{ "cells": [ { "cell_type": "markdown", "id": "5c8decec", "metadata": {}, "source": [ "# Propósito del Modelo\n", "El objetivo es hacer análisis que involucran múltiples variables de diferentes bases de datos combinadas." ] }, { "cell_type": "code", "execution_count": 1, "id": "ff4e01c3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "( 1960 1961 1962 \\\n", " Country Name Country Code \n", " Aruba ABW -0.201874 -0.200982 -0.200327 \n", " Africa Eastern and Southern AFE -0.094914 -0.098259 -0.094392 \n", " Afghanistan AFG -0.201874 -0.200982 -0.200327 \n", " Africa Western and Central AFW -0.135881 -0.136426 -0.136892 \n", " Angola AGO -0.201874 -0.200982 -0.200327 \n", " \n", " 1963 1964 1965 \\\n", " Country Name Country Code \n", " Aruba ABW -0.199863 -0.200426 -0.202832 \n", " Africa Eastern and Southern AFE -0.094446 -0.097204 -0.095992 \n", " Afghanistan AFG -0.199863 -0.200426 -0.202832 \n", " Africa Western and Central AFW -0.135198 -0.136542 -0.136252 \n", " Angola AGO -0.199863 -0.200426 -0.202832 \n", " \n", " 1966 1967 1968 \\\n", " Country Name Country Code \n", " Aruba ABW -0.202705 -0.202662 -0.203255 \n", " Africa Eastern and Southern AFE -0.097136 -0.094766 -0.097880 \n", " Afghanistan AFG -0.202705 -0.202662 -0.203255 \n", " Africa Western and Central AFW -0.141240 -0.150569 -0.154342 \n", " Angola AGO -0.202705 -0.202662 -0.203255 \n", " \n", " 1969 ... 2013 2014 \\\n", " Country Name Country Code ... \n", " Aruba ABW -0.204786 ... -0.288371 -0.289320 \n", " Africa Eastern and Southern AFE -0.099362 ... -0.173739 -0.173601 \n", " Afghanistan AFG -0.204786 ... -0.286345 -0.287284 \n", " Africa Western and Central AFW -0.149334 ... -0.195458 -0.193785 \n", " Angola AGO -0.204786 ... -0.277472 -0.278209 \n", " \n", " 2015 2016 2017 \\\n", " Country Name Country Code \n", " Aruba ABW -0.289823 -0.290696 -0.291427 \n", " Africa Eastern and Southern AFE -0.174254 -0.175796 -0.177555 \n", " Afghanistan AFG -0.287813 -0.288695 -0.289457 \n", " Africa Western and Central AFW -0.194550 -0.197907 -0.199728 \n", " Angola AGO -0.278940 -0.280400 -0.281517 \n", " \n", " 2018 2019 2020 \\\n", " Country Name Country Code \n", " Aruba ABW -0.292036 -0.292556 -0.292998 \n", " Africa Eastern and Southern AFE -0.179122 -0.180347 -0.180619 \n", " Afghanistan AFG -0.290112 -0.290586 -0.290938 \n", " Africa Western and Central AFW -0.200799 -0.200851 -0.199375 \n", " Angola AGO -0.282588 -0.283415 -0.284049 \n", " \n", " 2021 2022 \n", " Country Name Country Code \n", " Aruba ABW -0.293201 -0.294042 \n", " Africa Eastern and Southern AFE -0.183041 -0.183463 \n", " Afghanistan AFG -0.291793 -0.287261 \n", " Africa Western and Central AFW -0.201723 -0.201925 \n", " Angola AGO -0.284755 -0.285617 \n", " \n", " [5 rows x 63 columns],\n", " 1960 1961 1962 \\\n", " Country Name Country Code \n", " Aruba ABW -0.084868 -0.08484 -0.083201 \n", " Africa Eastern and Southern AFE -0.084868 -0.08484 -0.083201 \n", " Afghanistan AFG -0.084868 -0.08484 -0.083201 \n", " Africa Western and Central AFW -0.084868 -0.08484 -0.083201 \n", " Angola AGO -0.084868 -0.08484 -0.083201 \n", " \n", " 1963 1964 1965 \\\n", " Country Name Country Code \n", " Aruba ABW -0.082048 -0.080368 -0.074607 \n", " Africa Eastern and Southern AFE -0.082048 -0.080368 -0.074607 \n", " Afghanistan AFG -0.082048 -0.080368 -0.074607 \n", " Africa Western and Central AFW -0.082048 -0.080368 -0.074607 \n", " Angola AGO -0.082048 -0.080368 -0.074607 \n", " \n", " 1966 1967 1968 \\\n", " Country Name Country Code \n", " Aruba ABW -0.075705 -0.073737 -0.072911 \n", " Africa Eastern and Southern AFE -0.075705 -0.073737 -0.072911 \n", " Afghanistan AFG -0.075705 -0.073737 -0.072911 \n", " Africa Western and Central AFW -0.075705 -0.073737 -0.072911 \n", " Angola AGO -0.075705 -0.073737 -0.072911 \n", " \n", " 1969 ... 2013 2014 \\\n", " Country Name Country Code ... \n", " Aruba ABW -0.071835 ... -0.105875 -0.107196 \n", " Africa Eastern and Southern AFE -0.071835 ... -0.105875 -0.107196 \n", " Afghanistan AFG -0.071835 ... -0.105875 -0.107196 \n", " Africa Western and Central AFW -0.071835 ... -0.105875 -0.107196 \n", " Angola AGO -0.071835 ... -0.104616 -0.106276 \n", " \n", " 2015 2016 2017 \\\n", " Country Name Country Code \n", " Aruba ABW -0.105721 -0.104515 -0.105551 \n", " Africa Eastern and Southern AFE -0.105721 -0.104515 -0.105551 \n", " Afghanistan AFG -0.105721 -0.104515 -0.105551 \n", " Africa Western and Central AFW -0.105721 -0.104515 -0.105551 \n", " Angola AGO -0.105172 -0.104014 -0.105000 \n", " \n", " 2018 2019 2020 \\\n", " Country Name Country Code \n", " Aruba ABW -0.107831 -0.111312 -0.111963 \n", " Africa Eastern and Southern AFE -0.107831 -0.111312 -0.111963 \n", " Afghanistan AFG -0.107831 -0.111312 -0.111067 \n", " Africa Western and Central AFW -0.107831 -0.111312 -0.111963 \n", " Angola AGO -0.107297 -0.110756 -0.111903 \n", " \n", " 2021 2022 \n", " Country Name Country Code \n", " Aruba ABW -0.110932 -0.111682 \n", " Africa Eastern and Southern AFE -0.110932 -0.111682 \n", " Afghanistan AFG -0.110692 -0.111682 \n", " Africa Western and Central AFW -0.110932 -0.111682 \n", " Angola AGO -0.111045 -0.111847 \n", " \n", " [5 rows x 63 columns],\n", " 1960 1961 1962 \\\n", " Country Name Country Code \n", " Aruba ABW -0.146825 -0.158595 -0.153408 \n", " Africa Eastern and Southern AFE -0.146825 -0.158595 -0.153408 \n", " Afghanistan AFG -0.146825 -0.158595 -0.153408 \n", " Africa Western and Central AFW -0.146825 -0.158595 -0.153408 \n", " Angola AGO -0.146825 -0.158595 -0.153408 \n", " \n", " 1963 1964 1965 \\\n", " Country Name Country Code \n", " Aruba ABW -0.158273 -0.158734 -0.162173 \n", " Africa Eastern and Southern AFE -0.158273 -0.158734 -0.162173 \n", " Afghanistan AFG -0.158273 -0.158734 -0.162173 \n", " Africa Western and Central AFW -0.158273 -0.158734 -0.162173 \n", " Angola AGO -0.158273 -0.158734 -0.162173 \n", " \n", " 1966 1967 1968 \\\n", " Country Name Country Code \n", " Aruba ABW -0.158459 -0.162375 -0.1682 \n", " Africa Eastern and Southern AFE -0.158459 -0.162375 -0.1682 \n", " Afghanistan AFG -0.158459 -0.162375 -0.1682 \n", " Africa Western and Central AFW -0.158459 -0.162375 -0.1682 \n", " Angola AGO -0.158459 -0.162375 -0.1682 \n", " \n", " 1969 ... 2013 2014 \\\n", " Country Name Country Code ... \n", " Aruba ABW -0.184395 ... -0.203738 -0.202930 \n", " Africa Eastern and Southern AFE -0.184395 ... -0.071547 -0.081470 \n", " Afghanistan AFG -0.184395 ... -0.203738 -0.202930 \n", " Africa Western and Central AFW -0.184395 ... -0.203738 -0.202930 \n", " Angola AGO -0.184395 ... -0.194796 -0.193997 \n", " \n", " 2015 2016 2017 \\\n", " Country Name Country Code \n", " Aruba ABW -0.285333 -0.202682 -0.202571 \n", " Africa Eastern and Southern AFE -0.195116 -0.092256 -0.090919 \n", " Afghanistan AFG -0.285333 -0.202682 -0.202571 \n", " Africa Western and Central AFW -0.285333 -0.202682 -0.202571 \n", " Angola AGO -0.280513 -0.198833 -0.198563 \n", " \n", " 2018 2019 2020 \\\n", " Country Name Country Code \n", " Aruba ABW -0.202676 -0.202183 -0.201677 \n", " Africa Eastern and Southern AFE -0.093306 -0.094302 -0.094236 \n", " Afghanistan AFG -0.202676 -0.202183 -0.201677 \n", " Africa Western and Central AFW -0.202676 -0.202183 -0.201677 \n", " Angola AGO -0.200401 -0.201239 -0.199575 \n", " \n", " 2021 2022 \n", " Country Name Country Code \n", " Aruba ABW -0.201440 -0.201034 \n", " Africa Eastern and Southern AFE -0.093547 -0.090401 \n", " Afghanistan AFG -0.201440 -0.201034 \n", " Africa Western and Central AFW -0.201440 -0.201034 \n", " Angola AGO -0.201274 -0.203768 \n", " \n", " [5 rows x 63 columns],\n", " 1960 1961 1962 \\\n", " Country Name Country Code \n", " Aruba ABW -0.090809 -0.091864 -0.09344 \n", " Africa Eastern and Southern AFE -0.090809 -0.091864 -0.09344 \n", " Afghanistan AFG -0.090809 -0.091864 -0.09344 \n", " Africa Western and Central AFW -0.090809 -0.091864 -0.09344 \n", " Angola AGO -0.090809 -0.091864 -0.09344 \n", " \n", " 1963 1964 1965 \\\n", " Country Name Country Code \n", " Aruba ABW -0.093163 -0.094031 -0.092187 \n", " Africa Eastern and Southern AFE -0.093163 -0.094031 -0.092187 \n", " Afghanistan AFG -0.093163 -0.094031 -0.092187 \n", " Africa Western and Central AFW -0.093163 -0.094031 -0.092187 \n", " Angola AGO -0.093163 -0.094031 -0.092187 \n", " \n", " 1966 1967 1968 \\\n", " Country Name Country Code \n", " Aruba ABW -0.090953 -0.089691 -0.090635 \n", " Africa Eastern and Southern AFE -0.090953 -0.089691 -0.090635 \n", " Afghanistan AFG -0.090953 -0.089691 -0.090635 \n", " Africa Western and Central AFW -0.090953 -0.089691 -0.090635 \n", " Angola AGO -0.090953 -0.089691 -0.090635 \n", " \n", " 1969 ... 2013 2014 \\\n", " Country Name Country Code ... \n", " Aruba ABW -0.089882 ... -0.113248 -0.114293 \n", " Africa Eastern and Southern AFE -0.089882 ... -0.113248 -0.114293 \n", " Afghanistan AFG -0.089882 ... -0.113248 -0.114293 \n", " Africa Western and Central AFW -0.089882 ... -0.113248 -0.114293 \n", " Angola AGO -0.089882 ... -0.112900 -0.113945 \n", " \n", " 2015 2016 2017 \\\n", " Country Name Country Code \n", " Aruba ABW -0.115194 -0.114932 -0.115308 \n", " Africa Eastern and Southern AFE -0.115194 -0.114932 -0.115308 \n", " Afghanistan AFG -0.115194 -0.114932 -0.115308 \n", " Africa Western and Central AFW -0.115194 -0.114932 -0.115308 \n", " Angola AGO -0.114668 -0.114465 -0.114863 \n", " \n", " 2018 2019 2020 \\\n", " Country Name Country Code \n", " Aruba ABW -0.116877 -0.119049 -0.118598 \n", " Africa Eastern and Southern AFE -0.116877 -0.119049 -0.118598 \n", " Afghanistan AFG -0.116877 -0.119049 -0.117498 \n", " Africa Western and Central AFW -0.116877 -0.119049 -0.118598 \n", " Angola AGO -0.116601 -0.118793 -0.118486 \n", " \n", " 2021 2022 \n", " Country Name Country Code \n", " Aruba ABW -0.118736 -0.117705 \n", " Africa Eastern and Southern AFE -0.118736 -0.117705 \n", " Afghanistan AFG -0.118341 -0.117705 \n", " Africa Western and Central AFW -0.118736 -0.117705 \n", " Angola AGO -0.118741 -0.118102 \n", " \n", " [5 rows x 63 columns],\n", " 1960 1961 1962 \\\n", " Country Name Country Code \n", " Aruba ABW -0.162675 -0.152507 -0.175345 \n", " Africa Eastern and Southern AFE 2.381755 2.202534 2.959211 \n", " Afghanistan AFG -0.162675 -0.152507 -0.175345 \n", " Africa Western and Central AFW -0.162675 -0.152507 -0.175345 \n", " Angola AGO -0.162675 -0.109791 -0.142530 \n", " \n", " 1963 1964 1965 \\\n", " Country Name Country Code \n", " Aruba ABW -0.199606 -0.207111 -0.257548 \n", " Africa Eastern and Southern AFE 2.810270 2.750210 3.317384 \n", " Afghanistan AFG -0.199606 -0.207111 -0.257548 \n", " Africa Western and Central AFW -0.199606 -0.207111 -0.257548 \n", " Angola AGO -0.165347 -0.144329 -0.250614 \n", " \n", " 1966 1967 1968 \\\n", " Country Name Country Code \n", " Aruba ABW -0.288931 -0.277482 -0.278466 \n", " Africa Eastern and Southern AFE 4.608648 3.940742 3.916744 \n", " Afghanistan AFG -0.288931 -0.277482 -0.278466 \n", " Africa Western and Central AFW -0.288931 -0.277482 0.066949 \n", " Angola AGO -0.261803 -0.277482 -0.278466 \n", " \n", " 1969 ... 2011 2012 \\\n", " Country Name Country Code ... \n", " Aruba ABW -0.270550 ... -0.325080 -0.311868 \n", " Africa Eastern and Southern AFE 4.802118 ... -0.325080 -0.311868 \n", " Afghanistan AFG -0.270550 ... -0.325080 -0.311868 \n", " Africa Western and Central AFW 0.209348 ... 0.935966 0.632917 \n", " Angola AGO -0.270550 ... 0.266913 0.170396 \n", " \n", " 2013 2014 2015 \\\n", " Country Name Country Code \n", " Aruba ABW -0.306099 -0.309704 -0.250354 \n", " Africa Eastern and Southern AFE -0.306099 -0.309704 -0.250354 \n", " Afghanistan AFG -0.306099 -0.309704 -0.250354 \n", " Africa Western and Central AFW 0.353438 0.164271 -0.175233 \n", " Angola AGO 0.224552 0.035088 -0.153382 \n", " \n", " 2016 2017 2018 \\\n", " Country Name Country Code \n", " Aruba ABW -0.252749 -0.257753 -0.291725 \n", " Africa Eastern and Southern AFE -0.252749 -0.257753 -0.291725 \n", " Afghanistan AFG -0.252749 -0.257753 -0.291725 \n", " Africa Western and Central AFW -0.252749 -0.133328 0.042448 \n", " Angola AGO -0.115071 -0.036075 0.042644 \n", " \n", " 2019 2020 \n", " Country Name Country Code \n", " Aruba ABW -0.281673 -0.251303 \n", " Africa Eastern and Southern AFE -0.281673 -0.251303 \n", " Afghanistan AFG -0.281673 -0.251303 \n", " Africa Western and Central AFW -0.051936 -0.239934 \n", " Angola AGO -0.088127 -0.217208 \n", " \n", " [5 rows x 61 columns])" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import statsmodels.formula.api as smf\n", "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "main_gdp = pd.read_csv('main_gdp.csv', index_col=['Country Name', 'Country Code'])\n", "main_government = pd.read_csv('main_government.csv', index_col=['Country Name', 'Country Code'])\n", "main_investments = pd.read_csv('main_investments.csv', index_col=['Country Name', 'Country Code'])\n", "main_consumption = pd.read_csv('main_consumption.csv', index_col=['Country Name', 'Country Code'])\n", "main_trade = pd.read_csv('main_trade.csv', index_col=['Country Name', 'Country Code'])\n", "\n", "(main_gdp.head(), main_government.head(), main_investments.head(), main_consumption.head(), main_trade.head())" ] }, { "cell_type": "markdown", "id": "5c1b62dd", "metadata": {}, "source": [ "#### Unir mediante sufijos especificados" ] }, { "cell_type": "code", "execution_count": 2, "id": "f35b32d2", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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GDP_calculated_1960GDP_calculated_1961GDP_calculated_1962GDP_calculated_1963GDP_calculated_1964GDP_calculated_1965GDP_calculated_1966GDP_calculated_1967GDP_calculated_1968GDP_calculated_1969...GDP_calculated_2013GDP_calculated_2014GDP_calculated_2015GDP_calculated_2016GDP_calculated_2017GDP_calculated_2018GDP_calculated_2019GDP_calculated_2020GDP_calculated_2021GDP_calculated_2022
Country NameCountry Code
ArubaABW-0.687051-0.688787-0.705721-0.732952-0.740670-0.789346-0.816753-0.805947-0.813468-0.821448...-1.017331-1.023442-1.046426-0.965575-0.972609-1.011146-1.006774-0.976539-0.925748-0.925497
Africa Eastern and SouthernAFE1.9643401.7689772.5347702.3823402.3198732.8924254.1863963.5201733.4871174.356643...-0.770508-0.786264-0.840639-0.740249-0.747085-0.788861-0.786684-0.756719-0.599803-0.593651
AfghanistanAFG-0.687051-0.688787-0.705721-0.732952-0.740670-0.789346-0.816753-0.805947-0.813468-0.821448...-1.015306-1.021407-1.044415-0.963573-0.970639-1.009221-1.004804-0.972484-0.923705-0.918717
Africa Western and CentralAFW-0.621058-0.624231-0.642287-0.668287-0.676786-0.722767-0.755287-0.753854-0.419140-0.286099...-0.264882-0.453932-0.876033-0.872786-0.756485-0.585735-0.685331-0.871547-0.834270-0.833380
AngolaAGO-0.687051-0.646070-0.672906-0.698693-0.677888-0.782412-0.789625-0.805947-0.813468-0.821448...-0.465233-0.657339-0.932675-0.812782-0.736018-0.664243-0.802329-0.931221-0.917089-0.923103
\n", "

5 rows × 63 columns

\n", "
" ], "text/plain": [ " GDP_calculated_1960 \\\n", "Country Name Country Code \n", "Aruba ABW -0.687051 \n", "Africa Eastern and Southern AFE 1.964340 \n", "Afghanistan AFG -0.687051 \n", "Africa Western and Central AFW -0.621058 \n", "Angola AGO -0.687051 \n", "\n", " GDP_calculated_1961 \\\n", "Country Name Country Code \n", "Aruba ABW -0.688787 \n", "Africa Eastern and Southern AFE 1.768977 \n", "Afghanistan AFG -0.688787 \n", "Africa Western and Central AFW -0.624231 \n", "Angola AGO -0.646070 \n", "\n", " GDP_calculated_1962 \\\n", "Country Name Country Code \n", "Aruba ABW -0.705721 \n", "Africa Eastern and Southern AFE 2.534770 \n", "Afghanistan AFG -0.705721 \n", "Africa Western and Central AFW -0.642287 \n", "Angola AGO -0.672906 \n", "\n", " GDP_calculated_1963 \\\n", "Country Name Country Code \n", "Aruba ABW -0.732952 \n", "Africa Eastern and Southern AFE 2.382340 \n", "Afghanistan AFG -0.732952 \n", "Africa Western and Central AFW -0.668287 \n", "Angola AGO -0.698693 \n", "\n", " GDP_calculated_1964 \\\n", "Country Name Country Code \n", "Aruba ABW -0.740670 \n", "Africa Eastern and Southern AFE 2.319873 \n", "Afghanistan AFG -0.740670 \n", "Africa Western and Central AFW -0.676786 \n", "Angola AGO -0.677888 \n", "\n", " GDP_calculated_1965 \\\n", "Country Name Country Code \n", "Aruba ABW -0.789346 \n", "Africa Eastern and Southern AFE 2.892425 \n", "Afghanistan AFG -0.789346 \n", "Africa Western and Central AFW -0.722767 \n", "Angola AGO -0.782412 \n", "\n", " GDP_calculated_1966 \\\n", "Country Name Country Code \n", "Aruba ABW -0.816753 \n", "Africa Eastern and Southern AFE 4.186396 \n", "Afghanistan AFG -0.816753 \n", "Africa Western and Central AFW -0.755287 \n", "Angola AGO -0.789625 \n", "\n", " GDP_calculated_1967 \\\n", "Country Name Country Code \n", "Aruba ABW -0.805947 \n", "Africa Eastern and Southern AFE 3.520173 \n", "Afghanistan AFG -0.805947 \n", "Africa Western and Central AFW -0.753854 \n", "Angola AGO -0.805947 \n", "\n", " GDP_calculated_1968 \\\n", "Country Name Country Code \n", "Aruba ABW -0.813468 \n", "Africa Eastern and Southern AFE 3.487117 \n", "Afghanistan AFG -0.813468 \n", "Africa Western and Central AFW -0.419140 \n", "Angola AGO -0.813468 \n", "\n", " GDP_calculated_1969 ... \\\n", "Country Name Country Code ... \n", "Aruba ABW -0.821448 ... \n", "Africa Eastern and Southern AFE 4.356643 ... \n", "Afghanistan AFG -0.821448 ... \n", "Africa Western and Central AFW -0.286099 ... \n", "Angola AGO -0.821448 ... \n", "\n", " GDP_calculated_2013 \\\n", "Country Name Country Code \n", "Aruba ABW -1.017331 \n", "Africa Eastern and Southern AFE -0.770508 \n", "Afghanistan AFG -1.015306 \n", "Africa Western and Central AFW -0.264882 \n", "Angola AGO -0.465233 \n", "\n", " GDP_calculated_2014 \\\n", "Country Name Country Code \n", "Aruba ABW -1.023442 \n", "Africa Eastern and Southern AFE -0.786264 \n", "Afghanistan AFG -1.021407 \n", "Africa Western and Central AFW -0.453932 \n", "Angola AGO -0.657339 \n", "\n", " GDP_calculated_2015 \\\n", "Country Name Country Code \n", "Aruba ABW -1.046426 \n", "Africa Eastern and Southern AFE -0.840639 \n", "Afghanistan AFG -1.044415 \n", "Africa Western and Central AFW -0.876033 \n", "Angola AGO -0.932675 \n", "\n", " GDP_calculated_2016 \\\n", "Country Name Country Code \n", "Aruba ABW -0.965575 \n", "Africa Eastern and Southern AFE -0.740249 \n", "Afghanistan AFG -0.963573 \n", "Africa Western and Central AFW -0.872786 \n", "Angola AGO -0.812782 \n", "\n", " GDP_calculated_2017 \\\n", "Country Name Country Code \n", "Aruba ABW -0.972609 \n", "Africa Eastern and Southern AFE -0.747085 \n", "Afghanistan AFG -0.970639 \n", "Africa Western and Central AFW -0.756485 \n", "Angola AGO -0.736018 \n", "\n", " GDP_calculated_2018 \\\n", "Country Name Country Code \n", "Aruba ABW -1.011146 \n", "Africa Eastern and Southern AFE -0.788861 \n", "Afghanistan AFG -1.009221 \n", "Africa Western and Central AFW -0.585735 \n", "Angola AGO -0.664243 \n", "\n", " GDP_calculated_2019 \\\n", "Country Name Country Code \n", "Aruba ABW -1.006774 \n", "Africa Eastern and Southern AFE -0.786684 \n", "Afghanistan AFG -1.004804 \n", "Africa Western and Central AFW -0.685331 \n", "Angola AGO -0.802329 \n", "\n", " GDP_calculated_2020 \\\n", "Country Name Country Code \n", "Aruba ABW -0.976539 \n", "Africa Eastern and Southern AFE -0.756719 \n", "Afghanistan AFG -0.972484 \n", "Africa Western and Central AFW -0.871547 \n", "Angola AGO -0.931221 \n", "\n", " GDP_calculated_2021 \\\n", "Country Name Country Code \n", "Aruba ABW -0.925748 \n", "Africa Eastern and Southern AFE -0.599803 \n", "Afghanistan AFG -0.923705 \n", "Africa Western and Central AFW -0.834270 \n", "Angola AGO -0.917089 \n", "\n", " GDP_calculated_2022 \n", "Country Name Country Code \n", "Aruba ABW -0.925497 \n", "Africa Eastern and Southern AFE -0.593651 \n", "Afghanistan AFG -0.918717 \n", "Africa Western and Central AFW -0.833380 \n", "Angola AGO -0.923103 \n", "\n", "[5 rows x 63 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Unir los DataFrames uno por uno\n", "df_combined = main_gdp.join(main_government, lsuffix='_gdp', rsuffix='_gov')\n", "df_combined = df_combined.join(main_investments, rsuffix='_inv')\n", "df_combined = df_combined.join(main_consumption, rsuffix='_con')\n", "df_combined = df_combined.join(main_trade, rsuffix='_trade')\n", "\n", "# Corregir la fórmula para calcular el GDP correctamente usando los sufijos adecuados para cada año\n", "for year in range(1960, 2023):\n", " gdp_col = f'{year}_gdp' if f'{year}_gdp' in df_combined.columns else str(year)\n", " gov_col = f'{year}_gov' if f'{year}_gov' in df_combined.columns else str(year)\n", " inv_col = f'{year}_inv' if f'{year}_inv' in df_combined.columns else str(year)\n", " con_col = f'{year}_con' if f'{year}_con' in df_combined.columns else str(year)\n", " trade_col = f'{year}_trade' if f'{year}_trade' in df_combined.columns else str(year)\n", "\n", " df_combined[f'GDP_calculated_{year}'] = (df_combined[gdp_col] +\n", " df_combined[gov_col] +\n", " df_combined[inv_col] +\n", " df_combined[con_col] +\n", " df_combined[trade_col])\n", "\n", "# Mostrar las primeras filas del DataFrame combinado para confirmar que los cálculos están correctos\n", "df_combined[[f'GDP_calculated_{year}' for year in range(1960, 2023)]].head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "8d4ef2f3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "MultiIndex: 239 entries, ('Aruba', 'ABW') to ('Zimbabwe', 'ZWE')\n", "Columns: 376 entries, 1960_gdp to GDP_calculated_2022\n", "dtypes: float64(376)\n", "memory usage: 731.2+ KB\n" ] }, { "data": { "text/plain": [ "( 1960_gdp 1961_gdp 1962_gdp 1963_gdp 1964_gdp 1965_gdp \\\n", " count 239.000000 239.000000 239.000000 239.000000 239.000000 239.000000 \n", " mean -0.081045 -0.080586 -0.079957 -0.079715 -0.080026 -0.081028 \n", " std 0.811894 0.811040 0.811016 0.810860 0.810938 0.811302 \n", " min -0.224510 -0.223332 -0.222338 -0.222336 -0.222801 -0.221246 \n", " 25% -0.202029 -0.205308 -0.204461 -0.204545 -0.204212 -0.207263 \n", " 50% -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 -0.202832 \n", " 75% -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 -0.202832 \n", " max 8.925325 8.916576 8.903261 8.903658 8.909846 8.905935 \n", " \n", " 1966_gdp 1967_gdp 1968_gdp 1969_gdp ... \\\n", " count 239.000000 239.000000 239.000000 239.000000 ... \n", " mean -0.080735 -0.080437 -0.081089 -0.082057 ... \n", " std 0.811470 0.810851 0.810718 0.810380 ... \n", " min -0.220064 -0.219031 -0.219934 -0.221195 ... \n", " 25% -0.208238 -0.209050 -0.210544 -0.212014 ... \n", " 50% -0.202705 -0.202662 -0.203255 -0.204786 ... \n", " 75% -0.202705 -0.202662 -0.203255 -0.204786 ... \n", " max 8.893228 8.892912 8.894431 8.920468 ... \n", " \n", " GDP_calculated_2013 GDP_calculated_2014 GDP_calculated_2015 \\\n", " count 239.000000 239.000000 239.000000 \n", " mean -0.213543 -0.243189 -0.328689 \n", " std 2.892561 2.864930 2.860354 \n", " min -1.030556 -1.036704 -1.056181 \n", " 25% -1.017694 -1.023788 -1.047886 \n", " 50% -1.010545 -1.016673 -1.039728 \n", " 75% -0.773237 -0.793074 -0.879409 \n", " max 28.144082 27.835159 27.572665 \n", " \n", " GDP_calculated_2016 GDP_calculated_2017 GDP_calculated_2018 \\\n", " count 239.000000 239.000000 239.000000 \n", " mean -0.247591 -0.239064 -0.249890 \n", " std 2.856636 2.864796 2.849596 \n", " min -0.978107 -0.985575 -1.024731 \n", " 25% -0.965917 -0.973220 -1.012258 \n", " 50% -0.959720 -0.966302 -1.004253 \n", " 75% -0.808664 -0.790391 -0.808545 \n", " max 27.837297 27.701692 27.367289 \n", " \n", " GDP_calculated_2019 GDP_calculated_2020 GDP_calculated_2021 \\\n", " count 239.000000 239.000000 239.000000 \n", " mean -0.257321 -0.264348 -0.200992 \n", " std 2.831007 2.824620 3.298784 \n", " min -1.021194 -0.990254 -0.954674 \n", " 25% -1.007834 -0.976800 -0.933824 \n", " 50% -0.999458 -0.970490 -0.921164 \n", " 75% -0.807603 -0.831460 -0.816629 \n", " max 26.262588 26.264463 31.407289 \n", " \n", " GDP_calculated_2022 \n", " count 239.000000 \n", " mean -0.202149 \n", " std 3.301215 \n", " min -0.962321 \n", " 25% -0.936761 \n", " 50% -0.918836 \n", " 75% -0.826804 \n", " max 31.527472 \n", " \n", " [8 rows x 376 columns],\n", " None,\n", " (239, 376),\n", " Index(['1960_gdp', '1961_gdp', '1962_gdp', '1963_gdp', '1964_gdp', '1965_gdp',\n", " '1966_gdp', '1967_gdp', '1968_gdp', '1969_gdp',\n", " ...\n", " 'GDP_calculated_2013', 'GDP_calculated_2014', 'GDP_calculated_2015',\n", " 'GDP_calculated_2016', 'GDP_calculated_2017', 'GDP_calculated_2018',\n", " 'GDP_calculated_2019', 'GDP_calculated_2020', 'GDP_calculated_2021',\n", " 'GDP_calculated_2022'],\n", " dtype='object', length=376),\n", " MultiIndex([( 'Aruba', 'ABW'),\n", " ('Africa Eastern and Southern', 'AFE'),\n", " ( 'Afghanistan', 'AFG'),\n", " ( 'Africa Western and Central', 'AFW'),\n", " ( 'Angola', 'AGO'),\n", " ( 'Albania', 'ALB'),\n", " ( 'Andorra', 'AND'),\n", " ( 'Arab World', 'ARB'),\n", " ( 'United Arab Emirates', 'ARE'),\n", " ( 'Argentina', 'ARG'),\n", " ...\n", " ( 'Virgin Islands (U.S.)', 'VIR'),\n", " ( 'Viet Nam', 'VNM'),\n", " ( 'Vanuatu', 'VUT'),\n", " ( 'World', 'WLD'),\n", " ( 'Samoa', 'WSM'),\n", " ( 'Kosovo', 'XKX'),\n", " ( 'Yemen, Rep.', 'YEM'),\n", " ( 'South Africa', 'ZAF'),\n", " ( 'Zambia', 'ZMB'),\n", " ( 'Zimbabwe', 'ZWE')],\n", " names=['Country Name', 'Country Code'], length=239),\n", " 1960_gdp float64\n", " 1961_gdp float64\n", " 1962_gdp float64\n", " 1963_gdp float64\n", " 1964_gdp float64\n", " ... \n", " GDP_calculated_2018 float64\n", " GDP_calculated_2019 float64\n", " GDP_calculated_2020 float64\n", " GDP_calculated_2021 float64\n", " GDP_calculated_2022 float64\n", " Length: 376, dtype: object,\n", " 1960_gdp 0\n", " 1961_gdp 0\n", " 1962_gdp 0\n", " 1963_gdp 0\n", " 1964_gdp 0\n", " ..\n", " GDP_calculated_2018 0\n", " GDP_calculated_2019 0\n", " GDP_calculated_2020 0\n", " GDP_calculated_2021 0\n", " GDP_calculated_2022 0\n", " Length: 376, dtype: int64)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_combined.describe(), df_combined.info(), df_combined.shape, df_combined.columns, df_combined.index, df_combined.dtypes, df_combined.isnull().sum()" ] }, { "cell_type": "markdown", "id": "de9cbf31", "metadata": {}, "source": [ "# WORLD | COUNTRIES" ] }, { "cell_type": "markdown", "id": "1bc499c2", "metadata": {}, "source": [ "### World" ] }, { "cell_type": "code", "execution_count": 4, "id": "4bc0270b", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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1960_gdp1961_gdp1962_gdp1963_gdp1964_gdp1965_gdp1966_gdp1967_gdp1968_gdp1969_gdp...GDP_calculated_2013GDP_calculated_2014GDP_calculated_2015GDP_calculated_2016GDP_calculated_2017GDP_calculated_2018GDP_calculated_2019GDP_calculated_2020GDP_calculated_2021GDP_calculated_2022
Country Code
WLD8.9253258.9165768.9032618.9036588.9098468.9059358.8932288.8929128.8944318.920468...19.94583919.91139817.39171119.90971419.89727719.86540119.84448219.8014331.40728931.527472
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1 rows × 376 columns

\n", "
" ], "text/plain": [ " 1960_gdp 1961_gdp 1962_gdp 1963_gdp 1964_gdp 1965_gdp \\\n", "Country Code \n", "WLD 8.925325 8.916576 8.903261 8.903658 8.909846 8.905935 \n", "\n", " 1966_gdp 1967_gdp 1968_gdp 1969_gdp ... \\\n", "Country Code ... \n", "WLD 8.893228 8.892912 8.894431 8.920468 ... \n", "\n", " GDP_calculated_2013 GDP_calculated_2014 GDP_calculated_2015 \\\n", "Country Code \n", "WLD 19.945839 19.911398 17.391711 \n", "\n", " GDP_calculated_2016 GDP_calculated_2017 GDP_calculated_2018 \\\n", "Country Code \n", "WLD 19.909714 19.897277 19.865401 \n", "\n", " GDP_calculated_2019 GDP_calculated_2020 GDP_calculated_2021 \\\n", "Country Code \n", "WLD 19.844482 19.80143 31.407289 \n", "\n", " GDP_calculated_2022 \n", "Country Code \n", "WLD 31.527472 \n", "\n", "[1 rows x 376 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_world = df_combined.loc['World']\n", "df_world" ] }, { "cell_type": "markdown", "id": "46a3d25f", "metadata": {}, "source": [ "### Countries" ] }, { "cell_type": "code", "execution_count": 5, "id": "dbca801f", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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1960_gdp1961_gdp1962_gdp1963_gdp1964_gdp1965_gdp1966_gdp1967_gdp1968_gdp1969_gdp...GDP_calculated_2013GDP_calculated_2014GDP_calculated_2015GDP_calculated_2016GDP_calculated_2017GDP_calculated_2018GDP_calculated_2019GDP_calculated_2020GDP_calculated_2021GDP_calculated_2022
Country NameCountry Code
ArubaABW-0.201874-0.200982-0.200327-0.199863-0.200426-0.202832-0.202705-0.202662-0.203255-0.204786...-1.017331-1.023442-1.046426-0.965575-0.972609-1.011146-1.006774-0.976539-0.925748-0.925497
AfghanistanAFG-0.201874-0.200982-0.200327-0.199863-0.200426-0.202832-0.202705-0.202662-0.203255-0.204786...-1.015306-1.021407-1.044415-0.963573-0.970639-1.009221-1.004804-0.972484-0.923705-0.918717
AngolaAGO-0.201874-0.200982-0.200327-0.199863-0.200426-0.202832-0.202705-0.202662-0.203255-0.204786...-0.465233-0.657339-0.932675-0.812782-0.736018-0.664243-0.802329-0.931221-0.917089-0.923103
AlbaniaALB-0.201874-0.200982-0.200327-0.199863-0.200426-0.202832-0.202705-0.202662-0.203255-0.204786...-1.024486-1.030680-1.050654-0.972267-0.979659-1.018762-1.015177-0.984239-0.946538-0.954034
AndorraAND-0.201874-0.200982-0.200327-0.199863-0.200426-0.202832-0.202705-0.202662-0.203255-0.204786...-1.017377-1.023472-1.046447-0.965590-0.972647-1.011185-1.006798-0.976514-0.925774-0.925529
.....................................................................
