From 489c65cbac999768d51242ce9a620159b800156f Mon Sep 17 00:00:00 2001
From: Babalola-a <147947002+Babalola-a@users.noreply.github.com>
Date: Fri, 31 May 2024 10:52:09 +0200
Subject: [PATCH 01/34] adding ddl and updated er diagram

---
 blog/content/team_posts/phase_3/globalddl.png | Bin 0 -> 2472189 bytes
 blog/content/team_posts/phase_3/index.md      |   0
 database/carbon.sql                           |  25 +++++++++++++++++-
 3 files changed, 24 insertions(+), 1 deletion(-)
 create mode 100644 blog/content/team_posts/phase_3/globalddl.png
 create mode 100644 blog/content/team_posts/phase_3/index.md

diff --git a/blog/content/team_posts/phase_3/globalddl.png b/blog/content/team_posts/phase_3/globalddl.png
new file mode 100644
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literal 0
HcmV?d00001

diff --git a/blog/content/team_posts/phase_3/index.md b/blog/content/team_posts/phase_3/index.md
new file mode 100644
index 0000000..e69de29
diff --git a/database/carbon.sql b/database/carbon.sql
index 6ddebce..3c3e064 100644
--- a/database/carbon.sql
+++ b/database/carbon.sql
@@ -240,6 +240,29 @@ CREATE TABLE IF NOT EXISTS EmissionTags (
     description VARCHAR(250)
 );
 
-
+DROP TABLE IF EXISTS NGOEnterprise;
+CREATE TABLE IF NOT EXISTS NGOEnterprise (
+   ngo_id INT,
+   enterprise_id INT,
+   PRIMARY KEY(ngo_id, enterprise_id,
+   FOREIGN KEY (ngo_id) REFERENCES NGO(id)
+       ON UPDATE CASCADE
+       ON DELETE RESTRICT,
+   FOREIGN KEY (enterprise_id) REFERENCES enterprise(id)
+       ON UPDATE CASCADE
+       ON DELETE RESTRICT
+);
+DROP TABLE IF EXISTS NGOUser;
+CREATE TABLE IF NOT EXISTS NGOUser (
+   ngo_id INT,
+   user_id INT,
+   PRIMARY KEY(ngo_id, user_id),
+   FOREIGN KEY (ngo_id) REFERENCES NGO(id)
+       ON UPDATE CASCADE
+       ON DELETE RESTRICT,
+   FOREIGN KEY (user_id) REFERENCES user(id)
+       ON UPDATE CASCADE
+       ON DELETE RESTRICT
+);
 
 

From 8520a4c6f1701eb19d113914810249de953ab6de Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Fri, 31 May 2024 15:53:54 +0200
Subject: [PATCH 02/34] Implemented proper page navigation, persona home pages,
 added a test api page with route to display sql data, updated landing page,
 added carbon data sql file

---
 api/backend/rest_entry.py              |  6 +--
 api/backend/test/test_routes.py        | 39 ++++++++++++++
 app/src/App.py                         | 24 +++++++--
 app/src/modules/nav.py                 | 75 ++++++++++++++++++++++++++
 app/src/pages/01_userhome.py           | 20 +++++--
 app/src/pages/02_enterprisehome.py     |  7 ---
 app/src/pages/02_userSurvey.py         |  7 +++
 app/src/pages/03_NGOhome.py            |  7 ---
 app/src/pages/10_enterprisehome.py     | 19 +++++++
 app/src/pages/11_enterpriseSurvey.py   |  7 +++
 app/src/pages/20_NGOhome.py            | 19 +++++++
 app/src/pages/21_NGOSurvey.py          |  7 +++
 app/src/pages/30_about.py              | 24 +++++++++
 app/src/pages/31_apiPage.py            | 22 ++++++++
 database/{carbon.sql => 00_carbon.sql} |  4 --
 database/01_carbon-data.sql            |  5 ++
 16 files changed, 261 insertions(+), 31 deletions(-)
 create mode 100644 api/backend/test/test_routes.py
 create mode 100644 app/src/modules/nav.py
 delete mode 100644 app/src/pages/02_enterprisehome.py
 create mode 100644 app/src/pages/02_userSurvey.py
 delete mode 100644 app/src/pages/03_NGOhome.py
 create mode 100644 app/src/pages/10_enterprisehome.py
 create mode 100644 app/src/pages/11_enterpriseSurvey.py
 create mode 100644 app/src/pages/20_NGOhome.py
 create mode 100644 app/src/pages/21_NGOSurvey.py
 create mode 100644 app/src/pages/30_about.py
 create mode 100644 app/src/pages/31_apiPage.py
 rename database/{carbon.sql => 00_carbon.sql} (99%)
 create mode 100644 database/01_carbon-data.sql

diff --git a/api/backend/rest_entry.py b/api/backend/rest_entry.py
index fe522c5..c9543ab 100644
--- a/api/backend/rest_entry.py
+++ b/api/backend/rest_entry.py
@@ -4,8 +4,7 @@
 from flask import Flask
 
 from backend.db_connection import db
-from backend.customers.customer_routes import customers
-from backend.products.products_routes import products
+from backend.test.test_routes import test
 import os
 from dotenv import load_dotenv
 
@@ -60,8 +59,7 @@ def getData():
     app.logger.info('current_app(): registering blueprints with app object.')
     # Register the routes from each Blueprint with the app object
     # and give a url prefix to each
-    app.register_blueprint(customers,   url_prefix='/c')
-    app.register_blueprint(products,    url_prefix='/p')
+    app.register_blueprint(test, url_prefix='/t')
 
     # Don't forget to return the app object
     return app
diff --git a/api/backend/test/test_routes.py b/api/backend/test/test_routes.py
new file mode 100644
index 0000000..fea64f4
--- /dev/null
+++ b/api/backend/test/test_routes.py
@@ -0,0 +1,39 @@
+########################################################
+# Sample customers blueprint of endpoints
+# Remove this file if you are not using it in your project
+########################################################
+
+from flask import Blueprint, request, jsonify, make_response, current_app
+import json
+from backend.db_connection import db
+
+
+test = Blueprint('test', __name__)
+
+# Get all the products from the database
+@test.route('/test', methods=['GET'])
+def get_testdata():
+    # get a cursor object from the database
+    cursor = db.get_db().cursor()
+
+    # use cursor to query the database for a list of products
+    cursor.execute('SELECT id FROM Country')
+
+    # grab the column headers from the returned data
+    column_headers = [x[0] for x in cursor.description]
+
+    # create an empty dictionary object to use in 
+    # putting column headers together with data
+    json_data = []
+
+    # fetch all the data from the cursor
+    theData = cursor.fetchall()
+
+    # for each of the rows, zip the data elements together with
+    # the column headers. 
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+
diff --git a/app/src/App.py b/app/src/App.py
index 47825df..bd96b15 100644
--- a/app/src/App.py
+++ b/app/src/App.py
@@ -1,4 +1,9 @@
 import streamlit as st
+from modules.nav import SideBarLinks
+
+
+st.session_state['authenticated'] = False
+SideBarLinks(show_home=True)
 
 def App():
     
@@ -11,18 +16,27 @@ def App():
 
     col1, col2, col3 = st.columns(3)
     with col1:
-        if st.button("General User"):
+        if st.button("Act as Dave, a general user and citizen"):
+          st.session_state['authenticated'] = True
+          st.session_state['role'] = 'citizen'
+          st.session_state['first_name'] = 'Dave'
           st.switch_page("pages/01_userhome.py")
         st.image('https://cdn-icons-png.freepik.com/256/552/552848.png?ga=GA1.1.1507691374.1717099387', width = 100)
 
     with col2:
-        if st.button("Enterprise"):
-          st.switch_page("pages/02_enterprisehome.py")
+        if st.button("Act as Farm to Table, a medium sized business"):
+          st.session_state['authenticated'] = True
+          st.session_state['role'] = 'enterprise'
+          st.session_state['first_name'] = 'FarmtoTable'
+          st.switch_page("pages/10_enterprisehome.py")
         st.image('https://cdn-icons-png.freepik.com/256/834/834504.png?ga=GA1.1.1507691374.1717099387', width = 100)
 
     with col3:
-        if st.button("NGO"):
-          st.switch_page("pages/03_NGOhome.py")
+        if st.button("Act as NoEmissions, a transport emission NGO"):
+          st.session_state['authenticated'] = True
+          st.session_state['role'] = 'NGO'
+          st.session_state['first_name'] = 'CarEmissions'
+          st.switch_page("pages/20_NGOhome.py")
         st.image('https://cdn-icons-png.freepik.com/256/3101/3101045.png?ga=GA1.1.1507691374.1717099387', width = 100)
 
 
diff --git a/app/src/modules/nav.py b/app/src/modules/nav.py
new file mode 100644
index 0000000..e2da76c
--- /dev/null
+++ b/app/src/modules/nav.py
@@ -0,0 +1,75 @@
+# Idea borrowed from https://github.com/fsmosca/sample-streamlit-authenticator
+
+import streamlit as st
+
+#### ------------------------ General ------------------------
+def HomeNav():
+    st.sidebar.page_link("App.py", label="Home", icon='🏠')
+
+def AboutPageNav():
+    st.sidebar.page_link("pages/30_about.py", label="About")
+
+def testAPINav():
+    st.sidebar.page_link("pages/31_apiPage.py", label="Test API")
+
+# user role
+
+def UserHomeNav():
+    st.sidebar.page_link("pages/01_userhome.py", label="General User Home")
+
+def UserSurveyNav():
+    st.sidebar.page_link("pages/02_userSurvey.py", label="General User Survey")
+
+# enterprise role
+
+def EnterpriseHomeNav():
+    st.sidebar.page_link("pages/10_enterprisehome.py", label="Enterprise Home")
+
+# NGO role
+
+def NGOHomeNav():
+    st.sidebar.page_link("pages/20_NGOhome.py", label="NGO home")
+
+# --------------------------------Links Function -----------------------------------------------
+def SideBarLinks(show_home=False):
+    """
+    This function handles adding links to the sidebar of the app based upon the logged-in user's role, which was put in the streamlit session_state object when logging in. 
+    """    
+
+    # add a logo to the sidebar always
+    # st.sidebar.image("assets/logo.png", width = 150)
+
+    # If there is no logged in user, redirect to the Home (Landing) page
+    if 'authenticated' not in st.session_state:
+        st.session_state.authenticated = False
+        st.switch_page('App.py')
+        
+    if show_home:
+        # Show the Home page link (the landing page)
+        HomeNav()
+        testAPINav()
+
+    # Show the other page navigators depending on the users' role.
+    if st.session_state["authenticated"]:
+
+        if st.session_state['role'] == 'citizen':
+            UserHomeNav()
+            UserSurveyNav()
+
+        if st.session_state['role'] == 'enterprise':
+            EnterpriseHomeNav()
+        
+        if st.session_state['role'] == 'NGO':
+            NGOHomeNav()
+
+    
+    # Always show the About page at the bottom of the list of links
+    AboutPageNav()
+
+    if st.session_state["authenticated"]:
+        # Always show a logout button if there is a logged in user
+        if st.sidebar.button("Logout"):
+            del st.session_state['role']
+            del st.session_state['authenticated']
+            st.switch_page('App.py')
+
diff --git a/app/src/pages/01_userhome.py b/app/src/pages/01_userhome.py
index 58277ce..960ab02 100644
--- a/app/src/pages/01_userhome.py
+++ b/app/src/pages/01_userhome.py
@@ -1,7 +1,19 @@
 import streamlit as st
+from modules.nav import SideBarLinks
 
-def App():
-    st.write("User home page")
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
 
-    
-App()
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    st.write("Logged in as:")
+    st.write("General user")
+    st.image('https://cdn-icons-png.freepik.com/256/552/552848.png?ga=GA1.1.1507691374.1717099387', width = 50)
+
+st.write("# To get started take the survey below")
+st.write('')
+st.write('')
+
+if st.button('Take user general emissions survey'):
+    st.switch_page('pages/02_userSurvey.py')
diff --git a/app/src/pages/02_enterprisehome.py b/app/src/pages/02_enterprisehome.py
deleted file mode 100644
index 7725169..0000000
--- a/app/src/pages/02_enterprisehome.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import streamlit as st
-
-def App():
-    st.write("Enterprise home page")
-
-    
-App()
diff --git a/app/src/pages/02_userSurvey.py b/app/src/pages/02_userSurvey.py
new file mode 100644
index 0000000..4b498c9
--- /dev/null
+++ b/app/src/pages/02_userSurvey.py
@@ -0,0 +1,7 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+st.write("# User survey")
\ No newline at end of file
diff --git a/app/src/pages/03_NGOhome.py b/app/src/pages/03_NGOhome.py
deleted file mode 100644
index a0dbb5b..0000000
--- a/app/src/pages/03_NGOhome.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import streamlit as st
-
-def App():
-    st.write("NGO home page")
-
-    
-App()
diff --git a/app/src/pages/10_enterprisehome.py b/app/src/pages/10_enterprisehome.py
new file mode 100644
index 0000000..abc5ea7
--- /dev/null
+++ b/app/src/pages/10_enterprisehome.py
@@ -0,0 +1,19 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    st.write("Logged in as:")
+    st.write("Enterprise")
+    st.image('https://cdn-icons-png.freepik.com/256/834/834504.png?ga=GA1.1.1507691374.1717099387', width = 50)
+
+st.write("# To get started take the survey below")
+st.write('')
+st.write('')
+
+if st.button('Take enterprise general emissions survey'):
+    st.switch_page('pages/11_enterpriseSurvey.py')
diff --git a/app/src/pages/11_enterpriseSurvey.py b/app/src/pages/11_enterpriseSurvey.py
new file mode 100644
index 0000000..ebd6d44
--- /dev/null
+++ b/app/src/pages/11_enterpriseSurvey.py
@@ -0,0 +1,7 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+st.write("# Enterprise survey")
\ No newline at end of file
diff --git a/app/src/pages/20_NGOhome.py b/app/src/pages/20_NGOhome.py
new file mode 100644
index 0000000..c390f99
--- /dev/null
+++ b/app/src/pages/20_NGOhome.py
@@ -0,0 +1,19 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    st.write("Logged in as:")
+    st.write("NGO")
+    st.image('https://cdn-icons-png.freepik.com/256/3101/3101045.png?ga=GA1.1.1507691374.1717099387', width = 50)
+
+st.write("# To get started take the survey below")
+st.write('')
+st.write('')
+
+if st.button('Take NGO general emissions survey'):
+    st.switch_page('pages/21_NGOSurvey.py')
diff --git a/app/src/pages/21_NGOSurvey.py b/app/src/pages/21_NGOSurvey.py
new file mode 100644
index 0000000..72b6230
--- /dev/null
+++ b/app/src/pages/21_NGOSurvey.py
@@ -0,0 +1,7 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+st.write("# NGO survey")
\ No newline at end of file
diff --git a/app/src/pages/30_about.py b/app/src/pages/30_about.py
new file mode 100644
index 0000000..9f41e61
--- /dev/null
+++ b/app/src/pages/30_about.py
@@ -0,0 +1,24 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+st.write("# About this app")
+
+
+st.markdown (
+    """
+    This app is being built as an exemplar for Northeastern University's 
+    Summer 2024 Dialogue of Civilization Program titled *Data and 
+    Software in International Government and Politics*.  The program is bein
+    led by Dr. Mark Fontenot and Dr. Eric Gerber from the Khoury College of
+    Computer Sciences.  
+
+    The goal of this project is to allow different user personas to calculate
+    and see their emission data. It provides different emission related tools 
+    to promote a greener world. 
+
+    Stay tuned for more information and features to come!
+    """
+        )
diff --git a/app/src/pages/31_apiPage.py b/app/src/pages/31_apiPage.py
new file mode 100644
index 0000000..944a09c
--- /dev/null
+++ b/app/src/pages/31_apiPage.py
@@ -0,0 +1,22 @@
+import streamlit as st
+import requests
+from modules.nav import SideBarLinks
+
+SideBarLinks()
+
+st.write("# Accessing a REST API from Within Streamlit")
+
+"""
+Simply retrieving data from a REST api running in a separate Docker Container.
+
+If the container isn't running, this will be very unhappy.  But the Streamlit app 
+should not totally die. 
+"""
+data = {} 
+try:
+  data = requests.get('http://api:4000/t/test').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
\ No newline at end of file
diff --git a/database/carbon.sql b/database/00_carbon.sql
similarity index 99%
rename from database/carbon.sql
rename to database/00_carbon.sql
index 6ddebce..21b1eeb 100644
--- a/database/carbon.sql
+++ b/database/00_carbon.sql
@@ -239,7 +239,3 @@ CREATE TABLE IF NOT EXISTS EmissionTags (
     id INT PRIMARY KEY,
     description VARCHAR(250)
 );
-
-
-
-
diff --git a/database/01_carbon-data.sql b/database/01_carbon-data.sql
new file mode 100644
index 0000000..fafe5f6
--- /dev/null
+++ b/database/01_carbon-data.sql
@@ -0,0 +1,5 @@
+USE CarbonConnect;
+
+INSERT INTO Country (id, emissions, name) VALUES (1, '5.8 million metric tons', 'CountryA');
+INSERT INTO Country (id, emissions, name) VALUES (2, '15.2 million metric tons', 'CountryB');
+INSERT INTO Country (id, emissions, name) VALUES (3, '20.7 million metric tons', 'CountryC');
\ No newline at end of file

From ff40eac689acd9eea85054006e7c3d35f90047d8 Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Sat, 1 Jun 2024 17:58:07 +0200
Subject: [PATCH 03/34] Added NGO match page and feature for Enterprises

Added feature, fixed syntax in sql file, added some dummy insert data
---
 api/backend/enterprises/enterprises_routes.py | 92 +++++++++++++++++++
 api/backend/rest_entry.py                     |  2 +
 app/src/modules/nav.py                        |  4 +
 app/src/pages/12_enterpriseMatch.py           | 33 +++++++
 database/00_carbon.sql                        | 26 ++----
 database/01_carbon-data.sql                   | 33 ++++++-
 6 files changed, 172 insertions(+), 18 deletions(-)
 create mode 100644 api/backend/enterprises/enterprises_routes.py
 create mode 100644 app/src/pages/12_enterpriseMatch.py

diff --git a/api/backend/enterprises/enterprises_routes.py b/api/backend/enterprises/enterprises_routes.py
new file mode 100644
index 0000000..cf5e21f
--- /dev/null
+++ b/api/backend/enterprises/enterprises_routes.py
@@ -0,0 +1,92 @@
+########################################################
+# Sample customers blueprint of endpoints
+# Remove this file if you are not using it in your project
+########################################################
+
+from flask import Blueprint, request, jsonify, make_response, current_app
+import json
+from backend.db_connection import db
+
+
+enterprises = Blueprint('enterprises', __name__)
+
+# get all of the enterprise tags from database
+@enterprises.route('/tags', methods=['GET'])
+def get_tags():
+    # get a cursor object from the database
+    cursor = db.get_db().cursor()
+
+    # use cursor to query the database for a list of products
+    cursor.execute('''
+    SELECT description 
+    FROM EmissionTags 
+    WHERE EmissionTags.id IN (
+        SELECT tag_id 
+        FROM EntTags 
+        WHERE EntTags.enterprise_id = (
+            SELECT id 
+            FROM Enterprises 
+            ORDER BY id DESC 
+            LIMIT 1
+        )
+    );
+''')
+    
+    # grab the column headers from the returned data
+    column_headers = [x[0] for x in cursor.description]
+
+    # create an empty dictionary object to use in 
+    # putting column headers together with data
+    json_data = []
+
+    # fetch all the data from the cursor
+    theData = cursor.fetchall()
+
+    # for each of the rows, zip the data elements together with
+    # the column headers. 
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+
+# get all of the matching NGO's based on tags
+@enterprises.route('/NGOMatch', methods=['GET'])
+def get_matches():
+    # get a cursor object from the database
+    cursor = db.get_db().cursor()
+
+    # use cursor to query the database for a list of products
+    cursor.execute('''
+    SELECT NGO.name, EmissionTags.description
+    FROM NGO
+    JOIN NGOTags ON NGO.id = NGOTags.ngo_id
+    JOIN EmissionTags ON NGOTags.tag_id = EmissionTags.id
+    WHERE EmissionTags.id IN (
+        SELECT tag_id
+        FROM EntTags
+        WHERE EntTags.enterprise_id = (
+            SELECT id
+            FROM Enterprises
+            ORDER BY id DESC
+            LIMIT 1
+        )
+    );
+''')
+    
+    # grab the column headers from the returned data
+    column_headers = [x[0] for x in cursor.description]
+
+    # create an empty dictionary object to use in 
+    # putting column headers together with data
+    json_data = []
+
+    # fetch all the data from the cursor
+    theData = cursor.fetchall()
+
+    # for each of the rows, zip the data elements together with
+    # the column headers. 
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
\ No newline at end of file
diff --git a/api/backend/rest_entry.py b/api/backend/rest_entry.py
index c9543ab..d893cf6 100644
--- a/api/backend/rest_entry.py
+++ b/api/backend/rest_entry.py
@@ -5,6 +5,7 @@
 
 from backend.db_connection import db
 from backend.test.test_routes import test
+from backend.enterprises.enterprises_routes import enterprises
 import os
 from dotenv import load_dotenv
 
@@ -60,6 +61,7 @@ def getData():
     # Register the routes from each Blueprint with the app object
     # and give a url prefix to each
     app.register_blueprint(test, url_prefix='/t')
+    app.register_blueprint(enterprises, url_prefix='/e')
 
     # Don't forget to return the app object
     return app
diff --git a/app/src/modules/nav.py b/app/src/modules/nav.py
index e2da76c..4a3cb2e 100644
--- a/app/src/modules/nav.py
+++ b/app/src/modules/nav.py
@@ -25,6 +25,9 @@ def UserSurveyNav():
 def EnterpriseHomeNav():
     st.sidebar.page_link("pages/10_enterprisehome.py", label="Enterprise Home")
 
+def EnterpriseMatchNav():
+    st.sidebar.page_link("pages/12_enterpriseMatch.py", label="Enterprise Match")
+
 # NGO role
 
 def NGOHomeNav():
@@ -58,6 +61,7 @@ def SideBarLinks(show_home=False):
 
         if st.session_state['role'] == 'enterprise':
             EnterpriseHomeNav()
+            EnterpriseMatchNav()
         
         if st.session_state['role'] == 'NGO':
             NGOHomeNav()
diff --git a/app/src/pages/12_enterpriseMatch.py b/app/src/pages/12_enterpriseMatch.py
new file mode 100644
index 0000000..04381ba
--- /dev/null
+++ b/app/src/pages/12_enterpriseMatch.py
@@ -0,0 +1,33 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+import requests
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+st.write("# NGO suggestions")
+
+st.write('')
+st.write('')
+
+st.write("## Your emission tags are: ")
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/e/tags').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
+
+st.write("## NGOs with these tags are: ")
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/e/NGOMatch').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
\ No newline at end of file
diff --git a/database/00_carbon.sql b/database/00_carbon.sql
index 3c3e064..d8691e6 100644
--- a/database/00_carbon.sql
+++ b/database/00_carbon.sql
@@ -17,7 +17,6 @@ CREATE TABLE IF NOT EXISTS NGO (
    logo VARCHAR(255) UNIQUE NOT NULL,
    website VARCHAR(255) UNIQUE NOT NULL,
    name VARCHAR(50),
-   mission_tags VARCHAR(50),
    contact VARCHAR(50)
 );
 
@@ -25,7 +24,6 @@ CREATE TABLE IF NOT EXISTS NGO (
 DROP TABLE IF EXISTS User;
 CREATE TABLE IF NOT EXISTS User (
    id INT PRIMARY KEY,
-   tags VARCHAR(255),
    emission_result INT,
    country_id INT,
    FOREIGN KEY (country_id) REFERENCES Country(id)
@@ -40,7 +38,6 @@ CREATE TABLE IF NOT EXISTS Enterprises (
    name VARCHAR(50),
    type VARCHAR(255),
    emission_result VARCHAR(255),
-   emission_tags VARCHAR(255),
    misc_emissions VARCHAR(255),
    country_id INT,
    FOREIGN KEY (country_id) REFERENCES Country(id)
@@ -188,6 +185,12 @@ CREATE TABLE IF NOT EXISTS CostTags (
        ON DELETE RESTRICT
 );
 
+DROP TABLE IF EXISTS EmissionTags;
+CREATE TABLE IF NOT EXISTS EmissionTags (
+    id INT PRIMARY KEY,
+    description VARCHAR(250)
+);
+
 DROP TABLE IF EXISTS EntTags;
 CREATE TABLE IF NOT EXISTS EntTags (
    enterprise_id INT,
@@ -201,8 +204,6 @@ CREATE TABLE IF NOT EXISTS EntTags (
        ON DELETE RESTRICT
 );
 
-
-
 DROP TABLE IF EXISTS UserTags;
 CREATE TABLE IF NOT EXISTS UserTags (
    user_id INT,
@@ -231,24 +232,15 @@ CREATE TABLE IF NOT EXISTS NGOTags (
        ON DELETE RESTRICT
 );
 
-
-
-
-DROP TABLE IF EXISTS EmissionTags;
-CREATE TABLE IF NOT EXISTS EmissionTags (
-    id INT PRIMARY KEY,
-    description VARCHAR(250)
-);
-
 DROP TABLE IF EXISTS NGOEnterprise;
 CREATE TABLE IF NOT EXISTS NGOEnterprise (
    ngo_id INT,
    enterprise_id INT,
-   PRIMARY KEY(ngo_id, enterprise_id,
+   PRIMARY KEY(ngo_id, enterprise_id),
    FOREIGN KEY (ngo_id) REFERENCES NGO(id)
        ON UPDATE CASCADE
        ON DELETE RESTRICT,
-   FOREIGN KEY (enterprise_id) REFERENCES enterprise(id)
+   FOREIGN KEY (enterprise_id) REFERENCES Enterprises(id)
        ON UPDATE CASCADE
        ON DELETE RESTRICT
 );
@@ -260,7 +252,7 @@ CREATE TABLE IF NOT EXISTS NGOUser (
    FOREIGN KEY (ngo_id) REFERENCES NGO(id)
        ON UPDATE CASCADE
        ON DELETE RESTRICT,
-   FOREIGN KEY (user_id) REFERENCES user(id)
+   FOREIGN KEY (user_id) REFERENCES User(id)
        ON UPDATE CASCADE
        ON DELETE RESTRICT
 );
diff --git a/database/01_carbon-data.sql b/database/01_carbon-data.sql
index fafe5f6..6a84fe7 100644
--- a/database/01_carbon-data.sql
+++ b/database/01_carbon-data.sql
@@ -2,4 +2,35 @@ USE CarbonConnect;
 
 INSERT INTO Country (id, emissions, name) VALUES (1, '5.8 million metric tons', 'CountryA');
 INSERT INTO Country (id, emissions, name) VALUES (2, '15.2 million metric tons', 'CountryB');
-INSERT INTO Country (id, emissions, name) VALUES (3, '20.7 million metric tons', 'CountryC');
\ No newline at end of file
+INSERT INTO Country (id, emissions, name) VALUES (3, '20.7 million metric tons', 'CountryC');
+
+INSERT INTO EmissionTags (id, description) VALUES
+(1, 'Tag Description One'),
+(2, 'Tag Description Two'),
+(3, 'Tag Description Three');
+
+INSERT INTO Enterprises (id, name, type, emission_result, misc_emissions, country_id) VALUES
+(1, 'Enterprise One', 'Type A', 'Result A', 'Misc A', 1),
+(2, 'Enterprise Two', 'Type B', 'Result B', 'Misc B', 2),
+(3, 'Enterprise Three', 'Type C', 'Result C', 'Misc C', 3);
+
+INSERT INTO EntTags (enterprise_id, tag_id) VALUES
+(1, 1),
+(1, 2),
+(2, 1),
+(2, 3),
+(3, 2),
+(3, 3);
+
+INSERT INTO NGO (id, logo, website, name, contact) VALUES
+(1, 'logo1.png', 'https://www.ngo1.org', 'NGO One', 'contact1@ngo1.org'),
+(2, 'logo2.png', 'https://www.ngo2.org', 'NGO Two', 'contact2@ngo2.org'),
+(3, 'logo3.png', 'https://www.ngo3.org', 'NGO Three', 'contact3@ngo3.org');
+
+INSERT INTO NGOTags (ngo_id, tag_id) VALUES
+(1, 1),
+(1, 2),
+(2, 1),
+(2, 3),
+(3, 2),
+(3, 3);
\ No newline at end of file

From 40d70ced060f1d8fc131b432067d6c8d11b6b7fe Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Sun, 2 Jun 2024 22:44:28 +0200
Subject: [PATCH 04/34] Added enterprise compare tool, added wireframe page for
 NGO info (not functional yet)

---
 api/backend/enterprises/enterprises_routes.py | 40 +++++++++++-
 api/backend/ngo/ngo_routes.py                 | 24 +++++++
 api/backend/rest_entry.py                     |  2 +
 app/src/modules/nav.py                        | 18 +++++-
 app/src/pages/13_enterpriseCompare.py         | 19 ++++++
 app/src/pages/20_NGOhome.py                   |  6 +-
 app/src/pages/21_NGOInfo.py                   | 63 +++++++++++++++++++
 .../pages/{21_NGOSurvey.py => 22_NGOTools.py} |  2 +-
 app/src/pages/31_apiPage.py                   |  3 +-
 database/00_carbon.sql                        |  5 +-
 database/01_carbon-data.sql                   | 29 +++++----
 11 files changed, 188 insertions(+), 23 deletions(-)
 create mode 100644 api/backend/ngo/ngo_routes.py
 create mode 100644 app/src/pages/13_enterpriseCompare.py
 create mode 100644 app/src/pages/21_NGOInfo.py
 rename app/src/pages/{21_NGOSurvey.py => 22_NGOTools.py} (86%)

diff --git a/api/backend/enterprises/enterprises_routes.py b/api/backend/enterprises/enterprises_routes.py
index cf5e21f..0b8f1bb 100644
--- a/api/backend/enterprises/enterprises_routes.py
+++ b/api/backend/enterprises/enterprises_routes.py
@@ -16,7 +16,6 @@ def get_tags():
     # get a cursor object from the database
     cursor = db.get_db().cursor()
 
-    # use cursor to query the database for a list of products
     cursor.execute('''
     SELECT description 
     FROM EmissionTags 
@@ -56,7 +55,6 @@ def get_matches():
     # get a cursor object from the database
     cursor = db.get_db().cursor()
 
-    # use cursor to query the database for a list of products
     cursor.execute('''
     SELECT NGO.name, EmissionTags.description
     FROM NGO
@@ -84,6 +82,44 @@ def get_matches():
     # fetch all the data from the cursor
     theData = cursor.fetchall()
 
+    # for each of the rows, zip the data elements together with
+    # the column headers. 
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+
+# get my emissions, my country's, and avg other companies in same country emissions
+@enterprises.route('/EntCompare', methods=['GET'])
+def get_comparison():
+    # get a cursor object from the database
+    cursor = db.get_db().cursor()
+
+    cursor.execute('''
+    SELECT AVG(Enterprises.emission_result) AS avg_enterprise_emissions, Country.name AS your_country, (SELECT e2.emission_result
+        FROM Enterprises e2
+        ORDER BY e2.id DESC
+        LIMIT 1) AS your_enterprise_emissions
+    FROM Enterprises
+    JOIN Country ON Enterprises.country_id = Country.id
+    WHERE Country.name = (SELECT Country.name FROM Enterprises
+    JOIN Country ON Enterprises.country_id = Country.id
+    ORDER BY Enterprises.id DESC
+    LIMIT 1)
+    GROUP BY Country.name;
+    ''')
+    
+    # grab the column headers from the returned data
+    column_headers = [x[0] for x in cursor.description]
+
+    # create an empty dictionary object to use in 
+    # putting column headers together with data
+    json_data = []
+
+    # fetch all the data from the cursor
+    theData = cursor.fetchall()
+
     # for each of the rows, zip the data elements together with
     # the column headers. 
     for row in theData:
diff --git a/api/backend/ngo/ngo_routes.py b/api/backend/ngo/ngo_routes.py
new file mode 100644
index 0000000..c870336
--- /dev/null
+++ b/api/backend/ngo/ngo_routes.py
@@ -0,0 +1,24 @@
+from flask import Blueprint, request, jsonify, make_response, current_app
+import json
+from backend.db_connection import db
+
+ngo = Blueprint('ngo', __name__)
+
+@ngo.route('/NGOadd', methods=['POST'])
+def add_new_NGO():
+    
+    recieved_data = request.json
+
+    name = recieved_data['name']
+    website = recieved_data['website']
+    email = recieved_data['email']
+
+    query = 'INSERT INTO NGO (website, name, contact) VALUES (%s, %s, %s)'
+
+    data = (name, website, email)
+    cursor = db.get_db().cursor()
+    cursor.execute(query, data)
+    db.get_db().commit()
+
+    new_id = cursor.lastrowid
+    return jsonify({'message': 'Success!', 'id': new_id}), 201
diff --git a/api/backend/rest_entry.py b/api/backend/rest_entry.py
index d893cf6..2ce5738 100644
--- a/api/backend/rest_entry.py
+++ b/api/backend/rest_entry.py
@@ -6,6 +6,7 @@
 from backend.db_connection import db
 from backend.test.test_routes import test
 from backend.enterprises.enterprises_routes import enterprises
+from backend.ngo.ngo_routes import ngo
 import os
 from dotenv import load_dotenv
 
@@ -62,6 +63,7 @@ def getData():
     # and give a url prefix to each
     app.register_blueprint(test, url_prefix='/t')
     app.register_blueprint(enterprises, url_prefix='/e')
+    app.register_blueprint(ngo, url_prefix='/n')
 
     # Don't forget to return the app object
     return app
diff --git a/app/src/modules/nav.py b/app/src/modules/nav.py
index 4a3cb2e..1754b00 100644
--- a/app/src/modules/nav.py
+++ b/app/src/modules/nav.py
@@ -28,10 +28,22 @@ def EnterpriseHomeNav():
 def EnterpriseMatchNav():
     st.sidebar.page_link("pages/12_enterpriseMatch.py", label="Enterprise Match")
 
+def EnterpriseSurveyNav():
+    st.sidebar.page_link("pages/11_enterpriseSurvey.py", label="Enterprise Survey")
+
+def EnterpriseCompareNav():
+    st.sidebar.page_link("pages/13_enterpriseCompare.py", label="Enterprise Compare")
+
 # NGO role
 
 def NGOHomeNav():
-    st.sidebar.page_link("pages/20_NGOhome.py", label="NGO home")
+    st.sidebar.page_link("pages/20_NGOhome.py", label="NGO Home")
+
+def NGOInfoNav():
+    st.sidebar.page_link("pages/21_NGOInfo.py", label="NGO Info")
+
+def NGOToolsNav():
+    st.sidebar.page_link("pages/22_NGOTools.py", label="NGO Tools")
 
 # --------------------------------Links Function -----------------------------------------------
 def SideBarLinks(show_home=False):
@@ -62,9 +74,13 @@ def SideBarLinks(show_home=False):
         if st.session_state['role'] == 'enterprise':
             EnterpriseHomeNav()
             EnterpriseMatchNav()
+            EnterpriseSurveyNav()
+            EnterpriseCompareNav()
         
         if st.session_state['role'] == 'NGO':
             NGOHomeNav()
+            NGOInfoNav()
+            NGOToolsNav()
 
     
     # Always show the About page at the bottom of the list of links
diff --git a/app/src/pages/13_enterpriseCompare.py b/app/src/pages/13_enterpriseCompare.py
new file mode 100644
index 0000000..234547e
--- /dev/null
+++ b/app/src/pages/13_enterpriseCompare.py
@@ -0,0 +1,19 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+import requests
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+st.write('# Comparison to average company in your country')
+
+st.write('')
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/e/EntCompare').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
\ No newline at end of file
diff --git a/app/src/pages/20_NGOhome.py b/app/src/pages/20_NGOhome.py
index c390f99..f8fa240 100644
--- a/app/src/pages/20_NGOhome.py
+++ b/app/src/pages/20_NGOhome.py
@@ -11,9 +11,9 @@
     st.write("NGO")
     st.image('https://cdn-icons-png.freepik.com/256/3101/3101045.png?ga=GA1.1.1507691374.1717099387', width = 50)
 
-st.write("# To get started take the survey below")
+st.write("# To get started we need some information from you")
 st.write('')
 st.write('')
 
-if st.button('Take NGO general emissions survey'):
-    st.switch_page('pages/21_NGOSurvey.py')
+if st.button('NGO information questions'):
+    st.switch_page('pages/21_NGOInfo.py')
diff --git a/app/src/pages/21_NGOInfo.py b/app/src/pages/21_NGOInfo.py
new file mode 100644
index 0000000..475e29a
--- /dev/null
+++ b/app/src/pages/21_NGOInfo.py
@@ -0,0 +1,63 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+import requests
+import json
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+col1, col2, col3 = st.columns(3)
+
+with col1:
+    st.write("Logged in as:")
+    st.write("NGO")
+    st.image('https://cdn-icons-png.freepik.com/256/3101/3101045.png?ga=GA1.1.1507691374.1717099387', width = 50)
+
+st.write("# To get started fill in the following information")
+
+st.write('')
+
+NGO_name = st.text_input("NGO name")
+
+Website_link = st.text_input("Website link:")
+
+Contact_email = st.text_input("Head contact email:")
+
+if st.button("Submit"):
+    if NGO_name and Website_link and Contact_email:
+        
+        api_url = "http://127.0.0.1:8501/n/NGOadd"
+        data = {
+            "name": NGO_name,
+            "website": Website_link,
+            "email": Contact_email
+        }
+        
+        try:
+            response = requests.post(api_url, json=data)
+            
+            if response.status_code == 201:
+                st.success("Data successfully inserted!")
+            else:
+                try:
+                    error_message = response.json().get('error', 'No error message provided')
+                except json.JSONDecodeError:
+                    error_message = response.text  # Raw response if not JSON
+                
+                st.error(f"Failed to insert data. Status code: {response.status_code}, Error: {error_message}")
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+    else:
+        st.error("Please fill in all the fields before submitting.")
+
+
+
+# Multiselect tags option
+options = ["Transport", "Flights", "Energy", "Heat"]
+
+selected_tags = st.multiselect("Select your associated tags", options)
+
+st.write("Selected tags:", selected_tags)
+
+if st.button('Proceed to tools'):
+    st.switch_page('pages/22_NGOTools.py')
diff --git a/app/src/pages/21_NGOSurvey.py b/app/src/pages/22_NGOTools.py
similarity index 86%
rename from app/src/pages/21_NGOSurvey.py
rename to app/src/pages/22_NGOTools.py
index 72b6230..d94b6b3 100644
--- a/app/src/pages/21_NGOSurvey.py
+++ b/app/src/pages/22_NGOTools.py
@@ -4,4 +4,4 @@
 # Show appropriate sidebar links for the role of the currently logged in user
 SideBarLinks()
 
-st.write("# NGO survey")
\ No newline at end of file
+st.write("# NGO tools")
\ No newline at end of file
diff --git a/app/src/pages/31_apiPage.py b/app/src/pages/31_apiPage.py
index 944a09c..92f7986 100644
--- a/app/src/pages/31_apiPage.py
+++ b/app/src/pages/31_apiPage.py
@@ -19,4 +19,5 @@
   st.write("**Important**: Could not connect to sample api, so using dummy data.")
   data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
 
-st.dataframe(data)
\ No newline at end of file
+st.dataframe(data)
+
diff --git a/database/00_carbon.sql b/database/00_carbon.sql
index d8691e6..0b1428f 100644
--- a/database/00_carbon.sql
+++ b/database/00_carbon.sql
@@ -13,8 +13,7 @@ CREATE TABLE IF NOT EXISTS Country (
 
 DROP TABLE IF EXISTS NGO;
 CREATE TABLE IF NOT EXISTS NGO (
-   id INT PRIMARY KEY,
-   logo VARCHAR(255) UNIQUE NOT NULL,
+   id INT PRIMARY KEY AUTO_INCREMENT,
    website VARCHAR(255) UNIQUE NOT NULL,
    name VARCHAR(50),
    contact VARCHAR(50)
@@ -37,7 +36,7 @@ CREATE TABLE IF NOT EXISTS Enterprises (
    id INT PRIMARY KEY,
    name VARCHAR(50),
    type VARCHAR(255),
-   emission_result VARCHAR(255),
+   emission_result INT,
    misc_emissions VARCHAR(255),
    country_id INT,
    FOREIGN KEY (country_id) REFERENCES Country(id)
diff --git a/database/01_carbon-data.sql b/database/01_carbon-data.sql
index 6a84fe7..ba7c359 100644
--- a/database/01_carbon-data.sql
+++ b/database/01_carbon-data.sql
@@ -5,14 +5,16 @@ INSERT INTO Country (id, emissions, name) VALUES (2, '15.2 million metric tons',
 INSERT INTO Country (id, emissions, name) VALUES (3, '20.7 million metric tons', 'CountryC');
 
 INSERT INTO EmissionTags (id, description) VALUES
-(1, 'Tag Description One'),
-(2, 'Tag Description Two'),
-(3, 'Tag Description Three');
+(1, 'Transport'),
+(2, 'Flights'),
+(3, 'Energy'),
+(4, 'Heat');
 
 INSERT INTO Enterprises (id, name, type, emission_result, misc_emissions, country_id) VALUES
-(1, 'Enterprise One', 'Type A', 'Result A', 'Misc A', 1),
-(2, 'Enterprise Two', 'Type B', 'Result B', 'Misc B', 2),
-(3, 'Enterprise Three', 'Type C', 'Result C', 'Misc C', 3);
+(1, 'Enterprise One', 'Type A', 10, 'Misc A', 1),
+(2, 'Enterprise Two', 'Type B', 20, 'Misc B', 2),
+(3, 'Enterprise Three', 'Type C', 30, 'Misc C', 3),
+(4, 'Enterprise Four', 'Type D', 40, 'Misc D', 3);
 
 INSERT INTO EntTags (enterprise_id, tag_id) VALUES
 (1, 1),
@@ -20,12 +22,14 @@ INSERT INTO EntTags (enterprise_id, tag_id) VALUES
 (2, 1),
 (2, 3),
 (3, 2),
-(3, 3);
+(3, 3),
+(4, 2),
+(4, 1);
 
-INSERT INTO NGO (id, logo, website, name, contact) VALUES
-(1, 'logo1.png', 'https://www.ngo1.org', 'NGO One', 'contact1@ngo1.org'),
-(2, 'logo2.png', 'https://www.ngo2.org', 'NGO Two', 'contact2@ngo2.org'),
-(3, 'logo3.png', 'https://www.ngo3.org', 'NGO Three', 'contact3@ngo3.org');
+INSERT INTO NGO (id, website, name, contact) VALUES
+(1, 'logo1.png', 'NGO One', 'contact1@ngo1.org'),
+(2, 'logo2.png', 'NGO Two', 'contact2@ngo2.org'),
+(3, 'logo3.png', 'NGO Three', 'contact3@ngo3.org');
 
 INSERT INTO NGOTags (ngo_id, tag_id) VALUES
 (1, 1),
@@ -33,4 +37,5 @@ INSERT INTO NGOTags (ngo_id, tag_id) VALUES
 (2, 1),
 (2, 3),
 (3, 2),
-(3, 3);
\ No newline at end of file
+(3, 3);
+

From 5932c4a4b24fd2552f731e9a6e6db0e1be88d3ca Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Mon, 3 Jun 2024 23:04:20 +0200
Subject: [PATCH 05/34] added working route for ngo information page with POST
 request in NGO table and NGO tags

added two get api calls for debugging purposes on the ngo information page, can delete for deployment
---
 api/backend/ngo/ngo_routes.py | 74 ++++++++++++++++++++++++++++++++++-
 app/src/pages/21_NGOInfo.py   | 44 +++++++++++++++------
 2 files changed, 105 insertions(+), 13 deletions(-)

diff --git a/api/backend/ngo/ngo_routes.py b/api/backend/ngo/ngo_routes.py
index c870336..47636c0 100644
--- a/api/backend/ngo/ngo_routes.py
+++ b/api/backend/ngo/ngo_routes.py
@@ -6,8 +6,10 @@
 
 @ngo.route('/NGOadd', methods=['POST'])
 def add_new_NGO():
+    current_app.logger.info('ngo_routes.py: POST /NGOadd')
     
     recieved_data = request.json
+    current_app.logger.info(recieved_data)
 
     name = recieved_data['name']
     website = recieved_data['website']
@@ -16,9 +18,79 @@ def add_new_NGO():
     query = 'INSERT INTO NGO (website, name, contact) VALUES (%s, %s, %s)'
 
     data = (name, website, email)
+    current_app.logger.info(query)
+    current_app.logger.info(data)
     cursor = db.get_db().cursor()
     cursor.execute(query, data)
     db.get_db().commit()
 
     new_id = cursor.lastrowid
-    return jsonify({'message': 'Success!', 'id': new_id}), 201
+    
+    selected_tags = recieved_data.get('tags', []) 
+    if selected_tags:
+        insert_query = 'INSERT INTO NGOTags (ngo_id, tag_id) VALUES (%s, %s)'
+        # for each tag gets the tag_id from the emission tags table, :)
+        # also inserts into NGO tags
+        for tag_name in selected_tags:
+            tag_query = 'SELECT id FROM EmissionTags WHERE description = %s'
+            cursor.execute(tag_query, (tag_name,))
+            tag_row = cursor.fetchone()
+            tag_id = tag_row[0]
+            cursor.execute(insert_query, (new_id, tag_id))
+            db.get_db().commit()
+
+    return 'Success'
+
+
+
+
+
+@ngo.route('/ngotags', methods=['GET'])
+def get_ngotags():
+    # get a cursor object from the database
+    cursor = db.get_db().cursor()
+
+    # use cursor to query the database for a list of products
+    cursor.execute('SELECT * FROM NGOTags')
+
+    # grab the column headers from the returned data
+    column_headers = [x[0] for x in cursor.description]
+
+    # create an empty dictionary object to use in 
+    # putting column headers together with data
+    json_data = []
+
+    # fetch all the data from the cursor
+    theData = cursor.fetchall()
+
+    # for each of the rows, zip the data elements together with
+    # the column headers. 
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+@ngo.route('/tags', methods=['GET'])
+def get_ngodata():
+    # get a cursor object from the database
+    cursor = db.get_db().cursor()
+
+    # use cursor to query the database for a list of products
+    cursor.execute('SELECT * FROM EmissionTags')
+
+    # grab the column headers from the returned data
+    column_headers = [x[0] for x in cursor.description]
+
+    # create an empty dictionary object to use in 
+    # putting column headers together with data
+    json_data = []
+
+    # fetch all the data from the cursor
+    theData = cursor.fetchall()
+
+    # for each of the rows, zip the data elements together with
+    # the column headers. 
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
\ No newline at end of file
diff --git a/app/src/pages/21_NGOInfo.py b/app/src/pages/21_NGOInfo.py
index 475e29a..b9b3e27 100644
--- a/app/src/pages/21_NGOInfo.py
+++ b/app/src/pages/21_NGOInfo.py
@@ -23,20 +23,48 @@
 
 Contact_email = st.text_input("Head contact email:")
 
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/n/ngotags').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/n/tags').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
+
+# Multiselect tags option
+options = ["Transport", "Flights", "Energy", "Heat"]
+
+selected_tags = st.multiselect("Select your associated tags", options)
+
+st.write("Selected tags:", selected_tags)
+
+
 if st.button("Submit"):
     if NGO_name and Website_link and Contact_email:
         
-        api_url = "http://127.0.0.1:8501/n/NGOadd"
+        api_url = "http://api:4000/n/NGOadd"
         data = {
             "name": NGO_name,
             "website": Website_link,
-            "email": Contact_email
+            "email": Contact_email,
+            "tags": selected_tags 
         }
-        
+                
         try:
             response = requests.post(api_url, json=data)
             
-            if response.status_code == 201:
+            if response.status_code == 201 or response.status_code == 200:
                 st.success("Data successfully inserted!")
             else:
                 try:
@@ -51,13 +79,5 @@
         st.error("Please fill in all the fields before submitting.")
 
 
-
-# Multiselect tags option
-options = ["Transport", "Flights", "Energy", "Heat"]
-
-selected_tags = st.multiselect("Select your associated tags", options)
-
-st.write("Selected tags:", selected_tags)
-
 if st.button('Proceed to tools'):
     st.switch_page('pages/22_NGOTools.py')

From b561677a76286729ebcc3bffd9ca4b4e15056095 Mon Sep 17 00:00:00 2001
From: aahil n <nishad.a@northeastern.edu>
Date: Tue, 4 Jun 2024 19:35:00 +0200
Subject: [PATCH 06/34] Added NGOMatch and seeing why it aint working

---
 api/backend/enterprises/enterprises_routes.py | 39 ++++++--------
 api/backend/ngo/ngo_routes.py                 | 54 ++++++++++++++++---
 app/src/modules/nav.py                        |  6 ++-
 app/src/pages/23_NGOMatch.py                  | 33 ++++++++++++
 database/00_carbon.sql                        |  7 +--
 database/01_carbon-data.sql                   | 12 +++--
 6 files changed, 111 insertions(+), 40 deletions(-)
 create mode 100644 app/src/pages/23_NGOMatch.py

diff --git a/api/backend/enterprises/enterprises_routes.py b/api/backend/enterprises/enterprises_routes.py
index 0b8f1bb..1ccab0f 100644
--- a/api/backend/enterprises/enterprises_routes.py
+++ b/api/backend/enterprises/enterprises_routes.py
@@ -22,12 +22,7 @@ def get_tags():
     WHERE EmissionTags.id IN (
         SELECT tag_id 
         FROM EntTags 
-        WHERE EntTags.enterprise_id = (
-            SELECT id 
-            FROM Enterprises 
-            ORDER BY id DESC 
-            LIMIT 1
-        )
+        WHERE EntTags.enterprise_id = 1
     );
 ''')
     
@@ -63,12 +58,7 @@ def get_matches():
     WHERE EmissionTags.id IN (
         SELECT tag_id
         FROM EntTags
-        WHERE EntTags.enterprise_id = (
-            SELECT id
-            FROM Enterprises
-            ORDER BY id DESC
-            LIMIT 1
-        )
+        WHERE EntTags.enterprise_id = 1
     );
 ''')
     
@@ -97,17 +87,20 @@ def get_comparison():
     cursor = db.get_db().cursor()
 
     cursor.execute('''
-    SELECT AVG(Enterprises.emission_result) AS avg_enterprise_emissions, Country.name AS your_country, (SELECT e2.emission_result
-        FROM Enterprises e2
-        ORDER BY e2.id DESC
-        LIMIT 1) AS your_enterprise_emissions
-    FROM Enterprises
-    JOIN Country ON Enterprises.country_id = Country.id
-    WHERE Country.name = (SELECT Country.name FROM Enterprises
-    JOIN Country ON Enterprises.country_id = Country.id
-    ORDER BY Enterprises.id DESC
-    LIMIT 1)
-    GROUP BY Country.name;
+        SELECT AVG(Enterprises.emission_result) AS 'Average Emission (by Country)',
+               Country.name                     AS 'Country',
+               (SELECT e2.emission_result
+                FROM Enterprises e2
+                WHERE e2.id = 1)                AS 'Your Emissions'
+        FROM Enterprises
+                 JOIN Country ON Enterprises.country_id = Country.id
+        WHERE Country.name =
+              (SELECT Country.name
+               FROM Enterprises
+                        JOIN Country ON Enterprises.country_id = Country.id
+               WHERE Enterprises.id = 1
+               LIMIT 1)
+        GROUP BY Country.name;
     ''')
     
     # grab the column headers from the returned data
diff --git a/api/backend/ngo/ngo_routes.py b/api/backend/ngo/ngo_routes.py
index 47636c0..2ceb525 100644
--- a/api/backend/ngo/ngo_routes.py
+++ b/api/backend/ngo/ngo_routes.py
@@ -3,6 +3,7 @@
 from backend.db_connection import db
 
 ngo = Blueprint('ngo', __name__)
+current_id = 1
 
 @ngo.route('/NGOadd', methods=['POST'])
 def add_new_NGO():
@@ -42,9 +43,6 @@ def add_new_NGO():
     return 'Success'
 
 
-
-
-
 @ngo.route('/ngotags', methods=['GET'])
 def get_ngotags():
     # get a cursor object from the database
@@ -71,13 +69,55 @@ def get_ngotags():
     return jsonify(json_data)
 
 @ngo.route('/tags', methods=['GET'])
-def get_ngodata():
+def get_tags():
     # get a cursor object from the database
     cursor = db.get_db().cursor()
+    cursor.execute('''
+        SELECT description 
+        FROM EmissionTags 
+        WHERE EmissionTags.id IN (
+            SELECT tag_id 
+            FROM NGOTags 
+            WHERE NGOTags.ngo_id = 1
+        );
+    ''')
+    
+    # grab the column headers from the returned data
+    column_headers = [x[0] for x in cursor.description]
 
-    # use cursor to query the database for a list of products
-    cursor.execute('SELECT * FROM EmissionTags')
+    # create an empty dictionary object to use in 
+    # putting column headers together with data
+    json_data = []
+
+    # fetch all the data from the cursor
+    theData = cursor.fetchall()
 
+    # for each of the rows, zip the data elements together with
+    # the column headers. 
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+
+# get all of the matching Enterprises based on tags
+@ngo.route('/EnterpriseMatch', methods=['GET'])
+def get_matches():
+    # get a cursor object from the database
+    cursor = db.get_db().cursor()
+
+    cursor.execute('''
+    SELECT Enterprises.name, EmissionTags.description
+    FROM Enterprises
+    JOIN EntTags ON Enterprises.id = EntTags.enterprise_id
+    JOIN EmissionTags ON EntTags.tag_id = EmissionTags.id
+    WHERE EmissionTags.id IN (
+        SELECT tag_id
+        FROM EntTags
+        WHERE EntTags.enterprise_id = 1
+    );
+''')
+    
     # grab the column headers from the returned data
     column_headers = [x[0] for x in cursor.description]
 
@@ -93,4 +133,4 @@ def get_ngodata():
     for row in theData:
         json_data.append(dict(zip(column_headers, row)))
 
-    return jsonify(json_data)
\ No newline at end of file
+    return jsonify(json_data)
diff --git a/app/src/modules/nav.py b/app/src/modules/nav.py
index 1754b00..0bb3a50 100644
--- a/app/src/modules/nav.py
+++ b/app/src/modules/nav.py
@@ -26,7 +26,7 @@ def EnterpriseHomeNav():
     st.sidebar.page_link("pages/10_enterprisehome.py", label="Enterprise Home")
 
 def EnterpriseMatchNav():
-    st.sidebar.page_link("pages/12_enterpriseMatch.py", label="Enterprise Match")
+    st.sidebar.page_link("pages/12_enterpriseMatch.py", label="NGO Match")
 
 def EnterpriseSurveyNav():
     st.sidebar.page_link("pages/11_enterpriseSurvey.py", label="Enterprise Survey")
@@ -45,6 +45,9 @@ def NGOInfoNav():
 def NGOToolsNav():
     st.sidebar.page_link("pages/22_NGOTools.py", label="NGO Tools")
 
+def NGOMatchNav():
+    st.sidebar.page_link("pages/23_NGOMatch.py", label="Enterprise Match")    
+
 # --------------------------------Links Function -----------------------------------------------
 def SideBarLinks(show_home=False):
     """
@@ -81,6 +84,7 @@ def SideBarLinks(show_home=False):
             NGOHomeNav()
             NGOInfoNav()
             NGOToolsNav()
+            NGOMatchNav()
 
     
     # Always show the About page at the bottom of the list of links
diff --git a/app/src/pages/23_NGOMatch.py b/app/src/pages/23_NGOMatch.py
new file mode 100644
index 0000000..2852a69
--- /dev/null
+++ b/app/src/pages/23_NGOMatch.py
@@ -0,0 +1,33 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+import requests
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+st.write("# NGO suggestions")
+
+st.write('')
+st.write('')
+
+st.write("## Your emission tags are: ")
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/n/tags').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
+
+st.write("## NGOs with these tags are: ")
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/n/EnterpriseMatch').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
\ No newline at end of file
diff --git a/database/00_carbon.sql b/database/00_carbon.sql
index 0b1428f..1183f54 100644
--- a/database/00_carbon.sql
+++ b/database/00_carbon.sql
@@ -90,9 +90,8 @@ CREATE TABLE IF NOT EXISTS Flight (
    id INT PRIMARY KEY,
    user_id INT,
    enterprise_id INT,
-   Date_taken DATETIME,
-   origin VARCHAR(250),
-   destination VARCHAR(250),
+   date_taken DATETIME,
+   distance INT,
    emission_tags VARCHAR(50),
    aircraft_type VARCHAR(250),
    FOREIGN KEY (user_id) REFERENCES User(id)
@@ -216,8 +215,6 @@ CREATE TABLE IF NOT EXISTS UserTags (
        ON DELETE RESTRICT
 );
 
-
-
 DROP TABLE IF EXISTS NGOTags;
 CREATE TABLE IF NOT EXISTS NGOTags (
    ngo_id INT,
diff --git a/database/01_carbon-data.sql b/database/01_carbon-data.sql
index ba7c359..000379a 100644
--- a/database/01_carbon-data.sql
+++ b/database/01_carbon-data.sql
@@ -1,8 +1,12 @@
-USE CarbonConnect;
+INSERT INTO Country (id, emissions, name) VALUES (1, '5.8 million metric tons', 'Austria');
+INSERT INTO Country (id, emissions, name) VALUES (2, '15.2 million metric tons', 'Belgium');
+INSERT INTO Country (id, emissions, name) VALUES (3, '20.7 million metric tons', 'Bulgaria');
+INSERT INTO Country (id, emissions, name) VALUES (4, '7.3 million metric tons', 'Croatia');
+INSERT INTO Country (id, emissions, name) VALUES (5, '12.1 million metric tons', 'Cyprus');
+INSERT INTO Country (id, emissions, name) VALUES (6, '18.5 million metric tons', 'Czechia');
+INSERT INTO Country (id, emissions, name) VALUES (7, '9.8 million metric tons', 'Denmark');
+INSERT INTO Country (id, emissions, name) VALUES (8, '14.3 million metric tons', 'Estonia');
 
-INSERT INTO Country (id, emissions, name) VALUES (1, '5.8 million metric tons', 'CountryA');
-INSERT INTO Country (id, emissions, name) VALUES (2, '15.2 million metric tons', 'CountryB');
-INSERT INTO Country (id, emissions, name) VALUES (3, '20.7 million metric tons', 'CountryC');
 
 INSERT INTO EmissionTags (id, description) VALUES
 (1, 'Transport'),

From 311f40cb66ab588a9913525bea19dad45c28e96a Mon Sep 17 00:00:00 2001
From: aahil n <nishad.a@northeastern.edu>
Date: Tue, 4 Jun 2024 19:41:01 +0200
Subject: [PATCH 07/34] Change name my b

---
 app/src/pages/23_NGOMatch.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/app/src/pages/23_NGOMatch.py b/app/src/pages/23_NGOMatch.py
index 2852a69..0167d00 100644
--- a/app/src/pages/23_NGOMatch.py
+++ b/app/src/pages/23_NGOMatch.py
@@ -13,15 +13,12 @@
 st.write("## Your emission tags are: ")
 
 data = {} 
-try:
-  data = requests.get('http://api:4000/n/tags').json()
-except:
-  st.write("**Important**: Could not connect to sample api, so using dummy data.")
-  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+data = requests.get('http://api:4000/n/tags').json()
+
 
 st.dataframe(data)
 
-st.write("## NGOs with these tags are: ")
+st.write("## Enterprises with these tags are: ")
 
 data = {} 
 try:

From 68ba99a23597c509e7b146f564ba842823ded37d Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Tue, 4 Jun 2024 22:17:43 +0200
Subject: [PATCH 08/34] added history pages to display survey history for user
 and enterprise

---
 api/backend/enterprises/enterprises_routes.py | 55 +++++++++++++
 api/backend/user/user_routes.py               | 77 +++++++++++++++++++
 app/src/modules/nav.py                        |  8 ++
 app/src/pages/03_userHistory.py               | 44 +++++++++++
 app/src/pages/14_enterpriseHistory.py         | 35 +++++++++
 app/src/pages/23_NGOMatch.py                  |  6 +-
 database/01_carbon-data.sql                   |  2 +
 7 files changed, 226 insertions(+), 1 deletion(-)
 create mode 100644 api/backend/user/user_routes.py
 create mode 100644 app/src/pages/03_userHistory.py
 create mode 100644 app/src/pages/14_enterpriseHistory.py

diff --git a/api/backend/enterprises/enterprises_routes.py b/api/backend/enterprises/enterprises_routes.py
index 1ccab0f..ce1b458 100644
--- a/api/backend/enterprises/enterprises_routes.py
+++ b/api/backend/enterprises/enterprises_routes.py
@@ -115,6 +115,61 @@ def get_comparison():
 
     # for each of the rows, zip the data elements together with
     # the column headers. 
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+
+# Get all the supply chain history for this enterprise
+@enterprises.route('/EntSupplyChain', methods=['GET'])
+def get_supplychain():
+    cursor = db.get_db().cursor()
+
+    cursor.execute('SELECT * FROM SupplyChain WHERE SupplyChain.enterprise_id = 1')
+
+    column_headers = [x[0] for x in cursor.description]
+
+    json_data = []
+
+    theData = cursor.fetchall()
+
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+# Get all the operating cost history for this enterprise
+@enterprises.route('/EntCosts', methods=['GET'])
+def get_costs():
+    cursor = db.get_db().cursor()
+
+    cursor.execute('SELECT * FROM operatingEmission WHERE operatingEmission.enterprise_id = 1')
+
+    column_headers = [x[0] for x in cursor.description]
+
+    json_data = []
+
+    theData = cursor.fetchall()
+
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+# Get all the flights history for this enterprise
+@enterprises.route('/EntFlights', methods=['GET'])
+def get_flights():
+    cursor = db.get_db().cursor()
+
+    cursor.execute('SELECT * FROM Flight WHERE Flight.enterprise_id = 1')
+
+    column_headers = [x[0] for x in cursor.description]
+
+    json_data = []
+
+    theData = cursor.fetchall()
+
     for row in theData:
         json_data.append(dict(zip(column_headers, row)))
 
diff --git a/api/backend/user/user_routes.py b/api/backend/user/user_routes.py
new file mode 100644
index 0000000..f126c0e
--- /dev/null
+++ b/api/backend/user/user_routes.py
@@ -0,0 +1,77 @@
+from flask import Blueprint, request, jsonify, make_response, current_app
+import json
+from backend.db_connection import db
+
+user = Blueprint('user', __name__)
+
+# Get all the cars history for this user
+@user.route('/UserCars', methods=['GET'])
+def get_cars():
+    cursor = db.get_db().cursor()
+
+    cursor.execute('SELECT * FROM Cars WHERE Cars.user_id = 1')
+
+    column_headers = [x[0] for x in cursor.description]
+
+    json_data = []
+
+    theData = cursor.fetchall()
+
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+# Get all the residential history for this user
+@user.route('/UserResidential', methods=['GET'])
+def get_residential():
+    cursor = db.get_db().cursor()
+
+    cursor.execute('SELECT * FROM ResData WHERE ResData.user_id = 1')
+
+    column_headers = [x[0] for x in cursor.description]
+
+    json_data = []
+
+    theData = cursor.fetchall()
+
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+# Get all the flight history for this user
+@user.route('/UserFights', methods=['GET'])
+def get_flights():
+    cursor = db.get_db().cursor()
+
+    cursor.execute('SELECT * FROM Flight WHERE Flight.user_id = 1')
+
+    column_headers = [x[0] for x in cursor.description]
+
+    json_data = []
+
+    theData = cursor.fetchall()
+
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+# Get all the public transport history for this user
+@user.route('/UserTransport', methods=['GET'])
+def get_transport():
+    cursor = db.get_db().cursor()
+
+    cursor.execute('SELECT * FROM PublicTransport WHERE PublicTransport.user_id = 1')
+
+    column_headers = [x[0] for x in cursor.description]
+
+    json_data = []
+
+    theData = cursor.fetchall()
+
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
\ No newline at end of file
diff --git a/app/src/modules/nav.py b/app/src/modules/nav.py
index 0bb3a50..b426551 100644
--- a/app/src/modules/nav.py
+++ b/app/src/modules/nav.py
@@ -20,6 +20,9 @@ def UserHomeNav():
 def UserSurveyNav():
     st.sidebar.page_link("pages/02_userSurvey.py", label="General User Survey")
 
+def UserHistoryNav():
+    st.sidebar.page_link("pages/03_userHistory.py", label="General User Survey History")
+
 # enterprise role
 
 def EnterpriseHomeNav():
@@ -34,6 +37,9 @@ def EnterpriseSurveyNav():
 def EnterpriseCompareNav():
     st.sidebar.page_link("pages/13_enterpriseCompare.py", label="Enterprise Compare")
 
+def EnterpriseHistoryNav():
+    st.sidebar.page_link("pages/14_enterpriseHistory.py", label="Enterprise Survey History")
+
 # NGO role
 
 def NGOHomeNav():
@@ -73,12 +79,14 @@ def SideBarLinks(show_home=False):
         if st.session_state['role'] == 'citizen':
             UserHomeNav()
             UserSurveyNav()
+            UserHistoryNav()
 
         if st.session_state['role'] == 'enterprise':
             EnterpriseHomeNav()
             EnterpriseMatchNav()
             EnterpriseSurveyNav()
             EnterpriseCompareNav()
+            EnterpriseHistoryNav()
         
         if st.session_state['role'] == 'NGO':
             NGOHomeNav()
diff --git a/app/src/pages/03_userHistory.py b/app/src/pages/03_userHistory.py
new file mode 100644
index 0000000..390500f
--- /dev/null
+++ b/app/src/pages/03_userHistory.py
@@ -0,0 +1,44 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+import requests
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+st.write("# User history")
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/u/userhistory').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/u/userhistory').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/u/userhistory').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/u/userhistory').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
\ No newline at end of file
diff --git a/app/src/pages/14_enterpriseHistory.py b/app/src/pages/14_enterpriseHistory.py
new file mode 100644
index 0000000..d1d2eeb
--- /dev/null
+++ b/app/src/pages/14_enterpriseHistory.py
@@ -0,0 +1,35 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+import requests
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+st.write("# Enterprise history")
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/e/EntSupplyChain').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/e/EntCosts').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/e/EntFlights').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+st.dataframe(data)
\ No newline at end of file
diff --git a/app/src/pages/23_NGOMatch.py b/app/src/pages/23_NGOMatch.py
index 0167d00..f0b5ac8 100644
--- a/app/src/pages/23_NGOMatch.py
+++ b/app/src/pages/23_NGOMatch.py
@@ -13,7 +13,11 @@
 st.write("## Your emission tags are: ")
 
 data = {} 
-data = requests.get('http://api:4000/n/tags').json()
+try:
+  data = requests.get('http://api:4000/n/tags').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
 
 
 st.dataframe(data)
diff --git a/database/01_carbon-data.sql b/database/01_carbon-data.sql
index 000379a..fe91ca5 100644
--- a/database/01_carbon-data.sql
+++ b/database/01_carbon-data.sql
@@ -1,3 +1,5 @@
+USE CarbonConnect;
+
 INSERT INTO Country (id, emissions, name) VALUES (1, '5.8 million metric tons', 'Austria');
 INSERT INTO Country (id, emissions, name) VALUES (2, '15.2 million metric tons', 'Belgium');
 INSERT INTO Country (id, emissions, name) VALUES (3, '20.7 million metric tons', 'Bulgaria');

From cbf80d7d0c90b6763e9198676bb476ff3268e982 Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Tue, 4 Jun 2024 22:44:50 +0200
Subject: [PATCH 09/34] Delete unnecessary routes and fixing typo

---
 api/backend/ngo/ngo_routes.py   | 27 +--------------------------
 api/backend/user/user_routes.py |  2 +-
 app/src/pages/21_NGOInfo.py     | 19 -------------------
 3 files changed, 2 insertions(+), 46 deletions(-)

diff --git a/api/backend/ngo/ngo_routes.py b/api/backend/ngo/ngo_routes.py
index 2ceb525..95711de 100644
--- a/api/backend/ngo/ngo_routes.py
+++ b/api/backend/ngo/ngo_routes.py
@@ -5,6 +5,7 @@
 ngo = Blueprint('ngo', __name__)
 current_id = 1
 
+# adds an ngo to the NGO table given filled out data
 @ngo.route('/NGOadd', methods=['POST'])
 def add_new_NGO():
     current_app.logger.info('ngo_routes.py: POST /NGOadd')
@@ -42,32 +43,6 @@ def add_new_NGO():
 
     return 'Success'
 
-
-@ngo.route('/ngotags', methods=['GET'])
-def get_ngotags():
-    # get a cursor object from the database
-    cursor = db.get_db().cursor()
-
-    # use cursor to query the database for a list of products
-    cursor.execute('SELECT * FROM NGOTags')
-
-    # grab the column headers from the returned data
-    column_headers = [x[0] for x in cursor.description]
-
-    # create an empty dictionary object to use in 
-    # putting column headers together with data
-    json_data = []
-
-    # fetch all the data from the cursor
-    theData = cursor.fetchall()
-
-    # for each of the rows, zip the data elements together with
-    # the column headers. 
-    for row in theData:
-        json_data.append(dict(zip(column_headers, row)))
-
-    return jsonify(json_data)
-
 @ngo.route('/tags', methods=['GET'])
 def get_tags():
     # get a cursor object from the database
diff --git a/api/backend/user/user_routes.py b/api/backend/user/user_routes.py
index f126c0e..f3c2905 100644
--- a/api/backend/user/user_routes.py
+++ b/api/backend/user/user_routes.py
@@ -41,7 +41,7 @@ def get_residential():
     return jsonify(json_data)
 
 # Get all the flight history for this user
-@user.route('/UserFights', methods=['GET'])
+@user.route('/UserFlights', methods=['GET'])
 def get_flights():
     cursor = db.get_db().cursor()
 
diff --git a/app/src/pages/21_NGOInfo.py b/app/src/pages/21_NGOInfo.py
index b9b3e27..7ca8f8b 100644
--- a/app/src/pages/21_NGOInfo.py
+++ b/app/src/pages/21_NGOInfo.py
@@ -23,25 +23,6 @@
 
 Contact_email = st.text_input("Head contact email:")
 
-
-data = {} 
-try:
-  data = requests.get('http://api:4000/n/ngotags').json()
-except:
-  st.write("**Important**: Could not connect to sample api, so using dummy data.")
-  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
-
-st.dataframe(data)
-
-data = {} 
-try:
-  data = requests.get('http://api:4000/n/tags').json()
-except:
-  st.write("**Important**: Could not connect to sample api, so using dummy data.")
-  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
-
-st.dataframe(data)
-
 # Multiselect tags option
 options = ["Transport", "Flights", "Energy", "Heat"]
 

From df590cc508e18dc0770fd56e9140eb13634745f7 Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Wed, 5 Jun 2024 17:28:00 +0200
Subject: [PATCH 10/34] Changed ngo info survey to PUT from POST

---
 api/backend/ngo/ngo_routes.py | 52 +++++++++++++++++++++++------------
 app/src/pages/21_NGOInfo.py   | 27 ++++++++++++------
 2 files changed, 53 insertions(+), 26 deletions(-)

diff --git a/api/backend/ngo/ngo_routes.py b/api/backend/ngo/ngo_routes.py
index 95711de..b7ad121 100644
--- a/api/backend/ngo/ngo_routes.py
+++ b/api/backend/ngo/ngo_routes.py
@@ -5,8 +5,26 @@
 ngo = Blueprint('ngo', __name__)
 current_id = 1
 
-# adds an ngo to the NGO table given filled out data
-@ngo.route('/NGOadd', methods=['POST'])
+# Gets my ngo data
+@ngo.route('/ngomine', methods=['GET'])
+def get_mine():
+    cursor = db.get_db().cursor()
+
+    cursor.execute('SELECT * FROM NGO WHERE id = 1')
+
+    column_headers = [x[0] for x in cursor.description]
+
+    json_data = []
+
+    theData = cursor.fetchall()
+
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
+
+# updates an ngo to the NGO table given filled out data
+@ngo.route('/NGOupdate', methods=['PUT'])
 def add_new_NGO():
     current_app.logger.info('ngo_routes.py: POST /NGOadd')
     
@@ -17,29 +35,27 @@ def add_new_NGO():
     website = recieved_data['website']
     email = recieved_data['email']
 
-    query = 'INSERT INTO NGO (website, name, contact) VALUES (%s, %s, %s)'
+    query = 'UPDATE NGO SET website = %s, name = %s, contact = %s WHERE id = 1'
 
     data = (name, website, email)
-    current_app.logger.info(query)
-    current_app.logger.info(data)
     cursor = db.get_db().cursor()
     cursor.execute(query, data)
     db.get_db().commit()
 
-    new_id = cursor.lastrowid
+    # new_id = cursor.lastrowid
     
-    selected_tags = recieved_data.get('tags', []) 
-    if selected_tags:
-        insert_query = 'INSERT INTO NGOTags (ngo_id, tag_id) VALUES (%s, %s)'
-        # for each tag gets the tag_id from the emission tags table, :)
-        # also inserts into NGO tags
-        for tag_name in selected_tags:
-            tag_query = 'SELECT id FROM EmissionTags WHERE description = %s'
-            cursor.execute(tag_query, (tag_name,))
-            tag_row = cursor.fetchone()
-            tag_id = tag_row[0]
-            cursor.execute(insert_query, (new_id, tag_id))
-            db.get_db().commit()
+    # selected_tags = recieved_data.get('tags', []) 
+    # if selected_tags:
+    #     insert_query = 'INSERT INTO NGOTags (ngo_id, tag_id) VALUES (%s, %s)'
+    #     # for each tag gets the tag_id from the emission tags table, :)
+    #     # also inserts into NGO tags
+    #     for tag_name in selected_tags:
+    #         tag_query = 'SELECT id FROM EmissionTags WHERE description = %s'
+    #         cursor.execute(tag_query, (tag_name,))
+    #         tag_row = cursor.fetchone()
+    #         tag_id = tag_row[0]
+    #         cursor.execute(insert_query, (new_id, tag_id))
+    #         db.get_db().commit()
 
     return 'Success'
 
diff --git a/app/src/pages/21_NGOInfo.py b/app/src/pages/21_NGOInfo.py
index 7ca8f8b..bb291ae 100644
--- a/app/src/pages/21_NGOInfo.py
+++ b/app/src/pages/21_NGOInfo.py
@@ -24,26 +24,24 @@
 Contact_email = st.text_input("Head contact email:")
 
 # Multiselect tags option
-options = ["Transport", "Flights", "Energy", "Heat"]
+# options = ["Transport", "Flights", "Energy", "Heat"]
 
-selected_tags = st.multiselect("Select your associated tags", options)
+# selected_tags = st.multiselect("Select your associated tags", options)
 
-st.write("Selected tags:", selected_tags)
+# st.write("Selected tags:", selected_tags)
 
 
 if st.button("Submit"):
     if NGO_name and Website_link and Contact_email:
         
-        api_url = "http://api:4000/n/NGOadd"
+        api_url = "http://api:4000/n/NGOupdate"
         data = {
             "name": NGO_name,
             "website": Website_link,
-            "email": Contact_email,
-            "tags": selected_tags 
-        }
+            "email": Contact_email        }
                 
         try:
-            response = requests.post(api_url, json=data)
+            response = requests.put(api_url, json=data)
             
             if response.status_code == 201 or response.status_code == 200:
                 st.success("Data successfully inserted!")
@@ -59,6 +57,19 @@
     else:
         st.error("Please fill in all the fields before submitting.")
 
+st.write('## My NGO data')
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/n/ngomine').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+
+st.dataframe(data)
 
 if st.button('Proceed to tools'):
     st.switch_page('pages/22_NGOTools.py')
+
+

From da9d0faab356f2bcd88750f9f2df76ea97e5fb72 Mon Sep 17 00:00:00 2001
From: Babalola-a <147947002+Babalola-a@users.noreply.github.com>
Date: Wed, 5 Jun 2024 17:44:20 +0200
Subject: [PATCH 11/34] adding format of survey

---
 api/requirements.txt           |  1 +
 app/src/pages/02_userSurvey.py | 12 +++++++++++-
 app/src/pages/22_NGOTools.py   | 21 ++++++++++++++++++++-
 3 files changed, 32 insertions(+), 2 deletions(-)

diff --git a/api/requirements.txt b/api/requirements.txt
index 23ea575..5d35f95 100644
--- a/api/requirements.txt
+++ b/api/requirements.txt
@@ -5,3 +5,4 @@ flask-login==0.6.2
 flask-mysql==1.5.2
 cryptography==38.0.1
 python-dotenv==1.0.1
+streamlit-survey
\ No newline at end of file
diff --git a/app/src/pages/02_userSurvey.py b/app/src/pages/02_userSurvey.py
index 4b498c9..0b0b777 100644
--- a/app/src/pages/02_userSurvey.py
+++ b/app/src/pages/02_userSurvey.py
@@ -1,7 +1,17 @@
 import streamlit as st
 from modules.nav import SideBarLinks
+import streamlit_survey as ss
+survey = ss.StreamlitSurvey()
 
 # Show appropriate sidebar links for the role of the currently logged in user
 SideBarLinks()
 
-st.write("# User survey")
\ No newline at end of file
+st.write("# User survey")
+
+survey.text_input("Text input:", id="Q1")
+st.write("## What country in Europe are you from?")
+survey.selectbox("Selection box:", options=["Albania", "Andorra", "Armenia", "Austria", "Azerbaijan"])
+
+
+carNumber = st.slider("How many cars do you currently have?", 0, 130, 25)
+st.write("I have ", carNumber , "cars")
\ No newline at end of file
diff --git a/app/src/pages/22_NGOTools.py b/app/src/pages/22_NGOTools.py
index d94b6b3..5488b85 100644
--- a/app/src/pages/22_NGOTools.py
+++ b/app/src/pages/22_NGOTools.py
@@ -4,4 +4,23 @@
 # Show appropriate sidebar links for the role of the currently logged in user
 SideBarLinks()
 
-st.write("# NGO tools")
\ No newline at end of file
+st.write("# NGO tools") 
+
+# make a page so that NGOs can edit their tags 
+
+st.button("Edit NGO Information")
+if st.button("Edit NGO Information"):
+    @ngo.route('/editinfo', methods=['PUT'])
+    def update_ngo():
+        current_app.logger.info('PUT /ngo route')
+         NGO_name = NGOInfo['name']
+         # current_app.logger.info(cust_info)
+         Website_link = NGOInfo['website']
+        Contact_email = NGOInfo['email']
+        selected_tags = NGOInfo['tags']
+        query = 'UPDATE NGOINFO SET first_name = %s, last_name = %s, company = %s where id = %s'
+        data = (name, website, email, tags)
+        cursor = db.get_db().cursor()
+        r = cursor.execute(query, data)
+        db.get_db().commit()
+        return 'ngo information updated!'

