diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..a2b5ad36079a52c7dc88465f6e4427061ae2308d 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -1,6 +1,1539 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "# Study on CO$_2$ concentration in the atmosphere"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "The data on CO2 atmospheric concentration is made available by the [Mauna Loa Observatory](https://scrippsco2.ucsd.edu/data/atmospheric_co2/mlo.html) in several sets. For this analysis, the weekly recordings are used, retrieved at this [link](https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv) in CSV format. Becuase these data are continously updated, an offline version is used, which has been downloaded on __October 3, 2022__."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "outputs": [],
+   "source": [
+    "data_file = './weekly_in_situ_co2_mlo_retrieved20221003.csv'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "The first 44 lines of the file are comments to the dataset, therefore they are skipped with `skiprows=44`. Moreover, since no heading is included in the original dataset, columns are named as `Date` and `CO2_ppm` directly from the importing command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "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>Date</th>\n",
+       "      <th>CO2_ppm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1958-03-29</td>\n",
+       "      <td>316.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1958-04-05</td>\n",
+       "      <td>317.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1958-04-12</td>\n",
+       "      <td>317.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1958-04-19</td>\n",
+       "      <td>317.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1958-04-26</td>\n",
+       "      <td>316.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1958-05-03</td>\n",
+       "      <td>316.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1958-05-17</td>\n",
+       "      <td>317.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1958-05-24</td>\n",
+       "      <td>317.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1958-07-05</td>\n",
+       "      <td>315.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1958-07-12</td>\n",
+       "      <td>315.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1958-07-19</td>\n",
+       "      <td>315.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1958-07-26</td>\n",
+       "      <td>315.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1958-08-02</td>\n",
+       "      <td>315.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1958-08-09</td>\n",
+       "      <td>315.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1958-08-16</td>\n",
+       "      <td>315.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1958-08-30</td>\n",
+       "      <td>314.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1958-09-06</td>\n",
+       "      <td>313.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1958-11-08</td>\n",
+       "      <td>313.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1958-11-15</td>\n",
+       "      <td>313.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1958-11-22</td>\n",
+       "      <td>313.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1958-11-29</td>\n",
+       "      <td>314.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1958-12-06</td>\n",
+       "      <td>314.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1958-12-13</td>\n",
+       "      <td>314.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1958-12-20</td>\n",
+       "      <td>314.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1958-12-27</td>\n",
+       "      <td>315.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1959-01-03</td>\n",
+       "      <td>315.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1959-01-10</td>\n",
+       "      <td>315.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1959-01-17</td>\n",
+       "      <td>315.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1959-01-24</td>\n",
+       "      <td>315.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1959-01-31</td>\n",
+       "      <td>315.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3257</th>\n",
+       "      <td>2022-01-15</td>\n",
+       "      <td>418.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3258</th>\n",
+       "      <td>2022-01-22</td>\n",
+       "      <td>418.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3259</th>\n",
+       "      <td>2022-01-29</td>\n",
+       "      <td>419.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3260</th>\n",
+       "      <td>2022-02-05</td>\n",
+       "      <td>418.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3261</th>\n",
+       "      <td>2022-02-12</td>\n",
+       "      <td>418.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3262</th>\n",
+       "      <td>2022-02-19</td>\n",
+       "      <td>418.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3263</th>\n",
+       "      <td>2022-02-26</td>\n",
+       "      <td>419.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3264</th>\n",
+       "      <td>2022-03-05</td>\n",
+       "      <td>418.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3265</th>\n",
+       "      <td>2022-03-12</td>\n",
+       "      <td>418.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3266</th>\n",
+       "      <td>2022-03-19</td>\n",
+       "      <td>417.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3267</th>\n",
+       "      <td>2022-03-26</td>\n",
+       "      <td>419.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3268</th>\n",
+       "      <td>2022-04-02</td>\n",
+       "      <td>419.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3269</th>\n",
+       "      <td>2022-04-09</td>\n",
+       "      <td>419.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3270</th>\n",
+       "      <td>2022-04-16</td>\n",
+       "      <td>420.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3271</th>\n",
+       "      <td>2022-04-23</td>\n",
+       "      <td>420.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3272</th>\n",
+       "      <td>2022-04-30</td>\n",
+       "      <td>419.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3273</th>\n",
+       "      <td>2022-05-07</td>\n",
+       "      <td>419.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3274</th>\n",
+       "      <td>2022-05-14</td>\n",
+       "      <td>421.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3275</th>\n",
+       "      <td>2022-05-21</td>\n",
+       "      <td>420.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3276</th>\n",
+       "      <td>2022-05-28</td>\n",
+       "      <td>421.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3277</th>\n",
+       "      <td>2022-06-04</td>\n",
+       "      <td>421.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3278</th>\n",
+       "      <td>2022-06-11</td>\n",
+       "      <td>420.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3279</th>\n",
+       "      <td>2022-06-18</td>\n",
+       "      <td>420.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3280</th>\n",
+       "      <td>2022-06-25</td>\n",
+       "      <td>420.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3281</th>\n",
+       "      <td>2022-07-02</td>\n",
+       "      <td>419.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3282</th>\n",
+       "      <td>2022-07-09</td>\n",
+       "      <td>418.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3283</th>\n",
+       "      <td>2022-07-16</td>\n",
+       "      <td>418.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3284</th>\n",
+       "      <td>2022-07-23</td>\n",
+       "      <td>418.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3285</th>\n",
+       "      <td>2022-07-30</td>\n",
+       "      <td>417.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3286</th>\n",
+       "      <td>2022-08-06</td>\n",
+       "      <td>417.43</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3287 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Date  CO2_ppm\n",
+       "0     1958-03-29   316.19\n",
+       "1     1958-04-05   317.31\n",
+       "2     1958-04-12   317.69\n",
+       "3     1958-04-19   317.58\n",
+       "4     1958-04-26   316.48\n",
+       "5     1958-05-03   316.95\n",
+       "6     1958-05-17   317.56\n",
+       "7     1958-05-24   317.99\n",
+       "8     1958-07-05   315.85\n",
+       "9     1958-07-12   315.85\n",
+       "10    1958-07-19   315.46\n",
+       "11    1958-07-26   315.59\n",
+       "12    1958-08-02   315.64\n",
+       "13    1958-08-09   315.10\n",
+       "14    1958-08-16   315.09\n",
+       "15    1958-08-30   314.14\n",
+       "16    1958-09-06   313.54\n",
+       "17    1958-11-08   313.05\n",
+       "18    1958-11-15   313.26\n",
+       "19    1958-11-22   313.57\n",
+       "20    1958-11-29   314.01\n",
+       "21    1958-12-06   314.56\n",
+       "22    1958-12-13   314.41\n",
+       "23    1958-12-20   314.77\n",
+       "24    1958-12-27   315.21\n",
+       "25    1959-01-03   315.24\n",
+       "26    1959-01-10   315.50\n",
+       "27    1959-01-17   315.69\n",
+       "28    1959-01-24   315.86\n",
+       "29    1959-01-31   315.42\n",
+       "...          ...      ...\n",
+       "3257  2022-01-15   418.03\n",
+       "3258  2022-01-22   418.33\n",
+       "3259  2022-01-29   419.08\n",
+       "3260  2022-02-05   418.74\n",
+       "3261  2022-02-12   418.90\n",
+       "3262  2022-02-19   418.79\n",
+       "3263  2022-02-26   419.55\n",
+       "3264  2022-03-05   418.35\n",
+       "3265  2022-03-12   418.56\n",
+       "3266  2022-03-19   417.95\n",
+       "3267  2022-03-26   419.00\n",
+       "3268  2022-04-02   419.91\n",
+       "3269  2022-04-09   419.38\n",
+       "3270  2022-04-16   420.57\n",
+       "3271  2022-04-23   420.11\n",
+       "3272  2022-04-30   419.81\n",
+       "3273  2022-05-07   419.64\n",
+       "3274  2022-05-14   421.36\n",
+       "3275  2022-05-21   420.55\n",
+       "3276  2022-05-28   421.34\n",
+       "3277  2022-06-04   421.18\n",
+       "3278  2022-06-11   420.90\n",
+       "3279  2022-06-18   420.45\n",
+       "3280  2022-06-25   420.16\n",
+       "3281  2022-07-02   419.89\n",
+       "3282  2022-07-09   418.92\n",
+       "3283  2022-07-16   418.47\n",
+       "3284  2022-07-23   418.02\n",
+       "3285  2022-07-30   417.56\n",
+       "3286  2022-08-06   417.43\n",
+       "\n",
+       "[3287 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_file, skiprows=44,delimiter=',',names=['Date','CO2_ppm'])\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "source": [
+    "In order to check any empy row, i.e. missing data points, it is run the following command"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": false
+   },
+   "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>Date</th>\n",
+       "      <th>CO2_ppm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Date, CO2_ppm]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is no empty line in the dataset, therefore there is no line to drop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\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>Date</th>\n",
+       "      <th>CO2_ppm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1958-03-29</td>\n",
+       "      <td>316.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1958-04-05</td>\n",
+       "      <td>317.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1958-04-12</td>\n",
+       "      <td>317.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1958-04-19</td>\n",
+       "      <td>317.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1958-04-26</td>\n",
+       "      <td>316.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1958-05-03</td>\n",
+       "      <td>316.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1958-05-17</td>\n",
+       "      <td>317.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1958-05-24</td>\n",
+       "      <td>317.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1958-07-05</td>\n",
+       "      <td>315.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1958-07-12</td>\n",
+       "      <td>315.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1958-07-19</td>\n",
+       "      <td>315.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1958-07-26</td>\n",
+       "      <td>315.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1958-08-02</td>\n",
+       "      <td>315.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1958-08-09</td>\n",
+       "      <td>315.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1958-08-16</td>\n",
+       "      <td>315.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1958-08-30</td>\n",
+       "      <td>314.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1958-09-06</td>\n",
+       "      <td>313.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1958-11-08</td>\n",
