Get the Number of Products in each Nutritiscore Grade for Selected Stores

StoresByNutritionscore.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare the Nutrition Score of Selected Stores\n",
    "1. Select stores & get keys\n",
    "2. Retrieve data from OpenFoodFacts (https://world.openfoodfacts.org/)\n",
    "3. Aggregate products to nutrition portfolio\n",
    "4. Plot result\n",
    "5. Bonus: Nutrition Score Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import openfoodfacts as off # off python api\n",
    "\n",
    "# Configurations\n",
    "sns.set()\n",
    "#plt.rcParams['figure.dpi'] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Select Stores & Get Store Keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In total OpenFoodFacts contains 4958 different stores\n"
     ]
    }
   ],
   "source": [
    "# Get all stores from OFF\n",
    "all_stores = off.facets.get_stores()\n",
    "print(\"In total OpenFoodFacts contains %s different stores\" % len(all_stores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>products</th>\n",
       "      <th>url</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Carrefour</td>\n",
       "      <td>20545</td>\n",
       "      <td>https://world.openfoodfacts.org/store/carrefour</td>\n",
       "      <td>carrefour</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Magasins-u</td>\n",
       "      <td>19461</td>\n",
       "      <td>https://world.openfoodfacts.org/store/magasins-u</td>\n",
       "      <td>magasins-u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Auchan</td>\n",
       "      <td>14498</td>\n",
       "      <td>https://world.openfoodfacts.org/store/auchan</td>\n",
       "      <td>auchan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Leclerc</td>\n",
       "      <td>11093</td>\n",
       "      <td>https://world.openfoodfacts.org/store/leclerc</td>\n",
       "      <td>leclerc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Delhaize</td>\n",
       "      <td>8857</td>\n",
       "      <td>https://world.openfoodfacts.org/store/delhaize</td>\n",
       "      <td>delhaize</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name  products                                               url  \\\n",
       "0   Carrefour     20545   https://world.openfoodfacts.org/store/carrefour   \n",
       "1  Magasins-u     19461  https://world.openfoodfacts.org/store/magasins-u   \n",
       "2      Auchan     14498      https://world.openfoodfacts.org/store/auchan   \n",
       "3     Leclerc     11093     https://world.openfoodfacts.org/store/leclerc   \n",
       "4    Delhaize      8857    https://world.openfoodfacts.org/store/delhaize   \n",
       "\n",
       "           id  \n",
       "0   carrefour  \n",
       "1  magasins-u  \n",
       "2      auchan  \n",
       "3     leclerc  \n",
       "4    delhaize  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select top 100\n",
    "stores_df = pd.DataFrame(all_stores)[:100]\n",
    "stores_df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select German stores (searched the id column)\n",
    "selected_stores = [\"lidl\", \"aldi\", \"netto\", \"rewe\", \"edeka\", \"kaufland\", \"penny\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ger_stores_df = stores_df[stores_df.id.isin(selected_stores)]\n",
    "\n",
    "plt.figure(figsize=(14,5))\n",
    "sns.barplot(x=ger_stores_df[\"name\"], y=ger_stores_df[\"products\"])\n",
    "plt.ylabel(\"labeled products\")\n",
    "plt.xlabel(\"store name\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Retrieve Data from OpenFoodFacts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function for retrieving products by facet (with pagination)\n",
    "\n",
    "def get_products(facet, value, page):\n",
    "    return off.products.get_by_facets({facet: value}, page=page)\n",
    "\n",
    "def get_all_products(facet, value):\n",
    "\n",
    "    products = []\n",
    "    page = 1\n",
    "\n",
    "    while True:\n",
    "        start = time.time()\n",
    "        tmp_products = get_products(facet, value, page)\n",
    "        if len(tmp_products) == 0:\n",
    "            break\n",
    "\n",
    "        products.extend(tmp_products)\n",
    "        print(\"Page %s: Added %s products (Total=%s)! → %s sec\" %\n",
    "              (page, len(tmp_products), len(products), round(time.time() - start, 2)), end=\"\\r\", flush=True)\n",
    "        page += 1\n",
    "        \n",
    "    return products"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Aggregate Products by Nutrition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_nutri_from_facet(facet, value):\n",
    "    \n",
    "    # Retrieve products from off\n",
    "    products = get_all_products(facet, value)\n",
    "    \n",
    "    # Transform & select\n",
    "    products_df = pd.DataFrame(products)\n",
    "    products_df.to_csv(\"products-\" + store + \"-\" + value + \".csv\")\n",
    "    products_df = products_df[[\"product_name\", \"nutrition_grade_fr\", \"nutriscore_score\"]]\n",
