compute_coverage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eabe8b9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d490cb4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_file=\"/data/digbose92/MuSE_Loreal_study/mica-MUSE-LoREAL/RP_data_screentime_metrics_filtered_data.csv\"\n",
    "rp_data=pd.read_csv(csv_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "38755b59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
       "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
       "       ...\n",
       "       '[6.1, 7.1)', '[7.1, 8.1)', '[8.1, 9.1)', '[9.1, 10.1)', 'light',\n",
       "       'medium', 'dark', 'light_screentime', 'medium_screentime',\n",
       "       'dark_screentime'],\n",
       "      dtype='object', length=173)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a53aa06",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_10506/2860436522.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rp_data['ProductAdvertiser'].iloc[i]='MAYBELLINE'\n"
     ]
    }
   ],
   "source": [
    "for i in np.arange(rp_data.shape[0]):\n",
    "    if(rp_data['ProductAdvertiser'].iloc[i]=='MNY'):\n",
    "        rp_data['ProductAdvertiser'].iloc[i]='MAYBELLINE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0b261d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ARMANI',\n",
       " 'GARNIER',\n",
       " 'LANCOME',\n",
       " 'LRP',\n",
       " 'MAYBELLINE',\n",
       " 'OAP',\n",
       " 'VICHY',\n",
       " 'YSL',\n",
       " 'CERAVE',\n",
       " 'KERASTASE',\n",
       " 'LOP',\n",
       " 'KIEHLS',\n",
       " 'NYX',\n",
       " 'PRADA',\n",
       " 'REDKEN',\n",
       " 'BIOTHERM']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bc88b47f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['(female, [0, 15), [-inf, 1.1))', '(female, [0, 15), [1.1, 2.1))', '(female, [0, 15), [2.1, 3.1))', '(female, [0, 15), [3.1, 4.1))', '(female, [0, 15), [4.1, 5.1))', '(female, [0, 15), [5.1, 6.1))', '(female, [0, 15), [6.1, 7.1))', '(female, [0, 15), [7.1, 8.1))', '(female, [0, 15), [8.1, 9.1))', '(male, [0, 15), [-inf, 1.1))', '(male, [0, 15), [1.1, 2.1))', '(male, [0, 15), [2.1, 3.1))', '(male, [0, 15), [3.1, 4.1))', '(male, [0, 15), [4.1, 5.1))', '(male, [0, 15), [5.1, 6.1))', '(male, [0, 15), [6.1, 7.1))', '(male, [0, 15), [7.1, 8.1))', '(male, [0, 15), [8.1, 9.1))']\n",
      "['(female, [15, 25), [-inf, 1.1))', '(female, [15, 25), [1.1, 2.1))', '(female, [15, 25), [2.1, 3.1))', '(female, [15, 25), [3.1, 4.1))', '(female, [15, 25), [4.1, 5.1))', '(female, [15, 25), [5.1, 6.1))', '(female, [15, 25), [6.1, 7.1))', '(female, [15, 25), [7.1, 8.1))', '(female, [15, 25), [8.1, 9.1))', '(female, [15, 25), [9.1, 10.1))', '(male, [15, 25), [-inf, 1.1))', '(male, [15, 25), [1.1, 2.1))', '(male, [15, 25), [2.1, 3.1))', '(male, [15, 25), [3.1, 4.1))', '(male, [15, 25), [4.1, 5.1))', '(male, [15, 25), [5.1, 6.1))', '(male, [15, 25), [6.1, 7.1))', '(male, [15, 25), [7.1, 8.1))', '(male, [15, 25), [8.1, 9.1))', '(male, [15, 25), [9.1, 10.1))']\n",
      "['(female, [25, 35), [-inf, 1.1))', '(female, [25, 35), [1.1, 2.1))', '(female, [25, 35), [2.1, 3.1))', '(female, [25, 35), [3.1, 4.1))', '(female, [25, 35), [4.1, 5.1))', '(female, [25, 35), [5.1, 6.1))', '(female, [25, 35), [6.1, 7.1))', '(female, [25, 35), [7.1, 8.1))', '(female, [25, 35), [8.1, 9.1))', '(female, [25, 35), [9.1, 10.1))', '(male, [25, 35), [-inf, 1.1))', '(male, [25, 35), [1.1, 2.1))', '(male, [25, 35), [2.1, 3.1))', '(male, [25, 35), [3.1, 4.1))', '(male, [25, 35), [4.1, 5.1))', '(male, [25, 35), [5.1, 6.1))', '(male, [25, 35), [6.1, 7.1))', '(male, [25, 35), [7.1, 8.1))', '(male, [25, 35), [8.1, 9.1))', '(male, [25, 35), [9.1, 10.1))']\n",
      "['(female, [35, 45), [-inf, 1.1))', '(female, [35, 45), [1.1, 2.1))', '(female, [35, 45), [2.1, 3.1))', '(female, [35, 45), [3.1, 4.1))', '(female, [35, 45), [4.1, 5.1))', '(female, [35, 45), [5.1, 6.1))', '(female, [35, 45), [6.1, 7.1))', '(female, [35, 45), [7.1, 8.1))', '(female, [35, 45), [8.1, 9.1))', '(male, [35, 45), [-inf, 1.1))', '(male, [35, 45), [1.1, 2.1))', '(male, [35, 45), [2.1, 3.1))', '(male, [35, 45), [3.1, 4.1))', '(male, [35, 45), [4.1, 5.1))', '(male, [35, 45), [6.1, 7.1))', '(male, [35, 45), [7.1, 8.1))', '(male, [35, 45), [8.1, 9.1))']\n",
      "['(female, [45, 55), [-inf, 1.1))', '(female, [45, 55), [1.1, 2.1))', '(female, [45, 55), [2.1, 3.1))', '(female, [45, 55), [3.1, 4.1))', '(female, [45, 55), [4.1, 5.1))', '(female, [45, 55), [5.1, 6.1))', '(female, [45, 55), [6.1, 7.1))', '(female, [45, 55), [7.1, 8.1))', '(male, [45, 55), [-inf, 1.1))', '(male, [45, 55), [1.1, 2.1))', '(male, [45, 55), [2.1, 3.1))', '(male, [45, 55), [3.1, 4.1))', '(male, [45, 55), [4.1, 5.1))', '(male, [45, 55), [6.1, 7.1))', '(male, [45, 55), [7.1, 8.1))', '(male, [45, 55), [8.1, 9.1))']\n",
      "['(female, [55, 65), [-inf, 1.1))', '(female, [55, 65), [1.1, 2.1))', '(female, [55, 65), [2.1, 3.1))', '(female, [55, 65), [3.1, 4.1))', '(female, [55, 65), [4.1, 5.1))', '(female, [55, 65), [6.1, 7.1))', '(female, [55, 65), [7.1, 8.1))', '(male, [55, 65), [-inf, 1.1))', '(male, [55, 65), [1.1, 2.1))', '(male, [55, 65), [2.1, 3.1))', '(male, [55, 65), [3.1, 4.1))', '(male, [55, 65), [4.1, 5.1))', '(male, [55, 65), [7.1, 8.1))', '(male, [55, 65), [8.1, 9.1))']\n",
      "['(female, [65, 75), [1.1, 2.1))', '(male, [65, 75), [1.1, 2.1))']\n"
     ]
    }
   ],
   "source": [
    "age_bins=['[0, 15)','[15, 25)','[25, 35)','[35, 45)', '[45, 55)','[55, 65)', '[65, 75)']\n",
    "for age_bin in age_bins:\n",
    "    \n",
    "    age_col=[col for col in rp_data.columns if age_bin in col]\n",
    "    print(age_col)\n",
    "    subsample_df=rp_data[age_col]\n",
    "    rp_data[age_bin]=subsample_df.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce75b897",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "af9ec7c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       169\n",
       "1       376\n",
       "2       205\n",
       "3       359\n",
       "4       291\n",
       "       ... \n",
       "7643    890\n",
       "7644    305\n",
       "7645    140\n",
       "7646    200\n",
       "7647    326\n",
       "Name: [25, 35), Length: 7648, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp_data['[25, 35)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9fcba26e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rp_data['[45, 75)']=rp_data['[45, 55)']+rp_data['[55, 65)']+rp_data['[65, 75)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5e7322a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "rp_data['[45, 75)']\n",
    "age_condensed_bins=['[0, 15)','[15, 25)','[25, 35)','[35, 45)','[45, 75)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5be396e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tqdm in /data/digbose92/envs/miniconda3/envs/py38env/lib/python3.8/site-packages (4.65.0)\r\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7648/7648 [00:00<00:00, 31267.40it/s]\n"
     ]
    }
   ],
   "source": [
    "!pip install tqdm\n",
    "#COMPUTE COVERAGE FOR EVERY ENTRY IN CSV DATA\n",
    "from tqdm import tqdm\n",
    "subsample_age_data=rp_data[age_condensed_bins]\n",
    "\n",
    "\n",
    "coverage_value_age=[]\n",
    "\n",
    "for i in tqdm(np.arange(rp_data.shape[0])):\n",
    "    age_faces=subsample_age_data.loc[i]\n",
    "    age_list=list(age_faces)\n",
    "    #count number of non zero entries\n",
    "    non_zero_count = len([x for x in age_list if x != 0])\n",
    "    coverage_age=non_zero_count/len(age_list)\n",
    "    coverage_value_age.append(coverage_age)\n",
    "    \n",
    "rp_data['age_coverage']=coverage_value_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "543240ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "sns.set_style('darkgrid')\n",
    "plt.rcParams[\"figure.figsize\"] = (15,10)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "number_coverage=dict(Counter(coverage_value_age))\n",
    "keys_list=[0.2,0.4,0.6,0.8,1.0]\n",
    "\n",
    "#plot the dictionary in terms of bar plot \n",
    "\n",
    "coverage_list=[number_coverage[k] for k in keys_list]\n",
    "ax.bar(keys_list,coverage_list,\n",
    "           edgecolor='black', align='center',\n",
    "           linewidth=2, width=0.1)\n",
    "\n",
    "for i, v in enumerate(coverage_list):\n",
    "    ax.text(keys_list[i], v+12, str(v), ha='center', fontsize=14)\n",
    "\n",
    "# set the title and axis labels\n",
    "ax.set_title('Coverage distribution of Age bins', fontsize=20)\n",
    "ax.set_xlabel('Coverage values', fontsize=20)\n",
    "ax.set_ylabel('Counts', fontsize=20)\n",
    "\n",
    "# get the x-tick labels\n",
    "labels = ax.get_xticklabels()\n",
    "yticks = ax.get_yticks()\n",
    "ax.set_yticks(yticks)\n",
    "ax.tick_params(axis='y', labelsize=20)\n",
    "ax.tick_params(axis='x', labelsize=20)\n",
    "filename=\"Age_based_coverage.png\"\n",
    "plt.savefig('plots/'+filename,dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f870bff2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canada\n",
      "t-test between India and Mexico: t=3.284632822639577, p=0.00144040861990715\n",
      "t-test between Mexico and Indonesia: t=1.1215955405062246, p=0.2656608344583311\n",
      "t-test between Indonesia and Spain: t=2.235473859621765, p=0.026252246725266007\n",
      "t-test between Spain and Brazil: t=0.31683116042826165, p=0.7516086586994274\n",
      "t-test between Brazil and UK: t=0.7208578145417996, p=0.4710923560090896\n",
      "t-test between UK and France: t=2.213856741491044, p=0.026927507034298852\n",
      "t-test between France and Italy: t=1.4329284227251466, p=0.1520711182194333\n",
      "t-test between Italy and Germany: t=0.011464331094170797, p=0.9908535584513487\n",
      "t-test between Germany and USA: t=0.8323273693409222, p=0.4052751354086156\n",
      "India > Mexico > Indonesia > Spain = Brazil = UK > France > Italy = Germany > "
     ]
    },
    {
     "data": {