+       "      <td>313.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1958-11-15</td>\n",
+       "      <td>313.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1958-11-22</td>\n",
+       "      <td>313.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1958-11-29</td>\n",
+       "      <td>314.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1958-12-06</td>\n",
+       "      <td>314.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1958-12-13</td>\n",
+       "      <td>314.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1958-12-20</td>\n",
+       "      <td>314.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1958-12-27</td>\n",
+       "      <td>315.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1959-01-03</td>\n",
+       "      <td>315.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1959-01-10</td>\n",
+       "      <td>315.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1959-01-17</td>\n",
+       "      <td>315.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1959-01-24</td>\n",
+       "      <td>315.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1959-01-31</td>\n",
+       "      <td>315.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3257</th>\n",
+       "      <td>2022-01-15</td>\n",
+       "      <td>418.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3258</th>\n",
+       "      <td>2022-01-22</td>\n",
+       "      <td>418.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3259</th>\n",
+       "      <td>2022-01-29</td>\n",
+       "      <td>419.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3260</th>\n",
+       "      <td>2022-02-05</td>\n",
+       "      <td>418.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3261</th>\n",
+       "      <td>2022-02-12</td>\n",
+       "      <td>418.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3262</th>\n",
+       "      <td>2022-02-19</td>\n",
+       "      <td>418.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3263</th>\n",
+       "      <td>2022-02-26</td>\n",
+       "      <td>419.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3264</th>\n",
+       "      <td>2022-03-05</td>\n",
+       "      <td>418.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3265</th>\n",
+       "      <td>2022-03-12</td>\n",
+       "      <td>418.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3266</th>\n",
+       "      <td>2022-03-19</td>\n",
+       "      <td>417.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3267</th>\n",
+       "      <td>2022-03-26</td>\n",
+       "      <td>419.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3268</th>\n",
+       "      <td>2022-04-02</td>\n",
+       "      <td>419.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3269</th>\n",
+       "      <td>2022-04-09</td>\n",
+       "      <td>419.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3270</th>\n",
+       "      <td>2022-04-16</td>\n",
+       "      <td>420.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3271</th>\n",
+       "      <td>2022-04-23</td>\n",
+       "      <td>420.11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3272</th>\n",
+       "      <td>2022-04-30</td>\n",
+       "      <td>419.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3273</th>\n",
+       "      <td>2022-05-07</td>\n",
+       "      <td>419.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3274</th>\n",
+       "      <td>2022-05-14</td>\n",
+       "      <td>421.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3275</th>\n",
+       "      <td>2022-05-21</td>\n",
+       "      <td>420.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3276</th>\n",
+       "      <td>2022-05-28</td>\n",
+       "      <td>421.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3277</th>\n",
+       "      <td>2022-06-04</td>\n",
+       "      <td>421.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3278</th>\n",
+       "      <td>2022-06-11</td>\n",
+       "      <td>420.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3279</th>\n",
+       "      <td>2022-06-18</td>\n",
+       "      <td>420.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3280</th>\n",
+       "      <td>2022-06-25</td>\n",
+       "      <td>420.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3281</th>\n",
+       "      <td>2022-07-02</td>\n",
+       "      <td>419.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3282</th>\n",
+       "      <td>2022-07-09</td>\n",
+       "      <td>418.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3283</th>\n",
+       "      <td>2022-07-16</td>\n",
+       "      <td>418.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3284</th>\n",
+       "      <td>2022-07-23</td>\n",
+       "      <td>418.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3285</th>\n",
+       "      <td>2022-07-30</td>\n",
+       "      <td>417.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3286</th>\n",
+       "      <td>2022-08-06</td>\n",
+       "      <td>417.43</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3287 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Date  CO2_ppm\n",
+       "0     1958-03-29   316.19\n",
+       "1     1958-04-05   317.31\n",
+       "2     1958-04-12   317.69\n",
+       "3     1958-04-19   317.58\n",
+       "4     1958-04-26   316.48\n",
+       "5     1958-05-03   316.95\n",
+       "6     1958-05-17   317.56\n",
+       "7     1958-05-24   317.99\n",
+       "8     1958-07-05   315.85\n",
+       "9     1958-07-12   315.85\n",
+       "10    1958-07-19   315.46\n",
+       "11    1958-07-26   315.59\n",
+       "12    1958-08-02   315.64\n",
+       "13    1958-08-09   315.10\n",
+       "14    1958-08-16   315.09\n",
+       "15    1958-08-30   314.14\n",
+       "16    1958-09-06   313.54\n",
+       "17    1958-11-08   313.05\n",
+       "18    1958-11-15   313.26\n",
+       "19    1958-11-22   313.57\n",
+       "20    1958-11-29   314.01\n",
+       "21    1958-12-06   314.56\n",
+       "22    1958-12-13   314.41\n",
+       "23    1958-12-20   314.77\n",
+       "24    1958-12-27   315.21\n",
+       "25    1959-01-03   315.24\n",
+       "26    1959-01-10   315.50\n",
+       "27    1959-01-17   315.69\n",
+       "28    1959-01-24   315.86\n",
+       "29    1959-01-31   315.42\n",
+       "...          ...      ...\n",
+       "3257  2022-01-15   418.03\n",
+       "3258  2022-01-22   418.33\n",
+       "3259  2022-01-29   419.08\n",
+       "3260  2022-02-05   418.74\n",
+       "3261  2022-02-12   418.90\n",
+       "3262  2022-02-19   418.79\n",
+       "3263  2022-02-26   419.55\n",
+       "3264  2022-03-05   418.35\n",
+       "3265  2022-03-12   418.56\n",
+       "3266  2022-03-19   417.95\n",
+       "3267  2022-03-26   419.00\n",
+       "3268  2022-04-02   419.91\n",
+       "3269  2022-04-09   419.38\n",
+       "3270  2022-04-16   420.57\n",
+       "3271  2022-04-23   420.11\n",
+       "3272  2022-04-30   419.81\n",
+       "3273  2022-05-07   419.64\n",
+       "3274  2022-05-14   421.36\n",
+       "3275  2022-05-21   420.55\n",
+       "3276  2022-05-28   421.34\n",
+       "3277  2022-06-04   421.18\n",
+       "3278  2022-06-11   420.90\n",
+       "3279  2022-06-18   420.45\n",
+       "3280  2022-06-25   420.16\n",
+       "3281  2022-07-02   419.89\n",
+       "3282  2022-07-09   418.92\n",
+       "3283  2022-07-16   418.47\n",
+       "3284  2022-07-23   418.02\n",
+       "3285  2022-07-30   417.56\n",
+       "3286  2022-08-06   417.43\n",
+       "\n",
+       "[3287 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = raw_data\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For later convenience, the date column is converted into `datetime` format directly through pandas, so it can more easily get handles in any other time unit. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['Date'] = pd.to_datetime((data['Date']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Altough there is no line to drop, some weekly observation can still be missing and not specified in the raw dataset. A consistency check is therefore performed. Between two consecutive weeks, the time difference should be zero or very small (depending on record hours). Here, since no time is specified in the dataset, it is sufficient to check whether a period larger than 7 days occurs between observations. A `missing_poins` list is built to track the missing information:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1958-05-03 00:00:00 --> 1958-05-17 00:00:00\n",
+      "1958-05-24 00:00:00 --> 1958-07-05 00:00:00\n",
+      "1958-08-16 00:00:00 --> 1958-08-30 00:00:00\n",
+      "1958-09-06 00:00:00 --> 1958-11-08 00:00:00\n",
+      "1959-01-31 00:00:00 --> 1959-02-14 00:00:00\n",
+      "1959-03-07 00:00:00 --> 1959-03-21 00:00:00\n",
+      "1959-05-23 00:00:00 --> 1959-06-06 00:00:00\n",
+      "1959-08-08 00:00:00 --> 1959-08-22 00:00:00\n",
+      "1962-08-18 00:00:00 --> 1962-09-15 00:00:00\n",
+      "1962-12-22 00:00:00 --> 1963-01-05 00:00:00\n",
+      "1963-02-09 00:00:00 --> 1963-02-23 00:00:00\n",
+      "1963-04-27 00:00:00 --> 1963-05-11 00:00:00\n",
+      "1963-11-16 00:00:00 --> 1963-11-30 00:00:00\n",
+      "1964-01-18 00:00:00 --> 1964-05-30 00:00:00\n",
+      "1964-06-06 00:00:00 --> 1964-06-27 00:00:00\n",
+      "1964-08-01 00:00:00 --> 1964-08-15 00:00:00\n",
+      "1966-07-09 00:00:00 --> 1966-08-06 00:00:00\n",
+      "1966-10-29 00:00:00 --> 1966-11-12 00:00:00\n",
+      "1967-01-14 00:00:00 --> 1967-02-04 00:00:00\n",
+      "1976-06-19 00:00:00 --> 1976-07-03 00:00:00\n",
+      "1984-03-24 00:00:00 --> 1984-04-28 00:00:00\n",
+      "1985-07-27 00:00:00 --> 1985-08-10 00:00:00\n",
+      "2003-06-07 00:00:00 --> 2003-06-21 00:00:00\n",
+      "2003-10-04 00:00:00 --> 2003-10-25 00:00:00\n",
+      "2005-02-19 00:00:00 --> 2005-03-26 00:00:00\n",
+      "2006-02-04 00:00:00 --> 2006-02-25 00:00:00\n",
+      "2007-01-20 00:00:00 --> 2007-02-03 00:00:00\n",
+      "2012-09-29 00:00:00 --> 2012-10-20 00:00:00\n",
+      "2020-01-11 00:00:00 --> 2020-01-25 00:00:00\n",
+      "\n",
+      "Total missing points: 29\n"
+     ]
+    }
+   ],
+   "source": [
+    "missing_points = []\n",
+    "for i in range(0,len(data)-1):\n",
+    "    if (data['Date'][i+1])-(data['Date'][i]) > pd.Timedelta(days=7):\n",
+    "        missing_points.append(str((data['Date'][i]))+' -- '+str((data['Date'][i+1])))\n",
+    "        print(str((data['Date'][i]))+' --> '+str(pd.Timestamp(data['Date'][i+1])))\n",
+    "print('\\nTotal missing points: '+str(len(missing_points)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It results that data relative to 29 weeks is not present in this dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 1: Make a plot that shows the superposition of a periodic oscillation and a slower systematic evolution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A first look at the data can easily come from sorting the rows in chronological order, using the date as index, and plotting the CO2 concentration column as follows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f210f953198>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data = data.set_index('Date').sort_index()\n",
+    "sorted_data['CO2_ppm'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One observes that there is a short-period oscillation and a long-term increase of CO2 concentration. In order to extract the long term evolution, it is possible to _smooth_ the overal data using the `rolling` library from pandas. Documentation can be found at this [link](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html#). The function is used to compute the mean value using `.mean()`, specifying the window in this case equal to `60` weeks and centering the windows with `center=True`. The raw data and the 60-days moving average are plotted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f210f930400>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['mov_avg'] = sorted_data['CO2_ppm'].rolling(60,center=True).mean()\n",
+    "\n",
+    "sorted_data['CO2_ppm'].plot(lw=.8,color='k',label='Raw data')\n",
