Global Emissions by Countries.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eba0021e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pd.set_option(\"display.max_columns\",None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f0bd27e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Annual CO2 emissions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1750</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1751</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1752</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1753</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1754</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64255</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.012170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64256</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.010815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64257</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.010247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64258</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.011341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64259</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.010374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64260 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country  Year  Annual CO2 emissions\n",
       "0      Afghanistan  1750              0.000000\n",
       "1      Afghanistan  1751              0.000000\n",
       "2      Afghanistan  1752              0.000000\n",
       "3      Afghanistan  1753              0.000000\n",
       "4      Afghanistan  1754              0.000000\n",
       "...            ...   ...                   ...\n",
       "64255     Zimbabwe  2015              0.012170\n",
       "64256     Zimbabwe  2016              0.010815\n",
       "64257     Zimbabwe  2017              0.010247\n",
       "64258     Zimbabwe  2018              0.011341\n",
       "64259     Zimbabwe  2019              0.010374\n",
       "\n",
       "[64260 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/annual-co2-emissions-by-region.csv\")\n",
    "\n",
    "#Drop Code column\n",
    "df.drop(\"Code\",axis = 1, inplace = True)\n",
    "\n",
    "#Rename columns\n",
    "df.columns = [\"Country\",\"Year\",\"Annual CO2 emissions\"]\n",
    "\n",
    "#Convert tonnes to Giga tonnes\n",
    "df[\"Annual CO2 emissions\"] = df[\"Annual CO2 emissions\"]/10**9\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7023a2e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Anguilla</th>\n",
       "      <th>Antarctica</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>Aruba</th>\n",
       "      <th>Asia</th>\n",
       "      <th>Asia (excl. China &amp; India)</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Austria</th>\n",
       "      <th>Azerbaijan</th>\n",
       "      <th>Bahamas</th>\n",
       "      <th>Bahrain</th>\n",
       "      <th>Bangladesh</th>\n",
       "      <th>Barbados</th>\n",
       "      <th>Belarus</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>Belize</th>\n",
       "      <th>Benin</th>\n",
       "      <th>Bermuda</th>\n",
       "      <th>Bhutan</th>\n",
       "      <th>Bolivia</th>\n",
       "      <th>Bonaire Sint Eustatius and Saba</th>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <th>Botswana</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>British Virgin Islands</th>\n",
       "      <th>Brunei</th>\n",
       "      <th>Bulgaria</th>\n",
       "      <th>Burkina Faso</th>\n",
       "      <th>Burundi</th>\n",
       "      <th>Cambodia</th>\n",
       "      <th>Cameroon</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Cape Verde</th>\n",
       "      <th>Central African Republic</th>\n",
       "      <th>Chad</th>\n",
       "      <th>Chile</th>\n",
       "      <th>China</th>\n",
       "      <th>Christmas Island</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Comoros</th>\n",
       "      <th>Congo</th>\n",
       "      <th>Cook Islands</th>\n",
       "      <th>Costa Rica</th>\n",
       "      <th>Cote d'Ivoire</th>\n",
       "      <th>Croatia</th>\n",
       "      <th>Cuba</th>\n",
       "      <th>Curacao</th>\n",
       "      <th>Cyprus</th>\n",
       "      <th>Czechia</th>\n",
       "      <th>Democratic Republic of Congo</th>\n",
       "      <th>Denmark</th>\n",
       "      <th>Djibouti</th>\n",
       "      <th>Dominica</th>\n",
       "      <th>Dominican Republic</th>\n",
       "      <th>EU-27</th>\n",
       "      <th>EU-28</th>\n",
       "      <th>Ecuador</th>\n",
       "      <th>Egypt</th>\n",
       "      <th>El Salvador</th>\n",
       "      <th>Equatorial Guinea</th>\n",
       "      <th>Eritrea</th>\n",
       "      <th>Estonia</th>\n",
       "      <th>Eswatini</th>\n",
       "      <th>Ethiopia</th>\n",
       "      <th>Europe</th>\n",
       "      <th>Europe (excl. EU-27)</th>\n",
       "      <th>Europe (excl. EU-28)</th>\n",
       "      <th>Faeroe Islands</th>\n",
       "      <th>Fiji</th>\n",
       "      <th>Finland</th>\n",
       "      <th>France</th>\n",
       "      <th>French Equatorial Africa</th>\n",
       "      <th>French Polynesia</th>\n",
       "      <th>French West Africa</th>\n",
       "      <th>Gabon</th>\n",
       "      <th>Gambia</th>\n",
       "      <th>Georgia</th>\n",
       "      <th>Germany</th>\n",
       "      <th>Ghana</th>\n",
       "      <th>Greece</th>\n",
       "      <th>Greenland</th>\n",
       "      <th>Grenada</th>\n",
       "      <th>Guatemala</th>\n",
       "      <th>Guinea</th>\n",
       "      <th>Guinea-Bissau</th>\n",
       "      <th>Guyana</th>\n",
       "      <th>Haiti</th>\n",
       "      <th>Honduras</th>\n",
       "      <th>Hong Kong</th>\n",
       "      <th>Hungary</th>\n",
       "      <th>Iceland</th>\n",
       "      <th>India</th>\n",
       "      <th>Indonesia</th>\n",
       "      <th>International transport</th>\n",
       "      <th>Iran</th>\n",
       "      <th>Iraq</th>\n",
       "      <th>Ireland</th>\n",
       "      <th>Israel</th>\n",
       "      <th>Italy</th>\n",
       "      <th>Jamaica</th>\n",
       "      <th>Japan</th>\n",
       "      <th>Jordan</th>\n",
       "      <th>Kazakhstan</th>\n",
       "      <th>Kenya</th>\n",
       "      <th>Kiribati</th>\n",
       "      <th>Kosovo</th>\n",
       "      <th>Kuwait</th>\n",
       "      <th>Kuwaiti Oil Fires</th>\n",
       "      <th>Kyrgyzstan</th>\n",
       "      <th>Laos</th>\n",
       "      <th>Latvia</th>\n",
       "      <th>Lebanon</th>\n",
       "      <th>Leeward Islands</th>\n",
       "      <th>Lesotho</th>\n",
       "      <th>Liberia</th>\n",
       "      <th>Libya</th>\n",
       "      <th>Liechtenstein</th>\n",
       "      <th>Lithuania</th>\n",
       "      <th>Luxembourg</th>\n",
       "      <th>Macao</th>\n",
       "      <th>Madagascar</th>\n",
       "      <th>Malawi</th>\n",
       "      <th>Malaysia</th>\n",
       "      <th>Maldives</th>\n",
       "      <th>Mali</th>\n",
       "      <th>Malta</th>\n",
       "      <th>Marshall Islands</th>\n",
       "      <th>Mauritania</th>\n",
       "      <th>Mauritius</th>\n",
       "      <th>Mexico</th>\n",
       "      <th>Micronesia (country)</th>\n",
       "      <th>Moldova</th>\n",
       "      <th>Mongolia</th>\n",
       "      <th>Montenegro</th>\n",
       "      <th>Montserrat</th>\n",
       "      <th>Morocco</th>\n",
       "      <th>Mozambique</th>\n",
       "      <th>Myanmar</th>\n",
       "      <th>Namibia</th>\n",
       "      <th>Nauru</th>\n",
       "      <th>Nepal</th>\n",
       "      <th>Netherlands</th>\n",
       "      <th>New Caledonia</th>\n",
       "      <th>New Zealand</th>\n",
       "      <th>Nicaragua</th>\n",
       "      <th>Niger</th>\n",
       "      <th>Nigeria</th>\n",
       "      <th>Niue</th>\n",
       "      <th>North America</th>\n",
       "      <th>North America (excl. USA)</th>\n",
       "      <th>North Korea</th>\n",
       "      <th>North Macedonia</th>\n",
       "      <th>Norway</th>\n",
       "      <th>Oceania</th>\n",
       "      <th>Oman</th>\n",
       "      <th>Pakistan</th>\n",
       "      <th>Palau</th>\n",
       "      <th>Palestine</th>\n",
       "      <th>Panama</th>\n",
       "      <th>Panama Canal Zone</th>\n",
       "      <th>Papua New Guinea</th>\n",
       "      <th>Paraguay</th>\n",
       "      <th>Peru</th>\n",
       "      <th>Philippines</th>\n",
       "      <th>Poland</th>\n",
       "      <th>Portugal</th>\n",
       "      <th>Puerto Rico</th>\n",
       "      <th>Qatar</th>\n",
       "      <th>Romania</th>\n",
       "      <th>Russia</th>\n",
       "      <th>Rwanda</th>\n",
       "      <th>Ryukyu Islands</th>\n",
       "      <th>Saint Helena</th>\n",
       "      <th>Saint Kitts and Nevis</th>\n",
       "      <th>Saint Lucia</th>\n",
       "      <th>Saint Pierre and Miquelon</th>\n",
       "      <th>Saint Vincent and the Grenadines</th>\n",
       "      <th>Samoa</th>\n",
       "      <th>Sao Tome and Principe</th>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <th>Senegal</th>\n",
       "      <th>Serbia</th>\n",
       "      <th>Seychelles</th>\n",
       "      <th>Sierra Leone</th>\n",
       "      <th>Singapore</th>\n",
       "      <th>Sint Maarten (Dutch part)</th>\n",
       "      <th>Slovakia</th>\n",
       "      <th>Slovenia</th>\n",
       "      <th>Solomon Islands</th>\n",
       "      <th>Somalia</th>\n",
       "      <th>South Africa</th>\n",
       "      <th>South America</th>\n",
       "      <th>South Korea</th>\n",
       "      <th>South Sudan</th>\n",
       "      <th>Spain</th>\n",
       "      <th>Sri Lanka</th>\n",
       "      <th>St. Kitts-Nevis-Anguilla</th>\n",
       "      <th>Sudan</th>\n",
       "      <th>Suriname</th>\n",
       "      <th>Sweden</th>\n",
       "      <th>Switzerland</th>\n",
       "      <th>Syria</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>Tajikistan</th>\n",
       "      <th>Tanzania</th>\n",
       "      <th>Thailand</th>\n",
       "      <th>Timor</th>\n",
       "      <th>Togo</th>\n",
       "      <th>Tonga</th>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <th>Tunisia</th>\n",
       "      <th>Turkey</th>\n",
       "      <th>Turkmenistan</th>\n",
       "      <th>Turks and Caicos Islands</th>\n",
       "      <th>Tuvalu</th>\n",
       "      <th>Uganda</th>\n",
       "      <th>Ukraine</th>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>United States</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela</th>\n",
       "      <th>Vietnam</th>\n",
       "      <th>Wallis and Futuna</th>\n",
       "      <th>World</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>0.000176</td>\n",
       "      <td>0.023488</td>\n",
       "      <td>3.098969</td>\n",
       "      <td>3.521460</td>\n",
       "      <td>0.040640</td>\n",
       "      <td>0.219304</td>\n",
       "      <td>0.006715</td>\n",
       "      <td>0.006434</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>0.015813</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>0.012693</td>\n",
       "      <td>5.604855</td>\n",
       "      <td>2.513159</td>\n",
       "      <td>2.090668</td>\n",
       "      <td>0.000608</td>\n",
       "      <td>0.002130</td>\n",
       "      <td>0.044114</td>\n",
       "      <td>0.338501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005249</td>\n",
       "      <td>0.000524</td>\n",
       "      <td>0.009589</td>\n",
       "      <td>0.795816</td>\n",
       "      <td>0.016115</td>\n",
       "      <td>0.074958</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.015426</td>\n",
       "      <td>0.002451</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.002004</td>\n",
       "      <td>0.003191</td>\n",
       "      <td>0.010082</td>\n",
       "      <td>0.042391</td>\n",
       "      <td>0.046628</td>\n",
       "      <td>0.003545</td>\n",
       "      <td>2.253429</td>\n",
       "      <td>0.507012</td>\n",
       "      <td>1.166074</td>\n",
       "      <td>0.640811</td>\n",
       "      <td>0.165646</td>\n",
       "      <td>0.038545</td>\n",
       "      <td>0.064950</td>\n",
       "      <td>0.360088</td>\n",
       "      <td>0.007937</td>\n",
       "      <td>1.222781</td>\n",
       "      <td>0.025270</td>\n",
       "      <td>0.288578</td>\n",
       "      <td>0.016301</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.008339</td>\n",
       "      <td>0.096265</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010278</td>\n",
       "      <td>0.008806</td>\n",
       "      <td>0.007273</td>\n",
       "      <td>0.025770</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002327</td>\n",
       "      <td>0.001205</td>\n",
       "      <td>0.050844</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.013297</td>\n",
       "      <td>0.009333</td>\n",
       "      <td>0.002029</td>\n",
       "      <td>0.003520</td>\n",
       "      <td>0.001120</td>\n",
       "      <td>0.233268</td>\n",
       "      <td>0.001301</td>\n",
       "      <td>0.003096</td>\n",
       "      <td>0.001735</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.481975</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>0.023244</td>\n",
       "      <td>0.002239</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.061028</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>0.022076</td>\n",
       "      <td>0.003886</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.006014</td>\n",
       "      <td>0.166367</td>\n",
       "      <td>0.004736</td>\n",
       "      <td>0.035839</td>\n",
       "      <td>0.005250</td>\n",
       "      <td>0.002035</td>\n",
       "      <td>0.113539</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>6.650501</td>\n",
       "      <td>1.238069</td>\n",
       "      <td>0.024621</td>\n",
       "      <td>0.007024</td>\n",
       "      <td>0.045350</td>\n",
       "      <td>0.452738</td>\n",
       "      <td>0.061604</td>\n",
       "      <td>0.170175</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.003004</td>\n",
       "      <td>0.010877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006364</td>\n",
       "      <td>0.006051</td>\n",
       "      <td>0.048696</td>\n",
       "      <td>0.112142</td>\n",
       "      <td>0.313099</td>\n",
       "      <td>0.052294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.105756</td>\n",
       "      <td>0.077726</td>\n",
       "      <td>1.622861</td>\n",
       "      <td>0.000926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.645409</td>\n",
       "      <td>0.009846</td>\n",
       "      <td>0.043747</td>\n",
       "      <td>0.000480</td>\n",
       "      <td>0.000905</td>\n",
       "      <td>0.062130</td>\n",
       "      <td>0.000755</td>\n",
       "      <td>0.034484</td>\n",
       "      <td>0.013618</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.000630</td>\n",
       "      <td>0.451590</td>\n",
       "      <td>1.152384</td>\n",
       "      <td>0.595394</td>\n",
       "      <td>0.001942</td>\n",
       "      <td>0.271687</td>\n",
       "      <td>0.019830</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020163</td>\n",
       "      <td>0.002733</td>\n",
       "      <td>0.043337</td>\n",
       "      <td>0.038733</td>\n",
       "      <td>0.028675</td>\n",
       "      <td>0.257156</td>\n",
       "      <td>0.005163</td>\n",
       "      <td>0.010638</td>\n",
       "      <td>0.283293</td>\n",
       "      <td>0.000502</td>\n",
       "      <td>0.002686</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.045121</td>\n",
       "      <td>0.029275</td>\n",
       "      <td>0.381332</td>\n",
       "      <td>0.070231</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.004522</td>\n",
       "      <td>0.223912</td>\n",
       "      <td>0.239671</td>\n",
       "      <td>0.422491</td>\n",
       "      <td>5.412432</td>\n",
       "      <td>0.006655</td>\n",
       "      <td>0.101791</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.170899</td>\n",
       "      <td>0.184436</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>35.209447</td>\n",
       "      <td>0.013006</td>\n",
       "      <td>0.004296</td>\n",
       "      <td>0.012170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.006745</td>\n",
       "      <td>1.337082</td>\n",
       "      <td>0.004496</td>\n",
       "      <td>0.148839</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.034111</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.190930</td>\n",
       "      <td>0.005159</td>\n",
       "      <td>0.000883</td>\n",
       "      <td>18.973575</td>\n",
       "      <td>7.028698</td>\n",
       "      <td>0.411032</td>\n",
       "      <td>0.067112</td>\n",
       "      <td>0.037278</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.031397</td>\n",
       "      <td>0.075857</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>0.060285</td>\n",
       "      <td>0.099807</td>\n",
       "      <td>0.000608</td>\n",
       "      <td>0.006740</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.020693</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.021733</td>\n",
       "      <td>0.006333</td>\n",
       "      <td>0.478443</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.007544</td>\n",
       "      <td>0.045354</td>\n",
       "      <td>0.003381</td>\n",
       "      <td>0.000443</td>\n",
       "      <td>0.009724</td>\n",
       "      <td>0.008260</td>\n",
       "      <td>0.564033</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.000993</td>\n",
       "      <td>0.084154</td>\n",
       "      <td>9.552517</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100801</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.003702</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.007924</td>\n",
       "      <td>0.011798</td>\n",
       "      <td>0.018105</td>\n",
       "      <td>0.028000</td>\n",
       "      <td>0.005390</td>\n",
       "      <td>0.007368</td>\n",
       "      <td>0.106629</td>\n",
       "      <td>0.001996</td>\n",
       "      <td>0.036989</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.024629</td>\n",
       "      <td>3.103516</td>\n",
       "      <td>3.502625</td>\n",
       "      <td>0.039505</td>\n",
       "      <td>0.233516</td>\n",
       "      <td>0.006638</td>\n",
       "      <td>0.006207</td>\n",
       "      <td>0.000660</td>\n",
       "      <td>0.017378</td>\n",
       "      <td>0.001180</td>\n",
       "      <td>0.014432</td>\n",
       "      <td>5.597849</td>\n",
       "      <td>2.501554</td>\n",
       "      <td>2.102445</td>\n",
       "      <td>0.000630</td>\n",
       "      <td>0.002019</td>\n",
       "      <td>0.047228</td>\n",
       "      <td>0.341026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000744</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005298</td>\n",
       "      <td>0.000531</td>\n",
       "      <td>0.009905</td>\n",
       "      <td>0.800510</td>\n",
       "      <td>0.016547</td>\n",
       "      <td>0.071368</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.016376</td>\n",
       "      <td>0.002744</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.002344</td>\n",
       "      <td>0.003235</td>\n",
       "      <td>0.009554</td>\n",
       "      <td>0.043388</td>\n",
       "      <td>0.047396</td>\n",
       "      <td>0.003498</td>\n",
       "      <td>2.392360</td>\n",
       "      <td>0.568194</td>\n",
       "      <td>1.185882</td>\n",
       "      <td>0.648061</td>\n",
       "      <td>0.194361</td>\n",
       "      <td>0.040030</td>\n",
       "      <td>0.063907</td>\n",
       "      <td>0.356556</td>\n",
       "      <td>0.008141</td>\n",
       "      <td>1.203167</td>\n",
       "      <td>0.023994</td>\n",
       "      <td>0.288421</td>\n",
       "      <td>0.017152</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.008860</td>\n",
       "      <td>0.101574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009640</td>\n",
       "      <td>0.014261</td>\n",
       "      <td>0.007221</td>\n",
       "      <td>0.026502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002360</td>\n",
       "      <td>0.001358</td>\n",
       "      <td>0.042839</td>\n",
       "      <td>0.000150</td>\n",
       "      <td>0.013328</td>\n",
       "      <td>0.009080</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.003359</td>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.246722</td>\n",
       "      <td>0.001444</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>0.001399</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.002763</td>\n",
       "      <td>0.004346</td>\n",
       "      <td>0.484629</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.004896</td>\n",
       "      <td>0.025324</td>\n",
       "      <td>0.002015</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.061032</td>\n",
       "      <td>0.007726</td>\n",
       "      <td>0.025469</td>\n",
       "      <td>0.004124</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.008841</td>\n",
       "      <td>0.166282</td>\n",
       "      <td>0.005312</td>\n",
       "      <td>0.034268</td>\n",
       "      <td>0.005448</td>\n",
       "      <td>0.002064</td>\n",
       "      <td>0.116774</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>6.516012</td>\n",
       "      <td>1.223744</td>\n",
       "      <td>0.027467</td>\n",
       "      <td>0.006987</td>\n",
       "      <td>0.044473</td>\n",
       "      <td>0.461420</td>\n",
       "      <td>0.060033</td>\n",
       "      <td>0.203146</td>\n",
       "      <td>0.000224</td>\n",
       "      <td>0.003232</td>\n",
       "      <td>0.010463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006679</td>\n",
       "      <td>0.007154</td>\n",
       "      <td>0.052360</td>\n",
       "      <td>0.122237</td>\n",
       "      <td>0.324011</td>\n",
       "      <td>0.050456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102173</td>\n",
       "      <td>0.075813</td>\n",
       "      <td>1.618304</td>\n",
       "      <td>0.001047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.565751</td>\n",
       "      <td>0.009859</td>\n",
       "      <td>0.045036</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000931</td>\n",
