data_prepartion_F_V.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f370f696-b22d-47fe-964e-ade963f9e2d8",
   "metadata": {},
   "source": [
    "# Data Preparation\n",
    "\n",
    "__In this notebook, we will go through the steps followed in order to prepare the data required for our application. <br>\n",
    "The data used in this user study is provided by the well-known online recipe website [allrecipes](https://www.allrecipes.com/). <br>\n",
    "The data is meant to use for research purposes. <br>\n",
    "In a nutshell, the steps are :__\n",
    "  1. __Read the csv file of each category and add categpry column to DF__\n",
    "  2. __Extract the recipes that has image, as images will be used in our study__\n",
    "  3. __Compute Fsa and Nutri score for each recipe as main decision factor for healthiness of each recipe__\n",
    "  4. __Split recipies into healthy and unhealthy ones based on Fsa threshold (8)__\n",
    "  5. __Prepare rating matrices for each category, to be usd in recommendation generation phase__\n",
    "  6. __save the data__\n",
    "  7. __Optional: Push the data into Database__<br>\n",
    "For more information about the dataset: __ayoub.majjodi@uib.no__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2497778a-3e04-4bd1-b7ea-1aad7c14ea65",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.set(font_scale = 2)\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7bd5a2de-a61c-4b47-9e76-b64780755833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data files\n",
    "def read_data(data):\n",
    "    sep = '\\t'\n",
    "    file = data + '.csv'\n",
    "    if data == 'images':\n",
    "        sep = ';'\n",
    "    row_data = pd.read_csv(file, sep = sep)\n",
    "    return row_data\n",
    "# size of each dataset\n",
    "def size(data, name):\n",
    "    return   print('number of recipes in {0}: {1}'.format( name, len(data)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "75ea3b95-0473-46fe-99c0-acc6c30b4cfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read each category\n",
    "bbq = read_data('bbq')\n",
    "fruit_veg = read_data('fruits_vegetables')\n",
    "meat_poultry = read_data('Meat_poultry')\n",
    "pasta_noodles = read_data('pasta_and_noodles')\n",
    "salad = read_data('salad')\n",
    "seafood = read_data('Seafood')\n",
    "soups_chili = read_data('soups_stews_and_chili')\n",
    "images = read_data('images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "fb4a2adb-a55f-4564-8d45-09d537b3ff00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of recipes in bbq: 1682\n",
      "number of recipes in fruit_veg: 19574\n",
      "number of recipes in meat_poultry: 12672\n",
      "number of recipes in pasta_noodles: 2692\n",
      "number of recipes in salad: 3031\n",
      "number of recipes in seafood: 3237\n",
      "number of recipes in soups_chili: 3605\n",
      "number of recipes in images: 162985\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size(bbq, 'bbq'), size(fruit_veg, 'fruit_veg')\n",
    "size(meat_poultry, 'meat_poultry'), size(pasta_noodles, 'pasta_noodles')\n",
    "size(salad, 'salad'), size(seafood, 'seafood')\n",
    "size(soups_chili, 'soups_chili'), size(images, 'images')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11c3d404-0082-4921-b819-e289fa089abd",
   "metadata": {},
   "source": [
    "## Add category column to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "07a8e5a4-c9a6-4567-8df7-f1d352c577ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "bbq['category'] = 'Barbecue'\n",
    "fruit_veg['category'] = 'Fruits and Vegetables'\n",
    "meat_poultry['category'] = 'Meat and Poultry'\n",
    "salad['category'] = 'Salad'\n",
    "pasta_noodles['category'] = 'Pasta and Noodles'\n",
    "seafood['category'] = 'Seafood'\n",
    "soups_chili['category'] = 'Soups and Chili'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbf2b3e6-3e35-48f7-93b8-b5ab7f60e3d2",
   "metadata": {},
   "source": [
    "# Extract recipes with images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "72f88312-ab8c-4a96-b220-8f2942e48ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def recipe_with_image(data, images):\n",
    "    images = images.drop_duplicates(subset=['recipe_id'])\n",
    "    data_with_image = pd.merge(data, images, left_on=  'URL',\n",
    "                   right_on= 'recipe_id', how='inner')\n",
    "    if data_with_image.isnull().values.any():\n",
    "           data_with_image= data_with_image.dropna()\n",
    "    data_with_image =  data_with_image.drop(['cook_id','recipe_id','date_added'], axis=1)\n",
    "    data_with_image['id'] = range(0, len(data_with_image))\n",
    "    first_column = data_with_image.pop('id')\n",
    "    data_with_image.insert(0,'id',first_column)\n",
    "    return data_with_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "1f7a0639-b987-47de-9fca-7c0b843fab2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_bbq = recipe_with_image(bbq, images)\n",
    "final_fruit =  recipe_with_image(fruit_veg, images)\n",
    "final_meat_poultry =  recipe_with_image(meat_poultry, images)\n",
    "final_salad = recipe_with_image(salad, images)\n",
    "final_pasta_noodles = recipe_with_image(pasta_noodles, images)\n",
    "final_seafood = recipe_with_image(seafood, images)\n",
    "final_soups_chili = recipe_with_image(soups_chili, images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b45952e1-65a5-43f1-b122-c60af9b58d23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of recipes in bbq: 1200\n",
      "number of recipes in fruit_veg: 12916\n",
      "number of recipes in meat_poultry: 8375\n",
      "number of recipes in pasta_noodles: 1810\n",
      "number of recipes in salad: 2062\n",
      "number of recipes in seafood: 1931\n",
      "number of recipes in soups_chili: 2199\n"
     ]
    }
   ],
   "source": [
    "size(final_bbq, 'bbq'), size(final_fruit, 'fruit_veg')\n",
    "size(final_meat_poultry, 'meat_poultry'), size(final_pasta_noodles, 'pasta_noodles')\n",
    "size(final_salad, 'salad'), size(final_seafood, 'seafood')\n",
    "size(final_soups_chili, 'soups_chili')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "60a8d180-a738-4648-be80-c53ce534a028",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_bbq.to_csv('./Results/bbq.csv', index=False)\n",
    "final_fruit.to_csv('./Results/fruit.csv', index=False)\n",
    "final_meat_poultry.to_csv('./Results/meat_poultry.csv', index=False)\n",
    "final_pasta_noodles.to_csv('./Results/pasta_noodles.csv',index=False)\n",
    "final_salad.to_csv('./Results/salad.csv',index=False)\n",
    "final_seafood.to_csv('./Results/seafood.csv',index=False)\n",
    "final_soups_chili.to_csv('./Results/soup_chili.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37ff8477-49cf-4d36-a29f-1cdbddd58c01",
   "metadata": {},
   "source": [
    "# Concat recipes in one DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "a0193364-0bab-4e34-965a-cf5f83837a2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "recipes = [final_bbq,final_fruit,final_meat_poultry,final_pasta_noodles,final_salad\n",
    "          ,final_seafood,final_soups_chili]\n",
    "final_recipes = pd.concat(recipes)\n",
    "final_recipes.to_csv('./Results/final_recipes.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "7f91002c-eaa1-40bc-81f4-bd7d2fdab660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fruits and Vegetables    12916\n",
       "Meat and Poultry          8375\n",
       "Soups and Chili           2199\n",
       "Salad                     2062\n",
       "Seafood                   1931\n",
       "Pasta and Noodles         1810\n",
       "Barbecue                  1200\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_recipes['category'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d94e3a-368a-4ca9-9e8c-ba9178143f8c",
   "metadata": {},
   "source": [
    "# Preapre nutrients for 100g and Nuri/Fsa score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "id": "1a648c05-2aae-4a72-866b-62295f505849",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'URL', 'Name', 'Fiber (g)', 'Sodium (g)', 'Carbohydrates (g)',\n",
       "       'Fat (g)', 'Protein (g)', 'Sugar (g)', 'Saturated Fat (g)', 'Size (g)',\n",
       "       'Servings', 'Calories (kCal)', 'Average Rating', 'Average Sentiment',\n",
       "       'Number of Ratings', 'Number of Bookmarks', 'Year of Publishing',\n",
       "       'category', 'image_link'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the data\n",
    "final_recipes = pd.read_csv('./Results/final_recipes.csv')\n",
    "final_recipes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "id": "8ae9c392-48af-4faa-a2b2-f751399060fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_recipes['fiber_100g'] = (final_recipes['Servings'] * final_recipes['Fiber (g)'] * 100) / final_recipes['Size (g)']\n",
    "final_recipes['fat_100g'] = (final_recipes['Servings'] * final_recipes['Fat (g)'] * 100) / final_recipes['Size (g)']\n",
    "final_recipes['sugar_100g'] = (final_recipes['Servings'] * final_recipes['Sugar (g)'] * 100) / final_recipes['Size (g)']\n",
    "final_recipes['saturated_100g'] = (final_recipes['Servings'] * final_recipes['Saturated Fat (g)'] * 100) / final_recipes['Size (g)']\n",
    "final_recipes['sodium_100mg'] = ((final_recipes['Servings'] * final_recipes['Sodium (g)'] * 100) / final_recipes['Size (g)'])*1000\n",
    "final_recipes['carbohydrates_100g'] = (final_recipes['Servings'] * final_recipes['Carbohydrates (g)'] * 100) / final_recipes['Size (g)']\n",
    "final_recipes['protein_100g'] = (final_recipes['Servings'] * final_recipes['Protein (g)'] * 100) / final_recipes['Size (g)']\n",
    "final_recipes['kj_100g'] = ((final_recipes['Servings'] * final_recipes['Calories (kCal)'] * 100) / final_recipes['Size (g)'])*4.1868\n",
    "final_recipes['Salt (g)'] = final_recipes['Sodium (g)'] * 2.54\n",
    "final_recipes['salt_100g'] = (final_recipes['Servings'] * final_recipes['Salt (g)'] * 100) / final_recipes['Size (g)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "id": "45965bfa-5dda-4912-b5e9-190ba512e0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename columns to match ones in database\n",
    "final_recipes = final_recipes.rename(columns={'Fiber (g)':'fiber_g','Sodium (g)':'sodium_g','Carbohydrates (g)':'carbohydrates_g',\n",
    "                                                           'Fat (g)':'fat_g','Protein (g)':'protein_g','Sugar (g)':'sugar_g',\n",
    "                                                           'Saturated Fat (g)':'saturate_g','Size (g)':'size_g',\n",
    "                                                           'Calories (kCal)':'calories_kCal', 'Salt (g)':'salt_g', 'Number of Ratings':'NumberRatings'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b084043-1c94-40bd-8743-b186044aa89a",
   "metadata": {},
   "source": [
    "# Fsa Score based on :\n",
    "\n",
    "- compute the FSA score and Nutri score:\n",
    "\n",
    "- `https://www.food.gov.uk/sites/default/files/media/document/fop-guidance_0.pdf`\n",
    "\n",
    "- `https://www.santepubliquefrance.fr/content/download/150263/file/2021_07_21_QR_scientifique_et_technique_V41_EN.pdf <br>`\n",
    "- Paper: __Development of a new front-of-pack nutrition label in France: the five-colour Nutri-Score__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "id": "9d783489-6a27-4745-9c2f-f789fc180c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in final_recipes.index:\n",
    "    fat_count = satfat_count = sugar_count = salt_count = 2\n",
    "    if final_recipes.at[i,'fat_100g'] <= 3:\n",
    "        fat_count = 1\n",
    "    if final_recipes.at[i, 'fat_g'] > 21 or final_recipes.at[i, 'fat_100g'] > 17.5: # here the mistake :) > 17.5\n",
    "        fat_count = 3\n",
    "    if final_recipes.at[i,'saturated_100g'] <= 1.5:\n",
    "        satfat_count = 1\n",
    "    if final_recipes.at[i, 'saturate_g'] > 6 or final_recipes.at[i,'saturated_100g'] > 5:\n",
    "        satfat_count = 3\n",
    "    if final_recipes.at[i, 'sugar_100g'] <= 5:\n",
    "        sugar_count = 1\n",
    "    if final_recipes.at[i, 'sugar_100g'] > 22.5 or final_recipes.at[i, 'sugar_g'] > 27:\n",
    "        sugar_count = 3\n",
    "    if final_recipes.at[i, 'salt_100g'] <= 0.3:\n",
    "        salt_count = 1\n",
    "    if final_recipes.at[i, 'salt_100g'] > 1.5 or final_recipes.at[i, 'salt_g']> 1.8:\n",
    "        salt_count = 3 \n",
    "    final_recipes.at[i,'Fsa_new'] = fat_count + satfat_count + sugar_count + salt_count\n",
    "    final_recipes.at[i, 'fat_count'] = fat_count\n",
    "    final_recipes.at[i, 'satfat_count'] = satfat_count\n",
    "    final_recipes.at[i, 'sugar_count'] = sugar_count\n",
    "    final_recipes.at[i, 'salt_count'] = salt_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "id": "7ee6c8bb-1707-49bc-b183-940415dda55c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0     1045\n",
       "5.0     2069\n",
       "6.0     3801\n",
       "7.0     3660\n",
       "8.0     4212\n",
       "9.0     4477\n",