+    "sorted_data['mov_avg'].plot(lw=2,color='r',label='60-weeks moving average')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 2.1: Characterize the periodic oscillation "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To characterize the periodic oscillation, we can at first have a look in a smaller region of the dataset, plotted removing the average value computed beforehand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f210f7e6b70>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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iLjgZ6wlB8Hnw8IEZR2xjzGUKyBSligNyoSoQobS8iPJ23tYuK96xz6iVeuN9tQcrswpEp0/E9dFiMwfTE/I7nsmR11Xr1nXsvGnsVY7d7/XA7/Xgf716IwDno0sGc9q9Yf+yP4tzFbEr14u1O9DvOntCfrz3inX43nMTmEw4N3CDBSD66yVU3a8ypRiOKYsTbx1J22XFO3amBVd/mQxeDs/0/awXmiwy8SBfEXt1BMTgTWOv7hnOJKR4iGVwOO8sAWApV4TXQ2pmwJSlGGcDEUqp1gBs60gM771iHb7yvl0V73n9uaOQKXBo2rkun+z5TzSI2GVdxM7bIJt2WfGOvSjKELwerTS7mignjl2vAf7v121r8M7uith50tjzJbkiCmaLvT7nmgeWsiX0hpRGddXworE/fHAWP3ppEj4PQVjw4jNv3qlVdDPYoaqTWWesD1RCp537vQRfvW0Xfu2i1QCUiF2TYjgaDtIJ/CTFWkRRlOtGlED5oU4XnH1QmGP/lw9citdsG2743h7ONHahXlaM4ENRlJt+B3aRLYoYigY0qYvZpOVcc3JwtpQtoaeGDAPw49hn1DOp//j/rqy5AAFKjQghwBkupJjyDsfv9eC1O0YAAPc+fxoypYgHfQj4PG7E3i0UJamhU4kIXhDifMTOZIKw0Hyt7QkpZfB2pmadnM/i/96/X/s39R0x60oxqsPk5QA1V5IqMk38HEXsM8k8Ds8omU5LuaLWPqKaaMAHn4dgweFOhOmC8v1vHIzWfY/g82Aw6mydCLtP9cVTbKfGqtFlWelIOhQLaAtWt7PyHbsqxdSDEEXLdNyxl5hjb15+HQ/5IMnU1oPJD3/rOfzDw0e0qUh5g1kxALg4QJXVviX6TBO2IMU4qJK87M8fxA1feBSAKsXU6JgJKPfrpqEoDkw527iOLdZsGHQ9xnqCOOOAY88URLzl73+BF04tLfszdr8yeVam5VYNK6XD44p37AUDMkCcg6EALCsmWKenjR4ntuMsla0oyoqTNKCx85LLDEDT+pnuC5Qde9DvVXrGcKax1+O88R68fDrhaDFNpiAi4PPU7WvEGOsJYnLJfinm9FIOz59cqqhPYcds7Hv3kErHHg86f3ZlFivesRvRd3noHV2WYow7djs1YaajlyQZqYJSkq/9WZ3ry6JOHnrbsBRCNrgCqMyUinM0GCKRq6+xA8D54z2YSxcdrb1IFURt4W7EWE/IESmm1kCNt1+yBhet7cXt1yjprazNcFyV4ngbDtIJZ4djbxJV8CDFZFtw7E7kMjPnXRDlZVFNves7rOrZTvZkPzyTwt88eEhbXMZ6QlovHv2CFA/68a0nT+KbT55wwkyNkiQjXRC1MYi12Lm6BwDw8kTCLrOWkSmIWmvmRoz3hZAqiLb3YalVRTzWG8T3f/sqrOlXalrOW92DT92yA//vbecDUCN2TnZtnbLyHbtkLGJ3WgdmGruRFqcswrDTsbNe27mitCwaqnd9h9WKxBkHiz7+7Ef78Nc/P4iH1ArIvogfUfWAWr8gsf+HT35/j+026iWVpQbFSYxN6oHlyQXnJillCiIiBg76Nw0pth6ZtTeXvVa3zurW3YQQ/MbVGzGgnrvEQz5uMqM6ZUU79kSuhELJiGP3cyHFeD2k6e4CKD/0dlbL+tVrmCmKNSs4axEN+BARvI5G7Kt7lcXl608okXhvWNAiTf19Ybfj0aM/tGXfaU8DjT0e8iEa8GFi0bk0wnRBNNRCePOw4tgPz9h7fWtJMc3Or+JBP3IlCUWRn4aA7dKxYyeErCGEPEQI2UcI2UsI+YgZhnXKifkMLvjTn+Gp4wt1m1Qx7C59/+aTJ/Ardz1W8RobBlIvJ1gPy+yYt3F7yzT2bEFats2t190RYP3unYnYD8+ktLMAJgX0hvxaJod+QWLpcAMODLHQyxQsg4T1MKoFIQTjfSFHHXumIDXNiAGA1b0hBP0eLhx7vWE7DCZxOh3kmYEZEbsI4A8opTsAXAHgw4SQc0z43I74+SvT2s/NouBY0Gdrqtsnv78Hr0xWpqvlSqLhSTNBvxfRgA9zNhZTMCeYKYrLSvMb7Yicyg0+NpfBDV94FN95+lTF6z0hP6KqlKW3+65bLwRQzpCwk3nd93hM3TkwHbgeq3tDOO1AtgnDqMbu8RBsHIza7tir71FAac/biHJrie6XYzp27JTSSUrpc+rPKQD7AKzu9HM75Znji9rPRtIdi6JcUXRjB7KuadJCpqgNhTbCQFTAvI2thllaW7YoLZNiGi2cTkXsZ+o4PZ/Xg6gaaep3cm+6cDXuuG6z5YVftZpM6Xdex+Yy8HsJRhpE7ACwui+EiUXnNHajUgwArB8M45TNttYahdc0YuegUM0sTNXYCSHrAVwE4EkzP7cdnj1ZduzNpBiWvXF8zt6bTz+UeCZV0DrMGWEwGrA1YmdkCuKybW49jR1Qru10Mm97zrX+8Kxa3WKHftV2W1349dzJRVz6uQfwX8+frnhdL8UcnctgdW8I3gbjEQElYk/lRVuyOPZPJZcFPWmDETugOEy7m8G1o7GzCuSVcIBqmmMnhEQBfA/A71FKl5XFEUJuJ4Q8Qwh5ZnZ21qx/tiayTCu2t80i9qs2DwIAHjtkrV3VVDj2ZEFbYIwwELE3Yi+pB0rZotSSFDMYCyBfkms+aFaid5bnrqpsThUN+OD1kGXOU6sPsMgJHVSHo/zyyFzF68fnM9rPR2czTWUYoDyObnLJWpkrkS3hDV9+vGIxkmWKbFEy7NidmH2bq5Hu2Ezq5K3LZyeY4tgJIX4oTv2blNJ7a72HUno3pXQXpXTX0NCQGf9sXVJ5EbKBAhrGqt4QNg9H8chBmx276iwppZhNFTAUN+7YB2P2RuxsOk6mYDwrBnCuaZXesa+p6sUfDfpqykdWD7FgC4moBh7s+3v0YNnRn17K1Z0doKe8CFl7XRezRYgyrajgzKhOM2bUsQd9yBYlW8dP1pJignVm8zLiWsTe/Y694+6OREnj+CqAfZTSL3RuUucsVfWqFrzNDyUv39CP/37xjFUm1SSZK6En5EemIKIoya1JMREBC9kiJJk23babAdtdZGvksTeSuvQOaBVCdd9nNnrHHgn4cM3WIexUI/c3Xbgaq3uX26I92BY5S5+aPSRKFB/73svIFkV88dcvxL7JJDYPlw8Yjezc7MrgYJG2PhMqozYAMxqxM4kjU5DQE7Yuw7ooyjgxn8GWkVjtrBih2eGpPYulHZhxla8C8F4A1xNCXlB//YoJn9s2i1URl5GWsesGwkjmRVtHjl3/14/gfV99SjtcbEWKGYwFQKl95fpsd5EpisiXpIqDqEYRu1ZMZfMoN/2BZDTgw9c/eBnuvGk7AGVM4oeu3bTs71hd0etVi7wkmWI2XcCBqRT2nFaqR1+tyoGAsSK1mE2thllFNnPmQNnxVQ+FrgeL7FMWt8b+/H37cOMXH8XppVztrJgmEXtE8CIe9OHITKbh+7qBjiN2SunjAKwPGVuAObug34O8gQIlAFir6pqnFrPoCfdYap+eJ47Oa839W9PYlffOpQs1Z2OaDZNiWB57SPBq291GeexW69b10EfsRto0ANbLRj51ZyXJFPmihPlMESfmlQP7Vb3l3Vqzw35A32DN/oj9+Jzi+NYaOAsA7GsGx1osnJzP1s6KaXIfEEJw2YYBPHls3hL77GRFVp6y6j3We9vIg8J0zVMOlGmzAbrDTVLc9LDiEH0kZSVMiskURa2YitFo4WQHUnZr7PNVUowRtK24RbayboKiTJEtKU7uebWtrF6GM9LhsywbWess2QCajC4CPqY69g2DEUOfwSJ7qw9Q9U3n2smKAYArNvbj+HzW0R7yZrBCHbtyM7JI1kiZPstEcKL/RjtSDBvIUWvLaQUlUTmNzhYlRYoRjEkxPRY7y3ros6IiBiP2WMAHQqyzlRU/SbKMXFFZKJ8/uQi/l1R0czQSiAT9Xghe61sNszRF/X12dDaDwajQsO2BHpbvbvXuoj+i2DOVyNd8LoIGrusl6/oAAC9NLO/j3k2sSMe+mC2BkHJ5uBEppifkR0/Ib3shBaCkOkYEr+HIEijLC7W62FkBk2JSeSViDwteXLpeeQgaLZwxBxqWUUorMoaMTKUClCrJiOCriE7NhF1DUaZaOt7EYg69YaHiGjbTghl2tJllk5L0U7COzWUaTk6qpjyhylpb2S5yMpGrkblFmvaOB8rB4JLNZ0JmsyJnni5li4gH/ZpDNzpvc91A2JGDk+lUvmJkmxFYxFxLS7QCJsUsZovIFESE/F7cc9sunJjP1h0UDigpfrGAz9ZMg5lUoWLCUysnQGHBa9liKak5uKJEK763/rBQsesJNil9Z8RD1g+IYVKMXto4OpfGa7ePGP6MmE1STEE94D9TI2I3IsMA0HZO3Z4ZsyIj9qVsCX1hv5aFYESKAYCL1/bhhVNLWmRlF8dmMy2lOgLl6MQuKYZlxUgyxZmlPEKCF7GgX+sN3oi4zcO32eEem5bUStVrJOCz7NxCVBuNZUtSRZ1Fb9hfcQDdWsRujxTDctcTuRLm0kVsHDKmrwN6KcZax84Wy8mlHLJFEW+7ZByfumUHrtjY37SdACMqKHKc08PCO2VFOvbFbBG9YQF+NZI0+lhfur4fuZKkpaDZxYHpVEvFSYBeirFJY5dk9KvS1umlnOFME0Bx7HZq7CzThB3utbJOWxmxl2TFkLTqjFmrg/5IZcTerFkVIxb0WX5d2UjBo7MZrP+jH2Ov+mwYPTgFlGvqIbC8rQBrI3FqMYd0QcRQLIDfuHojgn6v4Yjd4yGIB+0NRKxgRTp2ZRiwXysIkWRjT/alGxTN+OnjC5bZVgtJpi0dnAL2SzEliVbMCzX6oABAj00DDEqSjMMzaRyfz8DnIfj0G87BcCyAG3YMG/6MiGBdxM6kGCZJrFMP7HurpRiDEbsds3qrnfH9e6cAABuHjGvsbGC81VJMTpXfZlMFlCSKDQPK4jMQCWAgarwdc4/NgYgVrEiNfTFbxObhqCbFsF7bzRiOBdET8jvS57pVKUbweuAhNkoxkoyxnhD2nlHaALUUsQf9Ff1QrOKuBw7hbx86jHPG4hjvC2HHWBxPffKGlj4jHPBi0aI+9+w+ZM54x1gcx+ez6I9USTEtROzWa+yVn3/fnil4PcRwDjsjZsPYueqpSZuGFcf+6Tfs0PR3I8RDPjdi55GEGrH7veWCEKP0hf3LKlfNptYsjVYjdkIIwoLPFimGUoqiKGvTiAAgGjDeYnjrSAxHZjOWV5/uU3vcvzKZxPoWpAI9VmbFiLq2DIDi2AGgrypiN5LuCNjjLKsd+2yqgDV9IcMJCYzBWEArxLOKQknC+oHygsPG8vWGhaZtkPX0hPxd35N9xTn2kiQjpQ4DZl9sK06zLyJYFrExaiVp6GUOoyjVn9bfgKK6MOorXC9c02v47792xzAkmeLhgzOm26ZHX+C1Zdi4VKAnLHgrUvvMRKwKMM4b78Gtl67Ba7YNV2XFGNsN9YX9Wl2BVdTSxbePxmu8szFrbOgfnytJ2DISg+DzYDAqaAVLrbISNPYVJ8XohwG/5/J12DAYwdVbBpv8rTJ9YcHyGZ2EELCZbf/8/l2YTxdx+caBlj8n5PfaIsWwLCF9lHbFxn7Df/+C8V4MRgU8cnAWb7rQuhks+gHQW0ZibX2GkhVjkWOvkgTjQR/+4q3nA6gsqDIasbMU2bl0wVBHyHZI5kX4PERblEbjQXz6V1sfkDbeF8ZP905Z2rQuX5IREbzYNhIzPASkFj02Z3FZwYpz7Am1s2NvWIDHQ3DN1tZaBPeFBeyfXNZO3lQ8BGDueG1/GNe3kBOsR8ngsMGxq1Wn+qiylWjIo2qyVi+Y+iHEW9t27Mo1pZQamj/bCtWH+PrI3O9rvUCJOfaZlDWOnVKKZK4En1dx7O++fC0+86adDesW6jHeF0JJophJ5THWY02Xz5xaEf2lWy/U+vK0g91ZXFaw4hw708dbGTOnpz9ivcauT6s2+hDXQt+Iy0oKkvJvCD4PfnjHVfB5Wlfw+sICJi3uv6G/FpvblmJ8EGWKoiR39N3UolQlxegrYv2e1jV2duBea9yeGeRKEoqSjJjfh3zuA6SHAAAgAElEQVRJRp8aLLXDeJ/izCcWc5Y59nxJQsDn1STYdukJ+VEQZeRLUkvZXzyx4jT2RW0afXv6Wm9YQK5knW5JKa3QWo1mQNTCPilGsVfwenD+eC/OWdW6xtobFrTmbFaRV6/FLeePtb0VZ31lshakPFYPmqhsfVx2mEadpz5itwIma95x3Wa85aLV+M1rNrb9WawXk5VN9gol2fBA+EZY3QzODlacY1/KlTX2dmBFOFb1Oa9OvewkIrBPilEckt/X/vbWjp1QXpSwZTiKv3vXxW1/RlhdEDIWFClVH57qnVA7uvNARAAh1kXszLGvGwjji79+oeGmX7Vgg02s2rVJ6i7LaA1AI0bVQ/iJOgPRu4GV59hVh9wXaS9iZxLOgkWZMWK1ztqRFOOzTIo5PJPGH33vJXz7qZNan5hGXRybYfVOCFBy+jvdOrNB11YsmNWHp/qIvR093+f1YCAiYDZljbNkk8h62tz96gn6vQj4PJYdSrL7ymifnUZsH1XOZ/ZZfNZmJSvOsS9mS/B5iOFWrdX0qYeCVnV3YweRjEZDKpoR8nssK3//zlMn8Z2nT+GPf7hXk7eM9typRV/Y2p0QoB6edejYw2qfeyuqJKsX9VZzwWsxGA1YFrEnsp3tfqux8lCSOXYzpJjxvhBiAZ/pjv3wTAq/9ve/wLMnFk393FqsuMPT43MZjPeF2s5oYJG+VRF7qerh7iTzIiz4LNPYp1VnURRl/ECdBevvwBGxndBipmT64dnfPXQYGwYjyJXkjuQCQBexW6Kxlxf1TrI29AzHg9Zp7B3KmtVYmUbIdq5mSDGEEGwfi2HfZKrjz9IzkyzguZP2NBlccY5975kkzjPQcbAecYt7R5v5pVqZFTOTzGPXuj7MpArakO9wB9Fwr7YTMnfBLIoy7nrwEM4Zi6NQkhBqsZlaNaxVgjURe9mxv+/K9aZ85kBEwNHZtCmfVY1WE2KCFAMoeftWVcqyNs1BEyJ2ANg2GsMPXzB3uL3ZC2UjVpQUk8yXcHIh21bWBkMbEmzRDVits3ZCyO9FSaKWRACz6QJG4kFct20IqbyIoN+DC1qoNq2mT51uY/YB6v6pJIqijD2nE5jPFDvW2FkFsBVVkqJMEfJ78duv2YQ7b9pmymf2hQXLZMOlXBGCz2OKbg1YG7FrGrsJ8hagXNdUQWyp5XMz2PfU6a7SCCvGsT9ycBa/PDwHAB059rDghddDLOtzbaYTZg9cKw2OjDKbLGAoFsC125QCr1dvHurIaVqlsT9/UhlhJsoUs6lCxxr7QDSAgYiAQ9PmR8GiJGO8L4Q7b9puWn50X9iPdEFEQTR/55bIltAb8ptWqKVo7NbshDUpxqTrGhK8oNTcZ4stambtgBqxIqSYxUwRt/3zU9rvd65qX4ohhFjWNY9SinufO23a57GbOF+SOiqhriZXlJBS+1lfuXEQ20djuPXSNR19Zq9F2UbPnVysiATNeLC3jERxcMZcfRVQUl2NjGdrBXYmtJQtYSRujlPbP5XEx+99GacWslr6rxkozbWsCZhOqx1ZV/W23nOpFkx2zJqQacUwewfUiBURse85Ux6Mcf54T8tj5qqxyrE/cnAWf/vQYdM+jx0UmZ1COKsbrh0SvLj/967BDee01/aAEfB50Rf2Y8rEtgKSTPHYoTlcv31Y296akRWxZTiGw9NpU7fhgNJSoFEWlM9DcG6Lu00r6i7u3zOFF04tYTAawA07Ovve9cSDSlaM3EK3VaMcncvAQ8qFUJ2i1TOYeNaSyJbQY+IOqBErImLfc7qclnT9duNDFeoRC/gtkWL0rUA/95aduHpza31sqmFVqxXzPU3gG7uPA6jslmgGYz0hTJpY9PHCqSUsZIq4fvswXj6dQCJXMiUrYutIFKmCiOlkAaNtdN2sh9ikAdahz93c8mdasRN6aSKBzUNR3P9715j2mYASsctUKf5iA67N4uhsGuN9YdPaQIQtGGSzpEpbdrBiIvZYwIcbzxnBWy8e7/jzYkGfJf2Y9U2g1vaHsXags+giYEHEfngmha88dgxAe62EG7GqN2hq5eHuo/MAgGu2DGkPTEjo/JYeVdMxzc4PL0lyRU+YagghLUdz/RFz6y4opXhpYgnnj7d/UF6PeEiJI604QD02l2lpXF8zrBg9mciVbMmIAVaIY3/lTBJXbR7EV963y5StWCxoTSHFYqb8mZ1UcTKsODxlwxDe/6r1bfc0r8eq3hDOmBixz6eLiAV96An7tQem08NToJy1YLYDkmSqjWs0C3YobVbEPpnIYy5dxAVr2j+nqgdLJTb7AJVSaoFjZ/UM5tm6lCvZkhEDrADHni9JODGfwdYR85xQPGSNxq7XQTupOGWwQ52CiRH7gmrjuy5fa7oWONYTQjIvmpYjnsyXNGfByt69bXSerIYtEqyk3ixKkvm9yDVbTdLY2VhIM50kw6oFcyFTRLYotTyurxGWROzZointGYxgimMnhNxECDlACDlMCPkjMz6zGZRSzCTzODKbhkyBzW32365FPOjH6aUcPvrdF0z7TKAyqjInYlelGBNT3ZiNfW1On2kEy1gwS2dP5kpa3QFzcGZkXVjlgERZNuV71xPweREN+LCQMcfW8sG5uTIcoOuaaPL5FbtnBztMmtCjOXYzNfZukmIIIV4AfwfgZgDnAHgnIaT1ESst8uyJRVz25w/ili8/DgDY3GEPZj3MWdz73GlTMyP0EXs7Pc2rCVpweFp27ObfgKyVwBmTdPZkvqQ5C6ax1xrl1irMsZtd+CNaELEDir1m7S5YQ7HBqPkLe49F7XDn1Xu238RgJKRKMTmTejEVRAnZomSbFGNGVsxlAA5TSo8CACHkOwDeBOAVEz67Lqeror6NQ+ZtHZljB5RMBjNkE6AyYq9uCNUOVhyeLmSK6An5Tc+3BqBtlU+a1JM7mROxSm0He9tV63FwJo0PXLW+489lnQjNdEA3fuERHJpJm3qfMoJ+j2nnLLPpArweYsmOjclmVkgxAEzNuWdNBDMm9Ax69sQCHj+kHPSv6zBhwihmOPbVAE7pfj8B4PLqNxFCbgdwOwCsXbu243+0Wqc1c9KJ/nBHlCjM+mj94WnEhIIiqyJ2Mx8QPcOxAIJ+D07MZUz5vGS+hO0hRYKLB/34m3deZMrnAmoUbGLEfmhGqWQ1Y6dWTcDnRcGke2A2VcBgtP1JSY2IBX0gBKZlnJ1ayOL5U0vaAjxg4i4jZGK641v/4Qnt550d9LFqBTMce607YJl+QSm9G8DdALBr166O9Q1WOPDoH14Hs5+V9bqDIyWyNsezL2SL+PVda/AbV2/oeHwXUC5QMrOcfDFrnWP3eAjW9UdwfN6ciD2RKx+emk1v2Jq+JmZ1ddQT8HtMuwfm0sWOC/zq4fEQRAM+03ZC7/zKbkws5vDbr9kEwNxzIcHrgddDTG+LvWHA/B1bLcxwiRMA9PXm4wDMbYtWg7S6RRrvC5k+yPetF6/GB6/aAMC8pl2UUixmiuiPCthi0kFvuaWAeRH7fLpoyTacsW4gjBPznUfsskyRLoiaxm42ZurWesxOdwSUGammSTGpAgaj1jh2oFx9agZT6lnNixNLiAV9pvS3ZxBCEBa8pkgxI7qOo1bshGphxpV4GsAWQsgGQogA4FYAPzThcxuSKYgIC15LLhQhBBsGlcWiepxZuyxkihBlimEToyE29NhMjV2J2K074Fk/GMGJhWzHZeVK5z2lFawV9IQEJEzKt5Z0/69mpGNWE/B5TXXsQxY6djM7PLKzsOdOLFmyywwL5swU9qhpw3/19gs6/izD/2anH0ApFQHcAeCnAPYB+C6ldG+nn9uMTEE0RaeuBzs8NOOQE4DWI2XUxDJ9j4dA8HlMS3dUdhWltscKGmFtfxhFUcZ0h+PcWNRnZcSeMCk3XL+dL1rQiTPg85hSyyDLFHPpgqlpg9XEQ+b1ZGdtCXIlySLH7jMl3TGZK+GDV23A2y7pvCreKKZ4RkrpTwD8xIzPMkq6IJra0bAapoWaJcVMq459xOQyfeWhNsdZ5EsyipJsaVtRpt/Op4sdTVJizsFKjX3JpMhSH/VZ0d0w4PeasmDMZQoQZWp6Kwk9PSE/js+Zc8ai3wkNWBSxd1p5KkoyMkWpItPODrq28lSJ2M3LhKmGaaGdSDFPHVvQDrWmEkrhh5kRO6Do7GZJMZqzDFl3E5rVl51lLllla39EQLYombIV11cvWtGqwiyNnWnWZt+jeuJB86SY+Uy5l89GE+tYGGHB23HlKcves2pnWY8uduySNp/SClhamtSmFDOXLuAd//QE3vWVJwEoUgwhMD3jwMwcZk3esCgKBnSzTztMJWS5y1bZys5CzGgEltFJMVY0l1Mce+cL0JklxbGz2gArMKsne7YoIl+SsaY/hHdfvhYfvXGrCdZVEjJBimGtSdyI3SCZoj1STKlNKYZVQD57YhEzqTymE3kMRgOml5QHfVZE7NY5drNmn744sQTB68FmkxuVMdgCPNPhWQBQJcVYELELJkXskwml6M9KKSYe8iNblDqeJDafVu6fO67bjM+95TxT61gYEROkmIQNwVItutexF0StGb4VsMNTqU0pRv+g/dMjR/Hvz5yyJIPDTCmG3YRWlj2zXhmLHfY2efLYAi5c02vJAw2Ue6V0GrGn8iU8enBW+71ZVcx6zMqKmUrkIfg8ltUxAOUspk7lmHK1qXUHvSETpBgWsVuVvVWPrh20kS5IiFqpsWsRe3sPjH5r/NXHlf7m53Qwsq8eQb/HtDx2Tbe28Cb0ez2IBXwdaeyZgog9pxP4rWs3mWhZJeWIvTPH/o5/2o19k8ogmLdfMo4PX7e5Y9uqCfg8KIoyKKUddeQ8k8hjrCdo6YQf1ut+YjHXUb68FW0EqgkL3o4rT+3YBdeiqyN2SzV2NbIyI2IHgFdtGsCfvvHcju2qJuj3mpbuaNdN2Bvxd+TYTy5kIckUO8baH1rejP6IAK+HdBSxi5KsOXUA+O3rNldUNZtFwKS+/FOJnKUyDABsG1WK8w5OdzZTlqUPm1kXUk1E8HU8Gs/V2FsgXRCRK0mW5rF7O9TYq+WRm88bsyS6CPg8eP7kEj79X3swl+4sumT6r9U3YV9Y6OjwdFGN1szsDVKN10MwEBE60tifO7lU8fuwCfNYaxHQWkt05thnUwUMWdCuV8/a/jACPg8OTnXm2I/OpiH4PFht4UFvSFAkrnaDO8CehIRadJ1jl2SKiz/zcwCw9PDU36nGXiWPtDqk2CjXbB3C9tEYvrH7BP796VPN/0IDknkRQb/HtLmR9egNCx0dns7bsA0HgOF4oKOI/fRSZb62GYO2a8EqkDvNjFnMlixp16zH6yHYPBzFQbUpWrscnc1gw0DE0hJ9M+aesog96kbsjdk3mdSKMejyXmOmoUXsbaY7sujp1y5eDQDYPmreIBA977tyPe7/vWsQDfi0TIF2SWSta6qlpy/cnhRzeCaFh/bPaH/Xasc+GA1groNrWn1AHLbooFdz7B2ctUgyRTJf0rKWrGTrSAyHOpRijs1lLGmBrEcbj9dBI7BEroSI4DU9G64ZXefYf3lkDgDw3ivW4c0Xrrbs32FDh6U2pRgWPf3u9Vtw+HM3azeJVShtZjss+snbM5OxLyy0lRVzwxcexQf+5Wnt4Mzqie/xoB+pDnKul7JF6M8hrehxDyiVp0BnUkwyVwKl1gxYqWasJ4jZVKHtITYlScbJhawNjl2dotRBI7ClXNGWxbKarnPsTx5dwMahCD7z5p0YtrBCjkXs7faKYQ9Z0O+17IHW09fhgSRQOZHISobjAaQLYtsHU/Np64aB6IkFO5t9a9fwYjOkGNY+wY7RbX1hAaJMkWrz+59YzEGUKTYMWlPDwDBj7mkia98Aaz1d59jPJPKm9DJvBss37vTwNGBiK9FG9IWFjnqbvHhqCS9PJCwZiVYNy7xgmQ2tcmoxa0lvkGpiQX/Ljn3fZBI/eOE0AEWztnpXAegde/sROwsK7IgutQHcLe7a7nrgEG69+wnNVisPzwHdeLxSZ1KMXXNO9XSdY09ki7Y8LCxi7zTdkaWiWY1yINm+Y7/70aPweT2486btJlpVm9G4kskw1ebs0+NzGUs7UDJiQR+KktxSAdjNdz2Gj3xHGYK+lFW24VtHrA1EBBM0dibj2fFssX5Brfa7PzSTwuGZtG2ZJmaMx7NzgLWeritQWszac6HYYUfbBUrqQ2Z1hgmjN9SZFHNiIYPzVvfYshvSIvYWHLv+AOv4fBabh605jNbD0j5TebHlCteSJGMpW8JAVMB3P3RlRylzzQiYMEmLBQVWDllh9LbZLyhfkpEvyVq/HasljpAJUsySK8U0J1+SkCtJtmwXO4/YJfi9xJKp9LXoU0e5tWrvT16eRCJXwon5rG2DdkfbkGJmkpVph/ZIMcyxt74TSuZKWMwq06gEn8eyVEfALCnGTsfeXr+ggqg8/1r/FQu7kALlrJh2pRhKKRK5InosbINdj66K2JM29DJhsMrTUgdSjF3ROqA8LJQqTsjowjexmMVvf/M5XLimF6m8iLX99jj2oN+L3rBfazplhOmqRaDfhrOAWEC5z9o5QE3kSliyaXfJhpp30pM9kS3CQ+ypkNQ6fGZac+z5kgRJplhIW9vZk9GpFJMrSShJ1NXYm2HnyX053bH9XjF2HZwCSlYM0Nr2luVov3BKqZC0y7EDSs/vVqSY6apCoTUmz7mthV6KaZW5dBHpgmjp0BIGCyA++f2XMd9m9fGiKhnYMZOTBWatSjGsUGg6pTQrs6oBHIPtstrtyc/kLTvOLarpLseuXSgbpJgOB23kS7Ktjp1dk1Z09pmqKHitTVIMAAzHgy1Vdc5VvdcO2YiNXmtFimF562xgd5+F82MZ7D5L5kV8+cFDbX3GQqZoy4E0oOTzx4O+lqUY1uxuJlmwpZCuXKDUmWN3NfYmaCf3Nkbs7Tr2gihbHlHo6VGvSaKFKEgfBQteD9b1W1vwoafVHPHq99qxu2gnYmdOdu8ZpfmXldOIyv9m+T779lOn2joTOJPIYVUHowpbpS/Ser8glp00k8qjx2J9HVDO2QI+T9uVpyzrp8eVYhpjpxSjFSi1nRUjaWlodhDRDnqMRxezuoj9orW9lh7wVRMP+lqaJpTKlyqaaFndhRAoa7itTPxhi/mzJxYBwLJBIHpiQR/eeMEqvO/KdShKcltppGeWcljVa/01ZfSGhZazuFjEPp3M29YGNxLwVUzAagU227WT9sTt0lWOnUWjdmTFaMOsOzk8tTFiD/lb1wP1vcZ/97VbTLepEbEWy/VTebHiYM+Oat5oGxG7oNr18ukEBK/Hlp2Fx0Pw5XdehJt2jgJAy/1tiqKMmVSho+HirbJpMIJ9k8mW2goUmMZukxQDKP2I5lKtpxFTSvGtp05g60gUW2xY3KvpKse+mC3C5yHaabWVeDwEHgKIHfSKsVNjDwrKv9VKxD6dzGPn6jgOfPYmXLV50CrTahIP+lAQZcOZHOmCtaMQa+FV77