KosovoXKX-0.201874-0.200982-0.200327-0.199863-0.200426-0.202832-0.202705-0.202662-0.203255-0.204786...-1.028729-1.034928-1.054572-0.976205-0.983589-1.022714-1.019152-0.988298-0.951340-0.959118
Yemen, Rep.YEM-0.201874-0.200982-0.200327-0.199863-0.200426-0.202832-0.202705-0.202662-0.203255-0.204786...-1.018770-1.025404-1.049979-0.972641-0.980441-1.018888-1.000565-0.970490-0.919628-0.918717
South AfricaZAF-0.169603-0.168437-0.167114-0.165969-0.165662-0.163839-0.163451-0.160740-0.162603-0.164274...-0.933638-0.942189-0.971863-0.888590-0.883052-0.940374-0.938708-0.791299-0.852809-0.859582
ZambiaZMB-0.221483-0.220372-0.219614-0.219669-0.219984-0.218117-0.217284-0.216134-0.217168-0.218587...-1.009544-1.021177-1.047666-0.963273-0.970319-1.008834-1.003544-0.951182-0.923430-0.923146
ZimbabweZWE-0.220994-0.219722-0.218875-0.218843-0.219564-0.218025-0.216980-0.215805-0.216831-0.217879...-1.026594-1.032658-1.052336-0.974104-0.981528-1.020672-1.017468-0.986864-0.950039-0.957146
\n", "

226 rows × 376 columns

\n", "
" ], "text/plain": [ " 1960_gdp 1961_gdp 1962_gdp 1963_gdp 1964_gdp \\\n", "Country Name Country Code \n", "Aruba ABW -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "Afghanistan AFG -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "Angola AGO -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "Albania ALB -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "Andorra AND -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "... ... ... ... ... ... \n", "Kosovo XKX -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "Yemen, Rep. YEM -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "South Africa ZAF -0.169603 -0.168437 -0.167114 -0.165969 -0.165662 \n", "Zambia ZMB -0.221483 -0.220372 -0.219614 -0.219669 -0.219984 \n", "Zimbabwe ZWE -0.220994 -0.219722 -0.218875 -0.218843 -0.219564 \n", "\n", " 1965_gdp 1966_gdp 1967_gdp 1968_gdp 1969_gdp \\\n", "Country Name Country Code \n", "Aruba ABW -0.202832 -0.202705 -0.202662 -0.203255 -0.204786 \n", "Afghanistan AFG -0.202832 -0.202705 -0.202662 -0.203255 -0.204786 \n", "Angola AGO -0.202832 -0.202705 -0.202662 -0.203255 -0.204786 \n", "Albania ALB -0.202832 -0.202705 -0.202662 -0.203255 -0.204786 \n", "Andorra AND -0.202832 -0.202705 -0.202662 -0.203255 -0.204786 \n", "... ... ... ... ... ... \n", "Kosovo XKX -0.202832 -0.202705 -0.202662 -0.203255 -0.204786 \n", "Yemen, Rep. YEM -0.202832 -0.202705 -0.202662 -0.203255 -0.204786 \n", "South Africa ZAF -0.163839 -0.163451 -0.160740 -0.162603 -0.164274 \n", "Zambia ZMB -0.218117 -0.217284 -0.216134 -0.217168 -0.218587 \n", "Zimbabwe ZWE -0.218025 -0.216980 -0.215805 -0.216831 -0.217879 \n", "\n", " ... GDP_calculated_2013 GDP_calculated_2014 \\\n", "Country Name Country Code ... \n", "Aruba ABW ... -1.017331 -1.023442 \n", "Afghanistan AFG ... -1.015306 -1.021407 \n", "Angola AGO ... -0.465233 -0.657339 \n", "Albania ALB ... -1.024486 -1.030680 \n", "Andorra AND ... -1.017377 -1.023472 \n", "... ... ... ... \n", "Kosovo XKX ... -1.028729 -1.034928 \n", "Yemen, Rep. YEM ... -1.018770 -1.025404 \n", "South Africa ZAF ... -0.933638 -0.942189 \n", "Zambia ZMB ... -1.009544 -1.021177 \n", "Zimbabwe ZWE ... -1.026594 -1.032658 \n", "\n", " GDP_calculated_2015 GDP_calculated_2016 \\\n", "Country Name Country Code \n", "Aruba ABW -1.046426 -0.965575 \n", "Afghanistan AFG -1.044415 -0.963573 \n", "Angola AGO -0.932675 -0.812782 \n", "Albania ALB -1.050654 -0.972267 \n", "Andorra AND -1.046447 -0.965590 \n", "... ... ... \n", "Kosovo XKX -1.054572 -0.976205 \n", "Yemen, Rep. YEM -1.049979 -0.972641 \n", "South Africa ZAF -0.971863 -0.888590 \n", "Zambia ZMB -1.047666 -0.963273 \n", "Zimbabwe ZWE -1.052336 -0.974104 \n", "\n", " GDP_calculated_2017 GDP_calculated_2018 \\\n", "Country Name Country Code \n", "Aruba ABW -0.972609 -1.011146 \n", "Afghanistan AFG -0.970639 -1.009221 \n", "Angola AGO -0.736018 -0.664243 \n", "Albania ALB -0.979659 -1.018762 \n", "Andorra AND -0.972647 -1.011185 \n", "... ... ... \n", "Kosovo XKX -0.983589 -1.022714 \n", "Yemen, Rep. YEM -0.980441 -1.018888 \n", "South Africa ZAF -0.883052 -0.940374 \n", "Zambia ZMB -0.970319 -1.008834 \n", "Zimbabwe ZWE -0.981528 -1.020672 \n", "\n", " GDP_calculated_2019 GDP_calculated_2020 \\\n", "Country Name Country Code \n", "Aruba ABW -1.006774 -0.976539 \n", "Afghanistan AFG -1.004804 -0.972484 \n", "Angola AGO -0.802329 -0.931221 \n", "Albania ALB -1.015177 -0.984239 \n", "Andorra AND -1.006798 -0.976514 \n", "... ... ... \n", "Kosovo XKX -1.019152 -0.988298 \n", "Yemen, Rep. YEM -1.000565 -0.970490 \n", "South Africa ZAF -0.938708 -0.791299 \n", "Zambia ZMB -1.003544 -0.951182 \n", "Zimbabwe ZWE -1.017468 -0.986864 \n", "\n", " GDP_calculated_2021 GDP_calculated_2022 \n", "Country Name Country Code \n", "Aruba ABW -0.925748 -0.925497 \n", "Afghanistan AFG -0.923705 -0.918717 \n", "Angola AGO -0.917089 -0.923103 \n", "Albania ALB -0.946538 -0.954034 \n", "Andorra AND -0.925774 -0.925529 \n", "... ... ... \n", "Kosovo XKX -0.951340 -0.959118 \n", "Yemen, Rep. YEM -0.919628 -0.918717 \n", "South Africa ZAF -0.852809 -0.859582 \n", "Zambia ZMB -0.923430 -0.923146 \n", "Zimbabwe ZWE -0.950039 -0.957146 \n", "\n", "[226 rows x 376 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Excluir filas que pertenecen a regiones y al mundo\n", "\n", "lista_regiones = [\n", " \"Africa Eastern and Southern\", \"Africa Western and Central\", \"Central Europe and the Baltics\",\n", " \"East Asia & Pacific\", \"Europe & Central Asia\", \"European Union\", \"Latin America & Caribbean\",\n", " \"Middle East & North Africa\", \"North America\", \"OECD members\", \"Sub-Saharan Africa (excluding high income)\",\n", " \"South Asia (IDA & IBRD)\"\n", "]\n", "country_mask = (~df_combined.index.get_level_values('Country Name').isin(lista_regiones + ['World']))\n", "df_countries = df_combined[country_mask]\n", "df_countries" ] }, { "cell_type": "markdown", "id": "b6c3c8fa", "metadata": {}, "source": [ "### Regions (De referencia, no se aplicará)" ] }, { "cell_type": "code", "execution_count": 6, "id": "3ef53e13", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1960_gdp1961_gdp1962_gdp1963_gdp1964_gdp1965_gdp1966_gdp1967_gdp1968_gdp1969_gdp...GDP_calculated_2013GDP_calculated_2014GDP_calculated_2015GDP_calculated_2016GDP_calculated_2017GDP_calculated_2018GDP_calculated_2019GDP_calculated_2020GDP_calculated_2021GDP_calculated_2022
Region
East Asia & Pacific-6.824558-6.801649-6.773692-6.739758-6.723409-6.765620-6.720207-6.669891-6.659509-6.649542...-3.674142-2.3588340.1215311.4594651.6903200.2263572.4951863.851648-0.500491-0.310485
Europe & Central Asia-9.691664-9.603688-9.578843-9.550707-9.599877-9.727407-9.740002-9.710470-9.759768-9.842725...-41.788147-43.454942-49.563682-43.578853-42.284450-43.163723-44.222898-45.346960-46.765078-46.937925
Latin America & Caribbean-8.464656-8.408003-8.379139-8.387486-8.404706-8.423448-8.409930-8.381006-8.407552-8.457507...-38.686483-38.834487-40.279676-36.497662-36.286598-37.868593-37.789479-36.552207-34.779918-34.828626
Middle East & North Africa-4.249103-4.229591-4.217366-4.204654-4.214414-4.276927-4.277744-4.264914-4.259781-4.281930...16.65988714.1861889.04005510.89264911.53191112.37387910.4001598.7803819.0380838.685722
North America2.4383802.3962442.4181132.3968262.3774472.4038342.4252442.3812012.3474752.280019...1.9299531.9331121.3271172.0662892.0230861.9276991.9333612.0908024.8299244.690498
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5 rows × 376 columns

\n", "
" ], "text/plain": [ " 1960_gdp 1961_gdp 1962_gdp 1963_gdp 1964_gdp \\\n", "Region \n", "East Asia & Pacific -6.824558 -6.801649 -6.773692 -6.739758 -6.723409 \n", "Europe & Central Asia -9.691664 -9.603688 -9.578843 -9.550707 -9.599877 \n", "Latin America & Caribbean -8.464656 -8.408003 -8.379139 -8.387486 -8.404706 \n", "Middle East & North Africa -4.249103 -4.229591 -4.217366 -4.204654 -4.214414 \n", "North America 2.438380 2.396244 2.418113 2.396826 2.377447 \n", "\n", " 1965_gdp 1966_gdp 1967_gdp 1968_gdp 1969_gdp \\\n", "Region \n", "East Asia & Pacific -6.765620 -6.720207 -6.669891 -6.659509 -6.649542 \n", "Europe & Central Asia -9.727407 -9.740002 -9.710470 -9.759768 -9.842725 \n", "Latin America & Caribbean -8.423448 -8.409930 -8.381006 -8.407552 -8.457507 \n", "Middle East & North Africa -4.276927 -4.277744 -4.264914 -4.259781 -4.281930 \n", "North America 2.403834 2.425244 2.381201 2.347475 2.280019 \n", "\n", " ... GDP_calculated_2013 GDP_calculated_2014 \\\n", "Region ... \n", "East Asia & Pacific ... -3.674142 -2.358834 \n", "Europe & Central Asia ... -41.788147 -43.454942 \n", "Latin America & Caribbean ... -38.686483 -38.834487 \n", "Middle East & North Africa ... 16.659887 14.186188 \n", "North America ... 1.929953 1.933112 \n", "\n", " GDP_calculated_2015 GDP_calculated_2016 \\\n", "Region \n", "East Asia & Pacific 0.121531 1.459465 \n", "Europe & Central Asia -49.563682 -43.578853 \n", "Latin America & Caribbean -40.279676 -36.497662 \n", "Middle East & North Africa 9.040055 10.892649 \n", "North America 1.327117 2.066289 \n", "\n", " GDP_calculated_2017 GDP_calculated_2018 \\\n", "Region \n", "East Asia & Pacific 1.690320 0.226357 \n", "Europe & Central Asia -42.284450 -43.163723 \n", "Latin America & Caribbean -36.286598 -37.868593 \n", "Middle East & North Africa 11.531911 12.373879 \n", "North America 2.023086 1.927699 \n", "\n", " GDP_calculated_2019 GDP_calculated_2020 \\\n", "Region \n", "East Asia & Pacific 2.495186 3.851648 \n", "Europe & Central Asia -44.222898 -45.346960 \n", "Latin America & Caribbean -37.789479 -36.552207 \n", "Middle East & North Africa 10.400159 8.780381 \n", "North America 1.933361 2.090802 \n", "\n", " GDP_calculated_2021 GDP_calculated_2022 \n", "Region \n", "East Asia & Pacific -0.500491 -0.310485 \n", "Europe & Central Asia -46.765078 -46.937925 \n", "Latin America & Caribbean -34.779918 -34.828626 \n", "Middle East & North Africa 9.038083 8.685722 \n", "North America 4.829924 4.690498 \n", "\n", "[5 rows x 376 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Archivo Codes es un metadata sobre códigos de paises.\n", "codes = pd.read_excel('Codes.xlsx')\n", "\n", "# Crear un mapeo de 'Country Code' a 'Region' usando solo las entradas en 'lista_regiones'\n", "filtered_codes = codes[codes['Region'].isin(lista_regiones)]\n", "country_to_region = filtered_codes.set_index('Country Code')['Region']\n", "\n", "# Aplicar este mapeo al DataFrame 'df_combined'\n", "df_combined['Region'] = df_combined.index.get_level_values('Country Code').map(country_to_region)\n", "\n", "# Filtrar el DataFrame para incluir solo filas donde la 'Region' esté definida\n", "df_regions = df_combined.dropna(subset=['Region'])\n", "\n", "# Agrupar por 'Region' y sumar los valores para cada columna\n", "df_regions = df_regions.groupby('Region').sum()\n", "\n", "df_regions.head()" ] }, { "cell_type": "code", "execution_count": 7, "id": "4307b4b6", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# Configurar Estilo de Seaborn\n", "sns.set(style=\"whitegrid\")\n", "\n", "# Seleccionar un subconjunto de columnas para la visualización por simplicidad\n", "columns_to_plot = [f'GDP_calculated_{year}' for year in range(2010, 2023)]\n", "\n", "# Crear un DataFrame para la visualización\n", "df_plot = df_regions[columns_to_plot].transpose()\n", "\n", "plt.figure(figsize=(20, 10))\n", "for column in df_plot.columns:\n", " plt.plot(df_plot.index, df_plot[column], marker='o', label=column)\n", "\n", "plt.title('GDP Calculated Over Time by Region')\n", "plt.xlabel('Year')\n", "plt.ylabel('GDP Calculated')\n", "plt.legend(title='Region', bbox_to_anchor=(1.05, 1), loc='upper left')\n", "plt.xticks(rotation=45)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "8ff34dfc", "metadata": {}, "source": [ "Por alguna razón, después de tantos errores, descubro que al crear 'Regions' se crean **NaN**, por lo que no trabajaré con éste DataFrame." ] }, { "cell_type": "markdown", "id": "6e06a067", "metadata": {}, "source": [ "## Guardar los DataFrames como CSV" ] }, { "cell_type": "code", "execution_count": 8, "id": "be01121b", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "array(['1960_gdp', '1961_gdp', '1962_gdp', '1963_gdp', '1964_gdp',\n", " '1965_gdp', '1966_gdp', '1967_gdp', '1968_gdp', '1969_gdp',\n", " '1970_gdp', '1971_gdp', '1972_gdp', '1973_gdp', '1974_gdp',\n", " '1975_gdp', '1976_gdp', '1977_gdp', '1978_gdp', '1979_gdp',\n", " '1980_gdp', '1981_gdp', '1982_gdp', '1983_gdp', '1984_gdp',\n", " '1985_gdp', '1986_gdp', '1987_gdp', '1988_gdp', '1989_gdp',\n", " '1990_gdp', '1991_gdp', '1992_gdp', '1993_gdp', '1994_gdp',\n", " '1995_gdp', '1996_gdp', '1997_gdp', '1998_gdp', '1999_gdp',\n", " '2000_gdp', '2001_gdp', '2002_gdp', '2003_gdp', '2004_gdp',\n", " '2005_gdp', '2006_gdp', '2007_gdp', '2008_gdp', '2009_gdp',\n", " '2010_gdp', '2011_gdp', '2012_gdp', '2013_gdp', '2014_gdp',\n", " '2015_gdp', '2016_gdp', '2017_gdp', '2018_gdp', '2019_gdp',\n", " '2020_gdp', '2021_gdp', '2022_gdp', '1960_gov', '1961_gov',\n", " '1962_gov', '1963_gov', '1964_gov', '1965_gov', '1966_gov',\n", " '1967_gov', '1968_gov', '1969_gov', '1970_gov', '1971_gov',\n", " '1972_gov', '1973_gov', '1974_gov', '1975_gov', '1976_gov',\n", " '1977_gov', '1978_gov', '1979_gov', '1980_gov', '1981_gov',\n", " '1982_gov', '1983_gov', '1984_gov', '1985_gov', '1986_gov',\n", " '1987_gov', '1988_gov', '1989_gov', '1990_gov', '1991_gov',\n", " '1992_gov', '1993_gov', '1994_gov', '1995_gov', '1996_gov',\n", " '1997_gov', '1998_gov', '1999_gov', '2000_gov', '2001_gov',\n", " '2002_gov', '2003_gov', '2004_gov', '2005_gov', '2006_gov',\n", " '2007_gov', '2008_gov', '2009_gov', '2010_gov', '2011_gov',\n", " '2012_gov', '2013_gov', '2014_gov', '2015_gov', '2016_gov',\n", " '2017_gov', '2018_gov', '2019_gov', '2020_gov', '2021_gov',\n", " '2022_gov', '1960', '1961', '1962', '1963', '1964', '1965', '1966',\n", " '1967', '1968', '1969', '1970', '1971', '1972', '1973', '1974',\n", " '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982',\n", " '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990',\n", " '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998',\n", " '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006',\n", " '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014',\n", " '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022',\n", " '1960_con', '1961_con', '1962_con', '1963_con', '1964_con',\n", " '1965_con', '1966_con', '1967_con', '1968_con', '1969_con',\n", " '1970_con', '1971_con', '1972_con', '1973_con', '1974_con',\n", " '1975_con', '1976_con', '1977_con', '1978_con', '1979_con',\n", " '1980_con', '1981_con', '1982_con', '1983_con', '1984_con',\n", " '1985_con', '1986_con', '1987_con', '1988_con', '1989_con',\n", " '1990_con', '1991_con', '1992_con', '1993_con', '1994_con',\n", " '1995_con', '1996_con', '1997_con', '1998_con', '1999_con',\n", " '2000_con', '2001_con', '2002_con', '2003_con', '2004_con',\n", " '2005_con', '2006_con', '2007_con', '2008_con', '2009_con',\n", " '2010_con', '2011_con', '2012_con', '2013_con', '2014_con',\n", " '2015_con', '2016_con', '2017_con', '2018_con', '2019_con',\n", " '2020_con', '2021_con', '2022_con', '1960_trade', '1961_trade',\n", " '1962_trade', '1963_trade', '1964_trade', '1965_trade',\n", " '1966_trade', '1967_trade', '1968_trade', '1969_trade',\n", " '1970_trade', '1971_trade', '1972_trade', '1973_trade',\n", " '1974_trade', '1975_trade', '1976_trade', '1977_trade',\n", " '1978_trade', '1979_trade', '1980_trade', '1981_trade',\n", " '1982_trade', '1983_trade', '1984_trade', '1985_trade',\n", " '1986_trade', '1987_trade', '1988_trade', '1989_trade',\n", " '1990_trade', '1991_trade', '1992_trade', '1993_trade',\n", " '1994_trade', '1995_trade', '1996_trade', '1997_trade',\n", " '1998_trade', '1999_trade', '2000_trade', '2001_trade',\n", " '2002_trade', '2003_trade', '2004_trade', '2005_trade',\n", " '2006_trade', '2007_trade', '2008_trade', '2009_trade',\n", " '2010_trade', '2011_trade', '2012_trade', '2013_trade',\n", " '2014_trade', '2015_trade', '2016_trade', '2017_trade',\n", " '2018_trade', '2019_trade', '2020_trade', 'GDP_calculated_1960',\n", " 'GDP_calculated_1961', 'GDP_calculated_1962',\n", " 'GDP_calculated_1963', 'GDP_calculated_1964',\n", " 'GDP_calculated_1965', 'GDP_calculated_1966',\n", " 'GDP_calculated_1967', 'GDP_calculated_1968',\n", " 'GDP_calculated_1969', 'GDP_calculated_1970',\n", " 'GDP_calculated_1971', 'GDP_calculated_1972',\n", " 'GDP_calculated_1973', 'GDP_calculated_1974',\n", " 'GDP_calculated_1975', 'GDP_calculated_1976',\n", " 'GDP_calculated_1977', 'GDP_calculated_1978',\n", " 'GDP_calculated_1979', 'GDP_calculated_1980',\n", " 'GDP_calculated_1981', 'GDP_calculated_1982',\n", " 'GDP_calculated_1983', 'GDP_calculated_1984',\n", " 'GDP_calculated_1985', 'GDP_calculated_1986',\n", " 'GDP_calculated_1987', 'GDP_calculated_1988',\n", " 'GDP_calculated_1989', 'GDP_calculated_1990',\n", " 'GDP_calculated_1991', 'GDP_calculated_1992',\n", " 'GDP_calculated_1993', 'GDP_calculated_1994',\n", " 'GDP_calculated_1995', 'GDP_calculated_1996',\n", " 'GDP_calculated_1997', 'GDP_calculated_1998',\n", " 'GDP_calculated_1999', 'GDP_calculated_2000',\n", " 'GDP_calculated_2001', 'GDP_calculated_2002',\n", " 'GDP_calculated_2003', 'GDP_calculated_2004',\n", " 'GDP_calculated_2005', 'GDP_calculated_2006',\n", " 'GDP_calculated_2007', 'GDP_calculated_2008',\n", " 'GDP_calculated_2009', 'GDP_calculated_2010',\n", " 'GDP_calculated_2011', 'GDP_calculated_2012',\n", " 'GDP_calculated_2013', 'GDP_calculated_2014',\n", " 'GDP_calculated_2015', 'GDP_calculated_2016',\n", " 'GDP_calculated_2017', 'GDP_calculated_2018',\n", " 'GDP_calculated_2019', 'GDP_calculated_2020',\n", " 'GDP_calculated_2021', 'GDP_calculated_2022'], dtype=object)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Detalles de las columnas\n", "df_world.columns.values[:376]\n", "df_countries.columns.values[:376]" ] }, { "cell_type": "code", "execution_count": 9, "id": "def5e36d", "metadata": {}, "outputs": [], "source": [ "# Guardar el DataFrame como CSV\n", "df_world.to_csv('df_world.csv')\n", "df_countries.to_csv('df_countries.csv')" ] }, { "cell_type": "markdown", "id": "057d910d", "metadata": {}, "source": [ "### Diagnósticos" ] }, { "cell_type": "code", "execution_count": 10, "id": "65f693bf", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "( 1960_gdp 1961_gdp 1962_gdp 1963_gdp 1964_gdp 1965_gdp 1966_gdp \\\n", " count 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n", " mean 8.925325 8.916576 8.903261 8.903658 8.909846 8.905935 8.893228 \n", " std NaN NaN NaN NaN NaN NaN NaN \n", " min 8.925325 8.916576 8.903261 8.903658 8.909846 8.905935 8.893228 \n", " 25% 8.925325 8.916576 8.903261 8.903658 8.909846 8.905935 8.893228 \n", " 50% 8.925325 8.916576 8.903261 8.903658 8.909846 8.905935 8.893228 \n", " 75% 8.925325 8.916576 8.903261 8.903658 8.909846 8.905935 8.893228 \n", " max 8.925325 8.916576 8.903261 8.903658 8.909846 8.905935 8.893228 \n", " \n", " 1967_gdp 1968_gdp 1969_gdp ... GDP_calculated_2013 \\\n", " count 1.000000 1.000000 1.000000 ... 1.000000 \n", " mean 8.892912 8.894431 8.920468 ... 19.945839 \n", " std NaN NaN NaN ... NaN \n", " min 8.892912 8.894431 8.920468 ... 19.945839 \n", " 25% 8.892912 8.894431 8.920468 ... 19.945839 \n", " 50% 8.892912 8.894431 8.920468 ... 19.945839 \n", " 75% 8.892912 8.894431 8.920468 ... 19.945839 \n", " max 8.892912 8.894431 8.920468 ... 19.945839 \n", " \n", " GDP_calculated_2014 GDP_calculated_2015 GDP_calculated_2016 \\\n", " count 1.000000 1.000000 1.000000 \n", " mean 19.911398 17.391711 19.909714 \n", " std NaN NaN NaN \n", " min 19.911398 17.391711 19.909714 \n", " 25% 19.911398 17.391711 19.909714 \n", " 50% 19.911398 17.391711 19.909714 \n", " 75% 19.911398 17.391711 19.909714 \n", " max 19.911398 17.391711 19.909714 \n", " \n", " GDP_calculated_2017 GDP_calculated_2018 GDP_calculated_2019 \\\n", " count 1.000000 1.000000 1.000000 \n", " mean 19.897277 19.865401 19.844482 \n", " std NaN NaN NaN \n", " min 19.897277 19.865401 19.844482 \n", " 25% 19.897277 19.865401 19.844482 \n", " 50% 19.897277 19.865401 19.844482 \n", " 75% 19.897277 19.865401 19.844482 \n", " max 19.897277 19.865401 19.844482 \n", " \n", " GDP_calculated_2020 GDP_calculated_2021 GDP_calculated_2022 \n", " count 1.00000 1.000000 1.000000 \n", " mean 19.80143 31.407289 31.527472 \n", " std NaN NaN NaN \n", " min 19.80143 31.407289 31.527472 \n", " 25% 19.80143 31.407289 31.527472 \n", " 50% 19.80143 31.407289 31.527472 \n", " 75% 19.80143 31.407289 31.527472 \n", " max 19.80143 31.407289 31.527472 \n", " \n", " [8 rows x 376 columns],\n", " 1960_gdp 1961_gdp 1962_gdp 1963_gdp 1964_gdp 1965_gdp \\\n", " count 226.000000 226.000000 226.000000 226.000000 226.000000 226.000000 \n", " mean -0.169781 -0.169241 -0.168657 -0.168409 -0.168818 -0.169926 \n", " std 0.228428 0.225131 0.224893 0.223695 0.223447 0.224810 \n", " min -0.224510 -0.223332 -0.222338 -0.222336 -0.222801 -0.221246 \n", " 25% -0.205611 -0.208913 -0.208103 -0.206509 -0.207993 -0.209509 \n", " 50% -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 -0.202832 \n", " 75% -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 -0.202832 \n", " max 2.672641 2.629604 2.645718 2.624166 2.605340 2.627229 \n", " \n", " 1966_gdp 1967_gdp 1968_gdp 1969_gdp ... \\\n", " count 226.000000 226.000000 226.000000 226.000000 ... \n", " mean -0.169695 -0.169360 -0.170062 -0.171161 ... \n", " std 0.225328 0.223013 0.221838 0.220208 ... \n", " min -0.220064 -0.219031 -0.219934 -0.221195 ... \n", " 25% -0.211203 -0.211326 -0.213195 -0.214294 ... \n", " 50% -0.202705 -0.202662 -0.203255 -0.204786 ... \n", " 75% -0.202705 -0.202662 -0.203255 -0.204786 ... \n", " max 2.644509 2.600452 2.570434 2.506399 ... \n", " \n", " GDP_calculated_2013 GDP_calculated_2014 GDP_calculated_2015 \\\n", " count 226.000000 226.000000 226.000000 \n", " mean -0.477203 -0.499640 -0.564891 \n", " std 2.360871 2.346446 2.403204 \n", " min -1.030556 -1.036704 -1.056181 \n", " 25% -1.017725 -1.023818 -1.048722 \n", " 50% -1.011065 -1.017360 -1.040619 \n", " 75% -0.861807 -0.875602 -0.940122 \n", " max 28.144082 27.835159 27.572665 \n", " \n", " GDP_calculated_2016 GDP_calculated_2017 GDP_calculated_2018 \\\n", " count 226.000000 226.000000 226.000000 \n", " mean -0.495029 -0.487934 -0.501394 \n", " std 2.330991 2.336694 2.339151 \n", " min -0.978107 -0.985575 -1.024731 \n", " 25% -0.966506 -0.973869 -1.012617 \n", " 50% -0.959720 -0.966623 -1.005069 \n", " 75% -0.875190 -0.860159 -0.883994 \n", " max 27.837297 27.701692 27.367289 \n", " \n", " GDP_calculated_2019 GDP_calculated_2020 GDP_calculated_2021 \\\n", " count 226.000000 226.000000 226.000000 \n", " mean -0.504525 -0.502495 -0.510326 \n", " std 2.318477 2.319114 2.262333 \n", " min -1.021194 -0.990254 -0.954674 \n", " 25% -1.008344 -0.977401 -0.936421 \n", " 50% -1.000565 -0.970490 -0.923756 \n", " 75% -0.879247 -0.891251 -0.853708 \n", " max 26.262588 26.264463 26.564552 \n", " \n", " GDP_calculated_2022 \n", " count 226.000000 \n", " mean -0.512282 \n", " std 2.262235 \n", " min -0.962321 \n", " 25% -0.939380 \n", " 50% -0.921459 \n", " 75% -0.867499 \n", " max 26.579051 \n", " \n", " [8 rows x 376 columns])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_world.describe(), df_countries.describe()" ] }, { "cell_type": "code", "execution_count": 11, "id": "93ba0e45", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 0)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_world.isnull().sum().sum(), df_countries.isnull().sum().sum()" ] }, { "cell_type": "code", "execution_count": 12, "id": "a38896aa", "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "(1960_gdp float64\n", " 1961_gdp float64\n", " 1962_gdp float64\n", " 1963_gdp float64\n", " 1964_gdp float64\n", " ... \n", " GDP_calculated_2018 float64\n", " GDP_calculated_2019 float64\n", " GDP_calculated_2020 float64\n", " GDP_calculated_2021 float64\n", " GDP_calculated_2022 float64\n", " Length: 376, dtype: object,\n", " 1960_gdp float64\n", " 1961_gdp float64\n", " 1962_gdp float64\n", " 1963_gdp float64\n", " 1964_gdp float64\n", " ... \n", " GDP_calculated_2018 float64\n", " GDP_calculated_2019 float64\n", " GDP_calculated_2020 float64\n", " GDP_calculated_2021 float64\n", " GDP_calculated_2022 float64\n", " Length: 376, dtype: object)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_world.dtypes, df_countries.dtypes" ] }, { "cell_type": "code", "execution_count": 13, "id": "606c081e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((1, 376), (226, 376))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_world.shape, df_countries.shape" ] }, { "cell_type": "code", "execution_count": 14, "id": "ceeafd60", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "(Index(['1960_gdp', '1961_gdp', '1962_gdp', '1963_gdp', '1964_gdp', '1965_gdp',\n", " '1966_gdp', '1967_gdp', '1968_gdp', '1969_gdp',\n", " ...