      "image/png": 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ooRzr5Ja016vY2FglJSVJkmrWrJnv82T/uOzdu1ezZs3Szp07FRsbK39/f9133316+umnb7qeoKMfv1utoZr99SXz6ully5bp+PHjkqS6deuqb9++GjRokFxd+fEtN6pWrWrbTklJybJv0aJFtnvprF27VhUrVtQPP/yg5cuX68SJE7p8+XKWG8qlpKRo06ZN2rRpk/7880+dPHlSVqtVPj4+qlGjhh544AENGTJE/v7+dufy+OOPa9u2bbmee/abU2XenJKb3JUM165d06hRo2w90alTJ/3rX//i3RK3QfbX26tXr2r27NlavXq1Tp8+ratXr2Z5nbZardqwYYM2b96siIgInT59WklJSfL19VW9evXUsWNHDRo0SN7e3qY1s3/9Zn4v2rFjh2JjY1WuXDnde++9GjVqVK7Wtj18+LDmzZunbdu26fz587JarfLz81P9+vXVrl07Pfzww6pYsaLdYy9evKjvv/9ev/32m06fPi2r1aqAgAA1a9ZMjz32GBe13ETm5zGTvXuyZX+NPnXqlFavXq1t27bp8OHDiomJkXT9nRP33HOP+vfvrwceeCDPc4mNjdUDDzyg1NRUPfbYY7dcsmTdunV67rnnJF1/p19xufp05syZtuU+Q0ND8/QzYZkyZdSpUyfT/fn9WrD3M92qVau0YMECHThwQLGxsWrZsqXmzJljOvann37SgQMHlJiYqBo1aujRRx/VoEGD5ObmJun68nTh4eGaP3++jhw5IqvVqjp16mjgwIEaNGiQ3d8jpaLxetWvXz9FRkaqTp06t3yXaFxcnO6//36lpqZq8ODBevvtt286vqTLvAdUuXLlnHpT8cylXOrVq6egoCD5+flp8uTJSktL09KlS02zGtjHb2EokjIyMjR27FgtWbIky+PHjx/X9OnTtWbNGs2dO9f0L287duzQyJEjs9yw8uLFi1q/fr3Wr1/v8C+g6enp6tChgzIyMnLsi46O1rp167Ru3Tr9/PPP+uqrr276jaqkevjhhzVr1ixFRkbqr7/+Uv369bPsP3LkiPbv328be+DAgZueLzk5WePHj7d7Zfvhw4d1+PBh/fTTT/rss8+y/MDk7u6uTz/9VI888oisVqvGjh2ruXPn5gjtf//9d3333XeSpG7dumUJ0HPrwIEDGj16dJabdkjXbxh25MgRHTlyRD///LPdO3dHRkZq5MiRio6OzvL4hQsXtHLlSq1cuVIPPfSQPv300yJzA5Ci8nU6depUffHFFzm+HjM/5j/++KPeffdd9e3b95bn+uyzzzRt2rQsj505c0Y//fSTVq1ape+//970F9z89mgmex8P6fqyR5cuXdLhw4e1bNkylStXLk9/UCmOr1eZv0RIUlRUlFPO+fPPP2vixIlKS0uzPXb+/HktWrRI4eHh+uSTT9SjR48cx93uj19MTIyefvrpHK+J+/bt0759+7Rp0yZNmTKlyPzBoyi78Ya0VapUMR0XFxen0aNH3/T70D/+8Q/98ssvOR6/fPmyLl++rL179+r777/XlClT1LJlS8cmjhItISFBzzzzjHbt2iXp+s8cn332WZbXPdwex48f14gRI3TmzBnTMSNHjrT7B7C4uDht375d27dv1w8//KBp06blKgCfO3euPvjggyzfi6Kjo7VkyRKtXr1a3377rVq3bm332PT0dH3yySf67rvvbGtDZ4qJiVFMTIz++OMPRUVF2f2D+pIlSzRx4kRZrdYsj58/f16//vqrfv31Vz3yyCP65z//yR9rneDUqVPq0qWL3X1nz57V2bNntWLFCvXp00cffvhhnj7m/v7+6ty5s3799VetWLFCEyZMuOnvB4sWLZIk+fn5mc6pqDEMw3bBlbe3t1Nv4uqsrwXDMPT666/b5nkrb7/9tn788ccsjx06dEjvvfeetm3bpi+++ELp6el67bXXbMtvZIqMjNTbb7+tyMhIvfvuu3bPXxRerx555BG98847Onr0qPbs2aNmzZqZnn/p0qVKTU2VpHz97l3SZP4cEBMTo8uXL9/yYrTciImJsS2fm3kFepUqVdS6dWtt27btphc8wj6+O6JI+te//qXdu3erS5cu6tu3r6pUqaKYmBj98MMP2rBhg06cOKEPP/xQn3/+eY5jz549awuiXFxcNHDgQHXv3l0+Pj46dOiQvv32W3311Vdq3LhxvueX+YPrvffeqwceeECBgYHy9/fXtWvXdOrUKS1YsEC7d+/W5s2b9c477+jjjz/Od63iKigoSPXr19dff/2lsLAwvfbaa1n2Z/6wERgYqLvvvvum4YVhGHrxxRe1YcMGSVLHjh3Vo0cPVa9eXS4uLtq7d69mzpyps2fP6sUXX9SPP/6oJk2a2I5v0KCBXn31VX344YfavXu3vvnmG40ePdq2//Lly/r73/8uwzBUsWJF/fOf/8zz842KitLf/vY32w9jXbt2Vc+ePVW9enVlZGTo2LFj2rx5s9asWZPj2AsXLujJJ59UfHy8LBaL+vXrp169esnPz09HjhzRzJkzdfDgQa1atUrjxo0rMmv8FYWv07lz59rO7+/vr2eeeUYtWrRQenq6/vjjD02fPl1Wq1Xjxo1TuXLl1KFDB9NzzZ8/X7t371abNm302GOPqVatWrp69aoWL16sxYsXKzY2Vm+88YbmzZuX41hHezQlJUWhoaFKSEiQt7e3Bg8erODgYAUEBCg1NVWnT5/W7t27tXr16tx8anLMTSper1d+fn6qWrWqzpw5o4MHD2ratGl6+umn8x0cHzhwQOHh4fL399fIkSPVtGlTJScna+PGjfruu++UkpKisWPHqlq1alk+L9Lt//iNHj1aR44c0eOPP65OnTqpbNmyOnbsmKZMmaKoqCitX79e8+fP16BBgxyqU9JFRUXZvh6bNWum8uXLm46dMGGCDh8+rL59+6pnz54qX768zp07lyXUTEtLU/Xq1dW1a1c1adJEVapUUalSpXT27Fn9/vvvWrhwoe3q9fDwcAUEBGSp8cEHHygxMdF0DrGxsXrhhReUkJAgX19f+fr6OvYBQJF09epVPfXUU/rzzz8lXX8n3scff0xgWUhefPFFRUdH215vy5QpoxMnTmT5o1taWpoCAwPVqVMnNWnSRBUrVpRhGDpz5ozWrFmjFStW6PTp03rhhRcUFhZ20yBz06ZN2rt3rwIDAzVs2DAFBgYqOTlZq1ev1uzZs5WYmKjXX39dK1eutPuuhLfeeksLFy6UJFWoUEFDhw5V8+bN5evrq9jYWO3duzdH8JZp+fLlev3112UYhqpXr66hQ4eqbt268vf315kzZ/Tzzz9r48aN+vnnn+Xj45PjCmtcD/yio6P11FNPSZJefvll21XGmW587c/IyJCbm5vat2+vdu3aqV69eipbtqzi4+N17Ngx/fDDD/rrr79s75J98cUX8zSfRx99VL/++quuXLmi1atXq3fv3nbHxcbG2r4fhoSEFJt3vBw+fFiXL1+WJLVq1cppN1t25tfCd999p0OHDqlVq1YaPHiw7XeH7BdUSdJPP/2kP//8Ux06dNCjjz6qKlWq6Ny5c5o2bZr+/PNPrVq1SosWLdKhQ4e0cuVK9e7dWyEhIapQoYJOnDihr776SkePHtX8+fPVtWtXu+9gKAqvV3369NEnn3yipKQkLVq06KYheuYfdxo0aJDj5+87UaNGjXT48GEZhqG33npLH330kcMX5yxdulTp6emyWCwKCQmxPd6nTx9t27bN9IJH3IQB3EZbtmwxAgMDjcDAQOPLL7803RcYGGhMmTIlx/EZGRnGiBEjjMDAQCMoKMi4dOlSjjFjxoyxnWPp0qU59l+9etXo06dPllr2dOzY0QgMDDT+/ve/253H8ePHb/pc//WvfxmBgYFGgwYNjGPHjt10bElw4+dv4cKFhmEYxrRp04zAwECjQ4cORkZGhm1sRkaG0aFDByMwMND49ttvDcMwjIULF9qO37JlS5Zzz5s3zwgMDDQaNWpkbNy40W79y5cvG7169TICAwONQYMG5difkZFhDB8+3NY7u3fvtu0bPXq07XO1adMmu+f/8ssvb9ov/fr1MwIDA42GDRsa4eHhph+n2NhYIzExMctjN/bs/PnzcxyTnJxsPP7447YxGzZsMD2/MxSXr9NLly4Z99xzjxEYGGi0b9/eOHv2bI4x+/fvN5o1a2YEBgYa999/v5GSkpJl/6lTp7LUePPNN7P0aqYJEybYxuzfvz/Hfkd79Pfff7edf926dXaPNwzDSE1NNa5evZrj8ZL4ejV9+vQsn5uOHTsa7777rrFs2TLj5MmTuTpH5scl8/jo6OgcY/744w8jKCjICAwMNAYMGJBjvzM+fjf2Webr441ufH1p1KhRjtdAwzCMuLg447777jMCAwONkJCQm86nJLvxNWjixInGoUOHbP8OHjxobN++3Zg2bZrRrl07IzAw0GjZsmWW1/tMN37PMXvtvdGJEyfsvjZkOnjwoO21ZvLkyXl6TsnJycagQYOMwMBA4+6777b7GmL2mmwYWT8m9noHefP3v//d9vE8depUro7JfK3p2LFjjn2Z53r44YeN/v372/7/+uuvG+np6c6e/h3lZq8H2f/FxMQYhpH19bZhw4bGb7/9dtMat/qeuHnzZqNhw4Y3fR258bXmmWeeMZKTk3OMmTJlim3MqlWrcuxfs2aNbf9jjz1mxMfHm84p+89Dly5dMlq2bGkEBgYa48ePN1JTU+0e9/nnn9s+LlFRUTd72iXazX4OvtX38xtdu3bNuHDhgun+jIwMY9y4cUZgYKDRrFkz48qVKzedS/bX9/T0dNtrz/Dhw03rzJw503aOyMjIm865KAkLC7PNO6/fV80442sh++8Or7/+uunPB9nHvv/++znGWK1W2+exTZs2RoMGDYxZs2blGBcdHW00b97cCAwMNEaNGmW3XlF5vRo7dqztZ7Dsv/dm2r9/v+0cM2fOvOm8izJn/szw559/2j4/gYGBRqtWrYyxY8ca8+bNMw4cOGCkpaXleX6Zv08PGTIky+NXrlwxGjdubAQGBhoff/xxns97J+O9wCiSGjVqpFGj/l979x0V1bX+Dfw7dBEEFURRYwUEFWPkZ0MjIJaYGKTFhppobFdirokl5mpisKBXozGmmEKEq1ioUSxEDSoiCNYgiqgoFqQoSCcCwvvHvHMywMww9Pb9rMVa45y253jKPs/Z+9mLKn0vEomE7iYlJSW4du1auenPnj0TWvra2trKfCOvo6MjtwuUskQiUZU52ZYsWYL27dujrKwMYWFhtdpec/Xuu+9CRUUFKSkpiI6OFr6Pjo5GSkoKVFRUyr0RlaWsrAy//PILAHFOWXl5A/X09LBixQoAwNWrV4U8whIikQienp7Q19dHSUkJVqxYgfz8fAQEBODkyZMAgNmzZ8Pa2rravzMiIkJITTNr1iy8/fbbcudt3759uUHD0tLShGN29OjRcHV1rbSMhoYGNm3aJLRU8/X1rXYZ60Njn6eBgYFCy87Vq1ejS5culeaxsLDAggULAJTf17IYGhpi7dq1MvMMzp07V/h8+fLlctPq4hiV5MgEILcbNyAeaKZiPvSqNNfr1fvvv1+ua2dycjL27t2LZcuWwd7eHtbW1li2bBnCwsIqdWuXZdWqVTJTCw0fPlw47yQpU6Q19P5zc3OTOWiWvr6+0JX5zp07wgBBrdmBAwcwefJk4e/dd9/FzJkzsW3bNmRkZGDatGnw9/dX2AoKKH8MyPPaa6/JzUEKiFtRSdYhGRhKWV9++aWQ2mPlypU1yo9LTV98fDzi4uIAiFuge3p6Mi1THap4Paj4t3///krLODo6YtSoUQrX27NnT4XTR44cKaRoq+rc19TUhKenp8yWwLNmzRJ6v1SsZwAQ6hlt2rTBzp07FebLrVgfOnDgAHJzc2FkZIR169bJ7fnw0UcfwcjICKWlpUqnpyD5tLW15eamB8T1i1WrVkFVVRUFBQWIjIys1vpVVFSEekFUVBRSUlJkzifpvWBhYQFzc/NqbaMxvXjxQvgsb7wRQNziX5I2UdafJF0IUPfnQrt27eQ+O1TUpUsX4TlAWps2bYSUk1lZWRg0aBDmzJlTaT5DQ0OMGzcOAHDlyhWZ22gq1ytJfSg3N1d4zq5I0gpdXV1dSDPS2llaWsLDw0PYtzk5OTh8+DDWrl0LBwcHWFlZYe7cufDz86uUikiWhIQE3L59GwAq7WNdXV0hPWhISIjMtJUkG2tu1CRNnjxZ7s2of//+wufHjx+XmxYdHY1Xr14BgMK8aZaWlnXaZaW0tBRpaWm4f/++cMNOTEwUBiGUXLxaGyMjIyEYJF0BkXwePny4wkEWAXFe60ePHgEQ5w1VRDr4eP36dZnl2bBhAwDg0aNH+PTTT7Fx40YA4rQyFVPOKEvSRRKAzEqPIjExMcIx6+LiIne+bt26CQPcSC/TmBr7PI2KigIgrsBKKpWySAfHJMvIMnHiRLldXHv37i10I634e+riGJUO7koedupLc7leqaioYNOmTfjtt98wevToSg86z58/x/Hjx7F48WK4uLgI/wey6OnpVepyLU06WK/oGAHqf/8perEoOa/KyspkdhWmf5SWluL48eM4cOBApUFFK6rqZa4s2dnZePToEe7evSscB5Kg1r1798o9tCvi5eUlPEg6OzszL2ULJn2/vHbtWqUxUKjh1eTcz8zMRFJSUrkgnSTAV9X1f+TIkZVSPUno6OgIAbCK9YwXL14IdYa33nqryrpzRZKXuzY2NgpTeaipqQkvHSs2gKDaKy4uRmpqKhITE4VjJz09Xch7XJP6g4uLC1RUVFBaWipz3I64uDjcuXMHQPPLOZ2fny98btOmjdz58vLyFL5AS0tLE+at63PB1tZW6cYt48aNkzv2Rb9+/YTPssbnqThfdnY2cnJyqtxmY1yvAPHzjmS6pI4jraioCCEhIQDEA2sreknS2ri6uuLIkSNwcnKqlMqloKAAFy5cwNq1azF+/HiEh4crXJdkQFENDQ1MnDix0nRJYD09Pb3K5x/6BxPwUZPUu3dvudOkB1iQvrkCECoJAKrMqzVw4EDcvXu3ZgWEOIhx5MgRBAQEIDY2Fn///bfceaXfpLc2U6ZMQVRUFE6ePIkvv/wSAIR8jQ4ODlUuL2m1BQBTp05VervPnj2T+f24cePg4uKCgIAAnDlzBoD4xrJ169Ya5wi8desWAPEgHV27dq3WstLH4KBBgxTOO2jQIISHh6OwsBCPHz+usrVBfWvs81SyHgsLC4UDshkYGAj5taW3XVGvXr0UlkVPTw8FBQWVfk9dHKNDhgxB9+7d8fjxY2zatAkhISEYN24crKysMHDgwFrnr2zO1ytra2tYW1sjLy8PV65cwY0bNxAXF4fLly8LrbHj4uIwY8YMBAUFyWz1ZW5urjDnsLm5OdTV1VFcXIyEhIRK0xty/yk6r/T09ITPFY/D1sjd3b3SAMR///03Hj58iMOHD8PHxwc+Pj6Ii4uDl5eX3IdwMzMzpbaXkJAAb29vnD9/Xu49BhAH8HNycuQ+fEqcPXsW27ZtAyDO97pu3TqlykHN05AhQ2BkZIRjx44hOTkZc+bMwb59++QOvk3VI+t6UBVlz/0rV65g7969iIqKEnI0y1LV9V/R9R345xpf8fp++/ZtoceVlZWVEiX+x6tXr4Rg2aFDh2SO6yKLdA85qrni4mL4+fnh8OHDuHXrlsIXrDWpP3Tu3BmjRo1CeHg4goOD8a9//avcdEkAU0NDo0YvjRqTdABR0ZgiyqqPc0HZawiguJW49Dgoip5HpOfLz8+X2SOlsa9XEi4uLti2bRsuXryI5OTkcs/IYWFhQtma28udhtC7d294enrCw8MDcXFx+OuvvxAXF4dLly4hNTUVgPg5ctGiRfj111+FhnbSXr16JbyosLGxkXmsjBkzBvr6+sjKysLvv/9eox75rRFbolOTJJ3uoiLprq8Vu51I3yiqenhVNMhYVV6+fIkFCxZg5cqViImJURhQAVDl9JZs3LhxaNOmDfLy8vDnn3/i9OnTyM/Ph7a2NsaPH1/l8hkZGTXarqJ9vnr16nKD0yxZsqRcC4DqklRCavIgXNNjNjs7u9rbqmuNfZ5K9kFV6wD++b9RtN8UtXIB/vlNFX9PXRyj6urq2L17N/r06QNAnFZk+/btmDFjBv7v//4P8+bNEwaGqa6Wcr3S0dHBmDFj4O7ujt27dyMyMhKbNm0SKvHPnj3Dzp07ZS5b1TGipqYmvPipeIw09P5TdBxKn1dNoTdKU6SlpQUzMzOsXLlSeHF75coV7N69W+4y0i8n5PH394eTkxOCgoIUBtAlqjoO7t69i08++QSlpaXo2rUrdu3a1WwGe2vppFuMK5MqStn5VFRU8N///hf29vYAgKSkJMydO7fJvbhsTZQ593ft2oUZM2bgxIkTCgNSQNXnfU3rGdLHSHXrmtnZ2SgpKanWMkDTrQs0J1lZWZg6dSo8PDzw119/VdlD6eXLlzXajqTH5aNHj3Dp0iXh+6KiIhw9ehQAYG9vr9Tx3pRIN8jJzMyUO1+7du2QkJBQ7s/R0bHSfPVxLihKq1SRsvU7ZZ+vZNUDm8L1SsLR0RHq6uooKyur1EtC0uvWyMioypRaTV191RkA8fPh4MGD8f7772Pbtm04d+4cvL29hZ7ar169wldffSVzfREREUJ9VV66HHV1daHngyRGQ1VjS3SiGvjxxx+F7jNDhw7FjBkz0L9/fxgYGEBLS0u4qcycOVNmnrDWpG3btrC3t0dISAgOHz4sXOTt7e2VGmVd+sa8e/dupVt6KwqaBQUFlcsjFhkZiYULFyqVz46ansb+f6urY7Rv374ICQnBmTNnEBYWhsuXL+Phw4f4+++/ERERgYiICOzZswe//PKLUi8OJFrq9UpDQwPOzs7o1KkTPvzwQwDAqVOnsH79+kp5hmtzjLTU/dcauLi44Ouvv0ZWVhYCAwOxbNkymfNVlZc6MTER69atQ0lJCTp27Ih58+Zh+PDh6Nq1K9q2bSv0hAkICMB//vMfAIofkDIzM7Fo0SLhhfKPP/7IrsxNiKampvBZ2aCWpE5RVb1GTU0NO3bswL/+9S+cP38ed+7cwbx58+Dj41OuhSE1DFVVVYXTo6Ki8N133wEAunfvjrlz52LIkCEwNjZGmzZthB5OO3fuxA8//FDv5a0J6UCbq6srZs+erdRyinr4kXI2btwojJlkb28PZ2dnmJmZoWPHjtDU1BTqJjY2NkhJSVE6sFaRra0tDAwM8Pz5cwQFBQmpA0+fPi00DGiOrX2lGzjFx8fXen31cS5UdQ1pSE3temVgYAAbGxucOnUKwcHBWLJkCUQiEdLS0nDhwgUA4h7rTWkf1kR91hlkGTFiBH777TdMnjwZWVlZSEpKQnx8PCwsLMrNJ51K193dXakynTx5UuYLKCqPQXRqUaTfsGdkZMgcaFCipt0Uy8rKEBAQAEDcpdLHx0fuA3hTaC3cFEyZMgUhISHCDVPynTKkWyHo6urC1NS0VmW5d++e0H1eR0cHeXl5iI6Oxm+//YZ58+bVaJ3t27cHID+FjCLSv686x2xza00ira7OUz09PTx79kypc1nyf1Mf+60uj1FVVVXY29sLLRXT09Nx/vx5+Pr64ubNm7h58ya++OILfP/990qtrzVcr0aPHo0uXbogJSUF2dnZyMrKqhSQrOoYKSkpEVrsSB8jrWH/tWQqKiro0aMHsrKy8OzZM7x48UK4XldHcHAwSkpKoKqqir179wo9RipS5hgoLi7G0qVL8eTJE4hEImzbtq1a3cGp/klfA549e4a+ffsqnL+oqEhILaXMPUZDQwPfffcdFixYgOjoaNy8eRPz58+Hl5dXpfyn1Lj8/PwAiP9f/fz85L7squ/rv/R1q7p1zYr3tNrWo0k5eXl5OHHiBABx7n3Js4cstT1+1NXV4eDgAC8vL4SGhmLNmjVo27at0NrX2NhYZrqHps7U1FRINXH58mUUFhZW2TpakZZ+LjSV65U0V1dXnDp1Ck+ePEFMTAyGDRuGw4cPKzU2VnNR33UGWTp16oQxY8YIgfKHDx+WC6JLMgBU1++//84guhKYzoVaFOmb4Y0bNxTOK53HuDokD+OAeCBCeQGV/Px8PHjwoEbbaGlGjBgBQ0NDlJSUoKSkBJ06dcKIESOUWlZ6FPmrV6/WqhxFRUVYvnw5Xr58iTZt2uDQoUNCHvJvvvmmxgMCSm5aT58+RXJycrWWlR4486+//lI4b2xsLABx97ru3btXs5RNR12dp5L13Lp1S2H3zIyMDDx9+rTStutKXR6jFXXq1AnOzs44dOiQMKjk2bNnle5m3VquV7LyoEu7ffu2wmPk9u3bQjdr6WOktey/lkz6/72mKXDu3bsHQNwqTl4AHVCuXvHVV18J3e2XLVumcMBbahzSLzUkrUgVuX37tnBsKftCREtLCz/++CMGDx4MQDxw3aJFi5hCo4mRnPvDhg1T2Fukps8UyjI3NxdaLVe3x5OGhoZQ16zrOkprpGzPtqSkJKFeMWnSJLnzJSYmlusdW1OSlC4FBQUIDQ1FamoqIiMjAYgbLlXV46opEolEQhqKvLw8mQOnVkdLPxeayvVK2ujRo9G5c2cA/+Tnl7zcsbKyavTxvepCQ9QZZJF+9ql4XTpx4oRQn1i6dCm2b9+u8E8yXkJMTAxSUlJqXKbWovldTYkUGDZsmNAlSNGNNjY2VuEAg4pIP4QrGuTE39+/RnnXWiJVVVU4ODhAQ0MDGhoacHBwULoy179/f+Hm6+fnV+N8gYA4UC7pDrh69Wr07dsXW7duhba2NoqKirBixQoUFRVVe712dnbCZ29v72otO3ToUOGYlVQqZHn69KlQGZZepjmqq/NU8iImJycHJ0+elDtfQECA0EVW2Zc31VGXx6g86urqQvfckpIS5OTkKLVca7heFRYWCg8OOjo6MlsaZ2VlCQMJyyJ97kkfI61h/7VkhYWFSExMBCAOWtakFTrwTyBeUaAjPT0dYWFhCtfj7e0Nf39/AOKWiQsXLqxReah+DR06VOj2fvz48SpTLBw5ckT4XJ17TNu2bfHLL78IL0hjYmLg7u5eo3oI1Q9lzv1bt25V2QiitvT19YUXLidOnEBaWlq1lpfUU+/fv4/z58/XeflaE+nUDYrOVWXrDwcPHqyTcvXq1UsYdDYoKAjBwcEoLS2FSCRq1q19P/jgA2Gfb9++HY8fP67V+lryudBUrlfSVFRUhOPvjz/+QHh4OJKSkgA0zxRDstRlnaE6KZ2kX4ZUbFwnaaGup6eHBQsW4O2331b4J+mNX1paWq58JBuD6NSidOrUSWjVFRYWhuPHj1eaJz8/XxhsrCY6dOggDCJy9OhRmRWo2NhYuQPctVYrVqzAjRs3cOPGDSxfvlzp5VRUVIRAw+PHj7Fy5UqFlda8vDzs27ev0vfR0dHYs2cPAHEFaurUqQCAHj164PPPPwcA3LlzR2F3S3lGjhwpPATv27cPx44dkzvvixcvyrU0MzIyElJ3hIeHywwqFxUV4fPPPxdatMycObPaZWxK6uo8dXZ2Frp1btmyReZD5e3bt4UBBaX3dV2qi2NUkv9cnqKiIqH1qra2ttL5k5vr9So/Px+urq44c+aM3AGLAHFlb/369cJAOHZ2dnJbiXl6espM6xITEyN0ge3fvz8sLS2Fac11/5HYrl27hOvtqFGjavzyUdJS6uHDhzJbrxUWFuLTTz9V2Io4PDwc//3vfwEAlpaW2LhxY43KQvXPwMAAEydOBCBuVfbzzz/LnTcqKkoIgnXt2rXcS3Vl6OrqwsvLS+gBc/78eSxbtowv5ZqIHj16ABC3WpV1j87MzMTKlSsbpCzz588HIL7efPzxx0I6AFlSU1PL/Xv27NlC7t3Vq1fj7t27Crd19uzZGvfObOn09fWFHNmPHj2SO99rr70m1EeCg4NlBsfCwsLg6+tbZ2WTtEa/fPmyUM8cOnRos+69amxsjDVr1gAAcnNzMWPGjCp7Y5SVlcltbNKSz4WmdL2S5uzsDJFIhMLCQuGZu23btsJ9trmryzqDu7s7fH19q+ydEhQUhKioKADic0Q6lcuTJ0+Ec2Ts2LFKjW9hbm6O1157DUD5XOokG3OiU4uzatUqXLhwAfn5+Vi+fDkuXbqECRMmQEdHBwkJCfj555+RlJSEAQMG1Kg7k4qKCiZPngxfX18kJCRg+vTp+OCDD9CjRw/k5eXh3Llz2L9/P7S1tdGpUyfhbSvV3PTp0xEZGYlTp04hNDQUt27dwtSpU2FpaQldXV3k5eXh/v37iImJQVhYGDQ0NODm5iYsn5OTg88++wylpaUwMDCoFLxwdXXFuXPncOrUKfzvf/+Dra1ttVssb926FS4uLigoKMAnn3yC0NBQTJo0Cd27d0dpaSkePnyICxcu4I8//kBISAi6desmLPv555/j4sWLyM7Oxueff44rV65g0qRJaNeuHe7fv4/ffvtNaEH/1ltvYcyYMbXYm01DXZynHTp0wIoVK+Dh4YHU1FQ4OTlh/vz5eOONlseRWAAAFfhJREFUN1BSUoLIyEh4eXmhoKAAIpEI69evr7eBsmp7jEZFReGHH36AlZUVxowZAzMzM3To0AF///03kpKScPDgQaGLoIuLi9DioSrN+XoVGxuLRYsWCS8/Xn/9dRgbG0NHRwc5OTm4desWAgMDhd4Kurq6+Pjjj2Wuq1+/fkhMTISTkxMWLlyIgQMHoqioCOHh4fD29kZJSQnU1NQqvbhpzvuvNcjIyKjUW+Xly5d4+PAhfv/9d6GVmaamJpYuXVrj7bz77rvYu3cvSktLsXDhQsybNw9DhgyBpqYm4uLi4OPjg6SkJLzxxhsyg+zZ2dn45JNP8OrVK2hra+Pf//63wpdmgLhVIQf2azyrVq1CVFQUMjIysH37dsTExGDy5Mno1asXVFVVkZqaijNnzuD3339HSUkJVFRUsGnTphq9qGnfvj327NkDNzc3PHjwAKdPn8aqVauwdevWZpmCoSWZMmUKzpw5g4KCAri5uWHBggVCo4lr165hz549eP78OQYPHoxr167Va1ns7Ozg4uKCgIAAXLt2DZMmTYKbmxveeOMN6Ojo4MWLF4iLi8Px48fRr18/bN68WVjWwMAAW7ZswdKlS/Hs2TM4OzvD0dERb775Jjp37oySkhKkpqYiNjYWf/zxBx4/fozdu3eXG9iRxNTU1DBw4EBcvXoVgYGBsLCwgLm5uVAv09PTg76+Ptq3b48xY8bg7NmzOH/+PObOnYvp06fD2NgYGRkZOHnyJIKDg9G9e3fk5OQgMzOz1mWbOHEiNmzYgNzcXKHRQEto7fvee+8hLS0N3333HdLT0zFz5kwMHz4ctra2MDMzg56eHl69eoXnz5/j5s2bCA0NFYLjqqqq5e6lLflcaErXK2ndunXDyJEjceHCBSFF4ltvvVWjQTWbqrqqM6SkpMDDwwPbtm2DnZ0drKys0KtXL+jp6eHly5e4f/8+QkNDce7cOQDiNC6rV68u14Do8OHDwku78ePHK/0bxo8fj19//RWJiYmIjY0t16iIymMQnVqcbt264ccff8TixYuRn5+P/fv3Y//+/eXmkYwOXdOcYMuWLcPVq1cRHx+PuLg4fPrpp+Wm6+vrY9euXfj2228ZVKkDIpEIO3bswMaNG3Hw4EE8evQIW7dulTt/xVa6X331lZATe9OmTTJb8Xp4eOD69et49uwZVq1ahZCQkGoN9tGnTx/s3bsX7u7uSElJwcmTJxWmGJHWuXNneHt7Y+HChUhPT4e/v7/Q5V/a+PHjsWXLFqXL1JTV1Xk6c+ZM5ObmYufOnXj+/Dk8PT0rzaOhoYH169fX68uH2h6jgLhVdUxMDGJiYuQuN3bs2ErXm6o0x+uVmpoaDA0N8ezZM6SlpcHX11dha62ePXvi66+/LvdySpq5uTnc3Nywbt06eHh4VJqurq6OLVu2CGMkSGuO+6+1OHDgAA4cOKBwng4dOmDr1q21yjtpaWmJjz76CLt27UJOTg527NhRaZ65c+fCxMREZhA9NzdXaDVaUFCAuXPnVrnNP//8U+7xTPWvU6dO2LdvH9zd3ZGYmIiIiAhERETInLddu3bYunUrhg8fXuPtGRgYwMfHBzNmzMCTJ09w9OhRaGpqYuPGjUrnYKa6N3HiRDg5OSEoKAjp6enYsGFDuemqqqpYvXo1cnJyGiQo5eHhAS0tLfj6+iI9PR3bt2+XOZ+sgN/48ePxww8/YPXq1cjKysLBgwflphJRUVGp1QCOLd3ChQuxaNEiZGVlVaoTuLu746OPPgIArFu3DjNmzBBSMkrSMkoYGxvj+++/x4IFC+qkXFpaWnjnnXeE+6Kuri4mTJhQJ+tubB999BH69euHLVu24PHjx7h48SIuXrwod36RSIRRo0Zh5cqVMDIyKjetpZ4LTe16Jc3FxQUXLlwQ/t0SXu5Iq6s6Q+fOnXHz5k0UFBTg6NGjOHr0qNxt6urqYs2aNZUC5ZJ0LDo6OrC2tlb6N0yYMAG//vorAHEgnkF0+RhEpxZp2LBhOHbsGH766SeEh4cjPT0denp6GDBgANzc3DB69Gjs2rWrxuvX1dXFgQMHsGfPHpw4cQIPHz6EqqoqunTpgjFjxmDOnDlCjmSqG+rq6li3bh2mT58Of39/REdHIyUlBQUFBdDW1ka3bt3Qv39/vPnmm7C1tRWWCwkJEW5A06dPlxtI7dChAzw9PTF//nykpaXhyy+/xDfffFOtMg4YMAChoaHw9/fH6dOncffuXWRnZ0NDQwPdunXD4MGD8dZbb8kMjFhYWCA0NBS+vr44ffo0Hjx4gMLCQrRv3x6vv/46HB0dq91NvKmrq/N00aJFsLGxga+vLy5evIj09HSoqKigS5cusLa2xpw5cxokGFXTYxQQB+DMzMwQGRmJ+Ph4pKenIyMjA4A4uGJpaYkpU6bAxsam2uVqjtcrTU1NhIeH4/r164iMjMRff/2FBw8eICMjQxgYuFOnTujXrx/Gjh2L8ePHQ0NDQ+E6XV1dYWJiAm9vb1y5cgUvXrxAhw4dMGLECMyfPx99+/aVuVxz3H+tmbq6OvT19dG3b1+MGTMGTk5O1XohKo+7uzsGDhyI//3vf7hx4wYKCgrQsWNHWFpaYtq0abC2thYGzaKWoXfv3jhy5AiOHz+O06dPIy4uDpmZmSgpKRGOsdGjR+O9996Drq5urbdnZGQEHx8fuLm5ISUlBYGBgdDS0sIXX3xRB7+GasrT0xPDhw+Hn58f4uPjUVxcDENDQ1hZWcHNzQ2Wlpa1eqaoDlVVVaxduxZOTk44dOgQoqOjkZ6ejuLiYujr68PMzAyjR48WBmOsyM7ODn/++Sf8/Pxw7tw53Lt3D9nZ2VBVVYWBgQFMTEwwfPhwTJgwAV26dGmQ39Qc2djYwNvbW7gfvHjxQki5KK1Lly4ICgrCL7/8gj///BNPnz6FpqYmunbtCnt7e8yePbtO7k/SHBwchCD6pEmToKWlVafrb0zjxo2Dra0tTp06hYiICFy/fh0ZGRnIzc2FlpYW9PX1YWpqKjxvKUpj01LPhaZ0vZJmb28PbW1tFBQUoHfv3njjjTcavAz1rS7qDD/88APu37+PiIgIXL16Fffu3UNqaioKCgqgqakJfX19mJiYwNraGpMnT67UKOvatWtCgx4bG5sqn42kWVpawtjYGE+fPsWxY8fw2WefsUekHKKy6mSvJyIiImom7OzskJycDEdHx3Jd24mIiIhaGj8/P6xduxaAeNBztialpiApKUnoFbF8+XJhjAei5oiJ9oiIiIiIiIiImrHAwEAAgKmpKQPo1GRIjks1NTVMmTKlcQtDVEsMohMRERERERERNVOXLl3C9evXAQDTpk1r3MIQ/X85OTnw8/MDIB7bydDQsJFLRFQ7zIlORERERERERNSMJCcno6ioCPfu3YOnpycAwNDQsMUN3EjNS0ZGBvLy8pCeno5du3YhKysLIpEICxcubOyiEdUag+hERERERERERM3IrFmzkJycXO67NWvWtKgBRan52bp1K4KDg8t9N2PGDPTv37+RSkRUdxhEJyIiIiIiIiJqhtq2bQtTU1MsWrQINjY2jV0cIgCAuro6XnvtNbz33ntwc3Nr7OIQ1QlRWVlZWWMXgoiIiIiIiIiIiIioKeLAokREREREREREREREcjCITkREREREREREREQkB4PoRERERERERERERERyMIhORERERERERERERCQHg+hERERERERERERERHIwiE5EREREREREREREJIdaYxeAiIiIiKg1KyoqwsmTJxEeHo7Y2Fi8ePECeXl50NHRQdeuXTFw4EBMmDABw4cPh4oK28AQERERETU0UVlZWVljF4KIiIiIqDU6efIkNm/ejOTk5Crn7dmzJ1avXg0bG5v6L1gjsbOzQ3JyMhwdHbF58+bGLg4REREREQC2RCciIiIiahTff/89vv32W+Hf1tbWsLOzQ58+fdCuXTtkZ2fjwYMHCAsLQ2RkJJKSkrBjx44WHUQnIiIiImqKGEQnIiIiImpggYGBQgC9Y8eO+OabbzB06NBK840cORIzZ87EnTt34OnpiczMzIYuKhERERFRq8cgOhERERFRA0pLS8P69esBANra2ti7dy/69OmjcBlTU1N4eXkhJCSkIYpIRERERERSODIREREREVED8vb2RmFhIQBg6dKlVQbQJVRUVODg4CBz2uXLl7FixQrY2dlh4MCBsLKywpQpU7Bjxw6FrdeDgoJgZmYGMzMzPHnyRO58T548EeYLCgqqNP2zzz6DmZkZ7OzsAAA5OTnYuXMn3n77bbz++uuwsrLCzJkzceTIEZnrnzVrFszMzITc8MHBwcL2JH+zZs1SWJ6TJ09i/vz5GDVqFCwsLDBr1ixkZmZiwIABMDMzwxdffCH390mEhYUJ6z1+/HiV8xMRERFR68CW6EREREREDaSsrAzBwcEAxK3QXV1da7W+0tJSbNiwAb6+vuW+LyoqQnx8POLj4+Hr64udO3fC2tq6VttS1v379/Hhhx9WGiz18uXLuHz5Mq5fv65UQFtZZWVlWLlyJQ4fPlxpWocOHTB27FiEhobixIkT+M9//gNNTU2565IE5PX19WFvb19nZSQiIiKi5o1BdCIiIiKiBnL37l28ePECADBkyBDo6OjUan3btm0TAujdunXD/PnzYWFhgcLCQoSFhcHX1xe5ublYuHAhAgIC0K9fv1r/BkUKCwuxePFiZGVlYfHixRg5ciS0tbURHx+P7777DqmpqfD19YWtrS1Gjx4tLLdp0yYUFhZi3rx5SE9Px9ixY/Hvf/+73LrbtGkjc5s+Pj5ISEiAlZUVpk+fjp49eyI3N1doWe/q6orQ0FDk5OTg1KlTeOedd2SuJzMzE2fPngUATJ48GRoaGrXfIURERETUIjCITkRERETUQG7fvi187t+/f63WlZCQgD179gAQ50z39fVFu3bthOnDhg2DtbU1Fi5ciOLiYqxduxb+/v612mZVMjMzUVxcjEOHDsHExET4fsCAARg6dCgmT56Mly9fYv/+/eWC6N27dwcAqKurAwDatWsHU1NTpbaZkJCAKVOmYPPmzRCJRJWmjxw5El27dkVycjKCgoLkBtGPHDmC4uJiAICzs7NyP5iIiIiIWgXmRCciIiIiaiBZWVnC544dO9ZqXQcOHEBpaSkAYMOGDeUC6BJvvvmmEBCOjY1FbGxsrbapjI8//rhcAF2iR48eQoqUq1ev1tn22rVrh7Vr18oMoAPiXPJOTk4AgKioKKSkpMicLzAwEABgYWEBc3PzOisfERERETV/DKITERERETWQ/Px84bO89CTKioqKAgCYmJhg0KBBcud77733Ki1TX0QikdyW3sA/re+zsrKQk5NTJ9u0tbWtMi2Oi4sLVFRUUFpaKuSklxYXF4c7d+4AYCt0IiIiIqqMQXQiIiIiogbStm1b4XNhYWGN11NUVISkpCQAgKWlpcJ5zc3NhTQpkkBxfWnfvj3at28vd7qenp7wWfqFQm2YmZlVOU/nzp0xatQoAJAZRJcMKKqhoYHJkyfXSbmIiIiIqOVgEJ2IiIiIqIHo6+sLnzMyMmq8nuzsbOFzVWlh1NXVhe1KL1cfqmpdr6Lyz+PHq1ev6mSbstLYyOLq6goAePToES5duiR8X1RUhKNHjwIA7O3tywX6iYiIiIgABtGJiIiIiBpMv379hM83b96sk3XKywXeWqiqqio1n62tLQwMDAD80/IcAE6fPi28XGAqFyIiIiKShUF0IiIiIqIGYmJiIqQ7uXLlCvLy8mq0HunW0s+fP1c4b0lJiTCgacVW1tItw8vKyuSuozapZ5oKdXV1ODg4AABCQ0OFdDKSAUWNjY0xcuTIRisfERERETVdDKITERERETUQkUgER0dHAEBBQQH8/f1rtB4NDQ307NkTABAbG6tw3lu3bqG4uBgAYGpqWm6adI52RaleJPnXmztJSpeCggKEhoYiNTUVkZGRAIApU6aUe6lARERERCTBWiIRERERUQN6//33hdzh3377LRITE5VarrS0FEeOHBH+PWLECADA3bt3FQbSAwICKi0j0a1bN+FzXFyc3HVIcobXN01NTQDiPOX1oVevXrCysgIgTukSHByM0tJSiEQiODk51cs2iYiIiKj5YxCdiIiIiKgBGRkZYe3atQDELaJnzZqFmJgYhcvcu3cPH374Iby8vITvpk+fLrScXrt2rczUMBEREUIQ3dLSEpaWluWmm5iYCIOO+vr6ygxeHz9+HKGhocr/wFowNDQEIB78s75IWqNfvnwZ+/btAwAMHToU3bt3r7dtEhEREVHzptbYBSAiIiIiam2cnZ2RmpqKb7/9FhkZGZg1axZGjRoFOzs79OnTB+3atUN2djYePHiAc+fO4fz583j16lW5gUnNzMzwwQcfwMvLC7dv34ajoyPmz58Pc3NzFBYW4syZM9i7dy9evXoFdXV1eHh4VCqHmpoapk6dip9++gl37tzB7Nmz8eGHH8LY2BjPnz9HaGgogoODMXjwYFy7dq3e98vgwYMRHR2NGzdu4Oeff8abb74ptNrX0tKCkZFRrbcxceJEbNiwAbm5uUI+eQ4oSkRERESKMIhORERERNQIlixZAhMTE2zevBnJycmIiIhARESE3PlNTEywYsWKct8tX74chYWF2L9/Px49eiS0cJemq6uLb775Bubm5jLXu3jxYkRHR+P69eu4du0alixZUm760KFD8cUXX+Cdd96pwa+snhkzZuDgwYPIysrC119/ja+//rpcOfbu3VvrbWhpaeGdd97BgQMHAIj3z4QJE2q9XiIiIiJquRhEJyIiIiJqJOPHj4eNjQ3++OMPhIeH48aNG8jMzER+fj50dHTQtWtXDBo0CBMmTMCwYcMgEonKLa+iooIvv/wSb7/9Ng4ePIgrV67g+fPn0NDQQPfu3TFmzBjMmTMHHTp0kFuGNm3awMfHB97e3jh27BgePXoENTU19OrVC46Ojpg2bRpSUlLqe1cAEKe68ff3x08//YRLly4hNTUVL1++rPPtODg4CEH0SZMmQUtLq863QUREREQth6isrKyssQtBRERERETUUPz8/IRW+/7+/pVyxRMRERERSePAokRERERE1KoEBgYCAExNTRlAJyIiIqIqMYhOREREREStxqVLl3D9+nUAwLRp0xq3MERERETULDAnOhERERERtWjJyckoKirCvXv34OnpCQAwNDSEs7NzI5eMiIiIiJoDBtGJiIiIiKhFmzVrFpKTk8t9t2bNGg4oSkRERERKYRCdiIiIiIhahbZt28LU1BSLFi2CjY1NYxeHiIiIiJoJUVlZWVljF4KIiIiIiIiIiIiIqCniwKJERERERERERERERHIwiE5EREREREREREREJAeD6EREREREREREREREcjCITkREREREREREREQkB4PoRERERERERERERERyMIhORERERERERERERCQHg+hERERERERERERERHIwiE5EREREREREREREJMf/A6Kz9mYhnR0aAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#country wise average age coverage \n",
    "\n",
    "country_list=list(rp_data['Country'].unique())\n",
    "country_wise_age_coverage=[]\n",
    "country_filter_list=[]\n",
    "\n",
    "for country in country_list:\n",
    "    \n",
    "    rp_data_country=rp_data[rp_data['Country']==country]\n",
    "    if(len(rp_data_country)>=30):\n",
    "        age_coverage_country_mean=np.nanmean(list(rp_data_country['age_coverage']))\n",
    "        country_wise_age_coverage.append(age_coverage_country_mean)\n",
    "        country_filter_list.append(country)\n",
    "    else:\n",
    "        print(country)\n",
    "        \n",
    "#sort the bar plot by means \n",
    "pvalues_list=[]\n",
    "confidence=0.05\n",
    "\n",
    "age_coverage_ids=np.argsort(-np.array(country_wise_age_coverage))\n",
    "sorted_age_coverage=[country_wise_age_coverage[i] for i in age_coverage_ids]\n",
    "country_filter_list_sorted=[country_filter_list[i] for i in age_coverage_ids]\n",
    "\n",
    "for i in range(len(country_filter_list_sorted)-1):\n",
    "        \n",
    "        c_i=rp_data[rp_data['Country']==country_filter_list_sorted[i]]\n",
    "        c_j=rp_data[rp_data['Country']==country_filter_list_sorted[i+1]]\n",
    "        \n",
    "        t, p = ttest_ind(list(c_i['age_coverage']), list(c_j['age_coverage']))\n",
    "        pvalues_list.append(p)\n",
    "        print(f\"t-test between {country_filter_list_sorted[i]} and {country_filter_list_sorted[i+1]}: t={t}, p={p}\")\n",
    "\n",
    "significant = np.array(pvalues_list)/(len(country_filter_list_sorted) - 1) < confidence\n",
    "\n",
    "for j in range(len(country_filter_list_sorted)-1):\n",
    "    cat = country_filter_list_sorted[j]\n",
    "    sign = \">\" if significant[j] else \"=\"\n",
    "    print(f\"{cat} {sign} \", end=\"\")\n",
    "\n",
    "fig, ax = plt.subplots()    \n",
    "#plot the dictionary in terms of bar plot \n",
    "ax.bar(country_filter_list_sorted,sorted_age_coverage,\n",
    "           edgecolor='black', align='center',\n",
    "           linewidth=2, width=0.4)\n",
    "\n",
    "for i, v in enumerate(sorted_age_coverage):\n",
    "    ax.text(country_filter_list_sorted[i], v+0.01, str(round(v,2)), ha='center', fontsize=14)\n",
    "\n",
    "# set the title and axis labels\n",
    "ax.set_title('Coverage distribution of Age vs Country', fontsize=20)\n",
    "ax.set_xlabel('Country', fontsize=20)\n",
    "ax.set_ylabel('Average coverage', fontsize=20)\n",
    "\n",
    "# get the x-tick labels\n",
    "labels = ax.get_xticklabels()\n",
    "yticks = ax.get_yticks()\n",
    "ax.set_yticks(yticks)\n",