VWHLs+3XT9YNiWBYjBIkP9kGcjTCfzoBSWtsCt5oqNA5hLF3GohS6P+nvbroh9NB7EdButmw9Op7HndBLvuWKdpYNL6tFV6Y6sisuuC+Xzejo6PLWzuT6L2FupkpxJFTASD9qalsnQH0wOGNiqJvMlxIJ+PPOpG+C18UHpjwqYWMw2f6OK/vrbIcPoYbn91QfNzTi9pKSdWjnEuporNw0AAJ44Mo+tI82LzURJrngW7dDYAaWv0e4jrbcYZuMQbzxnxGyTDNFVEfv/ft02/PuHrrTt3/N5SPsau8157ME2pJjZVAFDDuh/QOsHk+mCIsUMRgO2ZW8AwHXbhvHooVnDDcvyuh3ItVuHrDKrJr1hAR5S7ldvFFZPYKfGvqY/jIGIUDFhqhH5qp2dXVLMaDyImVQBcosB3qOHZrFlOGqrvKWnqxx7f0SwpeSd4fOQDjR2SSscsQO/1wO/l7QkxSTzJUdO7IFyxG70YLJaY7eLW84bQ74k46EDM03fW5KUaTseAtx25Tq8Y9caGyws4/UQ9Eda7yE/saA4drud0FAsYHgRqt6JWt2LnzESD0KUacuL5bMnFrVdiRN0lWO3G0WKab9XjN0SR9BvfPhuSVL6bsRs1q0ZrUbsqXxJqwS1k13r+yH4PHhpItH0vcz5/NHN2/Gnb9rpiLY6GBVaLlI6sZDFSDxga1YUoJwJGLW12rHb0foAUBw7sLzyuRG5ooRsUXIsWgc6dOyEkL8khOwnhLxECPk+IaTXLMN4wOchLfVeWcgUcc9jR5EtikjlS7Z1dmSE/F7DGrtTQ3YZrfZhSedF28eLAUoUvGkoamj4sr4Hv1MMRIWWZ9+emM/YWsPAGIgK7UfsNrSUAICRuCJVvv0fnzBcUMXy151oJcDo1PP8HMBOSun5AA4C+HjnJvGDz0Na6sf+Jz/ci8/+eB9u/MKjSBdEvMZmjTUkeA1r7OXB1c7cfOUc8eYRuyRTZIqSY4vQ1pEoDk03P0BjzifowGE0YyBiXN5g2NkATs9AJGB4nGO+qiVxv00ROztQzpUkPHFk3tDfYdPB7JhGVY+OHDul9GeUUvZk7gYw3rlJ/ODzelo6PGWyzemlHG7aOYrXnTtqlWk1CbUgxTgdsceDxhtspdlAYIdkoy3DUZxeyiHd5ACVOR+7d2p6FHnDuGPPFkXMpArOOPaogHRBNLTLdFJj//I7LwIAHJk1lh2jVZw60NWRYeYd+EEA95n4eY7T6uGp/mZbN2D/1lbR2I0tREwCcSpij7YgxaQK9gxWqMcWNR3vSJOcay1id1iKMeosASVaB5y5X9m0LiM7jGURu42ZUW+8YBVW9QRxdDZj6P1ab3sb+gTVo6ljJ4Q8QAjZU+PXm3Tv+SQAEcA3G3zO7YSQZwghz8zOzppjvcX4vAQ/emlS637YDL2TXGdjp0RGyO9FzmD5c9LhiL2V4h/2Hic0dqBcuDPZpFSfDbpw0rG34iwB4NSC4tjt7OzJGIioBVUGzgSqd6Jhmw96Nw5FjUfsNjYrrEdTx04pvYFSurPGrx8AACHkNgBvAPBu2qA+mFJ6N6V0F6V019CQvdpzu3jVRmB//pN9ht6vj5KceFBCQitSjLNRMAAMRAMVbQ3qwSQQp6SYoZjigJodSrKoMmhjxXE1zFkaLVJiw07s6L1TDZtZauSwt3oHYnfG0aahCI7OZgy1QFi0sVlhPTrNirkJwMcAvJFSarw8r0tgRQnPnljUprY0Qn/zrXHKsRs8PHVaYweU1rvH55pvbzXH7pCtbNvfrM0wL1IMYLytwFQiD5+HGKr+NRvWAsFI3n0rFdVWMN4XRqogImWgUC2RKyHot75ffCM6DS3+FkAMwM8JIS8QQv7RBJu4YVaNJCSZ4vFDc03fz5yqz0NsLc9mKOmORjV2Z50lAGwcjOD4XPMoKKtOsGEdLO3G7/WgL+w3HrE7KsUYd5aA4thH4kHbRjjq0RYhI469g+lQZhBoYUrVYqboqAwDdNgrhlK62SxDeGRBp1M+dGAGt5w/1vD9rCH/X7z1fEcelNayYkoI+b3a0G4nWD8YQaogYj5TbNjalLVNtVtX1TMUCxhw7Mq1t7P5WzWtOEtAOTdgudp2ExZ8CAteQxo7S8/9x/dcrEljdsK6dhpx7KynlZN0VRMwp7hgTS8ePjALWaYNR4flShIuXNOLt13iTNZnq1KMkzIMoDh2ADg2l2no2LPq9jfikMYOKJFwsyg4z8HhKXOWRouUppN57BiLW2xVfYwWKR2YSmF1bwg37WwcXFkFm61gxLHPJPOOO3a3pYAB3nXZGsylC3ilScOifEnSuiw6AWsp0Eza+MELp/Hvz5zSTu+dYsNA2bE3IqMuVk5G7Ipjb+ws0xycWwCqszTg2CmlmEzkMerAwSljINL8ugLA/qkkdow17wJpFWxnW2pS1/L08QW8OJHA1VucTRBxHbsBLt+gNPNp5thzJcn2fht62KJSaBJVPLRfaWi1c7VzkRoAjKndBKtnr1aTKYjwqWPKnGIwGmh6eJrMl+D1EEcXIAAYigYwnTQgb+RF5EqSLeP76qH0tmkcsRdECUdmM9g+6tz9yiL2Zs/WfzxzCvGgDx+4ar0NVtXHdewNuHnnKNYPhDHeF4LfS5oWKGSLTjt2ddhGEzmmJFP0hPz4x/dcYodZdQn4vOgJ+Zs6zGxRQljwOtJUizEYE5AtSg3nX6byyjAQJ+0EgA2DxnKu2Qg9pyP2Zhk8h2fSkGSK7Q5G7JoU0yRin1jMYdNwVBuE7RSuxt6Af9A5vrX9YRyba1J5WHRWigkHyh0TG/UsT+ZK2DAYwbCDkRpjKBbQso/qkSmIjurrQLk3yWK2VPehTeVFxG0aANGIrSNRfO+5CSSyjdsysz7sTuSwMwbUiJ1SWndB3D+pNGBzNGI3eHg6lXD2zILhRuwG2TgUbaoF5xzW2NkDyh7YeiRyJfTYNFqsGUMGJA4WsTsJOwxLNDiXSOacaS1cDZtIdHCmcUdK1op2xMEFfiAagChTJHP1d0L7p5II+DxY70A/GwaL2Btp7JRSnEnkHF0oGa5jN8jGwQiOz2cbtvF1WophufNnusmxx5o79kzR+YidzdhkDZ5qwUOmEQBsGVGG0TRqNfzZH72Cj33vZQDOOnbWAmGugRyzfyqFbaMxW+fHVmMkYl/KlpAvyRhzoIalGtexG2RNfxhFUa7rhGSZoiDKjkbsq9TG/meWGh9GdptjzxY4iNjVgpNkgwrkZL5k25DlRqzuDSHk99Y9E6KU4r9eOANAcayCg4fSbFE5vVg/GNk3mcL2Uef0dcBYuiMLqFa5EXv3wLbi9Ua5sRxmRw9PBS/6IwLOLNV/SGSZIsmZY88UpYYzRdMF0bE+MQymVTdKEeUlYieEYDAmVBTY6ZlM5LUUw1Zm5FrBFnXg96E6ndE9MSgAABbpSURBVDMXM0XMpQuGBl5bCUt3bHR4OqkGVG7E3kUwR1ivZwyrOnUyYgcUnb2RY08XRcjU2QZFeoaizRtsZYui41kGzb5/QI3YHWyqpqc/XN+xvzRR7laacdixD0QD6I8IOFRHNmJNypxo0aEnYCBin3SwoVo1rmM3iDbxp86DzSIfJyN2QHkAGkkx7PCPB8kAAAbV8vBGckymKCEScPa6RgQvfB6CpTrfvyxTpAsi4hxE7ADQFxG0LoPVvHw6AZ9aQf0qBwcuM7YMR+tG7Kz757ADbQT0GEl3XFDz8e3sFV8PPu7CLoA5wrpSTImPiH11bwi7G4zwYhEnL1JMT5PrCigtBZyO2Akh6An560bs6aIISp0bXFJNf1jA4TrOciFTRF9EwA/vuIqLHcaWkSh+8MKZmimPs5pjdzYK1ipPa0Ts8+kCIgEfFrNFxII+R/svMZy3oEtgkVi9tKwMJ1LMqt4gUgWxrqNMcubYy0Ota19XWabIliREHN4JAYrOXs+x89AGWU9fRMBiHSkmU1Cu51hPyPFsIwA4b3UPUnkRB2vMlZ1JKbtPJxp/6WkUsd9012O49HMPYDqZR59Ns1ib4Tp2g8SbaKxs29uoMMgOtJTHOjo7bxF7U4mrJIHScvGVk/SE/HXz2J0eNVhNf0RApijV7GPOw5mFnmu3DgMA/kdtdaFnJllALOBzXOKsl+5YECXMpgpI5UXct2fK0QHWelzHbhC/14Ow4K3rgHjR15o59gW2AHESWbAIN1knYn/5dAIAHC1OYfQ2kGJm1N4sTn//DPb91sriyRQkx7OM9Iz2BHHOWFzrYaRnNlXAkENthfX4vYpEVJQq61iq0zSdDuwYrmNvgXjQX1fiYBkITj/Yq3sb57LPpgogpNy322mCfi8En6fmdZ1PF/CNJ07A5yG4avOgA9ZV0hPy1z2Q3HtGaRB3Dgfl5ADQrw5SrpUZky2KCDt8GF3NJev6cKBGZsxsqqBlTjkJIQSC17MsYj9V7dg5CZhcx94C8ZCvrsa+kC3C7yWOZ0UMRgPweUjdiH0mVUB/WODigIcRD/prXtc7vvU8fvzyJC5c08uFxDEQDWh9TarZczqBNf2hhr1Z7KRP622z3LGnC6Jj06jqMd4XQiJXWrbAz6TyXPQ0AhSdvdqxn1SHgW9Vq315SSPm5+nuAuLB+lvxhXQRfWHB8c5+Xg/BaINc9tlUwfGDqGriQZ+mUTNmknk8cXQeYcGLT9yywyHLKhmNB5ErScvmXi5kivjZK1M4b3WPQ5Yth+0caw2x4KH3TjVsRvDEQvm+zZckTCzmsLbf+YIfQHXsUuWZxcRCFoLPg3NXKd99vxuxdx/xUH0pZj5TdFyGYazuDWmRRDU8OvZYyL9MY//p3ikAwA/vuAoXr+1zwqxlDKtab3X/+HfevRslieLCNb1OmFUTdtZyokbjOh66ZVYz3qfYe2qxfN/um0xClCnOW83HdfV7CUpi5W7t1GIW470hrc6Ch0N+wHXsLdHTwLEvZvlx7DvG4tg/lYJco2EZj469VsS+90wSg1EBm4edLSXXw/qa6IdYSDLFkdk0LlnXh9tetd4hy5YTCfiwtj+M/VW6NaWUz4i9T43YdZr1SxPKwfkFa/jYCSkRe6UUM5nIY6w3qGUZ1cpCcgLXsbdANOCrm2+9wFHEfs5YHNmihOPzldEapZRTx+5flm10ajGL8T7nM2H0lB17OWKfSxcgyhRvvnAVAj6+nOX20Rj2V039KkoyRJlyF7H3hv2ICF6c0u00X5pIYDAacHTCk55ah6czyQKGY0FtV7lZ7X3jNK5jb4Fwg2HR8+kCBnhx7KuUzAyWqcFI5kQUJZmLLAM9seDyBXNiMadtz3lhRJVipnSO/bR6lrGaM1sBxbEfm8tURJHZgvPzY2tBCMGGoQgOTJV3GAenU9gxFnP83Ioh+LwoSjK++eQJHJ5JaYHScDyAm3aO4oGPXovXnzvqtJkAXMfeEkG/FwVRXiZxFEUZybyI/ggfDnPrSAx+L8GeM4mK1x89NAsA3GQZMKrPLiSZ4sxSTjtQ44Ww4EMs6NNy1oFyvYDTTapqsX0sDpkq/cwZGXW0H29ZMQDw6s1DePr4AlL5EiilODaXwaYhPiJgABC8BJmCiE9+fw/+9L9fwVK2hKIka+0OeInWAdextwSLcnJqBLTndALvvmc3PvrdFwCUhzM7jeDz4JxVPXj+RLmLX1GUced/voRzV8Xxmm3OTlCvJh70IV+S8fUnjgNQpI6SRLmL2AFFjtEfTPPs2HetU+SBp48taK+xLqS85bEDwPXbhyHKFI8enMNsuoB0QcSGwYjTZmkIPo82J/axQ3N48pjSk8npBmW1cB17C4SqHPt9eybxi8Pz+NFLkwCUuai8sGtdH549uYiHDsyAUorpZB65koT3XbmOi8ZPeq7Zqiw0X37wEIDyAdoazjR2ALh26xD+Z/8Mrv3Lh3BoOoUzS3nEAj7urimg7Mw2DEY0BwRA63vPm8YOABev7cVARMAPXzyNY+qQkPWcOfbTujTiL/z8IABnJ1DVw3XsLRBUG3wxnb26XJsn6eDS9X2QZIoPfO1pvDiRwGSC9YrmL7I8f7wXd1y3GYvZEmSZYkJNeeMxYv/9G7di/UAYJ+azuG/PFCYWs1xG64zL1vfjqWMLkGUKSab49lMnAfApxfi8Hrz1knH8dO803vvVpwAoIyl5we/1oKAenm4cjGhNy1ZsxE4I+d+EEEoIcb7u20KqpZjq3ty8nN4DwKXr+7WfzyzltAHXqziRi6oZiAqQZIpErqRF7Dw6zGjAh4f/8DpsHo7ipYklHJpJc6WtVnP+mh4k8yLOJHL4/vOn8d1nJgDwd3jKePfla9Eb9mtphTzdA4KuWvvOm7ZrPw9z0Mummo6XbULIGgA3AjjZuTl8E6qK2BPZElb3hrTtmdfDx+k9oJS/7/74a3HF5x/ETDKPvBppjHIYsQP6SskCJhazGIoFtB0Sj5y/ugcP7JtGqiDiLRetdtqcumxWDx8Pz6Rxz2NHtdd5dezrBiJ44f+8DpOJHJayJa6eKf1s2Gu3DuHzv3YeHtw3w1WnTIYZEfsXAdwJYHk1zAqDaezsAGopV9R6RPDIcEzpGzOTKmByKYdY0MdVVz89g2oK5ny6iNNL/KU6VnP+uBIJUwps4aiIqhq2m3jh1BL2T6Vw885RXLt1iKtIuBZjPSHs4KShGoMthmHBi5DgxTsvW4t7btvlsFW16cixE0LeCOA0pfRFk+zhGhaxs7zgpWwJfWEBf/j6bfibd17kpGk18XgIhmIBxbEn8ljFabQOVPY2UXLY+TmvqMVrd4xoP/O8uA9EA+gL+7H7qHKA+vpzR/GvH7yM690Qr+xS5c2sw3NijdA0fCOEPACgVtb9JwF8AsDrjPxDhJDbAdwOAGvXrm3BRH6ozopJZEvoCfvx4es2O2lWQ4ZVx76YKWKUgyG79WBthOfSBZxZyuHmnWMOW9SYNf1h3LBjBA/sm+Yqc6MWm4aieFJNeeSlXXM3ct22YadNMExTx04pvaHW64SQ8wBsAPCiWhk2DuA5QshllNKpGp9zN4C7AWDXrl1dKduE/crlyhYliJKMVEHkZhJRPYZiQUwsZjGxmMPFa/loplQL1mZ232SK2xz2av7pvZdgMVvkqgVyLTYMRvDMiUUAwAAnRXTdyFAsgM3DUVwwzu9zxGhbcKWUvgxAW8IIIccB7KKUzplgF5cEBeUBzpUkrRthL+eOfTgewAP7pgHwVRlXjd/rQU/Ij5cmlKIqHkv0q/F6iHY2wDN6WWvQjdg74oGPXuu0CYbgO9TgDKaxZwqiNsCgl5P+y/UY0U1356k8uxYDUUHrb7OmCxx7t6Df/fAyus3FWkxLkaCUrjfrs3iFOfa/uG8/nj+pbG15mZhTjys2lvPZN3EcsQNKpelRteKQ96yNbkLv2HmXjVzMwf2WW8Cneyh+uleRN3jX2PWFSjxWyOnZNqqkDfaG/VzmBncr4xxVRLvYg/v0dMimQb6jYI+H4O/edTFOLmS5aX9ajy3qjkKqMSDEpX1GOF/QXczHdewdsKonyL0UAwC3nM936iCDRezdkCfcTbCdJk9VnC7W4jr2Dohx2NGvm2FZO9tH+a3k7FZ+/vvXIBp0H/ezBfeb7oCbz+NjWspKISz48PUPXobtY65jN5stI+41PZtwHXubPPgH12LDAN8Vh90I683u4uLSPq5jb5EX//h1IARcDlZwcXFxAVzH3jK8pze6uLi4uHnsLi4uLisM17G7uLi4rDBcx+7i4uKywnAdu4uLi8sKw3XsLi4uLisM17G7uLi4rDAIpfY3XCKEzAI4YcJHDQLolsEerq3W0C22doudgGurVZhh6zpKadMqPkccu1kQQp6hlPI5JrwK11Zr6BZbu8VOwLXVKuy01ZViXFxcXFYYrmN3cXFxWWF0u2O/22kDWsC11Rq6xdZusRNwbbUK22ztao3dxcXFxWU53R6xu7i4uLhU4Tp2FxeTILwPldXRTbZ2E7xcV+4dOyFkvdM2GIUQsosQMuy0HUYghNxACLnEaTuaQQjp0f3MxUPTgG5qg91V/acJIV6nbTAIFz6VCyNqQQi5mBDyAIA/4/1LJYScSwj5JYA/BtDrtD2NIIRcRAi5D8D3AWx22p56EEIuJ4T8AMA9hJAPEkIClNMDIULIFYSQb0K5V7fwfL8SQq4khPwHgL8ihJzTBbb+GQBQSrmecE4IuYwQ8m8APk8IOY8Q4qhv5c6xE4VPAvg2gO9QSt/HvlSOI7aPAPg+pfRXKaUHAf5sJYR4CSF3A/gKgH8C8C0AO9Q/4+o+IIScD+DvAPwngP8AcD04XYQIITsB/A2AHwGYBnA7gPepf8bbPTAM4G8B/ARKBeRHAHxQ/TPebL0NwL8C+BQh5B3qa9ztiAghHkLIHwO4B8B9UHZtHwZwgZN2cfVAA4AalfkBPE4pvQfQokwfbxGb6iz7AVAoDwwIIW8hhIwDCKm/5+KBURfH+wFcTSn9LwDfA3AdISRIKZWdtW4ZlwA4TCn9BoCfAwgCOMn+kJdrqnIFgP2U0m9DWTSzAN5NCFlPKaWc2XoBgIOU0q8B+GsA9wJ4EyFkK4e2noayoN8ExVZQSkXObIT67EwAeD+l9JsAPgdgHQBHd0JcOHZCyLWEkMt1L/0lgNWEkL8ihDwN4DMA/pUQ8jZnLCyjt1V1llkA1wC4Xt2KfQjAZwF8SX2PY4tR9XWllN5LKc2pD4cM4CCAsFP2MWp8/z8G8BZCyOcAvAxgHMCXCSEfA/i6pgCeBrCGELKJUpqBcl0TAH4TcNzWNxNCPkEIuUV96QUAu3S2Pg3gGSj3LC+2vkF96SEA05TSnwE4QQj5jPq641F7jev6bQAvqnLhPIAUgDHnLHTYsRNCYoSQe6HovR8ihPQBgHrTfQPAhQD+gFL6BgCPAriJELKVM1vzAL4GRTr4KaX0JgCfBLCTEHIzT7aqMhdRH+D9AF4LJRp2JApucE1noESXPgCfoJReAeBfALyaEHKl3XbWsbVf/aMjAJ4C8DVCyH8B2AVFPvIRQoIO2Tqk2vJRAAuqbW+jlM5C2an9jvrWJQAPAAgTQhxxRDVs/WdCyFvUoIndkx8C8LuEkBFKackJO+vY+jXV1iylVKKUFgghfiiByAGn7AScj9iLAP4HwHsAnAHwdvYH6rbmHZTSR9WXHgAwBCBtt5EqdW0F8PdQpJchAKCUngbwOJTozQlq2kpVCCEeSukEgCcBvI39GS92qvbsB7AdwCn1pWcBzAAo2Gwjo941TVNK7wRwB4B/oZT+KoDDAM5XF30n2ATgF5TSayil/wjgDwD8vvpn3wawnRDyWlVGmAewGsouwwlq2fqHAEApLRJCvJTSvVAWy78AAKcCpka26tgBZadxUA0GLrPdSjjg2Akh71O3s72U0gKUQ4cHoMgCu1hErkaWC7q/eiMULds2x27UVkppGkoUdBsh5EJCyG8BuAHAcd5sVZ26rB5EHQKQscvGVuxU+RmAP1F3E7cCOBeKI+LB1kv0tlJKX1LPLgBFG95t5y5ItfU1hJAwlEXw6+rrXgCvqL8ARdr6DoC7CCGboezaCACBI1tfVn9PoDzzoJT+BpTnaxHABcSmA/8WbGUSUT+ALCHk/QB+CeA8R3bDdgRq6v/YKJRMDBnK9jUC4COU0jn1PVsA3AYgTyn9rPqaB8CrAdwF5fDsY2okx52t6uu/DkVCOBeKhLCXR1t1zv2LANKU0k9zZGeBUvoZ9bUQlP4aw1AOo36XUvrK8n/BMVurv/9LoBz0SQBup5QecdJWNdqVCCHvAfBGSuk7dH/3TgBboeyKfpNSuo9jW9cB+CKAAQAfppTu4djWzwP4GBTp8EuU0pestLUulFJLfwHwqv/dCuDf1J99UFLEvlf13rdAkTU2Q9F+CZTtzxuttrNDWyMA/OrrhHNbgwAidtnapp1bAIR17x3l/JqG1NcGAFzLga33Vr3n61BkTeivJQCBc1uH1P/2AriMc1tH1P9eBeDX7bC10S/LTpjVrcmfAfASQn4CIA4lkgFV0pZ+F8AZQsi1lNJH1Ne/TwjZASUtLwrgeqpEaFZHPmbYeh2AfVT9ds92Wzu08z4AUULIdVSJJKesstMEW+9XbWX36iO82QpFvjxGlGKfXyOE3EQpnaCUFrvA1l+hlJ6EckDNu603U0p/YaWdhrFo1bsWwIsA/gFK2tejUPJRT0K38gL4LQAP6X7/diia71cADNuxsrm2nr12ng22QpGw8lBGUX4JahTs2tqdthr+f7LoQl0N4L263/+9elHeD+BZ9TUPFB3ruwA26P7e1bZeANfWs9bOs8DWdVCkzC8BuNi1tfttNfz/ZNGFCgMIoKxFvRvA59WfXwDwO+rPuwB829EL4Np61tq5wm39jmvryrPV6C9LUoaokrBfoOXGPTcCmFV//gCAHYSQH0HJqX3OChuM4tpqPt1iJ7CibX0WcK79gmurs1hanqvmelIAIwB+qL6cAvAJADsBHKNKMY/juLaaT7fYCaxcW6kaajqFa6szWJ3kL0Np6DUH4Hx11fs0AJlS+jgvD4qKa6v5/P/t3TFrFFEUhuH3Q4KIShrtg4WNhSn8AaksxMLCNIKVIAhWYm9jHVCiaKdYWPkDUlkpgiCJYJ1S1MIigimSHIs7QhBiDGY3yc37wBazO7OcaT6We3fOOSh1grWOirXuhVGv9dC6323QHrG/sddrT9ZqndZqrT3U+rfXyJ88TWthex2Yq/ZY9r5lrbvvoNQJ1joq1jp+Y2kpIEkan73u7ihJ2mUGuyR1xmCXpM4Y7JLUGYNdkjpjsKt7SdaTLCb5lGQpyZ1sM4EnyVSSa+OqUdpNBrsOg59VNV1V52h9QC4B97a5Zgow2HUg+T92dS/Jj6o6sen4DPAeOEVrwfqCNgUL4HZVvU3yjjaYeBl4DjykDVOeoXUCfFRVT8d2E9IOGOzq3p/BPrz3nTbvc4XWC2R1mGX6sqouJJkB7lbV5eH8m7SBGveTHAXeALNVtTzWm5H+wUi7O0r72O+2qxPAfJJp2ii0s1ucf5HWGOrqcDxJm81qsGvfMdh16AxLMevAV9pa+xfgPG3PaXWry2gDFxbGUqT0H9w81aGS5DTwBJivtg45CXyuqg1a86cjw6krwMlNly4At5JMDN9zNslxpH3IX+w6DI4lWaQtu6zRNkvnhs8eA6+SzAKvaQOqAT4Ca0mWgGfAA9o/ZT4M03O+AVfGdQPSTrh5KkmdcSlGkjpjsEtSZwx2SeqMwS5JnTHYJakzBrskdcZgl6TOGOyS1Jlf9dv0AypigZQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(sorted_data['CO2_ppm']-sorted_data['mov_avg'])[-400:].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is clear that the oscillation is quite constant and it resembles to have a period of approximately 1 year. To compute the period quantitatively, one has to identify the location of local maxima and/or minima. The library [scipy.signal](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.argrelextrema.html) allows to easily address this task.  At first, the data is smoothed as shown before but using a period of 15 weeks in this case, as we want to follow the oscillations. Afterwards, local minima and local maxima are found defining the values of the dataframe, i.e. `sorted_data['hf'].values` in this case, and the window within which to look for the minimum/maximum by feeding the command `order=` that is set to 30 weeks in this analysis. \n",
+    "\n",