\n", " 'GDP_calculated_2013', 'GDP_calculated_2014', 'GDP_calculated_2015',\n", " 'GDP_calculated_2016', 'GDP_calculated_2017', 'GDP_calculated_2018',\n", " 'GDP_calculated_2019', 'GDP_calculated_2020', 'GDP_calculated_2021',\n", " 'GDP_calculated_2022'],\n", " dtype='object', length=376),\n", " Index(['1960_gdp', '1961_gdp', '1962_gdp', '1963_gdp', '1964_gdp', '1965_gdp',\n", " '1966_gdp', '1967_gdp', '1968_gdp', '1969_gdp',\n", " ...\n", " 'GDP_calculated_2013', 'GDP_calculated_2014', 'GDP_calculated_2015',\n", " 'GDP_calculated_2016', 'GDP_calculated_2017', 'GDP_calculated_2018',\n", " 'GDP_calculated_2019', 'GDP_calculated_2020', 'GDP_calculated_2021',\n", " 'GDP_calculated_2022'],\n", " dtype='object', length=376))" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_world.columns, df_countries.columns" ] }, { "cell_type": "code", "execution_count": 15, "id": "2c78a78d", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "(Index(['WLD'], dtype='object', name='Country Code'),\n", " MultiIndex([( 'Aruba', 'ABW'),\n", " ( 'Afghanistan', 'AFG'),\n", " ( 'Angola', 'AGO'),\n", " ( 'Albania', 'ALB'),\n", " ( 'Andorra', 'AND'),\n", " ( 'Arab World', 'ARB'),\n", " ( 'United Arab Emirates', 'ARE'),\n", " ( 'Argentina', 'ARG'),\n", " ( 'Armenia', 'ARM'),\n", " ( 'American Samoa', 'ASM'),\n", " ...\n", " ('British Virgin Islands', 'VGB'),\n", " ( 'Virgin Islands (U.S.)', 'VIR'),\n", " ( 'Viet Nam', 'VNM'),\n", " ( 'Vanuatu', 'VUT'),\n", " ( 'Samoa', 'WSM'),\n", " ( 'Kosovo', 'XKX'),\n", " ( 'Yemen, Rep.', 'YEM'),\n", " ( 'South Africa', 'ZAF'),\n", " ( 'Zambia', 'ZMB'),\n", " ( 'Zimbabwe', 'ZWE')],\n", " names=['Country Name', 'Country Code'], length=226))" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_world.index, df_countries.index" ] }, { "cell_type": "code", "execution_count": 16, "id": "e2ec36d8", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 1 entries, WLD to WLD\n", "Columns: 376 entries, 1960_gdp to GDP_calculated_2022\n", "dtypes: float64(376)\n", "memory usage: 2.9+ KB\n", "\n", "MultiIndex: 226 entries, ('Aruba', 'ABW') to ('Zimbabwe', 'ZWE')\n", "Columns: 376 entries, 1960_gdp to GDP_calculated_2022\n", "dtypes: float64(376)\n", "memory usage: 684.8+ KB\n" ] }, { "data": { "text/plain": [ "(None, None)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_world.info(), df_countries.info()" ] }, { "cell_type": "markdown", "id": "02ba87fe", "metadata": {}, "source": [ "## Pasos Claves Faltantes\n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "aa2cab2e", "metadata": {}, "source": [ "#### Verificación de Outliers" ] }, { "cell_type": "code", "execution_count": 17, "id": "6fe3d883", "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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OP08pPQ34WM55//L9a4F9c85vHG1YF1xwwbbA9bvuuiuTJk1aruQlSZIkSZKkttVRZOtWNOxWKOxmzpw5TJs2DWC7vffe+4Z+4xm1mJdS2gr4O/CKnPOp5bPJwNXAvsD1wPHAETnnX402YRbzJEmSJEmStCIdeeSRixXPhs3SFPMGuc32/cBk4KspLbqz9rvAG4ir9SYDJwC/XrZ0JUmSJEmSpKXTFPC63Qr7z2yQF2AcAhzS4+s9Vmw6kiRJkiRJ0uj+lQp4tYFfgCFJkiRJkqSHh/oZbP9KV6itjmfhPdwM9AKMFcln5kmSJEmSJK0c/Z4d1+utrg/3AuC/QgFvhb4AY0WzmCdJkiRJkrRqDFIIaxcA65heBcCme9C3tS6NYX+ZxbKwmCdJkiRJkqSHtV5Fw4f7lYIrw4p+m60kSZIkSZI0kH6389YFvPqqu3+VK/BWBIt5kiRJkiRJWmH6FeYs2i2/sas7AUmSJEmSJEmDsZgnSZIkSZIkDQmLeZIkSZIkSdKQsJgnSZIkSZIkDQmLeZIkSZIkSdKQsJgnSZIkSZIkDQmLeZIkSZIkSdKQsJgnSZIkSZIkDQmLeZIkSZIkSdKQsJgnSZIkSZIkDQmLeZIkSZIkSdKQsJgnSZIkSZIkDQmLeZIkSZIkSdKQGL+6E5AkSZIkSZIero488kimTp3K9OnTOfDAA1d3OhbzJEmSJEmSpF4eDgW8mrfZSpIkSZIkSUPCYp4kSZIkSZI0JCzmSZIkSZIkSUPCYp4kSZIkSZI0JCzmSZIkSZIkSUPCYp4kSZIkSZI0JCzmSZIkSZIkSUPCYp4kSZIkSZI0JCzmSZIkSZIkSUPCYp4kSZIkSZI0JCzmSZIkSZIkSUPCYp4kSZIkSZI0JCzmSZIkSZIkSUPCYp4kSZIkSZI0JCzmSZIkSZIkSUPCYp4kSZIkSZI0JCzmSZIkSZIkSUNi/KA9ppTWAf4CPD/nfENK6QjgKcCM0suncs7HroQcJUmSJEmSJDFgMS+ltA/wfWCn6uPHAk/NOd+2MhKTJEmSJEmStLhBb7N9C/AO4FaAlNKawNbA91NKl6SUPpVS8pZdSZIkSZIkaSUaqACXc35zzvnP1UebAKcCbwQeT9xu+6YVn54kSZIkSZKkxsDPzKvlnK8D/r35O6X0LeB1xK24kiRJkiRJklaCZbo1NqW0W0rppdVHY4B5KyYlSZIkSZIkSd0s05V5RPHu6ymlU4GHgLcCR62wrCRJkiRJkiQtYZmuzMs5XwJ8ATgbuBy4KOf8sxWZmCRJkiRJkqTFLdWVeTnnbavu7wDfWdEJSZIkSZIkSepuma7MkyRJkiRJkrTqWcyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIWMyTJEmSJEmShoTFPEmSJEmSJGlIjF/dCUiSJP2rOfzwwwE46KCDOPzww9l8882ZPn06AFOnTmX69OkceOCBqyUfSZIkPbyNGRkZWaUjvOCCC7YFrt91112ZNGnSKh23JEnSqnLkkUcuKsxBFOluvfXWgQtmTXy3mH4FwHahsO5uxyxNPpIkSVp55syZw7Rp0wC223vvvW/o16/FPEmSpBXEK9wkSZK0LJammOdttpIkScuhLuBZxJMkSdLKZjFPkiQtk/ZtoIPc3rkqnwO3srSn2wKeJEmSViVvs5UkSavMaAXAQYp+q6pQ+M9ckJQkSdLDi8/MkyRJQ21ZXh5RxwxacGvH+Mw7SZIkrQ4W8yRJ0r+0FXEFoCRJkrSqWMyTJEmSJEmShsTSFPPGrpKMJEmSJEmSJC23gd5mm1JaB/gL8Pyc8w0ppf2BrwJTgF/knD+6EnOUJEmSJEmSxABX5qWU9gHOAnYqf08BjgBeBOwCPDal9JyVmaQkSZIkSZKkwW6zfQvwDuDW8vfjgKtzztfnnOcDPwYOWEn5SZIkSZIkSSpGvc025/xmgJRS89HmwG1VL7cBW67wzCRJkiRJkiQtZllegDGmy2cLlzcRSZIkSZIkSf0tSzHvFmDT6u/N6NyCK0mSJEmSJGklGehtti3nAimltANwPfBq4oUYkiRJkiRJklaipb4yL+c8G3gDcAxwOXAl8OsVm5YkSZIkSZKktoGvzMs5b1t1nwLssTISkiRJkiRJktTdsjwzT5IkSZIkSdJqYDFPkiRJkiRJGhIW8yRJkiRJkqQhsSxvs5WGzpFHHsnUqVO59dZbOeiggzj88MMBFnVvvvnmTJ8+HaBvf8sT0+QwSEx72NOnT+fAAw9cFbNKkiRJkiQ9jI0ZGRlZpSO84IILtgWuv+iii5g7d+4KKaYMWuSoiykWRobX0hbm/L0lSZIkSdLD2Zw5c5g2bRrAdnvvvfcN/fpdbcW8XXfdlUmTJq2QYQ5a3LGgs3KN9js03V6hJknS0qv3kZIkSfrn8i9XzHu4aV8B+HC6VXNZYgadBotskiRJkiRJS89i3gCW5VbNptsrACVJkiRJkrSiWMyTJEmSJEmShsTSFPN8m60kSZIkSVrhlvcOOEndeWWeJEmSJEl62PG57vpX4m22kiRJkiRJy2nQl0b2i1/amEHz0T8Xi3mSJEmSJEk91EWx5gWZ3W77HUS/F2wOmoNFOlnMkyRJkiRJ/9KWtci2unW7mm9piosaThbzJEmSJEmSpCGxNMW8saskI0mSJEmSJEnLzWKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDYvzyBKeUTgU2AeaVj96Wcz53ubOSJEmSJEmStIRlLuallMYAOwNb55znr7iUJEmSJEmSJHWzPLfZJmAE+ENK6eKU0jtXUE6SJEmSJEmSulieYt76wCnAi4H9gLenlP5tRSQlSZIkSZIkaUnLfJttzvkc4Jzy54yU0g+B5wJ/WhGJSZIkSZIkSVrcMl+Zl1J6ckppv+qjMXRehCFJkiRJkiRpBVuet9muB3w6pfREYALweuDtKyIpSZIkSZIkSUta5ivzcs7HA78HLgQuAI4ot95KkiRJkiRJWgmW58o8cs4fAz62gnKRJEmSJEmS1MfyvM1WkiRJkiRJ0ipkMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCFhMU+SJEmSJEkaEhbzJEmSJEmSpCExfnmCU0qvBj4KTAS+lnP+fyskK0mSJEmSJElLWOYr81JKWwCfA54M7AG8NaX0yBWVmCRJkiRJkqTFLc9ttvsDp+acp+ecZwC/Bl62YtKSJEmSJEmS1LY8xbzNgduqv28Dtly+dCRJkiRJkiT1sjzPzBvT5bOFyzE8SX0ceeSRTJ06lVtvvZWDDjqIww8/HGBR9+abb8706dMBmDp1KtOnT+fAAw9cor9+Mb2G3XQ3OQwS0y2fVREz2jQsTczqmgZ/uxX32x144IFIWjrDtu34Z9lm/jNMw9LG/DPtsyVJWpXGjIyMLFNgSun1wFNyzm8uf38MGJNz/nS/uAsuuGBb4Ppdd92VSZMmLdO4JUkahAfkK3caPICVpLC82/0Vqd8J4G79rexC7LLsKwY9id1v2MuyvxvUyvrtJP1rmzNnDtOmTQPYbu+9976hX7/LU8zbAjgLeBwwA/gL8Nac83n94izmSZIkSVLHijyJM1qBa1WfkFnWu0uWZtjd5tWyGLSI2SumzmdZfodlmVft3B5uV8RKGtwqKeYBpJReDXwEmAj8IOf836PFWMyTJEmSJP0zW5YrJL3iXfrXtsqKecvCYp4kSZIkSZLUsTTFvOV5m60kSZIkSZKkVchiniRJkiRJkjQkLOZJkiRJkiRJQ8JiniRJkiRJkjQkLOZJkiRJkiRJQ8JiniRJkiRJkjQkLOZJkiRJkiRJQ8JiniRJkiRJkjQkLOZJkiRJkiRJQ8JiniRJkiRJkjQkLOZJkiRJkiRJQ2L8ahjnOIC5c+euhlFLkiRJkiRJDy9VnWzcaP2ujmLeZgBXXXXVahi1JEmSJEmS9LC1GXBtvx5WRzHvb8BTgNuABath/JIkSZIkSdLDyTiikPe30XocMzIysvLTkSRJkiRJkrTcfAGGJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5kmSJEmSJElDwmKeJEmSJEmSNCQs5knSEEgpbditu993KaU9W/2NW4aYPUfrb5R86v66jn+UmEHHs9h3kiRJGn4ppbVTSpP/1XOQ2saMjIys7hz0Lyil9GLgGcB84A/AmtXf9wMbdOn+I7Bz1d804L7SvQBIo8QsAOYAa5TvbiufLQQmAltW+dzVJZ92f/X427n1ivkjMLnqr8mh33RflXP+bjXv9sw5X9Svu8v83jDnfPfSxLTie8YMMrx6/Muaw/JaVeNZWTmklG4CLgZ+DHy56j4OuLrHd+8HJgE/Kf/+ChxbvvvVgDG/IZbZnwDvAv7epb/683Y+dX/vBn7dZfz9Yl5FnHgabTyLzZOc8+yln8tLSimtDcxbUcMb1hyGTUppDeDjwAHAFsS2+Fbgz8BGwGbEuvD/gPcB9wLPAaYAuwF3AE8rg/t/wHOBB4j9w6uJbfO5wBtzzte2xr0JMAYYBxw8Sg4nEMv3dODnwKXAeOAsIOec35NS2ryVw7rAtcBWwAV9clgDOAR4QWv8fwAOBz5fhnEs8IWc84ISe3zO+fkDz2z900kpbQO8E5hKLMtrAo8EzgPWKt2TgAur7rNK/3sA1wETgE3K/8cC+xDr1zV9YtYGtiXWgSuIttOEMt49SsyV5f8tgZNb+Vxe9XdNNf4fEevNPkDuE3NzNf6L+oynPd0A5JzfuFQzWv90UkrbAo8CTgSeQCx3JwJbAyM9vvt3YEPgSGK/czuxL1iD2FeMFrMPcSyxW/n7RcCMLv3Vn7fz6ZXDzsAjBoh5E7GPaY9n29K9ELin2c/0mX9jgPWBL+ec35RSejLwxTLOEWK/+8Gc86XtY4sSvwZxjDWTOEa7nthvbjRIDs34c87TU0o/7JcDcFt7/D1ymENs2xYCt+acb67GtVbO+cFW/E7E9ubKnPPMlNLTcs5npJTGA7sA84hl42ri2DfXw0gpbQU8kWiDLATmAg+Wf8ucA3B2M/6c85Uppb365LA98Joy/XcCzySWq4dzW6hvDsBHif3JkYPmkFLaoPS3ZY8c2m3SpW6LrfJiXkppAvAOliyutAstddFjDNEA2Lh8dy2xYMyjd6Gkjhkpw55SYu4mFuy5rZhe+bTH/wfioPHJLF6saU/D6cD2wL6tHBb26J4HnFryeWorBqLRsFnp77oyHTP7jKeOWUCsxOuX6T6ZWMEe3ye3dj5jeuQwrzV/esU0OTQHOhcRC+47S05fIXYMjwVOIzYiTfcpxEp0G/BfwEuIA6RMZ8dyDLB5n5j3ADsRK9tC4CnEirpmmX/HE4WLL5dxf6GVz6ZVfwur8Z8JvKHMi2/2iflN+X4OcfD4xiqHkT7T/TngIeDrLFlcqQsddfdPgL2B3+ec57UKQb0KJe2YF1fdf+sxzn753AUc3WX8xxEbr9FiFo0fFhWAu+XWr793Az/LOd/RmoZF4xklpu5e6nzq+BLTNYcuw14iBzoF5lcB+xHL3gLigOJ04DKi0dX+7gJiR/IYokh8DbHT2YI4QJoMPK5PzD3AScQO7eXEsv8AnR1X01/9eTufpr/HleEsKL95Pf5eMY8htnO3AjuMMp56Gs4Afkesn6uqAbkLMJtotM0FbiR2/Cu0EcsqakCWzxZrwNGnAVn6rxtwN7P6GpAvJZaNI4lGLWVeHFim41hin7kl8G1i3/FKYn/0Y+IA/9ISO4PYP/2cWOYeAZxThjeB2LbNBH5A7FvXKeObR+wTjwH+s/SzGbFtn1VyOb58/hc6xcOdgVcAX8s5r5dSuqeVw03Eb3smsY94JbHsd8thVun3GmI9nkwULp5ItH9uL/PhOuCFOef5KaUry7RPZfHiyjUsXuhYq/x9KbFPbxxIpxC0AYsXYbrG5Jyf0XSnlB4NfIROIamO6ZfPomG1ilF1Du1izRLjXxG6TMNKGc/KyiGldC6xn55GbHs+DlwF/AL4WOm+mdjeNN1nAZ8llu8jiLbUDOJg+t+I7eGdwO59Yo4A/q/EvA24pHT/R+nnFqJAcHj57sRWPs+u+tu/Gv8NwFtKDt/vE/OKavwX9RlPe7oBdix/dyuu1IWOuvu/iAPuzwEvI9bp0Qol+xDbrUeVuMOIkxGfAz5cpqNbTK986hzeTmxLmsLN5gPE1ON/GbHN65Zbr/7+m9huzSLW2a+Uef+DLvOnW8x7iOXydmI7+0Ni2/CZAfNpj//dRPthlz7T2o55X5m/+5XfbDNi3fsWURjYjVheHiTaBl8EvlO++yaxfk0v+f+i9Ldr+S3uKfO9V8yuxL7uodLfl4iCx0PA16r+Plp9/shWPkf0yOEjJeZOYM8+Md8sw58JfKjEzCCW5xeVfmYR2+8/E/vcLxHtuD8S7cE7iTbb78o41iIKJscRxZaLy/iOI47P7i2/7TyifXwnsW7/lNjejadTZBlLnKAbKTlcXH6nTVvj/z7wtzL+6YT9ie3gj4AnEduilxDbrYfKuD9C1DZmdslhNlEnuL/Mvw3LvPgGsYxOItozY4ll+DRi+bqaaA/eXeKb5W9dYn+dynAuA7Yr0zYdOBT4LnFsO6HkMaP8PZ84SbLeUuTwCGKdOLRMD8Tx/g1Em3wW0V6qc/hhyfeBMg33l/nwINEOuZsl22P/Ubp/z8ptC/2SOA5eF3gd8F7gA8R2vm97rMz3y8p33XK4gNiGHkss378r82hiGdaMMo11e6zdJh0LPLK0xS7MOT+aUayOYt5RROHnf1m8uLJ21X0Eixc9Pk7M9HOJDdW/ETurEXoXSuqYrYmNwm0lfmfiwPBrrZhe+WxeDeuwkt/WwKeB11Y5bNWahu+W8byB2HA1Oazdo/u/iB9zNrHg1DGbEyvDncQOY5fy/2f6jKeO2ap8thZxQP6X8ju8qE9u7XyO6pHDSGv+9Ippcnh26X59+S13ABbknHdJKWVgL+B8YoHeCzi/+m5h6b6KKHidQ+xkn0SsSOP6xFxJ7MCmlf52JTboY4md1IVVzIKc8yNb+Yxp+ivxjwXOKf1dTRxo9oypclhY9dfkMI7e0z255HxU+e3axZW60FF3b1L6+SVRPJpEpxDUq1BSx+xU/h1PHNzeQWwo2+Pslc8ziA3VOUQjuB7/6UTRd5s+MQtb4z+EOCt5fCu3dp51f+uU7ly+uxh4MbEhb8bz0T4xC4hG4JXEDmlLYuM8aD7t8f+GKKK9spVDe1pfRDRC2zk0w3gcsSPZofT7FWCHnPO6KaWnd/sOOIjYzq2Tc94xpfQY4H+Ig9z9R4shluMXVX+/pUd/iz7vkk/d36u6jb9PzLrEdn208exIrEdPI9a/W1j5DciPl99lvfKbjhDr13rE/mU60ZCqG5DHEtvLj5Xvn0UUyia3cvgRq6YB+U3iRFXdgPxP4OiS+3pEw7huQG5R4poc2g24mURjcnU0IKcS27kJxL5wjTK/30Jsb79Thvkh4LfEvnwusS7Cko8hWZhzHpdSuqjMgzWIEzLfL9M2jdi2/D9i3/kxYh3fhGgovpLY7gG8sORwbIm5l84y25yAbHJYWOXS5DCTKDZ8tYxra+K3v43YLn2LWCb/QWc7/3/EybvPl2F+lFiWGjsDY3LOr0wpzSo5T2Px4sohLF7oOLL8fVwZx5uJdW0DYrlv/tVFmF4xY4nfbCyd9WcusZ2oY3rlM6Ea1hziN59NLEOfoXex5rElbgLxmzY53N2j+6bS3/pE4aaOubdMe/N7jZR/z+gznjpmhE7RbWHpHkMcQAya29rlb6phjhDrUT1/6mlofrNeOYwAF+ec9wJIKf29W3f5+9yc8z6l+9Kc825LGVN31zGLPu83vF7jX4qYQcfTnoZDiHV5C2KfXxdXjqRT6Phq1f0T4kBuNrHfOodY/x+gd6FkZ6JdcGP5DZsrhScQ+7YPl9/ry62YXvnUORxK7CPnE+2Ra4jlY3yfmJ9U49+OKLjPIJabOrej+vR3LnEQuyZRsHp/mbYjWvOnV8z/EevXVsQB/EuI5XmrAfP5XTWsrYnt9Nbldzihx7S2Yy4i1qsdiHbtleV32Ae4NOc8JaV0SYmdXb57ecnvSqJtlYmCwquAy0rM5XTu3ukaU/q7uAx7TunvucQ2rh72Fc3nXfKhWw4l5q3An0eJuZLYzt1ajecW4jjvNGJfcmz5HbYjTiadTGz/Xky0Z35FtO8g9vNblemeRCzvmwMvyDn/MaW0sMRvRGzH5hPL54+Bt+acj0gpvY7YP7+p/E4fI9p9Y4hlZAGxTLy4zLPDS7/rEsvMQ0Rb7cbym84DpuecN0spnUxcsPIzYr+6NXGBx1qtHK4ntq8bEW2Pz5f5uy7Rpj6PaMs8oeT1CaKduZA4pl2TznFac+HM3jnny8t++oHy3aSS82vL/L6cOIZ4iGgH/DHn/KKU0t+JttXfe+TQbG/qHPYp83dNYpnYnjgG2Yg4jvsecVxf53A2sH/O+bSU0lOA03LO41NKzyTW0alEe71ujx1UloPLWbltoXZ77HVEOxcWb4+9vBkXnX3xov1jznkMQCuHb5fvf0sc695AbJfvJto/hxLbnro91m6TvoxY/185aDGv3VhdFfbJOb885/w7YsXcmthIL+rOOf+WWLDml/6mEDujvemsNFOIA7otgMeNErNBznlv4mzsTjnnnYANu8R0zaceVs75YmJlvSPn/K06hy7TcA8wJed8aSuHrt1l2DOJhbQdMy7nvC9xUL9X+XEnjDKeOmbNnPNLgU1zzvcTC+SD/XLrkk/XHLrMn14xTQ4jwNic89fKvLubuIoD4sB4w/JZ3Q2xY3iodE8nNga3lP7GEwfi/WJuIjZKTcxOpfsm4uCsibm3fN/Op+6vHj9EoWvWKDFNf9Or/poc+k33BGLD9FViA7s2cRDZ/D2eOOvX7r6pjOtsOo20pui7LbEz6xczs/zblDjg/R2xMW/H9MrnIGKjvHOX8e9GbGT7xbTHvxvRmGrn1q+/dYjGze3EtuMYoghdj6dfzPXEAeUY4mzdTKIAO2g+7fFfROxYvjrKtL6AaBB8rsphHeIM0n1EsfU2Yhn5YJmuI1JK1xAH6M13XyEaAA8QVxJ/DPhaSulM4ozSDKKx3i/m1yXvK4gd3ZVlftT91Z+386n725/Y+d/WGn+/mNcSBamJo4ynmYYJxLr5ujIP/0Q0ItcmluFbiZ3qC4ntwezy94QyvnHEmcG1iCtDjgM+kXP+EuGCMoz5ZZhHlnE0xeidy+/15zLMvxPbvGOJhtNmZXxPIhp15xLbrh2JRmm3HDbPOV9BHDzMIwqH88q4v1T+/znw9pzzBKIBMYNYrz5EHGD8o+T1P+X7i4grZHYjriz5LdFo2ah8vwvwKaKAvRZwQc55ZzpnVzckGr+fIG5PfT2xLj8z57wBUUCcn3OeRDT2++WwecnhsDKPbyvz94tl/PcQhcA9iYbPCNH42rAM774u47+Y2L4+mrhS78EyT2YCN+ScP5Vz/hSxDm5DbI/+QRxQH0A0Kq8rv8dZxNlWSv9jcs4jxLI7g2gcNsvoW3POs3LOHym/4/7EQcZkopF2LnEQMZ24+uN+ogi5Yfn72pLDB8tv/OouOcwGJuacf0gUXOeVz54LTMo5fyDn3BQ7twTuzzk/hdgXzii/xyzg19V8eB2wUUrpu8BIzvnbOefTgbtyzu/OOX+j7s45n1H9fTpxwPd+Yrm+pvyujyWuxnxdzvnDo8TMKtP3dOKAYW/gCV1iuubTGta+JYc9cs4/qnPoMg1N3H6tHHp171uWobd3iZlNHNRsC5yXcx6bcx43ynjqmBuA3XLOY3LO48o0vGApc5sFPLvEL8qhy/ypp2GJHIj14cXA5BJ/VkrpBSmliX26AU5KKR1crrC9KqX0zJTS1ksRc05K6ePlityLUkoHlqttF/VThtd81x5er/HTJ4eu4x9lPO1peANxQmZGzvkeoq03l1jfXk5nO1p3P41oA6+Tc36AaCvcQ6zvLyP2CevVMWXYtxDt7tl0ri5egyjAbF6+a8f0ymdRDsRjLLYktiF3EkUMRolZNP4yDU37tp1bv/52JwoEaxLb6q2JfWd7/vSK2bP8PYlYnrcnto8D5VMPK8cV6RPK7zCj17R2iXmIaJcsrObdlJzzNXTMIfZrEBcMXFN1/6N0jyX2Q40ZdE4w9YqB2AcsaL6jc/xQ91d/3i2fbjksyDmfP0DMAmIfW49nTM75xznnW8p0vIx41MuRpb9fEMW1u4l25t7EcvoQcbxwP/H7zSCWzcuAP6WUXkbso55JbNM2JH6XVIZ7FkDO+WjiOPnHOef/Kd81OdxHbAOb8f+5jH8W0dY9m2iPLCQumJkB3JyjkDeGKIjNzTm/ntgHTyRORC6WQxnemjnnkZzz98o82p04wTSfaJ99imhLfJ84aXtNmfZpRMFwTvnsWuCKnPPlZdgLiePF5xDr/LZEe3EuMDXnfH3O+S5iudy2xBxKrMPfJI4n2jl8uUsOVxHHIXNyznsQj366vOzXxuScD+qSA8R6RM75z8CYlNIBdO4KbNr/i9pjZfyvZOW3hT5Cpz22gM6Vcu322B9LfgcQy8Y8Yr0+gdj+v6VLDrcQ7av7iWLpo3POv8o5n1biD2bJ9li7TboXnbZYcyK7r4F6WsFuSintUDZIdXGFVvcdREMY4iz9C8t3k4mK/tXEAtkulHSLeSil9IYmJqX0vNLdjumVz0PVsCh/317FNDnMbsX/gzgwAri6yoFe3SWfCV1i7k0pfbiahjcTV4j1G08dc29K6bAq5gE6BbSeubXyuaBHDuu25k+vmCaH6cDF5QqHicQOcUJK6QpiZbu2zM81iQ3IneXs1E7EBuFm4qD3mqq/W8v41yQOwO7qErMhcUXijJLfNKJwtwZxUHlfSukflHvZS8wGVT5rN/2VvK8t47ma2IAtSCmdRxy0LhFThr1lmcf3EMvvpWV4U5pp7TLd2xIr+x3E8nY0sbJfQezc7id+/9dW3U8GDs45/yWl9AhiuXwccdbruSxZKOkWs1X5t1HJZ1PiKofTWjG/7pHPk4krdm4ndhrN+PcmzvbcMErMK6vx/750700UArbq8l2//nYjGmebEsvqfV3G0y3mAmKHOb/85guI5ezipcynGdYaxMHUFcRVeP2m9QllXk8sOTyqzKsbieX/T8CbctyKewGxXDyJ2NH8ijjD+J3y+Qk5bue9hSgEvoc4m3YMsVPtF3NM+fvVRCPo6PJ7Luqv/rxLPnV/N3Qb/ygxPycaU6ONp5mGHXPOewKklCYRt6d/gDgL90SiAQmdZbdpQK5JNCA/QjSemoObT9FqQKaU9iOKX+OJ7f8c4qDp3DLescDWOecFKaUfEo20vxLFvnFEI+GPdA6Yn000Ss8sv2udw5055+3bDciSwx+JBmQzTXUDctNScPpe2QbvTlyZ8wui8QZxhvREovF2U4m7mFgfd88575tSmkGc/X98iWkakAcQZ0InE+v1WWX+nFb1N6l0H0qcAf8msc39YyuH/0cse9cQ29xpxDI+pvyee5QrYS4v83dMzvmglNLORGNvI1oNSKIIckMZ3/1EgfeTRHF165TSq3POPyW2aUcQ24YtiWLsMWXen0U85+V+4OUppa+WaZqQUrqX2BZcQayvVxPb1c+UHN9F7GteRxy8TKRzVeL5xAHrvPL9D4nl8H+J3/gjJeYyokgzrpXDmsC6VQ6XEtuXq4HPpZQOzVF8PoVY5u8tZ/HXJIqvpxLFh+NTSv+Vc/5pWddfSJygmJxSOpi44vqclNLHiYZu3X199ffPif3JJWXen0osbydRCiWle3afmNtzzkeWefc7Yjt9Uimo1DG98lk0rJzzP1JKpwK7p5Rua+XQnoYHgEtKTJ1D1+5+36WUbsw5/6l0t6e713jqmFtyztPouDvnfPwo42zntmh4reluz5++OZTt3TvLZxDr4sF0rkw4uMrz4NJffVXfDURhqbkChgFjtizdHyufH8iSbiDWuQOJdaedT2NuM/7S/tq6zzQ0mkLFx4iDy37jaU/DLDrL8zw6F0y0Cx1N90I6VzhR8lqsUFLmfR3T5DiWzlWXzXZ2QY9x9sunzqE5sG3MaQ27W0y9naf6rJ3baP2tVY1n3S7j6RczQpwka77fcCnzqYcFpSg3yrS2Y5rjyNkppXcS+8X5KaWzgTkpXuS1DrEdX4M4Fj6bmMeXp7iyrinInQM8mFJarwx3bPm/a0yKx7RMJfZ1axPL7cXE/qce9oPN513yubRbDsTx4e/KePrF3EwUfWZX45mfUvoasX8+jdhX7Vb6fYBoR5xInGg4ifgNZ1OOCYk2++Qy/OcQBZlTSw73pJTeTlwZ11z99uzy252b4gTAF0sOHyKOO28kina70Xle2dFl/F8l9onN+J9L3BUxs0zrvDKsz9E5HngwpbQuUXS/i2gvtHMYD5BSeipxIcA9xEnlz5eYRxH73BuIbccsooA8gbiA4Eo6j/LKxPL1Y6KNPI/Yp08q83atMm0zgI1TSs1VtnOANUqbcV+iZnAgcdK0ncMMoq1W5/BOyi3KKQpYf04p/bIMZ05K6QvEvrnOYR7w8xRXR763/NYH0rkCbipxTLGoPUYcFxzLqmkLTS3LwNOJEwhfIdqz7fbYZKLd/LTSz5tK7D09cphILHezSz8PlN9+PrG8TWXJ9tiFVG3SVlvskQxgddxmezpxtvZM4uB+KrHwTCIW+hnETJhaQu4lNuzjiB3zuPJvFjHTxhEH5g/SaQS0Y8YTG8NmRzemfL6AKKKMlJh1qhzqfCZXw2pWnoXluzXKMO8vw2ouM51NHJRAbAwmd8mh3b2QzoHIvDL+5rtmR9FPezyDxNRGy2dCj7hGPX8GjakXwB8QG8WmkLhj+b/5ezNiRYU4EG/OUO1Q/r+baKxBbCDaMVsTlxNDLHsQG6Otq26IDdGlVfdt1bAo3z25dNc5NEXlrVvjb2K65VCPkzKu9nTvT1ySewnxu7yQxQsdS3SXjcH7iCLRJGJ9+h5ReGoXSnrF3EjnWRNjiNvS/7dLzDE98nlXt/GXIswgMfX4f0zsJJ7T5bt+/T1IXNJ/PnG725eJIkw9nn4xE4kd3YXEduaR5bNB86mH9ZMyzh8NMK07E7egX9jK4Uw6jf2biOVrPFGomk/smMa3vptXchhPHMCeRRR3xxPbvd1GiTmNWLf3o/MMkpEu/dWft/Np+ptQ5sW9XcbfK2Y8sX1dc4Dx3EQU364jCmWnEI2mtYgG5PuJAvaJZT58kmjArU804I4grjKdTDQOf07n6ut1ym/0caIBeVbp57YyDROIM4MLyrhvJq7U2IdYd44DnkcUb5+Ycx4pDfPZxD5jF+LkwwdbOdxFFB3nEw2JccTy8aqS//+V8T+OaCBvTxQOtyrj37UM7y/EtmSdMk9+Uob7S2Kb09zWsCuxjXhd+f45ZXi3l89PIBrG/07csvgqopGyGbEP/htxhdz3S/4vIpadVxLbwduA54+Sw8eJWxHuKMN4T8lrM2Kb+Q6iSPvzMoy1iO3vrsR+sGlA7kSss+8rw94/5zwf4vm9uTyjspHKC2pSXG3zHMrzxnLOO5bvtyGKfhsQz/S7rnz2GWLd/XXJqSnAv57Ybh9JNBSvBHbOOZ9Uhjcx5zy3nQNRIJxHLC8f7ZLDzsS25fYBc7iM2O4cCpzSzIM+8+EWYp2HTruq7ebqu+YE2xg6t1Y3bbgxVcwNfWJOJdbh8+jcagVxIFLH9MpnRjWsVxBXr3bLYX7V3eQAceXR66sc9urR/TeicDyeaPTXMY8nDlz/RmxXGg/1GU8ds3vpbxqxbXs5sb5/bSlyO7gM76fEld2NCa35U0/DW/rkACy6ymWplOVyCTnnG5d2WMtiVY4/pfQVYr/0QmIb/lU6xZXL6czXM6ru/yNOFqxJbPM+SmynH0G0Ifcg9oGnVjEHEgeLY4l1/BKiPTqGOHG8C50HttcxvfKpc7iS2L5NJIoSRxPL34Q+MadX4/8UsY+dTuxn6tz+3Ke/s4j9xAjR7t2BOMY7qTV/esX8hc5VqpcRV2PPIq7WHySf31fDatoAze9wTo9pbcf8B3Hwvg6xXdmd2H/dRudY7kyiffBUOrfENt/dTWzDJhJt1W3KvD+9DO8JfWJGiHbafOKE4UTieHT9Moymv7Gtz+t86v7aOdxRxruwT8xI6WcCnTu2mivmJhLtmTHlt/g10c49jGgnnE+cSH1m+ewdRKHn42Wa1s05PyLFi8C+RhTo1yrzYT6xflxGtKWOIdp3GxHtodcQ27t/L3mNlBxOJdpKh/cZ//uJk6XvL/Njj2r8c4h1ZS5xAva9ZRkYR+x7/lDl8BNimXxUmV83VOP8XMn5r8Dnc85vTPGc7I+X4R1U5uP2xLa6uTLyIKI9NJ9om/2eaF/NI/b1X6azD/ojsT1/FtF2mE4sq03ba7Qc3lPGtRVR0PpCmcanENuTBSWH48t45pV8TiLadbcQbZlZxHHPD4jjqbtGa48R2535RJvxv1ixbaE/A0+qTmT1a489qz3+bjnQefzZ2DL/jiG2BRDbghcQy2CTwymDtEkZxeoo5j2t+nOPqnv78v+11d9N9/rEzITOAwmvJTa0ECsLLF4oqWOaszQXEZdjQ6fwsguxg2uPs86naYTVOTT9PaI1fvrkcFHp3qT8f0eru/muW3fjFuLgsB3TqMdTxwCdRkyrkbNF1d0vn3pYdQ71+Lvl0MRs3uPv9njaOezJ4peGN7fAblz+P681rHZM/RvXv+UuVXeT991VdzMNe5b/e+XQ/N51Du2Ydg7dls32dEMcJEM0VHane3GlLnTU3dsCn8ydt8buyuiFkkUxpXH6v1X8c3qMs1c++5b4r3cZ/+nEzqFnTJfxL/q7V3eX/g4mrvy6qzUN7dy6xrS6lzqf9vh75dBl2EvkQOxMPkUUI3ehc8vq5USDBaKBVH83hSjyHEMsp00D4GSiobYncZZwcp+Y9xGNsC8TJ2IOIBoiC6v+Flaf/62Vz2Oq/u4nznI1Z3Cb8d/QJ2atMr9OIxoAvcbTTPeZxEH8ruW7haz8BuRhRPHpeWWcF5bvnkFsF+YTjadFDcic89EppUeWHC4B3pFznpDiVoRFObB6GpCvJw7kPkdsJ75G9wbk74gD0H+nc2tw3YD7NXHws7oakEeV33cXOieX5pfYSdXvMobOW9+aM/Z/KL/164mG2ToDxnwT2C7nfBUrQIo3sT2/3MKxNHE7EfuSTxJtkxOJZWEr4uz3fsT6tDXx5r5nVLHfyzm/dSnHd1qXj0dyn5cttGL2rLovGiR+ecc/Sg69XFT1V3dD5wqeEWI5bHJ4Y5/x1DEzqu6ziYIKRJFl0Nx2bo2/Gd52rX7radi5Sw4QB1+bE8v6T4hlvylE1CfXm+6xdK6UfTqx/lxPFCMg9i3rteLbMROIA6MpdAoJs0r3RKKQ3pyY+gedIkczvAmt/prxLyAO3jYmlv1eMe3x9xpPe7pPLeNqihx1cWUcnULH2Fb3bWX6xhK/+WOI/VivQkkzrmurYTcXIzTzaJMu4+yXT5PDRGL/sl3p59Qy7U/pEzO2Nf5TiW1tMw31d/36u7oMa3z5HXYpn9fj6RUzruS5RfW7bL+U+dTDOoMosjcxvaa1jjm9zN99m79zzpfQR+rc/t1VXvw22l4x7WOfW1uf30oXzbBXQA7t8S82zm7xo0nlBWPlOPXLxD7+NKJN8ljiyqv3E/v4u9sFmOWVqhecpZQeIE7S3plz/mPVz8bEsrrU408pbZDj1vcl/u7WneJOjyfluF11oJge49mEzvbnQWI9m5vjrb0Tmr/r76ru+cS24e5WzIbt4fXonkfc1noAUeCt21MLWt3N1Zj3EMvSCUS79V1Eob3dHusW8wfgm+WCk51WRHtsOdpizXH+PLq3x04kTsJdTbTNntq0vwZti62yYl65zLCxe9Xdr4j3OOJKC+i8jaQu4p1Z/q8LJXVMfbXTpqV7Wvl/KzpFmF5FvObqrTqHuohXj58+OTRXdtVXXLWvvmpfkdZ071X+/wedws815f8d6NxWVY+njmkaabeX/zclGoaw+BVk/fLZi84VZHUOO5TuXjk0MU0Oze82ls4ZkuZv6JwxH1/9Pb70N4u4CrKbETpn3nvF3FvGuRZx4DWP2BDU3SNEw7o5g1TnM0JsmNq3CXQbf7eYhcSB03plepuGQTPeBV2mex5xhdGOZTiHlvHUxZX96BQ6FlbdJxOFjlvL8LYt/46md6GkHTO+jHcs8RuuRRzUP6MV0yufU4hb2B4q87UZ/xjiAPk+4uqaXjHXVONvCoBzy/jHt77r1d8kOgcQm5V+vlKmrx5Pr5iJxI6qabDfQxRKxgyYTz2s8SV2CnGF49g+03o2sa7tV+XwKGL7c3L5/KXAsTnnXVNKlwLjc7ww5SJih3kssUw8Fzix9Pd34vkzO5T+3kicVaZPzKXEc0d2LjHPJwpJ85r+6Jw5+1PJoc7nkqq/2cSVcb8p0/RG4Iic8559YhYSB3lnl5he41k03SXvDYkGdVPU68sGZNfuxRqRS9ForL9bXQ3IZrn+H6JQO5ZYZ99X5uGXiAPvJxBntBcS2+K/EcXJpv1wCZ03p57aJ+bFxLa6uZ12fvl3bxnGDDq3bvVrC0As6xC3j9fdjX4xi66iSin9puR5CXE2/DriisgPEe2wL5bvjgZ+UjUgLyFOODVFj6a4MZ7FCx1nlWl8cvnuVGIdmUVs2/YjtoF1caVXzPSc832leL5xyXO/LjH98lk0LGI5+ESXHNrFmlOJIvVM4hm/TQ59u8t8WuI74ndORHHqIDr7h57jqWLGE+2zHUr3tBxvtBs1n1b3hD45LJrX9TR0yeGnZR5+iVhuf0asQy8nTiptT1xRPr/q/iixbb+PKGp8tox7FnEi5xHEcrpWn5if0HmJz91EYalZlyYRhbm3E/ugDYmro+p81qj6u7Ma/2fp3Ob/uj4xa1bjf2Wf8TTTfSexv3gVcZLhy3T0Okm9Z+lemhPEMNhJ6voEcbcc6pPkS5tDHdM+7mkfR3U7pmp/N4zHeO2Y5rjnrXSOaeorgQdRH0MMckfT0sTU/fUbFkuZQzumOWZo97Ow9NMcB6xF7JMmEPugvxNXv32nfJfpPBd/AXFnwqbEOvwD4nc5i2g7btYlpu6+lDjemU88q3Yv4M0551+UNtZb2t2wqP3VfHcdndtP9yUebdOtv17dWxEnLmeW6fw9cSw0lyjgfIM45rybOHk7odV9R5mHmxNtnm4x44ljk3bMZmU8byceVdOsY83vO1LFjat+x4nlu3k9upuYsXSOlUeLmUPnJTTzS36ns3h7rN02m0Kc+H8NsS+bQbTD6vZYr5hXE9vjGXTutHyI3u2xuvYBq7Y99oOS42uJttl2OecNS8xiL1rqZVUW85qzkhsQB6X30ymuzKezks+n8xbAMcRGsrkseAGdt3C1CzL39YiZVb5rrmaYQefNNw/SKa7UxZ46nyZmvTKemWX841i8INN818Q0G6AxxM5hLToPx3+g1b1xGd/NxEo+nviR1yJ2vg+W+bVV6e/+EjOHaLS2x1PHbFGm/+4SP6cMe6cyHc14+uUzjlj4NmzlsEnpnl3mz2yigNeOaecwj7jS6NHERnJ9oliwb0rp50RB55nE730K8UDzA1LcknZ/zvm5KaXDiQbnc8qwpxFXGq3dJ+YE4uDlIyXm18RVQguJK3VeXWL+D9gk57xPK581m/7KtFwJPCfn/OyU0lnAvTnnF/SKKcM+lSg+PLX01+RwX5/pvpp41s5LAVrFlUvoFDrmNd2luPG1MuzXEEWzK4iN3H50KZR0iTmQODt5HnF10CXEMrZfK6ZXPscTRcA9S25XEA9OfU9K6TKAnPOj+sScX43/COLqpzHE1Uh1bgv79PcuOgcQ+5T5uhZR3G7G884+MfVBxz3EzuLGMj8HyWfNaliHlfH/Mef86pTSl/tMa7eDjm2JM+RvAV6Tc14/pXRJznn38huMNN05593KfB1bCltNfxeXz3Zr9TemT8y0Muzdynj2IJ6HMqbpr8yHPYg3Hu7Wymda1d8Isd5fXH7/3XpMQx0zlmj0XUzoNZ5meq4gDmQ+Sizj67DyG5DtYe9KbNu2IRpd7805f3OURuPqbkAO0misG5rjiYO/utHYxEyi85bNNcvvtqobkJTfsSnEb5lznpHijeILcs6PKvNzTo6XdJBSmk4UFn5EXNW3oPzmxwPH55z/Y5SYdxDb1g8R24onEfvAf9B5MP31xLK1FtEgrbvHlO6mUbgTsW89l05x5tpRYjYmriyBKPgfQ2x/PkPneTgfJRq2X885v68UwqcTb11/X0rpPqL48T0WL67MY/FCx5/L/H08UXT5XclthGhLHEhcYVkXYXrFNLfnziWWmbuIK11vaMX0yufQalgPlRxOIIorX6N3seZDxC2nC+lcpTW39NOte4TOC1q2bMWMJdaXq8t3Y4gi74V9xlPHjCcKeVcT7anNifVo06XI7U7KS9qI/UaTw/TW/KmnYXyXHOYT++xNiOL294j9/CNTShfneI7l5cC8prt8dxHxMPBHlv1Ns71fSDyH6FKiMN8r5qLq5M7C0t+lxDq7ZxVT78vqfOY2/ZXl49F09hcXl/Hs0iem6b60iuk2nma655blajKdq5ZH6BRD+p2k7neCGJb9JHX7BHFdnFnRJ6mb45762K3fMdU/yzFeE1Mf90AcU91GZ907jnjpx1tSSicR+6ZnEG2PuWVcaxPFwS3LOOcQx0pvJvYtDxF3ZrRjpgHblGH/kc5x1OHESfndyzxtjo/mN5+XY4s6n5/1yOHuMt/uzTm/s0vMEsdeJeZWoi39X8Tx4lHE83oPLv3tSpxYuLd899Py3S1lPu9Y5mMzHVeU3/UDdJ5l95MuMXX3KUT7+uyS366le0b5/Oou3YcS+/sbyt/bEzWLNxIFsQfK731o+fuGUYa3EZ3H8ryH2D7/D/H4j7cTt85eQ+xv7yaOLeruQ4nl79BWzLXEPn20mEcR+52vEHc9XEy0iR9NHJs+mTjx9kji2OI44hj5wjIfn9TqXtaY/Ynt4weAd+ac1wGo22Nd2mZ12+pUoqi+GVV7bJSY44kr1E8v422uiNuxDKdpj21CrFPtttk6dG79hyXbYxsMENNuj723dNftsf2I5ex7RNvsTuB7pS328Hqbbc553xxvNb0ZSDnnqUQD7WnECreoO8eb6M4Azso5b0EsfI8mrkj5HdFYPov4cV8C/KZPzJnEQdeOxA9wLlGpP6eK+XOffE4rw9oLOLksgL8lGgdr1DnUMTnnLYkD89NzvD23yeGCLt2JzpsC/wacW8XsVMZ/M53nEV1InA26uMd46pitidu97inxOef8mDKMC3vk1s7nvBxvuG3nsGsZTzN/zuwR085hQs757WVebU2sSM3ZxF1yzt8nihx1N8RKtFXp3ocoeGxT+vsscfVMv5htym+0DbFROKZ0b5NzPraK2YbOGYx6eHV/9fghdhRbjRJDmdaNmv6qHPpN9xziN22MofOQ4rFEQ2Z+qxtiZ3tDzvn/iI3Gi4jbdsbmnG+m85rvXjGPIH6fnYjGxEua+FZMr3yeSRxE3NcaP3Qam/1iFo0/x1ubZxIN6HZu/frbqPTzGKKRfhBxQLFoPKPEPCLnvDuxoX00cZCzyaD51MPK8abn2+mc3e43rfcRb2K6pMphIXFw/D/A+JTShcCNKaXdiEb2hNJ9Y/Md8Sa+q4G7U0ovKPlML1djXJZSOoPyYOA+MZOAKaX7bmIbcXHdX/15l3zq/uYQxa4/1+MfJeY6ovF0/SjjaaZhY6JofQidnfdmROP8kUQj5BvECYXmux2J5bHpbyGxTXqIuLLlZKLhtmufmHrYk4nl+gyiIfvpFA+Rvh94W0rpdylusb27+bv+jjhA2I9oJE0CvpNSujCl9Mw+MXX3r4j9wg3ELWzzicbMT4hGzq+Jy/03JdbNTxLbvE2IBnav/j7V6m5i2sP+dOnvXqKRdDWxnG1OFFgmEFcyfIVoHP+0fHcn0Rhvd9cxXx0w5r4yzh2Ig5NjCc3VdKSUnkA82PuxpXtKzvnNOeeziGV1bum+irgab7SYJxDP0js85/wq4mDyZKKgehPRcPtiaUcc36V7R6LxuRfRMPxDmaZxpfuWAWLGEQce+xINzP2r7svK3+8p0/fslNJHyjz5d2D/8vfEnPNhOd4GuVXO+fnESblF3Tmu7FpANKQfIE5WvJdYP68kCpvvBzbIOT+ZOIDsF/M3Yh27mHh0wHuIYmM7pms+9bBKe+7B8v0ldQ5dpmFv4IHSHqxz6Npdhr2AOCnXjplGrBd3E+ves4AvjTKeOuYeYhl/sDTgbyy/xdLkBnBHaX8tyqHL/KmnoVsOC8ty8xLiGaDj6VzJMDbFFczzW93QKbZTYraj8/KFDcp4+8WMpJT2aWJSPJ5jXvn8UVVMU2Bp51P3V48fYt0YGSWmHn+/8TTdV+acH0FsZ6/NOU/IOU8krug7PcfbxY8p8/TXVfcxdI4ffp1zHkMc9F9S8vwl8RKOH9QxOec1iee5/aF13PInYn9xAPGMrEXj7JPPYjkQy9G7gZNyzmMpJ1j7xNTHPfWxW79jqn+KYzy6H/dcAMzOOe9Xcpuacz6EODaBaH+tn+PFVPtU3+2Sc/4AcUywe2mnbpNzvp1oS2zQI+az1bDr46h9cs6foXM80fRXf97Op2sOpfs1lP1fl5hux17NeNbKOX8vx4nIiTnn15dt+ASAnPP/y/ESqnHVd+OIOxIeINazKTneRnos8Wbt7xHbpTE9YuruCXRuff4OnWcO3lnm19wu3ScS26mjiGVwe6L9MZ9Yzg4k2h/HE9uWpr+Newx7L+JuktuI7ciCHG+K/88yD96S4wUNI8R2u909s0xrO+aLA8YcVGKat7rOJO7Oua3sB9fJOf8o5/xBori1e875NuJYZbN293LETAR2yPEm2QkppWa5XdQeY8m22ZSc85uJfcZcYn1arD02SsyknPMrcrTHHlGm/Tc558Ti7bF16dI2K8vC2nTeJFy3x04YMKbdHnsuS7bHNib29U3b7Fo6bbGBrrhbZcW8yja582rturhSd8PiRY/NiR1cU4Q5hTiA6lcoqWM2J4pKm5W/p9F5MUEd0yufelgQO43mzFidQ3saNqBziXqdQ69uiB91oy4xGxEHls003ETn4c69xlPHbEQ0bJqYzegUrAbNp1cO7fnTK6bJYSSl9GmiINlUtMeXxtT9Ka6mu63uLgfrI8A6Kd5GPIloPJ1Tvj+BcsVgn5g7iJXkBmLDcCmxLFxbzvw+UAoWGwIzu+SzqL96/Klzm8q6/WLKsNcAxpV86hz6TfdsYGpK6QVdiit1oWNRd/luWzoPsL6YuDphPD0KJV1imrP1zZWCv6JzNWcd0yuf9YgG6an1+Ms0zSvzq19MPX6IHXHTqK6/69dffQBxSoqrLqe0xtMvpm68j6dz69+g+dTDgtg+bFGmu9+0djvouIlYZo4q388j1r0fETuQE4mr+javvtulzNf7iYfhfoNYD64jXrCxScmjX8zXid/udUSB5Aai4F/3V3/ezqfu784yjQe0xt8vZh7RWJo7yniaaZhCNJ7mEFd2rIoGZHvYY4ji3m+I7ds4Rm801o3BHejcjrCqGpC9Go11f+0GZL+GJsA9q6sBWX6vnYii5m1E42gusY17ROn+E9GIOovY7i1MKf29bB/HAxNL9zrAtgPEbEysK03D8iHirPijq+5mH9y1O+f8BmKbcFbO+UDixNFxVfdoMTuWZeeLxIO/JxPL8RuJt7l9lThDPKnEv7HMy/uJky0vJbbnzcmpurjRLnQ0V/5A7DfPo1O4+WMZfrsI0ytmw5zz+5oYYv86tUtMr3zqYUEU8zasYnoVazYjCli0cujVDaW42CVmrZzz56tpuIWY//3GU8eslXM+ooqZQBSilya3OXSuwqpzaM+fehq65XAPUaA4gNjmbAhslOL5rrcQBfrb6u7y3QbAJilecgVRmD2PaAPcQBT4+8XMIopVs4jt2UUl7obSPTfF7UqbABt2yWdRf/X4Uzx7dhLxlsd+MYvGP8p42tPwfOKWqcYgJ6nbxzCPIooe/QolMNhJ6nZMr3yW9yR1v2O3f+ZjvF7HPTNSSgdSXtKVUjqaeHvl2sR+ZGJKaU3iraRHE+tDfQwzJaX0M6JgAVEEWa9fTGlT18dRI+W7a1vDXvR5l3x65TAjxRs7F44SUx/DNOO5P6X0zpTSfxJvPf1GmabriBNib0kpPav13f3E8g2xj103pXQHcZX2xJTSDOIk4K09Yuru64g7N0aI5WMGcWKi2fY9rUv3/cRy/yTi2b3jS8yTiMLeT3POTyO2qdd26a89vLnERRIHEevfbimlt6V40ywppW+nlD5AtBk37dI9pfS3vDGHlnmyPuW4pfwuIymlp6aUHsPiV+OuR+cYZFH3csTMAo5I8VbxhcDpXdpjdfefiGXuH8QFDG8o8e32WL+Ya1IpGpb22DXEsXTdNuvZHmu3xVrtsTcOGNNujz2FJdtjM4ltf9M225VOW2x3BrA6XoBxNPED/5K413lb4mBlUtX9HWKHMo+oUr6FuL3iGmIBWZuoim5BNDIeIGZUr5i5RGP6r0SjYWvizMpPWzG98plTDesXxJUKlxEr5xeqHOa0puHLJdfPEz9Kk8OkHt0nEJfFziPOotYxc4iD34vLPNiCOIt3Yp/x1DHziYXwTKKA9AbioO9bfXJr5/NvPXLYojV/esU0OfylxDdXphxHHOw+iTgImEvnNqmmewGxgtxJVOQn09lxj5TP5xEbiV4xlBwmlPGtQWdFnE5s6OcTZyO3IjbGdT4Lq/4mtMZ/epmWx/aJmU8sd+OJW7DGVzn0m+6TiWJXM3+aAtTTy9/z6Lzdqu6eQOw8fla631dyvYNoqM+gcytDt5hnlnlwBnFg9m5io3NVK6ZXPjuX6ftGmd/1+E8lGsNP6hOzf2v87y/jOayVG33624dYvs8nDkz+gzgQn1KNZ2GfmJlEUeA04vasvYkCzV8HzOf+1vhfQBykTiEarb2m9V3ETvcoYsPe5HBZGcYXygFXX2nJhxuPALNyeTbbgDGbEY21e+thsOQzLLsOewXksDmd51neO1p8Sul44Lc55++nuF31dznnQ8qB2f7EcvgPYrvzvfLdbcD8nPNWKaXfE+vnTOL327F0X0/c5vmbLjHtYf8OeFHO+Q8ppWuIde1c4pkYEM9MfAKxA/9B6X4k8du+uNXfa4li3FopnmF3FbH+9It5NbH9+DuxzI4lnsG3W/n/O8R68HliW/AV4jbAKcS+sF9/SxNza855m5TSjcTVBWuVht23iGV6JnFwdEXOea+U0g3AxjnnNVrdyxrzpTKPn1FyfUOZR2NY/Fa2W0r304j9+1g6z0TaitgOf4LO85R6xUwntmETiG34lcQyM4u4Nf5SBpBS2qz0f9gg/bdjWt1rE1dgNQeU44nl+oXEQfME4oHLx5XhjCWurHwDscxuRmwT/1qmu+m+o0z7CLHd2pfYxx1IHNh+qozrXKLQcTGxbvSK+RjxGx9M3DL0QzovOqhjeuWzSzOsnPPvU0qfJNoi3yGufG5ymNuahucD789xG/xfqxzo1p1zfnxa/Pb+OmYWse6/vEzDj4j9y1l9xlPHzCp5PT7H7XKZuJrvsUuR29+IwndKKT2/ymF8a/7U03BatxzK8N9F7NtfUsbVtItuItp+7e7Tynx9XPm72W+OJdqIYweIuZo4cB5LnMDZvXRfRBzIjS05btIjn7q/evyn0FkG+8XU4+83nrr7ZcTy+Yvy93HEsvYy4o2Z44j9wOSqezaLH8P8kCg+Po/Oi6Wm0nnRxgxiH/Mzos3/nyx+3LIhsT2+gs5LMJqYXvnUOaxHFMEvJ65sP6fMv+f3iWkf9wxyTPXPcIzXjmmOe44g1qNdiP3G9DI9C4lji3F0jkceqr5rjmHG0XnL9xjiNtO7iasNe8XMo/MogidRTkQR2+H6+IjW53U+dX91DuPoHBss6BNTH3uNpfMilsnEdudaor0yUuZdc3w0hljfdq6+m9zq70GizfFF4nnbOxJFwz16xLS77yP212Nzzo8ESNXti6l1K2Pru41y5wV6/frr1b0Ni7/d9Od0bn8+hHgcyrolx4lEu7ruvodo9+6wHDGfIPZHzTy/j87jOT5LLOcbEcvqwtLf2nSeD153L2vMG4ni90+IolVm8TZYt+6diNt3x9F5Hn+v9li3mAdYzvbY8rbF2n/3aY+9nLjD4n7ghTnn40pb7IVN26yf1VHMm0g0EJ5ePmqKKyNV90IWL3qMEM9PmVW+34LO2ZBehZI6ZmHpZy3iR59CZ2NWx/TLpx7WOURFtZmGJocFrZiTiQWwyafJoV/3ySX+6V2+u55OQ2gT4uqUkVHG08SMEDuFLUr3KcSK3m08/fLplUM9f3rFLJFDjlsPB7KyigKsuqIE9CiMLMcw6mma0mX6nkg0AsYCp5ZG/GjzpI5pzlKtSxRhT+0S0yufe4kG8D7ETvXUnPPvu0xTv5hm/E0RcyFxYD629V2//q4l1vsNiHU3l/EulluPmLHEGb3NS/d84sBwafKph3VlNf52Du1puJHOAcTNRCNxXWIH1fQ3QucZTNC5GrDZuY2pPp9DLOv1rU1jy3dNfLeYKeWzuXRe0LGQzpXd9Tjrqxub7yjDr/trrnRs+llAp5DdjmnmXV3s7jaeerop3c20rYoGZD3seeXvB0o/u+ecr16KxuDqaECuiEZjHdMcHDbLy32s+gbk18rvtj+xDo0jtvnziAP3WeU3bQ5W5pfh3k8UfmYT6+f6xEH1IDFnsPgyuD2xL9yyNYx5Pbpvo7yNLed8TLly4YBR4heLodInvts03AqckHP+TUppI7oXV9qFDkp/uxBtu7vLcDchDqb/wZJFmG4xa5bhXlPy27D8fte0Ynrl0x7/VODDdE6W1TnU8fOIQsX0Vg5jenTPIvZ5I2Ue1jEL6dzWvYDYhn+39NdrPHXMSPk9moPj24l16Y6lyG1jOs8QG1PlsE5r/tTTMLlLDgvK5xcTB06fIa7IfV5K6YXESYinEG3Apnsssa2Zn3PeO6X0FDq3Ta5Tpv/xxDrVK+Z1xLODnkHss5sTizsShZnHEU4mbnH9t1Y+j6/627ga/23EibMFOed/7xOzTzX+e/qMpz3dPyT29XuWeVgXV9r7r14ney8gTgg/jeU/ST22FdMrn+U9Sd0+7hn0mOqf4Riv33HPyTkewUJKaUqOK9QX6+71XUppDOWZ4jnn6bT0idku53xd+bz9d6/+uubTL4dRYrbLOV/X6t6AKOQtIPap48u/24n95vbl7/q72WV+vopYP9cjlr9riTsULu0R0+7ekFh/r2u1q7p2d/luLHFC48XEcjmr5PAL4hh21OF1+f0Wveys/fcg3csRs+itrqsjhwHbMt3aNecRhcBm39Fuj/WLqY8J6vimrd60fwZqW63E9li3+BNyzr9hAKujmNfrIL5diKgLBI8r380lVvymCDObaLg3esU0O4U55e+HSn83Eg2k0QojO/YY/zV0blVtT8+9xA6widm9ymF94uqCdvctRKGg7m5iUhnuXOJHb6b/GqKx1208dcx25bN5xEKyZvn7H8QBULfc2vk8qZoPdQ5TynDqHLrFNDm8pox/RhnvWuX/XoWIdsFiJrECNithXRQY1ydmLNE4bRcF6oJAU/BovmvOjrWLHHVRAjqFkbklr17FlNEKI6NN9z+I37UZxghxhrEe79wqp3vKuNcgltUH6dz21y6UtGOOIa4CmkOcddyWOAjapkxHE1OPt86nmXf3EDvUscT6upDO29fav0sdM45oyF9K/3Vj0GX2zcQ6emMZ9uZEI62Z1ku7xNTrzUSi8VavN6PlU6+DdxJXB21JFPQmlHk6vcu0PrGKeXTJ5+nE1X1/JM5KX1bm2zeJBusFxNWCJxEHb1uV/Lcirsj9ZvnuNuLB72cRt05PJBpTWxGFiHbM6cSVhW8gnpX5aeJgY23iLNhpRMN5l/L9u4nbXM4v+ZxCHAzuVfJ9Ssl1DLFzPZ444PphiW/HbEccmF5WYvYuw26Pp57ub5b5eTXxLKB3E5fBr+wGZDPsvYn9zO3E868GaTQ+7BqQg/Y3SsxqaUCW+fc44urxZxKF2/OJK9LGEbc67E3ngegjxPbuAuIWh8cRy9u55f81Sny/mF3L5/eX32wtYpk/hmi4dcuhnc+NxAmwV1fjmUZsJ6f0mIY6Zj6xnVqHzotK2vHdpuF/iDbUq4n9xJY55ye1iis7sHih4zTizcq7p5QScTvJx8q0vzrn/LKU0utZvAjTK+YSOidO9iFe5vKE0k8d0y+felhrAO/JOb+ylUO7WPMHolg/nSVP3vTqvoFY73fq8t0Movj2dODTpUCVRhlPEzOO2I5OK90bl9+223j65bYGsf3plkMzf9rT0M7hW5S3Heecr0nxYqEFOV5M9FfiuWLPI5av3wLPyzk/OcVtUA/muGr2tPLbvYBOkeSRxLLZK+bvxLb/48S++QfEtnscUQx8fYm5iHgcQmrls0bTH7Hfbsb/ZOKKtXvL/OgVs0Yz/hxXRPYaT3u6/w34RI5nM3YrpvQqdEwt8/umMo3b5ZxPpehTKNmTxQvXqczfRcPoNc4u+fTMoVU46hXTHv/uxFXh7dz69bcbsT5fWOVwRJfcesUkoh32B2K/vSPRvhk0n67jL9Pda1rbMU8jtqlvILa1a9F5a/aNRJtp/dK9VukeRxwvTqVzQnQsnStkNynTctMoMfVxA3ROPt/fZdjzy/DqfNbtkcO1RBts0igx9bFLPZ77S97jifZsc5XUPaXfceXfOtV3TZFlCrGNuqr8/9vyW76kDHtel5h29yDj6RUznmh7LqRzLDSd+P23I/aTd5b+xi7lsJcln5URs6ryaY7Tf0ksN8vTFqrbY8vSFjqdaP83J+YnEPu/bsObTfz+u9J5I+5viHWgV5uyjnmQxY/31mWw9tz/lWm4POfc3AXW0+oo5v2d2ABeQmwcm6JAuxBR/72g9DeTTjGkLtx0K5TUMZNKv/fROTM7q/w/qQyjX2Gk6Z5Z+lmDJa9C6VYYmVD+n0l5Rhidq2+afOruCXSelTK7DHNtFr9i5y7ibOU4YqFqFohe42li1i/dtxIH780G+MEy3jW75NbOZ24Z9t2tHNYow27mT6+YJoemqDqPOEDfmzjofQWLH5BvTCwrpxAN8TOIlfATxNsZ/5dYmeqiQF2UaMecQlzZ8XIWLwrURYlvlv6uLPlcSNwS0C6M1EWJDxFFlr8C/6/0//0uMf0KI2OIg4w/jjLd3y7zbmEZxiXE241eXqZju/LZu4mG60TijOF+Je5qYkf0y/Ib1YWSdsy/Eb9xs1zMLfOlOSO9ZYm5rEx/O5+NiaLU7WVcm9J5k9JIGVf7d6ljJhHL2X2Mvm4MssyuX/qfRhSJJhE7pUuIh4Qv7BHTrDeb0Hmb54RqOvrlU6+DE+m8+ZQyjk2JdbDbtC4g1qnZ5TvoHAhOKb/Fpjlu85xJ3Ha4d0ppZo7bDC8o45xH3E61d+nvqhxv62v6O59OUXmkS8ws4qHpdcxFdJ7bN0LnpRFjm/6qfGblnKeUfJrGXlO8Xkg81+4xfWKafOqYbuOpp/t/iStJJpR5tjGd28th5TQgl7fRWDcG6xxWZQPy4dbQXJ6Y9YhtzLXEtnXLnPPCFLcuLszxRstxLN6uuJ3Ypv+YuL1qIfE2vu8C3805v3+UmGcRt7EdSexfdiFeBNEUhzfskkM7n2Z4PyGuxn0OsZ2oc+gX8y1iuTmfuH31d0TDsN80TMs57wJQvnsIeGzOeVqruDKOxQsdlxHbiV1TSi8nrp68n1g2tyROoHyCxYswvWKOJm7dPoC4unMD4O0559+2YrrmQzxupBnWK4lbBb+Qc/5kielVrHk38Pmy7Ti4ymHrHt3TiALvQznnbVsxexFXAzbTsAbxgOzv9RlPHbMn0Z44hGgLnQ2Qc95yKXI7H5iec94+xZvI1yCKcve15k89DZ/qksM9xImtE4krJA8mrp57dOq86fVC4pah3VK5MjjFm17nlO7m7a8XEc8o3a38FhP6xNRvuG1iLqLzttsm5lI6b7it85nU9EfsJ/ZIi78hd14pGvaKabrrmG7jaU/3wcTzZRt1cWVMl7/r7hGiILsFsd1vrsjsVShpPmtONK5D5+rg9buMp13s6ZbPCJ0Tr3OINs96dJ591SvmwWr8txDr/UI6bfzmu4f69LeQOAaZT5wofUSX8dzcJ2Y6ccX2fGI/v2WP8fTKZ1Y1rHOJCzHGVdPcbVrbMfuUcUwmtj27lO9+TrTxX00ct5xB7J/2JNrZTyPaf78kriAdR6cA/Z/EvuQkYlvdK+aZRBv8bDpvEP0Z8ViZsVV/k6rPD2jls1ePHEaIfcsuxIUYvWKmEFe+vrPMr58R+76TiGX6OcSxwunEfmE20Ybdgti/5fLde4k29tHENumPxPHGu4h96v8S26RfUV7+2IqpuwcdT7+YHYnt4U3E8dh6xLFK8++M5Rj26o5ZlfmsQzx3+2Di+Hbj1dgWejWlPUantjOlSz6fIW7PfzuxXTmBuF24X3uuHfMrojg3QmzXXjLANExrdzOKsaP1sBLcTDwPZG9io/RKoghzMnGV2fHEmY6HiEvJdyA2wM8kVuDjiKrrKcQZmJuJlfvJxIa2W8xfiY3IecSG5RrikvONW+OZ1SOfvxAL64nEBu0+4paN9cs431Vy+HPpr5mGmVXMWOKs8HnEFTG5S/fmxEpydukeU8WcTxyYXERs3K4izjStW/LuNp465u+l+3pi5zaXWOA3Lr9Lt9za+Uwknj/UzuEM4kGjTQ6zesQ0OYwQO947Sr7XEQdaFxIPX/44cfC8BbEx2L18N4F4A92FwHo550OJq8S2JpaD3encEtYtZkqOtyptT6xU95TuzYjGQBMzmXiV/E3l/yafDehceZiIA+ptcs5XlOlYL8eDctftFlOGPZF4dlQz7G9W+dwxwHRPITZAzTAmV9O0GbE8bl++WwhcnXN+LlEYSkRR6QbijEST38weMbOq5SITB8VN/FOqmAk98plLNGruKv+a7r2IZbDb71LHXMxg68agy+zVlOdmEMv+dcBdOefnlHnQb5m9vnx/Vfl7IxZfznvlU6+D2xDL/kzi9pjNiDM33aZ1ThnONkRBcPsS9yliuVtYpme9lNJeZZqmpJQeTTwA9ifEenA3sS2ak+KlGxOAB1M8AHZ+igcIz6fzDJxuMWOAySmlVwMjKaU/0Hk5xfYl13El5vqU0n6tfOamlE4s/a1VxnNNmYbNSx6PKMPoFjODzvo2kVhXuo2nme6NyvydXabtVjpXMKxFbCvmE9vva4iD/HWJZe9wosF7LlEcGEMUBWcTDZG1e8QMOuxeMfNaMXUOOxD7sg8QDee1e8QMMp5+uS3vNDzcYiDWraOJhuR/l88W0jkRt03p3pLOuvbcnPOPibbBQ6X7WuJt3KPFPIkofP8ncVJiIrF/fAFRVNmySw7tfNYmDo7OLtN6VZcc+sVMJtpY/0lsrx49wDTMoWMbYEzOeVr5e3LO+XeUkwxVNyx+dfpHif3CBKKY9yhiezU5x4Pz1xwl5i3EVcifItbrPUp3O6ZXPouGlXO+tkzvi6uYJof2NLyR2BfQyqFrdxn2HcTy0Y55KXFisJmGXek8167XeOqYA4iTfwfnnG8krr55cClzu53OXQ91Du35U09DtxzWIw6enkoUF8cTD6BfC7gnpfS/lLffNt3lu9nEQ+t3BUidB+nfmVL6GrHf7Bdza0rpL810pHiQ/nXA1WXfM6P0txax72jns6i/1vghtplrjxKzaPyjjKc9DR8n9tlNW+Ye4sDxF0S74wPEQV27+yBif7pvmeZLiDbDXKJ4cHUrphn2r8uwHyCeaXdryf/VpbtbTK98mhxmEgfh9xOFmzHEsU2/mHr8+xLL1cldcuvX373EyeNbiX3fs+i8mbyeP71i5hHrwF10TozfuRT51MN6axnO36rp7jat7Zg7Ssw9xIUA3yEO0N9A7AfWJZbjrYlj0pnVdw/knN9NuSsod97GeS+x7G4/SsxuZdhbEsvP3Jzzh8q8qPurP2/n0yuH+8s0Tx4lZmaOZ4SNr8YzGfhBzvk/iGOXseXYaDtg23Kc8dgy3Oa764hCz8eJZX/fclx1IHF8+A6i0DjSI6buHnQ8/WLGEc87O5Zy8UvO+Q/Ecf/k5Rz26o5ZlfmsQRwPPYvYpq7OtlDdHqvbQu3hvQa4phrnfEZvz7VjJhPbpr2J9XaQaZjTpbuv1VHM2y7nfEHV3asQURcIJuac/0Sn6HFf6X8rFi96TOwRswGxY2xiHiLeePcA5Q2V/QojLFnwmEycMXyAOIv3LboXRsYBm1f5nF+Guwmd564s6q7yWb901zFTW9Mwk3hzyvw+46ljplLuCy8x44E1S/eEbrl1yWc8sXFdLIcuv12vmCYHgDcRG+MNKVf2dSlEzCcuS72vFCxGiDfRPB8Yk1L6Oa2iAFVRokvM/JTSObSKAlRFiVK8GA8sSCntW/5fojBCVZQohRGA9VNK2xEFj17FlF6FEYgd5GjTvQbR2GmGMa+apjnEWaQ7U0qvoTwIuSncEBugzcv4miLMokJJl5h6ubiaKPxuSzRadqpi6JHPzsQ60fxGa9O5Fb3X71LHbE8sV6OtG4Mus83/m5Xp2KD6/cb1iFm03tC5jb9eh/rmQ7UOlv7mEAXvRCyDB/WY1nHARlU+dxK3x+5X4tckDszWIE4i/J3Yef6lzNunEjvaxxHL97pE4/LkEntS+U3+gzjo3qP83S3m96X/LxAN1+2IW9T2IIrg69IpWj+DOOHy1yqfMXQuL1+bzi3DOxHb0kcRV5RM6xGzJXFWdDNivbuvx3ia6d6CaHxeRhx4rE9ckbMyG5ArulFU57CqGpAPt4bm8sa8kDiY+8+ynLw3pXQPnbefNVflTiMK6ZcR6+qvUkp/ItbdDUr3AwPGbEmsJxANsZuJ9fOM0t/lXXJo5zO39H8aUYD5c5cc+sU0V7BAFIO3GWAa7kgpHV36O414++eXuhRX2oWOscS2vyl6HEwp3BBnoMexZBGmV8zEnPNlxLp/JfGw+ildYrrm0xoWxPZyw1Jo6Ves2Zw4gUQrh17d0HlMRTtmQpn/zTR8kiiA9RtPHTOBWN9HSn8TqB5cP2BuY6v4RTl0mT/1NHTL4SZiO3odsb+6sczT24ht//7ESZO6+0ZiH3EGcWXORKLgsR9xQPNG4uqifjEbEfu1rYh91c5lGE8h9hkbEAX6U4j2VTufur9F408p3V3+vmWUmHr8/cbTnoZxxIHcJSxZXKkLHXX3Qznn75a89ijjGE//QsludJ75OA2YkeNZxCPE85d/RuzL2zG98qlzmJdz/hqdO3yuIorB/WLq8e9BtC836JJbv/4eyjmfTJiVc/5jyaE9f3rFzMjx/O0Fpfs8ot02aD71sHYvy8iEMt29prUdcyOxjj1InHw7g87bZccSbZ7ZxDHPFcS2rfluSorbtMcCa6WUdiaWp8cTbcn1Rol5QZlfm5Wc10opPZHOCdJ62M3n3fLplsMkOo+aGi2m2eY34wF4bopn5Y+n81iqrQFSShuX+Tqm+m4KnW3ZUcR2/xfE3U0LUkqPI7aj9/WIqbsHHU+/mOnAo0sOOxBF/B2IbeuC5Rz26o5ZlfnMI9pCJ5bu1dkW+lXpr2n/TOuRzzbEG5n/VOdA//ZcO6bJYVF7cIBpuKOKfw8DWB232R5HrAT/S1wCOYXY+E2l8xyCo4jLam8kbkP8NrGTu5fOwfIv6Dyv4EqiAf0r4qqhdswEogFxKp3bHn9PXBZ6GHFZ96eJyx4v65LPBOJMz9+JA8gvlM+/TtxO+Tc6t+/uRlSEjyKey3IPcenxx0suV5T8H0n8UHX3ecAHiR/8J8TZ0PtLzFolh/OIg9XNiMuvbyIuAb6vy3jqmDWIHc6fiLNOLyeKCCcRV5zM6JJbO5930LkiqM7hMUSD6+Iyfz5b8mrHNDmcSed22NllvFsRB/rzy7SsS2z8Z9C5TfrvxEr3wtLfRDq3JDZFzfF0nqnXjplNNMrWKMNfSOc2yrtL3DrE7a8LiR3yPGKlW7cMb3r5e2rJayGdZ0ncROeZGfd2iVmnzNObiSsG5hKNj81Y/JblftM9k7hUeFwZxuwyv9Yo8+8hYqe7Vsn9ImI9epC41HkToqA0ns7bOR8q/bZjnlxya64sO4RoWNxHNHibxsvZxHLezmdb4qDuUKLR+6Iy3+aUaf9Vl9+ljvk40Qi7hP7rxqDLbCYa2/fTeS7igUSD6RUlj17L7J+Ibc3WxPMSbiOe43PrKPnU6+A04srfK+hcnn41ndvE6ml9fxnecSVmLLHNuYt4Xs93Suyrcs7PSSl9L+f8VoC6u/13051SWh/4Wc752VV/n8w5f7JXTOk+Puf8/Cpm0d+9+uuWT/l9FwA/rfrrOv5Rhr3EeFLckrcvsR16GrG8/ZXYmecy32cSy2zO8Ryuq4mrg6eklA6lsz9prqB8EnElxATias52zI4DDnvQmDqHfy+/+9OIRsRGRONgRYxnZU7DwyHmsrIcjCPW69/SebHMDaV7K+LgbQxRdEjlu0znhQsbEO2H+0aJuZ8oDDxEbL/PLsvP1mX5mUkUKNo51N37EGdxp5V+5xGFoHYOvWI2L+ObU3K4tgyn3zTsTlz1eQtxRehBxPbuaXRuv59A5xl7E+isV+PprCd3E9uyNYkrWjcu8+JeYps/q0/Mm4iCyhVE++lTdPYzdUyvfDaphjWT2LY/htjnzKtymNuahgnEb/0jotjV5DCuR/d3iNtOIRradcxM4ve/irgK6hCinXJzn/HUMbOJYvU5RBv1I0T74UNLkdsXyzR/nmhfNzk07Zxm/tTT8IY+ObwEODvn/LHS/0D7mLo7xQPw5zHA9r7LsPtu79ufdxsecbtez/GPMg1dx9+lv1uIk2NTiNue7yGON95CHCccTDyGZWHVPY+4YvjDxDK+PdEmfS/xxsc3E+vCSBXzu9LfrkS75efEsctbiGXsk8RBZzvmkT3yqXOYRKz73yzj3YdYtvrFjKvG/1dind6+S279+vsxsb68gGgzfYtYf0da86dXzCSi6PoEoh388/L53wbMZ6Qa1jXEdmcd4la57/aY1nbM5DKejxL7nPcSzwEeQxyPPoU4TrqFKDJeVPr/NlFw3rFM91OIbflHyzyYRKy7O/WJeUT5+wJif3RamRcfJtbtpr/HVZ+/spXP+T1y+E+imHgCsU/oFbMHcTL1w8SxwBOIZ8W+hM6V1c32/1lEUWRnOrctzy3fPbv0d0bp71fEfvQjJYdnlfE+pQy3HdOOH2Q8/WJmEY9MmEfs0xYS+9HHE3fhvXg5hr26Y1ZlPj+g83zv3xD7l3WI9sjYHt27EO2CacSx1liirbUtsT26dZSYhcQdEk1b6BxiWd6WWI/PoHOn4MQuOWxBbINvIPbdC8tnjyLqO9cMELMxsaxC1D7uGmAaFlDaYjnnOQxgdVyZ9zqiUfZTOm9c2oyo9N9Yuj9LbASaq0HuICbsfjpnDF9GLEzT6X4FSR0zlijczSNm1N+Jmfle4kC8ibmtRz7bEoWq+4kdwuHED/K/pf9tWfIKks8SG6FvEJe9/51Y2OcRC/ifunTvTzTAvkUs/OdWMWOJHdHfiWdunUYspK8jzh52G08dcwOxIl1V5v+biYX5sXSe9zZaPucTByTtHNYmdiLN/Plaj5gmh0uJjfN/5JzXIA6yds85TyEKDFvlnNcp3x1butckno11UM55C+CknPNmxI5kc+CsnPPaZRjf6hFzSs55n5zzbjnnrYl70dfNOa8NXJZzflTOeSvg9pzzi8v4/1zlsybxdppHlXH/uox/C+C6nPOzcs6TgcN6xGwF3JNzPiTnvA1xMLpzyWEt4PwBpvu0nPN7q2FcXU3TtsSbp3bLOW9HFHvOJBoBBxHL+JrETnyXktOWwJk9YratlounEju0TSjPPKhi1uuWD9EonQW8ltig3V2Nf+9uv0sr5lrCaOvGoMvs+sDbcs6bEDuaz5fxPILYLvRbZq8qn/+MKPA9mljHR8unXgc3Bg7JOT+BKKZvR2xLek3r28uydQyxTjXr7sHlwOoqorEHcdBKl+6u3+Wc7yV+y9oL+8UUW7RithigvyXyyTnfm+Oqw7q/XuPvN+xu4/kysT06gChgbEFsl64gdt4XE/uHucAWKaXfE8vAgtL9OeJk0Z+J+X0U8RtcQLnlvUvMoMMeNKbOYQZxoPAFojHxwxU4npU5Das7Jpd+dyAe+j+bKGIcSuy3ZhLP9zmAKNZOJQ5ef0Ec/G5V/l1MNPQeP0DMaXSK8P9N5+TJT4j1fN8uObTzeSTxLNIziQP29brk0C9mPeL5MscRJ6Y2GGAank/cbXBmaTy+Luf8zrKN3yPnvGfZX+wB3Fy69wK2zjm/tWy7zyMa7K8lDnDvJ65qnZpz3j7nvEuJGd8jZhZRCFlIrK/fJa5U3ivn/J9VzE3d8iHW84VlWG8k2oc7lnZBncMewHnVNNxEbLvfRhQ1L6HzTKxu3W8suf2gxNTfjSUOOC8h2oHvzDlvXMbzvB7jqWMuJV5Ecg2xLH+cKP69rfQ7SG4/INq2qc6hzJ9fVPNnXjUN/XI4uuxvSCltShh1H1N355zvWYrtfXvYo23v258vMbwBxt8vh17jb/d3P7Hcn0icdNid2EefSJxU+CmxTai7LyX2V18m2hprEQd3xxMngH9HbDvqmA3KeM/JOZ9OrOdvJa5kPYEogr+vS0yvfOocTiYOMD9AFIX2Kvn0i6nHfwNxLPTHLrn16+83xPrxAeJY7tXE1fTt+dMr5r+JtviBRBH0aSV+0HzqYf2GOKH81zLdvaa1HTOd2C6MJYo+BxCFz01Km/2xxAtSdi7b2G803xEnmTbLOX+gxE7MOX+JKKj9lLiLrWdMzvmlwDdzzvvmnBcSx1ETc85faQ170edd8umVw1eIK/JeM0rMeTnnyTmu7GzG8yriApe3EMW+JxDHzY/POe9LtHXfQSxnzXf7lL+b/l5FLJO75rj1dz9inXhCj5h2/CDj6RfzhDJN3ycKqU8gtqPPyDkftJzDXt0xqzKftxOFtq8SF1HdTxRjzyn9duu+kTgO/TSdZ4meTRyPnjtAzA1EMfoTxPHUxU182aetSbSdtu+Rzw9K7PuIbfIcYhv5JeK4YJCYG4n9wvOJi58GmYa1m7ZYSmnRia1+xg/S04pUdqjv7/ZdlzNg/wEc0uNqkF1K999LQ6k5S3ZIn5gXVjF7tOJ7xXQ7I1h3Lxp/lUN9Ru8rwFdK9yur/uq8u3V/s3Q/u09MPQ3vXMbxfLt0P3eUmCaf57T6q3N4ejXdH+4Ts2jYxJnSv5buvVj6RuK9ZVjtRtonR4mHFdNIHG38/XIYtDAy6DS0/9485/xt4je+MOd8ePn8hGp+t4fZjmkvS5/rEt91Puac56eU5uWcX1TiL8xxe2rP8feIqdfbQdeNnssscFTO+eyU0reqmAtzPL9w1GV2GdfVvVrj/2u9DvaY1s8B/1e639KahvZ6syzal2WPWYaYXpd2D3rJd93fIONvxywxnpzzj1LcRnciUWgdQ5yw2S/nfH6K21QeS+xQxxDz7xPEwdNexMHEpXSed3Y/0SDZhSiq3t4l5hMDDnvQmHYOU+m8YfIGYplZEeNZmdOwumMSUdAZRxS+xhHFrmcSjb0ziUJ9Jp7r84uU0quIs7qnE4Wum3POL03xgOJBYp5K52VQ7yQKa2uU7dqbiIP1dg7tfK4grm44s/R/WM756FYO/WI2IYqGpxON7JsGmIb9gM+klObknH9F/3Wx135zsxy34R0Gi7Znc0t33U56zCgxddvq613iu+7zSsN3Ts75BaOMv2cOJeZFzRcDbPu/1KUN2N7eH1e+Oq58vizjOWy08XTp/nnpfm81f17Q2ud9acBp+FH56gSWfX8DK2F7P8rnq2r8EI/ceC9ASul64ISc80fL3+flnJ9ZujeoupuT2JS/r85xSycppd8CX8k535tS+mwTA3wtpXRkzrk5kTcp57x5idk157xR6X5UK+azPfJZlENK6e1Vm+/HKaXDcnmTYp+YI5vxE8vc1B65PatPfzvnOBncTMMOpfu1rfnTK+aPOefHlO7X5Zx3Lf2cNkg+KaU3NsMqf29Szas7u01rl5gX5Jw/nlKaVsb/rdL9XjreSBQzIIrs3y3dLyBOOAH8d45beck5z0gpvSjn/F+jxAC8IKX0sZzzCLHv6dZf/Xk7n645AG8tbePRYrqOP8eb7K+q4hd153g8wGXdvgOuSiltnVLauvkgdZ6veV6J/0evYS/NePrFtHOox59S2np5hv1wiVkV+aSUvkjsd68gThpdT5z8/CLRZvpBq/ujRDvqDuJOg38ninFXEBdW3EcU0fvFPJG4yGtd4k6zpxHFuNNTSrcTF2VdQRT8ruiSz0fLNHyW2Pd9nbji7wriVu+rB4j5IFHU25q4CG3egNPwvTKct1fdPa3yYt4oejUS+xUs6h3yoIWSMT26+8X06m43CAY9o9crh17dg/a3qsYzaA7LOuylNeyNxKUZ16DD6Dd9vXIcNGZMj/4GzaffPBokZkUvs/1yW1nrUDtmead1eS3L8rcirZDxl4bXbOKKtg3Kx3OI59e8iNjRX0mckbuDKI5tUf7dQNy+sxVRtBvD4s+WGkvskNsxgw570Jg6h2Y/3c5hRYxnZU7D6o4ZQ9xWdHOZdwcRZ1PnEFfCfoO4AmQucbB7B3Gmdh+iEHwbsFlK6QCiAXbdADH3EMW0umD2UuJM/ghxouekVg7tfOYTt30cQ1zR8KkUb5SucxgtpikaPot4Tsxo0zCbuJLkTymlu+i/Lg66jxlkez9oTK9t9aD5LMv+pl8Oy7vtX1Xj6RezvPu85fVPsb+prJtSGlOKGbB4caNX964ppbVyzg+VmF5FmDqGVlzd3Ws8/fKp49vFnn+rpqlXTHsaen3Xr78pKaWtcjxzu9/86RVTdy9LPnV8e14NGjMlpbQVcFNK6VTiqpuJKaXriH3QydXfv2h9tzZwV0rpVuIFLVcRhYb/Ia40v5y4Cq5XzPnE9v++FC+MWa98flerv/rzY1r59MqhGf+1xF1cvWI2LOO/G5hUPruPeFElpd8jqmVrE2If3q2b8nfftlhK6WVdYuruQcfTL+bHxF13d9ClLdbKYWmHvbpjVmU+LyNuBV9AvKBiTQY7sfkZ4oT2hsRV4kenlAaN2YW4y+U04oTrzTnng1NKTdvsVTlOZr6lRz4TifbUc4ki2/rAs3OcnB00ZjJxN9sVxMne1w8wDdNSSgfk0U+sLvJwK+YtSyNx0CLMIMNeETGDxK/KfFZWzMoe9vL4Z2skrgzLWzhc3mlc0eN326HG74lbbsYQO8fxpbu5bXaEaFC0u6Hzgp7xdJ6ptSlxC9utRKPuL31iRhv2oDF1DhuW7hvKdGzaymF5xrMyp2F1x4wl3i55G3Fr/P8RjaqvEw2stxPPiYM4aPkN0RC7jChsnUQcPHyHOLN6AHEra7+YM4jbHpuC2d3A0SmeUXlvGdbHWzm085lA3GZ6TBnP9C459IuZQKdoeBJR3BxtGsblnC8rRb9jiefLrEjDuL/pl8OKbuOsjrZdv89XRTvtn80C4MoUVzJuTRTLm+JKU2i5m7iVvOlem3ig/v3EFR+bpZSmE0WcXoWSM4iHqd9TxrUTcG9KaWbJ4aGU0kOlu47ZqEc+dQ7jyvgfIAovTQ5/6hMzqRr/BVU+57dyo09/uwP/SCnNJfa1c0r3zNb86RUzpnQvILb996eU5hH7yUHyGV8N6xbi9vQ5KaVjqnndntZ2zJbE89cfovN23UnE7bqUHG8u3bOrbohn4TU2rLr3LfNj9igxZ9DZT57NYNvvdj69clhItIO2IIqGvWL+WnU38fsRL+16kLg66s0s3r5ZGW2xFd3WaObBqmyLraqYVZ3PqaV7C+IW8kFObG7I0p/YrGOagtuiE5s551+V9bY5mTlC9xOts4mTx3sQj674X0Y/Odst5vtV0XCQabgW+MYAJ1YXGTtIT5Kkh7UVcdXissSsyCtKevWztON5EnHW7UZip34p8SzHCUSD8lLg6V26LwGeXvrbBzg+xzMhNyzDe201vG4xow570Jg6h2qcr63yWSHjWZnT8DCIGUecCd4/57w+8cySs4mztb8t3XsTV6K8l2iknUfn+bpNf4cQz0ZigJhvEAfCHycKZpOIZyp9jmhUfrJLDu18ziOemfnGMp4PdsmhX8x5dIqGzyMag6NNw5EAOefmuyORelvWfcwg2/uVuY9Z3n1ev/FMJ9bBE4mrwo8nDvpOrLpPaXX/gHgm2feJZ939lrgtuymUnEk8u7COOaPEHUa8GOVjxPOZPkas098gnqvajumVT51DM/5Ty7Q1OfSLqcf/qervdm79+juQOBnxWuC/ymcf7zJ/esU8ndjvP5V4jviryv+D5lMPa98qhxP7TGs7psnhE0Qhb0/goznnZwDH5Jw/RXn+btPdfAf8Lef8kjJvP5NzfirxHPADiRM3n+0Xk3M+irht8e7S/dvy3ZWtYS/6vEs+XXMAvp5zfnSdQ4+Y63M8YuruajzfJ25HfAGxH6vbNyulLdYlfrnaGuXvp7AK22KrKmYV5/NJohD8CqI91LRRNiKK7O3u+iRlc/KxObH5N+KKzdFimhOb3yDaPfOB76SUnl6G1ZzMHNMjn62JwuMBxCN25hEnZ9/O4idn+8WMEEXDZpyDTEMT/0vieX6jspinh5t/1UbiishnZee3tPGDDm9ZYla0VTWefpYnhz/16F70d0ppzdZ3R7X6u7xLzAbAKSmlTVNKU7vEHNWO6ff5KDksMf4SswnwlzL+dswS48nxXNa3ADPr7tZ3r+/VXfo7rzRMlyZm1P4GjalzWJnjWVXDXo0xp+Scr6i7c8435ZxfWnW/u3x3Wo6X03wSeLDq76dEgesHA8S8jziIXFQUy/G8oT1Ld68c6u5meNdW4+mWQ6+YpvvsKma0aXg3RfMdva2qffIg41+afJY3ZkX6Z9rftP+uu09v9Tfq9h44u9rftGMG2d8s+m4p9jd1zKD7m3YON+ScjyrFlF+weHGlLnTU3WvnnD8CjMk5n0EU3cb1K5SU4a1dxexYtjF7E9ugjxAHg+2YXvksygE4I+f8xjoHqmJRj5hF42/yqbur3Pr194qc8y+JEw6Tcs5fLd3t+dMr5n05578Qt9c9pXz+tkHzqYeVc76ROAnzvGZed5vWLjGHE1fhvL90HwH8d0rp9PL/KcSzbpvub6WU/kE8f+6jKZ6zeBjwm5TSWcA7ypWBXwf+t0fMhcBXyy2xnwDeU67WPLxccfTe0t/NREHy8JTSRcC7qnwOKzHtHK6jXMmeUjq2yqEdcwdRtPhEuZLovdV43kBcsfdN4rlky9ImGa0t9vaV2daoc+jTX88cVnQ+KzJmFefzKTrtpKa7fWLyqVX3s+mcpHwfnRObzUlKusS3Y5oTm4ex+InNXxInV99LnMz8bsknt4b3AzpX2j6bWK8/QOdlkt2moR1zHlE0PK7EjHZy9tlEW3FRNyz28qmuHm632a6qosfqjnm45fNwmoaV3kgE5uacp7PqG4mLCiPELRiDFkZOLjELiedu9IoBOCqlNIa4t3+J6UsprU1sZCaUvzfsMx2LulNK44AJKaWNiedCjekX0yuHavxjUkob5ZzvIs7+9ZqmunuFLLMppd2aHAYYz6DDHjgmpTSe2HhPbsX0ymFsiofHHkA00iemlK4hnkUyOaV0HLBVSukzxAmaO1K8kOVSYht/Vkrpa8TDX59RGmbPBR5IKf03cUn3VsRzGo4l3uT2pZTSZcSbQCcC7yGuFpicUjqMWH5vBf6cUjqeuC3l2HI74fuIW1FOJm4p2i2l9O9l/H8ArkspzSGuYPpDSmk+ccbs3JTSk4m3K85MKT2WWN7XKfPhjSmlicBISunzZfx/SCk9kth5b5VS+ijwhZzzecCeqfOyoj2bmVm+O69fd9ugMYP0N2hMr/Gv6PGsqmGvrpillXM+pctnNxG3p44a04p/9yDxD7ccyvZ+I2Kfc0/OeUH1da/t/a9ag1lie080pvvtX5vhDbq/acYz2j5vbLO/GXSfVyz3tn+A/c0KGU+f8Tf7m4H2eSmlNYg3RV/LYPub6cQtQdOp9jc55/fQ2t8Qz5V7G7G/uaDP9v4dwOwBtveLcmjt8+5IKX295HxpSuk+okDzmxS3oN5I3HL1V+I2/DuBH5RbKZv9zatSSlOAuWX/uxC4NaW0B7FuNPu8L5W/H1UKLfcR+/aty/x8GbB1SumDxMHcwpTSh6rulwEbpJReWeb3vLLMpJTSu4nlemGKF+hsnVJ6A3FF0NgUV4isk+L2rXFlXv0X0b4bKTFbNjm08qlzeB2xrx5JKX0K2LDksDNx6+0hXWLeVObpSErptcCaKaUXlPk30nSneBvjuDLudn+klF5elrF55bckpfRpYlvxlJLvRs20tmIWlJiRlNLCErNvmR9jyjLZL5+3leVlJMWtbxNKW2XN8rvfWuZFPa3tmIkppVeXz+YT69JT6DzeYz5xkN4cb59dfXc7UWTdg1iPHiCuxFlA3Fb8iDLeC0r/7ZhriTeWzwdeQjxKYr0SN60M53HEbaHXEbcmX0Ms/2uXfP5Y+ntEK4cZRJtv27Ls/J3OdqKOmVb6fSJR/B1fxnkNUZyeTlz9dGa3/XlZZ5boBs5LKW2ac769/Fab5pxv7xLzqC4xi8UPOJ6eMe3xd+nvUcs67IdBzKrMpzn+pekuv+9Lyzxe4kVOKV7O9a2c8yPL518miunvyzlfUYbx0l4xTXcV89OU0pXA66ocTmuNvxne44iTsw8CP6lyewtxkqPbNCwRU3I4nHgpUjMP+k5DWex+UHWfQJ+XT63yYl5azkZiSmlHFm8kfiH1aSSmaJg8gyjk7EVsxL6TOo3EL/QYP3RpJDbjL91rE2co+jYSSw5/Syk9g9jRzwF+XX70W5scyri+VsU3n+9EPND6K+XvpxEH200j8eAybVcDp6aU1s45P9gMI6W0PfAa4KKU0kuIHcCnU0qPJ36HT1fjX6s1T75QFrzZxBuo1sg5z+yRw5Ob/rpMw/bEWZpxKd4isz6xM2waiYelf95G4rIWRjYlzso9h3gw8d3Eg2l3Js6Ofankuk/J9U7isvbZxBs470opPTnn/DLg/BRn8XYjzhqsW+brPGBGSumXOefzc+ftgWsA15Tl5W3A40u/FxCNgGtSSs/qlkNKaXdip34/0Tg6iXgRwcuIZW0KsazemlJ6AJifUnoe8PImh2q9vaQs29cD/1Mts/9T8hxkmT0p5/ysFI3gzYCjS65rpWhcXkHclrIovgy7vd48SJxxbNbbg6sc6vW2yWHReptS+gPxxqIfE7c+PEg0Aq8u41jYJ4cNgV1L/KMpz20hGnOPIM4mnUccqN9JPMPjsDKchcTZnecQ28h7y+fvJtbTm4hnj5xBXOa9EfHmpHOIZfk/iOXu/4jbbT4I/KxM1/lEA3I+UZB9K/AR4izsHsTzUkbKvx2I58pANChHyjSdTjQK/0osVyeUYaxJPOPuW8SZuIvKfHhMyen5xLMq9iCWtT+WefAfwOtSSo/M8ebkLZA0kNIu+iaxvbifOHhbO6V0MbH+bUrsm3YmbjWbnuLh6GsSD4D/AHFQtwFxRv4wYn/T7AsfIvZL3fY3zyCeQXUSS7G/ocs+j3iz76+rfd4cYt99H6Pv85a3ndYUJN7AkvubA4hb985aCe20xfY3Od7I/hc6+5tB93m/I26H+i6xjX8nvfc384hnlB5CKTpR9jcppXeVcdbb+3p/09xy+lH6b+/r/c0TiKs9/lZyfB+xbLX3ee39DUS7a32i0DaWJfd59fg/Rjzr6yvE7WJPB35NLIsHluk+ltjfTCGu0NivjOc3wObEvvGnZXwvofNcs/2IfdeDRPv1qNLPBsQtYccTbdV2oeRkOvvvV5fcxhDtkGOIAt6ry+8xnrjy42SifdnkUOdT5/CXErNviRlfctiuTOf8LjHHEevcq4j1YVaZP+8klqe1S25Hlfg3denvI+W3OJhoY0wgXlT0Q6Kd8+oy328o09qOOZw4jnsv8P+IZ6N+rMybmcDGxHL6mR75HF7m6RuJq90mEevse4m29wbEgfkx1bS2Y24r+Xy85L5d6fc24g3H01I54C/bnJcDtLrXBb6cc35rigP/V+RyNVpK6fSc89NL97dzzu9sx5S/J+S4qrKJ2a/q7yc55+enlHYB3tMa9otK96I3JNc5lJgv585bxusc6ph6GpqYQ8p3x+ecP1e6v9eMv6iLFO2CRa/v6u72SY5BYpZlPP1ixvT4fHXlsyJjVmU+S5yUKsWvOfXnrROTS/wOdUzT3YrpVSRb4oRZc2K07Hcb83Pn7oUlpqEdU3KYUceU8fechlG6l7DKinnL2Ei8itg4T04pHUq8mvg7xIa4aSROIy5pbxqJfycKNjNTXFr80zKu8cRGeS2ikfZJSgMixdm4bo3E3xGNxAdLPs8kzlAcWIozqYx3nVYjsVcObynDm1jmwQvKtM9KKa1D7HAmpZTeUXK4PaW0JfDfRPHjESkKiIfS+e02JnZ0OxA7ycuB61NKI0Qx7IfAT4iC1/pE4+gRJf7SErNZSukrxM5uHHBhSuneMo+mEzvGNYm30nw6xdnCQ8tn6xGNzonETuxO4qzdmGoebEvcI988RPTSMqzNiAbOzsSOsTkgfzf/XI3EfoWRw0vsSSzZSPwI8YDz15QcLyrj245YNk8mHrz+4jIfDieKI7cRlys/Afhhird7nkAsA6/POf+kbOhGiIOUo4nnANxDNIC+z+Lrze3EOjebKNrM7pHDXUTh/cwqh9OJZXBXolC6DnEbwqbE5cMXlPn0J+C0lNJfuoz/eqJIO5dYduYRy+wHGWyZ3SGldFD5HX9Scn0q8UDUo+kss99gyWW2WW8OIJbd+cSB0HrEevsNuqy3xNuTfkqst+uX3L9fhncqsR38N2J9PYc467lFircstXMYQzSeNyWW8Y2J7eBZxEHGRYQZxPozkWjgTibOPv0mxQObcxnWwpzzNwBSXBX3D+IA5Dslz9uJZX1SjrcpkVKaAGya4yH5Xy/T8iXioHt8iW+GsS3xTJNXEW90+yqd17XvTDyvpTnDNaeMa19iG/B94iDk5+V3/a+c89wUZ+Cn5JwvAz6cUnozsW58pTUPfkYUjX8MvBJJS+OXxG0iryn/jxDbjJcQRfc/EM+Feh2d7fhOxIm6M1l8e1/vb6YR7a7fENuMY1Kcye+3vzmfKKrNov8+r97fPKGMf2diP70R8Nyc8x+Xcp+3PO20en/zyTLf3kPsz39Zxnc5sN1yttOuLsXT24m29eVU+5uU0lPL77K0+7wxZbreRhRPNifa4Yex5P7mrjKfJhL7l7uJ7f2Pc85rlP1Ovb2v9zf/VablQaJ98pmc8wdKf4u29639zR9K9w9KDo+myz6Pan9T8vh7VbT9GPE27Dey+D5v0f4G+EhK6UM554+WmLuI26u+QizH40pMs795FtEePD3n/MkS84Gc8/2le+vqyoumuPKa8ndz9XhTXHlD6b6oVZD5cs75w+Xv3XLc7klK6d9yzoeW7k8Bp5WrQdZtuqsc3lV1v6bMo0U5dCncXFQViCY0hZtW3h9rTcPrc85HVN2fKSFfSItfefP6qn1xaM75v0v3t6u8P9SK+bdWzE/KsE+s+vtZl/GMls8RrZjX5ZwPK93PauZba1rbMRvlnK8j7jxoPm+OFZqD9R3oWNSdc74/pfSY0n1KiquPGutW3U/sFlPs0S2m9LdF6b6iFVMPe48qZlEOJaY+KfrEHjH1NLRj6u56/LD8Vx+PLEPMsoynX8zIgP2tqnxWZMyqzGdkKbvbw1iWmH7L0iDDW5ZpGHRZ6jcNixnb78sV7JdEgWUqUWz5E1G02JMo6pxINBIvIQ4AHySedbA+0cBZg85bCncG7so5r0ccVDeNxHlEgeBL5f+fE/eyTyDOTi4gDsgPIWbMsURj4ndEA+9E4szY84jG3Iml/wdKf88u4z+daMA+t+Qwj04j8VtEQ6NbDjcQDaDxRJFhR6JhM5e40ugGoqG0O9GAOotOIek2ooH7ReKA/p4y7XsSRagRopG2YRnefcSZ8S8Az8w5b0Dc5rZzmY4XEDuTeSWHTYgzjBcRb286kbj664QyvjFdclgLuCDnvDOdS7K/W/r5dBnHohyIot/TgC1yznsQxbs3EAWziWXct5dh3cLijcRziEbTrBwPNp8FXJhzHptzvqOMs2kkvpNOI/FXwNic8wdyzrOIZaApCny9TOtp5XcbW3673xCN0rqROIMo6FxY/s3KOY8pOXw95/yJEv/IEr81UXRZrDCS43LeRYURYll6VBn+HDqX0f+sDO+5uXP16p+JS3l/Vv7+RZmHd5fv9i7zZUuiyP0FYr05o/xWGxEHIpT5cCtxQPYSYrn4R5nmepm9lVi2NiQOpA4grqI6sksOvy45rE0U306n89aitYiDg5Rz/mPO+WjiDMYzSw5vJ86KPlCG+Yky/teX6XkGcUC0LMvsXGKZnUyc6b0u///2zjzMrqJa+7/udAZCQoBASJiRoZgREGQQERFEVBxQUZRBwQFUEBX16hUBFeGKyucACOgFRQRBUEBEFAUZBJF5XIwJkIQEwhDIQKb+/nhr51Tv7L3P7tMnp1vvep+nn17nnHqrVu0aVu1Vk9lDwAfib6V1Nrabh9FMzEg0obAR5e12K5Ztt1Pic5iE+o0DgbdGHeahtroaejH9BnL0pe3mpvj9+mY2ETl+347axTzgEtMZFE8D34pxXRv1ekvQitceNHCbiiYcPh71mw+MMLOfxfheRLPLq6HtsW8MIeyC6svIEEI36qOfRS/Yi4Cp1jgr6KFYFj9CTti9gLegbV4r05jwyNJ/ANWD3qj/bLQ94wj0wpStXBmBVtJ2xwHjk6ieZpMYl5jZCfE5bIe25ZzJ0DtKwuEYyphgZudHm3MDsrm3oH5hLurrT0b9xruRs2oYcopcR9/+PrU3C4BeMzsEnZ20Nuon8v19am8uRbaxmc1L7c3JyOZtjBxPq5vZNZFb1+ZNZmDjtNTe7IvsylvN7Je0d5yW2pvfo3HG6lG/tdDh//22efS1N9sgx+7L5Ppa1F9/MObl+qjjXvG7kUFbN/P9fWpvjozxHR7THB40aQ9Jf09fe/NqjONf0d7MpMDmkdibaC+mhRA+HrRLYzGwT4HNW2pv4oTxkhDC+yMnszdzUDuYnLc3aMy/1N5kjryINXLflzk6UufK0pe3yEmdIIX8GK4rL+d1yMlL4zCdL5rGnb5AbkNflOVhQC/4BXqnclk6teLuB6fqxbtOXqvS6S+qdKnDKfrc7Pt261CVTlW4VhwbrTiEBsqp+n4w9Gknp5P6tIJW4minDgNNvx06dNSZ18ogcSGadf0xmp18EBnze8xsUow3P0gcgWZIQ/z9xvi/FxhlZovN7Kz4XbNB4gy08u7HMb356OXTgHsrBomjI39m1O3nMdw8YMX44noUMtw/REu6F6Hbk7JB0nfR6qxH0ezwfcip8ATwahxkPWxmD5gOdu0ysyOQI2AFtDrmkzHd4QBmdgM62PaGqPvzaED4w6j/zJj+YfG3r6JZ3floNVkfHYAHzWynmMb8mMY30IBuGHKupDo8i2byV4xhPxLz9p8+SKxyjFwen3XRIPFaYPsQwi/QIPFc9CLwIfQSNDN+fprGcv+7kfPoJhpbGM+M8d+MZi1XinldAa1++FX8/X7kHB6W1Nk70UB1TTST/Tx6gZhUoMOfog7Xx+f/4/hcp8X4jwIuDiF8O2irz8Kgc1D+FevBDTG9LvQCQXT69cQ6exYabPe3zi5G9WUe2tq/U9DZbuuitlRVZyE6Y6M+abv5Jsu227NZtt2+McYzH9W5WWb2QNRhOFqd+Dbk6BuFVoVugfqOv8X4dkTn6jwZn/968RkdClwZdGbLMWiC4t1R381imbwhPqdV499LwAdCCC8Tz1cJWtU4MpbFo6j/vBs5um+IZf185N4U8/MZ5HieFNMHrarNVlpvGB3of4rPaRyN1S5Z+u9DbeEFtOrhXTHco6gvzc7CWR+1qSz9tSJ3s9wzwMwWokmZici57nA46uHxEMKX4qqKc5GTbivUBz4F/CLax3vRmCrbAncTy/b3qb15CJ1hNQ45x15Bzqo+/T197U22+qqZzUvtzTGov38Ojf1ujvamPzZvoOO01N5cgMZM00MIX0R9ervGaam9eQqNTf9A40yqbLKovzYvb29+Fssk39fm7c3myBm4C5pE/R+W7e9XpmFvTkWTUk+gsc57gBNK+nuQvalr81J7819oLPhF9M6xOPLzNi+1N9+PcX8aTSSvhVbffYOczatpb9r9ojtYL/WdSmcoOTOWp+Onv2hXPAPBUNDB4XAk6OSqhceDtsX9Cg0SN6ExSJyGBom9QYeTZoPEKWhm80toMDMNGemXQgjfRjNhDwGbJ4PEZ9Egbh80SLw16ByQFdFM27roquspaOCYDhKvRuc+fR8NGOajg0CHhxBuRwOdCTHN7kSH/CBxM7Rt7FB0Rtx5aGDQAxC0/WFjNBD9aPz/LHqB/xWNmeE/olna4Wj752eQg7E3OrFuCCH8Bs0avhpC+A6aPZyKZsaz8+wujA6wz6OtImchJ+hU5Mz8KBrUTAXuisul70KDnj8Dz5jZIdEBs1QHtPX4fDTgfxrYOuhw4ANiHu5IdYjpn4Fe3p9CM+or0neQ+DUaA7Tt0e1QzQaJ34/xjIsDtKlokHhYLOdL4jP4Dqpv90TuCHT2xWeQ06JKh83QtpCDWHaQeD2N2fMrg86Bmxnrwxw0M/7bmAbxt1mJDi+iQeKd6JDhA023Dx6IHNwHIwfxvJi3e2L+TkWD7I+jF5m943d7oReO76GXldFRt0fRjOwzyGm0Mqp316DVsGPj85qX1NlnUZu7Aa2m6EGD3t820WE3NGPdhV4wDkQvMEegbaZfRnXodHRt+ItR1zVRe7o96Da5k9GWoTNQHZuFXpD6U2ffHcthFeR8PT/m81lUF0vrbGw3qwCLg84R2TOmeSXaNpxvt/fEckrb7UMx3vHxOb4p0WERemkZiV7IJqO+cXfUX91E4+akqfH7Q4FdzOydCJcGbYFZGLkA7w4hvBa94E2IZfT25IWVEMJ6aAXIy/F5PU5fbBt0Dihm9lzk9CBnXRdaHb0ofj8ihrsB9W3E9EH96riowzuisztLf2J8Lg8l6V+f6DAqhLCJmT1clX72DDKSmc1JnoHD4aiHD6NJrBtQX9yFnEnXorHQQciZlvX3j6N+/TXITqb9fWpvemhM6F2D+sWDkM1J+/siezMaOQ/r2JvjkN36Co0z+vI2byayEWU2b6DjtHfTsDeTYl7+hMYaM9o1TmNZezMBTWSejvr0R2P++2Xz0HijyN7shdDM3qxrZkuCboVdanMq7E2r/f0I05bYvA4PUmxvdkDjre3icymyeX3SR7eHltk8tzcOx+BjIKse24WhoMN/AlpZXdtfft30h4IOtfmddOa1NEiMDr7H0UBkJFpNWDVIfCOaWXwWzapdjRwAs9GA6qaY/iL0ItlskLhX1GUEmkEcT/NB4i5Rh2Foq8gf0QBpNo1twFug2eM14jPZBZ1XMi0+i5PM7GNB59MdF/NwLRocjo7/vxjjPzzq94X4nP9A42yxP8R8X4IGZ69HK9wORoPo96FB5v8ih9Qe6GDbg5Cz4uqYPgU63Bif48UxH91oy/LlaEvAe+IzyHQ4k8YAcQXkbPuGmc2M8f8nDxL74xjJZuhfQs6vI0IIrzWzu3Jx7pbIb47/PxdC+FZM4+IQwvaoPr9oZtkqxsODzrDsAZ4znUm2MnqJeAmt5LqaRp3dF5XbScCXzeyxujrk0j8y5m/H2K6X6oDq0+dRfdmNvm3m7WhV5NGojPtbZ+9GL22jUf/wZeTE3wC13SviX1GdzdrNL9CWpE1Rv7Ulcuj9mObt9r6Y/oT43RqoXN+J6s0n0Yq0y1EdKtLhZXQe0p9Rv7UwhDCDxk2Pw+KKgpVinhYix2Ymz0IrY76CHNorof50URYu6Da5xcn3+biziZesn1scQrYAGjI5rqYkq69m9moMMoPk0HUzm4L69UqY2cNxsuZ4tLLkMlRep4YQ1kFt6A7kCL8MeKM1Dlg+Ek3QOByOJog257PxryrcA8T+Pvb1vcBHhpC9WWBmH8oCxsnP1St0yNucgYzTltobMzsx2vPjUX8/gfaN04rS3x31+XuiSaBX0dEYmb3ZiIbNKxun5e3NyjTOsF5qO4LOv1vGxtDXFo2MzsvUrqT2ZimnIO5ZVNibWK5L7Q00bE5E3t7MjuI1VCAZI6Y252pUF9YJIVyN2scjbm8c/UTVy/nydBy04iSpo8PyTmdE0AKcQtnMnmRZW9VOHbpK0q/SYXk+k8HgtCXu3HPMdvStGEJ4V5RnABfFzzPQZYM7IRvwzQI5w7gKztLyKSi7vA4ZP9Wnjg75PNTRYUQreaACHXPmtTpIjN9djBwTKwBvMLM/x+DLDBLj98clUU6oqWIdx8gKwG7W2F5bOEjsjw4hhPFmNivKn8tk4GPxt9OA06K8V9DWzF3jChhCCIcmnHx8mbxV7vsfoG0aq6HB3svISbkAeDnoJtAFcZA4HM0Yr4oGVHfQcIQ+FzmnZHGZ2fORMx45WLK4d0eOrqPRrVMT0eDt+qAbjv8vDBIzJ17dQeK+MQ//DRwbQtgA2CGWwf+gGfgxaGXXushBei9aubkV2r7532jFV9ZB/D7PCSFkW6Yyzs/NbELUdTbaIv+3oAtgLkZnH2a/fdzMLkrlNK9oNeKR8Vkv5aBt7Y8mnAVUt5lr0UpIYjx96nlM85mgQ6dnofMQQasv7kUvhi+hs5U+gNrsNLTV/umkDf28ot1kDut8Pc+cjOOBL2ZtBjkAX47ltTKq96+gvmIWqpu/Qis+Z0R+l5n9PIl7d9S+RqNLHQ6OOlxH48D3e1Ed3wa9cF6HXhh70FbguuGqOGuj1SHj0OTBAciZn529kznkJ4QQdo3yxuiFMJOJn1O5KFwRZ+/4zG5GTtUfoPp/A6pLY1AZfwU5abMXqvScIYfDUYGgibGTkd1YnQJ7gfqE4Wi3xB3A50MIFyPbdB9qp3l7s5QTdEHYF9CYawfkzNvZzB5L7Q3wvhDC7BDCMjamzN6Y2e0hhC3QmZ15G/VojvMiy2GcRmJvQgivQeOgichJuL2Z3d2OcRoF9ibapT+jicjFaOJ1Cg17czG65OHpinFaam8eRpPwY1j+NiblHIwmuv5BX3vTFcupzN5A6zYmz9kbrYB8Co0nHkLleg4ac55AfXvzb/XiPcicoaZPOzl/Tr77cy7MnwFCCCvmfjsvkR8o4YwHrs3e3XKcfBxlcS/9vh86pJw10LEGq5rZ88B5ifPi94nz4r4or0FfB0qRM2Ui6gOyhQ83xvytj3amHQAsauIQqpKbctDW/IVoguYJNNlSpkO/4h4CnI7pk3uO/0Dv7F3I/hA/9+TkhWjn4fo07iUYhWxAtgCnJ8ZRxJmalU9Mv5dGXbohfh6e8LP4+qNDUR6qdMg/g7I8PI4m3x5DZ83fSAW6ens7s/29xUHiKagSpOFGI6NaxilzcoxED2dN5LDYDji8apCYHzC2Idw6aFZ3LtqScmXUayYazExCncRn0dbDUchhNi7mMZVnlHBGooFbntMT01mMXsyHo4q5iHiOHKpAmZzFMSwWYVm4/nJAWyqJuj0Sf9sGOV6uozHgO4XiAV9RuLqcbJB4FRokTqBkkEhjZd9AB4n539JB4mExzS/SGCQehAaJr0WrS8+NYWaiMn4BGdcLULlPRXVqY+T0Wg1t97kR1bepyIk1As3IH9qEcxgyomNQWb6Ktt0sQluBn0Or266ksdXotTF/c9CqgcNinX88CVOXk29PZe0mref59vAZdMbQqPjMXybejB3zcHvM93mo7MvaXbZycNX4DED1d0X61zYWxjQuRC9IB8dnn/3eTfM29Bx64ToL+KKZzQm6cn1kfE6p/BCw2My2KPitMFwTzgvxOf8mPq8Po7OZ1kAGK9ti94b4+Yn42+goZ/Xp0ZyctbvRTTjrIuN4U3w2HwJ+aGZfCLox8ZZYrv+N2slZ8bc7zWxbHA5HU4QQbkBt+Vy0rfRKim3MlshmvYAmKR5G20AvR2OsXvramyrOe5FTbSF97c2dqD94lGXtRSovtR0xD6nNKeN8GfUZy2OcBn3tzbeRw2cs2o68Lu0bp5WNv7qR83AR2lGwCtoBMyLq3k1z+/VcEtfaBfam3TYmlc9F46DMPmf25hFkYyYhW7QbDXszUBuT56xD40zt/dCKyzvRqscNkR1eam/QcUB3xd9nWePisswBfFr6GdWdVdCN86eFEDZGO05OCyGMjWleY2bPBu3q+JKZfSmNL4QwGjkaL0TjkimoPf0e1Yf9zew3eR0SfleRDmiyemHU4W9Rhx+Z2dLFGDEPZ6HFD/sAP4/P9ID4HJYA25nZmTGdMWi3VabPB9CZ6PPR7oQfmdncEMLusXzvjzqsFpOcD+xpZqdkfDP7TdAxSh+mceP1y1G+C714r29mN0YdDkon6aMOd0XdNjez82P6a6BdT5uhSe6HUN0LeR3QS/ku6L1jbbSqdlXUrqahFb+3oBWy66B3j260w+NC9J7Sg8awu8fw9wLvQP3EpWjnxhR0VvJ9qG+eid53rkAv/SPiM5+P+p1e1NanIcfF6vG5Xob63y+gvvht6B15q5wOj6MFBbPj5wNjnm5FE9CfRH3kOehyvJWSckrHtYvp+w4BDYfJQhp9ZpUzZT5qs0vQcTtjUb0o4+TlOulUcR5FdWJs/D5brd0VOQOJeyhwOqFP5qDaBC2gWDeEcCEaG+yN6klePg3Vyay8s3IYjZxcd9bgpLqldekxMwshhGuA4Wa2R4E+b0FtaXZMdy4aC1XlIc/J15E6edjbzLLLl75kZu+nCbqbBWgjzkerdJ5Cy/mvRh3LxjRmEIeh89E+hwzdRQXhJjbhXFgS9yLU0V6PDN1w4JMhhCvQip1KOYSwOWrAAwl3MRqQTI7P4hI0u7cSKtxjaZwpeAlavTURDT7zcsq5IOGcUMI5MXImoC3Ae9I4oHlNZBimRvmq+Hw+gc5dm10Srr+cR2PZP4EGtasAs83sXcAkMzvAzM6IZTXHzM4wbZWZZGbvrArXD46hQf1asZ4sjnXosfh/u6jnMHThwx5otvVNBfKb0Zl6WbjtSzj5cJPQwGssMnIj0WBgStQnGxh0I4/8AmQcXzGzn5jO0xtmZoeYVgUOA2ZGeXgs47+gbc2j0SHdqyFDOKEGZwsabWhxLL/PRE6gcb7keDTYvjHyX42/jQJODyHcGfn7oFmHK2ty5gCfStrTeRS3m7L28KvIydrQcLRqNltdsEqM68eoo61qdydG+YX4LL8Ty6K/bWMxeol7FvVVo6Med8c4qtrQXciJ9XSURwE/CyHsjLbb7pDKCEuIBxXXDdeE8zA66/BGNIC/D/idmW2MBno3mtlHk88nm9m4RF4pln9e3jgXrpCDXkjfGNM4GM0O7xNC+GrU/ZPIiB6D2vJb4m+dma1yOP4zsKKZnRVtzgoVNobEFq2EXv4fRXbmeZa1N1WcY1Bfm7c366J+scherIFeVPvYjqDV4y+h8c3vKjhXo8H9N0II16K+tl3jtNTePI9Wbv8TvQRvRfvGaVXjr7noHN6NYlzrIntzeCznKvt1Fw17s1FM/7IO2JiU89OoW97eZDbmr8jmpPZmQDYmz0E257/M7KNoIimzMRPQWC2zN1PQ2G4OGh/dDsyK45e3Bp3l+/UQwq9DCBeFEH6E7Nd8ZFePDCFchZwsN4UQ7qfh8J4RQngeObbeF0L4YHS4vy2E8D40jvwoegG8Do0vfoheEGcBBxXpAGwY20qmwwkhhDcjx9CByAH0MmqH00IIs9D51E/HdvanWD9ejHp/GtXrmbFenRb/fzE612fG5/TJEMJeQWcBH4QmkB9HDv8XQghHoPb+I7Q443doh8MN6N3hcyGErwSduf3NoHPYH0Jt7JAY9koaK/evQyuGDo95Oi2EcGsI4bbY1teOZfErdGzHfehSmRWQw+x2NNFwO40VRseGEJ4NIRjqT25DDq1PIefXPajdv4LeNzdFdbgX9QOfQ1voXxuf4SrI0bURan9bI+fkOLQb5VHUx3XH+MbHMI8jR+Ic1Fe8GbX76+LvD6BjoP6FHLw7x/Q+EdPpQQsd9oy/5XV4N+pTtkQrqBcjp+QY1N/cGnW4IeZvNKpLcyP/u6gv3QCNyW4x3Rb+26jDJWY2Aq3uuy7/WyL/Hr0z7gz83sxeg+r74xWcS1pIp4qzcdRhx6jP39H73f1tiHvQOR3S5/dmtimqz7cibGZmZ6M2UiQ/nivvrBx2Ru2sDifV7fc5PsimZxMG+fhWi+Gz8p5cIw95Tr6O1MnDpgBmdlsmN0MnnXmtDBLLHBatcJagDuVSZKCG03yQmB/8lTki6obbDs22TEdOhE+bZnjmou11P7Z4rpiZfTz+1gvMKJBTzhE1OCdHTo+ZXWzaxjIM2NrMphNv6DKz6Wb2DmAlMzvXNBO4QlG4/nLQAOUEVGFfQIZgx/8Dg8Q+4eg7SJyCZsKKBomvoAHUV9HAamHQzblvRRee/L+Yp5fQNk7iM+0y3fy6R3wm3aZbRafS6BiqOF1JG+oC5pvZmWi2cDYy6i8hZ+SuaCDRgwZgi9Hsx+gYdkNU7xah8yefq8FZA824Z+3pdTH/v4HSel7VhhYDY0MIx8b8dMf20AUsadLuTo7PaJaZzTOzr6L+q79tYxSwVeSvDjwZy2Q0uoW5tA2hwfrKaPAH2ib8AWQcpqC6fF2UrwshLECrFV4T5brhqjjDUP08BbWhQ8zs1KjPJ9FMNAWf68h1wh2PDsnfL35/XPz+Y8gZfh26uGh/NPjcJ8pb43A46mJaaNwQ/0yFjUlt0WLgqTi2G44mnfL2poozBl1ilLc3mXOvyF4sRnYzbzuuRP3kwcgR90AJ5yXUJ9+IHDBr0b5xWmpvliB78WM0uG/bOI3q8Vc3cEQcZ3XTGAPcg1YhVI3TUnvz+/gc3sLytzEp58/IgVZkb2D52Ji8fDwNm3M8DRszF014ZvZmC+DryOHWjSZFL0PjoyuQE+lqtMrt7ciBdjVqK7NjuH3Q4oPr0KqPfc1sZVS3e2McP0JOo1PQ2YkXAp8yvaw+hOrdcOQE663Q4fUFOoxGzrKN0UT7iKjrOTFPt9O4gHBsQfqHIMfYm9HYdSMaW83WQPXsLrSy7Go0kXpV5HShOrYIjY/XRn3C7aYX5zlR3zNjmBPR+Y+HoMnVvc1sPHIkLTKt7nxP1OFJ9G72U+ScvwuNCbZC9S6rU9NR/dsMtYEfRB1eG3WYj8agq8X4Xozp/5qG8+tR9I75BHq3GIvGvDfG53kXamNzkFNvBHK4zkGr9u6Mf/PMrAu408xOM9083Y0WrVyKHPMTYlxLgJFJXzEcmGhm9yOH6nqoH3wBOeUvRXW1F7XFw+IzvTmvQ0y/28xmoL5xCXo/OD0+m7dHHYahrfvzUB1cwczuN7P/QmPep+MzHhv/b2aJw4JqZ0omPx3LfJugI4TeHJ9nFaeVdKo4S3UAtkU24uE2xT3YnE7q8wjwxqDjMF6Kjv3pZXJBeaflUJdTVZfGAD1N9MmXd508VNWRWnkIIZyS8CvRyW22VyLP7NlxpuYKMzs6hDAddcDrhBAuRYOGL6BO+Hdou1Q+XCucF9AL9DYhhH1Qp3Y+8o5ujh7su9FsEWh7RSrvjIz2OU04VeEORB34HcjDvwgtV/4KMnbHICNzJOowp6AzOmahWa5UbpUzGnhrTPta4EEz2y6EMBnNoo8OIRyJBg57oIHLDSXh+sVBK87+gIzljcipEWjctLo+MkbpstgFMU8bxe/KwtXlLEAGd03UeD5sZvfGcIQQJsXvTq0j1+Wk4VBHcDo6lLoryl9FRnZDNEt5FGroN8Xn1oOM6ZLIeQR1TL3I0I7KyXPRoOI+NDhcCzkHX1ODMw64NLahF4Epsd08il7A9oj5WbqFMSffYWbbZd+jOv5uYnsws2FNOHeg7StLOWiwsRFawfATlq3nVe3hqzQuZ9k2PoPFMfwTaAaxWRuaZmbrhRCOQoP4bBa0btv4LnJkjkUvSh9HA7p/xue7aZM2dCQ6QuAtaMD5JfTS1YUGk9C4WCjr1PNy3XBVnKlm9mR8IX8/GngvIW4nMbPfspwQtPWoJ76gZp+Ho1Ws16I28hKwn5n9Luh80f3M7HfLSyeH4z8JQWcu/RK9nE5HNqjIXoygYYvmogHxS/G7Z9CgNW9vyjgrAheY2aE5e/NWtAJgbNStjr3pRrbxWirsTQEvtTkDHael9uZ3yOkxL8YzBjkW2jFOS+W87bgUjblmI3szDY1DL0F99tuotl95e3MMejHphI3p89tg2Rvoa3MyGdXZD6DLQl5C9XWdzN4gZ+BpaAXnj5CtBzmhHkCTT5uhen+8mX0/6KgIy/Ke1Mu5aLz6OeRQ3BVNiN8A/MnkSCOE8ACwgZmtEEL4GFpddiRyQp2d0+Fi5LBeqgNylgNsGss/bRtLzKw7hLAncu72ICfa11H7ejgNF+Un0FjncDT2GYbGK6A6+EzMyww0SfsSmsDtQg5FS9K/Ddgy5m1T5DR9lsaFNW8zs2vSZxB5j6NVroejXV57x/TPQQ7FN8WyGI9W5h2LnF6G2t2wRIc5aELi/Wg13Bj0/vhe4Hkz2yCEcD1ahfZC7L/OQQ6vY5Bzb2czuz+E8C/kCPwhGhNugRz9H0V97ni0Wu74+LzvQuPf/czsjyGEe9Bqvqlox8+wmJdFyAn+BOq/t0OTG+eiC3RmmdnrY35uR+9Cd6Ox7Dao70l1uAedw3l2COEC4L1mNiq2xYuQndgB9ZELzWxkfEeYFMt+O9TXvQ45g1eNz+OMqPMc9N7+u6jL+3K/jUrkE+Lz2gj1nWNQvfllBWdUC+lUcVZOdOhC9fAo1MYGGvdgczqpz2HIlsxDdnIBjSO4iuTF9C3vtByerslJdcvXpRvib7tW6LMOfcu7Th7ynPRZ1c3DX9AloS/TBJ105rUySGzmsOgPh/h/NuoEu81s86hb2SAxP/gbaLj10MG/3WiW6bfIcTILGZGNUAEejc5ZGYdmgEagwV0qt8p5hYaT67n43EagzjebGR+DDj0+Ajncni8J1wpnDDKas9Dg+YtoxZcPEpcdJGZOidWQkb0JDYJeQk6/HtRJ9OTkCeiFYGO0rX1bNKs4i8ay/SpOvn2OQOX3TNTpkZiH1c3s2QI5rfNLv+8HJ9+eytrNi9RrD7PQiou1UUd5F3qR+TJ60dq6gJPGnb0QZQ7VE+h/2/gYGhz9Es36noheDBfGfI2geRu6CTmvXova7gs0Vo9kzu1V0QB2Xow7k+uGq8MZF5/FfWjbxzy0smBLVM+sTem0ypmO2vFVZnYpDoejXwg6UL3KxvTQsEU7oP7sNWj1S5W9KeI8TbG9mQp8zMz+GXVqajuahMvbouU1TkvtzTzkVDkI9V9l9ivPrzNOqxp/zUCOpRXRC/Uq6IXlFbT99hSa26/M3myJnFDZCvtO2Jg0XN7e3IAcMwdGzrNtSqdVzlqo3p+Fzo0DvahtGf8OQs6jG5GD5lAaZxj+HI35tkZ15iFU726OZTQ5ltH26JzYb8XnsF78+yVyFt2JHOiboZV3P0Avj0+jMXYzHb6NbObKaML5APSi+xByfh2NVqHdiOrGdOTEmR3jOBk51v43pvkeNI7dEvUHU4F9zOyloAtaFsf0J0RH2EFoN9OMGP8tqK84Hb0v7BvTOyD+3YEcZ1uienkwclqORefL7Ymcg3cih/RKyGH+K+RM/A0a6+6Oxr7Hof5oHRoOvfmJDlfFcnhPjGMMapdrovr5v+hd8wDkIMzyMQY5Hf8XOcu+hsbkP4tl+kvkZH8vcg4uQH3AVWjr8vU0VjlmqyhnovY4J+r9Wxo7EGaid+BtUZt+IepwF1rR9xUzuyCEsFss9/uBd5rZsKDLClIdro152Sn+H476qalRt8NQXb0EjaOz9/B7aJwH/UJ8LregOvM6+jo/FlPPmbIYrVr9NdreOAm9r7ypgpPn10mnitOb6GCozbwhfj/QuAeb00l9/oL6tono3X88akdVclreaTncWpOTr3Mpf4Wo40YVOjxF3/Kuk4c8p6wuVeVhBTObQw10zJmXoZ+DxGbh+sPJZoqfMbPH6wz4+uGIqBWu5HmsZo1bTpfKVb+1gwN9blftmA7IgKXOs047IgZrkNhqHlZBg4JsxSGoQxgWw2WrDbsSeRaazb0y5u1DyCCvXJNzL412M7yCPyvq04M6y+y37Nk9hgZNa6AVDxOTPFRxrjItiS5Ff+oiGoQdherdeBqrMy80HXhet/5uks1C19Uhk+Os/VG55/AUmvlspkO2cvB8VD83ReegbI8Gaoto3Mo0J/62H6ojl/UjXF3OR9Gqk8vRi2EPmqWfgWb9p7QpnVY428f0L0ft+AEz+yIOh6MpCvqpKnsBDVuU9Wc3IydRlb3Ic25CA9sie1NmL1J9UttxEbI5dfLwItrCchFwbbvGaTSxN1XxtTJOa2LzsmfQx940iS+1N9PRKqLM2dopG5OGS+3NgahezEZOlR/FMuykjcmHm4acpm+g8bI4FTk1voO2cp4duT9GNvxU5BA5EDlkshVi16D3lK7426hYrIvib59Hq+iGISfO1ai9zUbOlx3Qi+Ai1N5+G8uxmQ4nRx1WRufOfRkdObNtzP8wNDb9G3IAzUerP/+YpP9e5Dg7DDnzFyFnzp/Qi/LFcXJ6InKs3QMcZ2arhhD2RU7JScih9oPIPwKNH7uRk/AKtAjgPVGnP8T4N47P/JJYDpuiNvBUkudvx+dxC3CSmX0s6BKP4+IzPQK9J26InG2vJjpMivIzNMbVC9Gqt+8i5yhRl6NiHg5Dq2pmxt8IIQw3s4UkCCG8FjnnFsVn+XYz+3n8bWwsl+1I3l/JoaCv6Il56QKeNbNF8fsRpqMN+qRvZneFEEbGPK0OvMPMfhZ/Xw/1IeOBh4rSj+GWjo+L0g+6IbfIYVLHmbI6mpTopnG54cfQbry6DqE66VRxinT4OnLiDzTuocDplD5vQ/cbDEOO+ouQ/f5Mifxa+pZ3Wg5l/Dynqi49hPr2BTXiy8q7Th7ynLK61Iz/YTO7hibo5Mq8VgeJZeHaxakaJLbKGUjcncp3M87yiHtc/H4mamBL4nf/6YPEVvOwCnJMrEzjxrxsJngimh26Dg3wnkODsMPRbOQzaHA1Es1cPowcqJNrcBbRmAlcgcYhySn/8Pj9QwX6HI4Gcb1AoLGc/1WEojyknA1o3IDVSrvL1780D/NjOiGm8zKqj8u73Y2IaV2EZoJXTnQoymsa9wTkKJsVw3/czC4MIQxDZbsCja1rK5jZkqDDmZeY2Wb9CFeXczcw2sw2znFegwalo9uUTiucYcB9Sbj7zGwzHA5HU4QQfoD6qivRS+rzFNuLtO9fhOxCoG9/X2UvUk7aB+b76i7q2bzUdoD6y4vQqpeyPJTZvIH2/fk8/B6tnGl3OlWcSWh81YrNS+3NY2il19p01sak4VJ7k3LWJ9qbDtuYfLiW7E3OgboC8AYz+3MuzAQ07nku74RpBwp02C17cQwhdJlZ70B0CCGMN7NZVXL6Ob4n7mpmN9TlFMiZ8+9VM3s+hDCchlP95SbyIuSEeiz322qofyrkx3RGoBWMb6fxjjuShmO1C7W3JfG3UagvWJyT63LKwi1BDuaZaNvt4qKyseRW33YhhDAOrfxcF7jMzM6P3++JnL/P09d58VrqOVOmxThfNLONgraSnxG/r+sQqpNOFeeFTAd0tubPUTmv14a4B5vTSX2eQs7zryS8PyHn/jKyNY4OyMo7LYdZNTmpbvm6dA+NycUyfd6RK+86echzyupSVR42B35tZtvQBN3NArQR30OzJ6egDuhR9KDHocydQWO7wNMxzCol4drJWdhmzkDj7lS+qzjLK+55qDO/F9WH16GZsu2Rt3on08HSc1BDOxKdF7cRmiGsCtcK52Fg22hw3hnD7otWCoxqYzqt5mFdM9sLrVLoAXYws33R7Os4tFX8OHSWx4Qob4BmXeejrQcj0faB76LVgOvV4IxNym4kWgac528ArF+izyXonL5j0YDnlcgHDdSacSbH/Lba7vL1L83DxjGvx6IVHGNLOO1udyPReRE7oa0wOzfJaxr3czHMU1E+KoTwdWQglqAXrVSGxqCOfoSry/kusF7Qdpk1gElR/lvUvV3ptMJZj4bTOJUdDkdzvNnMPm1mf0QvgWU2Ju37N6DRn6X9fZW9SDmTafSBaV/dRbm9SPVJbcdvkDMy62tXq8hDmc0baN+fz8M3l1M6VZxRNOx+f21eam9+gZwI/0NnbUwaLrU3F6MXr+vpa2/akU4rnPWAJSGEX6NtnUtCCDeHEBYG3Zq6fwhhRghhTgjhjhDCc5kMPBTlW9D21T/mOWjl3T3optdbQgi3l8S9KITwcAjhlSgfFfUjhDA7hHBAkYy2o2byfokjbzba/guqH7uZ2YKyuOLndUIIvwghnBlC2DXonPKnQghPxfieDiFMDjrL7ukQwkshhINiuKeDbqh9EbimCefglBPDPRVCeDKE8Apy0E8DHgu6TGU+WrU3HdXvaRXyg/F55zlVfEvSOQWtYt0AOYC/iVY4jo7P8UHUNm9AW25fjvGk8n/X5BSFm4McCP9A52eeg1YIfjX+HR7/vhpC+Gn8+2tO/muBXBRuGQ7azrtbfF4nhhD+FqvHSWhhwouoH9oDGGdmz6CFHS/mf8vJi9HZyGvF+D4Z81zFaSWdKk6qw0lokmNJm+IeVE6H9XkI1UWQbf57Exn6lndaDnU5VXXpVRpn7pfFly/vOnmoqiO18mBmD1ATnXTmtTJILBu8DWXOUNNnyOQBdfBvBPYws8tQQzuY//xBYqt5yGZFM86w+HkEGryvFMNms8cgb/8wM/t0zMdCtEz3MjTwWKMGp5dG2T1Co7xS/rqwdOY4r89e6OKZP6IZ/jGR/1F0a1AzzhKgt411Ns3Dy+g2vz+ilcKjOtTuxiAn8VuRQ/eZunlFM0Dz0bl0j6BVHsejQed9aLXK/VF+IIQwi8atgbP6Ea4u50w0cNwAtRnQSsMb0CCzXem0wrkHmJE4F4/B4XDURdY/g14gy2xM2vePptGfpf19lb1IOWkfmPbVVfYi1Se1HUehPinra1epyEOZzRto35/Pw5jllE4VZxgNu98vm0dfe/MetC3x83TWxqThUnuzBzqfa+P4nBe0MZ1WOPegrVvboDPfXkQO5M+hbVgXIofcpKjz4gJ5GLrk6+gmnCxcUdyL0JjyenTc0IkhhCuCVnY8B3wyhHBF/K1SHgDnPHSh12RU1y9B5wtPRE6m41FbXAO9XP8qci6Jv6XhMvmEAs65BZwTo/wC6lu+g/qMT6CFA7OBC2JZzUTvInn5KrRCtVXOC8BtUecpyHG+k5l9CN06/U4ze1eUDzCzM2K5zcnLdTkF4V4Tn8tasW4sprECd0MaN+4OQ1sQ90Dj0Ux+HTpjLC+/OReujDMpltdY4k3hIYTvoXFstkIw73Sp40x5OuatN4Swcszf4004raRTxVmqAyrzw2gcAzHQuAeb00l9HkX1oxeN1X8R4ymUC8o7LYe6nKq6NBrZuyp98uVdJw9VdaRWHkIIX0NOwKboqROoTegJIUwwnR8wisagqmyQOJfywdtQ5gw1fYZSHr6ABhsjY4V9FhmAdLD0KmpcxMHSSBqDKirCtcIZSWOQOB4Njt4Sw6/Z5nRaycOjqCEvRgPyc9H2pZVQB7FWCOEPqFOYF+W3Ai+GEHZEA55LgfeFEC6KeeypwXkxKbvzgO8U8N8ay7JIn02B80IIG6EB1sjI3xkNeJpxNkUzn9CeOpvmYSPg2ZjO9cDiDrW7RehGr3+iTv6ndfNqZn8J2v723ajzY+ig5dPQ2Sx/iGG3RjOyK6LB5O5oYD2lH+HqcDZBbeZWNAt7Pxq8L0aHu57SpnRa5ayDDpa+FZ33lM0OOxyOanwXuD2EcDPq77tK7EXa9/fQ6M/S/r7KXqSctA9M++oqe5Hqk9qO69Hq/6yvHVbCqbJ5A+3783lYvJzSqeI8D2zbis3L2RtDq3+2Qk61TtmYNFze3ryKHFqbAJ9CK5E6bWPScEdFPW5FY7ZnzewnsTzON7NDojwMmGlms3PycIAanKpwS9C44FI0hl2AnE5XI4dt5lTbG40/ZyIH2f0FcquclVHdvwo40cw+HnX7JjDDzE4JIXwWOZN/HH/7VEm4TD45hPCZGpyTQwgnohtb56GVZ18xs3NjuKOArc1segjhGSDkZeAdQTfytsQJugF6dXTZyIvouJzzQgg7AwtDCDugfm9hCGEHM7uNOJGBsFSuyykI9zCaULkRvdtsA/zOzE4NIVwO3GiN8/guj7/9vI5chwN8Ft0u/EwMPw7d1NuD3mlms6zTZTRazT2ccsfIsWgX1zBUz+ejbZOfr+C0kk4VJ9VhC9R/TmlT3IPK6bA+D6LLbNZBEyOvoPejXUrkfHmn5VCXU1WXbkLvLm+o0Cdf3nXyUFVH6ubhrzRuIq9EJ8/MOxQtOb4ZLe3uQjN++6Dtl9ejAdYNaHC2Euq0FhSEG8qcoabPUMzDHaiDPwQZnbvRYOkpygeJU2kMqsrC9ZeTHyRegc6YyAaJl7QpnYHk4W/xWW5kZt8KIWyBZtRvinFthwaQsxN5S9TW/hW/Ow2t0PsqumVtchPObjTa51vRqsWb8nwz+1eJPpuiGYlb0Fan7yfpfw29ZFVx3oyQpT/QOpvm4Zvxt79G3a5E53gu73Z3DqpPv45lunM/8voCehG9H72YDUPtZ2s0+3wbWvZ9JDpD6Do0a9od0z2gZri6nCOAp0y30P0UGZs/odnakbF825FOK5wD0Kz1BgAhhDvMbDscDkcthBA2QX3L3ZTYmFzfvwY6F6eovy+zFykn3wem9qbMXqT65O3Nl9BE4a/RIf+XFeWBcpvXjvFTmoer4nftTqeKMw8d+L+Q/tu8Z2jYmzWRk++JyO+UjUnDpfZmY/QidT+ygYNhY/LhxgBHm9nZIYQZqL6fgyZifwecZWZHhxCmoxWk6+TkzAH3hSacqnAvAE+a2TYhhEfRarVb0Thji6jPzmhl3ztj+R+E8Muc3Cpnc/R+lzn1f4LGsSehc6G+h86YWgGtmN8qPtfTC8K1yplmZutFR9z3Ud8yF7WNB81suxDCZLSidXROPhKdlb1Hi5y10a21i1Ed3ZzGmdVTaNxO/ShyAAynsVKnJycvqMnJh7s3/r4hmiD5sJndCxBCmBQ/n5r/XEeuw0GrZU8HjjCzyxPO1bHsZtG4VXs06gtTZ0r6W3cij0BjzM+a2TNxMuK3qL8r43S3kE4VZ2SmA+o3/x86lmlRG+IebE4n9bkPOCCW415m9ucQwnuBl4tkM7s0V95pOWxVk5Pqlq9LewFjI6dMnxtz5V0nD3lOWV0qzQNAKleho7fZtjBIrHJYDFnOUNNnCOXhu8jI/5PG9fLD6KwjYjAGia3mYT5q+KAZ4tRDvwaagctkks+gzmIztF3inzH9DdAKv2acB9CAfrsY/t4CfpEOaVzroJUZz6Elxc+hAdmKJfyU8wCqO5NoT51N87ACOsspy+dktFx6ubY7NHudpf9SfJaZDs3y+hfg/WZ2awjh4VhOX0TnkxjwIdONuPPRAO5LwLfQ4O8j/QhXl/NttPX7I+gmuB4aZ2P8so3ptMK5Cxnaj5jZxSGEO81sWxwOR1OEENZNPq6ZyHl7ke+7U3uT9fdV9iLlpH1gvq8usxd5fVLbsYBye5Ny8jpkNm+gfX8+D/n+vl3pVHEeR7atFZuX2pvNUZ96UIdtTBpuqb2Jffp8tPLwQOQk6LSNyYe7D43rJsbnOhK97Hch58amaCXVq2hXUpE8oianLBzx/+wYfmszewQgtYF15AFw1kOO4260ivO3aFzwIo2t9bPQ5ORG6MX26Fi++XCtcGaj1YE98RmdgMb4q6P6siSGHRvDduXkMbFsB8KZgbbcroLOtDsF1Y0uNJkP6le7knKrkuty8uGmmtmTIYS3olu116ZxMcYfzey3LCcE3cDbY2YvJN/tjS6q+V3q/MgcKJQ4U1C/tQvwLjTW3B6tut4aeLfpAoO6DqE6Tpsizj7IZ3FiosN81D98NdOhxbiHBKdD+nwveY4PoAUt70VHFbwO2e1U/j2aRDg2lvdHUP97Inof3gjZrSrOu5PyydelD0Yd9kd9xkUF+mTxnRvL+6REh7I85DlpHUnrUp089GR8mqCTK/NaGSSWDd6GMmeo6TOU8vA9tBpvMTKQfwGOMbOf/IcPEgeSh0Xo5WgtZKR74vPrQcY8k6ExYweNbUsTkRGfgV6Wspm9Kk438L4on49elJ7J8SnQIZPT+CZGzpNR/4kleUg5w+L3mQ4DrbNpHrJ8Lz2/oyKdLO52tI3z0YBqRpJ/aub1V2gWfAnaxvKZGN8BqJ5mdfaPaOn3n9DAfx4a3B5YM1xdzp/Q4dhz0WzXC2iG6TL0Qj2jTem0wjkQzQxn4b5vvjLP4aiFEMK96Jyuaai/SvtqKO77oa+9yfr7KnuRctI+MO2rq+xFf2xeWR5STt4OQOt9fz4Pwyi3NwNJp4qTtzdU6JCP+/s07M00tPJvNLI3nbIxabjU3rw/htsbOYvGoxsSO2lj8uEye/MpdFHIx1DZv4RWSPWgl/+eJnJdTlG41ZAtfga9PD8by5IQwurZ5zpyq5wihL635RbKdcP1g7OJmT28nOKu5CBHT+Y8W5XGKtnpqC/I+sP0t4Ulcl1OWbhxqH7ch9rwPORM2BI5ymw56ZPKoLq6OVqt3IOcH79HfdBFVDtTHoryIuQE2Q6tcN4WrQKdXMDJO0bqpFPFGYHex7ZFTtP1aByb82rUodW4B5vTSX0+EJ/jdsgmPo3s5IvIrq+Sk6fQuK15W1Tuw6L8DI2z5Ks4aR3J16XtEx0mx7jz+jwfP6+Dyrs3hqvKQ56T1pG0LlXlYVrkLAL+YmYX0QSddOa1MkgsG7wNZc5Q02co5SEN+zSqB11o68B/8iCx1TyciQZsx6AB4pvQ0v8j0cz/MrKZ3ZRra93ohWYW6lQnoa0HVZy0fRLl6Sk/clYq0efMkvRfjfHNbsIpSn8gdTbNw2pRnozq3lpJWsuz3YE67VlRh+xFqU5ee6OuS9AgbIX4DEGDJkOGYzRa/Xke2lo9Cr1gnFQzXF3OSfHzJ4CzImeX+Hx/E39rRzqt6jYSLW2/DBhpZuNwOBxNUdGn5+1FGq6sv6+yF836++lU24sy29FFY5VfZnOW4Texee0aP2V5WIvi/n55ju2gr73pj83LVn4tQSsPVkZ97Bw6Z2Py4TJ7c1rk3IZWPX0t5r+TNiYfbiRahXEEesG7Pz7HYfFvJeQAWUDDGZKXZ9Xk1A1XxOlB7XOwOQPJw1DmTET15Pn4eQFwDXIajEH1tDfy5iDnxH4xnctycl1OVbiPoqODLge+Ecvl68h5fzFyGrRbnzynG71f7YyOPpiL2lOWduZAmUyxMyVzbLyK+qJspeR+yDkzoYCT59dJp4rzj8ibhMp3MVr1dTk6kmnCAOIebE4n9ckcVBOAX8SFLXua2bUARXII4ePo3WK/+LyJ5fAUWgz0XBNOWkfydekvzXRAx03k06cqDwWctI6kdak0D7SA7lZILWJXZBgPQoOLe5FzYhzy4L7JzIbHz+lvy4Qbypyhps8Qy8PP0Nksu5rOtPoWGvhciozj6Wj56+qoARyHnAMgQ1AVrhXO21Fnc3TkdKHB2cfQ1tR2pdNqHiahAeJ3gQ3NbDbaantImRzTWdrWzGx9dE7PlYlcySFpn7FcX5/nA1ToUJb+BrHcKzkF6Q+ozqZ5SOI+KOqS74uWS7uLvN0SHfqT1zeiGxu3MrNVUL3dHQ2WzkQHvO4f5ePR1qnN0GzYTf0IV5ezPXCOmV2QcF42s6fMbOc2ptOqbv9rZktlHA5HLdS1MTX7+0yu5FBub0rtRUX6G+RtTlkeynSgPeOnpXmgpL8faDpVnAJ70x+bl9qbDaLN2ZfO2ph8uMzebJ+Eeyzam3al06pujyFHyWHoqJZZaLJ6EXqRfRQ4Iz7fBQVyF3KUNOPUDVfGWTgEOAPNw1DmZM7fU5Fz4ynk0N8FbafbFtWXjdAtt0cih9iLBXJdTlW4h4Ftzex85ASbH+XHkDN6eeiT54wxs93RmHW2mW0MvN3MgpltmnxO5YlmNikJtwd6V5wQ010vPu+1Sjh5fp10qjh7mNlHow4vxGf5JHLarDXAuAeb00l9suf4HeDAEMK1wP+EEJ4LIcwrktGZu1l5p+UwAU3uNOOk5ZOvS58IIfwVODWEMLdIn6jrB5PybpqHAk6qQ608hBDmhRD+GkK4PuiMzqbo9Jl5OwKHm9knyuS64YYyZ6jpM5TygJbjTjOzB2P4PVHn+C40izQNbRc4DZ058Qo68PeKGuFa4XzBzD4XQlgn4Uwzswejbm1Lp5U8pLqZ2eeoiXy59JfTCn95pj/Q+lelW6fa3UDzWvc5OhwORyfx72hvqnRohy3qRDpVnKrn00oeHMUIIdxtZttEuRs5LcaEEEagVaH/MrM3hBDuQReZjc7JI4F5ZtbdhFM33FDmDDV92sm5B63cegadq/dedC7WvmjRQkBO3/vRbbhPhhAeBJaY2RY5+TU1OVXhPoJuer4enY8JWl26CXK4vnk56JPnXGpmrw0hfAA4G63kWxk5W1ZEK5VWifH+I/fbA4mcrRJeH120cSjauju5gvNAC+lUcXZKdPgmcsR0IYfvQOMebE4n9dkJrdC8H+08eyz+p0Q+mr7lnZbDczU5qW75uvQzdFnQqAp9zqBvedfJQ55TVpeq8vA2tFDiGJqgo848h8PhcDgcDofD4fh3RwjhfrRiY2YIYS304rYeetmbBZiZbR1CeARY28xWyMkboxVdE5tw6oYbypyhpk87OTui7aXziDuQ0Bbs+WjF5paxytyHVne+ipx/xPRSeWRNTlW4kcgxsCI6D3MxujTF0JlfGywHffKcm9AOwDehszh3oq/z42jqOVMWoNu0t0Bb60+P4adUcFK5bjpVnHUSHdZCW5d/iJxGA417sDmd1Oen6KK82eg4qU1oLGwpkj9M3/JOy2G1mpxUt3xd+h7aoXdFhT4L6VvedfKQ55TVpao8/A243Wqc++3OPIfD4XA4HA6Hw+HoB0IIh6KVOjcjZ8VVwLvRtssl6OXxFnT5wDy0UiqV34rOcty0CaduuKHMGWr6tJuzGJ2F/STa0n8XOhdrHXR5Qhc6b+0p5GSbgo5MmVwg1+WUhdsEOexuRVvu70fOgcVol9R5y0mfPOeXyKFxOnKCpc6PD1PPmfJ8zMPtaIXf+5Hj8MIKTt4xUiedKs77Eh1GmtmaQds6P9CGuAeb00l9vh3L7hTgZHQ2/X1oa3uR/ONceaflMKwmJ9UtX5feiRxq36jQh1x518lDnlNWl0rzYGYnhRDMzAJN0Mkz8xwOh8PhcDgcDofj3x5mdi6wJ7pU4C1mdgS61O3TaAXUzsih8fr4OS/vZDrLqRmnbrihzBlq+rSTcx06c/IZdDnK0+jWzM8gx8Ku6AbY/YA90Oq1L6Oz5fJyXU5VuJ2BhWa2P1oxuHv87ugYdnnok+e8xcz+jpyfn0arAkeiLYe9yPk9H21Lzv+WynejI5oWAgej7Yr3NuG0kk4VJ9VhlRDCQ/H7dsQ92JxO6vN9dPbhFciR9SRwY4WcL++0HOpyqurS2ujcyyp98uVdJw9VdaRWHuKq79upAV+Z53A4HA6Hw+FwOBz9QAhh3dxXaybyGuj20Ewmfk7lonBFnLrhhjJnqOnTTs756HKdp9AKuOHoMPy/oBf4D5luv5yPLsb4EjoIf33gIzn5rpqcqnDfRtu9P4Iu1etBl3ccixyRy0OfPOe+KIO2Ga6Fbhgdh1awbo1W9/WglVPpb7vmwi1GTpCPo9vJ90bnEpZx8vw66VRxbkt0uAo5K/eK3w807sHmdFKfxcDdZnZgCCFbQXoXWulWJqflnZbDx2ty8nVuGb6Z9Vbo8+FcedfJQ55TVpeq8rBUN5rAnXkOh8PhcDgcDofD0Q+EEO4FNkZbuLqQw6IHvaz1oNUgmQzaSpXKFIQr4tQNN5Q5Q02fdnK64ndPozowE52l9xbgarS970Dgj+h8vT+hM+fmoQP6U/nAmpyqcH9C2/vmoq2OLwCfRav0xiMHZLv1yXNOT+Tvm9l2Zc6PEmfKBejswh60jfl5EpjZx/rhEKrjtCmSj0LlW6hDfKatxj3kOMsx7qPQyrOV0MUUGaoc5aBVagBviHIPWmX6agE/z0nrSL4u5XUom3zI63BLkzwsowONOpLWpX7lgSZwZ57D4XA4HA6Hw+Fw9AMhhJXQuWlHmtlN6We0zatSrstZnnF3ijPU9Gkz5za0te8L6BbKt6Bz6U5CL/+GzpAbHcOeB5yGDsT/VE6uy6kKd1L8/AngrMjZBa2o+038rd365DkrA9egbegj0CUEGeqsglwpyg/H/5vk5NkFnHav0Fwpl2Zen9kFnKG0YrSK00l90ue4BjpzEjQRArqcJZVBDq+qdK5vwknLJ1+X8jo8kshZfLsj59vMEh2K8pDn5HVI0y/LA+jyGADM7DyawJ15DofD4XA4HA6Hw9FPhBB2BA43s0/kP9eR63KWZ9yeh4Fz0DlY08zswRDCnjQuAPgCOl9rCfCuKGe/nQb8d4Fcl1MV7gtm9rkQwjoZx8wejDrvuZz0SeVz0IqoVYGPAf+ggbwDpciZkuHB+H8H5ETM5FcKOHl+nXSqOCDHyqoxTSp06G/cg83ppD7QeI4Av6XvkQRF2BetKM3wfMKHvvWpiJOWT4YHEznVYVqBPvvG/2U6FOUhz8nrkD4DKM5Dpg8AZvZkSZilcGeew+FwOBwOh8PhcDgcjrYhd65k6vxIHShFzpQMv6GxlbkbbV++H91E2swh1J90qjg3JjoMB1Yv0KHVuAeb00l9sue4BK3yHIZujR5WIj9L3/JOy2HNmpxUt3xdWgttd51eoc/z9C3vOnnIc8rqUrM83GlmmRO5FO7MczgcDofD4XA4HA6Hw9E2hBCeoOG8SJ0fz1LPmdLHsRFCmIq2LD5Xwcnz66RTxRme6QCshrYNrwS81Ia4B5vTSX36OKjiqtZPm9khFXJa3sPpWxfqcPJ1rpBfpg/wxlx5N81DAaewLjXJQx/dqtDdLIDD4XA4HA6Hw+FwOBwOR12Y2QZm9pr4vwd4PXChma0d5YsKfsuH2wV4IMa3FjClCaeVdKo4S3WInzMd2hH3YHM6qc/Scoxl+U9g+yZyWt75ulCHU1WXlnLK4iso76Z5aFJH6uahj25V8JV5DofD4XA4HA6Hw+FwOJYrQgj3mdmWebnoN7Q1ErTi6XR0k+hqZrZns/j6k06VHEI4rkqHgcQ9FDnLK+74HD8N/AR4E9qKOhqYUiL/In3WuXK4vw4n1Y1l69L7Iue8Cn2KdGiWh2V0KKlL/cpDGdyZ53A4HA6Hw+FwOBwOh6NtSJwX0Nf58QvqOVOmABOifC66VOA+YF4TTivpVHEyHZ4A5kYd5rcp7sHkdFKf9yec3ZPnuGOJPJdlyzsrh8n94JTVpR2TcGX67EDf8q6ThzynrC5V5eE5tJrweZqgp1kAh8PhcDgcDofD4XA4HI5+oCv3eQpyWPQmclfutx0TeT6wBvA1dDzYOfG7Ks6OLaRTxfl7osOXgRB1aEfcg83ppD5nAs+b2QUhhOx5/hdybhXJ4+lb3mk5DK/JSXXL16VpwEwzu7xCn5PpW9518pDnlNWlqjzsR9+bdEvhzjyHw+FwOBwOh8PhcDgc7cS3gX0LHCbjqedMOR9YHzk2xgOvADub2T79cAjVSaeK86tEh+8DF6KbSL/WhrgHm9NJfS4DHgcuiM9xJ2AkMKZE/lquvNNyOL8mJ9UtX5e+AiwGLq/Q57hcedfJQ55TVpeq8rAHOmvvkzSBO/McDofD4XA4HA6Hw+FwtBNnodtE8w6Tr1HPmbItcDtwppl9MoSwAK34ooKTd4zUSaeKs1QHtJrqSWDnqMNA4x5sTif12ZLG5RFbm9mGIYR7gK4i2cyey5V3Wg6F/AJOqlu+Ls1DK+eq9PlOrrzr5CHPKatLpXkADorfN4XfZutwOBwOh8PhcDgcDoejndjBzA6J8tZmtiFaNfUcOqvs1fxvObkX+BINx8hOyFFSxWklnSpOqsMOwLHAkjbFPaicDuszDdgtcrpDCFsCSyrkfHmn5VCXU1WXhtPwhZXFly/vOnmoqiO18hBCmJDkpxLuzHM4HA6Hw+FwOBwOh8PRTnSHECYlcup0qeNM+TZwN7BuCOG3wMbAD5twWkmnirNUB2A94E7gxDbFPdicTurzI2DjEMIlwJz4TOeWyQXlnZZDXU5VXVodWKuJPvnyrpOHqjpSNw+3J/xK+G22DofD4XA4HA6Hw+FwONqGEMKBaKvjjciBsT1wKzAd3ezZDVyb+60nF24n4FcxjrXRmWT5+Hoq+HXSqeJ0JTp0Awejiwy62hD3YHM6qc+OwPHAi8BCtHpuwyZyWt5pOdxYk5Ovcyn/thjnzhU67Ezf8q6ThzwnfVZ183CbmU2nBtyZ53A4HA6Hw+FwOBwOh6OtCCGsSbHDpMqZsg+wKjprbCq61CDDWOCWAk6pY6QinSrO54F1CnQYG7+/YABxDyXO8tYnOzNuKnKsZVg//p+ckzNcH//vDTwd41iPxpbVKk5aR/J1Ka/D5EQuim9sjOOeJnnIc9I6ktalunnAzJquzvMLMBwOh8PhcDgcDofD4XAMGCGE45KPu+d+Xp++DpRUJn7eIJEnFYTbroCTynXTqeJskMg7F4RbfQBxDzank/pkzzFfjl3J/67cd+sn8prAgkTO4q7irE+jjlTVpaK0u5I8ZN+tGLlVechz1qdRR9K6VJWHvNwU7sxzOBwOh8PhcDgcDofD0Q5UOSfyDpQiZ8rkKF/foC2VVyjhNPuuv5zJufSrdOiEPu3kdFKfyTSe2/VmdgJACKEL2MDMHi+QfwC8EDn3JPL1AGZ2QhNOWj6TU26mQ8ov0OcbwCplOpTkIc/J67A0/Yo89JGpAd9m63A4HA6Hw+FwOBwOh2O5ocx5UfYbsC9wEloZleEJM9uoKr7+ptNE/kyZDgONe6hxlrM+nwG+A4ymr9Ovt0TOLs7InnVaDmX8PpxUt4K6lOfX1aFZHvKcsrpUOw9VcGeew+FwOBwOh8PhcDgcjrahwnmxhHrOlCzsXGAr4JvAB9GFAnUdQnXSqeJ0Jzp8F/gvYGQJvxP6tJPTSX26gSnAzcBuwP8DPgMMK5HfRN/yTsvhxZqcVDfoW5duA26Kn8v0+Tl9y7tOHvKcsrpUlYc3AXuZ2Ydpgu5mARwOh8PhcDgcDofD4XA4+oEvANsAF6HLA45FzpANgVnA5QW/pfJdwGHAo8gB8wbgwSacVtKp4qQ6HAlsjrZStiPuweZ0Up/HkfPsHmCumZ2Kjnwrk/PlnZZDXU5VXXoc3XxbpU++vOvkoaqO1MqDmZ0LBGrAnXkOh8PhcDgcDofD4XA42omZZvYEyzo/6jpTspVQY4B3IgfJik04raRTxUl16Abmxby1I+7B5nRSnynA6yJnTAjhvWhFWpmcL++0HOpyqurSQnShRZU++fKuk4eqOlIrDyGEiej8vaZwZ57D4XA4HA6Hw+FwOByOdmJOCGEPlnV+1HWmPAwcgHwW45Fz5I9NOK2kU8VJdQCtqrqkTXEPNqeT+pwArAFcDTwAXAD8T4WcL++0HOpyqurS1vG7Kn3y5V0nD1V1pG4eHgJ+RA24M8/hcDgcDofD4XA4HA5HO3EUcmjknR91nSndwIeRY+NjwKeABU04raRTxUl12A85io5oU9yDzemkPr8HzjazJcD7gKPN7AcVcr6803Koy6mqS+8BPt9En3x518lDVR2pm4f9zew0asCdeQ6Hw+FwOBwOh8PhcDjaBjO7D/hlgfOjrjPlSUVjp6EbRA8CRvfTIVQnnSpOqsMUdDPqcW2Ke7A5ndTnj+j8RIBvAd8JIRxfIefLOy2HupyquvS/aPVclT758q6Th6o6UjcPX49yU/TUCeRwOBwOh8PhcDgcDofDUQchhJOB7YC9kcPiwyGEScBPkTNlIbokIP1tfCK/DXgmhHC8mR0fQrgVXTDwqQrO+BbSqeIs1QEYhc5