    "ax.tick_params(axis='y', labelsize=20)\n",
    "ax.tick_params(axis='x', labelsize=20)\n",
    "plt.tight_layout()\n",
    "filename=\"Country_based_average_age_coverage.png\"\n",
    "plt.savefig('plots/'+filename,dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "66015315",
   "metadata": {},
   "outputs": [],
   "source": [
    "skintone_condensed_bins=['light','medium','dark']\n",
    "subsample_skintone_data=rp_data[skintone_condensed_bins]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "54931db7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 7648/7648 [00:00<00:00, 28966.04it/s]\n"
     ]
    }
   ],
   "source": [
    "coverage_value_skintone=[]\n",
    "\n",
    "for i in tqdm(np.arange(rp_data.shape[0])):\n",
    "    sk_faces=subsample_skintone_data.loc[i]\n",
    "    sk_list=list(sk_faces)\n",
    "    #count number of non zero entries\n",
    "    non_zero_count = len([x for x in sk_list if x != 0])\n",
    "    coverage_sk=round(non_zero_count/len(sk_list),2)\n",
    "    coverage_value_skintone.append(coverage_sk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c24572e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0.33: 3916, 0.67: 2497, 1.0: 1235}\n",
      "['0.33', '0.67', '1.0']\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rp_data['skintone_coverage']=coverage_value_skintone \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "number_sk_coverage=dict(Counter(coverage_value_skintone))\n",
    "#rename the keys \n",
    "print(number_sk_coverage)\n",
    "\n",
    "keys_list=[0.33,0.67,1.0]\n",
    "\n",
    "sk_coverage_list=[number_sk_coverage[k] for k in keys_list]\n",
    "keys_str_list=[str(k) for k in keys_list]\n",
    "print(keys_str_list)\n",
    " \n",
    "ax.bar(range(len(keys_list)),sk_coverage_list,\n",
    "           edgecolor='black', align='center',\n",
    "           linewidth=2, width=0.15)\n",
    "\n",
    "for i, v in enumerate(sk_coverage_list):\n",
    "    ax.text(i, v+20, str(v), ha='center', fontsize=14)\n",
    "\n",
    "# set the title and axis labels\n",
    "ax.set_title('Coverage distribution of Skintone bins', fontsize=20)\n",
    "ax.set_xlabel('Coverage values', fontsize=20)\n",
    "ax.set_ylabel('Counts', fontsize=20)\n",
    "\n",
    "# get the x-tick labels\n",
    "plt.xticks(range(len(keys_list)), keys_str_list)\n",
    "yticks = ax.get_yticks()\n",
    "ax.tick_params(axis='y', labelsize=20)\n",
    "ax.tick_params(axis='x', labelsize=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "filename=\"Skintone_based_coverage.png\"\n",
    "plt.savefig('plots/'+filename,dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2d62e333",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Canada\n",
      "Indonesia\n",
      "t-test between Brazil and UK: t=0.6127920847006144, p=0.5400926817248028\n",
      "t-test between UK and USA: t=0.4071359788974423, p=0.6839442165234115\n",
      "t-test between USA and France: t=2.7866619316469383, p=0.0053917847352547\n",
      "t-test between France and Italy: t=0.5274987692975748, p=0.5979194598730952\n",
      "t-test between Italy and Spain: t=0.05234211291179285, p=0.9582662374569365\n",
      "t-test between Spain and Germany: t=3.744279481480676, p=0.0001838842921587814\n",
      "t-test between Germany and India: t=0.0593370848579168, p=0.9526872729268752\n",
      "t-test between India and Mexico: t=0.8770686602778593, p=0.38270938814364375\n",
      "Brazil = UK = USA > France = Italy = Spain > Germany = India > "
     ]
    },
    {
     "data": {
      "image/png": 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MGTO0bNky089EtqlcXnrppSyv1bd3714tWbJEBw4cUGhoqFxcXOTr66vmzZvr1VdfVenSpdOUj4+PV48ePXTmzBl5enpq+fLlpos2jx8/XjNmzJAkffXVV+rRo4ex7e7XNDPx8fFasmSJNm3apJMnTyo8PFyurq7y9fVV/fr11aFDBzVv3tzuNfDOnTuaM2eONm3apAsXLig6Olo+Pj6qX7++unXrplatWmXpd5TfPZzvBpCvpV7IqGzZsqblwsPDNWLECJ08edK0zL/+9S8tWbIk3fMRERGKiIjQ0aNH9fvvv2vKlClq2LDhvTUcyEBUVJRee+01HTp0SJLUvn17fffdd8abeTy6bH0gNDRUERERmb5xz4rQ0FDj9kPbCPSyZcuqcePG2rdvX4ZvGPFgBAYGavDgwQoODjYtM2zYMLtf7IaHh2v//v3av3+/5s6dq6lTp2YpAJ8zZ46++uorJSYmGs+FhIRo+fLl2rBhg3755Rc1btzY7r5JSUn697//rZkzZxpz6NqEhoYqNDRUe/bsUUBAgN0PiMuXL9cnn3yi6OjoNM9fu3ZNa9eu1dq1a9WzZ0999tlnD214lV9cvnxZbdu2tbvtypUrunLlitasWaPOnTtr/Pjx2fp7+fj4qE2bNlq7dq3WrFmjjz76KMOFjxcvXixJ8vb2Nm0Tcib1+5GAgACHHHPhwoX65JNP0lxjrl27psWLF2vlypX697//rY4dO6bbLykpSS1btlRycnK6bSEhIdq8ebM2b96shQsXavLkyRkGllkRGhqqv//97+k+Uxw7dkzHjh3Tzp07NWXKlIcusLJarUZIVKhQIYcuMOyoa7zVatX7779vtDMzn376qf744480z50+fVpffPGF9u3bp//85z9KSkrSe++9Z0x5Z3PixAl9+umnOnHihD7//HO7x88Lr8M9e/bUuHHjdP78eR05ckT169c3Pf6KFSuUkJAgSWkCt0dZcnKyRo8ereXLl6d5PjAwUL/99ps2btyoOXPmmN71eeDAAQ0bNizNYpM3btzQli1btGXLlnseOHe/r3+Pqi5dumjGjBk6ceKEzp49q2rVqqXZfu7cOR0/ftwom1HmJKVMozlmzBi7I9vPnDmjM2fO6M8//9R3332X5stHd3d3ffvtt+rZs6eio6M1evRozZkzJ11ov3v3bs2cOVNSSo6Qk/+fT548qREjRigoKCjN8wkJCTp37pzOnTunhQsXphsYKqVcH4cNG6aQkJA0z1+/fl3r1q3TunXr9Pzzz+vbb7/N8H3cw4BPJchTAgICtHXrVklS/fr1VaJECdOyH330kc6cOaOuXbvqhRdeUIkSJXT16tU0HwISExNVvnx5tWvXTnXr1lXZsmXl4uKiK1euaPfu3Vq0aJExen3lypUqXrx4mjq++uorxcTEmLYhLCxMb7zxhqKiolS4cGEVLlz43n4BeCjdvn1bQ4YM0V9//SUp5dvsb775hmAIkqTatWvrzJkzslqtGjt2rL7++ut7fkO8YsUKJSUlycnJKc2COZ07d9a+fftM3zDiwXnzzTcVEhKiAQMGqHXr1ipSpIguXryY5svkxMREWSwWtW7dWnXr1lWpUqVktVoVHBysjRs3as2aNQoKCtIbb7yhZcuWZfgmdufOnTp69KgsFosGDhwoi8WiuLg4bdiwQbNmzVJMTIzef/99rVu3zu7dMmPHjtWiRYskSSVLllT//v315JNPqnDhwgoLC9PRo0fTBRQ2q1ev1vvvvy+r1ary5curf//+qlKlinx8fBQcHKyFCxdq27ZtWrhwoby8vNKNksa9WbFihUJCQjRkyBBJ0ttvv22M5LRJ/X4oOTlZbm5uat68uZo1a6aqVauqaNGiioyM1IULFzR37lydPXvWGD315ptvZqs9vXr10tq1a3Xr1i1t2LBBL730kt1yYWFhxnvETp06cReXg3l7e8vX11fBwcE6deqUpk6dqr///e85Do5PnjyplStXysfHR8OGDVO9evUUFxenbdu2aebMmYqPj9fo0aNVrlw51a1bN82+ti/mnn76abVo0UIWi0U+Pj66c+eOLl++rAULFujw4cPatWuXxo0bp2+++eaezn3EiBE6d+6ccf0tWrSoLly4oClTpiggIEBbtmzR/Pnz1bt373uqJ685c+aMIiIiJEmNGjVy2OL2jrzGz5w5U6dPn1ajRo3Up08fVapUSbdv304XAknSn3/+qb/++kstW7ZUr169VLZsWV29elVTp07VX3/9pfXr12vx4sU6ffq01q1bp5deekmdOnVSyZIldfHiRU2ePFnnz5/X/Pnz1a5dO7t31+SF1+HOnTvr3//+t2JjY7V48eIMQ3TbF4/Vq1dP9//Zo+r777/X4cOH1bZtW3Xt2lVly5ZVaGio5s6dq61bt+rixYsaP368Jk6cmG7fK1euGAG6s7OzXn75ZXXo0EFeXl46ffq0fvnlF02ePFl16tTJcfvu9/XvUVWrVi1Vq1ZNZ8+e1bJly/Tee++l2W774s5isahmzZoZhuhWq1Vvvvmm8R6lVatW6tixo8qXLy9nZ2cdPXpU06dP15UrV/Tmm2/qjz/+SPP/Y/Xq1fXuu+9q/PjxOnz4sH766SeNGDHC2B4REaEPPvhAVqtVpUqV0meffZbt8w0ICFDfvn2NLzbbtWunF154QeXLl1dycrIuXLigXbt2aePGjen2vX79ul599VVFRkbKyclJ3bp104svvihvb2+dO3dO06dP16lTp7R+/Xp9+OGHD/96GVbgPti7d6/VYrFYLRaL9ZNPPrGePn3a+Dl16pR1//791qlTp1qbNWtmtVgs1oYNG1oPHz6c7jiLFi0yjmOxWKzz58/PsN6LFy9ak5OTTbefOnXKWr9+favFYrFOmjQpW+cUFxdn7d27t9VisVhr1qxp3bZtW7oytnb+8MMP6bal/p3s3bs3W3XD8T744APj73H58uUs7dOqVSurxWKxtmrVKt0227G6dOli7d69u/Hv999/35qUlOTo5uM+c2R/+euvv6w1atQwjteoUSPr6NGjrfPmzbOePHnSmpiYmO32de7c2WqxWKz9+vVL8/ytW7esderUsVosFus333yT7ePCXEavc3f/hIaGWq1Wq/WHH34w9qlRo4Z1x44dGdZx4cKFDLfv2rXL6Etmr4+pX0Nfe+01a1xcXLoyU6ZMMcqsX78+3faNGzca21955RVrZGSkaZuuXLmS5t83b960NmzY0GqxWKxjxoyxJiQk2N1v4sSJxu8lICAgo9OGidR98u73IZcvXza2LVq0KMPj3Llzx3r9+nXT7cnJydYPP/zQarFYrPXr17feunUrw7bc/Z4nKSnJuD4OGjTItJ7p06cbxzhx4kSGbUbO/Pbbb2muEa1atbJ+/vnn1lWrVlkvXbqUpWPY/pa2/UNCQtKV2bNnj7VWrVpWi8Vi7dGjR7rtycnJ1sDAwAzr+f77760Wi8VavXp1u9fGzPp46utv7dq17b4XDw8PtzZt2tRqsVisnTp1yrA9+dGyZcuM30F2PweZccQ1PvXfzvbe2ezz3N1lv/zyy3RloqOjjX751FNPWatXr26dMWNGunIhISHWJ5980mqxWKzDhw+3W19eeR0ePXq08Zk5JibGbj3Hjx83jjF9+vQM2/0wyOg1L/U2i8VinTJlSrr9k5OTrYMHD7ZaLBZrrVq1rDdv3kxXZuTIkcYxVqxYkW777du3jffgth97bP3xgw8+sNuOe73+Ib3UfcD2mjB16lSrxWKxtmzZMs01Jjk52dqyZUurxWKx/vLLL1arNW0Odffrxbx584zXEnuZkNVqtUZERFhffPFFq8Visfbu3Tvd9uTkZOugQYOM/pc6CxsxYoTx9965c6fd46d+TbOnW7duxjV35cqVpr+nsLCwdNeU1P3e3nUtLi7OOmDAAKPM1q1bTY//MHi47klDvvDHH3+oU6dOxk/nzp3Vr18/ffvtt7p586Z69+6tBQsWZPitupTy7WyvXr0yLFOhQgXTOe2klG/9bMewLe6XVZ988okxNcf777+fo7lA8fA7efKk/P39JaWMQB8/fvxDdzsw7k29evU0btw44y6aW7duadmyZRo7dqy6dOmiRo0aafDgwZo/f36626LtOX36tE6dOiXp/0/lYlO4cGFjvroVK1bYvVUU9+7u17m7f+bOnZtun27duql58+YZHrdSpUoZbm/atKlxi2hmr2keHh4aP3683dG8AwYMMPrjgQMH0m3/5ZdfJEkFCxbU999/n+Fc/o899liaf//xxx+6ffu2SpcurU8//dT0jpyRI0eqdOnSSk5OzvJt/Mgdnp6epvPaS5KTk5M++OADubi4KDo6Wrt3787W8Z2dnY1pJPbs2aOrV6/aLWe786FWrVqqWbNmtupA1rz66qtpbhEPDg7W7NmzNWrUKLVt21bNmjXTqFGjtHnz5nTTONnzwQcf2J0SIfV7eNuUKak5OTllOjf3G2+8oWLFislqtWrz5s1ZOT1T/fv3t7uYpre3t9E3z5w5YyzW/bAIDw83HputDyWl3I1im47A3o9tuhDJ8df4IkWKaOzYsRl+nrN57LHHNHr06HTPFyxYUF27dpWUMqLziSee0N/+9rd05UqWLKl27dpJkg4ePGi3jrzyOmz7/+f27dtav3693ePbRqG7ubmlez/4KKtdu7aGDx+e7nknJydjqsPExEQdPnw4zfYbN24Yo3RbtWpl964pLy8v06mAsup+X/8eZZ07d5azs7OuXr0qPz8/43k/Pz9dvXpVzs7Oae7otcdqtRrviwcMGGCaCRUtWtS4Ph06dMhYe8PGyclJ48ePl7e3txITEzV69GjduXNHCxcuNP4fHzhwoJo1a5bt89y5c6cxNc2AAQP04osvmpYtVqyYChQoYPz7+vXrRr9/9tln7eZv7u7u+uqrr4zr/Zw5c7LdxvyEJAd5SnJyslavXq0//vgj3aKid8vsgmZPZGSkLl26pLNnzxpv/Gwf/s+dO5fmTWBGfvvtN+ONSY8ePZhbGKZSv+k/fPhwunnEACnlw9Dy5cvVvXv3dFO5REdHa9euXRo7dqyef/55bd++PcNj2RYUdXd3V4cOHdJtt32QCgkJ0Z49exxzArhnOXlNCwsLU2BgYJowwxaE2L5IMdO0adN0U5jZeHl5GUHB5cuX02wLDw/XkSNHJEkdO3ZMt0BSZmwf9p577rkMp+NwdXU1vky/+4MsHqyEhARdu3ZNAQEBRr8LCQkx1nPIrO/Z07NnTzk7Oys5OdnuWjb+/v46c+aMJOb1zU3Ozs766quvNG3aND377LPpAtDQ0FCtXr1ar7/+unr27KlLly6ZHqto0aLppglKLfXfMbPXouTkZF2/fl3nz583+lxAQIDKlCkjKWd9LrWMrr+1a9eWlBKU2JtCJD+7c+eO8bhgwYKm5aKiojL8Yvj69etGWUdf41u1aiUvL68snU+7du1M1xqqUaOG8djePPx3l4uMjNStW7cyrfNBvA5LUuPGjY3tts+kqcXHx2vFihWSUhaxzOhLkkdNp06dTL+Usf3/LqX/vfv5+SkpKUmSMlw/oF69eg6dLjG3r3+PstKlSxtfoKb+Ms/2+Omnn870fe65c+eM18L27dtnWDb1+ga299J3t+eLL76QJF26dEnvvvuuvvzyS0kp08rcPeVMVtmmmZFk9wvEjOzbt8/o9z179jQtV65cOWNR3dT7PIyYkBf3nW0l8tRiY2N18eJFLVu2TDNnztTMmTPl7++v3377zfRNnW1l88ycPn1aM2bM0I4dO3Tjxg3TcsnJybp165bpmxmbrVu36ttvv5WUMn/go7rKObKmYcOGKl26tFatWqXg4GD97W9/0++//266WA0eXZUrV9b48eM1btw4+fv766+//pK/v7/279+va9euSUoZBTN8+HD9+uuvxhuV1JKSkowPTc8995zdEcItW7aUt7e3IiIitHTp0hyNaEDG7L3OZSarr2kHDx7U7NmztWfPHmMuW3tSjzC0p3LlyhluL1q0qKS0IYuU8mHNNgK1UaNGWWjx/5eUlGR82Js3b57mzZuXpf1CQ0OzVQ8cLyEhQfPnz9eyZct04sSJDAcdZNb37ClTpoyaN2+u7du3a8mSJfrHP/6RZrstJHJ3d8/RF07InmbNmqlZs2aKiorSwYMHdezYMfn7++vAgQPGaGx/f3/17dtXixcvtnunQs2aNTNc+6VmzZpyc3NTQkKCTp8+nW671WrV8uXLtXDhQh09elSxsbGmx8pJn0sto+uh7Voopb8e5nepv7TPaA2orMqNa3xWXxuljEeJp1636vHHH89SuTt37th9H/WgX4dtevbsqW+//VZ79+5VcHCwfH19jW2bN2822sYXj2ll9Hu3fRkspf+9277IlZTp/PJ169bV2bNnc9ZA3d/r36Oua9eu2rNnj9avX69PPvlEkow1fbp06ZLp/rY7ziXplVdeyXK9ZrlUu3bt1LNnTy1cuFBbtmyRlPLeZ8KECTleC+bEiROSpLJly6a5TmRF6n78xBNPZFj2iSee0Pbt2xUTE6PLly9neudOfsVIdOQJBQoUUPXq1fX+++8bF6+DBw/qf//7n+k+qd/UmlmwYIG6d++uxYsXZxig22T0AiWlXETeeecdJScny9fXV5MnT2Zhq4dE6hEJWblFOavlnJ2d9e9//1tt27aVlLLy++DBg3nDk8/lVn+RUm67ffLJJ/Xqq6/q22+/1bZt2zRjxgxjVEtSUpI+++wzu8fbuXOnca0zu3XXzc3NGIW1cePGhy4UyK+y8po2efJk9e3bV2vWrMnwg7uU+etZRqMOJRnTTt095U/qa1d2vwyMjIxUYmJitvaRMj8X5K6IiAi98sorGjdunP76669M79qLi4vLUT22W4QvXbqk/fv3G8/Hx8dr5cqVkqS2bdtm6f8VOIaXl5datmypESNG6H//+592796tr776yvgb3LhxQ99//73dfTMblOLq6moEVpGRkWm2xcXFaejQoXr//fe1b9++TK8B93qNyOh6mHoKvodtZF3qwDAsLMy0XJEiRXT69Ok0P926dUtXLjeu8RlNF3a3rP4dU09VkFE5e3/vvPA6bNOtWze5ubnJarWmu4PHNv1V6dKlM50q7lGT1b//3b/31H/vzK5vJUqUyFnjdP+vf4+6du3aqWDBgoqKitKmTZuMz0aenp56/vnnM93/5s2bOao3o7/bmDFj0iz0/MYbb6S5mya7bO/dczKIL6f9/u7X9YcJI9GR5/Ts2VPfffedIiIitGjRIo0aNcpuuczmlQ4ICNCnn36qxMREFS9eXEOGDNHTTz8tX19fFSpUyLjdb+HChfroo48kZRxyhYWFafjw4cZF9aeffuLWuIeIh4eH8TirAYBtfurUL3L2uLq6atKkSfrHP/6hHTt26MyZMxoyZIhmzpyZZsQL8o/c7C/2PPPMM5o2bZo6deqkiIgIBQYG6uTJk6pVq1aacqlvRUy9qntGbVq/fr3dD8O4v1xcXDLcvmfPHv3444+SpPLly2vw4MFq2LChypYtq4IFCxojPr///ntNmTIl19ubE6kDiV69emngwIFZ2s/s9nzcH19++aUxl2bbtm3Vo0cPVa9eXcWLF5eHh4fxpeJzzz2nq1evZvkLw7u1atVKJUqUUGhoqBYvXmzc9rxx40bjwxgjKh8sd3d39ejRQ6VKldLf//53SdKGDRv0+eefp3tfnpU5rM389NNPxtRlTz31lPr27avatWurRIkSKlCggFFXv3797M4XjaxJHcqcPHnyno+XG9f4zF4b76e89jpcokQJPffcc9qwYYOWLFmiN954Q05OTrp+/bp27dolKWWUbV76HSJzXP/ur0KFCqlt27ZasWKFli1bZryHadu2bZY+s6X+suV///tflkd6ZxRIL168OM06WLt379awYcPu6XUVjkOIjjzH2dlZFStWVEREhG7cuKHw8HAVK1Ys28dZsmSJEhMT5eLiotmzZ6tKlSp2y2XlW7KEhAS9+eabCgoKkpOTk7799tts3V6IvC/1yLYbN26oatWqGZaPj483bmnOyqg4d3d3/fjjjxo6dKj8/Px0/Phxvfbaa/rtt9/SzYGNvC+3+4s9pUqVUsuWLY2g/OLFi2lCdNsIiuxaunQpIXo+MH/+fEkp/Wf+/PmmX+Lm9siP1K/HWbnDK7XUfd9qtcpisTisXcgdUVFRWrNmjaSUeWRt09nZc699z83NTV26dNFvv/2mtWvX6uOPP1ahQoWMEZVly5a1O40V7r9nn31Wjz32mK5evarIyEhFRESkuyZlNg1TYmKiMcLt7mvDwoULJaVMGTVz5kzTgTMP80i3+8FisRjTux04cEAxMTGZjo7OyMN+jc8rr8Op9erVSxs2bFBQUJD27dunJk2aaNmyZVmauxvZk7p/37x5M93C6anldBo6rn8PRteuXbVixQrjyyfbc1mR+o6ewoUL3/N179y5c8Z7LS8vL0VFRcnPz0/Tpk3TkCFDcnRM23v37L5vl9KeX3b6/cN81yDTuSBPSn0rYE5vnTx37pyklFEWZgG6lHYeKzOfffaZcWvxqFGjMlwoCflT6i9FbCPuMnLq1Cmjb2b1C5UCBQrop59+0pNPPikpZSGl4cOHcxtePnQ/+os9qeedvXs0wpo1a4y+9Oabb2rixIkZ/tjmFd63b5+uXr2a4zbh/rC9pjVp0iTDu6Cy8pp2L2rWrGn0veyOgHJ3dzemJTp06JDD24asy+popsDAQGP6lhdeeMG0XEBAQJpRUzllm9IlOjpaa9eu1bVr17R7925JKR9oM7sLEfePvXnQUzt16lSGU3ucOnXK6FupQwfbIBpJ6tChg+nf/M6dO7pw4UJ2m41UnJycjKnfoqKi7C7qmx0P+zU+r7wOp/bss88aC0za1o6wffHYqFGjh3ZO4gch9XXq2LFjGZbNaR/g+vdgPPPMMypZsqQSExOVmJioUqVK6ZlnnsnSvjVr1jQe3+t1Lz4+Xu+9957i4uJUsGBBzZs3z5iH/D//+U+OF5G1Dbq6cuWKgoODs7Vv6kVy//rrrwzLHj16VFLKVFXly5fPZivzD96JIs+JiYlRQECApJTQMSej0KX/H8Rn9KEuJCTEWEXezIwZM7RgwQJJKaOwhg0blqP2IG976qmnjNswV69enent6MuXLzceZ/VFVkq5ZeyXX34xVn/ft2+fRowYofj4+By0Gg+KI/tLdqY+SP2m/O43J7YR6kWLFtXQoUP14osvZvhjG82QnJycpn3Im7LymnbixIlM3+DeK29vb+OLwDVr1uj69evZ2r9169aSpPPnz2vHjh0Obx+yJvWUVBm9/qQeyJDRwoN//vmnQ9r1+OOPGwvWLl68WEuWLFFycrKcnJwYUZmHxMTEGIGil5eX3ffqERERxqJo9tiCPint62JW+9yCBQtyNP820ho0aJBxPZg4caIuX758T8d7mK/xeeV1ODVnZ2fj2rhu3Tpt375dgYGBkpj+ytGaNGliTI2T0RdOR48eTbMIaXZw/XswXFxc1KVLF7m7u8vd3V1dunTJ8pf2tWvXNr7Imj9/fo7XhZFSgnLb1FpjxoxR1apVNWHCBHl6eio+Pl6jR4/OUWZguy5LKdlWdjz11FNGv0/9un23K1euGIMeUu/zMCJER54zefJkYzRl8+bNc/w/oO2b94sXL9r9VjAmJkbvvvtuhqOAt2/frn//+9+SpHr16unLL7/MUVuQ95UoUUIdOnSQlDKyeOrUqaZl9+zZYwQGvr6+aV6YsqJw4cL67bffjBENO3bs0KhRo3gzlI84sr+MGDFCc+bMyXQU5+LFi7Vnzx5JKdMapJ7KJSgoyBgV3KZNmyzNIV2zZk1VqFBBUtq51JE3VaxYUVLKKJeLFy+m2x4WFqb333//vrTltddek5TyOvrWW28ZUxXZc+3atTT/HjhwoDHH5JgxY3T27NkM69q6dWuOR97AnLe3t3GduHTpkmm5ChUqGKPWlyxZYvdLv82bN2vOnDkOa5ttNPqBAwf0+++/S0r5QPYwj2rKC+7cuaNevXppy5YtpgsZSilfvH7++efGotStW7c2vbNh/Pjxdqc12LdvnzE1Ru3atVWvXj1jm4+Pj7GY5MqVK+0GBkePHjVd0BTZU7ZsWX388ceSpNu3b6tv376Z3mVktVp169Ytu9se5mt8XnodTq1Hjx5ycnJSTEyM/vnPf0pKGbRje58KxyhVqpRxN/rmzZu1evXqdGXu3LmjTz75JMd1cP17cEaPHq1jx47p2LFjeu+997K8n7OzszHI8vLly3r//fczDLqjoqKM9zap+fn5afr06ZJSXldfeeUVSSnXHdv/12fOnMlwWj0zTZs2NQbw/f7771q1apVp2fDw8DT5WOnSpdW2bVtJKdmYvS+Q4uPj9c9//tO4u6xfv37ZbmN+wpzouO9u3ryZ7tvZuLg4Xbx4UUuXLjVGLXh4eOjNN9/McT2dO3fW7NmzlZycrGHDhmnIkCFq2LChPDw85O/vr5kzZyowMFANGjSwG7JHRkbqnXfeUVJSkjw9PfX222/bfcOU2uOPP84CaPnYBx98oD179ujmzZuaOHGi9u3bp06dOunxxx+Xi4uLrl27pi1btmjp0qVKTEyUs7Ozvvrqqxx90VOsWDFNnz5d/fv314ULF7Rx40Z98MEHmjBhArer5xOO6i9Xr17VuHHj9O2336p169Zq1KiRHn/8cRUtWlRxcXE6f/681q5dq23btklKuf16zJgxaUKL1AvhZGUleZvnn39ev/76qwICAnT06NE0QQbylq5du2rLli2Kjo5W//79NXToUOMN8eHDhzV9+nSFhobqySef1OHDh3O1La1bt1bPnj21cOFCHT58WC+88IL69++vBg0ayMvLS+Hh4fL399fq1atVo0YNff3118a+JUqU0DfffKM333xTN27cUI8ePdStWze1aNFCZcqUUWJioq5du6ajR49q3bp1unz5sv73v/+lWQAP987V1VV169bVoUOHtGjRItWqVUs1a9Y07rApWrSovL29VaxYMbVs2VJbt27Vjh07NHjwYPXp00dly5bVzZs3tX79ei1ZskTly5fXrVu3FBYWds9t69Chg7744gvdvn3bCGAZUXl/HD16VMOHDzc+NNevX19ly5aVl5eXbt26pRMnTmjRokXG+/jChQvrrbfesnusGjVqKCAgQN27d9ewYcNUt25dxcfHa/v27ZoxY4YSExPl6uqaLnBydnZWp06dNGfOHJ0+fVp9+vTRoEGDVLFiRUVFRWnbtm2aO3euPD09VapUKWPULXLu5Zdf1vXr1/Xjjz8qJCRE/fr109NPP61WrVqpevXqKlq0qJKSkhQaGqrjx49r7dq1Rjju4uKS5rPPw3yNz0uvw6mVK1dOTZs21a5du4ypQDp27JijheyRsQ8++EC7du3SnTt39N5772n//v1q3769vLy8dPr0aU2dOlWBgYGqU6dOjqZ04fqXP/Xp00e7d+/Whg0btHbtWp04cUKvvPKK6tWrp8KFCysqKkrnz5/Xvn37tHnzZrm7u6t///7G/rdu3dKHH36o5ORklShRIt3AzV69emnbtm3asGGDZs2apVatWmXrTnhJmjBhgnr27Kno6Gi98847Wrt2rV544QWVL19eycnJunjxonbt2qV169ZpxYoVKleunLHvP//5T+3du1eRkZH65z//qYMHD+qFF15QkSJFdP78eU2bNs0YQd+xY0e1bNnyHn6beR8hOu67P/74Q3/88UeGZXx8fDRhwoR7mju4Xr16GjlypCZPnqxbt25p0qRJ6coMHjxY1apVsxui37592xhdFx0drcGDB2da56ZNm9JccJC/lCpVSr///rtGjBihgIAA7dy5Uzt37rRbtkiRIpowYYKefvrpHNdXokQJzZw5U3379lVQUJBWrlwpDw8Pffnll6y+nQ84qr+UKVNGx48fV3R0tFauXKmVK1ea1lm4cGF9/PHH6YJy23QsXl5eatasWZbPoX379vr1118lpQTxhOh5V4cOHdS9e3ctXrxYISEh+uKLL9Jsd3Fx0ZgxY3Tr1q378uF93LhxKlCggObMmaOQkBBNnDjRbjl7wcjzzz+vKVOmaMyYMYqIiNCff/5pOh2Is7PzPS10B3PDhg3T8OHDFRERoXfffTfNthEjRmjkyJGSpE8//VR9+/Y1btW13a5rU7ZsWf33v//V0KFDHdKuAgUK6KWXXjLeKxYuXFjt27d3yLFhztXVVSVLltSNGzd0/fp1zZkzJ8M7DCpVqqTvvvvO9H1vzZo11b9/f3366acaN25cuu1ubm765ptvjPleUxs1apQOHTqkkydPyt/fP13/9Pb21uTJk/XDDz8QIjnIyJEjVaNGDX3zzTe6fPmy9u7dq71795qWd3JyUvPmzfX++++rdOnSabY9rNf4vPY6nFrPnj3TLIrIF4+5o1y5cvrpp5/0+uuv686dO5o7d67mzp2bpswbb7whJyenHM+LzvUv/3FyctKkSZP05Zdf6s8//9SlS5c0YcIE0/J3r6nw2Wef6cqVK5Kkr776yu6aC+PGjdORI0d048YNffDBB1qxYkW2Fu+sUqWKZs+erREjRujq1atav3691q9fn6V9y5QpoxkzZmjYsGEKCQnRggULjOmOU3v++ef1zTffZLlN+RUhOvIENzc3eXt7q2rVqmrZsqW6d+/ukBV9R4wYobp162rWrFk6duyYoqOjVbx4cdWrV0+9e/dWs2bNjEVYAEmqXLmyli9frtWrV2vjxo3y9/dXWFiYEhMTjT767LPP6uWXX1bhwoXvub7SpUtr5syZ6t+/v65evapFixapQIEC+te//uWAs0Fuc0R/mTJlis6fP6+dO3fq0KFDOnfunK5du6bo6Gh5eHjI29tb1apVU7NmzdSpU6d0b6wOHz5svIl+7rnn5O7unuX216tXT2XLltWVK1e0atUqffjhh9xNk4eNHz9eTz/9tObPn6+TJ08qISFBJUuWVKNGjdS/f3/Vq1dPkydPvi9tcXFx0dixY9W9e3fNmzdPfn5+CgkJUUJCgry9vVW9enU9++yzxqJ1d2vdurU2bdqk+fPna9u2bTp37pwiIyPl4uKiEiVKqFq1anr66afVvn17PfbYY/flnB41zz33nGbMmGG8RwoPDzduxU3tscce0+LFi/XLL79o06ZNunLlijw8POTr66u2bdtq4MCBDnnPllqXLl2MEP2FF15QgQIFHHp8pOfh4aHt27fryJEj2r17t/766y9duHBBN2/eNBY5K1WqlGrUqKE2bdro+eefz/T1plevXqpWrZpmzJihgwcPKjw8XD4+PnrmmWf02muvqWrVqnb3K1y4sP744w9Nnz5da9as0cWLF+Xi4qLHHntMLVu21N/+9jdjDlo4Trt27dSqVStt2LBBO3fu1JEjR3Tz5k3dvn1bBQoUkLe3tywWi5588kl17NgxwymWHtZrfF56HU6tbdu28vT0VHR0tCpXrqwGDRrc9zY8Kpo0aaJVq1bp559/1vbt2xUSEqKiRYuqTp066t+/v5599tl76gNc//InNzc3ffrpp+rTp48WLFggPz8/Xb16VdHR0fL09FS5cuVUu3ZttWjRQq1atTL2W7FihTGAqk+fPqajuH18fDR+/Hi99tprun79uj755BP95z//yVYb69Spo7Vr12rBggXauHGjzp49q8jISLm7u6tcuXLGtd3el+O1atXS2rVrNWfOHG3cuFEXLlxQTEyMihUrpvr166tbt27ZnuI2v3KyZmdFMwAAAADIRfPnz9fYsWMlpSygxl0y+Ufr1q0VHBysbt26pZnKCUDuCQwMNO7Yee+994y1SwAAjsXEuwAAAADyjEWLFkmSLBYLAToAZMJ2zXR1dVXXrl0fbGMA4CFGiA4AAAAgT9i/f7+OHDkiSerdu/eDbQwA5HG3bt3S/PnzJUlt2rRRyZIlH3CLAODhxZzoAAAAAB6Y4OBgxcfH69y5cxo/frwkqWTJkiyOBwB23Lx5U1FRUQoJCdHkyZMVEREhJycnDRs27EE3DQAeaoToAAAAAB6YAQMGKDg4OM1zH3/8MQuKAoAdEyZM0JIlS9I817dvX9WuXfsBtQgAHg2E6AAAAAAeuEKFCslisWj48OF67rnnHnRzACBPc3NzU4UKFfTyyy+rf//+D7o5APDQc7JardYH3QgAAAAAAAAAAPIiFhYFAAAAAAAAAMAEIToAAAAAAAAAACYI0QEAAAAAAAAAMEGIDgAAAAAAAACACUJ0AAAAAAAAAABMEKIDAAAAAAAAAGCCEB0AAAD3xb59+1S9enXj59ChQw+6SQAAAACQKUJ0AAAA3BdLlixJ8++lS5c+mIYAAAAAQDYQogMAACDXxcbGat26dZIkT09PSdLatWsVHx//IJsFAAAAAJkiRAcAAECu27Bhg+7cuSNJ+vjjjyVJkZGR2rx584NsFgAAAABkihAdAAAAuc42dUv16tXVo0cPPf7442meBwAAAIC8yvVBNwAAAAAPt5CQEO3Zs0eS1LlzZ+O/33//vXbu3KmwsDD5+Pik2WfMmDFavHixPDw8tHv3bnl5eWVYR/v27RUYGKi6detq4cKF6bYnJSVp+fLlWrt2rY4fP66IiAgVKlRIlStX1vPPP68+ffqoQIECdo89YMAA7du3T0899ZRmz56twMBAzZo1Szt37tT169cVGxurTZs2qVy5csb5btiwQX5+fjp16pRCQkKUmJioYsWKqU6dOurUqZM6dOggZ+eMx7MkJiZq7ty5Wr58uc6fPy9nZ2dVqFBBnTt3Vt++fRUSEqI2bdpIksaPH6/u3bubHmvjxo1avny5jh49qps3b8rDw0MVKlRQ69atNWDAABUtWjTDtmRFcnKyVq9erXXr1unYsWMKCwuTJD322GOqU6eO2rVrpzZt2sjNzS3dvvHx8VqwYIHWrl2rs2fPKioqSkWLFlWtWrX00ksvqVOnTul+X8HBwWrTpo2sVquGDx+uUaNGZdi+lStX6t1335UkTZ06VS1btkxX5uLFi5ozZ4727NmjK1euKCEhQSVLllTjxo3Vr18/1a1b1+6x/fz8NHDgQEnSrFmz1LhxYy1evFjLli1TQECAwsLC1LVrV3399dfG78rPz0/bt2/X4cOHdeHCBUVFRalgwYLy9fVV06ZNNWDAAJUtWzaT37p05swZ/frrr9q7d6/Cw8NVvHhxNWrUSIMGDVLt2rX14YcfasmSJfL19c3wzo8bN27o999/144dOxQUFKTo6GgVL15c9evX1yuvvKKmTZtm2hYAAICHFSE6AAAActWKFSuUlJQkZ2dnderUSZLUqVMn/fDDD0pISNDKlSuNANKmc+fOWrx4seLi4rRhwwZ169bN9PjHjh1TYGCgcdy7XblyRa+//rpOnTqV5vmIiAgdOnRIhw4d0h9//KGff/7ZGCFvZuPGjRo9erSio6Ptbk9KSlLLli2VnJycbltISIg2b96szZs3a+HChZo8ebIKFSpk9zhRUVEaMmSIjhw5kub548eP6/jx41q1apXGjRuXYVullClz3nzzTe3duzfN8/Hx8cax5s6dqylTpqh+/fqZHs9MUFCQRowYoZMnT6bbFhgYqMDAQK1cuVKzZs1SkyZN0u372muv6fz582meDw0N1fbt27V9+3bNmzdPU6ZMkbe3t7Hd19dXDRo00MGDB7Vy5cpMQ/QVK1ZIknx8fNSsWbN023/77TdNmjRJCQkJ6doXFBSkpUuX6vXXX9dbb72VYT1xcXEaMmSIdu/ebVrmv//9r3788cd0z9++fVunTp3SqVOn9Mcff2jChAlq166d6XGWLVumjz76KE2br169qhUrVmjt2rX6/PPPM2yrzfLly/XJJ5+k69fXrl3T2rVrtXbtWvXs2VOfffaZXF35CAkAAB49vAMCAABArlq2bJkk6amnnlLp0qUlSeXLl9eTTz6pQ4cOaenSpelC9CZNmqhUqVIKCQnRihUrMgzRV65cKUlycXHRiy++mGZbeHi4+vbtq6tXr8rd3V0vv/yyGjduLF9fX0VHR2vXrl2aNWuWLl68qNdee01LlixR4cKF7dZz5coVjR49WgUKFNDrr7+uRo0aycXFRceOHTMWS7VarZKkp59+Wi1atJDFYpGPj4/u3Lmjy5cva8GCBTp8+LB27dqlcePG6ZtvvrFb16hRo4wAvUGDBhowYIAqVKigsLAwLV++XCtWrNAnn3yS0a9d8fHxGjRokI4fPy4XFxe99NJLatmypcqVK6eEhAQdOHBA06dP182bNzV06FBjtHJ2hYaGqk+fPgoJCTHOvVu3bqpcubKklBB67969xsKyqd25c0evvvqqLl++LElq27atevTooVKlSikoKEhz5szRvn37dPDgQQ0fPlxz5syRi4uLsX+nTp108OBBBQUF6dChQ2rQoIHdNoaHh2vXrl2SpI4dO6YLgn/99VdNmDBBUsqUQ3369FGlSpVUuHBhXbhwQXPmzNHhw4c1ZcoUFStWLF1/Te3bb7/V6dOn1bp1a3Xv3l1ly5ZVaGiosSaAlHKXQcmSJdWuXTvVr19f5cuXl4eHh65evarDhw9r7ty5io6O1rvvvqslS5aoSpUq6eo5dOiQxowZo6SkJBUsWFCvvvqqmjdvLnd3dx07dkxTp07V2LFjVa1aNdO2StLq1av1/vvvy2q1qnz58urfv7+qVKkiHx8fBQcHa+HChdq2bZsWLlwoLy8vjRkzJsPjAQAAPJSsAAAAQC45ceKE1WKxWC0Wi3XhwoVpts2dO9fYdvbs2XT7jh8/3mqxWKw1a9a03rhxw+7xk5KSrM2bN7daLBbr4MGD021/5513rBaLxdqqVSvrpUuX7B7j+PHj1vr161stFot14sSJ6bb379/faGfz5s2twcHBpuebnJxsDQwMNN1utVqt33//vdVisVirV69uvXDhQrrtGzZsMOobMWKENSkpKV2Z3377zShjsVisixYtSldm4sSJVovFYm3UqJH12LFjdtsSFBRkbdasmdVisVjfeeedDNtt5o033jDaMXXqVNNyUVFR1oiIiDTPff3118a+kyZNSrdPcnKy9d133zXKzJkzJ832sLAwa+3ata0Wi8X62Wefmdaduq8dOnQozbazZ88ax/jhhx+sycnJ6fZPSkqyvvfee1aLxWKtX79+uvPYu3dvmr+HvXNJ7fLly9b4+HjT7VevXrU+++yzVovFYn3vvffslunSpYvVYrFY69SpYz1y5Ei67aGhodY2bdoYbWrVqlW6Mjdv3rQ2bNjQarFYrGPGjLEmJCTYrcvWl2rUqGENCAjI8NwAAAAeRiwsCgAAgFxjWzi0QIECat++fZptHTt2NObHtrfAqG1qlqSkJK1atcru8f38/IwR0HdP5RIUFKQ1a9ZIksaOHavy5cvbPUatWrXUt29fSdLixYszPJ933303w3mqnZycVLFixQyP8cYbb6hYsWKyWq1256j+888/JaX8zj777DO7c6fb5rs2c+fOHc2ZM0eS9NZbb6lOnTp2y/n6+uof//iHJGndunWm09SYOX/+vDZu3CgpZRT5a6+9Zlq2UKFCaeZej4+PN+avr1atmkaOHJluHycnJ3366afGNC62c7IpVqyYmjdvLklas2aNEhMT7dZtm8rFdgdEatOmTVNCQoLq1KmjESNGyMnJKd3+zs7OGjt2rNzd3RUdHW13VL1NpUqV7J5LauXKlbM7N7xNmTJlNGTIEEnS5s2bjTscbP766y9j6pz+/fvriSeeSHeM4sWLZzpq/I8//tDt27dVunRpffrpp6ZTtYwcOVKlS5dWcnKycWcJAADAo4QQHQAAALkiMTHRmGqlVatW6RYH9fb2NhZ3XLFiRbp5xGvXrm1MCWI7zt1s4WiBAgXUtm3bNNu2bdtmTHXRokWLDNvauHFjSSnzll+5csVuGTc3N3Xs2DHD49wtOTlZ169f1/nz53XmzBmdOXNGAQEBKlOmjCSlm6c9MTFR+/fvlyQ9++yz6RZctXFycjIWabVn//79un37tiSl+/LibrZzT0hI0PHjx7N2Yv9n27ZtRsD76quvZmtff39/3bp1S5LUrVu3NNO0pObl5WX83s+dO2d8aWJj+z2EhYUZU7akduXKFR06dEiS9NJLL6XbvmXLFkkpvyd7AbpNkSJFZLFYJEmHDx82LffCCy+YnouZqKgoXb58WWfPnjX6ScGCBY1tQUFBacqnnm+9S5cupsd97rnn0swjfzfblzjPPfec3N3dTcu5uroac+ZndO4AAAAPK+ZEBwAAQK