diff --git a/module3/exo3/exercice_en.ipynb b/module3/exo3/exercice_en.ipynb
index dc1f0f108f5aefc2ca7f9462609dc8537c5a941a..0e66845232f88232fe4f1645871e726abc93aa50 100644
--- a/module3/exo3/exercice_en.ipynb
+++ b/module3/exo3/exercice_en.ipynb
@@ -7,7 +7,7 @@
     "hidePrompt": true
    },
    "source": [
-    "# Title of document"
+    "# Covid-19"
    ]
   },
   {
@@ -9163,7 +9163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 157,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -9190,138 +9190,33 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Label</th>\n",
-       "      <th>Period</th>\n",
-       "      <th>2020-01</th>\n",
-       "      <th>2020-02</th>\n",
-       "      <th>2020-03</th>\n",
-       "      <th>2020-04</th>\n",
-       "      <th>2020-05</th>\n",
-       "      <th>2020-06</th>\n",
-       "      <th>2020-07</th>\n",
-       "      <th>2020-08</th>\n",
-       "      <th>...</th>\n",
-       "      <th>2022-06</th>\n",
-       "      <th>2022-07</th>\n",
-       "      <th>2022-08</th>\n",
-       "      <th>2022-09</th>\n",
-       "      <th>2022-10</th>\n",
-       "      <th>2022-11</th>\n",
-       "      <th>2022-12</th>\n",
-       "      <th>2023-01</th>\n",
-       "      <th>2023-02</th>\n",
-       "      <th>2023-03</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Number of deaths – France</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>57355</td>\n",
-       "      <td>51413</td>\n",
-       "      <td>63128</td>\n",
-       "      <td>66948</td>\n",
-       "      <td>49140</td>\n",
-       "      <td>46295</td>\n",
-       "      <td>47220</td>\n",
-       "      <td>49317</td>\n",
-       "      <td>...</td>\n",
-       "      <td>48379</td>\n",
-       "      <td>54724</td>\n",
-       "      <td>52838</td>\n",
-       "      <td>49600</td>\n",
-       "      <td>55868</td>\n",
-       "      <td>54743</td>\n",
-       "      <td>70566</td>\n",
-       "      <td>64000</td>\n",
-       "      <td>52100</td>\n",
-       "      <td>55300</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 41 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                       Label  Period  2020-01  2020-02  2020-03  2020-04  \\\n",
-       "0  Number of deaths – France     NaN    57355    51413    63128    66948   \n",
-       "\n",
-       "   2020-05  2020-06  2020-07  2020-08   ...     2022-06  2022-07  2022-08  \\\n",
-       "0    49140    46295    47220    49317   ...       48379    54724    52838   \n",
-       "\n",
-       "   2022-09  2022-10  2022-11  2022-12  2023-01  2023-02  2023-03  \n",
-       "0    49600    55868    54743    70566    64000    52100    55300  \n",
-       "\n",
-       "[1 rows x 41 columns]"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fr_death = pd.read_csv('https://app-learninglab.inria.fr/moocrr/gitlab/082aa0aa9507b80b621099010e33de9d/mooc-rr/raw/master/module3/exo3/monthly_values.csv')\n",
-    "fr_death"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {
-    "hideCode": true,
-    "hidePrompt": true
-   },
-   "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>Label</th>\n",
-       "      <th>2020-01</th>\n",
-       "      <th>2020-02</th>\n",
-       "      <th>2020-03</th>\n",
-       "      <th>2020-04</th>\n",
-       "      <th>2020-05</th>\n",
-       "      <th>2020-06</th>\n",
-       "      <th>2020-07</th>\n",
-       "      <th>2020-08</th>\n",
-       "      <th>2020-09</th>\n",
+       "      <th>Country/Region</th>\n",
+       "      <th>2020-01-31</th>\n",
+       "      <th>2020-02-29</th>\n",
+       "      <th>2020-03-31</th>\n",
+       "      <th>2020-04-30</th>\n",
+       "      <th>2020-05-31</th>\n",
+       "      <th>2020-06-30</th>\n",
+       "      <th>2020-07-31</th>\n",
+       "      <th>2020-08-31</th>\n",
+       "      <th>2020-09-30</th>\n",
        "      <th>...</th>\n",
-       "      <th>2022-06</th>\n",
-       "      <th>2022-07</th>\n",
-       "      <th>2022-08</th>\n",
-       "      <th>2022-09</th>\n",
-       "      <th>2022-10</th>\n",
-       "      <th>2022-11</th>\n",
-       "      <th>2022-12</th>\n",
-       "      <th>2023-01</th>\n",
-       "      <th>2023-02</th>\n",
-       "      <th>2023-03</th>\n",
+       "      <th>2022-06-30</th>\n",
+       "      <th>2022-07-31</th>\n",
+       "      <th>2022-08-31</th>\n",
+       "      <th>2022-09-30</th>\n",
+       "      <th>2022-10-31</th>\n",
+       "      <th>2022-11-30</th>\n",
+       "      <th>2022-12-31</th>\n",
+       "      <th>2023-01-31</th>\n",
+       "      <th>2023-02-28</th>\n",
+       "      <th>2023-03-31</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Number of deaths – France</td>\n",
+       "      <td>France</td>\n",
        "      <td>57355</td>\n",
        "      <td>51413</td>\n",
        "      <td>63128</td>\n",
@@ -9349,26 +9244,29 @@
        "</div>"
       ],
       "text/plain": [
-       "                       Label  2020-01  2020-02  2020-03  2020-04  2020-05  \\\n",
-       "0  Number of deaths – France    57355    51413    63128    66948    49140   \n",
+       "  Country/Region  2020-01-31  2020-02-29  2020-03-31  2020-04-30  2020-05-31  \\\n",
+       "0         France       57355       51413       63128       66948       49140   \n",
        "\n",
-       "   2020-06  2020-07  2020-08  2020-09   ...     2022-06  2022-07  2022-08  \\\n",
-       "0    46295    47220    49317    49377   ...       48379    54724    52838   \n",
+       "   2020-06-30  2020-07-31  2020-08-31  2020-09-30     ...      2022-06-30  \\\n",
+       "0       46295       47220       49317       49377     ...           48379   \n",
        "\n",
-       "   2022-09  2022-10  2022-11  2022-12  2023-01  2023-02  2023-03  \n",
-       "0    49600    55868    54743    70566    64000    52100    55300  \n",
+       "   2022-07-31  2022-08-31  2022-09-30  2022-10-31  2022-11-30  2022-12-31  \\\n",
+       "0       54724       52838       49600       55868       54743       70566   \n",
+       "\n",
+       "   2023-01-31  2023-02-28  2023-03-31  \n",
+       "0       64000       52100       55300  \n",
        "\n",
        "[1 rows x 40 columns]"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "fr_death_clear = fr_death.drop(columns=['Period'])\n",
-    "fr_death_clear"
+    "fr_death = pd.read_csv('https://app-learninglab.inria.fr/moocrr/gitlab/082aa0aa9507b80b621099010e33de9d/mooc-rr/raw/master/module3/exo3/monthly_values.csv')\n",
+    "fr_death"
    ]
   },
   {
@@ -9943,32 +9841,6 @@
     "type(grouped_france_data)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-137-a21411c718ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Resample the data to get the last available value for each month\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmonthly_france_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouped_france_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mffill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mmonthly_france_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mresample\u001b[0;34m(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base, on, level)\u001b[0m\n\u001b[1;32m   5520\u001b[0m                      \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloffset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5521\u001b[0m                      \u001b[0mconvention\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvention\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5522\u001b[0;31m                      base=base, key=on, level=level)\n\u001b[0m\u001b[1;32m   5523\u001b[0m         return _maybe_process_deprecations(r,\n\u001b[1;32m   5524\u001b[0m                                            \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36mresample\u001b[0;34m(obj, kind, **kwds)\u001b[0m\n\u001b[1;32m    997\u001b[0m     \u001b[0;34m\"\"\" create a TimeGrouper and return our resampler \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    998\u001b[0m     \u001b[0mtg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTimeGrouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 999\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_resampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1001\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36m_get_resampler\u001b[0;34m(self, obj, kind)\u001b[0m\n\u001b[1;32m   1114\u001b[0m         raise TypeError(\"Only valid with DatetimeIndex, \"\n\u001b[1;32m   1115\u001b[0m                         \u001b[0;34m\"TimedeltaIndex or PeriodIndex, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1116\u001b[0;31m                         \"but got an instance of %r\" % type(ax).__name__)\n\u001b[0m\u001b[1;32m   1117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_grouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'"
-     ]
-    }
-   ],
-   "source": [
-    "# Resample the data to get the last available value for each month\n",
-    "monthly_france_data = grouped_france_data.resample('M').ffill()\n",
-    "monthly_france_data"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -9976,7 +9848,7 @@
     "hidePrompt": true
    },
    "source": [
-    "## 2nd: france_dead I GIVE UP???? help me huhuhu. why i can not see only france. i try filter etc,. and only loc() is work. but I hate this visualize"
+    "## 2nd: france_dead "
    ]
   },
   {
@@ -10463,70 +10335,7 @@
     }
    ],
    "source": [
-    "monthly_death_data\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
-    "hideCode": true,
-    "hidePrompt": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2020-01-31         0\n",
-       "2020-02-29         2\n",
-       "2020-03-31      3526\n",
-       "2020-04-30     24349\n",
-       "2020-05-31     28805\n",
-       "2020-06-30     29846\n",
-       "2020-07-31     30268\n",
-       "2020-08-31     30646\n",
-       "2020-09-30     31978\n",
-       "2020-10-31     36827\n",
-       "2020-11-30     52818\n",
-       "2020-12-31     64758\n",
-       "2021-01-31     76200\n",
-       "2021-02-28     86579\n",
-       "2021-03-31     95798\n",
-       "2021-04-30    104676\n",
-       "2021-05-31    109693\n",
-       "2021-06-30    111259\n",
-       "2021-07-31    112061\n",
-       "2021-08-31    114926\n",
-       "2021-09-30    117474\n",
-       "2021-10-31    118625\n",
-       "2021-11-30    120112\n",
-       "2021-12-31    124729\n",
-       "2022-01-31    131937\n",
-       "2022-02-28    139382\n",
-       "2022-03-31    143307\n",
-       "2022-04-30    146967\n",
-       "2022-05-31    149366\n",
-       "2022-06-30    150572\n",
-       "2022-07-31    153023\n",
-       "2022-08-31    155133\n",
-       "2022-09-30    156152\n",
-       "2022-10-31    158034\n",
-       "2022-11-30    159990\n",
-       "2022-12-31    163003\n",
-       "2023-01-31    165274\n",
-       "2023-02-28    166004\n",
-       "2023-03-31    166176\n",
-       "Name: France, dtype: int64"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "france_dead_data = monthly_death_data.loc['France']\n",
-    "france_dead_data"
+    "monthly_death_data"
    ]
   },
   {
@@ -10787,7 +10596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 158,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -10814,33 +10623,33 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Label</th>\n",
-       "      <th>2020-01</th>\n",
-       "      <th>2020-02</th>\n",
-       "      <th>2020-03</th>\n",
-       "      <th>2020-04</th>\n",
-       "      <th>2020-05</th>\n",
-       "      <th>2020-06</th>\n",
-       "      <th>2020-07</th>\n",
-       "      <th>2020-08</th>\n",
-       "      <th>2020-09</th>\n",
+       "      <th>Country/Region</th>\n",
+       "      <th>2020-01-31</th>\n",
+       "      <th>2020-02-29</th>\n",
+       "      <th>2020-03-31</th>\n",
+       "      <th>2020-04-30</th>\n",
+       "      <th>2020-05-31</th>\n",
