From 8047f291d966b80c68f0a5c5b155e174283a8609 Mon Sep 17 00:00:00 2001
From: malenamalka <malkagom@dickinson.edu>
Date: Tue, 28 Jan 2025 09:12:10 -0500
Subject: [PATCH 1/7] added a meme to ideas

---
 .DS_Store              | Bin 0 -> 6148 bytes
 ideas/just a meme.jpeg | Bin 0 -> 9891 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 .DS_Store
 create mode 100644 ideas/just a meme.jpeg

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..fec7c912fc114d69bc4fd923a115399c606958e4
GIT binary patch
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literal 0
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diff --git a/ideas/just a meme.jpeg b/ideas/just a meme.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..1e8008d5140cc7d03452c823b1253a71a13bb357
GIT binary patch
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From ce64ba97d8dacca3e70a5d6d022da87850a056a6 Mon Sep 17 00:00:00 2001
From: malenamalka <malkagom@dickinson.edu>
Date: Wed, 29 Jan 2025 21:28:07 -0500
Subject: [PATCH 2/7] added idea 1

---
 .DS_Store          | Bin 6148 -> 6148 bytes
 ideas/firstidea.md |  32 ++++++++++++++++++++++++++++++++
 2 files changed, 32 insertions(+)
 create mode 100644 ideas/firstidea.md

diff --git a/.DS_Store b/.DS_Store
index fec7c912fc114d69bc4fd923a115399c606958e4..17feb40272bd0ace9f6eadb0e8e9471988d26c86 100644
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diff --git a/ideas/firstidea.md b/ideas/firstidea.md
new file mode 100644
index 0000000..95a7d3b
--- /dev/null
+++ b/ideas/firstidea.md
@@ -0,0 +1,32 @@
+# Idea #1
+### Economics Discipline - Malena Malka Goldman
+
+**Research Question: How does the changes of inflation trends affect the price of the canasta basica nacional in Argentina**
+The Canasta Básica Nacional (CBN) in Argentina refers to a set of essential goods and services used to measure poverty and indigence levels. It consists of two key components:
+
+_Canasta Básica Alimentaria (CBA) – Basic Food Basket_
+* Represents the minimum amount of food required to meet basic nutritional needs.
+* A household whose income is below the CBA is considered indigent (indigencia), meaning they cannot afford even essential food items.
+ 
+_Canasta Básica Total (CBT) – Total Basic Basket_
+* Includes the CBA plus other essential expenses like housing, transportation, education, and healthcare.
+* A household whose income is below the CBT is considered poor (pobreza), meaning they can afford food but not other basic necessities.
+
+**Tractable data**
+For this project, I will be utilizing official information provided by both local and international statistical entities. On the one hand, I will be using a dataset from the National Institute of Statistics and Censuses from the Argentine Republic (INDEC) that contains information regarding the price of both the CBA and CBT from April of 2016 to December of 2024. This data is collected by the national office and is official and approved by the state. I will cross-examine this data with Argentina's monthly inflation rates.
+
+**Data retrieval**
+Although it is difficult to access the data sets provided by the Federal Reserve or the International Monetary Fund, I will be accessing the Inflation rates data through the trading economics website, which replicates multiple economic indexes. The link to access the data is the following:
+https://tradingeconomics.com/argentina/inflation-cpi
+And the link to access the CBA data is the following:
+https://www.indec.gob.ar/indec/web/Nivel4-Tema-4-43-149
+My goal would be to create a unique data frame combining both pieces of information and run my analysis.
+
+**Correct specification of the model**
+I'm not entirely sure what type of analysis would be most beneficial regarding this information, I guess I would need to look at the data first before making any decisions. Initially, I would like to run a linear regression model to be able to predict what prices would both the CBA and CBT have depending on the Inflation rate that month. However, I recognize that the price of the basket keeps rising, so maybe being able to cluster specific Interest rates with the percentage change in price of the CBA or CBT could also be interesting. I am open to suggestions :)
+
+**Implications for stakeholders**
+The findings of this study have significant implications for multiple stakeholders, including policymakers, economists and social organizations. For government entities and policymakers, understanding the relationship between inflation and the Canasta Básica Alimentaria can inform targeted economic policies, such as social assistance programs and wage adjustments, to mitigate the impact of rising costs on vulnerable populations. In addition, businesses and labor unions can use these insights to advocate for fair wage policies that align with inflation trends. Additionally, financial institutions and investors can leverage this analysis to assess economic stability and forecast consumer purchasing power. 
+
+**Ethical, legal, societal implications**
+This research can enhance public awareness of inflation’s impact on basic living costs, potentially influencing social movements and public discourse. However, the responsibility extends to ensuring that the findings do not contribute to fear-mongering but instead provide constructive insights that contribute to economic stability and improved social welfare policies. From an ethical standpoint, ensuring transparency and accuracy in data handling is crucial, as misleading conclusions could influence public perception and policy decisions. Given the socio-economic sensitivity of this issue, researchers must be cautious about potential biases in data sources and analysis, ensuring that findings are presented responsibly to avoid misinterpretations that could exacerbate economic anxieties.

From c592b6e964dbfde6b5007adee23d413af6a2712d Mon Sep 17 00:00:00 2001
From: malenamalka <malkagom@dickinson.edu>
Date: Thu, 30 Jan 2025 09:10:27 -0500
Subject: [PATCH 3/7] add idea1

---
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diff --git a/mini projects/.DS_Store b/mini projects/.DS_Store
new file mode 100644
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diff --git a/ideas/firstidea.md b/mini projects/ideas/idea1.md
similarity index 100%
rename from ideas/firstidea.md
rename to mini projects/ideas/idea1.md

From a554279cfce4ea1868e931b9c5721358d8d66568 Mon Sep 17 00:00:00 2001
From: malenamalka <malkagom@dickinson.edu>
Date: Wed, 5 Feb 2025 14:47:01 -0500
Subject: [PATCH 4/7] uploaded idea 2

---
 .DS_Store                    | Bin 6148 -> 8196 bytes
 mini projects/.DS_Store      | Bin 6148 -> 6148 bytes
 mini projects/ideas/idea2.md |  29 +++++++++++++++++++++++++++++
 notes/.DS_Store              | Bin 0 -> 6148 bytes
 4 files changed, 29 insertions(+)
 create mode 100644 mini projects/ideas/idea2.md
 create mode 100644 notes/.DS_Store

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diff --git a/mini projects/ideas/idea2.md b/mini projects/ideas/idea2.md
new file mode 100644
index 0000000..596b085
--- /dev/null
+++ b/mini projects/ideas/idea2.md	
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+# Idea #2
+### Study of International Student OPT - Malena Malka Goldman
+
+**Research Question: What are the most common majors among international students in undergraduate education, and how do these majors influence their likelihood of participating in Optional Practical Training (OPT)?**
+
+Optional Practical Training (OPT) is a temporary work authorization program that allows international students on F-1 visas to gain practical work experience in their field of study in the U.S. after graduation. OPT is typically available for up to 12 months, but students in STEM (Science, Technology, Engineering, and Mathematics) fields may apply for a 24-month extension, allowing them to work for a total of 36 months.
+
+**Tractable data**
+For this project, I will be utilizing official data from reputable sources that track international student trends in the United States. Specifically, I will analyze datasets from Open Doors and SEVIS (Student and Exchange Visitor Information System).
+The Open Doors Academic Level and Places of Origin dataset provides information on the distribution of international students by academic level (undergraduate, graduate, non-degree) and country of origin. Additionally, the Field of Study by Place of Origin dataset offers insights into the most common majors pursued by students from different countries. These datasets, published by the Institute of International Education (IIE), offer comprehensive and state-approved statistics on international student enrollment trends.
+To deepen the analysis, I will cross-examine this data with SEVIS statistics, which provide real-time tracking of international students in the U.S., including participation in Optional Practical Training (OPT). This comparison will help identify correlations between students’ academic backgrounds, country of origin, and their likelihood of engaging in OPT after graduation.
+
+**Data retrieval**
+As mentioned before, I will combine and transform data from Open Doors and SEVIS to analyze international student trends in the U.S. By merging academic level, field of study, and country of origin data with OPT participation statistics, I will create a unified framework for cross-examination. This transformation will allow for a deeper understanding of how academic and demographic factors influence students’ post-graduation choices. The links to access the data are the following:
+_Academic Level and Places of Origin Data_
+https://opendoorsdata.org/data/international-students/academic-level-and-places-of-origin/
+_Field of Study by Place of Origin_
+https://opendoorsdata.org/data/international-students/fields-of-study-by-place-of-origin/
+_SEVIS OPT data_
+https://studyinthestates.dhs.gov/sevis-data-mapping-tool/january-2024-sevis-data-mapping-tool-data
+
+**Correct specification of the model**
+To analyze international student trends and OPT participation, I will use a clustering analysis approach to group students based on academic level, field of study, and country of origin. The two main clustering methods I will consider are k-means clustering, which is efficient for large datasets, and hierarchical clustering, which can capture more complex relationships between groups.
+
+**Implications for stakeholders**
+This research project and its findings could offer significant benefits for students, universities, and potential employers. Prospective college students could use the insights to inform their decisions on which major to declare, considering how OPT participation may impact their ability to stay and gain work experience in the U.S. Universities, in turn, could tailor their academic and career support services to better align with the trends in OPT participation, enhancing their international student experience. Additionally, employers seeking to hire international talent could use the findings to refine their recruitment strategies, focusing on fields with higher OPT retention rates and ensuring they are tapping into the most promising student pipelines.
+
+**Ethical, legal, societal implications**
+One of my main concerns with this research is its political and social sensitivity, especially given the shifting administration and heightened debates around immigration. Public perceptions of international students are particularly vulnerable to policy changes, making it crucial to approach this study with care. The findings could influence discussions on OPT policies, student retention, and immigration, potentially shaping public opinion and institutional decisions. While the insights may help students make informed choices about their academic and career paths, there is also a risk of unintentionally reinforcing stereotypes based on nationality, major, or career prospects. To ensure fairness and equity, the research must be conducted with a neutral, data-driven approach, avoiding unintended biases. Additionally, maintaining strict data privacy and anonymization will be essential to protect student information and uphold ethical standards.
\ No newline at end of file
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From 97762b99e3e5e290f94b1f7f03993f32f5bbe204 Mon Sep 17 00:00:00 2001
From: malenamalka <malkagom@dickinson.edu>
Date: Thu, 20 Feb 2025 00:18:00 -0500
Subject: [PATCH 5/7] uploaded miniproject