k279NcQ82p5P67AecGqvGHsBE4HZ02+syckF5p+VQyG9SR/J1aTa6YbZKn3x518lDVR2pm4e3AHcCx9MEvjLP4XA4HA6Hw+FwOBwORzvxDuTAgIbzYn8zmw7MBubmf8vJAO9K5HcATzThtJJOFSfV4R1opVVvm+IeVE6H9XkKrZQDLSgbh5xpZTL0Le+0HOpyqurScOREq9InX9518lBVR+rmYUQiV8KdeQ6Hw+FwOBwOh8PhcDjaiR5ghUROHRZ1nCk/BW4DJoYQTgU2An7ehNNKOlWcpTqgiwxuA85oU9yDzemkPj8HNozlOB14GphRJheUd1oOdTlVdWklYLUm+uTLu04equpI3Tyk/Ep09fbWcvo5HA6Hw+FwOBwOh8PhcDRFCOEYtN3xCmB7YBfg78Dd6CKALuD83G89uXDvQVsO/w6siy7UuALYHV0ukOfk+XXSqeKQ6NCNtg1nZ60dCiwaQNyDzemkPm+L5fYs2nY6Czm1FgJjgfkF8uuA10feBGBXGnWhjJ9y0jqSr0s3RB3fiG6ovbsgvl2BndAlGkT+xU3ykOccSqOOZHE0y8OzZvZrgBDCdmZ2ByVwZ57D4XA4HA6Hw+FwOByOtiKEsANymOSdH88Cj1LtTFnGsQEMi5zPAV8p4fTh10ynivNrM7sz6vCR+N1CdGPpwQOMe7A5HdOnzEEVQrjDzLYrkoFPJuW9X1IOS+NowknrSKEOKb8gvoeAs7PyNrPNauShD4e+deTXdfJQJBeit7fX//zP//zP//zP//zP//zP//zP//zP//xvuf9tsskmdxTJVb/l5Dtb4LSSThXnzuUY96ByOqzPnf2Rm5RDXU5VXaqjTyt5qFuXSvOQ//Mz8xwOh8PhcDgcDofD4XB0Cl0lctVvqZzfXliH00o6VZzemuE6pU87OZ3Up7efcj6OVjhVdalOfK3koW5dqspDH7gzz+FwOBwOh8PhcDgcDkenUOWwaMWx0YpDaKCcqu8HQ592cjqpTytoJY526jDQ9NuhgzvzHA6Hw+FwOBwOh8PhcDgcjn8XuDPP4XA4HA6Hw+FwOBwOx78L8lsWBwNDQYf/BLSyNbe//LrpDwUdavN7akbucDgcDofD4XA4HA6HwzFQVJ67FkJ4J7AJsGJJuP1rxtfy+W5RhzVCCDub2T+a6DCUz78b1DPz0ucIrJaTNwG+lZT3txL+/gn/qhxnxSpOAX9pXSrQJx93HR2WyUMNHfKc2nkoQ1dvbzu2LDscDofD4XA4HA6Hw+H4v47ESXEzDefFzWb2j/jbTsCV8bdU3iR+fi1wO7AHcDzwdEG4NO7JwIJMNrPf1kinijMJ2A14CNge+B3wWC5cqkN/4m5Fn7ZyOqVPLMcPxuf4JmAecGsi/xV4eyK/EfhmLO9NgC1iOdyeC/cOYG4JJ60XKX+P+H/jnD5FOmTlfTOwb8xDqkM+D304ST1PdcinuUwezOwM+gF35jkcDofD4XA4HA6Hw+EYMEII32RZ50fmsLgLOVPyv6Xy/sAXzOyHIYT1gFuAV5pwWkmnipPq8EPgE8hZ0464B5vTSX26gA3NbG4I4RHgZTPbrkLOl3daDnU5VXXpIWB8E33y5V0nD1V1pG4eLjWz7ekH/Mw8h8PhcDgcDofD4XA4HO3A+4BtzOyDwDPA01HeGa2OKvotlQ04BMDMpgCr1OC0kk4VZ6kOwF7Aw22Me7A5ndRnnJnNjc/xFWBYlVxQ3mk51OVU1SVq6JMv7zp5qKojdfPQ7yPw3JnncDgcDofD4XA4HA6Hox2YX+T86IczZQF9HRtLWnAI1UmnipPqMB9Y1Ma4B5XTaX0S9AKLm8jQt7zTcqjLqapLvTm5KL5lyrsVTgt5SOVa8AswHA6Hw+FwOBwOh8PhcLQDZQ6Tqt9SeRLQG0I4Ln4enshroJVXVfy66VRxUh3WQJdyZDoMy3H6G/dgczqpT1p2GwOEEK5F58gVyQAjc5yX4+cyfp6T1pF8XRqZcLYAFhTEtyF9yzuvT5EOeU5aR1IdqvKwccLHzE6kCfzMPIfD4XA4HA6Hw+FwOBwDRghhOpAd5H9s/P/P+H8P4G9RfgNatfTPKAPcCGwArAXcFL/bNScXcVK5bjpVnA2A1YHbYppT0YUOoEsMBhL3YHM6qc8baJTd+jSe4frx/+ScDH3Le30a5VCXk9aRfF3K61CkT7688/rU4byJ4rpUlQeA6zPBzE6gCXxlnsPhcDgcDofD4XA4HI524Ex0aQDIgUHyeXIiT00+T03CTY5/Sx0bOTkLN7VErptOFWcycF4uzVSHgcQ92JxO6nMTjed2fR0HVQjhG8nHZepAURw5ToaiutRUh4K4+ujQz/Shb10qzUMr8JV5DofD4XA4HA6Hw+FwOIYEQggfBT4NbIpuJn0A+LGZXfx/SYf/BOSe4xLk5OoBFpbIr5A865r8PpyK9OcBs4DhaDturfjarEO/+WVwZ57D4XA4HA6Hw+FwOByOtqDCefEKzZ0po+J/A84GrgaORjeCdqMtlHUcQnWcNmWctdG5Z5kOKwCHo62Rc9EKtFbjHgqcTumTnZ/30xh+P+Aq4EB0vt6vc/I+wIXAxFjew2nUhUdRfcrz85y0juTr0prAAVGXR4D1SuJ7CNg6lvdCdLlFVR7ynLSOpHWpWR4eAv4LONPMzqQJ/DZbh8PhcDgcDofD4XA4HANGCOFY4BjgFHR23nTgHLTd8A7kGOsGbk5+m40cIOcgh8mpwInAocC30ZlkRwMvlXBSuW46VZyX0HlvJ6Jz/z4NfAWdhTZngHEPNqeT+syN5bYn8GFgFzM7BngRmFEgvxn4OI3yXpyUw97AWTU4aR3J16WPANsBuwC7lehzMXKuZeVNjTzkOWkdSetSVR7eZWYXAW8DPkENuDPP4XA4HA6Hw+FwOBwORztwKLBH3Cq4Lw3nx5uBN9LcmfIIsHfi2Ng3xnc2sFIJJ5XrplPFGWNmt0QdXgFmmdnFZnYDMG6AcQ82p5P6jDGzn8VyXNnMZsU6Mo/Gqr2lsplNB8Yn5b0kKYfHkDOtGSetI/m6tLKZzYqc3hJ99gceTsp7SY085DlpHRlTMw/k5WZwZ57D4XA4HA6Hw+FwOByOdmBh4vBYKqcOlCbOlAVZRAXhyjh5x0iddKo4KVIODDzuQeV0WB/ycsSSEplc3CkWJHIVJ5XzdakMaXwL6VveZfwqTlldqspDv+G32TocDofD4XA4HA6Hw+FoB6ocNXU4k4DeEMJx8XNPlLNz2NqVThVneJL+BIASHVqJe7A5yzPuPCd9jiNDCNdGeQtUxtfm5K4Y7ricDLBxTU5aPvm6NDLhjCrRZ8OEk9ehLA95TqrD8Jp52CjhT8w/5CL4BRgOh8PhcDgcDofD4XA4BowQwnR0Vh7ovLl/RrkLnSf2t5wMOlOsF7gJ2ABdVHBz/C0f7sYCTirXTaeKs0uS/q5JuFSHVuMebE4n9UmfYyqvH/9Pzsmg552V8QbJ9+vTqBdVnLSO5OtSXoc07iy+XdHlFU8U6FCWhzwnrSN5TlkeAK6L/7vM7ASawFfmORwOh8PhcDgcDofD4WgHzkRODIDbEhnkuOgqkKfG/13x+4XADOBBYHt0/tgS4M4STirXTaeKczO6xOAV5JhKdbhjgHEPNqeT+qTP8TvAR9HFELPj/y0K5BdoPOvzgddE/nXoYpVmnLSOTKZvXdoE3Ti7AJ0B+JcCfV4BRifx/RKtvKvKQ56T1pH0GVTlYW6MexE6n68pfGWew+FwOBwOh8PhcDgcjrYghHAkMN3MLgshTKbh/JgLrEW5M2U4MAwYiS5TmI/OIjsfWDf+tmcBJy/XSaeK8yt0o+jsqMNY4DhgBPAN5KhpNe6hwOmUPmOAlZEz7WLgU8A7kUNrX2CvnLwluvX1+7G8t0UOuCzeFYC3N+GkdSRfl8YBBvw18jct0GdLdBPzMbG8T47xVOUhz0nrSFqXmuVh2xj3tmZ2GE3gF2A4HA6Hw+FwOBwOh8PhGDBCCF8B3gPcH+XxwAHA/0Nnhj0InA38OvntAWCNKC9Cq6neEDnrALsD3wPeUcJJ5brpVHE+DjwVdXgAWBWtqjodOacGEvdgczqpzwS0Wu3U+EwfN7PrkINuUYF8OnKcZuW9dVIOi4D5NThpHcnXpVXRqrcTYtxF+owFRiXlna24q8pDnrMyxXWpKg8rmNmUKO9EDbgzz+FwOBwOh8PhcDgcDkc7cAjwHjN7OMqPJw6LUTR3pjxHX8fGiBifASuVcPKOkTrpVHFGoZt4p8RwM8zsYTNbglZpDSTuweZ0Up8uM3sw4Z+IsBiYnpfj8+1NyntJUg7Poe2qzThpHcnXpSVm9krkLCnR52DgyaS8qZGHPCetI2ldqspDKr9KDbgzz+FwOBwOh8PhcDgcDkc7sNjMXslkovMjOikW1XCmdAPP5DkhhHHx9zoOoTrpVHF60UqsLNyzAFGHgcY9qJxO6xNCGJs802tiuB607bSPHJ/v4lx5Z+XQDTxfk3Niwknr0uIQwtgkXJE+vegMvqXlXSMPy3BK6lJVHl5M5FrwCzAcDofD4XA4HA6Hw+FwtAPd0fnxMnJeXAMNp0sIYSyNRUVFjpFLgKNCCCsRLxEIIUwEfgK8VMJZxrlTI50qzgvA+0IIV8Rwi0IIY4CfA88PMO5B5XRYn+eAX4QQPhuf6S/ieYqLgGEhhEk5+cfAS0l5/41GOWT1ohknrSP5uvQCOsOuN8ZdpM/awHeS8v5LjTzkOWkdSetSVR7OT/jnUwN+AYbD4XA4HA6Hw+FwOByOASOE8DXgdWiL7WejfCRyWKyAnBi96JKEV+Nvf47f7R3DrQlshS4Q6EIXKZyLbid9XQEnz6+TThXn70AADqThvFqMbjadCrx+AHEPNqeT+tyMbms9GJiGtpyuDsyM5VokLwAmovL+FHBGLIcFMd0xTTiT6Vvn0rr0MjApcu8E1i/QYU6S1i9RHT67SR7ynLSOpHWpKg+Tgc2BX5rZkdSAO/McDofD4XA4HA6Hw+FwDBghhGE0HDAP0NdhMhnd2FnlTMnCbQHcAJwDvBd4V0F8ZQ6huulUcXoTHYYBuwL3x+8HGvdgczqpz+bApcDvEJ5GK9nK5G76lndaDmfV5KS6Tc7xb0eO2R1LdFgX+CR9y7tZHoo4ZXWpLA8At5vZk9SEO/McDofD4XA4HA6Hw+FwtA0hhLUodpg0c6YUOjYq4ivl10ynirNUh1z67Yh7sDkd06c/DqoM+eddJ46qOtdfHdqQfmldahfcmedwOBwOh8PhcDgcDofD4XD8m8Bvs3U4HA6Hw+FwOBwOh8PhcDj+TeDOPIfD4XA4HA6Hw+FwOBwOh+PfBO7MczgcDofD4XA4HA6Hw+FwOP5N4M48h8PhcDgcDofD4XA4HA6H498E/x/9XmmI8zuB8QAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "def plot_boxplots(df, title):\n", " plt.figure(figsize=(22, 6))\n", " df.boxplot()\n", " plt.xticks(rotation=90)\n", " plt.title(title)\n", " plt.grid(False)\n", " plt.show()\n", "\n", "plot_boxplots(df_world, \"Boxplot for World Data\")\n", "plot_boxplots(df_countries, \"Boxplot for Countries Data\")" ] }, { "cell_type": "code", "execution_count": 18, "id": "2c4ee1f1", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Outliers in World Data:\n", " 1960_gdp 0\n", "1961_gdp 0\n", "1962_gdp 0\n", "1963_gdp 0\n", "1964_gdp 0\n", " ..\n", "GDP_calculated_2018 0\n", "GDP_calculated_2019 0\n", "GDP_calculated_2020 0\n", "GDP_calculated_2021 0\n", "GDP_calculated_2022 0\n", "Length: 376, dtype: int64\n", "Outliers in Countries Data:\n", " 1960_gdp 82\n", "1961_gdp 58\n", "1962_gdp 58\n", "1963_gdp 74\n", "1964_gdp 65\n", " ..\n", "GDP_calculated_2018 38\n", "GDP_calculated_2019 33\n", "GDP_calculated_2020 41\n", "GDP_calculated_2021 34\n", "GDP_calculated_2022 34\n", "Length: 376, dtype: int64\n" ] } ], "source": [ "# Función para calcular outliers basados en IQR\n", "def calculate_outliers(df):\n", " Q1 = df.quantile(0.25)\n", " Q3 = df.quantile(0.75)\n", " IQR = Q3 - Q1\n", " outliers = ((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).sum()\n", " return outliers\n", "\n", "# Aplicar la función a cada DataFrame y mostrar resultados\n", "outliers_world = calculate_outliers(df_world)\n", "outliers_countries = calculate_outliers(df_countries)\n", "\n", "print(\"Outliers in World Data:\\n\", outliers_world)\n", "print(\"Outliers in Countries Data:\\n\", outliers_countries)" ] }, { "cell_type": "markdown", "id": "88dae679", "metadata": {}, "source": [ "## Decisiones:\n", "- En este caso, no se requiere realizar ninguna corrección de outliers. En contextos económicos, los outliers pueden representar situaciones económicas reales (como crisis o booms económicos) y pueden ser críticos para ciertos análisis.\n", "- Dado que mi objetivo de análisis se centra en comprender el comportamiento general de la economía, considero poco relevante los **outliers** y no se requiera una transformación especial.\n", "- Además, los outliers son pocos y se distribuyen aleatoriamente, su efecto en el modelo puede ser mínimo." ] }, { "cell_type": "markdown", "id": "4b5794bc", "metadata": {}, "source": [ "### Escala de Estimación NO APLICAR\n", "- Los datos presentan estacionalidad, tendría que desestacionalizar antes de aplicar la Escala de Estimación, ya que esta técnica es sensible a los patrones estacionales.\n", "- La Regresión Robusta M-estimada minimiza una función de pérdida robusta en lugar de los mínimos cuadrados, como la **regresión Huber** que es menos sensible a los outliers.\n", "- Sin embargo, como se han realizado cambios en los datos después de la estandarización original, puede ser necesario volver a estandarizar los datos antes de aplicar la Escala de Estimación.\n", "- Entonces, el siguiente código queda **reservado** y no se aplicará:" ] }, { "cell_type": "code", "execution_count": null, "id": "64ae51cf", "metadata": {}, "outputs": [], "source": [ "#import statsmodels.api as sm\n", "\n", "#X = df_countries[['GDP_calculated_2019', 'GDP_calculated_2020']] # Ejemplo\n", "#y = df_countries['GDP_calculated_2021']\n", "\n", "# Añadir constante para el intercepto\n", "#X = sm.add_constant(X)\n", "\n", "# Crear y ajustar el modelo de regresión robusta\n", "#robust_model = sm.RLM(y, X, M=sm.robust.norms.HuberT())\n", "#results = robust_model.fit()\n", "\n", "#print(results.summary())" ] }, { "cell_type": "markdown", "id": "7667d088", "metadata": {}, "source": [ "### Si se hubiese aplicado...\n", "La regresión robusta mostró que el modelo ajustado es estadísticamente significativo y los coeficientes para **GDP_calculated_2019** y **GDP_calculated_2020** como predictores de **GDP_calculated_2021** eran ambos significativos con p-valores extremadamente bajos (prácticamente cero).\n", "\n", "**Interpretación de los Coeficientes**:\n", "\n", "**const**: El intercepto de **0.0103** indica el valor base de GDP_calculated_2021 cuando los predictores son cero.\n", "\n", "**GDP_calculated_2019**: El coeficiente de **-0.3149**, osea que si todo lo demás es constante, un incremento de una unidad en el GDP_calculated_2019 está asociado con una disminución de 0.3149 unidades en el GDP_calculated_2021. Esto podría interpretarse como un efecto retardado negativo o una corrección en el crecimiento del PIB.\n", "\n", "**GDP_calculated_2020**: El coeficiente de **1.2905** indica un fuerte impacto positivo en el GDP_calculated_2021 por cada unidad incrementada en el GDP_calculated_2020. \n", "\n", "**Consideraciones Estadísticas**:\n", "\n", "**Escala de Estimación (Scale Est.)**: Indica que el modelo es resistente a la influencia de los outliers.\n", "\n", "**Iteraciones (No. Iterations)**: El modelo convergió después de **24** iteraciones.\n", "\n", "**Intervalos de Confianza**: Los intervalos de confianza para los coeficientes son estrechos, indicando precisión en las estimaciones de los parámetros del modelo.\n", "\n", "Este modelo proporciona una base sólida para inferencias sobre cómo los valores pasados del PIB calculado podrían estar influyendo en los valores futuros, bajo el contexto de un modelo robusto a outliers. Estos resultados pueden ser útiles para tomar decisiones económicas informadas o para realizar proyecciones futuras basadas en tendencias pasadas." ] }, { "cell_type": "markdown", "id": "6fc9ead7", "metadata": {}, "source": [ "### Análisis de Componentes Temporales\n", "Para series temporales, es crucial entender tendencias y ciclicidad, especialmente si los datos se usarán para proyecciones o análisis predictivos." ] }, { "cell_type": "code", "execution_count": 19, "id": "d1c6857a", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def decompose_time_series(series, title):\n", " if len(series) > 2: \n", " decomposition = sm.tsa.seasonal_decompose(series, model='additive', period=1) \n", " \n", " fig, ax = plt.subplots(4, 1, figsize=(16, 10), sharex=True)\n", " series.plot(ax=ax[0], color='b', title=title)\n", " ax[0].set_ylabel('Original')\n", " decomposition.trend.plot(ax=ax[1], color='r')\n", " ax[1].set_ylabel('Trend')\n", " decomposition.seasonal.plot(ax=ax[2], color='g')\n", " ax[2].set_ylabel('Seasonal')\n", " decomposition.resid.plot(ax=ax[3], color='k')\n", " ax[3].set_ylabel('Residual')\n", " plt.tight_layout()\n", " plt.show()\n", " else:\n", " print(f\"Not enough data to decompose {title}\")\n", "\n", "decompose_time_series(df_countries['GDP_calculated_2021'], 'Countries GDP 2021')\n" ] }, { "cell_type": "markdown", "id": "8326609e", "metadata": {}, "source": [ "Los gráficos muestran la descomposición de la serie temporal del PIB calculado para el año 2021 para los países. \n", "\n", "**Original**: Los picos pueden representar crecimientos económicos extraordinarios o caídas, posiblemente debido a eventos económicos específicos como reformas, crisis, o booms de recursos naturales.\n", "\n", "**Tendencia**: Algunas variaciones significativas indica que en esos países hubo cambios sustanciales en el tiempo en su actividad económica. \n", "\n", "**Estacional**: La componente estacional es esencialmente plana y cercana a cero en todos los casos, ya que sólo estamos viendo un año.\n", "\n", "**Residual**: Los residuos parecen ser pequeños para la mayoría de los países, sin embargo, los picos en los residuos pueden indicar modelos de comportamiento económico que no se explican completamente por la tendencia y que podrían ser objeto de una investigación más detallada." ] }, { "cell_type": "markdown", "id": "942ae41b", "metadata": {}, "source": [ "### Estandarización de Datos No Aplicada\n", "Si los nuevos DataFrames son derivados de DataFrames que ya han sido estandarizados, normalmente no necesitarías re-estandarizarlos, a menos que las transformaciones realizadas en los datos (como sumas o agrupaciones por región) pudieran haber alterado la escala o la distribución de los datos. Las estadísticas descriptivas muestran valores óptimos.." ] }, { "cell_type": "code", "execution_count": null, "id": "d84a8cce", "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "\n", "# Seleccionar las columnas numéricas para la estandarización\n", "columns_to_scale = df_countries.columns[df_countries.columns.str.contains('GDP')]\n", "\n", "# Inicializar el objeto StandardScaler\n", "scaler = StandardScaler()\n", "\n", "# Estandarizar las columnas seleccionadas\n", "df_countries.loc[:, columns_to_scale] = scaler.fit_transform(df_countries[columns_to_scale])\n", "\n", "# Verificar los cambios\n", "print(df_countries[columns_to_scale].head())\n", "print(df_countries[columns_to_scale].describe())" ] }, { "cell_type": "markdown", "id": "bb9fe7f1", "metadata": {}, "source": [ "### Verificación de Estacionariedad\n", "Si los nuevos DataFrames son derivados de otros que ya han verificado la ausencia de estacionariedad, generalmente no necesitarías volver a verificarlo para las mismas series temporales. Sin embargo, si se han agregado nuevas transformaciones o datos (por ejemplo, sumas o promedios de nuevas variables), sería prudente realizar una verificación de estacionariedad sobre las series resultantes para asegurar la validez de los análisis temporales." ] }, { "cell_type": "markdown", "id": "19cf4012", "metadata": {}, "source": [ "#### ADF Test 2021 Countries" ] }, { "cell_type": "code", "execution_count": 22, "id": "0d63434e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Augmented Dickey-Fuller Test on \"GDP_calculated_2021\" \n", " -----------------------------------------------\n", " Null Hypothesis: Data has unit root. Non-Stationary.\n", " Significance Level = 0.05\n", " Test Statistic = -5.1314\n", " No. Lags Chosen = 3\n", " Critical value 1% = -3.46\n", " Critical value 5% = -2.875\n", " Critical value 10% = -2.574\n", " => P-Value = 0.0. Rejecting Null Hypothesis.\n", " => Series is Stationary.\n" ] } ], "source": [ "from statsmodels.tsa.stattools import adfuller\n", "\n", "def test_stationarity(series, signif=0.05, name='', verbose=False):\n", " r = adfuller(series, autolag='AIC')\n", " output = {'test_statistic': round(r[0], 4), 'pvalue': round(r[1], 4), 'n_lags': r[2], 'n_obs': r[3]}\n", " p_value = output['pvalue'] \n", " def adjust(val, length= 6): return str(val).ljust(length)\n", "\n", " # Imprimir Resultados de la Prueba\n", " if verbose:\n", " print(f' Augmented Dickey-Fuller Test on \"{name}\"', \"\\n \", '-'*47)\n", " print(f' Null Hypothesis: Data has unit root. Non-Stationary.')\n", " print(f' Significance Level = {signif}')\n", " print(f' Test Statistic = {output[\"test_statistic\"]}')\n", " print(f' No. Lags Chosen = {output[\"n_lags\"]}')\n", "\n", " for key, val in r[4].items():\n", " print(f' Critical value {adjust(key)} = {round(val, 3)}')\n", "\n", " if p_value <= signif:\n", " print(f\" => P-Value = {p_value}. Rejecting Null Hypothesis.\")\n", " print(f\" => Series is Stationary.\")\n", " else:\n", " print(f\" => P-Value = {p_value}. Weak evidence to reject the Null Hypothesis.\")\n", " print(f\" => Series is Non-Stationary.\")\n", " \n", " return output\n", "\n", "# Ejemplo de aplicación\n", "series = df_countries['GDP_calculated_2021']\n", "result = test_stationarity(series, name='GDP_calculated_2021', verbose=True)" ] }, { "cell_type": "markdown", "id": "b9415edc", "metadata": {}, "source": [ "#### ADF Anual Countries" ] }, { "cell_type": "markdown", "id": "b6127d83", "metadata": {}, "source": [ "## Regresión lineal" ] }, { "cell_type": "code", "execution_count": 26, "id": "db0c4b7e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean Squared Error: 0.1084106968304278\n" ] } ], "source": [ "import pandas as pd\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_squared_error\n", "\n", "# Variables predictoras: PIB de 2016 a 2020\n", "predictors = ['GDP_calculated_2016', 'GDP_calculated_2017', 'GDP_calculated_2018', 'GDP_calculated_2019', 'GDP_calculated_2020']\n", "X = df_countries[predictors]\n", "y = df_countries['GDP_calculated_2021']\n", "\n", "# Dividir los datos en entrenamiento y prueba\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", "\n", "# Modelo de regresión lineal\n", "model = LinearRegression()\n", "model.fit(X_train, y_train)\n", "\n", "# Predicciones y evaluación\n", "y_pred = model.predict(X_test)\n", "mse = mean_squared_error(y_test, y_pred)\n", "print(\"Mean Squared Error:\", mse)" ] }, { "cell_type": "markdown", "id": "a285371e", "metadata": {}, "source": [ "Estos MSE son relativamente bajos dado que los datos están estandarizados. Estos son relativamente pequeños errores considerando la escala de los datos." ] }, { "cell_type": "code", "execution_count": 27, "id": "2f3ae8c4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance of GDP_calculated_2021: 5.095504994892357\n", "Root Mean Squared Error (RMSE): 0.3292577969166832\n" ] } ], "source": [ "import numpy as np\n", "\n", "# Calculando la varianza de GDP_calculated_2021\n", "variance = np.var(df_countries['GDP_calculated_2021'])\n", "\n", "# Calculando el RMSE\n", "rmse = np.sqrt(mse)\n", "\n", "print(\"Variance of GDP_calculated_2021:\", variance)\n", "print(\"Root Mean Squared Error (RMSE):\", rmse)" ] }, { "cell_type": "markdown", "id": "957a5aaa", "metadata": {}, "source": [ "- La varianza de los datos estandarizados es más alta que 1, lo cual puede indicar una distribución más dispersa de lo esperado para datos puramente estandarizados. Tal vez porque los datos tienen variabilidad inherente que podría estar afectando el desempeño del modelo.\n", "- Dado que el RMSE es considerablemente menor que la raíz cuadrada de la varianza de los datos (~2.26), esto indica que el modelo está haciendo un buen trabajo al capturar la variabilidad de los datos. Sin embargo, el RMSE todavía representa un error significativo en términos de la escala de los datos." ] }, { "cell_type": "code", "execution_count": 28, "id": "91f8f9ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Resultados de MSE de cada fold: [0.6766614 0.08180557 0.59952202 0.26042559 0.16903139]\n", "MSE promedio: 0.35748919398012907\n", "Desviación estándar de MSE: 0.23722981798694917\n" ] } ], "source": [ "import pandas as pd\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import cross_val_score\n", "import numpy as np\n", "\n", "predictors = ['GDP_calculated_2016', 'GDP_calculated_2017', 'GDP_calculated_2018', 'GDP_calculated_2019', 'GDP_calculated_2020']\n", "X = df_countries[predictors]\n", "y = df_countries['GDP_calculated_2021']\n", "\n", "model = LinearRegression()\n", "\n", "# Cross-Validation\n", "# Usamos 5 pliegues (folds) y medimos el error cuadrático medio negativo\n", "scores = cross_val_score(model, X, y, cv=5, scoring='neg_mean_squared_error')\n", "\n", "# Convertimos los scores a positivo porque 'neg_mean_squared_error' devuelve valores negativos\n", "mse_scores = -scores\n", "\n", "# Calculamos el promedio y la desviación estándar de los MSE para evaluar la consistencia del modelo\n", "print(\"Resultados de MSE de cada fold:\", mse_scores)\n", "print(\"MSE promedio:\", mse_scores.mean())\n", "print(\"Desviación estándar de MSE:\", mse_scores.std())\n", "\n", "# Esto proporciona una visión de cuán variados son los resultados del modelo entre diferentes subconjuntos de datos." ] }, { "cell_type": "markdown", "id": "bb38b6a8", "metadata": {}, "source": [ "- Con el MSE de cada fold muestra inestabilidad en cómo el modelo se comporta con diferentes subconjuntos de datos.\n", "- La Desviación estándar de MSE es alta, hay que ver la división entre subconjuntos de datos." ] }, { "cell_type": "markdown", "id": "eaf77199", "metadata": {}, "source": [ "### Regresión de Cresta (Ridge Regression)\n", "La regresión de cresta ajusta un modelo de regresión lineal que también incluye un término de penalización L2. Esta penalización puede ayudar a reducir la varianza del modelo sin aumentar significativamente el sesgo, lo cual es útil en situaciones de alta variabilidad entre los folds de validación cruzada." ] }, { "cell_type": "code", "execution_count": 29, "id": "f5f6dcda", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE de Regresión de Cresta: 0.46595401888464466\n" ] } ], "source": [ "from sklearn.linear_model import Ridge\n", "from sklearn.model_selection import cross_val_score\n", "\n", "# Definir el modelo de regresión de cresta\n", "ridge_model = Ridge(alpha=1.0) # Alpha es el parámetro de regularización\n", "\n", "# Aplicar Cross-Validation\n", "ridge_scores = cross_val_score(ridge_model, X, y, cv=5, scoring='neg_mean_squared_error')\n", "print(\"MSE de Regresión de Cresta:\", -ridge_scores.mean())" ] }, { "cell_type": "markdown", "id": "39e0670a", "metadata": {}, "source": [ "La Regresión de Cresta ha mostrado un MSE más alto en comparación con tus modelos lineales iniciales. Recordando que el MSE inicial más bajo fue de aproximadamente 0.036 y otro fue 0.108, el MSE de 0.466 sugiere que la regresión de cresta no ha mejorado el rendimiento y podría estar demasiado regularizada o no adecuadamente configurada para los datos. " ] }, { "cell_type": "markdown", "id": "7719f1ea", "metadata": {}, "source": [ "### Regresión Lasso\n", "Lasso es similar a la regresión de cresta, pero utiliza una penalización L1, que puede llevar a coeficientes a cero, ofreciendo una especie de selección automática de características. Esto puede ser útil si algunos predictores son redundantes o menos relevantes." ] }, { "cell_type": "code", "execution_count": 30, "id": "a8ad4630", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE de Lasso: 0.317814287588202\n" ] } ], "source": [ "from sklearn.linear_model import Lasso\n", "\n", "# Definir el modelo Lasso\n", "lasso_model = Lasso(alpha=0.1) # Alpha controla la cantidad de shrinkage\n", "\n", "# Aplicar validación cruzada\n", "lasso_scores = cross_val_score(lasso_model, X, y, cv=5, scoring='neg_mean_squared_error')\n", "print(\"MSE de Lasso:\", -lasso_scores.mean())" ] }, { "cell_type": "markdown", "id": "1a878093", "metadata": {}, "source": [ "Aun es más alto que los modelos lineales básicos anteriores. Parece que no todos los predictores son igualmente útiles o que la penalización está eliminando información valiosa. " ] }, { "cell_type": "markdown", "id": "9d5dcc1b", "metadata": {}, "source": [ "### Modelos de Árboles de Decisión\n", "Los árboles de decisión son más flexibles que los modelos lineales y pueden capturar patrones no lineales y complejidades que los modelos lineales no pueden." ] }, { "cell_type": "code", "execution_count": 31, "id": "cbb236ec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE de Árbol de Decisión: 1.2236968662198517\n" ] } ], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "\n", "# Definir el modelo de árbol de decisión\n", "tree_model = DecisionTreeRegressor(max_depth=5) # Controla la profundidad del árbol\n", "\n", "# Aplicar validación cruzada\n", "tree_scores = cross_val_score(tree_model, X, y, cv=5, scoring='neg_mean_squared_error')\n", "print(\"MSE de Árbol de Decisión:\", -tree_scores.mean())" ] }, { "cell_type": "markdown", "id": "5a2874d8", "metadata": {}, "source": [ "Es un MSE mucho más alto (1.47) en comparación con cualquier otro modelo probado. Esto sugiere que, aunque los árboles de decisión pueden modelar relaciones no lineales, pueden estar sobreajustándose a los datos de entrenamiento o simplemente no se adaptan bien a la estructura de tu conjunto de datos específico. La naturaleza de los árboles de decisión los hace muy sensibles a la variabilidad en los datos, lo que puede llevar a un rendimiento inconsistente." ] }, { "cell_type": "markdown", "id": "6d609bcf", "metadata": {}, "source": [ "### Random Forest\n", "Random Forest es un método de ensamble que utiliza múltiples árboles de decisión para reducir el riesgo de sobreajuste, lo que es común en árboles de decisión simples. Ofrece un buen balance entre sesgo y varianza y es muy efectivo en muchos problemas prácticos." ] }, { "cell_type": "code", "execution_count": 32, "id": "2c8a1243", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MSE de Random Forest: 1.8480368646500238\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestRegressor\n", "\n", "# Definir el modelo de Random Forest\n", "forest_model = RandomForestRegressor(n_estimators=100, random_state=42) # n_estimators controla el número de árboles\n", "\n", "# Aplicar validación cruzada\n", "forest_scores = cross_val_score(forest_model, X, y, cv=5, scoring='neg_mean_squared_error')\n", "print(\"MSE de Random Forest:\", -forest_scores.mean())" ] }, { "cell_type": "markdown", "id": "05ed4eb0", "metadata": {}, "source": [ "Aunque generalmente es un modelo robusto y efectivo para muchos problemas de regresión, ha mostrado el peor rendimiento con un MSE de 1.848. Esto es sorprendente ya que los modelos de ensamble como Random Forest suelen mejorar el rendimiento de los árboles de decisión individuales a través de la agregación de resultados. En este caso, el alto MSE podría indicar una mala configuración de hiperparámetros, una necesidad de más árboles en el ensamble, o simplemente que los datos no son adecuados para este tipo de modelado debido a su estructura o el tipo de relación entre las variables." ] }, { "cell_type": "markdown", "id": "61ca8060", "metadata": {}, "source": [ "**Conclusiones**\n", "Basándote en estos resultados, parece que los modelos más simples, como el lineal básico y Lasso, están funcionando mejor en este conjunto de datos. Esto podría indicar que la relación entre las variables predictoras y la variable objetivo es más lineal, o que la naturaleza de los datos no se presta a la complejidad adicional introducida por los árboles de decisión o los modelos de ensamble." ] }, { "cell_type": "markdown", "id": "7e64852a", "metadata": {}, "source": [ "# Mi Perspectiva como Economista" ] }, { "cell_type": "markdown", "id": "5896a646", "metadata": {}, "source": [ "1. Naturaleza de los Datos Macroeconómicos\n", "Los datos macroeconómicos, como el PIB, el consumo, la inversión, y los gastos del gobierno, a menudo exhiben características como tendencias a largo plazo, ciclicidad, y posibles no linealidades. Además, estos datos pueden estar sujetos a cambios estructurales debido a políticas económicas, crisis financieras, o cambios tecnológicos significativos.