZq9iC0HVNEsIOnXqhGnTpuH999/HqFGjMGzYMBw9ehQLFy5E//79FYuSDRo0CCkpKRBCAAC8vb1xxx13mFzzzJkzAIAWLVqoHiciIrKnLg27KPa/vP1LjO0wVqNoXJtOp0OoX6jUrSOnOAd+QUwIyH7MzV7FRcmqplmXIQBYsGAB5s2bh8OHD2PatGlYtWoVHn30Ufzyyy/w8NA0NCIioird2fFOXNf0OgT5BGHNuDWY2HmiNBUpmQrzC5O22W2I7C01N1W1vF1EOydHUvto2snR09MTs2bNwqxZsyzWM7z5r0psbKzUikBERORo3p7e+OO+P1BaUcpEwAryhEC+aBSRPaw7sU61vHtUdydHUvvwNTwREVENMRmwjnxwJ1sIyJ5SslOwK3WX6rHu0UwIqsKEgIiIiJyCXYbIUVJyrq5z0adJHwxrNQwAMLTlUDQIbKBVWLUG50UjIiIip1B0GeJaBGRH8jUIIgMjsXrsahy8eBCdGnTSMKragwkBEREROQW7DJGjKBKCgEh4eXihW1Q3DSOqXdhliIiIiJyCXYbIUTIKMqTtiIAIDSOpndhCQERERE7BWYaoOs7lnMOBCwfQLKwZArwD0LJeS5M68hYCJgS2Y0JARERETiFfIIotBGSNoxlH0X1xdxSVF0llGyZuwJAWQxT1MouUXYbINuwyRERERE7BFgKy1YK/FyiSAQC4acVNuO+n+/DzsZ8BAL+e+BUrDq6QjrOFwHZMCIiIiMgpOIaAbFGhr8Cqw6tUj31+4HOM+34cUnNTMXLVSMUxJgS2Y0JARERETsFZhsgWxy4fQ25JrtnjReVF+Pq/r1GmL1OUNwlp4ujQ6hwmBEREROQUXIeAbLE3bW+Vdf4+/7dJWVRwlCPCqdOYEBAREZFTsMsQ2WJf+r4q6/ye/LtJmYeOj7e24leMiIiInCLYNxg66AAAeaV5qNBXaBwRubLk7OQq6zCxtA8mBEREROQUHjoPhPiGSPt8mCNL0vLSbD6nb0xfB0RS9zEhICIiIqdpGNRQ2r6Qf0HDSMjVpeen23zO4lsXOyCSuo8JARERETlNdHC0tF2dBz5yD3qhN5swps9S/7mZM2AOOjTo4Miw6iwmBEREROQ0UUFXZ4A5l3NOw0jIlWUWZqJcX656rFFQI9XViCMDuUJxdTEhICIiIqeRJwT3r72f4whIlXz8QLuIdujdpDcA4P74+wGoLz4W7hfunODqICYERERE5DTGc8R/fehrjSIhV5ZRkCFtNwpqhG33bsO+hH1YMmIJAPXWAK5QXH1MCIiIiMhp5LMMAcD2s9s1ioRcWWFZobQd6BMIH08fdI3qKq0xoPbw37FBR6fFV9cwISAiIiKnGRw3WLF/8spJjSIhV1ZUXiRtB3gHmBy/vun1JmXyGazINkwIiIiIyGniwuOw9s610v5/l/6zWP901mmcuHzC0WGRiykqu5oQ+Hv5mxx/uOfDqO9fX9of0mKIU+Kqq5gQEBERkVPd2vpWabu4vBh6oVettz1lO9q83wZtP2iLxDOJToqOXIG8y5BaQuDj6YOj046iTf02CPENwTPXPuPM8OocL60DICIiIvei0+ng6+mLkooSAEBJeQn8vU0f+vp/3l/aXnZgGQbEDnBWiKQxeZchtZ8NoHJg8dFpR1FaUQpfL19nhVYnsYWAiIiInE7+AGdIDOSMpyOVvzGmuk/eZUhtDIGBTqdjMmAHTAiIiIjI6Xw9rz7EFZcXmxy/XHhZsd8kuInDYyLXoWghUOkyRPbFhICIiIiczs/LT9ouKTdtITBOEsr0ZQ6PiWyTXZyNxXsX498L/9r92opBxWa6DJH9cAwBEREROV1VXYaMy8r15Q6PiWwz47cZWP7vcgR4ByB1ZirC/MLsdu2qBhWTfbGFgIiIiJxO3mVIrYXAuKysgi0ErkQIgeX/LgdQ+fC+49wOu16/qnUIyL6YEBAREZHTyVsI1MYQmLQQCLYQuJIz2WcU+/IuYGrOFp7FpYJLVl/fmlmGyH6YEBAREZHTKcYQqHQZMhlDwBYCl7I3fa9iX97n39h3h7/DPXvuQdP5TZGSnWLV9atamIzsiwkBEREROZ2tXYY4hsC1nM05q9iXv9E3Nvb7sQAqE78nNz1p1fXZZci5mBAQERGR09naZYizDLkW4++ZpRYCuRNXTlhVTzGomF2GHI4JARERETmdooVAbZYhDip2aSYJgYUWAkU9KxMHdhlyLk47SkRERE5n6zoE7DLkWoy/P+ZWks4oyLB43q7zu3Do0iFEB0fjStEVjGk/Br5evhxU7GRMCIiIiMjpbF2HgF2GXIu1XYbu+fEes+elZKeg3+f9UFpRKpWl56XjyWufVFyPYwgcj12GiIiIyOnkXYZUxxBwULFLs6bLkBAC60+uV5RdLLiIhJ8TIITAZ/s/UyQDAPDU5qcghMDlostSWahvqB0jJzVMCIiIiMjpbO0ypPUYgqKyIuxN2wu90Gsah6swbsFRayHIK81TPXfJviXYeGojzueeVz2eW5IrdUEK8A5AiG9IDaOlqjAhICIiIqerclCx8cJkGrYQ6IUePZf0RI8lPTB9/XTN4nAl1owhSM9LN3v+9pTtOHTpkOqx9Pyr50UFRUGn01UzSrIWEwIiIiJyOvkYgud+fw7Pb3le8VBpMsuQhmMI9qfvx+GMwwCAD/Z8oFkczrbp1CZMWTsFe9P2mhyzpsuQ/MHemKeHJw5ePKh67I5v75C2o4OjrQ2XaoAJARERETmdvIWgQlTgtT9ew+cHPpfKXKmFwPizc0tyNYrEeYrLizHq21FYun8pRqwaoThWUl5ikhDklOSYXCMtL83s9X9P/l21ZQiAlHwBQFRwlC1hUzUxISAiIiKnk48hMJj26zRp25XGEBj3hU/OStYoEuc5n3se+aX5ACof7AtKCwBUtuYEvRGEjac2Kur/cPQHjPt+nKLMUpehHed2WBVHVBATAmdgQkBERERO5+VhOvO5/OHPlVoIcoqVb79PZ53WKBLnuVJ0RbGfnJ2MHed24I0/3zD7vfj28Lc4m3NW2r9UcEnaviHyBnw/5nub44gJibH5HLIdEwIiIiJyOrUZaOTdQ1xpDIFxF6FTWac0isR5MgszFft/pPyBaz+7tsrzUnNTpW3597hTaCfEN4q3OY7hbYbbfA7ZjgkBEREROd3NLW82KWsU1EjadqkWAqP+8RfzL2oUifMYJwRv/vWmVefJxw3IEwJ/T380CWli0jJ0TeNrpEShY4OOOP/4eQT5BAEAhrUahtb1W1cnfLIREwIiIiJyup6Ne+KtwW8pyjx1ntK2K40hMG4hyCrO0igS58koyFDsy7sCWSKfWSiv5GpCEOAZAF8vX3SI7KCo37tJb6y9cy0+uuUj/Dr+VzQOaYzt927H+ze/jy9v/7IGd0C2YEJAREREmniy75MI8wuT9uVTV7rSSsXGYwjcISEwbiGwlrkWggDPAABA96juivrdorohJjQGU3tMRUxo5XiBrlFdMa3XNNTzr1etGMh2TAiIiIhIEzqdDmvvXCvty1e7La0oVdR1pTEEWUVMCOReueEVafuNP99Ahb4Cr21/DVuSt0jlAV6VCcGA2AFSmYfOA9c1va7mwVKNmQ7xJyIiInISf29/aVu+MFmFqFDUc6UxBO7QQnCxwPpxEm3qt1HsT/hhAr45/I2izN+z8vs8vtN4nM46jf0X9mNsh7GIC4+rebBUY0wIiIiISDP+XlcTAnmXIeMEwKXGELhBC4G5VYTVDGkxRLFvnAwAVxMCTw9PzB4wu2bBkd2xyxARERFpJsA7QNqWdxmq0CtbCCpEBYQQTotLLrs4W7Ff11oINpzcgO8Ofyd9zS8XXkZKTopV53p7eCPULxTPXfecxXqGMQTkmpgQEBERkWbkXYYstRCYK3MG4/70uSW5JglLbbU9ZTuGfjUUY78fi+X/LgcA7L+w3+rzDStOt4tsZ7EeEwLXxoSAiIiINCPvMmRpDAGg3cBitQG2xq0GtdXTm5+WtievnQwASMm2rnUAAAJ9AgEoV5k25uvpq7oyNbkOJgRERESkGUULQZnrtRCUVpSaDCoG6k63IflaAQAghLBphqFwv3AAQHRwtNk6hoXGyHUxXSMiIiLNeHt4w1PniQpRgQpRgbKKMnh7eqt2ydFiYPHlwsuq5XVlYLGhy4/BrI2z4KGz/n1xuH9lQhAVbKGFwMu3esGR07CFgIiIiDSj0+lUxxG4SguBubfldaHLUHpeOvam71WUzf97vuKe20e2t3gNQwtBqG8ofD3VH/y9PbxrGCk5GhMCIiIi0pTaOAL5AGMDLcYQZBRmqJbXhS5DL297WbVcfs83tbjJ4jUMLQQ6nQ6RgZGqde5of0c1IyRnYUJAREREmjIeR/DExidwIf+CST1XaiGoC12GPtn7iWr5pYJL0vbNLW+2eA1DCwEARAREmBxvGtoU/7v+f9WMkJyFCQERERFpSt5CcKXoCt7Z+Y5qPZcaQ1AHWghCfUNVy09dOSVtNwtrhoHNB0r7HSI7KOrKE4LIAGULwZwBc7AvYZ/UikCuiwkBERERaUo+JeWVoitm62nRQpBXmqdaXhdaCGJCY1TL5clOZEAkvh79NQY1H4SbWtyEB7o9oKgrf9g3biEY02EM6gfUt2PE5CicZYiIiIg0JU8ILL1512IMgXxazqigKKTnpwMA/jz3J/ot64fW9Vtj8fDFNs3M4yoKSguk7WCfYJPkx9fTF6F+ofDQeWDz3ZsBAH+d/UtRRz5LUZhfmOJYbFisfQMmh6l9P71ERERUp3h6eErbrtZCkF+aL203DW0qbe84twN/nP0Dn+7/FIv3LnZ6XPYgv7cmIU1MjseFx5kkOn1i+ij2Gwc3lraNB4IbT2lKrosJAREREWnK2i5DWowhkL81N9fFxtzgXFdXUHa1hUAtIWge3tykzEPngTXj1sDH0wedGnTC4LjB0rHcklzHBEoOx4SAiIiINOWpc90WAnlCIH8bLpeUmeSscBRyS3Lx5MYnMfevuRBC2HSuXuilKV4BoHGI6b3FhcWpnjuy7UjkP5uP/Q/uV8wQNb3XdGl7ctfJNsVD2uIYAiIiItKU1S0EGo8haBDYQLVOcXmxs8JRmPvXXMzbOQ8A0C6iHYa3GW71uUVlV7v3+Hv5o76/6eDfuHD1hAAAvD1NFxvr16wf3h3yLpKzk/FCvxesjoW0p2kLgV6vx/z589G2bVv4+fkhJiYGs2bNQkFBQZXnHj9+HC+++CJ69+6NyMhIBAcHIz4+Hq+99ppV5xMREZFrkI8huFykPs0noP0YAuNpNeW06M702h+vSdtv73jbpnPl9xXoE6j68D+izQibrqnT6fB4n8ex8OaFZhcpI9ekaULw+OOPY+bMmWjfvj0WLVqEMWPGYOHChRg+fDj0er3Fcz/77DPMnz8fLVq0wIsvvoi3334bbdq0wfPPP4++ffuiqMh0hUMiIiJyPbVlDIGlh1xzC5g5inFyZOsAXvn4gSCfIHSL6qY4/mTfJ9GiXovqB0i1imZdhg4fPoxFixZh1KhRWL16tVTevHlzTJ8+HatWrcL48ePNnn/HHXfg2WefRWjo1UU1pk6dilatWuG1117Dp59+ikceecSh90BEREQ159LrEMi6DFlqIcgszERUcJQzQgIAHMs8ptiXjwewhnzK0UDvQHRp2EVxfEiLIdUPjmodzVoIvv76awghMGPGDEX5lClTEBAQgBUrVlg8v0ePHopkwGDcuHEAgP/++89usRIREZHjWDuoWJMxBKVVjyEAgD6f9sHwr4ebXdnY3gzrIRiczjpt0/nGXYb8vf2lJKBBYAP