preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Travel's restrictions between countries caused by COVID-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "from pycountry_convert import country_alpha2_to_continent_code, country_name_to_country_alpha2, country_name_to_country_alpha3\n",
    "from tqdm import tqdm\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 30)\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First look\n",
    "\n",
    "We start by looking at the data, verifying if there is errors in some columns and fixing it. The most import columns from the data are the name of the country that determined the closure (we created the column with the name of the continent), the start and end date of this restriction (we also create a duration of the restriction), and for the restriction we have columns that describe this restriction, it can be a complete or partial restriction, it can be a restriction of travels by air, by sea or by land, it can also restrict the entrance based on if the traveler is a citizen, a refugee, the travel history of the citizen and and based on the traveler visa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of dataframe: (1274, 48)\n",
      "Columns:  ['ID', 'COUNTRY_NAME', 'ISO3', 'ISO2', 'POLICY_TYPE', 'POLICY_SUBTYPE', 'START_DATE', 'END_DATE', 'AIR', 'AIR_TYPE', 'TARGETS_AIR', 'LAND', 'LAND_TYPE', 'TARGETS_LAND', 'SEA', 'SEA_TYPE', 'TARGETS_SEA', 'CITIZEN', 'CITIZEN_LIST', 'HISTORY_BAN', 'HISTORY_BAN_LIST', 'REFUGEE', 'REFUGEE_LIST', 'VISA_BAN', 'VISA_BAN_TYPE', 'VISA_BAN_LIST', 'CITIZEN_EXCEP', 'CITIZEN_EXCEP_LIST', 'COUNTRY_EXCEP', 'COUNTRY_EXCEP_LIST', 'WORK_EXCEP', 'SOURCE_QUALITY', 'SOURCE_TYPE', 'INTERNAL_GOVT_SOURCE', 'AIRLINE_SOURCE', 'INSURANCE_SOURCE', 'GOVT_SOCIAL_MED_SOURCE', 'EXT_GOVT_SOURCE', 'INTERNAL_MEDIA_SOURCE', 'EXT_MEDIA_SOURCE', 'OTHER_SOURCE', 'END_SOURCE', 'COMMENTS', 'OLD_ID', 'DURATION', 'START_DATE_NUM', 'END_DATE_NUM', 'CONTINENT']\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/policy_list.csv')\n",
    "df.START_DATE = pd.to_datetime(df.START_DATE, format = \"%m_%d_%y\", errors = 'coerce')\n",
    "df.END_DATE = pd.to_datetime(df.END_DATE, format = \"%m_%d_%y\", errors = 'coerce')\n",
    "#fixing wrong dates\n",
    "df.loc[878, ['END_DATE']] = pd.to_datetime('2021-01-10')\n",
    "df.loc[69, ['END_DATE']] = pd.to_datetime('2020-04-28')\n",
    "df.loc[421, ['START_DATE']] = pd.to_datetime('2020-08-17')\n",
    "df.loc[595, ['END_DATE']] = pd.to_datetime('2021-01-04')\n",
    "df.loc[918, ['END_DATE']] = pd.to_datetime('2021-02-01')\n",
    "df.loc[1196, ['END_DATE']] = pd.to_datetime('2021-09-21')\n",
    "\n",
    "df = df.sort_values('START_DATE')\n",
    "\n",
    "#remove data with no duration\n",
    "df['DURATION'] = df.END_DATE - df.START_DATE\n",
    "df = df[df.DURATION > pd.Timedelta(seconds = 0)]\n",
    "\n",
    "#create columns with START_DATE and END_DATE with numbers\n",
    "basedate = df.START_DATE.min()\n",
    "df['START_DATE_NUM'] = (df['START_DATE'] - basedate).dt.days\n",
    "df['END_DATE_NUM'] = (df['END_DATE'] - basedate).dt.days\n",
    "\n",
    "#fixing policys with country equal to 'European Union'\n",
    "european_union_countries = ['Austria',  'Belgium', 'Bulgaria', 'Croatia', 'Cyprus',\n",
    "                            'Czech Republic', 'Denmark', 'Estonia', 'Finland', 'France',\n",
    "                            'Germany', 'Greece', 'Hungary', 'Ireland', 'Italy', 'Latvia',\n",
    "                            'Lithuania', 'Luxembourg', 'Malta', 'Netherlands', 'Poland',\n",
    "                            'Portugal','Romania', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'United Kingdom']\n",
    "df_wrong_country = df[df.COUNTRY_NAME == 'European Union']\n",
    "new_rows = dict([(a, []) for a in df.columns])\n",
    "\n",
    "for row in df_wrong_country.iterrows():\n",
    "    for i, country in enumerate(european_union_countries):\n",
    "        for col in df.columns:\n",
    "            if col == 'COUNTRY_NAME':\n",
    "                new_rows[col].append(country)\n",
    "            elif col == 'ISO2':\n",
    "                new_rows[col].append(country_name_to_country_alpha2(country))\n",
    "            elif col == 'ISO3':\n",
    "                new_rows[col].append(country_name_to_country_alpha3(country))\n",
    "            else:\n",
    "                new_rows[col].append(row[1][col])\n",
    "df = df[df.COUNTRY_NAME != 'European Union']\n",
    "df = pd.concat([df, pd.DataFrame(new_rows)])\n",
    "\n",
    "#creating continent name column\n",
    "unique_countries = df.COUNTRY_NAME.unique()\n",
    "continents = {}\n",
    "helper = {'Holy See (Vatican City State)': 'EU',\n",
