├── stock-risk-profiling
    ├── requirements.txt
    ├── LICENSE
    └── notebooks
    │   └── 01_pca_clustering_stocks.ipynb
├── LICENSE
└── README.md


/stock-risk-profiling/requirements.txt:
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1 | pandas
2 | numpy
3 | matplotlib
4 | seaborn
5 | scikit-learn
6 | yfinance
7 | 


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/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2025 Cristiane Silva
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/stock-risk-profiling/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2025
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Stock Risk Profiling using PCA and Clustering
 2 | 
 3 | Project combining **Finance** and **Data Science** to segment stocks by their risk–return profiles using **PCA (Principal Component Analysis)** and **Clustering**.
 4 | 
 5 | ---
 6 | 
 7 | ## 🎯 Objective
 8 | Group stocks according to risk and performance metrics (mean return, volatility, drawdown, etc.) using **PCA** to reduce dimensionality and **K-Means** (optionally hierarchical clustering) for segmentation.
 9 | 
10 | ---
11 | 
12 | ## 🧠 Methodology
13 | 1. **Data collection** via `yfinance` (adjusted daily prices).  
14 | 2. **Feature engineering** for risk and return metrics:  
15 |    - Mean daily and annualized return  
16 |    - Daily and annualized volatility (standard deviation)  
17 |    - Maximum drawdown  
18 |    - Downside deviation  
19 |    - Sharpe ratio (rf ≈ 0% for simplicity, adjustable)  
20 |    - *Optional:* Beta vs. market index  
21 | 3. **Standardization** of variables (z-score).  
22 | 4. **PCA** for compressing correlated variables and analyzing explained variance.  
23 | 5. **Clustering** (K-Means) with *k* selection based on **silhouette score**; comparison with hierarchical clustering.  
24 | 6. **2D visualization** (PC1 × PC2) with color-coded clusters.  
25 | 7. **Export** of tables containing PCA scores, cluster labels, and metrics.
26 | 
27 | ---
28 | 
29 | ## 📊 Data
30 | - **Source:** Yahoo Finance via `yfinance`.  
31 | - Supports stocks from **B3** (Brazil, suffix `.SA`) or **European/American** markets.  
32 | - The notebook includes an editable list of stock tickers.
33 | 
34 | ---
35 | 
36 | ## 🗂️ Project Structure
37 | ```
38 | stock-risk-profiling/
39 | ├── data/                  # raw and processed datasets
40 | ├── notebooks/             # Jupyter notebooks with analyses
41 | ├── src/                   # reusable Python scripts
42 | ├── results/               # figures, tables, final outputs
43 | ├── requirements.txt       # dependencies
44 | └── README.md              # this file
45 | ```
46 | 
47 | ---
48 | 
49 | 
50 | [See the notebook here](stock-risk-profiling/notebooks)
51 | 
52 | 
53 | 
54 | ---
55 | 
56 | ## ⚖️ License
57 | MIT License — free to use, modify, and share with proper attribution.  
58 | Includes no warranty.
59 | 
60 | ---
61 | 
62 | *Last updated: 2025-10-07*
63 | 


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/stock-risk-profiling/notebooks/01_pca_clustering_stocks.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "cells": [
  3 |     {
  4 |       "cell_type": "markdown",
  5 |       "id": "1d4affde",
  6 |       "metadata": {
  7 |         "id": "1d4affde"
  8 |       },
  9 |       "source": [
 10 |         "# Stock Risk Profiling with PCA and Clustering"
 11 |       ]
 12 |     },
 13 |     {
 14 |       "cell_type": "code",
 15 |       "execution_count": 1,
 16 |       "id": "f7aa1e3d",
 17 |       "metadata": {
 18 |         "id": "f7aa1e3d"
 19 |       },
 20 |       "outputs": [],
 21 |       "source": [
 22 |         "import warnings\n",
 23 |         "warnings.filterwarnings('ignore')\n",
 24 |         "\n",
 25 |         "import os, sys, math\n",
 26 |         "import pandas as pd\n",
 27 |         "import numpy as np\n",
 28 |         "import yfinance as yf\n",
 29 |         "import matplotlib.pyplot as plt\n",
 30 |         "import seaborn as sns\n",
 31 |         "\n",
 32 |         "from sklearn.preprocessing import StandardScaler\n",
 33 |         "from sklearn.decomposition import PCA\n",
 34 |         "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
 35 |         "from sklearn.metrics import silhouette_score\n",
 36 |         "\n",
 37 |         "sns.set(context='notebook', style='whitegrid')"
 38 |       ]
 39 |     },
 40 |     {
 41 |       "cell_type": "code",
 42 |       "execution_count": 2,
 43 |       "id": "c30710c3",
 44 |       "metadata": {
 45 |         "id": "c30710c3"
 46 |       },
 47 |       "outputs": [],
 48 |       "source": [
 49 |         "TICKERS = [\n",
 50 |         "    'PETR4.SA', 'VALE3.SA', 'ITUB4.SA',  # Brazil\n",
 51 |         "    'AAPL', 'MSFT', 'GOOGL',             # USA\n",
 52 |         "    'MC.PA', 'NESN.SW'                   # Europe\n",
 53 |         "]\n",
 54 |         "START = '2020-01-01'\n",
 55 |         "END = None\n",
 56 |         "PERIODS_PER_YEAR = 252\n",
 57 |         "RISK_FREE_PER_PERIOD = 0.0\n",
 58 |         "RANDOM_STATE = 42"
 59 |       ]
 60 |     },
 61 |     {
 62 |       "cell_type": "code",
 63 |       "execution_count": 3,
 64 |       "id": "30cf05f3",
 65 |       "metadata": {
 66 |         "id": "30cf05f3"
 67 |       },
 68 |       "outputs": [],
 69 |       "source": [
 70 |         "def compute_drawdown(return_series: pd.Series) -> float:\n",
 71 |         "    wealth = (1 + return_series.fillna(0)).cumprod()\n",
 72 |         "    running_max = wealth.cummax()\n",
 73 |         "    drawdown = (wealth / running_max) - 1.0\n",
 74 |         "    return float(drawdown.min())\n",
 75 |         "\n",
 76 |         "def downside_deviation(returns: pd.Series, mar: float = 0.0) -> float:\n",
 77 |         "    downside = np.minimum(0, returns - mar)\n",
 78 |         "    return float(np.sqrt((downside ** 2).mean()))\n",
 79 |         "\n",
 80 |         "def sharpe_ratio(returns: pd.Series, rf: float = 0.0, periods_per_year: int = 252) -> float:\n",
 81 |         "    excess = returns - rf\n",
 82 |         "    mean = excess.mean() * periods_per_year\n",
 83 |         "    vol = excess.std(ddof=1) * np.sqrt(periods_per_year)\n",
 84 |         "    return float(np.nan) if vol == 0 else float(mean / vol)"
 85 |       ]
 86 |     },
 87 |     {
 88 |       "cell_type": "code",
 89 |       "execution_count": 4,
 90 |       "id": "dae22e45",
 91 |       "metadata": {
 92 |         "id": "dae22e45",
 93 |         "outputId": "a7869cc3-25b8-4966-e632-010bdecd9bc9",
 94 |         "colab": {
 95 |           "base_uri": "https://localhost:8080/"
 96 |         }
 97 |       },
 98 |       "outputs": [
