From eda3adb9c295333c0709fe15e1f21dbf20f0f196 Mon Sep 17 00:00:00 2001 From: Jess Date: Tue, 26 Aug 2025 18:14:43 +0200 Subject: [PATCH 1/5] new stuff --- notebooks/project_starter.ipynb | 2078 ++++++++++++++++++++++++++----- scripts/Imputing.py | 28 + scripts/Scaling.py | 31 + scripts/encoding_func.py | 32 + scripts/tache0.1.py | 0 scripts/tache0_alex_gueydan.py | 176 --- 6 files changed, 1878 insertions(+), 467 deletions(-) create mode 100644 scripts/Imputing.py create mode 100644 scripts/Scaling.py create mode 100644 scripts/encoding_func.py delete mode 100644 scripts/tache0.1.py delete mode 100644 scripts/tache0_alex_gueydan.py diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb index 58b2214..e61e1eb 100644 --- a/notebooks/project_starter.ipynb +++ b/notebooks/project_starter.ipynb @@ -23,10 +23,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 143, "metadata": {}, "outputs": [], "source": [ + "os.chdir(r'c:\\\\Users\\\\jessi\\\\Documents\\\\OFFProject\\\\scripts')\n", + "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..'))) \n", + "sys.path.append(\"../../\")\n", + "\n", "\n", "import os\n", "import sys\n", @@ -34,34 +38,38 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", - "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..'))) \n", - "from scripts import Data_filter_Jess as dfj\n", "\n", - "#os.chdir(r'c:\\\\Users\\\\jessi\\\\Documents\\\\OFFProject\\\\')" + "from scripts import Data_filter_Jess as dfj\n", + "from scripts import encoding_func \n", + "from scripts import Imputing\n", + "from scripts import Scaling\n", + "from encoding_func import *\n", + "from Scaling import *\n", + "from Imputing import *\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 144, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_21628\\3952692224.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " df = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n" + "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_2260\\1388454853.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n" ] } ], "source": [ "path = \"https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz\"\n", - "df = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)" + "df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 145, "metadata": {}, "outputs": [ { @@ -768,6 +776,11 @@ "rawType": "float64", "type": "float" }, + { + "name": "psicose_100g", + "rawType": "float64", + "type": "float" + }, { "name": "starch_100g", "rawType": "float64", @@ -783,6 +796,21 @@ "rawType": "float64", "type": "float" }, + { + "name": "isomalt_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "maltitol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sorbitol_100g", + "rawType": "float64", + "type": "float" + }, { "name": "fiber_100g", "rawType": "float64", @@ -1124,7 +1152,7 @@ "type": "float" } ], - "ref": "10afa48f-f9b4-41cc-84df-75656488a697", + "ref": "7c77a633-d656-4b66-8f7f-1e287ed93561", "rows": [ [ "0", @@ -1337,6 +1365,10 @@ null, null, null, + null, + null, + null, + null, null ], [ @@ -1486,6 +1518,10 @@ null, null, null, + null, + null, + null, + null, "17.0", null, null, @@ -1699,6 +1735,10 @@ null, null, null, + null, + null, + null, + null, "7.1", null, null, @@ -1958,6 +1998,10 @@ null, null, null, + null, + null, + null, + null, "0.0113351004464306", null, null, @@ -2122,6 +2166,10 @@ null, null, null, + null, + null, + null, + null, "2.2", null, null, @@ -2193,7 +2241,7 @@ ] ], "shape": { - "columns": 210, + "columns": 214, "rows": 5 } }, @@ -2362,7 +2410,7 @@ " \n", " \n", "\n", - "

5 rows × 210 columns

\n", + "

5 rows × 214 columns

\n", "" ], "text/plain": [ @@ -2408,10 +2456,10 @@ "3 NaN \n", "4 NaN \n", "\n", - "[5 rows x 210 columns]" + "[5 rows x 214 columns]" ] }, - "execution_count": 3, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -2436,7 +2484,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 146, "metadata": {}, "outputs": [ { @@ -2449,7 +2497,119 @@ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n" ] - }, + } + ], + "source": [ + "filtered_df = dfj.filter_nutriscore_data(df)\n", + "cat_df = dfj.categorical_filter(filtered_df, cat_keep= True)\n", + "num_df = dfj.numerical_filter(filtered_df, num_drop= True)\n", + "final_df = dfj.final_df(cat_df, num_df)\n", + "final_df.head()\n", + "target = df['nutriscore_score']\n", + "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.2 Enconding " + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['unknown', 'Beverages', nan, 'Fruits and vegetables',\n", + " 'Composite foods', 'Sugary snacks', 'Cereals and potatoes',\n", + " 'Salty snacks', 'Fat and sauces', 'Milk and dairy products',\n", + " 'Fish Meat Eggs', 'Alcoholic beverages'], dtype=object)" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['pnns_groups_1'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " pnns_groups_1 PNNS_pro\n", + "6 unknown NA\n", + "9 unknown NA\n", + "11 Fruits and vegetables Plant_based\n", + "12 unknown NA\n", + "14 Composite foods Processed\n", + "... ... ...\n", + "9946 Composite foods Processed\n", + "9955 Milk and dairy products Animal_based\n", + "9961 Sugary snacks Snacks\n", + "9970 Sugary snacks Snacks\n", + "9990 Fat and sauces Processed\n", + "\n", + "[1208 rows x 2 columns]\n" + ] + } + ], + "source": [ + "pnns_mapping = {\"unknown\" : 'NA', \n", + " 'Beverages' : 'Drinks',\n", + " 'nan' : 'NA',\n", + " 'Fruits and vegetables' : 'Plant_based',\n", + " 'Composite foods' : 'Processed',\n", + " 'Sugary snacks' : 'Snacks',\n", + " 'Salty snacks' : 'Snacks',\n", + " 'Cereals and potatoes' : 'Plant_based',\n", + " 'Fat and sauces' : 'Processed', \n", + " 'Milk and dairy products' : 'Animal_based',\n", + " 'Fish Meat Eggs' : 'Animal_based',\n", + " 'Alcoholic beverages' : 'Drinks'\n", + "}\n", + "\n", + "final_df['PNNS_pro'] = final_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n", + "print(final_df[['pnns_groups_1', 'PNNS_pro']])" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "filtered_df = one_hot_encode_column(final_df, 'PNNS_pro')\n", + "filtered_df.head()\n", + "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### maybe we should reduce pnns_groups by : plant_based; animal; processed" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ { "data": { "application/vnd.microsoft.datawrangler.viewer.v0+json": { @@ -2459,35 +2619,10 @@ "rawType": "int64", "type": "integer" }, - { - "name": "categories", - "rawType": "object", - "type": "string" - }, - { - "name": "pnns_groups_1", - "rawType": "object", - "type": "string" - }, - { - "name": "pnns_groups_2", - "rawType": "object", - "type": "string" - }, - { - "name": "brands_tags", - "rawType": "object", - "type": "unknown" - }, - { - "name": "ingredients_analysis_tags", - "rawType": "object", - "type": "unknown" - }, { "name": "code", - "rawType": "int64", - "type": "integer" + "rawType": "float64", + "type": "float" }, { "name": "additives_n", @@ -2500,27 +2635,27 @@ "type": "float" }, { - "name": "environmental_score_score", + "name": "energy_100g", "rawType": "float64", "type": "float" }, { - "name": "last_image_t", + "name": "fat_100g", "rawType": "float64", "type": "float" }, { - "name": "energy_100g", + "name": "saturated-fat_100g", "rawType": "float64", "type": "float" }, { - "name": "fat_100g", + "name": "trans-fat_100g", "rawType": "float64", "type": "float" }, { - "name": "saturated-fat_100g", + "name": "cholesterol_100g", "rawType": "float64", "type": "float" }, @@ -2554,106 +2689,102 @@ "rawType": "float64", "type": "float" }, + { + "name": "vitamin-a_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-c_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "calcium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "iron_100g", + "rawType": "float64", + "type": "float" + }, { "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", "rawType": "float64", "type": "float" }, { - "name": "nutrition-score-fr_100g", + "name": "PNNS_pro_Animal_based", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Drinks", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_NA", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Plant_based", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Processed", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Snacks", "rawType": "float64", "type": "float" } ], - "ref": "ddb52a72-2f10-4174-87ba-1958fb6c0741", + "ref": "e41ab0a0-0d29-4939-a11a-44b60451cee2", "rows": [ - [ - "5", - "Hazelnut spread", - "Sugary snacks", - "Sweets", - "xx:tai-shan", - "en:palm-oil-content-unknown,en:vegan,en:vegetarian", - "3", - "0.0", - "20.0", - "31.0", - "1750615523.0", - "255.0", - "0.0", - "0.012", - "6.1", - "1.5", - "2.2", - "9.0", - "19.0", - "7.6", - "0.0", - "20.0" - ], [ "6", - "Nutrition drink mix", - "unknown", - "unknown", - "xx:pg-tips,xx:green-organic", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - "4", + "4.0", "0.0", "15.0", - null, - "1748094869.0", "2401.0", "12.0", "10.5", + "0.0", + "0.0", "13.0", "9.0", "36.0", "23.0", "0.3", "0.12", + null, + null, + null, + null, "0.0", - "15.0" - ], - [ - "8", - "Plant-based foods and beverages, Plant-based foods, Breakfasts, Cereals and potatoes, Cereals and their products, Breakfast cereals, Flakes, Cereal flakes, Condiment", - "Cereals and potatoes", - "Breakfast cereals", - "xx:roger-s", - "en:palm-oil-free,en:vegan,en:vegetarian", - "5", "0.0", - "11.0", - "38.0", - "1746375622.0", - "1620.0", - "1.6", - "0.2", - "6.7", - "1.3", - "2.9", - "82.0", - "1.7", - "0.68", - "50.0", - "11.0" + "0.0", + "0.0", + "0.0", + "0.0", + "1.0" ], [ "9", - "Boissons et préparations de boissons, Boissons, en:cinnamon roll", - "unknown", - "unknown", - null, - null, - "6", + "6.0", null, "4.0", - null, - "1749357657.0", "1520.0", "11.0", "2.0", + "0.0", + "0.01", "25.0", "0.98", "9.0", @@ -2661,41 +2792,110 @@ "0.95", "0.38", null, - "4.0" + null, + null, + null, + null, + "0.0", + "0.0", + "0.0", + "0.0", + "1.0", + "0.0" ], [ "11", - "Aliments et boissons à base de végétaux, Aliments d'origine végétale, Aliments à base de fruits et de légumes, Fruits et produits dérivés, Produits déshydratés, Aliments à base de plantes séchées, Fruits secs", - "Fruits and vegetables", - "Dried fruits", - "xx:mg-ricarica", - "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown", - "7", + "7.0", "0.0", "4.0", - null, - "1748262414.0", "4.0", "1.0", "1.0", + null, + null, "1.0", "1.0", "1.0", "1.0", "1.0", "0.4", + null, + null, + null, + null, "0.0", - "4.0" - ] - ], - "shape": { - "columns": 21, - "rows": 5 - } - }, - "text/html": [ - "
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" + ], + "text/plain": [ + " code additives_n nutriscore_score energy_100g fat_100g \\\n", + "6 4.0 0.0 15.0 2401.0 12.0 \n", + "9 6.0 1.8 4.0 1520.0 11.0 \n", + "11 7.0 0.0 4.0 4.0 1.0 \n", + "12 8.0 1.0 6.0 1510.0 2.0 \n", + "14 9.0 0.6 -11.0 293.0 0.5 \n", + "\n", + " saturated-fat_100g trans-fat_100g cholesterol_100g carbohydrates_100g \\\n", + "6 10.50 0.000000 0.000000 13.0 \n", + "9 2.00 0.000000 0.010000 25.0 \n", + "11 1.00 0.129619 0.011300 1.0 \n", + "12 0.50 0.112994 0.016745 6.7 \n", + "14 0.06 0.129619 0.009760 2.0 \n", + "\n", + " sugars_100g ... vitamin-c_100g calcium_100g iron_100g \\\n", + "6 9.00 ... 0.058186 0.176374 0.010165 \n", + "9 0.98 ... 0.058186 0.171798 0.008665 \n", + "11 1.00 ... 0.058186 0.196138 0.015799 \n", + "12 1.70 ... 0.071429 0.178571 0.008929 \n", + "14 0.24 ... 0.090000 0.171798 0.008665 \n", + "\n", + " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", + "6 0.000000 \n", + "9 20.400223 \n", + "11 0.000000 \n", + "12 0.000000 \n", + "14 20.000291 \n", + "\n", + " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA PNNS_pro_Plant_based \\\n", + "6 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "11 0.0 0.0 0.0 1.0 \n", + "12 0.0 0.0 1.0 0.0 \n", + "14 0.0 0.0 1.0 0.0 \n", + "\n", + " PNNS_pro_Processed PNNS_pro_Snacks \n", + "6 0.0 1.0 \n", + "9 1.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "14 0.0 0.0 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5)\n", + "imputed_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 2.4 Scaling" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": shape of df with only numeric features=(2298, 24)\n" + ] + } + ], + "source": [ + "\n", + "work_df = scaler_numeric(imputed_df, 'nutriscore_score')" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.microsoft.datawrangler.viewer.v0+json": { + "columns": [ + { + "name": "index", + "rawType": "int64", + "type": "integer" + }, + { + "name": "code", + "rawType": "float64", + "type": "float" + }, + { + "name": "additives_n", + "rawType": "float64", + "type": "float" + }, + { + "name": "nutriscore_score", + "rawType": "float64", + "type": "float" + }, + { + "name": "energy_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "saturated-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "trans-fat_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "cholesterol_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "carbohydrates_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sugars_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fiber_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "proteins_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "salt_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "sodium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-a_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "vitamin-c_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "calcium_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "iron_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Animal_based", + "rawType": "float64", + "type": "float" + }, + { + "name": "PNNS_pro_Drinks", + "rawType": "float64", + "type": "float" + }, + { + "name": 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" + ], + "text/plain": [ + " code additives_n nutriscore_score energy_100g fat_100g \\\n", + "6 -0.000011 -0.500 15.0 1.610997 -0.044477 \n", + "9 -0.000011 0.625 4.0 0.315789 -0.102999 \n", + "11 -0.000011 -0.500 4.0 -1.912967 -0.688222 \n", + "12 -0.000011 0.125 6.0 0.301088 -0.629700 \n", + "14 -0.000011 -0.125 -11.0 -1.488092 -0.717484 \n", + "\n", + " saturated-fat_100g trans-fat_100g cholesterol_100g carbohydrates_100g \\\n", + "6 1.090501 0.000000 -1.000000 -0.290718 \n", + "9 -0.498878 0.000000 0.063830 -0.061053 \n", + "11 -0.685864 0.129619 0.202137 -0.520383 \n", + "12 -0.779357 0.112994 0.781344 -0.411292 \n", + "14 -0.861631 0.129619 0.038326 -0.501244 \n", + "\n", + " sugars_100g ... vitamin-c_100g calcium_100g iron_100g \\\n", + "6 0.101640 ... -0.100514 0.578616 0.000000 \n", + "9 -0.280702 ... -0.100514 0.541098 -0.189521 \n", + "11 -0.279748 ... -0.100514 0.740648 0.711839 \n", + "12 -0.246377 ... 0.099131 0.596629 -0.156182 \n", + "14 -0.315980 ... 0.379107 0.541098 -0.189521 \n", + "\n", + " fruits-vegetables-nuts-estimate-from-ingredients_100g \\\n", + "6 -0.386901 \n", + "9 0.021101 \n", + "11 -0.386901 \n", + "12 -0.386901 \n", + "14 0.013102 \n", + "\n", + " PNNS_pro_Animal_based PNNS_pro_Drinks PNNS_pro_NA PNNS_pro_Plant_based \\\n", + "6 0.0 0.0 -0.5 0.0 \n", + "9 0.0 0.0 -0.5 0.0 \n", + "11 0.0 0.0 -0.5 5.0 \n", + "12 0.0 0.0 2.0 0.0 \n", + "14 0.0 0.0 2.0 0.0 \n", + "\n", + " PNNS_pro_Processed PNNS_pro_Snacks \n", + "6 -1.0 4.0 \n", + "9 1.5 -1.0 \n", + "11 -1.0 -1.0 \n", + "12 -1.0 -1.0 \n", + "14 -1.0 -1.0 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "work_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3. Predicting the nutriscore" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 3.1 : Using random forest regression" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:528: FitFailedWarning: \n", + "540 fits failed out of a total of 1620.\n", + "The score on these train-test partitions for these parameters will be set to nan.\n", + "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", + "\n", + "Below are more details about the failures:\n", + "--------------------------------------------------------------------------------\n", + "240 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n", + " estimator._validate_params()\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n", + " validate_parameter_constraints(\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n", + " raise InvalidParameterError(\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "300 fits failed with the following error:\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n", + " estimator.fit(X_train, y_train, **fit_params)\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n", + " estimator._validate_params()\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n", + " validate_parameter_constraints(\n", + " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n", + " raise InvalidParameterError(\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", + "\n", + " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", + "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan -15.43366587 -11.6164328 -11.27435849\n", + " -14.5820768 -11.23554016 -11.36335203 -15.76857583 -12.44221896\n", + " -12.12038863 -14.66678277 -11.81629708 -11.61178998 -15.03447951\n", + " -12.10550901 -11.79386245 -15.61337517 -12.82035289 -12.67222122\n", + " -17.47154562 -13.5015248 -13.25917603 -17.47154562 -13.5015248\n", + " -13.25917603 -14.9627059 -13.17550421 -13.18977974 -15.43366587\n", + " -11.6164328 -11.27435849 -14.5820768 -11.23554016 -11.36335203\n", + " -15.76857583 -12.44221896 -12.12038863 -14.66678277 -11.81629708\n", + " -11.61178998 -15.03447951 -12.10550901 -11.79386245 -15.61337517\n", + " -12.82035289 -12.67222122 -17.47154562 -13.5015248 -13.25917603\n", + " -17.47154562 -13.5015248 -13.25917603 -14.9627059 -13.17550421\n", + " -13.18977974 nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan -15.91594522 -12.16781458\n", + " -12.06318706 -16.250399 -11.91238305 -12.01762806 -16.13582335\n", + " -12.50536945 -12.63605136 -15.5718352 -12.82225453 -12.11801577\n", + " -14.72627438 -12.28481043 -12.25241906 -16.73890135 -12.97095\n", + " -13.08337804 -16.24138596 -13.48690631 -13.3459846 -16.24138596\n", + " -13.48690631 -13.3459846 -16.35859837 -13.62628501 -13.44813366\n", + " -15.91594522 -12.16781458 -12.06318706 -16.250399 -11.91238305\n", + " -12.01762806 -16.13582335 -12.50536945 -12.63605136 -15.5718352\n", + " -12.82225453 -12.11801577 -14.72627438 -12.28481043 -12.25241906\n", + " -16.73890135 -12.97095 -13.08337804 -16.24138596 -13.48690631\n", + " -13.3459846 -16.24138596 -13.48690631 -13.3459846 -16.35859837\n", + " -13.62628501 -13.44813366 nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan -15.91293058\n", + " -11.5947586 -11.45373592 -14.44348788 -11.32279156 -11.47162727\n", + " -15.49815865 -12.46546805 -12.09982189 -14.27964486 -11.78431829\n", + " -11.5571342 -14.91128126 -12.11213898 -11.79477435 -15.61337517\n", + " -12.82248465 -12.66730308 -17.47154562 -13.5015248 -13.25917603\n", + " -17.47154562 -13.5015248 -13.25917603 -14.9627059 -13.17550421\n", + " -13.18977974 -15.91293058 -11.5947586 -11.45373592 -14.44348788\n", + " -11.32279156 -11.47162727 -15.49815865 -12.46546805 -12.09982189\n", + " -14.27964486 -11.78431829 -11.5571342 -14.91128126 -12.11213898\n", + " -11.79477435 -15.61337517 -12.82248465 -12.66730308 -17.47154562\n", + " -13.5015248 -13.25917603 -17.47154562 -13.5015248 -13.25917603\n", + " -14.9627059 -13.17550421 -13.18977974 nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " nan nan nan nan nan\n", + " -15.43366587 -11.6164328 -11.27435849 -14.5820768 -11.23554016\n", + " -11.36335203 -15.76857583 -12.44221896 -12.12038863 -14.66678277\n", + " -11.81629708 -11.61178998 -15.03447951 -12.10550901 -11.79386245\n", + " -15.61337517 -12.82035289 -12.67222122 -17.47154562 -13.5015248\n", + " -13.25917603 -17.47154562 -13.5015248 -13.25917603 -14.9627059\n", + " -13.17550421 -13.18977974 -15.43366587 -11.6164328 -11.27435849\n", + " -14.5820768 -11.23554016 -11.36335203 -15.76857583 -12.44221896\n", + " -12.12038863 -14.66678277 -11.81629708 -11.61178998 -15.03447951\n", + " -12.10550901 -11.79386245 -15.61337517 -12.82035289 -12.67222122\n", + " -17.47154562 -13.5015248 -13.25917603 -17.47154562 -13.5015248\n", + " -13.25917603 -14.9627059 -13.17550421 -13.18977974]\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Meilleurs paramètres trouvés : {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n", + "MSE : 15.536399122952776\n", + "R² : 0.8255571826701679\n" + ] + } + ], + "source": [ + "### Random forest \n", + "\n", + "X = work_df.drop(\"nutriscore_score\", axis=1) \n", + "y = work_df[\"nutriscore_score\"]\n", + "\n", + "from rf import *\n", + "best_rf, X_test, y_test, y_pred = random_forest_GS(X, y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'plot_learning_curve' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[170], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mplot_learning_curve\u001b[49m(best_rf, X, y)\n\u001b[0;32m 2\u001b[0m plot_mse(best_rf, X_train, X_test, y_train, y_test)\n", + "\u001b[1;31mNameError\u001b[0m: name 'plot_learning_curve' is not defined" + ] + } + ], + "source": [ + "\n", + "plot_learning_curve(best_rf, X, y)\n", + "plot_mse(best_rf, X_train, X_test, y_train, y_test)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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4YeSQUFATOk1NQ53LifokV+A0g+eX6nartzc/rweqUlNQmtOQ0a1JS4VumMiqqYFX0lGaBp/dDq0ycOweK4thmoHr7EI9uiCyvNQhQY1l7mpg7ipgCDvBJSo2DyAiorikZEs6f0l2Y3NJCZhkHST7Ej7pXuaqRJdfCQmArKBGMkVVVVWq/GrnnXdW22bPnh3zHClTiw4O5D1YJXClpaVoK/X19aEALZq1zdrH+j3evkIaFYTv++uvv6qStujrJnNyrPebKOSaS0mjRbJl8lmGZ5/kbyOclNLFu7+uxtzy0xO08NFQZBlVbLCwGa9jCZaDtUj0OTWmFdeVqXM6YwaOUs61SaGSNsnWqNSQKneL90xNskfy34zM+H/LDp8vtBiq9U6X5XVBjdMFn2ZDbWpyzHNqUwL/LXFKzV2guBAeLfDvze5rOAsHPDGldjHWlTX7b7clpDCwKTeKxMCGiIjiuvLKK9Ug9KSTTlKT3u+8806VKZH5L80l82BEr169Yh6Lt01ImZpkWfbcc0/sv//+arK9nIeorIxdtlzKzKJJ9uLss89Wc3UOOeQQnHbaaWpuipR+tSYrKJPBTzRrW3g2Sn6Pt6+QwCB8X2lIIA0MrMYDFinNa6ztdHvJyclRgZlFAi8pQbRIMCdzp8JFZ5ys+2P7a40EHdYtcj5Li8Z8VimYEXW88ExPaFvYyNyM85y4pBwrKrix5vdEP1WVupnxsz+NCd8l3rnEO0Rj5xxnu1NNZIp0xuzfwp4T9Xzr/FVmy8raBH5W6xoqJCsUxm6YKMrJwvKCXPRfXRhzDmpeTmk57L7AedjkfGRekdOJd3Y+EHn1lbAHg5eI48r+8tzC8kDVot9EWW6gbHNDZwmgAq9Tg3TUIrLhiD98qJziAg7codl/u9R2WIpGRERx7bfffmqOjUxynz59OqZMmaLKoHbaaSc8+eSTG+3iJFmWzfHDDz+o9scyf+Tqq69WTQtkICFBlZSVyWTuaNFlaxaZjyLNBaTzmMzPkfcg83fOOOMMXHrppWgNVgmaVZIWziors0rShAQq8fa1jhG+77Zqv546nhkD3PSbgeJaa8AcNphWc2PCJvPL7/5NDeqjHrMm/ofH7tZjMuHeEdzHajKgjhFWjib7WA0KVDlXWPma3LeaCVgBlFU2ZgYnvKh5LcHMRr1PPaYl24IT88OCg02xzsl6T0Kdl0x6kQYGYVkoux7KooTIvuoLjGBJmnr/fmTXSS1gpCy3tHUOq8dTQV7YuVpBTdSpV+gaNug21GpAvmGg1GFHst9AT7cXL43eFfvNWoTc8iqUZDUEwnavF+m1dahOTlLBijQNsHn90P1+rMzMQWFaFtKrq9V+vuAcPM0wMHDZGvRfUYjVeZlweA3omgHDZke/ZWuRWV+CLklLUF2fC58rBcvy90K31VORibJQkJOCamhdsoEPr23a9ad2w8CGiIgalZmZicMOO0zdJJiQxSal65hM1N99990bzZ5IViHeN5fx2iPH2yaLVMo3/dLNLDxgWb48OKm3maT8TTJPcpMMiARH8j4kgyNZhS1NAhUJRmbNmhXzmLVt8ODBoW0SwEnnM+ngFt5AQO5LYBPeWU2yMhJk1tbWRmRtpFGCZMbCMyJbm/OG6+oWbUahgQo3MDQPWFUpndNMvDLbCjg2kukI7zWgAoew7RGPBSfkyDwX2S9q7kkooBJWMwMhgYw1Tcbnx8BcDUvLJP4JBjmyrw6ctr2Oe8Y4sabCQK0P2KmTBrffgfzUwOusrzKRlRRYS2ZZmYHFJQYOmhTnywMT6JVhYkgnXXU427Ub8OcqE0srAnHToFzgv6OdqPUFUkS7d9dVTCNr+uQlA8V1QJd0DbVeqJus77O4xI+PZvvQO9uB/v/XMNfLUlBTiWF5JmYVBufPWIGbYSLVZuDQ3jremx/b7tmj6/jrwhSkOjXc/04lqkv8GDnAgRNHZ6Bkgw+Tp2Zg9V91sAcDIwMmevRJRsbO/eAsdiO3kxOr1vth9/iQbHhQWmig2ONArcsBh88Pl7seuWWV6LuqECluL9Ztl42BeyRjp9MGwd+nK0o/mAdbrRdZEw6APashq6g+tpqz4a3xIbUgtqyNEhsDGyIiiptxkYFz+CBZMjSDBg1Sv0v7Zek6JqUY0iVNgh4rg7N69WpVshY90B8yZIgKiORxa56NDMbffPPNmNfXg98Sh5e9yWtI44HmkDkdEhjJXB6LBEy9e/dWWSgJylojsBEHH3ywygxJS2YrMJHrKp3b5LpKiV34vhLYyLWw1rER1rUJ76629957448//lCPhTcP+PDDD9X73ZoDm8bs2Kkh2MlLAV4+PHDb+1Uv/lgbaGvcKQ2odAODc4EJwzWsqDSxqAz4ax0wuhfwn901DJpkosJabzIUq5j46FibyhhluAD9f3EW7Ay2RK69xonj3/HgC4m/rYG8laXRdCy4IHIeVZXbRLJDkiaBfbumxW8l3Dm9ISiQ9WjkBsQu0Cmu3tuBibvGn68Vj8QiXTOCrx+czZ/qDNwkuuuaoWOfPjHT/EOcpoGfL0rHpR/U4rWZ3mDnMyA7WceaG9Px5QI/3lsQm+kRfxeZuHBPFyZdkxexvU8vYNSoNFx8rok1671IdukoyG/akPXvd1ejZGoJBo/pjB5jRsXdR65epwtHNnoMW6pD3ajjYWBDREQxJKiROSkyIJdgJjs7W2UD3nvvPdVS2Bqon3DCCXjqqadUSZc0BpDuXu+//z769esX0aJYyNo30pJYBuPSelnmW8g8GgluRHhp24EHHqjK0S644ALVblr2kaAofBJ9U0ybNg133323ahstc3kkwzFv3jxVjrb99turAKc5JBiSm5DjiHfeeSc0af+cc84J7Svzg7777jvcfPPNql22lKfJejVyXWRb+OKh0vhAAhZZs0aCE2kJLZkdOU8JanbccceIJgEffPCBuu4SJFrtnuW1ZH2ezS0D3Jr8enrzBqdTTjdx/c8+fLZYLduishhPHGTD0QPDAo54629Khy9dQ7JDQ0l9I1miOGVk6a6WT/6+bDfgkT9jt1+4c+sNyOO+dQliUnS8eloaXj0NWF3uV8FS5/TANTtkUOPvcXX5xufr2e0aenVvepAmRo7rDsiNtkkMbIiIKIZkOaTrlpQ8WWVPknWRgGb8+PGhOSQyeJeBuJSOSecx6Up2yy23qEF/dGAzcuRIVcr2xBNPqMU3JbMgLZslgDrrrLMiJplLBkNe84033lCT/WVfeW1ZB0eCnqYaMGCAajwg5ybto2XQL6Ve8h6kDK25JDslC4+GkzV1LOGBjSwiKhkmec8S/NTV1anr89///hcHHXRQzLH/97//qf0lcyPXU0rZJLCTaxNO5hpJUCPXRYI9CQ4lSJN5T9IKu7nBHzUYmKPhg7EtCAzCytFUh+ZQy+XW8/CRqZi6tga/rwyEG1L9NfOSJNia0mRgC6vzSuYp8LrdsyKzTinOxs/noEHMijSOHc9aQjPjzcAkIiJqI99//z2uu+46lVmRgIZaRoI26RwnQY4EU9Q6tP/zNjLmNGFe78So59z4e0P8Qal5Y/OyD5siC9m+8OJL8Jo6LphwRsyitVuaqR0bN2NTXvs+spMbH4hrV5XHOZiJL89PxyGD+B17PKZ2RpP208xXWv1cOhK2eyYiojYh36OFr2kipMRMyq9kXRbJ6FDTxMvKSAmgtOfedddd2+Wcthmb+CJ9eiNBTWvRNRMuvX3LD9ObE69Z36drMn+HWQnashgmExFRm5B1Wo488khVeibzXaQBgZRRyfwQKWmTUrf2IPOCNkXm0DTWTro9SHZLgsQddthBlabJfBwptZM5NmPHjm3v09tmSUnW1lwG09gcmy8WGThqu/iND2KElejt0KWJzyFqIgY2RETUJqQzmXQCk3khVjAhAY6UoY0bN67dzksCrU257bbbVFCWKCQr8+6776o5OTIXSbrTSVMBmZMT3pSA2pIGjwHITDF3qGV02Ho1W0HEU+1wIsMbuZDsLz23Q2ndxt9csh2oi1o389lxW7Ysb2tjNnGODXNekRjYEBFRm5ByMwkQEo00M9gU6fKWSI444gh1o8Ti1IFRXYDJ66I6o1nr4HRwEtR81n84xiybA5vhx289BuHYcZdhYXjXuDgq/puJne6vwJwiwK4Bdx3qxLm7NazBRLSlMLAhIqJtGuek0JbQLRWqM9jS8q34a/TT98XOH/yD8Uedj+/6DsV2xevw2XfPIu/+Wzb6NIdNw+zrstrsNGnbxcCGiIiIaDPtWhBYcHOdtcDn1hjcvHIZOuERvPHak4HyupF9gen3tvdZbaW2xj+g1sfAhoiIiGgz7VgAuMObk4WvZWOaSNpaxqmvXBa4ESUgtnsmIiIiaqL8uM3xTFy1p468FA2H9gsOrWRKjWEGbibwxtFtfKJE2yAGNkRERERNNOfc2KFT30wNKc7ABPo3jrbh0L6Rj4/bTsPYwewCRs2hNfFG4ViKRkRERNRE+ak2rL9Ew3Ef+FWjgMtHAdfu7gg9npWk4YuTAkHM2ioTucmAS1qBEVGrY2BDRERE1AydUnX8dvqmi166pjOgIWpLLEUjIiIiIqIOjxkbIiIiIqIEYnL+TIswY0NERERERB0eAxsiIiIiIurwGNgQERERbQGmaaLOKwvYEG0utntuCc6xISIiImomw2/i7P8V4VtPCjQb0CXfhmnrZaBpAjYN7x1nw3FDuXYNUVtixoaIiIiomQ67ZDW+qE1Ctw1VSC6tw/TVBuCTmwm4DRz/vh8V9czeELUlBjZEREREzfDIL/Xw+oCTFq7FbhsqcOiaEhyzqgiaGRbIeAxc8IWnPU+TaJvDwIaIiIioGd75vhqDy6ojtnWrc6NPdV3DBhP4czUzNtQyZhNvFImBDREREVEz6G533AFUt7r6sJ00lNZgq+VfX4HabjehRr8E1RnXwDd5cXufEhEDGyIiIqLmGJltxN3u04JdquSHTUOlH1ut+i43QVtbAd00Yauqg2evh+Gvcbf3adE2joENERERUTPsnh8oAvLqGqqcDQ1mV2SmAg4dcNgACXI0oNLdMQqGfv+6BI/csBi/flG8yX3rn/olZgApsVz5qPtb7fy2PWz33BJs90xERETUDJpNwz+ds/BX9xx4bTbk1rrRp6gSa9OSI3c0gAq3iQxXYg9Arz91NjzB6UErFqzHp6+sx71vbd/o/p57v4Mjznb7/PWtd5JETcCMDREREVEzfF+ThN965augRpSkuDCtR17sjoaZ8AOtf/8sCwU1Fp8H+OPbxjM3jvr43d50mKivYCc4aj+J/u+NiIiIKKH8WhVn4U1bVFZGzT1xY3UVEtpbj6+Ju/2jF5qffXFDg9/bMUrvEp0JrUk3isTAhoiIiKgZVhlxAhsjakCvaXDAwA4FiTv49HkN1NfGf8y7scSLLEIahw4dqXmuLXNyRC3AwIaIiNrctGnTMGrUKHz66adNfs67776L4447Drvvvrt67tq1a1v1HIka45VvysMDGVmY0xPbKU1CmmRHYgY2z966BDeOmw0Y8Tu8bVS8CTaKDndhVF0bURti8wAionb2xhtvID09HUceeSQSRaKdkwRC99xzD/bdd1+ceeaZsNvtyM7ObvYx/v77b5xyyinqvbXEpEmTMH/+fHVbs2YNunTpstHgbPbs2XjyySfVT03TsMMOO2DixIkYNGhQzL4bNmzAY489ht9//x11dXXo27eveq+jR49u0bnSlvfxHA+u/sIDj+EIBDPW18M+A/DHZjHSsxqNACJ4vQbOuXE91pcZsGsmxu6XinNOzUFr+ee3UiyeWa2yShJ2Nbt4zBu/j7UJA/aMpr1notbAwIaIqJ29+eabaoCcKEFEIp7TX3/9pX7eeuutyMzMbNExJKh57rnn1HtqaWDzxBNPqNeXwKSqauOTJ2bNmoXzzz8f+fn56qd45513cO655+LFF19E//79Q/tWVFTgnHPOQWlpKU499VQUFBTgq6++wvXXX6/e81FHHdWi86WmKasz8MQ/BnbspOGIfoGGAHf/7MHdv/lQZwSyMzYT8EsL5yQpQzMbMjaqrXOc8MA0UaQ7oN1ZhyQbUG+zQ4eB3l4vjs9145yxmSjItiHTpeHQS9ch3W0gJfjUT7+vQXqajhOPzmqV9/vu/5YC9kA5ncRmfgnSrDV4msKMv68LftiSE2Noafr8MErqYOuUho4pMTN9iS4x/vqIiKhV+Hw++P1+uFwdu+69uDjQoamlQc2W8tFHH6F79+7q9xNOOEFlVhpz3333weFwqGBKAhUxZswYjBs3Dg899JAKkiwvvfSSygA9+OCD2GeffdS2o48+GuPHj8cjjzyisjYpKdawl7aka7734v6fPYFSMl0DpHRM1qER7mAAk6TDn2RvGPzrJvQ6H3ZaX4GeNXWodDnwd24myh32QOZGniPH8srOGuol5rGZMGw2LLXZcG+lE889XYeCGg/Sa+vRxW3Ao2uwScOBYCJo0idVWPB7CXLXVaHQr8M5OAfOzsno19OJsaPToOsa1q10o7IoB6nZFfDW+jD1zdWoNXV0H+DC8k9XwucFUvtnYofjeyCrcxLWTimGz2/EVp/FCWqSKqvx1PZfQPf7YeiAX7ehNiVJXZ/t0wZg/5J/Y55TgyR8mf8qMu1lsHv88CanI6VTMrQVXth8fvi7piBrSAZcq8qxcDXgqbch1fCjWzc/nItWIs1TDCd88NhSsDa/L1ypTiTXVMBZXg63zwWXvw7Z5hqkoFK9ng+pMOCErntg9O8MZ+F66BVVKsSUm7wr9VZ1Xf3u0x2w+b1yNxCG+hsuhKnpQF4aMKIf0LsztLP2hbbbwIY39+9yYHkRsM8Q4Je5wMs/AQM6AzcdD6Tz32aiYGBDRNRMbrdbDUS//vprFBYWqsFrp06dsMcee+Cyyy5T+3zzzTf48ssvsXDhQvUtvAxKd9xxR1xwwQUYMGBA6FgyV0SsW7cu9Lv45JNP0LVrV7XtiCOOwO233x5xDlL+dMcdd+Dpp58OPe+ZZ55Rg+i3334bH3/8Mb777jsVEEgplOyzpc5JzJ07V2Ud/vnnH9TW1qrszuGHHx4qEwv3008/4dlnn8Xy5ctV+Zi8n5122qlJ11rm0YRnK6zzGTFiROiYb731FqZPn47169erIK5Pnz44/vjjccwxx4SeJ9fvs88+U7+HH0+yJ1Y2pSmsoGZTVq1apa6RvJYV1Aj5/cADD1Sfn3w2eXmBFsHytyTHtoIaYbPZcOKJJ+K2227D5MmTVVBkWbRoER5++GHMnDlTBa177bUXrrjiChUAxft7ocbd/5MnsKhmqpSXBbubWQN9lw2o9QLhQY3QNOy3vhRDNwQG2KiuR++KWrzarws8ph7WJS34HDmu2wCcJlDnV8ctS0tWNxgZsPv88DntsBkG+pfUYPCGaqR4/Rjy/DS4PD70lL+pPzLx1EGj4Jth4JmvanBqbz/+/lVefyh03Y//+2wOapNdyC8swaKq6uDrmtB+Wod/31gOPdMF+/pKlOVkwpebHaqii1eGllFRjcFzV0CXTI4EBxpQkZWMyuQMGA47inILgBWxz5vbsw9Wde6CQm++uq97/eixuBTZlT6o4rWKGnjmlSALZRgOYGVyLopcmaheWI4dvAvloiEJFdD8QM76NViL7eCHC9XIQB3ky5lMlCEHfTAb6SiDE1XwIQ2m4YBr4aJQjsO68lZwY0VyDsMd2MEfmw/RTAOQz/PrfwIbnvkS5kWHQXt8AnDGo8BrPzccPPyiPfApMP0+YFjvTf6tUetjYENE1Ewy10MG+TKQl7IhGUzLQHbq1KmhfaTkSLILY8eOVYPX1atX48MPP8SECRPw2muvoWdPGaoA//nPf9S39FlZWTj77LNDz2/u/JFwt9xyixrsyrnJvA5r8Lylzum3337DNddcgx49euC0005DRkaGKruSwEqCJrk+lh9//BHXXnutCoik1EoG6zKol2M0hbymnI+cpwRR8rvIyckJzZuRoEYG9vIa9fX1KqC76667UFZWpjIe4thjj0VNTY06nyuvvFK9NxEe0G1Jc+bMUT9lTk20YcOGqb8fmacj5y0BTlFREQ499NC4+woJkqzAZuXKlepamqaJk046SZW6SeBzySWXtMp72ZqtqfQGghcJXOKRYCbFEZPR0P0GtisOBjVBq1KTYOg2wCbpgDilXTIYllSBwwSctrCDaSqoEX5dx4L8dGTVeTFi8ToV1Fh6bKjArovWYPLgnqipsoKaAMOwoSI1BSl1dci0gprg+Zs2Hc46N6qSXEhy2FGaG1nepoIA0wyM1YPn3H3VhlBQo07RBFxuHwx74LzrHM5GmirY4PQ2nLNkoLIqg8FEUC2S4IYdLvjQra4Uxa4MFKZmwyh3IBVlof1cqEM+lmMtBgWDmsC5+eDEcgzB9vhdNTy2oRYGXHELtza7mOupL4E9BjYENfEiQZ8fGP8EMO0+bEls5dwyDGyIiJpJMhCSnZGMSWNkEnhycuQq5BIIycR1mZgvcyfEYYcdhqeeekoN1OX3LSEtLU1laaIzJ1vinCRbdeedd2L77bdX+1ivId3KJEiQEiur45kEfPfff78KfF5++eVQMCH7yoC8KeR85RymTJmiApvo85Hzl+xMOHk/koWSrNrpp5+uzlECDJnTIoHNfvvtF8o8tXbpnAQd0axtEsxYTQMa29fK9lj7CvlsJUh7/vnnVcZNSGbnhhtuwLx585BIJDOYmpoaKoWsrq5WAZk1x8nj8ai5Srm5uaHnSKZQMoCN3ZfMnGRIJWjf3NeYLZdeAptmMqOClgqHHd92z2/Y3th8Fa3x1+tZVotulXXw2HRUuuwYsji261/3kkAwk+7zxTm2Bqc7To9mORfTgG6YcCc5YUpZVlhHNy0YgPjCztsRFpyE9jMbHk9upBd0ijcyiLH7jLjDcz/kGvhggwmH4YfbJg0HYruzJaMKfpVbijyKF0nwIEkFPxIC6GidRUEl4MPkJvybWrGhSX+71PrY7pmIqAWBw9KlS7F48eJG97ECCBlgyUCrvLxcZR969eqlOmS1JhnYRwc1W+qcZBJ/SUmJmoBvHcO67bnnnqF9hAyypVRPyrGsoMa6fhLcbAnhgZoEXXIelZWV2G233dTgX0rV2oNkjoTTGfvNtjUAt/bZ2L7WNmsfCRYlOzN06NBQUGORDF2ikeA4fH6XfPbhjRvk/YUHHCJ6IBh9v3PnzqGgZnNfwyM1Vi1g6hoW5GWE7q9OS4oJdmI49UYDnv7F1RixrgKdajzoUVmPASU1WNy7U8x+SzoF/h1VOOJ0HjNNeJJcsaVlko2x2eC32+Cqq1fZpmhqS9i5reoeG2R7ZN5RMItTnpIRt4RtdWZD2aWoS3YEnhdGGihIWCLqdQfcuj0QQMQJgdxIVcFPNBs8cCDwb0KOZqhAacszXU5g7G6b3nFUvyb97VLrY8aGiKiZpJRJ5j1I1qFbt24qO7H33nur+RG6lJoAqsxI5r9IJ67oCebynNZklZRF2xLntGzZMvXTKgmLRwIfIZPhhQRO0WQeTDgZsEvpWLikpCQ1SN0Ymd8jc22+/fZbFURFkyCnPci5W9mCaBKAhe+zsX2tbdY+co3ks4t3TXv3Zo1/cx3UJ5A5aJQMuNW4Oqq0zDTxY+8ClLsc6FVajXLVKS0OaUQgz5P5NtJMQNT7ApFESnDejmGib1lNxNPkvyKFeZmoTk1CWk29OoU5vQowvV9X9dpD+7swulsOfvi0FIYfsDvc6FNYg1UpqSjJyUJuaXkgTJDzN0zU56QhN0uDr8RAfmExijo3BC5yKkaos1tAaV4mCksrkV9apRqg1aY41bY6pwMurw81riT4dJvKtoQuiQReXbojtaoO9mDwJJmhwgIXeq2vgOG3Ixn1yEI1dJio0x1YlhoI3rrUlaIM3WCHO5iFkU/FgQ3opfZ1wgMPAtdYg4EeWKjK0HyqrE1KZGuRAk/U9Bd5ph1aMIiKmHPTBKaU2712BTBmOHDtMcCDnwbKzlx2wB32N9M5C3jzyiYelVobAxsiomaSUiaZIyHfnMv8DimTksn6MiFeyoRkYH/eeeep8hiZvyIDThmYyrfMDzzwwEY7aTWVBAKNsQbB4aR8Z0uck2R7hDRJGDgwrGNQmHglVZtiZXbCNWUS/E033aTm68i8IWkoIHOIJLiUz0bK64yWLD64BVjzmqwys3DWNqvMzLpe8fa1StDCGxDQluOya7h7Lw03TQ4OeU1TxR+m3wwkJ6xRsIzuZaKJKusyVUMBw+1HtceA4TfRtaYO/+ZmwBc2bNbtGtL9ftQ5bPBI+ZdpIqXWA63Cow6XX+yF12VDuc0G3ReblehcVI7Uu3aGw+NHPewYs0MGDshxoWueDT0KAhmbPQ/KwGsvfYik9GqMP/ssVM+qUvN1DL8fq/8sRv7wLGgpLhT0S0VSmh2liyphc9lw1yWLY5ohRNA0LO3fDclF64DSenidNqQ7DWSUlsHp8WD3JVMighr1FAAHz/0Dv/TfBaipRWZdDQan1iNzhxQYvXOQZauCmZ8N+5F7wVxUjtJX5yPZAPIGZ6DX6TvBNm0paiZnomzeBhiaAyXd+yBpaD5y9XJgaRGqijyqAUM6SmCrdWG9exi8fjscdjcyB+agbpd9kfTeD6qRgxSnmaYEQjqQmQHfrkNhllYCbi/MWi9cehX0FUWqJbQEX9IQDd1ygb2HwNhzMNC/G/TRQwNzosQ9ZwBXHAmsKQWG9wZWbgD+WAhs1w0YGcjWbHmcY9MSDGyIiFpABtAy30NuMtiX+SuvvPIKfv75ZzVAlUyCTMAP7ypmrVcSXXIUXlYT73XkOdGsbEhTydySLXFOVjZISsB23XXXjb6mlQVasWJFo5kfi5QKhbc/bkqAJPMmJKiRz+DGG2+MeEyCzWgbu85bmpSKiX///TeiO5uQRgtyLtttt10oCJLARbZHs7YNHjxY/ZTSQbn28a5pe5XddXQ37m7Htbua+H6FiQHZQN+swGD2tl99eOIfE4Zhor/Lg+mFqmOzKuXSHBqOW7YOy/LT8duATnD6Dey/thg/9soPBTcyjSWtrBa7JBtY2S0DtWV+ZKUA+41MRvcsG47Yzo4heYF9z7ysGpVhyyJphoF9903DUefEz75aMrLsSM4MNAuQv6mckQ0ld513jF3gM2dAoHzOmaTBmg4TSEjFaXZgs2HCH4fEfd26vB/jbh9cuBKj1l+Lpsi/a//IDacPka8zQnejZ8FFv5u4jd9fPLNZg12tOXM0OmcHbqJv58CNEg7n2BARNTNTEr0wowworJXkJUiwytGs7IZFOntZZVrhZKDaWMmUBBIyuLXmWAjZVzJGzbGlzmn33XdXcxpkYn68gEvOU+a2WINxmeQt5ypzXywyN+f999+PeJ7MkZBAKfzWt2/fFr0nmbgv681Es9aBaYvyNOkYN2TIEHz//fcRmRj5XbbtvPPOoayOOPjgg1WXul9++SXib01ad8t8EWv+knSVk8YV0nVtxowZEa/5+uuvt/r72lrZdQ0H99FDQY24Y287ii91oPRyJ6ZcmArf7anYcEMybjnMhfyKWiwuyMDknvkoTk3C2owUfNe/M/qWVAFeM3DzmajsloZv7i7A/IuSsPKmVPx7TSoePSIJ1+7lCAU14qWHu2GHIU5omgkXDJxzShaOuSXw35TWcOn9DYvDKs0N+tvwSwKi5mDGhoioGSTrccghh6j5NBLMyDfostbKe++9p7p/yXaZQyEZHFkxXhZxlIGprDfy+++/q7VKosvIpKWvlLJJlzGZeyKBkhxHggt5vrRvli5fkpmQoEoG7TIpNV5A0hgZGG+pc5JucFdffbVqACDlYzKIl/OSjIFkhmRhSskKySBc1laRbl2yvo1kLmSbBDqSiZLyuM0hZXXSJEDW5pHASLIk0oXogw8+UNmi6MBLOrmJRx99VLVWlixVv379VLe0pvr888/VawgJ1rxer+pOJqy1fCxXXXWV+tykNbN0LRMSqEh53OWXXx5xXLk+0qb65ptvVk0AJFsla9tIm2fZJu/VcuGFF+LPP//EpZdeqj5LyfZI5soKHtsyM7WtyUjSceMeTiz6w8RHtYHsh8wn8emaah6wIT2y66BMyWgK+czuuDq2WUBr6dQjBUef1wWfPLcubhOATYrTnIS2LLZ7bhn+ZRIRNYPMSzn55JNVqZPcJNCRb95l0C9rpljlUzJ4ltKqSZMmqczC8OHD1Tov9957b2hgbLnooovUIPzdd99VAYJkIGTwL0GEDMDlW35Zg0ZaKcuAXQbKcszmdFeT4GVLnZNkbaR9s9wkqJAJ7RLUyWvIoDx8bRhZMFJeSwb/Mslfsj3WAp0TJ07c7M9DWk9LwPbrr7+qoEOCLDl36QoX3Y5buojJWi8S+Mg6NxLMyQKdzQlsJNiTeVXhpCGDkDk+4YGNdX0lOJSbDF6l7bSs8xM9P0m6xr3wwgvqvchnLXOeJKD873//i4MOOihiX5kfJdfykUcewZtvvhlaoPO6667D0UcfHdEhjFrHQrcNGW4vDl+4Fjn1Xrh1DZN75GF+dlpEL4LcBP4o9jwsX92uPDa2BHKTGonYPJqGhhCcqO1pZnQOn4iIiDocaa8t6/ZIwHjWWWe19+ls1freUoaDF6xXa21a5Nc3+3dFSVJDNDMkF5gzMbaZx5YiGUP5okLIFyuOeG2gN+G28bNRFVtViuRU4O5XA1nOaHX518Isro3JKczo1Ae7r7+q2edAsdzahU3az2U+1ern0pFwjg0REVEHEz7nSsh3lNK8QmyqqQNtPqfXFxHUCBnkj9gQGSHIVJtEd9HdkrGMPdHzb4tsyR7BKW2UY5WkNqxXRdQeWIpGRETbLPnGO14ThGgyl0rmByUKWYRVGhBIGZ2UrUkp3j///IMxY8aEOqhR69krxw9zSWxXLZlrEy6RS9Esnbomoe/QZCyd0xAs9xzgQs/+Gykqy00D1sY24TD0xPk3QtsmBjZERLTNkgYKMsF/U2R+Udeu0Q1o28++++6rgpkvvvhCzRWSc5P3wRK0tjEqz8TcqG2S85iaH5mxqI2zPk0imnhnf1SV+7BgZhUG7pCGjOyNl7TZHj0OtaOfhsvvDW2rcibDncYZNtS+GNgQEdE2SybxR6+fE4+ss5NIZIFUuVH7eHe9E4GVihpIrmZwWTWmdgqudQJgwYb2WSC2JdKz7Bi1b8O5b4xzv0God2ooM9NUN7jS5AyUJqdh+P8F2pITtRcGNkREtM2Sbm6ck0LNNdVMxkBNgyOq/9Ka1MhGAe6WNVPuENKr74f3lNfg/XI+kvJc6P3kseh1YOJkNTs6tntuGQY2RERERM2Q7POroEaaHsusEglf/s1Jx9q0yHVsMpO23mGWpuvIfesM9Xvn9j4ZoiB2RSMiIiJqhmH9XPDomgpqhFrHpnNOzH5X7MZv3YnaEgMbIiIiomZ4/kgH1mYlw6cFAheXYWLPdaUx+92yj7Mdzo62DloTbxSOgQ0RERFRM/TO1PDhUz0xYmwe9HwnkganYexpeUgOVp7lpQBrrrBDDwY+RNQ2tt7iTyIiIqJWYtM1nHNyHiC3oMv3addTItrmMbAhIiIiIkog7IrWMixFIyIiIiKiDo+BDRERERERdXgMbIiIiIiIqMPjHBsiIiIiooTCOTYtwYwNEREREYWYtR74/l0Ds6oOGHEVYDsOSDsZ+OD39j41oo1ixoaIiIiIlOqjXoDx6Vz1uwvFcKEq8ECNGzjufmDK/4CdB7bvSRI1ghkbIiIiIoJn8tJQUCMNh+2oid1pv9va+rS22XbPTblRJGZsiIiIiDqQWq8Jtw9Ia+IozjRNTHy4FNMX+dTUjSP3cOHGU7Ni9qs/4oWwexpq0BNOlCEJFaGtRq2b34pTwmJgQ0RERNQBSIBS8JAXxfWmup9sB25PS0KWvX6jzzv+tg1YUxx4Dkzgk8lueHzluP3MqOCmPPo4GjzIhhOV0OWJgacTJSwG3UREREQJzjBNpN4nQY3ckxIkDXU+DTeXH7fJ54aCmjBf/eVu4itr8CK5+SdM1A4Y2BAREREluCen+1Hni93ubeXiGw0+2OAJ3V+fktmqr0e0OViKRkRERJTgPlpoNPJIyyaQN6WkTJoHJKMo4hVc3oYghyjRMGNDRERElOAG5bZ8yNZYEPPVfO9Gn5WEkpiwKd3X1BI2orbHwIaIiIgowe3aJc5GnwHU+3DRqpPw8O8bC1LiW1zs38ijJnTEPm5qOurcbCHQ2tjuuWUY2BAREREluLfn+KWDQOBmmoDHD3gDgYdfs+Pab0289k/zsilnjnRu5FEdPrjibDdRVL6xgIio/TCwISKirc55552HI488sr1Pg6hJ3D4THr+p2jnH88sKA1/MNwB/MKixghtdBzQtcNM1jP/I16xZOOnJGx8G1iMPfjgitumGie75tia+M6K2xeYBRES0Tfj0009RVVWFU045pUXPLy4uxttvv4358+dj3rx5KC8vxxFHHIHbb7+90ed89tlneOONN7BixQqkpqZi7733xsSJE5GdnR2z7+zZs/Hkk0+qn5qmYYcddlD7Dho0qEXnS+3fnvmvtUBxrQ+FNRpGdNbVzSJBzAFv+fDTCglUJO1iWl2coZsmkm3AJbvYcPcBdpz/mScQvNiCIYr8cNga7nsl6JHKtKaXiDWliMmAEzXojiSsgxOBNW50GLDpLIFqfbzGLcHAhoiItpnAZt26dS0ObJYvX45JkyahU6dOGDJkCH7//feN7v/666/joYcewogRI3DVVVehqKhIbZs1axZefvllJCc3rA0i284//3zk5+ern+Kdd97BueeeixdffBH9+/dv0TlT6/D6TXy93ETfTGBIXmzW45/1Puz6hgmvJFAk1pDsiiblW75AtiWYYVE3e3AAqzIxgZ/yo8YP/G+yD//70y8TWwIjNitukZ8S1FgBhssG1Af2W1tpICdZQ5JjSw6MG47FgSMlMv59EhERNcHgwYPx7bffqmyLZGtGjx7d6L7y+FNPPaUCIPlpswVKd+T+lVdeiTfffBNnn312aP/77rsPDocDzz33HAoKCtS2MWPGYNy4cSo4euKJJ9rgHdLGVHlMVLlNvDLbwA2TG7ZLBmPHXBNzS4D68ISJClLC7qvfpWxM4hspI5Osi2yyggYJVIKP1QdbO0vwYguWm/mNyOOFMjxaw74eE93uqVHBk24Y6O31IhsG7KYJj6ap1Wh0Ky4C0OO2MqytNmHzm8g0/fgoJw/DSouj3rkJO9woTM3G3E590Lt0DdJLffAVlmH672Wo7VyA3UalYEO5gfJKP9YX+5Hm9yI1y46BA1OQmaTDa5iorDBQVulDv84mNmzwIyU/BanJGhwwsOr9OUg13cg8cjjScpPg9xtYOKcWSWl25OTYUVnuhWvFOuTv1Bl+j4lahwtOO+D3A4ULylEyeRXQIxfDdsuCbfYauDtnIrmuBrYRveHXdCy98XdUra1Dz8N7wFdUBvdPi6ClOKGdvTtq/y1CxexirLZlwt8tF3uMABzz1mCVLRdmeiqGndxLXU9fpRcp3VKg2XR4Stywp9th1PlQM6UQaaMKYMtO2qJ/b9QyDGyIiKjdud1uvPTSS/j6669RWFioBvmSGdljjz1w2WWXqX2++eYbfPnll1i4cCFKS0uRkpKCHXfcERdccAEGDBiw0ePLfBvJ1ohRo0aFtj/99NMR9zdGSsnk1hQ//fQT6uvrceKJJ4aCGrHPPvugW7du6n1Ygc2qVaswd+5cHHXUUaGgRsjvBx54oMo0SRlcXl5e6LHvv/8ezz//vCpxk0Dr6KOPxvDhw3HxxRfjtttu4/yiLTz/ZfxXfrw1PxhXyP8LS4ZICDI9PBawAo1482XkIW8wM+MMzo2xjmmRAMWuBzqeyU9rn3jL2Ej2xypHM4zALXhyBnQUazbkV3uw3OWEoWno6fWrwKbUpqNE11BeLbvrMOxAsaHj0jFH4Me3X4p8CWh4bfgYvLfTIfDabHD6/eh0zgy4nS6UpElJZTWe/ahaxVkZhrwq4NU01Og6OtcVI8kwUOJyosYeGHLK2ZnqPVXDZphI9XpxxLx/cfL0zzGnywC8vf8JWGemqu5rcg23W78E1/z4PAx/GqZ0GoYPdt4LpmZHiiewno5X12EzDGxX9Au2m/M1kjw10OCED3Z40l1YW98LyV4D5clZ+PfThfAaqehUVwWHYaD+zU+QgWr0Rjm2s1XC47Rhvb0AS219AaxBdbID8/47A3ow65Zs05CV5UTdoio4XQbqTV0FTi7Dh2GXD0L323dt7p8XbWEMbIiIqN3dc889+OSTT3D44Yfj1FNPhd/vVwP+qVOnhvaR0qzMzEyMHTtWDfJXr16NDz/8EBMmTMBrr72Gnj17Nnp8KQV7/PHHVSZFMiaWPn36tMr7mTNnjvop82SiDRs2TAVwtbW1Kjjb1L5yXWRez1577RUK8G666SZ0795dlapJ4CRzeX799ddWeS/buvummnhzftiGeBVeMlCPCnjiMsMCFDXCtyIaLfJYrmBgs8lqsrAgymMEmglY2zUTlYYNf2WkhfausdkwtN6DHL+BZU5nWLZIUjkauldVxryCHHFyn53hCQYmbrsda7M6QTcbIi2XYcIZFsg5TBOphgGPTYfXpqPG0dCAICLppGvqeJ9svz92WzET/YtXYr2RAtN6H5qGooxcwHCiwt4Zr+95oAqGsuoC832E0zCQ7KnFuJmfwGEEmifY4YEGA2tqeiHJAOpsLqxMKYDd50ff6lLYTBP1sCMfpUhHJbKwDppEZnVAHsqQ7qzFP6nDYTO1sI9IQ5285ZU1cMJEjWkLXT+3bsesh+aj04QhcPRIx5bAVs4tw8CGiIjanWQ4JDtzxx13NLrPY489FjEvRUggJHNmZIL+9ddf3+hz99tvP7WPZIYOO+wwtDbJsAiZMxNNtsnE8Q0bNqBXr16b3FfI/Bzh8/lUaZpkaWSeTkZGhtp+/PHH4+STT27V97St+np5vFRJHNHjUBW4NLKfbJcpN401F1NzcILr1DiDO8l8mvDmADLilsdl+Rpp/awCq40PhmttOsptOtIMQ2Vwov3Sszd8mqZK1yxeHViZ25BJFD6bjqw6D6qD2ch4vdUkuPHoejA70zi/JvsC/3TbDhXJmTD0yItSmpqNVRk9UJaUj3qnC7lVkmaK1Lt0ZSiosdjgQ5LhhRcuVNmToRkmkvx+FdSo15UsLGrhRK1aESZcd88azEgZBr8tdpjsc+hwyuSpqPfltjlQ8f0q5J01ZKPvl1oX2z0TEVG7S0tLw9KlS7F48eJG97GCGgkKqqurVfZFBvgSHEgnsUQiZWjCKd+KR3G5XBH7NGdfydxIQCTd2KygRkjm59hjj0WikZJBCSYt8rlJZzqLx+NBSUlJxHOsksHG7q9fvz6iLXJrv0a/rGZ8cx5efiYjrPCnyn1b1O9qcBwnArIaCch8G1mrRjUcCJaehd9UN7RgM4ImksBFAgkpxYpW7krGyvTMsFP2wOuqjhuchAcD8eI3CRyS/Absm+jUpsq8AHSuKkZBVfT8HsDh8yKnthTZ1dXQTFMFVdFKU7LjZjwChXdAqq9evQcpW2vIkYXNbYoi5XfqkTjnLu2uTWnmEEXK4Wr76hv9u6LWx8CGiIjanZSHyWD0pJNOUvNF7rzzTpXFMcIGXzKov/zyy9U8FcnAyOR9uUkwFD6QTQRJSUmhQXU0axBu7dOcfdesWaN+SjAXLd629paTkxMKzqwANj29oVRHgrnc3NyI53Tp0mWj9zt37qzaYbfVa9y4q47seOtUhgchag5G+P1gsKHmy8jPYMZFlZlJiiJsfo36ERbcyHOlUYCM0FRJmjQT8AduEsRYa9nIc+zS8lkPzMWRW5To4EXaSGf5/erVent8DYGY9ZqSbUxJsd4IbHCjoK4EQwqXRxwns74ePcsDf4uiStek31vDJZFgXNeR6vMhw+tVg/6I6xV2PpLZGVi0DHst+wd9S1ejZ2nDccXwNfOQ7d2AgpoN2GPhHFS7XPCHff5ytMKMTvin67CI58niorqjVu2R6vegi7scPl1DmSvwBYkDflQiDR6kqPlI4Za5eqs5Punu6sjz9Ztwug1opga7L+wdmyb6jClAz3222+jfVXNIYNaUG0ViKRoREbU7CVRkLsnkyZMxffp0TJkyBR9//DF22mkntbaLfOMui27K5H2ZU9O7d2812JfB5wMPPIC6ujokEmuiv2RXevToEfGYbJPztsrMwveNZm0LbypAbWtgjoal59rw8hwD/xQCMwpNzIxMADUMfsOTLyq4Cf4ekfEIdkYLZz0vdJxg4CNpDwlYJDMTb05P+H0Zm6uFO60yNT+chgx9DRUIJBkmenh8cAafl6ReS/azojINQ4rWY0ShlWXQ4EU6DDhw72cP4tWRR2NGt0HoVlGMzpftAd86N1KnLMP0nD5IS3XBoZlIrvPB5jOQ4atGtSMZfXfMRNcUDSXlfiwt8qss2K6pZejrqIZt536o1JKRZPqQ9vxqzNtlN9QdOAKXXbILvnyvGHNn1SI7z46C/XbFV7k+DF0yC8cOLMPOwyuxsls32HQd6ckmav5cikXzvPhtn8Ph6rU3ui1finX5nZExIAtdTh+B0pu+gHfSNPTxVSKjZ2esNTujtiJZxZwL8nuj19rVyKtKR7JWAb8DsKU7UOdNQW5yGTb06YNhR/SEUW/As64Wvcb2RFqfdGz4Zi1cXZPhr/Gg8vdCdB7bCzkHdN+MvzLaUhjYEBFRQpDGADL/RW4yAJI5Na+88gp+/vlnNcCXyfYPPvhgTBezioqKuGVc0cK/5W9tQ4cOVY0N/v3335jARtaskeyKlI9Z+wrZ95hjjonZV857u+0C3wR37dpV/ZRuaNHibaMtIytJw2UjG+Z+LK8wcOOvge/579/Phs5pgW/8K9yBbMr9f/lx15SNlKlZGZeIx8N+t9a6kZ9yaMn6qAAk2DVtI/N3jhig4YjtnDh9Rxs8hoasZA27XVQYs2uKnOoDOXB7TSxc5UVmho70/Ltjpv34kQTNtGP8tI+AacGNPx4OIBu4EC0QJwAYHbm21FkXBf7OQ84+DoDcAJnBEjGL5bhuODDqcA3FdEDukycAcpMMCoDtm3CGUa8eI/WCsEVzT+YaU4mEpWhERNSupANadCmZDOYHDRoUClz0YJek8LkVQoKH6PkTjZFAorKyMuYYrWHfffdV5VHSyU3en+WXX35R5WSHHHJIaJsEPrK+jbRwDs/ayO+ybeeddw5ldWQtHflduqDJe7FI0PfBBx+0+vuigN6ZOt44wo7XjrCHghqR6dLUui137utA1WU2nLodkOsCsp0mzh0GlF1ih3mtA+6rHTh/mKnmjATm04RlWlS5WSBI2buPBs+NLugS1EjE4QiWnDUWpOsaPj0zBefv6kCKS1dBzaa4HBqG9XWiZ569kcKmQNbH0sRWCkTtghkbIiJqVzIol4G+zJ2RYEYaAqxduxbvvfeemiAv22WuiWRwbr31VpxwwglqDsXMmTPx+++/q7bH4cFDY7bffnvVEvnee+9VrZUlWJKgQeZoNJWsHRM+92XRokWhbSNGjFA3Ie/hwgsvxMMPP4yLLroIBx98sApUpC21lNFJJ7fodtSyHs8555yj1r4Rb7/9tppjJPOKLHa7Xd2/+eabceaZZ6r5SNLuWda6kYyXBE1tmZmixqU5dbx2ZPzvj502DU8f7MDTBwMzCw0sLZf1cvyYVqTjgB4aRnXRUZDa8DleuacN90/2NXwdbXVIi5jXI8mczfvs45+thDU6dBVtESU2BjZERNSuZK6MtCqWeTVyk0BHshIS0IwfPz40F+XRRx/FE088gUmTJqmgRBakfOaZZ1Sg0pTuQ7I+jgz8JQvy/vvvq6BBFuhsTmAj+4dbsGCBuglZU8YKbMRpp52mgg1pM33//fer+UHS7OCSSy4JlaFZrPfy1FNPqZsEJxJ8yfo+AwcOjNhXgkAJcCSgkufI+UuAI4uUXnPNNRET6SnxDe+kY3gn+U3HSYGqxBj3HeTE4HwdE74ICy4kc2NlfKTlM4B3T2isf/TmMGOCGsMwoUtwRZRgNLMtcvJERETUqiQbJBkiCfxkYU/a+mh3uQPZmnBqcU4fHj9Yx8V7RK7zZNn1osLYfgWmic/vyUdORkMwVKldFefZJlKxCrZgcCO9wIo3vIPOefxuvDVVaQ0LCW9Muvlgq59LR8I5NkRERB2I1+uNKb2TLNe7776rMkRWowHadjzT7XWct3PzAg3NBCZ9GrvYZTgdbqSFBTWBbUBOZmtkhigc2z23DMNtIiLaZklAILeNkTksMmcmUUg53aWXXoqDDjpIdUkrLi7G559/rrZff/31cDgc7X2K1AHIsHju0ti1k8IlY0PcuTVOWYeHKAExsCEiom3Wq6++iueee26j+8giezI5P1FkZWWpRghffvklysrKVODVv39/TJw4EWPGjGnv06PWZi3+GbrfssPI2jaDem08CK5DvgpubNYkHpb6UIJjYENERNusww8/HDvuuONG90m0yfgS2Pz3v/9t79OgdpDs0FAnrZ/VOjdNjzQay6+cdWT6Rp9nwIVadEIaVrPoqc3xircEAxsiItpmSatouRF1BLPOtaH/0/6wMa+JY5KsVTMb57Sr/gIRenXWUZC96bkyJhww4IjI2hAlKmYUiYiIiDqAfjk21F7jwGUjgRMHA4vP03Bo2pxNPu+z/+Uh2dlwvyALePOWQBv1CK54w0IThlodlCjxMWNDRERE1IHK0R4+2BnqkNcUGSk2/PhwJ9TUG7DpGpKc8cuc7PccDt/lsfPJ7KhvuMP1ayiBMWNDREREtA1ITdIbDWpEymX7ATlJYVt8SMHayNkeT57bmqdIQWz33DIMbIiIiIhIySi5G0mfng37+bsj+c1TYXcaDQ+etCdw/iHteXpEG8VSNCIiIiIKcR4xVN2Uk94BfH7Aznk2lPgY2BARERFR4xjUtDmWmbUMS9GIiIiIiKjDY2BDREREREQdHkvRiIiIiIgSCkvRWoIZGyIiIiIi6vAY2BARERFtIWsqDezzigd9nvDghRlNW0CzTa0rA36eG+h0RrSVYSkaERER0RawtNSHfk83rPtyzhcmnp/hwR9nOZEQBl4KLCpquP/omcAlh7bnGRFtUczYEBEREW0BO70Qtphl0J9rkRhueisyqBGXvtxeZ0ObYDbxRpEY2BARERFtAZXNqDx7ZaYHufe5kXNfPR78ow1K1v77UfztH09p/dcmaiMMbIiIiIja0InvunHmJwZK3UCZW8NV3/lx6ofuVn3N2FxSgPfXBa36ukRtiYENERERURspqjbxznwT0MLa+Woa3pjTuoVFjR399wdKMfWwb1v1tan5TGhNulEkBjZEREREbeSvNY3kTlp5woTRyEu6kYHSr9eh9M/C1j0BojbAwIaIiIiojQzNb59v2Rt7VSfq1c+/D/++Tc+HqDUwsCEiIiJqI31zGht6tW7AozWyzQ6P+t0o97Xq6xO1Ba5jQ0RERNSmWhbEzPi7Cq8/X4h6uw17H5aLYw5M2uxX9MEJmFFzfigB8PNoCQY2RERERAnuqVsXY8U/VXAEB28/P1+DBYsL0MnVtOf745TpVCENbj21NU6XqF2wFI2IqA0deeSROO+887b4cT/99FOMGjUK06ZNQ0d/L0QUyesxVFCjh32Xn+nxYvHv5fD5bc36/l+aCBjQsAF5mKbt0mrnTNQemLEhIqKEs2DBAvz0008qeOratSsSwfLly/HRRx9h/vz56lZdXY1zzz0X559/ftz9DcPAm2++iQ8++ADr1q1DdnY2Ro8ejQsuuADJyckx+//222948cUXsXDhQjidTuy888649NJL0a1btzZ4d9S2zGaVGv39XXHcb6Iz693wmzrsVSa+Ov8vlMyvRLc98nHgPaOg2yKPbz2/Fqn4W9sF9UgKlJ+xDC0hsZVzyzBjQ0RECUcG98899xzWrl2LRDFr1iy8/vrrKCwsxODBgze5/4MPPoiHHnoIffv2xTXXXIMDDzwQb731Fq644goV9IT74Ycf1Pb6+npcdtllOP300/HPP/9gwoQJ2LBhQyu+K2oL2v95kPGAB1PXSkFYY0x4/SZWVpjwGybK6008PMWHI16owYvT4i/e6fD7UV9jh/FCHtb8VYr6Ch+WfLkOzw7/tNFXSUUN9jR/QW8shd30NAQ3jfCX1aPm22XwzVi10f2IEgEzNkRErczv98Pr9SIpqekTfbcmNTU1SE3t+HX8++yzjwpA0tPTMXfuXJxxxhmN7rtkyRK8/fbb2H///XHfffeFtkv26f7778c333yDQw45RG3z+Xxqn06dOuH5559HSkqK2r7HHnuoAOfZZ5/FTTfd1AbvkJrCNE2sLzeQm67DaQ98q15YbeDl2Y2tTxPIzlS5gV1e8sFmeABdj123xuOH86bqQCJHAzS/AYeuwSP7atno3duFsSvXwWUYqpzMpwUemzJ1FHZxLUe6xxs6lE/Tcf9e32Pk0V1Q+sUKeBdXYqBzGEZ4ZqnDz9BGoETLb3htTYMOD37LvA9OGOhWUwqX34dKpKAUsp+GLBQjB8WoRTYyUAgHaqBlZACDe8K/6xB4F5fAmLISeqdkpI7Mgj6iF7wrauCevBr243aA68ih8C8sgq1TGuyjegL2ppXQoaIGeOlHoNYNTDgQKMhq/odG2wwGNkS0TZOA44033sDXX3+NFStWwG63o2fPnjjiiCNw4oknqn3kG/PXXnsNU6dOVSVFbrdblQcdfvjhauBps9ki5rrccccdeOKJJ9Q3/HJ//fr1uPnmm1VZlUVKmR5++GHMmTMHDocDe++9t/qmPicnJ+L8ysvL8cwzz+CXX35BSUkJcnNz1QBbyp+ysrLiDrpeffVVvPfeeygqKkKXLl1w9tlnq/djvd9DDz1UvUcpe4r2yiuv4NFHH1WD6REjRqhtcv5yrn/88Ye6L9uvuuqquNdT5vnIax122GHqvCXzItkNOV5Tr6M8T7I1Qsq2LHLc22+/Xf3u8XjUsb766iusXr1alW7ttNNO6rpst912oedIZkSyJJ988onK/miapq7hjjvuiBtvvFF93k2VmZnZ5H3l70k+i1NOOSVi+9ixY/H444/jiy++CAU2f//9t7o28l6toEYMGjQII0eOVEHQddddF3Gu8vnK361cx86dO+Okk05Sz5W/vaefflp9DrR5vlpm4L9/GdhQC4wbqOGW3XX8OMeD099xozTFBb9dil7MQNyysURMeJmXpsHvCn6Obn9DcOPxB+6H9gNMhx0eXQOSbcip9WLfolIkBTN98i/FZphwGX7YNQ2rendHzzXrkVTvQUVeFnxOBxx1bqx6fE6goEnXYSabkM7ONUiJDGqCqtKTsTonG3bDh2UZXdCvsAx53g1IQg1qkA0ddiSrsKYIbqSgGt1gVCYBf9Uh6a9vkIJadWZ6cS1sczzQXjHhRwrq0BPGlJ9hXvczNGjqLZvQ1U1m+yTrFXAd3B+pb46Hfur9wOfTG7+WN74O7NQH8PqBlcWAwwYcPhIY2BV45mugpBrISQOuOQa49HB0ZCxFaxkGNkS0zZJB/sSJE9XAcrfddlMDfhkgL168GD/++GMosFm0aJG6v99++6F79+7qG3YZ5MsAdc2aNXG/TX/kkUfUfjKQlWxFr169Qo9JwHHhhRfigAMOUOVJEuTIwHvevHkqsLAyOzKHQ4KSVatW4aijjlIDdpl7IoNaCQ5efvnlmEyIBFQSMBx77LHqvci+EgzIectgXoIoCRAkKJA5I7179454vpyHBD1WUFNVVaUaBEj5lRxTyqqmT5+uAgh5nXgkmyGZjWOOOSYUUDXnOsp1KS4uxocffojx48ejT58+ars8R8jzLrnkEvz7778qgDrhhBPUtZL9pXRLgqIhQ4aofSV4k4G+BI7HHXccdF1XAY4EihIcNSewaQ65BvJaQ4cOjdjucrkwcOBA9Xj4vmLYsGExx9l+++3VZy1Bd79+/dS2l156SV0