    "         'Timor-Leste': 'AS',\n",
    "         'Republic of Kosovo': 'EU',\n",
    "         'Sint Maarten (Dutch part)': 'NA',\n",
    "         'European Union': 'EU'}\n",
    "for i in range(len(unique_countries)):\n",
    "    try:\n",
    "        continents[unique_countries[i]] = country_alpha2_to_continent_code(country_name_to_country_alpha2(unique_countries[i]))\n",
    "    except:\n",
    "        continents[unique_countries[i]] = helper[unique_countries[i]]\n",
    "df['CONTINENT'] = df.COUNTRY_NAME.apply(lambda x : continents[x])\n",
    "\n",
    "\n",
    "print(f\"Size of dataframe: {df.shape}\")\n",
    "print(\"Columns: \", list(df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique countries: 221\n",
      "Initial start date: 2020-01-24 00:00:00\n",
      "Last end date: 2021-10-01 00:00:00\n",
      "Policy with smallest duration: 1 days 00:00:00\n",
      "Policy duration mean: 59 days 06:14:07.723704867\n",
      "\n",
      "Type of policys:\n",
      "PARTIAL     824\n",
      "COMPLETE    450\n",
      "Name: POLICY_TYPE, dtype: int64\n",
      "\n",
      "Closures:\n",
      "Total of air closures: 505/1274\n",
      "Total of land closures: 214/1274\n",
      "Total of sea closures: 178/1274\n",
      "\n",
      "Partial closures params:\n",
      "citizen: 71/1274\n",
      "history_ban: 140/1274\n",
      "refugee: 1/1274\n",
      "visa_ban: 41/1274\n",
      "\n",
      "Complete closure exceptions:\n",
      "citizen_excep: 408/1274\n",
      "country_excep: 186/1274\n",
      "work_excep: 208/1274\n"
     ]
    }
   ],
   "source": [
    "print(f\"Unique countries: {len(df.COUNTRY_NAME.unique())}\")\n",
    "print(f\"Initial start date: {df.START_DATE.min()}\")\n",
    "print(f\"Last end date: {df.END_DATE.max()}\")\n",
    "print(f\"Policy with smallest duration: {df.DURATION.min()}\")\n",
    "print(f\"Policy duration mean: {df.DURATION.mean()}\")\n",
    "print(\"\\nType of policys:\")\n",
    "print(df.POLICY_TYPE.value_counts())\n",
    "print(\"\\nClosures:\")\n",
    "print(f\"Total of air closures: {df[df.AIR == 1].shape[0]}/{df.shape[0]}\")\n",
    "print(f\"Total of land closures: {df[df.LAND == 1].shape[0]}/{df.shape[0]}\")\n",
    "print(f\"Total of sea closures: {df[df.SEA == 1].shape[0]}/{df.shape[0]}\")\n",
    "print(\"\\nPartial closures params:\")\n",
    "for col in ['CITIZEN', 'HISTORY_BAN', 'REFUGEE', 'VISA_BAN']:\n",
    "    print(f\"{col.lower()}: {df[df[col] == 1].shape[0]}/{df.shape[0]}\")\n",
    "print(\"\\nComplete closure exceptions:\")    \n",
    "for col in ['CITIZEN_EXCEP', 'COUNTRY_EXCEP', 'WORK_EXCEP']:\n",
    "    print(f\"{col.lower()}: {df[df[col] == 1].shape[0]}/{df.shape[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.sort_values(['CONTINENT','START_DATE'])\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 2, figsize = (16, 9))\n",
    "continent_config = {'AF':{'color': '#e41a1c' , 'label': 'Africa'},\n",
    "                   'AS': {'color': '#377eb8', 'label' : 'Asia'},\n",
    "                   'EU': {'color': '#4daf4a', 'label' : 'Europe'},\n",
    "                   'NA': {'color': '#984ea3', 'label': 'North America'},\n",
    "                   'OC': {'color': '#ff7f00', 'label':'Oceania'},\n",
    "                   'SA': {'color': '#ffff33', 'label': 'South America'}}\n",
    "\n",
    "for i in range(df.shape[0]):\n",
    "    if not (pd.isnull(df.END_DATE.iloc[i]) or pd.isnull(df.START_DATE.iloc[i])):\n",
    "        cur_continent = continents[df.COUNTRY_NAME.iloc[i]]\n",
    "        cur_color = continent_config[cur_continent]['color']\n",
    "        if df.POLICY_TYPE.iloc[i] == 'PARTIAL':\n",
    "            ax[0].plot([df.START_DATE.iloc[i], df.END_DATE.iloc[i]], [i, i], color = cur_color, alpha = 0.6)\n",
    "        elif df.POLICY_TYPE.iloc[i] == 'COMPLETE':\n",
    "            ax[1].plot([df.START_DATE.iloc[i], df.END_DATE.iloc[i]], [i, i], color = cur_color, alpha = 0.6)\n",
    "\n",
    "legend_elements = []\n",
    "for key, value in continent_config.items():\n",
    "    legend_elements.append(Line2D([0], [0], color = value['color'], lw = 3, label = value['label']))\n",
    "plt.suptitle(\"Closures time interval (start date | end date)\")\n",
    "for i in range(2):\n",
    "    ax[i].set_xlabel(\"Date\")\n",
    "    ax[i].set_ylabel(\"\")\n",
    "    ax[i].set_yticks([])\n",
    "    ax[i].set_xlim((df.START_DATE.min(), df.END_DATE.max()))\n",
    "    ax[i].legend(handles=legend_elements)\n",
    "\n",
    "ax[0].set_title(\"Partial closure\")\n",
    "ax[1].set_title(\"Complete closure\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  First Network: restrictions as edges\n",