 99 |         {
100 |           "output_type": "stream",
101 |           "name": "stderr",
102 |           "text": [
103 |             "[*********************100%***********************]  8 of 8 completed\n"
104 |           ]
105 |         }
106 |       ],
107 |       "source": [
108 |         "prices = yf.download(TICKERS, start=START, end=END, auto_adjust=True)['Close']\n",
109 |         "prices = prices.dropna(how='all')\n",
110 |         "returns = prices.pct_change().dropna(how='all')\n",
111 |         "\n",
112 |         "features = {}\n",
113 |         "for col in returns.columns:\n",
114 |         "    r = returns[col].dropna()\n",
115 |         "    if r.empty:\n",
116 |         "        continue\n",
117 |         "    mean_daily = r.mean()\n",
118 |         "    vol_daily = r.std(ddof=1)\n",
119 |         "    dd = compute_drawdown(r)\n",
120 |         "    ddv = downside_deviation(r, mar=0.0)\n",
121 |         "    shrp = sharpe_ratio(r, rf=RISK_FREE_PER_PERIOD, periods_per_year=PERIODS_PER_YEAR)\n",
122 |         "    features[col] = {\n",
123 |         "        'ret_daily_mean': mean_daily,\n",
124 |         "        'ret_ann_mean': mean_daily * PERIODS_PER_YEAR,\n",
125 |         "        'vol_daily': vol_daily,\n",
126 |         "        'vol_ann': vol_daily * math.sqrt(PERIODS_PER_YEAR),\n",
127 |         "        'max_drawdown': dd,\n",
128 |         "        'downside_dev': ddv,\n",
129 |         "        'sharpe_ann': shrp,\n",
130 |         "        'n_obs': len(r)\n",
131 |         "    }\n",
132 |         "\n",
133 |         "feat_df = pd.DataFrame(features).T.dropna().sort_index()"
134 |       ]
135 |     },
136 |     {
137 |       "cell_type": "code",
138 |       "execution_count": 5,
139 |       "id": "30e9b08b",
140 |       "metadata": {
141 |         "id": "30e9b08b"
142 |       },
143 |       "outputs": [],
144 |       "source": [
145 |         "X = feat_df.select_dtypes(include=[np.number]).copy()\n",
146 |         "scaler = StandardScaler()\n",
147 |         "X_scaled = scaler.fit_transform(X)\n",
148 |         "\n",
149 |         "pca = PCA(n_components=min(5, X.shape[1]))\n",
150 |         "X_pca = pca.fit_transform(X_scaled)"
151 |       ]
152 |     },
153 |     {
154 |       "cell_type": "code",
155 |       "execution_count": 6,
156 |       "id": "6793f2f2",
157 |       "metadata": {
158 |         "id": "6793f2f2"
159 |       },
160 |       "outputs": [],
161 |       "source": [
162 |         "explained = pd.DataFrame({\n",
163 |         "    'PC': [f'PC{i+1}' for i in range(pca.n_components_)],\n",
164 |         "    'Explained_Variance_Ratio': pca.explained_variance_ratio_,\n",
165 |         "    'Cumulative': np.cumsum(pca.explained_variance_ratio_)\n",
166 |         "})"
167 |       ]
168 |     },
169 |     {
170 |       "cell_type": "code",
171 |       "execution_count": 7,
172 |       "id": "480e67ea",
173 |       "metadata": {
174 |         "id": "480e67ea"
175 |       },
176 |       "outputs": [],
177 |       "source": [
178 |         "sil_scores = {}\n",
179 |         "max_k = min(10, len(feat_df))\n",
180 |         "for k in range(2, max_k):\n",
181 |         "    km = KMeans(n_clusters=k, random_state=RANDOM_STATE, n_init='auto')\n",
182 |         "    labels = km.fit_predict(X_pca[:, :2])\n",
183 |         "    sil_scores[k] = silhouette_score(X_pca[:, :2], labels)"
184 |       ]
185 |     },
186 |     {
187 |       "cell_type": "code",
188 |       "execution_count": 8,
189 |       "id": "4bd3b576",
190 |       "metadata": {
191 |         "id": "4bd3b576"
192 |       },
193 |       "outputs": [],
194 |       "source": [
195 |         "import pandas as pd\n",
196 |         "sil_df = pd.DataFrame({'k': list(sil_scores.keys()), 'silhouette': list(sil_scores.values())}).sort_values('k')\n",
197 |         "best_k = int(sil_df.loc[sil_df['silhouette'].idxmax(), 'k']) if not sil_df.empty else 3\n",
198 |         "\n",
199 |         "km = KMeans(n_clusters=best_k, random_state=RANDOM_STATE, n_init='auto')\n",
200 |         "labels_km = km.fit_predict(X_pca[:, :2])\n",
201 |         "\n",
202 |         "agg = AgglomerativeClustering(n_clusters=best_k)\n",
203 |         "labels_agg = agg.fit_predict(X_pca[:, :2])\n",
204 |         "\n",
205 |         "result = feat_df.copy()\n",
206 |         "result['PC1'] = X_pca[:, 0]\n",
207 |         "result['PC2'] = X_pca[:, 1]\n",
208 |         "result['cluster_km'] = labels_km\n",
209 |         "result['cluster_agg'] = labels_agg"
210 |       ]
211 |     },
212 |     {
213 |       "cell_type": "code",
214 |       "execution_count": 9,
215 |       "id": "84f50b7c",
216 |       "metadata": {
217 |         "id": "84f50b7c",
218 |         "outputId": "27d4b5d2-85ba-442e-f576-d000ebbbd869",
219 |         "colab": {
220 |           "base_uri": "https://localhost:8080/",
221 |           "height": 1000
222 |         }
223 |       },
224 |       "outputs": [
225 |         {
226 |           "output_type": "display_data",
227 |           "data": {
228 |             "text/plain": [
229 |               "<Figure size 600x400 with 1 Axes>"
230 |             ],
231 |             "image/png": 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\n"
232 |           },
233 |           "metadata": {}
234 |         },
235 |         {
236 |           "output_type": "display_data",
237 |           "data": {
238 |             "text/plain": [
239 |               "<Figure size 600x400 with 1 Axes>"
240 |             ],
241 |             "image/png": 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\n"
242 |           },
243 |           "metadata": {}
244 |         },
245 |         {
246 |           "output_type": "display_data",
247 |           "data": {
248 |             "text/plain": [
249 |               "<Figure size 600x500 with 1 Axes>"
250 |             ],