0adLH3KlUB2mWEOzZswceHh7o1auXotzPzw/x8fHYs2dPta57/vx5AEDDhg1rHCMRERE5nmJhMgsrAGs+hsBCl6GCsgL8cvwXvPHnG84IS/GGH6hMEErKS6w/v0zZQgAAX4z8Au/f/D623rMVgT6B9gmUagXNEoK0tDRERETA19fX5Fjjxo2RmZmJ0tJSm65ZUVGBV155BV5eXha7GxEREZHrkA8qLqkw/1Dr7DEEFfoKRVecEN8QRWuGmvd3v+/osAAoExUDSwOyjckTiiCfIABAw6CGmNZrGtpHtq95gFSraDaGoLCwUDUZACpbCQx1fHx8rL7mjBkzsHPnTrz++uto06aNxbqLFy/G4sWVKwueP38eiYmJVn+OveTn52vyua7Cne/fne8d4P278/27870D7n3/lu79Sqb5bkJyh44cQuIV9WvY27G8Y/g0+VNpP8QrBNu3bYePhw+KKsxPXOLn4ad6n/b+3u9L22dStmH7BjQPNF1MTM2f6X9K28U5xQ7/ueTPfqLWYVikWUIQEBCAS5cuqR4rLi6W6ljrhRdewPvvv4+EhAQ8++yzVdZPSEhAQkICgMrxCAMGDLD6s+wlMTFRk891Fe58/+587wDv353v353vHXDv+7d070uuLAEyqr5Gy9YtMaCb+jXsbdy8cbhUcPU5pWdMTwwYMACBewJRVGg+IagfVF/1Pu39vd+/cz9wQlkW2z4W1ze7XtHiYs66jeuk7T5t+jj855I/+wO0DsMizboMRUdHIzMzEyUlpk2DqampiIiIsLp14KWXXsKrr76K++67Dx9//LG9QyUiIiIHqqobjoGzugzlFOcokgEA6B7VHUDlIl6W2Dr9Z3XJxwAYDPxiIKLfjcbBiwctnrvz3E5pQTPA8gJk5B40Swh69uwJvV6P3bt3K8qLi4tx4MAB9OjRw6rrvPTSS3j55Zdxzz33YOnSpdDpdI4Il4iIiBxEPqjYko/3fowz2WccGwyA5Oxkk7L4RvEAqn7gzy7Olra3JG/B90e+r/Fg6Av5F7Bk7xKkZKdIZWpjCADgUsEldPm4C7KKspCWl4aFuxbi1e2v4u/zf0t1PtjzgeKc5uHWdTOiukuzhGDcuHHQ6XRYsGCBonzJkiUoLCzEhAkTpLJTp04hKSnJ5Bpz5szByy+/jEmTJuGzzz6Dh4em66wRERFRNVjbQnDw4kEM/mKww2cbUpvCs3FIYwCmCcHNLW/GrD6zpP2MggwIIfDX2b8w6ItBGPPdGHy2/7MaxTNy1Ugk/JKAISuGoEJfAcB0liFjU36eghFfj8Bjvz2GF7a+gGs/uxbHLx8HAKw8tFJRly0EpNkTdKdOnTBt2jT88MMPGDVqFJYuXYpZs2Zh5syZ6N+/v2KWoEGDBqFdu3aK8z/44APMnj0bTZs2xeDBg7Fy5UqsWLFC+m/Tpk3OviUiIiKqBmtbCADgVNYpJGWaviSsiYLSAoz9biwGLh+IcznnVBMCw6Jk3p7eivJejXth3pB5CPCuHPdYpi9DXmkexv9w9TnmkV+rv1Bqfmk+dqXuAgAcv3wcxy5XLkim1mVIbvXR1dibvlfa1wu9NANS9+juirrRwdHVjo/qBs0GFQPAggULEBsbi8WLF2PdunWIiIjAo48+ijlz5lT5tt+wTsHZs2dxzz33mBzv378/brzxRofETURERPZjzSBYubS8NHRs0LHGn5uclYxAn0BsSd6C7458BwAYsmIIOjXoZFI3IiACQOX4Armnr30aQOViXobuTIcvHcbZnLNSnZosqJacpey+tC99H9pHtjfbZcgSw2Jm8rUe1k9YDw8de1i4O01/Ajw9PTFr1iwcO3YMJSUlSE1NxbvvvougoCBFvTNnzkAIoSj7/PPPIYQw+5+rT+9ERERElWxpIQBsX5VXzfoT6xG3MA4x82Ow6r9VUnlSZpKUHMjV868HAMgqVi6c5u9dOch4QOwAqezaz65V1GkR3qLacRqPZ9ibVvnWv6oWAjVHMo4AADILM6WyXo17matOboQpIREREWlKi4RgxKoRAIDSilL8dOynKusbWjHMraQ8sdNEaVtA+RIz3D+8umGa3OuCXQuwN21vlWMI1BzJOIJbV96KnJLKVg5PnSfC/MKqHRvVHUwIiIiISFPWDio2UJsFCADK9eUmPQrU6IW+2gOT5Q/78ilIY0JjzJ6TW5Jbrc8CTLsMAcCkNZOq1UIAAOtOXF1/oEJUsLsQAWBCQERERBqztYUgryTPpGzbmW2IfDsSXT/pisKyQrPnpmSnoMXC6nfhkZO/+Q/3M98KYDzuwBYXCi6YlB3NPIorReZXd5YnWM9e9yx2PbCr2p9P7kHTQcVEREREtg4qLi4vNikbsHwAgMp1AL46+BWmdJ+iem7CLwk2r2XQsl5Lafu2NrdJXYzu7XKvVG6p601NWgjk/f3lLHWbWjl6JSICIuDj6YM+TfrA08MT93S5B8v/Xa6oN7Tl0GrHRXULEwIiIiLSlK0tBMYJgXE3oXO558yeu+mUbdOS92rcC4tvXSztLxi6ANnF2ajnXw/P93teKvf29EaQT5Dq7D9F5UXV7qKUUZBh8XiobyiKyotQWlGqKBvYfKCi3tgOY00Sguevfx5EABMCIiIi0pitYwiME4LzuecV+01Cmpg913jAryULblqAx3o/piiLDYtF4r2JqvXD/cLNTge6NWMrBmOw1Z9tYK6FwKBbVDccu3wMaXlpUpnaasp9Y/oizC9MWkl59djVuLbptSb1yD1xDAERERFpqqYtBAcvHlTsy9+W14StswOF+IaYPfbeifds/nwhhFUJgWFKVAPDVKhyYX5hSLwnES/1fwnfjfkOt7e93eZ4qO5iCwERERFpytaEoKSiRLGfmpeqPF6uPF5dlgYKq8koNN+9p6DC9lmBcktypUXNAr0D8UTfJ/DytpcVdVqEt0B9//qKMrUWAgDo0qgLujTqYnMcVPexhYCIiIg0VZNBxXkleXjwlwcVx40ThuoK9Qu1qf6lgkuK/WGthtXo8+WtAxEBEWgc3NikTlRwlLSKsoF8OlQiazAhICIiIk3VpMvQh3s+NDlurxYCbw9vm+pHB0dL232a9MHCoQtrFJc8IYgMjETz8Oaqn9koqJGizFwLAZE5TAiIiIhIUzUZVPzM789YPG6rXo17AQBa1WslbVvrg2EfAKhMJJaOWIoW9ZTdeWydflTeBSkiIAJx4XEmdaKCohSJCKA+hoDIEo4hICIiIk1Vp4Xg+yPfY2TbkfDUeaJCVCiO16TL0OJbF6O4vBgdG3S0uSvTyLYjcfjhwwj2CZZWLg71C8XlossAgJySHEQGRlp9PeMuQzEhpqshNwpqhKigKEUZWwjIVmwhICIiIk3Z+uANAGO+G4Mle5dID95yNekyFOgTiGuaXINAn8Bqnd8+sr0iJvnMQ7auWKzoMhQQCW9PbwT7BCvqeHt6IyqYCQHVDBMCIiIi0pStLQQGD//6sMmiZABQXFH9LkP2fpgO9b06MNnmLkMFyi5DADCm/RipbFS7UYpjBtX9epL74k8MERERaaomD7Bq8/SbayFQSx6M2XuGHnkLwfaU7QjzC4OHzgPRwdFVdh8ybiEAgHduegddo7qirKIMk7pMAgCTMQREtmILAREREWnK3KDicL9wPND1AYvnFpSZzu9vbgyBNQuW2buFQJ4QvLTtJXRb3A3xn8SjzfttkJKdYvFc40HFQOUCY4/0egSP93lcKosOjsbkrpPhqfPE7P6z7Ro/uQcmBERERKQptRaCro26Im1WGp67/jmbr2euhUAteTBm74QgvzRftTyrOEt1hiQ540HFliwdsRS5z+bipQEv2RwjERMCIiIi0pTaoGJfL1/4efmheXhzfHn7l3ig6wNWP6ybm3Y0rySvWrHUhKXFzVb9t8piq4UtCQEABHgH2BYc0f9jQkBERESaUmshkC8KNrHzRCwZsQSdGnSy6hrmugydzz1vMQ4ddFWFarPJXSfDQ2f+cevHpB/NHssuzpa26/nXs2NUREpMCIiIiEhTamMIfDx9TMosdflpEtJE2jbXZeh01mmLcTQLa2bxeHX0a9YPRx4+Yvb4L8d/MXssp+TqNKXysQhE9saEgIiIiDSl2kLg6W1SdiTD/IO1PCEw12XIOCEwHoDbpn4bi3FWV5sI89fdm75XtbykvETqTuTt4c21BcihmBAQERGRptQSAmv6zMs1Dm4sbZvrMnQ6+2pC8Mmtn+ClAS9JSYGHzgNzb5xr02faYnrL6dL2bxN+k7oRJWUmoaDUtOXDuHVAp7N/dyYiA65DQERERJpSG8jbrVE3k7K48Diz3X6q6jJUVlGGb/77RtpvHtYcAPD0tU+jSUgTxIXHoXPDzjbHbq1bo25F947dUd+/Pm5qeRPa1G+Do5lHoRd6HM44jF6NeynqyxcxszQwmcgemBAQERGRptRaCLpHdzcp+/iWjzHq21GqU3kqEgKVFoIbv7xRUd6pYeUAZX9vfzzQzfJaB/bg7eGNu7vcLe03C2uGo5lHAQCXCi6Z1M8p5vgBch52GSIiIiJNqQ0qbh/Z3qTsxhY34uITF9EhsoPJMXmXoQv5F/D1oa+l/ayiLGxL2aao3yioUU1CrjF5lyi11ZY5oJiciQkBERERaUqthcDX01e1boB3AIJ9g03K5S0EADD+h/FIykwCAKTlpSmOdWzQsbqh2k1kQKS0nVGQYXJc0WXIl12GyLGYEBAREZGm1MYQqCUJBvEN403KWtZraTLf/1cHvwJgmhA8e92z1YjSvqpsIWCXIXIiJgRERESkKbWHf0srBt/f9X7FfrBPMCICIhDmF6YoLyovQmZhJt748w2prF1EO4zvNL5mAduBPCF4e8fbOHnlpOI4WwjImZgQEBERkabUEgJLLQQ9onso9j09PKHT6RDuF64oLy4vxrjvx2Hrma1S2c0tb65htPYh7zIkIHDdZ9ehsKxQKuMYAnImJgRERESkKbVBxcbdf+SM5+TPLs4GYPrgXFBWgC3JWxRl0cHR1YzSvozXWbhYcBHtPmgnLap27PIx6ZirxEx1FxMCIiIi0pRxa4Cl1gGDIS2GSNvDWw8HYJpEnMs5Z3JeVHBUdUK0u8jASJOyszlnMXvrbOSW5GJv2tUVjLtFma7JQGRPTAiIiIhIU8bjBdRaDIx9OOxDRAREoL5/fbw1+C3VOieunDApc5W37a3qtVItn7tjLiLfjpTWKPDQeSC+UbwTIyN3xISAiIiINFWdFoIW9VogbWYaUmemol1kO9U6Z3POmpRFBblGC4Gnhyde7Pei6rHSilJpu3X91gj0CXRWWOSmmBAQERGRpoxbBCzNMCTn7ekNXy/19QrMcZUWAgB47vrnUN+/vsU6Leu1dFI05M6YEBAREZGmqtNCUF1qi5ppxdfLF8mPJVusExcW56RoyJ0xISAiIiJNGScA1owhUDO63Wh7hONUwb7B2Juw1+zx2LBY5wVDbosJAREREWnKuItQdVsIHu/zOEa2HWmHiJyrQ2QHs8dcqUWD6i4mBERERKQpkxYCK8cQGPPz8sOacWtQz7+e6vH/Xf+/al3X0Xy9fNGrcS/VY7e2vtXJ0ZA7YkJAREREmjLuIlTTMQQV+gqTMi8PLzx73bM1uq4jfT/mezzR5wlF2Y77d6BRUCONIiJ3woSAiIiINFWddQgsqRCmCcHjvR936ek7Y0Jj8PaQt3Ff/H3QQYdnr3sWfWL6aB0WuQnHDeMnIiIisoLxCsM6na5G1yvXl5uUCSFqdE1n+ey2z7Dw5oUI8gnSOhRyI2whICIiojolLtx0qk5vT28NIqkeJgPkbEwIiIiIqE75bMRnin0vDy/c2fFOjaIhcn3sMkRERER1yjVNrsHJR09CL/QoKi9CoHcgWtRroXVYRC6LCQERERHVOUwAiKzHLkNERETkUmrLAGCiuoIJARERERGRG2NCQERERETkxpgQEBERERG5MSYERERERERujAkBEREREZEbY0JAREREROTGmBAQEREREbkxJgRERERERG6MCQERERERkRtjQkBERERE5MaYEBARERERuTEmBEREREREbowJARERERGRG2NCQERERC5FQGgdApFbYUJAREREROTGmBAQEREREbkxJgRERERERG6MCQERERERkRtjQkBERERE5MY0Twj0ej3mz5+Ptm3bws/PDzExMZg1axYKCgqccj4RERERkTvTPCF4/PHHMXPmTLRv3x6LFi3CmDFjsHDhQgwfPhx6vd7h5xMRERERuTMvLT/88OHDWLRoEUaNGoXVq1dL5c2bN8f06dOxatUqjB8/3mHnExERERG5O01bCL7++msIITBjxgxF+ZQpUxAQEIAVK1Y49HwiIiIiInenaUKwZ88eeHh4oFevXopyPz8/xMfHY8+ePQ49n4iIiIjI3WmaEKSlpSEiIgK+vr4mxxo3bozMzEyUlpY67HwiIiJyDVFBUdJ2l4ZdNIyEyP1oOoagsLBQ9WEeqHzLb6jj4+Nj9/MXL16MxYsXAwDOnz+PxMREW8Ovsfz8fE0+11W48/27870DvH93vn93vnfAve+/qnuf3Xo2njz4JHw9fDEhbEKd+zq58/cecO/7rw33rmlCEBAQgEuXLqkeKy4uluo44vyEhAQkJCQAAHr06IEBAwZYG7bdJCYmavK5rsKd79+d7x3g/bvz/bvzvQPuff9V3fsADMDdQ++Gl4cXvD29nReYk7jz9x5w7/uvDfeuaZeh6OhoZGZmoqSkxORYamoqIiIizLYO2ON8IiIich3+3v51MhkgcnWaJgQ9e/aEXq/H7t27FeXFxcU4cOAAevTo4dDziYiIiIjcnaYJwbhx46DT6bBgwQJF+ZIlS1BYWIgJEyZIZadOnUJSUlK1zyciIiIiIlOajiHo1KkTpk2bhvfffx+jRo3CsGHDcPToUSxcuBD9+/dXLCo2aNAgpKSkQAhRrfOJiIiIiMiUpgkBACxYsACxsbFYvHgx1q1bh4iICDz66KOYM2cOPDyqbsCo6flERERERO5M84TA09MTs2bNwqxZsyzWO3PmTI3OJyIiIiIiU3yFTkRERETkxpgQEBERERG5MSYERERERERujAkBEREREZEbY0JAREREROTGmBAQEREREbkxJgRERERERG6MCQERERERkRtjQkBERERE5MaYEBARERERuTEmBEREREREbkwnhBBaB6G1iIgIxMbGOv1zMzIyEBkZ6fTPdRXufP/ufO8A79+d79+d7x1w7/t353sHeP/ufP/yez9z5gwyMzM1jsgUEwIN9ejRA//884/WYWjGne/fne8d4P278/27870D7n3/7nzvAO/fne+/Ntw7uwwREREREbkxJgRERERERG6MCYGGEhIStA5BU+58/+587wDv353v353vHXDv+3fnewd4/+58/7Xh3jmGgIiIiIjIjbGFgIiIiIjIjTEhICIiIiJyY0wIiIiIiIjcGBMCIiIiIiI3xoSgDuH4cCIiIiKyFROCOsCw+p1Op9M4EiIixzN++SGE4AsRIqIaYEJQy+3fvx+9evXChx9+CADQ6/UaR+Q8RUVFKCoq0joMIk2Ul5cDAPLz8zWORBuZmZnYunUrkpKSoNPp3OaFyMWLF3H+/Hmtw3AZ7vQ3j66+DDD8/nMnJ0+exIEDBxx2fSYEtdy0adMAAB999BEOHToEDw/3+ZbOnDkTH374IU6fPs0/Cm7MHd8Mb9myBaNGjUJ8fDyefvppJCcnax2SU33yyScYPHgwBg0ahF69euGjjz5S/BzU5Z+JiRMn4osvvkBxcbGivC7fs7GkpCT8/vvvuHTpkvQ3z53uH3DPRGjPnj245557MHjwYLz33nvIy8vTOiSnGj16NN566y2T+zb8LNT434CgWmvZsmXCy8tLPPzww0Kn04kOHTqI/fv3CyGEKC8v1zY4B1u9erXQ6XTCy8tL9O/fX3z//ffi0qVLWoflNGfOnBGnTp3SOgzNHD58WPz999+KMr1er1E0zvXTTz+JFi1aiHr16olevXoJDw8P8dBDD6nWrYtfk++++05ERkaKPn36iOeee040adJEtG3bVly+fNmkbkVFhQYROs6yZcuETqcTv/76a5V16+L3/sCBA9LfO51OJ/z9/cXcuXO1Dstpdu/eLX7++WdFWV37GTfnl19+Ee3atRN+fn4iNjZW6HQ68fLLL6vWrYs/+8uWLRM+Pj4O/bfPlYprsYiICIwdOxYvvPAC3nvvPcydOxcJCQn4+OOPtQ7N4fbt24eBAwdi8ODBOHnyJI4ePYpx48ZhypQp6Nq1K4KCggAAly5dQmRkZJ3rTtCuXTt06NABDzzwAK655hqEh4drHZJTtW7dGhEREbj33nsxdOhQNG3aVDomhKhz32+5mJgY9O3bFzNnzkTXrl3x9NNPY9OmTTh06BD279+PkpIS1K9fH61bt9Y6VIeIiYnB0KFD8eyzzyIuLg6bNm3ClClTsH79emzfvh3Hjh3DNddcg3HjxgGoWz8PERERuOOOO/Dqq68iIiICBQUFOHXqFH7//Xf4+/ujdevWaN26NZo0aaJ1qA5x3XXXISUlBaNGjYKPjw++/vprpKWlYenSpbj//vu1Ds/h4uLiUFFRgUmTJuGuu+5Chw4dAFx9M1xXfs7VxMbGokuXLnj88ccRFxeHWbNm4dy5c/j7779x8uRJeHt7w9fXF40aNdI6VIeIiIjA6NGj8eqrryIyMhIXL17EP//8g88//xxhYWGIjo7GLbfcgl69elX/Q6qVRpDmnnzySdGwYUPFW9L77rtP6HQ6MX36dJGbmyuEqJuZckVFhSgpKRE33nij6NKli1i3bp2YOnWqCA4OFg0aNBDPPfecOHbsmFizZo3o2LGjOHbsWJ36OsybN0/odDoREBAgwsPDxWOPPSZ27dolioqKtA7NKebPny90Op0IDw8XXl5e4vbbbxc//PCDyMrKkurUpe+33LvvvitiY2PFH3/8IZWtWbNGxMfHiz59+ghvb2/h5eUlOnbsKN555x3F16QuWLp0qYiJiRFbtmwRQlS2hB47dkw0adJERERESG+OdTqd6NWrlzhy5IjGEduP4Xf+rl27pLKHH35YhIeHS/ccHBws7rzzTrFz504hRN36d/C///1PNGnSRKxdu1YqO3z4sGjRooWIj48XeXl5dbplfOHChUKn04lGjRoJnU4nunfvLhYtWiTS09OlOhUVFXXqe27w1ltviebNmyt+7y1btkxcf/314pZbbhGhoaEiNDRUDBs2TKxatUoUFxfXqa/DU089JaKiosQ///wjld12220iMDBQ+Pv7i7CwMKHT6USTJk3Ee++9JwoLC6t1/0wIaqFz584JnU4n3njjDcVD4P79+0WbNm1EYGCgWLNmjXYBOsmuXbtEXFyc2Lx5sxCisivF8OHDhaenp+jYsaNo3ry56NChg0hOTtY2UDvKy8sTN9xwg+jYsaP46quvxIQJE4S3t7do3bq1mDdvnjh27JhJE3JhYaHIy8vTKGL7ys/PF0OHDhUdOnQQW7duFU8//bQICAgQ9evXF4888oj4448/RGlpqeKcvLw8ce7cuVrftF5WVibGjx8vrrvuOpGZmSmVGxKkW265RXz22Wfi1VdfFfXr1xeBgYFi5cqVGkZsf6+88oqIi4sTJ0+elMo+/PBD4ePjI55++mmRmZkp/vzzT/HQQw8JnU4nbr31VpGfn69hxPaRmpoqdDqdmD17tvRv+bnnnhNeXl7izjvvFJs2bRLff/+9ePjhh4W/v7/o27evuHDhgsZR28/ly5dFq1atxHPPPSd9Pw3/zufOnSt0Op30sGR4EKrt/97lCgoKxPDhw0X79u1FUlKSWLRokQgNDZV+xr///nuTF0JXrlwRBw8eNPl9WNuUlJSIESNGiJtuuklkZWVJ31fD9/3aa68Vs2fPFlOnThWhoaGiadOmYuvWrdoGbUeGf/sPPvig1C3y6aefFn5+fuKZZ54Rx44dExkZGeLTTz8VERERIjIyUmzbtq1an8WEoBa65557RHx8vDh+/LjJsQMHDojmzZuLevXqifXr1wsh6tZbImNjx44V8fHxori4WFRUVIjTp0+LZcuWiaZNmwoPDw/RvXt38dNPP6n2L66N9uzZI8LDw8Xdd98thBAiLS1NrFy5Ulx77bVCp9OJ/v37i5UrV4rU1FTpnMWLF4t7771XXLx4Uauw7ebgwYMiIiJCjB49WghR+VCwc+dOMXLkSKHT6UTr1q3F66+/Lv777z/pnMWLF4tbbrmlToy5mDhxooiOjpb2jx8/Lho0aCCmTp2q+Bk/ceKEaNOmjWjSpInIzs7WIlSHeOONN4ROpxM//fSTSE1NFYmJiSIiIkJMmzZN5OTkKOpOnTpV6HQ6cfDgQY2itZ/XXntN6HQ6MXToUPHff/+JkydPCk9PT/H6669LrUDl5eUiLy9PvP7660Kn04lXXnlF26Dt6McffxRxcXHi22+/NTmWmpoqAgICxHPPPaco37Bhg3j22WdFQUGBs8J0mKSkJNGwYUNx8803S2Xp6ekiISFB6HQ6ERISIhISEhRv0D/55BPRq1cvceDAAS1Cthu9Xi9uu+020bFjR6ksOTlZNGzYUCQkJIiMjAypfOPGjaJevXoiPj6+zrSYL126VOh0OhEbGysWLFggEhMThbe3t3jrrbeklwOGZ7ydO3cKf39/MWTIkGq1ljEhqGXKy8vFkiVLxNatW03egFRUVIiysjKxaNEiKXOW/2Opi3bs2CGCg4PF0qVLpbJz586JiIgI0aVLF9G0aVOh0+nE66+/rmGU9nPy5EkxZcoUxQNveXm5OHnypHj77bdFXFyc8PX1FePHjxd//PGH2LFjh+jcubOIjY3VMGr7uXTpknj11Vel+zf8IszPzxffffed6Natm9DpdKJfv35ixYoVYvPmzSI+Pl60aNFCy7DtZsmSJUKn04mePXuK+++/X3Ts2FEEBASInTt3Sr8PSkpKhBBC3HvvvaJRo0aKLgW13cmTJ0WbNm1EeHi4aNasmWjQoIHw9/cXP/74o1TH8EZ0yZIlwtvbu9pvy1zJP//8I15++WXRuHFjUb9+fdGiRQvRu3dvkZKSIoRQvvQpKSkRDRs2lJLmuuCbb74ROp1O6gJmuF+9Xi9KS0vFgAEDRHx8vPS9T0tLE4MHDxZRUVGaxWxPRUVF4osvvpB+78nf+u/evVsMHDhQ6HQ6ERcXJ1577TXx888/i27duonWrVtrFbJdzZ49W+h0OjFmzBjxzDPPiN69e4ugoCCxZ88e6cHX8PtvxIgRolWrVnXqRcjWrVull35BQUGib9++Jj0fDD8Tffv2Fddcc40oLy+3+WUwE4Jaqqrm0HfeeUfodDpx22231fnZdx555BERHx8vNSXPnDlTREREiM2bN4tt27aJu+66S9H3rra7cuWKavZfVFQk9uzZIx555BERGhoqGjZsKK677jqh0+nEjh07NIjUMQoKCsz+/F+4cEG89dZbIioqSvj7+4uOHTvWqftPT08Xd999t6hXr56IiIgQ/fv3F71795a6hxj+KOj1ejFt2jQRFxcnzp49q2XIdmP447Z+/XoxZcoU0b17d/H888+LUaNGiQ0bNgghhPQ2uLi4WLz55psiPDxcHDt2TLOY7SkvL09s27ZN3