z+Xu4+OKLVfmaBLMyh4e2jBlFJo780IAvmIS5808TNV4/XvumDkW5aQ07yuP+ZpRnyb5yUJcOeI3AfQlWfFHHkLtGYGibWVIH0/BjWUoSanQN/atrA0Ne+X8mkOL1YUNqClb16IJOpeUqqBEF60oihsblzkBgLo0D4vE6bNix7F9sXzEXDtMfMQvIhyTo8ENXbxhIQi0cWIUS9Fd72WCDiWTocMOOhv8uOFGLTKxFKfpCR2CdHDmmIQGhumdDnZEFx5ez4Bl6HZLWrNr0NfxnWeT9V36KvC+ZncteAJx24IKDN3082qowsCGibZZ84y1BjQyeZYAYLnwOhAzyP/74Y/Vtv0W+ib/lllvUdhnk5+XlRTxfBpty/HjlZ5JhuPLKKyO+zZeAQeZjSHbhrLPOUtskcFm5cqX6tn7cuHGhfWVgfO+996ogSAKkcDJYl+0SwAgJnI4++mi88847KrAREmxJYCPnLlkiy4wZM1SwI0GDRY4lgcCtt96qgish5/LAAw+oifHxLF26VAVYu+66a8T2pl7HAQMGYIcddlCBihwjOvsgJV7yuT322GPYfffdQ9uPP/54FYxKdkkyREICKQmM5NqGC3+PrUEyMJJRk+AyWkFBgQrKJLCWz8maQyPb4+1rHU8Cm4qKChW49e/fHy+88IIKlIQEkRK40Zbx5ryGoMby2jQvqpJjP89mk+NaQY2/kfI1YQJ2w4Qp820cTvxSkKs2D6ysxnGr10fs6tU1OHQbvK6G83O6G0rTxNqkTih05iI5rGQtXJa3DDtVzArdDw9/7KgPTstu2GqDXwUuBhywh4KWwGKf4VyoCn9LwaME8jZW3V09MpG+ZgG2KAl4GNhsc9g8gIi2WVLGlJGRgXPOOSfmMfm23SLBiTUYl8GoDC6lREwG1RIAhX/7Hj7IbmxOjWRZwgMVIfdluwzELdIVTL6Fl0AknGROZHv4vuHHsYIaa2AsGRjJ+lgkeyRBhpRDSfbDIsGFlIOFZ1nkHKR0S8rFwp155plojARe0UFNS69jPF9++aXKNEmJmzzfusl7kdedOXOmCixFWlqaypBJ0NaW5PXDP4dwVrBjnaP1M97+0fv+9ddfKlMmf19WUCMkIJSMY6IpLS2NyOxJZk2ygOGBuJRYhpPyuo3dl9JIKfNrzdcw3dUx7yUpWYe2OXPn5ZyD2R27N5ipscRLougaXD4/KqLmoizMSMOaZFeoiq3GYYddGpvJU3wN5Wx6dNCkaZictgfW6QWAGVs7V+Ar2vjpx0wKkhjNrjI5DfvEzpvxoeHvNBDQNNyz2ODd4qVXHqeecH9X1PqYsSGibZZkQ2QeQ/gAMR4ZMEv5jwQCEiCE/4+fqKysjHmOBBONkXkl0YNYGcDKdinJskimRAbv0eVS1jwgKWGLd+x4c0Pkf7SjgyOZ9yMthqU0TCb4f/fdd6pkSwIZi5yPlHWFzyOyBtIyiT6ext57S65jPMuWLVMDDmmd3BgJdGTuiWTirr76ahW85ufnqzkre+21l8pkNRZ4bAkSxJWVlcV9TAZE1j7hPyXY29S+Vpe48NJGS7xt7S16zpgEmtF/9+F/b0LmhW3svnyurf0al+yWgRcW+lEaiCeVa/Zw4OUfvZhmhLVHlp+2TZSjyd+5PCcszkgy/Kg1TRiO4L9tCUjCy9HsOhymiVS3D9VJsVmiCocD3ercKHM5UZfkRLLXj07rNyC3vAobuuap86pPSYKr3g1T06CZJmxeA5nuSuShDLq5FEu1AQ3XzKyD09xI9khNI0qCPSwjU4/0YImaD6Y0EgjMmIGBulDZmQQrlZD/JplwwKOCHCtXE7w46sip2ADP3rsg+de/sEVoGpzXHptwf1fU+hjYEBFtgpQxSfnTmDFj1JwXyZZIcCGBhZRDRQ/QRXt1QAvPNIWLPkeZxyIBj2RpJLD59ttvUVdXp0qaNldj770l17ExUool7ZEbY803kZI2WXtG5vLI4qVSwiaZOinjkg5kzWkI0BwSREkAJoFJdDmaZJCkTM0KrGRfa7s1nyh83/B9qG30yNAw5TQbHv8n2DxgkIaj++uYOCINp71dh4+WaXDbdCS7dKTagPXV1ryYOAeL0065XrPBTLarrIzi0IF6H+T7gy6VbjjrDFQ5bHCaJuyGAV/Yv2u537tGJuoHC7n8JgYsWo7UerfK0nRdthaVmenYkJ+JnssLVVBjakCv+lXYs+Zv9bxclKCzsQ4r0Btr9J7wSMhSmwIPXHAG58hYhWLy04uM4K1eZW68SIUb8sWGARdkLk8dTKSo5gBu5KocjuwnJWZSqpaMSvjggLQTsKMOhuaC4dLh0GqRJFW8E89E0jX7Ax/+BVw9CVhVEggG5dolOYCasPl8x+0GZKYCM5YBmSnA2F2BoT2BRz8DFq4D+ncBrj0G2GvTLdlp68PAhoi2WfINt8wpiTf4DCcZBind+r//+7+I7eHlXc0hWRBrfoVFzkG2h0/ml+yLTBqXTEd41kbuS7ZpcxZulPcr5WUSaMj8DQlwpGwtfM6KdQ7yPqVldXjWRib3h5dlNEVzrmP4PJxoPXr0UNkQWcCysUAunHQLkwyN3MS7776Le+65R73njbVs3hyS5frzzz9V1zvp1maRTJN0irOaM1j7CumiF13CN3v27IjmE9Y3wPJ3Ie8/nGyjLadfloaH9o8trXrtxNjFVS3af2PnmCjBDmoSKZw0CHhrTmy3NN1pQ25VHVZnBo/vN5FbW488jw+l0iHNpiPL48WY9RuQEiwzq3bYkVNdDbvhV8GL6tSmaUipqUHOiC4Y+8WxqJpXhpm3TsfIbyPLMdNRjaGYjfVmV3g0ByrNDKzBECQnVSPFVgtPnQN2F6B5DejBbJIPybClAza3F+kDdCQ/MQ7olgOzyg2PPRmeX1YieexgJHdOVZ3LUsvqgLIa6IObmLk4drfALZ56T6AhQGP/5g+Ibb5B2x7OsSGibZa025XyJ/n2Plp49kAGz9HZBMluSHOAlpCyLxlch5P7sl2yJ5Z9991XDeAl4xBO7st2WSNlc8jcHQlYpL2zDKplbk10yZmcg9SRf/755xHbpbFBczXnOiYnJzdaniYBmZyTLJYZT3jdu5SkRbPaQTe19K0lDjroIBWcRb83aYgg82WsVs9CyuOktE8+19rawDfxQgIgyTBJyZ0V2ErgY3W7C6//l0BT5h5RYuqfB9w32gbjeifePDZ+6ath01F4fw7MO1ICt7tSsf6+HHx3ZTpeHViGq+YtwQWLV6iuaBaX348Bh82AN0ODz25XZWd+XYcj24Xxz+yClEwHOu1WgIO+OQRJjfWkDv6brLenoo95CzrX3YOM6seQ538QWbUPItP7MNLNR9Qt03wEaZWPIM39BJJn3wHsuz3Qvyu0nfrANawz0i/eBfau6YHgw+WA3jmj6UHNpkhJXhO+yNhaSBlfU24UiRkbItpmnXzyyfj1119VYCMT12XQKPNtpKuXfPv95JNPqv3km/4PPvgAN9xwA3bZZRc1cJb1aVpaxiRti6WzlSziKHNopM2ztFmWbI2sRxI+Qf/7779XHdCkzbPMB5KfkmmQb/A3N9sgZU/SKU0GxDIIt7qehZPXkNKtu+++W52ndOaSwbZ09Yq3js7GNOc6SptkCYSkXbMEIBLoSPZI2h/L5yaT6KWltrRClsyFZDVkHpHcl4G/rIUjZJK9tFGW40k5l9VGWrJlEnw0h0welq51Qo4j/vnnH1XSZgWB0tHNKpWTRg7Sje6aa67BnnvuqUrT5PmSrQkPbCRokXlAcl1kLpAEnBLkSlAkJXXSLc4i1/zcc89VXeektbU0DJBASd6T/E3I3/HGsl3UPn4+2YGu6ZEZmqaw2zQM7eWEOSwDMz6NnbOV7vbAZ9iRfvpqHNDvMCz8eA36HdgFg45ufI5fODkLO7zwm/YmnxNRImNgQ0TbLBncynog0vpYFlSUQEYGxTL5PXwxTWnNLANnmYfy888/qxXiZfApJUQXXXRRs19XSr7+97//qbbE8rpyHjLQvfzyy0OZCmuyqgRd1gKdEvzIZFVp6yuD3eg1bFpC3od0DJOWytY6MeGka5wM3B988EFVSiZkYC7nFN1qelOacx1lIq+0mJbMkFwrKb+TjJIENhIIyLWTrIWckxXESOAiAUx4V7fTTjsNkydPViV3EpjIhGA5hrT4lu5tzSEBlqyJE07m7chNyPuxAhshi5h27dpVBXPSpEGCEmlHLQtxRpfQSVZGgmr5vOW9yd+hBGyXXnppTBtoOXe5jhIkyd+vXCtZ4FSyYRLYbKoZBrW9jxf5ceGIlg+5Bu+cBRMrYr6fN5N1JDsDHQ567dMJ/Q+M/TdskXxNdGGd5Gp8mjM4D6jFp0eUMDSzObM1iYhoqyJBhmQK7rrrrogsAnU8ktmTDJFk2KLXVaK20dgcm4tH6Xj8oIbARrsr/uK25s2NB6WfP78Kv31c3DCh367h6me3wycfvhIKeDfW6c+vnRQKbOqQjHnaEJQjGz5ICVsg0D7Y3zpzzqj5irRbmrRfgXlnq59LR8KMDRHRNkzm9kgmQbqkUccgc2uiszJSGifzoKRUkEFN4qnfeCflJjn8nB7Y97jOmPpNMXoOTUO/7dPjtghvjJXtkaBoujYK1Vr8du1EHRkDGyKibYwsPDdlyhRVgjZ9+nRMnDhxo13htmZSnmYtftkY+Ra8tdpCt4TMcZL5RRKMSpmarG0jjQekEcMll1zS3qdHcRzSd8tMek/LdmD/E1s2Gd86g2qkxQ9qYhvAEXU4DGyIiLYx0hxBFueUBTZlvo7MQ9lW3X///fjss882uo/MKXr22WeRKKTdtcyHkoYBFRUVKiiVeUpnnXVWTLtoSgzH9E+cifkOWTxTZiFENQvI3I2ZPur4GNgQEW1jpFGANeF9Wydd36Sz2MZIA4VEIoHNAw880N6nQfHECRhkm91aiLMdWQtuJqEe3bAaa9Aj9Jhm+jHyk8A6T5Qo2v9vpiNiYENERNusvn37qhtRqwU2CdJG2QpsxFBzFnLMEpRpOfCaGrp8cBocWeymRx3ftrPSEREREVE7+HFFZPeAn06LndDy2EFtNySTAKcr1mKoORvb37odOo3t1WavTdSamLEhIiIiai06sLIqcmWNfXvbUXiFjou+9KHGAzx2iB39c1o5sEl1ATWRbablrOwn7dK6r0stwrVYWoYZGyIiIqIt4LiBWuArYz2YFrFp6veD+8QOtwpSdbx3vBNfnuJs/aBGTumnW2MHyzL3Z3DDXBuijo6BDREREdEW8M44JwpcwYDGoatR1nW7AJ1TE2Cezah+0J45B6bDFghwumZBK3qmvc+KaItiKRoRERHRFqBrGgovc2JhiYF/N5g4tK+OVGcCfYd83mho541u77MgajUMbIiIiIi2oIG5OgbmtvdZUEdmst1ziyTQ1whEREREREQtw8CGiIiIiIg6PJaiERERERElEJaitQwzNkRERERE1OExsCEiIiLaytStq8Cfj/+Jb6aUw+fnco+0bWApGhEREdFWpHqvm+GdugxVnftgfeqv+NZbh91XVUN3+4Ge2UibcQX07NT2Pk3aKJaitQQzNkREREQdQEW9CZ+xiezLXwvgnrYCmZ56jFk5F6fO/xM/de6PBWnZgcdXlqE679Y2OV+itsbAhoiIiCiBTV3rg+P/3Mh6wAPH/3mwzyvuxnceew+y3TXQEQiAbKaJO/74COtSU0K7mAbw5osrsL7U1xanT9RmGNgQERERJbBdXvLDZ2qAFrj9ugqY+KUn/s6F5TGDuyS/D2NWLowocvrp/bU45uYSXPZYSeuePFEbYmBDRERElKA2VHul928kTcMTfzdSkmbEbpI9DdjU739364kjz7kcM3v1Vff/mudDcSUzN4nY7rkpN4rEwIaIiIgoQf21Zkt1NAsMgkeuWYmjZv8T8cgtz5Vuodcgal8MbIiIiIgS1LKyLRPSaPCH7p88/U84/A1ZmmXr2A6atg4MbIiIiIgS1KgucTaagUDEv6kOaUEG7DDgDN1P87iR5PU27MDRYMIxm3ijSPxTJiIiIkpQmclxNkoTAROo2EhzNIsfdtSiU8S6KP926Y6qpIYDGw3JHKIOjYENERERUYLy+IOBTDTNRE5yw/Zar4nvl/pR7nBF7WjCgCNiS1FaRsR9PdBXgKjDY2BDRERt4plnnsGoUaOwdu3ajW4jogZZrobSs8a8VrIzsu7xYfTLHnS/4nF49YbhnR+SmYkMjPZdsgBJ3oZ20XaOBmkrYW/vEyAiIuoIamtr8dprr2HevHlYsGABioqKMGLECDz77LONPue3337Diy++iIULF8LpdGLnnXfGpZdeim7dusXsu3z5cjz22GOYPn06vF4vtttuO5x//vnqObT18vhNFbfIdBmnDbDpkUFIac3Gn1/rt+PXukGhrM7eqxbCYcTp+Ry0Pj0DL++8F9y2hiHgRnandsJWzi3DwIaIiNrNhAkTcNZZZ6lBf6IrLy9XQUxubq4KOkpKNr6w4Q8//IDrrrsOAwYMwGWXXYbq6mq8+eab6j2/+uqryM/PD+27evVqtd1ms+GMM85AWloaPvzwQ0ycOBGPPvoodt111zZ4h7SluH2mClK8hgQr8Qeor/7rxVlfmSqgiZ4FflR/4KIdNbwz34+fl8iD8VMqhmniqQ17RZSqFaVmRuwjM2wkbwPYVCbn6qNOQlFGVsQ+fs6xoa0EAxsiImo3drtd3TqCvLw8fP755+jUSSZiA3vvvXej+/p8Ptx3331q3+effx4pKSlq+x577IHTTz9dBUg33XRTaP/HH38cVVVVKuAZNGiQ2nb44YfjhBNOwD333IP3338fWrx5FtQuXp7tx/nfmHAbQJIO7NcDqDeABcUm1kmGRVIw8nnJR6ayIWZgvr8pG4KPSUQjtzif6ycLgU9m+wIBj7yAFRyF2mGZgNuPpDsNwOgSGM0Fy9Wmd+mNTwfshCMXBdaq0VU/tAp4kIOpPfvEBDWKnE6ww5oelTFqjHRki84ubQ7TNFG6qg5puU64Uu3qvrpUW/A1aOvXMf7XhIiIWo3b7cZLL72Er7/+GoWFhXA4HGpALoNwyTRYPvroI7z77ruqZEqCke233x7nnnsudtxxx4jjGYaBl19+WWUciouL0b17d4wfPz7ua8scm+eeew6ffPIJunbtqrbdfvvt+OyzzzBt2rSY/WU+zhFHHKH2ETI356ijjlLn0bdvX0yaNAkrVqxQ2ZCzzz5bPbZ+/Xo8+OCD6ngScOy77764/vrrkZqa2qzrJFklK6jZlL///hsbNmzABRdcEApqhAQtI0eOxDfffKOyOXId6+rq8Msvv6jtVlAj5HnHHHMMnn76acyZM0dd7/DjSzAkJW6S3RkzZgzGjh2LE088UV0LKWGjLaPeZ+K5f018vMjAvxtMbKgPBhfBgEQCmq+WB6exqO2yNRjUBH+V/6fiDhWgqAgHkAG7FeBEk90ceiAokuVmNDPwXHmOzwgkYGQejTxXSsrkp1+2SzBg4uhjLsX3b/4f9l+9ECX2NHzUby90r6rEsuy8uO+xpMbEsIlFyPb5kePxwOU3UJLkhEfTkOd1w+aXU9Hgdtjh1zTopok6XVMtCVTAbZroXrEBN3w3CdnuGnw2dH9M6TcKjhQb9tk/EznZOqa8vhieai+6Vxbh4Fm/IKW2Fr93H4X16IrU6jp4kwxUJyerY2sGYNpsMHQdSW4Phq1ZhnxtFWyoR4nZGRXoDJ9DR2luGtJ274K9H9sNKTlJ2LowoGsJBjZERNs4yQhIYCEZglNPPRV+vx+rVq3C1KlTQ/tIOdQrr7yCoUOH4qKLLlLzTSRwkQH0Aw88gL322iu070MPPaRKrmT+ySmnnILS0lL1GvHmlWwpMpflgw8+wPHHH4+MjAx8/PHH+M9//qOCtCeeeELNU5Hznjt3rnqvEqTccsstrXY+8jpi2LBhMY9JgCLXVgKwfv36YdGiRfB4PI3uax3P+n3GjBmqRE3e55lnnon09HR8++23mDlzZqu9n23Z8Z8Y+HxJcBKKlV2JHnOG35egwx62wQp45P+p6MY6RjDAaYwZ9bvPRDCSCNSOWcGVipXk/+mAHngN02vg2LGXYeXTVyHJcKN/+QbsVLgBPldyo4PBLHmeTUdxcpKadOOxBVqlrbbbkeT3wykJprDsUpLPB7/NFjhNTcOqrE54eJ+TcPnkd/Fnn5EwoMNXa+Lrz8tVfGbqeUA6sDy9C+Zm9cQdXzyEQxd/g7+du2NW1z5wpwQCE7kkGVX1cEgdHwCvQ0c/7yxk+SpQj1RUIg25qIP8X0FlDeb4NLx/xI84bfIhzGoSAxsiom3dTz/9pLIzd9xxR9zHJUMjJVLDhw9X2QMJFoRkE8aNG6eClt13313ND5F933rrLRVISEZBtokDDjhAlWC1lmXLlqlsUpcugdUMDzroIBWo3XrrrSrrdNppp4X2lZIvKSm76qqrIrIpW5Jka0RBQUHMY9Y22UcCm6bua5HskwzgXnjhBZUNE/I5nHfeea3yXrZlc4pNfL40UBLVpC/QVSalkcesICRwJ+JHXFaGxzqeBDbRk/wlgyM3ydaokq3AAZ2mgae/nqQOkuw3MKJwHUzYMWL1Ctj9fviC/y4be1lv1ON+TQraIoMwt80GW9RbWJjfC98O2gtGVP/oUGwXVJqajSm9hmO/xX+hn3cJpiUPCD2WXOsNBTVCfl/q6ocRvukoRVeY6lUbjtu9pBz/pqdg3bQSdN05fkaKth1s8EdEtI2TUqalS5di8eLFcR//+eefVb27TGq3ghoh5V5HHnkk1q1bp7qEhe8rmR8rqBEy2b41J8Dvt99+oaBGZGdno1evXtB1Xc1TCSelc1KS1potpuvrpV4JEdfLYjVKsPZpzr7SsECyN1JOZwU1QkraTj75ZCQaydZJqaNFGihIYGmRTFV0Ewb5e9rYfSktVPMv2uA1Khs6Im9aS5aBl2AnXnMBM5j1kZsVvDiCGR6rdE0laeJnfTxSTuquQbrXDR1+pKBQbS9OS99oUNOctxF3P01T5WNNUecIZGh0MzJac0ipXZQye7b6acT5Pt4WbOlWU1Gd8H9Xze2K1pQbRWJgQ0S0jbvyyivV/2CfdNJJOProo3HnnXeqLI7MlRFWACDZhWjWtjVr1kT87N27d8y+ffr0abX3EK/MTUq0ZMJ/dMc1KeESFRUVrXY+SUmBQZu0bY4mA6LwfZqzr/VZSNAWLd629paTkwOXyxURRMvnYpHPRrrMhQsPUOPd79y5c0TJUWu+xq5dgAEyptaaMapSdVdhw/7Q742EDHJsa/6Mel/Bn3q8jE/wRMJL3RoRXjZmgwc63JjVpXujpxD+FrSoYEkG/DL3JZzTkFk3kTLrKnHIvF+gG5Ft1qLfud3vxaiV/6qB+UpHb7jqZCJRgDfOojrp/srA8YMBWriizDQkJ+kYMLp3wv9dUetjYENEtI2TbIfMO5E5KVJCJvM/rr76ajV/Jt5gu7U1VicvWZbGSGamOdtF+LezW5rVylnWuolmbbP2ac6+1LZ0TcNXx9lw7AAN6Y6weTLBjl0RN9XCKxglyJ+qTORXk/oRMbE/xIxTlqZvamRmZWviBD5Rkr3uqGfq0BtZsEadotrJhMvvR0FdfSATYppI93jQrbYGWXXV6jGH369+yn8ZvDIvx0ow+X2486tn0L94BS75eRK6lK9Xxy7Is2F4ZzeSvG44fB50rtiAC395Eyn1BqZ22hV/9xiM9Jp6ZFRXq7k9/mDPhIj3UmPDekd31KqFfqrh1m2os9uxIi8L/qEFGPvBfhu/GLTN4BwbIiJCZmYmDjvsMHWTAb8sFCnNAqS0zMqGLFmyJKL8SUgJm7D2sX7KXJvofWUeTFOEZ1TkvCxWNqgjGDJkiPo5a9asmBK82bNnq45sVoalf//+6ttf2Tea7Bt+POsbYGk8EC3eNtp8fbM0vH9M5HDp4m99eNLq1WACA7OBynpgfW0wQJGMigQxKpgJy8qoEbsRG9yEd1JTt2BQFF6mpsrQwk5CdUjzx20XLcqT00K/e5ECEw4MX7uqoRV1mKxkDZ/fW4DCEh8659lV6+eVpX70yLHDJSVwllXFMJKdKEYKkpM0pKfZ1H8v5CZfSGjmA8DKDRiamYo7stPUcSLaR0uwpL5s2F3d3QXAzkZggdIZby9HWZEHO5/aG+m5ThT9VYS6Ug96HtINuq0hiisIluKxDTTFw4wNEdE2TDqghdeNCxmgWG2HJbjYZ5991DZpIBCeNZFWzp9++qkabFv7y9wP2ff1119Xx7bMnz8fU6ZMadI59ezZU/2M3v+1115DRyGtm6UMTlpkSwc5i7RnllbNo0ePDq3fIw0MZE0c2S6PW+R58ny5HtKNTsgxJciRgFMW9bTI5yKd6KhtPDHGDvNqO9xX2GBeY8eCc+xYN9EB81oHZpxpw3MHAQvP0bDgPDt819vhvc6B0kvtWHieDd5rHDCvc2KXAjPYECBszZtg+qNTiomj+gSyJyrbI/NOvFEZRk3DXxN05KEs5vwKqiuw3/JAZz4fnKhDoAlFt8py7LQ6NgCWuf4Ou4bunRyw2zQ4HTr6d3JEBjWiRx70vAwU5NlVUBM4DU1lRlWmVYKW3p2A7LT4a+LEyaBKgKLbNIw4pQ8OvHwQMvJdalun3Tuh9+E9IoIaa/9tIagxm3ijSMzYEBFtw2TwfMghh6jgRYITmXQv8zjee+89lTmR7VIGJR3NJIMja6TImilWu2f5KXNyrEYBMrdGOnS98847uPDCC1U3NJmEK/cHDBgQajKwMQcffDCefPJJ3H333SrzI+fxxx9/oLy8HO3t7bffDgWCEkzIhGNZgFMMHDhQXS8hQYuU891www0455xz1BozNTU1eOONN9Q1jl5nRto3Swmg/JQW2ZLRkesr3dAefvjhiPI86fJ28cUXY8KECaq9tdT+S7tnK+hky9u244wz8X94J13domUnya1h/7/Gu7C+xsSniwz0zDCxpEJD/ywTo3vbVAmcZdZ6D3Z4xh82B6fBTl1smJA7GfcUHx4qaetVVoSP3nkYyT6rjLThC4alOfn4p0fs/Ld4S+kQdUQMbIiItmEyKV26aUl2RG4SqEhWQAbosqimNbfj0ksvRY8ePVRLZWnjLB28JItw1113Yaeddoo4pgzoZVKtDMwfeeQR9TxZjHLlypVNCmxkoC7Pk7bGsuBmcnKyCpAkgNp///3RniRrFN7pSIJAaYEtZOFQK7ARkpWRycfSllmCEyk3kzlMci2jWzvLNZL9pARQFkuVuU3SSU7WD4ouZZNskOwn6/PI9ZEJzRJsSoB61llnRUx4psTWOVXDuTtuvEvZJwuC83PkJhPrJX6R34NBVV9XGTK1GlSYgSzJzqsXY8fChqyMH9J4IrDvmqxAdzGirZVmtubsSSIiImoT33//vQogJdMlWS/aOuwzyY1fVxiAQ2/I2Eh5mgl4brCp4Fak73oGXpoJvHDxRehe3ZDddCMdbgTWdylPSsapp18Ib7AM0pKTBnxxb6e2fFu0CSu0/zZpv17mja1+Lh0J59gQERF1IPJ9ZPj6GkLK0GRek5QESkaHth6jpF9EeFAjZN5JVBXc8UNs+PqMpIigJqBhx6z6Olz//WfIqWlY80VELSVD1GGxFI2IiLZZsvClLLy3KVKelyhkbRtZGFVKz6SzmjR4kDk2ixYtwplnnplQ50qb76zhGh6aHueBJs6lsqMO4WHw3ksXoktFGS46YXxoWxPX1CRKeAxsiIhomyUBwR133LHJ/aZNm4ZEIY0J9txzT9UZTTrTCQlwpAxNGjfQ1mVu5OL2zWaDF0koQT1yVPZG5h/U2B3QDANmMKIx2W8i4cjipdR8DGyIiGibtfvuu6tJ+B2JlJvddttt7X0a1Eb+DaxzGasZ414fHKEnyP8fXrgWObXVKEkLrBmVn8VBNG0dGNgQEdE2S8q2WLpFiax/biMPNCMWscONhhWoAhyhdtDA7WdltezkiBIMqyqJiIiIEtSZwyXbEmv8sKZHNg5UQ4sKbbRgT9zueRr6dHFu3kkSJQgGNkREREQJyqZrmCTrb4bpmwW8eGQjwUi6rFsTSUIgLWyhTolpduit46GLM/HefyLXVKJEoTXxRuFYikZERESUwM4a7sQZO5iYssZAr0wNXdI38r30IxOAsyPnjRmww0BDICTD4Tv+b7vWPGWidsGMDREREVGC0zUNu3W3bTyoEeMPBHJSIzaVONIaumxpgOuLCa14pkTthxkbIiIioq1J8SvAc9+g7pPpWHPw7uh54b5wGH6Y5XXQC9Lb++yoCYJToKiZGNgQERERbU1k8c7zDkbyeQejf2ijHRqDGtrKsRSNiIiIiIg6PAY2RERERETU4bEUjYiIiIgogYSaPVCzMGNDREREREQdHgMbIiIiog6u3gesqDBhmuynRdsulqIRERERtcCvq3w49H2gxgdkOYHfTtbw/UoT904BuqUD7xypo1dm63+H/FzVPrjgccDU/LD7/Hh6dx8m7Bu5lg11LCxFaxlmbIiIiIiaye0zsM/bgaBGlHuA7V82cdmPwJoaYMp6oPdzBpaUBndoJXPdnTDN6AdTWjwD8NltOPcvB3x1rfu6RImIgQ0RERFRM415x2jSfju90rrn8XzNAYF1a8KYuo6HXimC22finXl+zCxq2rkSdXQsRSMiIiJqpt/XNm2/qlZOnPj8WtzR3DuFDlx7vzcQ9Jgmcpwmiq9wQosKgigxsRStZZixISIiImomPxJDl6ry2I2mib8dGQ2ZHE1DqVfH8a9Wtfn5EbUlBjZEREREHZTL64ndaAbK0aL9sCRRwjGi1sHAhoiIiKiDWpjXRWVowulm/Dk1Ll+cIIhoK8LAhoiIiKiZEmUGhGmzxTQPMHQd3SpqIrZl1HvQubKsjc+OWsps4o0isXkAERERUTMlyqAy2etBnSspYptmmjhq8VpUuBxYkp2GdI8P2xVX4LeuWe12nkRtgYENERERUQc0feoG1DnzY7Y7DBPJPr+6da6pD213seszbeVYikZERJTARo0ahdtvv729T4MS0NVfNAQt4VLddViTFpnFkZimONnWRmdGm09r4o3CMbAhIiIi6oDmOnJi5teIw//9F5M7Z2Jleoq6X+2w4+t+XbAiK6MdzpKo7bAUjYiIiKgDqnM4A7/UeIBab+B3hw1vDBkBI82BWV4/ZuUkY0GnPBUAHTV7KhYfuQT9Pz29Xc+bqLUwsCEiIiLqYG75shbDVy2Gz2fDtMxuDQ/4fcjwefHkS59gx9WFatPfvbvgslMOQa+SCvxY6MHqPg9i38WXQ7OxcCdRmSwzaxEGNkRERM3k9Xrxxhtv4Ouvv8aKFStgt9vRs2dPHHHEETjxxBND+61duxZPPfUU/vrrL1RVVaGgoAAHHXQQJkyYgKSkyDkQS5YswcMPP4x//vkHTqcTe+yxB6688spGz+Gbb77B22+/jUWLFsHv96N///44/fTTMXr06FZ979R8l/3gR7YLyEoCUmzAskqgsBY4cRCwsNTEOwuAMjewZ1dgXQ2wohJIdwJzigGJPRweDwo3eJFTWYvdl6/BPoXz0dmoxTXrgJvHHBDzejd+NzkU1IiRy9fhnnd+wBMH7YVf+4yEZhq4dsfHcPDCGfi190ikeOqx1/LpKHcV4Md99oL7kCHIPbAPVtfasH8/G7Yr4Nwc6hgY2BARETUzqJk4cSL+/vtv7Lbbbjj00ENVILJ48WL8+OOPocBm3bp1OPPMM1FdXY3jjz9eBT7ynEmTJmHmzJl48sknVUAk1qxZg3PPPRcejwcnnHACOnXqhF9//RWXXHJJ3HOQ57744osq+Lngggug67p67euvvx7XXnutOgYljkenx28OPWl25P05JXF2KqsLlZmVOF34bGAfXD79C/jKesIPG4oy0mOeMmxtQ1Bjya6tC/1uajqe2fkIHDf3Vxy18AfM6DYU7w89HnvNmY89vl2I81P74utCeU2vmsLz6FFJmLhHsOyNKIExsCEiImoGydRIgDJ+/HhcfPHFEY8ZRkM/3SeeeAJlZWUqC7PXXnupbePGjcMjjzyCV199FZ999hmOOeaYUKBSWVmJp59+WnVBExKcXHPNNViwYEHEa8yfP18FNdGvf9JJJ+Gqq65Sr3v44YcjNTW1Va8DtQGvv2HujMUEbNU5cCOQRUnTTFT6/fDKQp0yxcbvQ4Fng/RGi3jaytzsiPvlyemocSQjy12DHdfMwe+9d0a9w4E5+bn4emD/hpczgRu/qsf4UQ6kOlkeRYmNxZVERETN8NVXXyEjIwPnnHNOzGOSObECnF9++QWDBg0KBTWWs846S+33008/hfaV7MyQIUNCQY3QNA1nnHFGzGt8+eWX6jEJXsrLyyNu++yzD2pqajBr1iwkgtLSUrjd7tB9yV5JSZ5FMlQlJZFpCsl0bez++vXrYcpou51fA/Cj1fniLDyjafDoDd9Lj1i5Aa9/8SYOXLMaO5eUYfKbd2OHmllwoabhKboHXw4fFHGY7QuXqaDGkltbhnqnE0tzYhfxrHID6yrNhP48OsJrNHeOTVNuFIkZGyIiomZYuXKlClhcLlej+0impra2Fn379o15LDMzE3l5ear8zBpAyb69evWK2Tfe85ctW6YGYFLe1pjoAVd7ycnJibiflpYWcV9K+HJzcyO2denSZaP3O3funCCv4UOrc8bObdFME73qirFUS1YlZcf/Ng+fHTAcz33xNl7b9TC49FTY4UUfTEcdMiTBgxWZvaG5XLAbBny6jmHrl+LO718KHdOAhpKkPGTVrMHuK9fAZhjwB4N00TdHU7fE/jwS/zWo9TGwISIi6mAkY/Poo4+GMkTR+vXr1+bnRM2na4ARf/pNgHQOyE4CyutVCZpwGD4k2zZgO7eJNfY82Gu8KNTzccapp+LoWbOwLrUr8tIq0Km6EC7Uw4801CYlI8PvR7rfj4yaCoyf8g26V0i5GlDjSMLvvXbDfv/KhB8T3SorcerM2Xhrx6HwaDYMzNPx+snJ0OVkiRIcAxsiIqJmkMzK8uXLVSmKfGsbT3Z2tprjsnTp0pjHZC5NcXExBg4cGNo3JSVFdVeLFu/5PXr0wO+//66+Ye7Tp88WeU/UupacoyPJriHFHghkfIaJDXXA4FwNpXVAnd9EtRvonqGpOS2rq0z0zNDw3QrJpTjRM82JJ373qCfvXlmJr0fsjN2mL0CqvRioz1YZlgU53XDN0f1x25efYb/Fs1Dh7IYvh+6BRZ16Iq2+FrstWQDdY6LbyjKsdxZgXmof9OiVhqqjRmBYkQe+tXVwnzoCqcM74f78VDyXl4QNNSa6ZmgqkKa2tbF4lxrHwIaIiKgZDjnkEJUteeGFF3DhhRdGPCYlYjIIlEzK3nvvrebjSBAi3cssL730kppXs99++6n7NptNzcOR9s3Tpk0LzbORY73yyisxr3/YYYepNs/SJOCee+5Rz48uQ4sukaH21TcrOrOmoSA4tz8vJXAfYc3NhrgCgcQxAxo+2+ePs4ZsqcCE3uq3UefOwx2vTsOY6cuwMj8TbpuO73psj+4jy2Dac7GkoKfar97hgmYY6LloFZ4fvQNe2bMSnc+/QD2WuZHz7pbJgIY6FgY2REREzXDyySeryf4S2MydOxe77rqrmm8j2RXJukiHMyEdy2T9mquvvlrNh5FMy/Tp0/Htt99ixIgRas0by0UXXaQCoMsvv1y1i5b1buQ1ZK5OtKFDh+K8887Ds88+i1NOOUWtW5Ofn6+yQPPmzcPkyZPx559/tuk1ofaRYRqoTnLg2gkHwusMDOkmb98bc3oX4LzpSyOmlpu6ji933Q5P3NYNnftFzhch2lowsCEiImoGh8OBxx9/HK+99ppaoFMCGSlJk3VqjjzyyIiJw5KdkRbO0slMOirJ+jTSplkW6LTWsBHdu3fH888/j4ceekhlY6wFOv/zn/+oBT2jSWAjXdTeeustvPnmm6irq1OTnWVujQRStG2oTknGtzv1CQU1lsqUZOiGoYKZcMdPmYUB/SK7oxFtTTQzvLcdEREREW2Sdn/Tu6KZV7fO98inPLoai2d5MHVQoOQs3KFzlmGHkoZ2zsXJLvRavhi3/NUQfFPimqc91KT9BptXtPq5dCTM2BARERF1QE9O6IQd7yyM+9iXg3phfnk1+pZVo8LlwL+dstG5ZxZuafOzJGo7DGyIiIiIOqBUJ7AyM1c6TaiFOy02vx9+aFiWna5uFk1rg0VFidpR/Ab4RERERJTwdL+hghpZuNOiFteM06J5t5WL2vjsqKVMaE26USQGNkREREQdlCGLeMpAODyQ0TTk1FbF7Dt8/cq2PDWiNsfAhoiIiKiDighowvQpKYTT5w3dP2DxLNQ64i8oS7S14BwbIiIiog7K4ffDG9Y63LI8rRNOnPk7BhWvRY+yEizK64xdztixXc6Rmo8ti1uGgQ0RERFRM7l0wG2091kANgNoyMs02BM1+HPkKLxqpsDh9+HW7iU48vQe7XCGRG2HgQ0RERFRM12zM3DXX5ve77h+rXseWUnVWA9X5EbTxAXHZOLQ3TKCG6QELaV1T4QoAXCODREREVEz3bm3HY6oUVSSLfJ+z3TgvbGt+x3yVWlfAkZk4VKq2xsW1BBtO5ixISIiImqBmstsuPQHP75dDhw3CPi/vW3QNQ3/Fhnokgbkp7T+98cZNg8uS/kSL/kOQa3bRM8M4M+Lklr9dal1sZVzyzCwISIiImoBh03DU2Nih1I7FLRtQcwQ53psOF+Dg13PaBvHUjQiIiIiIurwmLEhIiIiIkogLEVrGWZsiIiIiIiow2NgQ0REREREHR4DGyIiIiIi6vA4x4aIiIgogc3b4McuL/pQ7QXsGvDUoTrOGeGI2W/J0ipo6cnom8/hXUcXuTIRNRX/8omIiIgS2JBnfKHffSZw7hcGfIYbxwwITDCvWmLDX31vQnFyBpZn5qGoX0/c9MqhSHVyAjptW1iKRkRERJSgnv3bHXf7hV8BXR43cX/pQRj9wkwM3bAGdUlJyHNXY8fp03Hrw4va/FyJ2hszNkREREQJ6vnpcTZKIkYLZGMW+bvAafow+NIHUZiepbYle9zYYe7SNj5T2pLY7rllmLEhIiIiSlDFdY0HNZYrDjsjFNSIOqcLs7v1xi0/1LfRWRIlBgY2RERERAlK5tREiApq5P6vvQfHPC/NW4+7fmvdcyNKNAxsiIiIiBJUavSkATMq0jFNFFRXxDwv2edt3ROjVi9Fa8qNIjGwISIiIkpQ/uiMjRkd3Gj44tV7MXbuFGiGAd0wcMrM3zB8DefY0LaHzQOIiIiIElTtphIvpoH+ZYX44K2HUJSaAZthILeuGlePPqmNzpAocTBjQ0RERJSg7NEjNV2LnGej6/il13bq14KaShXUqOcFUjtteapE7Y4ZGyIiIqIE5fFvep/1YR3RLPU2B+OaDowfXcswsCEiIiJKUDVN6AEwfO1y9XNFZh6+HDAc3StL8WMwi0O0LWFgQ0RE1AS1tbV47bXXMG/ePCxYsABFRUUYMWIEnn322Uaf89tvv+HFF1/EwoUL4XQ6sfPOO+PSSy9Ft27dYvZdvnw5HnvsMUyfPh1erxfbbbcdzj//fPUc2nbp0aVo0jggvBTNMHDjwSejwpmMX3tvF+qUlSUladGtoYm2cgxsiIiImqC8vFwFMbm5uSroKCkp2ej+P/zwA6677joMGDAAl112Gaqrq/Hmm29iwoQJePXVV5Gfnx/ad/Xq1Wq7zWbDGWecgbS0NHz44YeYOHEiHn30Uey6665t8A5pS6n3GjjkHT9+WxOILQyJP6S2KBhnfDxWw2F9bWq6jL6J4CPdAZS743RF06x6JQ2fDhoZ87zy5DT1s9ptIM2lw+M34bQx0Oko2Mq5ZTTTjG6ITkRElFhqamqQmprarufg8XhQVlaGTp06qft77703Bg8eHDdj4/P5cOSRR6pA5Z133kFKSoraLpme008/HUcffTRuuumm0P7XX3+9CoQk4Bk0aFAoQ3TCCSeoTM/7778Pjd++J5xqj4nF5cBfa3y49pdABzO1oKYKYkKRR4D1qxXgWB+ninrUiEzdleCje6qJFZWAPxQRRR0j/EDqZcKiptDDcYZ3sk2OF9rfgEMDTF2D064j2Q64dBPrqg2YVn8p6/XVOQdew+Y3sMvqxfBrQFlKOvo46vFnTi9U+XV1Vna3F7stWw2H38Csbp1QnpqEfvVu2KBhXUYSfB5DtbHO9PkwdjcXrtovGe46A73ybUhx8e9cTNeeaNJ+I8yLW/1cOhJ2RSMioojBu5ROyYB6jz32wH777YcrrrgC8+fPj9hv2rRpGDVqFD799FN88sknav/dd98dRxxxBF5++eW4x547dy6uvvpqHHjggWrfY489Fi+88IIKAsKdd955KiiQLMa1116LAw44APvuu2/o8b///hvjx4/HnnvuiYMPPhj3338/lixZos7nmWeeUfvI+cr9J56IPziQDIocs66ursnXRgIMK6jZFDnHDRs24JhjjgkFNUKClpEjR+Kbb74JvW85h19++UVtt4IaIc+T569cuRJz5syJOf6mrgG1rpdnG+j6tB87vezDBd8BlV4NPiu4CAWhwQ5m6tawKRQkyE1GYrZgtzNdg8cwsbRag996jtz0eCO24AGDWZtGWa9jBSkSUWg6YNPVTy90+DQban1ASb2GtTUaTM0WqIGT58l+crPuQ4PfZsMfvQbhmEUz8MwXk/BrandUGrZglkGDz+nAb4P64Mch/VCXkoSxa4qxe1EFdikqx8FLi6AbJmoddqxLTsIrU3w49NZiHPu/Mux7Uwk+nVq/RT8n2rawFI2IiBQZaF9yySX4999/cdhhh6lgRcqnpCRKyqSee+45DBkyJOI5kkkoLS3FUUcdhfT0dHz55ZdqnogEAIccckjEXJNrrrkGPXr0wGmnnYaMjAzMmjVLDcJl/sk999wTcVzJVsj8kh122AEXXXSReg0xY8YMVZ4lzz/zzDPVa3777beYOXNmxPOlVEyyKZ9//jkuuOAClTmxyNyYP//8U51zcnJyq1xLCeLEsGHDYh7bfvvtMXXqVKxYsQL9+vXDokWLVEDZ2L7W8azfm3oNqPUU1pg471sDHknPmMGgJESL/VUyJOEJFPV7cK5MdGLFCkKs51rHbk6BjbVv+LFUgCMBSlh2R4IVK4Nj7Wtlmxp7vbBE1B37jsXaByfipNm/Y9KO+0W+h+Dzt6+oRqpKPQUkGQZ2KK/GrwXZ6n610wGXvwZeuwPV9SZueaMKew12IjttW//unZmrlmBgQ0REyttvv60yARKYSEbFcvzxx+PEE0/Eww8/HFN2tX79erz33ntqToiQEivJ2sixrMDG7XbjzjvvVAPzp556CnZ74H96jjvuODX/5KGHHgplgCwVFRXqcQlqwj344IOqJEsyPd27d1fbxo0bp7I80caOHYv//ve/+OOPP7DXXnuFtkuWye/3q3NtLZKtEQUFBTGPWdtkHwlsmrpvS64BtY6p680mtWEO2VhMElWx1vCc5swUCO5rbiRAsrZJvNAQZwQCJ58fcNiaPZ52251YnZGD7Lqa2AeDwU2GN/ZCZXgjs7Qemx56WbcXmL3Sh72HOJt+IkRB23o4TEREQZJt6d27t8p0yER56yaZHJm8LhmB+vrIMhEpGbOCGpGUlKQyD1I+Zfnrr7/URHvZVzJA4ceWUiprn2gyFyWcHEMyF1JCZg3ohQRKJ598cszzJbCScq6PP/44tE2mlUrpXP/+/UMZkNZgXSeHwxG3pC18n+bs29xr0N4k0yaBrUU+/6qqqtB9yVRFN2FYt27dRu9LMB0+Pbg9XqN3ch20pqw0srFdwufcNEV4OVu8x2KO31jGJc5cnOhsUhODqm6VpRiyYQ0KUzMbff21ya6Yh2K3NZyTVL1l2EoS7jPfEq9BrY8ZGyIiUpYtW6b+h3z06NGN7iPBSOfOnUP347UtzszMVBmX8OOK//znP40eN3qAkJ2drUqswq1du1b97NWrV8zz422ToEbmn0iGRib9yzElI7VmzRpcddVVaE0S4Alp2xxNBkTh+zRn3+Zeg/aWk5MTcT88CLYCN+kyF65Lly4bvR/+99der7F911Tcsrsfd/5hBgbDUs4VNrm+SRrbNbqdc/RzYkrXAj96l2/A6MWzsCQrHz/23b7xY4Q3IxAeH+Cyx29wEIfu98PQbRhQsg6vfPQ03h26K97efrfY9xA0NzMVmV4f+lfVqvurkl2Yn5aiXsNmGOhUUx8okQtevosPS8HwQakJ95lvideg1sfAhoiIQiSTIc0CGiPBQbjwuSuNsb4FlQn7AwcOjLtPeOvj8IH85pJyNJkjJHNtZG6PZG9kQCJziFqT9X5kPk+fPn0iHpNt4fuE7xstel9KHHfsacOpg03M3GDir9V+PP4P4A7PfoTm18QJSEK/x8mMGBocNhNeI/iwFTSFHyv8PoBTZv6Glz94CnYjUGP2+YDhOPKUa2DKv8+I+TxmIJDxGcE+1CbgCv4bNg01/UY3zUBnNyPqRYIZI5vfJy110bmqAtcecAqmdesJl9cLnyNYBGS9jjpvHaZh4K+sNEzJSYUdOvw2LfDeDANJhh9j9k7DEf10pBoGhvRwqM5oxHbPLcXAhoiIFJnYL5kNWRBSj1kVsOV69uypfspE/c1Zj8X69lMm3UeLt01IswPpNCYBjcypkZbKUsYlWaXWZDVZkAYJ0e959uzZqnW1lWGRYFKCLdk3muwbfryWXANqPQNzNHUbN0jH/Qc2bF9d6cfh7xmYtQEww8enwcDks+OA3brakOrUkGTXYJimahWd5owdzA5+wo35ZVEbrYYFpgmb348HvnotFNSIwxfNxFHzpuHjobvgnWPtOLCvjiVlwMiuGnStaV8a+AwTbh/UOUayuvzttMlj1HhMJNmlvIyDdGobnGNDRETK4YcfrkrCXn/99biPb2pBysZIIwIp63jppZciStQsMn9E1qnZlLy8PDXA//nnn1UraIvMAZKFLzeWtZFyuHvvvVeV2kkL5dYmrZvlfD/66CPV4c0iHeCkHE7K/awmClIyJ2viyHZ53CLPk+dLYDh06NDNugbUtrpn2DDzbAeM6xwwrw3cii+xYe3FdvX74f0dyE3RVVAjZJHOeEGNCGsoFim4SGdWfQ06V8f+u5o49Rv1c9z2duSk6Ni5m77JxUDD2XUtTlDTPPJ8BjXUlpixISIiRSafyyT+Rx55RLUjlsyNZBZkUq3cl6xCS9ZIkUzNHXfcodawkU5n0mZZskMyEXf58uX48ccfcd9990V0RWuMlLNdfPHFqv20dGuTundpdWytCRNvEUtpIiDvSZojyJygXXbZBS0l3d6sCcTymnJtnn/+eXVfyuz22Wcf9bsELfJ+b7jhBpxzzjkquJLg7Y033lDlfNLKOpy0b5ZrLD9POeUUdd2lhE66oUk3uvD31ZJrQO0vN7ll3yVXb6z7mqbBp8cv3TJYytShNacnHjVgYENERKHBuAyipX3zF198EQpiZH6HZAykjXNLSdZGFu6UmwQYUvIm67BIZ69TTz1VtX1uaiZE2lHLwpuTJk1SDQbGjBmjgpezzjoLLldsByYZ+Ms+0g1NOrNtzsD/tddei+h0JJP5n376afW7XB8rsBGSlZHzkbbMcl0lMJRg8dJLL41p7SyBnuwn700yW9JIQNbiefTRR2NK2VpyDajjsqauxAh2L6t0Jced6//lgB3b4OyIEotmhve2IyIi6oC+//57XHfddbj77rtVJ7Ro//vf/1QGRIIbWTx0W7wG1DENf9qNf4vjPBCMZLJqqlH2f+dGPOTVdbw1dDeccfxEmLdumUYc1LamaU81ab9R5oWtfi4dCefYEBFRhyHfxYWvLSGkBEvmBUmHNslmRJP1JyRLtMcee2wVQU1LrgF1XGqOTfRX0DJvRXUp0+Bx2GMedhgGFuR1bcOzJEoMLEUjIqIOQ9Z1kXIyKbuSrmLSjEDmlyxatAhnnnmmmlxvWbx4MRYsWKBaPctE/PHjx8dtXCCBz6aEH7cjXQPq+Pzxgprwu8HWwNELhhamZbXB2VFrYbvnlmFgQ0REHWoe0J577qm6ghUXB+pzZHAvJVjjxo2LKc167rnn1HwWeXyHHXaIOZ4EBNLYYFOmTZuGjngNqOOrkXVbNzLGrXYlY2anHtipcGVoW3FKGt4f0vImGUQdFefYEBHRNksCgyVLlmxyv81Zf4doc/R52I3l4UlFtUBnWKRjmvjnsWsxpVt/7FC0CkuyC3D3PsdgXkH3wMOcY9MhTdUCTUk2ZWfzglY/l46EGRsiItpmSdkWS7cokTnt8buhqQAn2A6txpmEIRvW4PgTLsOazFxopqEW7jxh+/itoCnxMevQMgxsiIiIiBJUUrzYRIv82buiGPnVFbj55w/w4k77Id1Tj0MWzsA1d0R2SyPa2jGwISIiIkpQE0YCl30T5+v8YBOBdNSga2WZinEu+PtHdZNGavrxu7XL+RK1J7Z7JiIiIkpQl+4Sf8HV4weZ+PZE4P68d7Fgly4RpUt6ihN455o2O0fa8gxoTbpRJAY2RERERAnsnwm2UEmaJGoeHqPh3eNc2LdnYGD7y4Th8C17CrjhWODzG4GatyIbDBBtI1iKRkRERJTAduxiR90NmxiydcsB/ntaW50SUUJixoaIiIiIiDo8ZmyIiIiIiBKIyfkzLcKMDRERERERdXgMbIiIiIiIqMNjKRoRERERUQIJb99NTcfAhoiIiCiBmIaJ6ienoOr9RSifVQW/oaHTrbui4PKd2vvUiBIaAxsiIiKiBGH6Daxx/gd+wwkfNJiQBTp1rL/iV1T/tAp9PzqqvU+RKGFxjg0RERFRgqhOvgguoxpZWIkklKnV5U01XDNR/fHy9j49ooTGjA0RERFRIli6Dk5vFdJRpO5moghZKMISSAka2/9uS9juuWWYsSEiIiJKBBc+CyfqIjalohJpKGvR4TxeEz/NqMeydb4tdIJEiY0ZGyIiIqIE4F9WBFuc7TY0PzD54Nca3Ptmdeh+VpqGL+/Jh6YxE0BbL2ZsiIiIiBKA1x8bdPhhQxVymn2s8KBGlFebuOG5lmV+qH1K0Zpyo0gMbIiIiIgSgB1++KGhFpkqoKlHGoowAI5mZmyKyjxxt/84w7uFzpQoMbEUjYiIiCgBrEvJRrXeA+mGBy5Uw4tkdXPBBzeMJn8fXVUTP4Dhoo+0tWNgQ0RERJQA5mf3xTBjPjphmSo0EpUoQBl6As0IbMzgLbpQiYFNx8HPqmVYikZERESUAEaULkA+loeCGpGBIjhQq8KUpg52i2rjN4eW0Ihoa8bAhoiIEtYzzzyDUaNGYe3ate19KkStLteogy1O+FGXbGLyiCGY2787Hjn17yZnbKJxqjlt7ViKRkREW9ynn36KqqoqnHLKKdhaFBcX4+2338b8+fMxb948lJeX44gjjsDtt9/e6HM+++wzvPHGG1ixYgVSU1Ox9957Y+LEicjOzo7Zd/bs2XjyySfVT2nJu8MOO6h9Bw0a1MrvjBJFjaEjNWqbBCgzug1FTUoSSrLTYWzw4sIT5qJb72RcfUe3uMfJS4rtl+WX4wP4eJEfRw+I11SaqONjxoaIiFolsHnzzTc3+zgTJkzA5MmT0aVLF7S35cuXY9KkSVi6dCmGDBmyyf1ff/11FfSkpaXhqquuwrHHHotvvvkG559/PurqIhdhnDVrFs477zysWbNGPS6/r1y5Eueeey4WL17ciu+KEsk8V3eVrzGCwzP5WYbuqElJRl2yC6auq4DF5vVj5SovrjlvedzjBPaKJKHMKpcDxzxbDe3OWmh31WPcO3Vwew0sKfHD5+esjkTCds8tw4wNERFtVE1Njco2tAe73a5uiWDw4MH49ttvVbZFsjWjR49udF95/KmnnlIBkPy02QLfkMv9K6+8UgV9Z599dmj/++67Dw6HA8899xwKCgrUtjFjxmDcuHF46KGH8MQTT7TBO6TW5nf7UV/hxaKXZyNtyRpg775wDuuJjDQNS8/9BKUlNvzcZw84K1LQtXItbD4nTNix27yFWFHQGQgurmk3TdQDcFfX4qU/R2N1Wjr+d2UZTNNAvcuOgnovkqBjtcuGwhQX/El2JBsm0mo9qDFMwGMAfj/e+8fEe7N8gN0GePzIyQAOG+TEJbvYsEtXO4qqDWQmaXDZOYCmjiEx/teCiIhaNXtyxx13qMHxjBkz1P2SkhL06tUL48ePx8EHHxza98gjj1TZERl8/z979wHeVPm2AfzOaLpbyt57I4iIGxARRcWBipuh/h04cO+9Picq7j1x74V7IIgiKooIyt4bCt0j67vut5ySpmmb7qS9f16R9uQ0ORlt3uc8z/u8jz/+uMkkpKam4pNPPjHXz5s3D88//zwWLlwIj8eDzp07m8H3mDFjStzGxo0bzdecH2N5+umni79nNoKD+Llz5yIjIwMtWrQwgQIzFfHx8SXm2HA/3n/btm1LbHvvvfcwffp0c9mxY4c5losuughDhgwpVQ72zjvvmPvkMTdr1gz9+/c3WZRQJWFlYXAXboA3Y8YM5Ofn45RTTikOamjYsGFo164dvvjii+LAZu3atVi0aBGOPfbY4qCG+PWhhx5qXi+WwTVv3rz4uu+++868Dixx42M47rjjsOeee5rHf+utt5rXQCLLnIt+we8/7EBKbjYKYmLgiXEi9ZN/4XIvRKHLiSVdOmPxiANh9xZgZ1yiKUEbuuIvTJg1G3Cnot+Gf3HYsh/wV5u++LXT3tjgaYF1aSlwu+LgMskWG2BzIK7Qj4XNUuDKKkAeA6E8j7nkJbmQ1ywBsO8q1vH4gPQ8wO0DbD4gMQbpGT68tsCL1+YUFE3I4XvX7UGTRAfuOzIO5+2jYaNENr1DRUQaiccee8yUQI0dO9Z8zwHzjTfeiMLCwhID4c2bN+OCCy4wgcaIESOQm8uOTMDMmTNx9dVXm8Bg3LhxSEhIMKVVd911lymh4qCaGDAwKGLWggGSpUuXLuZfzk+ZNGkSkpOTTXkWB/BLlizBW2+9hfnz5+PZZ58NK0vDMi/ux2Nxu90mC3LVVVfhgw8+KA6CGPRwv7322svcZ2xsrHl8LG9LT0+vVGBTGQz8iPNkgjGo+uqrr8zzyuewon0Z1HFejxWw8Tnn69a+fXtTqsbAicHbrFmzauWxSPWteGMFNr65Es6WCSh0OpGXlICWG3cirpAzXwBnXiEGLlqDLU2SsaLN7gD2x+57o23WVuz7zyYcv+gzzOx2AD4ecGTx9S1z8vBnanKJ+2IYnVjoQeau7E6xnEIgIaZo1o7dBjjtJphBZiHg9wNc+ybWCeR5gdRYYHseYLMDLid2Zntw/kcF6NrUhpHdND+nLqjMrGoU2IiINBIMNBg8cM4HMcA59dRTTakTy57i4uLMdgYpN910U4ksjNfrxf3332+yKa+88orJsNDJJ59s5oRwG4Ojjh07Yvjw4WbCfEFBAY466qhSx3HHHXeY7MOrr75aIgOy7777msCJ2YxwMg5NmjQxx86J9sRs0MSJE01gw0n3VuaE98FysMBgiUFObWKGhaznKRC3+f1+bN261WTNKtqXtmzZYv5lxomPmQEZn/OUlJTi1/K0006r1cckVbf6rRXwOOxwFXrgcTph8/vgchcFNYE6rd9aIrChP9r1wZELfjPD3Fnd9i9xXZkhRkDJWqrPhwKbDdnM1DCA4S3xX36ZVbj7Z1iiVsCyNDsQ49i1r7mxXdf78N5CrwIbiWhqHiAi0khw8GsFNcSvTzzxRGRmZuKPP3a3kGXpWXBgwSzLpk2bTLlU4ACc80ImTJgAn8+HH3/8scJj4ET4pUuX4ogjjjBZFgZb1mXgwIEmcJozZ05Yj4dBmRXUUL9+/UwGhCVngY+RJWE//fSTCSbqCu+TXC5XqeuYNQrcpzL7MnPDgIjd2Kyghvi4mf2KNMyKMcC1ZGdnm255FmYLWRYZyCpjLOt7vg8DX8touI+E9olweH3w24tWo/E6nfAFZ1T4OseWfg+0y9yC3Jii8kyPvWRQwe9aFAQEJzwJwSAm2YVUrw8DC9zo7vaiX6EHPd2eokwN8aExWxT8K8HghhtZphYU1zBYapnYMF6P+roPqX3K2IiINBKcgxLMKg9jlsbCOSCB80LIWkema9eupW6jW7dupW6jLCtXriyeJ8NLKBxQhIOlWMEYlHHOjoVziDgviCVqvG7QoEE46KCDTIaqNhsiWNkvDn6sry3WYMnaHrhvsOB9reeYmZ5gobbVt6ZNm5b4PjCwtoI5ljYGCu6AF/x969ato+4+Bty2FzZ9tAbbUpugWUYm/DYgJ8mFlMyC4rghLzYGKzu0QEJhPnJdRa93al4WDls0D+8NGo3JPz+P/VbPwze9hxffLofhLTxeeN1u5NntcPn8+KdVCnwuJzr5fCUyOmleH9Jy3diRuCt4ChFYGSxP25lflLnhLgxybDa0SrFh0j5OtEmJ/tejvu6jMtSjrmoU2IiISAnBA/GaZJ0R5byYAw44IOQ+gZmI8titSdBl3AexNO7dd981TQp+++03E+RwTpDVgCBUcFQTrIn+zK506NChxHXcxkyTlfkK3DeYtS2wqYBEn/iWcRg1/1i0ue0v/DmrAE23pSM/MRaFcYlwFvjhcQG+BB/2WbYEB6+djYXN9kCCOw/91m7ED3vsi7y4JLy8zxkYsuJnHLJ4Fv5qtwd2xiVhc3IS4jz5aAk/Cm3AvNQk077Z5vUh3lt6oc8Etxc7+AUDFnY6YwbHZGl24fcZBSaoSUt1Ii3Wj1aJNhzdNw7/2zsGrZI070MimwIbEZFGguuwlJVBYZamPNb1XMMlmLUt8DYCS8QCMdCwgpL99tsPdYFnVjnx3pp8z7K0yy67zKwzc+2119bKfbIs7sMPP8Tff/9dKrBhpzlmV1g+Zu1L3DdwXpO1L5/L3r17m++tpgjshhYs1DaJHHEt4rHvEwdg3zKu9+V7MPegR9FpewaSt6/btdWFgxb9ixl79seWJi3wwZ7Hma3bExOQGxeHRJ8P+xzwA+JXFaDbMSdjW/NkLE/34Z1F+chfazMZnECZcc6ioIa/nzwv4LIB7qL0wIEdgJkXJcPh0CwFiV5694qINBJsj8w6cQu/fv/99013sr333rvcn+XAmqUaVuthCyezT5s2zQy+Dz744OLtHLRz7k7wvJZevXqZ0jXe77p11uANJW4vsJSsujh3J9RjoZq8n2B8Ljg/hm2m2XjBws5yLCfjHCMLAx+ub8MWzoFZG37Nbfvss09xVodr6fBrdkHj82thhzU2TZDoZY9zItmdAzdKzrOJL3TjgEX/ldjm2XXiYOy4pvC77MjtGY/BBzbHMf1icdnQeLx4tBO2oKCGXGz9XOBFO5cX7xwF+P8vBf77U+F/IBWzL0lVUCNRTxkbEZFGgl3E2DXMagzAIIUTZtkBraLyM865ueaaa0zXMt7G8ccfb4IXLljJrALnsljZGNpjjz1M+2F2UmMbY2ZoOEBn3Tq7orGdNLt4sRkB5+1wcjwDne+//950NKupdVjYgpqBG9s9t2rVykz+5eNmIBaqY1tFuHZM4NwXNkKwtnH+Di/ErmV8jFOnTsWFF15o1gpioPLaa6+ZuU6nn356idtli2x2ajvnnHPM2jf09ttvm6YMzC5Z2NmN3/M14+vA9Wv42vAxcQ4Rg6aysmUS+damtkILW3rxBAsPHChADGJyfOi0bjNWt29lrhp7QXscMCwVdrsXL71U+nb8Xj/Y2DlYq9xCbLsnvFJPqV9q91w1CmxERBqJyZMnmwU6OeeEE/QZiHC+SWD2oDxcXPLJJ5/ECy+8YLI07GrGQXpwa2g644wzzCCbGQdmZzhA5wKdDGyYtWEZ2EsvvWQyGLyeE/k50ZYBDQOgmuwEx+CL2QxmaDj45/0zSAtcPDRcfAyBFi9ebC7ENWWswMaaR8T7Y+vrKVOmmMfItYH4OlhlaBYursl5P2xLzQuDEwaE9913H3r27FliX75eDHAYUPFn+JwywOnRo4cJPK1OahJ99ti6DG7/rg5osCMHPOFQNMAd+O9qNMnIxaCvj0L7fqlmmztEy2iKK6Mjc5wn9P4iDYXNX5f9L0VEpM7xbP7tt99uBuVVGcxLdGA2iBkiBoxc2FOiz84uk+FclYcCJGML2pUqS+OAbU//JcXf8+QCX29i1pTt1+mfFTk4Z8ruslML2wnMfbJVrT8Oqb4fbCFScSEc4j+r1o8lmqiYUkREJIpwMBs4b8eaY8NMHDNE1hwiiT5bXSlIRDqaYTWSUHKNlErx+0O2Cy7dJ00ilT/Mi5SkUjQREWm0GBDwUh7OYeGcmUjBEr9LLrkEhx9+uOmSxmYO06dPN9uvu+664rP2En0yYxOLZ1Y0xUbsANdW2T3XItxZF01TnCH39WrahjRwCmxERKTR4lwhrmdTHs79YTlfJDWBYHOGL774Ajt27DCBV/fu3U3TBS48KtHLvWthTkpEJrpiPrahHXKRDA84dyq8yKRVs1hzNr/U3rFlTL4RaSAU2IiINHCckF9TXcYamtGjR2PgwIHl7hNpk/EZ2Nx99931fRhSC7yxLrjtDsT4ikoNk7DTXBakDAQyXWEHNg67DU2b2bFj++7iM371yPnqiCYNmwIbERFptNq3b28uIpGgfc5mfNrvEJyw4NvibfPb9kLc1lgUmNAk/KnR029vjtvezMI3fxQgNt6OuyamYEhPlSlGC7V7rhoFNiIiIiIRIGZEP3i/ysOdh12A3ltWYFNKC7Rfn4dB7lUoMKVo4bPbbbjjjBTccUatHa5IxFFgIyIiIhIBWt57Eo55eDz6rByEjU2aot9/K5BYUAivydSoka1IRfRbIiIiIhIBnC4Hsq89Ge3ylqPnxvVIMEGNbddCnZVoiyYNohQtnIuUpMBGREREJEI0v3cM7E/8j0uoIxPxyEYC/CjqZtb1l5Pq+/BEIppK0UREREQiSOqFg82Fsr5bC+/OAqQe3w02u87Qi5RHgY2IiIhIhEo+tEN9H4LUg92NuqUyVIomIiIiIiJRT4GNiIiIiIhEPQU2IiIiIiIS9TTHRkREREQkgvjVKKJKlLERERERqSs7soD/PQ6Mnwps2hH2j/l9fvzz3RbM+2QjvB5NLRcJRRkbERERkbowaxEw7Kbd3782E3jrCuCUIeX+WNbWAjx22h9ctdH48pGVGP9wP7Tuk1DLBywSXZSxEREREakLw28uve3Uhyr8safO/LM4qLFMu3xhDR6YRBq/LbyLlKTARkRERKQu+IKikzB58otKz/jTBU4n8mNizOXB8Qvgr9pNijRICmxEREREokCh0wm/3Q7YbOaSm+HDtp871fdhiUQMzbERERERiQI2vx82nw++XYENFWxpCmB1fR+a1DB1RasaBTYiIiIiUcDl9Zp/WX1WGBMDP4ObKpa3iTREKkUTERERiWDBoQvP5Ts9nqKvfT54C3V2X4SUsRERERGJFru6Bdj9fti9XlOWlrOwSX0flUhEUGAjIiIiEkVBjW3XJdbjgdtux/bslOJdPF4/pv7iwcYsP24Y5kSzRBXnRCO/XrYqUWAjIiIiEiWCi86cPh9su2rVNmd50fr+/OK9HprtxhPHOHHhfrF1f6Ai9UDxoIiI1Lr169fjyiuvxMiRIzF48GDcdtttxf8GCrVNRMrGECbZlmu+7v1oAWDb3Q4adjsu+rRoLo5IY6CMjYiI1Lrbb78dS5cuxdlnn41mzZqhffv2+OyzzxBNVq1ahY8++gj//fefuWRnZ+Pcc8/F+eefH3J/n8+HN998Ex988AE2btyItLQ0E9hNmjQJ8fHxpfb/6aef8OKLL2LJkiVwuVzYZ599cMkll6Bdu3Z18OgkGnhsNjjZ8jlgG5M18709cdqt25FhTwaC2wTb7YiZ4kb3VD9mnu5Ai0RHXR+2VIHfoYYQVaHARkREalVhYSH+/PNPnHzyyRg/fnzx9tmzZ8PhiJ5B1oIFC/D666+boKxPnz747bffyt3/oYcewltvvYVDDjkE48aNw8qVK833ixcvxpNPPgk7F1rc5fvvv8e1116LHj164NJLLzVBE4Oi//3vf5g2bRpatGhRB49QIh3fMT4AjqDAZkNCMvIybEhLcCM9PqjszO+Hp9CP/7b60fLhQmy7nPNuYur60EXqhAIbERGpVenp6fD7/UhJ2T3BmWJj67fu3+v1wu12Iy4uLqz9hw0bZgKQ5ORkLFq0CBMmTChz3+XLl+Ptt982Qc0DDzxQvL1t27aYMmUKvv76axxxxBFmm8fjMfu0atUKzz//PBISEsz2Aw880ASCzz77LG688cZqP16JDgtm78Qnr25AfGEB2i/4G87N2XB06QdfjMt0Qgs+j++zASvi45DoB/xeX1GDAWZtXHbAsWudmwI3kFdUktb8LjeQ5MXR3YC3TohFokuZAWk4NMdGRERqDefLHH300ebr5557zsyh4eX3338vdz7Nr7/+ijPPPBMHHXQQRo0aZYKB3NyieQSBmNl49NFHMWbMGBxwwAGm1OuGG27AunXrSuz36aefmvvj7TJ4OO6440zg8M0334T9WFJTU01QE46vvvrKBHOnn356ie3HH3+8CaQ+//zz4m1//PEHtm7dah6DFdRQr169sPfee5sgiMFPoPfeew8nnHCCecy8TQZR1mPkcyuRwf35v8jZ8z7kOi6C23Z68Xo0/NeLeHiQAg+SMKPT47h/xLeYevBn+Oq6uejw879I35KPH7rtj1mDh6D3jn+R7QI2piQh1+XCxiap2JiWipxYF5x+YN/MHDQv9MDNsKfQA8Q5AOeuwMblAJJcQJO4om1eP5DrxmdrHEi6vxC2ewphu68QyVMK8NtGzceR6KaMjYiI1BoOvnv27GnKspi94IW6dOlS5s9w/sp3331nBvqjR482A3WWcDEL8sQTTxSXcDGo4ZydTZs24dhjj0XXrl2xbds2M+hnUMQSrjZt2pS47UceecQECQwGEhMT0alTp1p53Mzo8Dj79etXKkvF54PXB+5L/fv3L3U7e+yxhyl5W716Nbp162a2vfzyy3j88cfRu3dvXHTRRcjPzzePlXN4JHK4P/4HeWNe2vVdHDxojWSsMt95kQQ/dmcK+2xbiTcPOBS9167HiXPm4sXDRmJ7Uqq5Ls8Vh08GjITfZkOezYbcBF9RYwAAOXFxaJKVhVWxLqyMc6GQGxnEMGNjekLvysbYdgU4qXFAvgfIKSy6nhkenuN22JENO/Z91Ye8K/2IcyqLU998wXOlJCwKbEREpNYMGDAAzZs3N4FN9+7dcdRRR1X4M8uWLTMZmuHDh5vvTzrpJPM9gxtmWJjBoaefftp0W3vppZdMsGA55phjcOqpp+KZZ54plRFiEPDGG2+EXX5WVczANGnSxDQBCNayZUv8/fffpgwuJibG7GttD7WvdXsMbDIyMkzmi8/lCy+8UFzOxyDwxBNPrNXHJJVT+OTsoC02+GCHHX74UbIMs2luFgauXYIRvy9BvisW21KLghry2m0mqEkoKESszQZ3TMn5MVuTk7Eyvuj97I/h7BsrmAlxUNzmchZldRjsxNgBt78oi8OBtN2G+3/x4JahmoMj0UmlaCIiElGYRbGCGgszMDRjxgzzL8u8vvjiC+y1115m8L9z587iCzuOMdMxZ86cUrc9duzYWg9qrACKQUsoVrDDfQL/DbV/8L4spSsoKDCPI3COEoPHI488EpE4v4rHa2GWLSsrq0Rjie3bt5f4GXaQK+97Zuj4+kf6fRTmFr1mgWzFxWilMXBJKCxEfEEB4vN33xfFFRSibfpOuLzeUj9XGNCEwuHz7Trdzwk3Ie7E2pa4K+D2BO1ns2FHdn6DfD0i4T6k9iljIyIiESVUmRoH7pzfwgwN7dixw2QvGLxwXk0ogV3HLB07dkRdYPDEYwyFAyJrn8B/mcGpaN8NGzaYf0OV0NVWWV11NG3atMT3SUlJpQI3tv8OFFw+GPx969ato+I+kiYfjLyfppUKbEyFGApLZG0y4hIxp2tf7PXfOqTl5GLk/Pn4bJ/B8NvtsPv8SM3NK1qvJjfflJ8FygsoWYop9CLfzeDHUdREgBkZqxyN3++Ke8x2fl/oA2Idu9us+Xz4v5FJDfL1iIT7qAy/Ug9VosBGRESijnVmdd9998XEiRPD/rm6yNYQ2zOzvTMDk+BytC1btpgyNStDY7Vy5vbgoI7bAveR6BFz8kDzb+HtXwKL1yPGuzvQdSALPnjhRwz88GFmlyFonp2BGQN64/A//kH/1WvQYesWrG+dgMxEF/5r2cf8XGJhIVrv2ImMhHgTIuXGxiDN60XbgkJsiHWZMpy4jDzkx8UUNQ7weIEEF8DvrWSEFcTkeIo6p7Erms2GWPjw8Vg7EmI0t0OilwIbERGJKAwIgrEpAMtArMUqOVGeGZycnBzst99+iDR9+/Y12aSFCxeacjkLS1u4AOegQYNK7GutkxP8WP75558STQ6sM8BsJsAFPANxm0RecGMFOIbthKJ/THyxu8vfCYsmoOga4L/v98JXDy5GXnI8YrN2wJueCTtLzGxFp/ATCwrNhRkXt8OBjPg4dItxwsHMjR/YHu/CxoQY9jMH4mKLOqHZds2hoV2Zmh4tgNlnutAiUakBaTj0bhYRkYjCAbo1l8byyiuvmH8PPvjg4jIzrgPDwOHbb78NeTuska8vhx9+OGw2m2lUEOjDDz8082WsNWyILZ1ZavfRRx+VaGnNAIitoFlq53QWnYdk4MMMEDu/Bdb/M/DjnCOJfr1HtMY50w/G5Lf2xXnTR+GCX06CbVdQE4xzbprm5GKny4VUnx/eGAc2J8ax7Rpgc4DJF5tp+2w3a+CgwIM1kxzw3xSHJRfFK6iJYH42jQjjIiUpYyMiIhGFHb9uvvlm0+mLc2LY7pntn5nlYMBgYavj+fPn4/rrrzfXs10yy7s4YXf27Nno06dPmevkVAUnD7MzmxVI0J9//mnWxbGCrh49ehQ/BnZze+edd3D11Veb9XiYieLP83EEBjYMWq666irzOM455xzTipqZKAZFzEydf/75xfuyhO3cc881ba//97//mYYBDJQYMDGrw9bRDKik8XD4/Wi6cxuWd2uHf52710Fi9sZM23J7kJRix4Ed7Xj9uDg0T9D7QxouBTYiIhJRuD7L5ZdfjieffBIffPCBKcU6+eSTTSAT2BCAk3lffPFFvPbaa6YN9MyZM+FwOEyXtIEDB5rAqCZlZmaaFtOBGHRZC2K2atWqOLChK6+8Em3btjWP4aeffjJBySmnnIJJkyaVamzArAy7nLGF89SpU01WhqVml1xySak20GeddZZ5ThgkcT0bTnoeP368mXfEwCawW5o0DJweExiOWNNlbLu+3j9mOd67vQ86TXVjY1ZQ4GIHsq7Se0IaB5s/sLediIiIRKX777/fZIi+/PJLU9omEWjXHJtS/B+U+2N3H/pzqW1uu90ENvls0dwxB48+NRT5XgfS7iuA1wqD/H58fKoTx/bWujTR5uO0kmWsZTlux+mINuvXrzcnotgchetvtW/fHl6v13S6TE1NNSeoqkoZGxERkSjCuTXBWRmWxk2fPt0s4qmgpvGUoBXGxMDp8WBRXNFrnhxnh+fWeHz2nwcbs/2YONAJl1OlZ9HI3wBfNr/fbzLZzDR7PB5TNssSYgY2LPXt3Lkz7rjjDlx22WVVvg8FNiIi0mjxw9Ra/LIsnLfDs4iRgg0FHnnkEYwYMcKUqXFtGzYeyMvLw+TJk+v78KSO+AMGck0cJRf0PLq3hncSeR544AHzt+vaa6/FoYceisMOO6z4Ov6NPeGEE/D+++8rsBEREamKKVOm4LPPPit3H072f/bZZxEpOnToYM5wsmEASzc4H4cto88888yIbH0ttRPUeHeV63DNzQO6/gcgoK20SAR67rnnMGHCBNx9993Yvn17qesHDBhQ7e6OCmxERKTR4ocsO4uVJyUlBZGEgc2DDz5Y34chdYjdmx27ghif3Q6v3Q4/59j4fMhxOtE2Pq++D1FqWENs5bx27VoceOCBZV7Ppihs0lIdCmxERKTR6tq1q7mIRLJ8pwOJHm9R+ZnV88nvN8FNXHFRmkhkY+ksg5vyymzZ4r86tDKTiIiISASz+XcP2pix8XOtol0BTnyHrfV7cCJh4hwatsxfsWJF8TZr3a2vv/4aL7/8sln/qzoU2IiIiIhEAQ4BXV4v4txuOL1etOgYi+aD19X3YYmE5fbbb0ebNm3MOmMsA2ZQc99992HIkCGmJJhzbG644QZUhwIbERERkbrgrNqwyxVi4oDN78ekx3tX/5gkIvls4V2iSWpqKubMmYNrrrnGrGUTFxeHH3/8ETt37sStt96KWbNmISEhoVr3ocBGREREpC78/XDpbT/cXuGPXfzCADOTJvBy2j0KaiT6xMfH46abbsJff/2FnJwc06b+n3/+wS233GKuqy41DxARERGpC306AAVvA49OB/LdwJXHAvElF1sNpWmbeFz31f7446ONKMjxYv9T2iEmzgG3210nhy0SLRTYiIiIiNQVVwxw1ZhK/5jDace+Y9vVyiFJ5GmI7Z7PPvvsCvfhvJsXXnihyvehwEZERERERGrV999/X9wFzeL1erFx40bzb4sWLcxaNtWhwEZERERERGrVqlWrQm5nSeUzzzyDqVOn4ptvvqnWfah5gIiIiIhIBPHbwrs0BDExMbj44otx+OGHm3+rQ4GNiIiIiIjUqz333BMzZ86s1m2oFE1EREQkCnm9fmRsbwKXzQ2/n02gRaIXy9Cqu46NAhsRERGRKDP7j1yc/GImtsWOhNtmw21XZ2DR5Ylo07l6A0OR2nLHHXeE3M4FOpmpmTdvHq677rpq3YcCGxEREZEo8erUdfjzl0ysdcRgU5sW8O1qC5zlcOLlQ7/H8IR8HLBgbH0fplSTP6h7WENw2223hdyelpaGbt264emnn8a5555brftQYCMiIiISBW69YDF2bvWar5elJhQHNeR12PH+3j0x9N0fMX/0l9hz+hH1eKQipfl8PtQ2NQ8QERERiQJWUEO5jtJDuO2JcXA77dj61XrNuZFGSRkbERERkSjTLTcfS5OK5tMMWLcVIxavRYcdWciJc8Fnt+Od9u/j+KVj4ErQUC8a+RpAJdqaNWuq9HMdO3as8n3q3S4iIiISZXrm5CErPQNN123H1V//XlyCwwFxRmIc4rMLMP2YH3D8d4fV85FKY9W5c2fYqjBXyOvdnZmsLAU2IiIiIlFoUEY2Rs+cX2Jegd0PxBZ6kBfnQv6/GfV4dNLYvfjii1UKbKpDgY2IiIhIFOCsGWuYWGCzYV6TZBwZYuBo2zW9xh3QXECii78BvHZnnnlmnd+nmgeIiIiIRJlYvx9778xCXkzJc9SMaQpjHObrpOz8ejo6kfqhjI2IiIhIFHL5/djcrjlaZq6B38b5NTbkxcbA43QUl6WJRJrZs2ebxTgzMjJKtYBm6drNN99c5dtWYCMiIiISpTa1ScOAf9cgJ9aFApeGdRK50tPTMXr0aMydO9e0I2cQY7Ult76ubmCjUjQREYkK69evx5VXXomRI0di8ODBZa5iLdKYxOa7zb+J+YWIzy+E0+OFq9BT34cl1cQMXDiXaHL11Vfj77//xhtvvIEVK1aYQOarr77CkiVLMGnSJAwcOBAbNmyo1n0otBcRkahw++23Y+nSpTj77LPRrFkztG/fvlI//8wzz6BXr14YPnx4le7/n3/+wRdffIF///3XHEdeXh5uvfVWHHPMMSH3LywsNF2BPv/8c2zduhUtW7Y0+3JCrdNZ+uP3s88+Mx/4q1evRmJiIoYOHYqLL74YaWlpVTpeiU4PznHjwbk+5HuBHW6OXItGr7F2P84LsX/Orknm/H9CoQeFXh9yY2Pq+KhFKsa/heeffz5OOeUUbN++3Wyz2+3o3r07nnjiCZxwwgm47LLL8Oabb6KqFNiIiEjEY5Dw559/4uSTT8b48eOrdBvPPfccjj766CoHNqwLf/fdd83aDD169DBnHstz/fXX48cff8Sxxx6LAQMGmP2ffvpprFu3rlS26fXXX8fDDz+MQYMGmazUli1bzLYFCxbglVdeQXx8fJWOWSLbku1eDHvNi83ZfjjtgIfhCWtpbEEFNXYbCmDDxz3boM+2LBQ4HeiyIxvxbi/mt2mGIXExpmuA32aHJ8bOuh7zY4yL1szYhKQuyUjrkICCNZnY9vpCJO3ZEv6WKciYvx35bi/aH98FSW0SS95nbgFQ4IZ75U4UfrgQMSO7wTW0K0eidfgMSUOyc+dO9OvXz3ydlJRk/s3Ozi6+/vDDD8cNN9xQrftQYCMiIlFRm82yhZSUlHo7hrFjx2LChAkmyPj222/LDWx++uknE9ScccYZuPzyy822MWPGIDk52QQsxx9/PPbcc8/iD/unnnoKffv2Nf86HEUTv/n9FVdcYc5eMksl0Yvv3c9W+DF3ox+DW9vQJMaPMe95sDN31w42wMO6IgYkocqLfH6zfU1aEtY0TTabZvpa4tBlG9EqOx+PnzIcTd1exLo9aL9hG3otXQdfjB0eux2fTv4TNr8PKZm5SMzPQdv0zUj1ZcLtcGJjk5bIdCVh9a0/odOObWjrXYN4ZMGJAnjggAdN4IUTPjjhvwvI4b/IhwseeJEEPxxm3xhkw2nLBxx2+BOSgeZNYNurHeydk4seTvMUYMQewKx/AY8XOG0o0LFFXb4EUcdfx+u/1IW2bdti06ZN5uvY2FiTxZ4/fz6OO+644nLj6q57o8BGREQiGrMbLNOysi68ELMfK1euxIwZM0y99o4dO5Camop9990XF1xwgfkQJdZsM2tCvB3rtuj3338P+zhY/hYu1o3TaaedVmI7v2dgw5I2K7Dh8efn55vyDCuooWHDhqFdu3Zm38DAhqtyv/TSS/joo49MwNexY0dzPZ8LPjeffPJJ8WOXyHDu1z68sGBXizJ2gXL7ADaDMhOnOVliV/DCURm3B69hYu1X9I0JgFxeHzLjY9FzRzZszPDY7SiMjcGKLm2QlRSHIbMXIz7Pg5TMfHjiit5XHnscVrXoiH03zcO/LbsjK6YoSMqNSURuTBxabNyEFBSYbTHwmv88SIMDbhPm2OFBAZJRiN3vU17jhx0xfg88njjYMz1A5jb4V2wzP+1EZukn5K73gBl3Ant3q50nXCIS/6Z98803uPHGG833/Jt3//33m7977I42depUjBo1qlr3ocBGREQiGuuue/bsiYceegiHHHKIuVCXLl1w5513Yo899jAfkAxqli9fbgb8v/32G9566y00adLEzFG54447cMstt2CvvfYy2ZLatnDhQnM2snXr1iW28/sWLVpg0aJFJfYllqsF69+/vwmScnNzkZCQYLZxIPD++++bBgrjxo0zGZ/77rtPwUyEWrHTjxetoIa8vqJApkSwsguDGwQFNvwysPzL70eTvEIc998GxHl98DiciPf6kM+uUrvOdjfdnmuCGh/L22JLlo7Z/TbkJMQWBzUWt8OBpthYYlssdqLQZG1i4DBhDoOY3UGNORzY4EGsyezYzAMLxAyPHTYTrQXg+jr3vA+8d00Fz540JFdccYUJbAoKCkzGhiet+PfP6oLGwOexxx6r1n0osBERkYjGAX/z5s1NYMNJpkcddVTxdQxeguef8MPxwgsvxMcff4yJEyea6/kzDGyYAQn8+dqybds2E3iFwsCGc2gC97W2h9qXZUxsPtCpUycTuDGoOeCAA/DII4+YibfETnGnn346Ig0zSmyEwEGMVU/Px8OSPGvuVFZWVols2MaNG9GmTZsyv2cpS6tWrYpLViL9PtZnF8UxYfP4AMeusjQKLs2x2TBw004T1BRvYobF60PhrvVrmqYXzVswXbNClPY4bKW7piW7s0sFJiamQiF8iIEPdhPEhLarZW/Ia0IENrQ+vcG+5mV939j179/fXCw86cSyXp6cYdbGem6rQ4GNiIhELSuoYRkDsxoej8dkdzgxlV3M6gtLy1wuV8jrOFDi9YH7Uqj9rUGVtc+sWbPMv6eeempxUEMM+Pbff3/8/PPPiCRNmzYt8b01YdjCxxxc4hc8EAz+PjgLFun3sV8boHUisCln1xUOe1HWpkw2mJZo7CbAOMVeMkNCyQWlA5PAoMTjKhqcO7yA3eODj7e1i88GNM3JQKukzdgc12r3Y/Dlwws7HAFBSFE2Jm5XeOIz9+Ex91MyhHEhzwQvzOYEXuPfdQshHbtPqeeqobzmZX1fGXydGppFixaZuYPBmFmvKQpsREQkarHkjPNKWM7A8oZAPJtaX+Li4swZ3VB4nLw+cF/i/oHbrX0D97HWeGD2Jhi3RVpgI4DLYcOnxztwwTde/L4ZGNTGhjSnDd8ts8rRdmVVnAEjWcYWnIfDJWoSd3c5s8S5SwcLeTY7Oq7bilbbdqBp7nbkJjqRkONBfKYHeUlOeFx2eB022Hw+E8AcsGUu1iS3x3ZXUzQt2IGW2TnF4RHvzQcHctHSBCvM2jiQAzdidjUWYBMPZmJYpJaHGOTADq/5maLgxm+yRQ5/NmwxDoCZpD7tgaUbi4K6c0YCV4+p9edeIgvLhnnhiRl2uOQJmZqmwEZERKISgxmu88L1bPgv55gww8FSErYMZRanvrB0juVjoVhr2gTua23v0KFDqX35eEKVqUn0YCe038Y74fP7Yd8VpOS5fdjz2UIs3eLb1eLZUbL8jOVojDIKvEDsruv8fnRLz0bTvEJsiY9D84Ki4HlbrAv2rDzs98dSNM/KwY6EeHidDmSlOGGL8cKbYkebo9ti1cydQHoB5rQ8EPEFefB7/fDn++F0JSF5XFtkpPjhOqAjCj9fAO+ni4BcG+z+fDh82ciPdSK7Sx/4m6Uirp0LrlWb4OPv29F7wDFuT9gKCuDo1KIoKPL5YOdaTfwdZGaRAdyu4y9+fNLoPPXUU3jnnXdMWTDn1XBBTivICXWypioU2IiISFT68ssvTYewRx991MydsXDhzPrM1hDXamA3M9btB5a48HsGK5wHFLjvhx9+aNpHBwc2XMeGH/hW4wCrQQAX8QxeoJTbJLJZQQ3Fx9ix5KI4bMv14fV/fBjW0YasPA8OecsPX3HbZz/7QAP5HiDGgfP+XGHm1mQ5HViTGI+1CUWZPNM0IDEe+a4Y+EyHNNuueTd+jEnfve7T7nddBc4YGHJzariP0yqTtP4ta76QNKp2z+eff765bN682awJxiDnuuuuMxd2s2SQc9JJJ1WrEYpWWRIRkahktUbmhN5AL774YshsDYODjIyMOjk2q2Vp8Ara1vdHHnlk8baDDz7YZJr4Ic9AzTJz5kyzrsMRRxxRvG3o0KHFTRMCH+OyZcswZ86cWnxEUluaJ9hx6b5O7NXagWFdYuG9Pg7+G2Lhvz4WcybG4PphMXj+uBismOQsbhhgveU5+A0cAH8+oCvsfiC2sKhUTYM8iURs0sAsO//GrVmzBg8++KDJTHNx4upmbpSxERGRqDR8+HC88cYbuPTSS00L55iYGPz6669mkB9qMipru+fOnYuXX37ZZFH4QVqZNRPY4Wj69Onma66bQ/xg5tlHGj16dPFk4SFDhpgghGvWsJsSOwEx+8JObQxqWIIR2BmI6+5wDQd2c+MxMavz2muvoXPnziW6nXXr1s08VmZ4uC+fA3YU4tnPXr164d9//632AncSOfZr68B+IU5esxu0J2gQx5Dn1/bNcR4TI5VqwyZSf/g3k1nrPn36mIYvOTlWl42qUWAjIiJRicEB13R5/vnnzWKdzHqwnOHZZ5/FueeeW2p/ljtwvRcubml9eFYmsGH2hPcT6IcffjAX63gCuyDde++9eOGFF0xJ2ueff27m1UyaNAlnnnlmqdvmejRch4eB2pQpU0ybWbZwnjx5cnEZWuDj4JwbBkls+cwznNzGOUcMbKxOatJwxXu82OZ0IM3rQ6zfb3oMrI2NQetM9iEDCjlhX6KaadXdQPn9frMw8dtvv21O0rDlPU/wsBSNa5JVh80fnMMXERGRqHP55ZebLnE//vhjcZmeNCyXjt29sOufKUmYlZaMGNjg3rVezY2f/4ojF61GZkIs3LvWtDk244x6PGKpqle6vBfWfhNXjkW0mDVrlim5fe+998xaXikpKRgzZowJZngix8mGE9WkjI2IiEgU4Zo2wW2hly5dalo9H3jggQpqGokmBYUYuSUdhVn58NjtOOqflRi+dL25zuX2Fgc2IpGC8wm5HtAxxxxjghnOHyxrva+qUmAjIiKNFpsJuN0s5Ckbg4jgxfnq02effWZK2w466CBTvrFq1SpTzsGznew4JI1Dl4JClt3g0B/no+nOkvMSfJyEIxJhOBeQcxGDT8zUJAU2IiLSaF199dWYN29eufscffTRuO222xApevfuXVyfzsCM83EGDx6M8847z1wnjQc7ov0yoAuOmLUQjl0zC7w2G/Jjdg3vNMqLWqbldwNz4okn1vp96C0vIiKNel5KZmZmuftE2uKY7O72+OOP1/dhSIRY2rEV5p/cAvsuXYu+67ciZWdO8cTzfd4ZXt+HJ1KnFNiIiEijxRajItFse6wLmckx+HS/vti6bA1Gz1oIm8+PHrcMQJtDdy9cK9IYaO0mERERkSjCorM8hx1LkxOR6Yop3j67Uxuk5LvRzO9G72sG1OsxSvUw6xbORUpSYCMiIiISBa57uIv5l+PZn5qnYUv87knYPr8fq2KKgpxBi06qt2MUqU8qRRMRERGJAm06xOOR9/oic6cH+e9m4fMFXiT7fMiz27DB6cQF6WuxX/aZcCbuzuKINCbK2IiIiIhEkZQmTjxzbhqmTkjA1kQ3suIL8coZsZj68gAFNQ2o4104l2iTmZmJe++9F6NGjcJee+2FuXPnmu3p6el46KGHsGzZsmrdvjI2IiIiIlHo2D1jsH3edPP1iQPOqu/DESnXunXrzCKda9euRY8ePfDff/8hOzvbXNe0aVM888wzWL16NR555BFUlQIbERERERGp9XXDsrKy8Ndff6Fly5bmEmjMmDFmAeLqUCmaiIiIiIjUqq+//hqXXHIJ+vbtC1uIMrquXbuabE51KGMjIiIiIhJBonH+TEXy8vLKXfCY2ZzqUsZGRERERERqFTM1M2fOLPP6jz76yDQUqA4FNiIiIiJRJs/tR6GHS3WKRIfLLrsMb731Fu677z5kZGSYbT6fz3RCGz9+PH755Rdcfvnl1boPlaKJiIiIRIlNmV60uzcXPrvdrNTp9J6MJzq/U9+HJTXM3/Aq0TBu3DjT9eymm27CjTfeaLYdccQR8Pv9sNvtuPvuu00Dgeqw+XlrIiIiIhLxbNdnAQ474PEVbXA54PQXIPeWJMTEaA2bhuK5nh+Gtd+5S45HtFmzZg3ef/99k6lhxqZbt2444YQTTPOA6lLGRkRERCRa2G0AJ5YnuYq+d3vhQWx9H5VIuXJzczF06FCce+65mDRpUrVLzsqiwEZEREQkmgKbhIDMDLM3BZ76PCKRCiUkJGDlypUh2zzXJDUPEBEREYkWMY7S25wazjU0frstrEs0OeKII/DVV1/V6n3oN0FEREQkWoSaGr1rm8/nxzdfpOPXXzLr/rhEKnDzzTdjyZIlpgPaTz/9hPXr1yM9Pb3UpTrUPEBEREQkSthuyC6aX2OV9HAYl+vGz0cU4vmntpfY95GnOyMpWbMOotGzvT8Ka7/z/qteF7G6xM5nlvJK0rxeb5XvQ+92ERERkWjh8wNZBbtL0rw+s+25J7eVGixedsEqPP9a9/o5TqkWfy3PRakPt9xyS63PsVFgIyIiIhJNWGtTuOusNk+CO2xc0qb0bqrJkQhy22231fp9aI6NiIiISLQIjmC4nE1ZZ8EV2Ugjo4yNiIiISLQIFatYi3VKgxFtHc/Ccccdd1S4D0vV2GSgqhTYiIhIRDjvvPOwceNGfPrppxXuy31uv/12PP300xg8eHCZ28rz+++/m4Xibr31VhxzzDE18hhE6oUN8NhspQZ1DXGehjTMUjSbzQb2M6tuYKNSNBERabAWL16MZ555Bhs2bKjvQxEp4Zm/PHBO8cA2xYO9XvaYQV2V+YHFaUmlkjnZ1T1IkRrk8/lKXTweD5YvX47LL7/cnJDasmVLte5DgY2IiDQIRx11FGbPno1BgwYVb+OaCc8991zIwIb7cX/+nEhdevYvDyZ9C1hNbf/aBjgf9OKlBR7kun1YmeGDrzKBjh9w8Wx30OZEnw+Th3yLV7s/g/uO/hGzX16KrVsKseXGH7HMdr+5LE96GJ6MvJp8eCKVagHdpUsXTJkyBT169MDkyZNRHSpFExGRBsHhcJhLZT5QY2Nja/WYpPHYkuPFZd/7kesFDmgN/LgOSI0F8n1A2q5lZ/LcwMx1wPqc0j/PWTJnf8VL0HwZK8AJFecwkomLAZx2/BtjQ3KBG63zCot/rvX6zUgoALbFtIZ93UZ89H4imr04C87CLBzU0oduWzdiTsu9MHvIdCQk5qOd04d2A1uj3X2HwJEYUwvPkoStEZYRDhs2DNdee221bkOBjYiIVFtOTg5eeeUV/Prrr1i3bh1yc3PRqlUrHHrooTj33HMRFxdXvG9mZiYeffRR/PDDDygoKEDfvn1NGUJZPvzwQ7z22msm68LbPPnkk5GUlFRqv+A5NixBY7aGOJfGcvTRR5ta7+A5NitXrsRJJ52E008/HVdccUWp27/hhhvw/fff44svvkBaWprZtm3bNnMfXEV7+/btaNKkCYYOHYoLLrgATZs2Lf7ZjIwMPP/885g5cya2bt2K+Ph4tGnTBocffjgmTJhQqeeaj42P4YQTTsDjjz+ORYsWmQBt+PDhuPLKK5GQkFCp25Pqm/qHF5f/sDvy+HhZLQxwrXFuszggsxBw+4BEF+AoKr7ZlhCLz7u2xoAtGThww3Z43B7M7NMNHpsdKYVuHDf3b3Rdsg4urw8rmqVi7Cln4cz5fwDJzZETm2huw+71ovVf27FXl1ew32uHIeXwTjX4QETKx7/JgYt4VoUCGxERqTYO1j/++GOMGDECRxxxhMmczJs3D6+++qqZ58IBOLGe+uKLLzaDcZaA9e/f35SLXXjhhUhNTS11u2+88QYeeugh9OzZExdddBHy8/NNkGMFFuXhsTDwYGB01llnmXIHat++fcj9eT2DrK+++gqXXnppiexPdnY2fvzxRxx44IHF971p0yZzu263G8cdd5y53bVr1+L99983H9DTpk0rDsCuu+4683yceOKJptyCAR0DqT/++KPSgQ3xOWMwyIBs1KhR5nb4/HNQcOONN1b69qTqct1+XDWjDtsqM9BJdgE7C4qDmsDr/m7VBD22Z2JxizTk7Rokpjvt+Gxgb5w0Z775vuv2DJzzy9/4tX9f9M7YnT7yORzISE7Gwi6d0fG095GyvXSAL1JV/DwIZefOneakzwcffIBzzjkH1aHARkREqq1du3aYPn06nM7dHyvMrDz11FN44YUX8M8//2CPPfbAJ598YoIaZnHOP//8EkEFAxhmMSxZWVl48sknzXUvvvhicdaHg/mxY8dWeEwMIAYMGGACm/322y+sTmnMhNx///345ZdfMGTIkOLt3377rQlGeL2F+zFQe/31100myTJy5EgT8HA7HyODot9++80c8zXXXIOasHTpUrz00kvmOSUGTMya8fllwBMpWZv09HQkJiYWl/zxueAk+eTkZPN9YWGheZ2bNWtW/DPsjBf4Pgj+ngEln29rBfP6vo8VOwFvXS8Xw1bAsY6iMrUQJUub4mOLgxqrO9qqJsnw2mxw7Cpt67gjC8uzS8+t8Tgc8DodyCrww1fgxeb0LVH1ekTyfTT2ds9nnnlmmdc1b97cnAC65ZZbqnUfah4gIiLVFhMTUxzUcLDPcjOehdt3333NNgY2NGPGDJMJOeOMM0r8PAf9HEQEmjNnjsnQsDwssJSNgw9mhWoDsx98LAzSAn3++ecmo8QyM2uQw/Iz1oRz4MPHal3atm1rsjcsyyNe73K5zHNQU93ZmOmyghrLPvvsA6/XG1Ed4FiOFziPiRksa2BIfF4CB4YUPBAM/r5169bFg89IuI/uaUBMXY+mGJy4nGXOwwi1qg3bQQfKiXFiZWrJ3zmKKyiE0+NBSpIN9lhH1L0ekXwfjd3KlStLXVatWmVKddkN7e677y7xt74qlLEREZEa8e6775oyrBUrVpg2noF4ZpPWr19vzswFz5HhIIFZH2s/a1/q3Llzqfvq2rVrrTwGBi/M1LAsgsELj5OBwp9//mmCLwY9xA9jPkaWf/ESCh8P8Wc4Z+fBBx/Esccea46d2SPOibECv8qybjv42ImDBKk7cU4bXhhlx4Qv6nqRTGZeSgc2rTJz0SUrF1uCGmOk5eYVZ2vosz264sT536GZLwF/dewL9lRLzMtHWmYm9lqxBO0+Pa1OHoU0HjabDS1atDBzDEPJy8szZc0dO3as8n0osBERkWrjvJepU6di//33x6mnnmqCFw7o+SHFifrBgU4kGz16tGlswPKzMWPGmGwNS1C4PdiRRx5ZojwtUODZXgZFDGSY5eF8mO+++w7vvPMODjvsMNxzzz2VPsbyur9Vaz0UqZLx/ew4vocNj8/zIs8DjO4KvL8EaJEA831CDJDgBAp9wNergM9XVvIOrNeUcQzn1lhfJ4To6lfoMdmZVrn52Bwfa7I6CW4P9knPwOa2zZGSsRlNcjJw//TP0SSvEB640Oe/tfDv0wTxnVug7fEd0OysSbDFhN9hUCQcLCvm3EM2aAmFpbS8jpnnqlJgIyIi1cbBP0uw2O0ssKvNzz//XCrTwBItKxtiYb06MzQpKSkl9rWyI8GZDWaFwhFYWhIuZmzY3YzlaFZgw6xRYOkXS8142yy74/ydcDDY4+3xwg9u1pKzUcG4cePQr1+/Sh+nRJYklw3X7b97WLVv29D7Xbo3MHOtBwe/XXJ7agzwz1l2tE+xm4YEnELjCDHPwnZdftEXZczB6J2bjySbDb3yC9CMJWVbNqJ5ciyOiFmPoff1Q+IBw7DjqXlIv+gbeOGEMxUYvG4y7ElqfR5JOC+qofFXcNKFjViq2xVNc2xERKTamEHgQD/wg4uD/pdffrnEfgcffLAZ1HNifaD33nvPTH4PxICBWQ+WuHGujWXz5s0mIAiHVfLAOT/h4lwhzuH566+/8OWXX2LNmjWlsjIMfA466CDT/nnBggWlboPPw44dO8zXPPbA47eeLzY3qOyxScMwrIMT88YBzWMBFjdO3gvYeanTBDWUEGMLGdQY1mb+roUYKDYp9BTv1sTjxWtfHIip7+yNI945FokHdDPXpV0wCN1816Kb/zp03nmNghqpNfz7xr+hvBDb4lvfB17+/vtvvPXWW9Wel6SMjYiIVBvXq2FL50suuQSHHHKICVIYfAR2SSPOMWGXMq79wgwNu5axHTTLvpgFCSxBYPaG68GwxO3ss8827aEZILAlaIcOHczPVYSZEJ4BZFc1fsAy0GEmKHjifTAGMvyQZZkYf54lZ8HYwYetSdnhjWVqvXr1MiV3fFyco8PjZVe01atX47zzzjPPS7du3cwEZGahGMzxWPbaa69KPdfSMOzV2omtVVlknQEP27AxpinwcpJP8VVdd+agWb67+PuGd85fos3DDz+MO+64w3zNk1+XXXaZuYTCE0J33XVXte5PgY2IiFTb+PHjzYcSJ9Jzkjy7BXH+CAMZdjWzcN7NE088gUceecSsC8OMB9eO4TYGMGyPGohlWgxGmOHhPuyIxm0sY7M+LMvDTkcs+eLioffee6/JIjFoqSiw6d27twlCli9fbsrgAts5B9425xbxtvlYuHAnmyBwX3ZP4+Mnfs/ngXNr2BWO5RacQHv88cdj4sSJ1e4CJI2MLyBLU+Ap+j7WgRFrt6JTdlBmsO6PTmqI39YwiqoOP/xw8/eanw9sd3/aaadh0KBBJfZhwMOumHvvvXdYbfnLY/NrlqGIiIhIVLBdl1WUpXHtClvcPjjyCzFhzZZSGRoO8F58vXt9HKZU0xMDvwhrv4v+Kp1NjlS33367WXOrohNL1aGMjYiIiEi0YEATGzB8czlg9zlVdiYR79Zbb631+1BgIyIiUs/YaKCiFqcJCQnmIo0cA5tCL+De9X6JdcIdGOgEiNEoL2r5y2oe0QDMnj0b8+bNM2tuBS8FwLK0m2++ucq3rbe8iIhIPZswYUKp+UXB2KSAzQikkWNAw6YBFk8hkBiDM89Lw8vPFnXiszz6bO0sZCtSFenp6abRyty5c82cm8BOmtbXCmxERESi3J133omCgl0LL5bBWtdHGjlma4IVeLH/gSkYenAz/DE3C2lNnejaPfTq7iL15eqrrzZtnd944w3Tzr9r166meyYX7mT3tF9++cU0YakOBTYiIiL1bODAgfV9CBItQrV88u4u59l73+Q6PRyRcHGxY2adTznlFLOeDbGdfvfu3U3XyxNOOMG0gn7zzTdRVQ2jl5yIiIhIY9WA52M0Vn6WZoVxiSY7d+40a4sRW0BTdnZ2idbQ4S6+XBYFNiIiIiLRItRYNsoGuNI4tW3bFps2bTJfx8bGomXLlpg/f37x9VzcmHNsqkOlaCIiIiJRgp3O3O6S25wOD9ul1dchiYRl2LBh+Oabb3DjjTea71mSdv/998PhcJjuaFykedSoUagOBTYiIiIiUWL7jYlodW8u8gqKJtu4XB481u5dAGfV96FJTWqASbgrrrjCBDZslMKMzW233YaFCxcWd0Fj4PPYY49V6z4U2IiIiIhEieQ4O3JvSzKtcT0eD1566aX6PiSRsPTv399cLGlpafj222/N3BtmbZKTq9/4QoGNiIiISJSp7lwEkUjRpEmTGrstNQ8QEREREYkgDbErGq1ZswaTJk1Cr1690LRpU8ycOdNs37ZtGy655BL8+eefqA5lbEREREREpFYtWrQIQ4cONY0CuEDnsmXLTDklNW/eHD/99BNycnLwwgsvVPk+FNiIiIiIiEituuaaa0zZ2Zw5c0wpJds9Bxo9ejTefvvtat2HStFERERERKRWsezsggsuQIsWLULOEevYsaNZy6Y6lLERERERiUQ//gM8+DEwYyGQWwAM7g7MuBOI05o1DZ3fHn3zZyrCErSEhIQyr9+6datpA10dytiIiIiIRJpuFwDDbwE+/QPIyge8fuDXpUCTcfV9ZCJVMmjQIEyfPj3kdZxr89Zbb2H//fdHdSiwEREREYkkMxYAKzaHvq7AA6zeUtdHJFJt119/Pb788ktTjvbPP/+YbZs3bzZr2Rx++OH4999/cd1111XrPhTYiIiIiESKlZuAQ24tf5/XZtTV0Ug9aYjtno888ki8/PLLpkHAiBEjzLZx48aZoGbevHl49dVXMWzYsGrdh+bYiIiIiESKrhdWvM/LPwA5hcCNJ9bFEYnUmPHjx+OEE07A119/bdo9c95Nt27dMGrUKCQnJ1f79hXYiIiIiESCN4oWK6zQss3APR/Cce9HcD04HIWJaiYgkemGG27AqaeeigEDBhRvS0xMxPHHH18r96dSNBEREZFI8OQXldjZD7vfhzG3/VKLByRSPffee2/xfBravn07HA4Hvv/+e9QGZWxEREREIkFeYaV/JCkzr1YORepXtM2fqQy/34/aooyNiIiISCTw+ir9I45aORCR6KTARkRERCQSOCofptTeuW+R6KNSNBEREZGIoDBFGl4p2qpVq0w7Z8rIyDD/Ll26FE2aNClzIc+qUmAjItJA3Xbbbfjss8/w+++/I1Idc8wxaNOmDZ599tn6PhSRCFD5UjQfbNjmTkTTJ4BstxsOG9A0Dkh02fDIIcCxPTTUk/p18803m0ugCy+8MOTcG5vNBq/XW+X70rtdRKQRmTFjBhYvXozzzz+/vg8lKs86fvTRR/jvv//MJTs7G+eee26ZzyXXZ3jzzTfxwQcfYOPGjUhLS8PIkSMxadIkxMfHl9r/p59+wosvvoglS5bA5XJhn332wSWXXIJ27drVwaOTiGALpxTNFpDZsSEjNg6PbTwM2alFZ/i9fmBrnh9b84DjPvJjcCsPfpug4Z7Uj5deeqlO70/vdBGRBuqmm27C9ddfXyqwYRYnUgKb999/35yhiwYLFizA66+/jvbt26NPnz747bffyt3/oYcewltvvYVDDjnErK69cuVK8z0DyyeffBJ2++5prmx9eu2116JHjx649NJLTdDEoOh///sfpk2bhhYtWtTBI5R65/dVslzNjxVNW6FFTiY2NWkasNn6nbLh981+tHjUjSO6AKO62LAiA5i3GbjjIGBAKw0DI1VDKUWbOHFind6f3tEiIg2U0+k0l0jGzES0GDZsmAlAuDr2okWLMGHChDL3Xb58Od5++20T1DzwwAPF29u2bYspU6aYVbePOOIIs83j8Zh9WrVqheeffx4JCQlm+4EHHmhW6WaZ3o033lgHj1DqVE4+4PGy/gb4ezXQvxPgrlwJzubEFBx6/i3Iik8EfCHm59g5OLZhWyHw2n9+vLaYqUS/Sfp8vIzXu+GCDT2bAm8eDezRsujvRaHXbw6lwOuH1+fHkp1AcgzQtYkdSS4bCjx+kxlKiGkYg29pOCL7E09EREqYPXu2OaN/1VVXmdWcg5111llYu3YtvvzyS9x1110l5ticd955xRM4Bw8eXPwzt956q5nrwlIrZhS4z6ZNm0ydc5cuXTB27FiMGTOmxP0888wzeO655/DOO+/gww8/NAN1Zhm4ujQzD507dzZBwAsvvGBut2nTpubYTjjhhArn2FjbuGL1ww8/jD///NNkdfbbbz9cc801aN68eVjPFY/piy++MKVd6enpJmAYOHCgKQVjZqSyUlNTw973q6++MvXip59+eontXG378ccfx+eff14c2Pzxxx/YunWrOS4rqKFevXph7733No+Dz2lgkPree+/hjTfeMCVurVu3Nu8F/uztt9+Op59+usTrKxFmRzYw4VHgs3lVmlMTqFVOJsb/+ROePHBU0QbGNoHxjY1BzK7gw/rXBDu7KtoYxAD4Jx3o/4ofsQ4PLtkbeOJPINdTouJtFy86JgNbc4FCHzC2pw0vjLKb+TwikUCBjYhIFNl///3RrFkzTJ8+vVRgs2bNGlMuxe2hMjVnn322GWwzULjjjjuKtzMYIQZADGqGDBliMgv5+fn49ttvTYC0Y8cOE5iEalDA+SK8bufOnXjttdcwefJkM0h/9NFHTVCUkpKCjz/+GHfffTe6du1qgouKcKDPcrnhw4ebeSbsoMO5Kjk5OXjiiSfCeq4YdDEYYTDBYGjdunUmCGN5F4+zY8eOqC3M6LDUrF+/fiW2x8bGomfPnub6wH2pf//+pW5njz32MCVvq1evRrdu3cy2l19+2QRHvXv3xkUXXWReJ5arcQ6PRIErX6mRoMbSPmN76Y2OXYFMZRZCtNlQ4AMeCKywDBGvrMna/fXbi/1om+TDQ4doNR2JDApsRESiiMPhwFFHHWUGsitWrDCBgoXBDh199NFlBkXM5DCw4W0EGz16tAlEAjHjwCCFg2mWRQUHTAyyOJfEmifD9p0stbr//vtNKRazCXT44Yeb22ewEU5gw6zTPffcg8MOO6x4GwOFd99912SAmBGqyGOPPVZqkj6PgY+J2Y7rrrsOtYWBGZ+LUKV2LVu2xN9//w23242YmBizr7U91L7W7TGwYatUZsq6d+9usmEMlIgZtRNPPLHWHo/UoC/+rNGbm9uhKOAtFdRQHczT+HylHw8dUut30+g0lDk2dU0LdIqIRBkOzgMDGWImhmVXHPzyTH5VBAYBBQUFJgOTmZlpAiJmShhQBDvllFNKTP63ghbOR7GCGmI2oVOnTiZgCQcnywcGNWSVV4V7G9bj4XPDMjk+Hus4/vnnH9QmZlEYtIRiBTvcJ/DfUPsH7/vrr7+a14YBqBXUEDNSRx55JCINSwB5vBa+DllZu0/5FxYWYvv2khkHlteV9z3LJPmaRu19dCkdwFZHv83rSpah1fGAuG1cYXS/HnV4H1L7lLEREYkyPFvP4IXZF5YiMZPBErINGzaYsq2qys3NNXNdvvnmG2zevLnU9QxygrFDWCCWnRFL2YJx0j0HD+EI1eLYmuNiLfDGjIf1tYXzTKx5KmzJzPkmnMOSl5dX4e3XpLi4OFO+FwoHRNY+gf/y8VS0L19jYnAWLNS2+sa5VYGSkpJKBW7M+gXi/Kryvg8MmKPyPu46DTjybqDQXSPlaC4PJ8ME4OC8qsGN349WiTZszrW+D12OZkmKAe45JL7EyY2oez3q8D6k9imwERGJ0qzNgw8+aOZfcFI9szdWmVpVsfMW11LhnBSu/MxAgkETGxawdIvrsgQLbFkczvbAM6LlKevnA29j/vz5pkwukLWuDAMoNktITEw0c2pYusbggAMwPm/BgU5NY8aJ7Z0ZmASXo23ZssWUqVkZGquVM7ezWUPwvoH7SAMwoj+w9FHgtZnAf+uBVZuBDelAt9bArEVAXukAtzxtsnYWfVHUAK10MBIY6JT1++cHklzA96fY0LeZA+8t8eOvLX6syvBjUw6Q4wHS4oDDOtlwTDcb/tgM5LiLmge0SVLJVG1QKVrVKLAREYlC7Kj1yCOPmIBmzz33xHfffWcCnIo6hpW1ZgxLLBjUMDBiN7JAc+fORSTiJPzgRgJWJuaHH34wGSjO/wnuEMYsT223me7bty/mzJmDhQsXYq+99ireztIWdmlj4Bi4L7HxA1/DQCyZY3BmZWOsM8BsJsAFPANxm0SJji2AG0LMidr7KmDeikrd1LH/zMXFY85GoTOmdEBjBTlWK2gr+IENPZoAnx5vR69mpU8iTNzDhvJWH9mzZqvpRGqMAhsRkSjEuSJc54QDeA6SOQfGmnsTzrwTDu4D2xdbGZLgjMq2bdvw0UcfIRKx7C04EKjo8bArGuvia7tEhM0SuOI2M12BgQ3vn/NlrFbPxJbODEj5PLOxgVVKxwCIZXRsf201beDjZVDGds/cbs2z4evEOVYS5WyVL01Lzc/FXhtWYlmz1tieWFQKWhzR2IE4ux95V4Se7yXS0CiwERGJUux+NnPmTLPWC+u/2Rq5ImwpzM5k9957r2nrzAEzWwoz08EmARwcc7DMNsWc+MoWy7wueC5LpDvooINMV7RbbrkFJ598spnfw9K1n3/+2cwL4ho9lcXJw1znxwokiB3muKgmHXzwwcXr43Ae1EknnWSe66uvvtocD0vT+PMMRAMDG74GXJfo+uuvxznnnGNKARmoMihiAMvSOgtL2Fhux0wVS+zYMICBEgMmZnXYOrqsrJxEAa56WUmxfh/WN03F9oTkog0+n1mqxuewY1BLG+aO01AvGvmt9YakUvRuFxGJUkOHDjVZFwYdbPcb2CWrLKNGjcLixYvNoo8sX+O8GS7QyeDlzjvvNMHArFmzTIlbhw4dcOGFF5qBNxd+jCYMXriODgMAZk6YwWHJHhcWZSvqqnQrYvMENiMIxLV/rAVQW7VqVWLhzyuvvNI0UWBwyDI/BiXsIsd5QcFziEaOHGleP7Zwnjp1qsnKsNSMzSCC20BzzSCWpzFI4no2nPTMVtzMTjGwCed9IBGqnLll5bm5/eeYePLpWDd7Ldr1SENcz5IT4UUaC5s/3JmcIiIiErEYsDFDxG55Fc21kgg1+Argj9Jt1cvDQdxzzxxhAt6yWoxL9Ln/4Jlh7XfNj8Nq/ViiidaxERERiSKBa2tYWBrHLBvXMVJQE810rlmkOlSKJiIijRbnzViLX5aFZ8EDGy3UNzYUYEe8ESNGmDI1rm3DxgNsYT158uT6Pjyp4/PNCoUaJrV7rhoFNiIi0mhNmTIFn332Wbn7cLI/Fy6NFJz7xDlEbBhgta5my+gzzzyzzC5xEi0qP5jduavToYgosBERkUZswoQJprNYRW2lIwkDGy4yKg3QqL2AP1ZUKlvz7eSBtXpIItFEgY2IiDRaXbt2NReRiHDHqcDd74eX2GmTBs+0S7Fz2Zy6ODKpYypFqxo1DxARERGJBA4H8MhZFe934wnA+heAoX3q4qhEooYCGxEREZFIcckxwHmHlb/PqEF1dTQiUUWBjYiIiEgkeXpS+dcP6VtXRyISVRTYiIiIiEQSzq9Y+RTQqUXJ7Q4b8Ms99XVUUsdzbMK5SElqHiAiIiISaTq3AlY9U/S13w8UuIE4V30flUhEU8ZGREREJJLxzLyCGpEKKWMjIiIiIhJBVGZWNcrYiIiIiIhI1FNgIyIiIiIiUU+laCIiIiIiEUSlaFWjwEZEREQkimW7XTjjqSxkLc1Fz64uPHRJczgcKsqRxkfvehEREZEole+JwesLR+L3TTbMa5aKv5YV4qwzFiM/z1vfhyZS5xTYiIiIiESpTxfuCQ9sOOivVdhz8Qbs8Prxd5umuGvq5vo+NJE6p1I0ERERkSjVbLUdLbEFbwzfAz67DTafH+22ZWLukhwsSfejZ1PN1YhGmmNTNcrYiIiIiEShW77Kx+AVG/DWsH7w24DUnEL02J6FBLsNv3RrjXF3bEZWob++D1OkziiwEREREYlCM97ZguUtm8Be4EO3DVnouzUD6xPjsSQ1CSl5HmxKiMc7/2qujTQeCmxEREREoszSrfn4tUsbPHvwQPg8ftjy87E81oWcGAdgs2FDcjwKHHa8Mbugvg9VqoAZuHAuUpLm2IiIiIhEmeVrC+G3xaBdehaun/4LWmXmwgfgx36d8OSowWaOxpbEOKzKzK/vQxWpM8rYiIiIiESZAg7hbDacM3O+CWqsQd0hC1dj/2XrzPexHi86rd5Wz0cqUncU2IiIiIhEmXWFRUU3PTall7qu54aibSOWrEWr7Vl1fmwi9UWBjYiIiEiUSY0v+ndDWnKp61a0aoIRqzdj3zWbkWPTUC8asZQwnIuUVOl3+/r163HllVdi5MiRGDx4MG677TbUlA0bNpjbfOaZZ2rsNqW0Tz/91DzPv//+e1j7n3feeTjmmGNq/bik6vg7w9eUv0MN3c6dO3HLLbfgiCOOMI+Z789o0tB+n/h3hK8D/66Ut01EalbivKIFONumZyIj3lW8fUm7FshLSERyvgd/dWyD3zq0qsejFInw5gG33347li5dirPPPhvNmjVD+/btUZuysrLwxhtvYO+99zYflI0ZBwt//PEHTj/9dCQnlz5DI1ULCHr16oXhw4cj0s2YMQOLFy/G+eefj0jF31W+N2tz4P7www/jm2++MX+D2rVrh6ZNm9bafUl0YADFzwr+baxN77//Pv7880/8+++/WLt2LXw+X7kniFatWoXHHnsM8+bNg9vtRu/evc3v7z777FNq3+zsbDz55JP44YcfkJGRYT5bTz75ZJx44omw6ayshOBZwxKzVpjTqwNeOnQgjvx7DfJiXdjaJAkOlqolxAM2PzzOmPo+VJHIDGwKCwvNH3X+sR0/fnyNH0ybNm0we/ZsOBz8lSzCD6vnnnvOfN3YAxsGNXwuOGhUYFMz+HweffTRURPYfPbZZyEDm//9738488wz4XLtPmtXH958803ze1ybgc2vv/6K/fffH+eeey6i0RNPPAG/v2EvmDdo0CDzt9zpdNZZYLNx48ZaD2xefvllE3TwZEh+fj42by46Yx7KunXrzO8lP88mTJiApKQkfPjhh7j44ovx6KOPYr/99ivel0HPhRdeaE5cnHLKKejSpQt+/vln3Hvvvdi+fXtEn8yQ+mPvHAfHci8WtWuGOK8fa1qVPMnj9AOL0pKQFePEKQ9uxdtXtqi3Y5XKU5lZ1VTqUyc9Pd18IKekpFS4b05ODhITEyt1MDwrFRsbW6mfERGYAWRdDSLrGwd6qampYe1blb9DtS0mpv7Pntb282K32xvk33JmeFu3bm0e32WXXVZuYPP444+bE3PTpk0zgRCNHj3anBi87777TPbHysR89NFHWLRoEa666iqceuqpZtvxxx+Pq6++Gi+99BKOPfZYc8JA6ojPB2TlAdsygTZNi4r2F28AdmYDQ/vxDV72z/E1raUBqWdjJuy5BbB1bY75z/+FZ2Y5Ye/rwKwBneH0lD5Z4rEBOS4n4Ac+yU7Eez9lYOyQ8P52ikSrsEdCnEvDs8XWWW4ri3Lrrbea8jSePeVZpldffRUrV67EYYcdZn6G9eQ8kxZca825APxjzZ+zzkYFb2OKf9KkSaXuk3/grdvjMb3zzjtYs2YNPB6PKY/r37+/mQeUlpZW5uO5/vrrTcr/yy+/RJMmTUqVD4wdOxannXaauR3L119/jbffftuU4nm9XnTv3t1krjjfKBCv44cRP6wYDHbs2NGUzfB54WP45JNP0LZt2+L9t23bZrb/9NNPZtDG4xk6dCguuOCC4jKbwOefz5HFeq62bt2K1157Db/99pt5vgsKCkyZDj9IeYyBWbDA4+QHNZ9L3m+nTp1w1llnYdSoUQgHn3Me99y5c81ZzBYtWpjngq95fPyuWY0ANm3aZO6Hx8b74ZnLDh064IQTTjDZkopY76EXX3zRlCH98ssvJnu41157mQ9+HreF9xPqOSZmEfjeefbZZ4vfa8Tn1XpuySot4evB9/Py5cvN2Vm+Ln379jVnXAPvszzhvmcqui8+ByxnCc5c8vePjyvU47a28feDZ4p5LCx3GTBgAK699lp07twZ33//PV544QXznud7ja8/X5fgx/DFF19gyZIl5v2ckJCAgQMHmt/NHj16FO9nHRdfq8BjDDwmDt74OjLzm5uba14PvkcnTpxYYWBmPZ7g18x6DniffD8dddRRZl8eb58+fczrbWW8+BxzOweUPHaeSQ/O1lnvEw4yp06digULFiAuLs7c7uTJk83r+NRTT+Grr74y7/t+/frhhhtuMH//whHqb2Jl3uPE9y/34+8esVSXf6v4mljv8cDXpbznpTKvCZ9D/hzfL/z7ytvlMQaz/nZbr42FJ8Y4oOffRv49ZIDA9zn/jgW+ZwI/C3g9X/dly5aZTDUfx0UXXVR8bLx9PnfWY7U8/fTT5nv+TvGY//77bzM/iyfm+N7n7+GQIUNQGcF/U8qSl5eHmTNnmtfFCmqIvztjxowxx7Zw4ULsscceZjs/h/geYzATiBkofk7xd5CvR1Ve/4jFIOCBj4A3fwLSkoBrxgBHDiq6LrcAuO1t4PM/gPhYIL8QYJcvhx1IcAEbdxYFD0cNAq4+Dvi/94FVW4ADewFbM4GZiwCvD0hNAAo8QF4h4PEA+W7AFxAAxMYAjEG474j+wGEDgKterfjYE2OLjnnd9vAeqxXn+Hd9vV8P4JA9gA/mAhszgTy32e5PiIE3PgXuLDsc2dmwwYtCOGCDz/zfjUR44YQTHizuPgjXpxfi+s9+xlv798ezh+yDzUkutMouLL6rZc0S4YuxAx4f8p0OvPHkenhO+RzpbZsg8eb9MPFYlfFKIw5sONjp2bMnHnroIRxyyCHmQhy0048//mgGcKwH5qUmzgZyoHDFFVeUuk9+OND06dPNgJ8frPyDzjOEPIPGEggOwMoLbPjBzTp9Dk6Y+g/E27X2sbD2mR/+Bx54oLkvfiDzA+e6667DNddcY87CWe6//37z4c0P1XHjxpkPU56hC/WhyEE/B5MsRTjuuONMXTVrt/nzHBzwbB8DAT7/PMvK++RzYgVj1sCSA2dex0Eab4NBHgdHPGvIhg833nhjqftm7Tc/gBnEEQda3I8DqopKiVhjzueBAw0eW8uWLc2A6a233sL8+fPNBysHHjwODkIYePF++H7h4JqDFA6kwglsiMfJQQ6DVt4eHxPvix/mfN+FCtzKw/fGHXfcYSah8/0TPKBg2R+f527dupnXh68BA1AOJPj6hBPYhPueCee+GBhzUMjnjMdtYZBSEf6OMNDkbfO9yACYA3QeE0ti+LpwsPfxxx/j7rvvRteuXU3gYmFgxAwJn6PmzZubEhsGSiyz4W1ZfwN4XPxd5XuTxxv4XFvBGwfpDGr5e8H7ZNBgDbb5O1KeESNGmJ8Nfs0CnwMO0hmscfAY+N569913ze1zQHvOOeeYbQyMGLwwKAkO5rZs2WLeZzxBw/tl+dvrr79u3mcrVqwwJw440GRgw99Rvg/fe+898xpXVbjvcb6G3I8nCfi3ln8n+b7g68nbCKWs56Uyrwnfu3zf8u8Yn0MeD/9m8DbCxdeOf3MPPfRQ8zeGf/cYNPPx8u/mwQcfXGJ//i3n88rHyUCHnzN8vvl3x3qP8fnh3zk+L/w9svB54TaeICLeBrMt3Ma/X//880+lA5tw8e8x/47ytQxmBTN8Tfg15+n8999/Zv5NcJaLQTODcO5rqcrrH5HufLcoeLHMWgT8cg+wTw/gvKeA12dWfBsf/gp8+psZuBsL15a8PqNobZcyFbh3f/3VX0WXcOQUFF3C5Q/6es7SoguYvd2d3bEV5sOxk69hEmxmux3xyIQHSXAjxWyJgQe5SMTey3avTXP1F7ORGR+Htw4YgC0pbsS7fdiRGAOf9feIwQ3HaT3boltGL5z85Rykj9+IT38Zh2P61m/5spTNp1K02g1sOHjgoIYDF5515lkzsrow8awYP4TDPWsZDmZfOFAPvs/As4cMoHj2NPDMopXlKc8BBxxgbp9BTGBgw8EjP2h5f/ygIX7ocIDKgSE/gC0sGeCHKmvmGQTxWPg8MCjh7T/yyCPFAx2eoQ9V/80Pcw7+OWhq1Wp35xLuz/vjdmZk+PzzmKzgJThIYk07B6aBk0x5fzfffLPZztvg6xeIH5B8zTiQJg5w+Zh4JpADOp5BLAsHsbw9ngEPDGL33XdfM1Dic8iBC8/Krl692gykA884VhaPlWdYA2+DA2YOzBkA8PmuDA70+X7iQIuZreD3FgdQHHDwtQ2cnG4NiitSmfdMOPfFOSU8q8sBTPCxVoTvc/4OWe8NBh5Tpkwx7z0OmDnYo8MPP9wcEwOZwMCGAXBgBo64H99fbBbAQI14XPxd5GMIPkYGAnfeeacZyAX+vnJgxuCc7zmrk1ZZuB8vZb1mxKCDz2Pg/IXMzEzzPmHAzzkSge/3M844w2Rl+H4PnLfG4I3zG6zMGvflwJ+DamZTGbRazyeDPj6fDH4q+z6synv8lVdeMSdw+HweeeSRxcfHvzc8vlBCPS+VeU2YpeJjZODD+7dOrHBfq3SqIvzbxb8LwYEkf56/Jw8++CCGDRtW4m8Yj5vvR+vvHe+Pf6/5vrUCG/495PuQjyfU7zFPct1zzz3mNa4rPJFDPOETzNpm7cP3J4891L6cM8fn2tq3qq9/RHrp+5LfM2sy7Udgj47A27PDvx0rqIk6fJ8HD1xt8MO5K6hhDOSFA164UfQ3i1u4Rx6KTu4GOvqvxSawyY2LQW4ZH93pSbFY17wJ1rVKQ+eN2/HhW2twzB3da/qBidSrGmtuzjNfNRnUhIMDFJbt8IxhZSfj8mwjPxR4JoxlFRaePWcWJfCsJj+M+WHLwRwHH4EXfhAzk8KznDRr1qziD+vAs7cMSjg4DcTMBY+dt8EzdYG3yw9yDsQ4WAoHgxBrQMCzoDyTzNvhYIiD5sAzfhZ+GFqDPOLXHDjwg5bPQ1mYbeEZSbbb5X0FHjcHxBwEz5kzp/g2reeVA4yq4nMZPICyOguxJK6mWcfNs9wMPCurMu+Z6t5XRTgQDBwsWkELj8MKaqxBNLNDzBIFsoIa/o7xPcvHYO3Ls97h4PuYZ5gZ7Fq3YV0OOuig4n2qi1nlwMG7dbs8k833T/D7ndtYfhV83xxkBpcL8nnjc1DW81nd92G473H+jeFJheCS0fIaupT1vIT7mjDDwcE0syaBpbvW34xwfP755yaQZyASeF+8bwaLPEkW/BwGn8Th885Ai8fN160i1uvNifi8n7rCz6Wy5lNZDT6sfcrb19rf2qeqr3994t99Bm4Wvg6ce4SA9sTF4lym3MwXU7kMfGPC0MaO0sHczoTw5rS57TY4WAbIv+1Jzt2vxy7MNPL3K5BV6lnW9xwzBY7BynzNdR9SB2pstrFVjlKXeJaP8w5YTsKzpsxa8AOZZ+asLAI//II/ALkvP0QYvLCUhlkb66w6v2bQw0G7hVkHvtmtkq1QrDe3lcEKVarEbfyAtTCgYtDBjAovofDMdDg4IObZaA4eODANDvQYrARjWU4wKzhlGUxZ+HwQy1XKWnPICmJY780zqzw2PqccYHGwxkEjyywsLL0KxNcncII45+8El2lY1zOIq2ksE+PZXp61Z8Zizz33NCVlHExYpVUcbAQPljiQYpBZmfdMOPdVHcEt2a3mH6FKI5m14B/34OwT5wQwOA0udQn3/Wm9ZwLL6Mp6Psr7na3K3yHrvcwSu2DWtuD3e1nPTajHbD2f1vuQ2Y0dO3aU2IfvicCgKpRw3+P8G8PfneCyN2bKyuqWGOp5qcxrYj0/of6uhXtCi3/vGNAzM1gW/t0IvI9Q76/A58QqSy4L557w5AJL5niygfN1GODxMyLU+6GmWNlunvgJxsFQ4D7l7WvtH5g9r8rrX5+C27EX/x5cfgxw/tMBV8QB544EXDGwX3QkMCX0Z2IpyXFA1u7AL3rwM5oBRuDr6NZ/e1IAAImiSURBVDPzaXyINVkbGxxmdk0MsuEG3/dFJ1SSkIU8xBf/rNthx9pWLRHr9qAgpuxhXWyhB103paP95h1Y1rYZjj+rA5KSHKUCaWb5AwU3rgj+PvAEWbmvue5DoimwKatsqaz++/zwry5+WLN2nmUanJjOIOeuu+4qnmTMAR1T89aE4+BJpcyicKDNDzy22mRkzrPm/OALLtvi42BJSFk19JwfUVXMHJU11yTczkIsG2F5Bj+wGUhwQMzSEg5KOViuyfay1m2xNKes0pvAznl8bnmml9mpv/76ywRxfF04cfuSSy4x+wQGksQgNXACbHlzFwIfW3nrPVTmPcez0iyzY+kXz1rzX5Zz8b3Fkg+WBnKOFhtnBAqcLB3ueyac+6qOsu6/rO2BzyeDHE5s54kCzqlhMGxlB1k6FG5Nv3Wbl156qfmdK2tgT+X9zlakvPLJyijv/VbR82ZlNgLx97uixYzDfY9XRajnpTKvSU3g/fHvEv9GlyX472hNPCf8HWU2gyeV+LvFk1ksE+V8nOD5lTXFet44VyuYtc3ah38r+Xc+1L4MapjV4t/DBue8w4FWTYA3ZxVNxJ98FNBj1wmF+ycAfdoD0/8oCnjYPODf9UWT9ju1AP5ZU9QE4OIjgWP2AaZ+CqzaWjQhf2sG8MlvgNtbdPuFbiA9u6hxQHpW0bwbvnXsNqBba4Bdw+iS0UVlcIfcAhRWkDnfqwswsDPw/pyi22VDg4w8vil37+PYVWrmcgAtUoA8D5CZC6TEA1cdB3RsAbz1E7B4E7Ajp6h5QJsm8MSlwpsN2BetNuV5BWgKOzxwmrk2ieYrP/xIxXbkIRFvDhyIFw7YG0vbtTANFdpuy8GG5qHnOO+/YiO6bd2Efyfujf1uHYjuLZQZi/TsnFRerfeH5R9tDq6DlZcRCFTRwmSMmFkGZ00C5eCZbTg5N4Wdn3i2LnC+AAV+iHPAwUEka8mZNeAZxeAggxNr+aHIaL6is5PWmV7OKwk+U85tgXg9Hx+zLcFlIpV9Lpip4Ycfa8kDBZcVBQoswQs+i1vemXjr7C8HHeEct/VYWWbDCwNIzrnhYJ7BEc+KsP4/UDgtxUOxfo4ZqsCz7rxPvr6VWVCWmTsOpq0BNcvveLzsIsaAg0Fd8HFbA7PKvGfCuS+qj0X6OC+C2RP+jgQHFjxjHrxuTlnHaL1nWNZW0Xumot/ZyrJec87X4Bywyr7fK4tn8ILfFzUZIPAMoLU4ZODAn9mOwDKJilTmNbGen+C/YYHPYUX4O8FSM06oryjTUlkV/W7wJBYvPJnC54jzmNhwgNnS2vi94n3xd8MqNw1klW8ye0R8DTmfk2vYMJAJ/J1i5zQGcOxiV9Ovf0Q4bt+iSzC+JmcfWnQJx4Nnlfz+9tOqfkwF74S/74uTUS2nlmxeUdQcoOhiCfwLG3iaMy/Pg/cP+wI3jRgBT6wTiHUgId+NllkF5vnbmBYPP4O3XRxeHz58rCPS4ut2yoBI1M6xKQvLChgsBNbi8w8yJ3uGw6rvD1VKxTNZwawJ/1bpBgc1/NAOvAQOmpkp4KCSJWi8MNUY3JnHmpDKwUqos/6BNZasFSdOyufjDJyXYs07sfBMPUvnmCUK9QHID7TAkhZrMBDqueAHXPAZTJ5NL+95ZrehwFIqfs3GByxnYAlHWdi+lAN47stJ1sEYqFnPP28zeN4Iz05aZXDWYwl+jQI/yCvDKmMJnjPB5yHw9Qh8TkOVsoV6b1nZCuuYmdULPm4r01eZ90w49xX4u1AbpXdlsQZOwe8tdkULri22jjHU+5NBIANYa4HDYCzr49+JcH5nK4s/z+NiRtO6D+LX3Mb3QPD8t+rg+zv4+Guy7Ilzoxiks7tYoMpOHK/Ma8LfRzY3YevuwPer9TcjHAxY+TvIgCKUUO+ncPE15Psu+H3KxxX8e8+/bwzU+PgC6+drEo+HnwUs32R3OQtPErDVNYPKwFJclp3yeD744INSf7f4+RRYvldTr79Et/h4JzpfPADH/r4EcBb9nS50OkwyqmVmPvqv2YEuG7PQJj0XzgI3/tfNi7R4ZWik4av1jA1bsjL1z05ZPFvPOvnvvvsu7LIgDv55po99/Dng4QcxByn84855MfyQYutXfujybBVrqXkGLtzOUbw9zmfgMfFsGUtIQrXcZDkOS6PYCYrzQ3gGlh8unFTLlqRW0MIBPx8zB34swbImyrJkjgEB9w88Q8iOUux+xfad/ODnPvwgZkaL6yDwcVjr/FhtQlnexPI1ntnj/fHsINun8kOR6/PwrDQHCXwuylvIkM8tz1xapVPcn6VHN910U7klPTx+1uWzjSrX+uFzxoEbP5gZ6DBQ4/orvF1mwv7v//7PtMxl0MEPfD4HLEfj4wk1z6c6+Nh5Pyzj4qCGWRu2n2bgGLxeEfEYWMrIwR2zK3xsHGSwXIalIRyU8gwpB0AsPeNAL7ANeFkq854J9754ppsdojgXhxlKlhry+Gsy2xCMgTdLGdmJjGe3+fvG55PZKP4+Bv8e8xj52rLLFjNVfD75u8rfWZYEcT6c1bqXv9f8nWXmkJmhBx54IKxys8riMbPkka2LzzzzzOKMLNs988w3u3RVNP8lkvB3lh3y+HzyjD5/h1hixXVa+B4PNwNRmdeEg+vLL7/c/H3h/bNtNLcx0OHfmOB5WaHwd4B/E/geZhafA38eL9/7PHb+7ShrrmFF+HvASfXs9MfSTQbknMvH54nBAZcK4PuVvzMsWWYr/Io6P4bCv8lWoGJlw59//vni91lgaRv/BrJEmv/ybwDLOfm5wA5n7MQX+DrxM4N/f1lSzMnG/N3h3wi+BiwBDcw+19TrL9Ev/vdNyIrfPa/K47Rjc5M4tN6ZD7sfSM13w+X3ISvWjqdPjKyFiqVifv0uR2Zgw0EX24SyPSrr5PkhyME6P0TLm1gdiG0tWQrDs98cPHPwx8ESf54DQA7oOYjlbTMw4FoLlRkgcaBjdTMra9DKQSpLB5iJefPNN002hEERAwsODAIxWOEglh/SLCPiQJvb+CHEQW1g4MTBNAM/tvDkBHLO92HAwkCNH/yBLUpZnsMSLj5eDoY5qGRAxMCG9eL84OTzwdvhz/PDksfMACsU3hbnvDDoshYS5e0Gz3cJhc8zy/24ECk/7HnWlvfP14aDF6ubE9vGclDBM5f8MOYx8zGz8QNLrWoaB1t8r/A9x7PxDKR5Np4BBgcIwfi6cMDLx2GdnWZgw/coBxrM4jFrxsfG4I37MogMR7jvmXDvi8fFchUG+QzEGQBzTk9tBjYcDDKQ5u8enyMOGNncgIEjB5HBHV/4XuPvIt9THCDzDDoHvxxEM0PA9zkvfJ/zsTITw/tg2+XAxT5r2kknnWSyaYHzd1jexvdJ8AKdkY6DVw6mOTjmc8uBLDOs/PvKUqtw5+VRZV4TBiZ8/Xnf/H3ie9laoJOD93Dw/cq/zRzg82QCJ8yzdI+Z9sC26JXFY+XJIP5e8G8Rfzf4fPB54e8M/77zpAL/PjBIYLly4Npj4eJJm8DFfIn3Q/zbFxjYMEhkKSlPDFiPlY+Tv0/BpX/8O8XPSF6shV/5GvCEYPBx1uTrL9Ftc8dm+D6hLeD28cPPbNuUloCMBBcSCjzIczmRG+tAjNergFcaDZu/JmeVS7l4xpNn8Bh4VHZBSRGR8jAzzOCDa8QwCyWNi17/xufFj9Jxwe8uFLITmlnP04Y4+NBzZw5cXh9WNUnEtsRY027AfVXFXSUlstx8VNnLbgS68/Oypw40RrU+x6YxClxzwMLJ4CzfYSZDQY2I1PTfGGZdKNyGHhK99PoL+VrGm3k1Bk9Re/2IyfOi/+YM7LE1E6OXbkTnHTmI99b8+mhSN6Vo4VykjkvRGiOWKrBLGecnsL0p69VZesH6bmu+jIhIVbFFM0ufWNrEsitmglluxfkl0VZaV59CrTkUSrjrKNUVvf5CS9eyA1rJeWJZLidWJsejZ2auSeIM2LwTq3L43i1qPiPS0CmwqQX8sJkxY4aZ48Faac6ZYF0551xYXdtERKqK8+84J4uTy9lsgnPqOGeNc+6UEQ5fqDWHQgl3HaW6otdfqKmrdKdPyg94D6QUuOGK03tCGg/NsRERkUaJQQEbqFSE7a6r03JcpDas3ZmPjlP9Rev+7GLz+zFm9WakuovKzwrsNvzcIQ2r/q9pPR6pVMVNo+eFtd9d0xvgAr7VoIyNiIg0StaaQyLRqEOTODTJ24mdCbvL0Vrn5SPG7zdTbuiX9s0wdHDNLogrdUPzZ6pGgY2IiIhIFBrfx4+Zv6VjfsumcHh9SLc78E9qImLtwKq0RHhcDrwyJnLmh4nUNgU2IiIiIlHorrGJGPpHAZDoBJdL9sKFRc2KMjSu/AIUXFe5RWhFop3aPYuIiIhEofhYG7q3WAYET5f2+/HoITp3Hc38tvAuUpICGxEREZEoNar1IrTIydq9we9HK4cX5x8UW5+HJVIvFM6LiIiIRLG72r6HlsPOxNuLbbhobxuGtFcJmjROCmxEREREotzobjaM6a1GAdK4KbAREREREYkgPrV7rhLNsRERERERkainwEZERERERKKeStFERERERCKIX6VoVaKMjYiIiEgUy/a6cOF3wD1zPPD6gta0EWlElLERERERiVLv5u6Nb90DgH+4YqMfN8x0Y+0kB9qnOOr70ETqnDI2IiIiIlHI60NRUGOVLfFfux29n3XX96FJDZSihXORkhTYiIiIiEShR+d6dwc1AXJ8ytZI46TARkRERKQu5RcCPl+1b+baOQ5TflaKTuRLI6U5NiIiIiJ14fflwL5XA1YsMrgb8NsDVb893o5dUYyIRRkbERERkbqwT0BQYwU6lz5XpZvyef3lZmb8oTI5EjV8NltYFylJgY2IiIhIbduZFXr7o19U6ebsjvIHtSMezazS7YpEMwU2IiIiIrVtxfq6uy+bDXMzNdtAGh+960VERERqXR11KmNTArsduTEa4kUzv6rMqkQZGxEREZHa5vfVwm2W3uRy+5CQV6jGaNIoKbARERERqW01HGl4fX7EF3hKbU/OLkCz9DzE5RTW7B2KRAEFNiIijdDvv/+OwYMHl7gMHToU48aNw5tvvgmv12v2+/TTT4uvnzNnTqnb2bBhg7nuvvvuK7H9mGOOMdv/97//hbz/2267zVy/c+fOEtuXLFmCG264AWPGjMGBBx6IQw89FKeeeir+7//+D//99x8aCut54+WJJ54IuQ+fw5NPPrnM23jvvffMzx988MHIz8+vxaOVGmGr2SEXuzynZubDZq2H4/eboCY1uxB2rw8xPnVFk8ZHBZgiIo3YqFGjcNBBB5nWsFu3bsVnn32GBx98ECtWrMCNN95YYt/HH38c++23H2yVaDE6f/58zJgxA8OHD69w31mzZuGqq65CkyZNMHr0aHTo0AFZWVlYs2YNZs+ejY4dO6J3795oaBhInnLKKWjevHmlfu7jjz9G+/btsW7dOnz77bc4+uija+0YpQbUcPtlxi35sQ4TxLTelA2nzwcnW0BzNo8fSHC7a/T+pG75VUxYJQpsREQaMQYKRx11VPH3Y8eOxUknnYSPPvoIkyZNKt7et29fLFq0CF999RWOOOKIsG67TZs2JpPw5JNPmmyQw1H+5GkGTrGxsXj11VfRqlWrEtf5fD5kZGSgrnk8HpO94nHVBut5feaZZ0oFkuVhZuvff//F7bffjjfeeAOffPKJApuIV7OBzZBz1qCjKwZLXU0Q5y7KsFo4JC5waognjY9K0UREpFhSUhL69+9vMjjr1+9uT8uMQsuWLfHUU0/BHeaZ4Pj4eFOKxuwPS9oqsnbtWnTq1KlUUEN2ux1paWmVfDQwpVose/v1119x5plnmuwUs1RTpkxBbm5uiX0ZXHD/5cuX46GHHjIBH8vhFixYYK5n2RxL7phN2n///c2//D64nK4y+vXrh0MOOcQEJqtWrapUtiYhIQEjRowwJWvz5s0zz59EqRG3AMNuBFLPAGwnlH1pOREfHfsJzhq7AFvj47CmeRLcMXYEtyXw2mzouCMDXSZvRadLtqLTldvR84kC9H7BgxtmebV4pzRYCmxERKQYBzwsbSKWhFmYsTjvvPNMsPP++++HfXsnnngi2rVrh2effbbCeSAsq2IQxPK1msS5OSxxY8B22WWXYeDAgXjrrbdw5ZVXmkxQsJtvvtkEM2eccYbZnyVi2dnZOPvss828FgY1/NkDDjjAfH/OOecgJyenysd30UUXmX/LmmsTrLCwEF9++aWZf8TgkRk0p9NpgiOJZOWUFv3wDzDrXyAzr9xbcG/PxeKsVMzq3BpJhYWm9KzjhiwzmLNCFY/dhh1pcbjmux9Mg+kYPxBT6EPuimws3gHc86sfh79bMsMjkcdns4V1kZIU2IiINGIMNphx2LFjB5YuXWom6bPMiUEA57QEYmagS5cueOGFF8IeyMfExOCCCy7Ali1bTDBRHgZOHLQzy8OGAXfffbfJTHCifXUsW7YMd955pwlGWGbHLAtv/7fffsM333wTMmv13HPPmcDm9NNPR+fOnfHKK6+YuT7XXHONCXx4OzfddBOuvvpqk2lh+VxV8faPPfZY/PDDD8XZofJwzhLL8pgxsgLQIUOGmPlRVtMHiUTVH4TG+Lzou3kJuqdnomt6umkUYN0q/82Jd2Jzy0RMmL8Mizp3L/GzcYU+xO7qovbtGiCrUFkbaXgU2IiINGIsvxo5ciQOO+wwnHbaaeas/7Bhw0ypVjDOkWF2gUHQtGnTwr4Pln5xLg+Dg/LmyfA4GFAwE7F582Z88MEHJiDhoP+KK64w91sVLG8Lbl7AsjQrSAjGYIYZkEDcj6Vwxx9/fIntJ5xwgtnOoKQ6GNTFxcXh0UcfrXBfBntt27bF3nvvXbyN82vY/OGXX35BJElPT0dBQUHx98x8sSGEhYHs9u3bS/zMxo0by/1+06ZNJUqpouY+Nm1FTYj15CMx342MhMTS1xV4MXj9NrTKzkOczw5XcKAbEMvkuiP4uWqg9yG1TzPLREQaMQ7UGVCw0xnLmpilSU1NLXN/Bgh77rknXn/9ddNoIBy87YsvvthcXnzxRVx++eVl7ssyMV44wGCGhG2pWe41c+ZMkylhg4HKYpYpGMvLkpOTS8wjsgRnqohZoz59+pQKePg9969uK+oWLVqYwPKll14yj5XBZSgcKDHTdNxxxxWXDFrBW2Jiogl6mL2JFE2bNi2VDQvkcrnQrFmzUk0nyvu+devW0XkfrUreRlX4YMPiFj0QX+DFjsSY0tc7bBi6ZFPxPJvAUiW3w4aC2KIGHu2SgFaJvC5Cn6sGeh+V4VeZWZUoYyMi0ohxUM4Wzvvuu68pPysvqLFMnjwZeXl5JrsSLs5L4X28++675sxoOMEQB+uco/Pyyy+beTpcR4eZnNrGzEl9mDhxonn+Odcm1NwfYkaN13344YcmKLUuLI1jeSBbZlc1syW1rZyugAmxQEz5XQPJBj988KNldja67MxEQczuYZzN78dRK9Yi0V1UbraweRN4bPwJoNBhw6bWybDbbOjdFPj5dA3/pGFSxkZERCqFGRUuCsmW0OzoFa5LLrkE48ePN53VKrMWDhsX9OzZ02RXWG4VqmtaeVauXFlq27Zt20xZCQOmcHC/1atXm/bPgVkbfs/MUri3Ux6eEeb8InZk43yZYMxicTufCzYyCMaymAceeADTp083C61KhLGVM6cl582S3xe4gR3ZQOtdnQBZImWzmXk0lzL7UuDFjaPnIjMlFh6HA3YuamMHlrmbINbvxy/tWyDDbseih5rBFUbAJNJQKGQXEZFKY1kZcY2acHGezeGHH44vvvjCTOgP9vPPP4dsQ8sMxN9//23m+HDRzspiQBI8l4bzfYgBWji4H4+DwVwgfs/tlQnwysPMC+fPcO4Ta/gDsWU1S9HYhprlg8EXtuTmz6o7WgMQG7M7qKGgEwExsQ6Mu6c3kgrdpt1zdqIL2fEurEhLxh+t0pDncGJ18xQFNdLoKGMjIiJVmrfCCeuc01EZ7JD2/fffh5yTcu2115q6ds4R4e0zM8Iszeeff26yEeeee25YpXLBunfvbubnjBkzxpTecd7Od999h0GDBplAK9wyMf7M/fffj8WLF6NXr17mXz5+lsxNmDABNYFd5Lgw6i233GK+D3y81nPNtWvKwutee+01012NpYUSQWq4CdmAfdIw6t4F+L1TSyxs2QROrw8bUhKQ53Ki85YMJBSGLmeU6KA5NlWjjI2IiFTJ+eefb8rEKoNr1XDeTCi33nqrCTY4OZ7zTO655x4zmO/WrZtp0cz7qwpmitjljVmfqVOn4s8//8TJJ5+Mhx9+2Cz8GW6ZGNtcswva7NmzTckX/+Vj4XZO3K8pRx55pCk3C8Rucj/++KN5LMzKlMUKepS1iUQ13155U0oCTvl7Bbpsy0JythvxeV5sSYzHnG5toMbf0hjZ/Fp+VkREGqjBgwebzNJtt91W34cijd28JcDe14W+zv9BlW4y/u48DFy9FX92aImCOCecbi+aZOZje1oC/PzvGlf1jlnqzSUn/RvWfo++26fWjyWaqBRNREREJArlOxz4o1MruF1Fc2k8MQ5sa5qAxNxC5MSXbgct0cOnSrQqUWAjIiJRhZP1vcELDwZJSEgwl7rEYwqn1TLnzXAujTQyNVwfYwpubDDNA+Lz3GZORj7XqWGLZ87PYKc0kUZGgY2IiEQVTtSvaEVvNhqo6pycquIaO8cee2yF+z399NOmRE4amebJNXpzXh8Q4/Gh7ZZsOL1FQUxerAMbWyab+TVpbjfbq9XofYpEOgU2IiISVe68804UFBSUu4+1rgw7oNUVrkLOpgcVCW4MII1EpzJWoe9RcsX7cDEp03RnfnFQQ/EFRXNs3C4bvj9XQU00U1e0qlFgIyIiUbdAaCRih7j99tuvvg9DItlZI4CXvt/9vd0GLHq0SjflsNsQW1i6JDPG7UVBnAsDW6vcURoftXsWERERqQsvXgxsehG4+Ehg2mTA+z7grPo5ZjOnptQ2J/K1MKc0UsrYiIiIiNSVVk2Ax86tkZvq3isea/7xItZdtBhndnwMMpNjYfdpcU5pnBTYiIiIiEShT0+xo8XWZDi9PvhsNtPumWK0RGHU87HlnVSaStFEREREolCyC2iH7SiMcRQHNTafHz9N1HlraZwU2IiIiIhEqZvSPsWpsbPR1uXFnk19WHW+HYPbKLCRxknvfBEREZEodkjcErx61kFa+LUBUbvnqlHGRkREREREop4CGxERERERiXoKbEREREREJOppjo2IiIiISATxaYpNlShjIyIiIhKNtmbCUeit76MQiRjK2IiIiIhEk+/+hn/kbWAPtLMBbG+dAJx1Vn0flUi9U8ZGREREJFq8NcsENValEv9tvikX9vFT6/nApCb5bLawLlKSAhsRERGRKOE/7eHioCaQ/e1f6uFoRCKLAhsRERGRaHDqg/V9BCIRTXNsRERERCLdXlcAf60Kma2RhsevMrMqUcZGREREJNL9taq+j0Ak4imwERERERGRqKfARkREREREop7m2IiIiIiIRBCfpthUiTI2IiIiIiIS9RTYiIiIiEQ5f30fgEgEUGAjItKA3XbbbRg8eDAi2THHHIPzzjuvvg9DJKqxcmnSpwXocEsmet+fjbnrvfV9SFINftjCukhJmmMjItLIzJgxA4sXL8b5559f34cSVebMmYPvv/8e//33H5YtW4bCwkI8/fTTZQaO2dnZePLJJ/HDDz8gIyMD7du3x8knn4wTTzwRtqA1Knw+H95880188MEH2LhxI9LS0jBy5EhMmjQJ8fHxdfQIJZr5+B79Kh05TZKQudOLYx7aiS1p8UhOcOLug+24eG8N+aThU8ZGRKQBu+mmmzB79uxSgc1zzz2HSPH+++/jiSeeQKT78ssv8cknn8Dr9aJz587l7ut2u3HhhReax3bYYYfh6quvRqdOnXDvvffi2WefLbX/Qw89hIcffhhdu3Y1+x566KF46623cPnll5ugR6Q8O5GGL5OOwqgFq5CSUwAv7CiIcSKu0IeC9Hzc9+Z2dH8qH16fCtakYVP4LiLSgDmdTnOJZC6XC9GAgcoNN9xgjnfatGlYsmRJmft+9NFHWLRoEa666iqceuqpZtvxxx9vgpaXXnoJxx57LNq0aWO2L1++HG+//TYOOeQQPPDAA8W30bZtW0yZMgVff/01jjjiiDp4hBKR/lsHXPK8mUNTVuFRIrLxX6+ueHjoALgdAeesY+1AjB3rUuJhy/TBOcVTfCPNcwqQVODBpiQX/H4/knxetM7Kx6CYAuw9rCk2xMVhUGvguO52xDlV8lTXfEFZXQmPMjYiIlGGGRiWP/GMfihnnXWWKWPyeDyl5thwLstnn31mvuZ26/Lpp5+abatWrTJZBZZMDRs2DAcddBDGjRtnBurBnnnmGfOzK1aswIMPPohRo0aZ/S+44AJzO8TSrTPOOMNs51wallqFM8fG2sbbufTSS82xHHzwwbjmmmuwbdu2Sj1fv//+u7kNZkEOPPBAHHfccbjjjjuwc+fOSt1Oy5Ytww7CmN2Ji4szwUyg008/3bwuDFYsX331lRlY8rpA/Fnexueff15ie35+vsnwWM/3mWeeiblz50bFfCqppCkfA30uAb75u8ygZkHLDnh9wP5Y2NZXOqhxOQAOkO02+O128y+/P2BtOoav2oZclxP5LhcKYmOxPT4BC1s2xbS0NrhsQSzu/82PUz/xoffzHqzPUqZHokNkn8YTEZFS9t9/fzRr1gzTp08vzgZY1qxZgwULFpjtoTI1Z599thlE//nnn2ZwbxkwYEBxEDBv3jwMGTLEZAw4iP72229x1113YceOHSZoCsYBNeeB8DoGC6+99homT55s5oc8+uijGDt2LFJSUvDxxx/j7rvvNuVWAwcOrPBxbt261cwDGj58OC655BIsXbrUBEY5OTlhl66xFIyBGoMSzm1hlmTTpk2YNWsWNm/ejCZNmqCmsXSM83B69+6N2NjYEtf169fPzK9hNsfCr+12u7kuEH+2Z8+eJfala6+91gS3fF723XdfbNiwwWSC+HpJA7IzB7h+Wrm73DDiZNwz5LjQV8YEnbvelQFokZ2Pjhl5WJWagC1JceUfg82G1Zl+TJnrw8OHOip3/CL1QIGNiEiUcTgcOOqoo0w5FLMlDBQsDHbo6KOPLjMoYjaBgQ1vI9jo0aNNIBKImQQGKS+//DLGjx9fKmBikMUMgjUhnsECS6juv/9+U2LVunVrs/3www83t//OO++EFdisXbsW99xzj5mjYmEA8O6775pMTkXzXBi48Di434svvojk5OTi65hVqq25K5mZmSgoKDDBVDBmfPj8MGiz8GtuC5UN4m38/fffZs5OTEwMfvrpJxPUjBkzxsyfsjBTc9lllyHSpKenIzExsTjAY0MFBtbWa8EGDFlZWeY9ZGHzBKtML9T3DExbtWpV/H5rqPeR+99qJHjKfo+ubNIC9x50TMmNfn9xAFNW7VpKgcf8mxkb/hDw33R/RD9X0XIfUvtUiiYiEoUYIAQGMsQP3S+++ALdunUz2YKqCOzAxcE5MzAcqDMgYqbEKjELdMopp5To8mUFLSwfs4IaYqcvTqBnwBKOFi1alAhqyCq1Cuc2mGliQHDuueeWCGoCg6TawCwXMRAJhQGMtY+1f3n7Bt4mM03E8r5AzLB16dIFkaZp06YlslZJSUklXgs+vsCBIQUPBIO/53sq8P3WUO8jYe+eQJMElOW/5m3ht4XIyjCxwpIzt6900ANgS1KsiXla5RQgXCM62iL6uYqW+6jsHJtwLlKSAhsRkSjUvXt3E7ww+2JlHlhCxrIkK+ipitzcXEydOtXcBudvcK4OL2xbTAxygrGNcSCWnVGo0igODNj6OBzt2rUrtS01NdX8a90GAxfOuQm88DEEBj+9evVCXeK8GOvYQuGZXmsfa//y9g28Tb6+DMg6dOhQal8GjdKAxDiBj68HXM4Si29aX++/bini3SWDkyZ5OZj829dAohNw2oFCL+D1AV6/CWxcbg+yXE780SYVaXmF6LclE3arU9quwKf4Hsz3fpzWx4ZL99ZwUaKDStFERKIUgw9O2v/tt9+w3377meyNVaZWVTfeeKMpd+LE9UGDBplAggNplj+98cYbIcu3ysp8lLWdmaVwlJdRsW5j/vz5pkwuEDM09blGDwM7nundsmVLyECFWTA+t4GZqZUrV5rrgsvReBssUysroyMN3LB+QO6bsP2xAtjvWrPJOkeflp+LVz96GhccdRa2JaagWW4Wrp35MTbEp8KRmQdvQmxRcGO3IT6vEHsu3oA5/RgQ+7G5RSK2903Akak+HBbvR5IL6NXchpwcPw7rZkOMy4E8tx9p8Ta0SFBWQKKHAhsRkSjFFsCPPPKICWj23HNPfPfddybAad68ebk/F7w4pIX14gxqGBixrXEgdt2KRJxcH9xIwMr0dOzY0fzLtsx1mc1gQMZsGhdBDQ5WFi5caIKyPn36FG/r27evWfyT1+21114lSgF57IFBEEtbGFwyGxVcerZ69epaf2xSDxwOYN8eIa8a++9cHLNkHtakNkMGErHvebfA5vPDzwRgrhuIdwA+O/IcDlx0XUf8skdlWqsroKlPPj39VaLcoohIlOKcFbYv5sr2LEnjHJhwytCseTTBJWFWhiQ4o8LyrlDtniMBsyMM5gIvVmkc2zsz08HFSDnxt6qZo6pgK2bOiwlub82sF7NqbKRg4dcMNnldoA8//NDcRuAaNpy3ZN1OIAakzPpI4xPr9aBH+mYMTF8B2OzwsXTNaUe824Nz93Lg4sF27LgiBuMqFdSIRCdlbEREohi7n82cOdOsWs/JrWwBXJH+/fubzmRsg8xJ5+xytscee5hMB5sEsAEBS6nYfphdfTg453Xhzo2JFOxwdOWVV+K+++4z7a8Z9DHjwfKuH3/8Ebfcckul5t+w3TR/jtipjLjGzF9//WW+5n3wNSCW8nFtIL4ufA6ZXWE5H4PQ//3vfyXmH3G+1EknnWReE7Zt5twmBilcp4jZmsDAhtcdcMABJuhhSZvV7pmvUY8ePcwxSuPE0xI5tyfi1d8K0aGpA0f1TqzvQxKpcwpsRESi2NChQ808GAYdbAEcvG5KWdkElklxkUiWr7G06dZbbzXBy5133onHHnvMdN9iiRsnqV944YUm+Ln99tsRbdi6mhmcV1991QQKnKTPOS377LOPCXwqg2vTPP300yW2ffLJJ8Vfs4TPCmyYKWLDBV64ACdfHx4HAxcufhqMARiDHQYozL5wXg27zXH+UOBcI2Z22Ebbut2ff/7ZBEZsa8022FzHSBqv+Bgbzj+w4r8BIg2VzV+buXgRERGpEwyEPB6PWZRUGiDbCeVezbYedn/J0keJXqdOLN1aP5S3Xil/Pa/GRnNsREREokjgGjgWZnmWL19u5hiJiDRWKkUTEZFGa8eOHfB6veXuk5CQYC6R4vnnnzelhHvvvbcpfWPnNJbEsSRx4sSJ9X14Uk/UREtEgY2IiDRiEyZMMJP7y1Pf6+IEGzhwoFm/Z9q0aabbGwOaESNG4IILLqj0vCERiUz+MtryS/kU2IiISKPFZglcL6Y81ro4kYKd7HgREZGSFNiIiEijxeyHSFR4ZTIw8bH6PgqRiKbmASIiIiKRbsIhwOYX6/sopI74bOFdpCQFNiIiIiLRoGUTIC30wpt+p4Z0IvotEBEREYkW658v1QKNCxJ6P7u+vo5IJGIosBERERGJFvGxgO8D4LSD4I91Ii8pBtMnDwZGDKjvIxOpd2oeICIiIhJt3rgSHrcb0156qb6PRGqBT+2eq0QZGxERERERiXoKbEREREREJOqpFE1EREREJIL4gjtESFiUsRERERERkainwEZERERERKKeStFEREREotCvG4HrM0/CTiTg/Ee4xYO9WgBzxzvgtKuUSRofZWxEREREIoHHC1z/GnDIzcA3f5W769crPRj6DpCOZPjggLVq559bgXZPeuvogKW2eG3hXaQkZWxERERE6pvHA7hOBvy7vp+xENinGzD3gZC7H/U+/x96ZLslv/YOUySSKWMjIiIiUt+G3rA7qLH8thzICx2leP3BO4uIAhsRERGR+jZnWejtT35R+dtS0BP1fDZbWBcpSYGNiIiISKRasbm+j0AkaiiwEREREYlUdg3VRMKl5gEiIiIikUpxTaPkU5VZlejXRURERKSB8WmejTRCCmxEREREIpW3CgGKzQa7JpZLI6TARkRERCRS2bn4ZggKXERKUWAjIiIRb/HixbjgggtwyCGHYPDgwXjmmWfq+5BE6obfW8Z2lZo1ZD7YwrpISWoeICLSAIOAGTNm4JhjjkHbtm0R7TweD6655hrz76RJk5CcnIwePXpU6jaysrLwxhtvYO+99zaBUVXMmTMH33//Pf777z8sW7YMhYWFePrpp8u8vezsbDz55JP44YcfkJGRgfbt2+Pkk0/GiSeeCFvQ2Xafz4c333wTH3zwATZu3Ii0tDSMHDnSPN74+PgqHa80FDoHLRIuBTYiIg3MkiVL8Nxzz5lBfEMIbNavX28ul112GU455ZQq3QYDGz4nVNXA5ssvvzSXbt26oXPnzuZ5Lovb7caFF15ogkwec5cuXfDzzz/j3nvvxfbt23H++eeX2P+hhx7CW2+9ZTJS48aNw8qVK833/HkGR3a1/G289NKLhE2BjYhII+b1es0gPC4uDpGKgQClpqbW63EwULnhhhvgcrkwbdq0cgObjz76CIsWLcJVV12FU0891Ww7/vjjcfXVV+Oll17CscceizZt2pjty5cvx9tvv22CmgceeKD4NhiUTpkyBV9//TWOOOKIOniEUid8PmDxemBbNuC0A4O6ArExZe7uf+UH2Kb9CH+hB4U5gB9O+NNSgOumljvPxjbFg+9PAlolOtCtCRDrVNlSNPFqDlWVKLAREWlAOPfEykywjMly9NFHmwzO7bffjieeeAILFizAp59+ik2bNuGmm24yZWsstfr444/NgHzbtm2IiYlBv379cPbZZ5ufDXTeeeeZkqkXX3wRDz/8MH755RdTmrXXXnuZwXunTp2K9y0oKMDLL7+Mr776Cps3bza326pVKxx44IG49NJLy308vJ958+aZr3nsvNAnn3yC1q1bmyCBx71mzRpT7tWsWTMMGTLEzMdp0qSJ2ff3338vfi743FjPDwMLPgfhatmyZdj7MrPDYJHBTKDTTz/dlKYxWJk4caLZxufF7/eb6wLxZx9//HF8/vnnJQKb/Px8k8Xhz7HcjWV5DLq432effWYer0So+SuBY+4B1m7bvS3eBbQqO2i3ZeSDs2k4zHWZfwuBHbk4Y+4cvL7fAeUGNyPe5f+9SIsDXhxlx5geSv9Iw6bARkSkARkxYoQJSj788EOcddZZpgSKOL9j9erV5utHHnnEzFfhwDkxMbE4COEgn8HBUUcdZQKPLVu2mECHg2bOJWHQEigvLw/nnnsu+vfvj4suusiUi7F86sorrzQZCIejqJvTfffdZwKR0aNH44wzzjBZorVr1+K3336r8PEwqNpzzz1NAMPjtY6Bc1CYaWLmhI/54IMPNoEEgzIe819//YXXXnvNBFF8Dq644gpT7sWsCC+UkJCA2sD5MpyH07t3b8TGxpa4joEi59fwOC38mqVmvC4Qf7Znz54l9qVrr70Ws2fPxvDhw7Hvvvtiw4YNJphsCGWHDd6Zj5cMaiivEFi1tdwfs0KXwBBmc0qTsDuj7cgHJnzhw/pONiS7lAmQhkuBjYhIA8Kz9wMGDDCBzX777VdiPokV2PCMPyfSB5efMXMTPFGdE9054Z2BRXBgs3PnTowfP74482AFHI8++ijmzp2LAw44wGxjIwNmZ6xsS2Xsv//+cDqd5v75uBh0WZjlsDIjgbjfXXfdZe73sMMOM1kcBgEMbLp3717iNmpDZmamyVKFyvCwjI2ZpK1bdw9k+TW38bpgvI2///7bBHEM0n766ScT1IwZM8a8Xha+zpyDJBEsMxf4a2WN3dzOSgbmWYXAvM3AwR1q7BCkFvkUf1aJcpIiIo3M2LFjQ86pCQxqcnNzTeDCrMsee+yBhQsXltqfWQZr/ohln332Mf+yNMySlJSEFStWmE5iNYmZD+txMAvEBgE8ZusY/vnnH9QHBo7EQCQUBjDWPtb+5e0beJuzZs0y/zLzFYjld1Z2LpKkp6ebIM/C0jm+ThaWL1pzqCwscSzve5ZPMqiNuvvI2gF0bI6a0nPd2krtH2MHWtp2Rsdz1UDvQ2qfMjYiIo1Mx44dQ25ft26dmX/DOSuBH+AU3J6YWrRoUarUyprgz5I2C8vAbr31VhMEtWvXzmQXhg4dimHDhhV3++L+zEoEat684kHgN998Y0rO2D2M5XXBmZP6YAVbwY8ncEAUGFjy6x07dpS5b+BtsuyMz1mHDqVPu7OkkN3UIknTpk1LfM8gNzhwY0YtkNVUoazvObcqKu+jXTvg0XOAsQ8AnqC1aVxOoLDk+7ciC5u0QIdtW7G2WfMKS9J47W0H2tGnfVr1H0dDeT3q4T6k9imwERFpZEJla5ih4XwZzps57bTTTMkW598woOHE/1DzYcprQRx45pNlYJxjwxIqNgJgmRrnwbC0jZPgma3gHBGrSYCloknwXFPm+uuvN3NT2H2M84I42OAcl8mTJ5c4hrqUkpJiAj7OUQoVqDCrNGjQoBIBIgMSXhdcjsbbYJlaWRkdiTLH7Qusew74cA6wYA3QPBkYdzDQPAVoOiHkj1iNA7yww4M4+BFjLlubNseGtKahg5pd7/2JfW3Yv50NIzra0bOpapuk4VNgIyLSwITKrlSEwQbnetxyyy2mFXGgp556qtrHxEwO57bwwoDjsccew6uvvooff/zRLER5+eWXVzrDwi5gDCDYCS4wWFu1alWNPCdVxYCPjQOYRQoOVljSx8ffp0+f4m19+/Y1WTJeFziPiWUwbCkdGATxDDADNzZfCC49s+ZQSYRr1QSYFH77btvkI4BHzwNbcRS14yiy4QF3uZma3MsciGf9mUQlb4lWERIuveNFRBoYa65MZQIFq4NZcJaDA+7qzFWx5r4EBxm9evUqUbLGgT6bHQReKmJljDjQt/D4X3jhhRp5Tqpj1KhRZl7MBx98UGI7mzbwuT788MOLt/FrPie8LhAbQPA2Als9s3zPup1AbCoQaWVoUkP8VRjg2mwKaqRRUsZGRKSBYWkWB/1cY4YDeQ7qObelPAMHDjT14lOnTjUTXtmNi9kCZkVYllbVif8scePAnANyBjPsmsZ5Iu+9954p2bIG6lVx6KGHmnI0rlHDVtKcY8MMUODEfAvLuTgvhevHsPU16+f5vFTm/pcuXWpun9ipjPj8sLU0cQ6RVYfP1tRsn801fvh8MrvCUjyuYfO///2vRGtmPr8nnXQS3nnnHVOSd9BBB5kgha2zma0JDGx4HbvNMehhSZvV7pkBFDvi8RilgalKfFJPZZgi9U2BjYhIA8NJsCwpe+WVV3DvvfeaAb+1QGdZkpOTzWKQbNXMNWiYaWE5Fde84XyYqgY2LBHjnB2WuvHCQIdNARhQcJ0dzi+pTlaEt8fsBY+Tj4G3e/HFF5ugJ9idd95pWj6zQQKDH5Z1VSaw4do0XM8nEOcOWVhmZwU2nBPD+UPWQprMTDGgYuDC9tnBuPYPgx0GKMy+MBA75ZRTTNAWOJeJmZ3777+/+HZ//vlnExhNmTIF7777boludNJAlBWjVFBeyexlXZZgSs3y6qWrEpu/vmZXioiISI1hIMQg9v3336/vQ5GqsJ0QevvFRwKPnVt69ynldFHz++G7yqnAJooNnRReq+hZT6vzWiAVYIqIiESRUKV2zPIsX748rLlJEmVsu+eQiUj5VIomIiKNFtePYdldeRISEswlUjz//POm4xpLC1n6xrlQLIlj57mJEyfW9+FJhCxB7/EBMYFt1EQaAQU2IiLSaE2YMKHC1cG5vs/555+PSMFGD/Pnz8e0adPMaugMaEaMGIELLrjArOUjDUwVq8liHCpDi2Y+lRFWiQIbERFptNhQgOvFlKeijnJ1bciQIeYiIiIlKbAREZFGi9kPkYjWNq3yP6Oz/dJIqXmAiIiISH3rW0Zm8OLRdX0kEgG8NltYFylJgY2IiIhIfftjSultXVsByZHTuEIk0imwEREREalvcbFA9hvA2AOAHq2Bp84Dlj9V5u6vH8H/h16KMEHd0KSR0hwbERERkUiQGAe8e3VYu56+hxMOuxtnfl6AfLh2bbUhKQbYfKHOW0e7cpZflXIosBERERGJQif0ADJSXjdfnzzuLDicDiS7NO9CGi8FNiIiIiJRLjEGiIlRUCONm3KVIiIiIiIS9ZSxERERERGJIGrlXDXK2IiIiIiISNRTYCMiIiIiIlFPgY2IiIhINMkrAIbeAGfyGRh760zEZuTX9xFJDfPYwrtISQpsRERERKJJ4unAT//BVuBB0025mHDNDMAferFOkcZEgY2IiIhIlPCd8RBWJHTEb00HYnVCezCc4Yl728jb6/vQROqdAhsRERGRaLA9Ez9/XQi3PQadc9Yi25mIuU0Hmavss/6t76OTGuSBLayLlKR2zyIiIiJRYPt+d6F/xmakurPM9y0KtmNzbHNkOxKQ4M2t78MTqXfK2IiIiIhEuvs/hHftjuKgxtKqYJvJ4Pjq7cBEIocyNiIiIiKR7tppaLbrS3/xEM5hvktxZyIfcRrUSaOnjI2IiIhIFGAYU8QFIHZXcBMDO+KQgHz4rpxWr8cnNcdtC+8iJSmwEREREYkSu7M1wcM5J9wPT6+XYxKJFApsRERERBqAlc1b1/chiNQrlWOKiIiIRLji9WrM196gIRyv9SI9LrUej1BqktumOrOqUMZGRETqze+//47Bgwfj008/re9DEYkiBbsu7l2XfNjgR7ft6+v7wETqlTI2IiIiYdi2bRvefvtt/Pfff/j333+xc+dOHH300bjtttvK/JnPPvsMb7zxBlavXo3ExEQMHToUF198MdLS0krt+88//+DJJ580/9psNgwYMMDs26tXr1p+ZBJtis7le0ptT87PqYejEYkcytiIiEi9GTRoEGbPno2jjjoKkW7VqlV46aWXsGLFCvTt27fC/V9//XUT9CQlJeHKK6/ECSecgK+//hrnn38+8vLySuy7YMECnHfeeVi/fr25nl+vWbMG5557LpYtW1aLj0oaDhtWJ3bAtQfPwM5NWqxTGidlbEREpN7Y7XbExrJtbdn8fr8JBBISElCf+vTpg2+++cZkW5itGTlyZJn78vqnnnrKBED81+EoatTL76+44gq8+eabOPvss4v3f+CBBxATE4PnnnsOLVu2NNsOO+wwnHTSSXj44YfxxBNP1MEjlIiSVwDExqAg3w93gQ+JYfxIn6wVGLxqKd4eko5bjx+CvKR4FDgd8Lrs6O7yYofdgViHDe8cZ8d+7WLq4EFIVbHAUCpPGRsREYmYOTaB37/zzjtmYH/ggQdi2rSi9Tk8Hg9efvnl4u2HHnoorrrqqlJZjQ0bNpjbeeaZZzBr1ixMmDDB7D9q1Cg88sgj5nYqi6VkoUrIQpkxYwby8/NxyimnFAc1NGzYMLRr1w5ffPFF8ba1a9di0aJF5rFYQQ3xa26bO3euKYML9N133+G0004zj2n06NF49tln8euvv2q+UkOwaC18bc/BZ3u/iJuO/hN3jv0L945bgJf2Pc20CPDtuoTisdvhcwEFSQkYumozsuLjUBDrgsfmxH+FLmzOd2JNjgP7vwHY7i3APtM8iH3Ig5iHPOj6rAfvLS7rlkWigzI2IiIScZjRyMjIwJgxY9CsWTO0atXKbL/55ptN1mS//fbDiSeeiO3bt+Pdd9/FWWedZbIdvXv3LnE7LHN77733zL7HHnssfvzxRxMkJScnl8iY1LSFCxeafzlPJlj//v3x1VdfITc312ShKtr3k08+MfN6hgwZYraxnO3GG29E+/btTakaAyfO5WEAJw3AiFsxO7EP5nTZr8Tm0Qu/Nv/6YUOBPRYJvvwS17/d/wBcOOYcpCckomV2Hk74dxWGr9yIH7q1LdqhRJctG+B04PeNfsBetH1lJnDypz780cSGvVqpI5dEJwU2IiIScTZt2mQCkqZNmxZvmzNnjglqWKJ19913mwn2xO/Hjx+PKVOm4Pnnny9xO5wPw8xP27ZFgzsGOMyisAlAbQY2VoalRYsWpa7jNpbXbd26FZ06dapwX9qyZYv5l5kmlqYxc/TKK68gJSXFbB87dqzJ4EgDKD/bvBMLDupTYnOsuwCtcrabr7fFNkOrgpIZvPUpaRh3ysXw7MoObkmKxzv9uuCiuYvwW/sWyI4to+xsV1BjYUbo/aU+7NVqd5ZR6keu2j1XiUrRREQk4rC8KjCoscq7iAGJFdRQz549Tbexv/76Czt27CjxM8OHDy8Oaog/x3ItZnqYMaktLEMjl8tV6jprTpG1T2X2ZeaGARG7sVlBDTHzw+YEkSY9PR0FBWxLXCQ7OxtZWVnF3xcWFprXItDGjRvL/Z5BLwPDBnkftqIMSkJhyeYShU6Wk1lDNr8JQAJ9161/cVBTfHsJcciIi0XT3N3HXUrA/VvifLnR8VxF4X1I7VNgIyIiEadjx46ltnHeDJsNdOnSpdR1Xbt2Nf+yq1ggzmcJlppatIghS91qS1xcXPHgJ5g1WLL2qcy+1uNjpidYqG31jcFpYHMIdohjGaCFwRxLDQO1adOm3O9bt25dIrBtUPfB1/nUIRi2/GfAt3u+i9/GVWp4AVoWbMeahPYlbqt5Zuk2zy6PF3afDxtSEooCmBBBTLAOScCF+yRV/3E0lNejhu9Dap8CGxERiTjWQL66GAiVJfDsbE1r3ry5+ZfZlWDcxgGUVWZW0b4U2FRAGrjXL0fXe47ExdnfoKtjOxw2P2LcBXD4vWb9Gl5a523GysQOWJ3QDuviW+Pw5fMw5p/fStzMQWs24ZOeHeFx+4D8Xc0y+J73+IBCL0a28+KdYx0Y092G4R2A2w+04Y8JDjSNVwlUJMizhXeRkjTHRkREogKzLz6fDytXrkSPHj1KXMdt1j6RoF+/fvjwww/x999/o0OHDqXWrGF2xWpfzX2J+7JZQvC+DIKspghWWR0X/AwWaptEqfMOR+vzDkfgLDB/wCA21u9Gl5y1yLXH4c+0/vgvpReO/WkdnJnx2JocD7/Hiw0HdEDTRCfGd7fh+qGxiHXakFHgR57Hj9aJuwP+k7T+qzQgytiIiEhUOPjgg82/XCQzMNvCVs8zZ87EwIEDw27HXBfHyjIWNi7wer3F23mcLCc74ogjircx8OH6NmzhHJi14dfcts8++xRndbiWDr9mF7TMzMzifTlf6IMPPqizxyeRgZ3RemYtQ6HdDrvNi1MWzsXHz/fEj2/3w3+XpeC3cxNw2yFFQQ2lxtpKBDUiDY0yNiIiEhX2339/0wGN7Y45iZftj612z6x353o2tc3qumbNfVm6dGnxtkGDBpkLMcC64IILMHXqVFx44YVm/RwGKq+99ho6d+6M008/vcTtXnnllZg0aRLOOecc07WN2LmNGarLLruseD+n02m+v+mmmzBx4kQcd9xxpt0z167h3CEGTYHzBKRhY3jfonAHjtr4A7a5muKHtvsgNV6BizReCmxERCRq3HnnnejVq5fJWDBoiI+PN8EEg4ju3bvX+v0//fTTJb5fvHixuRDXlLECGxo3bpwJNt544w3TipoLfI4cORKTJ08uLkOz7LnnnmYx0aeeespcGJxwXZv77rvPdH0LxGwPAxwGVPwZTnJmgMPyvKuvvrrEhGdpWEGMLejrwBC2eWE6+uxcXk9HJzWtsMSrK+Gy+Wtz9qSIiIjUCWaDGOyxVI8Le0rD4nachBjf7rLGUFantEWnjMfr7Jik9tguSw9rP//Ukm3xGzvlK0VERKKI2+0uMW/HmmPDkjxmiKxGA9Kw2MM4D+126Cy/NG4qRRMRkUaLAUFFC3VyDkukNCUgzqO55JJLcPjhh5suadu2bcP06dPN9uuuuw4xMWWsMi9RLT0hFS1ySi5AG2hlWgdkJakMscFQjFolCmxERKTRmjZtGp577rly9+Eie5ycHymaNGmCPfbYA1988QV27NhhAi/OL7r44otNcwVpmL7oPRxn/PEhHGVc32nHWsT//GwdH5VIZNEcGxERabTWrVtnMh3l4WR8tpIWqU8f7fUsErMycNjyX0Jez+JEh18tvxsK2+VhzrF5WHNsAiljIyIijVb79u3NRSTSHfPTRKxre2k5e6h2SUTNA0REREQinCMxFp0uPMi0eg7GbfmIq4ejklrD9ajCuUgJCmxEREREosE94+ELsZnD25wmSfVwQCKRRYGNiIiISJRwZL5WKmvD7zMfOa+ejkgkciiwEREREYkWyQnImTymOHPDoGZ+7x7odNqAej4wkfqn5gEiIiIiUSTp0QnAvadg5+yl+ODvWfAkO9Cvvg9KJAIoYyMiIiISbRJikTi8lwlqRKSIMjYiIiIiIpFEHc+qRBkbERERERGJegpsREREREQk6imwERERERGRqKfARkRERCTKvL7Qg8GvA1/mqR9ag2QL8yIlqHmAiIiISBSJf8iDfK/fTDD/278vPnQPxln1fVAiEUAZGxEREZEo8f2q3UGNwX/9dpz1OZfqFGncFNiIiIiIRIHvV3tx6HsBQY3FZsM3v++AJ3E8PAfcCOzIrq9DlBqjWrSqUGAjIiIiEuEu/86DQ9/1hxzMxrkLcdZvP8KZmwPnnH/haarCNGmcFNiIiIiIRLipf5Z93QevTMG9X71V/L0TXrivf6NuDkwkgiiwEREREYlS/TatwZGL/yq13fvYl/VyPFJDVIlWJQpsRERERKJUUkF+yO3ZNledH4tIfVNgIyIiIhKl5nbojsUt2pTYlueMxYbklvV2TCL1RYGNiIiISJTy2+0Ydc7/t3cf4FGUWxiAz6YXQg29914FpEovgiIdKUoTQSwockUUBQQVEUVRqigoiiBNAQuKgvSqgI1epUNCCenJ3Of7cZbZzW6yCUt2J/ne59lLdnayOzM78f5nzvnPvCyH8xWyLgtMjJOikec9ul1EnsAbdBIRERGZ2Mk8+cUvKcnmqnVQvOMSNTIJzp/JEGZsiIiIsoDdu3dL3bp1ZfXq1Z7eFMpsFot8XL+9RAblur3IJ9mjm0TkCczYEFG2HAAOGzbMZllwcLCULFlSOnbsKD179hRfX181QJwwYYJ6/cMPP5QGDRrY/M7Zs2elU6dO0qNHDxk9erR1+YMPPijnzp2TmjVryscff5zi88ePHy9r1qyRdevWSe7cua3LDx06JAsWLJC///5bLl68qLYpf/78Ur16denWrZtUqlRJsgL9uBkFBgZK0aJFpXXr1vLoo49KUFCQx7aPyIz+KVhVZjYqKLXP7JMOB3+SJD8O8Sj74VlPRNlWu3btpHHjxqJpmly6dEkFG++8844cO3ZMXn75ZZt1Edjce++9YrG/43cq9u3bJxs2bJDmzZunue6mTZtk1KhRKtBBcFW8eHG5ceOGnDp1SrZs2SIlSpTIMoGNDscT+wqRkZHy008/ydy5c2X//v3qeBORizRNSkXeUJmb34vWlEKXLkruhIsS5untojvAWrSMYGBDRNkWAoUOHTpYn3fv3l1lX77++mubjE6VKlVUFmXt2rXSvn17l967cOHCEhsbKzNnzpSmTZuqDFBqMJBH1uKzzz6TggUL2ryWnJws165dk8yWmJgoSUlJarvuBgRrxuPfq1cvla3Zvn27/PXXX1K1alWHv3fz5k0JDQ29K9tEZEoWi6ypUlL67TksgfFJsidfPfGzxEkZT28XUSZjYENE9J8cOXKosq9ffvlFzpw5YzPgnjFjhsyaNUtatWol/v7+ab4Xysj69u0rU6dOVSVtnTt3TnX906dPS9myZVMENeDj4yN58uRJ9/5gvsUDDzwg999/v9r2w4cPq31s06aNDB8+XEJCQqzrzpkzRz766CNZsmSJfPPNN6pM7vLlyyoww/tcvXpVrbNx40a5cuWK5MuXT+677z4ZOnSoTTndnfDz85P69eurkjwcDwQ2jz/+uCrrw/ZPnz5dlRFev35d/QvYJ2zX77//LjExMaqcDfvcr1+/FMEk9mf+/PmyefNmVeqHY1G+fHkVTBnLDJElw7HYuXOnCihRDogSOWwLvlfd+fPn1Wfv2rVLHRO8HzJtXbt2VdugB6WLFy+WVatWqRI8ZPxw7GrVqiUvvfSS2mcdgudPPvlE7Ut0dLQKjpHR6t+/v816gEwgslsnTpxQ5wY+r3bt2m75Hsj7nI9yPl/GkqxJ3shoqXX8ovgkJkmyj0Xig/2k4MFI2RT2rhwLKyMnyoWLn79FQgJE/CwWSYjXpECRQGk9tqIUqsC8DmUdDGyIiP6DkrR///1X/YzB+smTJ9XPyFhgUDtp0iRZvny5PPzwwy69H+bFfPnll2oAikxPavNGihUrpkrgUL6GuTnucuDAAfn5559VYIVBMgICDLSPHj2qgjUETUavvPKK2l8EZRiEh4eHS1RUlAwaNEgFG5gbg0zXwYMHZdmyZWpQ/+mnn7otg4KgAozBEgb5CKBq1KihArKIiAhrIIDvBYN+ZNoQMKCk74MPPlABD74vHYKKwYMHq99FlghZOARCf/zxhwpg9MDmn3/+Udm6sLAwFaAUKFBABVo4Zvhu8F3i85DNevLJJ1UJIzJ9yD7hOB05ckQFJnpgg0Bl9uzZKmuH8wHHG9uCADE+Pt4asCDY+t///qcCIwRlOXPmVNuGwAmf/9Zbb1n3Zf369fLCCy9IkSJF5LHHHrPOB8N7UNaTrGlSZb7zwEbzsUiFq1elRsQ1ET9fQW+0q/nDJC44QLr9tkP+rFJB/AJuBfnRCfpvWeTk2QSZP/xPeXx+LclX/HbATmRmDGyIKNtCqRgyEQhocDUf2QoMIpG1wUAVA1ljQ4AvvvhCNQPAz64M5JHZeeKJJ2Ts2LFqYDxgwACn62KAPmbMGDX4LleunBrEI2NRr149NYDNKAy0kTXS5/kgAMBzbA/mtGCekRGyDsjSGDMECIAQcKBBAn5fV6FCBZkyZYoqn8N+phcG9jj++hyb77//Xg34sb916tSxroesCYICBDVG2I+EhASVhUHmRc+u4Tj+8MMPKghDBggmT56sghAEPQ0bNrR5H2RVdK+99poK5rBPxu8Y74PAA9uI7//48eMq8H366adVRsUZBCGlS5eWadOm2SzH7+ni4uJk4sSJUq1aNZWZ0o899hn7hd/VO56hNBD7jcAHAaUeAGJdVwNuMpdvjyVLZFzq65S+ejPFspjQAInUwqV8xFHZEZbP4e/hzP991Xlp/WRpd20uuQun2GQI2z0TUbaFq+EoMUJpVu/evVW5EMqrMHC0h6viuEKPAfjChQtd/gwEDshwYBCa2jwZbAfKn1DqduHCBVmxYoUa7GJwPnLkSPW5GYFOb/bNC/QAC+VM9vr06eOw7AnlTl26dLFZjowGlmPwnhEoecN+44GACdkNBDSYbxQQEGCz7iOPPGLzHJkXNBnA96UHNYAsE7JLoG8Xjvu2bdukUaNGKYIa0LNWCAKR6UF2DQETgi79gdIxlKFh/o8eAMKePXusGSRHsB7K3vbu3et0nR07dqhSNgRMyPoYPxfNLfR19IwSzg+cF8asFj4HwY23wbFB4KbD/qEphjG4xb4bofQwtecoAcTFiOzyGXGJkqaccdZUzG0Wi4T5XZObAalfhImNjssyx8rbP4PuPmZsiCjbwkAdg2oMhjFoRZYmV67b94GwhwABZWLI3KD8yBV476eeeko9MHB/7rnnnK6LwTMe+D9XZEhwlR7lXshioEQsI53CkC2wh4wESq2M84h0OAb2UDpVuXLlFAEPnmN9lLtlRLNmzVRrbRwjBDIow0I5mT0ET9he+22CMmXKONxnBCv6/qGEDse0YsWKqW4PsjB6wIuHI3oQg/kvCKDQnhuBELJXyK7hfDI2PUAwjG53KBnDXJ177rlHmjRpYjNXS/9cZIuc0QdQ+j4hYHW0394mb968Ns/1gFCH793+O8exTe15oUKFstVnPFROkxC/JIl2EuDkuRkr4TeiUyz3i0uUUJ8bciD8duCfkiZ1uxTPlP3gZ1BmYGBDRNkWBuVoOZweKCHCIBXZldRKkIwwfwOlTEuXLlWZobRgoI+BKx6Yq4HBPzIFuFLvqLmAO2Xm/WMwf8WV459Z26RfrcUcF0eZHUAJmA6lccicYG4LMjLIQCGbh2YEzzzzjFoHJYXosoeMEQJVZHhQJoeSxnnz5qlAWv/cESNGqADJEQRFlD35+1pkVz8fqbrA8TybUlejJDwiWqLCAkV8b2UffROTpfnBDfJJlR6S60K0nCsaIMFasgT6aiKaRVB9mSunj7R+sZIUKscOg96JtWgZwcCGiCgdkFFBpgGD1RYtWrj8exjoopwKcyjScy8cTOTHYBdX6jFHJL2BjZ4NMMJ8IpRUoIOYK7Ae5pNgwrwxa4PnyCy5+j7upM87QsMFe+gUhnkz+nYhE4RjjoYHqdGzVcj2uBrwoukD5rbggbIVBL6Yn4PgSL/ii+5zyNDgAQhw0QwAgRCCIP1zkTVM63P1fdIbW6T1XVPWUCUcAYvjwCZPdJxE5QkRn4QkCbwRJ7E5AiXJz0eiYnLJS7vaZPq2EnkS59gQEaUTysoAk+xdhXk2bdu2VZPPMZfD3tatW23qu3WYW4O5JJjjgwF6emEAbD+XBvN9AAGaK7AetgPBnBGeY3l6Ajx3QdCAbAjK9IzHE8cQzQRA3y5kRTC/BsdYn6tipB93lKqh5TY63+nd8YwQyOnzpFBvj+f2QWipUqXUz2hJDXpzBCP9Rqv6OsgOYX9Q1uZoHhaaXODePYCSQAS3mA9mfG9sD7absp9D+W+Vzyb7+0pMziDVJc0/KUHi8nOIR9kPMzZEROmEuQwoEcMV9/RA5zDcI8fRnBR0HMPgFvMv8P7IjCBL891336n5FUOGDEl1/o8z6LCG+Tlo94zMAMqh0P4Zk/QRaLkCJXf4HXRAQ9YDAQD+xf6jXA5ZB0/A3BV0k8Ox0ds9oywMZV+Y96J3RAO0R8acGGTO8N0hQEDAgBuBog4ey5HVwTwXfE8oGUSZGebwYD0EOvjuENRikj+O4+uvvy4tW7ZUxwBZGUzsxzFBdzM9wMFcLHTZw7wblJMhW7Zy5Uo1v0Y//sjUTJgwQe0PGgDgcxHEIquG7BOaILz99tuqKxoCXMzTQuc3fC/4XrEMgQ7OD0yApuzl39w5ZHXlEtLhwGnx/S9IT/ALkBw+MZ7eNLoTrETLEAY2REQZgPuqYK6EsWuOK2VLGLii1bK9cePGyZYtW9R9YRDM4N4tGKji6j66oullTOmF38dAGNkldFpDC2PM2cGkdvt72DiDSbOYE6LfoBODaAQR2BccB3fdwya9cC8aNGTAdqHJgn6DTpSDoRTMCMsx/wXzWnCcv/32WzVfBh3VjN3eELShOQSyPthXZEGwfwh+ENCgQQDg95AR0ufMoA0zJh8PHDjQ5rPxMz4PrcSRVUHwisAH6xnn0yBrg0waHsjqIROG7cM5g3sKGTu/oUEBvjvsC+6rg/fUb9CpZxMpe9lRsqBUuhAp5SNud+3KEZOyoQBRVmfRHNU+EBGR6eEKPwa848eP9/SmENEdskx13vc5Z2y8PP/rfmvGJjAxTh7d/rkUiJ2XiVtI7mR58XaQmhptsm3HyOyOGRsiIiIiE0vCHBtLtOSI0yQ8OkJaHd4goUkpb9pJlNUxsCEiMhGUKKHsKTWY74FHZsI2uXITUZTX6fdvISL3uBnoL732rpDKl27d3wmigjL3vwHkZpxjkyEMbIiITAQT9dO6mzUm02PuS2bCPXYw6T0ts2fPViVyROQ+OWOipcJl2/8uJFjYFY2yHwY2REQmMnHixDQbFuj3OkHnrsyCZgIzZsxIcz1nN6Akooy7HhIqr7XqKhPW3W75nRCU26PbROQJDGyIiEx2g1BvhHu4uHpTSyJyv+8q1bEGNmghYBl9u9sfmRFr0TKCeUoiIiIiL/fS7dsyOfTQnzutQc2RwEqSf3Tm3ziXyNMY2BARERF5udfv85Nd/ZxfxX+/8YOyv0ATOfhAHylzhS3eKXtiYENERERkAnUL+cruvo5f69woTGpcGCmVVncX39CAzN40Iq/AwIaIiIjIJO4p7Ce5U8QtSTKzjWe2h+4Si4sPssHmAUREREQmEvmMn/xwNFEm79Sk+KWN0jToqIgM9PRmEXkcMzZEREREJtO+rJ/81F3+C2qICJixISIiIiLyJhbWmWUEMzZERERERGR6DGyIiIiIiMj0GNgQEREREZHpcY4NERERkUkdTCggSeLr6c0g8goWTdM0T28EEREREbnuws0kKTQrWUQfxVlEDg+0SLl8vGadFVhevunSetrroXd9W8yEpWhEREREJlNkVpJIsmGBJlJhPq9VU/bGsJ6IiIjIZJKTNBFf2+vTWpIx0iFTY7fnDGHGhoiIiMgbRcWI/O9TkbdXiiTbBS0WB0M4H46GKXtjxoaIiIjI28xfJzJo5u3nLywU+fcjkaL5bj1nDEOUAjM2RERERN7GGNToSg1V/5Sdm5j520OZzOLig4wY2BARERGZQWKyXIpOlGPXMabloJbIHgMbIiIiIpMYvtbTW0DkvTjHhoiIiMgkjkV4egsoUzAhlyHM2BARERGZhObr6S0g8l4MbIiIiIhMws/mrpx2kjVp8ll8Zm4OkVdhYENERORF5syZI3Xr1pWzZ896elPIC9nfzsaGj0W2nsvEjSHyMpxjQ0R0l+3evVuGDRtmsyw4OFhKliwpHTt2lJ49e4qvr6+sXr1aJkyYoF7/8MMPpUGDBja/g4Fup06dpEePHjJ69Gjr8gcffFDOnTsnNWvWlI8//jjF548fP17WrFkj69atk9y5c1uXHzp0SBYsWCB///23XLx4UW1T/vz5pXr16tKtWzepVKmSZBWXL1+Wzz//XLZu3Srnz58Xi8UiefPmVfvYpk0badmypac3kcgl/v63rkn7JSRJop+P5LoRJ0FxiRLv7yux/r4S7+8jBy8nSsVwDvFMjXNsMoRnPRFRJmnXrp00btxYNE2TS5cuqWDjnXfekWPHjsnLL79ssy4Cm3vvvVcNwF21b98+2bBhgzRv3jzNdTdt2iSjRo1SgQ6Cq+LFi8uNGzfk1KlTsmXLFilRokSWCWwQ9PXv319u3rwp7du3l+7du6vlp0+flj179qiAkoENmUUMKs00TQpdjJJEfx8JjdHvaZOgitRuBvlKu7l+cuKlnB7eUqLMx8CGiCiTIFDo0KGD9TkG2Mi+fP311zYZnSpVqqgsytq1a9VA3BWFCxeW2NhYmTlzpjRt2lRlgFKDwCkwMFA+++wzKViwoM1rycnJcu3aNclsiYmJkpSUpLbLnRYuXCgREREydepUh0EfsjlEZqCJSOcvvpR/WvWQwMRk9TBCLif/9Rh55euv5PBr5+W8byGxxARKQE6L+N1bVgp2LSo+3+yS60dixHJPWSkytp7kqJrHY/tD5G6cY0NE5CE5cuRQZV/I4Jw5c8a6vFevXlKgQAGZNWuWJCQkuPReKCMbPHiwyv4gA5EWZCtQCmcf1ICPj4/kyZP+wQ7mhaDsbceOHTJgwACVnUKWCgFFdHS0w3kkR48elXfffVcFfI0aNZI//vhDvX716lV56623VDYJJXn4F8+xPL2wr1C/fn2Hr4eHh9s8R2nf448/LidOnJARI0bIfffdJ82aNZMXXnghRRCEzNu0adOkT58+0qJFC7UPCFZR4ocgzR6+z08//VStj+OD933kkUdkyZIlqe4D3uuNN96QevXqqd/XIev36KOPqoCtSZMm8tBDD8nYsWMlMjIyXceIzAH525ZH/5LAOD1Lk1JsQIBsK1tLSicck6bRm8Qnd7RcvxYsyT/ul5Bh0+Tk99fl9OEQObX4nGyvvkrOLzmeqftA6fm2XXmQEQMbIiIPQUDz77//qp+Nc1+QscDAGsHO8uXLXX4/zIspWrSozJ07V2VvUlOsWDEVBKF8zZ0OHDigStwQsD377LNSq1YtWbx4sTz//PMqE2TvlVdeUcFM37591foIMqKiomTQoEGybNkyFdTgdxs2bKieP/bYY6qkLD2wr7By5Up1zF2BgGXo0KFSqFAheeaZZ1TmbP369TJu3Dib9Q4fPqyWI0h74okn5KmnnlK/g4zY5MmTUwQ1eP2DDz5Q83uQpRs+fLjK5OE9nMF3iaBq1apVag4Wyurg22+/VYEkzhe8F47T/fffLydPnlQZKsqa1pS7V0JiEyQyZ6AqPYvz95UkH9sB7g9lKsv6Mk3UzzWu75cEfz+5JnklSnJKlOS6vaImcvTl3zN7F4juGpaiERFlEgxQkXHA4BpX/nGVHhP4EQRgTosxyEDW4IsvvlDNAPBzaGhomu/v7++vBte4Yo9gAlkTZxA4jRkzRmV5ypUrJzVq1JCqVauqjECRIkUyvI9HjhyxKflC9gLPsT0//fSTyuDYZ61QPufnd/v/jmbMmKHm+qBBAn5fV6FCBZkyZYoqn8N+ugpB03fffacyK4sWLZLatWurcj/8W7lyZadZnjfffFM1FjBmspYuXaoyOaVKlVLL6tSpI998843NXChkYxCwYTmCIz0jhM/GnJ6BAwfKk08+afN5joI+QEngc889p47re++9Z9NQAvOpcF4gs2c8fvaNKjwJARa2US8vRNCK8z8sLEw9j4+PV3O78uXLZzMnCqWVzp6j+QMyjfoxz6qfoTm5Hr+leFXxSRaJyBMiV3MGSbKvj5pzk+darOS9duuCRqKPj/xToIK0OfKrBCbHSUKwr/gn+EqshKR4v9hTUdafzXqszPIZdPcxY0NElElQftW6dWs1WO7du7e6Ao8yJwz87WGODAa/KCnCHBFXIXBABgDlSqnNk8F2fPTRR9KqVSu5cOGCrFixQiZOnKi6ro0cOTLDpUwob7Ofx6IHWBiI20MQYByU6+uhFK5Lly42y7t27aqWp5bdcJax+fLLL61B0g8//KDK31AC9vDDD8s///yT4nfQHc4Y1ACyMsbSNggKCrIOhJCRwTFH8IoME4IVzJXS4XNz5sypsk72EDTZw6AIgScyd8jC2XfJQ1CIYHnz5s0uZ6IyGzJTxjlT2GZ9YAgBAQE2A0OwHwjaP0dGzBhIZtXPcFZkVPBmpEQH3fqbUUGNWtkikbmDJS7g1tw6/+RkyRV3K2A5lqO0BEfFS6BES7ik7AUd/kDxu7ofWeX7cMdn0N3HjA0RUSbBQB0BBf7PE3NikKXJlctQFmIHAQJaOCNzo3fySgveG+VOeHzyySfqar8zKBPDA4NiZEjQlhrlXhs3blQZB5RTpVfp0qVTLEPGAgMC4zwiHY6BPbS1RibFPuDBc6yPcrf0QhYKGSA8kC3bu3evKuVCdziUwH311Vc23wVK+uzprxsDRjQ8wHwaZIQQ8NgHGNevX7f+jGNcsWJFl5sjIMDE+yPbha519pD5+e2331TpH7YN2SPM20FA5kqGj8wHZ9egvT/K2ipVHb4eE+gnAfFJ0vDcRWl4bKccDi4vh5OqSJjEikWS5EJQGSkX97ec1MpIggRKvlYFpfJs24CZvASnz2QIAxsiokyCQTlaOKfH008/ra7wI7uiz61IC67sY6I8yqaQGXIlGEKmBY8HHnhA3Vdn+/btKpPjqLmAOyHjkdkQaCHAxANle8ikoMW1sWOdowyKzhi8oLwNJYUIJjAvCBklBGAIvjCX5k4yKci+IZOGcsRXX301xTbhfMJ3vHPnTtm1a5cKciZNmqQygzhf9LlFlLXGuq+OGinREX6q/AyZGiP/hCS5lDdY/owOk5B5wyW0eJAUDfaVgAp5RRI08Qv1V79XMjZBNB9f8QlMvXsikdkwsCEi8mLIqKBzFlpCo+uWqzDhHaVWmH+RnnvhIJuAuSzIrmACfXoDm+PHU3ZYQoYEteiOsiCOYD1MgEe2wpi1wXNkPVx9H1dUq1ZNBTa4QWlGIFODTAnm4xgZy9V0CBwxPwe1+ShjSQtK+BCcTJ8+XXVFQ6MA+zbeeB90Q8MDUJaGDBSyfMabuFLW4eMfKOLv+LUreYIl0ddHOnQrKMU62M2n0U85i0UswQFMCFCWxDk2REReDmVlgEn2rsI8m7Zt28r333+vJp7b27p1q8NsAubW7N+/Xw2gHZU/pQUBif1cGr09MQI0V2A9bAeCOSM8x/L0BHiAEjtHXeIwBwalaFCmTBnJCGRR7I9jTEyMahRgD53VUJqGDIw9Z5kdtHJGSRq+R2SXENzpHLW+1m+q6on7EFHm0FKJSBIC/MQvMVnG2wc1RNkEMzZERF4O81ZQIoYuW+mBzmG//PKLwzkpuJqPybG40o/3R2YEWRpkIK5cuSJDhgxJdf6PM+iwhvk5nTt3VqVSCCp+/vlnldVAoOUKlNzhd9AB7eDBg2peCv7F/iPrgcF+enz++eeq4xxuXIqBPyYBYx9xbNA4AE0B9IxHeqH5AsrF0GEO5X94X9xHyNGxQ1kgAikENmgqgLJEZMjQdhsBobPAFQ0W0PEOxwOBDbJD+L7QXAJzl9DdDZk1ZMXw2cjQGcvqKGtRnZ0dlKEpWO7jnY0kiDIDAxsiIhNA22CUTMXFxbn8Oyhjwr1tMPncHu7HgnklmJuBYAY30MRgHAN/ZAgwYM8I/D4aFmCQjgE/JrFjzg4G4anNWzFC4IHBP+aKoJEBuseh+xD2BcchvRPj0Vls3bp18vvvv6u5Q8hmoHkDAjqUbWH7XN02ezhW2B60sv71119VgIEmEWgnjXvUGCE4QUMGBFpr165VxwilZAgA0dI7NejohmAGN+nEPW1ws1I0lMDn4jhjn/D9IQjE63oHN8p6NHQGd1Je6peQLBeedlKnRpQNWDRv7RFJRESmgsE0MkuYC0JEd8jS1eHiegu+kt0XHWdswm7EyfUJ7IiXFVjGx7i0njY++K5vi5lwjg0RERGRSZRJSDm3CvwSksQnKSnTt4fIm7AUjYiInMJkfXTkSk1ISIh6ZCZskys3EUV5FkrAiLKKAY1DZelqTTS7jE1YVJwExjOwoeyNgQ0RETmFifrnzqW8W7kRGg1g7ktmwj12OnXqlOZ6s2fP5nwTylLurxwsoUtuSlSO2zd69UlKlpggPwmJud01jyg7YmBDREROTZw4Mc2GBfp9ZdABLbOgmcCMGTPSXA/35CEypSJ5Rc5G2C4b1k79s3lIoLSYGS1xgX7ik6xJskUkOjRQIjlrOutIx/3H6DY2DyAiIiLyRq0niPyy71aP51GdRCb3t77k/2acJPrb3rAVmZuk0Wnf/JW8n2VCyntvOaKNC7rr22ImzNgQEREReaN145y+lOib8op+srrJDVH2xa5oRERERERkegxsiIiIiEymXXiiiHE2gaZJi4K4eydR9sXAhoiIiMhkfhgUIk+UTpDAuHgJiE+Q4RUS5ZdHb3dKI8qOOMeGiIiIyITef8hfas2fr34e2GGgpzeH3InTpTKEGRsiIiIiIjI9BjZERERERGR6DGyIiIiIiMj0OMeGiIiIiMircJJNRjCwISIiIjKZuERNtGRDu2ciYmBDREREZBZHLidI+XdiRSy4oq9JgHSVD0qt8PRmEXkFzrEhIiIiMonyU2Nv/YBsTbJIfHKgDDvWQ5KTeXPOLMXi4oNsMLAhIiIiMotbiRobmvhJ8Lg4T20RkddgYENERERkFk6m1WiayJnI+MzeGiKvwsCGiIiIyOwsFum76L8yNaJsioENERERkdlpmhy+4umNIPIsdkUjIiIiMjuLRXIGeXojiDyLGRsiIiKiLDByC+GojrI5ZmyIiIiIzNQ8wMdyu9Uv2j7/11AgNIcnN4zciq2cM4SxPREREZFZ4MacCGwAAY26UectiYme2ywib8CMDREREZFZOMjUKL4WuRHnpBc0UTbBjA0RkZebM2eO1K1bV86ePev298b7jh8/XrITHEfsN44rkZkkxSXdvmmNfQyTpEliAgMbyt6YsSEiortq9+7dsmfPHunTp4+EhYWJmW3fvl1++eUXOXDggBw5ckTi4+Nl9uzZKlByJCoqSmbOnCnr16+Xa9euSbFixaRnz57SrVs3sRhKiCA5OVm+/PJLWbFihZw7d07y5MkjrVu3lmHDhklwcHAm7SF5M58An9uBjQMHoy3yxV+J0rcqh3eUPTFjQ0REdxWCmo8++khu3LghZvfDDz/IqlWrJCkpSUqVKpXqugkJCTJ8+HBZvny5tGnTRv73v/9JyZIlZfLkyTJ37twU67/77rsybdo0KVOmjFq3VatWsnjxYnnuuedU0EOkgmEENQ5OB4tFkzwWkVcWXpPzNzjZhrInhvRERGRaCDAQQAQFZc4NPBCovPTSSxIQECALFy6UQ4cOOV3366+/lr///ltGjRolDz/8sFrWpUsXFbTMnz9fOnXqJIULF1bLjx49KkuWLJEWLVrI22+/bX2PIkWKyNSpU+XHH3+U9u3bZ8Ieksdduymy+6hIpaIiRfOpRVGHLsnSWX/K1d8uSq5aLeVasO35btE0KRUdJYWuRsuO0iWk+pQb0uD4EbmQJ0zuzREjEeXLyqCysdIq6YJI/XIiickie46KVC4mUiSvh3aUyP0Y2BAReRAG5YsWLZK1a9fKyZMnxc/PT0qUKCEPPPCA9OrVy2ZdlD3NmDFDvv32W4mMjFQZgyeffFKaNGlis15iYqJ8/vnnar0zZ86oMqbatWurkqZy5cq5tF07duyQzz77TP766y/1udim7t27q4fRvn375OOPP5aDBw+qjEyuXLmkfPnyMmTIEKlevbqav7NmzRq1LgbyOrw+dOhQa7nWJ598okq8Lly4IKGhoVK/fn0VRKB0S7d69WqZMGGCOgZ//PGHen7+/HkZO3asPPjggxITE6O25aeffpKLFy9Kzpw55d5775UnnnjCGkDcqQIFCqQru4OAC8GMEUryUJqGYKV///5qGb5/TdPUa0b43Q8//FC+++47m8AmNjZWlbjh93D8cMxxvLAejjfK/8iEVmwXeXS6yM1YEV8fkXE9Zdv1MBkQXU0mLj4rOfx8JLpeyqGbFuInx/OFy3EfH7Eka3I5V5isqVVbvbYLGZ4IiyyKCJGnN++Q6T++fqvxQFyCiJ+vyIReIi/Z/l2TF7ArVSXXMLAhIvJgUPPUU0+pUq0GDRrI/fffrzIBmLuBga99YIMgAYFPv3791O9iPgayAZiTgSv7uldeeUUN7jGox1yOK1euyNKlS2XgwIGqJKxSpUqpbhfe780331SByaBBg1RghEAHJVQIlEaMGKHWO3HihAqs8uXLpzISefPmlYiICNm7d6/KZOD3u3btKjdv3lT7M3LkSMmdO7f6XQzEAYNyfAYCFAQ+KMO6fPmyLFu2TAYMGKCyIvZByfvvv6+CNwz6EQShvAvPcSwRaKGEC8fo1KlTqgxMD9IKFiwomQWlY5iHg2MdGBho81rVqlVVSRGyOTr87OPjo14zwu9WqFDBZl0YPXq0bNmyRZo3b66CQDREQCbIeB6QycTGizw+61ZQA0nJIq8ulql9npVBu/dLjoQkuZQjUBL8HAzdYpJE4pNFcgWK5mMRn6QkSfb1TTFA/qDJ/TJw13qpffbErQWJSSJjvxTp2ViknHuCfyJPYmBDROQhyNQgqEHAgQDByNGcCgQFmIOhTzrHhHVc8UcggkG9PrkdQQ3mdLzxxhvWdfH8kUceUWVN8+bNc7pNCCqwTtu2beX111+3Lu/Ro4da/sUXX6hgCZkUfBYyB1ivWrVqDt+vRo0aKkuEwAaDcPuBNybeI1hCaRYG8DpkYBAsoXOZfdc2fCaOnbH8bOXKlSqowT7qgRcguHv22WdV1mPixImSWa5fvy5xcXEOMzwIXvFdXrp0yboMP2MZXrOH99i/f78KZv39/WXz5s0qqOncubPKVulwPmBfvQkCXQSfenCHQBaZKb2JBLKByPQhONahcYIxmLV/jiAYQap+bmeVz7j6+yHJfSXlPLSrASFS8/QxiQn2l5gAf+cHO0kTiU4QyRGgJlA7m5W1r0jJ24ENIKODsrRyhU1zrMz6GXT3sXkAEZGHoFQJ5VKPPfZYitdw9d4eBvrGTlq4uh8SEqIyE7oNGzaof5EFMa6LoKFp06Yqm4IyNmfWrVun/g/7oYcekqtXr9o88PsIuHbu3KnWzZHj1m3Of/31VzWITy8MEr7//ntVJofBu/GzkCVCsITgyR7K4ezn1CBwwjFDkGiEMj3s+8aNGzN1Aj6CL0Ag4ggCGH0dff3U1jW+56ZNm9S/ffv2TbGvpUuXFm+CLJ4xY4VzxtgZD/tmHBiC/UDQ/nmhQoVszu2s8hm561QUCc9psw6yLbnjo+VIwTwSEhsvuWLiUh+5Jd7qllYk8qrDly3JydLk+IEUnyF1y5nqWJn1M9LF4uKDbDBjQ0TkIQhIKlasmKJUyRnjfBMd5rSgjbAOJUkY4Dsa4KLMC4EPMiRoJewIyssA8zWcwZVMQFYHczqQbUEGBaVnKKlr166dS/+HjgAL247gBW2NHXEU4GG+jz3sd/78+VWgaK9s2bKqNA4BEwYrmUEPvJBlcQTBozE4w8/OAk6sa3xP/TsuXrx4inVRlnf8+HG37ANlskB/kbnDbs2xiYq9Nf9lXE8Zkxgvw4KqysQvN0lQTILce/ys7ChVJOV9bMDfRwISk+Sp9evkhe49bwUtyMhYLBKYEC+Tv1sk5WIiRYL8RWL/m2MzsbdI2UIe2GEi92NgQ0RkEo4G+Xrmw13098Ik/fDwcIfrFC1a1HrFEhPY//zzTxWc/Pbbb6p0DPN4Jk2apDp8ufJZmCOiT6J3RWZ1QLsTCLAQsKKJgaNABUFWnTp1rMsQlCEgwWv25Wh4D5SpOcvoUBbSpYHImRq3SsMqFlUdy3CHpK3nrsryohXl2taLcqhgXskVEyvX7P4OciUmSqNDZ+WFdd/IxPvvl0YHD0kOLVbahUfLyWrVZUiFOKnWsIFIvX635tb8duxW57XC7IpGWQcDGyIiD8HVdWRIHA1mMwpBB0quMEjWJ+jr9Cv5emDiiJ4FwEAa81NcgZIxfY4N6tRRIjVr1ixrYGN/I0odskYo7UBzAVc/yxns07Zt21TNu/1NQI8dO6Zq5fXGBZkVhKJxALrF2X+/6DSHoK5y5crWZVWqVFHBIV5DaZ4OJX7INhmDIGTD8B2fPn06RWYOnfXI5HKGiLSobrMooHBu6f1aI/XzEy/azcPRNMkXGyfP7NstwVqcDOvTR7ZNKih5Qh0FwoYGGnafQZQVcI4NEZGHoH0vJpmjRbG7sjDNmjVT/6I8zPge6LSGeSa1atVyWoamNxnAIByZF+McEB0m0OqlUcg62MPkW7y/sTwO84AA+2o/+McxwGAec3tSK3tLCxoTYLC/YMECm+WYZI/g4r777nOa8bpbUJKHY4jmDkYo2/P19VWlfDr8jAAQrxmhKQLew9jqGfuiv48RmgqwDC0bsljkSnCQjGvQREY3bClvDnMW1BBlfczYEBF5SO/evdVEcAQ2aOeLrAXKl5BhwJV3lHmlF+a4IDjBPVKQvcCEcr3dMwIWtIdODQKTF198UZWSoRNahw4dVIYA8z8QHGGODt4L3c2w3cgy4DOQMUEghf1BFurRRx+1vqeezZk+fbq1pTXmvaBbGrrBoZvZmDFj5Oeff1bzdFByhW5CCEqQ1bDviuYIuqjh/i2ffvqpmoOCDAcyGmgbjQm+9l3nMurw4cOqWQKgUxlgnhGaMugNHvSmCmhHjXvtoJMd9gfZFewTGh0MHjzYpkMcjgWO91dffaXaNjdu3FgFKYsXL1b7Ygxs8FrDhg1V0IPgUm/3jAAKWTpsI2VPNfNbpEtFBjWUfTGwISLyEAzg0YYYN9PEjRYRyGDQj8nxGKhnFNoaoykBBvrvvfee6jCGwTFuVOnKDTpxPxlsA7YLg2UESCjjQukc3kPvBITsENpDI9uCzAqCMpSyoQUxuqrpkCV6+umn1XshYEpKSlI36MS2IAjAzTnxWWhTjawSshnokobfQ0tjV+D+PjiW+g06ETygJA33tEEjBHQ8cgfcmwYtqo1WrVpl/RmBoB7Y4PvFd6rfSBNZLDSAQODSs2fPFO/9/PPPq2AHxwnZFxxz3MsIN1Y1ZpuQ2ZkyZYr1fbdu3aqOJdpxI+g0dsmjLAiVnZrjZYH/3bqGKLuyaO6cdUpEREQeg0AINyvFjUkpa7K8dONWYKN3PNNpIqVDRI69ajvHjMzJ8uatkt+0aGPcMz8zq+AcGyIiIpNxNP8JWZ6jR4/ecSMGMgEfy62Hr8+tf/+Lb2Ic9oAmyj5YikZERNka5g+hPC41aICgN0HwBvPmzVNNEe655x5V+obOaSiJw32N0tM6m0wImRpjp0H1XFPBTcHoK2ir5smtI/IoBjZERJStodEBJvenBnOChg4dKt4C84/QdGHhwoWqUx0CmpYtW6o5UGgAQdnMf2Vp8aGhnt4SchvHbfIpdQxsiIgoW0OzBdwvJjWp3fvHE9CJDg/KhtT8Gvtlt0rQgsKCPbJJRN6CgQ0REWVryH4QmUbyf/NoLIbn/y3Ky4QNZXMMbIiIiIjMGNwYaZpM7RDoia0h8hoMbIiIiIhMLkDTpFZRBjZZBqfYZAjbPRMRERGZmG9yvNyYxPk1RAxsiIiIiEziyKggm+fBlliZWW65WIwtoImyKZaiEREREZlE2XB/0Sb7S3yiJlpSgnz66Zee3iQir8GMDREREZHJBPhZxMeHWRoiIwY2RERERERkeixFIyIiIiLyJkzGZQgzNkREREREZHoMbIiIiIiIyPQY2BARERERkekxsCEiIiIiItNjYENERERERKbHwIaIiIiIiEyP7Z6JiIiIiLwJ2z1nCDM2RERERERkegxsiIiIiIjI9BjYEBERERGR6TGwISIiIiIi02NgQ0REREREpsfAhoiIiIiITI/tnomIiIiIvImF/Z4zghkbIiIiIqIsZvz48ZIjRw7JThjYEBERERGR6bEUjYiIiIjIm7ASLUOYsSEiIiIiymb++OMPadeunYSGhkquXLmke/fucurUKevrgwcPlqZNm1qfX758WXx8fKRevXrWZVFRUeLv7y9Lly4Vb8DAhoiIiIgoGzl9+rTcd999cuXKFfn8889l9uzZ8ttvv0mzZs3kxo0bah28vmvXLomNjVXPN27cKIGBgfL7779b19m6daskJiaqdb0BS9GIiIjI7TRNsw5+6O5ISEiQmJgY9fP169fVlXPyTmFhYWLxok5n06ZNU+fPjz/+KHnz5lXLateuLVWqVJEFCxbI008/rYKVuLg42bFjhwp4ENh06dJF/c6WLVukffv2almFChWkYMGC4g0Y2BAREZHbIahBeQtljmeffdbTm0CpuHbtmuTMmdPl9bVRd3eIvmnTJmnZsqU1qIFKlSpJzZo1ZfPmzSqwKV26tBQrVkwFL3pgM2zYMBVM//rrr9bAxluyNcDAhoiIiO7KFWoM5tKCGv2OHTvKt99+m+1a07oDj585jh/+HrxJZGSk1KpVK8VyZF4iIiKsz/WABhnBffv2qSDm5s2bsmzZMpXN2blzpwwZMkS8BQMbIiIicjuU3bhyhRqTkX19fdW6HJinH4/fncmuxy9v3rxy8eLFFMsvXLigSst0CGRGjhwpGzZskPDwcJXVQWAzevRoWb9+vQpujA0GPI3NA4iIiIiIspEmTZrIzz//rDI3uoMHD8r+/fvVazo9Q/Puu+9aS86Q6QkODpbJkydL8eLFpVSpUuItmLEhIiIiIsqCkpKSVNmYvREjRsj8+fOlbdu28vLLL6vOZ2PHjpUSJUrIgAEDrOshQ1OgQAE1p2b69OlqGTJcjRs3lu+//1769u0r3oSBDREREXlMQECAqtHHv5R+PH53Jqsfv9jYWOnRo0eK5QsXLlTByqhRo1RwgmClTZs2KjNjPx8ImRoER8YmAZh7g8DGmxoHgEVDP0YiIiIiIiIT4xwbIiIiIiIyPQY2RERERERkepxjQ0RERF5h/PjxsmbNmhTLMWm5UaNGHtkmb3XixAmZMmWK6mIVGhoqHTp0kOHDh4u/v7+nN83rrV69WiZMmJBief/+/dWNKcm8GNgQERGR1yhatKhMmjTJZhnugE634WaJuAM8Oli9/fbb6n4k06ZNUxPFcX8Rcs0HH3xgc++a/Pnze3R76M4xsCEiIiKvERgYKNWrV/f0Zni15cuXq3uLIKjJlSuXta3vW2+9JYMGDeIA3UWVK1eW3Llze3ozyI04x4aIiIjIRLZu3Sr169e3BjWAVr3Jycmyfft2j24bkScxsCEiIiKv8e+//6p7ZDRo0ED69esnGzZs8PQmeeX8Gvu7vePeI+Hh4eo1ck3Pnj1VgPjQQw+pm1Ui60XmxlI0IiIi8goVK1aUKlWqSJkyZSQqKkrdFBA3EJw8ebK0bt3a05vnVXNs7G+iCFiG1yh1CACHDh0q1apVE4vFom5UOWvWLDVXiXOUzI2BDREREd0VCE4uX77sUsMAdPPq3bu3zXLc1RxzRubMmcPAhtymYcOG6qFDdjAoKEgWLVokgwcPVoEPmRMDGyIiIror1q1bl6LDmSPIzNiXVoGPj4+0bNlStXtGxy8MPkkkZ86cKmi0d+PGDfUapR8C54ULF8rBgwcZ2JgYAxsiIiK6Kzp37qwe5F4IAu3n0ujZMUcBIlF2weYBRERE5JXQ5QtZH8y5YbbmNtysdOfOnSpDo8NxQoYLZVWUfj/++KP4+vqqeV5kXszYEBERkcedO3dOxo0bJ+3atZPixYurSfC4X8s///wjU6ZM8fTmeZVu3brJkiVL5Pnnn1dzkDDp/f3335euXbvyHjYueOqpp6Ru3bpSrlw59Xzjxo2ycuVKefjhh1mGZnIWTdM0T28EERERZW/Xrl2TCRMmqDkOERERqpkAbqA4YMAAm4nedMvx48fVDTr37dsnoaGh0rFjRxk+fLg6bpS6qVOnqnsBXbhwQTAMLlGihCqZ7NWrl+qSRubFwIaIiIiIiEyPc2yIiIiIiMj0GNgQEREREZHpMbAhIiIiIiLTY2BDRERERESmx8CGiIiIiIhMj4ENERERERGZHgMbIiIiIiIyPQY2RERERERkegxsiIiIKFMMGDDAa+7s/ueff4qfn5/89NNP1mUbNmxQ27dgwQKPbht5Hs4BnAs4JzKC55Jje/fuFR8fH/n111/lbmBgQ0REdAeOHTsmjz/+uFSqVElCQkIkT548UrlyZenfv7+sX7/eZt1SpUpJtWrV0hz4X7582eHr//zzj3odj02bNjl9H30d/REUFCTly5eXkSNHSkRExB3sbdaBY9G4cWNp06aNZAcnTpyQ8ePHq4ElZQ9Xr15V33lGg7O7ca7VqlVLOnfuLM8//7xomub2z/Zz+zsSERFlE7t375ZmzZqJv7+/PProo1K1alWJiYmRw4cPy48//ihhYWHSokULt33exx9/rN4zODhYPvnkE2natKnTdTGAwOABEMx89913Mm3aNJWh2LNnjwQEBEh2tW3bNnUcvv76a5vl9913n/r+8H1mNRhsTpgwQQXXODcoewQ2EyZMUD83b97ca861Z599Vv13E/9N6tixo1s/m4ENERFRBuH/vKOjo9WVyZo1a6Z4/fz58277rISEBFm4cKH06NFDcuXKJXPnzpXp06erQMeRokWLSr9+/azPn3nmGXnwwQdlzZo18s0336j3ya5mzpwp4eHh0qFDB5vlKJFBdouI7h5ckEHQM3v2bLcHNixFIyIiyiBkZvLly+cwqIFChQq57bNWr14tFy9eVCVuKFm7efOmLFmyJF3v0a5dO/XvkSNHnK4za9YsVb62atWqFK8lJydLsWLFbK7CIjPVq1cvKVOmjMok5c6dW9q2betyDT2uJGOQ4+iqL7YDJS1GKF/BNt5zzz2q9C9HjhwqK2Zf9udMYmKiytS0bt06RWbG0bwI4zIERBUrVlTBT/Xq1VWQCH/88Ye0b99ecubMqc4HBJEIRB3tJ0oXH3roIRWcYv0uXbqoZfbH+fXXX1cZJJxDyK6VKFFCnnjiCbly5YrD/Vq+fLn6DBx/HBdsJ7YjPj5ebbueORw4cKC1RNGVq/j4Hh555BEpWLCgBAYGStmyZeWll15SAb0Rvie858GDB9XrOE+wPv42cGU+PfNafv75Z3nttdekZMmS6py69957Zfv27WodnFdNmjSR0NBQKVy4sEycONHhe+E7Rqkh1sM5gp8R0Dvy0UcfqVJSbG+5cuXkvffec1omde3aNRk9erRaD+vnz59fevfuneI7TC9Xj3Nq89QsFot6XT9vS5cubb0Ao3/n+t+a8e/ryy+/lBo1aqjzGucZluHvJCN/p66ca3iO/xb98MMPEhUVJe7EjA0REVEGYfCBgdyKFSuka9euLv1OUlKS0zk0cXFxqZahYaCCq50YGNSuXVuVoz322GPpCsQA2QpnHn74YXnuuefks88+k06dOtm8hgHnmTNnrCVu+kAGpW4oxcNgFq/PmzdPWrVqpYKN1MrlMgKDPwzEunfvrgZOOGZffPGFmiuD78F+m+2hDA+Dqfr166frc2fMmCGRkZHqeGMAiGwZgpKlS5fKkCFD1OAWcwcQ6H3wwQdSoEABGTt2rM17IBjFAA8D9TfffFN9HwiWMGj//fffrYEwgpG3335bunXrpoIgDM537dqlzoHNmzenKCV8+eWX5Y033pAqVaqo7w4D/qNHj6pgBwECAiQMkrEO5oPp3wkG0ak5efKkOk4YzA8fPlzN08KAGdu+ZcsWdT6gAYMRAm8EjKNGjVL7gSABx+XQoUMOB8aOvPjii+rvZMSIEeo93nnnHRUs45wcPHiw2oe+ffvKV199Ja+++qr6uzBmJ3FMn3zySRWs4HX9PMV2zJkzR/2+DtuHY4YADMcHgcTUqVPV92cPx6FRo0Zy6tQpGTRokCo9PXfunPo8fKcoTUUwll4ZOc5pqVy5sio9xb7hPNX/+4QgzwgXMBCU4Xjh/MNzBELYpvnz56d7X1w91xo2bKi+C5zPuCjgNhoRERFlyNatWzV/f39c2tXKly+vDRw4UJs5c6b2999/O1y/ZMmSat20HpcuXbL5vTNnzmi+vr7auHHjrMvee+89ta6jz8Lytm3bqvfB49ChQ9q7776rtjVXrlzahQsXUt2v7t27a4GBgVpERITN8n79+ml+fn42vx8VFZXi98+fP6/ly5dPu//++22W9+/fX22bUbNmzdRxsXf8+HG1rnGfV6xYoZbNmTPHZt2EhATtnnvu0UqVKqUlJyenum+ffPKJeo9vvvkmxWvr169Xr82fPz/FsiJFimhXr161Lt+3b59abrFYtOXLl9u8T506dbRChQql2E+sP2LECJvl+j4NHTrUugz7EB0dnWL75s2bp9ZdsmSJddmOHTvUshYtWmgxMTE26+N99OPhaN/S0qdPH/U73377rc3yUaNGqeXYHh2+Jyzr2LGjzXewc+dOtfzFF19M8/OwbVi3du3aWlxcnHU5vissx7m3a9cu63Ksg+PcoEED6zKcs6GhoVrZsmW1a9euWZfj5zJlymg5cuTQIiMj1TL8GxISolWuXFm7efOmdd3Tp0+r98Bn4rjpnnnmGS0oKEjbu3evzXafOHFCCwsLU+e3Lj3HOz3H2dHfkE5EbLbB0d+Q/Ws+Pj7anj17rMvx3XXu3Fm9tm3btgz9nbqy75s2bVLrTJ06VXMnlqIRERFlEK464uo5rlLjaiuucOKKK66c48qlo/IUXLXGxHVHD1yVdgRXm1GehKyIDlescWUcWRtHkDlAmQweFSpUUF3AsF1Y7uhqtBH2B5kQY6kbshwrV65UV1eNv49sgnEdlEr5+vqqK9g7duwQd/r888/VnCJceUfWS39gkjTmD6EsRs9KOXPp0iX1b968edP12SjxQfmYDqU7KCUrUqRIimwdSqUwv8pRmQ2yEUa4mo6yMWMjA2TkUIIFyFxg/7CfLVu2VMuMxxXZKsDVffv5QXoZUEbgfMPVe2QG7ecijRkzRs1HwvlgD1kW42fWq1dPZQnS+l6MUHJnzEjpV/1xTtWtW9e6HOsg02F8b/wdITOGMjx8Pzr8jGX4TtatW6eW4W8BGRpkK1C+p0PmEX9fRogbcKzxd435a8bzD38DDRo0UO+XWcfZXdq0aSN16tSxPsd398ILL6if7+bnomQTUF7rTixFIyIiugOYa6HPyUD5BuYAoBQL7ZhRRmRfNoRBEOZ3OBu428OACsELBtIYBBnnx2DeABoKYFBrX6qCQeCkSZPUz6jZR4kM6uddoQcvKP0ZNmyYWoayJgwYjcEVoOQJpVBr165VA3Ajd9+zBu2ub9y4kWoJ1YULF1Qg54y+TeltNYs5RPbQ2rt48eIOlwOCPGPpD+a/OJp3hbIhBDY4vnqgiDIrlGChRM1+vg5K4nQY1GOfnM3zyigEgAgCUG5lD0Ehyt0cBe6OjhMGsc7mBjli/x768dTnjNi/Znzv48ePq38dbbe+TN9u/V+UrNnDRQD744HP0S8YOIIgJLOOs7tUrlzZ6b7fzc/V//7c/d8IBjZERERuguABA3/MA8FVZtTH79y5U13BzygESggeALX3jmASO7IYRphH4yyASguCpD59+qj5BwikMFEaQQ4GkcY5LBiQ4Qo2BuRo4YogDxkVDPAQbP3yyy9pfpazgY395GV9MIRB5aJFi5y+X2r3CQJ9UJre+/kgC5We5ZDR+3RgrhAaMiAb8f7776vgCdkYZG8QdCLAdVdmxt2cHY/0HIuMHOu7Td9+/E2heYCnpOfvxZs/V//7cxYkZhQDGyIiorswCEDGBIENJtPfCWRrkHFBYOHoivDQoUPVpHL7wOZOoRwNgQ0+F5PjMZkZk4GxLTpMaj579qzaRkzkN7KfOO8Mrkojq2XP0dViBHaYhI6yH/tJ0K7SA5/0lEa5CzJaKFGzz9ogE4UMmZ6tQRYOgQyaLxhLpA4cOJDiPZGd+v7772Xfvn2pNkRIb+CDASeC1L/++ivFa8gYYdK8N94PR8/2YLvRwMLo77//tllH/xfH1dm6xuOBjNv169czfMHAHcdZL6FEYGAsp3T092Jx4TvHuWfP/jil9+/Ulc/VM89pXYhIL86xISIiyiDU8zu6YombPOr19vYlLemBeTvLli1Tc2969uypOoHZP5BBwcAWAyB3wmAK5W8oj8NAG1kCBDuOrqDbX43Hvrs6vwYDc5SXIbOlw2eho5M9ZMPwGuYeOCtDSwvmMmC+hd4+OLNNnjzZ5jnmMaCznjEwxXHF4NCYmcEx1ksLjZBZA3SiQgcxe/p3oweCrmaqEERj3hJK4dCW134fsG2YH+RtMGcEASI60+G80uFnLMNxwDr6upjLhI53xrbK//77b4qsII4H5t3gPMXfpCMZmS+S3uOsl1nq84R0KFu058p3jv+G/fbbbzbny5QpU9TPxnMyPX+nrnwu/v6QGUY5rTsxY0NERJRBaKWKunsEFyjDwtX106dPq0ERMgsYiGN5RqGtMYIktP11Bq9hjs+nn36aYmL6nUIgg9bOb731lhrYIFNihBI7ZB+wDibuY9I1blaKQAj7jfu7pAVZIAzKMHjDxHPMR8LA0VHAqLd4/vDDD9Vg7IEHHlAldxiIbtu2TV0FTmteAIIGTPbHnBY0SDBmoO42bCvKzJDlQttnvd0z5gwZ79eD/cScJjQLwDmEOTbYXvt7mgCyNCiNwneESeAoYcN3grkmOI4YiCLTgAAbmQF8Hs5TLEOWSG9I4Aha9mLgiwEummKgJHHjxo2qqQRKEO0DXW+A/cLAHA0BkDXV7+uCvxGcH2gxrDeBQGkl7oOD1tRo44xjjWOMG0ciO4hgwwj3FkIWFhcZ8MDfA85XzK3DvXpwbyXjPZBclZ7jjLbiCGLxd4NMEzIpCIgctZDPly+feq/Fixer1vQ4zxD0IZDSYW4WzgEcL8znwb1+EDShnBbNUTLyd5rWuYbgCduMssqMZl6dcmuPNSIiomxk7dq12vDhw7UaNWqo9sZoyZw3b16tefPm2scff6wlJSXZrI92qVWrVnX6fnorV73dc926dVWLW/u2y0axsbGq1WyFChWsy/S2u3cKbZvx+Xi/SZMmOVwHbY/btWun5c6dW7XSRVvYjRs3OmxL66xVLdrc1qxZUwsICNAKFy6svfDCC9qBAwectqr97LPPtCZNmqj9RltqHNcuXbpoixcvdmm/9BbJy5Ytc7nds6PWtfhc7K89vfUxWuHat8s9evSo1qlTJ7XtOF74+fDhwyneY+7cuaoNMfYPLY2HDBmiXblyJUVLX92iRYu0Ro0aqfdEC+OKFSuq1tLGtsk4zmiljPfE+zjadnvHjh1Tbb7z58+v2oWXLl1aGzNmjE17ZGf7nNZxctbu2dhiWedsv52dU2ij3bBhQ3Us8MDPK1eudPi5s2fPVn8/OP/QJnratGnWtuD224L9fu2117Rq1aqp1s843pUqVdIee+wxbfv27db10tte29XjDPgcfNf4HvHfHZwbaF0tDo4RznWsi2OA1/WWzcY2zTh3qlevrva/WLFi2iuvvKLFx8ff0d9paufahg0b1LI1a9Zo7mbB/7g3VCIiIiLybrhajKYH6F6XGZChQVYLDyJPO3HihOoyN27cOJtsYWZA1geZbdx01t1NLzjHhoiIiLIdlNWgfC0j9x4hooxBeR/K3fD3dzc6+XGODREREWU7uG/I3W6RS0Qpm3fYtyt3J2ZsiIiIiIjI9DjHhoiIiIiITI8ZGyIiIiIiMj0GNkREREREZHoMbIiIiIiIyPQY2BARERERkekxsCEiIiIiItNjYENERERERKbHwIaIiIiIiEyPgQ0REREREZkeAxsiIiIiIhKz+z/HBOXXrqmoCgAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import shap\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Take the optimized model\n", + "model = grid_search.best_estimator_\n", + "\n", + "# Initialize SHAP explainer\n", + "explainer = shap.TreeExplainer(model)\n", + "\n", + "# Compute SHAP values\n", + "shap_values = explainer.shap_values(X_train)\n", + "\n", + "# === 1. Global importance plot (summary plot) ===\n", + "shap.summary_plot(shap_values, X_train)\n", + "\n", + "# === 2. Bar plot of average importance ===\n", + "shap.summary_plot(shap_values, X_train, plot_type=\"bar\")\n", + "\n" ] } ], diff --git a/scripts/Imputing.py b/scripts/Imputing.py new file mode 100644 index 0000000..adb8424 --- /dev/null +++ b/scripts/Imputing.py @@ -0,0 +1,28 @@ +import pandas as pd +import numpy as np +from sklearn.impute import KNNImputer +#this function imputes missing values from any df with KNNImputer +def knn_impute_numeric(df, n_neighbors=5): + + #select only the numerical variables to do the imputer + numeric_cols = df.select_dtypes(include=['float','int']) + print(f": shape of df with only numeric features={numeric_cols.shape}") + + #Impute the numerical variables + imputer = KNNImputer(missing_values=np.nan, n_neighbors=n_neighbors, keep_empty_features=True) + imputed_numeric_data = imputer.fit_transform(numeric_cols) + + # Create a new DataFrame with the imputed numeric data + imputed_numeric_df = pd.DataFrame(imputed_numeric_data, columns=numeric_cols.columns, index=df.index) + + # Combine the imputed numeric columns with the non-numeric columns to ensure that we keep them + non_numeric_cols = df.select_dtypes(exclude=['float','int']) + imputed_df = pd.concat([imputed_numeric_df, non_numeric_cols], axis=1) + + # Ensure the column order is the same as the original dataframe + imputed_df = imputed_df[df.columns] + + return imputed_df + +#imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5) +#imputed_df.head() \ No newline at end of file diff --git a/scripts/Scaling.py b/scripts/Scaling.py new file mode 100644 index 0000000..5a041db --- /dev/null +++ b/scripts/Scaling.py @@ -0,0 +1,31 @@ +#This function scales numerical values +from sklearn.preprocessing import RobustScaler +import pandas as pd + +def scaler_numeric(df, target_col=''): + + #separate the nutriscore and the rest of the values to do the scaling + X = df.drop([target_col], axis = 1) + y = df[target_col] + + #select only the numerical variables to do the scaling + X_numeric = X.select_dtypes(include=['float','int']) + print(f": shape of df with only numeric features={X_numeric.shape}") + + #scale the numerical values and put the scaled numerical data into a dataframe + scaler = RobustScaler() + X_scaled = scaler.fit_transform(X_numeric) + X_scaled_df = pd.DataFrame(X_scaled, columns=X_numeric.columns, index=X_numeric.index) + + #combine the scaled df with the nutriscore + X_non_numeric = X.select_dtypes(exclude=['float','int']) + X_processed = pd.concat([X_scaled_df, X_non_numeric], axis=1) + scaled_df = pd.concat([X_processed, y], axis=1) + + # Ensure the column order is the same as the original dataframe + scaled_df = scaled_df[df.columns] + + return scaled_df + +#scaled_df = scaler_numeric(imputed_df, target_col='nutriscore_score') +#scaled_df.head() \ No newline at end of file diff --git a/scripts/encoding_func.py b/scripts/encoding_func.py new file mode 100644 index 0000000..7c712ff --- /dev/null +++ b/scripts/encoding_func.py @@ -0,0 +1,32 @@ +from sklearn.preprocessing import OneHotEncoder +import numpy as np +import pandas as pd + + +def one_hot_encode_column(df, column_name): + """ + One-hot encodes a specified column in the DataFrame. + + Parameters: + df (pd.DataFrame): The DataFrame containing the column to encode. + column_name (str): The name of the column to one-hot encode. + + Returns: + pd.DataFrame: The DataFrame with the one-hot encoded columns added. + """ + # Replace NaN values with 'unknown' for consistency + df[[column_name]] = df[[column_name]].replace('', np.nan) + df[[column_name]]= df[[column_name]].fillna('unknown') + + enc = OneHotEncoder(handle_unknown='ignore') + enc.fit(df[[column_name]]) + encoded_array = enc.transform(df[[column_name]]).toarray() + encoded_df = pd.DataFrame(encoded_array, columns=enc.get_feature_names_out([column_name])) + + return pd.concat([df, encoded_df], axis=1) + + +# Example of usage +#filtered_df = one_hot_encode_column(filtered_df, 'pnns_groups_1') +#filtered_df.head() +# filtered_df = filtered_df.drop(columns=['pnns_groups_1']) diff --git a/scripts/tache0.1.py b/scripts/tache0.1.py deleted file mode 100644 index e69de29..0000000 diff --git a/scripts/tache0_alex_gueydan.py b/scripts/tache0_alex_gueydan.py deleted file mode 100644 index a424a8c..0000000 --- a/scripts/tache0_alex_gueydan.py +++ /dev/null @@ -1,176 +0,0 @@ -""" -Module pour la manipulation du dataset Open Food Facts. - -Contient la classe `Tache0` permettant de : -- Sélectionner différentes colonnes (numériques, ordinales, non ordinales). -- Optimiser la mémoire avec un downcasting des nombres. -- Filtrer les variables catégorielles selon un seuil. -""" - -import pandas as pd - - -class Tache0: - """ - Classe permettant de manipuler et traiter un dataset Open Food Facts. - """ - - def __init__(self, file_path="datasets/en.openfoodfacts.org.products.csv", sample_size=10000): - """ - Initialise la classe et charge un échantillon du dataset Open Food Facts. - - Arguments : - file_path (str) : Chemin du fichier CSV à charger. - sample_size (int) : Nombre de lignes à charger (par défaut : 10 000). - - Attributs : - df (DataFrame) : Un DataFrame contenant l'échantillon du dataset. - """ - pd.set_option("display.max_columns", None) - pd.set_option("display.max_rows", None) - - self._df = pd.read_csv( - file_path, - sep="\t", - on_bad_lines="skip", - nrows=sample_size, - low_memory=False, - ) - - @property - def df(self): - """Retourne le DataFrame chargé.""" - return self._df - - def select_numeric_columns(self): - """ - Sélectionne et retourne les colonnes numériques (int64, float64). - - Retour : - DataFrame : Un DataFrame contenant uniquement les colonnes numériques. - """ - return self.df.select_dtypes(include=["int64", "float64"]) - - def select_ordinal_columns(self, cols): - """ - Sélectionne les colonnes ordinales spécifiées. - - Arguments : - cols (list) : Liste des noms de colonnes ordinales. - - Retour : - DataFrame : Un DataFrame contenant uniquement les colonnes ordinales spécifiées. - """ - if all(col in self.df.columns for col in cols): - return self.df[cols] - return None - - def select_non_ordinal_columns(self, ordinal_cols): - """ - Sélectionne les colonnes non ordinales (catégoriques). - - Arguments : - ordinal_cols (list) : Liste des colonnes ordinales à exclure. - - Retour : - DataFrame : Un DataFrame contenant uniquement les colonnes non ordinales. - """ - categorical_cols = self.df.select_dtypes(include=["object"]).columns - return self.df[[col for col in categorical_cols if col not in ordinal_cols]] - - def select_non_ordinal_columns_without_date(self, ordinal_cols): - """ - Sélectionne les colonnes non ordinales, en excluant celles qui contiennent "date". - - Arguments : - ordinal_cols (list) : Liste des colonnes ordinales à exclure. - - Retour : - DataFrame : Un DataFrame avec uniquement les colonnes non ordinales sans "date". - """ - categorical_cols = self.df.select_dtypes(include=["object"]).columns - return self.df[ - [col for col in categorical_cols if col not in ordinal_cols and "date" not in col.lower()] - ] - - def downcast_numerics(self): - """ - Réduit l'utilisation de mémoire en convertissant les nombres en types plus petits. - - Retour : - DataFrame : Le DataFrame avec les colonnes numériques optimisées. - """ - for col in self.df.select_dtypes(include=["int64", "float64"]).columns: - if self.df[col].dtype == "int64": - self._df[col] = pd.to_numeric(self.df[col], downcast="integer") - else: - self._df[col] = pd.to_numeric(self.df[col], downcast="float") - return self.df - - def numbers_variables(self, threshold): - """ - Sélectionne les colonnes catégorielles ayant un nombre de catégories unique inférieur ou égal à `threshold`. - - Arguments : - threshold (int) : Nombre maximum de catégories uniques autorisé. - - Retour : - DataFrame : Le DataFrame avec les colonnes filtrées. - """ - categorical_cols = self.df.select_dtypes(include=["object"]).columns - filtered_cols = [col for col in categorical_cols if self.df[col].nunique() <= threshold] - return self.df[filtered_cols] - - def unique_categories_count(self, column_name): - """ - Retourne le nombre de valeurs uniques d'une colonne spécifiée. - - Arguments : - column_name (str) : Nom de la colonne. - - Retour : - int : Nombre de valeurs uniques dans la colonne. - """ - return self.df[column_name].nunique() if column_name in self.df.columns else None - - -# Example usage of the Tache0 class methods - -# Initialize the Tache0 class -tache = Tache0() - -# Select numeric columns -print("Selecting numeric columns...") -numeric_df = tache.select_numeric_columns() -print(numeric_df.head()) - -# Select ordinal columns -print("\nSelecting ordinal columns...") -ordinal_cols = ['column1', 'column2'] # Replace with actual ordinal column names -ordinal_df = tache.select_ordinal_columns(ordinal_cols) -print(ordinal_df.head() if ordinal_df is not None else "Some columns are missing.") - -# Select non-ordinal columns -print("\nSelecting non-ordinal columns...") -non_ordinal_df = tache.select_non_ordinal_columns(ordinal_cols) -print(non_ordinal_df.head()) - -# Select non-ordinal columns without date -print("\nSelecting non-ordinal columns without date...") -non_ordinal_no_date_df = tache.select_non_ordinal_columns_without_date(ordinal_cols) -print(non_ordinal_no_date_df.head()) - -# Downcast numerics -print("\nDowncasting numerics...") -downcasted_df = tache.downcast_numerics() -print(downcasted_df.head()) - -# Select categorical columns with a limited number of unique values -print("\nSelecting categorical columns with <= 100 unique values...") -category_df = tache.numbers_variables(100) -print(category_df.head()) - -# Count unique categories in a specific column -print("\nCounting unique categories in a specific column...") -unique_count = tache.unique_categories_count('column_name') # Replace with actual column name -print(f"Unique categories count: {unique_count}") From 9e71b4d5f40b5fff5c6eeed35f654be80b500026 Mon Sep 17 00:00:00 2001 From: Jess Date: Tue, 26 Aug 2025 18:15:03 +0200 Subject: [PATCH 2/5] new scripts --- scripts/rf.py | 109 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 109 insertions(+) create mode 100644 scripts/rf.py diff --git a/scripts/rf.py b/scripts/rf.py new file mode 100644 index 0000000..427ca4e --- /dev/null +++ b/scripts/rf.py @@ -0,0 +1,109 @@ +from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve +from sklearn.ensemble import RandomForestRegressor +from sklearn.metrics import mean_squared_error, r2_score +import matplotlib.pyplot as plt + +def random_forest_GS(X, y): + # Séparer en train/test + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + + # Définir le modèle + rf = RandomForestRegressor(random_state=42) + + # Grille des hyperparamètres à tester + param_grid = { + "n_estimators": [5, 50, 100], + "max_depth": [None, 10, 20, 50], + "min_samples_split": [2, 5, 10], + "min_samples_leaf": [1, 2, 4], + "max_features": ["auto", "sqrt", "log2"] + } + + # Grid Search avec validation croisée + grid_search = GridSearchCV( + estimator=rf, + param_grid=param_grid, + cv=5, + n_jobs=-1, + scoring="neg_mean_squared_error", + verbose=2 + ) + + # Entraînement + grid_search.fit(X_train, y_train) + + # Prédictions + y_pred = grid_search.best_estimator_.predict(X_test) + + # Meilleurs paramètres + print("Meilleurs paramètres trouvés : ", grid_search.best_params_) + + # Évaluation + print("MSE :", mean_squared_error(y_test, y_pred)) + print("R² :", r2_score(y_test, y_pred)) + + return grid_search.best_estimator_, X_train, X_test, y_train, y_test, y_pred + + + +# Courbe d'apprentissage / Learning curve (Lr): +def plot_learning_curve(model, X, y, cv=5): + train_sizes, train_scores, test_scores = learning_curve( + model, + X, y, + cv=cv, + scoring="neg_mean_squared_error", + n_jobs=-1, + train_sizes=np.linspace(0.1, 1.0, 10), + shuffle=True, + random_state=42 + ) + + # Moyenne et écart-type + train_scores_mean = -np.mean(train_scores, axis=1) + test_scores_mean = -np.mean(test_scores, axis=1) + + plt.figure(figsize=(8,5)) + plt.plot(train_sizes, train_scores_mean, "o-", label="Erreur entraînement") + plt.plot(train_sizes, test_scores_mean, "o-", label="Erreur validation") + plt.fill_between( + train_sizes, + test_scores_mean - np.std(test_scores, axis=1), + test_scores_mean + np.std(test_scores, axis=1), + alpha=0.2 + ) + plt.xlabel("Taille de l'échantillon d'entraînement") + plt.ylabel("MSE") + plt.title("Courbe d'apprentissage - Random Forest") + plt.legend() + plt.grid(True) + plt.show() + + +# Loss en fonction du nombre d'arbres / Loss function // number of threes +def plot_mse(best_rf, X_train, X_test, y_train, y_test, n_estimators_range=[10,50,100]): + errors = [] + + for n in n_estimators_range: + rf_tmp = RandomForestRegressor( + n_estimators=n, + max_depth=best_rf.max_depth, + min_samples_split=best_rf.min_samples_split, + min_samples_leaf=best_rf.min_samples_leaf, + max_features=best_rf.max_features, + random_state=42, + n_jobs=-1 + ) + rf_tmp.fit(X_train, y_train) + y_pred_tmp = rf_tmp.predict(X_test) + mse = mean_squared_error(y_test, y_pred_tmp) + errors.append(mse) + + plt.figure(figsize=(8,5)) + plt.plot(n_estimators_range, errors, marker="o") + plt.xlabel("Nombre d'arbres (n_estimators)") + plt.ylabel("MSE sur test") + plt.title("Évolution de la loss (MSE) vs nombre d'arbres") + plt.grid(True) + plt.show() + From 5637256a4d2cb7e2d0c055317b9c261dd051761c Mon Sep 17 00:00:00 2001 From: Jess Date: Tue, 26 Aug 2025 18:16:26 +0200 Subject: [PATCH 3/5] small correction notebook --- notebooks/project_starter.ipynb | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb index e61e1eb..14335ed 100644 --- a/notebooks/project_starter.ipynb +++ b/notebooks/project_starter.ipynb @@ -23,17 +23,29 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'os' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241m.\u001b[39mchdir(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc:\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mUsers\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mjessi\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mDocuments\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mOFFProject\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mscripts\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 2\u001b[0m sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mappend(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mabspath(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(os\u001b[38;5;241m.\u001b[39mgetcwd(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m..\u001b[39m\u001b[38;5;124m'\u001b[39m))) \n\u001b[0;32m 3\u001b[0m sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../../\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'os' is not defined" + ] + } + ], "source": [ + "import os\n", + "import sys\n", + "\n", "os.chdir(r'c:\\\\Users\\\\jessi\\\\Documents\\\\OFFProject\\\\scripts')\n", "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..'))) \n", "sys.path.append(\"../../\")\n", "\n", - "\n", - "import os\n", - "import sys\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", From 30ae7fd9790f65734d45ed98941c887090d84f82 Mon Sep 17 00:00:00 2001 From: Jess Date: Tue, 26 Aug 2025 18:30:28 +0200 Subject: [PATCH 4/5] update --- notebooks/project_starter.ipynb | 78 ++++++++++++++------------------- scripts/rf.py | 1 + 2 files changed, 35 insertions(+), 44 deletions(-) diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb index 14335ed..a4d1d94 100644 --- a/notebooks/project_starter.ipynb +++ b/notebooks/project_starter.ipynb @@ -23,21 +23,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'os' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241m.\u001b[39mchdir(\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc:\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mUsers\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mjessi\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mDocuments\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mOFFProject\u001b[39m\u001b[38;5;130;01m\\\\\u001b[39;00m\u001b[38;5;124mscripts\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 2\u001b[0m sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mappend(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mabspath(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(os\u001b[38;5;241m.\u001b[39mgetcwd(), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m..\u001b[39m\u001b[38;5;124m'\u001b[39m))) \n\u001b[0;32m 3\u001b[0m sys\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../../\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[1;31mNameError\u001b[0m: name 'os' is not defined" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import sys\n", @@ -62,14 +50,14 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_2260\\1388454853.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_2820\\1388454853.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n" ] } @@ -81,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -1164,7 +1152,7 @@ "type": "float" } ], - "ref": "7c77a633-d656-4b66-8f7f-1e287ed93561", + "ref": "eceb6b15-d8f8-4433-bc01-cdf72187b033", "rows": [ [ "0", @@ -2471,7 +2459,7 @@ "[5 rows x 214 columns]" ] }, - "execution_count": 145, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -2496,7 +2484,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -2530,7 +2518,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -2542,7 +2530,7 @@ " 'Fish Meat Eggs', 'Alcoholic beverages'], dtype=object)" ] }, - "execution_count": 147, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -2553,7 +2541,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -2598,7 +2586,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -2619,7 +2607,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -2757,7 +2745,7 @@ "type": "float" } ], - "ref": "e41ab0a0-0d29-4939-a11a-44b60451cee2", + "ref": "679ac620-7cf6-4366-8bf5-14623c478ef3", "rows": [ [ "6", @@ -3119,7 +3107,7 @@ "[5 rows x 25 columns]" ] }, - "execution_count": 150, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -3137,7 +3125,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -3282,7 +3270,7 @@ "type": "float" } ], - "ref": "08b41fb3-0d58-4688-8622-f6ca3df547d0", + "ref": "ddd582f5-0aef-4492-866f-f210751317d7", "rows": [ [ "6", @@ -3644,7 +3632,7 @@ "[5 rows x 25 columns]" ] }, - "execution_count": 151, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -3664,7 +3652,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -3682,7 +3670,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -3820,7 +3808,7 @@ "type": "float" } ], - "ref": "45473ebf-b911-4682-bee0-28fbb5b64923", + "ref": "d29ff53c-83a8-40a1-9740-b71f708d6d39", "rows": [ [ "6", @@ -4182,7 +4170,7 @@ "[5 rows x 25 columns]" ] }, - "execution_count": 153, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -4207,7 +4195,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -4228,7 +4216,7 @@ "\n", "Below are more details about the failures:\n", "--------------------------------------------------------------------------------\n", - "240 fits failed with the following error:\n", + "219 fits failed with the following error:\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", @@ -4238,10 +4226,10 @@ " validate_parameter_constraints(\n", " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n", " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", "\n", "--------------------------------------------------------------------------------\n", - "300 fits failed with the following error:\n", + "321 fits failed with the following error:\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", @@ -4251,7 +4239,7 @@ " validate_parameter_constraints(\n", " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n", " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", "\n", " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan\n", @@ -4338,8 +4326,9 @@ "X = work_df.drop(\"nutriscore_score\", axis=1) \n", "y = work_df[\"nutriscore_score\"]\n", "\n", - "from rf import *\n", - "best_rf, X_test, y_test, y_pred = random_forest_GS(X, y)\n" + "from scripts.rf import random_forest_GS\n", + "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X, y)\n", + "\n" ] }, { @@ -4349,13 +4338,14 @@ "outputs": [ { "ename": "NameError", - "evalue": "name 'plot_learning_curve' is not defined", + "evalue": "name 'np' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[170], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mplot_learning_curve\u001b[49m(best_rf, X, y)\n\u001b[0;32m 2\u001b[0m plot_mse(best_rf, X_train, X_test, y_train, y_test)\n", - "\u001b[1;31mNameError\u001b[0m: name 'plot_learning_curve' is not defined" + "Cell \u001b[1;32mIn[20], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mplot_learning_curve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbest_rf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m plot_mse(best_rf, X_train, X_test, y_train, y_test)\n", + "File \u001b[1;32mc:\\Users\\jessi\\Documents\\OFFProject\\scripts\\rf.py:57\u001b[0m, in \u001b[0;36mplot_learning_curve\u001b[1;34m(model, X, y, cv)\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_learning_curve\u001b[39m(model, X, y, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m):\n\u001b[0;32m 51\u001b[0m train_sizes, train_scores, test_scores \u001b[38;5;241m=\u001b[39m learning_curve(\n\u001b[0;32m 52\u001b[0m model,\n\u001b[0;32m 53\u001b[0m X, y,\n\u001b[0;32m 54\u001b[0m cv\u001b[38;5;241m=\u001b[39mcv,\n\u001b[0;32m 55\u001b[0m scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mneg_mean_squared_error\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 56\u001b[0m n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m---> 57\u001b[0m train_sizes\u001b[38;5;241m=\u001b[39m\u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;241m1.0\u001b[39m, \u001b[38;5;241m10\u001b[39m),\n\u001b[0;32m 58\u001b[0m shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 59\u001b[0m random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m\n\u001b[0;32m 60\u001b[0m )\n\u001b[0;32m 62\u001b[0m \u001b[38;5;66;03m# Moyenne et écart-type\u001b[39;00m\n\u001b[0;32m 63\u001b[0m train_scores_mean \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39mnp\u001b[38;5;241m.\u001b[39mmean(train_scores, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined" ] } ], diff --git a/scripts/rf.py b/scripts/rf.py index 427ca4e..ba53ac3 100644 --- a/scripts/rf.py +++ b/scripts/rf.py @@ -2,6 +2,7 @@ from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error, r2_score import matplotlib.pyplot as plt +import numpy as np def random_forest_GS(X, y): # Séparer en train/test From bab244e7f0a6221cddd445d275bf67b31e710e05 Mon Sep 17 00:00:00 2001 From: Jess Date: Tue, 26 Aug 2025 18:56:52 +0200 Subject: [PATCH 5/5] new update --- notebooks/project_starter.ipynb | 120 +++++++++++++++----------------- 1 file changed, 56 insertions(+), 64 deletions(-) diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb index a4d1d94..d7cf0fc 100644 --- a/notebooks/project_starter.ipynb +++ b/notebooks/project_starter.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -50,14 +50,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_2820\\1388454853.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n", + "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_23760\\1388454853.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n", " df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n" ] } @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -1152,7 +1152,7 @@ "type": "float" } ], - "ref": "eceb6b15-d8f8-4433-bc01-cdf72187b033", + "ref": "c91cbbb4-7de4-4f19-b68f-80b1ffb03bdb", "rows": [ [ "0", @@ -2459,7 +2459,7 @@ "[5 rows x 214 columns]" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -2484,7 +2484,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -2518,7 +2518,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -2530,7 +2530,7 @@ " 'Fish Meat Eggs', 'Alcoholic beverages'], dtype=object)" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -2541,7 +2541,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -2586,7 +2586,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -2607,7 +2607,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -2745,7 +2745,7 @@ "type": "float" } ], - "ref": "679ac620-7cf6-4366-8bf5-14623c478ef3", + "ref": "ca6b955b-dab1-4e55-bb09-51fec154a3ab", "rows": [ [ "6", @@ -3107,7 +3107,7 @@ "[5 rows x 25 columns]" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -3125,7 +3125,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -3270,7 +3270,7 @@ "type": "float" } ], - "ref": "ddd582f5-0aef-4492-866f-f210751317d7", + "ref": "f13568b6-b0da-4dca-bee9-18c1152773e3", "rows": [ [ "6", @@ -3632,7 +3632,7 @@ "[5 rows x 25 columns]" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -3652,7 +3652,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -3670,7 +3670,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -3808,7 +3808,7 @@ "type": "float" } ], - "ref": "d29ff53c-83a8-40a1-9740-b71f708d6d39", + "ref": "10f8f3ab-25d3-467f-be48-16f8d3033a97", "rows": [ [ "6", @@ -4170,7 +4170,7 @@ "[5 rows x 25 columns]" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -4183,19 +4183,35 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 3. Predicting the nutriscore" + "### 3. Predicting the nutriscore" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "##### 3.1 : Using random forest regression" + "##### 3.1 : Decision tree" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "### code for that" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 3.2 Random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -4216,7 +4232,7 @@ "\n", "Below are more details about the failures:\n", "--------------------------------------------------------------------------------\n", - "219 fits failed with the following error:\n", + "268 fits failed with the following error:\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", @@ -4226,10 +4242,10 @@ " validate_parameter_constraints(\n", " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n", " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", "\n", "--------------------------------------------------------------------------------\n", - "321 fits failed with the following error:\n", + "272 fits failed with the following error:\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n", " estimator.fit(X_train, y_train, **fit_params)\n", @@ -4239,7 +4255,7 @@ " validate_parameter_constraints(\n", " File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n", " raise InvalidParameterError(\n", - "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n", + "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n", "\n", " warnings.warn(some_fits_failed_message, FitFailedWarning)\n", "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [ nan nan nan nan nan\n", @@ -4333,31 +4349,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'np' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[20], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mplot_learning_curve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbest_rf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m plot_mse(best_rf, X_train, X_test, y_train, y_test)\n", - "File \u001b[1;32mc:\\Users\\jessi\\Documents\\OFFProject\\scripts\\rf.py:57\u001b[0m, in \u001b[0;36mplot_learning_curve\u001b[1;34m(model, X, y, cv)\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_learning_curve\u001b[39m(model, X, y, cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m):\n\u001b[0;32m 51\u001b[0m train_sizes, train_scores, test_scores \u001b[38;5;241m=\u001b[39m learning_curve(\n\u001b[0;32m 52\u001b[0m model,\n\u001b[0;32m 53\u001b[0m X, y,\n\u001b[0;32m 54\u001b[0m cv\u001b[38;5;241m=\u001b[39mcv,\n\u001b[0;32m 55\u001b[0m scoring\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mneg_mean_squared_error\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 56\u001b[0m n_jobs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m---> 57\u001b[0m train_sizes\u001b[38;5;241m=\u001b[39m\u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;241m1.0\u001b[39m, \u001b[38;5;241m10\u001b[39m),\n\u001b[0;32m 58\u001b[0m shuffle\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 59\u001b[0m random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m\n\u001b[0;32m 60\u001b[0m )\n\u001b[0;32m 62\u001b[0m \u001b[38;5;66;03m# Moyenne et écart-type\u001b[39;00m\n\u001b[0;32m 63\u001b[0m train_scores_mean \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39mnp\u001b[38;5;241m.\u001b[39mmean(train_scores, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", - "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined" - ] - } - ], - "source": [ - "\n", - "plot_learning_curve(best_rf, X, y)\n", - "plot_mse(best_rf, X_train, X_test, y_train, y_test)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -4382,17 +4374,21 @@ } ], "source": [ - "\n" + "\n", + "from scripts.rf import plot_learning_curve, plot_mse\n", + "\n", + "plot_learning_curve(best_rf, X, y)\n", + "plot_mse(best_rf, X_train, X_test, y_train, y_test)\n" ] }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -4416,19 +4412,15 @@ "import matplotlib.pyplot as plt\n", "\n", "# Take the optimized model\n", - "model = grid_search.best_estimator_\n", + "model = best_rf # ou grid_search.best_estimator_ si tu gardes grid_search\n", "\n", "# Initialize SHAP explainer\n", "explainer = shap.TreeExplainer(model)\n", "\n", "# Compute SHAP values\n", - "shap_values = explainer.shap_values(X_train)\n", - "\n", - "# === 1. Global importance plot (summary plot) ===\n", - "shap.summary_plot(shap_values, X_train)\n", - "\n", - "# === 2. Bar plot of average importance ===\n", - "shap.summary_plot(shap_values, X_train, plot_type=\"bar\")\n", + "shap_values = explainer(X_train) \n", + "shap.summary_plot(shap_values.values, X_train, feature_names=X_train.columns)\n", + "shap.summary_plot(shap_values.values, X_train, feature_names=X_train.columns, plot_type=\"bar\")\n", "\n" ] }