\n", "\n", "- Tendencias y Estacionariedad: Muchos indicadores macroeconómicos son no estacionarios, es decir, tienen propiedades estadísticas que cambian con el tiempo. Esto puede incluir una media o varianza que varía con el tiempo. Los modelos lineales y algunos modelos no lineales asumen estacionariedad, lo que puede llevar a predicciones sesgadas o incorrectas si se aplican directamente a datos no estacionarios.\n", "\n", "- Cointegración: En macroeconomía, es común que series temporales múltiples compartan una tendencia común a largo plazo a pesar de ser no estacionarias individualmente. Esto se conoce como cointegración. Modelos que no consideran la posibilidad de cointegración pueden no captar adecuadamente la dinámica a largo plazo entre las variables.\n", "\n", "2. Relaciones Entre Variables\n", "En macroeconomía, las relaciones entre variables a menudo son complejas y pueden ser influenciadas por numerosos factores externos e internos.\n", "\n", "- Endogeneidad y Exogeneidad: Las relaciones entre variables macroeconómicas pueden ser endógenas, lo que significa que pueden influirse mutuamente. Por ejemplo, el consumo y el PIB pueden afectarse mutuamente. Los modelos que no manejan adecuadamente la endogeneidad pueden dar lugar a estimaciones sesgadas.\n", "\n", "- Cambios Estructurales: Los cambios en la política económica, grandes eventos económicos (como crisis financieras), o innovaciones tecnológicas pueden causar cambios estructurales en las relaciones económicas. Si un modelo no puede adaptarse a cambios estructurales a lo largo del tiempo, puede perder precisión a medida que el contexto económico subyacente evoluciona." ] }, { "cell_type": "markdown", "id": "261ccc0b", "metadata": {}, "source": [ "## Verificación de Supuestos Económicos y Estadísticos\n", "Sin embargo continuaré con otros supuestos, como la prueba de Breusch-Pagan o la prueba de Durbin-Watson." ] }, { "cell_type": "markdown", "id": "bd3f1488", "metadata": {}, "source": [ "### Modelo ARIMA\n", "El modelo ARIMA es útil para modelar series temporales que muestran patrones claros de tendencias o estacionalidad." ] }, { "cell_type": "code", "execution_count": 33, "id": "3a65bf0a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: 2014 No. Observations: 226\n", "Model: ARIMA(1, 1, 1) Log Likelihood 47.490\n", "Date: Sun, 12 May 2024 AIC -88.980\n", "Time: 21:25:40 BIC -78.732\n", "Sample: 0 HQIC -84.844\n", " - 226 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "ar.L1 -0.0259 0.471 -0.055 0.956 -0.948 0.896\n", "ma.L1 -1.0000 21.365 -0.047 0.963 -42.874 40.874\n", "sigma2 0.0375 0.800 0.047 0.963 -1.531 1.606\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 72165.12\n", "Prob(Q): 0.95 Prob(JB): 0.00\n", "Heteroskedasticity (H): 2.19 Skew: 8.56\n", "Prob(H) (two-sided): 0.00 Kurtosis: 89.05\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "from statsmodels.tsa.arima.model import ARIMA\n", "import statsmodels.api as sm\n", "\n", "data = pd.read_csv('df_countries.csv') \n", "time_series = data['2014']\n", "\n", "model_arima = ARIMA(time_series, order=(1,1,1)) \n", "results_arima = model_arima.fit()\n", "print(results_arima.summary())" ] }, { "cell_type": "markdown", "id": "4232f806", "metadata": {}, "source": [ "- Un valor P alto (0.95) sugiere que no hay autocorrelación significativa en los residuos, lo cual es bueno.\n", "- Jarque-Bera: Prueba la normalidad de los residuos; un valor P de 0.00 rechaza la hipótesis de normalidad, indicando que los residuos no son normales.\n", "- Heteroskedasticity: Con un P de 0.00, hay evidencia de heterocedasticidad.\n", "\n", "**Interpretación**\n", "El modelo SARIMAX aplicado no parece capturar todas las dinámicas de los datos, como se evidencia por los coeficientes de AR y MA no significativos y las pruebas de diagnóstico que muestran problemas con la normalidad y la heterocedasticidad de los residuos. \n", "\n", "**Opinión personal**: Esto puede deberse a la necesidad de un modelo más complejo o diferentemente especificado, o puede reflejar la naturaleza desafiante de modelar datos macroeconómicos con estructuras subyacentes complejas." ] }, { "cell_type": "markdown", "id": "7b038680", "metadata": {}, "source": [ "### Modelo GARCH\n", "Utilizado para modelar la volatilidad de series financieras o económicas." ] }, { "cell_type": "code", "execution_count": 34, "id": "ac33d14a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 5, Func. Count: 33, Neg. LLF: -4.4343562005323705\n", "Iteration: 10, Func. Count: 68, Neg. LLF: 74.82844976640274\n", "Iteration: 15, Func. Count: 101, Neg. LLF: 74.88308704571364\n", "Optimization terminated successfully (Exit mode 0)\n", " Current function value: -59.97551055030441\n", " Iterations: 23\n", " Function evaluations: 123\n", " Gradient evaluations: 19\n", " Constant Mean - GARCH Model Results \n", "==============================================================================\n", "Dep. Variable: 2014 R-squared: 0.000\n", "Mean Model: Constant Mean Adj. R-squared: 0.000\n", "Vol Model: GARCH Log-Likelihood: 59.9755\n", "Distribution: Normal AIC: -111.951\n", "Method: Maximum Likelihood BIC: -98.2689\n", " No. Observations: 226\n", "Date: Sun, May 12 2024 Df Residuals: 225\n", "Time: 21:28:17 Df Model: 1\n", " Mean Model \n", "========================================================================\n", " coef std err t P>|t| 95.0% Conf. Int.\n", "------------------------------------------------------------------------\n", "mu -0.1672 1.910e-02 -8.755 2.046e-18 [ -0.205, -0.130]\n", " Volatility Model \n", "=============================================================================\n", " coef std err t P>|t| 95.0% Conf. Int.\n", "-----------------------------------------------------------------------------\n", "omega 4.0612e-04 1.371e-03 0.296 0.767 [-2.280e-03,3.093e-03]\n", "alpha[1] 5.3858e-08 3.473e-02 1.551e-06 1.000 [-6.807e-02,6.807e-02]\n", "beta[1] 0.9994 2.271e-02 44.018 0.000 [ 0.955, 1.044]\n", "=============================================================================\n", "\n", "Covariance estimator: robust\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Oscar Murgueytio\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\arch\\univariate\\base.py:311: DataScaleWarning: y is poorly scaled, which may affect convergence of the optimizer when\n", "estimating the model parameters. The scale of y is 0.03733. Parameter\n", "estimation work better when this value is between 1 and 1000. The recommended\n", "rescaling is 10 * y.\n", "\n", "This warning can be disabled by either rescaling y before initializing the\n", "model or by setting rescale=False.\n", "\n", " warnings.warn(\n" ] } ], "source": [ "from arch import arch_model\n", "\n", "# Ajuste del modelo GARCH\n", "garch_model = arch_model(time_series, vol='Garch', p=1, q=1) \n", "results_garch = garch_model.fit(update_freq=5)\n", "print(results_garch.summary())" ] }, { "cell_type": "markdown", "id": "aabf3a75", "metadata": {}, "source": [ "**Interpretación**\n", "El modelo GARCH(1,1) indica que la volatilidad de la serie para el año 2014 es altamente persistente (beta cercano a 1), lo que es típico en datos financieros donde la volatilidad tiende a agruparse. Sin embargo, los choques específicos en la serie (medidos por alpha) no parecen tener un impacto significativo en la volatilidad futura, lo cual es atípico para un modelo GARCH y podría sugerir que los datos no exhiben mucha volatilidad de choques o que el modelo necesita ser ajustado o complementado con otros componentes." ] }, { "cell_type": "code", "execution_count": null, "id": "6fcbb25e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "c14f784f", "metadata": {}, "source": [ "### Homocedasticidad\n", "Puedes verificar la homocedasticidad utilizando pruebas como la prueba de Breusch-Pagan o visualizando los residuos de un modelo regresivo contra los valores ajustados. La homogeneidad de la varianza en los residuos es crucial para inferencias confiables en la regresión." ] }, { "cell_type": "code", "execution_count": 35, "id": "9c333f73", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Oscar Murgueytio\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " return _pandas_is_categorical_dtype(dt)\n", "C:\\Users\\Oscar Murgueytio\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " return _pandas_is_categorical_dtype(dt)\n", "C:\\Users\\Oscar Murgueytio\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\patsy\\util.py:672: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n", " return _pandas_is_categorical_dtype(dt)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "p-value de la prueba de Breusch-Pagan: 5.3068079966277775e-05\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import statsmodels.api as sm\n", "from statsmodels.stats.diagnostic import het_breuschpagan\n", "from statsmodels.formula.api import ols\n", "import matplotlib.pyplot as plt\n", "\n", "modelo = 'GDP_calculated_2022 ~ GDP_calculated_2021'\n", "\n", "model = ols(modelo, data=df_countries).fit()\n", "\n", "residuos = model.resid\n", "\n", "# Prueba de Breusch-Pagan\n", "_, pvalue, _, _ = het_breuschpagan(residuos, model.model.exog)\n", "print(f\"p-value de la prueba de Breusch-Pagan: {pvalue}\")\n", "\n", "# Visualización de residuos\n", "plt.figure(figsize=(20, 6))\n", "plt.scatter(model.fittedvalues, residuos)\n", "plt.axhline(0, color='red', linestyle='--')\n", "plt.title('Residuos vs Valores Ajustados')\n", "plt.xlabel('Valores Ajustados')\n", "plt.ylabel('Residuos')\n", "plt.show()\n", "\n", "# Si el p-value es menor a 0.05, existe evidencia de heterocedasticidad." ] }, { "cell_type": "markdown", "id": "344a7fc4", "metadata": {}, "source": [ "El bajo **p-value** de la prueba de **Breusch-Pagan (5.31e-05)** indica que hay evidencia significativa de heterocedasticidad en los residuos del modelo. E\n", "\n", "La visualización también muestra algunos signos de heterocedasticidad, dado que los residuos no parecen distribuirse uniformemente en torno a la línea roja horizontal, especialmente evidente con algunos puntos alejados de la línea central hacia los valores ajustados más altos." ] }, { "cell_type": "markdown", "id": "20f52e52", "metadata": {}, "source": [ "#### Weighted Least Squares (WLS)\n", "Utiliza los residuos de un modelo OLS para estimar los pesos y aplica WLS para considerar la heterocedasticidad. Los pesos pueden basarse en el inverso de los residuos al cuadrado de un modelo OLS preliminar." ] }, { "cell_type": "code", "execution_count": 36, "id": "19baf524", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " WLS Regression Results \n", "===============================================================================\n", "Dep. Variable: GDP_calculated_2021 R-squared: 1.000\n", "Model: WLS Adj. R-squared: 1.000\n", "Method: Least Squares F-statistic: 1.160e+06\n", "Date: Sun, 12 May 2024 Prob (F-statistic): 0.00\n", "Time: 21:31:57 Log-Likelihood: 794.87\n", "No. Observations: 226 AIC: -1580.\n", "Df Residuals: 221 BIC: -1563.\n", "Df Model: 4 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.0760 0.005 -15.237 0.000 -0.086 -0.066\n", "2020_gov 1.1297 0.007 150.802 0.000 1.115 1.144\n", "2020 2.6438 0.025 106.767 0.000 2.595 2.693\n", "2020_con 0.8706 0.006 153.978 0.000 0.859 0.882\n", "2020_trade 0.3443 0.012 27.622 0.000 0.320 0.369\n", "==============================================================================\n", "Omnibus: 16.899 Durbin-Watson: 1.819\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 49.479\n", "Skew: -0.154 Prob(JB): 1.80e-11\n", "Kurtosis: 5.271 Cond. No. 153.\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "import statsmodels.api as sm\n", "\n", "X = df_countries[['2020_gov', '2020', '2020_con', '2020_trade']] # G + I + C + T\n", "y = df_countries['GDP_calculated_2021'] # Traget = GDP\n", "\n", "# Constante\n", "X = sm.add_constant(X)\n", "\n", "# Modelo OLS para obtener los residuos\n", "model_ols = sm.OLS(y, X).fit()\n", "residuos = model_ols.resid\n", "\n", "# Inverso de los residuos al cuadrado\n", "pesos = 1.0 / (residuos ** 2)\n", "\n", "# Aplicar WLS con los pesos obtenidos\n", "model_wls = sm.WLS(y, X, weights=pesos).fit()\n", "\n", "print(model_wls.summary())" ] }, { "cell_type": "markdown", "id": "23b90234", "metadata": {}, "source": [ "#### Normalidad de los Residuos\n", "Utiliza pruebas como Shapiro-Wilk o Kolmogorov-Smirnov después de ajustar un modelo, o incluso visualiza un Q-Q plot de los residuos. La normalidad es esencial para la validez de muchas pruebas estadísticas, incluyendo aquellos en regresiones lineales. Un p-value pequeño en la prueba de Shapiro-Wilk (típicamente menor que 0.05) sugeriría que los residuos no se distribuyen normalmente, lo cual puede ser una indicación de que el modelo no captura toda la complejidad de los datos o que existen outliers." ] }, { "cell_type": "code", "execution_count": 37, "id": "ecbf4b03", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shapiro-Wilk Test:\n", "Statistic: 0.22526339595297296\n", "p-value: 1.2498590524151944e-29\n" ] } ], "source": [ "import scipy.stats as stats\n", "\n", "residuos = model_wls.resid\n", "\n", "shapiro_test = stats.shapiro(residuos)\n", "\n", "print(\"Shapiro-Wilk Test:\")\n", "print(\"Statistic:\", shapiro_test.statistic)\n", "print(\"p-value:\", shapiro_test.pvalue)" ] }, { "cell_type": "markdown", "id": "f401cf53", "metadata": {}, "source": [ "El test de Shapiro-Wilk tiene un valor muy bajo para la estadística y un p-valor extremadamente pequeño, es una falta de normalidad lo que afecta la confiabilidad de las pruebas de significancia y los intervalos de confianza para los coeficientes del modelo." ] }, { "cell_type": "markdown", "id": "c4792089", "metadata": {}, "source": [ "#### Ausencia de Multicolinealidad\n", "Antes de realizar un modelado regresivo, revisa la multicolinealidad entre variables independientes. Esto se puede hacer calculando el Factor de Inflación de la Varianza (VIF). Un VIF mayor a 10 (o en casos más estrictos, mayor a 5) puede indicar problemas significativos de multicolinealidad." ] }, { "cell_type": "code", "execution_count": 38, "id": "38853032", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " feature VIF\n", "0 const 1.666738\n", "1 2020_gov 16.399994\n", "2 2020 1.052399\n", "3 2020_con 16.402942\n", "4 2020_trade 1.050951\n" ] } ], "source": [ "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", "\n", "# VIF para cada variable en el modelo\n", "vif_data = pd.DataFrame()\n", "vif_data[\"feature\"] = X.columns\n", "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n", "\n", "print(vif_data)" ] }, { "cell_type": "markdown", "id": "94696e37", "metadata": {}, "source": [ "Los resultados del **Factor de Inflación de Varianza (VIF)** muestran que las variables **2020_gov** y **2020_con** tienen VIFs muy altos (mayores de 10), lo que sugiere una fuerte multicolinealidad. Esto significa que estas variables predictoras están altamente correlacionadas con otras predictoras en el modelo, lo que puede hacer que los coeficientes del modelo sean inestables y difíciles de interpretar. Tal vez los excluya para reducir la multicolinealidad." ] }, { "cell_type": "markdown", "id": "e71f383b", "metadata": {}, "source": [ "### Regresión Robusta\n", "Para mejorar el modelo usaré usando el método RLM (Robust Linear Model) de StatsModels" ] }, { "cell_type": "code", "execution_count": 39, "id": "f09082b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Robust linear Model Regression Results \n", "===============================================================================\n", "Dep. Variable: GDP_calculated_2021 No. Observations: 226\n", "Model: RLM Df Residuals: 221\n", "Method: IRLS Df Model: 4\n", "Norm: HuberT \n", "Scale Est.: mad \n", "Cov Type: H1 \n", "Date: Sun, 12 May 2024 \n", "Time: 21:33:28 \n", "No. Iterations: 50 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.0950 0.000 -194.216 0.000 -0.096 -0.094\n", "2020_gov 1.1203 0.001 790.858 0.000 1.117 1.123\n", "2020 2.8960 0.002 1504.097 0.000 2.892 2.900\n", "2020_con 0.8781 0.001 619.751 0.000 0.875 0.881\n", "2020_trade 0.0496 0.001 60.150 0.000 0.048 0.051\n", "==============================================================================\n", "\n", "If the model instance has been used for another fit with different fit parameters, then the fit options might not be the correct ones anymore .\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import statsmodels.api as sm\n", "\n", "X = df_countries[['2020_gov', '2020', '2020_con', '2020_trade']] \n", "y = df_countries['GDP_calculated_2021'] \n", "\n", "# Constante\n", "X = sm.add_constant(X)\n", "\n", "# Regresión Lineal Robusta\n", "model_robust = sm.RLM(y, X).fit()\n", "\n", "print(model_robust.summary())\n", "\n", "import matplotlib.pyplot as plt\n", "plt.figure(figsize=(20, 6))\n", "plt.scatter(model_robust.fittedvalues, model_robust.resid)\n", "plt.axhline(y=0, color='r', linestyle='--')\n", "plt.xlabel('Valores Ajustados')\n", "plt.ylabel('Residuos')\n", "plt.title('Residuos vs Valores Ajustados en Regresión Robusta')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3b666de7", "metadata": {}, "source": [ "Valores p extremadamente bajos para los coeficientes del modelo. Esto sugiere que los predictores son altamente significativos para explicar la variabilidad en el PIB calculado para 2021.\n", "\n", "En cuanto a la gráfica de residuos vs. valores ajustados, la dispersión de los residuos parece más controlada comparada con la regresión OLS tradicional, pero todavía se pueden observar algunos valores atípicos pronunciados, especialmente para valores ajustados bajos. Estos residuos podrían indicar que, aunque el modelo maneja mejor los outliers en comparación con OLS, aún podrían existir características no capturadas por el modelo actual." ] }, { "cell_type": "markdown", "id": "9d2fe2f7", "metadata": {}, "source": [ "### Transformación de Datos NO APLICARÉ\n", "Logaritmo para reducir la asimetría y estabilizar la varianza de los predictores que sean estrictamente positivos.\n", "Raíz cuadrada para reducir el efecto de los valores atípicos en los predictores con amplias variaciones." ] }, { "cell_type": "code", "execution_count": null, "id": "6941d2ad", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "88b99bd8", "metadata": {}, "source": [ "## Verificación de Supuestos y Diagnóstico del Modelo" ] }, { "cell_type": "code", "execution_count": 40, "id": "bf66ec6d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Robust linear Model Regression Results \n", "===============================================================================\n", "Dep. Variable: GDP_calculated_2021 No. Observations: 226\n", "Model: RLM Df Residuals: 221\n", "Method: IRLS Df Model: 4\n", "Norm: HuberT \n", "Scale Est.: mad \n", "Cov Type: H1 \n", "Date: Sun, 12 May 2024 \n", "Time: 21:34:32 \n", "No. Iterations: 50 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.0950 0.000 -194.216 0.000 -0.096 -0.094\n", "2020_gov 1.1203 0.001 790.858 0.000 1.117 1.123\n", "2020 2.8960 0.002 1504.097 0.000 2.892 2.900\n", "2020_con 0.8781 0.001 619.751 0.000 0.875 0.881\n", "2020_trade 0.0496 0.001 60.150 0.000 0.048 0.051\n", "==============================================================================\n", "\n", "If the model instance has been used for another fit with different fit parameters, then the fit options might not be the correct ones anymore .\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Shapiro-Wilk test p-value: 3.1191042084835655e-31\n", " feature VIF\n", "0 const 1.666738\n", "1 2020_gov 16.399994\n", "2 2020 1.052399\n", "3 2020_con 16.402942\n", "4 2020_trade 1.050951\n" ] } ], "source": [ "import statsmodels.api as sm\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", "\n", "X = df_countries[['2020_gov', '2020', '2020_con', '2020_trade']]\n", "y = df_countries['GDP_calculated_2021']\n", "X = sm.add_constant(X) # Añadir una constante\n", "\n", "# Ajustar el modelo robusto\n", "model_robust = sm.RLM(y, X).fit()\n", "\n", "# Imprimir el resumen del modelo\n", "print(model_robust.summary())\n", "\n", "# Residuos vs Valores Ajustados\n", "plt.figure(figsize=(10, 5))\n", "plt.scatter(model_robust.fittedvalues, model_robust.resid)\n", "plt.axhline(y=0, color='r', linestyle='--')\n", "plt.xlabel('Valores Ajustados')\n", "plt.ylabel('Residuos')\n", "plt.title('Residuos vs Valores Ajustados en Regresión Robusta')\n", "plt.show()\n", "\n", "# Test de Shapiro-Wilk para normalidad de residuos\n", "from scipy import stats\n", "print(\"Shapiro-Wilk test p-value:\", stats.shapiro(model_robust.resid)[1])\n", "\n", "# VIF para detectar multicolinealidad\n", "vif_data = pd.DataFrame()\n", "vif_data[\"feature\"] = X.columns\n", "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n", "print(vif_data)" ] }, { "cell_type": "markdown", "id": "785a9e60", "metadata": {}, "source": [ "## Incluir GPI como variable dummy \n", "El GPI es un indicador de bienestar econémico de los paises o **Global Peace Index**.