H///SIqKkrcfffdorCwUDpu+PoUFxeL9u3bizFjxtSZt6RFRUVi7dq1Zu/H0IJy4cIFUVJSIpYtWyY8PDzEn3/+6eRIHae8vNzk9578ge+rr74SLVq0EDqdTsTExNSp33snT54U/fr1k2aW6tKli+jTp4/UOlZWVibVnTRpkmjXrp2ilbwuKCsrE0uXLhUtW7YUN9xwg2o34PLycjFo0CDRr1+/avWKYEJQyxh+IZhrDjIcP3/+vLjllluETqcTL730Uq3vR6jGcK+7du0S3t7eYu7cuWLfvn3Cx8dHvPjii9IvibrypsDaPrFXrlwRW7ZsEf379xc6nU7qXlTbWfu2Q6/Xi9OnT4vbb79d6HQ6cc899zg2MA3s3r1bHD58WFy4cEE8+OCD4rXXXlP8Udy/f79o27atmDRpkoZROk5FRYX0O3Du3Lmib9++iofFP//8U7Rp00aMHTtWqxAd5sKFC+Lrr78WmzZtEkKY/rvYu3eviIuLE08++aQW4dmd4f4MLV9q9u3bJ3Q6nVi/fr1ISUkR7du3F7fffruzQnQoeWuIpeMGkydPFjqdTtx7770Oj83ZfvvtN/Hnn3+KEydOiPvuu08sW7ZM8Sxk+L133333aRil/cl/t1+4cEGaTMb4e79//37RsmVLMW3atGp9DhOCWsb4B8DSQ2J2drbo3bu3CAwMrBP9KC2ZOXOmiI+PF7179xadO3cWSUlJWoekCcPPx6VLl8Rdd90lvL2962y3MbU/kIayzMxMMXnyZOHl5VVn799g+vTpIjQ0VLz55pti586d4o8//hA33HCDCAkJqRPjRoQw/V7L/0AmJiYKDw8PERsbK9566y0xe/Zs0bZtW9GoUaM62zoq/3ro9Xrp70BGRoZ46qmnhJ+fn1vcu0FOTo5o0qSJuOOOO8Srr74qPD0968zPvjG1v/mGssuXL4tHHnnELX7vjRo1SsTFxYlVq1aJpKQkceDAAXHzzTeLwMDAOvO9N/5ZV3sRbPjeX7x4UTzxxBPCz8+v2vfvZb9ZUsmRrly5gp9++gnbtm1Dbm4uHnnkEQwcONDsysQVFRUIDQ3FzJkzkZ+fj4CAACdHbD/CinnEH3jgAaxevRpnz57F4sWL0aJFCydF53jZ2dlITEzEqVOnkJ2djX79+uHaa6+Fn5+fyfff8HXavHkzVq1ahdmzZyMiIkKLsO2moqICmzZtwvHjx1FeXo6bb74Z7dq1U/2ZMJT99ttv+Oyzz2r9/at97/v27Qs/Pz94enoCqFytfMuWLXj22Wel8xo3bowFCxagQYMGWoVuF+Z+73l5Xf3Tdc0112DmzJlYtGgRnnnmGans9ddfR2RkpFahO5T8Z1+n00n7y5cvx5dffokXXnih1t97cXExdu7cidTUVBQXF6NLly7o2bOn6r/74OBg9OrVC1u3bsWGDRvw1FNP1fqf/cLCQvz666/Yt28fCgsLMXHiRPTo0UP1b76hbN26dfjggw9q/e89c997uaeffhrjxo3D3XffDZ1OB71ej/DwcLz77ru1/ntv7vee4Xe+nOF7P3fuXHz55Zd46aWXqn//1UojyKnOnz8vbrzxRmnuecOc0y+//LIoLS2tU9OrGUtLSxPLly+3mPEasujdu3eLFStWiPPnzzsrPIdLSUmR+k56e3sLDw8PodPpRHx8vPjggw9EcnKy6tvT5cuXi86dO2sUtf0Y+szL55fv0KGD+O2334QQ5lvIfvnlF9G9e3dnhmp31nzvDW+Mzpw5I+bPny+GDh0qnnzySfHXX39pHH3N2fp7b+fOnWL58uVi7dq1tX79hfT0dJGYmGjTOYsXLxaenp7ijjvucFBUznPu3Dmpy6vh594wi9ovv/wiiouLhRDKefc/++wzqf98bZeWlibNnGa4/9DQUPHpp58qWoSM7dy5U1xzzTVOjta+qvreG7oG6vV6sW3bNjFr1izRo0cPMWHCBGlmxdqsOs97a9euFZ07dxYJCQk1+mwmBLXAiBEjRNeuXcVHH30kiouLxerVq0VsbKxo2LChOHHihNnz6kKiYJg94bbbbhObNm2qck5xeVeCumDIkCEiPj5efPrppyI/P19s2LBBvPzyy6Jnz55Cp9OJAQMGKOZcNvxxLCgoUMxVX1uNHj1atG3bVjz11FNi586dYs6cOcLb29vsoCq52j6g0tbvfV1j7e+9uvB7zphh/M/kyf/X3nlHRXG9f/i9u/S2oAiiomJDkRB7R8XeEhvRqDFKNGKLBUsSTQxiYo3mp4aoRGMHS0xi7GJv8auS2At2RRRFEET68vn9wdnJLs2S3R13733O8aizA+d9dubemffWISXW8QX5888/cevWLQNGZhw6duyId955B/PmzcPNmzexfv16BAUFoUKFCtJqM5oVljQkJCQgMDBQZ+MyU6VHjx7w8/PDrFmzEBcXh6VLl8Le3h61a9c2m+EwxfEm1x4wn3rgTd/3YmJi/vMwQZEQvOUcOHAA9vb2WLdunc74sRMnTsDKyuqNJ4+YAqdPn4adnR38/Pzg4eEBGxsbBAcH49y5c4UmSZtLZaDNqVOnYG9vj7Vr1+pc++zsbFy+fBlz585FxYoVYW9vj6VLl8oYqWGIjo6Gvb09Vq1apXN8zpw5YIxh5cqVxf6sqe9Y+jrXfsmSJTo/aw77jvBc7508eRLW1taoWrUqrK2t4ejoiLCwMLNZHOFlaNZSX79+vc69nJGRgYMHD2LkyJGwt7eHi4sLfv31V52fNYfvaN++fVLZ12bTpk1gjOHbb78t8ufy8vJKnHhtCrzOtS+4J4U5vAPIXe+JhOAtZ8iQIQgICJAyQ83SY2lpaWjbti2qVq2K9PR0qWU8NzcXt27dMvkXIgAIDQ2FjY0N1q1bh2PHjmHAgAGwtbVFuXLlMHv2bNy7d0+n0njx4gUuXLhgFu5A/sY69vb20moiOTk5OpXeixcvsHPnTjRp0gRKpRIbN26UK1SDEBgYiM6dO+POnTsA/n3Jj4uLQ6VKlfD+++8D+Hf1kdzcXLPoFQFe/9pv2LBBrlANAs/13pQpU+Do6Ig1a9YgKioK/v7+0qZ7kZGRhXbiTU9PL7LF1FSJjIyEs7Mz/vrrLwCF7/2nT5/i559/Rrly5VC2bFkcOXJErlANwoABA9C+fXupp0dzjycmJqJOnTpo3LixtP8CkH/vm0uvAe/X/k3rPX2NjBAJwVtMYmIiWrRogS5duhT5eUREBBhjOt1I586dQ9myZfH9998bK0yDkJKSgmHDhulsKBUXF4e1a9dKw4jq16+PyMhIJCUlAQB27twJa2tr/PDDDzJFrV/Onj0LxhgmTJig0yNSsAX45MmTqFSpEpo0aaKzLrkpc+/ePdStW7fQ8nGaF76hQ4eiVKlSSEtLk76PCxcuoE6dOli3bp3R49U3PF97nuu9xMRE9OzZE1WqVJGOXblyBd999x28vb3BGEP79u111pfftm0bLC0tzaaX8MiRI2CMYe7cuYVWU9Jm9+7dcHFxwfvvvw+1Wm0WLcQPHz5Eo0aN8MEHH+gc17iHhoZCqVTqDA05e/Ys3NzcEB4ebtRYDQHP1/5tqPdEQvAWk52djZEjR0pb0Bds/bp9+zYYY1JF8Pz5c0ydOhXW1tZm0XUaHR2N7du3A9B1v3r1Kr7//nvUqlULCoUCvXv3xh9//IH+/fvDxsbGLNyB/N1327dvD0tLS6xZs6ZQd7D2hLpFixbBxsYG8fHxcoSqdx4/fox27dohIiICQOHu4D///BOMMakFPS0tDV9++SWsra2RkpJi9Hj1Dc/Xnvd6LyoqClFRUQB03Y8fP47Ro0ejdOnSUCgUGDVqFM6cOYNPPvnErOq9+Ph4+Pr6ws3NTacFWHtHWs29P2nSJDg7OyM1NVWWWPVNamoq+vbtKzVqFaz3Tp06BcaY1Busfe+b+kR6gO9r/zbUeyIheMvJyckptqCnp6ejVq1a6Ny5MwDg8OHDcHJywsyZM40YoWEprpUgKysLx48fx/jx41GmTBk4ODiAMYZ58+bJEabBuH79Ot599104ODggLCys2JfdZcuWwdXVFadOnTJyhIZBrVbjzJkzxe6ye+vWLTg7O2PixIkA8teiV6lUZnXv83rtAVHvadd1BYeKbdmyBT179oRSqYSLi4vUompOnDhxAhUrVkTp0qWxYsUK6XjBluL58+ejfPnyOH/+vLFDNBj379+Xdh4vyJMnT1C+fHlpw8FDhw6Z3b3P87WXu94TCYGJM2zYMLi7u+PKlSvo06ePWSy59jK0K4bMzExERETAzs4OtWrVkjEq/aN5ETh8+LC0DJmfnx+ioqIQHx8vrbJz8+ZN9OvXDzVr1pQzXINQ3ATZ3NxctGzZEo0bN8bjx4/Ru3dvVKxY0cjRGQ5x7UvGXOu9kiaEa3+WnJyMsLAwMMbg4+NjjNCMRl5eHnJzc7F27VrUrFkTjDE0b95cGialGUJ379499OvXD7Vr15YzXINQ0n3Qq1cvVK9eHffu3UNgYKBZ1Xvi2peMoes9kRCYODt27ABjDF9++SUsLCzw+++/yx2SUdBuNZs/fz4YY9ixY4eMERmWS5cuYfLkyahYsaK0Fv1HH32EESNGwNfXF46Ojmbtr43mYTlz5kxYWFggPDwclpaW+OOPP2SOzDCIa18YUe8BCxYsAGMMO3fulDEiw3LixAn07dsXFhYW0vyJqVOnIjQ0FC1atIC9vb00rJQXVq5cCcYY5syZA0tLS7O998W1L4yh6z2REJg4CQkJcHV1BWMMnTp1kjsco3Px4kV4eXmhadOmcodiEAoOHThy5AhCQ0PRsmVLachAx44dzW6FoVfh8OHDsLS0BGMM7dq1kzscvSOuffHwXu+dPXsWVapUQfPmzeUOxSBo3/u5ubn47bffMHjwYNSqVQuMMVhZWaFZs2bSHCOeuHLlCuzt7cEYQ4cOHeQOR++Ia188hq73GAD8ly2WBcbnyZMn0rb0mZmZ1L17d4qOjqYbN25QlSpVZI7OsGi7ExE9ePCAVqxYQYMGDaJKlSrJGJlhycvLI4VCQchP4gkAPXnyhLKysqhs2bJkbW0td4hGQa1WS9u3JyQkUMuWLen69etmfe+La58Pz/VeWloaOTg4SP+/fPkyLViwgKZOnUpeXl4yRmZYtMt7eno6paSkUGpqKqWmppKPjw/Z2dkRY0zmKA2P9vVPTU2ldu3a0ZkzZ8z63hfXPh9j1nsiITARABBjjLZu3Urbt2+nWbNmkaurKxER3b17l65fv07t2rWTOUrDUJI7UX4hsbGxkTFCw6Lx37ZtG8XExNDkyZPJ1taWi8qQ6F//PXv2UHx8PA0YMICsrKxIrVZTZGQk5ebmUlBQkNxhGgRx7UW9t2PHDoqJiaGQkBCdpCAlJYVUKpWMERoWjf+uXbsoPj6ePvnkk0L3veYcc6Sk63/06FF6/Pgx9e7dW+YoDYO49vLUewq9/0aBQWCMUVZWFg0bNozUarXOg6FSpUpm+1AkKtmdiMw6GSD61z8oKIju3r1LSqXSLCvC4tomNP4ffvghHTlyRDquVCpp4MCBZpsMEPFz7YuDh3ovLy+vyOMa90GDBtHt27fJ0tJS53NzTgaI/vXv378/HTt2jLKzs4s8x1wp6fr7+/ubbTJAJK69XPWeSAjeIgCQWq0u8jgR0Zw5c0ipVNL48ePN/iWYiKThEUR8uWu/IGj8Z8+eTVZWVhQSEmJ2Q0SePXtGREVX8Nr+tra2FBISQlZWVkRU/IuUucDDtS+Idv1nzmVfO/lVKAo/hnm89toUVe7Nzb+kwRklXX9zHtSh/cw352tfkLem3tP7rATBa3P16lUcOnRI