---
 .DS_Store                                     |  Bin 8196 -> 8196 bytes
 mini project/.DS_Store                        |  Bin 0 -> 8196 bytes
 .../ideas/idea1.md                            |    0
 .../ideas/idea2.md                            |    0
 .../mini_project}/.DS_Store                   |  Bin 6148 -> 6148 bytes
 .../analysis-checkpoint.ipynb                 | 1214 +++
 .../Mini Project Presentation.pdf             |  Bin 0 -> 1465602 bytes
 mini project/mini_project/analysis.ipynb      | 1214 +++
 .../mini_project/data/arg_inflation.csv       |    5 +
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 .../mini_project/data/exchange_rate.csv       | 8082 +++++++++++++++++
 .../mini_project/data/gdp_deflator.csv        |   20 +
 .../data/imf-dm-export-20250216.csv           |    5 +
 .../mini_project/data/unemployment.csv        |  271 +
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 rename {mini projects => mini project}/ideas/idea2.md (100%)
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new file mode 100644
index 0000000..7ac184f
--- /dev/null
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "THIS IS FOR ANALYSIS OF MY MINI PROJECT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cba_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/canasta basica .csv'\n",
+    "inflation_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/arg_inflation.csv'\n",
+    "exchange_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/exchange_rate.csv'\n",
+    "cba_df = pd.read_csv(cba_file_path)\n",
+    "inflation_df = pd.read_csv(inflation_file_path)\n",
+    "exchange_rate_df =pd.read_csv(exchange_file_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Canasta Basica Alimentaria</th>\n",
+       "      <th>Canasta Basica Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1667.951111</td>\n",
+       "      <td>4033.322222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>1975.602500</td>\n",
+       "      <td>4831.954167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>2667.066667</td>\n",
+       "      <td>6676.768333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>4166.851667</td>\n",
+       "      <td>10401.310000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>6092.989167</td>\n",
+       "      <td>14718.236667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>9178.533333</td>\n",
+       "      <td>21524.225000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>16117.950833</td>\n",
+       "      <td>36373.939167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>40833.848333</td>\n",
+       "      <td>88191.855833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>126722.385000</td>\n",
+       "      <td>281322.264167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2025</td>\n",
+       "      <td>146726.230000</td>\n",
+       "      <td>334535.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year  Canasta Basica Alimentaria  Canasta Basica Total\n",
+       "0  2016                 1667.951111           4033.322222\n",
+       "1  2017                 1975.602500           4831.954167\n",
+       "2  2018                 2667.066667           6676.768333\n",
+       "3  2019                 4166.851667          10401.310000\n",
+       "4  2020                 6092.989167          14718.236667\n",
+       "5  2021                 9178.533333          21524.225000\n",
+       "6  2022                16117.950833          36373.939167\n",
+       "7  2023                40833.848333          88191.855833\n",
+       "8  2024               126722.385000         281322.264167\n",
+       "9  2025               146726.230000         334535.800000"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Data Cleaning - CBA\n",
+    "cba_df[\"Year\"] = cba_df[\"Month - Year\"].str.extract(r'(\\d{2})$').astype(int) + 2000\n",
+    "cba_yearly_df = cba_df.groupby(\"Year\")[[\"Canasta Basica Alimentaria\", \"Canasta Basica Total\"]].mean().reset_index()\n",
+    "cba_yearly_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Inflation Rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>25.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>34.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>53.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>48.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year  Inflation Rate\n",
+       "0  2017            25.7\n",
+       "1  2018            34.3\n",
+       "2  2019            53.5\n",
+       "3  2020            42.0\n",
+       "4  2021            48.4"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Data Cleaning - Inflation \n",
+    "inflation_arg_df = inflation_df.iloc[1, 1:].to_frame().reset_index()\n",
+    "inflation_arg_df.columns = [\"Year\", \"Inflation Rate\"]\n",
+    "inflation_arg_df = inflation_arg_df.dropna()\n",
+    "inflation_arg_df[\"Year\"] = inflation_arg_df[\"Year\"].astype(int)\n",
+    "inflation_arg_df[\"Inflation Rate\"] = pd.to_numeric(inflation_arg_df[\"Inflation Rate\"], errors=\"coerce\")\n",
+    "inflation_arg_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert date column to datetime format (if applicable)\n",
+    "cba_df[\"Date\"] = pd.to_datetime(cba_df[\"Month - Year\"], format=\"%B-%y\", errors='coerce')\n",
+    "cba_df = cba_df.sort_values(by=\"Date\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month - Year</th>\n",
+       "      <th>Canasta Basica Alimentaria</th>\n",
+       "      <th>Canasta Basica Total</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>April-16</td>\n",
+       "      <td>1514.53</td>\n",
+       "      <td>3663.66</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-04-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>May-16</td>\n",
+       "      <td>1561.35</td>\n",
+       "      <td>3830.77</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-05-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>June-16</td>\n",
+       "      <td>1614.32</td>\n",
+       "      <td>3942.67</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-06-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>July-16</td>\n",
+       "      <td>1666.48</td>\n",
+       "      <td>4033.76</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-07-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>August-16</td>\n",
+       "      <td>1675.05</td>\n",
+       "      <td>4041.87</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-08-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>September-24</td>\n",
+       "      <td>138744.31</td>\n",
+       "      <td>312174.70</td>\n",
+       "      <td>2024</td>\n",
+       "      <td>2024-09-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>October-24</td>\n",
+       "      <td>140653.57</td>\n",
+       "      <td>319283.60</td>\n",
+       "      <td>2024</td>\n",
+       "      <td>2024-10-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>November-24</td>\n",
+       "      <td>142148.73</td>\n",
+       "      <td>324099.10</td>\n",
+       "      <td>2024</td>\n",
+       "      <td>2024-11-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>December-24</td>\n",
+       "      <td>145408.96</td>\n",
+       "      <td>331532.43</td>\n",
+       "      <td>2024</td>\n",
+       "      <td>2024-12-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>January-25</td>\n",
+       "      <td>146726.23</td>\n",
+       "      <td>334535.80</td>\n",
+       "      <td>2025</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>106 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Month - Year  Canasta Basica Alimentaria  Canasta Basica Total  Year  \\\n",
+       "0        April-16                     1514.53               3663.66  2016   \n",
+       "1          May-16                     1561.35               3830.77  2016   \n",
+       "2         June-16                     1614.32               3942.67  2016   \n",
+       "3         July-16                     1666.48               4033.76  2016   \n",
+       "4       August-16                     1675.05               4041.87  2016   \n",
+       "..            ...                         ...                   ...   ...   \n",
+       "101  September-24                   138744.31             312174.70  2024   \n",
+       "102    October-24                   140653.57             319283.60  2024   \n",
+       "103   November-24                   142148.73             324099.10  2024   \n",
+       "104   December-24                   145408.96             331532.43  2024   \n",
+       "105    January-25                   146726.23             334535.80  2025   \n",
+       "\n",
+       "          Date  \n",
+       "0   2016-04-01  \n",
+       "1   2016-05-01  \n",
+       "2   2016-06-01  \n",
+       "3   2016-07-01  \n",
+       "4   2016-08-01  \n",
+       "..         ...  \n",
+       "101 2024-09-01  \n",
+       "102 2024-10-01  \n",
+       "103 2024-11-01  \n",
+       "104 2024-12-01  \n",
+       "105 2025-01-01  \n",
+       "\n",
+       "[106 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cba_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/z8/02768t9119xbgk92bl8mhrrm0000gn/T/ipykernel_8944/923394105.py:51: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from current font.\n",
+      "  plt.tight_layout()\n",
+      "/var/folders/z8/02768t9119xbgk92bl8mhrrm0000gn/T/ipykernel_8944/923394105.py:51: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from current font.\n",
+      "  plt.tight_layout()\n",
+      "/Users/malenamalka/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from current font.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n",
+      "/Users/malenamalka/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from current font.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set Seaborn style\n",
+    "sns.set_theme(style=\"darkgrid\", palette=\"magma\")\n",
+    "\n",
+    "# Create figure and axis\n",
+    "fig, ax = plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n",
+    "\n",
+    "# --- CBA Price Visualization ---\n",
+    "ax[0].plot(cba_df[\"Date\"], cba_df[\"Canasta Basica Alimentaria\"], color=\"turquoise\", lw=2.5, alpha=0.9, label=\"CBA Price\")\n",
+    "ax[0].fill_between(cba_df[\"Date\"], cba_df[\"Canasta Basica Alimentaria\"], alpha=0.2, color=\"turquoise\")  # Gradient effect\n",
+    "ax[0].set_title(\"📈 Canasta Básica Alimentaria (CBA) Price Trend (March 2016 - Present)\", \n",
+    "                fontsize=14, fontweight=\"bold\", color=\"turquoise\")\n",
+    "ax[0].set_ylabel(\"Price (ARS)\", fontsize=12, color=\"turquoise\")\n",
+    "ax[0].legend(loc=\"upper left\")\n",
+    "ax[0].grid(True, linestyle=\"--\", alpha=0.5)\n",
+    "\n",
+    "# Highlighting max and min points\n",
+    "max_idx = cba_df[\"Canasta Basica Alimentaria\"].idxmax()\n",
+    "min_idx = cba_df[\"Canasta Basica Alimentaria\"].idxmin()\n",
+    "ax[0].annotate(f\"Max: {cba_df['Canasta Basica Alimentaria'][max_idx]:,.0f} ARS\",\n",
+    "               xy=(cba_df[\"Date\"][max_idx], cba_df[\"Canasta Basica Alimentaria\"][max_idx]),\n",
+    "               xytext=(-50, 30), textcoords=\"offset points\",\n",
+    "               arrowprops=dict(arrowstyle=\"->\", color=\"black\"), fontsize=10, color=\"black\")\n",
+    "ax[0].annotate(f\"Min: {cba_df['Canasta Basica Alimentaria'][min_idx]:,.0f} ARS\",\n",
+    "               xy=(cba_df[\"Date\"][min_idx], cba_df[\"Canasta Basica Alimentaria\"][min_idx]),\n",
+    "               xytext=(-50, -30), textcoords=\"offset points\",\n",
+    "               arrowprops=dict(arrowstyle=\"->\", color=\"black\"), fontsize=10, color=\"black\")\n",
+    "# --- CBT Price Visualization ---\n",
+    "ax[1].plot(cba_df[\"Date\"], cba_df[\"Canasta Basica Total\"], color=\"royalblue\", lw=2.5, alpha=0.9, label=\"CBT Price\")\n",
+    "ax[1].fill_between(cba_df[\"Date\"], cba_df[\"Canasta Basica Total\"], alpha=0.2, color=\"royalblue\")  # Gradient effect\n",
+    "ax[1].set_title(\"📊 Canasta Básica Total (CBT) Price Trend (March 2016 - Present)\", \n",
+    "                fontsize=14, fontweight=\"bold\", color=\"navy\")\n",
+    "ax[1].set_xlabel(\"Year\", fontsize=12, color=\"black\")\n",
+    "ax[1].set_ylabel(\"Price (ARS)\", fontsize=12, color=\"navy\")\n",
+    "ax[1].legend(loc=\"upper left\")\n",
+    "ax[1].grid(True, linestyle=\"--\", alpha=0.5)\n",
+    "\n",
+    "# Highlighting max and min points\n",
+    "max_idx = cba_df[\"Canasta Basica Total\"].idxmax()\n",
+    "min_idx = cba_df[\"Canasta Basica Total\"].idxmin()\n",
+    "ax[1].annotate(f\"Max: {cba_df['Canasta Basica Total'][max_idx]:,.0f} ARS\",\n",
+    "               xy=(cba_df[\"Date\"][max_idx], cba_df[\"Canasta Basica Total\"][max_idx]),\n",
+    "               xytext=(-50, 30), textcoords=\"offset points\",\n",
+    "               arrowprops=dict(arrowstyle=\"->\", color=\"black\"), fontsize=10, color=\"black\")\n",
+    "ax[1].annotate(f\"Min: {cba_df['Canasta Basica Total'][min_idx]:,.0f} ARS\",\n",
+    "               xy=(cba_df[\"Date\"][min_idx], cba_df[\"Canasta Basica Total\"][min_idx]),\n",
+    "               xytext=(-50, -30), textcoords=\"offset points\",\n",
+    "               arrowprops=dict(arrowstyle=\"->\", color=\"black\"), fontsize=10, color=\"black\")\n",
+    "\n",
+    "# Adjust layout and show\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge Data\n",
+    "merged_df = pd.merge(cba_yearly_df, inflation_arg_df, on=\"Year\", how=\"inner\")\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[\"Inflation Rate\"].values\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to calculate linear regression coefficients\n",
+    "def linear_regression(X, y):\n",
+    "    n = len(X)\n",
+    "    mean_x, mean_y = np.mean(X), np.mean(y)\n",
+    "    \n",
+    "    # Calculate slope (m) and intercept (b)\n",
+    "    numerator = np.sum((X - mean_x) * (y - mean_y))\n",
+    "    denominator = np.sum((X - mean_x) ** 2)\n",
+    "    slope = numerator / denominator\n",
+    "    intercept = mean_y - slope * mean_x\n",
+    "    \n",
+    "    # Compute R-squared\n",
+    "    y_pred = slope * X + intercept\n",
+    "    ss_total = np.sum((y - mean_y) ** 2)\n",
+    "    ss_residual = np.sum((y - y_pred) ** 2)\n",
+    "    r_squared = 1 - (ss_residual / ss_total)\n",
+    "    \n",
+    "    return slope, intercept, r_squared, y_pred\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Perform Linear Regression\n",
+    "slope_cba, intercept_cba, r_squared_cba, y_pred_cba = linear_regression(X, y_cba)\n",
+    "slope_cbt, intercept_cbt, r_squared_cbt, y_pred_cbt = linear_regression(X, y_cbt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CBA Regression: Coeff: 542.5835715530903, Intercept: -2952.776089081708, R²: 0.387222922199141\n",
+      "CBT Regression: Coeff: 1191.6223071323811, Intercept: -4255.5547739709, R²: 0.3688196432010893\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print Regression Results\n",
+    "print(f\"CBA Regression: Coeff: {slope_cba}, Intercept: {intercept_cba}, R²: {r_squared_cba}\")\n",
+    "print(f\"CBT Regression: Coeff: {slope_cbt}, Intercept: {intercept_cbt}, R²: {r_squared_cbt}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jtK3VatO0Nt+ff/7JpEmT+PPPP7GxsaFMmTK4u7sDqV/jLzIyUk0w/u5FWVRUlFpma2ubIl749w6tdevW6t2/QoUKcezYMb766it1/9y5c1myZAl79+7l559/RqvVUqdOHSZOnEixYsVS9RlefOa/L0HyT+Hh4djb26tXX3fv3s2cOXN4/PgxBQoUoHz58il+H6n5zOHh4Xz55ZccPHgQjUZDiRIl1JOUF7+D9957D4PBwMaNG1m4cCHz5s3D3d2dYcOGqc+g/VN8fDyTJ09m165dJCcnU7RoUapXr46lpWWqfreenp5GV/Jr1qyJp6cnnTt3Ztu2bfTo0SNV71G9enWWLVvG999/z8qVK1myZAmurq706dPH6Cr4C0+fPqVAgQL/mgT+/vvvTJs2jdu3b2Nvb0+5cuWwt7c3arN/+u233wgNDWXHjh3qc8t/9+uvv6p3P1LjVbOd/vN3/XdPnz4lJCSESpUqvXJ/SEiImmC/6jvzX7+z1atXs3TpUiIiIihYsCCVKlXC1tZWTfBfeNX/q3+Xmu+jECL7kPMAOQ+Q84DUnQf8l8jISAD1osILlpaWODk5pehP/y613wVhWpJgi2wnJiaG3r17U65cOfbs2aPe2Tp8+DD79u1LdT358+cnMDAwRXlISAjAK69Ip4WPjw+FCxdm7969FC5cGEtLS5o3b67ud3R0ZMSIEYwYMYLbt29z6NAhFi1axKRJk1ixYkWq3qNChQoULFiQI0eO0KVLl1ceM3HiRE6cOMGRI0e4dOkSo0aNws/Pj169eqlX4L/55hvOnDmTps83fPhwbt26xerVq6lRowZWVlbExcWxbds2o+NatmxJy5YtiY6O5ujRoyxfvpwRI0ZQs2ZN9Wr4302dOpV9+/bx7bffUqdOHezs7IDns6GmV4UKFQC4e/dumt7D19cXX19f4uLiOHHiBGvXrmXatGlUq1bN6K4vPP99Pn36FIPBYNS5XrlyheTkZPLnz8+AAQNo0qQJS5cuVYcubtiwgd9///21sW/fvh13d3ejodcvDB48mM2bN6cpwU4rR0dHSpYsyaxZs165P7XDEl/lhx9+YMaMGQwbNoz27dvj7OwMwGeffabeBUmt1H4fhRA5n5wHvCTnAamTk88D/suLi9whISFGfXJSUtJrH2GA53fcTfVdEGkjs4iLbOf27ds8ffqUjz/+mLJly6p/xI4cOQK8vLL6b1cQ4fnQn4cPH6b4I7J7927y5cuHl5dXhuLUarW0bNmSQ4cO8fPPP9OkSRP17uHDhw9p0KCB+nxq6dKl6dOnD3Xq1FGfp0rte3Tv3p3ffvuNQ4cOpdh/+vRpfvnlF5o3b46NjQ3nzp3DYDAwePBg9Q+pXq/n2LFjQOqHmAGcOXOG5s2b4+Pjg5WVFZDydzBkyBB1aJyjoyPvvvsun376KXq9Xp2F85+/pzNnzlC7dm2aNm2qdniXLl0iPDw83bOHnj9/HoCSJUum+j2+/vpr2rdvj6Io2Nra0qhRI3Xo3uPHj1O8R82aNUlKSuLw4cNqmaIojBs3jsWLF3Pp0iUSEhLo16+f2qkCaqf6qivXoaGh/P7777Ro0UKdQfzv/9577z3++OMP7t+/n652SY1atWrx+PFjXFxcqFKlivrv+PHjrFixIk3D01/1u3Z0dKRv375qch0bG8uZM2fS/LtOzfdRCJE7yHmA8XvIecB/y6nnAanxYg3xH374waj8xfKeL0YV/LOdTfldEGkjd7BFtlOqVCkcHBxYsmQJlpaWWFpasm/fPnVZjRfPBul0OuD5pCL169fHw8PDqJ62bduyceNGBg4cyODBgylWrBi//PIL/v7+DBw4UH19RrRu3ZqVK1diYWHB4sWL1XJ3d3fc3NyYMmUKMTExFC9enEuXLnH48GH69eunHnf+/HmcnZ2N/hD/U/fu3Tl9+jSDBw+mQ4cONGzYEK1WS0BAAOvWraNs2bJqh/DiZOGrr76iXbt2REVFsX79eq5evQo8f47qVUOIX8XLy4sffviBSpUq4ebmxrlz51i6dCkajUb9Hfj4+PDll1/y9ddfU79+faKioli4cCElS5akfPnywPPf0+XLlzl16hReXl54eXmxd+9eNm3ahIeHB1evXmXx4sVG9f6bK1euqEtDKYrCrVu3mD9/Pq6urrRp00aN/b/e46233mL16tWMHj2a999/n6SkJFasWEGBAgXw8fFJ8b4NGzakevXqjBkzhs8++4wSJUrwww8/cP36db744gucnZ2xtLRk5syZ9OzZk8TERHbs2MFvv/2mtv0//e9//yM5Ofm1w+jatGnDxo0b2bp1K8OGDfvPtkmPtm3bsn79enr06MEnn3xCkSJFOHbsGMuXL8fPz498+fKlui6dTkdoaCiHDx+mQoUKeHl5sWnTJmbMmEGjRo0IDg5m5cqVhIaGqlfkUys130chRO4g5wHG5DzAWG46D0iNMmXK0KZNGxYuXEh8fDy1a9fmypUrLFy4kNq1a+Pr66u2M8CePXuoWrWqSb8LIm0kwRbZjqOjI4sWLeKbb77hs88+w97engoVKrB+/Xr69OlDQEAAjRs3pnbt2tSpU4fZs2dz/Phxli1bZlSPra0t69atY/bs2cyfP5+YmBhKly7N1KlTad++vUli9fT0pEKFCjx58oS6desa7Vu4cCFz5sxh3rx5REREUKRIEQYOHEjfvn3VYzp27EibNm2YMWPGa98jX758LFq0iC1btrBr1y727t1LYmIiRYsWpV+/fnTt2lV9vqd27dpMmDCB1atX8/PPP1OwYEFq167NwoULGTBgAGfOnHnt5B3/NGPGDCZPnszkyZOB51eFJ02axO7duwkICACgU6dOJCUlsXnzZjZu3IiNjQ1vvfUWI0aMUBOznj17Mm3aNHr16qV2ZElJSXz77bfq5+jfvz83b97kl19+Qa/X/+td07/Pqv3i+aPatWvz2WefqbPBpuY96tevz6xZs1i1ahUDBw5Eo9Hw5ptvsnbtWrWev7OwsGD58uXMnj2bBQsW8OzZM8qXL8+KFSvUyXxmz57NwoUL6d+/P/nz56datWqsW7eOrl27EhAQQLly5Yzq/N///kfZsmXVk5B/8vLyonTp0vj7+zNo0KBU/d7Sys7Ojg0bNjB79mxmzpxJdHS0+vxcz54901RX27ZtOXz4MAMGDGDw4MH06dOHBw8e4O/vz8aNGylcuDANGjSgc+fOfPHFF9y8eZMyZcqkqu7UfB+FELmDnAcYk/MAY7npPCC1pk6dSokSJfD392flypUUKlSIrl27MmDAAPXO9dtvv82uXbsYPXo07du3Z+LEiSb7Loi00SgyO4wQQgghhBBCCJFh8gy2EEIIIYQQQghhApJgCyGEEEIIIYQQJiAJthBCCCGEEEIIYQKSYAshhBDiX4WFhTFixAh8fHyoXr06ffv25ebNm+r+MWPGUK5cOaN/9evXV/cbDAbmz5+Pr68vVatWpWfPninWJ75y5Qp+fn5Uq1aNhg0bsnLlSqP9pqhDCCGEyGySYAshhBDiX/Xv35/79++zfPlytm/fjo2NDd27d1eXu7l27RqffPIJR48eVf/t3LlTff2iRYvYvHkzU6ZMYcuWLWg0Gvr06UNiYiIAERER9OjRg5IlS6qz5s+bNw9/f3+T1iGEEEJkNkmwhRBCCPFaERERFC1alMmTJ1OlShU8PDz49NNPCQkJ4caNG+j1em7evEmVKlVwdXVV/zk7OwOQmJjIqlWrGDRoEA0aNKB8+fLMnTuXJ0+ecODAAQC2bt2KlZUVEydOxMPDg3bt2tG9e3eWL19usjqEEEKIrCAJthBCCCFey8nJiTlz5lC2bFkAQkNDWblyJW5ubpQpU4a7d++SkJCAh4fHK19/9epVYmNj8fHxUct0Oh0VK1bk9OnTAAQEBODt7Y2lpaV6jI+PD3fu3CEsLMwkdQghhBBZwfK/DxGZRVEUDIa8sQy5VqvJM581q0nbZg5p18wjbWtMq9Wg0WjMHUaqfPHFF+qd4sWLF2NnZ8f169fRaDSsWbOGI0eOoNVqadCgAUOGDMHR0ZGgoCAAihQpYlRXoUKFePz4MQBBQUF4enqm2A/w6NEjk9Th4uKSrs8sfbUwBWnbzCHtmnmkbY2lpa+WBNuMDAaF8PBYc4eR6SwttTg52RMV9YzkZIO5w8lVpG0zh7Rr5pG2TcnZ2R4Li5yRYHfr1o2OHTuyadMmBgwYwMaNG7lx4wZarRZ3d3eWLFlCYGAgX3/9NdevX2fNmjXqc9pWVlZGdVlbWxMZGQlAfHz8K/cDJCQkmKSOjLCwyDsD/nLKdzEnkrbNHNKumUfaNn0kwRZCCCFEqpQpUwaAyZMnc/78edavX8+0adPo3r07Op0OAE9PT1xdXenYsSN//vknNjY2wPPnqF/8DM+TXltbWwBsbGzUycr+vh/Azs7OJHWkl8GgEBX1LN2vzyksLLTodLZERcWh18vFL1OSts0c0q6ZR9o2JZ3ONtUXWyXBFkIIIcRrhYWFcfz4cd59910sLCwA0Gq1eHh4EBwcjEajUZPrF14M1Q4KClKHdQcHB1O8eHH1mODgYMqXLw+Am5sbwcHBRnW82C5cuDDJyckZriMj8tJoC73ekKc+b1aSts0c0q6ZR9o2ffLOmCchhBBCpFlwcDDDhg3j1KlTallSUhKXL1/Gw8ODYcOG0atXL6PX/Pnnn8DzO97ly5fHwcGBkydPqvujoqK4fPkyNWvWBMDb25szZ86g1+vVY44fP06pUqVwcXExSR1CCCFEVpAEWwghhBCvVb58eerVq8ekSZMICAjg+vXrjBo1iqioKLp3707Lli35448/WLx4Mffu3ePw4cOMHTuWli1b4uHhgZWVFX5+fsyaNYtDhw5x9epVhg4dipubG82aNQOgXbt2xMTEMG7cOG7evMmOHTtYs2YN/fr1AzBJHUIIIURW0CiKItPDmYleb8hTk5xFRMTKMBMTk7bNHNKumUfaNqXnk5xl7+vd0dHRzJ49m4MHDxIdHU3NmjUZPXq0unTXvn37WLJkCbdv38bR0ZFWrVoxZMgQdZIxvV7PnDlz2LFjB/Hx8Xh7ezNhwgSKFi2qvsfFixeZOnUqly9fxtXVlZ49e+Ln56fuN0Ud6SF9tcgoadvMIe2aeaRtU0pLXy0JthlJpy0yStr23xkMBvT65DS/zsJCQ/78dkRGPkOvlz+RppTX2tbCwhKt9t875JyQYOdl0leLjJK2/XfSV2c/ea1tTd1XyyRnQohcR1EUoqLCiYuLSXcdoaFaDAY5EcoMea1tbW0d0Omcc8xa10IIkRWkr87e8lrbmrKvlgRbCJHrvOiwHRycsLKyTtcfSwsLTZ64amsOeaVtFUUhMTGBmJgIAPLnl4m2hBDiBemrs7e80raZ0VdLgi2EyFUMBr3aYTs46P77Ba9haamVoXyZJC+1rZXV82eQY2IicHR0+s8haEIIkRdIX5395aW2NXVfLT29ECJXebFEz4s/lkKY24vvYnqeMRRCiNxI+mqR3Ziyr5YEWwiRK8nzriK7kO+iEEK8mvx9FNmFKb+LMkRc5EgGg4HbNx4SHRWLo86e0mXdZeilEEIIkY1IXy2EyIskwRY5zsWzN9i24QCBtx6RmJiMlZUlJTzeoEOXZnjVKGvu8ITIFLGxMbz/fnPs7OzZseNH8uXLl+rX3r59i6Cgx9SpU89k8bRv34p3321Jr179Xrm/Xr2aRttWVla4uhaiceNm9OrVG0vL1A8LTE5Oxt9/Cx07dslQzEKIrCN9tciLpK+WvhpkiLjIYS6evcH8rzdx/a9A7HV2FHEviL3OjuuXA5n/9SYunr1h7hCFyBQHD+7HycmZ2NhYDh/+JU2vHTVqKFeu/JVJkb3e4MHD2LXrZ3bt+pm1a7fQq9cn/PTTDwwfPoTk5NQ/43TgwM8sWDA3EyMVQpiS9NUir5K+WvpqkDvYIgcxGAxs23CA6MhY3EsUUp+VsLOzwba4NQ/vBbN9w0EqV/OQIWjCZLLLEMcff9xN7dpvERISzM6d/jRt2jzVr1UU8yyz4eDggItLQXW7aNFiFC9egj59Pubnn3+kZcsPUlWPueIXQqSd9NXCHKSvTj/pq01P/rKJHOP2jYcE3nqEc6H8KSYi0Gg0OBfMz91bD7l946GZIhS5zcWzN/hyxBK+GrWUr7/8nq9GLeXLEUuy/O7L3bt3uHz5Et7etWnUqCnnz5/l7t07Rsds376Zjz5qS+PGdfHz68C+fT8Bz4eHBQU9ZvXq5Qwc2Bd4PiTsp59+MHp9+/atWLlyqbr944+76dGjM40b16Vp03oMHNiXa9euZvizlC9fgapVq3HgwD617OLF8wwZ8inNmzegUaO36Nr1Qw4c+BmAn376gWnTJqlxnz0bgKIobNy4li5d2tOo0Vs0b96AkSOH8OiR/L8vhLlJXy2ymvTV0ldnN5JgixwjOiqWxMRkbKytXrnf2saKxMRkoqNiszgykRtdOHM92wxx/PHH3dja2uLjUxdf34ZYWVmxc6e/un/jxnUsXryAjz7qyrp1W2jb9kOmTZvE6dMnWL58LYUKFaZTJz+mTZuZqvc7fPhXZs2azocfdmbjxu3Mm7eEhIQEZsz4yiSfx8OjDDdvXgMgJCSYoUMHUKaMJytXrmf16o1UqFCJ6dO/Ijw8jCZNmjF48DAAdu36mSpVqrJ160bWrl1N//6D2bRpB9Onz+bevUAZmiZENiB9tchK0ldLX50dSYItcgxHnT1WVpbEJyS+cn9CfCJWVpY46uyzODKR2xgMBraueznE0c7OBq2FFjs7G9yLFyI6KpbtGw5iMBgyPZbk5GT27/+JOnV8sbGxwcHBAR+fuvz884/Ex8cDsHXrRjp0+Ij332+Du3tR2rbtQL9+A9HrDTg5OaHVarG1tUWny5+q98yfPz+jRo3n3Xdb4uZWhEqVKtOqVWtu3bppks/k4OBITEwMAImJifTs2ZdPPx1M0aLFKFmyFF279iAxMZH79+9hbf38MwO4uBQkX758uLsXY/z4idSrVx83tyLUqFGTRo2acuuWPNcphLlJXy2yivTV0ldnV/IMtsgxSpd1p4THG1y/HIhtcWujoWeKohAeGkm5iiUpXdbdjFGK3OD2jYfcuZm6IY5lyhXL1FhOnPiDsLAwmjR5Wy1r0uRtjhz5lYMH91GvXgNCQ0OoVKmy0es6d+6a7vesVq0Gd+/e4fvvV/DgwX3u3Qvk5s3rJjtJiY2NUTtid/eitGjxAf7+W7l79zb379/j+vXnV8z1ev0rX1+vXn3++usSK1cu5f79ewQG3uH27Vu4uhYySXxCiPSTvlpkFemrpa/OruQOtsgxtFotHbo0w1Fnz8N7wTyLjUevN/AsNp6H94Jx1NnTvktTmTRFZNjzIY5J2WKI448/Pn/+6osvRtGgQW0aNKjNV1+NB2DXLn/y5Xt+nfSfJxf/5Z+Tkfx9ptCDB/fRrVsnHjy4T4UKlfj008EMHDg0Ix/DyNWrV/D0LA9AYOBdPvqoLUePHuGNN9z56CM/5s5d+K+v37BhDYMG9SMiIoLq1d9k2LAxfPRR+k9ShBCmI321yCrSV0tfnV3JHWyRo3jVKMvgUR+pa2tGhEVhZWVJuYolad+lqaytKUzi+RDHfMQnJGJnZ5Nif1YNcYyIiOD48aO8914rOnUyXldy69ZN7NmziwcPHlCwoCtXrlymXr0G6v7x40dSsKArQ4aMSNGhW1paEhsbo27HxsYQERGubq9bt5pWrVozfPgYtez33w8Dzzv7tJ4g/N3Vq1e4dOlPxo2bCMD//rcNZ2dn5s1bpB5z9OgRo9f88/3WrFlFz5598PPrrpZt2rRWZjAVIpuQvlpkBemrpa/OriTBFjmOV42yVK7mkS2WYxC5U+my7pQq8wZX/7pr1iGO+/b9iF6vp0uXbpQoUdJo38cf92Tv3j3s3LkdP79uLF26iGLFilOlSlVOnDjG778fZu7c7wCwtbXlwYP7hIeH4ezsQpUqVdm1awdVq9YgXz5Lli9fjKXly+6gUKHC/PnnBa5du4qDgwNHjx5mx46twPPnsKytrVMVf0xMDGFhoQDExcVx+fJfLFmygDffrMnbb7+rvldw8BOOH/+DUqVKc+3aFb79dpb6Xi/ih+cdfqlSpShUqDCnT5+kbt36WFho+fnnnzh8+FecnV3S2dJCCFOTvlpkNumrpa/OruSvnMiRtFotZcoVo7p3ecqUKyYdtjAprVbLh13NP8Txp59+oGbNWik6bIA33nCnfv1GHDy4j3feaUmPHr1ZuXIpXbt+yO7dO/jqq+nUqFETgPbtO3Hs2O8MHToQgOHDx+DiUpD+/XsyfPhnVK/+JpUrV1XrHjp0JE5Ozgwc2Je+fbtx7NhRxo9/vvzG5cuXUh3//Pmz+eCDd/jgg3fo1cuPjRvX0q7dh8yZswALCws1tsaNmzF58gS6du3ImjUr6dv3U4oUeUN9rxo1vKlYsTL9+/fkjz+O8sUXXxEfH0/v3l0ZMKAvt2/fZPjwMUREhPP48aN0tbUQwvSkrxaZSfpq6auzK42SF+7TZ1N6vYHw8Ny/TIWlpRYnJ3siImJJTs78mRzzEmnblJKSEgkLe4yLSxHy5Xv1c1mpYWmp5eypa+oQx8TEZKysLCnp4S5DHDPI0lKbp76v//WddHa2x8JCEo/sSvpqkVHStilJX539SV9tLC19tQwRF0KI15AhjkIIIUT2Jn21yG4kwRZCiH/xYoijEEIIIbIn6atFdiKXdoQQQgghhBBCCBOQBFsIIYQQQgghhDCBbJVgL1q0iK5dX78A+fjx42ncuLFRmcFgYP78+fj6+lK1alV69uxJYGCg0TFXrlzBz8+PatWq0bBhQ1auXGnyOoQQQgghhBBC5G3ZJsH+/vvvmT9//mv3Hzx4kG3btqUoX7RoEZs3b2bKlCls2bIFjUZDnz591DXZIiIi6NGjByVLlsTf359BgwYxb948/P39TVqHEEIIIYQQQoi8zewJ9pMnT+jduzfz5s2jVKlSrzwmODiYL774glq1ahmVJyYmsmrVKgYNGkSDBg0oX748c+fO5cmTJxw4cACArVu3YmVlxcSJE/Hw8KBdu3Z0796d5cuXm6wOIYQQQgghhBDC7An2X3/9Rf78+dm9ezdVq1ZNsV9RFEaPHs0HH3yQIsG+evUqsbGx+Pj4qGU6nY6KFSty+vRpAAICAvD29sbS8uWE6T4+Pty5c4ewsDCT1CGEEEIIIYQQQph9ma7GjRuneK76777//ntCQkJYsmQJS5cuNdoXFBQEQJEiRYzKCxUqxOPHj9VjPD09U+wHePTokUnqcHFx+e8P+hqWlma/xpHpXizKntrF2UXqSdumZDBoMlyHRvPyv4qS4erE3+TltrWw0OSJv/lCCCFEXmb2BPvfXL16lYULF7JhwwasrKxS7I+LiwNIsc/a2prIyEgA4uPjX7kfICEhwSR1pJdWq8HJyT7dr89pdDpbc4eQa0nbvhQfb0FoqNYkyYxcuMg8ealtDQYNWq2W/PntsLGxMXc4QgghhMhE2TbBTkhIYPjw4fTv35/y5cu/8pgXJyqJiYlGJy0JCQnY2tqqx7yYrOzv+wHs7OxMUkd6GQwKUVHP0v36nMLCQotOZ0tUVBx6vcHc4eQq0rYpJSYmYDAY0OsVkpPT1yYazfO21esNZr3LOnBgX86fP2tUli9fPlxcCuLr25BPPhmAtXXOStj+2bYDB/alSJE3GDduYqa9Z716NRk79kvee6/VK/dndgx6vYLBYCAy8hlxcfoU+3U62zx1wUEIIXKT3NhX/1Ne6KtNKdsm2BcuXODGjRssXLiQ7777DoCkpCSSk5OpXr06kyZNomTJksDzSdCKFy+uvjY4OFhNyt3c3AgODjaq+8V24cKFSU5OznAdGZHeBCAn0usNeerzZiVp25f0+oxnxC+S6uwwhLlx42Z89tkwdTsuLo5Tp04wf/5s9PpkPv98lBmjS7t/tu20aTPRai3MF1AWxpCRiz5CCCGyr9zWV/9TXuqrTSHbJtheXl7s37/fqGzdunXs37+fdevW4eLigpWVFQ4ODpw8eVJNjqOiorh8+TJ+fn4AeHt7s3nzZvR6PRYWz38px48fp1SpUri4uODo6JjhOoQQIrNYW1vj4lLQqKxo0WJcvXqZgwf35/hOW6fLb+4QskUMQgghci7pq/NGDKmVbRNsGxsbSpQoYVSWP39+LC0tjcr9/PyYNWsWzs7OuLu7M3PmTNzc3GjWrBkA7dq1Y8WKFYwbN47evXtz8eJF1qxZw6RJk4Dnz15ntA4hRA6gKPAsDY9kWGrBVHcb7exezu5lIlZW1mi1L4cVJyUlsXz5Yvbv30tsbAylSnnQu/cn1Kr1coWEU6dOsGTJQu7evcMbb7jTqVMXZsyYzLZtuylS5A3at29F/foNOXXqBOHh4UyZ8jXVq7/Jxo1r2blzB+HhoRQrVoLOnbvy9tvvqvVu3LiOnTu3ExISTMGCrrRo8T7duvVCo9EQHx/Pt9/O5Nixo8TERFOiREl69uyDr28jIOWQr0uXLrJs2SKuXbuCpaUl9eo1YMCAIeh0OgDat29F69btuHLlL06dOoGVlRXNm7/Hp59+ZrTSQ1r8PYaffvqBVauW0atXP77/fgXBwU/w8CjLkCHDqVzZK9VtLYQQIh2kr5a++jVyUl+dbRPs1Bo8eDDJycmMHz+e+Ph4vL29WblypTopmYuLCytWrGDq1Km0adMGV1dXRo4cSZs2bUxahxAiG1MUCrR8m3ynT5rl7ZNq+fD0h30m6biTk5M5deoE+/b9xPvvv/wbNHXqRO7cuc2ECZNxdS3EH38cYeTIIUybNos6depx48Y1Roz4jI4dOzNx4hRu3LjOrFkzUtS/c6c/X389F0dHR0qXLsOyZYvYv38vn38+ipIlS3H+/FlmzZpBTEwMbdt24OjRI6xdu4rJk6dTrFhJ/vrrIlOmfEmRIm/QvPl7LF++mFu3bjBz5jwcHR354YedjB8/hs2bd1CkyBtG73358iUGDepHq1atGTp0JBER4cyd+w2ffz6QZcu+V09SVq1aRv/+g/nkk0GcOnWcb7+dhadned55p0WG2xcgNDSEnTv9+eKLyeTLl49Zs6YzZcqXbNq0A41G859tLYQQIh2kr5a+Og2yc1+drRLsGTNSfoH+btCgQQwaNMiozMLCghEjRjBixIjXvs7Ly4stW7a8dr8p6hBCZHMmviqdVfbv38tvvx1StxMSEihcuAidO3ela9ceADx4cJ+DB/exYsVaypevCECnTn7cvHmDjRvXUqdOPbZs2Uj58hX59NPPAChevCQREeF8++0so/fz8amLt3dt4PkzZFu2bOSLLyZRt64vAO7uRQkKeszGjWtp27YDjx49wNraiiJF3HFzc8PNzY2CBQtRuLAbAI8ePcDOzgF396I4ODjQu/cn1KhRA0dHXYrPunnzBjw8yqpD6UqVKs3EidPo1q0TJ08e56236gJQu/ZbdOjQCYBixYqzZ88uLl48b7JOOzk5meHDR1O2bDkAPv64B2PGDCcsLIz4+Lj/bGshhBDpJH219NWplJ376myVYAshRKbQaJ5flU7DsDNLS63pJqTKwLCzevXq07//YAwGA5cvX2LBgjnUrFmLrl17qMOsrl+/BsCgQf2MXpucnIyDg+P/H3NV7YxfqFq1eor3K1q0mPrz3bu3SUxMYMqUL5k27eUjMXq9nsTERBIS4nn77ff48cfddOrUBg+Psnh716ZBg0a4uT3vtLt06caoUUNp1aoZlSpVoVYtH5o3fwcHB4cU73379k28vY2Hbnl4lMHBwZFbt26onXaJEqWMjrG3d1AnrDSVv7+Hvf3zWJOTk1LV1kIIIdJB+mrpq9Mou/bVkmALIfIGjQbs07DuvCmf68oAOzt7tSMtXrwEhQoVZsiQT7GwsGD48NEAKMrzOL/7bjl2dsaf8cVQLQsLCwyG/54W3draWv35xfFffTWDEiVKpjg2Xz4rrK1tWL16I5cuXeT06ZOcPHmczZvX06tXP3r06EPlyl7s2PEjp0+fJCDgFHv27GLlymXMnj2fmjVrGdWnKMorz20UxWD0zFa+fPlecYxpp3x/8YjQP98jNW0thBAinaSvlr46DbJrXy1nA0IIkYPUqFGTTp26sHPndk6cOAZAqVIeAISGhlK0aDH1348/7ubHH3cDUKaMJ3/99adRXX/9delf36tEiZJYWFgQFPTYqN7jx/9g06Z1aLVa9u37if/9bzteXtXo1asfy5Z9T6tWrTl06PkqECtXLuXixfPUq9eAIUNGsGnTDooWLcpvv/2S4v1Kly7DhQvnjcpu3LhObGwsJUuWTld7mVpq2loIIUTeJn21eZm7r5YEWwghcphevT6haNHizJw5jWfPnlG6tAd16vgya9Z0jh49zMOHD9i4cR3r13/PG2+4A/DRR35cu3aFxYsXcO9eIEeO/MaKFUsA0LxmSJyDgwOtW7dj+fLF/Pzzjzx8+IC9e/ewePF8nJ2fL1GYkJDAd9/N4+eff+Tx40dcuHCOc+fOqLN4Pnhwn5kzp3PmzGmCgh7z668HCQp6TJUqXiner2PHzty8eZ05c77m7t07nDt3hq++Go+nZ7kUV9DT6tatm5w4cczo33+dtLxKatpaCCGEkL467XJLXy1DxIUQIoextrZm1KhxDB78CcuWLWLIkOF89dV0li37jpkzpxMdHcUbb7gzcuQ4WrR4H3h+xXnq1JksXbqQrVs3Urx4Cdq27cCqVcuwtEw5jOuFQYM+x8nJmZUrlxIaGoKrayF69OiDn193AN5/vw3R0VHqMhmOjo40bNiE/v0HAzB8+GgWLpzHV199QVRUJG5uRfj008E0b/5eiveqXNmLWbPmsXz5Enr27IKdnT2+vg3p339gupf1eGHLlg1s2bLBqKxKlaosXrwyzXX9V1sLIYQQ0lenXW7pqzWKqQfDi1TT6w2Eh8eaO4xMZ2mpxcnJnoiIWNNNRCEAadtXSUpKJCzsMS4uRciXL+WzOall0olTsoErV/7CwsICT8/yatn+/T8zY8ZX7N9/JMOdYlrktrb9L//1nXR2tsfCQgaUZVfSV4uMkrZNSfrqV5O+2nxM2VdLjy6EEHnAjRvXGTz4E44ePUxQUBBnzpxm1aqlNGnydpZ22EIIIYR4Nemrcwf5TQkhRB7QqlVrwsJCmTdvDqGhwTg5OdO06dv06tXvv18shBBCiEwnfXXuIAm2EELkARqNhh49+tCjRx9zhyKEEEKIV5C+OneQIeJCCCGE+FdhYWGMGDECHx8fqlevTt++fbl586a6/8qVK/j5+VGtWjUaNmzIypXGE9IYDAbmz5+Pr68vVatWpWfPngQGBhodkxV1CCGEEJlNEmwhhBBC/Kv+/ftz//59li9fzvbt27GxsaF79+7ExcURERFBjx49KFmyJP7+/gwaNIh58+bh7++vvn7RokVs3ryZKVOmsGXLFjQaDX369CExMREgy+oQQgghMpsMERdC5EqyQILILnL6dzEiIoKiRYvSv39/ypYtC8Cnn37KBx98wI0bNzh+/DhWVlZMnDgRS0tLPDw8CAwMZPny5bRr147ExERWrVrFiBEjaNCgAQBz587F19eXAwcO0KJFC7Zu3ZrpdQghsp+c/vdR5B6m/C7KHWwhRK5iYWEBQGJigpkjEeK5F99FC4uceU3bycmJOXPmqMl1aGgoK1euxM3NjTJlyhAQEIC3t7fRDLc+Pj7cuXOHsLAwrl69SmxsLD4+Pup+nU5HxYoVOX36NECW1CGEyD6krxbZjSn76pzZ2wshxGtotRbY2joQExMBgJWVNRqNJs31GAwa9Hq5sp4Z8krbKopCYmICMTER2No6oNXm/GvaX3zxhXqnePHixdjZ2REUFISnp6fRcYUKFQLg0aNHBAUFAVCkSJEUxzx+/BggS+pwcXFJ34fm+Xqwud2L9V1lTXbTk7Z9FS329o7ExDwF0tdXazTP+xODQUFuhJtWXmrbl331U+ztHbGykgRbCCFS0OmcAdQkOz20Wi0Gg8FUIYm/yWtta2vroH4nc7pu3brRsWNHNm3axIABA9i4cSPx8fFYWVkZHWdtbQ1AQkICcXFxAK88JjIyEiBL6kgvrVaDk5N9ul+f0+h0tuYOIdeStjVWoIAdjx8/5unTpzx7Zu5oRF6m0YCLixNFihRJ102Zf5IEWwiR62g0GvLnd8HR0Qm9PjnNr7ew0JA/vx2Rkc/yxJ3WrJTX2tbCwjJX3Ll+oUyZMgBMnjyZ8+fPs379emxsbNSJxl54kdDa2dlhY2MDQGJiovrzi2NsbZ8nHFlRR3oZDApRUbn/7N/CQotOZ0tUVBx6fd65AJYVpG1fz9Y2P9bWDiQn64G09QkWFlocHGyIiYmXdjWxvNW2GiwtLdBqLXj69PV/63U621SPQpEEWwiRa2m1WrRaq/8+8B8sLbXY2NgQF6cnOTm3dyxZS9o25wkLC+P48eO8++676nOTWq0WDw8PgoODcXNzIzg42Og1L7YLFy5McnKyWla8eHGjY8qXLw+QJXVkRF76rur1hjz1ebOStO3raNBq056SWFi87E8URdrVlPJa2xoMmHRkXe65rC6EEEIIkwsODmbYsGGcOnVKLUtKSuLy5ct4eHjg7e3NmTNn0Ov16v7jx49TqlQpXFxcKF++PA4ODpw8eVLdHxUVxeXLl6lZsyZAltQhhBBCZAVJsIUQQgjxWuXLl6devXpMmjSJgIAArl+/zqhRo4iKiqJ79+60a9eOmJgYxo0bx82bN9mxYwdr1qyhX79+wPPnpv38/Jg1axaHDh3i6tWrDB06FDc3N5o1awaQJXUIIYQQWUGjyAJ0ZqPXGwgPjzV3GJnO0lKLk5M9ERGxMjTKxKRtM4e0a+aRtk3J2dk+288uHB0dzezZszl48CDR0dHUrFmT0aNHq0t3Xbx4kalTp3L58mVcXV3p2bMnfn5+6uv1ej1z5sxhx44dxMfH4+3tzYQJEyhatKh6TFbUkR7SV4uMkrbNHNKumUfaNqW09NWSYJuRdNoio6RtM4e0a+aRtk0pJyTYeZn01SKjpG0zh7Rr5pG2TSktfbVMciaEEEKkkcFg4PaNh0RHxeKos6d0WfdcNVu4EEIIIdJHEmwhhBAiDS6evcG2DQcIvPWIxMRkrKwsKeHxBh26NMOrRllzhyeEEEIIM5LL7UIIIUQqXTx7g/lfb+L6X4HY6+wo4l4Qe50d1y8HMv/rTVw8e8PcIQohhBDCjCTBFkIIIVLBYDCwbcMBoiNjcS9RCDs7G7QWWuzsbHAvXojoqFi2bzho0rU0hRBCCJGzSIIthBBCpMLtGw8JvPUI50L50Wg0Rvs0Gg3OBfNz99ZDbt94aKYIhRBCCGFukmALIYQQqRAdFUtiYjI21lav3G9tY0ViYjLRUbl/xmkhhBBCvJok2EIIIUQqOOrssbKyJD4h8ZX7E+ITsbKyxFFnn8WRCSGEECK7kARbCCGESIXSZd0p4fEG4SGRKIpitE9RFMJDIynp4U7psu5milAIIYQQ5iYJthBCCJEKWq2WDl2a4aiz5+G9YJ7FxqPXG3gWG8/De8E46uxp36WprIcthBBCZAMGg4Gb1+5z7vRVbl67n2WTkKZ7Hexnz57x+PFjYmJicHJyonDhwlhbW5syNiGEECJb8apRlsGjPlLXwY4Ii8LKypJyFUvSvktTWQdbCCGEyAYunr2h9tWJiclYWVlSwuMNOnRplul9dZoS7MTERPz9/dm9ezd//vkner1e3WdhYUHNmjV59913adOmDVZWr54ERgghhMjJvGqUpXI1D27feEh0VCyOOntKl3WXO9dCCCFENnDx7A3mf72J6MhYnAvlx8XaiviERK5fDmT+15sYPOqjTE2yU51g/+9//2P27NnEx8fTqFEj3n33Xdzd3bGzsyMyMpKgoCDOnDnDnDlzWLhwIYMHD6ZDhw6ZFrgQQghhLlqtljLlipk7DCGEEEL8jcFgYNuGA0RHxuJeopC6rKadnQ22xa15eC+Y7RsOUrmaR6ZdGE9Vgv3JJ5/w5MkTxo0bR5MmTV57d7p79+4kJiby448/snr1avbv38/y5ctNGrAQQgghhBBCCPFPt288JPDWI5wL5VeT6xc0Gg3OBfNz99ZDbt94mGkXylOVYDdp0oT27dunCPJVrKysaNOmDR988AHbtm3LcIBCCCGEEEIIIcR/iY6KJTExGRfrV98QtraxIiIsiuio2EyLIVX3xTt06JCq5NqoYq2Wjh07pisoIYQQQgghhBAiLRx19lhZWRKfkPjK/QnxiVhZWeKos8+0GDI88PzmzZv8/PPP3Lp1yxTxCCGEEEIIIYQQaVa6rDslPN4gPCQSRVGM9imKQnhoJCU93Cld1j3TYkh1gn3w4EFatWrFunXr1LKvv/6aVq1aMWTIEFq2bMlXX32VoWAWLVpE165djcp++eUX2rVrR/Xq1WncuDFff/018fHx6n6DwcD8+fPx9fWlatWq9OzZk8DAQKM6rly5gp+fH9WqVaNhw4asXLnSaL8p6hBCCJG3WO/0x3b+HIiJMXcoQgghhOD5KOoOXZrhqLPn4b1gnsXGo9cbeBYbz8N7wTjq7GnfpWmmrvyRqppPnjzJoEGDsLKyokyZMgD88ccfrF69mpo1a7Jz505mz57Nzp078ff3T1cg33//PfPnzzcqCwgIYODAgTRv3pydO3cyceJE9u7dy6RJk9RjFi1axObNm5kyZQpbtmxBo9HQp08fEhOfDwuIiIigR48elCxZEn9/fwYNGsS8efOM4jRFHUIIIfIIRcGpbk10fXvgMGUiVsePmjsiIYQQQvw/rxplGTzqIzwrliA2+hlBD0OJjX5GuYolM32JLkjlJGerV6+mXr16LF26VM32N2/ejEajYdq0aRQrVozy5ctz48YNtm7dSrt27VIdwIvZyc+cOUOpUqWM9m3evBkfHx/69u0LQIkSJRg6dChjx45Vk+xVq1YxYsQIGjRoAMDcuXPx9fXlwIEDtGjRgq1bt2JlZcXEiROxtLTEw8ODwMBAli9fTrt27UhMTMxwHUIIIfIGTWgoBSuWNipLbNDYTNEIIYQQ4lW8apSlcjUPbt94SHRULI46e0qXdc/UO9cvpOodLly4QIcOHdSADAYDx48fp0yZMhQr9nJ681q1anHjxo00BfDXX3+RP39+du/eTdWqVY329ezZk5EjR6Z4TXJyMjExMVy9epXY2Fh8fHzUfTqdjooVK3L69Gng+V1wb29vLC1fXkvw8fHhzp07hIWFmaQOIYQQuV++Xw4YJdf6Im8QEvQUXrN0pRBCCCHMR6vVUqZcMap7l6dMuWJZklxDKhPs6OhonJ2d1e1r164RExND7dq1jSvTajEYDGkKoHHjxsyePdsoUX+hYsWKlC9fXt1OTExk9erVVKpUCWdnZ4KCggAoUqSI0esKFSrE48ePAQgKCsLNzS3FfoBHjx6ZpA4hhBC5m8PngyjQ6eWIpdhR4wi/cBWyqLMWQgghRM6QqiHiBQsWNEokjx8/jkajMbrrC88nAnN1dTVthP8vOTmZkSNHcvPmTTZs2ABAXFwc8Hzt7b+ztrYmMjISgPj4+FfuB0hISDBJHRlhaZn7T84sLLRG/xWmI22bOaRdM0+Oa9vERJzcnI2Kog4dQV+9Ruo6UCGEEELkKak6P6hbty5r1qyhSZMmGAwGtmzZgoODA76+vuoxT58+Ze3atdSpU8fkQcbExDBkyBBOnjzJ/Pnz1aHkNjY2wPM72y9+hudJr62trXrMi8nK/r4fwM7OziR1pJdWq8HJKfPWYMtudDpbc4eQa0nbZg5p18yTI9r26lWoUMG4LCYGnX3e+bsthBBCiLRJVYI9YMAAPvzwQ+rWrYtGoyEuLo4vv/xSvYu7cOFC/P39iYqKol+/fiYNMDg4mD59+vDgwQOWL19udNf8xbDu4OBgihcvbvSaF0PL3dzcCA4OTlEnQOHChUlOTs5wHellMChERT1L9+tzCgsLLTqdLVFRcej1aXuEQPw7advMIe2aeXJK21qvWIrdyGHqduI77xG7cSskAomxJn0vnc4259zRF0IIIcS/SlWC/cYbb7Bz5062bNlCWFgYDRs2pH79+ur+HTt24ObmxsKFC1/5LHV6RUZG0q1bN2JiYti4cSPlypUz2l++fHkcHBw4efKkmhxHRUVx+fJl/Pz8APD29mbz5s3o9XosLCyA50PcS5UqhYuLC46OjhmuIyOSk7PvCaap6fWGPPV5s5K0beaQds082bZtFYUCbzck34VzalHU0lUktGkP2TFeIYQQQmQrqX6ErGDBggwYMOCV+w4ePJgps7JNnz6d+/fvs2LFCpydnQkJCVH3OTs7Y2VlhZ+fH7NmzcLZ2Rl3d3dmzpyJm5sbzZo1A6Bdu3asWLGCcePG0bt3by5evMiaNWvUZb5MUYcQQoicT/M0goKeJYzKws7+haGo6S4cCyGEECKLGAwUaNEUxcGR6HmLMLzhniVva5I5Wl4k1zExMSxYsIAxY8ZkuE6DwcBPP/1EUlIS3bp1S7H/0KFDFC1alMGDB5OcnMz48eOJj4/H29ublStXqpOSubi4sGLFCqZOnUqbNm1wdXVl5MiRtGnTRq3LFHUIIYTIufL9fpgC7Vqp2wZdfsKu3YX/H7UkhBBCiJxDG3gXF28vddvi9q0sS7A1iqIoqTlw69atbN++HYDWrVvTuXNno/07d+5k1qxZhIWFceXKFdNHmgvp9QbCw037LF92ZGmpxcnJnoiI2Ow5JDQHk7bNHNKumSc7tq392BHYrViqbj/7bBix477Msvd3draXZ7CzMemrRUZJ22YOadfMk9Pb1mblMhzHDFe3k8tXIOLwCdBo0l1nWvrqVN3BXrduHVOnTqVIkSLY2NgwefJkLCws6NixI3fv3mXs2LGcO3cOnU7H+PHj0x24EEIIkWWSkyno7oLmb9eZI348QLJ3bTMGJYQQQoh0URSca1XFIvCuWhQ9ZwHxfilHQ2emVCXY/v7++Pr6snjxYiwtLfnmm29YvXo1np6e9OnTh2fPntGxY0eGDBlCgQIFMjlkIYQQImO0d27jUruaUVno7YcoDo7mCUgIIYQQ6aa9fw+XNysblZlrHpVU3ee+d+8eHTt2xNLyeT7etWtX7t69y5AhQyhSpAhbt25l4sSJklwLIYTI9mw2rjNKrhN9GxASHCXJtRBCCJED2axZZZRcJ5cpS8iTSLNNUpqqO9hxcXG4urqq2y+WpipevDjLly/HxsYmc6ITQgghTCj/B+9idfwPdTtq/mISOnUxY0RCCCGESBdFwanOm1jeuqkWRX8zl/juvcwYVCoTbEVR0PztofAXa0H369dPkmshhBDZniY6ioIeRY3Kwk5dwFCylJkiEkIIIUR6aR/cx6VGJaOysIA/MRQv8ZpXZJ0MLdPl5ORkqjiEEEKITGF54jhO7zdXt5V8+QgNfAKWJlmpMtuKi4sjICCABw8eEB0djZOTE+7u7tSsWVNdhlIIIYTIaWw2rMVx6EB1W1+iJOEnz4M2e6zIkaGzC00GpjoXQgghMpv95C+xWzBX3X7Wtz+xU742Y0SZ78qVKyxbtoyDBw+SlJSUYr+trS2NGjWib9++lC9f3gwRCiGEEOmgKDg18MHy6ssloaOnzyK+V18zBpVSqhPsAQMGpLji/cknn5AvXz6jMo1Gw8GDB00TnRBCCJEeej0uZYqhjY1Ri57+70eS6vqaMajMFRMTw5QpU/jhhx/w8fFhwoQJVKlShaJFi2Jra0tkZCRBQUEEBATw+++/065dO959912++OIL8ufPb+7whRBCiNfSPnqIS7UKRmXZ9VGvVCXYbdq0yew4hBBCCJN41VIdoTfuoeQvYJ6AssgHH3xA48aN+e2334wmJn3B2dkZZ2dnKlasyMcff8yDBw9YsWIFbdq04ZdffjFDxEIIIcR/s968Ad3g/uq2vmgxwgP+zDZDwv8pVQn29OnTU11hcnJyuoMRQgghMsJ6+xZ0n/ZRt5Pe9Obp3kNmjCjrLFu2DA8Pj1QfX7RoUSZOnMitW7cyMSohhBAinRSFAk3rk+/PC2pRzJQZxPX91IxB/TeTzfDy8OFDtm7dir+/P0ePHjVVtUIIIUSq5O/UFqtfXj6iFD3zW+K79TRjRFkrLcm1KV4nhBBCZBZt0GNcvMoZlYWdOIehdPbvszKUYCuKwq+//srmzZv5448/0Ov1lC5d2lSxCSGEEP8tJgbX0m8YFYUfO4O+TFkzBWReYWFhaDQanJ2dAUhMTGTbtm3cunWLcuXK0aZNG5lFXAghRLZlvXUTuoH91G29WxHCz1/JtkPC/yldCXZwcDDbtm1j+/btBAUFodPp6NixI61bt8bLy8vUMQohhBCvZHk2AKd3GhuVhTwMg39MwJkXKIrC9OnT2bhxI0OHDqVXr17o9Xq6d+/OuXPn0Ol0bN26la1bt7J+/XpsbW3NHbIQQgjxkqJQ4J1G5Dt3Vi2KmTSNuP4D/+VF2U+aLgP88ccfDBo0iMaNG7No0SJKlXo+a9vChQuZMGGCJNdCCCGyjN0304yS67iuPQgJjsqTyTXA9u3bWbduHV26dKFp06Zq2dmzZ+ncuTMnTpzg4MGDREZGsnz5cjNHK4QQQrykefIE18L5jZLr8ONnclxyDalMsFesWMHbb79Nr169uHnzJoMHD+a3337j22+/RVGUzI5RCCGEeMlgwKVSGexnzVCLnm7eQczseWYMyvz8/f3x8/NjzJgxlChRAoA9e/aQL18+hgwZgkajwc3NjR49evDzzz+nqe6nT58yYcIE6tevT40aNfjoo48ICAhQ948ZM4Zy5coZ/atfv76632AwMH/+fHx9falatSo9e/YkMDDQ6D2uXLmCn58f1apVo2HDhqxcudJovynqEEIIkf1Y+2+lYJWXj3UZChYk5HEEeo+c+ahXqhLsWbNmYWtry7p169i7dy99+/bF1dUVjUaT2fEJIYQQKm3QY1zdCqANCVbLQq/eIalxUzNGlT3cvHmTevXqqdsJCQmcO3cOLy8vHB0d1fJy5crx8OHDNNX9+eefc+HCBebMmcP27dupVKkSvXr1Umcgv3btGp988glHjx5V/+3cuVN9/aJFi9i8eTNTpkxhy5YtaDQa+vTpQ2JiIgARERH06NGDkiVL4u/vz6BBg5g3bx7+/v4mrUMIIUQ2oigUeK8puv691aKYL74i7PJtsLAwY2AZk6oE+/333+fevXv07t2bfv36sXfvXrVDE0IIIbKC1Z7dRjOKJleoRMiTSBRnFzNGlX0kJSVhY2Ojbl+4cIHk5GRq1apldFxcXBz50jCMPjAwkD/++IMvv/ySmjVrUrp0acaNG0fhwoXZs2cPer2emzdvUqVKFVxdXdV/f59kbdWqVQwaNIgGDRpQvnx55s6dy5MnTzhw4AAAW7duxcrKiokTJ+Lh4UG7du3o3r27OpTdFHUIIYTIPjTBwc+HhAecUsvC/wggbtAQ8wVlIqlKsL/55huOHj3K2LFjefr0KUOHDsXX15fp06ej0WjkTrYQQohMpevZlfw9/dTtmMnTiTh8HKT/URUtWpTr16+r20eOHEGj0VC3bl2j406ePIm7u3uq63VycmLZsmVUrlxZLdNoNCiKQmRkJHfv3iUhIeG1y31dvXqV2NhYfHx81DKdTkfFihU5ffo0AAEBAXh7e2Np+XLuVR8fH+7cuUNYWJhJ6hBCCJE9WO/0p2DlMuq2IX+B50PCy3qaMSrTSfUs4vb29nTs2JGOHTty69Yttm/fzg8//ICiKIwaNYqWLVvSokULPD1zR8MIIYTIBuLicC1R2Kgo/MhJ9OUrmCmg7Oudd95h6dKllClTBoPBwNatWylWrBg1a9ZUj7l48SIbN26kW7duqa5Xp9PRoEEDo7K9e/dy79496tWrx/Xr19FoNKxZs4YjR46g1Wpp0KABQ4YMwdHRkaCgIACKFCliVEehQoV4/PgxAEFBQSnOHwoVKgTAo0ePTFKHi4uMdBBCCHPL/8G7WB3/Q92OGfclcZ8NM2NEppeuZbo8PDwYNWoUw4cP59dff2X79u2sXLmSZcuWUbZsWXbv3m3qOIUQQuQxFn9exLlJPaOykPshYG1tpoiyt969e3P69Gl69OgBgJ2dHdOnT1f39+jRg5MnT+Lh4UHv3r1fV81/OnPmDGPHjqVJkyY0btyY+fPno9VqcXd3Z8mSJQQGBvL1119z/fp11qxZQ1xcHECKtbetra2JjIwEID4+/pX74fmz5KaoIyMsLXPG2qsZYWGhNfqvMB1p28wh7Zp5MqNtNaEhFPAsZVQWeew0hvIV0peQZmMZ+jwWFhY0bdqUpk2bEhYWxo4dO4wmNRFCCCHSw3b+XBymfKlux7fvSPQieZb239ja2rJ27VoCAgIIDQ2lVq1a6nPQAAUKFKBPnz706tULe3v7dL3HwYMHGT58OFWrVmXOnDkADBo0iO7du6PT6QDw9PTE1dWVjh078ueff6rPhScmJho9I56QkKCuxW1jY5NibpcXSbGdnZ1J6kgvrVaDk1P62isn0ulkffTMIm2bOaRdM4/J2nb7dujQ4eW2gwNERJDfMrel1s+l6lPdvn2b0qVL/+sxLi4u9OnThz59+qhlt27deu0zWUIIIUQKioJzrapYBN5ViyLXbSGx+bvmiymH+fuQ8L+bO3duhupdv349U6dOpVmzZsyaNUu9W6zRaNTk+oUXQ7WDgoLUYd3BwcEUL15cPSY4OJjy5csD4ObmRnBwsFEdL7YLFy5McnJyhutIL4NBISrqWbpfn1NYWGjR6WyJiopDrzeYO5xcRdo2c0i7Zh5Ttq1Dm5bkO/ybuh03ZjzxI0ZDdAKQsdFFWUmns031Hf1UJdh9+/alcePG9O3bl4IFC/7n8Y8fP2b58uUcPnyYQ4cOpSoQIYQQeZsmONho0hOA0Es3Uf7/OVqROi+GU7+4s/tP58+fZ/LkyWlavmrjxo1MnjyZrl27MnbsWLTalycZw4YN4+nTp0ZrTv/5558AlClThmLFiuHg4MDJkyfV5DgqKorLly/j5/d84jpvb282b96MXq/H4v+XZjl+/DilSpXCxcUFR0fHDNeREcnJeefkXa835KnPm5WkbTOHtGvmyUjbasLCKFjBeEh4+OET6CtUhFz++0pVGr5z506ioqJo2LAhvXr1Ytu2bVy9epWYmBgMBgNPnz7l6tWrbNiwgX79+tGsWTMiIyPZsWNHZscvhBAiF7Dav9coudaXLPV8CS5JrlPt2bNnDBs2jDfffJM333yTIUOGqMk2QHh4OGPGjKFz585cu3Yt1fXeuXOHadOm0axZM/r160dYWBghISGEhIQQHR1Ny5Yt+eOPP1i8eDH37t3j8OHDjB07lpYtW+Lh4YGVlRV+fn7MmjWLQ4cOcfXqVYYOHYqbmxvNmjUDoF27dsTExDBu3Dhu3rzJjh07WLNmDf369QMwSR1CCCGyhtWPPxgl14qNDSGPwp8n13mARlEUJbUH//XXXyxdupRffvkFvV6fYr+1tTX169enb9++Rst5iFfT6w2Eh8eaO4xMZ2mpxcnJnoiIWLnCaGLStplD2jXzvKptHQf2w2brJvWYmPETiRv8ublCzHLOzvYmmUhm2rRprF27lhYtWmBvb8+uXbvo3Lkzo0aN4qeffmLSpElERkbi7e3N+PHjKVeu3H9XCixZsuS1w8vbtGnDjBkz2LdvH0uWLOH27ds4OjrSqlUrhgwZok4yptfrmTNnDjt27CA+Ph5vb28mTJhA0aJF1bouXrzI1KlTuXz5Mq6urvTs2VO9O22qOtJD+mqRUdK2mUPaNfNkpG3zd2yD1a8vRzDHDhvFs1HjTB1ilktLX52mBPuFZ8+eERAQwP3794mJicHJyYk33niDmjVrGk0+Iv6ddNoio6RtM4e0a+YxatvYOFyLuRrtjzj0O8lVqpopOvMwVYLdrFkzGjVqxNixYwH43//+x+zZsxk8eDATJkygcOHCjBo1ivfeey/D75WXSF8tMkraNnNIu2ae9LStJiKcguVKGpWF/3oMfaXccdM1LX11uqZus7Ozo379+ul5qRBCCIH2ymVc69YyKgsJfAKveW5Y/Lfg4GB8fX3V7QYNGjBmzBimTJlCu3btGDt2bLpnDxdCCCFex+rnn8j/cSd1W7GwIPReMOTLZ8aozEcWjhNCCJG15s0j/9+S64SWHxASHCXJdQYlJCSQP39+dfvFzN4ffPABU6dOleRaCCGEyem6dDBKrmOHDif0cUSeTa4hg+tgCyGEEKmmKDjWqw2X/1KLIleuJbFVa/PFlItpNBrg+XPSQgghhClpnkZQ0LOEUVn4oaPoq3iZKaLsQxJsIYQQmU4THkbB8sbLdYRduIqhyBtmiijveDHRmBBCCGEKVgd+Jn+XD43KQh6EgpWVmSLKXiTBFkIIkany/XqIAh3/dhe1cGEi/rqBQeakMbmQkBAePXoEoK72ERoaqpb93RtvyMUNIYQQaaP7+COsf/5R3X42aCixX0wyY0TZj8kT7OjoaBwdHU1drRBCiBzIYcRQbNesVLfjRozG9pvpEBGLZNimN3DgwBRln3zyySuPvXLlSmaHI4QQIpfQRD6lYNniRmURBw6TXLW6mSLKvtKcYCcmJvL9999z6tQpkpKSeLHKl6IoPHv2jJs3b3LhwgWTByqEECIHSUrC1d3FqCji51+gVi1kKrPMMX36dHOHIIQQIhfK98sBCnRqZ1QWcj8E5BGkV0pzgv3NN9+wfv16PD09CQ8Px9raGmdnZ65fv05SUtIrr54LIYTIOyxu3cD5rTeNykJuPwIHB3kuKROlZTKzq1evZmIkQgghcgtdr4+x/mGnuv2s/yBiJ001X0A5QJqX6dq/fz/du3dn9+7ddO3alcqVK7Nt2zb279+Pu7s7BhnyJ4QQeZbNmlVGyXVi46bPl+BycDBjVAKej0DbuXMnnTp1kpnFhRBC/LvISJycHYyS64h9v0pynQppTrDDw8Np0KABAOXKlePPP/8EoHDhwvTt25effvrJtBEKIYTIEQq82xjHEUPU7ajvlhG5eYf5AhIA3LlzhxkzZuDr68vo0aO5c+cOHTt2NHdYQgghsinLXw5BgQJGZSH3gkmu/uarXyCMpHm0nqOjI4mJiQCULFmSx48fExMTg4ODg7othBAi73jVxCdhZy5hKFb8Na8QmS05OZkDBw6wefNmTp06BUCtWrX46KOPaNy4MVaylIoQQohXcOzXA5v/+avbz/r2J3bK12aMKOdJ8x3smjVrsm7dOp49e0bRokWxtbXlwIEDAJw7dw4HGQYohBB5Rr5jR42Sa4O9AyGPIyS5NpOHDx8yd+5cGjZsyNChQwkLC2PAgAHA8xnG33nnHUmuhRBCpKCJica1kM4ouY7a94sk1+mQ5gR74MCBnD9/nn79+mFpaUnnzp2ZMGECbdu2Zd68eTRv3jzdwSxatIiuXbsalV25cgU/Pz+qVatGw4YNWblypdF+g8HA/Pnz8fX1pWrVqvTs2ZPAwMAsr0MIIfIa+y/GUKD1e+r2s4FDCLvzCCwszBhV3tW3b1/efvttNm/eTOPGjdmyZQt79uyhW7du6oofQgghxD/lO/IbBUu7Gxc+e4beu5Z5Asrh0pxglytXjr1796rrag4bNowBAwZQsGBB+vfvz8iRI9MVyPfff8/8+fONyiIiIujRowclS5bE39+fQYMGMW/ePPz9X15ZWbRoEZs3b2bKlCls2bIFjUZDnz591GHsWVWHEELkGcnJFHR3wW7pd2pRxO59xE74yoxBiSNHjlCmTBlmzpzJhAkTqFq1qrlDEkIIkc05DuhLgfbvq9txvfoSER4DtrKoZnqla8UUV1dXXF1dAdBoNGqynR5Pnjxh3LhxnDlzhlKlShnt27p1K1ZWVkycOBFLS0s8PDwIDAxk+fLltGvXjsTERFatWsWIESPUidfmzp2Lr68vBw4coEWLFllShxBC5BXau3dwqWWcuIXevI+iy2+miMQLU6ZMYceOHfTt2xedTkeLFi1o27YtJUuWNHdoQgghspuYGFxLv2FUFLHnAMm1asuSmhmUqvZbuHAhHTp0oHDhwixcuPBfj9VoNOrzXqnx119/kT9/fnbv3s13333Hw4cP1X0BAQF4e3tjafkyTB8fH5YuXUpYWBgPHz4kNjYWHx8fdb9Op6NixYqcPn2aFi1aZEkdLi4uqf68QgiRU1lv3oBucH91O/GtukTu2mvGiMTftW/fnvbt23Pnzh38/f3ZvXs3mzdvpnjx4mg0GqKioswdohBCiGwg3x+/U6BNC6OykLtBYGdnpohyl1Qn2PXr18+UBLtx48Y0btz4lfuCgoLw9PQ0KitUqBAAjx49IigoCIAiRYqkOObFbOZZUUdGEmxLyzSP0s9xLCy0Rv8VpiNtmzmkXVNyaNOSfId/U7dj531HYtduab7KLW2b+UqVKsXw4cP5/PPPOXz4MP7+/jx8+JCBAwdStWpVWrVqxbvvvouzs7O5QxVCCJHFHD77FNtN69XtuG69iJk514wR5T6pOje6evWq+vNff/2FRRZNYBMfH59itlNra2sAEhISiIuLA3jlMZGRkVlWR3pptRqcnOzT/fqcRqeTZzkyi7Rt5pB2BaKjQaczLrtxA/syZcjIXy9p28yn1Wpp1KgRjRo1Ijw8nF27drFjxw4mT57M9OnTuXTpkrlDFEIIkVViY3EtZXxDMWL3PpJ93jJTQLlXmofYd+jQgQEDBtCkSZPMiMeIjY2NOtHYCy8SWjs7O2xsbABITExUf35xjO3/P5ifFXWkl8GgEBX1LN2vzyksLLTodLZERcWh1xvMHU6uIm2bOaRdn7M4dRLdO8Z/6yOCn4KlJUTEpq9OadsUdDrbTL+j7+zsTI8ePejRowcXL15kx44dmfp+Qgghso98x/+gwAfvGpWF3HkM9nnnRl9WSnOCff/+/Sxb69rNzY3g4GCjshfbhQsXJjk5WS0rXry40THly5fPsjoyIjk575xg6vWGPPV5s5K0bebIy+1qN+0r7L+dpW4/692P2Gkzn2+YoE3ycttmlu3bt9O+ffv/PM7LywsvLy8AFEVh27ZtfPjhh5kdnhBCCDNwGDYY23Xfq9txXT4mZu6/P/IrMibNl8xbtGjB0qVLuX//fmbEY8Tb25szZ86g1+vVsuPHj1OqVClcXFwoX748Dg4OnDx5Ut0fFRXF5cuXqVmzZpbVIYQQuYbBgEvZ4kbJ9VP/H14m1yLbOnToEG3atGHfvn0pRl39U2JiIrt376Z169YcOnQoiyIUQgiRZZ49w7WQzii5frrzJ0mus0Ca72DfvXuXgIAA3n77bWxsbFJMkqLRaDh48KBJgmvXrh0rVqxg3Lhx9O7dm4sXL7JmzRomTZoEPH9u2s/Pj1mzZuHs7Iy7uzszZ87Ezc2NZs2aZVkdQgiRG2gfPsClekWjstDrgSgFnMwUkUiLxYsXq89Yjxs3joYNG+Ll5UXRokWxtbUlOjqax48fc/bsWU6cOEG+fPkYNGgQHTt2NHfoQgghTMjy5AmcWr1tVBZy+xFk0SjkvC7NCXaRIkVo1apVZsSSgouLCytWrGDq1Km0adMGV1dXRo4cSZs2bdRjBg8eTHJyMuPHjyc+Ph5vb29WrlypTkqWVXUIIUROZv2/7ej69VS3k6pW5+n+30CjMV9QIs3atm1Ly5Yt2bZtGz/88AN79+41Gn1lYWFBjRo1GDRoEO3btzeae0QIIUTO5zByKLbfr1S34zt2JnrBEjNGlPdoFEVRzB1EXqXXGwgPT99EQTmJpaUWJyd7IiJi5ZlLE5O2zRx5rV11XTtive/letbRM2YT37NPprxXXmvb1HB2ts+0Sc5iY2N5/Pgx0dHRODk5UbhwYXUCT5E60leLjJK2zRzSrv8QF4drCeO5oZ76/0CSb4M0VyVtm1Ja+uo038F+ITQ0lKSkJF7k5waDgbi4OAICAvjoo4/SW60QQois8oolO8L/CEBf1tNMAQlTs7e3p0yZMuYOQwghRCayPH0SpxbNjMpCbz9EcXA0U0R5W5oT7KtXr/L5559z586dV+7XaDSSYAshRDZneeEcTs2Mr2qHPAiF/380RgghhBDZn/3YEditWKpux7f7kOjFK8wYkUhzgv3NN98QFRXFqFGj+PXXX7GysqJRo0YcOXKEI0eOsHbt2syIUwghhInYzfkG+xlT1G1ZskMIIYTIYeLjcS1eyKjo6bZdJDVoZKaAxAtpTrAvXLjA6NGj6dChA3Z2duzatYvOnTvTuXNnBg8ezLp169TlrYQQQmQjioJztQpYPH6kFkVu2k5ik7f/5UVCCCGEyE4szwbg9E5jo7LQWw9QHHVmikj8XZpnVUlMTKRUqVIAlC5dmmvXrqn72rZty/nz500WnBBCCNPQPgnCtXB+o+Q69PJtSa6FEEKIHMT+izFGyXV867aEBEdJcp2NpDnBfuONN7h//z4AJUqUICYmhgcPHgDP15SOjIw0bYRCCCEyxOrHH3Cp8nLismTPcoQ8iUQpWNCMUQkhhBAi1RIScC2kw27pd2rR0807iF72vfliEq+U5iHib7/9NrNmzcLW1pZ33nmH0qVLM3fuXPr27cuqVasoVqxYZsQphBAiHRz7dsdm5w51O2biVOI+HWTGiERmKV++PJpUrluu0Wi4fPlyJkckhBDCFCzPn8Xp7YZGZaE376Po8psnIPGv0pxgDxw4kMDAQPz9/XnnnXcYM2YMAwcO5KeffsLCwoI5c+ZkRpxCCCHS4hWTn4T/egx9pcpmCkhktgEDBqQ6wRZCCJEz2E/6Arvv5qnbCS3eJ2r1ejNGJP5LmhNsa2tr5s+fT1JSEgC+vr7s2bOHS5cuUalSJYoXL27yIIUQQqSexV+XcG5Ux6gs5F4w2NiYKSKRFQYNkpEJQgiRayQm4lrU+FEumZg0Z0jzM9hjxozh/v375MuXTy0rVqwY7777LsnJyXzyyScmDVAIIUTq2X433yi5jm/TjpDgKEmu86D4+HguXLhAQEAAp0+f5vTp05w8eZLffvuNWbNmmTs8IYQQr2F58XyK5Dr0xj1JrnOIVN3BfvTo5ayzO3fupGnTplhYWKQ47siRIxw7dsx00QkhhEgdRcGpbk0sb95QiyLXbCLx3RZmDEqYy4kTJ/jss8+Iiop65X57e3uGDx+exVEJIYT4L/ZTJ2E3b7a6nfDOe0St3WzGiERapSrB/uqrrzh8+LC6PXDgwFcepygKdevWNU1kQgghUkUTGkrBiqWNykL/vIFSuLCZIhLm9u2331KgQAGmTJnC7t270Wq1tG3bliNHjrBp0yaWL19u7hCFEEL83auGhK/bQmLzd80UkEivVCXYkyZN4tixYyiKwtixY+nfv3+KZ621Wi06nY7atWtnSqBCCCFSsjq4j/ydO6jbeveihJ/9C2Syqzzt2rVrTJ48mWbNmhETE8PGjRtp0KABDRo0ICkpicWLF7Ns2TJzhymEEAKwuPQnzo2Nb1KGXruL4uRspohERqQqwS5cuDBt2rQBni/t0aBBA5yd5RcuhBDm5PDZp9huejmTaOyYL3g2dIQZIxLZhcFgwM3NDYBSpUpx8+ZNdV/z5s0ZNWqUuUITQgjxN3YzJmM/Z6a6ndD0baI2bjdjRCKj0jyLeJs2bVAUhcuXL/Ps2TMURUlxjLe3t0mCE0II8QqvGEYWceAwyVWrmykgkd0UL16ca9euUbNmTUqUKEFcXBy3bt3Cw8OD5ORkYmNjzR2iEELkbUlJFCxeCI1erxbJ3Cm5Q5oT7IsXL/LZZ58RFBQEoCbYGo0GRVHQaDRcuXLFtFEKIYQAwOL6NZzrGV/EDLkbBHZ2ZopIZEetWrVi1qxZGAwGunbtSuXKlZkyZQpdu3ZlyZIllClTxtwhCiFEnmVx+S+cG75lVBZ69Q6Ks4uZIhKmlOYEe9q0aVhaWjJ9+nTc3NzQatO80pcQQoh0sFm5DMcxL2d+lplFxev07t2biIgILl68CMCXX35Jnz59+PTTT3FwcGDx4sVmjlAIIfImu5nTsZ85Xd1OrN+IyO27zBiRMLU0J9iXL19mzpw5NG3aNDPiEUII8U+KQoGm9cn35wW1KGrZahJatzNjUCI702q1Rs9ZV6lShYMHD3L79m1Kly6Ng4ODGaMTQog8KDmZgqWKoElIUIsiV60nseX7ZgxKZIY03352cXGRu9ZCCJFFNBHhuBbOb5Rch52/Ism1+Fcff/wxt27dMipzcHDAy8uLBw8e0KpVKzNFJoQQeY/F1Su4vuFslFyHXr4tyXUuleZMuXPnzixbtoxnz55lRjxCCCH+X77Dv1KwXEl12+DsTEjQUwxvuJsvKJFtBQQEcPr0aU6fPs2pU6fUn//5b/fu3dy/fz9NdT99+pQJEyZQv359atSowUcffURAQIC6/8qVK/j5+VGtWjUaNmzIypUrjV5vMBiYP38+vr6+VK1alZ49exIYGGh0TFbUIYQQWc1u7kyc679cxjixri8hwVEoBQv+y6tETpbmIeKBgYHcunWLunXrUrZsWWxsbIz2azQa1qxZY7IAhRAiL3IYPQzbVcvV7dihw3k2ZoIZIxLZ3fbt29m5cycajQaNRsOkSZNSHPNiYtKWLVumqe7PP/+csLAw5syZg7OzMxs3bqRXr17s2LEDZ2dnevToQdOmTZk0aRLnz59n0qRJFChQgHbtno+0WLRoEZs3b2b69OkULlyYmTNn0qdPH/bs2YOVlRURERFZUocQQmSZ5GRcPEugjYlWiyJXrCHx/TZmDEpkhXQl2OXLl1e3/7lM16uW7RJCCJFKSUm4uhvPIhrx4wGSvWu/5gVCPDdu3Djatm2Loih069aNCRMmpJgtXKvVotPpKFu2bKrrDQwM5I8//mDTpk3UqFFDfa8jR46wZ88ebGxssLKyYuLEiVhaWuLh4UFgYCDLly+nXbt2JCYmsmrVKkaMGEGDBg0AmDt3Lr6+vhw4cIAWLVqwdevWTK9DCCGyyqtW/Aj96xaKq6uZIhJZKc0J9rp16zIjDiGEyPO0t2/h4mO8lnXo7YcoDo5mikjkJI6OjtSqVQuAtWvXUrFiRZNMZubk5MSyZcuoXLmyWvZiac7IyEguXbqEt7c3lpYvTyl8fHxYunQpYWFhPHz4kNjYWHx8fNT9Op2OihUrcvr0aVq0aEFAQECm1+HiIsvfCCEyn+38OThMmahuJ/rUIXLXXtBozBeUyFJpTrBfiIyMJCAggODgYJo3b87Tp08pVaoUGvnyCCFEmtmsX4Pj54PUbVm2Q2RErVq1CA8PZ/bs2Zw8eZKoqCicnJyoWbMm3bt3T1OyqdPp1LvGL+zdu5d79+5Rr1495s6di6enp9H+QoUKAfDo0SOCgoIAKFKkSIpjHj9+DEBQUFCm15GRBNvSMvdP7mphoTX6rzAdadvMke3aVa8nf9mSaJ9GqEUxy1eT1K5D+hMuM8l2bZvDpOv3vXjxYpYuXUp8fDwajQYvLy/mzp3L06dPWbVqFTqdztRxCiFErpX//XewOnFM3Y5asISEjp3NGJHI6YKCgujUqRNhYWFUq1aNihUrEhISwurVq9m5cyfbt2+ncOHC6ar7zJkzjB07liZNmtC4cWOmT5+OlZWV0THW1tYAJCQkEBcXB/DKYyIjIwGIj4/P9DrSS6vV4ORkn+7X5zQ6na25Q8i1pG0zR7Zo12vX4G+P0ALw+DEObm7micdEskXb5kBpTrDXr1/PggUL6NevH40aNeLDDz8EoFu3bgwfPpx58+bxxRdfmDxQIYTIbTRRkRQsU8yoLOz0RQwlSponIJFrzJw5EwsLC3766SeKFXv5Hbt//z49e/Zk7ty5zJgxI831Hjx4kOHDh1O1alXmzJkDgI2NDYmJiUbHvUho7ezs1MlQExMTjSZGTUhIwNbWNsvqSC+DQSEqKvevnGJhoUWnsyUqKg693mDucHIVadvMkV3a1Xr+t9hNHK9uJ9esRfS+Q8+HhEfEmi2ujMgubZud6HS2qb6jn65nsPv27ctnn32GXq9Xy319fRkyZAjLli2TBFsIIf5DvhPHKPD+O+q2Ym1N6N0gsLAwY1Qitzh69Chjx441Sq4BihUrxoABA/jmm2/SXOf69euZOnUqzZo1Y9asWerdYjc3N4KDg42OfbFduHBhkpOT1bLixYsbHfNi0tSsqCMjkpPzzgmmXm/IU583K0nbZg6ztatej0sVT7ShIWpR1HfLSOjQCfQKkPMnfpbvbPqkeWD9o0eP1ElU/ql06dKEhoZmOCghhMjN7CeON0qun30ykND7IZJcC5PR6/U4OTm9cp+zszMxMTFpqm/jxo1MnjyZLl268O233xoNxfb29ubMmTNGF92PHz9OqVKlcHFxoXz58jg4OHDy5El1f1RUFJcvX6ZmzZpZVocQQpiKxe2buBZxMkquw/68/jy5FnlemhPsIkWKcO7cuVfuu3TpUooJSIQQQvw/vZ6CJQpjt2i+WvR050/EfjXNjEGJ3KhcuXLs2vXqSfJ27tyZYjKwf3Pnzh2mTZtGs2bN6NevH2FhYYSEhBASEkJ0dDTt2rUjJiaGcePGcfPmTXbs2MGaNWvo168f8Py5aT8/P2bNmsWhQ4e4evUqQ4cOxc3NjWbNmgFkSR1CCGEKtku/w9mnhrqdVLU6IU8iMRTO2c9bC9NJ8xDx9u3bs2DBAmxsbGjYsCEAz549Y9++fSxdupQePXqYOkYhhMjxtPcCcalZxags9MY9lPwFzBOQyNU+/fRTevXqxdOnT2nVqhUFCxYkNDSUH374gWPHjjF//vz/ruT/7du3j6SkJA4cOMCBAweM9rVp04YZM2awYsUKpk6dSps2bXB1dWXkyJG0adNGPW7w4MEkJyczfvx44uPj8fb2ZuXKleqdcBcXlyypQwgh0s1gwLlaBSyCHqtFUfMXk9CpixmDEtmRRlGUND0goCgKX375Jdu2bVO3XyzN1apVK2bMmIFWK1O6p4ZebyA8PGdOfpAWlpZanJzsiYiIlec4TEzaNnOYul2tt21GN6Cvup30pjdP9x7KcL05kXxnU3J2tjfJUigff/wxX375JR4eHgDs2rWLmTNnGj26VbBgQYYNGyZJZxpIXy0ySto2c2Rlu2rv3MaldjWjsrALVzEUeSNT39dc5DubUlr66jTfwdZoNHz11Vf06NGDEydOEBkZiaOjI7Vq1aJs2bJpDlYIIXKz/B3bYPXry2Q6etY84j+WkT7C9E6dOkVs7MtE8IMPPuD999/n9u3bREZGkj9/fkqXLq1eFBdCCPHfbFYswXHsSHU7uWJlIn794/ks4UK8QrrXPS9VqhSlSpUyZSxCCJF7xMTgWtr4ynb4ibPoS5cxU0AiL9JoNOodbSGEEGlgMODs7YXF/XtqUfS33xHfuasZgxI5QZoT7MTERNavX8/Zs2eJiopKsV+j0bBmzRqTBCeEEDmR5ZnTOL3bxKgs5GEY5MtnpoiEEEIIkVrawLu4eHsZlYWdv4LhDXczRSRykjQn2F999RXbt2+nbNmyFChQIMX+ND7SLYQQuYrd11Oxn/21uh3XrRcxM+eaMSKRl0ycOBEHB4f/PE4uhgshxKvZrFqO4+hh6nZyufJEHDkpQ8JFqqU5wT5w4ACffPIJQ4YMyYRwhBAihzIYcKnkgTYsTC16unUnSQ0bmzEokRel5kK3XAwXQoh/UBScfKpjeee2WhQ9ez7xXbubLyaRI6U5wdZqtXh7e2dGLEIIkSNpHz3EpVoFo7LQq3dQnF3MFJHIqyZOnIiXl9d/HyiEEEKlvX8PlzcrG5WFnf0LQ9FiZopI5GRpXhekdevW+Pv7YzDIlO1CCGG1+39GyXVypSqEPImU5FoIIYTIAWzWrDJKrpM9yhDyJFKSa5Fuab6DPWTIEFq3bk3z5s2pXLkyNjY2Rvs1Gg3Tpk0zWYAASUlJLFy4kF27dhEZGUmFChUYPnw4NWrUAODKlStMnTqVS5cuUaBAAbp27UqvXr3U1xsMBhYuXMi2bduIiorizTff5Msvv6REiRLqMaaoQwiRt+h6+GH94251O2bq18T16W/GiIQQQgiRKoqCUz1vLG9cV4uiv5lLfPde//IiIf5bmhPsWbNmcefOHWxtbblw4UKK/ZmxvubixYvx9/dnxowZFCtWjOXLl9OnTx9++uknrKys6NGjB02bNmXSpEmcP3+eSZMmUaBAAdq1awfAokWL2Lx5M9OnT6dw4cLMnDmTPn36sGfPHqysrIiIiMhwHUKIPOTZM1xLuhkVhf9+Cn258mYKSAjw9vbG3t7e3GEIIUS2p31wH5calYzKwgL+xFBcbpyJjEtzgr179266devGyJEjsbCwyIyYUjh06BAtW7akXr16AIwePZpt27Zx/vx57t69i5WVFRMnTsTS0hIPDw8CAwNZvnw57dq1IzExkVWrVjFixAgaNGgAwNy5c/H19eXAgQO0aNGCrVu3ZrgOIUTeYPnnBZya+BqVhdwPAWtrM0UkxHPr1q0zdwhCCJHt2WxYi+PQgeq2vnhJwk+dB22an5wV4pXS/E3S6/U0btw4y5JrgAIFCvDrr7/y4MED9Ho9W7ZswcrKigoVKhAQEIC3tzeWli+vFfj4+HDnzh3CwsK4evUqsbGx+Pj4qPt1Oh0VK1bk9OnTACapQwiR+9nOm22UXMd37ExIcJQk10IIIUR2pyg4NfAxSq6jp88kPOCiJNfCpNJ8B7tZs2bs3buX2rVrZ0Y8rzRu3DiGDh1KkyZNsLCwQKvVMm/ePIoXL05QUBCenp5GxxcqVAiAR48eERQUBECRIkVSHPP48WMAk9SRXpaWuf9/aAsLrdF/helI22aOFO2qKOiqVcLi/j31mOjN20l++520/xHN4+Q7K4QQIqu9arWPsFMXMJQsZaaIRG6W5nPDqlWrMnv2bK5evUr16tVTPO+l0WgYMGCAyQIEuHXrFjqdju+++47ChQuzbds2Ro0axfr164mPj0/xDLT1/99NSkhIIC4uDuCVx0RGRgKYpI700Go1ODnlnefldDpbc4eQa0nbZg6dzhaCguAfF9d48gTH/78IJ9JHvrNCCCGygvXmDegGv5yAVP+GO+Fn/5K71iLTpDnBnjhxIgDnz5/n/PnzKfabOsF++PAhI0aM4Pvvv6dmzZoAVKlShZs3b7JgwQJsbGxITEw0ek1CQgIAdnZ26izniYmJRjOeJyQkYGv7/ATPFHWkh8GgEBX1LN2vzyksLLTodLZERcWh18vybqYkbZs5XrTrs83bsPvoQ7VcX9qDqNPnQaOBiFjzBZiDyXc2JZ3OVu7oCyGEqSkKBZrWJ9+fLydljpk8nbh+pr0RKMQ/pTnBvnr1ambE8VoXL14kKSmJKlWqGJVXrVqVI0eO8MYbbxAcHGy078V24cKFSU5OVsuKFy9udEz58s9n/HVzc8twHemVnJx3TjD1ekOe+rxZSdo2E/j5Ybdhg7oZ88VXxA0aAnoFUMwWVm4h39nMFxQUxNmzZ40uIBsMBuLi4ggICGDu3LlmjE4IITKPNugxLl7ljMrCTpzDUNrDTBGJvCTbPz744rnna9eu4eXlpZZfv36dEiVKUK1aNTZv3oxer1cnXjt+/DilSpXCxcUFR0dHHBwcOHnypJocR0VFcfnyZfz8/IDnS5tktA4hRC6RkIBTIVejovBDR9FX8XrNC4TIfvbu3cuIESNITk5Wl89UFEX9uXTp0uYMTwghMo311k3oBvZTt/WFChN+8ZoMCRdZJlUJ9scff8yXX36Jh4cHH3/88b8eq9FoWLNmjUmCA/Dy8qJmzZqMGjWKL7/8Ejc3N3bu3Mnx48fZuHEjxYoVY8WKFYwbN47evXtz8eJF1qxZw6RJk4Dnz037+fkxa9YsnJ2dcXd3Z+bMmbi5udGsWTMA2rVrl+E6hBA5n8WVyzg38DEqCwl8Ahl4FEQIc1i6dCkVK1Zk4sSJbNiwgeTkZPr27cvhw4eZO3cuY8eONXeIQghhWopCgXcbk+/sGbUoZuJU4j4dZMagRF6UqgRbUZRX/vxfx5qCVqtl0aJFfPvtt4wZM4bIyEg8PT35/vvvqVatGgArVqxg6tSptGnTBldXV0aOHEmbNm3UOgYPHkxycjLjx48nPj4eb29vVq5cqU5a5uLikuE6hBA5m+3S73D4YszLgg4diFi6GmQYs8iB7ty5w6xZs6hYsSJvvfUWK1aswMPDAw8PD8LCwliyZAl169Y1d5hCCGESmidPKFilrFFZ+PEz6D3KvuYVQmQejWLqjFikml5vIDw890+UZGmpxcnJnoiIWHnm0sSkbU1AUXCqXxvLay/nl4hZswGHjztLu2YC+c6m5Oxsb/JJzqpXr86yZcvw9vbm0qVLdO7cmfPnz6PVajlx4gRDhw7l+PHjJn3P3Er6apFR0raZ40W7xixfjUPfnmq5wcWFsEs34f8f+xRpJ9/ZlNLSV8vDCEKIPEsTFoZr4fxGyXXYxWsktfrAjFEJkXGlS5fmzJnnwyRLlixJUlISV65cAZ7PIfLPlTOEECLHURSoW9couY4ZP4mwK3ckuRZmlaoh4uXLl1cnRkmNF524EEJkV/l+OUiBTm3VbX1hN8IvXAWtVq48ihyvU6dOfPnllzx79ozPP/+c2rVrM3bsWNq3b8/69eupVKmSuUMUQoh00wQH41S5jFFZ+B8B6Mt6mikiIV5KVYI9YMCANCXYQgiRnTkMG4ztuu/V7dgRY3g2YszrXyBEDtOhQwcSExN58OABAJMnT6ZPnz5MnToVd3d3xo0bZ+YIhRAifax3+qPr20PdNujyE3btrty1FtlGqhLsQYNSP/teVq+TLYQQqZaUhKu7i1FRxL5fSa7+ppkCEiLzdOnSRf25WLFi7N69m5iYGJydnc0YlRBCpF/+1u9hdezoy4KpU4ns/5lMSCqyFZOMhExMTGTnzp106tTJaOZtIYTILixu3kiRXIfceSzJtci1Fi9eTK9evdTtc+fO0aJFC77//nvzBSWEEOmgCQ3FtZDOKLmOPHYaZMlBkQ1lKMG+c+cOM2bMwNfXl9GjR3Pnzh06duxoqtiEEMIkbL5fiXOdl4l0QrPmhARHgb29GaMSIvOsWLGChQsX4un58nnEEiVK8MEHHzB79my2bNlixuiEECL1rH7YScGKpdVtxc6OkEfhGMpXMGNUQrxeqoaI/11ycjIHDhxg8+bNnDp1CoBatWrx0Ucf0bhxY1kXWgiRfSgKBd5tTL6zZ9SiqCUrSWjbwYxBCZH5tm7dytChQ+ndu7da5ubmxujRo3F2dmbt2rVyQVwIke3lb/c+Vr//pm7HjhzLs+GjzRaPEKmR6gT74cOHbN26FX9/f0JDQylTpgwDBgzgu+++Y+DAgXh7e2dmnEIIkSaapxEU9CxhVBZ29i8MRYuZKSIhss6TJ09eO1N4lSpV+O6777I4IiGESD1NWBgFK5QyKgs/fAJ9hYpmikiI1EvVEPG+ffvy9ttvs3nzZho3bsyWLVvYs2cP3bp1Q1GUzI5RCCHSJN/RI0bJtcFRR8jjCEmuRZ5RrFgxjh079sp9J0+exM3NLYsjEkKI1LH68Qej5FqxsiLkYZgk1yLHSNUd7CNHjlCuXDmGDRtGnTp1sLRM88hyIYTIEvbjRmK3fIm6/WzQUGK/mGTGiITIeh999BHTpk0jOTmZpk2b4uLiQnh4OAcPHmTt2rUMHz7c3CEKIUQK+Tu1xeqXg+p27OcjeTZ6vBkjEiLtUpUpT5kyhR07dtC3b190Oh0tWrSgbdu2lCxZMpPDE0KIVEpOpmAxVzR6vVoU8cN+kmv7mDEoIcyjS5cuBAUFsXr1aqNZwy0sLOjWrRvdu3c3W2xCCPFPmohwCpYraVQW/ssf6CtXMU9AQmRAqhLs9u3b0759e+7cuYO/vz+7d+9m8+bNFC9eHI1GQ1RUVGbHKYQQr6W9eweXWlWNykJvPUBx1JkpIiHMb9iwYfTt25dz584RGRmJTqfDy8sLJycnc4cmhBAqq59/Iv/HndRtRasl9H4I5MtnxqiESL80LdNVqlQphg8fzm+//caiRYsoW7YsFhYWDBw4kE6dOrFhwwbCw8MzK1YhhEjBetN6o+Q6sa4vIcFRklwLATg6OlK/fn1atWpFgwYNJLkWQmQrOr8PjZLr2CHDCQ16Ksm1yNHS9TC1VqulUaNGNGrUiPDwcHbt2sWOHTuYPHky06dP59KlS6aOUwghUsjfpgVWf/yubkfNX0xCpy5mjEgI82nSpAnfffcd5cuXp3Hjxmg0mtceq9FoOHjw4Gv3CyFEZnrVSh8Rh34nuUrV17xCiJwjw7OVOTs706NHD3r06MHFixfZsWOHKeISQojX0kRHUdCjqFFZ2KkLGEqWes0rhMj9atWqhb29vfrzvyXYQghhLlYHfiZ/lw+NykIehIKVlZkiEsK0TDoduJeXF15eXqasUgghjFiePIFTq7fVbcXC4vmzWrK6gcjjpk+frv48Y8YMM0YihBCvpuveBeufflC3nw0cQuyEr8wYkRCmJ2ekQogcw37KROzmz1G3n/X5hNip35gxIiGyr5iYGGJjYylcuDCJiYmsXbuWoKAgmjdvjre3t7nDE0LkIZqoSAqWKWZUFnHgMMlVq5spIiEyT5omORNCCLPQ63Ep7W6UXD/dsUeSayFe4+LFizRu3Jh169YBz5fbnDVrFrt376Zbt24cOnTIzBEKIfKKfL8cSJFch9wPkeRa5FqSYAshsjXtg/u4FnFCGxOtloVeDySpXn0zRiVE9jZ37lxKly5Nx44diY+P54cffqBz586cOnWK9u3bs2TJEnOHKITIAxx7d6NAp3bq9rNPBhISHAXW1maMSojMla4h4kFBQZw9e5bExES1zGAwEBcXR0BAAHPnzjVZgEKIvMt6xzZ0n/RSt5NqvMnTvb+ATN4kxL+6cOECc+fOpVixYvz666/Ex8fzwQcfAPDee++xe/duM0cohMjNXjUZacS+X0mu/qaZIhIi66Q5wd67dy8jRowgOTlZnaFUURT159KlS5s2QiFEnqTz+xDr/T+r29FfzyG+R28zRiREzqHVarH6/xl5Dx8+jE6nUychjYmJwcbGxpzhCSFysXy//UKBD1sblYXcCwb5uyPyiDQn2EuXLqVixYpMnDiRDRs2kJycTN++fTl8+DBz585l7NixmRGnECKviI3FtVQRo6LwY2fQlylrpoCEyHkqV67M9u3bsbGxYe/evTRs2BCNRkNYWBjLly+ncuXK5g5RCJELOX7SE5sd29VtmYxU5EVpTrDv3LnDrFmzqFixIm+99RYrVqzAw8MDDw8PwsLCWLJkCXXr1s2MWIUQuZzluTM4NW9kVCZrYwqRdiNHjqR37978+OOPODs7079/fwBatmyJwWBg5cqVZo5QCJGbaGKiKVja3ags4qeDJNesZaaIhDCfNE9yptVqKVCgAAAlS5bk9u3bGAwGAHx9fbl586ZJAxRC5A12M6cbJddxXbs/nwhFkmsh0qxixYrs37+fLVu2cPDgQUqWLAnAxIkT2bNnT4buYC9atIiuXbsalY0ZM4Zy5coZ/atf/+VEhAaDgfnz5+Pr60vVqlXp2bMngYGBRnVcuXIFPz8/qlWrRsOGDVNcBDBFHUII08v3++EUyXVI4BNJrkWeleYEu3Tp0pw5cwZ4nmAnJSVx5coVAKKioowmPhNCiP9kMOBcxRP7mdPVoqeb/YmZPd+MQQmR8zk4OFC1alXs7OzUsubNm+Pq6sqtW7fSVef333/P/Pkp/9+8du0an3zyCUePHlX/7dy5U92/aNEiNm/ezJQpU9iyZQsajYY+ffqo5wwRERH06NGDkiVL4u/vz6BBg5g3bx7+/v4mrUMIYVqOA/pSoF0rdTuuR+/nF8dtbc0YlRDmleYh4p06deLLL7/k2bNnfP7559SuXZuxY8fSvn171q9fT6VKlTIjTiFELqQNeoyLVzmjstArd1BcXMwUkRC5Q2RkJLNnz+b06dMkJSWhKArwfFLSZ8+eERkZqV4cT40nT54wbtw4zpw5Q6lSpYz26fV6bt68yaeffoqrq2uK1yYmJrJq1SpGjBhBgwYNgOfLiPn6+nLgwAFatGjB1q1bsbKyYuLEiVhaWuLh4UFgYCDLly+nXbt2JqlDCGFCMTG4ln7DqCjih/0k1/YxU0BCZB9pvoPdoUMHxo0bR1JSEgCTJ08mISGBqVOnkpyczLhx40wepBAi97Has9souU4uV56QJ5GSXAthAtOmTcPf35+SJUtiYWGBo6MjVapUISkpiaioKL766qs01ffXX3+RP39+du/eTdWqVY323b17l4SEBDw8PF752qtXrxIbG4uPz8sTb51OR8WKFTl9+jQAAQEBeHt7Y2n58rq/j48Pd+7cISwszCR1CCFMI98fv6dIrkPuBklyLcT/S9c62F26dFF/LlasGLt37yYmJgZnZ2eTBSaEyL0c+3THZtcOdTtm8nTi+g0wY0RC5C6///47AwcOpH///qxevZqTJ0/y7bffEhsbi5+fX5rnS2ncuDGNGzd+5b7r16+j0WhYs2YNR44cQavV0qBBA4YMGYKjoyNBQUEAFClivDpAoUKFePz4MQBBQUF4enqm2A/w6NEjk9ThIhfvhMgwh88+xXbTenU7rmsPYmbPM2NEQmQ/6UqwFy9eTEBAgDp5yLlz5xgyZAj9+vWje/fupoxPCJGbxMXhWqKwUVH44RPoK1Q0U0BC5E5RUVG8+eabAJQtW1btr+3t7enZsycLFy5kzJgxJnmvGzduoNVqcXd3Z8mSJQQGBvL1119z/fp11qxZQ1xcHIC6LvcL1tbWREZGAhAfH//K/QAJ/9fefcdHVaV/HP/MJKRn0ghEQy8JIk0giq4IIsj+BFRgXUSCSFcQEEFEcVEERKQpSu8iiAVEdLEACrKKSBHBpYemoSQkIZPeZn5/ZDM6hpYwk0n5vl8vXuaeO3PnmcdJzjz3nntOVpZDjnEj3N2LPOCvzHFzM9r9VxynXOQ2LY2g6vb9d8q/vyL3zr8Vr5hwgHKR11JKub0xRf6dWLx4Me+88w6PP/64ra1mzZo89NBDzJgxA29vb3r06OHQIEWk7HP79QDB7eyX8Iv/LR7+9wVYRBwnKCiIlJQUIL+PTkhIICkpiaCgIKpWrcqFCxcc9lrDhg3jiSeewGQyARAREUFoaCg9evTgwIEDeHl5Afn3Yhf8DPlFr/f/JkLy8vIqNElqQVHs4+PjkGMUl9FoICjIt9jPL2tMJk1O5SxlNrfbt8OfVgUAIDUVf9/S8XtRZvNaBii3xVPkAvvDDz9k5MiRDBgwwNYWFhbG2LFjCQ4O5t1331WBLSJ2vN9+E7+J423bmd0eIWW+ls8RcZY777yT+fPnExkZSbVq1QgMDGTdunX079+fb7/9lqCgIIe9lsFgsBXXBQqGap8/f942rDsuLo4aNWrYHhMXF0eDBg2A/O8RcXFxdsco2K5atSq5ubk3fIzislismM3pxX5+WeHmZsRk8sZsziAvz+LqcMqVspxbn2eexvPd5bbtrF6Pk/72XMgGstNcFheU7byWdsptYSaT93Vf0S9ygX3hwoUrzhTeuHFj5syZU9RDikh5ZbUSfEcz3E6dtDUlv7uG7L8/4MKgRMq/ESNG0Lt3b8aOHct7773HoEGDmDp1KgsWLCAlJYWhQx0358GoUaO4dOmS3ZrTBw4cAKBevXpUr14dPz8/du7caSuOzWYzBw8eJDo6GoCoqCjWrFlDXl4ebm5uAOzYsYPatWsTEhKCv7//DR/jRuTmVpwvmHl5lgr1fktSmcptejqhtcLsmi598m9y/tYaStl7KFN5LWOU2+IpcoFdvXp1fvjhB+68885C+3bu3ElYWNhlniUiFY0hPp7Kt9rPKnzxwDGsN3AlSUSuT3h4OBs3buTUqVMA9O3bl8qVK7N3716aNGlC165dHfZanTt35qmnnmLevHl06tSJkydP8uqrr9K5c2fbzOLR0dFMnz6d4OBgwsPDmTZtGmFhYXTo0AGA7t27s3jxYsaNG8eAAQPYv38/K1asYMKECUD+vdc3egwRuT7uO38kqMv9dm3xJ86Cn5+LIhIpW4pcYPfs2ZPXXnuN3Nxc2rdvT0hICImJiWzevJl3332X0aNHOyNOESlDPDZ9SUCvf9q282rUJHHXfjAYXBiVSMXi5eVlGz4N0KVLF7p06eLw17n33nt56623mD9/PvPnz8ff358uXbrwzDPP2B4zfPhwcnNzeemll8jMzCQqKoolS5bYJiULCQlh8eLFTJ48ma5duxIaGsqYMWPsTgQ44hgicnV+zz+L97LFtu3Mf/Yk5Z0FLoxIpOwxWK1Wa1GfNGPGDJYtW0ZeXp6tzc3NjT59+vDcc885NMDyLC/PQmKia+9fKQnu7kaCgnxJSkrTMBMHK4259R/+FF5rVtm2U8e9TMaIUS6MqOhKY17LC+W2sOBgX4fO1BoTEwNgu3p86NAh3n77bU6fPk2tWrXo378/zZs3d9jrlXfqq+VGlYncXmaVj0trPyOndRsXBXRtZSKvZZRyW1hR+upizaw/atQoBg0axM8//0xycjImk4kmTZo4dNIUESljsrIIrR5q15S0+TtymzRzTTwiFUxSUhLDhg1jz549ALRq1Ypx48YRHR2Nm5sbNWrU4IcffmDbtm0sWbKEO+64w8URi0hp4L77J4IeaG/XdvFELFY/fxdFJFK2FXvpOn9/f+7565T9IlIhuR05THDr2+3a4k+dhxtYGkdEimbWrFmcOnWKyZMnYzKZmD9/PtHR0URERLBkyRJ8fHxITU1lwIABLFy4UAW2iOD74nP4LP5jCLhW+RC5cddVYN93333MmTOHBg0a0K5dOwxXuY/SYDCwefNmhwVYYP369SxcuJDffvuNGjVq8PTTT/N///d/QP7wt8mTJ/Prr78SGBhI79696d+/v+25FouFd955h48++giz2UyLFi14+eWXqVmzpu0xjjiGSEXkvWgefuOet21nPdAF8/JVV3mGiDjDtm3bGD58ON26dQOgWrVqPPzww/Tp08e2DrSfnx8DBgxg3LhxrgxVRFwtM5PQGlXsmi59uJ6ctu1cFJBI+XFdBfbtt9+O7/8Wk7/99tuvWmA7w6effsqLL77I888/T9u2bfn888959tlnCQsLo1atWvTt25f27dszYcIE9u3bx4QJEwgMDKR79+4AzJ07lzVr1jBlyhSqVq3KtGnTGDhwIJ9//jkeHh4kJSXd8DFEKhyrlaB2d+P+3wO2puTFK8h+UBMKibhCQkICtWvXtm0X/FywDnWB0NBQUlNTSzQ2ESk93PfuJujv9oX0xZjfsfqbrvAMESmK6yqwp0yZYvv59ddfd1owl2O1Wnnrrbfo06cPffr0AWDo0KHs3buXn376iZ9++gkPDw9eeeUV3N3dqVu3LqdPn2bRokV0796d7Oxsli5dynPPPUebNvkTNcyaNYvWrVuzadMmOnXqxIcffnjDxxCpSAyJCVRuUNuuLWHfISw3h7soIhHJzc3F09PTtu3u7m733z+zWDRpjUhF5PuvF/BZMMe2nflQN1IWLXddQCLlULGmLU1NTeXChQsAZGdns3jxYiZNmsSuXbscGhzAiRMniI2NLbS0yJIlSxg8eDC7d+8mKirK7gtEq1atOHnyJAkJCRw+fJi0tDRatWpl228ymWjYsKEtXkccQ6SiqLT1G7vi2lK5MvHnL6m4FhERKa2ysgitYrIrri+tWaviWsQJijzJ2f79+xkwYAD//Oc/GT16NJMmTeLDDz/EZDKxevVq3n77be677z6HBXjq1CkA0tPT6d+/PwcPHqRatWo89dRTtGvXjvPnzxMREWH3nCpV8u8pOXv2LOfPnwcKD5GrUqUK586dA3DIMUQqAr8xI/Fe/sfkJ2mjnif9ed3LKVJabNu2jRMnTgD5V6kNBgNbt27l2LFjtsecOXPGVeGJiAu479tL0P1t7douHv8NqynANQGJlHNFLrBnzZpFnTp16NGjB5mZmXz22Wc89thjjB8/nvHjxzN//nyHFtgF94k9//zzPP3004wePZqvvvqKIUOGsGzZMjIzMwvdA10wRC4rK4uMjAyAyz4mOTkZwCHHKC53d8etfVpaFawZ58h1XiVfieU2J4egqvbL8Jm//pa8llHFX4qgFNNn1nmUW+eaM2dOoba33367UFtJz6UiIq7hO+Ff+Mx5y7ad1elBzMvec2FEIuVfkb8b//LLL8yaNYvq1avz7bffkpmZyUMPPQTAAw88wIYNGxwaYKVKlQDo378/XbvmT550yy23cPDgQZYtW4aXlxfZ2dl2z8nKygLAx8cHLy8vIH8oe8HPBY/x9vYGcMgxisNoNBAU5Fvs55c1JlPxcyVX59TcHjsGfxnhQUoKJj8/571mKaHPrPMot463ZcsWV4cgIqVFdjah1SrbNSWv/ojs9h1dFJBIxVHkAttoNNqu5G7btg2TyUSTJk2A/KvNfy5AHSEsLAyg0BDuevXqsXXrVsLDw4mLi7PbV7BdtWpVcnNzbW01atSwe0yDBg1sr3GjxygOi8WK2Zxe7OeXFW5uRkwmb8zmDPLyNLGOIzk7tx7Ll+L77HDbds6995G69lPIAZLSHP56pYU+s86j3BZmMnk75Ip+eLjmQRARcD/wC0H3tbZru3j0NNbAoCs8Q0QcqcgFdqNGjfj444/x8vLiiy++oG3bthgMBhISEli0aBGNGjVyaIANGzbE19eXX375hZYtW9rajx49So0aNWjevDlr1qwhLy8PNzc3AHbs2EHt2rUJCQnB398fPz8/du7caSuOzWYzBw8eJDo6GoCoqKgbPkZx5eZWnC+YeXmWCvV+S5IzchvYqQOVdu20bZvnLCTrkUehAv0/1GfWeZRbERHH8508AZ+3Zti2szr+H+aVH7gwIpGKp8gF9pgxYxgwYAD//ve/CQ4O5qmnngKgc+fOWCwWlixZco0jFI2XlxcDBgxgzpw5VK1alSZNmvDvf/+b77//nuXLl1OvXj0WL17MuHHjGDBgAPv372fFihVMmDAByL9vOjo6munTpxMcHEx4eDjTpk0jLCyMDh06ANC9e/cbPoZIeWFIvkTl+jXs2hJ2H8BSo6aLIhIREZGryskhNDzEril55Qdkd/w/FwUkUnEZrFartahPSk1NJSYmhvr16+Pj4wPAV199RfPmzQkNDXV4kADLli3jvffe48KFC9StW5dhw4bRvn17IH9m88mTJ3Pw4EFCQ0Pp16+f3ZXlvLw8Zs6cybp168jMzCQqKorx48dTrVo122MccYyiysuzkJhYfofZFnB3NxIU5EtSUpquWDmYo3Nbacf3BD70R2ds9fHhYkws/G9kR0Whz6zzKLeFBQf7atK3Ukx9tdwoZ+fW7dcDBLf7m13bxSOnsAYFO/y1ShN9Zp1HuS2sKH11sQrsq4mJiaFu3bqOPGS5pU5bbpQjc+s7/kV85r9j204fMpy0VybdaIhlkj6zzqPcFqYCu3RTXy03ypm59Xl9Ir4zp9m2s9u1J3nNOoe+Rmmlz6zzKLeFFaWvLvIQ8eTkZGbMmMGuXbvIycmhoD63Wq2kp6eTnJzMoUOHinpYEXGVvDwq1wrD8L+Z8wEubfiSnFZ3uTAoEXEmnQwXKeNycqhcsyqG/03EC5C8fDXZD3R2YVAiAsUosF977TU+//xz7rnnHk6cOIG3tze1atViz549mM1mXn31VWfEKSJOYDxzmpCWje3aLh7/DaspwEURiYgj6GS4SPnlduggwW1a2bVdPHwSa3DIFZ4hIiWpyGPStm/fztNPP828efN49NFHCQsL48033+TLL78kMjKS48ePOyNOEXEwzw9W2xXXOXfcSXycWcW1SDnw2muvsXbtWmrVqoWbmxv+/v40btyYnJwcnQwXKcN8pk2xK66z77k3v+9WcS1SahS5wDabzbRo0QKA+vXr8+uvvwLg6+tLv3792Lp1q0MDFBHHC/jHQ5iGPWnbTpn5Npc++8qFEYmII+lkuEg5k5tL5RpV8J02xdaUvGQlyR9/6sKgRORyilxgBwUFkZKSAkDNmjVJSEggKSkJgKpVq3LhwgXHRigiDmNITSG0igmP7761tSX8+DOZ0X1cGJWIOJpOhouUH25HDhN6czCGzExb28WDJ8ju8pALoxKRKylygX3nnXcyf/58fv/9d6pVq0ZgYCDr1uXPVvjtt98SFBTk8CBF5Ma57/6JynXC7drizyZiqaOJjkTKG50MFykffGZNI7j17bbt7Lvuzh8SXrmyC6MSkaspcoE9YsQIEhISGDt2LAaDgUGDBjFt2jRuv/12li9fTvfu3Z0Rp4jcAJ/XJxL0QHvbdka/gcTHmcG9yPMcikgZoJPhImVcbi4hdcLxnTLR1mRetJzk9RtdGJSIXI8if7sODw9n48aNnDp1CoC+fftSuXJl9u7dS5MmTejataujYxSR4rJYCGlYB2Nioq3p0scbyLmnretiEhGnGzFiBL1792bs2LG89957DBo0iKlTp7JgwQJSUlIYOnSoq0MUkStwO3aU4L+1tGu7+N8YrKGhLopIRIqiWJevvLy8aNCggW27S5cudOnSxWFBiciNM56NJaTZLXZtF4+cwhoU7KKIRKSk6GS4SNnkPXsWfpNetm3n3N4qfxJSg8GFUYlIURRpiHhMTAwxMTG27UOHDjFkyBA6derE0KFD2bt3r8MDFJGi8/x0nV1xndOkGfEXklVci1QQ69evJyMjo9DJ8Jdffpm7776bRYsWuTA6ESkkL4+QyJp2xbV5/hIuff61imuRMua6CuykpCSio6Pp3LkznTt3pm/fvhw/fpzo6Gh2796Nt7c3P/zwA48//jg7d+50dswichWmx3tiGviEbTtlyjQubf5OHbRIBfLCCy/w22+/XXbfoUOHmD17dglHJCJX4hZzjNCbgjD+byJCgIsHjpHV7REXRiUixXVdQ8RnzZrFqVOnmDx5MiaTifnz5xMdHU1ERARLlizBx8eH1NRUBgwYwMKFC7njjjucHbeI/FV6OqG1wuyaEv+zi7yISBcFJCIlafDgwbb1ra1WK0OHDsXDw6PQ4xISEqhRo0ZJhycil+E9ZzZ+E16ybee0aMmljVt0UlykDLuuAnvbtm0MHz6cbt26AVCtWjUefvhh+vTpg4+PDwB+fn4MGDCAcePGOS9aEbkst1/2EXTv3XZt8b9fhMt8uRaR8mnw4MF89NFHAHzyySc0bNiQ4GD720KMRiMmk8nWn4uIi+TlEdI4AuPFeFuTec5Csh551IVBiYgjXFeBnZCQQO3atW3bBT/fdNNNdo8LDQ0lNTXVgeGJyDVNnozppT/Ofmf0jCb1rbkuDEhEXKF58+Y0b97ctj1kyBCqV6/uwohE5HKMJ2IIaXWbXVvC/iNYwm66wjNEpCy5rgI7NzcXT0/PP570v7Vz3S+zhq7FYnFQaCJyVVYrAY0bQOzvtqbk1R+R3b6jC4MSkdJgypQpAKSlpeHr6wvAF198wfnz52nXrh01a9Z0ZXgiFZb3gjn4/esF23ZO09u49PVWDQkXKUeKNIu4iJQOhgsXCK0agPFPxfXFgydUXIsIACdPnuT++++3zRY+a9YsRo4cydSpU3nwwQfZs2ePiyMUqWAsFgJurW9XXJtnz+PSpm0qrkXKmeteB3vbtm2cOHECyL9KbTAY2Lp1K8eOHbM95syZM46PUETseHzxbwL69PyjITKSpB92Y82zui4oESlVpk+fjpubG/fddx85OTm8//77PPDAA7z66quMHTuWN998k5UrV7o6TJEKwXjqJDRvbHdVK+GXw1huutllMYmI81x3gT1nzpxCbW+//XahNoPOwok4jf+T/fBa97FtO33CZHzGvwhJaYAKbBHJt2vXLiZPnkzjxo3ZsWMHKSkp9OjRAz8/Px599FGGDRvm6hBFKgSvxfPxf3GMbTv3lltJ2vqDrlqLlGPXVWBv2bLF2XGIyNVkZhJao4pdU+K3P2Bo2gQfF4UkIqVXTk4OAQEBQP4ING9vb1q0aAFAXl7eZedQEREHslgIjmqC229/jO5Mmz2X9EejXRiUiJSE6+phw8PDnR2HiFyB28H/Etz2Tru2+DNx4OV1/UNQRKRCiYyM5Ouvv6ZWrVps3LiRu+++G3d3d3Jycli1ahURERGuDlGk3DKePkVIVBP7xjNnyPYLhlxNBixS3mmSM5FSzHveO3bFdebD3YiPM4OXlwujEpHSbvjw4Xz88ce0adOG5ORkBg4cCEDHjh358ccfGTp0qIsjFCmfvJYttiuucyMiSUpIAS2ZJ1Jh6AKYSGlktRJ0dxTux47ampKXryb7gc4uDEpEyoq77rqLzz77jAMHDtC0aVPbSLQ+ffrQqlUrIiMjXRyhSDljtRLU6jbcT56wNaVMf4vMx/virvutRSoUFdgipYzh4kUqN6xj15Zw4CiWqmEuikhEyqLq1atT/S9Xzfr06QNASkoK/v7+rghLpNwx/naGkBaN7NoS9vyKpXoNF0UkIq6kAlukFKn0zSYCH+1u2867OZzEnw9qtlERKZLs7GyWL1/OTz/9RE5ODlZr/ioDVquV9PR0jh8/zi+//OLiKEXKPq93l+E/eoRtO7dOXZJ27FW/LVKBObzAjomJoW7duo4+rEi55zfyabxXvWvbThv7EunPjrnKM0RELu+NN97gvffeIyIigsTERDw9PQkODubo0aPk5OTw9NNPuzpEkbLtMrdypbw+g8x+A10YlIiUBkUusJOTk5kxYwa7du267Fnx5ORkDh065PBARcqt7GxCq1W2a0ratI3cpre5KCARKeu+/vprnnjiCcaOHcuCBQs4ePAgb731FhcuXCA6OhqLRTMZixSXMfZ3Qm5raNeWsGs/lpq1XBOQiJQqRZ5F/LXXXmPt2rXUqlULNzc3/P39ady4MTk5OZjNZl599VVnxClSLrkdO1qouI4/dV7FtYjckMTERNq0aQPkL9l14MABAKpWrcqgQYPYuHGjK8MTKbO8Vr1rV1zn1ahJ/PlLKq5FxKbIBfb27dt5+umnmTdvHo8++ihhYWG8+eabfPnll0RGRnL8+HFnxClS7ngtWUjw31ratrM6/l/+Elw+Pi6MSkTKA39/f7KzswGoVasW586dIzU11W5bRIrAaiWoTSv8R/5xe0XKa2+QuPsAGLXqrYj8och/EcxmMy1atACgfv36/PrrrwD4+vrSr18/tm7d6tAARcodq5XADm3wf2G0rcm8YCnmlR+4MCgRKU9atmzJypUrSU9Pp1q1anh7e7Np0yYAfv75Z/z8/FwcoUjZYTx3ltCqAbgfOmhrS9i5j8wBT7owKhEprYpcYAcFBZGSkgJAzZo1SUhIICkpCcgfenbhwgXHRihSjhguJRFaNYBKv/xsa0v4+SBZXf/hwqhEpLx5+umn2bdvH4MHD8bd3Z3HHnuM8ePH061bN9566y06duxY7GPPnTuX3r1727UdOnSI6OhomjVrRtu2bVmyZIndfovFwuzZs2ndujVNmzalX79+nD59usSPIVJUnmtWEdK0gW0776ab84eE165zlWeJSEVW5AL7zjvvZP78+fz+++9Uq1aNwMBA1q1bB8C3335LUFCQw4MUKQ8qbd9G5Yiatm1LYGB+Jx1ezYVRiUh5FBkZyRdffMGTT+ZfYRs1ahRDhw6lcuXKPPXUU4wZU7wVCpYvX87s2bPt2pKSkujbty+1atVi7dq1DBs2jLfeeou1a9faHjN37lzWrFnDpEmT+OCDDzAYDAwcONA2jL2kjiFy3axWAu9rjWn4U7am1IlTSPzlsIaEi8hVFXkW8REjRtC7d2/Gjh3Le++9x6BBg5g6dSoLFiwgJSWFoUOHOiNOkTLN98Xn8Fm8wLad9sxo0l8c78KIRKS8Cw0NJTQ0FACDwWArtovjwoULjBs3jj179lC7dm27fR9++CEeHh688soruLu7U7duXU6fPs2iRYvo3r072dnZLF26lOeee8428dqsWbNo3bo1mzZtolOnTiVyDJHrZTx/jpAmkXZtCT/+jKWOlqEVkWsrcoEdHh7Oxo0bOXXqFAB9+/alcuXK7N27lyZNmtC1a1dHxyhSduXmEnpzsF1T0r83kRt1h4sCEpGK4uTJk2zbto309PRCy3IZDIYinRD/73//S0BAABs2bGDOnDnExsba9u3evZuoqCjc3f/4StGqVSsWLFhAQkICsbGxpKWl0apVK9t+k8lEw4YN2bVrF506dSqRY4SEhFz3+5WKy/OjNZiGDrJtW0KrkLD/CLi5uTAqESlLilxgr1+/njZt2tCgwR/3o3Tp0oUuXboQHx/PokWLGDhwoEODFCmLjCdPEHJHM7u2iydisfr5uyYgEakw1q9fzwsvvIDVar3s/qIW2O3ataNdu3aX3Xf+/HkiIiLs2qpUqQLA2bNnOX/+PAA33XRToccUzGZeEse4kQLb3b38Dwl2czPa/bfCsVrx73Av7nt325rSJ0wma9iIon9Z/osKn1snUV6dR7m9MUX+m/HCCy/wwQcfXPZe60OHDjF79mwV2FLhea1eif8zf3x5zW7dluS1G1wYkYhUJPPmzeOuu+5i0qRJhIWFYTAYnPZamZmZeHh42LV5enoCkJWVRUZGBsBlH5OcnFxixyguo9FAUJBvsZ9f1phM3q4OoeSdPw9/OXnDkSP4RETgyIUzK2RuS4Dy6jzKbfFcV4E9ePBg2/rWVquVoUOHFurEABISEqhRo4ZjIxQpYwIe+j88dnxv2zbPnkfWo71cGJGIVDRnz57llVdeKXTF1xm8vLxsE40VKChofXx88PLyAiA7O9v2c8FjvL29S+wYxWWxWDGb04v9/LLCzc2IyeSN2ZxBXp7l2k8oJyqt/RC/gf1s25bgYJKPnMwfEp6U5pDXqKi5dTbl1XmU28JMJu/rvqJ/3QX2Rx99BMAnn3xCw4YNCQ62v6/UaDRiMpno1q1bEcMVKR8MKWYq17WfETzhp1+w1Kp9hWeIiDhH7dq1bUOnnS0sLIy4uDi7toLtqlWrkpuba2v780n4uLg42+1mJXGMG5GbW3G+YOblWSrG+7VaCezSkUo//WhrSn1pAhnDR4IVcEIOKkxuS5jy6jzKbfFcV4HdvHlzmjdvbtseMmQI1atXd1pQImWN+487CHrwj3VlrZ6eXDx1XpOiOIDFYuHEsVhSzGn4m3ypUz8co5ZIEbmqUaNGMXHiRMLDw2nWrJltqLQzREVFsWbNGvLy8nD739+8HTt2ULt2bUJCQvD398fPz4+dO3faimOz2czBgweJjo4usWOIFDDEx1P5VvsZwRO/301e/YgrPENE5PoV+VvqlClTqF69Omlpfwyb+eKLL1i2bBmnT592aHB/dfLkSW677TbbutuQf993dHQ0zZo1o23btixZssTuORaLhdmzZ9O6dWuaNm1Kv379CsXpiGNIxeU78WW74jp98FAu/hav4toB9u89xsvPzefV5xcw9eXlvPr8Al5+bj779x5zdWgipdrkyZNJSEjgiSeeoFmzZtxyyy12/xo2bOiw1+revTupqamMGzeO48ePs27dOlasWMHgwYOB/Pumo6OjmT59Olu2bOHw4cOMHDmSsLAwOnToUGLHEAHw2PCJXXFt8TcRfzZRxbWIOEyRJzk7efIkgwcP5oEHHuCZZ55h1qxZLFiQv77vm2++ydKlS2nRooXDA83JyWH06NGkp/9xH1RSUhJ9+/alffv2TJgwgX379jFhwgQCAwNta17OnTuXNWvWMGXKFKpWrcq0adMYOHAgn3/+OR4eHg45hlRQeXmE1KuOMS3V1nRp/UZy7rrbhUGVH/v3HmP21PdJSU4juEoAIZ4eZGZlc/TgaWZPfZ/hz/ekSfP6rg5TpFR68MEHS+y1QkJCWLx4MZMnT6Zr166EhoYyZswYu2U7hw8fTm5uLi+99BKZmZlERUWxZMkSWx9aUseQii3g4Qfw+OE/tu20F/5F+sjnXBiRiJRHBuuV1vC4gqFDh3LixAneeOMNGjRowN/+9jfuvvtuXn31VcaOHUtycjIrV650eKAzZ85k37597Ny5kylTptCtWzcWLFjAqlWr+Oabb2zrXs6cOZOvv/6aL7/8kuzsbFq1asVzzz1Hz549gfwhZa1bt+a1116jU6dODjlGceXlWUhMdMwEGqWZu7uRoCBfkpLSys19HMbfzhDSopFd28VjZ7AGBJZoHOUxt5A/auTl5+Zz9L+nCa9ZxW4GZKvVSuyZOCIb1uKVaYOdMly8vOa1NFBuCwsO9tVSKKWY+uqyz3DxIpUb1rFrS/xuJ3kNbimR1y/PuXUl5dV5lNvCitJXF7lH37VrF88++yyNGzdm9+7dpKSk0KNHD/z8/Hj00Uf59ddfixzw9bzmBx98wNSpU+3ad+/eTVRUlK0wBmjVqhUnT54kISGBw4cPk5aWRqtWrWz7TSYTDRs2ZNeuXQ47hlQsnh9/YFdc57SIIj7OXOLFdXl24lgsp2POElwloNDyQgaDgeDKAZyKieXEsVgXRShS+mVmZvLLL7+we/dudu3axa5du9i5cydbt25l+vTprg5PpER4fPapXXFt9fbOHxJeQsW1iFQ8RR4inpOTQ0BAAADbtm3D29vbNiQ8Ly/PrlB1BLPZzJgxY3jppZcKLTdy/vx5IiLs75mpUqUKkL9Eyfnz5wEKPa9KlSq22VUdcQypOAIe7YbHN5tt2ynT3iSzT7+rPEOKI8WcRnZ2LiGel78Fw9PLg6QEMynm8n9VSaQ4fvzxR0aMGIHZbL7sfl9fX0aPHl3CUYmUrIDuD+KxfattO+25F0h/7gWXxVOeaUJSkT8UuRqOjIzk66+/platWmzcuJG7774bd3d3cnJyWLVqVaFi9Ua98sorNGvWjC5duhTal5mZWege6IKZUrOyssjIyAC47GOSk5Mddowb4e5e/v/4FAynKNNDIFNTCaoRZteU/NPPWOrVL/ovkQOVi9xeRlCwPx4elcjOysHH16vQ/sysHDw8KhEU7O+U36HymtfSQLktGW+++SaBgYFMmjSJDRs2YDQa6datG9999x3vv/8+ixYtcnWIIk5jSEig8i32S2Qmbt1BXsNbXRRR+bZ/7zE+WrWJ0zFnyc7OxcPDnZp1b+aRXh00V4pUSEWuDYYPH86QIUNYtWoVHh4eDBw4EICOHTty8eJF5s+f77Dg1q9fz+7du/nss88uu9/Ly4vs7Gy7tqysLAB8fHzw8sr/Yp6dnW37ueAx3t7eDjtGcRmNBoKCfG/oGGWJyXRj+XKZn36CO+6wb8vOJqBSJdfEcxllNrdX0OL2CCIb1uC/+09gCvApdA92UqKZRk3r0OL2CKeeIS9veS1NlFvnOnLkCBMnTqRDhw6kpqayevVq2rRpQ5s2bcjJyWHevHksXLjQ1WGKOJzHxs8JeOIx27a1UqX8ZTNLUZ9dnmhCUpHCilxg33XXXXz22WccOHCApk2bEh4eDkCfPn1o1aoVkZGRDgtu7dq1JCQk0LZtW7v2l19+mSVLlnDzzTcTFxdnt69gu2rVquTm5traCtbNLNhu0KABAGFhYTd8jOKyWKyYzenXfmAZ5+ZmxGTyxmzOIC+vbE2U4PX6ZLzfmGLbznqiH+kzZ0NqNpB95SeWkLKc22t5uMe9nDpxjpPHzxESGoCnlwdZmdkkxCfjH+DDQ/+8l+TkDKe8dnnOq6spt4WZTN4Ov6JvsVgIC8sfdVO7dm2OHz9u29exY0eef/55h76eSGlg6tkdzy2bbNtpz44hfexLLoyofLNYLHy0ahMpyWl2E5L6+HjhXcOT2DNxfLxqM42a1dVwcalQijW6tXr16lSvXt2urU+fPgCkpKTg7+9/45EB06dPJzMz067t/vvvZ/jw4TzwwAP8+9//Zs2aNeTl5eH2vzWHd+zYQe3atQkJCcHf3x8/Pz927txpK47NZjMHDx4kOjoagKioqBs+xo2oSDPz5eVZys77tVgIaVQf48V4W9OlD9eT07YdlML3UKZye51ubVqPYWN62oadJVxMxsPDnYiGNflHr/bc2rSe099zecxraaHcOleNGjU4cuQILVu2pGbNmmRkZBATE0PdunXJzc0lLU3zF0j5YUhKpHJkLbu2xG++J69RY9cEVEEUZULSepHVr3AUkfKnyAV2dnY2y5cv56effiInJ4eCVb6sVivp6ekcP36cX375xSHBVa1a9bLtISEhhIeH0717dxYvXsy4ceMYMGAA+/fvZ8WKFUyYMAHIv286Ojqa6dOnExwcTHh4ONOmTSMsLIwOHToAOOQYUr4Yz50lpKn96ISLh09iDQ5xUUQVV5Pm9WnUrK4mThEpoi5dujB9+nQsFgu9e/emUaNGTJo0id69ezN//nzq1avn6hBFHMLjqy8I6N3Dtm01GLj4+0UNCS8BmpBU5PKKXGC/8cYbvPfee0RERJCYmIinpyfBwcEcPXqUnJwcnn76aWfEeVkhISEsXryYyZMn07VrV0JDQxkzZgxdu3a1PWb48OHk5uby0ksvkZmZSVRUFEuWLLFNWuaIY0j54fHZpwT0723bzm3YiKRvv4e/nJmVkmM0GnXmW6SIBgwYQFJSEvv37wfyb60aOHAgQ4YMwc/Pj3nz5rk4QpEbZ+rdA8+vvrBtp48YRdq4l10YUcXib/LFw8OdzKxsfHwKT0ialZmNh4c7/qaKM9+QCIDBWnAJ+jrdc889PPDAA4wdO5YFCxZw8OBB3nrrLS5cuEB0dDQPPfRQiRbZZVlenoXExPJ/Vq+sLFZv6tcbz88/tW2nTnqdjEFDXBjRtZWV3JY1yqvzKLeFBQf7lsis6qmpqZw4cYI6derg5+fn9NcrL9RXlz6GS0lUjqhp15a0ZTu5jZu6KKKrK0u5LQqLxcLLz83n6MHThNeoUmhC0tgzcUQ2rMUr0wY7ZeRZec1raaDcFlaUvrrIn/bExETatGkD5C/ZdeDAASB/OPegQYPYuHFjUQ8p4loZGYRWMdkV14nf7Sz1xbWIyJWkpxeeQDMmJoYmTZqouJYyzWPzV4WK6/jfL5ba4ro8MxqNPNKrA/4mX2LPxJGelklenoX0tExiz8Thb/LlH73a67YuqXCK/In39/e3LWtVq1Ytzp07R2pqqt22SFnhdmA/oTXt7/WP/y2evAa3uCgiEZHiO3ToEA8//DDLly+3a09OTqZnz5506tSJmJgY1wQncoNMT/Qi4LFHbNvpQ0cQH2cG3bLnMk2a12f48z2JaFiTtJR0zsdeJC0lnciGtbREl1RYRb4Hu2XLlqxcuZKoqCiqVauGt7c3mzZtomvXrvz88886My5lhvfsWfhN+uNercx/9iTlnQUujEhEpPh+++03nnjiCXx8fApNYubh4cGLL77I4sWLeeyxx/j0009ty3iJlHYGczKV69nPxZH09VZymzV3UUTyZ5qQVMRekT/5Tz/9NPv27WPw4MG4u7vz2GOPMX78eLp168Zbb71Fx44dnRGniONYrQRHNbErrpPf+0DFtYiUaQsXLiQoKIhPPvmE+++/326ft7c30dHRrF27Fh8fH+bPn++iKEWKptI3mwsV1/G/xau4LmUKJiS9LaoB9SKrq7iWCq3IV7AjIyP54osvOHr0KACjRo3Cz8+PvXv30q5dOwYNGuTwIEUcxRAXR+VG9ld2Lv56HGuVKi6KSETEMXbs2MGTTz5JYGDgFR8TEhJC3759WbVqVckFJlJM/gP64LXhE9t2+uChpE2c4sKIRESurcgFNkBoaCihoaFA/kLyTz75pEODEnGGv66VmVerNok792kJLhEpF+Lj46lZs+Y1HxcREcH58+dLICKR4jGkmKlct5pdW9KX35DbvKWLIhIRuX7FKrBPnjzJtm3bSE9Px2Kxn7rdYDAwdOhQhwQn4ij+Qwfh9dEa23bqSxPIGD7ShRGJiDhWcHAwcXFx13xcYmLiVa9yi7hSpa3fEPjPh+3a4s/EgVfhdZZFREqjIhfY69ev54UXXuBKy2erwJZSJSuL0Oqhdk2JW/5DXuMmLgpIRMQ5oqKiWLduHZ06dbrq49avX88tt2ilBCl9/J/sj9e6j2zb6QMGk/baNBdGJCJSdEUusOfNm8ddd93FpEmTCAsLs1tUXqQ0cTt8iOB77rBriz99Aby9XRSRiIjz9O7dm549e/L6668zcuRIPD097fZnZ2cza9Ystm/fzsKFC10UpUhhhtQUKtcJt2tL2riZ3Ja3uygiEZHiK3KBffbsWV555RVuuukmZ8Qj4hDeC+fi99JY23ZWl4cxL3nXhRGJiDhX48aNeeGFF3jttdf49NNPufPOO6lWrRp5eXmcPXuWnTt3kpSUxIgRI2jdurWrwxUBoNL2bQR272LXppPhIlKWFbnArl27NufOnXNGLCI3zmolqO1duB/6r60peclKsrs85MKgRERKRq9evWjQoAFLlixhy5YtZGVlAeDr68vdd99Nv379aNq0qYujFMnn//RgvD5837ad8UR/Ut+Y5cKIRERuXJEL7FGjRjFx4kTCw8Np1qxZoSFoIq5iSEig8i217doS9h/BEqbRFiJScbRo0YIWLVoAkJSUhNFoJCAgwMVRifxJaiqhdW62a0r67Gty72jlooBERBynyAX25MmTSUhI4IknnrjsfoPBwMGDB280LpEiqfTtFgJ7dLVt51WpSuL+I2A0ujAqERHXCgoKcnUIInYq/fAfAh9+wK4t/tR58PFxUUQiIo5V5AL7wQcfdEYcIsXmN2oE3iuX2bbTRo8lfcyLLoxIRERE/srvmaF4r15p287o/QSpM2a7MCIREccrcoH99NNPOyMOkaLLySE0PMSuKenLb8ht3tJFAYmIiEghaWmE1ra/XevShi/JaXWXiwISEXGeIhfYAJmZmRw5coScnBzbetgWi4WMjAx2797N6NGjHRqkyF8ZEhOo3MD+fuv4k+fA19dFEYmIiMhfuf+8h6CO99q1qb8WkfKsyAX2jz/+yIgRIzCbzZfd7+vrqwJbnMp9315M/R+3bWfd1wHz+2tdGJGIiIj8lcfXXxAQ3cO2ndEzmtS35rowIhER5ytygf3mm28SGBjIpEmT2LBhA0ajkW7duvHdd9/x/vvvs2jRImfEKQKA13sr8Bs7CkN2Nrm162Betoq8hre6OiwREREpkJOD7+QJ+Mz94/7qS5/8m5y/af11ESn/ilxgHzlyhIkTJ9KhQwdSU1NZvXo1bdq0oU2bNuTk5DBv3jwWLlzojFilIsvIwO+F0bbJUbL+3omUd+ZjNWnpGRERkdLCGPs7poFPUGn3TwCkD3qKtPETwcPDxZGJiJSMIq9hZLFYCAsLA6B27docP37ctq9jx45aokscznjmNIFdOuK9eiVWo5HUcS9jXr5KxbWIiEgp4rH5K4La/Y1Ku3/CYgogedkq0iZNVXEtIhVKkQvsGjVqcOTIEQBq1qxJRkYGMTExAOTm5pKWlubYCKVCq/TNJoI63EOl/fuwhISQ/MEnZIwYpfWtRURESoucHHwnvkzAY49gTEoip9ltJG3ZTnanLq6OTESkxBW5SunSpQvTp09n5cqVBAUF0ahRIyZNmsQ333zDnDlzqFevnjPilIrGYsFnxlQCev4jv7O+rTlJm74jp829136uiIiIlAjj2VgCu3bC5+1ZAKQPGMylz77GUrOWawMTEXGRIt+DPWDAAJKSkti/fz8AL7/8MgMHDmTIkCH4+fkxb948hwcpFYvhUhL+QwfhuekrADIe70fq5Kng6eniyERERKSAx5av8R86CGNiIhZ/EylvvkN2l4ddHZaIiEsVucA2Go08//zztu3GjRuzefNmTpw4QZ06dfDz83NogFKxuB3YT0C/aNxOn8Lq5UXKG7PIerSXq8MSERGRArm5+E6djM9bMwDIadIM86LlWGrXcXFgIiKuV+QCOz09HR8fH7u2mJgYmjZt6rCgpGLyXLMK/zEjMWRmklejFuZlK8ltrM+ViIhIaWE8dxb/wf3w+PEHADL6DSR1wmsaZSYi8j/XfQ/2oUOHePjhh1m+fLlde3JyMj179qRTp062yc5EiiQrC7/nRmIa/hSGzEyy2t9P0qatKq5FRERKkUrfbCao3d/w+PEHLH7+mBctJ/X1GSquRUT+5LoK7N9++40nnniC5OTkQpOYeXh48OKLL5KWlsZjjz3G+fPnnRKolE/G2N8JfOjveK9YgtVgIG3Mi5jf+xBrULCrQxMRERGA3Fx8prxKQM/uGBMSyGnUhKTN35H1UDdXRyYiUupcV4G9cOFCgoKC+OSTT7j//vvt9nl7exMdHc3atWvx8fFh/vz5TglUyp/ATh0Iua0hlfbuwRIYiHn1R6SPHqsluEREREoJjy1fE3pzML6zpmOwWsl4oj+XNm7GUqeuq0MTESmVruse7B07dvDkk08SGBh4xceEhITQt29fVq1a5ajYpLzKyyP0piC7pqRN32lJDxERkVIksP09VNq/z7ZtXrCUrK7/cF1AIiJlwHUV2PHx8dSsWfOaj4uIiNAQcbkqt0MHCW7Tyq7t4q/HsVap4qKIRERExE5GBqE1q9o1XVqzjpx27V0UkIhI2XFdY3GDg4OJi4u75uMSExOvepVbKjavyRMKFdfxF5JVXIuIiJQS7pu+LlRcJ+w/ouJaROQ6XVeBHRUVxbp16675uPXr13PLLbfccFBSDhkMeM+YZtvM6BlNfJwZDAYXBiUiIiI2d96Jfw/7icvi48xYwm5yUUAiImXPdRXYvXv3ZufOnbz++utkZWUV2p+dnc3UqVPZvn07vXr1cniQUnYZki8RFOxn15b02dekvjXXRRGJiIiInczM/L76xx9tTanjJ+afCBcRkSK5rnuwGzduzAsvvMBrr73Gp59+yp133km1atXIy8vj7Nmz7Ny5k6SkJEaMGEHr1q2dHbOUER5fbiTg8Uft2uLPxIGXl4siEhERZ4mNjaVdu3aF2idNmsQjjzzCoUOHmDx5Mr/++iuBgYH07t2b/v372x5nsVh45513+OijjzCbzbRo0YKXX37Zbg4YRxxD7FX6dguBPbratSXsO4Tl5nAXRSQiUrZdV4EN0KtXLxo0aMCSJUvYsmWL7Uq2r68vd999N/369aNp06ZOC1TKloB/PITHd9/+0RAWRtLB45BrcV1QIiLiNEeOHMHT05PNmzdj+NPtP/7+/iQlJdG3b1/at2/PhAkT2LdvHxMmTCAwMJDu3bsDMHfuXNasWcOUKVOoWrUq06ZNY+DAgXz++ed4eHg45BhiL+DBv+Px4w92bUmJqVjUV4uIFNt1F9gALVq0oEWLFgAkJSVhNBoJCAhwSmBSRlkshIYF2jWlvzEDn+eehaQ018QkIiJOd/ToUWrXrk2Vy0xcuWLFCjw8PHjllVdwd3enbt26nD59mkWLFtG9e3eys7NZunQpzz33HG3atAFg1qxZtG7dmk2bNtGpUyc+/PDDGz6G/E9WFqHVQ+2aMv71Ct6vvqy+WkTkBl3XPdiXExQUpOJa7LjFHCtUXCfs/S9ZAwa7JiARESkxR44coV69epfdt3v3bqKionB3/+O8fqtWrTh58iQJCQkcPnyYtLQ0WrX6Y6UJk8lEw4YN2bVrl8OOIVBp+7ZCxXXC3v+SOXK0iyISESlfil1gi/yZ91szCL6zhV1b/IVkLNWquygiEREpSUePHiUhIYHHHnuMu+66i549e7J9+3YAzp8/T1hYmN3jC650nz17lvPnzwNw0003FXrMuXPnHHaMii6gexcCu3exa4uPM6uvFhFxoCINEXeVS5cuMXPmTLZu3UpqaiqRkZGMGjWKli1bAo6Z9EQTpxRf5ZuDMeTm2rYzH+pGyqLlrgtIRERKVHZ2NqdOncLb25sxY8bg4+PDhg0bGDhwIMuWLSMzM7PQPdCenp4AZGVlkZGRAXDZxyQnJwM45BjF5e5exq9HZGcTFBZs15QxdhyZY16wfRF0czPa/VccR7l1DuXVeZTbG1MmCuxnn32WhIQEZs6cSXBwMKtXr6Z///6sW7eO4OBgTZziKqmphNa52a7p0scbyLmnrWviERERl/Dw8GDXrl24u7vb+sRGjRoRExPDkiVL8PLyIjs72+45BZOl+vj44PW/1SWys7NtPxc8xtvbG8AhxygOo9FAUJBvsZ/vctu2Qdu29m0nT+JdqxaXy4rJVPxcydUpt86hvDqPcls8pb7APn36NN9//z3vv/8+zZs3B2DcuHF89913fP7553h5eWniFBeotPUbAv/5sF1b/Mlz4FuGv4SIiEix+fj4FGqLiIjgP//5D2FhYcTFxdntK9iuWrUquf8bBRUXF0eNGjXsHtOgQQMAhxyjOCwWK2ZzerGf70p+/+xGpc1f27UlJab+7wf7yczc3IyYTN6YzRnk5WkWcUdSbp1DeXUe5bYwk8n7uq/ol/oCOygoiIULF9KoUSNbm8FgwGq1kpyczK+//nrZSU8WLFhAQkICsbGxV530pFOnTlecOKUox6hITE/0wnPjZ7Ztq5cXF8/EXeUZIiJSnh0+fJiePXuyaNEi2+1bAL/++iv16tXjlltuYc2aNeTl5eHm5gbAjh07qF27NiEhIfj7++Pn58fOnTttxbHZbObgwYNER0cDEBUVdcPHKK7csrZsVU4OoeEhdk1pzz5H+th/XXO5zLw8S9l7v2WEcuscyqvzKLfFU+oLbJPJZLtqXOCLL77gzJkz3H333cyaNYuIiAi7/cWZOOVGj1FcZeq+LquVoBB/u6b0VyaSNXzkVT9Iuo/DeZRb51BenUe5LZ8iIiKoX78+EyZM4OWXXyYoKIgPP/yQffv28fHHH1O5cmUWL17MuHHjGDBgAPv372fFihVMmDAByB9iHh0dzfTp0wkODiY8PJxp06YRFhZGhw4dAOjevfsNH6MiMJ6NJaTZLXZtCTv3Yaldx0URiYhULKW+wP6rPXv28OKLL3LffffRrl07pkyZUmYnTilT93WlpUED+w6bY8fwqVePwoMCL0/3cTiPcuscyqvzKLfli9FoZP78+UyfPp1nnnkGs9lMw4YNWbZsGZGRkQAsXryYyZMn07VrV0JDQxkzZgxdu3a1HWP48OHk5uby0ksvkZmZSVRUFEuWLLH1vSEhITd8jPLOfeePBHW5364t/kIyGAwuikhEpOIpUwX25s2bGT16NE2bNmXmzJmAYyY9cdXEKWXlvi5jzHH8Hn8Mt99/t7UlXTSD0VjoHq7L0X0czqPcOofy6jzKbWFFua+rNAsODua111674v4mTZrwwQcfXHG/m5sbzz33HM8995xTj1EuWSx4z5mN72sTbE2pk6eSMfApFwYlIlIxlZkC+7333mPy5Ml06NCB6dOn285Gl+WJU6D039flsfFz/Ic9iTHFTF6VqqQsXkFOq7vAAliKFrvu43Ae5dY5lFfnUW5FHMOQmID/sCfx3PQVAJndHiF1+ptY/fyv8UwREXGGMnHKfPXq1UycOJFevXrx5ptv2g31ioqKYs+ePeTl5dna/jzpSYMGDWyTnhQomPSkYCIWRxyj3MnNxXfSKwQ88RjGFDM5d9zJpS3b84trERERcTn3XTsJuq81npu+wurpScr0t0iZt1jFtYiIC5X6AvvkyZO89tprdOjQgcGDB5OQkEB8fDzx8fGkpKTQvXt3UlNTGTduHMePH2fdunWsWLGCwYMHA/aTnmzZsoXDhw8zcuTIQhOn3OgxyhPDxYsE9OiGz+z8Yfjpg4dyad3nWKqGuTgyERERwWrFe85sAh/6P9xifye3Tl2SvviGzMf76n5rEREXK/VDxL/66itycnLYtGkTmzZtstvXtWtXXn/9dU2c4kDue3dj6tcbt7OxWH18SXnzHbIe7u7qsJzCYrFw4lgsKeY0/E2+1KkfjtFY6s85iYhIBWZISsR/+FN4fvUFAJldu5M6Y7auWouIlBIGq9VqdXUQFVVenoXExGtPElYirFa8VizF76XnMWRnk1uvPuZlq8iLvLF7zCF/KbKgIF+SktJKzT2X+/ce46NVmzgdc5bs7Fw8PNypWfdmHunVgSbN67s6vOtWGnNbHiivzqPcFhYc7FsuJjkrr0pTX+2++ydMg/ri9vtvWD09SZ34Opl9+jnkqnVp/d0sDyfDS2tuyzrl1XmU28KK0leX+ivYUgIyMvAfMxKvD1YDkNXpQVJmz8Xqb3JxYM6xf+8xZk99n5TkNIKrBBDi6UFmVjZHD55m9tT3Gf58zzJVZIuISDlnteI9fw6+E8djyM0lt3YdUhavILdxU1dH5lTl5WS4iFQsZesUoDic8dRJAjt1wOuD1ViNRlLHT8S8dGW5La4tFgsfrdpESnIa4TWr4OPjhdHNiI+PF+E1qpBiTuPjVZuxFHGGdBEREWcwXErC1Ocx/F5+EUNuLpkPduXS5u8qRHE9e+r7HP3vaXxNPtwUXhlfk4/tZPj+vcdcHaKIyGWpwK7APDZ9SVCHNlT6dT+WypVJ/ngDGU+PKNcTpJw4FsvpmLMEVwnA8Jf3aTAYCK4cwKmYWE4ci3VRhCIiIvnc9+4mqP09eH75b6weHqS8PoOURcvL7UnwAjoZLiJlmQrsiigvD5+pkwno9U+MyZfIaRFF0ubt5Nx9j6sjc7oUcxrZ2bl4eV5+cjpPLw+ys3NJMZeO++1ERKQCslrxXjiXwC4dcTtzmryatbi0cTOZ/QaW65PgBXQyXETKMt2DXcEYEhMwPTUAj2+3AJDRbyCpr06BcjYb+pX4m3zx8HAnMysbHx+vQvuzMrPx8HDH3+TrguhERKSiMyRfwn/EUDw3fgZAVueHSHnzHaymABdHVnIKToaHXOVkeFKCWSfDRaRU0hXsCsT9l58J6tAGj2+3YPX2xjxnIamvz6gwxTVAnfrh1Kx7M4nxyfx1An2r1UrixWRq1Q2nTv1wF0UoIiIVlfu+vQTddw+eGz/DWqkSKVOmYV7yboUqrsH+ZPjl6GS4iJRmKrArCK/VKwnsfD9uv50hr1ZtkjZuIeuRR10dVokzGo080qsD/iZfYs/EkZ6WSV6ehfS0TGLPxOFv8uUfvdqXuSVARESkDLNa8Vo8n8BOHXA7c4q8GrW49O9NZPYfXCGGhP+VToaLSFmmKqK8y8zEb9Rw/J8ZiiEri6yO/0fSpm3k3drI1ZG5TJPm9Rn+fE8iGtYkLSWd87EXSUtJJ7JhLS3RJSIiJcpgTsbU/3H8XxyDISeHrAe6kLTlO3KbNXd1aC6jk+EiUpbpHuxyzPjbGUz9elPpl5+xGgykv/Av0oc/C+qQaNK8Po2a1eXEsVhSzGn4m3ypUz9cnbWIiJQY9/37MPV/HLfTp7BWqkTaK5PIGPBkhbxq/VcFJ8ML1sFOSjDj4eFOZMNa/KNXe50MF5FSSwV2OVXp2y2YnuyHMSkJS3Aw5nlLyLn3PleHVaoYjUbqRVZ3dRgiIlLRWK14LVuM3/gXMGRnk1ejJuaFy8ht3tLVkZUqOhkuImWRCuzyxmLB560Z+Lw+CYPVSk6z2zAvWYmleg1XRyYiIlLhGczJ+D07HK8NnwCQ9fdOpMyeizUwyMWRlU46GS4iZY0K7HLEkHwJ/6cH4/nVFwBk9O5L6uSp4FV4OSoREREpWe4HfskfEn7qJFZ3d9JenkjGoCEaEi4iUo6owC4n3P77KwF9e+V32p6epE6dSeZjvV0dloiIiFiteK1Yit+/xmLIyiKvWnXMi5aT2yLK1ZGJiIiDqcAuBzw/WoP/6BEYMjLy7+NaupLcJs1cHZaIiIikpeE/cihe69cBkPX3B0h5ay7WoGAXByYiIs6gArssy87Gb/wLeC9dlL/Zrj3muYuwBoe4ODAREREB8F6xFK/16/KHhP/rVTKeHKoh4SIi5ZgK7DLMe8USW3GdNnos6aOeBzc3F0eVz2Kx2Gb9DAr2p8XtEa4OSUREpMRld+hIxtHDZEb3Ibfl7a4Ox476ahERx1OBXYbl3N6KrPb3k9lvINntO7o6HJv9e4/Z1q3Mzs7Fw6MSkQ1r8HCPe7m1aT1XhyciIlJi8upHkPrmHFeHUYj6ahER51CBXYblNr0N8+qPXR2Gnf17jzF76vukJKcRXCWAEE8PsrNy+O/+E5w6cY5hY3rSpHl9V4cpIiJSYamvFhFxHqOrA5Dyw2Kx8NGqTaQkpxFeswo+Pl4Y3Yz4+HpRs3YYKcnpfLxqMxaLxdWhioiIVEjqq0VEnEsFtjjMiWOxnI45S3CVAAx/mcDFYDAQEhrAqZhYThyLdVGEIiIiFZv6ahER51KBLQ6TYk4jOzsXL0+Py+739PIgOzuXFHNaCUcmIiIioL5aRMTZVGCLw/ibfPHwcCczK/uy+7Mys/HwcMff5FvCkYmIiAiorxYRcTYV2OIwdeqHU7PuzSTGJ2O1Wu32Wa1WEuKTqVU3nDr1w10UoYiISPlmsVg4fuQ3ft51mONHfit0L7X6ahER59Is4nLd/rxepr/Jlzr1wzEa/zhHYzQaeaRXB2ZPfZ/YM3EEVw7A08uDzKwckhLN+Af48I9e7e2eIyIiIo5ReOktd2rWvZlHenWwzQquvlpExLkM1r+evpQSk5dnITGxbNzjdD2d9pUfW4kGt9bgoX9qbU1Hc3c3EhTkS1JSGrm5mvHVUZRX51FuCwsO9sXNTcVMaVVW+uq/Lr3l5elBZlY2ifHJ+Jt8Gf68/dJb6qtLjv7uOYfy6jzKbWFF6at1BVuu6XLrZWZmZXP04GlmT32/UKfdpHl9GjWra7vaHRTsT4vbI0hOztAvqYiIiIP9demtgtnBfXy88K7hSeyZOD5etZlGzerarkyrrxYRcQ4V2HJVxem0IX8IWr3I6kD+WTANNRMREXGOay29FVz5j6W3CvpmUF8tIuIM+ksqV1WUTltERERKnjOX3rrWpGkiImJPV7Dlqgo67ZCrdNpJCeZid9pXmzRNREREru3PS2/5+HgV2l/cpbeKMv+KiIjkU4EtV6VOW0REpHQrWHrr6MHTeNfwtBtxZrVaSbyYTGTDWkVaequo86+IiEg+XS6Uq7rWepmJF4u+XmZBp330v6fxNflwU3hlfE0+tk57/95jjn4bIiIi5VbB0lv+Jl9iz8SRnpZJXp6F9LRMYs/E4W/yLdLSW3+df8XHxwujmxEfHy/Ca1QhxZzGx6s2a7i4iMhlqMCugIpyP5U6bRERkZJX1HufmzSvz/DnexLRsCZpKemcj71IWko6kQ1rFflqs+ZfEREpPg0Rr2CKMzS7oNMueF5SghkPD3ciG9biH73aO63T/vNMpyIiIhVFcW+j+uvSW8Wd38SZ86+IiJR3KrDLqOJMEHYj91Op0xYRESmaku6rwX7preJy1vwrIiIVgQrsMqg4Z7aLu571n6nTFhERuT6u6qsdwRmTpomIVBS6B7uMKe4EYaXlfipnTJomIiJSmpT1vtrR86+IiFQk+stYhtzIBGEFQ7O9rjI0Ozs71+lDs9Vpi4hIeVYe+mpw7KRpIiIViYaIlyE3MkFYaRqa7chJ00REREqT8tJXg+PmXxERqUhUYBeBxWLhnXfe4aOPPsJsNtOiRQtefvllatasWSKvfyMThJW2+6nUaYuIiDOor3YsR8y/IiJSkaiaKYK5c+eyZs0aJk2axAcffIDBYGDgwIFkZ2eXyOv/+cz25VztzHZpHJpd0GnfFtWAepHVVVyLiMgNU18tIiKupL/Q1yk7O5ulS5cybNgw2rRpQ4MGDZg1axYXLlxg06ZNJRLDjU4QpvupRESkPFNfLSIirqYh4tfp8OHDpKWl0apVK1ubyWSiYcOG7Nq1i06dOjk9hoIz27Onvk/smTiCKwfg6eVBVmY2iReTr+vMtoZmi4hIeaW+WkREXE0F9nU6f/48ADfddJNde5UqVTh37lyxj+vuXrTOsvntkYx88TE+XLmJk8cLJgirxC2NavNIdHuatoi4jqMYaXBrydyLBuDmZrT7rziOcuscyqvzKLfiTOqri0+/m86j3DqH8uo8yu2NUYF9nTIyMgDw8LCftMTT05Pk5ORiHdNoNBAUVPSZQNu2v4172jXl2OHfSL6USkCgH/UblP57mE0mb1eHUG4pt86hvDqPcivOoL76xul303mUW+dQXp1HuS0eFdjXycsrf7mM7Oxs288AWVlZeHsX78NnsVgxm9OLHVOVmypT5abKACQnZxT7OM7m5mbEZPLGbM4gL6/wup9SfMqtcyivzqPcFmYyeesqgYOory4+/W46j3LrHMqr8yi3hRWlr1aBfZ0KhpvFxcVRo0YNW3tcXBwNGjQo9nFzcyvOhzYvz1Kh3m9JUm6dQ3l1HuVWnEF99Y3T76bzKLfOobw6j3JbPDplfp0aNGiAn58fO3futLWZzWYOHjxIy5YtXRiZiIiIgPpqERFxPV3Bvk4eHh5ER0czffp0goODCQ8PZ9q0aYSFhdGhQwdXhyciIlLhqa8WERFXU4FdBMOHDyc3N5eXXnqJzMxMoqKiWLJkSaHJVERERMQ11FeLiIgrGaxWq9XVQVRUeXkWEhPTXB2G07m7GwkK8iUpKU33cTiYcuscyqvzKLeFBQf7apKzUkx9tdwo5dY5lFfnUW4LK0pfrR5dRERERERExAFUYIuIiIiIiIg4gApsEREREREREQdQgS0iIiIiIiLiACqwRURERERERBxAs4i7kNVqxWKpGOl3czOSl6dZCJ1BuXUO5dV5lFt7RqMBg8Hg6jDkCtRXiyMot86hvDqPcmuvKH21CmwRERERERERB9AQcREREREREREHUIEtIiIiIiIi4gAqsEVEREREREQcQAW2iIiIiIiIiAOowBYRERERERFxABXYIiIiIiIiIg6gAltERERERETEAVRgi4iIiIiIiDiACmwRERERERERB1CBLSIiIiIiIuIAKrBFREREREREHEAFtoiIiIiIiIgDqMAWh4qNjSUyMrLQv48++giAQ4cOER0dTbNmzWjbti1LlixxccSl39y5c+ndu7dd27XyaLFYmD17Nq1bt6Zp06b069eP06dPl2TYpd7l8vrCCy8U+uzec889tv3K65VdunSJ8ePHc88999C8eXN69uzJ7t27bfv1mRUpPdRXO576audQX+1Y6qtLiFXEgbZs2WJt3Lix9cKFC9a4uDjbv4yMDGtiYqL1jjvusI4bN856/Phx68cff2xt3Lix9eOPP3Z12KXWsmXLrJGRkdbo6Ghb2/Xk8e2337beeeed1q1bt1oPHTpk7devn7VDhw7WrKwsV7yNUudyebVardauXbtaZ86caffZTUhIsO1XXq+sb9++1gcffNC6a9cua0xMjHXixInWJk2aWI8fP67PrEgpo77asdRXO4f6asdTX10yVGCLQ82bN8/64IMPXnbf/Pnzra1bt7bm5OTY2mbMmGHt2LFjSYVXZpw/f97av39/a7Nmzax///vf7TqXa+UxKyvLetttt1lXr15t25+cnGxt0qSJ9fPPPy+5N1EKXS2vubm51saNG1s3bdp02ecqr1d26tQpa0REhHXPnj22NovFYu3QoYP1zTff1GdWpJRRX+0Y6qudQ321c6ivLjkaIi4OdeTIEerVq3fZfbt37yYqKgp3d3dbW6tWrTh58iQJCQklFWKZ8N///peAgAA2bNhA06ZN7fZdK4+HDx8mLS2NVq1a2fabTCYaNmzIrl27Suw9lEZXy+upU6fIysqibt26l32u8nplQUFBLFy4kEaNGtnaDAYDVquV5ORkfWZFShn11Y6hvto51Fc7h/rqkuN+7YeIXL+jR48SGhrKY489xqlTp6hZsyZDhgyhdevWnD9/noiICLvHV6lSBYCzZ88SEhLiipBLpXbt2tGuXbvL7rtWHs+fPw/ATTfdVOgx586dc0K0ZcfV8nr06FEMBgMrVqzgu+++w2g00qZNG5555hn8/f2V16swmUy0adPGru2LL77gzJkz3H333cyaNUufWZFSRH21Y6ivdg711c6hvrrk6Aq2OEx2djanTp0iNTWVZ555hoULF9K4cWMGDhzIjh07yMzMxMPDw+45np6eAGRlZbki5DLpWnnMyMgAuOxjlOcrO3bsGEajkfDwcObPn8/zzz/Ptm3bGDJkCBaLRXktgj179vDiiy9y33330a5dO31mRUoR9dUlQ3/3nEN9teOor3YeXcEWh/Hw8GDXrl24u7vbfvkaNWpETEwMS5YswcvLi+zsbLvnFPxC+vj4lHi8ZdW18ujl5QXkf4kq+LngMd7e3iUXaBkzbNgwnnjiCUwmEwARERGEhobSo0cPDhw4oLxep82bNzN69GiaNm3KzJkzAX1mRUoT9dUlQ3/3nEN9tWOor3YuXcEWh/Lx8Sl0ZisiIoILFy4QFhZGXFyc3b6C7apVq5ZYjGXdtfJYMHTnco8JCwsrmSDLIIPBYOuwCxQMlTp//rzyeh3ee+89hg0bxj333MOiRYtsHbA+syKli/pq59PfPedQX33j1Fc7nwpscZjDhw9z22232a2nB/Drr79Sr149oqKi2LNnD3l5ebZ9O3bsoHbt2rqnqwiulccGDRrg5+fHzp07bfvNZjMHDx6kZcuWrgi5TBg1ahT9+/e3aztw4AAA9erVU16vYfXq1UycOJFevXrx5ptv2n1512dWpPRQX10y9HfPOdRX3xj11SVDBbY4TEREBPXr12fChAns3r2bmJgYpkyZwr59+3jyySfp3r07qampjBs3juPHj7Nu3TpWrFjB4MGDXR16mXKtPHp4eBAdHc306dPZsmULhw8fZuTIkYSFhdGhQwcXR196de7cme+//5558+Zx5swZtm3bxosvvkjnzp2pW7eu8noVJ0+e5LXXXqNDhw4MHjyYhIQE4uPjiY+PJyUlRZ9ZkVJEfXXJ0N8951BfXXzqq0uOwWq1Wl0dhJQfiYmJTJ8+ne+++w6z2UzDhg0ZPXq07czW/v37mTx5MgcPHiQ0NJR+/foRHR3t4qhLt7FjxxIbG8vKlSttbdfKY15eHjNnzmTdunVkZmYSFRXF+PHjqVatmiveQql0ubx+9dVXzJ8/nxMnTuDv70+XLl145plnbJN8KK+XN3/+fGbNmnXZfV27duX111/XZ1akFFFf7Xjqq51DfbXjqK8uOSqwRURERERERBxAQ8RFREREREREHEAFtoiIiIiIiIgDqMAWERERERERcQAV2CIiIiIiIiIOoAJbRERERERExAFUYIuIiIiIiIg4gApsEREREREREQdQgS0iAFitVleH4FDl7f2IiIiUt76tvL0fEVCBLVIqvP3220RGRhb5eTt37qRjx440atSI/v37F+s458+fZ/DgwcTGxtra2rVrx9ixY4scT3G0a9eOyMhIu3+33HILUVFR9O7dm++//77Ix9yzZw+DBw92WIy5ubl069aNH374AYDU1FSGDx9OixYt6Nq1K/v377d7fFxcHLfffju//fZboWP17NmTL774wmGxiYhIyVBfrb5a5Hq4uzoAESm+qVOnYrFYWLhwISEhIXz99ddFPsYPP/zA1q1b+de//mVre+edd/Dz83NkqFfVpk0bhgwZYtvOzc3lzJkzLFy4kMGDB7N27doifRn56KOPOH78uMPimzdvHlWqVOGuu+4CYM6cORw+fJiZM2eyadMmRowYwVdffYWHhweQn78HH3yQ6tWrFzrWuHHjGDRoELfffjshISEOi1FEREon9dWXp75ayitdwRYpwy5dukTz5s256667inVW/UoaNmxIjRo1HHa8awkODqZZs2a2fy1btqRbt27MnTuXnJwcPv300xKL5a/i4uJYuHCh3ZeKHTt28Oijj9KmTRuee+45zp49y+nTpwGIiYnhyy+/tHv8nzVq1Ihbb72VefPmlUj8IiLiWuqrnU99tZQmKrBFSqF169bRsGFDfvnlF3r06EHjxo1p27YtixYtAuD3338nMjKS2NhY1q9fT2RkJDt37ix0nLy8PBYuXEjnzp1p0qQJzZo149FHH2XHjh2213nhhRcAuO+++2xDzf467CwlJYUpU6bQvn17GjduTOfOnfn444/tXqtdu3bMnj2bqVOnctddd9GkSRP69+/PyZMni50Hf3//Qm2JiYlMmDCBe++9l0aNGnH77bczdOhQfv/9dwDGjh3LJ598QmxsLJGRkaxbtw6ArKws3njjDdq0aUOjRo3o0qULGzduvGYMy5Yt46abbqJJkya2NoPBgKenJwCVKlUC8nMNMGPGDB5//HGCg4OveMwHH3yQjz/+mMTExOvMhIiIlDbqq/OprxaxpwJbpJSyWCw888wzPPDAAyxcuJAWLVowffp0tm/fTpUqVfjggw8IDQ2lTZs2fPDBB9x6662FjjF9+nTmzJlDjx49WLx4Ma+++ipJSUmMGDGC9PR02rZty1NPPQXkD5W63JnczMxMHnvsMTZs2EC/fv2YO3cuLVq0YNy4ccyfP9/use+++y4nTpxgypQpTJo0iV9//fW67g+zWq3k5uba/mVmZnLkyBHGjh2Lu7s7nTt3tj1u8ODBfP/994waNYolS5YwZMgQfvjhB8aPHw/AkCFDaNOmDaGhoXzwwQe0bdsWq9XK0KFDWbNmDX379mXevHncdtttjBw5kvXr1181ts8++4y///3vdm3NmjXjyy+/JDExkbVr1xISEkLt2rXZs2cP+/bto2/fvlc95n333UdeXh6bNm26Zm5ERKT0Ul+tvlrkr3QPtkgpZbVaGTJkCI888ggALVq0YNOmTWzdupXWrVvTrFkzPDw8bEO2LicuLo6RI0fSu3dvW5uXlxfDhg3jyJEj3HbbbbbhZbfccgvVqlUrdIx169Zx9OhRVq9eTYsWLQBo3bo1ubm5zJ07l0cffZTAwEAATCYTc+fOxc3NDYAzZ87w9ttvk5SURFBQ0BXf6/r16wt1nu7u7jRq1IilS5fSsGFD2/vx9vbm+eefp2XLlgDccccd/P7776xZswaAGjVqEBwcjIeHhy0v33//Pdu3b2fWrFk88MADtveQkZHB9OnT6dy5M+7uhf8cxsTEEB8fb3dGHGDYsGGMGDGCO++8k9DQUKZNm4anpyfTpk1jyJAhZGVlMWbMGE6cOEGHDh0YMWKELScAPj4+1K1blx07dtCjR48r5kVEREo39dXqq0X+SgW2SCl222232X4u6KDT09Ov+/kzZswA8odqnT59mpMnT/LNN98AkJOTc13H+OmnnwgPD7d12AUKhk798ssvtGnTBoDGjRvbdU5hYWEAZGRkXLXTvvfeexk6dChWq5VTp04xc+ZMqlatyttvv02VKlVsj6tatSrvvvsugO1eqpiYGPbu3XvV97Njxw4MBgNt2rQhNzfX1t6uXTs2bNjAsWPHuOWWWwo9r2Bm0b9+mQkODmblypWkp6fj4+MDwNdff83Fixfp0aMHI0eOxNfXl9mzZ/PMM89QtWpVevXqZXeM8PBw21A5EREpu9RXq68W+TMV2CKlmJeXl9220Wgs0pqRBw4cYMKECRw4cAAvLy/q1atHeHg4cP1rTyYnJ1O5cuVC7QVtZrPZ1ubt7V0oXsgfQnc1gYGBNG7cGIAmTZpw66230r17dwYOHMiHH35ou4cKYMOGDcycOZNz584RGBhIgwYNCuXpry5duoTVaqV58+aX3R8XF3fZTjslJeWy76tAQYedm5vLzJkzeeaZZwD45ptvWLNmDfXr1+fhhx/myy+/LNRpe3t7244vIiJll/pq9dUif6YCW6ScSk1NZcCAAURGRvL5559Tt25djEYj27Zt46uvvrru4wQEBNhm3fyz+Ph4gKue7S6uunXrMmLECF5//XXmzJnDs88+C8Du3bt5/vnniY6Opn///raz7m+88QZ79uy54vH8/f3x8fGxnVH/q5o1a162veC9/fmLyeV8+OGHeHt706lTJy5evEheXh4BAQFAfv4uXrxY6Dlms9kpuRMRkbJDffUf1FdLeaFJzkTKqRMnTnDp0iUef/xx6tevbztD/d133wF/nKkuaL+SqKgoYmNjC3WKGzZsoFKlSoXueXKU3r17ExERwdKlS22zm/78889YLBaGDx9u67Dz8vL44YcfgCu/p9tvv5309HSsViuNGze2/Tt27Bhz5syxG4r2ZzfffDMA58+fv2Kc6enpzJkzh1GjRmEwGAgKCsJoNNq+1MTFxV12Dc1z587ZrlCIiEjFpL76D+qrpbxQgS1STtWuXRs/Pz/mz5/P1q1b+c9//sO//vUvVq9eDeTfawX5k50AbNq0iZiYmELH6datG/Xq1ePpp5/m/fff5z//+Q+vvvoqa9euZfDgwbbnO5q7uzsvvvgiOTk5TJo0CcD2BeHVV1/lxx9/5Ouvv6Zv374cPnwYwHbPm8lk4uLFi2zbto24uDjatGlDVFQUQ4YMYfXq1ezcuZNFixbxyiuvYDQar7hMR506dbj55pvZu3fvFeNcunQp9erV4+6777bF/be//Y158+axdetW1q5dy3333Wf3nJSUFI4fP07r1q1vLEkiIlKmqa9WXy3ljwpskXLK39+fuXPnYrVaGTFiBGPGjOHs2bO89957+Pr6snv3biB/Zs+77rqLGTNmMHXq1ELH8fb2ZuXKlba1M5966in27NnD5MmTGTZsmFPfw5133knHjh35z3/+w+bNm7njjjsYP348P//8MwMHDmTKlCncfPPNvPPOOwC2M/fdunUjPDycoUOHsn79eoxGIwsXLqRTp04sWLCA/v37s2bNGp544glmzZp11Rg6duzItm3bLrsvISGBpUuXMmrUKLv2CRMmkJGRwejRo/nb3/5W6J6u7du3U6lSJdq2bVvMzIiISHmgvlp9tZQ/BmtRZmEQEalgLly4QIcOHVi6dKltuZEb1bt3bxo0aMC4ceMccjwREZGKTH21lCa6gi0ichVVq1alT58+LFy40CHH++WXXzhy5AiDBg1yyPFEREQqOvXVUpqowBYRuYZhw4Zx4cIFtm/ffsPHmjJlCuPHjyc0NNQBkYmIiAior5bSQ0PERURERERERBxAV7BFREREREREHEAFtoiIiIiIiIgDqMAWERERERERcQAV2CIiIiIiIiIOoAJbRERERERExAFUYIuIiIiIiIg4gApsEREREREREQdQgS0iIiIiIiLiACqwRURERERERBzg/wFGZbR84babsAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot results\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# CBA Regression\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.scatter(X, y_cba, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cba, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"Inflation Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Alimentaria (ARS)\")\n",
+    "plt.title(\"Inflation vs. Canasta Basica Alimentaria\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# CBT Regression\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.scatter(X, y_cbt, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cbt, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"Inflation Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Total (ARS)\")\n",
+    "plt.title(\"Inflation vs. Canasta Basica Total\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge Data\n",
+    "merged_df = pd.merge(cba_yearly_df, inflation_arg_df, on=\"Year\", how=\"inner\")\n",
+    "\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[[\"Inflation Rate\"]].values\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values\n",
+    "\n",
+    "# Initialize and fit the models\n",
+    "model_cba = LinearRegression().fit(X, y_cba)\n",
+    "model_cbt = LinearRegression().fit(X, y_cbt)\n",
+    "\n",
+    "# Generate predictions\n",
+    "y_pred_cba = model_cba.predict(X)\n",
+    "y_pred_cbt = model_cbt.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CBA Regression: Coeff: 542.5835715530902, Intercept: -2952.776089081701, R²: 0.387222922199141\n",
+      "CBT Regression: Coeff: 1191.622307132381, Intercept: -4255.554773970885, R²: 0.3688196432010893\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot results\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# CBA Regression\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.scatter(X, y_cba, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cba, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"Inflation Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Alimentaria (ARS)\")\n",
+    "plt.title(\"Inflation vs. Canasta Basica Alimentaria\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# CBT Regression\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.scatter(X, y_cbt, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cbt, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"Inflation Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Total (ARS)\")\n",
+    "plt.title(\"Inflation vs. Canasta Basica Total\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print Regression Results\n",
+    "print(f\"CBA Regression: Coeff: {model_cba.coef_[0]}, Intercept: {model_cba.intercept_}, R²: {model_cba.score(X, y_cba)}\")\n",
+    "print(f\"CBT Regression: Coeff: {model_cbt.coef_[0]}, Intercept: {model_cbt.intercept_}, R²: {model_cbt.score(X, y_cbt)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the UE dataset\n",
+    "gdp_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/API_SL.UEM.TOTL.ZS_DS2_en_csv_v2_105.csv'\n",
+    "gdp_df = pd.read_csv(gdp_file_path, skiprows=4)  # Skipping metadata rows\n",
+    "\n",
+    "# Filter only for Argentina\n",
+    "gdp_arg_df = gdp_df[gdp_df[\"Country Name\"] == \"Argentina\"].drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"])\n",
+    "gdp_arg_df = gdp_arg_df.melt(var_name=\"Year\", value_name=\"GDP\").dropna()\n",
+    "gdp_arg_df[\"Year\"] = gdp_arg_df[\"Year\"].astype(int)\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[[\"GDP\"]].values\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values\n",
+    "\n",
+    "# Initialize and fit the models\n",
+    "model_cba = LinearRegression().fit(X, y_cba)\n",
+    "model_cbt = LinearRegression().fit(X, y_cbt)\n",
+    "\n",
+    "# Generate predictions\n",
+    "y_pred_cba = model_cba.predict(X)\n",
+    "y_pred_cbt = model_cbt.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CBA Regression: Coeff: -5247.52437954192, Intercept: 55358.7346156688, R²: 0.44375349416189824\n",
+      "CBT Regression: Coeff: -11130.89159422853, Intercept: 118841.43585629479, R²: 0.43562319352554424\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot results\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# CBA Regression\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.scatter(X, y_cba, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cba, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"UE Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Alimentaria (ARS)\")\n",
+    "plt.title(\"UE vs. Canasta Basica Alimentaria\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# CBT Regression\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.scatter(X, y_cbt, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cbt, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"UE Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Total (ARS)\")\n",
+    "plt.title(\"UE vs. Canasta Basica Total\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print Regression Results\n",
+    "print(f\"CBA Regression: Coeff: {model_cba.coef_[0]}, Intercept: {model_cba.intercept_}, R²: {model_cba.score(X, y_cba)}\")\n",
+    "print(f\"CBT Regression: Coeff: {model_cbt.coef_[0]}, Intercept: {model_cbt.intercept_}, R²: {model_cbt.score(X, y_cbt)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Canasta Basica Alimentaria</th>\n",
+       "      <th>Canasta Basica Total</th>\n",
+       "      <th>Unemployment Rate %</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1667.951111</td>\n",
+       "      <td>4033.322222</td>\n",
+       "      <td>8.085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>1975.602500</td>\n",
+       "      <td>4831.954167</td>\n",
+       "      <td>8.347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>2667.066667</td>\n",
+       "      <td>6676.768333</td>\n",
+       "      <td>9.220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>4166.851667</td>\n",
+       "      <td>10401.310000</td>\n",
+       "      <td>9.843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>6092.989167</td>\n",
+       "      <td>14718.236667</td>\n",
+       "      <td>11.461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>9178.533333</td>\n",
+       "      <td>21524.225000</td>\n",
+       "      <td>8.736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>16117.950833</td>\n",
+       "      <td>36373.939167</td>\n",
+       "      <td>6.805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>40833.848333</td>\n",
+       "      <td>88191.855833</td>\n",
+       "      <td>6.139</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year  Canasta Basica Alimentaria  Canasta Basica Total  Unemployment Rate %\n",
+       "0  2016                 1667.951111           4033.322222                8.085\n",
+       "1  2017                 1975.602500           4831.954167                8.347\n",
+       "2  2018                 2667.066667           6676.768333                9.220\n",
+       "3  2019                 4166.851667          10401.310000                9.843\n",
+       "4  2020                 6092.989167          14718.236667               11.461\n",
+       "5  2021                 9178.533333          21524.225000                8.736\n",
+       "6  2022                16117.950833          36373.939167                6.805\n",
+       "7  2023                40833.848333          88191.855833                6.139"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df[\"Unemployment Rate %\"] = merged_df[\"GDP\"]\n",
+    "merged_df= merged_df.drop(columns=[\"GDP\"])\n",
+    "merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Canasta Basica Alimentaria</th>\n",
+       "      <th>Canasta Basica Total</th>\n",
+       "      <th>Unemployment Rate %</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1667.951111</td>\n",
+       "      <td>4033.322222</td>\n",
+       "      <td>8.085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>1975.602500</td>\n",
+       "      <td>4831.954167</td>\n",
+       "      <td>8.347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>2667.066667</td>\n",
+       "      <td>6676.768333</td>\n",
+       "      <td>9.220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>4166.851667</td>\n",
+       "      <td>10401.310000</td>\n",
+       "      <td>9.843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>6092.989167</td>\n",
+       "      <td>14718.236667</td>\n",
+       "      <td>11.461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>9178.533333</td>\n",
+       "      <td>21524.225000</td>\n",
+       "      <td>8.736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>16117.950833</td>\n",
+       "      <td>36373.939167</td>\n",
+       "      <td>6.805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>40833.848333</td>\n",
+       "      <td>88191.855833</td>\n",
+       "      <td>6.139</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year  Canasta Basica Alimentaria  Canasta Basica Total  Unemployment Rate %\n",
+       "0  2016                 1667.951111           4033.322222                8.085\n",
+       "1  2017                 1975.602500           4831.954167                8.347\n",
+       "2  2018                 2667.066667           6676.768333                9.220\n",
+       "3  2019                 4166.851667          10401.310000                9.843\n",
+       "4  2020                 6092.989167          14718.236667               11.461\n",
+       "5  2021                 9178.533333          21524.225000                8.736\n",
+       "6  2022                16117.950833          36373.939167                6.805\n",
+       "7  2023                40833.848333          88191.855833                6.139"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df[\"Unemployment Rate %\"] = merged_df[\"GDP\"]\n",
+    "merged_df= merged_df.drop(columns=[\"GDP\"])\n",
+    "merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'tools' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/z8/02768t9119xbgk92bl8mhrrm0000gn/T/ipykernel_8944/2877963409.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m })\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mtools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay_dataframe_to_user\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Linear Regression Results\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataframe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mregression_results\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'tools' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the UE dataset\n",
+    "ue_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/API_SL.UEM.TOTL.ZS_DS2_en_csv_v2_105.csv'\n",
+    "ue_df = pd.read_csv(ue_file_path, skiprows=4)  # Skipping metadata rows\n",
+    "\n",
+    "# Filter only for Argentina\n",
+    "ue_arg_df = gdp_df[gdp_df[\"Country Name\"] == \"Argentina\"].drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"])\n",
+    "ue_arg_df = gdp_arg_df.melt(var_name=\"Year\", value_name=\"GDP\").dropna()\n",
+    "ue_arg_df[\"Year\"] = gdp_arg_df[\"Year\"].astype(int)\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[[\"GDP\"]].values\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values\n",
+    "\n",
+    "# Initialize and fit the models\n",
+    "model_cba = LinearRegression().fit(X, y_cba)\n",
+    "model_cbt = LinearRegression().fit(X, y_cbt)\n",
+    "\n",
+    "# Generate predictions\n",
+    "y_pred_cba = model_cba.predict(X)\n",
+    "y_pred_cbt = model_cbt.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GDP Deflator Data:\n",
+      "   Year  GDP Deflator\n",
+      "0  2004          40.4\n",
+      "1  2005          44.5\n",
+      "2  2006          50.6\n",
+      "3  2007          58.2\n",
+      "4  2008          71.7\n",
+      "\n",
+      "Canasta Basica Data:\n",
+      "  Month - Year  Canasta Basica Alimentaria  Canasta Basica Total\n",
+      "0     April-16                     1514.53               3663.66\n",
+      "1       May-16                     1561.35               3830.77\n",
+      "2      June-16                     1614.32               3942.67\n",
+      "3      July-16                     1666.48               4033.76\n",
+      "4    August-16                     1675.05               4041.87\n",
+      "\n",
+      "CBA Regression: Coeff: 3.529727491318894, Intercept: -172.53302711188826, R²: 0.9992866323328055\n",
+      "CBT Regression: Coeff: 7.883988022720947, Intercept: 335.4708741546783, R²: 0.9994505433323989\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load datasets (assumed structure)\n",
+    "gdp_deflator_df = pd.read_csv('/Users/malenamalka/Documents/DATA 400/mini_project/gdp_deflator.csv')  # Replace with actual file path\n",
+    "# Inspect the first few rows of the datasets\n",
+    "print(\"GDP Deflator Data:\")\n",
+    "print(gdp_deflator_df.head())\n",
+    "\n",
+    "print(\"\\nCanasta Basica Data:\")\n",
+    "print(cba_df.head())\n",
+    "\n",
+    "# Merge Data on \"Year\"\n",
+    "merged_df = pd.merge(cba_yearly_df, gdp_deflator_df, on=\"Year\", how=\"inner\")\n",
+    "\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[\"GDP Deflator\"].values  # Independent variable\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values  # Dependent variable 1\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values  # Dependent variable 2\n",
+    "\n",
+    "# Function to calculate linear regression coefficients manually\n",
+    "def linear_regression(X, y):\n",
+    "    n = len(X)\n",
+    "    mean_x, mean_y = np.mean(X), np.mean(y)\n",
+    "    \n",
+    "    # Calculate slope (m) and intercept (b)\n",
+    "    numerator = np.sum((X - mean_x) * (y - mean_y))\n",
+    "    denominator = np.sum((X - mean_x) ** 2)\n",
+    "    slope = numerator / denominator\n",
+    "    intercept = mean_y - slope * mean_x\n",
+    "    \n",
+    "    # Compute R-squared\n",
+    "    y_pred = slope * X + intercept\n",
+    "    ss_total = np.sum((y - mean_y) ** 2)\n",
+    "    ss_residual = np.sum((y - y_pred) ** 2)\n",
+    "    r_squared = 1 - (ss_residual / ss_total)\n",
+    "    \n",
+    "    return slope, intercept, r_squared, y_pred\n",
+    "\n",
+    "# Perform Linear Regression\n",
+    "slope_cba, intercept_cba, r_squared_cba, y_pred_cba = linear_regression(X, y_cba)\n",
+    "slope_cbt, intercept_cbt, r_squared_cbt, y_pred_cbt = linear_regression(X, y_cbt)\n",
+    "\n",
+    "# Print Regression Results\n",
+    "print(f\"\\nCBA Regression: Coeff: {slope_cba}, Intercept: {intercept_cba}, R²: {r_squared_cba}\")\n",
+    "print(f\"CBT Regression: Coeff: {slope_cbt}, Intercept: {intercept_cbt}, R²: {r_squared_cbt}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set Seaborn theme for aesthetics\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Create figure for plots\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# Colors\n",
+    "scatter_color_cba = \"crimson\"\n",
+    "scatter_color_cbt = \"royalblue\"\n",
+    "line_color = \"black\"\n",
+    "\n",
+    "# Plot CBA Regression\n",
+    "sns.regplot(x=X, y=y_cba, scatter=True, color=scatter_color_cba, line_kws={\"color\": line_color}, ax=ax[0])\n",
+    "ax[0].set_xlabel(\"GDP Deflator\", fontsize=12, color=\"black\")\n",
+    "ax[0].set_ylabel(\"Canasta Basica Alimentaria (ARS)\", fontsize=12, color=\"black\")\n",
+    "ax[0].set_title(f\" GDP Deflator vs. CBA\\nR² = {r_squared_cba:.4f}\", fontsize=14, fontweight=\"bold\", color=\"darkred\")\n",
+    "ax[0].grid(alpha=0.3)\n",
+    "ax[0].set_facecolor(\"#f9f9f9\")\n",
+    "\n",
+    "# Plot CBT Regression\n",
+    "sns.regplot(x=X, y=y_cbt, scatter=True, color=scatter_color_cbt, line_kws={\"color\": line_color}, ax=ax[1])\n",
+    "ax[1].set_xlabel(\"GDP Deflator\", fontsize=12, color=\"black\")\n",
+    "ax[1].set_ylabel(\"Canasta Basica Total (ARS)\", fontsize=12, color=\"black\")\n",
+    "ax[1].set_title(f\" GDP Deflator vs. CBT\\nR² = {r_squared_cbt:.4f}\", fontsize=14, fontweight=\"bold\", color=\"navy\")\n",
+    "ax[1].grid(alpha=0.3)\n",
+    "ax[1].set_facecolor(\"#f9f9f9\")\n",
+    "\n",
+    "# Adjust layout and show the plots\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/mini project/mini_project/Mini Project Presentation.pdf b/mini project/mini_project/Mini Project Presentation.pdf
new file mode 100644
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diff --git a/mini project/mini_project/analysis.ipynb b/mini project/mini_project/analysis.ipynb
new file mode 100644
index 0000000..7ac184f
--- /dev/null
+++ b/mini project/mini_project/analysis.ipynb	
@@ -0,0 +1,1214 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "THIS IS FOR ANALYSIS OF MY MINI PROJECT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cba_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/canasta basica .csv'\n",
+    "inflation_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/arg_inflation.csv'\n",
+    "exchange_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/exchange_rate.csv'\n",
+    "cba_df = pd.read_csv(cba_file_path)\n",
+    "inflation_df = pd.read_csv(inflation_file_path)\n",
+    "exchange_rate_df =pd.read_csv(exchange_file_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Canasta Basica Alimentaria</th>\n",
+       "      <th>Canasta Basica Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1667.951111</td>\n",
+       "      <td>4033.322222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>1975.602500</td>\n",
+       "      <td>4831.954167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>2667.066667</td>\n",
+       "      <td>6676.768333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>4166.851667</td>\n",
+       "      <td>10401.310000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>6092.989167</td>\n",
+       "      <td>14718.236667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>9178.533333</td>\n",
+       "      <td>21524.225000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>16117.950833</td>\n",
+       "      <td>36373.939167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>40833.848333</td>\n",
+       "      <td>88191.855833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2024</td>\n",
+       "      <td>126722.385000</td>\n",
+       "      <td>281322.264167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2025</td>\n",
+       "      <td>146726.230000</td>\n",
+       "      <td>334535.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year  Canasta Basica Alimentaria  Canasta Basica Total\n",
+       "0  2016                 1667.951111           4033.322222\n",
+       "1  2017                 1975.602500           4831.954167\n",
+       "2  2018                 2667.066667           6676.768333\n",
+       "3  2019                 4166.851667          10401.310000\n",
+       "4  2020                 6092.989167          14718.236667\n",
+       "5  2021                 9178.533333          21524.225000\n",
+       "6  2022                16117.950833          36373.939167\n",
+       "7  2023                40833.848333          88191.855833\n",
+       "8  2024               126722.385000         281322.264167\n",
+       "9  2025               146726.230000         334535.800000"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Data Cleaning - CBA\n",
+    "cba_df[\"Year\"] = cba_df[\"Month - Year\"].str.extract(r'(\\d{2})$').astype(int) + 2000\n",
+    "cba_yearly_df = cba_df.groupby(\"Year\")[[\"Canasta Basica Alimentaria\", \"Canasta Basica Total\"]].mean().reset_index()\n",
+    "cba_yearly_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Inflation Rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>25.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>34.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>53.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>48.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year  Inflation Rate\n",
+       "0  2017            25.7\n",
+       "1  2018            34.3\n",
+       "2  2019            53.5\n",
+       "3  2020            42.0\n",
+       "4  2021            48.4"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Data Cleaning - Inflation \n",
+    "inflation_arg_df = inflation_df.iloc[1, 1:].to_frame().reset_index()\n",
+    "inflation_arg_df.columns = [\"Year\", \"Inflation Rate\"]\n",
+    "inflation_arg_df = inflation_arg_df.dropna()\n",
+    "inflation_arg_df[\"Year\"] = inflation_arg_df[\"Year\"].astype(int)\n",
+    "inflation_arg_df[\"Inflation Rate\"] = pd.to_numeric(inflation_arg_df[\"Inflation Rate\"], errors=\"coerce\")\n",
+    "inflation_arg_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert date column to datetime format (if applicable)\n",
+    "cba_df[\"Date\"] = pd.to_datetime(cba_df[\"Month - Year\"], format=\"%B-%y\", errors='coerce')\n",
+    "cba_df = cba_df.sort_values(by=\"Date\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month - Year</th>\n",
+       "      <th>Canasta Basica Alimentaria</th>\n",
+       "      <th>Canasta Basica Total</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Date</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>April-16</td>\n",
+       "      <td>1514.53</td>\n",
+       "      <td>3663.66</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-04-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>May-16</td>\n",
+       "      <td>1561.35</td>\n",
+       "      <td>3830.77</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-05-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>June-16</td>\n",
+       "      <td>1614.32</td>\n",
+       "      <td>3942.67</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-06-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>July-16</td>\n",
+       "      <td>1666.48</td>\n",
+       "      <td>4033.76</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-07-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>August-16</td>\n",
+       "      <td>1675.05</td>\n",
+       "      <td>4041.87</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-08-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>September-24</td>\n",
+       "      <td>138744.31</td>\n",
+       "      <td>312174.70</td>\n",
+       "      <td>2024</td>\n",
+       "      <td>2024-09-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>October-24</td>\n",
+       "      <td>140653.57</td>\n",
+       "      <td>319283.60</td>\n",
+       "      <td>2024</td>\n",
+       "      <td>2024-10-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>November-24</td>\n",
+       "      <td>142148.73</td>\n",
+       "      <td>324099.10</td>\n",
+       "      <td>2024</td>\n",
+       "      <td>2024-11-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>December-24</td>\n",
+       "      <td>145408.96</td>\n",
+       "      <td>331532.43</td>\n",
+       "      <td>2024</td>\n",
+       "      <td>2024-12-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>January-25</td>\n",
+       "      <td>146726.23</td>\n",
+       "      <td>334535.80</td>\n",
+       "      <td>2025</td>\n",
+       "      <td>2025-01-01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>106 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Month - Year  Canasta Basica Alimentaria  Canasta Basica Total  Year  \\\n",
+       "0        April-16                     1514.53               3663.66  2016   \n",
+       "1          May-16                     1561.35               3830.77  2016   \n",
+       "2         June-16                     1614.32               3942.67  2016   \n",
+       "3         July-16                     1666.48               4033.76  2016   \n",
+       "4       August-16                     1675.05               4041.87  2016   \n",
+       "..            ...                         ...                   ...   ...   \n",
+       "101  September-24                   138744.31             312174.70  2024   \n",
+       "102    October-24                   140653.57             319283.60  2024   \n",
+       "103   November-24                   142148.73             324099.10  2024   \n",
+       "104   December-24                   145408.96             331532.43  2024   \n",
+       "105    January-25                   146726.23             334535.80  2025   \n",
+       "\n",
+       "          Date  \n",
+       "0   2016-04-01  \n",
+       "1   2016-05-01  \n",
+       "2   2016-06-01  \n",
+       "3   2016-07-01  \n",
+       "4   2016-08-01  \n",
+       "..         ...  \n",
+       "101 2024-09-01  \n",
+       "102 2024-10-01  \n",
+       "103 2024-11-01  \n",
+       "104 2024-12-01  \n",
+       "105 2025-01-01  \n",
+       "\n",
+       "[106 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cba_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/z8/02768t9119xbgk92bl8mhrrm0000gn/T/ipykernel_8944/923394105.py:51: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from current font.\n",
+      "  plt.tight_layout()\n",
+      "/var/folders/z8/02768t9119xbgk92bl8mhrrm0000gn/T/ipykernel_8944/923394105.py:51: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from current font.\n",
+      "  plt.tight_layout()\n",
+      "/Users/malenamalka/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128200 (\\N{CHART WITH UPWARDS TREND}) missing from current font.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n",
+      "/Users/malenamalka/opt/anaconda3/lib/python3.9/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128202 (\\N{BAR CHART}) missing from current font.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set Seaborn style\n",
+    "sns.set_theme(style=\"darkgrid\", palette=\"magma\")\n",
+    "\n",
+    "# Create figure and axis\n",
+    "fig, ax = plt.subplots(2, 1, figsize=(12, 10), sharex=True)\n",
+    "\n",
+    "# --- CBA Price Visualization ---\n",
+    "ax[0].plot(cba_df[\"Date\"], cba_df[\"Canasta Basica Alimentaria\"], color=\"turquoise\", lw=2.5, alpha=0.9, label=\"CBA Price\")\n",
+    "ax[0].fill_between(cba_df[\"Date\"], cba_df[\"Canasta Basica Alimentaria\"], alpha=0.2, color=\"turquoise\")  # Gradient effect\n",
+    "ax[0].set_title(\"📈 Canasta Básica Alimentaria (CBA) Price Trend (March 2016 - Present)\", \n",
+    "                fontsize=14, fontweight=\"bold\", color=\"turquoise\")\n",
+    "ax[0].set_ylabel(\"Price (ARS)\", fontsize=12, color=\"turquoise\")\n",
+    "ax[0].legend(loc=\"upper left\")\n",
+    "ax[0].grid(True, linestyle=\"--\", alpha=0.5)\n",
+    "\n",
+    "# Highlighting max and min points\n",
+    "max_idx = cba_df[\"Canasta Basica Alimentaria\"].idxmax()\n",
+    "min_idx = cba_df[\"Canasta Basica Alimentaria\"].idxmin()\n",
+    "ax[0].annotate(f\"Max: {cba_df['Canasta Basica Alimentaria'][max_idx]:,.0f} ARS\",\n",
+    "               xy=(cba_df[\"Date\"][max_idx], cba_df[\"Canasta Basica Alimentaria\"][max_idx]),\n",
+    "               xytext=(-50, 30), textcoords=\"offset points\",\n",
+    "               arrowprops=dict(arrowstyle=\"->\", color=\"black\"), fontsize=10, color=\"black\")\n",
+    "ax[0].annotate(f\"Min: {cba_df['Canasta Basica Alimentaria'][min_idx]:,.0f} ARS\",\n",
+    "               xy=(cba_df[\"Date\"][min_idx], cba_df[\"Canasta Basica Alimentaria\"][min_idx]),\n",
+    "               xytext=(-50, -30), textcoords=\"offset points\",\n",
+    "               arrowprops=dict(arrowstyle=\"->\", color=\"black\"), fontsize=10, color=\"black\")\n",
+    "# --- CBT Price Visualization ---\n",
+    "ax[1].plot(cba_df[\"Date\"], cba_df[\"Canasta Basica Total\"], color=\"royalblue\", lw=2.5, alpha=0.9, label=\"CBT Price\")\n",
+    "ax[1].fill_between(cba_df[\"Date\"], cba_df[\"Canasta Basica Total\"], alpha=0.2, color=\"royalblue\")  # Gradient effect\n",
+    "ax[1].set_title(\"📊 Canasta Básica Total (CBT) Price Trend (March 2016 - Present)\", \n",
+    "                fontsize=14, fontweight=\"bold\", color=\"navy\")\n",
+    "ax[1].set_xlabel(\"Year\", fontsize=12, color=\"black\")\n",
+    "ax[1].set_ylabel(\"Price (ARS)\", fontsize=12, color=\"navy\")\n",
+    "ax[1].legend(loc=\"upper left\")\n",
+    "ax[1].grid(True, linestyle=\"--\", alpha=0.5)\n",
+    "\n",
+    "# Highlighting max and min points\n",
+    "max_idx = cba_df[\"Canasta Basica Total\"].idxmax()\n",
+    "min_idx = cba_df[\"Canasta Basica Total\"].idxmin()\n",
+    "ax[1].annotate(f\"Max: {cba_df['Canasta Basica Total'][max_idx]:,.0f} ARS\",\n",
+    "               xy=(cba_df[\"Date\"][max_idx], cba_df[\"Canasta Basica Total\"][max_idx]),\n",
+    "               xytext=(-50, 30), textcoords=\"offset points\",\n",
+    "               arrowprops=dict(arrowstyle=\"->\", color=\"black\"), fontsize=10, color=\"black\")\n",
+    "ax[1].annotate(f\"Min: {cba_df['Canasta Basica Total'][min_idx]:,.0f} ARS\",\n",
+    "               xy=(cba_df[\"Date\"][min_idx], cba_df[\"Canasta Basica Total\"][min_idx]),\n",
+    "               xytext=(-50, -30), textcoords=\"offset points\",\n",
+    "               arrowprops=dict(arrowstyle=\"->\", color=\"black\"), fontsize=10, color=\"black\")\n",
+    "\n",
+    "# Adjust layout and show\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge Data\n",
+    "merged_df = pd.merge(cba_yearly_df, inflation_arg_df, on=\"Year\", how=\"inner\")\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[\"Inflation Rate\"].values\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to calculate linear regression coefficients\n",
+    "def linear_regression(X, y):\n",
+    "    n = len(X)\n",
+    "    mean_x, mean_y = np.mean(X), np.mean(y)\n",
+    "    \n",
+    "    # Calculate slope (m) and intercept (b)\n",
+    "    numerator = np.sum((X - mean_x) * (y - mean_y))\n",
+    "    denominator = np.sum((X - mean_x) ** 2)\n",
+    "    slope = numerator / denominator\n",
+    "    intercept = mean_y - slope * mean_x\n",
+    "    \n",
+    "    # Compute R-squared\n",
+    "    y_pred = slope * X + intercept\n",
+    "    ss_total = np.sum((y - mean_y) ** 2)\n",
+    "    ss_residual = np.sum((y - y_pred) ** 2)\n",
+    "    r_squared = 1 - (ss_residual / ss_total)\n",
+    "    \n",
+    "    return slope, intercept, r_squared, y_pred\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Perform Linear Regression\n",
+    "slope_cba, intercept_cba, r_squared_cba, y_pred_cba = linear_regression(X, y_cba)\n",
+    "slope_cbt, intercept_cbt, r_squared_cbt, y_pred_cbt = linear_regression(X, y_cbt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CBA Regression: Coeff: 542.5835715530903, Intercept: -2952.776089081708, R²: 0.387222922199141\n",
+      "CBT Regression: Coeff: 1191.6223071323811, Intercept: -4255.5547739709, R²: 0.3688196432010893\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print Regression Results\n",
+    "print(f\"CBA Regression: Coeff: {slope_cba}, Intercept: {intercept_cba}, R²: {r_squared_cba}\")\n",
+    "print(f\"CBT Regression: Coeff: {slope_cbt}, Intercept: {intercept_cbt}, R²: {r_squared_cbt}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot results\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# CBA Regression\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.scatter(X, y_cba, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cba, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"Inflation Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Alimentaria (ARS)\")\n",
+    "plt.title(\"Inflation vs. Canasta Basica Alimentaria\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# CBT Regression\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.scatter(X, y_cbt, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cbt, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"Inflation Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Total (ARS)\")\n",
+    "plt.title(\"Inflation vs. Canasta Basica Total\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge Data\n",
+    "merged_df = pd.merge(cba_yearly_df, inflation_arg_df, on=\"Year\", how=\"inner\")\n",
+    "\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[[\"Inflation Rate\"]].values\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values\n",
+    "\n",
+    "# Initialize and fit the models\n",
+    "model_cba = LinearRegression().fit(X, y_cba)\n",
+    "model_cbt = LinearRegression().fit(X, y_cbt)\n",
+    "\n",
+    "# Generate predictions\n",
+    "y_pred_cba = model_cba.predict(X)\n",
+    "y_pred_cbt = model_cbt.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CBA Regression: Coeff: 542.5835715530902, Intercept: -2952.776089081701, R²: 0.387222922199141\n",
+      "CBT Regression: Coeff: 1191.622307132381, Intercept: -4255.554773970885, R²: 0.3688196432010893\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot results\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# CBA Regression\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.scatter(X, y_cba, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cba, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"Inflation Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Alimentaria (ARS)\")\n",
+    "plt.title(\"Inflation vs. Canasta Basica Alimentaria\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# CBT Regression\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.scatter(X, y_cbt, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cbt, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"Inflation Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Total (ARS)\")\n",
+    "plt.title(\"Inflation vs. Canasta Basica Total\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print Regression Results\n",
+    "print(f\"CBA Regression: Coeff: {model_cba.coef_[0]}, Intercept: {model_cba.intercept_}, R²: {model_cba.score(X, y_cba)}\")\n",
+    "print(f\"CBT Regression: Coeff: {model_cbt.coef_[0]}, Intercept: {model_cbt.intercept_}, R²: {model_cbt.score(X, y_cbt)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the UE dataset\n",
+    "gdp_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/API_SL.UEM.TOTL.ZS_DS2_en_csv_v2_105.csv'\n",
+    "gdp_df = pd.read_csv(gdp_file_path, skiprows=4)  # Skipping metadata rows\n",
+    "\n",
+    "# Filter only for Argentina\n",
+    "gdp_arg_df = gdp_df[gdp_df[\"Country Name\"] == \"Argentina\"].drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"])\n",
+    "gdp_arg_df = gdp_arg_df.melt(var_name=\"Year\", value_name=\"GDP\").dropna()\n",
+    "gdp_arg_df[\"Year\"] = gdp_arg_df[\"Year\"].astype(int)\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[[\"GDP\"]].values\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values\n",
+    "\n",
+    "# Initialize and fit the models\n",
+    "model_cba = LinearRegression().fit(X, y_cba)\n",
+    "model_cbt = LinearRegression().fit(X, y_cbt)\n",
+    "\n",
+    "# Generate predictions\n",
+    "y_pred_cba = model_cba.predict(X)\n",
+    "y_pred_cbt = model_cbt.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CBA Regression: Coeff: -5247.52437954192, Intercept: 55358.7346156688, R²: 0.44375349416189824\n",
+      "CBT Regression: Coeff: -11130.89159422853, Intercept: 118841.43585629479, R²: 0.43562319352554424\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Plot results\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# CBA Regression\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.scatter(X, y_cba, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cba, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"UE Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Alimentaria (ARS)\")\n",
