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moneebullah25/stock-prediction-pytorch.ipynb

Created June 13, 2023 09:29
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Prediction AMZN Stock Prices using LSTM
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stock-prediction-pytorch.ipynb
{
"cells": [
{
"metadata": {
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"trusted": true,
"id": "HgGP8i-p-V6E"
},
"cell_type": "code",
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"
],
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true,
"colab": {
"base_uri": "https://localhost:8080/",
"height": 363
},
"id": "ecRchAsN-V6O",
"outputId": "1baebe80-c812-4dda-835d-a5097cac3f21"
},
"cell_type": "code",
"source": [
"filepath = './AMZN_2006-01-01_to_2018-01-01.csv'\n",
"data = pd.read_csv(filepath)\n",
"data = data.sort_values('Date')\n",
"data.head(10)"
],
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Date Open High Low Close Volume Name\n",
"0 2006-01-03 47.47 47.85 46.25 47.58 7582127 AMZN\n",
"1 2006-01-04 47.48 47.73 46.69 47.25 7440914 AMZN\n",
"2 2006-01-05 47.16 48.20 47.11 47.65 5417258 AMZN\n",
"3 2006-01-06 47.97 48.58 47.32 47.87 6154285 AMZN\n",
"4 2006-01-09 46.55 47.10 46.40 47.08 8945056 AMZN\n",
"5 2006-01-10 46.41 46.75 45.36 45.65 9686957 AMZN\n",
"6 2006-01-11 45.65 45.70 44.26 44.93 8497383 AMZN\n",
"7 2006-01-12 44.79 45.09 44.09 44.36 5818301 AMZN\n",
"8 2006-01-13 44.48 44.85 44.00 44.40 4432237 AMZN\n",
"9 2006-01-17 43.95 44.32 43.66 44.00 5635225 AMZN"
],
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" <div class=\"colab-df-container\">\n",
" <div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2006-01-03</td>\n",
" <td>47.47</td>\n",
" <td>47.85</td>\n",
" <td>46.25</td>\n",
" <td>47.58</td>\n",
" <td>7582127</td>\n",
" <td>AMZN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2006-01-04</td>\n",
" <td>47.48</td>\n",
" <td>47.73</td>\n",
" <td>46.69</td>\n",
" <td>47.25</td>\n",
" <td>7440914</td>\n",
" <td>AMZN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2006-01-05</td>\n",
" <td>47.16</td>\n",
" <td>48.20</td>\n",
" <td>47.11</td>\n",
" <td>47.65</td>\n",
" <td>5417258</td>\n",
" <td>AMZN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2006-01-06</td>\n",
" <td>47.97</td>\n",
" <td>48.58</td>\n",
" <td>47.32</td>\n",
" <td>47.87</td>\n",
" <td>6154285</td>\n",
" <td>AMZN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2006-01-09</td>\n",
" <td>46.55</td>\n",
" <td>47.10</td>\n",
" <td>46.40</td>\n",
" <td>47.08</td>\n",
" <td>8945056</td>\n",
" <td>AMZN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2006-01-10</td>\n",
" <td>46.41</td>\n",
" <td>46.75</td>\n",
" <td>45.36</td>\n",
" <td>45.65</td>\n",
" <td>9686957</td>\n",
" <td>AMZN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2006-01-11</td>\n",
" <td>45.65</td>\n",
" <td>45.70</td>\n",
" <td>44.26</td>\n",
" <td>44.93</td>\n",
" <td>8497383</td>\n",
" <td>AMZN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2006-01-12</td>\n",
" <td>44.79</td>\n",
" <td>45.09</td>\n",
" <td>44.09</td>\n",
" <td>44.36</td>\n",
" <td>5818301</td>\n",
" <td>AMZN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2006-01-13</td>\n",
" <td>44.48</td>\n",
" <td>44.85</td>\n",
" <td>44.00</td>\n",
" <td>44.40</td>\n",
" <td>4432237</td>\n",
" <td>AMZN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2006-01-17</td>\n",
" <td>43.95</td>\n",
" <td>44.32</td>\n",
" <td>43.66</td>\n",
" <td>44.00</td>\n",
" <td>5635225</td>\n",
" <td>AMZN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a33f5495-fc7b-4b15-80b1-94541413426c')\"\n",
" title=\"Convert this dataframe to an interactive table.\"\n",
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" fill: #174EA6;\n",
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"\n",
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" const dataTable =\n",
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" [key], {});\n",
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"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
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" element.appendChild(docLink);\n",
" }\n",
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]
},
"metadata": {},
"execution_count": 23
}
]
},
{
"metadata": {
"trusted": true,
"colab": {
"base_uri": "https://localhost:8080/",
"height": 673
},
"id": "ye0_NCGT-V6Q",
"outputId": "245eec71-f561-4870-c16f-54059a662b51"
},
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"sns.set_style(\"darkgrid\")\n",
"plt.figure(figsize = (15,9))\n",
"plt.plot(data[['Close']])\n",
"plt.xticks(range(0,data.shape[0],500),data['Date'].loc[::500],rotation=45)\n",
"plt.title(\"Amazon Stock Price\",fontsize=18, fontweight='bold')\n",
"plt.xlabel('Date',fontsize=18)\n",
"plt.ylabel('Close Price (USD)',fontsize=18)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x900 with 1 Axes>"
],
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},
"metadata": {}
}
]
},
{
"metadata": {
"id": "78Hwc8Yi-V6R"
},
"cell_type": "markdown",
"source": [
"## Normalize data"
]
},
{
"metadata": {
"trusted": true,
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WzUek2Mv-V6U",
"outputId": "8e7703a5-e47b-4bfe-94ca-7706e979cd6d"
},
"cell_type": "code",
"source": [
"price = data[['Close']]\n",
"price.info()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 3019 entries, 0 to 3018\n",
"Data columns (total 1 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Close 3019 non-null float64\n",
"dtypes: float64(1)\n",
"memory usage: 47.2 KB\n"
]
}
]
},
{
"metadata": {
"trusted": true,
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "X2G2m8dv-V6W",
"outputId": "9da85ba2-7929-4182-cc04-3837ab96ed0e"
},
"cell_type": "code",
"source": [
"from sklearn.preprocessing import MinMaxScaler\n",
"\n",
"scaler = MinMaxScaler(feature_range=(-1, 1))\n",
"price['Close'] = scaler.fit_transform(price['Close'].values.reshape(-1,1))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"<ipython-input-5-77e3f41ac3d3>:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" price['Close'] = scaler.fit_transform(price['Close'].values.reshape(-1,1))\n"
]
}
]
},
{
"metadata": {
"trusted": true,
"id": "VSTwiDZs-V6X"
},
"cell_type": "code",
"source": [
"def split_data(stock, lookback):\n",
" data_raw = stock.to_numpy() # convert to numpy array\n",
" data = []\n",
"\n",
" # create all possible sequences of length seq_len\n",
" for index in range(len(data_raw) - lookback):\n",
" data.append(data_raw[index: index + lookback])\n",
"\n",
" data = np.array(data);\n",
" test_set_size = int(np.round(0.2*data.shape[0]));\n",
" train_set_size = data.shape[0] - (test_set_size);\n",
"\n",
" x_train = data[:train_set_size,:-1,:]\n",
" y_train = data[:train_set_size,-1,:]\n",
"\n",
" x_test = data[train_set_size:,:-1]\n",
" y_test = data[train_set_size:,-1,:]\n",
"\n",
" return [x_train, y_train, x_test, y_test]"
],
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true,
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8pSjpwkV-V6Y",
"outputId": "eb252859-7d7a-48d1-9a3b-51ef74f8c084"
},
"cell_type": "code",
"source": [
"lookback = 20 # choose sequence length\n",
"x_train, y_train, x_test, y_test = split_data(price, lookback)\n",
"print('x_train.shape = ',x_train.shape)\n",
"print('y_train.shape = ',y_train.shape)\n",
"print('x_test.shape = ',x_test.shape)\n",
"print('y_test.shape = ',y_test.shape)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"x_train.shape = (2399, 19, 1)\n",
"y_train.shape = (2399, 1)\n",
"x_test.shape = (600, 19, 1)\n",
"y_test.shape = (600, 1)\n"
]
}
]
},
{
"metadata": {
"trusted": true,
"id": "MS7nxwk6-V6a"
},
"cell_type": "code",
"source": [
"import torch\n",
"import torch.nn as nn\n",
"\n",
"x_train = torch.from_numpy(x_train).type(torch.Tensor)\n",
"x_test = torch.from_numpy(x_test).type(torch.Tensor)\n",
"y_train_lstm = torch.from_numpy(y_train).type(torch.Tensor)\n",
"y_test_lstm = torch.from_numpy(y_test).type(torch.Tensor)\n",
"y_train_gru = torch.from_numpy(y_train).type(torch.Tensor)\n",
"y_test_gru = torch.from_numpy(y_test).type(torch.Tensor)"
],
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true,
"id": "ydPCvYFs-V6b"
},
"cell_type": "code",
"source": [
"input_dim = 1\n",
"hidden_dim = 32\n",
"num_layers = 2\n",
"output_dim = 1\n",
"num_epochs = 100"
],
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true,
"id": "jXZYNVoK-V6c"
},
"cell_type": "code",
"source": [
"class LSTM(nn.Module):\n",
" def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n",
" super(LSTM, self).__init__()\n",
" self.hidden_dim = hidden_dim\n",
" self.num_layers = num_layers\n",
"\n",
" self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)\n",
" self.fc = nn.Linear(hidden_dim, output_dim)\n",
"\n",
" def forward(self, x):\n",
" h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n",
" c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n",
" out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n",
" out = self.fc(out[:, -1, :])\n",
" return out"
],
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true,
"id": "5dU0IlR9-V6c"
},
"cell_type": "code",
"source": [
"model = LSTM(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)\n",
"criterion = torch.nn.MSELoss(reduction='mean')\n",
"optimiser = torch.optim.Adam(model.parameters(), lr=0.01)"
],
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true,
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sLmaWwIk-V6d",
"outputId": "fc47bb81-1105-47aa-f7f3-37fb3eed1b3b"
},
"cell_type": "code",
"source": [
"import time\n",
"\n",
"hist = np.zeros(num_epochs)\n",
"start_time = time.time()\n",
"lstm = []\n",
"\n",
"for t in range(num_epochs):\n",
" y_train_pred = model(x_train)\n",
"\n",
" loss = criterion(y_train_pred, y_train_lstm)\n",
" print(\"Epoch \", t, \"MSE: \", loss.item())\n",
" hist[t] = loss.item()\n",
"\n",
" optimiser.zero_grad()\n",
" loss.backward()\n",
" optimiser.step()\n",
"\n",
"training_time = time.time()-start_time\n",
"print(\"Training time: {}\".format(training_time))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 0 MSE: 0.7489547729492188\n",
"Epoch 1 MSE: 0.5571785569190979\n",
"Epoch 2 MSE: 0.3550248146057129\n",
"Epoch 3 MSE: 0.11383534967899323\n",
"Epoch 4 MSE: 0.08119436353445053\n",
"Epoch 5 MSE: 0.12229330837726593\n",
"Epoch 6 MSE: 0.06215396523475647\n",
"Epoch 7 MSE: 0.03346097841858864\n",
"Epoch 8 MSE: 0.039741095155477524\n",
"Epoch 9 MSE: 0.053013842552900314\n",
"Epoch 10 MSE: 0.057974714785814285\n",
"Epoch 11 MSE: 0.05394367873668671\n",
"Epoch 12 MSE: 0.04579221084713936\n",
"Epoch 13 MSE: 0.03839847072958946\n",
"Epoch 14 MSE: 0.034636400640010834\n",
"Epoch 15 MSE: 0.034985631704330444\n",
"Epoch 16 MSE: 0.037958238273859024\n",
"Epoch 17 MSE: 0.04114700108766556\n",
"Epoch 18 MSE: 0.042586181312799454\n",
"Epoch 19 MSE: 0.0416870191693306\n",
"Epoch 20 MSE: 0.03920357674360275\n",
"Epoch 21 MSE: 0.036471668630838394\n",
"Epoch 22 MSE: 0.03458753973245621\n",
"Epoch 23 MSE: 0.03397942706942558\n",
"Epoch 24 MSE: 0.034425970166921616\n",
"Epoch 25 MSE: 0.035337742418050766\n",
"Epoch 26 MSE: 0.036085665225982666\n",
"Epoch 27 MSE: 0.03623966872692108\n",
"Epoch 28 MSE: 0.035673633217811584\n",
"Epoch 29 MSE: 0.034549515694379807\n",
"Epoch 30 MSE: 0.033214230090379715\n",
"Epoch 31 MSE: 0.032047249376773834\n",
"Epoch 32 MSE: 0.03130275756120682\n",
"Epoch 33 MSE: 0.03099999576807022\n",
"Epoch 34 MSE: 0.030914366245269775\n",
"Epoch 35 MSE: 0.03069041110575199\n",
"Epoch 36 MSE: 0.030031777918338776\n",
"Epoch 37 MSE: 0.028864549472928047\n",
"Epoch 38 MSE: 0.027374034747481346\n",
"Epoch 39 MSE: 0.025889569893479347\n",
"Epoch 40 MSE: 0.024676289409399033\n",
"Epoch 41 MSE: 0.023721199482679367\n",
"Epoch 42 MSE: 0.022597597911953926\n",
"Epoch 43 MSE: 0.02060474455356598\n",
"Epoch 44 MSE: 0.017353758215904236\n",
"Epoch 45 MSE: 0.01332901418209076\n",
"Epoch 46 MSE: 0.00948617234826088\n",
"Epoch 47 MSE: 0.005405229516327381\n",
"Epoch 48 MSE: 0.0007158811204135418\n",
"Epoch 49 MSE: 0.006055368576198816\n",
"Epoch 50 MSE: 0.006936908699572086\n",
"Epoch 51 MSE: 0.006602602545171976\n",
"Epoch 52 MSE: 0.0017514880746603012\n",
"Epoch 53 MSE: 0.0020253045950084925\n",
"Epoch 54 MSE: 0.0011081402190029621\n",
"Epoch 55 MSE: 0.001454756478779018\n",
"Epoch 56 MSE: 0.0027977118734270334\n",
"Epoch 57 MSE: 0.0029427658300846815\n",
"Epoch 58 MSE: 0.002594469115138054\n",
"Epoch 59 MSE: 0.002709457650780678\n",
"Epoch 60 MSE: 0.0024910939391702414\n",
"Epoch 61 MSE: 0.001553592155687511\n",
"Epoch 62 MSE: 0.0010085636749863625\n",
"Epoch 63 MSE: 0.0009618732728995383\n",
"Epoch 64 MSE: 0.0005022502737119794\n",
"Epoch 65 MSE: 0.0005154761020094156\n",
"Epoch 66 MSE: 0.0010875585721805692\n",
"Epoch 67 MSE: 0.001025943667627871\n",
"Epoch 68 MSE: 0.0012045904295518994\n",
"Epoch 69 MSE: 0.001157596125267446\n",
"Epoch 70 MSE: 0.0006737074581906199\n",
"Epoch 71 MSE: 0.0006192333530634642\n",
"Epoch 72 MSE: 0.0004676888056565076\n",
"Epoch 73 MSE: 0.00033939912100322545\n",
"Epoch 74 MSE: 0.0005248804227449\n",
"Epoch 75 MSE: 0.0005872068577446043\n",