7YuXOnQkNDJck08O3cubM2btyoa9euyc/PT88880ya7Z06ddL333+vo0eP6uLFi2lGecfHx2vDhg2SpNatW6cL6f39/SVJMTExqlWrVpbbHRoaane0eaVKleTh4ZHp/larVcuXL9fChQt19OhRxcbGmpYNDw9P8+9Lly4Z5TMaaS7JdHS59P/PXZIxUjsrbty4keWyknTixAlJKV8w2BsNnZGzZ88ajzPb94knntAff/xh7FeqVCljW+vWrVWoUCHduXNHK1asML6YsVm5cqUR9N99t0JwcLDCwsIkSd99952+++67LLXd1q/tqV69epaOERwcrGnTpmnLli0KDg7OsGx4eHiaOylsvzt3d/cM5zx3cXFRzZo1tWfPnnTbkpKSjC9x5s2bp3nz5mWp3RmdOwAAwMOKkegAAADIFbYpWry9vfXss8/aLdOqVSsVKVIkTfnUUoeetlHnNlu3bjVGMt8djkrSzZs3c9JsxcTE2H3e1s6MxMXFaejQoXr//fe1b9++DAN0Sem2285Hkuko9Kxsz+m5Z9beu9m+BChatGiGI5ntiYyMNB5ndq4lSpSwu5+UchdCu3btJEmbNm1K9/ez9ZvatWunW6AzN35PqaesMbNt2za9+OKL+v333zMN0O3VZ/sdFC1aNNNR72a/28jISNPpb7LTFgAAgEcBI9EBAADgcLdv3zamioiIiMhw1LTN+vXr9cknn8jT09N4zjaH9eHDh7VixQqNGDHC2Gab4sUspE9KSpKUMm/2rFmzstz2cuXK2X0+K1N0/PTTT9q+fbsk6amnnlLfvn1Vu3ZtlShRQgUKFDDmN+/Xr58OHDiQ5TZll+3cJWnJkiWmc13fzTbNzP2W0TQqWdGpUyctXbpU0dHR2rRpkzFti216FFuZu6WeQuiNN95Qhw4dslSfbaoVe+zNYZ9aWFiY3nvvPcXExMjT01NDhgxR8+bNVaFCBXl5eRlfRuzZs8eYIufuOdEdIXUf6dWrlwYOHJil/TKayx0AAOBhRYgOAAAAh1uzZo3i4uKytU90dLTWr1+vrl27pnm+U6dOOnz4sAIDA3Xs2DHVrVtXUVFR2rp1qySpQ4cOdoM921zQd+7cUZUqVbI9T3V2Wa1WY6HMRo0aaebMmaaB6t2jqW1Sj3a3TTNiJqPtxYoVMx77+PjkWjhuqycyMlLx8fHZGo2eesT2zZs39fjjj5uWTT2FiL2R3s8884xKlCih0NBQrVixwgjRbaPQnZ2d9cILL6TbL/V84a6ursac57lp3bp1xh0H//3vf9W0aVO75cz6iPT/fweRkZFKSkrKsG+b9ZPUv0er1Xpfzh0AACC/YjoXAAAAOJxtapaSJUtq4sSJmf7YQt5ly5alO1bHjh2NkdS20efr1q0zQnp7I4wlGfOgx8fHp5kjPLdEREQYc4p36NDBNEC/c+eOLly4YHdbhQoVjHnXM1vkM6NzqlmzpvHYtqhmbrDN256QkKAjR45ka9/Uc3n/9ddfGZY9evSo3f1sXFxcjJB8165dCg8Pl9VqNfpLkyZNVLp06XT7lS9fXoULF5aUu7+n1M6dOycpJcA3C9CljP++VatWlZTSt1PPLX+3pKQknTx50u621POp369zBwAAyK8I0QEAAOBQly9fNkK59u3b68UXX8z05/nnn5ck7d27V9evX09zPB8fHzVr1kyStGrVKiUnJxvhqK+vrxo2bGi3Ha1atTKmCZk5c2aunGtqqafHMJtXXZIWLFhgOhe1q6urGjduLEnasWOH6Shi2+KlZpo2bWpMOTJr1qxcmQ5Eklq2bJnj33GdOnXSzIefemqV1KKiorRmzRpJKeFx6kVFU7N9mZKQkKA1a9bo0KFDxnzjZl+0uLi4GAuR7tq1SwEBAdk6h5yw/e3j4uJMzzkmJsbuF0o2qRfgzajc1q1bFRERYbq9devWkqTz589rx44dGTUbAADgkUaIDgAAAIdatmyZEdq2b98+S/vYyiUnJ9sNBW0h6I0bN7RixQr5+flJkl566SXT+bQrV65szHG9atUqTZ8+PcM2XL582Qjnc8LHx8cIhVeuXKn4+Ph0ZY4eParvv/8+w+O88sorklIWcPzkk0/sBq3Tp0/PcKR6kSJF1K9fP0nS4cOH9dVXX5kGtlLKdCkLFizIsF32PP7448ainhs3btSvv/5qWjY6OjrNFCXu7u7q2bOnJOnMmTOaMmVKun2sVqs+//xzYwFT2znZU69ePVWqVElSyjQutr+lh4dHhv1w6NChcnFxUXJyst58801du3bNtGxSUpKWL1+eYZnM2NoYExNjfDlwdx0ff/yxQkJCTI/x5JNPqnr16pKk33//3e5I/rCwMI0fPz7DtgwcONBYg2DMmDEZjmqXUkL5U6dOZVgGAADgYeRkza1hKQAAAHgktWvXTpcuXVLx4sW1c+fOTBdalFLC8xYtWujGjRuqVq1aujA7OjpazZo1U3R0tIoUKWLMKb1y5Uq703vYREREqGfPnrp8+bIkqXHjxurSpYuqVasmd3d3RURE6NSpU9qxY4f27t2rdu3a6YcffkhzjAEDBmjfvn166qmnNHv27AzPY9y4cZozZ46klJHWgwYNUsWKFRUVFaVt27Zp7ty58vT0VNGiRRUYGGh6zCFDhmjnzp2SpAYNGmjAgAGqWLGiwsLCtHz5ci1fvlz16tUzpjn5+uuv1a1btzTHiI+PV//+/Y2AtUaNGnr55ZdVo0YNeXp6KjIyUufOndPu3bu1fft2WSwWLV68OMPzsyc0NFTdunUzQt+nn35a3bp1U+XKleXk5KTg4GD5+flp9erV+uGHH9SkSRNj36ioKHXt2tX4+7Rv317du3dXyZIlFRQUpN9//1379u2TlBIcz5kzJ8P5v3/88UdNnjxZTk5O8vLy0u3bt9W+fft0f9O7zZgxwwic/197dxfSZB/GcfwnmhJsaTJDyxd6w4woWxGjRFFykWGkwgjBVKKoDqKDCISEXqATD4JeKKQQFC2USCVL7cWEPFguEUkCkcQssCYzW0KW2HMgjaxtTz3PQ1Of7+dw//u+uf5jR7/97+syGo2y2WyyWCwymUyamJjQmzdv1N3drebmZs8fOd/3ELfb7Z7BnJWVlTP2+KPh4WFlZmbq8+fPCgsL0759+7Rt2zYZDAb19/erqqpKvb29MpvNnjc6vD3T4XCooKBAU1NTWrhwoYqKipSSkqLQ0FA9f/5c5eXlGhkZ0apVq/TixQstW7bMM+z3e62trTp69Ki+fv2qsLAw5eTkKDU1VdHR0ZqcnNTw8LB6enrU0tKioaEhXb16Venp6X6/TwAAgPmGEB0AAAD/mWfPnik/P1/S9InqM2fO/PK9p0+fVk1NjSTp1q1bWrdu3Yz148ePewZFStOhsL9WFt84nU4dO3ZMDofjb6/Nzc396fTu74TobrdbBQUFPvtQR0RE6NKlS7pw4YLfZ3748EH79++f0Qv8e2vXrtXZs2eVl5cnSTp//rzXwZkfP35USUmJWltb/dYtTfcNr6ys/NvrvBkaGtKRI0fU19fn9zpvYfDr16914MABvXz50ud9ZrNZV65cmTEI1JvBwUFPa6BvLl++rO3bt/vfgKTa2lqdO3fObyseSVqwYIGampqUkJDg+ex3QnRp+vd98uRJn28HZGVlyWazqaioyO8zb9++rdLSUn358uWntZCQEJ06dUqdnZ1qaGjQihUrvJ58l6RHjx6ppKTEb+sXaXpAa0VFhSwWi9/rAAAA5puQQBcAAACA+eP7UPtXW7l8Y7VaPSF6fX39TyF6dnb2jBDdV5/rH0VFRam6ulqPHz/WnTt31N3drZGREU1OTspoNCohIUEbN25URkaGpx/5P2U0GnXjxg1VVFTo3r17GhwcVHBwsGJiYpSWlqbCwkLPEFV/Fi1apJqaGlVXV6uxsVEDAwMKCgpSfHy8srKyVFhYOCN0NhgMXp9jMBh08eJFORwO1dfXy+Fw6N27d5qYmJDBYFBcXJzWr1+vtLQ0paSk/ON9x8XFqb6+Xo2NjWpublZvb6/ev3+v4OBgRUdHKzk5WVarVZs3b/7p3tjYWDU0NKiurk7Nzc3q6+vT+Pi4wsPDlZSUpOzsbGVnZ//SGw0JCQkzTuiHh4crNTX1l/Zgs9mUkZGhmzdvqqOjQwMDA3K73QoNDdWSJUuUmJiorVu3ymq1KjIy8ve+oB/k5eVp+fLlun79urq6uuR2uxUREaE1a9YoNzdXWVlZnpZF/uTk5CgpKUnXrl2T3W7X6OioIiMjZTabVVxcrA0bNqitrU2SPANUvcnIyNDDhw9VW1ur9vZ29ff3a2xsTMHBwTKZTFq9erUsFot27NihmJiYf7V3AACAuYiT6AAAAMAc1NDQoBMnTkiS7t+/r/j4+ABXhNnoW3ul3bt3q6ysLNDlAAAAzEkMFgUAAADmoKamJknTA03j4uICXA1mo56eHr169UqSlJycHNhiAAAA5jBCdAAAAGCWefv2rT59+uRzva6uTu3t7ZKkPXv2KCgo6E+VhllkcHDQ59ro6KhKS0slSaGhodq5c+efKgsAAGDeoSc6AAAAMMt0dHSorKxMu3bt0pYtW7R06VJNTU1paGhId+/e1YMHDyRJJpNJBw8eDHC1CJTi4mLFxsYqMzNTiYmJMhqNGhsbU1dXl2pqauR0OiVJhw8f/td93AEAAP7PCNEBAACAWcjlcqmqqkpVVVVe16OiolReXq7Fixf/4cowm9jtdr9DSPPz83Xo0KE/WBEAAMD8w2BRAAAAYJZxuVxqaWnRkydP1N/fr9HRUY2Pj8toNGrlypVKT0/X3r17ZTAYAl0qAujp06dqa2tTZ2ennE6nXC6XQkJCZDKZtGnTJtlsNpnN5kCXCQAAMOcRogMAAAAAAAAA4AODRQEAAAAAAAAA8IEQHQAAAAAAAAAAHwjRAQAAAAAAAADwgRAdAAAAAAAAAAAfCNEBAAAAAAAAAPCBEB0AAAAAAAAAAB8I0QEAAAAAAAAA8IEQHQAAAAAAAAAAH/4CiwWqAGqptcoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_wise_skintone_coverage=[]\n",
    "country_filter_list=[]\n",
    "for country in country_list:\n",
    "    \n",
    "    rp_data_country=rp_data[rp_data['Country']==country]\n",
    "    \n",
    "    if(len(rp_data_country)>30):\n",
    "        sk_coverage_country=np.nanmean(list(rp_data_country['skintone_coverage']))\n",
    "        country_wise_skintone_coverage.append(sk_coverage_country)\n",
    "        country_filter_list.append(country)\n",
    "    else:\n",
    "        print(country)\n",
    "\n",
    "fig, ax = plt.subplots()   \n",
    "\n",
    "#sort the bar plot by means \n",
    "pvalues_list=[]\n",
    "confidence=0.05\n",
    "\n",
    "skintone_coverage_ids=np.argsort(-np.array(country_wise_skintone_coverage))\n",
    "sorted_skintone_coverage=[country_wise_skintone_coverage[i] for i in skintone_coverage_ids]\n",
    "country_filter_list_sorted=[country_filter_list[i] for i in skintone_coverage_ids]\n",
    "\n",
    "for i in range(len(country_filter_list_sorted)-1):\n",
    "        \n",
    "        c_i=rp_data[rp_data['Country']==country_filter_list_sorted[i]]\n",
    "        c_j=rp_data[rp_data['Country']==country_filter_list_sorted[i+1]]\n",
    "        \n",
    "        t, p = ttest_ind(list(c_i['skintone_coverage']), list(c_j['skintone_coverage']))\n",
    "        pvalues_list.append(p)\n",
    "        print(f\"t-test between {country_filter_list_sorted[i]} and {country_filter_list_sorted[i+1]}: t={t}, p={p}\")\n",
    "\n",
    "significant = np.array(pvalues_list)/(len(country_filter_list_sorted) - 1) < confidence\n",
    "\n",
    "for j in range(len(country_filter_list_sorted)-1):\n",
    "    cat = country_filter_list_sorted[j]\n",
    "    sign = \">\" if significant[j] else \"=\"\n",
    "    print(f\"{cat} {sign} \", end=\"\")\n",
    "\n",
    "ax.bar(country_filter_list_sorted,sorted_skintone_coverage,\n",
    "           edgecolor='black', align='center',\n",
    "           linewidth=2, width=0.4)\n",
    "\n",
    "for i, v in enumerate(sorted_skintone_coverage):\n",
    "    ax.text(country_filter_list_sorted[i], v+0.01, str(round(v,2)), ha='center', fontsize=14)\n",
    "\n",
    "# set the title and axis labels\n",
    "ax.set_title('Coverage distribution of Skintone vs Country', fontsize=20)\n",
    "ax.set_ylabel('Country', fontsize=20)\n",
    "ax.set_xlabel('Average coverage', fontsize=20)\n",
    "\n",
    "# get the x-tick labels\n",
    "labels = ax.get_xticklabels()\n",
    "yticks = ax.get_yticks()\n",
    "ax.tick_params(axis='y', labelsize=20)\n",
    "ax.tick_params(axis='x', labelsize=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "filename=\"Country_based_average_skintone_coverage.png\"\n",
    "plt.savefig('plots/'+filename,dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e78e6615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
       "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
       "       ...\n",
       "       '[0, 15)', '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)',\n",
       "       '[65, 75)', '[45, 75)', 'age_coverage', 'skintone_coverage'],\n",
       "      dtype='object', length=183)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7af72aec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rp_data['ProductAdvertiser'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "092a6619",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test between KERASTASE and OAP: t=-8.994665646156344, p=8.905530578368363e-19\n",
      "t-test between OAP and MAYBELLINE: t=4.568996254953636, p=5.26882900059179e-06\n",
      "t-test between MAYBELLINE and KIEHLS: t=-2.4588717937611295, p=0.01408007736223959\n",
      "t-test between KIEHLS and GARNIER: t=2.8295283913484033, p=0.004702968390701104\n",
      "t-test between GARNIER and LOP: t=1.0686021015571976, p=0.28538242839659783\n",
      "t-test between LOP and YSL: t=-3.8836446041149175, p=0.00011235955017889407\n",
      "t-test between YSL and BIOTHERM: t=6.064876510880156, p=2.1737947881710166e-09\n",
      "t-test between BIOTHERM and ARMANI: t=-5.0719966124244085, p=5.946832252693954e-07\n",
      "t-test between ARMANI and NYX: t=-3.2837567351661128, p=0.0010818784242638403\n",
      "t-test between NYX and VICHY: t=14.497723098037856, p=1.962143060492919e-42\n",
      "t-test between VICHY and LANCOME: t=-5.9874557255623895, p=2.956168478195772e-09\n",
      "t-test between LANCOME and CERAVE: t=5.058698988394206, p=5.327263789186426e-07\n",
      "t-test between CERAVE and PRADA: t=-8.923175352913495, p=3.538952600650442e-17\n",
      "t-test between PRADA and LRP: t=4.976871320295588, p=8.109331305720386e-07\n",
      "t-test between LRP and REDKEN: t=1.843655339830084, p=0.0656821742245973\n",
      "['KERASTASE', 'OAP', 'MAYBELLINE', 'KIEHLS', 'GARNIER', 'LOP', 'YSL', 'BIOTHERM', 'ARMANI', 'NYX', 'VICHY', 'LANCOME', 'CERAVE', 'PRADA', 'LRP', 'REDKEN']\n",
      "KERASTASE > OAP > MAYBELLINE > KIEHLS > GARNIER > LOP > YSL > BIOTHERM > ARMANI > NYX > VICHY > LANCOME > CERAVE > PRADA > LRP > "
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#compute age coverage average versus product advertiser\n",
    "\n",
    "brand_wise_age_coverage=[]\n",
    "brand_filter_list=[]\n",
    "\n",
    "for brand in list(rp_data['ProductAdvertiser'].unique()):\n",
    "    \n",
    "    rp_data_brand=rp_data[rp_data['ProductAdvertiser']==brand]\n",
    "    if(len(rp_data_brand)>=30):\n",
    "        age_coverage_brand_mean=np.nanmean(list(rp_data_brand['age_coverage']))\n",
    "        brand_wise_age_coverage.append(age_coverage_brand_mean)\n",
    "        brand_filter_list.append(brand)\n",
    "    else:\n",
    "        print(rp_data_brand.shape)\n",
    "        \n",
    "        \n",
    "#sort the bar plot by means \n",
    "pvalues_list=[]\n",
    "confidence=0.05\n",
    "\n",