+    "plt.title(\"UE vs. Canasta Basica Alimentaria\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# CBT Regression\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.scatter(X, y_cbt, label=\"Actual Data\", alpha=0.7)\n",
+    "plt.plot(X, y_pred_cbt, color=\"red\", label=\"Regression Line\")\n",
+    "plt.xlabel(\"UE Rate (%)\")\n",
+    "plt.ylabel(\"Canasta Basica Total (ARS)\")\n",
+    "plt.title(\"UE vs. Canasta Basica Total\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Print Regression Results\n",
+    "print(f\"CBA Regression: Coeff: {model_cba.coef_[0]}, Intercept: {model_cba.intercept_}, R²: {model_cba.score(X, y_cba)}\")\n",
+    "print(f\"CBT Regression: Coeff: {model_cbt.coef_[0]}, Intercept: {model_cbt.intercept_}, R²: {model_cbt.score(X, y_cbt)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Canasta Basica Alimentaria</th>\n",
+       "      <th>Canasta Basica Total</th>\n",
+       "      <th>Unemployment Rate %</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1667.951111</td>\n",
+       "      <td>4033.322222</td>\n",
+       "      <td>8.085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>1975.602500</td>\n",
+       "      <td>4831.954167</td>\n",
+       "      <td>8.347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>2667.066667</td>\n",
+       "      <td>6676.768333</td>\n",
+       "      <td>9.220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>4166.851667</td>\n",
+       "      <td>10401.310000</td>\n",
+       "      <td>9.843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>6092.989167</td>\n",
+       "      <td>14718.236667</td>\n",
+       "      <td>11.461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>9178.533333</td>\n",
+       "      <td>21524.225000</td>\n",
+       "      <td>8.736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>16117.950833</td>\n",
+       "      <td>36373.939167</td>\n",
+       "      <td>6.805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>40833.848333</td>\n",
+       "      <td>88191.855833</td>\n",
+       "      <td>6.139</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year  Canasta Basica Alimentaria  Canasta Basica Total  Unemployment Rate %\n",
+       "0  2016                 1667.951111           4033.322222                8.085\n",
+       "1  2017                 1975.602500           4831.954167                8.347\n",
+       "2  2018                 2667.066667           6676.768333                9.220\n",
+       "3  2019                 4166.851667          10401.310000                9.843\n",
+       "4  2020                 6092.989167          14718.236667               11.461\n",
+       "5  2021                 9178.533333          21524.225000                8.736\n",
+       "6  2022                16117.950833          36373.939167                6.805\n",
+       "7  2023                40833.848333          88191.855833                6.139"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df[\"Unemployment Rate %\"] = merged_df[\"GDP\"]\n",
+    "merged_df= merged_df.drop(columns=[\"GDP\"])\n",
+    "merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Canasta Basica Alimentaria</th>\n",
+       "      <th>Canasta Basica Total</th>\n",
+       "      <th>Unemployment Rate %</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1667.951111</td>\n",
+       "      <td>4033.322222</td>\n",
+       "      <td>8.085</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>1975.602500</td>\n",
+       "      <td>4831.954167</td>\n",
+       "      <td>8.347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>2667.066667</td>\n",
+       "      <td>6676.768333</td>\n",
+       "      <td>9.220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>4166.851667</td>\n",
+       "      <td>10401.310000</td>\n",
+       "      <td>9.843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>6092.989167</td>\n",
+       "      <td>14718.236667</td>\n",
+       "      <td>11.461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>9178.533333</td>\n",
+       "      <td>21524.225000</td>\n",
+       "      <td>8.736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>16117.950833</td>\n",
+       "      <td>36373.939167</td>\n",
+       "      <td>6.805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>40833.848333</td>\n",
+       "      <td>88191.855833</td>\n",
+       "      <td>6.139</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year  Canasta Basica Alimentaria  Canasta Basica Total  Unemployment Rate %\n",
+       "0  2016                 1667.951111           4033.322222                8.085\n",
+       "1  2017                 1975.602500           4831.954167                8.347\n",
+       "2  2018                 2667.066667           6676.768333                9.220\n",
+       "3  2019                 4166.851667          10401.310000                9.843\n",
+       "4  2020                 6092.989167          14718.236667               11.461\n",
+       "5  2021                 9178.533333          21524.225000                8.736\n",
+       "6  2022                16117.950833          36373.939167                6.805\n",
+       "7  2023                40833.848333          88191.855833                6.139"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_df[\"Unemployment Rate %\"] = merged_df[\"GDP\"]\n",
+    "merged_df= merged_df.drop(columns=[\"GDP\"])\n",
+    "merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'tools' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/z8/02768t9119xbgk92bl8mhrrm0000gn/T/ipykernel_8944/2877963409.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m })\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mtools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay_dataframe_to_user\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Linear Regression Results\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataframe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mregression_results\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'tools' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the UE dataset\n",
+    "ue_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/API_SL.UEM.TOTL.ZS_DS2_en_csv_v2_105.csv'\n",
+    "ue_df = pd.read_csv(ue_file_path, skiprows=4)  # Skipping metadata rows\n",
+    "\n",
+    "# Filter only for Argentina\n",
+    "ue_arg_df = gdp_df[gdp_df[\"Country Name\"] == \"Argentina\"].drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"])\n",
+    "ue_arg_df = gdp_arg_df.melt(var_name=\"Year\", value_name=\"GDP\").dropna()\n",
+    "ue_arg_df[\"Year\"] = gdp_arg_df[\"Year\"].astype(int)\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[[\"GDP\"]].values\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values\n",
+    "\n",
+    "# Initialize and fit the models\n",
+    "model_cba = LinearRegression().fit(X, y_cba)\n",
+    "model_cbt = LinearRegression().fit(X, y_cbt)\n",
+    "\n",
+    "# Generate predictions\n",
+    "y_pred_cba = model_cba.predict(X)\n",
+    "y_pred_cbt = model_cbt.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GDP Deflator Data:\n",
+      "   Year  GDP Deflator\n",
+      "0  2004          40.4\n",
+      "1  2005          44.5\n",
+      "2  2006          50.6\n",
+      "3  2007          58.2\n",
+      "4  2008          71.7\n",
+      "\n",
+      "Canasta Basica Data:\n",
+      "  Month - Year  Canasta Basica Alimentaria  Canasta Basica Total\n",
+      "0     April-16                     1514.53               3663.66\n",
+      "1       May-16                     1561.35               3830.77\n",
+      "2      June-16                     1614.32               3942.67\n",
+      "3      July-16                     1666.48               4033.76\n",
+      "4    August-16                     1675.05               4041.87\n",
+      "\n",
+      "CBA Regression: Coeff: 3.529727491318894, Intercept: -172.53302711188826, R²: 0.9992866323328055\n",
+      "CBT Regression: Coeff: 7.883988022720947, Intercept: 335.4708741546783, R²: 0.9994505433323989\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load datasets (assumed structure)\n",
+    "gdp_deflator_df = pd.read_csv('/Users/malenamalka/Documents/DATA 400/mini_project/gdp_deflator.csv')  # Replace with actual file path\n",
+    "# Inspect the first few rows of the datasets\n",
+    "print(\"GDP Deflator Data:\")\n",
+    "print(gdp_deflator_df.head())\n",
+    "\n",
+    "print(\"\\nCanasta Basica Data:\")\n",
+    "print(cba_df.head())\n",
+    "\n",
+    "# Merge Data on \"Year\"\n",
+    "merged_df = pd.merge(cba_yearly_df, gdp_deflator_df, on=\"Year\", how=\"inner\")\n",
+    "\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[\"GDP Deflator\"].values  # Independent variable\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values  # Dependent variable 1\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values  # Dependent variable 2\n",
+    "\n",
+    "# Function to calculate linear regression coefficients manually\n",
+    "def linear_regression(X, y):\n",
+    "    n = len(X)\n",
+    "    mean_x, mean_y = np.mean(X), np.mean(y)\n",
+    "    \n",
+    "    # Calculate slope (m) and intercept (b)\n",
+    "    numerator = np.sum((X - mean_x) * (y - mean_y))\n",
+    "    denominator = np.sum((X - mean_x) ** 2)\n",
+    "    slope = numerator / denominator\n",
+    "    intercept = mean_y - slope * mean_x\n",
+    "    \n",
+    "    # Compute R-squared\n",
+    "    y_pred = slope * X + intercept\n",
+    "    ss_total = np.sum((y - mean_y) ** 2)\n",
+    "    ss_residual = np.sum((y - y_pred) ** 2)\n",
+    "    r_squared = 1 - (ss_residual / ss_total)\n",
+    "    \n",
+    "    return slope, intercept, r_squared, y_pred\n",
+    "\n",
+    "# Perform Linear Regression\n",
+    "slope_cba, intercept_cba, r_squared_cba, y_pred_cba = linear_regression(X, y_cba)\n",
+    "slope_cbt, intercept_cbt, r_squared_cbt, y_pred_cbt = linear_regression(X, y_cbt)\n",
+    "\n",
+    "# Print Regression Results\n",
+    "print(f\"\\nCBA Regression: Coeff: {slope_cba}, Intercept: {intercept_cba}, R²: {r_squared_cba}\")\n",
+    "print(f\"CBT Regression: Coeff: {slope_cbt}, Intercept: {intercept_cbt}, R²: {r_squared_cbt}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set Seaborn theme for aesthetics\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Create figure for plots\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# Colors\n",
+    "scatter_color_cba = \"crimson\"\n",
+    "scatter_color_cbt = \"royalblue\"\n",
+    "line_color = \"black\"\n",
+    "\n",
+    "# Plot CBA Regression\n",
+    "sns.regplot(x=X, y=y_cba, scatter=True, color=scatter_color_cba, line_kws={\"color\": line_color}, ax=ax[0])\n",
+    "ax[0].set_xlabel(\"GDP Deflator\", fontsize=12, color=\"black\")\n",
+    "ax[0].set_ylabel(\"Canasta Basica Alimentaria (ARS)\", fontsize=12, color=\"black\")\n",
+    "ax[0].set_title(f\" GDP Deflator vs. CBA\\nR² = {r_squared_cba:.4f}\", fontsize=14, fontweight=\"bold\", color=\"darkred\")\n",
+    "ax[0].grid(alpha=0.3)\n",
+    "ax[0].set_facecolor(\"#f9f9f9\")\n",
+    "\n",
+    "# Plot CBT Regression\n",
+    "sns.regplot(x=X, y=y_cbt, scatter=True, color=scatter_color_cbt, line_kws={\"color\": line_color}, ax=ax[1])\n",
+    "ax[1].set_xlabel(\"GDP Deflator\", fontsize=12, color=\"black\")\n",
+    "ax[1].set_ylabel(\"Canasta Basica Total (ARS)\", fontsize=12, color=\"black\")\n",
+    "ax[1].set_title(f\" GDP Deflator vs. CBT\\nR² = {r_squared_cbt:.4f}\", fontsize=14, fontweight=\"bold\", color=\"navy\")\n",
+    "ax[1].grid(alpha=0.3)\n",
+    "ax[1].set_facecolor(\"#f9f9f9\")\n",
+    "\n",
+    "# Adjust layout and show the plots\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/mini project/mini_project/data/arg_inflation.csv b/mini project/mini_project/data/arg_inflation.csv
new file mode 100644
index 0000000..52d9ed6
--- /dev/null
+++ b/mini project/mini_project/data/arg_inflation.csv	
@@ -0,0 +1,5 @@
+"Inflation rate, average consumer prices (Annual percent change)",2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029
+,,,,,,,,,,,,,
+Argentina,25.7,34.3,53.5,42,48.4,72.4,133.5,229.8,62.7,31.8,17.5,10.4,8.9
+,,,,,,,,,,,,,
+"©IMF, 2024",,,,,,,,,,,,,
\ No newline at end of file
diff --git a/mini project/mini_project/data/canasta basica .csv b/mini project/mini_project/data/canasta basica .csv
new file mode 100644
index 0000000..977e993
--- /dev/null
+++ b/mini project/mini_project/data/canasta basica .csv	
@@ -0,0 +1,107 @@
+Month - Year,Canasta Basica Alimentaria,Canasta Basica Total
+April-16,1514.53,3663.66
+May-16,1561.35,3830.77
+June-16,1614.32,3942.67
+July-16,1666.48,4033.76
+August-16,1675.05,4041.87
+September-16,1711.22,4089.82
+October-16,1739.34,4191.81
+November-16,1762.65,4247.99
+December-16,1766.62,4257.55
+January-17,1789.15,4311.85
+February-17,1821.02,4425.08
+March-17,1876.56,4560.04
+April-17,1915.47,4692.9
+May-17,1937.24,4746.24
+June-17,1956.42,4793.23
+July-17,1984.64,4862.37
+August-17,2021.98,4933.63
+September-17,2049.39,5000.51
+October-17,2079.23,5073.32
+November-17,2125.84,5187.05
+December-17,2150.29,5397.23
+January-18,2197.26,5493.15
+February-18,2261.23,5675.69
+March-18,2294.56,5782.29
+April-18,2308.11,5908.76
+May-18,2418.65,6095
+June-18,2537.45,6343.62
+July-18,2627.37,6515.88
+August-18,2701.48,6753.7
+September-18,2931.88,7300.38
+October-18,3150.62,7845.04
+November-18,3276.02,8157.29
+December-18,3300.17,8250.42
+January-19,3423.03,8557.58
+February-19,3597.77,8922.47
+March-19,3767.01,9304.51
+April-19,3833.28,9544.87
+May-19,3911.58,9818.07
+June-19,4016.09,10080.39
+July-19,4133.91,10334.77
+August-19,4290.72,10683.89
+September-19,4502.88,11257.2
+October-19,4596.2,11536.46
+November-19,4886.34,12166.99
+December-19,5043.41,12608.52
+January-20,5332.94,13065.7
+February-20,5432.32,13200.54
+March-20,5615.94,13590.57
+April-20,5791.79,13784.46
+May-20,5785.01,13941.87
+June-20,5834.66,14178.22
+July-20,5929.29,14408.17
+August-20,6081.69,14717.69
+September-20,6288.17,15280.25
+October-20,6702.33,16152.62
+November-20,6981.61,16755.86
+December-20,7340.12,17542.89
+January-21,7677.09,18271.47
+February-21,7953.14,18769.41
+March-21,8312.33,19700.22
+April-21,8633.31,20374.61
+May-21,8874.89,20855.99
+June-21,9195.41,21517.26
+July-21,9386.04,21869.47
+August-21,9454.13,22122.66
+September-21,9713.21,22826.04
+October-21,10008.2,23419.19
+November-21,10266.79,23921.62
+December-21,10667.86,24642.76
+January-22,11111.27,25444.81
+February-22,12108.08,27122.1
+March-22,12900.45,29026.01
+April-22,13762.77,30828.6
+May-22,14400.84,32257.88
+June-22,15056.75,33727.12
+July-22,16008.28,36018.63
+August-22,17148.8,38756.29
+September-22,18359.84,41493.24
+October-22,20098.92,45222.57
+November-22,20715.93,47232.32
+December-22,21743.48,49357.7
+January-23,23315.04,52925.14
+February-23,26046.32,57301.9
+March-23,28388.12,61886.1
+April-23,30468.76,65812.52
+May-23,32055.87,70522.91
+June-23,33730.51,75219.04
+July-23,36130.15,80570.23
+August-23,42262.25,92131.7
+September-23,47857.79,103372.83
+October-23,51974.93,111746.1
+November-23,59886.86,126361.27
+December-23,77889.58,160452.53
+January-24,92414.67,193146.66
+February-24,104482.59,223592.74
+March-24,115873.35,250286.44
+April-24,120726.29,268012.36
+May-24,125235.49,275518.08
+June-24,127287.79,282578.89
+July-24,131293.57,291471.73
+August-24,136399.3,304170.44
+September-24,138744.31,312174.7
+October-24,140653.57,319283.6
+November-24,142148.73,324099.1
+December-24,145408.96,331532.43
+January-25,146726.23,334535.8
\ No newline at end of file
diff --git a/mini project/mini_project/data/exchange_rate.csv b/mini project/mini_project/data/exchange_rate.csv
new file mode 100644
index 0000000..7cb0b7a
--- /dev/null
+++ b/mini project/mini_project/data/exchange_rate.csv	
@@ -0,0 +1,8082 @@
+indice_tiempo,tipo_cambio_bna_vendedor,tipo_cambio_a3500,tipo_cambio_mae,volumen_mae,tipo_cambio_implicito_en_adrs,futuro_rofex_usd1m,interes_abierto_1m,futuro_rofex_usd2m,interes_abierto_2m,futuro_rofex_usd3m,interes_abierto_3m,futuro_rofex_usd4m,interes_abierto_4m,futuro_rofex_usd5m,interes_abierto_5m,futuro_rofex_usd6m,interes_abierto_6m
+2002-03-05,,1.9917,,,2.1801242236024847,,,,,,,,,,,,
+2002-03-06,,2.0508,,,2.222222222222222,,,,,,,,,,,,
+2002-03-07,,2.1375,,,2.343949044585987,,,,,,,,,,,,
+2002-03-08,,2.2033,,,2.2277777777777774,,,,,,,,,,,,
+2002-03-09,,2.2033,,,2.2277777777777774,,,,,,,,,,,,
+2002-03-10,,2.2033,,,2.2277777777777774,,,,,,,,,,,,
+2002-03-11,,2.2525,,,2.2539682539682535,,,,,,,,,,,,
+2002-03-12,,2.365,,,2.367816091954023,,,,,,,,,,,,
+2002-03-13,,2.3317,,,2.3485714285714283,,,,,,,,,,,,
+2002-03-14,,2.365,,,2.3431952662721893,,,,,,,,,,,,
+2002-03-15,,2.3407999999999998,,,2.362068965517241,,,,,,,,,,,,
+2002-03-16,,2.3407999999999998,,,2.362068965517241,,,,,,,,,,,,
+2002-03-17,,2.3407999999999998,,,2.362068965517241,,,,,,,,,,,,
+2002-03-18,,2.3175,,,2.4266820011500863,,,,,,,,,,,,
+2002-03-19,,2.3783,,,2.460227272727273,,,,,,,,,,,,
+2002-03-20,,2.432,,,2.7302631578947363,,,,,,,,,,,,
+2002-03-21,,2.3883,,,2.5705128205128203,,,,,,,,,,,,
+2002-03-22,,2.5683,,,2.9214285714285717,,,,,,,,,,,,
+2002-03-23,,2.5683,,,2.9214285714285717,,,,,,,,,,,,
+2002-03-24,,2.5683,,,2.9214285714285717,,,,,,,,,,,,
+2002-03-25,,3.205,,,3.196428571428571,,,,,,,,,,,,
+2002-03-26,,2.9917,,,3.350877192982456,,,,,,,,,,,,
+2002-03-27,,2.8517,,,3.1746031746031744,,,,,,,,,,,,
+2002-03-28,,2.8517,,,3.1746031746031744,,,,,,,,,,,,
+2002-03-29,,2.8517,,,3.1746031746031744,,,,,,,,,,,,
+2002-03-30,,2.8517,,,3.1746031746031744,,,,,,,,,,,,
+2002-03-31,,2.8517,,,3.1746031746031744,,,,,,,,,,,,
+2002-04-01,,2.8517,,,3.1746031746031744,,,,,,,,,,,,
+2002-04-02,,2.8875,,,2.7399999999999998,,,,,,,,,,,,
+2002-04-03,,2.9032999999999998,,,3.1788617886178865,,,,,,,,,,,,
+2002-04-04,,2.8,,,2.6474820143884887,,,,,,,,,,,,
+2002-04-05,,2.6733000000000002,,,2.888,,,,,,,,,,,,
+2002-04-06,,2.6733000000000002,,,2.888,,,,,,,,,,,,
+2002-04-07,,2.6733000000000002,,,2.888,,,,,,,,,,,,
+2002-04-08,,2.7042,,,2.8292682926829267,,,,,,,,,,,,
+2002-04-09,,2.7467,,,2.8412698412698414,,,,,,,,,,,,
+2002-04-10,,2.7708,,,2.9933110367892977,,,,,,,,,,,,
+2002-04-11,,2.7883,,,2.825,,,,,,,,,,,,
+2002-04-12,,2.8592,,,2.947826086956522,,,,,,,,,,,,
+2002-04-13,,2.8592,,,2.947826086956522,,,,,,,,,,,,
+2002-04-14,,2.8592,,,2.947826086956522,,,,,,,,,,,,
+2002-04-15,,2.9233000000000002,,,2.8188976377952755,,,,,,,,,,,,
+2002-04-16,,2.8533,,,2.632,,,,,,,,,,,,
+2002-04-17,,2.805,,,2.75609756097561,,,,,,,,,,,,
+2002-04-18,,2.9116999999999997,,,2.8962962962962964,,,,,,,,,,,,
+2002-04-19,,3.1167,,,3.2109374999999996,,,,,,,,,,,,
+2002-04-20,,3.1167,,,3.2109374999999996,,,,,,,,,,,,
+2002-04-21,,3.1167,,,3.2109374999999996,,,,,,,,,,,,
+2002-04-22,,3.1167,,,3.2109374999999996,,,,,,,,,,,,
+2002-04-23,,3.1167,,,3.2109374999999996,,,,,,,,,,,,
+2002-04-24,,3.1167,,,3.2109374999999996,,,,,,,,,,,,
+2002-04-25,,3.1167,,,3.2109374999999996,,,,,,,,,,,,
+2002-04-26,,3.1167,,,3.2109374999999996,,,,,,,,,,,,
+2002-04-27,,3.1167,,,3.2109374999999996,,,,,,,,,,,,
+2002-04-28,,3.1167,,,3.2109374999999996,,,,,,,,,,,,
+2002-04-29,,3.0217,,,3.120967741935484,,,,,,,,,,,,
+2002-04-30,,2.9167,,,2.8129496402877696,,,,,,,,,,,,
+2002-05-01,,2.9167,,,2.8129496402877696,,,,,,,,,,,,
+2002-05-02,,3.0358,,,2.977443609022556,,,,,,,,,,,,
+2002-05-03,,3.16,,,3.0625,,,,,,,,,,,,
+2002-05-04,,3.16,,,3.0625,,,,,,,,,,,,
+2002-05-05,,3.16,,,3.0625,,,,,,,,,,,,
+2002-05-06,,3.1633,,,3.1439393939393936,,,,,,,,,,,,
+2002-05-07,,3.1717,,,3.134920634920635,,,,,,,,,,,,
+2002-05-08,,3.1863,,,3.1031746031746033,,,,,,,,,,,,
+2002-05-09,,3.18,,,3.0491803278688523,,,,,,,,,,,,
+2002-05-10,,3.1767,,,3.2410714285714284,,,,,,,,,,,,
+2002-05-11,,3.1767,,,3.2410714285714284,,,,,,,,,,,,
+2002-05-12,,3.1767,,,3.2410714285714284,,,,,,,,,,,,
+2002-05-13,,3.2667,,,3.345454545454545,,,,,,,,,,,,
+2002-05-14,,3.2557,,,3.025,,,,,,,,,,,,
+2002-05-15,,3.225,,,3.184210526315789,,,,,,,,,,,,
+2002-05-16,,3.2228,,,3.079646017699115,,,,,,,,,,,,
+2002-05-17,,3.33,,,3.233009708737864,,,,,,,,,,,,
+2002-05-18,,3.33,,,3.233009708737864,,,,,,,,,,,,
+2002-05-19,,3.33,,,3.233009708737864,,,,,,,,,,,,
+2002-05-20,,3.5167,,,3.4,,,,,,,,,,,,
+2002-05-21,,3.4933,,,3.7307692307692304,,,,,,,,,,,,
+2002-05-22,,3.4208,,,3.180212014134276,,,,,,,,,,,,
+2002-05-23,,3.3883,,,3.325581395348837,,,,,,,,,,,,
+2002-05-24,,3.4283,,,3.3333333333333335,,,,,,,,,,,,
+2002-05-25,,3.4283,,,3.3333333333333335,,,,,,,,,,,,
+2002-05-26,,3.4283,,,3.3333333333333335,,,,,,,,,,,,
+2002-05-27,,3.4483,,,3.1975308641975304,,,,,,,,,,,,
+2002-05-28,,3.4516999999999998,,,3.7361111111111116,,,,,,,,,,,,
+2002-05-29,,3.5433,,,3.52,,,,,,,,,,,,
+2002-05-30,,3.5917,,,2.8181818181818183,,,,,,,,,,,,
+2002-05-31,,3.575,,,3.0439560439560442,,,,,,,,,,,,
+2002-06-01,,3.575,,,3.0439560439560442,,,,,,,,,,,,
+2002-06-02,,3.575,,,3.0439560439560442,,,,,,,,,,,,
+2002-06-03,,3.5942,,,3.5066666666666664,,,,,,,,,,,,
+2002-06-04,,3.6083,,,3.8571428571428568,,,,,,,,,,,,
+2002-06-05,,3.6267,,,3.72712146422629,,,,,,,,,,,,
+2002-06-06,,3.6367000000000003,,,3.7936772046589025,,,,,,,,,,,,
+2002-06-07,,3.63,,,3.666666666666667,,,,,,,,,,,,
+2002-06-08,,3.63,,,3.666666666666667,,,,,,,,,,,,
+2002-06-09,,3.63,,,3.666666666666667,,,,,,,,,,,,
+2002-06-10,,3.5324999999999998,,,3.735849056603773,,,,,,,,,,,,
+2002-06-11,,3.4558,,,3.62,,,,,,,,,,,,
+2002-06-12,,3.445,,,4.075,,,,,,,,,,,,
+2002-06-13,,3.4717000000000002,,,3.8684210526315788,,,,,,,,,,,,
+2002-06-14,,3.5042999999999997,,,4.279069767441861,,,,,,,,,,,,
+2002-06-15,,3.5042999999999997,,,4.279069767441861,,,,,,,,,,,,
+2002-06-16,,3.5042999999999997,,,4.279069767441861,,,,,,,,,,,,
+2002-06-17,,3.5042999999999997,,,4.279069767441861,,,,,,,,,,,,
+2002-06-18,,3.5078,,,4.916666666666667,,,,,,,,,,,,
+2002-06-19,,3.5362,,,4.205882352941176,,,,,,,,,,,,
+2002-06-20,,3.5833,,,3.3220338983050848,,,,,,,,,,,,
+2002-06-21,,3.636,,,3.6938775510204085,,,,,,,,,,,,
+2002-06-22,,3.636,,,3.6938775510204085,,,,,,,,,,,,
+2002-06-23,,3.636,,,3.6938775510204085,,,,,,,,,,,,
+2002-06-24,,3.7058,,,3.58,,,,,,,,,,,,
+2002-06-25,,3.85,,,3.6862745098039214,,,,,,,,,,,,
+2002-06-26,,3.8675,,,4.093023255813953,,,,,,,,,,,,
+2002-06-27,,3.8092,,,3.9999999999999996,,,,,,,,,,,,
+2002-06-28,,3.8037,,,3.6346153846153846,,,,,,,,,,,,
+2002-06-29,,3.8037,,,3.6346153846153846,,,,,,,,,,,,
+2002-06-30,,3.8037,,,3.6346153846153846,,,,,,,,,,,,
+2002-07-01,,3.7912,,,3.6981132075471694,,,,,,,,,,,,
+2002-07-02,,3.69,,,3.7058823529411766,,,,,,,,,,,,
+2002-07-03,,3.5766999999999998,,,3.9375000000000004,,,,,,,,,,,,
+2002-07-04,,3.5558,,,4.375,,,,,,,,,,,,
+2002-07-05,,3.5667,,,3.88135593220339,,,,,,,,,,,,
+2002-07-06,,3.5667,,,3.88135593220339,,,,,,,,,,,,
+2002-07-07,,3.5667,,,3.88135593220339,,,,,,,,,,,,
+2002-07-08,,3.5317,,,3.8360655737704925,,,,,,,,,,,,
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+2002-07-10,,3.5303,,,3.391304347826088,,,,,,,,,,,,
+2002-07-11,,3.555,,,3.6935483870967745,,,,,,,,,,,,
+2002-07-12,,3.5875,,,4.08,,,,,,,,,,,,
+2002-07-13,,3.5875,,,4.08,,,,,,,,,,,,
+2002-07-14,,3.5875,,,4.08,,,,,,,,,,,,
+2002-07-15,,3.5933,,,3.846153846153846,,,,,,,,,,,,
+2002-07-16,,3.5907,,,3.4761904761904763,,,,,,,,,,,,
+2002-07-17,,3.4967,,,3.7000000000000006,,,,,,,,,,,,
+2002-07-18,,3.5417,,,3.294117647058824,,,,,,,,,,,,
+2002-07-19,,3.5845000000000002,,,3.3823529411764706,,,,,,,,,,,,
+2002-07-20,,3.5845000000000002,,,3.3823529411764706,,,,,,,,,,,,
+2002-07-21,,3.5845000000000002,,,3.3823529411764706,,,,,,,,,,,,
+2002-07-22,,3.5978,,,3.4193548387096775,,,,,,,,,,,,
+2002-07-23,,3.6383,,,3.6333333333333337,,,,,,,,,,,,
+2002-07-24,,3.645,,,3.4923076923076923,,,,,,,,,,,,
+2002-07-25,,3.6267,,,3.5333333333333337,,,,,,,,,,,,
+2002-07-26,,3.6558,,,3.4166666666666665,,,,,,,,,,,,
+2002-07-27,,3.6558,,,3.4166666666666665,,,,,,,,,,,,
+2002-07-28,,3.6558,,,3.4166666666666665,,,,,,,,,,,,
+2002-07-29,,3.65,,,3.3934426229508197,,,,,,,,,,,,
+2002-07-30,,3.6508000000000003,,,3.444444444444444,,,,,,,,,,,,
+2002-07-31,,3.7008,,,3.4687500000000004,,,,,,,,,,,,
+2002-08-01,,3.6333,,,3.523809523809524,,,,,,,,,,,,
+2002-08-02,,3.615,,,3.428571428571429,,,,,,,,,,,,
+2002-08-03,,3.615,,,3.428571428571429,,,,,,,,,,,,
+2002-08-04,,3.615,,,3.428571428571429,,,,,,,,,,,,
+2002-08-05,,3.6163,,,3.3870967741935485,,,,,,,,,,,,
+2002-08-06,,3.6208,,,3.5000000000000004,,,,,,,,,,,,
+2002-08-07,,3.6278,,,3.6166666666666667,,,,,,,,,,,,
+2002-08-08,,3.6408,,,3.4461538461538463,,,,,,,,,,,,
+2002-08-09,,3.6367000000000003,,,3.3726067746686303,,,,,,,,,,,,
+2002-08-10,,3.6367000000000003,,,3.3726067746686303,,,,,,,,,,,,
+2002-08-11,,3.6367000000000003,,,3.3726067746686303,,,,,,,,,,,,
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+2002-08-13,,3.6108000000000002,,,3.4090909090909087,,,,,,,,,,,,
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+2002-08-15,,3.6417,,,3.4411764705882355,,,,,,,,,,,,
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+2002-08-18,,3.6391999999999998,,,3.671428571428572,,,,,,,,,,,,
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+2024-03-13,868.5001,849.75,849.5001,,971.3292788879235,865.5,858330.0,910.0,837225.0,969.0,127219.0,1030.0,83944.0,1105.0,44223.0,1164.0,11502.0
+2024-03-14,869.0001,850.25,850.0001,,971.3292788879235,866.0,893864.0,914.0,850379.0,977.0,135562.0,1038.0,87758.0,1105.0,44218.0,1170.0,11013.0
+2024-03-15,869.5001,850.75,850.5001,,971.3292788879235,864.5,901808.0,910.0,870060.0,973.0,152258.0,1036.0,91351.0,1104.0,47342.0,1160.5,17780.0
+2024-03-16,869.5001,850.75,850.5001,,971.3292788879235,864.5,901808.0,910.0,870060.0,973.0,152258.0,1036.0,91351.0,1104.0,47342.0,1160.5,17780.0
+2024-03-17,869.5001,850.75,850.5001,,971.3292788879235,864.5,901808.0,910.0,870060.0,973.0,152258.0,1036.0,91351.0,1104.0,47342.0,1160.5,17780.0
+2024-03-18,869.5001,852.25,852.5001,,971.3292788879235,862.0,880903.0,907.0,884475.0,965.5,152237.0,1027.0,94888.0,1086.5,47343.0,1146.0,17834.0
+2024-03-19,871.5001,852.75,853.0001,,971.3292788879235,865.5,816154.0,913.0,905621.0,970.0,173436.0,1028.0,101960.0,1082.5,52360.0,1140.0,18944.0
+2024-03-20,872.0001,853.25,853.0001,,971.3292788879235,862.0,796927.0,912.5,917154.0,965.0,174062.0,1020.0,104309.0,1079.0,51455.0,1128.0,20993.0
+2024-03-21,872.5001,854.25,854.0001,,971.3292788879235,861.0,761013.0,912.5,927460.0,964.5,171846.0,1026.0,103958.0,1090.5,63281.0,1155.0,22532.0
+2024-03-22,873.5001,854.75,854.5001,,971.3292788879235,860.5,717430.0,914.0,964887.0,964.0,171564.0,1023.0,116583.0,1086.5,67157.0,1145.0,22752.0
+2024-03-23,873.5001,854.75,854.5001,,971.3292788879235,860.5,717430.0,914.0,964887.0,964.0,171564.0,1023.0,116583.0,1086.5,67157.0,1145.0,22752.0
+2024-03-24,873.5001,854.75,854.5001,,971.3292788879235,860.5,717430.0,914.0,964887.0,964.0,171564.0,1023.0,116583.0,1086.5,67157.0,1145.0,22752.0
+2024-03-25,875.0001,856.25,854.5001,,971.3292788879235,859.0,640853.0,913.5,1041323.0,965.5,175633.0,1019.0,118960.0,1073.0,66713.0,1122.5,22795.0
+2024-03-26,875.5001,856.75,854.5001,,971.3292788879235,858.0,584775.0,909.0,1172664.0,962.0,189547.0,1016.0,126352.0,1075.0,65660.0,1125.0,23978.0
+2024-03-27,876.0001,857.4167,858.0001,,971.3292788879235,857.4,584775.0,892.0,1324467.0,940.0,209178.0,994.0,128493.0,1054.0,65755.0,1105.0,22942.0
+2024-03-28,876.0001,857.4167,858.0001,,971.3292788879235,857.4,584775.0,892.0,1324467.0,940.0,209178.0,994.0,128493.0,1054.0,65755.0,1105.0,22942.0
+2024-03-29,876.0001,857.4167,858.0001,,971.3292788879235,857.4,584775.0,892.0,1324467.0,940.0,209178.0,994.0,128493.0,1054.0,65755.0,1105.0,22942.0
+2024-03-30,876.0001,857.4167,858.0001,,971.3292788879235,857.4,584775.0,892.0,1324467.0,940.0,209178.0,994.0,128493.0,1054.0,65755.0,1105.0,22942.0
+2024-03-31,876.0001,857.4167,858.0001,,971.3292788879235,857.4,584775.0,892.0,1324467.0,940.0,209178.0,994.0,128493.0,1054.0,65755.0,1105.0,22942.0
+2024-04-01,876.0001,857.4167,858.0001,,971.3292788879235,857.4,584775.0,892.0,1324467.0,940.0,209178.0,994.0,128493.0,1054.0,65755.0,1105.0,22942.0
+2024-04-02,876.0001,857.4167,858.0001,,971.3292788879235,857.4,584775.0,892.0,1324467.0,940.0,209178.0,994.0,128493.0,1054.0,65755.0,1105.0,22942.0
+2024-04-03,880.0001,861.25,861.0001,,971.3292788879235,889.0,1355736.0,933.0,232059.0,986.0,128658.0,1045.0,65919.0,1097.0,22948.0,1157.5,12349.0
+2024-04-04,880.5001,861.75,861.5001,,971.3292788879235,889.0,1436801.0,931.0,255342.0,977.5,147048.0,1036.0,68160.0,1093.0,26664.0,1145.0,12449.0
+2024-04-05,881.5001,862.75,862.5001,,971.3292788879235,886.5,1460611.0,924.5,304317.0,963.0,164844.0,1013.0,73476.0,1073.0,50897.0,1123.0,13539.0
+2024-04-06,881.5001,862.75,862.5001,,971.3292788879235,886.5,1460611.0,924.5,304317.0,963.0,164844.0,1013.0,73476.0,1073.0,50897.0,1123.0,13539.0
+2024-04-07,881.5001,862.75,862.5001,,971.3292788879235,886.5,1460611.0,924.5,304317.0,963.0,164844.0,1013.0,73476.0,1073.0,50897.0,1123.0,13539.0
+2024-04-08,883.0001,864.25,864.0001,,971.3292788879235,881.5,1471926.0,918.0,331439.0,951.0,172770.0,999.0,69044.0,1050.0,42812.0,1100.0,13654.0
+2024-04-09,883.5001,864.75,864.5001,,971.3292788879235,883.0,1448568.0,920.5,367546.0,956.0,183121.0,1006.0,75603.0,1058.0,53031.0,1105.0,16750.0
+2024-04-10,884.0001,865.25,865.0001,,971.3292788879235,883.0,1460676.0,921.5,377703.0,958.0,184669.0,1007.0,79679.0,1060.0,56387.0,1115.0,17177.0
+2024-04-11,884.5001,865.75,865.5001,,971.3292788879235,883.0,1422827.0,919.0,408380.0,954.0,198192.0,1001.5,80356.0,1050.0,56777.0,1100.0,17390.0
+2024-04-12,885.5001,866.75,866.5001,,971.3292788879235,883.0,1329885.0,920.5,418239.0,955.5,194390.0,1000.0,89103.0,1048.0,56027.0,1103.0,18231.0
+2024-04-13,885.5001,866.75,866.5001,,971.3292788879235,883.0,1329885.0,920.5,418239.0,955.5,194390.0,1000.0,89103.0,1048.0,56027.0,1103.0,18231.0
+2024-04-14,885.5001,866.75,866.5001,,971.3292788879235,883.0,1329885.0,920.5,418239.0,955.5,194390.0,1000.0,89103.0,1048.0,56027.0,1103.0,18231.0
+2024-04-15,887.0001,868.25,868.0001,,971.3292788879235,883.0,1297572.0,922.5,423926.0,960.5,199538.0,1010.0,94924.0,1065.0,67170.0,1120.0,18351.0
+2024-04-16,887.5001,868.75,868.5001,,971.3292788879235,881.5,1305192.0,922.5,408237.0,964.0,200383.0,1018.5,98353.0,1072.0,67672.0,1133.5,18417.0
+2024-04-17,888.0001,869.25,869.5001,,971.3292788879235,881.0,1331389.0,920.5,425916.0,960.0,203834.0,1015.0,101078.0,1068.0,68082.0,1128.0,18956.0
+2024-04-18,888.5001,869.25,869.5001,,971.3292788879235,881.5,1331389.0,921.0,425916.0,960.0,203834.0,1015.0,101078.0,1068.0,68082.0,1128.0,18956.0
diff --git a/mini project/mini_project/data/gdp_deflator.csv b/mini project/mini_project/data/gdp_deflator.csv
new file mode 100644
index 0000000..d7d5488
--- /dev/null
+++ b/mini project/mini_project/data/gdp_deflator.csv	
@@ -0,0 +1,20 @@
+Year,GDP Deflator
+2004,40.4
+2005,44.5
+2006,50.6
+2007,58.2
+2008,71.7
+2009,82.7
+2010,100
+2011,123.7
+2012,151.3
+2013,187.5
+2014,263.1
+2015,333
+2016,470
+2017,592.2
+2018,841.1
+2019,1254.9
+2020,1757.9
+2021,2704.3
+2022,4583
\ No newline at end of file
diff --git a/mini project/mini_project/data/imf-dm-export-20250216.csv b/mini project/mini_project/data/imf-dm-export-20250216.csv
new file mode 100644
index 0000000..58362df
--- /dev/null
+++ b/mini project/mini_project/data/imf-dm-export-20250216.csv	
@@ -0,0 +1,5 @@
+"Inflation rate, average consumer prices (Annual percent change)",1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Argentina,no data,no data,no data,no data,no data,no data,no data,no data,no data,no data,no data,no data,no data,no data,no data,no data,no data,no data,0.9,-1.2,-0.9,-1.1,25.9,13.4,4.4,9.6,10.9,8.8,8.6,6.3,10.5,9.8,10,10.6,no data,no data,no data,25.7,34.3,53.5,42,48.4,72.4,133.5,229.8,62.7,31.8,17.5,10.4,8.9
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+"©IMF, 2024",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
\ No newline at end of file
diff --git a/mini project/mini_project/data/unemployment.csv b/mini project/mini_project/data/unemployment.csv
new file mode 100644
index 0000000..41b4cce
--- /dev/null
+++ b/mini project/mini_project/data/unemployment.csv	
@@ -0,0 +1,271 @@
+"Data Source","World Development Indicators",
+
+"Last Updated Date","2025-01-28",
+
+"Country Name","Country Code","Indicator Name","Indicator Code","1960","1961","1962","1963","1964","1965","1966","1967","1968","1969","1970","1971","1972","1973","1974","1975","1976","1977","1978","1979","1980","1981","1982","1983","1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020","2021","2022","2023",
+"Aruba","ABW","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",
+"Africa Eastern and Southern","AFE","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","8.17962867204006","8.27072384795289","8.26632715332635","8.13829095077232","7.90844555546911","7.8239078932856","7.78365441542562","7.81273363600731","7.84987781203905","7.78831690275509","7.67695520899306","7.63233006754872","7.58688283691332","7.39564818381754","7.21879262795258","7.15895834527989","7.10223115357479","7.07670978821918","7.15588066169117","7.40306130661224","7.42793993618157","7.18160807948002","6.98673270309414","6.94701073401333","7.03635682492423","7.19466599460041","7.34633063442169","7.3605131044333","7.58441879909928","8.19139491127414","8.57738499231761","7.98520225155097","7.80636542181196",
+"Afghanistan","AFG","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","8.07","8.011","7.888","7.822","7.817","7.867","7.863","7.89","7.903","7.935","7.953","7.93","7.88","7.899","7.885","7.914","7.817","7.878","7.754","7.753","7.784","7.856","7.93","7.915","9.052","10.133","11.184","11.196","11.185","11.71","11.994","14.1","13.991",
+"Africa Western and Central","AFW","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","4.15868027881975","4.25110173142729","4.36980488975891","4.39378131016294","4.39974946558807","4.34069121352357","4.31373545449228","4.32404850194229","4.51215767270693","4.55111905984559","4.47997712328447","4.2858537189934","4.18011071095659","4.09473808884134","4.10069950284306","3.97409484853549","3.95064260570811","3.96854217730079","4.000386882162","3.9915945374515","3.96902733010755","3.98216256621059","3.70385320108226","3.88139571396106","4.16446707837468","4.15757437388452","4.27419620395302","4.32363095413829","4.39527121076194","4.8523927005513","4.73673176237333","3.65857318082157","3.27724546605141",
+"Angola","AGO","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","16.855","16.978","17.399","17.4","16.987","16.275","16.172","16.371","16.593","16.682","16.7","16.488","16.498","16.378","16.36","16.206","16.153","16.228","16.431","16.618","16.77","16.562","16.492","16.406","16.49","16.575","16.61","16.594","16.497","16.69","15.799","14.602","14.537",
+"Albania","ALB","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","10.304","30.007","25.251","20.835","14.607","13.928","16.872","20.042","20.835","19.023","18.57","17.891","16.985","16.306","15.966","15.626","15.966","13.06","13.674","14.086","13.481","13.376","15.866","18.055","17.193","15.418","13.616","12.304","11.466","11.69","11.474","10.137","10.108",
+"Andorra","AND","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",
+"Arab World","ARB","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","11.9145083824283","12.1603853600891","12.9182743533617","13.0251716620291","13.516862533383","12.4771777786255","12.011082437016","11.9646898565116","12.1448058645914","12.6274870577085","12.5058820000806","12.512529502018","12.4563510932562","11.4330020298883","11.2407250571004","10.5232380711626","10.1508222413371","9.88041977711216","9.46639460138819","9.57747395203385","10.6932959358852","11.074941957709","11.0437281019774","11.0374017204054","11.1489135724281","10.8562902038262","11.1131075474704","10.6597396349929","10.2160980238311","11.3256125416623","10.8896587837575","9.9535706462442","9.59035028813167",
+"United Arab Emirates","ARE","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1.625","1.713","1.905","1.836","1.8","1.834","1.904","2.068","2.197","2.25","2.444","2.661","2.908","2.965","3.12","2.963","2.936","2.821","2.944","2.811","2.596","2.327","2.126","2.002","1.793","1.64","2.462","2.236","2.331","4.294","3.105","2.872","2.151",
+"Argentina","ARG","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","5.44","6.36","10.1","11.76","18.8","17.11","14.82","12.65","14.05","15","17.32","19.59","15.36","13.522","11.506","10.078","8.47","7.837","8.645","7.714","7.18","7.217","7.1","7.268","7.579","8.085","8.347","9.22","9.843","11.461","8.736","6.805","6.139",
+"Armenia","ARM","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1.783","1.8","5.3","6.6","6.7","9.3","10.8","9.4","11.2","11.095","10.992","10.833","10.705","10.617","10.54","10.465","10.394","17.1","19.295","19.523","18.884","17.511","16.576","17.498","18.261","17.617","17.704","18.966","18.304","18.175","15.469","13.379","13.245",
+"American Samoa","ASM","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",
+"Antigua and Barbuda","ATG","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",
+"Australia","AUS","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","9.586","10.733","10.879","9.724","8.473","8.509","8.367","7.684","6.876","6.288","6.747","6.375","5.933","5.399","5.036","4.785","4.381","4.242","5.565","5.214","5.083","5.225","5.663","6.078","6.055","5.711","5.592","5.3","5.159","6.456","5.116","3.728","3.668",
+"Austria","AUT","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","3.42","3.59","4.25","3.535","4.345","5.282","5.15","5.483","4.699","4.687","4.007","4.852","4.779","5.969","5.682","5.32","4.909","4.198","5.371","4.883","4.637","4.909","5.367","5.674","5.802","6.064","5.561","4.933","4.56","5.201","6.459","4.992","5.264",
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+"Viet Nam","VNM","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","2.142","2.11","2.038","1.972","1.955","1.93","2.87","2.29","2.33","2.26","2.76","2.12","2.25","2.14","2.078","2.046","2.026","1.889","1.737","1.114","0.999","1.027","1.316","1.256","1.848","1.848","1.874","1.161","1.681","2.103","2.385","1.523","1.645",
+"Vanuatu","VUT","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","7.997","7.964","8.288","8.103","8.153","8.108","8.24","8.24","8.308","8.283","8.434","8.621","8.682","8.447","8.126","8.006","6.854","5.661","4.565","4.685","4.821","4.951","5.058","5.15","5.254","5.261","5.287","5.326","5.444","4.046","4.766","5.143","5.082",
+"World","WLD","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","5.13377969924987","5.29010950422846","5.55969439262019","5.80049888718587","5.93004401353698","5.99253885959213","6.02239065084773","6.17097447113532","6.28861473444324","6.12304074094227","6.20224642581375","6.42824413217503","6.49549540304053","6.3375954079904","6.22712811677286","5.97625026239581","5.77753757637344","5.83230971050102","6.41961494141816","6.28633469019008","6.15688583531364","6.159641648439","6.10721944572915","5.96595956419962","6.00655926883703","5.97827384301346","5.90723141254145","5.76898308254956","5.59893079520961","6.60880501698705","6.06444597897447","5.26071088886287","4.91657221632923",
+"Samoa","WSM","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","2.1","2.44","2.69","3","3.245","3.516","3.78","4.128","4.49","4.734","4.96","4.975","5.037","5.162","5.212","5.321","5.426","5.539","5.647","5.621","5.68","8.752","8.773","8.72","8.9","9.062","9.4","8.639","7.838","7.435","6.612","5.048","4.977",
+"Kosovo","XKX","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",
+"Yemen, Rep.","YEM","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","8.409","8.342","8.344","8.34","8.988","9.585","10.223","10.829","11.46","11.557","11.724","11.84","12.015","12.132","12.215","12.35","12.488","12.657","12.763","12.779","13.311","13.473","13.673","13.467","17.9","18.416","18.603","17.584","17.202","17.953","18.287","17.363","17.091",
+"South Africa","ZAF","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","23.002","23.262","23.179","22.942","22.647","22.48","22.518","22.673","22.791","22.714","22.605","22.547","22.629","22.538","22.461","22.324","22.287","22.407","23.523","24.683","24.639","24.727","24.561","24.89","25.149","26.536","27.035","26.906","28.468","29.217","34.007","33.268","32.098",
+"Zambia","ZMB","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","18.9","19.544","19.7","18.648","16.828","15.3","13.305","12","12.495","12.93","13.415","14.131","14.705","15.33","15.9","13.229","10.561","7.93","10.582","13.19","10.545","7.85","7.264","6.571","5.942","5.239","4.529","5.033","5.542","6.033","5.199","5.995","5.905",
+"Zimbabwe","ZWE","Unemployment, total (% of total labor force) (modeled ILO estimate)","SL.UEM.TOTL.ZS","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","4.813","4.938","4.99","4.96","5.571","6.163","6.93","6.39","6","5.684","5.318","5.007","4.735","4.39","4.622","4.759","4.991","5.357","5.453","5.447","5.368","5.061","4.943","4.774","5.377","5.886","6.344","6.793","7.373","8.621","9.54","10.087","8.759",

