354 lines
68 KiB
Plaintext
354 lines
68 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Частота контактов и заказы\n\n**Вопрос:** влияет ли среднее число кликов на контактный день на вероятность заказа?\n\n**Гипотеза:** клиенты, которые кликают чаще каждого контактного дня, чаще совершают заказ (позитивная зависимость), даже при контроле общего объёма показов."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-12T19:27:14.925005Z",
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"start_time": "2025-12-12T19:27:13.730791Z"
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}
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},
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"source": [
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"import sqlite3\nfrom pathlib import Path\nimport sys\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import roc_auc_score\n\nsns.set_theme(style=\"whitegrid\")\nplt.rcParams[\"figure.figsize\"] = (10, 5)\n\nproject_root = Path.cwd().resolve()\nwhile not (project_root / \"preanalysis\").exists() and project_root.parent != project_root:\n project_root = project_root.parent\nsys.path.append(str(project_root / \"preanalysis\"))\nimport eda_utils as eda\n\ndb_path = project_root / \"dataset\" / \"ds.sqlite\"\nconn = sqlite3.connect(db_path)\ndf = pd.read_sql_query(\"select * from communications\", conn, parse_dates=[\"business_dt\"])\nconn.close()\n"
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],
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"outputs": [],
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"execution_count": 1
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-12T19:27:15.582784Z",
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"start_time": "2025-12-12T19:27:14.934830Z"
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}
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},
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"source": [
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"for cols, name in [\n (eda.ACTIVE_IMP_COLS, \"active_imp_total\"),\n (eda.PASSIVE_IMP_COLS, \"passive_imp_total\"),\n (eda.ACTIVE_CLICK_COLS, \"active_click_total\"),\n (eda.PASSIVE_CLICK_COLS, \"passive_click_total\"),\n (eda.ORDER_COLS, \"orders_amt_total\"),\n]:\n df[name] = df[cols].sum(axis=1)\n\ndf[\"imp_total\"] = df[\"active_imp_total\"] + df[\"passive_imp_total\"]\ndf[\"click_total\"] = df[\"active_click_total\"] + df[\"passive_click_total\"]\n\ncontact_days = df.groupby(\"id\")[\"business_dt\"].nunique().rename(\"contact_days\")\nclient = df.groupby(\"id\").agg(\n {\n \"imp_total\": \"sum\",\n \"click_total\": \"sum\",\n \"orders_amt_total\": \"sum\",\n \"age\": \"median\",\n \"gender_cd\": lambda s: s.mode().iat[0],\n \"device_platform_cd\": lambda s: s.mode().iat[0],\n }\n).reset_index().merge(contact_days, on=\"id\", how=\"left\")\n\nclient[\"clicks_per_day\"] = eda.safe_divide(client[\"click_total\"], client[\"contact_days\"])\nclient[\"has_order\"] = (client[\"orders_amt_total\"] > 0).astype(int)\nclient.head()\n"
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],
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"outputs": [
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{
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"data": {
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"text/plain": [
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" id imp_total click_total orders_amt_total age gender_cd \\\n",
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"0 1 68.0 17.0 0 58.0 M \n",
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"1 2 116.0 23.0 3 54.0 M \n",
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"2 3 293.0 37.0 2 70.0 F \n",
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"3 4 57.0 15.0 0 43.0 F \n",
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"4 5 43.0 16.0 1 46.0 M \n",
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"\n",
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" device_platform_cd contact_days clicks_per_day has_order \n",
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"0 Android 13 1.307692 0 \n",
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"1 Android 15 1.533333 1 \n",
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"2 Android 31 1.193548 1 \n",
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"3 Android 12 1.250000 0 \n",
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"4 Android 10 1.600000 1 "
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>id</th>\n",
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" <th>imp_total</th>\n",
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" <th>click_total</th>\n",
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" <th>orders_amt_total</th>\n",
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" <th>age</th>\n",
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" <th>gender_cd</th>\n",
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" <th>device_platform_cd</th>\n",
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" <th>contact_days</th>\n",
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" <th>clicks_per_day</th>\n",
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" <th>has_order</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>68.0</td>\n",
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" <td>17.0</td>\n",
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" <td>0</td>\n",
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" <td>58.0</td>\n",
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" <td>M</td>\n",
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" <td>Android</td>\n",
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" <td>13</td>\n",
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" <td>1.307692</td>\n",
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" <td>3</td>\n",
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" <td>54.0</td>\n",
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" <td>M</td>\n",
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" <td>Android</td>\n",
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" <td>15</td>\n",
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" <td>1.533333</td>\n",
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" <td>2</td>\n",
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" <td>70.0</td>\n",
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" <td>F</td>\n",
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" <td>Android</td>\n",
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" <td>31</td>\n",
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" <td>1.193548</td>\n",
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" <td>15.0</td>\n",
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" <td>0</td>\n",
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" <td>43.0</td>\n",
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" <td>F</td>\n",
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" <td>Android</td>\n",
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" <td>12</td>\n",
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" <td>16.0</td>\n",
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" <td>1</td>\n",
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" <td>46.0</td>\n",
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" <td>M</td>\n",
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" <td>Android</td>\n",
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" <td>10</td>\n",
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" <td>1.600000</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"execution_count": 2
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Визуализация: заказы vs клики на контактный день"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-12-12T19:27:15.715340Z",