    "age_coverage_ids_brand=np.argsort(-np.array(brand_wise_age_coverage))\n",
    "sorted_age_coverage_brand=[brand_wise_age_coverage[i] for i in age_coverage_ids_brand]\n",
    "brand_filter_list_sorted=[brand_filter_list[i] for i in age_coverage_ids_brand]\n",
    "\n",
    "for i in range(len(brand_filter_list_sorted)-1):\n",
    "        \n",
    "        c_i=rp_data[rp_data['ProductAdvertiser']==brand_filter_list_sorted[i]]\n",
    "        c_j=rp_data[rp_data['ProductAdvertiser']==brand_filter_list_sorted[i+1]]\n",
    "        \n",
    "        t, p = ttest_ind(list(c_i['skintone_coverage']), list(c_j['skintone_coverage']))\n",
    "        pvalues_list.append(p)\n",
    "        print(f\"t-test between {brand_filter_list_sorted[i]} and {brand_filter_list_sorted[i+1]}: t={t}, p={p}\")\n",
    "\n",
    "significant = np.array(pvalues_list)/(len(brand_filter_list_sorted) - 1) < confidence\n",
    "print(brand_filter_list_sorted)\n",
    "for j in range(len(brand_filter_list_sorted)-1):\n",
    "    cat = brand_filter_list_sorted[j]\n",
    "    sign = \">\" if significant[j] else \"=\"\n",
    "    print(f\"{cat} {sign} \", end=\"\")\n",
    "    \n",
    "fig, ax = plt.subplots()    \n",
    "#plot the dictionary in terms of bar plot \n",
    "ax.bar(brand_filter_list_sorted,sorted_age_coverage_brand,\n",
    "           edgecolor='black', align='center',\n",
    "           linewidth=2, width=0.4)\n",
    "\n",
    "for i, v in enumerate(sorted_age_coverage_brand):\n",
    "    ax.text(brand_filter_list_sorted[i], v+0.01, str(round(v,2)), ha='center', fontsize=14)\n",
    "\n",
    "# set the title and axis labels\n",
    "ax.set_title('Coverage distribution of Age vs Brand', fontsize=20)\n",
    "ax.set_xlabel('Brand', fontsize=20)\n",
    "ax.set_ylabel('Average coverage', fontsize=20)\n",
    "\n",
    "# get the x-tick labels\n",
    "labels = ax.get_xticklabels()\n",
    "yticks = ax.get_yticks()\n",
    "ax.set_yticks(yticks)\n",
    "ax.tick_params(axis='y', labelsize=20, rotation=90)\n",
    "ax.tick_params(axis='x', labelsize=20, rotation=90)\n",
    "plt.tight_layout()\n",
    "filename=\"Brand_based_average_age_coverage.png\"\n",
    "plt.savefig('plots/'+filename,dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bbff34db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.49139465875370925,\n",
       " 0.5112328767123288,\n",
       " 0.4226190476190477,\n",
       " 0.39366085578446913,\n",
       " 0.5392156862745098,\n",
       " 0.5454545454545454,\n",
       " 0.46731898238747555,\n",
       " 0.4955954323001632,\n",
       " 0.4200836820083683,\n",
       " 0.5850746268656717,\n",
       " 0.509090909090909,\n",
       " 0.5146853146853148,\n",
       " 0.4919075144508671,\n",
       " 0.49020979020979016,\n",
       " 0.39523809523809544,\n",
       " 0.35333333333333333,\n",
       " 0.4925]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_wise_age_coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea11ff1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ARMANI',\n",
       " 'GARNIER',\n",
       " 'LANCOME',\n",
       " 'LRP',\n",
       " 'MAYBELLINE',\n",
       " 'OAP',\n",
       " 'VICHY',\n",
       " 'YSL',\n",
       " 'CERAVE',\n",
       " 'KERASTASE',\n",
       " 'LOP',\n",
       " 'KIEHLS',\n",
       " 'NYX',\n",
       " 'PRADA',\n",
       " 'REDKEN',\n",
       " 'BIOTHERM']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_filter_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d101655d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.33 1.0\n",
      "Counter({0.33: 144, 0.67: 122, 1.0: 71}) 0.5942433234421365\n",
      "0.33 1.0\n",
      "Counter({0.33: 910, 0.67: 645, 1.0: 270}) 0.5492876712328767\n",
      "0.33 1.0\n",
      "Counter({0.33: 309, 0.67: 115, 1.0: 80}) 0.5139285714285714\n",
      "0.33 1.0\n",
      "Counter({0.33: 370, 0.67: 179, 1.0: 82}) 0.5135182250396196\n",
      "0.33 1.0\n",
      "Counter({0.33: 375, 0.67: 268, 1.0: 111}) 0.5494827586206896\n",
      "0.33 1.0\n",
      "Counter({0.67: 384, 0.33: 330, 1.0: 166}) 0.6047500000000001\n",
      "0.33 1.0\n",
      "Counter({0.33: 367, 0.67: 133, 1.0: 11}) 0.4329158512720157\n",
      "0.33 1.0\n",
      "Counter({0.33: 241, 0.67: 196, 1.0: 176}) 0.6310766721044047\n",
      "0.33 1.0\n",
      "Counter({0.33: 191, 0.67: 32, 1.0: 16}) 0.4203765690376569\n",
      "0.33 1.0\n",
      "Counter({0.33: 225, 0.67: 83, 1.0: 27}) 0.4682388059701493\n",
      "0.33 1.0\n",
      "Counter({0.33: 56, 0.67: 45, 1.0: 9}) 0.5239090909090909\n",
      "0.33 1.0\n",
      "Counter({0.33: 198, 0.67: 135, 1.0: 96}) 0.586923076923077\n",
      "0.33 1.0\n",
      "Counter({0.33: 101, 1.0: 100, 0.67: 85}) 0.6653146853146853\n",
      "0.33 1.0\n",
      "Counter({0.67: 44, 0.33: 22, 1.0: 18}) 0.6516666666666667\n",
      "0.33 1.0\n",
      "Counter({0.33: 22, 0.67: 7, 1.0: 1}) 0.4316666666666667\n",
      "0.33 1.0\n",
      "Counter({0.33: 55, 0.67: 24, 1.0: 1}) 0.44037500000000007\n",
      "t-test between NYX and PRADA: t=0.4057478589865518, p=0.685163681314382\n",
      "t-test between PRADA and YSL: t=0.6560623308590463, p=0.5120011533205355\n",
      "t-test between YSL and OAP: t=1.9462148594214366, p=0.05181657669915981\n",
      "t-test between OAP and ARMANI: t=0.6604759169687335, p=0.5090736651362494\n",
      "t-test between ARMANI and KIEHLS: t=0.38245063743853513, p=0.7022335034581946\n",
      "t-test between KIEHLS and MAYBELLINE: t=2.4588717937611295, p=0.01408007736223959\n",
      "t-test between MAYBELLINE and GARNIER: t=0.018524798558297004, p=0.9852216286469135\n",
      "t-test between GARNIER and LOP: t=1.0686021015571976, p=0.28538242839659783\n",
      "t-test between LOP and LANCOME: t=0.3843475628976206, p=0.7008543694440124\n",
      "t-test between LANCOME and LRP: t=0.0279542954238056, p=0.9777035267462502\n",
      "t-test between LRP and KERASTASE: t=2.896105449912335, p=0.0038636257370147937\n",
      "t-test between KERASTASE and BIOTHERM: t=1.0858565313424475, p=0.2781758530730924\n",
      "t-test between BIOTHERM and VICHY: t=0.36357051733908463, p=0.716309202035177\n",
      "t-test between VICHY and REDKEN: t=0.03877940327007441, p=0.9690806250660815\n",
      "t-test between REDKEN and CERAVE: t=0.3030495374151746, p=0.7620880923251547\n",
      "['NYX', 'PRADA', 'YSL', 'OAP', 'ARMANI', 'KIEHLS', 'MAYBELLINE', 'GARNIER', 'LOP', 'LANCOME', 'LRP', 'KERASTASE', 'BIOTHERM', 'VICHY', 'REDKEN', 'CERAVE']\n",
      "NYX > PRADA > YSL > OAP > ARMANI > KIEHLS > MAYBELLINE = GARNIER > LOP > LANCOME = LRP > KERASTASE > BIOTHERM > VICHY = REDKEN = "
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "brand_wise_skintone_coverage=[]\n",
    "brand_filter_list=[]\n",
    "\n",
    "for brand in list(rp_data['ProductAdvertiser'].unique()):\n",
    "    \n",
    "    coverage_data_brand=coverage_data[coverage_data['ProductAdvertiser']==brand]\n",
    "    #print(rp_data_brand['Coverage_skintone_10_bins'].min(),rp_data_brand['Coverage_skintone_10_bins'].max())\n",
    "    #print(Counter(list(rp_data_brand['Coverage_skintone_10_bins'])),rp_data_brand['Coverage_skintone_10_bins'].mean())\n",
    "    if(len(coverage_data_brand)>=30):\n",
    "        skintone_coverage_brand_mean=np.nanmean(list(coverage_data_brand['skintone_coverage']))\n",
    "        brand_wise_skintone_coverage.append(skintone_coverage_brand_mean)\n",
    "        brand_filter_list.append(brand)\n",
    "    else:\n",
    "        print(rp_data_brand.shape)\n",
    "        \n",
    "        \n",
    "#sort the bar plot by means \n",
    "pvalues_list=[]\n",
    "confidence=0.05\n",
    "\n",
    "sorted_skintone_coverage_ids_brand=np.argsort(-np.array(brand_wise_skintone_coverage))\n",
    "sorted_skintone_coverage_brand=[brand_wise_skintone_coverage[i] for i in sorted_skintone_coverage_ids_brand]\n",
    "brand_filter_list_sorted=[brand_filter_list[i] for i in sorted_skintone_coverage_ids_brand]\n",
    "\n",
    "for i in range(len(brand_filter_list_sorted)-1):\n",
    "        \n",
    "        c_i=rp_data[rp_data['ProductAdvertiser']==brand_filter_list_sorted[i]]\n",
    "        c_j=rp_data[rp_data['ProductAdvertiser']==brand_filter_list_sorted[i+1]]\n",
    "        \n",
    "        t, p = ttest_ind(list(c_i['skintone_coverage']), list(c_j['skintone_coverage']))\n",
    "        pvalues_list.append(p)\n",
    "        print(f\"t-test between {brand_filter_list_sorted[i]} and {brand_filter_list_sorted[i+1]}: t={t}, p={p}\")\n",
    "\n",
    "significant = np.array(pvalues_list)/(len(brand_filter_list_sorted) - 1) < confidence\n",
    "print(brand_filter_list_sorted)\n",
    "for j in range(len(brand_filter_list_sorted)-1):\n",
    "    cat = brand_filter_list_sorted[j]\n",
    "    sign = \">\" if significant[j] else \"=\"\n",
    "    print(f\"{cat} {sign} \", end=\"\")\n",
    "    \n",
    "fig, ax = plt.subplots()    \n",
    "#plot the dictionary in terms of bar plot \n",
    "ax.bar(brand_filter_list_sorted,sorted_skintone_coverage_brand,\n",
    "           edgecolor='black', align='center',\n",
    "           linewidth=2, width=0.4)\n",
    "\n",
    "for i, v in enumerate(sorted_skintone_coverage_brand):\n",
    "    ax.text(brand_filter_list_sorted[i], v+0.01, str(round(v,2)), ha='center', fontsize=14)\n",
    "\n",
    "# set the title and axis labels\n",
    "ax.set_title('Coverage distribution of skintone vs Brand', fontsize=20)\n",
    "ax.set_xlabel('Brand', fontsize=20)\n",
    "ax.set_ylabel('Average coverage', fontsize=20)\n",
    "\n",
    "# get the x-tick labels\n",
    "labels = ax.get_xticklabels()\n",
    "yticks = ax.get_yticks()\n",
    "ax.set_yticks(yticks)\n",
    "ax.tick_params(axis='y', labelsize=20, rotation=90)\n",
    "ax.tick_params(axis='x', labelsize=20, rotation=90)\n",
    "plt.tight_layout()\n",
    "filename=\"Brand_based_average_skintone_coverage.png\"\n",
    "plt.savefig('plots/'+filename,dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7576b9d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#box plot of coverage across brands \n",
    "brand_filter_list=[]\n",
    "brand_skintone_list=[]\n",
    "\n",
    "for brand in list(rp_data['ProductAdvertiser'].unique()):\n",
    "    \n",
    "    rp_data_brand=rp_data[rp_data['ProductAdvertiser']==brand]\n",
    "    brand_skintone=rp_data_brand['skintone_coverage']\n",
    "    \n",
    "    brand_filter_list.append(brand)\n",
    "    brand_skintone_list.append(brand_skintone)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fb2ef5fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.boxplot(brand_skintone_list, labels=brand_filter_list, \n",
    "          patch_artist=True, \n",
    "          boxprops={'linewidth': 2},\n",
    "          whiskerprops={'linewidth': 2},\n",
    "          medianprops={'linewidth': 2, 'color': 'red'})\n",
    "\n",
    "# set the title and axis labels\n",
    "ax.set_title('Skintone coverage vs Brand',fontsize=16)\n",