+r9mu2ptHj58CMYYQkND9bZN9dvC8+fPcfv2bVy6dAnPnz+XllTU8ODBA7N1B/LXHk5JSdFZc197YlV8fDwYYwgLCzM7/2vXrmHUqFFYtGhRoY1YNN/B/fv3wRjDjBkzzM7/ZZjztQeApKQknDhxAhcuXCjyc3Ot91JTU7F37150794d06ZNw99//w1At9xr6j1zvfYvIy4uzmzLfUxMDBYtWoTMzMxizzHnsp+RkYHk5GTcvn27yM/Nvc5PT0/HrVu3cPXqVemY9jufXPWeSAjeAjTr7fbv3x93796VjmvfCHFxcVi4cCHi4uLkCNFgrF69Go0bN4a1tTUYY/D19cVnn32GTZs2SbvvPXr0CAsXLsSDBw9kjlb/7N+/H126dIGHhwcqVaqEiRMn4unTpzrnnD17FtOmTTNL/zZt2qBp06bYtm1bseccPHgQw4YNM7t7Pykp6aXnmPO1X7lyJerWrQvGGBhjGD16tM6uo2q1Wqr3zM1/1KhRcHV1hYWFBSwtLdGgQQMkJibqJAQXLlzA1KlTze6+f1X27duHESNGmKV/lSpV0LJlSyQmJhb5uVqtxj///GOWZX/dunVo1aoVSpUqBScnJzRr1gyLFy/G2bNnpQTpwIEDGD58uFle+61bt6JVq1awsbGBQqFA37598fDhQ51z4uLisHjxYqPvPi8SApm5fv06SpUqhTJlykClUsHOzg6hoaE62WJJm5SYMuvWrYOzszMaNWqEWbNmISgoCDVq1ABjDDVq1MCQIUNw8uRJAIW38TYH/vjjD1StWhVubm4IDAyEm5ub9GJU0Dc9PV2mKA3HqlWr4OjoiI0bN0r3+N27d7FhwwYsWLAAhw4dQmxsLID8FiVzIjIyEh06dMCBAwfw4sWLEs81x2u/ceNGlCtXDv7+/pg9ezbq1asHxhj27t0rd2gGZ9WqVVCpVJgyZQpiYmIQGRkJpVKJAwcOIDc3F8nJyYUaBcyJ9evXo3///rh169ZLzy1u11ZT5ueff4a9vT127dqlczw1NRU3b97UefZrJ8jmwJo1a6BSqdCwYUNMnjwZ/fv3h5+fH5RKJXx9fbFgwQKpp/xVGkxMjS1btsDT0xNVqlTBqFGj0KBBAzDG0LlzZ6kBVIMc7zwiIZCZ7OxsDBkyBJ6envjmm2+ktXYrV66MyMhI6bxp06bpDCsyB8qWLYtPP/0Ud+7cAQCkpaVh69atKF++vNRq6O/vj4sXL8ocqWEoV64cBgwYgJiYGAD5Q6fee+892NnZ4eDBgwBQaOiYOSWH/v7+6N+/v9Q6smvXLtSuXVu69hYWFmjWrBlWr14ttRyZi7+HhwcYY1CpVBg5ciTOnz9fqGvYXFyLomzZshg6dChu3LgBADh+/Djc3Nwwbdo0HD58GEFBQfjyyy+xb98+aXdSc6FGjRoYOXIkEhISAOT3gPr7+2PChAlo2LAh3N3d0aJFC8yZMwf3798HULgeMGU0DR+urq747rvvkJKSUuicgve+OZUFFxcXBAcH48mTJwCAy5cvIzQ0FO7u7nB2dkbFihUxb948maM0DOXLl8fIkSOll/7MzEzExsZi9erVaNiwIRQKBT744AOz7BkA8p/5gwcPxqVLlwDkN/Z8+umnUCqV2LJlCwB5y7pICN4CLly4AC8vL+zevRsJCQmYOHEi3N3dwRhDQEAA/u///g+MMaxfvx6ZmZlmUTlu3LgRKpVK2l1WuxCEhYWhefPmmD59OmxtbfHuu+8WO9bQVJk3bx68vLxw/PhxAP/6nz59Gra2thg+fDiAoh+E5nD9nz59ikaNGmHMmDEA8h8M7u7uqFevHpYtW4a1a9fi66+/RoUKFeDs7IyoqCiZI9YfkZGRcHBwwKBBg/Dxxx/D1tYWFSpUwNy5c3H//n2zuL4l8d1338HT0xPHjh0DkH/vX7t2DeXLl4e7uzu6desGb29vODs7w83Nzayu/a5du+Du7o4NGzYAyG8FzMjIQNWqVVGmTBm0bdsWwcHB8Pb2hkKhwKRJk2SOWL+sWbMGdnZ2CAwMRMuWLcEYQ82aNbFhw4aX3vfmUC5CQkJQsWJFac5MXl4e6tSpAxsbG7Rt2xbdu3dHtWrVwBhDkyZNcP36dZkj1h/79++HSqXCunXrAOi2gKvVaty5cwdff/017Ozs8M477+iMrzcHfvjhB3h5eeHo0aMA/n3m379/H25ubujRowcAee9zkRC8JYwePRo+Pj5Sa9jRo0fx4YcfwsrKCowxlCtXDv/884+8QeqRyMhI2NjY4K+//gKQXwg07itXroSnpyeuX7+Ob7/9FowxzJ07V85w9cqLFy/QrVs3BAYGFjk0oF+/fvDw8EBWVpZ0LCkpCb/88ovUqmQO1KpVCyEhIQCAOXPmwN3dHefOnZM+T09Px5EjR1ClShW4u7ubxQMiKysLY8eOhUKhwOXLl/H8+XOsWrUKrVu3BmMMDRs2xIYNG5CcnCw9MG7fvo2wsDBp4qkp8+zZM7Rs2RLBwcE6QwLCw8NhY2ODGTNmAMhvNd+5cyc8PDzg5uZmFtceyK/XFQoFduzYIR1bvHgx7OzsdHacfvToEXr27AnGGH799Vc5QtU7mZmZCA4Ohr29PWJjYxEXF4ewsDBUr14djDF07NhRGiKq4datW/j+++/Not57+PAhFAoFmjRpIvWKT5gwAaVKlcL69eul8y5cuIDhw4eDMSY1mJgDly5dAmMMq1evlo4VbA1PSkrCokWLoFQq8emnnxo7RIORkZGBPn36oFu3bkXey+PHj4etra1OnZiUlISffvrJqPW+SAhkRlMgYmJi4OjoiLCwMJ3PBw8eDIVCAcYYXFxcEBoaCsD0W0v27dsHxhiGDx9eqIB07doVbdu2BZD/EGnfvj18fHzMZhz5w4cPUbNmTfTr10/nuOZeiIiIAGMM//vf/6TPdu7cCcYYVqxYYdRYDYGmZWjs2LFwdnbG/v37MWHCBAQEBCAlJaXQvf3zzz+bzfjytLQ0fP755xgwYIDO8cuXL2Pu3LlSy3CvXr1w4sQJvHjxAkuWLAFjDKtWrZIpav1x48YNNGvWDDNnzpSOpaSkgDGGkJCQQsNHNL2jmt4EU+fKlStwdXWFj48PlixZgilTpsDe3h6ffPKJ5K4ZOrZv3z4oFAqEh4cDMP06PykpCf3795daQjUcPXoUI0aMgIuLC5RKJUaOHClNpjSne//o0aOoXbs2HB0dUaNGDcyaNQuOjo5YsGBBoSGROTk56Ny5M+zs7KShZabOs2fPULt2bVSrVk1qCNRQ8N4eM2YMlErlK80zMQWePn0KPz8/dOvWTee4xnvr1q1gjGH37t3SZ3I880VC8BYxffp01KpVS5pc8uTJE3h4eKBfv34ICwuDq6srGjRoIHOU+iE1NRWdOnWCQqHAuHHjsHv3buzZswfjx4+HQqHA9u3bpQfjmDFjUK1aNaSnp5v8QxHInzeyYsUKqeuwoFNsbCwYY1iwYAGA/Im2nTp1Qv369Y0eqyE5efIkVCoV2rVrhwEDBsDHx0f6TK1WSz1GBw8ehEqlwtq1a+UKVa9oLzGr3W2emZmJ48ePY+zYsShdujScnJwwePBg1KhRA40aNZIrXL2TkJAgLS2sVquRkJCAoUOH4tq1a9I5mu9l7dq1sLOzQ3R0tCyx6pvs7Gx8/fXX0jyZsmXLwtPTUydB0rj/9ddfKFOmjM5npk5iYiJu3rwJQHcVvbS0NGzZsgXdu3eHUqmEm5sbJk+eDD8/P7O69xMTEzF79mzUqlULCoUC7777bqGef029N3HiRJQuXdqsxtP/8MMPYIyhXbt2OHbsWKH5QZpe8Y0bN8LJyQmnTp2SI0yDEB0dLc0NLPjMf/LkCWxsbDBlyhQAwL1799CpUyfUq1fPqDGKhOAtQHNzxMbGonTp0lI34YgRI1CmTBlpGMWlS5ekSWamjKYl/NKlS+jZsyesra1haWkJxhgsLS2lYQNAfuX42WefoUGDBnj8+LFcIeudjIyMIifTAfkvBA0aNED79u0BACtWrABjDCdOnDBmiEZh06ZNKF26tPSCFBwcrLPMXmZmJsLDw2FtbV3sWvWmRHEJrfbxxMRE7Ny5E7169ZJ6B0+fPm2sEA2K5iWwpIlzms8yMzMRFhYGDw8PafKxKaN9jU+fPo3w8HCcPn0aERERGDp0qM5qUy9evEB4eDisrKx0htGZMsWtmqL9vdy/fx/Lli1Dw4YNpTrBXF4KtROgS5cuYfjw4ejVq1eh1WWA/OfeqFGjUL16dbN45muzcOFCqFQqVKpUCQsXLsS9e/d0hsdmZmZi6dKlcHJy0mkkMGU093hJoxw6dOggNfjK9cwXCYERKe5lIDc3V/osNDQUlStXxo4dO2BjY4Pp06cX2qjLFCnO/erVq9i0aRO+/vprTJo0SWo113D27Fn4+PhgyJAhxgjTYJR07Yti4sSJcHJywoEDB1CrVi0EBgYaMjyDU5x/VlYWNm7ciE6dOsHW1haMMbRv3x4//fQTrl27hmnTpsHT07PQ8CpzpOB3tGHDBlhaWmLw4MEyRaQfXnbvF5ccnD59Gt7e3oWGV5kSr9Kj+ddff8HW1lZnnsiKFStQqVIl9O3b19AhGpRXrfcKnqd5IQoKCjJYbMbgZddfMySm4HlnzpxBjRo1TN5fG005T0tLw9q1a6Wkr1GjRpg7dy527NiB5ORkhIeHo2rVqtzc+xrmzp0LpVKJ33//HT4+Pujdu7chwysSBpjxPthvIdnZ2XT06FEiyt+yPiAgQOfz58+fU9OmTeny5ctUt25dioqKoho1asgRqt7Rdiciatu2bZHn5eXlkUKhoJs3b9KMGTNo27ZtdO3aNXJ1dTVWqAbjzJkzZGtrS2q1mvz8/KTjAIgxJv39559/Uo8ePahLly60Z88eio+PpzJlysgYuX4oyj8vL4/+97//0f79+2nnzp108uRJnZ957733aM2aNaRSqeQIWW+o1WpSKpWvdG5iYiKNHz+eNm3aRPHx8VS6dGkDR2dYXlbvadB8R3fv3qVZs2bR5s2bTb7sv8w9Ly+PPvzwQ/r111+pbNmyxBijxMREql27Nu3fv59cXFzkCFuvvKze0yYxMZEmTZpEkZGRZnHvE72av+bev3//Ps2ePZvWr19PN27cMOl7n6joeg8A/f3337Rjxw7asmULXbhwQefzNm3a0ObNm03+3n/Veo+I6Pjx4+Tv70/+/v504sQJeZ75Rk9BOOaXX36RNqJgjEGpVKJ69epYsmSJzsTaH3/8Ef7+/oiMjDSbNbiLcq9WrRrCw8Ml97y8PJ2seuzYsahTpw6WLVsmV9h6IyoqCgEBAVAoFLCyskKZMmXg7++PP//8UzpHrVZLrSgJCQlwcXEBYwyzZ8+WK2y9UZy/ZtlZIH8fhuvXr+PgwYP44osvsGjRIuzbt++lG3e97Wgvmat9jUvi4sWL8PX1xdSpUw0YmXEoqd7T3qlVU/bz8vLQvHlzeHh4SBNqTZVXdb9z5w6+/fZbNGrUCBUrVsSXX34p7U9iyrxuvQcAp06dgpeXF7766is5QtYrr+qv/dzr2LEjXF1dsXDhQjlC1hvF1XvarhkZGbh58yb27t2LkJAQzJ49G9u2bTOLTcletexrD5H09PSUdVVFkRAYiWvXrsHJyQldu3ZFVFQUtm7diuDgYPj6+oIxhjp16mDbtm3S+Xfu3ClybKEp8rrumi61ixcv4vDhw3KFrTeuXbuGUqVKoVWrVpg7dy6WLVuGTp06wdXVFYwxtGrVqtD4+JycHISEhJjFhLqX+fv7+5vNOOmCXL9+HUqlEkFBQdJSg8DLd6FMS0srNHzOFHndsp+dnY0bN27gyy+/NPlk4HXdMzIykJeXZzaryrxuvad5MUpMTJT2aTBlXtc/NzcXN2/exBdffGHyjUBvWu+ZC2/6zjNv3jw0b95crrBFQmAsPvjgAzRu3FhaYUHDuXPnMHPmTGkt5jFjxphNr4CGV3UPCQkptFurOdCjRw80bty40ASpQ4cOITg4GA4ODlAoFJgzZ45OhRkfH28WE8pe1X/27Nlm98Do06eP1Drk4eGBWbNm6ZRvc/MtyJuW/czMTJOvC17Vfdy4cSbvWhRvWu+ZC2/qn56erjPJ1hR51XpP01ugSQbNpRy8ab2XkZFR5N5ExkIkBEYgKSkJLVu2RK9evaRj2oUjJycH+/fvR7du3cAYw4QJE8xieU3g9d0nTpxoNu5A/p4D9erVw9ChQ6Vj2pV9VlYWoqKi8M4778DCwgI//fSTHGEaDJ79L1++DC8vL7z33nsICwtDpUqVpNahTZs2Sefl5eVJD4XU1FS5wtU7b1L2X2U4lSnAc50P8F3uAb79Rb1nuu88IiEwEl27dkWVKlUKLTWpfSPEx8ejS5cucHBwMJtlBgG+3dVqNZo2bYomTZogJydHZ7yo9svPzZs3UbduXVSoUAGxsbFyhat3ePbXbDAXFRUFIL8bOTg4GI6OjmCMoVu3bjr3+rNnz/Dtt98iODhY2qjI1OG57PPsznO5B/j2F/We6ZZ9kRAYifnz54Mxhm+++UbKirW7zTSVxL1792BlZYVZs2bJFqu+4dVdU/jHjh0Lxhh++eUX6bOi/I8fPw7GGCIiIowfrAHg3f/48eOoW7duoZ24Dx48iE6dOoExBgcHB4wZMwbJyck4efIkSpUqhWHDhskUsf7htewD/LrzXu559xf1numWfZEQGImcnBwEBgZCqVRi+PDhhTao0FQicXFx8PHxkTYnMwd4dgfyJ8n5+/tDoVBg2rRpOp9p70Fx+/ZteHl54euvv5YjTIPBu7+GguOkV65cCT8/PzDGUKlSJfj5+cHOzg5paWkyRah/eC77PLsDotzz7q9B1HumU/ZFQmAENNng+fPn0bt3bzDG4O7ujlWrVhU6Nzo6GqVKlcLy5cuNHaZB4Nkd+Nd/7969qF+/PhhjaNiwIbZu3Vro3L1798LZ2bnI78ZU4dm/uImS2sefPXuGb775Rlp55McffzRWeAaH57LPszvAd7kH+PYX9Z7pln2xMZmRiYuLo6VLl1JERIS0+Uz//v2pTJky9PDhQ1q/fj1ZWloW2qjDHODZnYgoNjaWZsyYQevXrycioqZNm9KoUaPIxcWFbt68Sb/88gtlZmbS5cuXZY7UMPDurw0Ays3NJUtLS0pISKCOHTtSTk4OXbp0Se7QDALPZZ9ndyJR7nn310bUe2952Zc3H+GXEydOYMyYMfD19YWFhQUYY7CyskJgYKC0fb25wpt7wQ3XduzYgffffx+lSpWSNi1RKBRo3749Tp06JWOkhoF3/5LIzMzEnDlzwBjDzp075Q7H4PBW9rXhzZ33cs+7f0mIeu/tLPuih8DIFNzG+/r165Sbm0tXr16lypUrk7e3N9nZ2ckYoeHg2Z2IKDc3lywsLIgov6Xkr7/+ohcvXtDNmzfJy8uLGjVqZPJbtZcE7/5FkZiYSKNGjaLMzEzaunWr3OEYDJ7LPs/uRKLc8+5fFKLeezvLvkgIZGLp0qX0/vvvU7ly5eQOxejw7E5EtHnzZurduzcpFAq5Q5EFnv1///136tKlC1lbW0vHEhMTycLCgpydneULzEjwXPZ5difiu9wT8e0v6j3TKPv83ZlGoLgcS3N89erVNHLkSIqLizNmWEaBZ/eS0Pbv27cv/f333zJHZFx49td27927t8542by8PHJ1dTXrhyLPZZ9ndyK+yz0R3/6i3jPBsm/UAUpmTmpqaqFtyguSkZGBKlWqIDg4GC9evDBSZIaHZ/eiKGrH1YyMDHh5eWH48OHC34z9eXXXjJcuapURcy/7PLsDL/fn/d43Z3+e3V+GqZV9kRDokR49ekChUODYsWMAin4xmDJlChwcHHDx4kVjh2dQeHYH8gt+dHQ0oqKiit1tcerUqVCpVMLfzPx5dtempF1GzbnsA3y7A0X7a14Ueb33efHn2f1lmFrZt5C7h8JcOH78uDQ5Zu7cubRhwwaytbXVOScjI4OePHlCc+bMIW9vbznCNAg8uxPlTxSaPn06bdq0iXJzc6ls2bK0Y8cOqlu3rnROamoqxcTEUGhoqPA3I3+e3YmInj59SocPH6YlS5ZQTk4OWVpaUv/+/alp06ZUoUIFcnBwoOzsbLMs+zy7ExElJCTQ/v37KSIigoiIrK2tacCAAdSmTRtyd3cnS0tLSk9Pp5iYGJo+fbrwNyN/nt1fFZN855E7IzEXfHx80LVrV4waNQqMMQwaNAjPnj0rdN6jR49MouvodeDZHQACAgJQpUoVTJgwAdOmTUOpUqXw2WefAYDOsnP379/H8+fP5QrTYPDsz7M7APTp0weOjo6oWbMmfH19peUUGzRogNmzZ+PKlSsAgAcPHpjVTqQA3+4A0K1bN6hUKtStWxcNGjSAh4cHGGPw9vbGggULcPfuXQDAjRs3kJKSInO0+odnf57dAWD06NHYs2fPS8979OgR0tPTjRCRfhAJgR5YunQprK2tsWfPHly7dg3NmjUDYwyLFi2SztF+OTAneHYHgNWrV6NUqVLYtGmTdOzjjz/G4MGDsWXLFgwbNgxDhw7F9u3bkZqaCsC8vg+e/Xl2B4Dly5fDwcEBCxculIYN/Pbbb6hWrZr0chwQEIBLly7JHKn+4dkdAFauXAknJyesXLlSGh4aExOD8PBwtGjRAowxtG3bFv/884+8gRoInv15dgeAdevWSWX8o48+euncSVNCJAT/kby8PDg7O2PUqFFSJnz79m3Uq1cPjo6O+P333+UN0IDw7K6hVatWCAwMREJCglQ5LliwAPb29rC0tISbmxvs7e1ha2uLsWPHyhusAeDZn2d3AKhTpw769euHJ0+e6Bw/c+YMSpUqhcDAQNjb26N8+fLSxkvmkhDx7A4AHTp0QIcOHQr55+bm4urVq5g5cybc3d3h7OyMrVu3yhSl4eDZn2d3AGjdujU8PT3RtWtX2NrawtHREdOnT0dycrLOeQXnUZpC+RcJwX9kxIgR8PT0RExMDIB/Z9pv2rQJSqUSnp6e0k50RU20NWV4dgeAp0+fonnz5hgwYIB0LCcnB76+vmjcuDEOHDiAFy9e4NChQ+jXrx8YYwgNDQVgGpXDy+DZn2d3ALh16xaqVauG0aNHS8fUajXUajXu3r2LWrVqITw8HDNnzgRjDIMHD5YxWv3CszsAZGdno3v37mjQoIF0TK1W69zXz58/x/r161GuXDn4+PggMTFRjlANAs/+PLsD+TsOu7m5YfDgwUhLS8PKlSvRqlUrMMZQvXp1rFu3rtC7zr59+3DixAmZIn49RELwH3jy5AlUKhW+/fZbqdtYu2CsWLECNjY2aN++faFs2tTh2V2bpk2bonLlyjh48CBiYmIwceJE2NjY4OjRozrLsN29exceHh7o2LGjjNHqH579eXbPyclB2bJl0bFjx0LLDV64cAGMMWlljdGjR0OlUuH06dNyhKp3eHbX8NVXX4Exho0bNyI7O1s6XjDZ/f3338EYw/fff2/sEA0Kz/48u+/cuRMuLi44ePCgdCw2NhYzZ85EzZo1peFSR48eBQDcu3cPzZo1g7W1tUwRvx4iIfgPJCQkYMeOHXj48KHOcU3BSE9Px/Dhw8EYQ1BQkDTR1hxay3l2B/71XLJkCRhjcHJygp2dHZycnNCkSRM8fvwYQP7Lg4ZWrVrB39/fLBIknv15dgf+LcNjx44FYwwhISFITU1FVlYWLl68iFatWqFp06bSMMLDhw+DMYZdu3bJGbZe4Nldm1OnTsHOzg6+vr6Ijo4u9LnmRTE7OxtVqlTBuHHjjB2iQeHZn2d3IL/FPzs7u1BjwIkTJ/DZZ5+hdOnSYIzh008/xYwZM6BUKrF27VqZon09REJgYJKTk9GtWzcolUqEh4fLHY5R4cV9//79+OCDDzB06FDMmzcPAwcORFxcHIB/XyCuX7+OevXqITAwUM5QDQLP/jy7nz9/Hq1btwZjDO7u7mjcuDGcnZ1hb2+v00W+f/9+lCtXDr/++quM0eoXnt01bN++HZ6enlCpVJg1a1aRq8ncuXMH9erVw6effipDhIaFZ38e3Ysb6qndyPnixQv89ttv6NOnD2xtbcEYQ9OmTY0V4n9GJAQGRHOjXLlyRVqWbvXq1WbTSl4SPLs3adIEkydPRlZWFoD8luKwsDAoFAqzGzpQFDz78+Z+48YNTJkyBQEBAahWrRqCgoJw4MAB6fOMjAwsXrwYKpUKV69elTFS/cObu6bu1rSMpqenY/PmzWjevDkYY6hduzaWLVuGc+fOISUlBWlpafj6669haWmJM2fOyBm6XuDZn2d34F//kt5ftBOGmzdvokuXLmCM4ezZswaPT1+IhOANed0X271794Ixhs8//9xAERkPnt2B4lcP0Pw9efJkMMbQrl07jBs3DgEBAXB0dMTw4cONHqsh4NmfZ3eg+LKfnJyM3NzcQq1ohw4dQs2aNXUmX5sqPLsD+S+BKSkpyM3N1fkuzp8/jy+++ALe3t5gjMHDwwOVK1eGSqWCi4sLQkJCZIxaf/Dsz7M7oOtf0qIQmu/m8OHDcHNzw8CBA40Vol4QCcFrUvBmeJ2X49WrV5v05kQ8uwOv7n/nzh0EBQWhbNmyYIzBwcEB48aNM/lN2Xj259kdKN6/pDrg2LFjqFmzJnx8fAotyWdK8OwO5Hvu2bMHXbp0QYUKFdCrVy/89ttvOmOo8/LycOrUKSxfvhwDBw5Es2bNMGTIEOzatUvqLTNVePbn2R0o3r8k0tLSMHXqVFhYWODp06dGilQ/MACQe7dkU2L+/PkUFxdHI0aMoBo1ahARkeYrZIyV+LN5eXmkUCgMHqOh4Nmd6PX8U1NT6ezZs2RpaUkqlYp8fHyMHq++4dmfZ3eiNyv7jx49op9++olat25Nbdq0MVqs+oZndyKimTNn0uLFi4mIqGzZsvTw4UPKysqiiIgI+uCDD0itVpNSqdT5maysLLK2tpYjXL3Dsz/P7kQv9y8KtVpNe/fuJQDUpUsXY4b735ErEzFFYmNjwRiDlZUV/P39ERERobPGbnEtRnl5eSa/9jjP7sDr+Rf0Ff6m7c+zO/DmZd8c4NkdAC5fvgyVSoXRo0cjOTkZz58/x86dO1GhQgVUrVpV2oFbm4LD6EwZnv15dgfezN/UEQnBazBu3DioVCr06dMHdnZ2sLCwQGBgILZt26azxGDB5ajMAZ7dgdf3N4cKURue/Xl2B/gu+zy7A0BgYCD8/f0RGxurczwiIgKMMWn4hLkmRjz78+wO8OkvEoJXJC4uDg0aNMA777wDAHjw4AH69+8PxhhcXV0xZswYaYt6DZcuXcKKFStw584dOULWGzy7A8KfZ3+e3QG+/Xl2B/KXy/Xz88NXX30lJT+al5+EhAQ4ODggODgYwL9J8KNHjxAVFSX8TdyfZ3eAX3+RELwid+/eRf369QutlHP48GE0adIEjDHUrFkTM2fOxIMHD5CXl4cJEybAyckJ9+7dkylq/cCzOyD8efbn2R3g259ndyB/A6rSpUtj3bp1AAr3fHXv3h2+vr7SGvTZ2dn44YcfoFKpcPfuXaPHq2949ufZHeDXXyQEr0heXh5iY2OlcWNqtVrnJlmxYgXKly8PxhjatGmDSZMmwdnZGUFBQXKFrDd4dgeEP8/+PLsDfPvz7A4A9