From 5c63a6b46d5349e00541fb57789761b21ab79776 Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Wed, 5 Jun 2024 18:03:17 +0200
Subject: [PATCH 12/34] Updated all persona home pages for consistency, better
 page navigation

---
 app/src/App.py                                |  4 +--
 app/src/modules/nav.py                        | 20 ++++++--------
 app/src/pages/01_userhome.py                  | 12 ++++++---
 app/src/pages/04_userMatch.py                 |  7 +++++
 app/src/pages/10_enterprisehome.py            | 12 ++++++---
 app/src/pages/12_enterpriseMatch.py           | 13 ++++++++++
 app/src/pages/13_enterpriseCompare.py         | 19 --------------
 ...riseHistory.py => 13_enterpriseHistory.py} |  0
 app/src/pages/20_NGOhome.py                   | 11 +++++---
 .../pages/{23_NGOMatch.py => 22_NGOMatch.py}  |  0
 app/src/pages/22_NGOTools.py                  | 26 -------------------
 11 files changed, 56 insertions(+), 68 deletions(-)
 create mode 100644 app/src/pages/04_userMatch.py
 delete mode 100644 app/src/pages/13_enterpriseCompare.py
 rename app/src/pages/{14_enterpriseHistory.py => 13_enterpriseHistory.py} (100%)
 rename app/src/pages/{23_NGOMatch.py => 22_NGOMatch.py} (100%)
 delete mode 100644 app/src/pages/22_NGOTools.py

diff --git a/app/src/App.py b/app/src/App.py
index bd96b15..ca16ff2 100644
--- a/app/src/App.py
+++ b/app/src/App.py
@@ -18,7 +18,7 @@ def App():
     with col1:
         if st.button("Act as Dave, a general user and citizen"):
           st.session_state['authenticated'] = True
-          st.session_state['role'] = 'citizen'
+          st.session_state['role'] = 'General User'
           st.session_state['first_name'] = 'Dave'
           st.switch_page("pages/01_userhome.py")
         st.image('https://cdn-icons-png.freepik.com/256/552/552848.png?ga=GA1.1.1507691374.1717099387', width = 100)
@@ -26,7 +26,7 @@ def App():
     with col2:
         if st.button("Act as Farm to Table, a medium sized business"):
           st.session_state['authenticated'] = True
-          st.session_state['role'] = 'enterprise'
+          st.session_state['role'] = 'Enterprise'
           st.session_state['first_name'] = 'FarmtoTable'
           st.switch_page("pages/10_enterprisehome.py")
         st.image('https://cdn-icons-png.freepik.com/256/834/834504.png?ga=GA1.1.1507691374.1717099387', width = 100)
diff --git a/app/src/modules/nav.py b/app/src/modules/nav.py
index b426551..5950480 100644
--- a/app/src/modules/nav.py
+++ b/app/src/modules/nav.py
@@ -23,6 +23,9 @@ def UserSurveyNav():
 def UserHistoryNav():
     st.sidebar.page_link("pages/03_userHistory.py", label="General User Survey History")
 
+def UserMatchNav():
+    st.sidebar.page_link("pages/04_userMatch.py", label="NGO Suggestions")
+
 # enterprise role
 
 def EnterpriseHomeNav():
@@ -34,11 +37,8 @@ def EnterpriseMatchNav():
 def EnterpriseSurveyNav():
     st.sidebar.page_link("pages/11_enterpriseSurvey.py", label="Enterprise Survey")
 
-def EnterpriseCompareNav():
-    st.sidebar.page_link("pages/13_enterpriseCompare.py", label="Enterprise Compare")
-
 def EnterpriseHistoryNav():
-    st.sidebar.page_link("pages/14_enterpriseHistory.py", label="Enterprise Survey History")
+    st.sidebar.page_link("pages/13_enterpriseHistory.py", label="Enterprise Survey History")
 
 # NGO role
 
@@ -48,11 +48,8 @@ def NGOHomeNav():
 def NGOInfoNav():
     st.sidebar.page_link("pages/21_NGOInfo.py", label="NGO Info")
 
-def NGOToolsNav():
-    st.sidebar.page_link("pages/22_NGOTools.py", label="NGO Tools")
-
 def NGOMatchNav():
-    st.sidebar.page_link("pages/23_NGOMatch.py", label="Enterprise Match")    
+    st.sidebar.page_link("pages/22_NGOMatch.py", label="Enterprise Match")    
 
 # --------------------------------Links Function -----------------------------------------------
 def SideBarLinks(show_home=False):
@@ -76,22 +73,21 @@ def SideBarLinks(show_home=False):
     # Show the other page navigators depending on the users' role.
     if st.session_state["authenticated"]:
 
-        if st.session_state['role'] == 'citizen':
+        if st.session_state['role'] == 'General User':
             UserHomeNav()
             UserSurveyNav()
             UserHistoryNav()
+            UserMatchNav()
 
-        if st.session_state['role'] == 'enterprise':
+        if st.session_state['role'] == 'Enterprise':
             EnterpriseHomeNav()
             EnterpriseMatchNav()
             EnterpriseSurveyNav()
-            EnterpriseCompareNav()
             EnterpriseHistoryNav()
         
         if st.session_state['role'] == 'NGO':
             NGOHomeNav()
             NGOInfoNav()
-            NGOToolsNav()
             NGOMatchNav()
 
     
diff --git a/app/src/pages/01_userhome.py b/app/src/pages/01_userhome.py
index 960ab02..0229803 100644
--- a/app/src/pages/01_userhome.py
+++ b/app/src/pages/01_userhome.py
@@ -8,12 +8,18 @@
 
 with col1:
     st.write("Logged in as:")
-    st.write("General user")
+    st.write(f"{st.session_state['role']} {st.session_state['first_name']}")
     st.image('https://cdn-icons-png.freepik.com/256/552/552848.png?ga=GA1.1.1507691374.1717099387', width = 50)
 
-st.write("# To get started take the survey below")
+st.write("# Navigate to your desired tool")
 st.write('')
 st.write('')
 
-if st.button('Take user general emissions survey'):
+if st.button('Retake user general emissions survey'):
     st.switch_page('pages/02_userSurvey.py')
+st.write('')
+if st.button('See my survey history'):
+    st.switch_page('pages/03_userHistory.py')
+st.write('')
+if st.button('See my NGO recommendations'):
+    st.switch_page('pages/04_userMatch.py')
diff --git a/app/src/pages/04_userMatch.py b/app/src/pages/04_userMatch.py
new file mode 100644
index 0000000..c80f041
--- /dev/null
+++ b/app/src/pages/04_userMatch.py
@@ -0,0 +1,7 @@
+import streamlit as st
+from modules.nav import SideBarLinks
+
+# Show appropriate sidebar links for the role of the currently logged in user
+SideBarLinks()
+
+st.write("# User match")
\ No newline at end of file
diff --git a/app/src/pages/10_enterprisehome.py b/app/src/pages/10_enterprisehome.py
index abc5ea7..74e8667 100644
--- a/app/src/pages/10_enterprisehome.py
+++ b/app/src/pages/10_enterprisehome.py
@@ -8,12 +8,18 @@
 
 with col1:
     st.write("Logged in as:")
-    st.write("Enterprise")
+    st.write(f"{st.session_state['role']} {st.session_state['first_name']}")
     st.image('https://cdn-icons-png.freepik.com/256/834/834504.png?ga=GA1.1.1507691374.1717099387', width = 50)
 
-st.write("# To get started take the survey below")
+st.write("# Navigate to your desired tool")
 st.write('')
 st.write('')
 
-if st.button('Take enterprise general emissions survey'):
+if st.button('Retake the enterprise general emissions survey'):
     st.switch_page('pages/11_enterpriseSurvey.py')
+st.write('')
+if st.button('See my survey history'):
+    st.switch_page('pages/13_enterpriseHistory.py')
+st.write('')
+if st.button('See my tags, matches, and comparison to average country'):
+    st.switch_page('pages/12_enterpriseMatch.py')
diff --git a/app/src/pages/12_enterpriseMatch.py b/app/src/pages/12_enterpriseMatch.py
index 04381ba..1b2eaf6 100644
--- a/app/src/pages/12_enterpriseMatch.py
+++ b/app/src/pages/12_enterpriseMatch.py
@@ -30,4 +30,17 @@
   st.write("**Important**: Could not connect to sample api, so using dummy data.")
   data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
 
+st.dataframe(data)
+
+st.write('## Comparison to average company in your country')
+
+st.write('')
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/e/EntCompare').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
 st.dataframe(data)
\ No newline at end of file
diff --git a/app/src/pages/13_enterpriseCompare.py b/app/src/pages/13_enterpriseCompare.py
deleted file mode 100644
index 234547e..0000000
--- a/app/src/pages/13_enterpriseCompare.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import streamlit as st
-from modules.nav import SideBarLinks
-import requests
-
-# Show appropriate sidebar links for the role of the currently logged in user
-SideBarLinks()
-
-st.write('# Comparison to average company in your country')
-
-st.write('')
-
-data = {} 
-try:
-  data = requests.get('http://api:4000/e/EntCompare').json()
-except:
-  st.write("**Important**: Could not connect to sample api, so using dummy data.")
-  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
-
-st.dataframe(data)
\ No newline at end of file
diff --git a/app/src/pages/14_enterpriseHistory.py b/app/src/pages/13_enterpriseHistory.py
similarity index 100%
rename from app/src/pages/14_enterpriseHistory.py
rename to app/src/pages/13_enterpriseHistory.py
diff --git a/app/src/pages/20_NGOhome.py b/app/src/pages/20_NGOhome.py
index f8fa240..bad0d07 100644
--- a/app/src/pages/20_NGOhome.py
+++ b/app/src/pages/20_NGOhome.py
@@ -8,12 +8,17 @@
 
 with col1:
     st.write("Logged in as:")
-    st.write("NGO")
+    st.write(f"{st.session_state['role']} {st.session_state['first_name']}")
     st.image('https://cdn-icons-png.freepik.com/256/3101/3101045.png?ga=GA1.1.1507691374.1717099387', width = 50)
 
-st.write("# To get started we need some information from you")
+st.write("# Navigate to your desired tool")
 st.write('')
 st.write('')
 
-if st.button('NGO information questions'):
+if st.button('Update NGO information questions'):
     st.switch_page('pages/21_NGOInfo.py')
+
+st.write('')
+
+if st.button('View my user and enterprise matches'):
+    st.switch_page('pages/22_NGOMatch.py')
diff --git a/app/src/pages/23_NGOMatch.py b/app/src/pages/22_NGOMatch.py
similarity index 100%
rename from app/src/pages/23_NGOMatch.py
rename to app/src/pages/22_NGOMatch.py
diff --git a/app/src/pages/22_NGOTools.py b/app/src/pages/22_NGOTools.py
deleted file mode 100644
index 5488b85..0000000
--- a/app/src/pages/22_NGOTools.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import streamlit as st
-from modules.nav import SideBarLinks
-
-# Show appropriate sidebar links for the role of the currently logged in user
-SideBarLinks()
-
-st.write("# NGO tools") 
-
-# make a page so that NGOs can edit their tags 
-
-st.button("Edit NGO Information")
-if st.button("Edit NGO Information"):
-    @ngo.route('/editinfo', methods=['PUT'])
-    def update_ngo():
-        current_app.logger.info('PUT /ngo route')
-         NGO_name = NGOInfo['name']
-         # current_app.logger.info(cust_info)
-         Website_link = NGOInfo['website']
-        Contact_email = NGOInfo['email']
-        selected_tags = NGOInfo['tags']
-        query = 'UPDATE NGOINFO SET first_name = %s, last_name = %s, company = %s where id = %s'
-        data = (name, website, email, tags)
-        cursor = db.get_db().cursor()
-        r = cursor.execute(query, data)
-        db.get_db().commit()
-        return 'ngo information updated!'

From b6bbfe872cc69035de756973cf453f4c9ac5590f Mon Sep 17 00:00:00 2001
From: aahil n <nishad.a@northeastern.edu>
Date: Wed, 5 Jun 2024 19:01:03 +0200
Subject: [PATCH 13/34] Big Change

- started user survey
- fixed about a lil
- changed carbon sql
---
 api/requirements.txt           |  3 +--
 app/requirements.txt           |  2 +-
 app/src/App.py                 | 15 ++++++++-------
 app/src/pages/02_userSurvey.py | 24 +++++++++++++++++-------
 app/src/pages/30_about.py      |  8 ++++----
 database/00_carbon.sql         |  6 ++++--
 6 files changed, 35 insertions(+), 23 deletions(-)

diff --git a/api/requirements.txt b/api/requirements.txt
index 5d35f95..7661e6e 100644
--- a/api/requirements.txt
+++ b/api/requirements.txt
@@ -4,5 +4,4 @@ flask-restful==0.3.9
 flask-login==0.6.2
 flask-mysql==1.5.2
 cryptography==38.0.1
-python-dotenv==1.0.1
-streamlit-survey
\ No newline at end of file
+python-dotenv==1.0.1
\ No newline at end of file
diff --git a/app/requirements.txt b/app/requirements.txt
index ceef4d5..604aa12 100644
--- a/app/requirements.txt
+++ b/app/requirements.txt
@@ -37,4 +37,4 @@ toolz==0.12.1
 tornado==6.4
 typing_extensions==4.12.0
 tzdata==2024.1
-urllib3==2.2.1
+urllib3==2.2.1
\ No newline at end of file
diff --git a/app/src/App.py b/app/src/App.py
index ca16ff2..e17900f 100644
--- a/app/src/App.py
+++ b/app/src/App.py
@@ -8,7 +8,7 @@
 def App():
     
     st.title('Welcome to Carbon Connect!')
-    st.header('Choose your persona below')
+    st.header('Act as...')
 
     st.write('')
 
@@ -16,26 +16,27 @@ def App():
 
     col1, col2, col3 = st.columns(3)
     with col1:
-        if st.button("Act as Dave, a general user and citizen"):
+        if st.button("Natalie Allard"):
           st.session_state['authenticated'] = True
           st.session_state['role'] = 'General User'
-          st.session_state['first_name'] = 'Dave'
+          st.session_state['first_name'] = 'Natalie'
           st.switch_page("pages/01_userhome.py")
         st.image('https://cdn-icons-png.freepik.com/256/552/552848.png?ga=GA1.1.1507691374.1717099387', width = 100)
 
+
     with col2:
-        if st.button("Act as Farm to Table, a medium sized business"):
+        if st.button("EcoForward Enterprises"):
           st.session_state['authenticated'] = True
           st.session_state['role'] = 'Enterprise'
-          st.session_state['first_name'] = 'FarmtoTable'
+          st.session_state['first_name'] = 'EcoForward'
           st.switch_page("pages/10_enterprisehome.py")
         st.image('https://cdn-icons-png.freepik.com/256/834/834504.png?ga=GA1.1.1507691374.1717099387', width = 100)
 
     with col3:
-        if st.button("Act as NoEmissions, a transport emission NGO"):
+        if st.button("EcoUnity Europe"):
           st.session_state['authenticated'] = True
           st.session_state['role'] = 'NGO'
-          st.session_state['first_name'] = 'CarEmissions'
+          st.session_state['first_name'] = 'EcoUnity'
           st.switch_page("pages/20_NGOhome.py")
         st.image('https://cdn-icons-png.freepik.com/256/3101/3101045.png?ga=GA1.1.1507691374.1717099387', width = 100)
 
diff --git a/app/src/pages/02_userSurvey.py b/app/src/pages/02_userSurvey.py
index 0b0b777..21c9476 100644
--- a/app/src/pages/02_userSurvey.py
+++ b/app/src/pages/02_userSurvey.py
@@ -1,17 +1,27 @@
 import streamlit as st
 from modules.nav import SideBarLinks
-import streamlit_survey as ss
-survey = ss.StreamlitSurvey()
+
 
 # Show appropriate sidebar links for the role of the currently logged in user
 SideBarLinks()
 
-st.write("# User survey")
+st.header("User Survey")
+st.write("##### Let's take a look at your Carbon Footprint!")
+st.write("Please complete the survey to the best of your ability.")
+country = st.selectbox(
+    "Country :flag-eu:",
+    ("Austria 🇦🇹", "Belgium 🇧🇪", "Bulgaria 🇧🇬", "Croatia 🇭🇷", "Cyprus 🇨🇾", "Czechia 🇨🇿", "Denmark 🇩🇰", "Estonia 🇪🇪", "Finland 🇫🇮", "France 🇫🇷", "Germany 🇩🇪", "Greece 🇬🇷", "Hungary 🇭🇺", "Ireland 🇮🇪", "Italy 🇮🇹", "Latvia 🇱🇻", "Lithuania 🇱🇹", "Luxembourg 🇱🇺", "Malta 🇲🇹", "Netherlands 🇳🇱", "Poland 🇵🇱", "Portugal 🇵🇹", "Romania 🇷🇴", "Slovakia 🇸🇰", "Slovenia 🇸🇮", "Spain 🇪🇸", "Sweden 🇸🇪", "Iceland 🇮🇸", "Liechtenstein 🇱🇮", "Norway 🇳🇴", "Switzerland 🇨🇭"))
+st.write("You selected:", country)
+
 
-survey.text_input("Text input:", id="Q1")
-st.write("## What country in Europe are you from?")
-survey.selectbox("Selection box:", options=["Albania", "Andorra", "Armenia", "Austria", "Azerbaijan"])
+with st.expander("Residential Data"):
+    electricityUsage = 0.0000036 * 12 * st.number_input("How much electricity do your household use per month (kWh)?", 0.0, None, 6320.0)
+    st.write("I use approximately ", round(electricityUsage) , "terajoules per year.")
+
+    electricityUsage = 0.0000036 * 12 * st.number_input("How much electricity do your household use per month (kWh)?", 0.0, None, 6320.0)
+    st.write("I use approximately ", round(electricityUsage) , "terajoules per year.")
 
 
 carNumber = st.slider("How many cars do you currently have?", 0, 130, 25)
-st.write("I have ", carNumber , "cars")
\ No newline at end of file
+st.write("I have ", carNumber , "cars")
+
diff --git a/app/src/pages/30_about.py b/app/src/pages/30_about.py
index 9f41e61..2d2ed84 100644
--- a/app/src/pages/30_about.py
+++ b/app/src/pages/30_about.py
@@ -11,14 +11,14 @@
     """
     This app is being built as an exemplar for Northeastern University's 
     Summer 2024 Dialogue of Civilization Program titled *Data and 
-    Software in International Government and Politics*.  The program is bein
-    led by Dr. Mark Fontenot and Dr. Eric Gerber from the Khoury College of
-    Computer Sciences.  
+    Software in International Government and Politics*. 
 
     The goal of this project is to allow different user personas to calculate
     and see their emission data. It provides different emission related tools 
     to promote a greener world. 
 
-    Stay tuned for more information and features to come!
+    Check out our blog for feature changes and fun!
+
+    - The Wafflers: Aahil, Anjola, Justin, Michael
     """
         )
diff --git a/database/00_carbon.sql b/database/00_carbon.sql
index 1183f54..624d869 100644
--- a/database/00_carbon.sql
+++ b/database/00_carbon.sql
@@ -120,9 +120,11 @@ DROP TABLE IF EXISTS ResData;
 CREATE TABLE IF NOT EXISTS ResData (
    id INT PRIMARY KEY,
    user_id INT,
-   elec_usage VARCHAR(250),
+   elec_usage FLOAT(5),
    emission_tags VARCHAR(250),
-   heat_gas VARCHAR(50),
+   heating FLOAT(5),
+   water_heating FLOAT(5),
+   cooking_gas FLOAT(5),
    FOREIGN KEY (user_id) REFERENCES User(id)
        ON UPDATE CASCADE
        ON DELETE RESTRICT

From 0297e8244100b18b416ed83e625db3daa7956cbd Mon Sep 17 00:00:00 2001
From: aahil n <nishad.a@northeastern.edu>
Date: Wed, 5 Jun 2024 21:31:45 +0200
Subject: [PATCH 14/34] a bunch  of changes

added user survey
type sh
---
 api/backend/rest_entry.py       |   2 +
 api/backend/user/user_routes.py |  41 ++++++++++++
 app/src/pages/02_userSurvey.py  | 107 ++++++++++++++++++++++++++++++--
 database/00_carbon.sql          |   4 +-
 database/01_carbon-data.sql     |   8 +++
 5 files changed, 154 insertions(+), 8 deletions(-)

diff --git a/api/backend/rest_entry.py b/api/backend/rest_entry.py
index 2ce5738..ce13cf4 100644
--- a/api/backend/rest_entry.py
+++ b/api/backend/rest_entry.py
@@ -7,6 +7,7 @@
 from backend.test.test_routes import test
 from backend.enterprises.enterprises_routes import enterprises
 from backend.ngo.ngo_routes import ngo
+from backend.user.user_routes import user
 import os
 from dotenv import load_dotenv
 
@@ -64,6 +65,7 @@ def getData():
     app.register_blueprint(test, url_prefix='/t')
     app.register_blueprint(enterprises, url_prefix='/e')
     app.register_blueprint(ngo, url_prefix='/n')
+    app.register_blueprint(user, url_prefix='/u')
 
     # Don't forget to return the app object
     return app
diff --git a/api/backend/user/user_routes.py b/api/backend/user/user_routes.py
index f3c2905..5178c58 100644
--- a/api/backend/user/user_routes.py
+++ b/api/backend/user/user_routes.py
@@ -40,6 +40,47 @@ def get_residential():
 
     return jsonify(json_data)
 
+# Get all the residential history for this user
+@user.route('/UserAddRes', methods=['PUT'])
+def add_residential():
+    current_app.logger.info('user_routes.py: PUT /UserAddRes')
+    
+    received_data = request.json
+    current_app.logger.info(received_data)
+
+    elec_usage = received_data['elec_usage']
+    heating = received_data['heating']
+    water_heating = received_data['water_heating']
+    cooking_gas = received_data['cooking_gas']
+    
+    query = "UPDATE ResData SET emission_tags = 'residential', elec_usage = %s, heating = %s, water_heating = %s, cooking_gas = %s WHERE user_id = 1"
+
+    data = (elec_usage, heating, water_heating, cooking_gas)
+    cursor = db.get_db().cursor()
+    cursor.execute(query, data)
+    db.get_db().commit()
+    return "success"
+    
+
+# Get all the residential history for this user
+@user.route('/UserCountry', methods=['PUT'])
+def add_country():
+    current_app.logger.info('user_routes.py: PUT /UserCountry')
+    
+    recieved_data = request.json
+    current_app.logger.info(recieved_data)
+
+    name = recieved_data['name']
+    emissions = recieved_data['website']
+
+    query = 'UPDATE NGO SET website = %s, name = %s, contact = %s WHERE id = 1'
+
+    data = (name, website, email)
+    cursor = db.get_db().cursor()
+    cursor.execute(query, data)
+    db.get_db().commit()
+
+
 # Get all the flight history for this user
 @user.route('/UserFlights', methods=['GET'])
 def get_flights():
diff --git a/app/src/pages/02_userSurvey.py b/app/src/pages/02_userSurvey.py
index 21c9476..84afcce 100644
--- a/app/src/pages/02_userSurvey.py
+++ b/app/src/pages/02_userSurvey.py
@@ -1,6 +1,7 @@
 import streamlit as st
 from modules.nav import SideBarLinks
-
+import requests
+import json
 
 # Show appropriate sidebar links for the role of the currently logged in user
 SideBarLinks()
@@ -13,15 +14,109 @@
     ("Austria 🇦🇹", "Belgium 🇧🇪", "Bulgaria 🇧🇬", "Croatia 🇭🇷", "Cyprus 🇨🇾", "Czechia 🇨🇿", "Denmark 🇩🇰", "Estonia 🇪🇪", "Finland 🇫🇮", "France 🇫🇷", "Germany 🇩🇪", "Greece 🇬🇷", "Hungary 🇭🇺", "Ireland 🇮🇪", "Italy 🇮🇹", "Latvia 🇱🇻", "Lithuania 🇱🇹", "Luxembourg 🇱🇺", "Malta 🇲🇹", "Netherlands 🇳🇱", "Poland 🇵🇱", "Portugal 🇵🇹", "Romania 🇷🇴", "Slovakia 🇸🇰", "Slovenia 🇸🇮", "Spain 🇪🇸", "Sweden 🇸🇪", "Iceland 🇮🇸", "Liechtenstein 🇱🇮", "Norway 🇳🇴", "Switzerland 🇨🇭"))
 st.write("You selected:", country)
 
-
 with st.expander("Residential Data"):
-    electricityUsage = 0.0000036 * 12 * st.number_input("How much electricity do your household use per month (kWh)?", 0.0, None, 6320.0)
-    st.write("I use approximately ", round(electricityUsage) , "terajoules per year.")
+    
+    houseHoldMembers = st.number_input("How many people live in your household?", 1, None, 2)
+    
+    electricityUsage = 0.0000036 * 12 * st.number_input("How much electricity does your household use per month (kWh)?", 0.0, None, 6320.0)
+    st.write("I use approximately ", electricityUsage / houseHoldMembers , "terajoules per year.")
+
+    heating = 0.0000036 * 12 * st.number_input("How much heating does your household use per month (kWh)?", 0.0, None, 3000.0)
+    st.write("I use approximately ", heating / houseHoldMembers, "terajoules per year.")
+
+    waterHeating = 0.0000036 * 12 * st.number_input("How much water heating does your household use per month (kWh)?", 0.0, None, 2000.0)
+    st.write("I use approximately ", waterHeating / houseHoldMembers, "terajoules per year.")
+
+    cookingGas = 0.0000036 * 12 * st.number_input("Per month, how much energy is used cooking? (kWh)?", 0.0, None, 2000.0)
+    st.write("I use approximately ", cookingGas / houseHoldMembers, "terajoules per year.")
+    
+    st.write("Total Residential Usage (TJ): ", (electricityUsage + heating + waterHeating + cookingGas) / houseHoldMembers)
+    
+    if st.button("Submit"):
+        if houseHoldMembers and electricityUsage and heating and waterHeating and cookingGas:
+            api_url = "http://api:4000/u/UserAddRes"
+            data = {
+                "elec_usage": electricityUsage,
+                "heating": heating,
+                "water_heating": waterHeating,
+                "cooking_gas": cookingGas}
+            
+            try: 
+                response = requests.put(api_url, json=data)
+                if response.status_code == 201 or response.status_code == 200:
+                        st.success("Data successfully inserted!")
+                else:
+                    try:
+                        error_message = response.json().get('error', 'No error message provided')
+                    except json.JSONDecodeError:
+                        error_message = response.text  # Raw response if not JSON
+                    
+                    st.error(f"Failed to insert data. Status code: {response.status_code}, Error: {error_message}")
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+        else:
+            st.error("Please fill in all the fields before submitting.")
+   
+# data = {} 
+# try:
+#   data = requests.get('http://api:4000/u/UserResidential').json()
+# except:
+#   st.write("**Important**: Could not connect to sample api, so using dummy data.")
+#   data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
+# st.dataframe(data)
+
+with st.expander("Car Data"):
+    
+    numCars = st.number_input("How many cars do you own?", 0)
+    
+    if (numCars > 0): 
+        fuelType = st.select_slider(
+        "",
+        options=["red", "orange", "yellow", "green", "blue", "indigo", "violet"])
+    st.write("My favorite color is", color)
+    
+    electricityUsage = 0.0000036 * 12 * st.number_input("How much electricity does your household use per month (kWh)?", 0.0, None, 6320.0)
+    st.write("I use approximately ", electricityUsage / houseHoldMembers , "terajoules per year.")
 
-    electricityUsage = 0.0000036 * 12 * st.number_input("How much electricity do your household use per month (kWh)?", 0.0, None, 6320.0)
-    st.write("I use approximately ", round(electricityUsage) , "terajoules per year.")
+    heating = 0.0000036 * 12 * st.number_input("How much heating does your household use per month (kWh)?", 0.0, None, 3000.0)
+    st.write("I use approximately ", heating / houseHoldMembers, "terajoules per year.")
 
+    waterHeating = 0.0000036 * 12 * st.number_input("How much water heating does your household use per month (kWh)?", 0.0, None, 2000.0)
+    st.write("I use approximately ", waterHeating / houseHoldMembers, "terajoules per year.")
 
+    cookingGas = 0.0000036 * 12 * st.number_input("Per month, how much energy is used cooking? (kWh)?", 0.0, None, 2000.0)
+    st.write("I use approximately ", cookingGas / houseHoldMembers, "terajoules per year.")
+    
+    st.write("Total Residential Usage (TJ): ", (electricityUsage + heating + waterHeating + cookingGas) / houseHoldMembers)
+    
+    if st.button("Submit"):
+        if houseHoldMembers and electricityUsage and heating and waterHeating and cookingGas:
+            api_url = "http://api:4000/u/UserAddRes"
+            data = {
+                "elec_usage": electricityUsage,
+                "heating": heating,
+                "water_heating": waterHeating,
+                "cooking_gas": cookingGas}
+            
+            try: 
+                response = requests.put(api_url, json=data)
+                if response.status_code == 201 or response.status_code == 200:
+                        st.success("Data successfully inserted!")
+                else:
+                    try:
+                        error_message = response.json().get('error', 'No error message provided')
+                    except json.JSONDecodeError:
+                        error_message = response.text  # Raw response if not JSON
+                    
+                    st.error(f"Failed to insert data. Status code: {response.status_code}, Error: {error_message}")
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+        else:
+            st.error("Please fill in all the fields before submitting.")
+   
+    
+    
 carNumber = st.slider("How many cars do you currently have?", 0, 130, 25)
 st.write("I have ", carNumber , "cars")
 
diff --git a/database/00_carbon.sql b/database/00_carbon.sql
index 624d869..80616ef 100644
--- a/database/00_carbon.sql
+++ b/database/00_carbon.sql
@@ -5,7 +5,7 @@ USE CarbonConnect;
 
 DROP TABLE IF EXISTS Country;
 CREATE TABLE IF NOT EXISTS Country (
-   id INT PRIMARY KEY,
+   id INT PRIMARY KEY AUTO_INCREMENT,
    emissions VARCHAR(255),
    name VARCHAR(50)
 );
@@ -118,7 +118,7 @@ CREATE TABLE IF NOT EXISTS Cars (
 
 DROP TABLE IF EXISTS ResData;
 CREATE TABLE IF NOT EXISTS ResData (
-   id INT PRIMARY KEY,
+   id INT PRIMARY KEY AUTO_INCREMENT,
    user_id INT,
    elec_usage FLOAT(5),
    emission_tags VARCHAR(250),
diff --git a/database/01_carbon-data.sql b/database/01_carbon-data.sql
index fe91ca5..c03f621 100644
--- a/database/01_carbon-data.sql
+++ b/database/01_carbon-data.sql
@@ -45,3 +45,11 @@ INSERT INTO NGOTags (ngo_id, tag_id) VALUES
 (3, 2),
 (3, 3);
 
+INSERT INTO User (id, emission_result, country_id) VALUES
+(1, 100, 1),
+(2, 200, 2),
+(3, 300, 3);
+
+
+INSERT INTO ResData (id, user_id, elec_usage, emission_tags, heating, water_heating, cooking_gas) VALUES
+(200, 1, 1000.0, 'res', 2000.0, 1.0, 11.0)

From b02052ddab98d19150965cb7bb63e9372fd6ff56 Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Wed, 5 Jun 2024 21:50:00 +0200
Subject: [PATCH 15/34] Added usermatch feature for NGO

---
 api/backend/ngo/ngo_routes.py | 36 +++++++++++++++++++++++++++++++++++
 app/src/modules/nav.py        |  2 +-
 app/src/pages/22_NGOMatch.py  | 11 +++++++++++
 database/01_carbon-data.sql   |  9 ++++++++-
 4 files changed, 56 insertions(+), 2 deletions(-)

diff --git a/api/backend/ngo/ngo_routes.py b/api/backend/ngo/ngo_routes.py
index b7ad121..c432732 100644
--- a/api/backend/ngo/ngo_routes.py
+++ b/api/backend/ngo/ngo_routes.py
@@ -125,3 +125,39 @@ def get_matches():
         json_data.append(dict(zip(column_headers, row)))
 
     return jsonify(json_data)
+
+
+# get all of the matching users based on tags
+@ngo.route('/UserMatch', methods=['GET'])
+def get_usermatches():
+    # get a cursor object from the database
+    cursor = db.get_db().cursor()
+
+    cursor.execute('''
+    SELECT DISTINCT User.id
+    FROM User
+    JOIN UserTags ON User.id = UserTags.tag_id
+    JOIN EmissionTags ON UserTags.tag_id = EmissionTags.id
+    WHERE EmissionTags.id IN (
+        SELECT tag_id
+        FROM NGOTags
+        WHERE NGOTags.ngo_id = 1
+    );
+''')
+    
+    # grab the column headers from the returned data
+    column_headers = [x[0] for x in cursor.description]
+
+    # create an empty dictionary object to use in 
+    # putting column headers together with data
+    json_data = []
+
+    # fetch all the data from the cursor
+    theData = cursor.fetchall()
+
+    # for each of the rows, zip the data elements together with
+    # the column headers. 
+    for row in theData:
+        json_data.append(dict(zip(column_headers, row)))
+
+    return jsonify(json_data)
diff --git a/app/src/modules/nav.py b/app/src/modules/nav.py
index 5950480..6707e4c 100644
--- a/app/src/modules/nav.py
+++ b/app/src/modules/nav.py
@@ -49,7 +49,7 @@ def NGOInfoNav():
     st.sidebar.page_link("pages/21_NGOInfo.py", label="NGO Info")
 
 def NGOMatchNav():
-    st.sidebar.page_link("pages/22_NGOMatch.py", label="Enterprise Match")    
+    st.sidebar.page_link("pages/22_NGOMatch.py", label="Enterprise/User Match")    
 
 # --------------------------------Links Function -----------------------------------------------
 def SideBarLinks(show_home=False):
diff --git a/app/src/pages/22_NGOMatch.py b/app/src/pages/22_NGOMatch.py
index f0b5ac8..2960604 100644
--- a/app/src/pages/22_NGOMatch.py
+++ b/app/src/pages/22_NGOMatch.py
@@ -31,4 +31,15 @@
   st.write("**Important**: Could not connect to sample api, so using dummy data.")
   data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
 
+st.dataframe(data)
+
+st.write("## Users you matched with")
+
+data = {} 
+try:
+  data = requests.get('http://api:4000/n/UserMatch').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+
 st.dataframe(data)
\ No newline at end of file
diff --git a/database/01_carbon-data.sql b/database/01_carbon-data.sql
index c03f621..f88173f 100644
--- a/database/01_carbon-data.sql
+++ b/database/01_carbon-data.sql
@@ -50,6 +50,13 @@ INSERT INTO User (id, emission_result, country_id) VALUES
 (2, 200, 2),
 (3, 300, 3);
 
-
 INSERT INTO ResData (id, user_id, elec_usage, emission_tags, heating, water_heating, cooking_gas) VALUES
 (200, 1, 1000.0, 'res', 2000.0, 1.0, 11.0)
+
+INSERT INTO UserTags (user_id, tag_id) VALUES
+(1, 1),
+(1, 2),
+(2, 3),
+(2, 4),
+(3, 1),
+(3, 3);
\ No newline at end of file

From 94335ac9085174a446dbddb5fe81053d39bed095 Mon Sep 17 00:00:00 2001
From: aahil n <nishad.a@northeastern.edu>
Date: Thu, 6 Jun 2024 00:04:05 +0200
Subject: [PATCH 16/34] Added Car Survey

yippee
---
 api/backend/user/user_routes.py | 47 +++++++++++++-----
 app/src/pages/02_userSurvey.py  | 87 ++++++++++++++++-----------------
 database/00_carbon.sql          |  6 +--
 database/01_carbon-data.sql     |  7 ++-
 4 files changed, 85 insertions(+), 62 deletions(-)

diff --git a/api/backend/user/user_routes.py b/api/backend/user/user_routes.py
index 5178c58..695b9ea 100644
--- a/api/backend/user/user_routes.py
+++ b/api/backend/user/user_routes.py
@@ -22,6 +22,27 @@ def get_cars():
 
     return jsonify(json_data)
 
+# Get all the residential history for this user
+@user.route('/UserAddCar', methods=['PUT'])
+def add_car():
+    current_app.logger.info('user_routes.py: PUT /UserAddCar')
+    
+    received_data = request.json
+    current_app.logger.info(received_data)
+
+    fuel_type = received_data['fuel_type']
+    fuel_used = received_data['fuel_used']
+    
+    query = "UPDATE Cars SET emission_tags = 'car', fuel_type = %s, fuel_used = %s WHERE user_id = 1"
+
+    data = (fuel_type, fuel_used)
+    cursor = db.get_db().cursor()
+    cursor.execute(query, data)
+    db.get_db().commit()
+    return "success"
+    
+
+
 # Get all the residential history for this user
 @user.route('/UserResidential', methods=['GET'])
 def get_residential():
@@ -62,23 +83,23 @@ def add_residential():
     return "success"
     
 
-# Get all the residential history for this user
-@user.route('/UserCountry', methods=['PUT'])
-def add_country():
-    current_app.logger.info('user_routes.py: PUT /UserCountry')
+# # Get all the residential history for this user
+# @user.route('/UserCountry', methods=['PUT'])
+# def add_country():
+#     current_app.logger.info('user_routes.py: PUT /UserCountry')
     
-    recieved_data = request.json
-    current_app.logger.info(recieved_data)
+#     recieved_data = request.json
+#     current_app.logger.info(recieved_data)
 
-    name = recieved_data['name']
-    emissions = recieved_data['website']
+#     name = recieved_data['name']
+#     emissions = recieved_data['website']
 
-    query = 'UPDATE NGO SET website = %s, name = %s, contact = %s WHERE id = 1'
+#     query = 'UPDATE NGO SET website = %s, name = %s, contact = %s WHERE id = 1'
 
-    data = (name, website, email)
-    cursor = db.get_db().cursor()
-    cursor.execute(query, data)
-    db.get_db().commit()
+#     data = (name, website, email)
+#     cursor = db.get_db().cursor()
+#     cursor.execute(query, data)
+#     db.get_db().commit()
 
 
 # Get all the flight history for this user
diff --git a/app/src/pages/02_userSurvey.py b/app/src/pages/02_userSurvey.py
index 84afcce..2ad7688 100644
--- a/app/src/pages/02_userSurvey.py
+++ b/app/src/pages/02_userSurvey.py
@@ -16,30 +16,30 @@
 
 with st.expander("Residential Data"):
     
-    houseHoldMembers = st.number_input("How many people live in your household?", 1, None, 2)
+    household_members = st.number_input("How many people live in your household?", 1, None, 2)
     
-    electricityUsage = 0.0000036 * 12 * st.number_input("How much electricity does your household use per month (kWh)?", 0.0, None, 6320.0)
-    st.write("I use approximately ", electricityUsage / houseHoldMembers , "terajoules per year.")
+    electricity_usage = 0.0000036 * 12 * st.number_input("How much electricity does your household use per month (kWh)?", 0.0, None, 6320.0)
+    st.write("I use approximately ", electricity_usage / household_members , "terajoules per year.")
 
     heating = 0.0000036 * 12 * st.number_input("How much heating does your household use per month (kWh)?", 0.0, None, 3000.0)
-    st.write("I use approximately ", heating / houseHoldMembers, "terajoules per year.")
+    st.write("I use approximately ", heating / household_members, "terajoules per year.")
 
-    waterHeating = 0.0000036 * 12 * st.number_input("How much water heating does your household use per month (kWh)?", 0.0, None, 2000.0)
-    st.write("I use approximately ", waterHeating / houseHoldMembers, "terajoules per year.")
+    water_heating = 0.0000036 * 12 * st.number_input("How much water heating does your household use per month (kWh)?", 0.0, None, 2000.0)
+    st.write("I use approximately ", water_heating / household_members, "terajoules per year.")
 
-    cookingGas = 0.0000036 * 12 * st.number_input("Per month, how much energy is used cooking? (kWh)?", 0.0, None, 2000.0)
-    st.write("I use approximately ", cookingGas / houseHoldMembers, "terajoules per year.")
+    cooking_gas = 0.0000036 * 12 * st.number_input("Per month, how much energy is used cooking? (kWh)?", 0.0, None, 2000.0)
+    st.write("I use approximately ", cooking_gas / household_members, "terajoules per year.")
     
-    st.write("Total Residential Usage (TJ): ", (electricityUsage + heating + waterHeating + cookingGas) / houseHoldMembers)
+    st.write("Total Residential Usage (TJ): ", (electricity_usage + heating + water_heating + cooking_gas) / household_members)
     
-    if st.button("Submit"):
-        if houseHoldMembers and electricityUsage and heating and waterHeating and cookingGas:
+    if st.button("Submit Residential Data"):
+        if household_members and electricity_usage and heating and water_heating and cooking_gas:
             api_url = "http://api:4000/u/UserAddRes"
             data = {
-                "elec_usage": electricityUsage,
+                "elec_usage": electricity_usage,
                 "heating": heating,
-                "water_heating": waterHeating,
-                "cooking_gas": cookingGas}
+                "water_heating": water_heating,
+                "cooking_gas": cooking_gas}
             
             try: 
                 response = requests.put(api_url, json=data)
@@ -63,42 +63,35 @@
 # except:
 #   st.write("**Important**: Could not connect to sample api, so using dummy data.")
 #   data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
-
 # st.dataframe(data)
 
 with st.expander("Car Data"):
     
-    numCars = st.number_input("How many cars do you own?", 0)
-    
-    if (numCars > 0): 
-        fuelType = st.select_slider(
-        "",
-        options=["red", "orange", "yellow", "green", "blue", "indigo", "violet"])
-    st.write("My favorite color is", color)
+    # numCars = st.number_input("How many cars do you own?", 0)
+    # if (numCars > 0): 
+    fuel_type = st.select_slider(
+         "Fuel Type",
+        options=["Gasoline/Hybrid", "Diesel", "Electric"])   
     
-    electricityUsage = 0.0000036 * 12 * st.number_input("How much electricity does your household use per month (kWh)?", 0.0, None, 6320.0)
-    st.write("I use approximately ", electricityUsage / houseHoldMembers , "terajoules per year.")
-
-    heating = 0.0000036 * 12 * st.number_input("How much heating does your household use per month (kWh)?", 0.0, None, 3000.0)
-    st.write("I use approximately ", heating / houseHoldMembers, "terajoules per year.")
+    if (fuel_type == "Gasoline/Hybrid"):
+        fuel_capacity = st.number_input("How many liters of gasoline does your vehicle hold?", 0.0, None, 50.0)
+        fuel_used_monthly = st.slider("How many times a month do you fill up your tank?", 0, 10, 5)
+        fuel_used = st.number_input("Total fuel used per year (liters): ", 0.0, None, fuel_capacity * fuel_used_monthly * 12) * 1.11302E-6
+    elif (fuel_type == "Diesel"):
+        fuel_capacity = st.number_input("How many liters of diesel does your vehicle hold?", 0.0, None, 50.0)
+        fuel_used_monthly = st.slider("How many times a month do you fill up your tank?", 0, 10, 5)
+        fuel_used = st.number_input("Total fuel used per year (liters): ", 0.0, None, fuel_capacity * fuel_used_monthly * 12) * 1.1571E-6
 
-    waterHeating = 0.0000036 * 12 * st.number_input("How much water heating does your household use per month (kWh)?", 0.0, None, 2000.0)
-    st.write("I use approximately ", waterHeating / houseHoldMembers, "terajoules per year.")
-
-    cookingGas = 0.0000036 * 12 * st.number_input("Per month, how much energy is used cooking? (kWh)?", 0.0, None, 2000.0)
-    st.write("I use approximately ", cookingGas / houseHoldMembers, "terajoules per year.")
+    elif (fuel_type == "Electric"): 
+        st.write("Please include charging data in residential data.")
     
-    st.write("Total Residential Usage (TJ): ", (electricityUsage + heating + waterHeating + cookingGas) / houseHoldMembers)
     
-    if st.button("Submit"):
-        if houseHoldMembers and electricityUsage and heating and waterHeating and cookingGas:
-            api_url = "http://api:4000/u/UserAddRes"
+    if st.button("Submit Car Data"):
+        if fuel_type and fuel_used:
+            api_url = "http://api:4000/u/UserAddCar"
             data = {
-                "elec_usage": electricityUsage,
-                "heating": heating,
-                "water_heating": waterHeating,
-                "cooking_gas": cookingGas}
-            
+                "fuel_type": fuel_type,
+                "fuel_used": fuel_used }
             try: 
                 response = requests.put(api_url, json=data)
                 if response.status_code == 201 or response.status_code == 200:
@@ -115,8 +108,12 @@
         else:
             st.error("Please fill in all the fields before submitting.")
    
-    
-    
-carNumber = st.slider("How many cars do you currently have?", 0, 130, 25)
-st.write("I have ", carNumber , "cars")
+# # View Car Data   
+# data = {} 
+# try:
+#   data = requests.get('http://api:4000/u/UserCars').json()
+# except:
+#   st.write("**Important**: Could not connect to sample api, so using dummy data.")
+#   data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
 
+# st.dataframe(data)
\ No newline at end of file
diff --git a/database/00_carbon.sql b/database/00_carbon.sql
index 80616ef..91966c5 100644
--- a/database/00_carbon.sql
+++ b/database/00_carbon.sql
@@ -6,7 +6,7 @@ USE CarbonConnect;
 DROP TABLE IF EXISTS Country;
 CREATE TABLE IF NOT EXISTS Country (
    id INT PRIMARY KEY AUTO_INCREMENT,
-   emissions VARCHAR(255),
+   emissions FLOAT(5),
    name VARCHAR(50)
 );
 
@@ -105,11 +105,11 @@ CREATE TABLE IF NOT EXISTS Flight (
 
 DROP TABLE IF EXISTS Cars;
 CREATE TABLE IF NOT EXISTS Cars (
-   id INT PRIMARY KEY,
+   id INT PRIMARY KEY AUTO_INCREMENT,
    user_id INT,
    fuel_type VARCHAR(50),
    emission_tags VARCHAR(50),
-   distance INT,
+   fuel_used FLOAT(20),
    FOREIGN KEY (user_id) REFERENCES User(id)
        ON UPDATE CASCADE
        ON DELETE RESTRICT
diff --git a/database/01_carbon-data.sql b/database/01_carbon-data.sql
index f88173f..842c1b5 100644
--- a/database/01_carbon-data.sql
+++ b/database/01_carbon-data.sql
@@ -59,4 +59,9 @@ INSERT INTO UserTags (user_id, tag_id) VALUES
 (2, 3),
 (2, 4),
 (3, 1),
-(3, 3);
\ No newline at end of file
+(3, 3);
+
+INSERT INTO Cars (id, user_id, fuel_type, emission_tags, fuel_used) VALUES
+(1, 1, 'Gasoline/Hybrid', 'car', 100.0),
+(2, 2, 'Diesel', 'car', 200.0),
+(3, 3, 'Electric', 'car', 304.0);
\ No newline at end of file

From 5c105da5134d9243443b34cc59648149c47f7eb0 Mon Sep 17 00:00:00 2001
From: mehallhm <michaelhmehall@gmail.com>
Date: Wed, 5 Jun 2024 08:59:50 +0200
Subject: [PATCH 17/34] migrated data to sdmx, new lin reg model

---
 eda/eda.ipynb  | 19675 +++++++++++++++++++++++------------------------
 eda/eda3.ipynb |  4480 +++++++++++
 2 files changed, 14307 insertions(+), 9848 deletions(-)
 create mode 100644 eda/eda3.ipynb