    "\n",
    "We create a initial temporal network where the countries will be represented by the nodes and each node will represent a restriction, so when two countries are linked, there isn't the possibility of travel between them. We start by creating a dataframe where each row represent a restriction between two contries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3], dtype=int64), array([ 31, 787, 203, 253], dtype=int64))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking if there is more than one restriction for each row\n",
    "sum_of_rest = np.sum(df[['AIR', 'LAND', 'SEA', 'CITIZEN', 'HISTORY_BAN', 'REFUGEE', \n",
    "                         'VISA_BAN', 'CITIZEN_EXCEP', 'COUNTRY_EXCEP','WORK_EXCEP']].values,\n",
    "                     axis = 1)\n",
    "np.unique(sum_of_rest, return_counts = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>time_start</th>\n",
       "      <th>time_end</th>\n",
       "      <th>DF_ROW_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>China</td>\n",
       "      <td>HISTORY_BAN</td>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>Italy</td>\n",
       "      <td>AIR</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>China</td>\n",
       "      <td>HISTORY_BAN</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Italy</td>\n",
       "      <td>HISTORY_BAN</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>China</td>\n",
       "      <td>CITIZEN</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Source Target         TYPE time_start   time_end  DF_ROW_ID\n",
       "0             Seychelles  China  HISTORY_BAN 2020-02-07 2020-08-05          1\n",
       "1             Cabo Verde  Italy          AIR 2020-02-27 2020-03-18          2\n",
       "2                 Angola  China  HISTORY_BAN 2020-03-03 2020-03-18          3\n",
       "3                 Angola  Italy  HISTORY_BAN 2020-03-03 2020-03-18          3\n",
       "4  Sao Tome and Principe  China      CITIZEN 2020-03-05 2020-03-20          4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cols to consider for restrictions\n",
    "cols_boolean = ['AIR', 'LAND', 'SEA', 'CITIZEN', 'HISTORY_BAN', 'REFUGEE', \n",
    "                'VISA_BAN', 'COUNTRY_EXCEP']\n",
    "cols_type = [col for col in cols_boolean if col+'_TYPE' in df.columns]\n",
    "\n",
    "# cols names with the data for each boolean\n",
    "cols_names = {}\n",
    "for col in cols_boolean:\n",
    "    if 'TARGETS_'+col in df.columns:\n",
    "        cols_names[col] = 'TARGETS_'+col\n",
    "    else:\n",
    "        cols_names[col] = col+'_LIST'\n",
    "\n",
    "# add values in a list to a dictionary\n",
    "def add_dict(row,dic):\n",
    "    for i, key in enumerate(dic):\n",
    "        dic[key].append(row[i])\n",
    "\n",
    "# parse the string to get the countries involved\n",
    "def parse_country_list(string):\n",
    "    string = str(string)\n",
    "    possible_seps = ['or','and']\n",
    "    for sep in possible_seps:\n",
    "        string.replace(sep, ',')\n",
    "    list_string = string.split(sep=',')\n",
    "    for i in range(len(list_string)):\n",
    "        list_string[i] = list_string[i].split(sep='(')[0].strip()\n",
    "    list_string = [string for string in list_string if string in countries]\n",
    "    return list_string\n",
    "\n",
    "# only considering these countries\n",
    "countries = df['COUNTRY_NAME'].unique()\n",
    "\n",
    "# our main data object\n",
    "edges_remove = {'Source':[],'Target':[],'TYPE':[],'time_start':[],'time_end':[],'DF_ROW_ID':[]}\n",
    "count = -1\n",
    "# iterate over each row, finding out which type is TRUE and what countries are in the restriction\n",
    "for index, row in df.iterrows():\n",
    "    count += 1\n",
    "    country1 = row['COUNTRY_NAME']\n",
    "    for col in cols_boolean:\n",
    "        if row[col] == 1:\n",
    "            if (col not in cols_type) or (col in cols_type and row[col+'_TYPE'] == 'Specific'):\n",
    "                countries2 = parse_country_list(row[cols_names[col]])\n",
    "                for country2 in countries2:\n",
    "                    add_dict([country1,country2,col,row['START_DATE'],row['END_DATE'],count], edges_remove)\n",
    "            elif row[col+'_TYPE'] == 'All':\n",
    "                if country1 in countries:\n",
    "                    add_dict([country1,'EVERYONE',col,row['START_DATE'],row['END_DATE'],count], edges_remove)\n",
    "\n",
    "edges_remove = pd.DataFrame(edges_remove)\n",
    "\n",
    "# remove possible errors of same source/target\n",
    "edges_remove.drop(np.arange(edges_remove.shape[0])[edges_remove['Source'] == edges_remove['Target']], axis=0, inplace=True)\n",