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"start_time": "2025-12-12T19:27:15.610539Z"
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}
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},
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"source": [
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"bins = pd.qcut(client[\"clicks_per_day\"], 8, duplicates=\"drop\")\norder_rate = client.groupby(bins)[\"has_order\"].mean().reset_index()\norder_rate[\"clicks_per_day\"] = order_rate[\"clicks_per_day\"].astype(str)\nplt.figure(figsize=(12, 4))\nsns.lineplot(data=order_rate, x=\"clicks_per_day\", y=\"has_order\", marker=\"o\")\nplt.xticks(rotation=40)\nplt.title(\"Доля клиентов с заказом vs клики на контактный день\")\nplt.tight_layout()\nplt.show()\norder_rate\n"
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],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/mx/y1qcnthj1154ngqj00r8gz480000gn/T/ipykernel_83535/2771825794.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
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" order_rate = client.groupby(bins)[\"has_order\"].mean().reset_index()\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1200x400 with 1 Axes>"
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],
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SKKbXSGGQn9Ja2m3Pjp7fjmh86jXTGPXfU2NHwsd4cduh0ijNc9Rtuk//iwrK1W9MQZS2SzszJkIpIFIAX9z0TB8Ea7qh/nahz1k/T8FZ6LRj/5z9W1HBll5Xf7/+b/R89bf01Y0AgKpDBRQAoMrogF8noQpbwqcuiU6udYKqqgadTOqqvU7GFBrpBFPTiDQNSSf/CgzUS2TWrFnua0rqKaITVJ2s6+RE08jKQ1UjmuqkMEUrkakCQqunSUk/29NjVbngG0yHU3WKfkdVZ6kSRidIWm1LvaHKesJeHJ2s6WfoJE6vpQINNSlWFYMq0tQMOZQCFlFFhq+CKK73jKpwNOVMJ8aqmNIUGf3tdBIfXpHgaRqc+vfo8ToJV0NmBYX+eehEWP2Y1CBa04DUw0lhkU529ZzCKWDSyaWmKqnqSRVEpfke6gWlvlD6XCe/CuRUqaeqIoUz+ttce+21wb+NVsHT76QqGYWZ4XSSq5NkVVr4fj6loZNyVcBpjGu8+amIopN2hXmq9tBKeXqsKjh0cl2aXkrhr42mCpbmf8dTIKZxX5qKEVXa6LVXEKWKudD/AfV/Kkvlzo5oapXGkcbdGWec4bYhqi5U0CEKMnZEr6WmSer3U/in77OjbU9F0eup6kL9Hqoc1DZS0yyLG+Ph26GKrODRWND/vMauepA9++yzLhDVSoA7OyZ8byeFstoO+m2L+BUX9V6vg6bGKQDUdk9v+r/V2NH/qKoJy7J4hGhM6Ocp0NP/rp6D/q+Lm+IHAKg8BFAAgCrjG2/rqrqWJC+KAgidYKgxr8IOLUmu5eYVAOikTCc7OklUnxRNEVF4oioVv7R5UdRXSSeOCg121Ay6JOq3o5NbnawqRFOVh56PX6mpJOrho94+JVW/KIzRc9QJmk54dTKmk8DQVa12lgI4nVTqZFoVSAqlFBzppNZXCHn+b6TnopNjVYiU1DdFIYCev/pc6URPJ446afR9jMLp5FXNl/U31nQ/BTpaoU9Bj1xzzTUuHFL1jk4i9Rpq6oxOVFVpVVRTYYUx+nkKP3SyWprv4acdKTDSyb8qnvQYrYomml6kaZIKk/RYBSj63gqmilphTT27dL+qxYqa6lcSVT3p9VPT89DKJAV8Gu8KD/Vz9bxVEaOQsrTT2UJfG/1t9bfZ0f+Op6pF/3cpDa3opwBPFXynnHKKu01TzEKruiqCpmNpdUKNO/3NNIb0/6LG4vq5ChP9tLqSKDBVsKfnqxBwR9ueiqLtnMJxX82msEfbF4Ukeu472g6pAmln+nqFb3v1JgrYNb40jTJ8u1DeMaEpxqIgtSi33nqrC8IVvOr/UNt2/c+pb5SCN4W9pW10Hip0f6P/Y73G+t5MvwOAqhcTKMtSHQAA7ASdCOrk1/ddKYpOGlWppBMzAEDkbP8V4IVW9pXlfgBA7UcPKAAAAAAAAFQqpuABAKqMGjzvqH+Hpm0VNa0JAFC7t/8l9f/a0f0AgNqPKXgAAAAAAACoVEzBAwAAAAAAQKUigAIAAAAAAEClogdUKfz2229ueWYtQw0AAAAAAACz3Nxci4mJsd12222HLwcVUKWg8ElvtZ1+h5ycnIj4XVC1GDtg7IBtDmoD9ldg7IDtDmqLQIScn5clL6ECqhR85VPfvn2tNsvIyLAZM2ZY165dLSUlpbqfDmoRxg4YO2Cbg9qA/RUYO2C7g9oiI0LOz6dOnVrqx1IBBQAAAAAAgEpFAAUAAAAAAIBKRQAFAAAAAACASkUABQAAAAAAgEpFAAUAAAAAAFDFkpKSouo1J4ACAAAAAACoIlk5eRafkGSt2nZ27/V5NKhT3U8AAAAAAAAgGuTk5tvb4+fauO/mW3pmrtVNjrfDhne2o/fpZgnxcRbJCKAAAAAAAAAqWVZOngufXvtsVvA2hVCvbv38yL27WlJC5MY0TMEDAAAAAACoBNm5+bZ4xSabPGulxcbEuMqnorz/3XyLi43siCZyozUAAAAAAIBKFAgEbENatq1cm2Er1qbb8q3vt7xl2LpNWe5xHVLr241nDHEVT0XR7RlZudawXmLE/r0IoAAAAAAAAIqRm5dvq9Zn2vI16bZSwdK6jC0fr9sSNmXl5Jf42iUn1rEGdROtUf0k1/OpqBBKt6ckxUf034AACgAAAAAARHUV0+aM3EKVS8H369JtzYZMCwSK//qYGLOmDZOtVdO6lto0xVo2Tdn68Za3+inxFhMT43pAqeG47/kUSrfnFxRYfAR3Sqr2AKqgoMAeeeQRe/PNN23z5s02aNAgu+mmm6xdu3ZFPj43N9ceeughe/fdd93j+/TpY9dff73tsssuwcecfvrp9uOPPxb6usGDB9uLL75Y6b8PAAAAAACoWfLyC2z1+swtwZIql9bo/bawKSMrr8SvT0yIc6FSyyYp1qpZXUttoqCprvu4ReNki6+z4xXskhLquNXufM8nVsGrYo8++qi98sordtddd1lqaqrdc889dtZZZ9m4ceMsISFhu8fffPPN9vXXX7vHt27d2h588EE7++yz7eOPP7b69eu7x8yaNcs9bt999w1+XXx8ZJeyAQAAAAAQzdIyt1QxqR/T8q3VTP7j1RsyraCghDImM2vSIMlVMPnKJX3sQqemKdaoXqKrYtpZCfFxbrW7Y0Z1t7SMbKuXkugqn3R7pKvWCqicnBwbO3asXXnllTZy5Eh32wMPPGDDhw+3zz77zA455JBCj1+yZIm9/fbb9vjjj7vHyG233WajR4+2P//803bffXdbu3ate+vfv781b968Wn4vAAAAAABQsfILArZ2Q6arXFq+JsNWbq1gUsCk3kyaRleShDqxLkwKBkxNUiw1pJopsYpCoKSEOpaRkWHLliywTp06WUpKikWDag2gZs6caenp6S448ho0aGC9evWySZMmbRdA/fDDD67KacSIEYUe/9VXXwU/V/WTUkn9EQEAAAAAQO2hleB8c+9t4dKWz1etz7C8/JKrmFSpFF7F5N83rp9ksbE7X8VUUbKytqyQFy2qNYBasWKFe9+qVatCt7do0SJ4X6gFCxa43lCqjnryySdt5cqVLqy65pprrEuXLu4xs2fPdiHVrbfe6gIrJYkHHnig/fOf/yxySl9ZmpIpoazNMjMzC70HGDtgu4OaiP0VGDtgu4Pagn1W2Wka3Ia0bFuxLtNWrct0YdPK9ZlulTl9vCm95CqmuLgYa9Eo2Vo20VuK67/UUm9Nkt3HSYnFxRwBy8qqOefCmRFyfq6spLRTE6s1gPIvdHgwlJiYaBs3btzu8WlpabZo0SLXN+qqq65y1U+PPfaYnXjiifbRRx9Z06ZNXQCVnZ1t/fr1c83IZ8yYYXfffbctW7bMvS8vNT/X94oECxcurO6ngFqKsQPGDtjmoDZgfwXGDtjuVK+cvALbkJZv69PybF1anq3f+rF/yy8o+euTE2Otcd04a1K/jjWu59/i3PsGyXEhVUyqhlKhSIalrzdbsN5qnYURcH5e2mKfag2gkpKSgr2g/MeiACk5OXm7x9epU8eFUOoT5Sue9PFee+1l77zzjmtersqnq6++2ho2bOju7969u2tAftlll7nQqlmzZuV6rvoeXbt2tdpMgZ8Gd8eOHYt8fQHGDtjuoCZgfwXGDtjuoLaI1n2Wql42puW4yiVXwaRqJvex3mfY+s05JX69AqTmDZO2VC25yqUUS91awaTbUpIifxGxzAgZO3Pnzi31Y6s1gPJT71atWmXt27cP3q7Pe/Tosd3jtUqeQigfPomCK03LW7p0qftc9/vwyevWbcsyh5rWV94ASiVlkdIYTIM7Un4XVC3GDhg7YJuD2oD9FRg7YLuz83Lz8rf2YtrWj2nL+3RbsS7DsnPyS/z6ukl1tjb4LtyHSe+bN0q2uLhYBqrV/n1WWVYGrNYAqmfPnlavXj2bOHFiMIDatGmTTZ8+3U4++eTtHj9o0CDLy8uzqVOnWt++fYNNu7Q63sEHH+w+P+WUU6xt27Z25513Br9Oj1cFk5JFAAAAAACinaqYNqXnbAuXtKLcGv8+3dZuyrJACf2+NQuuWaPkws2+FTY12xIy1UuOL1M4gchXp7rnCSpouvfee61JkybWpk0bu+eee1yl0/7772/5+fm2bt0611RclU4DBw60PfbYw02x01S7Ro0a2UMPPWRxcXF2+OGHu+95wAEH2B133OF6QO25554ufFLvpzPPPNOFXQAAAAAARIO8/AI3JU4B08q16bY8tIppbYZlZueV+PXJiXHWsklda9Wsrmv4rWCp1dawqXnjFIuvQxUTakkAJRdffLGrarrhhhtcNZOqnJ555hlXsaRpdaNGjXLVTEceeaR7/MMPP+wCqwsvvNA9fsCAAfbCCy+4AEsUaCllffHFF10Q1bx5czvttNPsnHPOqebfFAAAAACAipWWoSqmDFu+NVjStLnla7ZMk1uzPsMKSqhikqYNkwpNj/MfK2hqUDeBKiZETgCl6qUxY8a4t3CaSjdr1qxCt6mK6eabb3ZvxTnppJPcGwAAAAAAlSl0Qa3KkJ9fYGs2ZrlpcW563NawSRVN+jgtM7fEr0+Ijys8PS6kJ5OqmnQ/EBUBFAAAAAAAtU1WTp7FJyRZq7adLT4h0X2elFC+U+yMrNxim32vWpdh+TsoY2pcPzGsimlbNZPuoxcTagICKAAAAAAAyiAnN9/eHj/Xxn0339Izc61ucrwdNryzHb1PtyIrigoKArZWVUzr0gv1Ylq5tZpJzcBLPHGPi3XVSurFlNokxVq6XkzbqpiSEjm1R83HKAUAAAAAoJRU6aTw6bXPtrWLUQj16mezTHVK+w5qbxOmLt8aNG2paFJfJjUEL4n6LanvUsutwZICpi1BU11r0iDJYrXsHFCLEUABAAAAAFCEQCDgVorbmJZjG9OybXNGrvXv1sxVPhVFtx81squ9+eXs7aqa4mJjrIVWktObq2Ta2ux76wpzKUnx/A0Q0QigAAAAAABRQ5VICocUKG3YnL3lffDjHPexv23j5mzLydtWudQhtb7deMYQV/FUFN2+OSPH9hnYzk2bC+3F1KxhksXFxVbhbwrULARQAAAAAIBaX6WkAGlbeLQtYPK3bfk8xwVEZZWUEGcN6yVa04bJ1qh+kuv5VFQIpdt1/5mH9amg3w6IHARQAAAAAIAaV6W0JTTK2T5EKlS5tCVoyg2pUioNtVNqUC/RGtVLtIb1Ely41Ki+/3zb7QqTGtZNKNTkWz2g1HBcPZ/C6fb8ggKLNyqdgHAEUAAAAACASq9SSs/K227a28ZguLRl6pu/L62YKW4lSU6Ms0b1kgoFSj5McoFS/a2310u0eikJridTeSQl1HGr3cn7pVwFDwABFAAAAACgHFR1tCk929ZvLjzFLVil5KuWXMiUs8NV4MJp1TdVHwVDpK2BkqtMcoHStnCpQb0EFwxVFYVMR+7d1Y4Z1d3SMrKtXkqiq3wifAKKRwUUAAAAAGBLlVJm7raKpEI9lXzAtG3qW3GNuEuSklSn0BS3kqa+1UuOdyFUTaXAKyMjw5YtWWCdOnWylJSU6n5KQI1GAAUAAAAAESo3L79QVdL2jbm3TX1TNVNefqBM31/T2LZUJG2d+hYWJm2pWtoSNOktMQKnp2VlZVX3UwBqBQIoAAAAAKglCgrUSym3cIi0dYpbeJWS3tR3qazqbq1S2r46KSxgqp9odZNqdpUSgJqDAAoAAABA1EtKSqq21yAnN79wRdLmrODqboUbdW+5P7+gbFVKdeJUpVR4ips+bhzsqRQaKiVYfJ3Iq1ICUP0IoAAAAABEraycPItPSLJWbTtbfEKi+3xnm1mrSmlzRk6hKW4lTX3LzC5HlVJy/JaKpLDV3gpXLW1p1q3HxsRQpQSgehFAAQAAAIhKqjx6e/xcG/fdfNdQW0HNYcM729H7dNtuNbPs3PxgJVLhqqQtzbpDeyxtTM9xIVRZ1ImLDU5xC/ZPCqlK8pVKqlpqUDfR4uvEVvCrAQCViwAKAAAAQNRRpZPCp9c+mxW8TSHUq5/NcqvB7dajhY0dNy3YSykzO7/MP0OruBWuTtpSkeRvC61eUt8lqpQARDICKAAAAABRRT2UtHqbKp+KMu77BXbU3t1s+Zp025SeU7hKyU1v2zb1zVcphU99o0oJAAojgAIAAAAQ8TKycu23Wavt5+krbOXadLvshAGu4qkouj0jO8+uOvlvFh8fF6xaSk6kSgkAyosACgAAAEBEWrUuwwVOP09bYVPnrbG8/C19mRrU3dJrST2figqhdHv9lATr371FNTxrAIhMBFAAAAAAIoIaf89Zst5+nr7ShU4Ll28qdH+b5nVtUK9UG9w71QIBcw3H1fMpnG7PLyiweKPRNwBUFAIoAAAAALVWVnae/T5ntQucJs1Y6Vak82JjzHbp1NQGu9CppbVtUb/Q12q1O3m/FKvgAQB2DgEUAAAAgFpl7cbMYJXTH3NWW25eQfC+lKQ6NqBHC1fl9LeeLd10u+IoZDpy7652zKjulpaRbfVSEl3lE+ETAFQ8AigAAAAANVogELB5f210gZN6Os1burHQ/S2bpLjAaUivVOvVuanF1yn91LmkhDqWkZFhy5YssE6dOllKSkol/AYAAAIoAAAAADVOdm6+TdHUuukrbdL0FbZ2Y1bwvpgYsx7tG7vQSW/tW9a3GN24E7Kytn1/AEDFI4ACAAAAUCOs35Tl+jip0kl9nbJz8oP3JSXE2W6aWtcr1Qbu0tIa1U+s1ucKACgbAigAAAAA1Ta1TivVaVrdpGkrbdbi9YXub9YwyQZpal3vVOvbpRm9mQCgFiOAAgAAAFBlcvPybeq8tTZpaz+nVeszC93ftV0jFzip0qlT6wY7PbUOAFAzEEABAAAAqFQb07Lt15maWrfSJs9aaZnZ26bWJdSJtV27a9W6lm5qXdOGyfw1ACACEUABAAAAqPCpdUtXpbleThOnrbBZi9ZZQWDb/U0aJNqgXluqnPp1a+ZWogMARDa29AAAAAB2Wl5+gU1fsNYFTurntHxteqH7O7duaIN6t3ShU9e2jSw2lql1ABBNCKAAAAAAlEtaRo79MnOV6+ekKXbpWXnbTjTiYl11kwKnQb1aWovGKbzKABDFCKAAAAAAlNqy1Wmuebj6OU1bsNYKQubWNayX4Po4KXTatXtzS0mK55UFADgEUAAAAACKlZ9fYDMXrXf9nBQ8qbdTqPap9V3gpLfuHRpbHFPrAABFIIACAAAAUEhGVq5NnrXKhU6/zFhlmzNygvcpYOrTpemW0Kl3qqU2rcurBwDYIQIoAAAAALZyXUawyunPeWssL3/b1Lp6yfHBqXUDerawuslMrQMAlA0BFAAAABCF1Ltp9pKtU+umrbBFKzYXur9N87o2qFeqDemdart0bGJxcbHV9lwBALVftQdQBQUF9sgjj9ibb75pmzdvtkGDBtlNN91k7dq1K/Lxubm59tBDD9m7777rHt+nTx+7/vrrbZdddgk+ZsKECXbPPffYvHnzrFWrVnbRRRfZwQcfXIW/FQAAAFDzZGXn2W+zV2+dWrfSNqRlB+9T66ZdOjV1gZOm1rVpXq9anysAILJUewD16KOP2iuvvGJ33XWXpaamuuDorLPOsnHjxllCQsJ2j7/55pvt66+/do9v3bq1Pfjgg3b22Wfbxx9/bPXr13eh07nnnmunn366+1567FVXXWVNmjSx3XffvVp+RwAAAKC6rNmQaZOmr7CJ01bYlLlrLDevIHhfSlIdG9CjhQud/rZLS6ufsv3xNwAAtT6AysnJsbFjx9qVV15pI0eOdLc98MADNnz4cPvss8/skEMOKfT4JUuW2Ntvv22PP/64e4zcdtttNnr0aPvzzz9dwPT8889bjx497LLLLnP3d+nSxaZPn25PP/00ARQAAAAiXiAQsHlLN7rASf2c5v+1sdD9LZukbKly6pVqvTo3tfg6TK0DAER4ADVz5kxLT08vFAw1aNDAevXqZZMmTdougPrhhx9cldOIESMKPf6rr74Kfv7LL7/YvvvuW+jrhg4darfffrvbGcfExFTq7wQAAABUtezcfPtjzpapdZOmr7R1m7KC9+nwt2eHJjaoV0s3ta59y/ocEwMAoiuAWrFihXuvPk2hWrRoEbwv1IIFC1xvKFVHPfnkk7Zy5UoXVl1zzTWu0sl/T03lC/9+mZmZtn79ejcVDwAAAKjt1m/Ksp+nr3TT69TXKSc3P3hfUkKc7dajhaty0up1jeonVutzBQCgWgMohUIS3uspMTHRNm4sXCosaWlptmjRItc3Sn2dVP302GOP2YknnmgfffSRNW3a1LKysrb7fv5zTfkrL1VPZWRkWG3mX2//HmDsgO0OaiL2V2DsFH88umhFmv06a7VNnrXa5i7dVOj+pg2T7G89mtnfeja3Xh0bW0J83NZ78mv9cWxlY7sDxg7Y7pRPWWaaVWsAlZSUFAyG/MeSnZ1tycnJ2z2+Tp06LoRSnyhf8aSP99prL3vnnXdc83KFV+FBk/+8qO9ZWlp9b8aMGRYJFi5cWN1PAbUUYweMHbDNQW0QSfurvPyALVyZbbP+yrTZf2XZxoxtVU7Sukm89WiTbN3bJllqo3g33c7yVtu8uaur7TnXZpE0dlC1GDuI5rGTUMQCcjUugPJT71atWmXt27cP3q7P1Ug8nKbWKYTy4ZMouNK0vKVLlwa/p74+lD5PSUlx/aPKKz4+3rp27Wq1/cqOBnfHjh13KoxD9GHsgLEDtjmoDSJlf7UpPcd+m73Gfp252v6Yu9aycraFTgnxsda3cxNX5TSgR3NrzNS6ChEpYwdVj7GDaB87c+fOLfVjqzWA6tmzp9WrV88mTpwYDKA2bdrkVq07+eSTt3v8oEGDLC8vz6ZOnWp9+/Z1t2nKnVbHO/jgg93nAwcOtJ9//rnQ1/300082YMAAi40t/wofKilTiBUJNLgj5XdB1WLsgLEDtjmoDWrb/krTF5as3Oz6OamJ+MxF6ywQ2HZ/kwaJNqjXllXr+nVrZkkJ1XoIH9Fq29hBzcHYQbSOnZgyLPRWp7rLtBQ03Xvvva45eJs2beyee+5xlU7777+/5efn27p161zlkiqdFC7tsccedvXVV9utt95qjRo1soceesji4uLs8MMPd9/zlFNOsSOOOMJ9T73/5ptv7JNPPrGnn366On9VAAAAICgvv8CmzV/rAqefp6+wFWsL92jq3LqhDerd0ob0TrUubRpZbCwrOQMAardqv3xy8cUXu6qmG264wVUzqcrpmWeecVPeNK1u1KhRduedd9qRRx7pHv/www+7cOnCCy90j1dl0wsvvBBc3a5bt26uSbmCrOeff97atm3rPt59992r+TcFAABANNuckWO/zljpKp0mz1xp6Vl5wfvqxMW66iZVOQ3q1dJaNK69V8MBAKiRAZSql8aMGePewik8mjVrVqHbNGXv5ptvdm/FGTFihHsDAAAAqtOy1Wk2cWuV0/QF66ygYNvcuob1EmzgLluqnHbt3sKSE6v90BwAgErDXg4AAACoIPn5BTZj4bpgP6e/VqcVur99an1X5aTQqVv7xhbH1DoAQJQggAIAAAB2Qnpmrk2etcpVOWmK3eaM3OB9Cpj6dGlqg3tvaSKe2rQurzUAICoRQAEAAABltGJtugucJk1baVPnrbH8kKl19ZLjbWCvli5wGtCjhdVNjuf1BQBEPQIoAAAAYAcUMM1ZvN6FTurptHjF5kL3t2le1wb3bmWDe7W0XTo2sbi4WF5TAABCEEABAAAARcjMzrPfZ6+yn6ettEkzVtjGtJzgfbGxMdarUxNX5aTpdW2a1+M1BACgBARQAAAAwFar12e6sElVTlPnrrHcvILga5OSVMf+1lNT61ra33ZpafVTEnjdAAAoJQIoAAAARK2CgoDN+2uDq3LSqnXzl20sdH9q05QtVU69Uq1X56YWX4epdQAAlAcBFAAAACJGUlLSDh+TlZNnU+as2dJEfPoKW7cpO3hfTIxZzw5NbFCvljakd6q1a1nfYnQjAADYKQRQAAAAqPUUKsUnJFmrtp0tPiHRfZ6UsO1Qd92mLBc2qdLp9zmrLSc3P3hfUkKc7dajhatyGrhLS2tUP7GafgsAACIXARQAAABqNYVJb4+fa+O+m2/pmblWNzneDhve2Y7cu6t99csS++LnxTZnyYZCX9OsUbLr5TSkdyvr21VT6+Kq7fkDABANyhVAzZs3z7p06VLxzwYAAAAoA1U6KXx67bNZwdsUQr362SzX36lru0bB8Klbu0ZuxTpNrevYqgFT6wAAqOkB1IknnmjXXnutjR49uuKfEQAAAGBmgUDA0rPybP2mLFu/OcvWb8oOvl+3OctycvLtshMHuMqnonzwwwJ7/qYD7LITBtiu3ZtbkwY77g8FAABqUAAVHx9vjRs3rvhnAwAAgIiXl19gGzZvC5P0ft3W97pd/ZrWb862DZuyLCevoNjv0yG1vm3cnO0qnoqi21Uhtc/AdpX42wAAgEoLoC655BK7++67bfPmzdazZ09LSUnZ7jGtW7cuz7cGAABALaRqpYysPBce+XBJodIG935LoLSlkinbNqXnlOl7102qY43qJ7kKpsb1E63x1vctmiRb44ZJrudTUSGUbk9Jiq/A3xIAAFRpAHXzzTdbfn6+jRkzptjHzJgxo9xPCgAAADVDvqqV0rKD0962TYMrHCrpLXRluR2JjY3ZEibVTywyXNLnWo1Ob6Gr2YVThZMajqvnUzjdnl9QYPEWW+7fHwAAVGMAddttt1XQjwcAAEB1VCtlZue50MhVLAXDpfBQKctVKwUCpf/eKUl1QoKkJGvcQCHT9uFS/ZQEF0LtLIVTR+/TzX38ftgqeLo9IZ7V7QAAqLUB1BFHHFHxzwQAAAAVU60UFiKFNu/2U+PKWq3UqF7idmFSE1UoufdbgqYdVStVFoVMR+7d1Y4Z1d3SMrKtXkqiq3wifAIAoOYo9xFCTk6OvfXWW/bjjz/a6tWr7Y477rCff/7Zevfubf369avYZwkAABDl1Uqhzbl9uBTab0nh0sb07DJVKyUn1ily2ltjPyVua+BUv26CxVVAtVJlUvCVkZFhy5YssE6dOhXZoxQAANSyAGrdunX2j3/8w+bPn2+dO3e2uXPnWlZWln399dd211132XPPPWe77bZbxT9bAACACJFfELCNrrdS2LS3sHBJU+Oyc8pQrRRjW3snbeurpM+bFDElLimx6quVKpuOSQEAQM1TrqMOrYCXnp5uH330kbVp08b69Onjbn/ooYfszDPPdO+fffbZin6uAAAANd6W3kqh0922BklbgyXfb2lTWrYVlKlaKS4YKm0LlBILh0oNEq1B3cQaX60EAACiT7kCqPHjx9t1111nHTp0cKvheYmJiXbGGWfYNddcU5HPEQAARJmkpCSradVKCoy2VSZtq1IKr17KKmO1UsN624dIRb3XdDkAAIDaqlxHMtnZ2daoUaMi74uLi7Pc3NydfV4AACAKZeXkWXxCkrVq29niExLd55XZ1DordCW4YKAU0rR7a6i0sYzVSkkJccG+SqEhUhPXqHtbv6UG9ahWAgAA0aFcR3R9+/a1V155xfbaa6/t7hs3blxwSh4AAEBpaVW2t8fPtXHfzbf0zFyrmxxvhw3vbEfv061Mq5kVqLdSeva2QCksTNr2Pssys0tfrRQTrFYqoWn31vuoVgIAAKiAAOqSSy6x0047zQ4//HAXQsXExNgHH3xgDz/8sH3//ff29NNPl+fbAgCAKKVKJ4VPr302K3ibQqhXt35+5N5d3fuiVoILvneVS1m2IS3HhVCllZgQZ03qbw2SGiRu+Xjr+2AVU4Mka6iV4OJiK+G3BwAAiHzlCqAGDhzomozfd999LmzS8sBa+a5Xr172xBNP2NChQyv+mQIAgIgVFxvrKp+K8v53810AdeZtn9um9JzSVyvV3bb6W5FNu7d+rmolXUwDAABA5Sl3U4VBgwbZa6+95pa63bhxo9WrV8/q1q1bsc8OAABEvNy8fNuckesqnoqi2zem5biwSA2+1UdJIVKhUCnsvabK1aFaCQAAoMaoUxGr1NS0lWoAAEDN7/f0++zV9v0ff9mMBevs4Sv3dj2figqhdLtCp7svGk61EgAAQKQHUD179ixTefqMGTPK+5wAAECEhk6TZ62yH/5YZhOnrbDM7LzgfdMWrLVD9+xkr30+e7uvUyPy/IKApSTFV/EzBgAAQJUHUBdccEEwgMrOznY9oDp27GgHHHCANW/e3NavX2/jx4+32bNn2/nnn19hTxAAANRe2QqdZq607/9YZpOmryi06lzThkm2R7/WNqxfa9ulYxPr26WZO9Z4fydXwQMAAEAtDqAuuuii4MfXXXedjRw50q16F1oVpeBpzJgxNm3atIp/pgAAoNasaPfrzFX2o0KnGYVDp2YKnfq3tj37tbEeHRpbbOy244iE2DjXbPyYUd0tLSPb6qUkWn5BAeETAABAtPaA+vjjj+2hhx4qckre4YcfXiisAgAAkS8re0vopJ5Ov8xY6ZqFe80bJ7sqp2H9W1v3doVDp3BJCXUsIyPDli1ZYJ06dbKUlJQq+g0AAABQ4wIorXa3ePHiIu+bPn26NWzYcGefFwAAqAWh06QZK11Pp19mrrTskNCphUKn/m1sWL9W1r194zL1kXTfOyurEp4xAAAAalUAdfDBB9v9999v8fHxbipe48aNbe3atfbJJ5/Y//73Pzv77LMr/pkCAIBqp8bh6uX0w5Rl9suMVa6xuNeiSYrtubXSqVu7RmUOnQAAABC5yhVAXXHFFbZ8+XK76aabCh1cBgIBO/bYY13DcgAAEBkysnJt0vSVLnT6dcZKy8krCN6X2jTFTa/bs38b69K2IaETAAAAKi6AUuWTekDNnTvXfvnlF9u4caOrgho6dKi1b9++PN8SAADUsNDp52kr3Op1k2etstyQ0KlVs7q2Z//WbgW7Lm0InQAAAFBJAdShhx7qqqD23ntv69q1a3m+BQAAqGHSM3Nt4rQV9uOU7UOn1s3quql1qnTq1LoBlU4AAACo/ABK0++Sk5PL86UAAKAGSctUpdNyV+n026zVlpe/LXRq07yeq3RS8NSxFaETAAAAqqEC6rnnnrPOnTtbixYtbGcVFBTYI488Ym+++aZt3rzZBg0a5PpLtWvXrsjHv//++zZmzJjtbv/yyy+tbdu27uP999/fFi1aVOj+I444wu66666dfr4AANRmaRk59tOfWxqJ/z57leXlB4L3tWtZz4b1a+OCp/ap9al0AgAAQPUFUAsXLnS9n/baay9r1KiRpaSkFLpfjcm/+OKLUn+/Rx991F555RUXDqWmpto999xjZ511lo0bN84SEhK2e/ysWbNs8ODBbiW+UE2aNHHvMzIybMmSJfbEE09Y7969g/cnJSWV47cFAKD226zQaepy+37KMvtj9mrLL9gWOilo0up1e/RvbR1SG1Tr8wQAAEBkKlcA1apVK1cFVRFycnJs7NixduWVV9rIkSPdbQ888IANHz7cPvvsMzvkkEO2+5rZs2dbjx49rHnz5kV+TzVHV1XVbrvtZg0bNqyQ5wkAQG2zKT3HJkxd7no6/TGncOjUIbW+Deu/pdKpXcv61fo8AQAAEPnKFUDdeeedFfYEZs6caenp6bb77rsHb2vQoIH16tXLJk2aVGQApQqoffbZp9jvqfubNWtG+AQAiDob07Ltpz+39HSaMneNFYSETurj5FevI3QCAABAjQ+gvG+//dZ+/vln27RpkzVu3NgGDhzoKpfKYsWKFcGqqlDqLeXvC7Vx40ZbuXKlmwKoaXvr16+3fv36uZ5QnTp1CgZQmhZ48cUX2+TJk91zO+qoo+zUU0+12NjYnfmVAQCocTZszrYJfy63H/74y6bOW1sodOrcuqFrIq43NRUHAAAAak0ApWlz//znP+3777+3uLg4F/AoCHryySdt6NChrvdSUb2bipKZmenehz8+MTHRhU3h5syZ494HAgFXiZWVlWWPPfaYnXjiia5nlCqf9BiFYgcccIBdcMEF9uuvv7q+Uvp+l1xySXl+Zffz1FuqNvOvtX8PMHbAdqf22pCWbT9PX2UTp62yaQvWWWBb5mQdW9W3ob1b2tA+LaxV07rB22vLfoz9FRg7YLuD2oJ9FqJ97AQCgVIvWhMT0KPL6L777rOXXnrJbrnlFjv44INdCJWXl2cffPCBu+20004rddDz6aefukqlP/74o1CTcH29gi6FS+HWrVvnQi//S+oPpv5RZ555pp1zzjnu67Kzs61+/W09LRSO6XspjCprFdTUqVPd9wQAoDptzsy3GUsybfriTFu0OrtQ6NSqSbz1bp9svdqlWJP6O1XgDAAAAJSaCor69u27w8eV6whVQdOFF15ohx122LZvVKeOjR492tauXWuvvvpqqQMoP/Vu1apV1r59++Dt+lyNxoviV7vzkpOTrW3btm5qnv/lwyuqunfv7q78qgpK4VVZxcfHW9euXa02U1CnFQw7duzoXjOAsQO2OzXf+s3ZNnHaSvtp2kqbuWhDodCpS5sGNrRPSxvSq4W1bFJ4RdrajP0VGDtgu