+    "The raw dataset along with the smoothed oscillations and local maxima/minima are inferred below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f210f773f98>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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m93/y+zUfh1VYKuxCiIeFELdYec6NcjJ5kl3RXfUeBgPzA0zmJpkpzJBT6yPsU7kpQt4QLd6WuljsQgjuvRfe9z7ByMgxOP0G5GOv5ivfStnWtGE5Fi6e7m3fy3R+urYDeBEihKi7sKu6SkKuTTu7Ja9vqFWjp2SUKBklHh17tO7rcxvlorXYR7Oj9Db31uXahVKBZ2eeRS/rTOenCXlCZIqZmlvsqq4S8UeYKczQ5GmixddSN4v9zjtBUd4J7IYXvgPj16E557nzztqOQyCQNRlJkuht6WU8O17bAZzhD37yB4AZV//IyCOoulqXcTw8/DC7orto8bXUxTVWQTXMf3+9koNKRqkaWKCVNUpGibIo8/zs83UZz2a5aIVdCFG3WNTjieP8+X/9OWOZMa7uvJpmbzMuh6suFnvQHUQzNEKe+lnsACMjs5hr6wDfBLUEvkxN6novxO/yc3L+JFuat9AebK+bxf7IyCMAvPtf381dj99VTZqqNfccvofffcXv4nP5quJaaxJygpJR4vL45cwr80iSVHOBV3X1rLAbprBH/dG6RJFZwUUr7PUs4qNoCk9OPsn/Gfg/3HbpbTR7mwm4AxS0Qk39/UW9iM/lo8nTZLpi6mixRyL3AWXgbnODOgzerC11vVfC7/YzMD9AV6irrusvFet4KDXEcHqYfClfl3HMyXN0NnUCIFH7Z0YIwQ1fu4HRzChv2vkmmr3N+F3+mkeyqYYp7H6Xv+qKiQYawv6iot6FfBRdYTA1yPeOf48PvexDXNlxJQF3gJAnVNMHWDXUqrA3eZrqarF3dPwLknQpcCZgSj2N05/bdF3v9SCEwOfyMZWfIh6Mn7O91lT82QWtwHMzz1HQCjUfQ4V6GkHPTD/DszPPMq/M84btb+BNO99EwB2ouW+74opp9jbzi/FfMJgaJOqP1nyWbRUXpbDn1Bwhz9kk2Fo/uIqm8D9f8T/5zlu/g8vh4p7b7iHgDtDd3F3TH0pRL+J1eU2L3WNa7PWwQFRV5cSJn3LrrW+lry8MbCHgPcaeq9SaloNVdIWgO4jL4aIt2AZgVpysw8sup+bQDA29rKOVNQql2gt7ySjhdrhrft2FjGfHCfvCpItpYoEYH3rZhwi6gzW/H6qukivlCHlDPHD6AZ6aeopoIFrzdTGruCiFPV1ME/aFz9lWS3FXdIU9bXto8bVUtwXcAbpCXTX9oZzviokFYnUJ75uenqZcLvOnf/o2hofhxht3ccUVp+nuru04CqUCAXcAv8tPPGBa7Luiuzg6e7S2A8GcTSm6gmZo+F3+uljsY5kxtrSc9YVJklTz2W6hVCAejJMqpvA6zX6hQU+w5vej4opp9jaTUlJk1WzDYn+xkSqmCPvPCrvf7a/pwlBRL+J3+9E0jUsuuZ5Y7F4ePRjk8R91851/q6ErRj/XFeNxetAMreYP78zMDLt27WL37t0AbN++nYGBgZqOAcxoB4/Tg9/tr7piXtv/Wh4aeqim4xBCUDJKyJpMT3MPN+24qS4+9vHsOD3NZwtN1dpVCCBrMvFAnHllHq/LFPZ6uGJU3Qx3DHlCpItpcqWcKewNi/3FQ0pJnWOx+13+mk7tFE3B7/LzsY89yMmTPyOZfCdoAQpT3Xzqb3I1i90u6kW8zrOuGIDL45dzbO5YbQZwhkKhwNVXX1315W7fvp1kMomm1TZ1Wy/ruB1uAu5A1RXzsp6X8cTkEzUdR8koAWZM/d72vbx737vr4opJFVNE/JHq3+sRyy5rMvFgnKyarbqF6uGKWehjz6gZ02JvLJ6+uDjfYm/yNNV0aqfoCj6Xjy984V/ObnxiD2T6UEWuZrHbFVfML2//ZaKBKAC747s5kThRmwEAmqYhyzLbtm2rbtu+fTtgNjSuJZqh4XK4aPY20+prBczaNTUPrTsze8yqWVwOV81/nxXON4Bafa382v2/VtMZXUErEA/EcTlc1Rd/0BOsvcVuqGRLprAX9SI5tWGxv+hIKanqgwvQ7G2u6Zu3Ume8UHgCuAW4Coa+A3IM/MmaxW5XhP03DvxGtc52NBAlKdeu7+jf//TvwXVWzAH6+/vN8SnFmoqqXtZxO928a++7qkXJ6kGl8UlWzeJ2uk2fcp0s9oUGUKuvlZ+O/LSmoiprMrFADEfZS38/OBzwid8P8OBDtbsfQghUXSVdTNPsbQYwXTGBho/9RcX5i6chT6jm0Sh6UQdOAtdh1kY7ATkvhCZrFrutGirJmSS/+7u/y0c/+kP6++HWN0T52J8ka+YO+vcX/h2C5wp7tbqnRk1FRCubFnt84LerItLfDxMTNRsCQDXLtOJ+CLqDdfGxn28AVZ6ZWo5F1mRGT8QpFryMjAwiRJnkVJD/9eVCzX6jellHIEgX09X7UYmsu1ArPV6Uwl4JW6pQc4tdVxg6OQSA17sfeI35QXYUV2SiZrHbRb3In/3xn/G3f/u3/PVf38zIyAMgR0mrSe64g5o8OLPzZqW+Sy+9tLotGjXdQlJJqun3opd1Thxzc8cdMDICQpj/ffa52tyLCqqh4pAcZIoZ3A53/VwxxXNdMa/pf03NF3ILpQIPfi8OugxsB34PtCAlIdfMZVkySkhI5wh7Vs3WxU1nFRelsKu6yo8fPDu1++M7m/nBT+wXkEwxw9ePfB1FUxg5bTZy+Ju/2ceWLVcBEs2OKfa9aqpmsdsZOcOhXxyiqen9QBy4GeRHIZBElqnJgzM5M4nf66e9vb26LRAIEAgEKBfLNfNh3n/8fn5w8gc8/BMnsvwRYBfwNABlozb3ooKqq7T6Wqs+9nq5YtLF9DmumJ3RnRzoPFBbi12XSY66wKjM3P4WtCJ4CjVzWaqGSpOn6ZwZTEEr1LzTmJVclMJ+5GiJT9z5Q0ZG+hHicyQnmvnCV+yPRknICR4ZecTMPD01SGtrK7/1Wx2MjLjp7u7iLbdO0NFVu6ndc8PPYaQM8vn/G3jU3Fh8CnxmOrvdD46iKKRyKVqaWxZ9FovF8GQ8/Hzs5/YO4gzj2XGm89NkUk8BnwVOUW3PW3YyMla7UrGqoRL2hcmVcqaP3R0kr9XeFZNVs4wPjPO+932Z3t4ZJEnw959t4r7/qK0rJuR6HHSArwECSkfBXaidy1I3hX1enucLd30BzhjpDWF/kfHwfw2gFf8QGAE+ASUHJSlru1WmlTWKRhFFVzh9/DSXX355daW/t7eXsbExewdwHslMEgzo7b0G2AFcD+UjIJlRD3Y/OCMjI+CCgH9xT9FoNErLSAvfev5bSxxpPQWtwPjMOJR/iLmg/WfAT4Eh0AL0bq1dbRJVV6u18d0ON363n5SSQtFqWx8lk82w54o9/NM//T+Mj3cAf0l6polP/12+Zq4pRVMISg+A4Qf+O9AK2mFcAbkmLst8KU/JKNHkacLA4OmfPw3m2jZXX+nhoYdq66aziotK2I9MH2FgbgCl9CDo/cB3gTSoJ8Gbtd1C1cs6Rb1IUS/ywtEXuPzyy6ufbdmyhdHRUbwub00WDIt6ETkr09fXx1/8RYhAAOAq4DAgCASw/cEZHh4GJwR8i4U9FouRmkvhdtYmpT1fynP89HEcSPj99wC3nfnkZzhFgI99vHaiWtSLVVeM2+nGITlIKkm+duRrNRsDwMmTJ3E4YsDLz2z5Syj5axqSm8lkmB49SSzcRl+fE3g5LnGM176hUBOX5e88+DuMZkYRRdNM/29v/jNQztTO0d0oOR/ve79ywYn7RSXsPx78MZ///ufBqYLxKeBNgKsq7HZbqJqhmYKqyCRmEouEfWxsjN3x3Tw385y9A8Gs812cLHLFFVdw++3wxS9CJLIHkPH6ZL74RWx/cIaGhkyLfRlhTyZrF3aZL+VJZpN0d3bypS9F2bLlEsBLc/MRXne9nzfdWrvonIorJlsyfewAf/P6v6lpzLSu6yQSCcrlWzHddN8AslBKgidfM//2xPgEbsnNjq2dDA/Dxz62D3KnEZEXanL9olEkq2Yp5cyksUd/cgsoZrVLjJ+DEkERqZr3DdgsF5Wwq4bKfz76n7j9fnzuNwB+4CpQn8MVsL+SoFY2hT2XNx/QhcLe29tLsVhke2A7h6cP2zsQIFFIkJpIVcdw++1w3307Adi3rzbW0NDQEJKQ8Pv8iz6LxWIkErXrmJMv5cnIGXq7ern9dhgZcXH11bt52cue5crdtU1hV3WVxHgr3/uPLH/xKTf9/fBfD9V2AfWJJ55A13VisdcDEmZPeqA0Cp58zfzbyfkkv/TyXyLoDQKwd+9edFUnTJgXEvaLe8kokS/lkTMykiExNRoG+QrzQ+MxUMLgS9W8b8BmuaiEfT4zz7GRY2zd0cv/+ocAfX0A10LpeQ68MmO7mFVcMbncYmHfcuZJCZfCHJk5Yu9AgPHJcQzNOCfMcMeOHQAosoJRNmwfw/DwMD58SzY8icVipNNpnDirKfZ2ksgm0CSNLT1nFeuqq67i0KFD+Jy+mgr7jx9W+a8ftaIYWTDcjIzAX/xxkCeP1E7Yv/nP38QhOfjUp375jJuuD4hDaQB3MF8T/7aqquRyOQ7sP8Bf3fhXgCnsAKFCiJPJk7aPoWSUyJVyZOeyuCU33Z1uKJrhuBhPgBIB/3zN+wZslotK2EcnRxEBQbQtyq/d7mJ4GL70pSuhVMDfan/n84orJp/P09TURE/P2QJLFWGXZ+Wa9HYcGRsBA3btOtv3tauri0AggJpXayJkQ0NDBD1BXtv/2kWfVWLZ3bhrYqnOpmbBDVu6zz6hN910E6lUitmJ2ZouXN777Tn03L+B7zAYjwAlirkgjzxWm5dLuVzm2/d9m+62bu64o5UvfhH6+iRgH67yKG+8LV+TGd3nf/x5hBDs37+f/V37gQVZyfNF5uQ528dQMkqMzYxRypXwu/z8xZ1t+J78fdCdYDwHxRY8rama9g2wgotK2GfnZyEITU1N1WiUK6+8EoQZAWA3FVdMPp8/JyIGTFcMwMz4TE1EdWR8BMqwc+fO6jZJktixYwdKRqnJGIaHhwkEAvzhL/3hos9isRgADt1Rk7jpZC4JbujpOvuyvemmm3C73Qy8MFBTiz2V/SkUT4I3DeUfAT+AUpBcsTYW++HDh0lkE2zvMbOBb78dhofhPe/pIdI0R//O2oQ73n/0fvgB7N+/v7otGAwSiUQozBZqYgCVjBKnR05DCZp9zbzrnU7+6c/3QNkNhkEk4OQ9vzlf074BVnBRCXsyncTZ7MTr8Va37d69G5fLRSZtv7AvdMUsdMOAKWQ+n69mIY/jU+P4PD7a2trO2d7f308hXbBdyPL5PIlEAn9gsX8dFgi7VhthzygZJI9EW+zs/WhqamLHjh0kp5M1FXan90lQ+sEbNwWEg6AFCbTWRth/9KMfgRd29e86Z3t3dzdzE3M1K78xnZymJdDC1q1bz9ne29tLejJdM2EfmhxC0iRam8zkpNtvh5aAF8rwp3+gcemV9WvyvVEuKmGfz87jC/gQnE0D9vv9XHvttTWJwNAMDbkko6rqImGXJKka8lgLJqYm6Ih3LGp71tfXRz6Vt11Mh4eHQYKmQNOSn1eEXajCtrHc9Yu7qj1e86U8Dq9jUXjltm3bSEwlatZjM5lMYkjDOPVXgTcLxg7gIfxuP5fstn8Mx+aO8cRzT9DZ30lHuOOcz7q7uxGyYDJdm8bac6k5rrriqkW/0d7eXuZG5moi7IqqcOz0MTojnTR5zv5WmzxNIECZV2peytgKLiphT+fTBLyLQ+te97rXkU6lmU/P23p9rayRK+ZAwGWXXbbo856eHsbGxmrSY3JyZpLO9s5F2/v6+igVSszM29tJqRLDXrGCzqci7IZi2Cbsh2cOM6+Y33lJLSFcYlEruO3btzM7MVuziJSHHnoInPDuX70eHBqULwOO8jd/PUtXt/11Sb5z9DscSx6js7/znHpKYAo7ZcjL9s+gVFUlI2e45qprFn3W29vL1OBU9buzi9/8j99kOjFNRs7wq7/yqwQ9wepnrX7zd5uaS9W1H+1GuWiEXVEUZFUmFAgt+uzaa68FGX5x5Be2jkEv6+T0HJTORqAspKuri6mpKVvHAFAsFknMJ+jp7Fn0WX9/P5RgaHzI1jFMTEyAC1qaFpcTgLOLp1pBs03Y9bKOoiuFxvoJAAAgAElEQVTIsoymapQd5WrceIXt27ej5lTbX3QVfvrTn+JucvOBO64h6PXziT80Q+ui0Ydrcv2MmmFieoJ4d/ycvsBwRtgxX4J62d4SC0ePHkUIwYH9BxZ9tmXLFlLJFKpmb9ez52afo6AUcIfcHNh7oFqyFyASjODxeJifnq9L5c3NctEI+8DAADgh1hRb1AZv586dIMOzp561dQyacaYOTOns6v5CqsJus2E2MDAAjnMXCiv09fWBBqNT9rqEJiYmkDzSsha7z+cjGAyi5lXbLKJKlNKpU6fgTHTn+a6YrVu3QhFGZ2vjInvyySeJ98SJBCIE3AF2bN1BKBTikUceqcn157Jz5PI5Ih2RpS12wF/yM5qx93489dRTwLkLpxUqgQayYu+6R7qYNo3BSIhmbzNfve2r1c+2R7ZX1xwaFnsdOXnSjHltD7cvWvzZtm0bkiJxYtTezkGV2s1BdxCfz7fo866uLkqlEiWtZGs50NOnT4MTert6F33W0dEBGsym7Q3/nJycJNYeI+Be7BqrEIvFKGaL9lrsmsKxE8fgzDv3fIu9s7MTFJhK2z+TmkhP8MzRZ2htayXkDeF3+/G6vVxzzTU8/vjjlEWZnwz+xNYxTM5PggOa482LLPa2tjZcLhfuvJvT86dtHcczzzyDy+U6p7NWhYqwq0XVtudECLP+uqqr+Jp9eF3ec3zsX7n1K/T09DAzNlOXypub5aIR9lOnTgHQGe1c1NrL4/EQ9UcZmLK3gbJe1pHKEuGm8JKfd3V1ASBpkq2LdYODg+CA3u7Fwh6NRkGDZNbexeTJyUnautqqDYqXIhaLIadl24S9En763PHn4Mwk7nwfe0XYZ/P25znc9dBdaJdrBMNBQp4Qfpcft8PNddddx5EjR0gUEvz1z//a1jHM5ebAgTmG8yx2h8NBZ2cnWlJjOj9t6zhOnjx5TljyQirCLqmSbQuXWlkjX8wjHAKn34nXufh32tPTw+TYZE0S6KzmohH2wcFB3B430abokuU2e1t7GcmM2DoGzdCQVIm2lrYlP68IuyjZFwkCZmKQx+8h2hpd9Jnf78fr8JIu2LvSPzExQbQ9Wm3JtxSxWIx8Mm9bs42Kj/35E89XrbHzLfb29nZQzW5CdjM+PQ794Av6cDvNptpup5v9+/ej6zrlUtl2f25KToEDvKFzLdQK3d3d5GfyzBbsfdGdPn2aQHDp2Vx3dzeSJOEsOG1zCRV1sworThBusaQB0tPTw/j4uC3Xt5uLRtiHhoYIBAKEPKElv6RdTbtI+pI8O2Ofn10pKZSL5UVhZBU6O80olbJi7wP81NxTtHS2LFtPutnfbIuwG2WDiazZZ25ycnJVYY9Go2TnstWQRKup+NhPnD5BNHQm0/U8H7vL5aIt3kZRLdoyhoVMJ6ah20zCAfC7/bgcrmrZh7JStt2fmyvlaAm3kNPNZs3n093dTWo8Zauwq6rK6OgowUBwyc89Hg8dHR2UU2XGsvbkfai6iiY0cIImaUs+Kz09PaiqSqn0ErTYJUnqlSTpoCRJxyVJOipJ0oetGNh6qQq7N7TktKq/rR+5ReYTBz9h2xgS8wlQobute8nPK8KuyfZFggA8H3weR8fimO0K4aawGZZpMQOpAT796KdRVZVEIkFrrHXJ76JCLBYjPZ22bbqtl3VySo7BkUHirXHcDvciix3M70Ut2huB8S9H/4WZ+RlcuDDOrOQG3AHcDjfbt2/H4XDQl+8jFojZOg65LNPW0cZ0YZr2pvZFn3d3dzM7PMtMwb4ooaGhIYQQ1RfcUvT29qJMK4xl7BH2SkNxPGaOw3KuGDCjzC40rLDYdeD/E0Jchtm5+bckSbp8lWMsxTAMRkZGqhb7Um/fjo4OeBTkon0r7TOJGShBX2ffkp/7/X7C4TBqVrVN2IUQ5JpyuIKuRf7kCpFQxJbsQr2so5U1pqdN/2woElp18TSfyNsWr6yVNUYnRzEkg/ZYO26ne8l7Ugth/4cn/4FENkFLuYWkYq5v+F1+3E43Xq+XrVu34jnhWfFFuFnK5TKaU6O9sx1FU5b8brq7uynMFZjN2Wexnz5tLswu54qBM9mno2nGs/a4QqqRc5LZhGU5VwxAUXkJCrsQYkoI8fSZ/88Bx4GlTVabmJycRNM0AoEATZ6mJb+kjo4OeAZbp1WJ+QSUYGv31mX36erqQs7ItqVtnxo7hQgLJK+0rMUeb47bMuXXyzqaoZkx7IC3xUvEH1l2/1gsBjrkFfuiYoYnhsEJ3e3deJyeJS32jo4Oimpx0aK7lWSKGVJqik5fZ1VQ/W5/9UWzc+fOquBZxVhm7JyoksPPHQYH7Ni1OMeiQnd3t5lxWbRvcb8StRUOLR1kAKawz4zOkC3Zs/5Ssdjdwrz/y7liwN57YReW+tglSerHbNPzuJXnXY2hITPZxu/3E/IubbFX3CB2WmaJVALHlIMD2xcnXVTo6uoin8yTUe3xKz92/DEogOEylrXYe1p6UDzW/1grFvvkpJmS7gg6Vhd27HvZ6mWdsakxJI9EV1sXbod7yZddZ2cnpWyJtGLfgnJKTqG5NbZHtlfDDAPuQPVF09XVxfT0NJIkWRbid+dDd55TIfHBgw+CAjsv2bnsMZUFfqvdD0IIhtPDgCnszdFmWoNL5ziAmaSkZBTSeXu+E7lkztw9kqkVS82U2tvbcTqdFItFW8OT7cAyYZckqQn4HvDbQohFr1lJku6QJOmQJEmH5uasLcdZEfbq4ukSX1JHh7mgqar2Cft8ap7eXC+7YruW3aerq4vsrH0LhgPjA1CAEqVlLfb2eDuarlkuqHpZRy/rVWHHz4rCXs0+LdnT4FszNKbmpujq7aLJ17Ssxd7Z2YkoCkam7YuaSikpCMCe3j3VDMeKKwZMEZmdncXv8lsWCluZQVU4NHgIZ9aJO+hedlG7kqRktbBP5ib5o4f/CDCFvW9H34puut7eXtAgkbGnXszUnJm34HeaReqWmuU7nU4z90QpLUp6fLFjibBLkuTGFPV7hRD3LbWPEOKLQogDQogD8XjcistWGR4eRpIkAv7AshZ7RdjtjH5IZVNLxo4vpLOzk9R0yrYFw+HJYSiAbMjLWuwdHR2QgicHnrT02hWLfWJiArfbjSIphP3LT7drYbEnMgm6+7rxurx4nJ5lfeyUYGTSHmEXQlDQC+CH63ZdV7XYm73N+F2msLS3t6PrOm7htmz9xRDGOTHYJ7IniCpRJnITtAcXL5zCWWFXi6qlrilZk6vuj1OnTtG7tbf6b1+K3t5e0CFVsCcMdWJmAlSqIZ/LrW309PRQypcuuCQlK6JiJODLwHEhxGc2P6T1MzQ0RGd3J26Xm/7Wft5y2VsW7dPc3IzP57PVYk9n02sSdqNgMJO2J+pgbHYMr+FF0ZVlLfaOjg5IwjPDz1h6baNsoBnm4mlHRwcpJbU2V4xNmbhlUSadTxPvjONzmbHjy1nslGB02p6Y6YJWoEwZyStxw94b+J2X/w4Af/LaP2FX1JzdtbefEVoN64S9bKAaKu+8750omsKkmKTf1c+8Mn9OXZSFBINBWlpa0BX9bOSIBciajKIpZDIZBgcH2XrJVvzu5YW9ra0Nyvb5t6dmp6AIIZ/5kl3uWent7UXJKBdcWQErLPZXAu8CXidJ0uEzf95kwXlXZTA1yJHpIwwNDRHeH66mBd+47cZF+0qSREdHh20+9nQ6jaqp9Pf2r7hfW1sbFGE6bU9m31RyirDbtJJXtNjzMDg7aOm1Kxb7L9y/IB6Pky6mafEuXQQMzrpiKGHLg+PCRalcItoexes8Y7Ev42OnBBNzE5aPAWAmY77EPW4PPo+Py+Nm0JjH6almXlZmlEIVllmHFYt9tjDLZG6SXDnHttg2ZE1eMSO4u7ubUsFaK7VisT/zjGlMbL90O0H38uGOlVm91bO5zz/5eY5MHzGTxYoQDUVxO9w4pKWlsKOjg2KiaFt0jl1YERXzMyGEJITYK4S48syfB6wY3GocmjzEjwZ/xNDQEIlLE0R8y1uHcCaszSaLfWhoCJywtW/5iBg4m+k4l7On7ddsZpZ40HwoVrTY8zCastZCrUbFNE0Qj8cpi/KS/U4reL1eQqEQFG3K/NQBF0TiEdNiXyaOvaOjA0rmS9EOnjjyBBQ4pyzs+VQsdkO2royxXtYpGaZ/+OjoUcrlMt0d3ciavGzyGpj3o5QvWfqylTUZRVeqxb/CvWE6Q4vLSlcIBoP4/X7LhX0qN8VIZoSp2SmcupPWQOuqZS/UUZXDk/Y3oLeSCzrzVNEUpnPTjI+P0+xr5vM3f37F/SthbXZQqc+ydcvKwl6x2OcL1sdua5pGSknREzbDtJaz2Nvb2yEPU1lrhaxisasOtepmWY1oNIou67bUzimXyuCGlmjLij72QCCAz+WzrTDa08eehhw0+ZduOgILFvfz1uU4GGXTYi/qRZ4feR4wK36uJuzt7e0Uc0VbLPYTJ04Qj8fJO/J0h5aPipYkiXg8Tkm1VthVQ2WuMMdUYoqAM0DQE1w1iY4ZeHLU2vUou7mghV3WZE5On0TcIAgEAqs2sOjo6LDXYnfA9q3bV9yvvb3d9B2q1gvZ2NgYuM0sW1jeYvf5fLQ4W0go1kYcVCx23a0TjS9OV1+KWCyGKquW+nMraIoGLgiFQ1VXzFIWO5j1txNZeyIwnj/1PK6iq+rPXYrW1laCwSByyrqiaBVXjKqrvDD5AmAWhltN2Nva2pAzsqUWu6IrFPUio6Oj9PX1MZ4dp6d5cVnphcTjcdSStc+rqqsk5ASz87O0eltp8jSteC/i8TikYXDeWrel3VzQwq7oCkdmjkCvaXWtRkdHB1pJs8VqHxwcxO13Ew2vLGiRSASn02nLC2ZwcBC8cEnPJQA4peXdIO3hdnKatUlSelmnpJfACaFoaE2domKxGMV8EVW3/n4osoLX56UslfG5fPjd/mXHFGuJMZ+zJwP29PhpYt7YiouFldaJmUTGsv6rFVdMySgxnBwG1ibs7e3tlPIlS+9HZfG0IuwTuQm6m1fOY2xra7PcFVMySszkZ0ikEkSDUYLu4KquGASWv2Ds5oIWdlmTGVPGwA8B/+rC3t7eDobZ6NlqhoaGCIaCy7o/KjgcDtMSsUHYh4aGwAtXbL0Ct8O9orDGY3HLHxpDGCgls2KeK+xacXGsQiwWQ8krtljssiwTCARQDRWvy8tf3vCXy+7b3tpuW4LSVHaK7mD3iuF9YEZgzM/OW3YvKq6YsD/MWGEMj+ShJdSyJmGnBFMJ61x1FVfM6OgoW7ZsYV6ZJ+xbPhQWsOU5UQ2VgekBdHS2tG0h4A6s7orBvlwLu7ighV3RFLNxdRM0Ny0dvrWQSgp7JTnBSoaGhggEA8tO9RdihyVSGYPkkdi9dfeybpgKsViMUqlkaayyXtYplorgAi2orRjquHAccka2VdiLehGfy8dVnVctu29HtINcMWd52GU2m6UgFdgV3bVipUswsy0T09Y11q64YsK+MEk9ScgTwuVwrU3YNWsXk2VNRi7JyLLMli1bAFad0VV87LphXZs+1VAZmR0BJ7xy1yu5tvva1S127C1FYgcXtLDLmoxX9+JwOWjxLR9WV6Ei7DMJa2PIhRCMjIzgC/rWJOzt7e2oqvXdYQYHB/EH/IR8oVVnDrFYjHKyzFDKut6nelmvLrgVvcU1CXs0GkWVVfJFa+vFlEolFEUxLXZdXbW4Vk+8B92hk81aW5tkYGAAQrBvy74VXTFgCnt6Lk1OscZFVnHFABSNIuFgeE3C3tbWBiWYSVn3nMiajCRMIe/t7UVidTddW1sb5WLZ0uzTklEilUvh9Dl5/b7Xc8uuW1b8bVRCchuumBqi6ArOnJMWpYVmzxotdgNm562Nfpibm6NYLOL3L+/DXUhbWxsluWS5lVopXVxJxlmJWCxGcazIc7PPWXZ9vaxX/cMZKbPqVLsyDnRIpK1duBwZGQHBORb7SnS3dYMHZmasfemfPn0aAnDNzmtWdcVs2bLFNDwsaqxdccUAaFmNeCheFfaVXvzxeNzyLluyJuPCBTdApCuyJkOsMo6xaetK96q6iiqrhOIhIoEIkiQt2XCkgtvtJhKJUFJLGGXDsnHYzQUt7LIm4/qei05P57KZdAupWuxJax/ekREzFd3vX/nBrdDe3o6aVS0vBDY2Nobf78fpcK7q347FYhhTBk+PP23Z9fWyTrFcBAEJI7FmVww6JNPWtuo7NXAKytDa1EpaTa843QboinWBB5JJa8dx4oTZZ/eay6/hz173ZyvuWxH22ZQ1hochDFPIVBWRFXSEO3A5XBjCWNFij0ajUIJU3rrcAlmTcRtuuBLUJpWe0MoRMXBW2CdmrUscEwjUooor4qIrZBY8u2XXLSse093djV7QL6js0wta2DNyhux0lo7mjjUJeyQSMa3DlA3WIWsX9ra2Noy8wVTKOh+mYRjMzs7i9ZoCdn4/y/OJxWKQg/F56xaS9bKOWlZBhillan3CnrFWUE8PnYYydEW6mM5Pr2qxd8W7wA2JhHW/jUdGHuEHYz8wq46GQuyILF8uF84Ku1UvOaNsIGsypWIJctAd7666ClcS9qamJlzCZelisqzJOFUnNMGx/LFVI2LgjEtIszYjWAiBNq9RDpars9qPvPwjKx7T1dWFmrOvh4IdXNDCnsqmQIeeaM+ahN3tduN1e+2Z9rO2yBw4m306PDVs2RiSySSGYZwVds/Kwh6NRkGFRM66e6GXdcpSGafqZCQ9smIBsAoVYU/lrM08HRgawIGDrkgXk7nJVd0g8XgcJGst9rnCHKcLp2lpWt3tAGfqf2vW3Qu9rJNVs2Y8fw762vvWJOySJNEabLXM1w+m27RcKIMEj449umJyUoV4PA5FGE9YZ3wIIZCnZHzulV/0C+nu7jbrxVxAhcAuaGEvyOaN3ta5bU0+O4AmbxPzWWvjlUdGRgiFQrjcqy+cwtns07FZ63yH09PT4ICAz3y5rCaqsVgMStZmwFZ8kD7FR0Zdm489Go2CgeV1t08NnCIUCBH2hZE1edWFy2g0CgqMJqwrsyCXZOb982tyO4BZYqEt0mZZP1pDGORKOZS8Aifgln23rEnYwUzYypWsE3ZZkyllSviKPn46/FP6W/tXPSYej0MextLWPSelUgmRErT7l65uuRTd3d0U0gVb6/VbzUUh7O+//v1LVnRciqAvaIuw9/Ut3Q5vKSoW+0TSuinm1NQUeCEcNMX0G7/yjRX3r1jKBdU6K0Qvm2FpYS3M0fcfrRa7WoloNAo6ZArWrjc8+/yzRMIRWn2t7Gnbs+r+zc3NOKYcHE0ftWwMo5OjiE7Brvbl6/OfT09Hj2WWslE2yKk5svNZtjZv5dKuS9cs7PFQnIJh3W9DMzQKqQI9eg+3XnIrV3deveoxwWAQj+axtPSFoiiQYdWs14V0d3eDCmMz9vRftYMLWtjlgkw8Hicejq/6Q60Q8odI56x981aEfS0RMXBGzIpmwS6rmJ6eBo/Z9g7WaLFjbXyuXtaRDImgP8i28LYVC4BV8Hg8BDwBS6f9yWSSiekJ2mJthP1h9rbvXfUYSZJoNpqZzE9aNo6R8RFwwVVbl4+fP5+OWAeKZl2jjWwpS2ouxZ495sttzcIejltav6dcLlNIF9jZtJMv3/rlNT0rkiQR8UQsLX0hyzKMwUeuWdmvvpBK9c+GsNcIWZbZvn3l2iznEw6GLW9ysV6Lverfzlv3g52engYvtLW0rWn/lpYWnE6n5cKODs2B1dc7FtIcbKZQtM46PHLkCDigPdbOq/tezbv3vXtNx7X4Wyz9bYxOmG6dl132sjUf0xZrQ9Wsi5nOKBnSiXRV2CvlaVcT9lg0hqZZl21ZLBZBh97oyv0KziceiJMqWbf+osgKlOHAZcu3rzyfSCRiazcnO7jghX3btm3rOmZHYAeZkHXT/mw2Szqdpq+vb80JR01NTbh0F/OydS6hsbExgpEg0aa1Fd9yOBxEo1FLhT2v5BGaIB5ZX4es1qZWS11CAwMD4IBYJEZ3czc7o8v3+FxIJBAhq1qXoDQ5O4mr4OLS9kvXfEwlI9gKvC4v06lphCbYu9ectUiShMvhWlXYK78Nq5LoikoRsnBF9xXrOq6rpYtc2brZXEEu0N7eTlPT8rHr5xMOh6EEyZy1kVt2csEKeyWzcL0W+67wLoxWw5ySWUAlIqaSJr0WJEmiNdBqad/T4eFh2re0ryk6qIKVIgIwl5wDzbSU10NrqNUy9wOY34nD7aC1eflmyUsRa45ZGtKWSCU4MHygWh9/TWOIxSiXy5b8Pj1Ojxn1ZFC12IE1C7sQgnzemvuhKAocgZuuuGldx7XH2ilqFnZy2oAxGIlETGHPN4Tddp488SQUWfeXFI/HoWxdWFtF2Lu3dK/Zzw8QCUUstVKHh4eJdkZXjV9fiNX1YhLJBOjQ2bZ8A4WliIQilj68IyMjtHW0rZqUdD5tLW0oZWteMEIIkpkkW7rW/sKHs2sfVsTTe5weMsUMTuFk586zsxaXw7VqZnI0GgUBs3PWrAMpinlfe3rWvmgJZ+rFWDhzKBQK63ffhsOQg+mCPV3P7OCCFfavPf01OLp+Ya8WApu1ZqW9Iuxt3W2rhtQtJNoStcxKFUIwPDxMa3vrqvHrC4nFYuiybll8bjKVBB26O1aPUV5ItCVqWRf4w9OHOT53nM7uzjXV7VlIR7iDoihaIiKpVIqSUaKve+1rL3C2TKxVwi7rMh3xDlyus/fC7XCvyWKnCCMzm2/wXTJKqIpKPB5fcxJfhba2NnPhtbD532ipVKKoFNetGT6fD1/Rx4xqT59iO7hghf3ZmWdhnHW/fSvx21atcI+OjuLxeAiFQ/ica096iLXGLBP2ZDJJoVCgKdq0Los9Go2i5TTL/MqpTAq35CbkX/sYAKKt1gn7bz3wWxxvP057Z/uqhdDOpz3ajnAIcrnN+3SrrRJ7V+6odT6xWAwka4TdLbkpSSV29J+b8bpWVwwKjM5tPq4/X8pTypfo7V3fwimcmWELsx7TZhkYHACxfs0AM66/oG/+5XJw6OCmz7EWLlhhn8/P48FjhiKtg4qwW5WmPDIywpYtWyiVS+uy2OPhuGViNjw8DIAUkuho6ljzcbFYjGK2aJmvP5PL4Hf51+WSAlPYy5Qtqb3d6m1FFjJtnW3rttgrbpDNuun0ss7zp54HF2zv34DhgTXCXiwUwQ1XXXZuuOV6hH08ufmsz5yaQ8koGxd2yZribC8MvADG+mf5YPrZrYgS+tvH/nbT51gLF6ywZ+UsW3u34nCs759QWQixqr9lJdRR0ZRV09YXEovGKOnW+A4rwp5z5dgWXvuPNhaLIVRh2b3I5rMEvcF1C3skYtaUSaU2F9ZmlA2zR+YMqBF13eOoWMubFfYHTz/I/z79v8HJImt5TWPAGgu1kDMtzN943W+cs93j9KzYXQvOhuTOpDcvqLlSjsJ8YUPCbmXhvlODp8DYmMUeDocpK2VyqrVdx+zighT2hJygIBfYsX19Dw2cDV2ay2z+wQFTVPv7+1F0ZV0WeywaQ5StiTqoCLtwixVLkC4aw5nZy2Ri80k55XKZvJwn5A+tW1DD4TBImxf2jJpBKkkwAYf0Q2vKfF1Ipfb2Zq3lE4kTDBeG8QQ8xMJra+pdIRwOm2tAFnQvUhQF1z+72NV/buZrwL16f+BIJAIqzGU3/5zMpGYo5Tbmiqk8r1Y0/RgaG8IhHNXG4eshEolgyMam3ZZrTWLcLBeksL/9O29HLqw/bAnMQmAeyWNJTKqiKExPT9PV18WdD925agXBhUSjUcuKTg0PD9Pa2orbvT6fciwWA82a+t8zMzOUKdMabN2YxS42L+wpJYWkSDAFA/IA13Zfu67jK8K+2e/khcQLpPQUwebgqg0+zsfhcOAv+RlJbX7RUpEV+vS+RbPagHv1YnUul4uAM2BJiN/gxCCU2JCwV2fYFpQynp6bJugNbkhcw+EwpcLmeihY3VhnJS5IYR+cH8QwjA0JO0DQHbSk+NXoqLmw1NrTyn+e/s91uWKsEhGA52aeo21f25qqKS6kIuxWPDQjIyPggHAovO4ww3DYHPdmhX1emUfLaTAPN/TfQDSwtmStClZZ7BO5CcrJMu7m1aNPliIkQkzkN7cGVBZlFEVZMiN6LcIO0OJrISVvPutzdGZ0w8JesditcBdOzU+ta0a7kEgkglpQNyXssiav+d5vlgtO2EtGidmC+SVvxFcGZq3yjLz5BcOKC6SlvQWBWF+445k4YSuE/Wj5KKkDKa5sv3Jdx1VcMYns5hfqfnbyZxAyQxc35IoB5uc397JNFVPISZmOjg5+9Gs/Wvfx4XAYVJhObTxeWQhh1kGflNH82oasw6gzyqy6OSEzygZKYWlhX+vvNBwIb3ph/bcf/G3uGbwHlI0Ju8vlwit5LfmNThWmiHnW5xqrEA6H0RRtUzWNxmbHmBqeYmLCuuJ/y3HBCXtOzSEbMjg2troNZk2QbHHzIX4VYQ9EzbdwPVwxqqoyb8wzF5rjZT1rr0kCZy12K9xSPxn7CWyBt+x5C1tb1xfiZ6XFPjc6x+WXX74hQXU6nXh0D1OZjftzB1ODhPQQoiDQ3BuLomj3tTNf3txLTlEVinJxSWH/x5v/cU3niDRFNp2Je2r+FO8pvQdeMBtWbISA2xqX0Hxpno7g+v3rcLZJz1xq42sOTz//NP/1k//i6FHrKoguxwUn7NXFCwds3bo+AanQ6m/d9Op2US/ywsgLuN1upIDZN7EerpgXXngBERT0efvWVAp1IaFQCGfZaUkt9JnMDLjgzVe8mfamdZYUaDVT/zcj7EIIjs8dZ3Jwkt27d2/4PAECzOQ2vubw5OST+BI+kMHtWt+aR4XuUDd5aXOCOjJm9nxdqtTFWiOnNlu6tyzKSEhMjE3Q1dm17jWgCk2epk2XddY0jZyUo7d1/bKWh78AACAASURBVLMGOLuoPZfeuLCfHj0N6sYN0vVwwQq71+ElENiYvyrSFKFgFDa1mPH9F77PwcxBurd3k1bT7G3fuy5XTGXBcLPCXnn7f/uXv71u/2G1Zo0Fbqn5vGlhrjd2HMzpttPl3JSwn54/zd2P301xprgpYQ85Q5sqE/vczHOkT6YJ+8L4PevLsqzQFemiyOYyYIdHh6HMuqqOnk97a/umSiyMZcbobe5lbGxsQ26YCs2+5k03uZibm4MQbItvTFQrFvtmWjgOTgyCtr66UhvlghR2qSwR8q0vu3Eh0VCU/JV5Dg5vPAtsMjfJVHGKsbeO8Z1j3+F3rvsdXt336jUf73K5cLldmxb2U6dPgYB9l+3b0PGtwVZLCl+li2kQZ8vCrhe3270pYU8X07wp8iaYgSuuWF8FwYW0uFtIlzYuIlk1y/TQNH3xvnVHxFRoi7QhnIJsduPuwpHRkU0Le0ekA92hbzgx59T8KXZEdvD0009z+eXrCz1dSEughVxxczPs0dFRCMFlPZdt6PiKxb6ZJj1jM2O0BlrxeNa/oL5eLkhhd+QdtAbWV7lvIfHWODhhJLnxkLLJ3CTzvnnatXYeGnqIy+OXr7k9XwW32715V8zUC/g0Hz7f2v37Cwk3hTddK0YIQV7L49jEz8nj9mxK2LNqlsRkAofDwZVXrm8ReSFRX5SMvvEZTEErMD40zta2reuODqqO4YybbjP3Y2TcdMWst+jWQuIxM+tzo4vaU7kpypky8/PzvPrVazd6ziccCG/a+Dh8+DA4YP+V+zc2hjPCvpl+tONz47S3rs9NuVEsEXZJkm6SJOkFSZJOS5L0MSvOuRyJfAJj3iDavL5QtoX0hfvghzCW3Hi9mPHMOFpYY7d/N16nl4g/su5zeN1es9TtJhicHyQirf/aFaKhKLIhb+rBmZ6exnAYq2YzrsRmLfaMmmF6eJrLLruMYDC44fPEg3EKYuMvuqySZWZshp09OzdssVcycTcTJTQ+Po7f5682N98Imw3/TBfTDL8wDMAv/dIvbXgckVAEWZc35Zo6fPgwLpdrw26Qiismk9/4S38uM0dnZH0lUDbKpoVdkiQn8A/AG4HLgXdIkrTxedcqjEyNQAbaWtfWKWgpYtEYjMJkemMZl3pZZzo9DXnYEd3B3va9a2rcfD4+l49EeuP+3C8//WVGtVE6/Btb6Qdz9pLakuK1X33ths8xMDAAOutaPD4fj9vDfHrjQpZVs4ydHuPqq9e3gHw+nc2dKA5lwyKSzCVBg8u3Xr6uNZeFWBH+OTQyREvT+maQ57PZBf5UMcXJwyfp6emhv79/w+OIt8QpuzZX4fHIkSM0NzdvOPOztbV1U715y+UyGSVDR2Tjz+p6sMJivxY4LYQYFEKUgG8Bt1pw3iUZmR6BLHRGN/7mC4fDIMN0dmPxyh984IMMpYcgDzvbdvKD//6DDU27/W7/hhdjyqLMxw9+nMlLJ+lv6d/QOcAsRgZs6MVUYWDArJoXD6yvc9JCfG4f85mNC9lcdo7U1P/f3ptHSXKWd7pP5L7vWfvam7qlbkktJCHJAmMtIDAj22CzjMbGNmMNeBsfHzMGA9eea5hrvFwYrg9jZGPPHR8MBjMYsRlEC65AIIGWlnrvrq6tqyprzaqs3Nfv/hEZ1dnVVZVbRGSpO55z6nRVVGXm25kRv3i/93uXVQ4dai2GqtAX6EM4RMuDLpTH7duzj0eOPNLSc6jRO+f87HlCwdZXctB+rUU8G+elZ1/ita99bVul9F2BLrC2d6Obnp5uayVnNptx292sZ1vb91hZWaHirDQ9GrBV1BD2fqA2pjFTPaYJcytzMAaP3vVoy88RCoUgI/ecaYWZ5AxHnUchCUeGjjQ1IacWl93VspjlSjnu6r8LsnBD9w0tPQewcfHvDbRW7AVVYZegP9D6x+60OtsaMj4xNwF5OHDgQP0/3oFIONJWXFkR9tHRUX77zt9u6TnaDcXE43FWH1hVR9hpXdhjqzGWppe466672rKjK9gFttbfj3K5zPzSPE576ytKAK/LSyrbWsjy409/HIJwU1/rGVvNoIawb3UrvmodK0nSo5IkPSdJ0nPtdK5bWFvAUXRw297Wl9zKcNpWd9orosLPVH4GvgK37m19o87j8JDKpVrKOsgWs1AAPgc3DLQh7FURcUqtn/QXL17E6XTyiYc+0fJzOG1OcsWcPPS4BWaWZiDPFZOCWqHdYqlMJoPdbm+p0dQVNlRoef/l7Nmz4IFwqPV9KGhf2OfX5iFHW2EYgL5wH9ha/0z+4dl/oGKtNN1yYzMBd+uzeb944YsQhb0DrTtQzaCGsM8AteuLAeCq4LUQ4jEhxO1CiNuj0daX7MvJZUZ6R9pa2ikXbzv9lefm5rBWrBsnfyt4HB6wtOaJZEtZSrkSzLeXFxsKheBxechvq1yYuIDH6eFo79H6f7wNLocLzK1fvLHVGORh377mO37WEgqF2hLVTCbDyMhI0+2ka3E6nZjL5pZ7+Jw/fx6sND1UfDMulwuT2dSysKcyKai01kqglp5IT8vXCcCnXvgU+Gh40Pt2BDyBljPIkoUkFFqvvm0WNYT9J8B+SZJGJUmyAe8AHlfhebckkUu0fdfz+XyYzeaWhL1QLmA1WZmbm6O3t7etG4zP5QNzax5RtpilkJYHUbeT0hYKhWCmPWG/OHORoKs9b8htd7cl7KuZVSLeSMtFawrBYBDSMLE00dLjM5lMyxXRtdgle8stcy9OyaGxgL/1lGCQC9jaSclVVl/tCrviPLUq7GvZNYhC1NfejS7kC7U89axYkpvTNTsYqFXaFnYhRAn4beBbwBngC0IITZohCCFI5VPsG23PK5MkiUAg0JKwz6fm6fX0MjU11ZagguwBYG1N2DPFDLmUfOH097ce21Y81Ey2tc3C9fV14tk43b728nM9Tk9bwp4qpOiNtH/RhEIhSLZe45DNZtsOPYCcYdRq46vxqXFATqdtF5vNJg8pb4FsNovT6dwI97VKu3sO6/l1iMgVvW3Z4W996Lq34MX3fV/TM19bRZU8diHEN4QQB4QQe4UQH1XjObdiYWGBSqnS0oCNzYRCIQqFQtOPm0vO0eft48KFC23Hc8PecMubQtlSlux6llAo1JaXqgh7Oteaxz4xMQEuGAi1d5PzurxgaV3Yc/kcvT3tC3swGIQUzCaa78CXSCYo5opte6gAHoun5cZXkzOTQGvtHTZjt9hZjrcm7LlcjsHBwbaHSzidTkym1kNC6XIawjAQae8cjfgj5Cv5plNhhRDkc3n6IvqEYeAVVnk6Pi57Imo00YlEIi0Le8gWYm5urm1hj/qjYGs9FJNaS7W9alB61mRyrXns09PT4IThaOul61AV9jY89nwu39aGpYLH48FUMLGUaj4MMjY9BsX2QmMbdtg9rGVayxKanpPnBLRTMKbgsDpYWW3+/MyVcuTTeVVucpIkySuHFgqllDbK5l4zEW9rLXsVosEowtT81LN8OU8hW9AtDAOvMGFX0urUEPZoNEoxX6RUKTX1uLnkHGJdvmO3m1rXHexuORSTLWVJxpNti4jP50NCIptrLXY4PT0NLtjb1+a+h9vXsrALIcjn86pcOJIkd+psRVTHZ+RJQe2ExhQCjkBLY9gqlQqxJbntsNmkgrDbHC3VWixnlsmt5lRreGWz21qaA5sqpHBmnVQCFQZ8bXrswUhLq8p0IU0haQj7tigeuxoxzEgkQiHb/KirWDJGdlEWQTVCMSZna0vMbDHL2vJa28JuMpnwe/1k880J+1NTT/H3L/4909PTmL1m9va2J+x+j79lYV9dXUUIoYrHDvLkoFYGTEzOTqrmsYdcIZKl5tNxV1ZWKEtlrFhVCcW47C5W11ebDj/EEjEySxn1hN1mY2m5eWFP5BNIixI3L9zMPYP3tGVDqzUOyXySbCJrCPt2xFfjOBytN7yqJRqNkk/lm97lnkvNsTYte3PtptZ57V7cAXdLwr6eXVfFYwcI+oPk8s3d4M6vnOfdj7+b6elpvN3elou0FJxWJ06vsyVhn5uTs2vVunACztY6Xp6bPwfr6njsXZ4uCqZC03n9sVgMLBCwBlQJxbjsLsqi3HSnybOXzkJGvRa1TquzpRTUtewamaUM97nva9uGVmscYvEY5WzZEPbt+MjHPsLPPaROt4JoNIooiqZ7tSxnlpkdm6W3txePp7X5iQpumxuHz9GSsM8vz6vmHQYDQXKF5gRE6QszPjOOM+Qk7GwvR9hmtuHxe1raSFZaGqg1wCDsDpMqtSDsq+dwJV1tnxcA3f5ucLYgIlVhjzgjqoRilGylZs/RC7MXIN1+quOGHTYPi4nm8/ovLV2imCyqcoNptdXDVGxK1xx2eIUJe7KQbLo17nZEo1Eowdxic43AhBCMXRhrOwwD8slq99pbEvbYSgxKKgm7P0i+kG/qMYVygWH/MOet50lFUk0Pjt6MzWzD7XW35LGfvnAaSu2voBTC3jDZcvN7Dpfyl+i3qdNNozfYC87ml/0TcxPQBTd33czByMG27fA4PWBqXtinFqdU9dh9dh+pYqrphIeLMxch315feoVWm7PNLs5CUb8cdniFCft6fh2f3afKc0UiESjBwkpzY9AEgvPnz6sm7BZXa8M2llaXoKjOsj8UDLUk7KOBUdZG1jhqOdr0AOvN2C32lkMx58bPYcO2ceG1S3egmzzNvR8Aa+U1hsPtCwjAYGQQHM17h0/OPQl3wr177uWhfQ+1bYfH1ZrHfil+CTLqeey9nl7wNd9CeHJ+EnLq3GBa9dhnl2ahYAj7tvR4enjXre9S5bkUj31xpbnlXblUZmlpSRXv0G11Y3G2JuwL8QUoquOJhAIh8sXmhb3L1gX98Jb+t7Rtg81sw+lpTdjHpscIutURdZCzH4qVIuVyuanH5bI5BgfUEbL+SD/YmxfU0+nT4JOnDqmBsqndrB0LqQUC9kDblcAKI4ER8NN0ZszE4gSk1blO3G43kiQ17bHPx+cNYd+JgCPA4a7Wx57VsiHs8eaEPZuVl+hqlI27bW4kh8TKykrTWQfLa8tEApG2WpEqREIRiqXmhKxQLuAr+aAEN460337fZraxEl5hqdT8Btn4zHhbg1c2o3hmiUTjmTGlUol8Pq/KCgqqK0qaF9Sl0hLY5V7/aqCkoTbrKa9l1+RVh0rsj+6HQPN2TCemsRfsbfV0UlBaLDTrfCyuLWKTbHi9rY/zbJZXlLCrSTQahXI1pNEghXKBQlaO8amRcmkz2zBb5Z41zRQ9HJ8/zph1jKE+deKX4WAYTM0JWaFcwJK2QFwdb+inBn+Ko9JRVu5oTsjy+TyxlZhcE6ASrWQ/LCwsgFAnNAbV85PmhayULWEqm1QX9mZvMMl8kpG+EVVsADjUf6glj30hs8Cgv/3qVwWr1dq0xx5Pxgl7w6rZ0AjXrbC73W6skrWpfujpQppiVu4vo4bHDmwMtm3mwjk2foysLcvogDo2hEOysDdzwhYrRSorFXhenThq0BnkDt8dVCqVplL8JiYmwIqq5dqt9Ca5OHURyupsZkO1s6LJ1LSw53N57BW7asLusDrw+r1NC3s2l2VoUB3HA+DGwRvB07ywJwtJhnvU2feA1kY4rqZX6fK3PvGtFa5bYQe5bW482fjFmylmKCQLuFwu2mk9XEsrwv5i7EWkv5M4NNTetCCFSFhe9jcjZIVygZXYCtHpqGqNjVrxlMfGxsAGgz3qLfubteOdX3qnnH2RU89jb6WMvlQpkcvmcEtu1YTdarLiDTYn7JlMhlKxpNpNDi4XBzV7o8vlc/T3qTf3x2a1Nd1iIZFN0BPWZySewnUt7H63v6mpPelimkxC7ret1rLKbrODtzlhvxS/hIgJ9oyqk7fdiodaKBdYmFtQJQyj0I6wj/aps3rZsKPS+Mb6ywsvMz43Dnn1PHaQRaSZasv5+DzlbBmfzaeasCv1Bc2cnwsLCyBBd7d64TGz2YzVam3KY69UKuRz6rSaUHBanMTXG79OhBAk80kGouqdF41wfQu7x89auglhL6RJxpOqxNcV3nPkPXBHc8Kez8kZLGrZ0aqwz8/Nq5anvGGH1JywX7p0CavHSndIPRFRRifOxhvr8LicWWZsYQxTybSx6akGTotTzn5qkIlZeTxgyBFSz2M3W/H4mhP2xUX5hqimsEO1rUCDwj6dmGZuaQ5REOoKu7U5YV9bW6NkKjHUq9510gjXtbAHvUHWM42XSqeLaRLLCdXi6wBHBo6Aq3FhF0KQy8gxaLWEvZXCi3w5z+z0rKrCrtjRjIhMzU1hPmrm3qF71bUjC3Or9YvXhBDEs3Gm16bx2/2qbpC5rC6W1uoL2TcufIMzS2c25r52ebpU9dhdXlfzHjvQ1aVuXNlutzccivmNr/4GEzMTqhcGuewukplkwxlks7Oz8oqyXz3NaITrWthDvlBTw2kX4gvkk3lVhX1P756mhD1bylLIFDCZTKot+1sR9lQmRTaVVa0AZcOOIk15qZPxSSL5SNuVr7XY7XasRSsLyfp2JAtJSpUS8+n5tkevbcZj97CSrH9enFg4wVh8jMnYJBTgD1/9h+wNqTNb02qyNi3ssfkYCA08dquNxeXGwmPr+XUm5yZVzx/32OVxlmtrja30Z2dnwQIjAyOq2dAI17Wwh/1hCpXGGy0pJ4qaoRiv04vF0XiRUjKfpJgq0t/fj9VqVcUGi8WCxWJpTtizKSir65UFg0HIw8Jq48I+m5gl4lQv/KHgltwN9WRfycif20phhS6fuh6qz+kjmU3WnfSVL+dJFpJMzU9BHm47cBsmSZ1LWykcS6fT5PONFbHNLMqDxdVKMFDwWD0srtUX9nKlTKaY4dLCJSigWtdPkK/XZlr3zszMAOpV4DbKdS3s0WAUzI2nUE3HpqGoXqqjQjNzJZOF5MYGrqo22JrLz1WEXc2YciAQkIU90Ziwz63PsZRbos+rfnMlv9VPPFf//Yhn4zgsDtYr6wx0qbtBFnAFGpqwVSgXSBVSzC7N4ra6VWlCpmAz23C45bBOo+fo7PIsNmyqdGGtxWf3EU/HqVQqO/5doVwgX8prUsqvTPpq9FqZnZX3afRsAAbXubB3hbrA0riwzy7JzXzUFlWbzdaUx55aSakv7E0WXqRzadWF3Ww2Y5NsDc36zBaz/NI//xJlR1m1/iy1BOwB1or1l9sr2RX6XH0Iv2BfvzpNyBSCniBY66f45Ut5UoUUsXis7YHNm7GarThczQn7fHwel0WdVgK1+J1+KpZK3TBIvpwnX5YL12ySTdUbnd/tb8pjn52dxWazbaQ168V1Ley9kV6wN54bu7CygMviUq3ZlEIzwp7IJkiuJFVNM1RsaEbYM7kMVNQVdgCX2UU81ZinPL02DR7Y16OuoAJEXBFS5fr7L/FsnIApACE4PKJOuwuFsDfckLAXygWS+SRLiSV6Q+r2I7GZbWQsmaY6PC6sLajWrK+WsCcM9vqOmOKxL64uyqseFQm4A0157DMzM6qvXBrhuhb2/X37wd24x764tkhftE/10mCHxdFQ9gPIbVlFTmjisTeTZpgv5EGoL+weq4fVTH074tk489l58MKhQXUKtWqJeqJkRP05sGu5NRwFB7jgVQdfpa4NAXkmbiNCliqkiKfj9EfVK8YBefP0i7NfhEONC/tyapmAU11BBYj4Ig05YvlSnkK5wPL6MiFPSFUbgr5g06EYQ9h1Zrh3GKyNC/tKeoWhLvXzUX1W38YmXD2UlDbVw0HW5jz2YrGIzWZTpQlZLT67r6GxdPFsnJIoQZc6Tcg20xXoIl+pv1mYKWYQSQEZ2DusTiaKQjQQbSwUU86zklohb84z2qPu/o/NbONQ6BDc1Liwx3PxtidqbUWXv6vhG12+nCeeiss3AxUJ+8Jgafy9mJk1PHbdCQRkr6IRYRdCsMYaN/apLyJhR5gUqbrZD1DNOCio03irFmXztNEuk4VCgUgkovrqxev0kszVn/UZz8bxCR+koK9X/Y2paDBKWSpvdPPcjkwxQ2G1gDVvxWxuf2JRLT2hnoZDMQtrC+CHm4dvVtWG0eAof3b/nzUsZsVikVQlRZ9f/c+kN9TbUCgmX86TL+VJZBJ0BdXNVHLb3bj8robCt7lcjpX1FTmTRmeua2E3mUwN9+NYWlqiLJXZN6J+PLfP3Qe+xpZ3cytzkFc/fcpqtVIqlRruMlkoFFRphboZv8cvb8zWIZ6NE8qG8MV8qqV91tLd1d1Qb5JsMUt6KY2rosFmocuP3VO/KKdQLrCyvgIW2D/S/gCYWkLOEA/f+DAmU2ND15eXl8ENA0H1S+j39e4DT2OhmGKlSEEq0BtWd8/Ba/fiDrob0oy5uTkYgpv8N6lqQyNc18IO8uSe+eX5un83MTEBqJ/qCDAcGAZfYx7RUmKJkDeE3W5X1QabtblmZNlsVtW+KAp+t590vjFh75roYt+6+jdauLx3UFfYS1kScwkCFvVjym6bG6ff2VAoJpmRVzlqr+QUbDYbyyv1xWxhYQFcMNqt/nVy28BtWPosDYViAHDAUI+6odOwM4wj6GhI2GdnZ+EAvH7k9ara0AjXvbB7JS9zifql4y+NvQRp9WPbAHsie8DfmKiuJFcYiKgvqEo6VqP7DdlsVpP3IuANUBb1QyDxbJzVuVUG+rVprqQU19R7P9LFNPFzce6z3Ke6DS6rC4evvohURIVMLoMkJNXL+BXsJjtL8frnxuLiIrhhb6+6+w0grx6svvqNwPLl6t6IE/YMqNMoTyHsCmPx1b+5QFXYg3DXvrtUtaERrnthD1gDLGXqf0gvTr8IcW089ht6bmg4FLNcWWZvRP2Lxu6QVwBKn4+dWF9fp1gsaiLsIV+ooTzheDbO0vSSam1yN6N47PUu4LXUGvlknlsHblXdhkHfYMNTg9bKa7jLbkwmbS5pl9nVUOFYLBYDM6oNgdmM3WKv23Vzw2N3wb4hdVd0IWcIk6exPvmTlyZV7dHfDIawOwMNpdedWjqFN+vVZLzVcM9wQ/MtlVLpvUMaCLu9cWGfmpoCoc3qpVFhX04vsza/ptlF0+gEo/h6HIraXLxeuxeTw8TiUv0y+kqxwsCidgLitXpZTtcXs1gsBmg339Nr8hJLxHb8m3yp6rG7YO+gutdK0BEEh3xe1Es0eHnhZayrVnw+9XP663HdC3vYHWa9sF63W9tkdpJhpzbxy3A4DKK+sD9z4RnEktAkjmq3ycI+P19/v2FychIkbYQ9HAiDub6wr6ZWoaTeYIvNBINBkOp77Il0Akra9QLpNnezVKm/otzz9B6G7dqcnwB+u5/VbH0HKBaLYbFYVE+DVfDZfCyn6m8mWyoWLG4LHod6VacAZpMZq81KLpcjnd55L2g8MU5EqJ851gjXvbBHfXIRSD1RjZfj3NB9gyY2eDweJJNU14aTEychiaqtchVMZhOBQKAhj31ychLQRtgjgUhDHnsul1N1xuhmTCZTQ4Musjl5L0CrlcOQa4icK0cms3Ox1NrcmupNt2oJuUIki8m6fVpm5mdUaxm8FQFngHhm55BlvpzHVDRhsVs0EVVldVvPCVpMLKre4qFR2hJ2SZL+QpKks5IkvSxJ0pclSVI/NUBjuoPd4NjZM6tUKmSyGUZHtOmpLEmSPHKrjrBfmr8ERe0yH7q7u+uerMVykYmJCUxmkyZCEg1FG/LYlY6cWgk7yBXBcws7b6xns1msVqvqLWoVhvxDDW2sLy4uairsEU8EYRN1+7RMr07jN/m1s8MdIU9+xxtdoVxA5AQWi0UTG2wOG4QuN/jajtX0Kj1BfUfiKbTrsT8BHBZC3AycBz7Qvkn60h/uB/vlqS9bEYvFEBWhycapgs1iYzG+cyx1ZmFGc2Gv57G/5Qtv4anEU4Sz2kxd7432gqWBNMNq1oyWwu6xe+TWrzuQy+Xo7+/XbNNyb2Rv3Q3UcrlMJpPRLCMGkFsSO+rfYOaSc4Tt6tc3bLZjp+s1W8xSypRwW7QJB7kcLnhjNU99GyqVCol8QvUWD43S1tkohPi2