diff --git a/eda/eda.ipynb b/eda/eda.ipynb
index 23eb276..953ac2d 100644
--- a/eda/eda.ipynb
+++ b/eda/eda.ipynb
@@ -1,9850 +1,9829 @@
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scraper\n",
-    "import pandas as pd\n",
-    "import plotly.express as px\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Get the data\n",
-    "### Carbon data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "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 tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th>table</th>\n",
-       "      <th colspan=\"10\" halign=\"left\">1</th>\n",
-       "      <th>...</th>\n",
-       "      <th colspan=\"9\" halign=\"left\">65</th>\n",
-       "      <th>country</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time</th>\n",
-       "      <th>1995</th>\n",
-       "      <th>1996</th>\n",
-       "      <th>1997</th>\n",
-       "      <th>1998</th>\n",
-       "      <th>1999</th>\n",
-       "      <th>2000</th>\n",
-       "      <th>2001</th>\n",
-       "      <th>2002</th>\n",
-       "      <th>2003</th>\n",
-       "      <th>2004</th>\n",
-       "      <th>...</th>\n",
-       "      <th>2014</th>\n",
-       "      <th>2015</th>\n",
-       "      <th>2016</th>\n",
-       "      <th>2017</th>\n",
-       "      <th>2018</th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "      <th>2021</th>\n",
-       "      <th>2022</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>416329.05</td>\n",
-       "      <td>411485.90</td>\n",
-       "      <td>413749.45</td>\n",
-       "      <td>419976.61</td>\n",
-       "      <td>426307.90</td>\n",
-       "      <td>416848.48</td>\n",
-       "      <td>398982.10</td>\n",
-       "      <td>412683.08</td>\n",
-       "      <td>384901.69</td>\n",
-       "      <td>EU27_2020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>705507.53</td>\n",
-       "      <td>680904.05</td>\n",
-       "      <td>668832.49</td>\n",
-       "      <td>646120.22</td>\n",
-       "      <td>672691.73</td>\n",
-       "      <td>674474.81</td>\n",
-       "      <td>601207.60</td>\n",
-       "      <td>615594.09</td>\n",
-       "      <td>559118.99</td>\n",
-       "      <td>BE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>371707.83</td>\n",
-       "      <td>401866.25</td>\n",
-       "      <td>435213.07</td>\n",
-       "      <td>426494.22</td>\n",
-       "      <td>423259.56</td>\n",
-       "      <td>393891.37</td>\n",
-       "      <td>391756.15</td>\n",
-       "      <td>402387.56</td>\n",
-       "      <td>386451.29</td>\n",
-       "      <td>BG</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>314720.03</td>\n",
-       "      <td>316332.36</td>\n",
-       "      <td>329274.64</td>\n",
-       "      <td>336949.27</td>\n",
-       "      <td>354343.90</td>\n",
-       "      <td>350563.81</td>\n",
-       "      <td>330125.94</td>\n",
-       "      <td>354575.61</td>\n",
-       "      <td>323965.99</td>\n",
-       "      <td>CZ</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>15662060.68</td>\n",
-       "      <td>17983724.22</td>\n",
-       "      <td>16436561.98</td>\n",
-       "      <td>16404974.32</td>\n",
-       "      <td>15656406.16</td>\n",
-       "      <td>15526465.86</td>\n",
-       "      <td>15575519.99</td>\n",
-       "      <td>15742121.31</td>\n",
-       "      <td>17348358.89</td>\n",
-       "      <td>16463321.94</td>\n",
-       "      <td>...</td>\n",
-       "      <td>415245.59</td>\n",
-       "      <td>421010.26</td>\n",
-       "      <td>481782.63</td>\n",
-       "      <td>511782.05</td>\n",
-       "      <td>503125.28</td>\n",
-       "      <td>487706.80</td>\n",
-       "      <td>507731.62</td>\n",
-       "      <td>502221.23</td>\n",
-       "      <td>479131.26</td>\n",
-       "      <td>DK</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 1821 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "table            1                                                      \\\n",
-       "time          1995         1996         1997         1998         1999   \n",
-       "0              NaN          NaN          NaN          NaN          NaN   \n",
-       "1              NaN          NaN          NaN          NaN          NaN   \n",
-       "2              NaN          NaN          NaN          NaN          NaN   \n",
-       "3              NaN          NaN          NaN          NaN          NaN   \n",
-       "4      15662060.68  17983724.22  16436561.98  16404974.32  15656406.16   \n",
-       "\n",
-       "table                                                                   ...  \\\n",
-       "time          2000         2001         2002         2003         2004  ...   \n",
-       "0              NaN          NaN          NaN          NaN          NaN  ...   \n",
-       "1              NaN          NaN          NaN          NaN          NaN  ...   \n",
-       "2              NaN          NaN          NaN          NaN          NaN  ...   \n",
-       "3              NaN          NaN          NaN          NaN          NaN  ...   \n",
-       "4      15526465.86  15575519.99  15742121.31  17348358.89  16463321.94  ...   \n",
-       "\n",
-       "table         65                                                         \\\n",
-       "time        2014       2015       2016       2017       2018       2019   \n",
-       "0      416329.05  411485.90  413749.45  419976.61  426307.90  416848.48   \n",
-       "1      705507.53  680904.05  668832.49  646120.22  672691.73  674474.81   \n",
-       "2      371707.83  401866.25  435213.07  426494.22  423259.56  393891.37   \n",
-       "3      314720.03  316332.36  329274.64  336949.27  354343.90  350563.81   \n",
-       "4      415245.59  421010.26  481782.63  511782.05  503125.28  487706.80   \n",
-       "\n",
-       "table                                     country  \n",
-       "time        2020       2021       2022             \n",
-       "0      398982.10  412683.08  384901.69  EU27_2020  \n",
-       "1      601207.60  615594.09  559118.99         BE  \n",
-       "2      391756.15  402387.56  386451.29         BG  \n",
-       "3      330125.94  354575.61  323965.99         CZ  \n",
-       "4      507731.62  502221.23  479131.26         DK  \n",
-       "\n",
-       "[5 rows x 1821 columns]"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_carbon_df, tables = scraper.get_eurostat_dataframe(\"env_ac_ainah_r2\", lambda raw: np.arange(1, 66))\n",
-    "full_carbon_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>country</th>\n",
-       "      <th>EU27_2020</th>\n",
-       "      <th>BE</th>\n",
-       "      <th>BG</th>\n",
-       "      <th>CZ</th>\n",
-       "      <th>DK</th>\n",
-       "      <th>DE</th>\n",
-       "      <th>EE</th>\n",
-       "      <th>IE</th>\n",
-       "      <th>EL</th>\n",
-       "      <th>ES</th>\n",
-       "      <th>...</th>\n",
-       "      <th>RO</th>\n",
-       "      <th>SI</th>\n",
-       "      <th>SK</th>\n",
-       "      <th>FI</th>\n",
-       "      <th>SE</th>\n",
-       "      <th>IS</th>\n",
-       "      <th>NO</th>\n",
-       "      <th>CH</th>\n",
-       "      <th>RS</th>\n",
-       "      <th>TR</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1995</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>15662060.68</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8862865.42</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12571199.07</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5183706.31</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1996</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>17983724.22</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8821817.28</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>13419521.77</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5238820.25</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1997</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>16436561.98</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8693803.51</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14045761.23</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5112279.33</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1998</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>16404974.32</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8542440.41</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14424047.34</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5276437.22</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1999</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>15656406.16</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8310800.34</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>15414605.09</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5237620.84</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 33 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "country  EU27_2020  BE  BG  CZ           DK  DE  EE  IE  EL  ES  ...  RO  SI  \\\n",
-       "time                                                             ...           \n",
-       "1995           NaN NaN NaN NaN  15662060.68 NaN NaN NaN NaN NaN  ... NaN NaN   \n",
-       "1996           NaN NaN NaN NaN  17983724.22 NaN NaN NaN NaN NaN  ... NaN NaN   \n",
-       "1997           NaN NaN NaN NaN  16436561.98 NaN NaN NaN NaN NaN  ... NaN NaN   \n",
-       "1998           NaN NaN NaN NaN  16404974.32 NaN NaN NaN NaN NaN  ... NaN NaN   \n",
-       "1999           NaN NaN NaN NaN  15656406.16 NaN NaN NaN NaN NaN  ... NaN NaN   \n",
-       "\n",
-       "country          SK  FI  SE           IS  NO          CH  RS  TR  \n",
-       "time                                                              \n",
-       "1995     8862865.42 NaN NaN  12571199.07 NaN  5183706.31 NaN NaN  \n",
-       "1996     8821817.28 NaN NaN  13419521.77 NaN  5238820.25 NaN NaN  \n",
-       "1997     8693803.51 NaN NaN  14045761.23 NaN  5112279.33 NaN NaN  \n",
-       "1998     8542440.41 NaN NaN  14424047.34 NaN  5276437.22 NaN NaN  \n",
-       "1999     8310800.34 NaN NaN  15414605.09 NaN  5237620.84 NaN NaN  \n",
-       "\n",
-       "[5 rows x 33 columns]"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_carbon_short = full_carbon_df.xs(1, level=\"table\", axis=1).T\n",
-    "df_carbon_short.columns = full_carbon_df.xs(\"country\", axis=1)\n",
-    "df_carbon_short.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>country</th>\n",
-       "      <th>EU27_2020</th>\n",
-       "      <th>BE</th>\n",
-       "      <th>BG</th>\n",
-       "      <th>CZ</th>\n",
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-       "      <th>SE</th>\n",
-       "      <th>IS</th>\n",
-       "      <th>NO</th>\n",
-       "      <th>RS</th>\n",
-       "      <th>TR</th>\n",
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-       "    <tr>\n",
-       "      <th>time</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2008</th>\n",
-       "      <td>8626928.75</td>\n",
-       "      <td>9840862.32</td>\n",
-       "      <td>8374278.10</td>\n",
-       "      <td>12088284.26</td>\n",
-       "      <td>18472875.79</td>\n",
-       "      <td>10115618.26</td>\n",
-       "      <td>15495691.70</td>\n",
-       "      <td>11599639.01</td>\n",
-       "      <td>11023011.51</td>\n",
-       "      <td>7309667.12</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6489677.15</td>\n",
-       "      <td>6792814.40</td>\n",
-       "      <td>8093935.35</td>\n",
-       "      <td>8115433.15</td>\n",
-       "      <td>12652929.15</td>\n",
-       "      <td>6147808.43</td>\n",
-       "      <td>17704691.59</td>\n",
-       "      <td>12308807.83</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2009</th>\n",
-       "      <td>7851179.61</td>\n",
-       "      <td>8740690.23</td>\n",
-       "      <td>7277828.03</td>\n",
-       "      <td>11049662.42</td>\n",
-       "      <td>17176547.55</td>\n",
-       "      <td>9321295.50</td>\n",
-       "      <td>12786692.77</td>\n",
-       "      <td>10448510.06</td>\n",
-       "      <td>10354690.72</td>\n",
-       "      <td>6435677.13</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6178248.19</td>\n",
-       "      <td>5751828.40</td>\n",
-       "      <td>7375583.00</td>\n",
-       "      <td>7304968.32</td>\n",
-       "      <td>12003558.20</td>\n",
-       "      <td>5514200.84</td>\n",
-       "      <td>16180654.34</td>\n",
-       "      <td>11368305.58</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2010</th>\n",
-       "      <td>8024312.00</td>\n",
-       "      <td>9243216.52</td>\n",
-       "      <td>7673499.85</td>\n",
-       "      <td>11228220.11</td>\n",
-       "      <td>16767331.74</td>\n",
-       "      <td>9667309.77</td>\n",
-       "      <td>16307459.75</td>\n",
-       "      <td>12043627.47</td>\n",
-       "      <td>9989268.71</td>\n",
-       "      <td>6098359.65</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5739881.60</td>\n",
-       "      <td>5581717.18</td>\n",
-       "      <td>7437534.55</td>\n",
-       "      <td>7305820.30</td>\n",
-       "      <td>13441161.42</td>\n",
-       "      <td>6147726.61</td>\n",
-       "      <td>16184246.73</td>\n",
-       "      <td>11861900.26</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2011</th>\n",
-       "      <td>7901995.31</td>\n",
-       "      <td>8519668.04</td>\n",
-       "      <td>8424931.82</td>\n",
-       "      <td>10997932.47</td>\n",
-       "      <td>16608386.02</td>\n",
-       "      <td>9668751.22</td>\n",
-       "      <td>16334728.68</td>\n",
-       "      <td>11509752.46</td>\n",
-       "      <td>9620522.32</td>\n",
-       "      <td>6223872.41</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5697688.64</td>\n",
-       "      <td>5895533.44</td>\n",
-       "      <td>7280437.24</td>\n",
-       "      <td>7294510.54</td>\n",
-       "      <td>11997445.75</td>\n",
-       "      <td>5607249.35</td>\n",
-       "      <td>15554875.78</td>\n",
-       "      <td>12700895.28</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2012</th>\n",
-       "      <td>7728876.39</td>\n",
-       "      <td>8149775.00</td>\n",
-       "      <td>7808042.43</td>\n",
-       "      <td>10544785.70</td>\n",
-       "      <td>15347310.62</td>\n",
-       "      <td>9767753.13</td>\n",
-       "      <td>15694048.49</td>\n",
-       "      <td>11892306.47</td>\n",
-       "      <td>9558049.09</td>\n",
-       "      <td>6157580.45</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5624083.12</td>\n",
-       "      <td>5748566.83</td>\n",
-       "      <td>6938559.08</td>\n",
-       "      <td>6765294.90</td>\n",
-       "      <td>10873832.79</td>\n",
-       "      <td>5236450.88</td>\n",
-       "      <td>15875505.99</td>\n",
-       "      <td>12425877.26</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 32 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "country   EU27_2020          BE          BG           CZ           DK  \\\n",
-       "time                                                                    \n",
-       "2008     8626928.75  9840862.32  8374278.10  12088284.26  18472875.79   \n",
-       "2009     7851179.61  8740690.23  7277828.03  11049662.42  17176547.55   \n",
-       "2010     8024312.00  9243216.52  7673499.85  11228220.11  16767331.74   \n",
-       "2011     7901995.31  8519668.04  8424931.82  10997932.47  16608386.02   \n",
-       "2012     7728876.39  8149775.00  7808042.43  10544785.70  15347310.62   \n",
-       "\n",
-       "country           DE           EE           IE           EL          ES  ...  \\\n",
-       "time                                                                     ...   \n",
-       "2008     10115618.26  15495691.70  11599639.01  11023011.51  7309667.12  ...   \n",
-       "2009      9321295.50  12786692.77  10448510.06  10354690.72  6435677.13  ...   \n",
-       "2010      9667309.77  16307459.75  12043627.47   9989268.71  6098359.65  ...   \n",
-       "2011      9668751.22  16334728.68  11509752.46   9620522.32  6223872.41  ...   \n",
-       "2012      9767753.13  15694048.49  11892306.47   9558049.09  6157580.45  ...   \n",
-       "\n",
-       "country          PT          RO          SI          SK           FI  \\\n",
-       "time                                                                   \n",
-       "2008     6489677.15  6792814.40  8093935.35  8115433.15  12652929.15   \n",
-       "2009     6178248.19  5751828.40  7375583.00  7304968.32  12003558.20   \n",
-       "2010     5739881.60  5581717.18  7437534.55  7305820.30  13441161.42   \n",
-       "2011     5697688.64  5895533.44  7280437.24  7294510.54  11997445.75   \n",
-       "2012     5624083.12  5748566.83  6938559.08  6765294.90  10873832.79   \n",
-       "\n",
-       "country          SE           IS           NO  RS  TR  \n",
-       "time                                                   \n",
-       "2008     6147808.43  17704691.59  12308807.83 NaN NaN  \n",
-       "2009     5514200.84  16180654.34  11368305.58 NaN NaN  \n",
-       "2010     6147726.61  16184246.73  11861900.26 NaN NaN  \n",
-       "2011     5607249.35  15554875.78  12700895.28 NaN NaN  \n",
-       "2012     5236450.88  15875505.99  12425877.26 NaN NaN  \n",
-       "\n",
-       "[5 rows x 32 columns]"
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-     },
-     "execution_count": 96,
-     "metadata": {},
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-   ],
-   "source": [
-    "# Drop the rows until 2008 - when the data really begins\n",
-    "df_carbon_short.drop([str(i) for i in np.arange(1995, 2008)], inplace=True)\n",
-    "df_carbon_short = df_carbon_short.drop(\"CH\", axis=1)\n",
-    "df_carbon_short.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 313,
-   "metadata": {},
-   "outputs": [
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-          7823731.5,
-          8175206.26,
-          7600569.84,
-          7453706.39,
-          6577156.14,
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-           "caxis": {
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-            "linecolor": "white",
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-          "title": {
-           "x": 0.05
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-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
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-           "zerolinecolor": "white",
-           "zerolinewidth": 2
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-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
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-   "source": [
-    "# Plot the carbon emissions scatterplot\n",
-    "fig = px.scatter(df_carbon_short)\n",
-    "fig.show()"
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-  },
-  {
-   "cell_type": "code",
-   "execution_count": 314,
-   "metadata": {},
-   "outputs": [
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                       {\"responsive\": true}                    )                };                            </script>        </div>'"
-      ]
-     },
-     "execution_count": 314,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import plotly\n",
-    "plotly.offline.plot(fig, include_plotlyjs=False, output_type=\"div\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.PairGrid at 0x365c99e90>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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ykPpzlzqCPCzMkru+/b249uUXxn+6+sXx2z/1oviZSy+Iv/5fD2sXEuQ6FyhNk/c25iBd1tbzCLqDag3bVOrPJUJqcmvGfktTRm3DWgVoxyDzN7frIJrVxvpw/QCMwr6WFjOdpnSpfV2lxXNnwCBWmhl3fvt78df/6+HY++BB8wAKN8i1g+sEaE8V/XXpPhVyNWrrOgdOVdVMcB9AKlLY5/QA9OJnywMApGVd2wcAo5icmV9y47Bg997JeOTQfByaP7bkBmPX9om48codce62TU0dKmTt/gOH421/+v/Ebfc+cvJtl154dvz7q3bGWz62J3bvnYzJmfkY3zzW4lECDGa164i25tr9Bw67dqF2g6yzVFsBANLS7/WFawsoX52du2eGfDV5DWBWpKmt60DrAdJXx3zQPtRj2F41SS5Sfu5SR5CPyZn5uOvb34t/f9XO+Mgd++K3PnfvyfddeuHZ8ZLvO9vXQSAxrnOB0jR1b2MO0nVtPI+gO6jWKE2l/FwipCinZuy3NGmUNqxVgHYMOn9zug6ieU2vD9cPwKjsa+kw02lSV9rXVXo8dwYMYrmZsXnsjJPfz33DJ75y8u3mAZSp32sH1wnQnqr668p9KuSqitZ1DpyqipngPoCUtL3P6QHoxc+WBwBIz9q2DwBGMX3kaM/3Hzt+YslNSMRjNxbX33JPTM3O13l4UISp2fnHOjrlReYjIu6495H4yB374vWXXRAREQdX6REgNatdR7Qx107OXNcu1GjQdZZiKwBAWga5vnBtAeWrq3P3zJC3pq4BzIp0tXEdaD1AHqqeD9qH+gzTqybJSarPXeoI8jJ95Gi8/rIL4iN37Is7lvmey1/61N/qFhLjOhcoTRP3NuYgNP88gu6gWqM2lepziZCqXJqx39K0YduwVgHaMcz8zeU6iHY0uT5cPwBVsK+lwUynaV1oX1dp8twZMIjlZsZK389tHkCZ+rl2cJ0A7amyvy7cp0Kuqmpd58CpRp0J7gNITZv7nB6AXvxseQCANHmhebK2deP6nu8/dvzEkpuQBbv3TsbkjCcrYDWTM/MrdnTHvY/EzvO2RUTEWav0CJCa1a4j2phrvWauaxeqMug6S7EVACAtg1xfuLaA8tXVuXtmyFtT1wBmRbrauA60HiAPVc8H7UN9hulVk+Qk1ecudQR52bpxfew8b9uSH0q44DbdQnJc5wKlaeLexhyE5p9H0B1Ua9SmUn0uEVKVSzP2W5o2bBvWKkA7hpm/uVwH0Y4m14frB6AK9rU0mOk0rQvt6ypNnjsDBrHczOj1/dzmAZSnn2sH1wnQnir768J9KuSqqtZ1Dpxq1JngPoDUtLnP6QHoxc+WBwBIkxeaJ2sTW8Zi1/aJZd+3a/tEzM4/2vP3HzxytI7DgqJMr9LJ3KPHY9f2iZjYMtbQEQFUY7XriDbm2moz17ULVRh0naXYCgCQlkGuL1xbQPnq6tw9M+StqWsAsyJdbVwHWg+Qh6rng/ahPsP0qklykupzlzqCvPQzK3QLaXGdC5SmiXsbcxCafx5Bd1CtUZtK9blESFUuzdhvadqwbVirAO0YZv7mch1EO5pcH64fgCrY19JgptO0LrSvqzR57gwYxHIzY+7R4z1/j3kAZenn2sF1ArSnyv66cJ8KuaqqdZ0Dpxp1JrgPIDVt7nN6AHrxs+UBANK0ru0DgFGMbx6LG6/cEdffck/s3jt58u27tk/E+67cEbPzx3r+/rM2rq/7ECF7W1fpZNum9fG+K3fE+GY360BeVruOaGOurTZzXbtQhUHXWYqtUJ79+/fH5OTk6h/Ygq997WttH0IRUv4cR0RMTEzE+eef3/ZhQLYGub5wbQHlq6tz98yQt6auAcyKdLVxHWg9QB6qng/ah/oM06smyUmqz13qCPIyvnksnv6ETT0/RreQFte5QGmauLcxB6H55xF0B9UatalUn0uEVOXSjP2Wpg3bhrUK0I5h5m8u10G0o8n14foBqIJ9LQ1mOk3rQvu6SpPnzoBBLDczNqxb2/P3mAdQln6uHbZunO/5Z5gLUJ8qr9O7cJ8KuaqqdZ0Dpxp1Jni+kNS0uc/pAejFz5YHAEhTUS80v3v37vjVX/3VuOuuu+KBBx6IW2+9NV7zmtf09XvvuOOO+IEf+IG46KKL4u677671OKnWuds2xQev2hmTM/Nx8MjROGvj+pjYMhbjm8dianY+dm2fWHRjsWDX9omY2OLmAlYzsWVsxY4u3z4Rz3ryljhn68YWjgxgdL2uI9rQa+a6dqEqw6yz1FqhLPv374/nPOe5cfjwbNuH0tPRud7fJM7Kcvgcb9q0Of7u777mxeZhSINeX7i2gPLV0bl7ZshfE9cAZkXamr4OtB4gH1XOB+1DvQbtVZPkJsXnLnUE+XnK1o26hcy4zgVKU/e9jTkIj2nyeQTdQbWqaCrF5xIhZTk0Y7+lDcO0Ya0CtGPY+ZvDdRDtaWp9uH4AqmJfa5+ZThtKb19X6fLcGTCI02fGEzabB9A1q107uE6A9lTdX+n3qZCrKlvXOXCqUWaC+wBS1NY+pwegFz9bHgAgTWtOnDhxou2DqMpf/uVfxh133BEXX3xx/NiP/VjfLzR/4MCBuPjii+PCCy+MBx98cOAXmp+eno7x8fGYmpqKrVu3Dnfw1Ob+A4fj+lvuWXQzsmv7RLzvyh3x1G2bWjwyUqLj3nRELrRMCcxcLTfBOqMJ/bb85S9/OS6++OK45PXvjK1PfWZzB9inB77yxfjbP/vduOwXPhRPe+7Otg9nWX+//+vxV//umrjrrrviRS96UduHs0Tqn+PpB74VX7rp3cmev7bZl+mX64u0aZlSdH3WaBn6k/qs0HKzUl8P5EvLadM+/dJyMzRJnbrSsY4oXYkt65YuKrHlXnROqbrWMsMzB9Om5TLprnu0XC9N0RQt58VsYCWptWytwnBSa5n8mL9p0PJwrF9So2UYXiozXceUJJWu2lBiy13+fNJdJbZcBfOA3Gi5fuYCTdDy8vRHbrQ8HK2TGi0TYTaVQMvV0QNt0nL6zAj6oWUog5YhH+vaPoAqXXHFFXHFFVcM/Pve9KY3xU/+5E/GGWecEZ/85CerP7AOmJqdj8mZ+Zg+cjS2blofE2eOxfjmsST+3nO3bYoPXrUzJmfm4+CRo3HWxvUxsaWZ44McLdfV6R1t3bQ+ztywLmaOPBp79n+v0e6BPNV1rdDWNUidXLvQxLrutc5K7Io8bH3qM+OJ5/+jtg9jiekHvtX2IRQj1c8x9Mse2VvV17HON1WynspRxayxHqiaNbW6ps+R59fS1MQ66OdrXNYDjKapmT7q36N9WF6dDff6szVJCpq+L6n679MRpGWlxk99+/im9fGrP/6CmDnyqG6hBavtxVXs1fZn4FRd/HqRObiyLq6HHJTweUm5uxLOL2VJ+d8d64U6WV++1kx3LKzVA7NH49D8o3Fo/lhs27Q+No+d0fahUTGzHdLqYGH+PnRwLqYOH43NY2fEmRvWmb/UrooOzhw7I37pVc+LA4ePxpaxM2Lz2LrYtnm9fYXipbSPpM65yofnLzhdP9/PpuvedJW2ftfywsfNzB2Nf/uai2L+0eNxaO5Rn09oQGp7zqnH80uvel6MnbE2pg7Px5kbzANoSh1zwfd/Q7ra/l6t1K5FIAd1dTNq63qGanWhqbavQyA3VfaQ6r0/8Jhhempjz9Q9AEBvRb3Q/DA+8pGPxDe/+c34wz/8w3jPe97T1++Zm5uLubm5k7+enp6u6/CycP+Bw/G2W+6J2/ZOnnzbru0TceOVO+LcbZuS+HvHN7sRYDEdL2+1rhY6uv/A4Xjrn/w/jXcPp9NyHuq6VmjrGqQJXbt20fLjmlzXy62zkruiflqGMmh5efbI/lR1Het8j07Lj7OeyjPKrMltPWg5fbmtqTa0dY5Sen5Ny82sg36/xgXD0nJzM72qvyelvYB0dLnlOhvu58/WJFUZpuOm70vq+vt0REly3pNXavw9r7kofvm/fjX+7689tOjtN165I5715C1tHCrULtWWV9uLq9yr7c+UINWWc9LlrxeZg0u1tR603FtJnabYXUnnt21arkbK/+5YL93QVsvWl681U60c9uVD88fi7Z/62053XzqzfXQ5tExvKXZwaP5Y/PJ//WpSx1S6rrdcRQe9/ozxzZUfMiyrjZZT3EdS5Vzlp43nL7q+J6dquX5/8LlPjl961fPi33zS8waD6Mrzgrm13O8eZS+ja1JqObX+Ujse6CWllqtUR4e+/5uUldpyv9r+Xi17P1XpUst1dzNs63qmCl1qeTVdaKrt6xDqo+V6VdFD6vf+pEHL7Rmlpyb3TN3nQctQBi1Dvta2fQBt2rt3b1x//fXxh3/4h7Fu3bq+f9973/veGB8fP/nfeeedV+NRpm1qdn7JRXdExO69k3H9LffE1Ox8UX8v5dDxUv12pT9SouX01TUzzKKyaPkxba/rtv9+8qdlKIOWl7JHNsv5roaWH2M9caoc14OW05bjmmqac/SYrrfcxDqw1miClpvpTM/Urast19mWbmnaoB03vUY1Af3JdU/u1fi/vvUr8Zynbl3ydu1TshRbXm0vfnD6iL0aTpNiyzlxD8Cp2lwPWl6ZTuvl/FZLy6NLeU2mfGxUq42WrS/ngOqlvi9b8+XzOa5G6i3TW4odpHhMXdDllqtYc9YtqWi6ZWu/f84V/erynpyqlfr9R0/dGjfc+hVds6ycWvZzMWFlqbScWn+pHQ+sJpWWq1RHh9omdSW23K+2+2z776csXWk51W5SPS7y05WWV9OFprrwGLtMy2lz70+/tNyOXHrK5TjRMpRCy5Cvzr7Q/LFjx+Inf/In493vfnc8+9nPHuj33nDDDTE1NXXyv/vuu6+mo0zf5Mz8kovuBbv3TsbkTD0X3m39vZRDx0v125X+SImW01fXzDCLyqLlx7S9rtv++8mflqEMWl7KHtks57saWn6M9cSpclwPWk5bjmuqac7RY7rechPrwFq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xe+KWmNm+pdc1lcY94mjjdDz0fTT066dMpZ3J0JQ+Z24fv3fJLWc+/zJfG/2YtOWh7cmhXS/1KO1c1kpdvF7lKK3lttl7wzKk13toLWczpL1aopLuf18tl3QP+uZe0ISSz2V7fGXuCysppeUh7s8hXjPtKKXjo3Wxx3VEJqW2PIqNtKhbsvN9r7LWAuOq+VzeCL22w30tx1Bahuz8fcfuuX+0oaRz2R4fj/tGRHMtt7mf7FVYn/fYtMEe6V4T57LX7SD3gT6V9HkZ2tLlnB3y/zaQ84y2DGVvDeWzco1rho0YSstNGOp1U4dJ3mPb2+Nz72iac3l0Q71u6tDk976GvteHfv30q5TvYzdFT81xL+sybste59G4T3Ql27k8FGYER9My5KBlqFfaB83fdNNNcd9998VrX/vaVX/OvffeG6eccsoRP3bKKafEvffeu+a/+6qrroorrriiiWVO7Nv3PRBvvfHO+Ozdc4d/bM+u2bj6ot1x6snbelxZOdwjVrLRjoe+j4Z+/ZSrpDMZmtLnzO3r9y615cznX+Zroz+TtDy0PTm066UuJZ3LWqmL16ssJbXcNntvWIb2eg+p5WyGtldLU9r976Pl0u5Bn9wLmlLquWyPr8x9YTUltDzE/TnEa6Y9JXR8tC72uI7IpsSWR7GRFnXLEAz9+14lrQUmUeu5vBF6bYf7WpYhtAxD4O87dsv9oy2lnMv2+HjcNw5pouU295O9CqPxHpum2SP9mPRc9rod5D7Qt1I+L0NbupyzQ/7fBnKe0ZYh7a0hfFaucc2wUUNouQlDvW7qMe57bHt7fO4dbXAuj2ao1009mvre19D3+tCvn/6V8H3spuipOe5lfcZp2es8GveJLmU6l4fCjGAlWoYctAz1mlpeXl7uexFteOlLXxpbtmyJP/7jP17152zZsiX+4A/+IC6++OLDP/Z7v/d7ccUVV8R3v/vdVX/d4uJiLC4uHv7nhYWF2LlzZ8zPz8f09HQzFzCC+f1L8YaPfemIN9iH7Nk1G++/+JyY2b6ls/WUyD1iNRvpeOj7aOjXT9lKOZOhKX3O3D5/7xJbznz+Zb42+jVuy0Pbk0O7XupTyrmslbp4vcpTSstts/eGZYiv91BazmaIe7UkJd7/rlsu8R70xb2gSSWey/b4ytwX1tJ3y0Pcn0O8ZtrVd8dH62KP64iMSmt5FBtpUbcMxZC/71XSWmBSNZ7LG6HXdriv5cneMgyFv+/YHfePNpVwLtvj43HfeKxJW25zP9mrMDrvsWmSPdKfSc5lr9tB7gMlKOHzMrSlyzk75P9tIOcZbRna3sr+WbnGNcM4srfchKFeN3UZ5z22vT0+9462OJfXN9Trpi5NfO9r6Ht96NdPGfr+PnZT9NQc97JOG23Z6zwa94muZTmXh8KMYDVahhy0DPXa3PcC2nDPPffEpz/96fjkJz+55s978pOffMwD5b/73e/Gk5/85DV/3datW2Pr1q0Tr3NSc/uWVnyDHRFx291zMbdvafBvst0jVrORjoe+j4Z+/ZStlDMZmtLnzO3z9y6x5cznX+Zro1/jtjy0PTm066U+pZzLWqmL16s8pbTcNntvWIb4eg+l5WyGuFdLUuL977rlEu9BX9wLmlTiuWyPr8x9YS19tzzE/TnEa6ZdfXd8tC72uI7IqLSWR7GRFnXLUAz5+14lrQUmVeO5vBF6bYf7Wp7sLcNQ+PuO3XH/aFMJ57I9Ph73jceatOU295O9CqPzHpsm2SP9meRc9rod5D5QghI+L0NbupyzQ/7fBnKe0Zah7a3sn5VrXDOMI3vLTRjqdVOXcd5j29vjc+9oi3N5fUO9burSxPe+hr7Xh379lKHv72M3RU/NcS/rtNGWvc6jcZ/oWpZzeSjMCFajZchBy1CvTX0voA3XX399POlJT4oLLrhgzZ/3vOc9Lz7zmc8c8WO33HJLPO95z2tzeY1ZePDAml+/f52vD4F7RBOGvo+Gfv0AXepz5pr3R8p8PzJfG3Ua2p4c2vXCuLRSF68XfbH3hsXrTS3s1X65/+7BY7kXZGePr8x9oWRD3J9DvGaGpYs9riMow0Za1C20o6S2SloLsDa9tsN9BeifWTwZ94/s7PHxuG80qc39ZK9CO7TFeuyROnndDnIfANrV5Zwd8kwf8rXTLntrdTXemxrXDG0bahdDvW7ys7fH595RgqHuw6FeN8Mz9L0+9OuHJumpOe7lMHidR+M+AWsxIwAAyrS57wU07ZFHHonrr78+XvOa18TmzUde3iWXXBJPfepT46qrroqIiDe+8Y3xwhe+MN773vfGBRdcEB//+MfjL//yL+ODH/xgH0vfsOkTjl/z6yet8/UhcI9owtD30dCvH6BLfc5c8/5Ime9H5mujTkPbk0O7XhiXVuri9aIv9t6weL2phb3aL/ffPXgs94Ls7PGVuS+UbIj7c4jXzLB0scd1BGXYSIu6hXaU1FZJawHWptd2uK9wpL1798bc3Fzfy1jV7OxsnHbaaX0vg4aZxZNx/8jOHh+P+0aT2txP9iq0Q1usxx6pk9ftIPcBoF1dztkhz/QhXzvtsrdWV+O9qXHN0LahdjHU6yY/e3t87h0lGOo+HOp1MzxD3+tDv35okp6a414Og9d5NO4TsBYzAgCgTOkeNP/pT3869u7dG5dddtkxX9u7d29s2rTp8D+ff/758Yd/+Ifxjne8I3791389du3aFTfddFM85znP6XLJY5vdsSX27JqN2+4+9n+IZM+u2ZjdsaWHVZXFParD/P6lmNu3FAsPHojpbcfH7IlbYmZ7Oa/N0PfR0K+f0ZXeMtSgzZm7XqPm/ZEy34/M18ajajqXh7Ynh3a9TKamlpumlbp4vdY25JbbZu/VbyN9eL1pQhcz2V7tl/vvHjyWe5GX99gH2eMrc1/qMcSWh7g/h3jNQzPElh+riz2uI9o29I5HtZEWdUsfhtBySW2VtBZyGULLXdPrePyZYxjd3r1741nPenY88MD+vpeyqm3btsff/M1XPWw+GbN4Mu7fcAz1PbY9Ph73rVw1ttzmfrJXKV2NzUZoi/XZIwfV1rjX7SD3gaPV1jKMos993eWcHfJMH/K1064+91bpZ3KN3dW4Zuqn5TIN9boZX+ktH2Jvj8+9G4bSWx7qPhzqdWdSelulGPpeH/r1cyyzY3w19VT661zTvWR8XufRlHyfSp8lMAQlzwgeZV4CwPBMLS8vL/e9iNotLCzEzMxMzM/Px/T0dKe/97fveyAuv/HOI95o79k1G9dctDuecvK2TtdSKveobN++74F46413xmePen2uvmh3nNrh67Nex0PfR0O/ftZXS8tQgzZm7qiNljLvS2m5lPvRhszXRp3n8tD25NCul/HU2HLTtFIXr9fKtNw+e69e4/Qx9Nc7c8td6HImD32v9q30+99Fy6Xfgy65F/l4j30ke3xl7kv5htzyEPfnEK95KEpouYQzuYs9riPaUkLHEWW0PIqNtKhbujSklktqq6S1kMOQWu6aXjemtj9zzMoytlyqO+64I84777x47mXviumnnN73co6x8J1vxl98+Iq4/fbb49xzz+17OWyQv+/YLvcvv6G/x7bHx+O+lafmltvcT/YqpSql2dV4j82khr5HSml8o+fy0F+3Q9wHDqm1ZVhLCfu6yzlbykz357DJpI+9VcLsisj5WbnGNVMvLZdtqNfNxtXS8iH29vjcu9xqaXmo+3Co151BKW11bdzvfQ19rw/9+nlUKbOj5v8eVUNPpbzO66nhXrK2UVr2Oo+mxPtUyyxhcjWfy0NR4ozgUaXMSy1DDlqGevT+oPmPfOQj8S//5b+MrVu39rmMifQ99Ob3L8XcvqW4/8EDcdIJx8fsji0xs31L5+somXtUpvn9S/GGj33piA8hh+zZNRvvv/iczl6nUToe+j4a+vWzutpahho0OXM32mgJ876klku4H23JfG1DVvO5PLQ9ObTrZWNqbrlpWqmL1+tIWu6OvVefSfoY8uudveU29TGTh7xXS1Dy/e+q5ZLvQdfcizy8x16ZPb4y96VcWh7m/hziNWdXSsulnMld7HEd0bRSOo4op+VRbKRF3dKFIbZcUlslrYW6DbHlrul1NDX+mWNWlrXlEh160PxPvf36ePxpZ/W9nGP8YO9dccuVl3rQfKX8fcf2uX95eY99kD0+HvetHBlabnM/2auUpqRmV+M9Nk0Y6h4pqfFxzuWhvm5Hcx+ovWVYSUn7uss5W8JM9+ewyabrhkuZXVk/K9e4Zuqj5ToM9boZXW0tH2Jvj8+9y6m2loe6D4d63TUrqa2uTfK9r6Hv9aFfP2XNjtr/e1TJPZX0Oo+i5HvJ+kZt2es8mpLuU22zhMnUfi4PRUkzgkeVNC+1DDloGeqxue8FXHrppfGyl70snvSkJ/W9lGrNbPemej3uUZnm9i2t+CEkIuK2u+dibt9SUa/b0PfR0K+f1dXWMtSgyZm70UbN+yNlvh+Zr23Iaj6Xh7Ynh3a9bEzNLTdNK3Xxeh1Jy92x9+ozSR9eb8bRx0y2V/vl/rsHj+Ve5OE99srs8ZW5L+XS8jD35xCvOTstH6mLPa4jmqbj8WykRd3ShSG2XFJbJa2Fug2x5a7pdTT+zDFAfcziybh/eXmPfZA9Ph73rRwZWm5zP9mrlCZDsxHaYn1D3SO1Nz7U1+1o7gO1twwrKWlfdzlnhzzTh3zttKvLvVXS7BpFjd3VuGbqo+U6DPW6GV1tLR9ib4/PvcuptpaHug+Het01q62tUgx9rw/9+jE7mlRyT7W9ziXfS5rjdR5NSfeptlkCQ1DSjOBR5iUADNemvhewvLzc9xKAniw8eGDNr9+/zteBMmgZyqZRGBbNQw5ahhy0DKvTB12z5wByMM8hBy1DDlqG+ukYctAy5KBlSmEvAgBZeF8DOWgZ6qJZyE3jkIOWyci+BmpkdkEOWoYctAw5aBnaoS1gHGbHMHidgSaYJQCjMS8BYLh6f9B8RMTU1FTfSwB6MH3C8Wt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BnAN6AMoi093m9Qdoh/6FbpJ9ACAHu9s+gCotLS3FmTNnbv732bNn48knn4wXv/jFcccdd8T73ve++LM/+7P4tV/7tZvf8+STT978s9/85jfjySefjKmpqXjVq17V9OFDREQsXr7a9/GLAx4HNpMrgM10I11hrEMZZBmYlB6BMsk2GxkTQM50GORDXqF8cg7tkkFglT6A9WQCukv+YTyyA+RCX0E+5BXoCn0HQFeY8yAtMgnpkEfInxwDEboAukDOAT0AZZHpbvP6A7RD/0I3yT4AkIOibjT/xS9+Mb73e7/35n+/5z3viYiIt7/97fHJT34ynn766Th37ty6P3P06NGb//+xxx6L3/iN34hv//Zvj69+9auNHDNsdHDfnr6PHxjwOLCZXAFsphvpCmMdyiDLwKT0CJRJttnImABypsMgH/IK5ZNzaJcMAqv0AawnE9Bd8g/jkR0gF/oK8iGvQFfoOwC6wpwHaZFJSIc8Qv7kGIjQBdAFcg7oASiLTHeb1x+gHfoXukn2AYAc7Gz7AKr0hje8IVZWVjb975Of/GRERHzyk5+Mz3/+8+v+zFbf7ybztGluZiqOHZ7b8rFjh+dibmaq4SOC/MkVwGa6ka4w1qEMsgxMSo9AmWSbjYwJIGc6DPIhr1A+OYd2ySCwSh/AejIB3SX/MB7ZAXKhryAf8gp0hb4DoCvMeZAWmYR0yCPkT46BCF0AXSDngB6Assh0t3n9Adqhf6GbZB8AyEFRN5qHiIiF5V58+ZmleOLcs/Hlby7FwnKv7UMayez0VDx0/MimzcSxw3PxoeNHYnbaRoL0pJ47uQJS1WZ/6kZWpT6PT8pYpymlZ6ltsgzdVGW36hEYTS5rG9lmo5LHRC65hEl1eayX3GHQtLq7RF7hBaXO3XLeLaWO45zJICXRMZPRB2yly7mSCeiuSfLf5d6EpudOeQPGtbGvpqd2xbu+7674jR//7vhHf+twzD/X0ykUKce5096crsoxr0xG3wGkz/xcDXMe/chZ82QS0tFUHnUtkzKGtmdeJVVy2yxdQOl0ipyTH7mtnh6gBLrhBTLdbSW9/nIN5KSk/t2OXqZJuYy3LmQfAMjfjpWVlZW2DyJ3i4uLMTs7GwsLC3Hw4MG2D6fTnrpwKd77yKl49PT8za8dOzwXDx0/Ercd2t/ikY1uYbkX80u9uHj5ahzYtyfmZqZsImokx+PLKXdyVT5ZJiep9GeK3SjLzUllHDYhxbFeui5luUtZapssN69LWSYtdXVrV3tElhlFjmubrmRblodX2pjIMZdsT5a3Z6zfUFqHlUqW09Vkl8hr/mR5Ml2Yu+U8fZPmuAvjOGcy2B2lzsk6pjr6IA9NZFmubpAJ6lTqvFyKUfOvN7tLltdrYu6UN+ogy92zsNyL88/1YiUifv63vhSPnjl/8zGdki9Z3lruc6e9efd0Ocu555XJlNZ3Xc4ylESWzc91KG3Oy0HqWZazdslkHlLPMdWoM4+6Ng05Z9kYGo55tRtyybLctkcX5CGXLKdCp6wn5+mQ5e3Jbb30QLVkuTm6YWsyXY1cs5z76y/XVC3XLJOf3Pt3O6n0six3QyrjbRSlZr8usgxlkGXIhxvNV0DppWFhuRfv+tQT6zYLq44dnouP3n/UQpxtyfF45I7UyDK50J/9yXIzjEPq1pUsyxKl60qWSYturZ4sMyz5S5ssd5NclkeWt2askxtZTpMuYVSyPD55IxWT5Ng4hnSUOCfrGLqo7izLFTSjxHm5q/Rmt8lys+SNushyN+mU8sjyZsY5OepqluWV0nQ1y1CarmfZ/EwpUs6ynMFwUs4x6dO16cg1y8YQrJdDluUWBsshy6nQKaRMlrcmt+RGlpuhG6ibLDdPrqmDLMP4UuplWS5fSuON+sgylEGWIR872z4AqMr8Um/LzUJExInT8zG/1Gv4iKB8cgcwHv1JCoxDqIYsAVRPt0J75A/SI5d0hbEOVEGXQHPkjRIYx0CddAxUT64ARqM3oTnyBlRJp9AFxjnkQ14BID3mZ6ifnAHUT9cyKWMI8iO3QJV0CuRHboGt6AYoj1wDpEUv0yTjDQCgem40TzEWL1/t+/jFAY8Do5M7gPHoT1JgHEI1ZAmgeroV2iN/kB65pCuMdaAKugSaI2+UwDgG6qRjoHpyBTAavQnNkTegSjqFLjDOIR/yCgDpMT9D/eQMoH66lkkZQ5AfuQWqpFMgP3ILbEU3QHnkGiAtepkmGW8AANVzo3mKcXDfnr6PHxjwODA6uQMYj/4kBcYhVEOWAKqnW6E98gfpkUu6wlgHqqBLoDnyRgmMY6BOOgaqJ1cAo9Gb0Bx5A6qkU+gC4xzyIa8AkB7zM9RPzgDqp2uZlDEE+ZFboEo6BfIjt8BWdAOUR64B0qKXaZLxBgBQvd1tHwBUYWG5F9dXVuLfvv11sWPHjnj83LPxiS+cjeXetYiIOHZ4LuZmplo+SijDwnIv5pd6sXj5aszs3R0f/Lt/Nf6P//O/3czbKrkDcrG21w7u3xNzt0zF7HS9/TU3MxXHDs/FidPzmx7TnzRl1HHYRlYgB5N2umwBXdWv/6yXoT1V5s86B6pR2ryoG9huDJQ21oHRVTFH6BJohs9pUYqt5o3pqV3xwL13xuv/8q2xcKkXX/7mkn0LMJaZfbvjN378u+PCpauxb8+udfOluRLGU/eez7VLoDTWI9AM18qg2+rYR3i/i5KtZmbhUi8+9fe/O05++fy6eTPCOKebUr4uZV4CgPqNuhYwP0P95Ky/lPcwQH2qzr6uZVh+FhHK0URurVWhO/xMGOSn6rWAeR/KsF03TE/tip/5wVfF9ZWVeOLcs3IOGalr/2/upzTGNE3xfgp12thlM/t2x9/+zpfEZ//omU3fa7wBAIzHjebJ3lMXLsV7HzkVj67ZmN5z163xkfuPxrs/9US87ttfFB86fsSFEajAVnm77/BcfOIdfy0e+OQfrPtFSHIH5GCrXjt2eC4eOn4kbju0v7Z/d3Z6Kh46fiQefOTUuovr+pMmjTIO28oK5GCSTpctoKsG9Z/1MrSnqvxZ50B1SpoXdQODxkApYx0YXVVzREnzJqTK57QoycZ5Y3pqV3zk/qPx8Mmz8bHPnbn5ffYtwKj6zZf/7r+ci1/44VebK2EMde75XLsESmM9As1wrQy6ra59hPe7KNVWmbl3zby53LtmnNNJqV+XMi8BQL3GWQuYn6F+cra91PcwQD3qyL6uZRh+FhHKUnf3W6tCt/iZMMhPlWsB8z6UY6tumJ7aFZ94x1+LX/7cmXjf/+sPb36vnEMe6tj/m/spjTFNk7wnR12267IPvPnVERHrbjZvvAEAjG/HysrKStsHkbvFxcWYnZ2NhYWFOHjwYNuH0ykLy71416eeWLdxWHXf4bn4wA+/Og5N77FZYCA5Hqxf3o4dnov/44dfHc8u9+LAvj0xNzMld7RClhnFoF776P1Ha++yheVezC/14uLlq/pzDVlu1qBxmEJWyFPXsjxqp8sWuehalqnfKP1nvVwdWWZUk+TPOqc+stxtuc+LuuEFXc3ysGMg97FOd3Q1y3WoY47QJQxLlkfjc1qkqIocr84b11dW4hf+3/81Hj1zftP3dG3fAk0raU4eNF/+87fcHS89uK+FI4P6NZXlqvd8rl3CeiXNy11lPUKELDfBtTKaIMvpamIf4f2ucsjy4HnzZ3/wVbFzxw7jnKTVkeWcrkuZlyiFeRnKUEqWJ10LmJ/JXQ5ZlrP1ctrD0Iwccszk6s6+rm1fqln2s4gwmlSzvJU6cmutSilyynIq/EwYKZLl/iZdC5j3aYosN2ttN7xoeip++jNfikfPyDmTk+X2VLX/N/cTUVaWjWnaksL7KSVluesGddk/e8vdsXT5ee/fFUqWoQyyDPnY3fYBwCTml3pbbhwiIh49PR/PX19pdbOwullevHw1Du7fE3O32LyQr355O/E/8vaaO1400b8hM0CTnrl4pW+vzS/1au+g2Wk9R/sGjcNBa4AqsmINQAlG7fQmsjUqWQSaMEr/Nble1oGw3iT5m2SdI4vkoo2xmvt1pBT3QDRr2DGQ+1gflzmQLqtjjqijS+QU0v+c1iRkvNtW540vP7O05S8Uikhr32K8QtoGzZdLl5+Pu/61gQABAABJREFUl3bk52v0FXWpes/n2iXQlrrmSusR2F6VuSv5WhmwtbUdsn9qV+37iK6+d0592rxWM2je3LljR7ziJTONHAukJKfrUuYlquB9A4D1Jl0L5Dw/mxPIRao5aytDOe1hgOEM0yd1Zz/VrqV9fhZxPWtoSlJHbnNZq8oyVK/unwmTW6jepGuBtud9vQD1WNsNN+b1dtf3sk7KchmfVe3/2577oWrG9GRy6cAUdeX9FJoxqMuWLj+f5c+H6BgAIEVuNE/WFi9f7fv4xQGP1+mpC5fivY+cWre5OXZ4Lh46fiRuO7S/teOCcdWdN5kBmvTUhUtx7i+W+35Pm+sISIk1ANQjtf2sLAJNSa3/InQgVG3cnMsiuTBWx5PiGoBmGQPb0yt0XQ79IKdwQw55HYeMsyqHMW68Qvpy6JIm6Cty8uxyr+/jXckt0Kw650rrEdha1bmTNeiWjR3yK299bd/v1wGkpu1rNeZN2Jps0CVtz0UAKerqWsCcAJNpM0Nd7S0o1bB9Ivu0xdh7gTU0DJZDZ8gy1KuOHpBbSFOb875egGa0vb6XdVLWxfHZdidA1Yzp8XWxAyFVJXaZjgEAUrWz7QOASRzct6fv4wcGPF6XheXepg1ARMSJ0/Px4COnYmHAL6SDFNWZN5kBmrTaOYO0tY6A1FgDQD1S2s/KItCklPovQgdCHcbJuSySC2N1fKmtAWieMbA1vQLp94OcwgtSz+s4ZJy1Uh/jxivkIfUuaYK+IicLy73oPX+97/d0IbdAs+qeK61HYLM6cidr0B1bdcje3f1/NF8HkJIUrtWYN2FrskFXpDAXAaSoi2sBcwJMpu0MdbG3oFSj9Ins0xZj74a253/IReqdIctQv6p7QG4hXW3N+3oBmtPm+l7WSVlXx2fqe34YlTE9nq52IKSqtC7TMQBAytxonqzNzUzFscNzWz527PBczM1MNXxEN8wv9TZtAFadOD0f80s2AeSnzrzJDNCk1c554usX4p67bt3ye9pcR0BqrAGgHintZ2URaFJK/RehA6EO4+RcFsmFsTq+1NYANM8Y2JpegfT7QU7hBanndRwyzlqpj3HjFfKQepc0QV+Rk/mlXvzuV85v+znC+zqSW6BZdc+V1iOwWR25kzXojq06xM8jkZMUrtWYN2FrskFXpDAXAaSoi2sBcwJMpu0MdbG3oFSj9Ins0xZj74a253/IReqdIctQv6p7QG4hXW3N+3oBmtPm+l7WSVlXx2fqe34YlTE9nq52IKSqtC7TMQBAytxonqzNTk/FQ8ePbNpAHDs8Fx86fiRmp0ffPCws9+LLzyzFE+eejS9/cykWlkdfsC9evtr38YsDHocUDZO3cfMjM1CvKua2kqx2zie+cDbeec+dm365030TrCOgNAvLvTj/XC9+7n/5n+K+Ctfcq6wB6LI69rPbGbQWkEWgSU323zCG7UD7KhjeODnPYT2iB4i4MVanp3bFu77vrvi3b39d/MpbXxufeMdfi3d9310xPbUribGaqtTWADQvxTGQQrfnMAdCE37hh19dyzXoKsgpvCDF+XwQ18cZxbBjvK11pPEKeahrvkxhDzssfUU/qY3lxctXt/0c4T133Rrv/1/+pyTXuUA6xum1uufKHPfvdFsT64M6cidrkI66e2SrDtluH6EDGFed4ziFazXmTdiabDCO1K6xDiOFuQggRTmtBaqaf8wJ0F/qn3fMqbegK5r4fX+yT1vGHXs5Xjvpp+35H3LR1nw1bOfIMtRvkh7YKstLV+QWUtXWvL9xPt/4u4d6z1/Lfv8BqZg055NcG7B2J2VVjc/crp+5Rk1pjOnB7NMhfSV12cJyL648f23T7xVeS8cAAG3a3fYBwKRuO7Q/Pnr/0Zhf6sXFy1fjwL49MTczNdbG4akLl+K9j5yKR0/P3/zascNz8dDxI3Hbof1D/z0H9+3p+/iBAY9DqvrlbZL8yAzUp6q5rSSrnbPcuxbv/tQT8cC9d8YD99wZV56/Hnt374y7vmUmXtbRcwNrre2P6ald8cC9d8b/7W++Ivbu2RmH9k+NveZeyxqArqtyP7udYdYCsgg0rYn+G9agDjy4f499FYxh1Jynvh7RA6ya3b8nPnL/0Xj45Nn42OfO3Pz6PXfdGh+5/2gc3G/t3E9KawDakdIYSKXbU58DoW6rWXzsa8/GA/feGe94/csjIuIvvWh/fOvBfUnMEXIK66U0nw/i+jjjGDTG21xHGq+Qj6rny1T2sMPSV2wnxbF8cN+ebT9H+MTXL7RyTEA+xu21JubKnPbvdFtT64O6cidr0L4memSrDlm7j/iZN70qLl+9pgMYW93jOJVrNeZN2JpsMIoUr7EOI5W5CCBFOawFqpx/zAmwvVw+75hDb0FXNPn7/mSftow69nK9dtJPCvM/5KLp+WqUzpFlaMY4PbBdln/hh18d01O7Yrl3bcs/J7fQrjb2qWvn8+mpXVv+7qHc9x+QknFzPum1AWt3UlbF+Mz1+plr1JTGmN6efTrko4Qu26pzVn+v8Ls/9cTNztExAECbdrZ9AFCF2empeMVLZuI1d7woXvGSmbE2DgvLvU0L+IiIE6fn48FHTsXCcm/ov2tuZiqOHZ7b8rFjh+dibiafjQ1stFXeJs2PzEA9qpzbSrK2c5Z71+JjnzsTP/arX4x/8OuPx6/+7lfj0LSLdbCxP1az8qMf//34l//f05VdrLcGgGr2s9sZdi0gi0Ab6uy/UQzqwFv27ravgjGNkvOU1yOur7DWLXt3x8Mnz8bJM+fXff3kmfPxyZNn45a9u1s6snyksgagPSmMgZS6PeU5EOq2Notr36/5sV/9Yvzif/qjtg/vJjmFzVKYzwdxfZxJbDfG215HGq+Ql6rmy7a7Zxz6iq2kOpZXx+vGzxH+2K9+MU59/ULceovxCmxtkl5raq7MYf9OtzW5Pqgzd7IG7WmqR7brkOXetTj19Qvxstl9OoCxNTGOU7pWY96ErckGw0j1GuswUpqLAFKU8lqg6vnHnABby+3zjin3FnRFG7/vT/Zpy7BjL+drJ/2kMv9DLpqar0btHFmG5ozSA/2y/LO/9aX4mR981ZZ/Tm4hDU3vU9fO5w/ce+eWv3so9/0HpGbUnFdxbcDanZRNOj5zv37mGjWlMaY3s0+H/OTcZdt1zskz5+Phk2fjgXvvjAgdAwC0z43m4X+YX+ptWsCvOnF6PuaXhr/AOTs9FQ8dP7Lpguuxw3PxoeNHstrcwDAmzY/MQD2qnNtKonNgsKb6Qx6hXsNmWRaBLhvUgUuXn7evggakvB5xfYW1li4/v+kHvVZ94cz5WLr8fMNHBIwjpW5PeQ6EuqWUxX7kFPLk+jh1aHvuMl6hm9runnHoK7aS6lg2XoFxTdJrugduaHJ9IHdQJj/zQAmaGMfGMEAZUr3GOgxzEUC+qp5/zAmwNZ93BEbl9/3BZjlfO+lHXiFNo3aOLEOaBmX5tXccklvgprXz+dHbD237u4dy3n9A7qq4NmDtTsomHZ+lXj8DymGfDjSpX+ecPHM+jt5+SMcAAEnY3fYBwCQWlnsxv9SLxctX4+D+PTF3y9TYC+zFy1f7Pn5xwOMb3XZof3z0/qMxv9SLi5evxoF9e2JuZvzjg6aMk6sq8iMzUL2q57YUjbsW0DmwvYXlXlx5/lr8yltfG/v27IrHzz0bn/jC2VjuXbv5PVX2hzxSoir3qpMYZS0gi0BqmuzSfh34xLln+/7ZEvZVkIqm1iOj9ksXrq8wvI3jYXpqVzxw751x9PZDceX569F7/losLPeso6Elw3Z8at1uT05XbZfF1fn1yvPX4olzz7Z6fW2VnMJgqVwXX+X6OBtVMUZTWEcar5C2OubDFLpnHPqKjZoey6Pk0XgFxrG21za+X7Rvz664vrLS98/rHmh+fXDL1K74mR98VVy4dDVmpnbF9NTuODS9R+4gY02+12Tupi5Vz4fb7YeNYYB8rXb7+ed68Yl3/LUtf9YzIt33C1aZiwAm09Zns+q4hmdOoGmpfbZxKz7vCIxq0verI/QJ5Vm6cjXe9X13rcvC2usoqV876UdeyUEO6+4qjbNflmWYXNVdMyjLl3rX5BYy0dRaZHU+/9Nnlvp+X877D2jbJHmu6n0ta3dSNur4XJupvbt3xru+764tP3cUYf4CRmefDlQh1c/Fze7fEx+9/6iOAQBa50bzZOupC5fivY+cikdPz9/82rHDc/HQ8SNx26H9I/99B/ft6fv4gQGPb2V22oUF8jJurqrKj8xAteqY21Iy6VpA58BmW+XqnrtujY/cfzTe/aknbn4QoOr+kEdKUvVedRKjrgVkEUhFG126XQeWvq+C1NS9HhmnX/QAa60dD9NTu+Ij9x+Nh0+ejY997szNr7e1/oeuG6XjU+x2e3K6aKsspjy/yilsL6Xr4qtcH2etqsZoKutI4xXSVNd8mEr3jENfsVaTY3mcPBqvwKhWe22761n3HZ6LDw1YB+geui6V9cHsdGX/DNCwpt9rMndThyrnw0H7YWMYID/D/qxnRNrvF6wyFwGMp83PZtV1Dc+cQFNS/GzjVnzeERhVFe9XR+gTyjK7fyqeOPfsuiysvY6Sw7WTfuSVlOWy7q7SuPtlWYbx1dE1w2RZbiF9Ta9FZqen4sUDeiH3/Qe0ZdI8V/m+ljUAKRt2fI7yuaMI8xcwGvt0oAopfy7uRfoGAEjEzrYPAMaxsNzbtNiPiDhxej4efORULCz3Nn3/l59ZiifOPRtf/ubSpscjIuZmpuLY4bkt/71jh+dibsYCnrL1y9XP/daX4huLl7fNkfxAmkrO5qhrgdwMs3aBSW0cZ99YvBw/+1tf2pSrk2fOx8Mnz8YD994ZEfn3B9RplPmpia4veS0ApKmKbkttra9LIT3jds24/aIHWGvteHjg3jvj4ZNn4+SZ8+u+p5TrU7lzfa1bRn2Pa2bf7vjb3/mSLf8u3Q7N2WqdVeX8ai6AZgy712o6k/ZyrKryemPb48rcBunaqmump3bFkdsPxVfnn4vHv/YXY+e27e6BqjQ1lpt+r9H8DN212mvbXc96tCPvF+lBJlHq+qBucgcvqOK9JpmibVXNh6XNd23TDeTCWC3bdt2+8Wc9I7xfAFCyqtf6o64fvF9NznLaK8sa47An7DbvV9dDrvK1sNyLn/nMl25mYXpqV7zr++6KB+65M3ZExKf+/t+ImX272z1IKFQO6+46+t0aHppV1zWyhUu9+NTf/+541/fdFdNTu9Z9jyxDPaqel9tai1gLQH/jZL2KPMsmvGCUzx1FdCMjrv/CCybNQ13rcHM5pKGpObPt9xd0DgCQC594IkvzS71Ni/1VJ07Px/xSL2anbyy6n7pwadPm4NjhuXjo+JG47dD+m1+bnZ6Kh44fiQcfORUnNnzvh44fufn3Qam2y9X01K74v/z1O+Kn/v2T8eiaD5SvzZH8QJpKzuYoa4HcDLt2gUlsNc7uOzwXb3/9y+N3v3w+lnvX1n3/yTPn44F77iyiP6BOw85PTXV9yWsBID1VdVtqa31dCmmZpGvG7Rc9wFprx8PR2w/Fxz53Zsvvy/36VO5cX+uecd7j+sCbXx0REZ/9o2fWfV23Q3O2WmdVNb+aC6A5g/Za55/rxXO9a41n0l6OVVVeb2xzXJnbIG0bu2Z6ald85P6j8fDJs+vWt+Pk1pxGKZoay02+12h+hm5b7bWvzj/X2feL9CCTKnF9UDe5g/Umfa9JpkhBVfNhSfNd23QDuTBWy9ev21d/1jPC+wUApatyrT/O+sH71eQsp72yrDEqe0K8X109ucrb/FIvHj1z47Wr8vObwGCpr7vr6ndreGhW3dfI7r3r1vjI/Ufj3Z96IpZ712Q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RMCxzhBJYxwD0yp4B6dInJbO+oRlaA/plbsBiugCaZOZAGbQMDMr8gDRpEwDyZi8HmmTmAAC5WNf2AUAqJqZnF13Az7t9/0RMTM/G2OaRho8K8qAfSJM2gUGZH1AGLQMMzywFzAFOZj1AGbQMadAiUCczhjpYV0AbzB5KYS0DpTHXYHg6AoZljlAC6xiAXtkzIF36pGTWNzRDa0C/zA1YTBdAk8wcKIOWgUGZH5AmbUL7vv3tb7d9CMsaHx+PHTt2tH0YwArs5UCTzBwAIBc+aJ6sTc7MxsT0bEwdPR6jm9bH+OkjA19oTx09vuLth1e5HUoxSFf6gTR1oc0qrwWAZ03OzMaxp56OT/6z3bFx/Wmx78ATceOdD8bM7NMn/kwJ8wPqlMr+1IVrAaBcZilQl37niznAyRauh80jp8VVl54fu7Zvi2NPPROzTz0dkzO+KQza0uuMN9shDcu1OL+/Hnvq6bj3wBPe/4FMpPJazjz7PQtVsUatK2A1deyHZg+laHotp3Z9CpTn5Lm28P2ijetPi2fm5lo8OsiD6wNgWN5rogRV74f2O4DyzM/2x5+cjRvf8uIlf9YzwvsF0LSTr7s2rFsbV7/ygiXbjNAn1WjrWt/71ZQgh+fKWgP65f1qWGz62PG4+pUXnNLCya+j2E+hXjlcd1fJNTy0o+pZo2UoR9PXIuYH1GeYnrUJ7Tky+XhErIk3vvGNbR/KsjZt2hzf+c63fdg8VMjzdKAKvi8OAGBlPmiebD1y6Ei855b74o79Eyd+b8/O8bj+iovinG2b+v7/jW5cv+LtW1e5HUowaFf6gTSV3mbV1wLA0l1dcsGZ8dErd8Uvf+beE//IQe7zA+qU0v5U+rUAUC6zFKjLIPPFHOBkJ6+HzSOnxUev3BU33fVgfPzLD5z4fa9PQTv6mfFmO6RhqRbtr5CnlF7LmWe/52RVrVHrClhJXfuh2UMpmlzLKV6fAuWZn2vLvZ512c7xuMHcgRW5PgCG5b0mSlDlfmi/AyhPrz/rGeH9AmhSP21G6JPhtXmt7/1qcpfLc2WtAf3yfjUsNrZpJO498MQpLZz8XM1+CvXJ5bq7Sq7hoXl1zBotQxnauBYxP6Aew/asTWjP8ZnDETEXf/cN74m/df5PtX04i0wdfCjuvvEDMTEx4YPmoSKepwNV8H1xAACrW9v2AcAgJmdmF13sR0Tcvn8irrnlvpicme37/zm+ZST27Bxf8rY9O8djfMvIQMcKuRimK/1Amkpus45rAei65bq664HH46a7HoyrLj0/IvKfH1Cn1Pankq8FgHKZpUBdBp0v5gAnO3k9XHXp+XHTXQ/GXQ88fsqf8foUNK/fGW+2QxqWatH+CvlJ7bWcefZ75lW5Rq0rYDl17odmD6Voai2nen0KlGd+ri33etYd5g6syvUBMCzvNVGCqvZD+x1AeXr9Wc8I7xdAk/ppM0KfDK/ta33vV5Oztvvph9aAfnm/Gk41OTMb7/vCNxe1MP9c7X0/+wL7KdQkp+vuKrmGh2bVNWu0DPlr61rE/IDqVdGzNqF9W87aEWfseF5y/42efV7bdw0UxfN0oAptv79g5gAAufBB82RpYnp20cX+vNv3T8TEdP8X/GObR+L6Ky5adCG/Z+d43HDFRTG22UU8ZRumK/1Amkpus45rAei6lbq664HHY9f2bUXMD6hTavtTydcCQLnMUqAug84Xc4CTnbwedm3ftugfnpjn9SloVr8z3myHNCzVov0V8pPaaznz7PfMq3KNWlfAcurcD80eStHUWk71+hQoz/xce+n/cqbXs2BArg+AYXmviRJUtR/a7wDK08vPekZ4vwCa1mubEfqkGm1f63u/mpy13U8/tAb0y/vVcKqJ6dm444Hln6vt3rHNfgo1yem6u0qu4aFZdc0aLUP+2roWMT+gelX0rE0AaIbn6UAV2n5/wcwBAHKxru0DgEFMHT2+4u2HV7l9Oeds2xQfu3JXTEzPxuGjx2PrxvUxvmXEBTydMGxX+oE0ldpmXdcC0GWrdTW2aX187Mpd2c8PqFOK+1Op1wJAucxSoC7DzBdzgJPNr4f//tj0in/O61PQnEFmvNkOaVjY4vFn5lb88/ZXSE+Kr+XMs98TUf0ata6ApdS9H5o9lKKJtZzy9SlQnnO2bYpHJ4+s+GfMHViZ6wNgWN5rogRV7If2O4DyrDbbt25cH3/x7pd5vwAa1svPYX/h7S/1fh6VSeFa3/vV5CqFfvqhNaBf3q+GH1lt3z8y+3RDRwLdk9t1d5Vcw0Nz6pw1Woa8tXktYn5AtarqWZsAUD/P04EqpPD+gpkDAOTAB82TpdGN61e8fesqt69kbLOLdrqpiq70A2kqsc06rwWgq1br6jkFzhKoWqr7U4nXAkC5zFKgLsPOF3OAk41tHokzVlkPXp+C5gw64812SMPJLX73sekV/6z9FdKT6ms58+z31LFGrStgoSb2Q7OHUtS9llO/PgXKM7bJ+0UwLNcHwLC810QJht0P7XcA5Vlttp95+kj8xFlbGjoaYF4vP4etTaqUyrW+96vJUSr99ENrQL+8Xw3PynHfh1J0vT/X8NCMumeNliFfbV+LmB9QnSp71iYA1MvzdKAKbT+nn2fmAACp80HzZGl8y0js2Tket++fWHTbnp3jMb4lr4vwyZnZmJiejamjx2N00/oYP733JxLDfC2crLSuqpZKa6kcB92Q8nozs6B6Xeqq1/mW8hwkTV3qKCWDtKpvSFfds1T/5MR67U2v91NXrtWsm+b0sqY8HtCMrsz4QVQ5h8w0mpBSz02seV1RgpS6zdFyc8B8qI41WgatkDqzpl7eD6YfesxDio2meEwpc3/9iLlTLWurWU3f3209vjqFbtF8eZbaPyLCNcMC1v5iVV17uEYF2tLEbF9txpmBsFiK112+z61sKa65FKW6RlM9rq5YqZ/Ldo7HutPWxOTMrMdkCNY4tK+Na4VS2i/lPHhWDtfNVaw565YU5dAfvfPeLKkya3ozOTMbh2aOx5OzT8WTs0/Htk3r46ytGzRL0cyHU5X0nKGkc6E3Xey5C+u8C+cIkNOsq+pYu7hvA9XLfZb4HmwAoCk+aJ4sjW0eieuvuCiuueW+Uy769+wcjxuuuCiri+NHDh2J99xyX9yx4Dyuv+KiOGfbptq+FhYqqauqpdJaKsdBN6S+3uZn1sJjvOSCM+Ptr7ggZmafjrHNLR4gZKgr1wK9zrfU5yBp6kpHKRmkVX1D2mZmn463v+KCeHpuLu564PETv39ZBbNU/+TEeu1NP/dTF67VrJtmrbamZmafjv/D4wGN6MKMH0SV+4I9hqak0nMTa15XlCKVbnO01Bx41fPPivf97AvivV/4pvlQEWs0f8vtmb/xugvjg1/8Vvw/337slN/XCm0wa+rj/WD6pcf0pdhoiseUMvfXqcyd6lhbzWr6/m7z8dUpdIvmy7LU/nHZzvF4xysuiKtu/m8xM/t0RLhmiLD2F6rq2sM1KtCmumf7ajPODISlpXbd5fvcypfamktRqms01ePqkuX6ueSCM+PNLz0vLv/tO+JFf/s5HpMBWeOQhqavFUppv5Tz4EdSv26uYs1Zt6Qq9f7o3cI5s3nktLjxLS+OT3z5gbjjAbOHdpk1qzt46Ej8zQ9n4mNf3r/kv9elWUplPvxISc8ZSjoXete1nruwzrtwjgA5zboqj7Vr+zZQj5xnie/BBgCatGZubm6u7YPI3dTUVIyNjcXk5GSMjo62fTidMjkzGxPTs3H46PHYunF9jG8ZSfpif6HJmdm4+jP3nnJxP2/PzvH42JW7lj2fYb6WxXT8I7l3VbVUWkvlOFKn5Wrkst4mZ2bjz775aJy1dUMce+qZ2LBubdz78KG48c4H40V/+znJHCf903K7Sr4W6HW+5TIHU9fllkvuKCWDtKrv/nW5ZZo33+jev3kirrr0/Ni1fduJa/3HDh+L11z4YwM32vX+tZyXrq/XXg16P+V8rbZSy9ZNe5ZaUxHh8WBZ9uX65Dzjq1blvmCPWZqW69Vmz02seV2lQ8vVsQ/3Z7k5cPUrL4h7Dzxxyj/oMs98WFqvHVujeVppz7z0gjPj7+54Tnz8yw+c8vtayVMpe7JZUy3vB+cnpZb1mKYUG03xmNrm/ajBmDvDsbaql1LLqTy+OiVHKV1j50bz+Vtp/7jkgjNj14LXBlO+ZmiyZWu/umuPVK5hSId9mbbUMdtXm3Ef+fkXxq9+9q+KnIFapiopXHd1+fvcuthyCmsuRamu0VSPKzVNtTw5MxuPHT4WB344ExFx4t+kmZl9OiI8JoOwxpnXxT05VU1cK5TSfinnUaWSWk7xurmKNWfd0ou2W06xP3q31Jzx803taLvl1Jk1S5v/N3m/eN8jmk2ElpvX9flQ0nOGlM5Fy+3oQs8prfO6pHSOvba8b9++uPjii+NV770pztjxvEaOrR8P3f3ncfeNH4hL3/WJOPf5u9o+nEVSP74fHrg/bvvwW2Pv3r2xe/futg+HAaS4L6c061ZT17F2Yd+mWim2TPtymyVd/h7seVqGMmgZ8rGu7QOo0u233x4f+chHYu/evXHw4MG49dZb43Wve92KX3Ps2LH44Ac/GH/wB38Qjz76aJx99tnx/ve/P6666qpmDpqhjG1O+wJ/NRPTs0te3EdE3L5/IiamZ5c9v2G+FlaSe1dVS6W1VI6DbshlvU1Mz8a1n//GkreldJyQm5KvBXqdb7nMQdJVckcpGaRVfUPaTm504YcDRUT8vfPOGLhR/ZMT67U3g95PpV6rWTftWWpNffexaY8HtKDUGT+IKvcFewxtaLPnJta8riiRfbg/y82BXdu3Lfm6WIT5MCxrNE8r7Zl3PvB4vPWS8xf9vlZok1lTLe8HMww9pinFRlM8ppS5v5Zn7gzH2mpW0/d3Ko+vTqFbNJ+/lfaPux54PK5a8Nqga4ZnWfvVXXukcg0DUMdsX23GPfGkGQirSeG6y/e5dUsKay5Fqa7RVI+rq+b/7Yhf/P17lrzdY9I/axzS08S1Qintl3IeLC3F6+Yq1px1Sw5S7I/eLTVn/HwTKTJrljYxPRtnbd2w5IfMR2iWbuj6fCjpOUNJ58JgutBzF9Z5F84RIKdZV9exdmHfBuqX2yzxPdgAQNOK+qD5J598Ml74whfGVVddFa9//et7+ppf+IVfiB/84Afxe7/3e3HBBRfEwYMH45lnnqn5SOFZU0ePr3j74RVuH+Zrgd6l0loqx0E35LLecjlOIB29zg3zBfIwSKv6hrTV2aj+yYn12hv306ncH2nxeABtq3IOmWl0TRNrXlfAcnPg2FMrf9+i+UDXrLZnLteMVqAM3g+G8qTYaIrHlDL3F3WxtprV9P3t8QVgEIO8NmhPIaK6aw/XMEDJVptxU0efWvF2MxDS4PvcIN01mupxdZnHpFruT+imUtov5TzIRxVrzroF6rbUnPHzTZCPqaPHNQsdV9JzhpLOBZbThXXehXMEyGnW5XSsAKnzPdgAQNOK+qD5yy+/PC6//PKe//yXvvSl+OpXvxrf+9734owzzoiIiPPOO6+mo4PFRjeuX/H2rSvcPszXAr1LpbVUjoNuyGW95XKcQDp6nRvmC+RhkFb1DWmrs1H9kxPrtTfup1O5P9Li8QDaVuUcMtPomibWvK6A5ebAhnVrV/w684GuWW3PXK4ZrUAZvB8M5Umx0RSPKWXuL+pibTWr6fvb4wvAIAZ5bdCeQkR11x6uYYCSrTbjRjeu/M8QmYGQBt/nBumu0VSPq8s8JtVyf0I3ldJ+KedBPqpYc9YtULel5oyfb4J8jG5cHz98cnbFP6NZKFtJzxlKOhdYThfWeRfOESCnWZfTsQKkzvdgAwBNW/md+8L9yZ/8SbzoRS+K3/zN34xzzz03fvInfzJ+9Vd/NY4cObLi1x07diympqZO+Q+WMjkzG999bDruPfBEfPd/TsfkzKlvvI9vGYk9O8eX/No9O8djfMvIsv/vYb4WHadqtWbakEprqRxHarRcj1zW27DHmeLM6Sot01SPvc6NXOZgarScv9z2xkFa1ffqtEyb6mx0pf/3q55/VmzZuC6rGbgaLeety/tVP9cjXbif+mm5C/dHSup8/4fy2JfpRdXPyaucQ2bas7TcHUut+c0jp8XVr7wgPv2///2YPDI7dKe6ao+WWU7Tr48vNwfuffhQXHrBmUt+jfnwLB3n937OMFbaMy+94My49+FDi35fK3nQcjcMO6+8H5y+klru0v7aphQbTfGYmub9qPaYPT9ibQ0v5ZZTenx1R+pKusYukRnSrJX2j0uWeG0wpWuGVFvuyhqu6tojpWsY2pFqy1CF1Wbcc06vZwa2sRdpmTbVveabuF5J5ZpIy+Wpqo9U1uhCqR5X29psubTHpO3n+KXdn/TOnlyeLv48bynnMQwtN6uKNTe+ZSSue/3fid9784vik/9sd9z4lhfH1a+8IDaPnNaZdctiWs5T29fyy1lqVt378KG4xM831U7L6Uu125ONbxmJxw4f02yLtNwtKc6Fkp7rtnkuWuZkdbZeUrPL0TIwrBxazmme53Ssq0nxepzl5dAynKyXGdPW92C3SctQBi1Dvjr9QfPf+9734s4774xvfvObceutt8Z//I//MT73uc/F29/+9hW/7rrrrouxsbET/23fvr2hIyYnjxw6Eld/5t746d/6avyTT/5l/PS//2q88zP3xiOHjpz4M2ObR+L6Ky5adJG/Z+d43HDFRTG2efkL/GG+Fh2nqJdm2pBKa6kcR2q0XI9c1tswx5nqzOkqLXdbkz32OjdymYOp0XLectwbB2lV36vTMm2qs9Hl/t+vev5Z8b6ffUH86mf/KqsZuBot562r+1W/1yNduJ/6abkL90cq6n7/h/LYl1lNHc/Jq5xDZtqztNwdC9f85pHT4qNX7op7DzwRb/i/747X/87Xhu5UV+3RMktp4/Xx5ebA/Qen4v/6J3/HfFhB1zvO8f2cYay0Z/5f/+TvxP0Hpxb9vlby0PWWu6CKeeX94PSV0nLX9tc2pdhoisfUNO9HtcPsOZW1NbyUW07l8dUdOSjlGrtEZkjzlts/Lts5Hu985c648c4HT/xeatcMKbbcpTVc1bVHKtcwtCfFlqEqq824545urHwGtrUXaZm2NLHmm7heSeWaSMtlqbKPVNZoLsfVtjZbLukxSeE5fkn3J/2xJ5elqz/PW8p5DEPLzapizT05+3T82X0H4xd//554+x/ui6tu/m9x74En4sa3vDh+syPrlsW0nJ8UruWXs9SsuvHOB+Odr9wZl3V4z2yCltOWcrcnG9s8Ei//yb8V73zlzkUfNn+ZZhuh5e5IdS6U9Fy3zXPRMvPqbr2kZpejZWBYObSc0zzP6VhXkur1OMvLoWWY1+uMaeN7sNumZSiDliFfa+bm5ubaPog6rFmzJm699dZ43etet+yfefWrXx133HFHPProozE2NhYREZ///Ofj537u5+LJJ5+MTZs2Lfl1x44di2PHjp349dTUVGzfvj0mJydjdHS00vMgT5Mzs3H1Z+6NO/ZPLLptz87x+NiVu065eJ+cmY2J6dk4fPR4bN24Psa3jPR8cT/M13aZjtPSbzNtSKW1VI4jFVquVy7rrd/jzGHmdI2Wu6utHnudG7nMwVRoOV+5742DtKrv5WmZFNTZ6ML/95aN6+JXP/tX2c7A5Wi5DF3ar4a5Hin5fhqk5ZLvjxQ0+f4P5bAvs5K6n5NXOYe6PtO03D3za/6Zubn44J/+ddzxwOOL/sywnXa9qzZomYXafn18uTlgPiyvyx23vV7bpJXydLnlLqh6Xnk/OF0ltNzl/bVNKTaa4jE1xftRzTN7lmdtDS6Hltt8fHVHLkq4xi6RGdKupfaPiEj6miG1lru6hqu69nCN2l2ptQx1WG3GVTlL29qLtEwbml7zTVyvtH1NpOVy1NVH22s0t+NqSwot5/6YpPYcP/f7k/6l0DHV8PO85ZzHILTcjkHXXGr7P+nQcl5yaTnH92Zzp+V05dLtySZnZuPQzPF4cvapmJl9OsY2rY+ztm5I7jhLpOVuyGEulPRct41z0TIRzbZeUrPLyanlffv2xcUXXxyveu9NccaO59V6jIN46O4/j7tv/EBc+q5PxLnP39X24SyS+vH98MD9cduH3xp79+6N3bt3t3049CCnfTmneZ7TsS6Uw/U4i+XUMt02yIxp6nuwU6BlKIOWIV/r2j6ANp199tlx7rnnnviQ+YiI5z//+TE3Nxf/43/8j9i5c+eSX7dhw4bYsGFDU4dJhiamZ5d8AhARcfv+iZiYnj3lAn5s8+AX9MN8bZfpOC39NtOGVFpL5ThSoeV65bLe+j3OHGZO12i5u9rqsde5kcscTIWW85X73jhIq/penpZJQZ2NLvx/f/ex6axn4HK0XIYu7VfDXI+UfD8N0nLJ90cKmnz/h3LYl1lJ3c/Jq5xDXZ9pWu6e+TX/3ceml/yQ+YjhO+16V23QMgu1/fr4cnPAfFhelztue722SSvl6XLLXVD1vPJ+cLpKaLnL+2ubUmw0xWNqivejmmf2LM/aGlwOLbf5+OqOXJRwjV0iM6RdK702mKrUWu7qGq7q2sM1anel1jLUYbUZV9UMbHMv0jJtaHrNN3G90vY1kZbLUVcfba/R5aR6XG1JoeXcH5PUnuPnfn/SvxQ6php+nrec8xiEltsx6JpLbf8nHVrOSy4t5/jebO60nK5cuj1Zl69x26blbshhLpQ0B9o4Fy0T0WzrJTW7HC0Dg8qp5ZzmeU7HulAO1+MsllPLdNsgM6ap78FOgZahDFqGfK1t+wDadMkll8QjjzwS09PTJ37vv//3/x5r166NH//xH2/xyMjd1NHjK95+eJXboWs0AzTJzIF06BHSoEWgy8xASIMWyYW1ClTNXIH06RTKpnFyYr0CuTCvyIn1CrTB7IHm6Q4YhhlC7qxhANpmL6JrrHlYnj5gOBoCqmKeQD70CmXQMuRHt8BC5gJ0g9YBIE32aKBOZgwAkLJ1bR9Alaanp+OBBx448esHH3wwvv71r8cZZ5wRO3bsiGuvvTa+//3vx6c+9amIiHjDG94QH/rQh+Ktb31rfOADH4iJiYn4tV/7tbjqqqti06ZNbZ1G3yZnZmNiejamjh6P0U3rY/z0kRjbPNL2YXXa6Mb1K96+dZXbSZPW6qMZoElmzsrsd6dyf9RLj5xMb+3RIikzG6ibGVgGsyJ/WiQX/axVs4m2WYN5sAdSBb3XS6fUQbfp0DhVaaJr6xXSYB9fnXlF3ars0HqF7khpDzd7oHkldZfSPCMN1kT9SpohdM/kzGxsWn9afPKf7Y6N60+LfQeeiBvvfDBmZp8+8WesYaiXvRryu57SLcPKbc13hbbToA8YjobSZ79phvt5eOYJ/dJde/RKlbTcHi1TB03XS7fUTcP5MRfoh8bzpXWAvNmDy2WPBupkxgAAKSvqg+bvueeeeMUrXnHi1+9+97sjIuLNb35z3HzzzXHw4ME4cODAidu3bNkSt912W7zzne+MF73oRXHmmWfGL/zCL8Rv/MZvNH7sg3rk0JF4zy33xR37J0783p6d43H9FRfFOds2tXhk3Ta+ZST27ByP2096XObt2Tke41tWf0HJC1Fp0Vq9qmimF7oCIpqbObmYn43Tx47H2KaReN8Xvhl3PGC/i7D/N6HJHl0HpE1v7cppb9Ryt5gN1GHhHNmycV02M5ClmRVlGPZ6xDUCC9W1Jnpdq2YTbbMG87HSXLls53isO21NTM7M2tdYlt7rt9r+v27tmrj3wBOuQ+mZbtOyWuNbNq6L7z427fkmK2qq65zez6mK13xIjX28N3XMK/OAeVV3mOv+qgnoT2p7+DCzR/8wmC0b18VlO8dPmQPzUt7zF0ptntE+a6IZfuaBXC01Iy654Mz46JW74pc/c2/MzD6d1T6YGr3SC3s1Jahi3uX0OqxuqUJVa971RnW0nY6U9wTNkYOUG6pajk3ab5rhfq5GP/Mkxx6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",
-      "text/plain": [
-       "<Figure size 8250x8250 with 1122 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sns.pairplot(df_carbon_short)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Energy data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "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 tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th>table</th>\n",
-       "      <th colspan=\"10\" halign=\"left\">AFC</th>\n",
-       "      <th>...</th>\n",
-       "      <th colspan=\"9\" halign=\"left\">FC_OTH_HH_E</th>\n",
-       "      <th>country</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time</th>\n",
-       "      <th>1990</th>\n",
-       "      <th>1991</th>\n",
-       "      <th>1992</th>\n",
-       "      <th>1993</th>\n",
-       "      <th>1994</th>\n",
-       "      <th>1995</th>\n",
-       "      <th>1996</th>\n",
-       "      <th>1997</th>\n",
-       "      <th>1998</th>\n",
-       "      <th>1999</th>\n",
-       "      <th>...</th>\n",
-       "      <th>2014</th>\n",
-       "      <th>2015</th>\n",
-       "      <th>2016</th>\n",
-       "      <th>2017</th>\n",
-       "      <th>2018</th>\n",
-       "      <th>2019</th>\n",
-       "      <th>2020</th>\n",
-       "      <th>2021</th>\n",
-       "      <th>2022</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>218484.0</td>\n",
-       "      <td>178925.0</td>\n",
-       "      <td>175263.0</td>\n",
-       "      <td>175606.0</td>\n",
-       "      <td>169048.0</td>\n",
-       "      <td>181335.0</td>\n",
-       "      <td>193531.0</td>\n",
-       "      <td>194520.0</td>\n",
-       "      <td>187928.0</td>\n",
-       "      <td>214365.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>415.685</td>\n",
-       "      <td>628.687</td>\n",
-       "      <td>542.327</td>\n",
-       "      <td>611.261</td>\n",
-       "      <td>560.179</td>\n",
-       "      <td>687.335</td>\n",
-       "      <td>1220.556</td>\n",
-       "      <td>1316.017</td>\n",
-       "      <td>1457.559</td>\n",
-       "      <td>EU27_2020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>146824.0</td>\n",
-       "      <td>137183.0</td>\n",
-       "      <td>133881.0</td>\n",
-       "      <td>141083.0</td>\n",
-       "      <td>144691.0</td>\n",
-       "      <td>152598.0</td>\n",
-       "      <td>152735.0</td>\n",
-       "      <td>160130.0</td>\n",
-       "      <td>160913.0</td>\n",
-       "      <td>181350.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>49.422</td>\n",
-       "      <td>84.733</td>\n",
-       "      <td>45.143</td>\n",
-       "      <td>51.247</td>\n",
-       "      <td>61.076</td>\n",
-       "      <td>64.421</td>\n",
-       "      <td>50.728</td>\n",
-       "      <td>65.282</td>\n",
-       "      <td>176.339</td>\n",
-       "      <td>EA20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4785.0</td>\n",
-       "      <td>4998.0</td>\n",
-       "      <td>5849.0</td>\n",
-       "      <td>7590.0</td>\n",
-       "      <td>9053.0</td>\n",
-       "      <td>9398.0</td>\n",
-       "      <td>9637.0</td>\n",
-       "      <td>9975.0</td>\n",
-       "      <td>7828.0</td>\n",
-       "      <td>9059.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>BE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5387.0</td>\n",
-       "      <td>3083.0</td>\n",
-       "      <td>3289.0</td>\n",
-       "      <td>1630.0</td>\n",
-       "      <td>1173.0</td>\n",
-       "      <td>1961.0</td>\n",
-       "      <td>1803.0</td>\n",
-       "      <td>785.0</td>\n",
-       "      <td>564.0</td>\n",
-       "      <td>1670.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>BG</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>8179.0</td>\n",
-       "      <td>7194.0</td>\n",
-       "      <td>6156.0</td>\n",
-       "      <td>5952.0</td>\n",
-       "      <td>5415.0</td>\n",
-       "      <td>6722.0</td>\n",
-       "      <td>8811.0</td>\n",
-       "      <td>9013.0</td>\n",
-       "      <td>8383.0</td>\n",
-       "      <td>8983.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.307</td>\n",
-       "      <td>1.961</td>\n",
-       "      <td>2.288</td>\n",
-       "      <td>4.047</td>\n",
-       "      <td>4.520</td>\n",
-       "      <td>5.012</td>\n",
-       "      <td>3.610</td>\n",
-       "      <td>11.808</td>\n",
-       "      <td>23.064</td>\n",
-       "      <td>CZ</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 166 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "table       AFC                                                              \\\n",
-       "time       1990      1991      1992      1993      1994      1995      1996   \n",
-       "0      218484.0  178925.0  175263.0  175606.0  169048.0  181335.0  193531.0   \n",
-       "1      146824.0  137183.0  133881.0  141083.0  144691.0  152598.0  152735.0   \n",
-       "2        4785.0    4998.0    5849.0    7590.0    9053.0    9398.0    9637.0   \n",
-       "3        5387.0    3083.0    3289.0    1630.0    1173.0    1961.0    1803.0   \n",
-       "4        8179.0    7194.0    6156.0    5952.0    5415.0    6722.0    8811.0   \n",
-       "\n",
-       "table                                ... FC_OTH_HH_E                    \\\n",
-       "time       1997      1998      1999  ...        2014     2015     2016   \n",
-       "0      194520.0  187928.0  214365.0  ...     415.685  628.687  542.327   \n",
-       "1      160130.0  160913.0  181350.0  ...      49.422   84.733   45.143   \n",
-       "2        9975.0    7828.0    9059.0  ...       0.000    0.000    0.000   \n",
-       "3         785.0     564.0    1670.0  ...       0.000    0.000    0.000   \n",
-       "4        9013.0    8383.0    8983.0  ...       1.307    1.961    2.288   \n",
-       "\n",
-       "table                                                             country  \n",
-       "time      2017     2018     2019      2020      2021      2022             \n",
-       "0      611.261  560.179  687.335  1220.556  1316.017  1457.559  EU27_2020  \n",
-       "1       51.247   61.076   64.421    50.728    65.282   176.339       EA20  \n",
-       "2        0.000    0.000    0.000     0.000     0.000     0.000         BE  \n",
-       "3        0.000    0.000    0.000     0.000     0.000     0.000         BG  \n",
-       "4        4.047    4.520    5.012     3.610    11.808    23.064         CZ  \n",
-       "\n",
-       "[5 rows x 166 columns]"
-      ]
-     },
-     "execution_count": 189,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_energy_df, energy_tables = scraper.get_eurostat_dataframe(\"nrg_cb_e\", lambda raw: [\"AFC\", \"FC_IND_E\", \"FC_TRA_E\", \"FC_OTH_CP_E\", \"FC_OTH_HH_E\"])\n",
-    "full_energy_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>country</th>\n",
-       "      <th>EU27_2020</th>\n",
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-       "      <th>AL</th>\n",
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-       "      <th>UA</th>\n",
-       "      <th>XK</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>time</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1990</th>\n",
-       "      <td>218484.0</td>\n",
-       "      <td>146824.0</td>\n",
-       "      <td>4785.0</td>\n",
-       "      <td>5387.0</td>\n",
-       "      <td>8179.0</td>\n",
-       "      <td>11973.0</td>\n",
-       "      <td>31669.0</td>\n",
-       "      <td>1475.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1330.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>511.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>323.0</td>\n",
-       "      <td>392.0</td>\n",
-       "      <td>176.0</td>\n",
-       "      <td>15401.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1991</th>\n",
-       "      <td>178925.0</td>\n",
-       "      <td>137183.0</td>\n",
-       "      <td>4998.0</td>\n",
-       "      <td>3083.0</td>\n",
-       "      <td>7194.0</td>\n",
-       "      <td>3075.0</td>\n",
-       "      <td>30416.0</td>\n",
-       "      <td>2222.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1498.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>523.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>235.0</td>\n",
-       "      <td>759.0</td>\n",
-       "      <td>18317.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1992</th>\n",
-       "      <td>175263.0</td>\n",
-       "      <td>133881.0</td>\n",
-       "      <td>5849.0</td>\n",
-       "      <td>3289.0</td>\n",
-       "      <td>6156.0</td>\n",
-       "      <td>8647.0</td>\n",
-       "      <td>28418.0</td>\n",
-       "      <td>254.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>967.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>483.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>189.0</td>\n",
-       "      <td>15417.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1993</th>\n",
-       "      <td>175606.0</td>\n",
-       "      <td>141083.0</td>\n",
-       "      <td>7590.0</td>\n",
-       "      <td>1630.0</td>\n",
-       "      <td>5952.0</td>\n",
-       "      <td>6279.0</td>\n",
-       "      <td>33628.0</td>\n",
-       "      <td>221.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1093.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>623.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>213.0</td>\n",
-       "      <td>15773.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1994</th>\n",
-       "      <td>169048.0</td>\n",
-       "      <td>144691.0</td>\n",
-       "      <td>9053.0</td>\n",
-       "      <td>1173.0</td>\n",
-       "      <td>5415.0</td>\n",
-       "      <td>1781.0</td>\n",
-       "      <td>35908.0</td>\n",
-       "      <td>315.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>816.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>73.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>12378.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 43 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "country  EU27_2020      EA20      BE      BG      CZ       DK       DE  \\\n",
-       "time                                                                     \n",
-       "1990      218484.0  146824.0  4785.0  5387.0  8179.0  11973.0  31669.0   \n",
-       "1991      178925.0  137183.0  4998.0  3083.0  7194.0   3075.0  30416.0   \n",
-       "1992      175263.0  133881.0  5849.0  3289.0  6156.0   8647.0  28418.0   \n",
-       "1993      175606.0  141083.0  7590.0  1630.0  5952.0   6279.0  33628.0   \n",
-       "1994      169048.0  144691.0  9053.0  1173.0  5415.0   1781.0  35908.0   \n",
-       "\n",
-       "country      EE   IE      EL  ...  BA  ME  MD     MK  GE     AL     RS     TR  \\\n",
-       "time                          ...                                               \n",
-       "1990     1475.0  0.0  1330.0  ... NaN NaN NaN  511.0 NaN  323.0  392.0  176.0   \n",
-       "1991     2222.0  0.0  1498.0  ... NaN NaN NaN  523.0 NaN    0.0  235.0  759.0   \n",
-       "1992      254.0  0.0   967.0  ... NaN NaN NaN  483.0 NaN    0.0    0.0  189.0   \n",
-       "1993      221.0  0.0  1093.0  ... NaN NaN NaN  623.0 NaN    0.0    0.0  213.0   \n",
-       "1994      315.0  0.0   816.0  ... NaN NaN NaN   73.0 NaN    0.0    0.0   31.0   \n",
-       "\n",
-       "country       UA  XK  \n",
-       "time                  \n",
-       "1990     15401.0 NaN  \n",
-       "1991     18317.0 NaN  \n",
-       "1992     15417.0 NaN  \n",
-       "1993     15773.0 NaN  \n",
-       "1994     12378.0 NaN  \n",
-       "\n",
-       "[5 rows x 43 columns]"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_energy_short = full_energy_df.xs(\"AFC\", level=\"table\", axis=1).T\n",
-    "df_energy_short.columns = full_energy_df.xs(\"country\", axis=1)\n",
-    "df_energy_short.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2008</th>\n",
-       "      <td>305128.218</td>\n",
-       "      <td>245213.218</td>\n",
-       "      <td>17158.0</td>\n",
-       "      <td>3097.0</td>\n",
-       "      <td>8520.0</td>\n",
-       "      <td>12815.0</td>\n",
-       "      <td>41670.0</td>\n",
-       "      <td>1369.0</td>\n",
-       "      <td>753.031</td>\n",
-       "      <td>7575.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1884.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2733.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2434.0</td>\n",
-       "      <td>8875.0</td>\n",
-       "      <td>789.0</td>\n",
-       "      <td>2101.0</td>\n",
-       "      <td>708.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2009</th>\n",
-       "      <td>292276.753</td>\n",
-       "      <td>237029.753</td>\n",
-       "      <td>9486.0</td>\n",
-       "      <td>2662.0</td>\n",
-       "      <td>8586.0</td>\n",
-       "      <td>11208.0</td>\n",
-       "      <td>41859.0</td>\n",
-       "      <td>3025.0</td>\n",
-       "      <td>939.217</td>\n",
-       "      <td>7600.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1227.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1438.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1886.0</td>\n",
-       "      <td>5184.0</td>\n",
-       "      <td>812.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>768.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2010</th>\n",
-       "      <td>291540.094</td>\n",
-       "      <td>241227.094</td>\n",
-       "      <td>12395.0</td>\n",
-       "      <td>1167.0</td>\n",
-       "      <td>6642.0</td>\n",
-       "      <td>10599.0</td>\n",
-       "      <td>42962.0</td>\n",
-       "      <td>1100.0</td>\n",
-       "      <td>759.887</td>\n",
-       "      <td>8517.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>732.0</td>\n",
-       "      <td>3033.0</td>\n",
-       "      <td>1420.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1986.0</td>\n",
-       "      <td>5620.0</td>\n",
-       "      <td>1144.0</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>819.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2011</th>\n",
-       "      <td>321114.673</td>\n",
-       "      <td>260179.673</td>\n",
-       "      <td>13189.0</td>\n",
-       "      <td>1449.0</td>\n",
-       "      <td>10457.0</td>\n",
-       "      <td>11694.0</td>\n",
-       "      <td>51003.0</td>\n",
-       "      <td>1690.0</td>\n",
-       "      <td>731.622</td>\n",
-       "      <td>7180.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1993.0</td>\n",
-       "      <td>3146.0</td>\n",
-       "      <td>2749.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3262.0</td>\n",
-       "      <td>6701.0</td>\n",
-       "      <td>4556.0</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>3135.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2012</th>\n",
-       "      <td>349454.141</td>\n",
-       "      <td>277236.141</td>\n",
-       "      <td>16848.0</td>\n",
-       "      <td>2353.0</td>\n",
-       "      <td>11587.0</td>\n",
-       "      <td>15920.0</td>\n",
-       "      <td>46268.0</td>\n",