    "\n",
    "edges_remove.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get some dates data to fix some values\n",
    "dates = np.concatenate([edges_remove['time_start'].dropna(),\n",
    "                        edges_remove['time_end'].dropna()])\n",
    "dates = np.sort(np.unique(dates))\n",
    "min_date = np.min(dates)\n",
    "max_date = np.max(dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>time_start</th>\n",
       "      <th>time_end</th>\n",
       "      <th>DF_ROW_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>China</td>\n",
       "      <td>HISTORY_BAN</td>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>Italy</td>\n",
       "      <td>AIR</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>China</td>\n",
       "      <td>HISTORY_BAN</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Italy</td>\n",
       "      <td>HISTORY_BAN</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>China</td>\n",
       "      <td>CITIZEN</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Source Target         TYPE time_start   time_end  DF_ROW_ID\n",
       "0             Seychelles  China  HISTORY_BAN 2020-02-07 2020-08-05          1\n",
       "1             Cabo Verde  Italy          AIR 2020-02-27 2020-03-18          2\n",
       "2                 Angola  China  HISTORY_BAN 2020-03-03 2020-03-18          3\n",
       "3                 Angola  Italy  HISTORY_BAN 2020-03-03 2020-03-18          3\n",
       "4  Sao Tome and Principe  China      CITIZEN 2020-03-05 2020-03-20          4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop edges with no start_date\n",
    "edges_remove = edges_remove.dropna(subset=['time_start'])\n",
    "\n",
    "# change edges with no end_date to max date from dataset\n",
    "edges_remove['time_end'] = edges_remove['time_end'].fillna(max_date)\n",
    "\n",
    "edges_remove.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>time_start</th>\n",
       "      <th>time_end</th>\n",
       "      <th>DF_ROW_ID</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seychelles</td>\n",
       "      <td>China</td>\n",
       "      <td>HISTORY_BAN</td>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>2020-08-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cabo Verde</td>\n",
       "      <td>Italy</td>\n",
       "      <td>AIR</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>China</td>\n",
       "      <td>HISTORY_BAN</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Italy</td>\n",
       "      <td>HISTORY_BAN</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>China</td>\n",
       "      <td>CITIZEN</td>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Source Target         TYPE time_start   time_end  DF_ROW_ID  \\\n",
       "0             Seychelles  China  HISTORY_BAN 2020-02-07 2020-08-05          1   \n",
       "1             Cabo Verde  Italy          AIR 2020-02-27 2020-03-18          2   \n",
       "2                 Angola  China  HISTORY_BAN 2020-03-03 2020-03-18          3   \n",
       "3                 Angola  Italy  HISTORY_BAN 2020-03-03 2020-03-18          3   \n",
       "4  Sao Tome and Principe  China      CITIZEN 2020-03-05 2020-03-20          4   \n",
       "\n",
       "   Weight  \n",
       "0     0.0  \n",
       "1     1.0  \n",
       "2     0.0  \n",
       "3     0.0  \n",
       "4     0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remembering cols boolean, weights are the respective values to each type of restriction for an edge\n",
    "# cols_boolean = ['AIR', 'LAND', 'SEA', 'CITIZEN', 'HISTORY_BAN', 'REFUGEE', 'VISA_BAN', 'COUNTRY_EXCEP']\n",
    "weights = np.array([1, 0, 0, 0, 0, 0, 0, 0]) # prioritizing only AIR for a test\n",
    "weights = weights/weights.sum()\n",
    "type_weight = {cols_boolean[i]:weights[i] for i in range(len(cols_boolean))}\n",
    "edges_remove['Weight'] = [type_weight[type_i] for type_i in edges_remove['TYPE']]\n",
    "edges_remove.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain and remove EVERYONE rows\n",
    "rows_ind_everyone = edges_remove['Target'] == 'EVERYONE'\n",
    "rows_everyone = edges_remove.loc[rows_ind_everyone].reset_index()\n",
    "edges_remove = edges_remove.drop(edges_remove[edges_remove['Target'] == 'EVERYONE'].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# explode everyone values\n",
    "def return_countries_except(country):\n",
    "    return countries[(countries != country).ravel()]\n",
    "rows_everyone['Target'] = rows_everyone['Source'].apply(return_countries_except)\n",
    "rows_everyone = rows_everyone.explode('Target')\n",