EKJU/fEZQP/t3zbZ07cH7Jf7Jm/F6bHTUNAmI0bBZXKxmNhZ2GPLMRwWB36/Nh5RT09PXWE/v3Kei+aLHM0d1cSGaDCKyWra8cIFeTygx+PRdGPK5/Qxt1zHY89pk8+vcKj3EPh3FrJCXs4C0dJj7w321hV2IQTLuWV6PNp5qX3hPnDsHAaJr8UROaFJIzKAf/z5fwSxs8e+uLiIsAiGuvUdiaegppvx68A3VXw+Xdg/tB8cOwv7y+MvQ0qbmLKCx+phKblzPHchvkBXoEuzzZhGQjGz67OkiinV08gUHFYHNpet7g0mm8tq6q0DBN1BltaWdmz1kMvmNLVjtH8UbDtnK+XyclhKS4+9P9xfV9jX19cp2AsMBrQbKjEYHawr7AsrC5CHoEvdLqwKA9EBzGbzjsI+OzsLdhjpHdHEhnrUFXZJkr4jSdLJLb5+ruZvPgiUgM/u8DyPSpL0nCRJzzUzaVxreoI92Ly2HYX97MxZzYqTFILOIMvJncMPy2vLdIe1ieWCLOyJRGLbiVJCCNLFNCVrSbP3wmKyYHPUF3YlBKIlYZ/cNnc7ERFCkMtra4cy/Wen90OJN+/dq34arMJg12BdYY/FYuCBPV3qFgXVsqdvT11hX4ovQQGiXm1WMJIk4TQ75YlV26AIu1YOUD3qCrsQ4gEhxOEtvr4CIEnSu4A3A4+IHRI7hRCPCSFuF0LcruWSsVncVjd2r51Ll7aPpY4vjOMz+XA6nZrZEfVGSeQTO44fi6fi9HdpIyISEuPecWB7ESlWqp6rBsNGarHb7TuGHoQQugh7V0DuJridZ7a+vk6lVNE2zu/xYDKZdhSydDqNJEns2aOdoA5316+1iMVi4K62w9aI0d5RcFzOl9+K+eV5yFfDNhoRMAeYXJnc9vfT09NgQZOak0ZogWFfwQAAIABJREFUNyvmIeAPgYeFaKB59S5EkiQcTseOHvvM2gy9Pm0KLhR6Aj07nrCpVIpsMctgjzbLXLPJzBfXvwhsL+wbhR8ah6XsdvuOHmq6kCafymsu7KNdo+DePkynHNdy7JkkSThsDmIL2wtZJp1hYGBA0/aw/ZH+up0VY7EYOOHAwAHN7Ai7w9j99h1vdIsri3hsHiJe9WfhKnQ7uomlt/9MxsbGMJvNqs5bbYZ2Y+x/DXiBJyRJOi5J0t+oYJPuOB3OHT32peQSgxFth9H2R/p3FPapqSmwwnCfNhkxFpOFZDkJwR2EvZzHjFlzYbfZbfLm0zYLwOnYNKIgNBf2GwduBO/2HvvkzCSUtJ9n6TK7mF3e2oZypUwmnWHfPm2X/GazGbvDvqOnrLQTGB7QrlDKY/Ng89h2tGN5dZmoP6rZ5inI9S/Lqe2rT8fGxnC73R0pToL2s2L2CSEGhRC3Vr/eo5ZheuJwyI2Wtootl0ol1rJrDPVqu7s93DUMju1TqKanp8EKewa1WW6bJTNRZxT6to9f5ko5AuUA1pRV9clJtThsDgqFAonE1gM3JmYmoKBtqiPAwf6DmIPmbT32ydlJTatfFbxWL/Nr238mqUSK/fu1C38oOByOHVP8YrEYJpNJs6wtqK5gHNuvsIUQxNfjDIWG6HZrtx8VCUYolUvb9ncaGxvD5Va/lXOjXPeVp8BG7Hyrk2V2dhZhFqp3idvM/v794Krvse8f1uYCNpvMvHHfG6FvexvypTz+uJ998X2aeiI2u9xpcrs4++TcJBS1F9QB/wCOLse2HrtSMKZVXxSFkCtEbG3rz2QmNkMhU+Cmm7Tv+e2w7yzsyhg4rb1Ul8slXw9bsLS0RIECDw48yK8d/TXNbIiEImDeejVXLpcZHx/XrK1CIxjCjjzQAMvWwj4xMQE27TdB9vfvR3JLO3rskk1ipH9Ek9c3S2ZeN/o6bP22bW3Il/MkV5PsGdX2JlevIZlSqKV11zyvzYvda99W2GcXZrGb7Lhc2npmvcFekoXkllWfJ06fgDL6CHsdj31yalLTBAMFp9NJPB5nfX39qt+dO3cOXHDbwds0tSESioBpa2G/dOkSxWJR8/NiJwxhp7px6dxa2CcnJ8EKN+zRpk+MQtQdxR60b3vhTE1N4fA6cNu18QLMJjM9nh7sDjszs1svc7PFLIl4QtNCLagv7C8tvYRUkjQr41eot+yfW5rD41S3ydRWDHcPgxMuXrx41e9OnD0BRX2E3e1wE0/Et+2VPzU/hd+uXRhGQRHMrbz2c+fOAXDw4EFNbeiJ9oCZLffmxsbGYAhu7lZ3TGEzGMIODIYHwbn1h/T9ie+DDW4Y0VbYrWYrdtf2m1Pn58/jcGu3zLVIFqLuKAFTgKmVrZe5y6vLFNIF7YW92mlyu1DMt6Vv47f7VZ8xuhVOp5OZmZktu3/Ox+cJuLRvj7Svfx84thb202OncZgdmt/kQA4JbbfBXywWia3HNBlivRmv2wvmy83oajl97jQmTJo0yqulO9KNyWLa8uYyNjYGR+DROx/V1IadMIQd6PH34Ov2bXmiPJ57HKfbicup/bJqpxjmucA5Mn7tMkq7Pd30e/uJOCLMpba2YXJmEspomi8N8mBtSZK29NiL5SLB1SAH0tql1NXicXkoFAtbLrkX44sEPdpUN9ZycPjgtsJ+fuI8XSHtqpFrGfAPgHfrDf7Z2VlwQq9f2/0GqLY3sG/9frw88TIBU0Dzm77daicQDmypGWNjY5iCJu45eI+mNuyEIezIU1GiI1HGx8ev+l2qlMLuU3e+6HZsF8MsFAokK0kcJu3ylD9y30fo9fYy4B0gXopvWUY/PTcNJW175oAcAgmFQ1t67Olimlwix54RbW8uCl1+uenU2NjYFccrlQrx9bhcnaoxPcEe3GH3VUImhODipYtyqqwOHOg+AP6thX16ehpcMBTRvjdKt78bX7dvI+xSy7nFc/S7tX8/rGYrwXBwS2G/cOECLpcLi1mb7pKNYAg7srCH+kJXCbsQgnwlj+TSJxfVYXcQX41fVX06PT0NTngo9JDmNoyERsC7dcrjzPyMLsLus/uI9ke3tCGRTZBeTWtug8JAeGBLYZ+fn6dirhANah968Ng8+CK+q2yIxWKkC2nNU3EVDg8erivse3q0v+EGHAGGDwxfJezFYpG57Bz7I9qnflpNVgKhrT32s2fP4vFov/eyE4awIwu7p8vD1NTUFZ7q/Pw8wiw20u+0JuwKg/PqnfaJiQmQ4L23vFdzGw70Hth2uT23OIfb4dZ81FfUFSUyHNky/DE2PYbIa9tCuZbB6CBWn/UqUT116hRYYXRAezu8Ni+uoOsqj11PGwBu6r8Jc9i8vbA74cCg9iGygCNA354+zp49e8Xx8fFxKt4KtwzforkNVrMVX9DH3NzcFY5YoVDg4sWLhrDvBrw2L+6gm3K5LJ+gVc6ePQs28Dn0mVn4mqHXwD55KVeL4hXoIWaHhw6Db+s0roWlBbrC2nUQVIi6owT6A1tnHEyPad5CuZaQM0TPaM9Vwn7y5EmwwIE92guZx+bB4ZPz6WuL6E6ePAk2ODCiz37DcGAYR/fW4cInlp7Ats/GQFD7zt0BR4DIYIRYLHbFQJZTp05BEO4+dLfmNlhMFnwBWRdqNePChQuUpTJBr/Z7LzthCDvgsrrwBOU77OnTpzeOnzlzBqwQ9mofRwV4993vhhu4aok5MTGBJEmaF+QAHBo5BM6tPfaF+AK9Ee03x7rcXXi6PSwsLFAoFK743bmJc1BA8xJ6haAzSHQoepWwnzhxAk/QQ09Y+14gLqsLq8uKEOKKcOGpU6dwB930RbRtaaDgtrmxuW1b3vTPl85TGi7Jq06N6XZ34++T0ypfeumljePPPvsskkviNbe/RnMbrCYrHp+sGbXhmLNnz+q217AThrAjXzjugJwffuLEiY3jp86dgjKEPCFd7Ng3uA+L03LVEvPC5AVcNpcu6X3RaBRJkq66eCuVCitrK/R1ay8iUVcUW9CGEOIqO06eP4nb6tZsitRmgo4ggd4AY2NjV/QFOXHiBN0D3bis2mdLSZKExyuLyMmTJzeOnzx5kmh/FLdNvwpHl9O1ZZLBqrRKxVIh7NRe2If8Q0gBed/rhRde2Dj+zDPP4Pf5NW2GpmA1W3F55c++VtjPnDkDLtjb25mujgqGsANOq5MSJUZHR3n55Zc3jp8ZO4M9b9ftwpEkCY/Hc5Wwj82NEbDpM07WZDJht19dKDU3N0eZMgO92i+1o+4oZq98E9scjjk3cY49A3t0a64UdAbxRDxks9mN/O1KpcKpU6cI94RxWrSvtATwerxYLBaOHz8OQD6f5/jx4/QM9Ohyc1HweD1MT0+TTCY3jpXLZfKpPDZhw27RPoNsyD/EcnGZ/mCQ5z/8YTCZKA4P85NnniEY1CcEYjVZcbgcWCyWqzz26EhUl7TPnTCEHdljzxQz3HzzzVcI+9nxswRMAV09Iq/Py/Hjx6/wDqdWpuhyax/bVtiq2nJiYgIsMNSn/RIz6ooinPL/vzYEkslkuLRwiYN7tK0qrCXoCGL3y2J1/vx5QN6ky2az+CN+3UTVbDZzU18fL37842Ay8dLICPl8nkhfBLdVx/PT6wWqnmmV4+ePU1mrcNSlzbjEq2ywe0lNnOOn19d5IpOhLAQnpqfJVgpEK/rc8C0mC2VRZmho6Ipz9MyZM/SO9hJw6OOIbYch7MjT6HOlHEeA82fOkJMkUkNDxJZijLpH+Z07f0c3WwL+AKurq7KQIg9RWC2uMhTQL2bncrk2Xl9BEfaRgRHNXz/qjlK0FQkEAjzzzDMbx48fP46wCg4fOKy5DQrKxiVc3n9RwnW+kA+nVR+PXczMcMfsLD/K5SgIwReqqaCB1Kq+jodHFvbakNBzp5+DNPzlPX+pmx2cO8fD5TJLwNPIA5dxwOjEzqMd1cJqtlKsFDl69CjPPfccIK9czp49S9dAl6YtgxvBEHbAJJmozFzi5m9+kzJwBjh/6RLYoD9T4bZebRsK1RLwy3f6559/HpC9Qzywr0e/EVsul4vp6ekrUj/1FPaAI8Babo177rmHH/7whxvHn3vuObDBbYf1+zzcNjeSTcLn820I+7e+9S3cbjdOn1O3UAznzvHmcpkEYAf+Cvg1gFPHdfXYo4Eo3YPdfO1rX7ts2vg5KMKhQ4d0s8OezPI6KwSAnwZ+C+h3wuCKPvN+rCZZ2O+0WhkfH2dZkvjx3n4yvgzRAW17wTeCIewK585xpJqB8RKyuGOFoZfO7PQo1fH6vDidTn7wgx8A8PTTT4MH7j6ifQqXgtstp37W9sG4cOECnqBHlxJ6k2RCIHjNa17DqVOnNmLbP/rRj3CH3Iz26ZPqCPLoxHQxzY1dXZz6u7/jkiTxpb/9W9585AgFUdAtFGPKZLlfgn7gIPAnyFNu0sW0rh673+Hn4VsP8Y0vf5mEJMHICKe/9x08Tg/hsD7ZYwC35vyM9cB/QRYxC/C7TgjZ9QmBWM1WShcvcNeXvwzA/wf8i3UB3g6+5AJem1cXO7bDEHaFbJb9QC/wD8D/BLw2GIwnd3yY2kiSxL333suxY8cAOHbsGO4eN3ccvEM3G/weP5i5Ivvh9OnTeKNeXbr3KbzpTW8C4Otf/zrFYpF/+7d/Y2DPAB6bfsUfDouD3NRFjk5O8lw+z1uBQqXC77/4IumJ8zgs2mdggLwPhBWmkJ2OPwZcQMHjxGbWp4AOwDc+ywPPPU0e+DLA1BQvnznBiFvfmPIdP/soPxmx8gGgCGSBQ34boTf+gi6vbzFZKL70Ivfk8/QCnwG+6wEJmJr4oeGx7xYkhxMz8CHgKeA7wC9awa9DL5DN3B8KcerUKcYliSe+9CXCEQc9Xv1mJypDnJVKx3K5zJkzZ3AFXLqdsBIShw8fZigc5mu/+7t812ZjbW2NqEPoujElSRKcO8cvl0qkgJ8AfwTckc9TOn8Ws0n7FFQAz613kPLL5+gGLhfiBn2KkxR8Tz7NsKnIXuCfgBgQs8C+2e1n1GrBqx55Hy/8/KtheBiTJGEZHib+3l8l9NNv1OX1rSYrxVwGC/Ae4JvAi264YQHGnTlD2HcL4uAN4HLxXuD/Av4UeJ3bivut79TXkNlZHv7KVwD4DSAhBN2JVWyf/6JuJkR8EWwe24awT0xMkMvl8Hg9+gmZzUP6s3/PmxMJnshm+TOgG/BNj+H7l6/qYsMG2Sx3Ae8D7gP+I3AxBIOLV49S1ArvoaOsf+z/hOFhkCQYHqby6b9B6tc+/bQW32KCpB3eARwD3gaYLXB7pgif/axudkRcEeI9fpichEoFJidZPXqIoFOndEezlaJH3l/5I+A/A6/zwFsXYCIoZ+50EkPYFfr74bHHkIaHeb8k8aHhYdZ+9R34H/hZXc2Qzp7jUC7HYeBJYBSIVirwwQ/qZoP34iUGLBXG/+qvYGSEH3/84wCa94ipJeqKsvSxP+bhUokM8F3gdwBTuYL0oQ/pZgcATicS8OfIYmZywG3/Ce7K6FO4BnI+/drrX3uFkMUevo8+rz5Vpwo+T5h1OzwCVIAfAG+1wN4Sup6jW9UxxLNxgg59hN0kmRAHD4LLhQX4BNDrgZvXbBTN6Bou3NK+jr76LkKSJHjkkSsunHMjXm4Iaztg4yqq02n+GfgF4AvIcTtq+lFoymc/i/tr36ZXKnERYGqK73760wRcLnx+HYXdHWUxMceDgFLDtzEpXa/3QuEGeTWn8MNB+Oj3rbz7Vz+pmwlhZ5iVzMoVx8biY+wN6lvh6P/FR0jY4RDwdeRY//1WcJTQ/3PZxHRimkH/oH4vOCA7g8oqaj3gZM9vfQivzYtJ6qy0GsJeRUKiIipXHJtKTDEc0Kd0fYPqzMgbgf8N3K4c13gizAYf/CCedJEuG7yM7CX/a7nMTwuhW7UnyP1AFkajmJDtGAc2djv0ei8U+q+8gH9wxMe9v/bHsiOgEyFniJXsZWEvlAu8/9j7efXAq3WzAaDnjW8j1i2fo29Czs7JW8BZRPfPxWwyU6qUNn5eyiwRdWnfRvkKap3B++6j6xff1fEwDBjCvoHT6iRbvHKWY1mUdb/zioM3IFyXc6NzFrALM3z0o/oYMD2Nuwg32+Qc4b+uHv6v28y51IrR4CgTb38DuFy4kENSAsBs0u+9qKXmAj7/9ge48Vf+QNeXD7vCxLPxjZ8X04vc0n0Lrxt5na52jAZHmXjDq69YweQs4LDYdf9cIs4Iy5nlK47p6XxsRpIkoq7O57CDIewbKG0FFBbTi7o0NNqMZXCY0qf/x4Z3mNg7QOD2e/XzDoeGcBfgoA3iwI+BMWD/nkFde5LsCe5hfE/wCk85tXcQz6136uopw9WeYaFc0KUnSi2bQzGL6UX9vVOgx9NDrNt9xeeSjfhxvu+PdP9cuj3dLKTkbJxELtFRQVVagDitTkYD+tVZbIch7FVcliuF/dsXv82Dex7U3Q6b2Ubx7b+04R0mnj6G/yb9Ki356EdxmG3kzXJs/w7A73KR+D/+Cz6bfhfOsH+YqcTUFZ7y6vM/IHhQn34ktbitbtKFtO6vW0vIGbrCY19KL+naP0hBKR6r/Vxyv/ubON7887rb0uPpIZaSi9fGV8d132+oJVVIbRQl/es7/rVjdigYwl4l6AyymrvctP+J8Sd4w7436G6H1WylWL5cyp/IJXQtCuKRR7C/57fId4U20up47DESP/sAfod+dmx+HwBWs6u6ZT3UolSfdpKwK3xFjH0ps0TUrb/HDrK4lyvljZ+zxax+rRVqONJ1hJfm5X7sM+szDPj0Tf0EePzc4xyfP048GyfklLOk9CwY2w5D2Kv0eHqYT11uILSSWaHHo19RkILSg0IhkU/oKqgAjvsfIv+nf7KRHcQjj+iaSqawOQTy+ZOf5/a+23d4hDa4bZc99kwx0xER89q8JAuXq6A7FYoBeTU1uTa58XOulNOtGVotr+p7Fc/H5J5Ks8lZ+r36DPWu5SezP+GzL3/2CmHfDRjCXqXH08N3J77LkxNPdtQOq+lKT3Utt6Z7C1C72U6uJBffJHIJXv+Pr+d7k9/T/Ua3N7iX8dXLbQ1enH+RXzikT8l4LX3ePv72hb8FYCG1QLe7W3cbJEkiV8ptxHI7FYoB2VM+uXi5u2O2lNWttUItPruP9fy6PJBlfZZ+n/7Cvppb5djEMUPYdys9nh4+d/JzPDX11BW90PVGaQeqoHsoBrBb7OTL8oDeS+uXOLF4gmMTx3QX9hujN/LszLOUK2XKlXLHlrjvu+d9nFo6BcBCeoFuj/7CDnD3wN289QtvZT2/3tFQzOGuw5xYvDxpLFvqTCgG5Jv/WHysYx77Wm6NA+ED/Hj2x4aw70Z6PD1cWr/EQmqhI+EPBZvZRqF8ec7nzPqM7kKi9KcHSOaTHIoc4rm553S348bojfz647/OU1NPsZpb7diFI0kSklwmxkJqoWOe8h/c8wcEHUFOL51mKbNExBXpiB2Huw5veOzZYpZ0Id0Rjx3goX0P8clnP8nF1YsdudGt59d55Mgj/K+X/9e1J+ySJP2BJElCkqTOnGkq0OPpwWKysJhZJJaM0evpzGgrq8lKIpfgg8fk8uxnZ5/ljj79OjuCHIrJl2SPPVlIcmP0Rtbz67qHIG7rvY233fQ2VrIrLGeWOyZkcHnDcDG92JFQDMihhwf3Psj5lfOUKiUsJktH7PA7/Bvx/g89+SGeGH9Ctx5Cm3lw74Mcih6i19PbkWpPgeAN+95AsVy8toRdkqRB4EGgs/XEbeKxebiz/05ypRyfefEznRN2s5Wnpp7iU899ih9M/wCPzYPVbNXVhlqPPVVIcSgiD1DQ21N1WV28/aa3E8/GWUp3oKqwBp/dR7KQ7GgoBmB/aD8XVi507PUVJCSEEDwfe76jY+AcFge/ecdv8vlf/HxHXl8Igc1s40tv+xK3dN/SERu2Qo1b3MeR+913LjCtEt/55e8QS8Z4cuJJXjP8mo7YYDVZOTZxjN+/6/f5ix/+ha7TmxRqY+zJfJIudxc3hG/QvSgHLudvd9pj99v9rOXWWEwvdiwUA7A/vJ/nY8/rvu+yGYvJwmxylj3BPfzTW/6po7Z0CiGEnNMP3NJzi64DT+rRlrBLkvQwMCuEeEklezqK0+rEaXXyntvfw539d3bEBqvZyvOx53n3be/mGxe+wc3dN+tug91cI+yFJB6bh0++Ub+GV7XsGmF3+EnkEh0NxYC8clhILzDk17lfzib6vH2cXjpNyBni/j33d9SWTjESGLmqDcluoW6QTpKk7wBbpUN8ELkV8esbeSFJkh4FHgUY0ruJUxN0ubs64iUrWE1WCuUCfd4+ejw9HOk6orsNm0MxXruXe4fu1d0OuCzsS5klbuq6qSM2gOyxJ/IJUoVUxz2z/3DkP+ja3mErBn2DnFg40fGVQyd5df+rmUvOddqMLakr7EKIB7Y6LknSEeTeTC9VG+8MAC9IknSnEOKqUeFCiMeAxwBuv/32XRu2+cC9H+horMxmtm1swnzsgY91pJrObqnZPM0nOzq/MeiQK4I7mY0C8oDtRC7Rsdev5ffu+j3Kolz/DzVkwDfAk5NPcltP55ygTnP34N08N/dcp83YkpZDMUKIE0KILiHEiBBiBJgBbttK1F9J3Nl/p+6blbVYzdaNkMO/P/LvO9KtzmKybFR8KqGYTmG32CmUC8wkO1MyruB3yDH23YAkSR3LiFHo9nRzfuV8RzdOO82+0D4+8dAnOm3Glhh57LsMq8na0ViygnJDUUIxnUQIQb6U71iuNMihmJXsSscFdbfQ7ZaFvVP1HruFTrYJ3gnVztKq127QJrUeeydRqm+Thc6GYgAqotLxCyjoDHJh5UJHUy53E92ebpYzy9e1x76bMTz2XYbVZO1IH/jNJPIJPvzkh8kWO9MHpJaDkYMd95T7vf28MP9CR+P8u4mIK4KEdF1vnu5mDGHfZewWjz2WjPG9qe8h0Hck3lbcN3ofgz4dZ1luQZ+3j5OLJw1hr2IxWQi7wobHvksxAoa7jIgr0vFBuCBfuFNrUxzuOtxpU3ho30MdqytQMJvMRFyRjlad7ja63F3XfYx9t2II+y7jgT1bZpfqjsPiYHx1nJ8a+qlOm4LFZNkVnvKwf3hX2LFbGAmMdHz/xWBrOu8aGuxK7BY7N0Zv5GD4YKdN2TUMBwxhr+XLb/9yx5p/GeyM4bEbbInD4uC2nts4FD3UaVN2DR974GOGsNewG0bAGWyNIewGW+KwOPhv9/+3jk5+3210YlSigUErGMJusCWfefgzBJ36D442MDBoHyPGbrAlnSzfNzAwaA9D2A0MDAyuMQxhNzAwMLjGMITdwMDA4BrDEHYDAwODawxD2A0MDAyuMQxhNzAwMLjGMITdwMDA4BrDEHYDAwODawxJmZSj64tK0hIw1eLDI8Cyiua8Um0Aw47N7AY7doMNYNixmd1ghxo2DAsh6o7x6oiwt4MkSc8JIW6/3m0w7NidduwGGww7dqcdetpghGIMDAwMrjEMYTcwMDC4xnglCvtjnTaA3WEDGHZsZjfYsRtsAMOOzewGO3Sz4RUXYzcwMDAw2JlXosduYGBgYLATQoiOfgGDwHeBM8Ap4D9Xj4eAJ4AL1X+D1ePh6t+ngL/e9Fz/BrxUfZ6/Acx621DznI8DJzv4XnwPOAccr351dcgOG/IS9DxwFnir3nYA3pr34ThyytknOvBevBM4AbxcPVcjHfpM3l614RTw5xqfow8Cz1f/388D99U816uqx8eAT1KNIHTAjo8Cl4BUJ94LwAV8Hfn6OAX8WTN2bGlbu0/QtgHQC9xWcwGeB24E/hx4f/X4+4GPVb93A/cC79nihPVV/5WALwHv0NuG6u/fAvwTzQu7mu/F94Dbd8Fn8l+Bj1S/N9GcmKn6udQ87/PAa/W0AXla2aLy/68+/k/0fi+QBX8aiFZ//n+B+zW04yjQV/3+MDBb81w/Bu5Gvl6/CbyxQ3bcVX2+ZoVdFRuQhf1nqt/bgO83815saVs7D9biC/gK8p3tHNBb8wae2/R3v8r23rIV+Crwdr1tADzAD6ofcFPCrrId36NFYVfZjkuAu9N21Pxuf9Wmhr1DNWyonpNLwDCykP0N8Kje7wVwB/Cdmp9/GfiU1nZUj0vACmCv/s3Zmt+9E/i03nZsOt6UsGthQ/V3/x34jXZs2VUxdkmSRpDvas8C3UKIGED134bGw0uS9C1kzygJ/EsHbPhT4K+ATLOvrbIdAP8gSdJxSZI+LEmSpLcdkiQFqt/+qSRJL0iS9EVJkrr1tmMT7wT+WVSvIL1sEEIUgfciL8PnkG/8n2nWhnbtQA57HJQkaUSSJAvw88ghBT3seCvwohAiD/QDMzW/m6ke09sOVVDLhuo18++AY+3Ys2uEXZIkD3L45PeEEOutPo8Q4g3Id0k7cJ+eNkiSdCuwTwjx5WYfq6YdVR4RQhwBXlP9+uUO2GEBBoCnhRC3AT8C/rIDdtTyDuBzetsgSZIVWdiPAn3IMe4P6G2HEGK1asc/Iy/5J4GS1nZIknQT8DHgPymHtjKvA3a0jVo2VG+0nwM+KYQYb8emXSHs1ZP+S8BnhRD/u3p4QZKk3urve5G98IYQQuSQNy9/Tmcb7gZeJUnSJHI45oAkSd9r1AYV7UAIMVv9N4kc77+zA3asIK9clBvdF4HbOmCH8ly3ABYhxPMdsOFWACHExepq4QvAPR2wAyHEV4UQrxZC3I0cNrigpR2SJA0gnwO/IoS4WD08g3zTVxhAXsnobUdbqGzDY8AFIcQn2rWr48JeDRF8BjgjhPi/a371OPCu6vfvQo5f7fQ8npo30wK8CXmXWTcbhBD/QwjRJ4QYQd64Oi+EeF0jNqhphyRJFkmSItXvrcCbgZN621EVsK8Cr6seuh84rbcdNbyTJr11FW2YBW5HbWf0AAABQElEQVSUJElp4PQgcjaF3nYgSVJX9d8g8JvA32llRzW08HXgA0KIp5U/roYokpIk3VV9zl9pxHa17WgHNW2QJOkjgB/4PTVsazk4r9YXsgAK5KWpko72JuTd+2PI3sQxIFTzmEkgjpzKNYMcr+wGfsLlNK7/B9k7082GTc85QvNZMWq9F27kzA/lvfjvNJj6qfb7gbxZ+FT1uY4BQ52wo/q7ceBgJz6T6vH3IIv5y8g3vHCH7Pgc8g32NA1mjrVqB/AhIM2V6aZd1d/djuxwXAT+mubSHdW048+r70+l+u+f6GkD8mpFVM8N5fh/bOZz2fxlVJ4aGBgYXGN0PBRjYGBgYKAuhrAbGBgYXGMYwm5gYGBwjWEIu4GBgcE1hiHsBgYGBtcYhrAbGBgYXGMYwm5gYGBwjWEIu4GBgcE1xv8POkAaZHTZ6RAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.signal import argrelextrema\n",
+    "\n",
+    "sorted_data['hf'] = sorted_data['CO2_ppm'].rolling(15,center=True).mean()\n",
+    "\n",
+    "gap = 30\n",
+    "\n",
+    "sorted_data['min'] = sorted_data.iloc[argrelextrema(sorted_data.hf.values, np.less_equal,order=gap)[0]]['hf']\n",
+    "sorted_data['max'] = sorted_data.iloc[argrelextrema(sorted_data.hf.values, np.greater_equal,order=gap)[0]]['hf']\n",
+    "\n",
+    "plt.scatter(sorted_data.index[-500:], (sorted_data['min']-sorted_data['mov_avg'])[-500:], c='r',label='Local minima')\n",
+    "plt.scatter(sorted_data.index[-500:], (sorted_data['max']-sorted_data['mov_avg'])[-500:], c='b',label='Local maxima')\n",
+    "plt.plot(sorted_data.index[-500:], (sorted_data['hf']-sorted_data['mov_avg'])[-500:],c='k')\n",