From cf030ab43b2f080bbab60388c47cbaa6729bc29c Mon Sep 17 00:00:00 2001
From: malenamalka <malkagom@dickinson.edu>
Date: Thu, 20 Feb 2025 08:06:34 -0500
Subject: [PATCH 6/7] updated miniproject

---
 .DS_Store                                     | Bin 8196 -> 8196 bytes
 mini project/mini_project/.DS_Store           | Bin 6148 -> 6148 bytes
 .../analysis-checkpoint.ipynb                 | 170 ++++++++++-
 .../Mini Project Presentation.pdf             | Bin 1465602 -> 1621748 bytes
 mini project/mini_project/analysis.ipynb      | 170 ++++++++++-
 mini project/mini_project/data/.DS_Store      | Bin 0 -> 6148 bytes
 mini project/mini_project/data/GDP_data.csv   | 271 ++++++++++++++++++
 7 files changed, 601 insertions(+), 10 deletions(-)
 create mode 100644 mini project/mini_project/data/.DS_Store
 create mode 100644 mini project/mini_project/data/GDP_data.csv

diff --git a/.DS_Store b/.DS_Store
index aef7b5c88e7c42a46d0599271521c4a8b6d18a7d..3e4f7465bfee2a52d22832d9364b532668bc61ca 100644
GIT binary patch
delta 274
zcmZp1XmQw}DiAlLX*L4`0}F#5LpnnyLrHGFi%U{YeiBfOBSyH&rE>EzM^yO~yz&JZ
zhQZ1CxdlKy3=DHFY;G11U}WT(yg^7}@;X6zDC>n_BfF`Ej)Jj)(c}Uldx)q=PCAlS
zbH@`v1Hc+3klke0;;I036HF4}AaqF)6xWy^0J#aQYjc-yEz86LzRm0s-&i(ti~eDn
HSilVcsNYF5

delta 249
zcmZp1XmQw}DiBxk;|Bu+0}F#5LpnnyLrHGFi%U{YeiBfO!$)Z8t=}t;Iikv^;FT}P
zFbq!4&n*DzVPL2!*xW22z{tou`HY|h6GQRjHG=X8&J)2#b_+`#1tUx2$vHyy5D77c
z;+%9OBg`#N01bj0@Igo%VSs`#lmpa+Vz2oIknJ!{n`?w?SvIpvd}G=CUWA<)0G16x
A&j0`b

diff --git a/mini project/mini_project/.DS_Store b/mini project/mini_project/.DS_Store
index e02537d2c8dca71ece44ed91fdf76d1b434943bc..bdeab242791faaa2bfa3f72782daaf44096e36dd 100644
GIT binary patch
delta 358
zcmZoMXfc@J&&azmU^g=(@8kv+4S}3=!{Frn+yVv=;5@q<D5y0#kENN3^TOoMEQR$+
z<;4X_Ir&LI+3iUMIhn;J1_sv{nV4Bv+1NSQIk-7ugER8WgG&-iN{gKmi=siiko^3d
zBp5p}DJ(O!JYGP=IX|x?F*7f<2&^G9B^9V7COk7QCBNJ$zceqU7_2uKBEiAQ!5J?g
zUR_^pWNN0PU|~?Jqfl*WWT2y9VrEiX%gG_CtZy9@pPiGNm){9?I|Cylgl6D{(lDwE
z$Y5beVMt^sVMs*ruSjn%vRrPyiwo4R9R6n*|E=GAZ1QAQNmej>@<vt}X^{sYL*a&%
Z1sCPz<maUW#TYkVU=3j0%+B$b9{>n^V}}3$

delta 124
zcmZoMXfc@J&&a(oU^g=(_v8i^4S}3=!{Frn+yVv=;IPgF3TjQxV`*mMaG3m=rBD||
zUO=!8sEUOlg&~olgds6E-^C><CqD_K)A8Qn4;7OW*rZt@tOhn2W`O{P&D+@m7&j}h
HKM?=`OdunD