    "edges_remove = edges_remove.append(rows_everyone, ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# given a row, return the ordered relation Source, Target\n",
    "def merge_list(row):\n",
    "    row_list = [row['Source'],row['Target']]\n",
    "    row_list.sort()\n",
    "    return (row_list[0],row_list[1])\n",
    "\n",
    "edges_remove['Country_Duo'] = edges_remove.apply (lambda row: merge_list(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# given a group of edges of two countries, merge weights and separate dates\n",
    "def aggregate(df):\n",
    "    df_dates = np.concatenate([df['time_start'].dropna(),\n",
    "                               df['time_end'].dropna()])\n",
    "    df_dates = np.sort(np.unique(df_dates))\n",
    "    c1, c2 = df['Country_Duo'].values[0]\n",
    "    new_df = {'Source':[],'Target':[],'time_start':[],'time_end':[],'Weight':[]}\n",
    "    for i in range(df_dates.shape[0]-1):\n",
    "        cur_time = df_dates[i]\n",
    "        weight = (df['Weight'][(df['time_start']<=cur_time) & (df['time_end']>cur_time)]).sum()\n",
    "        add_dict([c1, c2, cur_time, df_dates[i+1], weight], new_df)\n",
    "    return pd.DataFrame(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>time_start</th>\n",
       "      <th>time_end</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source          Target time_start   time_end  Weight\n",
       "0  Afghanistan         Albania 2020-03-15 2020-06-01     0.0\n",
       "1  Afghanistan         Algeria 2020-03-17 2020-06-01     2.0\n",
       "2  Afghanistan         Algeria 2020-06-01 2021-06-01     1.0\n",
       "3  Afghanistan  American Samoa 2020-03-25 2020-04-01     0.0\n",
       "4  Afghanistan  American Samoa 2020-04-01 2020-05-01     0.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = edges_remove.groupby(['Country_Duo'])\n",
    "edges_remove_list = []\n",
    "for i in grouped:\n",
    "    edges_remove_list.append(aggregate(i[1]))\n",
    "edges_remove_final = pd.concat(edges_remove_list, ignore_index=True)\n",
    "edges_remove_final.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save\n",
    "edges_remove_final.to_csv('data/edges_rest.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gephi network of restrictions\n",
    "\n",
    "With our new dataframe with data from the restriction between pairs of countries, we now transform the dataframe to be able to open it on Gephi.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>time_start</th>\n",
       "      <th>time_end</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source          Target  time_start    time_end  Weight\n",
       "0  Afghanistan         Albania  2020-03-15  2020-06-01     0.0\n",
       "1  Afghanistan         Algeria  2020-03-17  2020-06-01     2.0\n",
       "2  Afghanistan         Algeria  2020-06-01  2021-06-01     1.0\n",
       "3  Afghanistan  American Samoa  2020-03-25  2020-04-01     0.0\n",
       "4  Afghanistan  American Samoa  2020-04-01  2020-05-01     0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load\n",
    "edges_rest_gephi = pd.read_csv('data/edges_rest.csv')\n",
    "edges_rest_gephi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>time_start</th>\n",
       "      <th>time_end</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2020-09-22</td>\n",
       "      <td>2020-10-19</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2020-10-19</td>\n",
       "      <td>2020-12-26</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source               Target  time_start    time_end  Weight\n",
       "1  Afghanistan              Algeria  2020-03-17  2020-06-01     2.0\n",
       "2  Afghanistan              Algeria  2020-06-01  2021-06-01     1.0\n",
       "5  Afghanistan               Angola  2020-09-22  2020-10-19     1.0\n",
       "6  Afghanistan               Angola  2020-10-19  2020-12-26     1.0\n",
       "7  Afghanistan  Antigua and Barbuda  2020-04-23  2020-06-01     1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove weight 0 edges\n",
    "edges_rest_gephi = edges_rest_gephi.loc[edges_rest_gephi['Weight']!=0]\n",
    "edges_rest_gephi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>[2020-03-17, 2020-06-01, 2.0]</td>\n",
       "      <td>[2020-03-17, 2020-06-01]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>[2020-06-01, 2021-06-01, 1.0]</td>\n",
       "      <td>[2020-06-01, 2021-06-01]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Angola</td>\n",
       "      <td>[2020-09-22, 2020-10-19, 1.0]</td>\n",
       "      <td>[2020-09-22, 2020-10-19]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Angola</td>\n",
       "      <td>[2020-10-19, 2020-12-26, 1.0]</td>\n",