"Epoch 76 MSE: 0.0005701836198568344\n",
"Epoch 77 MSE: 0.0006695101037621498\n",
"Epoch 78 MSE: 0.000646014348603785\n",
"Epoch 79 MSE: 0.0005136264953762293\n",
"Epoch 80 MSE: 0.0004840493493247777\n",
"Epoch 81 MSE: 0.0004234964435454458\n",
"Epoch 82 MSE: 0.0003203470550943166\n",
"Epoch 83 MSE: 0.00035055080661550164\n",
"Epoch 84 MSE: 0.0003733080520760268\n",
"Epoch 85 MSE: 0.00036719426861964166\n",
"Epoch 86 MSE: 0.0004335067351348698\n",
"Epoch 87 MSE: 0.0004231045313645154\n",
"Epoch 88 MSE: 0.00038690774817951024\n",
"Epoch 89 MSE: 0.00038401116034947336\n",
"Epoch 90 MSE: 0.00032881161314435303\n",
"Epoch 91 MSE: 0.00030802885885350406\n",
"Epoch 92 MSE: 0.0003193531301803887\n",
"Epoch 93 MSE: 0.0003063012263737619\n",
"Epoch 94 MSE: 0.00032423206721432507\n",
"Epoch 95 MSE: 0.00034152469015680254\n",
"Epoch 96 MSE: 0.000329547212459147\n",
"Epoch 97 MSE: 0.00033179562888108194\n",
"Epoch 98 MSE: 0.00032275510602630675\n",
"Epoch 99 MSE: 0.00029945571441203356\n",
"Training time: 26.38035488128662\n"
]
}
]
},
{
"metadata": {
"trusted": true,
"id": "_DL2eBsd-V6e"
},
"cell_type": "code",
"source": [
"predict = pd.DataFrame(scaler.inverse_transform(y_train_pred.detach().numpy()))\n",
"original = pd.DataFrame(scaler.inverse_transform(y_train_lstm.detach().numpy()))"
],
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true,
"colab": {
"base_uri": "https://localhost:8080/",
"height": 425
},
"id": "w00IAORy-V6e",
"outputId": "c25cb79d-0a26-4354-a4c2-a1c08771588e"
},
"cell_type": "code",
"source": [
"import seaborn as sns\n",
"sns.set_style(\"darkgrid\")\n",
"\n",
"fig = plt.figure()\n",
"fig.subplots_adjust(hspace=0.2, wspace=0.2)\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"ax = sns.lineplot(x = original.index, y = original[0], label=\"Data\", color='royalblue')\n",
"ax = sns.lineplot(x = predict.index, y = predict[0], label=\"Training Prediction (LSTM)\", color='tomato')\n",
"ax.set_title('Stock price', size = 14, fontweight='bold')\n",
"ax.set_xlabel(\"Days\", size = 14)\n",
"ax.set_ylabel(\"Cost (USD)\", size = 14)\n",
"ax.set_xticklabels('', size=10)\n",
"\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"ax = sns.lineplot(data=hist, color='royalblue')\n",
"ax.set_xlabel(\"Epoch\", size = 14)\n",
"ax.set_ylabel(\"Loss\", size = 14)\n",
"ax.set_title(\"Training Loss\", size = 14, fontweight='bold')\n",
"fig.set_figheight(6)\n",
"fig.set_figwidth(16)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1600x600 with 2 Axes>"
],
"image/png": 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pCvYfRXL66aeTnp6OYRgsXrz4sMLJUIqLi/MbTv7555/7VYC+9957QW9DTEyMb755gAULFlBeXu57Pm/ePL/9vffue/fuJTs7m5iYGIYNG8bll1/OX/7yF/71r3/59t21a5dvRM+6detwu920a9eOs846i8mTJzN9+nROOOEE3/6//fZbMN6iiNRCFZQiEjZ27NjBs88+y8MPP8ygQYPo3r07iYmJ5Ofn8+mnn+J0On37Hn/88X7Hpqens3PnTsAzqXZ+fj7R0dH07NmTYcOGcd555/Hvf//bdzM5ZcoUzj//fOLi4vjoo498n8J6Jw0Hz1yLkyZN8i3ysmfPHsaOHcuYMWNISUlh586dfPHFF7z++uv06NGj1vd08803k52d7ZtsfPr06cTFxfmuEYg5c+aQm5tLt27d+O6773zzT4JnAZ2Gcvnll/PWW2/5bgRvu+02Pv74Y3r06EFBQQFLlixhyJAh3HvvvVgsFq666irfjd8nn3zC9u3bOe6442jWrBlZWVmsXr2alStXMmjQIE499dQGa6eIiIhIqLRr1w6LxeIb3fPQQw+xdu1a8vPzGyXEC6bx48fz2GOPAZ5RSxdffDEnnngi69at48svv2yQa+y/oKXXRRddxEUXXcRVV13lG3m0c+dOLrjgAr9VvL3at2/PiSeeCHhWW7/99tsZNGgQHTt2pEWLFrjdbj7//HPf/jabzVcRe/PNN1NcXMzQoUNp0aIFiYmJbNu2za9woXnz5g3yfkXk0BRQikjYqaio4Mcff+THH3+s9fXx48czZMgQv22nnnoqr776KuBZcW/69OmAZ1LzYcOGER8fz7PPPsuf//xnCgsLKS8vZ9asWX7nsFgs3HHHHX7nnjRpElu2bPEFjHl5ebz55psBvZ9p06aRm5vru6F75JFHiIuLC3i4yIknnsgXX3zBF1984be9V69e/OlPfwroXAeTmZnJ9OnTueWWWygpKcE0zRrXrf41uuaaa9i0aZOvwnP16tX1Xl1SREREpClISUlh/PjxzJ49G4Ddu3f7RsoMGzaMTZs21WtxnHBw+eWX8+WXX/Lzzz8D+M3leMIJJ/gFePWdn7H63JDV7du3D/AsDrl27Vrfat7r16/3+3AeoEWLFjz77LN+I6ncbjdLly5l6dKltZ5/woQJfgvuZGVl8dFHH9W6b2JiIhdeeGHd35SIHBYFlCISNq644gq6du3KTz/9xOrVq8nOziY3NxeXy0VycjK9evXivPPOY/To0TWOveWWW3C73Xz22WdkZWXhcrlq7DN48GA++ugjXn31Vb7//nt27NiB0+kkLS2NQYMGcfnll9OvXz+/YwzD4MEHH2TMmDHMmTOHX3/9lezsbGw2G+np6QwZMoT09PSDvi+r1cq//vUvrrrqKpYtW4Zpmvz1r38lNjaWMWPG1Pnr85e//IUTTjiBN998k23btpGQkMCYMWO46aab/G60GsLIkSOZP38+M2fOZOHChWzfvh2Hw0FSUhI9e/b0WwXdYrHw+OOPc8YZZzB37lx+/fVXcnJyMAyDtLQ0unbtyrBhwwJ6ryIiIiLh7q9//SstWrRg7ty57Nu3j7S0NMaMGcOUKVMYO3ZsqJtXbzabjZdeeonnnnuODz/8kJycHFq3bs3FF19M3759G63C8K677uKEE05g9uzZLF++nLy8PGw2G+3ateOkk05i4sSJJCUl+fYfNGgQt9xyC8uXL2fTpk3k5ORQUVFBfHw83bp145xzzuG8887z7X/bbbexcOFCVq1axb59+8jPzyciIoKMjAyOPfZYJk2apPnTRRqRYdZ3CTAREQmqxYsXM3HiRN/zL7/88rBWEhcRERERqYvy8vJaPwB/7LHHePnllwHPHPGLFy/2Ww1cRKS+VEEpIiIiIiIiIj4TJ06kTZs2DBo0iIyMDAoKCvj+++/9Fk+8+OKLFU6KSINRQCkiIiIiIiIiPhUVFXz00UcHnJ/xxBNP5JZbbmnkVonIkUwBpYiIiIiIiIj4TJgwgQULFrB+/Xry8/MxTZPk5GR69+7N2WefzWmnnRbqJorIEUZzUIqIiIiIiIiIiEjIWELdABERERERERERETl6KaAUERERERERERGRkFFAKSIiIiIiIiIiIiGjgFJERERERERERERCRqt4H0JOThHBWkbIMCAlpXlQryENQ33VdKivmg71VdOhvmoa9u8n73Np+oL9s6ef8fClvglv6p/wpb4Jb+qf8BWMvqnrPakCykMwTYL+A9MY15CGob5qOtRXTYf6qulQXzUN6qcjT2P1qb53wpf6Jrypf8KX+ia8qX/CVyj6RkO8RUREREREREREJGQUUIqIiIiIiIiIiEjIKKAUERERERERERGRkNEclIfB7XbjcjnrfbxhQHl5OQ6HXfMuhDn1VfizWiOwWPSZi4iIiIiIiEhTo4CynioqysjLywIOL63KzbXgdrsbplESVOqrcGeQlJRGdHRMqBsiIiIiIiIiIgFQQFkPbrebvLwsIiOjiYtLwDCMep/LajVwuVSS1xSor8KXaZoUFxeQl5dFenrrUDdHRERERERERAKggLIePMO6TeLiEoiMjDqsc0VEWHA6VZXXFKivwltcXAK5uWWHNe2CiIiIiIiIiDQ+Tdh2GA6nclJEGpZ+HkVERERERESaJgWUIiIiIiIiIiIiEjIKKEVERERERERERCRkFFCKiIiIiIiIiIhIyGiRnKPIQw/dzyeffASA1WolPj6BTp06c8oppzF27FlYLHXLqz/++EOmT/8HCxZ8E8TWioiIiIiIiIjI0UAB5VFm6NDh3HPP33C73eTm5rJ48Y88/fQ/+OabL3n00X8SEaFvCRERERERERERaTxKoxqIaZpUOAI/LsJt4nSa9bpmlC3wlYsjI22kpKQCkJbWgm7dutOrVx9uuul6PvnkI84661xmz36Djz/+kF27dhIfn8Dw4cfz5z9PITY2ll9++ZmHH54GwIgRxwBw1VVXM2nStSxYMJ933pnNtm1biYmJYeDAY7jppttISkqu1/sTEREREREREZEjnwLKBmCaJn+bUcQf212Net1uba1M+7/mAYeU+xs0aDCdO3fl22+/4qyzzsVisXDzzXeQkdGKXbt28o9/PMq//z2d22+/iz59+jFlym3MmPE8b745F4CYmFgAnE4nf/rTdbRt2468vDyeffZfPPTQ/Tz55PTDfq8iIiIiIiIiInJkUkDZQA4zIwy5du3asXHjBgDGj7/Utz0joxVXX309Tz75CLfffhc2m424uDgMw/BVYnqdeeY5vseZma25+ebb+dOfJlJaWkpsbGzjvBEREREREREREWlSFFA2AMMwmPZ/zes3xDvCgtPprtd16zPE+0BME8BzrqVLF/PGG6+ydesWSkpKcLlc2O0VlJeXEx0dfcBzrFu3lpdffoENG/6gqKgI0/S8r71799ChQ8cGaaeIiEhT5nSabNrton1LK5G2Jv7pphwRTNNk4y4XcfH1ux8VERERaQgKKBuIYRhERwZ+XESEgdMS+l9Qtm7dTKtWrdi9exd33nkL5557Pldf/Wfi4+NZuXIFjz76AA6H44ABZVlZGbfdNpkhQ4Zx330PkpiYxN69e7j11sk4nfVIbkVERI4w2flubvhXAQCDu9u4/ZK4ELdIBH75w8Hjb5Zw7kiTS0+2hbo5IiIicpRSQCksW7aUjRs3MH78pfz++1rcbjeTJ9+CxWIB4KuvPvfbPyLChsvl/yn71q1bKCgo4LrrJpOe3hKAdet+a5w3ICIi0gSs3lz1gd3SdfrwTsJDcZlnscZdWU5AAaWIiIiEhgLKo4zd7iAnJxu3201ubi6LF//IzJmvMnz48Zx++hls2rQRp9PJu+++zXHHHc+qVb/ywQfv+Z0jIyODsrJSfv55CZ07dyU6Opr09JbYbDbmzn2bc845n82bN/Lqqy+F6F2KiIiEnwhr6EdMiOwvOtLzfVlWYYa4JSIiInI0s4S6AdK4Fi/+kXPOOZ0LLjiL2267kV9++Zmbb76dRx/9B1arlS5dunLjjbcwa9ZrTJx4EZ99toBrr73B7xx9+vTj3HPP57777ubMM09h1qzXSEpK4p577uPrr7/k8svH88Ybr3HDDTeH5k2KiIiEIYdTAZCEn6qAUnNQioiISOgYpmnqbvkgsrOL2P8r5HDYycnZTUpKBjZbPSaerOZwFsmRxqW+Cm/en8vU1AwyMlJq/dmV8GIYkJraXH3VBKivGsZdzxeyebfL9/ztaUkNev79+8n7XJq+YP7s/b7Nyd9mFNEqLYKnbmyun/Ewo39/w5v6J3ypb8Kb+id8BaNv6npPqgpKERERkSBzu02/cFIkXMREqYJSREREQk8BpYiIiEiQ5Rf7fwSdkaJbMAkP0ZWDgTQHpYiIiISS7o5FREREgmz/8Gd3jptyBUISBrxzUJZXmLjd+p4UERGR0FBAKSIiIhJktcU+L80vbfR2iOzPG1ACVDhC2BARERE5qimgFBEREQm2WhLK73+1N347RPZjiwBL5W8E5XZVUIqIiEhoKKAUERERCbLaRs4O6mZr/IaI7McwDF8VpeahFBERkVBRQCkiIiLSSCKrZZInD4oMXUNEqvEulKMKShEREQkVBZQiIiIiQeY2PcFPlK1qvr82Layhao6InxjvQjkKKEVERCREIkLdABEREZEjXmXuE2GFmy9shsNl0iJJAaWEh6qVvEPcEBERETlqqYJSDssFF5zFnDlv1nn/X375mREjjqGoqCiIrWpc+7+njz/+kNNPP/GwztkQ56irbdu2cPbZp1FaWtIo16uPn376kSuvvBS32x3qpoiI1Iu3Ls0wYFjvSE7oFxXS9ohUF60KShEREQkxVVAeJUaMOOagr1911dVMmnRtwOd98cXXiYmJqfP+ffr044MPFhAXFxfwtQLxyy8/M2XKdb7nSUnJ9O3bjz//+SYyM1sH9donn3wqw4YdV+f9L7jgLMaPv4Tx4y+t9zkOx/PPP8f5548nNrYZUPW1++STr2nevHmN/cvLy3n11Zf46qvPyc7OIjY2lvbtO3LRRZfSuXNXLrzw7INe75577qNlywymTLmOuLjmfPDBAqKiqn5RX7t2DVdffQUACxf+DMCxxw7npZee57PPPuH0089oqLcuItJoKkd4Yxx8N5GQiI5SQCkiIiKhpYDyKPHBBwt8j7/88nNmzHieN9+c69sWExPre2yaJi6Xi4iIQ397JCUlBdQOm81GSkpqQMccjjffnEtsbDN27NjG448/xJ133sprr72F1eo/rC6Q93woUVHRREVFh/wcdbFnzx5+/PF7brnljjof88QTD/Pbb6u55ZY7aN++IwUFBaxe/SsFBQW0aJHu97321ltvsHjxjzz11L992+Li4lizZjUAsbGxfPfd15x66um+1z/66APS01uyd+8ev+uOGXMm7777tgJKEWmSfAGloYhSwo93kRyt4i0iIiKhoiHeDcU0wV7euH/Mut9EpqSk+v7ExcVhGIbv+datWxg9+gQWLfqB//u/CZx00jBWrlzBzp07uOuuWznrrNGceurx/OlPE1m6dLHfefcf4j1ixDF8+OE87r77dk4++Tguvvg8Fi781vf6gYZDL168iMsuu4BTTz2eW2+9kezsbN8xTqeTp556gtNPP5GxY0/m3/+ezoMP3sfdd992yPedlJRMamoq/fsP5Morr2bLlk3s3Lnd147937Pb7WbmzFe48MKzGTXqOK644hK+/voLv3MuWrSQiy8ex6hRx3HjjdeyZ89uv9drG569cOF3/OlPExk1ajhnnHEyd999OwCTJ1/Dnj27mT79n4wYcYyv0rW2c7z//ruMH38OJ554LJdcMo4FC+b7vX6or31tvvrqczp37kpaWotDfi29fvjhOyZO/D+GDRtBRkYrunfvwQUXXMyZZ56D1Wr1+16LiYnBao3w21Y9eB0z5kzmz/+f73lFRTlffvkZY8acWeO6xx13AuvW/cbOnTvq3FYRkXBRFVCGth0itdEQbxEREQk1VVA2BNMk8b9/xbb190a9rKNdN/KvfaDBftt5/vlnmTz5Jlq1ak3z5s3Zu3cvxx57HNdc82dstkgWLJjPnXfeyptvzqVly5YHPM8rr7zI9dffyA033MS7777NtGl/Ze7cD4mPT6h1//Lyct56ayZ//evfMQwLDzzwV5577inuu+9BAGbNeo3PPlvA3XffR/v2HXjnnbf4/vtvGDjw4MPW9+cdRuxwOA/4nmfOfIXPPvuE22+/m9at2/Drr8t54IG/kZiYxODBg9m7dw/33juV8867kHPOOY9169by7LNPHfS6P/64kHvvvYOJE/+Pv/xlGg6Hg59++gGAhx9+giuvvJSzzz6Ps84694Dn+Pbbr3n66SeZMuU2jjlmCD/++D2PPPJ3WrRI9/s6BPq1X7lyOd2796jjV9AjOTmFRYt+YOTIk3zDwuvrtNPG8tZbM9mzZw8tW7bkm2++omXLVnTt2r3Gvi1btiQ5OYVff10e9GH6IiINTbGPhDMFlCIiIhJqqqAUnz/96VoGDz6WzMzWxMcn0KVLV84993w6duxMmzZtufrq68nMzOSHHw5elTdmzJmceurptG7dhmuvvYGyslJ++23NAfd3Op3cccc9dO/ek27dujNu3HiWLVvqe33u3DlMmHAlI0eeRLt27bnllqnExdWcG/FgsrOzmT17JmlpLWjbtl2t7zk6OoaZM1/h7rv/xtChw8jMbM3YsWcxevQYPvjgPQDmzZtLq1atufHGW2jbtj2jR4+ptdqvutdff5mTTx7NpEnX0r59B7p06crll18FQHx8AhaLhdjYWF+FYW1mz57JmDFnMW7chbRt246LL57ACSecxFtvzfTbL9Cv/Z49e0hNTavT19Br6tR7Wb16JWPHnsyf/jSR6dP/wcqVKwI6h1dSUjJDhw7nk08+BGD+/P9xxhkHnsMyNTW1RsWqiEhT4K2gtKiCUsJQTOUclGX2EDdEREREjlqqoGwIhuGpZHRUBHxoRIQFp7OeKxPbohp0rFj37j39npeWlvLyyy+waNFCcnKycblcVFRU1JgbcH+dOnXxPY6JiaFZs2bk5eUecP/o6Gi/iriUlFTf/sXFxeTm5tCzZy/f61arlW7demCah/66jRs3FtM0KS8vp3Pnrjz44OPYbLZa3/OOHdspLy/nlltu8DuHw+GgS5duAGzZstmvLQC9e/c5aBvWr//9oNWRdbFlyxbOPnuc37Y+ffrxzjuz/bYF+rWvqCgnMjIyoLb07z+QOXM+YM2aVaxa9SvLli3lnXdmM2nStVx55Z8COhfAGWeczdNP/4PRo8ewevUqHnjgUX79dUWt+0ZFRVFeXh7wNUREQs03K4sCSglDqqAUERGRUFNA2VAMAyLrsahJhAUs9QwoG1h0tP9q3M899xRLly7mhhtupnXrNkRFRfGXv9zpN0S6NvsvNGMYBuZB5ssMdP9APPfcizRrFkdSUlKtw5Grv+eysjIAHn/8qRpzMlYPNQPVGIvdeAX6tUxMTPTNBxrodfr1G0C/fgOYMOFKXn31JV599SUuu+yKgL9Wxx47nCeeeIhHH32A4447noSExAPuW1hYSGJiYAsziYiEA++/xKqglHCkgFJERERCTUO85YBWrfqVsWPPYuTIk+jUqTPJySns2bOrUdsQFxdHcnIKa9f+5tvmcrn44491dTo+IyOTzMzWdZorsUOHDkRGRrJ37x5at27j9yc93TPnZvv2HVi71n/ItHdF6gPp1Kmz35D1/UVE2HC5Dh5St2/fnpUrf/XbtmrVr3To0OGgxx1Kly7d2LJl02GdA6BDh464XC7s9vpUEUdw2mlnsHz5soMO766oqGDnzh107drtcJoqIhISDfXBm0gwRHum6aZCq3iLiIhIiKiCUg6odeu2fPvtVxx33PGAwUsv/Qe3u/FvXM8/fzxvvPEKrVu3pl279rz77tsUFRXS0OPkYmObcfHFE3jmmX9imiZ9+/anuLiYVatW0KxZHGeddTbnnHM+s2e/wXPPPc1ZZ53DunXrfPMnHshVV13NzTf/mczM1px88mhcLheLFi1kwoQrAcjIyODXX3/hlFNGY7NFkpiYWOMcl1wykb/97S66du3GMccM4YcfvuO7777mX/967rDe85Ahw3jssQdxuVxYrVa/1zZt2kBsbGy1LQZdunRl8uRrOOWU0+jevScJCQls2bKJ//73OQYOPIZmzeLq1Y6rr76eSy+dSEJC7Yv5AKxZswqbLZLevfvW6xoiIqGkVbwlnHkrKMtUQSkiIiIhooBSDujGG2/hkUf+znXX/R8JCYlcdtkVlJSUNHo7LrvsCnJzc3jwwfuwWKycffZ5DBkyDIul4QuAr776ehITk5g58xV27dpJXFxzunbtzsSJnkVtWrZsyYMPPs4zz/yTuXPfpkePXlxzzQ088sjfD3jOgQOP4YEHHuXVV1/ijTdepVmzZvTrN8D3+qRJ1/HEEw9z0UXnYrfbWbjw5xrnOOGEE7npptt5662ZPP30k2RktOLuu/8W8Erm+zv22OFYrVZ+/nkJQ4cO83vthhuu9ntutVr59tvFDB06jAUL5vPCC/+mvLyc1NRUhg8/nquuCnz+SS+bzVZrMFvdF198yujRpxMd3XhD5kVEGooCyiPDrFmzmDFjBllZWXTv3p2//vWv9O1b+wdnl19+OUuWLKmxfeTIkbzwwgvBbmpAqoZ4h7ghIiIictQyTI05Oqjs7CL2/wo5HHZycnaTkpKBzRbYAiP7O6xFco5Sbrebyy67gFGjTuXqq69vtOseqX01d+4cfvjhO/75z2dD3ZQDys/P59JLz+ell16nVavMWvfx/lympmaQkZFS68+uhBfDgNTU5uqrJkB9dfhWb3bwwKvFtE6z8I/JB64WPxz795P3uTSMjz/+mKlTpzJt2jT69evHa6+9xoIFC1iwYAEpKSk19s/Pz8fhcPg9P+ecc3jwwQcZN25cjf0PJtg/ext2OLn3xSJaJFl45ubgfH9K/ejf3/Cm/glf6pvwpv4JX8Hom7rek6qCUsLenj27WbLkJ/r3H4jD4WDu3LfZvXsXp556eqibdkQ455xxFBcXUVpaUqe5OkNhz55d3HbbnQcMJ0VEwp4qKJu8V155hfHjx3P++ecDMG3aNL755hvmzp3LNddcU2P//UcGzJ8/n+joaE4/PfzuX6KjKod4aw5KERERCZGwCyifeeYZnn3Wv5KrQ4cOLFiwAPAslPHoo4/y8ccfY7fbGTFiBPfddx+pqam+/Xft2sX999/P4sWLiY2N5dxzz+W2226rscKxNA2GYfDJJx/y3HNPYZrQsWMnnnrq37Rvf3gLxIhHREQEV1wxKdTNOKju3XvSvXvPUDdDRKTe3L6AUgllU2S321mzZg3XXnutb5vFYmH48OEsX768TueYO3cuZ5xxxn7zO9dNsL9tYioXySm3mwrRw4y3P9Qv4Un9E77UN+FN/RO+gtE3dT1XWCZ2Xbp04ZVXXvE9r754x8MPP8y3337LU089RfPmzXnggQeYPHkys2fPBjwrPF977bWkpqYye/Zs9u3bx5133onNZuPWW29t9Pcihy89vSX/+c/LoW6GiIjIYdONeNOUl5eHy+WqMZQ7JSWFTZs2HfL4lStX8scff/DQQw/V6/opKcEdqh8Z4wIKcTghKSkOq1XfqOEm2N8DcnjUP+FLfRPe1D/hKxR9E5YBpdVqJS0trcb2oqIi5s6dy5NPPsmwYZ4FPR5++GHGjh3LihUr6N+/PwsXLmTDhg288sorpKam0qNHD2666SaefPJJJk+eTGTk4c0ZKSIiIhIoXwVlaJshIfLuu+/StWvXAy6ocyg5OcGdo8vpqjr5jl2FNItp+IUIpX4Mw/NLYrC/B6R+1D/hS30T3tQ/4SsYfeM956GEZUC5detWRowYQVRUFP379+e2226jVatWrF69GofDwfDhw337durUiVatWvkCyhUrVtC1a1e/Id8jRozg/vvvZ8OGDfTsGdgw0doqHbzbtL6QSPjw/jxquEDTob5qOtRXh8/7pTOM4H0d9+8n9VfDSUpKwmq1kpOT47c9JyfH756zNqWlpcyfP58pU6bU+/qmSVB/gYuwGkRYwemCsgqIjQ7etaR+gv09IIdH/RO+1DfhTf0TvkLRN2EXUPbt25dHHnmEDh06kJWVxXPPPcdll13Ghx9+SHZ2Njabjfj4eL9jUlJSyMrKAiA7O7vGjaL3uXefQNSW8jocDnJz9wIuIiIO/xPmhjiHNA71Vfiy211YLBZSUjz/Pmi4QNOhvmo61Ff1F7enDCjGZrMGfWVt9VPDi4yMpFevXixatIhTTjkFALfbzaJFi5gwYcJBj12wYAF2u52zzz67MZpabzFRFopK3ZTb9ZuiiIiINL6wCyhHjhzpe9y9e3f69evHSSedxCeffEJ0dON/nFtbWatpmlitkRQU5AEWDKP+oZXVauBy6UawKVBfhS/TdJOfn0dERCQFBWWkpto0XKAJ0NCOpkN