+       "      <th>2020-06-30</th>\n",
+       "      <th>2020-07-31</th>\n",
+       "      <th>2020-08-31</th>\n",
+       "      <th>2020-09-30</th>\n",
        "      <th>...</th>\n",
-       "      <th>2022-06</th>\n",
-       "      <th>2022-07</th>\n",
-       "      <th>2022-08</th>\n",
-       "      <th>2022-09</th>\n",
-       "      <th>2022-10</th>\n",
-       "      <th>2022-11</th>\n",
-       "      <th>2022-12</th>\n",
-       "      <th>2023-01</th>\n",
-       "      <th>2023-02</th>\n",
-       "      <th>2023-03</th>\n",
+       "      <th>2022-06-30</th>\n",
+       "      <th>2022-07-31</th>\n",
+       "      <th>2022-08-31</th>\n",
+       "      <th>2022-09-30</th>\n",
+       "      <th>2022-10-31</th>\n",
+       "      <th>2022-11-30</th>\n",
+       "      <th>2022-12-31</th>\n",
+       "      <th>2023-01-31</th>\n",
+       "      <th>2023-02-28</th>\n",
+       "      <th>2023-03-31</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Number of deaths – France</td>\n",
+       "      <td>France</td>\n",
        "      <td>57355</td>\n",
        "      <td>51413</td>\n",
        "      <td>63128</td>\n",
@@ -10868,90 +10677,651 @@
        "</div>"
       ],
       "text/plain": [
-       "                       Label  2020-01  2020-02  2020-03  2020-04  2020-05  \\\n",
-       "0  Number of deaths – France    57355    51413    63128    66948    49140   \n",
+       "  Country/Region  2020-01-31  2020-02-29  2020-03-31  2020-04-30  2020-05-31  \\\n",
+       "0         France       57355       51413       63128       66948       49140   \n",
+       "\n",
+       "   2020-06-30  2020-07-31  2020-08-31  2020-09-30     ...      2022-06-30  \\\n",
+       "0       46295       47220       49317       49377     ...           48379   \n",
        "\n",
-       "   2020-06  2020-07  2020-08  2020-09   ...     2022-06  2022-07  2022-08  \\\n",
-       "0    46295    47220    49317    49377   ...       48379    54724    52838   \n",
+       "   2022-07-31  2022-08-31  2022-09-30  2022-10-31  2022-11-30  2022-12-31  \\\n",
+       "0       54724       52838       49600       55868       54743       70566   \n",
        "\n",
-       "   2022-09  2022-10  2022-11  2022-12  2023-01  2023-02  2023-03  \n",
-       "0    49600    55868    54743    70566    64000    52100    55300  \n",
+       "   2023-01-31  2023-02-28  2023-03-31  \n",
+       "0       64000       52100       55300  \n",
        "\n",
        "[1 rows x 40 columns]"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 158,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "fr_death_clear"
+    "fr_death"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hideCode": true,
-    "hidePrompt": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hideCode": true,
-    "hidePrompt": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hideCode": true,
-    "hidePrompt": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hideCode": true,
-    "hidePrompt": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "hideCode": true,
-    "hidePrompt": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 143,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
    },
-   "outputs": [],
-   "source": []
-  },
-  {
+   "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",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2020-01-31 00:00:00</th>\n",
+       "      <th>2020-02-29 00:00:00</th>\n",
+       "      <th>2020-03-31 00:00:00</th>\n",
+       "      <th>2020-04-30 00:00:00</th>\n",
+       "      <th>2020-05-31 00:00:00</th>\n",
+       "      <th>2020-06-30 00:00:00</th>\n",
+       "      <th>2020-07-31 00:00:00</th>\n",
+       "      <th>2020-08-31 00:00:00</th>\n",
+       "      <th>2020-09-30 00:00:00</th>\n",
+       "      <th>2020-10-31 00:00:00</th>\n",
+       "      <th>...</th>\n",
+       "      <th>2022-06-30 00:00:00</th>\n",
+       "      <th>2022-07-31 00:00:00</th>\n",
+       "      <th>2022-08-31 00:00:00</th>\n",
+       "      <th>2022-09-30 00:00:00</th>\n",
+       "      <th>2022-10-31 00:00:00</th>\n",
+       "      <th>2022-11-30 00:00:00</th>\n",
+       "      <th>2022-12-31 00:00:00</th>\n",
+       "      <th>2023-01-31 00:00:00</th>\n",
+       "      <th>2023-02-28 00:00:00</th>\n",
+       "      <th>2023-03-31 00:00:00</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country/Region</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Belgium</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>705</td>\n",
+       "      <td>7594</td>\n",
+       "      <td>9467</td>\n",
+       "      <td>9747</td>\n",
+       "      <td>9841</td>\n",
+       "      <td>9895</td>\n",
+       "      <td>10016</td>\n",
+       "      <td>11625</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31918</td>\n",
+       "      <td>32228</td>\n",
+       "      <td>32516</td>\n",
+       "      <td>32673</td>\n",
+       "      <td>32902</td>\n",
+       "      <td>33061</td>\n",
+       "      <td>33228</td>\n",
+       "      <td>33557</td>\n",
+       "      <td>33717</td>\n",
+       "      <td>33814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>China</th>\n",
+       "      <td>213</td>\n",
+       "      <td>2837</td>\n",
+       "      <td>3323</td>\n",
+       "      <td>4697</td>\n",
+       "      <td>4708</td>\n",
+       "      <td>4713</td>\n",
+       "      <td>4733</td>\n",
+       "      <td>4796</td>\n",
+       "      <td>4813</td>\n",
+       "      <td>4814</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14927</td>\n",
+       "      <td>15046</td>\n",
+       "      <td>15240</td>\n",
+       "      <td>15713</td>\n",
+       "      <td>15952</td>\n",
+       "      <td>15986</td>\n",
+       "      <td>17102</td>\n",
+       "      <td>97663</td>\n",
+       "      <td>101048</td>\n",
+       "      <td>101056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>France</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3526</td>\n",
+       "      <td>24349</td>\n",
+       "      <td>28805</td>\n",
+       "      <td>29846</td>\n",
+       "      <td>30268</td>\n",
+       "      <td>30646</td>\n",
+       "      <td>31978</td>\n",
+       "      <td>36827</td>\n",
+       "      <td>...</td>\n",
+       "      <td>150572</td>\n",
+       "      <td>153023</td>\n",
+       "      <td>155133</td>\n",
+       "      <td>156152</td>\n",
+       "      <td>158034</td>\n",
+       "      <td>159990</td>\n",
+       "      <td>163003</td>\n",
+       "      <td>165274</td>\n",
+       "      <td>166004</td>\n",
+       "      <td>166176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Germany</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>583</td>\n",
+       "      <td>6288</td>\n",
+       "      <td>8500</td>\n",
+       "      <td>8973</td>\n",
+       "      <td>9141</td>\n",
+       "      <td>9298</td>\n",
+       "      <td>9488</td>\n",
+       "      <td>10452</td>\n",
+       "      <td>...</td>\n",
+       "      <td>141105</td>\n",
+       "      <td>143855</td>\n",
+       "      <td>147404</td>\n",
+       "      <td>149948</td>\n",
+       "      <td>153544</td>\n",
+       "      <td>157791</td>\n",
+       "      <td>161465</td>\n",
+       "      <td>165711</td>\n",
+       "      <td>168086</td>\n",
+       "      <td>168935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iran</th>\n",
+       "      <td>0</td>\n",
+       "      <td>43</td>\n",
+       "      <td>2898</td>\n",
+       "      <td>6028</td>\n",
+       "      <td>7797</td>\n",
+       "      <td>10817</td>\n",
+       "      <td>16766</td>\n",
+       "      <td>21571</td>\n",
+       "      <td>26169</td>\n",
+       "      <td>34864</td>\n",
+       "      <td>...</td>\n",
+       "      <td>141389</td>\n",
+       "      <td>141998</td>\n",
+       "      <td>143867</td>\n",
+       "      <td>144426</td>\n",
+       "      <td>144576</td>\n",
+       "      <td>144633</td>\n",
+       "      <td>144685</td>\n",
+       "      <td>144749</td>\n",
+       "      <td>144845</td>\n",
+       "      <td>144933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Italy</th>\n",
+       "      <td>0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>12428</td>\n",
+       "      <td>27967</td>\n",
+       "      <td>33415</td>\n",
+       "      <td>34767</td>\n",
+       "      <td>35141</td>\n",
+       "      <td>35483</td>\n",
+       "      <td>35894</td>\n",
+       "      <td>38618</td>\n",
+       "      <td>...</td>\n",
+       "      <td>168353</td>\n",
+       "      <td>172086</td>\n",
+       "      <td>175595</td>\n",
+       "      <td>177092</td>\n",
+       "      <td>179101</td>\n",
+       "      <td>181098</td>\n",
+       "      <td>184642</td>\n",
+       "      <td>186833</td>\n",
+       "      <td>188094</td>\n",
+       "      <td>188322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Japan</th>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>67</td>\n",
+       "      <td>481</td>\n",
+       "      <td>898</td>\n",
+       "      <td>974</td>\n",
+       "      <td>1013</td>\n",
+       "      <td>1300</td>\n",
+       "      <td>1575</td>\n",
+       "      <td>1770</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31281</td>\n",
+       "      <td>32613</td>\n",
+       "      <td>39942</td>\n",
+       "      <td>44918</td>\n",
+       "      <td>46765</td>\n",
+       "      <td>49652</td>\n",
+       "      <td>57274</td>\n",
+       "      <td>68099</td>\n",
+       "      <td>72395</td>\n",
+       "      <td>72997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Korea, South</th>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>162</td>\n",
+       "      <td>248</td>\n",
+       "      <td>271</td>\n",
+       "      <td>282</td>\n",
+       "      <td>301</td>\n",
+       "      <td>324</td>\n",
+       "      <td>415</td>\n",
+       "      <td>466</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24555</td>\n",
+       "      <td>25068</td>\n",
+       "      <td>26876</td>\n",
+       "      <td>28445</td>\n",
+       "      <td>29209</td>\n",
+       "      <td>30568</td>\n",
+       "      <td>32219</td>\n",
+       "      <td>33486</td>\n",
+       "      <td>33988</td>\n",
+       "      <td>34093</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Netherlands</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1040</td>\n",
+       "      <td>4811</td>\n",
+       "      <td>5975</td>\n",
+       "      <td>6132</td>\n",
+       "      <td>6166</td>\n",
+       "      <td>6252</td>\n",
+       "      <td>6457</td>\n",
+       "      <td>7459</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23000</td>\n",
+       "      <td>23122</td>\n",
+       "      <td>23239</td>\n",
+       "      <td>23292</td>\n",
+       "      <td>23459</td>\n",
+       "      <td>23568</td>\n",
+       "      <td>23697</td>\n",
+       "      <td>23702</td>\n",
+       "      <td>23705</td>\n",
+       "      <td>23705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Portugal</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>160</td>\n",
+       "      <td>989</td>\n",
+       "      <td>1410</td>\n",
+       "      <td>1576</td>\n",
+       "      <td>1735</td>\n",
+       "      <td>1822</td>\n",
+       "      <td>1971</td>\n",
+       "      <td>2507</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24149</td>\n",
+       "      <td>24592</td>\n",
+       "      <td>24855</td>\n",
+       "      <td>25031</td>\n",
+       "      <td>25228</td>\n",
+       "      <td>25450</td>\n",
+       "      <td>25714</td>\n",
+       "      <td>26022</td>\n",
+       "      <td>26117</td>\n",
+       "      <td>26266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Spain</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8464</td>\n",
+       "      <td>24543</td>\n",