+7dw0cffYRLly4B+PelSNNSunz5cjDGpF6Ss2fPokqVKia31GJx8OzPszvAr79ICP4j2g+I5ORkTJ06FTY2NmCMwcnJCenp6TJGZ1h4dgeEP8/+PLsDfPvz5p6RkVHoWF5eHi5dugTGGBYtWoTs7GyMGzcOdnZ2Jr/EbkF49ufZHeDPXyQEb0jBLiTtSWWzZ88GYwzLli0zdlhGgWd3QPjz7M+zO8C3P0/uRU2ML8rf29sbPXv2xIEDB+Do6Ijvv//eWCEaFJ79eXYH+PYX+xC8hLS0NHr69Cnl5OSQl5cX5ebmSmvsApDWodb8+8KFCzR27FiKj4+nq1evyhn6f4ZndyLhz7M/z+5EfPvz7E70av6av4ODg2nz5s1Uo0YNevz4Md26dUvm6P87PPvz7E4k/EUPQQmsWLECTZo0gb29PRhjqFevHqZMmYJ9+/YV25X0448/gjGGXbt2yRCx/uDZHRD+PPvz7A7w7c+zO/Dq/prekT///BNKpRKMMURHR8sVtt7g2Z9nd0D4A2LIULFERUXB0dERLVu2xIwZMxASEoLWrVtDoVDAw8MDEyZMwNmzZ4v82bVr1xo5Wv3Cszsg/Hn259kd4NufZ3fgzfyvX78OV1dX9OjRQ6ao9QfP/jy7A8Jfg0gIisHLywvDhg3DgwcPAACZmZlISkpCdHQ0WrduDcYYAgICcPz4cQD5LUXmskEFz+6A8OfZn2d3gG9/nt2BN/MH8tdlT05OlitsvcGzP8/ugPDXIBKCIjhx4gRKlSolTRArWOlnZ2cjPDwcrq6uqF69Os6fPy9HmAaBZ3dA+PPsz7M7wLc/z+7Am/trNm0y9d25efbn2R0Q/tqIhKAI7t+/D6VSifnz50vHCt4kOTk52LJlCxwdHdG5c2dkZmYaO0yDwLM7IPx59ufZHeDbn2d3QPjz7M+zOyD8tREJQRFkZmaiRYsWKFOmDPbu3avzWcFsMDQ0FIwxs2kx4tkdEP48+/PsDvDtz7M7IPx59ufZHRD+2ijkXuXobcTa2ppGjBhBiYmJNH78eNq8eTM9e/aMiEhadionJ4eIiJo2bUqOjo6UkJAgY8T6g2d3IuHPsz/P7kR8+/PsTiT8efbn2Z1I+GsjEgIt8vLypH/379+fVq9eTQkJCTRq1CiaO3cunTlzhtLT04kxRpaWlpSTk0NxcXGUm5tL7u7uMkb+3+HZnUj48+zPszsR3/48uxMJf579eXYnEv5FIlPPxFtJUlIS4uPjceHCBWnCyMGDB9G5c2colUp4e3tj3LhxWL9+Pe7du4e5c+eiWrVqCAwMlDny/w7P7oDw59mfZ3eAb3+e3QHhz7M/z+6A8C8KkRAgf5zY6tWr4e3tDTs7O9SsWROTJ09GfHw88vLycOPGDSxfvhz+/v5gjOn86dy5MxITE+VWeGN4dgeEP8/+PLsDfPvz7A4If579eXYHhH9JMACQu5dCbmbOnElLly4llUpFTZs2pZMnT9LFixcpLCyMvvrqKyIiUqvVlJGRQVeuXKG9e/eSm5sbValShZo0aUL29vYyG7w5PLsTCX+e/Xl2J+Lbn2d3IuHPsz/P7kTCv0Tkzkjk5tatW7C3t8ekSZPw9OlTAEBiYiI6deoES0tLnDx5UuYIDQfP7oDw59mfZ3eAb3+e3QHhz7M/z+6A8H8Z3CcEn3zyCRo3bowrV64A+HeZqb1790KhUCAsLAwAkJWVBaDw+rSmDM/ugPDn2Z9nd4Bvf57dAeHPsz/P7oDwfxlcrzIUHx9Ply9fpvr161PVqlV1Pmvfvj01atSItm/fTkREVlZWRET06NEjmj59OsXExBg9Xn3CszuR8OfZn2d3Ir79eXYnEv48+/PsTiT8XwWuE4LMzEx6/PgxVaxYkSwtLQkAMcakzzt27EhXrlyhq1evEhFRbm4ubdu2jcLCwsja2lqusPUCz+5Ewp9nf57difj259mdSPjz7M+zO5HwfyVk6pl4K8jKysKyZcsKdR9puokOHjwIhUKBlStXAgDOnTuHGjVq4MMPP5QlXn3Cszsg/Hn259kd4NufZ3dA+PPsz7M7IPxfBa4TAg3Z2dlFHn/69CkqVqyIoKAgAMDkyZNhZWWF5ORkI0ZnWHh2B4Q/z/48uwN8+/PsDgh/nv15dgeEf0mIhOAlfPjhh6hWrRqio6NRunRpzJgxQ+6QjAbP7oDw59mfZ3eAb3+e3QHhz7M/z+6A8BcJQTFoupN+/PFHWFhYwMfHBxUqVJA5KuPAszsg/Hn259kd4NufZ3dA+PPsz7M7IPw1WMg9h+FtRTPZpEWLFmRra0tXrlyhHTt2yByVceDZnUj48+zPszsR3/48uxMJf579eXYnEv4aRELwEry9valHjx6kUCioc+fOcodjVHh2JxL+PPvz7E7Etz/P7kTCn2d/nt2JhD8DALmDeNtRq9WUmZlp3ltWFwPP7kTCn2d/nt2J+Pbn2Z1I+PPsz7M7Ed/+IiEQCAQCgUAgEAg4huuNyQQCgUAgEAgEAt4RCYFAIBAIBAKBQMAxIiEQCAQCgUAgEAg4RiQEAoFAIBAIBAIBx4iEQCAQCAQCgUAg4BiREAgEAgEnHDp0iBhj0h+lUkkuLi7k6+tLgwYNot27d9N/WXju7NmzFBoaSnfu3NFf0AKBQCAwOGJjMoFAIOCMfv36UZcuXQgAPX/+nK5du0Z//PEHrVmzhtq1a0ebN28mZ2fn1/69Z8+epenTp1Pr1q2pcuXKeo9bIBAIBIZBJAQCgUDAGfXq1aOPPvpI59iCBQto8uTJtGDBAurXrx/t2rVLpugEAoFAYGzEkCGBQCAQkFKppPnz51OLFi1o9+7ddOzYMSIiio+PpwkTJlCdOnXIxcWFbGxsyMfHh+bMmUNqtVr6+dDQUAoKCiIiooCAAGlY0uDBg6VzsrKyaObMmVS7dm2ysbEhZ2dneu+99+iff/4xqqtAIBAIdBE9BAKBQCCQGDJkCB07dox27NhBLVq0oPPnz9Nvv/1GPXv2pKpVq1JOTg7t3r2bvvjiC7p16xYtW7aMiIh69epFDx8+pIiICJoyZQrVqlWLiIiqVq1KREQ5OTnUqVMnOnHiBA0cOJBGjx5NKSkp9PPPP1Pz5s3pyJEj1KBBA9m8BQKBgGdEQiAQCAQCCT8/PyIiio2NJSKiVq1a0a1bt4gxJp0zbtw4GjhwIC1fvpxCQ0PJw8OD/Pz8qGnTphQREUHt27en1q1b6/zeH3/8kQ4dOkS7d++mjh07SsdHjhxJvr6+NHHiRDp06JDB/QQCgUBQGDFkSCAQCAQSTk5ORESUmppKRES2trZSMpCdnU1JSUmUmJhIHTt2pLy8PDpz5swr/d5169ZRzZo1qX79+pSYmCj9yc7Opvbt29OxY8coIyPDMFICgUAgKBHRQyAQCAQCCU0ioEkMcnNzafbs2bRmzRq6ceNGoWVJk5OTX+n3XrlyhTIyMqhMmTLFnpOYmEienp5vGLlAIBAI3hSREAgEAoFA4vz580RE5O3tTUREISEhtHjxYurbty9NnTqV3NzcyNLSkv7++2/6/PPPKS8v75V+LwB65513aMGCBcWeU1KyIBAIBALDIRICgUAgEEisWLGCiIi6du1KRERr166lli1b0oYNG3TOu3HjRqGf1Z5nUJDq1avTkydPqE2bNqRQiNGqAoFA8DYhamWBQCAQkFqtpokTJ9KxY8eoS5cu1Lx5cyLKX4604DChFy9e0A8//FDodzg4OBARUVJSUqHPPv74Y3r06FGxPQQJCQn/VUEgEAgEb4joIRAIBALO+Pvvv2ndunVERDo7Fd+9e5c6dOhAkZGR0rmBgYG0bNky6tu3L7Vr144SEhLol19+odKlSxf6vQ0bNiSFQkHfffcdJScnk729PXl5eVHjxo1p7NixFB0dTZMmTaIDBw5QmzZtyMnJie7du0f79+8nGxsbOnjwoNG+A4FAIBD8C0PBph+BQCAQmCWHDh2igIAA6f8KhYIcHByoQoUK1KBBA+rXrx916tRJ52fS09Ppm2++oU2bNlFCQgJ5enrSkCFDqGHDhtSuXTtauXKlzuZjq1evpjlz5tCNGzcoJyeHBg0aRKtWrSKi/AnKP/30E61du5YuX75MRETlypWjRo0a0aBBg6hDhw4G/w4EAoFAUBiREAgEAoFAIBAIBBwj5hAIBAKBQCAQCAQcIxICgUAgEAgEAoGAY0RCIBAIBAKBQCAQcIxICAQCgUAgEAgEAo4RCYFAIBAIBAKBQMAxIiEQCAQCgUAgEAg4RiQEAoFAIBAIBAIBx4iEQCAQCAQCgUAg4BiREAgEAoFAIBAIBBwjEgKBQCAQCAQCgYBj/h8bEfh1MtdnCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=files[16]\n",
    "real=pd.read_csv('Weekly_data/'+f)[:1001]\n",
    "print(f, real.columns)\n",
    "smooth=7\n",
    "ser=real[real.columns[1]].values\n",
    "ser=np.delete(ser, np.where(ser=='.'))\n",
    "ser=ser.astype(float)\n",
    "\n",
    "# #ser=MovingAverage(ser, numb=len(ser)//smooth)[smooth+5:-3]\n",
    "ser=Norm01(ser)[0]\n",
    "bound=150\n",
    "row=ser[:bound]\n",
    "t=np.arange(len(ser))\n",
    "plt.figure(figsize=(12,8), facecolor='w') \n",
    "#plt.title(gr.upper(), size=20)\n",
    "plt.plot(ser, lw=3, c='g', label='For incremental learning')\n",
    "#plt.plot(row, lw=3, label='Initial series')\n",
    "# plt.plot(x+data.mean())\n",
    "# plt.plot(t, a*t+b)\n",
    "xt=np.arange(0, 1001, 100)\n",
    "plt.xticks(xt,real[real.columns[0]].values[xt], size=18, rotation=60)\n",
    "plt.xticks(size=18)\n",
    "plt.yticks(size=18)\n",
    "plt.xlabel('Date', size=18)\n",
    "plt.ylabel('Value', size=18)\n",
    "#plt.legend(fontsize=14)\n",
    "plt.grid(axis='both')\n",
    "#plt.savefig('Nreal_NUGLCBW027NBOG_class.png', dpi=300, format='png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 10)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Initial Graph'''\n",
    "traject=make_traject(row)\n",
    "G=make_gr(traject)\n",
    "tG=from_networkx(G, group_node_attrs=['points', 'mean'], group_edge_attrs=all) \n",
    "cent=np.asarray(list(nx.degree_centrality(G).values()))# degree_centrality to node features\n",