9dfgKCxwAuJwusrOLgnKN/fuprsNppG6uuuoq7rzzTnr37k3fvn157bXXKCsrY9y4cQBMnTqV9PR0brvtNr/j3n33XU455RSSkpJC0ew6i4k2KCqFMgWUIiIiEgJhF1DuLz4+nvbt27Nt2zaGDx+Ow+GgsLDQr4oyJyfHN2dlamoqK1eu9DtHdnY2QK3zWh5K7WWtBgkJyeTk7KmspKw/i8WC2+0+rHNI41BfhTfDsBAfn4x3IKWGCzQd6qumQ31Vf+5q01AE+2uofgqOsWPHkpuby/Tp08nKyqJHjx689NJLvpE6u3fvxmLx/9B606ZNLFu2jJdfDv/F/mKiLIBLFZQiIiISEmEfUJaUlLB9+3bS0tLo3bs3NpuNRYsWcdpppwGeG79du3bRv39/APr378/zzz9PTk6Ob6XFH3/8kbi4ODp37txg7YqIsNGiRWucTke9z2EYkJTUjLy8Ev0iEebUV+EvIsKGoQnXRCRM+f7v0D9TTdqECRMOOKR75syZNbZ17NiR33//PdjNahAxUZ5vzgoFlCIiIhICYRdQPvbYY5x00km0atWKffv28cwzz2CxWDjzzDNp3rw5559/Po8++igJCQnExcXx4IMPMmDAAF9AOWLECDp37szUqVO54447yMrK4qmnnuKyyy5r8BW8DcPAZqv/OQ0DoqOjsdkcCr3CnPpKREQOh/e/DosCSglTsZUBZVlFiBsiIiIiR6WwCyj37NnDrbfeSn5+PsnJyQwaNIg5c+aQnJwMwD333IPFYmHKlCnY7XZGjBjBfffd5zvearXy/PPPc//993PRRRcRExPDeeedd1grJ4qIiIgcDn24JeHOM8QbDfEWERGRkAi7gPJf//rXQV+Piorivvvu8wsl95eZmcmLL77Y0E0TERERqRdvQKmZKCRceYd4K6AUERGRUKj/8tMiIiIiEhDNlSvhKiba82tBWYUCShEREWl8CihFREREgszuUOgj4U2L5IiIiEgoKaAUERERCSLTNHn2vVIA1m11hrg1IrXTHJQiIiISSgooRURERILI7gh1C0QOLTbaOwdliBsiIiIiRyUFlCIiIiJBVKaKNGkCvBWU+n4VERGRUFBAKSIiIhJE5dUWHTFMN/FvPEnCjL+DvTyErRLxp1W8RUREJJQUUIqIiIgEUfWKtB7mZqLWLCZywypif/g4hK0S8RetgFJERERCSAGliIiISBBVVJuDsrO53ffYqCgNQWtEahfrXSSnQgGliIiIND4FlCIiIiJB5HRVBT7nddoHQNngkykZfUmomiRSQ9UQ7xA3RERERI5KCihFREREgsjp8vzdPsNKSyPHs61NF7BYQ9gqEX/eRXI0xFtERERCQQGliIiISBC5KisoI6xgKcoHwN08MXQNEqlFbLSnglKreIuIiEgoKKAUERERCSJvBWWE1cBSnA8ooJTw462gdLnA6VRIKSIiIo1LAaWIiIhIEDmcnr9tFjeW4gIA3HGJoWuQSC28c1CChnmLiIhI41NAKSIiIhJE3kVymlOK4XYD4G4WH8omidRgtRrYIjyPNcxbREREGpsCShEREZEg8g7xjqMMADMyCiJsIWyRSO2iI7WSt4iIiISGAkoRERGRIPJWUDarDCjd0bGhbI7IAVUFlKqgFBERkcalgFJEREQkiLwVlM0oB8CMUkAp4ck7D2VZhQJKERERaVwKKEVERESCyBtQxpqVQ7yjYkLYGpEDi470/F2hCkoRERFpZAooRURERILIO8Q7xhtQRiuglPCkId4iIiISKgooRURERILIV0HprhzirTkoJUx5A8qyihA3RERERI46CihFREREgmjVRgcA0e7KRXI0B6WEqegoVVCKiIhIaCigFBEREQmiDTs9JZR5WSWA5qCU8KUh3iIiIhIqCihFREREGkNZKaA5KCV8eRfJUUApIiIijU0BpYiIiEgjqFokp1mIWyJSuxhVUIqIiEiIKKAUERERaQSxZuUiORriLWFKQ7xFREQkVBRQioiIiDSCrqmepZE1xFvCVdUiOSFuiIiIiBx1FFCKiIiIBJHhyXxobvFUUGoVbwlX3iHeZRWqoBQREZHGpYBSREREJEhcLhOzMuux2r1zUCqglPCkId4iIiISKgooRURERILE6ap6bKmoDCg1B6WEKa3iLSIiIqGigFJEREQkSJyuqqDHUlEKaA5KCV9Vc1AqoBQREZHGpYBSREREJEgclRWUNtOB4XICGuIt4atqiHeIGyIiIiJHHQWUIiIiIkHiHeIdby33bTMjo0PUGpGDqz4HpWmqilJEREQajwJKERERkSBxOj0hT6LVM7zbHRkNFmsomyRyQN6A0u0GhzPEjREREZGjigJKERERkSDxVlCmkweAOz4phK0ROTjvIjmgeShFRESkcSmgFBEREQkS7yI5voAyMS2UzRE5KIvFIMrmeayAUkRERBqTAkoRERGRIPEukpNglADgbhYfwtaIHJp3mHdZhQJKERERaTwRoW6AiIiIyJFm6x4n2/a5SE3wfBYcjyegNGPjQtkskUOKjjIoKDG1kreIiIg0KgWUIiIiIgHYsMPJ/34oZ8LoGFok1b7gzdT/FAFw1vAoAJqblRWUMQooJbxVX8lbREREpLEooBQREREJwL0vesLHvbluHrv+4EO2f9/uWQrZG1CqglLCnXehHAWUIiIi0pg0B6WIiIhIPWzPch1yH+8w2ThVUEoTEaMKShEREQkBBZQiIiIi9eA6dD7pW2gkzq0KSmkaorwBpRbJERERkUakgFJEREQkSApL3QDEulVBKU1D1RyUIW6IiIiIHFUUUIqIiIg0oBUbHL7HFZUhTzOXKiiPBLNmzWLUqFH06dOHCy+8kJUrVx50/8LCQqZNm8aIESPo3bs3p512Gt9++20jtbZ+vAFlmYZ4i4iISCPSIjkiIiIiDejxN4v9nltMFzHuMkAVlE3Zxx9/zCOPPMK0adPo168fr732GpMmTWLBggWkpKTU2N9ut3PVVVeRkpLC008/TXp6Ort27SI+/uALK4VaTJTmoBQREZHGp4BSREREpAF892sFKzc6a8xNGU+J77GpgLLJeuWVVxg/fjznn38+ANOmTeObb75h7ty5XHPNNTX2nzt3LgUFBcyePRubzQZA69atG7XN9aFVvEVERCQUFFCKiIiINIDn3iutdbs3oHTHNAOrtTGbJA3EbrezZs0arr32Wt82i8XC8OHDWb58ea3HfPXVV/Tv35+///3vfPnllyQnJ3PmmWdy9dVXYw3w+8AwDqv5dT6/YUB0tQrKYF9XDq1630j4Uf+EL/VNeFP/hK9g9E1dz6WAUkRERKSelv1up2d7m29YbG0STM+Qb3ds88ZqljSwvLw8XC5XjaHcKSkpbNq0qdZjtm/fzk8//cRZZ53FCy+8wLZt25g2bRpOp5PJkycHdP2UlMb53klJaU5aigGU4TatpKbqezZcNNb3gNSP+id8qW/Cm/onfIWibxRQioiIiATAFgEOp+fx42+WkJZo4dlbEg64fwKegNJUQHlUMU2TlJQUHnjgAaxWK71792bv3r3MmDEj4IAyJ6cIM4gjrg3D84tITk4RzsqVnQqLnWRnFwXvolIn1fsmmN8DUj/qn/Clvglv6p/wFYy+8Z7zUBRQioiIiAQgwloVUAJk5bu56L68A+4f762gbBbei6PIgSUlJWG1WsnJyfHbnpOTQ2pqaq3HpKWlERER4Tecu2PHjmRlZWG324mMjKzz9U2TRvkFzjQhqtoq3vqlMXw01veA1I/6J3ypb8Kb+id8haJvLI17OREREZGmLTIisEl5qiootUBOUxUZGUmvXr1YtGiRb5vb7WbRokUMGDCg1mMGDhzItm3bcLvdvm1btmwhLS0toHCyscVUBpQVWiRHREREGpECShEREZEARAQ4/iTerFwkRxWUTdpVV13FnDlzeP/999m4cSP3338/ZWVljBs3DoCpU6fyj3/8w7f/JZdcQn5+Pg899BCbN2/mm2++4b///S+XXXZZqN5CnWgVbxEREQkFDfEWERERCUBqgoWcAtch94uNNigtN0mPLIZyLZLT1I0dO5bc3FymT59OVlYWPXr04KWXXvIN8d69ezcWS9Vn/xkZGcyYMYNHHnmEs88+m/T0dCZOnMjVV18dqrdQJ9HeId4VCihFRESk8SigFBEREQlAfKwFOHRAOemMWEb0jST+tQpYp0VyjgQTJkxgwoQJtb42c+bMGtsGDBjAnDlzgt2sBhVduSJ9hQPcbhOLJbApDURERETqQ0O8RURERAJQ17qyzpmexVEspZ6VkFVBKU2Bt4LSNMHuPMTOIiIiIg1EFZQiIiIigahjQtl65cdEVRRgKfCs/Gw2U0Ap4S8yAgzDE1CWV5i+wFJEREQkmBRQioiIiATgQPlkSrxBTqHn1Qwzi6TPXvd73ZWYFuSWiRw+i8Ugygbldi2UIyIiIo1HQ7xFREREAmAeILOZeHos8c081WZXtfmtxuvuhORgNkukwcRUzkOpgFJEREQaiyooRURERAJgHiCh7NjKyvO3JWC1GsS9uwuAsiGngtuJvdtAsFgbs5ki9eYZ1m1Sbg91S0RERORooYBSREREJAD7x5PnjIiifUYELRIMItcuwdmmCxG7twBg79IPe++hjd5GkcMRVTnvZJkqKEVERKSRKKAUERERCcR+mc0x7ex07RRFzKJPiPvoVb/XnG27NF67RBpITKSGeIuIiEjjUkApIiIiEgBvZDOwq43+zXYz7K0HsZSXYhr+qx07Mtrjjte8k9L0REd6/lZAKSIiIo1Fi+SIiIiIBMA7BeWw3jYuzH8HS3kpAMZ+c1OWDzu9sZsm0iCivRWUFQooRUREpHGoglJEREQkAN4c0mI6idy42rfd0b47hRdNwZ2YBm6XFsWRJitaq3iLiIhII1NAKSIiIhIAb2STkL8Dw+nAHd2MnL++DJZqA1MUTkoT5qug1CreIiIi0kg0xFtEREQkEJUJZfqeVQA4W3fyDydFmjhvQFmmId4iIiLSSHQ3LSIiIhIAE7CYLjqv+wQAe5e+oW2QSAPTKt4iIiLS2BRQioiIiATANKG7uYWoikJMi5Wy4WND3SSRBqVVvEVERKSxKaAUERERCYBpQgdzFwD2zn0hwhbiFok0LC2SIyIiIo1NAaWIiIgclNNl8smiYvbluULdlLBgAmlmLgDuxNTQNkYkCKI1xFtEREQamVbxFhERkYP6apmdlz4qxTBg9v1JoW5O6JnQgjwA3IkpIW6MSMPTKt4iIiLS2FRBKSIiIjWUV5hs2e0EYN02z9+miqkAMDFJNT0BpStBFZRy5PEFlFrFW0RERBpJWAeUL7zwAt26deOhhx7ybauoqGDatGkMHTqUAQMGcOONN5Kdne133K5du7jmmmvo168fw4YN47HHHsPpdDZ280VERJqse18s5M7ni1ixwUFcjOHbXlLmDmGrwoNpQhylnsexcSFujUjD8y2S41BAKSIiIo0jbAPKlStXMnv2bLp16+a3/eGHH+brr7/mqaeeYubMmezbt4/Jkyf7Xne5XFx77bU4HA5mz57No48+yvvvv8/06dMb+y2IiIg0SflFbnZkeYLIjxeVU1qtiur/Hi0gK//onovSNCHWLPc8jowOcWtEGl6U5qAUERGRRhaWAWVJSQl33HEHDz74IAkJCb7tRUVFzJ07l7vuuothw4bRu3dvHn74YZYvX86KFSsAWLhwIRs2bOCJJ56gR48ejBw5kptuuolZs2Zht2siHRERkUP5aFG57/GvG5x8t8L//8+f1jgau0lhxQRiqPA8jooJbWNEgsA7xNvuALdbIaWIiIgEX1gukvP3v/+dkSNHMnz4cP7zn//4tq9evRqHw8Hw4cN92zp16kSrVq1YsWIF/fv3Z8WKFXTt2pXU1Ko5oUaMGMH999/Phg0b6NmzZ0BtMYxD71Nf3nMH8xrSMNRXTYf6qulQX4WvQ/WJxTjK+82sHlBGh83XYv+fqXBplzQ93oDSNMHurBryLSIiIhIsYRdQzp8/n99++4133323xmvZ2dnYbDbi4+P9tqekpJCVleXbp3o4Cfiee/cJREpK84CPCcdrSMNQXzUd6qumQ30VfhLjXVAZwNUmrnkUqalHb79ZrcW+gDI5IxUSw+troZ8pOVyR1X5DKLebvsBSREREJFjCKqDcvXs3Dz30EC+//DJRUVGhbg4AOTlFQVu11DA8v0QE8xrSMNRXTYf6qulQX4WvouKDT4lSWlpBdnZRI7UmDNkrsOKZozOn2InpDI+vxf4/U97nIoGyWAyiIqHCrnkoRUREpHGEVUC5Zs0acnJyGDdunG+by+Vi6dKlzJo1ixkzZuBwOCgsLPSroszJySEtLQ3wVEuuXLnS77zeVb69+wTCNAn6L86NcQ1pGOqrpkN91XSor8LP3tyDL4JjcHT3WaS7ao5Oty3KMyllGNHPlDSE6EiDCrtJhQJKERERaQRhFVAee+yxfPjhh37b7r77bjp27MjVV19NRkYGNpuNRYsWcdpppwGwadMmdu3aRf/+/QHo378/zz//PDk5OaSkpADw448/EhcXR+fOnRv1/YiIiDRFP66uuQjOcPcKznAt5MmIyzGMo3thmEjTM7zbZY0EizXErREJjmibQQEm5VpjUkRERBpBWAWUcXFxdO3a1W9bbGwsiYmJvu3nn38+jz76KAkJCcTFxfHggw8yYMAAX0A5YsQIOnfuzNSpU7njjjvIysriqaee4rLLLiMyUjN8i4iI1Ka41M3y9Q6G9qz6vzLBLOI49wqGuVdynPkrAH9yvU+e8edQNTMsRLs8FZROW3SIWyISPFGV805qiLeIiIg0hrAKKOvinnvuwWKxMGXKFOx2OyNGjOC+++7zvW61Wnn++ee5//77ueiii4iJieG8885jypQpIWy1iIhIePvPB6X8vM7Bb1ucxDczSC3axrPOR4nGv5qyrbmH/KN8vYwo0xNQuhRQyhHMu3J3hUMBpYiIiARf2AeUM2fO9HseFRXFfffd5xdK7i8zM5MXX3wx2E0TERE5Yvy8zhNEfvWLnbgYg5PdS2uEkwBdzG2sCrdJFxtZQU4ZoIBSjmzRqqAUERGRRmQJdQNEREQkvDicJu3NXQDMbzGO82z/4J6IyQBE4qTD9h9C2byQWrPZQQyVc1AqoJQjmG+Id0WIGyIiIiJHBQWUIiIi4qfCAW3MPQD0PrkXnbsn85Olr+/1ZiVZoWpayK3Z7CSmcoi32xYV4taIBI+3glJDvEVERKQxKKAUERERPzbTQQaeEDKufWvuvCwOgDcsYwCIrigMWdsaywffl/PFzzVLxyKs+CooYxJiG7tZIo1GQ7xFRESkMYX9HJQiIiLSuFqRhRUTd1QM7uZJeNfEKTCaAxBlLwpd4xrBnlwXb37hmWfy5EGRGEbVqkDldpMYPBWURpSGeMuRK9rm+VsBpYiIiDQGVVCKiIiIn7aVw7tdaa2gMpyLtBmU4AnkbM7ykLUt2CrsJjc9XVUh6nBWveZ2m3ywsIIY01NBaUYqoJQjl3cOygoFlCIiItIIFFCKiIiIn07u7QC4WrT2bXv0hjTKDE8gF+EoC0m7GsNHP/qHr3ZnVTjz/Uo7UDXE21QFpRzBqoZ4h7ghIiIiclRQQCkiIiLeQkmucP6Pie75ADjadPG93qVNJKVHeAWl220y52v/91a9gvLf75cCEIfnbzMyptHaJtLYNAeliIiINCYFlCIiIkc5t9vENKGLeytXuD/ybXd06u173CzG4gvkIhxHZkD52dKqRXEyzb2c5FqCvcKTUFYPaXqYmwFwVqswFTnSREd6/tYq3iIiItIYtEiOiIjIUc47hHOM+wfftvndrmVIWqbffolpsbAVIpxH5hDvVz72vK9I086/HP8glXx2LSqCsy4gt9ANQJqZS3tzN24MHB17hrK5IkGlCkoRERFpTKqgFBEROcqVVngCiL7megDeaHcDPcaPqrGf3eIZ4m2tpYKyuNRNYYk7iK1sPKe7fySVfADiNq/ANE3KKr9GtztfB2B7ZFvM2OahaqJI0EVpDkoRERFpRAooRUREjnJlFSYW0007cxcAYy7pQ/PYmrcIFZUBZYTLDi6Xb7vbbTLpsQKufrwAexMdDup2V7X7fNeXvse2PVu4/ok8tu1zgWnSxdwGwG8xvRq9jSKNKVqreIuIiEgjUkApIiJylCurMEmkECsmpmHgbp5Y634V1qpVqw171TDv0vKqAKOgiVZRVjg8f6eZubRhr297jFlBXPEenp9XShv2kkgxdiL4X+LZIWqpSOPQEG8RERFpTAooRUREjnJlFSYpZgEA7rhEsFhr3c9ttWGvnL7aqKgKKIurBZROV43DmgRvsNrbvQGAXbHtWW10AqCr21M12df9BwC/GR1xGLYQtFKk8URVfosroBQREZHGoIBSRETkKFdWYZJMZUB5gOpJAAMoI8rzuKJqHsri0qoAo6kOB/12uWeivd7mRgB2JnThD6MdAOe6vybT3Oubo/NXoysDuiiglCObb4i3w38KBBEREZFg0CreIiIiRzm/CsrmSQfczzCglGgSKPGroNyeVVU2We4IXjuDpbjUzXvfeQLX3qangnJHfBfW7qsAoJe5iRccDxKJ5811OKE3nU+Irv1kIkcIb0AJYHdAdFQIGyMiIiJHPFVQioiIHOVKq1dQxh8ioDQ8wZw3oHzuvRKen1fKQPdvvOD4O4lrfwh+gxtYbpGnOizGLKejuQOA7c268IOlP1uMDM9rVGDFjduw0OOkXtgijAOeT+RIEGnz/MyDhnmLiIhI8CmgFBEROcr5V1AmHnA/wzAowxtQeioOv/vVMzT6JudbdDZ30P3Hl4Pb2CBwujzhSw9zE1ZMnIlpFEUlUW5EcW3EX3jKeimuylum3PaDIFLVk0erWbNmMWrUKPr06cOFF17IypUrD7jve++9R7du3fz+9OnTpxFbe3gMw9A8lCIiItJoNMRbRETkKOZwmixd52CSmQ+AOz75oPuX4l9BCdDMLPWtfG2zl4JpVpVeNQF2p+fvLuZ2AJxtOvtecxg2/mc9kVVGZ44xf+Pkc8Zy4BpTOZJ9/PHHPPLII0ybNo1+/frx2muvMWnSJBYsWEBKSkqtx8TFxbFgwQLfc6MJ/VyAZ5h3ud2k3KGAUkRERIJLFZQiIiJHsZmflrFlt4tU8gFwx9cetABYLNUXySnDVVl5OMS92m8/o7Q4OI0NEqfT8z5SvCFtYmqNfTZbWvN+5GgSW8Q1ZtMkjLzyyiuMHz+e888/n86dOzNt2jSio6OZO3fuAY8xDIO0tDTfn9TUmt9b4cw7D2W5PcQNERERkSOeKihFRESOYp8u8SwEk2bmAeBOOEQFpRENJlgqynBUVh4ON/2HuVqKcnE1a97wjQ0SR+UaP22ii6DUs1BQdEnNSrf/Gxvb5CrgpGHY7XbWrFnDtdde69tmsVgYPnw4y5cvP+BxpaWlnHTSSbjdbnr27Mmtt95Kly5dAr5+sL/tvOff/zpR3pW87WZTKoo+ohyobyQ8qH/Cl/omvKl/wlcw+qau51JAKSIicpSzmQ6SKQTAdZAh3hYDimgGgFFSiMNlYjFdDHWv8t+vMA9Xy3bBa3ADc1RWUCa5q1Yyj3VV3UllpFjYneOmXxdbSNonoZeXl4fL5aoxlDslJYVNmzbVekyHDh14+OGH6datG0VFRbz88stcfPHFzJ8/n5YtWwZ0/ZSUxgn8979O82algIvI6GhSU2MbpQ1Su8b6HpD6Uf+EL/VNeFP/hK9Q9I0CShERkaOQ2236KiAzzX0AOGwxmM3iD3yQAbuMNACsuXtxOKE1+4ijDLs1mpXuDhxjrsVamIsj2G+gAW3d4ymhTHDlA56VzE/rGsVXyyo4pnskE0bHUFJuktRcM+NI3Q0YMIABAwb4PR87diyzZ8/m5ptvDuhcOTlFmEGcBtIwPL+I7H8dq+H52cjKKSU72xW8BsgBHahvJDyof8KX+ia8qX/CVzD6xnvOQ1FAKSIicpTZvNvJXc8XYYuASBu0K98NQEli5kHHYFiqB5Q5e7A7TdqaewDIa5bBvuJkz/Dvwrzgv4kG9O43nhXJmzkqKyjjEmkea+G5WxN8Q7ojbRqDdDRLSkrCarWSk5Pjtz0nJ6fO80rabDZ69OjBtm3bAr6+adIov8Dtf52oyu/7sgpTv0CGWGN9D0j9qH/Cl/omvKl/wlco+kalACIiIkeZu54vAsDhBLsDuplbAMiPb33IY7cbnqGp1qxdZO8upE1lQFkYl0EuCQBYippWQAkQZVYQhyeodMcnAk1vxWUJnsjISHr16sWiRYt829xuN4sWLfKrkjwYl8vFH3/8QVpaWrCa2eCifXNQhrghIiIicsRTQCkiInKUG2SuBSCmT5+D7mcxYK+RwjbSMUw3n81e6Qsoi+MzyDc8QzeMksJ6t8XuMPl0STnZBe56nyNQ6UkW4ikBwLRaMaM0157UdNVVVzFnzhzef/99Nm7cyP33309ZWRnjxo0DYOrUqfzjH//w7f/ss8+ycOFCtm/fzpo1a7jjjjvYtWsXF154YajeQsCqVvFWeYuIiIgEl4Z4i4iIHMVspoMO5k4AIrr15GCxoLeg8DdLR9q699LLvZGOlceWJmaSXxnyWQ4joJz1eRkLFlfw/a92Hrz6IPNhNiC3CXFmKQBmdDMtKSm1Gjt2LLm5uUyfPp2srCx69OjBSy+95BvivXv3biyWqs/+CwsL+etf/0pWVhYJCQn06tWL2bNn07lz51C9hYBFRXr+VkApIiIiwaaAUkRE5CjW2txLBG6ckbG4E1IOuq93yPNyozuns4jT3ItIqlz9u6hFRwqMLcDhBZQLFlcAsH5H4y3I4XSZNKMMAHe0qiflwCZMmMCECRNqfW3mzJl+z++55x7uueeexmhW0PiGeDsUUIqIiEhwaYi3iIjIUSrdzOZ895cAlKe0PmTloPfl7y0DcWIhjXxPuBmbgDM+lTw8Q7wPJ6CsrriscYZ5O10QVxlQmtHNGuWaIk1B1RDvEDdEREREjngKKEVERI4y3mGbf3f+h7HuHwCwp2Ye8jhr5V1DuRHFZqNqf1ebTthsFgq8c1CWFoE78HDRrFwqcIxrIU84/om7qDjgc9SHw2nSzPQGlKqgFPHyruKtId4iIiISbAooRUREjjKZqVYiTTtdzO2+bY6UQweUEdaqx+uM9r7HzrZdibBCAXEAGG43RllJwO166HVPIHmH63UGmeuIX/5FwOeoD4cL4qicgzJGAaWIV3SU5+8KBZQiIiISZAooRUREjjKmiW9xGy9nh26HPC7CWjUEfLVRtdBHRc9jiIwwcBoRFBMD1G+Y96pNTqLNct9zw15+kL0bhmmauFxoDkqRWmgVbxEREWksCihFRESOMm4TWpALwC5SucI2DeoQUFqr3TV8axnEAstw5iWPw9WyHe7K4dn5hzkPZSszy/fYcDrrdY5AVDbbfxVvEQE0xFtEREQajwJKERGRo4xpQopZAMB6oy3bjQxsEYc+zlptiLfdiOTxiCv5LuNMz/PKLLHAqBzmXY+Ask0LC5lUBZSWsqKAzxEod2Xu0gzNQSmyv6pVvEPcEBERETniKaAUERE5yrhNk+TKgDLHSATAOMQK3gARlpr7xEZ5tvVq70k4q1byLgi4XdGRBulmju+5tTzweSwD5q2g1CreIjVoiLeIiIg0FgWUIiIiRxnThBTyAcgxEup8XPVFcrx2ZntW67ZFGLRMtrDPSAHAmrsv4Ha53PiCUwBLWfBX8fZVUJqag1Jkf9GRnr8VUIqIiEiwKaAUERE5ypgmJJie6kTvytt14XTX3LYr2+V7bLXCTiPN8zh3T8Dtcrkhmaqh4dby4AeUXtHYATAjoxrtmiLhzltBaXeA262QUkRERIJHAaWIiMhRxjQhtnJIcwkxjOwfWafjlq6119jWPLZq2HeE1WCXN6DMqUdA6TL9Kigj9gso9+W5ePvLMgpLaklK68mbuURVBpTY6va1EDkaeANK0DyUIiIiElx1mBJfREREjiRuE2IpB6DUiCaxjncDXdpEsH2fJ8izRYDDCdeeXTVnY4QV9pEMgKUgN+B2udyQVL2Ccr8h3rc+W4jDCYYB40fFBHz+Wu0XUJoKKEV8bBGenzfT9Azzjok69Fy1IiIiIvWhgFJEROQoY5pVcy6WEkNqRN1Ch8tOjSE7382xvSI5eVAUJWVumsVUDcaIsMLuyjktLaVF4HRAhK3O7XK6INmsCigtLgc4KsDmGXbtcri4zvUeRUtawqiz63zeg/FWUEaanvIw06Yh3iJehmEQHQllFZqHUkRERIJLAaWIiMhRxqxWQVlCNMnN6zbjS/NYC/dObO57Xj2cBM8Q70LicGDFhgtLcT7uxLS6t8vlJAH/qklLaTHuhChenl/KCe5fGO/+HAogyz4aIqPrfO5DiVYFpUitoiMNyipMKhRQioiISBBpDkoREZGjjNtt+gLKnj2ac/rQhqkajLIZYBjkEg+ApTA/oOPjXEVYMHFhUIBn6LhRVozLZfLpkgoGmut8+9o2r22QNrtNT+gS6Z2DMkIBpUh13nkoy2tOQSsiIiLSYOpdQVlWVsby5cv55Zdf2LNnD3l5eURHR5OcnEzXrl0ZMmQI7dq1a8i2ioiISAOwuB1E4gTgyvNSMW0NM69cTGXOmWMkkm7mYSkKbB7KeKdneHc+zSkhhgRKsJQWU1hqgmky2L3Gt69txwYc3QYcdptN3xyUlUO8tYq3iJ8omzegVAWliIiIBE/AAeXy5cuZPXs2n376KRUVFZhm7TcrhmHQqVMnLr74Ys4991zi4uIOu7EiIiJy+KLd5b7HZlQDDpOurLTKpXIeygArKONdnv1zjQTs2MD0VFA6nCat2Uc6VYFnxM6NdTpncakbhwuSDjCM3TTBYrqw4fI8VwWliB/vz3WFQwGliIiIBE+dA8r169fz+OOPs3DhQqxWK0OGDKF///707t2b1NRUEhISKC8vp6CggM2bN7NixQoWL17Mgw8+yLPPPsuf//xnLr30UiIiNO2liIhIKMVUBpQuWxRYrA12Xm+QkWMkgAmWojxM08QwDl2hmVPgJs7uqaDMJR535Sw0ltJi7A7o4d7kaTMGVkwidmyqU5smPVYAwCt3JxIbXbMdpllVPQmag1Jkf9GVPxKqoBQREZFgqnNaeM4559CqVSvuvfdexo4dS3Jy8gH3HTJkCBdddBEAS5YsYc6cOTz22GOUlJRw/fXXH36rRUREpN6i3Z4VvN2RsQ163lapnrAzx0gEoGh3Ltc8VsDE02MY2f/AQ6dN0+TP/yzgEiqHeFsSMEy3r4LS7jTpam4D4BPLCMa6F2ItysNSmIs7/sD3I05XVaCyL99F+5Y1b3tMIIpqk+sFsOq4yNEgyjsHZYUCShEREQmeOgeU06ZN47zzzgu4AnLIkCEMGTKEyZMns2fPnoAbKCIiIg0rxqysoIyMadDzdm/ruUfIqRzivXNDFsUWk3+/X1ojoHS6TJb97qBHuwhf5WWy6al2LLDGY3V6qhotpcXMmF/KZHMrAKuMzvS3baK1YycROzdhP0hAuXqT0/fYZq29irN6BaUZYQOL1g8Uqc63SI7jEDuKiIiIHIY6p40XXnjhYV2offv2tG/f/rDOISIiIocvxltBGdWwAWXrFlZsEbDX7QkN09w5cIC874Pvy5nztScovf5cTyVncmUFZYE1gQiXp42luUVs2u6gi7kdgD8s7dhka+cJKHdsxN7jmAO256l3ijFMN53MHZiu7kDN4eyegNJTQanh3SI1+eag1BBvERERCSKVCYiIiBxlLHbvEO+GDSgBLjwphj1GKgAt3DlVy2Tv59tfq4ZV/2deKUPdKznJ/TMAedZkCmnmebynkG7mFmKooJhottOSTRHtgUMvlFNWAZNc83jB+SCtP3+p1n1ME6JMb0CpFbxF9hdVOeuB5qAUERGRYKr3ijXl5eX89NNPrFq1iry8PACSk5Pp06cPxx57LFFRuskXEREJN0vX2mlG5RDvBq6gBIiMgCySAIjGTgLFFNC8xn72/VYEPtP1ve/x9uh2pJd5xpM6i4roUjn/5GqjM27DwuKy9lwJ2Db/hlFRhnmA95GWaOHSfQs8j3/7hixuqLGPaUKkd4i3KihFavAN8VZAKSIiIkFUr4Dyvffe48knn/QFk2a16gjDMEhJSeH222/n3HPPbZBGioiISMP48Mdy+uOpoDSjGnaRHIDICAOHYSObRFLJp6WZQ4FRM6CsqJzPboLrI/q613OMuRaAl6znkheZSoyxG4BYZwltzL0A7LBmAPA77dhOOm3se4la+SPlg0+utS0Oh8vveW1hpolJtHeRHAWUIjVUBZQhboiIiIgc0QIOKN944w0eeughTNPkmGOOYciQIbRo0QKAffv2sWTJEn7++WfuvvtuysvLufjiixu80SIiIlI/JeUmzSoXybHERDf4+W2Vw0H3GCmkmvlMc/6H6RGXACf57VdabpJu5vB/rv/5bX/HciotLVBUOcS7mbuENqZnkb0OA9rCr4Bh8In1OK5xvUfUL9/WGlC63SaWkiK/bdasXThbd/Lb5j8HpUZ/iOwvSnNQioiISCMIKKDct28fTzzxBImJiTz99NMMGTKk1v2WLFnCjTfeyGOPPcYpp5xCampqgzRWREREDs+OfW7iKQbAElezsvFw2SI8YcZeI4Xe5kZakMeDzn+TVS2gLC51AzDCvdy3rYxI3rCegcOwYRgGRYanurOZu4Q2eALK1r3aeAJK4EvLEK5xvYdt6zosBTm4E1L82rF1r4tUd67fNkvePqgloIw0NcRb5ECqVvFWQCkiIiLBE9AiOfPmzcNut/PQQw8dMJwEGDJkCI888ghlZWV88MEHh91IERERaTiJpqey0Gye0ODnjqz86HMP/oEh9grfQ+/w7qHuVQA8Zx3PWbbpvGUdA4DFwLdITgwVZJADgDWzNeeM8FR9ZhnJrDI6YZgmUasW1WjHXc8X0YI8v23WvH019jNNfEO8zQgFlCL7i678sdAclCIiIhJMAQWUS5cupW3btowaNeqQ+44aNYp27drx008/1btxIiIi0vASKyso3XENH1BWVVAm+223lBT4Hlc4TDBNepqbAPjF6I7bqLolMQwoIQYTw7fNFd0Ms1k8ZRVVIcn3loEARC3/DtzuGm1JM/0rKK25tQSUVC2SozkoRWqK0iI5IiIi0ggCCijXr1/PgAED6rz/gAED2LBhQ8CNEhERkeBJ8FZQNotv8HNHVgaUKy1dcVULGC3Fhb7H5XaTeEqIxVNVucNI9zuHYYBpWHBENvNtc6a2AsPAqDolX1sGY7dGYdu1mZjvao7YaGF6KihL8FRdHqiCsmoOSgWUIvuL9s1BGeKGiIiIyBEtoICyoKAgoPkk09LSyM/PD7RNIiIiEiTpSRYS8QSU7iAElLbKId7bjAwutz1ELp5rWEqqAsoKh0mKmQ9APnE4DJvfOSyVIWR5VNUcma60VgBEWKv2yzES+Sz1bACif/6qRlvS8FRQrjS6eM6bl1VjH7cCSpGDilYFpYiIiDSCgALKsrIyoqPrvuJnVFQU5eXlATdKREREgsN0uUigBAhWQFlV4rjHSGWj0RoAo9oQb4cTUskHwNU8CYAe7SJoHmtw9+VxWCtDyJXFVZWV7haZAJw9wv8+ZEbusQBYc/aC3XPPYZqeIMVbQbnS4gkorXn7PCWT1ZkQpUVyRA5Ic1CKiIhIYwgooBQREZGmZ8c+F//9oIR9eS5iXZ75J00MzNi4Br9WpH8xJPmGpwqyegWly42vgjIuI5W3pyVx//8158WpCfTvbGPDDhcAm43MqmMqKygT4yy8PS0Ja+UdTIHRnFziMTCJ2LsdqJqO0jsH5W9GJ9wYGE4HlqJ8v/ZVr6DEFnVY713kSBRl83zo4HCC262QUkRERIIjItADvvnmG7Kzs+u076pVqwJukIiIiDSsOV+XEbX6JwqX/kTHmBMAcMbEgcV6iCMDF1mtghIgn5oBpdttkoqnotIdn+TbblROMOmdZ3KRpS8XuxfgJAJH+55+533m5gT+/E/POTYbrUg2C7Hu24GzTRecLrCYbt81dhupFEQmk2TPwZK3z++aoCHeIgfjHeINUG6H2LoPphIRERGps4ADytWrV7N69eo6728YxqF3EhERkaBZtqaMj5wziMRJ55KtADhjGn54N1TNQel1oArK1MoKSne8/2rfADee34x/zSlhnaUDf7L9jcT4CP7SrLnfPikJVYNAthkZDDLXEbFvJxWA02WSSCFW3LgxyCWeXFsqSfYcrLn7cLbr5jvWbZpEVa7ibUYqeRHZny0CLBZPZXK53SQ2Wvf2IiIi0vACCihff/31YLVDREREgmDTLic9zM1E4gQgrTIYdMUGJ6CMsPqHF3neCsqCXN+26kO8awsoB3WrGie+3cggJf3gtytbjQwArFk7AHC6IMX0VE9WRMfjdlvZ7k6hE2DN328lbxOizMoKyghVUIrszzAMomxQVqF5KEVERCR4AgoohwwZEqx2iIiISAMzTZO7/1vERebGGq85myUE5ZrR+2V8242WAFizdvq2ud1Vi+TsP9waPCt1G0bVejYdW9V+u3LigEi+WW5nm/caez3XcLogGU/FZllUIpTBZnsqJwKWXP+A0m1CtHeId6QCSpHaREcalFWYVDgUUIqIiEhwaJEcERGRI9TObM9qMX3d62u85kxIC8o1DcPgpgub+Z5v81Y3FuRglJcC4HKbB62gNAzDb6h4cnzttytXnxVLq1SLr4IyIncPO3cUU+EwSa6soDQSPQHoHlI87cjbV+M8WiRH5OC881CqglJERESCpcECSofDwWuvvcb111/P9ddfz4wZM6ioqGio04uIiEiAnE4Tw3TTu5YKSldicAJK8FRAehUZzdgX0QIA26Y1FJW6+c/cIpIqKxxdtVRQAtgdVY9jomqf8y7CanDu8dHkEY+z8pamz3OTKLebJFcukBOZUhlQGqkAWA9WQamAUqRWUb6AMsQNERERkSNWQAHlvHnzOPHEE1m0aJHfdrfbzdVXX82jjz7K119/zddff82TTz7JhAkTsNt1JyMiIhIMbrfJqk0OSstrr2oqLjNJoYDmlOLCwmzLaN9rzsQWQWuXuV9ztkR1AMCavZvHZhUzyFyLFROHYcOsw1Bzc/8TVhMdaYBhsMFo67kGbkpzi3xzUBKfCHhW8gawFOSwYl05brfnnB/9UF61ireGeIvUyltBWaEKShEREQmSgALK77//ntLS0hpzUX7wwQf89NNPtGjRgocffpinnnqK/v37s3r1ambPnt2gDRYRERGPT36q4MHXinnkjaJaX1++3kEr01MxuIcUXrGew1zLKD6wjKSiY6+gtcuy391FdoS3enEv63e4GO/6DIAdMe1r7lyL0ooDhyLeyq4nrZf7ti35fodviLe7uaeCMocE7ERguF2UzJyB8+kHyd2Zy6I1jqpFclRBKVIr79yyGuItIiIiwRJQQPnbb78xePBgrFar3/b//e9/GIbBk08+ybhx4zj99NN5+eWXSUhI4LPPPmvQBouIiIjHNys8wdof2121vm4YkGlmAbA3ogUOw8ZzERfzdMRlWKOCF8b172yjaxsrLZM9txlrij3zP1py92ExXfQxNwDwQYsL63S+wd0PXNnorezaZGnDaqMTACn2LN8iOd5FeEzDwl48812e6/6GzH0riV78ueccGuItclBRNs1BKSIiIsEVUECZk5NDhw4d/La53W5WrFhBy5YtGTx4sG97TEwMJ554IuvX15yYX0RERA7P0nV2tu11EW8WcYrrJywFOTX2cTihVWVAWRaf7vdabHTt8zo2BFuEwQN/iufKsbEA7DM8waArN4dM9hGJkzKi+N3S4WCnAaBtuvWAi+QAxFTLLr3DuNtHZFdVUMYl+l7fa6T4HRu9ZzNQtUiOadMQb5HaVA3xDnFDRERE5IgVUEBZWlpaYx6ojRs3UlZWxsCBA2vsn56eTklJyeG1UERERPzsynbx5FslWEw3zzge5x7XyyRNvwNLYa7ffnaHSSaeId75McGbc/JAjMoMNIdEAGzFuXR07wRgi5FBfumhQ9LmsQffp/oCOrvwLPyTULqHFni+Fu7EqlByl+G/MFB8ziYwTV8FJZGqoBSpjVbxFhERkWALKKBMTk5m69atfttWrFgBQO/evWvsX15eTnx8fP1bJyIiIjX8+33Ph3/XuObShr0AWEqLiPrlW7/9qldQ5sf4V1A2BktldphteBbCsVWU0N3cAsAmozWxB1idu7rUhIPfqniDE6iqoGxXuI4I3LgsEbjjk32vf2kZip0I3/OY0jy6m5ux4saJBXdMXJ3el8jRRnNQioiISLAFFFD279+fb7/9lo0bNwKeVTXnzZuHYRgMGzasxv7r168nPb3xfyESERE5kjmckGQWcr77C7/tkb8v93tud7h9i+TkRVVVUPbtFEFj8FZQFhNLOTYABpurAdgTnck5I6IPeOxdE+IY2tPGhNExB71Gs+jqAaWnQrKF0xPKFiW0BouVuBjPPqssXbjM9jAX2x5hs9EKgPPcXwOw0WgDGuItUivvYlQVDgWUIiIiEhwBBZRXXHEFTqeTiy++mMmTJ3PuueeybNkyBg4cSPfu3f32LSkpYdmyZfTt27dBGywiInK0a5tupbe5ASuesOBK2zQAbNt+xyirmlolsqKIOMoxMciJrBrefNvFjVMp6A0oMQy2Gy0B6GjuAmDcJd0Y1vvAgeCALjZuvSiO+GYHv1WxWg16dfAErt4KSq/S5DYA/PvWBN+2HCORfUYKvxvtADjVvRiA9UbbOr4rkaOPhniLiIhIsAUUUA4aNIiHHnoIt9vNF198we+//06vXr144oknauw7b948KioqOP744wNq0JtvvslZZ53FwIEDGThwIBdddBHffls1ZK2iooJp06YxdOhQBgwYwI033kh2drbfOXbt2sU111xDv379GDZsGI899hhOpzOgdoiIiIQru9OknbkbgE8tw9hmZLCVlhhuN7YNK337JZR5qifLYpOxGzbf9urDooPJUu0y71pO8XvNldG+wa6T0MxzoWwSWWj0923P7j4C8FR/nTrYf37JPyoDSl97Uls2WHtEjjTRvlW8Q9wQEREROWIFFFACjBs3jh9++IE5c+bw+eefM3fuXFq1alVjv5EjRzJv3jxOOOGEgM7fsmVLbr/9dt577z3mzp3Lscceyw033OBbDfzhhx/m66+/5qmnnmLmzJns27ePyZMn+453uVxce+21OBwOZs+ezaOPPsr777/P9OnTA32rIiIiYcnuwBdQbjUyAFhu8YxksG1f79svscwzP2VZ88ZfIAcgMqIqofzcOozlRlcANif1wWzWvMGu4wsfDYO/2f7MKbb/cL7tSUo7VI3isFn9j/ndaO/3fPhJ/oGlSG1mzZrFqFGj6NOnDxdeeCErV6489EHA/Pnz6datG3/+85+D3MLgiI7yruKtCkoREREJjoADSoDo6Gj69u1LmzZtDrhP69at6d69O5GRgc3nNGrUKEaOHEn79u3p0KEDt9xyC7GxsaxYsYKioiLmzp3LXXfdxbBhw+jduzcPP/wwy5cv9y3Ws3DhQjZs2MATTzxBjx49GDlyJDfddBOzZs3CbtfHviIi0vTZnSZppmeV6t14hjVvMDz/J0fs3gLAwpV2orI9K2aXJaRjMRqnarK6SJv/NR+O+BMvWMexsM9VDXqdnu1tTLmgme+527CSZ8T75s0DiNgvoNxotMZZ7TbI2jKjQdskobV7924WLVpEWVmZb5vb7eaFF17g4osv5sorr+Sbb74J6Jwff/wxjzzyCDfccAPvv/8+3bt3Z9KkSeTk5Bz0uB07dvDYY49xzDHH1OethAUtkiMiIiLB1jiz5NeTy+ViwYIFlJaWMmDAAFavXo3D4WD48OG+fTp16kSrVq1YsWIF/fv3Z8WKFXTt2pXU1Kp5qEaMGMH999/Phg0b6NmzZ0BtCObvc95zh+B3RgmQ+qrpUF81Heqr+nM4TZLMQgDyjHigcpEXIGLPNkzT5Jm5JbxorgKgqFV3rFVTUwb8Na9vX0XZ/J/nGInMtp7OlckxDd7vLZJqfuYaE2n4rhNh9b+g3YgkhwTSyQPAnZre5L8X9++npv5+DsfTTz/N119/zcKFC33b/vOf//DMM8/4ni9dupS33nqrzvOlv/LKK4wfP57zzz8fgGnTpvHNN98wd+5crrnmmlqPcblc3H777dx4440sW7aMwsLCer2fYPflob5nqs9BeTR/X4WCfp7Dm/onfKlvwpv6J3wFo2/qeq6AAsqJEyce9HWLxULz5s3p1q0bZ511Fu3a1W+41O+//87FF19MRUUFsbGxPPfcc3Tu3Jm1a9dis9mIj4/32z8lJYWsLM+KndnZ2X7hJOB77t0nECkpDTcELZTXkIahvmo61FdNh/oqcG6zmGT8A8qtlYvQWIoLiLW4STNz6WTuwIUFY+AI4n4xAM9IgtTU+n3NA+6rCCdQM5BpmRZLamqzmvsfhuziCqDIb1u71s2JifYElztyymock2fEk256AsrUjNQarzdV+pmCX375hWHDhmGzeVJy0zSZNWsWHTt25OWXXyYrK4urrrqKGTNm8PTTTx/yfHa7nTVr1nDttdf6tlksFoYPH87y5csPeNxzzz1HSkoKF154IcuWLav3+2msPj3QddKLK4Bi7C6j3v9+yOHRz3V4U/+EL/VNeFP/hK9Q9E1AAeWSJUvqtN/nn3/O888/z9SpUw8ZatamQ4cOzJs3j6KiIj799FPuvPNO3njjjYDP0xBycoowgzSaxTA8nR7Ma0jDUF81HeqrpkN9VX/O0lKaUQ7Apedn8O8FUFQazR5SaEkOWavW0dn0fCi3xWhFodPKGccafL/C4JRjosjOLjrY6Wuob18Vl7lr3Z6bV0Z2du2v1VdhYc3F8IqLiikp9nxkW1DoqPH689YLecz5NOtaHEtmgF+TcLR/P3mfH41ycnL85khfu3Ytubm5TJ48mZYtW9KyZUtOOeWUOt/b5uXl4XK5SElJ8duekpLCpk2baj3m559/5t1332XevHn1fh9ewf538lA/4xWlLgDKyt0B//shh0f/V4Y39U/4Ut+EN/VP+ApG39T1njSggPLLL7886Otut5u8vDyWL1/OjBkzePTRR+nRoweDBw8O5DJERkb6qi979+7NqlWreP311xkzZgwOh4PCwkK/KsqcnBzS0tIAT7Xk/hOWe1f59u4TCNMk6D8wjXENaRjqq6ZDfdV0qK8CF2P3VCW6rDYG9k2g6L18wFNF2dLMwdy1g/amZ168zUYrmrkhNcHKC3ckYLEY9f56B9pXURG1j+fo0S6iwft8/yvFRHm2eq/jrCUPXWnpynm2fzKqW3OuOIK+B/Uz5bknNat9EZYsWYJhGBx77LG+benp6b57xIZWXFzM1KlTeeCBB0hOTj7s8zVWnx7oOlG2qiHeR/v3Vqjo5zq8qX/Cl/omvKl/wlco+iagRXIyMzMP+qdNmzb07duXK664gjlz5hATE8PMmTMPu5Futxu73U7v3r2x2WwsWrTI99qmTZvYtWsX/fv3B6B///788ccffhOW//jjj8TFxdG5c+fDbouIiEioNXMUAOCMTQDD4O7L4wDYbGQCELF9Ax1MzwI5Zsu2dMzwrBBjsTTuRD8REQaPXdecpOb+122Vaj3AEfW3/9w2j13nPx1M2/Sqa3ZuXfW43IjCZqvXmoESxlq1auX3gfUXX3xBWloaHTt29G3LysqqMW3QgSQlJWG1WmssiJOTk1NjaiGA7du3s3PnTq6//np69uxJz549mTdvHl999RU9e/Zk27Zt9XxnoRFVuUiOwwkul36TFBERkYYXtEVyWrZsycknn+wXJtbFP/7xD0444QQyMjIoKSnho48+YsmSJcyYMYPmzZtz/vnn8+ijj5KQkEBcXBwPPvggAwYM8AWUI0aMoHPnzkydOpU77riDrKwsnnrqKS677LKAVxQXEREJR80dngpKZ1wiAP0722iZbGFFdjcudn9Gytbl9HB7/s8bcnJH7I0cTFbXPiOCrm0iWPybZ4j1Q1cHZ8jx/uFrerJ/CDphdAxx0QbH94skqbmFqx8v8L0Wud9iPtL0jR49mueff54pU6YQGRnJsmXLuOyyy/z22bhxI61bt67T+SIjI+nVqxeLFi3ilFNOATwfoC9atIgJEybU2L9jx458+OGHftueeuopSkpKuPfee2nZsmU931loeBfJAahwQGzDf8YgIiIiR7mgruLdqlUr8vLyAjomJyeHO++8k3379vkW3JkxYwbHHXccAPfccw8Wi4UpU6Zgt9sZMWIE9913n+94q9XK888/z/33389FF11ETEwM5513HlOmTGnQ9yYiIhIKbrdJc6cnXHNXBpQAiXEWlud0pzQ2hWalOXiXoHG2bNv4jdxPpK0q3OjcOji3HtUrKCePi63xelyMhQmn1dwOEHmAoejSdE2aNIkffviBzz77DIBu3bpx4403+l7fuXMnK1euPODq27W56qqruPPOO+nduzd9+/bltddeo6ysjHHjxgEwdepU0tPTue2224iKiqJr165+x3urNfff3hREWMFqAZfbM8w7Nlo/MyIiItKwghpQFhcXExMTE9AxDz/88EFfj4qK4r777vMLJfeXmZnJiy++GNB1RUREmoINO10kuD0VlJaERN/2xOYGDsPGsvSTOH7zuwA4I6JwJwY+/3JDa4woo3oBZXJ8YEO2LRrhfcSJi4tjzpw5/PHHHwB06tQJq9W/7O+ZZ56hT58+dT7n2LFjyc3NZfr06WRlZdGjRw9eeukl3xDv3bt3YzlCv5kMwyAq0qC03KTcriHeIiIi0vCCGlD++OOPfnP9iIiIyOHZtMtJmlk5OiGhavGNZpUVTa9v78HxlduKEluHRfrWGBNsVw8ooyIPHYlecGI0737jWQl9xXoHZw6PDlbTJIQOVK3onT89UBMmTKh1SDdwyHnXH3300YCvF06iI6G0HAWUIiIiEhRB+a2loKCAe++9l82bNzNmzJhgXEJEROSoVFpu0srMAsCV3MK3PTbKE8ptNFpTUDnAO6fNgMZvYC3cjZBQVh/iHR976ICyV4eqz2hPOSYqGE2SECouLmb79u04HA6/7R9//DG33XYb9957L7/99luIWtc0eeehVEApIiIiwRBQBeXEiRMP+rppmuTn57N582acTif9+/evMSG5iIiI1F9ZRbWAMqVqoY3oyoASw+AvEZMZYK6la/8xJISikfs5Y1g0P6xyMLRn8FajqaiWQ8U3O/Tnr05X1eMhPbRKzpHmiSee4H//+x8//vgjNpunf998800eeOABzMrAfP78+cydO5dOnTqFsqlNRlTlXLIV9hA3RERERI5IAQWUS5YsqdN+GRkZnHvuuVx33XW+m0IRERE5fPYyO2l4hni7ktN9270VlABrLJ1YQyceblb7ojCNrVNmBC/ckUDzOlQ21leLxKpQMroOQ7zbtKiaj3D/FcCl6Vu6dCnDhw/3mwv9xRdfJD09nSeffJLs7GzuvPNOZsyYccj5z8XDV0HpUAWliIiINLyAAsovv/zyoK9bLBaaN29OXFzcYTVKRESkqTJNkw07XWSmWoOy0q2Rm4UFE4c1CjOuqj4yIa5m1WBd5mJsLLW1ryFFRxm8ODUBWx1X5E5qbuGfk+O1GvERKisri+OPP973fOPGjezevZs77riDY445BoBPP/2Un3/+OVRNbHKiIz1/a4i3iIiIBENAAWV9JhMXERE5miz+zcG/5pTQKtXCv25s2AHWLpeJa8c2AEqTMv0mXuzXqeZ/6VFH2SCGugztri4zzXronaRJstvtfqN4lixZgmEYHHfccb5tbdq04auvvgpF85ok7wce5RUKKEVERKThhX5pTxERkSPIwlWeCdp2Zbsb9LyLVtv5cY2dNuVbAYho39Hv9bhYC2/dl+i3zTtnnMjRpmXLlvz+++++59988w0JCQl0797dty0/P5/Y2PCYBqEp8A7xrnAcYkcRERGReqhzBeX8+fM544wz6n2h3bt3s2vXLgYNGlTvc4iIiIS7hp7OcNMuJ3f/t8j3/BHTU0Hpbt2h5rX3u3hd5mIUORIdf/zxvPnmmzz22GNERkby/fffc8455/jts3nzZjIyMkLUwqZHq3iLiIhIMNW5gvKOO+7grLPO4v3336ekpKTOF1i1ahV/+9vfGD16NIsXL65XI0VERJoKo4EzwafmeP7PtZkO+rr/oL/5BwDOzNpXHj5pQKTvsS2giVxEjhzXXnstGRkZvPLKK/z3v/8lJSWFm266yfd6Tk4Oy5cvZ/DgwSFsZdPirchWQCkiIiLBUOdfXd59910eeeQR7r77bv7+979z/PHH07dvX3r16kVqairx8fFUVFSQn5/P5s2bWblyJT/88ANbt24lLi6OKVOmcMUVVwTzvYiIiIRcQweU+SWeoeI3u2Yxxv0jAGWWGJwZ7Wvd///OiOXUwVFkpFgxGroxIk1EWloa8+fPZ9GiRQAMHjzYbxHHvLw87rjjDkaMGBGqJjY53kVyKhRQioiISBDUOaDs2bMnM2fOZNGiRbz11lt89dVXfPbZZwf85cc0Tdq0acNtt93GhRdeSEJCwy4UICIiEo4aeog3JlhMNye5l/o2rUwdTkdr7Qu8RNoMOmWqdFIkOjqak046qdbXOnfuTOfOnRu5RU2bhniLiIhIMAX8G8ywYcMYNmwYhYWFLF26lF9++YU9e/aQn59PdHQ0ycnJdO3alcGDB/tNRC4iInI0MAyDvu4/aGlmA1VzN2fnu9m828kxXSwYVmudSy1NoJu5hWg8K1O8aD2PiIFn0vHgh4lIpb1797J27VqKi4uJi4ujR48epKenh7pZTY5vFW97iBsiIiIiR6R6l1jEx8dz8sknc/LJJzdke0RERJo0wzR5yvkkACVflFJ6yoUA3P1CIQnFuznR8RC57QYSdd2tfse9/10ZcTEWTh0c5bc9wmpwqvsnAD63DOUt6xj+3T8OETm4rVu3cv/99/PTTz/VeG3YsGHcd999tGvXLgQta5pUQSkiIiLBpDFgIiIiDaiZPd/3OGrlQl9AWVhicoXra2KpIHbrInIKcnAnpFBuN9md42L2l+UANQLKKEvV8O7PLccCVXPBiUjtdu/ezaWXXkpOTg4dO3bkmGOOoUWLFmRlZfHzzz/z448/ctlll/HOO+9oJe868s1B6VBAKSIiIg1PAaWIiEgDcLtN/jajiORtm3zbIrJ2YcnPxpWQAqbJSPfPVa/t2kJ582SueCi/xnks1SayzDT2kUAJ5dj4xfBMneKtZBKR2j377LPk5ORw3333cfHFF9eYM3327Nncf//9PPfcczz44IMhamXTogpKERERCSZLqBsgIiJyJCguM1m/w8W5rq/9tts2r8XhhAyySabIt92atZPS8pq/6Lv329TWvRuArUYr3IZnYRyrVQGlyMEsXLiQk046iUsuuaTWBR0vvvhiTjrpJL777rsQtK5pilJAKSIiIkGkgFJERKQBlFaYRJvlHGOuAWCx0QuADd+sZPaXZXR3b/bb35q1k6Iyzy/6KWY+I10/c5FrAW6nm7VbHTz9TjF5RW46VmwEYIvRqhHfjUjTlpOTQ9euXQ+6T9euXcnNzW2kFjV93grKCi2SIyIiIkGgId4iIiIN4NPFFXQ2t2PFJJtEPrSOZKhzDe32/UKnbDjX/Q0ABTQjgRIisnZSVGqSbOYzy3EPkTgByF7dlvv/15Mks5Cvf/2Qq12fAdD62J4cb49keB9NQClyKMnJyWzYsOGg+2zYsIHk5ORGalHTpyHeIiIiEkyqoBQRETlMWfkuPv6pgq7mNgD+MNqyyuiCG4MUCn3hJMBnlmEAWLN2UVxmMtC9zhdOAuxZtZkI08kzjke51vUeFkwWG70o7nsCk89vxsCutkZ9byJN0YgRI/jqq6945513an393Xff5euvv+b4449v5JY1XdGV//Q4XeB0KaQUERGRhqUKShERkcPkHfLY1dwKwO+W9hQZzdhJGm3Y57fv55ZjudD9BZbSIl6es5vLzI1+r9v3ZdHD3EwrsgFYZXTiwYireSo1OvhvROQIMXnyZL7++mv+9re/8frrrzN48GBSUlLIyclh6dKlbNiwgcTERCZPnhzqpjYZUdUW57I7IMIawsaIiIjIEafeFZQnn3wyr7/++kH3mTVrFieffHJ9LyEiItIkuCpXtunq9gSUfxhtAfjR0h+ArbTkF6M78ywnssFowx5SADij7Cv6uv8A4FtjIABpjn0MdK8FYKnRk5siplJixBLfTIMeROqqVatWvPXWWwwePJj169fz5ptv8swzz/Dmm2+yfv16hgwZwltvvUVGRkaom9pkRFjBu96Q3aEKShEREWlY9a6g3LlzJ4WFhQfdp7CwkF27dtX3EiIiIk2CwwXRZjlt2QPAH0Y7AN6wjmW90YbFlj6UGLHYIgAnbDNa0tLMYaJ7vu8cX1sHM9L5C0nl++hnesLI7yyDwDC49aJmjf6eRJq69u3b8/rrr7N7927Wrl1LcXExcXFx9OjRg4yMDF544QV++OEHXnvttVA3tUkwDINIm6divEIBpYiIiDSwoA7xLioqIjJSk/mLiMiRzemETuYOLJi44pPIK08AoMSI5SvrUN9+fTvZWPa7g01GJkMqV/sGcGOwzOgBQJyzkP54PgBcaenM6MFRDO2p/0tF6isjI6PWSsnNmzezZMmSELSo6YqyGVTYTSocoW6JiIiIHGkCCiiXLl3q93znzp01tgG4XC727NnDhx9+SPv27Q+rgSIiIsFWbje5+vF8Euy53HdVc9I6tKjzcf+aU0yUzaC9uRsAV8t2sKX2/du3tLLsdwdzrafQ1txDprmPduzhbctplBix5BNHIsWAZ7Xv7bSku6lKJREJD1E2AzA1xFtEREQaXEAB5eWXX45ROfmMYRjMmzePefPm1bqvaZoYhsFtt9122I0UEREJpjc+LSXZns0Mx/0YL8DUxL8y8Pj2nDH84AvT/PWlIrbtdQFwnekZ3u1MyzxgQJme7Bm6nWMk8hebZ3GOOLOEYmIB2GS0ZqC5zvM4rjsxhsFxfVU9KSLhIbJyJW8N8RYREZGGFlBAecMNN2AYBqZp8txzzzF48GCGDh1aYz+LxUJCQgLHHnssnTp1arDGioiINLQdWS4+/9nOee5ficGzHPf4grks/bgX5jFjMCKjDnjstr0uIkwnHcydtPNWULbIJKGZQUFJzV/gE5pZsFrB5araVmxUzS/5lWUwA12egDL95ON4ZWii74NBEZFQi4zw/Htkd4a4ISIiInLECSigvPHGG32PlyxZwvnnn8+5557b0G0SERFpNG98WgpAX/d637YR5gpGuFaw+1M7EWeNP+jxk1zzuMj9me+5Ky2Tf9+awHe/2vnv/zznTmpu0CzGoGubCOKia4aXtghwOGGBZTgJFOPCyrjBwxVOikhY8Qzx1ireIiIi0vAs9T1w5syZCidFRKTJc5uAadLH3FDjteYblh38YNP0CyfBM8Q7IsLgxAFVQ7OnXhrHP25IILaWcBLgn5PjSWhm4DasvGUdwxzraAyrtV7vR0QkWDTEW0RERIKl3qt47969my1bttC/f39iYmIAcLvdvPTSS3z11VdER0dz5ZVXcuKJJzZUW0VERBpcSryFTPaRTCF2Irgu4l5ecD5ABG6iCvZRZppwgErGvkl5sM9/m9ksHgCLxeCSU2IoLnXTIePgYWPzWAuWen9kKCIAV199dUD7//HHH0FqyZHLW0FZYQ9xQ0REROSIU++A8umnn+brr79m4cKFvm3/+c9/eOaZZ3zPly5dyltvvUXfvn0Pr5UiIiJBEmUz6FM5vHud0Z4tlkzOsD3Dx44bsVUUYynKwx2f7HfMnlwXX/xcQUbe737bt/U7i5hqYea5x9dcZOfc46OZ932573l8M4PoSP853a49O7Yh3prIUeX7778P+BhNoxCYSA3xFhERkSCpd0D5yy+/MGzYMGw2z1gP0zSZNWsWHTt25OWXXyYrK4urrrqKGTNm8PTTTzdYg0VERBpShcPkmMrh3auNzgA4DBs7aEE79mDds61GQDnjo1JWbnDwD+ePgGdxm++NAZw+/Hg6HuJ6qQlVpZKv3J2I1eIJSUrKqn7hH9jN1gDvTOTo8uWXX4a6CUc87xBvu1MBpYiIiDSsegeUOTk5tGrVyvd87dq15ObmMnnyZFq2bEnLli055ZRTWLJkSYM0VEREJBgqHNDN3ALAaktn3/YtRivamXuI2LsdR9f+vu3ZBW5WbnRyknspA0xPBeX7llGssXTi7JhDB4sn9Ivkh1V2+naKIDa69uqtmEhVdYkEKjMzM9RNOOL5hng7QtwQEREROeLUO6B0u92YZtWnp0uWLMEwDI499ljftvT0dLKzsw+vhSIiIkHktDtobe4FYLNRFXBssrRmpOsXInZu8tv/fws9w7OvdP0PgLcto1lj6QRAVB0KH6MiDe7/v+YH3SdSBZQiEoa8/8ZpiLeIiIg0tHpPyd+qVStWrlzpe/7FF1+QlpZGx45Vg9uysrKIj48/vBaKiIgEUfOSfdhw4YyIYh9Jvu2/Gl0BiPxjBbhcvu1WCzQzS2lTuTrOG9axvte81UWHS/PiiUg4ivRVUCqgFBERkYZV74By9OjR/PLLL0yZMoXbb7+dZcuWMXr0aL99Nm7cSOvWrQ+7kSIiIsFQWOKmfNsOAIoTMrnxwuakJVpIiDNYbXSixBqHpayYiO3rfcc4nCYtzRwA8mhOiVG1oE30YQzN7tjq4Ct9i4iEmoZ4i4iISLDUO6CcNGkSffr04bPPPuOjjz6ia9eu3Hjjjb7Xd+7cycqVKxk6dGiDNFRERMLT79ucvDy/lNLypldR8/DMYnq7PQvkFCS24bg+kTx7SwLHdLPhNqysdrcDICJrp++Y4nKTVmQBsMdI8QsWbfWeOAVuvzgOgAFdDuMkIiJBFFn5z5OGeIuIiEhDq/dvQXFxccyZM4c//vgDgE6dOmG1+ld/PPPMM/Tp0+fwWigiImHtbzOKAHCb8KczYw+xd/jILXSzeZeT49wrANjbqj8dKl/zVgntNtLABGvuXt9xJWUm3UzP/MrxbTMY2T+STbvKgMMbmp2SYOG1exI1/6SIhK3IyipxBZQiIiLS0A67TKNr1661bs/MzNRqiiIiR5FVG5vWmL93vymjHbvJJAsHEbQdOcj3WlVAmQqApVpAmVvkJqMyoEzs0JKWyQ03NDs6SnNPikj40hBvERERCZYGGUe2bNky1q1bR3FxMXFxcXTv3p1BgwYd+kARETli7Ml1h7oJAVmy1sGZ7l8BcHftg7VZjO+1di09oeMuIw2oqqC0O0x27HOTYXqGeLuT0+nXOYLxo6Jpm645JEXkyKZVvEVERCRYDiug/OWXX7j77rvZtm0bAKZp+oa3tWvXjkceeYQBAwYcfitFRCRsbNntZM1mJ6cPjcJqbZoVf0WlbopKTYZXBpT2nsf4vd4q1TNF8248FZTWPM+K3dkFnhC2VWUFpSs5HcMwOH9kDCIiRzpbhFbxFhERkeCod0C5fv16Jk2aRFlZGccddxxDhw4lLS2NrKwsFi9ezA8//MD/s3ffcVZU5x/HPzO3bG/sssDSixTpWBBEsdfYSzRiC4kYaxKNpmgU+y/RWKKxa4ydWGLsJWKnqDRBkCa9bWXrbTPn98fs3t0rCyywDfm+Xy/de+eemTmzZ9m989znnGfixIlMmTKFfv36NWefRUSkDV37kLfmZEqSxWH7JLVxb3bOyx+FyDHlDDLfAxAZmJj13z3fR+cONsXF2QDYVRXgxKgKgWVcOuNV8XY65Ldqv0VE2lLdFO9IrI07IiIiIj86Ox2gfOCBB4hGozzyyCMcfPDBCa9ddNFFfPLJJ1xyySU88MAD3H333bvcURERaV/WFTlt3YWd9tGcMOPdedgYSnJ642blJrxu2xa3XZTBxNtjONj4cLEryqisySKXzQSIYWwfbmbuVs4gIvLjoyneIiIi0lLsnd1x5syZHH300VsEJ+scfPDBHH300cyYMWOnOyciIu2LMfU3pWkpW07vbvh6e9Y1z8dodz4A9vB9G22TFLQwls0KqwsA7rLFFJW5FNStP5mdBz6tOykie46giuSIiIhIC9npAGVFRQXdunXbZptu3bpRUVGxs6cQEZF2xt1KHRzbuJzofMTUf89t3Q7tgGkLIlz6t83876sw6wujjDSLADADhzfa3u+zCPhhjjXQ2/+Vr1myJhav4O106NQ6HRcRaSfqq3jvHh9GiYiIyO5jpwOU+fn5zJkzZ5tt5s6dS36+1ucSEfmxcBoEKI2BtYXeNO+LnJf5tfMcR8++F5z2OfX7nilVFG12eeT1arqFVpFJNbFgKrFuW18nOTloMcv2ApQnup8wb05RvIK31p8UkT1NsHaKt+OA4yhIKSIiIs1npwOUhx12GDNnzuSee+4hHA4nvBYOh7nvvvuYMWMGhx9++C53UkRE2odYg9ija2DlRodkE+ZU938AZFOJXVHaRr1rugPdOQBU9Ri8zWnargtzrf5U4xUDmhh7lS4og1JE9kx1GZSgQjkiIiLSvHa6SM4ll1zCRx99xMMPP8yLL77IsGHDyM3Npbi4mG+++YaSkhK6d+/OJZdc0pz9FRGRNhRrkDHjulBVY+hEMX7qUyut6grIzmuL7m1TShLUhGGQu5yz3XcACPcfyZYradarChmwUnjcdzKXOy9ygDuP9VZHQAFKkT3Rs88+y+OPP05hYSEDBw7k+uuvZ9iwYY22fe+993jooYdYtWoVsViMnj17cuGFF3LyySe3bqebUaDBnUM4akhJ2tZvUBEREZGm2+kMypycHF588UVOPvlkqqur+fjjj3nllVf4+OOPqaqq4tRTT+XFF18kOzu7GbsrIiJtqeEU7+qQIRQx5JmyhDZ2dWWL92P5uhifzA1vv2EDWWnen7yfOW/jw+VrayDOfuObtO/r9nhqSKIDFQw2ywFwNcVbZI/y1ltvcfvtt3PppZfy6quvMnDgQCZOnEhxcXGj7bOysvjVr37Fiy++yH//+19OPfVU/vjHP/Lpp5+2cs+bj2VZquQtIiIiLWKnMygBOnTowO23385NN93E8uXLqaysJD09nT59+hAIBJqrjyIi0k40XF5yXbFDWorVSICyvMX78YeHvQJsWWk2w/s17e9N51ybwuJIvDjO9AE/5czk4Db3OfHAJP77eZiY5WeONYAxZl78NSdHAUqRPcmTTz7JmWeeyWmnnQbA5MmT+eijj3j55Ze56KKLtmg/evTohOfnn38+//nPf/j666856KCDWqXPLSEYsAhHjSp5i4iISLPapQBlnUAgwIABA5rjUCIi0o41nOI9d2mMJWscTiFxzUmrquUzKOvc9WIl//pTTqOvRWOGd2eG6d/dT//ufiygv1lFKmEiwTTOmDBou8cP+OunL35l780YxwtQRpPTMSnpzXINItL+RSIRFixYwKRJk+LbbNtm7NixzJ49e7v7G2OYPn0633//PVdfffUOn99q4ZnUdcdvynnqCuVEY6bF+yU7NjbS+jQ+7ZfGpn3T+LRfLTE2TT3WDgcoH3zwQWpqarj88su3miUZiUS4//77SU9Pb/QTZRER2T3Faqd455oyLnGm8HnVcDpukUFZ0Wr9icbAdQ22veVfvZc/CvHqpyF6dPJx1U/TmL0kxtnmOwDcvQZjbaM4Th1/gyZf2XtDbQZpNKeL3lGJ7EFKS0txHIfc3NyE7bm5uSxfvnyr+1VUVHDwwQcTiUSwbZsbbriBAw88cIfPn5ubscP77IymnCc1uYLizTGSU1PIy0tuhV4JtN7PgOwcjU/7pbFp3zQ+7VdbjM0OBSi/+OIL7rvvPn73u99tcwp3MBgkJyeHv/zlLwwbNowDDjhglzsqIiJtz3EMtnG5O3on3djEoXzFCroAUE4qmVRjtfAU72gssVDPb/5ezl8vySQYSAwYfr3Ym3+4aqPD7x/2+jTAXQFArOfAJp2rYQblauqL4rjZHXaq7yKyZ0lLS+M///kP1dXVTJs2jTvuuIPu3btvMf17e4qLKzAtuOSjZXk3Ik05j9/2GhQWVVOUo3neLW1HxkZan8an/dLYtG8an/arJcam7pjbs0MByv/85z9kZmYyYcKE7bY955xzePjhh3nllVcUoBQR+ZGIOdDDrKcbm+LberEegO+sXuxnvm3xIjmhSOJfyg0lLuuKHHp1qf+TZoxh1UaHbmYj5zuv8193PN/Ye9HPrPauo0uvJp2rYQYllsVffedxjPs5nY47b5vVv0XkxyUnJwefz7dFQZzi4mLy8vK2up9t2/Ts2ROAQYMGsWzZMh555JEdDlAaQ6vcwDXlPHVTvEMRo5vKVtRaPwOyczQ+7ZfGpn3T+LRfbTE2O1TFe/bs2YwdO5ZgcNtFBcDLohw7diyzZs3a6c6JiEj7UhUyFFDY6GvfWn0AsCs3t2gfGlYSB+jnroJN6xK2LVgRA+B3sX9yuDuT38WeItXUUEARALEuPZt0rqA/MQz5tm8cVwauxcpTgRyRPUkwGGTw4MFMmzYtvs11XaZNm8bIkSObfBzXdYlEIi3RxVaTVJutrireIiIi0px2KEC5adMmunfv3uT23bp1o7Cw8RtZERFpn1ZuiHH1A5uZtXjLqXtVNYZcs2UA0rF8fGP3A8Aubdnf+26DAOVA93seit3K8Beuxd7sZTat2uhw8z8rSTU1DDXLALzp6O5XAIRSczBpmU06l7/BPIOsdOVMiuzJLrzwQqZMmcKrr77KsmXLuPHGG6mpqeHUU08F4JprruGuu+6Kt3/44Yf5/PPPWb16NcuWLeOJJ57gv//9LyeeeGJbXUKzqPvgJqLZ3SIiItKMdmiKt23bRKNNfzcSjUax7R2KgYqISBu77+Uq1mxy+b9nK3lxcmKF7MoaQxZeEZw15Menelem5bM27K3R6CsrBNcBe/tFaHZGw6kGp7n/w8YQJEZw0deERh/Fc+9XA3CAOy9hv6ucp72+Zndr8rmSg/VBybRki82VyhgS2VMdd9xxlJSUcN9991FYWMigQYN47LHH4lO8169fn/C+t7q6msmTJ7NhwwaSk5Pp06cPf/3rXznuuOPa6hKaRd0U77AyKEVERKQZ7VCAMj8/nyVLljS5/ZIlS8jP1zQ4EZHdSWV1/U2nMQarQbXqimqXjsZbY3KOPYBurhegDGd2pLAwhxg+/E4Mu7wUN3vr67LtioYByqFu/d+k4OI5hEYfRbj2c7QT3Y8b3b8ytydpTTzXPv0D9O/uo2+Bn29XxnayxyLyYzFhwoStrsX+9NNPJzz/zW9+w29+85vW6FarqpvirQCliIiINKcdSm/cZ599mD59OmvWrNlu2zVr1jB9+nT222+/ne6ciIi0vvyc+j8NoQZLpZVWuLz4YYgsvADlKqszsyyvGnbJkENxLZtNdi4AvpKNLdY/tzZCme8vJ5/S+Pbg0nkQjRD0Q1ezkWFmKQ42t/gmJuy/fu8jmnwuv9/i5l9kcsFxqeRna0aAiEgwoCneIiIi0vx26G7rnHPOIRaLccUVV1BSUrLVdqWlpVx55ZU4jsPZZ5+9y50UEZGW5bqGxatjhMKGSAwwBr+J4Tj1GTIPv1YFQJbxpnhvJp0/+S/l3MDNVA8eA8BqvKz5aa/P550ZoRbpa10GZW93LQBr6Ugh2ViRMJ+/Mps5S2Mc6n4JwNfWIKba+7EOL5vz1/6riXXotFPnveC4VPbu5ee3P21q/qWIyI9PkqZ4i4iISAvYoSnegwcP5vzzz+epp57i+OOP56yzzmL06NF07twZgI0bNzJt2jSmTJlCSUkJF154IYMHD26RjouISPP57+dhnv+gJv78986TjHHnUbLhZujTA4DFqx0AsmvXoCyzMghbSaylE3WzwD+2RjGa+YzcMJXz3zyUo/ZLwrabt7hMXYCyp1kPwPdWAZVWKse406ieNx98e3GY4wUo0w8eh5lmc1XgKrJMBYvtXlyYunP9ycuyueHCjGa5BhGR3VU8g1KrXoiIiEgz2qEAJcDvf/97kpKSePzxx3nooYd46KGHEl43xuDz+Zg0aRK//vWvm6ufIiLSghoGJwMmylHudADMpy8R7vNbAJKCUBWC7No1KDeTHt+nLgb5kb0vk5yX6UwxP3PepqL63Gavfu3WBii7uxsAWGkVsMbqxDFMY7hZTG+zll6sx7X9+EaMhmkxNlq5bLS86eeZOxmgFBGRhlO8lUEpIiIizWeHA5SWZfHb3/6W008/nZdffpnZs2dTVFQEQF5eHqNGjeLUU0+lR48ezd5ZERFpeYPM8vhju7IM8IrjlJQbMIbM2jUoN1v12YR1AcqQlcyrvsO4wHmd8903WLl4CIzap1n7Vxeg7GC8vhVaOcy1+4MDg81yHond4vW5z0jSc9OBsvi+p41PJj1Va0mKiOwsTfEWERGRlrDDAco6PXr0+FFWJhQR2dONd2fFHwfLNlIDvPqJt55kDhUEiWGwKCYr3q5BoW/essdxgfM6AGmLvsQ0c4DSxAOUmwEosbLYQC4byaETpfhwAagafTTBgEVqskV1yPD3X2eSn+Nr1r6IiOxpklQkR0RERFqA0khERPZwoUh9Fkw3s5ET3Y/jz4OVJRANs7nKa1NgCgGIZuQQs+o/47Ib/DUpsnK4tbZydrBkfbP3ty5AmWPKAbxAqWXxsb1vvM3j/a/FP3gYAI/8Loun/pit4KSISDOom+KtDEoRERFpTgpQiojs4coq3Pjjc5038OHyjdWXGpIA8JVsik/p62W8ytnRjt3Jrl1bMingLf/RULHlZVf6q8qavb+u8aaaZ7teBuUVPy8A4J++E3nGPo7/DP8tJ164b7xPAb9FcpLWnRQRaQ7B2r8HWoNSREREmpMClCIie7jqsHeT2dVs5DB3JgCPpZ7NGisfAF/JxnjGTF+zBgDTpQcTf5JK93yvsvUPC3XXBSgD1WXN3l/XhWQiBPFKyHYoyKZLrk3ISuIJ/8nkjtmv2c8pIiKepHgGZRt3RERERH5UFKAUEdnDVYW8AOXJzlR8GFbkDeebaA/W0REAX/FGYo7XtndtBqVb0JP9BwW589Is+nb1J6xBue+AACW161MGItUQDTdrf42BNLyq48a2IZhMcrC+AwZl9YiItBRV8RYREZGWoACliMgerro2QDnWtxCA7KOPBGBdPINyA+HadSp7WhsBcDp1SzhGwwzKQ0YG6ds3g0htHTa7oqxZ+2sMpNYFKJNSwLI464iUhNdFRKRlqIq3iIiItAQFKEVE9gA1YcOaQqfR16rDhhQTokvUK2gT67M3xx2QxDrLy6C0izcSjhqSTZhs1ytM43TolHCMhhmUPp9FMGjHq3w3d4DSNZBm6gKUqQCM6Bdgr24+CvJs+nfzb2t3ERHZBUG/qniLiIhI89NdnIjIHuDGJypYscHh1l9m0O8HAbzqGkMnUwKAm5KGSc0g4K9hVW2A0leygXDA0NkUe22S0zAp6QnHaJhB6bO9DJsSK4suphi7onSX+j7j2wjT5ke4+OQ0koNW7RTvEAAmOTXe7pZfZmKM2aJgj4iINJ+6NSgjMWVQioiISPNRBqWIyI+c6xpWbPCyJ2cujGzxenG5Sz61wcfsPAACfuIZlFbxJiJhly4UAuB0yN/iGHaDCKXP561RFs+gLN+1AOXfXqxi2oIoE+8oA34wxTs5JaGtgpMiIi2rrop3NOb9fRERERFpDu0uQPnwww9z2mmnMXLkSMaMGcMll1zC8uXLE9qEw2EmT57M6NGjGTlyJJdffjlFRUUJbdatW8dFF13E8OHDGTNmDP/3f/9HLBZrzUsREWkXvl9fP7U7PWXLX/uLV8foaLwgopPlBSiDfotCcojiw2cc0muK6Gy837NuIwFK6wcZlEG/RYlVN8V71wKUdeoK9bjGxKd4u0kp29hDRESaW10GJWiat4iIiDSfdhegnDlzJueccw5TpkzhySefJBaLMXHiRKqrq+NtbrvtNqZOnco999zD008/zaZNm7jsssvirzuOw6RJk4hGo7zwwgvccccdvPrqq9x3331tcUkiIm3GGMPcpfV3kD+ckmeMYV2xWz/FOysXgO9Wx3Atm2WWVwwnd8O3FNQGKJ2c7QUoLYIBKLZ2fQ3KxrJz3K1M8RYRkZYXaLBKiArliIiISHNpdwHKxx9/nFNPPZW99tqLgQMHcscdd7Bu3ToWLFgAQEVFBS+//DK///3vGTNmDEOGDOG2225j9uzZzJkzB4DPPvuMpUuX8te//pVBgwYxfvx4rrzySp599lkikS2nN4qI/Fi98UWYFz8MxZ//MNulsMylqsbQyarNoKyd4p2S5EUcp9nDAdjfnR/PoHRyEgvkQOIalH6fl0FZN8XbtwsZlNEfJL4bY2rXoEwskiMiIq3Dtq14kFIBShEREWku7b5ITkVFBQBZWd6N7vz584lGo4wdOzbepm/fvhQUFDBnzhxGjBjBnDlz6N+/P3l5efE248aN48Ybb2Tp0qXsvffeTT5/Sy5nVndsLZnW/mmsdh8aq0TPvFcTf9zDrGfJis5AcnytxqqQd3PZxS4BB0x2HpYFJ41L4rN5EeZb/QDoY9ZQYyUD4ObkbfH99TX4uMtbgxJWN5ji3dh4NGWsYk7ize/36x0wkGpqMyhTUjXWrUD/rnYPPxwnjZe0lKSARTRmiGj1JBEREWkm7TpA6bout912G6NGjaJ///4AFBUVEQgEyMzMTGibm5tLYWFhvE3D4CQQf17XpqlyczN2tvvt6hzSPDRWuw+NVR0ve3Gku4i7Yn9jwfd9mDFvMj853CuAs2FzGKigo+u1y+jZg4y8DPDHgAo2Wh0AyKOMGpMEQFbPbpCX+P31AollXtvcdHKyaygiGwB/5Wby8rY+HtsaK1MWAzbHn//7owg/PTKTmtoMytScbFK3cWxpXvp3tXvQOElLSwpAZQ1ElEEpIiIizaRdBygnT57MkiVLeO6559qsD8XFFZgWeu9lWd5NREueQ5qHxmr3obFq3Gj3GwAGm+U4L9zEZ1mTGdgrQFFxFMu4dHC8NShLSMEtqqCi2gWIT9NOIUIK3hIZxW4ypqgi4fjGGLrn21TWGJLtGiLhSLxIjluxma/nFtG7a1LCPk0Zq40lXmWcbFPOT533qLEOoKxsYHyKd6XjI/SDvkjz07+r3cMPx6nuuUhzCwYswGiKt4iIiDSbdhugvOmmm/joo4945pln6Ny5c3x7Xl4e0WiU8vLyhCzK4uJiOnbsGG8zb968hOPVVfmua9NUxtDiN2OtcQ5pHhqr3YfGCjaU1Ffv7mdWxx8PM0v4zWNf88ebDyASg2wqCBDDWBZORg4Y8NcuKhm2kqgglQy8QmXG58dNzYQtvrcWf/lVJq6pLZLjh82kE8PGj8tfH1rL/ZN7x6eW16modvngqzCjBwVIa6TCeLh22eBfOq9wrPsFZt77vD/s8foq3smpe/w4tyb9u9o9aJykpdVV8g6rireIiIg0k3ZXJMcYw0033cT777/PU089Rffu3RNeHzJkCIFAgGnTpsW3LV++nHXr1jFixAgARowYweLFiykuLo63+eKLL0hPT6dfv36tch0iIm0tFPEiFJZxGcBKABZYfQA4z3kDjOG9mWG61Ba/cTM7gD8AJFZpLbKy44/dzJytLmxn2xZ+n/da0G9hLJuS2gzMjqaUWYsT72SnL4hw0tVrePi1an5+x+Ytjgf1VcfHunO9a8GQ9P2C+iI5quItItLqgt6fCk3xFhERkWbT7gKUkydP5r///S933XUXaWlpFBYWUlhYSCjkFUTIyMjgtNNO44477mD69OnMnz+fP/7xj4wcOTIeoBw3bhz9+vXjmmuuYdGiRXz66afcc889nHPOOQSDwTa8OhGR1ldAIWmmhgh+7vGdg4vFSPMdFXO+YdbiaDxA6eTWZ6v7fPVByLq1JAHcrNwmnbMuwLnCKgDgLOddps4KJ7T524tV2z1ORZUh3VSRRX3b1DWL4xmUClCKiLS+YG0GZUQZlCIiItJM2l2A8vnnn6eiooJzzz2XcePGxf9766234m3++Mc/csghh3DFFVcwYcIE8vLy+Pvf/x5/3efz8dBDD2HbNj/96U/53e9+x8knn8wVV1zRFpckIrJLjDHEYjuepeLUzvA+IGkZAIutniyzu/OmPQ6AyIfvAF4AE8DpkJ+w/+DeXpSxYQalk5PYZmvqbl7/6xsPwEFmNv0WvL7Nfb5bFaOozE3Y9uB/qigwicXNOpd8pwxKEZE2VD/FWxmUIiIi0jza3RqU33333XbbJCUlccMNN3DDDTdstU3Xrl159NFHm7NrIiJt4m8vVrFwZYx7rsgkPcWmqMxlY6nD4N6Bbe7n1Mb69o4tAWB52gCIwLv2WE5wP6Xn5oWAobvZ4LXPK0jY/88XZPDuzBBFr+XUH7NDpyb12e/zvn5hj+Ap+yec777B2c7b1IRPgaSURvf58+NesZunr8uOBzg3VxlG1GZ4FpFFHpspqFiKD+/i3K0cS0REWo6meIuIiEhza3cZlCIikmjmwigV1Yavv/Pm0v3+4XJu+mclC1due26d43o3jj2dNQCMP2lvnr8hm/QB/aghSEq0kl5mPT3Neq99x65bHMNvW6y38uqP2cQAZSRW//hfvp9QRDbphPCvX7HdfcsqE7Mo6zI8v7b3ZrlVEA9OgjIoRUTagorkiIiISHNTgFJEpB1rmJ3i91lEooaKam/b9AXbC1B6BXK6xmoDkJ26Y9sWOTlB5ltewbDD3Rn0MBu91/O7bXEMnw8+sUdRTCZRXxLRPoOb1O/0lPo1LI1ls9Tyju3f6FUTd92tZ92Eait3h8KG5CDxKd7rrTw+s0YmtDXKoBQRaXVBf90alMqgFBERkeahAKWISDv15aII599WlrCtYXbhOzPCbIvjQBeKSDIRjD+Ak+tlP1oWvOE7GIBz3LdJIoLx+RtdX9Lng2orhUmB63jnqP/Dzc7bok1j+nVNXEHke8vLzvRvWAUkZlheHnuev0fvIN14hXBCEcPUWWHOv62MUIR4EZ/15PGG72AKa4v2VHfpF686LiIirSdJU7xFRESkmSlAKSLSTt35fBVug9nONWFDeVXizWB1aMubw9IKF9c1zF0abTB9uwBsb2HIos0un1sjWEN9QDLaZ7AXjfwBv+1lyZRY2YQzmxacbExdNW/fxtoAZe1NbbIJcYo7lcFmOWc77wJegPKh16rj+9ZlUK6zOlJk5XBF4Fpm9T2Rmgm/3un+iIjIzgtqireIiIg0MwUoRUTamdmLo/zfs5VbbH/98xAzFybeDW6uSlyvcdGqGBffuZmzJ5fx5rQw3Wunb8carC+5Vzc/rmUzxXdkfFvN2OMa7UvDmKW9C38xltdN8d6wGowhFPEClIPMinibs913eDR6E/5Na+Lbkk2YjpQAcO6E3gBstHJJOeMc3CauhykiIs1LVbxFRESkubW7Kt4iInu6u16sJNpgCvQwdzHXxx7hucLjePWzwxLa1oQTbw5f+bgm4Xm3uvUl87rEtx13QBJ+Hzz/zoEMNUsJBm327j+i0b747IaPrUbbNMUqqzMOFr6aSuyKUiLRLAAGm6UJ7fqaNRS9/i8IXAHAIPM9PgxOdh69B+RxwbEhasKGvCx9viYi0lZUxVtERESam+7wRETamYbByaCJ8PvYk+RSzuXOC1jGy5g803mPpyLX41+9JGHfH075rg9QFtQfM2Dxk7HJHD46jdv9E9l82mVbTY/0+eqDkr5d+IsRtQLxad4rv5jPVQ+UA3Cg/S0Ab9tj421HmkWkGW+K92CzzNu/xwAAjj0gmVPHqzCOiEhbqpvi3XA9YREREZFdoQCliEg7klDd2hhuij1IZ4rjm3qbdQRNhIudl+jORnp/8WzC/vk5ietINhagrHPBsSncd2UmY4YEt9qfpAY1aHY0QHn6IckAnHBgEgBzLS/IuPxTLyiZaSrZK+oFIP/pO5EjA//ge6uAIDEOdmeBMYx0FwIQ7Tlgx04uIiItRlO8RUREpLlpireISDvwzfIoWWk2+Tn1UcDBZhn7mwU4lg+fcQAYbr4jk/r1KTOLV1AajUAgiDGGz7+JxF9LNTXksRlInOJdx7YtOnXYsjBOQ+kp9f3Z0Snepx+SzNghQbrk2rz+eZhFVi/Am7btfV2OjWElnSm0OgDwgT2aXzqvcoL7CT3NekaaxRjLItpv2A6dW0REWo6qeIuIiEhzUwaliEgbW1vocMtTlfzuH+UUlXlTuJNNiN86zwCwuvc4nvCdBMCR7gz6mdXxff1OmEDtNO+1hfUFc37ifMwb0SsBcDp0wqSk7VTf0lPqg5LWDi5BaVkWXTv6sG2LgT39fGv3AWAvs4qgidDfeBW9v7N7AdCjk48P7f1xsBhoVnCm+z4AlSf/Eie/a6PnEBGR1qcq3iIiItLcFKAUEWljXy6qv8OrW5vxZ8479DbrcANBVo48mY/sfQEYaFYw0v0uYX//qsUAlNVW9O6TWcWv3Rfir1cddfZO9y0ztT4qWVzubqPltt14YTrr6EgRWQSJMdB8Tx/Xq9bddURfThufzB2TMihPzuVB35nx/UL7HEJo/yO3dlgREWkDKpIjIiIizU0BShGRNrZiQ2KVAcu4HOVOA6DqxIkkF3RmjdWJNeQDMMbMA2CWNRCAwEovYBmqreg93szGrp0SvvKk3xIefuBO983ns+jUwftTMbjXzq8KYlkWWBbz7P4ADDNL6G3WAtBtRG/OPCwFn8/iyT9k0/PsE9lw+PmERhxE5dHn7PQ5RUSkZdStQakApYiIiDQXrUEpItLGkgOJc6cHmBXkU4obTCY0fBy9/d46kQvsvnRzN8XbveY7hFGxRV4GpTHURLwbxb6xFQBUHXoqqQeM2eX+/eVXmZRXuVsU4NkZ86y9OIwv+bnz3/g2p1O3+GPLsjhgcBAG/4SKXT6biIi0BE3xFhERkeamDEoRkTZSWe0yb1mUSCwxA2W8OwuAyMB9IBDEsiwO3yfIAqtPvI2T2YGZvqFE8OOrrsAu3kAo7L1WEF3ntenUo1n6mRy0miU4CTDLHpTw3Ok/HDezQ7McW0REWkddkRxV8RYREZHmogCliEgbufK+cm79VyWff+OloJx/dDLHOJ/zU/c9AMJDD4i3PWRkEgusvvHnsR79CZsASywvCBlYtZiasAFj6BT2pk7H8uszE9uDq89KY43ViXt9Z7OCLlSe+HN8V9++49V3RESkTdVN8Y7GwHUVpBQREZFdpwCliEgbKKt0qaxJvKkbvGYq1zhPAeDkdCTSf2T8Nb8Pvre68onlbavZ73CAeNDSv/I7Pp4bJpfNpDg1GMvC6VjQGpfSZPsNCvLXSzKZ1+0I5p97J6Gxx4KtP0MiIruboL/+g6VIbBsNRURERJpIa1CKiLSy6QsifLUoceEuy7gMWPIWAKFhB1L5kwsgmBR/Pej3iszc6L+Y606Hof07AKUssPuAC9b3i1mz2WUfUzu9O7cz+AOtdUlN1qOTjzsuzmzrboiIyC4INvjzEokakoPKhBcREZFdowCliEgrmvFthLunVG2xfbhZTEblRtzkVCpOuxiCyQmv5+fUZhpaFhn5GfHt39ZmUCYVriIlEKKnWQ+A06l7C12BiIjs6WzbIuD3pnhHVChHREREmoHm1omItKLv1zuNbt/bLAfwpnX/IDgJXsXU849J4fgxSfTs5BWsufy0NIqtbDbSARvDQLOCXrUZlO1t/UkREflxqa/krTUoRUREZNcpQCki0opC4cQbuSxTQcBEGeiuACDWrW8je3mOG5PMecekYtUWlRk3LAjgTfMGBpul9LVrMyjzlUEpIiItR5W8RUREpDkpQCki0kqMMazYUJ9BuZe7khej13Jv7K8MYgWw7QBlYw4ZGWSONQCA0e58eji1GZSdlEEpItLcnn32WQ477DCGDh3KGWecwbx587badsqUKfzsZz9jv/32Y7/99uOCCy7YZvvdTV2hHE3xFhERkeagAKWISCt5Z0aYhSvry52e5H5EkBgDzQpyTRnGsogV9N6hY158UioLM4YDMNgsJ81UY3w+nI5dm7XvIiJ7urfeeovbb7+dSy+9lFdffZWBAwcyceJEiouLG20/Y8YMjj/+eP71r3/xwgsv0KVLF37+85+zcePGVu55y6ib4h2JKYNSREREdp0ClCIireSfb9fEHyeZMOPdrxNedzp2xSSl7NAxLcvi9FO6sdjqEd8W69yrXVbwFhHZnT355JOceeaZnHbaafTr14/JkyeTnJzMyy+/3Gj7u+66i3POOYdBgwbRt29fbrnlFlzXZdq0aa3c85ahKd4iIiLSnFTFW0SkhRljCEchLcWiqsaAMfzO+RdphBLaxbru2PTuOmnJFtOsYfQ3qwCI9hu6y30WEZF6kUiEBQsWMGnSpPg227YZO3Yss2fPbtIxampqiMViZGVl7fD5a5cebjF1x9+R8yQF6qd4t3T/9mQ7MzbSejQ+7ZfGpn3T+LRfLTE2TT2WApQiIi3o5Y9qmDI1MRA51szlMPdLAD6094s/jgwcuVPnSEu2mOrbjwnuW+APUH3gcbvWaRERSVBaWorjOOTm5iZsz83NZfny5U06xp133kl+fj5jx47d4fPn5mbs8D47Y0fOk55WA8QIJiWRl5fecp0SoPV+BmTnaHzaL41N+6bxab/aYmwUoBQRaUE/DE4CHOlMByDauSe3Ff+cJVYPBha47D10x29aAQrybHqN6MmL1k0cMzYVk5GzS30WEZHm9cgjj/DWW2/xr3/9i6SkpB3ev7i4AtOCM6kty7sR2aHzGK/oW3FpDUVFmubdUnZqbKTVaHzaL41N+6bxab9aYmzqjrk9ClCKiLSyQeZ7AKpOuBD3KR8v+o7mkIIge+9kHr1tW1x6ahowAGe7rUVEZEfl5OTg8/m2KIhTXFxMXl7eNvd9/PHHeeSRR3jyyScZOHDgTp3feKuDtLgdOU/dFO9wtHX6tqdrrZ8B2Tkan/ZLY9O+aXzar7YYGxXJERFpQfk5ib9mc00Z+ZRiLIto1z7kZXmvD+mtz4tERNqrYDDI4MGDEwrc1BW8GTly68tzPProo/zjH//gscceY+jQH9f6wPEq3iqSIyIiIs1Ad8QiIi3IcRNv3AbWZk86nbpDUgrXnhNkQ4nD/oOCbdE9ERFpogsvvJBrr72WIUOGMGzYMJ566ilqamo49dRTAbjmmmvo1KkTV111FeBN677vvvu466676Nq1K4WFhQCkpqaSlpbWZtfRXFTFW0RERJqTApQiIi0oEk18PtadC0C0+14A9Ojko0cnX2t3S0REdtBxxx1HSUkJ9913H4WFhQwaNIjHHnssPsV7/fr12HZ91vwLL7xANBrliiuuSDjOZZddxuWXX96qfW8JDat4i4iIiOwqBShFRFpQNFabWWIMg8z3HOV60wPDww5sw16JiMjOmDBhAhMmTGj0taeffjrh+YcfftgaXWozgdq7CE3xFhERkeagAKWISAupDhlCEcAYrnce5VD3KwCiXXoR7ffjWotMRET2LPVFchSgFBERkV2nIjkisscLhQ0bS5q//vVdL1YCsK/5Nh6cBAgdcHSzn0tERKQ1aYq3iIiINCdlUIrIHu/qf5RTWOZy16WZdMtvvvUgl6yOAfAz920AQiMPpuqos3Cz8prtHCIiIm0hWFvbTRmUIiIi0hyUQSkie7zCMheAWYubNw2kQ6aNbVyGWl7l7upDTsXN7giW1aznERERaW2a4i0iIiLNSQFKEdmjGVN/Y7VwZazZjlsdMqwvdulMET4nivEHcPI6N9vxRURE2lLQXzvFu/n+dIqIiMgeTAFKEdmjvfFFOP64OTMol6zx7th6mXUAxDp2Bbv5po+LiIi0pWDA+6oq3iIiItIcFKAUkT2W6xqeea+mRY797kwv8LmXbz0ATn63FjmPiIhIW9AUbxEREWlOClCKyB6rpLzlbqqKNnvrWu6buQEAp1P3FjuXiIhIa1MVbxEREWlOClCKyG7FdQ33v1zFyx/veuZjRY27xbaGa1LuirIK79i93TUAxDr3bJbjioiItAd1U7yVQSkiIiLNQQFKEdmtrNjg8Om8CFM+DFET3rWboj89WrHFtuZYhzIUNmyuMvhNjJTS2jUou/TY5eOKiIi0F8EGGZTN9eGeiIiI7LkUoBSR3UooUn8TtLHEwXEMU2eF2VDi7NBxjDE4tbskmxB3R//KHdF7mbewapf7uKnMy54cmLwRy3Vwk1Nxs/J2+bgiIiLtRd0Ub4CoKnmLiIjILvK3dQdERHZERXV9gLKixvCzm8oA6NfVx60XZTb5OA3XzPpF13kMX7EEgNjGt4BzdqmPj73hBTnrpnc7nXuAZW1rFxERkd1KsMFdRDhq4hmVIiIiIjtDGZQislupbBCgbPh46dody6CsCtXve3zSrPjjvVdNhV2cqvbdKq8vnWtq15/spOndIiLy4+LzWfh93uOwCuWIiIjILlKAUkR2KxU19cHDNYWJQUnHaVpgMRYzXHbPZgBSTQ3BpfPir2W7m7E3FzdDT2FIUt36kyqQIyIiPz7161BqDUoRERHZNQpQishupbK6vvL2Sx+FEl5bvKZpi2B9sSASX3/yAHcelhOjPKOAZVZXAPzrvt/p/jUsFDDAtxZQBqWIiPw4JdVW8laAUkRERHaVApQisltwXUNphcvrX4S32ubGJyq3mUX59XcR/vlWNdMX1M9FG+9607uL++7PUssLJH7830U7XSE8HPG+ppsqgpUlADidu+/UsURERNqzugxKTfEWERGRXaUApYjsFv71Tg0X37k5/jzXlJFstgxW/u/ryFaP8Zfnqnh7Rpivv/PupJJNiP3NfABK+o1mSW2AMrd8JVNnbT0Qui3ltRme/WuzJ53sjpjktJ06loiISHuWFA9QKoNSREREdo2qeIvIbuHtGfUBw7HuHG6J/YNCsvmD/wp6JZVwWPUnvOcbw+pNY5t8zNHufJKIEsvtTLRTT5Za3tqT/cwq5oZ27marsnaNzIF107u7aHq3iIj8OAU1xVtERESaiQKUItLulZS7Cc+Pdz4FoCNl3Bq7nxynkqCJMDY2jyc3dwI6bXGMtYVbVvk+xP0KgMiQA/D7LZZa3lTsTpQSqCkHUprUv6VrY7zxeYh+3fzkZHiJ6b1drT8pIiI/bkG/pniLiIhI81CAUkTavbnL6u98fCbGPtZ3UJus0YmS+GOA/vNfA7bMorz2ofKE5+OdrxhvvPUnw0MOICPVptpKYSWd6ckGzBcfsmTYGbw3M8zZR6TQIXPrK2L86ZEKAKY1WNuyt7MaAEcVvEVE5EcqXiQnpgxKERER2TVag1JE2r2Kqvobn6HBNQTdCG5KGu/YY+Lb/xf0gpIHmHksm7tqi2NEGxT4to3DL51XAIjsNZxY1z7xAOSLvqMAOMn5iOsf2cwncyM8+nr1FlmcdTaWbJmZ6TMxuke9AGW0oM+OXKqIiMhuo65ITiSiAKWIiIjsGgUoRaTdK63wgoPHjE7ipkHetOxon8H81z6EGDZVwSw2jv8ZX1mD8GGY+cArfLuiPpvRcQyWdw/FvRNt3nJ+TQFFuMFkNk/4HfEXgQ/t/akkhU6UMNQsAWDW4ii/umszsxZvOYetYUXwOr3MegImhpucipvbudm+DyIiIu1Jkqp4i4iISDNRgFJE2q3qkOHbFVGWrvXSH/unlZI2ZyoANWOP48ATBvOn3n+j6PI7OeTgfP7jOxSA49xPue3xEjbUZjcuX+9gDPhs6PvlcwRdr+BO9ZFnQTAp4ZwRK8jn9ggAxrjfJLz21NvVW/SxqpFiOnuZlQDECnonBD9FRER+TOqmeKuKt4iIiOwqBShFpN36y3OVTH6yksWrvUDjgYuewYpFifQeRLT33hy1fxJXX9SV1LxsAn6Lib8/iI3kkEUVI82ieGGcebVrWB7Uq5qUWR8BUHn8+dQceFzC+caPCAIwwxoCwGiTGKA0tfdfS9fG+HhOGNc1hH44rc0YBtYFKLtqereIiPx4xad4K0ApIiIiu0hFckSk3Vq4sn7hyBQTInfVbAAqT5zYaGZiepqf6fYQTnA/ZZS7iGjMW6OyvHYNy/HmayzXJdqtHzXjfrLF/j8/PpWhffz88+W9cRyLXmY9nUwxG61cAEorvanm979cxfpil3AEasLesbvk2pxvv8Oha18lgBcYjfYY0FzfChERkXYnHqCMbaehiIiIyHYog1JEdgtDzFJs18Hp0Amn89YrY8+yBgFwoDuHcMQLKIZrsxz7FHsBzvCwLat8AyQHLQ4ankSllcYCqy8Az0f/wH7ufAAiUfhqUYT1xd5xv1wUobo2QPnLXgs5ct3L8eCksSyiPRWgFBGRHy9N8RYREZHmogxKEdkt9DO1VbG799tmu9n2ABzHooAirOkf8nb4MJatcwiYKF1KFwNe5e7t+Z89mmHOUgD+L3Yf86y9qCKZu5+bAFYOAPOWxfD5IMtUcNDMv2MZQ7T7XsTyuxHr2heTkb0LVywiItK+aYq3iIiINBcFKEWk3crPsdlU6mUr9jFrAYhtI3sSIBzM4GXnCM5032fg6o+5fIM3zXu4WY7fieCmZ+F06r7NYyQH4fXwwbiBIFeFngRgWG1Fb2LP8qfAZfG2jgMHuN/gcyLE8rtR9ssbIRDcmcsVERHZraiKt4iIiDQXTfEWkXar4ZSxPmYNwDandwPc9IsMpviOxMVisFlOJ1MEwCh3EQCRvkO2W1n7hgsz2HdgkPGTjuIG/6SE18aYefQ06xK21RXTCQ85QMFJERHZYwRrp3grg1JERER2lQKUItJuOd5yjgRMlB5mAwCxLtsOUPbu4ud3l+3FHMtb//EY5wsARpmFAET7DdvuefsU+Pndz9Lp2dnP6oL9udL/O6Ye8ie+ql3fcqi7JN7WNg77uN6xIwNG7sDViYiI7N6S4lO827gjIiIistvTFG8RabdMbULGoOB6fFEXNyUNN7PDdvfbb1AyN9sHM8pZxBnu+yx1uzOIFQBE+g7doT7c+PMMSiv2oSDPxweffs2+zkL6mTUMLHDpvmYGhzhfk0E1bmoGsW59d/QSRUREdlvB+BRvZVCKiIjIrlGAUkTaLbc2Qnn1mPXwAcS69dvu9GwA27bIHDuaDdNfp3N0PTfHHgQg1qk7bk7HHepDSpJFSpIPgJX+buBAf7OCfTbfQ9fYoni7yF7Dwfbt0LFFRER2Z6riLSIiIs1FU7xFpN1yvfo4pG1YBkB0BzIUzz0+E/+1txIaeXB8W83oo3epPysDPQAYaFbStdQLTkYLehPr1J3qw0/fpWOLiIjsboKa4i0iIiLNRBmUItJuubUJGSm1AcpYt347tL9Jy6DizMupOvKnYAxuh0671J/CQCdqSCKFMADVBx5P1U8u2KVjioiI7K6C/toAZUwZlCIiIrJrlEEpIu2WayDZhEkq9ip47+waj25O/i4HJwFKqixmWoPjzyODR+/yMUVERHZXmuItIiIizUUBShFpt1wX9jKrsIzByezQpAI5Le1p309YYnWnaPgRRHsNbOvuiIiItJmGVbyN2XaQcmOJw30vVbKuyGmNromIiMhuRgFKEWm3jPEClLDz2ZPNbbndjUmB64mdcVGTCvaIiIj8WNWtQWkMRGPbbvvaZyE+/ybKG1+EWqFnIiIisrtRgFJE2iW3dgHKHmY94FXgbms/Py4FgD+dl47Pp+CkiIjs2YKB+seR7Uzz/m61F8FcV+S2ZJdERERkN6UiOSLSLtXNFOtuNgDgdOzahr3xHD06maNHJ7d1N0RERNoFv8/CZ4PjQmQbGZRVNS5rC73A5LpiTfEWERGRLSmDUkTapboK3gWmCAAnt3Mb9kZEREQaE2xCoZwla534B4+bKw1VNcqiFBERkUTtLkD55ZdfcvHFFzNu3DgGDBjABx98kPC6MYZ7772XcePGMWzYMC644AJWrFiR0KasrIyrrrqKUaNGse+++/LHP/6RqqqqVrwKEdlVrgsYQwc2e88zc9q2QyIiIrKFukI54cg2ApSrE9Mr1xUrQCkiIiKJ2l2Asrq6mgEDBnDDDTc0+vqjjz7K008/zY033siUKVNISUlh4sSJhMPheJurr76apUuX8uSTT/LQQw/x1Vdf8ec//7m1LkFEmoExkEkVAbypYG56dtt2SERERLaQl+XdTny/YetTtxf/MECpSt4iIiLyA+0uQDl+/Hh+85vfcOSRR27xmjGGf/3rX/zqV7/iiCOOYODAgfzlL39h06ZN8UzLZcuW8emnn3LLLbcwfPhw9t13X6677jrefPNNNm7c2NqXI+3MF/Mj3P9KFdHYthdyl7bnGuhgvOxJJzUD/IHt7CEiIiKtbXg/7+/z3CXRRl93XcOSNV6Asn93HwDrVShHREREfmC3KpKzZs0aCgsLGTt2bHxbRkYGw4cPZ/bs2Rx//PHMnj2bzMxMhg4dGm8zduxYbNtm3rx5jQY+t8VqwUK9dcduyXNIonv/7U3179vVx3EHNL3Yicaq9RljyK2b3p2R3eTvvcZq96Gx2n1orHYPPxwnjZe0hhF7BXj54xDzlsdwHIPPl/iDt6bQpSYMyUHYf1CQxatrWKsMShEREfmB3SpAWVhYCEBubm7C9tzcXIqKvEIaRUVFdOjQIeF1v99PVlZWfP8dkZubsZO9bV/nkDqlAETdAHl5O/5911i1nkClE8+g9HfI2+Hx0ljtPjRWuw+N1e5B4yStqW+Bj7QUi6oaw9K1DgN6JN5e1E3v7tfNT7d8L4NSlbxFRETkh3arAGVbKC6uiFcdbG6W5d1EtOQ5pHE1NRGKiiq22cYYg1WbfqKxal0r1se49qEKflqbQRlOzqByO+NVR2O1+9BY7T40VruHH45T3XNpPs8++yyPP/44hYWFDBw4kOuvv55hw4Y12nbJkiXcd999LFiwgLVr1/KHP/yBCy64oHU73Ap8PothffxMWxBl7tLoVgOU/bv56ZrnrS61scTFdQ22rTRfERER8exWAcqOHTsCUFxcTH5+fnx7cXExAwcOBCAvL4+SkpKE/WKxGJs3b47vvyOMocVvxlrjHHsyYwz/+TRE51xffJvFtr/nXy6M8PB/q7n01DRG7lW/9qHGqnVc+5B3c51r6qZ45+zw911jtfvQWO0+NFa7B41Ty3jrrbe4/fbbmTx5MsOHD+epp55i4sSJvPPOO1vM7gGoqamhW7duHHPMMdx+++1t0OPWM2KvANMWRJmzNMqZh6UkvBYPUPbwk5dlE/BDNAaFZS6dOvgaO5yIiIjsgdpdkZxt6datGx07dmTatGnxbZWVlcydO5eRI0cCMHLkSMrLy5k/f368zfTp03Fdd6ufcMuP28oNDi/8L8Q9U6ri2364LldZhcs/Xq3i2xXeAu93vlBFRbXhjmcqW7OrP2qu27S75dWbnPiNdQfqA5QiIiJt6cknn+TMM8/ktNNOo1+/fkyePJnk5GRefvnlRtsPGzaMa6+9luOPP55gMNjKvW1ddYVylq9zKK+qL4BTXuWyvth7vlc3H7Zt0bmDd/uxToVyREREpIF2l0FZVVXFqlWr4s/XrFnDwoULycrKoqCggPPOO48HH3yQnj170q1bN+69917y8/M54ogjAOjbty8HHXQQ119/PZMnTyYajXLzzTdz/PHH06lTp7a6LGkDxhhe/DDEq5+EtnjthwHKPz5STnG54eM5ESZP1HS45vb0u9V8PCfCX36VSYfMxj8X+ffUGt6cFiLWYFmqujUo3czsVuiliIhI4yKRCAsWLGDSpEnxbbZtM3bsWGbPnt3i52/pgke7WlipQ6ZNz84+Vm5wmLcsykHDkwBYutbLnuyaZ5OR6v39L8jzsXqTy7pih1FWYKvHFI+KXrVvGp/2S2PTvml82q+WGJumHqvdBSjnz5/PeeedF39eNyXmlFNO4Y477uCXv/wlNTU1/PnPf6a8vJx99tmHxx57jKSkpPg+d955JzfffDPnn38+tm1z1FFHcd1117X6tUjbWrHBSQhOJpswIcv7Ofnhv4/i8vrsvhseb9pah9J0b3wRBuDNaSHOPTq10TYvfZQYSB7vfMVwswRQBqWIiLSt0tJSHMdptFDj8uXLW/z8rbWW6K6cZ+wwh5Ubylm4Gk453DvO6s/LABi2V0q82F2/Hg4zvo1SWunbqYKFeyqtJ9u+aXzaL41N+6bxab/aYmzaXYBy9OjRfPfdd1t93bIsrrzySq688sqttsnOzuauu+5qie7JbmTpmvpUvJ867/IL51WmWcN40Xc0FnsntC3IsxOnGhnD8e5nbPZnAeNbqcc/flv75KSsInGaV0+zjuucxwFwAknE8ru1dNdERETarZYuUNUchbAGdPX+ls9cUMOmTeXYtsXc76oB6JFv4sUJc1K9rMpla0LbLVgoKlLW3ml82i+NTfum8Wm/WmJsmlq4sd0FKEWay6qNXoDyd3kfcOw6b32ocWYO42JzeP/90cQO/A3+gI9p8yPx4ORQdwldzUaCxPi18xw4ULasA7F+Q9vsOn5MfFup1vns+zXxx11MIXdG/4YPh2jPAWw+5ypMWmZrdVFERGQLOTk5+Hw+iouLE7YXFxeTl5fX4udvrcJHu3Kevbr7SUmC8irD8nUOvTr74lO8+3fzx4/bpbZo4boiRzelO0DFr9o3jU/7pbFp3zQ+7VdbjM1uVSRHpKlijmHO0ig9zHqOXTcFgO+tgvjrR7oz2PjQowA8+JpXPGeIu4Q7Y3/jGudfXnCyVvJH/23FnterrHH5dkWUUHj3/o1dUl6fHenbym+cDSVeMLm3u4Z/Ra8nl3IiwVTKf3YVRtO7RUSkjQWDQQYPHpxQqNF1XaZNmxYv1Lin8/sshvTx1pScszTKyo0O4SikJVsU5NW/ASjI8wKUZZWG6tDu/R5HREREmo8ClPKj9IeHK9hU6nKq8yEAm3uP5KqcyfzcfwOfWN6NxIh1/yP03XfsP8irrHmm+YAA9dPC1+FlRASXfYMVqqK1XfOPciY/WcnFd5W1+rl3xpvTQvz0hlKKyhKna89cGIk/fvnjEDO+jfxwV3y2hW1c7sx8Bh/e/htOugI3U8FJERFpHy688EKmTJnCq6++yrJly7jxxhupqanh1FNPBeCaa65JWGIoEomwcOFCFi5cSCQSYePGjSxcuJCVK1e21SW0uBG11bznLImyeLWXPVlXvbtOarJFdrr3fF2xs+VBREREZI+kKd6yW4pEDf98u5pR/QPsOzCY8Nprn4VYtdEh3VRxpOtlOpjDT+ThvtlU1mQy8Y6u/D72BEe50/E9/xAMvRm/ibG/tRCAf/pOoCwln7Xd9ufyRX+mh9lIYPFc6Na5Va+xrnBPTRhc1yS8uW+P/vWON0370rs38+Lk+sDi29PDCe3+9mIVFxzrcvT+SfFrisQMo8xCckqW4ySl8u15d9K5T8fW67yIiMh2HHfccZSUlHDfffdRWFjIoEGDeOyxx+JTvNevX49t13/2v2nTJk4++eT48yeeeIInnniC/fffn6effrq1u98q6gKUS9Y4pCZHAW/q9w8V5Pkoq4yxrsihX1fdjoiIiIgClLKb+nhOhP997f334uTEAOVztesZnuB+QgoRYp17Eu0zGID0FJu//zqTP999Gge7X1MQXkP25lUMNWUEYyFKyORp+3i6ZfsZkh/gi8XD6eG+R/DbL+Gwo5ul79Uhw/L1Mfbu6acmbEhL2X4i86V3b+bvV2bh97d9kHJjiYNlQX6OL77t2xXRhDaVNS7ptde1ocQl25Tz29gzpFGNg4933xjLOW/tx/nHp3H0/kksW+twlfs1AJER4xScFBGRdmnChAlMmDCh0dd+GHTs1q3bNgs//hjlZdt062izptBlzpLa9ScbCVB2ybX5dgWJBQpFRERkj6YApeyWQpH6NYsaBsNijrc9xYQ403kPgOpxP0koH52XZVNqZTHbGsgY8w19S+fRzd0MwMq8YZhym58elkJJucvn9kjOct8juGgWxBKDcDvr1n9VsHStQ58CH8vXOVx+WhrjhgW3aBfwQ9R7b09JueHNaWFOOii5Wfqws6IxwxX3lgPw9HXZBAPe97XuJqS7Wc/xzmd8/eoI/vHdXlxwbAoApzhTGWfmxI+zr7OQ090P+OcbJ/D11/2wTRLj3NkAhIce0IpXJCIiIs1peL8Aawq92ROWRaMZkl3z6gvliIiIiIDWoJTdVF5W/Y/uN8ti8cfri71P4o93PyWLKmJ5XQiPOChhX9u2GLGXnxm2V5l7UMVcDnC/AaDXEftzzxWZ7DswQI/OPhZavSkhEztUDYvm7lAfjTGsKXRwHIPrGt7/MsyK