+       "      <td>27127</td>\n",
+       "      <td>28355</td>\n",
+       "      <td>28445</td>\n",
+       "      <td>29094</td>\n",
+       "      <td>31791</td>\n",
+       "      <td>35878</td>\n",
+       "      <td>...</td>\n",
+       "      <td>107906</td>\n",
+       "      <td>110719</td>\n",
+       "      <td>112600</td>\n",
+       "      <td>114179</td>\n",
+       "      <td>115078</td>\n",
+       "      <td>115901</td>\n",
+       "      <td>117095</td>\n",
+       "      <td>118434</td>\n",
+       "      <td>119380</td>\n",
+       "      <td>119479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>US</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5359</td>\n",
+       "      <td>66638</td>\n",
+       "      <td>107857</td>\n",
+       "      <td>127432</td>\n",
+       "      <td>153969</td>\n",
+       "      <td>182794</td>\n",
+       "      <td>205957</td>\n",
+       "      <td>230545</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1017377</td>\n",
+       "      <td>1030088</td>\n",
+       "      <td>1046240</td>\n",
+       "      <td>1059507</td>\n",
+       "      <td>1070376</td>\n",
+       "      <td>1080466</td>\n",
+       "      <td>1092764</td>\n",
+       "      <td>1108688</td>\n",
+       "      <td>1119917</td>\n",
+       "      <td>1123836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>United Kingdom</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5145</td>\n",
+       "      <td>39062</td>\n",
+       "      <td>52569</td>\n",
+       "      <td>56284</td>\n",
+       "      <td>57434</td>\n",
+       "      <td>57984</td>\n",
+       "      <td>58875</td>\n",
+       "      <td>64308</td>\n",
+       "      <td>...</td>\n",
+       "      <td>201790</td>\n",
+       "      <td>205481</td>\n",
+       "      <td>207831</td>\n",
+       "      <td>209268</td>\n",
+       "      <td>212350</td>\n",
+       "      <td>214179</td>\n",
+       "      <td>217054</td>\n",
+       "      <td>220064</td>\n",
+       "      <td>220721</td>\n",
+       "      <td>220721</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>13 rows × 39 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                2020-01-31  2020-02-29  2020-03-31  2020-04-30  2020-05-31  \\\n",
+       "Country/Region                                                               \n",
+       "Belgium                  0           0         705        7594        9467   \n",
+       "China                  213        2837        3323        4697        4708   \n",
+       "France                   0           2        3526       24349       28805   \n",
+       "Germany                  0           0         583        6288        8500   \n",
+       "Iran                     0          43        2898        6028        7797   \n",
+       "Italy                    0          29       12428       27967       33415   \n",
+       "Japan                    0           6          67         481         898   \n",
+       "Korea, South             0          16         162         248         271   \n",
+       "Netherlands              0           0        1040        4811        5975   \n",
+       "Portugal                 0           0         160         989        1410   \n",
+       "Spain                    0           0        8464       24543       27127   \n",
+       "US                       0           1        5359       66638      107857   \n",
+       "United Kingdom           1           3        5145       39062       52569   \n",
+       "\n",
+       "                2020-06-30  2020-07-31  2020-08-31  2020-09-30  2020-10-31  \\\n",
+       "Country/Region                                                               \n",
+       "Belgium               9747        9841        9895       10016       11625   \n",
+       "China                 4713        4733        4796        4813        4814   \n",
+       "France               29846       30268       30646       31978       36827   \n",
+       "Germany               8973        9141        9298        9488       10452   \n",
+       "Iran                 10817       16766       21571       26169       34864   \n",
+       "Italy                34767       35141       35483       35894       38618   \n",
+       "Japan                  974        1013        1300        1575        1770   \n",
+       "Korea, South           282         301         324         415         466   \n",
+       "Netherlands           6132        6166        6252        6457        7459   \n",
+       "Portugal              1576        1735        1822        1971        2507   \n",
+       "Spain                28355       28445       29094       31791       35878   \n",
+       "US                  127432      153969      182794      205957      230545   \n",
+       "United Kingdom       56284       57434       57984       58875       64308   \n",
+       "\n",
+       "                   ...      2022-06-30  2022-07-31  2022-08-31  2022-09-30  \\\n",
+       "Country/Region     ...                                                       \n",
+       "Belgium            ...           31918       32228       32516       32673   \n",
+       "China              ...           14927       15046       15240       15713   \n",
+       "France             ...          150572      153023      155133      156152   \n",
+       "Germany            ...          141105      143855      147404      149948   \n",
+       "Iran               ...          141389      141998      143867      144426   \n",
+       "Italy              ...          168353      172086      175595      177092   \n",
+       "Japan              ...           31281       32613       39942       44918   \n",
+       "Korea, South       ...           24555       25068       26876       28445   \n",
+       "Netherlands        ...           23000       23122       23239       23292   \n",
+       "Portugal           ...           24149       24592       24855       25031   \n",
+       "Spain              ...          107906      110719      112600      114179   \n",
+       "US                 ...         1017377     1030088     1046240     1059507   \n",
+       "United Kingdom     ...          201790      205481      207831      209268   \n",
+       "\n",
+       "                2022-10-31  2022-11-30  2022-12-31  2023-01-31  2023-02-28  \\\n",
+       "Country/Region                                                               \n",
+       "Belgium              32902       33061       33228       33557       33717   \n",
+       "China                15952       15986       17102       97663      101048   \n",
+       "France              158034      159990      163003      165274      166004   \n",
+       "Germany             153544      157791      161465      165711      168086   \n",
+       "Iran                144576      144633      144685      144749      144845   \n",
+       "Italy               179101      181098      184642      186833      188094   \n",
+       "Japan                46765       49652       57274       68099       72395   \n",
+       "Korea, South         29209       30568       32219       33486       33988   \n",
+       "Netherlands          23459       23568       23697       23702       23705   \n",
+       "Portugal             25228       25450       25714       26022       26117   \n",
+       "Spain               115078      115901      117095      118434      119380   \n",
+       "US                 1070376     1080466     1092764     1108688     1119917   \n",
+       "United Kingdom      212350      214179      217054      220064      220721   \n",
+       "\n",
+       "                2023-03-31  \n",
+       "Country/Region              \n",
+       "Belgium              33814  \n",
+       "China               101056  \n",
+       "France              166176  \n",
+       "Germany             168935  \n",
+       "Iran                144933  \n",
+       "Italy               188322  \n",
+       "Japan                72997  \n",
+       "Korea, South         34093  \n",
+       "Netherlands          23705  \n",
+       "Portugal             26266  \n",
+       "Spain               119479  \n",
+       "US                 1123836  \n",
+       "United Kingdom      220721  \n",
+       "\n",
+       "[13 rows x 39 columns]"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "monthly_death_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": true
+   },
+   "outputs": [],
+   "source": [
+    "monthly_death_data.to_csv('monthly_death_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<a href='monthly_death_data.csv' target='_blank'>monthly_death_data.csv</a><br>"
+      ],
+      "text/plain": [
+       "/home/jovyan/work/module3/exo3/monthly_death_data.csv"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import FileLink\n",
+    "FileLink('monthly_death_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Country/Region    France\n",
+       "2020-01-31         57355\n",
+       "2020-02-29         51413\n",
+       "2020-03-31         63128\n",
+       "2020-04-30         66948\n",
+       "2020-05-31         49140\n",
+       "2020-06-30         46295\n",
+       "2020-07-31         47220\n",
+       "2020-08-31         49317\n",
+       "2020-09-30         49377\n",
+       "2020-10-31         58197\n",
+       "2020-11-30         66237\n",
+       "2020-12-31         64295\n",
+       "2021-01-31         66778\n",
+       "2021-02-28         57262\n",
+       "2021-03-31         58660\n",
+       "2021-04-30         57387\n",
+       "2021-05-31         52604\n",
+       "2021-06-30         46221\n",
+       "2021-07-31         48754\n",
+       "2021-08-31         51649\n",
+       "2021-09-30         50068\n",
+       "2021-10-31         54057\n",
+       "2021-11-30         53914\n",
+       "2021-12-31         64231\n",
+       "2022-01-31         66722\n",
+       "2022-02-28         57023\n",
+       "2022-03-31         57292\n",
+       "2022-04-30         56141\n",
+       "2022-05-31         51226\n",
+       "2022-06-30         48379\n",
+       "2022-07-31         54724\n",
+       "2022-08-31         52838\n",
+       "2022-09-30         49600\n",
+       "2022-10-31         55868\n",
+       "2022-11-30         54743\n",
+       "2022-12-31         70566\n",
+       "2023-01-31         64000\n",
+       "2023-02-28         52100\n",
+       "2023-03-31         55300\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "france_row = fr_death.iloc[0]\n",
+    "france_row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([57355, 51413, 63128, 66948, 49140, 46295, 47220, 49317, 49377,\n",
+       "       58197, 66237, 64295, 66778, 57262, 58660, 57387, 52604, 46221,\n",
+       "       48754, 51649, 50068, 54057, 53914, 64231, 66722, 57023, 57292,\n",
+       "       56141, 51226, 48379, 54724, 52838, 49600, 55868, 54743, 70566,\n",
+       "       64000, 52100, 55300], dtype=object)"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "france_values = france_row.drop('Country/Region').values\n",
+    "france_values"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 138,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -10998,8 +11368,269 @@
     "plt.xticks(rotation=45)\n",
     "plt.legend()\n",