    "    \n",
    "    # Count products for scores\n",
    "    scores = products_df[\"nutrition_grade_fr\"].value_counts()\n",
    "    scores.sort_index(axis=0, inplace=True)\n",
    "    \n",
    "    # Calculate shares\n",
    "    scores_r = round(scores/sum(scores),3)*100\n",
    "    scores_r.index = [\"A\", \"B\", \"C\", \"D\", \"E\"]\n",
    "    \n",
    "    return scores_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Asking for all products of lidl ...\n",
      "Asking for all products of aldi ...8811)! → 8.88 sec\n",
      "Asking for all products of netto ...094)! → 8.24 secc\n",
      "Asking for all products of rewe ...2879)! → 8.21 sec\n",
      "Asking for all products of edeka ...68)! → 8.14 seccc\n",
      "Asking for all products of kaufland ...)! → 7.9 secc\n",
      "Asking for all products of penny ...)! → 7.68 secec\n",
      "Page 14: Added 19 products (Total=279)! → 8.42 sec\r"
     ]
    }
   ],
   "source": [
    "nutri_scores = {}\n",
    "\n",
    "for store in selected_stores:\n",
    "    print(\"Asking for all products of %s ...\" % store)\n",
    "    nutri_scores[store] = get_nutri_from_facet(\"store\", store)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Plot Nutrition Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"dark\")\n",
    "\n",
    "def plot_scores(scores, store, save=False):\n",
    "    fig = plt.figure(figsize=(12, 6))\n",
    "    plt.rcParams[\"axes.axisbelow\"] = \"line\"\n",
    "    \n",
    "    # Plotting no of products per grade\n",
    "    plt.bar(height=scores, x=scores.index, \n",
    "                 color=[\"#058b49\", \"#74c928\", \"#fbc801\", \"#f4730e\", \"#f02d1a\"])\n",
    "    \n",
    "    # Configuring axis & field\n",
    "    plt.ylim(top=35)\n",
    "    ax = plt.gca()\n",
    "    ax.yaxis.set_major_formatter(mticker.FormatStrFormatter('%d %%'))\n",
    "    ax.tick_params(axis='x', labelsize=30, direction=\"in\", pad=-35, labelcolor=\"white\")\n",
    "    \n",
    "    # Labeling plot\n",
    "    plt.title(\"Nutriscore von \"+ r\"$\\bf{\" + store + \"}$\" + \" - Produkten\",\n",
    "              fontdict={\"fontsize\": 20, \"fontweight\": 10, \"verticalalignment\":\"bottom\"})\n",
    "    plt.ylabel(\"Products in Grade\")\n",
    "    \n",
    "    if not save:\n",
    "        plt.show()\n",
    "    else:\n",
    "        plt.savefig(\"Nutriscore_\" + store, dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1ZMkSbdq0qcxhPZV9P6nurzfgcsUYceAydeedd+qNN94o8yxy+/btNWbMGD311FOus8+dOnXStGnTSoz1PV+PHj1cD+AxDEOBgYGlnoRXVcHBwXr99dc1c+ZM1a5dW1arVU6nUxERERo2bFiJZf/2t78pNDRUcXFxOnbsmGvZwMDAUg/ICQsL08KFC7VgwQKlp6e7njz46KOPlrpjhrvatGmjOXPmaOnSpZo1a5Zq1aoli8UiX19f3X777SVKqTs6dOig9PR0hYaGqmHDhuUu54ljqS7Dhw8vUZTi4+P1xhtvuN6PHDlSd955p1atWqWdO3e6Sl3jxo111VVXqWvXroqIiCh1t4tu3bqVGDJxfvm+5ZZbZLVaXff+rlWrVpm35ruY/V/qqvqZaNGihZYsWaL58+dr7ty5stlsatmypV544QV9+OGHZd5C9EK+R/yer6+vXn75ZQ0fPrzUBa+VfT+p7q834HJlOKty6TkAAACAasHQFAAAAMAEFHEAAADABBRxAAAAwAQUcQAAAMAEFHEAAADABF65feHw4cN15MgRWSwW+fn56fnnn1dYWJik4gd52Gw21a5dW5IUHR2t8PDwUttITEzUnDlzJEnjxo1zLbN48WKFhISoV69e3jgUAAAAoFp4pYjHxsaqfv36kqRPP/1UEydO1Jo1a1zzFy1apDZt2lS4jYULF2rp0qWSiu8dGx4erpSUFO3atUujRo3yXHgAAADAA7xSxM+VcEnKy8tzPQmtKnx8fFRQUCCp+PHRkhQTE6PnnnuuekICAAAAXuS1B/pMmjRJX375pZxOp5YtW6bQ0FBJxUNT6tWrJ6fTqVtuuUVjxoyRv79/qfWTkpIUGxsrSZowYYIOHjyoY8eOacSIEd6IDwAAAFQrrz9Zc+3atdq4caPrEbqpqakKDg5WYWGhZs6cqfz8fM2dO7fCbWRnZ2vEiBFavny54uPjdejQIYWEhGj06NHeOAQAAADgopnyiPsOHTooMTFRDRs2LDF97969GjZsmLZs2VLh+hMnTlSfPn3kcDi0bt06zZo1S+PGjVPfvn3VtWtXt3NkZeWrqMjrhw8AAIArgMViqGHDuuXO9/gY8fz8fOXk5Cg4OFiStGXLFjVo0EABAQGy2+1yOByqX7++nE6nNm3a5LqbSnmSkpIkSV26dNHmzZtd480tFovsdnuVshUVOSniAAAAMIXHi3hBQYGioqJUUFAgi8WiBg0aaMmSJTIMQ5mZmRo1apQcDoeKiorUqlUrTZkypdxtFRYWKi4uTvHx8ZKk8PBwJSQkqGfPnmrevHmZtz0EAAAALkWmDE25VGRm5nFGHAAAAB5hsRgKDKxX/nwvZgEAAADwfyjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAlqeWMnw4cP15EjR2SxWOTn56fnn39eYWFhkqSUlBSNHz9e2dnZCggIUGxsrK699tpS20hMTNScOXMkSePGjVN4eLgkafHixQoJCVGvXr28cSgAALitkX8tWWv7mh0D53GcLtDJnLNmxwAkSYbT6XR6eie5ubmqX7++JOnTTz/Viy++qDVr1kiSHn30UT3wwAPq3bu31q1bp9WrV+utt94qtY2//vWvio+PlySNHDlS77//vlJSUjRr1iy98sorF5QrMzNPRUUeP3wAwBWqSZP6yo9uYHYMnKfu3FM6fjzX7Bi4QlgshgID65U/3xshzpVwScrLy5NhGJKkzMxMJScnKzIyUpIUGRmp5ORknTx5stQ2fHx8VFBQoIKCAtlsNklSTEyMJk6c6IUjAAAAAKqXV4amSNKkSZP05Zdfyul0atmyZZKk1NRUNW3aVFarVZJktVoVFBSk1NRUNWrUqMT6Y8eO1fjx4yVJEyZM0Jo1a9SxY0eFhIR46xAAAACAauO1Ij5z5kxJ0tq1azV79my9+uqrVVq/c+fOSkhIkCRlZ2dr3rx5Wr58uebPn69Dhw4pJCREo0ePrvbcAAAAgCd4/a4pffr00VdffaWsrCwFBwcrPT1dDodDkuRwOJSRkaHg4OAKtzF79mxFRUVp586dysjI0IIFC5SWlqbt27d74xAAAACAi+bxIp6fn6/U1FTX+y1btqhBgwYKCAhQYGCgwsLCtGHDBknShg0bFBYWVmpYyvmSkpIkSV26dJHdbneNN7dYLLLb7R48EgAAAKD6eHxoSkFBgaKiolRQUCCLxaIGDRpoyZIlrgI9depUjR8/Xi+99JL8/f0VGxtb7rYKCwsVFxfnuntKeHi4EhIS1LNnTzVv3tx1S0MAAADgUueV2xdeqrh9IQDAk7h94aWH2xfCmy6J2xcCAAAAKIkiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmKCWp3eQlZWlZ599VocOHZLNZlNISIimT5+uRo0aSZIiIiJks9lUu3ZtSVJ0dLTCw8NLbScxMVFz5syRJI0bN861zOLFixUSEqJevXp5+lAAAACAauPxIm4Yhp588knddtttkqTY2FjNnTtXMTExrmUWLVqkNm3aVLidhQsXaunSpZKkkSNHKjw8XCkpKdq1a5dGjRrluQMAAAAAPMDjQ1MCAgJcJVySOnbsqGPHjlV5Oz4+PiooKFBBQYFsNpskKSYmRhMnTqy2rAAAAIC3ePyM+PmKioq0atUqRURElJgeHR0tp9OpW265RWPGjJG/v3+pdceOHavx48dLkiZMmKA1a9aoY8eOCgkJ8Up2AAAAoDoZTqfT6a2dTZs2Tenp6YqPj5fFUnwyPjU1VcHBwSosLNTMmTOVn5+vuXPnVrid7OxsjRgxQsuXL1d8fLwOHTqkkJAQjR49ukp5MjPzVFTktcMHAFxhmjSpr/zoBmbHwHnqzj2l48dzzY6BK4TFYigwsF75870VJDY2VgcPHtSCBQtcJVySgoODJUk2m02DBg3SN998U+m2Zs+eraioKO3cuVMZGRlasGCB0tLStH37do/lBwAAAKqTV4p4XFycdu3apRdffNE1vluS7Ha7cnOLfyp1Op3atGmTwsLCKtxWUlKSJKlLly6y2+0yDEOSZLFYZLfbPXQEAAAAQPXy+Bjx/fv3a8mSJbr22ms1cOBASVLz5s314osvKjMzU6NGjZLD4VBRUZFatWqlKVOmlLutwsJCxcXFKT4+XpIUHh6uhIQE9ezZU82bNy/ztocAAADApcirY8QvNYwRBwB4EmPELz2MEYc3XTJjxAEAAAD8hiIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJiAIg4AAACYgCIOAAAAmIAiDgAAAJigSkW8qKhIGRkZnsoCAAAAXDHcKuI5OTl65pln1KFDB919992SpM2bNysuLs6j4QAAAICayq0iPmXKFNWrV09btmyRj4+PJKlTp0768MMPPRoOAAAAqKlqubPQtm3btHXrVvn4+MgwDElSo0aNlJmZ6dFwAAAAQE3l1hnx+vXrKysrq8S0Y8eOqUmTJh4JBQAAANR0bhXxfv366amnntL27dtVVFSkb7/9VuPGjdPAgQM9nQ8AAACokdwamjJ06FDZbDZNnz5dZ8+e1cSJEzVgwAA99thjns4HAJe9Rg2tstbyMzsGzuM4a9fJLIfZMQBc4dwq4oZhaPDgwRo8eLCH4wBAzWOt5aezX/HYhktJrduKJOWaHQPAFa7cIr5t2za3NtCtW7dqCwMAAABcKcot4pMmTSrx/tyDfAICApSdnS1Jatq0qTZv3uzBeAAAAEDNVG4R37Jli+v1kiVLlJ2draioKPn6+qqgoECLFi1SQECAV0ICAAAANY1bgxbfeOMNPfPMM/L19ZUk+fr6asyYMVq+fLlHwwEAAAA1lVtF3M/PTz/88EOJaf/9739dxRwAAABA1bh115SnnnpKTz75pCIiItSsWTOlpaXps88+0+TJkz2dDwAAAKiR3Criffr00Q033KCPP/5YGRkZuu666zRs2DC1bt3a0/kAAACAGsmtIi5JrVu3pngDAAAA1cTtIr5582bt2LFDWVlZcjqdrumzZ8/2SDAAAIDLUaN6tWTlOrpLjqOgQCfzzpodowS3inh8fLz++c9/6r777tNHH32kAQMGaMOGDbrvvvs8nQ8AAOCyYvX11akOV5sdA7/T4IejUt6l9URdt+6asnr1ar3++uuaOHGifHx8NHHiRC1ZskRHjhzxdD4AAACgRnKriOfk5KhNmzaSJB8fH505c0YdOnTQjh07PBoOAAAAqKncGppyzTXXaP/+/QoNDVVoaKhWrVolf39/NWjQwNP5AAAAgBrJrSL+9NNPKzs7W5IUHR2tZ555Rna7XVOmTPFoOAAAAKCmqrSIFxUVyWaz6aabbpIkdejQQZ988onHgwEAAAA1WaVjxC0Wi4YPHy6bzeaNPAAAAMAVwa2LNW+99VZ99913ns4CAAAAXDHcGiN+1VVXaejQobrrrrvUrFkzGYbhmhcVFeWxcAAAAEBN5dYZ8dOnT6t79+4yDEPp6elKS0tz/alMVlaWhg4dqh49eqhnz54aOXKkTp486ZqfkpKiAQMGqEePHhowYIAOHDhQ5nYSExMVGRmpyMhIbd261TV98eLFWr9+vTuHAQAAAFwy3Doj/o9//OOCd2AYhp588knddtttkqTY2FjNnTtXMTExkqQpU6Zo0KBB6t27t9atW6fJkyfrrbfeKrWdhQsXaunSpZKkkSNHKjw8XCkpKdq1a5dGjRp1wfkAAAAAM1R6RvzMmTOu10lJSdqxY4frz9mzZyvdQUBAgKuES1LHjh117NgxSVJmZqaSk5MVGRkpSYqMjFRycnKJM+bn+Pj4qKCgQAUFBa4LR2NiYjRx4sRKMwAAAACXmgrPiL/zzjv69ttvNWfOHEnSkCFDFBAQIEn69ddfFR0drX79+rm9s6KiIq1atUoRERGSpNTUVDVt2lRWq1WSZLVaFRQUpNTUVDVq1KjEumPHjtX48eMlSRMmTNCaNWvUsWNHhYSEuL1/AAAA4FJRYRFft26dpk2b5npvs9mUmJgoSdq9e7emTp1apSI+Y8YM+fn56eGHH65y0M6dOyshIUGSlJ2drXnz5mn58uWaP3++Dh06pJCQEI0ePbrK2wUAAADMUOHQlCNHjqhdu3au961atXK9bteunQ4fPuz2jmJjY3Xw4EEtWLBAFkvxboODg5Weni6HwyFJcjgcysjIUHBwcIXbmj17tqKiorRz505lZGRowYIFSktL0/bt293OAwAAAJipwiJut9tlt9td7//5z3+6Xp8br+2OuLg47dq1Sy+++GKJBwMFBgYqLCxMGzZskCRt2LBBYWFhpYalnC8pKUmS1KVLF9ntdtetFC0WS4msAAAAwKWswiIeGhqqL7/8ssx5W7duVevWrSvdwf79+7VkyRJlZGRo4MCB6t27t0aMGOGaP3XqVK1YsUI9evTQihUrSgyF+b3CwkLFxcVp7NixkqTw8HBlZWWpZ8+eys7OVnh4eKV5AK/iUQgAABwcSURBVAAAgEtBhWPEH3vsMU2bNk2GYSgiIkIWi0VFRUXavHmzZsyY4bp4siKhoaHau3dvufNbtWrlGvtdGZvNppUrV5Z4v2TJErfWBQAAAC4lFRbx+++/X+np6Ro7dqzOnDmjgIAAZWdny8fHRyNGjHDddhAAAABA1VT6QJ8nnnhC/fv317fffqusrCwFBASoU6dOql+/vjfyAQAAADWSW0/WrFevHuOvAQAAgGpU6ZM1AQAAAFQ/ijgAAABgAoo4AAAAYAK3xoifk5mZWeqhOS1atKjWQAAAAMCVwK0i/sUXX2jSpEk6ceKEnE6na7phGNq9e7fHwgEAAAA1lVtFfPr06Ro+fLj69u2rOnXqeDoTAAAAUOO5VcRzcnI0cOBAGYbh6TwAAADAFcGtizUfeOABrV692tNZAAAAgCuGW2fEv//+e7399tt69dVX1bhx4xLzVq5c6ZFgAAAAQE3mVhHv16+f+vXr5+ksAAAAwBXDrSLet29fT+cAAAAArijlFvG1a9eqT58+kqT33nuv3A08+OCD1Z8KAAAAqOHKLeIbN250FfF169aVuYxhGBRxAAAA4AKUW8RfffVV1+u3337bK2EAAACAK4Vbty8EAAAAUL0o4gAAAIAJKOIAAACACSjiAAAAgAncuo/4Tz/9pICAADVu3Fj5+fl67bXXZLFYNGTIEPn6+no6IwAAAFDjuHVG/JlnnlFOTo4kKTY2Vjt27NB3332nyZMnezQcAAAAUFO5dUb86NGjatmypZxOpz799FNt2LBBderU0V133eXpfAAAAECN5FYRt9lsysvL088//6xmzZqpUaNGOnv2rE6fPu3pfAAAAECN5FYRj4yM1GOPPab8/Hw9/PDDkqTk5GQ1b97co+EAAACAmsqtIj5x4kT9+9//Vq1atdS1a1dJxY+3nzBhgkfDAQAAADWVWxdrvvDCC/rjH//oKuGSdOONN2rLli0eCwYAAADUZG4V8ffff7/M6evXr6/WMAAAAMCVosKhKe+9954kyeFwuF6fc/jwYQUEBHguGQAAAFCDVVjE161bJ0k6c+aM67VUPD68cePGio2N9Ww6AAAAoIaqsIi//fbbkqS4uDiNHj3aK4EAAACAK4FbY8RvvfVWpaSklJj2yy+/6Msvv/RIKAAAAKCmc6uIT58+XXXr1i0xrW7dupo+fbpHQgEAAAA1nVtFPDMzU0FBQSWmBQUF6fjx4x4JBQAAANR0bhXxFi1aaNu2bSWmffXVVzxZEwAAALhAbj1Zc+TIkRo1apQefPBBtWjRQocPH9b777+vmJgYT+cDAAAAaiS3zoh3795dr7/+uux2uxITE2W327Vs2TJ1797d0/kAAACAGsmtM+KS1KFDB3Xo0OGCdhIbG6uPP/5YR48e1QcffKA2bdq45kVERMhms6l27dqSpOjoaIWHh5faRmJioubMmSNJGjdunGuZxYsXKyQkRL169bqgbAAAAIAZ3CriCxcuLHdeVFRUpevfddddevTRR/XQQw+VOX/RokUlynl5GZYuXSqpeKhMeHi4UlJStGvXLo0aNarSDAAAAMClxK0inpaWVuL98ePHtWPHDreHpnTu3LnqyX7Hx8dHBQUFkiSbzSZJiomJ0XPPPXfR2wYAAAC8za0i/o9//KPUtC+++EIbN26slhDR0dFyOp265ZZbNGbMGPn7+5daZuzYsRo/frwkacKECVqzZo06duyokJCQaskAAAAAeJNbF2uW5Y9//KM+/fTTiw6wcuVKrV+/XqtXr5bT6Sz3IUGdO3dWQkKCEhIS1LJlS7333nsaOnSo5s+fr6efflpxcXEXnQUAAADwFreK+OHDh0v82bdvnxYsWKDg4OCLDnBuGzabTYMGDdI333xT6TqzZ89WVFSUdu7cqYyMDC1YsEBpaWnavn37RecBAAAAvMGtoSl/+ctfZBiGnE6nJMnX11dhYWGaNWvWRe3cbrfL4XCofv36cjqd2rRpk8LCwipcJykpSZLUpUsXbd68WYZhSJIsFovsdvtF5QEAAAC8xa0ivmfPnovayQsvvKB//etfOnHihB5//HEFBARo48aNyszM1KhRo+RwOFRUVKRWrVppypQp5W6nsLBQcXFxio+PlySFh4crISFBPXv2VPPmzcu87SEAAABwKTKc505zX4EyM/NUVHTFHj4AL2nSpL7OfnXBl+TAA2rdVqTjx3M9vp8mTeorP7qBx/cD99Wde8rj//ZNmtTXqQ5Xe3QfqLoGPxz1ytf9+SwWQ4GB9cqdX+4Z8TvvvNM17KMin3/++QUFAwAAAK5k5Rbxc0+xlKT//ve/Wrt2rR555BFdddVVOnbsmFasWKE+ffp4JSQAAABQ05RbxLt06eJ6PX36dL322mtq2rSpa9odd9yhJ598Uk888YRnEwIAAAA1kFuDFjMyMuTn51dimp+fn9LT0z0SCgAAAKjp3LprSkREhIYNG6Zhw4apWbNmSk1N1SuvvKKIiAhP5wMAAABqJLeK+LRp07R48WJNmTJFGRkZatKkie69916NHDnS0/kAAACAGsmtIl67dm1FR0crOjra03kAAACAK4JbRVyStm/frnXr1ikjI0NBQUHq1auXunXr5slsAAAAQI3l1sWaCQkJGj16tJo0aaK//OUvCgoKUnR0tN59911P5wMAAABqJLfOiC9btkzLly9Xu3btXNPuvfdePfXUU+rfv7/HwgEAAAA1lVtnxLOzs9WqVasS01q2bKlTp055JBQAAABQ07l1Rvzmm2/WrFmzFB0dLV9fX9ntds2fP1+dOnXydL4ap24Dm/xstc2Ogd+xF55W/qlCs2MAAIAriNu3L3zmmWfUuXNnNWjQQKdOnVKnTp00b948T+ercfxstWWbe5/ZMfA7hdGblC+KOAAA8J5Ki7jT6dTp06e1fPlynThxwnXXlGbNmnkjHwAAAFAjVTpG3DAM9ezZUxaLRc2aNVOHDh0o4QAAAMBFcutizbCwMKWkpHg6CwAAAHDFcGuMeJcuXTR06FD17dtXzZo1k2EYrnkPPvigx8IBAAAANZVbRfybb77R1Vdfra+//rrEdMMwKOKAm/wb+qh2rTpmx8B5Tp/9VTlZZ8yOAQC4QrlVxN9++21P5wBqvNq16mjs12Fmx8B55nTZLYkiDgAwR4VFvKCgQC+//LL27dun66+/Xv/7v/8rm83mrWwAAABAjVXhxZrTp0/XZ599ppYtW+rjjz9WbGyst3IBAAAANVqFRXzr1q167bXX9Oyzz+rVV1/VZ5995q1cAAAAQI1WYRG32+0KCgqSJAUHBysvL88roQAAAICarsIx4g6HQ9u3b5fT6ZQknT17tsR7SerWrZtnEwIAAAA1UIVFPDAwUBMnTnS9DwgIKPHeMAxt3rzZc+kAAACAGqrCIr5lyxZv5QAAAACuKG494h4AAABA9aKIAwAAACagiAMAAAAmoIgDAAAAJqCIAwAAACagiAMAAAAmoIgDAAAAJqCIAwAAACagiAMAAAAmoIgDAAAAJqCIAwAAACbwShGPjY1VRESE2rZtq3379pWYl5KSogEDBqhHjx4aMGCADhw4UOY2EhMTFRkZqcjISG3dutU1ffHixVq/fr0n4wMAAADVzitF/K677tLKlSt19dVXl5o3ZcoUDRo0SB9//LEGDRqkyZMnl7mNhQsXaunSpVq6dKni4uIkFZf4Xbt2qVevXh7NDwAAAFQ3rxTxzp07Kzg4uNT0zMxMJScnKzIyUpIUGRmp5ORknTx5stSyPj4+KigoUEFBgWw2myQpJiZGEydO9Gx4AAAAwANqmbnz1NRUNW3aVFarVZJktVoVFBSk1NRUNWrUqMSyY8eO1fjx4yVJEyZM0Jo1a9SxY0eFhIR4PTcAAABwsUwt4lXRuXNnJSQkSJKys7M1b948LV++XPPnz9ehQ4cUEhKi0aNHm5wSAAAAcI+pd00JDg5Wenq6HA6HJMnhcCgjI6PMYSznmz17tqKiorRz505lZGRowYIFSktL0/bt270RGwAAALhophbxwMBAhYWFacOGDZKkDRs2KCwsrNSwlPMlJSVJkrp06SK73S7DMCRJFotFdrvd86EBAACAauCVIv7CCy/ojjvuUFpamh5//HHdf//9rnlTp07VihUr1KNHD61YsULTpk0rdzuFhYWKi4vT2LFjJUnh4eHKyspSz549lZ2drfDwcI8fCwAAAFAdvDJG/LnnntNzzz1X5rxWrVq5xn5XxmazaeXKlSXeL1mypFoyAgAAAN7EkzUBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABNQxAEAAAATUMQBAAAAE1DEAQAAABOYXsQjIiJ0zz33qHfv3urdu7e2bt1a5nKJiYmKjIxUZGRkiWUWL16s9evXeysuAAAAUC1qmR1AkhYtWqQ2bdpUuMzChQu1dOlSSdLIkSMVHh6ulJQU7dq1S6NGjfJGTAAAAKDaXBJF3B0+Pj4qKCiQJNlsNklSTEyMnnvuOTNjAQAAABfkkiji0dHRcjqduuWWWzRmzBj5+/uXWmbs2LEaP368JGnChAlas2aNOnbsqJCQEG/HBQAAAC6a6UV85cqVCg4OVmFhoWbOnKnp06dr7ty5pZbr3LmzEhISJEnZ2dmaN2+eli9frvnz5+vQoUMKCQnR6NGjvR0fAAAAuCCmX6wZHBwsqXi4yaBBg/TNN99Uus7s2bMVFRWlnTt3KiMjQwsWLFBaWpq2b9/u6bgAAABAtTC1iNvtduXm5kqSnE6nNm3apLCwsArXSUpKkiR16dJFdrtdhmFIkiwWi+x2u2cDAwAAANXE1KEpmZmZGjVqlBwOh4qKitSqVStNmTKl3OULCwsVFxen+Ph4SVJ4eLgSEhLUs2dPNW/eXOHh4d6KDgAAAFwUU4t4ixYttHbtWreXt9lsWrlyZYn3S5Ys8UQ0AAAAwKNMHyMOAAAAXIko4gAAAIAJKOIAAACACSjiAAAAgAko4gAAAIAJKOIAAACACSjiAAAAgAlMvY84Lk+v9IjS4zf2kCQVOYvUbtkQHTiVbnIqVKc5XXaXO6/IWaTCIrtyCjN0JH+Xvjv5oXZnf+69cPA+n6tkNHpARoMIGb7tpVqNJUtdyXFKKjwiZ36SnNkfypm9UXKeMTstLkDduafKnO48e1r6NVfOX3PkzDosx9HvVXQoSY7kj6Wzv3o5JTypwQ9Hq7yOY8+Pyut/twfSXDko4qgSP5/aeqDNb08wtRgWPXp9d03/z8oK1kJNYjEsqmOtpzq+9RTk21I3N+6ln3O+1ls/Rcl+NtvseKhOVn9Zmr8gI+hJGZY6pedbmkg+TWTU7SQFDZXzTIaKjs6UM+NlyXnW+3lR7YxataV6tWXUayw1bilr6J2SJKc9W2eT3lHhv/4h/Zpjckrg8kURR5U80OaP8q/tV2LawxTxGu2NfSNLvDcMi+rWCtA19W5Sp8D75WOpo1b+XTQ4NF4v7X7YpJSodrVbydp2vQzfMNckZ95Xcp76VM7TB4rPhtcKlFG7lYyAHjL8bpThEyTrtQvlsP8gZ26iedlxUX5dPui3N4Yho46/5Bsgy9U3ytrydlkahcjwC5DPHcNl7dBLp1cMUdGB7eYFRrXLj3rCreWcebkeTlLzUcRRJY/e8BdJUqHjjN7bu1WD2kfo2gZN9edrbtJnh743OR084cfszWVO/+p4gr5Ie1Mj27+jOtZ6uq7+LWrbIFx7T231ckJUu1qNZA37VEbtEEmSM/97OQ4Mk/JKly2nJB1+Vqp7qywtZsjSgF9TX+4cP26scL613V9k6z1LliatZQlorjpD3lVB/N1ypu/xUkJ42tnPPjY7whWDizXhtusaNFN48xskSf9K2akFSWtc8x77v4KOK0t6wX59ffw91/uW9W81MQ2qi6XlG7+V8Nz/yLH7jjJLeAn5O1S05x45Do6W01nohZQwi2PPJypY8Cc5fvmPJMnwbaA6j74pGYbJyYDLD0Ucbnv0hu6yGMUfmRXJm/Vdxs/68cQBSVKf0D/I3+ZXwdqoqTIKfnG99rXWNzEJqkW9rrI0jJQkOR05cvw0SHK4/+tnZ9pCKW+bp9LhUnE6V6ffHiynvfi6EEvTdrLe9FdzMwGXIYo43Pbw9d0lSVm/5mrDz19JklYmb5Ek+fnUUb92d5iWDeapW6uh63VW4TETk6A6WJpFuV47j78hFR4yLwwuac7cdJ356g3Xe58uj5gXBrhMUcThlohrOirEP0iStHrvv1XoKL4jwjvJn8lR5JDE8JQrUS3Dpk6BPV3v9+dwJvRyZ/jf5XpddOJtE5PgcuD4JsH12nJtF8nCpWdAVVDE4ZbzS/aK5N8u3juWl6nPD/8gSep6VZjaNWrh9WzwLkOG/GoFKCzgT/p72Ftq5tdakrTj+Ps6kr/L5HS4KHXayfBpLElyOuxS/ncmB8KlrigtWc7TeZIkw1ZXlqbtTE4EXF740RWV8rf5qXdoN0nSL9mp+s/R5BLzV/64WXeFdJJUPI584hfLvZ4RnlPRw30kKc2+XztOrNYXaW96KRE8xbBd/dubwoOSHKZlwWXCWSTnqWMygtoUv6/X2Nw8qBbuPtzH/txonVn/rofT1GycEUelBoT9SX4+xQ/zWLX7s1Lz39//pfIKCyRJg9pHuC7oxJXhrPOMCh0FMsQdEy57tQJ/e83DmeCmcxdsSpLh18jEJMDlhzPiqNT5w1JW/ril1Hz7mdNat3+bHro+QlfVC9Q913XWpl++9mZEeNDvH+gjSbWtfmpSp6U6Bt6r5nXbq/l109Sh0T1avn+4zhTx2GvgimI5/+SL07QYqD7uPtDHsfu/Hk5S81HEUaGwwBbqEtxWkrT92G79lF32XTFWJG/WQ9dHSCou7hTxmqO8B/pI0qfHXlL/62bq5sY9Fdqgm3pfM0nvHXjei+lQrc5m/va6VoB5OXBZMeo0cL122rNMTILqwgN9vIcxBKjQo+efDU8ufTb8nC0Hv9OR3BOSpPtbdVGgr7/Hs8F8DucZvX9gmgrOFt9nunOTPmpga2ZyKlwo55nzftC2hUiympYFlwnDIiPgqt/e550wLwtwGeKMOMplNSwaFBbher+4+wgt7j6i0vVsVh/9LezPiv9mnSfj4RJxuihfh/K+U9uAcFmNWmrtf5t2nuDf/rJUsFvOMydk+DSWYfWT6naU8neanQqXMEvw9TJsdSVJztN5KuIx90CVcEYc5brnus4KrndhF948dkP3ak6DS1n+eRf2+fsEmZgEF8uZ89tQJEtjHtCCilk79XO9LjrwtVR01sQ0wOWHM+Io1/kXab616xMdzMmodJ2B7f6k0EZX66agVuoY1FLfZfxS6Tq4/NU9bzxxYVGBiUlwsYrSFskSOECSZDQZLKXO5+maKJNRv6l8bnvM9f7M1zwACqgqijjKFOjrr/tadZEk5Zy2a+SnL+rXs4WVrpf1a57mR/yvpOLx5d9tecWjOWG+2hY/XVOvo+t9RgE/fF3W8rapKGujLA3vl2H1l7X1O3LsuUcqynNrdaPpU3Lm75DyeMpqjVa7nmo/8oYMv+IfwovS98jxwxqTQwGXH4amoEyDwv4sm9VHkrRm/5dulXBJ+n97PtcZR/GvJge2+5N8eNxxjWYxaumv106Rb636kqRThen6JXeHyalwsYp+GSzn6cOSJKP+H2Rtv1Wqd1vFK9XtLEu7j2S9doEMw+aFlDCLtV13+UZ9LmvLP0iSnAWn9Otbj0lObl0IVBUtCWWq7N7h5TluP6VPDn6j+1p2UWO/BurZ+ja9v+9LT0SEl1wfcFepaTarr4LqtNRNgfepSZ0QSVKR06G1B1+Qw3nG2xFR3c5myrGnu6xt1svwbSuj7k2qdf02OfO2y3nqUzlPH5AcOVKtRjJqt5IR0EOGXwezU6OaWK+//7c3hiGjdj3Jr6EsV90oa8vbZQm81jW7KPuITq8YIicXadYotf7cw+1lz27dLJ3l2oALRRFHKR2DWqlDUEtJ0pHcE0o8/EOV1n/nxy26r2XxsJZHb/gLRfwyN7hNfKXL5J/N1vsHpmlX1qdeSASv+HW/HD92laVFjIwmT8iw1JZRr6uMel3LXcVZmKqioy/ImftvLwZFdavz+DuVLuO0Z+vszlUq/Pgf0q+nvJAK3lR34etuL3vq9jApN8eDaWo2ijhKOf9s+P/b/bmcVXxS2vqft+vU6Xw1qF1Xd197i4LrNlJq/snqjgkTFToKZHecUpp9n/ae+rd2nlivAgf/Gdc4jlMqOjBCOhYjo9GDMvwjZPi2l2o1lix+kuOUVHhIzrwkObM3ypm9SZLD7NSoRs6zhdLpXDl/zZUz65CKjnwvx6EkOZI/ks7yFF3gYlHEUcroLUs0esuSC17/17OFarK4X+UL4pI19uswsyPgUlJ4VM60hXKmLTQ7CTwkP7pB5QuhRjvV4WqzI1yRuFgTAAAAMAFFHAAAADABRRwAAAAwAUUcAAAAMAFFHAAAADABRRwAAAAwAUUcAAAAMIHpRTwlJUUDBgxQjx49NGDAAB04cKDM5RITExUZGanIyEht3brVNX3x4sVav369l9ICAAAA1cP0Ij5lyhQNGjRIH3/8sQYNGqTJkyeXudzChQu1dOlSLV26VHFxcZKKS/yuXbvUq1cvb0YGAAAALpqpRTwzM1PJycmKjIyUJEVGRio5OVknT5Z+HLqPj48KCgpUUFAgm80mSYqJidHEiRO9mhkAAACoDqY+4j41NVVNmzaV1WqVJFmtVgUFBSk1NVWNGjUqsezYsWM1fvx4SdKECRO0Zs0adezYUSEhIRe8f4vFuPDwFyHEP8iU/aJi3vg8NLRd5fF9oGq89n3AduHfq+AZ3vq3Nxpe45X9wH3e+Lc3rmru8X2g6rzd/Srbn+F0Op1eylLKrl27NG7cOG3cuNE17b777tOcOXN0/fXXl7tedna2RowYoeXLlys+Pl6HDh1SSEiIRo8e7Y3YAAAAwEUzdWhKcHCw0tPT5XA4JEkOh0MZGRkKDg6ucL3Zs2crKipKO3fuVEZGhhYsWKC0tDRt377dG7EBAACAi2ZqEQ8MDFRYWJg2bNggSdqwYYPCwsJKDUs5X1JSkiSpS5custvtMoziU/4Wi0V2u93zoQEAAIBqYOrQFEn6+eefNX78eOXk5Mjf31+xsbFq2bJlmcsWFhbq8ccfV3x8vBo2bKjCwkI99dRTOnr0qJo3b65FixbJx8fHy0cAAAAAVJ3pRRwAAAC4Epl+H3EAAADgSkQRBwAAAExAEQcAAABMQBEHAAAATEARBwAAAExAEQcAAABMQBEHAAAATEARBwAAAEzw/wGFMqN1UmcavwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0v8rLy9P777+vzz//vFLb6927t1avXq0PPvhA999/f5nLuWNfqqKsq6bMmjVLhw8fdi736KOPFjvp9PDhw0pISNC///1vXXvttapXr16p27/vvvs0bNgw5wmsx48f1xtvvKG5c+fq1Vdf1ZYtW1x+79WvX1/Lli1TSEiIJOns2bN65pln9O2338rLy0vPPPOMc9nTp09r6dKlWrJkiUwmk3OI1JWqsu+XiRMnasqUKcUuOZmSkqLY2Fg9+OCDpb5GVb4/fs/Hx0cJCQnatGlTiaE9FX3XVPdnEbiSMEYcuELccccdWrFiRalHkdu2bauRI0dq+PDhzqPPHTt21JQpU7RkyZJSt9etWzfnDXhMJpOCgoJK3O2uskJCQrRs2TJNnz5dtWrVksVikcPhUGRkpAYNGlRs2b///e9q2bKl4uLidPz4ceeyQUFBJW6QEx4ergULFmj+/PlKT0933l3w8ccfr/Jt2lu1aqXZs2dr8eLFmjFjhry8vGQ2m+Xj46PbbrtNnTt3rtT22rdvr/T0dLVs2bLMcuqufXEnLy8vLV68WK+++qrWrVun9PR0NWjQQN26ddPQoUPVs2fPMo86Dx06VHfccYfefvttJScnO4tegwYN1KRJE3Xp0kWRkZFlnoR5sSZNmui1117TI488oqysLNlsNg0cOFArVqzQgAEDVKdOHb3++us6fPiw6tSpo9tuu02jRo264m8nX9n3S7NmzZSQkKB58+Zpzpw5slqtat68uaZNm6YPPvig1MuLVuX7ozQ+Pj565ZVXNHjw4GInvVb0XVPdn0XgSmJyVObUdgAAAADVgqEpAAAAgAEo4gAAAIABKOIAAACAASjiAAAAgAEo4gAAAIABPHL5wsGDB+vo0aMym83y9fXVP/7xD4WHh0u6cGMPq9WqWrVqSZJiYmIUERFRYhtJSUmaPXu2JGn06NHOZRYtWqTQ0FD16NHDE7sCAAAAVAuPFPGZM2eqTp06kqRPPvlE48aN05o1a5zzFy5cqFatWpW7jQULFmjx4sWSLlybNiIiQgcOHNDu3bs1bNgw94UHAAAA3MAjRfzXEi5JZ86ccd5prTK8vb2Vn58v6cLtpCUpNja23FssAwAAAJcrj93QZ/z48friiy/kcDi0dOlStWzZUtKFoSn+/v5yOBy66aabNHLkSAUEBJRYf+fOnZo5c6YkaezYsTp06JCOHz+uIUOGeCI+AAAAUK08fmfNtWvXauPGjc7b5qampiokJEQFBQWaPn268vLyNGfOnHK3kZ2drSFDhmj58uWKj4/X4cOHFRoaqhEjRnhiFwAAAIBLZsgt7tu3b6+kpCTVq1ev2PS9e/dq0KBB2rJlS7nrjxs3Tr169ZLdbte6des0Y8YMjR49Wr1791aXLl1czpGVlaeiIo/vPgAAAK4CZrNJ9er5lTnf7WPE8/LylJOTo5CQEEnSli1bVLduXQUGBspms8lut6tOnTpyOBzatGmT82oqZdm5c6ckqXPnztq8ebNzvLnZbJbNZqtUtqIiB0UcAAAAhnB7Ec/Pz1d0dLTy8/NlNptVt25dJSQkyGQyKTMzU8OGDZPdbldRUZHCwsI0adKkMrdVUFCguLg4xcfHS5IiIiKUmJio7t27q2nTpqVe9hAAAAC4HBkyNOVykZl5hiPiAAAAcAuz2aSgIP+y53swCwAAAID/jyIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGIAiDgAAABiAIg4AAAAYgCIOAAAAGMAjRXzw4MHq0aOHevXqpX79+mnPnj3OeQcOHFDfvn3VrVs39e3bVwcPHix1G0lJSYqKilJUVJS2bdvmnL5o0SKtX7/e3bsAAAAAVCuTw+FwuPtFcnNzVadOHUnSJ598opdeeklr1qyRJD3++OP629/+pp49e2rdunV677339MYbb5TYxgMPPKD4+HhJ0tChQ7V69WodOHBAM2bM0KuvvlqlXJmZZ1RU5PbdBwAAwFXIbDYpKMi/7PmeCPFrCZekM2fOyGQySZIyMzOVkpKiqKgoSVJUVJRSUlJ06tSpEtvw9vZWfn6+8vPzZbVaJUmxsbEaN26cB/YAAAAAqF5ennqh8ePH64svvpDD4dDSpUslSampqWrUqJEsFoskyWKxKDg4WKmpqapfv36x9UeNGqUxY8ZIksaOHas1a9aoQ4cOCg0N9dQuAAAAANXGY0V8+vTpkqS1a9dq1qxZWrJkSaXW79SpkxITEyVJ2dnZmjt3rpYvX6558+bp8OHDCg0N1YgRI6o9NwAAAOAOHr9qSq9evfTVV18pKytLISEhSk9Pl91ulyTZ7XZlZGQoJCSk3G3MmjVL0dHRSk5OVkZGhubPn6+0tDRt377dE7sAAAAAXDK3F/G8vDylpqY6n2/ZskV169ZVYGCggoKCFB4erg0bNkiSNmzYoPDw8BLDUi62c+dOSVLnzp1ls9mc483NZrNsNpsb9wQAAACoPm6/asrJkyc1ePBg5efny2w2q27duho9erTatWsnSdq/f7/GjBmjnJwcBQQEaObMmWrevHmp2yooKNBTTz2l+Ph41atXTwUFBRo+fLiOHTumpk2bauHChfL29nY5G1dNAQAAgLtUdNUUj1y+8HJFEQcAAIC7XBaXLwQAAABQHEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMICX0QEAAKip6gd