251 |             "image/png": 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VlZmZiYKCArm21q1bQywWV/g9nzZtGlJTU7Fo0aIS2wCKxuxER0eXuX7z5s0RFxcnd3x37tzB1atX5ZarzOey+OGripdVu3btCn19fWzduhX5+fklanhz32V5mz4iqiqeQaIGz8jICBs3bsQnn3yCwYMHyz1J+/bt2zh69KhcIEhKSsLhw4cBFD1pG4AsQFlaWlbqjEy/fv2wevVqrF69GsbGxiV+o160aBHS0tLg7u4Oc3NzPHnyBP/973/h4OBQ6rOaqmPevHl4/PgxvvrqK5w4cQLe3t4wNDTE06dPcfz4ccTFxeHDDz8EAKxatQpRUVHw9vZGamqq7PiLDRo0qEZqqoiOjg5CQ0MxatQoLFy4EAYGBujRo0el1m3WrJns0lx5vvzyS4wcORIDBgzAv/71LzRv3hwvXrzA9evX8ezZM/zyyy8AgA8++AAbN27E/Pnz4eLign/++QdHjhyRnSmpqosXL2LZsmXo27cvrK2tUVhYiMOHD0NNTa3CR1D4+vri7t272LJlC27fvo3+/fvLnqR99uxZXLhwAWvWrClz/WHDhmHnzp0YP348hg0bhpcvX+Lnn39Gq1at5MY2VeZz6eDgADU1NYSFhSEjIwOamppwd3dH48aNsWTJEnz++efw8/ODr68vTExM8OTJE8TExKBjx45YvHix0vqIqKoYkIhQNEblyJEj2L59O06fPo3Dhw9DLBbD1tYWn3zyidwt+cW3Gb+peNrV1bVSAalp06ZwcXHB1atX4e/vX+LhfgMHDsS+ffvw008/IT09HU2aNEG/fv0wderUMseyVJWOjg7CwsJw4MABHDp0CJs2bUJOTo7sXWzffvutbIxR8cMPo6OjSz0T8a4CElA0sHv79u0YOXIkZs6cibCwsBLvmHsbrVq1QkREBEJCQnDw4EGkpqbCxMQEjo6OcrfuT5o0CRKJBEeOHEFkZCQcHR2xdevWcoNIeezt7eHl5YXo6GgkJydDR0cH9vb2CAsLg7Ozc4Xrz5w5E+7u7ti1axf27NmDtLQ0GBoaokOHDti0aVO5QdLOzg6rV6/Ghg0bsHLlSrRq1QrffPMNjh49isuXL8uWq8znskmTJli6dCm2bt2KhQsXorCwED/++CMaN26MAQMGwMzMDKGhodi+fTvy8vJgbm6O999/v1Jj0d62j4iqQiSUNpqQiIiIqAHjGCQiIiIiBQxIRERERAoYkIiIiIgUMCARERERKWBAIiIiIlLAgERERESkgM9BqkHXrl2DIAglnmlDRETUkOTn50MkElXpqft1Dc8g1SBBEGSvHhAEAXl5eaW+tJJqBvtY+djHysX+VT72sfKV1sdv/jxUVTyDVIOKzxw5OTkhOzsbsbGxaNWqFXR1dWu5svqJfax87GPlYv8qH/tY+Urr45s3b9ZyVW+PZ5CIiIiIFDAgERERESlgQCIiIiJSwIBERERvJTMzEy4uLliwYEGJeY8fP0abNm2wceNGufYDBw6gU6dOGDRoED788ENMmDABT548AQDMmzcPO3fufBelE5WJAYmIiN7KsWPH0LZtW/z222/IysqSmxceHg53d3ccOHCgxF1Nbm5uOHz4MH799VdYW1tj5cqV77JsonLxLrZaUFhYiPz8/NouQ+Xl5ubK/haL607W19DQgJqaWm2XQfTOhIeHIygoCD///DMiIyPh7+8PoOj/uoMHD2LHjh2YNWsWLl68CA8Pj1K30bVrV/znP/95l2UTlYsB6R0SBAHPnj1DampqbZdSL0ilUqirq+PJkyd1KiABgLGxMZo2bQqRSFTbpRAp1f379/H06VN4eXmhoKAAYWFhsoB07tw5NG3aFK1atcKwYcMQHh5eakAqLCzE8ePH0bZt23ddPlGZGJDeoeJwZGZmBl1dXf7wfEuFhYXIzc2FlpZWnTljIwgCsrOzkZKSAgCwsLCo5YqIlCs8PByDBw+Gmpoaunfvji+//BIPHjyAnZ0dwsPDMXToUADAgAEDsGHDBqSlpcHIyAgAcOnSJQwaNAgA4OjoiLlz59bacRApYkB6RwoLC2XhqHHjxrVdTr1QWFgIANDW1q4zAQkAdHR0AAApKSkwMzOrU7UR1aT8/HwcPnwY6urqOHr0KABAIpEgPDwcgYGBOH36NG7cuIGtW7cCAAoKCnDkyBGMGjUKQNEYpE2bNtVa/UTlYUB6R4rHHPFJrg1D8fc5Pz+fAYnqraioKDRv3hz79u2TtT148AAff/wxGjVqhJ49e2Lt2rWyeTExMVi7dq0sIBHVZXVr4EYDwMtqDQO/z1QfJKQmYf/fR7Hz2n7s//soElKT5OaHh4djwIABcm12dnYwNzfHoUOHSszz9PRESkoKbt26VeG+Q0JC0K1bN9mfY8eOvf0BEVUBzyAREZGcZxkpCLn0A/55GQexSAwRRBAgYP+tX2FvaovJrgFoamCGsLCwUtc/ePBgqe3q6ur4/fffAQBt27aFn59fqcutWrWqZg6E6C3wDBJV26lTpzBu3Di4urqiXbt28PHxweLFi/Hw4UPZMvb29ti+fXuN7jc9PR3BwcG4f/9+jW63LBkZGViwYAFcXV3h4uKCadOmyQZhE9U3zzJSMP/katx/FQ8AkApSFAqFkApSAMC9l/GYf3I1nmXw3wDVbzyDRNXy7bffIiwsDH369MHy5cthYmKChIQEREREYObMmTh06JDS9p2eno6QkBDY2dnByspKafspNmPGDNy/fx9LliyBlpYW1q1bh8DAQEREREBdnf+ESHX5+PhAIpHgzJkz0NDQAAAs3rUSv6+PhKm7FSz7tMKT4/eREfcaIrEIglSKxh0tYe5ljW+ObcSxL/ehdevWsu3169dPdiksOzsbycnJsLGxAVA0ILu0J20T1VX8312FxT9Nx4UbT5CZkw99bQ14tLeEtYWh0vcbExODsLAwBAUFYfr06bL2zp07Y+jQoYiOjlZ6DTVJEATk5+dDU1OzxLxr167h3Llz2L59O7y8vAAANjY28PX1xYkTJ+Dr6/uuyyWqURYWFoiKikKfPn2QkJqEv6KvQMfSAADw/EIi8jNyYR/UGSI1MaT5hch7LYFUkCLu9SPo6Org8OHDctubNGkSgKJb+FesWFFiPpGq4CU2FfTkRSY+Cz6Dqd9G4+eT/yDy/EP8fPIfTP02Gp8Hn8WTF5lK3f+OHTtgamqKoKCgUud7e3uXua6Pjw+WLVsm13by5EnY29sjMTFR1hYaGopevXrByckJ7u7uGDNmDB4/fozExET06NEDADBz5kx07NgRjo6OsnXz8vLw3XffwdvbG+3atUO/fv1w5MgRuf3NmzcP/fv3R0xMDAYOHAgnJydERUWVWu+ZM2dgaGgIT09PWZutrS0cHBxw5syZcnqJSDUMHToUERERAICYu78jOzEdBu8VPYokPz0H6nqaEKkV/agQa6hB20wfACASiVEoLaydooneAZ5BUjFPXmRi9rozyM4tAABIpQKkb8y/m/Aas9edwZoZ3WBpql/j+y8oKMDVq1fRu3dv2Sn5mnbo0CGsX78e06ZNg7OzMzIyMnDlyhVkZWXB1tYWISEhmDJlCmbMmAFnZ2doaWnBzMwMADB9+nRcvXoVkydPhp2dHWJiYvDZZ5/B0NAQ3bt3l+0jJSUFX331FT799FNYWFjA0tKy1Fri4uJgY2NT4q40W1tbxMXFKeX4id6ljh074qeffkJycjL+iLmMRm3Ngf/7uDd+3xJxP/6FOw9fQ6+lEfRtTWDc1gwisQgiiJAryZU96BEAtmzZwoejUr3BgKRi1u65iuzcAkilQqnzpVIB2bkFWLfnGr6Z2rXG95+amoq8vLwyA0VNuHHjBuzt7TFx4kRZW8+ePWVfOzg4AABatmyJ9u3byx4UefHiRURFRcldDvP09MTz588RHBwsF5DS0tIQFhaGDh06lFtLeno6DAwMSrQbGRnh77//fqvjJKorBg0ahIMHD+LvmGsw6d8cr64/AQBom+mjzQwPZCWkIjshDclRD/H6+jPYftwBAgRo6WjxEhrVW7zEpkLin6bjTvzrMsNRMalUQGz8K8Q/TVdaLcp8zo+joyNu376NlStX4s8//6z0i33Pnz8PY2NjuLu7o6CgQPanS5cuiI2NlT15Gyh6V1pF4YiooRg0aBB27doFE31jaJpoy80Tq4thYGsC8w9sYDfOBRn3XqIgOx+CIIWamA9BpfqLZ5BUyIUbTyAWiyoMSAAgFotw4ebTGh+0bWxsDC0tLTx58qRGt/smPz8/ZGVlYd++fdi5cycMDAwwePBgzJkzB9ra2mWu9/r1a6Smppb5wsvnz5+jadOmAABTU9NK1WJoaIhnz56VaH/zfVJEqs7c3ByzZs2Cra0tIl6eRLKo6FEdmfGvodVYFxoGWgAAyZMMqOmoQ0NHE830miFRxLOoVH8xIKmQzJx8iEWQG3NUFrEIyJTk1XgN6urq6NixIy5evIiCgoIq3+auqalZ4oxQWlqa3LRYLEZAQAACAgKQnJyMX3/9FWvWrEGjRo0wefLkMrdtZGQEExMThIaGljrfxMRE9nVlz4DZ2triwoULEARBbp2HDx/K3d5MpOqKXyprkdEMFw7EoFBUgPy0XDw5dg/SAilEamKINdVg+29n6Grp4GNHP5zBr7VcNZHyMCCpEH1tDVTi5BEAQCoA+jolb1uvCWPHjsUnn3yCLVu2YMqUKSXmx8TEyI33eVPTpk3x4MEDubbz58+XuS9zc3OMGzcOR48elQ2KLh4cnpubK7dsly5dsG3bNmhoaKBNmzZVOqaydOvWDZs2bcKFCxfQpUsXAEXh6Pbt25gwYUKN7INIGRJSk3Ap8Rqy8iXQ09CBm5ULWhg3k1umtLs3mxqYYffqHdh4+UfcffEAjZ0tZU/SlgpS2JvaYbLraDQ1MMOff/5Z5v7d3Nw4PolUGgOSCvFob4mfTtyt1LJSqYAuTsq5m6R79+6YMGGC7GnWH374IRo1aoTExEREREQgIyOjzIDUp08fLFmyBCEhIXBxcUFMTAyuX78ut8zixYthaGgIZ2dnGBoa4urVq7hz5w5GjBgBAGjSpAkMDQ0RGRkJMzMz6Ovrw8HBAZ6envD29saECRMwYcIE2NvbQyKR4P79+3j06BG+/vrrKh+ri4sLvLy8sGDBAsydOxdaWlpYu3Yt7O3t0bt37ypvj0jZKvuakPI0NTDD8h5zkJCahMtJ15GZlw19TV24NnMuEbKI6isGJBVibWGINtaN8E9CarnjkMRiEexbNEJLJT408rPPPoOLiwt2796NBQsWQCKRwMzMDF5eXhg/fnyZ6/n7+yMhIQF79uzBzp074evri1mzZmH27NmyZVxcXLBv3z7s378fEokEzZs3x/z58+Hv7/9/xyfGypUrsWbNGkyaNAl5eXk4deoUrKyssGHDBoSGhmLPnj1ISkqCgYEB3nvvvTLf+VQZ69atw8qVK7F48WIUFBTAy8sLixYt4lO0qc4pfk2IJD8HAGSvBylW/JqQlT3nVhiSAKCFcTMGImqwRIIgVPKiDVXk5s2bAAAnJydkZ2cjNjYWDg4O0NXVRU5ODh4+fAgbG5tyBxpX5M3nIJUWksRiEXS11JX2HKS6pLCwEDk5ObLb/OuSmvp+1zbFzzHVrJru30Un/4P7r+JLBKM3iUVivNfYBst7zHnr/akCfoaVr7Q+fvPnoaribf4qxtJUH2tmdIN9i0YAigKRupoIYnHRAGL7Fo0aRDgiInkJqUn452VcueEIKDqrdPfFAySkJr2jyohUE68RqCBLU318M7Vr0bvYbj5FpiQP+jqa6OJkodTLakRUd11KvAaxSFxhQAKKziJdTrrOy2dE5WBAUmHWFobv5OW0RFT3ZeVLIELlHl8hggiZedlKrohItfESGxFRPaCnoQMBlRtSKkCAvibH4xCVR+XPID169Ajbt2/HX3/9hXv37sHW1hZHjx6tcD0fHx8kJZW8Bn/jxg1oaWkpo1QiIqVxs3LB/luVe3CjVJDCtZmzcgsiUnEqH5Du3buHmJgYdOjQAVKpFFW5Ka9Pnz4YN26cXJumpnIerkhEpEwtjJuhdWPbSt/FxvFHROVT+YDk4+Mje9P7vHnzqvSGdVNTUzg7OyupMiKid2uKW4DsOUilhSSxSAwdDW1Mdh1dC9URqRaVH4MkFqv8IRAR1YimBmZY2XMu3mtsA6AoEKmJ1CAWFf0/+V5jm0o/JJKooVP5M0hv48iRI9i3bx80NDTw/vvvY86cObC3t6/tsoiIqo2vCSGqGQ02IPn4+KB9+/awtLTE48ePsWXLFowcORKHDh1C8+bNq71dQRCQnZ0NiUQCALK/c3NzIZVKUVhYiMLCwho5hoaueLyZIAh1rk8LCwshlUohkUgglVb8XJq6SvFzTDVLmf1rqtkIvjbecm3Z2Q3v1n5+hpWvtD4WBAEiUeUeO1FX1atXjRSPQarMXWyKUlJS0K9fPwwYMABLliyp1v5v3ryJvLy8Muerq6ujefPm9eYuuZiYGOzduxe3b99GdnY2zMzM4O7ujo8//hgtW7YEAHTs2BEzZszA6NE1N+YhIyMDu3fvRu/evWFra1tj2y1Nfn4+Nm7ciJs3byI2NhY5OTk4deoUGjVqVO56ubm5ePz4MQoKCpRaHxFRXaWpqanSrxppsGeQFJmZmaFTp064devWW21HQ0MDrVq1gkQiQXx8PKytraGjo4Pc3Fw8efIEWlpaKv1urmLfffcdtm3bht69e2PZsmUwMTFBQkICDhw4gPnz5+PAgQOyZdXV1Wv0mF++fInQ0FC0adMGtra20NLSUtpvKnl5eTh06BDatWuH999/H+fOnYO2tnaljkddXR0tWrRQ6UCs+DmmmsX+VT72sfKV1sf379+v5areHgNSDROJRHIvRNTR0YGuri7EYjHEYjHU1NRq7MWqeSmPkHXnEgpzs6CmpQe9Nm7QNGtZI9suT0xMDLZt24agoCBMnz5d1u7m5gZ/f39ER0fLHWPxcdeU4oH5xaFIJBK91fYFQUB+fn6pj3ho1KgRLl++DJFIhAMHDuDcuXOVOh41NTWIxWLo6OjUi0Bc/Dkm5WD/Kh/7WPne7GNVv7wG1IO72GpKcnIyrly5ohKnA/NfPUXSDwuQGDYLr8/tR/qfx/D63H4khs1C0g8LkP/qqVL3v2PHDpiamiIoKKjU+d7e3qW2A0Vjv5YtWybXdvLkSdjb2yMxMVHWFhoail69esHJyQnu7u4YM2YMHj9+jMTERPTo0QMAMHPmTHTs2BGOjo6ydfPy8vDdd9/B29sb7dq1Q79+/XDkyBG5/c2bNw/9+/dHTEwMBg4cCCcnJ0RFRZVZc334h05ERFWj8meQJBIJYmJiAABJSUnIzMzE8ePHAQCurq4wMTFBQEAAnjx5gt9++w0AcPToUURHR6N79+4wMzPD48ePERoaCjU1NYwdO7bWjqUy8l89RdL38yDN/b/BloIUb75dIDfpHpK+n4dmY1dBw8SixvdfUFCAq1evonfv3tDQ0Kjx7QPAoUOHsH79ekybNg3Ozs7IyMjAlStXkJWVBVtbW4SEhGDKlCmYMWMGnJ2doaWlBTOzotuWp0+fjqtXr2Ly5Mmws7NDTEwMPvvsMxgaGqJ79+6yfaSkpOCrr77Cp59+CgsLC1haWirlWIiISDWpfEB6+fKl3GUeALLpH3/8EW5ubrK7x4pZWVkhJSUFK1asQEZGBgwMDODu7o5p06a91R1s70LKkeCicFTWk3IFKaS52Ug5EoJmAV/X+P5TU1ORl5en1EBx48YN2NvbY+LEibK24oeBAoCDgwMAoGXLlmjfvj20tbWhpqaGixcvIioqCtu3b4eXlxcAwNPTE8+fP0dwcLBcQEpLS0NYWBg6dOigtOMgIiLVpfIBycrKCnfv3i13mV27dslNOzs7l2hTBXkpj5CbWP6xAgAEKXIT7yAv5ZHSxiQp87KTo6MjfvrpJ6xcuRK9evVChw4dKnW26vz58zA2Noa7u7vc3WNdunTBkiVLUFhYKBs7ZGxszHBERERlUvmA1JBk3bkEiMRlnz16k0iMrLuXajwgGRsbQ0tLC0+ePKnR7b7Jz88PWVlZ2LdvH3bu3AkDAwMMHjwYc+bMKXfA8+vXr5Gamoq2bduWOv/58+do2rQpgKLXzBAREZWFAUmFFOZmASKR3JijMolEKMzJqvEa1NXV0bFjR1y8eBEFBQVQV6/aR0hTUxP5+flybWlpaXLTYrEYAQEBCAgIQHJyMn799VesWbMGjRo1wuTJk8vctpGREUxMTBAaGlrqfBMTE9nXHHhNRETl4V1sKkRNSw+o7HM9BQFq2npKqWPs2LF4/vw5tmzZUur84kHzpWnatCkePHgg13b+/Pkylzc3N8e4ceNgb2+PuLg4AJBdbsvNzZVbtkuXLnj16hU0NDTg5ORU4k9pt/ETERGVhmeQVIheGze8Pru3cgsLUujZuyulju7du2PChAkIDg7G/fv38eGHH6JRo0ZITExEREQEMjIy5AZEv6lPnz5YsmQJQkJC4OLigpiYGFy/fl1umcWLF8PQ0BDOzs4wNDTE1atXcefOHYwYMQIA0KRJExgaGiIyMhJmZmbQ19eHg4MDPD094e3tjQkTJmDChAmwt7eHRCLB/fv38ejRI3z9dfUGrcfExEAikeDvv/8GAERHR0NPTw+tWrVCq1atqrVNIiKq2xiQVIimWUtoWdkjN+le+eOQRGJoNWsNTbMWSqvls88+g4uLC3bv3o0FCxZAIpHAzMwMXl5eGD9+fJnr+fv7IyEhAXv27MHOnTvh6+uLWbNmYfbs2bJlXFxcsG/fPuzfvx8SiQTNmzfH/Pnz4e/vD6DoEtzKlSuxZs0aTJo0CXl5eTh16hSsrKywYcMGhIaGYs+ePUhKSoKBgQHee+89+Pn5VftYly5diqSkJNn0ggULAABTpkzB1KlTq71dIiKqu+rVu9hq282bNwEATk5OyM7ORmxsLBwcHKCrq4ucnBw8fPgQNjY2b/VkZbnnIJUWkkRiiLV0lfYcpLqksLAQOTk5stv865Ka+n7XNsXPMdUs9q/ysY+Vr7Q+fvPnoariGCQVo2FigWZjV0GrWeuiBpEYEKsV/Q1Aq1nrBhGOiIiIlImX2FSQhokFmgV8XfQutruXUJiTBTVtPejZuyv1shoREVFDwYCkwjTNWr6Tl9MSERE1NLzERkRERKSAAYmIiIhIAQMSERERkQIGJCIiIiIFDEhEREREChiQiIiIiBQwIFG1nTp1CuPGjYOrqyvatWsHHx8fLF68GA8fPpQtY29vj+3bt9foftPT02XvgVO2GzduYP78+ejVqxc6dOiA3r17Y82aNcjOzlb6vomIqPbwOUhULd9++y3CwsLQp08fLF++HCYmJkhISEBERARmzpyJQ4cOKW3f6enpCAkJgZ2dHaysrJS2HwA4duwYHj16hAkTJsDa2hr379/Hhg0b8Ndff+HHH39U6r6JiKj2MCCpsITUJFxKvIasfAn0NHTgZuWCFsbNlL7fmJgYhIWFISgoCNOnT5e1d+7cGUOHDkV0dLTSa6hJgiAgPz8fmpqaJeYFBgbCxMRENu3m5gZDQ0PMmTMHf//9N9q1a/cuSyUioneEl9hU0LOMFCw6+R/M+d9XiLh9DP+7F4OI28cw539f4YtT/8GzjBSl7n/Hjh0wNTVFUFBQqfO9vb3LXNfHxwfLli2Tazt58iTs7e2RmJgoawsNDUWvXr3g5OQEd3d3jBkzBo8fP0ZiYiJ69OgBAJg5cyY6duwIR0dH2bp5eXn47rvv4O3tjXbt2qFfv344cuSI3P7mzZuH/v37IyYmBgMHDoSTkxOioqJKrffNcFTM0dERAJCSotx+JiKi2sMzSCrmWUYK5p9cDUl+DgBAKkjl5t97GY/5J1djZc+5aGpgVuP7LygowNWrV9G7d29oaGjU+PYB4NChQ1i/fj2mTZsGZ2dnZGRk4MqVK8jKyoKtrS1CQkIwZcoUzJgxA87OztDS0oKZWdGxTp8+HVevXsXkyZNhZ2eHmJgYfPbZZzA0NET37t1l+0hJScFXX32FTz/9FBYWFrC0tKx0fVeuXAEA2Nra1uyBExFRncGApGJCLv0ASX5OiWBUTCpIIcnPwcbLP2J5jzk1vv/U1FTk5eVVKVBU1Y0bN2Bvb4+JEyfK2nr27Cn72sHBAQDQsmVLtG/fHtra2lBTU8PFixcRFRWF7du3w8vLCwDg6emJ58+fIzg4WC4gpaWlISwsDB06dKhSba9evUJwcDB69OgBa2vrtzhKIiKqy3iJTYUkpCbhn5dxZYajYlJBirsvHiAhNUlptYhEIqVt29HREbdv38bKlSvx559/Ij8/v1LrnT9/HsbGxnB3d0dBQYHsT5cuXRAbG4vCwkLZssbGxlUOR/n5+Zg1axYAYMmSJVVal4iIVAvPIKmQS4nXIBaJKwxIACAWiXE56XqND9o2NjaGlpYWnjx5UqPbfZOfnx+ysrKwb98+7Ny5EwYGBhg8eDDmzJkDbW3tMtd7/fo1UlNT0bZt21LnP3/+HE2bNgUAmJqaVqkmQRCwYMEC3LhxAz/99JPskh4REdVPDEgqJCtfAhEqd+ZGBBEy82r+WT3q6uro2LEjLl68iIKCAqirV+0jpKmpWeKMUFpamty0WCxGQEAAAgICkJycjF9//RVr1qxBo0aNMHny5DK3bWRkBBMTE4SGhpY6/80B11U9A7Z69WocO3YMYWFhaNOmTZXWJSIi1cNLbCpET0MHAoRKLStAgL6mrlLqGDt2LJ4/f44tW7aUOj8mJqbMdZs2bYoHDx7ItZ0/f77M5c3NzTFu3DjY29sjLi4OAGSDw3Nzc+WW7dKlC169egUNDQ04OTmV+FPabfyVERoaip07d2LVqlXw8PCo1jaIiEi18AySCnGzcsH+W79WalmpIIVrM2el1NG9e3dMmDBB9jTrDz/8EI0aNUJiYiIiIiKQkZEhNyD6TX369MGSJUsQEhICFxcXxMTE4Pr163LLLF68GIaGhnB2doahoSGuXr2KO3fuYMSIEQCAJk2awNDQEJGRkTAzM4O+vj4cHBzg6ekJb29vTJgwARMmTIC9vT0kEgnu37+PR48e4euvv67ysR45cgRr1qzBwIEDYWVlJVdrixYtSn0MABERqT4GJBXSwrgZWje2xf1X8eWOQxKLxHivsY1SHxr52WefwcXFBbt378aCBQsgkUhgZmYGLy8vjB8/vsz1/P39kZCQgD179mDnzp3w9fXFrFmzMHv2bNkyLi4u2LdvH/bv3w+JRILmzZtj/vz58Pf3Lzo+sRgrV67EmjVrMGnSJOTl5eHUqVOwsrLChg0bEBoaij179iApKQkGBgZ477334OfnV63jLD679csvv+CXX36Rm7dy5cpqb5eIiOo2kSAIlbtmQxW6efMmAMDJyQnZ2dmIjY2Fg4MDdHV1kZOTg4cPH8LGxqbcgcYVefM5SKWFJLFIDB0NbaU9B6kuKSwsRE5Ojuw2/7qkpr7ftU3xc0w1i/2rfOxj5Sutj9/8eaiqOAZJxTQ1MMPKnnPxXmMbAEWBSE2kBrGo6Fv5XmObBhGOiIiIlImX2FRQUwMzLO8xBwmpSbicdB2ZednQ19SFazPnd/IuNiIiovqOAUmFtTBuxkBERESkBLzERkRERKSAAYmIiIhIAQMSERERkQIGJCIiIiIFDEhEREREChiQiIiIiBSofEB69OgRFi9ejEGDBsHR0RH9+/ev1HqCICA0NBQffPAB2rdvj48++qjEO8GIiIioYVL5gHTv3j3ExMSgZcuWsLOzq/R6YWFh2LBhA8aMGYOtW7eiSZMmGDduHB4/fqzEauuXU6dOYdy4cXB1dUW7du3g4+ODxYsX4+HDh7Jl7O3tsX379hrdb3p6uuxFucr2+PFjTJw4Ed26dYOTkxO8vLwwbdo0uWMkIqL6R+UDko+PD2JiYrBhwwa0bdu2Uuvk5uZi69atGDduHMaMGQMPDw989913MDY2rvEf5vXVt99+i6CgIOjr62P58uX4/vvvMXnyZNy/fx8zZ85U6r7T09MREhKCBw8eKHU/AJCVlQVTU1PMmjUL27Ztw7x58/Dw4UOMHj0ar169Uvr+iYiodqj8k7TF4qpnvKtXryIzMxP9+vWTtWlqaqJXr1747bffarI8pcqKf4SXFy+hIDML6vp6aOzuBj3rlkrfb0xMDMLCwhAUFITp06fL2jt37oyhQ4ciOjpa6TXUJEEQkJ+fD01NzRLz2rRpg6+//lqurV27dujTpw/Onz+PAQMGvKsyiYjoHVL5M0jVERcXBwCwtbWVa7ezs8OTJ0+Qk5NTG2VVmuTpU9yYuwDXp8/C47378ezYMTzeux/Xp8/CjbkLIHn6VKn737FjB0xNTREUFFTqfG9v7zLX9fHxwbJly+TaTp48CXt7eyQmJsraQkND0atXLzg5OcHd3R1jxozB48ePkZiYiB49egAAZs6ciY4dO8LR0VG2bl5eHr777jt4e3ujXbt26NevH44cOSK3v3nz5qF///6IiYnBwIED4eTkhKioqEofv7GxMQAgPz+/0usQEZFqUfkzSNWRnp4OTU1NaGlpybUbGhpCEASkpaVBW1u7WtsWBAHZ2dmQSCQAIPs7NzcXUqkUhYWFKCwsrHbtOU+f4ebc+SjMLtoupFII0v8/P+Ofe/hrzlw4rV4JbYum1d5PWQoKCnD16lX06tULYrG4UsdSfNxAUf8IgiC3XvHXxcsdPnwY69evx5QpU+Ds7IyMjAxcuXIF6enpsLW1xYYNGzBt2jTMmDEDzs7O0NTUROPGjVFYWIhp06bh6tWrCAoKgp2dHc6cOYPPPvsM+vr66Natm6yG5ORkLF++HJMmTYKFhQUsLCzKPZbi2lJSUrBu3To0bdoUPj4+Za5TWFgIqVQKiUQCqVRa6jKqQPFzTDWL/at87GPlK62PBUGASCSqrZJqRIMMSMqUn5+P2NhY2XR8fLzsa3V1deTm5r7V9v9Zt6EoHJX1Q1cqRWG2BPfWB+O9JV+81b5K8/LlS+Tl5cHMzKzSZ9oKCgpkywqCIDcN/P8zMbm5ucjJycG1a9fQqlUrjB49WraMp6cngKKgUnzmz9LSEu3bt5e1nz17FtHR0di4cSM8PDwAAB07dsSzZ88QHBwMV1dXAEXhpXigt5OTk2wf5R3PwoULcezYMQCAlZUVNm/eDA0NjTLXyc3NRUFBgexspap783NMNY/9q3zsY+VT7OPShi2okgYZkAwNDZGXl4fc3Fy5s0jp6ekQiUQwMjKq9rY1NDTQqlUrSCQSxMfHw9raGjo6OsjNzcWTJ0+gpaVV7bNT2Y8SkH2vEnduSaXI+ucepMkp0G3Zolr7Kktxf6mrq1f6ON5cViQSlVhXQ0NDtm1tbW04OTlh//79WL9+PXr27In27dvLllGsoXhaJBLhjz/+gJGREbp27Sq3/65du2Lp0qXQ0NCAmpoa1NTUYGxsjM6dO1f6uGfOnIkxY8bg6dOn+PHHH/Hpp5/iv//9LywtLcs97hYtWpQ4U6lKFD/HVLPYv8rHPla+0vr4XdxlrGwNMiAVn4F4+PAh2rRpI2uPi4uDpaVltQMMUBQAdHV1ZdM6OjrQ1dWFWCyGWCyW/YCujteX/wDE4rLPHr1JLMbry3/AwNamWvsqS+PGjaGlpYVnz55V+jiKjxso6h+RSCS3bvHXxcsNGzYMEokE+/btww8//AADAwMMHjwYc+bMgba2tmxgfvHp2+LtpaWlIS0tTXZWSdGrV6/QtGlTiEQimJqaVun70LJlS7Rs2RLOzs744IMP0Lt3b3z//fdYvHhxqcurqalBLBZDR0fnrT5PdUXx55iUg/2rfOxj5Xuzj1X98hrQQANSx44doa+vj2PHjskCUn5+Pk6cOCEbp1IXFWRmQSQWyY05KotIJEJBZlaN16Curo6OHTvi4sWLKCgokJ3FqSxNTc0Sg5vT0tLkpsViMQICAhAQEIDk5GT8+uuvWLNmDRo1aoTJkyeXuW0jIyOYmJggNDS01PkmJiayr9/mH6+Ojg7s7Ozw6NGjam+DiIjqNpW/i00ikeD48eM4fvw4kpKSkJmZKZsufk5NQEAAevXqJVtHS0sLEydOxI4dO/DDDz/gwoULmD17NlJTUzF+/PjaOpQKqevrQZAKlVpWEASo6+sppY6xY8fi+fPn2LJlS6nzY2Jiyly3adOmJZ5fdP78+TKXNzc3x7hx42Bvby8bz1N8uU1xPFeXLl3w6tUraGhowMnJqcSfmroenpmZibt376J58+Y1sj0iIqp7VP4M0suXL+WexQNANv3jjz/Czc1N7i6qYoGBgRAEATt27MCrV6/g4OCA7du31+kfeo3d3fB4z97KLSyVorGHu1Lq6N69OyZMmCB7mvWHH36IRo0aITExEREREcjIyED37t1LXbdPnz5YsmQJQkJC4OLigpiYmBKveFm8eDEMDQ3h7OwMQ0NDXL16FXfu3MGIESMAAE2aNIGhoSEiIyNhZmYGfX19ODg4wNPTE97e3pgwYQImTJgAe3t7SCQS3L9/H48ePSrxPKPKCA4ORkZGBjp27AgTExMkJSVh165dyMvLQ0BAQJW3R0REqkHlA5KVlRXu3r1b7jK7du0q0SYSiTBx4kRMnDhRWaXVOD3rljBoY4+Mf+6VPw5JLIZB69bQq+EB2m/67LPP4OLigt27d2PBggWQSCQwMzODl5dXuWfh/P39kZCQgD179mDnzp3w9fXFrFmzMHv2bNkyLi4u2LdvH/bv3w+JRILmzZtj/vz58Pf3/7/DE2PlypVYs2YNJk2ahLy8PJw6dQpWVlbYsGEDQkNDsWfPHiQlJcHAwADvvfce/Pz8qnWcjo6O2LlzJw4fPozs7GyYm5ujc+fOWL9+fZ0O00RE9HZEgiBU7poNVejmzZsAACcnJ2RnZyM2NhYODg7Q1dVFTk4OHj58CBsbm7catCt5+hQ35sxDQXZ26SFJLIa6ri7af7sKOhYW1d6PKigsLEROTg60tbWrPfBdWWrq+13bFD/HVLPYv0Xy8/OxdetWHD16FGpqatDQ0IClpSWmTp0KBwcHPHjwAKtXr5ZdZrezs8Pnn38u9/7N6OhohISEIDMzEwUFBfD09MTnn38OsViM2NhYtGnTBgcPHsTevXuRn58PHR0dNGrUCJ988gnc3d2RmJiIwYMH488//6ytblBZpX2O3/x5qKpU/gxSQ6NjYYH2367CvXUhyLhzBxCLIRKJIAgCIJXCoHVrvDdjSr0PR0RUf8yfPx/Z2dnYu3ev7DErv//+Ox4+fAgTExOMGjUKCxYskL3a5+jRoxg9ejQOHTqEJk2a4MyZM1i8eDG2bt0KR0dHFBQUYOXKlZg4caLspo1NmzbhypUrCAsLQ9OmRQ/RvXHjBm7dugV3d+UMRyDVxoCkgnQsLNB+9dcl38Xm4a7Uy2pERDUtPj4eJ0+exOnTp+WeQdelSxcAwNq1a+Hq6ir33sP+/fvjt99+w08//YTp06dj8+bNmDRpEhwdHQEU3W07d+5c9OzZE3/88Qc0NDTw448/4tChQ7JwBADt27cv87EgRAxIKkzPuuU7eTktEZGy3L59Gy1atJC947C0+cVP0n+Ti4sLfv/9d9kyX3wh/+YATU1NtG3bFrGxsTAxMYGmpqbcJTmiijAgERFRnZGQkICpU6ciJycHHTt2VMo+cnJy8NFHHyE/Px9mZmbYuXOnUvZDqk3ln4NERESqy9HREQkJCbIHxrZo0QKHDx/GxIkTkZaWBkdHR1y7dq3EeteuXZNdUittmby8PNy6dQtt2rSBlZUV8vLyZIO8tbW1cfjwYXz55Zd4/fq1ko+QVBUD0jvGmwYbBn6fiSrH2toaPXr0wMKFC5Geni5rL34z/MiRI3Hp0iUcOXJENu/o0aO4fPkyRo4cCQCYNGkSNm/eLHtReEFBAVavXo1mzZrB1dUV2traGDVqFBYtWoTk5OQS+yAqDS+xvSPFT3/Ozs7mCxMbgOzsbACQe8kuEZVu5cqV2LJlC/z9/aGurg5DQ0OYmJggMDAQ5ubm2LVrF1avXo1169ZBJBLBxsYG//3vf2FmZgag6OG1S5YswcKFC5GVlYWCggJ06dIFoaGhstcKTZkyBeHh4ZgwYQLy8/PRqFEj6OrqYtasWbI6MjMz5V43ZWFhgb17K/lwXqp3+BykGlTec5AA4OnTp0hNTYWZmRl0dXXrxcv8alNhYSFyc3OhpaVVZ56DJAgCsrOzkZKSAmNjY1io+OMW+Jwe5WoI/ZuQmoRLideQlS+BnoYO3Kxc0MK42Tvbf0Po49rG5yDRWyu+vTQlJaWWK6kfpFKp7IW5YnHdulpsbGwsdzsxUUPzLCMFIZd+wD8v4yAWiSGCCAIE7L/1K+xNbTHZNQBNDcxqu0yiMjEgvUMikQgWFhYwMzMr8UZ7qjqJRIK4uDi0aNGiTl221NDQqDNntIhqw7OMFMw/uRqS/BwAgFSQf+r/vZfxmH9yNVb2nMuQRHUWA1ItUFNT4w/QGiD9v1etaGlpqfTrPIjqm5BLP0CSn1MiGBWTClJI8nOw8fKPWN5jzjuujqhy6tZ1CSIiUmkJqUn452VcmeGomFSQ4u6LB0hITXpHlRFVDQMSERHVmEuJ1yAWVe5Hi1gkxuWk68otiKiaGJCIiKjGZOVLIELl7tAVQYTMvGwlV0RUPQxIRERUY/Q0dCCgck+PESBAX5O33lPdxIBEREQ1xs3KpcLxR8WkghSuzZyVWxBRNTEgERFRjWlh3AytG9tWOA5JLBLD3tTunT40kqgqGJCIiKhGTXELgI6GdpkhSSwSQ0dDG5NdR7/jyogqjwGJiIhqVFMDM6zsORfvNbYBUBSI1ERqssD0XmMbPiSS6jw+KJKIiGpcUwMzLO8xBwmpSbicdB2ZednQ19SFazNnXlYjlcCAREREStPCuBkDEakkXmIjIiIiUsCARERERKSAAYmIiIhIAQMSERERkQIGJCIiIiIFDEhEREREChiQiIiIiBQwIBEREREpYEAiIiIiUsCARERERKSAAYmIiIhIAQMSERERkQIGJCIiIiIFDEhERERECtRru4C39eDBA3z11Ve4du0a9PT0MGjQIMyYMQOamprlrufj44OkpKQS7Tdu3ICWlpayyiUiIiIVoNIBKS0tDQEBAbC2tkZwcDCSk5OxatUq5OTkYPHixRWu36dPH4wbN06uraJgRURERPWfSgekn3/+GVlZWQgJCYGxsTEAoLCwEEuXLsXEiRNhbm5e7vqmpqZwdnZWfqFERESkUlR6DNKZM2fg4eEhC0cA0K9fP0ilUpw/f772CiMiIiKVptIBKS4uDra2tnJthoaGaNKkCeLi4ipc/8iRI2jXrh1cXFwQGBiIu3fvKqtUIiIiUiEqfYktPT0dhoaGJdqNjIyQlpZW7ro+Pj5o3749LC0t8fjxY2zZsgUjR47EoUOH0Lx582rXJAgCsrOzIZFIAED2N9U89rHysY+Vi/2rfOxj5SutjwVBgEgkqq2SaoRKB6S3sWjRItnX77//Pjw9PdGvXz9s374dS5YsqfZ28/PzERsbK5uOj49/iyqpMtjHysc+Vi72r/Kxj5VPsY9V/aYnlQ5IhoaGyMjIKNGelpYGIyOjKm3LzMwMnTp1wq1bt96qJg0NDbRq1QoSiQTx8fGwtraGjo7OW22TSsc+Vj72sXKxf5WPfax8pfXx/fv3a7mqt6fSAcnW1rbEWKOMjAw8f/68xNikd0UkEkFXV1c2raOjIzdNNY99rHzsY+Vi/yof+1j53uxjVb+8Bqj4IO1u3brh999/R3p6uqzt+PHjEIvF8PT0rNK2kpOTceXKFTg5OdV0mURERKRiVPoM0vDhw7Fr1y5MnjwZEydORHJyMr755hsMHz5c7hlIAQEBePLkCX777TcAwNGjRxEdHY3u3bvDzMwMjx8/RmhoKNTU1DB27NjaOhwiIiKqI1Q6IBkZGeGHH37A8uXLMXnyZOjp6WHYsGGYOXOm3HJSqRSFhYWyaSsrK6SkpGDFihXIyMiAgYEB3N3dMW3atLe6g42IiIjqB5UOSABgZ2eHnTt3lrvMrl275KadnZ1LtBEREREVU+kxSERERETKwIBEREREpIABiYiIiEgBAxIRERGRAgYkIiIiIgUMSEREREQKGJCIiIiIFDAgERERESlgQCIiIiJSwIBEREREpIABiYiIiEgBAxIRERGRAgYkIiIiIgUMSEREREQKGJCIiIiIFDAgERERESlgQCIiIiJSwIBEREREpIABiYiIiEgBAxIRERGRAgYkIiIiIgUMSEREREQKGJCIiIiIFDAgERERESlgQCIiIiJSwIBEREREpIABiYiIiEgBAxIRERGRAgYkIiIiIgUMSEREREQKGJCIiIiIFDAgERERESlgQCIiIiJSwIBEREREpIABiYiIiEgBAxIRERGRAgYkIiIiIgUqH5AePHiAsWPHwtnZGZ6envjmm2+Ql5dX4XqCICA0NBQffPAB2rdvj48++gjXr19XfsFERERU56l0QEpLS0NAQADy8/MRHByMmTNnYt++fVi1alWF64aFhWHDhg0YM2YMtm7diiZNmmDcuHF4/PjxO6iciIiI6jL12i7gbfz888/IyspCSEgIjI2NAQCFhYVYunQpJk6cCHNz81LXy83NxdatWzFu3DiMGTMGANCpUyf07dsX27dvx5IlS97NARAREVGdVO0zSBcuXMDu3bsRGRmJzMzMUpe5fv065s+fX+3iKnLmzBl4eHjIwhEA9OvXD1KpFOfPny9zvatXryIzMxP9+vWTtWlqaqJXr144c+aM0uolIiIi1VDlgJSXl4eAgACMGzcOy5cvx6xZs+Dt7Y29e/eWWDYhIQGHDh2qiTpLFRcXB1tbW7k2Q0NDNGnSBHFxceWuB6DEunZ2dnjy5AlycnJqvlgiIiJSGVW+xLZ9+3b88ccfmDJlCnr16oXnz59j27ZtWLJkCW7duoUlS5ZALH43Q5vS09NhaGhYot3IyAhpaWnlrqepqQktLS25dkNDQwiCgLS0NGhra1erJkEQkJ2dDYlEAgCyv6nmsY+Vj32sXOxf5WMfK19pfSwIAkQiUW2VVCOqHJAiIyMxZMgQTJ48GQDQunVreHp6YsuWLVi/fj1evHiBdevWQVNTs8aLVQX5+fmIjY2VTcfHx9deMQ0E+1j52MfKxf5VPvax8in2sarngCoHpMTERIwePbpE+6RJk9CiRQvMnTsXY8eOxZYtW2qkwPIYGhoiIyOjRHtaWhqMjIzKXS8vLw+5ublyZ5HS09MhEonKXbciGhoaaNWqFSQSCeLj42FtbQ0dHZ1qb4/Kxj5WPvaxcrF/lY99rHyl9fH9+/druaq3V+WAZGRkhFevXpU6z9fXF0ZGRpgyZQr+/e9/Y+DAgW9dYHlsbW1LjDXKyMjA8+fPS4wvUlwPAB4+fIg2bdrI2uPi4mBpaVnty2sAIBKJoKurK5vW0dGRm6aaxz5WPvaxcrF/lY99rHxv9rGqX14DqjFI28HBAdHR0WXO9/T0xM6dO5GSkoK1a9e+VXEV6datG37//Xekp6fL2o4fPw6xWAxPT88y1+vYsSP09fVx7NgxWVt+fj5OnDiBbt26KbVmIiIiqvuqHJB69eqF69evl/vU6Q4dOmD37t1o0qTJ29RWoeHDh0NPTw+TJ0/GuXPnEBERgW+++QbDhw+XewZSQEAAevXqJZvW0tLCxIkTsWPHDvzwww+4cOECZs+ejdTUVIwfP16pNRMREVHdV+VLbEOGDEHfvn2hoaFR7nJ2dnb49ddfkZqaWt3aKmRkZIQffvgBy5cvx+TJk6Gnp4dhw4Zh5syZcstJpVIUFhbKtQUGBkIQBOzYsQOvXr2Cg4MDtm/fjubNmyutXiIiIlINVQ5IimNsyqOnpwc9Pb0qF1UVdnZ22LlzZ7nL7Nq1q0SbSCTCxIkTMXHiRCVVRkRERKqqypfYMjIyMH78+ArvUtu8eTMmTJiArKysahdHREREVBuqHJD++9//4tq1a/jXv/5V7nL/+te/cO3aNezevbvaxRERERHVhioHpN9++w0ffvghTExMyl2ucePG+PDDD3HixIlqF0dERERUG6ockB4+fIh27dpVatm2bdviwYMHVS6KiIiIqDZVOSAJgqDU5YmIiIhqW5UDkoWFBW7dulWpZW/dugULC4sqF0VERERUm6ockD744AP88ssvFb74Lz4+Hr/88gs++OCDapZGREREVDuqHJAmTJgAbW1tjBo1CpGRkSgoKJCbX1BQgMjISIwePRra2tp8MjURERGpnCo/KLJx48YIDQ3FlClTMHv2bGhra8Pa2hp6enrIyspCfHw8cnJyYGpqitDQUJiamiqjbiIieguBgYHo3r07Ro0aJdc+cOBATJkyBQ4ODujVqxemTp2KyZMny+YfOHAAJ0+exKZNm+TWS0xMRK9evdC6dWtZm6amJvbv34/Hjx9j+vTpKCwsRGFhIWxtbbF8+XIYGRmVqOvp06dYunQpkpKSAABisRjz5s2Dh4eHbJmLFy8iICAAq1evxuDBg2uiO4hKqHJAAoD27dvj119/xc8//4yoqCjExcUhMzMT+vr6sLe3h4+PD4YPHw5DQ8OarpeIiGrAsGHDsHXrVrmAdPPmTTx//hze3t4ICQmBu7s7Dhw4gKCgoEq9nV1PTw+HDx8u0W5ubo6ffvoJ2traAICvvvoKwcHBWLRoUYlllyxZAg8PD4wZMwYA8OrVK+Tk5MgtEx4eDg8PD0RERDAgkdJUKyDl5ubi7NmzEAQBQ4YMwQcffAAzM7Oaro2IiJTEx8cHS5YswZ07d9CmTRsAkAUOsViMgwcPYseOHZg1axYuXrwodwanqjQ1NWVfFxYWQiKRlPnKqmfPnsn9PFF85l56ejpiYmIQGRmJgQMH4tGjR2jZsmW1ayMqS5UD0suXLzF8+HAkJiZCEASIRCJoa2tj48aN6NKlizJqJCKiGqahoYFBgwYhIiICCxcuRG5uruzKwLlz59C0aVO0atUKw4YNk52xqUhWVhYGDRokm27VqhXWrFkDAMjLy4O/vz+ePHkCe3t7bN68udRtBAYGYuHChfjxxx/h7OyMHj16oHPnzrL5R44cgZeXF5o0aYKBAwciIiICs2bNesveICqpyoO0N23ahKSkJIwZMwZbt27F/PnzoaWlhcWLFyujPiIiUpJhw4bhyJEjyMvLw4kTJ2BnZwc7OzuEh4dj6NChAIABAwYgJiYGaWlpFW6v+BJb8Z/icAQUnUU6fPgwzp8/D1tbW/z888+lbqN///44ffo0xo0bBwAICgrCtm3bZPMjIiJktQ0dOhQHDx5EYWFhtfuAqCxVPoN07tw5DBo0CHPnzpW1mZqaYvbs2YiLi4OtrW2NFkhERMrRqlUrtGzZEtHR0bLg8erVK5w+fRo3btzA1q1bARTdnXzkyJESA7qrQ1NTE35+fvjiiy8QGBhY6jJGRkbo3bs3evfuDScnJ2zZsgUTJkxAbGws7t69iy+++EI2Jur169c4c+YMvL2937o2ojdV+QzS06dP0alTJ7m2Tp06QRAEvHz5ssYKIyIi5SserH3jxg34+vri0KFD6NmzJ2JiYhAVFYWoqCisX78e4eHh1d5HUlISJBIJAEAqleL48eNyd7u9KTo6WrasIAi4ffs2WrRoAaBocPbYsWMRHR0tq23BggVvVRtRWap8BikvLw9aWlpybcUD8BSfiURERHVbv379sGLFCvTr1w96enoIDw/HnDlz5Jbx9PTE/PnzZW9ROHfuHLp16ya3jY8//rjEGCQA2L17N+7evYu1a9cCKAo9jo6OcnewDRo0CKGhoTA3N8fly5fxzTffQF1dHYIgwMbGBosXL0Zubi6OHDmCXbt2laj/m2++wYsXL/hYGapR1bqLLSkpSe51IxkZGQCAR48elXprf9u2batZHhERVUf803RcuPEEmTn50NfWgEd7S1hblPz/WV9fH9euXZNNR0ZGllhGXV0dv//+O4Ci/8/9/PxK3WdsbGyp7T4+PvDx8Smz1jcfDTB37ly5IRxvunz5com2Ro0a4fr162Vum6i6qhWQ1q9fj/Xr15doX7p0qdx08V1uZf2jISKimvXkRSbW7rmKO/GvIRaLIBYBUgH46cRdOFibYMYIF1ia6td2mUR1XpUD0sqVK5VRBxERvaUnLzIxe90ZZOcWDXeQSgVI35h/N+E1Zq87gzUzujEkEVWgygFpyJAhyqiDiIje0to9V5GdWwCpVCh1vlQqIDu3AOv2XMM3U7u+4+qIVEuV72IjIqK6J/5pOu7Evy4zHBWTSgXExr9C/NP0d1QZkWpiQCIiqgcu3HgCsbji96UBgFgswoWbT5VcEZFqY0AiIqoHMnPyUcl8BLEIyJTkKbcgIhXHgEREVA/oa2uggqtrMlIB0NfRrHhBogaMAYmIqB7waG9Z4fijYlKpgC5OFkquiEi1MSAREdUD1haGaGPdqMJxSGKxCA7WJmhZykMjiej/Y0BSYT4+Pujbt6/cK178/Pxw6dIlBAcHw93dHYMGDZL92bx5M4Ci9+lNmjQJAwYMwIABAzBo0CBcuHABAHDgwAHY29vj0KFDsm1GR0fj448/LrWG9PR0zJkzB/3798eAAQPQv39/HDlyRFbfn3/+KVt24cKFck/TLSgogIuLCxISEmqsT4gaspkjOkJXS73MkCQWi6CrpY4ZI1zecWVEqqdaT9KmuiMvLw/h4eEYPnx4iXkDBgzAwoULS7QvWbIEHh4eGDNmDADg1atXyMnJkc1v1qwZNmzYAF9fX9l79sqybt06mJiY4MiRIxCJRMjMzMSLFy8AAG5ubrh8+TLef/99AMCVK1dgbGyMxMREWFlZ4ebNmzA2Npa9iJKI3o6lqT7WzOiGdXuuITb+ldyTtKVSAfYtGvFJ2kSVxICk4qZOnYq1a9di0KBB0NHRqdQ6z549g5mZmWzaxMREbn6bNm2gpqaGn376SRaiytuWi4sLRKKi31j19fWhr1/0n6+bmxsOHTqEoKAgPH36FEZGRujSpQsuX74MKysrXL58GW5ublU4WiKqiKWpPr6Z2rXoXWw3nyJTkgd9HU10cbLgZTWiKuAlNhXXpk0buLm5YefOnSXmHTlyRO4SW/FLKAMDA7Fw4UIMHz4cq1atwh9//FFi3ZkzZyIsLAyZmZnl7n/06NHYtm0b/Pz8sGzZMkRHR8vmubu749q1a8jLy8OlS5fQuXNnuLq64tKlSwCAS5cuwd3d/S2OnojKYm1hiBG97RE4yAkjetszHBFVEQNSPTB9+nT8+OOPeP36tVz7gAEDcPjwYdkfX19fAED//v1x+vRpjBs3DgAQFBSEbdu2ya1ra2sLHx8fhIWFlbtvd3d3REdHY/r06TAwMMDixYtlLy1u2rQpzM3NcePGDVy6dAmurq5wdnbGtWvXkJ+fj2vXrvEMEhER1Um8xFYPWFlZoX///rJB2JVhZGSE3r17o3fv3nBycsKWLVswYcIEuWWmTJmCQYMGoVmzZuVuS1dXF927d0f37t3h7e2NcePG4csvvwRQdJnt4sWLuHr1KhYtWgQdHR2YmJjg119/RZMmTWBhwVuNqW7z8fFBixYtZL+APHjwAM2aNYO2tjYAYPfu3ejUqRP++OMPGBoayq23ceNGODg44OOPP0ZSUhIMDAxQUFAAS0tLrFy5Erq6unL7mjdvHg4ePFhiW8XS09OxbNky3LlzByKRCIIgYOLEiRgwYIBsmcePH6NXr16YOnUqJk+erIwuIWoQGJDqiU8//RS+vr5QV6/4WxodHQ13d3fo6OhAEATcvn271IHS5ubm8Pf3x5YtW8oMSefOnYOTkxOMjIwAALdu3ZLblpubG4KDg2FsbAw9PT0AgKurKzZv3syzR6Qy5s6dCwcHBwBFwWfdunWy6cpasGABevbsCQD48ssvsX37dkydOlU2/8SJExX++y3vpohi4eHhcHd3x4EDBxAUFCQbH0hEVcNLbHVUXsojvD6zDy9++x6vz+xDXsqjcpc3MTHBxx9/jOfPn8vaFMcgrVixAgBw+fJl+Pn5yW7zT0hIwOLFi0vd7ieffIKsrCy5tsDAQNy8eRMA8M8//2DUqFGy2/yjoqLwn//8R7asq6sr4uPj0blzZ1lb586dER8fz/FH1CAVFBQgOztb9ksFALx8+RJbtmzBvHnzyl332bNnaNKkidxNEdbW1rL5hYWFOHjwIBYtWgQ9PT1cvHhRKcdA1BDwDFIdk//qKVKOBCM38S4gEgMiESAIeH12L7Ss7GE2YCo0TIouS0VFRcmtO3nyZNkpdTc3N7nfTt80d+5czJ07t9R5fn5+8PPzk00bGBjIBlUXe3Nc0rhx42RjmUpjZmaGu3fvyrV17dq1RBtRfbdixQoEBwcjOTkZTZo0kfulZNmyZfjss89kd4CWZfTo0Zg+fTqOHTsGZ2dndO3aFd7e3rL5586dQ9OmTdGqVSsMGzYM4eHh8PDwUNoxEdVnPINUh+S/eoqk7+chN+leUYMgBaSFRX8DyE26h6Tv5yH/Fd/CTaQK3ry8tWDBAhw+fBgXLlxAr169sGDBAgBFl7wtLCwqFWTKuykCKLq8NnToUABFN2nExMQgLS2tho+KqGFQ+YAUFRWFgQMHwsnJCX369EFERESF6yQmJsLe3r7En3/961/voOKypRwJhjQ3WxaIShCkkOZmI+VIyLstjIjKZWJigtTUVLm2169fl3jGGFAUmnx9fXH27FkAReP2Tp8+DR8fH9mT5gcOHIjbt2+Xuq/imyJmzpyJ4OBgHD58GEDRA19Pnz6NTZs2wcfHB0OHDkVBQYHsyfZEVDUqfYntzz//xJQpUzBs2DAsWLAAFy9exMKFC6Gnp4e+fftWuP6sWbPkBgoXDyKuDXkpj4ouq1VEkCI38Q7yUh5B06yl8gsjogp5eXnh559/xueffw4AOHToEKysrOQeyPqmCxcuwMbGBkDR3aIODg6yO9rs7e3xyy+/lHoXW3k3RRw6dAg9e/bE2rVrZcvHxMRg7dq1GDVqVM0dLFEDodIBafPmzWjfvj2WLVsGoOj08+PHj7Fhw4ZKBaSWLVvC2dlZyVVWTtadS0Vjjso6e/QmkRhZdy8xIBHVEQsWLMCKFSswYMAAiMVimJqaYv369XLLFI9BkkqlMDIywqpVqyq17cDAQEybNg1OTk74559/sHr1agiCAJFIBDMzM9lNEeHh4ZgzZ47cup6enpg/fz5u3bqFtm3b1szBEjUQKhuQip/OrPgfgq+vL44ePSp735eqKMzN+r8B2ZVYWCRCYU5WxcsRUbnin6bjwo0nyMzJh762BjzaW8Ja4YnTijdDKE4DQKNGjeTu3lS0a9euUtuzs7NLtCnewFDZmyKKn5T/JnV1dfz+++9l1kVEZVPZgJSQkID8/HzY2trKtdvZ2QEA4uLiKgxIS5YswcyZM2FsbIwePXpgzpw5MDY2VlbJ5VLT0gOEyqQjAIIANe3auxxIpOqevMjE2j1XcSf+tdwLXX86cRcO1iZ8oSsRqW5AKr4zQ/E6ffF0eXduaGpqYsSIEfDy8oKhoSH++usvbNmyBX///Tf2798PDQ2NatclCAKys7MhkUgAQPZ3RcTWHYCzeyu5EynELTuU+ttnQ1LVPqaqq499/OxlNhZsvQRJTiGAorfcv3lh++6j15i1LgYrJrqhaWPd0jdSQ+pj/9Y17GPlK62Piy8Dq7I6FZAyMjKQkpJS4XLNmzd/q/2YmZlhyZIlsmlXV1e89957mDhxIn777TfZO8uqIz8/H7GxsbLp+Pj4Sq+rb9wM6mlPICrnTJIgEqHAqBnuP88EnseWuVxDUpU+puqpT3287UQKsnMKyjxhKxUEZOcU4Nv//oHxvUsfZF3T6lP/1lXsY+VT7GNNTc3aKaSG1KmAdPz4cSxatKjC5SIjI2V3cWRkZMjNS09PBwC5p9RWRvfu3aGrq4tbt269VUDS0NBAq1atIJFIEB8fD2tra+jo6FRq3QKLmXj505cQ8iSlD9YWiSHW1IHFkBlQNzavdo31RXX6mKqmvvVxwrMMJL5IrHA5QQAev8iDXiMrtGhqoLR66lv/1kXsY+UrrY/v379fy1W9vToVkPz9/eHv71+pZfPy8qChoYG4uDh07dpV1h4XFwcAJcYmvSsikUjuBZQ6OjolXkhZJl0b6IxbjZQjIchNvCP3JG0IUmg1aw2zAVNkT9KmIlXqY6qW+tLH1+4lQCwWQSqteLyfWCzCtfupaGOr/F9G6kv/1mXsY+V7s49V/fIaUMcCUlVoamrCzc0N//vf/xAQECBrj4yMhJ2dXZXvYIuOjkZ2djacnJxqutQq0TCxQLOAr5GX8ghZdy+hMCcLatp60LN3h6ZZyRfKElHlZebkFw3IrsSyYhGQKclTek1EVDepbEACit5gP3r0aCxZsgT9+vXDpUuXcPToUbkHpQGAo6MjBg8eLHtZ66pVqyASieDs7AxDQ0PcuHEDW7duRbt27WRv265tmmYt+Zwjohqmr62BSpw8AlB0V5u+jmqPoSCi6lPpgPT+++8jODgY69atQ3h4OCwtLfHVV1+hX79+cssVFhZCKv3/vzPa2dlhz5492LdvH3JycmBubo5hw4Zh2rRpUFdX6S4honJ4tLfETycq96JkqVRAFydeziZqqFQ+DfTo0QM9evQodxnFB69VZawTEdUf1haGaGPdCP8kpJY7DkksFsG+RSO0tCj5ug8iahhU/mW1RERVMXNER+hqqUMsLn0QqVgsgq6WOmaMcHnHlRFRXcKAREQNiqWpPtbM6Ab7Fo0AFAUidTWRLDDZt2iENTO68UnaRA2cyl9iIyKqKktTfXwztWvRu9huPkWmJA/6Opro4mTBy2pEBIABiYgaMGsLwxIvpyUiAniJjYiIiKgEBiQiIiIiBQxIRERERAoYkIiIiIgUMCARERERKWBAIiIiIlLAgERERESkgAGJiIiISAEDEhEREZECBiQiIiIiBQxIRERERAoYkIiIiIgUMCARERERKWBAIiIiIlLAgERERESkgAGJiIiISAEDEhFRJfn4+MDDwwP5+fmytosXL8Le3h5ff/01ACAhIQHTpk2Dj48P/Pz8MGzYMOzfv7/U7fn6+mL27Nn46KOP4Ovri927d8vmZWZmwsXFBQsWLFDuQRFRqRiQiIiqwMLCAlFRUbLp8PBwtGvXDgDw/PlzjBw5El5eXoiKisKBAwewfft2FBQUlLm9qVOnYu/evQgLC8N3332HO3fuAACOHTuGtm3b4rfffkNWVpZyD4qISmBAIiKqgqFDhyIiIgIAkJGRgb/++gtdu3YFAOzevRudOnXCv/71L9nyRkZGGDFiRIXbbdasGWxsbBAfHw+gKHgFBgbi/fffR2RkZM0fCBGViwGJiKgKOnbsiKSkJCQnJ+Po0aPo27cv1NTUAAC3bt2Ci4tLtbZ79