       "10.0    5908\n",
       "11.0     862\n",
       "12.0     198\n",
       "Name: Fsa_new, dtype: int64"
      ]
     },
     "execution_count": 564,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## FSA score distribution\n",
    "final_recipes['Fsa_new'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "id": "e7ee5d33-30db-4577-ab38-cb1109987df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Genereate the Nutri-score annotation for a given value\n",
    "def NutriScore_string(score):\n",
    "    str_score = ''\n",
    "    if score in range(-15, 0):\n",
    "        str_score = 'A'\n",
    "    if score in range(0,3):\n",
    "        str_score = 'B'\n",
    "    if score in range(3,11):\n",
    "        str_score = 'C'\n",
    "    if score in range(11,19):\n",
    "        str_score = 'D'\n",
    "    if score in range(19,41):\n",
    "        str_score = 'E'\n",
    "    return str_score    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "id": "4764bdfb-1f1d-437c-8c7e-0b2bcb5f9535",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute nutri- score\n",
    "for i in final_recipes.index.to_list():\n",
    "#     print(i)\n",
    "    # assign a point to enrgyA\n",
    "    energy = final_recipes.at[i,'kj_100g']\n",
    "    if energy > 3350:\n",
    "        kj_point = 10\n",
    "    else:\n",
    "        energy_level = energy % 335 # if the rest of is > 335 move to next point\n",
    "        if energy_level <= 335:\n",
    "            kj_point = energy // 335\n",
    "        else:\n",
    "            kj_point = (energy // 335) + 1\n",
    "    # assign a point to Sugar\n",
    "    sugar = final_recipes.at[i, 'sugar_100g']\n",
    "    if sugar > 45:\n",
    "        sg_point = 10\n",
    "    else:\n",
    "        sg_level = sugar % 4.5\n",
    "        if sg_level <= 4.5:\n",
    "            sg_point = sugar // 4.5\n",
    "        else:\n",
    "            sg_point = (sugar // 4.5) + 1\n",
    "    # assign a point to saturated_100g\n",
    "    sat_fat = final_recipes.at[i, 'saturated_100g']\n",
    "    if sat_fat > 10:\n",
    "        sf_point = 10\n",
    "    else:\n",
    "        sf_level = sat_fat % 1\n",
    "        if sf_level <= 1:\n",
    "            sf_point = sat_fat // 1\n",
    "        else:\n",
    "            sf_point = (sat_fat // 1) + 1\n",
    "    # assign a point to sodium_100mg\n",
    "    sodium = final_recipes.at[i, 'sodium_100mg']\n",
    "    if sodium > 900:\n",
    "        sdm_point = 10\n",
    "    else:\n",
    "        sdm_level = sodium % 90\n",
    "        if sdm_level <= 90:\n",
    "            sdm_point = sodium // 90\n",
    "        else:\n",
    "            sdm_point = (sodium // 90) + 1\n",
    "\n",
    "    N_point = kj_point + sg_point + sf_point + sdm_point # negative point\n",
    "\n",
    "    # positive point \n",
    "    fiber = final_recipes.at[i, 'fiber_100g']\n",
    "    protien = final_recipes.at[i, 'protein_g']\n",
    "    # fiber\n",
    "    if fiber > 4.7:\n",
    "        fb_point = 5\n",
    "    else:\n",
    "        fb_level = fiber % 0.9\n",
    "        if fb_level <= 0.9:\n",
    "            fb_point = fiber // 0.9\n",
    "        else:\n",
    "            fb_point = (fiber // 0.9) + 1\n",
    "    # protien\n",
    "    if protien > 8:\n",
    "        pr_point = 5\n",
    "    else:\n",
    "        pr_level = protien % 1.6\n",
    "        if pr_level <= 1.6:\n",
    "            pr_point = protien // 1.6\n",
    "        else:\n",
    "            pr_point = (protien // 1.6) + 1\n",
    "    if N_point > 11:\n",
    "        P_point = fb_point\n",
    "    else:\n",
    "        P_point = fb_point + pr_point\n",
    "\n",
    "    # Nutri score\n",
    "    Score = N_point - P_point\n",
    "#     print(Score)\n",
    "    final_recipes.at[i, 'Nutri_score'] = NutriScore_string(Score) # fct above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "id": "2c277a30-f311-4082-adb6-2a361fcc5d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    9059\n",
       "B    6133\n",
       "C    5706\n",
       "D    4387\n",
       "E     947\n",
       "Name: Nutri_score, dtype: int64"
      ]
     },
     "execution_count": 569,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nutri score distribution \n",
    "final_recipes.Nutri_score.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "id": "6bb742f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Prepare Recipe rating matrix to benchamrk RS algorithms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "f98df789-45c4-44d5-8ebe-3cab7ad9e67b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userid', 'recipeid', 'rating', 'Name'], dtype='object')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_profile = pd.read_csv('Rating-Profile_Merged.csv', sep=';')\n",
    "rating_profile = rating_profile.rename(columns = {'name':'Name'})\n",
    "rating_profile = rating_profile[['userid','recipeid','rating','Name']]\n",
    "rating_profile.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "bc99392c-e764-4564-bcd2-9429a01cd9b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Meat and Poultry         39332\n",
       "Fruits and Vegetables    17328\n",
       "Pasta and Noodles         5924\n",
       "Barbecue                  5665\n",
       "Seafood                   5382\n",
       "Salad                       68\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_recipes = final_recipes.merge(rating_profile, on = 'Name')\n",
    "final_recipes.category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "94d49316-dd63-45d6-b17e-7b0530719210",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select recipes \n",
    "final_recipes = final_recipes[['recipeid','userid','rating','URL','Name','fiber_g','sodium_g','carbohydrates_g','fat_g','protein_g','sugar_g','saturate_g', 'size_g','Servings',\n",
    "                  'calories_kCal','category','image_link','fat_100g','fiber_100g','sugar_100g','saturated_100g','protein_100g','sodium_100mg',  \n",
    "                  'carbohydrates_100g','kj_100g','Nutri_score','Fsa_new','salt_100g','salt_g','fat_count','satfat_count','sugar_count','salt_count','NumberRatings']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa86e87b-0ab3-48b6-8829-41ec780f956a",
   "metadata": {},
   "source": [
    "### Split based on Fsa score, with a thershold 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "dfd5b0e6-9fa0-4bc1-a97b-99b2f1c732fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The split is based on what is considered healthy and less healthy from UK food standard agency view point\n",
    "final_healthy = final_recipes[final_recipes.Fsa_new <= 8]\n",
    "final_unhealthy = final_recipes[final_recipes.Fsa_new > 8]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be998c50-ccb7-48be-aa5f-b2299f89d8d7",
   "metadata": {},
   "source": [
    "### Split based on category\n",
    "\n",
    "- As the recommender algorithm, "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bb6f630",
   "metadata": {},
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "8559e216-e3ed-44a0-a82b-793e678a5b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data(category):\n",
    "    h_cat_data = final_healthy[final_healthy.category == category][['recipeid','userid','rating']]\n",
    "    unh_cat_data = final_unhealthy[final_unhealthy.category == category][['recipeid','userid','rating']]\n",
    "    \n",
    "    # save data h/unh rating matrix\n",
    "    h_cat_data.to_csv('./recipe_recommender/rating_matrix/h_'+category.replace(' ','_')+'.csv', index=False, header=False)\n",
    "    unh_cat_data.to_csv('./recipe_recommender/rating_matrix//unh_'+category.replace(' ','_')+'.csv', index=False, header=False)\n",
    "    \n",
    "    return \"ok :)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "98aa9f8d-3eb5-4e1c-a8d8-79c19b19651f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for cat in final_healthy.category.value_counts().index:\n",
    "    split_data(cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "1a20c0ca-683c-4c8e-8a38-a6cbc6f0227e",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_healthy.drop_duplicates(subset=['recipeid'], inplace=True)\n",
    "final_unhealthy.drop_duplicates(subset=['recipeid'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9057b41e-b66c-49b1-b881-9d813b469f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['recipeid', 'userid', 'rating', 'URL', 'Name', 'fiber_g', 'sodium_g',\n",
       "       'carbohydrates_g', 'fat_g', 'protein_g', 'sugar_g', 'saturate_g',\n",
       "       'size_g', 'Servings', 'calories_kCal', 'category', 'image_link',\n",
       "       'fat_100g', 'fiber_100g', 'sugar_100g', 'saturated_100g',\n",
       "       'protein_100g', 'sodium_100mg', 'carbohydrates_100g', 'kj_100g',\n",
       "       'Nutri_score', 'Fsa_new', 'salt_100g', 'salt_g', 'fat_count',\n",
       "       'satfat_count', 'sugar_count', 'salt_count', 'NumberRatings'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_healthy.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "164f3884-80c3-4781-9a5a-16717ad1fe21",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_healthy = final_healthy.rename(columns={'recipeid':'id'})\n",
    "final_unhealthy = final_unhealthy.rename(columns={'recipeid':'id'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c0428793-a178-424d-aee6-455cfa91cd85",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_healthy.drop(columns=['userid','rating'], inplace=True)\n",
    "final_unhealthy.drop(columns=['userid','rating'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "304d74a1-bbcd-4d9e-b3d9-373c2b6cb056",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_healthy.to_csv('./Final_recipes/F_V_healthy.csv', index=False, sep=';')\n",
    "final_unhealthy.to_csv('./Final_recipes/F_V_unhealthy.csv', index=False, sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a11627-7e58-4c24-9ccc-e822d5617b74",
   "metadata": {},
   "source": [
    "- rating matrices for each food category is saved here, `recipe_recommender/rating_matrix`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcedeef7-1ea6-4f88-8f5a-eb7fe2f2c2f5",
   "metadata": {},
   "source": [
    "# Prepare to push to database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "3298eab7-3e85-47df-9608-bd60ef8c3dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyodbc\n",
    "import psycopg2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "af69c272-924b-472a-b722-984142eabfc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = psycopg2.connect(\"host=localhost dbname=##### user=### password=###\")\n",
    "cur = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "990cc864-408b-4f5e-be18-bf7dabe36eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv \n",
    "\n",
    "with open('./Final_recipes/F_V_healthy.csv') as f:\n",
    "#     reader = csv.reader(f)\n",
    "#     next(reader)\n",
    "    next(f)\n",
    "#     for row in reader:\n",
    "#         cur.execute(\n",
    "#         'INSERT into \"Labels_Nudges_recipes\" Values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',\n",
    "#             row\n",
    "        \n",
    "#         )\n",
    "    cur.copy_from(f, 'HealthyRecipe', sep=';',columns=(\n",
    "                     'id','URL','Name','fiber_g','sodium_g','carbohydrates_g','fat_g','protein_g','sugar_g','saturate_g', 'size_g','Servings',\n",
    "                  'calories_kCal','category','image_link','fat_100g','fiber_100g','sugar_100g','saturated_100g','protien_100g','sodium_100mg',  \n",
    "                  'carbohydrates_100g','kj_100g','Nutri_score','Fsa_new','salt_100g','salt_g','fat_count','satfat_count','sugar_count','salt_count'  \n",
    "                  ))\n",
    "conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "81434bd1-6794-4399-82ca-efb7f91b36bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv \n",
    "\n",
    "with open('./Final_recipes/F_V_unhealthy.csv') as f:\n",
    "#     reader = csv.reader(f)\n",
    "#     next(reader)\n",
    "    next(f)\n",
    "#     for row in reader:\n",
    "#         cur.execute(\n",
    "#         'INSERT into \"Labels_Nudges_recipes\" Values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',\n",
    "#             row\n",
    "        \n",
    "#         )\n",
    "    cur.copy_from(f, 'UnhealthyRecipe', sep=';',columns=(\n",
    "                     'id','URL','Name','fiber_g','sodium_g','carbohydrates_g','fat_g','protein_g','sugar_g','saturate_g', 'size_g','Servings',\n",
    "                  'calories_kCal','category','image_link','fat_100g','fiber_100g','sugar_100g','saturated_100g','protien_100g','sodium_100mg',  \n",
    "                  'carbohydrates_100g','kj_100g','Nutri_score','Fsa_new','salt_100g','salt_g','fat_count','satfat_count','sugar_count','salt_count'  \n",
    "                  ))\n",
    "conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "213ad9cb-6732-4337-a3e9-af4cdac33850",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c23d460-bf22-474c-b362-fa9d13f6409f",
   "metadata": {},
   "source": [
    "# New Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fec92509-c81a-42a8-8f21-b997fd0b9604",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "ac27c6cf-b2e7-48a3-acd7-d1559628920a",
   "metadata": {},
   "outputs": [],
   "source": [
    "h = pd.read_csv('./Final_recipes/F_V_healthy.csv', sep=';')\n",
    "uh = pd.read_csv('./Final_recipes/F_V_unhealthy.csv', sep=';')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e04d8f4-1911-48e4-ac34-f32ccfbcd55b",
   "metadata": {},
   "source": [
    "## compute new Fsa-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e8c9f2cc-77fd-451a-b647-f65c39ba44d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fsa_score(final_recipes):\n",
    "    for i in final_recipes.index:\n",
    "        fat_count = satfat_count = sugar_count = salt_count = 2\n",
    "        if final_recipes.at[i,'fat_100g'] <= 3:\n",
    "            fat_count = 1\n",
    "        if final_recipes.at[i, 'fat_g'] > 21 or final_recipes.at[i, 'fat_100g'] > 17.5: # here the mistake :) > 17.5\n",
    "            fat_count = 3\n",
    "        if final_recipes.at[i,'saturated_100g'] <= 1.5:\n",
    "            satfat_count = 1\n",
    "        if final_recipes.at[i, 'saturate_g'] > 6 or final_recipes.at[i,'saturated_100g'] > 5:\n",
    "            satfat_count = 3\n",
    "        if final_recipes.at[i, 'sugar_100g'] <= 5:\n",
    "            sugar_count = 1\n",
    "        if final_recipes.at[i, 'sugar_100g'] > 22.5 or final_recipes.at[i, 'sugar_g'] > 27:\n",
    "            sugar_count = 3\n",
    "        if final_recipes.at[i, 'salt_100g'] <= 0.3:\n",
    "            salt_count = 1\n",
    "        if final_recipes.at[i, 'salt_100g'] > 1.5 or final_recipes.at[i, 'salt_g']> 1.8:\n",
    "            salt_count = 3 \n",
    "        final_recipes.at[i,'Fsa_new'] = fat_count + satfat_count + sugar_count + salt_count\n",
    "        final_recipes.at[i, 'fat_count'] = fat_count\n",
    "        final_recipes.at[i, 'satfat_count'] = satfat_count\n",
    "        final_recipes.at[i, 'sugar_count'] = sugar_count\n",
    "        final_recipes.at[i, 'salt_count'] = salt_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69bfc6e8-3906-4efa-92cb-d723c6994a1b",
   "metadata": {},
   "source": [
    "# compute new Nutri_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "b7695ba2-a865-4c58-b9a2-ef85faa04965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    204\n",
       "B    159\n",
       "C     74\n",
       "D     20\n",
       "Name: Nutri_score, dtype: int64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h.Nutri_score.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "36919ba5-05d9-427b-a36f-0b549245bc04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C    225\n",
       "D    151\n",
       "B    119\n",
       "A     30\n",
       "E     10\n",
       "Name: Nutri_score, dtype: int64"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uh.Nutri_score.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "b43da41f-7df2-4479-8d25-62f7632d7a91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "535"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(uh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "7efaace3-9399-4e6a-9403-52c735f455da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "662"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Correct_un_nutri = pd.read_csv('./Correct_nutri_fsa.csv', ';')\n",
    "len(Correct_un_nutri)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60bae10b-3921-4a8f-88cd-4b289a548dd5",
   "metadata": {},
   "source": [
    "# Based on French excel (Alain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "0aa9b12d-d046-4310-b1a9-849f2aca3ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nutriscore_C    268\n",
       "Nutriscore_D    198\n",
       "Nutriscore_B    168\n",
       "Nutriscore_A     18\n",
       "Nutriscore_E     10\n",
       "Name: Nutri-Score, dtype: int64"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Correct_un_nutri['Nutri-Score'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b6a5d1-0098-4462-b275-14991db9c2e6",
   "metadata": {},
   "source": [
    "# based on my script (consediring if N_point >= 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "4bba2a16-3679-48be-9749-82f6ab1fb3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C    225\n",
       "D    151\n",
       "B    119\n",
       "A     30\n",
       "E     10\n",
       "Name: Nutri_score, dtype: int64"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uh.Nutri_score.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "b4bd205e-c30c-4e98-a100-ffe1fe74a9c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in uh.index:\n",
    "    uh.at[i, 'Nutri_score'] = \"Nutriscore_\" + uh.at[i, 'Nutri_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "42f7646f-1c40-4761-9140-ff4c6b2b1713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mg = uh.merge(Correct_un_nutri, left_on='Name',right_on='Product')#[['Name','Nutri_score','Product' ,'Nutri-Score']]\n",
    "len(mg[mg.Nutri_score != mg['Nutri-Score']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "d3a0ccba-4d8f-4da5-b390-7d5ce09baaf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in mg.index:\n",
    "    if  mg.at[i,'Nutri_score'] != mg.at[i,'Nutri-Score']:\n",
    "        mg.at[i,'Nutri_score'] = mg.at[i,'Nutri-Score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "43ec763b-dea9-4b2a-8041-640fc3ee7f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "mg = mg[uh.columns.to_list()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "85c5d04a-2a5c-48db-a7dc-5327a0c81a4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in mg.index:\n",
    "    mg.at[i,'Nutri_score'] = mg.at[i,'Nutri_score'].replace('Nutriscore_','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "7ed04a77-16a5-47ba-ab8b-187343875128",
   "metadata": {},
   "outputs": [],
   "source": [
    "mg.to_csv('./Final_recipes/F_V_unhealthy.csv', index=False, sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "907a1635-156e-451e-a9ce-4f51f16ab53a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'URL', 'Name', 'fiber_g', 'sodium_g', 'carbohydrates_g', 'fat_g',\n",
       "       'protein_g', 'sugar_g', 'saturate_g', 'size_g', 'Servings',\n",
       "       'calories_kCal', 'category', 'image_link', 'fat_100g', 'fiber_100g',\n",
       "       'sugar_100g', 'saturated_100g', 'protein_100g', 'sodium_100mg',\n",
       "       'carbohydrates_100g', 'kj_100g', 'Nutri_score', 'Fsa_new', 'salt_100g',\n",
       "       'salt_g', 'fat_count', 'satfat_count', 'sugar_count', 'salt_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mg.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "fd2bac02-bc95-4940-bb13-e086c414c9d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "C    225\n",
       "D    177\n",
       "B    110\n",
       "A     13\n",
       "E     10\n",
       "Name: Nutri_score, dtype: int64"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mg.Nutri_score.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "5ce3e671-c3ec-44b1-8d0a-9d132a47e9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "457"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6d619f8b-5dc9-4ec8-bb4e-ef4e1c02a273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "535"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(uh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f68e8e98-985c-4ef4-8d5c-b442f9686cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'URL', 'Name', 'fiber_g', 'sodium_g', 'carbohydrates_g', 'fat_g',\n",
       "       'protein_g', 'sugar_g', 'saturate_g', 'size_g', 'Servings',\n",
       "       'calories_kCal', 'category', 'image_link', 'fat_100g', 'fiber_100g',\n",
       "       'sugar_100g', 'saturated_100g', 'protein_100g', 'sodium_100mg',\n",
       "       'carbohydrates_100g', 'kj_100g', 'Nutri_score', 'Fsa_new', 'salt_100g',\n",
       "       'salt_g', 'fat_count', 'satfat_count', 'sugar_count', 'salt_count',\n",
       "       'NumberRatings'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "638b7b06-c05f-4d05-b532-7a5fc572d75b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1986, 1293, 1217, 1129, 1094, 836, 823, 731, 707, 674]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(h['NumberRatings'], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "cf298add-8310-4a10-ad5d-fdd0874250ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2399, 1995, 1889, 1712, 1410, 1152, 1047, 908, 885, 858]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(uh['NumberRatings'], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "3aeceaa8-ce32-42ea-82e2-0e534b3644f4",
   "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>id</th>\n",
       "      <th>NumberRatings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>58</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>41</td>\n",
       "      <td>1293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>134</td>\n",
       "      <td>1217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>160</td>\n",
       "      <td>1129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>200</td>\n",
       "      <td>1094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>454</td>\n",
       "      <td>836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>652</td>\n",
       "      <td>823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>34</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1136</td>\n",
       "      <td>707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>761</td>\n",
       "      <td>674</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  NumberRatings\n",
       "442    58           1986\n",
       "326    41           1293\n",
       "299   134           1217\n",
       "286   160           1129\n",
       "333   200           1094\n",
       "355   454            836\n",
       "272   652            823\n",
       "456    34            731\n",
       "396  1136            707\n",
       "81    761            674"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h[[\n",
    "    'id',\n",
    "    'NumberRatings'\n",
    "]].sort_values(by=['NumberRatings'], ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d03796",
   "metadata": {},
   "source": [
    "### Data description for the paper\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "690ea980-fb2d-444c-baec-de035def8bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Read healthy recipes \n",
    "\n",
    "h_recipes = pd.read_csv('./Final_recipes/F_V_healthy.csv')\n",
    "h_recipes['Healthiness'] = 'healthy'\n",
    "## Read unhealthy recipes\n",
    "unh_recipes = pd.read_csv('./Final_recipes/F_V_unhealthy.csv')\n",
    "unh_recipes['Healthiness'] = 'unhealthy'\n",
    "\n",
    "\n",
    "# concat all \n",
    "all_data = pd.concat([h_recipes, unh_recipes], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "f38f906b",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Plot distribution\n",
    "def plt_data(data, title, x,y,pallete,hue=None, ax=None, legend = None):\n",
    "    plt.figure(figsize=(7, 7))\n",
    "    sns.set(font_scale = 2)\n",
    "    widths = 2\n",
    "    #sns.set_style(\"darkgrid\", {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
    "    splot = sns.barplot(data = data, x = x, y =y, palette =pallete,dodge=False, hue=hue ,ax=ax)\n",
    "                        #palette = \"colorblind\")\n",
    "    # if legend:\n",
    "        # splot.get_legend.remove()\n",
    "    if title != 'All':\n",
    "        for p in splot.patches:\n",
    "                splot.annotate(format(p.get_height(), '.0f'), \n",
    "                           (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                           ha = 'center', va = 'center', \n",
    "                           xytext = (0, 9), \n",
    "                           textcoords = 'offset points', fontsize=14)\n",
    "    plt.xticks(rotation = 8)\n",
    "    splot.set_xticklabels(splot.get_xticklabels(),rotation = 0)\n",
    "    plt.savefig('./'+title+\"_.eps\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "8617b8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_Df(data,column, ctitle, f=None):\n",
    "    data_dist = data.value_counts().sort_index()\n",
    "    data_freq = data.value_counts(normalize=True).sort_index()\n",
    "    \n",
    "    data_dist = pd.DataFrame(data_dist).reset_index()\n",
    "    data_dist.columns = [column,'Count']\n",
    "    data_dist['freq'] = data_freq.values\n",
    "    data_dist['Healthiness'] = 'healthy'\n",
    "    if f:\n",
    "        data_dist.loc[data_dist['FSA score'] > 8, 'Healthiness'] = 'less healthy'\n",
    "    return data_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "c2677cf4",
   "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>FSA score</th>\n",
       "      <th>Count</th>\n",
       "      <th>freq</th>\n",
       "      <th>Healthiness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.004036</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>0.043391</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>102</td>\n",