-       "      <td>2710.0</td>\n",
-       "      <td>783.605</td>\n",
-       "      <td>5954.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1440.0</td>\n",
-       "      <td>3279.0</td>\n",
-       "      <td>2741.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2538.0</td>\n",
-       "      <td>5781.0</td>\n",
-       "      <td>5827.0</td>\n",
-       "      <td>89.0</td>\n",
-       "      <td>2773.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 43 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "country   EU27_2020        EA20       BE      BG       CZ       DK       DE  \\\n",
-       "time                                                                          \n",
-       "2008     305128.218  245213.218  17158.0  3097.0   8520.0  12815.0  41670.0   \n",
-       "2009     292276.753  237029.753   9486.0  2662.0   8586.0  11208.0  41859.0   \n",
-       "2010     291540.094  241227.094  12395.0  1167.0   6642.0  10599.0  42962.0   \n",
-       "2011     321114.673  260179.673  13189.0  1449.0  10457.0  11694.0  51003.0   \n",
-       "2012     349454.141  277236.141  16848.0  2353.0  11587.0  15920.0  46268.0   \n",
-       "\n",
-       "country      EE       IE      EL  ...  BA      ME      MD      MK  GE      AL  \\\n",
-       "time                              ...                                           \n",
-       "2008     1369.0  753.031  7575.0  ... NaN  1884.0     NaN  2733.0 NaN  2434.0   \n",
-       "2009     3025.0  939.217  7600.0  ... NaN  1227.0     NaN  1438.0 NaN  1886.0   \n",
-       "2010     1100.0  759.887  8517.0  ... NaN   732.0  3033.0  1420.0 NaN  1986.0   \n",
-       "2011     1690.0  731.622  7180.0  ... NaN  1993.0  3146.0  2749.0 NaN  3262.0   \n",
-       "2012     2710.0  783.605  5954.0  ... NaN  1440.0  3279.0  2741.0 NaN  2538.0   \n",
-       "\n",
-       "country      RS      TR      UA      XK  \n",
-       "time                                     \n",
-       "2008     8875.0   789.0  2101.0   708.0  \n",
-       "2009     5184.0   812.0    25.0   768.0  \n",
-       "2010     5620.0  1144.0    23.0   819.0  \n",
-       "2011     6701.0  4556.0    32.0  3135.0  \n",
-       "2012     5781.0  5827.0    89.0  2773.0  \n",
-       "\n",
-       "[5 rows x 43 columns]"
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-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "# Drop the rows until 2008 to match carbon dataset\n",
-    "df_energy_short = df_energy_short.drop([str(i) for i in np.arange(1990, 2008)])\n",
-    "df_energy_short.head()"
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-   "execution_count": 198,
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-       "      <th>2008</th>\n",
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-       "      <td>651.0</td>\n",
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-       "      <td>8994.0</td>\n",
-       "      <td>15460.0</td>\n",
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-       "time                                                                      \n",
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-       "\n",
-       "country       IE      EL      ES  ...       PT      RO      SI       SK  \\\n",
-       "time                              ...                                     \n",
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-       "time                                                     \n",
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-    "# Match countries w/ the carbon dataset\n",
-    "countries = list(df_carbon_short.columns)\n",
-    "df_energy_short = df_energy_short[df_energy_short.columns.intersection(countries)]\n",
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-   "metadata": {},
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{\"legend\":{\"title\":{\"text\":\"country\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#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                       {\"responsive\": true}                    )                };                            </script>        </div>'"
-      ]
-     },
-     "execution_count": 317,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plotly.offline.plot(fig, include_plotlyjs=False, output_type=\"div\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Merge Datasets\n",
-    "And standardize the data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>country</th>\n",
-       "      <th>carbon</th>\n",
-       "      <th>energy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>EU27_2020</td>\n",
-       "      <td>8626928.75</td>\n",
-       "      <td>305128.218</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>EU27_2020</td>\n",
-       "      <td>7851179.61</td>\n",
-       "      <td>292276.753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>EU27_2020</td>\n",
-       "      <td>8024312.00</td>\n",
-       "      <td>291540.094</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>EU27_2020</td>\n",
-       "      <td>7901995.31</td>\n",
-       "      <td>321114.673</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>EU27_2020</td>\n",
-       "      <td>7728876.39</td>\n",
-       "      <td>349454.141</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     country      carbon      energy\n",
-       "0  EU27_2020  8626928.75  305128.218\n",
-       "1  EU27_2020  7851179.61  292276.753\n",
-       "2  EU27_2020  8024312.00  291540.094\n",
-       "3  EU27_2020  7901995.31  321114.673\n",
-       "4  EU27_2020  7728876.39  349454.141"
-      ]
-     },
-     "execution_count": 200,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_carbon_long = pd.melt(df_carbon_short)\n",
-    "df_energy_long = pd.melt(df_energy_short)\n",
-    "df_merged = pd.concat([df_carbon_long, df_energy_long[\"value\"]], axis=1)\n",
-    "df_merged.columns = [\"country\", \"carbon\", \"energy\"]\n",
-    "df_merged.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    }\n",
-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>country</th>\n",
-       "      <th>carbon</th>\n",
-       "      <th>energy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>EU27_2020</td>\n",
-       "      <td>8626928.75</td>\n",
-       "      <td>305128.218</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>EU27_2020</td>\n",
-       "      <td>7851179.61</td>\n",
-       "      <td>292276.753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>EU27_2020</td>\n",
-       "      <td>8024312.00</td>\n",
-       "      <td>291540.094</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>EU27_2020</td>\n",
-       "      <td>7901995.31</td>\n",
-       "      <td>321114.673</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>EU27_2020</td>\n",
-       "      <td>7728876.39</td>\n",
-       "      <td>349454.141</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     country      carbon      energy\n",
-       "0  EU27_2020  8626928.75  305128.218\n",
-       "1  EU27_2020  7851179.61  292276.753\n",
-       "2  EU27_2020  8024312.00  291540.094\n",
-       "3  EU27_2020  7901995.31  321114.673\n",
-       "4  EU27_2020  7728876.39  349454.141"
-      ]
-     },
-     "execution_count": 201,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_merged.fillna(0)\n",
-    "df_merged.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>carbon</th>\n",
-       "      <th>energy</th>\n",
-       "      <th>country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.170545</td>\n",
-       "      <td>4.622992</td>\n",
-       "      <td>EU27_2020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-0.025678</td>\n",
-       "      <td>4.412569</td>\n",
-       "      <td>EU27_2020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.018115</td>\n",
-       "      <td>4.400507</td>\n",
-       "      <td>EU27_2020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.012825</td>\n",
-       "      <td>4.884745</td>\n",
-       "      <td>EU27_2020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>-0.056614</td>\n",
-       "      <td>5.348760</td>\n",
-       "      <td>EU27_2020</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     carbon    energy    country\n",
-       "0  0.170545  4.622992  EU27_2020\n",
-       "1 -0.025678  4.412569  EU27_2020\n",
-       "2  0.018115  4.400507  EU27_2020\n",
-       "3 -0.012825  4.884745  EU27_2020\n",
-       "4 -0.056614  5.348760  EU27_2020"
-      ]
-     },
-     "execution_count": 202,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_merged_s = pd.DataFrame()\n",
-    "for feat in df_merged.columns:\n",
-    "\tif feat == \"country\": continue\n",
-    "\tdf_merged_s[f'{feat}'] = ((df_merged[feat] - df_merged[feat].mean()) / df_merged[feat].std())\n",
-    "df_merged_s[\"country\"] = df_merged[\"country\"]\n",
-    "df_merged_s.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>carbon</th>\n",
-       "      <th>energy</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>country</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">AT</th>\n",
-       "      <th>carbon</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.752501</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>energy</th>\n",
-       "      <td>-0.752501</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">BE</th>\n",
-       "      <th>carbon</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.201674</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>energy</th>\n",
-       "      <td>-0.201674</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BG</th>\n",
-       "      <th>carbon</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.215923</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SI</th>\n",
-       "      <th>energy</th>\n",
-       "      <td>-0.566730</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">SK</th>\n",
-       "      <th>carbon</th>\n",
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-       "    <tr>\n",
-       "      <th>energy</th>\n",
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-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">TR</th>\n",
-       "      <th>carbon</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>energy</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>64 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  carbon    energy\n",
-       "country                           \n",
-       "AT      carbon  1.000000 -0.752501\n",
-       "        energy -0.752501  1.000000\n",
-       "BE      carbon  1.000000 -0.201674\n",
-       "        energy -0.201674  1.000000\n",
-       "BG      carbon  1.000000 -0.215923\n",
-       "...                  ...       ...\n",
-       "SI      energy -0.566730  1.000000\n",
-       "SK      carbon  1.000000 -0.755988\n",
-       "        energy -0.755988  1.000000\n",
-       "TR      carbon       NaN       NaN\n",
-       "        energy       NaN  1.000000\n",
-       "\n",
-       "[64 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_merged_s.groupby(\"country\").corr()"
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-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_merged_s = df_merged_s.drop(df_merged_s[df_merged_s[\"country\"] == \"EU27_2020\"].index, axis=0)"
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{\"legend\":{\"title\":{\"text\":\"country\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"energy\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"carbon\"}}},                        {\"responsive\": true}                    )                };                            </script>        </div>'"
-      ]
-     },
-     "execution_count": 319,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plotly.offline.plot(fig, include_plotlyjs=False, output_type=\"div\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 304,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(465, 33)\n"
-     ]
-    },
-    {
-     "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>carbon</th>\n",
-       "      <th>energy</th>\n",
-       "      <th>country_AT</th>\n",
-       "      <th>country_BE</th>\n",
-       "      <th>country_BG</th>\n",
-       "      <th>country_CY</th>\n",
-       "      <th>country_CZ</th>\n",
-       "      <th>country_DE</th>\n",
-       "      <th>country_DK</th>\n",
-       "      <th>country_EE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>country_NL</th>\n",
-       "      <th>country_NO</th>\n",
-       "      <th>country_PL</th>\n",
-       "      <th>country_PT</th>\n",
-       "      <th>country_RO</th>\n",
-       "      <th>country_RS</th>\n",
-       "      <th>country_SE</th>\n",
-       "      <th>country_SI</th>\n",
-       "      <th>country_SK</th>\n",
-       "      <th>country_TR</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>0.477605</td>\n",
-       "      <td>-0.092076</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>0.199320</td>\n",
-       "      <td>-0.217693</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>0.326432</td>\n",
-       "      <td>-0.170063</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>0.143413</td>\n",
-       "      <td>-0.157062</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>0.049850</td>\n",
-       "      <td>-0.097152</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 33 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      carbon    energy  country_AT  country_BE  country_BG  country_CY  \\\n",
-       "15  0.477605 -0.092076           0           1           0           0   \n",
-       "16  0.199320 -0.217693           0           1           0           0   \n",
-       "17  0.326432 -0.170063           0           1           0           0   \n",
-       "18  0.143413 -0.157062           0           1           0           0   \n",
-       "19  0.049850 -0.097152           0           1           0           0   \n",
-       "\n",
-       "    country_CZ  country_DE  country_DK  country_EE  ...  country_NL  \\\n",
-       "15           0           0           0           0  ...           0   \n",
-       "16           0           0           0           0  ...           0   \n",
-       "17           0           0           0           0  ...           0   \n",
-       "18           0           0           0           0  ...           0   \n",
-       "19           0           0           0           0  ...           0   \n",
-       "\n",
-       "    country_NO  country_PL  country_PT  country_RO  country_RS  country_SE  \\\n",
-       "15           0           0           0           0           0           0   \n",
-       "16           0           0           0           0           0           0   \n",
-       "17           0           0           0           0           0           0   \n",
-       "18           0           0           0           0           0           0   \n",
-       "19           0           0           0           0           0           0   \n",
-       "\n",
-       "    country_SI  country_SK  country_TR  \n",
-       "15           0           0           0  \n",
-       "16           0           0           0  \n",
-       "17           0           0           0  \n",
-       "18           0           0           0  \n",
-       "19           0           0           0  \n",
-       "\n",
-       "[5 rows x 33 columns]"
-      ]
-     },
-     "execution_count": 304,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_dummies = pd.get_dummies(df_merged_s, dtype=int)\n",
-    "print(df_dummies.shape)\n",
-    "df_dummies.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 305,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO Fix later\n",
-    "df_dummies = df_dummies.fillna(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 306,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.        , -0.09207599,  0.        ,  1.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ],\n",
-       "       [ 1.        , -0.21769314,  0.        ,  1.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ]])"
-      ]
-     },
-     "execution_count": 306,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X = np.pad(df_dummies.iloc[:, 1:].to_numpy(), ((0,0), (1,0)), mode=\"constant\", constant_values=1)\n",
-    "y = np.array(df_dummies[\"carbon\"])\n",
-    "X[0:2,]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 307,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.63397514, -2.21470212, -0.39824008,  0.3802898 , -1.22706076,\n",
-       "       -0.72706076,  0.65881969,  0.6805806 ,  0.64455475,  0.27293924,\n",
-       "        0.45161452, -1.07691537, -0.14824008, -1.6197102 , -1.32691537,\n",
-       "       -1.60544525,  0.53720418,  1.03720418,  0.13778577, -0.56265042,\n",
-       "        1.48014441,  0.25867429, -1.93426594,  1.05896509, -0.54838548,\n",
-       "        0.15881969, -1.28412054, -1.99132571, -2.28412054, -1.82691537,\n",
-       "       -1.04838548, -1.32691537, -2.37720616])"
-      ]
-     },
-     "execution_count": 307,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m = np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, y))\n",
-    "np.set_printoptions(suppress=True) #this just prevents python from printing it out in inconvenient scientific notation\n",
-    "m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 308,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-1.74058105 -2.29707018 -2.06447086 -2.21869755 -2.17957681]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "-704.6"
-      ]
-     },
-     "execution_count": 308,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# get the residuals\n",
-    "res = y - np.matmul(X, m)\n",
-    "print(res[0:5])\n",
-    "round(sum(res), 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 309,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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yyiv567/+a17/+tfzF3/xF+TzeW688UZOOeWUCQLal770pZimyatf/Wre+973ks1m+fd//3daWlqmNOaOJ9deey1f+9rXuPrqq3nkkUdYv3493//+97n33nu54YYbqKmpAYLwy9atW7n11ls55ZRTaGhoYNu2bWzbtm3G/V9xxRV86lOfIhwO8653vWuCFimTybBmzRr++I//mB07dhCPx/nlL3/JQw89xBe+8IXKel/5yle47rrruOeee2bsk/bDH/6QH//4x3zhC1+ohI0AzjrrLN73vvfxla98hauvvprnP//5s56Xiy++mDVr1vDjH/+YSCRSKRlQ5lWvehXf+ta3SCQSbN26lfvvv59f/vKXM5YqKPPud7+b73//+7zsZS/jzW9+M/v27ePb3/72JB3a2972Nm677Tb+9E//lHvuuYcLL7wQz/PYvXs3t912G3fccQfnnHMOn/3sZ/ntb3/LK1/5StatW8fg4CD/9m//xpo1a7joootmHY9EMm+W2SMlkZzUTBcaEyIId2zatEls2rRJuK4rhBBi37594u1vf7toa2sThmGIzs5O8apXvUp8//vfr2z393//9+Lcc88VdXV1IhKJiC1btojPfe5zwrbtyjpTpboXCgXxF3/xF6KxsVHEYjHx6le/WvT09EwZKrrzzjvFtm3bhGma4tRTTxXf/va3p9znT37yE3HGGWeIcDgs1q9fLz7/+c+L//qv/1ry0JgQQTjqmmuuEU1NTcI0TbF9+3Zx8803T9r2vvvuE2effbYwTbPqMNmePXsqaey/+93vJnxmWZb46Ec/Knbs2CFqampELBYTO3bsEP/2b/82Yb3y+ZsprJbJZMSaNWvEmWeeWbkmxpNOp0VHR4d43vOeN+XnU/HRj35UAOLNb37zpM/GxsYq5ywej4vLL79c7N69e1Jq/FShMSGE+MIXviA6OztFKBQSF154oXj44Yen/G1s2xaf//znxemnny5CoZCor68XZ599trjuuutEKpUSQghx9913i9e+9rWio6NDmKYpOjo6xFVXXSWeffbZqr6nRDJfFCFm6MwnkUgkEolEchIjNUISiUQikUhWLdIQkkgkEolEsmqRhpBEIpFIJJJVizSEJBKJRCKRrFqkISSRSCQSiWTVIg0hiUQikUgkqxZZUHEWfN/n6NGj1NTUTNtbRyKRSCQSyYmFEIJMJkNHR8ekhszjkYbQLBw9enRCdVeJRCKRSCQrh56eHtasWTPt59IQmoVyaf6enh5qa2uXeTQSiUQikUiqIZ1O09XVVZnHp0MaQrNQDofV1tZKQ0gikUgkkhXGbLIWKZaWSCQSiUSyapGGkEQikUgkklWLNIQkEolEIpGsWqQhJJFIJBKJZNUiDSGJRCKRSCSrFmkISSQSiUQiWbVIQ0gikUgkEsmqRRpCEolEIpFIVi3SEJJIJBKJRLJqkZWllwHfF/QmC+Rsl5ip01kXQVVlQ1eJRCKRSJaaFecR+upXv8r69esJh8Ocd955PPjgg9Ou+/Wvfx1FUSb8C4fDSzjayewdzHDjr/fxpbue5ct37+FLdz3Ljb/ex97BzLKOSyKRSCSS1ciKMoRuvfVWPvzhD/PpT3+aRx99lB07dnD55ZczODg47Ta1tbX09fVV/h06dGgJRzyRvYMZbr73IDuPpqiLGmxsilMXNdh5NMXN9x6UxpBEIpFIJEvMijKEvvjFL/Ke97yHa665hq1bt3LTTTcRjUb5r//6r2m3URSFtra2yr/W1tYlHPFz+L7gjp0DjOZsNrfEqQkbaKpCTdhgc0uc0ZzNnbsG8H2xLOOTSCQSiWQ1smIMIdu2eeSRR7jssssqy1RV5bLLLuP++++fdrtsNsu6devo6urita99Lbt27VqK4U6iN1lg31CW9kR4UidcRVFoT4TZO5ilN1lYlvFJJBKJRLIaWTGG0PDwMJ7nTfLotLa20t/fP+U2p556Kv/1X//Fj3/8Y7797W/j+z4XXHABR44cmfY4lmWRTqcn/FsMcrZL0fWImlPr0yOmhuV65Gx3UY4nkUgkEolkdlaMITQfzj//fN7+9rdz5plncvHFF/ODH/yA5uZmvva1r027zfXXX08ikaj86+rqWpSxxEydsK6Rn8bQKdgeIV0jNo2hJJFIJBKJZPFZMYZQU1MTmqYxMDAwYfnAwABtbW1V7cMwDM466yz27t077Tqf+MQnSKVSlX89PT0LGneZzroIm5rj9KWKCDFRBySEoC9VpLslTmddZFGOJ5FIJBKJZHZWjCFkmiZnn302d999d2WZ7/vcfffdnH/++VXtw/M8nnzySdrb26ddJxQKUVtbO+HfYqCqCpdva6UhZrJnMEum6OD6Ppmiw57BLA0xk5ee3irrCUkkEolEsoSsqDjMhz/8Yd7xjndwzjnncO6553LDDTeQy+W45pprAHj7299OZ2cn119/PQCf/exnecELXkB3dzfJZJL/83/+D4cOHeLd7373soy/u6WGay5czx07B9g3lGUgXSSka2zvTPDS01vpbqlZlnFJJBKJRLJaWVGG0BVXXMHQ0BCf+tSn6O/v58wzz+T222+vCKgPHz6Mqj7n5BobG+M973kP/f391NfXc/bZZ3PfffexdevW5foKdLfUsPGSuKwsLZFIJBLJCYAijhWsSCaQTqdJJBKkUqlFC5NJJBKJRCI5vlQ7f68YjZBEIpFIJBLJYiMNIYlEIpFIJKsWaQhJJBKJRCJZtUhDSCKRSCQSyapFGkISiUQikUhWLdIQkkgkEolEsmqRhpBEIpFIJJJVizSEJBKJRCKRrFpWVGVpiUQikUgkJwe+L06ILgvSEJJIJBKJRLKk7B3MVPpuFl2PsK6xqTnO5duWvu+mNIQkEolEIpEsGXsHM9x870FGczbtiTBRM0Ledtl5NMXRVIFrLly/pMaQ1AhJJBKJRCJZEnxfcMfOAUZzNptb4tSEDTRVoSZssLklzmjO5s5dA/j+0rVBlYaQRCKRSCSSJaE3WWDfUJb2RBhFmagHUhSF9kSYvYNZepOFJRuTNIQkEolEIpEsCTnbpeh6RM2plTkRU8NyPXK2u2RjkoaQRCKRSCSSJSFm6oR1jfw0hk7B9gjpGrFpDKXjgTSEJBKJRCKRLAmddRE2NcfpSxURYqIOSAhBX6pId0uczrrIko1JGkISiUQikUiWBFVVuHxbKw0xkz2DWTJFB9f3yRQd9gxmaYiZvPT01iWtJyQNIYlEIpFIJEtGd0sN11y4nm0dCZJ5h4PDOZJ5h+2diSVPnQdZR0gikUgkEskS091Sw8ZL4rKytEQikUgkktWJqip0NUSXexgyNCaRSCQSiWT1Ig0hiUQikUgkqxZpCEkkEolEIlm1SENIIpFIJBLJqkUaQhKJRCKRSFYt0hCSSCQSiUSyapGGkEQikUgkklWLNIQkEolEIpGsWqQhJJFIJBKJZNUiDSGJRCKRSCSrFtliQyKZA74vTojeOBKJRCJZHKQhJJFUyd7BDHfsHGDfUJai6xHWNTY1x7l8W+uSd0uWSCQSyeIgDSGJpAr2Dma4+d6DjOZs2hNhomaEvO2y82iKo6kC11y4XhpDEolEsgJZcRqhr371q6xfv55wOMx5553Hgw8+OOP63/ve99iyZQvhcJjt27fzi1/8YolGKjlZ8H3BHTsHGM3ZbG6JUxM20FSFmrDB5pY4ozmbO3cN4PtiuYcqkUgkkjmyogyhW2+9lQ9/+MN8+tOf5tFHH2XHjh1cfvnlDA4OTrn+fffdx1VXXcW73vUu/vCHP/C6172O173udezcuXOJRy5ZyfQmC+wbytKeCKMoE/VAiqLQngizdzBLb7KwTCOUSCQSyXxRhBAr5jX2vPPO4/nPfz5f+cpXAPB9n66uLj7wgQ/w8Y9/fNL6V1xxBblcjp/97GeVZS94wQs488wzuemmm6o6ZjqdJpFIkEqlqK2tXZwvIllR7O5P8+W797CxKY42hTDa9X0ODuf4wKWb2dImrxGJRCI5Eah2/l4xHiHbtnnkkUe47LLLKstUVeWyyy7j/vvvn3Kb+++/f8L6AJdffvm06wNYlkU6nZ7wT7K6iZk6YV0jb7tTfl6wPUK6RsyUkjuJRCJZaawYQ2h4eBjP82htbZ2wvLW1lf7+/im36e/vn9P6ANdffz2JRKLyr6ura+GDl6xoOusibGqO05cqcqwDVQhBX6pId0uczrrIMo1QIpFIJPNlxRhCS8UnPvEJUqlU5V9PT89yD0myzKiqwuXbWmmImewZzJIpOri+T6bosGcwS0PM5KWnt8p6QhKJRLICWTG+/KamJjRNY2BgYMLygYEB2traptymra1tTusDhEIhQqHQwgcsOanobqnhmgvXV+oIDaSLhHSN7Z0JXnq6rCMkkUgkK5UVYwiZpsnZZ5/N3Xffzete9zogEEvffffdvP/9759ym/PPP5+7776bD37wg5Vld911F+eff/4SjFhystHdUsPGS+KysrREIpGcRKwYQwjgwx/+MO94xzs455xzOPfcc7nhhhvI5XJcc801ALz97W+ns7OT66+/HoC//Mu/5OKLL+YLX/gCr3zlK/nud7/Lww8/zP/9v/93Ob+GZAWjqgpdDdHlHoZEIpFIFokVZQhdccUVDA0N8alPfYr+/n7OPPNMbr/99oog+vDhw6jqc7KnCy64gFtuuYVPfvKT/M3f/A2bN2/mRz/6Edu2bVuuryCRSCQSieQEYkXVEVoOZB0hiUQikUhWHiddHSGJRCKRSCSSxUYaQhKJRCKRSFYt0hCSSCQSiUSyapGGkEQikUgkklWLNIQkEolEIpGsWqQhJJFIJBKJZNWyouoISarD94WsfiyRSCQSSRVIQ+gkY+9gptIPq+h6hHWNTc1xLt8m+2FJJBKJRHIs0hA6idg7mOHmew8ymrNpT4SJmhHytsvOoymOpgpcc+F6aQxJJBKJRDIOqRE6SfB9wR07BxjN2WxuiVMTNtBUhZqwweaWOKM5mzt3DeD7spC4RCKRSCRlpCF0ktCbLLBvKEt7IoyiTNQDKYpCeyLM3sEsvcnCMo1QIpFIJJITD2kInSTkbJei6xE1p452RkwNy/XI2e4Sj0wikUgkkhMXaQidJMRMnbCukZ/G0CnYHiFdIzaNoSSRSCQSyWpEGkInCZ11ETY1x+lLFRFiog5ICEFfqkh3S5zOusgyjfA5fF/QM5pnd3+antG81C1JJBKJZNmQ7oGTBFVVuHxbK0dTBfYMBlqhiKlRsD36UkUaYiYvPb112esJnSjp/bLWkkQikUhAGkInFd0tNVxz4fqKoTGQLhLSNbZ3Jnjp6ctfR+hESe8/UYwxiUQikSw/0hA6yehuqWHjJfETzttxbHp/ObOtJmwQD+nsGcxy564BNjbFj+tYTxRjTCKRSCQnBtIQOglRVYWuhuhyD2MCc0nvP15jP1GMMYlEIpGcOEhDaJWy1BqZ59L7pxZrR0yNgXTxuKb3z8cYWw4tkdQvSSQSydIhDaFVyHJoZMan99eEjUmfL0V6/3hjTAhBpuhiez6mplIT1icZY8txnqR+SSKRSJYWaQitMpZLI1NO7995NEU8pE/wyJTT+7d3Jo5ren/ZGDuazNOfshjN27i+j66qNERN2hKhijG2HOdJ6pckEolk6ZF1hFYRy9mPrJze3xAz2TOYJVN0cH2fTNFhz2B2SdL7O+si1EUNHjo4xkC6QNhQqY+ahA2VgXSBhw6OURc1aK8NL/l5kr3iJBKJZHmQhtAqYrn7kZXT+7d1JEjmHQ4O50jmHbZ3JpbO21G2I475/uW/FeBo6vidp+mKSS73byORSCSrFRkaW0WcCILl5Uzv700WSBYcnr++nr6UxVjeJmu56KpKa22YttoQY3mH/cO543KeZtL/uL5Y9t9GIpFIViPSEFpFnAiCZVi+9P6yIbixKc6a+ugksbQnBAeHcwCLfp5m0/+8bFvbCfHbSCQSyWpDhsZWESupH9lCmC78NN4QVBSF2ohBUzxEbcRAUZSKsbGhKbao56ka/c8TPSk2NsVO+t9GIpFITjTk6+UqYqX0I1sIM4WfNjbFq8pc66qPLup5qkb/s28oyxue10lfunjS/jYSiURyIiINoVXGid6PbCFUk35erYGzmOepWm1WU03opP1tJBKJ5ERFGkKrkBO1H9lCqLZ9xp9evKlqY2OxztNctFldDdGT7reRSCSSExlpCK1STsR+ZAthLunnczFwFuM8zbWY5Mn220gkEsmJjDSEJCcc8+m1NVP4SQiB6wmGshb7hrKV/Y03NsoC6+PhhVkN2iyJRCJZqUhDaBmQTTWnZ769tqYLP43mLPYN5hjIFCk4Ht954DC7etMT9rcU/b1OZm2WRCKRrGRWjCE0OjrKBz7wAX7605+iqipvfOMb+Zd/+Rfi8fi021xyySX85je/mbDsve99LzfddNPxHu60yKaa07OQXltThZ9GcxaP9STJWy6uD2vro3TUhSfsD1iy/l4nozZLIpFIVjorxhB661vfSl9fH3fddReO43DNNddw7bXXcsstt8y43Xve8x4++9nPVv6ORpdPeyGbak5PtWLnjU3xabU848NPbbUh9gxkSRccdE2lNqKzubWG2ohJTdhgz2CWO3b2I2Dex5wPUv8jkUgkJxYrwhB6+umnuf3223nooYc455xzAPjXf/1XXvGKV/DP//zPdHR0TLttNBqlra1tqYY6LQud6E925iJ2ns6QGB9+eqI3Sc9YnrCh0VobZlNznIaYOWF/TxxJgRJ4k+Z7zOONDKNKJBLJ8WVFGEL3338/dXV1FSMI4LLLLkNVVR544AFe//rXT7vtf//3f/Ptb3+btrY2Xv3qV/O3f/u3M3qFLMvCsqzK3+l0elG+w2JM9Cczi9UHrRx++u2eIf7jd/vZ2BinLmpMOucRUyPvBPuKTtO24nj296rGwJFhVIlEIjn+rAhDqL+/n5aWlgnLdF2noaGB/v7+abd7y1vewrp16+jo6OCJJ57gr//6r3nmmWf4wQ9+MO02119/Pdddd92ijb3MYk30J6uHYDH7oKmqwqbmOC3xMLqmTDKCyvuLGjooLHl/r2oMHBlGlUgkkqVhWQ2hj3/843z+85+fcZ2nn3563vu/9tprK/+/fft22tvbufTSS9m3bx+bNm2acptPfOITfPjDH678nU6n6erqmvcYyizGRH8yewjmWmtnMfZ3xpoEAth1NL0ox6yGagycjU3xZQ2jnqzGtkQikUzFshpCf/VXf8XVV1894zobN26kra2NwcHBCctd12V0dHRO+p/zzjsPgL17905rCIVCIUKhUNX7rJb5TPTjJ6ShjMX/e7KfsfzkCbQ3mecV29tpqgmt2IlrsWvtVLO/y7cF105famn6e1WrE3vlGeqyhVFPZmNbIpFIpmJZDaHm5maam5tnXe/8888nmUzyyCOPcPbZZwPwq1/9Ct/3K8ZNNTz22GMAtLe3z2u8C2GuE/34CanguBwaKeB6PuduqK94lGrCBrbr8+DBUR7vSbK2MUbEWLkT12LX2ql2f0tV36dandiB4dyihFHnigzHSSSS1ciK0AiddtppvOxlL+M973kPN910E47j8P73v58rr7yykjHW29vLpZdeyje/+U3OPfdc9u3bxy233MIrXvEKGhsbeeKJJ/jQhz7Ei170Is4444xl+R7VTszHTkhxT+fZgSye5/P4kRRndik0xEKM5mweP5LCcnw8TaE5HkLXlBU9cS12rZ1q9rdU9X2q1YkBi6aXqhaZ1SiRSFYrK8IQgiD76/3vfz+XXnpppaDil7/85crnjuPwzDPPkM/nATBNk1/+8pfccMMN5HI5urq6eOMb38gnP/nJ5foKwOyT7lQT0nDWQgGaakIk8w77hnLURQz2DmYp2C7NNSapgoMnBPVhc8VPXItda6ea/S1FfZ9qdWIbm2KLqpc6lqk0QDKrUSKRrFZWjCHU0NAwY/HE9evXI4So/N3V1TWpqvRKYKoJydRUdE3F9SEe1hnN2fSliozlbeJhA9cXaKqKqanA3CcuKY6tjoWep2p1Ymvqo8etN9l0GqDNrfFlCcdJJBLJcrNiDKGThdnEqFOFT2rCOvVRk6FMkbqogef75B0P1/PRQxrJvEtLbZia8HM/Z7UT12KLY09Wo2oxztNcdGLHozfZTBqgZwcz2K6/5KUEJBKJZLmRT7UlpBox6lThE0VR6G6Jk7VchjI2uqYQ1jQEMJyxqIkYbGqOTfAwVJuOv5ji2JM142im8zTXjL25GDjz1S5NZYwCM2qAnh3IYLk+R5NFTmldmlICEolEciIgDaElolox6rUv3Dhl+KQhZrJjTYIHD45iqAoFxyWka7iKwo41CRpiz6X8VzNxLbY49mTNOJrpPM03Y68aA+dYY+aUlpqqf4epjNEzuhIzaoA66iIcHs0T0tUlKSUgkUgkJwrSEFoiqhWj9qWL04ZPRnI256yrr3ggyrWFRnI2pq5V1juaLBIyVLpbg8l2Ki/CYopjT+aMo+nO00Iz9mYSZ8/XszaTMbqrL0W26NIxjWEcMTVCusord7Szpz973EsJSCQSyYmCNISWiPHaHyEEmaKL7fmYmkpNWJ+g6dnSVltV+GRLG7QnwhPWs1wfy/FxfZUf/aGX2/X+yiS6sek5L0R/qkjB8ehYBHHsyZxxNJVmSwhx3DL25utZm80YffxIkuGsTc5yqI2Yk7Yvh1JPa6vlsi2tJ6XOSyKRSKZCGkJLRFn7czSZpz9lMZq3cX0fXVVpiJq0JUITND3V6kPGr/d0X5qfP9mHrnp01EWImnplEn26P01LKf2+6Hp4nqBnLE/EUOlqiE0a71zEsTnbpeC4xD2d4axVMe7Kk/FKzjiaSrOVKbqLlrE3noV41mYzRjc2xRjKWOwfzrFjjTGjBmi+pQROVqG8RCI5uZGG0BLRWRehLmpw11MDGJpC2NAI6RpCCPrTBXrG8rxka+sETU+1E5KqKnTWRfjJY0exXZ9TWmsmaVl+8+wQEUPjwu4mOkIRcpbDgZEcDx0cI2pqNMbDlf3NVRw7lLE4NFLg2YEsCqBrKvVRk+6WOA0xc0VnHE2V8m57/qJk7B3LQjxrsxVrjIZ0muIhYiWDarE1QCerUF4ikZz8qMs9gFWFAMcTJPMOvaMFDo/k6B0tkMw7OJ5gIe/O002iQgj2DeVQFYXyYk1VqI2YnLu+AYAHD4yRLgQeqkzRYc9gtuqJce9ghv/3ZD+u5+N5PnVRg7ChMZQp8lhPkpGsRV+qSHdLfEVmHJVT3htiJnsGs2SKDqpCJWMvYmrzytibiueMmam3i5galutNaWCN91xNRcH2aIqHeMu5a9nWkSCZdzg4nCOZd9jemViQmL0cztt5NEVd1GBjU5y6qMHOoyluvvcgewcz89qvRCKRLAUr7xV9hdKbLHB4LI+mQN4JQlMCgYKC7itEDI1Do/mKuHmuIYbpPALlME5d1CBvB7qkMo3xEM9fX8/u/ixHU0V0VZmTOLYcyhnL25y7oZ7Hj6RI5h3iYZ26qMFQxubBg6Ocs65+RWccHZvyXlxgxt5U+L4gXXCwHJ+BdIH2RGSSV2gmA6vaYo0XbGrigk1NixbCWgyhvAypSSSS5UQaQktExnLYO5glbTmENAUjpKMQeBYc1yNd+nxXX4qfPHZ0ziGG6do3lMM4pq5M0LKUaa+LUHQ83vz8tbQlwnOaiMZ7oWrCBmd2KewbzDGat/F8H11TMFSFV2xvX/HhkWM1W9Nl7M0nzFQOK+0dzNAzmufpPpd1jVG6W2poiAXC5tkMrLk29V0s0fpChfILCalJA0oikSwG0hBaIjIFh9GcDQJiYYPxc4apqWSKDsMZi+8/3IOhaXOuxTOdR8DUVHRVIZ136KiPTtCyQOBlCBs6m5rjc54cj/VCNcRC1K83KxlxWqlPWlNNaJY9zZ3lmATHa7amytgL6RrbOhKc0ZXA9QU9o/lZx3Vsltg56+t59PAY+4dzjOZsnreunoihVWVgHY9q1LOd52obyU4VzltI7SmpSZJIJIuFNISWiJztIQQz6oBsz2cs73DhpsScQwzTeQRA4AOegI1N0UWtGDxdFezaSDm7yiFsLL5I+kSZBI/1Eg1nLB47nOSHj/ZWNa6pwko1YYPnr29g70CWQ6N5Hj00xtb22qqNmflWo56Kas5ztY1kj70GFhJSO1mLd0okkuVBGkJLhKooREwV1xMUbK9kEQkgUN6WxcyNUXNSiAEgHtJ55NAoDx+q55x1DVNObNN5BF7Y3cRAxmIk5yw4jDOeanUpiymSXkidnfkaBzNtW/YS7R3M8P929s9pXNOFlRpiIZ6/wWRNQ5TRnMVV562d9jefivmmv4+nfJ5HshbxkI6mKGSLDg8cGKY3WeCdFwXfZ77XwHxDaidz8U6JRLI8SENoidjQFKMpHqY/XSBXdHCe0yxjqBAO6YQ0ldba8ITtRnMW+wZzDOcs0gWHr/1mPw+tHZvWyzCdR2D/cHZRQyYwd13KQpnvJLgQD1I12853XDOFlRRFoaU2RN52qY0Yiz6pz2Tclb/P4ZE86aJDb7JA0fEACOsah0YKhA2VT75y64zXwNFkgZCh0d0yucL5fENqJ3PxTolEsjxIQ2iJ6KqP0pEI8exABiHA1AIPkBDg+oKc5dISD6GPEzOP5iwe60lSsD1MXSURMWiIGbN6P8Z7BMoTnusLXrWjHeELDo7mgcA466qf/2Th+4KQrnHxqc08fGCUoYzFQNo/bm0Z5jMJLlSHUs22852c5xtWWiizGXe9yQJ/6BnjyFiekZyNAoR0FSHAcj2KrscdO/t58ZYWLtrcPKUn0nJ9LNfH9QU/eqyX23f2TzjGfL/7QjRJEolEMhXSEFoifF8wlnfQVEAoVNTSCugqQSq9An3JQkXQvG8wR8H2qI8ajOUdWmrDtCeCCaCaEMCxE55dar8RMlRMXV2QtubYfYc0leaaQOx7WnvtcREvz3USXEgYZS7bzndyPlFDi7bnc3g0T6rgIIRAAHnbRwgIwrkwlrf55VMDXLCpCVVVJlY470/z88f70FWfjrrwhArn5WNsbIrP67svl/EokUhOXmRBxSXi0Z4xBjMWa+ojNMVNQpqKpiqENJWmuElXQxRfgCcEewaz9KWKDOcsTF1lLO8QMYPMLkVRJnkZpuLYInd1EYMjY3meLaVo10XMeRe9m6qAXn3MpGcsz2+eHcJyveOiz6imaOD4SXAunppjmcu2cx1XmamKNc6nqGW1HGvc1YQNNDUQaG9uiTOas7lz1wDpgkOm6GB7Hq4vsF2/9L2D7+55AtvzebI3NeHclSuc7+nPYns+p7ROfwxgXt+9bDz2pYqIwDKrUDagVmrxTolEsjxIQ2iJGMnZOF4QNlIUBU1T0NTgv4oSFDIEOL+7kW0dCUZLmiDPF7TUhjmzq65SUwZmrjJ87IQXD+kcHM7jeoK19RE8X3BwJEc8pE+YnHxfTNrXbPuebqKrZl/T7b9nNM/u/jQ9o/kJ+5nrJLiQSs1z2XYhk3M5rLTY1Z6nolrjrmB7mJpK0QlqUAnA8Xxs18f2fHwAAcNZi4zlzOsYvcnCvL77UhuPEonk5Ef6j5eIxpIRczRZQFEUTF1FUxQ8IcjZHlnLJWwEdWjOWdfAw4fq+dpv9tMQM+ZcZfjYyShdcBjN28TDOqqqEg/rjOZsMsVAiDsXgWl532214Uq9oPFNVhciVp1NuzJXcfZCwihz2XahovHFTHmfiWpDeIqi0FQT4shoAV8BRQRhWwXwfaDkGcpZLtmiO69jlI3P+Xz341EvSSKRrF6kIbREnNlZR0jXGMvbNESfywLSFQVVVxjNu9RFTc7srENVFc5Z18BDa8fYeTQ1aV+z6UeOnYxsz8f1fQwt+LkNTSVrPdduYy4C05ztMpy1OJoskCwEb+O6qtIQNdnUEqM2YsxLrFqtMHkuk+BCNDhz3Xahk/NipLzPRrXG3YamGFvba3m8J4Xvi4qoX1FAU0r10Ev91qKmNq9jjDc+5/Pdl8p4lEgk1bNSq71LQ2iJGMhadNSFSRZs0kWPiAmGquD4olLduT0RZiBr0dUQXZCX4djJKKgurWK7HgoKBcfD94Pjw9wEpsMZi57RPEII6mImhqbjeD6DmSIZy2FzS3zOYtW5ipqrnQQXcg7ns+1iTM7H80Eyk3Hn+z77hrJsbIqjADu66vj+I0cAgaooqGV9kBAgFEK6iqGp5G2v6mMstgB8KYxHiURSHSdKodv5IA2hJSJnu9THTC7qbuaJI0lSBYeCL9BUhca4ybbOBEppvTLz9TIcOxnVhHXCusrh0TwKUHR9IqbG7v40bYkIw1mL0zsStB9Tw+hYfF/w2OEkhq7i+QJTUyv6JjOmMpK12Nmb5rVnds5poptP+nm1k+BCPDXHbtufKuIJQXsizKWntbKxKT5pm4VMzsfjQXKsYfWSrZONu75kgSd7UxRsj2zR5XM/f5rW2hANUZOM5eB4AtcXKCK4XmsjBqauEDV14qGpBeBLVVtKIpEsPyu92rs0hJaIspemLmrwujM76EsVyTseUSPoK5azPZJ5Z5InZT5ehmMno4jxXCjM8wURQyMe0nm6L8sTR1LURgzChsbXfrt/xkm3N1lg/3CObR217BkMemHFwzqGpuJ4Pq4PvhCc0ZWY00Q3k65ECIHrCYayFvuGsvPykEx1Dttrw/Sli+zuT0/4+9hzXN72vn3D/PKpQfpSBQbSRX74aC9P9KRmNVKq9fAcjwfJdIbVi7e0sLsvw76hLHsHs+wbzFJ0fUK6wmDGYjhr8cyAguP7NMRMfAG6GmQ56qqC7fnoJYNvqvCX1PBIJKuHk6HauzSElojxXpru5hg1YYOQoVW6wc/WWXw+GoprLlzP7U/284ud/WQtl6a4ie8HKfpjORtPBF6dltoQXfXRWSfdssGysSlOLGSwdzDLWN4ma7noqkpbIkxIV2meY5PV6XQl5araA5kiBcfjOw8cZldvel4ekvHncO9ghq/9dj/7hrIUHI900SFvu0RNndqwQcSY6InZP5zlF0/20ZssUB81qQ0baCqznq9qPTzlB8lI1qKtNozl+gjhUhMOsvrm8yCZzbB6xwXreJXezpfuepanPR8hBHlbAB4QhMJsT6CrKmsbIiQLLp7vI1BoqQkKfz5vbf20nj+p4ZFIVgcnQ7V3aQgtEWUvzdP9aW7fNYDlehUBakjXOLWtZtFDBt0tNbzmTJUnj6Y4tTVOXdQkZmrct38EVbFoiBooikKxVCdmtkl3vMHSEDN5/vr6CZljIEgV3DkXs5tKV1Kuqp23XFwf1tZH6agLL9jVOt5AiBgqR8cKHBjJYbkeIV1lfWOM7pb4cwbD+ev57oOHefjQGCrQO1ZA11TqoyabmmOMlMoFjD9fvi+4d98w33nwMDnLZWNTnI7Q9B6eciXnsZzNwZE8rudXjtHdEp/zg8R1fW576AiHRnJ0N8cr53T8G9ovnxrkFdvbeLI3RdHxgppWhlbJZLQcD98XZIsOph5je2cCTVXwfEGm6NIYnz3EJTU8EsnJz8lQ7V0aQktMpugykrUousFEo6oKYV0jUzw+BeDypUmusz6Kpgap9HnbozEeIqSr+EKQswNjRlEU2mpDPN6T5Ld7htjUHJ/wFj+VwVLuNC9KhSDnI4Q9NpTXVhtiz0CWdMFB11RqIzqbW2uojZjUhI15u1rHu3AbYwaP9SQ5miyiKFAfNSjYPkeSBXwBO7oSHBnL88937Oap/jSaolIfMzE0BccTDGWKZC2XzS2xCUbK3sFMxQs3mrNIRAwcV7CpJUZDLETM1HiiN8W3f3+Iay7cQFd9lKf70zw7kMFQVWoiBkZYn3CMbZ2109Y7Opa9gxlue6iH/7ezH01VGM7aFYOqIWZOeEN76MAog+kiqgJRU68UO9cVBc3UEb6DK6ApHsbzBXnbJaRrnLFGhrgkEknAyVDt/cQd2UmG7wtueeAwz/SnEQSaC6EGtVkE8Ex/mlseOFxpZLlYHHuR2l5QJM8otfFwPB9NVTE1ldGczbMDaY6MFfjP3+2nOR6eVMfnJVtbeXYgw6OHx2hPRGiuCVF0Fi6EHa8reaI3Sc9YnrCh0VobjKFcTHIhrtbxNZCe6c+QKbooCkR0DU1VCJsKruczmrP53z3D6Ar0Zyw8T9AYN/F9H1XXCekKZsxkNGfTmyzSGDPI2W7F23RkLI/lerTWhlEUKhl1G5piDGdsBjLFoHp4MgiHHhoNil3WRzVCehAqHX+MZwcyrK2PzvogKR//0EgOTYXGuInnUzGoykU5y29ofekiri+ImhrHeLRRFNB1Fdv2eMGmBs7f2CRDXBKJZBLL0SposZGG0BJxZCzPb54ZJGu5GJpK1NRQFfAFWK5P1nL57TODHLlgPWsbY4t23GMvUlNT0TUVxxOYGmSLLi21YRzP5/EjKdIFh7ChsaExjq4pE0I5AHc9NUDOdhnMWBwayRMxNdY2RHne2voFewm6W2pY/6IYP3ysl6FMkfUNcTrrw6jqxALo83W1ll24cV9nNG8TNjTSRZfynK4pCgVP4HounhC0lLROuq6Sszxcv0hbbZiIGdzs8bDOcNYiEdZJ5m1u39nPkbE8HYkwvWMFTF1FVRTMmEp/usgD+0eJh3ViIa3yPR46NMqhkTx1EYOc5VUqj0Ng9MVCOn3JIuesa5j0IBkvxI4YGrfv7Gc0Z9PdHGc4a+OVmuKWDap9Q1nqo/WVN7S22jC6quB6AqEzwRgSAlxXoKsKzTUhGeKSSCRTcjJkikpDaInYO5RlKGOVQmEKvhC4QV06wrqCJ4KMnb1D2UU1hKYKOyUiOv0pC12FaEhnY1OUfUM58raLrkJrbZi6kn6orCm55YHDFB2PsbzD2oYop7bWMJgp0pcqEgvpXLa1ZcGhkrK4+IkjSfrTFqmCS18qRFsiTLRkxNWE9Xm7WsvesXJbhqihoSoKvgBNAc8XOJ6PoakYmhKELhWFmKlhewLH9RnNO3QYgbGiqwqpvMNwzua//vcAO/vSRAyNTCEwpBxPENKDm99xfXK2S1si8BLpqoLleHi+T6rgBCUOVG1SJl626KBrCmevq5/wIDlWiO15gp6xPFvaaqiNGDRETQYzRcyYWjHaRnM26YLDQMZie2eC529ooKU2zEC6GIS9yhohX1CwA21Wc43JhkW8HiUSycnHSs8UlYbQEjGStXF8QUgNvAuOLypiaUNVUFQV2/cZydqLfuxjL9KQHnijNFWtpDsOZop4nk9NxGBTc2yCV6KtNsTv94/QXBNix5q6ymcddVHaE5GK+La7uWbeVv94EXNHXZhUweHwaJ7dAxme7k9TGwmyuaKmhoLC8zc0zFr36FjK3rEHD46gKUoQFjNUcrZHWFfI217FKFIAxxOEDY36mMlozsFyPHKWS9HxUVUYTBcpuoEGKxrSiRgasZBGqmCTtz08P8gCs10fy/NLYuPgN87bHkeThUpT02ctl60dtdSEQwymLRzfx1BV6qIG9bEQp7XXTnmuyhlhR8byjORsnhnIEAvpbGqJkbGcimGlqQpFx2PvUJZ1jTFeenor6xpiXLy5mV/s7Mf2PCzHx/ODfmJCgKoo6KrKzx7v42Xb2+b8MFupVWYlEsncWcmZotIQWiIa4yYKQX8mTVXQ1MAIEgRpyp4ThMwa4+as+5oPx16kQxmLx3uS7B/KcXAkR9H26GqMsrklTkMsCAkJEWQIDWVtRrITa0SUWYz0yKnqULTUhnl2IIvt+qgKFGyXgu1xcMRFVxVChjpr3aNjKXvHepMF+pJFknmHRNQga3kM5xwQAl9AxvMxdJWGmEZzOES26NJaE2I0H3hUkgWbiK5iuYLGeIgXbGgga3kYmoqqqDTGQzipIpbrM5KzS+EnH1UJvH6pQhCOC+kqvh80NbU8weM9KRriBhFDL10bgnTRY0dXtBIWm65mR33UpD5ikCu67BvKcc66es7sqmPfYI7RvE3RcfF8OL0jwZvOWVM5Z295wVoGsxbP9KfJFl3SRS8I55XKIJyxJsGuvjR96eKcMvWmKx3wktNbiBj6intQSiSS2VmpmaLSEFoiNjbFCBkqluuhCoDnlNJC+PhCEDZUIoZaKfK32JPE+It0SxtcuKmpIiD+zoOH6UhEKllg5Ro+weRvky46HBrJUxsxqY8aE9LmI6ZadVbTVBxbh0IIwVDGIh7WiHgqGcslmXcxdZX6qIGhBUX9nuydeyp9d0sN77xoPWFD5Z7dgwykLOySV0dVFCzHR1HA1IOQ0tqGGAeGcxRsl0TYIKSpdLfWkC442J7grLV1qKpKTVihPmoylAli4g1xk1TBIRExSOZtbFcE58n2UBWIhzRyto8vFAwt8D75AkayDnURQXNtGMvxcTyPwYzF/uEs3S019IzleeJIkoipkSm6lWa3NWGd+liIo8kCI1mLTNGlIRaifr1JuuCwdyjL1o5aPvrSU9F1dcL5+OBlm/l/T/bx48eOYns+iYhBa22Y7pYaGmJmJSOw2ky96WoY/X7/CHc+1U9zTQhTV1dUCX6JRHLysmIMoc997nP8/Oc/57HHHsM0TZLJ5KzbCCH49Kc/zb//+7+TTCa58MILufHGG9m8efPxH/AxqIpCQ8yk6HgIIfB8UflMUUDXgsnl5t8dRF/ESWKm8ETZMOqsi7CrN10SVGscKbVccFyfuoiBrqlEjEC/8vv9I8RCGkXHr9S7iZlB+Gi+6ZHH1qHIFN2gOW0shKHC4bECjufTWRemLmpiez45y2Nbh8nBkfyEVPRqDMfulho++cqtXHJqMzfctYcjyTxxUwUl6J2lKtCRCJMsBA1md6xJsG8oy6GRPDVhnZip0VEXxtRVOuoCw1JRFLpb4mQtl9GcTTSkoyrQVR8hHtLRNRWVQDQfM3WKjo/vB2Jk1wdNEYFRDBQcL/D4NMdprQ0znLO47eEe3nDmGr778GGePJoiYmgY42oNNcSC/6aLDiNZi7G8TTQUCBYHMhbrGmO8+ZyuCUbQ+PPxmjNVdvamiZga9VGzYmCVv1u1Xr/pPFaOJxjL2wxlLXRN4QUbGik43oopwS+RLDcy1Hz8WDGGkG3bvOlNb+L888/nP//zP6va5p/+6Z/48pe/zDe+8Q02bNjA3/7t33L55Zfz1FNPEQ7PTV+yUPKOR2ddBAUYzQUZPQKBQqkInwiEudGQzpr66KL0aZlLeGJ8scfhrIXleJh64I1pjIVoaDAZyVoMpIvomsLa+mhQ78YNau+4peax82GmFH/H9bFdn5CulWrdKBilVP/HelJkLbeSin7GmrqqDUdVVdjQFGddY5TuljiGHpQQCLLnkiQLgQdqKFOkLhIYNad31PLH56xha3sCXwhuuOtZBtIFTF3DUBU0BdY1RjmaLJDK2xRdn6Lr84KNjZzaVsPN9x3gwEgOUwSibEUh6OGlgKqq6GogsG6ImqiaQtH12D2QoeC47B3M8r/PDlMTfk6LpCrqpNT4U1vj7BaBMXVwOFe1YLHgeGiawppSvaljqTZTb6oqs0II9g5mKToebbUhcpZH3vaojaycEvwSyXKykhuargRWjCF03XXXAfD1r3+9qvWFENxwww188pOf5LWvfS0A3/zmN2ltbeVHP/oRV1555fEa6pTETJ2meIimuElfqkhvsoDtBqElAXi+T23YoD5qoqnKgvu0zDU8saU9uJkcz69kTillEROCroYIfekijuejKkEhRscL0v6b4ya1EYNfPj1Ad8vs4zz2zaa9VCtoqhT/spi4LmpglrwZmaJDuuCgAIloEMqLmNqcDcec7WJ5fqXYJATXTXdznP3DOYazFsm8TabokogYdDXo7B3Isak5ju/DcDZISddVhbwTGIFRUyOsq/gCnr++gQ+/5BTWlDxVqYLDEz2pSs83XVMqGWp520MogYEWMjVGsjYKQUPeqGmyfyjPYKaIqoSpCetkiy4NMY2GcanxdZE6Co7PK7e38eodHeQdr+o3x8UqijZVldmyhy8eNkrf1cb2gmrmK6UEv0SyXKz0hqYrgRVjCM2VAwcO0N/fz2WXXVZZlkgkOO+887j//vunNYQsy8KyrMrf6XR6UcZTzlj6/YERhBAY2nMhinwxSF1uiIeIh7SS/iQwktpqQ3OeJGYNT2QsfF+wtaMWIeCJI0l+9sRRFBXWNURwXI9E1MQsTdJjeYfeZLGUsQU5yyVZcIiaOi21YTY1xzA0tapxTvdms6W9ZsoUfyF8DE2phGqEEAymgzIErbVh7JLRVg7nzMVwPHbyH83Zlf5puaLDWM7GF4K1DVHOWVdP0fXZeTTF0/2la6KkJRrJBteLoir4Frhe0KXd9QW251fG8ZozOvjBI0fY1ZdGBcxSr7kgTOriehA1VPKWiy8EDTGTkK6RLjr4QtBeE8Fy/cCANZ5LtY+GdAbSRZ7oTbGmPsrl29rmXIJhsYqiTWVQTfDwjSvgWaYab5MMC0hWIydDQ9OVwElrCPX39wPQ2to6YXlra2vls6m4/vrrK96nxURVFba01/DDx3rJFB0aYyZ1UYNkziZru/gEWUQPH0oylrcr+ptERCdUqn3TM5qvaiKYKTyRLjooCA6N5klbQfZSpuAwlndQFYUDGng+WK6goy6MqmqVGjQIaKkJkQvpbOtM0DBOS+L6/qyT2WxvNuO7opdT/HVNo6POwHJ8io5LMu/gC2irDTLbygUhy+OYi3dh/ORvu0FByYIdZKWlCg5FN0h5f2Ygg+35bO+so7s5xu27BnA8nxdsqGckY5HWVJRyHSLXIx7SeGF3E6N5Z8JDStdVrrloA9f/4mn600Usx0NXg+28UimF+pjJUNYiagS3pu/7ZIsuuqYQC2m4vkrB8TmtvYaBlMVo3sbxPIqOz8amOG99wdp5vR0uVlG0qQyqioevVDi0/HuVsxKD610QMbRprxsZFpCsRk6GhqYrgWU1hD7+8Y/z+c9/fsZ1nn76abZs2bJEI4JPfOITfPjDH678nU6n6erqWvB+fV+wuy9De22Y5rjJWN4hVXAAhZqQgaLAU31p4qZGTcSs9JvqT1m4vs9/3XuATNGtdEnf3pngZdumru2SsRxGckVsL6hZU18KH/WnC+SsoMdZuX7RcCnDyBOgqYKoYZC1A4+P5/t0NUQJGRoQzNTpgkNHfZR1DdEJN+ZsoZNq3mye6c/w3hdtpK9kUJVT/B/rSfLsQIahjEciYuALgakHHpGIqbOp+bn9TeddmM6jEKTT53nw4CiW4xMPaxxNFsnZHqamUBPSsT3B4VIbjNZEiOGsheP5/O/eETJFl6ipURcxMHQVzxN4vo/l+oR0lcd7gnYh60oemktPCwzzf7tnL7v7M4zlnaBsQik7azBjYbs+muJyaCSPqkJt2CARMXB9MDSVnBVcA+esj1YMiYLjcc2FC6tKvhhF0aY2qAJB/ZFkgeZ40Kx2LO8E3recxVjBoSlm8tPHj066pmVYQLJSOB5ey5OhoelKYFkNob/6q7/i6quvnnGdjRs3zmvfbW1tAAwMDNDe3l5ZPjAwwJlnnjntdqFQiFAoNK9jzkTZst/cGidmavSliuQdj4ih0TuW5+n+DJ4jaKsJVfpNmRr4Iqg8fM/TgySiRqkNgsKBoRy7+zN88LLNkyeO3x3gscOpihA3bJS0JFm71O1exfUha7klrwf4XuAJUlWF2rBOquiRtYPU7ZaaUKUCsydgY1N0zqGTat9s+tLFKVP8n+5P8/CBUQ6O5NnZmySZD5qJbmyKVww9mNogm82j8Irt7TzWk8RVoT9VxHLK4bhAl6RqparSOZujqQJF28XUNcKGRtZysRyfUeHQVhtGVaE/Y/PIoTHUUhHDm+89wJ+8YF3ld7r0tFYu3tzMT544ym+eGWQs7yCEYP9QjpARhB9VJfAyIUBXIR42yBYdYiGtEloqp833p4vsWFPHmvqFvxFOVxQNqNojeaxBZbke9TET1xfURgxylsszA1myRRcFQVPc5NTWGnYdTdOXeq5ekQwLSJaa+Rozx8treTI0NF0JLOvZa25uprm5+bjse8OGDbS1tXH33XdXDJ90Os0DDzzAn/3Znx2XY85E2bIvOiq7+zKM5m1c30dXg7Rqz/PxBRRdH9MIhMiBBygQ1lrCJ2RoxEOBziJTcHi8JzmhUevewQw3/HIPj/ckSy0ggl5TlutzZCyP7frESx6OkK6WUvkDL4MvfDw/MGpChkZNCHK2R7bo4vqC1poQ521oZDBrMZJzMHVtTqGT+b7ZlFP8uxqiXLalld/tHeJLd1n0JvPkLJen+9P0p4t0twQG0bEG2bEehYgRZihjcf/+YZ4dyPC+P+qmqSbEusYYEV3jDz1j1McUxnJ25btoioIN2K5P0fGCVHi1FPJRVXQ10MEMZIq4pVYcEUNDL7XXODCc4+Z7D07wXui6yhuet4bXndnJkbE8//W7g6iqwrb2Gu7bP8Zw1qKhJBAfyztAYND2py3W1EdKdYSc49LL59iiaPN5yE9lUBVsjzt39fOLnf2M5iwSEYPGeIhNzTEaYqFJ9YqmC/GWa1jFQxp7BjIyLCBZFOZrzBxPr+XJ0NB0JbBizMjDhw8zOjrK4cOH8TyPxx57DIDu7m7i8TgAW7Zs4frrr+f1r389iqLwwQ9+kL//+79n8+bNlfT5jo4OXve61y35+GOmju36PHp4DNcTpX5SgVEzkrXxROCpcTxBMm+jqSqJiMFo1gpSszUVvVT0L6RrmHGVgXSRB/aPcGQsz5r6KLc/2c+zAxlMXaWrPsxAxiq1iVBxiz6+DznLIxHRqQnr5G2PIC0s2K+vCCw3ELOqSqnVhAJb2mv40xdt4oJNTewfzs4rdLIYbzb7h7PcsWuAeFinIWbilKpOD6aLjOYsWmrCrG2MVoyCYz0KY3m7YoQ6nsf+oRx/97OneM+LNhAxNHwRiJzrIgZFO+gNphkanggK/JTbXohSC46YGdRXytkuugLJnA2KQl0kaKw6lndorQ1zRmeCvUO5Kb0XqqqgKArposOm5ji6HuivHutJkrNdFFUhGgqKJ9ZHDdpqw9RFDJ7uS6EqKt0tcd54dudxCw8t5CE/VZXZsKHy5NEUp7bGqZulXtGxxvN4MbtbEqErwNP9aWkISRbEfK/z4+21PBkamq4EVowh9KlPfYpvfOMblb/POussAO655x4uueQSAJ555hlSqVRlnY997GPkcjmuvfZakskkF110EbfffvuS1xACaC9VCh7LO6ytj1Q6qod0jfqowVDGImpqvHBzI64PpqYykrPYUzJsQEEb9zagKAqJqMFI1mb/cA5FUXiyN4XnC2rCBiFdpU1RGM05FByvcqMEVZM1jJInSoiglo2mKoR0tZKl5JT6TUUNjfbaMG2JMKqqzLufzELfbMY/cM7qqmMsb1cqXyuKIFP0aKkRXH56K64v6BnNI4SoeBTG8jaP9SQp2B7xcGAI5nSXfUNZvv/IEZrioUoqvOtDQ8zA9jwKtosvIGRo5G0HhEJTTQhDU0kW3FKFaIdUwcX2QVcEBcdn33COxljg7VBVdUZR47ETfkPM5Myuusqk73iBJ2pbZxMXdTfx4IFR9g1l8YRgMF3krl2DqIqy6MbQ8XjI552gindnFfWKxhvPjidKv58bpOGH9SB7Me/w88f72NgUk1ohybxYyHW+FGLmld7QdCWwYgyhr3/967PWEBJCTPhbURQ++9nP8tnPfvY4jqw6+tJFQoZKXcRgLO9M6DCeK03OjhcUWGwq9RsbzQfp244vqA3plTo6z/HcjZezXXJOEFYytLJwWKfD0LBdH8f3OTKaB0WhuSaENc44QoChq0RMDd8XgQdK1dAU2NgcI1P0JoR25tNPZqFvNsc+cMrtI8phkuGsRc9onv/+/WE0TSGsa9SGDYazFu2JMLv7MhRsj4aYWXlgxUKBl240Z9McD9GRiNCXKjKWs2muMWmImQymLRTK3emhPqpzUXcTiqKwdzBLf7oQtMco/RohXQkm+ImX4oyixqm8ZQ0xk+evr39ODG17/NGWFu7cFTyw1zXGiJr6cRUOH4+HfNTQ8HxB71h+kkcIJnoGy8bzk71JUgWHvOUQD+l4vo/wwXI81jVGsVxfaoUk82Yh1/lSiZlXckPTlcCKMYRWOjk7qFR89roGDgznGMvbZC0XXVVprQ1zalsNO3tT7B3KckprDRFTw9SUoHihD/VRY5IXJZV3SERMNjTFUBWFWCnl2vEEIf25cEPI0MCF2oiBQCER0WltDdpBPHkkxXDWwhcC1/NxXQGqgiqgpTbEts466qPmoghTF/JmM9UDR1EUaiMGozmr0n19TX2U2rCB7wv2D2fpGc0TNTVG80HNnfHn0CmVKAg8Rg6vf14nEUPjnmcGOTJWIBbSOaW1JmjMWgxaZ8RCQTHJkKZy9toE9+33cD2B63kUXUFLbYiIrmHqaqnQYY76qDlj6G86b9l4MfQZaxI8eSS1pMLhhTzkpxKd7h/OcvuT/fSMFp7TCMVCbGp5TiM03jNYNp6fHcyw99AYAMmCg+sHLWpiIZ3Ta0I0xedea0siKbOQ63wpxcwrtaHpSkAaQktE+YYJG2rlTb9cNLEmrJO1XGzXZ2NTjOGszUC6iKmpnNIavAXkSy0Qyl6kTDEounf+xga6StlC2zsTHBjJkSk6mOM8H0IIMgWHiKFz4eZGGqMh9g/nUBWFLe21jOWCHlBDmaAwYEjVaK8Pc/ba+kon+mre/qvJuJjvm810DxwhBPsGc4zlbDwvqJWkqqCrKnVhHR94ui+NrgZGxfjtyjWImmtCHBrJ01wT4pOv2sofndbC3U8P0JcqoikKYUOjsy5CMmfzh54kvxobJBbSqQnpjORsNAUS8RC6ppItupjhIKOrXH8pXXAYyFjThv6q8Zbt6KrjB4/2Lmk9kfk+5KcSndZFDAazFp4v2NIW55mBoAZUbzJPqmhzamsNBcef5BnsbqnheWvr+PXuQTwh0JXA4xYJ65iawoHhPLGQvqCmv5LVzXyvc98X+EJQG9HZN5TljM5ERfIAUsy8kpCG0BIx/q1/c0u80uUdnrthnre2nmtf+FwdnZipU3Bcvnz3Xp4dyJApPveg11SFHV11XHXe2sqk8bLtbeweyPB4T5KBtFVqPxF4jnwBOzpqeOt56yoZOeNbXNz6yGH+7Vf7AIGmKQg/SOdWFGiIhWZ18c4l42I+bzbTeU0yRZejqTyZoouhqdSENUxdw/F8hnM2ClBwA72TqtqYuhqEVTyfaKkGUdHxKw86VVV44ebmStp+znYZzlj84sk+Cq7PWWvrOJosMpS16BnLU7A9ulvj7FhTByg81pOsVHzWVIWi47J3KMu6xtiMob/ZvGWuL5a8nsh8dF1TiU5zlsPv9g1TsD0uPqWJxniYWMio1BEaydo8IzK8YnvHpOvF9wW9Je9cIhI0r9UUpRImHs3ZPDuQYW19VKYQS+bFfK/z8r1aDsv3JYts66ylvS4ixcwrDPnkWCKq1cjoujrJSPjgZZu5/cl+nuxNkXdcoobOGWsSXH5M8bnulho+eNlmbvn9YX5/YLTS+qEuYnDexkbect5zVYePTY9+8MAYAqiLmsRKKfqDmSIZy+HMrjoMTZ3WxbsURe+mO3+jOYvhrI2qKHTWRQiXwoMhXcOMBe0vNEVBKIIjYwXUkkchETE4vSNGfdRgz2B20oOubKz5vuDup4JaP+WQ1Jr6oJDhSM7i9/tHMNSgxYeiKBNEzkXHw/Nha0ctbz6na9ZzMJO3rGc0v+T1ROaq65pOdAoKKoHOav9wnoZYaEoN1Kt2tFcKT5bpTRYYygQ6r1TBoSasTZioYiGdvmSRc9Y1yLduybyY63V+7POuoy5CU9xk59E0fzicZDhr0xQPSTHzCkIaQkvIfDUy3S01/PkfzRxOKoelXF/wjgvX8/YL1nFwJA/AxqZYpfHnsZQnr7LwdChjEQ89Z0iUU5YTEZMz1kx28S5l0bupzl+m6KIpgcA8Gpp4OZfHkio4bGyOEikJx8NGIAp/pj/NWM6ekHJ/LFMJKcvapJqwTu9Ygb5UkXTBIRE1KxN8uuCwdyjL6R0JPvrSU9EnCd2nZjpv2XLVE5nLNTud6NRygxYgph6UfEgXA21b+TxGQxoHh3MUSo1rx1NujHtqWw1P9qYq3rZyiDhbdNA1hbPX1cu3bsm8qfY6n+5519UQo7MuwhO9KTY2xbnmwvXTPnOXAtmbb25IQ2iJma9GZqZw0kKqmpYnr466CM01YbKWN2GyMXWVQyN5nr8hPKWxsJS9cHw/KBD5R1uaef6GeuLhoNnoP/z8afxSMcjxY/B9n9G8g6LAts4EuqpWUu5dPDJFl9ZaeMcF66Y9TzMJKRVF4ZS2Goay1gSRe8H2GMhYrGuM8aZz1lRtBM3EctYTqfaanepcjeasSu0mBYEv4A+Hk5yxJlHRn83kzXpOW6dxZldd5ffLWS6aqlIXM6mPmpzWXrvo31uyuqjmOp/peaeqKpua4yTzDoqiLNq9WI1RM36dcmui/UM52ZuvSqQhtAwspvp/oWGp8ZOXpk4M7WQtF7WUufTKM9qn3M9ipI9Wc6NPZ+yd0ZVgXWOMntH8JG/BWM7G9QX1UYOwrlMbMSak3Ntu0BU9Ykx/G8wmpIwYGqe01kwQuR+vGh/LWU+kmmv22HM1mrN4rCdJ3nKJhjTytoemQLJU0+nMriAjcSZv1rHaunPG/X6GqtCftqb0VEok82G263ype39V85I7lV7J0FS2ddaysSkue/NVgTSEVjCLEZY6dvIar90IjIUgPfy0tqnfuBeaPlrtjT6dsdebLLC2Porl+Lh+ULCyXJYgETXIWh6d9dFKxlg5HAPg+j4Hh3MzPrSqCUlNJXI/Xq7oE7meyPhzFTM19g3mKNgejfEQUdvjkJVH1RTqoibZosNTfWma42Ea49N7s6byhEVDGooNfanijNtKJIvNUqbLV/OSC1TWaasNcTRZCHpC+j57BrPEQjoNsZDszTcLC/q1bNtmcHAQ3/cnLF+7du2CBiWpjsUIS0010ZeNhXLvp5m0JwvRrlRzo69viHHbQz0cGsnR3RyvHGO8sdeeCNPVEGE0Z7OmPvBseb5gIG2RKbh0THF+YPJDa6YO9fMRuR8vTtR6IuPP1RO9KQYyRWIhDdvzKTgerbVhYiGNouPjCRhMW5y9rn5WIbmsrCuZiuXQwSyVVq+al9w7dg4ghKiskym6JAsO9TETU1Mm1DE7XiU2ThbmZQjt2bOHd77zndx3330Tlpc1Gp43WfQoWXzKbtqIESZdcCbUJVIUpSo37UK1J/Pdvpob/TsPHEZVFW7f2Y+mwnDWpiFqVgrwlW/uZN7hDc/r5PGeFPuGsuRtl5Cucd6GBrpb4vSlipP0Q8c+tGbzTE03EV+2tYWQrrG7P31CeWeWi/K5+vbvD7FnMAsETX1basNsag4a42aKLgXHZSBd5HVnVdcn7UT2hEmWnuPV7X02lkqrV81L7hO9SRDQWR9BURRsLwj1G6Xnf7mOWaboUhsxjkuJjZOFeRlCV199Nbqu87Of/Yz29vYp37Ylx59yI9ff7x8hZ3u4pUrJ9VGT7pY4hhZ0SE8XnBkn6oW+cc9n+9lu9Iih8qvdgzTETTRVoTFu4vliQkr/+PpGTTUh/uySTVNWM7753oMzPrTK6xzrmXqyN8WzAxleuaOd09pqee+LjqnxZHvctWvpH8YnOsH1sIG+ZJGIqVF/TCuN2oiBokB9NERNaHJ4YTpOVE+YZGlZinIdM7EUHspqtEh520MgiJY82qamomtqpbOAoankrEDiAMenxMbJwrzOyGOPPcYjjzzCli1bFns8q4JqXbqzrVewPYYyFv3pIu21YYxSv7KhTJFM0SGkq0RDOt954DCW5884US/0jXuu2890owsRfO+C49FZFyFnBfV4xqf0T9W6YqqJcraH1samODf+et8kz5Tj+aTyNodG8+zuT7O1vZbulhou39bKlrZa9g5m+Mb9y/cwPtHpqo9yxpo6dh5NTeonJivuSubLUpbrmInj7aGsRosUNTUQVNapCevUR02GMkXMmInj+Wiqiqmp8p6bhXkZQlu3bmV4eHixx7IqqNalO9N6G5viHBnLc8sDhzF1laaYQdZyiStBxlQspHFkrAAobO2ooT5mVtWgc6Fv3HPZvnyj5ywHUCaE9TJFl+GsHQj9ouaEm3surSvKzFao8FjPVDnbqWB71EUNPF+ga0rl3L3jgnXctWtw2R/GJzLLle4v66ec3CxFuY5qr6Hj6aGsRot0RmcdQgh29aUr63S3BD0kR3I2rufTXhcBAq2nrHI9PfMyhD7/+c/zsY99jH/4h39g+/btGMZEi7W2Vtb0mIqyS3cka1ET1qkNG3i+z5O9yQnGyUyu36f707TUhOhNFth5JEXY1KgJ6xgaFB2fXCnlPaSruD5saopX3ihOpIm6sy5CXcTgd/uGUQHXF5WwXkPJsNvQFKM2YlRu7vm0rigz3UPrWM9UuXdZuVO9AMbyNqausTkRYc9glv95pJfBdHFJ+36tRJZa5LxcuhHJ0nG809dPlGuomheJy7e1AtCXLlbWqY3obG6JsfNoGt8PpBGpgisTC2ZhXobQZZddBsCll146YbkUS09P2aV7eCQfpG2P5MdpegxylseduwZY3xCb1vVruz6/eXaIiKGxtaOGsKkSD+lkiy5hQ+O0thqiIR3L8XiiN4Xteji+mDCOE2Wi3j+cZTBrUSjVlqmNGigoHE0WODicRVeVSrZXQ8xcUOuKmTjWBZ0puhM61duuh15yL48/d54QdNZPfe6kKPE5lkrkvNy6kdmQnqrF4Ximr59o11C1LxJTrfPaHR3s6KqjqSYkr7cqmJchdM899yz2OE56epMF/tAzxmCmiOcL4mFjnKbHQlMVHj08xqM9Y1O6foUQ7BsKOsYrCuiaiqFppaaoJqM5m4GMxdn1EQ6PuiXjSMXQJl/8yz1Rl41CzxdcfEoT+4fyjOZtPN8LxqxC2NQYSFvUhg1qI8aCW1dMx7EuaNsL6hEZmj6hQ325DlHE1PCFj6YoS9r363gw14q1832gHm+R84miG5mOE8XLcDJwvNLXT9RrqJoXCZlRuXDm9aS++OKLF3scJz0Zy+HwaB7PEzTGzcqNFtIVzJjJSNamZzTPUNaa0vVbbk5ZFzXI2y6mrtIQNRnMBG7SeFinL1Xg3r0eyYLNaM4mYmo805+huyVOfXRiRWVTU5dtoh4f5w+KOIYqY8tbLodGcxxNFhnNORwYztGeCHNKWw0RQzvurSviIR1VUYJsC9cnUupQX/69CrZHXcSkuSZEz1hhSft+LSZzrVh7Ik/gS9nmZa6caF6Glc58tGfVGPMnyjU03VhnO6bMqFwY854Jk8kk//mf/8nTTz8NwOmnn8473/lOEonEog3uZCJbdCnY3qQMGghutJChkim6CCEqrt94SK8YCFnLxXE9TF1BU1VCmsamlhgZy2E0Z6OqMJK1KNoehqaSiBhoqsJgulgSHgeF7BzPo2D7bGqOU3AW7hGaTwZcf6pIwXHpKBl75QKOozmbvUM58lbgzdranmAgXaQvVWQoa3FKaw3PW1t/XFtX7B3MAJDMO6xrjNLdUkNDzAQmGjmXndbKN+6fPi3/stNaT9g3tGf7M3z1nr2M5Cw6EhE2NMYoON60FWtP9Al8qdseVMt4L0N3c4ys5QV6M02luznG3qHcsmv1ViJz0Z5Va8yfCNfQSnnxOBmZlyH08MMPc/nllxOJRDj33HMB+OIXv8jnPvc57rzzTp73vOct6iBPBuIhnYihYTkeMVPD8QSeEGiKgqEpWE6QDrmhIc6m5gK/3z9SaRnhej5CQKrgUnA81jfFKwZVWTuzuz+D7Qqa4hptiQhN8RAHhnMk8zZjmQKGqtCeiIBQqCm1mPjGfYcWNKHNNwPO8wU9owXCukZd1Kz0jdo7mKFgu8TDGpar0lobprslXgmHbWyOce0LNy6KJ+hYxruXn+5L8/Mn+7AcD0NTcH1/0hvnTA/jU9tquOupE/OB9uxAmr/72dPsG8oSMdUJRSo3t8RLFWv7EXDChQmmYynbHsyFspchYqg8cigZNPv1fXQ18Oa2JULLrtVbqVQTDpqLN265ryHpOVxe5vWrfuhDH+I1r3kN//7v/46uB7twXZd3v/vdfPCDH+S3v/3tog7yZKAmbLC2Mcq+oSz7hnKMlzArBEXmNjREqY0abGmv4YeP9ZIpOjTGTBJRg4LlUXQ9Cg40xozK5NQQC3FKK+wbytHWFOa8DQ2lgnUKtWGd3zw7hC/A8QWW69OaeK7C70ImtGpv3KnWy1kuu/sy/Gr3IPUxE11T8H1IFx2a4iY5y6vochRFIRE1OaW1huGMTV+6eNwmjbJ7uashysbm2KxvnFM9jAuOyzfuO3RCPtD2Dmb46j372DeUpS5qEAvpOJ4/oUhleyLME0dSoAR6jBMt1DQVS9X2YK7kbJfhrMVIzsJy/FJD4OfOeapo0xgLSVH9PJkpHDRXzc9yXkMnqj5prqzkhIB5e4TGG0EAuq7zsY99jHPOOWfRBncy0VkXYW19lJ1H0wAIAQoCgYKiQtH1WdcQpb02zE8eO0p7IkxzzGSs4JAuOGhq4E7vT1s8M5ClKR4iGgqqG+8byhE2VM5aW08ialaOaWgqUVMjHoqSs122rUmwriFaudHmO6FVe+NOlwEXiJGD2kFjeYe1DRGKrkfOcrEcj7ZEhE3NsQkPo6UOb1QrQBz/MPZ9MWVxxqkeaMCSPjTKv9lI1iJqasRKWqhji1Se2VVHvhQyjU7z9rvcYvtjWa6aRbMRNTSGsxZ5KxDcP6cLDM75QLoIIlhPsrjMVfOznNfQiaJPWgizRQdOdCNpXoZQbW0thw8fnlRZuqenh5oa6b6bFgUMVSEaNQgbGoqiIISg6Hg4nkAAvangptjcEp+gESoXGzwylmd3f4ajySK6FkxkWztqCRsaYWNiyMj2fDxfEAqpRAmKEy6GcVHtjTtVBly5Tg/A2oYoIzk7qBwtgvCYqirEQ0FbhvEs1DU9nxtxrgLEas/LffuGK33Rlip0Nn5sw1kb2/VRoBKejYU0RnM2QxmLqKGDwgkXapqJE7Exa+D1VRBMd50Fn4lpPpXMn/lofpbrGjoR9EkLYbbowIu3tLC7L3NCSgXKzOtJdsUVV/Cud72Lf/7nf+aCCy4A4N577+WjH/0oV1111aIO8GShN1kgmXd4/vp6+lNWkC7ueWiqSlsiQlttiGQ+yJIq3xRlEfF42usiFB2fNz+/i7ZEmJip014b5mu/3T/JrWtqKrqqkM47dNRHKyngZeY7oVV7447k7Enrja/TY2gqni84vSNBLKSxuz/DSNYib3uVRoGwcNf0XESIxxpM7bXhCf3F5ts2pHxe9g5mueXBwwjBkobOymPb0BgjpGfpGc2jKOALUBWFsKGiKdCXKnD+xkYEsOto+oQKNc3GiZZGXHA8muImikKlGKihqTieT7boEg/rNMZMCo6su7bYzFfzsxzX0HLrkxbCbNGBP/Qk+fLde2hPhOmoi5xQUoHxzOvM/vM//zOKovD2t78d1w2sVMMw+LM/+zP+8R//cVEHeLJQnog2NsVZUx+d5OnxhODgcOApme2mCBvBRD7eWzGVW1cIH8vzKbg+LTUTPSwLmdCqvXEbY+ak9cp1enRVI2u5uL7A0BSa4iG2tis8cGCU4axFb7JAxNQoOgtzTc9FhHiswWS7PpbjEzJUTF2d9U1mtvOStwLNiKLAjjV1S6oFKI+tL1Ugb3u4fuCnCBkqCgrpgoMnoLtV5fJtbQD0pYKKtW21IVxfVEo4dNZFTthS/SdSGnHM1GmKh2iKm/SlLMbyNlnLRVdVWmrDtNWGAOWEnOBWOgvR/Cz1NXSiatyqYSYvOATPvKGMxVlddSdkh4My87oDTdPkX/7lX7j++uvZt28fAJs2bSIaPTEeQCci4yfJmKmTsRwKtkfE1IiXtD4hXWNjU2xeN8Wxbt29g1mGszaaoqAADx4Yo2e0UKnHsxDjor02TFM8xFN9Kbqb4xVxthBiQsHDMzvreLB5lIcOjtJZFyGkaxiagucLjowVyFpBPaSdvSn2DmURgiBM6AZtRw6O5FjbEJ13yvxcRIjHdqAvOhqPHBolWXCojxo8b209YUOb8U1mtgfa/uEcoLCxKbbkWoDOuggbm2L85Imj+EKwriHKWN6h4Hj4wscXgqihs64hVnkwXXPhem75/WF+f2CUVMEGAXVRg03N8UUd28nK+OvhnHV1ZC2v8vITD2nsHcqdsBPcSudE1Y2t9LEey0xe8EzRJVN6xp+oHQ7KLOhVJBqNUldXV/l/yfSUH4q/emaAwVSRVNHF8wWaqpAI67Qkwly6pZU19dF53xRlt+69+4b5zoOHURTY2BTHcj2e6c8sSj2estdk/3CWwyN59g8FBQ9bE2EGUkHNH11VCBsa/3jHbpI5m8MjeZ7pzxAL6cRMjWTeqRiEHYkIri/YPxR4w+IhjdM6aumqj9KfLhIL6Vy2tYWNTXF6RvNzcldXq9k5MpafYDABPN03hucL1tZHGMs7HBzJc866+kqK+VRvMrM90OIhHSEgFprsLYLjqwVQVYUz19bxP48eQQiBFtZpS4QqYchoTYitHbWkCs6EB1PR9WmuMTm1NehZp6kKfakiN9978Li7teej61qqatnVMP562Fu6T+qiBgXbY+9Q7oSe4E4GTkTd2HSspLGOZyYvuO35WCUpgqlNLnlyImmf5mUIua7Lddddx5e//GWy2SwA8XicD3zgA3z605+e1IRVEjwUayM6ewayWK5H1NSJmiqWKxjMWqSKLq87sxNVVRZ8UzzRk0KI8eEXg6Z4aMH1eMaHmdY2RGmpCfFMf4bDo3meGcgQNXW6GiKc2lpD0fG466kBAE5ti5MtegxmiuwfzuH6gqihEdYDvUQybyN8Hx8FT8DmljiN8SCmvGcwy60P9lAfM9k/lCt5MATtiTCXntbKhZuaFqTZGUgHYxpvMKXyNgPpYknPISpC4rJuaaY3mZl+u+1rEvzw0d5l0wI01YToaohiuz7JgoPn+2iqSldDlE3NQYPbg8M5crZb8aaN5e0JYTyAmvDxd2tXq+sab9QMZyweO5xkf0lnd7yrZVdjUK3UCe5k4UTTjc3EShprmZm84Iaq4LiCprg+SZ8KJ5b2aV4j+MAHPsAPfvAD/umf/onzzz8fgPvvv5/PfOYzjIyMcOONNy7qIE8GXNfnzl0DGJpCTcik6ApsV6AqCk0xk6Lrc9dTA1z1/LXoujrvm2I6L8j4ejxDaYtHe8aojRhzetM+NsxUEzZojJn4QnB4NE9nXZgLNjahKPDwwTFMTQFFIW/7PH99PX2pIg8fGiNvu6iKgl0KkVmuh66pxENBur+haZUxRwyVX+0eZG1jlIaoQV+ywHDO5pFDY/x69yAv3tLKW16wdl6anfKNCFQMptGczRNHkgxmAkOoLCTW1SDdH2Z/k5nut4PASF0uLUBZs5KIPKfXKmvUFEUhU3QqD6blTOmdS42qsoExnLXoGc1j6CrbOmrZ2BSftA3MXC37HeevJ2JqVd1vczGoVuIEd7xYjjTqE0k3Nhsraawwsxe8P23RXBMKslCP4UTTPs3LELrlllv47ne/y8tf/vLKsjPOOIOuri6uuuoqaQhNwaM9YxwcydFaGw6ae7p+JXXZ1FWylsuB4Ry3P9XHxqY4WSvIKqkJGZzSUlP1w2I2L0jR8djVl+Zrv9lPyJhdAFxmuokxa3kUHJ/W2jAFJ2gFAjCatysVrEdzNlnLI2Ro+L7A9QWO67GmPorn+xxNFYNmsgT6obLBIUTw0Cw4HkLAfftHyVkumqqgKQpjeZe7nxmk6Hq886INc9bslG/EDU0xwrrG0WSePYM50gUHXQ0a1qqKQtZyUVDIWy7EQxUDKmJo04brpnugLacWYPz5GK+ZOvZ8dNZFeHYwsywpvdXqunwfvnF/YNS01YY5miwghMDzBXsGc8RCQaPe8jazVcv+Q0+Sv/vZUzTFTSzPn/G+mE8V4JU2wR0PZAuJk5PpvJ5nrEnwpnPW8Kvdgye89mlehlAoFGL9+vWTlm/YsAHTNCdvIGEkZ+N4PhFTK/UWm1xEbSxv839/s5+i41NwAiH12oYoZ3XVV/2wmMkLMpqzeOTQGNmiS0PMoLW2+lTG6Qws2/NxPZ9E1CBdcCpGTLmDO0DWcittNPJOkLGkq0qpDpJOSNfQVLBcH2EHLlUIxHbDWRtDVXiqL03B9oiHdDQ1SPsuOB6pvM2zA5l5aXbKN2JXfbQiJPZ8QUuNiecLcrZLxAjE5qoK/ekia+oj9KWKdCTC/PTxo+wfmj4MMxXLGSqZiyhzuVJ6q/FE7RnIkMzbFaMmU3RJFhzqYiamVi4OmaU+Wl/ZZqZq2WN5m8F0kUzRpS3RSGf9ZG9S+Xc5WaoALzWyhcTJzUxez3WN0RM+NDyvp9j73/9+/u7v/o6bb76ZUCgEgGVZfO5zn+P973//og7wZKExZmJoaqnx6kRtTsF2OZos4Hg+Q5kimqoSMTU8T9Azmsdy/KofFtN5QYQQ7B3Ikiw4bGyK0Z6IVMJb1TzAj50YhQjSqbOWixBQsIKaSGVRnK4G+p/y/5uaihBB5oDwBaquVrxhEUMjZznlCnSV7LOBdIFk3sbQ1aBHW0hD14KxaUpQkTdtueQsjz0DmTlrdsbfiOOFxI4vAlGr45IqBA1gG+MhBjMWT/SmqAkbDGQsjqaK83qoL2eopNrzsVwpvdXoug4M26SLDusag+y7ckkGQwvGGQ/rEzRdEVObtlq27wt2HU2Ts1wMTcHQVDR1+vviZKgCvNRI43F1MJ3XcyWEhqs2hN7whjdM+PuXv/wla9asYceOHQA8/vjj2LbNpZdeurgjPEl4Xlc96xtjPDuYIWqo5Gy/0oAxmbfI2R4qkCo4hAydguMT1lU0P2j6OZK1uGPnAMYOlYLjzdjyYaq3/sG0xaHRPHVRk+6Wmkn6odke4OMnRtv12TeUYyxv47geqUIweXU3xyqiuIaoyUC6AIpCa6lv2EjOJmpoFGwX1wtCYLqmEg9rpIsOQgiEL7hv/wiW65O3PPK2h+b4oAQG1Xh8wFBViq5HsmDPWbMz/twdKyR2PZ+oqWNoflD40fMpuj4bGmOopcyphTzUlzNUUs35WK6U3mo8Uaqi4glRMWqCwqGB4R2UaFArXsjyNlNVyx7NWew6mmbPQAZfCEDhsZ4kz1tbT2M8NOV9sdKrAC8H0nhceSy2lutEDw1XbQglEokJf7/xjW+c8HdXV9fijGgaPve5z/Hzn/+cxx57DNM0SSaTs25z9dVX841vfGPCsssvv5zbb7/9OI1yenRd5eoL1/PJH+3kid50MOmXPvN8UBXQNIVoyMDUggd93vFQXYX+dJHasM7PnzjKA/tH8BVBzNDZ3pngZdvbphRnHvvWX3Q8asI6Z6+tpyE2OXw52wO8PDE+3Z/mN88OoSoKdVEDU1coOB55B44kizwzkGFTc5y2RIiesTyu7xMxVMbyNkPpAmN5O9ihAkfGCuiaQiJi0FEXYSBdJFl0SBddDF2hIWIQNkMMposgBJauEi6FFIUA2/WJhTSEEKiKOmOYZrYbcbyQOFmw2T+UwxdOadINBryuMcrLt7dx60NHVvxDvZoH03KE8arxRHW3xBlMFytGTU04aB8zmClixgKDaLwXsi9V5Iw1iQnVssfyNo/1JBnKWNiuH1TYVgWHR/KM5GzO29DIhqbYpPtiJkNNCMFgukjR8UkXHHxfnFBvvcuFNB6XhsUyXlajlqtqQ+jmm28Ggpu9p6eH5uZmIpGlU3vbts2b3vQmzj//fP7zP/+z6u1e9rKXVcYOVEJ5y4ko/UM89/8C0FQFU1NRFNAVBc0IHrijOZtdR9MkCw5pyyVUSns/MJJj90CGD162edZMlXTB4TsPHJ7Uj6xMNZqPjU1xWmpCRAwNpfR2Heh9VOoiOumiy/37RjgwlKMxbtJSGyKZs9nZm+YPh5M4no+qqNSENToSYfJOUMNGCMgW7YooOaQraKpK3vGJGEH4rOh4ZIuBUNrzBa4nMHW10o+tuyW+oDBNeQL+/YERxnIW6ULgUSg6gQbK9nxytstT/emT/qF+7AP1vS/aWHWbkYVSjSfqjWd3cteuwQnG0qaWGBnLYSRr4frQlggDgj2DWRpi5oRq2c8OZBnKFEnm7OD3LVU3rwkbgTi+6PLAgRFqwzqmrk64L6Yz1EZzNnsHMxwayVMT1vnOA4d56MDYST15VMtKbiGxmCy2l2X8/oYyFo/3JEslRlx8H9oTES7b2sIFM5QYOZbVquWa85UnhKC7u5tdu3axefPm4zGmKbnuuusA+PrXvz6n7UKhEG1tbcdhRHPDdX2+fu9BfCHY0VlLzvZxfB/H9ekdKyAIPByu52OMq++joJC1HAqOi6lq1IR14iEdxxNkig6P9yT5zgOH+f9euXXWzugPHRhbkOaj3C/twu4mAIayRZ46mkYgqIuaNMZCpIoOhqZyNFWkMWaQiJr4wmI455Y8OCqGrpIsOMTDOq21QS2ivB30W4qFNMKGjgLYrke66BA2NISAvO0xnLVRFTA0FV1TGM7arKmP8MazOxfsun3J1lbufKqfvlQR1xdYTlBx2RcCVQnCljf/7iDttWFipkZbbXhCteCasL7iH+ozvQ1uaaud1z7nOgFU44lSFWWCsVQbMdjcEmdnbxpfCEK6SqrgTvJeXXPhem57qIedvUmylofn+0RLCQxG6SUkFtLIWy5PHU3RUhvmjDV1lftiKkOt4Hg8emhsTlXIVxMruYXEYrHYXpYpS0doKmsbI2SLHkNZi8eOJPnNs4P80ZYW3nLe1CVGxrOatVxzflqrqsrmzZsZGRlZUkNovvz617+mpaWF+vp6XvziF/P3f//3NDY2Tru+ZVlYllX5O51OL8o4yunzjTETTdOojQQhnpzlomsWnu/j+cHbka6puH6gkSm6Hq4Pig/hEOhK0CQzpCuYMZOBtMX9+0fpGcuzrjE27fEXQ/NRdnF3hCIk8zZP9KQYzdsYmkreLmmaNAVNg2zRpWC51EQMIqaOWXQxS2JxH2itMckUXFzfLxVJDMKDluPjeEH6esRU8R0f2/HQVAVdDeoKlZuF5iyPWEjnj89ewymt85ukxxMxNZriJkeTBfKWixACXVMJ6YGANm+59I4VSJaykiKmhq4Fom9dU6mPGuiqyvmbGlfkQ/14vA3OdwKYTcc0nbH02jM7OaMrQXNNaEqjq7ulhtc9r5PHe5P0pyxihoamKQykixQcD7P0WwP0jBXoaoxNui/GH3vvYIan+tJkii4bm2J0t8RpiAVe55N98qiWldxCYjFY7Ptq/P7aakMcTRaCLFrb5dFDSeJhnYZYiPqowXDG4nd7hik6Pu+8aObjrGYt17xeW//xH/+Rj370o9x4441s27Ztsce0aLzsZS/jDW94Axs2bGDfvn38zd/8DS9/+cu5//770bTJ6esA119/fcX7tJiMT58fT9COQqXoCLySbihTDNLQff+59YxSccKBjEWbohAx9VKRRIORrMWB4dyMhhAsXPNRdnEfTebZdTTNaN4mbGgVTVPWcvFF8GahKZB3PNrrIiWvSvBdy4X7hO/TGA/hegLPD0KDZZ2UgoLj+XhWYFxlLZ94SCcRMaiN6PgCFKA+ahAthcbmq8cY763oTxVxXB/H8zE0hbCho2sKnifI2R4oCojgd7Icn4zlEjE0mmpC2K7HnkGLRMTg1Lbq6z4tJgtxvR+Pt8GFTgCz6Zjmm41SEzKImwYKFrGwjqootNWGGc0Fvdfc0o1XE9Z55fb2KcdYPvbDh0b52m/20RgL0TZFEdOTefKYC6u1wvZi31fH7q9SOqI0D1iuT8QXmFrwrK2LmRRsj95kftbjrGYt17wMobe//e3k83l27NiBaZqTtEKjo6NV7efjH/84n//852dc5+mnn2bLli3zGSZXXnll5f+3b9/OGWecwaZNm/j1r389bXbbJz7xCT784Q9X/k6n04siBJ8ufT7QuRhBBpUIHr7JglOptQMKvhDETI2IqVNwPEbzDh2GVrqpxLTHnIqFpDJ21kXY2BzjJ48dJW8H6cZlTZNGMHHhByE7VVHQ1GDsmqLgC0qp9oGRVB71ULZY+X9fgOUITB10TcH1BAXHx/MDz8ym5hinttbg+KISispa7rwnmmf7M3z/kR72DWXxSuLrXUdTZIouugo5m0qRvspZFkG9o/qoSQSVZMHl8Eg+6E5vaLi+4L69w/zRqS1Lagwt1PW+2G+DS+Vmn082SjlU83RfGsf1CRnBvdVhaFiuz2jOJhEx6G6Jc1r79J7GoG2OQcjQaKmduvv2yTx5zJWVkEa92Cz2fVXeX1ttiEzRZSBTpGC7GFqQaRwxNYqOj126rg1NJSdc6qPmrMdZzVqueX2jG264YVEO/ld/9VdcffXVM66zcePGRTlWeV9NTU3s3bt3WkMoFAodF0H1+PT5mKnheKJSWbouojOSs4iHdE5pjfPsQBZVBQSYus5o3qLcvNfUA2PKdn1MXSWVd6iLGGxsmtkbNJ7ZJo/pPAuqqrCjq47/eeQIoCBEkNqvKAq2G6SZx6MaA2kL8AkbGpqiYGgKolRR2ih5hXwReMncQBqEAugquH7QeiSoFySwneDz2rBOd0sNiejEjLepJppqPCO/fGqAL9z5DMM5C1NTMVSFrO0FobuSet0VgVdOENQtIliMW8ry84VCSA8MwY5EhLChMZZ3+NXuQS7Z0sILNzdX/ZsshMVwvS/22+CJ7GZXVYU/PqeTBw6M0Jcu0lYbwtQ1HM8nZ7kkIjr1saAdzWwhztU8ecyHEz2NerFZ7PsqZ7sMZy2OJgskCw5F22Ms75C13aAvohkY816pZpvjBf0Ea8MGIzlrxuOsZi3XvO7Od7zjHYty8ObmZpqbl2ayADhy5AgjIyO0t7cv2THLlNPnr/vpUzzdn0FTlHKkBU8I4iGd5poQzwxkGcvbmJpKfdRkW2ctPWNBl/e87WLqGr4IdDWZooMv4LyNjaypX5yHy2yeheZSvR3L9dg3lCNneYSNIHW9IWZi6gojWZuC41MTDjK+bNcPDCmeM3Js10dTlcDg88seIgVDB9sJ1it7YQyVKbsXw+SJphrPyF1P9fO3P9pJMu9g6gqe55PyAs9PSFNwPIEdlC5CVcATwT+VIDpGybtl6hrxkE7RDQTuIUOjuUbhyFiBu58emLEh7GIxm+fl2YEstz3cw+vO6qQmZEz7Br7YE/qJ7mY/pbWWv7h0M1++ew9DGQtTVzB1jUTUIGrqrG2IVqVbWc2Th2R2Fvu+GsoEwmhfQH3MJB7SKLo+qYIdyAMU0NRAtyiEIFt0aakNo6nMepzVrOVa8GtKsVjEtu0Jy2prFy5cPZbDhw8zOjrK4cOH8TyPxx57DIDu7m7i8TgAW7Zs4frrr+f1r3892WyW6667jje+8Y20tbWxb98+Pvaxj9Hd3c3ll1++6OOrhnWNUTY0RclZLpbrIXyBUgohQTDJdjVE0JRgohAoHBzJs6EpRs7yGMlZWE6Q6VJ0ghtoR0cNbzlv7aJcnNV4Fp6rt6PTkYhUCiwGNYVUcpaLqasoioLjBWEk1xe4vo9AQfCckaMQlAyImWola8wbp4vSlUAb1VkfZSRv84fDY5w1rg7SsRNNNeP3heCLdz7LWN6hJqRh6hq252NZDpoCiqZhaAI7KB5U8cRBqcxBSZ9UdHxMTStllAW9zyDwaMVCOn2p4pJ4O2byvIzlbYYyRXYdTfHsQIaGaGjacNliT+grwVNy6WlBe5VyeNQXUBcx2NxaU7VuZTVPHpLZWcz7yvcFj/ckgwKvvl/RAbXUhHC9oLBt1vJoqQl0qKM5m4ipsbEpSn/aquo4q1XLNa+nUC6X46//+q+57bbbGBkZmfS553kLHtixfOpTn5pQHPGss84C4J577uGSSy4B4JlnniGVSgGgaRpPPPEE3/jGN0gmk3R0dPDSl76Uv/u7v1uWWkLlN/eoqXPV89fQn7bIOx6O6/N4zxgjOYdk3sbU1aCujuXR1RChUHKFnrehgb2DGfYP54iaJqe21nDGmjpesrWVkK6xuz+9oJh7tZqOa1+4cULjznhYZ99gjtG8Tc6yyNs+p7XXctlpLfzPo70MZSxc38dyfFQFwoaKAqXaLYHI2vMhFtIxVUhZPkINHhLRUODNOb0jwWM9YwxlLZ7qS3HehgaKjj9hogFmHf8dOwdI5m2GsxZhXS0ZbCWRdukNyhdBfSLX81BVKqGxMmVVlicgW3TQNJVEyQgsv4E114TQFGVJvB3TeV5GcxaP9STJl5rUttVGiJrTp3Qv9oS+Ujwlp7TV8PGXn7Yg3cpqnTwks7OY91VvssD+oRzbOmvZM5hlNGcTD+uEDJWmmhBF1y/1qQwSOZprQnQkwozknDkdZzVqueZlCH3sYx/jnnvu4cYbb+Rtb3sbX/3qV+nt7eVrX/sa//iP/7jYYwSC+kGz1RAq97ICiEQi3HHHHcdlLPNh/Ju7pml01kcZzVn8755hRvMOqhKUJgiXBJt5x+PAcJ62RIjhrEVnXZi6qMn5myK8cns7p7XXUnBc7tq1OLUpqtV09KWLk27sM9fWMZSx6EsVaIyHeN8l3ZzSVsMLNjXyvYeOcPfuAbKWh/D9kvdLwfM8hBu4fxwhMDSNhngI27fwRaAnaktE2NaZoCEW4qy19Tx1NM1g2uLpvjT10dCEiebwSI4nepNEDY1M0aUm/NzkWx7/40eSDGeKKEECHp4PuhYYN8EyBdcXpR5oQejSQaAKcEuXlq4ppX5k4AgQfiBMTBcdXE8QMYNikaAsibdjKs+LEIJ9g7mgSW3YCDJJDG1WofJiTugryVOyGLqV1Th5SKpjse6r8kvPxqY4sdD4F1AXTVXZ1lFLzvLY0BzDcv2Sl1qZ9/27mrRc83pS//SnP+Wb3/wml1xyCddccw0vfOEL6e7uZt26dfz3f/83b33rWxd7nCueY9/chRDsHcwylrNRUDA1BU8EoaLasEHWcnA8QTLnYOgqozmHs9fVVy7ovYMZvnHfoUWrTTEXTceWttoJN7blBjf2BZuaJtxwp7TW8rbz13F4LE9LTYin+zOBsWCo+EIErQ18gaIE8e6iKyqNWpviIc7d0FCpydIQC3Hexkae7kvz5ud3sa0jUZlo9g5m+Pb9h9l5JEXYVAOjKmqyqSVGfdQkU3TpTwchonypB5XrB2G7eEgPmm2qCrYn8HwfVVepjRjkLY9oJNA4eb4ohckEbqnmEUC4pIEaztqc0hJnY3OMkZxTlbdjMSrNTuV5yRRdRvM2sZBGzgo0AuUecLMJlRdzQl9tnpLVNnlIAma6j8ufub7g1TvaETBjr8iZGP/S0xALUb8+eLaVC7qCIFVw+cvLNqOWPNIxU6e9NkxfurjgqMHJzLwModHR0Uo2V21tbSVd/qKLLuLP/uzPFm90JxHjL+KYqbN3KMP+oSxOyUviU/JKlNY3dQ0hguKKG5tivPfijZyzrgFVVY5LavJcNR3VTph5x0MtvZkkIga+H6TEqyWxuKaCoqjEQjpr6iLkrMCbc8GmJhpjE0OYRccLBOQdicqEU9YFHRnLEza1kjEAg5kiQ1mLWEgjVXAYyliVppyB100ElasLDvGwQchQsT0PT0Bc1ziltYbHepIUbA9T11BVgVFq7BlRFRIRndGcQ8zUiZiBkLuzPlK1G3qxKs1OV+m44LjYjoKhazTEzAlesmr6ylWbIj/b7y89JScOi93iYTVTPpdP96V5+OAYQ5kiludPuI+Bae/x+RjMU7301Eae8wLvGcyyvTNBV3208rvuHczwtd/uX1V9w+bDvAyhjRs3cuDAAdauXcuWLVu47bbbOPfcc/npT39KXV3dIg/x5KB8Ef/qmQEGU0VG806lonI5OylqBuLbfNGttN+wXZ+iE6EualQu7uORmjwfTUc1E2bM1PGFYChrBVllWsnDIgLvTzLvkLVcBtNF2mvDbCnVbTm2MexUY/B9we1P9nNkLE9HIkym6JIqODTGTCKmxqGRPLpaSt8XUB81sVwPywmyvFRFw3K8IBtPK3c019jSVoOhKcRDesVgS+YddFVQEzFoKP0WiqJQFzErvdxGczZnr2uY1dtxbGXYuK+TLjo8eHCE3mSed160YU4PqWM9L2N5u6LJEgrs7kuzt5SF2N0Sx9CUBQuV52LISU/J8rMaG2keL8rn8g89Yzzbn8H1Be2JMKe21RI2VHYeTfF0f9CRwCt9thhV2ucabl6tfcPmw7yehNdccw2PP/44F198MR//+Md59atfzVe+8hVs2+ZLX/rSYo/xpCAovqazZyCLVXoQ6UpJZ0KQneR4Asd3ET6gBK00DF1FVeEb9x2qXLjHIzV51pssarJ9TaJUB2luRRjbE2Ee70lSHzVQFIVQpYO8wPGCB0UspPOWF6yjIxHhG/cfrOpGv3ffML/Y2Y/levSOFfCEIG8FNZYczwcRNGe1XAgbGi01YYTwOTRawPcFETMQbtuej6ZCd3OcD75kM1vbE2Qshx892svhkTyxsMajh5JESh4noPRwiXD22jr600VGc84Er910jPfmNcZMdvcHhovr+eiqQl+qSMTQpuwdNxPjPS+7jqb4l1/uCQoDhg0MXcXxBEOZIpmiQ33UnLYNSDVegxPpASu9HAEznYcT6fda6ZTP5UjWZixnY2gKDTGTVMHhyd4UZ3bV0d0c446nBkDA5ae3oqqBx3gxiolWG25ezX3D5sO8DKEPfehDlf+/7LLL2L17N4888gibN29m+/btiza4kwnX9blz10DQ5TpkUizV1lGEQCVIyw66s1PpsB5UU45z/sZG9g7lKhfu8UpNnu4ma0+EQcAPH+2d89ukqipcelorv3lmiKGMTX3MwNCCEFO26BI1dTY0xQCFTc1xuhqiVd3oewczfOfBw4zmLFprw5ilyd7zLPK2R8Hx0FUFxw8KPZaNKv7/9u48SrKyTPD/966x5r5XVVYVtVGsimyCGwgN1dq2jNDOoNJCM0j3Eec0Mj3AUQ9tc/qn9nCmnXZQexvEVsdxQZu2bRQF6REBEdkKqorKhKKK3NfY48Zd3t8fNzMqsyq3qFyj8vmckxwq8kbmjbiR9z73eZ/3eTHYUKcYzpVI2CYRM5xp8c6dLXzsXdumrVlmn6+XT3qNCZvxiXXVskUX0zg65JQpepy7pYHztjQCcGQ0P+uFeTKbF7N0nn8jRaHkkYxaWNFwId2xXOmEGzLqusbG+hgPPtdLc00EQw/7HSU1E8vQSUQM+tMOhq5x+enHd75eSNZgLZ1gT+YsRyUB3lzvw9bGBN95+g1eH8mxoyVZzvbKBbFyUz/77bURDo3kqIlZREwD29QZnViDcFdrEn+i70bW8amNTVlEewmaiS5kuHktNzRdiyq6Uj7yyCPccsstPPnkk9N6BW3ZsoX6+nouvvhivvrVr/KOd7xjyXe02k0uutpWGyUZMSl5AbmSz2C6SN71USqcUh6zdEDD1KG9Llz5Wtf1aR/c5ZyafOwf2XDG4ccv9jGWd0/4bvJt25u59NRWftk9TLHkk1Uepq7TWhtlW3OCkVxp2v7O94c+eUIKuwBbE1Pgw4Vo2+ui9I4XcP2A1lobxw0bOJrG0feoJmbhBYozNtShaWHx4n+5bCebj1mrbWpg+OyRgKGJjq6WoWEr2NuTwnH9idl8TTzePczzR8Z5dSg364U5V/IouB5jOZdCyaMxYZePX8TUaKmxF9WQcfIEuLM1ievHj5tZsqkhFi4PYk3/019o1mCtnGBP5ixHJQHeXO/Dvv40McvgyVdHMHSN4WypPDw6+bk7WS+Iy5EpnPrZD/ujBVjG0UkIyajJaK7EWD5sh6+hKE1tjDZhKZqJzjfcvNYbmq41FQVCX/ziF7nppptmbJhYV1fHzTffzP/4H/9DAqEZzLToatQKA5yxfInxvItHGAwloyabGmKctbG+XCsz9YO73FOTJ//IgkDx85cHGcu7i7r713WND711M0XPp2e8QEPcDjsyuz6HRnI0JSNcftr0/Z3rD33yhLStOYnrKQYzRexE2MRR08JZd+mCS97x2dKUQBF2ZJ3cxvWD8mrxAxmHN22qn7Uz99Sg7JH9A3z710dIFVyYaAmpaTCcc/jqL7rR9XDK/Jkba9nWnJzxwpywTYIAhrIONVHruGBisQ0Zp54ADV07bmZJWDuVO25JkoVmedbCCXau/U3YBi/0pPjGk69zw9tOmVY4Wg0qCfDmeh9KXsBjrwxNNPuEpqSNH8BQpkjW8XhzZ3huORkviMuVKZz62VcTN3OTEzCAMFvseOUZLwptxo74K9FMtBoamq4lFb0Lzz///JyLpF5xxRXcc889i96pk9HkoqvjE4WsuZKPHygMXSNu6TQlLIZzJc7cWMeZG+qojU2/SM40a2u5pyYv5d3/jtYa/ujtp5SLDF/uS1Mo+cTtcBbXwy8PoOssaL8nT0gbIjG2tybIOG65uZhl6JhGOAvPU7CtJYGmaWQdj9FcOKU8W/SpT1j0p4s0JSPTgsbZ7iQ31sfIFsPAqr02wnCuxMGBDIau0WIbHB4roBRETJ2Dg1kSEZPGROS4QCKsmYrx3BthzdRUS9GQ8dgT4NSZJQCZonvcCbCS47wWTrCz7e9ozqF7MMdApsjBwSx940XO3lRfNUNllQ47zvY+KKXoHsqVgyBN18PlY0wDO2GXh3Aa4g0n3QVxOTOF0z/7Jo1xe9pNmOsHmLpOfdQM+6UpSEaMaT9jpZqJVktD07Wiok//wMAAlnX8ya/8w0yToaGhRe/UyegtnQ201kTY25tCBeroolVo5IoKTddoTkZoStjHBUGzfXCXe2ryUt/972itIThD8cpghtaaCB11UVprwunelZyoju2n8ebO+mlDQIGC9toom5vijOTCIb2zNtZxoD9DX6qIaWg0xG3O3lR/XN3RbHeSEdOgeyjLhvpwaPPAQBY/UDQlbEre0fR3TdQi53h0D+VoiM88/HD56a089sogwxmH+okAebJmarENGU/kBFjJcd7VWrPqJ9iZ9neyk3ah5JOYuPjE5uikvRZVeuMx23HLFD3G8iXq4+FnMRExyRa98gV7cggnXXAZyCxs6YVqsNz1a1P/tna2JqfdhE29wRrIOOxqmzinDOVWpZloNTU0XQsqOtNu3LiRvXv3smPHjhm//8ILL6zKgqbVQNc1GhIWnq8mpsyriUVXw3W3dKXY0pSgKRmp6IO7nFOTl/ruPwgUD780SMkLeMvmhqMnKkOv6ER17MV+anMxZ2L47YKtTfzOGa08/NLgRNNHn82Ncc7d2sB5Wxs5rb22opk179rVUr7oTF5okhMZl8mVnjXCJTomLzSZokdtzDouYLx4ezOX7m7llweHKZR8ciqs3wlrpuILbsg49X2dGgz/zumVnQArOc5r4QR77P5O7aTdmLAp+WGBfEPcpiZaPQXBld54zHbcSn6A5wfYphb2IWtJ0DVlSQZD1yi6Pl1DWbY0JU6aC+Jy16/N9Nmf6wYLWNVmouutoeliVBQIvec97+Ezn/kMe/bsIRqNTvteoVDgrrvu4vd+7/eWdAdPFm+M5XltKIdl6Hh+gK/CmWIAhgamoTOULvInl2znxTfSa+KDu9Tp1aU6Uc12MdY0GMu7bGqIl4dDdrTUzJsxW8id5DOvjxExdPIlr3yhsSa6NU8uuKoIF1+1jHDx2clCyWMDRl3X+NCFmym6AT3jeRriNrVRC0OH/rRTUTAxWxbr3btb2d+XWdDnqNLjvNon2GP3d7KTdnLieEyuuD3ZQLJaCoIrvfGY7bjZho6pa6TzLhsa4nQ2xElOWZKh6Hr4AZyxoY4/OG/TSXNBXIn6tWM/+/PdYK12M1FpaLowFQVCn/70p3nggQfYtWsXt9xyC6eeeioA+/fv595778X3fT71qU8ty45Wu66hbFiwa+k0xE1cn/LK5ZYBOTdgMOPgBYo/uWT7mvjgLvXdf6Unqrlmfiz0YryQjNlCArTBdJGWmghHxgq01UQwjXC6fsQMGzZqgKaDZYS1AoauYxv6nMOaf/T2o/s/knMqDibmy2J99OIt/L61Yd7P0Ykc59U8wR67vxFTx/X98hTmmG2yveVoQFstBcGVBqSzHTdQBIQLA29rjqNpWjlrmi64dA1lOX1DLX92xamY5vHFvNVqperXKvnsr4VmomthH9a6ij4RbW1t/OpXv+JP/uRPuPPOO8uLnGqaxpVXXsm9995LW1vbsuxotRvJlnADRcI00DQdQw/7B2mEM50sHfKex0i2tKY+uEt591/JiWohMz+W6mK8sAAt4LxTGsmVhuhPF0nYBuN5FxU1yDk+TUkb0BjNu3h+QEddjKzjcnAwM+OsuMXu/0KyWD97eZA/ftf2Ba84XelxXs3P6dT9faFnnKIbAB5ttVG2tySndSavloLgEw1IZzpu79jRzEDGYSTnYptG+ecMZBy2NCX44HmdJ1UQBCtbILyWztFi8So+M2zZsoUf//jHjI2N0dXVhVKKnTt30tDQsBz7d9JoStqYuk7B9XC8cJVzNVEzbeoagVKYuj5xQV1blirgWOiJquB6C15QdilOSAsN0E5rr2Vbc6I8820o6zCU8emoj3JqWw1F12dvb5qiq+hPh9Pf55sVd6L7vxz1ENWWRp/c3zfG8vzvXx7itZEsZ2+sK3fyheqbIXMiAelsx+3V4ey6qg9ZC/Vrojqd8C1SQ0MD559//lLuy0ltR0uS+rhF33gBTQsb/Jl6WGhbdAOUUmyoj7CjJbnauzqjpQg4FnKiuvy0Nh5+aWU7F1dyJ6nrWvmis68/zW9eG2Uo45AqhNPS37a9iddH85S84IRnxS3EctVDVNudrq5rbG5K8JGLNnPf44dWZZbOUjfvW0hAOtPvPPa4VVtguxRWu35NVKe1nSs+iWyoi9FaE2EwU8T3FKVATUyeD78MU6OlNsKGurV/17oYs52oztxQy5s66+lNFXjhjXE21K9c5+JK7yQng4XOxjiX724rX2jilsGDz/XSn3Y4c0PdCc+KW4i10M9nLVmtC+ByNe+bKyCVBW/nth4DQLE46+MsuQb0pYs0JGyipkHKO/4uPWEaNMRt+tLFeU9c1b7Q5LEnqqGMw/NHxnngtz0MZYt0D+VIFVx2ttUctwr9chW+nuiFdOqF5shonleHcyuy/IQ0TDveSl8AV2OZj5N5aZGltB4DQHHiJBBaIbmSx6HhLBln5gt4xgm/P98F/mRYaHJqIDeUcfi3F/sZy4cn9mTEpHe8SF+qSK7kl5cCmLQUmY7ZAsnFXkhXcvkJqYeY2UpdAFdj8dm1tOCtECcTCYRWiKHD6yMFAgWmFmYIFAoNDaUUngq/P8PSNGXVfjcYBIpfdQ/zs5cH6UsV0DTF4dEinh9wwSkN5eZ4bTVRBtIF8iWvvBSApmlLkumYL5BczIV0ruEqpRSDaYei65MuuASBWvTFSuohVs9qLD67Vha8FeJkI4HQCnn84DBuoCb6zUz0nZlcnU/T0HyFFygePzjMztbjF7Wt9rvBrsEM33rqMI/uHyTv+iQjJjVRk0zRxdDg+TdSvGlT2IywMWkzkguDhoF0kbF8CcvQF53pWO5AcrbhqtGcQ9dAltdH89RETf7PU4d5+rWxJcniST3EygsCRfdQlsFskWTERCl1XGCyHEO4a2HBWyFORhIIrZDe8WK5MDoIwkaKTCw3Fig1+b/0jhdnfH613A0GgeLIWJ7XhnMAbGtOUPR8vvb46/zm0ChKKTobYngBjGQdxvIubTU2I9kS//7KEHHbwFMKP1AEgSLjuhwaydGciIQdXLc0EDGNijMqKxFIzjRcVXR9nnl9jPGCS33c5tzNDUQtfUmzeFIPsXImM4ov9Izz2mCOvvEirTVRdrQuf+8iKZAXYnnIX8wK2dgQRdfC5TTQwr5BKgj7CIUrFSt8FW43k2q4G+wazPCtJw/z5GujjBdKaArq4hYxK1z+QtOgPmFj6Dolz6Pk+RRdnyNjhfJddWdDjMZkBNcLGMwU0dDY0ZrE1DQG00V++FwPD+3tr7guaqUCyanDVV2DGV7qS5MtemxrTrCj9WjxdzVk8cR0UzOKG+pipPIu/akCg5kiWccr17MtV7G6FMgLsTwkEFoh//Etm/kfP+0i7bjEDFDo5YaKGoqCB3VRi//4ls0zPr/Su8GlmFm2kJ8xuc2+/jTffuowBwez6JpGSzKCQjGSLXFoOE9DwsLQNWpjFoWSR1+qiOP5GDq4niL8sYqxvIth6OSKHpmihxcoHnimh9baKGdtrGVbc/KEhrOWO5A89r26+Z3b+O2RMf72sVdpTFh01MWmXbjWUhZPzG+mjOLOtiS5kkfe8UgXXF4ZyHB6R03F68UtlBTIC7E8JBBaIdGoyR9evIWv/KKbvKvQUOXvKcLu0tddtIVodOZDstC7wY7aKP/v4BA/3zdAX6qIrmnErMpnli1kdtrkNl2DGfb2puhPFbEMnY31UaKWAUBDHMZyJbKOh6VrZIsugxmHrOOhaxq+H66LpBRYOjiuz5HRPLqmEbUMIkqR93xKns/BwRyJiEVjwq44o7KcwwqzvVc725JELJ222thxWShYG1k8sTAzZRQbExHe3FlP92COgUyRN8by1Mcs3tRZv2zF6lIgL8TSk0BoBb3/zRt4ZP8AB/ozeMHRx00dTm1P8v43b5j1ubqu8TtntPLKYIbfHh6joy5KS02EohuU7wZPba/h//vxPh49MEjB9UlETFqSEWL10YoyKAspKgbK2yQjBn4QDm35gWIg49CuacRsE1PXiFgGrh9QcgPSTg7XC2uiDD388v3w9/oBKBRKKZqTEepjFoNZB8vQqYma5EseL/Wm2N1eQ8Q0aK+NLDijslzDCnO9V68MZih5gdR0nARmyyhOLmY6nnd5bSTHtRdu5p07W5Y1KyMF8rOr9h5rYnXIGXiFTKbWNzcmeNeOJl7qy5J2XGojFmd0JHlttDhndqNrMMPDLw2SczwG0w6vj+SJ2wadjXHesrmBU9tr+Pm+AX7z+hiBgk0NMbxAMZx1yJU83rSpjpFcad4MykKKin+ytx8F5W1GciVcX2HoGlFTp+gFjOZdNlgGtqljGRrpgo+uh5kfBega+IHCm6gZNw0wdJ1AKWxDpzlplwMrUw/XZss5Yd+hkZxDzDKpj1nYpr6gjMpyDCvM9169MpDB8QJ6x4vsapOajmo2V0ZxcsmclmSE7S0rU+8lBfLHOxl6rInVIYHQCpmaWrcsizdvnr5IbUcddA1meWMsj6Zpxy2eOJl12NwY59S2GoYyDr2pAgnb5N2ntvLz/YP0jBfQgYaJgmRDBzuhM5or8epwnlPbkvNmUHrGC3QNZkhGDEZyJeyJbIymaeW6lhfeSIEWZlk0TcM2dKyJBkgKsE2dQsmn5AXYpo7rBfgKAp/ygKCvJpYXUWFWKGIaGLpGpuhhGzrGxMKZfhAGRiPZEq4foGmQjFhYhkZfqoCuaQxnHGif/xgs9bDCfAXYG+pjHB7NEzF1qemoclKovLZVe481sbokEFohCynW7RrM8r9/eYh00S3f0WxrTjCaLx2Xdeioj9FeF+XgYJYHnnuDobRDQ9ymZ6yAZUwvyk1GTUZzJfwAHM+fM4Oyrz/NS31pNMIgxDR0GuJ2eXpwzDbIu+Hz4xNDOjVRk9aaCOP5Eo4XEDXDzE7B9RnKOowVwu3VDL8vETHChpKBIl/y8VW4jwPpsN4oYZsUXR9dC4MlL1BYehh8mYaOoWs8f2Sci7c3LyigWMphhYUc04ip8943dXCwPys1HUtopYdApFB57ar2Hmti9UkgtELmK9btGy9wZDSPpsH2lmT5jubp10c5PJLnnM31s0777h7M4ivF5oYEpqHj+oqIeXRby9DJOR7pojtnTUrXYIZ/fb6PbNGjPm5RG7NwfcXQlOnBlqERt0wUMJguYpk6tqGzY2KIbLIBousrMgUXb4boZ7JnkgKKno9GOLQw0UUADY1M0SUZsTilJcH+vjR+oCh6YSNGhWI0VyJum+xsTdA9lKto5tVSDSsstAD7tPbaaYuzSu3C4qzWEIgUKq9N1dJjTaxdEgitkLlS60EQsLcnjWXqnLWhllwpYCwfDkttqIuyvz9Db6rIpob4jB1sAwWGFg6FNcRthjJF7IRd3tb1A3RNYyxf4sJTmmZM30/eVTlewJbGOENZh2QEIqaOnbAZzZXoGsxQF7PYWB/llcEse3tSxGwdyzBojNs0xC16xnIUZ4p+YLKPNpoGugIfcH0ARczSicYsGmIWmqbh+mE1ed7xiNsGRTcIX6eu4XiK1too21uS1MZMDg3nyDguR0bz0wINYFmDj0qGS6SmY2ms9hCIFCqvPdXQY02sbRIIrZC5UuvdQ1lcX7G9Nc4zh1OM5Ut4foBp6MQsA9vQGco4ZIoetbHpmYdCyac+ZtFSE+XIWJ7tLQmyjhfO5oqGs7bGciUipsGm+vis6fvJu6oN9VFaamyypaM/wzJ0bFPn9ZE8uztqGMyWAIhaOjnHxzYChjNFUgW3PBvOmPi5/jG/R038ZzIYCoC4pVEft6mJWpwzUTvVNZhlMFNkIO2g67C1Oc6WxgTxiDmtbilTdHG8gB/+tofhbKmcIaiPW6BgvOAuW9ZAhktW1loZApGgdm2RjttiseSTsYJmS62f0pwg53j0pRwc1ycZtbCiJq6vSBfCuptCycfxfODoH/rUrMPlp7dy/69eZyRXYkdLnNeGc2Fg4PokbYO372zmQxdunjUImHpXZehauT/KaL5EzvHKtUY1E6vDp4ouw9kShZJHoCCYnA1GOAPMNgxKvl+eGg/Ta4Q0LSyYBohaBhsb4mxvObpMwflbGxjNldjfn6Y+blHyAjY1xND1o6vSKqU4OJAlXXQxdZ0N9WGGoHc8z8MvD5R/zok2YVwIGS5ZOSsxBCLTr6uPFLKLxaqKQOjQoUPcfffdPPLII/T397NhwwY+8pGP8KlPfQrbtmd9XrFY5LbbbuPb3/42juNw5ZVX8uUvf5m2trYV3PvpdrTWsPWdCX57ZIyRXImmhE1T0uYT33qOnOPRVhsp/yFHTA07GaHgFij4AT3jBaKWMWPWYUdrDR+9eAt//9irPHN4nKzjggq7Vb9lSwP/6fzZgyA4/q5qsj9KphgGSL0TNUz/fnCYQins+KwRLpKqaZArhamgADBUeALyj00HTVCEdUCmrggUnLWxnjd11qMU9IznKZR8XF8xnC0ylCmhaTCcKdGfcjhzYy0d9TEKpXCf0kWX2pjFrrZkeYX6/pSDbYRrevSnHTY1xJc1ayDDJctrMjjZ25tiNB8Oic1ksUMgMv26OklmVixWVQRC+/fvJwgC/vZv/5YdO3awd+9ebrrpJnK5HPfcc8+sz7v11lv513/9V7773e9SV1fHLbfcwgc+8AEef/zxFdz76WY62TYlbRzXm9ZteqqwQNliV1sNI9nStKzD5ae3EjENHtrbx4PP9fDs4XGKXkDCNmiuiXBKUzhUdv8Th+bMhMx0V6VpGiM5h1+/OsJQtlTO4EyKmmHgowJVrv9RTGR6jt14Co0wULJMnYa4RdTSeW04y/NHUowXXFw/wPUCNF1jS2OcC7Y20ZcqsLc3zbOHxxnOlmhORtjSlMALFJsbj9ZOZYoeo/kSNRNDiKO5UnlIcTkLJ9fjcMlKZE+m/r2M5h26B3NkCi6nb6ilMRGZtu1iu4PL9OvqJZlZsRhVEQjt2bOHPXv2lP+9bds2Dhw4wFe+8pVZA6FUKsU//uM/8q1vfYt3v/vdANx3332cdtppPPnkk7z1rW9dkX2faraT7b6+NHk3IGHr0+pyXD8gW/RIRk2aEhH+w1s2UhOxyJU8YpbB68N5vvXkEZ5/Y4zuwSz5sPKYRMTEU4rRbDhlfiHNFGe6qxpIF3nslSHypbABkMH0mp/SxOy0yWGxcjCkwFMKQwcVzDxtXhEWdv/H8zfxs32DHBzIolAkLB1Pm6wlUvSni7zUm+KU5iRXnNbKi71ptjUnueFtW8k6Hv/r0a7yNP5wnwK8IMAywseyjkfJP9rGu9oKJ9fqUM1KZE+O/XvpqIuSKXi8MV7A8XzO2dxQDoYWMwSyVmqPxOJIZlacqKoIhGaSSqVobGyc9fvPPPMMruty+eWXlx/bvXs3mzdv5oknnljxQGiuk+2OliSvDuWwTYO6qMVYwSXneBi6TmttlPbaCKBRE7HobIzTNZjh/l8d4tH9g6QLLtmSjx8ETCxsj+MFKAW+qSBf4tXh3JzNFCcvtl6g2HNmO88fGadrMMuTr45QcH1MPazB0NDwp6wNEqhwRpplaGj+0WBIn6j/mbqMyFS6BgnbYHNDjIf29tM1mMMNwoVXsypAKYVt6miEd/lPvTZKX6pIUyJCe12EVMFF0zRqotZxRZK2oWPqennWmamH0/snzZQ1WM/Bxonu13JnT2b7ezl9Qx2OFzCUdXi5L82FpzRRdBc3BCLTr08e6zEzKxavKgOhrq4uvvSlL805LNbf349t29TX1097vK2tjf7+/lmf5zgOjuOU/51Opxe9vzD9ZAuQLriU/KA8A6qjLkpfqsh5mxvIez6Fkk/U0klYJq+O5DhjQ7igatdghv/9y0P85tAoQRBgmjpB0QsbICqIGOGq9kEQNiksBQEjWQevJTFjM8WZLrbbmhOctbGOX3YNETcNLFMjXwrQtTCICaakePwgXCtt8nFNCxeQDXw1LRNkEjZerImFGS2n5HFoJAcTdT3WxPIb/sQP94MAXQs7VvuBwg8Ug5kiqWKJpkSEXMljV2vNccN5NVGTxrjNQLoAmkZbbZSaiYVsZ8oarOdg40SsVPZktuCkMWFzzuYGXu5NMZh22NeXpiFuL2oIRKZfC7G+rWogdMcdd/CFL3xhzm327dvH7t27y//u6elhz549/MEf/AE33XTTku/T5z73OT772c8u+c+dPNkWXYN9fWPTpsg3xG3a66L0pgo8emBgortyQGkis5OMmkQtg68+1s1ovkTPeNh4MR61SI0X0LVwnS7fC8LOy4aGrxRRTcP1AoqeT6boHZcJme1i+1Jfmozj4vkKwwDT0NG0IFxTSddwpwQ5CsL1wUwdU9couD5eMD0IsgyIWybxiEld3CJVcCkFYJlhpiZQR4fVJofZlA8xOyyqdrxwgzDAKYKCuGXMWiTZXhfhyFgeULTXRvCVouB4x2UNFhJsbGteuVT7ZGYqU3T54bO9jGQddrXVrKmhmqXMnsyViZsrOGlM2Fy4rZF9fWk+eH4nZ26oW9RxkenXQqxvq/qXfdttt3H99dfPuc22bdvK/9/b28ull17KxRdfzN/93d/N+bz29nZKpRLj4+PTskIDAwO0t8++MNWdd97JJz/5yfK/0+k0nZ2dc7+QBUjYJiUv4JnXR/EDNW2K/FCmSH+qQN7xKXo+rqeYHFUyNEhGDWqjZrnL9LaWJL5SmFqY/dG08CJkTCxSaunaxOMK11doiuOaKc53Z//UayP4E8NVGpQDIFMPf8dkLbQBWIYxMRymaIyHs+BeHcrhTmxk6DqGoZF1PFJFlyAIF2g1dJ3xvDttSA3CzFZAmBXStHBWmlWeNq+hppSVz1Yk+Tunt5X7CB0azh1XOLmQzMa3njpMY9zm1eHcgrNFlQyzTd12KOPw/JFxXh0KWxZ0D2ZprYnQUhMttxSA1R+qqSR7Mtd7MV8mbr7gpOgGNMQjnLmhbtHvgUy/FmJ9W9VAqKWlhZaWlgVt29PTw6WXXsq5557LfffdN62fzEzOPfdcLMvi5z//OVdffTUABw4c4PDhw1x00UWzPi8SiRCJRGb9/onqqI3iuAHjBZfNU/rhREwNK27xYk+Kkq84erkPKWA467KvL82bO+vZ359hNOdgahqBUpiGhheEw0eWoeF7qrwSfFjDo9B0jY31sWn1E/Pd2Z/WXsOrQzlyjo+GT8wy8JVPMBFoTQ59TX6Zhk5j1OLtO5rY15fBMnWSUZ2SF+B4AfmST8IyIIBAU+gKCq4/MZQWDrFxTLwQjkQERCfGzUYmCsmbEjYF92jZ9mxFkpOvc6YL8XyvP2bpPLp/kM1N8WlLnsw1NFXJMNvUbYezDkdG81iGzpkba2mvjXJoOMt43uW5I+O8ubN+WjC0mkM1C82eDGccfv7y4IzvBbCgTNxKBScy/VqI9a0qcr09PT1ccsklbNmyhXvuuYehoaHy9yazOz09PVx22WV8/etf54ILLqCuro4bb7yRT37ykzQ2NlJbW8snPvEJLrroolWZMdaXLhKxwuniY3l32sywkUyR0kQWSNcmui5r4bCQr8L6mNeG85zeUUcyYpIuuNTFbVL5EjHLwPUVgQom6nW08vT0bNEjZptcuquFD711y7SL8Xx39omoxZamOD3jRcbzJXzlEzE1iq4KAzYd6qMW525t5E2b6nj+yDgbG2KARq4UznRz3ICYFa4q7wfQWhtFqYDXRvLYho7jhkGOFrNI5V3cY4qrJ//pK0VPqkhdzOKU5ji1Ufu4YYrZiiRnyxbM9fqVUvSOFym4PhvrY+UL/lxDU5XU9Ezdtr02Qu94gUCFGbCDg1l2tCaJWiYRUyPneHQPZWmIN5SDgdUcqllI9qSjLsqPX+xjLO8e9170jOeJWsa8NUZ//K7kigYnMv1aiPWrKgKhhx9+mK6uLrq6uti0adO07ykVDpK4rsuBAwfI5/Pl7/31X/81uq5z9dVXT2uouBpyJQ/b1HnL5gYODefLHZsNXcc2TQLCAm3TCIegJmeAGZqG7yscLyBVKNGctCeWuoiG/XZ8F8vQUUrhBEdnXNmWQUsywg1vP4UPntt53AVjIXf2mxsTXHNeJ999+ggHB7MUSgGaBnUxm3M213HD207h4u3NvDKY4aW+NImIxVi+hK8USdskUyiS9QM0wsaJgxmHmGUQNQ0MDfKej22AqYWLrgaeOm6qvalDa010IrDyOdCf5YrT2xadCZjr9WeKHkNZh0TEJGIa074309BUJQXEwLRtM0WP8YJLQ8LGNjRGcyUGUg4NcYuhTLgPU3shrfZQzbzZk7gNCsby7ozvxfNvjDOccbjglKZ5a4xWOjiR6ddCrE9VEQhdf/3189YSbd26tRwUTYpGo9x7773ce++9y7h3CzN54Y1aBudtbSBT9Mqzxvb1pekezk0EPxqadvR1TAZEgYJcKcxQDKYdso7HztYkveNFeib6qliGTkPCYlNDnLM31XHNWzrZ1b7wBoqTpl5sP3huJ9ecs4lnDo/yykC2vP9bGhPlC8TUoMKemOWVLrgYuoat6Xi+QlOKguvhB4qYreP5UPI8RrwAXdfKx27qkGCY2YKRnEPc1qd9b7Hmev2O55NzPLY2JcozzqY6dmiqkgJiYNq2JT/A8wOsibXTklGT0XyJ0zpqyDoe2aKLr6Dgemgaa2KoZq4A5axNdfzgtz3T3gulVPnzHjENxvJueXbgsY59b1c6OJHp10KsP1URCJ0Mpl54d7Ympy2emoiElUGTBcP6RNGzPrE6qZqYVZWwDQpuwLt3t9KQCAuSGxM2dXGL9toIb+psYHd7DTVRa96LRSV1EbquceG2Zi7c1jzva9venMD3FUU3oC5mopQiVwqHyGqjBkfGCph6WEv0xGujDGecclG1RriQqz8x60wDUGE2bDBTYnd7DR11Ucbz7qILhed6/T3jBWKWyYb64wMbOH5oqtLp11O3tQ0d09BxJ5pTWoZOzvGI2yZv7qzn5d40gxmHgXSRhnhkzQzVzBagvDKYmfb6RnNOec06LwjwA0WmGB6/hsTxy+PMNOwnwYkQYjlJILRC5rrwlryAiKHhBQrXDzB0fWIhU0UwERREDI2Sr2irs7n2ws1LMq17qYYepr62F3tTKNREfYsPGkQsnfq4yVCmhGnoJKMGaBqNCZtUwSVwg3DBVg1sXSMfKCxDJ2EbaEDe9UnYJqe21ZCImhwazi1JofBsr/+CrU1sb3boSxdRSs1bqFvp9Oup29ZETRriNkOZInbCLh//yf5SLTVRzt3awFXnhF3F19JQzUwBytT3wvUDnjsyTqHkT9TEmWSLLgD7+tJ01EVoSh5dN2y1h/2EEOuTBEIraLYL71u3tRAxDR5/dRTPD++aNY5Oodc1aKuNcuEpTdNmIM3WIbqS4Giphh4mX9s3nnydg4NZEhGrPLMrbhm4fnjh3FgTI110+feDQ5S8AJ2JITDCafN5Nxwqi1k6tqnjBYqIqWMY4AZqyQuFZ3v9rw5nue/xQwsq1K10+vWx2+5oTZJ1PEZyYW+pjvoYoDg4mKUpafPB8zpXPQO0UJPvxYs9KVL5EoWST2PCLi+I6/qKbS1JRnMlfv3aGG/b0UQ8YsoMLSHEqpFAaIXNfuFt4+4fvczenjQF10MpDRNFxDTZ3prg1st3cfH25lkvEIvpkLxUQw9hMHQKfeNFYrZB/cTwnxuEM9he6k2BUozlXDQNaqMWug4qV6LghQXipqFhTgwNBoEKs2WWTtQ0sHRtWTIGk69/MpB8ZTBDwjb56MVbePilwXmzZZVOvz5229qYyY6WBM8eGcedeN3jeXfNDINVYvK9eGUgw+ujeerjFgooeX55FuPZm+rJOS77+7P0poqYuiYztIQQq0ZTx1YYi2nS6TR1dXWkUilqa2uX9Xd1DWb4txf6ePrQGNmSS9K2uOCUBvac1THnxeH4qdsm+dLRTsoruRxDECi+8ovuci3UZHYkXXD5VfcwvakCjhvQELcwJ9YAc/2AkYnV7aOmhm3q6Fo4ey5q6cQtk9a6CC3JCE3JyLK8ntkCyd85vY2YbSwoWzb1ZzhemLna0Zqc8eJ+bB+h4WwJVEAyalETtdjekuSa8zayq23mz9xaXR9t0k9f7ueenxyYWPpFYeo6jQmb7S1JGhM2XhDw2lCWD56/mfa66Jp8DUKI6rbQ67dkhNaQHa01fPzdlQ1TrbWVs2fLjkBY9Oz6YeNHY0pDTFPXiNkGRden5CkgmFhVfKIzth6uVH/2pvplyRi8MpDm3ke7Gck6dNRFOaUpQcH1p/UA2t0+fxBcyTDj5LaPdw/zf359GE2Dbc1JEpEwiD0yluf+X72+6MaNq+W09lrO6KjFNDRs0yjXPE3thRS1TLa3JKUQWgixqiQQWmMqHaZaiytnz1YLdU5nPb/sGqbg+hOtAzR8BSUvIG6btNdGGMyUsAyNzU0x2muitNZFOW9rI6e1155QxmC+zMkr/Rnu/tE+uoeyxG2D4WyJhniRHa1JdrYmKw4kKz1+LxxJoRS8aVP9goLYtboY67E21sfY0VoTZgbrYrJshRBizZJAqMqt1ZWzp2ZHMo5LtuiRczwGMg6vDWfJFD38IBwCS9jmREEtJEsB7zq1mesu2rroWVLzZU66BjPc+2gX3UNZ6uMWicjRtd+yjsebO+uXNZCsNIhda9m/uciyFUKIaiGBUJVbyytn67qG4/k8um+I7qEsBddjPO9i6Rp1URNPQU3EpC5m4QUB/WmH9tooN7ztlFlrYxZqvszJZCH0SM4hZuskIia6phExNeyEzWiuRPdQljd31oUNFmcIJBdbp1NpEDtX4ASQjBg88/oYv3l9lPO2NK56kCHLVgghqoEEQlVuLa+cfWww0mFFyRQ9Do/mcX0f29DJO+FyFhHTYENdlE9ctnPRQdBCMifff6aHwXSRDXUxhrMl3Imux8DRDs+5EkMZZ8ZAcinqdCoNYmcLnEZzJboGs4zkHNIFl799rJunN4+tiZohWbZCCLHWSSBU5dbqEMRkMDKSLU3U/ji8OpSlZ6yA5/sEAXiaojZqEihoStjc8u4dXHZa26J/90KHnHylOKOjlsa4zWCmiJ3Qy9tbhk62GM68u3h787RAcqnqdCoNYmcKnEZzpYmmheFadnUxi6ZEZE3VDElnaCHEWqbPv4lY6yaHIM7cUMd43uXQcK7ch2a1LoQ94wWePTLGUKbIYweH+PdXhugazFJ0fWqiFrUxE02DmGXyth3NbGmK88pAlmCWNagqcTRzMnOcH7MNAhVgaFBwfba3JojZ4YrojucTKEXO8ciXfJqSkWmB5LHZppqohaFr1EQtdraGjQJ/+tLAgl7HZBDbmLA5OJglU3TxgoBM0eXgYHbWxo19qbDjtVKKrsEshZJHQ9yi5AU0JSO010Ur3hchhFivJCN0klhrQxD7+tK80p/BNDSKJR9QTCY8ciWfhG0QMQ18pRjMOJzallyyouSFDDnVx2xaaiIcGSuwszXJmzvry2tiZYsu+VLAjtYkH790+7RAcqln6VVSRzMZOPWM53n+jXEipk5/ukDU1BnLu8Rsg+0tifJ+rcaMQSGEqDYSCJ1E1soQRBAofnNoDC9QJCImqYKHZeiUPIWph2uq5Us+UUsv1+J4gZq1KLlSCx1yuvy0Nu5/4ugyGm/eXM9QxqEvVaApGeHjl+xgV9v0bNpSz9ILAkXENLj0tBbOP6WBZMScd9HcqGUwlHEYyjhkih7JiMmmxhhnbayb6L90YvuyUGu9maMQQlRCAqEqtNYuRMfuj1LhFPSOuiiDGQc/CLBNA03zy4urlvyAmqhJwjYYL7hkil7Fs9tmex8WWjd1bDbG8cJszMXbm2ed1bSUs/TmKrie6XhOrU268JQmUoUST782ikLD8xXhqm0nti8LVQ3NHIUQohISCFWZlbwQLSTgmml/aqMWI7kSp7bXknVGGc+XCAKFoWm4fgAa6JpGTdTECxSmpjGWDy/uC53dNt/7sNAhp0qHFJdqll6lBdczzYSriZr0pxwG0gUKrk/3UJaGeEN5gdOlnjFYLc0chRCiEhIIVZGVvBAtJOCabX+6h7IcGc3TnLQ5f2sj//7KEKP5UhhcBIAiXEjV0hnLuUQsnY31sQXPblvo+7DQIKeSIcWlmKV3Io0RZ6pN0jSN7a0JMo5LuuAykC4yli9hGfqSzxispmaOQghRCZk1tkYFgeLIaJ79/WmOjObxvGDJZivNZzLQ2Nuboj5usa05SX3cYm9vivseP0TXYGbO2VNnb6zDMnX29qRpiFu8c1c4/bwuZrGxPkrcNjB1jcG0g65pvGNHM3/09lMWFMRVOmtrMsjZ3V5LZ2N8SS7Si52lV0nB9aTZZsI1JiITHbBjFF2fQyPLM2PwRPZZCCGqgWSE1qCZsjHNyQivDmfZ3Bhf1jXFFnrn/96z9VkvjLquc+aGWp49PM4LPSm2tyQ5d0sDB/oz9KWK1EQtOpvi7GxNctlpbbxte/OCA5S1srbaYmbpnUjB9Vy1SY2JCKd1aNTFLa69YDPbW5JLXje2VpdyEUKIxZJAaI2Zbdjn5b4Uh0fytNZEZizSXaoL0UIDjdeGc3NeGDvqw47NpzQnGM+7OJ7P5sY4525tWNQiqou9IC9loXmls/Qmf3d/qogfhL2KamMLK7ierzapP+3wpk31vHNny7IMTa3lpVyEEGIx5Ky1hsyVjdnRkuTVoRwHBjI0JyPHBSlLdSFaaKABzHthbE5GuOFtp6Br2pLNcFvMBXk1ZzxN/d0F1+PIaIHXhnJccEoDTcloebvZipxXu4P4Wl7KRQghFkMCoTVkrmxMbcyioy5K33iRdMGlLm6Xv7eUF6KFBhrbmhMLujB2NixNXc6kE70gr8aMp8kM0L6+NP/6Yh+O67OhPsYGO0bMMnj60BiPvTLM+Vsb6KiPzRvUrOYipqsdiAkhxHKRQGgNmSsbo2kau9prGMo6dA1l2dVWsywXooUGGpsa4ityYZxpKGvy974ykKEmamLoGn6gyBS945bEmPwZKz3jaTID1DWY4aXeNFnHY0tTnJaaKIau0dmYIG6b/PrQKAf6MxTdgKg1f1Czmh3EZTV5IcTJSAKhNWS+bEzMMtjVVsO25gTD2dKyXIgqufNf7gvjKwNpvvebHrqHsvgqoCFms6O1hivPbOPdu1v52uOHeKk3jesHWIbO1uYEf7C79bjfu9IF1lOzTzWRcE21+rjFUMYh6/i8ubOexoRNUzLC27Y30Tte5IPndy64yHk1O4ivtaVchBBisSQQWkMWko15y+YGPvaObfRNFAQvx4WokgBntgsjwJHR/Anv48/3DfA3Pz/IUMbBNnUipk6m4DGcLbGvPw1AImJy0bYmdF0jCBTposcj+wfZ0hSfto/HZtqUCrNHJT/ANnRitr6g5T0WUmh9bPZpJFfCV4raiEUyEq4WP7XxYTxiYhoa7XXRNbE8ykKslaVchBBiKUggtIYsNBtjmvqyX4gqufM/9sK42KLkV/oz/M3PD9KfLtJRG8UydVxfkSq4OF7A4dE8EUvnytPb0PWjrbDalZpxmGtqps31wxXbx/IlPD/ANHQStkFDwp6z0Hyhr+nY7JNt6Ji6jusHREyjvLZaphjOGJPZVkIIsbrk7LvGrKU6jBO5819sUXIQKL73zBGGMg7ttREilgFAxNSwEzYDaYdM0cXQLbKOT23saCA02zDXZKbtyVdHGMuXKLo+yaiFFTVxvYA3xgt4gaJQ8hf9mo7NPtVETRrjNoOZInZCxzJ0sk6YjZLZVkIIsfokEFqDqrUOYymKkiczKrapYZvGtO9pmkbU0hnJBXiBouQHxz1/pj5Cuq7xO2e08tOX+xnKhgGWZYTrnmUdj5akTW3M4mf7BtjRmlxUofWxdV5Tl8EYzZWwTR1d0yh5PgcHszLbSgghVpkEQmtUNdZhLEVRcq7k4SuImEZ5OGmqiKmjFAQKbOP4FWJmG2qKWSYtNRFMXSPreKQKLqDRmLA5vaMG2zRm3LdKX9NMdV6Ty2B0DWR5fTQfLjbrK5ltJYQQa4AEQuKEHVs8nCm6C2rGmCm6sxZSJ2yThphFpuCSKrjYCX1aAKJpGroGlqGRjEwPkuYaasqVPGxTZ1dbDQcGMpS8AKUg73h0D+XZ2hyfsWC60k7Ws9V5WYZOXdzm/Loo7z2744Q7awshhFhaEgiJEzLbemglL5g2/X/qDK2S51N0fX74bC/DWWfGouON9TF2tNYwnCvheD6juRLJqIll6JQ8n/60w4b6ODtaE3QN5Rbcvyhhm5S8gN8eHsMPFHVxe2J4TDGUKTKac+hsjB+XSTqRTtaz1XmdvUkyQEIIsdZIICQqNlvx8OHRHEMZB8cLOKeznrG8O22GVqroYmgajhewq61m1qLjyYwKhBmbjONR8kqUPEVHbZRPXLaTLU3xaYGGbejhWmZbGoiYBkGgpgVDHbVRHDdgvOCyuSFWnm0WMTWsuMXhsQJtXkBHbXTaaz3RTtbVWuclhBDrjQRCoiJzFQ/vajPJOT7pgstvD48zlCni+YqIpRMohQrARTGed3F9haFrMxYdT82odA1mGC+46Bpsb01yzVs62dUeZlQmA419/Wl+89oog+kiP3yuh4f29h83tb0vXSRi6TTELcbybjnL5PoB2aJHfczCNnX60sVpNUKLWVpiIXVeS7kIrBBCiMpVRSB06NAh7r77bh555BH6+/vZsGEDH/nIR/jUpz6FbduzPu+SSy7hsccem/bYzTffzFe/+tXl3uWT1nzFwzvbkrw+kiNdcMkUPeK2TqCgPm6jgNpoGCxNbSo4U9HxQjIquq7heD6PHRiakp0yZ8wyTdYIvWVzA4eG84zmS+QcD0PXaa2NsrUpTqrgzthUca6WBpef3krENNjfn644kFnNRWCFEEKEqiIQ2r9/P0EQ8Ld/+7fs2LGDvXv3ctNNN5HL5bjnnnvmfO5NN93EX/zFX5T/HY9X10ystWYhxcOBUtTELN7enMQydWxDx/F9fv3aKLZpoGnatKaCk8+badr7XBmVSqa2T9b6RC2D87Y2TOssXRM1yToeRTeYtbHhTIFZoeTz8EsnFsisxiKwQgghjlcVgdCePXvYs2dP+d/btm3jwIEDfOUrX5k3EIrH47S3ty/3Lq4bCyke1jUdXylaa8MFRgHSBcodlqc2FZz6vEo7LFcytX1qrc/O1mQ5AIO5a32mmhqYdQ1muP+J6YFMzvH49aERXupLce0Fm3nb9uYZs0Mn0m9JhtCEEGJ5VEUgNJNUKkVjY+O8233zm9/kG9/4Bu3t7bzvfe/jM5/5zJxZIcdxcByn/O90Or0k+3uyWEjx8I7WJIPp4rRgaWqH5UTExNT1ch+gE+2wXMnU9sXU+hxrpkBmNOfQPZhjJOeQKrgcGs7znjPb2XNW+6IXgZUhNCGEWD5VGQh1dXXxpS99ad5s0Ic+9CG2bNnChg0beOGFF7j99ts5cOAADzzwwKzP+dznPsdnP/vZpd7lk8ZCAoqrz93Iwy8NTguWJjssp4sl+tNFNjXEiNk6maJbcSAyqdKp7Uu1fMmxgcxozuG5I+MUSj7JqEnEMsg5Hk+/PkpfunjcMFclAVw1DKFJtkoIUc1WNRC64447+MIXvjDnNvv27WP37t3lf/f09LBnzx7+4A/+gJtuumnO537sYx8r//9ZZ51FR0cHl112Gd3d3Wzfvn3G59x555188pOfLP87nU7T2dm5kJezbiwkoNA1bcamgg2JCIau0xC3eX0kv6h11E5kavtSTGufGsgopegezFEo+TQmbDRNI1CKfCncv9Fcac5FYOcK4GKWwY+e71vUkiXLTbJVQohqt6qB0G233cb1118/5zbbtm0r/39vby+XXnopF198MX/3d39X8e+78MILgTCjNFsgFIlEiEQiFf/s9Wa+gGK2YOmibU1cflobMdtYdAbhRIe7Frt8ydRARikYzYdNHycDFdcPMHWdiGnQUXf80h0LDeA0WPSSJcupGrJVQggxn1UNhFpaWmhpaVnQtj09PVx66aWce+653HfffeWGeJV47rnnAOjo6Kj4ueJ48wUUK9FUcKmGuyoxNZBpiFt4QYBlhH9KSimyRY/W2ig1URNfqRlnwy0kgMu7fkXLeyy1uYa8lmKBXSGEWAuqokaop6eHSy65hC1btnDPPfcwNDRU/t7kjLCenh4uu+wyvv71r3PBBRfQ3d3Nt771Ld7znvfQ1NTECy+8wK233so73/lOzj777NV6KevOSiweu9JdnKcGMm+M5VEKHM9H1zSyRY+YbbK9JQwOCo4342y4hQRwR0bzFS/vsVTmG/JaigV2hRBiLaiKQOjhhx+mq6uLrq4uNm3aNO17SikAXNflwIED5PN5AGzb5mc/+xlf/OIXyeVydHZ2cvXVV/PpT396xfdfLL+VCLimmgxkHtrbz1Cmj8G0Q33MorU2yvaWJI0Je97ZcPMFcCe6vMdiLWTIywvUqmarhBBiqWhqMpIQM0qn09TV1ZFKpaitrV3t3RFrTBAoftU9zLd+fZic47GtOUE8Yk4b5lpMrcyxQcmxQ2hLXYcTBIqv/KK73G/p2ODr4GCWszbW8Xtnd/DFnx2kPm7NmK3KFF3G8y63/s4uyQgJIVbFQq/fVZEREieHk3Gata5rvH1nC+110fJQ0mDGWbI6pZWugVrokJeCVclWCSHEUpNASKyIk32a9XLWKa1kDdRCexwVXH/JGlQKIcRqkkBILLv1Ms16OeuUVqoGqpImlZ2N8RWfsSeEEEtNAiGxrGSadXWptEB7pWfsCSHEUpNASCwrmWZdXU6kSeVKz9gTQoilVHlXQiEqcLTmZOaYO2YbOJ4v06zXkMkC7TM31DGedzk0nGM873LWxrqTZhhTCCEmSUZILKtKF0YVa4MMeQkh1gu5+ohltVpNAcXiyZCXEGI9kKExsawma04aEzYHB7Nkii5eEJApuhwczMo0ayGEEKtKAiGx7OaqOfnoRVuJmAb7+9McGc0TBNLoXAghxMqRoTGxImaqOSm4Hg+/tDRNFk/GrtVCCCGWnwRCYsVMrTnpGsxw/69eX5Imiyd712ohhBDLR4bGxIqbbLI4ki3RVhPB8QJyjkcyYrKzNclorsRPXxpY0DDZZNfqvb0p6uMW25qT1Mct9vamuO/xQ3QNZlbgFVWXIFAcGc3LcKQQQiAZIbEKesYLPHtkjLFciUMjObwgwNR1GuM221sTC26yKF2rKyfZMyGEmE4CIbHi9vWleaU/g2Vo1MQsLMPE9QMGM0UyTlhEvZAmi9K1ujLrZc03IYSohAyNiRUVBIrfHBrDCxQ1UYuIaaBrGhHToDFhUyj5HBjIYBv6vE0WpWv1wh2bPauJWhi6Rk3Uqng4UgghTiYSCIkV1TNeYChTpKMuStbxUOrohVfTNBIRg77xIi21kXmbLE7tWj0T6Vp9VCXZMyGEWE8kEBIrKlfycPyAU9tridkmo7kSjucTKIXj+WSLPqahcd7Wxnnreia7VvelitMCKjjatXpHa1K6ViPZMyGEmI0EQmJFTWZxopbOmzvraamJUnQDxvIlim5AfcJiV1sNp7XXzvuzpGv1wkn2TAghZiZnPbGipq49trM1yflbG8gUPUp+gKVr9KeLnL2pfsFZnMmu1ZMzoQbSRSKmwVkb67jiDJkJNUnWfBNCiJlJICRW1GQWpzdV4OBgWLMSjxhoJehLFWlKRirO4shK6fOb6X2P2QaFkk9fqijZMyHEuqWpY4srxDTpdJq6ujpSqRS1tfMP14iFmdrPxvHCYZkdrUnJ4iwzed+FEOvFQq/fEgjNQwKh5SPrg60Oed+FEOvBQq/fMjQmVs3UtcfEypH3XQghjpJZY0IIIYRYtyQQEkIIIcS6JYGQEEIIIdYtCYSEEEIIsW5JICSEEEKIdUsCISGEEEKsWxIICSGEEGLdkkBICCGEEOtW1QRCv//7v8/mzZuJRqN0dHRw3XXX0dvbO+dzisUiH//4x2lqaiKZTHL11VczMDCwQnsshBBCiLWuagKhSy+9lO985zscOHCA73//+3R3d3PNNdfM+Zxbb72Vf/mXf+G73/0ujz32GL29vXzgAx9YoT0WQgghxFpXtWuNPfjgg1x11VU4joNlWcd9P5VK0dLSwre+9a1ywLR//35OO+00nnjiCd761rcu6PfIWmNCCCFE9Vno9btqMkJTjY6O8s1vfpOLL754xiAI4JlnnsF1XS6//PLyY7t372bz5s088cQTs/5sx3FIp9PTvoQQQghxcqqqQOj2228nkUjQ1NTE4cOH+ed//udZt+3v78e2berr66c93tbWRn9//6zP+9znPkddXV35q7Ozc6l2XwghhBBrzKoGQnfccQeaps35tX///vL2f/Znf8azzz7LT3/6UwzD4A//8A9Z6pG9O++8k1QqVf46cuTIkv58IYQQQqwd5mr+8ttuu43rr79+zm22bdtW/v/m5maam5vZtWsXp512Gp2dnTz55JNcdNFFxz2vvb2dUqnE+Pj4tKzQwMAA7e3ts/6+SCRCJBKp+LUIIYQQovqsaiDU0tJCS0vLCT03CAIgrOmZybnnnotlWfz85z/n6quvBuDAgQMcPnx4xsBJCCGEEOvPqgZCC/XUU0/x9NNP8/a3v52Ghga6u7v5zGc+w/bt28tBTU9PD5dddhlf//rXueCCC6irq+PGG2/kk5/8JI2NjdTW1vKJT3yCiy66aMEzxoQQQghxcquKQCgej/PAAw9w1113kcvl6OjoYM+ePXz6058uD2O5rsuBAwfI5/Pl5/31X/81uq5z9dVX4zgOV155JV/+8pdX62VUtSBQ9IwXyJU8ErbJxvoYuq6t9m4JIYQQi1K1fYRWynrsI3Rs0FNwPR5+aZDuoSxFzydqGmxvSXLlmW3saK1Z7d0VQgghjrPQ63dVZITEyukazPCTvQPloKfkBQxlHGqjFjvbksTtGPmSx97eFL2pAje8basEQ0IIIapWVfUREsurazDDfY8fYm9vivq4xSlNCcZyJfrTRcbyJVxfYegaNVGLna1JRnMlfvrSAEEgSUUhhBDVSQIhAYTDYT/ZO8BorsTO1iQ1UYt8ySdX8mmvjVB0fbqHsuW+TZqm0VEXpWswS894YZX3XgghhDgxEggJAHrGC3QPZemoi6JpYRF0yQ/wggDbNEhGTUZzJTJFr/ycmG3geD65kjfbjxVCCCHWNAmEBAC5kkfR84nbR8vGbEPH1HVcP8AydLwgoOQH5e8XSj4R0yBhS6mZEEKI6iSBkAAgYZtETYP8lOxOTdSkMW6TLXqUvABT17GN8COjlKIvVWRHa5KN9bHV2m0hhBBiUSQQWueCQHFkNE/GcWlO2vSOF6fVAW1vTRC1dPrTRRIRg5itkym6HBzM0piwueKMNuknJIQQomrJmMY6NttU+Zzj0V4XxdA1/EDREI9g6Dr1MYt9fRl0Dba3JrnmLZ0ydV4IIURVk0BonZqcKj+SdaiJmtRGLfwgYDTncHAww8HBDAAa0FEX4927W8gWfV4dzuGrgKG0w8MvD6DrSDAkhBCiakkgtA5NTpU/PJLHCwIOjeTx/ABfKfKOh+sHNCYjxCyDVMHl0EiOV/5fhqZkhHM317OhPi5NFYUQQpwUpEZoHeoZL/DskTEGM0WGMg5Ry6A+blEoBWQdH1/BQMohXfBojNtYhobnB4znS+ztTXNoJIdSsKMlIU0VhRBCVDXJCK1DGcfl8Gge31c0JW00TcNxfVw/IGHrpB0fzw9QyuTIWJ50wSVQUPI88qUcA+ki7bVRmhIR2usi5aaKnY3x1X5pQgghREUkEFqHskWPQsmnJhoefsf1yZY8vCDAMHV8P8APoC9VJFDgq7BWSAG6UhRLYdA0mCmSKpZoSkSkqaIQQoiqJIHQOpSMmMQsg0zBJV1wKbgBnh9QcH2Kro8XhEGPmgiAIPw3QBCA0hTposcpTXEGMw4oiFvGKr0aIYQQ4sRJjdA6VBO1aErapIseY3kXTQuXyzA0cDxVDnoUEBzz3IAwQzSeL3FkrIDrKxQaUiEkhBCiGkkgtA511EYxdR3T0KmPmShF2Dna0Kd9IKZmhI7lB5AquBRcn7itU3D9ldh1IYQQYknJ0Ng61JcuErF0mpM2XqCoi+loOhQcHy8o4vrBtOGxY00GRwnbwDYN8qWAmAyNCSGEqEKSEVqHciUP29Q5d0sjrTVRfKXIOh6OHxC1DDbWRTF0MHWYGt/ogK6Fj9umRlttFMvQ0FCzZo6EEEKItUwyQuvQ5AKrUUtne0sCZ2J5DVMDQ9MYK3hYukbUMvB8RaD8sEiaMBgydA3b1Mk6HsmoSVMiQl6GxoQQQlQhCYTWoY31Mba3JHny1RHG8iWKrk/dROPETMHl9dE8Cg3b0ImY4HgBmhZmfUxDR9MgYhpsrI/TXhcBNBK2fJSEEEJUHxkaW4d0XeN3zmglXXQZyjokIgaWoU3UBik21kdpSliUfIVSELcNEhGTzY0x2mojbGyI8fadzZy7pZ6CG7CjNcnG+thqvywhhBCiYnIbv07FLJOWmgimrpEr+eScEoqwx9C2lgR1UZODgzk66mL0pQocGc3jeIqO+iinttUQtQy6hnI0JmyuOKMNXZcqISGEENVHAqF1arJg+q3bmuhNFXh1KEe26JFzPPb1ZaiPWdimzh+9fSs1UYt9/Wl+89ooQxmHVMGl6AactbGOK85okwVXhRBCVC0JhNapyYLpvlSBrsFsuORGzMQydFw/oC9VQNc0RnMlTt9QR2djnMt3t9EzXiBX8kjYJhvrY5IJEkIIUdWkRmid2lgfY1tLgr09afKOR2PCJmIa6FpYJG0aOpap8/yR8fLK8rqu0dkYZ3d7LZ2NcQmChBBCVD0JhNYpXdd4U2d9uXliyQ8IlMLxfEZzJeK2yZkbaukeytEzXljt3RVCCCGWhQyNrWMtNRE6G+OUvIDxgkvW8TB1ndbaKNtbktTGTA4N52RleSGEECctCYTWsYRt0pyMUBczAY2SH2AbOjVRE03TyBRdIqYhPYKEEEKctGRobB2bbKzYn3aoiYZBUW3MQtM0lFL0pYrSI0gIIcRJTQKhdUzXNa48s43GhM3BwSyZoosXBGSKLgcHs9IjSAghxElPAqF1bkdrDTe8bStnbqhjPO9yaDjHeN7lrI113PC2rdIjSAghxElNij8EO1pr2HZJUnoECSGEWHeqJiP0+7//+2zevJloNEpHRwfXXXcdvb29cz7nkksuQdO0aV9//Md/vEJ7XF2kR5AQQoj1qGoCoUsvvZTvfOc7HDhwgO9///t0d3dzzTXXzPu8m266ib6+vvLXX/3VX63A3gohhBCiGlTN0Nitt95a/v8tW7Zwxx13cNVVV+G6LpZlzfq8eDxOe3v7SuyiEEIIIapM1WSEphodHeWb3/wmF1988ZxBEMA3v/lNmpubOfPMM7nzzjvJ5/Nzbu84Dul0etqXEEIIIU5OVRUI3X777SQSCZqamjh8+DD//M//POf2H/rQh/jGN77Bo48+yp133sk//dM/8ZGPfGTO53zuc5+jrq6u/NXZ2bmUL0EIIYQQa4imlFKr9cvvuOMOvvCFL8y5zb59+9i9ezcAw8PDjI6O8vrrr/PZz36Wuro6fvSjH6FpCyvsfeSRR7jsssvo6upi+/btM27jOA6O45T/nU6n6ezsJJVKUVtbu8BXJoQQQojVlE6nqaurm/f6vaqB0NDQECMjI3Nus23bNmzbPu7xN954g87OTn71q19x0UUXLej35XI5kskkDz30EFdeeeWCnrPQN1IIIYQQa8dCr9+rWizd0tJCS0vLCT03CAKAadmb+Tz33HMAdHR0nNDvFEIIIcTJpSpqhJ566in+1//6Xzz33HO8/vrrPPLII1x77bVs3769nA3q6elh9+7d/PrXvwagu7ubu+++m2eeeYZDhw7x4IMP8od/+Ie8853v5Oyzz17NlyOEEEKINaIqAqF4PM4DDzzAZZddxqmnnsqNN97I2WefzWOPPUYkEgHAdV0OHDhQnhVm2zY/+9nPuOKKK9i9eze33XYbV199Nf/yL/+ymi9FCCGEEGvIqtYIVYNUKkV9fT1HjhyRGiEhhBCiSkxOdhofH6eurm7W7aqmoeJqyWQyADKNXgghhKhCmUxmzkBIMkLzCIKA3t5eampqZpymPxlxSsZo7ZJjtPbJMVr75BitfXKMplNKkclk2LBhA7o+eyWQZITmoes6mzZtmne72tpa+eCtcXKM1j45RmufHKO1T47RUXNlgiZVRbG0EEIIIcRykEBICCGEEOuWBEKLFIlEuOuuu8rT+MXaI8do7ZNjtPbJMVr75BidGCmWFkIIIcS6JRkhIYQQQqxbEggJIYQQYt2SQEgIIYQQ65YEQkIIIYRYtyQQOgGjo6N8+MMfpra2lvr6em688Uay2eyCnquU4nd/93fRNI0f/vCHy7uj61ilx2h0dJRPfOITnHrqqcRiMTZv3sx/+S//hVQqtYJ7fXK799572bp1K9FolAsvvJBf//rXc27/3e9+l927dxONRjnrrLP48Y9/vEJ7un5Vcoz+/u//nne84x00NDTQ0NDA5ZdfPu8xFYtX6d/RpG9/+9tomsZVV121vDtYhSQQOgEf/vCHeemll3j44Yf50Y9+xL//+7/zsY99bEHP/eIXvzjjUh1iaVV6jHp7e+nt7eWee+5h7969fO1rX+Ohhx7ixhtvXMG9Pnn93//7f/nkJz/JXXfdxW9/+1ve9KY3ceWVVzI4ODjj9r/61a+49tprufHGG3n22We56qqruOqqq9i7d+8K7/n6Uekx+sUvfsG1117Lo48+yhNPPEFnZydXXHEFPT09K7zn60elx2jSoUOH+K//9b/yjne8Y4X2tMooUZGXX35ZAerpp58uP/Zv//ZvStM01dPTM+dzn332WbVx40bV19enAPWDH/xgmfd2fVrMMZrqO9/5jrJtW7muuxy7ua5ccMEF6uMf/3j5377vqw0bNqjPfe5zM27/wQ9+UL33ve+d9tiFF16obr755mXdz/Ws0mN0LM/zVE1Njbr//vuXaxfXvRM5Rp7nqYsvvlj9wz/8g/roRz+q3v/+96/AnlYXyQhV6IknnqC+vp7zzjuv/Njll1+Orus89dRTsz4vn8/zoQ99iHvvvZf29vaV2NV160SP0bFSqRS1tbWYpizJtxilUolnnnmGyy+/vPyYrutcfvnlPPHEEzM+54knnpi2PcCVV1456/ZicU7kGB0rn8/jui6NjY3LtZvr2okeo7/4i7+gtbVVsttzkDN8hfr7+2ltbZ32mGmaNDY20t/fP+vzbr31Vi6++GLe//73L/curnsneoymGh4e5u67717wkKeY3fDwML7v09bWNu3xtrY29u/fP+Nz+vv7Z9x+ocdPVOZEjtGxbr/9djZs2HBcACuWxokco1/+8pf84z/+I88999wK7GH1kozQhDvuuANN0+b8WugJ4VgPPvggjzzyCF/84heXdqfXmeU8RlOl02ne+973cvrpp/Pnf/7ni99xIU5yn//85/n2t7/ND37wA6LR6GrvjgAymQzXXXcdf//3f09zc/Nq786aJhmhCbfddhvXX3/9nNts27aN9vb24wrTPM9jdHR01iGvRx55hO7uburr66c9fvXVV/OOd7yDX/ziF4vY8/VjOY/RpEwmw549e6ipqeEHP/gBlmUtdrfXvebmZgzDYGBgYNrjAwMDsx6P9vb2irYXi3Mix2jSPffcw+c//3l+9rOfcfbZZy/nbq5rlR6j7u5uDh06xPve977yY0EQAGGG/MCBA2zfvn15d7parHaRUrWZLMT9zW9+U37sJz/5yZyFuH19ferFF1+c9gWo//k//6d69dVXV2rX140TOUZKKZVKpdRb3/pW9a53vUvlcrmV2NV144ILLlC33HJL+d++76uNGzfOWSz9e7/3e9Meu+iii6RYehlVeoyUUuoLX/iCqq2tVU888cRK7OK6V8kxKhQKx1133v/+96t3v/vd6sUXX1SO46zkrq9pEgidgD179qhzzjlHPfXUU+qXv/yl2rlzp7r22mvL33/jjTfUqaeeqp566qlZfwYya2xZVXqMUqmUuvDCC9VZZ52lurq6VF9fX/nL87zVehknjW9/+9sqEomor33ta+rll19WH/vYx1R9fb3q7+9XSil13XXXqTvuuKO8/eOPP65M01T33HOP2rdvn7rrrruUZVnqxRdfXK2XcNKr9Bh9/vOfV7Ztq+9973vT/l4ymcxqvYSTXqXH6Fgya2xmEgidgJGREXXttdeqZDKpamtr1Q033DDtj/+1115TgHr00Udn/RkSCC2vSo/Ro48+qoAZv1577bXVeREnmS996Utq8+bNyrZtdcEFF6gnn3yy/L13vetd6qMf/ei07b/zne+oXbt2Kdu21RlnnKH+9V//dYX3eP2p5Bht2bJlxr+Xu+66a+V3fB2p9O9oKgmEZqYppdRKD8cJIYQQQqwFMmtMCCGEEOuWBEJCCCGEWLckEBJCCCHEuiWBkBBCCCHWLQmEhBBCCLFuSSAkhBBCiHVLAiEhhBBCrFsSCAkhhBBi3ZJASAix5l1//fVomnbc1549e1Z714QQVU5WnxdCVIU9e/Zw3333TXssEoks2+8rlUrYtr1sP18IsTZIRkgIURUikQjt7e3TvhoaGgDQNI1/+Id/4D/8h/9APB5n586dPPjgg9Oev3fvXn73d3+XZDJJW1sb1113HcPDw+XvX3LJJdxyyy386Z/+Kc3NzVx55ZUAPPjgg+zcuZNoNMqll17K/fffj6ZpjI+Pk8vlqK2t5Xvf+9603/XDH/6QRCJBJpNZ5ndFCLFYEggJIU4Kn/3sZ/ngBz/ICy+8wHve8x4+/OEPMzo6CsD4+Djvfve7Oeecc/jNb37DQw89xMDAAB/84Aen/Yz7778f27Z5/PHH+epXv8prr73GNddcw1VXXcXzzz/PzTffzKc+9any9olEgv/0n/7TcZmq++67j2uuuYaamprlf+FCiMVZ7VVfhRBiPh/96EeVYRgqkUhM+/rLv/xLpZRSgPr0pz9d3j6bzSpA/du//ZtSSqm7775bXXHFFdN+5pEjRxSgDhw4oJQKV+4+55xzpm1z++23qzPPPHPaY5/61KcUoMbGxpRSSj311FPKMAzV29urlFJqYGBAmaapfvGLXyzdGyCEWDZSIySEqAqXXnopX/nKV6Y91tjYWP7/s88+u/z/iUSC2tpaBgcHAXj++ed59NFHSSaTx/3c7u5udu3aBcC555477XsHDhzg/PPPn/bYBRdccNy/zzjjDO6//37uuOMOvvGNb7Blyxbe+c53nsCrFEKsNAmEhBBVIZFIsGPHjlm/b1nWtH9rmkYQBABks1ne97738YUvfOG453V0dEz7HSfiP//n/8y9997LHXfcwX333ccNN9yApmkn9LOEECtLAiEhxEnvLW95C9///vfZunUrprnw096pp57Kj3/842mPPf3008dt95GPfIT/9t/+G3/zN3/Dyy+/zEc/+tFF77MQYmVIsbQQoio4jkN/f/+0r6mzvuby8Y9/nNHRUa699lqefvppuru7+clPfsINN9yA7/uzPu/mm29m//793H777bzyyit85zvf4Wtf+xrAtIxPQ0MDH/jAB/izP/szrrjiCjZt2rSo1yqEWDkSCAkhqsJDDz1ER0fHtK+3v/3tC3ruhg0bePzxx/F9nyuuuIKzzjqLP/3TP6W+vh5dn/00eMopp/C9732PBx54gLPPPpuvfOUr5Vljx/YwuvHGGymVSvzRH/3Rib9IIcSK05RSarV3QgghqsVf/uVf8tWvfpUjR45Me/yf/umfuPXWW+nt7ZVGjEJUEakREkKIOXz5y1/m/PPPp6mpiccff5z//t//O7fcckv5+/l8nr6+Pj7/+c9z8803SxAkRJWRoTEhhJjDwYMHef/738/pp5/O3XffzW233caf//mfl7//V3/1V+zevZv29nbuvPPO1dtRIcQJkaExIYQQQqxbkhESQgghxLolgZAQQggh1i0JhIQQQgixbkkgJIQQQoh1SwIhIYQQQqxbEggJIYQQYt2SQEgIIYQQ65YEQkIIIYRYtyQQEkIIIcS69f8DdoNaITErDiQAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# x values versus residuals\n",
-    "plt.scatter(X[:, 1:2], res, alpha=0.5)\n",
-    "plt.xlabel(\"Energy\")\n",
-    "plt.ylabel(\"Carbon\")\n",
-    "plt.title(\"Residual Plot vs. X Values\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 310,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# residuals versus order\n",
-    "plt.scatter(range(len(X[:,1:2])), res, alpha=0.5)\n",
-    "plt.xlabel(\"Index\")\n",
-    "plt.ylabel(\"Residuals\")\n",
-    "plt.title(\"Residual Plot vs. Order\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "wafflers",
-   "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+	"cells": [
+		{
+			"cell_type": "code",
+			"execution_count": 1,
+			"metadata": {},
+			"outputs": [],
+			"source": [
+				"import scraper\n",
+				"import pandas as pd\n",
+				"import plotly.express as px\n",
+				"import numpy as np\n",
+				"import matplotlib.pyplot as plt\n",
+				"import seaborn as sns"
+			]
+		},
+		{
+			"cell_type": "markdown",
+			"metadata": {},
+			"source": [
+				"# Get the data\n",
+				"### Carbon data"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 2,
+			"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 tr th {\n",
+							"        text-align: left;\n",
+							"    }\n",
+							"</style>\n",
+							"<table border=\"1\" class=\"dataframe\">\n",
+							"  <thead>\n",
+							"    <tr>\n",
+							"      <th>table</th>\n",
+							"      <th colspan=\"10\" halign=\"left\">1</th>\n",
+							"      <th>...</th>\n",
+							"      <th colspan=\"9\" halign=\"left\">65</th>\n",
+							"      <th>country</th>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>time</th>\n",
+							"      <th>1995</th>\n",
+							"      <th>1996</th>\n",
+							"      <th>1997</th>\n",
+							"      <th>1998</th>\n",
+							"      <th>1999</th>\n",
+							"      <th>2000</th>\n",
+							"      <th>2001</th>\n",
+							"      <th>2002</th>\n",
+							"      <th>2003</th>\n",
+							"      <th>2004</th>\n",
+							"      <th>...</th>\n",
+							"      <th>2014</th>\n",
+							"      <th>2015</th>\n",
+							"      <th>2016</th>\n",
+							"      <th>2017</th>\n",
+							"      <th>2018</th>\n",
+							"      <th>2019</th>\n",
+							"      <th>2020</th>\n",
+							"      <th>2021</th>\n",
+							"      <th>2022</th>\n",
+							"      <th></th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>0</th>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>...</td>\n",
+							"      <td>416329.05</td>\n",
+							"      <td>411485.90</td>\n",
+							"      <td>413749.45</td>\n",
+							"      <td>419976.61</td>\n",
+							"      <td>426307.90</td>\n",
+							"      <td>416848.48</td>\n",
+							"      <td>398982.10</td>\n",
+							"      <td>412683.08</td>\n",
+							"      <td>384901.69</td>\n",
+							"      <td>EU27_2020</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1</th>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>...</td>\n",
+							"      <td>705507.53</td>\n",
+							"      <td>680904.05</td>\n",
+							"      <td>668832.49</td>\n",
+							"      <td>646120.22</td>\n",
+							"      <td>672691.73</td>\n",
+							"      <td>674474.81</td>\n",
+							"      <td>601207.60</td>\n",
+							"      <td>615594.09</td>\n",
+							"      <td>559118.99</td>\n",
+							"      <td>BE</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2</th>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>...</td>\n",
+							"      <td>371707.83</td>\n",
+							"      <td>401866.25</td>\n",
+							"      <td>435213.07</td>\n",
+							"      <td>426494.22</td>\n",
+							"      <td>423259.56</td>\n",
+							"      <td>393891.37</td>\n",
+							"      <td>391756.15</td>\n",
+							"      <td>402387.56</td>\n",
+							"      <td>386451.29</td>\n",
+							"      <td>BG</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>3</th>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>...</td>\n",
+							"      <td>314720.03</td>\n",
+							"      <td>316332.36</td>\n",
+							"      <td>329274.64</td>\n",
+							"      <td>336949.27</td>\n",
+							"      <td>354343.90</td>\n",
+							"      <td>350563.81</td>\n",
+							"      <td>330125.94</td>\n",
+							"      <td>354575.61</td>\n",
+							"      <td>323965.99</td>\n",
+							"      <td>CZ</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>4</th>\n",
+							"      <td>15662060.68</td>\n",
+							"      <td>17983724.22</td>\n",
+							"      <td>16436561.98</td>\n",
+							"      <td>16404974.32</td>\n",
+							"      <td>15656406.16</td>\n",
+							"      <td>15526465.86</td>\n",
+							"      <td>15575519.99</td>\n",
+							"      <td>15742121.31</td>\n",
+							"      <td>17348358.89</td>\n",
+							"      <td>16463321.94</td>\n",
+							"      <td>...</td>\n",
+							"      <td>415245.59</td>\n",
+							"      <td>421010.26</td>\n",
+							"      <td>481782.63</td>\n",
+							"      <td>511782.05</td>\n",
+							"      <td>503125.28</td>\n",
+							"      <td>487706.80</td>\n",
+							"      <td>507731.62</td>\n",
+							"      <td>502221.23</td>\n",
+							"      <td>479131.26</td>\n",
+							"      <td>DK</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"<p>5 rows × 1821 columns</p>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"table            1                                                      \\\n",
+							"time          1995         1996         1997         1998         1999   \n",
+							"0              NaN          NaN          NaN          NaN          NaN   \n",
+							"1              NaN          NaN          NaN          NaN          NaN   \n",
+							"2              NaN          NaN          NaN          NaN          NaN   \n",
+							"3              NaN          NaN          NaN          NaN          NaN   \n",
+							"4      15662060.68  17983724.22  16436561.98  16404974.32  15656406.16   \n",
+							"\n",
+							"table                                                                   ...  \\\n",
+							"time          2000         2001         2002         2003         2004  ...   \n",
+							"0              NaN          NaN          NaN          NaN          NaN  ...   \n",
+							"1              NaN          NaN          NaN          NaN          NaN  ...   \n",
+							"2              NaN          NaN          NaN          NaN          NaN  ...   \n",
+							"3              NaN          NaN          NaN          NaN          NaN  ...   \n",
+							"4      15526465.86  15575519.99  15742121.31  17348358.89  16463321.94  ...   \n",
+							"\n",
+							"table         65                                                         \\\n",
+							"time        2014       2015       2016       2017       2018       2019   \n",
+							"0      416329.05  411485.90  413749.45  419976.61  426307.90  416848.48   \n",
+							"1      705507.53  680904.05  668832.49  646120.22  672691.73  674474.81   \n",
+							"2      371707.83  401866.25  435213.07  426494.22  423259.56  393891.37   \n",
+							"3      314720.03  316332.36  329274.64  336949.27  354343.90  350563.81   \n",
+							"4      415245.59  421010.26  481782.63  511782.05  503125.28  487706.80   \n",
+							"\n",
+							"table                                     country  \n",
+							"time        2020       2021       2022             \n",
+							"0      398982.10  412683.08  384901.69  EU27_2020  \n",
+							"1      601207.60  615594.09  559118.99         BE  \n",
+							"2      391756.15  402387.56  386451.29         BG  \n",
+							"3      330125.94  354575.61  323965.99         CZ  \n",
+							"4      507731.62  502221.23  479131.26         DK  \n",
+							"\n",
+							"[5 rows x 1821 columns]"
+						]
+					},
+					"execution_count": 2,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"full_carbon_df, tables = scraper.get_eurostat_dataframe(\"env_ac_ainah_r2\", lambda raw: np.arange(1, 66))\n",
+				"full_carbon_df.head()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 3,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"text/html": [
+							"<div>\n",
+							"<style scoped>\n",
+							"    .dataframe tbody tr th:only-of-type {\n",
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+							"</style>\n",
+							"<table border=\"1\" class=\"dataframe\">\n",
+							"  <thead>\n",
+							"    <tr style=\"text-align: right;\">\n",
+							"      <th>country</th>\n",
+							"      <th>EU27_2020</th>\n",
+							"      <th>BE</th>\n",
+							"      <th>BG</th>\n",
+							"      <th>CZ</th>\n",
+							"      <th>DK</th>\n",
+							"      <th>DE</th>\n",
+							"      <th>EE</th>\n",
+							"      <th>IE</th>\n",
+							"      <th>EL</th>\n",
+							"      <th>ES</th>\n",
+							"      <th>...</th>\n",
+							"      <th>RO</th>\n",
+							"      <th>SI</th>\n",
+							"      <th>SK</th>\n",
+							"      <th>FI</th>\n",
+							"      <th>SE</th>\n",
+							"      <th>IS</th>\n",
+							"      <th>NO</th>\n",
+							"      <th>CH</th>\n",
+							"      <th>RS</th>\n",
+							"      <th>TR</th>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>time</th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>1995</th>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>15662060.68</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>8862865.42</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>12571199.07</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>5183706.31</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1996</th>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>17983724.22</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>8821817.28</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>13419521.77</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>5238820.25</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1997</th>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>16436561.98</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>8693803.51</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>14045761.23</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>5112279.33</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1998</th>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>16404974.32</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>8542440.41</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>14424047.34</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>5276437.22</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1999</th>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>15656406.16</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>8310800.34</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>15414605.09</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>5237620.84</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"<p>5 rows × 33 columns</p>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"country  EU27_2020  BE  BG  CZ           DK  DE  EE  IE  EL  ES  ...  RO  SI  \\\n",
+							"time                                                             ...           \n",
+							"1995           NaN NaN NaN NaN  15662060.68 NaN NaN NaN NaN NaN  ... NaN NaN   \n",
+							"1996           NaN NaN NaN NaN  17983724.22 NaN NaN NaN NaN NaN  ... NaN NaN   \n",
+							"1997           NaN NaN NaN NaN  16436561.98 NaN NaN NaN NaN NaN  ... NaN NaN   \n",
+							"1998           NaN NaN NaN NaN  16404974.32 NaN NaN NaN NaN NaN  ... NaN NaN   \n",
+							"1999           NaN NaN NaN NaN  15656406.16 NaN NaN NaN NaN NaN  ... NaN NaN   \n",
+							"\n",
+							"country          SK  FI  SE           IS  NO          CH  RS  TR  \n",
+							"time                                                              \n",
+							"1995     8862865.42 NaN NaN  12571199.07 NaN  5183706.31 NaN NaN  \n",
+							"1996     8821817.28 NaN NaN  13419521.77 NaN  5238820.25 NaN NaN  \n",
+							"1997     8693803.51 NaN NaN  14045761.23 NaN  5112279.33 NaN NaN  \n",
+							"1998     8542440.41 NaN NaN  14424047.34 NaN  5276437.22 NaN NaN  \n",
+							"1999     8310800.34 NaN NaN  15414605.09 NaN  5237620.84 NaN NaN  \n",
+							"\n",
+							"[5 rows x 33 columns]"
+						]
+					},
+					"execution_count": 3,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"df_carbon_short = full_carbon_df.xs(1, level=\"table\", axis=1).T\n",
+				"df_carbon_short.columns = full_carbon_df.xs(\"country\", axis=1)\n",
+				"df_carbon_short.head()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 4,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"text/html": [
+							"<div>\n",
+							"<style scoped>\n",
+							"    .dataframe tbody tr th:only-of-type {\n",
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+							"</style>\n",
+							"<table border=\"1\" class=\"dataframe\">\n",
+							"  <thead>\n",
+							"    <tr style=\"text-align: right;\">\n",
+							"      <th>country</th>\n",
+							"      <th>EU27_2020</th>\n",
+							"      <th>BE</th>\n",
+							"      <th>BG</th>\n",
+							"      <th>CZ</th>\n",
+							"      <th>DK</th>\n",
+							"      <th>DE</th>\n",
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+							"      <th>IS</th>\n",
+							"      <th>NO</th>\n",
+							"      <th>RS</th>\n",
+							"      <th>TR</th>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>time</th>\n",
+							"      <th></th>\n",
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+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>2008</th>\n",
+							"      <td>8626928.75</td>\n",
+							"      <td>9840862.32</td>\n",
+							"      <td>8374278.10</td>\n",
+							"      <td>12088284.26</td>\n",
+							"      <td>18472875.79</td>\n",
+							"      <td>10115618.26</td>\n",
+							"      <td>15495691.70</td>\n",
+							"      <td>11599639.01</td>\n",
+							"      <td>11023011.51</td>\n",
+							"      <td>7309667.12</td>\n",
+							"      <td>...</td>\n",
+							"      <td>6489677.15</td>\n",
+							"      <td>6792814.40</td>\n",
+							"      <td>8093935.35</td>\n",
+							"      <td>8115433.15</td>\n",
+							"      <td>12652929.15</td>\n",
+							"      <td>6147808.43</td>\n",
+							"      <td>17704691.59</td>\n",
+							"      <td>12308807.83</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2009</th>\n",
+							"      <td>7851179.61</td>\n",
+							"      <td>8740690.23</td>\n",
+							"      <td>7277828.03</td>\n",
+							"      <td>11049662.42</td>\n",
+							"      <td>17176547.55</td>\n",
+							"      <td>9321295.50</td>\n",
+							"      <td>12786692.77</td>\n",
+							"      <td>10448510.06</td>\n",
+							"      <td>10354690.72</td>\n",
+							"      <td>6435677.13</td>\n",
+							"      <td>...</td>\n",
+							"      <td>6178248.19</td>\n",
+							"      <td>5751828.40</td>\n",
+							"      <td>7375583.00</td>\n",
+							"      <td>7304968.32</td>\n",
+							"      <td>12003558.20</td>\n",
+							"      <td>5514200.84</td>\n",
+							"      <td>16180654.34</td>\n",
+							"      <td>11368305.58</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2010</th>\n",
+							"      <td>8024312.00</td>\n",
+							"      <td>9243216.52</td>\n",
+							"      <td>7673499.85</td>\n",
+							"      <td>11228220.11</td>\n",
+							"      <td>16767331.74</td>\n",
+							"      <td>9667309.77</td>\n",
+							"      <td>16307459.75</td>\n",
+							"      <td>12043627.47</td>\n",
+							"      <td>9989268.71</td>\n",
+							"      <td>6098359.65</td>\n",
+							"      <td>...</td>\n",
+							"      <td>5739881.60</td>\n",
+							"      <td>5581717.18</td>\n",
+							"      <td>7437534.55</td>\n",
+							"      <td>7305820.30</td>\n",
+							"      <td>13441161.42</td>\n",
+							"      <td>6147726.61</td>\n",
+							"      <td>16184246.73</td>\n",
+							"      <td>11861900.26</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2011</th>\n",
+							"      <td>7901995.31</td>\n",
+							"      <td>8519668.04</td>\n",
+							"      <td>8424931.82</td>\n",
+							"      <td>10997932.47</td>\n",
+							"      <td>16608386.02</td>\n",
+							"      <td>9668751.22</td>\n",
+							"      <td>16334728.68</td>\n",
+							"      <td>11509752.46</td>\n",
+							"      <td>9620522.32</td>\n",
+							"      <td>6223872.41</td>\n",
+							"      <td>...</td>\n",
+							"      <td>5697688.64</td>\n",
+							"      <td>5895533.44</td>\n",
+							"      <td>7280437.24</td>\n",
+							"      <td>7294510.54</td>\n",
+							"      <td>11997445.75</td>\n",
+							"      <td>5607249.35</td>\n",
+							"      <td>15554875.78</td>\n",
+							"      <td>12700895.28</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2012</th>\n",
+							"      <td>7728876.39</td>\n",
+							"      <td>8149775.00</td>\n",
+							"      <td>7808042.43</td>\n",
+							"      <td>10544785.70</td>\n",
+							"      <td>15347310.62</td>\n",
+							"      <td>9767753.13</td>\n",
+							"      <td>15694048.49</td>\n",
+							"      <td>11892306.47</td>\n",
+							"      <td>9558049.09</td>\n",
+							"      <td>6157580.45</td>\n",
+							"      <td>...</td>\n",
+							"      <td>5624083.12</td>\n",
+							"      <td>5748566.83</td>\n",
+							"      <td>6938559.08</td>\n",
+							"      <td>6765294.90</td>\n",
+							"      <td>10873832.79</td>\n",
+							"      <td>5236450.88</td>\n",
+							"      <td>15875505.99</td>\n",
+							"      <td>12425877.26</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"<p>5 rows × 32 columns</p>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"country   EU27_2020          BE          BG           CZ           DK  \\\n",
+							"time                                                                    \n",
+							"2008     8626928.75  9840862.32  8374278.10  12088284.26  18472875.79   \n",
+							"2009     7851179.61  8740690.23  7277828.03  11049662.42  17176547.55   \n",
+							"2010     8024312.00  9243216.52  7673499.85  11228220.11  16767331.74   \n",
+							"2011     7901995.31  8519668.04  8424931.82  10997932.47  16608386.02   \n",
+							"2012     7728876.39  8149775.00  7808042.43  10544785.70  15347310.62   \n",
+							"\n",
+							"country           DE           EE           IE           EL          ES  ...  \\\n",
+							"time                                                                     ...   \n",
+							"2008     10115618.26  15495691.70  11599639.01  11023011.51  7309667.12  ...   \n",
+							"2009      9321295.50  12786692.77  10448510.06  10354690.72  6435677.13  ...   \n",
+							"2010      9667309.77  16307459.75  12043627.47   9989268.71  6098359.65  ...   \n",
+							"2011      9668751.22  16334728.68  11509752.46   9620522.32  6223872.41  ...   \n",
+							"2012      9767753.13  15694048.49  11892306.47   9558049.09  6157580.45  ...   \n",
+							"\n",
+							"country          PT          RO          SI          SK           FI  \\\n",
+							"time                                                                   \n",
+							"2008     6489677.15  6792814.40  8093935.35  8115433.15  12652929.15   \n",
+							"2009     6178248.19  5751828.40  7375583.00  7304968.32  12003558.20   \n",
+							"2010     5739881.60  5581717.18  7437534.55  7305820.30  13441161.42   \n",
+							"2011     5697688.64  5895533.44  7280437.24  7294510.54  11997445.75   \n",
+							"2012     5624083.12  5748566.83  6938559.08  6765294.90  10873832.79   \n",
+							"\n",
+							"country          SE           IS           NO  RS  TR  \n",
+							"time                                                   \n",
+							"2008     6147808.43  17704691.59  12308807.83 NaN NaN  \n",
+							"2009     5514200.84  16180654.34  11368305.58 NaN NaN  \n",
+							"2010     6147726.61  16184246.73  11861900.26 NaN NaN  \n",
+							"2011     5607249.35  15554875.78  12700895.28 NaN NaN  \n",
+							"2012     5236450.88  15875505.99  12425877.26 NaN NaN  \n",
+							"\n",
+							"[5 rows x 32 columns]"
+						]
+					},
+					"execution_count": 4,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"# Drop the rows until 2008 - when the data really begins\n",
+				"df_carbon_short.drop([str(i) for i in np.arange(1995, 2008)], inplace=True)\n",
+				"df_carbon_short = df_carbon_short.drop(\"CH\", axis=1)\n",
+				"df_carbon_short.head()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 313,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
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+										"2013",
+										"2014",
+										"2015",
+										"2016",
+										"2017",
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+										"2015",
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+												"gridcolor": "white",
+												"gridwidth": 2,
+												"linecolor": "white",
+												"showbackground": true,
+												"ticks": "",
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+											"zaxis": {
+												"backgroundcolor": "#E5ECF6",
+												"gridcolor": "white",
+												"gridwidth": 2,
+												"linecolor": "white",
+												"showbackground": true,
+												"ticks": "",
+												"zerolinecolor": "white"
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+										"shapedefaults": {
+											"line": {
+												"color": "#2a3f5f"
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+										"ternary": {
+											"aaxis": {
+												"gridcolor": "white",
+												"linecolor": "white",
+												"ticks": ""
+											},
+											"baxis": {
+												"gridcolor": "white",
+												"linecolor": "white",
+												"ticks": ""
+											},
+											"bgcolor": "#E5ECF6",
+											"caxis": {
+												"gridcolor": "white",
+												"linecolor": "white",
+												"ticks": ""
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+										"title": {
+											"x": 0.05
+										},
+										"xaxis": {
+											"automargin": true,
+											"gridcolor": "white",
+											"linecolor": "white",
+											"ticks": "",
+											"title": {
+												"standoff": 15
+											},
+											"zerolinecolor": "white",
+											"zerolinewidth": 2
+										},
+										"yaxis": {
+											"automargin": true,
+											"gridcolor": "white",
+											"linecolor": "white",
+											"ticks": "",
+											"title": {
+												"standoff": 15
+											},
+											"zerolinecolor": "white",
+											"zerolinewidth": 2
+										}
+									}
+								},
+								"xaxis": {
+									"anchor": "y",
+									"domain": [
+										0,
+										1
+									],
+									"title": {
+										"text": "time"
+									}
+								},
+								"yaxis": {
+									"anchor": "x",
+									"domain": [
+										0,
+										1
+									],
+									"title": {
+										"text": "value"
+									}
+								}
+							}
+						}
+					},
+					"metadata": {},
+					"output_type": "display_data"
+				}
+			],
+			"source": [
+				"# Plot the carbon emissions scatterplot\n",
+				"fig = px.scatter(df_carbon_short)\n",
+				"fig.show()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 314,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"text/plain": [
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{\"legend\":{\"title\":{\"text\":\"country\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#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                       {\"responsive\": true}                    )                };                            </script>        </div>'"
+						]
+					},
+					"execution_count": 314,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"import plotly\n",
+				"plotly.offline.plot(fig, include_plotlyjs=False, output_type=\"div\")"
+			]
+		},
+		{
+			"cell_type": "markdown",
+			"metadata": {},
+			"source": [
+				"### Energy data"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 6,
+			"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 tr th {\n",
+							"        text-align: left;\n",
+							"    }\n",
+							"</style>\n",
+							"<table border=\"1\" class=\"dataframe\">\n",
+							"  <thead>\n",
+							"    <tr>\n",
+							"      <th>table</th>\n",
+							"      <th colspan=\"10\" halign=\"left\">AFC</th>\n",
+							"      <th>...</th>\n",
+							"      <th colspan=\"9\" halign=\"left\">FC_OTH_HH_E</th>\n",
+							"      <th>country</th>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>time</th>\n",
+							"      <th>1990</th>\n",
+							"      <th>1991</th>\n",
+							"      <th>1992</th>\n",
+							"      <th>1993</th>\n",
+							"      <th>1994</th>\n",
+							"      <th>1995</th>\n",
+							"      <th>1996</th>\n",
+							"      <th>1997</th>\n",
+							"      <th>1998</th>\n",
+							"      <th>1999</th>\n",
+							"      <th>...</th>\n",
+							"      <th>2014</th>\n",
+							"      <th>2015</th>\n",
+							"      <th>2016</th>\n",
+							"      <th>2017</th>\n",
+							"      <th>2018</th>\n",
+							"      <th>2019</th>\n",
+							"      <th>2020</th>\n",
+							"      <th>2021</th>\n",
+							"      <th>2022</th>\n",
+							"      <th></th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>0</th>\n",
+							"      <td>218484.0</td>\n",
+							"      <td>178925.0</td>\n",
+							"      <td>175263.0</td>\n",
+							"      <td>175606.0</td>\n",
+							"      <td>169048.0</td>\n",
+							"      <td>181335.0</td>\n",
+							"      <td>193531.0</td>\n",
+							"      <td>194520.0</td>\n",
+							"      <td>187928.0</td>\n",
+							"      <td>214365.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>415.685</td>\n",
+							"      <td>628.687</td>\n",
+							"      <td>542.327</td>\n",
+							"      <td>611.261</td>\n",
+							"      <td>560.179</td>\n",
+							"      <td>687.335</td>\n",
+							"      <td>1220.556</td>\n",
+							"      <td>1316.017</td>\n",
+							"      <td>1457.559</td>\n",
+							"      <td>EU27_2020</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1</th>\n",
+							"      <td>146824.0</td>\n",
+							"      <td>137183.0</td>\n",
+							"      <td>133881.0</td>\n",
+							"      <td>141083.0</td>\n",
+							"      <td>144691.0</td>\n",
+							"      <td>152598.0</td>\n",
+							"      <td>152735.0</td>\n",
+							"      <td>160130.0</td>\n",
+							"      <td>160913.0</td>\n",
+							"      <td>181350.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>49.422</td>\n",
+							"      <td>84.733</td>\n",
+							"      <td>45.143</td>\n",
+							"      <td>51.247</td>\n",
+							"      <td>61.076</td>\n",
+							"      <td>64.421</td>\n",
+							"      <td>50.728</td>\n",
+							"      <td>65.282</td>\n",
+							"      <td>176.339</td>\n",
+							"      <td>EA20</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2</th>\n",
+							"      <td>4785.0</td>\n",
+							"      <td>4998.0</td>\n",
+							"      <td>5849.0</td>\n",
+							"      <td>7590.0</td>\n",
+							"      <td>9053.0</td>\n",
+							"      <td>9398.0</td>\n",
+							"      <td>9637.0</td>\n",
+							"      <td>9975.0</td>\n",
+							"      <td>7828.0</td>\n",
+							"      <td>9059.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>BE</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>3</th>\n",
+							"      <td>5387.0</td>\n",
+							"      <td>3083.0</td>\n",
+							"      <td>3289.0</td>\n",
+							"      <td>1630.0</td>\n",
+							"      <td>1173.0</td>\n",
+							"      <td>1961.0</td>\n",
+							"      <td>1803.0</td>\n",
+							"      <td>785.0</td>\n",
+							"      <td>564.0</td>\n",
+							"      <td>1670.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>0.000</td>\n",
+							"      <td>BG</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>4</th>\n",
+							"      <td>8179.0</td>\n",
+							"      <td>7194.0</td>\n",
+							"      <td>6156.0</td>\n",
+							"      <td>5952.0</td>\n",
+							"      <td>5415.0</td>\n",
+							"      <td>6722.0</td>\n",
+							"      <td>8811.0</td>\n",
+							"      <td>9013.0</td>\n",
+							"      <td>8383.0</td>\n",
+							"      <td>8983.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>1.307</td>\n",
+							"      <td>1.961</td>\n",
+							"      <td>2.288</td>\n",
+							"      <td>4.047</td>\n",
+							"      <td>4.520</td>\n",
+							"      <td>5.012</td>\n",
+							"      <td>3.610</td>\n",
+							"      <td>11.808</td>\n",
+							"      <td>23.064</td>\n",
+							"      <td>CZ</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"<p>5 rows × 166 columns</p>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"table       AFC                                                              \\\n",
+							"time       1990      1991      1992      1993      1994      1995      1996   \n",
+							"0      218484.0  178925.0  175263.0  175606.0  169048.0  181335.0  193531.0   \n",
+							"1      146824.0  137183.0  133881.0  141083.0  144691.0  152598.0  152735.0   \n",
+							"2        4785.0    4998.0    5849.0    7590.0    9053.0    9398.0    9637.0   \n",
+							"3        5387.0    3083.0    3289.0    1630.0    1173.0    1961.0    1803.0   \n",
+							"4        8179.0    7194.0    6156.0    5952.0    5415.0    6722.0    8811.0   \n",
+							"\n",
+							"table                                ... FC_OTH_HH_E                    \\\n",
+							"time       1997      1998      1999  ...        2014     2015     2016   \n",
+							"0      194520.0  187928.0  214365.0  ...     415.685  628.687  542.327   \n",
+							"1      160130.0  160913.0  181350.0  ...      49.422   84.733   45.143   \n",
+							"2        9975.0    7828.0    9059.0  ...       0.000    0.000    0.000   \n",
+							"3         785.0     564.0    1670.0  ...       0.000    0.000    0.000   \n",
+							"4        9013.0    8383.0    8983.0  ...       1.307    1.961    2.288   \n",
+							"\n",
+							"table                                                             country  \n",
+							"time      2017     2018     2019      2020      2021      2022             \n",
+							"0      611.261  560.179  687.335  1220.556  1316.017  1457.559  EU27_2020  \n",
+							"1       51.247   61.076   64.421    50.728    65.282   176.339       EA20  \n",
+							"2        0.000    0.000    0.000     0.000     0.000     0.000         BE  \n",
+							"3        0.000    0.000    0.000     0.000     0.000     0.000         BG  \n",
+							"4        4.047    4.520    5.012     3.610    11.808    23.064         CZ  \n",
+							"\n",
+							"[5 rows x 166 columns]"
+						]
+					},
+					"execution_count": 6,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"full_energy_df, energy_tables = scraper.get_eurostat_dataframe(\"nrg_cb_e\", lambda raw: [\"AFC\", \"FC_IND_E\", \"FC_TRA_E\", \"FC_OTH_CP_E\", \"FC_OTH_HH_E\"])\n",
+				"full_energy_df.head()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 7,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"text/html": [
+							"<div>\n",
+							"<style scoped>\n",
+							"    .dataframe tbody tr th:only-of-type {\n",
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+							"\n",
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+							"        vertical-align: top;\n",
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+							"\n",
+							"    .dataframe thead th {\n",
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+							"</style>\n",
+							"<table border=\"1\" class=\"dataframe\">\n",
+							"  <thead>\n",
+							"    <tr style=\"text-align: right;\">\n",
+							"      <th>country</th>\n",
+							"      <th>EU27_2020</th>\n",
+							"      <th>EA20</th>\n",
+							"      <th>BE</th>\n",
+							"      <th>BG</th>\n",
+							"      <th>CZ</th>\n",
+							"      <th>DK</th>\n",
+							"      <th>DE</th>\n",
+							"      <th>EE</th>\n",
+							"      <th>IE</th>\n",
+							"      <th>EL</th>\n",
+							"      <th>...</th>\n",
+							"      <th>BA</th>\n",
+							"      <th>ME</th>\n",
+							"      <th>MD</th>\n",
+							"      <th>MK</th>\n",
+							"      <th>GE</th>\n",
+							"      <th>AL</th>\n",
+							"      <th>RS</th>\n",
+							"      <th>TR</th>\n",
+							"      <th>UA</th>\n",
+							"      <th>XK</th>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>time</th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>1990</th>\n",
+							"      <td>218484.0</td>\n",
+							"      <td>146824.0</td>\n",
+							"      <td>4785.0</td>\n",
+							"      <td>5387.0</td>\n",
+							"      <td>8179.0</td>\n",
+							"      <td>11973.0</td>\n",
+							"      <td>31669.0</td>\n",
+							"      <td>1475.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>1330.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>511.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>323.0</td>\n",
+							"      <td>392.0</td>\n",
+							"      <td>176.0</td>\n",
+							"      <td>15401.0</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1991</th>\n",
+							"      <td>178925.0</td>\n",
+							"      <td>137183.0</td>\n",
+							"      <td>4998.0</td>\n",
+							"      <td>3083.0</td>\n",
+							"      <td>7194.0</td>\n",
+							"      <td>3075.0</td>\n",
+							"      <td>30416.0</td>\n",
+							"      <td>2222.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>1498.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>523.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>235.0</td>\n",
+							"      <td>759.0</td>\n",
+							"      <td>18317.0</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1992</th>\n",
+							"      <td>175263.0</td>\n",
+							"      <td>133881.0</td>\n",
+							"      <td>5849.0</td>\n",
+							"      <td>3289.0</td>\n",
+							"      <td>6156.0</td>\n",
+							"      <td>8647.0</td>\n",
+							"      <td>28418.0</td>\n",
+							"      <td>254.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>967.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>483.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>189.0</td>\n",
+							"      <td>15417.0</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1993</th>\n",
+							"      <td>175606.0</td>\n",
+							"      <td>141083.0</td>\n",
+							"      <td>7590.0</td>\n",
+							"      <td>1630.0</td>\n",
+							"      <td>5952.0</td>\n",
+							"      <td>6279.0</td>\n",
+							"      <td>33628.0</td>\n",
+							"      <td>221.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>1093.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>623.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>213.0</td>\n",
+							"      <td>15773.0</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1994</th>\n",
+							"      <td>169048.0</td>\n",
+							"      <td>144691.0</td>\n",
+							"      <td>9053.0</td>\n",
+							"      <td>1173.0</td>\n",
+							"      <td>5415.0</td>\n",
+							"      <td>1781.0</td>\n",
+							"      <td>35908.0</td>\n",
+							"      <td>315.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>816.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>73.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>31.0</td>\n",
+							"      <td>12378.0</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"<p>5 rows × 43 columns</p>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"country  EU27_2020      EA20      BE      BG      CZ       DK       DE  \\\n",
+							"time                                                                     \n",
+							"1990      218484.0  146824.0  4785.0  5387.0  8179.0  11973.0  31669.0   \n",
+							"1991      178925.0  137183.0  4998.0  3083.0  7194.0   3075.0  30416.0   \n",
+							"1992      175263.0  133881.0  5849.0  3289.0  6156.0   8647.0  28418.0   \n",
+							"1993      175606.0  141083.0  7590.0  1630.0  5952.0   6279.0  33628.0   \n",
+							"1994      169048.0  144691.0  9053.0  1173.0  5415.0   1781.0  35908.0   \n",
+							"\n",
+							"country      EE   IE      EL  ...  BA  ME  MD     MK  GE     AL     RS     TR  \\\n",
+							"time                          ...                                               \n",
+							"1990     1475.0  0.0  1330.0  ... NaN NaN NaN  511.0 NaN  323.0  392.0  176.0   \n",
+							"1991     2222.0  0.0  1498.0  ... NaN NaN NaN  523.0 NaN    0.0  235.0  759.0   \n",
+							"1992      254.0  0.0   967.0  ... NaN NaN NaN  483.0 NaN    0.0    0.0  189.0   \n",
+							"1993      221.0  0.0  1093.0  ... NaN NaN NaN  623.0 NaN    0.0    0.0  213.0   \n",
+							"1994      315.0  0.0   816.0  ... NaN NaN NaN   73.0 NaN    0.0    0.0   31.0   \n",
+							"\n",
+							"country       UA  XK  \n",
+							"time                  \n",
+							"1990     15401.0 NaN  \n",
+							"1991     18317.0 NaN  \n",
+							"1992     15417.0 NaN  \n",
+							"1993     15773.0 NaN  \n",
+							"1994     12378.0 NaN  \n",
+							"\n",
+							"[5 rows x 43 columns]"
+						]
+					},
+					"execution_count": 7,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"df_energy_short = full_energy_df.xs(\"AFC\", level=\"table\", axis=1).T\n",
+				"df_energy_short.columns = full_energy_df.xs(\"country\", axis=1)\n",
+				"df_energy_short.head()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 27,
+			"metadata": {},
+			"outputs": [],
+			"source": [
+				"full_energy_df.xs(\"FC_OTH_HH_E\", level=\"table\", axis=1).to_clipboard()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 8,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"text/html": [
+							"<div>\n",
+							"<style scoped>\n",
+							"    .dataframe tbody tr th:only-of-type {\n",
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+							"    }\n",
+							"\n",
+							"    .dataframe tbody tr th {\n",
+							"        vertical-align: top;\n",
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+							"\n",
+							"    .dataframe thead th {\n",
+							"        text-align: right;\n",
+							"    }\n",
+							"</style>\n",
+							"<table border=\"1\" class=\"dataframe\">\n",
+							"  <thead>\n",
+							"    <tr style=\"text-align: right;\">\n",
+							"      <th>country</th>\n",
+							"      <th>EU27_2020</th>\n",
+							"      <th>EA20</th>\n",
+							"      <th>BE</th>\n",
+							"      <th>BG</th>\n",
+							"      <th>CZ</th>\n",
+							"      <th>DK</th>\n",
+							"      <th>DE</th>\n",
+							"      <th>EE</th>\n",
+							"      <th>IE</th>\n",
+							"      <th>EL</th>\n",
+							"      <th>...</th>\n",
+							"      <th>BA</th>\n",
+							"      <th>ME</th>\n",
+							"      <th>MD</th>\n",
+							"      <th>MK</th>\n",
+							"      <th>GE</th>\n",
+							"      <th>AL</th>\n",
+							"      <th>RS</th>\n",
+							"      <th>TR</th>\n",
+							"      <th>UA</th>\n",
+							"      <th>XK</th>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>time</th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>2008</th>\n",
+							"      <td>305128.218</td>\n",
+							"      <td>245213.218</td>\n",
+							"      <td>17158.0</td>\n",
+							"      <td>3097.0</td>\n",
+							"      <td>8520.0</td>\n",
+							"      <td>12815.0</td>\n",
+							"      <td>41670.0</td>\n",
+							"      <td>1369.0</td>\n",
+							"      <td>753.031</td>\n",
+							"      <td>7575.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>1884.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>2733.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>2434.0</td>\n",
+							"      <td>8875.0</td>\n",
+							"      <td>789.0</td>\n",
+							"      <td>2101.0</td>\n",
+							"      <td>708.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2009</th>\n",
+							"      <td>292276.753</td>\n",
+							"      <td>237029.753</td>\n",
+							"      <td>9486.0</td>\n",
+							"      <td>2662.0</td>\n",
+							"      <td>8586.0</td>\n",
+							"      <td>11208.0</td>\n",
+							"      <td>41859.0</td>\n",
+							"      <td>3025.0</td>\n",
+							"      <td>939.217</td>\n",
+							"      <td>7600.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>1227.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>1438.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>1886.0</td>\n",
+							"      <td>5184.0</td>\n",
+							"      <td>812.0</td>\n",
+							"      <td>25.0</td>\n",
+							"      <td>768.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2010</th>\n",
+							"      <td>291540.094</td>\n",
+							"      <td>241227.094</td>\n",
+							"      <td>12395.0</td>\n",
+							"      <td>1167.0</td>\n",
+							"      <td>6642.0</td>\n",
+							"      <td>10599.0</td>\n",
+							"      <td>42962.0</td>\n",
+							"      <td>1100.0</td>\n",
+							"      <td>759.887</td>\n",
+							"      <td>8517.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>732.0</td>\n",
+							"      <td>3033.0</td>\n",
+							"      <td>1420.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>1986.0</td>\n",
+							"      <td>5620.0</td>\n",
+							"      <td>1144.0</td>\n",
+							"      <td>23.0</td>\n",
+							"      <td>819.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2011</th>\n",
+							"      <td>321114.673</td>\n",