    "ax.set_xlabel('Brand',fontsize=16)\n",
    "ax.set_ylabel('Skintone coverage', fontsize=16)\n",
    "\n",
    "# get the x-tick labels\n",
    "labels = ax.get_xticklabels()\n",
    "yticks = ax.get_yticks()\n",
    "ax.set_yticks(yticks)\n",
    "ax.tick_params(axis='y', labelsize=16)\n",
    "\n",
    "# set the rotation angle for the labels\n",
    "ax.set_xticklabels(labels, rotation=90, fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.savefig('plots/box_plot_skintone_coverage_brand.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "21723bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#box plot of coverage across countries\n",
    "country_filter_list=[]\n",
    "country_skintone_list=[]\n",
    "\n",
    "for country in list(rp_data['Country'].unique()):\n",
    "    \n",
    "    rp_data_country=rp_data[rp_data['Country']==country]\n",
    "    country_skintone=rp_data_country['skintone_coverage']\n",
    "    \n",
    "    country_filter_list.append(country)\n",
    "    country_skintone_list.append(country_skintone)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6e5d7e62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.boxplot(country_skintone_list, labels=country_filter_list, \n",
    "          patch_artist=True, \n",
    "          boxprops={'linewidth': 2},\n",
    "          whiskerprops={'linewidth': 2},\n",
    "          medianprops={'linewidth': 2, 'color': 'red'})\n",
    "\n",
    "# set the title and axis labels\n",
    "ax.set_title('Skintone coverage vs Country',fontsize=16)\n",
    "ax.set_xlabel('Country',fontsize=16)\n",
    "ax.set_ylabel('Skintone coverage', fontsize=16)\n",
    "\n",
    "# get the x-tick labels\n",
    "labels = ax.get_xticklabels()\n",
    "yticks = ax.get_yticks()\n",
    "ax.set_yticks(yticks)\n",
    "ax.tick_params(axis='y', labelsize=16)\n",
    "# set the rotation angle for the labels\n",
    "ax.set_xticklabels(labels, rotation=90, fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.savefig('plots/box_plot_skintone_coverage_country.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "68fb8bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0     0.33\n",
       " 1     0.67\n",
       " 2     0.33\n",
       " 3     0.33\n",
       " 4     0.67\n",
       " 5     0.33\n",
       " 6     0.67\n",
       " 7     0.67\n",
       " 8     0.33\n",
       " 9     0.33\n",
       " 10    1.00\n",
       " 11    1.00\n",
       " 12    0.67\n",
       " 13    0.33\n",
       " 14    0.33\n",
       " 15    0.67\n",
       " 16    0.33\n",
       " 17    0.67\n",
       " 18    0.33\n",
       " 19    0.33\n",
       " 20    0.67\n",
       " 21    0.33\n",
       " 22    0.33\n",
       " 23    0.33\n",
       " 24    0.33\n",
       " 25    0.67\n",
       " 26    1.00\n",
       " 27    0.33\n",
       " 28    0.33\n",
       " 29    0.33\n",
       " 30    0.33\n",
       " 31    0.33\n",
       " 32    0.33\n",
       " 33    0.33\n",
       " 34    0.33\n",
       " 35    0.33\n",
       " 36    0.33\n",
       " 37    0.33\n",
       " 38    0.33\n",
       " 39    0.67\n",
       " 40    0.33\n",
       " 41    0.33\n",
       " 42    0.33\n",
       " 43    0.67\n",
       " 44    0.67\n",
       " 45    0.33\n",
       " Name: skintone_coverage, dtype: float64,\n",
       " 46    0.33\n",
       " 47    0.33\n",
       " 48    0.67\n",
       " 49    0.67\n",
       " 50    1.00\n",
       " 51    0.33\n",
       " 52    0.33\n",
       " 53    0.33\n",
       " 54    1.00\n",
       " 55    0.67\n",
       " 56    0.67\n",
       " 57    0.67\n",
       " 58    0.67\n",
       " 59    0.33\n",
       " 60    1.00\n",
       " 61    0.67\n",
       " 62    0.33\n",
       " 63    0.33\n",
       " 64    0.33\n",
       " 65    1.00\n",
       " 66    1.00\n",
       " 67    0.33\n",
       " 68    0.33\n",
       " 69    0.33\n",
       " 70    0.33\n",
       " 71    0.33\n",
       " 72    1.00\n",
       " 73    0.67\n",
       " 74    0.67\n",
       " 75    1.00\n",
       " 76    0.67\n",
       " 77    1.00\n",
       " 78    1.00\n",
       " 79    0.67\n",
       " 80    0.67\n",
       " 81    0.67\n",
       " 82    0.33\n",
       " 83    1.00\n",
       " 84    1.00\n",
       " 85    0.67\n",
       " 86    0.67\n",
       " 87    0.67\n",
       " 88    0.33\n",
       " 89    0.67\n",
       " 90    1.00\n",
       " 91    1.00\n",
       " 92    0.67\n",
       " 93    0.33\n",
       " 94    0.33\n",
       " 95    1.00\n",
       " 96    1.00\n",
       " 97    0.33\n",
       " 98    0.33\n",
       " 99    0.33\n",
       " Name: skintone_coverage, dtype: float64,\n",
       " 100    0.33\n",
       " 101    0.67\n",
       " 102    0.67\n",
       " 103    0.67\n",
       " 104    0.67\n",
       "        ... \n",
       " 797    1.00\n",
       " 798    0.33\n",
       " 799    1.00\n",
       " 800    0.33\n",
       " 801    0.67\n",
       " Name: skintone_coverage, Length: 701, dtype: float64,\n",
       " 328    0.33\n",
       " Name: skintone_coverage, dtype: float64,\n",
       " 802     0.33\n",
       " 803     0.67\n",
       " 804     0.67\n",
       " 805     1.00\n",
       " 806     1.00\n",
       "         ... \n",
       " 1614    0.67\n",
       " 1615    0.33\n",
       " 1616    0.67\n",
       " 1617    0.33\n",
       " 1618    1.00\n",
       " Name: skintone_coverage, Length: 817, dtype: float64,\n",
       " 1619    1.00\n",
       " 1620    0.33\n",
       " 1621    1.00\n",
       " 1622    1.00\n",
       " 1623    0.67\n",
       "         ... \n",
       " 4816    0.33\n",
       " 4817    0.33\n",
       " 4818    0.33\n",
       " 4819    1.00\n",
       " 4820    0.33\n",
       " Name: skintone_coverage, Length: 3202, dtype: float64,\n",
       " 4821    0.67\n",
       " 4822    0.33\n",
       " 4823    0.33\n",
       " 4824    0.33\n",
       " 4825    0.33\n",
       "         ... \n",
       " 5622    0.33\n",
       " 5623    0.67\n",
       " 5624    0.67\n",
       " 5625    1.00\n",
       " 5626    1.00\n",
       " Name: skintone_coverage, Length: 806, dtype: float64,\n",
       " 5627    0.33\n",
       " 5628    0.33\n",
       " 5629    0.67\n",
       " 5630    0.33\n",
       " 5631    0.67\n",
       "         ... \n",
       " 5849    1.00\n",
       " 5850    0.67\n",
       " 5851    1.00\n",
       " 5852    0.33\n",
       " 5853    1.00\n",
       " Name: skintone_coverage, Length: 227, dtype: float64,\n",
       " 5854    1.00\n",
       " 5855    0.33\n",
       " 5856    1.00\n",
       " 5857    1.00\n",
       " 5858    0.33\n",
       "         ... \n",
       " 7564    0.67\n",
       " 7565    1.00\n",
       " 7566    0.33\n",
       " 7567    1.00\n",
       " 7568    0.33\n",
       " Name: skintone_coverage, Length: 1715, dtype: float64,\n",
       " 7569    0.67\n",
       " 7570    0.33\n",
       " 7572    0.33\n",
       " 7574    0.67\n",
       " 7576    0.33\n",
       " 7580    0.67\n",
       " 7581    0.67\n",
       " 7582    0.67\n",
       " 7585    0.33\n",
       " 7586    0.33\n",
       " 7587    0.67\n",
       " 7589    0.33\n",
       " 7611    0.67\n",
       " 7612    0.33\n",
       " 7613    0.33\n",
       " 7614    0.33\n",
       " 7615    0.67\n",
       " 7616    0.33\n",
       " 7617    0.33\n",
       " 7618    0.33\n",
       " 7619    0.33\n",
       " 7620    0.33\n",
       " 7621    0.33\n",
       " 7622    0.67\n",
       " 7623    0.67\n",
       " 7624    0.67\n",
       " 7625    0.67\n",
       " 7626    0.67\n",
       " 7627    0.67\n",
       " 7628    0.67\n",
       " 7629    0.67\n",
       " 7630    0.67\n",
       " 7631    0.33\n",
       " 7632    0.33\n",
       " 7633    0.33\n",
       " 7634    0.33\n",
       " 7635    0.33\n",
       " 7636    0.33\n",
       " 7637    0.33\n",
       " 7638    0.67\n",
       " 7639    0.67\n",
       " 7640    0.67\n",
       " 7641    0.33\n",
       " 7642    0.67\n",
       " 7643    0.33\n",
       " 7644    0.33\n",
       " 7645    0.67\n",
       " 7646    0.67\n",
       " 7647    0.67\n",
       " Name: skintone_coverage, dtype: float64,\n",
       " 7571    1.00\n",
       " 7573    0.33\n",
       " 7575    0.33\n",
       " 7577    0.33\n",
       " 7578    1.00\n",
       " 7579    0.33\n",
       " 7583    0.33\n",
       " 7584    0.33\n",
       " 7588    1.00\n",
       " 7590    0.67\n",
       " 7591    0.33\n",
       " 7592    0.33\n",
       " 7593    0.67\n",
       " 7594    0.33\n",
       " 7595    0.33\n",
       " 7596    0.33\n",
       " 7597    0.33\n",
       " 7598    0.33\n",
       " 7599    1.00\n",
       " 7600    0.67\n",
       " 7601    0.33\n",
       " 7602    0.33\n",
       " 7603    0.33\n",
       " 7604    0.33\n",
       " 7605    0.33\n",
       " 7606    0.33\n",
       " 7607    0.67\n",
       " 7608    0.67\n",
       " 7609    0.33\n",
       " 7610    0.33\n",
       " Name: skintone_coverage, dtype: float64]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_skintone_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fe990406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.8\n",
       "1       0.8\n",
       "2       0.4\n",
       "3       0.8\n",
       "4       0.6\n",
       "       ... \n",
       "7643    0.8\n",
       "7644    0.6\n",
       "7645    0.8\n",
       "7646    0.8\n",
       "7647    0.8\n",
       "Name: age_coverage, Length: 7648, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp_data['age_coverage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "76d88ac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.33\n",
       "1       0.67\n",
       "2       0.33\n",
       "3       0.33\n",
       "4       0.67\n",
       "        ... \n",
       "7643    0.33\n",
       "7644    0.33\n",
       "7645    0.67\n",
       "7646    0.67\n",
       "7647    0.67\n",
       "Name: skintone_coverage, Length: 7648, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp_data['skintone_coverage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "75b0a302",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Filename', 'Advertiser', 'Date', 'Version', 'Country', 'Duration',\n",
       "       'Description', 'Brand/Product', 'ProductAdvertiser', 'Title',\n",
       "       ...\n",
       "       '[0, 15)', '[15, 25)', '[25, 35)', '[35, 45)', '[45, 55)', '[55, 65)',\n",
       "       '[65, 75)', '[45, 75)', 'age_coverage', 'skintone_coverage'],\n",
       "      dtype='object', length=183)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rp_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0a2eee74",
   "metadata": {},
   "outputs": [],
   "source": [
    "rp_data.to_csv('RP_metrics_LOreal_coverage_updated.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2497f14d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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