       "      <td>[2020-10-19, 2020-12-26]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>[2020-04-23, 2020-06-01, 1.0]</td>\n",
       "      <td>[2020-04-23, 2020-06-01]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source               Target                         Weight  \\\n",
       "1  Afghanistan              Algeria  [2020-03-17, 2020-06-01, 2.0]   \n",
       "2  Afghanistan              Algeria  [2020-06-01, 2021-06-01, 1.0]   \n",
       "5  Afghanistan               Angola  [2020-09-22, 2020-10-19, 1.0]   \n",
       "6  Afghanistan               Angola  [2020-10-19, 2020-12-26, 1.0]   \n",
       "7  Afghanistan  Antigua and Barbuda  [2020-04-23, 2020-06-01, 1.0]   \n",
       "\n",
       "                   Interval  \n",
       "1  [2020-03-17, 2020-06-01]  \n",
       "2  [2020-06-01, 2021-06-01]  \n",
       "5  [2020-09-22, 2020-10-19]  \n",
       "6  [2020-10-19, 2020-12-26]  \n",
       "7  [2020-04-23, 2020-06-01]  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join weight with time intervals\n",
    "edges_rest_gephi['Weight'] = edges_rest_gephi[['time_start','time_end','Weight']].values.tolist()\n",
    "edges_rest_gephi['Interval'] = edges_rest_gephi[['time_start','time_end']].values.tolist()\n",
    "edges_rest_gephi.drop(['time_start','time_end'], axis=1, inplace=True)\n",
    "edges_rest_gephi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>[[2020-03-17, 2020-06-01, 2.0], [2020-06-01, 2...</td>\n",
       "      <td>[[2020-03-17, 2020-06-01], [2020-06-01, 2021-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Angola</td>\n",
       "      <td>[[2020-09-22, 2020-10-19, 1.0], [2020-10-19, 2...</td>\n",
       "      <td>[[2020-09-22, 2020-10-19], [2020-10-19, 2020-1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>[[2020-04-23, 2020-06-01, 1.0]]</td>\n",
       "      <td>[[2020-04-23, 2020-06-01]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>[[2020-07-01, 2020-07-10, 1.0], [2020-07-10, 2...</td>\n",
       "      <td>[[2020-07-01, 2020-07-10], [2020-07-10, 2020-1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Austria</td>\n",
       "      <td>[[2020-03-20, 2020-06-15, 1.0], [2020-06-15, 2...</td>\n",
       "      <td>[[2020-03-20, 2020-06-15], [2020-06-15, 2020-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>[[2020-06-15, 2020-07-01, 1.0], [2020-07-01, 2...</td>\n",
       "      <td>[[2020-06-15, 2020-07-01], [2020-07-01, 2020-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>[[2020-07-01, 2020-07-16, 1.0], [2020-07-16, 2...</td>\n",
       "      <td>[[2020-07-01, 2020-07-16], [2020-07-16, 2020-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>[[2020-03-28, 2020-11-08, 1.0]]</td>\n",
       "      <td>[[2020-03-28, 2020-11-08]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Central African Republic</td>\n",
       "      <td>[[2020-03-27, 2020-04-11, 1.0], [2020-04-11, 2...</td>\n",
       "      <td>[[2020-03-27, 2020-04-11], [2020-04-11, 2020-0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Chad</td>\n",
       "      <td>[[2020-03-24, 2020-08-01, 1.0]]</td>\n",
       "      <td>[[2020-03-24, 2020-08-01]]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source                    Target  \\\n",
       "0  Afghanistan                   Algeria   \n",
       "1  Afghanistan                    Angola   \n",
       "2  Afghanistan       Antigua and Barbuda   \n",
       "3  Afghanistan                     Aruba   \n",
       "4  Afghanistan                   Austria   \n",
       "5  Afghanistan                   Belgium   \n",
       "6  Afghanistan                  Bulgaria   \n",
       "7  Afghanistan                   Burundi   \n",
       "8  Afghanistan  Central African Republic   \n",
       "9  Afghanistan                      Chad   \n",
       "\n",
       "                                              Weight  \\\n",
       "0  [[2020-03-17, 2020-06-01, 2.0], [2020-06-01, 2...   \n",
       "1  [[2020-09-22, 2020-10-19, 1.0], [2020-10-19, 2...   \n",
       "2                    [[2020-04-23, 2020-06-01, 1.0]]   \n",
       "3  [[2020-07-01, 2020-07-10, 1.0], [2020-07-10, 2...   \n",
       "4  [[2020-03-20, 2020-06-15, 1.0], [2020-06-15, 2...   \n",
       "5  [[2020-06-15, 2020-07-01, 1.0], [2020-07-01, 2...   \n",
       "6  [[2020-07-01, 2020-07-16, 1.0], [2020-07-16, 2...   \n",
       "7                    [[2020-03-28, 2020-11-08, 1.0]]   \n",
       "8  [[2020-03-27, 2020-04-11, 1.0], [2020-04-11, 2...   \n",
       "9                    [[2020-03-24, 2020-08-01, 1.0]]   \n",
       "\n",
       "                                            Interval  \n",
       "0  [[2020-03-17, 2020-06-01], [2020-06-01, 2021-0...  \n",