       "      <td>0.102926</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>150</td>\n",
       "      <td>0.151362</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>158</td>\n",
       "      <td>0.159435</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>199</td>\n",
       "      <td>0.200807</td>\n",
       "      <td>less healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>295</td>\n",
       "      <td>0.297679</td>\n",
       "      <td>less healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>0.024218</td>\n",
       "      <td>less healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>0.016145</td>\n",
       "      <td>less healthy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FSA score  Count      freq   Healthiness\n",
       "0          4      4  0.004036       healthy\n",
       "1          5     43  0.043391       healthy\n",
       "2          6    102  0.102926       healthy\n",
       "3          7    150  0.151362       healthy\n",
       "4          8    158  0.159435       healthy\n",
       "5          9    199  0.200807  less healthy\n",
       "6         10    295  0.297679  less healthy\n",
       "7         11     24  0.024218  less healthy\n",
       "8         12     16  0.016145  less healthy"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Fsa score distribution\n",
    "\n",
    "fsa_dist = prepare_Df(all_data.Fsa_new, 'FSA score','FSA distribution', 'f')\n",
    "fsa_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "930b6e4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_data(fsa_dist, 'Fsa_dist','FSA score','Count',(\"#2ecc71\",\"#e74c3c\"),'Healthiness')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "bf44889d",
   "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>Nutri score</th>\n",
       "      <th>Count</th>\n",
       "      <th>freq</th>\n",
       "      <th>Healthiness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>234</td>\n",
       "      <td>0.236125</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>278</td>\n",
       "      <td>0.280525</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>298</td>\n",
       "      <td>0.300706</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>171</td>\n",
       "      <td>0.172553</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>10</td>\n",
       "      <td>0.010091</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Nutri score  Count      freq Healthiness\n",
       "0           A    234  0.236125     healthy\n",
       "1           B    278  0.280525     healthy\n",
       "2           C    298  0.300706     healthy\n",
       "3           D    171  0.172553     healthy\n",
       "4           E     10  0.010091     healthy"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Plot the NutriScore\n",
    "Nutri_dist = prepare_Df(all_data.Nutri_score, 'Nutri score','Nutri distribution')\n",
    "Nutri_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "id": "2c3d83e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt_data(Nutri_dist, 'Nutri_score','Nutri score','Count',(\"#2ecc71\",\"#8BF541\",\"#F5CE07\",\"#F59007\", \"#F53907\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "2910863a",
   "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>Healthiness</th>\n",
       "      <th>str_condition</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Healthy</td>\n",
       "      <td>MTL</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Healthy</td>\n",
       "      <td>No label</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Healthy</td>\n",
       "      <td>Nutri score</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Less healthy</td>\n",
       "      <td>MTL</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Less healthy</td>\n",
       "      <td>No label</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Less healthy</td>\n",
       "      <td>Nutri score</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Healthiness str_condition  Count\n",
       "0       Healthy           MTL     58\n",
       "1       Healthy      No label     61\n",
       "2       Healthy   Nutri score     62\n",
       "3  Less healthy           MTL     42\n",
       "4  Less healthy      No label     39\n",
       "5  Less healthy   Nutri score     38"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## plot healhiness distribution \n",
    "healthy_dist = pd.read_csv('./healthiness_dist.csv')\n",
    "healthy_dist.rename(columns={'healthiness':'Healthiness'}, inplace=True)\n",
    "healthy_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "948583c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plt_healthiness(healthiness, title, hue=None, order=None, ax=None):\n",
    "    plt.figure(figsize=(7, 7))\n",
    "    #sns.set_style(\"darkgrid\", {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
    "    splot = sns.barplot(data = healthiness, x = 'str_condition', y = 'Count', hue=hue, palette = (\"#2ecc71\",\"#e74c3c\", ), order = order, ax=ax)#, legend = False)\n",
    "    splot.set_xticklabels(splot.get_xticklabels(),rotation = 0)\n",
    "    for p in splot.patches:\n",
    "                print(p)\n",
    "                splot.annotate(format(p.get_height(), '.0f'), \n",
    "                           (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                           ha = 'center', va = 'center', \n",
    "                           xytext = (0, 9), \n",
    "                           textcoords = 'offset points', fontsize=14)\n",
    "    plt.legend(bbox_to_anchor=(1.01,0.5), loc=2, borderaxespad=0., title='Healthiness')\n",
    "\n",
    "    # splot.axes.set_title(\"Title\",fontsize=50)\n",
    "    splot.set_xlabel(\"Condition\")#,fontsize=12)\n",
    "    # splot.set_ylabel(\"Count\",fontsize=20)\n",
    "    # splot.tick_params(labelsize=5)\n",
    "    plt.savefig('./'+title+\"_.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "822b206a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rectangle(xy=(-0.4, 0), width=0.4, height=61, angle=0)\n",
      "Rectangle(xy=(0.6, 0), width=0.4, height=62, angle=0)\n",
      "Rectangle(xy=(1.6, 0), width=0.4, height=58, angle=0)\n",
      "Rectangle(xy=(-2.77556e-17, 0), width=0.4, height=39, angle=0)\n",
      "Rectangle(xy=(1, 0), width=0.4, height=38, angle=0)\n",
      "Rectangle(xy=(2, 0), width=0.4, height=42, angle=0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_healthiness(healthy_dist, 'HealthinessDistribution', hue='Healthiness', order=['No label','Nutri score','MTL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "a7300a2f",
   "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>number_of_dietgoals</th>\n",
       "      <th>str_condition</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>MTL</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>No label</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Nutri-score</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>MTL</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>No label</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Nutri-score</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>MTL</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>No label</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>Nutri-score</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>MTL</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>No label</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>Nutri-score</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>MTL</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>No label</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>Nutri-score</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5</td>\n",
       "      <td>MTL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>No label</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>Nutri-score</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6</td>\n",
       "      <td>No label</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6</td>\n",
       "      <td>Nutri-score</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    number_of_dietgoals str_condition  Count\n",
       "0                     0           MTL     23\n",
       "1                     0      No label     23\n",
       "2                     0   Nutri-score     25\n",
       "3                     1           MTL     28\n",
       "4                     1      No label     27\n",
       "5                     1   Nutri-score     33\n",
       "6                     2           MTL     24\n",
       "7                     2      No label     14\n",
       "8                     2   Nutri-score     16\n",
       "9                     3           MTL     17\n",
       "10                    3      No label     22\n",
       "11                    3   Nutri-score     11\n",
       "12                    4           MTL      7\n",
       "13                    4      No label      9\n",
       "14                    4   Nutri-score     10\n",
       "15                    5           MTL      1\n",
       "16                    5      No label      4\n",
       "17                    5   Nutri-score      3\n",
       "18                    6      No label      1\n",
       "19                    6   Nutri-score      2"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "goal_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "c9d56e15",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Plot goals per condition\n",
    "goal_dist = pd.read_csv('./goals_dist.csv')\n",
    "# plot goal distribution\n",
    "goal_dist['max'] = [0,0,25,0,0,33,24,0,0,22,0,0,0,10,0,4,0,3,0,0]\n",
    "\n",
    "def plt_goals(goals, title, hue=None, hue_order=None, order=None, ax=None):\n",
    "    plt.subplots(figsize=(7,7))\n",
    "    sns.set_style(\"darkgrid\")#, {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
    "    splot = sns.barplot(data = goals, x = 'number_of_dietgoals', y = 'Count', hue=hue,  palette = (\"#e74c3c\", \"#34495e\", \"#2ecc71\"), capsize=.2 ,hue_order=hue_order,order = order, ax=ax)\n",
    "    splot.set_xticklabels(splot.get_xticklabels(),rotation = 0)\n",
    "    \n",
    "    splot.set_xlabel(\"number of diet goals\")#,fontsize=12)\n",
    "    splot.set(ylim=(0, 40))\n",
    "    #splot.legend_.remove()\n",
    "    plt.legend(bbox_to_anchor=(1.01,0.5), loc=2, borderaxespad=0., title='Labels')\n",
    "\n",
    "    plt.savefig('./'+title+\"_.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "e3ae36cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_goals(goal_dist, 'goals_condition', 'str_condition', hue_order=['No label','Nutri-score','MTL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "30ab0903",
   "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>number_of_dietgoals</th>\n",
       "      <th>avgfsa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8.619718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7.886364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>8.537037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>8.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>8.423077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>8.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_of_dietgoals    avgfsa\n",
       "0                    0  8.619718\n",
       "1                    1  7.886364\n",
       "2                    2  8.537037\n",
       "3                    3  8.620000\n",
       "4                    4  8.423077\n",
       "5                    5  8.375000\n",
       "6                    6  7.333333"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Fsa per goals\n",
    "all_goalS = pd.read_csv('./ayoub_data_goals.csv')\n",
    "fsa_per_goals = all_goalS.groupby('number_of_dietgoals')['fsa_score'].mean().reset_index(name='avgfsa')\n",
    "fsa_per_goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "e2891d3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='number_of_dietgoals', ylabel='avgfsa'>"
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data = fsa_per_goals, x='number_of_dietgoals',y='avgfsa', marker=\"o\", dashes=True, linestyle='--', linewidth = 3, color = 'red')\n",
    "#sns.scatterplot(x = 'number_of_dietgoals', y = 'avgfsa', size = 'avgfsa', data = fsa_per_goals, color = 'red').legend_.remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "id": "098563ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.257689458689459"
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Avg_muliple_goals = fsa_per_goals.loc[fsa_per_goals.number_of_dietgoals > 1]['avgfsa']\n",
    "Avg_muliple_goals.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "id": "2a0cb0c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Plot goals per condition\n",
    "# plot goal distribution\n",
    "goal_dist['max'] = [0,0,25,0,0,33,24,0,0,22,0,0,0,10,0,4,0,3,0,0]\n",
    "\n",
    "def plt_goals_both(goals, title, hue=None, hue_order=None, order=None, ax=None):\n",