       "      <td>0.040275</td>\n",
       "      <td>0.000762</td>\n",
       "      <td>0.034922</td>\n",
       "      <td>0.014417</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.460043</td>\n",
       "      <td>1.148425</td>\n",
       "      <td>0.617960</td>\n",
       "      <td>0.001726</td>\n",
       "      <td>0.260760</td>\n",
       "      <td>0.023028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019311</td>\n",
       "      <td>0.002326</td>\n",
       "      <td>0.042973</td>\n",
       "      <td>0.039193</td>\n",
       "      <td>0.028508</td>\n",
       "      <td>0.265284</td>\n",
       "      <td>0.006305</td>\n",
       "      <td>0.010535</td>\n",
       "      <td>0.281705</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.003098</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>0.029008</td>\n",
       "      <td>0.401240</td>\n",
       "      <td>0.070164</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.004805</td>\n",
       "      <td>0.234200</td>\n",
       "      <td>0.228583</td>\n",
       "      <td>0.399110</td>\n",
       "      <td>5.292268</td>\n",
       "      <td>0.006525</td>\n",
       "      <td>0.105222</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.163191</td>\n",
       "      <td>0.185432</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>35.220412</td>\n",
       "      <td>0.010426</td>\n",
       "      <td>0.004838</td>\n",
       "      <td>0.010815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.006860</td>\n",
       "      <td>1.390056</td>\n",
       "      <td>0.005404</td>\n",
       "      <td>0.153448</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>0.037471</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.187416</td>\n",
       "      <td>0.005485</td>\n",
       "      <td>0.000916</td>\n",
       "      <td>19.373878</td>\n",
       "      <td>7.166304</td>\n",
       "      <td>0.415097</td>\n",
       "      <td>0.069629</td>\n",
       "      <td>0.036498</td>\n",
       "      <td>0.001971</td>\n",
       "      <td>0.032771</td>\n",
       "      <td>0.080324</td>\n",
       "      <td>0.001171</td>\n",
       "      <td>0.060743</td>\n",
       "      <td>0.099456</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.007425</td>\n",
       "      <td>0.000630</td>\n",
       "      <td>0.001257</td>\n",
       "      <td>0.021811</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.021792</td>\n",
       "      <td>0.007059</td>\n",
       "      <td>0.484588</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.009552</td>\n",
       "      <td>0.047505</td>\n",
       "      <td>0.003938</td>\n",
       "      <td>0.000549</td>\n",
       "      <td>0.011186</td>\n",
       "      <td>0.007530</td>\n",
       "      <td>0.572834</td>\n",
       "      <td>0.000579</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>0.084062</td>\n",
       "      <td>9.750726</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.091670</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.003526</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.008235</td>\n",
       "      <td>0.011860</td>\n",
       "      <td>0.018738</td>\n",
       "      <td>0.025131</td>\n",
       "      <td>0.005093</td>\n",
       "      <td>0.007516</td>\n",
       "      <td>0.105642</td>\n",
       "      <td>0.002205</td>\n",
       "      <td>0.034722</td>\n",
       "      <td>0.000380</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>0.024255</td>\n",
       "      <td>3.127499</td>\n",
       "      <td>3.515587</td>\n",
       "      <td>0.039238</td>\n",
       "      <td>0.250240</td>\n",
       "      <td>0.006016</td>\n",
       "      <td>0.005980</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>0.018636</td>\n",
       "      <td>0.000986</td>\n",
       "      <td>0.015594</td>\n",
       "      <td>5.628105</td>\n",
       "      <td>2.507829</td>\n",
       "      <td>2.119741</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>0.002037</td>\n",
       "      <td>0.044673</td>\n",
       "      <td>0.345921</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000747</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004793</td>\n",
       "      <td>0.000535</td>\n",
       "      <td>0.009832</td>\n",
       "      <td>0.786655</td>\n",
       "      <td>0.013817</td>\n",
       "      <td>0.074853</td>\n",
       "      <td>0.000513</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.017214</td>\n",
       "      <td>0.002880</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.002318</td>\n",
       "      <td>0.003272</td>\n",
       "      <td>0.009899</td>\n",
       "      <td>0.042552</td>\n",
       "      <td>0.049685</td>\n",
       "      <td>0.003615</td>\n",
       "      <td>2.456848</td>\n",
       "      <td>0.531012</td>\n",
       "      <td>1.248596</td>\n",
       "      <td>0.724578</td>\n",
       "      <td>0.208713</td>\n",
       "      <td>0.038910</td>\n",
       "      <td>0.062467</td>\n",
       "      <td>0.351474</td>\n",
       "      <td>0.007798</td>\n",
       "      <td>1.187661</td>\n",
       "      <td>0.025467</td>\n",
       "      <td>0.307927</td>\n",
       "      <td>0.016533</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.008094</td>\n",
       "      <td>0.104784</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.009263</td>\n",
       "      <td>0.017914</td>\n",
       "      <td>0.007223</td>\n",
       "      <td>0.027935</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002528</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.043729</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.013546</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>0.003886</td>\n",
       "      <td>0.001402</td>\n",
       "      <td>0.248869</td>\n",
       "      <td>0.001499</td>\n",
       "      <td>0.003121</td>\n",
       "      <td>0.001553</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.003737</td>\n",
       "      <td>0.004536</td>\n",
       "      <td>0.461238</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.005292</td>\n",
       "      <td>0.034154</td>\n",
       "      <td>0.002103</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.063831</td>\n",
       "      <td>0.008147</td>\n",
       "      <td>0.023670</td>\n",
       "      <td>0.004098</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.010124</td>\n",
       "      <td>0.164445</td>\n",
       "      <td>0.005345</td>\n",
       "      <td>0.036153</td>\n",
       "      <td>0.005375</td>\n",
       "      <td>0.002006</td>\n",
       "      <td>0.130275</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>6.461226</td>\n",
       "      <td>1.207620</td>\n",
       "      <td>0.021754</td>\n",
       "      <td>0.007508</td>\n",
       "      <td>0.043563</td>\n",
       "      <td>0.467346</td>\n",
       "      <td>0.065511</td>\n",
       "      <td>0.232425</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>0.011962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006548</td>\n",
       "      <td>0.008015</td>\n",
       "      <td>0.052489</td>\n",
       "      <td>0.134519</td>\n",
       "      <td>0.337340</td>\n",
       "      <td>0.054720</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.105569</td>\n",
       "      <td>0.078077</td>\n",
       "      <td>1.646180</td>\n",
       "      <td>0.001051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000352</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.579417</td>\n",
       "      <td>0.009364</td>\n",
       "      <td>0.045483</td>\n",
       "      <td>0.000586</td>\n",
       "      <td>0.000938</td>\n",
       "      <td>0.039066</td>\n",
       "      <td>0.000751</td>\n",
       "      <td>0.036087</td>\n",
       "      <td>0.014265</td>\n",
       "      <td>0.000286</td>\n",
       "      <td>0.000638</td>\n",
       "      <td>0.466103</td>\n",
       "      <td>1.126973</td>\n",
       "      <td>0.620610</td>\n",
       "      <td>0.001491</td>\n",
       "      <td>0.274671</td>\n",
       "      <td>0.022984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021715</td>\n",
       "      <td>0.002520</td>\n",
       "      <td>0.042307</td>\n",
       "      <td>0.038182</td>\n",
       "      <td>0.026401</td>\n",
       "      <td>0.272450</td>\n",
       "      <td>0.006873</td>\n",
       "      <td>0.011019</td>\n",
       "      <td>0.286336</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.003049</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.039968</td>\n",
       "      <td>0.029620</td>\n",
       "      <td>0.425329</td>\n",
       "      <td>0.070193</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.005226</td>\n",
       "      <td>0.223232</td>\n",
       "      <td>0.186623</td>\n",
       "      <td>0.388088</td>\n",
       "      <td>5.253606</td>\n",
       "      <td>0.006174</td>\n",
       "      <td>0.109282</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.146673</td>\n",
       "      <td>0.182584</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>35.696349</td>\n",
       "      <td>0.010107</td>\n",
       "      <td>0.006520</td>\n",
       "      <td>0.010247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.010453</td>\n",
       "      <td>1.425734</td>\n",
       "      <td>0.005403</td>\n",
       "      <td>0.164309</td>\n",
       "      <td>0.000464</td>\n",
       "      <td>0.037679</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000506</td>\n",
       "      <td>0.185030</td>\n",
       "      <td>0.006297</td>\n",
       "      <td>0.000943</td>\n",
       "      <td>19.917214</td>\n",
       "      <td>7.369322</td>\n",
       "      <td>0.415954</td>\n",
       "      <td>0.066720</td>\n",
       "      <td>0.037488</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>0.031594</td>\n",
       "      <td>0.085719</td>\n",
       "      <td>0.001207</td>\n",
       "      <td>0.061872</td>\n",
       "      <td>0.100208</td>\n",
       "      <td>0.000612</td>\n",
       "      <td>0.007760</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>0.001662</td>\n",
       "      <td>0.022346</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.022086</td>\n",
       "      <td>0.006815</td>\n",
       "      <td>0.466649</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.009560</td>\n",
       "      <td>0.043552</td>\n",
       "      <td>0.004140</td>\n",
       "      <td>0.000568</td>\n",
       "      <td>0.015479</td>\n",
       "      <td>0.007567</td>\n",
       "      <td>0.586505</td>\n",
       "      <td>0.000610</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.085829</td>\n",
       "      <td>9.956569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.092228</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.003518</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.008249</td>\n",
       "      <td>0.012514</td>\n",
       "      <td>0.017719</td>\n",
       "      <td>0.026084</td>\n",
       "      <td>0.005269</td>\n",
       "      <td>0.007333</td>\n",
       "      <td>0.104411</td>\n",
       "      <td>0.002231</td>\n",
       "      <td>0.034651</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>0.000166</td>\n",
       "      <td>0.025305</td>\n",
       "      <td>3.054491</td>\n",
       "      <td>3.434629</td>\n",
       "      <td>0.041818</td>\n",
       "      <td>0.251461</td>\n",
       "      <td>0.006018</td>\n",
       "      <td>0.005906</td>\n",
       "      <td>0.000709</td>\n",
       "      <td>0.017711</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.016185</td>\n",
       "      <td>5.601973</td>\n",
       "      <td>2.555341</td>\n",
       "      <td>2.175203</td>\n",
       "      <td>0.000705</td>\n",
       "      <td>0.002124</td>\n",
       "      <td>0.045849</td>\n",
       "      <td>0.331725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004803</td>\n",
       "      <td>0.000563</td>\n",
       "      <td>0.009862</td>\n",
       "      <td>0.755362</td>\n",
       "      <td>0.014480</td>\n",
       "      <td>0.071798</td>\n",
       "      <td>0.000512</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.019411</td>\n",
       "      <td>0.003032</td>\n",
       "      <td>0.000309</td>\n",
       "      <td>0.002343</td>\n",
       "      <td>0.003367</td>\n",
       "      <td>0.010471</td>\n",
       "      <td>0.042506</td>\n",
       "      <td>0.049628</td>\n",
       "      <td>0.003675</td>\n",
       "      <td>2.591324</td>\n",
       "      <td>0.576584</td>\n",
       "      <td>1.267690</td>\n",
       "      <td>0.755402</td>\n",
       "      <td>0.211270</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.062213</td>\n",
       "      <td>0.348085</td>\n",
       "      <td>0.008010</td>\n",
       "      <td>1.135688</td>\n",
       "      <td>0.024924</td>\n",
       "      <td>0.319647</td>\n",
       "      <td>0.017137</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.008192</td>\n",
       "      <td>0.104218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010169</td>\n",
       "      <td>0.032262</td>\n",
       "      <td>0.007859</td>\n",
       "      <td>0.027565</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002426</td>\n",
       "      <td>0.001274</td>\n",
       "      <td>0.045206</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.013669</td>\n",
       "      <td>0.009569</td>\n",
       "      <td>0.002216</td>\n",
       "      <td>0.004188</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>0.249144</td>\n",
       "      <td>0.001565</td>\n",
       "      <td>0.003273</td>\n",
       "      <td>0.001532</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.004902</td>\n",
       "      <td>0.451081</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.005878</td>\n",
       "      <td>0.064508</td>\n",
       "      <td>0.002123</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.065367</td>\n",
       "      <td>0.008383</td>\n",
       "      <td>0.026096</td>\n",
       "      <td>0.004154</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.013410</td>\n",
       "      <td>0.160170</td>\n",
       "      <td>0.008209</td>\n",
       "      <td>0.035080</td>\n",
       "      <td>0.005377</td>\n",
       "      <td>0.002094</td>\n",
       "      <td>0.136078</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>6.639555</td>\n",
       "      <td>1.214673</td>\n",
       "      <td>0.038163</td>\n",
       "      <td>0.006981</td>\n",
       "      <td>0.043818</td>\n",
       "      <td>0.470415</td>\n",
       "      <td>0.071030</td>\n",
       "      <td>0.247425</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>0.003220</td>\n",
       "      <td>0.012096</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006786</td>\n",
       "      <td>0.008103</td>\n",
       "      <td>0.054210</td>\n",
       "      <td>0.138924</td>\n",
       "      <td>0.337706</td>\n",
       "      <td>0.051482</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109245</td>\n",
       "      <td>0.076951</td>\n",
       "      <td>1.691360</td>\n",
       "      <td>0.001080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000249</td>\n",
       "      <td>0.000362</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.576758</td>\n",
       "      <td>0.009690</td>\n",
       "      <td>0.046053</td>\n",
       "      <td>0.000605</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>0.038288</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.036088</td>\n",
       "      <td>0.014488</td>\n",
       "      <td>0.000298</td>\n",
       "      <td>0.000658</td>\n",
       "      <td>0.472001</td>\n",
       "      <td>1.096956</td>\n",
       "      <td>0.634934</td>\n",
       "      <td>0.001540</td>\n",
       "      <td>0.269654</td>\n",
       "      <td>0.022973</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022372</td>\n",
       "      <td>0.002552</td>\n",
       "      <td>0.041766</td>\n",
       "      <td>0.036895</td>\n",
       "      <td>0.025878</td>\n",
       "      <td>0.273105</td>\n",
       "      <td>0.007473</td>\n",
       "      <td>0.011502</td>\n",
       "      <td>0.292453</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>0.003167</td>\n",
       "      <td>0.000165</td>\n",
       "      <td>0.037866</td>\n",
       "      <td>0.030357</td>\n",
       "      <td>0.419195</td>\n",
       "      <td>0.078035</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.005385</td>\n",
       "      <td>0.231694</td>\n",
       "      <td>0.188541</td>\n",
       "      <td>0.380139</td>\n",
       "      <td>5.424882</td>\n",
       "      <td>0.006252</td>\n",
       "      <td>0.113938</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.129596</td>\n",
       "      <td>0.211774</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>36.419712</td>\n",
       "      <td>0.009945</td>\n",
       "      <td>0.006930</td>\n",
       "      <td>0.011341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.010720</td>\n",
       "      <td>1.448793</td>\n",
       "      <td>0.005579</td>\n",
       "      <td>0.171707</td>\n",
       "      <td>0.000470</td>\n",
       "      <td>0.038020</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000492</td>\n",
       "      <td>0.178940</td>\n",
       "      <td>0.006006</td>\n",
       "      <td>0.000919</td>\n",
       "      <td>20.241964</td>\n",
       "      <td>7.450834</td>\n",
       "      <td>0.411016</td>\n",
       "      <td>0.068495</td>\n",
       "      <td>0.039820</td>\n",
       "      <td>0.001979</td>\n",
       "      <td>0.034354</td>\n",
       "      <td>0.102162</td>\n",
       "      <td>0.001187</td>\n",
       "      <td>0.062484</td>\n",
       "      <td>0.099709</td>\n",
       "      <td>0.000633</td>\n",
       "      <td>0.007998</td>\n",
       "      <td>0.000632</td>\n",
       "      <td>0.001707</td>\n",
       "      <td>0.022575</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.026621</td>\n",
       "      <td>0.006316</td>\n",
       "      <td>0.465716</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.009089</td>\n",
       "      <td>0.042006</td>\n",
       "      <td>0.004301</td>\n",
       "      <td>0.000580</td>\n",
       "      <td>0.016027</td>\n",
       "      <td>0.007593</td>\n",
       "      <td>0.576651</td>\n",
       "      <td>0.000634</td>\n",
       "      <td>0.000308</td>\n",
       "      <td>0.001030</td>\n",
       "      <td>0.084267</td>\n",
       "      <td>10.174681</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102202</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.003457</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.008508</td>\n",
       "      <td>0.012946</td>\n",
       "      <td>0.017882</td>\n",
       "      <td>0.025988</td>\n",
       "      <td>0.005196</td>\n",
       "      <td>0.007316</td>\n",
       "      <td>0.101010</td>\n",
       "      <td>0.002283</td>\n",
       "      <td>0.032075</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.027379</td>\n",
       "      <td>2.916906</td>\n",
       "      <td>3.286785</td>\n",
       "      <td>0.040540</td>\n",
       "      <td>0.246643</td>\n",
       "      <td>0.006207</td>\n",
       "      <td>0.005634</td>\n",
       "      <td>0.000727</td>\n",
       "      <td>0.013888</td>\n",
       "      <td>0.000974</td>\n",
       "      <td>0.016255</td>\n",
       "      <td>5.448709</td>\n",
       "      <td>2.540065</td>\n",
       "      <td>2.170186</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>0.002257</td>\n",
       "      <td>0.041653</td>\n",
       "      <td>0.323747</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004705</td>\n",
       "      <td>0.000586</td>\n",
       "      <td>0.010287</td>\n",
       "      <td>0.701955</td>\n",
       "      <td>0.014960</td>\n",
       "      <td>0.067184</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.020513</td>\n",
       "      <td>0.003153</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.002390</td>\n",
       "      <td>0.003281</td>\n",
       "      <td>0.010928</td>\n",
       "      <td>0.041536</td>\n",
       "      <td>0.049101</td>\n",
       "      <td>0.003322</td>\n",
       "      <td>2.616449</td>\n",
       "      <td>0.617513</td>\n",
       "      <td>1.267690</td>\n",
       "      <td>0.779527</td>\n",
       "      <td>0.221384</td>\n",
       "      <td>0.037118</td>\n",
       "      <td>0.064172</td>\n",
       "      <td>0.337086</td>\n",
       "      <td>0.008014</td>\n",
       "      <td>1.106664</td>\n",
       "      <td>0.026072</td>\n",
       "      <td>0.313798</td>\n",
       "      <td>0.017315</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.009741</td>\n",
       "      <td>0.107532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011483</td>\n",
       "      <td>0.032815</td>\n",
       "      <td>0.008262</td>\n",
       "      <td>0.028202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002223</td>\n",
       "      <td>0.001322</td>\n",
       "      <td>0.046428</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.013483</td>\n",
       "      <td>0.009785</td>\n",
       "      <td>0.002065</td>\n",
       "      <td>0.004015</td>\n",
       "      <td>0.001466</td>\n",
       "      <td>0.250095</td>\n",
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       "      <td>0.001554</td>\n",
       "      <td>0.000163</td>\n",
       "      <td>0.004092</td>\n",
       "      <td>0.004687</td>\n",
       "      <td>0.438498</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>0.005958</td>\n",
       "      <td>0.065514</td>\n",
       "      <td>0.002461</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.071929</td>\n",
       "      <td>0.008706</td>\n",
       "      <td>0.026232</td>\n",
       "      <td>0.004168</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.013914</td>\n",
       "      <td>0.154827</td>\n",
       "      <td>0.008452</td>\n",
       "      <td>0.036541</td>\n",
       "      <td>0.005549</td>\n",
       "      <td>0.002135</td>\n",
       "      <td>0.140026</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>6.481175</td>\n",
       "      <td>1.196478</td>\n",
       "      <td>0.038762</td>\n",
       "      <td>0.008041</td>\n",
       "      <td>0.042441</td>\n",