       "1  [[2020-09-22, 2020-10-19], [2020-10-19, 2020-1...  \n",
       "2                         [[2020-04-23, 2020-06-01]]  \n",
       "3  [[2020-07-01, 2020-07-10], [2020-07-10, 2020-1...  \n",
       "4  [[2020-03-20, 2020-06-15], [2020-06-15, 2020-0...  \n",
       "5  [[2020-06-15, 2020-07-01], [2020-07-01, 2020-0...  \n",
       "6  [[2020-07-01, 2020-07-16], [2020-07-16, 2020-0...  \n",
       "7                         [[2020-03-28, 2020-11-08]]  \n",
       "8  [[2020-03-27, 2020-04-11], [2020-04-11, 2020-0...  \n",
       "9                         [[2020-03-24, 2020-08-01]]  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join same source/target data in a list for the cols Weight/Interval\n",
    "grouped = edges_rest_gephi.groupby(['Source','Target'])\n",
    "edges_rest_gephi_list = []\n",
    "for i in grouped:\n",
    "    cur_df = pd.DataFrame(i[1].iloc[0,:].values.reshape(1,-1),\n",
    "                          columns = i[1].columns)\n",
    "    cur_df.loc[0]['Weight'] = list(i[1]['Weight'])\n",
    "    cur_df.loc[0]['Interval'] = list(i[1]['Interval'])\n",
    "    edges_rest_gephi_list.append(cur_df)\n",
    "edges_rest_gephi = pd.concat(edges_rest_gephi_list, ignore_index=True)\n",
    "edges_rest_gephi.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>&lt;[2020-03-17,2020-06-01,2.0]; [2020-06-01,2021...</td>\n",
       "      <td>&lt;[2020-03-17,2020-06-01]; [2020-06-01,2021-06-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Angola</td>\n",
       "      <td>&lt;[2020-09-22,2020-10-19,1.0]; [2020-10-19,2020...</td>\n",
       "      <td>&lt;[2020-09-22,2020-10-19]; [2020-10-19,2020-12-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>&lt;[2020-04-23,2020-06-01,1.0]&gt;</td>\n",
       "      <td>&lt;[2020-04-23,2020-06-01]&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>&lt;[2020-07-01,2020-07-10,1.0]; [2020-07-10,2020...</td>\n",
       "      <td>&lt;[2020-07-01,2020-07-10]; [2020-07-10,2020-11-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Austria</td>\n",
       "      <td>&lt;[2020-03-20,2020-06-15,1.0]; [2020-06-15,2020...</td>\n",
       "      <td>&lt;[2020-03-20,2020-06-15]; [2020-06-15,2020-07-...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Source               Target  \\\n",
       "0  Afghanistan              Algeria   \n",
       "1  Afghanistan               Angola   \n",
       "2  Afghanistan  Antigua and Barbuda   \n",
       "3  Afghanistan                Aruba   \n",
       "4  Afghanistan              Austria   \n",
       "\n",
       "                                              Weight  \\\n",
       "0  <[2020-03-17,2020-06-01,2.0]; [2020-06-01,2021...   \n",
       "1  <[2020-09-22,2020-10-19,1.0]; [2020-10-19,2020...   \n",
       "2                      <[2020-04-23,2020-06-01,1.0]>   \n",
       "3  <[2020-07-01,2020-07-10,1.0]; [2020-07-10,2020...   \n",
       "4  <[2020-03-20,2020-06-15,1.0]; [2020-06-15,2020...   \n",
       "\n",
       "                                            Interval  \n",
       "0  <[2020-03-17,2020-06-01]; [2020-06-01,2021-06-...  \n",
       "1  <[2020-09-22,2020-10-19]; [2020-10-19,2020-12-...  \n",
       "2                          <[2020-04-23,2020-06-01]>  \n",
       "3  <[2020-07-01,2020-07-10]; [2020-07-10,2020-11-...  \n",
       "4  <[2020-03-20,2020-06-15]; [2020-06-15,2020-07-...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parse the data to the string type of gephi\n",
    "def gephi_dates(x):\n",
    "    string = '<'\n",
    "    for i in range(len(x)):\n",
    "        if len(x[i]) == 2:\n",
    "            string += '[' + x[i][0] + ',' + x[i][1] + ']'\n",
    "        else:\n",
    "            string += '[' + x[i][0] + ',' + x[i][1] + ',' + str(x[i][2]) + ']'\n",
    "        if i != len(x)-1:\n",
    "            string += '; '\n",
    "        else:\n",
    "            string += '>'\n",
    "    return string\n",
    "\n",
    "edges_rest_gephi['Weight'] = edges_rest_gephi['Weight'].apply(lambda x: gephi_dates(x))\n",
    "edges_rest_gephi['Interval'] = edges_rest_gephi['Interval'].apply(lambda x: gephi_dates(x))\n",
    "edges_rest_gephi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save so that we can load in the gephi software\n",
    "edges_rest_gephi.to_csv('data/edges_rest_gephi.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Second Network: open borders as edges\n",
    "\n",
    "Now with the same nodes, we will create a temporal network that is the complement of the network built before, the restrictions between two countries will be represented by the abscence of links between two nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of 3D adjacency matrix: (616, 221, 221)\n"