+5dxMXFoU2bNrh//z6ePn0KLy8vDBs2TBbIiOjdYUAiIqqiQYMG4eDBg4iIiMDQoUPfalvBwcH46KOP8OWXX2LFihWwtrZGeHg4Bg8eDDU1NXTv3h2JiYl48OBBDVVPRJWhXtsFEBGpmkGDBsHPzw/W1tawtraWtbdt2xbXrl3DmDFjKr2tqVOnol+/ftDV1QUA5Ofn4/Dhw1BXV8fRo0cBABKJBOHh4Zg7d25NHgYRlYNnkIiIqsjc3ByzZs3CnDlz5NpHjhyJP/74Q+6SWHp6On7++edKbzsqKgrNmzfH2bNnERUVhaioKOzbtw+HDx+Wu3uOiJSLAYmICED803Ts+d8dhB2+iT3/u4P4p+nlLj906NAS443MzMzw008/4fTp0/Dx8cGAAQMwZswYqKsXnaw/deoUFi5cWO52w8PDMWDAALk2Ozs7mJubIzo6uhpHRkTVIRIEQajtIuqLmzdvAgCcnJyQnZ2N2NhYODg4yE6dU81iHytfQ+jjJy8ysXbPVdyJfw2xWASxCJAKgFQqwMHaBDNGuMDSVF8p+24I/Vvb2MfKV1ofv/nzUFXxDBIRNVhPXmRi9roz+CchFUBRKCooFCCVFv3eeDfhNWavO4MnLzJrsUoiqg0MSETUYK3dcxXZuQWyQKRIKhWQnVuAdXuuvePKiKi2MSARUYMU/zQdd+JflxmOikmlAmLjX1U4JomI6hcGJCJqkC7ceAKxWFSpZcViES7cfKrkioioLmFAIqIGKTMnH5XMRxCLgExJnnILIqI6hQGJiBokfW0NVHB1TUYqAPo6msotiIjqFAYkImqQPNpbVjj+qJhUKqCLk4WSKyKiuoQBiYgaJGsLQ7SxblThOCSxWAQHaxO0tDB8R5URUV2g0gHp/PnzmD17Nnr27Al7e3ssW7as0utmZGRgwYIFcHV1hYuLC6ZNm4aUlBQlVktEdc3MER2hq6VeZkgSi0XQ1VLHjBEupc4novpLpQPS2bNncefOHXTu3BmGhlX77W7GjBk4f/48lixZgm+//RYPHz5EYGAgCgoKlFQtEdU1lqb6WDOjG+xbNAJQFIjU1USywGTfohHWzOimtCdpE1HdpV7bBbyNzz//HPPmzQMAXLp0qdLrXbt2DefOncP27dvh5eUFALCxsYGvry9OnDgBX19fpdRLRHWPpak+vpnaFfFP03Hh5lNkSvKgr6OJLk4WvKxG1ICpdEASi6t3AuzMmTMwNDSEp6enrM3W1hYODg44c+YMAxJRA2RtYQhrBiIi+j8qfYmtuuLi4mBjYwORSH7cga2tLeLi4mqpKiIiIqorVPoMUnWlp6fDwMCgRLuRkRH+/vvvt9q2IAjIzs6GRCIBANnfVPPYx8rHPlYu9q/ysY+Vr7Q+FgShxEkIVVOnAlJGRkal7iRr3rw5NDXr5kPb8vPzERsbK5uOj4+vvWIaCPax8rGPlYv9q3zsY+VT7OO6+nO6supUQDp+/DgWLVpU4XKRkZGws7Or9n4MDQ3x7NmzEu1paWkwMjKq9nYBQENDA61atYJEIkF8fDysra2ho6PzVtuk0rGPlY99rFzsX+VjHytfaX18//79Wq7q7dWpgOTv7w9/f3+l78fW1hYXLlwocQrw4cOHaN269VttWyQSQVdXVzato6MjN001j32sfOxj5WL/Kh/7WPne7GNVv7wGNNBB2t26dUNaWhouXLgga3v48CFu376Nbt261WJlREREVBfUqTNIVZWUlISbN28CKDrFl5CQgOPHjwMA+vbtK1vO0dERgwcPxooVKwAALi4u8PLywoIFCzB37lxoaWlh7dq1sLe3R+/evd/9gRAREVGdotIB6dKlS5g/f75s+uzZszh79iwA4O7du7L2wsJCSKVSuXXXrVuHlStXYvHixSgoKICXlxcWLVoEdXWV7hIiIiKqASqdBvz8/ODn51fhcm+GpWIGBgZYsWKF7KwSERERUbEGOQaJiIiIqDwMSEREREQKGJCIiIiIFDAgERERESlgQCIiIiJSwIBEREREpIABiYiIiEgBAxIRERGRAgYkIiIiIgUMSEREREQKGJCIiIiIFDAgERERESlgQCIiIiJSwIBEREREpIABiYiIiEgBAxIRERGRAgYkIiIiIgUMSEREREQKGJCIiIiIFDAgERERESlgQCIiIiJSwIBEREREpIABiYiIiEgBAxIRERGRAgYkIiIiIgUMSEREREQKGJCIiIiIFDAgERERESlgQCIiIiJSwIBEREREpIABiYiIiEgBAxIRERGRAgYkIiIiIgUMSEREREQKGJCIiIiIFDAgERERESlgQCIiIiJSoF7bBbyN8+fP48CBA/jrr7/w+PFj/Pvf/8bixYsrXC8xMRE9evQo0d6hQwfs27dPGaUSERGRClHpgHT27FncuXMHnTt3RlpaWpXXnzVrFtzc3GTTenp6NVkeERERqSiVDkiff/455s2bBwC4dOlSlddv2bIlnJ2da7gqIiIiUnUqPQZJLFbp8omIiKiOatAJY8mSJXBwcICHhwcWLVqE1NTU2i6JiIiI6gCVvsRWXZqamhgxYgS8vLxgaGiIv/76C1u2bMHff/+N/fv3Q0NDo9rbFgQB2dnZkEgkACD7m2oe+1j52MfKxf5VPvax8pXWx4IgQCQS1VZJNUIkCIJQ20UUy8jIQEpKSoXLNW/eHJqamnJtPj4++OCDDyp1F1tpTp8+jYkTJ2Lt2rXw9fWt1jZu3ryJvLy8aq1LRERUn2hqasLJyam2y6i2OnUG6fjx41i0aFGFy0VGRsLOzq5G9929e3fo6uri1q1b1Q5IAKChoYFWrVpBIpEgPj4e1tbW0NHRqcFKqRj7WPnYx8rF/lU+9rHyldbH9+/fr+Wq3l6dCkj+/v7w9/ev7TLeikgkgq6urmxaR0dHbppqHvtY+djHysX+VT72sfK92ceqfnkNaOCDtN8UHR2N7OxslT4dSERERDWjTp1BqqqkpCTcvHkTQNEpvoSEBBw/fhwA0LdvX9lyjo6OGDx4MFasWAEAWLVqFUQiEZydnWFoaIgbN25g69ataNeuHXr27PnuD4SIiIjqFJUOSJcuXcL8+fNl02fPnsXZs2cBAHfv3pW1FxYWQiqVyqbt7OywZ88e7Nu3Dzk5OTA3N8ewYcMwbdo0qKurdJcQERFRDVDpNODn5wc/P78Kl3szLAH1Y6wTERERKQ/HIBEREREpYEAiIiIiUsCARERERKSAAYmIiIhIAQMSERERkQIGJCIiIiIFDEhEREREChiQiIiIiBQwIBEREREpYEAiIiIiUsCARERERKSAAYmIiIhIAQMSERERkQIGJCIiIiIFDEhEREREChiQiIiIiBQwIBEREREpUK/tAoiIiKju8vHxgYaGBrS1tZGfn49///vf6N69O3r16oXWrVtDKpUiNzcXBgYGWLJkCRYtWoScnBxkZmYiJycHVlZWAIABAwbAxMQEX3/9NaysrFBYWIhGjRph2bJlsLGxkdvngwcP4Ofnh3/9619YuHBhqXWdOHECW7ZsQWFhIXJzc2FmZoadO3dCLP7/537mzp2L3377DefOnYOurm6VjpsBiYiIiMq1bt06ODg4ICkpCQMHDkSnTp2gp6eHw4cPIzs7G7GxsXBwcICuri4OHz6MmzdvIjo6Gnfu3MGmTZtk2zlw4ADc3NxkbatXr8aKFSsQFhYmWyY/Px9ffPEFevbsWWY9KSkp+OKLL3DgwAE0a9YMAHDr1i2IRCLZMpmZmYiOjkabNm1w/Phx+Pn5VemYeYmNiIiIKqVZs2awsbHB+fPna2R77u7uePLkiVzbxo0b0bdvX1hbW5e53suXL6GmpgYjIyNZW9u2beUC0tGjR+Hh4YGxY8ciPDy8yrUxIBEREVGl3L17F3FxcejRoweysrIwaNAgfPTRR5g/fz4WLFhQpW1JpVKcPHkSvr6+sra//voL169fx8cff1zuuvb29ujUqRN8fHwwefJkbNu2DcnJyXLLhIeHY+jQofjggw/w6NEjxMXFVak+XmIjIiKics2YMQPa2trQ0dHBihUroK6uXuoltsq4dOkSBg0ahKdPn8LIyAj79u0DAEgkEixduhTr16+XOxNUGrFYjODgYDx48AB//PEHzpw5gy1btiAiIgItW7bE3bt38fz5c3h5eUEsFmPgwIGIiIjAZ599VuljZkAiIiKichWPQSqWmJhY7W0Vj0GSSCSYNm0ali5dinXr1iEhIQFPnjxBQEAAACA9PR1SqRTp6elYvXp1qduys7ODnZ0dhg8fjvHjxyMqKkp2SS0rK0s2jik/Px+CIGDmzJlQV69c9GFAIiIiondOR0cHX331Ffr27Yvbt2/D0dERFy9elM0PDg5Genp6qXexJScnIzExEZ06dQIApKWlITExEc2bN0deXh5++eUX7N27F3Z2drJ1/P39cfr06XIHf7+JAYmIiIiqrHgMUvFt/lpaWtizZw/09fUrvQ1zc3OMHz8eGzZswJYtW8pd9tSpU4iKisLXX3+NgoICbNq0CYmJidDW1kZhYSGGDBmCnj17IjIyEs2aNZMLR0DRYwbCw8MrHZBEgiAIlT4SKtfNmzcBAE5OTiVue6Saxz5WPvaxcrF/lY99XL6s+Ed4efESCjKzoK6vh8bubtCzblmlbZTWx2/+PFRVPINERETUwEiePsW9dcHIuHMXEIshEosgSAU83rMXBm3s8d6MqdCxsKjtMmsVb/MnIiJqQCRPn+LGnHnI+OdeUYNUCqGgEJBKAQAZ/9zDjTnzIHn6tBarrH0MSERERA3IvXXBKMjOlgWiEqRSFGRn4966kHdbWB3DgERERNRAZMU/KrqsVlY4KiaVIuPOHWTFP3o3hdVBDEhEREQNxMuLlwBxJX/0i8VFyzdQDEhEREQNREFmFkTi8p9SXUwkEqEgM0vJFdVdDEhEREQNhLq+HgRp5Z7uIwgC1PX1lFxR3cWARERE1EA0dnerePxRMakUjT3clVtQHcaARERE1EDoWbeEQRv7ischicUwaNMGei1bvJvC6iAGJCIiogbkvRlToa6rW3ZIEouhrquL92ZMebeF1TEqG5AKCwsRFhaGf//733Bzc4Orqys+/vhj/Pnnn5VaPyMjAwsWLICrqytcXFwwbdo0pKSkKLlqIiKi2qVjYYH2366CQevWRQ1iMURqarLAZNC6Ndp/u6rBP0lbZV81kpOTg9DQUAwZMgSBgYEQi8XYt28fRo8eje3bt8PDw6Pc9WfMmIH79+9jyZIl0NLSwrp16xAYGIiIiAioq6tstxAREVVIx8IC7Vd/XfJdbB7uDfqy2ptUNgloa2vj5MmTMDIykrV5enqif//++OGHH8oNSNeuXcO5c+ewfft2eHl5AQBsbGzg6+uLEydOwNfXV+n1ExER1TY965ZVfjltQ6Gyl9jU1NTkwlFxm729fYWXys6cOQNDQ0N4enrK2mxtbeHg4IAzZ84opV4iIiJSHSobkEpTUFCAv/76C7a2tuUuFxcXBxsbG4hE8g/LsrW1RVxcnDJLJCIiIhWgspfYSrNt2zYkJydjzJgx5S6Xnp4OAwODEu1GRkb4+++/36oGQRCQnZ0NiUQCALK/qeaxj5WPfaxc7F/lYx8rX2l9LAhCiZMQqqZOBaSMjIxK3UnWvHlzaGpqyrWdP38ewcHBCAoKQrt27ZRVYoXy8/MRGxsrm46Pj6+1WhoK9rHysY+Vi/2rfOxj5VPsY8Wf06qmTgWk48ePY9GiRRUuFxkZCTs7O9n0rVu3MHXqVPTv3x9TplT83AZDQ0M8e/asRHtaWlqJcU1VpaGhgVatWkEikSA+Ph7W1tbQ0dF5q21S6djHysc+Vi72r/Kxj5WvtD6+f/9+LVf19upUQPL394e/v3+V1nn06BECAwPh4uKCr776qlLr2Nra4sKFCyVOAT58+BCti58LUU0ikQi6urqyaR0dHblpqnnsY+VjHysX+1f52MfK92Yfq/rlNUDFB2mnpKRg3LhxsLCwwIYNG6ChoVGp9bp164a0tDRcuHBB1vbw4UPcvn0b3bp1U1a5REREpCLq1BmkqsjJyUFgYCBev36NhQsX4t69e7J5mpqacHR0lE07Ojpi8ODBWLFiBQDAxcUFXl5eWLBgAebOnQstLS2sXbsW9vb26N279zs/FiIiIqpbRIIgCLVdRHUkJiaiR48epc5r1qwZoqKiZNP29vYYMmQIVq1aJWvLyMjAypUr8dtvv6GgoABeXl5YtGgRzM3Nq13T1atXIQgCNDU1IQgC8vPzoaGhUS9ONdZF7GPlYx8rF/tX+djHyldaH+fl5UEkEqFjx461XF31qWxAqouuXbsGQRAqfamPiIioPsrPz4dIJIKLi0ttl1JtDEhEREREClR6kDYRERGRMjAgERERESlgQCIiIiJSwIBEREREpIABiYiIiEgBAxIRERGRAgYkIiIiIgUMSEREREQKGJCIiIiIFDAgERERESlgQCIiIiJSwID0juTm5mL9+vXw8fFBu3bt8MEHH2D16tW1XVa99Pfff8PBwUGlX5JY1xQWFiIsLAz//ve/4ebmBldXV3z88cf4888/a7s0lfTgwQOMHTsWzs7O8PT0xDfffIO8vLzaLqveOHbsGD799FN069YNzs7OGDRoEMLDw8FXjypPVlYWunXrBnt7e9y8ebO2y6kR6rVdQEMglUoRFBSEx48fY8qUKbCyssKTJ0/w8OHD2i6t3hEEAcuXL4eJiQmys7Nru5x6IycnB6GhoRgyZAgCAwMhFouxb98+jB49Gtu3b4eHh0dtl6gy0tLSEBAQAGtrawQHByM5ORmrVq1CTk4OFi9eXNvl1Qs7d+5Es2bNMG/ePDRq1Ai///47vvjiCzx79gxTpkyp7fLqpU2bNqGwsLC2y6hRDEjvQEREBP766y9ERkbCzMystsup1yIiIvD69WsMHToUu3btqu1y6g1tbW2cPHkSRkZGsjZPT0/0798fP/zwAwNSFfz888/IyspCSEgIjI2NARSdoVu6dCkmTpwIc3Pz2i2wHti8eTNMTExk0x4eHkhNTcX333+PoKAgiMW8eFKTHjx4gJ9++glz587Fl19+Wdvl1Bh+St6B/fv3o2/fvgxHSpaeno41a9Zg/vz50NDQqO1y6hU1NTW5cFTcZm9vj5SUlFqqSjWdOXMGHh4esnAEAP369YNUKsX58+drr7B65M1wVMzBwQGZmZk8s6wEX331FYYPHw4bG5vaLqVGMSApWX5+Pm7fvg1LS0t8/vnncHZ2houLC6ZPn47nz5/Xdnn1yrp169C2bVt4e3vXdikNQkFBAf766y/Y2trWdikqJS4urkSfGRoaokmTJoiLi6ulquq/K1euwNzcHPr6+rVdSr1y/Phx/PPPP5g8eXJtl1LjGJCULDU1Ffn5+QgLC0NqaipCQkKwdOlSXL16FVOnTq3t8uqN2NhYhIeHY/78+bVdSoOxbds2JCcnY8yYMbVdikpJT0+HoaFhiXYjIyOkpaXVQkX1359//onIyEiMGzeutkupVyQSCVatWoWZM2fWy+DJMUjVkJGRUanLCs2bN4dUKgUA6OnpISQkBJqamgAAU1NTjB07FhcuXOD4jVJUpY81NDSwdOlSjBw5EnZ2du+guvqhKn1c/Lktdv78eQQHByMoKAjt2rVTVolEb+3Zs2eYOXMm3NzcMHr06Noup17ZvHkzGjdujKFDh9Z2KUrBgFQNx48fx6JFiypcLjIyEpaWlhCJROjYsaPcDxlXV1eoqanh/v37DEilqEof37lzB3FxcVizZg3S09MBFD1WASj6bV1LSwtaWlpKrVcVVaWP3wyet27dwtSpU9G/f3/eEVQNhoaGyMjIKNGelpZWYpwXvZ309HQEBgbC2NgYwcHBHJxdg5KSkrBjxw5s3LhR9nkuHt+VnZ2NrKws6Onp1WaJb40BqRr8/f3h7+9f6eWbNWtW5rziH+Qkryp9HBkZibS0NPj4+JSY17lzZwQGBmLOnDk1XaLKq+rnGAAePXqEwMBAuLi44KuvvlJSZfWbra1tibFGGRkZeP78Ocdz1aCcnBxMnDgRGRkZ2Lt3LwwMDGq7pHolMTER+fn5+OSTT0rMGz16NDp06IB9+/bVQmU1hwHpHfD29sbx48eRm5srO5Nx8eJFFBYWom3btrVcneobMmQIXF1d5doOHjyIyMhIhIWFwdLSspYqq19SUlIwbtw4WFhYYMOGDbxTsJq6deuGLVu2yI1FOn78OMRiMTw9PWu5uvqhoKAAM2bMQFxcHHbv3s1HJyiBg4MDfvzxR7m22NhYrFy5EkuXLoWTk1MtVVZzGJDegfHjx+Pw4cMICgrC6NGj8erVK6xZswadOnWCu7t7bZen8qysrGBlZSXXdvnyZaipqcHNza2WqqpfcnJyEBgYiNevX2PhwoW4d++ebJ6mpiYcHR1rsTrVMnz4cOzatQuTJ0/GxIkTkZycjG+++QbDhw/nD/IasnTpUkRHR2PevHnIzMzE9evXZfMcHR1LjKmjqjM0NCzz/9e2bdvWi1/+GZDeAQsLC/z4449YsWIFpk6dCh0dHfTo0QPz5s2DSCSq7fKIKvTixQvcuXMHAPDpp5/KzWvWrBmioqJqoyyVZGRkhB9++AHLly/H5MmToaenh2HDhmHmzJm1XVq9Ufw8qVWrVpWYd+rUqRK/UBGVRiTw5TREREREcjikn4iIiEgBAxIRERGRAgYkIiIiIgUMSEREREQKGJCIiIiIFDAgERERESlgQCIiIiJSwIBEREREpIABiYiIiEgBXzVCRHXSgQMHMH/+fNm0pqYmLC0t4enpiaCgIJiamsrmvXjxAtu3b0d0dDSePn0KkUgEW1tb9OzZE6NGjZK9FPbGjRs4cOAAbty4gbt376KgoAB3795958dGRHUfAxIR1WnTpk2DlZUV8vLycOXKFezZswcxMTE4evQodHR0cOPGDXzyySfIzs7GwIEDZS/J/PvvvxEWFoY///wTO3bsAADExMQgPDwcrVu3hpWVFeLj42vxyIioLmNAIqI6rVu3bnBycgIA+Pv7w9jYGN9//z1OnTqFbt26YcqUKVBTU8PBgwdhZ2cnt+7MmTOxb98+2fSIESMQGBgIbW1tLFu2jAGJiMrEMUhEpFLc3d0BAImJifj555+RnJyMefPmlQhHAGBqaoqgoCC5aW1t7XdWKxGpLgYkIlIpCQkJAABjY2NERUVBW1sbffr0qeWqiKi+YUAiojotMzMTr169wrNnzxAZGYmNGzdCW1sb3t7eiIuLg7W1NTQ1NWu7TCKqZzgGiYjqtDFjxshNN2vWDN9++y3Mzc2RmZkJPT292imMiOo1BiQiqtMWL14MGxsbqKmpwdTUFDY2NhCLi05+6+vrIysrq5YrJKL6iAGJiOq09u3by+5iU2Rra4vY2Fjk5eXxMhsR1SiOQSIileXt7Y2cnBycOHGitkshonqGAYmIVNbw4cPRpEkTrFq1Cg8fPiwx/+XLl9i0aVMtVEZEqo6X2IhIZRkZGWHjxo345JNPMHjwYLknad++fRtHjx6Fi4uLbPmkpCQcPnwYQNGTtgHIApSlpSUGDx78bg+AiOosBiQiUmkdOnTAkSNHsH37dpw+fRqHDx+GWCyGra0tPvnkE4waNUq2bGJiItavXy+3fvG0q6srAxIRyYgEQRBquwgiIiKiuoRjkIiIiIgUMCARERERKWBAIiIiIlLAgERERESkgAGJiIiISAEDEhEREZECBiQiIiIiBQxIRERERAoYkIiIiIgUMCARERERKWBAIiIiIlLAgERERESkgAGJiIiISMH/A4eQvcJ3ke2NAAAAAElFTkSuQmCC\n"
252 |           },
253 |           "metadata": {}
254 |         }
255 |       ],
256 |       "source": [
257 |         "import matplotlib.pyplot as plt\n",
258 |         "import seaborn as sns\n",
259 |         "\n",
260 |         "# Scree plot\n",
261 |         "plt.figure(figsize=(6,4))\n",
262 |         "plt.plot(range(1, len(pca.explained_variance_ratio_)+1),\n",
263 |         "         pca.explained_variance_ratio_, marker='o')\n",
264 |         "plt.title('Scree Plot – Explained Variance per Component')\n",
265 |         "plt.xlabel('Principal Component')\n",
266 |         "plt.ylabel('Explained Variance Ratio')\n",
267 |         "plt.tight_layout()\n",
268 |         "plt.show()\n",
269 |         "\n",
270 |         "# Silhouette plot\n",
271 |         "plt.figure(figsize=(6,4))\n",
272 |         "plt.plot(sil_df['k'], sil_df['silhouette'], marker='o')\n",
273 |         "plt.title('Silhouette Score by k (K-Means)')\n",
274 |         "plt.xlabel('k')\n",
275 |         "plt.ylabel('Silhouette Score')\n",
276 |         "plt.tight_layout()\n",
277 |         "plt.show()\n",
278 |         "\n",
279 |         "# PCA scatter with clusters\n",
280 |         "plt.figure(figsize=(6,5))\n",
281 |         "palette = sns.color_palette(n_colors=best_k)\n",
282 |         "for cl in sorted(np.unique(labels_km)):\n",
283 |         "    sel = labels_km == cl\n",
284 |         "    plt.scatter(result.loc[sel, 'PC1'], result.loc[sel, 'PC2'], label=f'Cluster {cl}', s=60)\n",
285 |         "for t in result.index:\n",
286 |         "    plt.annotate(t, (result.loc[t, 'PC1'], result.loc[t, 'PC2']), fontsize=8, xytext=(4,4), textcoords='offset points')\n",
287 |         "plt.title('PC1 vs PC2 – K-Means Clusters')\n",
288 |         "plt.xlabel('PC1')\n",
289 |         "plt.ylabel('PC2')\n",
290 |         "plt.legend()\n",
291 |         "plt.tight_layout()\n",
292 |         "plt.show()"
293 |       ]
294 |     },
295 |     {
296 |       "cell_type": "code",
297 |       "execution_count": 10,
298 |       "id": "9c07db21",
299 |       "metadata": {
300 |         "id": "9c07db21",
301 |         "outputId": "e2e2d37f-27bb-4700-8996-447449e91ed8",
302 |         "colab": {
303 |           "base_uri": "https://localhost:8080/"
304 |         }
305 |       },
306 |       "outputs": [
307 |         {
308 |           "output_type": "stream",
309 |           "name": "stdout",
310 |           "text": [
311 |             "Saved: ../results/pca_clustering_results.csv\n"
312 |           ]
313 |         }
314 |       ],
315 |       "source": [
316 |         "# Optional: save tabular results locally (not required for rendering on GitHub)\n",
317 |         "os.makedirs('../results', exist_ok=True)\n",
318 |         "result.to_csv('../results/pca_clustering_results.csv', index=True)\n",
319 |         "print('Saved: ../results/pca_clustering_results.csv')"
320 |       ]
321 |     }
322 |   ],
323 |   "metadata": {
324 |     "kernelspec": {
325 |       "display_name": "Python 3",
326 |       "language": "python",
327 |       "name": "python3"
328 |     },
329 |     "language_info": {
330 |       "file_extension": ".py",
331 |       "mimetype": "text/x-python",
332 |       "name": "python",
333 |       "nbconvert_exporter": "python",
334 |       "pygments_lexer": "ipython3",
335 |       "version": "3.11"
336 |     },
337 |     "colab": {
338 |       "provenance": []
339 |     }
340 |   },
341 |   "nbformat": 4,
342 |   "nbformat_minor": 5
343 | }


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