diff --git a/mini project/mini_project/.ipynb_checkpoints/analysis-checkpoint.ipynb b/mini project/mini_project/.ipynb_checkpoints/analysis-checkpoint.ipynb
index 7ac184f..866a823 100644
--- a/mini project/mini_project/.ipynb_checkpoints/analysis-checkpoint.ipynb	
+++ b/mini project/mini_project/.ipynb_checkpoints/analysis-checkpoint.ipynb	
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,9 +27,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '/Users/malenamalka/Documents/DATA 400/mini_project/canasta basica .csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/z8/02768t9119xbgk92bl8mhrrm0000gn/T/ipykernel_20358/811111655.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0minflation_file_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/Users/malenamalka/Documents/DATA 400/mini_project/arg_inflation.csv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mexchange_file_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/Users/malenamalka/Documents/DATA 400/mini_project/exchange_rate.csv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mcba_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcba_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0minflation_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minflation_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mexchange_rate_df\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexchange_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    676\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 932\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    933\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    934\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1214\u001b[0m             \u001b[0;31m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0;31m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m             self.handles = get_handle(  # type: ignore[call-overload]\n\u001b[0m\u001b[1;32m   1217\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    784\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 786\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    787\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/malenamalka/Documents/DATA 400/mini_project/canasta basica .csv'"
+     ]
+    }
+   ],
    "source": [
     "cba_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/canasta basica .csv'\n",
     "inflation_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/arg_inflation.csv'\n",
@@ -41,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -149,7 +167,7 @@
        "9  2025               146726.230000         334535.800000"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1182,6 +1200,148 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the GDP dataset\n",
+    "gdp_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/data/GDP_data.csv'\n",
+    "gdp_df = pd.read_csv(gdp_file_path, skiprows=4)  # Skipping metadata rows\n",
+    "\n",
+    "cba_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/data/canasta basica .csv'\n",
+    "cba_df = pd.read_csv(cba_file_path)\n",
+    "\n",
+    "\n",
+    "# Filter only for Argentina\n",
+    "gdp_arg_df = gdp_df[gdp_df[\"Country Name\"] == \"Argentina\"].drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"])\n",
+    "gdp_arg_df = gdp_arg_df.melt(var_name=\"Year\", value_name=\"GDP\").dropna()\n",
+    "gdp_arg_df[\"Year\"] = gdp_arg_df[\"Year\"].astype(int)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define independent (X) and dependent (y) variables\n",
+    "# Merge Data\n",
+    "merged_df = pd.merge(cba_yearly_df, gdp_arg_df, on=\"Year\", how=\"inner\")\n",
+    "\n",
+    "X = merged_df[[\"GDP\"]].values\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values\n",
+    "\n",
+    "# Initialize and fit the models\n",
+    "model_cba = LinearRegression().fit(X, y_cba)\n",
+    "model_cbt = LinearRegression().fit(X, y_cbt)\n",
+    "\n",
+    "# Generate predictions\n",
+    "y_pred_cba = model_cba.predict(X)\n",
+    "y_pred_cbt = model_cbt.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "CBA Regression: Coeff: 6.267458876554092e-08, Intercept: -23545.10790229818, R²: 0.2126220487420748\n",
+      "CBT Regression: Coeff: 1.3131414495969047e-07, Intercept: -47646.20263842166, R²: 0.20364163026858118\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[\"GDP\"].values  # Independent variable\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values  # Dependent variable 1\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values  # Dependent variable 2\n",
+    "\n",
+    "# Function to calculate linear regression coefficients manually\n",
+    "def linear_regression(X, y):\n",
+    "    n = len(X)\n",
+    "    mean_x, mean_y = np.mean(X), np.mean(y)\n",
+    "    \n",
+    "    # Calculate slope (m) and intercept (b)\n",
+    "    numerator = np.sum((X - mean_x) * (y - mean_y))\n",
+    "    denominator = np.sum((X - mean_x) ** 2)\n",
+    "    slope = numerator / denominator\n",
+    "    intercept = mean_y - slope * mean_x\n",
+    "    \n",
+    "    # Compute R-squared\n",
+    "    y_pred = slope * X + intercept\n",
+    "    ss_total = np.sum((y - mean_y) ** 2)\n",
+    "    ss_residual = np.sum((y - y_pred) ** 2)\n",
+    "    r_squared = 1 - (ss_residual / ss_total)\n",
+    "    \n",
+    "    return slope, intercept, r_squared, y_pred\n",
+    "\n",
+    "# Perform Linear Regression\n",
+    "slope_cba, intercept_cba, r_squared_cba, y_pred_cba = linear_regression(X, y_cba)\n",
+    "slope_cbt, intercept_cbt, r_squared_cbt, y_pred_cbt = linear_regression(X, y_cbt)\n",
+    "\n",
+    "# Print Regression Results\n",
+    "print(f\"\\nCBA Regression: Coeff: {slope_cba}, Intercept: {intercept_cba}, R²: {r_squared_cba}\")\n",
+    "print(f\"CBT Regression: Coeff: {slope_cbt}, Intercept: {intercept_cbt}, R²: {r_squared_cbt}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7u3fvWadjqalPQpKWQZKWYcqUqmtRV6SkFMqP+/QJr3L85ZdvgSQtgygug8m0GDk5c/HVVw/jpptc27th5swb/oy1CEeO5OGzz8pbWT3wQDxatAiQzxs3LhoXLz6DdevuxNixPdCyZQCKi43YufMUxo37FAsW7K/xe4uMbClX3/7++yVYrY4q5xQVGdCu3So88shufPXVmWrHiYgIRUREKDp1ao7u3Vth5Mhu+OijCfLxiqQzETUtTNASEbkocswYBP/Zu/Xkpk0oPHas2vMqV8K6SnX2LC4lJwMor3YNDq+6oK2ruMmT4RsSAgD4cfZsmFUqAM7tDexmMwr/+ANZ+/ej44034t6vv8ZTpaWYmpqK1r16AQCOvfMOREfVBenVCIKAuMmTAZR/f45v2FDlnKNvvglTcTFOf/IJLn7zjctjKyptOGbV6eTHlZOwV+OwWKpsnkZERETUGI0Z0wPt25evBzdtOoZjx/KrPa9yJayrzp4tRXJyLgBgyJAIhIeH1D7QK5g8uTdCQvwAALNn74NKZQZweXMwoHzjrT/+yMf+/Rdw442d8PXXk1BaugCpqbPQq1f5prrvvHMYDkfN2m4JgoDJk8vXwxqNBRs2HKlyzptv/g/FxeW9db/5xvWNdStvOKbTVS1kIKLGjz1oiYhcpPTzw+1JSdg9ZgzsZjN23nILBr/0ErrdcQeUfn4o/OMPHF29GgWHD191nIrNvgDAZjSi6Ngx/LJkidybdfBLL9VL/H7NmiF20iSkfPAB8g8dAuC8ORhQvtnZRwMHQnI40LJHD4x69120iIqC/tIlOfkpKJUQ/tysy6xSwWYov8UruH17KJRVd7OtMOjFF3Hms8+gz8vDweefh6m4GD0fegiS3Y7Tn3yCI6tWAQB8g4OReIUq3YrvG1Dey9aQn4///eMf8nMd/9worTQ9HZf+V97frG2/fvjbH39UGSv355+x/eabAQApGzdiwHPPXe3bR0RERNTg+fkpkZR0N8aM+QRmsx233LIVL710M+64ozv8/JT44498rF79Gw4fzrvqOBWbfQGA0WjDsWP5WLLkgNyb9aWXbq6X+Js188ekSQn44IM/cOhQeYyVNwcDyjc7GzhwIxwOCT16tMK7745BVFQrXLqkk5OfSqVCXs+qVCYYDOWb/bZvHwKl8sp1bC++OAyffZaGvDwdnn/+OxQXG/HQQwmw20V88slJrFpVvv4MDvbFggXVb+Bb8X0DynvZ5ufr8I9//Fd+7kobpRFR48YELRFRDUTeeSfGffopvn38cVg0Gvw0bx5+mjevynmCUoleM2YgfMCAKseutNkXAAxdvhyRd97p9rgr9J01y6nnauXqWQBo3qULbnr5Zfz84otQnT2Lf992W5UxhixdKm+YdmDuXLn6dGZODppFRFzxtYPatMHEffuwe+xY6HJy8NvKlfht5Uqnc3wCAzFu+3Y079KlyvWSw4H3O115wRrYpg0GL1kCwLl6NmHatGrPjxg2DGHx8Sg9dQpl6enI+e9/0enm+vkwQUREROQt7ryzBz79dCIef/wraDQWzJu3H/PmVb3lX6kUMGNGPwwY0KHKsStt9gUAy5cPx5139nB73BVmzUrEBx9c/uV75c3BAKBLlxZ4+eVb8OKLP+Ls2TLcdttHfx0CS5feLG+YNnfut3JP25ycuYiICK1yfoU2bYKxb98jGDv2E+TkaLFy5c9YufJnp3MCA32wfft96NKlRZXrHQ4JnTqtucr4QViyhOtRoqaICVoiohqKeeghdLzpJqR++CHO/ec/0GZlwarRIKBVK4TFx6PzyJGI/9vfrpqsrKD080Ng69ZoP3gw+s2ejc4jRtRr7G379kX7QYOQf+hQlc3BKgxatAht+vTBH+vWoeTkSRgLC+EbEoK2/fqh76xZ19yQ7Gra9OqFqamp+OPtt3F2926oMjIg2mwIiYhA19GjkTh/PlpERbk0lqBQQOHri6C2bdF55EgMWbYMzSIi4LDZkLZtG4Dy72/spElXHKPP3/+OH2fPBlC+WRgTtERERNQUPPRQAm66qTM+/PAY/vOfDGRlqaHRWNCqVSDi49tg5Mhu+Nvf+lw1WVnBz0+J1q2DMHhwBGbPHogRI7rWa+x9+4Zj0KCOOHQor8rmYBUWLRqGPn3CsW7dYZw8WYjCQgNCQvzQr184Zs0acM0Nya6mV692SE19Em+/fQi7d59GRkYpbDYRERGhGD06EvPnD0VU1FU2/K1EoRDg66tA27bBGDmyG5YtG+7S95yIGh9ButoWg0RERERERERERERUb7hJGBEREREREREREZGHMEFLRERERERERERE5CFM0BIRERERERERERF5CBO0RERERERERERERB7CBC0RERERERERERGRhzBBS0TkQX+88w42RkbiraAg/CsxEXm//urpkIiIiIiIXPLOO4cRGbkWQUErkZi4Eb/+mu3pkIiIGiQmaImI3GDf1KlYJQhV/ygUWBsSgqSePfH900/DUFAgX5P366/4cfZsQBAQPXEiio4dw5f33gtJFOs93tL0dHwxYQLWtWyJt4KC8PGQIbiwb5/L12uzs7H/73/Hph49sCYgABvat8feRx+F+sKFq163ffhwrBIE7Lz11mqPX0pOxufjx+P9Tp2wxt8fm7p3x38XLoTVYKhyrs1oxP9WrEBSz55YExCAjZGROPD887BoNC6/DyIiIiICpk79AoKwosofhWIFQkJeRc+e7+Dpp/eioEAvX/Prr9mYPXsfBEHAxIlxOHYsH/feuxOiKNV7vOnpJZgwYQdatvwngoJWYsiQJOzbd9bl67OzNfj7379Gjx7rEBDwCtq3fxOPPvo5LlxQVTlXozFj9uy96NhxNfz9X0FMzDtYsyYZklT1ff78cxZuv/0jtGjxOoKDX8VNN23Gd9+drzYGo9GGFSsOomfPdxAQ8AoiI9fi+ee/hUZjdv0bQUSNhiBV968KERHVyL6pU3Fq69ZrntciKgqTjxxBQMuWyPnpJ6R99BESpk1Dx6FD8WF8PFQZGZij08EnIKDeYtVkZeFf/fvDXFbm9LygUGD87t3oPn78Va8vTknBp8OGwarVVjkW0KoVJiUno1V0dJVjR9eswcHnngMAdB41Cg98/73T8dQtW/DN9OlANf9bajdgAB7++Wf5+2I1GPDZqFHIP3SoyrkRw4fjge+/h8LH56rvg4iIiIjKTZ36BbZuPXHN86KiWuLIkcfRsmUgfvopEx99lIJp0/ph6NBOiI9fj4yMUuh0ixAQUH/rsKwsNfr3/wBlZSan5xUKAbt3P4Dx42Ouen1KSiGGDfsQWq2lyrFWrQKRnDwD0dFhAACHQ8TQoZtx+HBelXOfe24w3nzzdvnrPXsyMH78djgczmtZhULAv/99PyZMiJWfMxisGDVqGw4dqjru8OFd8P33f4OPD+vpiJoS/sQTEbnZ5CNHMDMnBzNzcvBEVhYeOXwYHYYOBQCoz5/H8Q0bAACdhg/H7Rs3ouPQoUh++WWUpqWh+4QJ9ZqcBYBfly6FuawMSj8/jN+9G48cPozQrl0hiSJ+fPbZaqsBKjs4bx6sWi0EpRK3JyVheno6hr32GgDAXFaGnxcudDrfbrHg4Pz5cnK2OjaTCQeefRaQJIR07Ij7vvsOU1NTEX3//QCAwqNHkfLBB/L5/1u+XE7ODlq0CNPS0tBv9mwAQO5PP+HCnj01/r4QEREREXDkyOPIyZmLnJy5yMp6FocPP4ahQzsBAM6fV2HDhqMAgOHDu2LjxrsxdGgnvPzyT0hLK8aECTH1mpwFgKVLD6KszAQ/PyV2734Ahw8/hq5dW0AUJTz77LfXXMvOm/cdtFoLlEoBSUl3Iz39Kbz22igAQFmZCQsXXi4i2LbthJycXbZsOE6ffgpjxvQAALz11iGcO3e54OHZZ7+FwyGhU6dQ/PTTVPzww9/QunUQRFHCk0/uhcNx+S655csPysnZRYtuQlrak5g9eyAA4KefsrBnT4YbvlNE1JAwQUtE5GbB4eFoFhGBZhERCO3cGe0TE3HL6tXy8cpVnw6rFXunTMGvS5ciYvhw3L5pU73G5rBacXb3bgBA1N13o8eECWifmIgBzz8PANBmZiL/t9+ueL3NZEL2jz8CACLHjEGv6dPRqmdPDFq4EGFxcQCA7AMH5PNzfvoJm3v2xNFVqwBBuOK4eT//LLcmGPDcc+g6ejRax8fj1nfflc+pGNdhtSI1KQkA0H7QIAx79VWExcZi2KuvIu7RRzFw4UIEtGpV4+8NEREREQHh4SGIiAhFREQoOndujsTEjli9+jb5eOWqT6vVgSlTvsDSpQcxfHgXbNp0d73GZrU6sHv3aQDA3Xf3xIQJsUhM7Ijnnx8CAMjMVOO333KveL3JZMOPP14EAIwZ0wPTp/dDz56tsXDhTYiLawMAOHAgUz7/009TAQDt2gVj6dLhiIlpjX/+s7xVlyhK+OyzUwDKWyZUVPQuWXIzbr65C0aO7IZHHukFACgo0CMjo1R+D0lJxwAAgwZ1xKuvjkJsbBu8+uooPPpobyxceCNatQqs+zeLiBoU3v9JRHQdCJVut/cNCgIAiHY7vpgwARf37kX81Km47f33ofTzu+IY2QcPYuctt1zztYYsW4Ybly+v9lhZejps+vLeYe3695efDx8wQH5ccPQoOgwZUu31Sl9fTD5yBPq8PASGhTkdq653btGxY9BmZSGgZUvcuXUrPr+7+kV7+8GDMSk5Gfq8PLStFFd1YxanpMCsKu8P1nlUebWDWaWCMiAAY7Ztq3Z8IiIiIqq9yrfbBwX5AgDsdhETJuzA3r1nMXVqX7z//jj4+SmvOMbBg5m45ZZrtwRbtmw4li8fUe2x9PQS6PVWAED//uHy8wMGdJAfHz16CUOGdKr2el9fJY4ceRx5eTqEhTknQavrnXv06CUAQJ8+4VAoyosN4uPbICDAB2azHUeP5gMAOndujtLSBcjMVKNly8t3w1Vuo+DvX/55ICWlECpVeZ/ZUaO6AQBUKhMCAnywbduEauMmosaPCVoionpkt1igy87GgWeekZ/r9GeS9eTmzbi4dy8A4NSWLTi1ZQsAYFZ+PoLDw6uM5Q6azEz5cWDr1pcfV0q2arOyrni9wscH7fr1Q7t+/Zyezz5wAGXp6QCcE7/+LVti4Asv4IZnn73qe/IPDUWHwYOrPH98/Xr5ccW4qoxKt3xJEj4eMgT5v/0GQalEtzvvxOgNG9AsIuKKr0VERERErrFY7MjO1uCZZ76Rn7vllq4AgM2bj2Hv3vKNubZsOY4tW44DAPLzn0d4eEi9xJOZqZYft24dJD+unGzNyrryhrE+Pgr069ce/fq1d3r+wIGLSE8vAQD0719+TKu1yInUyq8lCAJatQrEpUs6ZGWpncbp2rUFgPINwPbuPYtPPjkJABgxoisiI1sCgFxJC5RvvTBkSBJ++y0XSqWAO+/sgQ0bxiIiIvSq3wcianyYoCUicrP3O1X/G3sA6HrHHeg1YwYAwC8kRG4tUJlvcHC113YYMgQzc3Ku+fp+oVde0FVUzwKAslKvW6W/v/zYqtNd8zUqKztzBl8/9JD8db+nn5YfJ0yZUqOxKsvYtQu/rVwJAPAJCJC/bxWtEADg8D//KVfZSg4HLnz9NT5NTcXf/vgDAS1b1vq1iYiIiJqqTp3WXPHYHXd0x4wZ5b+oDwnxk1sLVBYc7FvttUOGRCAnZ+41Xz801P+KxyqqZwE49bqtqE4FAJ2u6uZfV3PmTAkeemiX/PXTTydW81rOlcH+/so/X8uK6txyy1a5d+2wYZ2xY8d98jGNxiw//uc/f5Urdx0OCV9/nYHU1CL88ccTaNmSbQ6ImhImaImIroOut9+OuMmTEfvIIxD+7MUaO2kSYidNcnkMH3//OleGXmvThJoqPX0aO0eNgrGoCADQffx49JhQ91uzzvz739gzaRIkhwMAMOy11xDSofzWtcrvQVAoMH73bnS65Rb88fbb+PWll6DNzMTR1atx08sv1zkOIiIiIgJuvz0Kkyf3xiOP9JLXspMm9cKkSb1cHsPf36fOlaHuXsuePl2MUaO2oajIAAAYP768r21dX+viRZX8uKTEiB9/vIiHHkr4c9zL5ykUAnbvfgC33NINb799CC+9dACZmWqsXp2Ml18eWevXJ6KGh5uEERG52SOHD2NaWhqGrlghb4ylyshA2/795QVtbWQfPIhVgnDNP79eof8sUF61W8FhuVxdYDdf/k2+X7NmLsVTnJKC7cOHw5Bf3nsrfOBAjPnooxq+q6rSPv4YXz/0EESbDQDQ98knccOzz16Or9J7iBw3Dt3Hj4d/aCgGL16MkI4dAQCZ331X5ziIiIiImqLDhx9DWtqTWLFihLzHa0ZGKfr3b1+ntezBg5kQhBXX/LN8+cErjhEScnm/BovFIT82m+3y42bNrlyBW1lKSiGGD9+C/PzyO8wGDuyIjz6695qvVfn1mjWrfv+IPXsm4eTJWbjnnhicPl2Chx/ehT17MqqMO25cNMaPj0FoqD8WLx6Gjh3L1+HffXfBpfdARI0HE7RERG4W0r49wmJjMXTpUgxevBgAoLl4Ef++7TYY/qw09ZRmldovmEov978ylZTIj0O7dr3mOKXp6dg5ahRMxcUAgA5Dh+L+/fudkqe1kb5zJ/b97W9y5Wy/2bMx6p13nM5pHhkpPw5q21Z+LAiC/P4sKhWIiIiIqObat2+G2Ng2WLp0OBYvHgYAuHhRjdtu+5dcaeopnTo1lx+XlhrlxyUllx9X9IG9mvT0EowatQ3FxeXXDR3aCfv3P+qUPG3ePEBut1BaapKflyQJZWWmq75WYmJHJCS0RVLS5Q1yt21LAQC5Fy0AtG3r3Nu24v2pVJdfj4iaBrY4ICKqR0OXLUPmd9+h4PBh6PPysH/mTNzz+ee1GssdPWhbxcbCJyAAdrMZhb//Lj9fdOyY/Dh8wICrjm8qK8PuMWPkpG7E8OG4d88e+F2hd66rLv32W3ly9s+esgPmzcOIN96ocl7bfv3gExgIu8mEwj/+kJ+XJAn63FwAQAg3CSMiIiKqs2XLRuC77y7g8OE85OXpMHPm1/j88wdrNZY7etDGxrZGQIAPzGY7fv89X37+2LHLjwcM6HDV8cvKTBgz5mM5qTt8eBfs2TMJwcFVq2H79QvHTz9l4fjxAoiiBIVCQFpasVxRW/Fahw/n4Z13DiM3V4vXXhuFQYPK16JWa9Uq3379whEY6AOTyY4//iiQj0uShNxcLQBwkzCiJogVtERE9Ujh44M7t2yRN+E698UXOPPvf9dqrIoetNf643+VBK1vYCAix40DAJz/8kuc+/JLFPz+O46uXg0AaN6tG9oPGiSfr8vNhS43F8ZKFbb/feEFaC5eBAAEt2+P0Rs2wKJSyefq8vJq/N4kUcQ3U6fKbRe6jB6NG5555vKYlWLwCw5GzJ+bkhUePYr/LlyI0tOn8evSpdD9maB1Rx9cIiIioqbOx0eBLVvGy5tiffFFOv7977RajVXRg/Zaf66WoA0M9MW4cdEAgC+/PIMvv0zH779fwurVvwEAunVrgUGDOsrn5+ZqkZurdaqwfeGF/bh4UQ0AaN8+BBs2jIVKZZbPzcvTyufef38cAKCgQI9XXvkvzpwpwaJFPwAAlEpBPt68uT8++igFBw5kYtasPUhOzsGJEwWYNu1LeawxY7oDAIKD/eR+tEePXsLChd/j9OliLF16QE7QTpgQU4vvMBE1ZILk7i7bRERN0L6pU3Fq61YAwMycnCqbeR16/XX8vGgRACCkQwdMT093uderu6nOncMngwc7tTgAyjfcuufLLxH1ZwIXAFb92Wes86hReOD772EoKMD7nTvL/WGrIyiVeN5ur/bYX8erkLF7N/4zceJV4658jbG4GJ/eeCNUZ89WOS9i2DDc//33UPpV3xOMiIiIiJxNnfoFtm49AQDIyZlbpYLz9dd/kROTHTo0Q3r6Uy73enW3c+fKMHjwJqe2A0D5hltffvmQnMAFAEFYAQAYNaobvv/+bygo0KNz5zWw2cQrjq9UCrDblwIor4C96abNOHLkUpXzFiwYin/+c7T89ZIlP2Llyp+rHfOOO7rjq68eho9PeY1ccbEBN964GWfPllU5d9iwzvj++7/Bz095xRiJqPFhBS0R0XWQOH8+wgcOBADoL13CL0uWeCyWlt27Y1JyMqLvuw/+LVrAJyAA7QcPxr179jglZ6uT89NPV03O1lbW/v01Oj+oTRtMSk5G36eeQkiHDlD4+qJ5ZCQGL1mC+777jslZIiIiIjeaP38oBg4sr0y9dEmHJUt+9Fgs3bu3QnLyDNx3XxxatAhAQIAPBg+OwJ49k5ySs9X56afMqyZn/8rPT4nvv/8b5s4djA4dmsHPT4no6DCsXn0bXn/9VqdzX3llJPbsmYSRI7uhTZsg+Psr0bt3O6xZczu+/vpychYA2rQJRnLyDDz1VCI6dGgGX18FIiNbYsmSYfjuu0eZnCVqglhBS0REREREREREROQhrKAlIiIiIiIiIiIi8hAmaImIiIiIiIiIiIg8hAlaIiIiIiIiIiIiIg9hgpaIiIiIiIiIiIjIQ5igJSIiIiIiIiIiIvIQJmiJiIiIiIiIiIiIPMTH0wE0VceOHYMkSfD19fV0KEREREReyWazQRAE9OvXz9Oh0F9wLUtERER0dTVZyzJB6yGSJMl/GitJkiAIgqfDoGpwbrwT58V7cW68F+fGO7lrXhrzOqmh41qWPIlz4504L96Lc+O9ODfeyRNrWSZoPcTX1xeSJCEmJsbTodQLh8MBg8GA4OBgKJVKT4dDlXBuvBPnxXtxbrwX58Y7uXNe0tPT+aHFS3EtS57CufFOnBfvxbnxXpwb7+SptSx70BIRERERERERERF5CBO0RERERERERERERB7CBC0RERERERERERGRhzBBS0REREREREREROQhTNASEREREREREREReQgTtEREREREREREREQewgQtERERERERERERkYcwQUtERERERERERETkIUzQEhEREREREREREXkIE7REREREREREREREHsIELREREREREREREZGHMEFLRERERERERERE5CFM0BIRERERERERERF5CBO0RERERERERERERB7CBC0RERERERERERGRh/h4OgAiIiIiahgkUYQt9RwcZRooWzWHb0J3CAr+vp+IiIiIvJ8oSjifZ4dGL6J5iAJRHX2gUAieDgsAE7RERERE5ALzL8egffdT2M/lQLLZIfj6wKd7J4Q+9TACburn6fCIiIiIiK7oeIYFO38wIKfIDrsd8PEBOrX1wQOjgtE32t/T4bHFARERERFdnfmXY1AtXAPb6QsQggOhaNcKQnAgbKcvQLVwDcy/HPN0iERERERE1TqeYcHbO7W4cMmOQD8BrUIFBPoJuHjJjrd3anE8w+LpEJmgJSIiIqIrk0QR2nc/hag3QhHeGkKgPwSFAkKgPxThrSHqjdC++ykkUfR0qERERERETkRRws4fDDBaJLRuLsDfT4BCUf7fsOYCTJby46IoeTROJmiJiIiI6IpsqedgP5cDRYtQCIJzjy5BEKBoEQr7uRzYUs95KEIiIiIiouqdz7Mjp8iO0CCh2rVssyABOUV2nM+zeyjCcg0uQXvx4kX069cPu3fvlp87ffo0Jk+ejL59+2LEiBFISkpyukYURbz99tsYNmwY+vTpg+nTpyMrK8vpHHeMQURERNTYOMo0kGx2wN+3+hP8fSHZ7XCUaa5vYERERERE16DRi7DbAd8r7MLl6wPY7eXneVKDStDabDbMmzcPRqNRfk6lUmHatGno2rUrdu3ahdmzZ2Pt2rXYtWuXfM769euxfft2vPLKK9ixYwcEQcDjjz8Oq9XqtjGIiIiIGiNlq+YQfH0Ai636Eyw2CD4+ULZqfn0DIyIiIiK6huYhCvj4ALYrFMja/twwrHmIZ1OkDSpBu27dOgQHBzs9t3PnTvj5+WH58uWIiorCxIkTMXXqVGzcuBEAYLVasXnzZsyePRvDhw9HTEwM1qxZg8LCQuzfv99tYxARERE1Rr4J3eHTvRNEtRaS5NybS5IkiGotfLp3gm9Cdw9FSERERERUvaiOPujU1gc6o1TtWlZnlNCprQ+iOl6hxPY6aTAJ2iNHjmDHjh345z//6fT80aNHkZiYCB+fy9/IwYMH4+LFiygtLUV6ejoMBgMGDx4sHw8NDUVcXByOHDnitjGIiIiIGiNBoUDoUw9DERIEsaAEkskCSRQhmSwQC0qgCAlC6FMPQ1A0mGUlERERETURCoWAB0YFI9BfQKlGgsUqQRTL/1uqkRDoX35coRCuPVh9xunRV3eRVqvFggULsGTJErRv397pWEFBAcLDw52ea9u2LQDg0qVLKCgoAIAq17Vt2xb5+fluG4OIiIiosQq4qR9avj4XvrGRkAwmiEVlkAwm+MZGouXrcxFwUz9Ph0hEREREVK2+0f6Y80AounXwgckqoUwrwWSV0K2DD+Y8EIq+0f6eDhGerd910fLly9G3b1/cddddVY6ZzWb4+fk5PefvX/6NtVgsMJlMAFDtORqNxm1j1JbD4ajT9d7K4XBAFMVG+/4aMs6Nd+K8eC/Ojffi3FxfvkN6o9WgBNhSz0FUaaFoGQrfhO4QFAqnOeC8EBEREZG36Rvtj97d/XA+zw6NXkTzEAWiOvp4vHK2gtcnaL/44gscPXoUX331VbXHAwICqmzUZbFYAABBQUEICAgAUN5HtuJxxTmBgYFuG6M2JEmCwWCo9fXeTBRFmM1mAICCtzx6Fc6Nd+K8eC/Ojffi3HhIVEcAHeEAYPvzl9iVuXNeJEmCIHjHopmIiIiIGjaFQkCPTr6eDqNaXp+g3bVrF0pLSzFixAin55ctW4akpCR06NABRUVFTscqvm7Xrh3sdrv8XOfOnZ3OiYmJAQCEh4fXeYzaEAShyqZnjUVF1UxQUBCUSqWHo6HKODfeifPivTg33otz453cOS9MzhIRERFRU+D1CdpVq1bJVRgVbrvtNsyZMwdjxozBnj17sH37djgcDvlDQHJyMrp164awsDA0a9YMISEhOHTokJxc1Wq1SEtLw+TJkwEAiYmJdR6jthrzB0qFQgGlUtmo32NDxbnxTpwX78W58V6cG+/EeSEiIiIicp3X3w/Yrl07dOnSxekPAISFhaFjx46YOHEi9Ho9Fi9ejHPnzmH37t3YunUrZs6cCaC8b+zkyZOxatUq/PDDD0hPT8fcuXMRHh6O0aNHA4BbxiAiIiIiIiIiIiKqKa+voL2WsLAwbNq0CStXrsSECRPQpk0bLFiwABMmTJDPmTNnDux2O5YsWQKz2YzExEQkJSXJm365YwwiIiIiIiIiIiKimhIkSZI8HURTdPLkSUiSVKcett7M4XDAYDAgODiYtzd6Gc6Nd+K8eC/Ojffi3Hgnd85Leno6BEFAr1693BQduQvXsuQpnBvvxHnxXpwb78W58U6eWst6fYsDIiIiIiIiIiIiosaKCVoiIiIiIiIiIiIiD2GCloiIiIiIiIiIiMhDmKAlIiIiIiIiIiIi8hAmaImIiIiIiIiIiIg8hAlaIiIiIiIiIiIiIg9hgpaIiIiIiIiIiIjIQ5igJSIiIiIiIiIiIvIQJmiJiIiIiIiIiIiIPIQJWiIiIiKiemSz2bBmzRqMGDEC/fr1w6RJk/DHH3/Ix0+fPo3Jkyejb9++GDFiBJKSkpyuF0URb7/9NoYNG4Y+ffpg+vTpyMrKcjrHHWMQERERkWcwQUtEREREVI82bNiAXbt24ZVXXsEXX3yByMhIPP744ygsLIRKpcK0adPQtWtX7Nq1C7Nnz8batWuxa9cu+fr169dj+/bteOWVV7Bjxw4IgoDHH38cVqsVANwyBhERERF5DhO0RERERET16IcffsC4ceNw0003oUuXLli4cCH0ej2OHz+OnTt3ws/PD8uXL0dUVBQmTpyIqVOnYuPGjQAAq9WKzZs3Y/bs2Rg+fDhiYmKwZs0aFBYWYv/+/QDgljGIiIiIyHOYoCUiIiIiqkctWrTAgQMHkJubC4fDgR07dsDPzw+xsbE4evQoEhMT4ePjI58/ePBgXLx4EaWlpUhPT4fBYMDgwYPl46GhoYiLi8ORI0cAwC1jEBEREZHn+Fz7FCIiIiIiqq3Fixdj7ty5GDVqFJRKJRQKBdauXYvOnTujoKAA0dHRTue3bdsWAHDp0iUUFBQAANq3b1/lnPz8fABwyxi15XA46nS9t3I4HBBFsdG+v4aMc+OdOC/ei3PjvTg33slT88IELRERERFRPTp//jxCQ0Px7rvvol27dvjss8/wwgsv4KOPPoLZbIafn5/T+f7+/gAAi8UCk8kEANWeo9FoAMAtY9SGJEkwGAy1vt6biaIIs9kMAFAoeNOhN+HceCfOi/fi3Hgvzo13cue8SJIEQRBcOpcJWiIiIiKiepKXl4f58+djy5YtGDBgAACgV69eOHfuHNatW4eAgIAqG3VZLBYAQFBQEAICAgCU95GteFxxTmBgIAC4ZYzaEAQBwcHBtb7em1VUzQQFBUGpVHo4GqqMc+OdOC/ei3PjvTg33smd8+JqchZggpaIiIiIqN6kpKTAZrOhV69eTs/36dMH//3vf9GhQwcUFRU5Hav4ul27drDb7fJznTt3djonJiYGABAeHl7nMWqrMX+gVCgUUCqVjfo9NlScG+/EefFenBvvxbnxTp6YF9ZQExERERHVk4q+r2fOnHF6PiMjA126dEFiYiJ+//13pz5nycnJ6NatG8LCwhATE4OQkBAcOnRIPq7VapGWliZX5LpjDCIiIiLyHCZoiYiIiIjqSe/evTFgwAC88MIL+O2335CZmYm33noLycnJeOKJJzBx4kTo9XosXrwY586dw+7du7F161bMnDkTQHnf2MmTJ2PVqlX44YcfkJ6ejrlz5yI8PByjR48GALeMQURERESewxYHRERERET1RKFQYP369XjrrbewaNEiaDQaREdHY8uWLejbty8AYNOmTVi5ciUmTJiANm3aYMGCBZgwYYI8xpw5c2C327FkyRKYzWYkJiYiKSlJ3vQrLCyszmMQERERkecIkiRJng6iKTp58iQkSapz3y9v5XA4YDAYEBwczF4qXoZz4504L96Lc+O9ODfeyZ3zkp6eDkEQqvRvJc/jWpY8hXPjnTgv3otz4704N97JU2tZtjggIiIiIiIiIiIi8hAmaImIiIiIiIiIiIg8hAlaIiIiIiIiIiIiIg9hgpaIiIiIiIiIiIjIQ5igJSIiIiIiIiIiIvIQJmiJiIiIiIiIiIiIPIQJWiIiIiIiIiIiIiIPYYKWiIiIiIiIiIiIyEOYoCUiIiIiIiIiIiLyECZoiYiIiIiIiIiIiDyECVoiIiIiIiIiIiIiD2GCloiIiIiIiIiIiMhDmKAlIiIiIiIiIiIi8hAmaImIiIiIiIiIiIg8hAlaIiIiIiIiIiIiIg9hgpaIiIiIXCaKIjQajafDICIiIiJqNJigJSIiIiKXmEwm5OXlwWg0ejoUIiIiIqJasdlsng6hCh9PB0BERERE3k2SJKhUKqhUKjgcDoSEhHg6JCIiIiKiGrFarSgrK4MkSWjfvr2nw3HCBC0RERERXZHVakVxcTGMRiMkSfJ0OERERERENWK326FWq6HRaLy22IAJWiIiIiKqlkajQVlZmVfeBkZEREREdDUVeyeo1WrY7XavLjZggpaIiIiInDgcDhQVFcFgMEAURU+HQ0RERETkMofDAY1GA41G4/WJ2QpM0BIRERGRzGAwoLi4GFar1dOhEBERERG5zGazQaPRQKfTNZjEbAUmaImIiIgIoiiitLQUWq0WDofD0+EQEREREbnEarXKiVmHw9GgErMVmKAlIiIiauIsFguKiopgNpsb5IKWiIiIiJoei8UCtVoNvV7f4AsMmKAlIiIiaqIkSYJarUZZWVmDX9QSERERUdNgMpmgVqthNBobzRqWCVoiIiKiJshms6G4uBhGo5EbgRERERGR1zMajVCr1TCZTI0mMVuBCVoiIiKiJkar1aK0tBQ2m83ToRARERERXZEkSTAYDNBoNDCZTI22sIAJWiIiIqImwmKxoLS0lFWzREREROTVRFGETqeDTqeD2Wxu9GtXJmiJiIiIGjlRFFFWVgaNRtPobgcjIiIiosbDYrHIiVm73d5kNrBlgpaIiIioEdPr9SgtLYXVam0yC1wiIiIialj0ej20Wm2j7C/rCiZoiYiIiBohs9mMsrIytjMgIiIiIq+l1+sbfX9ZVzBBS0RERNSI2O12lJWVQafTNcnqAyIiIiLyfgaDAWq1usknZiswQUtERETUCIiiCI1GA7VaDZvN5ulwiIiIiIicSJIEo9EIjUbDu7z+gglaIiIiogbObDajuLgYZrOZfWaJiIiIyKuIoij3mDWbzUzMVoMJWiIiIqIGShRFqNVqqFQqtjMgIiIiIq9ScYeXVquFzWZjYvYqmKAlIiIiaoAsFguKi4thMplYNUtEREREXqMiMavRaGCz2bhWdQETtEREREQNiCRJctWs3W73dDhERERERACYmK0LJmiJiIiIGgir1YqSkhJuqkBEREREXsPhcECr1TIxWwdM0BIRERE1ABqNBmVlZbDZbJ4OhYiIiIgIVqsVWq0WOp0Odrudidk6YIKWiIiIyIvZ7XYUFxfDYDCwapaIiIiIPM5kMkGj0cjrUyZm607h6QBcUVpaivnz52Pw4MHo168fnnjiCZw7d04+fvr0aUyePBl9+/bFiBEjkJSU5HS9KIp4++23MWzYMPTp0wfTp09HVlaW0znuGIOIiIjInbRaLXJzc6HT6ZicJSIiIiKPMhqNuHTpEi5dugStVguHw8HkrJs0iATtrFmzkJOTg40bN+Lf//43AgICMHXqVJhMJqhUKkybNg1du3bFrl27MHv2bKxduxa7du2Sr1+/fj22b9+OV155BTt27IAgCHj88cdhtVoBwC1jEBEREbmL1WrFpUuXUFRUxLUGEREREXlURWI2Pz8fer0eDofD0yE1Ol7f4kClUiEiIgKzZs1Cjx49AABPPvkkxo8fj7NnzyI5ORl+fn5Yvnw5fHx8EBUVhaysLGzcuBETJ06E1WrF5s2bMX/+fAwfPhwAsGbNGgwbNgz79+/H2LFjsXPnzjqPQURERFRXkiRBrVZDrVaz1ywREREReZTRaIRareYGtdeB11fQtmzZEqtXr5aTsyUlJUhKSkJ4eDi6d++Oo0ePIjExET4+l3PNgwcPxsWLF1FaWor09HQYDAYMHjxYPh4aGoq4uDgcOXIEANwyBhEREVFdmEwm5OXloaSkhMlZIiIiIvIYk8kktzLQ6/VMzl4HXl9BW9lLL70kV7tu2LABQUFBKCgoQHR0tNN5bdu2BQBcunQJBQUFAID27dtXOSc/Px8A3DIGERERUW1YLBaoVCoYDAbeLkZEREREHmM2m6FSqWA0Grkuvc4aVIJ2ypQpePDBB/Hpp5/iqaeewieffAKz2Qw/Pz+n8/z9/QGUf+AxmUwAUO05Go0GANwyRm011r/wDocDoig22vfXkHFuvBPnxXtxbrxXQ58bm80GtVoNnU7X4N6DJElXjLmhzwsRERFRU2M2m6FWq1kw4EENKkHbvXt3AMDLL7+M48eP46OPPkJAQECVzTMsFgsAICgoCAEBAQDKN9uoeFxxTmBgIAC4ZYzakCQJBoOh1td7M1EUYTabAQAKhdd30mhSODfeifPivTg33quhzo0kSdBoNNBqtbDb7Q1y51sfHx8YjcZqY3fnvEiSBEEQ6jQGEREREVXPaDRCo9GwYtYLeH2CtrS0FMnJybjzzjuhVCoBlC/2o6KiUFRUhPDwcBQVFTldU/F1u3btYLfb5ec6d+7sdE5MTAwAuGWM2hAEAcHBwbW+3ptV/GAHBQXJ80begXPjnTgv3otz470a4tyYzWaUlJTAbDbDx8fHqf99QxIYGIigoKBqj7lzXpicJSIiInI/g8EgJ2bZX9Y7eP2ngqKiIjz//PMICwvDkCFDAJTfEpiWloaRI0eidevW2L59OxwOh/whIDk5Gd26dUNYWBiaNWuGkJAQHDp0SE6uarVapKWlYfLkyQCAxMTEOo9RWw3lA2VtKBQKKJXKRv0eGyrOjXfivHgvzo33aihzI0kS1Go1ysrK4HA4IAhCg04+CoJw1e95Q5kXIiIioqai4i5utVoNs9nMxKyX8fr7AWNiYnDTTTdhxYoVOHr0KDIyMvDCCy9Aq9Vi6tSpmDhxIvR6PRYvXoxz585h9+7d2Lp1K2bOnAmgvG/s5MmTsWrVKvzwww9IT0/H3LlzER4ejtGjRwOAW8YgIiIiqo7NZkN+fj5KS0t56xgRERERXVeSJEGr1SI3NxcFBQWsmvVSXl9BKwgC3nrrLbz55pt49tlnodPpMGDAAHz88cfo0KEDAGDTpk1YuXIlJkyYgDZt2mDBggWYMGGCPMacOXNgt9uxZMkSmM1mJCYmIikpSd70KywsrM5jEBEREf2VTqdDSUkJbDabp0MhIiIioiZEkiTodDqoVCrYbDYmZb2cIDXEnSkagZMnT0KSpDr1sPVmDocDBoMBwcHBvL3Ry3BuvBPnxXtxbryXN8+NKIooKSmBVqttlIvhkJAQ+Rflf+XOeUlPT4cgCOjVq1edxiH341qWPIVz4504L96Lc+O96nNuTCYTSktL2crgCrxxLev1LQ6IiIiIGhKz2Yy8vDxoNBouiEn2xRdfYMyYMejVqxfGjh2Lffv2ycdOnz6NyZMno2/fvhgxYgSSkpKcrhVFEW+//TaGDRuGPn36YPr06cjKynI6xx1jEBERUcNms9lQWFiIS5cusZVBA8MELREREZEbSJIElUqFvLw8mEwm8CYlqvDll1/ixRdfxIMPPoivv/4aY8aMwXPPPYdjx45BpVJh2rRp6Nq1K3bt2oXZs2dj7dq12LVrl3z9+vXrsX37drzyyivYsWMHBEHA448/DqvVCgBuGYOIiIgaLlEUoVKpkJubC41Gw30PGiCv70FLRERE5O1sNhuKi4tZqUBVSJKEtWvXYsqUKZgyZQoA4KmnnsIff/yBw4cP4/Dhw/Dz88Py5cvh4+ODqKgoZGVlYePGjZg4cSKsVis2b96M+fPnY/jw4QCANWvWYNiwYdi/fz/Gjh2LnTt31nkMIiIiangcDge0Wi00Gg1sNhsLBBowVtASERER1YFOp0Nubi70ej2Ts1TFhQsXkJeXh7vuusvp+aSkJMycORNHjx5FYmIifHwu100MHjwYFy9eRGlpKdLT02EwGDB48GD5eGhoKOLi4nDkyBEAcMsYRERE1HDY7XaUlpYiOzsbJSUlsFqtTM42cLWuoDUYDMjJyYFGo0Hr1q3RsWNHBAQEuDM2IiIiIq8liiKKi4uh0+mYmKUryszMBAAYjUbMmDEDaWlpiIiIwKxZszBy5EgUFBQgOjra6Zq2bdsCAC5duoSCggIAQPv27auck5+fDwBuGYOIiIi8n8PhgFqthlarhd1uZ1K2EalRgrbi9qiPPvoIhw8fduppoVQqcfPNN+OBBx7AlClT4O/v7/ZgiYiIiLyByWRCcXExLBYLF8Z0VXq9HgDwwgsv4Omnn8a8efPw7bff4sknn8SHH34Is9kMPz8/p2sq1tEWiwUmkwkAqj1Ho9EAgFvGqK3G2uPO4XBAFMVG+/4aMs6Nd+K8eC/OjfeqydxIkgStVgu1Ws1WBm4gSdIVv++e+plxOUG7detWLFy4ECaTCXfddRceeOABdO3aFcHBwVCpVMjJycEvv/yCRYsWYcWKFVixYgUee+yx+oydiIiI6LqyWCzQaDTQ6XT8oEMu8fX1BQDMmDEDEyZMAADExsYiLS0NH374IQICAqps1GWxWAAAQUFB8h1qVqvV6W41i8WCwMBAAHDLGLUhSRIMBkOtr/dmoijCbDYDABQKdoXzJpwb78R58V6cG+/lytwIggCj0QiVSgWLxcK7ttzEx8cHRqOx2kS3O39mJEmCIAiuxeTKSXfddRfy8vLw9ttv4+67775idezcuXNhsViwfft2rF69Grt378bevXtdj5yIiIjIC5lMJqjVahiNRiZmqUbCw8MBoEoLgu7du+PgwYPo2LEjioqKnI5VfN2uXTvY7Xb5uc6dOzudExMTI79GXceoDUEQEBwcXOvrvVnFz3lQUBCUSqWHo6HKODfeifPivTg33utac1N5/QmAd6q7UWBgIIKCgqo95s6fGVeTs4CLCdrx48djxowZLg3s7++PKVOm4NFHH8WmTZtcDoSIiIjIm0iSBKPRCI1GA5PJxMQs1UpcXByCg4Nx4sQJDBgwQH4+IyMDnTt3Rv/+/bF9+3Y4HA75Q0BycjK6deuGsLAwNGvWDCEhITh06JCcXNVqtUhLS8PkyZMBAImJiXUeo7Ya84d9hUIBpVLZqN9jQ8W58U6cF+/FufFe1c2N1WpFWVkZDAaDvP5k9bN7CYJw1Z8HT/zMuJSgrU2rAoVCgSeeeKLG1xERERF5kiiK0Ol00Gq1vJWM6iwgIACPPfYY3n33XbRr1w69e/fGnj178Ouvv2LLli3o3r07Nm3ahMWLF+Oxxx5DSkoKtm7dihUrVgAo7xs7efJkrFq1Cq1atULHjh3xxhtvIDw8HKNHjwYATJw4sc5jEBERkWdVbACm0Wjku1+o6ajRJmHVSUtLQ1paGhISEup0ixQRERGRJ9lsNmi1Wu6KS2735JNPIjAwEGvWrEFhYSGioqKwbt06DBo0CACwadMmrFy5EhMmTECbNm2wYMECuV8tAMyZMwd2ux1LliyB2WxGYmIikpKS5E2/wsLC6jwGEREReYYkSVCr1dwArIkTJBdn/osvvsBLL72EJ554ArNnzwYAzJ8/H6tXr5ab3s6aNQvvvPNOvQbcWJw8eRKSJDXapLbD4YDBYEBwcDBvo/AynBvvxHnxXpwb7+XOudFqtSgpKYHD4eCi2AUhISHo0KFDtcfcOS/p6ekQBAG9evWq0zjkflzLkqdwbrwT58V7cW68l8PhQElJCcxmM6xWK9eg15E3rmVdamJx8OBBTJw4EQEBAYiLiwMA7N+/H2+++SaGDRuGY8eO4ZNPPsG2bdvw4Ycf1il4IiIioutFkiQUFxejqKiIVbNEREREdF2YTCZcunQJhYWFMJlMXIOSay0O3nzzTdx+++34+uuv5cbE7733HgRBwObNmxEZGYk+ffrg1KlT+OCDDzBt2rR6DZqIiIiorux2O4qKimAwGLgoJiIiIqJ6JUkSDAYDdDodjEYjbDYbN6ElmUsVtIcOHcJjjz0mJ2dFUcQPP/yA+Ph4REZGyucNHz4cp06dqp9IiYiIiNzEbDYjLy8Per2eyVkiIiIiqjd2ux0qlQrZ2dkoKCiATqdjYpaqcKmCVqPRoE2bNvLXKSkp0Gq1GDFihNN5SqWSf8mIiIjIa4miCI1GA5VKxd1xiYiIiKheSJIEk8kErVYLo9HIfQ7omlxK0LZr1w7Z2dny1z/88AMEQcCoUaOczjt27Bjat2/v3giJiIiI6kiSJOh0OqhUKm7CQERERET1wuFwQKfTQafTwWKxQBRFT4dEDYRLCdrbbrsNb731FsaPHw+Hw4EPPvgAoaGhuP322+VzysrKsHbtWowePbregiUiIiKqKYPBAJVKBbPZzEUyEREREbmd2WyGVquFXq9ntSzViksJ2qVLl2Lw4MFo164dBEGA0WjE+vXrERAQAABYsWIFNm/eDLVajUWLFtVrwERERESucDgc8iZgTMwSERERkTuJogi9Xg+tVguLxcKWn1QnLiVoO3fujGPHjuGDDz5AYWEhxo0bhzvuuEM+vmXLFnTq1Amff/6506ZhRERERJ5gsVhQVFQEk8nk6VCIiIiIqBGxWCxyGwO73c5qWXILlxK0QHkf2pdeeqnaY+fPn4dCoXBbUERERES1pdfrUVxcDJvN5ulQiIiIiKiRMBqN0Gg08qZfRO7klqxqRXJWp9Phueeec8eQRERERDUiSRJKS0tRWFjI5CwRERER1ZkkSdDr9cjLy8OlS5eg0+mYnKV64XKCduPGjRg8eDAGDx6MDRs2VDm+bds29OzZE2vXrnVrgERERETX4nA4UFBQAJVKxUUzEREREdWZXq9HdnY2CgoKuKcB1TuXWhysW7cOzzzzDDp16oSgoCA8/fTTUCqVeOKJJ3D27FlMnz4d//vf/9CiRQusW7euvmMmIiIiAgAIggCDwYCysjLYbDb2ACMiIiKiOrFarSgtLWVSlq4rlxK0mzdvxh133IH//Oc/8PHxwYIFC7B69Wr06tULd955J/R6PWbOnIlXXnkFrVq1qu+YiYiIiOBwOFBSUsLNGYiIiIioziRJglqthlqtZrssuu5canFw/vx5zJw5Ez4+5fnc2bNnIyMjAw888AA6d+6MQ4cOYf369UzOEhER0XVR0QtMo9GwpQERERER1YnJZEJeXh5KSkqYnCWPcKmC1mAwIDw8XP66Xbt2AICoqCjs27cPgYGB9RMdERERUSVmsxlqtRp6vZ6Vs0RERERUJ1arFWVlZTAYDPylP3mUSwlaSZIgCIL8tVKpBAC8+OKLTM4SERFRvTMajdBoNDAajVw8ExEREVGdOBwOqFQqaLVa2O12T4dD5FqC9kpat27trjiIiIiIqjAYDNBoNDCZTEzMktsYjUb8/PPPuHjxIjQaDVq3bo0uXbpg2LBh8Pf393R4REREVE9EUYRGo4FarebdWORVXE7QVq6gvdpzRERERHUhSRIMBgPUajXMZjN3zyW3OX78OF5//XV88cUXsFqtVY4HBwdj3LhxWLRoEXr37u2BCImIiKg+mEwm6HQ6GAwGJmbJK7mcoL3nnnuqVBTcdddd8PPzc3pOEAScP3/ePdERERFRkyFJEvR6PdRqNSwWCxOz5DZarRZz5szBxx9/jJEjR+Ldd99FYmIiunbtiuDgYKhUKuTk5ODnn3/GN998gxtuuAEPPvgg1q1bh5YtW3o6fCIiIqoFu90OvV4PnU7HtSV5PZcStFOmTKny3PDhw90eDBERETU9oihCp9NBrVbDZrNx8Uxu17dvX9x9993Iyclx2vi2QuvWrdG6dWv069cPc+bMQWZmJv7v//4P/fr1Q2Zm5vUPmIiIiGpFkiQYjUbodDp57wJWy1JD4FKC9sMPP3R5QDZXJiIiIldU9ADTaDSw2WxcPFO92bNnD2JjY10+v2vXrli/fj1Onz5dj1ERERGRu9hsNuj1emi1Wv7CnxqkOm0SVllWVhY++OADfPjhh7h06ZK7hiUiIqJGxuFwQKvVMjFL101NkrPuuI6IiIiuD6vVCrVaDb1ez2pZatDqlKCVJAlff/013nvvPXz33XdwOByIiYlxV2xERETUSDgcDhiNRhgMBt5uRh5TVFQEQRDQpk0bAIDFYkFSUhLS0tLQu3dvTJkypcqeC0REROR9LBYLNBoNdDodHA6Hp8MhqjNFbS7Kz8/Hyy+/jK5du+Kee+7BoUOHMHPmTBw6dAhpaWnujpGIiIgaIFEUodVqkZ+fj6ysLBQWFkKr1XLnXLruJEnC3LlzERERga1btwIo/6XBrbfeitmzZ+PTTz/F7NmzcdNNN8FoNHo4WiIiIroSi8WCwsJC5ObmQq1WMzlLjUaNErT79+/HxIkT0aVLF/zjH/9Az549AQC7d+/GO++8g8TExHoJkoiIiBoWg8GA3NxcFBYWQqfTwW63sxcYeUxSUhLefvttPP3007jnnnvk53799Vc8+eSTKCkpwfnz51FWVoZ//vOfng2WiIiIqrBarSgqKkJubi40Gg0Ts9TouJSgfeONN9CjRw/cfvvtSEtLw8svv4ycnBzs3LmTFTBEREQks9vtKCwsREFBAcxmM9cJ5BU+/PBDzJ49G6tXr0b37t0BAJ988gn8/PzwyiuvQBAERERE4LnnnsNnn33m4WiJiIiogs1mQ3FxMStmqdFzqQftCy+8gN69e+PgwYO4+eab5ec1Gk29BUZEREQNhyRJ0Gq1KCsrg81m83Q4RE5OnTqFJUuWyF+bzWYkJydj0KBBaN68ufx87969kZmZ6YEIiYiIqDKHwwG1Wi1Xy/KX/tTYuVRBO3nyZJw7dw533HEHxo0bh88++wxWq7W+YyMiIiIvJ0kS9Ho98vLyUFxczOQseSWr1YrAwED560OHDsFms2HEiBFO5xmNRvj5+V3n6IiIiKiCKIooKytDdnY2ysrKuHcBNRkuJWi3bduGgoICvPXWWygtLcWDDz6I9u3b47nnnoMgCBAEob7jJCIiIi8iSRJ0Oh1yc3NRUFAAo9HIHrPktbp164aTJ0/KX+/btw+CIOC2225zOu/AgQPo2rXrdY6OiIiIRFGESqVCVlYWSktLYbPZmJilJsWlFgcAEBISgieeeAJPPPEETp8+jaSkJHz88ceQJAlTpkzBpEmT8NBDDyEhIaE+4yUiIiIPEkUROp0OarUaNpuNSVlqEO6//3689tpriIuLgyiK+OCDDxAZGYmbbrpJPufw4cNYv349nn32Wc8FSkRE1ATp9XqUlpbCarUyKUtNlksVtH8VGxuLVatWITc3F7t370ZCQgL+7//+D3369EGfPn3cHSMRERF5WEVVQ3Z2NoqKimCxWJicpQZjwYIFiImJwejRo3H77bfDbrdjy5Yt8vHRo0dj6NCh6NKlCxYsWOC5QImIiJoQm82G/Px8FBQUwGKxMDlLTZrLFbTVUSqVuOeee3DPPfegqKgIW7ZswdatW90VGxEREXmYw+GAVquFWq1mDzBqsIKCgvDjjz/il19+QUFBAYYPH442bdrIx8PCwrBw4ULMmzcPISEhHoyUiIio8ZMkCRqNBiqVivsXEP3JpQTtmTNn0LNnz6ue07ZtWyxYsMCp6iA9PR0xMTF1i5CIiIiuO1EU5Z1zmZilxqJyS4PKtm/ffp0jISIiaprMZjNKS0thMpl4NxZRJS61OBgzZgzmzp2LwsJClwbNycnB008/jTFjxtQpOCIiIrq+JEmCVqtFdnY2N2igRsdoNMJoNF7x+G+//YbExMTrGBEREVHT4HA4UFxcjLy8PBgMBiZnif7CpQTtsWPHoFKp0KlTJ9x+++1ISkpCSkoKdDodRFFEWVkZTpw4gfXr12PcuHGIioqCSqXC77//Xt/xExERkZsYjUbk5eWhqKiImzRQo2IwGPDII48gNDQUoaGhePDBB50StcXFxZg+fTpuuukmnDhxwoOREhERNS6SJEGtViM7OxtqtRoOh8PTIRF5JZdaHISGhmLLli2YM2cOXnvtNTz55JOw2+1VzgsICMCdd96J5ORk3HDDDW4PloiIiNzPZrOhtLQUBoOBi2ZqlJYsWYJPP/0UDz30EEJDQ7Ft2zYsW7YMb7zxBnbs2IEnn3wSKpUKN998M9atW+fpcImIiBo8SZJgNBqhUqlgNptZMUt0DTXaJKx///747LPPYDAY8PPPP+PChQvQaDRo3bo1unTpgmHDhiEwMLC+YiUiIiI3qqhoUKvV3KCBGrWvvvoKzzzzDNasWQMAGDJkCBYtWoTo6GjMnDkTHTt2xIYNG/DAAw94OFIiIqKGS5IkmEwmGAwGGAwG2O12JmaJXFSjBG2F4OBg3HHHHe6OhYiIiK4Tk8kkb9DAVgbU2F26dMlp7Tp27FhMmzYNc+bMwfTp0/HWW28hJCTEgxESERE1XBaLBVqtlklZojqoVYKWiIiIGia73Q6VSgWtVst2BtRkmM1mtGzZUv66RYsWAIBHH30UH3zwgYeiIiIiatisVitUKhX0ej3XlUR1xAQtERFRE2C1WqHRaKDT6eBwOFg1S02aIAgAgKlTp3o2ECIiogbIZrNBpVLJ60oiqjsmaImIiBoxi8XilJglossCAgI8HQIREVGDYbfboVarodVqq904nohqjwlaIiKiRshsNkOtVsNgMDAxSwSgoKAA2dnZACD/TFR+rrLOnTtf19iIiIi8mcPhcErM8k4sIvdze4JWo9GgefPm7h6WiIiIXGA0GqHRaGA0GpmYJapkwoQJVZ676667qj2XPztERESAKIpQq9XQaDRMzBLVsxonaC0WC9asWYOffvoJVqtV/gEVRREGgwGnTp2C0Wh0e6BERERUPUmSYDAYoNFoYDKZuHMu0V98+OGHng6BiIiowRBFERqNBhqNBjabjYlZouugxgnaBQsWYN26dejVqxeKiooQGBiINm3a4OTJk7BarVi+fHk9hElERER/JUkS9Ho9NBoNzGYzE7NEVzBlyhSXz01JSanHSIiIiLyXKIrQarVQq9VMzBJdZ4qaXrBr1y7MnTsXJ06cwJw5czBgwAAcOnQIZ8+eRdeuXfnhkIiIqJ5VLJ5zc3NRWFgIo9HI//8S1YHFYsG//vUv3HjjjejXr5+nwyEiIrquJEmCRqNBTk4OiouLne6WJqLro8YJ2qKiIowdOxYA0Lt3bxw+fBgA0LFjRyxatAjbt293b4REREQEALDZbFCpVMjJyUFRURHbGRDVUUZGBp5//nl07NgRU6ZMQXp6OmbOnOnpsIiIiK4LSZKg1WqRnZ2N4uJiWCwWJmaJPKTGLQ5atGgBi8UCAIiOjkZOTg50Oh2aNWuGHj16VLsTLhEREdVORX9ZnU4nV8py4UxUe3a7HZ9//jnee+89HDx4EAAwYsQIzJo1C3fffTf8/Pw8GyAREVE9E0URer0eKpUKNpuNv/An8gI1rqAdNmwY3n77bRgMBnTr1g3BwcHYvXs3ACA5ORnNmzd3e5BERERNjdVqRVlZGbKyslBQUACdTgeHw8HkLFEtZWVlYfHixejUqRMefPBBFBYWYtmyZQCAZcuW4b777mNyloiIGrWKzb8q7sayWCxMzhJ5iRonaJcvX47k5GSMGzcOPj4+ePLJJzFz5kzccMMNWLJkCSZOnOj2INVqNZYuXYqbb74Z/fv3x8MPP4yjR4/Kx0+fPo3Jkyejb9++GDFiBJKSkpyuF0URb7/9NoYNG4Y+ffpg+vTpyMrKcjrHHWMQERHVRUW1bH5+PnJyclBaWgqr1cqFM1EdjR07Ft27d8d7772H8ePHIzk5GampqXjmmWf4Sw8iImr07HY7SktLkZWVxcQskZeqcYK2V69eSE9Px4svvggAeO2117B06VKEh4djyZIlWLVqlduDfO6553DixAmsXr0a//73vxEfH48ZM2bg/PnzUKlUmDZtGrp27Ypdu3Zh9uzZWLt2LXbt2iVfv379emzfvh2vvPIKduzYAUEQ8Pjjj8NqtQKAW8YgIiKqLYfDAbVajdzcXOTn57NalsjN9u3bh/j4eHz88cd45513MGjQIE+HREREVO8MBgMKCwuRl5cntzPg+pLIO9W4By0AhIeHIzw8HAAgCIKcrK0PWVlZ+PXXX/Hpp5+if//+AIDFixfjv//9L77++msEBATAz88Py5cvh4+PD6KiopCVlYWNGzdi4sSJsFqt2Lx5M+bPn4/hw4cDANasWYNhw4Zh//79GDt2LHbu3FnnMYiIiGrKYrFAq9VCr9fDbrdzwUxUTzZt2oQPP/wQY8aMQYsWLfDwww9j2rRp6NGjh6dDIyIicitJkqDX66HRaGA2m2G322G32+Hr6wtBEDwdHhFdgUsJ2n/84x947LHH0KFDB/zjH/+46rmCIOCll15yS3AA0LJlS3zwwQdISEhweg1JkqDRaJCamorExET4+Fx+K4MHD8b777+P0tJS5OXlwWAwYPDgwfLx0NBQxMXF4ciRIxg7diyOHj1a5zGIiIhcUdHGQKvVwmQyweFweDokokZv+vTpmD59OjIyMrB582Z89NFHeO+999C9e3cIggC1Wu3pEImIiOpEFEXodDqo1Wpu/EXUALmUoF2+fDnuuOMOdOjQAcuXL7/que5O0IaGhspVqxX27duH7Oxs3HTTTVizZg2io6Odjrdt2xYAcOnSJRQUFAAA2rdvX+Wc/Px8AEBBQUGdxyAiIroah8MBnU4HjUbDRTORh0RHR+P111/Hq6++ir1792Lz5s3IzMzEhAkTMHjwYEyaNAkPPPAA2rRpU28xXLx4Effeey9eeukl3HvvvQDK90JYuXIlUlNT0aJFCzz66KOYMWOGfI0oinjnnXfw2WefQavV4oYbbsCyZcvQpUsX+Rx3jEFERA2LJEkwm80wmUzQarVsYUDUgLmUoK38IdJms0GpVNZbQNfy+++/48UXX8SoUaMwcuRIvPbaa1V23PX39wdQfuuoyWQCgGrP0Wg0AACz2VznMWqrsVZOORwOiKLYaN9fQ8a58U6cF+9V17mxWq1sY1BP+HPjOZIkXfH73hDmRaFQYNy4cRg3bhxKSkqwbds2bNmyBbNnz8Zzzz0Hi8VSL69rs9kwb948GI1G+bmKvRBuvfVWrFixAsePH8eKFSvQokULefPdir0QXnvtNbRr1w5vvPEGHn/8cXz99dfw8/NzyxhERNQwWK1WmM1mGI1G+W4sSZK4xiRq4Grcg3bQoEFYunQp7r777vqI56q+//57zJs3D3369MHq1asBAAEBAVU26qpYVAcFBSEgIABA+T9iFY8rzgkMDHTbGLVRcZtrYySKIsxmM4DyD0HkPTg33onz4r1qMzeCIMiVDJUXzuReoijK/7/mz8315ePjA6PRWO3fa3f+eyZJUr33y2vdujWee+45PPfcczh8+DC2bNlSb6+1bt06BAcHOz3njr0QuJ8CEVHjVpGQNRqNsFgsEEWRa0uiRqbGCdrz588jNDS0PmK5qo8++ggrV67E6NGjsWrVKvk3/eHh4SgqKnI6t+Lrdu3awW63y8917tzZ6ZyYmBi3jVEbgiBUWaQ3FhVVM0FBQR6tuKaqODfeifPivWo6N5U3ZXA4HPD19YWvr299h9kkVcxNQEAAf26us8DAQAQFBVV7zJ3/nrkrObt582ZMnz79mucNHDgQAwcOBFCeHN60aRMef/xxt8Rw5MgR7NixA1988QVGjBghP++OvRC4nwIRUeNjNpuh1+thMBhgt9uZlCVq5GqcoH344Yfx6quvokuXLujWrVt9xFTFJ598gpdffhmPPvooXnzxRadqjMTERGzfvh0Oh0P+EJCcnIxu3bohLCwMzZo1Q0hICA4dOiQnV7VaLdLS0jB58mS3jVFbjfkDpUKhgFKpbNTvsaHi3Hgnzov3utbciKIoJ2YrqhoqrqP6VTE3/F5fX4IgXPXfKm/79+zLL7/EO++8g8WLF2PcuHFyK6vqWK1WfPbZZ3jjjTcQERHhlgStVqvFggULsGTJkip7GrhjLwRP7qfgza0s6qIhtOpoqjg33onz4h6SJEGn00Gv18u/7K8rzo334tx4jje266pxgjYjIwP//e9/0b17dwQGBsqLvwqCIOD8+fNuC/DixYt49dVXMXr0aMycOROlpaXysYCAAEycOBGbNm3C4sWL8dhjjyElJQVbt27FihUrAJT3jZ08eTJWrVqFVq1aoWPHjnjjjTcQHh6O0aNHA4BbxiAioqZHFEVotVpu/EXk5b788kts2bIFTz/9NGbMmIFx48Zh4MCB6NatG4KDg6FWq5GTk4Nff/0VP/74o9wu4IknnnDL6y9fvhx9+/bFXXfdVeWYO/ZC8NR+CmzXRZ7AufFOnJfaEwQBdrsdOp0OOp1OrpZ1F7aE8l6cG8/xxnZdNU7QdurUCY888kiNg6qtb7/9FjabDfv378f+/fudjk2YMAGvv/46Nm3ahJUrV2LChAlo06YNFixYgAkTJsjnzZkzB3a7HUuWLIHZbEZiYiKSkpLkRWpYWFidxyAioqbD4XBAo9FAo9Fw4y+iBmLq1Kl4+OGHsWnTJnz88cfYsWOHU2WEUqnEjTfeiOXLl2PGjBl12megsi+++AJHjx7FV199Ve3xhryfAtt1kSdwbrwT56V2zGYztFotDAaDfEevu79/bAnlvTg3nuON7bpqnKD98MMPa3pJnfz973/H3//+96ue07t3b+zYseOKx5VKJebPn4/58+fX6xhERNS42e12qNVqubqBiVmihsXf3x9PPfUUnnrqKej1emRnZ0Oj0aB169bo2LHjFRfqdbFr1y6UlpY69Z0FgGXLliEpKQkdOnRosPspAGzXRZ7BufFOnBfXVNx9UHkzWaA8kVNfG2OyJZT34tx4hje266pxgrZCYWEhrFar/OFUFEUYDAb8/PPP10yoEhERNSSSJKG0tFTepIGJWaKGLyQkBHFxcfX+OqtWrZJvk6tw2223Yc6cORgzZgz27NnToPdTICIi14iiCJ1OB61W67RnARERUIsE7YkTJ/Dwww/jzJkz1R4XBIEJWiIiahQcDgfKyspQWFgIX1/feqtqIKLGq127dtU+HxYWho4dO3I/BSKiRo57FhCRK2qcoJ0/fz5UKhVWrVqFr7/+Gv7+/rjrrruwd+9e7Nu3DwcPHqyHMImIiK4fm80GnU4HjUYDq9UKm80GHx8fJmiJyO3csRcC91MgIvI+DofDKTHLO7CI6GoEqYb/SjRv3hyrV6/GjBkzsHHjRvzrX//Cf//7XwDAfffdB4VCgZ07d9ZLsI3JyZMnIUlSnft+eSuHwwGDwYDg4GD2IPIynBvvxHnxDmazGRqNBnq9HqIoQpIkiKIIk8mEwMBA9obyMpwbzwkJCUGHDh2qPebOf8/S09MhCAJ69epVp3HI/biWJU/h3HgnzstldrsdGo0GWq3WK1pjcb3kvTg3nuONa9kaV9BaLBZER0cDAGJiYpCSkiIfmzZtGtsbEBFRg3KljRqIiIiIiFxls9mg0Wi4mSxRDUkSkF1oh84oolmQAp3b+aA+b1zU6/WwWCxXTNB6So0TtJ07d8aFCxcwbNgw9OjRA1qtFpmZmejatSv8/f1RVlZWH3ESERG5lSRJ0Ov10Gg0MJvN7AdGRERERDVmsVjkxGzFHVhE5Jr0TCv2JptQUOqA3QH4KIHwMCXGDAlETNe6t2GyWCxIT09HamoqTp06hdTUVGRmZkKSJLz++uuYO3euG96Fe9Q4QTtx4kS88MILCA4Oxn333YeYmBgsXrwYCxcuxJtvvomoqKj6iJOIiMgtKnbQVavV3KiBqIlQKBQu95AWBAF2u72eIyIioobOYrFApVLBYDDwDiyiWkjPtGLbPgNMVgkhAQJCAgGbHcgtcmDbPgP+didqlKS12Ww4f/48UlNT5YTsuXPnrriu++qrrxp2gnbZsmU4d+4cNm/ejPvuuw9r1qzBhAkTsH37diiVSmzfvr0+4iQiIqoTu90ub/zFjRqImpalS5dykz8iInILk8kEtVoNo9HIxCxRLUkSsDfZBJNVQstmgtzSwM8P8PUVoNZJ2JtsQs8uftW2OxBFEVlZWU6Vsenp6bBYLC69flhYGObNm+fGd1R3NU7QBgQE4LPPPoPNZgMA3H777UhNTcXvv/+O/v37s4KWiIi8isVigVarhU6ng8PhYGKWqAlavny5p0MgIqIGTBRFec8Cs9nMxCxRHWUX2lFQ6kBIgFAlASsIQHCAgIJSB7IL7ejcTon8/Hynyti0tDTo9XqXXisgIACxsbGIj49HfHw8EhISEBcXh4iIiHp4Z7VX4wTt9OnT8dJLL6Fbt27yc5GRkYiMjMSZM2dw11134auvvnJrkERERDUhSRKMRiO0Wi2rG4ioCpPJhJSUFFitVvmXNhUfvn/++We8/vrrHo6QiIi8gc1mg06ng1arhd1uZ2ssIjfRGUXYHUBIYNVjZkMZSvPTkZ9zGkuOnkP2xdMu73fl4+ODHj16ICEhAQkJCYiPj0dUVBR8fJzTnwqFwh1vw61cStBmZ2fLj7du3Yp77rkHSqWyynl79+7F999/777oiIiIakAURbla1mKxcBFNRFUcOHAA999/P1QqVbXHmzVrxgQtEVETZzaboVarYTAYuPEXUT1oFqSAjxIwGvTQq86grOA0ygrSoSpMh1FX5NIYgiAgMjLSqTK2Z8+e8Pf3r+fo64dLCdqnnnoKe/fulb+eMGFCtedJkoTRo0e7JzIiIiIXVbQx0Ov1sNvtXEQT0RUtWbIEYWFh2LhxIz766CMolUpMmzYNe/fuxYYNG7Bv3z5Ph0hERB5iNBqhVqthMpl4BxaRm5nNZqSnp8utCn5NToG2LMfl6zt27OhUGRsXF4fg4OB6jPj6cilB+9577+H777+HJEmYPn06lixZUqXXrFKpRIsWLXDLLbfUS6BERESVSZIk9wLjIpqIXHXixAls3LgREyZMgFarxfr163HnnXfizjvvhNVqxSuvvII9e/Z4OkwiIrqO9Ho9NBoNTCYT78AicgObzYazZ88iNTUVaWlpSE1Nxblz51z+zOYfFIZevRIwaMDlhGzLli3rOWrPcilB27FjR0yZMgVAeQnx2LFj0bp163oNjIiIqDp2u13uBWaz2biIJqIaEUVR3hSiZ8+eOHXqlHzsvvvuw9/+9jdPhUZERNdRxS/71Wo1zGYz15REtSSKIjIzM5028UpPT4fVanXper+AZmjWuidCw2LQsl0MuveMx/23dUZMV796jty71HiTsClTpkCSJBw7dkzux/JXN998s1uCIyIiqmA2m+U2Bg6Hg20MiDxAkiRcunQJgiCgffv2EP667W4DEBUVhZMnT2LYsGHo0aMHjEYj0tPTERMTI28GQ0REjZckSdDpdNBoNNyzgKiGJElCXl6eU2VsWloaDAaDS9cHBgYiNjZW7hmbkJCAiIhOyClyQGcU0SxIgc7tfNAAl5h1VuME7eHDh3H//fcjNzcXAOQPyIIgQJIkCILA20yJiMgtKnZV12q1MJvN/P8L0XVmsViQlpaG48eP48SJEzhx4gRKSkoAAC+++CKWLVvm4QhrbvLkyViwYAEcDgdmz56NAQMGYPbs2ZgzZw5WrlyJ+Ph4T4dIRET1QBRF6HQ6qNVq3oVF5KKSkhK5MrYiGXuljVb/ysfHB9HR0U59YyMjI+HjUzUV2SW8xunJRqfG34G5c+fC19cXW7ZsQUREBBQKRX3ERURETVhFFZtWq4XdbucCmug6KSgocErGnj59Gna7vdpzf/nll+scnXvMnz8fJSUlOHz4MADIPWjHjx+P0NBQ/Oc///FwhERE5E6iKEKj0UCj0cBms/EuLKIr0Gg0OHXqlPwnNTUVhYWFLl2rUCgQGRnpVBkbHR0NP7+m1aagLmqcoP3jjz+wfft2jB8/vj7iISKiJsxkMkGj0cgtdLiAJnKBJMGWkQVRo4OieTP4RneBK/eFWa1WpKWlycnYEydOoKioyKWXjI6ObpDVs0D5B4g33nhD/nrAgAG4cOEC0tPT0bNnT4SGhnowOiIicheHwyEnZu12O9eVRJVUtHiq3Dc2Ozvb5esjIiLkRGxCQgJiY2MRFBRUjxE3fjVO0LZt25ZVs0RE5DaiKEKv18ttDFgtS+Q66++nof/4a9iz8yHZHBB8lfDp3B4hj4yD3w2xTucWFBQ4JWNPnz4Nm812zdcIDAxEQkIC+vTpgz59+qB3797o3LkzOnToUF9vq16NHDkS69evR0xMjPxcs2bNkJiYiJSUFEyePBkpKSkejJCIiGpLFEUYjUbo9XoYjUbuW0CE8rsTMzIy5KrY1NRUnD9/3uXPXW3btnWqjI2Pj0fz5s3rOeqmp8YJ2qeeegqvv/46Ro4cieDg4PqIiYiImgD2ASOqG+vvp6F5cwtEgwmK0BAIzX0Aqx228zkoeWMz8u8dhjSLRk7IFhQUuDRuRESEnIzt06cPoqOjq+0V1pD88ssv8r8xBw8exE8//VRttfDXX3+N8+fPX+/wiIioDv6alBVFketKarIcDgcuXrwoJ2NPnTqF9PR0l34pDwDNmzeXk7AV/23btm09R01ALRK0Z8+eRVpaGsLDwxEfH1+lhFkQBPzwww9uC5CIiBoXURSh1Wqh0WhgtVpZ1UBUG5IE/cdflydn27REmc2MU+pipOlKkaorRYZBDdupb645jL+/P+Lj450Ssq1bt74Ob+D62rRpE7Zt2wZBECAIAp588skq51T8WzRp0qTrHR4REdWCw+GQ15Tcs4CaIkmSkJub61QZe/r0aRiNRpeuDwwMRFxcnFNlbEREBAQXWmWR+9U4QXvu3Dn07dtX/vqvH6z5QZuIiKpjtVphMBi4QQNRHdlsNpz67gB+O3EIaVYd0nLVKLS6thDv0KGDnIjt27cvoqOj4evrW88Re97atWsxbdo0SJKEkSNH4t1330VcXJzTOUqlEi1atEB8fLyHoiQiIlfYbDZoNBrodDr2lqUmpaioyKkyNjU1FRqNxqVrfX19ERMT49SqoGvXrlAqlfUcNbmqxgnaAwcO1EccRETUCDkcDhgMBuh0Orm/LBfRRDVTUlLi1Ds2LS0NZrP5mtf5CgrERnVH/5uGyknZNm3aXIeIvU/z5s0xfPhwAOVr2f79+6NZs2YejoqIiFxls9lgNpthMBi4mSw1CRqNxqky9tSpUy5v5qpQKBAVFeVUGdtUfinfkNW6oZhKpcLPP/+MS5cu4b777kNpaSmio6NZCk1E1MRJkgSTyQSdTgeDwcDNGYhqwG63IyMjwykhm5ub69K1bfwCER8ShvhmrRDnF4pI+KPda8/Ct2fX+g26gRk+fDhKSkrw6quv4sCBA1Cr1WjdujWGDRuGuXPnss8aEZEXEEURJpMJZrMZRqMRVquVSVlqtIxGI9LT050Ssq6u/wCgc+fOTpWxMTExVdqRkverVYJ25cqVePXVV2EymSAIAgYOHIjFixejtLQU3333HVq0aOHmMImIyNs5HA7odDrodDpYLBb2ASNyQVlZGVJSUuRkbGpqKkwm0zWv8/X1RY+g5ohFAOLbdEBCszC08S9fiEuSBLFYBd+ojvCN7lLfb6HByc3NxdChQ1FUVIQhQ4agW7duyM/Px+rVq7Ft2zYcPnwYHTt29HSYRERNiiRJMJvNMJvNcmKWm31RY2S1WpGRkYHU1FScPHkSqampyMzMdPnvert27ZwqY+Pi4tC8efN6jpquhxonaN955x0sW7YML774Iu666y4MGjQIAPDss89i0qRJeOmll7Bu3Tq3B0pERN7JZDJBq9WyWpboGux2O86dO+dUHZudne3StW3btnXayCs2NhZC6gVo3txSvlGY5ANJFAGrHaJWD0VwIEIeGQfwzqYqXnjhBfj4+CAtLQ2RkZHy8xcuXMBtt92GxYsXY8uWLZ4LkIioCbFYLNBoNE7rSK4lqbFwOBw4f/68U2VsRkYG7Ha7S9dX9MavSMgmJCQ0ys1cqVyNE7Tr1q3DokWL8I9//AMOh0N+/vbbb8fKlSvx2muvMUFLRNTIiaIIvV4v95at/P8DIiqn0Whw4sQJHD9+HCkpKTh58qRLu+r6+PggNjbWKSEbHh5etY3UDbFo/vxU6D/+GvbsfEg6BwQfJXyjOiHkkXHwuyG2nt5Zw/btt9/irbfeckrOAkBkZCSWLVuGefPmeSgyIqKmw2w2Q61Wy4lZooZOkiTk5OQ49Yw9ffq0S3dGAUBwcDDi4uLkytj4+Hh07NiRbUSbkBonaLOysuRNFv4qJiYGhYWFdQ6KiIi8k9VqldsY2O123nZG9KeKConK1bGZmZkuXdu6dWunZGxcXBwCAgJcutbvhli06h8DW0YWRI0OiubNytsacDF/RXa7/YrVJ23atIFWq73OERERNR0mkwlqtRpGo5GJWWqwJElCUVGRnIit+K+rawg/Pz/ExMQgLi4O0dHR6NevHyIjI6FQKOo5cvJmNU7QdurUCcnJybj11lurHDt69Cg6derklsCIiMg7SJIEo9EIrVYLo9HIDRqIUF4de/LkSRw/fhwnTpzAyZMnYTAYrnmdUqlEz5490adPH/Tt2xd9+vRBhw4d6lYdIQjcCKwGevfujX/961+44447qhzbunUrevXq5YGoiIgaNyZmqSFTq9VOlbGpqakoKSlx6VqlUonu3bs7Vcb26NEDvr6+8mZ4gYGBTM5SzRO0M2bMwPLlyxEYGIhx48YBAPR6PXbt2oVXX30Vzz//vNuDJCKi608UReh0Omi1Wm76RU2aKIq4cOGCU3XshQsXXLq2VatWTtWx8fHxCAwMrOeI6Wpeeukl3H777SgtLcWkSZMQHh6OgoICfPzxx/j+++/x73//29MhEhE1GmazGSqViolZajAMBgPS0tKc+sbm5eW5fH3Xrl2d+sb27NmTaz9ySY0TtC+88AIuXryIF154AS+88AIA4JZbbgEAPPLII1i0aJF7IyQiouvKYrE4tTFgtSw1NTqdDidPnpSTsSkpKdDpdNe8TqlUIjo6Gr1795YrZCMiItg7zAuMHDkS69evR0xMDEaPHo1t27ZhwYIF+O677+RzwsPDsXnzZkyYMMGDkRIRNQ7sMUsNgcViwZkzZ5wqYy9evOjy55/w8HC5MjYhIQFxcXEIDQ2t56ipsapxglYQBLz//vt4/vnn8eOPP6KsrAwtWrTA8OHDER8fXx8xEhFRPZMkCQaDAVqtFiaTiQtpajJEUURmZqZTdez58+ddWpi3aNHCqTo2ISEBQUFB1yFqqqmDBw869YWbPHkyHnnkEZw5cwZlZWVo1aoVevbsyWQ6EVEd6XQ6GAwGbiJLXsdut+P8+fNOlbFnz56F3W536fpWrVrJidiKVgVX6mlPVBs1TtBWiI6ORnR0tDtjISKi68zhcECr1UKr1cJms7GNATV6BoMBKSkpTtWxrmzooFAo0L17d6eEbJcuXZjQa8AEQUBMTIynwyAiavBsNhs0Gg2Kiorg41PrFAOR24iiiOzsbKfK2PT0dJjNZpeuDwkJQVxcnFOrgvbt23PdR/Wqxv96Wq1WrFu3Dr/++ivUanWV44Ig4IcffnBHbEREVE/MZjO0Wi30ej0cDgfbGFCjJEkSsrKynKpjz54969Lf99DQUKdkbK9evRAcHHwdoqb6wg9VRETuY7PZYDAYYDQaYTKZYLfbYbFYoFAouNkRXVeSJKGwsFCuik1NTUVaWppL7akAwN/fHzExMU7VsV27duXfY7ruapygfeqpp5CUlISEhASEhYVVOc4P+URE3kkURbmNAW87o8bIaDQiNTXVqTpWpVJd8zpBEBAVFeWUkOXCvPGZNWuWS33hWGxARFQ9m80GvV4Po9EIs9kMURTlz//MA9D1olKp5ERsRXVsaWmpS9f6+PigR48eiIuLkytjo6Ki4OvrW89RE11bjRO0n3/+ORYvXoyXX365PuIhIiI3EkURRqNRrnBwOBxsY0CNgiRJyMnJwZEjR3D69GmkpKQgIyPDpb/fzZo1kzfyqqiObdas2XWImjxJkiSXEghMMhARXSZJEoxGo9xbtnJSlqi+6fV6pKWlOVXG5uXluXStIAjo2rWr0yZePXv2REBAQD1HTVQ7NU7QKhQK3HzzzfURCxERuYEkSTCZTNDr9fLOuUzKUkNnMpmqVMeWlZW5dG1kZCT69OmDvn37ok+fPujWrRurY5ugDRs2YODAgZ4Og4ioQaioluU+BXS9WCwWpKenO1XGZmZmuvwLgQ4dOjj1jI2Li0NISEg9R03kPjVO0E6ZMgWbN2/GqFGj+OGGiMiLWK1WuYUBF9LUkEmShLy8PJw4cQLHjx/HiRMnkJGR4VJbjuDg4CrVsc2bN78OURMRETVckiTBYrHAZDJV28KAyJ1sNhvOnz8vJ2NPnTqFs2fPwm63u3R9WFiYU2VsfHw8WrVqVc9RE9WvGidoX375ZfTr1w/R0dEYMGAAgoKCnI4LgoCkpCS3BUhERFcmSZKclDWZTFxIU4NkNpuRlpbmlJB1tZdYly5dnKpjo6KioFQq6zliIiKihs9ut8NsNsNoNDq1wuJaktxJFEVkZWU5Vcamp6fDYrG4dH2zZs2cesYmJCSgXbt23PyTGp0aJ2gXLlyIM2fOIDg4GIcOHapynD8kRET1TxRF6HQ6aLVaWCwWVstSgyFJEvLz8+VWBSdOnEB6erpLFRNBQUHo1asXevfujb59+yIhIQH+/v4IDAzkXT10VcOHD3dpgzAiosZMFEWYzWY5KWuxWCBJEteR5DYV67zKm3ilpaVBr9e7dH1AQABiY2MRHx8vV8d27tyZ6zxqEmqcoP3Xv/6FZ599Fm+88QYrVIiIrjNRFKHRaKDRaGCz2VjhQF7PYrHg9OnTTgnZoqIil66tqI6t+NO9e3entYcoijCZTPUVOjUiBw4c8HQIREQeYbPZ5LYFJpMJDofD5U0Tia6lpKREroqtaFXg6h4BPj4+6NGjh1wVGx8fj6ioKPj41DhNRdQo1PhvvsPhwN13383kLBHRdSKKIoxGIwwGA4xGI+x2OxfV5LUKCgqckrGnT5+GzWa75nWBgYFISEiQk7G9e/dmLzEiIqJasFgs8tqx4k4rrh2prnQ6nVMyNjU1FQUFBS5dKwgCIiMjnSpje/bsCX9//3qOmqjhqHGCdsKECdi5cydGjBhRD+EQERFQNSlb0ROMyJtYrdYq1bGFhYUuXRsREeFUHRsdHc2KCSIiolqq2CyWSVlyB7PZjPT0dKdWBZmZmS5f37FjR6fK2Li4OAQHB9dfwESNQI0/CQ0ePBgLFy7EiRMnMGTIkCr9vARBwEsvveS2AImImgpBEGCz2aDRaKDVamG325mUJa9SVFTklIxNS0uD1Wq95nUBAQGIi4uTN/Lq3bs3WrdufR0iJiIiarwq9iTQ6/Uwm81MylKt2Gw2nD17Vm5RkJqainPnzsHhcLh0fevWreVEbMV/W7ZsWc9REzU+NU7Qzpo1CwCQnJyM5OTkKseZoCUiqjmTyYTi4mJ5Yc3FNXmazWZDRkYGjh8/LidkL1265NK1HTt2RO/evdGnTx/07dsX0dHR8PX1reeIiYiImgaLxQKtVgu9Xs/WV1QjoigiMzPTqTI2PT3dpV+4A0BoaKhTIjYhIQHt2rWr56iJmoYaJ2hZzUVE5B6iKEKv10Or1cJkMkGv13M3evKYkpISp+rYU6dOwWKxXPM6Pz8/xMfHO7UraNOmzXWImKj2cnNz8euvv8JqtcqJDVEUYTAY8PPPP2P79u0ejpCIyJndbofRaIROp4PZbHa5upGaLkmSkJeXh5MnT+LEiRM4c+YMTp8+DYPB4NL1gYGBiI2NlROxCQkJ6NSpEwRBqOfIiZomNnsjIrrObDYbdDqdUxsD/vKLrie73Y6MjAycOHFCrpDNy8tz6drw8HA5Edu3b1/ExMSwOpYalM8++wyTJ0+GzWaTP2RKkiQ/jomJ8WR4REQym80m70lgMpnYwoCuqqSkRK6MTU1NRVpaGlQqlUvX+vj4IDo62qlvbGRkJPcHILqOXPppGzlyJNavX4+YmBiMHDnyqucKgoAffvjBLcERETUmJpNJvh2NC2y6nsrKyqpUx5pMpmte5+vri7i4OKfqWN7GRg3dq6++in79+mHDhg149913YbPZsHDhQuzduxeLFy/GW2+95ekQiaiJstlssFgssFgsMJvN7CtLV6TRaJCWlib3jE1NTXV5o1aFQoHIyEinytjo6Gj4+fnVc9REdDUuJWgr/w9BFMWrlrTzfx5ERJdVbmNQscgmqk92ux3nzp2Tk7HHjx9HTk6OS9e2bdvWqTo2NjaWi3VqdM6cOYOPP/4Y/fr1w6hRo/B///d/iI2NRWxsLIqKirBy5UqMHj3a02ESURNgtVphNpthMplgNpvlfrJcL1JlRqMR6enpTn1js7OzXb6+U6dOiImJQe/evZGQkIC4uDgEBQXVY8REVBsuJWgPHDggPz548GB9xUJE1GhYrVbodDrodDq5jQFRfVCr1UhJSZFbFZw8edKl6lgfHx/ExsY6Vce2b9/+OkRM5FkKhQJhYWEAgOjoaKSnp0MURSgUCtxxxx348MMPPRwhETVWoijKCVmj0Qir1coKWXJSsUlr5crY8+fPu/xZom3btk6VsfHx8WjWrBlMJhP3uiDycmwoQkTkJjabDQaDAQaDgbekUb1wOBxO1bEpKSnIzMx06drWrVujb9++cjI2Li4O/v7+9RswkReKjY3FL7/8gptvvhk9evSA1WrF8ePH0b9/f6hUKpc2xyMicpUoinIfWaPRCIfDwV/cE4DydV1mZqZTZWx6ejpsNptL1zdv3lxOwlb8t23btlXO4983oobBpQStQqGo0U593FGSiJqKihYGer2emzeQ22k0GqSkpMgJ2ZMnT7q0866Pjw969uzpVB3boUMH7rpLBGDmzJn4+9//Dr1ej1dffRW33HILpk+fjhkzZuCdd97BDTfc4OkQiaiBczgc8i/tK9aHTJI1bZIkITc316ky9vTp0zAajS5dHxgYiLi4OKfK2IiICK7tiBoRlxK0S5cu5Q8+EVElNpsNWq0WWq1W7hdGVBeiKOLChQtOm3lduHDBpWtbtWrllIyNj49HYGBgPUdM1DA99thjsFgsuHjxIgDggw8+wJgxY/DMM8+ga9euWLt2rYcjJKKGqKJSVq/Xy5WynlwfShKQXWiHziiiWZACndv5gB/pr5/i4mKnytjU1FRoNBqXrvX19UVMTIxTZWy3bt2gVCrrOWoi8iSXErTLly93ecCUlJTaxkJE5PVMJhM0Gg0MBgOrZalOtFotTp486VQdq9PprnmdUqlEdHQ0evfuLbcsYAUFUc089dRT8uPIyEikpKRAo9GgTZs2HoyKiBoah8MBs9ksJ2a9pX1BeqYVe5NNKCh1wO4AfJRAeJgSY4YEIqYrN/90N41G41QZe+rUKRQVFbl0rUKhQFRUlFNlbHR0NHx9fes5aiLyNm7pQWuxWLBz50689957+O2339jigIgalYo2BlqtFhaLhf/GUY2JoojMzEyn6tjz58+7lOBv2bJllepY7rxLVDcrV67Ezz//jG+++QYA8L///Q8PPPAAXnzxRTz77LOeDY6IvJIoirBYLPIfs9ks30XlDUnZCumZVmzbZ4DJKiEkQEBIIGCzA7lFDmzbZ8Df7gSTtHVgNBqRlpbmlJDNzc11+frOnTs7beIVExPDdR0RAahjgjYjIwPvv/8+tm7dirKyMrRs2RIzZ850V2xERB5ltVqh0+mg1Wq9piKCGga9Xo+TJ0/i+PHjcnWsVqu95nUKhQI9evRAnz595OrYTp06sTqWyI3eeOMNLF++3CkR2717dzz66KNYuHAhgoKC8MQTT3guQKL/b+/O46Mqz/7xf845s+9bFkwChJAQshEgIoiKlWqp8GhdWquVKtVK64JUxKpgRSngUxAeN9wX1Fqtv1paEWut5bHVLxWkhkBCjARZJQmEkD2Z7fz+yDPTDAkwE2YyZyaf9+uVV5I5y1zh5iT3XHOd66a48/v9cLvdcLvdwWSsx+MJ3j2l1DuoZBnYuLkTnW4ZdrMQbGmg0QBqtYDjrTI2bu7EmBEatjsIg9vtRk1NTUhl7J49e8J+TZCWlhZMxBYUFKCwsBBWqzXGURNRooo4Qev1evHHP/4RzzzzDP73f/8XAHDhhRfi5z//OS677DJoNHw3jogSV6B/WGtrKzo6OtjGgE5LlmXs27cP27dvDyZkd+/eHdb/G6vVipKSkmBCtqioCEajcRCiJhq6nnvuOSxfvhwLFy4MPpaZmYlHH30UKSkpeOyxx6KeoD1+/DhWr16N//3f/0VbWxvGjBmDBQsWoKysDACwa9cuLFu2DDt37oTNZsPs2bNx0003BY/3+/148skn8fbbb6OlpQUTJ07Egw8+iBEjRgT3icY5iIYqWZbR3d2Nzs5OdHV1oaurK9hDNpHmgfvrvahr9MGkE/okYAUBMOoE1DX6sL/eixHpUbmZNmn4fD7U1taGVMbW1NTA6/WGdbzNZgupjC0qKoLL5Ypx1ESUTML+rbxv3z4899xzeOmll1BfX4+CggI8+OCDeOihh/Dggw/iggsuiGWcREQxFaiWbW1thdfrZbUsnVRHR0dI79iKigocP378tMcJgoDRo0eHtCsYOXIkq2OJBtmhQ4cwYcKEfrdNmjQJDz/8cNSf86677kJjYyNWr14Nh8OBN954AzfddBPeeecdOBwOzJkzB9/+9rfx0EMPoby8HA899BBsNhuuuuoqAMDatWvx5ptvYsWKFUhLS8PKlSvx05/+FBs2bIBGo0FTU9MZn4NoqOm9qFdnZ2dCJmRP1Nrhh9cHmE6yTqhaBbR39ew3lMmyjAMHDoRUxu7atQudnZ1hHW80GlFQUBCsjC0qKkJGRgbndER0RsJK0M6cORN//etfYbFY8P3vfx9z5szBOeecg+bm5ogWECMiUhKfz4f29vbgxJzVsnQiWZaxf/9+fP7559i1axe2b9+Or776KqwEvtlsDlbHjhs3DsXFxTCbzYMQNRGdyqhRo/C3v/0N06dP77Nt06ZNyMzMjOrz7du3D59++il+97vfBRPDixYtwj/+8Q9s2LABOp0OGo0GS5YsgUqlQk5ODvbt24fnn38eV111FdxuN1566SUsXLgQ06ZNAwCsWbMG559/Pj788EPMnDkTv//978/4HERDgSzLwaRsR0dH0rWwMhtEqKSenrP9ve/i8fYsGGY2iIMfXBzV19cHE7GBz+G0ngIAjUaD/Pz8kOrYkSNHQhSH1r8hEcVeWAna999/HyUlJXjkkUfw7W9/GyoVb4cgosTUu1oiMDFnUpYCOjo6UFlZGVIde+zYsbCOzcnJCfaNDVTHcvJOpDw///nPMX/+fHi9Xnzve99Damoqjhw5gvXr1+Oxxx7DI488EtXns9vteO6551BUVBR8TBAEyLKM5uZm7Ny5E2effXbI/Hry5Ml49tln0djYiEOHDqG9vR2TJ08ObrdYLCgoKMDWrVsxc+ZMfP7552d8DqJkFZj7dXZ2or29PanvlBqepkK6U8LBBh/U6tA2B7IMtHfJyEyVMDwteV/PHz9+PKQytrKyEkeOHAnrWEmSMHr06JDK2NzcXKjV6hhHTUQUZoL2hRdewMsvv4xLL70UNpsN1157LebMmYPc3NxYx0dEdMaSvVqCBkaWZRw8eDCYjN2+fTtqamrg8/lOe6zJZOpTHWuxWAYhaiI6U7fddhsOHjyI1atXY/Xq1cHHVSoV5s+fj1/84hdRfT6LxRKsWg14//33sX//fpx33nlYs2YN8vLyQranpqYCAL755hvU1dUBAIYNG9Znn8OHDwMA6urqzvgcRMnE7Xajs7MzmJj1+/1DYu4nCMClU/R49f12HG+VYdQJUKt6Kmfbu2ToNAIunaJPmgXC2tvbUVVVFdI39tChQ2EfP3LkyJDK2DFjxkCvP0l/CCKiGAsrQfuTn/wEP/nJT1BTU4OXXnoJr7/+Op555hmMHj0agiCE1XuPiGiwBSol2trakrpagsLT1dUVUh27fft2NDY2hnXsiBEjUFpaGqyQzcnJYXUsUQJbsWIF7r33XmzevBnHjh2DzWbDOeecA6fTGfPn3rZtG+6//35Mnz4dF110EVasWNGnB6xWqwWA4KJFAPrdp7m5GUDP77czPcdAhfOmViIKvJmbrD9fIjtxbGRZRldXF7q7u9Hd3R1c4Guotq7KG67C9d8x4C//6kRdox/tXYBKBDJTRMyYrEfecFVM5sSxvma6u7vx5ZdfBqtiKysr8fXXX4c9xunp6SgsLAwmZMeOHdvvm+vJ+HqBv8+Ui2MTP7Isn/TfPV7jEtG9DXl5eXjkkUewfPlybNy4ES+99BL27t2LK664ApMnT8Z1112HH/zgB0hJSYlVvFi7di02b96M1157LfgYV74looCuri50dHSgtbUVHo8nKSdZdHqyLOPw4cPBRGx5eTm+/PLLsFbiNRgMKC4uDqmO1Wg00Ov1TMoSJRGr1YoZM2YM6nP+7W9/w913341x48YFq3d1Oh3cbnfIft3d3QB6fh/pdDoAPRWBga8D+wQqvaJxjoGQZRnt7e0DPl7J/H4/urq6AIC/+xUmcGdUe3s7urq6Qipkh2JCtj8j0oCf/pcKh47IaOuUYdILyEgRIIpedHaefi40EH6/P/h750yvGa/Xi6+//hq7du3Crl27UFVVhd27d4edLLHb7Rg7dmzIR39vwIW7KFiii+bYUHRxbOJHpVKho6Oj378b0ZwDyLIc9gKCA2o+I4oiZs2ahVmzZuHo0aN49dVX8corr+COO+7AXXfdFfwPFm2vvPIKHn/8cZx99tnBx6Kxai1XviVKbN3d3cFK2UBSlhP0oaW7uxu7du1CeXl5MCkbbr+xESNGBJOx48aNw+jRoyFJUnC73+8fMhN4omQ1atQo/PGPf8S4ceOQnZ19yomyIAiora2Negyvv/46li1bhosvvhirVq0Kzh/T09PR0NAQsm/g+7S0tOAbSw0NDRg+fHjIPvn5+VE7x0AIggCj0Tjg45UskAgyGAwhfxMoPvx+P9xuN7q6utDa2orm5mao1WqIosj1UU4hbxAvz8A1o9PpIrpm/H4/9u/fj8rKSlRVVWHnzp2orq4OJkdOx2QyYezYscHK2MLCQgwbNizshMhQMNCxodjj2MSPXq+HwWDod1s05wCR/C46479mLpcLd911F+666y5s2bIFr7zyypmeso/6+nosWrQI27ZtQ3Z2dsi2aKxay5VviRJL4Fa2QF/ZQPsCJmWHjrq6umAytqKiAlVVVWFVx+r1ehQVFWHcuHEoLS1FSUkJ7Hb7IERMRPE0bdq04K2s06ZNG/QX7m+88QaWLl2K2bNn4/777w+pxjj77LPx5ptvwufzBV8EbN68GdnZ2XA6nTCbzTCZTPjss8+CydWWlhZUVVXh+uuvj9o5BiqZX1CKoghJkpL6Z1Qiv98Pj8cT0rKg9xvwgVtPRVFkxZnCBK6Zk42LLMuor68P9ovduXMnqqqq0NraGtb5tVot8vPzg4nYwsJCLsoaptONDcUPxyY+BEE45d/3eMwBovp246RJkzBp0qRonhIAUFlZCavVij//+c946qmnQhp/R2PVWq58S6R8Xq83mJTt6OhgT9khxO12Y9euXcFWBdu3b+9TKXYyWVlZIdWxubm5rLQhGoJefvnl4NexKCY4la+//hrLly/HxRdfjLlz54b0vtbpdLjqqqvwwgsvYNGiRbj55ptRUVGBdevW4aGHHgLQ0zf2+uuvx6pVq+BwOJCRkYGVK1ciPT0dF198MQBE5RxE8RBIxrrd7mAy1u12Q5ZlvvmeBJqamoKJ2MBCXuH2/5ckCbm5uSGLeOXk5ECtVsc4aiKi+EiIV6kXXXQRLrroon63RWPV2niufJuszaDZ7Fq5EmVsfD4furq6gh9utzupV+BNlHEZDA0NDaioqAi2KqiqqoLH4zntcTqdDoWFhSgpKcG4ceNQUlLSb7+xSP8PcWyUi2MTP0pcWCESra2taGlpQUZGBtxuNx577DEcOHAAV199NS644IKoPtcHH3wAj8eDDz/8EB9++GHItiuuuAKPPPIIXnjhBSxbtgxXXHEFUlJScM899+CKK64I7jdv3jx4vV4sXrwYXV1dOPvss/Hiiy8G2yQ4nc4zPgdRLAUSsYGPQELW6/UyGZsk2traUF5ejq+++gpVVVWorKwMKaw6nezs7GBlbFFREcaMGRPSM5uIKNklRIL2VKKxam28Vr7lwgoUD0ocm8Ctpm63G52dnejs7ER3d7fiX+BH01BtEO/xeFBTU4OdO3dix44d2LlzZ/BNsdMZNmwYiouLUVRUhOLi4n6rY6PRO3aojk0i4NjEjxIXVgjXli1bMGPGDNxyyy145JFHMG/ePDz33HOw2WxYu3Yt3nnnHVx22WVRe76f/exn+NnPfnbKfUpKSvDWW2+ddLskSVi4cCEWLlwY03MQDZTf74fX64XP54PX64XX6w1JyAbmc7IsBz8ocXV3d6O6ujqkMnbv3r1hj2tGRgYKCgqClbFjx46F2WyOcdRERMqW8AnaRF75lgsrUDwoaWx8Ph+6u7vR0dGBzs7OkP5i8b4NXfb74f1qP/zNbRCtJqhyh0OIYQJoqDSIP3r0aLBvbKA6NpyFJTUaDQoLC4OVsePGjYPL5RqEiIfO2CQijk38KHFhhXAtWrQI+fn5mDt3Ljo7O/H666/j1ltvxZNPPom5c+di2bJlUU3QEiWLQLL1xARs77UAeidfmYRNDh6PB7W1tcFEbGVlJb766quwev8DPRX+vStjCwsL4XA4Yhw1EVHiGVAG5ODBg/j000+D/YGAnndN29vb8c9//hNvvvlmVIM8lURe+RbgwgoUH/Eam8CCD6dqWyAIQtxXXXVv24W2326Ad/9hyB4fBLUE1fBhMP1oFjQTx8bseZOtQXygOrb3Yl7h3uo2bNgwlJSUoLS0FOPGjUN+fn5ce44l29gkE45NfChxYYVwffbZZ3jrrbeQnZ2NDRs2oLOzE7NnzwYA/PCHP8Trr78e5wipP4H5OBeGGjy9F2Xt6OgIvvZjBWzy8vv92Lt3LyorK4MJ2erq6rDeTAcAk8kU0jO2qKgIaWlpcZ/bExElgogTtG+//Tauv/56eDye4C/a3refnWnCMlKJvvItUTKTZTnYJqS9vR3d3d3BPmNK5d62C82PvgJ/eydEiwmCVQW4vfDUHkDzo6/AuuDGmCZpE1ljY2Owb+z27dtRWVkZvM35VNRqNQoKCkIW80pLSxuEiIloKBJFMdjKauPGjbDZbMFFbltaWk5aGUzx1dLSguPHjwP4zxsAKpUq+FkUxeD3ga9FUWRiqB8+ny9Y+erxePpNuHq9XnR0dAR7SlPykWUZhw8fDlnEq6qqCm1tbWEdr9PpkJ+fH6yKLSgoQEpKCoxGI99EISIagIgTtMuXL8f48ePx9NNP46mnnoLH48G9996LjRs3YtGiRfif//mfGIR5clz5lkg5ZFkOLvwQSMoGJvYJUWkhy2j77Yae5GyK/T8v6nQaiFo7/Eea0PbbDXBMyAeG+As+r9eLr776KiQhe+DAgbCOTU1NDVbGjhs3DmPHjuUiNUQ0aMrKyvDCCy9Ar9fjrbfewqxZsyAIAhoaGvDII4+grKws3iHSSZzqlured+D0/hyo5FapVMEEbn9J3MDn/s4zUIH5T++7hQLJ5f7uGDqxOrV3HIIgBLcFztf7o/fjvRfdOnFb73ZSvZ+Tkt/Ro0dDKmMrKytx7NixsI5VqVTIzc0NVsUWFhYiJycnpCWZ3++PSu9/IqKhKuIE7Zdffonf/va3GD9+PKZPn47f/OY3GDt2LMaOHYuGhgYsW7ZsUJOW0Vi1livfEg2Mz+dDZ2cn3G43urq6ggt7Kb1K9mQ8Nfvg3X+4p3L2hBdNgiBAtJjg3X8Ynpp9UI8ZGZ8g46SpqQkVFRXBdgU7d+4MaxKuUqlQUFAQ7BtbWlqK9PT0QYiYiKh/K1euxIwZM/Dmm28iJSUFixcvBgAUFRXB7/fjgw8+iHOENBAnSzSemNTtL4l7sm3d3d3BXsr9JUj7e87edxj2/nyiQNJVFMU+86bex/SXJO7v3Cf7moam1tbWkERsJIuwCoKA7OzskL6xY8aMCd55QEREsRFxglYURTidTgBAXl4eqqur4ff7IYoiZsyYgZdffjnqQfb2yCOP9HmMK98SDY5AL7JAP7JAQjYRk7H98Te39vSctZ7kV6NGBbnVB39z6+AGNsh8Ph92794dUh27b9++sI5NSUkJaVVQUFDACT0RKcr48eOxe/duVFVVoaioKLhg69NPP42pU6fyTaQkF+4CVr2rAXm7NilZV1cXqqurQ1oV7N27N+zjMzIyQipjCwoKknYhayIiJYs4QTt27Fh88sknuOCCC5Cbmwu3243y8nJMmDABTU1NYTcQJ6LEEFjUq7OzE52dnfD5fEl7O5xoNUNQS4DbC+j6qY53eyGoJIhW8+AHF0PNzc19qmPb29tPe5xKpcKYMWOCydjS0lIMGzaM/f6ISPHMZjPOOeeckMeuuuoqAEB1dfWgr6lAJ+f3y9jzjRcNDR50tHuRlaoa6l2GaAjzeDz46quvQloV7N69Gz6fL6zjXS5XSGVsYWEh7HZ7jKMmIqJwRJygnTt3Ln72s5+hra0Ny5cvx7e+9S385Cc/wU033YQnn3wSEydOjEWcRDRI/H4/Ojo60NnZiY6ODni93sTpIXuG1HkjoBo+DJ7aAxC19pBEoyzL8Le0QZ2TBXXeiDhGeWb8fj9qa2tDqmO//vrrsI51Op19qmP1en2MIyYiiq6mpibcd999+Pjjj4Or0gM9vx/b29tx7NixsJMdFFvlNd34/UftqG/yQSe24XBdC1LtEi6dokf+SLYZo+Tm9/uxd+/ekMrY6upquN3usI63WCwhidiioiKkpqbyjXQiIoWKOEF78803o7u7O/iC/rnnnsOll16KO++8EyNHjsRjjz0W9SCJKLYCt/G1tbUN7RV7BQGmH81C86OvwH+kCaLFBGhUgNsLf0sbRKMeph/NSqgFwlpaWrBjxw5s374d5eXl2LFjR1ir80qShLy8vJDq2IyMDE7qiSjhzZ8/H2+88QYuvfRS7Nq1C0ajEXl5efjkk0/Q1NSE5557Lt4hEnqSs4//vgUd3TJcFhEWHXBMLeBggw+vvt+OH38XTNJS0pBlGYcOHQqpjK2qqgrrjiYA0Ov1GDt2bDARW1RUhKysLM7biIgSSMQJWgC47bbbgl+PGjUKFRUVaG5uRkpKStQCI6LYEQQhmJQNJGaHbFL2BJqJY2FdcCPafrsB3v2HIbf6IKgkqHOyYPrRLGgmjo13iCcVqLQIVMaWl5djz549YVU/2+32kOrYwsJCGAyGQYiaiGhw/eUvf8GSJUuwaNEirF69Gps2bcJbb72FtrY2XHDBBaisrIx3iEOe3y/j9x+19yRnrQI0agGiIECjAWySgOOtMjZu7sSYEZpEes+UKOjo0aPBythAMrapqSmsY1UqFfLy8kL6xo4aNQoq1YBe2hMRkUIM6Lf4smXL8M9//hN/+ctfAAD/7//9P/zgBz/A/fffj/nz50czPiKKIq/Xi9bWVhw7dgwA+qwaTD00E8fCMSEfnpp98De3QrSae9oaKOxVYFtbGyoqKoIJ2YqKCrS2nn4BM1EUQ6pjx40bxyoLIhoympqacN555wEAioqKsGrVKgCAyWTC3XffjSVLlmD16tXxDHHIqz3kxYEGLywGoc/fJkEAjDoBdY0+7K/3YkQ6k1KkbC0tLSGVsTt37kR9fX1Yx4qiiFGjRoW0KhgzZgw0GlaPExElm4hnNCtXrsSSJUtCErGjR4/G7Nmzce+998JgMOCWW26JZoxENEButzu4yFdXVxfcbje8Xi86Ojqg1+u5KvGpCALUY0bGO4ogWZZDqmO3b9+O3bt3h1Uda7VaQ5KxvVctJyIaalJSUtDc3AwAyM3NRX19PRobG+F0OpGRkYFDhw7FOUJqbvPD6wXUJ7mRQ60C2ruA1g6+yUzK0tHRgerq6pC+sfv37w/7+KysrGAitrCwEAUFBbyjiYhoiIg4Qfvcc89h+fLlWLhwYfCxzMxMPProo0hJScFjjz3GBC3RIPN6vfB4PPB4PPB6vXC73ejq6gq2LeidxBsKi30lg46ODuzYsQPl5eXB6thAQuFUBEHA6NGjg31jx40bhxEjRrA6lojo/0yfPh3Lli1DSUkJRo4cCafTiZdffhl333033n33XbhcrniHOORZTSJUKsDjBbT9FAp6vIBKAswGvtFM8ePxeFBTUxNSGVtbWxv23WmpqakhPWMLCwthtVpjHDURESlVxAnaQ4cOYcKECf1umzRpEh5++OEzDoqIQvn9fni93pCPQELW4/EEk7CBD0ossixj//792Lp1K3bt2oWKigp89dVXYU3wzWYzSkpKggnZ4uJimEymQYiaiCgxLV26FNOmTcMNN9yAjz/+GPfeey/uvvtuLF++HM3NzfjVr34V7xCHvJwMFbJSVfj6Gy80agD4z5uMsgy0d8nITJUwPI3tDWhw+Hw+7N27N6Qytrq6Gh6PJ6zjrVYrioqKUFBQEEzIpqamxjhqIiJKJBHPakaNGoW//e1vmD59ep9tmzZtQmZmZlQCIxpK/H5/SMI1UAnr9XpDFu9iEjY5dHR0oLKyMqRdQTgLQwiCgJycnJB2BSNHjmSrCiKiCIwYMQK7du1CTU0NAOCuu+5Ceno6Pv30U0yaNAk33HBDnCMkURTwg+lGPP77FjQ2y3BaZOh0MtxuoLVDhk4j4NIpeqW1hqckIcsyDh48GFIZu2vXLnR0dIR1vF6vD0nEFhYWIjMzk3czERHRKUWcoP35z3+O+fPnw+v14nvf+x5SU1Nx5MgRrF+/Ho899hgeeeSRWMRJZ6i7uxsejwcqlQqiKEKSJIiiyInCIDsxEet2u+F2u+HxeEKSr0zAJo/AJH/79u0oLy9HRUUFampq4PP5TnusyWTqUx1rNpsHIWoiouSm1+sxbty44PfXXXcdrrvuujhGRCcqzdNi3g8s+P1H7ahv8kH2Ad2ensrZS6fokT+SiyRRdBw5ciSkMrayshLHjx8P61i1Wo38/Pxgz9iioiJkZ2dDkqTYBk1EREkn4gTtbbfdhoMHD2L16tUhK9yqVCrMnz8fv/jFL6IaIEVHe3s7jh07BgDBpKwgCBBFESqVCpIkBT9OrMYLJAsD+wcSu4FEryAI/SZ8h3Ly98REbHd3d3CBLiZik1tXV1ewOjbQPzZw7Z3OiBEjUFpaGuwdm5OTw+pYIqIo2rVrFwRBQH5+PgCgvLwcS5YswVdffYW8vDwsXLgQ5557bpyjpIDSPC1KRmuw5xsvGhr86GjvRlaqipWzNGDNzc0hlbGVlZVoaGgI61hRFJGTkxNSGZuXlwe1Wh3jqImIaCgYUOOmFStW4N5778XmzZtx7Ngx2Gw2nHPOOXA6ndGOj6LoZP0s3W53yPe9E6u9E4gnS772Tvj2Tta63W4YjUZoNJrgY4EK3sDnRCbLcrA3bKAKNlAR2zsRG+5CAZR4ZFnGN998E9Kq4Msvv4TX6z3tsUajEcXFxcFWBUVFRdBoNNDr9Ql/bRARKU1jYyOuvPJKfPLJJwCAiy66CI899himTZsGSZIwevRofPjhh3jvvffw17/+FRdeeGF8A6YgURQwOlMNh0GNo0fZc5bC19HRgaqqqmBCtrKyEgcOHAj7+OHDh4cs4pWfnw+DwRDDiImIaCgb8CzHarVixowZ0YyFFOJkVZ29Hz9V5Wdg0arOzk643e7gLT69E7kAghW4kiSdsoq39/69k7vRqtDtr/+rz+cLJpwDH0DPAgGBvrA+ny/47xBYpIuSW3d3N6qqqoLJ2PLychw9ejSsY0eOHBlsVzBu3DiMHj065Pa3wDVDRETRt2jRItTU1ODFF1+E3W7HsmXLMG3aNBQXF+ODDz6A0WhES0sLZsyYgRUrVjBBS5Rg3G43ampqQipj9+zZE3ahRFpaWrAqNvBhtVpjHDUREdF/hJWgHTVqFP74xz9i3LhxyM7OPmViTBAE1NbWRi1ASnyBxOWJCUyfz9dn5dPTtUjor1r3xMTvqc7Ru61A4Gu2HaCTqaurC7Yp2L59O3bt2hVWdaxerw9Wx5aWlqKkpAQ2my32ARMRUb/ee+89PPzww7jxxhsB9LxpNn78eMyfPx9GoxEAYLFY8Mtf/hI33XRTHCMlotPx+XzYvXs3du/eHayQDfcOJgCw2WwhlbGFhYVISUmJcdRERESnFlaCdtq0abBYLMGvh3JvUYqtcKt0A05M8BINlNvtDqmO3b59e9g9ybKysoJ9YwPVsSoVb8MkIlKK+vp6jBkzJvh94OusrKyQ/dLT09Hc3DyosRFR/2QZ2Ffnwe49+/HNvirUHdyFyspKVFVVoaurK6xzGI1GFBQUhCzilZGRwdezRESkOGFlEF5++eXg16+88kqsYiEiGjT19fUhydiqqqqwEv46nQ6FhYXBhGxJSQn7bxMRKZzX64VOpwt+H1jUp7/Ffdg7nih+6uvrsXPnTnz8aQU++3wHjnxTDU93a1jHajQa5Ofnh1THjhw5kr39iYgoIQyoxKu1tRUtLS3IyMiA2+3GY489hgMHDuDqq6/GBRdcEO0YiYjOiMfjQXV1dUi7grq6urCOzcjICFbGjhs3jqv1EhEREUXB8ePHQ3rGVlZW4siRI2EdG1jcr3dlbG5uLudoRESUsCJO0G7ZsgUzZszALbfcgkceeQTz5s3Dc889B5vNhrVr1+Kdd97BZZddFotYaQBkvx+eqlp01zXA09UBde5wgLf0UJI7evRocBGvQHVsd3f3aY/TarUoLCwMSci6XK5BiJiIiGJt48aNqK6uBtBTJSsIAt577z3s3LkzuA/XUSCKjY6OjmASNpCUPXToUNjHm+1ZsKflQ2/LR07uWNw9ZyycTgerY4mIKGlEnKBdtGgR8vPzMXfuXHR2duL111/HrbfeiieffBJz587FsmXLmKBViK5PvkDLU7+D72A9mq0GHD90CFJGGkw/mgXNxLHxDo8oKjweD2pqaoLJ2IqKirAn/MOGDQsmYktLSzFmzBhWXhARJamHH364z2MPPvhgn8fYm5LozHR3d+PLL78MqYzds2dP2Avx6kypsKfmw3VWPhzpY2FPzYNGZwYAuN1At1tGY6sa7DBFRETJJOIE7WeffYa33noL2dnZ2LBhAzo7OzF79mwAwA9/+EO8/vrrUQ+SItf1yRdouncN/G0dkNJcEB0WCMca4ak9gOZHX4F1wY1M0lJCamxs7FMdG85CERqNBgUFBSHVsampqYMQMRERxdvXX38d7xCIkpLX60VtbW1IZexXX30Fr9cb1vF2uz2kZ6xkzsM7/9TCZhIg9FMcq1YB7V1AW2d4yV4iIqJEEXGCVhRFaLVaAD23itlsNkyaNAkA0NLSAoPBEN0IKWKy34+Wp34Hf1sHxHQXBJ0WgihA0GkgptjhP9KEtt9ugGNCPtsdkKJ5vV7U1NSELOZ18ODBsI5NTU0NLuQ1btw4jB07FhqNJsYRExGREo0YMSLeIRAlPL/fj/3794dUxlZXV6OzszOs400mEwoKCkISssOGDQupWt9X54VKaoHHC/Q3bfN4AZUImPR8DUNERMkl4gRtWVkZXnjhBej1erz11luYNWsWBEFAQ0MDHnnkEZSVlcUiToqAZ+dueHcfgGiz9LlNTxAEiBYTvPsPw1OzD+oxI+MTJFE/mpqaQpKxO3fuDGvSr1KpUFBQgJKSkmC7gvT09EGImIiIiCj5yLKM+vr6YFXszp07UVVVhdbW1rCO12q1yM/PR1FRUTAhO2LEiNP2jB2epkK6U8LBBh/UaiGklkSWgfYuGZkpIjJSmKAlIqLkEnGCduXKlZgxYwbefPNNpKSkYPHixQCAoqIi+P1+fPDBB1EPkiLjO9YM2eOFoD1JL02NCnKrD/7m8CZYRLHg8/mwe/fukITsvn37wjo2JSWlT3VsoLKfiIiIiCLT1NQUTMQG2hU0NjaGdawkScjNzQ2pjM3JyRlQX39BAC6doser77fjeKsMo06AWtVTOdveJUOnETBjsh6iGF4LBSIiokQRcYJ2/Pjx2L17N6qqqlBUVASj0QgAePrppzF16lRWrSmA5LBCUKuAbg+g7ydp5fZCUEkQrebBD46GrOPHj6OiogLl5eWoqKjAjh070NHRcdrjVCoV8vPzQ3rHnng7HBERERGFp62tDVVVVcFEbGVlZdgLrAJAdnZ2sDK2sLAQ+fn50Ol0UYsvf6QGP/4usHFzJ+oafWjvAlQSkJkq4dIpeuQNV6GzkwlaIiJKLhEnaAHAbDbjnHPOCXnsqquuAgBUV1cjPz//zCOjAVMXjYZqdBY8u/ZA1LnQO40lyzL8LW1Q52RBncd+bBQbfr8ftbW1wYW8tm/fjr1794Z1rNPpDCZiS0tLUVBQENVJPxEREdFQ0d3djerq6pDK2L1790KWw1tk66yzzgqpjC0oKIDJZIpx1D1J2jEjNNhf70Vrhx9mg4jhaSoIQs88k4iIKNlEnKBtamrCfffdh48//hhutzv4x93v96O9vR3Hjh2Dz+eLeqAUPkEUYbntWjTduwb+uqMQ0lyQLTrIXW74m1ogGvUw/WgWFwijqGlubsaOHTuCydgdO3agra3ttMdJkoS8vLxgMnbcuHHIyMhgdSwREcUNiw0oUXk8HuzZsyekVcFXX30Frze8alOn0xnSM7awsBAOhyPGUZ+cIAAj0gdUT0RERJRwIv6LN3/+fLzxxhu49NJLsWvXLhiNRuTl5eGTTz5BU1MTnnvuuVjESRHSnTce9kd+gZanfgffwXr4mwTInV1Q52TB9KNZ0EwcG+8QKUH5/X58/fXX2L59e7BCds+ePWEd63A4ggt5jRs3DoWFhTAYDDGOmIiI6D9YbEDJwO/3Y9++fSGVsdXV1eju7g7reLPZjIKCgmBlbFFREdLS0vgmOQ15sox+K7eJiGIt4gTtX/7yFyxZsgSLFi3C6tWrsWnTJrz11ltoa2vDBRdcgMrKyljESQOgO288tOeOg6eqFqhrgK+rA+rc4aycpYi0tbWhoqIiWB1bUVER1gq+oigGq2MDFbKZmZmc+BMRUVyx2IASjSzLOHz4cEhlbFVVVVh3KwGATqdDfn5+SHXs8OHDIYpijCMnSizVe93B3sdeX0/v43RnT+/j/JGaeIdHREluQC0OzjvvPABAUVERVq1aBQAwmUy4++67sWTJEqxevTq6UdKACaIITVEutGc5oT56NN7hkMLJsoy9e/cGk7Hl5eWora0Nq0+ZzWYLqY4tLi5mdSwRESkOiw1I6Y4ePYqqqqqQhOyxY8fCOlalUiE3NzdYFVtYWIicnByoVGwVQHQq1XvdePX9dnS6ZZh0Akx6wOMFDjb48Or77fjxd8EkLRHFVMR/qVNSUtDc3AwAyM3NRX19PRobG+F0OpGRkRHRCqBEFF/t7e0hvWMrKiqC1/epiKKI0aNHB5Ox48aNw4gRI1gdS0REisdiA1KS1tbWYIuCwOe6urqwjhUEAdnZ2SGVsWPGjIFWq41x1ETJRZaBjZs70emWYTcLwRtONRpArRZwvFXGxs2dGDNCw5tRiShmIk7QTp8+HcuWLUNJSQlGjhwJp9OJl19+GXfffTfeffdduFyuWMRJRGdIlmXs378f5eXl2LZtGyorK7F79+6wVsK1WCx9qmMHYwVfIiKiaGOxAcVLV1cXqqurQypj9+7dG/bxGRkZIZWxY8eO5XyMKAr213tR1+iDSSf0ScAKAmDUCahr9GF/vZcL1xFRzET822Xp0qWYNm0abrjhBnz88ce49957cffdd2P58uVobm7Gr371q1jESUQR6mhvx/a//B3lFeXYse9r7NyzG01NTac9ThAE5OTkhFTHjhw5kn3KiIgoKbDYgAaDx+PB7t27Qypjd+/eHfYCdC6XK6QytrCwEHa7PcZREw1NrR1+eH2ASd//drUKaO/q2S8coihCFEVotVoIggBZluH3+yHLcvADQJ/PAf21lwvcqSiKIgRBCDlff8f1/loQhOBH79d0gXP2fjxQvNPfuU4WLxFFR8QJ2hEjRmDXrl2oqakBANx1111IT0/Hp59+ikmTJuGGG26IepBEdGqyLOPgwYMoLy/v6R37ry3YvX8vfGH88TSbzX2qY81m8yBETURENPhYbEDR5vf7sXfv3pDK2Orqarjd7rCOt1gsIYnYwsJCpKWlsXUU0SAxG0ToNAIEQYReK0AUBUhSTzWtLANdXTLMRj9sJlXIdXni1xqNBgaDATqdDnq9PuICl94J3EACNpA8PTG52nt/n88X3N/v94d8BJKpXV1d0Ov1UKl6fgZJkiCKYvBz758lcJzP5wueM/AcvT98Pl/w41TxnniOwGO9f44Tv2YSmKJBlnsq5Fs7/DAbRAxPUym6TcmA6vP1ej3GjRsX/P66667DddddF7WgiOjUOjs7UVlZGewdu3379rAXj8g+KwNFEydgwoQJKC0txahRo1gdS0REQwaLDehMyLKMb775JqQytqqqCu3t7WEdr9frMXbs2GBCtqioCFlZWUzGEg2i3tWkarUaJWM0mFBkxOFGGTaLCoIgAZAgyxJkeNHS5kNeioiJxQb4/b7gsYEPSZIgSdIZL8YXiAsAJEmK6Oc4FZ/PB0mSYDQawzpv4Hzh7DsQp0om9/66d1I48HV/Sd7+krpM8FL1Xjc2bu5EXaMPXh+gkoB0p4RLp+gVu+BfRL9Bdu3aBUEQkJ+fDwAoLy/HkiVL8NVXXyEvLw8LFy7EueeeG5NAiYaqwAuBQCK2vLwcNTU18Hq9pz3WIKkw1uRAwf995HeJsOdmQ3vfz2EwGpmYJSKiIefVV1/FzJkz+y02qKurw29+8xvcc889cYyQlOTo0aOoqqoKVsdWVVWF1TIKAFQqFfLy8kL6xo4aNeqMkzhEFL7e1ZwajQZarRYajSb4EXg9NPOCbjz5dgt2H5JhNghQqwCP14/WDgF6rRrfmWKB1coF+KIhGgng/qp5T0zq9pfYDbSZYXI3uVXvdePV99vR6ZZh0gkw6QGPFzjY4MOr77fjx98FyoriHWVfYc0OGhsbceWVV+KTTz4BAFx00UV47LHHMG3aNEiShNGjR+PDDz/Ee++9h7/+9a+48MILYxkzUVLr6upCVVVVSHXs0aNHwzp2xFkZGNPmR6HFhSJHGkboLZB6VWTIXW74DhyGv/YgUDImVj8CERGRYs2ZMwf/+te/4HQ6+2wrLy/Hr371KyZoh6iWlhZUVlaisrISO3bswM6dO9HQ0BDWsYEe/oEWBUVFRRgzZgw0GmVW6RAlM0mSoNFoYDQag4lYtVp9ykr1cbla3Ha1Bb//qB0HGrxo7QBUKiD7LBV+MN2I0ryhmZz1+2XUHvKiuc0Pq0lEToYKohj/iv9A9fJA9JfUPZPk7skeo/iQZWDj5k50umXYzf9Z+E+jAdRqAcdbZWzc3ImJhfGNsz9hJWgXLVqEmpoavPjii7Db7Vi2bBmmTZuG4uJifPDBBzAajWhpacGMGTOwYsUKJmiJwiTLMurq6kKqY6urq8OrjjUYUFxcjJKSEpSWlqKkpAT6mgM4vuwFiC4rhP7+YGlUkFv88De3xeCnISIiUqZZs2ahqqoKQM/f3u9973vQavu+2K6vr0dOTs5gh0dx0NHRgerq6pC+sfv37w/7+KysrJCesQUFBTAYDDGMmIhORRRFqFQqmM1mmEymfn/Hn05pnhYlozWKTEjGQ3lNdzBh7fX2JKyzUhM/YR1I7g7kboYTK3f7S+hGmtzt3dqCztz+ei/qGn0w6YQ+/WYFATDqBNQ1+vDNUR8yMuIT48mE9T/yvffew8MPP4wbb7wRADBy5EiMHz8e8+fPh9FoBNDT3P6Xv/wlbrrpppgFS5To3G53n+rYcCszhg8fHlzIq7S0FKNHj+5zW4jHehyCWgLcXkDXT8WG2wtBLUK0mqLx4xARESWE+++/H88//zwAYO/evRg/fjxSUlJC9pEkCTabDXPmzIlHiBRDHo8HNTU1wZ6xO3fuRG1tbbCP4emkpqaG9IwtLCyE1WqNcdREFA5BEGA0GmGz2aDX68840SWKAnKz1FGKLnGV13Tj8d+3oKNbhsUgQG3ouUX862+8ePz3LZj3A0tCJ2kHKhqVu70/PB4POjo6gndb+P1+eL3ekAXY+mvHwErdk2vt8MPrA0z6/rerVUB7F9DWEd4cYDCFlaCtr6/HmDH/uR068HVWVlbIfunp6Whubo5ieESJrb6+PiQZW1VVBY/Hc9rjdDodioqKgsnYkpISOByO0x6nzhsB1fBh8NQegKi1h0xQZFmGv6UNqlGZEHMyz+jnIiIiSiTnnntuyDoJDzzwAEaNGhXHiChWfD4f9u7dG1IZW11dHdb8CwCsVmuwKjY3Nxfjx49Henp6jKMmooGQJAkOhwM2m40ViFHk98v4/Uft6OiW4bL+p7pTqwE0aqCxuWd7yWjNkK0uHoj+krs+ny/4JkN/PXkDC6d5vd5gJW7gI5DIDXwOLK4WOG6otl0wG0SopJ43FPrrMuTx9iwYZjIobz2esBK0Xq8XOp0u+L1arQ753Fu470QTJRuPx4Ndu3aFJGTr6urCOjYjIwOlpaXBCtm8vLyBLSAhCDD9aBaaH30F/iNNEC0mQKMC3F74W9ogGvUw/uhS+Lg4GBERDVEvv/wyAKCtrQ0mU88dJW+//TYOHDiAyy67DKNHj45neBQBWZZx8ODBYGVsZWUlqqqq0NHREdbxer0eBQUFIZWxmZmZEAQBfr8fnZ2d0OtPUoJDRHEjCAI0Gg1SU1N5jcZA7SEvDjR4YTH0vfVeEASYDcCBBi9qD3lZbRxjgiBAkqSwFlTr3V4hkNDt/bl3cvfEZG5/nxPV8DQV0p0SDjb4oFaHtjmQZaC9S0ZmqoSzXANfpC5WuIQo0QAdOXIkJBlbWVkJt9t92uO0Wi0KCwuDydhx48bB5XJFLS7NxLGwLrgRbb/dAO/+w5BbfRBUEtQ5WTD9aBZU48egs7Mzas9HRESUSGpqajBz5kz88Ic/xNKlS7F48WIsX74cALB48WJ8+OGHmDp1apyjpP4cOXIkpDK2srISx48fD+tYtVqN/Pz8kEW8srOzz2gVcSIafKIowmg0IjU1lddvjDS3+eH1AuqTtNVWq4DWjp79SDl699Y9XQ/mQGVuIFnbu0L3xErdU7Va6P1ZKQQBuHSKHq++347jrTKMOgFqVU/lbHuXDJ1GwKVT9H360ypB2AnajRs3orq6GkBPZl4QBLz33nvYuXNncJ/a2troR0ikAB6PB19++WVIQvabb74J69izzjorpHdsXl5ev9Xn0aSZOBaOCfnw1OyDv7kVotUMdd4I4P8qQoiIiIaqX/7yl1CpVLj88svh8Xiwdu1aXHPNNXj22Wdx4403YvHixdi0aVO8w6T/c+TIESxatAh//etfcfjw4bCOEUUROTk5IZWxgzH/IqLYEQQBarUadrudPaBjzGoSofq/hJb2ZLeIq3r2o8QUSWUu8J+WCSdW4faX0A1U68YzoZs/UoMffxfYuLkTdY0+tHf1tDXITJVw6RQ98kf28x9bAcJO0D788MN9HnvwwQf7PMbeL5QMjh492qd3bFdX12mP02g0KCgoCCZjx40b12cRkkEjCFCPGRmf56ahS5b7fWOAiEgpPv74Y7z44osoKyvDRx99hObmZsydOxcWiwU/+9nPcNVVV8U7ROrlsccew7p16065z/Dhw0MW8crPz4fBcJLSLyJKKIIgQKVSwWq1wmazDXiBJgpfToYKWakqfP2NFxo1+qxr0tohI/ssFXIyeEP2UCEIPe0uRFEM+83OExO3JyZ0e7ddCLRciGZSN3+kBmNGaLC/3ovWDj/MBhHD01SKfmka1hX19ddfxzoOorjxer2oqakJScgePHgwrGPT09NDWhWMHTuW1Rk0ZLm37fpPaw2PD4Jagmr4MJh+NAuaiWPjHR4REYCeu2ICC29u3LgRRqMR5513HoCexToG1AOeYubExdzS0tKCydhAuwJW0xElJ0mSgolZ/m4ePKIo4AfTjXj89y1obJZhNiB4i3hrhwy9tmc7FwijUwm0XIgkodtfVe7JWi6E00dXEIAR6YnzuyOsSEeMGBHrOIgGzbFjx1BRUYHy8vJg79hwerKq1WqMHTs2pDo2LS1tECImio9AhULvd0sDf2ADfxQDfzDdFTXofO1diB1d0A4/6z+L0zW3ouOldyBpfwh1SS6Ak78DqrT+RUSUnIqLi/HOO+8gLy8Pb731Fi655BKoVCp4PB48+eSTKC4ujneI1MtPfvITjB49GnV1dcjIyIjfnUlENChEUYQkSTCbzbBYLND0tww7xVxpnhbzfmDB7z9qx4EGL1o7etoaZJ+lwg+mG1Gad+oep0SR6t1DNxy9++iG03bhxCpdJd79nzipZKIB8Pl82L17dzAZu337duzfvz+sY1NTU/tUx56u2TZRohJFMZiI1Wq10Ol00Gg0UKlUUKvVp+xPJPv9aFz5W1g9IsSMzP/8sTMCXpsDvsbjEDf9G7bvXAAZ/+lh1N9H78RvoN9579gCXwOhCd0T/0AHvgb+c0tO768D5w/ntplAj6bez326Y4hIuR5++GFcfvnlePLJJ6HVanHvvfcCAPLy8lBXV4d33303zhHSiS644AIcO3YMR48ejXcoRBQjkiRBp9PBbDbDZDKxlYEClOZpUTJag9pDXjS3+WE1icjJULFylhQh0j66vV8rer3eGEc3MEzQUlI5fvx4sDq2oqICO3bsQEdHx2mPU6lUyM/PD1nMKz09XZHvqhCdiUCCMtDPS6PRQKvVQqPRQKPRQK1WR/z/3rNzN9xbKyEY9fAfaw59PgBSpxvyv3ZCVXsImpK8KP400dE7UdvfNq/Xi/b2dmi1WgiCEPatNkD/fZOY2CWKr29/+9vYuXMntmzZgsmTJwfvFJs/fz4uuugiVtASEQ0iSZJgMplgtVqh0+niHQ6dQBQF5GaxhR8lvkhbLsQDE7SUsHw+H2pra7F9+/ZgQnbv3r1hHetyuUKqYwsKCjghoKQmSRK0Wi3MZjO0Wu1pq2Ij4TvWDNnjhaA9yR87rRpycyt8JyRvleJUFRqBylmPxwODwRDWv1nvW2sCXwcqfHt/9H4s8PXJKosD4rEKKg1hSbzoX3Z2NrKzs0Meu/POOwEAzc3NSdvT1O/348knn8Tbb7+NlpYWTJw4EQ8++CDbmRHRoAu0MbDZbGxjQEQEJmgpgTQ3N6OioiLYqmDnzp1oa2s77XGSJGHMmDEhCdmMjAxWx1LSC9z2YTQaYbFYoNfrY/I8ksMKQa0Cuj2Avp82IN0eCCoVJEdyJjxOFHh3NhpObP1wqs+9PwLHnuoz0akk86J/3d3dWLNmDT7++GO43e7gNeH3+9He3o7Kysqw7r5JRGvXrsWbb76JFStWIC0tDStXrsRPf/pTbNiwgQkSIhoUTMwSEfUv6gna6upq5OfnR/u0NMT4/X7s2bMnmIzdvn079uzZE9axDocDJSUlwYW8CgsLY5aYIlKiwK0bFosFZrM55qveqotGQzU6C55deyDqXH36tPqPt0A9dhTURaNjGocSyX4/PDt3w3esGZLDCnXRaAgRJG8D7SgiTfj2twrqqRK6TOpSb+5tu9D86Cvwt3dCtJggWHsW/fPUHkDzo6/AuuBGYNrZ8Q5zwO655x488cQTKC4uRkNDA/R6PVJSUrBjxw643W4sWbIk3iHGhNvtxksvvYSFCxdi2rRpAIA1a9bg/PPPx4cffoiZM2fGOUIiSmaiKMJoNMLpdDIxS0TUj4hftTc1NeG+++47adXBsWPHgi/0iMLV2tqKHTt2BJOxFRUVaG1tPe1xoigiLy8vpHdsZmYmq2NpyAkk8QwGA8xmM4xG46BdB4IownLbtWi6dw38dUch2iyAVg10e+A/3gLRZIDltmsjSkwmg65PvkDLU7+Dd/eBnhYQahVUo7Ngue1a6M4bH9PnjnQVVKD/1gynSuqeuBIqk7pJQpbR9tsNPcnZFPt/fo/oNBC1dviPNKHttxvguKAsvnGegT/84Q/4xS9+gUcffRQrVqzAF198gd///vc4dOgQpk2bBr/fH+8QY6K6uhrt7e2YPHly8DGLxYKCggJs3bqVCVoiihm1Wg2n0wmLxRLvUIiIFCviBO38+fPxxhtv4NJLL8WuXbtgNBqRl5eHTz75BE1NTXjuuediESclEb/fj71794ZUx9bW1ob1Yt5ms4W0KigqKoLBYBiEqImUKZCEM5vNMJvNcatI0J03HvZHfvGfhGRzKwSVCuqxowYlIak0XZ980ZOwbuuAaLP09Oft9sCzaw+a7l0D+yO/UNy/yUBaM5yuUrd3Yrf398DpF1Bjgjc+PDX74N1/uKdy9oQ3eQRBgGgxwbv/MLxfHwIyMuIU5ZlpaGgIJiNLSkrw7LPPAgAyMjJw33334dFHH8WvfvWreIYYE3V1dQCAYcOGhTyempqKw4cPxyMkIkpyoijCZDLB5XLF/I4uIqJEF/Fvyb/85S9YsmQJFi1ahNWrV2PTpk1466230NbWhgsuuACVlZWxiJMSWHt7O3bs2IHy8vJgdWxLS8tpjxNFEaNHjw6pjh0+fDirY4nQ079Lp9PBYrHAaDRGrefpmdCdNx7ac8ed0S39yUD2+9Hy1O96krPpvVo+6LUQdS74646i5anfQXvuuIT/txlIpS7wn8RuOAunnerD5/NBpVIF42D17pnzN7f29Jy1nmRMNSrIrT74W07fA16pbDYburu7AQB5eXk4cOAAWltbYTabkZubi/3798c5wtjo7OwEgD5v5Gm1WjQ3D3wRx8G6cy7wRs9g6f2GEykLx0aZeo+LIAjQarVwOBwwGo3B7RQfvGaUi2OjTPEalwG1ODjvvPMAAEVFRVi1ahUAwGQy4e6778aSJUuwevXq6EZJCUOWZezbtw/l5eX497//jcrKSuzevTusCbXFYkFJSUkwGVtcXBz8g05EPdVrKpUquOiXTqeLd0h9CKIITUlevMOIK8/O3fDuPtBTOdtfBaLNAu/uA/Ds3D1k/62isZCaLMvweDxobW2FVtuzOF3vSt2TtWzo3ZYhcJ7en098fKgRrWYIaglwewFdPxX5bi8ElQTRYhr84KLk/PPPx+OPP44LLrgA2dnZMBqNeOedd3DDDTdg8+bNsFqTc0HDwN8Mt9sd8veju7t7wL36ZVlGe3t7VOI7na6urmCSeTD4/f5gIl8Jb4LSf3BslCkwLoG5qt1uB4BB+x1BJ+f3+9HV1QWA14zScGyUKZrjIsty2EWGESdoU1JSgu+y5+bmor6+Ho2NjXA6ncjIyMChQ4ciPSUlsI6ODuzcuRPbt29HeXk5KioqcPz48dMeJwgCcnJyQtoVjBw5kr+UiE5CkiRYrVbY7XZIkhTvcOgUfMeae3rOatX976BVQ25uhe/YwCvWqOfviCRJUKvV0Ov1YV8XJ6vMPbF692TVvCceHzjniZ8TNcGrzhsB1fBh8NQegKi19130r6UN6pwsqLITs70BACxZsgTnn38+Zs2ahU2bNuHWW2/F3Llz8fjjj6OiogI///nP4x1iTARaGzQ0NGD48OHBxxsaGga8wK8gCIP2ZvqZJJIHIlA1o9Pp+HdXYTg2yuT3+6HX63HWWWfBbDbHOxzqJXDNGAwGXjMKw7FRpmiOSyR3gEecoJ0+fTqWLVuGkpISjBw5Ek6nEy+//DLuvvtuvPvuu3C5XJGekhKELMs4cOBASO/YmpqasKpjzWZzsDp23LhxKC4u5h9uojBIkgSDwQCHwxGsEiRlkxxWCGoV0O0B9P2MWbcHgkoFyZGcVXpKF0jsRmsS3LtCV5ZleL3eYN9dr9cb8v2pEroBcU/sCgJMP5qF5kdfgf9IU0+lrEYFuL3wt7RBNOph+tEsIIHbDRUXF6O6uho7duwAAKxYsQIWiwWffvopLrvsMtx3331xjjA28vPzYTKZ8NlnnwUTtC0tLaiqqsL1118/4PMO1gtKSZIG/Y18URTj8rx0ehwbZZEkCSaTCTqdDiaTiYkmBQpcMxwb5eHYKFM8xiXiBO3SpUsxbdo03HDDDfj4449x77334u6778by5cvR3NyclIsqDFWdnZ3B6thA79hjx46FdeyoUaNQWFiICRMmYPz48cjOzuYEiigCoihCp9PBbrez1UeCUReNhmp0Fjy79kDUufpWIB5vgXrsKKiLRscxSoqWSPvwnthuIZDYjVZ7hmgkeDUTx8K64Ea0/XYDvPsPQ271QVBJUOdkwfSjWdBMHHvGzxFv6enpSE9PB9CTtL///vvjHFHsaTQaXH/99Vi1ahUcDgcyMjKwcuVKpKen4+KLL453eESUgERRhF6vh8PhgEajYTsDIqIzEHGCdsSIEdi1axdqamoAAHfddRfS09Px6aefYtKkSbjhhhuiHiTFnizLOHToUDAZW15ejpqamrCaIptMJhQXF/epju3s7IRer2diligMgQSPwWCA0WiETqeDwWDgongJSBBFWG67Fk33roG/7ihEmwXQqoFuD/zHWyCaDLDcdm3CLxBGA3Mm/Xf7a88QSPD2l9w9MckbOEfvzyd7TDNxLBwT8uGp2Qd/cytEqxnqvBEJXTnbW01NDd577z20t7f3uRNIEAQ88MADcYostubNmwev14vFixejq6sLZ599Nl588cU+C4cREZ1KoJDAZrPBZOrpSc5FjoiIzkzECdpXX30VM2fOxLhx44KPXXfddbjuuutQV1eH3/zmN7jnnnuiGiRFX1dXF6qqqkISso2NjWEdO3LkyJDesTk5OX3KvgdzlV2iRNV70S+tVgtZlmE2m3l7SxLQnTce9kd+gZanfgfv7gOQm1shqFRQjx0Fy23XQnfe+HiHSAnoTNsz9Nd798REbqAlQ6A9g1SQA6BvQjeR3zx69dVXMWfOnJNWGydzglaSJCxcuBALFy6MdyhElIAEQYBWq4XdbofJZErovwVEREoTcYJ2zpw5+Ne//gWn09lnW3l5OX71q18xQaswsizj4MGD2LRpUzAhW11dDa/Xe9pjDQZDSHVsSUkJbDZb7IMmSmKiKEKr1cJsNgcTsj6fj7eFJRndeeOhPXccPDt3w3esGZLDCnXRaFbOUtwMpHo3kKw9sUo3ke+OWbZsGS6++GI8//zzyMzMZIKBiCgMarUaNpsNVqs1of8GEBEpVVgJ2lmzZqGqqgpAT7Lve9/7Xr+L1dTX1yMnJye6EdIZ2bVrF6699lrs2rUrrP2HDx+OcePGobS0FOPGjcPo0aNZzUcUJZIkwWg0wmw2s33BECGIIjQlefEOg2jAknHRin379uHpp59GVlZWvEMhIlI8URRhMpngdDqhVqvjHQ4RUdIKK0F7//334/nnnwcA7N27F+PHj0dKSkrIPpIkwWazYc6cOdGPUiH8fj+efPJJvP3222hpacHEiRPx4IMPYsSIEfEO7aRefPHFkyZn9Xo9ioqKQqpjHQ7HIEdIlPwCfbqcTif0en28wyEiGtLGjBmD/fv3xzsMIiJFEwQBGo0GTqcz2GeWiIhiJ6wE7bnnnotzzz03+P0DDzyAUaNGxSwopVq7di3efPNNrFixAmlpaVi5ciV++tOfYsOGDYpdXOE73/kOnnnmGXg8HmRmZoZUx+bm5oa96jQRDYxarYbdbofVamXFLBGRAqxYsQJ33HEHRo4cicmTJ0On08U7JCKiuBMEAaIoQpIk6HQ66HQ6WCwWtjMgIhokEWfnXn75ZQBAW1tb8J20t99+GwcOHMBll12G0aNHRzdChXC73XjppZewcOFCTJs2DQCwZs0anH/++fjwww8xc+bMOEfYv4svvhiHDh1CQ0PDSRfDIKLokyQpeDsY3wghIlKOO++8E/X19Zg+fXq/2wVBCKtPPxFRMlCpVDCZTNDr9dBqtYotPCIiSnYRZw1qamowc+ZM/PCHP8TSpUuxePFiLF++HACwePFifPjhh5g6dWrUA4236upqtLe3Y/LkycHHLBYLCgoKsHXrVsUmaAHAarXC5/Ph6NGj8Q6FKOlJkgSDwQCbzcZ2BkRECnT99dfHOwQiorgLFBM4HA72liUiUoCIE7S//OUvoVKpcPnll8Pj8WDt2rW45ppr8Oyzz+LGG2/E4sWLsWnTpljEGld1dXUAgGHDhoU8npqaisOHDw/4vD6f74ziiuR5/H7/oDxX7+cbrJ+PwsexiT5BECBJEsxmMywWS7DyIJJ/Y46LcnFslItjo0xKH5cHH3ww3iEQEcWNKIowGo1wOBz9LvxNRETxEXGC9uOPP8aLL76IsrIyfPTRR2hubsbcuXNhsVjws5/9DFdddVUs4oy7zs5OAOhzy4dWq0Vzc/OAzinLMtrb2884tnB0dXUFf4bB4Pf70d3dDQDsW6QwHJvoEQQBKpUKVqsVZrMZkiTB4/HA4/FEfC6/34+uri4AHBel4dgoF8dGmaI5LrIsx6R/d2dnJyoqKuB2u4MtoPx+P9rb2/HPf/4TjzzySNSfk4gongRBgE6ng8PhgNFojHc4RER0gogTtB6PBw6HAwCwceNGGI1GnHfeeQB6KiaStddiYAEJt9sdsphEd3f3gG9jFgRh0P44nkmcAxGomtHpdJAkadCel06PY3PmeidmLRZLVP4dA+NiMBg4LgrDsVEujo0yRXNcYpGc3bRpE77//e+jqamp3+1ms5kJWiJKKiqVKrhoLd/QJCJSpoizqcXFxXjnnXeQl5eHt956C5dccglUKhU8Hg+efPJJFBcXxyLOuAu0NmhoaMDw4cODjzc0NCA/P3/A5x2sF5SSJA36H+PAKqCcBCgPx2ZgBEGAWq2G1WqNyQQ3MC5MNCkPx0a5ODbKpORxWbx4MZxOJ55//nm8/vrrkCQJc+bMwcaNG/H000/j/fffj3eIRERRIYoiDAYDnE4n2xkQESlcxAnahx9+GJdffjmefPJJaLVa3HvvvQCAvLw81NXV4d133416kEqQn58Pk8mEzz77LJigbWlpQVVVFRebIBoCJEmCzWaD3W5nYpuIKIFt374dzz//PK644gq0tLRg7dq1+O53v4vvfve7cLvd+PWvf4333nsv3mESEZ0RtVoNh8MBi8USk7sRiIgouiJO0H7729/Gzp07sWXLFkyePBkjRowAAMyfPx8XXXRR0lbQajQaXH/99Vi1ahUcDgcyMjKwcuVKpKen4+KLL453eEQUI6IoQq/Xw+VysfKAiCgJ+P1+ZGZmAgDGjBmDysrK4Larr74aP/7xj+MVGhHRGQtUzaakpECtVsc7HCIiCtOAGsZmZ2cjOzs75LE777wTANDc3Ayr1XrmkSnQvHnz4PV6sXjxYnR1deHss8/Giy++2GfhMCJKDqw8ICJKPjk5OdixYwfOP/985ObmoqOjA9XV1cjPz4fH40Fra2u8QyQiGhBJkuBwOGCz2Th3JSJKMBEnaLu7u7FmzRp8/PHH/a58W1lZiY6OjqgHqgSSJGHhwoVYuHBhvEMhohiSJAkmkwkOh4OVB0RESeb666/HPffcA5/PhzvuuANlZWW44447MG/ePCxbtgyFhYXxDpGIKCKCIECn0yElJSVkQWsiIkocESdo77nnHjzxxBMoLi5GQ0MD9Ho9UlJSsGPHDrjdbixZsiQGYRIRxZYgCFCpVDCbzbBYLKyMJyJKUgsXLsTRo0exZcsWAAj2oL388sthsVjw5z//Oc4REhGFTxRFmM1mpKSkcJ0EIqIEFnGC9g9/+AN+8Ytf4NFHH8WKFSvwxRdf4Pe//z0OHTqEadOmwe/3xyJOIqKYEAQBarUaVqsVFotFkSuOExFR9IiiiJUrVwa/Lysrw549e1BdXY0xY8bAYrHEMToiovCp1Wq4XC6YzeZ4h0JERGco4rfYGhoaMHPmTABASUlJsPogIyMD9913H958883oRkhEFCMqlQoulwvDhw+H3W5ncpaIaAhob2/v81hVVRXOPvtsJmeJKCEIggCTyYTMzEwmZ4mIkkTECVqbzYbu7m4AQF5eHg4cOBBcTCE3Nxf79++PboRERFEmiiJMJhMyMjJgt9t5OxgR0RBQXl6O8ePHY82aNSGPNzU1YerUqSgsLMSuXbviFB0RUXgkSYLL5cKwYcO4VgIRURKJOCtx/vnn4/HHH0d7ezuys7NhNBrxzjvvAAA2b94Mq9Ua9SCJiKJFrVYjNTUVw4YNg1arjXc4REQ0CL7++mtMnz4dx44dQ0FBQcg2rVaL//mf/0FrayvOP/98HDx4ME5REhGdnCAI0Ov1wQIDQRDiHRIREUVRxAnaJUuWYPPmzZg1axZUKhVuvfVWzJ07FxMnTsTixYtx1VVXxSJOIqIzIkkSLBYLsrKyYLFYOKklIhpCVqxYgZSUFHzxxRe48sorQ7YZDAbcfvvt+Pzzz2E0GrF8+fI4RUlE1D9RFGGxWJCRkQGdThfvcIiIKAYiXiSsuLgY1dXV2LFjB4CeCa/FYsGnn36Kyy67DPfdd1/UgyQiGqhAtYHD4YDBYIh3OEREFAcfffQRFi1aBIfDcdJ9UlNTsWDBAjz55JODGBkR0alxITAioqEh4gQtAKSnpyM9PR1AT/Lj/vvvj2pQRETRoFarYbPZYLPZWDFLRDSEHT58GKNHjz7tfsXFxWxxQESKIAgCjEYjUlJS2GuWiGgIGFCCtqamBu+99x7a29vh9/tDtgmCgAceeCAqwRERDYQkSTCZTHA6nVCpBvRrjoiIkkhKSgq++eab0+535MgROJ3OQYiIiOjkJEmCw+FgkQER0RAScebi1VdfxZw5cyDLcr/bmaAlongRRRF6vR5Op5P9uYiIKGjatGl45ZVX8MMf/vCU+61btw6lpaWDExQR0QkEQYBGo0Fqair0en28wyEiokEU8SJhy5Ytw8UXX4x9+/bB5/PB7/eHfPh8vljESUR0UoIgQKvVIj09nYsnEBFRH/PmzcPf//53LFiwAF1dXX22u91uLFy4EH/5y19w++23xyFCIhrqRFGE2WxGZmYmk7NERENQxBW0+/btw9NPP42srKxYxENEFDZBEKBSqWCz2WC1WiGKEb/nREREQ0BZWRnWrFmD+fPn47XXXsP06dORnZ0Nn8+Hffv2YdOmTTh69CiWLl2K73znO/EOl4iGGJVKFWxpQEREQ1PECdoxY8Zg//79sYiFiChskiTBYrHAbrezzywREZ3WbbfdhtLSUqxcuRJ/+tOfgpW0ZrMZ3/nOd7BgwQKcc845cY6SiIYSQRCg0+mQmpoKrVYb73CIiCiOIs5qrFixAnfccQdGjhyJyZMn81ZiIhpUoijCYDDA6XRyIktERBGZOnUqpk6dCgBobGyEKIqw2+1xjoqIhiJJkmC1WuFwOHgXGBERRZ6gvfPOO1FfX4/p06f3u10QBHi93jMOjIioN1EUodVqYbfbYTKZ4h0OERElOKfTGe8QiGgICiwElpKSAoPBEO9wiIhIISJO0F5//fWxiIOIqF+BPrN2ux1WqxWCIMQ7JCIiIiKiiAUWAnO5XJAkKd7hEBGRgkScoH3wwQdjEQcRUR+BW7/sdjsnsURERESUsNRqNVwuF8xmc7xDISIiBRrQyjqdnZ2oqKiA2+2GLMsAAL/fj/b2dvzzn//EI488EtUgiWhokSQJBoMBDoeDfWaJiIiIKGEJggCj0YiUlBSo1ep4h0NERAoVcYJ206ZN+P73v4+mpqZ+t5vNZiZoiWhARFGETqeDw+FgTy4iIiIiSmiSJMHhcMBms7FNFxERnVLECdrFixfD6XTi+eefx+uvvw5JkjBnzhxs3LgRTz/9NN5///1YxElESUwQBKjVajgcDpjNZk5giYiIiChhCYIAnU6HlJQU6HS6eIdDREQJIOIE7fbt2/H888/jiiuuQEtLC9auXYvvfve7+O53vwu3241f//rXeO+992IRKxElIbVaDavVCpvNBlEU4x0OEREREdGABRYCS0lJ4dyWiIjCFvFfDL/fj8zMTADAmDFjUFlZGdx29dVX49///nf0oiOipBVYACwzMxMOh4MTWCIiIiJKaGq1Gunp6UhLS+PcloiIIhLxX42cnBzs2LEDAJCbm4uOjg5UV1cDADweD1pbW6MbIRElFVEUYTKZkJGRgbS0NC6WQEREREQJLTC/zczMhMlkinc4RESUgCJucXD99dfjnnvugc/nwx133IGysjLccccdmDdvHpYtW4bCwsJYxElECU4QBGg0GjidTk5ciYiIiCgpcCEwIiKKhogTtAsXLsTRo0exZcsWAAj2oL388sthsVjw5z//OepBElHiEgQBKpUKNpsNVquVt3sRERERUcLjQmBERBRNESdoRVHEypUrg9+XlZVhz549qK6uxpgxY2CxWKIaIBElLkmSYLFY4HA4IElSvMMhIiIiIjpjgTmu0+lk8QEREUVFxAna9vZ2GI3GkMeqqqpwzjnnRC0oIkpsoijCaDTC4XBAq9XGOxwiIiIioqjQaDRwuVxs2UVERFEV9tt95eXlGD9+PNasWRPyeFNTE6ZOnYrCwkLs2rUr6gESUeIQRREGgwHDhg3DsGHDmJwlIiIioqQgiiLMZjMXAiMiopgIK0H79ddfY/r06Th27BgKCgpCtmm1WvzP//wPWltbcf755+PgwYMxCZSIlCuwAFhKSgoyMjL6VNkTERERESUqtVoNl8uF9PR0qFQR34RKRER0WmElaFesWIGUlBR88cUXuPLKK0O2GQwG3H777fj8889hNBqxfPnymARKRMqkUqngcDgwfPhwWK1Wrl5LRERERElBEAQYjUZkZGTAZrNxnktERDET1tt/H330ERYtWgSHw3HSfVJTU7FgwQI8+eSTUQuOiJRLkqRgn1mNRhPvcIiIiIiIokalUsFutzMxS0REgyKsBO3hw4cxevTo0+5XXFzMFgdESU4URej1ejgcDuj1+niHQ0REREQUNZIkQa/Xw+l0cj0FIiIaNGElaFNSUvDNN9+cdr8jR47A6XSecVBEpDyBPrMOhwMmk4mVBERERESUNCRJgsFggM1mYxECERENurB60E6bNg2vvPLKafdbt24dSktLzzAkIlISQRCCCyNkZWXBbDYzOUtERERESUGSJFitVmRmZmLYsGFMzhIRUVyElaCdN28e/v73v2PBggXo6urqs93tdmPhwoX4y1/+gttvvz3qQRJRfEiSBJvNhqysLNjtdohiWL8yiIiIiIgUSxCEYI/ZrKwspKWlsZ0BERHFVVgtDsrKyrBmzRrMnz8fr732GqZPn47s7Gz4fD7s27cPmzZtwtGjR7F06VJ85zvfiXXMRBRjoijCYDCw9xYRERERJY1AYtZiscBqtUKlCuvlMBERUcyF/RfptttuQ2lpKVauXIk//elPwUpas9mM73znO1iwYAHOOeecmAVKRLEX6DPrdDphMpniHQ4RERER0RkLtOyyWq2wWCyQJCneIREREYWI6C3DqVOnYurUqQCAxsZGiKIIu90ek8CIaHBJkgSLxQKn08lWBkRERESU8ERRhFqths1mg9ls5hyXiIgUa8D3dDidzmjGQURxIggCdDodXC4XF0UgIiIiooQniiK0Wi1sNhtMJhMXuCUiIsXjW4hEQ5hKpYLL5UJGRgaTs0RERDFy+PBh3HXXXZg6dSrOPvts3HTTTfjqq69C9tm8eTOuvPJKlJSU4JJLLsH69etDtnd3d+Ohhx7ClClTMH78eMybNw+NjY1RPwdRIgu060pLS0NmZibMZjOTs0RElBCYoCUagkRRhNFoREZGBux2O2/3IiIiihG3241bbrkFjY2NePbZZ/HGG2/AbDbjhhtuwLFjxwAAtbW1mDt3LqZNm4b169fjmmuuwf3334/NmzcHz7NkyRJ8+umneOKJJ7Bu3TocOHAAd955Z3B7NM5BlMgkSYLdbsfw4cOZmCUiooTDZSuJhhi1Wg2z2Yz09HQukEBERBRjn3/+OWpqavCPf/wDaWlpAIDf/OY3mDRpEv7+97/j6quvxrp165Cfnx9Mlo4aNQpVVVV44YUXMGXKFNTX12P9+vV49tlnUVZWBgBYvXo1ZsyYgfLycpSWlkblHESJSBRF6PV6uFwuaLXaeIdDREQ0ICybIxoiRFGE2WxGRkYGzGZzvMMhIiIaEnJzc/Hcc88Fk7MBsiyjubkZQE8Sd/LkySHbJ0+ejG3btkGWZWzbtg0AcM455wS3Z2dnIy0tDVu3bo3aOYgSiSAIUKvVSElJwVlnncXkLBERJTRW0BIlucDk1el0wmw2w+fzwe12xzssIiKiISElJQXTpk0LeezVV19Fd3c3pk6dCgCoq6tDenp6yD6pqano7OxEU1MT6uvrYbfb+ySgUlNTcfjw4aidYyB8Pt+Aj430efx+/6A8V+/nG6yfj8Ln8/kgiiJMJhNcLhdUKtWg/t+g/vGaUS6OjXJxbJQpXuPCBC1REpMkCSaTCU6nEyoVL3ciIqJoO3jwIKZPn37S7Z988glSUlKC3//1r3/FmjVrMHv2bOTn5wMAurq6oNFoQo4LfO92u9HZ2dlnOwBotVp0d3dH7RyRkmUZ7e3tAzo2Ul1dXejs7ByU5wIAv98f/Hdhr37lEEURarUadrsdJpMJ3d3dA/7/S9Hl9/vR1dUFgNeM0nBslItjo0zRHBdZlsPuic6MDVESCqxg63Q6YTKZ4h0OERFR0kpLS8PGjRtPut3hcAS//t3vfoelS5fi0ksvxX333Rd8XKvV9rm7JfC9Xq+HTqfr9+6X7u5u6PX6qJ0jUoIgwGg0DujYSJ1JnAMRqJrR6XTs2a8QKpUKNpsNZrMZnZ2d0Ov1HBsFCVwzBoOB46IwHBvl4tgoUzTHJZIFK5mgJUoykiTBarXC4XDwXTgiIqIYU6vVyMnJOe1+q1atwvPPP4/Zs2dj0aJFIRP2YcOGoaGhIWT/hoYGGAyG4MKex48fh9vtDqmCbWhoCLY1iMY5BmKwXlBKkjTo8xpRFOPyvBRKFEUYjUY4nU5oNJpgiwNJkpjQUBiOi3JxbJSLY6NM8RgXzjaIkkQgMZuVlQWXy8UXE0RERAqxcuVKPP/887jnnnuwePHiPtUUZWVl2LJlS8hjmzdvxoQJEyCKIiZOnAi/3x9c6AsA9uzZg/r6epSVlUXtHERKIggCtFot0tPTMWzYsH5bdBARESULZnCIElxgkYSMjAykpaVx8kpERKQgn332GV544QXMnj0bl112GY4cORL8CPRvnT17NioqKrBq1SrU1tbipZdewgcffICbb74ZQE8bhZkzZ2Lx4sX47LPPUFFRgQULFmDSpEkoLS2N2jmIlEKSJNjtdmRlZbFdFxERDQlscUCUgARBgCiKMBgMsFgsg9b/jYiIiCKzYcMGAMBrr72G1157LWTb7bffjjvuuAO5ublYu3YtVq5ciXXr1iEzMxMrV67ElClTgvsuXboUy5cvx+233w4AuOCCC7B48eLg9micgyjeRFGEXq+H0+mETqeLdzhERESDRpBlWY53EEPRjh07IMtycPXeWDt27BiOHj06KM8F9Kx6F2jez1vto0cURWi1WphMJphMJqjV6ojP4fP50N7eDqPRyD43CsJxUS6OjXJxbJQpmuNSXV0NQRBQXFwcpegoWjiXpWgSBAEqlQoOhwMWi+WUi6rwd78ycVyUi2OjXBwbZYrXXJYVtEQJINDGwGq1DuoKxkREREREsSRJEsxmMxwOB1QqvjwlIqKhiX8BiRRMEASo1Wq4XC723yIiIiKipCEIAvR6PRwOBwwGQ7zDISIiiismaIkUShRFmM1muFwu3u5ARERERElDrVbDZrPBZrOdsp0BERHRUMEELZHCsGqWiIiIiJJRoG2X0+kc0FoKREREyYoJWiIFCUxaU1JSWDVLRERERElBEARoNBo4nU4WIBAREfWDCVoihVCr1XA6nbBYLPEOhYiIiIgoKlQqVbCdgSiK8Q6HiIhIkZigJYozURRhMBjgcrmg0WjiHQ4RERER0RkLzHGdTie0Wm28wyEiIlI0JmiJ4kiSJDgcDi6QQERERERJIbCegtPphNlsjnc4RERECYEJWqI4EAQBOp0OKSkp0Ol08Q6HiIiIiOiMSZIEq9UKh8PBdgZEREQRYIKWaJBJkgSLxQKn08mJKxERERElPFEUodfr4XK52M6AiIhoABIuO7Ro0SLce++9fR7fvHkzrrzySpSUlOCSSy7B+vXrQ7Z3d3fjoYcewpQpUzB+/HjMmzcPjY2NUT8H0aloNBqkp6cjJSWFyVkiIiIiSmiBdgYpKSk466yzmJwlIiIaoITJEPl8Pvz3f/83/r//7//rs622thZz587FtGnTsH79elxzzTW4//77sXnz5uA+S5YswaeffoonnngC69atw4EDB3DnnXdG9RxEJyOKIiwWC7KysmA0GuMdDhERERHRGQm0M8jKyoLVauV6CkRERGcgIVoc1NbW4r777sOBAwdw1lln9dm+bt065OfnB5Olo0aNQlVVFV544QVMmTIF9fX1WL9+PZ599lmUlZUBAFavXo0ZM2agvLwcpaWlUTkHUX8CiyRYLJZ4h0JEREREdEYEQYBer4fT6YRer493OEREREkhISpot2zZgrFjx2LDhg3IzMzss/3zzz/H5MmTQx6bPHkytm3bBlmWsW3bNgDAOeecE9yenZ2NtLQ0bN26NWrnIOpNEAQYjUZkZGQwOUtERERECU+tVsPlciEjI4PJWSIioihKiAraa6+99pTb6+rqkJ6eHvJYamoqOjs70dTUhPr6etjt9j49kVJTU3H48OGonWMgfD7fgI+N9Hn8fv+gPFfv5xusn09pJEmC3W6H3W4HMHjjHI6hPjZKxXFRLo6NcnFslInjQpR8JEmC0WiE0+mEWq2OdzhERERJJ+4J2oMHD2L69Okn3f7JJ58gJSXllOfo6uqCRqMJeSzwvdvtRmdnZ5/tAKDVatHd3R21c0RKlmW0t7cP6NhIdXV1obOzc1CeCwD8fn/w32UoLYYlCAJ0Ol0wmT9Y4xsJv9+Prq4uAENrbJSO46JcHBvl4tgoUzTHRZZl9rUkiqPA3NbhcHAdBSIiohiKe4I2LS0NGzduPOl2h8Nx2nNotVq43e6QxwLf6/V66HS6PtsBoLu7O3hrTjTOEanALfCD4UziHIhA1YxOp4MkSYP2vPEkSRLMZjOcTqeiEwWBsTEYDENmbBIBx0W5ODbKxbFRpmiOC5OzRPGjUqlgs9lgs9kUPbclIiJKBnFP0KrVauTk5JzROYYNG4aGhoaQxxoaGmAwGGA2m5Geno7jx4/D7XaHVME2NDQE2xpE4xwDMVgvKCVJGvSJlSiKcXneeNBoNHC5XDCZTPEOJSyBsWFCQ1k4LsrFsVEujo0ycVyIEpcoisF2Bv3dQUhERETRlxSZs7KyMmzZsiXksc2bN2PChAkQRRETJ06E3+8PLvQFAHv27EF9fT3Kysqidg4aekRRhNlsRmZmZsIkZ4mIiIiITiQIArRaLdLT0zFs2DAmZ4mIiAZRUiRoZ8+ejYqKCqxatQq1tbV46aWX8MEHH+Dmm28G0NNGYebMmVi8eDE+++wzVFRUYMGCBZg0aRJKS0ujdg4aWtRqNVJSUjBs2DCoVHEvRiciIiIiGpDAArdZWVksOiAiIoqDpMgq5ebmYu3atVi5ciXWrVuHzMxMrFy5ElOmTAnus3TpUixfvhy33347AOCCCy7A4sWLo3oOGhoEQYDBYEBKSgorC4iIiIgoYYmiCL1eD5fLBa1WG+9wiIiIhixBlmU53kEMRTt27IAsy8jPzx+U5zt27BiOHj06KM8F9Kzg3NnZCb1en1Q9aAPVBXa7PWEXLvH5fGhvb4fRaGRvQAXhuCgXx0a5ODbKFM1xqa6uhiAIKC4ujlJ0FC2cyyY2QRCgVqvhcDhgNpsTal7L3/3KxHFRLo6NcnFslClec9mkqKAlijVBEKDT6eByuaDX6+MdDhERERHRgEiSBLPZDKfTyYQAERGRQjBBS3QagUmsy+VKygoKIiIiIkp+oihCp9PB6XSy4ICIiEhhmKAlOgWNRgOXy8XFEoiIiIgoYanVathsNthstoRqZ0BERDRUMEFL1A9RFGE0GpGSkgKVipcJERERESUeSZJgNBrhdDqhVqvjHQ4RERGdBDNPRCdQq9Ww2+2w2WzxDoWIiIiIKGKB9RMcDgeMRmO8wyEiIqLTYIKW6P8IggCDwQCXywWtVhvvcIiIiIiIIqZSqWC322G1Wrl+AhERUYJggpYIPbd/2Ww22O12TmSJiIiIKOEEFra12+1sZ0BERJRgmKClIU0QBGg0GqSmpnI1WyIiIiJKOJIkwWAwwOFw8C4wIiKiBMUELQ1ZkiTBZDIhJSWFVbNERERElFAC7bnsdjsMBkO8wyEiIqIzwAQtDTmBqlmn0wmTyRTvcIiIiIiIIiJJEhwOB2w2GwRBiHc4REREdIaYoKUhJVA163K5IElSvMMhIiIiIgqbIAjQ6/VISUlhOwMiIqIkwgQtDQmsmiUiIiKiRMZFbYmIiJIXE7SU9AIr2jqdTlbNEhEREVFCEQQBOp0OLpeLi9oSERElKSZoKWkFqmZdLheMRmO8wyEiIiIiioharYbNZoPVamXVLBERURJjgpaSkiRJsFqtcDgcnMwSERERUUIRRRFGoxFOpxMajSbe4RAREVGMMUFLSYW3gBERERFRohIEAWq1Gi6Xi+smEBERDSFM0FLS4MIJRERERJSoeAcYERHR0MUELSU8QRCg1+vhcrmg0+niHQ4RERERUdhEUYRer4fT6eRcloiIaIhigpYSmkqlgt1uh81mgyAI8Q6HiIiIiChsarUadrsdVquVc1kiIqIhjAlaSkiBSgOXywWtVhvvcIiIiIiIwiZJUnARMLVaHe9wiIiIKM6YoKWEo1arYbPZWDVLRERERAklsKCtw+GA0WiMdzhERESkEEzQUsIQRREGgwEulwsajSbe4RARERERhU2lUgWLDLgIGBEREfXGBC0lBLVaDYfDAYvFwqpZIiIiIkoYgSIDp9PJ1lxERETULyZoSdFEUYTRaITL5WJ/LiIiIiJKGIIgQK1Ww+l0wmw2xzscIiIiUjAmaEmxNBoNJ7RERERElHAkSYLVaoXD4WA7AyIiIjotJmhJcURRhMlkgsvlgkrF/6JERERElBhEUYRer4fL5WI7AyIiIgobs1+kGIHbwFwuF0wmU7zDISIiIiIKiyAIUKlUXDOBiIiIBoQJWlIESZJgNpvhdDohSVK8wyEiIiIiCgvnsURERHSmmKCluBIEAVqtFk6nE0ajMd7hEBERERGFRRAE6PV6OJ1O6PX6eIdDRERECYwJWoobLp5ARERERIlIrVbDZrPBZrOxnQERERGdMWbFaNAJggCDwYCzzjoLLpeLyVkiIiIaMj7//HOMHTsWn332WcjjmzdvxpVXXomSkhJccsklWL9+fcj27u5uPPTQQ5gyZQrGjx+PefPmobGxMernoFOTJAkWiwWZmZmw2+1MzhIREVFUMDNGg0qSJDidTpx11lm8FYyIiIiGlNbWVtxzzz3w+/0hj9fW1mLu3LmYNm0a1q9fj2uuuQb3338/Nm/eHNxnyZIl+PTTT/HEE09g3bp1OHDgAO68886onoNOLtDOID09Henp6VCr1fEOiYiIiJIIWxzQoBBFEXq9Hi6XC1qtNt7hEBEREQ26JUuWICsrC4cOHQp5fN26dcjPzw8mS0eNGoWqqiq88MILmDJlCurr67F+/Xo8++yzKCsrAwCsXr0aM2bMQHl5OUpLS6NyDupLEIRgOwOr1co7v4iIiCgmOMOgmFOr1cGqWSZniYiIaCj605/+hC+++AL3339/n22ff/45Jk+eHPLY5MmTsW3bNsiyjG3btgEAzjnnnOD27OxspKWlYevWrVE7B4VSqVSw2WzIysqC3W5ncpaIiIhihhW0FDOSJMFkMiE1NRUajSbe4RARERHFxcGDB7Fs2TKsXbsWRqOxz/a6ujqkp6eHPJaamorOzk40NTWhvr4edru9zxvdqampOHz4cNTOQT0Cc1itVgubzQZJkuIdEhERESU5JmgpJtRqNSwWC1JTU6FS8b8ZERERJaeDBw9i+vTpJ93+j3/8A/fccw+uueYalJWV4eDBg3326erq6vNmduB7t9uNzs7Oft/s1mq16O7ujto5BsLn8w342Eif58TevdEmSRL0ej1sNhs0Gg06OjoG7eej8AX+L3BslIXjolwcG+Xi2ChTvMaFmTOKKlEUYTQaYbfb4Xa7ubItERERJbW0tDRs3LjxpNvffvttdHR04I477jjpPlqtFm63O+SxwPd6vR46na7PdgDo7u4OLroajXNESpZltLe3D+jYSHV1daGzszMm55YkCQaDAVarFTqdDn6/Hx0dHejq6gIAtjZQGL/fz7FRII6LcnFslItjo0zRHBdZlsPOizFBS1EhCAJ0Oh3sdjtMJhN8Pl+/LwKIiIiIkolarUZOTs5Jt7/zzjtoaGgI9n6VZRkA8NOf/hSTJk3CCy+8gGHDhqGhoSHkuIaGBhgMBpjNZqSnp+P48eNwu90hVbANDQ3BtgbROEekBEHot2VDLJxJIvlkeidmTzx3oGrGYDCwxYHCcGyUieOiXBwb5eLYKFM0xyWSokUmaOmMcWVbIiIiov699tpr8Hq9we/r6+sxe/Zs/PrXvw4mbcvKyrBly5aQ4zZv3owJEyZAFEVMnDgRfr8f27Ztw5QpUwAAe/bsQX19PcrKyqJ2joEYrBeUkiRFbZ4pCALUajVcLhdMJtNJ9xNFEZIk8UWzAnFslInjolwcG+Xi2ChTPMaF2TQaMEmSYLFYkJmZyZVtiYiIiPqRkZGBESNGBD/OOussAD2tEdLS0gAAs2fPRkVFBVatWoXa2lq89NJL+OCDD3DzzTcH9505cyYWL16Mzz77DBUVFViwYAEmTZqE0tLSqJ1jKAjMX7Oysk6ZnCUiIiIaTKygpYiJogidTgeHwwGDwRDvcIiIiIgSWm5uLtauXYuVK1di3bp1yMzMxMqVK4OVrgCwdOlSLF++HLfffjsA4IILLsDixYujeo5kJggCNBoNnE4nE7NERESkOIIcaIRFg2rHjh2QZRn5+fmD8nzHjh3D0aNHz+gcgdvB7HY7LBbLKXtp+Hw+tLe3w2g0slRfYTg2ysRxUS6OjXJxbJQpmuNSXV0NQRBQXFwcpegoWhJpLitJEsxmM1wuV9h3fPH3i3JxbJSJ46JcHBvl4tgoU7zmsqygpbBIkgSr1Qq73c5fHERERESkeIGqWZfLNWiLmRERERENBBO0dEqiKMJoNMLhcECr1cY7HCIiIiKi0wr0mnU6nVwngYiIiBSPCVrqlyiK0Gq1cDgcrDggIiIiooQgCAK0Wi1cLhfXSiAiIqKEwQQthRAEASqVCna7HVar9ZR9ZomIiIiIlCLQksvhcLBqloiIiBIKE7QUxD6zRERERJRoBEGATqeDy+WCXq+PdzhEREREEWOClthnloiIiIgSEqtmiYiIKBkwQTuEsc8sERERESUiVs0SERFRMmGCdggSBAFqtRp2ux0Wi4V9ZomIiIgoYUiSBJvNBrvdzqpZIiIiSgpM0A4hgQXArFYrbDYbJ7RERERElDBEUYROp0NKSgrbchEREVFSYYJ2CAlUGqhUHHYiIiIiShyCIMDpdMJms/HuLyIiIko6zNQNEXa7nZNZIiIiIkpIVquVd38RERFR0uIsZ4hgcpaIiIiIEhWTs0RERJTMONMhIiIiIiIiIiIiihMmaImIiIiIiIiIiIjihAlaIiIiIiIiIiIiojhhgpaIiIiIiIiIiIgoTpigJSIiIiIiIiIiIooTJmiJiIiIiIiIiIiI4oQJWiIiIiIiIiIiIqI4SYgE7eHDh3HXXXdh6tSpOPvss3HTTTfhq6++Ctln8+bNuPLKK1FSUoJLLrkE69evD9ne3d2Nhx56CFOmTMH48eMxb948NDY2Rv0cREREREREREREROFSfILW7XbjlltuQWNjI5599lm88cYbMJvNuOGGG3Ds2DEAQG1tLebOnYtp06Zh/fr1uOaaa3D//fdj8+bNwfMsWbIEn376KZ544gmsW7cOBw4cwJ133hncHo1zEBEREREREREREUVCFe8ATufzzz9HTU0N/vGPfyAtLQ0A8Jvf/AaTJk3C3//+d1x99dVYt24d8vPzg8nSUaNGoaqqCi+88AKmTJmC+vp6rF+/Hs8++yzKysoAAKtXr8aMGTNQXl6O0tLSqJyDiIiIiIiIiIiIKBKKr6DNzc3Fc889F0zOBsiyjObmZgA9SdzJkyeHbJ88eTK2bdsGWZaxbds2AMA555wT3J6dnY20tDRs3bo1aucgIiIiIiIiIiIiioTiK2hTUlIwbdq0kMdeffVVdHd3Y+rUqQCAuro6pKenh+yTmpqKzs5ONDU1ob6+Hna7HVqtts8+hw8fjto5BsLn8w34WCXz+Xzw+/1J+/MlMo6NMnFclItjo1wcG2XiuBARERERRSbuCdqDBw9i+vTpJ93+ySefICUlJfj9X//6V6xZswazZ89Gfn4+AKCrqwsajSbkuMD3brcbnZ2dfbYDgFarRXd3d9TOESlZltHe3j6gY5XO7/ejq6sLACCKii/UHlI4NsrEcVEujo1ycWyUKZrjIssyBEGIRlhERERERIoV9wRtWloaNm7ceNLtDocj+PXvfvc7LF26FJdeeinuu+++4ONarRZutzvkuMD3er0eOp2uz3YA6O7uhl6vj9o5IiUIAoxG44COVbpA1YzBYIAkSXGOhnrj2CgTx0W5ODbKxbFRpmiOC5OzRERERDQUxD1Bq1arkZOTc9r9Vq1aheeffx6zZ8/GokWLQibsw4YNQ0NDQ8j+DQ0NMBgMMJvNSE9Px/Hjx+F2u0OqYBsaGoJtDaJxjoFI5heUoihCkqSk/hkTFcdGmTguysWxUS6OjTJxXIiIiIiIwpcQ9wOuXLkSzz//PO655x4sXry4TzVFWVkZtmzZEvLY5s2bMWHCBIiiiIkTJ8Lv9wcX+gKAPXv2oL6+HmVlZVE7BxEREREREREREVEkFJ+g/eyzz/DCCy9g9uzZuOyyy3DkyJHgR6B/6+zZs1FRUYFVq1ahtrYWL730Ej744APcfPPNAHraKMycOROLFy/GZ599hoqKCixYsACTJk1CaWlp1M5BREREREREREREFAnFJ2g3bNgAAHjttddw3nnnhXy89NJLAIDc3FysXbsWH3/8Mb73ve/h7bffxsqVKzFlypTgeZYuXYopU6bg9ttvx0033YRRo0bh8ccfD26PxjmIiIiIiIiIiIiIIiHIsizHO4ih6N///jdkWYZarY53KDHDlZeVi2OjTBwX5eLYKBfHRpmiNS4ejweCIGDChAlRiIqiiXNZiieOjTJxXJSLY6NcHBtlisdcNu6LhA1VgYFO5gsxmX+2RMexUSaOi3JxbJSLY6NM0RoXQRA4xgrFuSzFE8dGmTguysWxUS6OjTLFYy7LCloiIiIiIiIiIiKiOFF8D1oiIiIiIiIiIiKiZMUELREREREREREREVGcMEFLREREREREREREFCdM0BIRERERERERERHFCRO0RERERERERERERHHCBC0RERERERERERFRnDBBS0RERERERERERBQnTNASERERERERERERxQkTtERERERERERERERxwgQtERERERERERERUZwwQUtEREREREREREQUJ0zQ0hn7+uuvMX78eLzzzjsn3aepqQkLFizA2WefjbPPPhsPPPAAOjo6BjHKoSeccfnjH/+IMWPG9PnYt2/fIEY6NBw6dKjff+u333673/15zQyeSMeG183gWr9+PS699FIUFxdj5syZeP/990+6L6+bwRPJuPCaIaXjXFaZOJdVFs5llYtzWWXjXFaZlDiXVUX1bDTkeDwe3H333af9pTFv3jx0d3fjlVdeQUtLCxYtWoSHHnoI//3f/z1IkQ4t4Y7Ll19+iUmTJmH16tUhjzscjliGNyR9+eWX0Gq1+Nvf/gZBEIKPm83mfvfnNTN4Ih0bXjeD509/+hPuv/9+/PKXv8SFF16IDRs24K677kJ6ejrGjx/fZ39eN4Mj0nHhNUNKxrmsMnEuqzycyyoX57LKxbmsMil1LssELZ2RJ554Akaj8ZT7fPHFF9iyZQs2btyInJwcAMDDDz+Mm2++GXfddRfS0tIGI9QhJZxxAYCamhrk5+cjJSVlEKIa2mpqapCdnY3U1NTT7strZnBFMjaB/XndxJ4sy3jsscdwww034IYbbgAA3Hbbbfj3v/+NLVu29Jk88boZHJGOC8BrhpSNc1ll4lxWeTiXVS7OZZWJc1llUvJcli0OaMC2bt2Kt95667Tv5nz++edISUkJ/pIBgEmTJkEQBGzbti3WYQ454Y4L0PNO0OjRowchKork35rXzOCK9DrgdTM49uzZg0OHDuG//uu/Qh5/8cUXMXfu3D7787oZHJGOC8BrhpSLc1ll4lxWmTiXVS7OZZWJc1llUvJclglaGpCWlhbcc889WLx4MYYNG3bKfevr6/vso9FoYLPZcPjw4ViGOeREMi7Hjh3D0aNHsXXrVsyaNQvnnXcebrvtNnz99deDFO3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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set Seaborn theme for aesthetics\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Create figure for plots\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# Colors\n",
+    "scatter_color_cba = \"crimson\"\n",
+    "scatter_color_cbt = \"royalblue\"\n",
+    "line_color = \"black\"\n",
+    "\n",
+    "# Plot CBA Regression\n",
+    "sns.regplot(x=X, y=y_cba, scatter=True, color=scatter_color_cba, line_kws={\"color\": line_color}, ax=ax[0])\n",
+    "ax[0].set_xlabel(\"GDP\", fontsize=12, color=\"black\")\n",
+    "ax[0].set_ylabel(\"Canasta Basica Alimentaria (ARS)\", fontsize=12, color=\"black\")\n",
+    "ax[0].set_title(f\" GDP vs. CBA\\nR² = {r_squared_cba:.4f}\", fontsize=14, fontweight=\"bold\", color=\"darkred\")\n",
+    "ax[0].grid(alpha=0.3)\n",
+    "ax[0].set_facecolor(\"#f9f9f9\")\n",
+    "\n",
+    "# Plot CBT Regression\n",
+    "sns.regplot(x=X, y=y_cbt, scatter=True, color=scatter_color_cbt, line_kws={\"color\": line_color}, ax=ax[1])\n",
+    "ax[1].set_xlabel(\"GDP\", fontsize=12, color=\"black\")\n",
+    "ax[1].set_ylabel(\"Canasta Basica Total (ARS)\", fontsize=12, color=\"black\")\n",
+    "ax[1].set_title(f\" GDP vs. CBT\\nR² = {r_squared_cbt:.4f}\", fontsize=14, fontweight=\"bold\", color=\"navy\")\n",
+    "ax[1].grid(alpha=0.3)\n",
+    "ax[1].set_facecolor(\"#f9f9f9\")\n",
+    "\n",
+    "# Adjust layout and show the plots\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/mini project/mini_project/Mini Project Presentation.pdf b/mini project/mini_project/Mini Project Presentation.pdf
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diff --git a/mini project/mini_project/analysis.ipynb b/mini project/mini_project/analysis.ipynb
index 7ac184f..866a823 100644
--- a/mini project/mini_project/analysis.ipynb	
+++ b/mini project/mini_project/analysis.ipynb	
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,9 +27,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: '/Users/malenamalka/Documents/DATA 400/mini_project/canasta basica .csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/z8/02768t9119xbgk92bl8mhrrm0000gn/T/ipykernel_20358/811111655.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0minflation_file_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/Users/malenamalka/Documents/DATA 400/mini_project/arg_inflation.csv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mexchange_file_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/Users/malenamalka/Documents/DATA 400/mini_project/exchange_rate.csv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mcba_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcba_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0minflation_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minflation_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mexchange_rate_df\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexchange_file_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    676\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 678\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    679\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    680\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 575\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    931\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 932\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    933\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    934\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1214\u001b[0m             \u001b[0;31m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0;31m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m             self.handles = get_handle(  # type: ignore[call-overload]\n\u001b[0m\u001b[1;32m   1217\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    784\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 786\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    787\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/malenamalka/Documents/DATA 400/mini_project/canasta basica .csv'"
+     ]
+    }
+   ],
    "source": [
     "cba_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/canasta basica .csv'\n",
     "inflation_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/arg_inflation.csv'\n",
@@ -41,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -149,7 +167,7 @@
        "9  2025               146726.230000         334535.800000"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1182,6 +1200,148 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the GDP dataset\n",
+    "gdp_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/data/GDP_data.csv'\n",
+    "gdp_df = pd.read_csv(gdp_file_path, skiprows=4)  # Skipping metadata rows\n",
+    "\n",
+    "cba_file_path = '/Users/malenamalka/Documents/DATA 400/mini_project/data/canasta basica .csv'\n",
+    "cba_df = pd.read_csv(cba_file_path)\n",
+    "\n",
+    "\n",
+    "# Filter only for Argentina\n",
+    "gdp_arg_df = gdp_df[gdp_df[\"Country Name\"] == \"Argentina\"].drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"])\n",
+    "gdp_arg_df = gdp_arg_df.melt(var_name=\"Year\", value_name=\"GDP\").dropna()\n",
+    "gdp_arg_df[\"Year\"] = gdp_arg_df[\"Year\"].astype(int)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define independent (X) and dependent (y) variables\n",
+    "# Merge Data\n",
+    "merged_df = pd.merge(cba_yearly_df, gdp_arg_df, on=\"Year\", how=\"inner\")\n",
+    "\n",
+    "X = merged_df[[\"GDP\"]].values\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values\n",
+    "\n",
+    "# Initialize and fit the models\n",
+    "model_cba = LinearRegression().fit(X, y_cba)\n",
+    "model_cbt = LinearRegression().fit(X, y_cbt)\n",
+    "\n",
+    "# Generate predictions\n",
+    "y_pred_cba = model_cba.predict(X)\n",
+    "y_pred_cbt = model_cbt.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "CBA Regression: Coeff: 6.267458876554092e-08, Intercept: -23545.10790229818, R²: 0.2126220487420748\n",
+      "CBT Regression: Coeff: 1.3131414495969047e-07, Intercept: -47646.20263842166, R²: 0.20364163026858118\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# Define independent (X) and dependent (y) variables\n",
+    "X = merged_df[\"GDP\"].values  # Independent variable\n",
+    "y_cba = merged_df[\"Canasta Basica Alimentaria\"].values  # Dependent variable 1\n",
+    "y_cbt = merged_df[\"Canasta Basica Total\"].values  # Dependent variable 2\n",
+    "\n",
+    "# Function to calculate linear regression coefficients manually\n",
+    "def linear_regression(X, y):\n",
+    "    n = len(X)\n",
+    "    mean_x, mean_y = np.mean(X), np.mean(y)\n",
+    "    \n",
+    "    # Calculate slope (m) and intercept (b)\n",
+    "    numerator = np.sum((X - mean_x) * (y - mean_y))\n",
+    "    denominator = np.sum((X - mean_x) ** 2)\n",
+    "    slope = numerator / denominator\n",
+    "    intercept = mean_y - slope * mean_x\n",
+    "    \n",
+    "    # Compute R-squared\n",
+    "    y_pred = slope * X + intercept\n",
+    "    ss_total = np.sum((y - mean_y) ** 2)\n",
+    "    ss_residual = np.sum((y - y_pred) ** 2)\n",
+    "    r_squared = 1 - (ss_residual / ss_total)\n",
+    "    \n",
+    "    return slope, intercept, r_squared, y_pred\n",
+    "\n",
+    "# Perform Linear Regression\n",
+    "slope_cba, intercept_cba, r_squared_cba, y_pred_cba = linear_regression(X, y_cba)\n",
+    "slope_cbt, intercept_cbt, r_squared_cbt, y_pred_cbt = linear_regression(X, y_cbt)\n",
+    "\n",
+    "# Print Regression Results\n",
+    "print(f\"\\nCBA Regression: Coeff: {slope_cba}, Intercept: {intercept_cba}, R²: {r_squared_cba}\")\n",
+    "print(f\"CBT Regression: Coeff: {slope_cbt}, Intercept: {intercept_cbt}, R²: {r_squared_cbt}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set Seaborn theme for aesthetics\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Create figure for plots\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# Colors\n",
+    "scatter_color_cba = \"crimson\"\n",
+    "scatter_color_cbt = \"royalblue\"\n",
+    "line_color = \"black\"\n",
+    "\n",
+    "# Plot CBA Regression\n",
+    "sns.regplot(x=X, y=y_cba, scatter=True, color=scatter_color_cba, line_kws={\"color\": line_color}, ax=ax[0])\n",
+    "ax[0].set_xlabel(\"GDP\", fontsize=12, color=\"black\")\n",
+    "ax[0].set_ylabel(\"Canasta Basica Alimentaria (ARS)\", fontsize=12, color=\"black\")\n",
+    "ax[0].set_title(f\" GDP vs. CBA\\nR² = {r_squared_cba:.4f}\", fontsize=14, fontweight=\"bold\", color=\"darkred\")\n",
+    "ax[0].grid(alpha=0.3)\n",
+    "ax[0].set_facecolor(\"#f9f9f9\")\n",
+    "\n",
+    "# Plot CBT Regression\n",
+    "sns.regplot(x=X, y=y_cbt, scatter=True, color=scatter_color_cbt, line_kws={\"color\": line_color}, ax=ax[1])\n",
+    "ax[1].set_xlabel(\"GDP\", fontsize=12, color=\"black\")\n",
+    "ax[1].set_ylabel(\"Canasta Basica Total (ARS)\", fontsize=12, color=\"black\")\n",
+    "ax[1].set_title(f\" GDP vs. CBT\\nR² = {r_squared_cbt:.4f}\", fontsize=14, fontweight=\"bold\", color=\"navy\")\n",
+    "ax[1].grid(alpha=0.3)\n",
+    "ax[1].set_facecolor(\"#f9f9f9\")\n",
+    "\n",
+    "# Adjust layout and show the plots\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/mini project/mini_project/data/.DS_Store b/mini project/mini_project/data/.DS_Store
new file mode 100644
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diff --git a/mini project/mini_project/data/GDP_data.csv b/mini project/mini_project/data/GDP_data.csv
new file mode 100644
index 0000000..6cae4fc
--- /dev/null
+++ b/mini project/mini_project/data/GDP_data.csv	
@@ -0,0 +1,271 @@
+"Data Source","World Development Indicators",
+
+"Last Updated Date","2025-01-28",
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From 10f9f46504914aa9fd90688e89ec0264a9cc5417 Mon Sep 17 00:00:00 2001
From: malenamalka <malkagom@dickinson.edu>
Date: Tue, 25 Mar 2025 09:01:39 -0400
Subject: [PATCH 7/7] uploaded presentation

---
 .DS_Store                               | Bin 8196 -> 8196 bytes
 mini project/.DS_Store                  | Bin 8196 -> 8196 bytes
 presentation/Coke_vs_Pepsi_project.pptx | Bin 0 -> 2256390 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)
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