4yVLLx+gYuIj9XL5O5RQaHQOQRBEHAMBtLLV8lBdT1+gYuIjfnNOSco2OAUhiaAoAAABgCIo4AAAAYACKOAAAAGAAt48Rz8rK0gsvvKDDhw/LarUqNDRUU6dOVf369SVJkZGRslqtqlWrliQpJiZGERERJbaTlJSk2bNnS5JGjx7tXGbRokUKDQ1Vjx493L0rAAAAQLVxexE3mUwaMGCAbrnlFknSzJkzNWfOHMXGxjqXWbhwoVq1alXudhYsWKDFixdLkoYOHaqIiAgdOHBAu3fv1rBhw9y3AwAAAIAbuH1oSmBgoLOES1KHDh10/PjxSm/H29tb+fn5ys/Pl9VqlSTFxsZq3Lhx1ZYVAAAA8BSPXr6wqKhIb7/9tiIjI4tNj4mJkcPh0E033aSRI0cqICCgxLqjRo3SmDFjJEljx47VmjVr1KFDB4WGhnokOwAAAFCdTA6Hw+GpF5syZYrS09MVHx8vs/nCwfjU1FSFhISooKBA06dPV15enubMmVPudrKzszVkyBAtX75c8fHxOnz4sEJDQzVixIhK5cnMPKOiIo/tPgDgKtOwYR2uI36Z8ZtzWidOcB1xeIbZbFJQkH/Z8z0VZObMmTp06JDmz5/vLOGSFBISIkmyWq3q16+fvvnmmwq3NWvWLEVHRys5OVkZGRmaP3++0tLStH37drflBwAAAKqTR4p4XFycdu/erZdeesk5vluSbDabcnMv/FTqcDi0adMmhYeHl7utnTt3SpI6d+4sm80mk8kkSTKbzbLZbG7aAwAAAKB6uX2M+E8//aSEhAT98Y9/1MMPPyxJatq0qV566SVlZmZq2LBhstvtKioqUlhYmCZNmlTmtgoKChQXF6f4+HhJUkREhBITE9W9e3c1bdq01MseAgAAAJcjj44Rv9wwRhwA4E6MEb/8MEYcnnTZjBEHAAAA8BuKOAAAAGAAijgAAABgAIo4AAAAYACKOAAAAGAAijgAAABgAIo4AAAAYACKOAAAAGAAijgAAABgAIo4AAAAYACKOAAAAGAAL6MDXG386lrla61ldAz8jq3gnPJOFxgdAwAAXEUqVcSLiop08uRJBQcHuytPjedrrSXrnPuMjoHfKYjZpDxRxAEAgOe4NDQlJydHzz//vNq3b6+7775bkrR582bFxcW5NRwAAABQU7lUxCdNmiR/f39t2bJF3t7ekqSOHTvqgw8+cGs4AAAAoKZyaWjKl19+qW3btsnb21smk0mSVL9+fWVmZro1HAAAAFBTuXREvE6dOsrKyio27fjx42rYsKFbQgEAAAA1nUtFvE+fPho+fLi2b9+uoqIiffvttxo9erQefvhhd+cDAAAAaiSXhqYMHDhQVqtVU6dOVWFhocaNG6e+ffvqiSeecHc+AAAAoEZyqYibTCY9+eSTevLJJ90cBwAAALg6lFnEv/zyS5c2cOutt1ZbGAAAAOBqUWYRHz9+fLHnGRkZkqTAwEBlZ2dLkho1aqTNmze7MR4AAABQM5VZxLds2eJ8nJCQoOzsbEVHR8vHx0f5+flauHChAgMDPRISAAAAqGlcumrKihUr9Pzzz8vHx0eS5OPjo5EjR2r58uVuDQcAAADUVC4VcV9fX33//ffFpv3vf/9zFnMAAAAAlePSVVOGDx+uAQMGKDIyUo0bN1ZaWpq2bt2qiRMnujsfAAAAUCO5VMR79eql6667Th999JEyMjJ07bXXatCgQWrRooW78wEAAAA1kktFXJJatGhB8QYAAACqictFfPPmzdqxY4eysrLkcDic02fNmuWWYAAAAEBN5tLJmvHx8Zo0aZKKior04YcfKjAwUJ9//rkCAgLcnQ8AAACokVwq4u+9956WLVumcePGydvbW+PGjVNCQoKOHj3q7nwAAABAjeRSEc/JyVGrVq0kSd7e3jp//rzat2+vHTt2uDUcAAAAUFO5NEb8mmuu0U8//aSWLVuqZcuWevvttxUQEKC6deu6Ox8AAABQI7lUxJ977jllZ2dLkmJiYvT888/LZrNp0qRJbg0HAAAA1FQVFvGioiJZrVbdcMMNkqT27dvr448/dnswAAAAoCarcIy42WzW4MGDZbVaPZEHAAAAuCq4dLLmzTffrF27drk7CwAAAHDVcGmMeJMmTTRw4EDdddddaty4sUwmk3NedHS028IBAAAANZVLR8TPnTunrl27ymQyKT09XWlpac4/FcnKytLAgQPVrVs3de/eXUOHDtWpU6ec8w8cOKC+ffuqW7du6tu3rw4ePFjqdpKSkhQVFaWoqCht27bNOX3RokVav369K7sBAAAAXDZcOiL+z3/+s8ovYDKZNGDAAN1yyy2SpJkzZ2rOnDmKjY2VJE2aNEn9+vVTz549tW7dOk2cOFFvvPFGie0sWLBAixcvliQNHTpUEREROnDggHbv3q1hw4ZVOR8AAABghAqPiJ8/f975eOfOndqxY4fzT2FhYYUvEBgY6CzhktShQwcdP35ckpSZmamUlBRFRUVJkqKiopSSklLsiPmvvL29lZ+fr/z8fOeJo7GxsRo3blyFGQAAAIDLTblHxN966y19++23mj17tiSpf//+CgwMlCSdPXtWMTEx6tOnj8svVlRUpLfffluRkZGSpNTUVDVq1EgWi0WSZLFYFBwcrNTUVNWvX7/YuqNGjdKYMWMkSWPHjtWaNWvUoUMHhYaGuvz6AAAAwOWi3CK+bt06TZkyxfncarUqKSlJkrRnzx5Nnjy5UkX8xRdflK+vrx599NFKB+3UqZMSExMlSdnZ2Zo7d66WL1+uefPm6fDhwwoNDdWIESMqvV0AAADACOUOTTl69KjatGnjfB4WFuZ83KZNGx05csTlF5o5c6YOHTqk+fPny2y+8LIhISFKT0+X3W6XJNntdmVkZCgkJDqUyogAABzzSURBVKTcbc2aNUvR0dFKTk5WRkaG5s+fr7S0NG3fvt3lPAAAAICRyi3iNptNNpvN+fxf//qX8/Gv47VdERcXp927d+ull14qdmOgoKAghYeHa8OGDZKkDRs2KDw8vMSwlIvt3LlTktS5c2fZbDbnpRTNZnOxrAAAAMDlrNwi3rJlS33xxRelztu2bZtatGhR4Qv89NNPSkhIUEZGhh5++GH17NlTQ4YMcc6fPHmyVq5cqW7dumnlypXFhsL8XkFBgeLi4jRq1ChJUkREhLKystS9e3dlZ2crIiKiwjwAAADA5aDcMeJPPPGEpkyZIpPJpMjISJnNZhUVFWnz5s168cUXnSdPlqdly5bau3dvmfPDwsKcY78rYrVatWrVqmLPExISXFoXAAAAuJyUW8Tvv/9+paena9SoUTp//rwCAwOVnZ0tb29vDRkyxHnZQQAAAACVU+ENfZ5++mk99NBD+vbbb5WVlaXAwEB17NhRderU8UQ+AAAAoEZy6c6a/v7+jL8GgCqqX88ii5ev0TFwEXuhTaey7EbHAHCVc6mIAwCqzuLlq8KvKryRMTzI65YiSblGxwBwleN/BgAAAMAAFHEAAADAAJUampKZmVnipjnNmjWr1kAAAADA1cClIv7ZZ59p/PjxOnnypBwOh3O6yWTSnj173BYOAAAAqKlcKuJTp07V4MGD1bt3b9WuXdvdmQAAAIAaz6UinpOTo4cfflgmk8ndeQAAAICrgksna/7tb3/Te++95+4sAAAAwFXDpSPi3333nd58800tWbJEDRo0KDZv1apVbgkGAAAA1GQuFfE+ffqoT58+7s4CAAAAXDVcKuK9e/d2dw4AAADgqlJmEV+7dq169eolSXr33XfL3MCDDz5Y/akAAACAGq7MIr5x40ZnEV+3bl2py5hMJoo4AAAAUAVlFvElS5Y4H7/55pseCQMAAABcLVy6fCEAAACA6kURBwAAAAxAEQcAAAAMQBEHAAAADODSdcR//vlnBQYGqkGDBsrLy9Nrr70ms9ms/v37y8fHx90ZAQAAgBrHpSPizz//vHJyciRJM2fO1I4dO7Rr1y5NnDjRreEAAACAmsqlI+LHjh1T8+bN5XA49Mknn2jDhg2qXbu27rrrLnfnAwAAAGokl4q41WrVmTNntH//fjVu3Fj169dXYWGhzp075+58AAAAQI3kUhGPiorSE088oby8PD366KOSpJSUFDVt2tSt4QAAAICayqUiPm7cOH3++efy8vJSly5dJF24vf3YsWPdGg4AAACoqVw6WXPatGn685//7CzhknT99ddry5YtbgsGAAAA1GQuHRFfvXq1JkyYUGL6+vXrNX78+GoPBQAAcKWq7+8lC5d3vuzY8/N16kyh0TGKKbeIv/vuu5Iku93ufPyrI0eOKDAw0H3JAAAArkAWHx+dbv8Ho2Pgd+p+f0w6k2t0jGLKLeLr1q2TJJ0/f975WLowPrxBgwaaOXOme9MBAAAANVS5RfzNN9+UJMXFxWnEiBEeCQQAAABcDVw6WfPmm2/WgQMHik375Zdf9MUXX7glFAAAAFDTuVTEp06dKj8/v2LT/Pz8NHXqVLeEAgAAAGo6l4p4ZmamgoODi00LDg7WiRMn3BIKAAAAqOlcKuLNmjXTl19+WWzaV199xZ01AQAAgCpy6TriQ4cO1bBhw/Tggw+qWbNmOnLkiFavXq3Y2Fh35wMAAABqJJeOiHft2lXLli2TzWZTUlKSbDabli5dqq5du7o7HwAAAFAjuXREXJLat2+v9u3bV+lFZs6cqY8++kjHjh3T+++/r1atWjnnRUZGymq1qlatWpKkmJgYRURElNhGUlKSZs+eLUkaPXq0c5lFixYpNDRUPXr0qFI2AAAAwAguFfEFCxaUOS86OrrC9e+66y49/vjjeuSRR0qdv3DhwmLlvKwMixcvlnRhqExERIQOHDig3bt3a9iwYRVmAIwWUM9btbxqGx0DFzlXeFY5WeeNjgEAuEq5VMTT0tKKPT9x4oR27Njh8tCUTp06VT7Z73h7eys/P1+SZLVaJUmxsbGaMGHCJW8b8IRaXrU16utwo2PgIrM775FEEQcAGMOlIv7Pf/6zxLTPPvtMGzdurJYQMTExcjgcuummmzRy5EgFBASUWGbUqFEaM2aMJGns2LFas2aNOnTooNDQ0GrJAAAAAHiSSydrlubPf/6zPvnkk0sOsGrVKq1fv17vvfeeHA5HmTcJ6tSpkxITE5WYmKjmzZvr3Xff1cCBAzVv3jw999xziouLu+QsAAAAgKe4VMSPHDlS7M++ffs0f/58hYSEXHKAX7dhtVrVr18/ffPNNxWuM2vWLEVHRys5OVkZGRmaP3++0tLStH379kvOAwAAAHiCS0NT/vrXv8pkMsnhcEiSfHx8FB4erhkzZlzSi9tsNtntdtWpU0cOh0ObNm1SeHj5Y2h37twpSercubM2b94sk8kkSTKbzbLZbJeUBwAAAPAUl4r4jz/+eEkvMm3aNP3nP//RyZMn9dRTTykwMFAbN25UZmamhg0bJrvdrqKiIoWFhWnSpEllbqegoEBxcXGKj4+XJEVERCgxMVHdu3dX06ZNS73sIQAAAHA5cvk64pdiwoQJpV7dpFmzZlq7dq3L27FarVq1alWx5wkJCdWSEQAAAPCkMov4HXfc4Rz2UZ5PP/20OvMAAAAAV4Uyi/ivd7GUpP/9739au3atHnvsMTVp0kTHjx/XypUr1atXL4+EBAAAAGqaMot4586dnY+nTp2q1157TY0aNXJOu/322zVgwAA9/fTT7k0IAAAA1EAuXb4wIyNDvr6+xab5+voqPT3dLaEAAACAms6lkzUjIyM1aNAgDRo0SI0bN1ZqaqpeffVVRUZGujsfAAAAUCO5VMSnTJmiRYsWadKkScrIyFDDhg117733aujQoe7OBwAAANRILhXxWrVqKSYmRjExMe7OAwAAAFwVXL6O+Pbt27Vu3TplZGQoODhYPXr00K233urObAAAAECN5dLJmomJiRoxYoQaNmyov/71rwoODlZMTIzeeecdd+cDAAAAaiSXjogvXbpUy5cvV5s2bZzT7r33Xg0fPlwPPfSQ28IBAAAANZVLR8Szs7MVFhZWbFrz5s11+vRpt4QCAAAAajqXiviNN96oGTNmKD8/X5Jks9k0a9YsdezY0a3hAAAAgJrK5csXPv/88+rUqZPq1q2r06dPq2PHjpo7d6678wEAAAA1UoVF3OFw6Ny5c1q+fLlOnjzpvGpK48aNPZEPAAAAqJEqHJpiMpnUvXt3mc1mNW7cWO3bt6eEAwAAAJfIpTHi4eHhOnDggLuzAAAAAFcNl8aId+7cWQMHDlTv3r3VuHFjmUwm57wHH3zQbeEAAACAmsqlIv7NN9/oD3/4g77++uti000mE0UcAAAAqAKXivibb77p7hwAAADAVaXcIp6fn69XXnlF+/btU7t27fTMM8/IarV6KhsAAABQY5V7subUqVO1detWNW/eXB999JFmzpzpqVwAAABAjVZuEd+2bZtee+01vfDCC1qyZIm2bt3qqVwAAABAjVZuEbfZbAoODpYkhYSE6MyZMx4JBQAAANR05Y4Rt9vt2r59uxwOhySpsLCw2HNJuvXWW92bEAAAAKiByi3iQUFBGjdunPN5YGBgsecmk0mbN292XzoAAACghiq3iG/ZssVTOQAAAICriku3uAcAAABQvSjiAAAAgAEo4gAAAIABKOIAAACAASjiAAAAgAEo4gAAAIABKOIAAACAASjiAAAAgAEo4gAAAIABKOIAAACAASjiAAAAgAE8UsRnzpypyMhItW7dWvv27Ss278CBA+rbt6+6deumvn376uDBg6VuIykpSVFRUYqKitK2bduc0xctWqT169e7Mz4AAABQ7TxSxO+66y6tWrVKf/jDH0rMmzRpkvr166ePPvpI/fr108SJE0vdxoIFC7R48WItXrxYcXFxki6U+N27d6tHjx5uzQ8AAABUN48U8U6dOikkJKTE9MzMTKWkpCgqKkqSFBUVpZSUFJ06darEst7e3srPz1d+fr6sVqskKTY2VuPGjXNveAAAAMANvIx88dTUVDVq1EgWi0WSZLFYFBwcrNTUVNWvX7/YsqNGjdKYMWMkSWPHjtWaNWvUoUMHhYaGejw3AAAAcKkMLeKV0alTJyUmJkqSsrOzNXfuXC1fvlzz5s3T4cOHFRoaqhEjRhicEgAAAHCNoVdNCQkJUXp6uux2uyTJbrcrIyOj1GEsF5s1a5aio6OVnJysjIwMzZ8/X2lpadq+fbsnYgMAAACXzNAiHhQUpPDwcG3YsEGStGHDBoWHh5cYlnKxnTt3SpI6d+4sm80mk8kkSTKbzbLZbO4PDQAAAFQDjxTxadOm6fbbb1daWpqeeuop3X///c55kydP1sqVK9WtWzetXLlSU6ZMKXM7BQUFiouL06hRoyRJERERysrKUvfu3ZWdna2IiAi37wsAAABQHTwyRnzChAmaMGFCqfPCwsKcY78rYrVatWrVqmLPExISqiUjAAAA4EncWRMAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMABFHAAAADAARRwAAAAwAEUcAAAAMIDhRTwyMlL33HOPevbsqZ49e2rbtm2lLpeUlKSoqChFRUUVW2bRokVav369p+ICAAAA1cLL6ACStHDhQrVq1arcZRYsWKDFixdLkoYOHaqIiAgdOHBAu3fv1rBhwzwREwAAAKg2l0URd4W3t7fy8/MlSVarVZIUGxurCRMmGBkLAAAAqJLLoojHxMTI4XDopptu0siRIxUQEFBimVGjRmnMmDGSpLFjx2rNmjXq0KGDQkNDPR0XAAAAuGSGF/FVq1YpJCREBQUFmj59uqZOnao5c+aUWK5Tp05KTEyUJGVnZ2vu3Llavny55s2bp8OHDys0NFQjRozwdHwAAACgSgw/WTMkJETSheEm/fr10zfffFPhOrNmzVJ0dLSSk5OVkZGh+fPnKy0tTdu3b3d3XAAAAKBaGFrEbTabcnNzJUkOh0ObNm1SeHh4uevs3LlTktS5c2fZbDaZTCZJktlsls1mc29gAAAAoJoYOjQlMzNTw4YNk91uV1FRkcLCwjRp0qQyly8oKFBcXJzi4+MlSREREUpMTFT37t3VtGlTRUREeCo6AAAAcEkMLeLNmjXT2rVrXV7earVq1apVxZ4nJCS4IxoAAADgVoaPEQcAAACuRhRxAAAAwAAUcQAAAMAAFHEAAADAABRxAAAAwAAUcQAAAMAAFHEAAADAAIZeRxxXple7Reup67tJkoocRWqztL8Onk43OBWq0+zOe8qcV+QoUkGRTTkFGTqat1u7Tn2gPdmfei4cPM+7iUz1/yZT3UiZfNpKXg0ks59kPy0VHJUjb6cc2R/Ikb1Rcpw3Oi2qwG/O6VKnOwrPSWdz5TibI0fWEdmPfaeiwztlT/lIKjzr4ZRwp7rfH6v0OvYff9CZh+52Q5qrB0UcleLrXUt/a/XbHUzNJrMeb9dVU/+7qpy1UJOYTWbVtvirto+/gn2a68YGPbQ/52u98XO0bIXZRsdDdbIEyNx0mkzBA2Qy1y4539xQ8m4ok19HKXigHOczVHRsuhwZr0iOQs/nRbUzedWS/GvJ5N9AatBclpZ3SJIctmwV7nxLBf/5p3Q2x+CUwJWLIo5K+VurPyuglm+xaY9SxGu0FfuGFntuMpnl5xWoa/xvUMeg++Vtrq2wgM56smW8Xt7zqEEpUe1qhcnSer1MPuHOSY4zX8lx+hM5zh28cDTcK0imWmEyBXaTyfd6mbyDZfnjAtlt38uRm2RcdlySs8v7/fbEZJKpdoDkEyjzH66XpfltMtcPlck3UN63D5alfQ+dW9lfRQe3GxcY1S4v+mmXlnOcyXVzkpqPIo5Kefy6v0qSCuzn9e7eberXNlJ/rNtId15zg7Ye/s7gdHCHH7I3lzr9qxOJ+iztdQ1t+5ZqW/x1bZ2b1LpuhPae3ubhhKh2XvVlCf9EplqhkiRH3neyHxwknSlZthySdOQFye9mmZu9KHNdfk19pbP/sLHc+ZY2f5W15wyZG7aQObCpavd/R/nxd8uR/qOHEsLdCrd+ZHSEqwYna8Jl19ZtrIim10mS/nMgWfN3rnHOe+L/F3RcXdLzf9LXJ951Pm9e52YD06C6mJuv+K2E5/5X9j23l1rCi8nboaIf75H90Ag5HAUeSAmj2H/8WPnz/yL7L/+VJJl86qr2469LJpPByYArD0UcLnv8uq4ymy68ZVambNaujP364eRBSVKvln9SgNW3nLVRU2Xk/+J87GOpY2ASVAv/LjLXi5IkOew5sv/cT7K7/utnR9oC6cyX7kqHy8W5XJ1780k5bBfOCzE3aiPLDQ8Ymwm4AlHE4bJH23WVJGWdzdWG/V9JklalbJEk+XrXVp82txuWDcbx86rnfJxVcNzAJKgO5sbRzseOEyukgsPGhcFlzZGbrvNfrXA+9+78mHFhgCsURRwuibymg0IDgiVJ7+39XAX2C1dEeCtlq+xFdkkMT7kaeZms6hjU3fn8pxyOhF7pTAF3OR8XnXzTwCS4Eti/SXQ+Nv+xs2Tm1DOgMijicMnFJXtlym8n7x0/k6lPj3wvSerSJFxt6jfzeDZ4lkkm+XoFKjzwL3o2/A019m0hSdpxYrWO5u02OB0uSe02Mnk3kCQ57DYpb5fBgXC5K0pLkePcGUmSyeonc6M2BicCriz86IoKBVh91bPlrZKkX7JT9d9jKcXmr/phs+4K7SjpwjjycZ8t93hGuE95N/eRpDTbT9px8j19lva6hxLBXUzWP/z2pOCQJLthWXCFcBTJcfq4TMGtLjz3b2BsHlQLV2/uY5swQufXv+PmNDUbR8RRob7hf5Gv94Wbeby9Z2uJ+at/+kJnCvIlSf3aRjpP6MTVodBxXgX2fJnEFROueF5Bvz3m5kxw0a8nbEqSybe+gUmAKw9HxFGhi4elrPphS4n5tvPntO6nL/VIu0g18Q/SPdd20qZfvvZkRLjR72/oI0m1LL5qWLu5OgTdq6Z+bdX02ilqX/8eLf9psM4Xcdtr4Kpivvjgi8OwGKg+rt7Qx77nf25OUvNRxFGu8KBm6hzSWpK0/fge/Zxd+lUxVqZs1iPtIiVdKO4U8ZqjrBv6SNInx1/WQ9dO140Nuqtl3VvV85rxevfgPzyYDtWqMPO3x16BxuXAFcVUu67zscOWZWASVBdu6OM5jCFAuR6/+Gh4Ssmj4b/acmiXjuaelCTdH9ZZQT4Bbs8G49kd57X64BTlF164znSnhr1U19rY4FSoKsf5i37QtoZKshiWBVcIk1mmwCa/PT9z0rgswBWII+Iok8VkVr/wSOfzRV2HaFHXIRWuZ7V46+/hdyr+m3XujIfLxLmiPB0+s0utAyNkMXmpRcAtSj7Jv/0VKX+PHOdPyuTdQCaLr+TXQcpLNjoVLmPmkHYyWf0kSY5zZ1TEbe6BSuGIOMp0z7WdFOJftRNvnriuazWnweUs76IT+wK8gw1MgkvlyPltKJK5ATdoQfksHfs4Hxcd/FoqKjQwDXDl4Yg4ynTxSZpv7P5Yh3IyKlzn4TZ/Ucv6f9ANwWHqENxcuzJ+qXAdXPn8LhpPXFCUb2ASXKqitIUyB/WVJJkaPimlzuPumiiVqU4jed/yhPP5+a+5ARRQWRRxlCrIJ0D3hXWWJOWcs2noJy/pbGFBhetlnT2jeZHPSLowvnzXllfdmhPGq2X21TX+HZzPM/L54euKduZLFWVtlLne/TJZAmRp8ZbsP94jFZ1xaXVTo+Fy5O2QznCX1Rqtlr9qPbZCJt8LP4QXpf8o+/drDA4FXHkYmoJS9Qu/U1aLtyRpzU9fuFTCJenfP36q8/YLv5p8uM1f5M3tjms0s8lLD/xxkny86kiSThek65fcHQanwqUq+uVJOc4dkSSZ6vxJlrbbJP9byl/Jr5PMbT6U5Y/zZTJZPZASRrG06Sqf6E9laf4nSZIj/7TOvvGE5ODShUBl0ZJQqoquHV6WE7bT+vjQN7qveWc18K2r7i1u0ep9X7gjIjykXeBdJaZZLT4Krt1cNwTdp4a1QyVJRQ671h6aJrvjvKcjoroVZsr+Y1dZWq2Xyae1TH43yKvdl3Kc2S7H6U/kOHdQsudIXvVlqhUmU2A3mXzbG50a1cTS7v7fnphMMtXyl3zrydzkelma3yZz0B+ds4uyj+rcyv5ycJJmjeJ1ZzeXly3ctlkq5NyAqqKIo4QOwWFqH9xcknQ096SSjnxfqfXf+mGL7mt+YVjL49f9lSJ+hXuyVXyFy+QVZmv1wSnanfWJBxLBI87+JPsPXWRuFitTw6dlMteSyb+LTP5dylzFUZCqomPT5Mj93INBUd1qP/VWhcs4bNkqTH5bBR/9Uzp72gOp4El+C5a5vOzp28Kl3Bw3pqnZKOIo4eKj4f/e86kclbxT2vr923X6XJ7q1vLT3X+8SSF+9ZWad6q6Y8JABfZ82eynlWbbp72nP1fyyfXKt/OfcY1jP62ig0Ok47Ey1X9QpoBImXzaSl4NJLOvZD8tFRyW48xOObI3ypG9SZLd6NSoRo7CAulcrhxnc+XIOqyio9/Jfnin7CkfSoXcRRe4VBRxlDBiS4JGbEmo8vpnCwvUcFGfihfEZWvU1+FGR8DlpOCYHGkL5EhbYHQSuEleTN2KF0KNdrr9H4yOcFXiZE0AAADAABRxAAAAwAAUcQAAAMAAFHEAAADAABRxAAAAwAAUcQAAAMAAFHEAAADAAIYX8QMHDqhv377q1q2b+vbtq4MHD5a6XFJSkqKiohQVFaVt27Y5py9atEjr16/3UFoAAACgehhexCdNmqR+/frpo48+Ur9+/TRx4sRSl1uwYIEWL16sxYsXKy4uTtKFEr9792716NHDk5EBAACAS2ZoEc/MzFRKSoqioqIkSVFRUUpJSdGpUyVvh+7t7a38/Hzl5+fLarVKkmJjYzVu3DiPZgYAAACqg6G3uE9NTVWjRo1ksVgkSRaLRcHBwUpNTVX9+vWLLTtq1CiNGTNGkjR27FitWbNGHTp0UGhoaJVf32w2VT38JQgNCDbkdVE+T7wf6lmbuP01UDke+x6wVv27Cu7hqX97U71rPPI6cJ0n/u1NTZq6/TVQeZ7ufhW9nsnhcDg8lKWE3bt3a/To0dq4caNz2n333afZs2erXbt2Za6XnZ2tIUOGaPny5YqPj9fhw4cVGhqqESNGeCI2AAAAcMkMHZoSEhKi9PR02e12SZLdbldGRoZCQkLKXW/WrFmKjo5WcnKyMjIyNH/+fKWlpWn79u2eiA0AAABcMkOLeFBQkMLDw7VhwwZJ0oYNGxQeHl5iWMrFdu7cKUnq3LmzbDabTKYLh/zNZrNsNpv7QwMAAADVwNChKZK0f/9+jRkzRjk5OQoICNDMmTPVvHnzUpctKCjQU089pfj4eNWrV08FBQUaPny4jh07pqZNm2rhwoXy9vb28B4AAAAAlWd4EQcAAACuRoZfRxwAAAC4GlHEAQAAAANQxAEAAAADUMQBAAAAA1DEAQAAAANQxAEAAAADUMQBAAAAA1DEAQAAAAP8PxNJnHb148iSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot charts\n",
    "\n",
    "for store in selected_stores:\n",
    "    plot_scores(nutri_scores[store], store.capitalize(), save=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Calculate Nutrition Ranking\n",
    "* Award good grades:\n",
    "    * A: +2 & B: +1\n",
    "    * C: +/- 0\n",
    "* Punish bad grades:\n",
    "    * D: -1 & E: -2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = [2,1,0,-1,-2]\n",
    "ranking = pd.DataFrame(columns=[\"Store\", \"Score\"])\n",
    "\n",
    "i=1\n",
    "for store in selected_stores:\n",
    "    score = sum(nutri_scores[store] * parameters)\n",
    "    ranking.loc[i]= [store.capitalize(), score]\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Edeka</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kaufland</td>\n",
       "      <td>-18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lidl</td>\n",
       "      <td>-26.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aldi</td>\n",
       "      <td>-29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Netto</td>\n",
       "      <td>-29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rewe</td>\n",
       "      <td>-34.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Penny</td>\n",
       "      <td>-46.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Store  Score\n",
       "0     Edeka    3.1\n",
       "1  Kaufland  -18.0\n",
       "2      Lidl  -26.6\n",
       "3      Aldi  -29.3\n",
       "4     Netto  -29.3\n",
       "5      Rewe  -34.3\n",
       "6     Penny  -46.2"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranking = ranking.sort_values(by=\"Score\", ascending=False).reset_index(drop=True)\n",
    "ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "plt.figure(figsize=(14,7))\n",
    "plt.bar(x=ranking[\"Store\"], height=ranking[\"Score\"],\n",
    "                 color=[\"#74c928\", \"#fbc801\", \"#f4730e\",\"#f4730e\",\"#f4730e\", \"#f4730e\", \"#f02d1a\"])\n",
    "plt.title(\"Aggregated Nutrition Scores (more is better)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Note\n",
    "If you want to help improve the open food database you can do so easily:\n",
    "\n",
    "1. Register at https://world.openfoodfacts.org/\n",
    "1. Download the mobile app https://world.openfoodfacts.org/open-food-facts-mobile-app\n",
    "1. Start scanning :)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analytics] *",
   "language": "python",
   "name": "conda-env-analytics-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}