    "cent=torch.tensor(cent.reshape(len(cent),1), dtype=float)\n",
    "tG.x=torch.cat((tG.x, cent),1) \n",
    "tG.x=tG.x.float()\n",
    "pred = predict(tG)\n",
    "int(pred), len(G.nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.993499899595139]\n",
      "60\n",
      "150 0 6.993499899595139\n",
      "60\n",
      "210 0 8.793073301369951\n",
      "60\n",
      "270 0 8.265738181198332\n",
      "60\n",
      "330 0 8.436323407140895\n",
      "60\n",
      "390 0 17.380652467336628\n",
      "60\n",
      "450 0 10.114510752254985\n",
      "60\n",
      "510 0 18.02586784017119\n",
      "60\n",
      "570 0 12.66737594725208\n",
      "60\n",
      "630 0 19.585786986112826\n",
      "60\n",
      "690 2 200.94434437610795\n",
      "60\n",
      "750 2 47.27882269580334\n",
      "60\n",
      "810 2 23.86991273416873\n",
      "60\n",
      "870 1 25.18104842655486\n"
     ]
    }
   ],
   "source": [
    "from Libraries.Spectrum import MSSAExplore\n",
    "from Libraries.Localapp import LAprExplore\n",
    "from Libraries.ChooChoo import ChooChooExplore\n",
    "'''Graph is growing'''\n",
    "labels=[int(pred)]\n",
    "st=60\n",
    "mape=[MSSAExplore(pd.DataFrame({'data':ser[:bound+st]}),st,st,prds=[], predictors=None)[2]]\n",
    "print(mape)                \n",
    "centres=ages={}\n",
    "depth=25\n",
    "dist=.05\n",
    "for c in traject.cluster.unique():\n",
    "    centres[c]=traject[traject.cluster==c][traject.columns[1:-1]].values.mean(axis=0)\n",
    "    ages[c]=0\n",
    "for k in range(bound,len(ser)-st,st):\n",
    "    print(st)\n",
    "    #trj=make_traject(Norm01(ser[k-depth:k+st])[0])\n",
    "    trj=make_traject(ser[k-depth:k+st])\n",
    "    trj.drop('cluster', axis=1, inplace=True)\n",
    "    trj.step=trj.step.values+k\n",
    "    for i in range(st): \n",
    "        step=trj.loc[i, trj.columns[1:]].values\n",
    "        norms=[np.linalg.norm(step-centres[j]) for j in centres.keys()]\n",
    "        if min(norms) <= dist:\n",
    "            newclust=list(centres.keys())[norms.index(min(norms))]\n",
    "            added=False\n",
    "        else:\n",
    "            newclust=traject.cluster.max()+1 \n",
    "            added=True\n",
    "            ages[newclust]=0\n",
    "        traject=traject.append(trj.loc[i],ignore_index=True)\n",
    "        traject.loc[traject.step==trj.loc[i].step, 'cluster']=newclust\n",
    "        traject.cluster=traject.cluster.astype(int)\n",
    "        if added: \n",
    "            oldest=max(ages, key=ages.get)\n",
    "            traject.drop(traject[traject.cluster==oldest].index, inplace=True)\n",
    "            del ages[oldest]\n",
    "        centres={}\n",
    "        for c in traject.cluster.unique():\n",
    "            centres[c]=traject[traject.cluster==c][traject.columns[1:-1]].values.mean(axis=0)\n",
    "            ages[c]+=1\n",
    "    traject.cluster=traject.cluster.astype(int)\n",
    "    G=make_gr(traject)\n",
    "    tG=from_networkx(G, group_node_attrs=all, group_edge_attrs=all) \n",
    "    cent=np.asarray(list(nx.degree_centrality(G).values()))# degree_centrality to node features\n",
    "    cent=torch.tensor(cent.reshape(len(cent),1), dtype=float)\n",
    "    tG.x=torch.cat((tG.x, cent),1) \n",
    "    tG.x=tG.x.float()\n",
    "    pred = predict(tG)\n",
    "    labels.append(int(pred[0]))\n",
    "    err=min(MSSAExplore(pd.DataFrame({'data':ser[k-bound:k+st]}),st,st,prds=[], predictors=None)[2],\n",
    "           #LAprExplore(pd.DataFrame({'data':ser[k-bound:k+st]}),st,st,prds=[], predictors=None)[2],\n",
    "           ChooChooExplore(pd.DataFrame({'data':ser[k-bound:k+st]}),st,st,prds=[], predictors=None)[2])\n",
    "    mape.append(err)\n",
    "    print(k, int(pred[0]), mape[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class 0 - mean sMAPE 11.7%\n",
      "class 1 - mean sMAPE 25.2%\n",
      "class 2 - mean sMAPE 90.7%\n"
     ]
    }
   ],
   "source": [
    "row=ser[:bound]\n",
    "t=np.arange(len(ser))\n",
    "plt.figure(figsize=(12,9), facecolor='w') \n",
    "plt.plot(ser, lw=3, c='g', label='Incremental learning period', alpha=.5)\n",
    "plt.plot(row, lw=3, label='Initial series', alpha=.5)\n",
    "j=0\n",
    "plt.text(10, min(ser[:bound]), '*%i-%.0f'%(labels[j], mape[j]), size=18, c='k')\n",
    "for k in range(bound,len(ser)-st,st):\n",
    "    j+=1\n",
    "    plt.text(k+1, min(ser[k:k+st]), '*%i-%.0f'%(labels[j], mape[j]), size=18, c='k')\n",
    "    plt.axvline(k, ls='-.', c='r')\n",
    "plt.plot([], ls='-.', c='r', label='Periods of graph analyses')\n",
    "plt.plot([], '*', c='k', label='Predictability class label and sMAPE(%)')\n",
    "#xt=np.arange(smooth, len(ser)+1, 21)\n",
    "#plt.xticks(xt-smooth, real.date.values[xt], size=14, rotation=90)\n",
    "xt=np.arange(0, 1000, 100)\n",
    "plt.xticks(xt,real[real.columns[0]].values[xt], size=18, rotation=60)\n",
    "plt.yticks(size=18)\n",
    "plt.xlabel('Date', size=18)\n",
    "plt.ylabel('Values', size=18)\n",
    "plt.legend(fontsize=18, loc=3)\n",
    "plt.grid(axis='both')\n",
    "#plt.savefig('real_OG_class.png', dpi=300, format='png' )\n",
    "plt.show()\n",
    "res=pd.DataFrame({'l':labels, 'm':mape})\n",
    "for i in (0,1,2):\n",
    "    print('class %i - mean sMAPE %.1f%%'%(i, res[res.l==i].m.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.07689518696054243, 10, 1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import mannwhitneyu\n",
    "m0, m1, m2 = res[res.l==0].m.values, res[res.l==1].m.values, res[res.l==2].m\n",
    "U1, p = mannwhitneyu(m0, m1, use_continuity=True)\n",
    "U2=len(m0)*len(m1)-U1\n",
    "min(U1, U2), p, len(m0), len(m1)"
   ]
  },
  {
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}