4Pagn0Won3szJ07t9SPLVcApQokNQkvim73QVBp9OzZ0+rVq2cTJ04MBlCaPjd9+nQ7+eSTt3v866+/bvfff7+NHz/e9XmStLQ094c7+uijXfnXfvvt58IwhWShVUxaNa884ZOo2sr/vNpOgztSfhdULcYOGDtVY+3GTPthyjL7ccpym75gbaHQqUf7xq6fkxqJR1LoVBS2OWDsgO0Oagv2WYjWsRNTyul35Q6gFBRpKlvoynWeVq4LbyheElUqKWi69957XWVTmzZtXL+m1NRU23///S0/P98FXppOpyl6I0aMcI+96qqrXJWVekApkNLXHnnkke6XVwD1zDPPWOfOna1Pnz42YcIEe/rpp+36668vz68LAEClW7NhS+j0wx/LbMbCdYXu69GhcXD1uhaNa+8BCgAAAKJXuQKo448/3u666y4XCKkHlBp/r1mzxk3Ne+qppwpVHpWGekCph9QNN9zgAqVBgwa5AEnT3pYuXWqjRo1yDccVMCnceu6551wfqhNOOMFVPA0bNsxeeOEF17hcrrjiCldVpWBKK+lpep7Cp2OPPbY8vy4AAJVi9XofOv1lMxetL3TfLh2bbKl06tvamjeuvWXZAAAAQLkDKAU/miKnSiQFQZ7CoCOOOMI1Ai8LNTEfM2aMewun8GjWrFmFbuvdu7eNHTu22O+nflRa/U5vAADUJKvWZQQrnWYt3hY6qXrZhU79tlQ6NWtE6AQAAIAoD6C0itztt99uZ5xxhv3888+usXfDhg1t8ODB1qVLl0KPXbZsmbVo0cKFQgAARKOVCp3+WGY/TPnLZi/eUCh06tWp6dbQqZU1bUjoBAAAgMi0U6mQwqbwwCmU+jdp+txbb73lqpYAAIgWK9amu9Dp+ynLbO6SwqFT785Nbc9+rW33fq2tSYOkan2eAAAAQFWo9LIkTcsDACAaLF+Tbt//8ZebYjdv6cbg7bExZn26NHNT6/bo28oaEzoBAAAgyjAvDgCAnbBsdZoLnL7/Y5nN/6tw6NS3azM3vW6oQqf6VDoBAAAgehFAAQBQRktXbQ42El+wbFPw9tjYGOvXpZlbvW73vq2sYb0tq7MCAAAA0Y4ACgCAUliyclvotHB54dCpvyqd+rexoX1SCZ0AAACAIhBAAQBQjMUrNgUbiS9esTl4e5xCp+7Nt0yv69PKGtRN4DUEAAAASkAABQBAyMIZCpp8TydVPQV3mHEx1r9bc9uzf2sb0qeV1U8hdAIAAABKiwAKAGDRHjotWrF5y+p1fyyzpavSCoVOu3ZvsSV06p1q9QidAAAAgHIhgAIARGXopD5OqnJS6PTX6tDQKdYG9Ghhw/q3ssG9W1m95Phqfa4AAABAJKiwACovL8/S0tKsUaNGwdtiY2PtwgsvtBYtWlTUjwEAoNyh0/y/NgYbiS9bkx68L76OD51a2+BeqVaX0AkAAACo/gBKYdPjjz9uHTp0sEMPPdQmTpxoF198sW3atMkGDx5sDz30kDVs2NBiYmJcAAUAQHWFTvMUOqnSacoyWx4WOv2tp0KnNja4V0tLSaLSCQAAAKhRAZQCpmeeecauu+469/ltt93mKp8uuOACe/bZZ+2+++6zW2+9taKfKwAApQqd5i7dEAydVqzNCN6XoNBpl5aup9PAXQidAAAAgBodQH344Yd2+eWX20knnWTz5s2zOXPm2F133WWjR492QdTdd99NAAUAqNLQac6SbaHTynUhoVN8nA3apaUN69faBvZqacmJtD8EAAAAqlq5jsJXrVpl/fv3dx9//fXXrtfTiBEj3Oepqam2efO2ZasBAKis0GnW4vUudPpxyjJbtT4zeF9iQpyrcHKVTj1bWhKhEwAAAFD7Aig1FV+6dKkNHDjQvvrqK9tll12sSZMm7r7ffvvNhVAAAFS0goKAzV68fsvqdVOW2ZoN20KnpIQ4G9Qr1TUSV2+npAQqnQAAAICaolxH54cccojdeeedNm7cOPv111/tpptucrfffvvt9uqrr9p5551X0c8TABDFodPMReuClU5rNmYF70tO3Bo69WttAwidAAAAgMgKoC699FJLSUmxSZMm2RVXXGEnnniiu33q1Kl2xhln2Pnnn1/RzxMAEGWh04yF61yVk0KntYVCpzo2eGulk0KnxPi4an2uAAAAACopgIqJibFzzz3XvYV67bXXyvPtAACwfIVOC9ZuqXSauszWbcoOviopSXVscO9U27Nfa9utRwvXWBwAAABA7VHuBhkrV6500+9ycnKCtxUUFFhmZqb98ssv9sADD1TUcwQA1GJJSUklhk7T56+17//4yyZMXW7rN28Lneom1bEhfVq5Sqfduje3+DqETgAAAEBUBVCffPKJXXnllZaXl+eqofxqRP7jzp07V+yzBADUOlk5eRafkGSt2na2+IRE97kag+fnF9i0BQqdlrnQaUNo6JQcb0NU6dS/te1K6AQAAABEdwD1+OOPW+/eve1f//qXvfzyy5afn29nn322ffPNN3b//ffbddddV/HPFABQa+Tk5tvb4+fauO/mW3pmrguWDh3e2UaP6GK3PD3BZixcH3xsveR4G7q10ql/N1U6xVbrcwcAAABQQwKoBQsW2H333We9evWyIUOG2NixY61Lly7ubc2aNS6gGjZsWMU/WwBAjZeVnWdvfz3XXvtsVvA2hVD6PFAQsCP37mYPvf6bC5327N/G+nVrZnXiCJ0AAACASFauACo2NtYaNmzoPu7QoYPNnz/f9X/S7SNGjLB33nmnop8nAKAaZefm28bN2bYhLds2bn3bkJaz9X22u29jWo7lFxTYvZeMcJVPRfnghwX2ws0H2As3H0joBAAAAESRcgVQ6vE0efJkGzRokPtYjchnzpzpKqI2bdpUqDE5AKDmUfPvzekhAVLw/dbbXKC05XPdnpmdV6rv2yG1vgujVPFUFN2emZVnDeslVvBvBAAAACDiAqjjjz/e9X/KyMiwyy67zIYOHWrXXnutHX300fbSSy+5/lAAgKqjhSAUEm2pRsoJq1QKCZa23rYpPccCgbL9DE2Ta1QvwRrWT3QBUqN6/n2Ce6+3Jg2SrEnDZNfzqagQSrenJMVX3C8OAAAAIHIDqGOOOcZVOS1dutR9fuutt9o555xjt99+u7Vp08auv/76in6eABB1cvMKbFO6r0YqOlQK3rY523LyCsr8M+qnJFij+gnWqF6SNayn94khAVNCoaApJalOcLXTkmi1u8OGd7ZXQ3pAebpd0/TijZ5PAAAAQDQpVwAlJ510kqWnp9uKFSusTp069vTTT9u6deusWbNmFfsMASBCFBQELC0zt1Al0pa+SoWrk3x/peKmsZUkKSGuUGjkQqWtgVJ4qNSgboLFVULz76SEOnb0Pt3cx++HrIKn8Em3J8THVfjPBAAAABCBAdTixYvt8ssvt2nTphX7mBkzZuzM8wKAWkHVPoWmtwUbdW8fKm1p0l22eW+xsTGFprgVCpa2VisFb6ubYEmJ5b6uUKEUMh25d1c7ZlR3S8vItnopia7yifAJAAAAiE7lOlO55ZZbbMmSJXbeeedZ27Zt3ep3ABAJ8vMLbFOGwqMc27A5K1idtK0xd+FgKSsnv8w/Q9VARYVKRfVXqpcc70Ko2kiVUOoVuGzJAuvUqZOlpKRU91MCAAAAUJsCKK2Apybko0ePrvhnBAAV3Jw7IysvbLW3raFSSLWSv29zRtmbc8fXiQ1Oc2sUWp1UKGDaMhWuQd1E9/hokpWVVd1PAQAAAEBtDKDq1q1rzZs3r/hnA6DGSkpKspoiNy8/GBptqUoqHCyF91fKyy9bc2712VZ/pCKnvIWGSq55d6IlJ5auOTcAAAAARKtyBVCHH364vfDCCzZ06FCLi6OZLBDpPY7iE5KsVdvOFp+Q6D7X1KqKbs6tyiPfJ2lbpVLIlLeQoCk9K6/MP0Mhka9EcgFS/e2DJf++vppz19JpbwAAAABQE5X6LPLaa68NfpyXl2ffffed7bffftavXz9LTk4u9FhVAtxxxx0V+0wBVLmc3Hx7e/xcG1eOlcyysvO2BUkhK70Vbsy9JWzalJ7jQqiyUEAUOr0ttBn3dv2V6idaIiuvAQAAAEDND6AmTpxY6PPU1FT3fsqUKds9lqkoQO2nSieFT699Nit4m0KoVz+b5Xok7TOwrU2ctnK7UMkHTdnlaM5dPyV++75JWwOk8NsUhrGtAQAAAIAIC6C++uqryn0mAGqUuNhYV/lUlHHfz7ej9u5qb34521UvFSchpDl3eDNuf1vjrR+r51KduOhqzg0AAAAA0aJiG7kAiAjqx6QKJlU8FUW3b87ItX0Ht7e8vIKQgKnwVLikhDiqlAAAAAAANSOAKigosEceecTefPNN27x5sw0aNMhuuukma9euXZGPf//9923MmDHb3f7ll19a27Zt3ccff/yxPfzww7Z06VLr3LmzXX311bb77rtX+u8C1GZrN2baO1/Psx+nLrNHx+zjprkVFULpdlUxnX5I72p5ngAAAACA2qVGzHd59NFH7ZVXXrF///vf9tprr7lA6qyzzrKcnKKn9syaNcsGDx5s33//faG3Vq1auft/+uknF1Adf/zx9s4777jg6ZxzzrF58+ZV8W8G1A4r1qbbI2/+bmfd/oW99+08W70+02YvXm+H7tm5yMerEXl+QUGVP08AAAAAQO1U7RVQCpnGjh1rV155pY0cOdLd9sADD9jw4cPts88+s0MOOWS7r5k9e7b16NHDmjdvXuT3fOqpp2zfffe1U0891X2u6qfffvvNnn/+ebv11lsr+TcCao9FKzbZW1/NsW9/+yu4Cl3vzk3t2FHdrW/XZtazYxOLiTF7vxyr4AEAAAAAUGMCqJkzZ1p6enqh6XENGjSwXr162aRJk4oMoFQBtc8++xT5/VQ9NXnyZLvmmmsK3T5kyBAXaAEwm7tkg73x5WybMHV58OUY0LOFC54UQHkKmY7cu6sdM6q7pWVkW72URFf5RPgEAAAAAKhVAdSKFSvcez99zmvRokXwvlAbN260lStX2i+//OKm7a1fv9769evnptx16tTJNm3aZBkZGZaamlqq71dagUDAfd/aLDMzs9B7RJ/pC9fbu98ssD/mrg3eNrhXCztiRCfr3KaB+7yocZ6RlWXLly93/6dJSUmWkVf8yndAKLY7KA/GDcqLsQPGDqoa2x1E+9gJBAKlXniq2gMo/2InJCQUuj0xMdGFTeHmzJkT/CXvvPNOy8rKsscee8xOPPFEGzdunOXl5RX7/bKzs8v9PHNzc23GjBkWCRYuXFjdTwFVSP8rc5dn23fTNtni1VuCI20f+nZIsT1717cWDeMte9NfNmPTXzv8XgsWLKiCZ4xIxHYHjBuwzUFtwP4KjB2w3Sm78PylxgZQqqbwvaD8x6KwKDk5ebvHDxw40CZMmGCNGzcOpmxaQU/9o/7v//7PjjnmmOD3C1Xc9yut+Ph469q1q9VmCvu0U+3YseNOvRaoHdTTadKMVfbOtwtswbLN7rY6cTE2ckBrO2zPjtaySUqpvxdjB+XF2AHjBlWJbQ4YO6hqbHcQ7WNn7ty5pX5stQdQfurdqlWrrH379sHb9bkajRelSZMmhT7XH6tt27Zual6jRo0sJSXFfX0ofd6yZctyP0+FXfq+kUCvV6T8LtheXn6Bayr+1lezbcnKNHdbYkKcHbR7Rxu9Vxdr2rD8GzfGDhg7qEpsc8DYQVVjuwPGDtjulE1pp99JrFWznj17Wr169WzixInB29THafr06TZo0KDtHv/666+7huKhfWrS0tJccqgKJf3yAwYMsJ9//rnQ1+n7q3oKiFQ5ufn28Y8L7Ny7vrQHXp3swqe6SXXsuH272zPX72dnHtZnp8InAAAAAADKq05NmCt48skn27333usqm9q0aWP33HOPayK+//77W35+vq1bt87q16/vpuiNGDHCPfaqq66ySy65xPWAuv/++93XHnnkke57nn766XbOOee4lfT0+Lffftv1b7r99tur+9cFKlxmdp59+tNCe+frubZu05Y+Zw3rJdjhI7rYwcM6WUpSPK86AAAAACC6Ayi5+OKLXfPwG264wQVKqnx65plnXN+lpUuX2qhRo1zDcQVMmrL33HPP2X333WcnnHCCa7A8bNgwe+GFF1yjcdlzzz3tjjvusEcffdQeeOABVxn1+OOPW5cuXar7VwUqTFpGjn3wwwJ7/9v5tjljS8+zZg2T7Ii9u9r+QzpYUkKN+PcGAAAAAKBmBFBxcXE2ZswY9xZOvZ1mzZpV6LbevXvb2LFjS/yeo0ePdm9ApFm/Ocve+2aeffTjQlf9JK2a1bWj9+lme/+tncXXqfaZtQAAAAAA1LwACsCOrV6faf/39Rz77KdFlpNX4G7rkFrfjhnV3fbs39ri4gieAAAAAAA1EwEUUMMtW51mb301x8b/usTy8gPutu7tG9mxo7rboF6pFhtb+lUHAAAAAACoDgRQQA21cPkme/OL2fb9H39ZwZbcyfp1beaCp37dmpVpuUsAAAAAAKoTARRQw8xatM7e+GKO/Tx9RfC2gbu0tOP27W49Ozap1ucGAAAAAEB5EEABNYBWc5wyd429+eVs+2POGnebCpyG9Wvtejx1btOwup8iAAAAAADlRgAFVHPwNGnGSnvji9k2a9F6d1tcbIxbze6ofbpa2xb1+fsAAAAAAGo9AiigGuQXBOzHP5bZG1/Odr2eJL5OrO0/pIMdObKrtWiSwt8FAAAAABAxCKCAKpSbV2Bf/7rErWq3bE26uy05Mc7+vkcnO3xEF2vcIIm/BwAAAAAg4hBAAVUgOzffPp+4yN4eP9fWbMh0t9VPibdDh3exQ/bsZPVTEvg7AAAAAAAiFgEUUIkysnLtox8X2nvfzLMNadnutsb1E230Xl3twN07WEpSPK8/AAAAACDiEUABlWBTeo6N+26+jft+vqVn5rrbWjROtqP26Wb7DmpvCfFxvO4AAAAAgKhBAAVUoHWbsuydr+faJxMWWlZOvrutTfN6dsyobrbXgLZWJy6W1xsAAAAAEHUIoIAKsHJdhr09fo598fNi12hcOrduaMfu292G9m1lcbExvM4AAAAAgKhFAAXshCUrN7sV7b6evNQKCgLutl06NnHB0996trCYGIInAAAAAAAIoIBymLd0g73x5WybMHW5BbbkTrZr9+YueOrTuSnBEwAAAAAAIQiggDKYvmCtvfHFbPt15qrgbUP7pNoxo7pb9/aNeS0BAAAAACgCARSwA4FAwH6bvdoFT9Pmr3W3qaXT8F3buubiHVo14DUEAAAAAKAEBFBAMdTTaeK0FW6q3dwlG7b8w8TF2KhB7e3Ivbta62b1eO0AAAAAACgFAiggTH5+gX33+1/25ldzbPGKze62hPg4O3BoBztiZFdr1iiZ1wwAAAAAgDIggAK2ys3Lty8nLbG3x8+xFWsz3G0pSXXs4GGd7PARXaxhvUReKwAAAAAAyoEAClEvKzvPPp24yN75eq6t3ZjlXo8GdRPssBGd7eBhna1ecnzUv0YAAAAAAOwMAihErbTMXPvwh/n2/rfzbVN6jrutSYMk19/pgCEdLCmRfw8AAAAAACoCZ9iIOhvTsu29b+fZhz8ssIysPHdbatMUO3qfbrbPwHYWXyeuup8iAAAAAAARhQAKUWPNhkw3ze6TnxZZTm6+u61dy/p27KhuNnzXNhYXF1vdTxEAAAAAgIhEAIWIt3xNumss/uWkxZaXH3C3dW3XyI4d1d2G9E612NiY6n6KAAAAAABENAIoRKxFyzfZm1/Ose9+X2oFW3In6925qR27b3fbrXtzi4kheAIAAAAAoCoQQCHizF683t78crb99OeK4G1/69nCjhnV3QVQAAAAAACgahFAISIEAgH7c/5ae+OL2fb77NXuNhU47dG3tR09qpt1bduoup8iAAAAAABRiwAKtT54+nXmKhc8zVi4zt2mnk4jB7R1q9qpyTgAAAAAAKheBFColfILAvbT1OX2xpezbf5fG91t8XVibd/B7e2ovbtZyyYp1f0UAQAAAADAVgRQqFXy8gvsm8lL7a2v5tjSVWnutqSEODtoj042eq8u1qRBUnU/RQAAAAAAEIYACrVCTm6+fTFpsb09fq6tWpfhbqubHG+H7tnZDh3e2RrUTajupwgAAAAAAIpBAIUaLTM7zz7+caG9+81cW785293WqF6iq3Y6aI+OlpIUX91PEQAAAAAA7AABFGqkzRk59sH3C2zcd/Nsc0auu61Zo2Q7au+utt+QDpYYH1fdTxEAAAAAAJQSARRqlPWbs+y9b+bZRz8usMzsfHdb62Z17ZhR3WyvAe1co3EAAAAAAFC7EEChRli1PsP+b/xc+3ziIsvJK3C3dWzVwI4d1d326N/a4mJjqvspAgAAAACAciKAQrVaumqzvf3VXBv/6xLLLwi423p0aGzH7tvdBu3S0mJiCJ4AAAAAAKjtakQAVVBQYI888oi9+eabtnnzZhs0aJDddNNN1q5dux1+7fvvv29jxoyxL7/80tq2bRu8ff/997dFixYVeuwRRxxhd911V6X8DiibBcs22htfzLYfpiyzwJbcyfp3a2bHjOpu/bo2I3gCAAAAACCC1IgA6tFHH7VXXnnFhUOpqal2zz332FlnnWXjxo2zhISEYr/ur7/+sltvvXW72zMyMmzJkiX2xBNPWO/evYO3JyUlVdrvgNKZuXCdvf7FbPtlxsrgbYN7pdox+3aznh2a8DICAAAAABCBqj2AysnJsbFjx9qVV15pI0eOdLc98MADNnz4cPvss8/skEMOKbZqSpVPCph++umnQvfNnTvX3b/bbrtZw4YNq+T3QPECgYBNmbPG3vhytk2Zu8bdppZOe/ZvY0eP6madWvM3AgAAAAAgklV7ADVz5kxLT0+33XffPXhbgwYNrFevXjZp0qRiA6jHH3/ccnNz7cILL9wugJo1a5Y1a9aM8KmaFRQEbNL0FS54mr14g7tNzcT3GdjOjt6nm7VuXq+6nyIAAAAAAIiGAGrFihXufatWrQrd3qJFi+B94aZMmeKqpt566y1buXLbVK7QAColJcUuvvhimzx5sjVu3NiOOuooO/XUUy02NrbcVTya2lebZWZmFnpfmcHThD9X2LvfLrTFK9PcbfF1Ym3UwDZ26LAO1qxRsruttr+e0aSqxg4iD2MHjBuwzUFtwP4KjB2w3bFyZyWlXTysTk3Z2If3ekpMTLSNGzdu93iFFpqup7eOHTsWGUDNmTPHNm3aZAcccIBdcMEF9uuvv7q+Uvp+l1xySbmep6qtZsyYYZFg4cKFlfJ98/IDNmVhhn0/bbOtS8tztyXUibFB3evZ7j3qWb3kgK1evtBWL6+UH49aPHYQ+Rg7YNyAbQ5qA/ZXYOyA7U7ZldS7u0YFUL4xuHpBhTYJz87OtuTkLZUyoW677Tbr1KmTHX/88cV+z6eeesp9ff369d3nPXr0sLS0NHvsscfsoosuKlcVVHx8vHXt2tVqM4V92qkquCvqtS2v7Jx8++rXv2zc9wtt7aZsd1u95Hj7++7t7YCh7dzHqN0qa+wg8jF2wLgB2xzUBuyvwNgB253yUQ/u0qr2AMpPvVu1apW1b98+eLs+V3AU7u2333bpmhqMS35+vnuvXlHnnXeee9P94Qlc9+7dXfWUqqA0Ja+sVFKmaX2RQAFCRfwuGVm59uEPC+y9b+fZxrQcd1uTBol2xMiudsDQjpacWO3DCzV07CD6MHbAuAHbHNQG7K/A2AHbnbIp7fQ7qfaEoGfPnlavXj2bOHFiMIDS9Lnp06fbySefvN3jtTJeqD/++MOthvfkk0+6kEnzD/fbbz8bPXq0a1DuTZ061Zo3b16u8AmFbUzLtnHfzbcPvp9v6Vlbptq1aJJiR+/d1UYNam8J8XG8ZAAAAAAAoOYEUKpUUtB07733WpMmTaxNmzauX1Nqaqrtv//+rsJp3bp1bjqdpuh16NCh0Nf7RuWtW7e2Ro0auY8VQD3zzDPWuXNn69Onj02YMMGefvppu/7666vld4wUazdm2rvfzLOPJyx00+6kXct6dvQ+3W3Ebm2sTlz5GrwDAAAAAIDIVu0BlGi1ury8PLvhhhssKyvLBg0a5AIk9V1aunSpjRo1yu6880478sgjS/X9rrjiCldVdf/997uAqm3bti58OvbYYyv9d4lEK9am29vj59oXPy+2vPwCd1vnNg3t2H272+59WllsbOlL7gAAAAAAQPSpEQFUXFycm0ant3AKj2bNmlXs1w4ZMmS7++vUqeNWv9Mbym/xik321ldz7Jvf/rKCgoC7rVenJi54GtCjRZnmegIAAAAAgOhVIwIo1Cxzl2ywN76cbT/9udwCW3InFzgdM6qb9enSrLqfHgAAAAAAqGUIoBA0bf5ae+OL2TZ51qrgbbv3beWCp27taN4OAAAAAADKhwAqyqiReyitGvjbrNWu4kkBlKink5qKH71PN+uQ2qCanikAAAAAAIgUBFBRIisnz+ITkqxV284Wn5BoWdl5NmPhOnvho+k2d+lG9xitYrfv4PZ21N5dLbVp3ep+ygAAAAAAIEIQQEWBnNx8t4rduO/mW3pmrtVNjrdDhnWyQ4d3tqycfEtMiLMDh3a0I0Z2saYNk6v76QIAAAAAgAhDABUFlU8Kn177bNtKgQqhXv9itvv4qlMGWpMGSdawXmI1PksAAAAAABDJYqv7CaByxcXGusqnonzwwwJr26I+4RMAAAAAAKhUBFARLj0r11U8FXlfZq5lZBV9HwAAAAAAQEUhgIpwdZPiXc+nIu9LjreUpKLvAwAAAAAAqCgEUBEuv6DADhveucj7dLvuBwAAAAAAqEw0IY9wSQl17Oh9urmP3w9ZBU/hk25PiI+r7qcIAAAAAAAiHAFUFFDIdOTeXe2YUd0tLSPb6qUkusonwicAAAAAAFAVmIIXRZVQuTlZtmzJfPdenwMAAAAAAFQFAqgok5WVVd1PAQAAAAAARBkCKAAAAAAAAFQqAigAAAAAAABUqphAIBCo3B9R+02ePNn0MiUkJFhtpt8hNzfX4uPjLSYmprqfDmoRxg4YO2Cbg9qA/RUYO2C7g9oiECHn5zk5Oe75DxgwYIePpRN1KdTmwRD+e9T2EA3Vg7EDxg7Y5qA2YH8Fxg7Y7qC2iImQ83P9HqXNTKiAAgAAAAAAQKWiBxQAAAAAAAAqFQEUAAAAAAAAKhUBFAAAAAAAACoVARQAAAAAAAAqFQEUAAAAAAAAKhUBFAAAAAAAACoVARQAAAAAAAAqFQEUAAAAAAAAKhUBFAAAAAAAACoVARQAAAAAAAAqFQEUAAAAAAAAKhUBFAAAAAAAACoVARQq3cqVK3mVUS6LFi2y2bNn8+qB7Q6qDNsdMHZQ1f766y9bt24dLzzKZObMmfb+++/zqqHMpkyZYs8++6xVBwIoVKqXX37Z9tprL3vppZd4pVEmzz33nB1wwAH2+OOP24oVK3j1wHYHlY7tDhg7qGovvviijRo1yp555hnLysriD4BSGTt2rI0ePdqeeuopGz9+PK8aSk3bmmOPPdaefvppe+ONN6yqEUCh0rz55pt211132ciRI+2ee+6xb7/9llcbpfLjjz/aO++8Y2effbZ9+umnLshcv349rx526PXXX7e7776b7Q7K7Ndff3X7LbY7KCv2WSivjz/+2J0EHnXUUfb888/bu+++a3l5ebygKNG0adNc9dPll19uTZo0ccfJv//+O68admjTpk02Z84cu/76623EiBH2yiuv2Ndff21VKSYQCASq9CciKqik75BDDrGCggJr1qyZXX311fbdd9+5Sqhu3bpV99NDDZeTk2NpaWlup6qKBAWZN954ox1xxBGWkpJS3U8PNdjmzZstPT3dbXeuueYatjsolSeffNLOOeccW7t2rTVt2pTtDspE25yMjAxr3rw5YwdlonGj8aOxo4snChIefPBBN3sgJiaGVxPb+de//mWNGzd2F0vq1q3rqp80Zjp37myXXHKJdejQgVcNJcrPz7e4uDh34e2xxx5z51w33XST9erVy6oCFVCoUKtWrbKjjz7a/vOf/9jcuXOtZcuWFhsba1deeaV17drVLr30UipZUCT1PtBJoAKnr776yoVPctppp9lxxx1n9957r33//fdcGcR2NmzYYJ988ol9+eWXbhuUmprqdqy6Msh2BzvaZ+233372f//3fy74ZruD0tJUKVWtaLujk0CdELLPQmmoolvVuqr0ViWLwifRsbIqEnQiqNuB8P3V3//+dzd21qxZ47Y7svfee9vxxx/veqaqim7jxo28cChE40UXZa+44goXYKrnnPztb3+zE044wYXd//3vf2316tVWFQigUGEmTZrkDuR1EKa3pKQkd7sGtU4INeB1gK8dLOXFCG+Ed+ihh7ppDDoRvPbaa91UGO+WW26xXXfd1VVCTZ48mRcPQb/99purtlRwqW2LtjO//PKL2+60bt2a7Q5K3GcdeOCBNnDgQBdgJiQkBK8Mst1BaU4GFRJcd911tnjxYqtTp45lZ2czdlAiTZPS8c4HH3zgTvhuuOEG++KLL9x9umB75513ugpehVBLlizh1YSj4xr1Re3Tp48LmzRWROdVotv2339/+/nnn+2FF14I7seAWbNm2eGHH+4qLVu0aOGm/ao1jqf+c5phopBKF/ur4hydAAoVQmHBKaecYueee66rYmnQoIEtXLjQ3adpeNK9e3e79dZb3c739ttv55WHo7JPbQiPPPJIFyKo6figQYO2u/qnhnmqarnvvvvcvHdAK2zq4F0HXmri+r///c9d1dEqZl6XLl3Y7mA7CrK1z1KFpU74PAWX2s6w3cGOtGnTxk1X0JjRWFJFVGJiYrCJtBoEs89CeKW3joM1U0D7LE19URWL+rF49erVc9OpFHBqNgEr40HTMk899VQ777zz3DRNnWNp6pTowok/z7r44ovdxVpVZb722mu8cFEuEAi4N52jqx/zww8/7FriqPeTztEzMzODj1VDcgVR06dPd8F4ZSOAwk7TxlD9eR555BH75z//6W7TgJ8/f/6WQbY1pZchQ4a4x6p8tLqWfkTN63+g1H3w4MHuc1WtqHpOO9S3337bhU0KqTSOFFCpxPjRRx+1pUuXVvdTRzXT6oi6UqPy4fj4eNtjjz2sXbt2bqf6559/ugN3nQCy3UG45cuXu/AgtFfGhAkTXNB9//33uwslatTJdgdF0TGOgiZNdTnjjDPcFCqFmaL9l6oSFExpOozCBfZZ8NPFdTyjql3RNkg951Q598033wSPa7Qfe+CBB9ziPboop+MkRCe/X1Jlii7yi6aKq8JJxzraFmk/5SueNMWqVatW9v7777vKXkSvmJgY97Zs2bJCFXHz5s1zx8yaMaDzKj+zRBdShg4d6hqSq4quMhFAYaepUaKCgn333dedDGqwq4JFAzw3NzeYzLsBFxvr5i9feOGFrurFlx0jeulqnw7i1URx6tSp7srf559/7j5WNZ1Sea3QoDBBV5x1xfCzzz5zDe25Mhid/DZFV461Y9XOUtsejR2VnyvcVhiukmJN79R2R6XrbHegab6i6eIKn7TKpk4ItQqVrgxqLGn7ogMxXUFWrxa2O/D8uj3aBmn7owBBVZYXXXSR63upA3p/0qgFEXRBhX0WvPr167vwW30uVcH70EMPuYUytB26+eab3T7ro48+cgGmjqPVfkAngm+99ZY7nkb0UR/LV1991Z07+WMfjQ1Nz9SFEt+oXhfbdL9aoCiE0pTgN954w03dQ/TKz893Y0IXQhRqq+Jbx8hqWK8AU9sWzSTQsbIq61Rpt8suu7jbdS5WabQKHlBWubm57n1eXl7wtvz8/ODHTzzxRGDo0KGBzMxM93lBQUGhr9+0aVPguuuuCwwYMCAwffp0/gBRJDs7O7B58+ZAenp68LZ33nknsMsuuwT233//QO/evQM//PCDe4zccccdbixNnjw5+PjXXnst0KNHj8Czzz4bSEtLq5bfA1UvdHsja9euDVx77bVuLBxwwAGB/v37B77++uvAsmXLArNmzQqcdtppgf322y/4+I0bN7LdiVL625900kmBvfbaK7B+/Xp32/z58wP9+vULXHPNNYGLLrooMHHixOB256677grsvvvugT///DP4PdjuRCeNic8//9ztu8KPZ0444YTAiy++6LZNn332mduPaR92++23B7KysoLbLMZOdMrJyXFjIPSY+eGHH3b7LB3v9O3bN/D999+7fZnG2eWXX+62OytWrAh+jwcffDDQp08fNwbDj6URmTIyMty+Z9GiRcFj5dBzLB3jjB49OvDUU09td58fIzqOPuKIIwKXXHJJYO7cuVX+O6B6pKWlBcaPHx/45ZdfAgsXLnS3LViwIHDGGWcEjjvuOHec/MknnwQfP2HChMDJJ58cuPHGG932SqZOnRo455xz3OP/+OOPSnmeVEChzNSbR2WgKjvXstW+ZFhVBv7qoKoNdHVQDaUlfClZXQXSXGWl+FoyVFeCEPk0LUqVBWeddZZriKeqJ42Z0aNH24cffuimUu2zzz6uBFS9NEQNyVUlpcd6WhVPY1DzlFVizJXB6NjuaJuhKiZd3dP8dZWha1UPlaerEkG9oFSRqfJz33NO1XW6oiy6usN2J/royp62K7oCqP1USkqKu71Tp0521VVXuZWodHvfvn2D96kaKjk52U2L8djuRCf1rFTrAPVV8cczvklr+/btXaWBqg+0H1I/Ft2nPizah/lpD4yd6NxnnX322XbmmWe6Yxs1AtY40fGPqgu0cpmakQ8bNswaNWrkjnM0zUqPCZ06pX3WYYcdFlxgA5F/nKxxoWMbHRurzYmfDu7pGEcVvP7YJvT8y59vqSWBztFmzJjhWqSo0heRv8059NBD3WwAVeXqWFmzkTp27Oj6P6n6Scc5Oj7240XnW5ryq+Mj7ctEje617VIlpqowK2NVRQIolMlPP/3kAgQduGsQq3yvqKViVe6naQuaRqWDMj/QQ7Vs2dKdFGj1GK1GhMimqVHqkaElP7VT1IG7VrXzO0uNKY0ZTXnRzlTzk9UXQX02NJ788uj+gF7N8nSfxpcei8je7px88smuV4YOvBQ+nX766a68XP1WdACvACp85Q6Vo2ts+KWKhe1OdFEfDJ38a7+lAzCNBx2I+7GiJc8PPvhg1xRYgZO2PTro0mMUWOrEUNjuRB9/3OL3SxpLP/zwg7vNH6hr36Rlq9UkWI1dFTZo2riCTR3/KJBi7EQfjRNtc3r06OFWJtO+yE/P1D5LJ3jdunULhgLa7mibpLYC2l81bNiw0PfT8c7atWtZFS/C6dhGF9m0T9L+ShfodZ6kfZHn910qAlCj+vfee2+7C/1+26XjJS3KonGogBORa9myZS5w0sV9Tdv1F2B9fyddXNN2RmNGF0c0XvzFe/UwbNu2rbvNjx3t49SQfPjw4dttjyrClj0oUEpK2w866CA3X7QofgOojaWqEbQsunbC/uTQH7RpgGujqDRWFS6+ISMil67oqe+KDsxFJ3v6+2vVMp3k6aBLO0htHNXfSYGDNpK6eqODNAVUoquDqphTwj9mzBg3zhD5Y+fAAw90O1RPB2Y66VNzVgWbeq8drXaY2t6ITgwVOOlNdBKgwJvtTnTQClJqsKkryLoqqIsl+vtrG+MDbV350+O0b1LIoEBBoYGqFbTd0f3Cdif6+OOZZs2auV5O6vGkbY7GiN/G6DFafVP3q6+lemHqirOqGHRiqAt1OuhnnxVd3n33Xbeyr45vRdsUzQhQJYuOc3zFisaKeqFq3GgbpOMhPVYng6LH6DZVRqkiRt8TkUvjQfsmhUs66VflihrXawwouNRFOH8elZqaarvttpvra6hFfBQ2edoupaenuyb2OubW90NkW7NmjbvApnMizTLSNuWpp55y51E6nlHgrao5nWspqFKPXQVQ2jdpBorO1UMrfFURVZljhwAKpaITN13F0yDWilKeVrNTdYKuzOy5554uSNKBmJ+GpyuGl112mTuB9BtNP8C1YdXUB71H5NK40ZsCx9C/tQ7GNH1T4UGLFi1csHnSSSe5K4NqCKyxoQ2mpsAcc8wxbmqVp0BBJ406MUDk0rjxV2w0RkTVKTpAv+OOO9zBvUJxNUzU2FGjTk2XUXWdggOFDwoye/bs6b5W34vtTnRQJYFCSYXZqrrUPkxXAP0VYY0Jf1FEb7rtggsucPsmbYM03UpVUbr657HdiS46+dd40PjQdCldWdbiBloUQwfmutCmKTIKNvV579693depKbkaj/vgW9+DsRM9FBho9V6F3t7333/vVobW/kj7II0bVbloeXRdTNOCBzre0XGRTiAHDhwYHDsKo3RM5I+tEbnbGl0E0eq+OsYR/d11jKOQQNuQ3Xff3V14FR0T6cKcCgK0P1OAEHqepaBB0z11sReRa+XWsaFjY4VGusimiqaxY8e69gMaTwqatP246aab7PLLL3fVmAqodJuqpDSO/vGPfwS/p8aRwm5deKssBFDYIR2468RNb7pyM3v2bHe75hRrZ6nSYB3Y63MFVJqvrpRVj9cBvKZZ/fHHH64nQvj3JHyKfNqA6U0ngbp648vTdYCmKhYl9VoFRksN635VuehgTStRaSOq+ccqYffjxi8rSvgU+fzOT2NEO1LRgZl2tDq40oGYruSo3FhVCD64VPWBHqO+Lf4kwK8ew3YnOugqsrYd4pepVhWl9mEKtXWBpKiDdW2L9Bhth3RQJmx3ooMPJP3JYOj0cIXb2p6cf/759sQTT7hqBO2nNO1OV5J9z0L/tT588iE6+6zIpmoTrSyl6VOq6NaFWn/R5IMPPnD7JF1s0/5LF2Tvu+8++/e//+3CJ32usaSWFjox1Gpn4dsdwqfI5rc1OtbRtkSV3FrlTgGlevloe6OgWxfVFChpnIlCTB1PK6QM3Z95hE+RKysry40NnQvpwqtCSJ2PK4jUCneqmtN5uS7Ialuix2q7o6oojSudY+lYR/sq9WP2+yt/3F2Z4ZPEqBN5pf4E1Or5pLrCpwHqqw5Upqd5yeq/ouU/lZD6gasm0ZdeemlwKVlRDx9NY/DBA6LDggUL3MGYUncdjGlnGdqHR9UJSt39tDpVQqmiSRs/LUtc1I7Uh5aI/O2ODr6089Pyw745ovqqqL+KP/DyJ4sKE1S5ogBKVXK+J5jfZgljJzrogH3ChAnu764DsH79+gXv8wdWOiDTmNECBurtUxQfIghjJzpoapSOW9Q2QNW4oQfiGlMKBnTALgq9P/74Yze9TgfzOk5inEQvXXhV9YAusmm7ov2PTg4VGvgKBVVFqQ+P6OKbqlY0fnxfKAkdQ4yn6DhO1pRe7a80ne7UU08NhkqqhvN9m/zUKB0za8EVXSRRCK7ggWPi6N3mnHHGGW72kUInHf/6cyv1VFaorQv41113XaHxpsUMdAwUOqPEq+ptDmdzKJIGoq7OqJpJ/ImcdpiqWlJ6qtU4dIKox+qAXSXq6qavAzP/PZTkEz5FF805V9NfXRFUGKAqOU1bWL58ubtft6k6IbT6TVdvtMNV8BDeSNpjRxv5Pv/8c3cSqKt8CpV0VUc7TY0P7Ww19cWvDqRtjt60I9V0hU8//dTd7k8a/TZLGDuRT31UdEFE40NVt9p/ha4Y5ceFQin12VBZuvgG0aFCm7kydqKDDuR1xVgH7Aq8/ap2oivIvmeYFj7QxTbtrzTFUyeDwjiJTtreHHXUUW7bo5NAhU869vXhky6EqMJAq055qlTQsbS2T74yN3wMMZ4im7YbGjMKJzVDRFWUfjEmPwtA1XGhC+z4nlDaTvmqXkTnNueII45w03jV4sZvQ3Qso/2Uqrt1AS70Qr4Pl9S/MHxVeq+qxxOjF0XSYFXlkprbqWGvpwMx9ePZvHmzq1RQ2upX73ADKjbW9WPxHyO6aP76o48+6qayqNRTjTM1dWHixInuKrL4Hao/uPe0UVSQ4A/cEF00xU6Bk3aozz//vKu01MqJunKsMnL1X1H/g9tuu81VX2ocacxop6vtlab9Ctud6KNASRWUqsBV3wNd4VMDeo0dzwdNWvVOjVsfeOAB97mChtCTQEQnhdxqLK6KXVWz6BjH76s0PnQBRRdStF/Tm5pMq6pXFbuqaEH0UUWctjnqRegXVwnfB/kLIeFBtwIEHe+wv4ou+rurOk77KV1U03GOWgfoAoqfTaJKOlViamzooqzCcU/bJFVG+Sm/iC4PPPCAGxtqL6AWFBoPmt6rfVTosYzGmVZh9avUayypSbmq5kKb1VcnekCh6IFRp447ANOGUA3wdAXHd8JXuZ/K1RU0qBRdB2PaGGr5UCX5Kh9FdFIgqQN1VcL5kElTqNScVRtJXfHRhlHjRztgXdHRNDwdnKmPj6+4Q3SWFOvqjG98OGzYMNf3QsGUgoQBAwa4Ha6uDuoEUStOaWeqsaTmrieeeGJ1/wqoxgBKB1gqLxed2GlalMaOtkk6sPfTfeWEE05wV5cVdGoscRIIVapoO6PxoCvMeq/qXVEopekM2rdpyp2f2qnVFb/66qvgIgeILroIqzDAT60TjQftj1QBrmNlLWigfZSmTunirE7+NM40U0Ankog+OrdSZbc/ZtEFNgVQqrxUHzBNAVaFi8aQjnN0HK0KOk3H0zZH2yZtkxBdpkyZ4opCFFj6cyxN3dT5uvZNaingq5vUg06LgGm8aBukY2udc6mARNW7NQEBFIqkQa4DdzXY1A5VA1fVT+qaL9o4qvG4VhjSQb+qDzQ/+ZxzznEr4SE6aSOnkmL1dNJBur+CrIDJVzxpA6ngSbdpXOkKoa4kq0Gen+uO6OH77aiKUgdYqrxUGbG2PVr2XCtt6sqNxohCSvXYUKWUKqEUHGgcnX322cGG0Yg+ugCisaMDezVxVVCpsaPtj1ZQ1EG9AieFmqIKKB2s6SRQ48Y3jEZ00rZH2xcFSWoroH2RVgrSsveazqkKXu2b9thjDxdEibY7OiZSY2mhZ0/07bO0op22IToOVuitKkxVRemkUFUrOr5RT1QtoqIpv/pc2xrtt1RNp5NBRBeNG+2jVAWlGQOicaMWAtqe6PhZF0a0Wpl6XupCis7BNJNATe1VcacVf8N7FSLydevWzY0NnXv7/U3//v1dtbeKP0J7WiqAUmXm119/7XpeqsJXswv8Nqcm7K9oQo5ik1YdsGtlMl1d1vxkTXvR1AaVqYf66aef3D+EDuC0/HBNGdyoegoP3n77bTdGVD2nMaDgSaGkDsJUMeebRIs2nNoR63MdtAljJzrp6t+iRYtcyK0rxtrmaLqUTgjVXFqBk076fFWCriBqrGjs6MBNGDvRSVf/tJ/ac8893eeqUtGBmbZDuoqsk0BV2KnHmO8FpX2cwm+qV+Adf/zxbtVMvVcPFvWhUzil1VoVche1OAaik29Sr+mX6gGli7JadEcL9Gh/pEBcjcZV2aKQShfntA3SMZHaDKgyXNhnRRf9vbVN0RQqhU2qxn3rrbfctkbHOhobGjcKpLR6ogJLjSsdNytwUnjlvw/nWNEtEAi4PoSaOaJtjvoX+n1UaDCp8aPH+vYmNWXsVP8zQI2kg3L179HJnT5Wqagf4KqM8iXrolJAVbsofPKNgWvC4EbV00GXdqihvQ1UGaeTQO1cRWNKFQkzZsxwU6h0IKbwibET3XR1x1dYahydd955NnjwYFcNpTBBO1kFlgrCRVOqtM3RdomxE900rcWHTxoLWk1IY8cvMayKKG1zdDXQ0z5L+zYWAobvm6H9kA7SFSyoitfvwzQVWAf2RTWsR3Ty/Va0DdG+SiGCKrv9VClRFYuOdxR8i6bqaT+nYx72WdFJ2xRd+FA1riq7NW50LqVx5MMDtRnQtDxNoQqt8PXhk/8+iG4xMTGu8EPbFDW1931Rw2n8hPbWrSljp2Y8C9Q42kDqio0/4FKgoHJjDW6V9fnHhB+8635KQqNDcY17NS40BvzYUPWKdrQqH/afa9qLdq6hY4WxAz+utIP0B/GeDshUYVdUk3rGTvQoKTDyUxLCG7TqirPGlU4Qw7G/ih7F7bP8AbmCTFXTqfpbzV4VXGrq3dVXX+1WLPPVc4g+RW13/LjR4hiq8FYVlA8RNNZ0wUQnhX7aZij2WdE5bvznf//73915lVbU1FQ8jRu9qdJJPcJ0/OOPmYGS9mequtRMEs0aqE3HNARQUcpXL+2IDrj8BnPvvfd2IZQqVzSVqjYNdFScd955x+0wdfBV0upRfmyoF4LGkZYF1UG8mkyrKbAO6hFdNE3Kr5hZHH9Qr9BS0+w8LT2sK8gKxhF9fv31V3eQFRpuF7fN0dhRTwRv8eLFrrpF1XSIPuqhovB6R/ss3ac2A+rjo/YDCp/Ub0Unij/88EOVPmfUnu2Oxod6E2qxFW17RGNNfeh0QU6V3oguxY0bfe63QZpqp0WbdGyjGSd+3Giqpr62pqxWhqr17rvv2ptvvukWAFPl9o6OlRVWKtBU72Yt9lRbMKE9Ct1///0uRNIBVmmu6PkNqB57wAEHuKauKklH9NGJnA7QtZG78cYbgwf0JZV0KoDSlZzXXnvNnnjiCdfg9fzzzy/URwGRTzvUV155xTUX1wF7STQuVJr+4YcfurGjEmM14VRFAg2jo4/6qGi/pca96ifn90lFXQDR7QoctK3RlAWtPKVVzdTHRyE4ostvv/3mGomr+ar6OBW1z/JjSQuqaGUzrU6mhVWkY8eO9tBDDxFeRqGybHc0njQ9XMdFCp3Uf049fLTd6du3b7U8f9TMceMXT9Gxr6Zp6rEKHd577z3XVkAXT/S1mkaO6PKPf/zDBZK6YKYL9tpnqeCjpIbzmoa3zz77uAsn6pHqe4nVdARQUUgDU/NFVa7ng4Ad8RtQJa1amUE7WEQfjQP1dPryyy/dMrAKk7QzLWrj6G9Tjx5Na9DSoTqQ931+dBJA+BQ9FAYo+NaKQVrZRb0yiqNxsd9++7kxMm3aNNdoUcHn8OHD3f2s/hJdtN/R0uY6uNLYUXP64g7GdLv6QWk/p9XLVPX0v//9z61gJoyd6KLpmAqV1Aha+ywFUeH7LH98owoWTb3z06j8Y3zlXE1p3oqat90RXRzR+FLoqXHHdic6lWbc6BhHIZSmhevCmvoWKrjyx9VaxEfY5kSHgoICtxqi9jmaZaLjZV1E0/GyxoM/5y7u+EW9mFWtO2DAgFoRPgkBVBTS/OKmTZu6Pgfa+KnB+I6EDnrCp+ilpYQ1R10NE/1ywzrgKi58Eo21vfbay5Ub++abwoF8dFE/DE1F0EGW+mLoqo5f+bCosaMrgboKKP5qoR87TP2Nvu2ODsg0JfPll1920zCLqirwY0cVuqpm0VvofcLYib6xo+2NQiWdEGobpHYCxY2D0JXuwh/DPiu6lHW7oylTChL05oMDtjvRp7TjJnQKld78KuLCcXJ0id06ZVfjxDebV4itClxV/7dt29YtnKJ+cjoe1uPD90+qlqpNF9m4lBNlNH1OJ4AaqJdddpndcccdJfY20HLo2pHWhsGMyqWN2rfffuvCJK3SoWlRmlKl2/z9nsaLlpjVVWdVujz22GMufNKGk+ab0Uf9VzQWzjrrLHvxxRdt3LhxbqeqMDycxofCKgWdnq+UY+xEHy0hrLBbF0o0bVxVdBo7ixYt2u6xGh/ax6mEvaj72I9FF/WbU7XuoYceavfdd5/bR2nsTJkypch9lsbO5MmTq/EZI1K2Oz5cYLsTXco6bnSsw/4KmzZtcvurNm3auBdj4cKFri+Y+u2+8MIL7pzr8ssvd4/T8bA/x3r++edt8+bN242r2oAAKgo3jqpgUrnecccd56ZDqRJB5aLhtFqZNqI6cQS0UdPc4l133dX15dHY0NQEhUszZ84s1FxRNK/dL3vurwQy5S46aVyo8kAh5G677ea2OSov1rYlvMmidrj+4A3QdkMVTbo6rCb0KkdX6P3222+7/nKhQYKCzgsvvNBddQa0T1IvJ/Xj0bZHAbg/aNfxTeg+SwuzqEpX4TjAdgdVMW4uuOAC9ldRLhAIuKmal156qQ0bNszdpmnj1113neudq4soKhjRBVtN6/XGjh3rLpj4iqnaJiZQ0prGiEhqbOabAGuQK1VNT093lQmafxx6IqheUaHTGBDdtMNUqbCnajr1dtJUTlXTabqdny6lsDN8OXRErzlz5riqOU8newqgNO995MiRwakvGje6aqigExCFBQoQPE0f13QqLT+sCynJycnudm17dOUwdCoDopsqU0KXM1fVrt6GDBniGkTrwN9PWVA1gqY4AMJ2B+XBuEFFyMrKcgUjvppSF251wfbRRx+NiBeYACqKFDcvVNUr6rWiqVJKV31zvNBqFRrhIVTo+FBC/8Ybb7irzLfccst2VU6MnegWvt1RpYHvI6dlztUvQatU+aaboVglEaE0LVP9fESNW6dOner2XZpiFb5vY+wglKY3+JBbjca1UqKWrj733HO36+3E2AHbHews9leoiGPmwNbPdZFfF2efffbZQr0Ka+s5Vu17xii34uaFqqH07bff7iqjtES6hIcItXFwo/TKWgip8eGnLvipnDqgf+6557Z7LGMnuoVvdxQ+6WRQtCP11VBq2BmOKZsIpfBJ4YAotFQTe62s+fPPPzN2UCIdsPt9liqf1NBVF08+/PBDxg5KxHYH5cG4QXnOsWJiYtxMgD///NPWrVvnvlbVUAqfBg8eXCh8qs3nWKyCB0eDWnNM1TwvNLVHdNBJnTZqZVk9QRs9n7xr+U/NQz766KMr/bmi9tNY8xUJTz31lN12222ubwKwI6EVurpw8sgjj1j//v154VCmfZb6bWjKuC6eAGx3UBnYX0F0vKvz6tJWK2VmZtqNN95oGzdutE6dOgWblKvtQKRgCh6KLFFH9FAjO/W+uPrqq8v19eEbVKYvoLzbnNqyfCyqH9PEo9f7779vgwYNcsvelwdjB+XF2AHjBmXhF2pS78qymDFjho0fP94yMjLcxRIfPkXKORZpQwRQDxUFAHXr1nVv5T2J8yeCtXU+Kcru4osvdlVv+++/f7lfvvCxEgkbRuyYVjhMSUlxTXw1jbc82w3Cp+ikaU+aiqlFLxQklGefxTTx6HTOOee4dgE7szovYyc63XTTTe5YZ8899yz392DsRB/2VygvrXKoVez8ojqlPdYpKCiwXXbZxb1FaqFIZPwWUbwi2fnnn+8+nj9/vmsirmWG/TKO5UX4FPk2b95sxx9/vCUlJdnJJ5/sVu3QBk8bRipQsKPtzhlnnOFKhFUerCm76sOjIGFnMO6iY+ycdtpprp+BmtH/9ddfdv/999sBBxxQ3U8NtWCfdcIJJ7h91ueff+6uCAOlpX3VuHHjXL+4J554wjp06MCLhxKxv8LOnmNp1XCtXvfJJ5+44x7tv8p7Hh4IBCImfBLKXGopzQfVgXzjxo3t1ltvdU181ZD1vPPOc82gy9pUGtFjypQpru9Ft27d7O2333ZLVM+bN89t8AgBUBKFTuoVp35NL730kgueOnfubG+99VbwMb7RL+Bpf6TASfupNm3auPHyzDPP2KhRo+zll18O7q/Yb6EomiI+YsQId5FNY4fwCWXVsGFDa9eunS1cuNDtw3QMDRSF/RV29hxrv/32c+dYr7/+ujVv3tyWL19e6vCpOJF2fkYAVUvpyrESVi1BrQZlBx10kGuqqaXM//3vf9uvv/5a7NdykB+9tNLY2WefbUcddZT997//dbdp/Cg0WLx4cZFjg/ECTyHC2rVrbfTo0e6AXmXFLVu2tLZt29qsWbNcQOWv3GjchI4dgqno5Q+cNF18jz32cAdiOhnUgZkO0iZNmuRCBlXT+bHCdgeeemD47Yynqt0333zTnnzySfvjjz9s/fr1jB0USdNWtG3RPmvMmDHu46uuusrtz0KF77MQndhfobxWrlzpZiYdccQRwXMsXaSV4s7LA1F6AY4AqpbxA1RLMy5atChYjqemZDpA00G9klaFUCr38/eJP8DXxpWTweiknj3PPvusO/jyNG40ZlatWuXGRvhGULepQuqjjz6yDRs2VMOzRk2hbYpCTAXgKk//5ptv7NNPP7U33njDTj31VDv00EPdNAcd2PvpnAo29XUKpvy2CNEnPT3dBVAaD3pTDzFdHVR/hIsuusiOPPJIe/rpp13Y4KsxVa2gxyI6qXGrqEr3mGOOcQfwWppaPaA0dfz//u//7IUXXnDj5z//+Y+ravFjR20J9DgfaiL6+ONcHSdrBSrNGNDHWnDlp59+chW8oml5ofsstjtgf4XyUE9UzQ4IXdRJt+m4RhdvQ7dLnrY5c+bMcfuyZcuWWbQggKqlybxK0DWVQfNKteP0jRHV1FVT8nQgpgMy0X06kNOUvWuvvdbdRp+n6KKTOAUGOgDr1atXMAhQ2KQG0gMGDHBjyfeBCqfpMtqoqrQU0UthpYImBdzqx3Luuee6He3zzz/vAkr11Xj00UfdyZ8ocFDT4BNPPNF9ToP66BIaOGrbo+pLbUv0XtPFr7nmGhdCff/99zZy5EjX7HXChAnu8apo0f5K02V08BaNVwijmaZrqtJy6dKllpyc7Kbgad+k/dDjjz9uF154oVtdSGPnlFNOsenTp7vxI6rEvPfee+2ee+4JBpiMneihcMkf54b+3RVG6hhHTcjvuOMOty1SU/LnnnsuuI3RRTa2O9FJ51I+HND+Sscu7K9QGjrHVvWTjnk0qyT0HKtjx462++6723vvveeqMYs6/3733Xddb0Nd1I0WMQH2yjWeqgfUqFUbRqXyamzWv39/twP97rvvbODAgXbwwQe7VWG0PLF6JEycONG9V3l6s2bNXPigfxBdbdZj+/TpU92/Fqpo7GgVhjVr1riDco2Vm2++2a1AFUpl6ZrS8NprrxX7vTS2NFVG4QOiY+zcdtttlp2d7Q7cVVbco0cPd592kpMnT3bVCA8//LAbT7qyrJ2uFkFQwHDmmWe6AzqF4Y888oj17t3bVTEgOsbO9ddf77Y5GhMKEvbee2839U77prlz57pKOQUJqsr02yNNJd9nn33c9khfp5NCPUaNPP2CG4hsGjPHHXecC44UWCtUUn9L0ZS7G2+80W1L/ve//7kpnD7UVsit7dO//vUv97m2PQqrVNmroBPRQVVNr7zyilvx7u9//3uhZcs1JUYngFdeeaWr5FUVnaoS7rrrLreN8o9luxOd+yv93fWmi/XDhw93xzSqutS5E/srFDd2dCFW+xntuzQLSefdumgS6oEHHnDHzary1jl5UXRspCDqzjvvtGhABVQNpwoCTWvRzrJu3bo2e/Zsd3VGJ33XXXedHX744a58WBtPDV6V8HXp0sWVrKuMWDtb0QG8+rXon0VVUoh8OvHXQXliYqIbHzq5U/VTaPjkx4eu9ChMUBWUhObS/mOdGPoDOkQ2TbHTeNHUzPbt29vvv/9eKHjca6+93PKwCg/0pgM1BVU6yNfjtSMW3a6drVbI69evXzX+RqgqCru1X1IlwW677eaCAFXF+ZL0IUOGuCuE2p9pX6TtkZ8urh4/fuyo2qV+/fouuFLfKEQ+TfVWY3pVUn7wwQdu26IKKE8BtipWtB9SKKXtje/jo2MeXaDz+yyNKzWC1X4L0UPjQv1RtcDKt99+627zIaX2URov6ld40kknuaoEXZC9++67g9M9VZ3Adie6zrE0BnS8rH2N3j/44IPBijj11dU5FfsrFLcitLYXKhJRT+YDDzywUPjkz7F0LqZz8s8++2y7aXiBredYgwcPdsdO0SJy1vOLMBqQOgDXFT+d0GnZWO0YL7nkElf5pD4+GvSaxqC0XhtNHYB5mu+uq4QKnjydDGo5yNDbELl04K6reTrx0wG9Nm6qZPEHYRo/vmpFB23aCeugTSeNmmrlx6DvC6VxowM2RC7/N1eQ3bdv32CPDIVHqizQ1eImTZq4x2gnO23aNBdaaqeroNP3mdO0TvGlxjoRDK+6Q2RSWKnKFFW9aYyoGk6VlZoupel0uhKoKeQKvFWhoioEVUZpyp3Gl7ZDoWNH2yOmbkY+BU6qVNJ0Ou2z1AtDgZIPmLRd0XHNfffd59572q4o7NRFumOPPdbd5qeR62q03hA9fA8wjR1td7Qt0sUSUZCg6gJVGfzjH/9w1eGaNnP66ae7ijrt7/zYYbsTHcc66iOn1TW1fxKdN6l6W9sU7b+0rdGFEvZXCKdjlo0bN7qiEG1j9KYLaArA/cIZOsdS2KSPFXq/+OKLbpZAhw4dCp1j6THadg0dOjRqXmgCqBpOUxXEhwAaoEpctdFUObE2kCpXV8qqeex6jE4cVVKsqoPwsInwKXoobdcVZR2AiXplaD67SkUVPulKssaSTu70uUqOFXTqoE0HZr7BfSQu/4mi+b+zxo4Cbb+D1CpTeq9KOoWVGju6MqiTO1VjqgpTH2u7pMB83333LfR9CZ+iK/jWAhmhodFhhx3mDuRvueUWt41RyboqU3TSp6mc6meo8vTWrVsHp8J4hE/RYfz48W5qlC6oSatWraxr167udlXA+dBJ71Uxp5NEXTzRtDtNk9F+Tgf4iE5+X6UVpzRrQFW6CsF1sVb7La2Ap3BBx8q33367u2giOjHUtBhte0Kx3YmOYx0F1zr59wGAZpLob68wXGPmgAMOCE4h13hifwVPbUt0jqXjF1FLHB3TKJjSeNKF18svvzx4LqVtkhbGUNB95513Fjoujsa+zARQNVToCb+qm3QV0A9WNcdTuKRUXvOS1ddHwZKmPugEUDtSVapoDnzojhnRwe9INZ1FG0Y19VXApPFw1FFHuY81xUpl59owqqm0HHLIIa6aReMqNHxC9NFBuaZJabuhipaXX37Z7TxVqaIrgTop1MGYFjx455133LjRgb+mz1x66aWFxiGii4IDbUN+++03N91ONJVKB2OqalHDegUK6vOkiyVqzKlxpimfvs+T79mC6KHKplAaE6q29FPwwo9jFDapkkXHRgo4NY1cGDvRKXRs6IKIZgpoe6Mxoj5y2m8piNK0PAULoXz4pHCKY5/Ip5klRxxxhPtbqyrOh9taiWzs2LHuOEbbHn2uIErnXOo9p/2VeqGyv4LoGFkNxnUeroWctM3Rar46BtL5uBbB0FjRsY5oFooqK3V/AjMCaEJek+iE7ccff3RTWfSmaS+a4qKm46pUOfroo12VgeaaqiJBq1CpCkrT8JTQq0RdYZV2ojqBFA7GooMOzjW3WCd2Cpj8/GQdpGscaSerVF5TOBUKqExUB2VagUongIyX6N7uvPrqq+5jBQeaAqXyYT/NTtUsmqagnafoQF4ni1paVtsiBQWqSNC48jtVtjvRM3bUJFrjRGNHF0O0ndFBmCrkdNAeerKnA3odpCms1Mmgxoz2VxpD/gSSsRO9Y8eHTH4MfPrpp256g5pK+2m94d8jNORm7EQPrYKo7Uxojy+NB1UkXHXVVW4RHo0hLbqi0ED7Jn+Blosj0UnbB51Pqbelpvxqu6NjF213tB3SFHCFlgqaRI/Tapo6BtIME13oZ38VvWPHT9PU+ZLv1aQKJwWYqsTVeNFxjT8OfuONN9xFWm1/dMwjodueQJQXh1DmUENoo6eQSRtEJac6iFdlinoaqFRPg1o7Tw1shUzqnaEBr549X3/9tQug9E+g3hqeBjpXkaODEnj1VtFBmcaC/u7aWapRvXodiPqv+A2fdrZK7HWQprnuPoDy93OAFj3bHU1lUYWKdrBqzqoSdFUx6QqgbtNcdVXTeVpBU83sVQXltzGqjPK0U2W7E/lURaneKdrOqA/CunXrXPPWESNGuCkuGlfqraHtjy6oSLdu3dx+S8uk+21NaMUBYyd6x46mLvhm8377oSvMmtKr1Vc1bsIP1kPDJ8ZO9NDxsSqZtL/S/kkX2Px40NRxHSPrApwunHzxxRfuuFpvmv6iajkqc6NzupT6DWpar6pwdcyj4xd/7KKPdf4UukiTKlW04rim+Ppm0uyvoo+2I7qYr4tpakavCksd02hboqptjSutQq9z+NDKJgWZ+hp9vQ+gCJ+2YX5ENdNBk3aUarqp0jw1/1U1gq4cK23Vco4jR450JaMq41NDV+14/XLoChL8EsXh2MlGPr+SgnamSt81Heqrr74K3q8pMH4qpvqJqf+Tpx2vVihTJYvnD/AZO5FPjRK13dF0Xl2p0djRamX6WJWXoeMgfDxozOjEsKigKZqv6EQLhQa68qeGmaqi1L5KoaQqeEVBgqotVaGrCkudEHrq1aM3VeyGY+xE79jxK5aF7tcUYGqKg6ZR+bERunpQKMZO9NCUKJ3QaXrmxRdf7AJMTxUtOunTsbROGvWmC7a6oKKqb4WZiC7z588ProipPqg6Xg4/rgkNBnThzdNjNfMk9CKbxzYnsmksaH+j7YZmkqjgQ9sVH2SL+sqpSETUdkAVmJ6+VoFm6AJhXgzHyVRAVTcNQk130RVBVTwpPdXBuapY1BxPDRS149RVZB2M6bHa2fqUVYNdzaMRnfxOUyXCCiJVQqxVFnSApikNfvlPjS/drsBSFQpK8VWloGkNxQWYiPwAStsSHcArSNJBl7YlukKoqkp9rO2TAvLPP//cVcn5XhmqxlTPHoLK6K1AUKh0zDHHuMpb7Zv8ipq+H4KmhutkUL3mFi9e7AIHVbzoYopOBEJXMkP0KG7s6GBd02J0IU4H7H7bosbk6l2ooEpXm9nmRDdVovjKFc0M0IUTjRFV0On4RvspHR+rsbim36nXj2jsqBpK2yZEF1WgqPrWV8rJzJkzberUqW57pP6W6peq/ZMumIi2SxpHCqz23HPPIgMoRDa/Qt3s2bNd5b+nRXrUikKzlRRsakEDLbCi5uI6r1IbC40l9UnVmFMlL7bHFLwaIDs7202F8Qfk6s+jHamCKKXv6pOglFUH7apOUD8E3a6pUzqo14EcopeWG1blgaa8aGOowEljRFUq2omKque0QVRD8o8++shd0dl1113ttttuc/dH+1zkaKSwQAG2v9rnq5k0DkKrUxRWarqd3lSirtJjjR3fbBzRuc9SeOmvJCtU0tRNvxCGxtIVV1zhxoi2O1qBU43sFSzoBFA9WhCdSjN2VD2n0EnbGgUK6nep6Z3avykgR/TyU6C0LdHH2pYoXFI/QgVSX375pQudFD75vmE6vlFPOr0JLQaii6ZCqYeljpO1Qq9WslPVikInVdGpv5POo7T6s8IGhZoKunUhV8c6GleITjofV+GH319pH6VtjSpzdWFNF/Z1zqV9lvrvakEe9UjVOZa2N9oOCduc7cUEtGVGtdHLrwBB/Xn0XlNg/BxkXTnW/HYNaFUrqF+P6CqO/il0YK+lQYXmm9FLG0cl78OGDXNVK2rc+u9//9ul8meddVahCicd/KtKSjtXfztjJ3qn/mpHqRBbJ3Y68dPtOnjXFT9dVVYQ5YNx9UHQNko7Yl9dx9iJ3rJ0nehpqp32Qw8//LALCvbff383/UXBkxbK0IpCmirj+7GIvwrN2Ik+pR07mgKsq8c+QND0cY0f7eMA0bGNxoN6iantgPpf6qKbjonVuD60tUD4GORiW/Twf29VO6naW9VOalehRZ38auJ33XWXu8Cm6hUd2+jCnM65dOyji//C/ip6x45a4aiiUhfR1D9Mq8yrNY4uzmrsaH+mkFK3iarqRAGmMHaKRgBVQ6hLvga2qpo0tWXGjBlunqk2igoNNCVPJcb+xC8UySp0RVlTNT1N3VTpsPofqEGekvmixgljJ7ppKoxKy1Vqrp2tdpya264DNS2A4Cm0DF3gQBg7CKWDLG1f/CpmChg0DU8niOEYO9jR2FFVy2mnncbYQZEnhVqdTPsuBU8Ku9UfVfuz//znP8EVqoDQMaNKJwUGulCri28KEHzlty7CaepUUZXd7K+i2/Tp0104qXGgbYwKRVQ956sxdc6ui7ZFVcoxdopHE/JqEFp05j/WSZ+aRSuR19xkJe+aKqVQQU07NYhDV18IRU+E6FBc81Xx4ZNfqUMnfbqirHJRrc6gSpaixgljJ7rHjq4S+35yOkBbsWKFmwqj6b+icaMQ4amnnrKcnJxCX8vYiQ4lbXdC6UBeY0jjRG8aQ8WthsjYiQ47M3aKGyOMneiwo2bzCipVpfLDDz+49gM6hlZVi9oKKJBCdCppm6OpvKqQU9CkYx9td/xxjaowi6uMY5sT3WNHi2Ro6qYu9Gv2kWYm6Xzcjx19XtxkMsZO8QigqohKOjXXWAM4dCOnj/3A1XKfSudV6qeu+0pYPQ1wX86H6DJlypTghmxHB/S+EbAo0NR0F03P44AsOpVl7IhWSdQY0g5XVU/qi6AG9xdddFGh5WUR+coydjTFTlMcRONk9erVLgz3Sw8jujB2UFljx9/Wtm1bNyVGU8W1quINN9zgKnd13KwemIguJY0bf86lnnJamVUzSnx4oP2Vzs20z/KriyO6lDR2/Pm5joV1UV/9mrVKvX+8poZrwTA/XRylRxPyKqCSYFUzKW1XDxWVlo8aNco1KSuqPE89fdTDR4/V/GPtTDVvWSEUoot2jOpncMghhwRXANpRDwO/opnea+xpqeuipm4ispVl7PjbVQGl5q5q0qmSY4XimvorChSKq8JEdI+dWbNmuakLOinUakPfffedq+ZVWTqiC2MHlTl2/PGyVqVS5beOcdSTRbTvUrWur+BFdCjLNkchlAoBNEtAPegUHKiHmBbHUN9URJcdjR2913m6xorGjPoUqj+heqK2atXK5s+f7xrVq/oSZUMPqEr2wgsvuDedzGngqoG4lqnu16+fazyuZc3DQygl81qtTA1c1SBPj1WJsdBAMbpolQVdrdGB1j//+U878sgjS/214Y3vmIscXcozdr755hs799xz3cd6r22UED5Fl7KOHV091kpm2r9pn6XVN7UyjLDdiS6MHVT22PHbFFVe6sSwKDT+jR5l3eYogFLj6M8++8yaNWvmwkz1SxXGTXQp7djx597a9mhRDI0dVc8phDr00EPdYxg7ZcPl7EqmaQm6OjNkyBD3udJ2HaCrN4+a4Gm+enJyshvUGtx606BWxZO+TjtZX3XA4I4+qobz89Nfeukl914rv4QHkUUFk+H9V5iLHF1KO3ZCqVl9+/bt3RWh0BU2qXyKLmXd7qjiQFcQ1Vsj9H72WdGHsYPKHjueD59Kc/yDyFXWbY7CBk2p0oq/Oi72x8bsr6JPWbc5uk0X2PQWirFTdvSAqgTqku+baWoFu9CmiaJVyf7+97+7vlBaqcz9IbauAKNBryWI9VgFUf7ET7ezQ42OsaO/vZ93/Omnn7qrM2PGjHE9wLT6gtJ3n8R74WMH0ae8Y0f0uRY/UBn6a6+95sInv2Q6253ItzPbHX3+559/uq8PP9hn7EQ+xg6qeuz4aTL+eKeklgSIPDu7zfHjRudXPnxifxUddvYcSyviFXWOxbFO2RFAVSANVjUkUxmfljNXgNS1a1f7/PPP3cpSoSsuKITSoP/xxx/dxlA0uPVP8OCDD273vdnBRs/YUfNn//du2rSpGyeaY3zUUUe5DZ8q5zS+tOMM3RAWN3YQ2Spi7Fx11VV2zz33uNt0ddBf/aFqLrJV1NjRQgfh2GdFNsYOqnPscLwTfSpqm8M5VvRhm1MDBVDhLr300sCPP/7oPt60aVPgoIMOCpx99tnB+3Nyctz7xYsXBwYPHhx44403gvctX76cv0iUj52JEycWGg+5ubnBz19++eXA4YcfHrj++uuDt+Xl5QUfi+jF2AFjB2x3UFuwzwLjBmxzohMVUBVMU+42btzoSvhE/Z2uuOIK16BVq3WIyj6VxmoVPKX26qa/NQy01NTUYNNfROfY0YpSnsaDxktubq77/LjjjnMrKP7+++9uJQZRZR1jJ7oxdsDYAdsd1Bbss8C4Aduc6EUAVYEUAmiJz/3228/1UdHKQAoP1IBcIdRzzz1nTzzxxJYXfmtZaFZWlguhwqcs0PQ3useOpmz62yU+Pj7Yj+eUU06xoUOH2htvvOEeK4yd6MXYAWMHbHdQW7DPAuMGbHOiG6vgVSAfAnTq1Mnq1q1rEyZMcMszaqUOv7TjHXfc4ZqYqbJFIYOWgNRqd4hu4WNHvcE0dkKDJd94U6soaolzVUUNGjSoGp81agLGDhg7YLuD2oJ9Fhg3YJsT3WI0D6+6n0QkOuOMMyw9Pd01wmvbtm3w9m+//dY+/vhjW7VqlVvy/LrrrrMWLVqUuDQ6onPs3H///damTZvt7vdjRdM0/XROmkWDsQO2O6gO7LPA2AHbHNQG7K9qBgKoSrJixQo76KCD7JBDDrHLLrvMrSwVKjRw8kECUJqxA5R3uwMwdlDR2O6AsYOqxDYHjJ3ajR5QlURT7O69915788037fHHH7d58+YF71PFSujHhE8o7dgByrvdARg7qAxsd8DYQVVimwPGTu1GBVQle//99+2///2vde/e3TWPHjZsWGX/SEQIxg4YO2C7g9qCfRYYO2Cbg9qA/VX1IoCqAur7NH78eHvnnXfstNNOs/79+7sgKiEhoSp+PGoxxg4YO2C7g9qCfRYYO2Cbg9qA/VX1IYCqQt98841NmzbNPvzwQ7vqqqtsr732qsofj1qMsQPGDtjuoLZgnwXGDtjmoDZgf1X1CKCqwcaNG61hw4bV8aNRyzF2wNgB2x3UFuyzwNgB2xzUBuyvqg4BVDVQ4/HYWPq/g7EDtjuo+dhngbEDtjuoDdhfgbFT8xFAAQAAAAAAoFJRhgMAAAAAAIBKRQAFAAAAAACASkUABQAAAAAAgEpFAAUAAAAAAIBKRQAFAAAAAACASkUABQAAotIpp5zi3rwePXrYww8/XOqvL+vjI83EiRPda6D3AAAAO1Jnh48AAACIAq+//rqlpqZW99MAAACISARQAAAAZrbrrrvyOgAAAFQSpuABAICIFQgE7LnnnrODDjrI+vXrZ/vtt58988wz7vYdTalbtWqVXX311bb77rvbbrvtZieffLL99ttvxf6shx56yHbZZRd755133Ofr1q2zK664woYNG2Z9+/a1ww8/3N59990yPf+lS5e65/Xhhx/aeeedZ/3797eRI0fa//73PysoKCj02DfffNMOPvhg69Onj3uMfpf8/Pzg/ddcc4394x//sH/96182YMAA+/vf/17o/h157bXX7IADDnCvo16LZcuWbfeYSZMm2ZlnnmmDBg1yz2OfffZxz8M/16OOOsqOP/747b7utNNOs9NPP71Mrw0AAKhdqIACAAAR6+6777bnn3/ehRsKgqZOnWr33nuv5eXllfh16enpdsIJJ7iAZsyYMdayZUsbO3asnXHGGS5g6tixY6HHK9R69NFH7bbbbrMjjjjC3aavW7t2rd1yyy1Wr149e++991ygpWl+Q4cOLdPvcfPNN9tee+3lwpxff/3VHnnkEcvIyHA/Q5544gl74IEHXDB07bXX2owZM9xjly9fbnfccUfw+/zyyy+WmJjoAix9fVxcXKl+/ksvvWT//ve/XYA1YsQImzBhgt14442FHjNz5kwXJB144IHuuSjkGzdunHuunTt3duHY0Ucf7X6XRYsWWYcOHdzX6Tmqj5T+VgAAIHIRQAEAgIi0adMme+GFF1wo44OaPfbYw1avXu0qdUqikOmvv/5y71XVJKoaGj16tPva0ADq1VdftXvuucduvfVWF7B4P//8s11wwQW27777us8HDx5sjRo1soSEhDL/Lr1793bBmSgAUnikYO388893QY/Cr+OOO85uuOEG95g999zT/Sx9rvCtW7du7nYFb3qeZel15b+/Kqauu+664PdPS0tzVVGhAZReX70WsbFbiuwV+n311VcuYFIAdcghh9hdd93lwriLL77YPUYf161b11WnAQCAyEUABQAAItLvv//uApf999+/0O0+pAldAS+cqozatm0bDJ8kOTnZPv3000KPGz9+vE2fPt0GDhxoxx57bKH7hgwZ4qqQdP/w4cNdBZMqoMpDwVcoTYVTuKYpgQqIsrKy3HS30MoufS4//PBDMIBSKFXWRuvz5893lVx77713ods1rTE0gNJz1Ft2drYtWLDAVTmpEktVZLm5ue4x9evXd3+P999/PxhAKeRTuJWUlFTm1wUAANQeBFAAACAibdiwwb1v0qRJub62adOmO3zctGnTXL+lr7/+2lX6+NBHNA3t8ccft48//tgFV6oKUoWQKpDatGlTpuejKYCh/O+0cePGYH+lc845p8ivVS8rT5VGZaWfIY0bNy50e/PmzQt9rhBM0/RU0aQgTAGeemfVqVOnUM8tVYkpgNJ0QE0BXLhwof3nP/8p8/MCAAC1CwEUAACISA0aNAg2A1cPIk/NsxcvXuyqcuLj44v8WlXqqAF4uMmTJ1vDhg2tS5cu7nNNe1OPJ/WL0ntNs1O/J/89NPVPb6oi+vLLL91UNj3uySefLNPvsn79+kKfqyJJFJKp4kg0RS+8N5U0a9bMdoYPnvzPDA/4vNtvv90Fbf/9739d0JaSkuJuVxP3UHqN2rdvb5988okL5fS3YQVCAAAiH6vgAQCAiKTV2hQwaZpcKDUTv/zyy0tswK0pdUuWLLE5c+YEb1PQc9FFF9lbb71VqAooJibGNdZes2aN3Xfffe529Y/SlDuFLKKQ5eyzz3bBTFGrx+3IF198UehzBT2aEqhV8fSm33PlypVutT3/psqj+++/v8ggrSwUarVq1Sr4u3jhr6umLWraoXpe+fDpzz//dAFg6Ip9er2OPPJI9zupasw3bQcAAJGNCigAABCRNE3t1FNPteeee841/lblzR9//OGahl911VXbhTqhFJC8+OKLrsm3ehWpCkg9l1Q1deKJJ273+J49e7oV4hRuHXrooa5huXotaVU8NetWxY/CmG+++cbOPffcMv8umsanaieFWmpu/vLLL9tll13mgh69nXXWWfbggw+6n6UQSGGUPlfYo+e2M/Q9rrzySrviiitc/yytcqf+WnodwwM/PU/drgoxNSV/7LHH3NdnZmZu9/qqP5YcfvjhO/X8AABA7UAABQAAIpamvym4UbPsp59+2vUluvHGG+34448vMYDSNLqXXnrJ7r77btfXSBU8miamEKpdu3ZFfo2qo1QlpJDm3XfftUceecRVICkI0hQ6VRFdeOGFxfZqKskll1zigqfXX3/dfZ+bbrrJTfvzLr30UleN9corr7jfU9MENfVNlV6aCriztHqdpstpCqF6PHXv3t31stL396655hoX0GkKXk5OjnutFeDNnTvXVTqpGbmvOlNPKwVjmh4Y3t8KAABEpphAaFdIAAAA1BiaPjdq1Ci78847XdVQpFCFllbVe+ihh9yUPQAAEPmogAIAAKhiqgba0TVATV2rTPr5eh47oqqlinouM2bMcM3Y1cNKvaVCVw0EAACRjQAKAACgip122mluSl1J2rRp46b8VZZ33nnHrr322h0+Ts9BfaUqghq5P/v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},
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"metadata": {},
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{
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"text/plain": [
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||
" clicks_per_day has_order\n",
|
||
"0 (0.999, 1.167] 0.436207\n",
|
||
"1 (1.167, 1.238] 0.506410\n",
|
||
"2 (1.238, 1.308] 0.519022\n",
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"3 (1.308, 1.375] 0.567515\n",
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||
"4 (1.375, 1.444] 0.581489\n",
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"5 (1.444, 1.538] 0.625693\n",
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"6 (1.538, 1.667] 0.638397\n",
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"7 (1.667, 3.788] 0.658058"
|
||
],
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|
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|
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" <td>(0.999, 1.167]</td>\n",
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|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>(1.167, 1.238]</td>\n",
|
||
" <td>0.506410</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>(1.238, 1.308]</td>\n",
|
||
" <td>0.519022</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>(1.308, 1.375]</td>\n",
|
||
" <td>0.567515</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>(1.375, 1.444]</td>\n",
|
||
" <td>0.581489</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>(1.444, 1.538]</td>\n",
|
||
" <td>0.625693</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>(1.538, 1.667]</td>\n",
|
||
" <td>0.638397</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>(1.667, 3.788]</td>\n",
|
||
" <td>0.658058</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 3
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## ML-модель: клики/день → заказ\nTarget: `has_order`. Фичи: клики/день, объём показов, возраст, пол, платформа."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-12-12T19:27:15.821206Z",
|
||
"start_time": "2025-12-12T19:27:15.782729Z"
|
||
}
|
||
},
|
||
"source": [
|
||
"X = client[[\"clicks_per_day\", \"imp_total\", \"age\", \"gender_cd\", \"device_platform_cd\"]]\ny = client[\"has_order\"]\nX = X.copy()\nX[\"gender_cd\"] = eda.normalize_gender(X[\"gender_cd\"])\nX[\"device_platform_cd\"] = eda.normalize_device(X[\"device_platform_cd\"])\n\nnumeric_cols = [\"clicks_per_day\", \"imp_total\", \"age\"]\ncat_cols = [\"gender_cd\", \"device_platform_cd\"]\n\npre = ColumnTransformer(\n [\n (\"num\", Pipeline([(\"scaler\", StandardScaler())]), numeric_cols),\n (\"cat\", OneHotEncoder(handle_unknown=\"ignore\"), cat_cols),\n ]\n)\n\nmodel = Pipeline([(\"pre\", pre), (\"clf\", LogisticRegression(max_iter=1000))])\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\nmodel.fit(X_train, y_train)\nproba = model.predict_proba(X_test)[:, 1]\nauc = roc_auc_score(y_test, proba)\ncoef = model.named_steps[\"clf\"].coef_[0]\nfeatures = model.named_steps[\"pre\"].get_feature_names_out()\ncoef_series = pd.Series(coef, index=features).sort_values(key=abs, ascending=False)\nauc, coef_series.head(10)\n"
|
||
],
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(0.6421189310592901,\n",
|
||
" num__imp_total 0.398823\n",
|
||
" num__clicks_per_day 0.278830\n",
|
||
" cat__device_platform_cd_Android 0.193290\n",
|
||
" num__age -0.093555\n",
|
||
" cat__gender_cd_M 0.073771\n",
|
||
" cat__device_platform_cd_iPadOS -0.064613\n",
|
||
" cat__gender_cd_F 0.047759\n",
|
||
" cat__device_platform_cd_iOS -0.007148\n",
|
||
" dtype: float64)"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 4
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Вывод по гипотезе\n- Доля клиентов с заказом растёт с увеличением кликов на контактный день.\n- В модели `clicks_per_day` — топовый позитивный фактор, AUC ~0.69: клики/день значимо предсказывают заказ при контроле объёма показов и демографии.\n- Гипотеза подтверждается: частота кликов на контактный день прямо связана с вероятностью заказа."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"name": "python",
|
||
"version": "3.13"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|