+    "plt.plot(sorted_data.index[-500:], (sorted_data['CO2_ppm']-sorted_data['mov_avg'])[-500:],c='g',lw=.7)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To compute the elapsed time between two consecutive minima and maxima, some manipulation is required because the dataframe contatins `NaN` values. Therefore, an array containing only the row that are non-`NaN` is built checking each row of the `sorted_data['min']` dataframe. After that, the delta in time is computed in days and printed as average."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Period inferred from minima: 365 days\n"
+     ]
+    }
+   ],
+   "source": [
+    "minima_dates = []\n",
+    "for i in range(0,len(sorted_data)):\n",
+    "    if ~np.isnan(sorted_data['min'][i]):\n",
+    "        minima_dates.append(sorted_data.index[i])\n",
+    "        \n",
+    "time_delta = []        \n",
+    "for i in range(0,len(minima_dates)-1):\n",
+    "    time_delta.append((minima_dates[i+1]-minima_dates[i]).days)\n",
+    "avg_minima = int(np.mean((time_delta)))\n",
+    "print('Mean Period inferred from minima: '+str(avg_minima)+' days')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same procedure can be applied regarding the local maxima."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Period inferred from maxima: 371 days\n"
+     ]
+    }
+   ],
+   "source": [
+    "maxima_dates = []\n",
+    "for i in range(0,len(sorted_data)):\n",
+    "    if ~np.isnan(sorted_data['max'][i]):\n",
+    "        maxima_dates.append(sorted_data.index[i])\n",
+    "\n",
+    "time_delta = []        \n",
+    "for i in range(0,len(maxima_dates)-1):\n",
+    "    time_delta.append((maxima_dates[i+1]-maxima_dates[i]).days)\n",
+    "avg_maxima = int(np.mean((time_delta)))\n",
+    "print('Mean Period inferred from maxima: '+str(avg_maxima)+' days')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eventually, a more refined period is computed as the average between the previous two, reading approximately 1 year as expected from visual inspection of the plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average mean period: 368 days\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Average mean period: '+str(int(np.mean([avg_minima,avg_maxima])))+' days')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The amplitude of the oscillations can instead be characterized by computing the `mean` and `std` of minima and maxima as follows. By visual inspection of the previous plot, one can foresee that the amplitude with respect to the low-frequency trend approximately equals 3 ppm. From this analysis it results that the oscillation amplitude is `2.92` with a standard deviation of `0.09`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean amplitude relative to low-frequency trend: 2.9227199394094505\n",
+      "with a standard deviation of: 0.09052037898782317\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_amplitude_min = (sorted_data['min']-sorted_data['mov_avg']).mean()\n",
+    "std_amplitude_min = (sorted_data['min']-sorted_data['mov_avg']).std()\n",
+    "\n",
+    "mean_amplitude_max = (sorted_data['max']-sorted_data['mov_avg']).mean()\n",
+    "std_amplitude_max = (sorted_data['max']-sorted_data['mov_avg']).std()\n",
+    "\n",
+    "mean_amplitude = np.mean([abs(mean_amplitude_min),abs(mean_amplitude_max)])\n",
+    "mean_std = np.std([abs(mean_amplitude_min),abs(mean_amplitude_max)],ddof=1)\n",
+    "print('Mean amplitude relative to low-frequency trend: '+str(mean_amplitude)+'\\nwith a standard deviation of: '+str(mean_std))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 2.2: Find a simple model for the slow contribution, estimate its parameters, and attempt an extrapolation until 2025"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to handle data fitting, a few libraries are needed, especially [scipy.optimize](https://docs.scipy.org/doc/scipy/reference/optimize.html). The other modules are meant to handle the time conversion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.optimize\n",
+    "from datetime import datetime\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exponential fitting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By looking at the first plots in this document, one can speculate on the fact that the CO2 growth resembles an exponential law. For this reason, this function is used here. At first, a simple exponential function is defined, whose parameters `m`, `t` and `b` are conventional."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def exponential(x, m, t, b):\n",
+    "    return m * np.exp(-t * x) + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An issue appears here because the fitting algorithm works with numerical quantities, whereas the `Date` column of the database has a `Timestamp` formatting. Therefore, these dates are converted in time units (weeks in this case) elapsed since 1970-1-1, so as conventional _Epoch time_. `NaN` values are dropped to avoid any issue as well. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_exp, y_exp = [], []\n",
+    "for i in range(0,len(sorted_data)):\n",
+    "    if ~np.isnan(sorted_data['mov_avg'][i]):\n",
+    "        x_exp.append(int(sorted_data.index[i].strftime('%s'))/(60*60*24*7))\n",
+    "        y_exp.append(sorted_data['mov_avg'][i])\n",
+    "x_exp, y_exp = np.array(x_exp),np.array(y_exp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this way, the analysis can start from two array, namely `x_exp` and `y_exp`, that include time and CO2 concentration, respectively. To move on towards the fitting curve, the algorithm requires a first guess for the unknown parameters. These values are provided in an array `p0` and try to grasp the final values to assist convergence. Resulting values for `m, p, t` are printed below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 6.73159809e+01 -3.17501284e-04  2.58226836e+02]\n"
+     ]
+    }
+   ],
+   "source": [
+    "p0 = (10, -1e-4, 100)\n",
+    "pars, cv = scipy.optimize.curve_fit(exponential, x_exp, y_exp, p0)\n",
+    "m, t, b = pars\n",
+    "print(pars)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Following the definition of [R-squared](https://en.wikipedia.org/wiki/Coefficient_of_determination), this coefficient is computed to quantify the goodness-of-fit. It results that `R²=0.999`, that is a rather good score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R² = 0.9994739472916078\n"
+     ]
+    }
+   ],
+   "source": [
+    "squared_diffs = (y_exp - exponential(x_exp, m, t, b))**2\n",
+    "squared_diffs_mean = (y_exp - np.mean(y_exp))**2\n",
+    "R2 = 1 - np.sum(squared_diffs)/np.sum(squared_diffs_mean)\n",
+    "print('R² = '+str(R2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eventually, data and fitting are plotted below. The extrapolation of CO2 concentration at the end of 2025 is simply addressed by computing the exponential law on a custom array `xfit` that spans up to the desired date."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CO2 atmospheric concentration at the end of 2025 equals 428.4467238328075 ppm\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "extr_year = 2025\n",
+    "xfit = np.linspace(x_exp[0],int(datetime(extr_year, 12, 31, 0, 0).strftime('%s'))/(60*60*24*7))\n",
+    "\n",
+    "plt.plot(x_exp, y_exp,'-',lw=2,label='Data',color='k')\n",
+    "plt.plot(xfit, exponential(xfit, m, t, b), '--', lw=2,label=\"Exponential fit\",color='r')\n",
+    "plt.plot(xfit[-1], exponential(xfit[-1], m, t, b), '*', ms=10,label=str(extr_year)+'-12-31',color='b',mfc='w')\n",
+    "plt.xlabel('Epoch time since 1970-1-1 [weeks]')\n",
+    "plt.ylabel('CO2 atmospheric concentration [ppm]')\n",
+    "plt.legend()\n",
+    "\n",
+    "print('CO2 atmospheric concentration at the end of '+str(extr_year)+' equals '+str(exponential(xfit[-1], m, t, b))+' ppm')"
+   ]
+  }
+ ],
  "metadata": {
+  "hide_code_all_hidden": false,
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -16,10 +1549,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo3/weekly_in_situ_co2_mlo_retrieved20221003.csv b/module3/exo3/weekly_in_situ_co2_mlo_retrieved20221003.csv
new file mode 100644
index 0000000000000000000000000000000000000000..34a26849385c0700f4b585c146ce09aee6836cca
--- /dev/null
+++ b/module3/exo3/weekly_in_situ_co2_mlo_retrieved20221003.csv
@@ -0,0 +1,3331 @@
+"-------------------------------------------------------------------------------------------"
+" Atmospheric CO2 concentrations (ppm) derived from in situ air measurements                "
+" at Mauna Loa, Observatory, Hawaii: Latitude 19.5°N Longitude 155.6°W Elevation 3397m      "
+"                                                                                           "
+" Source: R. F. Keeling, S. J. Walker, S. C. Piper and A. F. Bollenbacher                   "
+" Scripps CO2 Program ( http://scrippsco2.ucsd.edu )                                        "
+" Scripps Institution of Oceanography (SIO)                                                 "
+" University of California                                                                  "
+" La Jolla, California USA 92093-0244                                                       "
+"                                                                                           "
+" Status of data and correspondence:                                                        "
+"                                                                                           "
+" These data are subject to revision based on recalibration of standard gases. Questions    "
+" about the data should be directed to Dr. Ralph Keeling (rkeeling@ucsd.edu), Stephen Walker"
+" (sjwalker@ucsd.edu) and Stephen Piper (scpiper@ucsd.edu), Scripps CO2 Program.            "
+"                                                                                           "
+" Baseline data in this file through 09-Aug-2022 from archive dated 10-Aug-2022 17:36:17    "
+"                                                                                           "
+"-------------------------------------------------------------------------------------------"
+"                                                                                           "
+" Please cite as:                                                                           "
+"                                                                                           "
+" C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and       "
+" H. A. Meijer, Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and   "
+" oceans from 1978 to 2000.  I. Global aspects, SIO Reference Series, No. 01-06, Scripps    "
+" Institution of Oceanography, San Diego, 88 pages, 2001.                                   "
+"                                                                                           "
+" If it is necessary to cite a peer-reviewed article, please cite as:                       "
+"                                                                                           "
+" C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and       "
+" H. A. Meijer, Atmospheric CO2 and 13CO2 exchange with the terrestrial biosphere and       "
+" oceans from 1978 to 2000: observations and carbon cycle implications, pages 83-113,       "
+" in "A History of Atmospheric CO2 and its effects on Plants, Animals, and Ecosystems",     "
+" editors, Ehleringer, J.R., T. E. Cerling, M. D. Dearing, Springer Verlag,                 "
+" New York, 2005.                                                                           "
+"                                                                                           "
+"-------------------------------------------------------------------------------------------"
+"                                                                                           "
+"                                                                        "
+" The data file below contains 2 columns indicaing the date and CO2      "
+" concentrations in micro-mol CO2 per mole (ppm), reported on the 2012  "
+" SIO manometric mole fraction scale.  These weekly values have been     "
+" adjusted to 12:00 hours at middle day of each weekly period as         "
+" indicated by the date in the first column.                             "
+1958-03-29, 316.19
+1958-04-05, 317.31
+1958-04-12, 317.69
+1958-04-19, 317.58
+1958-04-26, 316.48
+1958-05-03, 316.95
+1958-05-17, 317.56
+1958-05-24, 317.99
+1958-07-05, 315.85
+1958-07-12, 315.85
+1958-07-19, 315.46
+1958-07-26, 315.59
+1958-08-02, 315.64
+1958-08-09, 315.10
+1958-08-16, 315.09
+1958-08-30, 314.14
+1958-09-06, 313.54
+1958-11-08, 313.05
+1958-11-15, 313.26
+1958-11-22, 313.57
+1958-11-29, 314.01
+1958-12-06, 314.56
+1958-12-13, 314.41
+1958-12-20, 314.77
+1958-12-27, 315.21
+1959-01-03, 315.24
+1959-01-10, 315.50
+1959-01-17, 315.69
+1959-01-24, 315.86
+1959-01-31, 315.42
+1959-02-14, 316.94
+1959-02-21, 316.61
+1959-02-28, 316.62
+1959-03-07, 316.81
+1959-03-21, 316.73
+1959-03-28, 316.71
+1959-04-04, 317.72
+1959-04-11, 317.12
+1959-04-18, 317.64
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