     "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EREREjoDCeR1ycnJITEwkMzMTMwt3OSL7cM5RUFBATk4O3bt3D3c5IiIiIiJyBNStvQ5lZWWkpqYqmEujZGakpqaqZ4eIiIiISDOgcH4QCubSmOnzKSIiIiLSPCici4iIiIiIiISZwnkjt337diZMmEDPnj3p378/Z599NqtWrWrQfY4dO5aFCxfWuczjjz/Onj17ap6fffbZ7Nq164j3XVpayi233ELPnj0ZMGAAo0ePZt68eUe83aNhypQppKenM2TIEIYMGcI111wT7pJERERERKSZ0IBwjZhzjosuuohrr72W1157DYDFixeTm5tLnz59wlrb448/zlVXXUV8fDwA77777lHZ7k033UT37t1ZvXo1ERERrFu3juXLlx+VbR8Nl19+OU899dQB5wcCAVq10tdKREREREQOjVrOG7GPP/6YqKgobr311pppQ4YM4eSTT2bmzJmce+65NdN/8pOfMGXKFAAyMzO59957GTlyJNnZ2XzxxReMHz+enj178swzzwDUuX6o2267jezsbAYMGMD9998PwKRJk9i6dSunnHIKp5xySs0+8/Pz+cUvfsH//u//1qw/ceJEHnvsMQAeffRRhg0bxqBBg2q2FWrt2rXMmzePhx56iIgI76PZo0cPzjnnHAAuvPBChg4dyoABA3j22WcBqKqq4rrrrmPgwIEcf/zx/OlPf6rZ1plnnsnQoUM5+eSTWbFiBQBvvPEGAwcOZPDgwYwePbq+b0Wdxo4dy7333suYMWN44oknePvttxkxYgRZWVmcdtpp5Obm1rwWN9xwA2PHjqVHjx5MmjSpZhsvvfQSgwYNYvDgwVx99dUA5OXlcfHFFzNs2DCGDRvG7Nmzj0q9IiIiIiLS+KiJr57+5+2lLNtafFS32b9jEvefN+CA87/55huGDh16WNvu0qULc+bM4e677+a6665j9uzZlJWVMWDAgH3C/sE8/PDDpKSkUFVVxbhx4/jqq6+44447+OMf/8jHH39MWlraPstPmDCBu+66ix/96EcATJ06lf/85z+8//77rF69mvnz5+Oc4/zzz2fWrFn7BOSlS5cyZMgQIiMja63l+eefJyUlhb179zJs2DAuvvhiNmzYwJYtW/jmm28AarrW33zzzTzzzDP07t2befPm8aMf/YiPPvqIBx54gOnTp9OpU6fD6ob/+uuv89lnnwFw5513cv3119fs95NPPgFg586dzJ07FzNj8uTJ/P73v6/5gWLFihV8/PHHlJSU0LdvX2677TZWrVrFww8/zOzZs0lLS6OwsLBm+3fffTcnnXQSmzZtYvz48Y2qF4GIiIiIiBw9CufN1Pnnnw/A8ccfT2lpKYmJiSQmJhIbG3tIoXTq1Kk8++yzBAIBtm3bxrJlyxg0aNABl8/KymLHjh1s3bqVvLw8kpOT6dq1K5MmTeL9998nKysL8M4tX7169SG1Xk+aNIk333wTgM2bN7N69Wr69u3LunXruP322znnnHM444wzKC0t5fPPP+fSSy+tWbe8vByAUaNGcd1113HZZZfx/e9/v977rnagbu2XX355zeOcnBwuv/xytm3bRkVFxT7XID/nnHOIiYkhJiaGdu3akZuby0cffcQll1xS80NHSkoKADNmzGDZsmU16xYXF1NSUkJiYuIh1y0iIiIiIo2bwnk91dXC3VAGDBjAtGnTap3XqlUrgsFgzfP9r3UdExMDQERERM3j6ufV50XXtT7A+vXr+cMf/sCCBQtITk7muuuuq9c1tS+55BKmTZtWM5gdeOfP/+pXv+KWW26p83iXLFlCMBis6dZebebMmcyYMYM5c+YQHx/P2LFjKSsrIzk5mSVLljB9+nT+/Oc/M3XqVB5//HHatm3L4sWLv7OPZ555hnnz5vHOO+8wZMgQFi9eTGpqas38++67j3feeQeg1vUPJCEhoebx7bffzk9/+lPOP/98Zs6cycSJE2vmhb4XkZGRBAIBnHO1XhItGAwyZ84c4uLi6l2HiIiIiIg0TTrnvBE79dRTKS8v57nnnquZtmDBAj755BO6devGsmXLKC8vp6ioiA8//PCQtl2f9YuLi0lISKBNmzbk5uby3nvv1cxLTEykpKSk1m1PmDCB1157jWnTpnHJJZcAMH78eJ5//nlKS0sB2LJlCzt27NhnvZ49e5Kdnc3999+Pcw6A1atX869//YuioiKSk5OJj49nxYoVzJ07F4D8/HyCwSAXX3wxDz74IF988QVJSUl0796dN954A/B+GFiyZAngnYs+YsQIHnjgAdLS0ti8efM+NTz88MMsXrz4kIL5/oqKiujUqRMAL7744kGXHzduHFOnTqWgoACgplv7GWecsU8r/ZHUJCIiIiIijZtazhsxM+PNN9/krrvu4re//S2xsbFkZmby+OOP06VLFy677DIGDRpE7969a7qL11d91h88eDBZWVkMGDCAHj16MGrUqJp5N998M2eddRYZGRl8/PHH+6w3YMAASkpK6NSpExkZGYAXNJcvX87IkSMBaN26NS+//DLt2rXbZ93Jkyfzs5/9jF69ehEfH09qaiqPPvoogwYN4plnnmHQoEH07duXE044AfBC/vXXX1/TC+A3v/kNAK+88gq33XYbDz30EJWVlUyYMIHBgwdzzz33sHr1apxzjBs3jsGDBx/S61YfEydO5NJLL6VTp06ccMIJrF+/vs7lBwwYwH333ceYMWOIjIwkKyuLKVOmMGnSJH784x8zaNAgAoEAo0ePrhnQT0REREREmherbqFs6bKzs93+1/Zevnw5xx13XJgqEqkffU5FRERERBovM1vknMs+2HLq1i4iIiIiIiISZgrnIiIiIiIiImGmc84P4kAjaYs0BjotRUREREQam6qgo2B3OXkl5eSXVlBeWUVllSMQDHr3VUEqg959oMpRGfTuQ6fXLB8ImR+yfiDoaB3TiqevGhruwz1qFM7rEBsbS0FBAampqQro0ug45ygoKCA2NjbcpYiIiIhIM+eco3hvgLzSMnaUeME7r6ScvNJvH+eXVpBXUk7h7nKCh9mGFBVptIqIoFWkERUZQasI/76W59GRzasjuMJ5HTp37kxOTg55eXnhLkWkVrGxsXTu3DncZYiIiIhIE1cVdHyzpYivthSRV1xGnh+080rLyffDd0VV8DvrRUdGkJ4YQ1rraDq1jWVIlzakt44hPdG7pbaOIS4qklZ+6I6KNFpFevdR+4XwyAhr0Y2iCud1iIqKonv37uEuQ0RERERE5KhyzrE2bzefr83ns9X5zF1XQHFZAAAzSE2IqQndPdMTvLAdErrbJcaQ3jqWpLhWLTpQH00K5yIiIiIiIi3AtqK9zF5TwOdr8pm9Np/c4nIAOrWN48yBHRjVK41hmSm0S4yhVTPrMt4UKJyLiIiIiIg0Q0V7KpmzLp/ZawqYvTafdXm7AUhJiGZkz1RG9UxjVK9UuqbEq/W7EVA4FxERERERaQbKKqtYsKHQax1fm8/XW4pwDuKjIxnePYUrhnVlVK80+nVIJCJCYbyxUTgXERERERFpggJVQb7aUuR1U19TwKKNO6moChIVaWR1SebOcb0Z1SuNwZ3bEt1K3dQbO4VzERERERGRJiKvpJz3vtnGrFV5zFtXSEm5N4hb/4wkrj2xGyf2SmN4ZgoJMYp6TY3eMRERERERkUasaG8l05du563FW/l8bT5BB91S4zlvSEdG9UxjZM9UUhKiw12mHCGFcxERERERkUZmb0UVH67I5a3FW5m5Mo+KqiDdUuP58Sm9OH9wR3q3Twx3iXKUKZyLiIiIiIg0ApVVQT5dncdbi7fywbJcdldU0S4xhqtO6Mb5QzoyuHMbjarejCmci4iIiIiIhEkw6Ji3vpC3lmzlvW+2sWtPJW3iojh/SEfOG9yREd1TidTI6i2CwrmIiIiIiMgx5Jzj6y1FvLV4K//+ahvbi8uIi4rkjAHtOX9wR07una7R1VsghXMREREREZFjYM2OEt5avJW3lmxlQ8EeoiKNMX3ace85x3Hace2Ij1Y8a8n07ouIiIiIiDSQnJ17eHvJNt5aspXl24qJMBjZM5XbxvbkzAEZtImPCneJ0kgonIuIiIiIiBwleyuqWLx5Fws3FPLJqjwWbtwJQFbXttx/Xn/OGZRBu8TYMFcpjZHCuYiIiIiIyGHKLy1n4YadLNxQyIKNO1m6pYhA0AFwXEYS94zvy3mDOtI1NT7MlUpjp3AuIiIiIiJSD8451ufvZuGGnSzYUMiijTtZl78bgOhWEQzp3JYfju7BsMxkvtc1mbbx0WGuWJoShXMREREREZFaVASCLN1a5LWMbyxk4YadFOyuAKBtfBTZ3VK4bFgXhmUmM7BTG2JaRYa5YmnKFM5FRERERESA4rJKvti4k0UbvZbxxZt3UVYZBKBbajxj+qYzLDOFYZnJ9EhrTYSuPy5HkcK5iIiIiIi0OM45Nhbs4cvNO/li4y4WbtzJiu3FOAeREUb/jCSuGN6VYZkpZHdLpl2SBnGThqVwLiIiIiIizV7R3kqWbN7F4s27+HLTThZv3sXOPZUAxEdH8r2uydw5rjfDMlMY0qUtCTGKSnJs6RMnIiIiIiLNSqAqyKrcUr7cvJMvN3mBfM2OUgDMoFd6a07v356srskM6dKWPu0TiVQXdQkzhXMREREREWnSdhSX8cWmb1vFv8opYm9lFQApCdFkdWnLBYM7ktU1mUFd2pAUGxXmikW+S+FcRERERESajLLKKpZuLeLLTbtqWsW37NoLQFSkd6745cO6MKRLW7K6tqVrSjxmahWXxk/hXEREREREGpVAVZBtRWVs2bWXLTv31twv317Msq3FBIIOgE5t4xjStS3Xj8okq2syAzomERuly5lJ06RwLiIiIiIix1RZZdV3gnf1fc7OPWwvLsPP3zXSE2PomZ7ATSf3IKtrW7K6tNUI6tKsKJyLiIiIiMhRVVJWuU/oztnpB29/Wn5p+T7LR0YYHZJi6ZQcxwk9UumUHEentnF0So6jc3I8GW1i1SIuzZ7CuYiIiIiIHDbnHJsL9zJvfQHz1xcyb30hmwr37LNMdKsIL2y3jWNcv3Z0To7bJ4B3SIqlVWREmI5ApHFQOBcRERERkXpzzrE2b7cfxL1Avq2oDIC28VEMz0xhwvAudEmOrwnhaQkxROhSZSJ1UjgXEREREZEDCgYdK3NLmLeugPkbCpm/vpD80grAOw98RPcURnRPYXj3VHq3a60QLnKYFM5FRERERKRGoCrI0q3FNV3UF2wopGhvJeCNjj66dzrDu6cwokcqmam6TJnI0aJwLiIiIiLSgpUHqvg6p4h5fhhftKGQ3RVVAHRPS+DMAR0Y0SOF4d1T6JwcH+ZqRZovhXMRERERkRakKuj4KmcXs1blM3ddAV9s2kl5IAhAn/at+f73Onst491TdKkykWNI4VxEREREpJnLKyln1qo8PlmVx6er89i5pxIzGNAxiStHdGNEjxSGZaaQkhAd7lJFWiyFcxERERGRZiZQFeTLzbv4ZGUeM1ft4JstxQCktY7mlH7tGNu3HSf3SiNZYVyk0VA4FxERERFpBrYXlTFrlRfGP12dT0lZgMgI43td23LP+L6M6ZNO/4wkjaYu0kgpnIuIiIiINEEVgSCLNu5k5qodfLIyjxXbSwBonxTD2QMzGNM3nVG90mgTFxXmSkWkPhTORURERESaiC279jJzpRfGP19bQGl5gKhII7tbCr88qx9j+6bTt32iLm8m0gQ1WDg3s+eBc4EdzrmB/rSJwA+BPH+xe51z7/rzfgXcCFQBdzjnpvvThwJTgDjgXeBO55wzsxjgJWAoUABc7pzb4K9zLfBrfx8POedebKjjFBERERFpKMVllXy1ucgL5KvyWL2jFPCuN37+kI6M7ZPOib3SaB2jNjeRpq4hv8VTgKfwAnSoPznn/hA6wcz6AxOAAUBHYIaZ9XHOVQFPAzcDc/HC+ZnAe3hBfqdzrpeZTQB+B1xuZinA/UA24IBFZvaWc25nwxymiIiIiMjhKy0PsCF/NxsKdrMhfzfr8/fUPC7YXQFAdGQEI3qkcPmwLoztm07P9NZqHRdpZhosnDvnZplZZj0XvwB4zTlXDqw3szXAcDPbACQ55+bsoPvYAAAgAElEQVQAmNlLwIV44fwCYKK//jTgKfP+hRoPfOCcK/TX+QAv0L96FA5LREREROSQ7S4P+IH72+C9ocAL4vml5fss2yEplsy0eE7v357MtAT6tk9kRI8U4qPVOi7SnIXjG/4TM7sGWAj8zG/R7oTXMl4tx59W6T/efzr+/WYA51zAzIqA1NDptayzDzO7Ga9Vnq5dux7ZUYmIiIhIi7a3oiokeO/xWsH95ztK9g3g7RJjyExL4NR+6WSmJdA9NYHMtAS6pcYrhIu0UMf6m/808CBed/MHgceAG4Da+uS4OqZzmOvsO9G5Z4FnAbKzs2tdRkRERESkNpVVQb7ctItZq/KYtTqPr7cU4UL+okxrHUP3tHjG9PECeGZqAplp8WSmJpCgc8RFZD/H9F8F51xu9WMzew74t/80B+gSsmhnYKs/vXMt00PXyTGzVkAboNCfPna/dWYerWMQERERkZZrc+EePlmVx6xVecxZW0BJuXct8awubbn91N70btea7n4LeGKsLmEmIvV3TMO5mWU457b5Ty8CvvEfvwX8zcz+iDcgXG9gvnOuysxKzOwEYB5wDfBkyDrXAnOAS4CP/FHcpwOPmFmyv9wZwK8a+thEREREpPnZXR5gztoCZq3O49PV+azP3w14o6WfO7gjY/qkMbKnriUuIkeuIS+l9ipeC3aameXgjaA+1syG4HUz3wDcAuCcW2pmU4FlQAD4sT9SO8BtfHsptff8G8BfgL/6g8cV4o32jnOu0MweBBb4yz1QPTiciIiIiEhdgkHHsm3FzFrttY4v2riTyipHXFQkJ/RI4ZqR3RjdJ50eaQkaLV1EjipzTqdag3fO+cKFC8NdhoiIiIgcY/ml5Xy6Oo9Zq/L5dHUe+aXe5cv6dUhkTJ90RvdJJzszmZhWkWGuVESaIjNb5JzLPthyGolCRERERFqUikCQRRt31rSOL91aDEBKQjQn9UpjdJ90RvdOo11SbJgrFZGWROFcRERERJo95xwLN+5kyucbmLliB7srqmgVYXyvazI/P6MPo/ukM7BjGyIi1FVdRMJD4VxEREREmq2KQJB3vt7K859t4OstRbSJi+KCrE6M6ZPOiT1TNaK6iDQaCuciIiIi0uzkl5bzt3mb+OvcjeSVlNMzPYGHLxrIRVmdiI/Wn8Ai0vjoXyYRERERaTaWbyvmhdnr+efirVQEgozpk84Nl3bn5F5p6rIuIo2awrmIiIiINGnBoOOjFTt4fvZ6Pl9bQFxUJJdld+a6E7vTq13rcJcnIlIvCuciIiIi0iSVlgd4Y+Fmpny+gY0Fe8hoE8svz+rHhGFdaBsfHe7yREQOicK5iIiIiDQpmwv3MOXzDUxdsJmS8gBDuyVzz/i+jB/QgajIiHCXJyJyWBTORURERKTRc84xb30hL8xezwfLcokw45xBGVw/qjtDurQNd3kiIkdM4VxEREREGq3yQBVvL9nG85+tZ9m2YpLjo7htbE+uPiGTDm1iw12eiMhRo3AuIiIiIo1Ofmk5L8/dyMtzN5JfWkGf9q357feP58KsTsRGRYa7PBGRo07hXEREREQajTU7SvnLZ+v4+xdbqAgEObVfO24Y1Z1RvVIx06XQRKT5UjgXERERkbByzjF/fSHPfbqOGct3ENMqgkuGdubGk7rTM12XQhORlkHhXERERETCIlAVZPrSXJ6dtZYlOUUkx0dx57jeXD2yG2mtY8JdnojIMaVwLiIiIiLH1G7/+uR/mb2ezYV7yUyN56ELB3Lx9zoTF63zyUWkZVI4FxEREZFjYkdJGS9+voGX526iaG8lQ7sl8+tz+nPace2JjND55CLSsimci4iIiEiDWp1bwuRP1/Pml1uoDAYZ378DPxzdnaHdUsJdmohIo6FwLiIiIiJHnXOOueu8Qd4+WrGD2KgILh/WhRtP6k5mWkK4yxMRaXQUzkVERETkqAlUBXn3m+08N2sdX28pIjUhmp+e3oerTuhGSkJ0uMsTEWm0FM5FRERE5IiVlgd4fcFmnv9sPVt27aVHegK/+f7xXJTVidgoDfImInIwCuciIiIicthyi8uY8vkGXpm7keKyAMMzU5h4/gDG9WtHhAZ5ExGpN4VzERERETks7y/dzu2vfkllVZCzBmZw08ndyeqaHO6yRESaJIVzERERETlk/1q8hZ9OXcKgzm14/PIhdEvVIG8iIkdC4VxEREREDsmr8zdx75tfM6J7CpOvHUbrGP1JKSJypPQvqYiIiIjU218+W8+D/17GKX3TefqqoRrsTUTkKFE4FxEREZGDcs7x1EdreOyDVZw1sANPTMgiulVEuMsSEWk2FM5FREREpE7OOX73n5U888lavv+9Tvz+4kG0ilQwFxE5mhTORUREROSAgkHHxLeX8tKcjVx1QlceOH+gLpEmItIAFM5FREREpFaBqiC//MfXTFuUwy2je/DLs/phpmAuItIQFM5FRERE5DsqAkHufn0x73y9jbtP68Md43opmIuINCCFcxERERHZR1llFT965Qs+WrGD+84+jh+O7hHukkREmj2FcxERERGpsbs8wA9fWsicdQU8fNFArhzRLdwliYi0CArnIiIiIgJA0d5Krn9hPktyivjjZYO5KKtzuEsSEWkxFM5FREREhILScq55fj6rckv48w++x5kDO4S7JBGRFkXhXERERKSFyy0u48rJ89hcuIfnrslmbN924S5JRKTFUTgXERERacE2F+7hysnzKCgt58UbhnNCj9RwlyQi0iIpnIuIiIi0UOvySrly8jx2lwd4+aYRZHVNDndJIiItlsK5iIiISAu0YnsxV02ej3OO124eSf+OSeEuSUSkRVM4FxEREWlhlmzexTXPzycuKpJXfngCPdNbh7skEZEWT+FcREREpAWZt66AG19cSHJCFH+76QS6pMSHuyQREQEiwl2AiIiIiBwbH63I5doX5tM+KYY3bjlRwVxEpBFRy7mIiIhIM+ec4y+freeRd5fTv2MSU64fTlrrmHCXJSIiIRTORURERJqxikCQX//za6YuzOGsgR147LLBxEfrT0ARkcZG/zKLiIiINFOFuyu49eVFzF9fyB2n9uKu0/oQEWHhLktERGqhcC4iIiLSDK3KLeHGFxeQW1zOExOGcMGQTuEuSURE6qBwLiIiItLMfLQilzteXUxcdCRTbxnJkC5tw12SiIgchMK5iIiISDNRPfDbw+8uZ0DHJJ67JpuMNnHhLktEROpB4VxERESkGQgd+O3s4zvwh0s18JuISFOif7FFREREmriC0nJue/kL5m/QwG8iIk2VwrmIiIhIE7ZyuzfwW15JOZOuyOL8wR3DXZKIiBwGhXMRERGRJuqjFbnc/rcvSYhpxesa+E1EpElTOBcRERFpYpxzTP50PY+8p4HfRESaC4VzERERkSakPFDFr9/8hjcWeQO/PXbpEOKiI8NdloiIHCGFcxEREZEmoqC0nFtfXsSCDTu5Y1xv7hrXWwO/iYg0EwrnIiIiIk1A6MBvT16RxXka+E1EpFlROBcRERFp5D5cnssdr3oDv029ZSSDNfCbiEizE9FQGzaz581sh5l9EzLtUTNbYWZfmdmbZtbWn55pZnvNbLF/eyZknaFm9rWZrTGzSWZm/vQYM3vdnz7PzDJD1rnWzFb7t2sb6hhFREREGpJzjmdnreWmlxbSI701b/3kJAVzEZFmqsHCOTAFOHO/aR8AA51zg4BVwK9C5q11zg3xb7eGTH8auBno7d+qt3kjsNM51wv4E/A7ADNLAe4HRgDDgfvNLPloHpiIiIhIQysPVPFf077ikXdXcPbADKbeMpIObWLDXZaIiDSQBgvnzrlZQOF+0953zgX8p3OBznVtw8wygCTn3BznnANeAi70Z18AvOg/ngaM81vVxwMfOOcKnXM78X4Q2P9HAhEREZFGq6C0nKsmz+ONRTncOa43T16RpRHZRUSauXCec34D8HrI8+5m9iVQDPzaOfcp0AnICVkmx5+Gf78ZwDkXMLMiIDV0ei3r7MPMbsZrladr165HejwiIiIiR6Qq6PjX4i08On0lhbsrNPCbiEgLEpZwbmb3AQHgFX/SNqCrc67AzIYC/zSzAUBt1wZx1Zs5wLy61tl3onPPAs8CZGdn17qMiIiISENzzvHRih38/j8rWZlbwoCOSfzf1UMZ1Fnnl4uItBTHPJz7A7SdC4zzu6rjnCsHyv3Hi8xsLdAHr9U7tOt7Z2Cr/zgH6ALkmFkroA1eN/ocYOx+68xsoMMREREROSILNxTyu/+sYMGGnWSmxvPkFVmcc3yGrl8uItLCHNNwbmZnAr8Axjjn9oRMTwcKnXNVZtYDb+C3dc65QjMrMbMTgHnANcCT/mpvAdcCc4BLgI+cc87MpgOPhAwCdwb7DjwnIiIiEnYrthfzh+krmbF8B+mJMTx04UAuH9aFqMiGHK9XREQaqwYL52b2Kl4LdpqZ5eCNoP4rIAb4wL8i2lx/ZPbRwANmFgCqgFudc9WDyd2GN/J7HPCefwP4C/BXM1uD12I+AcAP9A8CC/zlHgjZloiIiEhYbS7cw59mrOLNL7fQOqYV94zvy/WjMomPDudQQCIiEm7m9yxv8bKzs93ChQvDXYaIiIg0UwWl5Tz18RpembsJDK4/MZNbx/QkOSE63KWJiEgDMrNFzrnsgy2nn2hFREREGlBpeYDJn67juVnr2FtZxaVDu3DX6b3JaBMX7tJERKQRUTgXERERaQDlgSr+Nm8TT320hoLdFZw5oAM/H9+XXu1ah7s0ERFphBTORURERI6iYNDxryVbeOz9VeTs3MvIHqn84qx+DOmiy6KJiMiBKZyLiIiIHAXOOT5e6V2rfMV271rlj1x0PCf3TsMfCFdEROSAFM5FREREjtCijYX87r2VzN9QSLfUeCZdkcW5ula5iIgcAoVzERERkcO0cnsJj05fyYzluaQnxvDghQOZoGuVi4jIYVA4FxEREamH/NJyVm4vYcX2ElZsK2ZlbglfbymidbSuVS4iIkdO/4OIiIiIhCirrGLNjlKWbyv+NoxvLyG/tLxmmbTWMfTrkMjtp/bm+hMzda1yERE5YgrnIiIi0iIFg46cnXtZsb2YFdtL/CBezPr83QSdt0xMqwj6dkjklL7p9MtIol+HRPp2SCStdUx4ixcRkWZH4VxERESavaI9lTUhfIUfwldtL2F3RRUAZtA1JZ5+HRI5Z1BH+nVIpF+HRLqlJhCpQd1EROQYUDgXERGRJi9QFWRbURmbC/ewqXAPG/376ue79lTWLNs2Pop+HRK5NLtLTUt4n/aJJMTozyIREQkf/S8kIiIiTULR3sqasL1pv/C9ZedeAtV90YGoSKNzcjxdUuIZ1LkNXVPi6dM+keMykmiXGKPrjouISKOjcC4iIiKNQqAqyNZdZbWG702FeyjaW7nP8ikJ0X74bsu5gzLomuKF8a4p8WS0iVN3dBERaVIUzkVERCRsivZWMnPlDmYs38HMlTsoKQvUzIuKNLr4rd9DurTdJ3x3SYkjMTYqjJWLiIgcXQrnIiIickxtKtjDjOW5zFiey/z1hQSCjtSEaM4a2IHsbil0TfUCePukWLV+i4hIi6FwLiIiIg0qGHQsydnlBfJlO1iZWwJA73at+eHoHpx2XHuGdGmrIC4iIi2awrmIiIgcdXsrqvhsTT4zluXy4Yod5JeWExlhDM9M4f+d25/TjmtHt9SEcJcpIiLSaCici4iIyFGxo6SMj5bvYMbyXD5dnU95IEhiTCvG9E3n9P7tGdunHW3idZ64iIhIbRTORURE5LA451iVW8qM5bl8sCyXxZt3AdCpbRxXDO/Kace1Z3j3FKJbRYS5UhERkcZP4VxERETqrSromL++kOlLt/Philw2F+4FYHCXtvz8jD6c1r89fdsn6jriIiIih0jhXEREROoUqAoyf30h73y9jelLt5NfWkFMqwhO6pXGj8b2Yly/drRLig13mSIiIk2awrmIiIh8R6AqyNx1XiB/f+l2CnZXEBcVyan92nH28Rmc0i+d+Gj9GSEiInK06H9VERERAaCyKsjcdQW8+/U2pi/NpXB3BfHRXiA/5/gMxvZtR1x0ZLjLFBERaZYUzkVERFqwyqogn68t4N2vtvH+su3s3FNJQnQk445rz9nHZzC2bzqxUQrkIiIiDU3hXEREpIWpCASZvTbfD+S5FO2tpHVMK047rh1nHZ/BmD4K5CIiIseawrmIiEgLUBEIMntNfs055MVlARJjWnF6//acdXwGJ/dOUyAXEREJI4VzERGRZqo8UMVnq71A/sGyXErKAiTGeoH8nOMzOKl3GjGtFMhFREQaA4VzERGRJqassoq8knLySsvJ9+/zSsrJ9++r5+0oLqc8ECQpthXjB3TgnOMzOLFXqgK5iIhII6RwLiIi0ghUVgUpKK3YN2Tvd18dxEvKArVuIyUhmvTWMaQlRjO0azLtkmIZ2TOVUT3TiG4VcYyPSERERA6FwrmIiMgxtKciwIrtJSzbWsyybcUs21rMpsI9FO6uqHX5xNhWpCfGkN46huM6JjG6dUzN8/RE75bWOobU1tFERSqAi4iINFUK5yIiIg3AOUdeSTlL/QC+bFsxy7cWs75gN855yyTFtqJ/xyTOGtiBdomxftCO3id0a5A2ERGRlkHhXERE5AgFqoKsz9/ttYT7YXz5tmLyS79tDe+SEkf/jCQuGNKJ/h2T6N8xiY5tYjGzMFYuIiIijYXCuYiIyCEoLQ+wcnvxPt3SV2wvoTwQBCA6MoI+HVpzar929M9I4riMJPplJNEmLirMlYuIiEhjpnAuIiJyEBWBIC/N2cAr8zaxIaRbetv4KAZ0TOLqE7rVtIb3TG+tc79FRETkkCmci4iI1OHjFTt48N/LWJe/m5E9Uvl+1rfd0jskqVu6iIiIHB0K5yIiIrVYm1fKQ/9exscr8+iRlsAL1w3jlH7twl2WiIiINFMK5yIiIiGKyyp58sPVvDB7A3FRkfz6nOO4ZmSmrhMuIiIiDUrhXEREBKgKOqYt2syj01dSsLuCy7O78LMz+pKeGBPu0kRERKQFUDgXEZEWb8GGQv7n7aV8s6WY7G7JTLl+OAM7tQl3WSIiItKCKJyLiEiLtXXXXn773greWrKVjDaxTLoii/MGZWiQNxERETnmFM5FRKTFKaus4tlZ63h65lqCznHHuN7cOqYH8dH6b1FERETCQ3+FiIhIi+Gc492vt/PIu8vZsmsv5xyfwS/P6keXlPhwlyYiIiItnMK5iIi0CMu2FvM/by9l3vpCjstI4rHLBnNCj9RwlyUiIiIC1COcm9mdwAtACTAZyAJ+6Zx7v4FrExEROWKFuyt47P2VvDp/E23ionj4ooFMGNaVyAidVy4iIiKNR31azm9wzj1hZuOBdOB6vLCucC4iIo1WZVWQv87ZyOMzVrG7ooprT8zkrnF9aBMfFe7SRERERL6jPuG8umnhbOAF59wS0zC2IiLSiM1alccD/17Gmh2lnNw7jf8+tz+92yeGuywRERGRA6pPOF9kZu8D3YFfmVkiEGzYskRERA5deaCKn05dwjtfbaNbajzPXZPNace106XRREREpNGrTzi/ERgCrHPO7TGzVLyu7SIiIo1GoCrI7X/7kveX5fKz0/tw85gexLSKDHdZIiIiIvVy0HDunAuaWS7Q38w0uruIiDQ6waDjv6Z9xfvLcpl4Xn+uG9U93CWJiIiIHJL6jNb+O+ByYBlQ5U92wKwGrEtERKRenHPc/9ZS/vHlFn5+Rh8FcxEREWmS6tMSfiHQ1zlX3tDFiIiIHKrfT1/JX+du5JbRPfjxKb3CXY6IiIjIYYmoxzLrAF13RkREGp0/f7yGp2eu5coRXfnlWf008JuIiIg0WQdsOTezJ/G6r+8BFpvZh0BN67lz7o6GL09ERKR2L83ZwKPTV3LBkI48eMFABXMRERFp0urq1r7Qv18EvLXfPNcw5YiIiBzc3xfl8N//Wsppx7XnD5cOJiJCwVxERESatgOGc+fciwBmdqdz7onQeWZ2Z0MXJiIiUpv/fLOde6YtYVSvVJ76QRZRkfU5Q0tERESkcavPXzTX1jLtuqNch4iIyEHNWpXHHa9+yeAubXn26mxio3QdcxEREWkeDhjOzewKM3sb6G5mb4XcPgYKDrZhM3vezHaY2Tch01LM7AMzW+3fJ4fM+5WZrTGzlWY2PmT6UDP72p83yfyTCs0sxsxe96fPM7PMkHWu9fex2sxq+3FBRESamIUbCrn5rwvp2a41U64bTkJMfS44IiIiItI01NVy/jnwGLDCv6++/Qw4sx7bnlLLcr8EPnTO9QY+9J9jZv2BCcAAf53/NbPq5pCngZuB3v6teps3Ajudc72APwG/87eVAtwPjACGA/eH/gggIiJNzzdbirj+hQV0bBPHSzcMp028LiIiIiIizcsBw7lzbqNzbqZzbqRz7pOQ2xfOucDBNuycmwUU7jf5AuBF//GLeNdQr57+mnOu3Dm3HlgDDDezDCDJOTfHOeeAl/Zbp3pb04Bxfqv6eOAD51yhc24n8AH1+zFBREQaoTU7Srjm+fkkxUXx8k0jSE+MCXdJIiIiIkfdQc85N7MTzGyBmZWaWYWZVZlZ8WHur71zbhuAf9/On94J2ByyXI4/rZP/eP/p+6zj/1hQBKTWsa3aju1mM1toZgvz8vIO85BERKShbC7cw1WT5xNhxss3jaBj27hwlyQiIiLSIOozINxTwBXAaiAOuAl48ijXUds1cFwd0w93nX0nOvescy7bOZednp5er0JFROTYyC0u48rJ89hbWcXLNw2ne1pCuEsSERERaTD1uv6Mc24NEOmcq3LOvQCccpj7y/W7quPf7/Cn5wBdQpbrDGz1p3euZfo+65hZK6ANXjf6A21LRESaiMLdFVw1eR4FpeVMuX4Y/TokhbskERERkQZVn3C+x8yigcVm9nszuxs43OaLt/j20mzXAv8KmT7BH4G9O97Ab/P9ru8lftd6A67Zb53qbV0CfOSflz4dOMPMkv2B4M7wp4mISBNQUlbJtc/PZ1PhHiZfO4ysrhrTU0RERJq/+lyH5mq8EP8T4G68VumLD7aSmb0KjAXSzCwHbwT13wJTzexGYBNwKYBzbqmZTQWWAQHgx865Kn9Tt+GN/B4HvOffAP4C/NXM1uC1mE/wt1VoZg8CC/zlHnDO7T8wnYiINEJ7K6q4ccpClm8r5tlrhjKyZ2q4SxIRERE5JsxrbD7IQmZxQFfn3MqGLyk8srOz3cKFC8NdhohIi1URCPLDlxYya3UekyZkcd7gjuEuSUREROSImdki51z2wZarz2jt5wGLgf/4z4eY2VtHXqKIiIgnUBXkrte/5JNVefzmouMVzEVERKTFqc855xOB4cAuAOfcYiCz4UoSEZGWJBh0/PIfX/Pu19v59TnHMWF413CXJCIiInLM1SecB5xzRQ1eiYiItDjOOR749zKmLcrhznG9uenkHuEuSURERCQs6jMg3Df2/9m77zC5yrLx498nW9J774Q0AgQSCAlFEaX3Ir2+CuIrKNXX9tP3VVFUQBAEURTFklDEgihVEFDBBEihhhBaGqSH9LK7z++Pc5ZMls1mtsyeLd/Pde21c56ZueeeM2eec+7znHMmhDOBohDCSOAS4OnCpiVJag1ueHQOdzz9Nud/ZBiXHTIy63QkSZIyk8/I+ReA3YBNwJ3AauCyQiYlSWr5bnvqDW56fC6nTRjM148eQ/KLmZIkSa3TDkfOY4zrgf+X/kmSVG93TpvH1Q/M5ug9+nP1SWMtzCVJUqtX48h5COG8EML0EMK69O+5EMK5jZWcJKnluX/WIr72pxc5aHRvbjh1HEVtLMwlSZK2O3KeFuGXAVcA04EA7AVcG0IgxvibxklRktRSPD57MZffPZN9durBrWftTWlxPmdXSZIktXw1bRVdBJwYY/xHjPH9GOOqGOPjwCfT+yRJytt/3lzO5343nTH9u3D7eRNoX1qUdUqSJElNRk3FeZcY49tVG9O2LoVKSJLU8rywYBUX/Po5BvfowK8/PZHO7UqyTkmSJKlJqak431DH+yRJ+sDri9dw3i+n0a1DCb87fxI9OpZmnZIkSVKTU9PV2seEEF6opj0AOxcoH0lSCzJ/xXrOvn0qxUVtmHzBJPp1bZd1SpIkSU1SjcV5o2UhSWpxFq/eyFm/mMrGLRXc89n9GNqzY9YpSZIkNVk1Fee3AQ8BD8YYZzdSPpKkFmDlus2cc/tUlq/dxOTP7Mvofp2zTkmSJKlJq+mc8/OAlcA30986vzWEcHwIoVMj5SZJaobWbirjv341jbeXr+fn501g3OBuWackSZLU5G135DzG+B5wB3BHCKENMAk4EvhSCGED8EiM8ZpGyVKS1Cxs3FLO+Xc8y0uLVvOzs/dm/+G9sk5JkiSpWajpsPYPxBgrgGfSv/8NIfQCDi9kYpKk5mVLeQUXT57OtLdX8KPTxnHIrn2zTkmSJKnZqOmwdgBCCNeEELqEEEpCCI+FEJYBR8QYJzdCfpKkZqC8InLlPbN4bPYSrjp+d44fNzDrlCRJkpqVHRbnwGExxtXAMcACYBTwPwXNSpLUbMQY+d/7XuIvsxbx5SN24ex9h2adkiRJUrOTT3Fekv4/CrgzxriigPlIkpqZHzz0GpOnzuNzBw3ncwcNzzodSZKkZimfc87vDyHMBjYAF4UQegMbC5uWJKk5+MkTc/npk29w9r5D+NLho7NOR5Ikqdna4ch5jPErwH7AhBjjFmA9cHyhE5MkNW2/feZtrnnoNY4fN4BvH7c7IYSsU5IkSWq28rkgXAfgYuDWtGkAMKGQSUmSmrY/zVjAN+57mUPG9OG6U/akTRsLc0mSpPrI55zzXwGbgf3T6QXAdwqWkSSpSXv0lcV88fcvsN/OPbn5zL0oKcpnVSJJkqSa5LNFNTzGeOvoZg0AACAASURBVA2wBSDGuAFwiESSWqGn5y7j4inT2X1gV35+3gTalRRlnZIkSVKLkE9xvjmE0B6IACGE4cCmgmYlSWpyZsxbyQW/eY5hPTvy60/tQ6e2+VxTVJIkSfnIZ8vq/4CHgMEhhMnAAcB/FTIpSVLTMvu91fzXr56ld+e2/Pb8iXTrUJp1SpIkSS3KDovzGOOjIYTpwL4kh7NfGmNcVvDMJElNwtvL1nHO7dNoX1LE786fRJ8u7bJOSZIkqcXJ52rtBwAbY4x/A7oBXwshDC14ZpKkzC1Zs5GzfjGVsvIKfnfBRAb36JB1SpIkSS1SPuec3wqsDyHsCfwP8A7wm4JmJUnKXHlF5LK7ZrJ83SZ+8+lJjOjTOeuUJEmSWqx8ivOyGGMEjgduijHeCLiFJkkt3E2Pvc7Tbyzn28fvzthBXbNOR5IkqUXL54Jwa0IIXwXOBg4MIRQBJYVNS5KUpX+9voybHn+dT+41iFMnDM46HUmSpBYvn5Hz00h+Ou38GON7wEDg2oJmJUnKzJLVG7ns7hmM6N2Jq07YLet0JEmSWoV8rtb+HnB9zvQ8POdcklqksvIKvnDnDNZtKufOz+xFh1J/y1ySJKkx5HO19n1DCM+GENaGEDaHEMpDCO83RnKSpMb1o7+/ztS3VvCdE3ZnZF8vLyJJktRY8jms/WbgDOB1oD1wAXBLIZOSJDW+J+cs5ZYn5nLqhEF8cu9BWacjSZLUquR1vGKMcW4IoSjGWA78KoTwdIHzkiQ1ovfe38jld89kVJ/OfOu43bNOR5IkqdXJpzhfH0IoBWaGEK4B3gU6FjYtSVJjKSuv4JI7Z7BxSzm3nLUX7UuLsk5JkiSp1cnnsPZz0sd9HlgHDAY+WcikJEmN54ePzmHa2yu4+sSxjOjTKet0JEmSWqXtjpyHEHoDvWOMr6RNG4FvhRB2B7wgnCS1AP+YvYRbn3iDMyYO5oTxA7NOR5IkqdWqaeT8x0DvatoHAjcWJh1JUmNZtGoDl98zkzH9u/B/x/p75pIkSVmqqTgfG2N8smpjjPFhYI/CpSRJKrQt5RV8fsp0tpRVcMuZ42lX4nnmkiRJWarpgnAldbxPktTEXffwa0yft4qbzhjPzr09z1ySJClrNY2cvx5COKpqYwjhSODNwqUkSSqkx15dzM+eepOzJg3huD0HZJ2OJEmSqHnk/HLgryGEU4Hn07YJwH7AMYVOTJLU8BasXM8V98xitwFd+MYxu2adjiRJklLbHTmPMc4BxgJPAjulf08Ce6T3SZKakc1lFXx+ygzKKyK3nLmX55lLkiQ1ITWNnBNj3AT8qpFykSQV0A8ems3M+av4yVl7sVOvjlmnI0mSpBw1nXMuSWohHn75PW7/11uct99QjhrbP+t0JEmSVIXFuSS1cPNXrOd/fj+LsQO78rWjx2SdjiRJkqqx3eI8hPBY+v8HjZeOJKkhJeeZTycCt5y5F22LPc9ckiSpKarpnPP+IYSPAceFEO4CQu6dMcbpBc1MklRvVz/wKrMWvM9Pz96bIT07ZJ2OJEmStqOm4vx/ga8Ag4Drq9wXgU8UKilJUv09+OK73PH023zqgJ04Yvd+WacjSZKkGmy3OI8x3gvcG0L4RozxqkbMSZJUT+8sX8eX7n2BPQd15atHep65JElSU1fjT6kBxBivCiEcBxyYNj0RY/xrYdOSJNXVxi3lXDxlOiHAzWfuRWmx1/6UJElq6na4xRZC+B5wKfBK+ndp2iZJaoKufuBVXlq4mutO2ZPBPTzPXJIkqTnY4cg5cDQwLsZYARBC+DUwA/hqIROTJNXeX19YxG+eeYcLPjKMw3bzPHNJkqTmIt9jHbvl3O5anxcMIYwOIczM+VsdQrgshPDNEMLCnPajcp7z1RDC3BDCayGEw3Pa9w4hvJjed1MIIaTtbUMId6ftU0MIO9UnZ0lqDt5ato6v/OFFxg/pxpeP3CXrdCRJklQL+Yycfw+YEUL4B8nPqR1IPUbNY4yvAeMAQghFwELgT8CngBtijNflPj6EsCtwOrAbMAD4ewhhVIyxHLgVuBD4D/AAcATwIHA+sDLGOCKEcDrwA+C0uuYsSU3dxi3lXDR5OsVFgZvP3IuSIs8zlyRJak52uPUWY7wT2Bf4Y/q3X4zxrgZ6/YOBN2KM79TwmOOBu2KMm2KMbwFzgYkhhP5AlxjjMzHGCPwGOCHnOb9Ob98LHFw5qi5JLdG37n+ZV99dzfWn7snAbu2zTkeSJEm1lNfQSozx3RjjX2KM98UY32vA1z8duDNn+vMhhBdCCL8MIXRP2wYC83MesyBtG5jertq+zXNijGXA+0DPqi8eQrgwhPBcCOG5pUuXNsT7kaRG96cZC7hz2nw+d9BwPrFL36zTkSRJUh1kdtxjCKEUOA74fdp0KzCc5JD3d4EfVj60mqfHGtpres62DTHeFmOcEGOc0Lt371pkL0lNw+uL1/C1P77ExJ16cOWho7JOR5IkSXWU5UmJRwLTY4yLAWKMi2OM5elV4X8OTEwftwAYnPO8QcCitH1QNe3bPCeEUExyEbsVBXofkpSJ9ZvLuGjydDqUFnHTGeMp9jxzSZKkZqvGLbkQQpsQwksFeu0zyDmkPT2HvNKJQOXr/gU4Pb0C+zBgJDAtxvgusCaEsG96Pvm5wH05zzkvvX0y8Hh6XroktQgxRr7+55eYu3QtPzp9HP26tss6JUmSJNVDjVdrjzFWhBBmhRCGxBjnNdSLhhA6AIcCn81pviaEMI7k8PO3K++LMb4cQrgHeAUoAy5Or9QO8DngDqA9yVXaH0zbbwd+G0KYSzJifnpD5S5JTcHvn1vAH6cv5JKDR/LRkZ6WI0mS1NyFHQ0ohxAeB/YBpgHrKttjjMcVNrXGNWHChPjcc89lnYYk7dCr767mhFv+zd5Du/Pb8ydR1MYfo5AkSWqqQgjPxxgn7Ohx+fzO+bcaIB9JUgNYu6mMiydPp0v7Em48fbyFuSRJUguxw+I8xvhkCGEoMDLG+Pf0kPSiwqcmScoVY+Srf3yRt5evY/IF+9K7c9usU5IkSVID2eGlfUMInwHuBX6WNg0E/lzIpCRJHzZ56jzun7WIKw8bzX7De2adjiRJkhpQPr+7czFwALAaIMb4OtCnkElJkrb10sL3+fb9r/CxUb353MeGZ52OJEmSGlg+xfmmGOPmyon0d8P9WTJJaiSrN27hosnT6dGxlBtOG0cbzzOXJElqcfIpzp8MIXwNaB9COBT4PXB/YdOSJEFynvmXfv8Ci1Zt4JazxtOjY2nWKUmSJKkA8inOvwIsBV4k+e3xB4CvFzIpSVLiV/9+m4defo8vHTGavYf2yDodSZIkFUg+V2uvCCH8GphKcjj7a3FHP44uSaq3GfNWcvUDr3LImD585qM7Z52OJEmSCmiHxXkI4Wjgp8AbQACGhRA+G2N8sNDJSVJrtWr9Zj4/ZQZ9u7Tjh6eMIwTPM5ckSWrJdlicAz8EPh5jnAsQQhgO/A2wOJekAqioiFx5zyyWrNnI7/97f7p2KMk6JUmSJBVYPuecL6kszFNvAksKlI8ktXo//+ebPDZ7Cf/vqDGMG9wt63QkSZLUCLY7ch5COCm9+XII4QHgHpJzzk8Bnm2E3CSp1Xn27RVc8/BrHDW2H+ftv1PW6UiSJKmR1HRY+7E5txcDH0tvLwW6FywjSWqllq/dxOenTGdQ9/Z8/5N7eJ65JElSK7Ld4jzG+KnGTESSWrOKishld89k5fot/PFz+9ClneeZS5IktSb5XK19GPAFYKfcx8cYjytcWpLUutzyj7n88/VlfPfE3dl9YNes05EkSVIjy+dq7X8GbgfuByoKm44ktT5Pv7GMG/4+h+PHDeDMiUOyTkeSJEkZyKc43xhjvKngmUhSK7RkzUYuuXMmw3p15OoTx3qeuSRJUiuVT3F+Ywjh/4BHgE2VjTHG6QXLSpJagfKKyKV3zmTtpi1MvmASHdvm0yVLkiSpJcpnS3AscA7wCbYe1h7TaUlSHd349zk88+Zyrj15D0b365x1OpIkScpQPsX5icDOMcbNhU5GklqLJ+cs5cf/mMvJew/ilAmDs05HkiRJGWuTx2NmAd0KnYgktRaLV2/k8rtnMqpPZ646fves05EkSVITkM/IeV9gdgjhWbY959yfUpOkWiqviFx+90w2bC7nlrPG0760KOuUJEmS1ATkU5z/X8GzkKRW4mdPvcHTbyzn+yeNZUQfzzOXJElSYofFeYzxycZIRJJauhnzVnL9I3M4emx/TtvH88wlSZK01Q6L8xDCGpKrswOUAiXAuhhjl0ImJkktyZqNW7jkrhn07dKOq0/y98wlSZK0rXxGzrc57jKEcAIwsWAZSVILE2Pk639+iYUrN3DPZ/eja/uSrFOSJElSE5PP1dq3EWP8M/7GuSTl7Y/TF3LfzEVcdsgoJuzUI+t0JEmS1ATlc1j7STmTbYAJbD3MXZJUg7eWreMb973ExGE9uPjjI7JOR5IkSU1UPldrPzbndhnwNnB8QbKRpBZkc1kFl9w5g5KiNvzotHEUtfE8c0mSJFUvn3POP9UYiUhSS3PdI6/x4sL3+enZezOgW/us05EkSVITtt3iPITwvzU8L8YYrypAPpLUIjw1Zym3PfUmZ00awhG798s6HUmSJDVxNY2cr6umrSNwPtATsDiXpGosW7uJK+6Zxai+nfjGMbtmnY4kSZKage0W5zHGH1beDiF0Bi4FPgXcBfxwe8+TpNasoiJy5T2zWL1xC7+7YCLtSoqyTkmSJEnNQI0/pRZC6BFC+A7wAkkhv1eM8csxxiWNkp0kNTO//PdbPDlnKd84egy79OuSdTqSJElqJmo65/xa4CTgNmBsjHFto2UlSc3QSwvf5wcPzebQXfty9r5Ds05HkiRJzUhNI+dXAgOArwOLQgir0781IYTVjZOeJDUP6zaVccmdM+jZsS3XfHIPQvBn0yRJkpS/ms45r/GQd0nSVt+6/2XeWr6OyRdMonvH0qzTkSRJUjNjAS5J9XT/rEXc89wCLj5oBPsP75V1OpIkSWqGLM4lqR7mr1jP1/74IuOHdOPSQ0ZmnY4kSZKaKYtzSaqjLeUVXHLXDABuOn08JUV2qZIkSaqb7Z5zLkmq2Y1/f50Z81bx4zPGM7hHh6zTkSRJUjPmMI8k1cEzbyznlifmcsregzh2zwFZpyNJkqRmzuJckmpp5brNXH73TIb17Mg3j9st63QkSZLUAlicS1ItxBj50h9eYPm6Tdx0xng6tvXsIEmSJNWfxbkk1cLv/vMOj76ymC8fsQu7D+yadTqSJElqISzOJSlPs99bzVV/e5WPjerNpw8YlnU6kiRJakEsziUpDxu3lHPJnTPo0q6E607ZkzZtQtYpSZIkqQXxZElJysN3/vYKcxav5Tefnkjvzm2zTkeSJEktjCPnkrQDD730Hr/7zzwuPHBnDhzVO+t0JEmS1AJZnEtSDRat2sCX//ACYwd25YuHjc46HUmSJLVQFueStB3lFZHL757JlvIKbjpjPKXFdpmSJEkqDM85l6Tt+Pk/32TqWyv44Sl7MqxXx6zTkSRJUgvmMJAkVWPV+s3c8vhcDhnTh5P2Gph1OpIkSWrhLM4lqRo/ffJN1m4u438O34UQ/Nk0SZIkFZbFuSRVsWT1Ru54+i1OGDeQ0f06Z52OJEmSWgGLc0mq4sePz6WsPHLZISOzTkWSJEmtRCbFeQjh7RDCiyGEmSGE59K2HiGER0MIr6f/u+c8/qshhLkhhNdCCIfntO+dxpkbQrgppMeehhDahhDuTtunhhB2auz3KKl5mrd8PXdOm8dp+wxmaE8vAidJkqTGkeXI+cdjjONijBPS6a8Aj8UYRwKPpdOEEHYFTgd2A44AfhJCKEqfcytwITAy/TsibT8fWBljHAHcAPygEd6PpBbgR4/NoahN4AufcNRckiRJjacpHdZ+PPDr9PavgRNy2u+KMW6KMb4FzAUmhhD6A11ijM/EGCPwmyrPqYx1L3Bw8IpOknZgzuI1/GnGQs7bfyf6dW2XdTqSJElqRbIqziPwSAjh+RDChWlb3xjjuwDp/z5p+0Bgfs5zF6RtA9PbVdu3eU6MsQx4H+hZNYkQwoUhhOdCCM8tXbq0Qd6YpObr+kfm0LG0mP/+2PCsU5EkSVIrU5zR6x4QY1wUQugDPBpCmF3DY6sb8Y41tNf0nG0bYrwNuA1gwoQJH7pfUusxa/4qHnr5PS47ZCQ9OpZmnY4kSZJamUxGzmOMi9L/S4A/AROBxemh6qT/l6QPXwAMznn6IGBR2j6omvZtnhNCKAa6AisK8V4ktQzXPfIa3TuUcP5HhmWdiiRJklqhRi/OQwgdQwidK28DhwEvAX8Bzksfdh5wX3r7L8Dp6RXYh5Fc+G1aeuj7mhDCvun55OdWeU5lrJOBx9Pz0iXpQ555Yzn/fH0ZF398BJ3blWSdjiRJklqhLA5r7wv8Kb0+WzEwJcb4UAjhWeCeEML5wDzgFIAY48shhHuAV4Ay4OIYY3ka63PAHUB74MH0D+B24LchhLkkI+anN8Ybk9T8xBi57pHX6NelHWfvOzTrdCRJktRKNXpxHmN8E9izmvblwMHbec53ge9W0/4csHs17RtJi3tJqsk/XlvC8++s5OoTx9KupGjHT5AkSZIKoCn9lJokNaqKisi1D89haM8OnDJh0I6fIEmSJBWIxbmkVuuvL77Lq++u5opDR1FSZHcoSZKk7Lg1KqlV2lJewfWPvMYu/Tpz7B4Dsk5HkiRJrZzFuaRW6Q/PL+Dt5eu58rDRtGkTsk5HkiRJrZzFuaRWZ+OWcm587HXGDe7GIWP6ZJ2OJEmSZHEuqfWZPHUe776/kS8dPpr0Zx0lSZKkTFmcS2pV1m4q4yf/mMsBI3qy/4heWacjSZIkARbnklqZX/3rLZav28wXDxuddSqSJEnSByzOJbUaq9Zv5ran3uTQXfsyfkj3rNORJEmSPmBxLqnV+OmTb7J2c5mj5pIkSWpyLM4ltQpLVm/kjqff4oRxAxndr3PW6UiSJEnbsDiX1Crc/I+5lJVHLjtkZNapSJIkSR9icS6pxZu/Yj13TpvHafsMZmjPjlmnI0mSJH2IxbmkFu+Gv8+hTQh84ROOmkuSJKlpsjiX1KLNWbyGP81YyHn770S/ru2yTkeSJEmqlsW5pBbt+kfm0LG0mP/+2PCsU5EkSZK2y+JcUos1a/4qHnr5PS746DB6dCzNOh1JkiRpuyzOJbVY1z3yGt07lHD+R4ZlnYokSZJUI4tzSS3SM28s55+vL+Oig0bQuV1J1ulIkiRJNbI4l9TixBi57pHX6NulLefsNzTrdCRJkqQdsjiX1OL847UlPP/OSi45eCTtSoqyTkeSJEnaIYtzSS1KRUXk2ofnMKRHB06dMDjrdCRJkqS8WJxLalH+9uK7vPruaq44dBQlRXZxkiRJah7ccpXUYpSVV3D9o3PYpV9njttzQNbpSJIkSXmzOJfUYtz7/ALeWraOKw8bTZs2Iet0JEmSpLxZnEtqETZuKefGx15n3OBuHDKmT9bpSJIkSbVicS6pRZg8dR7vvr+RLx0+mhAcNZckSVLzYnEuqdlbu6mMn/xjLgeM6Mn+I3plnY4kSZJUa8VZJyBJdRVj5KGX3uN7D85m+brNfPGw0VmnJEmSJNWJxbmkZmnm/FV892+v8OzbKxnVtxO/PX8i44d0zzotSZIkqU4sziU1KwtXbeCah2Zz38xF9OpUytUnjuXUCYMo9jfNJUmS1IxZnEtqFtZs3MKtT7zBL/71FgG4+OPD+dxBI+jU1m5MkiRJzZ9btZKatLLyCu5+bj43PDqHZWs3c+L4gXzx8NEM7NY+69QkSZKkBmNxLqnJeuK1JVz9wKvMWbyWiTv14PbzxrDn4G5ZpyVJkiQ1OItzSU3O7PdW892/vco/X1/G0J4d+OnZe3H4bv38/XJJkiS1WBbnkpqMJWs2csOjc7j72fl0blfCN47ZlXP2HUppsRd7kyRJUstmcS4pcxu3lPOLf77JrU+8waayCv5r/2FccvAIunUozTo1SZIkqVFYnEvKTEVF5L5ZC7n2oddY9P5GDt+tL185cgzDenXMOjVJkiSpUVmcS8rEtLdW8J2/vcILC95n7MCu3HDaOCbt3DPrtCRJkqRMWJxLalRvL1vH9x+czUMvv0f/ru244bQ9OX7PgbRp48XeJEmS1HpZnEsqmIqKyKL3NzB3yVrmLlnLK++u5v5ZiygtasMXDxvF+R/ZmfalRVmnKUmSJGXO4lxSvW0uq+Dt5euYu2QtbyxZy9ylSTH+5tJ1bNhS/sHjunUo4eS9B3P5oSPp07ldhhlLkiRJTYvFuaS8rd64hTeWrOWNpes+GA1/Y+la5q1YT3lF/OBxA7u1Z3ifTkwa1pMRfToxvHdHRvTpRM9ObTPMXpIkSWq6LM4lEWNkc3kFGzaXsz79W7J64wcj4JVF+OLVmz54TklRYKeeHdmlX2eO2aN/WoR3YufeHelQatciSZIk1YZb0FIzUlERWb+lnHWbyli3qYz1m5Pb67eU5xTWZR8U2BvS2xs2l7Mu5/b6zeVs2JI+dlM567eUbzPynatT22KG9+nER0b0Znifjozo3YkRfToxpEcHiovaNPIckCRJklomi3OpwNZvLmP52s2s21zGuk1pMb25jLWbkuK4si25v4x1lQX3pnLWVnns+s3lO37BHO1LiuhQWkSHtkV0KCmmfWky3a1DCe1Li+lQkt5XWkSH0uIPHt++tIhendoyok8n+nRuSwheSV2SJEkqJItzqZ7WbSpjwcoNLFy1ngUrN6R/W2+vWLd5hzHaBOjYtpiOpcV0bFv0we0B3drRsW0xHUqL6dS2KP1fTIe2RXRqmxTTHdtuLbo7lm693a64yJ8nkyRJkpoJi3NpB9Zs3MLCVRtYsGJr0b1w1dYifOX6Lds8vrS4DYO6t2dgt/bsNqArg7q3p3fntklRXVqU/t+2yG5b3MbRaUmSJKkVsziXUgtWrufRVxZvM/K9cNUGVlUpvtumxfeg7h3YY1BXBnXvwMDu7dO29vTq2NYRa0mSJEm1YnGuVm/x6o3c/Phc7np2HlvKI+1LipKR7+7tGT+kG4O6d/igGB/UvT09O5Y6yi1JkiSpQVmcq9VatnYTP33iDX77n3cor4icts9g/vtjwxnUvb3FtyRJkqRGZXGuVmfV+s38/J9v8qt/v83GLeWctNcgLj14JIN7dMg6NUmSJEmtlMW5Wo01G7fwy3+9zS/++SZrN5dx7B4DuPSQkQzv3Snr1CRJkiS1chbnavHWby7j10+/w8+eeoNV67dw+G59ufzQUezSr0vWqUmSJEkSYHGuFmzjlnKmTJ3HT56Yy7K1mzlodG+uOHQUewzqlnVqkiRJkrQNi3O1OJvLKvj98/P58WNzeW/1RvYf3pOfnTOKvYf2yDo1SZIkSapWm8Z+wRDC4BDCP0IIr4YQXg4hXJq2fzOEsDCEMDP9OyrnOV8NIcwNIbwWQjg8p33vEMKL6X03hfQS2yGEtiGEu9P2qSGEnRr7farxlZVXcO/zCzj4+if4f396iYHd2zPlgklM+cy+FuaSJEmSmrQsRs7LgCtjjNNDCJ2B50MIj6b33RBjvC73wSGEXYHTgd2AAcDfQwijYozlwK3AhcB/gAeAI4AHgfOBlTHGESGE04EfAKc1wntTBioqIn998V1+9Pc5vLl0HbsP7MK3P7U7B43q7U+iSZIkSWoWGr04jzG+C7yb3l4TQngVGFjDU44H7ooxbgLeCiHMBSaGEN4GusQYnwEIIfwGOIGkOD8e+Gb6/HuBm0MIIcYYC/CWlJEYI4+8spgbHp3D7PfWMLpvZ352zt4ctmtfi3JJkiRJzUqm55ynh5uPB6YCBwCfDyGcCzxHMrq+kqRw/0/O0xakbVvS21XbSf/PB4gxloUQ3gd6AsuqvP6FJCPvDBkypAHfmQopxsgTc5Zy/SNzeHHh++zcqyM3nTGeY8b2p00bi3JJkiRJzU9mxXkIoRPwB+CyGOPqEMKtwFVATP//EPg0UF21FWtoZwf3bW2I8TbgNoAJEyY4qt4MlFdEPnXHszw1ZymDurfnulP25IRxAyguavTLJ0iSJElSg8mkOA8hlJAU5pNjjH8EiDEuzrn/58Bf08kFwOCcpw8CFqXtg6ppz33OghBCMdAVWNHw70SN7anXl/LUnKVcdshILjpoBKXFFuWSJEmSmr8srtYegNuBV2OM1+e098952InAS+ntvwCnp1dgHwaMBKal566vCSHsm8Y8F7gv5znnpbdPBh73fPOWYcrUefTqVGphLkmSJKlFyWLk/ADgHODFEMLMtO1rwBkhhHEkh5+/DXwWIMb4cgjhHuAVkiu9X5xeqR3gc8AdQHuSC8E9mLbfDvw2vXjcCpKrvauZe+/9jTw+ewmf+ejOFuaSJEmSWpQsrtb+L6o/J/yBGp7zXeC71bQ/B+xeTftG4JR6pKkm6O5n51NeETlj4uAdP1iSJEmSmhGHH9UslFdE7n52Hh8d2YuhPTtmnY4kSZIkNSiLczULT7y2hEXvb+TMif7knSRJkqSWx+JczcKUqfPo3bkth+zaN+tUJEmSJKnBWZyryVu0agP/eG0Jp04YRIm/Zy5JkiSpBbLSUZN317PzicDp+3hIuyRJkqSWyeJcTVpZeQV3PzuPA0f2ZnCPDlmnI0mSJEkFYXGuJu3x2UtYvHoTZ05y1FySJElSy2VxriZtyrR59O3SloN36ZN1KpIkSZJUMBbnarLmr1jPk3OWctqEwRR7IThJkiRJLZgVj5qsu5+dTwBO87fNJUmSJLVwFudqkraUV3D3c/M5aHQfBnZrn3U6kiRJklRQFudqkh57dTFL12ziTEfNJUmSJLUCFudqkiZPnUf/ru04aHTvrFORJEmSpIKzOFeTM2/5rqjQZQAAIABJREFUev75+jJO28cLwUmSJElqHax81OTc+ew82gQ4bZ/BWaciSZIkSY3C4lxNyuayCn7/3Hw+sUtf+nf1QnCSJEmSWgeLczUpj76ymGVrN3PWJC8EJ0mSJKn1sDhXkzJl2jsM7NaeA0d5IThJkiRJrYfFuZqMt5et499zl3P6PoMpahOyTkeSJEmSGo3FuZqMO6fNo6hN4FQvBCdJkiSplbE4V5Owqayc3z+/gIN36UPfLu2yTkeSJEmSGpXFuZqER15ezIp1mznTC8FJkiRJaoUsztUkTJk6j0Hd23PgSC8EJ0mSJKn1sThX5t5cupZn3lzOGROH0MYLwUmSJElqhSzOlbk7p82juE3glAmDsk5FkiRJkjJhca5MbdxSzr3PL+DQXfvSp7MXgpMkSZLUOlmcK1MPv/weK9dv8UJwkiRJklo1i3NlavLUeQzp0YEDhvfKOhVJkiRJyozFuTIzd8kapr21wgvBSZIkSWr1LM6VmSlT51NS5IXgJEmSJMniXJnYuKWcP0xfwGG79aNXp7ZZpyNJkiRJmbI4VyYeePFd3t+whbMmeiE4SZIkSbI4VyamTJ3HsF4d2W94z6xTkSRJkqTMWZyr0c1ZvIbn3lnJGRMHE4IXgpMkSZIki3M1uilT51Fa1IaT9x6cdSqSJEmS1CRYnKtRbdicXAjuiN370aNjadbpSJIkSVKTYHGuRvXXFxaxZmMZZ07yQnCSJEmSVMniXI1qyrR5DO/dkUnDemSdiiRJkiQ1GRbnajSvvruaGfNWccbEIV4ITpIkSZJyWJyr0UyZOo/S4jacvPegrFORJEmSpCbF4lyNYv3mMv48YyFHj+1Ptw5eCE6SJEmSclmcq1HcP2sRazZ5IThJkiRJqo7FuRrFlKnzGNmnExOGds86FUmSJElqcizOVXAvLXyfWQve58xJXghOkiRJkqpjca6CmzJtHm2L23DSeC8EJ0mSJEnVsThXQa3dVMZ9MxZyzB4D6NqhJOt0JEmSJKlJsjhXQf1l5iLWbS73QnCSJEmSVAOLcxXUlGnvsEu/zuw1pFvWqUiSJElSk2VxroJ5YcEqXlq42gvBSZIkSdIOWJyrIBat2sANj86hXUkbThg/MOt0JEmSJKlJK846AbUc5RWRp15fyuT/vMPjs5cQgcsOHkWXdl4ITpIkSZJqYnGuelu6ZhP3PDefO6fNY8HKDfTqVMrnDhrO6fsMYXCPDlmnJ0mSJElNnsW56iTGyH/eXMHkqe/w8MvvsaU8st/OPfnqkWM4dNe+lBZ7xoQkSZIk5cviXLXy/vot3Dt9AZOnvsObS9fRtX0J5+63E2dMHMKIPp2yTk+SJEmSmiWLc+1QjJGZ81cxeeo87p+1iE1lFYwf0o3rTtmTY/boT7uSoqxTlCRJkqRmzeJc27VuUxn3zVzE5Knv8PKi1XQoLeKTew/irElD2G1A16zTkyRJkqQWo0UX5yGEI4AbgSLgFzHG72ecUrPw6rurmTz1Hf48YxFrN5WxS7/OfOeE3Tl+3AA6e+V1SZIkSWpwLbY4DyEUAbcAhwILgGdDCH+JMb6SbWZN08Yt5Tzw4rtMnjqP599ZSWlxG47Zoz9nTRrKXkO6EULIOkVJkiRJarFabHEOTATmxhjfBAgh3AUcDzTb4vy199bw+OwlDR538eqN/HnmQlat38LOvTry9aPHcPLeg+jWobTBX0uSJEmS9GEtuTgfCMzPmV4ATMp9QAjhQuBCgCFDhjReZnX08qL3+cFDsxs8bnGbwOG79eOsSUPYb3hPR8klSZIkqZG15OK8ugozbjMR423AbQATJkyI1Ty+STluzwEcNbZ/g8ctahMoKfJ3ySVJkiQpKy25OF8ADM6ZHgQsyiiXBlFc1IZif7VMkiRJklqcljxc+iwwMoQwLIRQCpwO/CXjnCRJkiRJ+pAWO3IeYywLIXweeJjkp9R+GWN8OeO0JEmSJEn6kBZbnAPEGB8AHsg6D0mSJEmSatKSD2uXJEmSJKlZsDiXJEmSJCljFueSJEmSJGXM4lySJEmSpIxZnEuSJEmSlDGLc0mSJEmSMmZxLkmSJElSxizOJUmSJEnKmMW5JEmSJEkZsziXJEmSJCljFueSJEmSJGXM4lySJEmSpIxZnEuSJEmSlDGLc0mSJEmSMmZxLkmSJElSxizOJUmSJEnKWIgxZp1DkxBCWAq808BhewHLGjhmoeIWKtdCxW5u+TbHuIWMbdzCxi1E7OaUq3EbJ24hYxu3sHELEbs55WrcxolbyNjGLXxs+4iGjTs0xth7Rw+yOC+gEMJzMcYJzSFuoXItVOzmlm9zjFvI2MYtbNxCxG5OuRq3ceIWMrZxCxu3ELGbU67GbZy4hYxt3MLHto8obM7b42HtkiRJkiRlzOJckiRJkqSMWZwX1m3NKG6hci1U7OaWb3OMW8jYxi1s3ELEbk65Grdx4hYytnELG7cQsZtTrsZtnLiFjG3cwse2jyhsztXynHNJkiRJkjLmyLkkSZIkSRmzOG/hQggh6xwkSVLz5baEpJrYRzQci/OWrzR3orl8eUIIrXLZbMzPp7XO4+oUal40ZNzm8t2F5pVrITXn71ihPsPmuGw4LwAozjoB5a+xlq1mtgyrsNplnUBtNdV1tOecZyiEMBjoTvI5zCpA/KOAi4BpwLIY40/S9hDr8cGHEMYB/YAX07ibGiDXMcA5wALg3zHGWfXNs0r8jkC7GOPyBoi1MzARWBljfDinvc75hhC6xRhXhRDaxRg3hhDaxBgr6ptrNa8zANgJeD3GuLQB444GjgLKgV8BG2OMWxog7jBgKVCczp8GmS8hhGOBA4ES4EcxxrfrG7NQcdNld0OMsaIhvxM58fcF1sQYX27AmJ1ijGtDCEUxxvIGitkX6BhjfLMh4lUTP8QYY0MsYyGEXsDqGOPmQn2XC6Xys0tvN1juIYT+JN/j+Q3ctxeqr9wdKKpcNzdUzoWIG0IYCHRL475Q3xyriX8I8BngNZJ1x28bIObuQB9gLvBuA60vRgJnACuAJ2KML9U3ZpX4HYDSGOOqBoo3DNiTZH35UAPF7BJjXJ2zLVGIdUY/YCCwMMb4XgPGHQkcQrL+/HED9hFDgOVASUN9dmncQ4GPAe2BaxtqXhQibjoP+gPEGKfWN1418Q8DLgFeAebEGH/RADH3JlnO3gJmN1AfMRr4NLAW+GuMcUZ9Y1aJ3wVoH2NcXJ84TXKPQWuQLiDPAJ8F/hBC+Hza8TdU/L2BG4FfA7OA80IItwKkG6B12tsZQjgeuIsk76uA74UQutcz16HA74Eykg2MR0IIhzdgx7wn8CDJfL4qLUbqGms48B9gHHBtGu8ISOZrHWOOIXnPPwNuDSHskhZiDfr9DCHsSrKj5mLg0RDCZ0MIuzRA3MHAk0ARcDTwXeD0EEL7esY9GngA+BFwRwhhUkPMlxDCPmnMqcBG4N4QwiEhhLZNLW66AXs3cFYIobg+393txD8YeBr4YQhhtwaKeSLwTgjhozHG8oZYjtPvyOvAFSGEsfVO8sPxR5H2ZfVdxtL5+BRwWwihfQMts2NCCBeFEDrVJ04er7Mr8GAI4WKAhuqH0rhPA78MIYxpqOU43Rn2jRBCt/rGqhL3GJK+8oIQwv5Qv/VmIeOmffjfgEuB+0MIZ9cnx2riHwncRLIOXQgclq5T6xPzWGAKcCXJ+uLQBshzV5Jtk84kRcjN6bZFg0jj/xn4Y7rer9eRBOnn9gCwP8n34ooGyHGXNL9fkPTpAwtQmO8CPEby2f2ncjlugD5uF+BPQFfgXOCn9Uy1Mu6RJNuXtwI/CsngSkPEPRq4FniHZGfCgw3RDxUibjpv/wqcAjwcQjipvnlWiX84cB3wO2AOML4BYh4J3AF8Evg2yTZ3fWOOAX4LrCapf/83hFCU3tcQ66MxwP3A70IIP6xXsBijf438R1LE/Aj4n3R6PPA48D9ApwZ6jb2A23OmO5CMdN9az7i/BI5Jb08EfkCysHevR8yjgHtzpk8m2ft2eDrdph6x2wOPAGcCA0g6kOuAT9Yx3jnAd9LbOwFfTuMdUcd4XUiK/fPTeF8EFgF71Pe9V3mdAHwT+Hw6fXT62f0fMKaesQ8DbktvlwAXANeTrGBL65hrP2Am8HGSlfX5JCPoH22AZeJM4Dc50xeQrLg+Xp/YDR2XZEfVsyQ7Pr5LMiJUXDmPGmCZaA9clsb9JskGzO71jDkS+DfJ0RPLgAMb4PMqTfO7Lf2ufbu+eVaJP4yk6HiSZGOge11zJuln/w78DLg6zbl9PZerQcDidH5+lgZaR1TzOj2Bl0h26E4BLsq5rz6fXzHJOuKnwH+TbICPaYC444GVJCOvlwNdG2g+tCPZOP4eSX/8PWC/phg3p588J50+JF3+etVn3ubE7wFMBg7LmZ4CHFuPmPsAr7J1Hfc14Lf1zLMzSWH+6XS6E3AzsH8DLRMj0/l8FjCcZEfTFfWcr88An0mn9wDeBMbVI+Zwku2mc4GPkKwzPpfe11DbEQNIB3vS6UuAFyr7uHrE7USy8+fidLotyU6s4+sZ90CSYnFfYHeSdcf56X11XocCfdN+7BM5bT8Hxtcz3waPS7KjalbO+76QZJ3fq4GWiV7AL4CD0umD0mX7bOCsOsbcn6RemZhO/wI4lWRboKSOMbuQ7DyonA97kqyXDqBh1kejgRlpH9ENmA3sVud4DfHh+FenD/LzJCPbndLpMSRF5CUNFH8syV7ZwTltHdOVyvl1jNmGZKP78znTOwHfT/9qXYilcUancYdWfjlICvQlwF71nA9dSXZ87JlO9wS+QLKRf2Ad4p2YfgEHptN9gS8B3wE61DHHnwCjqiwb7wK7NvAy9z1yNoJIVljfB75Q+XnWMe5uJBtb+6XTxSTF9I+A/vVY1n4C7JzTdna6TEys53wYlXbKY3PaPgNMz/2+ZB2XZIPzYJIN8IvS/uIM0pUTySGs9V0mBld+b0kKh3tINhTrtOEC9AZOTm+fB7zP1gK9Tvmmy8JIkp2aQ0kK3m+Rbtw3wDzYK/0eDEvnwW/YWqDXOuc03lBgF5IC4ba69g1pvI+ly/7eaV/2BXIK9Lp+b7fzWqemy8RhJBuJFzdQ3P5A5WGVX01j16t/I9nwPhQYATxKsqO0a879dV2GQ5pnSL/TV5HsaPlI1cdlHZdkB8V/58TvCzxMuuFd13lQ5TUmpX1Q5br5a8C36xFvDNvu+OmQfn596/GZlQInAd1y2m4FvtUA7z+QFLxfymnbH/hdXecxycb7yWnsyvn6a2DveuR4KfDlnLbPkrOzuCH+SHYAXFil7U/UY3AmjdEu/S63rexzSbYfTqvH/KicJ/+V035x5edWz3w7AYeTDEZUfn731re/LERcYFfSnWvp9BskR4BMJd150wDzo1/6vyfwBHALyTrrKeArdYg3DPhYersPMJ+knrmVZEd9rdenaR+xe870w2l+XyfZ+X1APefBqaQ729Lp6STbsF+mDtsq9f5Q/KvVh1eSc3siyUbbeLaOhu1Bcm5Fnfb2khS5H2FrwX8VyZ7UzjmPOQP4Yi3jFrG1IDiQZI/WUTn3fSRdseS9J45kz3H/ytgkG8TXkLOiIxld+GYDzPcvkRyWNyjnta8Crs7z+R2r5HUNySFdPdPpoSSj3yfXIbe26XLwzSrtl6edcr1GycgpLkg2fn4OnJrTdhjJHupBtYzbI50vlUXMl9N5WrkHspRkpf1/dcy7hOQojWurtF9IMnLTidptxI4kOXepN8nOg1uBr5CuVNLHXFfb5a0Qcav0E5XXBWnL1gL97LStThtEaT/x0dx+oUqu95CsZM+pfK18ljO2Fvkdc9rPZdsCfU+Saz/ku4wNoEqBTLJD8Gfp8taTpHio79EfndP/w9laoPdO23b4HUy/Cx8qkkkK9B8DP0+n+wJDa5lbW6BPensCWwv0rmlbnXaK5n521bSVsrVAr9x51wvoUou4lX1Etyrt/dPvyJ9Jdp4Oog4jhuky1y29Xblz+ys5bXnnmj5+ZJpLnyrto0l2vn4vXfZOBHbKMi7b9hG92Xb99GdgRHq7Zx2XidEk6/oPLfsk66afprdPIN0WyPPzquwjulS+D5JC9T/AkLRtGHmOjqXL2MAqbZVFzUXA19Lbe5Kzo7cO86MrW3fIF5GM/j9H2teRbsPlESe3bywip88AbgdOSm8PrcPy25lkp0/lOmMk8Jfqlpk6vP/c7YhepMVvOv0M6U6F9PPIa17kPL5DznzMnR9fJt2Jk36/B9Yibu56c2DO9B5se5Rmx3xj5szTwWy7E6hyefs6W9fNx1KL9XMh4lb9vNPl7STSHVZsPfLosHzzrBLvg+2InPnbk3QEPZ0+DvhBbZYzoG2V9/8F4NL09idItv/yPnIuXcYGVlm2RgNn5kxfTrK9We+dmWm8G0m230eT7Ki4qdYxGiIR//L6sEaRHDL5TeCUtO0qkhHjPdm6cfjd3IW7FvGPITkk8WGSQ2nHpu23Ai8Dw9PpK0gOXy3OZ0EkOeT8lyTntO2Ttp1Fshfr6JzHPUieI9EkI61PkGxkXkcy8tEN+BdJ4ds3fdx5wC11mBfD0+eeTnLo7tD0da5k6wq2D/A8OxjRTL9cj6fz8RckK6WPkxy2fQUwIH3cpeR51APJhthZwCHp9EBgHvDVdDqQbHD9nHocLpbm/rt0mbowbbswzf3knMf9glocpkhScDyTLhcPkmx4jiLZyPxWznLyyfS18xp9JOnoryQ5PDyQXCxxLjmdO0lx80tqsaGRfjempd+Ln6T57wTcR7IBULlh8UVqMdJSiLhs20+cWeW+9iRHVXybpIBcSu0Lvdx+4j7SI1PIKfBIRsZmk5zztsPDsti2j6h8z8VsXbmem+Z6I+kht3nEzO0jrslZpipHVXZO2+8E1gBH1nI+5PYRHXLaA0l/9EOSovpEktM/tlugU00fkXNfm/S9fCd9zGLSo3h2kN9OpH0EVXZmkBQGj6ffk6PSedCOuo3g5fYRn61meTuC5GiQn5H0l3ktb3y4jxhe5f5BJH3R8yQX5slr1IKtfcQZbC3CK5ez3UgK9M+Q9Md/IM8dF2z7Xf7Q4dDpfLoyne/rgF2yisvWPuJbwOlVlrWidL5Xbji/QDLqXZsdmdvrIyp30B+f5nxo+t5G5BGzso/4a068QNpPkJynWQqcli5vOzxFge33EZXLw1kkh12PTefDR2v53RgO/BfJebpVv4M9gEfS2wek86PGZY2t/cRPSXbIVxY0lfN1CknxtVc6/0flkeNObO0nSqvcNxKYmfO9qdPRjWnek0mO8jg/p71yR8s/0zz2Ifn+9ckzbmUf8Sty+gi29vHfIVnf7U2yHZDXod1sux3Rtcp9uwHPprfPJenn890RlPtdvoW0z8r5HK9Il99PkoxOD88qLtvZjqDKDh+SbYlan5LJdvqIah53BcmO7h3usGHb7YjtHjFL0q8flGee1fYR1TzubJILENZ2PlT2EaeRs61OWsOkt3uSHKUwrFaxa5uMf7X/Szu3V0g2GL5GzmHMJEXjrSSF+ikkG2+1Og+NZORoNls3jK8F7su5/yqSvel3p3nktdcJOJLkXJUjSQqMBenCWJQujC+TnLP6hfT2DkdfSTaqZ5HswRtKUihWFqU9SfY2/ZxkI/c14LhazotdSc7T/QnJSv5OkpX/4emX8xqSlcJuJHu++9UQa0T6vi4kuRjFHaQFOMmIwfdIzq+9iOTqsJ/II79d0s/qRyQbDJen7SPT+ft1ktHCj6af1U51XOZ2SeN/Jf2sniA5PLYbyaFdN5NscB+aLnN5HU5HshKuPK9tAPC/pKPbJIf6fZnkcJ4fkJwje3SecY8kueDXF0lGUb6StvdN268l2cj6FMlpBXkdpZHOx1dJzjcbRrJBWzkSOJqkAJtC0uHPIc/DjwoRlw/3Ez8m3SBh25GLW9N5e1ID9BN/yLk/d6N2FfkV5tX1EZWjdrmjK3emOeczH6rrI75YTZ7nklx475h69BG/ITlSIHfnRBuSwvRPwBZqOCKGD/cRv6zMlW331H+K5AiCHZ5DyYf7iNzTiCrf+0CS/nEFcGJt3n+V18ntIx6nmlHQ9P515HmdDj7cR3yDZCO4qMpyfEqaf147BvlwH3FlznJWuUHfheT6AfPzWda2813+JltH7HI/w++nsfM6HL8Qcam+j9grNybJBv6XSdZNtV1/1thHpG0fBzaTbHDucFuCGvqInMf8kmSd/zw5pwXVELPGPiJ9zKnpcjCDPNdDOc/dUR9RSrJ9cTxJkVLjfKbmfqJy2a0sZP5FPfuJ9H9fkh1v+6Xz4PDazIOc16ixjyD5bn+dpNDOqy+i5j6i8kjSs0kuPPcv8uzjqaGPSP8PIRmcOjtd1vI64oo8vsvp57cofd0G6yNqG5ca+ohqvusvAZNquUzk00e0Idn5PSOfeUwefUT6uJPI8zRB8ugj0sdNTGPWaicF1fcRbat53DiS04kH1Cp+bR7sX+3/SDrxX+V0Eh1JNtpzz186iqTz+x15HiJW5TXGk16gI+c1/kLOXrL0i78b6aFjecTsQrKH99ictmtIR/3T6YnpAn8zeYwGpc85lpzDydMYT7H1EPEOJHtgzyY9F4889/qTjLQ+zNaL4/RPvzTD0ulJJIe4TyfZcDm9hlhFJHvdL81pOxe4MWe6M8mhv1eSjoLvIL+qF4HZn5yLwJCssO4i2av+MnW8GArJKNoPybkYDMnK84Kcz3Zk+vneDJyQZ9xAMtJ4eU7b4Wx7mFgJyZ7us9l6DnqNnx/JCvNZ4NB0ehzJkQSV86UnSQd4czr/drjxlhP7rCr5Hkuyd7/yMLqu6eufSC1GoRs6LtvvJ67IeUwRySGFK9h6+GNtz089L2e6sp/oUKXt8nzmMdX3ET8gp49I2/Yl2ZDPt1jaXh/Rla0bWe1INjpOzVk28zkSqLo+4ldUGaVKc17C1otffig22+8jrs+ZLk5fczrphmtNebL9PmK3Ko8bQlIwbze/HcyH7fURn67yuKEkR1Acn8/rsP0+4v+3d95hd1TVGv+tFEISSggtNAlg6ISWAIHQQQIEBSWCgAFBAQUUEaQIinQvSDVYERC9CFKsFIULKCKKcKMXwQIil6YiF1EjLcm6f6w1nP1NTtlzvnPyfYes93nmOWfa2nt2eWdmzSrXl9pkccz66p3Jec3apRFHlNtlCqYEyg7EQ+O5vHhRL4zXrqBCDJROyyWDI3z7lZg1ybSqY4M8jtgOi4kyIUNeU47wOVR87X+OjK/wSVs24ojipWZ7Hws7V5wbTTnCx++ymDXQk8mYrNvOZPCEbzsBmE8t+F6/eMLHy2O+VFJO+PlNOYIaF3/b671Lq3oX+2nBEb5tOvBq7jgmgyOw58vnsBfz7JgXNJ/LQ5JtvwHWGSi55D1HjPT2fqTNcZHDEYX7VY6Cv+VzBDbnjvQ6Z3E7ec8RUzHLj6pKzNzniJ3IUN7VLaPqCbFUXzANy9rJZDsA+Gyd44qovrkvo+mXlMLEr7hxpEHLxlHRnMnlbIb59haa3fPr1buN9ljJf0dgfjZ3UTPrz/JHbSB3eewLVVFfwczlZpaOW5mMgDnY16nUh2tTr2vZBzb9CtJM3gjsa9GoROZXSUwdMVIeTs3/rt0AOVuSvBRipp431DlusZxykvqOwqwninG3nLdJMXYXMMPPkD0c+4I/nJrW/FpgUrmNyfTDo+QjV8jAvgR8v7yvQrt2Ra6fk8sTxUNip3miCFaV64bQiCMuKB23PBUDA9KcI4qYGosX7VChLRpxxEGl47YleShsJJ/GHCGl41bOqSsZHOHbplILmJN9/SUZuRyxUWbdW3FE0V+FT+GSufUngyN82yQyfYvJn8srpNc3UHL92JYcgb347VixzrkcsWJ6XRlyczliGhUzMNCcI0Zg99JJbbRxLkdcSb5lWEue8DkzOae+5D9L3EMbH3wSGS05AnsJ2iFTXjZH+JjZMLf/yHuOKGLh5H7Zzp3LY7CXzKzYPd2S6+fkcMTOVIxtRXWOyH1Wy+WIrchw9Sid04wjFvOyJ+SOsURuS47wY06lTQV6v/I0BhpDRIrotL9X1UdKu1/DiAkR2QL7snsdpiVEvSdbyN8bi/Y5T0S+pqp3Jrv/Dbyoqs+IyHuwwD7HeLmt5E7GBuw/VPUh31aMk9lJvfcDHlPVBzNkro+Zq4zCNLF/9et8VUSeA15V1X+KyDbA3iJyrqr+Xyu5ifylgddV9XkRuVotN+9wVX1dRGZ7exS5Hv+kqs8W55bbWkTGY+31IPCUqv4+2f0qMFYtd/O2mJnYqcAbMur1nYiMBub69d6g2iev7TDM5O0+z7/5qvfbU43kNWmHlTGTptnAL72eQ1R1PmYmNMSP2woj0htV9bVW5YjIW4EDRORh4FFVfdS3D/H6r6yqL4vI9sBhInIE8Eohs5Fsr++rqvoCFq03xTzML/WXXt8/A3/Cvgi1aodpwFQRUczE80Wvx3wReQHTHCMiM4HNReRk4OVWbd0NuRV4YjL20nFdaUy2aosqPLEbFhV2TguZuRzxbuAJVX0go55VOGIfETlbVYv2z+HLVhzxih+3DvCkqv6kOLdNjlDniGmq+gls/DabC7kcsQYwR1XvTc/P5YmKHLGaqn5LVf+nVRmZHPGKiGyH5fg+AuflFnJzOWJLrC9+mdkOleeyqv57IORWfJYYp6pfy2mDRH4VjthDRI5U1b+1kFnlOeJxVb0to55VOGJf4LRiPHSBI/6ImZHPERFpcN8fTx5PbIcpUz6N+RQ3q2MuT6yFmfTvqqqvNapjgzKqcMSyqvq95NyG5VTgiO2xF5/DVfXhzPrmcMQULJjxOzPHQ5W5vAX2tfrpgZBbkSNWVtVvt6pnSX4VjthdRD6oqv9oIbPKc8QfVfX+jHpW4Yh3YQGL/+DHdJIjJmDPq+f6/MmefwX/exz/AAAf3klEQVSGVDk4kAcfIL/AfEXuFJH3e2cVeAn4o2/7MmZmg5NfjvyJmL/z1zFN0CUicoiILKmqc1X1ZeAJESmCll2kqv/KkLs7ZgJzGHCeiFzq9Zrrh8wBXhGRGViwjhyZqwLFjXcjTLs7TUSW8G1Dgb+LyOGYL+3dWu3FfEPMr/c6ETkRMyNBVV/3QxQYIiKTMJ/79ZvIWgdrz8mY+fu5IrJbcsifgQdFZDPgIsysrNXD5YaY9vrLInINZgKU4jXgRRHZAPNZWaKVzAblFMGQjsci058rIssnY+pp4HERWQ8zIWrZdy53TaxNRmD+dceLyDFex/mYb/IvRGRXPNK3qua86L4DT7khIt8XkRV9uySHvSwib8fM2V/2MlvJnYz5Av43poU+GXsgGOWHDAH+5WPlY1jU4X8PhNyKPPEVnCcKZNS5Kk9cqK1fzKtwxNlA0xu0y6zKEXepv5jnoCJHXI+5/zSSVZUj7vOyGnJ7RY74FmbS+gYqPHRX5YiWfedyq3DEZ6lxxLwWcqtwxCzMtDanvm3N5YGQ28azxEvp+V3giPM7zBFnkadwrcoRd+Q88yTyq3DEDdgX3Tl+TL0X8yo8cSH2Av8GGsiswhPXYYHDWirfS2VU5Yi56flNXsyrPkfcUNS9hdwqHHEZZnWV8xJWdS7PUtVXB0JuGxyR/Xzt8qtyxAVdeI7oBkfcqaot5Sbyq3DEjZjF2Tw/ptLzPH5SLB1csMl1FjU/nX0wX+1TqZlPrI0FeHiIdnwRbFB8L1nfEfPfm+nrwzAflWfJ8wsrfN9uBt7j25bHbhhXJsfth70k/Zz8QBrTgf9M1o/BfKqnYxrCkZif0NNk+G2XZI/2Oh6Mmbx8EPN/Sn1VTsB8Sh6ghQkapq090/9PwPzJb8EDqWAvzi9gGrGW5myYOdKPvH6jsBtmnzQQWDC5mzAf+Czf7wZlXYj7FWHRY8/2sop0UBtj2vpKfkaY+dNF/n9FH2s3kkSmx3xqnqWWXq+VSd5K3idb+foXsQeezYpzsYBGP8ZuvFV8zPfFffl8HhzibXMwZuY3Cvvq8d9kRlzuhlx6jCcIjgiOaCw3OKILcgmO6EmO8OMr80SzOcFC4gmCI4IjFiwjOKILHNGyzP4KiKVuR34Wz2vr61N9whQTaH1My185hYGfvwIWOXwKNZ+HnbCoj0VQjhm5gzqReyp9c2APwyKaF2SyHhY5M1suZmpzNx7czbcdhaXmGOvr55IZCbgke3FMC1n4kIzFIjN+B0+thAXc+L+cyQgcUSKhMVhgs2swc7HRfi1ZE9vr912S6PuYL83XqeUHP9nHws6+3q6P+WeAE/3/ECyQ09lY+qpRWLC5x8iIKF+SuwcW8bLwMRqJkfNVWBCaoX492bkyvV2+R5LaBot2fiM1/6vTMHKu6mO0MXZjK4LRDcEelC5LruEiKuZV7oZcepAnCI4IjlhQbnBEl+QSHNFzHOHH9yRPEBwRHLGg/OCILnBEyzI7ISQWm2TJ/9UwM9R9km3TMTPoIlhCEaggN2DLhlgQkSLy+Jk+idekFvziA3g08bQ+rQZd8n8m5uuxbLJtFUwbW9xQcnIUL5seixHm8SQE6QP9stJ57dxMrgW+layPcbI7PWm37RrJx8ys0vyEt+KpwXx9PBb4ZYfi2ir220lOEGOTbd/Ag6r4WMkKAlNHtlAL0DEZ017unezb3Ou+mm/LCnyBRaFP63spppFe2teXx24mBxdtWLX+WIqPo0pj7Qo8GAqmFV8lU9ZamDZ+XNLmZ1ILJjMUyyF6csX27bhcepAnCI4IjlhQbnBEl+QSHNFzHFGMcXqQJwiOCI5YUH5wxIJl9Zsjqizhc94BiMjawFdF5AwRmamqT2EmJFPFAimgqt/H8h1P8tP+UkH+XlhKsDOA00RkR0ybuQxwNKYpAzMXGeXltfS/c7+PS0XkChGZqBZI5r+An4vIci7nGSzX71KqOkdbB4JZDzNZ+TJwka9fi5nW7CUWuAfMd7KPv4f6qG4hf00R2UdEDvRNhwBzReQCl/F3XDMpImNV9WFV/XE9+UldrxOR83zzR4BVi3VV/RPmHzbF9zcNQiUi40XkfSIyVUTGAndgQWF2FJExfth7/dhlVPUpzQiYVaec9bAb21UisrXLOB04XETeoYYHsRvkZD/tsWZ1T+T+ELhSRO7wzSdhwWW+JCJLqerzmNZ0cxEZivudtZA7VURO8r5bGfgBFqF392SsHYb5BI1S1ed87LVqh2kYoZ8LXCsib8E050OB/UVkqprfzx3AfLHgMy3RDbm9yBPBEcERDeQGRwRHFDIXaY4o1beneCI44g25wRE1+cERdJ4jKiPnDT6WptqUdTFznVMwovwxRr7LYrktZ2EmQTthAZ2qmsFshef2w0yBPoZFAATzWzoTC3zwQ8xnJzff+DTgtxjZfw4b1IVG7ALMxOQQLJjJo+Tlah6PaV33w6J9Hg98wvdNAc7BfFW+iPmnVMrjjZm6/BojiqdwbRhmtnMN5hs1FCOk+5vVGZvAs7GAFEX01aN834aYudR3gEO9rO0z6re+y7za63OGb5/h8g7FtJGTvE/H9WPMPYhpjI/DbvgbJGX9CjgWi1r5e0rphprIXROLZnqIr9+BBS0C00Bf6O3/cS8zywQN0+L+BjO1+lLSLntgpnofAXbBzIIexjXrGXK39uvbBstdeSrwad+3JZav+xGMvP9Cfn7MjsulB3mC4IjgiOCI4IjgiKZ1pkd5guCI4IjgiIXCEe0sHRW2qC2YH8LFwJG+PhTTOhaBEpbxTr4Si2y5TxtlbIcH6PD1DbCgA4WJzDCf+NuQb76zBBZQJPX5uLW4Dl8/0MnjG2TmHwXeA5ySrO8F/DBZX8Yn0vuALX1brpnNMk5E70/WH8BuUIKZmHwT00o+AuzbRNZQLHjKscm23UhyQbrMkzFyygkAsxzmI3OAr2/vbVr009uAT2HRJO9vVr8W5SyG3dzSQCqfBI5J1if7mLuCTP8av969S3LXIQnS4dv2w27cWT5nmEnXL6gFbdkWizhbmFlN8XF2MxbwJle5JD5GD0i2vRO4sXTcutjNYfWBkksP8gTBEcER9edGcERwRHBETV5P8gTBEcER9csIjugwR7S7dFzgorb4IF01WT8a+Gad4yr70yTnFoEihgHjnHxG+rbV2qz3Oj5xCg3W2cBH6xw3tKLctZL/y2G+NIv5+uh+tPPyxSRI6nw9JS00pkEs/GyaRT9dCyd2X9/FJ+DiDY5v5V81DIvguHSy7QYsp2axPsSJ7S39GAtDsNyXY4rzsZv+1Y36rQIprZCOJ8z35xE84Ew/+m7/tO8xM6NppWOGk6npTs5ZvDT3VgVuS9Yryeum3F7kieCI4Ig6soMjuiQ3OKLP8T3BEUVde40ngiP6nBMc0ffc4IgOc0Q7S/ictwkRy6Ooqj9V1aeTXU9i2hVEZEux/Iqo5ypU78kqUPNtQC0H4IvAXFV9WUTeC5wpIku1cQmPq+q/tJZX8Ek8b6aIvF1EtvVrzM0dW7TH42nVgZWB10VkO+BiEVm6OLYK1PyT7vL/RZ2fxnyhEM/rqKovqOpf/H/DtlbVx1U1zRv9B+DvqvqKX/tRpeMbyhKRoV6nL6jqSyIyzHf9i1qbrgmsrKovqer/tpLZpN7zgQdV9e/J+Q/gOYlFZCsR2UqTHMK55ajqX9X8l3DfpxeAl1X1RW+Ti9w3LAuF/5SqflNV5yTnzqfWb1NEZE1VfV1VX2okq0F9XynNveGYuRMi8j7gMhEZXnW8dVJuj/NEcERwRFl2cESH5QZH1NBrHFHUtdd4IjiiT32DI/rWOziiwxzRDuLlvE2UOyMZBE8DT4gFJ/gi8EoXip8jIhdjfkLnq+o/Wp1Q1C+p57zS+lBgiIi8E/PpeVodORVqcNyrwO8wM6eLgZv8htLWQFbVF7zOxbgdC4wQC/xwu7d5u3gBeEpEdgAuB1oGEUnqNc9/y9f1DPA3EVkfMzUaUz63HaQ3zGITRkpbYaZBS3egjPmq+iLwaxGZjvXfT+qU3VRGiRyL/08Bz4nInliglKw5Uh7DdYj3BeBhETkUOBL4jN+s+0ucbcvtJZ4IjmiJ4Ii+ZQRHdEBucEQNbwKOgB7hieCI4IhMBEd0niOyKhFLxYXmppCbYRPkN1RIRA+1dBaNyvNjhmMBFH5PZu7GtL6NzgHegd0A7iIz4EVy7mQ8vUadfT/DNGW7d6EfPo4Fl7gXmN5uH2JKqpWwfJhPALtl9PNqWICSEU2O+Qjmr/NLKgakqFB3waJn/g0LFNJWLsuiHUpyh2MBW57Hg7Y0a5OyjCbHnODz435gozbq2TDIifffw8D6bbTlCp2SmzF+BhVPBEe0bJPgCA2OCI54439wRP126RmeCI4IjmhWXnBEH9n94oi2ylwYhbxZlmTQLlFvu/+fgEXrzAp0kZyX5vubjgU8mIj7LZXKeH8zYmlSxsHAnZhJiZT2bQo8XoU0kuu9s8lE/BqwVz/afFNqEUSHlvYdgd0Ep5XbqEG/rUjfPKTpTWQ45te1W2a99sLSKOyD+7nUKe94zFRn+2b1yyxv85R0SuNhHBbZNZuUkjpOwKKrLlnaXuQ9vRjYNVPmWOAg//8BPH9pnePOwNKmrJVb36Ju2BeDZ+sRJBbk5kvAOm207wf8Wpfqr9xe5ongiOCIOnUMjqjVNzhiEeWIUr/1JE8ERwRH+HnBEQue3xGO6NTSVeFvxgVL2/AT4DRgl2T7Ahqj3A4EVsEiGE4B3oWlt/gWZr50Jh7cgAxtYpMytsGiexZRF8uDbzGqB9LYFDMtOryQUa89qrRFnTI+A9zeYN9kYHKOfCeg35Gk4Ci3Ka7xrNBv7wDuBvaldFP1/SOBndu9/oScN8XSbfyi3pgDRuA3qCrlYFFfn8QiTz4KrFunTZaq2CazMO3w/XiwjAbHZd1Q65WLpaw5DrvJDi21ReU5AszEHkrWrLNvSDtye5EngiOCI+rID47Q4IhE/iLNEX5MT/FEcERwRElmcET9MjrGEZ1Yul7Am2nBTEhuwvILnoulotg32T+8TbnLYOk4bsLyBy7h26dieQCLyIFVCDM1TVkCOMYH94fKx/SzTf4L+G2yXiniYqN6p+tYXsUpzerc7FqwaJGfx/J1bo3lKExTnVTuNywgS0Ged1O6qZZl9oMw9sDMzI7CHgZ+QKIdbLe9sRQZs6ilJjkFy026Wrtjzf9PxMyg7ir14RsPAG3Wd1ryfzKW6mKVfratYBFJL8JNBanzYNSG3J7gieCIPvuCIxaUGxwRHBEc0Xd/T/JEcMQbMoIjgiPq1qlTHNGpJQLCZUJEVsPC6T+kqtcAl2HmJFuLyP4Aqvp6RZlF1MEXsQAcP8C0WW/3Q+7Homdu6cdprtzk2BFqkRIvA84CNheRvQt57UQzFJGNRWSaiIxR1Z2Ax0XkLpc5T2oRRivD67SNiBwkIlP8Ov6KEUfDNmi0XURWxCb1SFW9FYtIehYwTkRm+blV+20l4CwROVxVb8GCWhwNTBeRxerJzO27OtgaOFNVZ2Ha0+uBY0VkZ5ebHVjF6z5UREZjN5KpeFBIVT0HuMPLaGus+bjaANOcPiMit4jIKN+/rst9NVdu8ev/PyoiN4vI4Zj/0/XAuSKyWJW2Tce7GuZivnA7iMhSqvqaH7eXiGyQKzeR3xM8ERzRp67BEX3rHhzhCI4Ijkjq28s8ERwRHBEckaAbHNFJxMt5JtRSQ3wHOEpEJqjqs5gpyDPANiIyroq8EhmthEU0vAo4GZgpItN8wj8HrCYiI3IGd0nuh4ErReQKEdleVb+ImTTtISL7+XVlk6f/7oRpi48DLheRo1R1T+BFEbnfZc5tLCkLS2K+UZ8UkU8CPwcOq0pyIrKyWqqDjwHbisi2Tmi/Bc4DVpQ2oi6qpU25FyOfQ5Kb6snAO9ohoTp1H+t/h2KaU/xaHgBeB44Ukc0qyCvqNExV5wDvA/4E7OgPHQD3YX412UjG2jHA6djN5FVVPQj4J3CtiHwUuEFEls2tazIuJ2BmVrthvlyrYBr1pYHVsZyqWSjNjWki8h4RWQP7ojAE2FNEVhSRGVgAkJZZEMroBZ4IjuhT5+CImrzgiOCIejIXaY7w+vYkTwRHBEfUqXNwRA0d44iOQxfC5/leW+hrPrMOsHGy7zSM2Nbx9XHA6v0o6wTgZuB2jOjGY8EH/oIFOPgOFSMautyjsGiIawDfxgh0L9/3EeBSSoEmMmRuCtySXPuuLqcIknAPsGU77e2/k7yM8b4+BtMYnoXl+Zzp21uas2ARU6+k5qPyXuAxYKqvD8F9a1rIWQX4nv9fA/h4su9AzAzmYF+fjpt39XP8reXtuh1mgvRV4ALfNxEj1c+TmDhltu80jODPxoLQrIqZNV2PPXTcD7y9jX5bw+UUPkYjkmNOwW6GG7bRDh/C/KGudfmFL9SePi7+F3hLG3KPwR4gPo1pz7fyuXcZ9mXkbmBizvXTwzxBcERwxILtGxyhwRGJzEWaI/y4nuQJgiOCIxYsKzhCO88R3VgGpNBeWXzyPor56DxAzTflRN/eTjTHYcn/vYAf+v/bgKv8/2gsOud1uaQB7AKclqwfixHycZhvyX6YWcyevn9MxXqPxHIzvkbNr2YU8Angkn60cRGIY08sHcRxWOTS6UV7YQEkjgUeAUZVkH2o990hvn4QltJju4p1vA8j2u2woCcfS/adgt2oD+3guFsS08ZfiAVbmQh83wnwD8DawKnApyrI3Bl4CDMV+woe+MJl3QJ8HdgiU9aqwLLU/MpG+hibkpIZ8Na0j3OuO/m/LRZcZbz3/2WYL1txY12cioTv562DPVgIcDh9fdqGASu3MTd6gicIjqgnOziiJjM4QoMjCI6oJ7/neILgCAiOCI6oye0qR3R6GbCCB+NCX83mW72D3oIltp+DmR+N9/2nAttUlL8XFg2xGCQzgEN8YtyGawqxvJcjqZOOoY7MIhfhI5j5zxnJ9rdiqQeW9W13psTUQu46wP44cfq2MT5BfgRs7dv2xLRtS1JBywSMTf6v72S0BpaC4WEsUue7SudcT6JVbNBvrTTSBwM7ZNYxjTb6Xa/jVOAK4ATfXtzwKqWFaFDeZkm7LomZd30Wv9lhN5gVge1pQdbYzWENaprCAzHznZ0x053Vfftol3sL9nDQNIomFlX2Z5iG9OeY1nhF4HPYTarQer8HuDpnDPvxa2HmfEVEzA2By4ux7L/fAA6s2KZj/BpX9PWR3q5fx7THxVw8DFg+U2ZP8QTBEeU+C47Q4IjSmA2OWMQ5ok6/9SRPEBwRHKHBEYmsjnLEwloGrODBugA/xUxfRvqgnYb5TozENEuPAWu0IXdXTIOXRovcGYsWeSseeREzC/omFaM9+sC9GjNVutS3jXFZG/pAPI8maSkSWYKR+euYz8dFmIZxmE/Oj2G+L+dhZFwptyBG4k8A5/n6aGA97GY1G4v4eDzwd+CdfsyGPtnravaoqJEmM+IijW+qtzpR/IaKOSYblLM48GHMHKqIFrk0cCNGdEUalTUxU7SNmsha1+s1g1oKkw96m/+MGoHuBpzu/zfGtJ7LNpG7I2a+tbmPrbWxG+sJmEnXjZjp2NVefsM61pG9mY+nc4BNsIeCx0nykGJfAGZWkLmH99GPsBvdKT6GL/XxsrofdwDw60Zjq4HsnuMJgiOCI2qygiM0OKKO7EWaI/yYnuUJgiOCIzQ4IpE/ni5wxMJYBqzgwbbQlzhvA+7x/8dgUS7BcgLOBjapKHsnzI+hyEe4OrCPT8CrsDQJ+wMfwIJKZPl8pIPfB9TNWOTF84CLffs5wDUYGWb7kmBmUPdimsibMX+fs30SDsNMQu6k5puRnRMRM2d6EvNNOj/Zfjg1LfK7Ma3yjr6+DLBci37rika6Thl3A2Mx851KZm2JnHrawhWwSK3fxbWkmBb1+8CE5LiGZlhORg8Dh5WvAdP635SMyUeBPZJjmhI0psk82v8vnpQ3G/NbGoVFhZ1Bpl8UickTFqH1TCydxzjvx+eBk7zsh/A5lDl+H/br3BCLQPosFgBlNcwk7WuYFv1XZPqy0WM8QXBEuc+CI4IjgiP6ylzkOaLBHB70PEFwRHDEgrKDI2qyO8oRC3MZ0MIH21JnwtyBkdrlmNnNXcDmbcidiEVHfBtGbvcl5DQO0w5divmaZJE+5vfxvE+OVTEN1AGY9mprjEBP92OH0UST2aSMb+O+JcBMzNTm55hPxruxgBI/Bia1Ift4zJfmP4BZvm1/TEP5UUxztbFvb5V7dGFopNMybsXNkcr7MmXV0xaOwCKqLoYF0fgfzJTpQWDb3HK8Ty4pjse0x0d4f22K+VzdiQXUKHyCJP2tV1///XwypoSaudQmPg4qjTEfw78HLsFyjq7k8+F07AYyzmWf6ON8vUy5O2FmV2v5eqElXgtLlXEw9gCwJXaDqaQhpUd4guCI4Ij6soMjgiOCI1r326DmCYIjgiPqyw2O6Cu7oxyxsJYBr8BgW0oT5mYskuP+PvH37ofcycCLwN+A/X1bQUoFaVUxLZnoA/hvGGl+Bdgd0wyugplu3ACc024bAFtgGsiNMe3hoT7hP+1ktxSmfVstQ+YawH7J+q4++XZyoijMTg72Mquar3T8y1XG2DimH7IaaguT/ecDu1aUuz1mKrWbk+a13ndfAb7ixyydlJNNRphZ1B34DQMj/uGY6dhNZPgXleRtAryCpRo5GgtS835M430i9uDRcmw1mBvzgBnFNRZzy8fbj8j0YaswFgYdTxAc0azPgiOCI4IjgiNa9dug5gmCIyA4op7c4Ijaesc5YmEsA16BwbiUJsyNwC319rUhdyOfMG9MFiemphrHJvLWw1IgfMoH970u/wjfvwGwUj/qu4IT0MuFTN8+Ovmfk7JoMSy9wnzM9GVvzGTlICfTjbEbwEWl86q2R8e+XLUqAzNr+kQ/ZdXTFv4C02xu1qbMUZi2cbYT5raYmc5EzPxqRD/qOxrTRv8HiUYX02zeRcWonH7u+phWdqaT6qEu6yEfLxf5+Kk6Fib5XPhg0W8+37bCHrj6nSKjF3giOKJpnwVHBEcERwRHtOq3Qc0TwRHBEQ3kBkd0kSO6vQx4BQbrwoIarQ93SG6h0TqyQ/I2x4IZ7OPr2+CRKjskfwvMX2OlcrtUlLMxFpjhHsz/5V5gFnCl75+I5RLtb6TSjn25alLGCJ/4/fI7o762cFffVtmcqVTG2NL69t7mbROoy1nFCfoezMzpDCx6Z9uRLX1OvEQtTc1QLLDLSWSaoDWQW9xYP5RsOwT7CjCyQ2Nh0PNEcETLPguO0OCIZFtwxCLOEQ36bVDxRHDEAjKDI+q3SXBElziim8uAV2AwL3TwC0hJ7uaYZud9HZK3hU+awzohryR7OBYs4d3tTpZE1kTgBWrazVmYr83amMnQkh2qc1f6rVTGsA7IaKQt7AjhJ/23Bxb4Y88OyRyJadLPwrTrlfNv1pFZ3EQ+1F9ZJbnFjXUG5js1m8ygLRXKGPQ8ERyxcPqsVEZwRHBEV8dbcET3OKKb/VYqo188ERzRR2ZwxIJygyO6yBHdWga8AoN9oZ9fQJrI3bQTZJTI6+gLf0n2FniuwQ7IKrSbB/j6mP7enBZmv3WpbfutLWwgezim3byDQepXU6pvMYYP7bDcSS73z/RDg96ijEHPE8ERC6fPutS2wREaHNFAbnBElziim/3WhbYNjtDgiAZygyO6yBHdWArfg0ATiMgwVZ070PVoBRHZFPi3qv5uoOvSDCIyGfgBcJaqXtrFcgZ9v4nIcOALwO3ADao6vwvyl1XVP4uI6CCf8N0awyKyPjCvm3OjR8ZbcETfcnqhz4IjEgRHdBfBEXXLGtT9FhzRF8ER3UVwRPcRL+eBAYGIbIlpYjcAnu70zaSXICJbYBq9+wa6LoHAYEFwRA3BEYHAggiOqCE4IhBYEL3KEfFyHhgwiMhSqvqPga5HIBAYnAiOCAQCzRAcEQgEmqEXOSJezgMDhsI8qhfMpAKBwMJHcEQgEGiG4IhAINAMvcgR8XIeCAQCgUAgEAgEAoHAAGPIQFcgEAgEAoFAIBAIBAKBRR3xch4IBAKBQCAQCAQCgcAAI17OA4FAIBAIBAKBQCAQGGDEy3kgEAgEAgEARGSeiMwWkd+IyK9E5DgRafqsICLjReSAhVXHQCAQCATerIiX80AgEAgEAgVeVtVNVHUDYFdgD+BTLc4ZD8TLeSAQCAQC/UREaw8EAoFAIACAiPxLVZdI1tcEHgCWA1YHrgFG++6jVfU+EbkfWA94ArgauBQ4D9gBGAHMUtUvLrSLCAQCgUCgRxEv54FAIBAIBIAFX85924vAusA/gfmq+oqITACuVdVJIrIDcLyqTvfjDwdWUNWzRGQE8FNghqo+sVAvJhAIBAKBHsOwga5AIBAIBAKBQQ3x3+HA50RkE2AesHaD498GTBSRfX19aWAC9mU9EAgEAoFAA8TLeSAQCAQCgbpws/Z5wF8x3/O/ABtjMWteaXQacIyq3r5QKhkIBAKBwJsEERAuEAgEAoHAAhCR5YEvAJ9T84FbGnhOVecD7wWG+qH/BJZMTr0d+KCIDHc5a4vIaAKBQCAQCDRFfDkPBAKBQCBQYKSIzMZM2OdiAeAu9H2XAzeKyAzgLmCOb/81MFdEfgVcBVyCRXB/SEQEeB7Ye2FdQCAQCAQCvYoICBcIBAKBQCAQCAQCgcAAI8zaA4FAIBAIBAKBQCAQGGDEy3kgEAgEAoFAIBAIBAIDjHg5DwQCgUAgEAgEAoFAYIARL+eBQCAQCAQCgUAgEAgMMOLlPBAIBAKBQCAQCAQCgQFGvJwHAoFAIBAIBAKBQCAwwIiX80AgEAgEAoFAIBAIBAYY/w/dicjm6wTPvgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Ensure that the date indices/columns are in datetime format\n",