    "    fig, ax1 = plt.subplots(figsize=(10,6))\n",
    "    #plt.subplots(figsize=(7,7))\n",
    "    sns.set_style(\"darkgrid\")#, {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
    "    #splot = \n",
    "    ax2 = sns.barplot(data = goals, x = 'number_of_dietgoals', y = 'Count', hue=hue,  palette = (\"#e74c3c\", \"#34495e\", \"#2ecc71\"), capsize=.2 ,hue_order=hue_order,order = order, ax=ax)\n",
    "    ax2 = ax1.twinx()\n",
    "    #splot.set_xticklabels(splot.get_xticklabels(),rotation = 0)\n",
    "    ax2 = sns.lineplot(data = fsa_per_goals, x='number_of_dietgoals',y='avgfsa', marker=\"o\", dashes=True, linestyle='--', linewidth = 3, color = 'red')\n",
    "    # splot.set_xlabel(\"number of diet goals\")#,fontsize=12)\n",
    "    # splot.set(ylim=(0, 40))\n",
    "    ax1.legend_.remove()\n",
    "    #plt.legend(bbox_to_anchor=(1.01,0.5), loc=2, borderaxespad=0., title='Labels')\n",
    "\n",
    "    plt.savefig('./'+title+\"_.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "id": "0e203235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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soFu3btStW5fGjRvz7rvvcuHChTTtz507ByQPdmakQoUKAJw5cyYP3mHesLkEWa/XExQUlGb4HWDnzp1s2bIFFxcXa0KccsODgoIy7CswMBCLxcL58+fzN+hCSOrQCiEyYilZivs//kzkht9JqlM3zX5L6TIqRiaEyI6KFSsyYcIEnJyc+Oabb2jSpAkNGjTggw8+oEaNGqxYsYJSpUpl2ceVK1eIiYnh9u3bfPTRRxgMBho1aoTBYGDjxo1069aNQ4cOWdvffjBV09fXN8P+UvbfvXs3j97l47O5BDm1hIQEhg0bRrt27XjrrbcoVqwYs2fPtk69uHXrFvDoG37nzp2CCViIIsjh7BncB7+B9oL8IVqUmBo2ImrLDu5/Ox2LbwliJk5OVwFD6icLUTBu3LjB1atX03xFR0dn2r5evXo0adIEFxcXGjduTNOmTXF1deX48eMsW7YM5RE/uynzj/38/Fi9ejVLly5l1qxZbN++nf79+xMbG8uIESNITEwEIO7BFCxnZ+cM+3NyckrTzhbYZBWLFNevX0/3kF1YWBgNGjQAIP7BalwpN/a/srrh0dHR6b559Ho9JUqUwNvbNV37Ii0pCVatAh8feP55taPJc/s2LcvzPn19i9AI/NCv4ZcVOK35BaZOhSFD1I5IFKQRQ+DtgXj/9//DJhO0aQPdu8OgQZCNuqpCiNx59dVXuXbtWpp9Q4YMYejQoenaHjlyhP79+1OmTBnWr19vnRMcHh7OkCFDWLRoEW5ubgwfPjzduSmee+45du3ahVarxS/VwkI6nY6RI0dy4MABQkND+eOPP2jfvr215G5mpeNSEvJHJeYFyaYT5JIlS7J//360Wi379u3jiy++YMKECcTFxTFo0KDHuuELFy5kxowZafbVq1eP5cuX5/G7sHMHDkCPHsn1bOvUgeeeK3RzS5u265Wn/eVHwm2zjh6FFSuSX5vN0EiWKC6SMhqk+Okn2LUr+eunn2DaNMjg2RIhxONbunQpZrM5zT4PD48M206cOJHY2Fi++OKLNA/M+fn58e233/L888+zYMECBg0alOmIr0ajyXQahlarpWXLloSGhnLixAnat2+Pi4sLkDwzICMpI82ZXU8NNp0gu7i4WG/qCy+8QKlSpejZsyc//fQTffr0yfYNT2mXWp8+fejSpUuafSnzniMjYzGZ0q8mVRRpPP0odusWGoCjR4n6ZR1JrZ5RO6w8k18jvbdv38+Xfm2Nx5hxGB68TnyhA9Hlq0IRee8iC4qC189zsS4ofvQotGxJwotdif34cyxlymZ1thAim3Q6Ld7ero+cM5wiISGBY8eO4e7uTu3atdMd9/f3JzAwkLNnz3L58mWqVq2aq7hSpsKm5Gcpq+RlNuX1UXOU1WDTc5D/64knnqBcuXLExMRw5cqVx7rhHh4elC1bNs2XrS1zaAuUYsVI6NXbuu0y43sVoxG2RHfoIIYtmwBQNBpiR3+gckTCZmg0RK3bQuyYD1FSDVA4/bYan6b1cflmEjyYIieEKDj3799HUZQsl5JOOZaUlJRpm6VLl/K///2Pffv2ZXj86tWrQPJMAHhYTCGluMJ/pRRTyKzoghpsKkFWFIXJkyczYsQITCZThm1SRnlNJpO1XEhGN1xRFC5cuICDgwMVK1bMv6CLgLi3hqA8mM6i37MT3fGjKkckbIHrl59bXyd2eQlz9UfXwhRFiLMzce+OIiL4bxK6PCwZpYmPx3XSF/g0b4h+43p5kE+IAlSsWDG8vLyIiori2LFj6Y6Hh4dz/vx5HB0draXXMnLlyhU2b97MmjVr0h1LTEy0Pj/21FNPAcmVM8qUKcPJkye5ceNGmvYWi4UdO3ag0Who3rz547y9PGVTCbJGo2H79u1s2rTJuhhIaleuXOHixYu4uLgQGBhovZHbt29P1/aff/4hIiKC+vXr4+bmlu+xF2aWgPIkdnrRuu38g4wiF3WOwXvR79kJgOLgQNzIsSpHJGyVpUxZ7v80n6h1WzDVqGXd7/DvZTz7vYrHa90lSRaigGi1Wrp16wbABx98kGaxkIiICN5//32SkpJ46aWXcHVNLlhw/fp1zp8/T0REhLVtt27dcHBwSLdicVJSEhMmTODatWu0aNGCmjVrWo/17NkTs9nMBx98kKZ4wvfff8+lS5do06YN5cqVy7f3nlM2lSADdO/eHYDPP/+cmzdvWveHh4fz7rvvYjKZ6NWrFwaDgYYNG1K5cmWCg4NZuXKltW1ERASffvopAP369SvYN1BIpV5+2rB2jSwvW5QpCq5fTrBuJvTohblixsXfhUiR1LgpkX/s4f7XU7H4+Fj3m2rUKnQP/gphy4YNG0bDhg05c+YMbdq0YcCAAQwcOJDnnnuOkJAQnnjiCUaPHm1tP3r0aNq1a8fSpUut+ypVqsSYMWOs/XXr1o1hw4bRunVrVq1aRYUKFfjqq6/SXLdv377Ur1+f4OBg2rZty7Bhw+jYsSOzZs2idOnSjB8/vmBuQDZpFFuqqUHyXx/vvPMOu3fvxsXFhXr16mE2mzl69ChxcXG0bNmSGTNmWKdaHDt2jD59+hAXF0edOnUoUaIEISEh3Lt3j+7duzNhwoRHXDE9eUgvY54vdUS/dzcAcW+8RewXk1WO6PH5+rrnSxWLwvyQnuOO3/HqmfyRueLoSMRf/2ApF6ByVMKeaCIjcPn6SwxbNxOxez/Ip3xC5ErKQ3o5lZSUxLJly1i7di0XLlzAYrFQvnx5OnToQN++fdMs1ta7d29CQkIyLBv3119/MWfOHI4dO0ZCQgKlS5fm+eefZ9CgQdYR6NTi4+OZPXs2GzZs4ObNm/j6+tKsWTOGDBlic8+B2VyCDGA2m1m2bBmrV6/m/PnzaLVagoKC6Nq1K927d7eWd0tx7tw5pk2bxoEDBzAajQQEBNCzZ09efvnlLCeiZ0YS5Iw57vgDr55dAVBcXLj7T2jyksJ2TBLkHFIUvNq2wvHoYQDi+79BzFffqByUsFtxcfCfKkPay5fwGPwGMR9NwNSosUqBCWEfcpsgi0ezyQRZbZIgZ0JR8H76KXQnTwAQO+ZD4t4dpXJQj0cS5BxSFBx37cD1qwnoTp0kIuQolpLZKy8kRHZ49HsNw8Z1ACR0fZnYjydgKVVa5aiEsE2SIOcfm5uDLGyYRkPcO8OA5BFk/lOUXBQBGg1JTz9L1JadRP6+R5Jjkae0V6+g3/G7ddtp9Sp8mtTHZeoUyKTevRBC5AdJkEWOJL74EjEff87df0KlckFRptFgrpK7AvJCZMZS1p+IPw+S0OnhIk6auFhcJ36WXBZuyyapeCGEKBCSIIuccXQk/p1hdj/3WAhhmyz+5bg/ZyFRazZiqvawtrbD5Ut4vt4Tzx5dcDgTpmKEQoiiQBJkIcQj6devxbDq/2RajSgwSU81J3L7Xu5/OQWLl5d1v37XDrxbNcH5px/UC04IUehJgiwem+b2bfnYszBLTMTto7F4vDMI71ZNZPROFBydjoQBg4jYf5j4vgOsK3pqTCZMVasnN3HQ4JUQjc/9O3glRKNzkJrKQojHJwmyyDWHM2G4/e8ditWthuP+jNdjF/bPafF8HK5dBUB79y7mMmVVjkgUNYpPMWImf0fkH3sxNnmKxPadSGr5NDoHDZ7/nsOx+VM4VKyAY/On8Pz3nCTJQojHplM7AGG/nH/6Aedli5Nf//A9SU2eUjkikediY3H9bop1M274u5BB8XchCoK5Zi3u/bYJYmMBcIu9h/bFF+Hyg5U9L19G++KLeM6bT8LKXzAHVsQcWAFzhYpYyvpDLuriCyGKJkmQRa7Fvz0UpyUL0SgKhm1bcDh9CnPVamqHJfKQ87yf0d6+BYC5VGni+wxQOSJR5Gk01pX3tEnGh8lxisuX0TpocfnpxzS7FUdHzAHlMVeoSNyocZhqP1FAAQsh7JFMsRC5Zq5UGePz7a3bLj9OUzEakdc00fdwmfGddTvu3VHg5KRiREKkZXHUQ8B/ljkPCICIiHRtNUlJ6M6dxbBtS/qHTRUFr9Yt8Hj1ZVzHj8Fp7mwcd25He+miPJgqRBElI8jiscQNGY5h8wYADL+uJHbseFn1qpBwnvUD2shIAMwB5Uno1VvliIRIK8bVE8/ffns4zSIgAMsvvxJ37SaacR+hvXgB3YXzaC9ewOFWuPU8c2CFNP1obt/G8diRDK9hHXl+MFXDXL4CCa/1AYMhH9+ZEEJtkiCLx2Jq0Iikho1xDNmPJikJ59kzif14gtphqcpoTsLX1z3P+ktIMnI/KjHP+ssOTcRdnGc9LKMVO3IsODoWaAxCPIrJrHCvXCXc9gajNSVh0TkS4+qJKSAImrZI01YTcx/txYs4XL6E4uWd5pjDxQuZXiNl5Fl37iwAil5PQt+0U420/17GefaPyUl0YMWHc5518itWCHslP73iscUN+R+er/cEwGnRfOJGvI/i4alyVOrROzjy7NaRedbf9ue+5j4FmyC7zPgebcx9AExBVUh8qXuBXl+I7DKZFaKcPB7uMGdcclJxc8dcqzbmWrXT91HnCSJ2BONw8TwOF87jcPHCw/+G30zT1hxQPt3DfroTx3GZPTPt9RwdMZcLeDjynPLAYOUgLP7lcvdmhRAFRhJk8diMbZ/HVDkI3dkzaO9H47RoAfFDhqsdlsglbfhNnOf+ZN2OHf2BPP0vCjcnJ8w1a2GuWSv9sZiY5ET5UnLSjLNzuiYOF86n26dJSkJ3/hy68+fS7E9q1ISo9VvTnn/6FNrrVzEHVkxOnmXkWQjVyU+heHxaLfGDh+E+YggAzrN/JH7Q26DXqxyYyA1Fryf+9f44L5iDqXIVjO07qR2SEOpxc8t05DlFUvMWxHz4SdrR5/+MPKcwVaiYbp/TyuW4zJgKgKLTJY88V6iYavS5giTPQhQw+UkTeSKhWw9cvpyAw61wHG7ewLBxHYlduqkdlsgFxduH2AlfEv/2EDR374JWit0IkRVTnbqY6tRNuzM2NjlZvnghzdSNjMrLpR6B1phM6C6cR5fBqLSi0xE7Zjzxw0ak2a+5exfF01OSZyHykPw0ibxhMBA/6G0M27YQ985wjM+9oHZE4jFZSpeB0mXUDkMI++Tqmvm0jf8w1aiJJioyOYm+eSPTdhqTCcXbO91+z7690B06+J85zw+rbljKBaRLnnUOmuSFVpKMWBz1yQ83ZjJ/W4iiSBJkkWfi3xlO/LB31Q5DCCHsStzIsTBybPJGbCwOly4+nK6R+sHBmzcwZzBFQ3vxQtqR5+2/pzlunbYRWIHY8Z+hqVUTz3/PWcvjOQQE4Pnbb9wrV0mSZCEekARZ5B15kMuuGZYvIanl08kjx0IIdbi6Yq5RE3ONmumPxcamL7eYmPjI//emTp5jP/480yW63fYGp60IIkQRJgmyEAKH06dw/987oNcT33cAsZ98IX/wCGFrXF3T7zMYiDh6+uHIc8pDgpdSlaq7cd3a3BxQHm1SbMZLdJuS8vkNCGE/JEEW+cNsRr9xHbrQ48SN/ShPuvR216NzktWr8oPrpC/QKAokJiY/MCTJsRD25REjzw6XLuJw7Qo4O2NJSMIhICBtkhwQgEUniwEJkUISZJHnNDH38Xq2ObqLF1A0GhJ79MJcodJj96tzMnD89a55EOFDtRatztP+7JHu6GEMG9dZt+PGfKhiNEKIPPef5DnG1RPP1WvQdu3ycInu1auJcfXMdKEVIYoaqd8k8pzi5m59kESjKDj/OEPliERWXL763Po6seOLmGrVUTEaIUR+M5kV7pWvjGXOXNi1C777jvgTp+UBPSFSkQRZ5Iv4If+zvnZasRTNrVvqBSMypTuwH8ODJ94VjYbYUeNUjkgIURBMZoXYvw9Dq1bQtSuOc35WOyQhbIokyCJfJDVtRtITyYXzNYmJOM/76RFniAKnKLh+NcG6mditB+YqVVUMSAhRkIztOlpfO+7biybirorRCGFbJEEW+UOjIS7VKLLzvJ8hJka9eEQ6jnt3ow/eCzxYoev9MSpHJIQoSJZSpUl6siEAGrMZ/dbNKkckhO2QBFnkG2P7TpgDygOgjYrCeflidQMSDykKrl9+Zt1MeKU3lsAKKgYkhFBDYvtO1teGDWtVjEQI2yIJssg/Dg7EvT3Uuuk86wcwmVQMSKTQ/74Fx0N/A6Do9cS9O1LliIQQakjs8DBB1u/eieZ+tIrRCGE7JEEW+Sqh56tYihUDwOHKvxjWrVE5IgGgOLtgqloNgPi+A7CUKatyREIINVgCypP0oHKNxmhEv22LyhEJYRskQRb5y8WF+AFvWjedZ3wPipQSUltS85ZE7txH9Mw5xA19V+1whBAqMqYaRTZsXK9iJELYDkmQRb6L7/8GiosLipMTpicbQEKC2iEJAAcHEl/qjuLnp3YkQggVJXboDJD8/2kXF5WjEcI22ORKemazmeXLl7NmzRouXLiA2WzG39+fdu3aMXDgQAyGh8sN37hxg1atWmXaV7169Vi+fHkBRC0yo/gUI/rnBSQ9UR/F11ftcIQQQqRirhzEveW/YGzSDCRBFgKwwQTZbDYzePBgdu3ahYuLC3Xq1EGn03H06FGmTZvG7t27WbhwIc7OzgCcPHkSgCpVqhAUFJSuv8DAwAKNX2TM2OZ5tUMQRiP6ndsxtn0eNBq1oxFC2BDjs23VDkEIm2JzCfKqVavYtWsXVapU4eeff8bvwce/ERERDB48mMOHD/Pjjz/y3nvvAXDq1CkABg4cSKdOnTLtV4iizmnZYtxHjSCpTl1ix39KUotWaockhBBC2CSbm4O8Zk1ylYNx48ZZk2MAHx8fPvnkEwA2btxo3Z8yglyjRo2CC1I8Ns3t22hu31Y7jKIjPh6XbycD4Hj0MLpjR1UOSAghhLBdNpcge3t7U6FCBWrXrp3uWPny5QG4deuWdd+pU6dwcXGRqRR2QvvvZdxGjaBY/Rq4fD9F7XCKDOeFc3G4eQMAcwk/4vu/oXJEQgibYzSi374NtxFDcP34A7WjEUJVNjfFYtasWZkeO378OAAlS5YEICoqiuvXr1OjRg3mz5/P2rVruXz5Mu7u7jz99NMMGTIkzSi0UJ/DubM4L5gLgPOShcS9OwrFp5jKURVyMTG4TPvWuhk34n15EEcIkY7u+FE8X+kGgMXbm9gPPwFHR3WDEkIlNjeCnBlFUZg2bRoAbdsmP0yQMv84NDSU7777jmLFitGoUSPMZjMrV67kpZde4sKFC6rFLNJLevpZTNVrAqCJi7MmyyL/uMyZhfbOHQDMZf1JeK2vugEJIWySqW59zKVKA6CNjMTxr2CVIxJCPXaTIH/77beEhIRQvHhxBg4cCDycf1y5cmU2b97M/PnzmT17Ntu3b6dDhw7cvn2b999/P8P+oqOjuXr1apqv1FM3RD7RaIh7Z5h103nOTxAfr2JAhZvmXhTOP0yzbse9NxpSlUkUQggrrZbE9h2tm4YNa1UMRgh12dwUi4x8//33zJ49G71ez9SpU/Hx8QGgb9++tG3bFldXV+s+ABcXFz7//HMOHjxIaGgoR44c4YknnkjT58KFC5kxY0aafSk1k729XfP9PeW1RKMRg15v830C8EZf+GoCXLmC9s5tfDevgTfffORpRZmvr3vuTpw2Ge5FJb+uVAn3IW/irrOLH3shhBpeewXm/ASA85aNOM+dDVq7GUsTIs/Y9G9Kk8nEZ599xooVKzAYDEyfPp0GDRpYjzs4OODv75/huc7OzjRu3Ji1a9cSGhqaLkHu06cPXbp0SbNP/yAZjIyMxWSy5O2byWe+vu40bdcrT/vct2kZt2/fz9M+Uzi/8TZuH40DwDRpMpGde4CDQ5bn5DpJLARy8++guXMHn++mWj8min5vDImRMlovhMhClToUK148eVrWzZtEbvwDU+MmakclMqHTaXM9qLd27VqWLl3KmTNnsFgsBAYG0rVrV1577TUcHvH7OMWRI0f4+eefOXz4MNHR0Xh5edGoUSMGDx5MxYoV07Xv06cP+/fvz7S/bdu2ERAQkKv3k9dsNkGOjY1l+PDh7N27Fw8PD3788cc0yXF2FC9eHID4DD7C9/DwwMPDI09iFTmX8FofXL6ZjPZeFLqLF9Bv3oixg9Sxzksu079DGxsDgKladRK7dFM5IiGEzXNwIPGFDjgvXgCAYeM6SZALocmTJzN37lz0ej0NGjTAwcGBv//+m4kTJxISEsKMGTPQPGJBqXXr1jFmzBjMZjPVq1enbt26nDt3jg0bNrB9+3Z+/vnndHnb6dOncXFx4dlnn82wT1dX2/kE3yYT5Hv37tGvXz9CQ0MpVaoUs2fPznCVvBkzZnDmzBneeecdqlSpku741atXgYdVL4TtUNzcie83ENepyaXeXGZ8h7F9R1nhLS+ZTSg6HRqTidjRH8rHpEKIbEls3+lhgrxpPbGfTZT/NxciYWFhzJs3Dx8fH5YtW2YtkxseHs4rr7zCH3/8wbZt23juuecy7SMiIoJPPvkERVH44YcfaN26NZBcUGH27Nl8++23jBo1im3btuH4oBLKtWvXiIqKomnTpkyZYvtlXm3uN6bRaGTQoEGEhoZSqVIl/u///i/D5BiS/5G3bt3K5s2b0x27e/cuwcHBODo60qhRo/wOW+RC/IA3UR5Ma3H85xCO+/epHFHhEvv5JCKC/yb23VEYX2ivdjhCCDuR1KwFFk8vAByu/Ivu6GF1AxJ5at++fSiKQqdOndKsIeHn50evXslTNQ8ePJhlH9u2bSM2NpZOnTpZk2MAjUbDm2++SfXq1bl+/TqHDz/83kmpPGYvC7vZXII8bdo0jhw5QqlSpVi8eHGWo789evQAYP78+Rw6dMi6PzY2lnHjxhETE0O3bt3w9fXN97hFzil+fiT0eDhv2rByuYrRFE6WwArEjflQRn+EENmn12Ns+7x107BhnYrBiLyWMnUiPDw83bHIyEgAvLy8suzDYrFQvXp1GjdunOHxlHnEqauD2dvKxzY1xSIqKorFixcDyUtLT5w4MdO2U6ZMoVmzZvTr14/58+fz2muvUa9ePby9vfn777+JjIzkySefZPTo0QUVvsiF+LeHogs9Ttw7wzG26/joE4QQQuS7xA6dcVr1fygaDdrr19QOR+Sh5s2b89VXX7FlyxZmz55Nt27d0Ol0bNu2jUWLFuHp6clLL72UZR+9evWyjjb/l8ViITQ0FEg7xTUlQb537x4DBgwgNDSUxMREatasyaBBg2jevHkevcO8YVMJ8rFjx0hISACSF/9IucEZSZm/MmbMGOrUqcOSJUs4efIkFouFcuXKMXDgQPr06WOd+yJsk7lSZaK27FQ7jEJDe+M6is4RRT41EUI8BmOrZ7g/6VuM7Tpg8ZPneAqTihUrMmHCBL744gu++eYbvvnmG+uxunXr8uWXX1KqVKlc97969Wr+/fdfSpQokaaCWMoUi48//pigoCAaNGjApUuXCAkJISQkhHHjxtGnT59cXzev2VSC3KJFC8LCwnJ83gsvvMALL7yQDxEJYV9cPx6HYdtW4ga9TfzgoShe3mqHJISwR87OJPQbqHYUIptu3LiB2WxOsy+ral316tWjSZMm7N+/n9q1a6PVajl69CjHjx9n2bJljBs37pFVLDJy4sQJvvjiCwDee+89dA/q7kdERHDz5k10Oh2TJk2iQ4cO1nM2bdrEyJEjmTRpEg0bNqRatWo5vm5+0CiKoqgdhBAiDxw9CqnrfR84AA0bqhaOEEKIgvHMM89w7VraqTBDhgxh6NCh6doeOXKE/v37U6ZMGWbOnEnZsmWB5DnJQ4YM4dixYwwePJjhw4fnKIZjx47xxhtvEBUVRc+ePfn000/THI+IiCA6Opry5cunO3fixIksXLiQHj168Nlnn+XouvlFEuQMyEIhyfJzoZAMWSzot27GecEcon9egOLhmeawr687x1/vmqeXrLVodb7ct2e3jsyz/rY/93W2/h08Xu+JYcsmABKfb0/0Ivt56NHX1z1P7xlk/74JIYS9SlkoJCcjyN27d+fo0aOsWrWK2rVrpzl25coVnn/+efR6Pfv27cPZ2TlbcezevZv//e9/xMXF0bVrV7744gu0OSgt+tdff9G3b19q1qzJr7/+mu3z8pNNTbEQRZtH31cxbNkIgNOiBcQPydlfr0WZ7tBBa3KsaDTEjv5A5YiEEIWCoqA7cQz9hrWYAyuS2PNVtSMSGcjunOGEhASOHTuGu7t7uuQYwN/fn8DAQM6ePcvly5epWrXqI/v8v//7Pz777DPMZjP9+vVj9OjROZ6ekVJtLOU5NFtgc2XeRNFlfO7hPHLn2T+C0ahiNPbF9avPra8TX+yKuUZNFaMRQhQWhnVr8H62Oa7fTcF5/s9qhyMe0/3791EUJculpFOOJSUlPbK/GTNm8PHHH6MoCmPHjmXMmDEZJsf79u1j5MiRLFiwIMN+bHFhN0mQhc1I6NYDcwk/ABxu3sCwepXKEdkHx31/ot+dXAlE0WqJGzVO5YiEEIWFsdUzKA8etHI8/A/aq1dUjkg8jmLFiuHl5UVUVBTHjh1Ldzw8PJzz58/j6OhIhQoVsuxr8eLFTJ8+HUdHR7755hv69u2baduEhATWrVvHokWLMJlM6Y7/9ttvADRr1ixH7yc/SYIsbIfBQPygt62bLj98Dxb7mgte4BQF14kPH2hI6NELc8XKKgYkhChMFE8vkpq3tG4bNsqiIfZMq9XSrVs3AD744IM0i4VERETw/vvvk5SUxEsvvYSrqysA169f5/z580RERFjbhoWFMWnSJAAmTZpEu3btsrxus2bNKFOmDNeuXePrr79OM1/6119/ZfPmzfj6+lpjswUyB1nYlIQ+/XH5bgra2Bh0YafRb9+Gsc3zjz6xiHLc+QeOIfsBUBwdiXtPFsYRQuStxA6d0e/cDiSvqhf/5jsqRyQex7Bhwzh27BghISG0adOGBg0aoNFoOHr0KNHR0TzxxBNpFlkbPXo0ISEhaapizJo1i6SkJDw8PNi5cyc7d2a8nkHPnj158skn0ev1TJkyhQEDBrBgwQJ27NhB1apVuXLlCqdOncLFxYXp06fj7u5eIPcgOyRBFjZF8fQioXdfXGbNAMB5xveSIGdGUXD98uHc44TX+mApF6BiQEKIwijxhQ64jfwfGosFXch+NOHhKH5+aoclcslgMDBv3jyWLVvG2rVrOXToEBaLhfLly/PGG2/Qt29f9Hp9ln2EhIQAEB0dzfr16zNt17RpU5588kkgufbymjVrmDlzJsHBwezcuRNvb2+6du3K4MGD8ff3z7s3mQckQRY2J/7NwTjPmYXGZEL/VzC6Qwcx1W+gdlg2R3vzBtqoSAAUJyfiRuRtmTQhhABQihcnqclT6IP3olEUDJs3kNB3gNphicfg6OhInz59srVy3eLFi9PtCw4OztV1y5cvb52aYetkDrKwOZYyZUns8nAekssP01SMxnZZSpUmYt8h7n8zjdiR47CUzP3SoEIIkZXE9h2trw0bZB6yKPxkBFkl3u56dE4GtcPIktGchK9v3s4HSkgycj8q8ZHt4t4ZjtOq/wNAv3EdDhfOgW/dPI2lUHB0JKF3X7WjEEIUcsb2nWDcKAAcg/egibiL4lNM5aiEyD+SIKtE52TI01Xhai1anWd9pdA7OObL6mb3eXSCbK5eg8Rn26Dfs4uEl3uiOGY9H0oIIUT+sZQqTVL9BjgeOojGbEa/dTOJr7ymdlhC5BuZYiFsVuznXxFx6AQxU3/A4l9O7XBshiY8HGSFeCFEAUvs0Nn6OqV6jhCFlYwgC5sl9XwzkJiI9/NPY/YvR9y4j0hq3FTtiIQQRURix85o7keT2KGzrNYpCj1JkIWwI06L5+Nw7SoO166i69+bu/+EgpOT2mEJIYoAS7kA4sZ8qHYYQhQImWIh7MfduzjfiXh0u8IqLg7X76Y83Bz+riTHQgghRD6QBFnYPE1UJK4fjIJy5Si352CRXX7aee5stLdvAWAuVZr4PlKHVAghhMgPkiALm6