     ]
    }
   ],
   "source": [
    "edges_remove_final = pd.read_csv('data/edges_rest.csv')\n",
    "for col in ['time_start', 'time_end']:\n",
    "    edges_remove_final[col] = pd.to_datetime(edges_remove_final[col])\n",
    "countries = np.unique(np.concatenate([edges_remove_final.Source.unique(), edges_remove_final.Target.unique()]))\n",
    "countries.sort()\n",
    "countries_to_int = dict([(c,i) for i, c in enumerate(countries)])\n",
    "int_to_countries = dict([(i, c) for i, c in enumerate(countries)])\n",
    "number_of_nodes = len(countries)\n",
    "basedate = edges_remove_final.time_start.min()\n",
    "edges_remove_final['time_start_num'] = (edges_remove_final['time_start'] - basedate).dt.days\n",
    "edges_remove_final['time_end_num'] = (edges_remove_final['time_end'] - basedate).dt.days\n",
    "number_of_days = max(edges_remove_final.time_start_num.max(), edges_remove_final.time_end_num.max())\n",
    "\n",
    "temporal_adj = np.ones((number_of_days, number_of_nodes, number_of_nodes))\n",
    "print(f\"Shape of 3D adjacency matrix: {temporal_adj.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for d in edges_remove_final.itertuples():\n",
    "    i = countries_to_int[d.Source]\n",
    "    j = countries_to_int[d.Target]\n",
    "    t1 = d.time_start_num\n",
    "    t2 = d.time_end_num\n",
    "    temporal_adj[t1:t2, i, j] = 0\n",
    "    temporal_adj[t1:t2, j, i] = 0\n",
    "with open('data/temporal_adj.np', 'wb+') as f:\n",
    "    np.save(f, temporal_adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = temporal_adj.sum(axis = 2)\n",
    "fig, ax = plt.subplots(nrows = 2, ncols = 1, figsize = (16, 9))\n",
    "\n",
    "#mean degree plot\n",
    "ax[0].plot(list(range(temp.shape[0])), temp.mean(axis = 1))\n",
    "ax[0].set_title(\"Time series of the mean degree of the network\")\n",
    "ax[0].set_ylabel(\"Mean degree\")\n",
    "ax[0].set_xlabel(\"Days since first restriction\")\n",
    "ax[0].set_ylim((0, 230))\n",
    "\n",
    "#countries plot\n",
    "continent_config = {'AF':{'color': '#e41a1c' , 'label': 'Africa'},\n",
    "                   'AS': {'color': '#377eb8', 'label' : 'Asia'},\n",
    "                   'EU': {'color': '#4daf4a', 'label' : 'Europe'},\n",
    "                   'NA': {'color': '#984ea3', 'label': 'North America'},\n",
    "                   'OC': {'color': '#ff7f00', 'label':'Oceania'},\n",
    "                   'SA': {'color': '#ffff33', 'label': 'South America'}}\n",
    "for i in range(temp.shape[1]):\n",
    "    country_continent = df[df.COUNTRY_NAME == countries[i]].CONTINENT.iloc[0]\n",
    "    ax[1].plot(list(range(temp.shape[0])), temp[:, i], color = continent_config[country_continent]['color'], alpha = 0.8)\n",
    "ax[1].set_title(\"Time series of the degree of countries\")\n",
    "ax[1].set_ylabel(\"Degree of country\")\n",
    "ax[1].set_xlabel(\"Days since first restriction\")\n",
    "\n",
    "legend_elements = []\n",
    "for key, value in continent_config.items():\n",
    "    legend_elements.append(Line2D([0], [0], color = value['color'], lw = 3, label = value['label']))\n",
    "ax[1].legend(handles=legend_elements)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def consecutive(data, stepsize=1):\n",
    "    return np.split(data, np.where(np.diff(data) != stepsize)[0]+1)\n",
    "\n",
    "gephi_df = {'Source': [], 'Target':[], 'Interval':[]}\n",
    "for i in range(number_of_nodes):\n",
    "    for j in range(i+1, number_of_nodes):\n",
    "        gephi_df['Source'].append(int_to_countries[i])\n",
    "        gephi_df['Target'].append(int_to_countries[j])\n",
    "        days_with_edges = np.where(temporal_adj[:, i, j] == 1)[0]\n",
    "        days_with_edges = consecutive(days_with_edges)\n",
    "        intervals_with_edges = [(np.min(t), np.max(t)) for t in days_with_edges]\n",
    "        gephi_df['Interval'].append(intervals_with_edges) \n",
    "gephi_df = pd.DataFrame(gephi_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_interval(x):\n",
    "    s = \"<[\"\n",
    "    for i, t in enumerate(x):\n",
    "        date_start = basedate + pd.Timedelta(days = t[0])\n",
    "        date_end = basedate + pd.Timedelta(days = t[1])\n",
    "        s += str(date_start)[0:10]\n",
    "        s += \",\"\n",
    "        s += str(date_end)[0:10]\n",
    "        if i != len(x) - 1:\n",
    "            s += \"]; [\"\n",
    "    s+= \"]>\"\n",
    "    return s\n",
    "\n",
    "gephi_df['Interval'] = gephi_df.Interval.map(adjust_interval)\n",
    "gephi_df.to_csv('data/edges_open_gephi.csv', index = False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}