+    "monthly_death_data.columns = pd.to_datetime(monthly_death_data.columns)\n",
+    "\n",
+    "plt.figure(figsize=(14, 8))\n",
+    "\n",
+    "# Plotting cumulative cases for France from monthly_death_data\n",
+    "if 'France' in monthly_death_data.index:\n",
+    "    plt.plot(monthly_death_data.columns, monthly_death_data.loc['France'], label='Cumulative Cases - France')\n",
+    "\n",
+    "\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Number of Cases/Deaths')\n",
+    "plt.title('COVID-19 Cases and Deaths Over Time in France - Linear')\n",
+    "\n",
+    "# Format x-axis to show month and year\n",
+    "plt.gca().xaxis.set_major_formatter(plt.matplotlib.dates.DateFormatter('%b %Y'))\n",
+    "plt.gca().xaxis.set_major_locator(plt.matplotlib.dates.MonthLocator(interval=1))\n",
+    "\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate cumulative values\n",
+    "cumulative_values = []\n",
+    "cumulative_sum = 0\n",
+    "\n",
+    "for date in date_indices:\n",
+    "    cumulative_sum += france_row[date]\n",
+    "    cumulative_values.append(cumulative_sum)\n",
+    "\n",
+    "# Plotting\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.plot(date_indices, cumulative_values, marker='o', linestyle='-', color='b', label='Cumulative Cases/Deaths - France')\n",
+    "plt.title('Cumulative COVID-19 Cases/Deaths Over Time in France')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Cumulative Number of Cases/Deaths')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.grid(True)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'date_indices_monthly' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-169-107f537779d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mcumulative_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mcumulative_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdate_indices_monthly\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mcumulative_sum\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mfrance_row\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%Y-%m-%d'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# Convert datetime to string format for matching\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mcumulative_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcumulative_sum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'date_indices_monthly' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Calculate cumulative values for France\n",
+    "cumulative_values = []\n",
+    "cumulative_sum = 0\n",
+    "for date in date_indices_monthly:\n",
+    "    cumulative_sum += france_row[date.strftime('%Y-%m-%d')]  # Convert datetime to string format for matching\n",
+    "    cumulative_values.append(cumulative_sum)\n",
+    "\n",
+    "# Plotting\n",
+    "plt.figure(figsize=(14, 8))\n",
+    "\n",
+    "# Plotting cumulative cases for France from monthly_death_data\n",
+    "if 'France' in monthly_death_data.index:\n",
+    "    plt.plot(date_indices_monthly, cumulative_values, marker='o', linestyle='-', color='b', label='Cumulative Cases - France')\n",
+    "\n",
+    "# Plotting cumulative cases from monthly_death_data\n",
+    "plt.plot(date_indices_monthly, monthly_death_data.loc['France'], marker='o', linestyle='-', color='r', label='Monthly Cumulative Cases - France')\n",
+    "\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Number of Cases/Deaths')\n",
+    "plt.title('COVID-19 Cases and Deaths Over Time in France')\n",
+    "plt.gca().xaxis.set_major_formatter(plt.matplotlib.dates.DateFormatter('%b %Y'))\n",
+    "plt.gca().xaxis.set_major_locator(plt.matplotlib.dates.MonthLocator(interval=1))\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.legend()\n",
+    "plt.tight_layout()\n",
     "plt.show()\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['1970-01-01'], dtype='datetime64[ns]', freq=None)"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fr_death.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "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>Country/Region</th>\n",
+       "      <th>2020-01</th>\n",
+       "      <th>2020-02</th>\n",
+       "      <th>2020-03</th>\n",
+       "      <th>2020-04</th>\n",
+       "      <th>2020-05</th>\n",
+       "      <th>2020-06</th>\n",
+       "      <th>2020-07</th>\n",
+       "      <th>2020-08</th>\n",
+       "      <th>2020-09</th>\n",
+       "      <th>...</th>\n",
+       "      <th>2022-06</th>\n",
+       "      <th>2022-07</th>\n",
+       "      <th>2022-08</th>\n",
+       "      <th>2022-09</th>\n",
+       "      <th>2022-10</th>\n",
+       "      <th>2022-11</th>\n",
+       "      <th>2022-12</th>\n",
+       "      <th>2023-01</th>\n",
+       "      <th>2023-02</th>\n",
+       "      <th>2023-03</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1970-01-01</th>\n",
+       "      <td>France</td>\n",
+       "      <td>57355</td>\n",
+       "      <td>51413</td>\n",
+       "      <td>63128</td>\n",
+       "      <td>66948</td>\n",
+       "      <td>49140</td>\n",
+       "      <td>46295</td>\n",
+       "      <td>47220</td>\n",
+       "      <td>49317</td>\n",
+       "      <td>49377</td>\n",
+       "      <td>...</td>\n",
+       "      <td>48379</td>\n",
+       "      <td>54724</td>\n",
+       "      <td>52838</td>\n",
+       "      <td>49600</td>\n",
+       "      <td>55868</td>\n",
+       "      <td>54743</td>\n",
+       "      <td>70566</td>\n",
+       "      <td>64000</td>\n",
+       "      <td>52100</td>\n",
+       "      <td>55300</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Country/Region  2020-01  2020-02  2020-03  2020-04  2020-05  \\\n",
+       "1970-01-01         France    57355    51413    63128    66948    49140   \n",
+       "\n",
+       "            2020-06  2020-07  2020-08  2020-09   ...     2022-06  2022-07  \\\n",
+       "1970-01-01    46295    47220    49317    49377   ...       48379    54724   \n",
+       "\n",
+       "            2022-08  2022-09  2022-10  2022-11  2022-12  2023-01  2023-02  \\\n",
+       "1970-01-01    52838    49600    55868    54743    70566    64000    52100   \n",
+       "\n",
+       "            2023-03  \n",
+       "1970-01-01    55300  \n",
+       "\n",
+       "[1 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fr_death"
+   ]
   }
  ],
  "metadata": {
diff --git a/module3/exo3/monthly_death_data.csv b/module3/exo3/monthly_death_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..11365ecbdcde28156226f75c3582c24407fff4d9
--- /dev/null
+++ b/module3/exo3/monthly_death_data.csv
@@ -0,0 +1,14 @@
+Country/Region,2020-01-31,2020-02-29,2020-03-31,2020-04-30,2020-05-31,2020-06-30,2020-07-31,2020-08-31,2020-09-30,2020-10-31,2020-11-30,2020-12-31,2021-01-31,2021-02-28,2021-03-31,2021-04-30,2021-05-31,2021-06-30,2021-07-31,2021-08-31,2021-09-30,2021-10-31,2021-11-30,2021-12-31,2022-01-31,2022-02-28,2022-03-31,2022-04-30,2022-05-31,2022-06-30,2022-07-31,2022-08-31,2022-09-30,2022-10-31,2022-11-30,2022-12-31,2023-01-31,2023-02-28,2023-03-31
+Belgium,0,0,705,7594,9467,9747,9841,9895,10016,11625,16645,19528,21092,22077,23016,24230,24955,25173,25241,25380,25602,25994,27015,28331,29052,30179,30826,31439,31754,31918,32228,32516,32673,32902,33061,33228,33557,33717,33814
+China,213,2837,3323,4697,4708,4713,4733,4796,4813,4814,4827,4881,4960,5009,5023,5030,5031,5033,5047,5056,5069,5079,5089,5103,5119,5655,12749,14621,14897,14927,15046,15240,15713,15952,15986,17102,97663,101048,101056
+France,0,2,3526,24349,28805,29846,30268,30646,31978,36827,52818,64758,76200,86579,95798,104676,109693,111259,112061,114926,117474,118625,120112,124729,131937,139382,143307,146967,149366,150572,153023,155133,156152,158034,159990,163003,165274,166004,166176
+Germany,0,0,583,6288,8500,8973,9141,9298,9488,10452,16248,33071,56945,70045,76342,82850,88442,90875,91637,92200,93638,95606,101344,111602,117786,122702,129391,135292,139000,141105,143855,147404,149948,153544,157791,161465,165711,168086,168935
+Iran,0,43,2898,6028,7797,10817,16766,21571,26169,34864,48246,55223,57959,60073,62665,71758,80156,84264,90630,107794,120428,126303,129830,131606,132454,136838,140204,141083,141315,141389,141998,143867,144426,144576,144633,144685,144749,144845,144933
+Italy,0,29,12428,27967,33415,34767,35141,35483,35894,38618,55576,74159,88516,97699,109346,120807,126128,127566,128063,129221,130921,132100,133828,137402,146498,154767,159383,163507,166697,168353,172086,175595,177092,179101,181098,184642,186833,188094,188322
+Japan,0,6,67,481,898,974,1013,1300,1575,1770,2152,3492,5753,7897,9176,10243,13059,14784,15193,16067,17651,18267,18360,18392,18815,23670,28124,29571,30623,31281,32613,39942,44918,46765,49652,57274,68099,72395,72997
+"Korea, South",0,16,162,248,271,282,301,324,415,466,526,917,1425,1605,1735,1831,1963,2021,2098,2292,2497,2858,3659,5625,6772,8170,16590,22875,24197,24555,25068,26876,28445,29209,30568,32219,33486,33988,34093
+Netherlands,0,0,1040,4811,5975,6132,6166,6252,6457,7459,9453,11525,14108,15688,16689,17398,17897,18028,18114,18368,18584,18850,19863,21392,21809,22142,22583,22855,22933,23000,23122,23239,23292,23459,23568,23697,23702,23705,23705
+Portugal,0,0,160,989,1410,1576,1735,1822,1971,2507,4505,6906,12482,16317,16848,16974,17025,17096,17361,17743,17975,18157,18441,18955,19905,21063,21695,22280,23115,24149,24592,24855,25031,25228,25450,25714,26022,26117,26266
+Spain,0,0,8464,24543,27127,28355,28445,29094,31791,35878,45069,50837,58319,69142,75459,78216,79953,80875,81486,84340,86415,87368,88052,89405,93225,99410,102218,104456,106341,107906,110719,112600,114179,115078,115901,117095,118434,119380,119479
+US,0,1,5359,66638,107857,127432,153969,182794,205957,230545,270601,350604,446602,511827,548349,572156,590261,600689,609480,637765,696889,744886,779427,825468,888905,950322,983142,996086,1007077,1017377,1030088,1046240,1059507,1070376,1080466,1092764,1108688,1119917,1123836
+United Kingdom,1,3,5145,39062,52569,56284,57434,57984,58875,64308,77759,95107,131791,148638,152949,154069,154499,154982,156844,160192,164640,169243,173763,177869,184634,188575,193028,198162,200321,201790,205481,207831,209268,212350,214179,217054,220064,220721,220721