       "      <td>0.467762</td>\n",
       "      <td>0.071685</td>\n",
       "      <td>0.248844</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.003303</td>\n",
       "      <td>0.012503</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007087</td>\n",
       "      <td>0.008272</td>\n",
       "      <td>0.054533</td>\n",
       "      <td>0.144263</td>\n",
       "      <td>0.322626</td>\n",
       "      <td>0.048598</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109345</td>\n",
       "      <td>0.075084</td>\n",
       "      <td>1.678367</td>\n",
       "      <td>0.001111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>0.000077</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.582150</td>\n",
       "      <td>0.009823</td>\n",
       "      <td>0.054667</td>\n",
       "      <td>0.000623</td>\n",
       "      <td>0.001027</td>\n",
       "      <td>0.038945</td>\n",
       "      <td>0.000753</td>\n",
       "      <td>0.033315</td>\n",
       "      <td>0.013696</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.478608</td>\n",
       "      <td>1.085107</td>\n",
       "      <td>0.611263</td>\n",
       "      <td>0.001585</td>\n",
       "      <td>0.252683</td>\n",
       "      <td>0.024841</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022981</td>\n",
       "      <td>0.002606</td>\n",
       "      <td>0.042767</td>\n",
       "      <td>0.037682</td>\n",
       "      <td>0.026961</td>\n",
       "      <td>0.262639</td>\n",
       "      <td>0.008980</td>\n",
       "      <td>0.011626</td>\n",
       "      <td>0.288280</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>0.003261</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.037864</td>\n",
       "      <td>0.031013</td>\n",
       "      <td>0.405126</td>\n",
       "      <td>0.085647</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>0.005531</td>\n",
       "      <td>0.223229</td>\n",
       "      <td>0.190683</td>\n",
       "      <td>0.369878</td>\n",
       "      <td>5.284697</td>\n",
       "      <td>0.006378</td>\n",
       "      <td>0.110246</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.116688</td>\n",
       "      <td>0.247709</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>36.441388</td>\n",
       "      <td>0.010255</td>\n",
       "      <td>0.006720</td>\n",
       "      <td>0.010374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 238 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Afghanistan    Africa   Albania   Algeria   Andorra    Angola  \\\n",
       "Year                                                                     \n",
       "1750        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1751        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1752        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1753        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1754        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2015        0.007904  1.312649  0.004532  0.150818  0.000465  0.033979   \n",
       "2016        0.006745  1.337082  0.004496  0.148839  0.000469  0.034111   \n",
       "2017        0.006860  1.390056  0.005404  0.153448  0.000465  0.037471   \n",
       "2018        0.010453  1.425734  0.005403  0.164309  0.000464  0.037679   \n",
       "2019        0.010720  1.448793  0.005579  0.171707  0.000470  0.038020   \n",
       "\n",
       "Country  Anguilla  Antarctica  Antigua and Barbuda  Argentina   Armenia  \\\n",
       "Year                                                                      \n",
       "1750     0.000000         0.0             0.000000   0.000000  0.000000   \n",
       "1751     0.000000         0.0             0.000000   0.000000  0.000000   \n",
       "1752     0.000000         0.0             0.000000   0.000000  0.000000   \n",
       "1753     0.000000         0.0             0.000000   0.000000  0.000000   \n",
       "1754     0.000000         0.0             0.000000   0.000000  0.000000   \n",
       "...           ...         ...                  ...        ...       ...   \n",
       "2015     0.000154         0.0             0.000480   0.192366  0.005072   \n",
       "2016     0.000154         0.0             0.000491   0.190930  0.005159   \n",
       "2017     0.000143         0.0             0.000491   0.187416  0.005485   \n",
       "2018     0.000147         0.0             0.000506   0.185030  0.006297   \n",
       "2019     0.000143         0.0             0.000492   0.178940  0.006006   \n",
       "\n",
       "Country     Aruba       Asia  Asia (excl. China & India)  Australia   Austria  \\\n",
       "Year                                                                            \n",
       "1750     0.000000   0.000000                    0.000000   0.000000  0.000000   \n",
       "1751     0.000000   0.000000                    0.000000   0.000000  0.000000   \n",
       "1752     0.000000   0.000000                    0.000000   0.000000  0.000000   \n",
       "1753     0.000000   0.000000                    0.000000   0.000000  0.000000   \n",
       "1754     0.000000   0.000000                    0.000000   0.000000  0.000000   \n",
       "...           ...        ...                         ...        ...       ...   \n",
       "2015     0.000898  18.870080                    6.933450   0.401555  0.066283   \n",
       "2016     0.000883  18.973575                    7.028698   0.411032  0.067112   \n",
       "2017     0.000916  19.373878                    7.166304   0.415097  0.069629   \n",
       "2018     0.000943  19.917214                    7.369322   0.415954  0.066720   \n",
       "2019     0.000919  20.241964                    7.450834   0.411016  0.068495   \n",
       "\n",
       "Country  Azerbaijan   Bahamas   Bahrain  Bangladesh  Barbados   Belarus  \\\n",
       "Year                                                                      \n",
       "1750       0.000000  0.000000  0.000000    0.000000  0.000000  0.000000   \n",
       "1751       0.000000  0.000000  0.000000    0.000000  0.000000  0.000000   \n",
       "1752       0.000000  0.000000  0.000000    0.000000  0.000000  0.000000   \n",
       "1753       0.000000  0.000000  0.000000    0.000000  0.000000  0.000000   \n",
       "1754       0.000000  0.000000  0.000000    0.000000  0.000000  0.000000   \n",
       "...             ...       ...       ...         ...       ...       ...   \n",
       "2015       0.037199  0.002041  0.032672    0.072835  0.001262  0.058633   \n",
       "2016       0.037278  0.001795  0.031397    0.075857  0.001285  0.060285   \n",
       "2017       0.036498  0.001971  0.032771    0.080324  0.001171  0.060743   \n",
       "2018       0.037488  0.002030  0.031594    0.085719  0.001207  0.061872   \n",
       "2019       0.039820  0.001979  0.034354    0.102162  0.001187  0.062484   \n",
       "\n",
       "Country   Belgium    Belize     Benin   Bermuda    Bhutan   Bolivia  \\\n",
       "Year                                                                  \n",
       "1750     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1751     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1752     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1753     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1754     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "...           ...       ...       ...       ...       ...       ...   \n",
       "2015     0.100981  0.000641  0.006076  0.000539  0.000966  0.019421   \n",
       "2016     0.099807  0.000608  0.006740  0.000619  0.001153  0.020693   \n",
       "2017     0.099456  0.000616  0.007425  0.000630  0.001257  0.021811   \n",
       "2018     0.100208  0.000612  0.007760  0.000649  0.001662  0.022346   \n",
       "2019     0.099709  0.000633  0.007998  0.000632  0.001707  0.022575   \n",
       "\n",
       "Country  Bonaire Sint Eustatius and Saba  Bosnia and Herzegovina  Botswana  \\\n",
       "Year                                                                         \n",
       "1750                            0.000000                0.000000  0.000000   \n",
       "1751                            0.000000                0.000000  0.000000   \n",
       "1752                            0.000000                0.000000  0.000000   \n",
       "1753                            0.000000                0.000000  0.000000   \n",
       "1754                            0.000000                0.000000  0.000000   \n",
       "...                                  ...                     ...       ...   \n",
       "2015                            0.000326                0.018461  0.005425   \n",
       "2016                            0.000330                0.021733  0.006333   \n",
       "2017                            0.000333                0.021792  0.007059   \n",
       "2018                            0.000343                0.022086  0.006815   \n",
       "2019                            0.000334                0.026621  0.006316   \n",
       "\n",
       "Country    Brazil  British Virgin Islands    Brunei  Bulgaria  Burkina Faso  \\\n",
       "Year                                                                          \n",
       "1750     0.000000                0.000000  0.000000  0.000000      0.000000   \n",
       "1751     0.000000                0.000000  0.000000  0.000000      0.000000   \n",
       "1752     0.000000                0.000000  0.000000  0.000000      0.000000   \n",
       "1753     0.000000                0.000000  0.000000  0.000000      0.000000   \n",
       "1754     0.000000                0.000000  0.000000  0.000000      0.000000   \n",
       "...           ...                     ...       ...       ...           ...   \n",
       "2015     0.495214                0.000213  0.006954  0.048194      0.003298   \n",
       "2016     0.478443                0.000213  0.007544  0.045354      0.003381   \n",
       "2017     0.484588                0.000169  0.009552  0.047505      0.003938   \n",
       "2018     0.466649                0.000174  0.009560  0.043552      0.004140   \n",
       "2019     0.465716                0.000169  0.009089  0.042006      0.004301   \n",
       "\n",
       "Country   Burundi  Cambodia  Cameroon    Canada  Cape Verde  \\\n",
       "Year                                                          \n",
       "1750     0.000000  0.000000  0.000000  0.000000    0.000000   \n",
       "1751     0.000000  0.000000  0.000000  0.000000    0.000000   \n",
       "1752     0.000000  0.000000  0.000000  0.000000    0.000000   \n",
       "1753     0.000000  0.000000  0.000000  0.000000    0.000000   \n",
       "1754     0.000000  0.000000  0.000000  0.000000    0.000000   \n",
       "...           ...       ...       ...       ...         ...   \n",
       "2015     0.000392  0.008446  0.007744  0.575907    0.000491   \n",
       "2016     0.000443  0.009724  0.008260  0.564033    0.000539   \n",
       "2017     0.000549  0.011186  0.007530  0.572834    0.000579   \n",
       "2018     0.000568  0.015479  0.007567  0.586505    0.000610   \n",
       "2019     0.000580  0.016027  0.007593  0.576651    0.000634   \n",
       "\n",
       "Country  Central African Republic      Chad     Chile      China  \\\n",
       "Year                                                               \n",
       "1750                     0.000000  0.000000  0.000000   0.000000   \n",
       "1751                     0.000000  0.000000  0.000000   0.000000   \n",
       "1752                     0.000000  0.000000  0.000000   0.000000   \n",
       "1753                     0.000000  0.000000  0.000000   0.000000   \n",
       "1754                     0.000000  0.000000  0.000000   0.000000   \n",
       "...                           ...       ...       ...        ...   \n",
       "2015                     0.000293  0.000696  0.081633   9.683201   \n",
       "2016                     0.000297  0.000993  0.084154   9.552517   \n",
       "2017                     0.000297  0.000997  0.084062   9.750726   \n",
       "2018                     0.000300  0.001008  0.085829   9.956569   \n",
       "2019                     0.000308  0.001030  0.084267  10.174681   \n",
       "\n",
       "Country  Christmas Island  Colombia   Comoros     Congo  Cook Islands  \\\n",
       "Year                                                                    \n",
       "1750                  0.0  0.000000  0.000000  0.000000      0.000000   \n",
       "1751                  0.0  0.000000  0.000000  0.000000      0.000000   \n",
       "1752                  0.0  0.000000  0.000000  0.000000      0.000000   \n",
       "1753                  0.0  0.000000  0.000000  0.000000      0.000000   \n",
       "1754                  0.0  0.000000  0.000000  0.000000      0.000000   \n",
       "...                   ...       ...       ...       ...           ...   \n",
       "2015                  0.0  0.086071  0.000169  0.003451      0.000062   \n",
       "2016                  0.0  0.100801  0.000202  0.003702      0.000062   \n",
       "2017                  0.0  0.091670  0.000238  0.003526      0.000070   \n",
       "2018                  0.0  0.092228  0.000246  0.003518      0.000073   \n",
       "2019                  0.0  0.102202  0.000253  0.003457      0.000077   \n",
       "\n",
       "Country  Costa Rica  Cote d'Ivoire   Croatia      Cuba   Curacao    Cyprus  \\\n",
       "Year                                                                         \n",
       "1750       0.000000       0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1751       0.000000       0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1752       0.000000       0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1753       0.000000       0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1754       0.000000       0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "...             ...            ...       ...       ...       ...       ...   \n",
       "2015       0.007407       0.009405  0.017841  0.029053  0.006251  0.006960   \n",
       "2016       0.007924       0.011798  0.018105  0.028000  0.005390  0.007368   \n",
       "2017       0.008235       0.011860  0.018738  0.025131  0.005093  0.007516   \n",
       "2018       0.008249       0.012514  0.017719  0.026084  0.005269  0.007333   \n",
       "2019       0.008508       0.012946  0.017882  0.025988  0.005196  0.007316   \n",
       "\n",
       "Country   Czechia  Democratic Republic of Congo   Denmark  Djibouti  Dominica  \\\n",
       "Year                                                                            \n",
       "1750     0.000000                      0.000000  0.000000  0.000000  0.000000   \n",
       "1751     0.000000                      0.000000  0.000000  0.000000  0.000000   \n",
       "1752     0.000000                      0.000000  0.000000  0.000000  0.000000   \n",
       "1753     0.000000                      0.000000  0.000000  0.000000  0.000000   \n",
       "1754     0.000000                      0.000000  0.000000  0.000000  0.000000   \n",
       "...           ...                           ...       ...       ...       ...   \n",
       "2015     0.104815                      0.002792  0.035181  0.000446  0.000176   \n",
       "2016     0.106629                      0.001996  0.036989  0.000398  0.000180   \n",
       "2017     0.105642                      0.002205  0.034722  0.000380  0.000161   \n",
       "2018     0.104411                      0.002231  0.034651  0.000390  0.000166   \n",
       "2019     0.101010                      0.002283  0.032075  0.000399  0.000162   \n",
       "\n",
       "Country  Dominican Republic     EU-27     EU-28   Ecuador     Egypt  \\\n",
       "Year                                                                  \n",
       "1750               0.000000  0.000000  0.009351  0.000000  0.000000   \n",
       "1751               0.000000  0.000000  0.009351  0.000000  0.000000   \n",
       "1752               0.000000  0.000000  0.009354  0.000000  0.000000   \n",
       "1753               0.000000  0.000000  0.009354  0.000000  0.000000   \n",
       "1754               0.000000  0.000000  0.009358  0.000000  0.000000   \n",
       "...                     ...       ...       ...       ...       ...   \n",
       "2015               0.023488  3.098969  3.521460  0.040640  0.219304   \n",
       "2016               0.024629  3.103516  3.502625  0.039505  0.233516   \n",
       "2017               0.024255  3.127499  3.515587  0.039238  0.250240   \n",
       "2018               0.025305  3.054491  3.434629  0.041818  0.251461   \n",
       "2019               0.027379  2.916906  3.286785  0.040540  0.246643   \n",
       "\n",
       "Country  El Salvador  Equatorial Guinea   Eritrea   Estonia  Eswatini  \\\n",
       "Year                                                                    \n",
       "1750        0.000000           0.000000  0.000000  0.000000  0.000000   \n",
       "1751        0.000000           0.000000  0.000000  0.000000  0.000000   \n",
       "1752        0.000000           0.000000  0.000000  0.000000  0.000000   \n",
       "1753        0.000000           0.000000  0.000000  0.000000  0.000000   \n",
       "1754        0.000000           0.000000  0.000000  0.000000  0.000000   \n",
       "...              ...                ...       ...       ...       ...   \n",
       "2015        0.006715           0.006434  0.000652  0.015813  0.000978   \n",
       "2016        0.006638           0.006207  0.000660  0.017378  0.001180   \n",
       "2017        0.006016           0.005980  0.000689  0.018636  0.000986   \n",
       "2018        0.006018           0.005906  0.000709  0.017711  0.000988   \n",
       "2019        0.006207           0.005634  0.000727  0.013888  0.000974   \n",
       "\n",
       "Country  Ethiopia    Europe  Europe (excl. EU-27)  Europe (excl. EU-28)  \\\n",
       "Year                                                                      \n",
       "1750     0.000000  0.009351              0.009351              0.000000   \n",
       "1751     0.000000  0.009351              0.009351              0.000000   \n",
       "1752     0.000000  0.009354              0.009354              0.000000   \n",
       "1753     0.000000  0.009354              0.009354              0.000000   \n",
       "1754     0.000000  0.009358              0.009358              0.000000   \n",
       "...           ...       ...                   ...                   ...   \n",
       "2015     0.012693  5.604855              2.513159              2.090668   \n",
       "2016     0.014432  5.597849              2.501554              2.102445   \n",
       "2017     0.015594  5.628105              2.507829              2.119741   \n",
       "2018     0.016185  5.601973              2.555341              2.175203   \n",
       "2019     0.016255  5.448709              2.540065              2.170186   \n",
       "\n",
       "Country  Faeroe Islands      Fiji   Finland    France  \\\n",
       "Year                                                    \n",
       "1750           0.000000  0.000000  0.000000  0.000000   \n",
       "1751           0.000000  0.000000  0.000000  0.000000   \n",
       "1752           0.000000  0.000000  0.000000  0.000000   \n",
       "1753           0.000000  0.000000  0.000000  0.000000   \n",
       "1754           0.000000  0.000000  0.000000  0.000000   \n",
       "...                 ...       ...       ...       ...   \n",
       "2015           0.000608  0.002130  0.044114  0.338501   \n",
       "2016           0.000630  0.002019  0.047228  0.341026   \n",
       "2017           0.000707  0.002037  0.044673  0.345921   \n",
       "2018           0.000705  0.002124  0.045849  0.331725   \n",
       "2019           0.000715  0.002257  0.041653  0.323747   \n",
       "\n",
       "Country  French Equatorial Africa  French Polynesia  French West Africa  \\\n",
       "Year                                                                      \n",
       "1750                          0.0          0.000000                 0.0   \n",
       "1751                          0.0          0.000000                 0.0   \n",
       "1752                          0.0          0.000000                 0.0   \n",
       "1753                          0.0          0.000000                 0.0   \n",
       "1754                          0.0          0.000000                 0.0   \n",
       "...                           ...               ...                 ...   \n",
       "2015                          0.0          0.000769                 0.0   \n",
       "2016                          0.0          0.000744                 0.0   \n",
       "2017                          0.0          0.000747                 0.0   \n",
       "2018                          0.0          0.000781                 0.0   \n",
       "2019                          0.0          0.000832                 0.0   \n",
       "\n",
       "Country     Gabon    Gambia   Georgia   Germany     Ghana    Greece  \\\n",
       "Year                                                                  \n",
       "1750     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1751     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1752     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1753     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1754     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "...           ...       ...       ...       ...       ...       ...   \n",
       "2015     0.005249  0.000524  0.009589  0.795816  0.016115  0.074958   \n",
       "2016     0.005298  0.000531  0.009905  0.800510  0.016547  0.071368   \n",
       "2017     0.004793  0.000535  0.009832  0.786655  0.013817  0.074853   \n",
       "2018     0.004803  0.000563  0.009862  0.755362  0.014480  0.071798   \n",
       "2019     0.004705  0.000586  0.010287  0.701955  0.014960  0.067184   \n",
       "\n",
       "Country  Greenland   Grenada  Guatemala    Guinea  Guinea-Bissau    Guyana  \\\n",
       "Year                                                                         \n",
       "1750      0.000000  0.000000   0.000000  0.000000       0.000000  0.000000   \n",
       "1751      0.000000  0.000000   0.000000  0.000000       0.000000  0.000000   \n",