+							"      <td>260179.673</td>\n",
+							"      <td>13189.0</td>\n",
+							"      <td>1449.0</td>\n",
+							"      <td>10457.0</td>\n",
+							"      <td>11694.0</td>\n",
+							"      <td>51003.0</td>\n",
+							"      <td>1690.0</td>\n",
+							"      <td>731.622</td>\n",
+							"      <td>7180.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>1993.0</td>\n",
+							"      <td>3146.0</td>\n",
+							"      <td>2749.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>3262.0</td>\n",
+							"      <td>6701.0</td>\n",
+							"      <td>4556.0</td>\n",
+							"      <td>32.0</td>\n",
+							"      <td>3135.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2012</th>\n",
+							"      <td>349454.141</td>\n",
+							"      <td>277236.141</td>\n",
+							"      <td>16848.0</td>\n",
+							"      <td>2353.0</td>\n",
+							"      <td>11587.0</td>\n",
+							"      <td>15920.0</td>\n",
+							"      <td>46268.0</td>\n",
+							"      <td>2710.0</td>\n",
+							"      <td>783.605</td>\n",
+							"      <td>5954.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>1440.0</td>\n",
+							"      <td>3279.0</td>\n",
+							"      <td>2741.0</td>\n",
+							"      <td>NaN</td>\n",
+							"      <td>2538.0</td>\n",
+							"      <td>5781.0</td>\n",
+							"      <td>5827.0</td>\n",
+							"      <td>89.0</td>\n",
+							"      <td>2773.0</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"<p>5 rows × 43 columns</p>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"country   EU27_2020        EA20       BE      BG       CZ       DK       DE  \\\n",
+							"time                                                                          \n",
+							"2008     305128.218  245213.218  17158.0  3097.0   8520.0  12815.0  41670.0   \n",
+							"2009     292276.753  237029.753   9486.0  2662.0   8586.0  11208.0  41859.0   \n",
+							"2010     291540.094  241227.094  12395.0  1167.0   6642.0  10599.0  42962.0   \n",
+							"2011     321114.673  260179.673  13189.0  1449.0  10457.0  11694.0  51003.0   \n",
+							"2012     349454.141  277236.141  16848.0  2353.0  11587.0  15920.0  46268.0   \n",
+							"\n",
+							"country      EE       IE      EL  ...  BA      ME      MD      MK  GE      AL  \\\n",
+							"time                              ...                                           \n",
+							"2008     1369.0  753.031  7575.0  ... NaN  1884.0     NaN  2733.0 NaN  2434.0   \n",
+							"2009     3025.0  939.217  7600.0  ... NaN  1227.0     NaN  1438.0 NaN  1886.0   \n",
+							"2010     1100.0  759.887  8517.0  ... NaN   732.0  3033.0  1420.0 NaN  1986.0   \n",
+							"2011     1690.0  731.622  7180.0  ... NaN  1993.0  3146.0  2749.0 NaN  3262.0   \n",
+							"2012     2710.0  783.605  5954.0  ... NaN  1440.0  3279.0  2741.0 NaN  2538.0   \n",
+							"\n",
+							"country      RS      TR      UA      XK  \n",
+							"time                                     \n",
+							"2008     8875.0   789.0  2101.0   708.0  \n",
+							"2009     5184.0   812.0    25.0   768.0  \n",
+							"2010     5620.0  1144.0    23.0   819.0  \n",
+							"2011     6701.0  4556.0    32.0  3135.0  \n",
+							"2012     5781.0  5827.0    89.0  2773.0  \n",
+							"\n",
+							"[5 rows x 43 columns]"
+						]
+					},
+					"execution_count": 8,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"# Drop the rows until 2008 to match carbon dataset\n",
+				"df_energy_short = df_energy_short.drop([str(i) for i in np.arange(1990, 2008)])\n",
+				"df_energy_short.head()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 9,
+			"metadata": {},
+			"outputs": [
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+							"      <th>country</th>\n",
+							"      <th>EU27_2020</th>\n",
+							"      <th>BE</th>\n",
+							"      <th>BG</th>\n",
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+							"      <th>SI</th>\n",
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+							"      <th>FI</th>\n",
+							"      <th>SE</th>\n",
+							"      <th>IS</th>\n",
+							"      <th>NO</th>\n",
+							"      <th>RS</th>\n",
+							"      <th>TR</th>\n",
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+							"    <tr>\n",
+							"      <th>time</th>\n",
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+							"    <tr>\n",
+							"      <th>2008</th>\n",
+							"      <td>305128.218</td>\n",
+							"      <td>17158.0</td>\n",
+							"      <td>3097.0</td>\n",
+							"      <td>8520.0</td>\n",
+							"      <td>12815.0</td>\n",
+							"      <td>41670.0</td>\n",
+							"      <td>1369.0</td>\n",
+							"      <td>753.031</td>\n",
+							"      <td>7575.0</td>\n",
+							"      <td>5881.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>10744.0</td>\n",
+							"      <td>921.0</td>\n",
+							"      <td>6225.0</td>\n",
+							"      <td>9412.0</td>\n",
+							"      <td>16107.0</td>\n",
+							"      <td>12754.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>3412.0</td>\n",
+							"      <td>8875.0</td>\n",
+							"      <td>789.0</td>\n",
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+							"    <tr>\n",
+							"      <th>2009</th>\n",
+							"      <td>292276.753</td>\n",
+							"      <td>9486.0</td>\n",
+							"      <td>2662.0</td>\n",
+							"      <td>8586.0</td>\n",
+							"      <td>11208.0</td>\n",
+							"      <td>41859.0</td>\n",
+							"      <td>3025.0</td>\n",
+							"      <td>939.217</td>\n",
+							"      <td>7600.0</td>\n",
+							"      <td>6751.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>7598.0</td>\n",
+							"      <td>651.0</td>\n",
+							"      <td>7780.0</td>\n",
+							"      <td>8994.0</td>\n",
+							"      <td>15460.0</td>\n",
+							"      <td>13765.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>5651.0</td>\n",
+							"      <td>5184.0</td>\n",
+							"      <td>812.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2010</th>\n",
+							"      <td>291540.094</td>\n",
+							"      <td>12395.0</td>\n",
+							"      <td>1167.0</td>\n",
+							"      <td>6642.0</td>\n",
+							"      <td>10599.0</td>\n",
+							"      <td>42962.0</td>\n",
+							"      <td>1100.0</td>\n",
+							"      <td>759.887</td>\n",
+							"      <td>8517.0</td>\n",
+							"      <td>5206.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>5814.0</td>\n",
+							"      <td>767.0</td>\n",
+							"      <td>8625.0</td>\n",
+							"      <td>7334.0</td>\n",
+							"      <td>15719.0</td>\n",
+							"      <td>14931.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>14673.0</td>\n",
+							"      <td>5620.0</td>\n",
+							"      <td>1144.0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2011</th>\n",
+							"      <td>321114.673</td>\n",
+							"      <td>13189.0</td>\n",
+							"      <td>1449.0</td>\n",
+							"      <td>10457.0</td>\n",
+							"      <td>11694.0</td>\n",
+							"      <td>51003.0</td>\n",
+							"      <td>1690.0</td>\n",
+							"      <td>731.622</td>\n",
+							"      <td>7180.0</td>\n",
+							"      <td>7932.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>6742.0</td>\n",
+							"      <td>3410.0</td>\n",
+							"      <td>7036.0</td>\n",
+							"      <td>11227.0</td>\n",
+							"      <td>17656.0</td>\n",
+							"      <td>12481.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>11254.0</td>\n",
+							"      <td>6701.0</td>\n",
+							"      <td>4556.0</td>\n",
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+							"    <tr>\n",
+							"      <th>2012</th>\n",
+							"      <td>349454.141</td>\n",
+							"      <td>16848.0</td>\n",
+							"      <td>2353.0</td>\n",
+							"      <td>11587.0</td>\n",
+							"      <td>15920.0</td>\n",
+							"      <td>46268.0</td>\n",
+							"      <td>2710.0</td>\n",
+							"      <td>783.605</td>\n",
+							"      <td>5954.0</td>\n",
+							"      <td>7787.0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>10766.0</td>\n",
+							"      <td>3903.0</td>\n",
+							"      <td>7452.0</td>\n",
+							"      <td>13472.0</td>\n",
+							"      <td>19089.0</td>\n",
+							"      <td>11682.0</td>\n",
+							"      <td>0.0</td>\n",
+							"      <td>4191.0</td>\n",
+							"      <td>5781.0</td>\n",
+							"      <td>5827.0</td>\n",
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+							"  </tbody>\n",
+							"</table>\n",
+							"<p>5 rows × 32 columns</p>\n",
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+						"text/plain": [
+							"country   EU27_2020       BE      BG       CZ       DK       DE      EE  \\\n",
+							"time                                                                      \n",
+							"2008     305128.218  17158.0  3097.0   8520.0  12815.0  41670.0  1369.0   \n",
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+							"2011     321114.673  13189.0  1449.0  10457.0  11694.0  51003.0  1690.0   \n",
+							"2012     349454.141  16848.0  2353.0  11587.0  15920.0  46268.0  2710.0   \n",
+							"\n",
+							"country       IE      EL      ES  ...       PT      RO      SI       SK  \\\n",
+							"time                              ...                                     \n",
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+							"2012     783.605  5954.0  7787.0  ...  10766.0  3903.0  7452.0  13472.0   \n",
+							"\n",
+							"country       FI       SE   IS       NO      RS      TR  \n",
+							"time                                                     \n",
+							"2008     16107.0  12754.0  0.0   3412.0  8875.0   789.0  \n",
+							"2009     15460.0  13765.0  0.0   5651.0  5184.0   812.0  \n",
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+							"2012     19089.0  11682.0  0.0   4191.0  5781.0  5827.0  \n",
+							"\n",
+							"[5 rows x 32 columns]"
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+					},
+					"execution_count": 9,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"# Match countries w/ the carbon dataset\n",
+				"countries = list(df_carbon_short.columns)\n",
+				"df_energy_short = df_energy_short[df_energy_short.columns.intersection(countries)]\n",
+				"df_energy_short.head()"
+			]
+		},
+		{
+			"cell_type": "code",
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+											"ticks": "",
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+											"zerolinewidth": 2
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+										"yaxis": {
+											"automargin": true,
+											"gridcolor": "white",
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+											"ticks": "",
+											"title": {
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+											"zerolinewidth": 2
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+								"xaxis": {
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+									"domain": [
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+									"title": {
+										"text": "time"
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+								},
+								"yaxis": {
+									"anchor": "x",
+									"domain": [
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+					"metadata": {},
+					"output_type": "display_data"
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+			],
+			"source": [
+				"fig = px.scatter(df_energy_short)\n",
+				"fig.show()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 317,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"text/plain": [
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{\"legend\":{\"title\":{\"text\":\"country\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#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                       {\"responsive\": true}                    )                };                            </script>        </div>'"
+						]
+					},
+					"execution_count": 317,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"plotly.offline.plot(fig, include_plotlyjs=False, output_type=\"div\")"
+			]
+		},
+		{
+			"cell_type": "markdown",
+			"metadata": {},
+			"source": [
+				"### Merge Datasets\n",
+				"And standardize the data"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 11,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"text/html": [
+							"<div>\n",
+							"<style scoped>\n",
+							"    .dataframe tbody tr th:only-of-type {\n",
+							"        vertical-align: middle;\n",
+							"    }\n",
+							"\n",
+							"    .dataframe tbody tr th {\n",
+							"        vertical-align: top;\n",
+							"    }\n",
+							"\n",
+							"    .dataframe thead th {\n",
+							"        text-align: right;\n",
+							"    }\n",
+							"</style>\n",
+							"<table border=\"1\" class=\"dataframe\">\n",
+							"  <thead>\n",
+							"    <tr style=\"text-align: right;\">\n",
+							"      <th></th>\n",
+							"      <th>country</th>\n",
+							"      <th>carbon</th>\n",
+							"      <th>energy</th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>0</th>\n",
+							"      <td>EU27_2020</td>\n",
+							"      <td>8626928.75</td>\n",
+							"      <td>305128.218</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1</th>\n",
+							"      <td>EU27_2020</td>\n",
+							"      <td>7851179.61</td>\n",
+							"      <td>292276.753</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2</th>\n",
+							"      <td>EU27_2020</td>\n",
+							"      <td>8024312.00</td>\n",
+							"      <td>291540.094</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>3</th>\n",
+							"      <td>EU27_2020</td>\n",
+							"      <td>7901995.31</td>\n",
+							"      <td>321114.673</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>4</th>\n",
+							"      <td>EU27_2020</td>\n",
+							"      <td>7728876.39</td>\n",
+							"      <td>349454.141</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"     country      carbon      energy\n",
+							"0  EU27_2020  8626928.75  305128.218\n",
+							"1  EU27_2020  7851179.61  292276.753\n",
+							"2  EU27_2020  8024312.00  291540.094\n",
+							"3  EU27_2020  7901995.31  321114.673\n",
+							"4  EU27_2020  7728876.39  349454.141"
+						]
+					},
+					"execution_count": 11,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"df_carbon_long = pd.melt(df_carbon_short)\n",
+				"df_energy_long = pd.melt(df_energy_short)\n",
+				"df_merged = pd.concat([df_carbon_long, df_energy_long[\"value\"]], axis=1)\n",
+				"df_merged.columns = [\"country\", \"carbon\", \"energy\"]\n",
+				"df_merged.head()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 12,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"text/html": [
+							"<div>\n",
+							"<style scoped>\n",
+							"    .dataframe tbody tr th:only-of-type {\n",
+							"        vertical-align: middle;\n",
+							"    }\n",
+							"\n",
+							"    .dataframe tbody tr th {\n",
+							"        vertical-align: top;\n",
+							"    }\n",
+							"\n",
+							"    .dataframe thead th {\n",
+							"        text-align: right;\n",
+							"    }\n",
+							"</style>\n",
+							"<table border=\"1\" class=\"dataframe\">\n",
+							"  <thead>\n",
+							"    <tr style=\"text-align: right;\">\n",
+							"      <th></th>\n",
+							"      <th>country</th>\n",
+							"      <th>carbon</th>\n",
+							"      <th>energy</th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>0</th>\n",
+							"      <td>EU27_2020</td>\n",
+							"      <td>8626928.75</td>\n",
+							"      <td>305128.218</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1</th>\n",
+							"      <td>EU27_2020</td>\n",
+							"      <td>7851179.61</td>\n",
+							"      <td>292276.753</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2</th>\n",
+							"      <td>EU27_2020</td>\n",
+							"      <td>8024312.00</td>\n",
+							"      <td>291540.094</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>3</th>\n",
+							"      <td>EU27_2020</td>\n",
+							"      <td>7901995.31</td>\n",
+							"      <td>321114.673</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>4</th>\n",
+							"      <td>EU27_2020</td>\n",
+							"      <td>7728876.39</td>\n",
+							"      <td>349454.141</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"     country      carbon      energy\n",
+							"0  EU27_2020  8626928.75  305128.218\n",
+							"1  EU27_2020  7851179.61  292276.753\n",
+							"2  EU27_2020  8024312.00  291540.094\n",
+							"3  EU27_2020  7901995.31  321114.673\n",
+							"4  EU27_2020  7728876.39  349454.141"
+						]
+					},
+					"execution_count": 12,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"df_merged.fillna(0)\n",
+				"df_merged.head()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 13,
+			"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>carbon</th>\n",
+							"      <th>energy</th>\n",
+							"      <th>country</th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>0</th>\n",
+							"      <td>0.041299</td>\n",
+							"      <td>4.622992</td>\n",
+							"      <td>EU27_2020</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>1</th>\n",
+							"      <td>-0.181105</td>\n",
+							"      <td>4.412569</td>\n",
+							"      <td>EU27_2020</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>2</th>\n",
+							"      <td>-0.131469</td>\n",
+							"      <td>4.400507</td>\n",
+							"      <td>EU27_2020</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>3</th>\n",
+							"      <td>-0.166536</td>\n",
+							"      <td>4.884745</td>\n",
+							"      <td>EU27_2020</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>4</th>\n",
+							"      <td>-0.216169</td>\n",
+							"      <td>5.348760</td>\n",
+							"      <td>EU27_2020</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"     carbon    energy    country\n",
+							"0  0.041299  4.622992  EU27_2020\n",
+							"1 -0.181105  4.412569  EU27_2020\n",
+							"2 -0.131469  4.400507  EU27_2020\n",
+							"3 -0.166536  4.884745  EU27_2020\n",
+							"4 -0.216169  5.348760  EU27_2020"
+						]
+					},
+					"execution_count": 13,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"df_merged_s = pd.DataFrame()\n",
+				"for feat in df_merged.columns:\n",
+				"\tif feat == \"country\": continue\n",
+				"\tdf_merged_s[f'{feat}'] = ((df_merged[feat] - df_merged[feat].mean()) / df_merged[feat].std())\n",
+				"df_merged_s[\"country\"] = df_merged[\"country\"]\n",
+				"df_merged_s.head()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 14,
+			"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></th>\n",
+							"      <th>carbon</th>\n",
+							"      <th>energy</th>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>country</th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"      <th></th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th rowspan=\"2\" valign=\"top\">AT</th>\n",
+							"      <th>carbon</th>\n",
+							"      <td>1.000000</td>\n",
+							"      <td>-0.752501</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>energy</th>\n",
+							"      <td>-0.752501</td>\n",
+							"      <td>1.000000</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th rowspan=\"2\" valign=\"top\">BE</th>\n",
+							"      <th>carbon</th>\n",
+							"      <td>1.000000</td>\n",
+							"      <td>-0.201674</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>energy</th>\n",
+							"      <td>-0.201674</td>\n",
+							"      <td>1.000000</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>BG</th>\n",
+							"      <th>carbon</th>\n",
+							"      <td>1.000000</td>\n",
+							"      <td>-0.215923</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>...</th>\n",
+							"      <th>...</th>\n",
+							"      <td>...</td>\n",
+							"      <td>...</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>SI</th>\n",
+							"      <th>energy</th>\n",
+							"      <td>-0.566730</td>\n",
+							"      <td>1.000000</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th rowspan=\"2\" valign=\"top\">SK</th>\n",
+							"      <th>carbon</th>\n",
+							"      <td>1.000000</td>\n",
+							"      <td>-0.755988</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>energy</th>\n",
+							"      <td>-0.755988</td>\n",
+							"      <td>1.000000</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th rowspan=\"2\" valign=\"top\">TR</th>\n",
+							"      <th>carbon</th>\n",
+							"      <td>NaN</td>\n",
+							"      <td>NaN</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>energy</th>\n",
+							"      <td>NaN</td>\n",
+							"      <td>1.000000</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"<p>64 rows × 2 columns</p>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"                  carbon    energy\n",
+							"country                           \n",
+							"AT      carbon  1.000000 -0.752501\n",
+							"        energy -0.752501  1.000000\n",
+							"BE      carbon  1.000000 -0.201674\n",
+							"        energy -0.201674  1.000000\n",
+							"BG      carbon  1.000000 -0.215923\n",
+							"...                  ...       ...\n",
+							"SI      energy -0.566730  1.000000\n",
+							"SK      carbon  1.000000 -0.755988\n",
+							"        energy -0.755988  1.000000\n",
+							"TR      carbon       NaN       NaN\n",
+							"        energy       NaN  1.000000\n",
+							"\n",
+							"[64 rows x 2 columns]"
+						]
+					},
+					"execution_count": 14,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"df_merged_s.groupby(\"country\").corr()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 15,
+			"metadata": {},
+			"outputs": [],
+			"source": [
+				"df_merged_s = df_merged_s.drop(df_merged_s[df_merged_s[\"country\"] == \"EU27_2020\"].index, axis=0)"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 318,
+			"metadata": {},
+			"outputs": [
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+												"showbackground": true,
+												"ticks": "",
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+												"gridwidth": 2,
+												"linecolor": "white",
+												"showbackground": true,
+												"ticks": "",
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+										"shapedefaults": {
+											"line": {
+												"color": "#2a3f5f"
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+										},
+										"ternary": {
+											"aaxis": {
+												"gridcolor": "white",
+												"linecolor": "white",
+												"ticks": ""
+											},
+											"baxis": {
+												"gridcolor": "white",
+												"linecolor": "white",
+												"ticks": ""
+											},
+											"bgcolor": "#E5ECF6",
+											"caxis": {
+												"gridcolor": "white",
+												"linecolor": "white",
+												"ticks": ""
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+										},
+										"title": {
+											"x": 0.05
+										},
+										"xaxis": {
+											"automargin": true,
+											"gridcolor": "white",
+											"linecolor": "white",
+											"ticks": "",
+											"title": {
+												"standoff": 15
+											},
+											"zerolinecolor": "white",
+											"zerolinewidth": 2
+										},
+										"yaxis": {
+											"automargin": true,
+											"gridcolor": "white",
+											"linecolor": "white",
+											"ticks": "",
+											"title": {
+												"standoff": 15
+											},
+											"zerolinecolor": "white",
+											"zerolinewidth": 2
+										}
+									}
+								},
+								"xaxis": {
+									"anchor": "y",
+									"domain": [
+										0,
+										1
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+									"title": {
+										"text": "energy"
+									}
+								},
+								"yaxis": {
+									"anchor": "x",
+									"domain": [
+										0,
+										1
+									],
+									"title": {
+										"text": "carbon"
+									}
+								}
+							}
+						}
+					},
+					"metadata": {},
+					"output_type": "display_data"
+				}
+			],
+			"source": [
+				"fig = px.scatter(df_merged_s, x=\"energy\", y=\"carbon\", color=\"country\")\n",
+				"fig.show()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 319,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
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{\"legend\":{\"title\":{\"text\":\"country\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"template\":{\"data\":{\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"energy\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"carbon\"}}},                        {\"responsive\": true}                    )                };                            </script>        </div>'"
+						]
+					},
+					"execution_count": 319,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"plotly.offline.plot(fig, include_plotlyjs=False, output_type=\"div\")"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 17,
+			"metadata": {},
+			"outputs": [
+				{
+					"name": "stdout",
+					"output_type": "stream",
+					"text": [
+						"(465, 33)\n"
+					]
+				},
+				{
+					"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>carbon</th>\n",
+							"      <th>energy</th>\n",
+							"      <th>country_AT</th>\n",
+							"      <th>country_BE</th>\n",
+							"      <th>country_BG</th>\n",
+							"      <th>country_CY</th>\n",
+							"      <th>country_CZ</th>\n",
+							"      <th>country_DE</th>\n",
+							"      <th>country_DK</th>\n",
+							"      <th>country_EE</th>\n",
+							"      <th>...</th>\n",
+							"      <th>country_NL</th>\n",
+							"      <th>country_NO</th>\n",
+							"      <th>country_PL</th>\n",
+							"      <th>country_PT</th>\n",
+							"      <th>country_RO</th>\n",
+							"      <th>country_RS</th>\n",
+							"      <th>country_SE</th>\n",
+							"      <th>country_SI</th>\n",
+							"      <th>country_SK</th>\n",
+							"      <th>country_TR</th>\n",
+							"    </tr>\n",
+							"  </thead>\n",
+							"  <tbody>\n",
+							"    <tr>\n",
+							"      <th>15</th>\n",
+							"      <td>0.389329</td>\n",
+							"      <td>-0.092076</td>\n",
+							"      <td>0</td>\n",
+							"      <td>1</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>16</th>\n",
+							"      <td>0.073914</td>\n",
+							"      <td>-0.217693</td>\n",
+							"      <td>0</td>\n",
+							"      <td>1</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>17</th>\n",
+							"      <td>0.217987</td>\n",
+							"      <td>-0.170063</td>\n",
+							"      <td>0</td>\n",
+							"      <td>1</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>18</th>\n",
+							"      <td>0.010548</td>\n",
+							"      <td>-0.157062</td>\n",
+							"      <td>0</td>\n",
+							"      <td>1</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"    </tr>\n",
+							"    <tr>\n",
+							"      <th>19</th>\n",
+							"      <td>-0.095499</td>\n",
+							"      <td>-0.097152</td>\n",
+							"      <td>0</td>\n",
+							"      <td>1</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>...</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"      <td>0</td>\n",
+							"    </tr>\n",
+							"  </tbody>\n",
+							"</table>\n",
+							"<p>5 rows × 33 columns</p>\n",
+							"</div>"
+						],
+						"text/plain": [
+							"      carbon    energy  country_AT  country_BE  country_BG  country_CY  \\\n",
+							"15  0.389329 -0.092076           0           1           0           0   \n",
+							"16  0.073914 -0.217693           0           1           0           0   \n",
+							"17  0.217987 -0.170063           0           1           0           0   \n",
+							"18  0.010548 -0.157062           0           1           0           0   \n",
+							"19 -0.095499 -0.097152           0           1           0           0   \n",
+							"\n",
+							"    country_CZ  country_DE  country_DK  country_EE  ...  country_NL  \\\n",
+							"15           0           0           0           0  ...           0   \n",
+							"16           0           0           0           0  ...           0   \n",
+							"17           0           0           0           0  ...           0   \n",
+							"18           0           0           0           0  ...           0   \n",
+							"19           0           0           0           0  ...           0   \n",
+							"\n",
+							"    country_NO  country_PL  country_PT  country_RO  country_RS  country_SE  \\\n",
+							"15           0           0           0           0           0           0   \n",
+							"16           0           0           0           0           0           0   \n",
+							"17           0           0           0           0           0           0   \n",
+							"18           0           0           0           0           0           0   \n",
+							"19           0           0           0           0           0           0   \n",
+							"\n",
+							"    country_SI  country_SK  country_TR  \n",
+							"15           0           0           0  \n",
+							"16           0           0           0  \n",
+							"17           0           0           0  \n",
+							"18           0           0           0  \n",
+							"19           0           0           0  \n",
+							"\n",
+							"[5 rows x 33 columns]"
+						]
+					},
+					"execution_count": 17,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"df_dummies = pd.get_dummies(df_merged_s, dtype=int)\n",
+				"print(df_dummies.shape)\n",
+				"df_dummies.head()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 18,
+			"metadata": {},
+			"outputs": [],
+			"source": [
+				"# TODO Fix later\n",
+				"df_dummies = df_dummies.fillna(0)"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 19,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"text/plain": [
+							"array([[ 1.        , -0.09207599,  0.        ,  1.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ],\n",
+							"       [ 1.        , -0.21769314,  0.        ,  1.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+							"         0.        ,  0.        ,  0.        ]])"
+						]
+					},
+					"execution_count": 19,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"X = np.pad(df_dummies.iloc[:, 1:].to_numpy(), ((0,0), (1,0)), mode=\"constant\", constant_values=1)\n",
+				"y = np.array(df_dummies[\"carbon\"])\n",
+				"X[0:2,]"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 20,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"text/plain": [
+							"array([ 1.5       , -2.63265928, -0.25      , -0.75      , -1.        ,\n",
+							"       -1.        ,  0.5       ,  1.75      ,  1.5       ,  0.75      ,\n",
+							"       -0.75      , -0.75      ,  0.75      , -0.25      , -0.75      ,\n",
+							"       -0.75      ,  1.        ,  1.375     , -0.25      , -0.5       ,\n",
+							"        0.875     , -1.375     , -2.        ,  0.875     , -0.75      ,\n",
+							"        0.375     , -1.5       , -2.        , -0.875     , -2.        ,\n",
+							"       -0.75      , -1.75      , -1.        ])"
+						]
+					},
+					"execution_count": 20,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"m = np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, y))\n",
+				"np.set_printoptions(suppress=True) #this just prevents python from printing it out in inconvenient scientific notation\n",
+				"m"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 21,
+			"metadata": {},
+			"outputs": [
+				{
+					"name": "stdout",
+					"output_type": "stream",
+					"text": [
+						"[-0.60307536 -1.24919755 -0.97973066 -1.15294323 -1.10126625]\n"
+					]
+				},
+				{
+					"data": {
+						"text/plain": [
+							"-738.4"
+						]
+					},
+					"execution_count": 21,
+					"metadata": {},
+					"output_type": "execute_result"
+				}
+			],
+			"source": [
+				"# get the residuals\n",
+				"res = y - np.matmul(X, m)\n",
+				"print(res[0:5])\n",
+				"round(sum(res), 1)"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 22,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"image/png": 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",
+						"text/plain": [
+							"<Figure size 640x480 with 1 Axes>"
+						]
+					},
+					"metadata": {},
+					"output_type": "display_data"
+				}
+			],
+			"source": [
+				"# x values versus residuals\n",
+				"plt.scatter(X[:, 1:2], res, alpha=0.5)\n",
+				"plt.xlabel(\"Energy\")\n",
+				"plt.ylabel(\"Carbon\")\n",
+				"plt.title(\"Residual Plot vs. X Values\")\n",
+				"plt.show()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": 23,
+			"metadata": {},
+			"outputs": [
+				{
+					"data": {
+						"image/png": 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",
+						"text/plain": [
+							"<Figure size 640x480 with 1 Axes>"
+						]
+					},
+					"metadata": {},
+					"output_type": "display_data"
+				}
+			],
+			"source": [
+				"# residuals versus order\n",
+				"plt.scatter(range(len(X[:,1:2])), res, alpha=0.5)\n",
+				"plt.xlabel(\"Index\")\n",
+				"plt.ylabel(\"Residuals\")\n",
+				"plt.title(\"Residual Plot vs. Order\")\n",
+				"plt.show()"
+			]
+		},
+		{
+			"cell_type": "code",
+			"execution_count": null,
+			"metadata": {},
+			"outputs": [],
+			"source": []
+		}
+	],
+	"metadata": {
+		"kernelspec": {
+			"display_name": "wafflers",
+			"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": "3.11.9"
+		}
+	},
+	"nbformat": 4,
+	"nbformat_minor": 2
 }
diff --git a/eda/eda3.ipynb b/eda/eda3.ipynb
new file mode 100644
index 0000000..f7cc1f5
--- /dev/null
+++ b/eda/eda3.ipynb
@@ -0,0 +1,4480 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import plotly.express as px\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandasdmx as sdmx\n",
+    "from functools import reduce"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Fetching & Basic Manipulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estat = sdmx.Request(\"ESTAT\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Carbon Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-06-04 23:17:27,003 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n"
+     ]
+    },
+    {
+     "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>geo</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1985</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1986</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1987</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1988</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1989</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year geo  value\n",
+       "0  1985  AT    NaN\n",
+       "1  1986  AT    NaN\n",
+       "2  1987  AT    NaN\n",
+       "3  1988  AT    NaN\n",
+       "4  1989  AT    NaN"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resp = estat.data(\n",
+    "\t\"ENV_AIR_GGE\",\n",
+    "\tkey={\n",
+    "\t\t\"unit\": \"THS_T\",\n",
+    "\t\t\"freq\": \"A\",\n",
+    "\t\t\"src_crf\": \"TOTX4_MEMONIA\",\n",
+    "\t\t\"airpol\": \"GHG\"\n",
+    "\t}\n",
+    ")\n",
+    "emission_df = resp.to_pandas(datetime={'dim': 'TIME_PERIOD'}).droplevel(level=['unit', 'freq', 'src_crf', 'airpol'], axis=1)\n",
+    "emission_df.reset_index(inplace=True)\n",
+    "emission_df[\"year\"] = emission_df[\"TIME_PERIOD\"].dt.year\n",
+    "emission_df.drop(\"TIME_PERIOD\", inplace=True, axis=1)\n",
+    "emission_melted_df = pd.melt(emission_df, id_vars=\"year\")\n",
+    "\n",
+    "emission_melted_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "px.scatter(emission_melted_df, x=\"TIME_PERIOD\", y=\"value\", color=\"geo\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Energy Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_melted_energy_dfs(code: str) -> pd.DataFrame:\n",
+    "\t\"\"\"\n",
+    "\tA quick helper to get, parse, and melt the dataframes for a given energy `nrg_bal`.\n",
+    "\n",
+    "\t:param code: The `nrg_bal` code to fetch and melt\n",
+    "\t:returns: A melted dataframe\n",
+    "\t\"\"\"\n",
+    "\tresp = estat.data(\n",
+    "\t\t\"NRG_D_HHQ\",\n",
+    "\t\tkey={\n",
+    "\t\t\t\"siec\": \"TOTAL\",\n",
+    "\t\t\t\"unit\": \"TJ\",\n",
+    "\t\t\t\"nrg_bal\": code,\n",
+    "\t\t\t\"freq\": \"A\",\n",
+    "\t\t}\n",
+    "\t)\n",
+    "\thousehold_energy_df = resp.to_pandas(datetime={'dim': 'TIME_PERIOD', 'freq': 'freq'}).droplevel(level=[\"siec\", \"unit\", \"nrg_bal\"], axis=1)\n",
+    "\n",
+    "\thousehold_energy_df.reset_index(inplace=True)\n",
+    "\thousehold_energy_df[\"year\"] = household_energy_df[\"TIME_PERIOD\"].dt.year\n",
+    "\thousehold_energy_df.drop(\"TIME_PERIOD\", inplace=True, axis=1)\n",
+    "\thousehold_energy_melted_df = pd.melt(household_energy_df, id_vars=\"year\")\n",
+    "\thousehold_energy_melted_df.columns = [\"year\", \"geo\", code]\n",
+    "\treturn household_energy_melted_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-06-04 23:30:27,284 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
+      "\n",
+      "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
+      "\n",
+      "2024-06-04 23:30:27,461 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
+      "\n",
+      "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
+      "\n",
+      "2024-06-04 23:30:27,608 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
+      "\n",
+      "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
+      "\n",
+      "2024-06-04 23:30:27,745 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
+      "\n",
+      "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "codes = [\"FC_OTH_HH_E_SH\", \"FC_OTH_HH_E_SC\", \"FC_OTH_HH_E_WH\", \"FC_OTH_HH_E_CK\"]\n",
+    "energy_dfs = [get_melted_energy_dfs(code) for code in codes]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Merge Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "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>geo</th>\n",
+       "      <th>carbon</th>\n",
+       "      <th>energy_heating</th>\n",
+       "      <th>energy_cooling</th>\n",
+       "      <th>energy_water_heating</th>\n",
+       "      <th>energy_cooking</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>86862.05</td>\n",
+       "      <td>211567.042</td>\n",
+       "      <td>21.359</td>\n",
+       "      <td>40387.965</td>\n",
+       "      <td>7011.298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2011</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>84795.76</td>\n",
+       "      <td>190731.846</td>\n",
+       "      <td>25.123</td>\n",
+       "      <td>41343.612</td>\n",
+       "      <td>6891.695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2012</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>81981.39</td>\n",
+       "      <td>195419.278</td>\n",
+       "      <td>34.215</td>\n",
+       "      <td>41839.251</td>\n",
+       "      <td>7026.774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>82304.13</td>\n",
+       "      <td>205494.885</td>\n",
+       "      <td>31.035</td>\n",
+       "      <td>41541.823</td>\n",
+       "      <td>7353.560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>78716.82</td>\n",
+       "      <td>178513.717</td>\n",
+       "      <td>27.645</td>\n",
+       "      <td>42495.391</td>\n",
+       "      <td>7152.047</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year geo    carbon  energy_heating  energy_cooling  energy_water_heating  \\\n",
+       "0  2010  AT  86862.05      211567.042          21.359             40387.965   \n",
+       "1  2011  AT  84795.76      190731.846          25.123             41343.612   \n",
+       "2  2012  AT  81981.39      195419.278          34.215             41839.251   \n",
+       "3  2013  AT  82304.13      205494.885          31.035             41541.823   \n",
+       "4  2014  AT  78716.82      178513.717          27.645             42495.391   \n",
+       "\n",
+       "   energy_cooking  \n",
+       "0        7011.298  \n",
+       "1        6891.695  \n",
+       "2        7026.774  \n",
+       "3        7353.560  \n",
+       "4        7152.047  "
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_merge_dfs = [emission_melted_df, *energy_dfs]\n",
+    "columns = [\"year\", \"geo\", \"carbon\", \"energy_heating\", \"energy_cooling\", \"energy_water_heating\", \"energy_cooking\"]\n",
+    "res_carbon_df = reduce(lambda l, r: pd.merge(l, r, left_on=[\"year\", \"geo\"], right_on=[\"year\", \"geo\"]), to_merge_dfs)\n",
+    "res_carbon_df.columns = columns\n",
+    "res_carbon_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "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>carbon</th>\n",
+       "      <th>energy_heating</th>\n",
+       "      <th>energy_cooling</th>\n",
+       "      <th>energy_water_heating</th>\n",
+       "      <th>energy_cooking</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>carbon</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.950484</td>\n",
+       "      <td>0.844733</td>\n",
+       "      <td>0.947675</td>\n",
+       "      <td>0.952764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>energy_heating</th>\n",
+       "      <td>0.950484</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.920472</td>\n",
+       "      <td>0.994126</td>\n",
+       "      <td>0.992522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>energy_cooling</th>\n",
+       "      <td>0.844733</td>\n",
+       "      <td>0.920472</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.924791</td>\n",
+       "      <td>0.929758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>energy_water_heating</th>\n",
+       "      <td>0.947675</td>\n",
+       "      <td>0.994126</td>\n",
+       "      <td>0.924791</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.994235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>energy_cooking</th>\n",
+       "      <td>0.952764</td>\n",
+       "      <td>0.992522</td>\n",
+       "      <td>0.929758</td>\n",
+       "      <td>0.994235</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        carbon  energy_heating  energy_cooling  \\\n",
+       "carbon                1.000000        0.950484        0.844733   \n",
+       "energy_heating        0.950484        1.000000        0.920472   \n",
+       "energy_cooling        0.844733        0.920472        1.000000   \n",
+       "energy_water_heating  0.947675        0.994126        0.924791   \n",
+       "energy_cooking        0.952764        0.992522        0.929758   \n",
+       "\n",
+       "                      energy_water_heating  energy_cooking  \n",
+       "carbon                            0.947675        0.952764  \n",
+       "energy_heating                    0.994126        0.992522  \n",
+       "energy_cooling                    0.924791        0.929758  \n",
+       "energy_water_heating              1.000000        0.994235  \n",
+       "energy_cooking                    0.994235        1.000000  "
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_carbon_df.iloc[:, 2:].corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Standardize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_carbon_df.drop(res_carbon_df[(res_carbon_df.geo == \"EU27_2020\") | (res_carbon_df.geo == \"EU20\")].index, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TEST\n",
+    "res_carbon_df.drop(\"year\", axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_carbon_df.to_clipboard()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "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>carbon</th>\n",
+       "      <th>energy_heating</th>\n",
+       "      <th>energy_cooling</th>\n",
+       "      <th>energy_water_heating</th>\n",
+       "      <th>energy_cooking</th>\n",
+       "      <th>geo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.270778</td>\n",
+       "      <td>0.005791</td>\n",
+       "      <td>-0.467261</td>\n",
+       "      <td>-0.075604</td>\n",
+       "      <td>-0.354672</td>\n",
+       "      <td>AT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.281369</td>\n",
+       "      <td>-0.049254</td>\n",
+       "      <td>-0.465859</td>\n",
+       "      <td>-0.063681</td>\n",
+       "      <td>-0.358060</td>\n",
+       "      <td>AT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.295793</td>\n",
+       "      <td>-0.036870</td>\n",
+       "      <td>-0.462471</td>\n",
+       "      <td>-0.057498</td>\n",
+       "      <td>-0.354234</td>\n",
+       "      <td>AT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.294139</td>\n",
+       "      <td>-0.010251</td>\n",
+       "      <td>-0.463656</td>\n",
+       "      <td>-0.061208</td>\n",
+       "      <td>-0.344978</td>\n",
+       "      <td>AT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.312524</td>\n",
+       "      <td>-0.081533</td>\n",
+       "      <td>-0.464919</td>\n",
+       "      <td>-0.049312</td>\n",
+       "      <td>-0.350685</td>\n",
+       "      <td>AT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     carbon  energy_heating  energy_cooling  energy_water_heating  \\\n",
+       "0 -0.270778        0.005791       -0.467261             -0.075604   \n",
+       "1 -0.281369       -0.049254       -0.465859             -0.063681   \n",
+       "2 -0.295793       -0.036870       -0.462471             -0.057498   \n",
+       "3 -0.294139       -0.010251       -0.463656             -0.061208   \n",
+       "4 -0.312524       -0.081533       -0.464919             -0.049312   \n",
+       "\n",
+       "   energy_cooking geo  \n",
+       "0       -0.354672  AT  \n",
+       "1       -0.358060  AT  \n",
+       "2       -0.354234  AT  \n",
+       "3       -0.344978  AT  \n",
+       "4       -0.350685  AT  "
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_merged_s = pd.DataFrame()\n",
+    "for feat in res_carbon_df.columns:\n",
+    "\tif feat == \"geo\": continue\n",
+    "\tdf_merged_s[f'{feat}'] = ((res_carbon_df[feat] - res_carbon_df[feat].mean()) / res_carbon_df[feat].std())\n",
+    "df_merged_s[\"geo\"] = res_carbon_df[\"geo\"]\n",
+    "# df_merged_s[\"year\"] = res_carbon_df[\"year\"]\n",
+    "df_merged_s.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dummies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(364, 33)\n"
+     ]
+    },
+    {
+     "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>carbon</th>\n",
+       "      <th>energy_heating</th>\n",
+       "      <th>energy_cooling</th>\n",
+       "      <th>energy_water_heating</th>\n",
+       "      <th>energy_cooking</th>\n",
+       "      <th>geo_AT</th>\n",
+       "      <th>geo_BE</th>\n",
+       "      <th>geo_BG</th>\n",
+       "      <th>geo_CY</th>\n",
+       "      <th>geo_CZ</th>\n",
+       "      <th>...</th>\n",
+       "      <th>geo_LV</th>\n",
+       "      <th>geo_MT</th>\n",
+       "      <th>geo_NL</th>\n",
+       "      <th>geo_NO</th>\n",
+       "      <th>geo_PL</th>\n",
+       "      <th>geo_PT</th>\n",
+       "      <th>geo_RO</th>\n",
+       "      <th>geo_SE</th>\n",
+       "      <th>geo_SI</th>\n",
+       "      <th>geo_SK</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.270778</td>\n",
+       "      <td>0.005791</td>\n",
+       "      <td>-0.467261</td>\n",
+       "      <td>-0.075604</td>\n",
+       "      <td>-0.354672</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.281369</td>\n",
+       "      <td>-0.049254</td>\n",
+       "      <td>-0.465859</td>\n",
+       "      <td>-0.063681</td>\n",
+       "      <td>-0.358060</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.295793</td>\n",
+       "      <td>-0.036870</td>\n",
+       "      <td>-0.462471</td>\n",
+       "      <td>-0.057498</td>\n",
+       "      <td>-0.354234</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.294139</td>\n",
+       "      <td>-0.010251</td>\n",
+       "      <td>-0.463656</td>\n",
+       "      <td>-0.061208</td>\n",
+       "      <td>-0.344978</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.312524</td>\n",
+       "      <td>-0.081533</td>\n",
+       "      <td>-0.464919</td>\n",
+       "      <td>-0.049312</td>\n",
+       "      <td>-0.350685</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     carbon  energy_heating  energy_cooling  energy_water_heating  \\\n",
+       "0 -0.270778        0.005791       -0.467261             -0.075604   \n",
+       "1 -0.281369       -0.049254       -0.465859             -0.063681   \n",
+       "2 -0.295793       -0.036870       -0.462471             -0.057498   \n",
+       "3 -0.294139       -0.010251       -0.463656             -0.061208   \n",
+       "4 -0.312524       -0.081533       -0.464919             -0.049312   \n",
+       "\n",
+       "   energy_cooking  geo_AT  geo_BE  geo_BG  geo_CY  geo_CZ  ...  geo_LV  \\\n",
+       "0       -0.354672       1       0       0       0       0  ...       0   \n",
+       "1       -0.358060       1       0       0       0       0  ...       0   \n",
+       "2       -0.354234       1       0       0       0       0  ...       0   \n",
+       "3       -0.344978       1       0       0       0       0  ...       0   \n",
+       "4       -0.350685       1       0       0       0       0  ...       0   \n",
+       "\n",
+       "   geo_MT  geo_NL  geo_NO  geo_PL  geo_PT  geo_RO  geo_SE  geo_SI  geo_SK  \n",
+       "0       0       0       0       0       0       0       0       0       0  \n",
+       "1       0       0       0       0       0       0       0       0       0  \n",
+       "2       0       0       0       0       0       0       0       0       0  \n",
+       "3       0       0       0       0       0       0       0       0       0  \n",
+       "4       0       0       0       0       0       0       0       0       0  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_dummies = pd.get_dummies(df_merged_s, dtype=int)\n",
+    "print(df_dummies.shape)\n",
+    "df_dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_dummies = df_dummies.fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1.        ,  0.00579135, -0.46726144, -0.07560401, -0.35467208,\n",
+       "         1.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ],\n",
+       "       [ 1.        , -0.04925367, -0.46585892, -0.0636813 , -0.35805979,\n",
+       "         1.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ]])"
+      ]
+     },
+     "execution_count": 234,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = np.pad(df_dummies.iloc[:, 1:].to_numpy(), ((0,0), (1,0)), mode=\"constant\", constant_values=1)\n",
+    "y = np.array(df_dummies[\"carbon\"])\n",
+    "X[0:2,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.37222448,  0.5223011 , -0.05161035, -0.61810575,  0.12303269,\n",
+       "       -0.36377837, -0.8083168 , -0.98725832, -1.43894754, -0.72166141,\n",
+       "        3.67563685, -0.82824913, -1.18894754, -1.10229215,  1.04523083,\n",
+       "       -0.90493835,  0.51144635, -1.31676292, -0.98725832, -0.88219065,\n",
+       "        0.85654786, -0.72729215, -1.32672908, -1.15060293, -1.47729215,\n",
+       "       -0.42639073, -0.9560645 ,  0.75277552, -0.86484897, -0.68179675,\n",
+       "       -0.78990451, -1.21169524, -1.04964596])"
+      ]
+     },
+     "execution_count": 235,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m = np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, y))\n",
+    "np.set_printoptions(suppress=True) #this just prevents python from printing it out in inconvenient scientific notation\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.30945998 -0.28344126 -0.30080717 -0.31654979 -0.28971399]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "64.1"
+      ]
+     },
+     "execution_count": 236,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get the residuals\n",
+    "res = y - np.matmul(X, m)\n",
+    "print(res[0:5])\n",
+    "round(sum(res), 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# x values versus residuals\n",
+    "plt.scatter(X[:, 1:2], res, alpha=0.5)\n",
+    "plt.xlabel(\"Energy\")\n",
+    "plt.ylabel(\"Carbon\")\n",
+    "plt.title(\"Residual Plot vs. X Values\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# residuals versus order\n",
+    "plt.scatter(range(len(X[:,1:2])), res, alpha=0.5)\n",
+    "plt.xlabel(\"Index\")\n",
+    "plt.ylabel(\"Residuals\")\n",
+    "plt.title(\"Residual Plot vs. Order\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {},
+   "outputs": [
+    {
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+           "bgcolor": "white",
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+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
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+          "mapbox": {
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+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "energy_heating"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "carbon"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "px.scatter(df_merged_s, y=\"carbon\", x=\"energy_heating\", color=\"geo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np_remove = lambda a, i: np.concatenate([a[:i,], a[i + 1:,]])\n",
+    "lin_reg = lambda X, Y: np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, Y))\n",
+    "\n",
+    "def loo_cv_pred(X, Y):\n",
+    "\t\"\"\"\n",
+    "\tPredict Y values using leave one out cross validation\n",
+    "\n",
+    "\t:param X: The X features array (including bias column)\n",
+    "\t:param Y: The true Y values\n",
+    "\t:return: An array of the predicted Y-Vals\n",
+    "\t\"\"\"\n",
+    "\ty_pred = []\n",
+    "\tfor i in range(len(X)):\n",
+    "\t\tholdout_X = X[i]\n",
+    "\t\t\n",
+    "\t\tloo_X = np_remove(X, i)\n",
+    "\t\tloo_y = np_remove(Y, i)\n",
+    "\t\tloo_b = lin_reg(loo_X, loo_y)\n",
+    "\n",
+    "\t\ty_hat = np.matmul(holdout_X, loo_b)\n",
+    "\t\ty_pred.append(y_hat)\n",
+    "\t\n",
+    "\treturn y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds = loo_cv_pred(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6267042262195361"
+      ]
+     },
+     "execution_count": 248,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y, preds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Experiment: Model per country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "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>carbon</th>\n",
+       "      <th>energy_heating</th>\n",
+       "      <th>energy_cooling</th>\n",
+       "      <th>energy_water_heating</th>\n",
+       "      <th>energy_cooking</th>\n",
+       "      <th>geo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>1.962098</td>\n",
+       "      <td>3.156258</td>\n",
+       "      <td>0.049519</td>\n",
+       "      <td>1.422028</td>\n",
+       "      <td>1.976399</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>1.838539</td>\n",
+       "      <td>2.430414</td>\n",
+       "      <td>1.000927</td>\n",
+       "      <td>1.636463</td>\n",
+       "      <td>2.337869</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>1.844259</td>\n",
+       "      <td>2.873586</td>\n",
+       "      <td>1.000302</td>\n",
+       "      <td>1.614002</td>\n",
+       "      <td>2.229544</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>1.841121</td>\n",
+       "      <td>3.118138</td>\n",
+       "      <td>1.073088</td>\n",
+       "      <td>1.635543</td>\n",
+       "      <td>2.142887</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>1.675800</td>\n",
+       "      <td>2.240820</td>\n",
+       "      <td>1.143476</td>\n",
+       "      <td>1.616531</td>\n",
+       "      <td>2.043686</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>1.699596</td>\n",
+       "      <td>2.474152</td>\n",
+       "      <td>1.236836</td>\n",
+       "      <td>1.629732</td>\n",
+       "      <td>2.059423</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>1.708232</td>\n",
+       "      <td>2.677761</td>\n",
+       "      <td>1.628912</td>\n",
+       "      <td>1.660820</td>\n",
+       "      <td>2.041405</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>1.722893</td>\n",
+       "      <td>2.583788</td>\n",
+       "      <td>1.621449</td>\n",
+       "      <td>1.696977</td>\n",
+       "      <td>2.089008</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>1.628270</td>\n",
+       "      <td>2.434724</td>\n",
+       "      <td>1.853032</td>\n",
+       "      <td>1.704964</td>\n",
+       "      <td>2.100902</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>1.581249</td>\n",
+       "      <td>2.432933</td>\n",
+       "      <td>2.190273</td>\n",
+       "      <td>1.693026</td>\n",
+       "      <td>2.052831</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>1.322488</td>\n",
+       "      <td>2.249065</td>\n",
+       "      <td>2.887347</td>\n",
+       "      <td>1.799113</td>\n",
+       "      <td>2.171680</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>167</th>\n",
+       "      <td>1.440005</td>\n",
+       "      <td>2.637342</td>\n",
+       "      <td>3.278921</td>\n",
+       "      <td>1.689769</td>\n",
+       "      <td>2.022219</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>1.383980</td>\n",
+       "      <td>2.214246</td>\n",
+       "      <td>3.167601</td>\n",
+       "      <td>1.415021</td>\n",
+       "      <td>1.610834</td>\n",
+       "      <td>FR</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       carbon  energy_heating  energy_cooling  energy_water_heating  \\\n",
+       "156  1.962098        3.156258        0.049519              1.422028   \n",
+       "157  1.838539        2.430414        1.000927              1.636463   \n",
+       "158  1.844259        2.873586        1.000302              1.614002   \n",
+       "159  1.841121        3.118138        1.073088              1.635543   \n",
+       "160  1.675800        2.240820        1.143476              1.616531   \n",
+       "161  1.699596        2.474152        1.236836              1.629732   \n",
+       "162  1.708232        2.677761        1.628912              1.660820   \n",
+       "163  1.722893        2.583788        1.621449              1.696977   \n",
+       "164  1.628270        2.434724        1.853032              1.704964   \n",
+       "165  1.581249        2.432933        2.190273              1.693026   \n",
+       "166  1.322488        2.249065        2.887347              1.799113   \n",
+       "167  1.440005        2.637342        3.278921              1.689769   \n",
+       "168  1.383980        2.214246        3.167601              1.415021   \n",
+       "\n",
+       "     energy_cooking geo  \n",
+       "156        1.976399  FR  \n",
+       "157        2.337869  FR  \n",
+       "158        2.229544  FR  \n",
+       "159        2.142887  FR  \n",
+       "160        2.043686  FR  \n",
+       "161        2.059423  FR  \n",
+       "162        2.041405  FR  \n",
+       "163        2.089008  FR  \n",
+       "164        2.100902  FR  \n",
+       "165        2.052831  FR  \n",
+       "166        2.171680  FR  \n",
+       "167        2.022219  FR  \n",
+       "168        1.610834  FR  "
+      ]
+     },
+     "execution_count": 279,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_be = df_merged_s.loc[df_merged_s[\"geo\"] == \"FR\"]\n",
+    "df_be"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.        , 3.15625764, 0.04951903, 1.4220275 , 1.97639861],\n",
+       "       [1.        , 2.43041402, 1.00092731, 1.63646348, 2.33786898]])"
+      ]
+     },
+     "execution_count": 280,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_be = np.pad(df_be.iloc[:, 1:5].to_numpy(), ((0,0), (1,0)), mode=\"constant\", constant_values=1)\n",
+    "y_be = np.array(df_be[\"carbon\"])\n",
+    "X_be[0:2,]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta_be = lin_reg(X_be, y_be)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.05395949  0.03933248  0.00048339 -0.0018462  -0.02048255]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 282,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res_be = y_be - np.matmul(X_be, beta_be)\n",
+    "print(res_be[0:5])\n",
+    "round(sum(res_be), 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# x values versus residuals\n",
+    "plt.scatter(X_be[:, 1:2], res_be, alpha=0.5)\n",
+    "plt.xlabel(\"Energy\")\n",
+    "plt.ylabel(\"Carbon\")\n",
+    "plt.title(\"Residual Plot vs. X Values\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# residuals versus order\n",
+    "plt.scatter(range(len(X_be[:,1:2])), res_be, alpha=0.5)\n",
+    "plt.xlabel(\"Index\")\n",
+    "plt.ylabel(\"Residuals\")\n",
+    "plt.title(\"Residual Plot vs. Order\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "energy_heating=%{x}<br>carbon=%{y}<extra></extra>",
+         "legendgroup": "",
+         "marker": {
+          "color": "#636efa",
+          "symbol": "circle"
+         },
+         "mode": "markers",
+         "name": "",
+         "orientation": "v",
+         "showlegend": false,
+         "type": "scatter",
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+         "xaxis": "x",
+         "y": [
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+         "yaxis": "y"
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+       "layout": {
+        "legend": {
+         "tracegroupgap": 0
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+        "margin": {
+         "t": 60
+        },
+        "template": {
+         "data": {
+          "bar": [
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+            "error_y": {
+             "color": "#2a3f5f"
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+            "marker": {
+             "line": {
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+             "pattern": {
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+              "solidity": 0.2
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+            },
+            "type": "bar"
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+          "barpolar": [
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+              "solidity": 0.2
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+            "type": "barpolar"
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+             "gridcolor": "white",
+             "linecolor": "white",
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+             "startlinecolor": "#2a3f5f"
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+            "baxis": {
+             "endlinecolor": "#2a3f5f",
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+             "linecolor": "white",
+             "minorgridcolor": "white",
+             "startlinecolor": "#2a3f5f"
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+            "type": "contour"
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+          "contourcarpet": [
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+            "colorbar": {
+             "outlinewidth": 0,
+             "ticks": ""
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+            "type": "contourcarpet"
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+          "heatmap": [
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+            "type": "heatmap"
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+          ],
+          "heatmapgl": [
+           {
+            "colorbar": {
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+            "colorscale": [
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+          "histogram": [
+           {
+            "marker": {
+             "pattern": {
+              "fillmode": "overlay",
+              "size": 10,
+              "solidity": 0.2
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+            "type": "histogram"
+           }
+          ],
+          "histogram2d": [
+           {
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+            "colorscale": [
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+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
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+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "energy_heating"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "carbon"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "px.scatter(df_be, x=\"energy_heating\", y=\"carbon\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds_be = loo_cv_pred(X_be, y_be)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8016784112747927"
+      ]
+     },
+     "execution_count": 287,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_be, preds_be)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "wafflers",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 39006d7cfe1e5126586b2c82a01458cb2a22846b Mon Sep 17 00:00:00 2001
From: mehallhm <michaelhmehall@gmail.com>
Date: Wed, 5 Jun 2024 21:53:11 +0200
Subject: [PATCH 18/34] more complete regression eda