9TGWrvXejC9f5339+8tbrm/puoZoDHq567gp+gCPR2+k2zsPQnnpDvWhuZVX1QeGZ3wbiQeKN5Y6+E2M26N/50z3fU7/5q/81HmHf75dA8ZwmDsTgK+sQTxjH0sEPwPMSm6P3c/k1X/iWPdzb7xSMoj22rtNrk1ERER23Yi9AvHH3fN9pCZvOfujrlBO3fs2EREREQUoZffU4L3uxtL6N7drNjlgDMc7nwJQM+4n4PP9cG/OOTKV6bUByr3CS+nORlzbhzNgGF1yfViWRWaqjWvZfGrXVuec+fEOdfGBV6q56v5yXv00xOwlUR57o5prH6pIaJNnShmQWrTFvnWZk39wHmecmUtvs45j3C+I3T2ZFaurEwrPtKaqBtU273+lmiferAagotpwgPmGAuqvZZLzCm9GLucn7id0pRATCNLzhj9QfMhZ3OC/mCVWd6L4yKKKq5xnAIgO2b/R8RIREZHdw6CefpJqY5T9uzf+N71LbVXv9cUOrqtK3iIiIqIApeym3AYfuM9dWj/1uqzSZX8zn55swE1KITx8XKP7Z6ZabLJy+cbqG99W020gJjkt/jwrzYuCfmjv722Y/TlEmxYYfHt6iE/neW3/PTXEpgZB1F7uOh6J3sT7kYuZEr2WB8v+SPj9txL2j8YMeaaUvcxqAJ6xj6OcVLqE1lLz4D387fkKlqyJ0ZrmLYvy3aoYPhNjgvMm/4jeyrFf/hW7tJCasOEQ90sApthH8rE1CoAUwvzWeRaA8N77Q1IK44YlMcMexqTA9VwYmEykdqUJg0Vo/yNb9ZpERESkeQX8FqP6exHKYX0DjbbJz7bx+7wPZIs2K4tSREREFKCU3ZRT+2l7pqmk87LP+XLKF/x7ag3/fLuGo2srd4f2ORSTnNro/umpXvDxYd/pVJPkHXPMET9oY9Opg818qy+byIGaaoKL5zSpf/982yvUc7TzBTdGH6TDhm8B6GDKmBz7B/3MGnzUvyHP/fhFiISpDhn+PbWGvzxfyTB3MQDlHfvweZ/T+LP/V0TxcbCZzZXOc6za0HrrNq1YH+PWf1Xy2BvVnOO+zc+d1xhoVrK/WUDGU3ewfm0VY9x5AFQPGcut/l/wJ/+lCccIjfaCjz06+bji9DQmT8ygNDmf+3xns8DqQ9E5fyDWre8W5xYREZHdyy9PSOX6C9LZf1DjAUrbtuic692GqFCOiIiIgAKUsptyXC84+UD0dq5zHue42Xez6cPP6WY2cqA7B4DwqIO3ur/f5wUov7X7cnbgDj74yV8Jj9gy2/KeyzMxls1H9r4AVLz/Hnc+X8GsxVsWzDHG8OncMK99GiI12WKY+x3XOv/kYDObI2fexSB3OX+L3kV3NuFgsZaOfGjvSzGZpDjVJC2Yyafzwrz0UYjvVjkMM0u8vg7Ym/HDk5hnD+Be388AONH9hB6rp+3S97CpHMfEp6Znm3JOdD4C4DNrOADBjat4MnoDKUQIZeazMqkXMcvPNHs49/h+Rgyb77odTLTXoPgxDxwaZGAPP/k5Pt7yHcTlgd/DkJGtcj0iIiLSstJSbIb0DmBZW64/Wacg15v+vVaFckRERAQFKGU35TpwrPsZXSmMb7sx9jBPRm8gSIxwjwHEujYtG6/CSmP4gb0afc22vTfW79hjcbDpumEu9rez+L9nKxPaVda4PPxaNfe/Us1zH9RQXePyS+fV+OtJJsIDsTvowUZq0vI4L3AL5wZv5Rb/RbzuGw9A8tcfsmS19yY92YTihXuivfdm34FeBsLb9oEstwoAGLzwVTAtv25TSYWX2XCI8yXPRv9EByoIZXTkJv8kHvKdDkA+XvGemlGHcPDIpPi+//UdwrGB+1l19MUJldTrnH5IMp072Pzx3PQWvw4RERFpP+KFchSgFBERERSglN3Q599EeGdmmGHuUgAe9p3KIqsnAD5cllrdqPzZb7Z7nAlHpdAt3+bh32Vtt+0Kuysv2d4U8Ntj9/NT5x2smvrq2/e/XMXU2RHSTDV3RO/lw+gkBpvlhK0gf/BfHm8XtYNU/+oGqtPz49vetcfiYhFcNp8h0UVgDFc7/yKfUir9mUT6DiEr3ebmX2RgLJsr/NdSQ5CsinUEVixq2jdtJ931QiWX3V1Oqqnh186zpBBmNflUT/wDF56YyRT7SG72/YL37AP4qMNRxA47iSG9A9z8iwwG9vTWlnQsP8P7+hs9/v6Dgtx7ZRbD+zU+BUxERER+nApqC+WsUyVvERERQQFKaceKN7vM+DaCaZAlGIsZ7nupitUboowwXnBurjWAm/0X8ZU1iHftMWy4YDJuVu52j3/CgcncdWkW2elN+2fwH98h8ceTnFfI+Psf8G1aA8DsJV7Bmp8577C/WRBvt3LQ0cywh3Kz7xfMtgbw3dgLcHM7c9PEDE4/JJmLTkhlo5XLO/ZYAEaseJPx7tcc5n6Fi0X43CshKQWA/t39HDoySLWVwsf2PgAEv53ZpL7vDGMMMxd6U9lPc/9HJtUUks3v82/C6dSdaMyAZTHVtz93+H9O54smQiAY7+svf5JKRqrFz45M2eYULxEREdnz1GVQrlMGpYiIiKAApbRjf3yknL+9WMUf//wNobf+i11eSlG5S7IJ8WL096TgVcleYnVnvdWRawK/Yd64ixnQP7NZ+zF2iJfdt9HK41HfKRThZVwmla4n/am/YBzvjXWB2cRp7gcALLO68Yp9GPZxpwEw1bc/VwWuIuWQwwHonOvjjENTGNTLyyx8zncsAH03z+d893UAag45Gbf/sIS+2LX/Yr+y9gYg8OVUrFAVLSFaWyS8gynjDOd9AN7tcTa//Vk2APsMqM96vOL0NHKzEn+ddMv38eg1WZw0LrlF+iciIiK7r4LaIjmlFYbqUMsvWSMiIiLtmwKU0m6VVRp+EXuFx2I30/3Tp8n+25W8/48PuNr5F3mUATA7bRS3XJwT3+fwfZK2crSdN+mkNC45xasG/rzvWM4M/pVzArdSTRLBkvUUfrWADFPFA9HbCRLjO6snv/Rfz/3+s8jKTYsf58ChATLTEv/Jde5QO73JymeB1QcbQy+zHoDQ/olVxcErDgTwkb0vK+lMIFxF0qxPmv2aARZ8HyPFhPhb9C7SqaEovTtHTzqEHp28jIf8HB8Xn5zKUfslccDejU/RVuakiIiINCYtxSYr3XufsL5YWZQiIiJ7OgUopV2KRA3D3O/4mftOfJsvXMNvqx7jMPcrHCwm+y/imsgv6FPgZ8JRKVxwbArd833N3pfkoMUhI5MY2CsY37be6hifZr3u3U85wp1BFlVUkMoTHS4Ay+K6873CL/dekckxo5M4+4iULY5t2xaH7+Md917fz6jAC4SuzR2Mm5O/RftR/b1AoGv5eK12ynny3M+a7Vob+vsrVfzMeZsebGQTOaw7/ar6FM5ah45MYuJPUvH5FIgUERGRHVNXyXudApQiIiJ7PAUopd3ZUOzw85sLucx5EYDX7YM4IvAgT9k/IYKfCH7+7jubj+19cSxvivQJByZz7AEtO5U4FE6cfvShvT8A42s+5xznLQCe9J3ISWcN5MXJOQzt4wUTO+f6uPC4VDpmNx48PetwL3C51O7BBYHJ/NN3ArP2ubDRtvsPCjBumBfQnGkPAcC/djnEort4dYnmfx8lVh3iWNcLfj6deRad9ipo1nOIiIjIni1eKKdQhXJERET2dApQSrtz95Qqfuq+Rz+zhjLS+W/mCbiWj6f8J3JS4G5ODNyD79BjAPjJ2Oaf0r01Dadn2zZ8bQ1ibbArQaJ0oJzNpBEaeRB9C3YsizMzzebnx3lBylIri3/5TsDq1KXRtpZlccnJXpblOjpSRjqWE8O/7vudvKrGPfxaNae4U+lABWVJeZx+xUHYtrIkRUREpPl0qc2gXKtCOSIiIns8BSil3akoq+ZM5z0AvtnnXG69plf8tbCVxC9OzebMQ5P56yWZnHPkltOmW8pvf9aBvgU+rj47jRsuzADL4l73dCL4qSHIS13O48LT8ncqkNcxJzGo2bvL1oOc8enUlsW3Vh8AAqsW7/A569SEDY+9Uc0HX4W9w1ZX0D26mp/VZoWGjjqT1LTG15gUERER2Vl9u3rvdxZ8H8NxVChHRERkT+Zv6w6INGSM4aDqaaRTQySnMwNPPcQLBF6RySufhDj9kGTya4N5dcVaWkuPzgFuvzgTY2BTqfdJ/1f2YE4O3I2LxUnDdr56eF6DCtg3TcyIX+P2zLf7MdaZR3DhV9SM+8lOnfvDr8O8/2WYLCoYMONdRqz5gL/iTbX63iogdf+Dd+q4IiIiItsyoLufjFSLimrDwpUxhvTZtQ9EXdcw49soi1bFGNEvwPB+fs0AERER2U0oQCntilu4ifNjrwFQOfqYeFGWzrk+LjklbVu7tqrs9PqAYsjyppmfeODOr4HZPd/mzMOSycuyGdCj6f8sp9r7cZHzCoHvv8UK12CSdjyjdFOZS7Yp58HorXRaU5rw2uajJpDub91AsIiIiOwZfD6LfQcGmDorwsyF0V0KUM5bFuW592v4fr33IfI7M8J0ybU59oAkxg9PIjlJgUoREZH2TAFKaXNVNS4fzorwyczN3LzpJjpTxRKrO5ljjmq3axAEA4lvck8/JHmLbTvCsixOG7/jwcWNVi7lwQ5kRkrwr1tBtPegHdrfdQ3vzAjzc+dDOlHKenL5m/9cllrdSaOG3wzqt8N9EhEREWmq0YOCtQHKCBccm7LDGY/L18V4/oMa5i2LAZAchH0GBJm1OML6Ypcn3qzhhf+FOHxUkJMOSiYjtb2+uxQREdmzKUApbe7dmWFe/DDEGc5HdGcTRWRzo/9i7gnuPuseHjw82KrnO/+YFJ56pwaAedHujKME/9rlOxygLK8yWMaNV+t+yH86X9t7A7CZDNKSlW0gIiIiLWdIHz8pSVBaYVi61qF/96bfnjz/QQ3/+TQEgM8HR+2bxCkHJ5OVblMTTuXjOWHenh5mQ4nL61+Embkoyh8npNM5V7NDRERE2ht9hChtbskaB9s4nOG8D8ATvhNZb3Vs415t38+P9zIeLzs1lU4dWveN7nFjkjnzUG9K+WK7JwD+Vd/t8HFKKlx6mXXkUk4NQaZZwxNeT0tRgFJERERaTsBvMaq/90HvzG8jTd7v6+8i8eDkuGFB7rk8kwuOSyWrdhmelCSLY0Ync/flmVzzszTyc2w2lrhc91gFS9bEmv9CREREZJcoQCltLjfTor9ZRR6bqSCVD+wD+N3Z7We9ya05ev9kXpycw0HDk9rk/OuKvUI2X1lexmPSoq/xbVpDYOk3YJpWCbO43GWE8QKbmzsNYPBeyZw2vn4tzaTdJ4lVREREdlOj9/becMxYGMU04T3M5kqXh1+rBuAnY5O4/LS0rRYYtG2LfQYEufkXGfQp8FFRbbjpnxV8tajpwVARERFpeZriLW0u6sBIswgAp+9grh6fzfC++tFsqkVWb1bQhV7R9XS4+zcAlJ8yifD+RzTa3tq0lpwnb8XN6kD1oMsZ6XoByvQRQ/njIRkADOzpJzloYVnKoBQREZGWNbxfgGAANpW6rNzg0KvL1t8HGmN45PVqNlcZuufb/PSwpq3hnZ1uc8MFGdzz70pmL4lx5wtVTDzecOR+bfNBs4iIiCRSBqW0uWgMhrhLAfAPGszIvQI7vED6niie6WhZvOsbm/Ba8mdvNZpF6TiG7+55El9ZIYGV33HYZ7czzswBINpnSLzdsL6BHVoDSkRERGRnJQcthvfzsihnLoxus+1HsyN8tSiKzweXnZa2Q0UKk5Msfnd2OoeNCmIMPPZGNc+8V43rNm3miYiIiLQcBSilzUVjhj5mjfe4a5827s3uoyCvfirTe/YYNlM/LT5YuBr/qsUJ7ZPmfUGH//sVB5q58W0dKtcCUONPI6bvvYiIiLSR0YPqpnlvfer1plKHf77tTe3+6WEp9Oq84x+m+nwWF52YGl/L+/XPw9zxbCWV1e5O9HrnxRyjwKiIiEgDSpGSNheIVNGJUgCczj3auDe7F9sG14VSK5PzAzfh4OMSZwrHul+Q8uUHVPQcAIB/7TIyn787vt9DvtPZ3Gs4k9bdT3bNRhblH0A3nypaioiISNsY1T+AzwdrNrmsK3ISPogFcF3DA69UE4p4S9GcMHbnp2ZblsVph6TQOdfHQ69VMXdpjD8+UsHVZ6fTo1Pzvh9yXa86+ZpNDuuKHNYWede3sdTFtiE7zSI7wyYnwyYn3aIgz8fQvgG6dbS11I6IiOxRFKCUNpdXtRqA6tRcTHL7L47Tnvz5gnRufKISgHLLWz/yPXsMx7pfEFw0y4te2ja+t6cA8G3mCK6rmUCZlcmwoJ+n9rmFpdOXMGCvvfhpm12FiIiI7OnSUmyG9vYzZ2mMGd9GOOXgxLUlX/ssxKJVMVKS4NJTUptlOaADhwbp2tHmzuer2Fjqct1j5VxychoHDA7u0nGNMSxZ4/DFNxGmLYhQVtl4pqTjQHG5objcAZwGr9SQk2ExtE+AYf38HLJv6i71R0REZHegAGVbC4faugdtrlO1N727Iqe71hzYQSlJW745X2D1xfEn4asqx7dhJU5RMZnLZhHD5v9qTqHMygRg3rIYORlBFtj9GJSkBeJFRESkbe0/KMicpTFmLozGA5TVIcNT71Tz0Wxv6vf5x6ZutWL3zujV2c/tkzK4999VfLM8xt1TqjhqvxjHj0mic27Tz2OM4fv1DtMXRPhifpTCsvop42kpFn0LfHTN81GQ56Ogo01Brg/XQFmFS2mlS1mFobTCZcmaGAtXxiitMHwyN8IncyPc/3I1fQt8jOwfYJ8BAXp19mm9dhER+dFRgLINpb71NHzyX9JHHkTFGZfDHjqNY6/qhQBU5vYks437srtJamRh+Jjlp6Tz3nRcPRt74VyW/G8uHYFX7MNZbXVJaPvxHO/Nvl+zu0VERKSN7TswwKNvwPJ1DoVlDuuLXR56rYrizQbLgpPGJXPIiF3LbmxMRqrNHyak8/wHNbz+RZj3vvT+G9bXz5H7JbFP/wA+35bvuVzXsHiNw8xvI8xcmBiUTArCfgODHDg0yLA+fvz+xt/n52Vt+fF8JGr4bnWMb5ZFmbcsxvfrHZat8/576aMQORleUaH+3fz06eqje74PfyP921GxmKG82lBe7VJeZaioNlSHDI5rcFxvYk7M8bJBU5MsUlMsUpMs0pJt0lIscjNtUpP3zPsZERHZdQpQtiGT5oXjkmd/SmifQ4n2HdrGPdo+X+FajM+P26FTsxwveeb77FM9C4DKTv0UoNxBjQUoAdZ2HErH1bPJ+t9zjK6t5v2G7+CtHicaa5HuiYiIiDRZVrrNoJ5+vl0R487nq1ixwZv23CnH5pJT0hjYs+VuXXw+iwlHpzK0b4C3p4eYszTGvGWx2hknFsP6BnBcL3gYiRrCUVhf7CRM304KwIi9AowZEmTUXgGSgjsXrAsGvOndQ/sEOOcosAOpfDCjlK+/i/LNsiilFYaPZkfiWaUBP/Ts7KN3Fz/Z6RZpyRZpKbVfk21ijiEUMdREDKEwhCKG8iqX0gpTm73pPa4K7XrRntRki7wsm47Z3n/5OTZdcn107uA9bo5AqoiI/DgpQNmGasafRFpoM0x9nbT3XqBs0t5gt99UttSpL5P23gsAhPfej4qTf4nJyIHaAFijGaDRMMHFc3Bz8okV9K7f7rqkfvAiaVNfAeA/9iF06DOKgpa+iB+Z4Fb+Bd/5TX+eAazasZlr7cUaa+tB5a4d2+/PnYiIiOw59h8U4NsVsXhw8qj9kjjnyBSSG1nWpiUM7xdgeL8Am0od/vd1hKmzwpRWmPiskx9KTbbYp3+A/fcOMLzvzgclt6VDlo/DRiVx6MgkojHDwpUx5i+PsXxdjOXrHKpChqVrHJaucbZ/sO2wbchIschIs8hM9TIi/T7w2WDbFn4bDFAT9rIrq0Le18oa77/qkGFVyGHVxi37YtvQMdumU05d8NIXD2LmZtpkplpbzTQVEZEfPwUo29pxP8V89i6BVYvJfuAP1Iz7CeGhY8AfaOueJQgumBEPTgIkffslwSXzcLJz8ZUWYoLJ1Iw5htA+h+Dm5APgX7WEjFcfwr9hFQCR/iMIDxiFZVySvplGYOV3ALySehz3R0/i1oBWoNxRqckWXXJt1he75GXZFG32phats/KpPPpn+D56k5qwy8O+0wE46/BkenXx85fnKnHrZyExZnD7+nkTERGRPdMBg4O8+kkIvw8mnZTG8H5t8x4lP8fH2UekcMYhyXz1XZS1RQ5Bv0VSwCIpCEG/F8Qb2H3r07dbQsDvZXMO6+t9X4wxbCxxWbbOCwpWVLsJQcPqkMHvh+SgRXLQIiVokRS0yEz1qod3yKitIp5uk51ukZps7fT6lqGIoajMpXCz630tc9hQ4rK+2GVjiUM4ChtLXDaWuFs9RkoSZKbZZKRapKdYBP0WwYBFwO99zwN+sC2vErtteUFPy/KWK/L7rPqvfm8aenpK3X+2d7wAqo4uItJOWcaYXc/l/xErKqqgpb5DlgV5eRmUf/QBGS/cixWLAuCmZRLa73Ai/YYRK+iN8QcIrPoONyUdp2NXCGx/7R3fpjUEv5tNtEd/Yj0H7FI/k2Z9TMarD2PFolSPPZbQvoeT8dIDBNZ9v0VbY1lE+wwG1yX4/bcJ260ffCONP0DFSb/k/Kkj2Vxp+MuvMujZuX3GzOvGqiV/HnZWJGqwLfD7LW5+qoL5y7352v/6UzZPv1vN+1+GCQYt7rsyi5wMLwhcWe3y6bwI+Tk2XfN8O7QIfHvXnsdKEmmsdh8aq93DD8ep7rns/lr63157+zceihiCflQIhvY3NjvLGENphWF9scOmMpfCUrfBV4fSCtMq15eSBB2zfeTXTkHvmONldHbt6KNTjr3DP3M/lvH5MdLYtG8an/arJcamqe9J22c0aA8xa3GUee8Uc/Zh+xO55h+kfPU/kqe/h6+8hNSPXiX1o1cBMLaNVZvuZiwLNyef8LCx1OxzKG5eYtETjCFp1kdkvPY4VjQMQNVhp1F9+Bk7NX38h9O6q447D3x+yi65zcuANAY3Kxf/mqWkTH+XwMrvCC6bH+9reNR4qo46GytUTdK8afhXL8GKRYjsNZzSvcZwyb+S4+vdBDSlY6cEG6xD+buz0jn/tjIA5i2LsrnKu0OdcGRKPDgJkJ5qc+wBya3dVREREZHtSm6BadLStizLokOmRYdMm8GNvO66XrZnebWpLdDjUhkyRKMQiRmiMUM05j02xivY4xpqHxtiLsQcr9BPzIGoY6gJe4V+qmqnnzsu1IRh1cbGp6AH/NAl10fXjt4H+N06+uja0UeXXFv3KSIirUAByjb0/pdhvv4uSveOqRw2KpvqQ0+j+uCTCS78kuS5n+NfuwxfaSGW6+ImpYBlY4eq8JVsjAcwQ0PHYMWiBJfMxemQDz4//vUrAXAysvFVlJH24cskz/mMsl/8OT79uin8qxaT+v6LgBfkLB53BjVVFh0yAZ/fy5Ss5eR1ITziIOyi9QSXzAV/gGivQTgda1eVzOxA9RFnJBz/28VRqkKV8ecZqfrDv6sars905wtV8ceZ6Zo+LyIiIiLtk21bpKdapKdCQV7zH98YQ00YSisSMzc3lXpTztcWOURjDYOX0QZ98wo1dc3z0am22E9+jo/82uxLERFpHgpQtqFenX18/V2UBd/HOGxUkrfR5yMy5AAiQw4AwKqpwqqpxM3uCJaFVbmZwMpFpEx/l+Cy+SR/My1+PP+mtQAYn5+qI39KzUEnkPzlh6S9+1w8qFl5yqRG+2KXl2CXFePfuAr/yu8IrFiIv3gDAKERB/FP30m8fEc5ANdfkM6Q3o2vB+TmdSHUIKvTGNPoOi/GGP73dTj+/PfnpJORqj/wLSUrTcFfEREREdkzWZZFajKkJvsaLQ7puobCMpe1RS5rNjmsKXRYW+SwttChJgzri721NBuTlV5OdrpFToZFhwybnAyb3AaVzHMz7YQZTyIi0jgFKNtQz87eH8fC0q1X3DMpaZiUtPrnGdnxAGZg8VxSZr6Pk9WB8PBx2JWbsasriPQfgZvZAYDQ6CNxOhaQ/eiNJM/5lKpjJ2CS648X+H4hKZ/+l+Cir7dYIxLA6dCJip9cyDv31QcT73+5ioeuzt7u9a0pdJj8ZAXdOvq46qw00lPqA5Av/C/EV4u8TyaPGZ3EyP4q0tKSMhX8FRERERFplG1bdOrgo1MHH6Ma3JfUrZ25tshhXaHDxtq1MzeVumwq9YKXmytdNlfCyg1bP352ukVBno9+XX306+Znr25+OmTq/bmISEMKULahtGTvk7TqsKGy2iU5aO1QFcBo/+FE+w/ffrveexPr1B3/xtWkvfcClSf8HCsSIuWzN0j7YEq8nZucRqxrb2Ld+hLtPRgnpyNOTkeWbvRRFaqItyutMMxeEuWuFyqJevVYuGliBgN61P84GWN44JUqyqsM31bF+M+nISYclcq8ZVFe/ijEolXejrlZFqccrLUQm9OQPv54oZw6egMkIiIiIrJjGq6dObRPYkKFMYaqkMG1U/h+dSXFm11KK1xKKlyK4pXMXcJRKKs0lFXG+HZFDPASP3IzLQb08DNmSJCRewW0zqWI7PEUoGxDaSneH6HVm1wm/t9mAK44PY0DhyZW6V60MsbmKpfRe2+/ejdARbXLv6eG2Kubj4OGJ4FlUXXMBLKeup2Uae/gX7EI/6Y1WI4XxAqNHE/1Iafg5HdNOE5phcvy5TH+8pwXnExL9taG2VjicsczlQlt//x4BUfuG+T4scksXRvj/perE15//fMwr38eTtjWr5uPW36R0egUcNl5F52QyhX3lsefn3JQMqnJ+h6LiIiIiDQXy7LISLXIywuSlRxotNqtMV6hnsIyl1UbHZasibFkjcPqTQ7F5YYv5kf5Yn6U1GSL0XsHGDc0yN69/KpgLyJ7JAUo21B6ypZ/eO57qYp1RQ5jBgfp3MHGsuCGJ7wA4fXnW/Tv7t/uGiZTPgzx3pdh3p0JYwYH8fstIgNHUfmTC0l7858E1q8AIJbbmepDTiW876GNHueOZypZsaF++vlZh6cwuLef395f3mj797+K8P5XkYRtAT/xLMsfmnh8qoKTLaBjdmK25FlHpLRRT0RERERE9lyWZZGZZpGZZtO3q59Da+sOhMKGZetizF4c5fP5EUrKDVNnRZg6K0JOhsUR+yZx5L5JZKnQpYjsQRSgbEMds22G9k3im2WJmYUvfRTipY9CW7S/+Skva/GeKzLpkuutXxkKGz6cFeaAwUE6ZNo88WY1731Zf7wZC6PxjMyaA48j2msAgaXfEO01iFjPAVvtW03YJAQnh/X1c9T+3h/U352dxt+mVLHfwACXn5rGNQ+Vs7Zwy0Wjk4Lw+LXZXHnfZoo313+keP4xKRx7QJKCky1En7iKiIiIiLRfyUkWg3sHGNw7wM+OTGHhyhiffxNh+oIopRWGf08N8eonIQ4cGuS4A5Lo1UW37SLy46ffdG3Isixuv7QjM+eVMainn02lLlfe13h2YkO/vq+cgB9+d3Y6tz3tBS2feqeGGy5M592ZicHOf75dTZ8CH51ybGzbIta1L7Gufbd7jqpQfUDx12ekJSwWve/AIM/9uX66+R2TMrn9mcraNVU8qckWt12UQcBvccekTJ55r4bD90micwdbnwSKiIiIiIjgJRfUBSsvPM4wc2GUN6eFWLbW4eM5ET6eE2HvXn6OGZ3EvgMC+HxKRhCRHycFKNtYarLNkD7emiWdc328cGM2n30T4Z0ZYZau8TIY+3Xz8f16B6dBse9ojHhwss7kJ+uf33tlJr+9v5zyKsOva4OeL9yYvd2sxaLNLu9MD5Gc5LXLSrMYM2Tba18GAxY3XJgBwIZih7wsO6HYT2aazSWnpG1td2kBx4xO4p0ZYX4yNqmtuyIiIiIiIk0Q8FscODTI2CEBlqxxeGt6iBnfRvl2hVdgp0OmxRH7JHH4PklkZyjpQ0R+XBSgbGcsy+KgYUkcNCwJYwxllYacDBvXNZRXG2rC9QHHrbn1lxl07uCjV2cfy9bWRzU3lLjxqeEAVTUuy9c7lFcZ5i6N4hr4dG7iGpLpqTv2CV3nBseXtnP+MSmM3CvAwB76Jy4iIiIisjuxLK/2QP/u6RRtdnn/yzAfzgpTUm6YMjXEyx+H2H/vAMeOTqZ/d5+WzhKRHwVFL9oxy7LIyfD+2Ni2RXa6RXY6PPCbLJ56p5qZC6MAHLlvkDWFLgtXxphwVAr9unnDOuGolISsyupQYmm5O56tZPFqh23Zb2DTKodL+2LbFiP2Cmy/oYiIiIiItFt5WTZnH5HC6YckM/3bCO/NDLN4tcO0+VGmzY/Sr6uP48YkM3rvAP5dmP5tjCEaA79Pa9qLSNtQgHI3lJdtc8XpaZx7SxmZqRZnHZFCesqWKf579wrw9HXZXPNgOeuLXSKx+gDlsrWxbQYnjx+TRG6mzdGjNUVYRERERESkLQX89TPtvl8f490ZYT77JsLStQ73vVRFbqbFMaOTOXyfIGmN3Bs2NHdplK+/i1Ja4VJW6VJWaSirdIl4+S/4fd4yXkkBSA5aHDA4yAljk7Z7XBGRXaEA5W4q4Lf45x+ycVyzzT8UwYBFMOB9AhaJelmU975UyZwlsUbbj9jLzy9PSCMvS398RERERBp69tlnefzxxyksLGTgwIFcf/31DBs2bKvt3377be69917Wrl1Lr169uPrqqxk/fnwr9lhEfox6d/Fz8cl+zj4ihfe+DPP+l2GKyw3Pvl/DK5/UcPyYZI4fk0xqcmIm5Loih6ffrWHW4ug2jx9zIOYYqkMAhlc/CfHel2FOOjCZY0YnkRRUhqWIND8FKHdjXiGb7f9xCNaO8qKVMRZ8H0sITmalWfzyhFQ65/pITbLIVWBSREREZAtvvfUWt99+O5MnT2b48OE89dRTTJw4kXfeeYfc3Nwt2s+aNYurrrqK3/72txx66KG8/vrrXHrppbzyyiv079+/Da5ARH5sstJtzjg0hZPGJfP5NxHemBZizSaXlz4K8faMMCeMTeKY0ckYAy9/XMPbM8I4DvhsOHRUkB75PrIzbLLTbbLTLTJSbWKOIRKFSMwQiRrWFbu8/HENaza5PPdBDW9ND3Hq+GSG9A4QjRkiMYjGDFEHkgKQk26TnWGTvI0gZixm2FjqsqHEYV2R97WozCXqgOuC4xpcF1zTMJvTIuj3HltWXTtwjdfWsiDgs/D7vH38foucLIdkf5TsdJucDJsOGd51NizoKiLth2WMMdtvtucqKqqgpb5DlgV5eRkteg6AC24rpSa85fasNIv7fp21zT8e4mmtsZJdp7HafWisdh8aq93DD8ep7rk0jzPOOIOhQ4fy5z//GQDXdRk/fjznnnsuF1100Rbtf/3rX1NTU8PDDz8c33bmmWcycOBAbrrpph06d0v/29O/8fZLY9O+tbfxcV3DjG+j/PujGtYWugBkpFrYFmyu8jo4qn+Ac49OoSCv6cVNXdfw2TcR/j01xKZSt0n7pCRBTm2gMhIzRKMQjhrCUUMoQpt+v4IBSE2ySE22SE2ySEmysG1vPC28WhC2BYEApAQtkoNem+Sgha/u29ag/5GYoTpkqAp5X6vDhnCk9ngN/gNwHC8AG3O8AKvjegeyaxvUtU2qPXdKkkVyktdPv8+qP16Dtj4bfLbXN58Ntu0dr+6xz/auyXFN7fm9DFnH9cbBNd5XY8wW41LX77p27g/2adjGqv2fzwbbqjt37fe29rWG/c7ISKaqKoRVm3Rl1263G1yDZXnbDYk/M8aAqRuEbfws1e33w/8a1aB/CY8baxofh8QxaXicH/ZjK6fcejtT/7yxPjfsW5MjOlbCFywLenb20blD/e+Dlvi91tT3pMqg3AMM7ROIF9Spc/MvMujW0afgpIiIiMh2RCIRFixYwKRJk+LbbNtm7NixzJ49u9F95syZwwUXXJCwbdy4cXzwwQc7fP6WLtAbv7nU28J2R2PTvrW38fH5LMYODXLA4ABfzI/y76k1rC/2AooFeTbnH5PKyP47XkjT57MYPyKJA4cE+d+sMG98HqYqZAj4vaXHAn4vezEUMZRWuISjUBOGmvDWg5nJQeiS66Nzrk2XXB/5OTZJgS2DavGMztrgZjgKmAZBrNpgGEAsVj89PeaCi5/1hWFKK1xKKwwlFS6OQ/x4ZZXtIKq8R6tp6w4IkJps8fi1Wfh89UHyhl+bQ1OPpQDlHuCMQ1MorzKs3BijJgzHjk6if3cNvYiIiEhTlJaW4jjOFlO5c3NzWb58eaP7FBUVkZeXt0X7oqKiHT5/bm7rZMK21nlkx2ls2rf2OD4nHwYnjDd8NKuaUMRw1Og0As0wtfmc47z/tsYYL4uwuNyhZLNDTdiQVJuFmBSwSApapCZ7U66tVo7suq6hssalqqbua+1/IRPPHjSAqZ1eHokaqkMu1WFDTcilOuTiNhLT9Pu86fHpqTbpKTbpKRbJSXZCVmLdfj7bC/j6a6ej+2qjq16mX33bcKT23KG6r94U+IbHNLUZjY4x8cxMxwXHqf/qGi9r0zUGn103Bb7+/LZdnwFoN8gcjF9m7Xms2ozGusxI27a2zGw0Jp5lWTdNvy5LtK6NaXDMumt2Xe+xl51Zv1/dY9c0yFBMyCCt//nZ2o+SqQ1m112fVZuVWde8YYZiw8nFdf1rLPUxMSvTxH9mGr5W93hbP+ENv8eNZTY2fN60A21bwrU22DC0XxKdOmVu0b4tfq8pSrUH6NHJx+SJ7e+PpoiIiIhsX3Fxy0/xzs3NaPHzyI7T2LRvu8P4DO/tfd1cVtmq5031QWqHxl9zI1Bc3LLn39bYBICcFO+/7aur0dD0KfFe5MfZgfZN6cOPq1bE7vBvZ09SVFQRf9wSY1N3zO1RgFJEREREZBtycnLw+XwU/+COuri4eIssyTp5eXlbZEtuq/22bHPNrGbUWueRHaexad80Pu2XxqZ90/i0X20xNj+uMLyIiIiISDMLBoMMHjyYadOmxbe5rsu0adMYOXJko/uMGDGC6dOnJ2z74osvGDFiREt2VURERGS3pACliIiIiMh2XHjhhUyZMoVXX32VZcuWceONN1JTU8Opp54KwDXXXMNdd90Vb3/eeefx6aef8sQTT7Bs2TL+/ve/M3/+fCZMmNBWlyAiIiLSbmmKt4iIiIjIdhx33HGUlJRw3333UVhYyKBBg3jsscfiU7bXr1+Pbdd/9j9q1CjuvPNO7rnnHv72t7/Rq1cvHnjgAfr3799WlyAiIiLSbilAKSIiIiLSBBMmTNhqBuTTTz+9xbZjjz2WY489tqW7JSIiIrLb0xRvERERERERERERaTMKUIqIiIiIiIiIiEibUYBSRERERERERERE2owClCIiIiIiIiIiItJmFKAUERERERERERGRNqMApYiIiIiIiIiIiLQZBShFRERERERERESkzShAKSIiIiIiIiIiIm1GAUoRERERERERERFpMwpQioiIiIiIiIiISJtRgFJERERERERERETajAKUIiIiIiIiIiIi0mYUoBQREREREREREZE242/rDrR3ltXyx27Jc0jz0FjtPjRWuw+N1e5DY7V7+OE4abx+PFp6LPUz035pbNo3jU/7pbFp3zQ+7VdLjE1Tj2UZY0zznVZERERERERERESk6TTFW0RERERERERERNqMApQiIiIiIiIiIiLSZhSgFBERERERERERkTajAKWIiIiIiIiIiIi0GQUoRUREREREREREpM0oQCkiIiIiIiIiIiJtRgFKERERERERERERaTMKUIqIiIiIiIiIiEibUYBSRERERERERERE2owClCIiIiIiIiIiItJmFKAUEREREdlDPfvssxx22GEMHTqUM844g3nz5rV1l/Y4Dz/8MKeddhojR45kzJgxXHLJJSxfvjyhTTgcZvLkyYwePZqRI0dy+eWXU1RU1EY93rM98sgjDBgwgFtvvTW+TePTdjZu3MjVV1/N6NGjGTZsGCeccALffPNN/HVjDPfeey/jxo1j2LBhXHDBBaxYsaLtOrwHcRyHe+65h8MOO4xhw4ZxxBFH8MADD2CMibfR+LSeL7/8kosvvphx48YxYMAAPvjgg4TXmzIWZWVlXHXVVYwaNYp9992XP/7xj1RVVTVbHxWgFBERERHZA7311lvcfvvtXHrppbz66qsMHDiQiRMnUlxc3NZd26PMnDmTc845hylTpvDkk08Si8WYOHEi1dXV8Ta33XYbU6dO5Z577uHpp59m06ZNXHbZZW3Y6z3TvHnzeOGFFxgwYEDCdo1P29i8eTNnn302gUCARx99lDfffJNrr72WrKyseJtHH32Up59+mhtvvJEpU6aQkpLCxIkTCYfDbdjzPcOjjz7K888/z5///Gfeeustrr76ah577DGefvrphDYan9ZRXV3NgAEDuOGGGxp9vSljcfXVV7N06VKefPJJHnroIb766iv+/Oc/N18njYiIiIiI7HFOP/10M3ny5Phzx3HMuHHjzMMPP9yGvZLi4mLTv39/M3PmTGOMMeXl5Wbw4MHm7bffjrdZunSp6d+/v5k9e3Yb9XLPU1lZaY466ijz+eefmwkTJphbbrnFGKPxaUt//etfzdlnn73V113XNQceeKB57LHH4tvKy8vNkCFDzBtvvNEaXdyjXXTRReYPf/hDwrbLLrvMXHXVVcYYjU9b6t+/v3n//ffjz5syFnW/1+bNmxdv8/HHH5sBAwaYDRs2NEu/lEEpIiIiIrKHiUQiLFiwgLFjx8a32bbN2LFjmT17dhv2TCoqKgDiWWDz588nGo0mjFXfvn0pKChgzpw5bdHFPdJNN93E+PHjE8YBND5t6cMPP2TIkCFcccUVjBkzhpNPPpkpU6bEX1+zZg2FhYUJY5ORkcHw4cP1e64VjBw5kunTp/P9998DsGjRIr7++msOPvhgQOPTnjRlLGbPnk1mZiZDhw6Ntxk7diy2bTfb8jD+ZjmKiIiIiIjsNkpLS3Ech9zc3ITtubm5W6x/KK3HdV1uu+02Ro0aRf/+/QEoKioiEAiQmZmZ0DY3N5fCwsK26OYe58033+Tbb7/lpZde2uI1jU/bWb16Nc8//zwXXnghF198Md988w233HILgUCAU045Jf79b+z3nNYIbXkXXXQRlZWVHHvssfh8PhzH4Te/+Q0nnngigManHWnKWBQVFdGhQ4eE1/1+P1lZWc32u04BShERERERkXZg8uTJLFmyhOeee66tuyK11q9fz6233soTTzxBUlJSW3dHGjDGMGTIEH77298CsPfee7NkyRJeeOEFTjnllDbunbz99tu8/vrr3HXXXfTr14+FCxdy++23k5+fr/GRRilAKSKyHWvWrOHwww9P2JacnExGRgZ9+/Zl1KhRnHLKKfTo0aONeigiIrJjcnJy8Pl8WxTEKS4uJi8vr416tWe76aab+Oijj3jmmWfo3LlzfHteXh7RaJTy8vKELL3i4mI6duzYFl3doyxYsIDi4mJOPfXU+DbHcfjyyy959tlnefzxxzU+baRjx4707ds3YVufPn14991346+DNxb5+fnxNsXFxQwcOLD1OrqH+stf/sJFF13E8ccfD8CAAQNYt24dDz/8MKeccorGpx1pyljk5eVRUlKSsF8sFmPz5s3N9rtOa1CKiDRRjx49uOyyy7jssss477zzOPjggykpKeEf//gHxxxzDH/7298wxrR1N0VERLYrGAwyePBgpk2bFt/mui7Tpk1j5MiRbdizPY8xhptuuon333+fp556iu7duye8PmTIEAKBQMJYLV++nHXr1jFixIhW7u2e54ADDuD111/nP//5T/y/IUOGcMIJJ8Qfa3zaxqhRo+LrG9ZZsWIFXbt2BaBbt2507NgxYWwqKyuZO3eufs+1glAohGVZCdt8Pl/8fknj0340ZSxGjhxJeXk58+fPj7eZPn06rusybNiwZumHMihFRJqoR48eXH755Vts/+qrr7jmmmt4+OGHsW2bX//6163fORERkR104YUXcu211zJkyBCGDRvGU089RU1NTUKmmLS8yZMn88Ybb/CPf/yDtLS0+FpeGRkZ8Rkbp512GnfccQdZWVmkp6dzyy23MHLkSAXAWkF6enp8PdA6qampZGdnx7drfNrG+eefz9lnn81DDz3Esccey7x585gyZQo33XQTAJZlcd555/Hggw/Ss2dPunXrxr333kt+fj5HHHFEG/f+x+/QQw/loYceoqCgID7F+8knn+S0004DND6traqqilWrVsWfr1mzhoULF5KVlUVBQcF2x6Jv374cdNBBXH/99UyePJloNMrNN9/M8ccfT6dOnZqlj5ZRuo+IyDbVTfEeN24cjz/+eKNtli9fzkknnYQxhvfff58uXbpQUVHB888/zyeffMKKFSsoKysjOzubsWPHctlllyVMCb/77rt56KGHuPvuuznuuOO2OP5LL73En/70J377298yadIkwJty9PDDDzNv3jyKiopIT0+na9euHHHEEfzqV79qmW+GiIj8qDzzzDM8/vjjFBYWMmjQIK677jqGDx/e1t3aowwYMKDR7bfffns8WBwOh7njjjt48803iUQijBs3jhtuuEFTiNvIueeey8CBA/nTn/4EaHza0tSpU/nb3/7GihUr6NatGxdeeCFnnnlm/HVjDPfddx9TpkyhvLycffbZhxtuuIHevXu3Ya/3DJWVldx777188MEH8anDxx9/PJdeeinBYBDQ+LSmGTNmcN55522x/ZRTTuGOO+5o0liUlZVx88038+GHH2LbNkcddRTXXXcdaWlpzdJHBShFRLajKQFKgGuuuYbXXnuN6667jnPPPZc5c+YwYcIERo8eTY8ePUhJSWH58uV88sknZGRk8Morr8SnoKxdu5YjjjiCMWPG8MQTT2xx7J/+9KfMnz+fjz76iI4dO7Jw4ULOPPNMfD4fhx9+OAUFBZSXl7Ns2TLWrl3L1KlTW+z7ISIiIiIiItKcNMVbRKSZ7L///rz22mt88803gJcG/9lnn5GdnZ3Qbvr06Vx44YU8+OCD3HLLLQB07dqVcePG8emnn7JmzRq6desWb79kyRLmzJnDEUccEf8k/rXXXiMSifDAAw9sMQWitLS0Ba9SREREREREpHmpSI6ISDOpq3hWFyDMyMjYIjgJ3mLr/fr144svvkjYftZZZ2GM4aWXXkrY/u9//xsgYbpKneTk5C225eTk7FT/RURERERERNqCApQiIi1oxowZXHLJJYwbN47BgwczYMAABgwYwOLFi9m0aVNC20MOOYROnTrxyiuv4DgOAJFIhNdee40uXbpw0EEHxdsee+yx2LbNZZddxh/+8AfeeOMNNm7c2KrXJiIiIiIiItIcNMVbRKSZ1AUcO3ToAMDbb7/Nb37zG1JTUxk3bhxdu3YlJSUFy7J49dVXWbt2bcL+Pp+PM844g/vvv59PPvmEQw89lA8++ICysjImTJiAbdd/pjR8+HCefvppHnroId544w1eeeUVAIYOHcrVV1/NAQcc0EpXLSIiIiIiIrJrFKAUEWkmM2fOBLwgIcD9999PUlISr7zyCr169Upo++abbzZ6jDPOOIMHH3yQKVOmcOihh/Lvf/8b27Y5oBaOVAAACGhJREFU7bTTtmi777778thjjxEKhZg7dy5Tp07lueeeY9KkSbzxxht07969eS9QREREREREpAVoireISDP4/vvvefvttwkGgxx55JEArFq1ir59+24RnNy0aRNr1qxp9DidO3dm/PjxfPLJJ8yaNYtp06Yxbtw4CgoKtnru5ORkRo8eze9//3smTZpEKBTi888/b7ZrExEREREREWlJClCKiOyir7/+mokTJxKJRLjooovo1KkTAAUFBaxcuZKioqJ423A4zI033kg0Gt3q8c466yxisRhXXnklxphGi+PMnj2bcDi8xfbi4mIAkpKSdvWyRERERERERFqFpniLiDTRqlWr+Pvf/w5ANBqluLiYefPmsXjxYnw+H7/61a+47LLL4u3PPfdcbr75Zk4++WSOOeYYYrEYX3zxBcYYBg4cyKJFixo9z0EHHUTXrl1Zu3YtHTt25NBDD92izaOPPsqMGTPYb7/96NatG8FgkG+//ZZp06bRvXv3eBaniIiIiIiISHunAKWISBOtWrWK+++/H/CmVWdkZNCnTx8uueQSTjnlFHr06JHQ/pxzzsHv9/PMM88wZcoUMjMzGT9+PFdddRVXXnnlVs9j2zYnnngiDz74IKeccgp+/5a/qs8++2wyMjKYO3cuM2fOxBhDQUEBF198Meeffz7p6enNe/EiIiIiIttx2GGHAfDhhx+2cU9EZHdjGWNMW3dCREQSTZo0iY8//ph3332Xnj17tnV3RERERKSVrFmzhsMPP3ybbbp27doug4AKUIrIzlIGpYhIO7N06VI+/vhjDjzwQAUnRURERPZQPXr04MQTT2z0tYyMjFbujYhIy1KAUkSknXj99df5/vvv+c9//gPApZde2rYdEhEREZE206NHDy6//PK27oaISKtQgFJEpJ2YMmUKX331FQUFBdx6662MGjWqrbskIiIiIu3cgAED2H///fnrX//KX/7yFz7//HNCoRCDBg3iiiuuYOzYsVvsU1JSwoMPPsj//vc/Nv1/e/cX0lQbwHH856xNYWpZETai7MKVYEbIornlRUFBhRcFdmV1Y5RFIHRR0lVURFGUUhSGUGGU3ViU6yawfxDVQozKG6GDEkmt7DSm1vK9CM/7jm30h3qPw+8Hxthzfufh7Fz+eM5zBgeVl5cnn8+n+vp6lZSUJOVHR0fV1tamGzduqK+vT5JUVFSkYDCoHTt2qKCgICEfjUZ14sQJhUIhffz4UcXFxaqvr9eaNWv+zk0AkPHYgxIAAAAAgAlifA/KQCCg8+fP/zDv9Xrl9XplmqamT58uv9+vSCSizs5OjYyM6NSpU1q1apWVj0QiqqmpkWEY8vl8WrJkifr7+3X79m05nU61tLSooqLCyg8PD2vr1q0Kh8OaP3++gsGgpk6dqtevX+vhw4e6fPmyFi1aJOn7HpRfvnyRx+PR0NCQ/H6/YrGYbt26peHhYbW0tCgQCPz5mwYg47GCEgAAAACACcYwDDU1NaU8Vl5erhUrVli/e3t7tW7dOh07dkxZWVmSpNraWm3cuFH79+9XIBBQTk6OJOno0aMyDEPbtm1TQ0ODNUdXV5fq6uq0b98+hUIhORwOSdLJkycVDodVXV2tw4cPKzs72zrHNE0rN25wcFBlZWW6cOGCnE6nJGn9+vXasmWLWltbKSgBpERBCQAAAADABGMYhpqbm1Meq62tTSgos7Oz1dDQYJWTkrRw4UJVV1fr2rVr6urq0urVqzU6OqqbN29q2rRp2r59e8KcVVVVqqys1IMHDxQOh1VRUaGvX7/qypUrysvLU2NjY0I5KaV/Wc/evXutclKSli9fLo/Ho+fPn//yfQAwOTh+HAEAAAAAAP+nQCCg3t7elJ/GxsaEbFFRkTweT9Ic449qv3jxQpLU19enkZERLV68WLm5uUn5ZcuWSZJevnxp5aPRqMrKypL2mUwnPz9fc+fOTRqfPXu2Pn369FNzAJh8KCgBAAAAAMhgM2fOTDk+Y8YMSdLnz58TvtPlZ82alZAzTVPS93LxZ6VbVTllyhR9+/btp+cBMLlQUAIAAAAAkMHevXuXcvz9+/eSJLfbnfCdLj8+Pp7Lz8+XJL19+/bPXSwApEBBCQAAAABABnvz5o0GBgaSxp88eSJJKi0tlSQtWLBALpdLPT09isViSflHjx5JkvVW7uLiYrndbvX09GhoaOhvXT4AUFACAAAAAJDJ4vG4jh8/rrGxMWvs1atX6ujoUGFhoaqqqiRJTqdTa9eu1YcPH3T27NmEOe7evav79+9r3rx5Wrp0qaTvj2XX1NTINE0dPHhQ8Xg84RzTNBWNRv/yvwMwGfAWbwAAAAAAJhjDMNTU1JT2eF1dnVwulyTJ6/UqHA5rw4YN8vv9ikQi6uzsVDwe14EDB5STk2Odt2fPHj1+/FhnzpzRs2fPVF5eroGBAYVCIeXm5urQoUNyOP5dy7R79251d3ero6ND3d3dCgaDcjqd6u/v171799TW1matuASA30VBCQAAAADABGMYhpqbm9Me37x5s1VQFhQU6Ny5czpy5Ija29sVi8VUWlqqXbt2qbKyMuG8wsJCXb16VadPn9adO3f09OlTud1urVy5Ujt37lRJSUlC3uVyqbW1VZcuXdL169fV3t4uh8OhOXPmaNOmTSnfHg4Avypr7L9rwAEAAAAAQMbwer3y+Xy6ePGi3ZcCAL+NPSgBAAAAAAAA2IaCEgAAAAAAAIBtKCgBAAAAAAAA2IY9KAEAAAAAAADYhhWUAAAAAAAAAGxDQQkAAAAAAADANhSUAAAAAAAAAGxDQQkAAAAAAADANhSUAAAAAAAAAGxDQQkAAAAAAADANhSUAAAAAAAAAGxDQQkAAAAAAADANv8AA7JX4WZNOjUAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VKGukOh4-V6f",
"outputId": "dfa2efb1-a65b-4882-f2e5-885b86a8d5de"
},
"cell_type": "code",
"source": [
"import math, time\n",
"from sklearn.metrics import mean_squared_error\n",
"\n",
"# make predictions\n",
"y_test_pred = model(x_test)\n",
"\n",
"# invert predictions\n",
"y_train_pred = scaler.inverse_transform(y_train_pred.detach().numpy())\n",
"y_train = scaler.inverse_transform(y_train_lstm.detach().numpy())\n",
"y_test_pred = scaler.inverse_transform(y_test_pred.detach().numpy())\n",
"y_test = scaler.inverse_transform(y_test_lstm.detach().numpy())\n",
"\n",
"# calculate root mean squared error\n",
"trainScore = math.sqrt(mean_squared_error(y_train[:,0], y_train_pred[:,0]))\n",
"print('Train Score: %.2f RMSE' % (trainScore))\n",
"testScore = math.sqrt(mean_squared_error(y_test[:,0], y_test_pred[:,0]))\n",
"print('Test Score: %.2f RMSE' % (testScore))\n",
"lstm.append(trainScore)\n",
"lstm.append(testScore)\n",
"lstm.append(training_time)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Train Score: 10.12 RMSE\n",
"Test Score: 259.75 RMSE\n"
]
}
]
},
{
"metadata": {
"trusted": true,
"id": "9Wl1XfQz-V6f"
},
"cell_type": "code",
"source": [
"# shift train predictions for plotting\n",
"trainPredictPlot = np.empty_like(price)\n",
"trainPredictPlot[:, :] = np.nan\n",
"trainPredictPlot[lookback:len(y_train_pred)+lookback, :] = y_train_pred\n",
"\n",
"# shift test predictions for plotting\n",
"testPredictPlot = np.empty_like(price)\n",
"testPredictPlot[:, :] = np.nan\n",
"testPredictPlot[len(y_train_pred)+lookback-1:len(price)-1, :] = y_test_pred\n",
"\n",
"original = scaler.inverse_transform(price['Close'].values.reshape(-1,1))\n",
"\n",
"predictions = np.append(trainPredictPlot, testPredictPlot, axis=1)\n",
"predictions = np.append(predictions, original, axis=1)\n",
"result = pd.DataFrame(predictions)"
],
"execution_count": null,
"outputs": []
},
{
"metadata": {
"trusted": true,
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "sfo0gwQE-V6g",
"outputId": "83fc4e5a-a189-475a-f2f0-54c0acc6d5b6"
},
"cell_type": "code",
"source": [
"import plotly.express as px\n",
"import plotly.graph_objects as go\n",
"\n",
"fig = go.Figure()\n",
"fig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[0],\n",
" mode='lines',\n",
" name='Train prediction')))\n",
"fig.add_trace(go.Scatter(x=result.index, y=result[1],\n",
" mode='lines',\n",
" name='Test prediction'))\n",
"fig.add_trace(go.Scatter(go.Scatter(x=result.index, y=result[2],\n",
" mode='lines',\n",
" name='Actual Value')))\n",
"fig.update_layout(\n",
" xaxis=dict(\n",
" showline=True,\n",
" showgrid=True,\n",
" showticklabels=False,\n",
" linecolor='white',\n",
" linewidth=2\n",
" ),\n",
" yaxis=dict(\n",
" title_text='Close (USD)',\n",
" titlefont=dict(\n",
" family='Rockwell',\n",
" size=12,\n",
" color='white',\n",
" ),\n",
" showline=True,\n",
" showgrid=True,\n",
" showticklabels=True,\n",
" linecolor='white',\n",
" linewidth=2,\n",
" ticks='outside',\n",
" tickfont=dict(\n",
" family='Rockwell',\n",
" size=12,\n",
" color='white',\n",
" ),\n",
" ),\n",
" showlegend=True,\n",
" template = 'plotly_dark'\n",
"\n",
")\n",
"\n",
"\n",
"\n",
"annotations = []\n",
"annotations.append(dict(xref='paper', yref='paper', x=0.0, y=1.05,\n",
" xanchor='left', yanchor='bottom',\n",
" text='Results (LSTM)',\n",
" font=dict(family='Rockwell',\n",
" size=26,\n",
" color='white'),\n",
" showarrow=False))\n",
"fig.update_layout(annotations=annotations)\n",
"\n",
"fig.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<html>\n",
"<head><meta charset=\"utf-8\" /></head>\n",
"<body>\n",
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