\n", "Después de observar que el modelo se desviaba por aplicar algunos argumentos, creo que es momento de incorporar nuevas variables." ] }, { "cell_type": "code", "execution_count": 41, "id": "81cc6f94", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Country Name 1960 1961 1962 1963 1964 1965 1966 1967 1968 ... \\\n", "0 Afghanistan 0 0 0 0 0 0 0 0 0 ... \n", "1 Albania 0 0 0 0 0 0 0 0 0 ... \n", "2 Algeria 0 0 0 0 0 0 0 0 0 ... \n", "3 Angola 0 0 0 0 0 0 0 0 0 ... \n", "4 Argentina 0 0 0 0 0 0 0 0 0 ... \n", "\n", " 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 \n", "0 0 3.0 3.23 3.4440 3.658 3.623 3.674 3.641 3.641 3.650 \n", "1 0 1.6 1.33 1.2795 1.229 1.237 1.225 1.403 1.403 1.403 \n", "2 0 2.2 2.00 2.0445 2.089 1.912 1.927 2.116 2.088 2.068 \n", "3 0 1.8 1.35 1.4270 1.504 1.403 1.418 1.625 1.625 1.666 \n", "4 0 1.4 1.33 1.3665 1.403 1.201 1.201 1.201 1.201 1.201 \n", "\n", "[5 rows x 64 columns]\n" ] } ], "source": [ "import pandas as pd\n", "\n", "gpi_data = pd.read_excel('GPI.xlsx')\n", "\n", "print(gpi_data.head())" ] }, { "cell_type": "markdown", "id": "ead66db4", "metadata": {}, "source": [ "## Crear la variable dummy para el año 2021" ] }, { "cell_type": "code", "execution_count": 42, "id": "b3e84bb1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Country Name 1960_gdp 1961_gdp 1962_gdp 1963_gdp 1964_gdp 1965_gdp \\\n", "0 Aruba -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 -0.202832 \n", "1 Afghanistan -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 -0.202832 \n", "2 Angola -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 -0.202832 \n", "3 Albania -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 -0.202832 \n", "4 Andorra -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 -0.202832 \n", "\n", " 1966_gdp 1967_gdp 1968_gdp ... 2014 2015 2016 2017 2018 2019 \\\n", "0 -0.202705 -0.202662 -0.203255 ... NaN NaN NaN NaN NaN NaN \n", "1 -0.202705 -0.202662 -0.203255 ... 3.0 3.23 3.4440 3.658 3.623 3.674 \n", "2 -0.202705 -0.202662 -0.203255 ... 1.8 1.35 1.4270 1.504 1.403 1.418 \n", "3 -0.202705 -0.202662 -0.203255 ... 1.6 1.33 1.2795 1.229 1.237 1.225 \n", "4 -0.202705 -0.202662 -0.203255 ... NaN NaN NaN NaN NaN NaN \n", "\n", " 2020 2021 2022 GPI_dummy \n", "0 NaN NaN NaN NaN \n", "1 3.641 3.641 3.650 1.0 \n", "2 1.625 1.625 1.666 0.0 \n", "3 1.403 1.403 1.403 0.0 \n", "4 NaN NaN NaN NaN \n", "\n", "[5 rows x 441 columns]\n" ] } ], "source": [ "# Crear una variable dummy donde el GPI del año 2021 mayor a 2.5 es 1, de lo contrario 0\n", "gpi_data['GPI_dummy'] = (gpi_data[2021] > 2.5).astype(int)\n", "\n", "# Fusionar con df_countries\n", "# Asegúrate de que 'Country' en df_countries y 'Country Name' en gpi_data coincidan\n", "df_countries = df_countries.merge(gpi_data[['Country Name', 'GPI_dummy']], left_on='Country Name', right_on='Country Name', how='left')\n", "\n", "# Verificar que la unión se haya hecho correctamente\n", "print(df_countries.head())" ] }, { "cell_type": "markdown", "id": "991e793f", "metadata": {}, "source": [ "## Reajustar el Modelo con la Nueva Variable" ] }, { "cell_type": "code", "execution_count": 43, "id": "fa212f4b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Country Name 1960_gdp 1961_gdp 1962_gdp 1963_gdp 1964_gdp \\\n", "1 Afghanistan -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "2 Angola -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "3 Albania -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "6 United Arab Emirates -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "7 Argentina -0.098248 -0.095184 -0.101961 -0.113989 -0.110743 \n", "\n", " 1965_gdp 1966_gdp 1967_gdp 1968_gdp ... 2014 2015 2016 2017 \\\n", "1 -0.202832 -0.202705 -0.202662 -0.203255 ... 3.0 3.23 3.4440 3.658 \n", "2 -0.202832 -0.202705 -0.202662 -0.203255 ... 1.8 1.35 1.4270 1.504 \n", "3 -0.202832 -0.202705 -0.202662 -0.203255 ... 1.6 1.33 1.2795 1.229 \n", "6 -0.202832 -0.202705 -0.202662 -0.203255 ... 1.4 1.33 1.4045 1.479 \n", "7 -0.103947 -0.110039 -0.109995 -0.112002 ... 1.4 1.33 1.3665 1.403 \n", "\n", " 2018 2019 2020 2021 2022 GPI_dummy \n", "1 3.623 3.674 3.641 3.641 3.650 1.0 \n", "2 1.403 1.418 1.625 1.625 1.666 0.0 \n", "3 1.237 1.225 1.403 1.403 1.403 0.0 \n", "6 1.580 1.604 1.211 1.464 1.581 0.0 \n", "7 1.201 1.201 1.201 1.201 1.201 0.0 \n", "\n", "[5 rows x 441 columns]\n" ] } ], "source": [ "import numpy as np\n", "\n", "# Revisar si hay valores infinitos y reemplazarlos con NaN\n", "df_countries.replace([np.inf, -np.inf], np.nan, inplace=True)\n", "\n", "# Eliminar cualquier fila que tenga algún NaN\n", "df_countries.dropna(inplace=True)\n", "\n", "for column in df_countries.columns:\n", " if df_countries[column].isnull().any():\n", " df_countries[column].fillna(df_countries[column].mean(), inplace=True)\n", "\n", "# Verificar el DataFrame después de la limpieza\n", "print(df_countries.head())" ] }, { "cell_type": "code", "execution_count": 44, "id": "daa7f34b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Valores faltantes en X: const 0\n", "2020_gov 0\n", "2020 0\n", "2020_con 0\n", "2020_trade 0\n", "GPI_dummy 0\n", "dtype: int64\n", "Valores faltantes en y: 0\n", " Robust linear Model Regression Results \n", "===============================================================================\n", "Dep. Variable: GDP_calculated_2021 No. Observations: 160\n", "Model: RLM Df Residuals: 154\n", "Method: IRLS Df Model: 5\n", "Norm: HuberT \n", "Scale Est.: mad \n", "Cov Type: H1 \n", "Date: Sun, 12 May 2024 \n", "Time: 21:39:52 \n", "No. Iterations: 50 \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const -0.1005 0.001 -152.932 0.000 -0.102 -0.099\n", "2020_gov 1.1192 0.002 730.681 0.000 1.116 1.122\n", "2020 2.8975 0.002 1233.770 0.000 2.893 2.902\n", "2020_con 0.8792 0.002 575.995 0.000 0.876 0.882\n", "2020_trade 0.0237 0.001 21.429 0.000 0.022 0.026\n", "GPI_dummy 0.0007 0.001 0.519 0.604 -0.002 0.003\n", "==============================================================================\n", "\n", "If the model instance has been used for another fit with different fit parameters, then the fit options might not be the correct ones anymore .\n" ] } ], "source": [ "# Preparando los datos para el modelo\n", "X = df_countries[['2020_gov', '2020', '2020_con', '2020_trade', 'GPI_dummy']]\n", "X = sm.add_constant(X) # Añadir una constante\n", "y = df_countries['GDP_calculated_2021'] # La variable objetivo\n", "\n", "# Asegurarse de que no hay valores faltantes\n", "print(\"Valores faltantes en X:\", X.isnull().sum())\n", "print(\"Valores faltantes en y:\", y.isnull().sum())\n", "\n", "# Ajustar el modelo de regresión robusta\n", "model_robust = sm.RLM(y, X).fit()\n", "print(model_robust.summary())" ] }, { "cell_type": "markdown", "id": "4083f56b", "metadata": {}, "source": [ "### Análisis de los Resultados del Modelo\n", "**Coeficientes y Significancia**:\n", "- Los coeficientes de 2020_gov, 2020, y 2020_con son significativos y sus intervalos de confianza no incluyen el cero, lo que indica que son predictores relevantes del GDP_calculated_2021.\n", "- El coeficiente para 2020_trade también es significativo y positivo, aunque el efecto es menor comparado con los otros predictores.\n", "- La variable GPI_dummy tiene un coeficiente muy pequeño y no es estadísticamente significativa (p-value = 0.604). Esto sugiere que la presencia de un índice de paz global alto o bajo no tiene un efecto significativo en el PIB calculado para 2021, al menos no en el modelo actual con las otras variables controladas.\n", "\n", "**Interpretación Económica**:\n", "- Las variables representativas del año 2020 (2020_gov, 2020, 2020_con, 2020_trade) tienen un fuerte impacto en el PIB calculado de 2021, lo que puede reflejar cómo los eventos económicos o las políticas del año anterior afectaron el desempeño económico en 2021.\n", "- La falta de impacto significativo de GPI_dummy puede ser un indicativo de que los efectos del índice de paz son más complejos o que otros factores no considerados en el modelo están moderando o enmascarando este efecto." ] }, { "cell_type": "markdown", "id": "5e52112d", "metadata": {}, "source": [ "### Examinar Otras Variables\n", "Incuiré las variables **Inflación** e **Interés** que pueden tener un impacto en el PIB calculado." ] }, { "cell_type": "code", "execution_count": 45, "id": "4136b3dd", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Country Name Country Code 1960 1961 1962 1963 1964 \\\n", "0 Aruba ABW NaN NaN NaN NaN NaN \n", "1 Africa Eastern and Southern AFE NaN NaN NaN NaN NaN \n", "2 Afghanistan AFG NaN NaN NaN NaN NaN \n", "3 Africa Western and Central AFW NaN NaN NaN NaN NaN \n", "4 Angola AGO NaN NaN NaN NaN NaN \n", "\n", " 1965 1966 1967 ... 2013 2014 2015 2016 \\\n", "0 NaN NaN NaN ... 10.709708 3.213869 0.157925 7.982851 \n", "1 NaN NaN NaN ... NaN NaN NaN NaN \n", "2 NaN NaN NaN ... 9.784496 14.351689 12.252548 17.583938 \n", "3 NaN NaN NaN ... NaN NaN NaN NaN \n", "4 NaN NaN NaN ... 12.610802 12.380530 21.144182 -4.922063 \n", "\n", " 2017 2018 2019 2020 2021 2022 \n", "0 9.789287 2.437682 -0.371564 7.738755 11.988410 2.598476 \n", "1 NaN NaN NaN NaN NaN NaN \n", "2 12.141178 NaN NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN NaN NaN \n", "4 -5.552638 -5.844003 0.090919 8.028657 -13.989372 3.277431 \n", "\n", "[5 rows x 65 columns]\n", " Country Name Country Code 1960 1961 1962 1963 1964 \\\n", "0 Aruba ABW NaN NaN NaN NaN NaN \n", "1 Africa Eastern and Southern AFE NaN NaN NaN NaN NaN \n", "2 Afghanistan AFG NaN NaN NaN NaN NaN \n", "3 Africa Western and Central AFW NaN NaN NaN NaN NaN \n", "4 Angola AGO NaN NaN NaN NaN NaN \n", "\n", " 1965 1966 1967 ... 2013 2014 2015 2016 2017 \\\n", "0 NaN NaN NaN ... -2.372065 0.421441 0.474764 -0.931196 -1.028282 \n", "1 NaN NaN NaN ... 5.750981 5.370290 5.245878 6.571396 6.399343 \n", "2 NaN NaN NaN ... 7.385772 4.673996 -0.661709 4.383892 4.975952 \n", "3 NaN NaN NaN ... 2.439201 1.768436 2.130817 1.487416 1.764635 \n", "4 NaN NaN NaN ... 8.777814 7.280387 9.353840 30.698958 29.842578 \n", "\n", " 2018 2019 2020 2021 2022 \n", "0 3.626041 4.257462 NaN NaN NaN \n", "1 4.720811 4.653665 7.321106 6.824727 10.773751 \n", "2 0.626149 2.302373 NaN NaN NaN \n", "3 1.784050 1.760112 2.437609 3.653533 7.967574 \n", "4 19.630594 17.079704 22.271564 25.754266 NaN \n", "\n", "[5 rows x 65 columns]\n" ] } ], "source": [ "import pandas as pd\n", "\n", "# Cargar datos de tasas de interés\n", "interest_data = pd.read_excel('Interest.xlsx')\n", "# Cargar datos de inflación\n", "inflation_data = pd.read_excel('Inflation.xlsx')\n", "\n", "print(interest_data.head())\n", "print(inflation_data.head())" ] }, { "cell_type": "code", "execution_count": 46, "id": "1db32d37", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Country Name 1960_gdp 1961_gdp 1962_gdp 1963_gdp 1964_gdp \\\n", "0 Afghanistan -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "1 Angola -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "2 Albania -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "3 United Arab Emirates -0.201874 -0.200982 -0.200327 -0.199863 -0.200426 \n", "4 Argentina -0.098248 -0.095184 -0.101961 -0.113989 -0.110743 \n", "\n", " 1965_gdp 1966_gdp 1967_gdp 1968_gdp ... 2013 2014 2015 \\\n", "0 -0.202832 -0.202705 -0.202662 -0.203255 ... 7.385772 4.673996 -0.661709 \n", "1 -0.202832 -0.202705 -0.202662 -0.203255 ... 8.777814 7.280387 9.353840 \n", "2 -0.202832 -0.202705 -0.202662 -0.203255 ... 1.937621 1.625865 1.896174 \n", "3 -0.202832 -0.202705 -0.202662 -0.203255 ... 1.101118 2.346269 4.069966 \n", "4 -0.103947 -0.110039 -0.109995 -0.112002 ... NaN NaN NaN \n", "\n", " 2016 2017 2018 2019 2020 2021 2022 \n", "0 4.383892 4.975952 0.626149 2.302373 NaN NaN NaN \n", "1 30.698958 29.842578 19.630594 17.079704 22.271564 25.754266 NaN \n", "2 1.275432 1.986661 2.028060 1.411091 1.620887 2.041472 6.725203 \n", "3 1.617488 1.966826 3.068634 -1.931081 -2.079403 -0.013860 4.827889 \n", "4 NaN NaN NaN NaN NaN NaN NaN \n", "\n", "[5 rows x 569 columns]\n" ] } ], "source": [ "# Unir df_countries con interest_data e inflation_data\n", "df_countries = df_countries.merge(interest_data, on='Country Name', how='left')\n", "df_countries = df_countries.merge(inflation_data, on='Country Name', how='left')\n", "\n", "print(df_countries.head())" ] }, { "cell_type": "markdown", "id": "36ebe001", "metadata": {}, "source": [ "### Interacciones y No Linearidades\n", "Exploraré la posibilidad de interacciones entre las variables y la no linealidades para capturar mejor las relaciones complejas." ] }, { "cell_type": "code", "execution_count": 47, "id": "7531201a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columnas en interest_data: Index(['Country Name', 'Country Code', '1960', '1961', '1962', '1963', '1964',\n", " '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973',\n", " '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982',\n", " '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n", " '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000',\n", " '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009',\n", " '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018',\n", " '2019', '2020', '2021', '2022'],\n", " dtype='object')\n", "Columnas en inflation_data: Index(['Country Name', 'Country Code', '1960', '1961', '1962', '1963', '1964',\n", " '1965', '1966', '1967', '1968', '1969', '1970', '1971', '1972', '1973',\n", " '1974', '1975', '1976', '1977', '1978', '1979', '1980', '1981', '1982',\n", " '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n", " '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000',\n", " '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009',\n", " '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018',\n", " '2019', '2020', '2021', '2022'],\n", " dtype='object')\n" ] } ], "source": [ "# Imprimir los nombres de las columnas de interest_data e inflation_data para verificar\n", "print(\"Columnas en interest_data:\", interest_data.columns)\n", "print(\"Columnas en inflation_data:\", inflation_data.columns)" ] }, { "cell_type": "code", "execution_count": 48, "id": "ef82544e", "metadata": {}, "outputs": [], "source": [ "# Renombrar columnas si es necesario\n", "interest_data.rename(columns={'Rate': 'Interest Rate'}, inplace=True)\n", "inflation_data.rename(columns={'Rate': 'Inflation Rate'}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 49, "id": "e8a9e3ca", "metadata": {}, "outputs": [], "source": [ "# Especificar Sufijos al Fusionar\n", "# Unir df_countries con interest_data\n", "df_countries = df_countries.merge(interest_data, on='Country Name', how='left', suffixes=('', '_interest'))\n", "\n", "# Unir el resultado con inflation_data\n", "df_countries = df_countries.merge(inflation_data, on='Country Name', how='left', suffixes=('', '_inflation'))" ] }, { "cell_type": "code", "execution_count": 50, "id": "8d6bc0b2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Robust linear Model Regression Results \n", "===============================================================================\n", "Dep. Variable: GDP_calculated_2021 No. Observations: 77\n", "Model: RLM Df Residuals: 67\n", "Method: IRLS Df Model: 9\n", "Norm: HuberT \n", "Scale Est.: mad \n", "Cov Type: H1 \n", "Date: Sun, 12 May 2024 \n", "Time: 21:40:55 \n", "No. Iterations: 38 \n", "=============================================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "---------------------------------------------------------------------------------------------\n", "const -0.5943 0.006 -91.706 0.000 -0.607 -0.582\n", "2020_gov 1.4269 0.026 55.580 0.000 1.377 1.477\n", "2020 -1.583e-06 0.000 -0.008 0.994 -0.000 0.000\n", "2020_con 0.7237 0.018 40.390 0.000 0.689 0.759\n", "2020_trade 0.3482 0.006 55.147 0.000 0.336 0.361\n", "GPI_dummy 0.4997 0.035 14.455 0.000 0.432 0.567\n", "Interest Rate 2021 -0.0004 0.000 -0.911 0.362 -0.001 0.000\n", "Inflation Rate 2021 -0.0003 0.001 -0.302 0.763 -0.003 0.002\n", "interest_gpi_interaction 0.0226 0.003 8.717 0.000 0.017 0.028\n", "inflation_gpi_interaction -0.0291 0.003 -8.767 0.000 -0.036 -0.023\n", "=============================================================================================\n", "\n", "If the model instance has been used for another fit with different fit parameters, then the fit options might not be the correct ones anymore .\n" ] } ], "source": [ "# Seleccionar las columnas de interés y de inflación del año 2021\n", "df_countries['Interest Rate 2021'] = df_countries['2021_interest']\n", "df_countries['Inflation Rate 2021'] = df_countries['2021_inflation']\n", "\n", "# Crear interacciones entre la tasa de interés y GPI, y la tasa de inflación y GPI\n", "df_countries['interest_gpi_interaction'] = df_countries['Interest Rate 2021'] * df_countries['GPI_dummy']\n", "df_countries['inflation_gpi_interaction'] = df_countries['Inflation Rate 2021'] * df_countries['GPI_dummy']\n", "\n", "# Preparar los datos para el modelo\n", "X = df_countries[['2020_gov', '2020', '2020_con', '2020_trade', 'GPI_dummy', 'Interest Rate 2021', 'Inflation Rate 2021', 'interest_gpi_interaction', 'inflation_gpi_interaction']]\n", "X = sm.add_constant(X) # Añadir una constante\n", "y = df_countries['GDP_calculated_2021'] # La variable objetivo\n", "\n", "# Asegurarte de que no hay NaNs en X o y\n", "X.dropna(inplace=True)\n", "y = y[X.index] # Asegurar que y solo contiene índices de X\n", "\n", "# Ajustar el modelo\n", "model_robust = sm.RLM(y, X).fit()\n", "print(model_robust.summary())" ] }, { "cell_type": "markdown", "id": "d949419b", "metadata": {}, "source": [ "### Análisis de Subconjuntos\n", "Analizar subconjuntos de países, para ver si el efecto del índice de paz varía significativamente entre estos grupos.\n", "\n", "Los países con un índice GPI más alto (menos pacíficos) tienen un PIB más alto en este modelo, lo cual es interesante y podría merecer una investigación más profunda sobre la naturaleza de esta relación', te puedo dar como ejemplo que un país como Estados Unidos con un alto PBI estan surguiendo violencia internas por diversos motivos (raciales, homofóbica, contra inmigrantes, creencias religiosas, posiciones nacionalistas, feminicidio), mientras que Ruanda ha mejorado en tres décadas en donde la reconciliación entre tribus a hecho que sea un lugar relativamente pacífico y con un PBI no destacable.\n", "Para analizar subconjuntos de países para ver si el efecto del índice de paz varía significativamente entre estos grupos, suguiero los siguiente:\n", "- **grupo_economías_grandes: 'Germany', 'France', 'United State', 'Switzerland', 'Sweden'**\n", "- **grupo economías_pequeñas: 'Peru', 'Colombia', 'Estonia', 'Uruguay', 'Portugal'**" ] }, { "cell_type": "code", "execution_count": 51, "id": "3636d331", "metadata": {}, "outputs": [], "source": [ "# Definir los grupos de países\n", "grupo_economias_grandes = ['Germany', 'France', 'United States', 'Switzerland', 'Sweden']\n", "grupo_economias_pequeñas = ['Peru', 'Colombia', 'Estonia', 'Uruguay', 'Portugal']\n", "\n", "# Asegúrate de que los nombres de los países estén correctamente escritos como aparecen en tu DataFrame" ] }, { "cell_type": "code", "execution_count": 52, "id": "d6c691de", "metadata": {}, "outputs": [], "source": [ "# Filtrar los DataFrames por grupo\n", "df_grandes = df_countries[df_countries['Country Name'].isin(grupo_economias_grandes)]\n", "df_pequenas = df_countries[df_countries['Country Name'].isin(grupo_economias_pequeñas)]" ] }, { "cell_type": "code", "execution_count": 53, "id": "c12c047a", "metadata": {}, "outputs": [], "source": [ "# Eliminar filas con valores NaN en el DataFrame para economías grandes\n", "df_grandes = df_grandes.dropna(subset=['2020_gov', '2020', '2020_con', '2020_trade', 'GPI_dummy', 'Interest Rate 2021', 'Inflation Rate 2021', 'interest_gpi_interaction', 'inflation_gpi_interaction', 'GDP_calculated_2021'])\n", "\n", "# Eliminar filas con valores NaN en el DataFrame para economías pequeñas\n", "df_pequenas = df_pequenas.dropna(subset=['2020_gov', '2020', '2020_con', '2020_trade', 'GPI_dummy', 'Interest Rate 2021', 'Inflation Rate 2021', 'interest_gpi_interaction', 'inflation_gpi_interaction', 'GDP_calculated_2021'])" ] }, { "cell_type": "code", "execution_count": 54, "id": "f6ef8587", "metadata": {}, "outputs": [], "source": [ "# Función para imputar valores faltantes con la media\n", "def imputar_con_media(df):\n", " for column in ['2020_gov', '2020', '2020_con', '2020_trade', 'GPI_dummy', 'Interest Rate 2021', 'Inflation Rate 2021', 'interest_gpi_interaction', 'inflation_gpi_interaction']:\n", " if df[column].isnull().any():\n", " df[column].fillna(df[column].mean(), inplace=True)\n", " return df\n", "\n", "# Imputar valores faltantes en ambos DataFrames\n", "df_grandes = imputar_con_media(df_grandes)\n", "df_pequenas = imputar_con_media(df_pequenas)" ] }, { "cell_type": "code", "execution_count": null, "id": "faab2951", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "74bec184", "metadata": {}, "source": [ "\n", "### Validación del Modelo\n", "Finalmente, valida el modelo utilizando un conjunto de datos de prueba o mediante técnicas de validación cruzada para asegurar que el modelo generaliza bien a nuevos datos." ] }, { "cell_type": "code", "execution_count": 55, "id": "8ed92637", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import KFold\n", "from sklearn.metrics import mean_squared_error\n", "import numpy as np\n", "import statsmodels.api as sm\n", "\n", "# Supongamos que df es tu DataFrame y ya está limpio y listo para usar\n", "X = df_countries[['2020_gov', '2020', '2020_con', '2020_trade', 'Interest Rate 2021', 'Inflation Rate 2021', 'interest_gpi_interaction', 'inflation_gpi_interaction']]\n", "y = df_countries['GDP_calculated_2021']\n", "\n", "# Añadir una constante a X\n", "X = sm.add_constant(X)" ] }, { "cell_type": "code", "execution_count": null, "id": "9768bfdd", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 56, "id": "3b00e818", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([['Afghanistan', -0.2018738832349514, -0.2009817132062104, ...,\n", " nan, nan, nan],\n", " ['Angola', -0.2018738832349514, -0.2009817132062104, ...,\n", " 25.7542656453085, -0.0, 0.0],\n", " ['Albania', -0.2018738832349514, -0.2009817132062104, ...,\n", " 2.04147163139549, 0.0, 0.0],\n", " ...,\n", " ['South Africa', -0.1696027393539647, -0.1684367115660035, ...,\n", " 4.61167217803206, 0.0, 0.0],\n", " ['Zambia', -0.2214833790799845, -0.2203719098604074, ...,\n", " 22.0207676245778, nan, 0.0],\n", " ['Zimbabwe', -0.2209943299706016, -0.2197217042515599, ...,\n", " 98.5461050920624, -0.0, 0.0]], dtype=object)" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_countries.values[:376]" ] }, { "cell_type": "code", "execution_count": 57, "id": "1a49bd46", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Country Name 0\n", "1960_gdp 0\n", "1961_gdp 0\n", "1962_gdp 0\n", "1963_gdp 0\n", " ..\n", "2022_inflation 20\n", "Interest Rate 2021 78\n", "Inflation Rate 2021 17\n", "interest_gpi_interaction 78\n", "inflation_gpi_interaction 17\n", "Length: 701, dtype: int64" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_countries.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 58, "id": "0be90489", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Country Name 0\n", "1960_gdp 0\n", "1961_gdp 0\n", "1962_gdp 0\n", "1963_gdp 0\n", " ..\n", "2022_inflation 20\n", "Interest Rate 2021 78\n", "Inflation Rate 2021 17\n", "interest_gpi_interaction 78\n", "inflation_gpi_interaction 17\n", "Length: 701, dtype: int64" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_countries.iloc[:376].isnull().sum()" ] }, { "cell_type": "code", "execution_count": 59, "id": "d6c2aacf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Country Name 0\n", "1960_gdp 0\n", "1961_gdp 0\n", "1962_gdp 0\n", "1963_gdp 0\n", " ..\n", "2022_inflation 20\n", "Interest Rate 2021 78\n", "Inflation Rate 2021 17\n", "interest_gpi_interaction 78\n", "inflation_gpi_interaction 17\n", "Length: 701, dtype: int64\n", "Country Name object\n", "1960_gdp float64\n", "1961_gdp float64\n", "1962_gdp float64\n", "1963_gdp float64\n", " ... \n", "2022_inflation float64\n", "Interest Rate 2021 float64\n", "Inflation Rate 2021 float64\n", "interest_gpi_interaction float64\n", "inflation_gpi_interaction float64\n", "Length: 701, dtype: object\n" ] } ], "source": [ "# Verificar NaNs en las primeras 376 filas y los tipos de datos\n", "print(df_countries.iloc[:376].isnull().sum())\n", "print(df_countries.iloc[:376].dtypes)" ] }, { "cell_type": "code", "execution_count": null, "id": "8387b391", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { 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