foDTitXgVxcehj4/C8fE3tkAqc5n40LjO+s27HvTtKRo+FEKrR3riO09yfcNy/T+1QhMgXkiAL2+fiQnz/QdZN39CzRa6Kg/NPP6KNTF41z1yuPAlSXkkIoRKn+XMoVqcq7mNH4rRgrtrhCJEv5CE9YRfi+w/CdcZUiI/HOfIerjdvE1uqhNphFYyICJxnzrBuxo4cA3qpC52f8mMhH1NCIpH3jXnapxBqMD3ZwPpa//tWSEwEg20vfCVETkmCLOyCUqwYDBgAM5ITRd/Qs0UnQf76a7T3owEwVQ4isVsPlQMq/PJ6IR94sJiPJMiiEDDVrI25XHkc/r2E9n40+r27MLZ+Tu2whMhTMsVC2I9330XRJL90v3ELp4goVcMpCN4RsTBtmnU7dvQH4OCgYkRCiCJPoyGxQyfrpn7DOhWDESJ/SIIs7EdgIPfKlbFu+oaeVTGYglH/n0sQHw+AqUYtjKlWshJCCLWkTpANWzaCyaRiNELkPUmQhV25XePh6nqel6/hGBOnYjT574/WNeD4cRLbdyJ27IeglR9ZIYT6TPWexFyyFADaiAgc9/2pckRC5C35bSvsSkIxb2JK+gKgURSKnzqnckQFoEYNoucvwdj2BbUjEUKIZFotxvYdrZuGDWtVDEaIvCcJsrA7qUeR3W7cAkvRKvkmhBC2IDHVlC/9pg1FdhEnUThJFQthd2JKlSCqfFnulylJVPkyoNWoHVKeczSaSNLLj6cQwnYlNW6KpXhxtHfu4HArHN3BEEyNGqsdlhB5QkaQhf3RaLjSvAFRFfwL5ZzcgMt3WPHaT/T6v/04xUtZMCGEjXJwIPGFDtZNw5aNKgYjRN6SISohbEzfxcF4RsczYMGflL90B15UOyIhhMhY4kvdAQ2J7TuS1KyF2uEIkWckQRaFh6KAxr6nW1Q+G06LPx+Wr1v1UgOeVTEeIYTISlLTZiQ1baZ2GELkOZtMkM1mM8uXL2fNmjVcuHABs9mMv78/7dq1Y+DAgRj+s6TlxYsXmT59OocOHSIqKopy5crRo0cPevXqhbYQfgQvUlEU3K6H4xt6lrtVKxCdqk6yPeq76GGppD3NKnO2sp+K0QghhBBFk80lyGazmcGDB7Nr1y5cXFyoU6cOOp2Oo0ePMm3aNHbv3s3ChQtxdnYG4PTp07z66qvExMRQr149atWqxYEDB5gwYQJHjhxhypQpKr8jkZ+KnzpHqUMnANCazUT7l7bbUeTqJ6/R+OBFACwaWPDaUypHJIQQQtgus9lMeHg48fHxKErailYmkwmj0citW7fYsWMHEydOzFHfNpcgr1q1il27dlGlShV+/vln/PySR9AiIiIYPHgwhw8f5scff+S9995DURRGjRpFTEwMkydPpnPnzta2ffv2Zf369bRp04bnnpM14gurqEB//A6fRGux4HInEpdbd4nzK652WLnSf+HD0ePtT1fncnn7fB9CiCIqNhb9jt8x1X4CS0B5taMRhdzPP//Mzz//zP3797PVPqcJss3NP1izZg0A48aNsybHAD4+PnzyyScAbNyY/KRscHAwYWFhNGzY0Jocp7T9+OOPAVi8eHEBRS7UYHJ2IqpCOeu2vS4/XffwZeoevQKAWath4WtNVY5ICCGyz3nmDIpXr4DngNdxWrlc7XBEIbdt2za++eYboqOjURQlyy9/f3/eeOONHF8jxwnyjBkzWL16dbbazp49m5EjR+aof29vbypUqEDt2rXTHStfvjwAt27dAmDv3r0AtG7dOl3b+vXrU6xYMQ4dOkRMTEyOYhD25Xb1SqR8sOJx7SaGqGhV48kxRUkzerz5uVrcKO2lXjxCCJFDllKl0MTHA2DYsE7laERht3LlSgA6duzI7t272b9/Pw4ODnTv3p3jx4/z+++/8+abb+Lg4IDFYmHQoEE5vkauEuRff/01W203b97M77//nqP+Z82axebNm3FxcUl37Pjx4wCULFkSgHPnkpcZDgoKyrCvwMBALBYL58+fz1EMwr4YPd2J9i9l3S5+0r5GkRuHXKD66RsAGB0dWNJLCu0LIeyLsXVblAcP0OtOheJw4ZzKEYnC7OTJkzg7O/PJJ5/g5+eHl5cXlSpVIjg4GEdHR/z9/RkxYgTDhw/n2rVrLFy4MMfXyHIO8tWrV9m9e3e6/bdv32bp0qVZdnzt2jXOnDmDm5tbjoPKiKIoTJs2DYC2bdsCD0eSfX19MzwnZf+dO3fyJAZhu+7UqIznleQk0+viFcKfqI7JxVnlqLKn44Yj1tcb2tXhtq+HesEIIUQuKG7uGJ9+FsOWTQDoN6wnftgIlaMShVV0dDQVKlTA1dXVuq9y5cps3LiRe/fu4enpCcDrr7/OzJkz2b59O++8806OrpFlglyiRAkWLVrEv//+a92n0Wi4cuUKn3/++SM7VxSFZ555JkcBZebbb78lJCSE4sWLM3DgQADiH3yc4+TklOE5Kfvj4uLSHYuOjiY6Ou1H8Xq9nhIlSuRJvKJgxfkWI9bXB9fbEWgtCsVPnedm/Zpqh5Utn4zvTMeNR+my9h+W9WykdjhCCJErie07WRNkw8a1kiCLfOPi4oLmPxWr/P39ATh//jz16tUDkvPA8uXLc/ny5RxfI8sEWa/X89FHHzFr1izrvoMHD+Lu7k7VqlUzPU+r1eLi4kKVKlVyNe/jv77//ntmz56NXq9n6tSp+Pj4WK8DpLtJKVJKfvy39AfAwoULmTFjRpp99erVY/ny5Xh7u6ZrL/KOr697vvR7u0YQrrv2A+Bz9iK3alXBonfMl2vlpSS9jtVd6vNbp7pYHDKe9ZRf96yws7X7ZmvxCJGnXu0OI4aAyYTj4X/wjY+EcuUefZ4QOeTv78/FixeJi4uzTskNCAhAURROnTplTZABEhISMJlMOb7GI8u8PfXUUzz11MN6rFWrViUoKKhAqkOYTCY+++wzVqxYgcFgYPr06TRo0MB6POWmJCQkZHh+YmJimnap9enThy5duqTZp9frAYiMjMVksuTJe8hMUf5Feft29kqy/Nej7tn9siVJ8HDDKToGhyQTPmcvcqdGxvPTbVFmyTHk/p7Zi3z7oymfvtdyq7D/O4qiTodnsxbod+0AIGbxcuIHDVY5psJNp9MWyUG9Zs2aERoayvjx4/n0009xc3OjVq1aAPz666+8/PLL6PV6jh07xqVLlwgMDMzxNXL8kN6iRYv48MMPc3yhnIqNjeWtt95ixYoVeHh4MHfuXFq2bJmmTcp0iMzmGN++fRvIeI6yh4cHZcuWTfMl0yvsnEbDneqVUTQaIiv4c790SbUjylwGn2oIIYS9S+zwsOSqXqpZiHzy+uuv4+3tzaZNm2jevDlGo5GKFSvSqFEjTp06RdeuXRk2bBj9+vUDkhPqnMpxgtywYUOqVauW4wvlxL179+jduzd79+6lVKlSLF26NM3IcYrKlSsDD6tZpKYoChcuXMDBwYGKFSvma7zCdkRV8CesS1uuPvUkid62+7Bbh01H+fLDX6h89qbaoQghRJ5JfKEDyoNpj44H/kITHq5yRKIwKlasGPPmzaNatWro9XrrDIAPPvgADw8Pzp07x7Zt24iNjaV06dIMHpzzTzJytZJeTEwMK1eu5MiRI8TExGA2mzOc5wvJ84NzUl7DaDQyaNAgQkNDqVSpEnPnzrWWdfuv5s2bM2fOHLZv386rr76a5tg///xDREQEDRs2zLNKGsL2KQ4OJLmmn1JjS/SJSfRetp/id2No+PclPv6wE382s5+pIEIIkRnF15ekxk3R/xWMRlEwbN5AQt8BaoclCqFq1aqxevVqbty4Yd0XFBTExo0b+eWXX7h+/Trly5fn5Zdfxt0959Pmcpwg3759m549e3L9+vVMk+LUMnuALjPTpk3jyJEjlCpVisWLF1sfyMtIw4YNqVy5MsHBwaxcuZLu3bsDyUtNf/rppwDW4XUhbEXHjUcpfjd58Zq73q4cfDLnc6OEEMJWJXZ9GcXTk8R2HTE+307tcEQhV6pUqTTbxYsX56233nrsfnOcIP/www9cu3YNZ2dn2rdvT2BgYKZl1nIqKirK+vCfj49PlutmT5kyBa1Wy8SJE+nTpw/jx4/nl19+oUSJEoSEhHDv3j26d++eZ2XmhH3SGpPwuHojzXLUanKKN/LKigPW7aWvNCLRyfYrbQghRHYl9OlPQp/+aochioj4+HicnR+ue3Dy5Ek2btyIxWKhRYsWNGnSJFf95jhB3rVrFxqNhvnz5/PEE0/k6qKZOXbsmLUiRWhoKKGhoZm2nTJlCgC1a9dm1apVTJs2jQMHDnD27FkCAgJ49913efnll/M0PmFf/A6HUizsAg5JJhLd3Yj3zfzTiILSde0/eN9Lrt8dXsKdTc+nX1JdCCGEEFnbsWMHX331FY0bN+azzz4DYPv27QwfPtw69XfBggX06NGDTz75JMf95zhBvnv3LpUqVcrz5BigRYsWhIWF5fi8SpUqWVfZEyKFY1w8DknJtQ99T57l35bqLsLhGpNA918OWrcX92pKkj5XjwEIIYQQRdbx48cZOnQoZrPZukCIoih88cUXmEwmKleuTK1atdiyZQsrVqygWbNmtG7dOkfXyHEVi+LFi2dad1gIW3K7emXra49/r6OPjlExGnj5179xj0muzX21tBfbWldXNR4hhMh3ZjO6/X9BjLr//xWFy4IFCzCbzbz66qv88MMPABw6dIjr16/j7u7O8uXLmThxIrNmzUJRFFatWpXja+Q4QX766ae5du0ap0+fzvHFhChIid6e3C/tB4AGKH4qfTnAguIZFcdLvx2ybi/s/RRmnYNq8QghRH5znj4VnzpV8e70HIbft6gdjihE/vnnHzw9PRkzZoz1ObidO3cC0LJlS2v1soYNG1KmTBmOHz+e42vkOEEeOnQofn5+jBgxIlcXFKIg3a7xcBTZ+9xldPHqfPrRc1UILvFJAFwoX5ydLTNfql0IIQoDTZIRh1vJdZANsmiIyEN3797F398fR8eHD7n/+eefaDSadIuCeHt7Ex0dneNr5KqKRc2aNfn999/p3r07np6e+Pn5pQkyNY1Gk6uhbSHyQqxfceKKeeFyNwqtxUKxsAuEP1GwUxvc7ifQacMR6/aC3k+haHNW/lAIIexNYofOuH71OQD67dsgLg5cbLtOfVGydu1ali5dypkzZ7BYLAQGBtK1a1dee+01HByy9wlneHg4P/zwA8HBwdy+fZtSpUrRqVMn3njjDeviHaklJCSwcOFC1q1bx9WrV3F3d6dVq1YMGzYsR6sZe3p6Ehsba92+desWYWFhaDSadFUrbty4kav1MHKcIC9ZssRa21hRFKKiooiKisq0fU7rIAuRpx4sP11ub/LDcT5hF7hdIwiLY8E9HBfj7sS7k3vQd1EwHtHxBDetVGDXFkIItZiDqmCqHITu7Bk0cXHod27H2L6j2mEJYPLkycydOxe9Xk+DBg1wcHDg77//ZuLEiYSEhDBjxoxH5m83b96kR48e3Lx5k+rVq1OjRg3++ecfpk2bxv79+5k3b16awdOkpCQGDx5McHAwpUqVomXLlly4cIFVq1axa9cuVq5cSenSpbMVf/ny5Tl06BDnz5+nYsWKrF+/HoCqVavi5+dnbbdu3Tru3r1Lo0Y5f0g/x1nCl19+meOLCKGme+VKk+jmiiEmFp0xCe9zl7lbrWCXHw+rUoqxX3TDOc4I8kejEKKISOzQCd13yWVZDRvWSoJsA8LCwpg3bx4+Pj4sW7aMwMDkxarCw8N55ZVX+OOPP9i2bRvPPfdclv188skn3Lx5k+HDh1uXco6Li+Odd95h3759LF68mP79H9bDXrJkCcHBwbRq1Yrp06dbR5i/++47Zs2axWeffcasWbOy9R46derEwYMHef3116lbt661BHG3bt0AuH79OrNnz+aXX35Bo9HQpUuXHN+nHCfIubmIEKrSarlTvRJlQo4CyQ/r3a0SCNocT8F/bPEu6T9yEkKIwsrYoTOuDxJk/bYtkJgIBoPKURVt+/btQ1EUOnXqZE2OAfz8/OjVqxdff/01Bw8ezDJBvnDhArt27aJcuXJpVq1zcXHhiy++oHXr1ixZssSaICuKwvz589FoNIwfPz7N9Ivhw4ezdetWdu7cyZUrV6xl27Ly8ssvc/z4cVauXMkff/wBQJs2bejVqxcAd+7c4f/+7/+A5BWVX3zxxezfoAcKPkMQQgWRFcthMiT/QGosFgz3Yx9xhhBCiMdlqlkbc7nyAGjvR6P/c7e6AQnr1Inw8PB0xyIjIwHw8vLKso8///wTRVF4+umn0f5nsKl06dJUr16da9euce5ccvWosLAwwsPDqVq1KmXLlk3TXqvVWlc93rNnT7prXbx4EaPRmG7/Z599xoYNG/j2229ZtWoV06dPt763ChUq0KNHD5YsWcLo0aOzfC+ZyfEI8sGDBx/d6D8aNGiQ43OEyEuKTsfNutVRtFrulfdHccj/vw3f+XE7N0p7sb5dHVkQRAhRNGk0JLbviMvM6QDoN6zD+GxblYMq2po3b85XX33Fli1bmD17Nt26dUOn07Ft2zYWLVqEp6cnL730UpZ9pCS+lStXzvB4hQoVOH78OGfOnKFSpUrZag9w5syZdMf69++Pm5ubdZ4xwG+//UaxYsVo3rw5lSqlf67Hzc2NTz/9NMv38Cg5/q3du3fvHD94d+rUqZxeRog8F1k58NGN8kiFC7fpuu4wAN1+/ZtBP/Yhxt2pwK4vhBC2IrFDJ2uCbNi8gZivp4JOBg3UUrFiRSZMmMAXX3zBN998wzfffGM9VrduXb788ktKlSqVZR+3bt0CyLTyhK+vL5A81QHg9u3bafZn1v7u3bvpjt29exdvb+80+8aMGUP9+vVp3rx5lnE+jhwPo3l6emb6ZTAYUBQFRVEAaNq0qXXYXIiipO+iP62vz1UsIcmxEKLIMtVvgLlkcsKljYjA8a9glSMqfG7cuMHVq1fTfGVV+7devXo0adIEFxcXGjduTNOmTXF1deX48eMsW7bMmsdlJj4+HsC6SMd/peyPi4tL819nZ+dstU/Nzc2Nc+fOsX//fpKSkrKMKy/l+E+4AwcOZHn8/v37bN++na+++oq4uLhsP5FoS7y9XQvkOrUWrc7T/vZtWpan/QFsf+7rPO/T19c91+fm9T0jKSnv71tICE/tP2/dfOqn1WyvXfuxunyce2YvCv33GkXj31GIDL3cDQ4cgK5d8WpUF+RnIU+9+uqrXLt2Lc2+IUOGMHTo0HRtjxw5Qv/+/SlTpgzr16+3zgkODw9nyJAhLFq0CDc3N4YPH57p9VLmHWc2oyAlwU75b07bp9asWTPWrVtHv379rPs0Gg3//PMP1apVyzTG1DQaDSdPnsxW2xR5/hmHu7s7L774IiVKlKB///78/PPP1vIf9iIyMhaTyZKv1/D1def4613zrL9ai1bTtF2vPOsPkhPuZ7eOzNM+tz/3Nbdv38/VuXl2zxQF15u38Q09i/szbWgakbf/1vtMd6yvd7aswuc3FsON3Pf3OPfMXvj6uhfO77VUai1aXej/HYXI1LjPIPXiE/KzkCd0Oi3e3q4sXboUs9mc5piHh0eG50ycOJHY2Fi++OKLNA/M+fn58e233/L888+zYMECBg0alOmIr8uDBV8SEjJenTYxMRF4OGKc0/apjRs3jvDw8EcO0GblUSPiGcm3SUBNmzalbNmyrF+/3u4SZFG4GaKiqfDHg4/4/u//KNm8PTed8+ZTgyfu3oKQ7QCYtRoWvPZUnvQrhBB2LZsrs4ncedSc4RQJCQkcO3YMd3d3amfwyaa/vz+BgYGcPXuWy5cvU7Vq1Qz7SZl7nDLH+L9S5hyntMtu+4zmKHt5ebFw4UKioqKIiIjAZDLRqVMnqlevzqRJk7J6u48lX2fJu7m5ceHChfy8hBA5lujtSUxJX9xu3gazmR4XT/N99fqP37GiMOjsMevm789W56q/z+P3K4QQQuSB+/fvoyhKlktJpxzLar5vSjWKlOoU/3X+fPI0w6CgoDT/zW77jHh5eaUpP+fs7JxpVYy8kG+1ri5cuMC5c+fw9PTMr0sIkWu3qz/8oep09TzuxsTH7rPRnZs8EZn8V3CSTsuiV5s+dp9CCFEYaS9egFx87C0eT7FixfDy8iIqKopjx46lOx4eHs758+dxdHS0ll7LSEr1iB07dmCxpJ2meP36dU6dOkWZMmWsJdgqVqxImTJlOHnyJDdupJ1zaLFY2LFjBxqNJttVKU6fPs3SpUuz1Ta3cjyCvHt35kW+FUXBaDRy8eJFFi9ejNlsplmzZo8VoBD5IaZ0CeK9PHCOisbZbKbrv+dYWKlG7jv8z+jxpudrEV5S/jgUQojUnGfOwOn/lqI7FUrk77sx1amrdkhFilarpVu3bsyZM4cPPviAOXPm4OfnB0BERATvv/8+SUlJ9OzZE1fX5KmH169fJz4+Hm9vb3x8kj8V9ff3p3nz5uzdu5fvv/+eESNGAMlVKD788EPMZnOah+oAevbsyTfffMMHH3zAjBkzrPOSv//+ey5dukTbtm0pV65ctt7H9evXs/2eHRwccHJyyvGAbY4T5DfffDNbdZAVRcHb25shQ4bk9BJC5D+Nhjs1KuMffAiAly+HsTywKsZczpNrfusa1e5FJG84ObH0lSZ5FakQQhQauuNH0Z0KBUC/cb0kyCoYNmwYx44dIyQkhDZt2tCgQQM0Gg1Hjx4lOjqaJ554Is3qc6NHjyYkJCRdVYyPP/6YV155hVmzZrFjxw4CAwP5559/uH37Ni1atOCVV15Jc92+ffuya9cugoODadu2LfXq1ePixYucOXOG0qVLM378+Gy/h2eeeSbHa3I4OjpSrVo1evbsSZcuXR7ZPsdTLEqXLk2pUqUy/fL396dGjRr07t2bX375hTJlyuT0EkIUiKjyZeHBmu8+xkReuHYx130dKubHT5Vrc1/nCIMHc7eYW16FKYQQhUZih87W14YNa2WahQoMBgPz5s1j3LhxVKpUiUOHDhESEkKpUqV47733WLx4sXV0Nyv+/v6sWrWKrl27EhERwa5du/D09OS9995jxowZ6P6zGIxer2fu3LkMHjwYZ2dndu7cSWxsLD169GDFihWZLjqSkdKlS1OsWDHr2huKouDq6kqJEiVwc3NLsz/ly2g0cvToUcaNG8dHH330yGvkeAR5x44dOT1FCNuk1cKIEfDuuwC8cvE06/0rYNHkfGp+nM6RhZVqsDqgMls//BBCJuZ1tEKk4+5lwMlRn6d9JiQZuR/1+HPyhciI8elnUVxc0cTFojt3Foew05irZq+Wrcg7jo6O9OnThz59+jyy7eLFizM9VqpUKb788stsX9fZ2Znhw4dnWWM5OzZv3szrr79OREQEffr0oVevXvg/GPACuHnzJqtWrWL27NlUrVqVqVOnkpCQwN69e5k2bRqrVq2iZcuWPPvss5leQ9Z6FEXbwIHcHzUad1MS5eLu0zz8GrtL+j/6vEzcd9TDf5bEFCK/ODnq86V+9H0kQRb5xNmZxNZtcVq3BkgeRY6TBFnk0MyZMzl27BgfffRRuqkcACVLlmTo0KEEBAQwevRo1q9fz1tvvUXFihUJDAzkzTffZNWqVVkmyI9VxWLv3r1MmDCBAQMG0LNnT/r3788nn3zCH3/8kauizEIUOHd3Vpd7WNHi1YunVAxGCCEKP2OHTtbXhg3rVIxE2KuNGzfi6+ubYXKcWqdOnShZsiRr1qyx7mvZsiUlSpR45Mp6uRpBvnv3Lv/73//4+++/gbQrlPz111+sWLGCunXr8t1331mfjhTCVv0SEET3y2EElyjD0sDsj2Q4ms0MP/UPvwQEccldKlYIIUR2GFu3RTEY0CQmojt5Au2F81gqVFQ7LGFHwsPDs6yZnFqxYsU4c+ZMmn0lSpRIt++/cpwgJyYm0r9/f8LCwnB2dqZt27ZUr14dFxcXYmJiOHHiBDt27OCff/7hrbfeYsWKFej1eTtHToi8dNfJmReffjF5ekQOdLx6nq5XztH5ynlWlQ9iWrV6+RShEEIUHoqbO8ZWz2DYuhlIHkWOHzZC5aiEPSlZsiTnz58nOjo60yW1IXlhlPPnz+P9n6mPERERFC9ePMtr5HiKxZIlSwgLC6Nq1aps2bKFSZMm0adPH15++WX69evHN998w+bNmwkKCuL06dOsXLkyp5cQosDlNDk2mE30PZ9cqsgBhVtOj37iVwghRLLE9qmmWWySaRYiZ1q0aEF8fDyjR48mISEhwzZGo5GxY8eSkJCQZgGSAwcOcP36desiJpnJ8Qjyxo0b0Wq1fP/995lOnyhZsiTff/897dq1Y/369bz22ms5vYwQNu2ly2cpnpj8Q3nb4Mzqcln/oAkhhHjI+Hw7FJ0OjcmE4z+H0F67iqVMWbXDEnZi0KBBbNmyhV27dvHCCy/QsWNHqlSpgrOzM7GxsZw5c4aNGzdy/fp1vLy8rPWbZ86cyc8//4xGo6FXr15ZXiPHCfLFixepVKkSAQEBWbYLDAykUqVKXLyY+9qyQqjBxZRExyvn2V6qHHcyGBl2SUritQsPJ/fPr1QDo4MUhBFCiOxSvLxJatYCh3NnSWzfUe1whJ0pUaIE8+fP5/333ycsLIyff/45XRtFUahcuTLffvutdUB3/fr1xMXF8fLLL9OqVassr5Hj3+qKouCQzdXGHBwcSEpKyuklhFBNhyvnGXL6MB6mJIolJvBj1SfStelxKQyvJCMA151d2VA28/XqhRBCZCz6p3koXt6QwxXRhACoXLkyv/32G9u3b2fHjh1cuHCByMhIXFxcqFixIs8++yxt27ZNk7P26dOHGjVqULNmzUf2n+MEuVy5cpw9e5Zbt25luepJeHg4586dIzAwMKeXEEI19/QGPEzJf9S9eOUcCyrWIM7R0Xrc3ZjIK5dOW7fnVqqJSZu75amFEKIoU7x91A5B2ClFUdBoNGg0Glq3bk3r1q2zdV6PHj2yfY0cJ8ht2rThhx9+YOTIkfzwww+4uaVfUjcmJoaRI0diNptp06ZNTi8hhGr+LFGGy67uBMTex82UROcr51he4WHpt1cvnsLtQQJ92dWdbaXLqxSputzc9Tg7GdQOQwghRBHUsmVLOnbsSMeOHalatWq+XCPHCXLfvn1ZvXo1ISEhPP/883Ts2JHq1avj6upKTEwMp06dYv369dy5c4fSpUvTr1+/xw5y9erVjB07lqVLl/Lkk0+mOXbjxo0s55HUq1eP5cuXP3YMomhQNBqWBVZj7IkQAHpeCmNV+SBMWgd8EuN5+fLDuok/V66NWftYa+3YLWcnA03bZf2AQ07s27Qsz/oSQtip2FhwdVU7CmEHbt26xbx585g3bx6VK1fmxRdfpH379nm69kaOE2R3d3fmz5/PW2+9xaVLl1iwYEG6NoqiEBgYyA8//IC7u/tjBXj48GEmTJiQ6fGUlVCqVKmSYdFomeIhcmpr6fK8cfYYxRMT8E2Mp+31y2wqW4He50/ibDYDcMbdi52PsSS1EEIIQFFwWrIQw/rfcDzwF3ePnJKpF+KRFi5cyIYNG9i2bRtnzpzh66+/5ptvvqFRo0Z06tSJNm3a4PqYf2zl6tH78uXLs2HDBjZu3Mju3bu5dOkSsbGxuLi4EBgYSMuWLWnXrh2OqeZu5sa2bdsYM2YMcXFxmbY5dSp5aeCBAwfSqVOnTNsJkV1GBwdWBVTh7TNHAeh18TSbywSyu6Q/VaMjqBN5h58r10aRB0uEEOLxaDQ4LVuE46HklXn1WzeT2PNVlYMStq5Ro0Y0atSIjz/+mL1797JhwwZ27NjBvn37+Ouvv/j000959tln6dixI82bN0ebi097c5QgJyQk4OTklHyiTkfnzp3p3Lmz9XhwcDA1atTAy8srx4GkdvPmTb799lvWrl2Ls7MzxYsX586dOxm2TRlBrlGjxmNdU4jU1pSrxOvnQ3E1m6gQc48mt6+zr0QZ3m7UmjqRtznq7at2iEIIUSgktutkTZANG9dJgiyyTafT8fTTT/P0008THx/P9u3b2bBhA8HBwdaBXB8fH9q3b8+4ceNy1He2UuqYmBi+/PJLnnrqKW7evJlhG5PJxLBhw2jVqhVffPFFlqO+jzJ16lTWrl1LzZo1WbFiBRUqZF5G69SpU9aRayHySoyjnnX+FaFRI1i9mi+++5yfRr5NzaqVOOpTQsoSCSFEHkns8PDTX/2uHWhi7qsYjbBXzs7OdOjQgVmzZvHXX38xcuRInJ2duXv3LosXL85xf49MkMPDw3n55ZdZtGgRsbGxhISEZNjuwoULJCYmkpCQwJIlS+jWrRu3b9/OcUAAFSpUYNKkSaxatYoqVapk2i4qKorr168TGBjI/Pnz6dSpE3Xq1KFZs2aMHz+e8PDwXF1fCIAjL3RE+eILGDECQ4sW1OrXm8ldn6NmlYpqhyaEEIWGJbACphq1ANAkJqL/favKEQl7deXKFWbPnk2/fv345ptviIuLQ6PRUK9evRz3leUUC4vFwuDBg7l48SLe3t4MGjSIFi1aZNg2KCiIkJAQVq9ezfTp07lw4QJDhw5l+fLlaHI42jZo0KBstUuZfxwaGsqZM2do0KABJUuW5Pjx46xcuZKdO3eyaNGiLEeghcjMay93QtOvN1y+nLzj8mW8er/G0PmLefPrmeoGJ4QQhUhih07oQo8DYNiwjsQu3VSOSNiL8PBwNm/ezMaNGzlx4gSQXCwiICCAzp0706lTJ8qWzfky5lkmyOvWrSM0NJSAgAAWL16c5cIgAC4uLrz22mu0bNmSXr16cfToUTZv3ky7du1yHFh2pMw/rly5MjNnzsTfP7mqQFxcHOPHj2fDhg28//77rF69Ot250dHRREdHp9mn1+sf+R5F0VHM1flhcpzi8uXk/UIIIfJMYvtOuE76AgD99m0QFwcuLipHJWxVREQEW7ZsYdOmTfzzzz8oioKiKHh5edGuXTs6d+5MnTp1HusaWSbIGzZsQKPR8MEHH+QocfT392f8+PEMGzaMdevW5VuC3LdvX9q2bYurqys+Pg/Lwri4uPD5559z8OBBQkNDOXLkCE888USacxcuXMiMGTPS7EupmeztLXUY85Ov7+OV/isod2PjKR0QkDZJDgjgbmx8gcdiL/fM1tjafbO1eDJjL3GKQqR4A6hSBcLC0MTF4Xv4L3jxRbWjEjaqRYsWmM1mFEVBr9fTqlUrOnfuTMuWLdHpclWgLZ0sezl16hQeHh6ZTqvISps2bfD09LQOd+cHBwcH66jxfzk7O9O4cWPWrl1LaGhougS5T58+dOnSJc0+vV4PQGRkLCaTJV9iTlGUfwHdvp27BzAK+p5NX7eNyYuX4NX7teQkOSCAqMVLmL664OfH5fae5Rd7+f61te+1vP53tJc4hcgOlxc64hoWBkDC0v/j/lPPqhyR7dPptEVyUM9kMlGvXj06d+7MCy+8gIeHR55fI8sE+d69exkuvpEdGo0Gf39/Tp8+navz80Lx4sUBiI9PP+Ln4eGRLzdUFB4nws4zavVWhs5fTDFXZ+7GxjN99VZOhJ1XOzQhhCh0jB064Tp1CgD6bVvAaIQHA1dCpPb7779nOkCakWvXrlGmTJkcXSPLBNnJyYmoqKgcdZja/fv3raOy+WHGjBmcOXOGd955J8NqF1evXgWgZMmS+RaDKNxOhJ2XB/KEEKIAmGrVwVwuAM3duxifbY0mKgpFngsSGfD39+fevXv88ssvnDt3joSEBCyWtJ/8m81mEhMTuXXrFufOnSM0NDRH18gyQa5QoQKhoaFERUXlePGPyMhIrl27RkBAQI7Oy4mwsDC2bdtGhQoV0iXId+/eJTg4GEdHRxo1apRvMQghhBAiD2g03Fv2C+ZyAfBgUTIhMnLnzh26detGeHg4iqIAyTMXUl6nbENyRYvczEvOsg5ys2bNsFgszJ8/P8cdL168GJPJRP369XN8bnb16NEDgPnz53Po0CHr/tjYWMaNG0dMTAzdunXD11dWPRNCCCFsnTmoiiTH4pHmzJnDzZs3cXZ2plu3bvTu3RtFUXjyySd588036dy5Mx4eHiiKQuPGjTNdwyMrWabUPXr0YO7cucybN4+aNWvSpk2bbHW6c+dOfvrpJzQaDd265V8tw2bNmtGvXz/mz5/Pa6+9Rr169fD29ubvv/8mMjKSJ598ktGjR+fb9YUQQgghRMHas2cPGo2G2bNn8+STTwIPK6+NGDECSJ5JMGDAAA4cOEBoaCgNGjTI0TWyHEH28/Pjww8/JCkpiREjRvDRRx9x4cKFTNufPXuWcePGMWTIEMxmM926daN27do5CiinxowZw9SpU6lXrx4nT55k7969+Pr6MnLkSBYsWICzs9SsFUIIIexSqo/MhUhx48YNSpYsaU2OAapXr87x48etc5GLFSvGl19+iaIouVpq+pGTMl5++WUuXbrEvHnzWLVqFatWrcLX15fKlSvj5eWF2WwmMjKS8+fPc/fuXSB5vkfHjh35+OOPcxxQRh71xl544QVeeOGFPLmWEEIIIVRkMqH/YxuGDWvRHT9K5M59oM1yPE8UMWaz2VqpLEVgYCD79u3j33//pXz58gBUq1aNsmXLcvTo0RxfI1uzlkeOHEmTJk349NNPuXLlCrdu3eLWrVvpJkQDVKpUicGDB+fb4iBCCCGEKNzcR7yD9sGgm+7QQUwN5GF78ZCPj491UDZFynLSZ8+etSbIAJ6enpw5cybH18j2Y33NmjVj27ZthISEsH//fi5cuMD9+/dxcnKiePHiBAYG0rJlSypUqJDjIIQQ+cfbXY/OyaB2GEIIkT06HYkvdMB5yUIADBvWSYIs0qhVqxZ//PEHBw8etM4trlixIoqiEBISYn1mzmg0cvXq1Vyte5GjuhcajYZGjRpJ2TQh7IjOycDx17vmaZ+1Fq3O0/6EECI1Y/uODxPkjeuI/eRzeFC2S4iuXbvy+++/8+abb9K7d2+GDh3Kk08+iaenJ8uXL6dmzZpUq1aNefPmce/evRw/oAePeEhPCCGEEKKgGZu3wuLhCYDDv5fRHc/5HFJReD399NO89NJLxMXFMW/ePBwcHHB2dqZv376YTCbGjBlD586dWbt2LRqNhoEDB+b4GpIgCyGEEMK26PUY2z7/cHPDOhWDEbboiy++4Mcff6Rnz57WRUHeeust3nzzTZycnFAUBXd3d0aPHk3Lli1z3H/OlxYRQgghhMhniR064/TLCgAMG9YSN3a8TLMQaTzzzDM888wz1u2UOshDhw4lIiKCYsWK4eDgkKu+ZQRZCCGEEDbH2OoZFBcXAHTnzuJwJkzliIS90Ol0lChRItfJMUiCLIQQQghb5OKC8dm21k3DhrUqBiOKGkmQhRBCCGGTEjt0sr42yDxkUYAkQRZCCCGETTK2eQ5Fr0fRarF4eUFcnNohiSJCHtITQgghhE1S3Ny5t2g5ptp1Uf6ztLAQ+UkSZCGEEELYrKRn2qgdgiiCZIqFEEIIIYQQqUiCLIQQQgghRCoyxUIIIYQQtk1R0P3zN4YN69DeCuf+D7PVjsguValSJVvtFi1aRKNGjTI9PmbMGNasWfPIfrp06cJXX31l3e7Tpw/79+/PtP22bdsICAjIVoz5TRJkIYQQQtg0ze3beLVrjUZRUDQaYj7+HKVECbXDsjsdO3bM9NiVK1c4cuQIbm5u+Pv7Z9lP3bp1MZlMGR6zWCxs3rwZi8VCtWrV0hw7ffo0Li4uPPvssxme6+rq+oh3UHAkQRZCCCGETVNKlCCpURP0+/ehURQMmzeQ0Ke/2mHZnSlTpmS4Pz4+npdeegmAyZMnU7p06Sz76dGjBz169Mjw2Pfff4/FYqFt27b06dPHuv/atWtERUXRtGnTTOOwJTIHWQghhBA2z5h60ZCNsmhIXpo4cSLnz5+ne/fumY7uZsfff//NrFmzKF68OBMmTEhz7NSpUwDUqFHjsWItKJIgCyGEEMLmJbZ/mCA7/rkHTVSkitEUHseOHWPVqlUUK1aMkSNH5rofi8XCZ599hsViYcyYMXh5eaU5fvLkSUASZCGEEEKIPGMpU5akevUB0JhM6LdsUjmiwmHixIkoisLQoUPx8PDIdT8rV64kLCyMOnXqZDjXOSVBvnfvHgMGDKBx48bUrVuX3r17s3fv3lxfN79IgiyEEEIIu5DYTqZZZOTGjRtcvXo1zVd0dPQjz9u9ezeHDx+mZMmSdOvWLdfXN5lMzJw5E4B33nknwzYpUyw+/vhjbt26RYMGDShbtiwhISEMHDiQhQsX5vr6+UGjKIqidhBCCCGEEI907hxUrpz82mCA27fB3V3dmGzAM888w7Vr19LsGzJkCEOHDs3yvH79+rFv3z7Gjh1L3759c339devWMXLkSKpWrcratWvTHY+IiKBJkybodDomTZpEhw4drMc2bdrEyJEjURSFX3/9NV3lC7VIFYsMREbGYjJZ8vUavr7uHH+9a571V2vRapq265Vn/QHs27SMZ7fmfj5SRrY/9zW3b9/P1bl5fc/APu7b49wzsI/7VlS+1x7n3zEjvr7uNnXfhMh3nn54V6+J7uQJSEwkevkvJHbJ/cinvdPptHh7u7J06VLMZnOaY4+aLnHu3Dn27duHu7s73bt3f6w4Fi1aBMCAAQMyPO7j48Nff/1FdHQ05cuXT3OsXbt2HDlyhIULF7J8+XI+++yzx4olr8gUCyGEEELYjcRU1Sz0G9erGIntKFWqFGXLlk3z9agEedOm5Dncbdq0wcXFJdfXvnLlCsePH8fFxYU2bdpk2s7Hxyddcpzi6aefBiA0NDTXceQ1SZCFEEIIYTcSO3S2vnb49xLITNFc+f3334HkEdy86Ofpp5/G2dk5V334+voCkJCQ8Fix5CWZYiGEEEIIu2GuUpWYLyZhbPE05ipV1Q7HLt24cYMzZ87g7u5OkyZNHquv3bt3A2Q5erxv3z7WrFlDjRo1MpzrfPXqVQBKliz5WLHkJRlBFkIIIYT90GiIf+NtSY4fw9GjRwGoXbs2Ol3ux0oVReHEiRMA1KtXL9N2CQkJrFu3jkWLFmW4RPVvv/0GQLNmzXIdS16TBFkIIYQQoghJSWpr1ar1yLbXr1/n/PnzREREpDt24cIFYmJi8PPzw8/PL9M+mjVrRpkyZbh27Rpff/11mgcKf/31VzZv3oyvr+9jlZrLazLFQgghhBCiCEmZ0uDv7//ItqNHjyYkJCTDsnHZ7Uev1zNlyhQGDBjAggUL2LFjB1WrVuXKlSucOnUKFxcXpk+fjrsNleyTBFkIIQpAotGIr6/t/M9fiMJAe+Xf5AVDzBbi3xmmdjh2I2U0+HHn/EZGRma7n3r16rFmzRpmzpxJcHAwO3fuxNvbm65duzJ48OBsJesFyS4S5NWrVzN27FiWLl3Kk08+me74xYsXmT59OocOHSIqKopy5crRo0cPevXqhVYrs0iEEOoz6PX5UnNbiKJKd/wo3s82B8BSrBjxbw6Gx5hPW5Sk1C3OjsWLF2d67MUXX+TFF1/Mdl/ly5dn0qRJ2W6vJpvPHg8fPsyECRMyPX769Gm6devGxo0bKV26NM2bN+fmzZtMmDCBUaNGFWCkQgghhCgophq1MPslj1xq797Fcf8+lSMShYlNJ8jbtm1jwIABxMXFZXhcURRGjRpFTEwMkydPZvny5cyYMYOtW7dSpUoV1q9fz9atWws4aiGEEELkO60WY7uHSxYbNq5TMRhR2Nhkgnzz5k1GjRrF0KFDsVgsFC9ePMN2wcHBhIWF0bBhQzp3flg43MfHh48//hjI+qMBIYQQQtiv1IuG6DeuB4tFxWhEYWKTCfLUqVNZu3YtNWvWZMWKFVSoUCHDdnv37gWgdevW6Y7Vr1+fYsWKcejQIWJiYvI1XiGEEEIUvKQmT2Hx8QHA4eYNdIcOqhyRKCxsMkGuUKECkyZNYtWqVVSpUiXTdufOnQMgKCgow+OBgYFYLBbOnz+fL3EKIYQQQkU6HYnPt7duGjbINAuRN2wyQR40aBAvvvjiIytQ3Lp1C3i4hvd/pey/c+dO3gYohBBCCJtg7NDJ+tqwcT0oiorRiMLCruuhxMfHA+Dk5JTh8ZT9GT3kFx0dTXR0dJp9er2eEiVK5HGUQgghhMgvxuatsLh7oL0fjcO/l9CdOIapVh21wxJ2zq4T5JQRZo1Gk+Fx5cFfkUoGf00uXLiQGTNmpNlXr149li9fjre3ax5HKlKTxRJyTu5Z7sh9yx1bum+JRiMGvd7m+xRqcodOHWHpUgC8d2yBZ5qpHJOwd3adILu4uACQkJCQ4fHExMQ07VLr06cPXbp0SbNP/+B/mJGRsZhM+fskrC39Aipot2/fz9V5cs9yR+5bzhXlewaP9/2W13x93fNlgRVbeo/i8elbt8PzQYKc9Pt2ooaPVjmigqHTaWVQL5/YdYJcokQJTp06xZ07d6hYsWK647dv3wYynqPs4eGBh4dHvscohBBCiPxlfPpZ4ga+ibFdR5IaN1U7HFEI2ORDetlVuXJl4GE1i9QUReHChQs4ODhkmDwLIYQQopBwcSF24tckNWshy02LPGHXCXLz5slrsG/fvj3dsX/++YeIiAjq16+Pm5tbQYcmhBBCCCHslF0nyA0bNqRy5coEBwezcuVK6/6IiAg+/fRTAPr166dWeEIIIYQQwg7Z9ecQWq2WiRMn0qdPH8aPH88vv/xCiRIlCAkJ4d69e3Tv3p1nnnlG7TCFEEIIUUA096PR/74VxcUV4/Pt1A5H2Cm7TpABateuzapVq5g2bRoHDhzg7NmzBAQE8O677/Lyyy+rHZ4QQgghCojj7p14vvoyGqORpPoNJEEWuWYXCfLixYuzPF6pUiWmTZtWQNEIIYQQwhaZatcBsxkAx0MH0V6/hqV0GZWjEvbIrucgCyGEEEKkULx9SHqqhXVbv2m9itEIe2YXI8hCCCFsn7e7Hp2TQe0wRBGX2KET+j07ATBsWEfCwLdUjkjYI0mQhRBC5Amdk4Hjr3fN0z5rLVqdp/2Jwi/xhQ64jX4XjaLguH8fmtu3UTJYMEyIrMgUCyGEEEIUGoqfH0mNmgCgsVgwbN6gckTCHkmCLIQQQohCxdihk/W1YeM6FSMR9koSZCGEEEIUKontOlpfO+7djSYqUsVohD2SBFkIIYQQhYqlrD9JdesBoDGZ0G/drHJEwt5IgiyEEEKIQiexfWfra8MmmYcsckaqWAghhBCi0Ens0AnHI/+Q2L4jxjbPqR2OsDOSIAshhBCi0LFUqEj0vKxX4hUiMzLFQgghhBBCiFQkQRZCCCGEECIVSZCFEEIIUbiZTDj+uQeHE8fVjkTYCUmQhRBCCFFo6Teso1itynh17YDLzOlqhyPshCTIQgghhCi0LGXLor17FyC5HrLRqHJEwh5IgiyEEEKIQstUpy5m/3IAaKPv4fjnHpUjEvZAEmQhhBBCFF4aTZqlpw0b16kYjLAXkiALIYQQolBL7JBqVb3NG8BsVjEaYQ8kQRZCCCFEoWZq0BCzX0kAtHfu4Lh/n8oRCVsnK+kJIYQoUozmJHx93fOsv4QkI/ejEvOsP5EPtFqM7TrgPH8OAIYNa0l6qrnKQQlbJgmyEEKIIkXv4MizW0fmWX/bn/ua+0iCbOsS23eyJsj6jevhi8mglQ/SRcbkO0MIIYQQhV5S02ZYfHwAcLh5A90/f6sckbBlkiALIYQQovDT6Uh8vr1107BBqlmIzMkUCyGEEEIUCYmdXkR39gyJHTqT2KGT2uEIGyYJshBCCCGKhKRn2hD1TBu1w1BNlSpVstVu0aJFNGrUKMs2JpOJunXrYsxkZUI/Pz/27Em7KEtCQgILFy5k3bp1XL16FXd3d1q1asWwYcMoUaJE9t5EAZEEWQghhBCiCOjYsWOmx65cucKRI0dwc3PD39//kX2dO3cOo9FIuXLlqFOnTrrjXl5eabaTkpIYPHgwwcHBlCpVipYtW3LhwgVWrVrFrl27WLlyJaVLl87xe8ovkiALIYQQQhQBU6ZMyXB/fHw8L730EgCTJ0/OVqJ66tQpALp27crbb7/9yPZLliwhODiYVq1aMX36dPR6PQDfffcds2bN4rPPPmPWrFnZfSv5Th7SE0IIIUSR5HAmDE14uNphqG7ixImcP3+e7t278+yzz2brnJMnTwJQo0aNR7ZVFIX58+ej0WgYP368NTkGGD58OIGBgezcuZMrV67k7g3kA0mQhRBCCFGkGH77Fe+nnsSnWQOcly5UOxxVHTt2jFWrVlGsWDFGjsx+ffCUEeTsJMhhYWGEh4dTtWpVypYtm+aYVqvlmWeeAUg3Z1lNkiALIYQQomixWNCdPQOAvoiXe5s4cSKKojB06FA8PDyydY6iKJw6dQpfX1927NhBt27dqFu3Lo0bN+bdd9/lwoULadqfO3cOgMqVK2fYX4UKFQA4c+bMY7yTvFUoEuTffvuNKlWqZPr13XffqR2iEEIIIWyEsc1zKA8+5nc8cQztpYsqR6SO3bt3c/jwYUqWLEm3bt2yfd6VK1eIiYnh9u3bfPTRRxgMBho1aoTBYGDjxo1069aNQ4cOWdvfvn0bAF9f3wz7S9l/9+7dx3g3eatQPKSXMsz/1FNP4fNglZzUqlWrVtAhCSGEEMJGKe4eGFs+jeH3rQAYNq4n/p1hKkeVezdu3MBsNqfZ5+Hh8cgR4QULFgDQr18/HB0ds329lPnHfn5+/PTTT9Y8y2Qy8c033zBv3jxGjBjB77//jsFgIC4uDgBnZ+cM+3NycgKwtrMFGkVRFLWDeFy9e/cmJCSEPXv24Ofnp3Y4QgghhLB18+dD//7Jrxs3hr/+Ujeex/DMM89w7dq1NPuGDBnC0KFDMz3n3LlztG/fHnd3d/bs2YOLi0u2r6coCjdv3kSr1abLuywWC926dSM0NJRvv/2W9u3bM3PmTKZOncrQoUMZMmRIuv72799Pnz59aNq0KfPnz892HPmpUIwgnz59muLFi+dZchwZGYvJZMmTvjLj6+vO8de75ll/tRatpmm7XnnWH8C+Tct4dmv2J+xnx/bnvub27fu5Ojev7xnYx317nHsG9nHf5Hstd+S+5Y6t/YwKdWiaPk0xBwc0ZjPs38/do6exlC6jdlg5otNp8fZ2ZenSpRmOIGdl06ZNALRp0yZHyTGARqOhVKlSGR7TarW0bNmS0NBQTpw4Qfv27a39JyQkZHhOYmIikPkIsxrsfg7ylStXiI6OztZTlEIIIYQQAIpPMZKaNrdu6zdvUDGax1OqVCnKli2b5utRCfLvv/8OQLt27fI8nuLFiwMPE+KUVfLu3LmTYftHzVFWg90nyCnzj4sVK8aECRNo06YNtWrV4rnnnuOHH36w/lUihBBCCJFaYodO1teGIlTN4saNG5w5cwZ3d3eaNGmS4/OXLl3K//73P/bt25fh8atXrwJQsmRJAIKCgoCH1Sz+6/z582na2QK7T5BTJoqvXr2a9evXU6lSJerUqUN4eDjTpk2jT58+mQ7pCyGEEKLoSmzXEUWjAcDxr2A0mYxwFjZHjx4FoHbt2uh0OZ9te+XKFTZv3syaNWvSHUtMTGTr1uSHH5966ikAKlasSJkyZTh58iQ3btxI095isbBjxw40Gg3NmzdP159a7D5BThlBfuGFF9i1axczZ85kyZIlbNiwgapVq3L48GGmTp2a7rzo6GiuXr2a5uvWrVsFHL0QQggh1KL4+WFq2BgAjcWCwY6nWeTEiRMnAKhVq9Yj216/fp3z588TERFh3detWzccHBxYv369NRkGSEpKYsKECVy7do0WLVpQs2ZN67GePXtiNpv54IMP0lSr+P7777l06RJt2rShXLlyefH28oTdP6Q3bdo0rly5Qrly5dIsXVi2bFm++uorunTpwooVK3jvvffSlDBZuHAhM2bMSNNXvXr1WL58Od7ergUWf1Hk6+uudgh2R+5Z7sh9yx25bzkn98yO9XgZbofDSy/h/twzuBeBf8uUKRD+/v6PbDt69GhCQkLSVMWoVKkSY8aMYeLEiQwbNoxatWpRunRpjh49ys2bN6lQoQJfffVVmn769u3Lrl27CA4Opm3bttSrV4+LFy9y5swZSpcuzfjx4/P+jT4Gu0+QDQYDlSpVyvBYtWrVKFmyJDdu3ODSpUtpVnDp06cPXbp0SdM+JcEuqCoWRdXjPCFfVD1uFYuiSr7XckfuW85JFQs71qMPvDoAHky1wI7+LVOqWORUymhwyhzh3Hj99depXLkyc+bM4dixY4SFhVG6dGneeustBg0ahKtr2rj0ej1z585l9uzZbNiwgZ07d+Lr60uPHj0YMmSI9UE+W2H3CfKjFC9enBs3bhAfH59mf3YKaAshhBCikMvBAhmFxaJFi7LddvHixZkea9KkSY4e8nN2dmb48OEMHz482+eoxa7nIMfExDB+/HiGDRuGyWTKsE3KxwiygIgQQgghhMgOu06QXV1d+f3339m6dSsHDx5Md3zPnj1ERkYSFBQkCbIQQgghHklz5w7851NnUfTYdYKs0Wjo3r07ABMmTCA8PNx67N9//+XTTz8F4O2331YlPiGEEELYB/26NXh2aU+xmpXQ/75F7XCEyux+DvLgwYP5+++/OXToEM8//zz169cH4MCBAxiNRvr165cvq8QIIYQQovDQhZ1GH7wXAMOGtRg7dXnEGaIws+sRZAAnJycWLFjAe++9R9myZTlw4ACHDx/miSeeYPr06YwZM0btEIUQQghh4xI7dLa+1v++DWSRsSLN7keQIbl0yKBBgxg0aJDaoQghhBDCDpmrVsNUoSK6C+fRxsag37UD4/PyCXRRZfcjyEIIIYQQj02jwZhqFNmwYa2KwQi1FYoRZCGEEMJeebvr0TkZ8qw/U0IikfeNedZfUZLYoRMu074FQL91MxiNkGqVXlF0SIIshBBCqEjnZOD4613zrL9ai1aDJMi5YqpTF3NZfxyuXkF7LwrHP/eQ9ExrtcMSKpApFkIIIYQQABoNie07WjcNG9epGIxQkyTIQgghhBAPJLZPNQ958wYwm1WMRqhFEmQhhBBCiAdMDRthLpG8+q72zh0cD/ylckRCDZIgCyGEEEKk0GoxtuuAxdOLhO6vYPHwVDsioQJ5SE8IIYQQIpXYseOJ+XySVLAowiRBFkIIIYRIRfH2UTsEoTKZYiGEEEIIIUQqMoIshBBCFCKJRiO+vu552md8QiIxRbm2cmJi8nQLjUbtSEQBkQRZCCGEKEQMej1N2/XK0z73bVpWJBNk/bbNGFavQr9tK1FrN2OuVVvtkEQBkSkWQgghhBAZMKxehdPqX9DG3Mewca3a4YgCJAmyEEIIIUQG0iwasnG9ipGIgiYJshBCCCFEBozPtEZxdgZAF3Yah7NnVI5IFBRJkIUQQgghMuLqivGZNtZNwwaZZlFUSIIshBBCCJGJxA6drK/1G9apGIkoSJIgCyGEEEJkwtj2eZQHK+o5Hj+K9tJFlSMSBUESZCGEEEKITCjuHhhbtLJuy8N6RYPUQRZCCCFEkefmrsfZyZDxwVd6wB/bkttt3YDbJx9kq88iv8CKHZMEWQghhBBFnrOTIdMFVjyMiWxo1Ajd6NHg48Pp7bv5dsMfnAg7n2WfRXWBlcJAplgIIYQQQmShXK3qKBO/hBEjoFUrqg7ow+Suz1GzSkW1QxP5RBJkIYQQQogsDO3UFsf+/eDy5eQdly/j1fs1hnZqq25gIt9IgiyEEEIIkYVirs4Pk+MUly8n7xeFkiTIQgghhBBZuBsbDwEBaXcGBCTvF4WSJMhCCCGEEFmYvm4bUYuXPEySAwKIWryE6eu2qRuYyDdSxUIIIYQQIgsnws4zavVWhs5fTDFXZ+7GxjN99dZHVrEQ9ksSZCGEEEKIRzgRdp43v56pdhiigMgUCyGEEEIIIVKREWQhhBBCZMloTsLX1z1P+0xIMnI/KjFX53q769FltuqdEHmg0CTI+/btY9asWYSFhZGUlESNGjUYNGgQzZs3Vzs0IYQQwq7pHRx5duvIPO1z+3Nfc5/cJcg6JwPHX++ap/HUWrQ6T/sT9q1QTLFYvXo1/fr14/Dhw9SuXZu6dety+PBhBg4cyIoVK9QOTwghhBBC2BG7H0G+desWH3/8Me7u7ixbtoygoCAAjh07Rr9+/fjiiy9o1aoVfn5+KkcqhBBCCCHsgd2PIC9ZsgSj0Ujfvn2tyTFA7dq1GThwIImJiTKKLIQQQgghss3uE+S9e/cC0Lp163TH2rRpA8CePXsKNCYhhBBCCGG/7DpBVhSFc+fOodVqqVChQrrj5cuXR6vVcu7cORRFUSFCIYQQQghhb+w6Qb537x5GoxEvLy/0en264zqdDm9vb+Lj44mNjVUhQiGEEEIIYW/s+iG9+Ph4AJydnTNt4+TkBEBsbCxubm7W/dHR0URHR6dpq9frKVGiBA4OBfN3g0vlqnnaX63qQY9ulEM1PMvneZ86Xe7vb17fM7CP+/Y49wzs477J91ruyH3LncL+M2oP9wyKxvfa435vZKWg8pWiSKPY8dyD8PBwWrRoQZkyZdixY0eGbZ5++mmuX7/Onj170lSymD59OjNmzEjTtmfPnnz66af5GrMQQgghhLBtdj2C7OLiAkBiYuaFxlOO/XeUuU+fPnTp0iVd+7i4OGu/QgghhBCi6LHrBNnNzQ0XFxciIyMxmUzodGnfjslkIjIyEoPBgIeHR5pjHh4e6fYJIYQQQghh15NXNBoNlSpVwmw2c+nSpXTHL168iMViSVMfWQghhBBCiKzYdYIM0Lx5cwD++OOPdMdS9rVs2bJAYxJCCCGEEPbL7hPkrl27YjAY+Pnnnzlx4oR1//Hjx5kzZw5OTk706tVLxQiFEEIIIYQ9sesqFimWLl3KZ599hqOjI40bN0ZRFA4cOIDJZGLSpEl07txZ7RCFEEIIIYSdKBQJMsDOnTuZM2cOJ0+eRK/XU6VKFd5++22aNGmidmhCCCGEEMKOFJoEWQghhBBCiLxg93OQhRBCCCGEyEuSIBcR+/bt4/XXX6dRo0bUq1eP3r17s3fvXrXDsiurV6+mSpUq/P3332qHYrPMZjNLlizhpZdeom7dutSuXZv27dvzww8/ZLmgT1FnNptZtGgRnTp1olatWjRs2JD+/fuza9cutUOzG1FRUTRr1owqVaqoHYrN++2336hSpUqmX999953aIdqka9euMW7cOFq0aEHNmjVp3rw548eP5/bt22qHJvKBXS8UIrJn9erVjB07Fr1eT+PGjbFYLBw4cICBAwfy2Wef0aNHD7VDtHmHDx9mwoQJaodh08xmM4MHD2bXrl24uLhQp04ddDodR48eZdq0aezevZuFCxemW9VSwNixY1m7di1ubm40adKEpKQkQkJCCA4OZtiwYbzzzjtqh2jzPv30U0lUsunUqVMAPPXUU/j4+KQ7Xq1atYIOyeYdP36cfv36cf/+fYKCgqhVqxYnTpxg5cqV7N+/n19++QVPT0+1wxR5SRGFWnh4uFKzZk2lfv36SlhYmHX/0aNHlXr16im1atVSbt68qWKEtm/r1q1K3bp1laCgICUoKEg5ePCg2iHZpOXLlytBQUFKx44d03xP3b17V+nRo4cSFBSkTJkyRcUIbdPGjRuVoKAg5bnnnlNu375t3X/mzBmlfv36StWqVZWLFy+qF6AdWL9+vfXnMygoSO1wbN5rr72mBAUFyf/7sykxMVFp27atEhQUpCxatMi6PyEhQRk6dKgSFBSkTJgwQcUIRX6QKRaF3JIlSzAajfTt2zfNioK1a9dm4MCBJCYmsmLFChUjtF03b95k1KhRDB06FIvFQvHixdUOyaatWbMGgHHjxuHn52fd7+PjwyeffALAxo0b1QjNpq1btw6A999/P833WOXKlenYsSMWi4Xg4GC1wrN54eHhTJgwgbp16+Lg4KB2OHbh9OnTFC9ePM3Pqcjcpk2buHTpEh07dqR3797W/QaDgbFjx1K8eHEuXryoYoQiP0iCXMilzDNu3bp1umNt2rQBYM+ePQUak72YOnUqa9eupWbNmqxYsYIKFSqoHZJN8/b2pkKFCtSuXTvdsfLlywNw69atAo7K9k2bNo3169fTokWLdMdiY2MBJPHLwgcffEBiYiKTJk1SOxS7cOXKFaKjo6lRo4baodiNbdu2AdCvX790x0qVKkVwcDBz584t6LBEPpM5yIWYoiicO3cOrVabYXJXvnx5tFot586dQ1EUNBqNClHargoVKjBp0iQ6deqEVit/Sz7KrFmzMj12/PhxAEqWLFlQ4dgNvV6f5tOdFDt37mTLli24uLhk+AeugGXLlrF3717Gjx9PQECA2uHYhZT5x8WKFWPChAns2bOHmzdvUrp0aTp16sTAgQMxGAwqR2lbTp48iaOjI1WrVuXGjRusX7+ef//9Fy8vL9q2bZvhoICwf5IgF2L37t3DaDTi4+ODXq9Pd1yn0+Ht7c3du3eJjY3Fzc1NhSht16BBg9QOoVBQFIVp06YB0LZtW5WjsW0JCQmMGjWKc+fOcf78eUqXLs3kyZNlek8G/v33X77++msaN27Mq6++qnY4duPkyZNA8sPbnp6e1K9fHz8/P06cOMG0adPYu3cvCxYswMnJSeVIbYPRaOTGjRuULFmSLVu28MEHHxAfH289/vPPPzNgwABGjRqlYpQiP8iwWCGW8kOcVdWAlP8JpnyUK0Re+/bbbwkJCaF48eIMHDhQ7XBs2vXr19m6dSvnz5+37gsLC1MxIttkNpsZNWoUGo2GL7/8Uj79yoGUEeQXXniBXbt2MXPmTJYsWcKGDRuoWrUqhw8fZurUqeoGaUNiYmKA5AGn0aNH07p1a7Zs2cLBgwf57rvv8PLyYu7cufIsTyEkCXIhlp1pAYospCjy0ffff8/s2bPR6/VMnTo1w5JS4qGSJUuyf/9+QkJCmDp1KklJSUyYMIHZs2erHZpNmTNnDocPH2bs2LGULl1a7XDsyrRp09i4cSOTJ0/GxcXFur9s2bJ89dVXaDQaVqxYQVJSkopR2o6U+u3x8fE0atSIKVOmEBgYiIeHB+3atbPOff/hhx/k92khIwlyIZbyP7+sFmhIOSa1aUVeMplMfPTRR/z4448YDAZmzJhBgwYN1A7L5rm4uODt7Y2npycvvPACM2bMQKPR8NNPP8lCKw+cPn2a6dOn07JlS15++WW1w7E7BoOBSpUqZTjtrlq1apQsWZK4uDguXbpU8MHZoNS/G1955ZV0x1u1aoWfnx/h4eFcvny5IEMT+UzmIBdibm5uuLi4EBkZiclkQqdL+89tMpmIjIzEYDDg4eGhUpSisImNjWX48OHs3bsXDw8PfvzxR0mOc+mJJ56gXLlyXL58mStXrlCpUiW1Q1Ldd999R1JSEiaTiffffz/NMYvFAmDdP27cOPnUIoeKFy/OjRs30syzLcrc3d1xdHQkKSmJsmXLZtimdOnShIeHExkZaa3YI+yfJMiFmEajoVKlShw7doxLly6l++V68eJFLBZLhk/QC5Eb9+7do1+/foSGhlKqVClmz54t319ZUBSFr7/+mhs3bvD111+n+yMWsI70mUymgg7PJsXFxQFkWRt6/fr1APzvf/+TBDmVmJgYJk2axL179/j2228z/H67evUqgNRIfsDBwYGKFSty+vRpwsPDqVq1aro2d+7cAZDvtUJGEuRCrnnz5hw7dow//vgjXYL8xx9/ANCyZUs1QhOFjNFoZNCgQYSGhlKpUiXmzp0rZd0eQaPRsH37di5dusSLL76Y7mfxypUrXLx4ERcXFwIDA1WK0rYsXrw402PVq1fHbDbLg42ZcHV15ffffycyMpKDBw/SpEmTNMf37NlDZGQkQUFBkiCn0qJFC06fPs2WLVvS/YxeuHCBa9euUaJECfz9/VWKUOQHmYNcyHXt2hWDwcDPP//MiRMnrPuPHz/OnDlzcHJyolevXipGKAqLadOmceTIEUqVKsXixYslOc6m7t27A/D5559z8+ZN6/7w8HDeffddTCYTvXr1ktq04rFpNBrr99uECRMIDw+3Hvv333/59NNPAXj77bdVic9W9ezZExcXF3777TfrpxOQ/InZhx9+iMVi4dVXX5V6+YWMRpHHLgu9pUuX8tlnn+Ho6Ejjxo1RFIUDBw5gMpmYNGkSnTt3VjtEu9C7d29CQkJYunQpTz75pNrh2JSoqChatmxJQkICNWrUyHLVwSlTphRgZLYvKSmJd955h927d+Pi4kK9evUwm80cPXqUuLg4WrZsyYwZMzJ8qEqkJSPIj5aQkED//v05dOgQLi4u1K9fH4ADBw5gNBrp168fY8aMUTlK27Np0yZGjhyJyWSiRo0alChRgiNHjhAZGUnjxo2ZM2cOjo6Oaocp8pAkyEXEzp07mTNnDidPnkSv11OlShXefvvtdB+xicxJgpy5PXv28MYbb2SrrSQv6ZnNZpYtW8bq1as5f/48Wq2WoKAgunbtSvfu3WVkKpskQc4eo9HIggULWL9+PZcuXUKv11O9enV69+4ti/lk4dSpU8ycOZOQkBDi4uLw9/enc+fO9OvXT5LjQkgSZCGEEEIIIVKRYQkhhBBCCCFSkQRZCCGEEEKIVCRBFkIIIYQQIhVJkIUQQgghhEhFEmQhhBBCCCFSkQRZCCGEEEKIVCRBFkIIIYQQIhVJkIUQBWL69OlUqVKFYcOGqR1Kgdu0aROdO3emTp06NGzYkE8++SRfrnPmzBmqVKlClSpV0uwfM2YMVapUYdKkSXlyHYvFwsWLF/Okr4KyevVqqlSpQteuXdUORQhhByRBFkKIfHTw4EFGjBjB6dOn8fLyomzZspQuXVrtsHLt+PHjdOvWjZUrV6odihBC5Bud2gEIIURhtmXLFgAaNGjAwoULcXBwKPAY3n33Xd544w28vb0fu6+lS5cS+v/t3XtUVVUewPHvvTwUlLRMjcWgpHDQukKiqI0SIj6Wo67SMqwhSVwzQyq2GldktlrlAzWNpTJm5og6NUliKPaaHjOp09SQIUwoxkMFvCioA47yUJ57/mCdM9y4PFRoEn+fv2zvffbZe3Nb63f22XufrCxGjx7dAS0TQoifJwmQhRCiE5WVlQEwfPjw/0twDNCvXz/69ev3f7m3EELcimSJhRBCdKL6+noAnJ2d/88tEUII0V4SIAtxi9E3G61YsYKysjJWrFjB+PHjsVgsBAcH88orr3DhwgWjfFFRkbFxq7Kysll9bW3s+utf/8qxY8eIiopi1KhRDB8+nCeeeIJ//OMfAFRVVbF+/XomTJiAxWJhwoQJbNiwgdra2hb7cPr0aRYtWkRgYKBR3wcffNBi+YqKCjZv3syMGTPw9/cnICCAOXPmkJSUZASgTU2YMAFfX1/OnDnDc889xwMPPEBgYCAxMTFtjm9bsrKyWLJkCUFBQVgsFsaMGUNUVBTffPONTTl9U+Jnn30GwObNm/H19WXChAk33YaLFy+yevVqQkND8fPzY+rUqbz99ts0NDTYLd/aJr3S0lJee+01pkyZgp+fH4GBgURERBhLQ3T672j//v0A7NixA19fX5YuXWpT7vjx4yxevJigoCD8/f2ZOXMmycnJxvX2+l9dXc3OnTt57LHHGD58OP7+/kyfPp2NGzdy+fJlu32qrq7m3XffJSIiggcffBCLxcLIkSOZPXs2O3bsoKampl1jqZRi7969PPXUU4wbN45hw4Yxfvx4lixZQmZmZrvqEEJ0PbLEQohb1IULF5g1axYlJSV4eHjg5eVFXl4e7733Hl999RUpKSnccccdN32fQ4cOkZKSgpOTE15eXlitVtLT0/ntb3/Lm2++SVxcHHl5eQwYMAB3d3fOnDnD1q1b+fe//01sbGyz+vLz83n88ceprKzEx8eHqqoq0tPTSU9PJzU1ldWrV9uULyoqIjIyksLCQhwdHfHy8qKhoYGMjAwyMjL4/PPP2bJli90Z2ueff55jx46haRolJSU3vTnu3XffJTY2lvr6enr16sWQIUMoKSnh4MGDHDx4kPnz5xtBuLu7OwEBAZw+fZr//Oc/uLu74+7uTt++fW+qDfn5+cybN4/i4mK6d++Oj48P58+fJzY2lpEjR15XXVlZWfzmN7+htLQUZ2dn7r33Xq5evUpqaiqpqanMmjWL1atXYzKZ6NatGwEBARQWFlJaWkr//v2N353uk08+ISYmhtraWu688068vb3Jz89n2bJlLT4YlJWV8fTTT5OTk4PJZGLQoEE4OzuTl5fHm2++yYEDB9i+fTuDBw82rikvLyciIoKsrCwcHByM397Zs2fJzMwkMzOTb775hu3bt7c5Bq+88gp79uzBZDIxcOBA+vXrR1FRER999BF/+ctfiI+PZ+LEidc1rkKILkAJIW4pycnJStM0pWmamjx5sjp+/LiRd/ToUeXv7680TVN//OMflVJKWa1Wo3xFRUWz+nJycoz8pl544QUjPTo6WpWXlyullCovL1ePPPKI0jRNDRkyRIWEhKisrCzjuoSEBKVpmho6dKi6dOmSkR4fH2/UN3HiRHXy5Ekj79NPP1UWi0VpmqY++ugjI72urs64V1RUlLp48aKRl5eXp6ZOnao0TVOrVq2yaXtISIjSNE1ZLBaVnp6ulFKqpqbG6MONSE1NVb6+vsrX11dt3bpV1dbWKqWUamhoUPv37zfan5SUZHNddHS00jRNxcfH3/C9dQ0NDSosLExpmqYiIyON8a2vr1cJCQnK19e31b/l2rVrjbQrV66o4OBgpWmaeumll2zGJi0tTY0bN05pmqZ27tzZZl1KKXXu3Dnl5+enNE1TGzduNMansrJSLV261GhXSEiIzXVz585Vmqap6dOn2/wmSkpKjLzJkyera9euGXlr1qxRmqapqVOnqqKiIiO9rq5O7dq1y7jX999/b+Tp/9/MnDnTSMvNzVWapqkxY8ao3NxcI726ulq9+uqrStM0FRoaav+PIYTo0mSJhRC3sHXr1nH//fcb/x0QEMC0adMA+Ne//tUh9+jVqxerV6+mZ8+eAPTs2ZMnnngCaDwP99VXX+W+++4zys+dOxdnZ2fq6+vJy8trVp/JZGLz5s02M4JTpkzhmWeeASAhIcFI//zzzzlx4gReXl5s3LiRu+++28jz9vZm48aNmM1mEhMTKS0tbXavyZMnM3z4cACcnJyMPtyILVu2oJQiLCyM3/3udzg6Ohr9eeSRR1iyZAkA8fHxdpd9dIS0tDQyMjK444472LBhA7179wbAbDYTGRnJww8/3O669uzZQ3FxMaNGjWLlypU2YzNixAhWrVoFwLZt21pdLqNLSEjg2rVrTJo0iWeffdYYH1dXV2JjYxk2bJjd/qSmptKtWze2bt1q85vo378/b7zxBvfccw8FBQUkJycbeUeOHMFkMvHiiy/i4eFhpDs4OBAREcGAAQMAOHnyZKttzs3NBRo3UPr4+Bjpzs7OxMTEMHbsWMaMGWN3aZIQomuTAFmIW1Tv3r3x9/dvln7vvfcCjet2O0JAQECzwFJfquDo6MiYMWNs8hwdHY3AzV5gMWLEiGbrnQEeffRRoPG1vx7sfvnllwBMmjSJbt26NbtG0zQ0TaO2tpbU1NRm+Q888EAbvWufyspK0tLSAHjyySftlgkLC8PZ2ZkLFy6QlZXVIff9sa+++gqA4OBgu8tnHnvssXbXpY/tr371K0wmU7P8hx56iF69elFaWtqu/hw8eBCAxx9/vFme2Wxmzpw5zdIPHToENPanaaCr69mzp/FhD70sNK7Dz8zMZNy4cc2uqampMcbm6tWrrbbZ09MTgMOHD7N9+3bOnz9v5Lm4uLBjxw5WrVpFjx49Wq1HCNH1yBpkIW5RLR3b1b17d4AOm8Xs379/szQnJyegMYCxt/ZXz1dKNcsbOnRoi/dxc3OjvLyc/Px8+vTpw6lTp4DGs4SPHj1q97qSkhIAu192u9n1vjqr1UpdXR1OTk42M41Nubi4MGjQILKzsykoKMDPz69D7t1UQUEB0Dh7bo+9B4+W6GP7zjvvtLhBUp85zs/Pb/Vho6amhqKiolbb0PQtg07vT0u/CcB4Q6KX1Tk7O3Px4kWOHj1Kfn4+RUVFnDp1iuzsbCMwtvf7a8rPz49JkybxxRdfsH79etavX4+vry/jxo0jJCSEESNGYDbLPJIQtyMJkIW4RelBaGdzdXX9yepzdXWlvLyca9euAf+bBbdarVit1lbrLS8vb5Zmb9b5Rugz4S4uLq0GTHrfOuuVvD4eLi4udvPd3NwwmUxtBoZN69ID5dbYG9umLl26ZPy7pb+vvVlYfZxam6G1N6aXL19m7dq1fPjhhzbLP3r37s3YsWPJzs42Ava2bNq0icTERPbu3Ut2djY5OTnk5OSQkJCAh4cHy5Ytk016QtyGJEAW4jZiL3DSg9GfSlVVVYt5ehCkvyLXA8H4+HimTJnS+Y1rgR7AXb16lYaGhhaDZD3o7OiHCp0+Li2NYXV1dbuCY2gc2/LycpKTk7FYLDfVrqb9raysxM3NrVkZew8N+nWtLQfSg3O9rFKKqKgo0tPTueuuuwgPD8fPzw9vb2/c3d0BmDNnTrsDZAcHB8LDwwkPD6e4uJh//vOffP311xw6dIizZ8+yePFikpKSbnqMhBC3Fnl3JEQXp2+WAuyeDdv0zOSfwo9fleusVisVFRWYzWYGDRoEwMCBA4HWZzkzMjLIzc3t1EDf09MTR0dHamtrjY1dP1ZVVWUs89Db3dH09eU//PCD3fz2zAbr2jO23377LadOnWrzTGE3NzdjKU5OTo7dMvbGra3+AMb6Z33jXUZGBunp6Tg6OvLee++xcOFCgoKCjOAY/rfspi0VFRVkZmZy9uxZoPFovlmzZhEXF8ehQ4fQNI36+no+/vjjdtUnhOg6JEAWootrupnL3jpdfbPWT+Xbb7+luLi4WXpiYiIAI0eONDYFjh8/HoCUlBSqq6ubXWO1WgkPD2fGjBlkZGR0Wpt79OhhnDGst/PHkpKSqK2tpXfv3jYni3Sk0NBQAP7+97/bbCjT6R/xaA99bJOSkuzOOqelpTF37lymTZvGuXPnjHR9Q9+Pr9HPOd63b5/d+zU9heLHbTh8+LARpDZVUVFBSkoKgLEhTy/Xo0cPuw8iX3/9tfH7qqurs9sW3bp165g9ezZvvfVWszw3Nzdj1rilD7AIIbouCZCF6OJcXV2NjVMbNmwwXlnX1dXx9ttvX1dQ1RFqampYtGiRTYC3d+9edu3ahclkYuHChUb69OnT8fLyorCwkOjoaC5evGjkFRQUsGDBAurq6hg6dCgPPvhgp7Z7wYIFmM1m9uzZw7Zt24zgSylFSkoKcXFxACxevLjT1odbLBZCQkK4evUqixYtspkp3bt3L7t37253XU8++SR33nknaWlpLFu2zGad8bFjx3juueeAxqC86cdA9OUmTYNmgPnz59O9e3c+/fRTtmzZYmwSrampYc2aNRw5cqRZG0aOHMno0aOpqakhKirKZjb7/PnzLFy4kPPnz+Pp6cns2bMBjLZcvnzZpr8NDQ188cUX/P73vzfS2pr5njFjBgDvv/8+Bw4csAn6jxw5YnxN8KGHHmq1HiFE1yNrkIW4DSxevJjo6Gi+++47goOD8fLyori4mLKyMiIiIti3b1+bG7E6SlBQEN999x2hoaH4+Phw6dIlY8YvJibG5tg4Z2dn3njjDebPn8/hw4cZP3483t7e1NbWUlBQQH19Pffccw9btmzp9HaPHj2al156idjYWOLi4khISGDAgAEUFxcbgXtERAS//vWvO7UdK1euJDIykszMTCZOnIimaZSVlVFcXExISIhx3Fpb+vTpwx/+8AcWLFjAvn37+Pjjj/H29qaiooLCwkKg8USKNWvW2FynP2x99tlnzJgxg1GjRvHyyy/j6enJihUrWLp0KZs2beKdd97Bw8ODwsJCrly5gsVi4fjx4zg4ONjUFxcXR2RkJLm5uUybNo3Bgwfj5OREXl4edXV1eHh4sHnzZuOtwrBhwwgNDeVvf/sby5cv56233uLuu+/m3LlzlJWV4eLigr+/P99//32by4cCAwOZN28eO3fuJCYmhtdeew13d3cuXbpkzFSHhYUxduzYdo2pEKLrkBlkIW4DEydO5E9/+hNBQUGYzWZOnz7NL37xC9atW8eyZct+0rbcf//9JCYmMnr0aAoKCrhy5Qq//OUv2blzJ/Pnz29W3tvbmwMHDvDMM88waNAgCgoKOHPmDAMGDCAyMpL9+/ff9Cek2ys8PJw9e/Ywbdo0nJyc+OGHHzCbzUyZMoVdu3b9JGPZt29fdu/ezcKFC/Hw8ODkyZOYzWYWLVrE66+/fl11BQYG8uGHHzJ37lzc3d05efIkJSUlaJpGdHQ0iYmJzc5bnjlzJk8//TR9+vShoKCA7OxsI+/hhx/mz3/+M8HBwdTX15Obm4unpyevv/468+bNA/53DGHT/iQlJfH8889z3333ce7cOQoLCxk8eDDPPvss+/fvZ8iQITbXbNq0iRdffJGhQ4dSXl5Obm4ubm5uhIWFkZKSQnR0NNC4dKOt5REvvPACa9euZdSoUdTX15OdnU1VVRVBQUHEx8ezYsWK6xpTIUTXYFLt3fIshBBC3KDdu3ezfPly42FICCF+zmQGWQghxE1bunQpM2fObHHTp/4VwNY+CiKEED8XEiALIYS4aT4+Ppw4cYK4uDibM4hramrYtm0bX375JU5OTsYnxYUQ4udMllgIIW4bK1eu5MSJE9d93csvv2z3U8k34vDhw2zduvW6rwsODiYqKqpD2tAZqqqqmDNnDjk5OTg4ODBw4EBcXFywWq1cuXIFJycnli9fLgGyEOKWIKdYCCFuG7m5uaSnp1/3dR15wkdpaekNtaGzPj7SUVxdXUlKSiI5OZkPPvgAq9VKUVERffv2JTQ0lKeeeqrTzocWQoiOJjPIQgghhBBCNCFrkIUQQgghhGhCAmQhhBBCCCGakABZCCGEEEKIJiRAFkIIIYQQogkJkIUQQgghhGhCAmQhhBBCCCGa+C+lU3tRcP6i/wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt_goals_both(goal_dist, 'goals_condition', 'str_condition', hue_order=['No label','Nutri-score','MTL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "id": "e16d533c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create combo chart\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(10,6))\n",
    "sns.set_style(\"darkgrid\")\n",
    "#bar plot creation\n",
    "#ax1.set_title('Average Percipitation Percentage by Month', fontsize=16)\n",
    "ax1.set_xlabel('Month', fontsize=16)\n",
    "ax1.set_ylabel('Avg Temp', fontsize=16)\n",
    "ax1 = sns.barplot(data = goal_dist, x = 'number_of_dietgoals', y = 'Count', hue='str_condition',  palette = (\"#e74c3c\", \"#34495e\", \"#2ecc71\"), capsize=.2)# ,hue_order=hue_order,order = order, ax=ax)\n",
    "ax1.tick_params(axis='y')\n",
    "#specify we want to share the same x-axis\n",
    "ax2 = ax1.twinx()\n",
    "ax1.legend_.remove()\n",
    "color = 'tab:red'\n",
    "#line plot creation\n",
    "ax2.set_ylabel('Avg FSA score', fontsize=16)\n",
    "# sns.set_style('white'\n",
    "# )\n",
    "ax2 = sns.lineplot(data = fsa_per_goals, x='number_of_dietgoals',y='avgfsa', marker=\"o\", dashes=True, linestyle='--', linewidth = 3, color = 'red')\n",
    "ax2.tick_params(axis='y', color=color)\n",
    "#show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f86d820",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "34dae432",
   "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>diet_goal</th>\n",
       "      <th>Count</th>\n",
       "      <th>freq</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Eat more fruit</td>\n",
       "      <td>44</td>\n",
       "      <td>21.359223</td>\n",
       "      <td>No label</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Eat less sugar</td>\n",
       "      <td>40</td>\n",
       "      <td>21.052632</td>\n",
       "      <td>Nutri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eat more vegetables</td>\n",
       "      <td>37</td>\n",
       "      <td>17.961165</td>\n",
       "      <td>No label</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eat more protein</td>\n",
       "      <td>36</td>\n",
       "      <td>17.475728</td>\n",
       "      <td>No label</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Eat more vegetables</td>\n",
       "      <td>35</td>\n",
       "      <td>19.021739</td>\n",
       "      <td>MTL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             diet_goal  Count       freq condition\n",
       "0       Eat more fruit     44  21.359223  No label\n",
       "1       Eat less sugar     40  21.052632     Nutri\n",
       "2  Eat more vegetables     37  17.961165  No label\n",
       "3     Eat more protein     36  17.475728  No label\n",
       "4  Eat more vegetables     35  19.021739       MTL"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Plot goals per condition\n",
    "diet_goals = pd.read_csv('./all_goals.csv')\n",
    "diet_goals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "id": "cefda0c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plt_diet_goal(diet_goal, title, palette,hue=None, order=None):\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    sns.set_style(\"darkgrid\")#, {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})\n",
    "    x = 'Count'\n",
    "    y = 'diet_goal'\n",
    "    if title == 'All':\n",
    "        x,y = y,x\n",
    "    splot = sns.barplot(data = diet_goal, x = x, y = y, hue=hue, palette = (\"#e74c3c\", \"#34495e\", \"#2ecc71\"), order = order)\n",
    "    splot.set(xlabel= 'Diet goals', ylabel = 'Count')\n",
    "    splot.legend_.remove()\n",
    "    if title  == 'All':\n",
    "        splot.set_xticklabels(splot.get_xticklabels(),rotation = 75)\n",
    "    splot.set(ylabel='Count')\n",
    "    plt.savefig('./'+title+\"_diet_goal.png\", bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "id": "a22bac5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "order  = ['No goals','Gain weight','Lose weight','Eat less sugar','Eat less salt','Eat more vegetables','Eat more fruit','Eat more protein']\n",
    "plt_diet_goal(diet_goals, 'All','ocean_r','condition', order=order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a9fafe2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}