       "1752      0.000000  0.000000   0.000000  0.000000       0.000000  0.000000   \n",
       "1753      0.000000  0.000000   0.000000  0.000000       0.000000  0.000000   \n",
       "1754      0.000000  0.000000   0.000000  0.000000       0.000000  0.000000   \n",
       "...            ...       ...        ...       ...            ...       ...   \n",
       "2015      0.000498  0.000260   0.015426  0.002451       0.000278  0.002004   \n",
       "2016      0.000498  0.000267   0.016376  0.002744       0.000293  0.002344   \n",
       "2017      0.000513  0.000278   0.017214  0.002880       0.000293  0.002318   \n",
       "2018      0.000512  0.000279   0.019411  0.003032       0.000309  0.002343   \n",
       "2019      0.000519  0.000288   0.020513  0.003153       0.000321  0.002390   \n",
       "\n",
       "Country     Haiti  Honduras  Hong Kong   Hungary   Iceland     India  \\\n",
       "Year                                                                   \n",
       "1750     0.000000  0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "1751     0.000000  0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "1752     0.000000  0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "1753     0.000000  0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "1754     0.000000  0.000000   0.000000  0.000000  0.000000  0.000000   \n",
       "...           ...       ...        ...       ...       ...       ...   \n",
       "2015     0.003191  0.010082   0.042391  0.046628  0.003545  2.253429   \n",
       "2016     0.003235  0.009554   0.043388  0.047396  0.003498  2.392360   \n",
       "2017     0.003272  0.009899   0.042552  0.049685  0.003615  2.456848   \n",
       "2018     0.003367  0.010471   0.042506  0.049628  0.003675  2.591324   \n",
       "2019     0.003281  0.010928   0.041536  0.049101  0.003322  2.616449   \n",
       "\n",
       "Country  Indonesia  International transport      Iran      Iraq   Ireland  \\\n",
       "Year                                                                        \n",
       "1750      0.000000                 0.000000  0.000000  0.000000  0.000000   \n",
       "1751      0.000000                 0.000000  0.000000  0.000000  0.000000   \n",
       "1752      0.000000                 0.000000  0.000000  0.000000  0.000000   \n",
       "1753      0.000000                 0.000000  0.000000  0.000000  0.000000   \n",
       "1754      0.000000                 0.000000  0.000000  0.000000  0.000000   \n",
       "...            ...                      ...       ...       ...       ...   \n",
       "2015      0.507012                 1.166074  0.640811  0.165646  0.038545   \n",
       "2016      0.568194                 1.185882  0.648061  0.194361  0.040030   \n",
       "2017      0.531012                 1.248596  0.724578  0.208713  0.038910   \n",
       "2018      0.576584                 1.267690  0.755402  0.211270  0.038803   \n",
       "2019      0.617513                 1.267690  0.779527  0.221384  0.037118   \n",
       "\n",
       "Country    Israel     Italy   Jamaica     Japan    Jordan  Kazakhstan  \\\n",
       "Year                                                                    \n",
       "1750     0.000000  0.000000  0.000000  0.000000  0.000000    0.000000   \n",
       "1751     0.000000  0.000000  0.000000  0.000000  0.000000    0.000000   \n",
       "1752     0.000000  0.000000  0.000000  0.000000  0.000000    0.000000   \n",
       "1753     0.000000  0.000000  0.000000  0.000000  0.000000    0.000000   \n",
       "1754     0.000000  0.000000  0.000000  0.000000  0.000000    0.000000   \n",
       "...           ...       ...       ...       ...       ...         ...   \n",
       "2015     0.064950  0.360088  0.007937  1.222781  0.025270    0.288578   \n",
       "2016     0.063907  0.356556  0.008141  1.203167  0.023994    0.288421   \n",
       "2017     0.062467  0.351474  0.007798  1.187661  0.025467    0.307927   \n",
       "2018     0.062213  0.348085  0.008010  1.135688  0.024924    0.319647   \n",
       "2019     0.064172  0.337086  0.008014  1.106664  0.026072    0.313798   \n",
       "\n",
       "Country     Kenya  Kiribati    Kosovo    Kuwait  Kuwaiti Oil Fires  \\\n",
       "Year                                                                 \n",
       "1750     0.000000  0.000000  0.000000  0.000000                0.0   \n",
       "1751     0.000000  0.000000  0.000000  0.000000                0.0   \n",
       "1752     0.000000  0.000000  0.000000  0.000000                0.0   \n",
       "1753     0.000000  0.000000  0.000000  0.000000                0.0   \n",
       "1754     0.000000  0.000000  0.000000  0.000000                0.0   \n",
       "...           ...       ...       ...       ...                ...   \n",
       "2015     0.016301  0.000062  0.008339  0.096265                0.0   \n",
       "2016     0.017152  0.000066  0.008860  0.101574                0.0   \n",
       "2017     0.016533  0.000066  0.008094  0.104784                0.0   \n",
       "2018     0.017137  0.000069  0.008192  0.104218                0.0   \n",
       "2019     0.017315  0.000073  0.009741  0.107532                0.0   \n",
       "\n",
       "Country  Kyrgyzstan      Laos    Latvia   Lebanon  Leeward Islands   Lesotho  \\\n",
       "Year                                                                           \n",
       "1750       0.000000  0.000000  0.000000  0.000000              0.0  0.000000   \n",
       "1751       0.000000  0.000000  0.000000  0.000000              0.0  0.000000   \n",
       "1752       0.000000  0.000000  0.000000  0.000000              0.0  0.000000   \n",
       "1753       0.000000  0.000000  0.000000  0.000000              0.0  0.000000   \n",
       "1754       0.000000  0.000000  0.000000  0.000000              0.0  0.000000   \n",
       "...             ...       ...       ...       ...              ...       ...   \n",
       "2015       0.010278  0.008806  0.007273  0.025770              0.0  0.002327   \n",
       "2016       0.009640  0.014261  0.007221  0.026502              0.0  0.002360   \n",
       "2017       0.009263  0.017914  0.007223  0.027935              0.0  0.002528   \n",
       "2018       0.010169  0.032262  0.007859  0.027565              0.0  0.002426   \n",
       "2019       0.011483  0.032815  0.008262  0.028202              0.0  0.002223   \n",
       "\n",
       "Country   Liberia     Libya  Liechtenstein  Lithuania  Luxembourg     Macao  \\\n",
       "Year                                                                          \n",
       "1750     0.000000  0.000000       0.000000   0.000000    0.000000  0.000000   \n",
       "1751     0.000000  0.000000       0.000000   0.000000    0.000000  0.000000   \n",
       "1752     0.000000  0.000000       0.000000   0.000000    0.000000  0.000000   \n",
       "1753     0.000000  0.000000       0.000000   0.000000    0.000000  0.000000   \n",
       "1754     0.000000  0.000000       0.000000   0.000000    0.000000  0.000000   \n",
       "...           ...       ...            ...        ...         ...       ...   \n",
       "2015     0.001205  0.050844       0.000160   0.013297    0.009333  0.002029   \n",
       "2016     0.001358  0.042839       0.000150   0.013328    0.009080  0.002000   \n",
       "2017     0.001215  0.043729       0.000156   0.013546    0.009250  0.002150   \n",
       "2018     0.001274  0.045206       0.000144   0.013669    0.009569  0.002216   \n",
       "2019     0.001322  0.046428       0.000146   0.013483    0.009785  0.002065   \n",
       "\n",
       "Country  Madagascar    Malawi  Malaysia  Maldives      Mali     Malta  \\\n",
       "Year                                                                    \n",
       "1750       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1751       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1752       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1753       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1754       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "...             ...       ...       ...       ...       ...       ...   \n",
       "2015       0.003520  0.001120  0.233268  0.001301  0.003096  0.001735   \n",
       "2016       0.003359  0.001263  0.246722  0.001444  0.003107  0.001399   \n",
       "2017       0.003886  0.001402  0.248869  0.001499  0.003121  0.001553   \n",
       "2018       0.004188  0.001470  0.249144  0.001565  0.003273  0.001532   \n",
       "2019       0.004015  0.001466  0.250095  0.001667  0.003394  0.001554   \n",
       "\n",
       "Country  Marshall Islands  Mauritania  Mauritius    Mexico  \\\n",
       "Year                                                         \n",
       "1750             0.000000    0.000000   0.000000  0.000000   \n",
       "1751             0.000000    0.000000   0.000000  0.000000   \n",
       "1752             0.000000    0.000000   0.000000  0.000000   \n",
       "1753             0.000000    0.000000   0.000000  0.000000   \n",
       "1754             0.000000    0.000000   0.000000  0.000000   \n",
       "...                   ...         ...        ...       ...   \n",
       "2015             0.000143    0.002887   0.004210  0.481975   \n",
       "2016             0.000143    0.002763   0.004346  0.484629   \n",
       "2017             0.000147    0.003737   0.004536  0.461238   \n",
       "2018             0.000153    0.003935   0.004902  0.451081   \n",
       "2019             0.000163    0.004092   0.004687  0.438498   \n",
       "\n",
       "Country  Micronesia (country)   Moldova  Mongolia  Montenegro  Montserrat  \\\n",
       "Year                                                                        \n",
       "1750                 0.000000  0.000000  0.000000    0.000000    0.000000   \n",
       "1751                 0.000000  0.000000  0.000000    0.000000    0.000000   \n",
       "1752                 0.000000  0.000000  0.000000    0.000000    0.000000   \n",
       "1753                 0.000000  0.000000  0.000000    0.000000    0.000000   \n",
       "1754                 0.000000  0.000000  0.000000    0.000000    0.000000   \n",
       "...                       ...       ...       ...         ...         ...   \n",
       "2015                 0.000143  0.004785  0.023244    0.002239    0.000059   \n",
       "2016                 0.000143  0.004896  0.025324    0.002015    0.000029   \n",
       "2017                 0.000143  0.005292  0.034154    0.002103    0.000029   \n",
       "2018                 0.000149  0.005878  0.064508    0.002123    0.000030   \n",
       "2019                 0.000159  0.005958  0.065514    0.002461    0.000029   \n",
       "\n",
       "Country   Morocco  Mozambique   Myanmar   Namibia     Nauru     Nepal  \\\n",
       "Year                                                                    \n",
       "1750     0.000000    0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1751     0.000000    0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1752     0.000000    0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1753     0.000000    0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1754     0.000000    0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "...           ...         ...       ...       ...       ...       ...   \n",
       "2015     0.061028    0.006306  0.022076  0.003886  0.000055  0.006014   \n",
       "2016     0.061032    0.007726  0.025469  0.004124  0.000044  0.008841   \n",
       "2017     0.063831    0.008147  0.023670  0.004098  0.000048  0.010124   \n",
       "2018     0.065367    0.008383  0.026096  0.004154  0.000050  0.013410   \n",
       "2019     0.071929    0.008706  0.026232  0.004168  0.000053  0.013914   \n",
       "\n",
       "Country  Netherlands  New Caledonia  New Zealand  Nicaragua     Niger  \\\n",
       "Year                                                                    \n",
       "1750        0.000000       0.000000     0.000000   0.000000  0.000000   \n",
       "1751        0.000000       0.000000     0.000000   0.000000  0.000000   \n",
       "1752        0.000000       0.000000     0.000000   0.000000  0.000000   \n",
       "1753        0.000000       0.000000     0.000000   0.000000  0.000000   \n",
       "1754        0.000000       0.000000     0.000000   0.000000  0.000000   \n",
       "...              ...            ...          ...        ...       ...   \n",
       "2015        0.166367       0.004736     0.035839   0.005250  0.002035   \n",
       "2016        0.166282       0.005312     0.034268   0.005448  0.002064   \n",
       "2017        0.164445       0.005345     0.036153   0.005375  0.002006   \n",
       "2018        0.160170       0.008209     0.035080   0.005377  0.002094   \n",
       "2019        0.154827       0.008452     0.036541   0.005549  0.002135   \n",
       "\n",
       "Country   Nigeria      Niue  North America  North America (excl. USA)  \\\n",
       "Year                                                                    \n",
       "1750     0.000000  0.000000       0.000000                   0.000000   \n",
       "1751     0.000000  0.000000       0.000000                   0.000000   \n",
       "1752     0.000000  0.000000       0.000000                   0.000000   \n",
       "1753     0.000000  0.000000       0.000000                   0.000000   \n",
       "1754     0.000000  0.000000       0.000000                   0.000000   \n",
       "...           ...       ...            ...                        ...   \n",
       "2015     0.113539  0.000007       6.650501                   1.238069   \n",
       "2016     0.116774  0.000007       6.516012                   1.223744   \n",
       "2017     0.130275  0.000007       6.461226                   1.207620   \n",
       "2018     0.136078  0.000008       6.639555                   1.214673   \n",
       "2019     0.140026  0.000008       6.481175                   1.196478   \n",
       "\n",
       "Country  North Korea  North Macedonia    Norway   Oceania      Oman  Pakistan  \\\n",
       "Year                                                                            \n",
       "1750        0.000000         0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1751        0.000000         0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1752        0.000000         0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1753        0.000000         0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1754        0.000000         0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "...              ...              ...       ...       ...       ...       ...   \n",
       "2015        0.024621         0.007024  0.045350  0.452738  0.061604  0.170175   \n",
       "2016        0.027467         0.006987  0.044473  0.461420  0.060033  0.203146   \n",
       "2017        0.021754         0.007508  0.043563  0.467346  0.065511  0.232425   \n",
       "2018        0.038163         0.006981  0.043818  0.470415  0.071030  0.247425   \n",
       "2019        0.038762         0.008041  0.042441  0.467762  0.071685  0.248844   \n",
       "\n",
       "Country     Palau  Palestine    Panama  Panama Canal Zone  Papua New Guinea  \\\n",
       "Year                                                                          \n",
       "1750     0.000000   0.000000  0.000000                0.0          0.000000   \n",
       "1751     0.000000   0.000000  0.000000                0.0          0.000000   \n",
       "1752     0.000000   0.000000  0.000000                0.0          0.000000   \n",
       "1753     0.000000   0.000000  0.000000                0.0          0.000000   \n",
       "1754     0.000000   0.000000  0.000000                0.0          0.000000   \n",
       "...           ...        ...       ...                ...               ...   \n",
       "2015     0.000224   0.003004  0.010877                0.0          0.006364   \n",
       "2016     0.000224   0.003232  0.010463                0.0          0.006679   \n",
       "2017     0.000231   0.003268  0.011962                0.0          0.006548   \n",
       "2018     0.000241   0.003220  0.012096                0.0          0.006786   \n",
       "2019     0.000257   0.003303  0.012503                0.0          0.007087   \n",
       "\n",
       "Country  Paraguay      Peru  Philippines    Poland  Portugal  Puerto Rico  \\\n",
       "Year                                                                        \n",
       "1750     0.000000  0.000000     0.000000  0.000000  0.000000          0.0   \n",
       "1751     0.000000  0.000000     0.000000  0.000000  0.000000          0.0   \n",
       "1752     0.000000  0.000000     0.000000  0.000000  0.000000          0.0   \n",
       "1753     0.000000  0.000000     0.000000  0.000000  0.000000          0.0   \n",
       "1754     0.000000  0.000000     0.000000  0.000000  0.000000          0.0   \n",
       "...           ...       ...          ...       ...       ...          ...   \n",
       "2015     0.006051  0.048696     0.112142  0.313099  0.052294          0.0   \n",
       "2016     0.007154  0.052360     0.122237  0.324011  0.050456          0.0   \n",
       "2017     0.008015  0.052489     0.134519  0.337340  0.054720          0.0   \n",
       "2018     0.008103  0.054210     0.138924  0.337706  0.051482          0.0   \n",
       "2019     0.008272  0.054533     0.144263  0.322626  0.048598          0.0   \n",
       "\n",
       "Country     Qatar   Romania    Russia    Rwanda  Ryukyu Islands  Saint Helena  \\\n",
       "Year                                                                            \n",
       "1750     0.000000  0.000000  0.000000  0.000000             0.0      0.000000   \n",
       "1751     0.000000  0.000000  0.000000  0.000000             0.0      0.000000   \n",
       "1752     0.000000  0.000000  0.000000  0.000000             0.0      0.000000   \n",
       "1753     0.000000  0.000000  0.000000  0.000000             0.0      0.000000   \n",
       "1754     0.000000  0.000000  0.000000  0.000000             0.0      0.000000   \n",
       "...           ...       ...       ...       ...             ...           ...   \n",
       "2015     0.105756  0.077726  1.622861  0.000926             0.0      0.000011   \n",
       "2016     0.102173  0.075813  1.618304  0.001047             0.0      0.000011   \n",
       "2017     0.105569  0.078077  1.646180  0.001051             0.0      0.000011   \n",
       "2018     0.109245  0.076951  1.691360  0.001080             0.0      0.000011   \n",
       "2019     0.109345  0.075084  1.678367  0.001111             0.0      0.000011   \n",
       "\n",
       "Country  Saint Kitts and Nevis  Saint Lucia  Saint Pierre and Miquelon  \\\n",
       "Year                                                                     \n",
       "1750                  0.000000     0.000000                   0.000000   \n",
       "1751                  0.000000     0.000000                   0.000000   \n",
       "1752                  0.000000     0.000000                   0.000000   \n",
       "1753                  0.000000     0.000000                   0.000000   \n",
       "1754                  0.000000     0.000000                   0.000000   \n",
       "...                        ...          ...                        ...   \n",
       "2015                  0.000234     0.000359                   0.000077   \n",
       "2016                  0.000238     0.000333                   0.000077   \n",
       "2017                  0.000242     0.000352                   0.000077   \n",
       "2018                  0.000249     0.000362                   0.000079   \n",
       "2019                  0.000242     0.000353                   0.000077   \n",
       "\n",
       "Country  Saint Vincent and the Grenadines     Samoa  Sao Tome and Principe  \\\n",
       "Year                                                                         \n",
       "1750                             0.000000  0.000000               0.000000   \n",
       "1751                             0.000000  0.000000               0.000000   \n",
       "1752                             0.000000  0.000000               0.000000   \n",
       "1753                             0.000000  0.000000               0.000000   \n",
       "1754                             0.000000  0.000000               0.000000   \n",
       "...                                   ...       ...                    ...   \n",
       "2015                             0.000213  0.000234               0.000114   \n",
       "2016                             0.000253  0.000245               0.000121   \n",
       "2017                             0.000256  0.000256               0.000125   \n",
       "2018                             0.000264  0.000268               0.000126   \n",
       "2019                             0.000257  0.000285               0.000129   \n",
       "\n",
       "Country  Saudi Arabia   Senegal    Serbia  Seychelles  Sierra Leone  \\\n",
       "Year                                                                  \n",
       "1750         0.000000  0.000000  0.000000    0.000000      0.000000   \n",
       "1751         0.000000  0.000000  0.000000    0.000000      0.000000   \n",
       "1752         0.000000  0.000000  0.000000    0.000000      0.000000   \n",
       "1753         0.000000  0.000000  0.000000    0.000000      0.000000   \n",
       "1754         0.000000  0.000000  0.000000    0.000000      0.000000   \n",
       "...               ...       ...       ...         ...           ...   \n",
       "2015         0.645409  0.009846  0.043747    0.000480      0.000905   \n",
       "2016         0.565751  0.009859  0.045036    0.000594      0.000931   \n",
       "2017         0.579417  0.009364  0.045483    0.000586      0.000938   \n",
       "2018         0.576758  0.009690  0.046053    0.000605      0.000988   \n",
       "2019         0.582150  0.009823  0.054667    0.000623      0.001027   \n",
       "\n",
       "Country  Singapore  Sint Maarten (Dutch part)  Slovakia  Slovenia  \\\n",
       "Year                                                                \n",
       "1750      0.000000                   0.000000  0.000000  0.000000   \n",
       "1751      0.000000                   0.000000  0.000000  0.000000   \n",
       "1752      0.000000                   0.000000  0.000000  0.000000   \n",
       "1753      0.000000                   0.000000  0.000000  0.000000   \n",
       "1754      0.000000                   0.000000  0.000000  0.000000   \n",
       "...            ...                        ...       ...       ...   \n",
       "2015      0.062130                   0.000755  0.034484  0.013618   \n",
       "2016      0.040275                   0.000762  0.034922  0.014417   \n",
       "2017      0.039066                   0.000751  0.036087  0.014265   \n",
       "2018      0.038288                   0.000773  0.036088  0.014488   \n",
       "2019      0.038945                   0.000753  0.033315  0.013696   \n",
       "\n",
       "Country  Solomon Islands   Somalia  South Africa  South America  South Korea  \\\n",
       "Year                                                                           \n",
       "1750            0.000000  0.000000      0.000000       0.000000     0.000000   \n",
       "1751            0.000000  0.000000      0.000000       0.000000     0.000000   \n",
       "1752            0.000000  0.000000      0.000000       0.000000     0.000000   \n",
       "1753            0.000000  0.000000      0.000000       0.000000     0.000000   \n",
       "1754            0.000000  0.000000      0.000000       0.000000     0.000000   \n",
       "...                  ...       ...           ...            ...          ...   \n",
       "2015            0.000293  0.000630      0.451590       1.152384     0.595394   \n",
       "2016            0.000289  0.000638      0.460043       1.148425     0.617960   \n",
       "2017            0.000286  0.000638      0.466103       1.126973     0.620610   \n",
       "2018            0.000298  0.000658      0.472001       1.096956     0.634934   \n",
       "2019            0.000318  0.000678      0.478608       1.085107     0.611263   \n",
       "\n",
       "Country  South Sudan     Spain  Sri Lanka  St. Kitts-Nevis-Anguilla     Sudan  \\\n",
       "Year                                                                            \n",
       "1750        0.000000  0.000000   0.000000                       0.0  0.000000   \n",
       "1751        0.000000  0.000000   0.000000                       0.0  0.000000   \n",
       "1752        0.000000  0.000000   0.000000                       0.0  0.000000   \n",
       "1753        0.000000  0.000000   0.000000                       0.0  0.000000   \n",
       "1754        0.000000  0.000000   0.000000                       0.0  0.000000   \n",
       "...              ...       ...        ...                       ...       ...   \n",
       "2015        0.001942  0.271687   0.019830                       0.0  0.020163   \n",
       "2016        0.001726  0.260760   0.023028                       0.0  0.019311   \n",
       "2017        0.001491  0.274671   0.022984                       0.0  0.021715   \n",
       "2018        0.001540  0.269654   0.022973                       0.0  0.022372   \n",
       "2019        0.001585  0.252683   0.024841                       0.0  0.022981   \n",
       "\n",
       "Country  Suriname    Sweden  Switzerland     Syria    Taiwan  Tajikistan  \\\n",
       "Year                                                                       \n",
       "1750     0.000000  0.000000     0.000000  0.000000  0.000000    0.000000   \n",
       "1751     0.000000  0.000000     0.000000  0.000000  0.000000    0.000000   \n",
       "1752     0.000000  0.000000     0.000000  0.000000  0.000000    0.000000   \n",
       "1753     0.000000  0.000000     0.000000  0.000000  0.000000    0.000000   \n",
       "1754     0.000000  0.000000     0.000000  0.000000  0.000000    0.000000   \n",
       "...           ...       ...          ...       ...       ...         ...   \n",
       "2015     0.002733  0.043337     0.038733  0.028675  0.257156    0.005163   \n",
       "2016     0.002326  0.042973     0.039193  0.028508  0.265284    0.006305   \n",
       "2017     0.002520  0.042307     0.038182  0.026401  0.272450    0.006873   \n",
       "2018     0.002552  0.041766     0.036895  0.025878  0.273105    0.007473   \n",
       "2019     0.002606  0.042767     0.037682  0.026961  0.262639    0.008980   \n",
       "\n",
       "Country  Tanzania  Thailand     Timor      Togo     Tonga  \\\n",
       "Year                                                        \n",
       "1750     0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1751     0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1752     0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1753     0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1754     0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "...           ...       ...       ...       ...       ...   \n",
       "2015     0.010638  0.283293  0.000502  0.002686  0.000121   \n",
       "2016     0.010535  0.281705  0.000495  0.003098  0.000128   \n",
       "2017     0.011019  0.286336  0.000498  0.003049  0.000158   \n",
       "2018     0.011502  0.292453  0.000520  0.003167  0.000165   \n",
       "2019     0.011626  0.288280  0.000554  0.003261  0.000175   \n",
       "\n",
       "Country  Trinidad and Tobago   Tunisia    Turkey  Turkmenistan  \\\n",
       "Year                                                             \n",
       "1750                0.000000  0.000000  0.000000      0.000000   \n",
       "1751                0.000000  0.000000  0.000000      0.000000   \n",
       "1752                0.000000  0.000000  0.000000      0.000000   \n",
       "1753                0.000000  0.000000  0.000000      0.000000   \n",
       "1754                0.000000  0.000000  0.000000      0.000000   \n",
       "...                      ...       ...       ...           ...   \n",
       "2015                0.045121  0.029275  0.381332      0.070231   \n",
       "2016                0.040052  0.029008  0.401240      0.070164   \n",
       "2017                0.039968  0.029620  0.425329      0.070193   \n",
       "2018                0.037866  0.030357  0.419195      0.078035   \n",
       "2019                0.037864  0.031013  0.405126      0.085647   \n",
       "\n",
       "Country  Turks and Caicos Islands    Tuvalu    Uganda   Ukraine  \\\n",
       "Year                                                              \n",
       "1750                     0.000000  0.000000  0.000000  0.000000   \n",
       "1751                     0.000000  0.000000  0.000000  0.000000   \n",
       "1752                     0.000000  0.000000  0.000000  0.000000   \n",
       "1753                     0.000000  0.000000  0.000000  0.000000   \n",
       "1754                     0.000000  0.000000  0.000000  0.000000   \n",
       "...                           ...       ...       ...       ...   \n",
       "2015                     0.000205  0.000011  0.004522  0.223912   \n",
       "2016                     0.000216  0.000011  0.004805  0.234200   \n",
       "2017                     0.000231  0.000011  0.005226  0.223232   \n",
       "2018                     0.000238  0.000011  0.005385  0.231694   \n",
       "2019                     0.000231  0.000012  0.005531  0.223229   \n",
       "\n",
       "Country  United Arab Emirates  United Kingdom  United States   Uruguay  \\\n",
       "Year                                                                     \n",
       "1750                 0.000000        0.009351       0.000000  0.000000   \n",
       "1751                 0.000000        0.009351       0.000000  0.000000   \n",
       "1752                 0.000000        0.009354       0.000000  0.000000   \n",
       "1753                 0.000000        0.009354       0.000000  0.000000   \n",
       "1754                 0.000000        0.009358       0.000000  0.000000   \n",
       "...                       ...             ...            ...       ...   \n",
       "2015                 0.239671        0.422491       5.412432  0.006655   \n",
       "2016                 0.228583        0.399110       5.292268  0.006525   \n",
       "2017                 0.186623        0.388088       5.253606  0.006174   \n",
       "2018                 0.188541        0.380139       5.424882  0.006252   \n",
       "2019                 0.190683        0.369878       5.284697  0.006378   \n",
       "\n",
       "Country  Uzbekistan   Vanuatu  Venezuela   Vietnam  Wallis and Futuna  \\\n",
       "Year                                                                    \n",
       "1750       0.000000  0.000000   0.000000  0.000000           0.000000   \n",
       "1751       0.000000  0.000000   0.000000  0.000000           0.000000   \n",
       "1752       0.000000  0.000000   0.000000  0.000000           0.000000   \n",
       "1753       0.000000  0.000000   0.000000  0.000000           0.000000   \n",
       "1754       0.000000  0.000000   0.000000  0.000000           0.000000   \n",
       "...             ...       ...        ...       ...                ...   \n",
       "2015       0.101791  0.000132   0.170899  0.184436           0.000022   \n",
       "2016       0.105222  0.000147   0.163191  0.185432           0.000026   \n",
       "2017       0.109282  0.000139   0.146673  0.182584           0.000026   \n",
       "2018       0.113938  0.000145   0.129596  0.211774           0.000027   \n",
       "2019       0.110246  0.000155   0.116688  0.247709           0.000029   \n",
       "\n",
       "Country      World     Yemen    Zambia  Zimbabwe  \n",
       "Year                                              \n",
       "1750      0.009351  0.000000  0.000000  0.000000  \n",
       "1751      0.009351  0.000000  0.000000  0.000000  \n",
       "1752      0.009354  0.000000  0.000000  0.000000  \n",
       "1753      0.009354  0.000000  0.000000  0.000000  \n",
       "1754      0.009358  0.000000  0.000000  0.000000  \n",
       "...            ...       ...       ...       ...  \n",
       "2015     35.209447  0.013006  0.004296  0.012170  \n",
       "2016     35.220412  0.010426  0.004838  0.010815  \n",
       "2017     35.696349  0.010107  0.006520  0.010247  \n",
       "2018     36.419712  0.009945  0.006930  0.011341  \n",
       "2019     36.441388  0.010255  0.006720  0.010374  \n",
       "\n",
       "[270 rows x 238 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.pivot_table(df, index = \"Country\", columns = \"Year\").T\n",
    "\n",
    "#Retain only Years as Index\n",
    "df.index = df.index.get_level_values(1)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ea4350e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[1800:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d1c38ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>EU-27</th>\n",
       "      <th>India</th>\n",
       "      <th>Russia</th>\n",
       "      <th>United States</th>\n",
       "      <th>Rest of world</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1800</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>0.026637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000978</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.026714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009640</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000289</td>\n",
       "      <td>0.026853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.030195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1804</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003642</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.030334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>9.683201</td>\n",
       "      <td>3.098969</td>\n",
       "      <td>2.253429</td>\n",
       "      <td>1.622861</td>\n",
       "      <td>5.412432</td>\n",
       "      <td>13.138554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>9.552517</td>\n",
       "      <td>3.103516</td>\n",
       "      <td>2.392360</td>\n",
       "      <td>1.618304</td>\n",
       "      <td>5.292268</td>\n",
       "      <td>13.261449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>9.750726</td>\n",
       "      <td>3.127499</td>\n",
       "      <td>2.456848</td>\n",
       "      <td>1.646180</td>\n",
       "      <td>5.253606</td>\n",
       "      <td>13.461490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>9.956569</td>\n",
       "      <td>3.054491</td>\n",
       "      <td>2.591324</td>\n",
       "      <td>1.691360</td>\n",
       "      <td>5.424882</td>\n",
       "      <td>13.701087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>10.174681</td>\n",
       "      <td>2.916906</td>\n",
       "      <td>2.616449</td>\n",
       "      <td>1.678367</td>\n",
       "      <td>5.284697</td>\n",
       "      <td>13.770288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>220 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country      China     EU-27     India    Russia  United States  Rest of world\n",
       "Year                                                                          \n",
       "1800      0.000000  0.001202  0.000000  0.000000       0.000253       0.026637\n",
       "1801      0.000000  0.000978  0.000000  0.000000       0.000267       0.026714\n",
       "1802      0.000000  0.009640  0.000000  0.000000       0.000289       0.026853\n",
       "1803      0.000000  0.000997  0.000000  0.000000       0.000297       0.030195\n",
       "1804      0.000000  0.003642  0.000000  0.000000       0.000333       0.030334\n",
       "...            ...       ...       ...       ...            ...            ...\n",
       "2015      9.683201  3.098969  2.253429  1.622861       5.412432      13.138554\n",
       "2016      9.552517  3.103516  2.392360  1.618304       5.292268      13.261449\n",
       "2017      9.750726  3.127499  2.456848  1.646180       5.253606      13.461490\n",
       "2018      9.956569  3.054491  2.591324  1.691360       5.424882      13.701087\n",
       "2019     10.174681  2.916906  2.616449  1.678367       5.284697      13.770288\n",
       "\n",
       "[220 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "\n",
    "#Retaining emissions values of top 5 countries\n",
    "df2 = df2[[\"China\",\"EU-27\",\"India\",\"Russia\",\"United States\",\"World\"]]\n",
    "\n",
    "#Calculate emissions from rest of the world\n",
    "df2.insert(5, \"Rest of world\",df[\"World\"] - (df[\"China\"] + df[\"EU-27\"] + df[\"India\"] + df[\"Russia\"] + df[\"United States\"]))\n",
    "\n",
    "#Drop World\n",
    "df2.drop(\"World\",axis = 1, inplace = True)\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e92a2b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C3wIe8CVHn2J92/sDrMTrthD30yexG2OaROR0rKZ4L4rI61j9OCuxHmiPx3pov81XvlFEvo417P4rIvIcVj9EN9a35udjJbLfMMYc7OTQ92L1D3w8hDD/iPXN/+0icgLwFtbD7ljgRKzBGY7vxtvurnigRET+jXX9yrD6gq3A6j/4cjfLBdOd+6QvYkdECoBxxphQ+5FVABtE5BGsLyYuxLomF3cwWMvDWHNDnoNV27GrR+/ocN25P27F+tw8KiLzsf52LMaqkayg675WYb9OwRhjPL6/C2cGa74oIudjTUIO1uc6UkSu9f2+zxjzRED5bOBU4CNjzOedHPpasabzeBtrxNU4rCasx2ONDnqfX4yfi8gDwGW+WF/FqjG+HOsLg96eBB4R+Qlwgy+2NcC5cuiUk6XGmDd6GmN3zqtYcyR+hPUlwV6sAbLOwZrC49fGmLcD9h2HNUXJIz0+AUqp4SWUoUL1pa+evLCaIRlgVhfldmDVBMTQ+fQG7+A3dHZ3yvotn4KVkDT5jvk0Vn+NAg4d1t0OXI1Vg+XE6oR/G9Z/8Ab4rV/ZrxDiVAq+8t/HGgCj1bfdY37r0oE/AYVYCVsh1pxfaaHupy9j920Ti1Vrug7rgdOFNUfWK1hJnT2gfCLwG6ymTA1YExxvxxpNMauLY92JVfN32PvvZBsH1kPYx1iJcSNW079/AieHeP8ccl38lj+Gr8Il2PnDas56M9bDW6Xv/BdgPZhN9tsu1HJBr0+o90kX7/EdDv08hRSTr2wFUBTCtWg7/kl88XDtxGqmdm4X277p2/b8blz7Dt9vd+8PX9nxWM2T67ESxZd8yyrwm4ZhoF6nTs7BfDqYMsB3PNPB650g5a/xrbuki2OeCbyGNUl8i++8f+bbPjpIeTvWnJw7fO+xCLiLXpgupoP4HuvkfXf03kOOsTvn1XeN/4WV/LVgdV94jYDpS/zKf9+3n5l9cW70pS99Db2XGNPVl5hKKdX/ROQerFqZE4wxOrDBACEiRwN5wEXGmEf78DivAgux5v5r7qvjdJev9UAF8BdjTEcDhAx4IvJfYIQx5svhjkUdGRH5BKsW8ZvhjkUpNThoH0Cl1IAjIvdi1Rpp8jfwnIKVAP69q4I9JdZUMacAT4Qz+ZPD54wE+H++n28EWTeYXAEsFJGTwx2I6jkR+QbWSKH/r4uiSinVTmsAlVIDioiMw2rS5sTqL9jmPWPMsrAEpfqFiCzgi35URwFHGWMKwhjPO1hNqDdiNfc7ETgd+ABYYqxRGpVSSqlBRQeBUUoNKMaYffT+JNVqcFiBNTXIHuC8cCZ/Pi9jxfMNrD7KhVj9Um/Q5E8ppdRgpTWASimllFJKKTVMaB9ApZRSSimllBomNAFUSimllFJKqWFCE0CllFJKKaWUGiY0AVRKKaWUUkqpYUITQKXUsCAiBb5h/cOyfSf7zRERIyK/7e199yYR+YovzgvCHctA0Vf3RG8RkWhfjDeGO5YjJSIjRaRJRL4f7liUUmqw0wRQqQFERJaIyL99D21OESkVkY0i8kcRmRDu+AYSEckVkYdFZKeINIpIi4jsE5HnReR8EYkId4x9RURiReTnIvKeiFSJiMt3r7wqIheIyLCb4kdEfutLUNteXt+5eUNETg93fEfCd60v6MGmK4Ek4A6/fSWLyM9E5HUROSAizSKyQ0QeEpExHRw/UUTuE5Ei3+dsi4isEBEJKNdn+zbGlAB/Bm4UkdgenAullFI+w+4hQamBSkRWAH/CmgPt78ABIB2YCVwIvOdbN+yJyDXA74Fq4ClgM9AKjAZOAh4HFgM/DFeMfUVEJgGvAFOANcDNQAWQgfXeHwWmA1eFK8Yw+w2wF+v/tylY98DLInKeMebJXj7WVKA/5lL6OVAAPBbqBiISA1wJPGqMqfZbtQBrLsM3gfux7p2ZWOfp2yKyyBiz1W8/kcAbwGzgPmAbsAzrb1Um8Nt+2jfAvb5zcSHwQKjnQiml1KE0AVRqAPDV2NwE7AdmG2PqAtbHYE1EHRYiEm+MqQ/X8f35akJuxHrIPMsYUxtQ5AYROQY4rp9D63O++2A1MAHrvT8fUORWEfkS8KVePOaAufYh+o8xZmPbLyLyHLAR+DXQYQLYk/dpjHH2OMq+dy5W7d/jAcu3A1ONMfn+C0XkFaxk7HfA2X6rLsa6ny43xtznW/aw77xeIyKPGmP29cO+McYUiMh7WAmlJoBKKdVD2gRUqYEhDeth7ePA5A/AGNNsjKnyXyYiI0TkZhHJ9zUXPSgij4vIuIByF/iaxH0lcL8i8o6IFAQsK/Atny0ir4lILfA/v/WTRORRESkUkVYRKRaRl0RkbsB+5onICyJS4Ytvh4j8OrB5oq854zQRGdnVSfLVGNwE1APfDpL8tZ2vz4wxIT0gisg3ROR9EWnwvd4XkTM7KT9HRN7yla0Skb+LSEZAmXgR+YOIbPB7/7tF5JYjbL52MVat051Bkj8AjDEfG2P+5BfLySLylIjs8TXJq/E10Vsa5L2947v+E0TkWRGpAg67H0Xkp2I1vW3x/fxpsFjEatL8hojU+o69SUR+0Mlxs0VklYhUi9Ws9zURmdKN8xPsfHwCVAKTfMdq73MpIv8nIp+ISDNWDVRbPBf7Ym32xf66iCwOEnfQPoCh3vu+sp1+nkTEAOOApXJoE9ecLt76t4CDxphPA85HQWCC5lu+BqjCqrHzdy7QBDwcsPweIAL4v/7Yt5//ALNEZFqQdUoppUKgNYBKDQylQAOwRESmGmN2dFbY9yD5GlYt17NYza4mAyuAk0VknjGm8AjiGQu8BTwDPAfE+Y47D6vmLQL4G1bTyxRgKbAI+MRX7jTgBWC3L7YqYCFWDcAxWA+nbeYDb2M1e72gi7iOA0YCjwcmxD0hIj/GqknYDvwBqznfBcCLIvJDY8xDAZuMxnr/z2Gd9znARcA8EfmSMabJV24UVrL2HFatkxvrHF2F1dztlB6G3FZ7EhhXZy7AukaPA4V+sb0pIscbY94LKB8HrAXex6o1ywhY/1MgC/gLViJ+DnCviKQYY25oKyQiZ2DdAwex7oF64DvAX0VkgjHm1wH7HQG8C3wIXAOMB34GvCQiM40xnm6853YikgYk++Lw9w3gcuBBrL5ldb7yt2Jdp498ccQDlwJvi8iZxphXuzheyPd+iJ+n84G7sZpT+g/mUt5JDHbf9m93FmvANom+97rZb5kN6x7fZIxpCdjkI8BLCLXNvbzv9b6fX8H63CqllOouY4y+9KWvAfACrsBKQNxYD0B/BM4DsoKUvcRX9raA5V/zLX/Cb9kFvmVfCbKfd4CCgGUFvvIXBywXrAe4FuDoIPuy+X5GYz1svws4Asr8IjAWrAc5AzwWwjn6qa/sL4Ksi8eqSW17pQZ5X+/4/Z6MlXTvBhL8licA+VgJS1KQ8/LzDt7Tr/yWRQIRQWL8va/sfL9lOb5lvw3h/VcCdd28r0YEWZaJlVC8GuR+MMAfgmzTdp3qgdEB7/UjwNW2HLAD+4AaIDug7PuAB5gc5LhXBRzzSt/yU0J4n7/1lT3Rd/2zgCXAOt/ymwPOtws4KmAfU7ESj3VApN/ybN97KQDsndxTId/7hPh5CnacEM7FeN+x7urGNrf7trnIb1mqb9lTHWxTBnzQn/vG+hLGAPd153OgL33pS1/6+uKlTUCVGiCMMXcCXwdexxrE43LgH0ChiPwtoOngcqwH1ZsD9vEK8Blwpu8b9p6qwhpMxN8xwAysQSX+F7iBMcbr++dXsRKMR4EkEUlrewFttScn+233jjFGjDEXhBBXgu/nYc0Sfccr93vtC1LG31exap3uNX7Nbn3/vg+rJuykgG3qsGqM/P3Jt3y53z5ajTEusGprxRodMQ1r0BawBsvoiQSCv/cOGWMa2/4tInEikoqVgG3oJI47OlgO8E/jV7tsjGnFqqFyAGf4Fs/FqkV+xBhTHFD2dqzuB4HNbL1Yg3z4e8v3c3In8QRag3X9S7BqMmcDdwHXBZR7xRizLWDZmViJ2W2+WNviLsYagGWcb38d6c69fwyhfZ56It33M6RachE5G+sLqNc49HPf9jeno76OLX5l+mvflb6fgTXTSimlQqRNQJUaQIwxL2ONWGjHSgJPxGoGdxFWzWDbqJbjgWJz6Oh+bbZgPVymYX2L3hP55vAmd20P4Z8GFg5wlO/nI52UyexRVF8kPwlB1t2A1ZQPrKZ3E7vY13jfzy1B1rU1VQucemOPCRj4wxjjFJE9gWV9zUt/hPWQH5iMJ3cRW0fqsGo6QyYiE7GaDp6C1c/UX7ARLMuNMTWd7DIwaQJoG9mx7Rz05NwWm8ObArY97Kd2Ek+gnwA7sRLKGmCbMaY5SLmdQZaFGvfGIOuhe/d+qJ+nnmi7rtJpKdqbrP4Tq7npt40x/vdEW5PmqA42j/Yr01/7bntP/TH6qlJKDUmaACo1APmSr8+Bz0Xkn8Au4Psi8mPfui4f7Px318m6jv4GHMmDV1u5K7FqI4Mp7mB5V9oewo8JXGGM+RzrnCEiwRLjQN05h+2HCWVfIrISKwl9HatWqxhrmopRWDVJPa2d3YzVT3SCMabLKUFEJA6rOeIIrIE1PsdqwukFrgZOCLJZhw/0PsHOQeC57Mm57ayPX3f295HxGwW0E53d4z3VnXu/LxOZtv6BKZ0VEpFTgeexEt6TzeEDUFUDzVj3beC2UViJ+dp+3nfbe+qwD6RSSqnOaQKo1ABnjCkXkXysARPSsAaMyQdOFZGkILU107Fqiip8v7c1Awv2MDgeqy9UKNoGpumsCRxYySpAo7FG/+tN72P1sVouIiuNMZVdbdCJttEKZ2ANxOFvuu9nYJI1UUQi/ZsH+h5Wx3PogBTnY/XbWubflM/3UHwknsPq13Yx1gAlXTkRq//aRcaYQ5r0isgfehjD9CDL2mq+2s6X/7ntaPuBOKelf9yBo1mGEnd37v1QP0/Q/STxANbfgA6bzorIKViD1WwHTgrWmsAY4xWRTcBsEYkKqP2ej/VFxmHJdl/uG99orvgNKKOUUqp7tA+gUgOAWFMhHDYsv2/dZKyHzwq++Nb7RazP768Cyi7DeqD8t1/i0dbU7aSAsudgJQehysP6Nv8iETnswV5E2mo0XsNqevorETks6RSRGBGJ9/s95GkgfIlX28iMT/lGFwwmlJqcN4BG4KcB8cRjDTbT4CvjLwH4ccCyH/uWv+i3zIP10N4eh2/k1kOuVw/8FStx+KV0MFWFiMz1NT9tiwMOr6E8mZ73QzxPREb77SsSa4ATD9YchQCbsOa0vFBEsvzKRvDFwC4v9fD4fenfWLFd6YsVAN+9eSFWv9LOmmx2594P9fME1r3YaW2eP18rgffo4Br7rv+LWH8bTjSdj6i7Cqsv3qUBy3+O1Sz96f7at8+xvp9Bax6VUkp1TWsAlRoYYoF3RGQz8F+smgQBpgHfw+oP8xO/pO4x4PvA//PNB/Yu1jfjP8aqIWyvHTLG7BCRNcAPfQ+Vn2E1oVyONQJm+4NuZ4wxRkQuxKot+0hE2oatT8Iatv6/WCPzNYrI97AeAneIyCO+4yT53s83fcd+x7fr7kwDgTHmURHJxhpRM19EnvLF4cKaIuJkYDFd1BAYY2pE5CqsaSA2iMhjvlUXYJ3LH5rD5xnMB64XkZlY/ZrmYvXP3M6hA5g8izVAz39E5HmsBPFcQq9t7SjmJhE5HXgFa6qK17GS1EqsgT+Ox+rrd5tvk3X4pmHw3SeFWNf+fKzmoLN6EMZOrPP1Z6zmpOdiDdf/e2PMAV+cHhG5DKsW6GMRechX9v+wHuBvMsbsCrr3MPJ9Vm7HmgbiXd+91TYNRBxwXpC+sf7bh3zvh/p58u36Q+AHIvJ7rD6YXuBl/wF+gngG+JqIzDfGfNS20Df1xEtYf18eBZYdmmuCMeYffr8+jJX83uW7h7YBp/nexx+MMXv7Y99+vgZ8bozRKSCUUqqnwj0Mqb70pS8D1pcxF2J9I74Dq/lWK1CE1Y/m+CDbjMBKMvb4ypYBTwDjgpTNwnogrMOqTfgPVrO9dwg+DcQ7ncQ6FWt00oO+4xZjPfDOCSg301euyFeuFPgAazTGFL9yXyHEaSAC9n8MVo3YLqz+XC1YtU4vAN8lYBqGjt4X1sPmB1i1gY2+f38jSLkC3/magzU6ZSNWP6YngMyAsnasPna7sUY53IeVlB1FwJQPdGMaCL9tYrFq3db5YnD5zu8rWMmd/1QFR2MlE9VYSdg7wJexvkQwAfs97H4Icp0uwBqhdpfvve0CftbBNkuxEtQ63/X5lIDpRTo7bnfODV9MAzGvi3Jd7hNrmpVPfTHX+d7Dlzu6J4IsD+neD/XzhDXi5XNYzbm9vvhzunif0VhfDNwXsPwC3/YdvoLsKwm43xebE2vQn8sA6a99+107L9aXYSH/rdCXvvSlL30d+hJjdCAtpZRSqrtE5ACwwxgTOF3IgCAiv8L6ImK86bwp5qAgIncD3wKmGGO6GqxIKaVUB7QPoFJKKdVNvr6PRzLVSn+4B6vm95dhjuOI+fqS/hD4tSZ/Sil1ZLQPoFJKKdUNvsnNz8RqZvlamMPpkLHmVcwJdxy9wRhzkC4mnVdKKRUabQKqlFJKdYOI7MH6AvUxrL6E3s63UEoppQYOTQCVUkoppZRSapjQPoBKKaWUUkopNUwMhz6AWsWplFJKKaX6g3RdRKnw0hpApZRSSimllBomNAFUSimllFJKqWFCE0CllFJKKaWUGiY0AVRKKaWUUkqpYUITQKWUUkoppZQaJjQBVEoppZRSSqlhYjhMA6GUUkoppVTYbdq06RSHw3G9MSYLrYhRvc8rIgfdbvcNc+bMea2jQmLMkJ8mb8i/QaWUUkopNSB0OA/gpk2bTomKiro/JyenNSYmpsVms+kzqupVXq9XmpubowsKCiKdTudlHSWB+s2DUkoppZRSfczhcFyfk5PTOmLEiGZN/lRfsNlsZsSIEc05OTmtDofj+g7L9WdQSimllFJKDUfGmKyYmJiWcMehhr6YmJgWXzPjoDQBVEoppZRSqu/ZtOZP9QfffdZhnqcJoFJKKaWUUkoNE5oAKqWUUkoppbrl73//e9Kxxx47JT4+/pjIyMg5OTk5My+//PLskpISx44dOyJFZO6qVasSO9vHypUrs5OTk3P7K2Zl0WkglFJKKaXUoOX1eqmpqcHtduPxeHA4HKSnp4c7rCHtkksuGf3II49knn322RWXX355aWJiomfz5s0xjzzySPq2bdti7r///gOh7OcnP/lJ+fLly2v6OFwVQBNApZRSSik1aJWUlFBdXd3+u4iQmpqKzTZIGrqlpeVSWdn/z+SpqW4qKvK6u9mTTz6Z+Ne//jXz7rvvLvj5z39e2bb8a1/7WsMVV1xR/vzzzyeEuq+JEye6Jk6c6OpuDOrIDJJPhlJKKaWUUodyu93U1NQcsswYw6Ca5zocyd8RHPfee+/NnD59epN/8tfG4XDw7W9/u67t98bGRtu55547Lj4+/pjMzMyjf/GLX2R7PJ728oFNQFevXh0vInNXr14dv2zZsgmxsbGzR48ePeuWW245pEp3zZo1I0444YRJGRkZR8fExMyeNm3a9AcffDClJ+9nONIEUCmllFJKDUqVlZWHJXsiHc7Fro6Q0+mUTz/9NO6EE06o67o0XH/99aNHjBjhefzxx/ecddZZlffcc8/IRx99NLmr7X7yk5+MmzVrVvM///nP/IULF9ZfffXVY99+++3YtvV79uyJXLhwYcN9992376mnntp9xhlnVF9++eU5f/nLXzQJDIE2AVVKKaWUUoOO1+ulqqoq3GEMK6WlpfbW1lYZO3asM5Ty8+fPr3/44YcLAZYvX1731ltvJb744ovJF198cXVn2y1fvrzqtttuKwE47bTT6tesWZP4zDPPJB9//PFNAJdeemn79l6vl2XLltUXFRVFPvroo2k//OEP9abogiaASimllFJq0Kmursa/OaHqP6HWsn71q189pKZw8uTJzcXFxZFdbbds2bL27aKiokxOTo6zqKiofbvy8nL7lVdemf36668nlZWVRbbdBxkZGdqfMASaACqllFJKqUHFGENl5WFd0FQfy8zM9ERGRpr9+/d3mcQBJCcnH5KhR0ZGGqfT2WUXtJSUFLf/7xEREcbpdLZnnd/5zndyPvvss7grrriieObMmS1JSUme+++/P/2NN95ICvGtDGuaACqllFJKqUGlurqa1tZWRGRwDfgyyEVFRZk5c+Y0vPXWW4lAcThiaGpqkrVr1ybddNNN+6+66qrytuX33nuvdv4MUVgHgRGRs0XkAxGpFJEWEdkhIteKSKRfmQIRMQGvg+GMWymllFJKhUdraysHD1qPgpr89b+f/vSnpVu2bIm97777UgPXeTwenn322ZCngeiJ5uZmm8fjISoqytu2rLq62rZmzZqkvjzuUBLuGsBU4G3gdqAGmA/8FsgCLvMr9yRwn9/vrf0TnlJKKaWUGiiMMRQWFuL1ersurPrEueeeW/v222+X/uIXv8j54IMP4s4888ya+Ph4z9atW6MfeeSRjNGjRztDnQi+J1JTUz0zZ85suu2227ITExM9NpuNO+64IysuLs7T2NioMxyEIKwJoDHmLwGL3haRBOAnIvJT88XXOiXGmA/7OTyllFJKKRUGLpeLpqYmHA4HERER7U09a2pqaGpqCnd4w97DDz9ceNxxxzU8+OCDGZdccsn4lpYW26hRo1pPPvnkmmuvvfZgc3NznyZiq1at2nPxxRePW7FixfikpCT3D37wg7Kmpib7o48+mt711koGWtW5iKwE/gCMMMYYESkAnjXG/LKHuxxYb1AppZRSSnWovr6eAwcO9LiWT0SYNm0adru9lyML7fAdrcjLyyvIzc2tOGxFWlpuWCaDT011U1GR1+/HVf0iLy8vLTc3NyfYunA3AQVAROxAFDAHuBx40ByamV4kIpcDzcAbwBXGmH39H6lSSimllOoLxhjKy8spKys7bJqBIT3YiyZhqp8NiAQQaMRKAAEeB670W/cS8CFQCBwFXA+8JyKzjDG1wXYmIpcClwLceOONLFq0CIAJEyYQHx9PXp71OUtNTWXGjBm8++67ADgcDhYvXsymTZuoq7OmH5k3bx6lpaUcOGA1ZZ48eTJRUVFs3rwZgIyMDKZMmcK6desAiIqKYuHChWzcuJGGhgYAFixYQGFhIUVFRQBMnToVu93O1q1bAcjKymL8+PGsX78egJiYGBYsWMCGDRtobm4GYOHChezdu7e90/P06dPxeDzs2LEDgFGjRjF69Gg2bNgAQFxcHPPmzWP9+vU4ndZcnYsXL2bnzp2UlZUBMHPmTJxOJ7t27QJgzJgxZGZmsnHjRgASEhKYM2cO69atw+22RuNdsmQJW7ZsaR96OTc3l/r6evbs2QNATk4OKSkpbNq0CYDk5GRyc3NZu3YtxhhEhKVLl5KXl0d1tTWH55w5c6iqqqKgoECvk14nvU56nfQ66XXS6zQMr1NBQQFxcXFERkbi9XopKSnBGENUVBTp6ent5w9g9OjRlJeXt8eVnp6O0+lsP+eJiYlUV1e3n+P+vE4xMTEoNdANiCagIjIHiMUaBOY3wJPGmB93UHYm8BnwS2PMPSHsPvxvUCmllFJKBVVbW0tRUVGvDewy6JqAKtUHBnwTUGPMJt8/14lIBfB3EbnTGJMfpOxmEdmB1VxUKaWUUkoNUg0NDYfU7iml+t5AHCq1LRkc30U5rdlTSimllBqkvF4vxcXFh/X3U0r1rYGYAB7n+7k32EpfE9CpwCf9FpFSSimllOpVVVVVtLa2Dt3BXZQaoMLaBFRE/gusAbYAHqzk7wrgKWNMvoh8DfgusBooBqYB1wL7gcfCEbNSSimllDoyLperfTAZpVT/CncfwI+BC4AcwA3sAa4G/uxbfwDIAO4BkoBK4L/ANcaYun6NVCmllFJK9YrS0tJeG/RFKdU9YU0AjTHXAdd1sv5/wIn9F5FSSimllOpLbrebmpqacIeh1LA1EPsAKqWUUkqpIapt3jylVHhoAqiUUkoppfpNfX19uENQPbRy5cpsEZkb7PWnP/0pZfXq1fEiMvfjjz+ODtx21apViSIyd8eOHZGdHeP2229PW7Ro0eTU1NTc+Pj4Y+bMmTPt+eefT/Avc++996Z2FMe55547rrff91AT7j6ASimllFJqmPB6vZoABvj536pzG52m35/JR0SJ+54fJOd1d7u4uDjPiy++uCtw+fTp052ffPJJzJHGddddd438yle+UrdixYryuLg47xNPPJFy9tlnT37iiSd2n3feebUAZ599du2MGTO2+2/3wQcfjPjNb34zZtmyZbVHGsNQpwmgUkoppZTqF01NTXi9XkREp3/wCUfydyTHdTgc5sQTT2zs7XjabNq0advIkSPdbb8vX768bvbs2dH33ntvZlsCmJ2d7c7Oznb7b/fYY4+lxsXFec4++2xNALugTUCVUkoppVS/aKv90+RPdcQ/+Wszc+bMpsrKyoiOtnG73bz66qvJp556ak1MTIzeXF3QGkCllFJKKdXnjDHU1eksXkOBy+U6bFlERIf52RH75JNPRkyePLm5o/X//ve/E6qqqhznnntuVZ8FMYRoAqiUUkoppfqc0+nE5XJp889BrqamxhEZGTk3cPn27ds/74vj3XPPPanbtm2LvfXWWws7KrNq1aqUlJQU9xlnnKHfMIRAE0CllFJKKdXntPnn0BAXF+d55ZVXdgYuHzdunGvXrl1Roe7H7XYfci8Eq0F87733Yq+++uqxF154YdkZZ5wRdPSglpYWee2115K+8Y1vVDkcmtqEQs+SUkoppZTqczr/39DgcDjMkiVLmoKti4iIMABut1sC13k8HmnbHmDcuHGziouL26eE2L59++dTp05tbft969atkcuXL5+8cOHC+oceeuhAR/E8++yzifX19fbvfve7lT1/V8OLJoBKKaWUUqpPGWNobu6wC5caIrKyslwAxcXFEcAhF7y4uDjCZrORmZnpBnjhhRd2t7S0tCeK48aNa+9YWFRU5Fi2bNmU7Oxs50svvbSns5q9p556KnnkyJGtJ510Up+NTDrUaAKolFJKKaX6lMvl0ukfhoGZM2c609LSXC+88ELSWWeddUh/vNWrVyfNmDGjMS4uzgDMnz8/6DcCtbW1tlNOOWUywH//+9/d8fHx3o6OV19fb3vzzTeTvv/975fZbDq5Qag0AVRKKaWUUn2qrfZPk7/Bz+12y5tvvjkicPmECRNax48f77ryyitLfvWrX40FOOOMM2pdLpesWrUq5f3330948sknd3e1/9NOO23ijh07Yu67776Cbdu2RW3btq29X2Hg/IOrVq1KbG5utp1//vk6+mc3aAKolFJKKaX6lDb/HDoaGhrsJ5100rTA5VdeeWXxbbfdVnLVVVeVx8XFeR544IHMZ555Js1ms5np06c3rVq1ate3v/3tLkfp/OCDDxIAVqxYMT5wnTHmE//fn3766ZScnJyWRYsW6Q3WDTIMvokZ8m9QKaWUUmogKygo6LdBYESEadOmYbfb++V4gYfvaEVeXl5Bbm5uReDyn/+tOrfRafq9UmZElLjv+UFyXn8fV/WPvLy8tNzc3Jxg67QGUCmllFJK9RkdAKZzmoSp/qa9JZVSSimlVJ9xu914PB5EOqwcU0r1I00AlVJKKaVUn9EBYJQaWDQBVEoppZRSvaq1tX0+b23+qdQAowmgUkoppZTqNTU1NezcuZPS0lIAWlpawhyRUsqfJoBKKaWUUqpXGGMoKysDoLy8nObmZq0BVGqA0QRQKaWUUkr1itraWlpbW9sHfCkoKMDtdusAMEoNIJoAKqWUUkqpI+Zf+9c24IvH4znkd6VU+GkCqJRSSimljlhg7Z9SamDSBFAppZRSSh2RYLV/auhZuXJldnJycu6R7ufjjz+OFpG5q1evjm9bJiJzb7rppvQj3bfqmiPcASillFJKqcGtqampvfZPE0DVE2vWrNk+ZcoUZ7jjGA40AVRKKaWUUkektrYW0Nq/nmja91ouXlf/P5PbItyx407J6/fjduDEE09sDHcMw4U2AVVKKaWUUj1mjGlPAFUPhCP564Xjrl69Or6tGeeyZcsmxMbGzh49evSsW2655bBmnLfcckt6VlbW0TExMbNPOOGESYWFhZGBZQKbgP7rX/9KXLRo0eSUlJTcuLi42bm5udOef/75hCOJWVk0AVRKKaWUUj3W0NCAx+PRwV+GqZ/85CfjZs2a1fzPf/4zf+HChfVXX3312Lfffju2bf0//vGPpKuvvnrsiSeeWPOPf/wjf/r06c0rVqzI6Wq/e/bsiTzttNNqH3744b1PPPFE/pe+9KWGb33rW5Nff/31EX36hoYBbQKqlFJKKaV6TJt/Dm/Lly+vuu2220oATjvttPo1a9YkPvPMM8nHH398E8Ctt9468stf/nLdP//5z/0AZ511Vl1FRYXjqaeeSutsv9dcc0152789Hg+nn3563fbt22Mefvjh9JNPPlmbix6BsNYAisjZIvKBiFSKSIuI7BCRa0Uk0q+MiMg1InJARJpF5F0ROSaMYSullFJKKcDr9VJXVxfuMFQYLVu2rP0GiIqKMjk5Oc6ioqJIALfbzbZt22JOP/30Gv9tvvnNb1Z3td/8/PyIb37zmzkZGRlHR0REzI2MjJz7/vvvJ+zZsyeq19/EMBPuGsBU4G3gdqAGmA/8FsgCLvOV+RVwHXAlsB1YCawRkZnGmIP9HK9SSimllPKpr6/H6/WGOwwVRikpKW7/3yMiIozT6RSA4uJih8fjkczMTJd/mZEjRx6yTSCPx8MZZ5wxqbGx0X711VcXT5kypSU+Pt573XXXZVdWVkb0/rsYXsKaABpj/hKw6G0RSQB+IiI/BaKwEsCbjTH3A4jIeqAAK0G8th/DVUoppZRSfnTwF9WZ7Oxst91uN6WlpYckbSUlJZ3mIFu2bInatm1b7DPPPLPr7LPPbq9hbGlp0fFLesFAPImVQFsT0EVAAvB020pjTCPwMrCs/0NTSimllFJg1dLU19eHOww1gDkcDqZNm9a8evXqJP/lzz//fHJn2zU2NtrAalLatmznzp2RmzZtiuuTQIeZcDcBBUBE7Fi1fXOAy4EHjTFGRKYBHmBXwCbbgP/r3yiVUkoppVSburo6HfhFdemqq64q+f73vz/xvPPOG3vWWWfVvP322/HvvPNOYmfbHHPMMS2ZmZmu//f//t/oxsbG4rq6OttNN900KiMjw9XZdio0A6UGsNH3eg9Yi9XfDyAZaDDGeALKVwOx/oPFKKWUUkqp/qPNP1Uovve979XceOON+9esWZN03nnnTfzf//4X86c//amgs21iYmLMU089tdtut3PBBRdMvPHGG0etXLmy5Nhjj9Uq514gA+GbGxGZA8RiDQLzG+BJY8yPReTXwC+NMckB5S8BHgIijTGHfRMgIpcClwLceOONcxctWgTAhAkTiI+PJy8vD4DU1FRmzJjBu+++C1jV1IsXL2bTpk3tI1rNmzeP0tJSDhw4AMDkyZOJiopi8+bNAGRkZDBlyhTWrVsHQFRUFAsXLmTjxo00NDQAsGDBAgoLCykqKgJg6tSp2O12tm7dCkBWVhbjx49n/fr1AMTExLBgwQI2bNhAc3MzAAsXLmTv3r0cPGiNezN9+nQ8Hg87duwAYNSoUYwePZoNGzYAEBcXx7x581i/fj1OpxOAxYsXs3PnTsrKygCYOXMmTqeTXbusCtYxY8aQmZnJxo0bAUhISGDOnDmsW7cOt9vqq7tkyRK2bNlCZWUlALm5udTX17Nnzx4AcnJySElJYdOmTQAkJyeTm5vL2rVrMcYgIixdupS8vDyqq60BoObMmUNVVRUFBQV6nfQ66XXS66TXSa+TXqdBcJ1EhNTUVFpbW9uvW1xcHLGxse2/R0ZGkpGRQVFRUXtN4ahRo6isrKSlpQWAtLQ0Wltb289XQkICUVFRlJeXt5+P9PR0CgsLaTN69GjKy8vb40pPT8fpdLbvIzExkcmTJ7ef4/68TjExMR1OhpiXl1eQm5tbEbi8ad9ruWGZDN4W4Y4dd0pevx9X9Yu8vLy03NzcnGDrBkQC6E9Evgf8HZgEnALcC0T51wKKyJXAb40xoUwEObDeoFJKKaXUIFdVVUVxcXG4wwhKRJg2bRp2uz0sh+9oRUcJoFJ9obMEcKA0AfW3yfdzPNa0D3asZNDfNN86pZRSSinVz7T5p1KD10BMAI/z/dwLfADUAd9qWykiscAZwH/6PzSllFJKqeHN5XLR2NgY7jCUUj0U1lFAReS/wBpgC9Zon8cBVwBPGWPyfWVuAa4TkWq+mAjeBtwXlqCVUkoppYYxrf1TanAL9zQQHwMXADmAG9gDXA382a/MLVgJ39VAKrAR+KoxprQ/A1VKKaWUUpoAKjXYhTUBNMZcB1zXRRkD3Oh7KaWUUkqpMHE6ne0jiyqlBqeB2AdQKaWUUkoNQFr7p9TgpwmgUkoppZQKiSaASg1+mgAqpZRSSqkutbS04HQ6Eelwqjul1CCgCaBSSimllOpSW+2fNTyDUmqw0gRQKaWUUkp1yhhDTU1NuMNQYbZy5cpsEZnb9kpLS8s9/vjjJ23YsCGmP+M466yzcmbOnHlUfx5zKAn3NBBKKaWUUmqAa25uxuVyISJaA9jLtr7+QK7H1dLvz+T2iGj39JN/ktfd7eLi4jwvvvjiLoD8/PzIm266adRpp502ZevWrZszMzM9vR/p4X73u9+VNDU1aVvkHtIEUCmllFJKdaq+vh7Q5p99IRzJ35Ec1+FwmBNPPLER4MQTT2ycOHFi60knnTTthRdeSPzRj35U1btRBjdjxgxnfxxnqNImoEoppZRSqlONjY3hDkENUAsWLGgCOHDgQCSAiMy96aab0v3LrFy5Mjs5OTm37feKigr7//3f/43LyMg4Oioqas7IkSNnfec73xnXtj4/Pz/itNNOm5CSkpIbHR09Z8yYMTN/9rOfZbetD2wCum/fvohvfetbOaNHj54VHR09JycnZ+bll1+e3dLSorWEQWgNoFJKKaWU6pDX66WpqSncYagBKj8/PxJg/PjxIdfKrVixYszGjRtH3HjjjQeys7Nd+/bti3zvvffi29afd95541taWmx//OMf9yUnJ3t2794duX379g77GZaWljqSk5PdN99884GUlBT39u3bo2+99dbsioqKiCeffHLfkb3DoUcTQKWUUkop1aG25E/7/6k2LpcLgJ07d0atWLFi7LRp05rPO++8mlC3/+yzz0ZcfPHF5Zdcckl127If//jH7c1H//e//43461//uufcc88NaeLJ+fPnN8+fP7+w7feTTz65YcSIEd6f/exnOS0tLfujo6P1xvWjCaBSSimllOpQQ0MDoP3/lKWmpsYRGRk5t+33pKQk9/r167fFxMSEfINMnz696f7778+02+3mtNNOqzv66KMPqT086qijmq6//vrRFRUVjmXLltVPnjy5tbP9eb1e/vCHP2T8/e9/Ty8qKopyOp3tTT93794dOXPmTO0z6Ef7ACqllFJKqQ5p/z/lLy4uzrN27dptb7755vbbb799n8vlsn3nO9+Z4PGEPgDo3/72t/2nnHJKzR133JGdm5s7c9y4cTMfeuih5Lb1zz777J6jjz668dprrx0zZcqUWdOmTZv+0ksvxXe0v9///vcZN9xww5jTTjut5sknn9z9zjvvbLv55pv3AzQ3N2s/wACaACqllFJKqaA8Hg/Nzc3hDkMNIA6HwyxZsqTphBNOaPzlL39Zceutt+7Ly8sb8cgjjyQDREZGmtbW1kNyjKqqKrv/72lpaZ7HHnvsQEVFRd6HH3649ZhjjmlcsWLFhE8++SQaYPz48a7nnnuuoLq6+rM1a9ZsT09Pd33nO9+ZdPDgwUP20+bFF19MWbZsWfV9991X9M1vfrNu6dKlTSNGjPD21TkY7DQBVEoppZRSQengL6orK1asqJo0aVLL3XffnQWQmZnZun379ui29R6Ph/fffz+ho+0XLFjQfO+99xZ6vV42b94c7b/Obrdz4oknNl5//fXFLS0ttt27d0cF20dLS4stMjLykITvX//6V8qRvbOhS/sAKqWUUkqpoLT5p+qKzWbjiiuuKFmxYsX4l156KX7ZsmU1jz/+ePott9zSNGnSJOdf//rXtIaGhkMqnebOnTv19NNPrzn66KObRYSHH344LSYmxrt48eLGyspK+wknnDD5nHPOqZw2bVpLS0uL7Z577slMS0tzHXPMMUGro5cuXVr36KOPZtxyyy2NkydPdv7jH/9I2bdvX3SwskoTQKWUUkop1YG2AWCU6szFF19cdcstt2TffvvtWa+88kp+eXm54+abbx4VERFhLrroorLp06e3PProo+1zA86bN69x1apVqXfccUeUzWYz06dPb3r++ed3TZw40dXc3CxHHXVU85///OfM0tLSiOjoaO8xxxzT+Morr+yKi4sLOtDMrbfeWlxRUeG4+eabRwGceuqp1bfffvv+c889d1J/nYPBRIbBiE5D/g0qpZRSSvU2j8fDtm3bwh1Gt4kI06ZNw24P2l2szw/f0Yq8vLyC3NzcisDlW19/INfjaun3Shl7RLR7+sk/yevv46r+kZeXl5abm5sTbJ3WACqllFJKqcNo88/+oUmY6m86CIxSSimllDqMJoBKDU2aACqllFJKqcNo/z+lhiZNAJVSSiml1CHcbjdOpzPcYSil+oAmgEoppZRS6hDa/FOpoUsTQKWUUkopdQhNAJUaujQBVEoppZRSh9AEUKmhSxNApZRSSinVzuVy4XQ6EelwSjul1CCmCaBSSimllGrXVvtnjAlzJEqpvqAJoFJKKaWUaqfNP1VH5s+fP/XUU0+dEGzdzJkzjzrrrLNyurO/HTt2RIrI3FWrViW2Lbv22mszV69eHX+EobZbtWpVoojM3bFjR2Rn5V577bW4RYsWTU5OTs6NiYmZPW7cuJlnnXVWTn5+fkRvxHbHHXekPfHEE0k92ba3aQKolFJKKaUAq9ZP5/9T/WXs2LGuNWvWbP/qV7/aftM98MADWW+99VavJYCheO211+K+9rWvTU1ISPDcf//9BU8++WT+JZdcUrZ79+7o/Pz89sTxSGJ77LHH0l988cWkXgv6CDjCHYBSSimllBoYampqcLlciIg2Ae0n+b/emett8vT7M7kt1u6eeOOUvP4+rr+YmBhz4oknhr3K+YEHHkifMGFC86uvvrrHZrPqx5YvX1537bXXlnm93jBH1/u0BlAppZRSSuH1eiktLQW0/19/Ckfy1x/HbWsu+uc//zll7NixM+Pi4mYvWbJksn+TysAmoKNGjZpVU1PjuPvuu0eKyFwRmdvW5NLj8XDNNddkjR07dmZkZOScnJycmffdd1/qIe/J62XlypXZKSkpuSNGjJi9fPnynNraWntXsdbV1dlTU1Pdbcmfv7ZlncV2/fXXZ86cOfOo+Pj4Y1JTU3NPOOGESZs3b47yPxdbtmyJff7551Pbtr333nvbY7/rrrvSJk2aNCMyMnJOdnb2rGuvvTbTP4aNGzdGf/nLX56cmJh4TExMzOwJEybMuPnmm9NDuxKHC2sNoIh8CzgfmAskAjuAO4wxq/zKFADjAjYtNcZk9VecSimllFJDXUVFBW63O9xhqCHks88+iystLY286aabCpuamuTqq68ee9FFF41bu3bt7mDln3766d2nnnrq1NNOO6360ksvrQCYPXt2M8AFF1ww9rnnnktduXJl8bx585pee+21hJ///Oc5aWlp7nPOOacW4MYbb8y45557Rl522WUlS5cubXjuueeSr7/++tFdxZmbm9t03333jbzyyitHXnjhhZXTp09v7U5shYWFkT/84Q/Lxo8f31pbW2t76KGH0pcuXTpt586dm1NTUz0PPvjgvm9961sTx44d67zuuutKAI466ignwHXXXZd58803j1qxYkXpCSecUP/xxx/H3nbbbaNiY2O911xzTTnA8uXLJ02cOLHloYce2hsdHe3dtm1bdF1dXZeJbUfC3QR0JbAX+AVQAZwGPCkiacaY+/zKPQn4/37YRVFKKaWUUj3jcrkoLy8PdxhqiGlsbLS9/vrru9LT0z0ABw8ejLj++uvHNDQ0SFxc3GHVzMcdd1yzw+Ewo0aNcvk3Dd28eXPUP//5z/Q//vGPBT/96U8rAb7xjW/UHzx4MOLGG2/MPuecc2rdbjf33Xdf1jnnnFN+7733FgOcddZZdYsWLZpcVlYWEXgsf9dff/3BDRs2xN1xxx3Zd9xxR3Z6errrpJNOqvnVr35VevTRRzs7iw3gb3/724G2f7vdbs4888y6zMzMY1atWpV02WWXVc6dO7clNjbWm5qa6vbftqqqynbnnXdm/+xnPyu58847S8BqetrU1GS76667sq+66qry8vJyR2FhYdRzzz2XP3/+/GaAM888s777V+ML4W4CeoYx5lxjzNPGmLeMMb8EVmElhv5KjDEf+r02hSFWpZRSSqkhx+VyUVhYqM0+Va+bNWtWU1vyBzBz5swWgIKCgk5H5Az0n//8J95ms3HeeedVu1wu2l4nnHBC/fbt22Pcbjf5+fmR5eXlEcuXL6/x3/bMM8+sCb7XL6SkpHg/+OCDnW+88cb2yy677OCYMWOcTz/9dPqxxx47fd26dbFdbf/mm2+OWLRo0eSkpKRjIiIi5sbHx89pamqy7dy5M6qz7d5+++245uZm27nnnnvI+zrppJPqKysrHXv27InMyMhwZ2Vltf7oRz8a9/DDDycXFRUdcQVeWGsAjTEVQRZ/CpzZ37EopZRSSg0nxhhqamooKSlhKA50oXqfw+EwHo9Hgq3zeDzicDgO+RYhISHhkDbFkZGRBqC5uTnoPjpSUVHh8Hg8pKamzg62ft++fRGFhYURAFlZWYccMzMz0xXKMWw2GyeddFLjSSed1AjwwQcfxJx88snTbrjhhpFvvPFGfkfb7dq1K/LMM8+ccvTRRzfefffd+0aPHt0aFRVlli9fPrmlpaXTyraysjIHwLx582YEW793797IKVOmtL766qu7fvWrX426/PLLc374wx/aZs+e3XDvvffuP+6445pDeW+Bwt0ENJhFwNaAZReJyOVAM/AGcIUxZl+/R6aUUkopNURUVFS0D/qiVChSU1NdBw4cCFqrVV5eHpGent4nnUhTUlI8drvdvPHGG9vt9sO7vo0aNcrtdrsF4ODBg4fkN6WlpZ02/+zIokWLmo877ri6Xbt2RXdW7qWXXkpoaWmx/fe//92dkJDgBatWPZTBZ1JTU90Aq1at2p2dnX1Yojpr1qwWgNmzZ7e89tpr+U6nU1577bW4a665ZvTy5csnl5SU/C/Y+ejKgEoAReRErNq/i/wWvwR8CBQCRwHXA++JyCxjTG3/R6mUUkopNbh5PB4qKoI1xFKqY8cdd1zDddddl7x3796I8ePHtycsb7311ojKykrHkiVLjqhvGkBERIRpaWk5pIbw1FNPrbv66qulpqbGsXz58rpg202cOLE1LS3N9cILLySdffbZ7WVeeumlpK6OWVRU5Bg1atQhyavX62Xfvn1RaWlp7cuDxdbc3GwTERMREdFe+/m3v/0tJbCmNCIiwjidzkNqBE844YTG6Ohob1FRUcR3vvOdLvOaqKgo8/Wvf72+pKSk9Ec/+tH4iooKe2Zmpqer7QINmARQRHKwBnt5yRjzWNtyY8zP/Iq9JyIfAJ8BFwL3dLCvS4FLAW688UYWLVoEwIQJE4iPjycvz5ryJDU1lRkzZvDuu+8C4HA4WLx4MZs2baKuzrpv5s2bR2lpKQcOWH07J0+eTFRUFJs3bwYgIyODKVOmsG7dOgCioqJYuHAhGzdubJ9IdcGCBRQWFlJUVATA1KlTsdvtbN1qVXRmZWUxfvx41q9fD0BMTAwLFixgw4YNNDdbNbsLFy5k7969HDx4EIDp06fj8XjYsWMHAKNGjWL06NFs2LABgLi4OObNm8f69etxOp0ALF68mJ07d1JWVgbAzJkzcTqd7Nq1C4AxY8aQmZnJxo0bAUhISGDOnDmsW7eufVSwJUuWsGXLFiorKwHIzc2lvr6ePXv2AJCTk0NKSgqbNlndNJOTk8nNzWXt2rUYYxARli5dSl5eHtXV1QDMmTOHqqoqCgoK9DrpddLrpNdJr5NeJ71O/XCdcnJy8Hg8lJWV0drayogRI4iPj29fHxERQWZmJsXFxe3NQ7Ozs6murm7fZ2pqKh6Ph5qamvZrGxsb234dIyMjycjIoKioqL1/4ahRo6isrKSlpQWAtLQ0Wltb289XQkICUVFR7QPSREVFkZ6eTmFhIW1Gjx5NeXl5+/2Tnp6O0+ls30diYiLV1dXt57g/r1NMTAxD2YoVKyr/9Kc/ZS5ZsmTqL3/5y5Lx48e3btmyJfr222/Pnj17duNZZ50VNDnrjgkTJrSsWbMmcfXq1XUJCQmeWbNmteTm5jrPPffc8gsvvHDCxo0bD86fP7+xubnZtnnz5uhdu3ZFP/XUU/scDgeXXXbZwd/97ndj0tLS3EuXLm149tlnk/Pz87u8KOeff36OMYZvfOMb1VOmTHFWVlY6HnvssdQdO3bEPPLII3s6i+2UU06p/+1vfyvf/va3cy6++OKKzz//POaBBx7IjI+PPyQxmzRpUsvatWsTnnvuuYT09HT3lClTnFlZWZ4rrrii+Nprrx2zb9++yK985SsNXq+X7du3R69duzb+jTfeyN+wYUPMypUrR5999tnVkyZNclZVVdnvvvvurKlTpzb3JPkDkIHQ4VdEUoD3gQbgK8aYTieEFJEtwCfGmO+FsPvwv0GllFJKqQHC6/Wyc+fOITvlg4gwbdo0etI0rjcO39GKvLy8gtzc3MOqXXf9Ytvcvg2pY5PvPuqT7m5TUFAQccUVV4x65513Emtra+2pqanuU089tfruu+8uSklJae9MOn/+/KkpKSmu//73v+0J1OrVq+PPOOOMKR999NGWL33pSy07duyInDZt2qwnn3xyd9tUDu+9917sZZddNnbnzp0xLS0ttpdffnnn6aefXu/1evnDH/6Q8fjjj6fv378/asSIEZ5Jkya1fP/736+47LLLKsG6t3/xi19kP/744+mtra22E088sea0006rXbFixfjt27d/PnXq1KAzCTz77LMJjz32WNqnn346oqKiIiI+Pt4zefLk5iuvvPLgN7/5zfaktqPY7r///tRbb711ZHl5eeTUqVOb7r777gPnn3/+hK997WvVDz30UCHA1q1bIy+++OKczz//PLahocH+xz/+seDyyy+vBPjTn/6U8sADD2Tm5+fHREZGenNyclq++c1vVv/2t78tLSoqcvzkJz8Zs3Hjxrjy8vKI+Ph4z8KFC+vuuuuuosmTJ3c4M0JeXl5abm5uTrB1YU8ARSQWWANkAguNMWUhbLMF2GiM+X4Ih9AEUCmllFLKp6qqiuLiYkRkSI78OdgSwPxf78wNx2Twtli7e+KNU/L6+7iqf3SWAIZ7IngH8AwwGTguxORvJjAV+Esfh6eUUkopNaQYY9r7/g3F5G8w0iRM9bdw9wH8E9bk7z8DUkTkWL91nwInAd8FVgPFwDTgWmA/8Fi/RqqUUkopNYgZYzh48CCtra1DtvZPKdW1cCeAJ/t+/jHIuvHAASADa7CXJKAS+C9wjTHmiDuZKqWUUkoNVR6PNT6E3W7HGENJSQlVVVWA1v4pNZyFeyL4nBCKndjXcSillFJKDRXNzc1UVlZSW1uLMYaoqCgcDgeNjZ2OsaeUGibCXQOolFJKKaV6SVlZWfs0DG2cTmf7lAlKKWXruohSSimllBroPB5P+/x5SinVEU0AlVJKKaWGgKqqKu3bp5TqkiaASimllFKDnNfrpbKyMtxhKKUGAU0AlVJKKaUGudraWtxud7jDUEoNApoAKqWUUkoNYv6TuyulVFc0AVRKKaWUGsTq6+txOp2ISLhDUUPcypUrs0VkbtsrLS0t9/jjj5+0YcOGmN4+1h133JH2xBNPJPXW/urq6mynn376hKSkpGNEZO69996b2lv7PhIiMvemm25K76zMqlWrEkVk7o4dOyJ745g6DYRSSiml1ADjdrtpaWkhLi6u03Iej4fi4mJAJ3cfrLZt25br8Xj6/Zncbre7jzrqqLzubhcXF+d58cUXdwHk5+dH3nTTTaNOO+20KVu3bt2cmZnp6a34HnvssfSpU6c2n3/++TW9sb877rgj/c0330x88MEH944ZM8Z11FFHDdu5UTQBVEoppZQaQIwxFBQU0NLSQk5OTqdJ4MGDB7Xv3yAXjuTvSI7rcDjMiSee2Ahw4oknNk6cOLH1pJNOmvbCCy8k/uhHP6rq3Sh7z44dO6LHjx/fcsEFF9SEOxaAhoYGiYuLC8u3NtoEVCmllFJqACkrK6OlpQWgvXYvmPr6eqqrq/srLKWCWrBgQRPAgQMHDmmeeNddd6VNmjRpRmRk5Jzs7OxZ1157bab/+o0bN0Z/+ctfnpyYmHhMTEzM7AkTJsy4+eab0wHmz58/dcuWLbHPP/98altz086abJaUlDi++c1v5iQlJR0TExMze/78+VPffffd2Lb1o0aNmvX000+nbdu2LbZtf8H2c88996TGxMTMdjqd7e2pMzIyjk5OTs71er2AVeseHx9/zJ133pnWVubf//53/NFHHz0tKipqTmpqau53v/vdsbW1te151urVq+NFZO5zzz2XcMIJJ0yKjY2dfdFFF40NFoPX62XlypXZKSkpuSNGjJi9fPnynNraWntH770nNAFUSimllBogmpqaDpnMvbW1tT0Z9NfY2EhRUZH2+1Nhl5+fHwkwfvz49iaV1113XeZVV101dtmyZTVPPfXU7gsuuKD8tttuG+Xf12358uWT7Ha7eeihh/b+61//2n3ppZeW1dfX2wEefPDBfePHj29ZunRp7Zo1a7avWbNm+9lnn13bUQynn376xHfffTfxhhtuOPC3v/1tj9frZdmyZVM3b94cBfD000/vXrp0ae348eNb2vYXbD8nnXRSQ0tLi+3999+PBfj888+jqqqqIhoaGuybNm2KBvjwww9jGhoa7CeccEIDwCeffBJ91llnTU5JSXH//e9/z/9//+//Fb/00kspp59++sTA/f/4xz/OmTVrVtO//vWv3ZdccknQkZtuvPHGjHvuuWfkd7/73fLHH388PyYmxlx//fWju74SodMmoEoppZRSA4DX66WwsBAROaQ/X3FxMRMmTACsxK+srIzGxsZwhTngSDXYdgjEgUkwmKRwRzT0uVwuAHbu3Bm1YsWKsdOmTWs+77zzagCqqqpsd955Z/bPfvazkjvvvLMEYPny5XVNTU22u+66K/uqq64qLy8vdxQWFkY999xz+fPnz28GOPPMM+vb9j937tyW2NhYb2pqqrutuWlHnn322YRNmzbFrV69esfXvva1BoDTTz+9fty4cbNuuummrCeffHLfcccd15yamuquqKiI6Gx/M2fOdKanp7veeeeduBNOOKHxzTffjJs6dWpTRESEeeutt+LnzZvX8vbbb8cnJye7Z8+e3QJw/fXXZ2dnZ7euWbNmt8NhpVYpKSnuSy65ZMKaNWtGnHTSSe3HO/3006v/+Mc/dlit73a7ue+++7LOOeec8nvvvbcY4KyzzqpbtGjR5LKysohOL0o3aA2gUkoppdQAUFpaSmtr62GDuTQ1NeFyuTh48CB79+7V5M+fGxxv2XBsseHYYCPiDTuON0QHxOlDNTU1jsjIyLmRkZFzZ86cOXPbtm2xL7zwwu6YmBgD8Pbbb8c1Nzfbzj333GqXy0Xb66STTqqvrKx07NmzJzIjI8OdlZXV+qMf/Wjcww8/nFxUVNTjSqkPP/xwRHJysrst+QNISEjwnnjiibUfffRR56MoBTFv3ryG999/Pw7gvffeiz/22GMbFi5cWL9u3bo4gHXr1sXNmzev/VifffbZiGXLltW0JX8AF1xwQbXdbjdr16495PhnnHFGTWfHzs/PjywvL49Yvnz5IeXOPPPMTrfrLk0AlVJKKaXCrKmpicrKyg7X79q1S+f6C8K+SbDVCK7FHrwZBs9YLybVQGu4Ixu64uLiPGvXrt325ptvbr/99tv3uVwu23e+850JHo81AGhZWZkDYN68eTPaEsXIyMi5Z5xxxhSAvXv3Rtrtdl599dVd6enprssvvzxnzJgxuXPnzp36/vvvd3s6iZKSkoi0tDRX4PKMjAxXT/rOLV68uP6TTz6J93q9fPTRR3FLliypX7JkScPHH38cB7Bx48b44447rr22sqKiIiIzM/OQ4zscDpKSkjxVVVWHJLbZ2dmdjthUWFgYAZCVlXVIucD9HyltAqqUUkopFUZer7e9P19HNVdtA1CoL0gp2DcLnile7Jtt2GoEELCL9o3sQw6HwyxZsqQJ4IQTTmiMiYnxXnbZZeMfeeSR5EsuuaQ6NTXVDbBq1ard2dnZhyUus2bNagGYPXt2y2uvvZbvdDrltddei7vmmmtGL1++fHJJScn/7PbQ87aRI0e6KisrD2seWVZWFpGYmNjtaSmOP/74hl//+tf2F198MaGoqCjqq1/9akNkZKQpKyuLfP755xMqKysdxx9/fHsNYFpamqst6W3jdrupqamxp6SkHJLIiUinVdOjR492ARw8ePCQ/ZWWlvZa80/QGkCllFJKqbCqqKjA6XRqs8U2rWArAFu+QEeP763geNcGsWAi8SV/KhxWrFhRNWnSpJa77747C6ykMDo62ltUVBSxZMmSpsBXcnLyId9mREVFma9//ev1P/3pT0vLy8sjKioq7AARERHG6XR2massXLiwsaqqyvGf//ynvbllfX297a233kqcP39+Q2fbBjN//vzm+Ph4z8033zxy/PjxLdnZ2e60tDTPpEmTmm+++eaRsbGx3oULFza1lZ89e3bDf/7zn2T/6Vgef/zxZI/HI0uXLu3W8SdOnNialpbmeuGFF5L8l7/00ktJwbfomR7VAIrIdGAuMAZ4xBhzUEQmAaXGmPrOt1ZKKaWUUmBN5VBWVhbuMAYEKQXHxzakDMRYCZ2JM7hzDd7JBtoqhRog4nUbUg/uxV4c67Q+I5xsNhtXXHFFyYoVK8a/9NJL8WeeeWb9FVdcUXzttdeO2bdvX+RXvvKVBq/Xy/bt26PXrl0b/8Ybb+Rv2LAhZuXKlaPPPvvs6kmTJjmrqqrsd999d9bUqVOb2yaTnzRpUsvatWsTnnvuuYT09HT3lClTnFlZWYd9JXDWWWfVzZkzp+F73/vehN/85jdFaWlp7rvvvjvL6XTarrnmmoPdfT92u525c+c2vPPOO4nnnHNO+5C8CxYsaHjiiSfSFy1aVBcR8UWF3PXXX1+ycOHC6SeffPKkH/7wh+UHDhyI+N3vfjd68eLFdf4DwITC4XBw2WWXHfzd7343Ji0tzb106dKGZ599Njk/P7/bTWM7061PjIjEicjTwOfAX4HfA9m+1TcB1/dmcEoppZRSQ1V9fT379+/X5ooGbNuEiFdtSAN4Zhjcs714RnkxERDxvo3IVTYc7wu2XULkv21II7i/7MXxia09WVThc/HFF1eNGzfOefvtt2cB/OEPfyi9884797355puJ55577qSLLrpowtNPP51y3HHHNYDV1DE9Pd195513jjz77LMnX3HFFeMmT57c/PLLL+9u2+cNN9xQPGnSpJYLLrhgwtKlS496+umnkzo6/ssvv5y/ePHiul//+tdjLrzwwonGGF599dUdM2fOdHa0TWcWLVrUALBkyZL2Grwvf/nL9QALFy48pFZv3rx5Lc8+++yuiooKx/e+972JN91006ivf/3rVatXr87vybGvu+66sssuu6zk8ccfTz///PMnNjQ02G644YbCnuyrI9Kd5gYi8hBwGnA+8D7QAswzxmwSkQuAXxpjZvZmgL1A21MopZRSakBpS/6GfbPPFnB8JNh32fCMMpg0gz1PEL5I6rzJBhMDtjIQt2DiDK5jvER8aEPcQZI/uzDh95Oxx/Tq3Nmh6jAbzcvLK8jNzT1sJJ9t27blejyefh+Xw263u4866qi8/j6u6h95eXlpubm5OcHWdfdm+ybwM2PM2yIS+KnaB4zrQXxKKaWUUsNG23x/wzr5awH754J9q4Ab3LO8SLXgyDu8cZqtWqAaTKTBM9qLSbBqBYdKzZ8mYaq/dTcBjAE6GqM4no676iqllFJKKaCurg6Px9PpqJ9DmgciX7JBA3hzDN5McGwRpKHzhE5aBXvh0Ej6lAqn7vaa/Rj4XgfrzgY+OLJwlFJKKaWGtqqqKoDhmfwBtn1Wsuc+1outUojYYOsy+VNK9Z7u1gBeC6wRkTXAM1j9604TkV9gJYBLejk+pZRSSqkho6Wlhaampq4LDmH2bVY/PluRDanXxE+p/tatGkBjzDrgRCAKuB+ro+sNwATgJGPMx70eoVJKKaXUENFW+zdcSTXYDgqeSQZbr45rqJQKVbdHHDLGvA98WURigGSgxhgzvL/KUkoppZTqgtfrpaamJtxhhJV9m2DsBjwMmUFcusHr9XrFZrMNz7a/qt94vV4BvB2t7/HMmcaYZmNMsSZ/SimllFJdq62txevt8Jls6HOBbbfgzTHYdw+75A8ROdjc3Bwd7jjU0Nfc3BwtIgc7Wt/jBFAppZRSSoXGGENFxWFTwA0rtnxBXIJJBWkefgmg2+2+oaCgILKxsTHGV0OjVK/yer3S2NgYU1BQEOl2u2/oqFy3JoI/bGOR5cAY4DVjzA6/5ZcZY+7v8Y57l1azK6WUUiqsamtrOXDgQLjDCKuI1TZoBSLBVtqH+c8AnQgeYNOmTac4HI7rjTFZaEWM6n1eETnodrtvmDNnzmsdFepxAigitwDHAv8DvgHcZYy5x7dukzFmTo923Ps0AVRKKaVU2Bhj2L17N06nM9yhhE8rRP7DhmeGwb5ZkM7zpCMzgBNApQaCbg8C4+drwGxjjFtEbgCeEZFRxpgr0ZtfKaWUUgqwJn53Op3Dd+J3wFbiG/Ql0vRt8qeU6tKRVD3bjDFuAGNMJXAqkCMifwt1vyLyLRH5t4gUiUiDiHwiIucElBERuUZEDohIs4i8KyLHHEHcSimllFL9whhDeXl5+7+HK1uRYBwGqQ53JEqpI0kAS0SkvZmnMaYV+D+sJpczQ9zHSqAB+AXwdeBt4EkR+alfmV8B1wG3Amf4yq8RkawjiF0ppZRSqs9VVVXR0tKCyPCu9ZIiwZsFtpLhfR6UGgiOpA/gaMBtjDlsiFERWWSM+SCEfaQZYyoClj0JLDTGjBeRaKAUuNMY8zvf+hFAAfAXY8y1IYQ6fL9uU0oppVRYGGMoKytrr/0b1uoh6mk77mO8OD7rh3FPtA+gUp06knkAC4Mlf751XSZ/vnLBxkP+FMjw/XsRkAA87bdNI/AysKxbASullFJK9QNjDEVFRZr8+diKrJzIePU7eaUGgpATQBE5RkT+KiI7RaTR99opIg+LSG4vxrQI2Or79zTAA+wKKLPNt04ppZRSakCpra2lpqYm3GGEjdSC4x1BaqzfbUWCiTXYy3XWA6UGgpBGARWRK4GbgXqsfnqv+FaNA84CLhCRa4wxtx9JMCJyInAmcJFvUTLQYIzxBBStBmJFJNLX9zBwP5cClwLceOONLFq0CIAJEyYQHx9PXl4eAKmpqcyYMYN3330XAIfDweLFi9m0aRN1dXUAzJs3j9LS0va5eyZPnkxUVBSbN28GICMjgylTprBu3ToAoqKiWLhwIRs3bqShoQGABQsWUFhYSFFREQBTp07FbrezdauV52ZlZTF+/HjWr18PQExMDAsWLGDDhg00NzcDsHDhQvbu3cvBg1al6/Tp0/F4POzYYU2/OGrUKEaPHs2GDRsAiIuLY968eaxfv7592OnFixezc+dOysrKAJg5cyZOp5Ndu6z8esyYMWRmZrJx40YAEhISmDNnDuvWrcPtdgOwZMkStmzZQmVlJQC5ubnU19ezZ88eAHJyckhJSWHTpk3WBUxOJjc3l7Vr12KMQURYunQpeXl5VFdbPcHnzJlDVVUVBQUFep30Oul10uuk10mv06C+Tscddxz79+/H4/FQWlpKRkYGTU1N7ec8KSkJu93eHmdMTAzJyckUFxcDYLPZyM7OprS0FJfL1X4d6uvraWxsbI9dRKiqqgIgNjaWxMRESkpKALDb7YwcOZKSkhI8HusRauTIkdTW1tLU1ARASkoKxpj29zpixAji4+Pbr2NERASZmZkUFxfj9XoByM7Oprq6uv3ap6am4vF42pPduLg4RkgsUa84sDdHwAEPnlOBIi/16c1UjKxk/MGxlE4opynJ2sfInRk4Y1upGm3tI7k4kZi6GIqnWXHE1EUzclcme+bsBzFghAmbxlIyuZTmhBYrru1ZNCc0U51da7234mSqa6vZvGFzh9eprz5PMTExKDXQddkHUETOAF4CbgNuMsbUBayPB64G/h/wdWPMK4fvJYRARHKADcAHxpjlvmW/Bn5pjEkOKHsJ8BAQaYxxdbFrbW+glFJKqX5RV1fH/v37h+eUDy6IeNWG1IAn12DfItAK4hXcc704PumnGkDtA6hUp0L5JF4B/N0Y86vA5A/AGFNvjLkGeBz4ZU+CEJEU4D/AfuC7fquqgXgRCfwEJwFNISR/SimllFL9om3Kh2GZ/BlwvGVDKsF9rBf75wItQDQYMdAY7gCVUm1CSQBnA/8Kody/gDldlgogIrHAaiAS+JpvkJc22wE7MClgs2m+dUoppZRSA0JTUxPNzc3DL/kDbAVgLxTccw2OT21Iq1gTvjeBJ8dg368VY0oNFKEkgHbAHUI5d4j7ayciDuAZYDKwzBhTFlDkA6AO+JbfNrFY8wH+pzvHUkoppZTqijGGAwcOsHfvXiorK9v7KYZi2I766QX7Jza8SQZbmSCNXyR7guDYa0OaNAFUaqAIZRCYzcDpwJtdlDvdV7Y7/gScBvwMSBGRY/3WfWqMaRGRW4DrRKQaq9ZvJVaieV83j6WUUkop1amqqipqa63BRNoGXcnIyCAjI6OzzWhoaGgfDGS4se0WbLWC61gPjg91pE+lBrpQEsA/Aw+JyFbgryZIuwYRuRj4MXBJN49/su/nH4OsG4814fstWAnf1UAqsBH4qjGmtJvHUkoppZTqkMvlorT08MeLsrIyEhISiI6ODrqd1+uluLh4ePb9c4Njk+BNM9j32axmn0qpAa3LUUABRORB4IdY8/G9DOzzrRoHfA2YAvzFGPPjPorzSAyzv8RKKaWU6i5jDPv376e+vj7o+sjISKZMmRJ0XUVFRfv0CcONfbPg2GDDtdBDxPqwjLp5OB0FVKlOhTQPoDFmhYi8htVU8ydAlG+VE1gP/MoY81LfhKiUUkop1bfq6uo6TP4AWltbqaysJDU19ZDlLperff7CYccN9jzBO9Jg361NP5UaLEJKAAGMMS8CL/qmZEjzLa4IMkm7UkoppdSgYYwJqQavpKSE2NjY9sm+W1paDpkofbixbxekRXDnDKDaP6VUl7pMAEUkFWvS9YeMMa/5Er7SgDKnAJcCK4KM5KmUUkopNWDV1NTgcrlC6sOXn59PTEwMUVFR1NTU9E+AA5Eb7J8L3iyDfZfW/ik1mITyif05MAF4vZMyr2MN2nJFL8SklFJKKdUv2iZvb/t3KJqbm4d38gfYdgrSJHjGe7FVaLc3pQaTUBLAbwN/Djb6Zxvfur8AZ/ZWYEoppZRSfa22tpbW1lZENIkJmQcc/xO8GQZ7vtb+KTXYhPKpHQdsDaHcNiDniKJRSimllOonPan9U2DbZU327pnoxVamibNSg00oCWAzkBBCuThfWaWUUkqpAa+urg6n06m1f91hrKkfvKkG+16t/VNqMArlk7sJ+HoI5c70lVVKKaWUGtDcbjfFxcWA1v51h+0A2GoF7ySDDM+pD5Ua9EJJAB8AfiAi3++ogIh8D7gQuL+3AlNKKaWU6islJSV4PDqTVXfZP7dhRhikDETnPFdqUOpyGghjzPMi8kfgURG5DPgvsB8wwFjgFGAecLcx5oW+DFYppZRS6kjV1dVRW1sb7jAGHakA20HBPceL/VNN/pQarEKaCN4Yc4WIvAP8AvglEOVb5QTeB840xqzukwiVUkoppXqJx+OhqKgo3GEMSvbNgokw0AJiNAFUarAKKQEEMMa8DLwsIg4g1be40hjj7pPIlFJKKaV6WU1NjTb97IkGsO0RPEcZ7Ls0+VNqMOvW8E0i8hsgwxhT6nu5/daN9K1XSimllBqQhvsE7j1l3ylgwEQbxKUJoFKDWXfH770eGN3BumzfeqWUUkqpAcfpdNLcrDNWdZsB+y7BZIN998Ce+sHY3Xhj68MdhlIDWshNQH0Ea/CXYEYD1UcWjlJKKaVU39Dav56REpAGwXOUF8fHAy8BNA4XnlEH8KaW402qQhrjMGZ2uMNSasDqMgH0Tf/QNgWEAR4UkbqAYtHALOD13g1PKaWUUurIGWM0Aewh+y5r8BepCXckhzORTlrnbMDENSANcdgPjMN47GBz0f16DqWGh1A+GU1Ape/fAtQCVQFlWoH/AH/qvdCUUkoppXpHU1MTLpcLEdGJ37ujFWwFgneCwbZ3YPX9M1HNVvIX5cSxZRae0fvxjC1AbHbEPrBiVWogCWUewGeAZwBE5FHgd8aYvX0dmFJKKaVUd9XU1FBRUUFMTAyxsbFER0djt9uprrZ6qWjy1z22AkHcgkkcWIO/eGOacM3ZgIlw4dgxA/fUreDQgemVCkVIdeMiMh5oNsZc6LfsxwHF6o0xT/RmcEoppZRSoTLGUF5ejtPppKWlpT3pUz1n3yV4Ewy2wgGU/MU20DrnI7B7cOyYjnvaFrDr1B5KhSqUPoCLgPeArwOv+JbZgfsDihoRKTXGaD9ApZRSSvW75uZmnE5n0Gae2vSzB+rAdlBwH+PFnte/CaDBWEmdx44g7ctMXD2tsz8CAceuabinbQa7t19jU2qwC6UG8KfAf4wxrwRZN88YswlARB4ALkEHglFKKaVUGHTWzFOTv+6z7xYr6cIgpm9H/zSRLXgyS/CmVmBiGjExzWDzXTOvzRqG0Oa1RqNwRuHYPcVK/mx6XZXqrlASwC8D/y+Ecq8DDxxZOEoppZRS3ef1eqmtrQ13GEOH/9x/e/ou+TMRTlwz8/CmVICANIxAGhKwVaVbBWxejBgwAkasRLQlSpM/pY5AKAlgOrDPf4ExxiMiVwIH/BZX+soqpZRSSvWruro6vF5tCthb+mPuP2N30zr7Y8yIBuz7JoDHjjetDG/GQRg4XQ6VGnJCSQDrgdTAhcaYOwMWpfnKKqWUUkr1Kx3wpXf19dx/Rry4jv4EE1ePY/sM3JO3Q4SO4qlUfwjlK51PgDNCKHeGr6xSSimlVL9pbW2lsbEx3GEMHS7f3H/jDbaCvqmKcx31Od7UShw7j8I9YZcmf0r1o1ASwAeBC0Tk/I4KiMh3ge+hE8ErpZRSqp9VVVWFO4Qhxba3be4/+mTuPxPdjDe7CPv+cXjG7INoZ68fQynVsVAmgn9RRO4D/i4iP8Ea7OUA1nhMo4GTgQXAH40xL/VlsEoppZRS/jwejyaAvcmAfWffzv3nySqy/uF2YEZoza1S/S2kieCNMb8QkXeAnwFXAlG+VU5gPbDcGPPvPolQKaWUUqoD1dXVOvhLL7JtE2ylgmueF8cnfZUAFiO1SXjTy/pk/0qpzoWUAAL4avde8k0Cn4o1PlOFMcbTV8EppZRSSnXEGENFRUW4wxgypBQcHwqe0QZbiSCm9xNAb1wdJq7Bmsdv0s5e26/NbmNEfBwj4uN7bZ9KDVXdHtfXGOMxxpQZY0p7I/kTkUki8hcRyRMRj6+mMbBMgYiYgNfBIz22UkoppQav2tpa3G43IjpnwBFrhoi3bBAH3jQv9qK+q/3DK3gdrl7bZ0pGGjlTJpGRPZLI6ChcrS6M0TkClepIyDWAfWgGcBrwIRDZSbkngfv8fm/ty6CUUkopNXD51/7pw34PtYJtj2ArBluxgBvci7w43uujef8weLKKsVWlYjKP/Ht8sdnIHDWSEfFx1NfU0traSkxsLIkpydjtA+ERV6mBaSB8Ol5uGzxGRJ7Fmk8wmBJjzIf9F5ZSSimlBqr6+npaWloQEU0Ae6IeIl63YasRTKzBO9rgzTA4NtiQPpqF3SRVQ3QLcmAc3rQja7rriIhg5JhRRERFUnGwjLiEeOKTEn1r+yaBVWqoCHsCaIzRnttKKaWUCpkxhoMHD7b/W/lpAakBkwhEQ7BcTsqt5A8vuBZ7sOfbsBUI9vy+TZw8WUXgsWNijmzkz6joKLLGjkZEKCs+SGpGGo6IiF6KUqmhL+wJYDdcJCKXA83AG8AVxph9YY5JKaWUUv2surqa1lbtCXIYD0T814at0sr6TJTBmwneHIN3rEEqwb5HsO0WiAH3MV4c622Ip+/7UBrx4skoxVaRjjejtNOyIoIjIgKb3YbxmvZRXm02ITIqivTsLDxuD1XlFaSPzMRm0xo/pbpjsCSAL2H1ESwEjgKuB94TkVnGmNqwRqaUUkqpfuPxeCgt7TyBGK7sHwu2SsF9jBdpANxgKxfs+79IkIzDWAlhksHxvq1PRvoMxptUDZGt2OoT8Qbp/2ez2UhKSyE+KRGHo/PHU2dzC/W19aRnZ+oAQEr1wKBIAI0xP/P79T0R+QD4DLgQuCewvIhcClwKcOONN7Jo0SIAJkyYQHx8PHl5eQCkpqYyY8YM3n33XQAcDgeLFy9m06ZN1NXVATBv3jxKS0s5cOAAAJMnTyYqKorNmzcDkJGRwZQpU1i3bh0AUVFRLFy4kI0bN9LQ0ADAggULKCwspKjImvh06tSp2O12tm7dCkBWVhbjx49n/fr1AMTExLBgwQI2bNhAc3MzAAsXLmTv3r3tTV6mT5+Ox+Nhx44dAIwaNYrRo0ezYcMGAOLi4pg3bx7r16/H6XQCsHjxYnbu3ElZmTXvzsyZM3E6nezatQuAMWPGkJmZycaNGwFISEhgzpw5rFu3DrfbDcCSJUvYsmULlZWVAOTm5lJfX8+ePXsAyMnJISUlhU2bNgGQnJxMbm4ua9euxRiDiLB06VLy8vKorq4GYM6cOVRVVVFQUKDXSa+TXie9Tnqd9Dp1ep1iYmIYMWIEAEVFRe1NQEeNGkVlZSUtLS0ApKWl0dra2n6+EhISiIqKory8vP18pKenU1hYSJvRo0dTXl7eHld6ejpOp/OQfURGRrYPPhMdHU1qamr7+RMRRo0aRVlZWXsNZUZGBk1NTe3nPCkpCbvd3n4/xcTEkJycTHFxMWAlQtnZ2ZSWluJyudqvQ319PY2Nje33mIhQVVUFQGxsLMl1STi2RFAzto46qWfs7lEcmFmEK8VNpDOSzMYMGhMaqYqpAY8hIz+NlhRDeY4VR0JZPIll8RyYacUR2RzJ6K0j2Xd0IZ4Ia9D3nM/GUD62ksaUJgAyd6fjjvRQOdaKI/FgAnFVIyiaXmKd48YoRm3PouCYA8THFRJjBE9sAxWeNFpMtHWd7OUkpSSTkZ5AhEOoqWvG3dpIRUsMHq8Q43CTGu3kYFMsXi94DYxJcFLmTmH3bqtmcMZoG7VNhsIq614Ymwop8TX8b/MWoH8/TzExMSg10MlAajvfNgiMMeYrIZTdAnxijPleF0UHzhtUSimlVI8ZY9i+fTsej05BfIhGiHzBhhkB3lSDY9fAahJpMDi//Ba2miS8yVZNYJu0rAwSU5Jpamiksb6RxJQkIqM6GxQ+FDZixn4VsYelX6BWSaoBb2D9heg+Te6UUkqpYaKpqQmPx6PN/vy5IGKNDTzgOcpg3zXwzo1JqoYoJ1KfeEjyl5KRRmJKMjWVVYgI6SMzeiH5U0p1ZVAmgCIyE5gKfBLuWJRSSinVP9qa2g2k1ku9qg4cb9qwfypIKdDVOOkGHO/akEpwH+vF8ZH02RQOR8KTcRA8NkxMU/uypNQUktNSqauuITI6ipgRsWGMUKnhJex9AEUkFmsieIBRQIKInO37/VXgeOC7wGqgGJgGXAvsBx7r12CVUkopFTb19fXhDqHveCHiHSuZkwIbbAIzwuA62YtJCVLeA/ZPBHuB4J7nxfGZDXENvOTPYPBkHMRWmYY3zerjabPbSclIo6GuHrvDQayvT6dSqn+EPQEEMoBnApa1/T4eOOArcw+QBFQC/wWuMcbU9U+ISimllAonl8s1pCd+t+cJtnLBtciLfTuYWIOtQoh4xYbrFC8mA2gA+07BVixIBYhH8EzxYisQpGHgJX8AJqnKmvy9cCxkWAlgfGI8IkJLcwtpmelhjlCp4SfsCaAxpoCuO8ye2A+hKKWUUmqAGsrNP6Uc7J8KngleK8GrEqgSDAZiIOI/NrzZYDsAGDBp4JliMDFebAcFW8XAS/68SZW4x+zDm14KLgcmqrl9XXxiIi3NLcTFx4UxQqWGr7AngEoppZRSXRmSzT+9YNsPjo9sEAsmzpqovY0gmGYDsWArBc90g4ky2PfYsG8TZIAO5eDJLMY16zNwRWA/MA5cUXhydgMQGRVFVEw0laXlpGSkhTdQpYYpTQCVUkopNaAZY9prAIcK23bB8ZkgjYIZYXDP9eJ47/CEThBMk8FkgH2HIO6BmfS1MXY3rsnbkPoEpCoFz+gDYP9i2o74pASM14vdbtfRXJUKk4H9V0QppZRSw15TUxNer3fIJAy2vRDxvjVvn2uBF28aON6zISb4+xMEW5kg7oH//t05uyHaif3AOLxjCw5J/gDiExNobGgkLjE+PAEqpbQGUCmllFIDW1vzz6HQ/09qrGTPm2bwJhgiNgyd7+K9MY14xu3FVpKNZ2zBYSM8xMbFYXc4aGlqJi5BE0ClwmXo/NVRSiml1JBjjKG2tjbcYfQOlzXPHzbwTDQ4dg+txzD31K3gtWGrT8TEHd5nMz4pAbfbTXRMdBiiU0q1GVp/eZRSSik1pDQ2NuJyuQZ/88+2SdtrwT3fi+PjQf5+AnhSy/CmlWMvmGg1Aw1gs9kYER9HQ20dsTr6p1JhpQmgUkoppQas6upqYPA3/2ybtN0zx+DYZEO8QycBNOLBPXUr0jgCE9kCka7DyoxIsOb+83g82Gz6+KlUOOknUCmllFIDksfjoa6uLtxhHDHbTsGRZ8Mz2YttvzXy51DiGbcXE9uEvWAC3jH7g5aJT0yg1ekkdsSIfo5OKRVIB4FRSiml1IBUW1s7uGv+msC+S7BvErwjDcYN9vKhlfyZqGbc4/OxlWfgGXXgsIFfABwRDmJGxFJVXkFyWmqfxOE1UFIfy66qRLzGxtfG9slhlBoSNAFUSiml1IDU1vxz0HGC433BViCIsZI/b4bBkTf0Gl65Ju0ADLaqVNxTtwUtE5eY0P7vjvpyVjVHkl+VSHlTNEel1TAuqR5bCLlyYe0I3j+QyWcHU2lyRQAwLrGeU7wQYe/221FqWNAEUCmllFIDTktLC83NzeE5eFulY08q64w1zYNtP3imG0y0F/se25BM/rzRTXizirHvz8E9Pr/DcvGJCTQ3NTEi/tCpHzxe+Kgog7f2jqKsMaZ9+Wu7x5AU7SQ3s5IpqbVMTKkjJuLQ+QQLauJ4fut49tXGE2HzMCuziswRzTS6HByojcfltRPRu29XqSFDE0CllFJKDThVVVX9f1ADtl2C4yMBB3hmGjxTDd3JJOxbBPs+wT3Xi32bIE1DL/Fr4xmzD4yAOwIiW4OWiYyOIjIqisrSclIz09uXby5L5sVtOZQ3xTAmoYFTJu7HY2wU1Y3Aa8DttfH+gSzW7stGMExMqePY0WXMyKjirb2jWJM/isToVk6bvI+G1gg+KU5nU4m1f8fQPeVK9QpNAJVSSik1oLhcrv5v/lkHEe/ZsB0UvOnGqsnbYMP+qcF1vBczuutdSBnYPxI8Ywy2IkGahlZ/P3/G7saTfQBbeabV9y+IqOhoMkePxOv1Ynd80R6z3hnBI5umkhrbwtnT88k7mMpr+Yd32kuIdJIV30RMhIcDtXH843+TEQwG4UvZZcRHtfLf3WPxmqF7npXqC5oAKqWUUmpAKS8v79/BX9wQ8boNaQbXfC+2IsFeJHiTjLXuHRut3/BCZ9PX1UPEWzYYASbFYB+CTT79eUYWQoQbqU2GzIOHrU9MSSI1MwOP2015SSlpWRnt6z4szMBjbCwaU8qzWyfQUVvbutYo6iqjfL8ZxiXWkxjdyuTUWtYfyKS4OCPodkqpzmkCqJRSSqkBIxy1f/bPBFut4FrswbHehnishMRWY9U3YbeSO9fp3uATaDVC5H9s4AL3Ai+OdUM7+TMYPGMKkNpEvBmHJ3/xiQmkZWXSWN+As7mFjOys9sFfvAbe35/FpJRaNhRmEHpHS2FfbTzUwv9K+2YkUaWGi6H9F0oppZRSg0pFRUW/1v5JJdj/J3gmebFv+SL5a1/vS1Bs5YL94yDJShNE/McGLeA+1mslkEO8SaI3tRwzognbwWxM4qHJus1mIzUznZamZowxpGSkHTLy55ayZKpbopiZUUVJg84JqFQ4aA2gUkoppQYEl8vVv4O/eK0RO4kGMwLsu4MnbuIRTJTBsdmGrcLgmeHFmwL27YJ9p4AH3It8yZ97aCd/YE38TksU3oSawyrwUjLSsdnt1NdWkj4y87Bt39s3ksQoJwXV8YetU0r1D00AlVJKKRV2Ho+Hffv29V/tnwcc6wRbpeBa5MHxYeeNosQpeJO9SL0Q8aY1oIkRg3cseEd5cXxoQ1xDP/nzxtfgTanEnj8Zz/jdh6yLio4mITmRuqoaktNSDtu2rDGaHZVJfHXCAd7aO6q/QlZKBdAEUCmllFJhZYzhwIEDtLS09M8BmyHiTRu2UsGd68W+w4Z4u07ebNU2q/9busGkG0ykwbHbhn3f8Jlx3J2zB1wOjMMFtkOT9bSRmXjcHrwYHBGHz52xbn8WdvEC4DHaC0mpcNEEUCmllFJhVVJSQkNDQ/8crBki/22DZnAt8mLfYg0AEypBsJcD5UO/ti+QN6YRb8ZB7PvH4xm1/5B1CclJRMdEHzbiZxu3R/i4KJ2ZGVV8pKN3KhVW+vWLUkoppcKmvr6+X/v92T8TaPT12fuke8nfcOcZtwe8NvDYwOFpX26320nJSKO5sYm4xPhDBn1p87+yFJpcEeQk11PbEnXYeqVU/9EEUCmllFJh4fV6KS4uDpow9IkGa+AW7ySDY5MNcWryFyoT6cQzsgjbwezDJn5PyUzHZrPRUN9ATGxs0O0/PJBJUrST3ZWJ/RGuUqoTmgAqpZRSKizKyspwuVz9NvCL41PfXHSJBmnU5K873KP3gc2LrSkOolrbl0fHxJCQlEhNZXXQgV8Aqpqj2FmZyNyR5WyrSOqniJVSHdEEUCmllFL9rqWlhYqKij7YMUgh4A5YXge2XYJnisGxRR9/usOIB8/o/dgq0/FkH1r7l5aVgcvlQgQcjuBDS1gTvoPDbvDq4C9KhZ0OAqOUUkqpfmWMobi4uPd37IWINdbonsZu8GaDSTMQAbZCARuYGIM0axLSHd6sEohsxVaVhnvKtvblsXEjiIqJpuJgGamZ6cG3NVYCOCm1lk9L0vorZKVUJ/QvoFJKKaX6VVVVFU1NTb2+X/tWsaZ2mOHFOwps1eD41IbjIxu2YsEzXWv/ustgcI8pQBpH4EktO2RdUloKbpeLqOiooP0465wRvLlnFNUtURyVVkNZY0x/ha2U6oTWACqllFKq37S2tnLw4MFe36/Ugn2j4BltsFUKtoOCsRm8vhpA4wBbkSCt2vevO0xiNSahDseuqbgn7WhfHh0bQ0xsLJWl5aRkHFqzV94YzeN5k9lfGw/AyPhGiuqDDw6jlOp/mgAqpZRSql8YYygqKur9QV8MON6zgR28ow0RH1q1fOIVpA+6GQ4n7rEF4HLgjWkEv9w5KTUFj9uN3eE4pPbP5REe/XQK1S1RfHVCISKGfTVxfFIcvImoUqr/aQKolFJKqX5RU1NDY2Njr+/XvsVq+ula6MWxUWv4eouJasabXoq9cNwhg79ERkUxIj6OqvIKklIPHfnzxe05FNXH8X8zdvPctgm4vdrkVqmBRj+VSimllOpzbre775t+7hfEpQlgb/FkF4LNgCvikInfk9NS8Ho82MSGzfbFo+SnJams2z+SJeOKWbN3tCZ/Sg1QYf9kisgkEfmLiOSJiEdE3glSRkTkGhE5ICLNIvKuiBzT/9EqpZRSqidKS0vxeDxdF+wO/6afYwz2Ik3+eovB4B51AKlMxZv1xYitEZERjEiIp666lvjkLyZ1r2iMYtXnExmbWE91cxSVTdHhCFspFYKwJ4DADOA0YKfvFcyvgOuAW4EzgAZgjYhk9UuESimllOqxpqYmqqure32/bU0/3XONNv3sZd7UcohuwV6ZjhnxRbPdpNQUjDF4jBe73Q6A2yM8+tlUbAKzMqr4vCw1XGErpUIwEBLAl40xY4wx3wK2BK4UkWisBPBmY8z9xpg1wLcAA1zWv6EqpZRSqjv6as4/Kfmi6ad9rzb97G2eUQegNRJPfG37MrvDQXxSIvU1dSQmJ7Uvf2lHDoV1cXxtyj7+mz8mDNEqpboj7AmgMcbbRZFFQALwtN82jcDLwLI+DE0ppZRSR6i6upqWlpZe3acUQsRrNkwceDO92A5q8tebTFQL3rQy7CXZmIzS9uVJqckAuFwuHA5rHMG8gym8u28ki8eWsLYgG08Y+/2J18PJb9zHWc9eG7YYlBoMBsMooNMAD7ArYPk24P/6PxyllFJKhaIvBn6x7QPHWzZMEngmenF8HPbvsoecQwZ/sVvf09vsNhKSk2ioqycxJQmAFreNp7dMZHRCA00uO+VN4ZvoPbN0Fxc+cRkT927k06OXYW9qhKj4sMWj1EA2GP5qJgMNxpjAnuPVQKyIRIYhJqWUUkp1obS0FK+3q4Y+3eAEx1obJhk8Y63kT9Dav95kIlpxj9qPVKXgzSppX56YnIzNZsPZ7CQiIgKAtQXZNLRGsHBMKZtKMsISr93dyqmv38Nvbv4KmaX5/PPbt5LQUEmU9PKAQ0oNIYOhBhCs/n6BpKN1InIpcCnAjTfeyKJFiwCYMGEC8fHx5OXlAZCamsqMGTN49913AXA4HCxevJhNmzZRV1cHwLx58ygtLeXAAWv+m8mTJxMVFcXmzZsByMjIYMqUKaxbtw6AqKgoFi5cyMaNG2loaABgwYIFFBYWUlRUBMDUqVOx2+1s3boVgKysLMaPH8/69esBiImJYcGCBWzYsIHm5mYAFi5cyN69e9u/SZ0+fToej4cdO3YAMGrUKEaPHs2GDRsAiIuLY968eaxfvx6n0wnA4sWL2blzJ2VlZQDMnDkTp9PJrl1W5eqYMWPIzMxk48aNACQkJDBnzhzWrVuH2+0GYMmSJWzZsoXKykoAcnNzqa+vZ8+ePQDk5OSQkpLCpk2bAEhOTiY3N5e1a9dijEFEWLp0KXl5ee0DAsyZM4eqqioKCgr0Oul10uuk10mv0xC5Tvn5+SQlJdHS0oLD4WhfHxERQWZmJsXFxe3JYXZ2NtXV1e37TE1NxePxUFNT035tY2Njaf2ohVRXMqUTysn6OJ2CYw7g9dVQjd80ltIJ5TQlWfsYuTMDZ2wrVaOtfSQXJxJTF0PxNCuOmLpoRu7KZM+c/SAGjDBh01hKJv//9u47Po78vu//6zMz27GLXggS7OTxeLx+0hWdJFtWteKiWLYiW7Gd3y+x4+QRJ45tOXai/By32ElcZceSE5fYsa0uWe18kq76uu545B3JYwFAECB638X2me/398cAIECCJAiCBAF+no/HHrCzs9/9DnY4t+/9tmGKmbDLavvxNoqZIpPt4Ti4hrN1xApRBveG51NyKkFrdzOn7+kFwAkcth/qoH/fEOVUeA5uPraJmYY8023h+9bY24BXcRnePQpAaiJJc28jPXeF76Nbddn22hbO7h+kkqiE5+mRdqZbcmRbcgA09zQiRhjZGa5ynx6roX6glt47wvcxUvboOLKZvgP9VGPhebz1tc1Mtk+Tawrf+5buJqxjGd0enteZkTSZ0STFB14g6paZLG6ivuY0g8EmDC5b6hPkZ/KM+XV0ngooBx6P9bazu36csbEpHm5+lbOFFkZLDdzdcByAXDXJ4albeLDpMK6E79Ozo3dxa203DdHw73Fkajc1kQLbUwPUjAxS+/SruJN5tvhnqCaSHHzrP+apzPt4S/MhBItFeHb0Lg7UnmJv9/M88L9+n/q+Ho7f8w66/ukP0W7yRCZrmcxmOXzo0HX/95RIrF0rqFLLJdYula3Whoh8Dmiy1n7Hgm3/CvgDILawFVBEfh74ZWtt6jLF3jgHqJRSSt0EjDF0d3ev7ti/AKKfcbAZoArOuLb8rSaLpXrgEKZtEO+N2/B3nYRoFYCa2gytmzcxOjRCc1vY0vfl49t4/HQ737+vhy8e33FVr900dob3Pfq7PPTC3+IZn4oXI5+qpyY/ScQvc2rXAzz51n/Gwbu+Bz8SIzUzwQ/83X/hrc/9X8brt/DV9/0sb3v2r9hxJvwShmgUhoehru6q6rVCemKqG956aAE8DrjAbuDEgu37Zh9TSiml1A3CWsvZs2dXfeIXp1uQguDfERB5wV3VsjcSGylj4yUkl1l291jrVfH3vIFpG8Tt2kOw7fR8+AOobaijUq6QTCUBmC5FePpMG3dvGuOpM5uuorKWdzz1v/jBL/xnrDg889BHyNa1ccuJfyCVn6ASiTPWvIOW0W7+xV/8JNma/8ird/0j7jn0FZKFab7xjp/COB4/8qmfxzP+yuuh1E1mPQTA54As4dIPvwYgIknC9QD/ZA3rpZRSSqkFrLX09fXNd6tbvYLBPSKYWovTtx6mL1i5sJPjyhqRgvoxqrcfgmgFyadw+ztwB7cg1aWnS7ASEGzpw99xCiJV3DM7CBpHIVmY3yeWiBNPJBgfHqWhpQmAR7s6CKzQni5wcLB5RXVNFKb5sb/+ae499FVeu+1ddO5+gHc+9sdkZsYW7dc61oMvLqd23o9xPR5+7q/o2Xo3zz70I7z/73+bxomzK3p9pW5max4AZ8Pcd8/e3QxkROSDs/e/bq0tiMhvAh8TkUnCVr9/TziBzceve4WVUkopNa9QKDA9PU0QBJTL5flxfKtJBsCZEKpvMnjf3rg97PyOHvzdJ8B3kXIcZ7oe7+StiL106LVYgm2n8XcfR/I1yFA7tm4Cf+9x/F2ncM924PXuhHIMomVMzQymdYCgZQgiPjLRiDvaSrD1NCQWv391DfUEQYDruYgIY/kYz/e1cP/mEZ7qWVnrnxjDz//e99I++AZf+ke/yJ5Tz/GP/+5XL7q/ZwP2dIdjfUcbtmJF+NG/+ZkVvbZS6gYIgEAL8Nnzts3d3wH0AL9JGPh+EWgEXgbeZa0dRimllFJrolqtcubMGYLgGs64aMF71cHGLc4EG3LWT4vF33OcYNtpZKIRqUSx0TJBxxmwQuTk/ks/d99Rgi29OMOtUEpitp4OR6LNpJBqlKDjTFgWhMs7APguzmgrzkwGv3kQ/5ZjF5Tteh6pTJrpiUkys+PpHunswBVLJlYlV1nZROy7u16go/8In/3AL/Ouxz9B3fTylwppnuileaJ3Ra+rlAqteQC01vZwmQGzNpyp5tdnb0oppZRaY9ZaBgYGrm34A5zTgjMsVN9s8F7eWOHPRkuYukmCTf2Y5hGcvq3hIuxzyy9UIgRbe3CytbhDmy98PhZ/3xGCLX24Z7ZjarLYbafP7VCTx5KHUhRnphYpJsE4YAUbLWOaRzCbBi5av0x9LQBBEOC4DgO5JK8MNPP27QM8fRVj/x586dOUYilahjuvKPwppVbHmgdApZRSSq0/09PT5HK5a/siPngvCabB4gwJYjZGADTpaar7jmBrw+UlCBzczj0EzSMwtw3CiVjKUaq3vg5isY7BxsLJdcSPYDJT4cQtPTsI6iagbnqJVwPiFUx89IrrmamrozCTJ10bBsGvn+wg5gUIUPRX9hEyUily38EvcfDO93Pfoa+uqAyl1NXRAKiUUkqpK1KtVhkYuHjL0WpxXxMkL/h3BXjPrv/JX6wT4O/oJNjWDdUIbvduEItJ5Am2noFo5cInxSpQ9aje9tpsISzqN+X27CSoH18cHFdBKpPGi3hMjRdIpWvomarh9ZFG3r2rjyd72ldc7l2vPUKiNMPA5v089NJnVrHGSqnl0gColFJKqWWbW+ZhbhH3ayYXBsBgu8E95qz7sX9B4wj+LcewyQLOwGYoxwm2d50bk3cpro8z2AbWxUYq2EgVCRzwI5jmYUjlV72+tfV1VCtVEslwYfNvdG4hGalS9D0qwcqX4XjwxU8zUdfO7lPPr1ZVlVJXSAOgUkoppZZtdHSUfH71A8ciPkSecEDANFsiPeuz9c9isZkp/B2dmOZRJJ/Ce+MAQccZbE3/8gtywGxaPFZuGbFxxSLRKIlUkonRMeqbGunPJjk62sC7d/bxxFW0/mWmh7ntjcf55jt+iu96UlfyUmqtaABUSiml1LLMzMwwMjJybV/EgveM4IwK1bcEeC+tv/BnsQQdPQSb+7A1M+C7uJ17MMkZ/H1HLjP13dqrbajDGoMgiAjf6t5MzA3wjVA1K2/9u//lz+NYQzGRwQuql3+CUuqa0AColFJKqcsql8ucPXsWESGcnPvacF8T3C4H/26D+7qDVG/wtLQEf9cJgh3dyHQtXudeTKxIsP00eP5aV+2yorEY6bpaZnIzpOtqGcnHeXWwibdvH+S5s20rLleM4a3P/iWnt93NXa89soo1VkpdqfX3tZpSSimlrhtrLRMTE3R2duL7/jULfzIF3hOC97JDsMPgDApOdv2Fv6B5iGBHN07/FiTw8HedxHT0rYvw57oubVs3Y4KAcqmMF/F4vHszrmOJuQGlFc78CXDbG4+zafgU3773H7Ot99DqVVopdcW0BVAppZRSSwqCgP7+frLZ7DV8EfCeFZxTAh74txkogTu0/sKfSc5Qve0wMl0LWEzD+FpXadlEhLaOzbiuy+jgMC3tbUyVorzU38ybt4zwbF/rVZX/zic+wVRtK1t7D93oPWCV2vC0BVAppZRSFyiXy3R1dV3b8GfAe9LBPeUQ7Lf4txncU4LXtf4+npjaSap3vQzGxR1qx2y+gkle1lgilWTTtg7iyQRjQyM0tbUgIjzT24axQmOixEwluuLy2weOc9sbT/APD32E+1798irWXCm1EtoCqJRSSqlFcrkcfX1913Ss39xkL26P4N9ncE4KTvbGCH6mbgKTmcZGyuD5uINbcLJ1S+5rvSr+7hMEW3qhFMfrvAV/39HrW+EFGpqbSKVrCEyACQJKhRLZqWlMECzaTxyhJpOhtqGeWDyGX/UZHRymvqkB13WpBA7P9bayv3mSF85eXevfdz3xCSqROJ5f1clflLoBaABUSiml1LxyuUxvb++1D38vCO4pB/9Og9N9Y4z3M4k8/t43MM2zM50aASsEm/vwTt2K27cNQbCxEkHjCKZlOOzmKQa3dxvWSjjL53LW9lttIrS0t5GuzVDMFwCIRKKkWtPUNzcyk83hV6og4EUi1GTSOI5DuVRmbGiEWDw23/IHcHCgiXw1wp7GaY6ONqy4WjW5MR586TO88KYP8vDzf70qh6qUujoaAJVSSikFhBO+9Pf3X9vwZ2Zb/k45+PsNzoDgTKx9+PO39ODvPQ5GcLv2YKMlbLKAjZYRx+LfcoygZQgilXBpB4BiAvdsB1JO4G/pgURpTeruOA5tHZtJpJKMj4ySqqkhnkxgjKFSKuP7/nzgs9ZijSGfzVEulUmla2hsbZ4PfgDWwlNnNtFWU+DYSP3KK2Yt3//V/0rELzPctoe0BkClbggaAJVSSikFwPj4OIVC4dq9QDA75q9Hwpa/gXC9v7Xmt/fh7zuGM9aMTNUTbO9eNGunBSSfxCbzyEwadzTsEmlqJ8Oun2vR4jdrbvKWeDLB6MAQtY0NRGPheD3HcYjGY0SJEQQBhZl8GPQEkjUp0nW1S5bZNZFhIJfie/b28JWT21Zct3c8+Se8/Zm/4Jvf+S95+Nm/WnE5SqnVpQFQKaWUUpRKJYaHh6/dC1Qh8i0HZ2B2zF+X4EyuffgLmofwb30dGW/EViKY3SeX3M+mClCNYGuyBI1j17mWF9e6pT0Mf4PDNDQ34UWW/mjnui7JmtSyynzyzCaSkSoj+TgrXbX+9iPf4EOf/08cuv09tIx00TbStaJylFKr78YYba2UUkqpNTU0NHTtun6WIfKIgwxC9X6De3ztw591ffyOHqoHDiHZWiSfxrYPXPpJkSrEKtengsvQsnkTqXQN48Mj1Dc1XDT8XYmne9p4fbiRhzqGeXWoeUVltA6f4if+7J9zdvNtjDVt586j37zqeimlVo+2ACqllFI3uUKhwMzMzLUpvDgb/qbBf9DgveogxbULf1YC/J2dBFvOQMRHpupxxpoIdp9aszpdqXgyQfOmVqKxGBMjY2Tq6ohEV75Mw5yDg4184Y0d3NY8wUg+TiVwr7gMMYYf/eufIXAjPPfAh/nQ537pquullFpdGgCVUkqpm9zIyMi1KTiAyGMOkgX/LQbvJQcpr23Ln7/3DYKOXpyRVmSqAdMyuG7CXywRp66hnpraDNVKlZGBIWob6ojGYwD4gfBIZwenJ9O01RRpT+fZ1zRFU6p8yXKNhcNDjfzfw3vYXpcjEyvz/NlNK6rjw8/9FXu7nudTP/DrfODvflUXfVfqBiTXdKavG8OGP0CllFJqpQqFAt3d3atf8Nw6fycdqg8ZvJcFqaxtHAja+qkeOIx7ZjumYRybzq1pfS7F9TziiTiu5+J6HqmaGmKJOCYwTE9O4jgOmfq6+dk7+7NJ/uq1PQzmUmxOzzBejFPyPQTLPZvGeOeuftrTiyf4mSpFebm/mef6WhkvxmlP57m9ZZxHu7auqM6100P8yq8+SN/mAwRuhP0nnrrqv8OKRKMwPAx1dWvx6pp51Q1PWwCVUkqpm9i1av1zj4bhz7/d4B5d+/BnUjmqt76OTNZjYqUbMvxFYzFqatMka2qIzbbqzSmXyowPj+J6Lpn6Olz3XPfMl/qb+dTru0hGfD50WydPnWnHE8OOuiyeY3h9pIFXBptpTRXoqJ2hIVHmxFgdZ6bTAOysz/Lw1kF6szU82tWx4vr/k8/+IpFqmVfu/T5++NO/sOJylFLXlrYAKqWUUjch3/eZmJi4+gBYBJkB20TY9lEB9xXBPSaYrUAAbv8ajvnDYppGqN5yDByD27uDYM/xNavP+RzXJV2bJl1bSywRx1pLqVCkWCggInieh+O6eJ5HNB5btF4fwFM9bXzhjZ3sbphmb8MUj3Z1ENjFc/zFXZ+tdTkEYSCXJFeJsiUzw63Nk7hYXhtpZCC3vBlCL+aeg1/mp/70n/Hl7/55vvOpPyWdn7iq8q6KtgAqdUkaAJVSSqmbSLVaZXR0lMnJyaue9VNGIfJoOK7Ppixmm8U5I5AHs89iA/BOrc2E49arYGqn8Ld3Y+snkEISt2cX/r4ja7pu35xEKkmmvo5UugYRoVwskc/N4LgOqXTNZSd1MRa+0bmFRzq3cqBlglSkwov9bZd93Zjn057OU/E9+nNJViOvpHOj/JdfewsTdZsZatnJ/Qe/dNVlXhUNgEpdknYBVUoppW4CQRAwNjbG2NjYqiz3IAMQ+aYDcfD3GJwxwTku2AwEDxu8ww6Su/6fhf1NfQTbu7GpfLihEsU7dQtBTRb/1tfX/ON5TW2GusYGYvEYge8zPTFFEBhqMikaWpou+3xj4dBgI492dTA0k+TeTaP4RpYV/gDKvsfpyaUXgF8Ra/mRT/088VKOp9/6Y3zkb3929cpWSl0TGgCVUkqpDaparZLNZpmZmSGfz2OMWZVynV7wHnPCsLfX4r0kCIJNWoiD94yDXOekZbH4u04S7OhCpmtxe3aCdbDxPP6uU+CszrGv1MKlGyrlMmNDI0TjMTL1tTjOha2k1oJvBM+xiMDQTIKDg028MtDEWCFBa6rAB249zetDDXSuZqC7Qm9+5Qvce+grfOn9/4Hv+8p/Xet8rZRaBu0CqpRSSm1A1WqV7u5uqtXqqpYrQxD5ewdbB8E2g3fw+oe981kJqO5/HbNpAKd/C9YJsJsG17ROc7xIhPrmRjJ1tVQrVabGJ0ila0ikkovG802VonROZOiZTDOQSzI4k6RQjYRlOAbfOAiWXQ1Zbm+Z4OR4LUdH61nLJs1YKcdv/PKbGG/YwkT9Zu499NU1q8si2gVUqUvSFkCllFJqgwmCgDNnzqx++BuHyDccbAqC7YbIK1e+UPhqs16Vyp2vYOsncLv2YOomsI3ja10t0nW1pOsyJJJJrLVMjo0j4tDU1oKIMFWKcnK8ls6JDF0TGcYKCQCibsCmmgK3t0wQ8wxVI1grpKMVAiu8PtzIF4/vWOOjC737sT8mkxvly//oF/jI3/7cWldHKbVM2gKolFJKbSDWWnp7e8nlVneZAxkOF3VHwL/T4j0va97yZ+IFqne9jE3m8Y7fRrClD5uZXtM6uZ5H6+ZNJFJJKuUyM9nwfcjU1VEhxquz3Ti7ZrttJjyfnQ1ZNqfzWGAol6Q3W8N0KcqN3JiUzo3yG//fvRzb9x20jnSxefDGmVlVWwCVujRtAVRKKaU2kLGxsdUNfz64BwX3iEASqncbIs+ufbdPk56ictfL4Bi8Y3fg7z4B8dKa1ilZk6KlvQ1xHEYHh0il09Q3NVI1Lk/0bOKx7s0UfY+WVJF37exDBM5mU5yeTHN0pGFN636l3v/IbxOplji15yHuOfy1ta6OUuoKaABUSimlNghrLePjq9T9MQCnW3APC860EOw12Chh+LNrG/6CpmGqtx9CKlHcrlvwbz0CbrAqZSeSSeqbG3Ech0qlQqVcJjeVJfD9iz4nEo3Q2NJMKpOmXCozPTFOpqmFnul63uir5+BgE9lylFubJtnXPMXhoQa+1b0Fu04bi5rGenj7M3/Bc/d/mHd964/WujpKqSukAVAppZTaILLZLL7vIyJXtdSDc1LwXhGkIJg6S/UtAe5RB3dq7QOLv7kXf98RJJfBGd60aks7RKJRmtpaSNak8KtVKuUK8USCdG2G+sZGJkZHmZ6Ymt8/GosRT8aJJxLU1GawxjIxMgZehCOlu/j7J7dSDlxcMexumObdu/p4/mwrX3zjxhi/dzW+92u/hXE8xpu20jA1sNbVUUpdIQ2ASiml1AYx1/p3VeHvuBB51sE0W/w7AtxeB+8G6PIJEDSO4u87gjPeBIUUwZ4Tq1JuIpWkbUs7FhgdGufI9FacWJqOTI6W2CQR19DU1kqmvh6RcGbPuRk8A98nNzVNteIzE93CZ4/tpS9bw76mSXY3ZBmeiXNqoo4Tx+pXpa5rrXmkm/u//Tke/45/wbse+59rXR2l1AqsiwAoIj8O/PkSD/2UtfYT17k6Siml1A2nWCxSKBSuqgynU/CeFYItFhuzRF5Y+1k+55h4geqBQ0i+BgopzNYzq1JuTW2GlvY2KuUKT3XV8/TQ/eQq0fnHXTG8ffsA797WidiAarVKkJvBGIsjQjQeoxJp4tG+7Xy7v5maaJUP7u/i5f5mvnpy26rU8Uby3Y/+Lr4XpRxNkipMrXV1lFIrsC4C4ALvAIoL7nevVUWUUkqptVQoFDh79iyxWIzW1tarHvvn9IL3tGDbwKYs3okLFydfK9YJqN5xELC4fdvx9x25qvISySSJmiSJZJJ4MkFupsQnD95NX74+7K7Z2sex0QY8J6ASeDx+egvHx+r54dtPEXUDJioJJooxJooxxgpxjsxO4PK2bYN4juGLx3cQmBvn77damsbO8OBLn+aph3+c73z6T9e6OkqpFVpvAfDb1tqZta6EUkoptZampqbo7+/HWkulUrn6WT+L4D3tYOvBNFm8IzdOeLFYqrcewWayeMcO4N/yxlWN+atvaqShpQlrLeViibNDBf7X0YcomSgfOtDJ832tfP6NXYufEy8xVYryP567a9F2Vwx18Qr3bBqlJVXiiZ528pXIyit3g3vfN34P43hUY0mSxexaV0cptULrLQAqpZRSG5K1llKpRKlUora2FsdZOoSNjo4yPDy8ii8M3jMO+BDss3jPrf1Yv4X8XScxm/pxu3cRbO++qtk+M/V1NLQ0MTZR5PXRJobL23llqI1kxOcDt5zm88d2Ugku7PY6WYrjiOFAyzhtNUWMFYq+w0w5yngxzqGhpiWft5E0jPfx0At/y7MP/DBv/Ye/WOvqKKWuwnoLgF0i0gh0Ab9jrf3kWldIKaWUWi5jDJVKBcdxcF2XarVKPp+fvwVBGG5yuRxbt2694Pn5fH51wx/huD+3V/DvNeHMnzfAZC9z/M29BDu6cPq3ENRPQHLlYxzTdRmaN7UyMAH//aV3YKwQ93x2N0yzp3GaT72++5LLMhjrcGSkkSMjK67Cuvbeb/4+AOVYkmRpFdeZVEpdd+slAA4CHwNeAlzgw8AnRCRprf3d83cWkZ8AfgLg13/913nooYcA2LlzJ+l0msOHDwPQ2NjIbbfdxtNPPw2A53k8/PDDHDx4kGw27Npw3333MTw8TF9fHwB79uwhFotx5Eg4/qClpYW9e/fyzDPPABCLxXjwwQd5+eWXmZkJe6vef//9nD17lv7+fgBuueUWXNfl2LFjALS1tbFjxw6ef/55ABKJBPfffz8vvvgixWI45PHBBx/k9OnTDA0NAbB//36CIODEiXAGtM2bN7NlyxZefPFFAGpqarjvvvt4/vnnKZfLADz88MOcPHmSkZHw/14HDhygXC5z6tQpADo6OmhtbeXll18GIJPJcM899/DMM8/gz65/9La3vY2jR4/OjzW58847yeVydHeHwzG3b99OQ0MDBw8eBKC+vp4777yTp556CmstIsLb3/52Dh8+zOTkJAD33HMPExMT9PT06Puk75O+T/o+bcj36e67755/T5Zq2TPGYK1lenqaVCrF5OQk7e3t839jz/N46KGH6O7uJggChoeHaW5uplAozP/N6+rqcF13vp6JRIL6+noGBgbmX7e9vZ3h4WFMzhCdidDoNOIeEop1JQYiw7TUNCJJYWTnGADpsRrqB2rpvSN8HyNlj44jm+k70E81Fv59tr62mcn2aXJNYT1aupuwjmV0e1iPzEia2pE0fQfCekSLUbYc28SZO84SRMLAu/1QB6Nbx8k3hAGvtbMZ0zqA13GScqWGSgoStTMM+1vCMqRCizvCgL8ZMxvaNrtnGTdNSDRBY9qlxi1gxcONJahLuUQ8YTQLv/3tB9iUmGBP3RDDlXaapIfxceGh5sM8O3oXB2pPURcNj+Xw5F7qo1m2psJzsCffzkw1yYG6TgAmKhnemN7JW5oPARBYh+fH7uTOuhOkI+GxvDqxj+b4BFuS4TnYNbOFchBlf214/oyV6ziV28qDTa8BUDERXho/wN31b5DywoXtX57Yz+bECJsS4ftyKreVwDrsy4Tn1HCpgTP5Tby58SgApSDGyxP7ua/hGHE3/Lfw0vhtbEsN0hqfAOB4djuuGPakewEYLDbRX2zhvobw32jej/Pq5K28ufEIdZMDvO35/8sLb/5B2rZWePJ//A8ADvzZn1Gur+fUBz4AQMcTT9B68CAv/+zPhu99by/3/MEf8Myv/Rp+PA7A2z76UY7++I8zvn8/AHd+8pPktmyh+/3vD8+Fb3yDhhMnOPhv/g0A9adOcecnP8lT/+2/YR0HMYa3f/SjHP7Jn2Ryzx4A7vn4x5m45RZ63v1uAHY++ijpbJbDh8L35Xpe9xKJBErd6ORqpopeSyLyaeCdQLO11lxi1/V5gEoppTaEkZGR+QC60NwyAhf7/3BbWxtNTU3z94eGhhgbG1t5RSrg9AjuScEZPtfSZVMW/3ZL5IUbY9yfFYO/5zjB1h5ksgHJpZc942dtQx0NLc2LQna1WiU/U+T0VB1/ffJN7KjPURcv81J/67U6hA3nn3zmP/Ad//DnfPO7/hXv/eYfrHV1Li8aheFhqKtbi1e/cZrQlbqI9dICuJTPAT8EbEdnA1VKKXUDymazS4Y/uPxafePj4/MBsFAorCj8OZ2C2yXIFDADgmDSFv8uA4FFSoLkBO+ltfvMarGY+nFsKo+NljENY9i6KdzebZhIeVnhz4tEaN7USrImxciU4Rtd7VRsjMAKk6Ukg/kajHXYWZ+lJVXkmd5N1+HINoba6SHe9uxf8sKbPsjDz/3VWldHKbUK1nMAnKMtfEoppW445XKZs2fPrvj51WqVarWKiNDX14eILH+BdwvuK4J32MFkLKbZYrcDMYszKLiHBbFr3+IXNIzh7zqBrZ0ON1igEsM7vp9gcx82femxZiJCXWMDdU0NWAuPn6zj77pupTZWIeaF3UszsSpvbRwiEQkYnolp+LtC7/nWH+IYn8mGLdTkJ9e6OkqpVbCeA+APAGPA6qwEq5RSSl0hYwyDg4OUSiW2bNlCLBYDoFKp0Nvbu/zAdhFDQ0NUKhWq1eoVVAq8ZwT3lEOwx2B9cHsEsTdOzzSTmcLffQLTMA6lON6JWzE1OWy0DNEK/p4Tl53tM1mToqmthUg0ysC45f8cvYPRQor37e5jIJekazIDQK4cpXOi9noc1oaTzo7wtn/4C1669x/z1mf/cq2ro5RaJesiAIrI5wkngHmNcBKYD83efvoy4/+UUkopAIIgQEQuurzCSsrr7e0ln88D0NnZSUdHB9Za+vv7Mebq//c0PT19xc9xXwzDn3+nwRkS3OEbKPilcuGyDi3DUIninroFGyvi7zkOzvLCshfxaGprIZVOUygFfPbVDl4c6mBrbY4f2N/N109tZaYSvcZHcnN492P/k4hfZmjTXh789mfXujpKqVWyLiaBEZHfIGzx6yAcXHsM+D1r7XI6o9/4B6iUUuqayufznDlzBtd12blzJ5HI1S3WXa1WOXPmDKVSacnHr6i75iqSYYh81cHcYpERwZm8McKfSRTwd57CtPVD4OH2bsc6AWZLL3jLX9cvGovSvn0rILxwppbPn7yNdKzKd27v5+hoAyfH667ZMdxsanJj/OZ/vpvDt7+X3V0v0DA1sNZVWj6dBEapS1oXLYDW2l8Cfmmt66GUUmr9yWaz9PX1Ya3FGENnZye7d+9ecQgMguCS4Q8uP8HLqsiDd0gwmy1mOxDMLuieBOuBewOEP4sl6OgJW/is4PZtxxqHYOvpKwp+AJFohE1bOygHHr/3wh1MFOO8Y0c/2XKELx3fcck1/NSVe9fjf0ykWqS343be/MoX1ro6SqlVtC5aAK/Shj9ApZRS50xPT8/PvOm6LoXChYuHu67Lnj178Lwr+x7UWktvby+53CovhG1ApkAmBMlBcKuF+MUqAc5xwfu2INUw9AQ7DTYJ3hGH6kMB3vPOmo/5s05A9dYjmE39OCMtSLaWYGsPRK9gPOMs1/PYvH0rgUT5nefvpD2dZ1NNgcd7NlPy18V32etKMj/Jb33sTo7e+g629r1G8/g6m25BWwCVuiS9aiqllNow5lr7LicIArq6uti7d+/8enyXY61lcHDw6sNfCWQCnAlBJkDGw2UaxCyoR8UQ3L/E95cGvMcc3F7BtFn8bQanE5zT4SQvwXaDe+L6hT+LJdh6GpvM44y34Iw3gVclaB4h2HIGm87hdu/G1E5idp9a0Wt4EY/2bR1YJ8LHX7ydXfXTDOeTvDrUvMpHo+a884lPEC/n6dr1Ju499JW1ro5SapVpC6BSSqkbnrWWQqGAMQZjDEEQEAQBvu8TiURIJBL4vr+s8LdQJpNh69atl91vZmaGoaGhS3b7vKRgdhH2N85bhD1hsQ1g0xbrAFVwBwSqUPmwueBrWveg4L3q4N9jcAYFZ3B2MfmYxbRabD14h6/P8g42VqRy4DC2fgICN5y1M3DAnZ38ppjAO72bYHsXNnlhK+xyeJEI7ds7sBLhD1+6neZUgZlylOPj9at4JGqh7WcO8nO/930c2/d2Ng2dom2kc62rdOW0BVCpS9IWQKWUUtecMQYRWXZr20JX0u3ySidfyWazjI+P09jYuOTjQRBw9uxZcrnciuoOIIMQecJBioJNW/w7DAhIAWRakCFw+i8MbU6nYPadOxY5C+6rQrDL4PQJzui5+khZcHsFeldUxSsWNI5Sve0QOAbvxH6C5qGwHn4EKaTAOJi6cfx9R5Y9u+f5ovEYmzq24OPxBy/cSXOyQMV3NfxdQ21DJ/npP/oQuZpGunfdz92vPbLWVVJKXQMaAJVSSl2xmZkZJiYmaGtrIxq9+JT7xWKRsbExpqencRyHaDRKKpWitbV1fjkGay1nz54lGo3S2tp6QRnj4+PL7na5kl4tg4ODJJNJEonEou3VapWenh7K5fKKy5ZBiHzDwabAvzPAOevgvn75NfmsZ3GPCeYWG7YnzEDkSQdbDzYK7ujaNDLMT+qy9w1kJo17dls4wYvnzz6+OuqbGqlvbqRQcfj9l+5kayZHyfd4fWTpoK4WcwIfx/i4gU85loJlfHnRMN7Hv/vDH8SKw9ff+zN85G9/7jrUVCm1FjQAKqWUWjZrLRMTEwwODgKQy+XYvXv3/ALoc3zfp7+/f1FwM8ZQKpUolUpUKhW2bdsGhAFvbr0713Vpamqaf06hUGBoaGhV6i79QAJsw4WPdXd3LzqOUqnEmTNnlrUAu4yBzIDZxqLOXwvDX7DP4L3gIMvtHeaDMxm2DhKByGMOGAj2W7xn1yj8icG/5SjBlj6ckRYoJcMWvquoTiwRJ5VOk6pJgUDgB7iuSzQe443hJH/5+m3c3TbGWCHOCW35uyTXr3DfwS/xzic+wfbew/Pbh5t38s13/BTPPfBhqtFzX3Ikiln2nXiKW088ze6uF9k8cIxyNMWnfug3+ad/8zM4usyyUhuWjgFUSim1LNVqleHhYaamphZtF5FF4Smfz9PX14fv+5csb8uWLSQSCTo7Oxe1ru3atYtEIjEfwnzfv7plFQJwXxK8Yw7Ws1TfabCbL9xNRNi+fTtTU1NMTk5evjtpEI7Jm2vRC/YY/IcsOOC+LrivCrZmNvy9eAXhb5Z1LDYDkgNi4N87GyKr1z8AWiegevurmOYR3DM7MKkctmlsRWXFkwlS6RpSmTSRSARjLSPTDhXfIRH1ccTha507eX2kkXfvPsuxkXq6p2pX+Yg2lrsPfYUf/swvUDc9zFDLbl478G48U8Exhl1dL9LRf5RcqoHhll0EXpRIpci2vsO4JqAUS9G9/V56O+4kX1PP+x/5HeLlmbU+pKujYwCVuiQNgEoppS4qCAJKpRKTk5MXBL/zRSIRotEo+Xx+2WPxEokExWJx0TbHcUgmk8zMrPBDaB6cMeav/u5rDs6oEOwzyFlBCuB/h8HsWFnxEC7Z4D3u4EwKwW4DJXDOCna2kcqZFIKtFtNs8F658vAHYMUiVjCtlqBjtpw1WNrBuj7VO17BNI7jndxH0N6PrbmymVATqWTY0pepwfM8AmPpn4zw8lArLw1spnjeUg77GifZ1zzFo50dFzymznECnw98+dd477c+zpmOO3n+gQ+x/+jjHHjjMZzZf38WOLv5NnLpJrAWN6gCQs+2uykn0rT3H2Nv5/NkcqNreiyrSgOgUpekAVAppdQipVKJ8fFxZmZmltUF8kYgU+B0CU6v4Ews/vxlIxb/zQb3qINMgU2FE7D4b7OY3SsY1zcKkUcdkLBVzj0WBkGbsBAAHvh3GdyTDs7Y1X0WDNoNNgbe6eszs+f5THqa6r4j2Mx0ONnL1p4LZ/QUQQSsufBvGY3HaG5rJZ5M4AfQNZ7khYF2jo404hthZ32WPY3T5CseZ7M1RFxDwvOZKMY5M52+Tke5PrUOd/KRv/1Z9p16hqfe8mO4fpmHX/zUZZ9ngcDxcI2/cZOKBkClLkkDoFJK3WSstYyPj+N5Hul0Gtd1CYKAXC7H5OQk+Xx+ft8rnVXzmvIBZ/ZWBpkMF053u8OlFaxYbDOYTRashZnwc5i44RIMC7tOmpQNQ+A7Debyq0DMk36IfMuBOPh3WrznZdH6fdaxmHZw+lnzhdivhknk8XefwLQOQSWC13kL/s5OiC9eBiOVrqGxrRXXdSnkcmSnpgmCgGgsRiKZJF2XoVR1+LsTO3h5oJnaeIUDLZMkPJ/RfIyuyVomSxdb8V4tJZ0d4Xse+e+87Zn/QzUS5wvf+zHe8sLfsK3vtbWu2o1DA6BSl6QBUCmlbiLWWgYGBpicnJzfFo/HKZfLVx70DGEYu+yLhgufYwEXbBKIXeY55z3fe0Fwj83OGjrbNXK+GhmL2WXn19pzsstc2D1hoQLV9xps22XqP0S4hl+PYOsg2LuycX03OiuGYOtp/J2nwAru2W1YMZgtvefW9yNcnL2prY1UOkV/NkHXRC1v3jxMPHLuHAoMPNfbwtdPbeWO1gkakxVOjNXSNZnBbrC/2/UQLed51+N/zHu/+QdEqiWeefAj5GqbecfjnyRZurIuuRueBkClLkkDoFJK3SSstQwODjIxMXF1BeXB+3YYhvy3WMyei1xmq+Fadu5RwZle0ErmWarvN9i5yT4NeP8g2CQE99oLQqV7WPBedgh2GmwUxIRLIeACRXCmBRnmyidZwUIcCKD63Qvqs4AMg/e8gzMu2Kgl2G3BDSd52Wjhz6SyVG97DZvJ4oy0ItO1BNu7IbJ4Mp+aTJrGtjYCPL52aitnJ6PUJQ2nJmrZUZ9DgMGZJKP5OHsbptjfMsXjpzczVbqS1K/mpLMj3P/y53nPtz5O3fQwr97xPrp3vIl3PvEJarMja129G5MGQKUuSQOgUkptcNba+fX4stnsyguqhK1g7iEBCzYTTnbiv8kQ3G7Pfeyx4Xg870VBSoJptAQ7LJK1IILbI9g4VL/fgAfuy4J3OEx9QYfF/04DkbAop1uIPOEQbDdIRXAGVvez1XwItLMhcG6JiFIYct2TDjZl8fdbZArc04L4G+vzncUSbO7D33sMfA+vew9B+1ls7fSi/cRxaGprJVOXoXuyhi8e7eDBjlG+PdhK92QtESdgW12OjtpCeM5VPfpzKc5ma9boyNavWCnH3Ye/zv3f/hz7jz+JYw1dO97Ei2/+IN/55J+yafjkWlfxxqYBUKlL0gColFIbVBAETE5OMjExQaVSufICDFAFKYFzSnDfEKQiBB0W027xDgk2Ck5Owta5ZrAJcE+GQc00WYI9BrfbWdRCZ7EIQnCrwXRYIt9wCfYYZBpkNJxJ07ZYJBeug2cbwdRZvJPXZiIUi53vkuo/bHD6BKdL5tfdw4B7/PKLt69HNlKhestRTNsgMt6IM9lIsLMTnMVrwEWiUZq3bCEWjfBIZwdjOaE+BU/0bMZswL/L9RKtFKifHKB+sp+GyX7qpwbYPPAGd7z+KLFqkfGGDl6++3spJ2o4cORb7Ox5Za2rvD5oAFTqkjQAKqXUBlEul6lWq/i+T6lUYmJiAmPCD/LLnsylCG6n4HQunk3TYjHbwLQZ3M6wS+QcU2ORIkgwG/AiFv8uizMkuH1LfxayUYtUBBux2DSYZot3wsHGbHjVFrA1YOstpsYSOeSu/A+zDHMhUMqC9Sxmh8XUg3dEkMLG+zxnXT8c67ftNDgB7undmNrJJdf2q8mkady0iZIf4a9f28ndrSM82beVfm3ZWxE3qHL/S5/lPd/6OO1DF7bkTWdaOHz7exlr2s6ek8+w/8RTuCZYg5quYxoAlbokDYBKKbXOVSoVhoaGlt29U6bAe8qBCATbLXaTRYYl7N44GM5eaZospnX28inhuD2vy0FyFwl0CYutDYMdsQtn3VzyObGwdc1/s8V79tyYOutabCPIDFC48rF9K2WxBLvC+rtdgpQ3zuc4KwbTMIapm8Sms5jaKYhUcUZbcCYa8bd1Q7w8v38sESddmyGZzhCJuPRM1fC1N1q5a3OWL76xk6q5toF8I0oUszz0wt/yzsf/mKaJPvo238bRW78TEYiWC8RLM8SLWWpzo7QPHidezl++ULU0DYBKXZIGQKWUWmeMMZTLZUqlEsVikcnJyWXP4CnDEPmGAw7YGIsmZzFpi9lmsTGLe8ZBxq5t+LJYzE6L07vxxtXdKExyhqDjDEHrAESrYAXJp5BsBidXR7C5b9Gi7smaFJnGJlKpOJVAODrSwKGhRmLkwY3xYv+lpktV5xNj2NHzCg98+zM8+OKniZfzdG2/j5fe/EHuOPwI+088pWnhWtAAqNQlaQBUSqkbnO/7ZLNZ8vk8pVKJcrl8+Sedrxi2ynkvCjYFwe0W74VwSQNbZzEZcAdnZ9PUMV3rnhWDv6OTYHsXWMEZa8HJ1hGkslA7jU3mF31MTdbUUNfcQiIRYaIY5YnT7YxlHXY15Znx4xwZbmC0kFi7A1pnOvpe5zv+4c+467VHyORGqbpRXr7n+zjbcTv3Hvw7Hct3rWkAVOqSNAAqpdQNyFpLNptlfHycQqGwskJK4J4QnO5z4/lMsyXYavBe2Xhr2KnZVtWmEfzdJ7A1MzhDm5BCDcHmMxBbPBGQiJBK15Cqb6ImFWU0H+cbne0Yv0JbreH5s22MF3WR9mWzlv3Hn+Q93/o4+48/RSma5Mht76Sv4w7qx/u4+/VHdNmG60UDoFKXpAFQKaVuINVqlVwux/j4+JW19FXDpRekDBiQLDinBQkE02IxbRbrWtxhZ9WXUlBLs1hsZpqg/SymYRSCCFKOIcUkzkQTzkQjEnir81quT9DRg7+5FxIlKMXxTu8m2NyLzYRjQ6PxGJFoFM/zcKMxUpkMUU8YK8R4rKsdCUrEYx7P97dRqEZWpV43A9ev8OZXvsC7v/VHbBk4xnSmlace/jFilTxvef5vqMlPrnUVbz4aAJW6JA2ASim1Rqy1VKtVisUihUKBmZmZK+/eacA5KXivLp6t0nrh+DqTAbd78YyeanVZt4rJZCFSwUaq2EQBm8xja7LYZBECB2eiCcRgo5Ww+6UXgBGkkAIrYBycbB3u4GYkW7vs1lmLxbQMUd17DOJlZKIBd6yFIDONbRkiVZsinq4nWZMg6p0rs+w7HBlp4PXBNI3xHLhxnulrp+SvTiDd6BrG+7j78Ne49cTT7O18lkRphoFNt/DMgx+hvf8I97/yRSL+CpZeUatDA6BSl6QBUCmlrgNrLZVKhXw+T6FQoFwuUy6X55dpmHPR5Rp8kHGQCcGZIJyNswiSD5cuMM2WYLfB6XeQKmBBJkAq+lnkWpmfYGXT2TDQzT8gSDGJ5GuQbAavoUxkSw6JBoBANYKpRPADH2N8rLVYYwhkBhyDzNQQOXwvTjF10de2ToBpHibY3IdpGEdyadyz2wi2nMGmc8STCdLN7WRSHlOlKMfH6uiZSFKuWhwxJCNVWmuqHBlr4cR4na7lB7QNnWTvqeeozY5Qmx3GAvlUPYVkHYVkHflkHZFqiYde/BS3Hn8Sx1pGmndwfM/DDLft5tY3nuS2N57QT/83Ag2ASl2SBkCllFohYwylUolSqUSlUsEYgzEGEcFxHBzHwfd9KpUKpVKJILhwLa9Lrs+XB/e04PTPLs+wYJ09mwGbAqIW02Bxex2cIf3csZRwCYRxzKYBMIIz1hx2v/SjV1ZOtISpn8DUTWDqJ7A1M2F5o21EvQSSqOBELE7SgbSLm/BIp1yiSzSqGQuFqodvHCKOIeYFiPUZ7MtTzo3gjDUTff2eC+sQqeDvOknQ1h+GznIMt28bJj1NfEeOWE2GaDJNOukwVYryeGcLCSeP9ZL0TqfpmcpQ1Fa+c6xlV/dLvPebf8Bdr//9/OZcTSMAycLUBWvwTdS188KbfpDAi3Dr8afYfuZVPONf12qry9AAqNQlaQBUSqnLMMbg++EHPGst+Xye6elp8vkrW6frsouxW2CGcAH1TkEGwmUYTK3FbLLYpEXygpMTZIrrukbeuSqG49pM4ygmNYPb34E72XRd63A5JpXDNA9jEyVIlDC1U1i3guM4WMAaE7aQZmtxxptxx5uQ6foL/pYWGwa+1gFMwzg2OTsZj+8iU/V4QQ2RHXGaOyzeecviFaouY/k4fdkU4zMuQRAgMvd+WaJuQMS1+DYStgBaSMSEt2wbZXp0mOz4BNHn34ZTqJmvS9Deh7/7BHg+zlA7UqjB1E6Q3OVT09hMTULwjdA1keH4aJqImSFnMrzQ346xzrX/w68X1tIy2s19B7/E/d/+PO1DJ5hJ1fP0W34Uxxrahk5Smx0lUZwmn6gjX9NAOZbEiEs1EidZmOaOI49q6LuRaQBU6pI0ACql1BKMMczMzDA9PU02m132OnvLYoHybPfNXNitUyYFZxQkP9vKV2MJds5O3NLvIKPXbnkGK7NdE+fKdwy4fjieLVaGaAmTzmEbpjCpLLjhB1/HcTDG4OTqcU/txplsRBYEDTt7+V3tkGrqJqF5EtwgrGvFw+Zj4WM7zxAkpufr53oe0VgUSTZwcrqDqGe5pW4AU86RzxWolIphoZUo7kgbznQdNl7EJAuYhjGIlSFw8Uq1OF4KpzZGvCFCusYS9Sxl3+HVwQbGsoJFsIBLQDpWpWRiDBUy9GXTjOYT2Mv+HSx3tY7w/bd0M3HmBM7gJqJH78Jiqd7+KqZ1CJmsxx3fRHT/IF59DTXpJIkonM0meam3Ds9WqKsxdE83cnSkgUqgC7YDtA8c57ue+ATb+g7TOtxFvBJ+edO54028fuDd1E2e5aEXP0OsWlzjmqpVoQFQqUvSAKiU2vDmxt+VSiV8PxxzNdddc+73uZ9BEFAul+db/OZcsvVuNtBRJRx/V13wuxUQG44LGwdnOPw5150zfLrFpsE22nBdPmNxxh2cgdUPffOtWh1nMZkcNloEp7qs50bjMWLxOG6shqFKK4VqhB2JHmYmxsLurdbBzdVB1cPWzGCixfDYrYB18YoZnPEm6G/FyacvrJvrY2MlSBWRmiKkihAJsIMNyGgTRCr49x4hSIxdtI5exCOWbuR0cQf5SoTAWILAZ3Mmx8GxbZyaqGdH3TQd6SxNNT77m8YxpSzTU3lKMzMg4ZhMCaI4bopUYw2NzXEcJwy206UIwzNJRvNRZkqWhMww5TfwzNkOyqsUtt69q5f7MweZHp8i+uzbCbadJug4Q+TsTmL7LPXtUaIeFKsuJ8Zr6RqNsqtukucGd3BivJ6b+fOnmIC66SEy2RESxSypwhT3v/w57j78dUrRJKd2P8BEw1byqXoscNdrj7B58PhaV1utNg2ASl2SBkCl1IZgraVcLlOpVPB9H9/3KZfL8+PzrvRad7mxeU6fhAFtWpAsiH/5/+dbx2IbwTRZmBuG5VukLGE5U4uD4UpZLLgBNlqGaAUbLYfdFzN5/KZRcEuI4xCPx/GiERwvgsBs+xVYcQisi0+EUhCnZBLMlBMUyxbHrZCJVTg53c4rw23sqZ9gf/M4O1ODuH6WYr6AMZZoLBq2vImDtZYgCCgVilQr4cyIXrkZ99u3QymG3dtDdVs31l5qBtTw7yICNtlG9/Q2jHUwFiIRSyLqE3GrjE2naKud4vXprXRONjBT8S7a/THu+uypn2BLZoaO+hJ7GyawgY8X8XAch0LVpWsiw0jWxfd9Ym6A6zoMFWoZyGcYyceZqVzZOMLliLkBH3v4eUZ6OpFCApssEKluYsvtGSxwaKiRM2MetbEigSQ4NNJGf+7iE8ZsVOncKAeOfouOs0doHzxO23AnddODF4zZKyRqefLhH0ewvO3ZvyRVmFqbCqvrRwOgUpekAVAptS4ZY6hUKpTLZWZmZsjlche02i102fF356uAjIHTKzh9gpQIQ5ss6KaZspj6sLsmEcKrjSGc4SOQcJyZCbfPNQQ6owIzy+sWad0qNlHExothgEuWw9ewgBEIPPBdSJXwa/PYRAGcCkgFkQuPVRwhnkjgR5vpn2rBSIVkwqfke+T9BI4ERCQAEaqBR9m4jBXTDBYyTJVj5Cuzf4ALa8quukn2NYwScS3WB2t9YhEfi0MYLcMyJfDpSJzFKQ0hCEIEY8t40Tg5aaEcxCn7ccrVCNVqBFzIJGdojo/h4tM5tof2ukn+9sy9ZMvnwlfECahPVChWXXIrCGU1kQp3tgzREC8g1ifqBMS8gLP5Jl4fa2GiGLvIsV8b33fLaQ7EXyU3NY3rN7Ht9gZeHWykbxRaaw2P9uxkYi0XabeW5tHTbBk4ypb+Y0zVbeKZBz+Cda7tWEMn8HnL83/NAy99lt3dL+JYQyUSZ7B1D4Nte8mnGqhG47g2IFrM4/ll3MDnrtcfIV6+sjG7ah3TAKjUJWkAVEpdN3Pr3pVKJarVKtVqFWMMkUiEaDQ634pXLpcJguCC7pnhZBl2/v75LhryyuHC6PNLJ/ic66Y5/7uEv1dAZs4tn2Adi2knnIDFBwzY2rCVzR2eHZu3glY7KyZcNy5ewqTzBJkiQaoA8RISKYJTQuTCWUMvxotEiEQjeJ6HuC5WopRMjFIQo1SNU6pEqQbg2RIkPL7UfYBCdfVbr5Yj4VV4x+ZT3JI4jtgqg+Wd+JMOJ6Md9M7Uki1H8c25ICFY2mryHGga5uh4CwO5C7uPbjSpaJWPvfVFshM5WlozvNzfSIM3zidfv5/yGs/iuWnwOD/8mV9g38lnFm0/vudh/vxH/4iJhi3X5HX3dD7Phz/zC3T0H2Vg0y0cPvAeqtE423oO0T50nMaJPpyN/5lGLYcGQKUuaV0EQBHZD3wceBCYAv438F+stcv5dHTjH6BS69TCsXRzM2XO3RaOqZsLe0ute7dSIjJfB4qEE6iMSRj2fKAiYZDLLr0WnpXZVjsPrEf4ewRsBEhYbDS8LzOEyzDMLrJuseD5mHgJky4TJCoE8SpE/HCcn3HCvTwz2w2zikTLiFdBnCoiFcCHJS5f4giRSAQvEnbLrJKgHCQoVROUq1H8AHAFZK790CBYfN9BjE8yWaZIlGrgUrERxko1dE41MVZMLgpUN4qmRJ63NXUx4DfywmA7+rlpsQ/d1slDW0d4daCeWmecPzz0EMEazubpVUt8/1f+K9/1xCcox2v41nf8JIlSlrqpIVpHumgd6cQ6Ln//rp/mpft+gLGmbZcsT4yhbnqQpvFeGib6GG3ewelt92Cdc2Mpk/lJ7nrt67zplS9x4I3HmajfzCPv/nfc8+qXueXUMxr41NI0ACp1STd8ABSReuAocAz4LWAX8NvA71pr/9MyirixD1CpG8DCsLYwyAVBsOj3uf18359vvbtSgmArYWijABKATQI1szvMgJMHqgIB4c2E+2GY3yYlkOzs+Lu5cHZ+qEuH3TRt1GIdA9UqAVWM+Fi3inFmCxYLzuwLOAZcA06AuAESKSNuBUcqQBWMz5VeVlzPxXU9cDyMRAhshMBE8YMIQRDF9yP4gYOIIRoJwLMUnQijlVq6s430ZmupBLp2280m5vk8vKWffbX9fPL1B9Y0xNdODfKv/uRH2XnmIM888GEmGjt4zzc+vmjWzFI0xVjTNrYMHAOgZ+tdTNW2YSX80sJI2B04WinQNH6GpvE+Iv7icZ/5RC1du+4nWi5QPzVA43gvnvEZb+jghfs+SKKU5e3P/MUF4/yUWkQDoFKXtB4C4C8CHwW2WWuzs9s+Cvwy0Da37RJu7ANUapnmgtfCbpCXu82Furlgt/Dnwu1XdB2YH4M22/XRJwxlVRvOhFkMw9lcF0unOruPH3bBvFiL3JWwYrERg6mpYFIVTE0eMhOQHkfcuXGAFqzB2gBWeJ0TERzXRdwIViIYwuAWBFGCIELgu1jjYIPZCVQcwTD7f38r4IBrDeJYqi7knQTTJslUNUH/TC1D+fSatuioG1/MDagaWbN1/Fy/wo6eg/zEn/2/JIpZPv3B3+Ddj/0Rm4ZPXfQ5A617GG3eQXpmHK9aRqyZbamziDVUI3HGGrcxVdtGMZHBwZCeHiGdG0XEoXn0NPlkLVN17UzXtjKTbmLPiWe45dSzuvaeWh4NgEpd0noIgE8DA9baf7Jg21bgDPC91tqvXKaIG/sA1Q1r7t/GwkB1/v3zw9ZSvy91u1g5S41zmxs3FwRX8Y23YUHrmZ0NbMwGMxv+Xpn7CRRtGOKKYeua4wviC+I7SLD8D6LWCcLWNDcIu0PGS9hEHpMohNP9R8tYJ0DKcZxyMqxqbPYxzwfHhC13czeZ+7n4dUQEx4tiJYadnV7TAta6WOthrIsxbvgh2gjGONjAwRo5F2Zng62Y2SlLXLCuUHGEEeroKjcwkK9lpnp9JwNRajW5foVEMUuyOE2yOE2iME2qMEXt9DB12SFqp4fJZEeozQ5TOz1MOj8BwHj9Fr7y/l/ghz73SyRLuWW9lgWK8TTWcTGOO78OomBJ5Sdx7NI9CKpejEokTrI4rf/S1MpoAFTqktZDn6J9wOMLN1hre0WkMPvY5QLgdTfXPW6hSwXtiz22kudcrrz5xyzz3ffmFmte9DQ7+x977vH5shfsd/5rWey5584y1mINWGvC8qwFM1eXucdm71s7Ww8bPs8azILAxMKirT3vpRbet4t2XfK3hZdoWeKnXMNruLXnQtnC3+dnkCTs5mgAP5y4xMnbcPbJahjGCJxzXSMt810l524yF/IqF19LzjLb9dENw5Wd7fo4F7isE0AkgISPcYNwAXA3wHqzP10/3OYZiBiI+OHYt0hYeSsCOICLxQsDGW74M/AwJgZicep9XHcGgMCPYaopTODMNeIhc38jwBphbm4UseHbJBYmvCSvFLfQNdM0+5pqnrV4fpl4OY/rh8swzC35IEtcLxZts/YS+y+x7Ur3xyLWhgF89vdzPznv/uy+4YVk/nGZve7Mbb9wH4NjDWICHGNm7wc4Jgh/n9tmFm9zbIAs+hnuJ8ace63ZOlkRjOMROC5eUCVWzhMr54mX88TKM8RLMyQL0ySLU8RLM7hBFdcEGMehFKuhFK/BOF54XXMcJus2M9y6i9HG7RSStRQTGcrRJGY2TBnHxYqLFUjPjFM/OUDt9BCpwhSJ4jTJYpZkYZpEafbn7LZYpXDR08R3o2QzzUxnWhhr3EbXjjdRjqfxvShiAn70r//tRUPbUgSWHRYXivjlC7qGKqWUWj3rIQDWE078cr7J2cduOCe+9gJ2V91aV2P9muti6BNO6FFh/sP/vKVnoj83Db89dwun4V/Y0iOL9zUL9jPnbT/vvtgltgfhWDWZG68WhHVfdH92X2ssiA2zpcw1N1lwbDgr5NxYNDds+ZK5FjB3rhXMnvs9Eo5rs47FSgBuWIaImS3LYOeSoA3A+lgT/iSoIkEVfB/xA6RSRSo+Uq3glKtIqYJTriDlMm6xFN4KBZxCEa9QJDIzg5vP483k8cqLP6jZJd4ce5Htq+3tV7DvUsFno3KMr2Om1kA5kqAcS1KOpSjHUuST9Yw2bacaiWMkDHGu8YlUS0QrxQWhN6Dj7GvcffirV/y+BY5LMVFLIZGhkKilkKwj29pMJZrAdyMYN0IlEg/r4LhEKwXipRmilSLJwhSZmTFqp4dpHzi+aHyfUuvKTXR9V2ol1kMAhKW7ccpFtiMiPwH8xOzdo0DpGtXrojY1t95b39x4vV92/bncNXpB8LpII9Z8ORc8bM/7ebnXW+5+y3rS+S2QF8uslsmZaerTtYueYzmXWWfbMjBWMHbBTxM2FoZPu949TqIgUUjXw8afkf+GUShMkkxe7Huvc2fbUmfDXMtV2IKzYF9rF39YknMl2NnHxYYtXmHLk4cV51wZ8y1vS9Vhbp+F8f+8580/vvhfi11wHOda/8zi582XKPP1trC4RXDRjnOtkwvqsuDaMf9PacFz545//m9o51ojDfNLLcrcFxwye52Sc/tWikhpxmICgwlM2Ka9DCIOXszzYzUSuN58YFz4fz7rOGEruw1bzdygihv4SLXIZDFrG+gLwtdc/hVN3XwmwG0Iv6rcMIJqNXijvv64XZtz/++tte9dg9dVatnWQwCcBOqW2F7L0i2DWGv/BPiTa1elyxORlwdGhu5byzqo9UFEXh4Y03NFXZ6IvDw9NajnirosEXn5rLV6rqjL0nNFqZvPehgoc5xwrN88EekAUrOPKaWUUkoppZRahvUQAB8B3iMiCzuafYhwFbGn1qZKSimllFJKKbX+rIcA+AnC1cW+ICLvnB3f98vA7yxjDcC1tKZdUNW6oueKWi49V9Ry6bmilkvPFaVuMjf8OoAAIrIf+EPgQcJxf/8b+GVr7YYatKyUUkoppZRS19K6CIBKKaWUUkoppa7eeugCet2JyG4R+aSIHBaRQESeXGKfTSLy5yLSLyIzIvKqiPzIEvvtF5HHRKQgIgMi8isi4p63j4jIL4lIn4gUReRpEbnr2h2hWi2rda6IyI+LiF3i9i/P20/PlXVqmedKnYj8mYhMzJ4rj4jI7iX20+vKBrZa54peVzY2EflBEfnygv+3vCIiHz5vn2W9t3pNUermogFwabcB3w2cnL0tIiIO8GXCdac/Cnwf8ALwf0XkAwv2qwe+RbgOzfcBvwL8LPBfzivyPwAfA34L+B5gBviWiLSt6lGpa2FVzpUF3kHY1Xnu9oXzHtdzZf265Lky69PAe4B/C/ww0Ag8JiKZuR30unJTWJVzZQG9rmxM/57wvfoZ4HuBJ4C/EZF/s2Cfy763ek1R6iZkrdXbeTfAWfD754Anz3t8H+GF8nvO234Q+PSC+79IuI5hZsG2jwKFuW1AHJgG/vOCfVLAKPBra/230Nt1O1d+fHa/mku8lp4r6/i2jHPlwdlz4B0LtrXOXi9+bsE2va5s8Nsqnit6XdnAN6BpiW1/A5y+kvdWryl609vNd9MWwCVYa81ldonM/pw+b/sUIAvuvw941C6erfRTQIKwRQjgISADfGbB6+eBr8w+X93AVvFcWQ49V9axZZwrdwE+C5a3sdYOA68B71+wn15XNrhVPFeWQ8+VdcpaO7bE5leBltnfl/ve6jVFqZuMBsCVOQK8CPyKiOwRkYyI/DjwFsJlK+bs47zF6q21vYTfqu1bsE8AnDrvNd5YsI9av5Z7rszpEhFfRE6IyE+e95ieKxtbHPDthbMbl4FbF9zX64pa7rkyR68rN4+HgGOzvy/3vdVrilI3GW+tK7AeWWutiLwP+DvOjc+oAv/MWvv4gl3rCVt6zjc5+9jcPjNL/I98EkiKSNRaW1m1yqvr6grOlUHCsRUvAS7wYeATIpK01v7u7D56rmxsnUBcRG631r4OICIJ4ACQXrCfXlfUcs8Vva7cRETkuwjH8P0/s5uW+97qNUWpm4wGwBWYndjjrwgH3X8IGCEcsP+nIjJurf37Bbsvtc6GnLf9Yvtc7DG1Tiz3XLHWPgo8uuCpj4hIDPhPIvL7C7qE6bmycT0KnAY+KSL/DMgCvwnUEn5psJBeV25uyzpX9Lpy8xCR7YTj//7OWvsXCx5a7nur1xSlbiLaBXRl/hHhOIvvt9Z+xlr7pLX2o8AXgf+2YL9JoG6J59dy7tu2SSB9/nTLs88rWGvP/+Cn1pflnitL+RzQAGyfva/nygY2++35PyGczOM4MADsBP4SGF6wq15XbnJXcK4sRa8rG4yINACPAL3ARxY8tNz3Vq8pSt1kNACuzD7CC975feFfBXYtuH+c8/rGi0gH4cxZxxfs4wLnr/V1QZ98tS4t91y5lLlvVvVc2eCstS8Rvr/7gN3W2rcSTujwwoLd9LqilnuuXLKI2Z96rqxjIpIEvgpEgffPTswyZ7nvrV5TlLrJaABcmTOEfd5vOW/7vUDPgvuPAO8RkYVjMj4EFDk3e9tzhN13fnBuh9kL+vfMPl+tb8s9V5byA8DYbBmg58pNwYZOWGu7RGQP8E7gTxfsotcVBSzrXFmKXlc2CBHxgM8Ce4D3WWtHzttlue+tXlOUusnoGMAlzF7Uvnv27mYgIyIfnL3/9dlbL/AlEfkVwnVw3g/8EPCvFxT1CeCngS+IyG8RdtH5ZeB35qZbttaWROQ3gY+JyCThN2n/njCcf/yaHaRaFat1rojI5wknaniN8FvWD83efnpunI6eK+vb5c4Va21BRD5G+L6OAbcTTuDxKWvtNxcUpdeVDW61zhW9rmx4/5PwPPm3QIOIPLDgsVev4L3Va4pSN5vrvfDgergRjo2wF7ltn91nN+E3bwPADHAY+ElAzitrP/A44Tdpg8CvAu55+wjwH4Gzs/v9A3D3Wv8d9Hb9zhXgN4AThNNuF4FXgH+6xOvpubJOb8s8V34P6Ceczr8T+AXAW6Isva5s4NtqnSt6XdnYN8JeJJc7T5b13uo1RW96u7luYq1O3KSUUkoppZRSNwMdA6iUUkoppZRSNwkNgEoppZRSSil1k9AAqJRSSimllFI3CQ2ASimllFJKKXWT0AColFJKKaWUUjcJDYBKKaWUUkopdZPQAKiUUhuQiHxBRDpFJL7EY4+KyBsiEl2LuimllFJq7WgAVEqpjemngVbgFxduFJEPAu8GfspaW1mLiimllFJq7ehC8EoptUGJyM8Cvw4csNZ2ikgKOA48bq39sWv0mglrbfFalK2UUkqpq6ctgEoptXH9PnAC+Pjs/f8PSAI/JyIHRORrIpKbvX1WRNrmnigiKRH5QxE5ISIFETktIn8kIpmFLyAiVkT+vYj8noiMAq9fr4NTSiml1JXTFkCllNrAROQh4BnC8Pcx4F8DTwCvAC8ThkMX+FUgD7zZWmtFpBn4FeAxYBToAP4j0Gutfc+C8i0wBDwN/DngWGu/fn2OTimllFJXSgOgUkptcCLyv4B/DjwHPAz8JfBm4Pa5cYAisoewe+j3Wmu/tkQZHnA/YZjcZq3tnd1ugUPW2ruvx7EopZRS6upoF1CllNr4/vvsz9+24bd+7wS+CBgR8WbD3WmgB7hv7kki8k9F5FURmQGqhOEPYO955V8QGJVSSil1Y9IAqJRSG1/lvJ9NwC8QhrqFt52EXT0RkQ8QthQ+D/wg8ADwgdnnn7+0xPC1qrhSSimlVpe31hVQSil13U0QtgD+7yUeG5v9+YPAi9bafzX3gIi8/SLl6VgCpZRSap3QAKiUUjefx4ADwCv24gPBE0D5vG0/ck1rpZRSSqlrTgOgUkrdfH4ZeAn4moj8GWGr32bgXcBfWGufBL4J/JGI/EfgReC7ge9ak9oqpZRSatVoAFRKqZuMtfakiDwA/BrwJ4Stff2ELYOds7t9knBM4L8lHPP3TeCHgReue4WVUkoptWp0GQillFJKKaWUuknoLKBKKaWUUkopdZPQAKiUUkoppZRSNwkNgEoppZRSSil1k9AAqJRSSimllFI3CQ2ASimllFJKKXWT0AColFJKKaWUUjcJDYBKKaWUUkopdZPQAKiUUkoppZRSNwkNgEoppZRSSil1k/j/AcA/wtZXLGK6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"font.size\"] = 15\n",
    "fig, ax = plt.subplots(figsize = (12,6))\n",
    "\n",
    "df2.plot(kind = \"area\",\n",
    "         stacked = True,\n",
    "         color = [\"red\",\"cornflowerblue\",\"wheat\",\"tan\",\"orchid\",\"lightgrey\"],\n",
    "         ax = ax)\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "\n",
    "plt.grid(axis = \"y\", linestyle = \"--\")\n",
    "plt.title(\"Annual total CO$_2$ emissions, by regions (1750 - 2019)\\n Source: Global Carbon Project (2020)\")\n",
    "plt.ylabel(\"GtCO$_2$e\")\n",
    "\n",
    "lgd = plt.legend(bbox_to_anchor = (1,1))\n",
    "plt.savefig(\"plots/Historical global emissions by countries.jpeg\",\n",
    "            dpi = 300,\n",
    "           #bbox_extra_artist = (lgd,),\n",
    "           bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f5126300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "China            219.985862\n",
       "EU-27            287.020302\n",
       "India             51.937016\n",
       "Russia           113.884449\n",
       "United States    410.238263\n",
       "Rest of world    569.101082\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cumulative emissions\n",
    "df2.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "23e3e08b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "China            10.174681\n",
       "EU-27             2.916906\n",
       "India             2.616449\n",
       "Russia            1.678367\n",
       "United States     5.284697\n",
       "Rest of world    13.770288\n",
       "Name: 2019, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[2019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c246e47b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.441387575"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[2019].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf1dbdd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>China</th>\n",
       "      <th>EU-27</th>\n",
       "      <th>India</th>\n",
       "      <th>Russia</th>\n",
       "      <th>United States</th>\n",
       "      <th>Rest of world</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1800</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.278075</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.899961</td>\n",
       "      <td>94.821964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.498886</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.956624</td>\n",
       "      <td>95.544490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1802</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.207790</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.786931</td>\n",
       "      <td>73.005279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1803</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.164999</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.942518</td>\n",
       "      <td>95.892483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1804</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.615122</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.971807</td>\n",
       "      <td>88.413071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>27.501712</td>\n",
       "      <td>8.801528</td>\n",
       "      <td>6.400069</td>\n",
       "      <td>4.609164</td>\n",
       "      <td>15.372102</td>\n",
       "      <td>37.315424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>27.122103</td>\n",
       "      <td>8.811696</td>\n",
       "      <td>6.792538</td>\n",
       "      <td>4.594791</td>\n",
       "      <td>15.026137</td>\n",
       "      <td>37.652736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>27.315752</td>\n",
       "      <td>8.761397</td>\n",
       "      <td>6.882630</td>\n",
       "      <td>4.611620</td>\n",
       "      <td>14.717489</td>\n",
       "      <td>37.711113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>27.338406</td>\n",
       "      <td>8.386916</td>\n",
       "      <td>7.115168</td>\n",
       "      <td>4.644080</td>\n",
       "      <td>14.895454</td>\n",
       "      <td>37.619976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>27.920674</td>\n",
       "      <td>8.004377</td>\n",
       "      <td>7.179883</td>\n",
       "      <td>4.605661</td>\n",
       "      <td>14.501908</td>\n",
       "      <td>37.787497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>220 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country      China      EU-27     India    Russia  United States  \\\n",
       "Year                                                               \n",
       "1800      0.000000   4.278075  0.000000  0.000000       0.899961   \n",
       "1801      0.000000   3.498886  0.000000  0.000000       0.956624   \n",
       "1802      0.000000  26.207790  0.000000  0.000000       0.786931   \n",
       "1803      0.000000   3.164999  0.000000  0.000000       0.942518   \n",
       "1804      0.000000  10.615122  0.000000  0.000000       0.971807   \n",
       "...            ...        ...       ...       ...            ...   \n",
       "2015     27.501712   8.801528  6.400069  4.609164      15.372102   \n",
       "2016     27.122103   8.811696  6.792538  4.594791      15.026137   \n",
       "2017     27.315752   8.761397  6.882630  4.611620      14.717489   \n",
       "2018     27.338406   8.386916  7.115168  4.644080      14.895454   \n",
       "2019     27.920674   8.004377  7.179883  4.605661      14.501908   \n",
       "\n",
       "Country  Rest of world  \n",
       "Year                    \n",
       "1800         94.821964  \n",
       "1801         95.544490  \n",
       "1802         73.005279  \n",
       "1803         95.892483  \n",
       "1804         88.413071  \n",
       "...                ...  \n",
       "2015         37.315424  \n",
       "2016         37.652736  \n",
       "2017         37.711113  \n",
       "2018         37.619976  \n",
       "2019         37.787497  \n",
       "\n",
       "[220 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.copy()\n",
    "for index in df3.index:\n",
    "    row_total = df3.loc[index].sum(axis = 0)\n",
    "    for column in df3.columns:\n",
    "        df3.loc[index, column] = df3.loc[index, column]*100/row_total\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a07abcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "China            27.920674\n",
       "EU-27             8.004377\n",
       "India             7.179883\n",
       "Russia            4.605661\n",
       "United States    14.501908\n",
       "Rest of world    37.787497\n",
       "Name: 2019, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.loc[2019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30e4c479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"font.size\"] = 15\n",
    "fig, (ax1,ax2) = plt.subplots(2,1, figsize = (12,12), sharex = True)\n",
    "\n",
    "df2.plot(kind = \"area\",\n",
    "         stacked = True,\n",
    "         color = [\"red\",\"cornflowerblue\",\"wheat\",\"tan\",\"orchid\",\"lightgrey\"],\n",
    "         ax = ax1)\n",
    "\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax1.spines[\"left\"].set_visible(False)\n",
    "\n",
    "ax1.grid(axis = \"y\", linestyle = \"--\")\n",
    "\n",
    "ax1.set_title(\"Annual total CO$_2$ emissions, by regions (1750 - 2019) \\n Source: Global Carbon Project (2020)\")\n",
    "ax1.set_ylabel(\"GtCO$_2$\")\n",
    "\n",
    "\n",
    "df3.plot(kind = \"area\",\n",
    "         stacked = True,\n",
    "         color = [\"red\",\"cornflowerblue\",\"wheat\",\"tan\",\"orchid\",\"lightgrey\"],\n",
    "        ax = ax2)\n",
    "\n",
    "#ax2.legend([\"\"])\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines[\"left\"].set_visible(False)\n",
    "\n",
    "ax2.grid(axis = \"y\", linestyle = \"--\")\n",
    "ax2.set_ylabel(\"% share\")\n",
    "ax2.get_legend().remove()\n",
    "\n",
    "#lgd = plt.legend(bbox_to_anchor = (1,1))\n",
    "plt.savefig(\"plots/Historical global emissions by countries.jpeg\",\n",
    "            dpi = 300,\n",
    "           bbox_inches = \"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11e14bf9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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