---
 eda/eda3.ipynb | 2754 ++++++++++++++++++++++++++++--------------------
 1 file changed, 1592 insertions(+), 1162 deletions(-)

diff --git a/eda/eda3.ipynb b/eda/eda3.ipynb
index f7cc1f5..9a5e343 100644
--- a/eda/eda3.ipynb
+++ b/eda/eda3.ipynb
@@ -194,29 +194,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 598,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 23:30:27,284 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "2024-06-05 21:23:32,675 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
       "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
       "\n",
       "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
       "\n",
-      "2024-06-04 23:30:27,461 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "2024-06-05 21:23:32,827 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
       "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
       "\n",
       "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
       "\n",
-      "2024-06-04 23:30:27,608 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "2024-06-05 21:23:33,017 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
       "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
       "\n",
       "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
       "\n",
-      "2024-06-04 23:30:27,745 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "2024-06-05 21:23:33,194 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
+      "\n",
+      "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
+      "\n",
+      "2024-06-05 21:23:33,390 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
       "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
       "\n",
       "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
@@ -225,10 +230,116 @@
     }
    ],
    "source": [
-    "codes = [\"FC_OTH_HH_E_SH\", \"FC_OTH_HH_E_SC\", \"FC_OTH_HH_E_WH\", \"FC_OTH_HH_E_CK\"]\n",
+    "codes = [\"FC_OTH_HH_E_SH\", \"FC_OTH_HH_E_SC\", \"FC_OTH_HH_E_WH\", \"FC_OTH_HH_E_CK\", \"FC_OTH_HH_E\"]\n",
     "energy_dfs = [get_melted_energy_dfs(code) for code in codes]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 599,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-06-05 21:23:35,400 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n"
+     ]
+    },
+    {
+     "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>geo</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2011</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>115.592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2012</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>100.745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>109.229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>109.229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>109.229</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year geo    value\n",
+       "0  2011  AL  115.592\n",
+       "1  2012  AL  100.745\n",
+       "2  2013  AL  109.229\n",
+       "3  2014  AL  109.229\n",
+       "4  2015  AL  109.229"
+      ]
+     },
+     "execution_count": 599,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resp = estat.data(\n",
+    "\t\"TEN00127\",\n",
+    "\tkey={\n",
+    "\t\t\"unit\": \"KTOE\",\n",
+    "\t\t\"freq\": \"A\",\n",
+    "\t\t\"siec\": \"O4652XR5210B\",\n",
+    "\t\t\"nrg_bal\": \"FC_TRA_ROAD_E\"\n",
+    "\t}\n",
+    ")\n",
+    "gas_df = resp.to_pandas(datetime={'dim': 'TIME_PERIOD'}).droplevel(level=['unit', 'freq', 'siec', \"nrg_bal\"], axis=1)\n",
+    "gas_df.reset_index(inplace=True)\n",
+    "gas_df[\"year\"] = gas_df[\"TIME_PERIOD\"].dt.year\n",
+    "gas_df.drop(\"TIME_PERIOD\", inplace=True, axis=1)\n",
+    "gas_melted_df = pd.melt(gas_df, id_vars=\"year\")\n",
+    "\n",
+    "gas_melted_df.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -238,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 703,
    "metadata": {},
    "outputs": [
     {
@@ -269,21 +380,153 @@
        "      <th>energy_cooling</th>\n",
        "      <th>energy_water_heating</th>\n",
        "      <th>energy_cooking</th>\n",
+       "      <th>total</th>\n",
+       "      <th>gas</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>2010</td>\n",
+       "      <td>2011</td>\n",
        "      <td>AT</td>\n",
-       "      <td>86862.05</td>\n",
-       "      <td>211567.042</td>\n",
-       "      <td>21.359</td>\n",
-       "      <td>40387.965</td>\n",
-       "      <td>7011.298</td>\n",
+       "      <td>84795.76</td>\n",
+       "      <td>190731.846</td>\n",
+       "      <td>25.123</td>\n",
+       "      <td>41343.612</td>\n",
+       "      <td>6891.695</td>\n",
+       "      <td>274577.058</td>\n",
+       "      <td>1632.260</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>2012</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>81981.39</td>\n",
+       "      <td>195419.278</td>\n",
+       "      <td>34.215</td>\n",
+       "      <td>41839.251</td>\n",
+       "      <td>7026.774</td>\n",
+       "      <td>279812.556</td>\n",
+       "      <td>1585.826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>82304.13</td>\n",
+       "      <td>205494.885</td>\n",
+       "      <td>31.035</td>\n",
+       "      <td>41541.823</td>\n",
+       "      <td>7353.560</td>\n",
+       "      <td>289591.863</td>\n",
+       "      <td>1554.830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>78716.82</td>\n",
+       "      <td>178513.717</td>\n",
+       "      <td>27.645</td>\n",
+       "      <td>42495.391</td>\n",
+       "      <td>7152.047</td>\n",
+       "      <td>261676.431</td>\n",
+       "      <td>1524.254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>81081.58</td>\n",
+       "      <td>193886.979</td>\n",
+       "      <td>25.714</td>\n",
+       "      <td>42104.965</td>\n",
+       "      <td>7179.133</td>\n",
+       "      <td>278096.422</td>\n",
+       "      <td>1539.983</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   year geo    carbon  energy_heating  energy_cooling  energy_water_heating  \\\n",
+       "0  2011  AT  84795.76      190731.846          25.123             41343.612   \n",
+       "1  2012  AT  81981.39      195419.278          34.215             41839.251   \n",
+       "2  2013  AT  82304.13      205494.885          31.035             41541.823   \n",
+       "3  2014  AT  78716.82      178513.717          27.645             42495.391   \n",
+       "4  2015  AT  81081.58      193886.979          25.714             42104.965   \n",
+       "\n",
+       "   energy_cooking       total       gas  \n",
+       "0        6891.695  274577.058  1632.260  \n",
+       "1        7026.774  279812.556  1585.826  \n",
+       "2        7353.560  289591.863  1554.830  \n",
+       "3        7152.047  261676.431  1524.254  \n",
+       "4        7179.133  278096.422  1539.983  "
+      ]
+     },
+     "execution_count": 703,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_merge_dfs = [emission_melted_df, *energy_dfs, gas_melted_df]\n",
+    "columns = [\"year\", \"geo\", \"carbon\", \"energy_heating\", \"energy_cooling\", \"energy_water_heating\", \"energy_cooking\", \"total\", \"gas\"]\n",
+    "res_carbon_df = reduce(lambda l, r: pd.merge(l, r, left_on=[\"year\", \"geo\"], right_on=[\"year\", \"geo\"]), to_merge_dfs)\n",
+    "res_carbon_df.columns = columns\n",
+    "res_carbon_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 704,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res_carbon_df.drop(index=72, axis=0, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 705,
+   "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>geo</th>\n",
+       "      <th>carbon</th>\n",
+       "      <th>energy_heating</th>\n",
+       "      <th>energy_cooling</th>\n",
+       "      <th>energy_water_heating</th>\n",
+       "      <th>energy_cooking</th>\n",
+       "      <th>total</th>\n",
+       "      <th>gas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
        "      <td>2011</td>\n",
        "      <td>AT</td>\n",
        "      <td>84795.76</td>\n",
@@ -291,9 +534,11 @@
        "      <td>25.123</td>\n",
        "      <td>41343.612</td>\n",
        "      <td>6891.695</td>\n",
+       "      <td>274577.058</td>\n",
+       "      <td>1632.260</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>1</th>\n",
        "      <td>2012</td>\n",
        "      <td>AT</td>\n",
        "      <td>81981.39</td>\n",
@@ -301,9 +546,11 @@
        "      <td>34.215</td>\n",
        "      <td>41839.251</td>\n",
        "      <td>7026.774</td>\n",
+       "      <td>279812.556</td>\n",
+       "      <td>1585.826</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>2</th>\n",
        "      <td>2013</td>\n",
        "      <td>AT</td>\n",
        "      <td>82304.13</td>\n",
@@ -311,9 +558,11 @@
        "      <td>31.035</td>\n",
        "      <td>41541.823</td>\n",
        "      <td>7353.560</td>\n",
+       "      <td>289591.863</td>\n",
+       "      <td>1554.830</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>3</th>\n",
        "      <td>2014</td>\n",
        "      <td>AT</td>\n",
        "      <td>78716.82</td>\n",
@@ -321,6 +570,20 @@
        "      <td>27.645</td>\n",
        "      <td>42495.391</td>\n",
        "      <td>7152.047</td>\n",
+       "      <td>261676.431</td>\n",
+       "      <td>1524.254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>AT</td>\n",
+       "      <td>81081.58</td>\n",
+       "      <td>193886.979</td>\n",
+       "      <td>25.714</td>\n",
+       "      <td>42104.965</td>\n",
+       "      <td>7179.133</td>\n",
+       "      <td>278096.422</td>\n",
+       "      <td>1539.983</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -328,36 +591,32 @@
       ],
       "text/plain": [
        "   year geo    carbon  energy_heating  energy_cooling  energy_water_heating  \\\n",
-       "0  2010  AT  86862.05      211567.042          21.359             40387.965   \n",
-       "1  2011  AT  84795.76      190731.846          25.123             41343.612   \n",
-       "2  2012  AT  81981.39      195419.278          34.215             41839.251   \n",
-       "3  2013  AT  82304.13      205494.885          31.035             41541.823   \n",
-       "4  2014  AT  78716.82      178513.717          27.645             42495.391   \n",
+       "0  2011  AT  84795.76      190731.846          25.123             41343.612   \n",
+       "1  2012  AT  81981.39      195419.278          34.215             41839.251   \n",
+       "2  2013  AT  82304.13      205494.885          31.035             41541.823   \n",
+       "3  2014  AT  78716.82      178513.717          27.645             42495.391   \n",
+       "4  2015  AT  81081.58      193886.979          25.714             42104.965   \n",
        "\n",
-       "   energy_cooking  \n",
-       "0        7011.298  \n",
-       "1        6891.695  \n",
-       "2        7026.774  \n",
-       "3        7353.560  \n",
-       "4        7152.047  "
+       "   energy_cooking       total       gas  \n",
+       "0        6891.695  274577.058  1632.260  \n",
+       "1        7026.774  279812.556  1585.826  \n",
+       "2        7353.560  289591.863  1554.830  \n",
+       "3        7152.047  261676.431  1524.254  \n",
+       "4        7179.133  278096.422  1539.983  "
       ]
      },
-     "execution_count": 199,
+     "execution_count": 705,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "to_merge_dfs = [emission_melted_df, *energy_dfs]\n",
-    "columns = [\"year\", \"geo\", \"carbon\", \"energy_heating\", \"energy_cooling\", \"energy_water_heating\", \"energy_cooking\"]\n",
-    "res_carbon_df = reduce(lambda l, r: pd.merge(l, r, left_on=[\"year\", \"geo\"], right_on=[\"year\", \"geo\"]), to_merge_dfs)\n",
-    "res_carbon_df.columns = columns\n",
     "res_carbon_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 706,
    "metadata": {},
    "outputs": [
     {
@@ -386,47 +645,79 @@
        "      <th>energy_cooling</th>\n",
        "      <th>energy_water_heating</th>\n",
        "      <th>energy_cooking</th>\n",
+       "      <th>total</th>\n",
+       "      <th>gas</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>carbon</th>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.950484</td>\n",
-       "      <td>0.844733</td>\n",
-       "      <td>0.947675</td>\n",
-       "      <td>0.952764</td>\n",
+       "      <td>0.953864</td>\n",
+       "      <td>0.860277</td>\n",
+       "      <td>0.953774</td>\n",
+       "      <td>0.958315</td>\n",
+       "      <td>0.955910</td>\n",
+       "      <td>0.996810</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>energy_heating</th>\n",
-       "      <td>0.950484</td>\n",
+       "      <td>0.953864</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.920472</td>\n",
-       "      <td>0.994126</td>\n",
-       "      <td>0.992522</td>\n",
+       "      <td>0.927136</td>\n",
+       "      <td>0.994901</td>\n",
+       "      <td>0.993171</td>\n",
+       "      <td>0.999384</td>\n",
+       "      <td>0.957210</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>energy_cooling</th>\n",
-       "      <td>0.844733</td>\n",
-       "      <td>0.920472</td>\n",
+       "      <td>0.860277</td>\n",
+       "      <td>0.927136</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.924791</td>\n",
-       "      <td>0.929758</td>\n",
+       "      <td>0.927544</td>\n",
+       "      <td>0.933284</td>\n",
+       "      <td>0.932756</td>\n",
+       "      <td>0.871485</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>energy_water_heating</th>\n",
-       "      <td>0.947675</td>\n",
-       "      <td>0.994126</td>\n",
-       "      <td>0.924791</td>\n",
+       "      <td>0.953774</td>\n",
+       "      <td>0.994901</td>\n",
+       "      <td>0.927544</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.994235</td>\n",
+       "      <td>0.994353</td>\n",
+       "      <td>0.996930</td>\n",
+       "      <td>0.958951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>energy_cooking</th>\n",
-       "      <td>0.952764</td>\n",
-       "      <td>0.992522</td>\n",
-       "      <td>0.929758</td>\n",
-       "      <td>0.994235</td>\n",
+       "      <td>0.958315</td>\n",
+       "      <td>0.993171</td>\n",
+       "      <td>0.933284</td>\n",
+       "      <td>0.994353</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.995190</td>\n",
+       "      <td>0.960897</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total</th>\n",
+       "      <td>0.955910</td>\n",
+       "      <td>0.999384</td>\n",
+       "      <td>0.932756</td>\n",
+       "      <td>0.996930</td>\n",
+       "      <td>0.995190</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.959333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gas</th>\n",
+       "      <td>0.996810</td>\n",
+       "      <td>0.957210</td>\n",
+       "      <td>0.871485</td>\n",
+       "      <td>0.958951</td>\n",
+       "      <td>0.960897</td>\n",
+       "      <td>0.959333</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -435,21 +726,25 @@
       ],
       "text/plain": [
        "                        carbon  energy_heating  energy_cooling  \\\n",
-       "carbon                1.000000        0.950484        0.844733   \n",
-       "energy_heating        0.950484        1.000000        0.920472   \n",
-       "energy_cooling        0.844733        0.920472        1.000000   \n",
-       "energy_water_heating  0.947675        0.994126        0.924791   \n",
-       "energy_cooking        0.952764        0.992522        0.929758   \n",
+       "carbon                1.000000        0.953864        0.860277   \n",
+       "energy_heating        0.953864        1.000000        0.927136   \n",
+       "energy_cooling        0.860277        0.927136        1.000000   \n",
+       "energy_water_heating  0.953774        0.994901        0.927544   \n",
+       "energy_cooking        0.958315        0.993171        0.933284   \n",
+       "total                 0.955910        0.999384        0.932756   \n",
+       "gas                   0.996810        0.957210        0.871485   \n",
        "\n",
-       "                      energy_water_heating  energy_cooking  \n",
-       "carbon                            0.947675        0.952764  \n",
-       "energy_heating                    0.994126        0.992522  \n",
-       "energy_cooling                    0.924791        0.929758  \n",
-       "energy_water_heating              1.000000        0.994235  \n",
-       "energy_cooking                    0.994235        1.000000  "
+       "                      energy_water_heating  energy_cooking     total       gas  \n",
+       "carbon                            0.953774        0.958315  0.955910  0.996810  \n",
+       "energy_heating                    0.994901        0.993171  0.999384  0.957210  \n",
+       "energy_cooling                    0.927544        0.933284  0.932756  0.871485  \n",
+       "energy_water_heating              1.000000        0.994353  0.996930  0.958951  \n",
+       "energy_cooking                    0.994353        1.000000  0.995190  0.960897  \n",
+       "total                             0.996930        0.995190  1.000000  0.959333  \n",
+       "gas                               0.958951        0.960897  0.959333  1.000000  "
       ]
      },
-     "execution_count": 200,
+     "execution_count": 706,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -459,43 +754,210 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 707,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Standardize"
+    "res_carbon_df.drop(res_carbon_df[(res_carbon_df.geo == \"EU27_2020\") | (res_carbon_df.geo == \"EU20\")].index, inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 708,
    "metadata": {},
    "outputs": [],
    "source": [
-    "res_carbon_df.drop(res_carbon_df[(res_carbon_df.geo == \"EU27_2020\") | (res_carbon_df.geo == \"EU20\")].index, inplace=True)"
+    "res_carbon_df.drop(\"year\", axis=1, inplace=True)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 230,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# TEST\n",
-    "res_carbon_df.drop(\"year\", axis=1, inplace=True)"
+    "## Missing Data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 289,
+   "execution_count": 709,
+   "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>geo</th>\n",
+       "      <th>carbon</th>\n",
+       "      <th>energy_heating</th>\n",
+       "      <th>energy_cooling</th>\n",
+       "      <th>energy_water_heating</th>\n",
+       "      <th>energy_cooking</th>\n",
+       "      <th>total</th>\n",
+       "      <th>gas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AT</td>\n",
+       "      <td>84795.76</td>\n",
+       "      <td>190731.846</td>\n",
+       "      <td>25.123</td>\n",
+       "      <td>41343.612</td>\n",
+       "      <td>6891.695</td>\n",
+       "      <td>274577.058</td>\n",
+       "      <td>1632.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AT</td>\n",
+       "      <td>81981.39</td>\n",
+       "      <td>195419.278</td>\n",
+       "      <td>34.215</td>\n",
+       "      <td>41839.251</td>\n",
+       "      <td>7026.774</td>\n",
+       "      <td>279812.556</td>\n",
+       "      <td>1585.826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AT</td>\n",
+       "      <td>82304.13</td>\n",
+       "      <td>205494.885</td>\n",
+       "      <td>31.035</td>\n",
+       "      <td>41541.823</td>\n",
+       "      <td>7353.56</td>\n",
+       "      <td>289591.863</td>\n",
+       "      <td>1554.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AT</td>\n",
+       "      <td>78716.82</td>\n",
+       "      <td>178513.717</td>\n",
+       "      <td>27.645</td>\n",
+       "      <td>42495.391</td>\n",
+       "      <td>7152.047</td>\n",
+       "      <td>261676.431</td>\n",
+       "      <td>1524.254</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AT</td>\n",
+       "      <td>81081.58</td>\n",
+       "      <td>193886.979</td>\n",
+       "      <td>25.714</td>\n",
+       "      <td>42104.965</td>\n",
+       "      <td>7179.133</td>\n",
+       "      <td>278096.422</td>\n",
+       "      <td>1539.983</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  geo    carbon energy_heating energy_cooling energy_water_heating  \\\n",
+       "0  AT  84795.76     190731.846         25.123            41343.612   \n",
+       "1  AT  81981.39     195419.278         34.215            41839.251   \n",
+       "2  AT  82304.13     205494.885         31.035            41541.823   \n",
+       "3  AT  78716.82     178513.717         27.645            42495.391   \n",
+       "4  AT  81081.58     193886.979         25.714            42104.965   \n",
+       "\n",
+       "  energy_cooking       total       gas  \n",
+       "0       6891.695  274577.058   1632.26  \n",
+       "1       7026.774  279812.556  1585.826  \n",
+       "2        7353.56  289591.863   1554.83  \n",
+       "3       7152.047  261676.431  1524.254  \n",
+       "4       7179.133  278096.422  1539.983  "
+      ]
+     },
+     "execution_count": 709,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "def fill_holes():\n",
+    "\t\"\"\"\n",
+    "\t\"\"\"\n",
+    "\tlin_reg = lambda X, Y: np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, Y))\n",
+    "\n",
+    "\tdfs = []\n",
+    "\n",
+    "\tfor name, group in res_carbon_df.groupby('geo'):\n",
+    "\t\tcols = [[name for _ in range(len(group.index))]]\n",
+    "\t\tfor i in range(1, len(group.columns)):\n",
+    "\t\t\td = group.iloc[:, i:i+1].to_numpy()\n",
+    "\n",
+    "\t\t\tmissing_mask = np.isnan(d) | (d == 0)\n",
+    "\t\t\tpresent_mask = ~missing_mask\n",
+    "\n",
+    "\t\t\tmissing_mask = missing_mask.reshape(1, -1)[0]\n",
+    "\t\t\tpresent_mask = present_mask.reshape(1, -1)[0]\n",
+    "\n",
+    "\t\t\tif not np.any(missing_mask):\n",
+    "\t\t\t\td = d.reshape(1, -1)[0]\n",
+    "\t\t\t\tcols.append(d)\n",
+    "\t\t\t\tcontinue\n",
+    "\n",
+    "\t\t\tif not np.any(present_mask):\n",
+    "\t\t\t\td = d.reshape(1, -1)[0]\n",
+    "\t\t\t\tcols.append(d)\n",
+    "\t\t\t\tcontinue\n",
+    "\n",
+    "\t\t\tx_present = np.pad(np.arange(len(d))[present_mask].reshape(-1, 1), ((0, 0), (1, 0)), mode=\"constant\", constant_values=1)\n",
+    "\t\t\ty_present = d[present_mask]\n",
+    "\n",
+    "\t\t\tw = lin_reg(x_present, y_present)\n",
+    "\n",
+    "\t\t\tx_missing = np.pad(np.arange(len(d))[missing_mask].reshape(-1, 1), ((0, 0), (1, 0)), mode=\"constant\", constant_values=1)\n",
+    "\t\t\ty_missing_pred = np.matmul(x_missing, w)\n",
+    "\n",
+    "\t\t\td[missing_mask] = y_missing_pred\n",
+    "\t\t\td = d.reshape(1, -1)[0]\n",
+    "\n",
+    "\t\t\tcols.append(d)\n",
+    "\t\t# print(cols)\n",
+    "\t\tdfs.append(pd.DataFrame(cols).T)\t\n",
+    "\t\t# print(dfs)\n",
+    "\tdf = pd.concat(dfs, axis=0)\n",
+    "\tdf.columns = res_carbon_df.columns\n",
+    "\treturn df\n",
+    "\n",
+    "res_carbon_df = fill_holes()\n",
+    "res_carbon_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "res_carbon_df.to_clipboard()"
+    "## Standardize"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 710,
    "metadata": {},
    "outputs": [
     {
@@ -524,53 +986,65 @@
        "      <th>energy_cooling</th>\n",
        "      <th>energy_water_heating</th>\n",
        "      <th>energy_cooking</th>\n",
+       "      <th>total</th>\n",
+       "      <th>gas</th>\n",
        "      <th>geo</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-0.270778</td>\n",
-       "      <td>0.005791</td>\n",
-       "      <td>-0.467261</td>\n",
-       "      <td>-0.075604</td>\n",
-       "      <td>-0.354672</td>\n",
+       "      <td>-0.278063</td>\n",
+       "      <td>-0.151182</td>\n",
+       "      <td>-0.520127</td>\n",
+       "      <td>-0.16618</td>\n",
+       "      <td>-0.443228</td>\n",
+       "      <td>-0.194221</td>\n",
+       "      <td>-0.20869</td>\n",
        "      <td>AT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-0.281369</td>\n",
-       "      <td>-0.049254</td>\n",
-       "      <td>-0.465859</td>\n",
-       "      <td>-0.063681</td>\n",
-       "      <td>-0.358060</td>\n",
+       "      <td>-0.292585</td>\n",
+       "      <td>-0.13864</td>\n",
+       "      <td>-0.516839</td>\n",
+       "      <td>-0.159931</td>\n",
+       "      <td>-0.439427</td>\n",
+       "      <td>-0.184739</td>\n",
+       "      <td>-0.222157</td>\n",
        "      <td>AT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.295793</td>\n",
-       "      <td>-0.036870</td>\n",
-       "      <td>-0.462471</td>\n",
-       "      <td>-0.057498</td>\n",
-       "      <td>-0.354234</td>\n",
+       "      <td>-0.29092</td>\n",
+       "      <td>-0.111683</td>\n",
+       "      <td>-0.517989</td>\n",
+       "      <td>-0.163681</td>\n",
+       "      <td>-0.430231</td>\n",
+       "      <td>-0.167029</td>\n",
+       "      <td>-0.231146</td>\n",
        "      <td>AT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.294139</td>\n",
-       "      <td>-0.010251</td>\n",
-       "      <td>-0.463656</td>\n",
-       "      <td>-0.061208</td>\n",
-       "      <td>-0.344978</td>\n",
+       "      <td>-0.309431</td>\n",
+       "      <td>-0.183871</td>\n",
+       "      <td>-0.519215</td>\n",
+       "      <td>-0.151659</td>\n",
+       "      <td>-0.435901</td>\n",
+       "      <td>-0.217583</td>\n",
+       "      <td>-0.240014</td>\n",
        "      <td>AT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.312524</td>\n",
-       "      <td>-0.081533</td>\n",
-       "      <td>-0.464919</td>\n",
-       "      <td>-0.049312</td>\n",
-       "      <td>-0.350685</td>\n",
+       "      <td>-0.297228</td>\n",
+       "      <td>-0.14274</td>\n",
+       "      <td>-0.519914</td>\n",
+       "      <td>-0.156581</td>\n",
+       "      <td>-0.435139</td>\n",
+       "      <td>-0.187847</td>\n",
+       "      <td>-0.235452</td>\n",
        "      <td>AT</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -578,22 +1052,22 @@
        "</div>"
       ],
       "text/plain": [
-       "     carbon  energy_heating  energy_cooling  energy_water_heating  \\\n",
-       "0 -0.270778        0.005791       -0.467261             -0.075604   \n",
-       "1 -0.281369       -0.049254       -0.465859             -0.063681   \n",
-       "2 -0.295793       -0.036870       -0.462471             -0.057498   \n",
-       "3 -0.294139       -0.010251       -0.463656             -0.061208   \n",
-       "4 -0.312524       -0.081533       -0.464919             -0.049312   \n",
+       "     carbon energy_heating energy_cooling energy_water_heating energy_cooking  \\\n",
+       "0 -0.278063      -0.151182      -0.520127             -0.16618      -0.443228   \n",
+       "1 -0.292585       -0.13864      -0.516839            -0.159931      -0.439427   \n",
+       "2  -0.29092      -0.111683      -0.517989            -0.163681      -0.430231   \n",
+       "3 -0.309431      -0.183871      -0.519215            -0.151659      -0.435901   \n",
+       "4 -0.297228       -0.14274      -0.519914            -0.156581      -0.435139   \n",
        "\n",
-       "   energy_cooking geo  \n",
-       "0       -0.354672  AT  \n",
-       "1       -0.358060  AT  \n",
-       "2       -0.354234  AT  \n",
-       "3       -0.344978  AT  \n",
-       "4       -0.350685  AT  "
+       "      total       gas geo  \n",
+       "0 -0.194221  -0.20869  AT  \n",
+       "1 -0.184739 -0.222157  AT  \n",
+       "2 -0.167029 -0.231146  AT  \n",
+       "3 -0.217583 -0.240014  AT  \n",
+       "4 -0.187847 -0.235452  AT  "
       ]
      },
-     "execution_count": 231,
+     "execution_count": 710,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -617,14 +1091,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 711,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(364, 33)\n"
+      "(335, 35)\n"
      ]
     },
     {
@@ -653,11 +1127,11 @@
        "      <th>energy_cooling</th>\n",
        "      <th>energy_water_heating</th>\n",
        "      <th>energy_cooking</th>\n",
+       "      <th>total</th>\n",
+       "      <th>gas</th>\n",
        "      <th>geo_AT</th>\n",
        "      <th>geo_BE</th>\n",
        "      <th>geo_BG</th>\n",
-       "      <th>geo_CY</th>\n",
-       "      <th>geo_CZ</th>\n",
        "      <th>...</th>\n",
        "      <th>geo_LV</th>\n",
        "      <th>geo_MT</th>\n",
@@ -674,16 +1148,16 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-0.270778</td>\n",
-       "      <td>0.005791</td>\n",
-       "      <td>-0.467261</td>\n",
-       "      <td>-0.075604</td>\n",
-       "      <td>-0.354672</td>\n",
+       "      <td>-0.278063</td>\n",
+       "      <td>-0.151182</td>\n",
+       "      <td>-0.520127</td>\n",
+       "      <td>-0.16618</td>\n",
+       "      <td>-0.443228</td>\n",
+       "      <td>-0.194221</td>\n",
+       "      <td>-0.20869</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -698,16 +1172,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-0.281369</td>\n",
-       "      <td>-0.049254</td>\n",
-       "      <td>-0.465859</td>\n",
-       "      <td>-0.063681</td>\n",
-       "      <td>-0.358060</td>\n",
+       "      <td>-0.292585</td>\n",
+       "      <td>-0.13864</td>\n",
+       "      <td>-0.516839</td>\n",
+       "      <td>-0.159931</td>\n",
+       "      <td>-0.439427</td>\n",
+       "      <td>-0.184739</td>\n",
+       "      <td>-0.222157</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -722,16 +1196,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.295793</td>\n",
-       "      <td>-0.036870</td>\n",
-       "      <td>-0.462471</td>\n",
-       "      <td>-0.057498</td>\n",
-       "      <td>-0.354234</td>\n",
+       "      <td>-0.29092</td>\n",
+       "      <td>-0.111683</td>\n",
+       "      <td>-0.517989</td>\n",
+       "      <td>-0.163681</td>\n",
+       "      <td>-0.430231</td>\n",
+       "      <td>-0.167029</td>\n",
+       "      <td>-0.231146</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -746,16 +1220,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.294139</td>\n",
-       "      <td>-0.010251</td>\n",
-       "      <td>-0.463656</td>\n",
-       "      <td>-0.061208</td>\n",
-       "      <td>-0.344978</td>\n",
+       "      <td>-0.309431</td>\n",
+       "      <td>-0.183871</td>\n",
+       "      <td>-0.519215</td>\n",
+       "      <td>-0.151659</td>\n",
+       "      <td>-0.435901</td>\n",
+       "      <td>-0.217583</td>\n",
+       "      <td>-0.240014</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -770,16 +1244,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.312524</td>\n",
-       "      <td>-0.081533</td>\n",
-       "      <td>-0.464919</td>\n",
-       "      <td>-0.049312</td>\n",
-       "      <td>-0.350685</td>\n",
+       "      <td>-0.297228</td>\n",
+       "      <td>-0.14274</td>\n",
+       "      <td>-0.519914</td>\n",
+       "      <td>-0.156581</td>\n",
+       "      <td>-0.435139</td>\n",
+       "      <td>-0.187847</td>\n",
+       "      <td>-0.235452</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>...</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -794,54 +1268,74 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 33 columns</p>\n",
+       "<p>5 rows × 35 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     carbon  energy_heating  energy_cooling  energy_water_heating  \\\n",
-       "0 -0.270778        0.005791       -0.467261             -0.075604   \n",
-       "1 -0.281369       -0.049254       -0.465859             -0.063681   \n",
-       "2 -0.295793       -0.036870       -0.462471             -0.057498   \n",
-       "3 -0.294139       -0.010251       -0.463656             -0.061208   \n",
-       "4 -0.312524       -0.081533       -0.464919             -0.049312   \n",
+       "     carbon energy_heating energy_cooling energy_water_heating energy_cooking  \\\n",
+       "0 -0.278063      -0.151182      -0.520127             -0.16618      -0.443228   \n",
+       "1 -0.292585       -0.13864      -0.516839            -0.159931      -0.439427   \n",
+       "2  -0.29092      -0.111683      -0.517989            -0.163681      -0.430231   \n",
+       "3 -0.309431      -0.183871      -0.519215            -0.151659      -0.435901   \n",
+       "4 -0.297228       -0.14274      -0.519914            -0.156581      -0.435139   \n",
        "\n",
-       "   energy_cooking  geo_AT  geo_BE  geo_BG  geo_CY  geo_CZ  ...  geo_LV  \\\n",
-       "0       -0.354672       1       0       0       0       0  ...       0   \n",
-       "1       -0.358060       1       0       0       0       0  ...       0   \n",
-       "2       -0.354234       1       0       0       0       0  ...       0   \n",
-       "3       -0.344978       1       0       0       0       0  ...       0   \n",
-       "4       -0.350685       1       0       0       0       0  ...       0   \n",
+       "      total       gas  geo_AT  geo_BE  geo_BG  ...  geo_LV  geo_MT  geo_NL  \\\n",
+       "0 -0.194221  -0.20869       1       0       0  ...       0       0       0   \n",
+       "1 -0.184739 -0.222157       1       0       0  ...       0       0       0   \n",
+       "2 -0.167029 -0.231146       1       0       0  ...       0       0       0   \n",
+       "3 -0.217583 -0.240014       1       0       0  ...       0       0       0   \n",
+       "4 -0.187847 -0.235452       1       0       0  ...       0       0       0   \n",
        "\n",
-       "   geo_MT  geo_NL  geo_NO  geo_PL  geo_PT  geo_RO  geo_SE  geo_SI  geo_SK  \n",
-       "0       0       0       0       0       0       0       0       0       0  \n",
-       "1       0       0       0       0       0       0       0       0       0  \n",
-       "2       0       0       0       0       0       0       0       0       0  \n",
-       "3       0       0       0       0       0       0       0       0       0  \n",
-       "4       0       0       0       0       0       0       0       0       0  \n",
+       "   geo_NO  geo_PL  geo_PT  geo_RO  geo_SE  geo_SI  geo_SK  \n",
+       "0       0       0       0       0       0       0       0  \n",
+       "1       0       0       0       0       0       0       0  \n",
+       "2       0       0       0       0       0       0       0  \n",
+       "3       0       0       0       0       0       0       0  \n",
+       "4       0       0       0       0       0       0       0  \n",
        "\n",
-       "[5 rows x 33 columns]"
+       "[5 rows x 35 columns]"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 711,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_dummies = pd.get_dummies(df_merged_s, dtype=int)\n",
+    "df_dummies = pd.get_dummies(df_merged_s, dtype=int, columns=[\"geo\"])\n",
     "print(df_dummies.shape)\n",
     "df_dummies.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 288,
+   "execution_count": 712,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/jk/vqmr1l1d1dnflmyh1qdjmhk40000gn/T/ipykernel_66892/3693932479.py:1: FutureWarning:\n",
+      "\n",
+      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "df_dummies = df_dummies.fillna(0)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 713,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_dummies.drop([\"energy_cooling\", \"energy_heating\", \"energy_water_heating\", \"energy_cooking\"], axis=1, inplace=True)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -851,29 +1345,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 714,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.        ,  0.00579135, -0.46726144, -0.07560401, -0.35467208,\n",
-       "         1.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "array([[ 1.        , -0.19422063, -0.2086896 ,  1.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "         0.        ],\n",
+       "       [ 1.        , -0.18473927, -0.22215674,  1.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ],\n",
-       "       [ 1.        , -0.04925367, -0.46585892, -0.0636813 , -0.35805979,\n",
-       "         1.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ]])"
+       "         0.        ]])"
       ]
      },
-     "execution_count": 234,
+     "execution_count": 714,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -886,22 +1380,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 715,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.37222448,  0.5223011 , -0.05161035, -0.61810575,  0.12303269,\n",
-       "       -0.36377837, -0.8083168 , -0.98725832, -1.43894754, -0.72166141,\n",
-       "        3.67563685, -0.82824913, -1.18894754, -1.10229215,  1.04523083,\n",
-       "       -0.90493835,  0.51144635, -1.31676292, -0.98725832, -0.88219065,\n",
-       "        0.85654786, -0.72729215, -1.32672908, -1.15060293, -1.47729215,\n",
-       "       -0.42639073, -0.9560645 ,  0.75277552, -0.86484897, -0.68179675,\n",
-       "       -0.78990451, -1.21169524, -1.04964596])"
+       "array([  56.20252722,  127.95282355,   43.70567339,  -24.29766787,\n",
+       "        -44.87242756,   24.04409072,   48.15089027,   -7.80834782,\n",
+       "       -715.48225259,   -2.65882844,   40.86351043,  -17.92484837,\n",
+       "       -162.67244527,    4.80913169, -438.8064785 ,   38.26739083,\n",
+       "         -0.98892811,   14.71399105, -353.58122047,   45.08681054,\n",
+       "         58.88487034,   33.86351043,   63.92759016,  -87.57534671,\n",
+       "         14.38389138, -198.7054641 ,    9.95865107,  -34.07436775,\n",
+       "        -45.87242756,   28.10817045,   43.28875074])"
       ]
      },
-     "execution_count": 235,
+     "execution_count": 715,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -914,23 +1409,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 716,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-0.30945998 -0.28344126 -0.30080717 -0.31654979 -0.28971399]\n"
+      "[ 1.78907501  1.14997642 -0.72151283  6.11610034  2.12409377]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "64.1"
+       "1173.1"
       ]
      },
-     "execution_count": 236,
+     "execution_count": 716,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -944,12 +1439,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 717,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -969,12 +1464,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 757,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# x values versus residuals\n",
+    "plt.scatter(X[:, 2:3], res, alpha=0.5)\n",
+    "plt.xlabel(\"Gas\")\n",
+    "plt.ylabel(\"Carbon\")\n",
+    "plt.title(\"Residual Plot vs. X Values\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 758,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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X6l4oFMTVV18t6uvrRSwWExdffLHo6OgYt5z8gQceEOvWrROmaYqTTz5ZfP/73x/3Pe+9915x6qmninA4LJYtWya++tWviu9+97sCELt3764cdzTl84eTBpjo/Xp6esRVV10lGhoahGmaYv369eKOO+4Y89onn3xSnHbaacI0zSMupd+xY4cABCAef/zxEc9ZliU++9nPig0bNohEIiFisZjYsGGD+Na3vjXiuPLv98gjjxz284QQore3V1xzzTWira1NhEIhUVNTIzZv3lwpmR9OuXz+Jz/5ybjvdc8994g1a9aIUCgk1q5dK376059OKJPw7W9/W5x22mkiEomIRCIh1q9fL/76r/9aHDhwoHLMkZ4niWQ2UIQY5qeWSCQSiUQiOYGQOUISiUQikUhOWKQhJJFIJBKJ5IRFGkISiUQikUhOWKQhJJFIJBKJ5IRFGkISiUQikUhOWKQhJJFIJBKJ5IRFCioeBt/3OXDgAIlEYlJy+xKJRCKRSGYeIQSZTIaFCxeOabA8HGkIHYYDBw6wePHi2R6GRCKRSCSSSdDR0cGiRYsmfF4aQoehLLXf0dFBVVXVLI9GIpFIJBLJkZBOp1m8eHFlHZ8IaQgdhnI4rKqqShpCEolEIpHMMw6X1iKTpSUSiUQikZywSENIIpFIJBLJCYs0hCQSiUQikZywSENIIpFIJBLJCcu8M4RuueUWli1bRjgc5owzzuDpp58+5PE333wzJ598MpFIhMWLF/PpT3+aYrE4Q6OVSCQSiUQyl5lXhtBdd93FNddcwxe/+EWee+45NmzYwEUXXURvb++4x//whz/k2muv5Ytf/CKvvfYa//Ef/8Fdd93F3/7t387wyCUSiUQikcxF5pUhdNNNN/GhD32Iq666irVr13LbbbcRjUb57ne/O+7xTz75JGeffTaXX345y5Yt48ILL+Syyy47rBdJIpFIJBLJicG8MYRs2+bZZ59l8+bNlcdUVWXz5s089dRT477mrLPO4tlnn60YPrt27eK+++7jbW9724SfY1kW6XR6xB+JRCKRSCTHJ/NGULG/vx/P82hubh7xeHNzM1u3bh33NZdffjn9/f2cc845CCFwXZePfOQjhwyN3XjjjVx//fVTOnaJRCKRSCRzk3njEZoMjz76KDfccAPf+ta3eO655/jpT3/KL37xC/7hH/5hwtdcd911pFKpyp+Ojo4ZHLFEIplP+L6gYzDP1u40HYN5fF/M9pAkEslRMm88Qg0NDWiaRk9Pz4jHe3p6aGlpGfc1n//853nf+97HBz/4QQDWr19PLpfjL/7iL/i7v/u7cbvRhkIhQqHQ1H8BiURyXNHem+FXW3rY2Zel6HqEdY0VjXEuWtdMW9OhextJJJK5w7zxCJmmyWmnncbDDz9cecz3fR5++GHOPPPMcV+Tz+fHGDuapgEghNy5SSSSydHem+GOJ/aw5UCKmqjB8oY4NVGDLQdS3PHEHtp7M7M9RIlEcoTMG48QwDXXXMOVV17Jpk2bOP3007n55pvJ5XJcddVVAFxxxRW0trZy4403AnDxxRdz00038brXvY4zzjiD9vZ2Pv/5z3PxxRdXDCKJRCI5Gnxf8KstPQzmbFY2xSsNHRNhg3hIZ0dvlgde6WF5QxxVPXSzR4lEMvvMK0Po0ksvpa+vjy984Qt0d3ezceNG7r///koC9b59+0Z4gD73uc+hKAqf+9zn6OzspLGxkYsvvpivfOUrs/UVJBLJPKczWWBnX5YF1eExXa0VRWFBdZj23iydyQKL66KzNEqJRHKkKELGiA5JOp2murqaVCpFVVXVbA9HIpHMMlu703zj4R0sb4ijjePxcX2fPf05/vL8laxukXOGRDJbHOn6PW9yhCQSiWQuEDN1wrpG3nbHfb5ge4R0jZg5rxzuEskJizSEJBKJ5ChorYmwojFOV6o4puhCCEFXqkhbU5zWmsgsjVAikRwN0hCSSCSSo0BVFS5a10xdzGRHb5ZM0cH1fTJFhx29WepiJhee0iwTpSWSeYI0hCQSieQoaWtKcNXZy1i3sJpk3mFPf45k3mF9azVXnb1M6ghJJPMIGcSWSCSSSdDWlGD5eXE6kwVytkvM1GmtiUhPkEQyz5CGkEQikUwSVVVkibxEMs+RoTGJRCKRSCQnLNIQkkgkEolEcsIiDSGJRCKRSCQnLNIQkkgkEolEcsIiDSGJRCKRSCQnLLJqTCKRSI4B3xeyhF4imcdIQ0gikUgmSXtvhl9t6WFnX5ai6xHWNVY0xrloXbMUVZRI5gnSEJJIJJJJ0N6b4Y4n9jCYs1lQHSZqRsjbLlsOpDiQKkiFaYlkniBzhCQSieQo8X3Br7b0MJizWdkUJxE20FSFRNhgZVOcwZzNA6/04Pvi8G8mkUhmFWkISSQSyVHSmSywsy/LguowijIyH0hRFBZUh2nvzdKZLMzSCCUSyZEiDSGJRCI5SnK2S9H1iJrjZxdETA3L9cjZ7gyPTCKRHC3SEJJIJJKjJGbqhHWN/ASGTsH2COkasQkMJYlEMneQhpBEIpEcJa01EVY0xulKFRFiZB6QEIKuVJG2pjitNZFZGqFEIjlSpCEkkUgkR4mqKly0rpm6mMmO3iyZooPr+2SKDjt6s9TFTC48pVnqCUkk8wBpCEkkEskkaGtKcNXZy1i3sJpk3mFPf45k3mF9a7UsnZdI5hEygC2RSCSTpK0pwfLz4lJZWiKZx0hDSCKRSI4BVVVYXBed7WFIJJJJIg0hiWSOIntYSSQSyfQjDSGJZA4ie1hJJBLJzCANIYlkjiF7WEkkEsnMIavGJJI5hOxhJZFIJDOLNIQkkjmE7GElkUgkM4s0hCSSOYTsYSWRSCQzizSEJJI5hOxhJZFIJDOLNIQkkjmE7GElkUgkM4s0hCSSOYTsYSWRSCQzizSEJJI5xvAeVkM5m1cPpNg7kGdJXZQrz1oqS+clEolkCpGJBhLJHKStKYG/FlIFm3TRwROC3nSRB1/pRVUUaQxJJBLJFCENIYlkDtLem+HOpwJRxaX1MaKmLkUVJRKJZBqQoTGJZI4xnqiiqoAQUBs12D+U51dbuqWookQikUwB0iMkkcwxRosqDuZs2nuzDOVtXM9HAH2Zbk5dXMMbVjbO9nAlEolkXiMNIYlklpiou/xBUcUIgzmbFzqSFGyXeNjACOvYrk9PusiPnt7HguqwDJHNASY6lxKJZO4jDSGJZBY4VHf5mKkT0lR6UgW292ZJFxyaEiaqGkSyFQWqIwY5y+WBV3pY3hCXi+4scqhzKY1UiWTuIw0hiWSGOVx3+fNObqQ/a7OtJ03e9tBVFc8X1MUMwoZGtujSVBVmeUOs0ndscV10tr/WCcnhzqVMapdI5j4yWVoimUEO111+30Ceb/66HQFEDQ0hQFMhZzl0Jgt0p4tETI0VjTGiIV32HZtFDncuB3M2D7zSI5PaJZI5zrwzhG655RaWLVtGOBzmjDPO4Omnnz7k8clkko9//OMsWLCAUCjEqlWruO+++2ZotBLJSEYnQgshSBcc+rMW6YJDznboy1i0NcZ43ZI6IqaG5wOKguX6AGxYVENdLCT7js0yo8/lcBRFYUF1uOKxk0gkc5d5NYPeddddXHPNNdx2222cccYZ3HzzzVx00UVs27aNpqamMcfbts0FF1xAU1MTd999N62trezdu5eampqZH7xEAmMSoYdXg/kiEFAMGRqOL1hcF2FlU5wDySKJsIYvAi+EoamVvmPrW6tl37FZYvi5HI+IqdGTLkqPnUQyx5lXhtBNN93Ehz70Ia666ioAbrvtNn7xi1/w3e9+l2uvvXbM8d/97ncZHBzkySefxDAMAJYtWzaTQ5ZIRlDuLn8gmWdHb25ENVi64JK3PVzPJ2+7NMRDtDUlyFoeBdslGtLJuQ5DeZvudFH2HZtlyucyb7skwsaY56XHTiKZH8yb0Jht2zz77LNs3ry58piqqmzevJmnnnpq3Nfce++9nHnmmXz84x+nubmZdevWccMNN+B53oSfY1kW6XR6xB+JZKporYmwvCHGlgNp8rZLXcwkpKuoikJYV9AUBQWFnpSFEIK6mMnGxTU0JsLkLJei41NwPNa3VstE3FmmtSbCisY4XakiQozMAyp77Nqa4tJjJ5HMceaNIdTf34/neTQ3N494vLm5me7u7nFfs2vXLu6++248z+O+++7j85//PP/yL//Cl7/85Qk/58Ybb6S6urryZ/HixVP6PSQnNqqqsHFJDY7r43k+tufjC4HlemQtl1hYJxrSGchZZIpBSKUuZrJpaQ2LaiNcuLaFv3vbGj5y7gppBM0yqqpw0bpm6mImO3qzZIoOru+TKTrs6M1Kj51EMk+YN4bQZPB9n6amJr797W9z2mmncemll/J3f/d33HbbbRO+5rrrriOVSlX+dHR0zOCIJScCDYkQi+uitFRHKDo+QzmLdCEIka1bWEVt1CBVCEJg5YW1vS/Hotoo7339EpbUx+TiOkdoa0pw1dnLWLewmmTeYU9/jmTekR47iWQeMW+C1w0NDWiaRk9Pz4jHe3p6aGlpGfc1CxYswDAMNE2rPLZmzRq6u7uxbRvTNMe8JhQKEQqFpnbwEskwYqZOQzxEdcQgmbfZ1Z8jW3TJWS6W66MgMDWV3oxFquBQEzFY31rNhadIgb65SFtTguXnxaWytEQyT5k3HiHTNDnttNN4+OGHK4/5vs/DDz/MmWeeOe5rzj77bNrb2/F9v/LY9u3bWbBgwbhGkEQyE5RzS9p7s+zozZKzXBIRg9qYCUKwb6hAquDgeD6qotCYCLN5bZM0guYwqqqwuC7K6pYqFtdFpREkkcwj5o0hBHDNNdfwne98hzvvvJPXXnuNj370o+RyuUoV2RVXXMF1111XOf6jH/0og4ODfPKTn2T79u384he/4IYbbuDjH//4bH0FiQRVVbjglCbSRYe+rEUspGFoCpmiw4FUEU2BWFhHUxWW1EXpGMpz55N7ae/NzPbQJRKJ5Lhj3oTGAC699FL6+vr4whe+QHd3Nxs3buT++++vJFDv27ev0o8JYPHixfzqV7/i05/+NKeeeiqtra188pOf5G/+5m9m6ytIJABEDJ3GRAhdVcjZHjnLIllw0TWF1uoImqaSzDsArGyKs6M3W+krBsgwjEQikUwRihhd9ykZQTqdprq6mlQqRVVV1WwPR3KcsLU7zTce3sFJ9THytsdg3mbL/hSJsE7I0PCFIJm3Of2kehriITJFh2Te4Y/+oJWXOlKywadEIpEchiNdv+eVR0giOV4oi/EVHI+qiIHt+SgKGHrg0XQ8H01VMbXg/xFTo703y4+e3ocQyAafEolEMkXMqxwhieR4YbQYn6mp6JqK4wmEEGSLgdhiIhzsVfKWS3/WJme5ssGnRCKRTCHSEJJIZoHRYnwgqI7oDOVsBrJWpcN8uTHrrv4cCoLlDXHZ4FMikUimEGkISSSzxHAxvlTBJaRrqApoqsrKpjhVEaOiUhwL6dTHQ0RNbUS3+nKKX8TUsFxPNviUSCSSo0TmCEkks8hoMb6+jMWLHUl29eXY058jpGusb63m1EXVfO/JPfx21wA528P1fXRVpS5qsqIphqGpssGnRCKRTAI5a0oks0xZjA9gdQucvaJhTHl8e1+GvoxFd7pIS1WIhG7ieD69mSLpok1tLMSZy+tlg0+JRCI5SqQhJJHMMYYbRgC+L3jwlV4SYYOC4zGYc0iEBNGQRiyk050uoqsqm9fIBp8SiURytEhDSCKZ43QmCzzfMUTBdnE8n5ztkio4GKpCVdRgUW2E2qhJxNQO/2YSiUQiGYE0hCSSOc5rXWm2d2cwNIXqiEF9zCRne2SLLqamsqopTrroykRpiUQimQSyakwimcP4vuCZPUO4viARNgjpGpqqUhU2WFAdxvMF23uzmJoqE6UlEolkEsiZUyKZI/i+GJMk3Zks0JcpsqA6TKrgYOpqRUdIURRiIY2uZJHTltbKRGnJccN494LMf5NMF9IQkkjmAO29GX61pWdMD7GVzXEsz+fklipe7kwxmLOJh3UMTcXxfLJFD11T2LSsTi4UkuOCie4F2U9PMl1IQ0gimWXaezPc8cQeBnP2mB5i23sz2K5P2FDZuLiG9t4sQ3mbrOWiqyo1MYPaqMmaFtkQWDL/Ge9eyFkOT+8Z4JWuFJefvoSzVjRIo18ypUhDSCKZRXxf8KstPQzmbFY2HWyfkQgbxEM623syWK7PgWSRVc1x/nBZLZmii+35GKpCd7rIqYtqZFhMMu8Z714YyFq82pVmMBcY/3v6c7xt/QLesq5FeockU4ZMlpZIZpHOZIGdfVkWVIfH7SG2sCZCSFcJ6So7erNkLZdoSCOkq/RkLOrjIS48ReoHSeY/o++F3f05Hny1hx09GYZyNq7n05Uq8tj2Pu54Yg/tvZnZHrLkOEF6hCSSWSRnuxRdj6g5vkcnYgZGz9s3LGBHd5adfVl60sVK640LT5F5E5Ljg/K9EDHC7O3P8nj7AAXbIxHS0DUVzxdkLZfBnMW+QY0HXulheUNcbgIkx4w0hCSSWSRm6oR1jZwVaADZno+pqSTCOoqiULA9QrrGmpYqNq9ulpU0kuOWmKljuz5P7exn90CedNFBVxQKjkJECTykYUPF9QR522VHT4bOZGGECrtEMhmkISSRzCKtNRFqogaP7+hHUcATotJMdXljlIGcw/rW6orRIyd9yfFKwQmaDncmC7iej64oaKqC4/l4RR9dU6mKBMUBmaJLsuBIEVHJlCBzhCSSWWRXf5bejEXB8Sg6PlFTx9QVOpN5Htvej6YqMgdIctxT7qdXFTaoiRi4vgAFUEBTFWzXx/F8IoaKLwSW46IqSBFRyZQgryKJZJYoV8l4vuDcVY3s7MsxlLdxfZ+IoeEDzYkQyxvisz1UiWRa2T+U56XOJA1xk4ihliQiPBxPoOCjqgqu59ObtgLPqS+oi5uyWlIyJUhDSCKZJYZXySTCBnUxs1Iab2oqIBjKOzIPQnJc096b4ftP7WPL/hRhU0VXVaKGhqooWK5P0fEC55ACqgpFx8fUVfKWz67+rCwWkBwz0hCSSGaJ0RVjiqJQFTEqz7u+T2/GknkQkuOWsoDi/qE8YVMjHtJRFCjYHq7n4wuBooAQAiECw6gqbHD6SbW4vpCVY5IpQeYISSSzRLliLD+BoVOuGJN5EJLjkeECiqe2VtOUCJOzXExNpaU6HISHfUGk1Gg4Zqqc1BBj00m1NMTDtFSFaO/N0pkszPZXkcxz5AwrkcwSrTURVjTG2XIgVdoJH9zVCiHoShUrFWMSyfHG8NCwqqq0NcVLOkFBP71YRCdZdIiGNKq0QGnd8wTbujLs1HJUR3RCuiY9ppJjRhpCEsksoaoKF61r5kCqwI7eYEGImBoF26MrVaQuZs75ijHZJVwyWUaHhuti5oh+epbjIwSEjcAjZLs+8bCBoSk4nqA7ZaEq0JexWN0yy19GMq+RhpBEMou0NSW46uxllW7b80k1WnYJlxwLw0PDiXCQG1cXMyv99AZzFq90pnAFKEJQHzMrXlNTA10FTVV5qSPF2bIRq+QYkIaQZN5yvHgj2poSLD8vPq++y3hdwvO2y5YDKQ6kClx19jJpDEkOyUShYUVRSIR1utNF/mBpLU/vGcLz/KDRsKbieD7Zoks0pLOyKc7OvqysrJQcE9IQksxLjjdvxHxSjR6vSzhAIhzkcezozcpqHslhOZLQ8LmrGulOW9iuHyhJWy6aqtJUFWZFY4yqiMGe/pzME5IcE9IQksw7pDdidhndJXw4iqKwoDpcqeaZL8adZHY4XGg4pGs0xENUl2QlRvfiyxQdWVkpOWbk1SOZV0hvxOwzOsl1NBFToyddlLt0yRFxqNCw74tK+Gz4/Q6yslIydUhDSDKvkN6I2We8JNfhSP0jydEyUWj4eKislMx9pKCiZF5x0Bsx/iIbMTUs15PeiGmknOTalSoihBjxXHmX3tYUl7t0yVHh+4KOwTxbu9N0DObx/eDaKofP1i2sJpl32NOfI5l3WN9aLcPgkilBbtkk8wrpjZh95C5dMtUcrvhhPlZWSuYPcrWQzCukGvPcYKIk13ULq9iwuAa3tLuXi5XkUPi+4Imd/fzo6X3kLJflDXEWhsYvfphPlZWS+YU0hCTzCumNmDuM3qX3ZSxe7Ejy0+c6jwtJg6PleNG1minaezPc/3I3923pZjBnUR0xcFzBiqYYdbGQLH6QzBjSEJJMK74v6BjKs7s/B8DyhhgLqyN0laqKJrNgzGc15uON8i69vTfD/Vu6T1hJg+NN12q6Gd513nI9mqvCKAr0ZopkLIeNi2uoi4Vk8YNkRpCGkGTaaO/N8MPf7uO3uwcZylt4no+qKsRDBs1VYaoiBhFjcguGzBmYO5zokgZS1+roGH69tNZE6BwqYOoqqqJgxlQGczY7+3LURk0pxSCZEaQhJJkW2nsz3PzQDl7sSOL5Asf1SRUdXA+6sdk7kGN5Y4xTFlZPesGQOQNzgxNZ0uBENwInw/DrRQjQNRXHE4R0BUVRiId1BnM2maKLoiCLHyTTjiyfl0w5vi+4/+VutvdkACg4Hqmii++DqoACuD7sHsjzcmea+pjBYM7mgVd6KiWzkvnDiSxpcDRGoCRg+PWSCOvURk2yRacixWBoKp7vY7melGKQzAjSEJJMOZ3JAi93pnA9H9vzKDgeCsEkpyqgqwqqEhhMfZkiu/rytFSF5IIxTxkuaTAex7OkwYlsBE6W4deLoii0NcWJmIEXqOi4ZIoOBcdnZ1+W2qghix8k0440hOYhEwmPzRVytkvOcXF9geX4IEAbvlsu/VNRAgn9nkwRz0cuGPOUE1lg8UQ2AifL6OulLmaycXENsZBO51CRPf15UvkgNBY2tNkeruQEYN4ZQrfccgvLli0jHA5zxhln8PTTTx/R63784x+jKArvete7pneA00x7b4ZbH93J1x/czjce3sHXH9zOrY/upL03M9tDqxAzdWKGji/A9QUgUBSo2EKltVJVAmPI8XzSsnnivKUsaVAXM9nRmyVTdHB9n0zRYUdv9riWNDiRjcDJUr5eaqMmL+5PsncgR6rgUHA8UKClOsSbVzdxxkn1dKWK3PHEnjk1v0mOP+aVIXTXXXdxzTXX8MUvfpHnnnuODRs2cNFFF9Hb23vI1+3Zs4fPfOYzvOENb5ihkU4P5eqULQdS1EQNljfEqYkabDmQmlOTRWtNhPWt1Riagi8EouQCUhUFX4BX8mAZqloJlQ3lbblgzGNO1DYIJ7IReLQM92R3JgvYnkfHYJ4n2/t5ZGsPXckCrTVh3rCykWUNcaoiBiub4jJ/UDLtzKvt90033cSHPvQhrrrqKgBuu+02fvGLX/Dd736Xa6+9dtzXeJ7He9/7Xq6//np+85vfkEwmZ3DEU8d8qk5RVYW3rG/hte40AxkL2/cRCiiKIPAPgaEqKIrAFwq6qrKoJioXjHnOiSppIHWtDo3vC37T3se9LxygK1UkVbDoGCzi+YKYqRHSFYquQsTQ8PyRrz3eqw4lc4N5YwjZts2zzz7LddddV3lMVVU2b97MU089NeHr/v7v/56mpib+/M//nN/85jeH/RzLsrAsq/L/dDp9bAOfIuZbiXJbU4JPX7CKkKby0NbeIPfHB00FXQ0cka4PdTGdc1Y2cPkZS074BeN44ESVNDhRjcCJKKtsv9KV4gdP7eOF/Ulsx8NH4HrBMQrgej4RU8PxfExNIV10KhpC5XlOaglJppt5Ywj19/fjeR7Nzc0jHm9ubmbr1q3jvubxxx/nP/7jP3jhhReO+HNuvPFGrr/++mMZ6rRwsDpl/NDRXJws2poS/PN7NvKb9j6+/9RetnVnsFwfVYF4SOeU1mouOW0RZ61oOGEXDMnxw4lqBJYpGz+vdad5ZvcgL+1P8nJnirwduHlUwBt2vKqAD+RtDwUouj6G7jOYtcgUXaoiQVNlmXAumW6O2ysrk8nwvve9j+985zs0NDQc8euuu+46rrnmmsr/0+k0ixcvno4hHhVzrev6eK0zFtVGxxg0qqpw7qom3tDWeETHSySS+UN5HniivZ9n9w7RlSyweyBHznIp2h7usFCXN/q1AlQRGEQCcDyfYmkes0sxsvEaKcuebpKpZt4YQg0NDWiaRk9Pz4jHe3p6aGlpGXP8zp072bNnDxdffHHlMd8Pbi5d19m2bRsrVqwY87pQKEQoFJri0R87c6nr+ujWGb4viIV0Tltay4feuJy2xsS4Rs/S+hhL62PTPj6JRDL9lOeBx7b30ZUu4rgenggMnNGoVIpFR+ALgaoqhA2NouNTdH3ino+mKGSKzphGyrKnm2Q6mDeGkGmanHbaaTz88MOVEnjf93n44Yf5xCc+Meb41atX8/LLL4947HOf+xyZTIZ//dd/nRNenqNhrnRdH906QwgoOh6pgkPnUJ7/295LS3WUoZyN5XpoqkpN1OD1y+tlHpBEcpxQngde2DdEpuhVpDEmKuzyx3msXDgBQRsN1/MRQNjU6M9ahI2RCeeje7pFjDC9mSJP7epne2+Gj79pBauaq6b+y0qOe+aNIQRwzTXXcOWVV7Jp0yZOP/10br75ZnK5XKWK7IorrqC1tZUbb7yRcDjMunXrRry+pqYGYMzj84XZrk4Z3TrD8Xws18cv7QJtD3oyDj2ZFAoQ0hViIZ1kAR58tYfejMWnNq+UxtAJhAxjHH8MnwdUVUFTBa4r8CZR3e6XxFZ938fzYVlDlL9961qaqkMjrpfRVbNDeYfXupIM5W1cz2dXX45/yNl8/u1rWdUi5xfJ0TGvDKFLL72Uvr4+vvCFL9Dd3c3GjRu5//77KwnU+/btQ1XnlTTSUTOb1SnDW2c4fmAEub6P6wvcUbOgADwBmaKLLyAe0tjeHbi1l583+yX+kumnHMZo780wVLDRFJUVjXEu2dQqd+7zmPI84PmCiKGTzDuBcKoIKsGOxh7SlMDbnbE8aqMGn7lwNW88uXHczyxXzQ7lHV7oSFKwXeJhAyOsY+ouO3uz3PJIO395fpvcbEmOinllCAF84hOfGDcUBvDoo48e8rXf+973pn5As8BsVacMb51hOx6+EEF4zBPjur4RoKhBIndYV/E0n5c6k3OmxF8yfZTDGPsG8uRtl6zlYrk+r3Wl+d3uAa4+fyXnr2k+/BtJ5hzleQAodYwPcn3g6AwhQ4WqiIGqKCyuDXHNhavYvHb8a6JcNRsxwrzWFRhBdbGDJfaxkI7tegzkrDmjpyaZP8w7Q0gyewxvneF4gRGkKjBRwb4vBLqiIkTQgd7UVfK27Cd2vFMOY+wbyDOUtyk6HvGwQVVEwXF9utJFvvHwDhbXRmUYYx5Sngcg0DCL6Co5K2ihowCI8XOCIPAAaapCXdRgSX2MRNigrSnOH592aC9huWq2N1NkKG8TDxsjCkYcz0fXNBZUR+aUnppkfiANIckRU26dsb03g+sL/JIhJMbZAirD/haIyvFRU+qBHO90Jgu092bI2y5Fxxuxcw8ZGi1VIfoyFnc/18G1b1kjd+7zjPI8sHsgR9ZyaYiHSBddiqVaecGw+7/kIlIUaIyHOGdlI+/Y0MLyhjh5xzvi0H65avapXf24no8RPjiHCCHIFl2aqsI0JkLsHcjJzZbkqJAr0jxmphNRh7fO6E9b5Hwf/RApWYpysDLE8wW6rnJqa43sJ3ack7Ndhgo2Wcsds3MHMHUNU1fYKXfu85LyPLC1J8OLHUlcT9BYFaY7VcB2gzY6GpCI6OgqKIrK6gUJPvzGFZMWTy1XzW7vzbCrL4epu8RCOo7nky26REyNFY0xio4UX5QcPfJqmafMlp5GuXWGqSr88tUeLMev5ASMmd4ElZLYUEhj7YJqLlon+4kd78RMHU1RsVyfqsjYc+14Pqau4QvIWA4dg3lZVTbPaGtK8KnNKyt6YqmCTVVYx3aD2UDXFEK6Rm3U4Iwpks5oa0rw8Tet4B+yNjv7stiuh65pNFWFWdEYozYaNL6dKT01yfGDIsR4gQ1JmXQ6TXV1NalUiqqquVHpMlpPI2rq5G23oic0E92+fV9w1zP7+Pb/7WIwZwcVZJ5AKZlFXtkVpAQL4wVrm/nYm1bIao4TANf1+fz/vMxDr/XSEA+RCB8UABVCMJizqY4a1EQMVjTG6c/aFBwX34cF1RE2r22qeA7KysU7+7IMZG0a4iYrGuMsrI7QVWopIw2o2WO0wvyy+sC7t2cgD0yPgvz27gzffGQHB5IF6mIhmqtC6KpCd9qasflPMj840vVbeoTmGXOlC72qKlx2+lJOW1LH3c928NL+JB1DBbJFF8/3MXWVeEhn9YIq/uz1SzmnrXHEeKS+zPFJ2VPZm7GwXZ/d/TlqIjoNiTC6ppAtuoSNIJ7an7Vx/Qy6qjCQs8kUXF7Yn+Sx7b28aXUTZ7c18MSOfh7b3kdvpojrC3RVoSZq0pQIURszMXVVqgvPIqqqjKsYv6whPo2fCQ2xEO29OQ6kUrxyAKojUrRVMnmkITTPGN2FXghBuuAwVHBAQMzU2NGTmbHci1UtCa596xo6kwUylkO64FCwPRRFmXA3eDT6MtJgmj8M91QurY8RNnR+t3uAZMElY2WpiRpUR0zChkam6FJ0fFIFh8GcjRCCWMigIW6SLTj8aks3v3y5i6Lr43hBG4aorlK0PbpSBXrSRRoSIf5waS3VEYMtB1IcSBWkN+A4Z/g1dsZJtXg+pItOpTpRIpkM0hCaZwzvQj+Ys3lpf6DLU54ETE0hYuo8+FoPF6xpnhHD4Wh0jQ6nL/OJN7exuqWKnO3Sl7F4sSPJrr6c7Cs0xxnPU5kIG9RGDZ7dO8jewTypvIsQCrmiw2DeIR7WEQTXj6mpFB2X/YMOvi/IO4FiuaIE13TC0DE0lYLt4fngIuhNF3mifYCVzXFWNMYYyNlSQ+Y4ZiJveG3MZElddMa84ZLjD2kIzTPKehoHknle7kzTky6iKAoxs1RBYbmkCi63PtLOk+39vG5x7ZwxHA6nL9MxlOfzP9vCuoXV2J5Px2AeQ1NZ11oVlNvartz5z1FGeyrLKIqCpqrURU0UYNOyWrZ2Z+nOWBRtL+gtZWj4QlB0PIruKIXykmZV1vLQFZ/CsOR8z4dM0WHvQJ6s5bKyKS41ZI5jJrrGILjOFlSH5fmXTIrjux/FcUhrTYTlDTFe7kzRn7XQVIWYqSIQFF0fIUBXg2qtoZzNy50p7nhiD+29mdke+rj6MiFdRVWUSrPGZN6hK1XAcr3S4ujyUmeKzmSeeEhnZVOcwdLO35+ow6NkxjnoqRyp79Lem6XoeLRUhwmbGj4KlusTNXQcL7hmXS8IkVnu+OfTE1BwfDIlwwmCCkVFAU8ICo5LquBwIFWk6EjBzuOV8a6x4URMDcuV519y9EhDaJ6hqgobl9RQsD1s10NTFYSAnO2V1FUhHjZAUUgWHFqqQnPGcJhIXyaoJLLwBJi6QjJvs28gT87ySBddupJFHt/Rz+/3DDKUt0fs/CRzg7KnMj9sEcoU3YoKsOsLNFUt9aMSRENaYMh4PjnLxfHEUfWogsAY0lQF3w9K8vvSRTwhZl1DxvcFHYN5tnan6RjMz/p9d7ww3jU2nIItNYQkk0NeMfOQhkSI5qowedsL8ilcH8cNKrVipoauqhQcF8fzcXwxZ1zGE+nL2G4Q8tBVBUcoDOVdLNcLFjpNRVfBcj0OJAtkLZf1rdVy5zfHKCv/bjmQIh4KyuVtL/D26CGNZD5Q/q2NGhi6hqFrWI5HwfGwPHF0nTo5eLipaYQNjaLj4XjBtT6bGjKzpe91IjDeNVZGCEFXqig1hCSTQnqE5iExU6chHqI2atJSFaapKkTE0KgKBQmlnggEfAxNxdTUOeMyLk9kdslwg2ACKziBN6tguViOh+N5QZKsrqKrCp4PRScw9Aq2x7aeDKamyp3fHKKs/FsXC0TtMkUnaL8C9GesivJvVcSgNmriej71cZOIqaMwtjfV4VJdFUBXFcJGcKTl+IR0lfPXzJ5gZ7kQYMuBFDVRg+UNcWqiQUXbXAlPz2fGu8Zc3ydTdNjRm6UuZnLhKVKwVXL0SENoHlLu9aNpCpbnEzU0dE3B8UTgXbFdFKA5ESYR1ueMy1hVFS7Z1EpjIkRXukgyb9GZLNCVKpItuhRKpdKKAiFdwycIo2hBRIVM0SVmanQlizRWheTOb47R1pTgqrOXsW5hNcm8w0DWIqRrmLrGhkXV1MVCKIpCW1OcsKGRLLgsqYvQEDfRlZH96XQVwoaCPsGaFjYCA9/zA2+hpiqc1VbP2SsaZurrjmB0RVMibKCpQeWczGubOkZfY3v6cyTzDutbq2e0gEKGP48v5JZ6HjK610+vFTQ8tB0fXwSGRHVEpSFhAswpl/Gq5iquPn8lX7t/Kx1DBRACQ1MImwpFRwRNWoWCqStYLri+QAiBqSkUHY+hgoOuKWxaVlfZ+UmtoblDW1OC5efFK+ejL2Pxy5e7GcjZmLpGxNQwNIXaqImmKjTETRzXJ28H8g9CBIaNrqloioKveKiC0jWi4fsCX8Di2gimrlJ0/UC3qC7Mh85ZMWvnXVY0zRyjr7GZvudl+PP4QxpC85Ryr59v/bqdR7b34Xg+AhFUkYV0oqbGtu4MQ3mHJXXROeUyftPJTTy+o59Ht/UiRCAW47g+/b6FrirYro/t+kR0jZzjIYRAQcV2fQxVZVlDlDUtgfCinJTmHsN1pVa3wILqcOUc9aSLhHSNM1fUs3ltExFD57XuND/+3T529GaD3nQW2I6PLXwUJdCJOXVRFX0Zm65UgZCu4gPpoovt+rTWRLj6/JWsapm98z1c32s8IqZGT6kliOTYORrtsqlkbHujiJT1OA6QhtA8ZnlDnOVNcQbyNtURg6wVVOkULA/XF2SKLs1VcOWZc+vm7EwWSBUczju5CQDb87Ecj2f3DpG3PVLCoej4GJpKVVjHL4U/IGjSaXuCgu3JSWmecLgd/OK6KMsbYpUGnq7v4/sCVVFZWBNm07I6IoaG62UrXiRfBM+3NcX549PGKpLPNMMrmhJhY8zzcyU8LTl6yh7njOXws+c6GcjarGqevfZGkqlH3pXzmM5kgV19OVY0xiuTrxCBAWR7Prbr4XqCiKnN8khHUt49LwxF0NSDJfSdySK9mSJVYY2utBUkfvvgleLvIS0Q5gP43pO7CRvarPdckxwZh9vBtzUl+Nw71lYaeB5IFtjdn6M/Y5EuOFiOP8KLNNfCoLKi6fhkuMd5MG+xszdHUyJEY8KkLhaqHCfDn/MbaQjNY8ZzxyuKQlUkMIpc32dPf27OuePH2z2Xk2izlku64FATMXCFIF9wgxwhXWVpQ4z1rdXURk1e3J+kP2Nx+kn1MidjDjKZvK3RDTznU+5XuaLpQKrAjt4gVyhiahRsj65UUVY0zUNGe5xNXWVPf55k3uaFjiQbF9eMMIZk+HP+Ig2hecx8dcePt3sWIugsvqQuwisHHMJG8L0aYia1MZOTGmMsro0e7C8UNWnvyVa8RaORk9LsMVV5W7OVBzJZyhVNo/Oh1rdWc+EpMmdtPjFeXzMhgnYwYV0la7ns7MtRGzUrc9JcnW8lh0eesXnMkbrjF1SF6RjMk7NdIoaGAuQdb9Z22aN3zxFDpTNZoD8bqE5HdJXljXEGsjanLKymJmqM8fokwjooQa+p2pg55jNOhElpLnpMTvS8rdmuaJJMDeNVASbCOrVRk75MkVhIYyBrcSBZIGRoGKpCd7rIqYtqZPhzHnL8rhInAIdzx9dGDerjJl++71W6UkXylsdAzgYEDfEQDfHQrFVYlXfPP/ztPh7Z1kvB8YiFdE5qiLGwOsxAzmYwZ5O1xjd0dFWhOmIymLdZXBc94XIy5mK13ETdwU+0vK355smSjGWitINy+H4wa5OxHIquj0Kgjt+YCPEnmxLH9bV9vCINoXnORO74BVUhOpNFvvHwDizXJ6KrWF4QfjI1hQEFGuLmrO7UlzfEqYubLKmP0loTwdQCfU/HFywL6RxIFtjSmS7tqA9qfwoh6E5bnLm8joLjn3A5GXPV6yK1dCTHCxOlHdTFTE5qiPFkqoDl+nieTyykUx83iZo6v97ay9L66HHt9TwekYbQccBod/zz+4a484k97BvK4wuIaApDjofnC+Ihjfp4hILt0Z22OG1JDe19uVnZqQ+venM8wbaeLEN5O+hPpakYmkqq4PBSZ4oVjfExhs5lZywBOKFyMuay10Vq6cw8vi/oGMqzsy/LQNamIW6yojHOotromPM/F0Opc5WJ0g6EEPRlimiKwpqWBKsXVBHStSBUDyeM1/N4QxpCxwlld/yDr3bzrw/tYDBn45cSkC0v0OrRVQXLFQzlHRpiZin05M3aTr28cBYdjZc7UxTsoCu9EdZxPEGm4OALQW3UJJl3JjR0TqScjLnsdZmvyfvzlfbeDD/87T4e295Hb6aI6wu0kgDl6pYq3ry6ibPbGlhUG6W9N8vdz3awsy+LJ6A2YtDWlJDCoxMwUdpBb7rI3oE8tTGDU1qrR1SNAdLrOU+RM9JxxPaeNDc9sJ2hvEPU1Cg6PqoStKnwRaDH4wtBwXbxoyae72N7PjVRY1Z26jFTJ6SpbOtOU7Bd6mIHKzBCuoIIaxQcj8aEyftev2zCBO8TKSdjLntdDpe8fyBZYGl9jEzRoWMwf1wbrNNNe2+Gmx/awTN7BslaHqqqYKoKectl/1CR/UNFHt3WS308RHNViMGcQ9H1MDWVuKmRzuvsTxZ4pSvF5acv4awVDfJcjGK8tIOi45MI6/zBktoxRhBIr+d8RRpCxwm+L7j7mU76sxZhXcXUVSw3aFGgawquJxAKuJ5AU0WpUWXQnX62duqtNREaE2F+t3uQpkRozMKZszwW1ITpz9goisLqltlVD54LzGWvy6GS93f0ZEkXHVxf8M1H2udEcvd8xfcF97/czbbuNJbrY2gKugoZy8MXQYNiAMeH7rRFd9oCQFMgFtLI2x5OqghCsENT2daV5s1rmvmTTYtmXaF7rjE67SBdcPjR7/YRNsYXqZVez/mJ7D5/nFAOmZi6iq4pKIChKngCFBH0IAu8Qj4IKDoedTGTeEijK1WkrSk+4xVWqqqwaVktuqqQKTpYrocvAiNtMGcTMTVObk5ge77cYZUoe126UsWgT9swytVys3Euy4zXHXzfYJ500aEqYrCkLsryhjg1UYMtB1Lc8cQe2nszszLW+UpnssDLnSks1wcIGhLbXtBv8BBN0D0B2aJHMu+QtTxytk+m6HIgWeR/Xujksz95iYdf65mhbzF/KHucV7dUsWlpHW1NiTl7/0kmhzRbjxNytosnfGKmhicEluMTMlRc28cToGkCzwGP4IaNh3RaqkK09+VmtcJqzYIqVrUkGMrZ5GyPnOWiqSpNVWFWNMYwNJWi48sdVon5oGA8fBdd7s+kq6rszzRF5GyXnONS1hIVgO0LhH/QGzQR/uj/C3B8n6ip0Z0OqkwX10ZntYHtXGa67j+ZyD67yNXlOCFm6tRGTDIFl6Lj46kC1xdEjKBru+UKBGCqCo2JMPXxEELAkroopy2tJaRrQaPLGb75WmsivG5xLS93plhfFcLxBaamjqjCOJ71gCbDfFAwLu+iOwbz9GdtFtbMveTu+UrM1IkZOuVb1fNFxRN0OEOojFI6thwSKDo+LYkQfRmLu5/r4Nq3rJEL8QRM9f03FzXBTjSkIXSc0FoToa0pQX/WpioS7Pts38cpCX4B1MUM/urCkzmnrYGtPRme2T1Ib7rIz17o5P4t3bNy8w3fYfVkrMoOK2u5c8bDMReZLwrGczm5e77SWhNhfWs1u/qzZADHEyhHedrFsL9NTQ3CaiiYusJOaZgelqm6/+aqJtiJhjSEjhOGGxQAIV0lXXTIKx6qotBaG+WaC1dxwdoW2nszPLatb9jNp8/qzTcfPBxzkblSLXcot/5cTu6eTqYz1KGqCm9Z38LWngyZPYNkikFj4vG8QWXPz0RoKsRCOp4f5OaZuoYvkIbpEXCs999c1gQ70Ti+Zp8TnOEGRXtvhmTBRlVU2pri/PFpraxqrpqSm286JvnD7bBkDH1ucji3/pH2wzueQp8zEepoa0rwqc0rKzpC3akCjncwA0gBVCVIkJ4IFagKG6iqgi8ERcejIRGiJmIcd4bpVDDVc9BoTTAhBJmii+35mJoa5HBK79yMMGVXezKZpKamZqreTjJJDmdQHKsg33RO8hPtsGQMfW5ypG79uZ7cPZXMZKijrSnB596xlitLytKvHkjzRHsfL+9PU3A8hAiMIQUQ4mCidPmXToQ1QoZK3vLQVIVESCdq6qxsThxXhunhOBIDZzrmoOFh48GcTXvvSGX96ohOSNekd24GmJQh9NWvfpVly5Zx6aWXAvCe97yHe+65h5aWFu677z42bNgwpYOUHB2HctkeLmcjbKgM5S22HEgBjJgUZiOePfozI0aYnnSBR7b18MzeQa46exnntDUeNwvpfOFoPIsnSuhzNkIdqqqwtD7G0voYb17dzMfOa+M37X18/6m9bOvOkLMCD4PvC4SiENZVEiGdjO1hOUEpvakptFRHqI2HWFIXPa4M08NxJAbOdM175bDxgWSeHb25Mcr63SkLVYG+jMXqlqn+5pLhTMoQuu222/jBD34AwIMPPsiDDz7IL3/5S/77v/+bz372szzwwANTOkjJsVPe9XSnini+IFd0QaHihk2EdYbyDq8eSNGbsfjx0/sI6xoLqiNsXtvE60+qn/FJfvTCMpR3eHZvP53JAkXHw/UEWw6keMf6hbz39UuOmwV1PnC0nsX5ktx9LMyF9ieqqnDuqibe0NZIx1Ce3f05fCGImRpDeYfn9g7Rl7EYyFp0pYr4AurjJq01EVY2J44rw/RwDDdwWqpCxH2ddNHh6T0DdCbzfOCck1jeEJ+2ea+1JsLyhhj3vnQAzxfUD1PWNzXQVdBUlZc6Upwtlb+nlUkZQt3d3SxevBiAn//857znPe/hwgsvZNmyZZxxxhlTOkDJsTN811NwXNp7szyzZ4hEWENTFYRQMDQFy/XIFj2qIzqpgsP+QoEX9id5bHsvm5bVMpR3WFIXnbFJfvjCMpR3+O2uAXrSRRRFIWbquL5PznL51ZZu+rIWn9q88oSZxGebyVSDTXVy91zLG5tLFXLDPUXDuXBtS+U3ixgaCkzYuuZ4Zvgmqz5msrV7WFhKVehKFYkYGleetWzajFtVVdi4pIZ7ntuPEALb8zFKFXzZoks0pLOyKc7OPpknNN1MyhCqra2lo6ODxYsXc//99/PlL38ZCJIfPc+b0gFKjo3Rbt2wo+H5GfK2S8F2iZg6rueRLcnzawrkHZfejEUsZNAQN8kVXZ7ePYgvoCkRGrf6Zzom+fLCEjHCvHpgiIGshaYqRAwVRVHQVA1fCFQVtncHxt7y82SFxUww29VgczFvbLZ/kyNhrlQazjblTVbEUHlx/9iGz0M5m19v7WVFU3xajduGRIjFdVFs1ydZcMYIylZFDPb052Se0DQzqTvy3e9+N5dffjkrV65kYGCAt771rQA8//zztLW1TekAJZNndGgJ4LWuIXRVobUmTMdQkUzRQVcVRKlHkU+gS2IYCnnbxUl5NMZD2J5PwXLZ1pOhIR4aszuajkm+vLD0Zor0ZiwEgSxA+bM9IVAVlaqIgeX6vNSZlDunGWI2q8GOJKQxG8bQiVghN1/J2S4Fx2Uo54zb8LkxYbJ/qMCze4cIaeq0GbcxU6c+ZqJrCs1eGIDaqEFV2EBRgtZDs208nwhM6tf9+te/zrJly+jo6OBrX/sa8XiwyHZ1dfGxj31sSgcomTyjcxbSBYehvE0spDOQtQgZKpTUpG3Px1DA8cDzBDnhoasqBUfgeD7VYYP6WIiuZJF0waE6alY+Z7om+fLC8tSu/kppcNnZIwTYrk+sVOliuRZ525M7pxlitlp9HGlI4+/evnbGPYPzof2JJCBm6vg+9GUtEiWjYziuL4iFdDJFh2X1MTqGCtNi3BYcl/6sHXinTBVD06iLmqxoilEbNWfMeC6HmTOWQ7boEg/pJMLGGAmTct4ZwPKGGItqo8fF9TwpQ8gwDD7zmc+MefzTn/70MQ9IMnWMzlmwXI+i7eH5PjnLI6yrWI6P5wt0VcXHx+dguW35+s5bHq4Pi+oi5CyX9r4sq5oT0z7JlxeW7b0Ztvdk8HyB5wOKwC513a6LGrh+MOqoKXdOM8lsVIMdaUjjvNVNvGFl45R//uE4USrk5jutNREWVEd4YX+S2uhIT48QgmzRpTERQldVNp1UR87um3Ljtr03w51P7gUgEdZxPYGqQE+6wEDOoqkqPCNVfNu7M9z9bAcv7U/Sk7Fw3CBXqbkqzKmLarhkUysIhe/8ZifP7h0iV5JbqIkavH55PZefMf8LVY541bj33nuP+E3f+c53TmowR8Itt9zCP/3TP9Hd3c2GDRv4t3/7N04//fRxj/3Od77Df/7nf7JlyxYATjvtNG644YYJjz/eGJ6z4HiC17oyDOZtfF9geT6mq6CqQad6XYW8HbyufM/5CHwBrgDheLxyIENN2KClOkwy78zIJN/WlODjb1rBQMbixf1JspZLSFeJhXTqogZhQyvlDqmc2lpzwoUdZjtheKarwY40pPHwaz2zVmkz+jcZnpTcMZivXKNzKdH7RENVFTavbeKx7b30ZyxqYuaIROWIqbGwOgworGmpYnlDbEqN2+GezdctqWEo71R0hIKQmEtzFVx55vSq/D/8Wg/feHgHB5IFCo6H7wftWnxf0J0u8GpXiv/v+Q4EkLWC/N+wphI2VJIFhwde6WbPQI4//cMlrFlQNW+v4yM2hN71rncd0XGKokxbwvRdd93FNddcw2233cYZZ5zBzTffzEUXXcS2bdtoamoac/yjjz7KZZddxllnnUU4HOarX/0qF154Ia+88gqtra3TMsa5RDm09NtdAwzlbQq2SzSkkbM8VA8s18fQVUxdRfEPCq4JAo+Q45U8MATGUabo4ng+L3YkefcfLOLcVY1j3KfTwarmKr5w8Sn84y9f49WuNLqqUhc1QRH0pIMS4A0LA+G++XgTTpa5kjA8kwm4RxrS6EoVZzVfrPybbO/O8IPf7mVnXxZPQG3EoDZqggLJvDNnEr1PRM5a0cCbVjfx+I5+CrZHThxMVF7eEGUg51TCUqqqTKnBPzptoS5m8ofLaivK0rYbyINETG2Kv/VBtvek+cbDO+hKFdBUBUVR8BG4ro/vB+tAwfGAg+u5qYGrKORsDwUPVwie2jnA7r4cGxbXsLIkoDrfruMjNoR83z/8QdPMTTfdxIc+9CGuuuoqINAz+sUvfsF3v/tdrr322jHHl7WOyvz7v/8799xzDw8//DBXXHHFjIx5NlFVhQtOaeKBV4Py8paqEBFTx3YKFAUoqgLCp2AL3JIWvwL4JVn+shGkle71smz/vsE8tz+2i960xXtfv2RGjI9VLQn+9u1r+OFv9/Hb3YMM5CwAaiIGZxwn7tmj4URt1jhRSEMIgeV4DOYd6uMmKrPfL6u82+7LWJi6QkjX6E0XGcw7GKrCHy6rZXlD/IQ4b3MRVVW4/IwlFB2fzmSe2qhJVdhAU6E7bVEXM9m8tmlaPHfjSS0oikJVJLimXd+f1mox3xfc/UwnfRmL+liInnQRIQS+ECNUyEfj+WAJD98fbh7B/qE8uqYwkLPn5XU8bxIqbNvm2Wef5brrrqs8pqoqmzdv5qmnnjqi98jn8ziOQ11d3YTHWJaFZVmV/6fT6ckPeg4QMfRSrDuw4j3fpzpqENJVUkUX2w1UZ3VNwRMCRQTeII+DUvyqAgIFTQ3eT1UEWdvj0e29WK7PB86ZmYu+3FLgeE3YO1KmSsF4tsNqk2G8kIbrCfpzFtmii1r6LWxXjFDknenvWt5td6eLtFSFMHUN2/XoGCzg+j7VEYPutMWi2qhssjmLtDUl+MA5B3O6BnJWJex1ckuCB1/pnRaP62xLLZQ9UqauoqoKri9wPR/fE5WN8Hh4Ymz/OpVg83wgWUSUnptv1/Gkf+VcLsdjjz3Gvn37sG17xHNXX331MQ9sNP39/XieR3Nz84jHm5ub2bp16xG9x9/8zd+wcOFCNm/ePOExN954I9dff/0xjXUukbNdTF3l9cvrydteRUk6Zqr8emsf+5MFXM8jETbIWC6uJ9BVhaLjoygQNzVcEShQh0phNABDEwhf0JnMz+hFP5FQ3ETMx8X+cEyFgvFsh9WO5bwMD2kk8w5DORtfCOJhg/qYQa7o4no+v3y5mwXVQUnyZL/rZMY5fLe9oCpMyAjCGwoKihL87fqCwaxFpuhSFTFmTHlaMpZyTtfwDZaqwP1behjKT4/HdbalFnK2iyeCOd0XpbwgcWgjaEIU0LVgozyYs6mOGOzoycyr63hShtDzzz/P2972NvL5PLlcjrq6Ovr7+4lGozQ1NU2LIXSs/OM//iM//vGPefTRRwmHwxMed91113HNNddU/p9Opysq2vOR8s6j4HgVtytAuuDgA4tqIliuz7pF1SgCXu1Okyk4gIPt+Siqgm17aCpETQ1FCfIwNFUBRaE2as7ZyXsmF/uZNLiOVcF4tsNqx3peyiGNgu3x1K4BYqFAi0VTS7orhkZbU4zOZJ4f/nYvBcfnQKpAXdSkIRZCU5Uj+q7lcbb3Zhgq2GiKyorGOJdsaqWtMXHYxsamrmKUNg4Q6F75AkKGiuX6FF0fe1jH+JlUnpaMZFd/doT6/t6BAq7nc/pJtRWPzVR67mZbaiFm6tRGTDIFl2TeJqKr5KwgL+hoUQBT04gYGrbnM5S3qYoY8+o6npQh9OlPf5qLL76Y2267jerqan77299iGAZ/9md/xic/+cmpHiMADQ0NaJpGT0/PiMd7enpoaTl0R7p//ud/5h//8R956KGHOPXUUw95bCgUIhQKHfN45woT7Txsz8f1fIQQNFdHWFpqnZEoWfNbuzMUHR/b9UudqTUMTa3o94QNlbCuUhU2GMhZc+6in+7FfrjuxtauDC90JOlOFbBcH109uGCuaq6awm8VcCxu9dloDDqcqTovbU0J3n7qAl7cn0RTFYquh+MJLNdDdX1e6cygKoKX9qeImTqxkEZnslBJtF/eGGUgZ0/4Xcvj3DeQJ2+7ZC2XouPxcmeSX73azeqWBFFDw/L8MYbc8N224wlCevDemqKgKgoKCp7vlxaQg4bSXFCePhEZfU3GPZ3tPVk8z+fF/Sk2LlaoiwVrwlR67iaSWli3sIr1i6rZP1Rg/1BhWsL/rTUR2poS9GdtLPfgPO9OwiVkaCoRUw02x56g6HioCvPqOp7USF944QVuv/12VFVF0zQsy2L58uV87Wtf48orr+Td7373VI8T0zQ57bTTePjhhysVbL7v8/DDD/OJT3xiwtd97Wtf4ytf+Qq/+tWv2LRp05SPa64z0c7Ddj3ytkdVRGdFY6yyINbFTE4/qY6qsMZvdw2iKIE7X1VUXD/Q79FVBUNTqYuH0FTm3OQ93Yt92VPwfMcQO3qz9GcshBAYmoKhaYR0lde60vx2Vz/vff1SNiyumVIv0WjjFqhUmxiqQne6yKmLxpcSmM3GoFN9XhoSIZbWx2iMh9g7mOOVA2ksx0NVlFIPPUGqEBgw8XCU2oiJ4/n0ZopkLIeVTfFxv2t5nPsG8gzlbYqOh64F13/R9unP5tk/WGBpfYT1rdVUR4wRhtzw3Xaq4GCWSvxNXSViqKQtF8/zqY2ZJMLB+ZPK07PDeNdkf9ZCIbi+knmHnX05aqPBORQiEJjtyxbZ2Zc95nt6tNRCX8bi16/18I+/3EayYKMIqJ4GvZ7h6wIEiv2uL+hOWxWvkDrqNeMlUId1hUQ4kIcolirdHFWwoik+r67jSQsqqmrwMzU1NbFv3z7WrFlDdXU1HR0dUzrA4VxzzTVceeWVbNq0idNPP52bb76ZXC5XqSK74ooraG1t5cYbbwTgq1/9Kl/4whf44Q9/yLJly+ju7gYgHo9X1LBPBMbbeZiayopS243aYSrRZTRN44K1zezozbK7P1cKOaiEjcAzVB0xWN4QpTttzbnJezoX++Gegp50gXTBqUyOtqdg6gLQSEQMdvbnuOEXr7GqJUFV2JgyL9HwSez5fUnytkvGChLfbdenMRHiTzYlxp2gZ7Mx6FSfl5ipEzE0MkWbrd0ZCrZHLKShqyqu7zOUC8K/KoEGSlXYIKRrmDGVwZzNgVSRuqg55rt2Jgu092bI2S6ZoouhQV/GwvN93JInxxOCfYN5BnI2JzXEWLewquJh+os3LB+x2x7M2cTDOoYW6F8N5GxMXWVpXRRPCAqWK5WnZ4nxrklTU0uGL8TDOoM5m0zRxfUF7b1ZejNFirbHj57exyud6WMOtZelFtp7M9z1+w5e7EiiKgqN8RACQTrv8OCrPfRmprax9PB1ob03Q1VEDzYVQ4GmkIIotV4a1n+J4J+qEswVqqKQd3w8T+C4PkIBU1dZ3jC/1tdJGUKve93r+P3vf8/KlSs599xz+cIXvkB/fz//9V//xbp166Z6jBUuvfRS+vr6+MIXvkB3dzcbN27k/vvvryRQ79u3r2KgAdx6663Yts0ll1wy4n2++MUv8qUvfWnaxjkXGU/4rmB73PnUnglj1FedvQzfh+/8ZidP7hzAcn2ihkZjVZiF1WEGcs6cnLyna7Ev7x4Hshau72O5Pr7v43g+ngh29bYbSBEEOhuQdT22daepi4Z4rSvN73YPcPX5Kzl/TfNhP+9QtDUlePPqpjHl2fVxk6ip8+utvSytj46ZNGezWmWqz0trTYTlDTHueX4/OStoC6BrB69DIQIjSAAFOzAUQ4aGoijEwzp9GYvqiDHmu+Zsl85kgX0DeRzPx/IC9XVNAdsLFgVVAVVR8H3BvoE8ridY1Rx4mLrSxRG77bKhajk2BcejqSrM6pbgvOzpz0nl6VlkvGsyEdapjZr0ZYrURA0836cva7G3FCb1PJ/F9VEWVkemNNR+/8vdbO/JYOoq9cOEQsNVgWjsdDSWHm9dyBVd/uOJXTy7d4hUvpQrCiQiBg2xEBnLIe94eJ4gWyqw8XyBAHRFwfMFtz+2k+09mXkjazKp2e6GG24gk8kA8JWvfIUrrriCj370o6xcuZLvfve7UzrA0XziE5+YMBT26KOPjvj/nj17pnUs843xhO+OpB3AV/94A79p7+PeFw7QlQq8SUIwZyfv6Vrsy7vHRFhnz0AeRVHI2x6eX1LjVsH3A0Vu2xOVRThveUQMl9qYSXe6yDce3sHi2iirWo5t4tzalWFBdYTXLa7B8QWmplZCLcPDTOWxl1WOlzfGeOVAesarVab6vKiqwsYlNfzo6X34vkAgECKQgShXPZq6guMLdM/HEwfzH3RVIWe5LKgOj/mu/RmLzqECeTtQGLa9QEvLdkXFw6QEtQKVKspM0RnhYVrdUsWVZy7j7mc7aO/NVMqTqyMmLdWhYEORCLNpWe28VuSd74y+JoUQZIou9XGTwZxFX8ZCUxX2D+VJFxx0NTAIVjbFqYoYJMJTk1fXmSzwcmcKzxdjhELLuZuZojstjaXHWxe++scbKlV0vhDETI1E2CARNijYHj/63V5+/nI3Qgg0VaAoCiFdpbU2Qjyk05+xeHxHP0Vn5uRVjoVJGULDc22ampq4//77p2xAkpnlSFok7OrPsmV/Gt8XmLqCpig0JsJsXts0Jy/w6SpNLe8eq8IGjutRsN0gnl5aFFGUYDEuHV9eNFVVoeD4iJxNbdSgL2Nx93MdXPuWNcesTLuwJjyuUVEOMz2xs5+XOlIjKrRqIgaaqsx4tcp0nJeGRIjW2kgp4VPgKh6KEvSdU5UgQTmQhQiaCXu+T9ZyGcjaqIrCutbqEe/n+4IX9iUxjUBfRYhhffcUoCQ2V544NVXB9QRhQxvhYWrvzfDgqz30ZSwylkt/1sLUNNa1VtFaEyVvu3QM5cnZLssbY9IImiWGX5O267OzL1dp4uv5gqzlEQ9p9KYtIqZGc1WYFY2xKU+eztkuOSfwhBra2GvB0IJt1Uw1lj6cTMlV55xEZyoQYewYKlCwXZoSIRRFqXheHc9n/1BuXmgKzZ0MV8mscagWCaMrKlprD07idz65d04qiE5XaWp59+j5PgIFy/UxdZWi4wdCYkKMKT8VBPochqbgeMHEamgKO6dg4jxcmKm9N8uPnt6HEIyo0OpKFdHUYAKfqZ5xMD3npWy4u75gIGuTCAc5QoYWdKJPWy6aCtVRnYLj0ZkqULQ9HF8Q0lW+9Ug7v9s1wIfeuJxVzVV0Jgvs6s+xobWax9sHyFhORSRumEMJ1wcQ5Ern09RUUgWLBdXhSsh5MGfTUhWiK6WiqyqqEuSYxEM6dbGQFFGcA5Svyde60zy2vQ9VCZqJmrpCOu8QMYLmo54vOGVhNTXRsW1dpiKvLmbqxIxgOR5eaVjG8eZWY+m846FrCg2xMB1DBWpjISxXMJgrkre9igdUQeHZvYN0DC0AmLNCuJP6RU866aQxF8Nwdu3aNekBSeYOs11qfSxMRxfw8u7x5c4k8bBGT1oQ0VUcV1Q0YsZDiCDsEzE18rZHbdTAF8fWAuJwYaa85dKftVEU2LCoZtxzVx8zueLMpewZyAMHJ6fp1ESa6vMyvAzYDvsUHY9YSMFyBZqmYjmBgOiSmij7k4URCZ26qtCdKvKLl7p4aX+Kv37LalprI/RnrdKuViVdDBam0adWVQJvkOV4+EJlIGcTMXTetLqJB189eM9kii7JgkNtzMTUFAZz9ogqJCmiOPUc7fW7vCFOUyIUNMdVgpwuTVVZWBtleUOUjqEC/aUQ2Xjr3lTk1bXWBBWIuweCwhRzWI6QEIJMwZlTjaXL80+66OB6Pq6q0JuxKDoefql4xPOhK12gO1Xgkz9+nnTBmbOd6yd15j71qU+N+L/jODz//PPcf//9fPazn52KcUnmALNZaj0ZRk+AyxvifHQKGyUO92gEoZXAMxA2NXLF8Y0aTQ0WXd8PmtwKEcgN1IyTpHs0HC7MtKs/h4JgeUN8wnP3fEeSVMGhP2uPCJuhwFDOrogILm+I8YZVDTRVhafEMFreEOcdG9Qp2R2OLgMeyFnsHypSdDxsN+iG5KkqL3YmcbxAQVfXVExNDQRCCRa+zmSBbzy8g8tOX0LHYB4hBI2JEDVRg86hAslCcH7LETJTV/F8gaoqKATn9i2ntHBSfYz/ef5A5Z4p63UZYb2SpF2uQqqKGFJEcYoZLtZZcFx8HxZUR9i8tomzVjSMe411Jgsk8w5ntzUAVNT34yGNrOVREzE4MFRgV392xKYCpi6vTlUV3rK+ha09GV7sSNKTtqiOGoAglXfwBZy6IM6GxdVs783Mukp+ef55es8AmgL9ucAIcv2gYSsCwoYKQpB1fF7sSGFqChFDq3Sun45KuMkyqZl4ItHEW265hWeeeeaYBiSZO8xmqfXRMlMq0mWPxv0vdzOYt+lJF1GAmKmSd3x8UWpcS/C3rgYiej4+RdvH0IOwTdsx6mwcLswUC+n4IqjmCPJTgkTq8iRecDy292QoOh6rmhNEzQgHknkefK0Hx/Opi5l4fpA4+vTuAX7y7H7ammIsqYsd0+86HeepfE5++Nt9dGzLoygQ0dVS8jRBvkepeZ5CYDCHQoHWEEDY1LEcjwPJAnc/uw9dC9oOmJpKSNdYWqfg9eco2B6+AE1TCGkK4XBQvu94AkNTedOaJvKON+KeKZdil8MdhqaSs9yKorQUUZw6hofxI4bKUM6hL2vxwv4kj23v5U2rm8b1QJTnuYWhSOWaGMzZPLM3yVDexvF8MkUHJaXgiyQrm+LTklfX1pTgU5tXHmwsnT3YWHpVcwJdU7njid14AmojgRzHxiU1NCRCM24YleefzmSefYN5UnkHVQHPL1VVqoGRmLOD61wQeFZ1TeA7PiEBmqFOSyXcZJjSu++tb30r1113HXfcccdUvq1klpjtxoBHyky3jGhrSvCxN8XZsKSGHz69j6FsMPG292YpOD4520ULRFZxfYHnBAuoJ0AXQXuTPQM5Hnqt55gqhg4VZqqPm9z55B4OJPsDL0hJUXlFU4zaqMn27gyuJ2hrjFeqZbpSwcQb9KQr0hQP4Xg+qqpQdFx29+WoChs8vWeAzmSeD5xz0lH9rtN5npY3xKmLmyytjxIzNZ7ePYSCQjys4QvBUM4pSRyApgqKrsDQgiR3rSQaqiiwf6jIxkU1dKaKFf0fVwQTf8TU8HxoqQ5V+obpmkpzJGhi3FhakIbfM8NLsc1YIOioqWqp8lKKKE4Vw8P49TGTF/enKNiBpELUVBnI2DyytZeC7fHnbxh53Y4+Z4M5mxc6ksHrw0G+EEBVWCddcNg3mCekq9OSVzdeY+muVIEfP90xQiajN13kuY4k9zy3n8V1URrioRntFVge6wfOOYm85dGV7KLoBn0qA4Vqn8Lo7qxQ6mYQ/J66FiisT0cl3NEypSvY3XfffcjO7pL5xWw3BjwSZiuPSVUV3rCykQXVYX61pYeX9ifxCbxkmhaUkkYMlf6sTbrgIhDoCiysCRM2NJ5oH+B3uwZZ1ZLgdYtrJz2BTaQP9b0nd5cSFgWNCRPXFxVF5RWNMbpSRRbUhCv95zJFl8GcheMHZefCFyQLTtBXrmQlJAsOz+0borkqTFeqSMTQ+Lu3r638rofKzZju89SZLLCrL8fyhhgvdKQoul5FV8j1gvYBfinXR0ClKkjXlFJZfZDwbLk+NTGTxqog7DuUtyk4LkJALKRXGhiHdK0SQoFAwbr8nUffM21N8aBSLRdUIy2oiQCCHb3ZOanDNR8ph/FbqkJs7c5SKElFDGQtCo6P43mkig6Pbe/D831u+KNT0UvSB8PPWaxUZFCwXepigdDsYM6juSrMaUtq2NGbZWl9jP+3cSGJsFGZ+zoG81OWU1eu2FpcG+U37X2VzUNrdRjT0MgUHfYPFYCg0bDt+WPUzWfSGLrmwlXsGcyxszcLQNH1KUWlR+AThOV9ERSQlPP3ZqoS7lBMWlBx9KLY3d1NX18f3/rWt6ZscJLZZbYbAx4Jh8pjAoiHNJ7dO8QzewfZtLRuysc6vHP1HU/sZnd/jkU1EXb15xnMWfgiyCfRFGhMhAgbOlnLJRHWKdguQzmblzuPbQIbXvXn+4JbH93JUN7h9GV1vLg/RTLvEA/r1JRK95/fl0RXFU5uTozoPVd0PBw36J1VdDxylovr+9ieqJSR5+3gMdeDX2/t5bzVTbxhZeNhQ17TnW9WDm/EfZ2hvI2mBt2wIUhqNktVe8FvBL56UHfIdoKS+5qoQarg4vuCuiqTP1xWS6boYrkeW7szDGQtwoZGSNcqBqQQgUFT3hCMd89URXRWNsXYciCN75crzNw5q8M1H8kUHQbzNrbn0ZnMoyoKB1JFfBHoebm+wHZ8+j2LX7zchUDhw+cGCuDDz9lLnSl6M0XiIR3b88kWAy2pFY0xVFVlYU2EvoxFImxU1KCnIyTf3pvh/pe7+dkLB9g/lCdsqAzkHGoiPsm8Exwkgvs9mbMBWNkUn5UClkW1UU5fVk9Pqojri1IFrYfrj23i6vkCs9TOQ8HH98WcqISb1KeXe32VUVWVxsZGzjvvPFavXj0V45LMEaaj+moqmSiPaTBn096bZSBnkS443P7YTn6/ZOiIJ6jhTVWzxcDFXt4Bjp5gyju4P3v90srubXVLnGQ+RN4ewjcFNREDt/SeQgQZRKoCeSfP4roIg4doAHo0DDc4EmGDjYtrKp4N1/fRNRUFhebqEGFDq7zO1FQUJWi6qKkCT0DR8Sol4yWZJHwBgzmnpCxu8/BrPTQnwpVy8YlCXq4vpjXfbHgVixCBi94XgRCioiiEDI2i65d2o8H59XyB5QRaUE1xk5qoSW0sRLro0iICkbjA4DFQFYXHtveVfg8RuP4n2BBMdM/8vw0L2bB4dnI6jmfaezP87PkDbO1Kk7Nc8nbgjgh6zpXC0yVXoCIERUfw6LZeLNfnE29ewarmqso5+/5T+2jvyaIg0DWNplG6QcOv0+kK9Zbfd/9QnqLjomvBXJHM2/TngnYWqhJUsDkFB7WUlD9bBSyqqnDJplZ+s6OPXf05IoaK7SlBi45hxwUyXCLIXfSCwhGhGKxfOPuh4UkZQl/84henehySOcyRiC7OFuPlMQ2P8Zt60BetPhY6ognK9wVP7Ozn4dd62NGbpS9jBaXvhsaS+ughw1ijF8D+nIXvCxbXRwHB8/tS+L5fmcQ0VcF3XbYcSHNqa/WUTGCjDcO62EHPhu35qAr0Z4o0V0foShUrTVuFEEQMjR7Xx3G9kkJkgKIE3qByPo3j+vRmLRKmVkow7jhsyOsdpy6Y1nyz4VUsYV3F8oLS+YihAoFRVB8PYTle4PURQcJ41AgWu/p4iCV1Ud68uolfb+0d4wEdyNlsWFxDU6kRZ2/GOuSGYC7fM8cTZaNhb38O1xO4vigl7YIrBE6pP5ZCUMEZ6HoFc8QT7X0M5W0+//a1rGpJlHJeltGVLhA1NGqi5ogCAzh4nUYMjZ+/2DXlod7hIeR4SA/uW9fHcgJVc98vKZoHFj6W65EpOuRtFwjNWgHLquYq/vT0xdz0wHYs18P3gwpNVRz0Ciml8+L7Pj6glbzMg3mbXf3ZWd1UH/Gsk06nj/hNq6qOramkZO5xKNHF2WS8TuzlGH9t1GAo79BUFaalOkwLHHKCau/N8MPf7uORbb1kig6WF0yqCVMjW/Ro7/FIFRz2D+V4+6kLx93ZD18Ad/YFgoYRXeX/2vtxPT9Q5lbVSjUTBC04JmoAerSMZxge9GwEIYSIaXD+mmbu39Jdadran7MZyFiBOCSgEnx3X4AiDnqDQIDnU8h5aGqIZM5hpx/kTRwq5CVgWvPNhlexdKWKqChBGw3bAxG02qiJGAwJwbJSD7ac7WJqKrVRg5XNiYpBs7Q+OqEHdHnDkRs3c/WeOV4Y3vvPE4KIqeH7PhnPH7dTOgTXcCmtC9+Hnb1Zbnmknb88v422pgSLaqOc2lrDlgOpMUbQ8OtUgRGh3nJrjnLeWEtVaFIbm/K8ETFUXjmQwfV9NBWGTwtB4UXQ+y4w8BR6UhaLa6OzWsDy5tXN/GZ7P12pAp3JQMJiuE/IcQ/m6KlAW1OctQsS7OzLctOD27n89CUTShxMN0f8a9XU1IybgzEenjdOppREMg2MzsmIhzQGchamrjKUdyrx/fK1O5HruL03w3cf380ze4cq+jB2qaFgphhUgaEEgni7+nL8fs8QK5sTRIyxOQHlBbC1JsKW/SnueX4/RcdD11TU0uRVactBMIn2pYvjNgA9Wo40wf3sFQ3Yrs83Ht7BgWTQbRolqIzJ2S6uT0UgsjxxleUAys1MPT+YuFuVCNEJxl3eoRYcb9rzzcpVLGFd45FtvdiuF4RHNAVTV8kUXRZUR7j6/JW86eSmCQ2aw3lzjifjZjrFM6eb4b3/tvVkcDyBV9ZIGIWASk9Ao9QrURAovg/krBGboyO5TofLJJTD8OXWHLqmUh3RCekaOds9qt84Z7sUHJehnIPredRGzVJOkFfp+u4Jgk2aKzB0hbqYyYFUgc6hMDnb5dRFsyO62FoT4XVLatE7VVY2JdjSlSZvufhCULRdHM9DBRIhjU3L6mipCvNad4bBnE3WctnTn+Nt6xfwlnUtM+4dOuJZ95FHHqn8e8+ePVx77bW8//3v58wzzwTgqaee4s477+TGG2+c+lFKJIdgeEjquX1DpAsO1RFjTHwfxs9FKe8sO5MFVIKS6FTGKrl3FfxS6wzFF2RtD8MNqlBOWVhFTXTiao3hTUGFH+SteD4IJVChVtVAYKzoBi0fxmsAerQc6UQOsLUrQ0tVOAiXZW3qogYhQyOZtyulu96whEe1lG+ja0FYL1LybmUtl5zlVrxOwxm+Q11cF532fLNy+fGb1jTx8Gs9HEgWsN2gZL2tKc4fn9bKqubAY30og+ZE8OYcLtF3eJ5coArsMphzaIibnNQQQ1UU8o43awZUOQyMoCJwGjY0IrrJQMHBcUem6moKmIaK5wXVgo7rYzlB2PvFjiQdQ3mW1seOKC+yYzBPWNc4kMyzozdXKbU3wjqOJ+hOWagKvNCR5OFXe484mTpm6vg+9GUtEhGTmB/0Fyu6XkkDKzhOCPAA1Q82N0II/m+Hy8KaCH+yKTErxuzwuWcwZ3Nqa9Cypi9jYbk+IV2wqDbCGcvrSRdcHnqtl7ztllIFoCtV5LHtfXSlijPeuumIDaFzzz238u+///u/56abbuKyyy6rPPbOd76T9evX8+1vf5srr7xyakcpkRyG8i7+mb2D3P7YTupjIVrGqVAaz3Vc3lnWRk32D+bJWi6eVyojFwKNgzkyorSr9HzYMxBMnIeq1hjeFNRyfVz8oFO8rhI1VFRFJVN0qI3pnL9mairwjnQi39mXpSqis3fQpz4eIlQqJ66JmtTHXTJFt+RhswnpKmFDK7WV8NEUheqISVtTjG09uSNW3Z2J3JmytMHZKxrmrbdjuhme6NtSFSLmaXSnizy8tZvn9g2wtrWaHd1ZulNF+jIWqYJTEoEUaKqCoWlUR3RaqsNUhU1WNMa5ZNNBI3MmiJk6pqrwalcG1/OJmlqgCaUqVIV0Bl3nYH4KlKQUBEIILEfgCrA8m6d3D2LqgVjhn71+KW1NicNep601EZY3xLj3pQN4vqB+WEsMUwNdDVTnf/S7fSyojrCw5siSqVtrIiyojvDC/iS1UQNN12hKhCk4XqDBQylXD9BKwp+266MANVGDqojBr7f2srQU/p1pRs899TGTmohJ2NDoShX4w2V1pAoOv9s1QLroEjEU9FJ5Z87yGMxZ7BvUZrzybVJ++KeeeorbbrttzOObNm3igx/84DEPSiKZDKqqsGlpHb9fMsSWAylaRj0/US5KeWfZEAshUCg6gYigV0q8RAk0aErixEFJvKqQtdxKq4SJQm6jm4IaGmQtr9KLxxFB752z2uo5e0XDlP0Wh5vIy9+5KmxUWkCUfyPb9YmHgjJ/Srk2UVMPKr9KTUaX1EdZ31pNVcRgIOcQKyWIHknIa6a8LYf7nPkcFjoWRosPPrcvELTLWy6O5+P48PDWfuBglEkJSn6AQCG44Likiy496SI1UZPXutL8bvcAV5+/kvPXNM/I9yg4Lp3JIvsGcwgBqaJLSPOJhjRMTa1IJpTzhTw/0Isqi5uWyVoumqPwzN4hHE9UDJRDXT9lb+89z+0P7hnPr+SkZYsuEUMlpCv0ZSxet7imkq93uGRqVVXYvLaJx7b30p+xqImZxMMaVWGdgZwd3IuGgu0HIb6wrlBwAp2shniIjYuqae+b3Y7v4809QghufmgHedvl2X1DDOXtUnsbBUUJNlZlQzVvu+zoycxo5dukDKHFixfzne98h6997WsjHv/3f/93Fi9ePCUDk0gmw2S0j8oJxpoK8bBOd6pQUkcNNDGGl46XZeR1LegxVW6VMFG1xnhNQZurQggRCI8N5myW1oX50DkrpnzSOtREXv7OXqmk3vEEvu8zmAsE6Fw/KG8tLx6mrlKlayTCOic1xlhUEyFreXQOFYgYGpf+4WK27E/PSYmF8ZiplixzkeEJub/bPUhPulhJwHVGZRmX7QUhxrwNAI4H2aLLkroo3eki33h4B4tro6xqmd7fsL03w51P7i3diyqKAMcXWK6P4wcJy2FTQ/cEtusR1lWKbiCZUP6KpgampuGUkqt7U0X2RfJHbEQ0JEIsrotiuz49mSLpglNqI6EgCOYcXVXpy1pBn7lS7zLb84mHtAkX+7NWNPCm1U08vqO/1NZFYOgqmhq05ykXLZiaiuMHSeJ1MZOhvEPW8uZED8jRc4/vC1Y0xnl0Wy8dg/lKCxu1lHNouT4mKhFDqzQqnsnKt0kZQl//+tf54z/+Y375y19yxhlnAPD000+zY8cO7rnnnikdoERytByt9tHwBOPlDTH2DeQrDTt9AYiD1Q5eyTDK2x6+sMlZLg3x0ITVGqObguZtl4wVlMTark9rTZC8O90Lx2jK3/nlzsAF3zkUJEy7fjBBeT4kwjqe55MI6zQnwpy6KPAADeWdoA9TzmKo4NAQM3l5f4oLT2nmncbCOe9hmemWLHONckLuYNZmIBvksvi+XxGcPBLKDiJFAdsLVMhbEiH6MhZ3P9fBtW9ZM23nfrhH69RF1QwVbAq2T8HxsNzA26oqgpZ4iEhIw3J8qiM6rx5I44mgpLtcven6Ak1TiBsaecdjKG+zoydDx1AgyjiRSnpnskB3qkjE1KiPmgzlbaKmTiKkEw1p9GdtUgULhOD5fUlCpdwkTVPQFKXSrPe17vQYY0VVFS4/YwlFx6czmac2GvT9e3bvEAUnKOCgpM4cC+kjcvtsz6cmasyZHpBlVFXhglOa+J8XO7Fdr6RWT2WzZZTyDjOWW+rPyIxWvk3qk972trexfft2br31VrZu3QrAxRdfzEc+8hHpEZLMCY4mF2VkV3mLhTVhDiQLuL7AKbVlUBg2+UMptCV4aucAnu/j+kxY/j3cMGvvzZAs2KjK2OTdmWT4d84WAxG6guMRNtRgolJLXqC4ScjQcD1BdzrIFdlyIBCuUxSoj5mc3JLglQPpSpLj6pa5K58xWy1Z5hLlhNyudBFBUBxQcLyxMsCHoHwfQGAMBaXSCqausHOavRHDRUPjIZ2Wqgi9mSLNCRNPCIqOj+v5/MGSWnJ2oOC9dmEV//LgNvYNFMjbQcuUsqclYqjoJaMoW3ToTBa444ndpAvuGG8hMKK7/b6BPL0Zi6ipsaA6DAS9BAdzNr4I2uq4vo9VCLzBYUNlQXUYX0Ay7/CLF7tY3hAbV4fqA+cc3MylrEAtfXlDnPq4yY6eLBFDI14q8bdcr9LDbq70gBxNxNBpjIfoSxfJO4HhrSkCQ9eIGIGga85yMXWNFcfYlPpomfQvtXjxYm644YapHItEMqUcTS7KcGMlmBCDaobAyAmqvBzXH9FVPmoGSsaP7ejnDW2Nhyz/nosCe+Xv/N+/3097XxbDV0qd1JVS3lOEFY0xDE1l32CeeEjlse0DZCwHXYGIGfTeioV0FtWa88KImO5WH/OBckLu7/cOIoQIDPyjMIKGoxCEiwVguR6mruELptUbMVw0dHgvt5ztEg/rVJd6jPVkiiytj3HRuhZCukZzIsJQNuiSrqkKuqqiqUHFUqCorlBwPDqThZLsRnyEt/C17kBLzytVeC40I/i+YO9ADsv10DUFy/EYzDvYjh94PIBU3iFsBnk+RTdokREyNJbWR7Fcf8J7ZvickbEcfvZcJ/sGC6xsipEpevRlikCQ15ctujRVhYmHNNr7crPeA3I8crZLVURnVUuCjsHAA+0LUUlLcH1BwQn6ul3yB4tndA45YkPopZdeYt26daiqyksvvXTIY0899dRjHphEMtMMn3he607zzO5BtnSmeGl/Es8XKKpCVFNQ1UAPCILmqp4viIZUljfED/n+c7Eku60pwbtet5DtvRmaq0JBLyAt6KxdFpRz/SCM15ezURRBa3WYWMhAVYL8kBc6kmxcXDMvjIiJWrIIIUgXHPqyRToG8zyyrYfzVjWxqDY6Z426yVJOyH3ote4gaVXRgorISb6foaroikLR8WhIhKiZAj2sQzFaNLQuZo5oJVN0PDwf1i6s4j2bFlekAFY0xnn1QIqwoWG5gVBhWTXddn1MLQh5NyR0Tm2tRlWDaqZE2CBmavzq1R4QcNEpzZXnGhJhGuIhkgWbrmQxUE/2BBFTRddU8paL5Qk0z8fTVbRS8+LWkE5bUxxDUw95zwyfM8w/VLnjiT209+VYUB0iXXToSVsEzVf1QMSxLzcnekCOR8zUiRg6rTUaedsnlbexfR/H9Sm6gfc9Yuhcdc5JM54qcMRX68aNG+nu7qapqYmNGzdW1DRHoyiKFFSUzFvKE8/iuiibVzfzfzv6+Oavd9CTDtzfEUPDKFWjeKXddKpgM5i157QBcCgSYYO6qEnM1Mdtf5G3XPqzNhFTpSpsUBszUculwrrKYM5mZ1+OjYtrsNy5lZswmvGUtweyFs/uG2L/YIGCE7Tf2PnLbdz55F7OXdXI5WcsOe5yhs5a0cAFa5u598Uuio5bcQmVl87hZecTGUhBWE2peD4SIZ2oqbOyOTGt3ojxREPLrWTSBYf2viynLKzmsxeeXOkwX+6H9bvdA+wfypf6/AWVXq4XtL2xXNBVlY2LaiqGTpms5ZUKB4J/V0WC501NDYoxHA3P92iIBwKIUVPFdgWi9IMWbB/Hc9BKgo7NVWF0VSVsqFjukXVfH680PSjmENTHQoAypwsUhp+3DYuq2dmXYzBnBRpJAhRV4U2rGnnPaTOfXnPEhtDu3btpbGys/FsiOd5RVYUVjXGqIyb9WZuqiFExAEKledJyPUKGPu3hgOnkcGrUu/pzKAhOqo+xxUrjeIKQHhyjKArxsM5gzqav1H9rruUmDGf0d90zkOeJHX2kCk6lpFpTAAQ96SL3vdxFb8biU5tXzsnFZbKoqsJ7X7+U3ozNc/uGcD2fouNTcFxKhZCUTYHRjTNVQChU7gVNgZbqMLWlfm3T7Y04VGVoT8ZiaX2MP9m0qGIElVnVXMXV56+sqKl7vh9oYqmB+nhD1KQqrNNaO3YzU64OVRCVf0NQUBA1dTqHCuhqIOioKEHVk+0Jhh2KUSoPLzoe7b1ZejMWMVOjNmYe8T0zOsQeMbSgeGMWhS2PlBG5mKXG1K4fI1N0GcrbtNZEuPz1S+d2i42lS5eO+2+J5HimvHC+1pXGcX1Cwzq2l2Pz1VFj2sMB08nhJAdiIR0hoLkqTGeySF+miDlMQM7QVLJFh65UgbNWNMy53IThDP+uz+0d4rWuNKmCUympVhUqJb2aCpbjsb07KLVfft7czX2aDG1NCT59wUp++Nt9/Hb3IP3ZIr6vYpcy4RQlCJNWR01Oba2irSXBju4s6ZK4YjLvIATUx01aayIj+rXNxNgno1J+/ppmFtdGufvZDtp7M+Qdn6ihsbI5wdlt9fzs+QPjNgY2S6J/AqXybwg2Aq21EXb0ZoLiCtfDcl2KTmA+qoBaElcsOkHTY00JKsbCusr+UlFGwT7yKMpcDLEfKaPPm+UGid1nnFQ/q56sSc3cd955Jw0NDbz97W8H4K//+q/59re/zdq1a/nRj34kDSXJccNwl3pXukhLVQhTD7RHskWXsKHOSDhgujnUwnLqomp++lwnBcerJKYO5mxiIQ0hAkG6rOWyduHczE0YTVtTgivPWsrf/XQL2bIXTwRqwIauoirBzt0XgbfDcj1e6kzO29DnoSi3JOkYyrO7P4cvBBFDJW97lXYaKxrjlVyp4SKUs+2NmGwBwqqWBNe+dc2Y1wG8vD89rmc0HgpU1RHBv4fTEDOJGBq6ppAuekGjYkpd4nUVX/iV3n0KEAvrlVL9xrhJVcTgodd6aGs6vgztiZiLhSOTMoRuuOEGbr31ViBQmf7mN7/JzTffzM9//nM+/elP89Of/nRKBymRzCbDXep9GQtTVzB1jeqoQdTUZyQccDT4vqgsbADLG2JHlPQ70QQF8GJHii0HUqxsirNxcQ0v7Q8Mg6Lj4XqCqoheylOYH0QMnURYpyZikCk11zU05WC4pySoqZV6zeXtI8vjmI+oqsLS+hhL62NHdOxcMgYnO56JXncoz+iq5sBbESQrH3yuO11kYU0EU1cYyNrEw4GOEELgCfC8g54hhUCx3heCmqjJqYuqD5swfTwy166jSRlCHR0dtLW1AfCzn/2MSy65hL/4i7/g7LPP5rzzzpvK8Ukk08LRtlcY7lLf2ZfFF1ATMWY0HHAktPdmKqGOZMFGEVAdNXj98vojSvo9kgUiYqi4nk9YVzE0lURI45TWarrTRe54Ys+8ECTM2S4+gSBdwfHx/ZGSykFfuaAvlaoEUgnzNfQpOXIOF3IDxjx36qIa/mRTgnue28++wQK1UYOQrpb0xgRGqSdY3NQouD41EQMhYFl99KgTpiXTw6Tu7Hg8zsDAAEuWLOGBBx7gmmuuASAcDlMoFKZ0gBLJVDPZ9goTudTniieovTfDzQ/t4MWOJKqi0BgPIRCk8w4PvtpzTEm/5QXi/pe7uW9LN4M5m+qIQX08xIrGGHWxEEKIeaElBEH1WG3EJB1xGMrZUGqdoqhUKmJ9X6DoBItda828Dn1KjpzDhW4mei6kq+zpz1F0gnwfy/WpihgkQjp9WRu3lHWezDsYusorB9LomnrUCdOSqWdSv/wFF1zABz/4QV73utexfft23va2twHwyiuvsGzZsqkcn0QypRxre4W55tIt4/uC+1/uZntPBlNXR3TDDlcFAnPHmvTb1pTgnRtVXj6Q4uTmODVRs6I1BPNLkHB4D7j6eIiuVBGnpCSuKMHfmhqIRq5qSXDRurkT+pRMP4druDrec2etaOBt6xfw+z2DrAnptPdmcX1BqNSAdSBnI0RQWdYQN0mEDRzXn1TCtGRqUQ9/yFhuueUWzjzzTPr6+rjnnnuor68H4Nlnn+Wyyy6b0gFKJFPF6PYKibCBpiokwgYrm+IM5mweeKUH35+stNzs0Zks8HJnCs8XJMLGiERPRVFIRAw8ISpJv5Ml7wTtN1pro1RFjDHqzBFTmxdu/nL12JL6KAuqIyytjxIxAxkE2xUoikJzVYS3rV9w3JXOS6YHVVV4y7oWFtVG8QWcsrCKhrhJ1vKwHK9UiaiyoDoUGEGeT6boUB3W0VSFe57bj+v6h/8gyZQzKY9QTU0N3/zmN8c8fv311x/zgCSSI+Foc3zg+G6vkLNdck5gfBja2N/B0FTg2JN+xxMkHM5c7XM0HqN7wDUlTCxHUBMzOaetnjesbDwulaUl08d4ooc1EZPE4hp2lYoX8rbHUM6i6Ahc38fQVGzX4r6XuxBC8J4/XDxlhvdk5skTkUnPVr/5zW+4/fbb2bVrFz/5yU9obW3lv/7rvzjppJM455xzpnKMEskIJpvjM1F7hTIRU5tzXZuPlJipEzOC23m44GEZxwu0YY416fdw4otdqeKc7HM0EXOxlFcyvxnvmsoUHb75SDsn1cc4kCzwaleadLFY6ryuEjWDfh+vdqWnrOBg9DwZ0lQaE2E2LatlzYIqeZ0PY1Iz4j333MP73vc+3vve9/Lcc89hWRYAqVSKG264gfvuu29KBymRlDlcjs+VZy0lYujjLmrHkzdjNK01Eda3VrN7IEem6IwQPBRCkM7bCBQWVEUqicCTmQQPJ744V/scHYq5mvclmb+MvqY6BvOEdY2u0n3Tl7FQFIWqsIYP5CwPRYGGuFkJ0R9LwUF7b4bvPr6HzmSeuqiJrihs78nyeHs/P3/pAGsXJnjd4rrDbh5PFCY143/5y1/mtttu44orruDHP/5x5fGzzz6bL3/5y1M2OIlkOKNzfMoLfSJsEA/pPL8vyT/8/DUaYiaW54/xFI32ZgBkii6252OoCt3pIqcump/VQaqq8Jb1LWztyfBiR5KetEV11AAE/RmLnOURD+vs6s9y80M7jsiDNhGTVfWVSE5UWmsiLG+Ice9LByg6gdET0VVUVUERgaK5oij0pm1ObokfU4je9wU//N0+ntkziKLA9p4MQzkHXwRaWb4QvLjfJ5V36Uzm+cA5J53w9+ykDKFt27bxxje+cczj1dXVJJPJYx2TRDIuh8rxGco79GaKZIouC9oaaK2NjlsNVvZmPL8vSd52yVgutht0V29MhPiTTYl55c0YTltTgk9tPtgyYSBr4Xg+jheIt522tIaFNeP/LpP5LBlSkkiODFVV2LikppIQ7foCUwPXF9iuj6Gr1MVMBvM2ns8xFRw8ubOfR7b2IkRQsZYqOLi+QBECO+jSymDWpmh79GYtIobG37197Ql9707KEGppaaG9vX1Mqfzjjz/O8uXLp2JcEskYJsrxEUIEpaqeIGqqGLpaqQaLh/QR2jZtTQnevLpphEp0SNeoj5tETZ1fb+1laX103u6QhrdM2NmX5RcvddGftTi1tbrSUXu832WyYTIZUpJIjoyGRIjFdVHSBYdd/TkKtoemqcRCOnVRg5ChkczbpIvOpEP0vi946NVe8o7HopownUMFHE+ga+B6gTdII2gnI+D/b+/ew6MqDzzwf885c81MZoaQTG5ck6CQctktFjbeSkt+gGIXq7+uF/osWMWHVlcr1i3UVm27+2CturauT2l3V6xbrdb9Fdp1K0pBUDGioKAgIEEQSDIZSDL327m8vz+GjAQSSCBkMjPfz/PkecicM5P3nTOc+Z73vBcEYyo27PVj1kQvrphQNuh1zhXnFISWLFmCu+++G08//TQkSUJrayuamppw77334oEHHhjsMhIB6LuPT/fqxVazDEPgtEURTx4NVu2xY29bGJVuO/52tAeqkV5YstiW/q+QKxMCnkn3kgmyJEGINtSWOTMhqFuuj5IjyjUOiwmlTitqSh2QZQnHwslMAJIkCUlNhyxJ6IqlMHP8yHO6Rd8SiKMtGIfTakJMNRDXDEgQMHRAiPR8OcaJYfw2swLdMBBJatiwpx2X1Zbm7DnvfJ1TEFq+fDkMw8Ds2bMRi8Vw5ZVXwmq14r777sNtt9022GUkAtD3iKWUbkDTDQghUO62Z0JNt5NHg3XfXqvy2HqEKSEEwgkNVpOMnUcCONIV69faS8NZPo+SI8o1J5+/6itd2KkHEU1pkGQJJllCVzQFq0nBKM/A1i48eYi8L5iAJAFlTitaAnEIkb7o0QwB+cRKsIYuoMgSzIoMVTfgNMloCyYK+oLonIKQJEm4//77cd9996G5uRmRSAT19fX49a9/jfHjx8Pn8w12OYn6HLGU0nTEUjpcdhNqyxyn9R86eTRYb+GgM5pCsz+CrlgKqm4goepYveUgvvl3Y3P2FhmQ36PkchnndilMJ5+/OqIpTPA60BKI43gkhWhSg92s4PIJpf1aE7DbqUPkdT294HKVx4Yiiwmd0SQkpFuDDKT/IUmAzSxD13WouoEiqwlJVUc4qV7I6g9rAzoDJpNJPPTQQ1i/fn2mBejaa6/F6tWr8fWvfx2KouCee+65UGUl6nXEkkWRUet1AgBGFFl67H/q3DYtgXiPcNAZTeGDw10IJzTYLTIsigQIGQePR3NmAdG+5NucP/ngXOfAovzQ14SLVR47Zk/y4tIB3J7qbSqRaFLFwY4o9vkimDbKhVhKw7FwAgInbo2dCEEQQFdcgyxLCCc0xFJRrH2/BZYvyQX5ORxQEHrggQfw61//Go2NjXj77bfxjW98A7fccgveeecdPPbYY/jGN74BRVEuVFmJAPQ+Yime0vHbpkOZliKbWcaxcBJtwQRGOq1orPdClqUe4cBhUfBRSwCtwTgggFAiPfFgicOCUR77oMznkU35OOdPLjvfde4oPwzGiMu+phJx2S2YMa4Emz85hk/ao5g+xoNdrSG0BuNIqQYkKR2IggkNigRUua2QJQkehwWHO+M5f/F3rgYUhF566SU8++yz+Pu//3vs2rULU6dOhaZp2Llz52m3I4gupN5GLHVfaX1wpAtHOmOIpXQUWRTYzArW7/ZDlqQeQ+jf+bQTB/wRyJIEs0mGrhuwmtKdij9sCWKC9/zm8xgOOOfP8HC2ObDyoZM+9d/5jrg801QiI51WfGncCOz1RRDXDFR57BAC6Iwm0y1DhoDZJKPCZYWmC5gUGZVuG0odVvhCiYL8HA4oCB09ehTTp08HAEyePBlWqxX33HPPkIagp556Cj//+c/h8/kwbdo0PPnkk5gxY0af+7/00kv40Y9+hEOHDmHChAn42c9+hquvvnrIyktDp85bDKM+PYFYWbEVVW47yoqtiKv6aVfdiy4di+X/8xFUXcBmTl8lFVlNKLaaYFJkhOIqWgJxjHRYc74zMef8yb58XueOht7ZBkJUeuxIqDr+4UtjUOG2wW5W8FlHFH/a0Yq3DxyHVZGR0gV0XcCAgb1tYZiUKBwWBe8f7iq4z+GAgpCu67BYPu+DYTKZ4HQ6B71QfXnxxRexbNkyrFq1CjNnzsQTTzyBuXPnYt++ffB6vaft//bbb+Omm27CypUrcc011+D555/Htddei/fffx+TJ08esnLT0DAMgfUftyOlG/jimBGfX3Ur8mlX3XazCaVOC8pdVtgtCiCASFLDsUgKhkivPh9KRFBfJedFZ2LO+ZNdHMFHg+nUgRDdo15TunFi+hABm9mE2jJn5v/92JEOlDqt6Iikl8Q61BGDJAFumxlmkwxVFwjEVByLJLHHFyqo88WAzvBCCCxevBhWqxUAkEgksHTpUjgcPYcZ//GPfxy8Ep7k8ccfx5IlS3DLLbcAAFatWoX/+7//w9NPP43ly5eftv8vfvELzJs3D/fddx8A4Kc//SnWr1+Pf//3f8eqVasuSBkpewZy1R1NaVAUCZVuO1oDccRVHZohYDXJkE8MNw3GVRwLJxFP6VmqUf9xJNLwxhF8NJhO7uuY0gwcOBZFVywFTTdgkiUYAK6oKz1tIERnNIX2UAK+UBIp3YBVkaEbQInDDLvFBGFTcCysY9uhTjROLJz+gwP6X7do0aIev3/zm98c1MKcSSqVwvbt27FixYrMY7Iso7GxEU1NTb0+p6mpCcuWLevx2Ny5c7F27doLWVTKkoFcdTssJtjNJtjcMg52RBFL6XBYFciSBF0IJNX0WmVuuxl/3dOOOu/wvWd+8kikuKrBMIBKtx2N9QMbhUIXDkfw0WDqHgixxxfC5k+OpTs8F5lhMUkIxVToAmgPJ/Hp8UimH2CzP4y/fNSGhGYgqRlwWtMTOUZTGlK6jnKXDfGUjkqPDcdCyYK6PTagILR69eoLVY6zOn78OHRdR3l5eY/Hy8vLsXfv3l6f4/P5et3/TPMcJZNJJJPJzO+hUOg8Sk1DaSBX3d1fTO8e6kCRWYEE6cS6XDpkCVAUCWNLijC5yjWs+26cPBLJbpbRFU03be84GsDmT/z4ykTvgOYloQuDI/hosNWUOuEttsJuViBJQCylQZFlVI0oQk1pETqiaqYrAAC8uqsdXTEV9ZUudESOI6UZsJoV2MwyYkkdLV3pmfcvLi9GMK4W1G1atsOeYuXKlfjxj3+c7WLQORjIVXf3F9PutiBiqo7y4vTt3qSWnlCx2GbG5Go3iqwm+MPJYXlSOHkk0kiHBTuPBhE/EQJHFJlxPJzEW/uPI6Ea+NblhTckdrjhCD4aTC2BOAIxFZfVlQJIz7BvPhGkVUPAaVWwvz2MlkAcADLdBoQARjotiKcMqLoBQwjIsgRZllBX7oTNrCChGgV1mzZnalpaWgpFUdDe3t7j8fb2dlRUVPT6nIqKigHtDwArVqzocTstFAph9OjR51FyGioDvequ8xbj5hljcOh4FNHUiZYgWUb1iCLUljlQ4rAifB4LIF5o3X2iKlzW9FDZlIYShyUTAD0OC+IpHS2BWEEOiR2OOIKPBkt3V4Aqqx2KLKEzmsK+9kimr5AsS5AA7PGFMKakKNNtQJaACpcd/nACTosFBgAJQCylw25WCvI2rXz2XYYHi8WC6dOnY8OGDZnHDMPAhg0b0NDQ0OtzGhoaeuwPAOvXr+9zfwCwWq1wuVw9fih3dF91T65yIxBTceh4FIGYiinV7l4nCru0thRXT6nEqBF2fGlcCRpqRuKSsSNQ4rBmWpHqvM5heVLoPhHqBtAVS8FpM/doBTMrMgwhMKLIkrm9R9nXPYJvYoULo0uKGILonJzcFaAzmsKOIwEcCydgNckosijQdANdsRRe3tGK4+FkZl9JklDndaLIYkI0pUORJUBKr0bfEogX5G3a4XeZewbLli3DokWLcMkll2DGjBl44oknEI1GM6PI/vEf/xHV1dVYuXIlAODuu+/Gl7/8ZTz22GOYP38+XnjhBWzbtg2/+c1vslkNusAGctUtyxLmTa5AWzBxYsZfBboQiCe1Yd93o/tEGEqo0HQD5lMWm1V1A4osw2UzoyM6PG/vEdG56e4K8FFLAMG4ingqvV5ZRySJuKojoRqwWxTs8YWxcU87asoc2N0agtNqQonDgr8Z7UmvsRhNIhBXMdJhwYxxIwtyuZecCkI33HADjh07hgceeAA+nw9/8zd/g3Xr1mU6RB8+fBiy/Hkj16WXXornn38eP/zhD/GDH/wAEyZMwNq1azmHUAEYyLw5udp34+QO3yZZgqoLWE3pwCaEQCShweuyQZExbG/vEdG56e4K8Ik/jM+OBmE3y/CFEkjpBoSRPhc4rQpiSQ0b9/qx9Ct1KHFYMt0GXHYTJlY48elxCTVeJ26eMaZgR5lKQpyYPY56FQqF4Ha7EQwGeZssz+XiXDzN/jCefusgtn3WhaRqoKzYAs1IhyC7RcG0UW50RNO3Bpd+uXbY14eIBua13T78fN1edMVUxDUdJkmCQHqBVUlK9xNK6QYaJ5XjO7PqsP7j9AVfUkuPoq3zOof1Bd/56O/3Ny8RiU7IxdmX67zF+Nbl42EzKXh9nx9Hu+JwWE0nlhixoSOqDuvbe0R0fiZVujC+1IFUexjFdhNCcQ2G6J4cFkjpAkIDtn3WhfZwAt+eVZtzF3wXGoMQUY6r8xbjh9fU4yuTvNiwpx1twQQUSQIgDfvbe0R0fqo9dlR5irDjaCA9q7QQsJtlSJIEIQDdMOC2m6DpBjbsacdltaU5d8F3oTEIEeUBWZZwxYQyXFZbyqs9ogIiyxIa67346x4fumIp2C0mAOllglKaAbMiwWU3Q9XTo2CH6+Sw2cQgRJRHcvH2HhGdn0trS3HJuBL89eN2GIaBhCEgSRIcVhNGFJkRT+koK7ZCObGkBvXEIERERJTDZFnC/zt9FHYcCUDVDTitJlhNCmQJiCTTAyeq3DYAEkeP9iJnJlQkosFjGAJHOmPY6wvhSGcMhsHBo0S57NLaUnx1ojcTdOKqhoRmwOuyYdooN+KqMWwnh802RkPKC7k49D1bTl6tPqHpsJkU1JY5C3IiNaJ8IcsSbp45BgnVQEsghhFFFrhsZigy4AslOXr0DBiEKOfxi73/Tl6tvtJtQ5HFjlhKw67WIFqD8V6XISGi3JCeTuPzyWE7osmcmBw22xiEKKfxi73/Tl6tfoLXmVmXrNhmhtNqwn5/hIuzEuU4Luw7cAxClLP4xT4w3avVV7ptPRZnBdIz0Fa6bZnFWTnyjCh3cfTowLCzNOWsgXyx0+er1Rf1MWrEblGQ1HQOryWigsIgRDmLX+wD071afayP9yOe0rk4KxEVHAYhyln8Yh+Y7tXq24IJnLrWshDpWWc5vJaICg2DEOUsfrEPjCxLmDu5HCUOC/b7IwgnVGiGgXBCxX5/hMNriagg8VKZclb3F3trMI79/nRfIbtFQTyloy2Y4Bd7L2pKnZg3uQIb9rSjJRCHIkmwmTm8dqhwviui4YdBiHJanbcYt1z2+bwZ7aEE583ow8nzLcVVDRCA12VDY70Xl9aW8gv5AuN8V0TDE4MQ5TzOm3F2p863VHVivqW2YAKv7PKhwm3jl/EFxPmuiIYvBiHKC5w3o2+cbym7+P4TDW/sLE2U5zjfUnbx/Sca3hiEKO8V+krrnG8pu/j+Ew1vvDVGeY0dVHvOt1RsM5+2nfMtXVh8/4mGN7YIUd7q7qC6qzUIT5EZNaVOeIrM2NUaxOoth9DsD2e7iEOC8y1lF99/ouGNQYjy0qkdVIttZiiyhGKbGRO8TnRGU3htd3tB3CbjRIrZxfefaHhjEKK8xA6qPXXPtzS5yo1ATMWh41EEYiqmVLs5dHsI8P0nGr54U5ry0ucdVHu/3WC3KGgPJQqqgyrnW8ouvv9EwxODEOUldlDtHedbyi6+/0TDD2+NUV5iB1UiIuoPBiHKS+ygSkRE/cEgRHmLHVSJiOhsCquDBBUcdlAlIqIzYRCivMcOqkRE1BfeGiMiIqKCxSBEREREBYtBiIiIiAoWgxAREREVLAYhIiIiKlgMQkRERFSwGISIiIioYDEIERERUcFiECIiIqKCxSBEREREBStnglBnZycWLlwIl8sFj8eDW2+9FZFI5Iz7/9M//RMuvvhi2O12jBkzBnfddReCweAQlpqIiIiGs5wJQgsXLsTu3buxfv16vPzyy3jjjTdw++2397l/a2srWltb8eijj2LXrl145plnsG7dOtx6661DWGoiIiIaziQhhMh2Ic5mz549qK+vx3vvvYdLLrkEALBu3TpcffXVOHr0KKqqqvr1Oi+99BK++c1vIhqNwmTq33qzoVAIbrcbwWAQLpfrnOtAREREQ6e/39850SLU1NQEj8eTCUEA0NjYCFmWsXXr1n6/Tveb0d8QRERERPktJxKBz+eD1+vt8ZjJZEJJSQl8Pl+/XuP48eP46U9/esbbaQCQTCaRTCYzv4dCoYEXmIiIiHJCVluEli9fDkmSzvizd+/e8/47oVAI8+fPR319PR566KEz7rty5Uq43e7Mz+jRo8/77xMREdHwlNUWoXvvvReLFy8+4z41NTWoqKiA3+/v8bimaejs7ERFRcUZnx8OhzFv3jwUFxdjzZo1MJvNZ9x/xYoVWLZsWeb3UCjEMER5wTAEWgJxRFMaHBYTqj12yLKU7WIREWVVVoNQWVkZysrKzrpfQ0MDAoEAtm/fjunTpwMANm7cCMMwMHPmzD6fFwqFMHfuXFitVvz5z3+GzWY769+yWq2wWq39rwRRDmj2h/HqrnYcOBZBQtNhMymoLXNi7uRy1HmLs108IqKsyYnO0pMmTcK8efOwZMkSvPvuu9iyZQvuvPNO3HjjjZkRYy0tLZg4cSLeffddAOkQNGfOHESjUfzXf/0XQqEQfD4ffD4fdF3PZnWIhlSzP4zVWw5hV2sQniIzakqd8BSZsas1iNVbDqHZH852EYmIsiYnOksDwHPPPYc777wTs2fPhizLuP766/HLX/4ys11VVezbtw+xWAwA8P7772dGlNXV1fV4rYMHD2LcuHFDVnaibDEMgVd3taMzmsIErxOSlL4VVmwzw2k1Yb8/gtd2t6Om1MnbZERUkHImCJWUlOD555/vc/u4ceNw8pRIs2bNQg5MkUR0QbUE4jhwLIJKty0TgrpJkoRKtw3N/ghaAnGMLinKUimJiLInJ26NEdG5iaY0JDQdRZber3nsFgVJTUc0pQ1xyYiIhgcGIaI85rCYYDMpiPURdOIpHVaTAkcfQYmIKN8xCBHlsWqPHbVlTrQFE6fdKhZCoC2YQJ3XiWqPPUslJCLKLgYhojwmyxLmTi5HicOC/f4IwgkVmmEgnFCx3x9BicOCOV8oZ0dpIipYDEJEea7OW4xbLhuHyVVuBGIqDh2PIhBTMaXajVsuG8d5hIiooLFjAFEBqPMWo2aWkzNLExGdgkGIqEDIssQh8kREp+CtMSIiIipYDEJERERUsHhrjIgoSwxD4EhXDAeORdARSaHUaUFtmROjRhSx/xbREGEQyiLDEOy8SlSgmv1hPP/OYWz+5Bj84QRU3YAiSxjhsOLvxo/ANdOqAKTPBzWlDoYjoguEQShLmv1hvLqrHQeORZDQdNhMCmrLnJg7uZzDmYnyXLM/jCf+uh/bDnUiktQhkF77LakZaOmK4//riuNPO1rhsJpQZDHBU2TG39WMxM0zx/D8QDTIGISyoNkfxuoth9AZTaHSbUORxY5oUsW7hzqwuy2Im2eMwaW1pbz6I8pDhiGw7iMf9vlCSGoGZAnQDAEBQJYBzUjvpxpAMK5BkSQE4sD6j9vhDyfx3cYJDENEg4hBaIgZhsCru9rRGU1hgtcJSZLQGU2h2R9BVzSJrriKz45HcdWUSsybXMETHlGeaQnE8VFLEEnNgBAChhAwBCAJA2ovS8JFUhpkWYJilvGJL92SXDPLyQslokHCIDTEWgJxHDgWQaXblglBO44EEE9pcNrMKDfLiCZ1vHeoE23BBGf+Jcoz0ZSGqKrBEIAhAN0QkCGgGoA4ZV9JAiCAlGbApEjQDQM7j3Zh22edcNnN7FtINAgYhIZYNKUhoekostghhECzP4xQXEWxTQGEgFlOn+wcFgVHu2J4dZePV39EecRhMcFhNqH7v7QhBBQpHYpOJp34gQSYlHT/IQnAvvYIfr35AKxmhX0LiQYBg9AQc1hMsJkUxFIauqLphS81XSCUUE9cHRoQAththCAk4EhnHOVuG2aMK0FM1XkFSJTjqj12TKl249PjEQDpACQBEKc2BwGQJQmyJEGRZcRSGpKqAYcVGOmwoqzYCn84gaZPj+MTfxh3fKUWF5W7hrYyRHmAQWiIVXvsqClzYNM+P9pDSUSTGoqtJggAkaSWaQKPazpSmkA0peLHf94Nr8uGao8dpU4rrwCJcpgsS5g3pQJ728MIxTuQUHWoujj9thgAQECWZKiahoSqw6zIqPbYEE1paP40glhSg2YIfHosip9GU/jR/HpcVMHzAtFAcGbpIfbp8Qg6wkk0+6NoD8Wh6QYiSRWREyc0kwwYBhCMq9B0HRBAUjNwLJzA8UgSgMCu1iBWbzmEZn8429UhonNQ5y3GdxsnYP6UKpQX23CiKxCAdAD6/LYZoOo6AnEdmpEebHG4I4bNnxzH/vYwAnENNrMMT5EZB/wRPPV6M88LRAPEFqEh1D1s/mhXDMU2E6wmCcGEhoRqQEDAogCQJBiGgKoByROnRhkCcaGjJRCH1STj8rpSNB+L4rXd7agpZf8holxU5y3GD6+px6LLxuHN/cewbpcP+9sjSOoGVM1AUtNhCEDTu1uHgKQukIqpUGQJVkVCLKXBHzbgLbahyCKjI5rkeYFogBiEhsjJw+arPXa0dMVR7rKiKJJCazCOpCaQ1IFTx43ISI8c0Q1A1QUOHo9iXKkDlW4bmv0RtATiXFGcKEfJsoSxIx0YO9KBm2eMxZYDx/HXj33Y0tyBQCwFq0kBIHAskkJKT58bBNIjzVQJEDCQANARTcJtM6PSbed5gWiAGISGyMnD5oUATIqMSFJHNKXDLMtQocM45TkSAOXEzUsdACCg6sCnx6K4rM6OpJZANNXLxCNElHNkWcIVE8owtqQIvlASRWYFQgi81dwBTRdQZEAYgIHPwxAASJKBcEKg0m1HWbEVn3VEeV4gGgD2ERoinw+bN6HYZoLHboY/lF5fqNimwGbp/VAIpPsJKHL6lplJlhBJajgWTsJqUuCwMMsS5ZOYqkORJVR57PisM70WoVlJ3wo7+W6XEOmh990tRVUeOxKqzvMC0QAxCA2Rk4fNS5KESrc9PaGaIWBAgknueSi6z3f6iWai9PxCgNUsA0KgLRhHndeJao99aCtCRBdU97nCH06gK5aCSZYgyxIkSYJZkTIn7e6LJAmA225GqcOCtmCC5wWiAWIQGiLVHjtqy5xoCyYghECRVYHLboLTaoKmG1B1A2ZFgs0k4eQujoqcbjJXtXSDuCJJCCc1lDgsmPOFcnaIJMozp54rzIoERZagi/S5QJEBk/x5KJIlYESRBb5QkucFonPA9tMhIssS5k4uR2swjv3+CJxWBTazApNFgiklwVNkhtWkIJHSEUqoCCU0GIaAzaxA1Q2oABQAXTEVLrsJIx3WbFcpJxmGQEsgfbuBk1PScNR9rvjEH8YBfwSKLEMy0hdCKV1AUSTYzQqSqoGELmBWZFS4bJg6yo05X+D8YkQDxSA0hOq8xbjlsnF4dVc7mv1hSAACcRVjRxahzusEIGHHkQBUQyChGlAsEoqtJnTFUlBkGQ6LghFFZnyh2g1fKIHVWw5xLbIBaPanF6w8cCyChKZzeQIatuq8xbjjK7XojKSw1xdKzy0EwGSST/QXTK9YX+K04M6v1GH2pHKGeqJzJAnR28Tu1C0UCsHtdiMYDMLlGpzp67tbJfb4Qvi/nW1IagaqPDbYLQraAnHsag0hmtBgUiQEYip0IVDqtKDSbUeFy4YiqwlmWYIvlMDUUR4s/XItT4Bn0T2HU2c0hQqXFboBhBIqumLp6Qy+dfl4hiEadj7xhfHU68040hVFShNIajpiKR0pzUCp04plcy7C/1Nfke1iEg1L/f3+ZotQFsiyhNElRRhdUoSaUkemlaI9lIDVpGDBtCpMG+2BahhY/dYhlDjMkJAOPnt8YWi6AZOSbiF6/3AX5ww5i5PncBrpsGCvL4KuWCr9PsoS2oIJ2M0K7p9fz0BJw8pFFcX4p9l1mVbkQDwFWZJR53Xi+unVXFuMaBAwCGVZnbcYNbOcvfZb2esLwWkzochixkctQcRTGhxWEyyKhKQmcCychD+cwB5fiEHoDLrncLKbZew8mn4fnTYzzDYTVF2gK5rCxr1+zJroxRUTyrJdXKIeznSOIKLzxyA0DHS3EJ3KYTHBqsjY5wshntJgNyvoiCQRVw0YQkAIAd0QWP+xD40TOVKkL9GUhriqoSuqIp5Kj7iTpPR7ZTVJKCu24GhXHBv2tOOy2lK+jzTs9HWOIKLzx+Hzw1i1x46yYhvaggmYZAnt4WR6JmpFgs0kQwhAkWW8d7ALWw4cz3Zxhy2HxQTDAI5FknDazJkQ1E0zBBxWE9qCCbQE4lkqJRERZQOD0DAmyxIuGTcCigS0h5NIano6AAFIaAasZhnVHhsSqo4Ne9phGOz33ptqjx2VbjsiSQ2mUz7xQghEEhrKiq1QJIlLExARFRgGoWFuUqULY0Y6IAGQICGpGdD0dAtGhcsGs0lma8ZZyLKExnoviswKjp8IlIZIj8DpjKZgtyiocttgM3NpAiKiQsOz/jBX7bFjgrcYzf4IvK70JIqKJMFyommjM5pia0Y/XFpbiq9M9OKt/ccRT+mICg2KLMPrsqGmtAgdURVTqt1cmoCIqMAwCA1z3a0Zmz/xIxxX4XFYYFZkpHQDkYSWac0AJLZmnIEsS7h55hgkVAMtgRhGFFngspmhyODSBEREBYy3xnJAd2uGxaQgntIRiKWQUA14XTZMG+VGXDW40GI/1HmL8a3Lx2HGuJGQJQkd0SSCcQ1Tqt2coZuIqECxCSEHsDVj8HBOFiIiOhmDUI7obs3onoW6I5qE1aRgSjUXWhwozslCRETdGIRyCFsziIiIBheDUI5hawYREdHgyZnO0p2dnVi4cCFcLhc8Hg9uvfVWRCKRfj1XCIGrrroKkiRh7dq1F7agRERElDNyJggtXLgQu3fvxvr16/Hyyy/jjTfewO23396v5z7xxBOnLatARERElBO3xvbs2YN169bhvffewyWXXAIAePLJJ3H11Vfj0UcfRVVVVZ/P3bFjBx577DFs27YNlZWVQ1VkIiIiygE50SLU1NQEj8eTCUEA0NjYCFmWsXXr1j6fF4vFcPPNN+Opp55CRUXFUBSViIiIckhOtAj5fD54vd4ej5lMJpSUlMDn8/X5vHvuuQeXXnopFixY0O+/lUwmkUwmM7+HQqGBF5iIiIhyQlZbhJYvXw5Jks74s3fv3nN67T//+c/YuHEjnnjiiQE9b+XKlXC73Zmf0aNHn9PfJyIiouEvqy1C9957LxYvXnzGfWpqalBRUQG/39/jcU3T0NnZ2ectr40bN+LAgQPweDw9Hr/++utxxRVXYNOmTb0+b8WKFVi2bFnm91AoxDBERESUp7IahMrKylBWVnbW/RoaGhAIBLB9+3ZMnz4dQDroGIaBmTNn9vqc5cuX47bbbuvx2JQpU/Bv//Zv+NrXvtbn37JarbBarQOoBREREeWqnOgjNGnSJMybNw9LlizBqlWroKoq7rzzTtx4442ZEWMtLS2YPXs2nn32WcyYMQMVFRW9thaNGTMG48ePH+oqEBER0TCUE6PGAOC5557DxIkTMXv2bFx99dW4/PLL8Zvf/CazXVVV7Nu3D7FYLIulJCIiolwiCSFEtgsxnIVCIbjdbgSDQbhcrmwXh4iIiPqhv9/fOdMiRERERDTYGISIiIioYDEIERERUcFiECIiIqKCxSBEREREBYtBiIiIiAoWgxAREREVLAYhIiIiKlgMQkRERFSwGISIiIioYDEIERERUcFiECIiIqKCxSBEREREBYtBiIiIiAoWgxAREREVLAYhIiIiKlgMQkRERFSwGISIiIioYJmyXQCioWAYAi2BOKIpDQ6LCdUeO2RZynaxiIgoyxiEKO81+8N4dVc7DhyLIKHpsJkU1JY5MXdyOeq8xdkuHhERZRGDEOW1Zn8Yq7ccQmc0hUq3DUUWO2IpDbtag2gNxnHLZeMYhoiIChj7CFHeMgyBV3e1ozOawgSvE8U2MxRZQrHNjAleJzqjKby2ux2GIbJdVCIiyhIGIcpbLYE4DhyLoNJtgyT17A8kSRIq3TY0+yNoCcSzVEIiIso2BiHKW9GUhoSmo8jS+x1gu0VBUtMRTWlDXDIiIhouGIQobzksJthMCmJ9BJ14SofVpMDRR1AiIqL8xyBEeavaY0dtmRNtwQSE6NkPSAiBtmACdV4nqj32LJWQiIiyjUGI8pYsS5g7uRwlDgv2+yMIJ1RohoFwQsV+fwQlDgvmfKGc8wkRERUwBiHKa3XeYtxy2ThMrnIjEFNx6HgUgZiKKdVuDp0nIiLOI0T5r85bjJpZTs4sTUREp2EQooIgyxJGlxRluxhERDTM8NYYERERFSwGISIiIipYDEJERERUsBiEiIiIqGAxCBEREVHBYhAiIiKigsUgRERERAWLQYiIiIgKFoMQERERFSzOLH0W3auWh0KhLJeEiIiI+qv7e7v7e7wvDEJnEQ6HAQCjR4/OckmIiIhooMLhMNxud5/bJXG2qFTgDMNAa2sriouLIUmDt0hnKBTC6NGjceTIEbhcrkF73eGuUOsNFG7dWW/WuxCw3sOv3kIIhMNhVFVVQZb77gnEFqGzkGUZo0aNumCv73K5ht2HZygUar2Bwq07611YWO/CMlzrfaaWoG7sLE1EREQFi0GIiIiIChaDUJZYrVY8+OCDsFqt2S7KkCrUegOFW3fWm/UuBKx37tabnaWJiIioYLFFiIiIiAoWgxAREREVLAYhIiIiKlgMQkRERFSwGISy5KmnnsK4ceNgs9kwc+ZMvPvuu9ku0qB66KGHIElSj5+JEydmticSCdxxxx0YOXIknE4nrr/+erS3t2exxOfmjTfewNe+9jVUVVVBkiSsXbu2x3YhBB544AFUVlbCbrejsbER+/fv77FPZ2cnFi5cCJfLBY/Hg1tvvRWRSGQIazFwZ6v34sWLTzv+8+bN67FPLtZ75cqV+NKXvoTi4mJ4vV5ce+212LdvX499+vPZPnz4MObPn4+ioiJ4vV7cd9990DRtKKsyIP2p96xZs0475kuXLu2xT67V+1e/+hWmTp2amSywoaEBr7zySmZ7Ph5r4Oz1zrdjzSCUBS+++CKWLVuGBx98EO+//z6mTZuGuXPnwu/3Z7tog+oLX/gC2traMj9vvfVWZts999yD//3f/8VLL72EzZs3o7W1Fdddd10WS3tuotEopk2bhqeeeqrX7Y888gh++ctfYtWqVdi6dSscDgfmzp2LRCKR2WfhwoXYvXs31q9fj5dffhlvvPEGbr/99qGqwjk5W70BYN68eT2O/+9///se23Ox3ps3b8Ydd9yBd955B+vXr4eqqpgzZw6i0Whmn7N9tnVdx/z585FKpfD222/jt7/9LZ555hk88MAD2ahSv/Sn3gCwZMmSHsf8kUceyWzLxXqPGjUKDz/8MLZv345t27bhq1/9KhYsWIDdu3cDyM9jDZy93kCeHWtBQ27GjBnijjvuyPyu67qoqqoSK1euzGKpBteDDz4opk2b1uu2QCAgzGazeOmllzKP7dmzRwAQTU1NQ1TCwQdArFmzJvO7YRiioqJC/PznP888FggEhNVqFb///e+FEEJ8/PHHAoB47733Mvu88sorQpIk0dLSMmRlPx+n1lsIIRYtWiQWLFjQ53Pyod5CCOH3+wUAsXnzZiFE/z7bf/nLX4Qsy8Ln82X2+dWvfiVcLpdIJpNDW4FzdGq9hRDiy1/+srj77rv7fE4+1FsIIUaMGCH+8z//s2COdbfueguRf8eaLUJDLJVKYfv27WhsbMw8JssyGhsb0dTUlMWSDb79+/ejqqoKNTU1WLhwIQ4fPgwA2L59O1RV7fEeTJw4EWPGjMmr9+DgwYPw+Xw96ul2uzFz5sxMPZuamuDxeHDJJZdk9mlsbIQsy9i6deuQl3kwbdq0CV6vFxdffDG+/e1vo6OjI7MtX+odDAYBACUlJQD699luamrClClTUF5entln7ty5CIVCPa64h7NT693tueeeQ2lpKSZPnowVK1YgFotltuV6vXVdxwsvvIBoNIqGhoaCOdan1rtbPh1rLro6xI4fPw5d13t8QACgvLwce/fuzVKpBt/MmTPxzDPP4OKLL0ZbWxt+/OMf44orrsCuXbvg8/lgsVjg8Xh6PKe8vBw+ny87Bb4AuuvS27Hu3ubz+eD1entsN5lMKCkpyen3Yt68ebjuuuswfvx4HDhwAD/4wQ9w1VVXoampCYqi5EW9DcPAd7/7XVx22WWYPHkyAPTrs+3z+Xr9THRvG+56qzcA3HzzzRg7diyqqqrw4Ycf4vvf/z727duHP/7xjwByt94fffQRGhoakEgk4HQ6sWbNGtTX12PHjh15faz7qjeQf8eaQYguiKuuuirz76lTp2LmzJkYO3Ys/vCHP8But2exZDQUbrzxxsy/p0yZgqlTp6K2thabNm3C7Nmzs1iywXPHHXdg165dPfq+FYK+6n1y/64pU6agsrISs2fPxoEDB1BbWzvUxRw0F198MXbs2IFgMIj/+Z//waJFi7B58+ZsF+uC66ve9fX1eXeseWtsiJWWlkJRlNNGFrS3t6OioiJLpbrwPB4PLrroIjQ3N6OiogKpVAqBQKDHPvn2HnTX5UzHuqKi4rRO8pqmobOzM6/ei5qaGpSWlqK5uRlA7tf7zjvvxMsvv4zXX38do0aNyjzen892RUVFr5+J7m3DWV/17s3MmTMBoMcxz8V6WywW1NXVYfr06Vi5ciWmTZuGX/ziF3l/rPuqd29y/VgzCA0xi8WC6dOnY8OGDZnHDMPAhg0betx/zTeRSAQHDhxAZWUlpk+fDrPZ3OM92LdvHw4fPpxX78H48eNRUVHRo56hUAhbt27N1LOhoQGBQADbt2/P7LNx40YYhpE5ueSDo0ePoqOjA5WVlQByt95CCNx5551Ys2YNNm7ciPHjx/fY3p/PdkNDAz766KMeQXD9+vVwuVyZWw/Dzdnq3ZsdO3YAQI9jnmv17o1hGEgmk3l7rPvSXe/e5PyxznZv7UL0wgsvCKvVKp555hnx8ccfi9tvv114PJ4ePexz3b333is2bdokDh48KLZs2SIaGxtFaWmp8Pv9Qgghli5dKsaMGSM2btwotm3bJhoaGkRDQ0OWSz1w4XBYfPDBB+KDDz4QAMTjjz8uPvjgA/HZZ58JIYR4+OGHhcfjEX/605/Ehx9+KBYsWCDGjx8v4vF45jXmzZsn/vZv/1Zs3bpVvPXWW2LChAnipptuylaV+uVM9Q6Hw+J73/ueaGpqEgcPHhR//etfxRe/+EUxYcIEkUgkMq+Ri/X+9re/Ldxut9i0aZNoa2vL/MRiscw+Z/tsa5omJk+eLObMmSN27Ngh1q1bJ8rKysSKFSuyUaV+OVu9m5ubxU9+8hOxbds2cfDgQfGnP/1J1NTUiCuvvDLzGrlY7+XLl4vNmzeLgwcPig8//FAsX75cSJIkXnvtNSFEfh5rIc5c73w81gxCWfLkk0+KMWPGCIvFImbMmCHeeeedbBdpUN1www2isrJSWCwWUV1dLW644QbR3Nyc2R6Px8V3vvMdMWLECFFUVCS+/vWvi7a2tiyW+Ny8/vrrAsBpP4sWLRJCpIfQ/+hHPxLl5eXCarWK2bNni3379vV4jY6ODnHTTTcJp9MpXC6XuOWWW0Q4HM5CbfrvTPWOxWJizpw5oqysTJjNZjF27FixZMmS04J+Lta7tzoDEKtXr87s05/P9qFDh8RVV10l7Ha7KC0tFffee69QVXWIa9N/Z6v34cOHxZVXXilKSkqE1WoVdXV14r777hPBYLDH6+Ravb/1rW+JsWPHCovFIsrKysTs2bMzIUiI/DzWQpy53vl4rCUhhBi69iciIiKi4YN9hIiIiKhgMQgRERFRwWIQIiIiooLFIEREREQFi0GIiIiIChaDEBERERUsBiEiIiIqWAxCRFSwJEnC2rVrs10MIsoiBiEiykmLFy/Gtddem+1iEFGOYxAiIiKigsUgREQ5b9asWbjrrrvwz//8zygpKUFFRQUeeuihHvvs378fV155JWw2G+rr67F+/frTXufIkSP4h3/4B3g8HpSUlGDBggU4dOgQAGDv3r0oKirC888/n9n/D3/4A+x2Oz7++OMLWT0iuoAYhIgoL/z2t7+Fw+HA1q1b8cgjj+AnP/lJJuwYhoHrrrsOFosFW7duxapVq/D973+/x/NVVcXcuXNRXFyMN998E1u2bIHT6cS8efOQSqUwceJEPProo/jOd76Dw4cP4+jRo1i6dCl+9rOfob6+PhtVJqJBwEVXiSgnLV68GIFAAGvXrsWsWbOg6zrefPPNzPYZM2bgq1/9Kh5++GG89tprmD9/Pj777DNUVVUBANatW4errroKa9aswbXXXovf/e53+Jd/+Rfs2bMHkiQBAFKpFDweD9auXYs5c+YAAK655hqEQiFYLBYoioJ169Zl9iei3GPKdgGIiAbD1KlTe/xeWVkJv98PANizZw9Gjx6dCUEA0NDQ0GP/nTt3orm5GcXFxT0eTyQSOHDgQOb3p59+GhdddBFkWcbu3bsZgohyHIMQEeUFs9nc43dJkmAYRr+fH4lEMH36dDz33HOnbSsrK8v8e+fOnYhGo5BlGW1tbaisrDz3QhNR1jEIEVHemzRpEo4cOdIjuLzzzjs99vniF7+IF198EV6vFy6Xq9fX6ezsxOLFi3H//fejra0NCxcuxPvvvw+73X7B60BEFwY7SxNR3mtsbMRFF12ERYsWYefOnXjzzTdx//3399hn4cKFKC0txYIFC/Dmm2/i4MGD2LRpE+666y4cPXoUALB06VKMHj0aP/zhD/H4449D13V873vfy0aViGiQMAgRUd6TZRlr1qxBPB7HjBkzcNttt+Ff//Vfe+xTVFSEN954A2PGjMF1112HSZMm4dZbb0UikYDL5cKzzz6Lv/zlL/jv//5vmEwmOBwO/O53v8N//Md/4JVXXslSzYjofHHUGBERERUstggRERFRwWIQIiIiooLFIEREREQFi0GIiIiIChaDEBERERUsBiEiIiIqWAxCREREVLAYhIiIiKhgMQgRERFRwWIQIiIiooLFIEREREQFi0GIiIiICtb/D9asxAISjp6mAAAAAElFTkSuQmCC",
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       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -994,7 +1514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": 719,
    "metadata": {},
    "outputs": [
     {
@@ -1005,7 +1525,7 @@
        },
        "data": [
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          "legendgroup": "AT",
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@@ -1107,40 +1623,38 @@
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           1
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          "title": {
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+          "text": "total"
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@@ -3119,12 +3581,55 @@
     }
    ],
    "source": [
-    "px.scatter(df_merged_s, y=\"carbon\", x=\"energy_heating\", color=\"geo\")"
+    "px.scatter(df_merged_s, y=\"carbon\", x=\"total\", color=\"geo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 753,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.cm as cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 756,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Carbon Emissions')"
+      ]
+     },
+     "execution_count": 756,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = cm.rainbow(np.linspace(0, 1, len(df_merged_s)))\n",
+    "plt.scatter(x=df_merged_s[\"total\"], y=df_merged_s[\"carbon\"], color=colors, alpha=0.5)\n",
+    "plt.title(\"Transportation Gas Usage vs Carbon\")\n",
+    "plt.xlabel(\"Oil & Gas Usage in Transportation (ktoes)\")\n",
+    "plt.ylabel(\"Carbon Emissions\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 720,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3133,7 +3638,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": 721,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy.linalg import det"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 722,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3164,7 +3678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 247,
+   "execution_count": 723,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3173,16 +3687,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 248,
+   "execution_count": 724,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.6267042262195361"
+       "0.8419084630885562"
       ]
      },
-     "execution_count": 248,
+     "execution_count": 724,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3200,7 +3714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 279,
+   "execution_count": 725,
    "metadata": {},
    "outputs": [
     {
@@ -3229,199 +3743,123 @@
        "      <th>energy_cooling</th>\n",
        "      <th>energy_water_heating</th>\n",
        "      <th>energy_cooking</th>\n",
+       "      <th>total</th>\n",
+       "      <th>gas</th>\n",
        "      <th>geo</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>156</th>\n",
-       "      <td>1.962098</td>\n",
-       "      <td>3.156258</td>\n",
-       "      <td>0.049519</td>\n",
-       "      <td>1.422028</td>\n",
-       "      <td>1.976399</td>\n",
-       "      <td>FR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>157</th>\n",
-       "      <td>1.838539</td>\n",
-       "      <td>2.430414</td>\n",
-       "      <td>1.000927</td>\n",
-       "      <td>1.636463</td>\n",
-       "      <td>2.337869</td>\n",
-       "      <td>FR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>158</th>\n",
-       "      <td>1.844259</td>\n",
-       "      <td>2.873586</td>\n",
-       "      <td>1.000302</td>\n",
-       "      <td>1.614002</td>\n",
-       "      <td>2.229544</td>\n",
-       "      <td>FR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>159</th>\n",
-       "      <td>1.841121</td>\n",
-       "      <td>3.118138</td>\n",
-       "      <td>1.073088</td>\n",
-       "      <td>1.635543</td>\n",
-       "      <td>2.142887</td>\n",
-       "      <td>FR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>160</th>\n",
-       "      <td>1.675800</td>\n",
-       "      <td>2.240820</td>\n",
-       "      <td>1.143476</td>\n",
-       "      <td>1.616531</td>\n",
-       "      <td>2.043686</td>\n",
-       "      <td>FR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>161</th>\n",
-       "      <td>1.699596</td>\n",
-       "      <td>2.474152</td>\n",
-       "      <td>1.236836</td>\n",
-       "      <td>1.629732</td>\n",
-       "      <td>2.059423</td>\n",
-       "      <td>FR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>162</th>\n",
-       "      <td>1.708232</td>\n",
-       "      <td>2.677761</td>\n",
-       "      <td>1.628912</td>\n",
-       "      <td>1.660820</td>\n",
-       "      <td>2.041405</td>\n",
-       "      <td>FR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>163</th>\n",
-       "      <td>1.722893</td>\n",
-       "      <td>2.583788</td>\n",
-       "      <td>1.621449</td>\n",
-       "      <td>1.696977</td>\n",
-       "      <td>2.089008</td>\n",
-       "      <td>FR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>164</th>\n",
-       "      <td>1.628270</td>\n",
-       "      <td>2.434724</td>\n",
-       "      <td>1.853032</td>\n",
-       "      <td>1.704964</td>\n",
-       "      <td>2.100902</td>\n",
-       "      <td>FR</td>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.056699</td>\n",
+       "      <td>0.056867</td>\n",
+       "      <td>-0.568738</td>\n",
+       "      <td>-0.197789</td>\n",
+       "      <td>-0.478802</td>\n",
+       "      <td>-0.034514</td>\n",
+       "      <td>-0.335104</td>\n",
+       "      <td>BE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>165</th>\n",
-       "      <td>1.581249</td>\n",
-       "      <td>2.432933</td>\n",
-       "      <td>2.190273</td>\n",
-       "      <td>1.693026</td>\n",
-       "      <td>2.052831</td>\n",
-       "      <td>FR</td>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.072725</td>\n",
+       "      <td>0.049004</td>\n",
+       "      <td>-0.550216</td>\n",
+       "      <td>-0.195549</td>\n",
+       "      <td>-0.478933</td>\n",
+       "      <td>-0.040696</td>\n",
+       "      <td>-0.344081</td>\n",
+       "      <td>BE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>166</th>\n",
-       "      <td>1.322488</td>\n",
-       "      <td>2.249065</td>\n",
-       "      <td>2.887347</td>\n",
-       "      <td>1.799113</td>\n",
-       "      <td>2.171680</td>\n",
-       "      <td>FR</td>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.073381</td>\n",
+       "      <td>0.04114</td>\n",
+       "      <td>-0.531693</td>\n",
+       "      <td>-0.193309</td>\n",
+       "      <td>-0.479064</td>\n",
+       "      <td>-0.046878</td>\n",
+       "      <td>-0.344678</td>\n",
+       "      <td>BE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>167</th>\n",
-       "      <td>1.440005</td>\n",
-       "      <td>2.637342</td>\n",
-       "      <td>3.278921</td>\n",
-       "      <td>1.689769</td>\n",
-       "      <td>2.022219</td>\n",
-       "      <td>FR</td>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.101798</td>\n",
+       "      <td>0.033276</td>\n",
+       "      <td>-0.513171</td>\n",
+       "      <td>-0.19107</td>\n",
+       "      <td>-0.479195</td>\n",
+       "      <td>-0.05306</td>\n",
+       "      <td>-0.319312</td>\n",
+       "      <td>BE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>168</th>\n",
-       "      <td>1.383980</td>\n",
-       "      <td>2.214246</td>\n",
-       "      <td>3.167601</td>\n",
-       "      <td>1.415021</td>\n",
-       "      <td>1.610834</td>\n",
-       "      <td>FR</td>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.078167</td>\n",
+       "      <td>0.025413</td>\n",
+       "      <td>-0.494649</td>\n",
+       "      <td>-0.18883</td>\n",
+       "      <td>-0.479326</td>\n",
+       "      <td>-0.059242</td>\n",
+       "      <td>-0.296488</td>\n",
+       "      <td>BE</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       carbon  energy_heating  energy_cooling  energy_water_heating  \\\n",
-       "156  1.962098        3.156258        0.049519              1.422028   \n",
-       "157  1.838539        2.430414        1.000927              1.636463   \n",
-       "158  1.844259        2.873586        1.000302              1.614002   \n",
-       "159  1.841121        3.118138        1.073088              1.635543   \n",
-       "160  1.675800        2.240820        1.143476              1.616531   \n",
-       "161  1.699596        2.474152        1.236836              1.629732   \n",
-       "162  1.708232        2.677761        1.628912              1.660820   \n",
-       "163  1.722893        2.583788        1.621449              1.696977   \n",
-       "164  1.628270        2.434724        1.853032              1.704964   \n",
-       "165  1.581249        2.432933        2.190273              1.693026   \n",
-       "166  1.322488        2.249065        2.887347              1.799113   \n",
-       "167  1.440005        2.637342        3.278921              1.689769   \n",
-       "168  1.383980        2.214246        3.167601              1.415021   \n",
+       "     carbon energy_heating energy_cooling energy_water_heating energy_cooking  \\\n",
+       "0 -0.056699       0.056867      -0.568738            -0.197789      -0.478802   \n",
+       "1 -0.072725       0.049004      -0.550216            -0.195549      -0.478933   \n",
+       "2 -0.073381        0.04114      -0.531693            -0.193309      -0.479064   \n",
+       "3 -0.101798       0.033276      -0.513171             -0.19107      -0.479195   \n",
+       "4 -0.078167       0.025413      -0.494649             -0.18883      -0.479326   \n",
        "\n",
-       "     energy_cooking geo  \n",
-       "156        1.976399  FR  \n",
-       "157        2.337869  FR  \n",
-       "158        2.229544  FR  \n",
-       "159        2.142887  FR  \n",
-       "160        2.043686  FR  \n",
-       "161        2.059423  FR  \n",
-       "162        2.041405  FR  \n",
-       "163        2.089008  FR  \n",
-       "164        2.100902  FR  \n",
-       "165        2.052831  FR  \n",
-       "166        2.171680  FR  \n",
-       "167        2.022219  FR  \n",
-       "168        1.610834  FR  "
+       "      total       gas geo  \n",
+       "0 -0.034514 -0.335104  BE  \n",
+       "1 -0.040696 -0.344081  BE  \n",
+       "2 -0.046878 -0.344678  BE  \n",
+       "3  -0.05306 -0.319312  BE  \n",
+       "4 -0.059242 -0.296488  BE  "
       ]
      },
-     "execution_count": 279,
+     "execution_count": 725,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_be = df_merged_s.loc[df_merged_s[\"geo\"] == \"FR\"]\n",
-    "df_be"
+    "df_be = df_merged_s.loc[df_merged_s[\"geo\"] == \"BE\"]\n",
+    "df_be.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 280,
+   "execution_count": 742,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1.        , 3.15625764, 0.04951903, 1.4220275 , 1.97639861],\n",
-       "       [1.        , 2.43041402, 1.00092731, 1.63646348, 2.33786898]])"
+       "array([[ 1.        , -0.03451352],\n",
+       "       [ 1.        , -0.04069572]])"
       ]
      },
-     "execution_count": 280,
+     "execution_count": 742,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "X_be = np.pad(df_be.iloc[:, 1:5].to_numpy(), ((0,0), (1,0)), mode=\"constant\", constant_values=1)\n",
+    "X_be = np.array(np.pad(df_be.iloc[:, 5:6].to_numpy(), ((0,0), (1,0)), mode=\"constant\", constant_values=1), dtype=np.float64)\n",
     "y_be = np.array(df_be[\"carbon\"])\n",
     "X_be[0:2,]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 281,
+   "execution_count": 743,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3430,14 +3868,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 282,
+   "execution_count": 744,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-0.05395949  0.03933248  0.00048339 -0.0018462  -0.02048255]\n"
+      "[0.015909982113676932 0.004008857952391476 0.007476999077431337\n",
+      " -0.016814565780291363 0.010940996033058412]\n"
      ]
     },
     {
@@ -3446,7 +3885,7 @@
        "0.0"
       ]
      },
-     "execution_count": 282,
+     "execution_count": 744,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3459,12 +3898,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 283,
+   "execution_count": 745,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3484,12 +3923,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 284,
+   "execution_count": 746,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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gl5ZFaG5/BQUFxpQpU4xu3boZFovFGDhwoLFixYpGYz///HMjMTHRsFgsLV5G4MCBA4YkQ5Lx6aefumyrqakx7r//fmPQoEFGcHCwERgYaAwaNMh46aWXXPqdfP8+/PDDX3w9wzCMwsJCY86cOUafPn0Mf39/IzQ01EhPT3cuF/BTJ5cOWLt2bZP7evvtt41+/foZ/v7+Rv/+/Y133nmn2SUili9fbiQmJhoBAQFGcHCwMXDgQOOBBx4w8vLynH1a+nMCPMVkGD855w0AAAAXzFkCAABwg7AEAADgBmEJAADADa8LS0uWLFFcXJysVqtSUlL05Zdfuu2/du1a9e3bV1arVQMHDnTepuGkxx9/XH379lVgYKDOOeccpaena9u2be15CAAAwIt4VVhas2aN5syZo8cee0y7du3SoEGDlJGR0WjZ/JM+//xzTZgwQVOnTtVXX32l0aNHa/To0crJyXH2Of/88/Xiiy/qn//8pz799FPFxcVp5MiRLuvQAACAzsurvg2XkpKiiy++WC+++KKkHxdUi42N1YwZMzRv3rxG/cePH6+Kigpt2LDB2TZkyBANHjxYy5Yta/I1SktLZbPZ9MEHHygtLa19DgQAAHgNr1mUsra2Vjt37tT8+fOdbWazWenp6crOzm5yTHZ2tubMmePSlpGRoXXr1jX7GsuXL5fNZtOgQYOaraWmpsZlJV2Hw6Hi4mJ17dq1Vbc/AAAAZ55hGCorK1P37t0b3Tj7p7wmLBUVFamhoUGRkZEu7ZGRkc77Nf1cfn5+k/3z8/Nd2jZs2KCbbrpJlZWVio6OVlZWlrp169ZsLZmZmVqwYEErjwQAAHQkubm5Ovfcc5vd7jVhqT3913/9l3bv3q2ioiK98soruvHGG7Vt2zZFREQ02X/+/PkuZ6zsdrt69Oih3NxchYSEnKmyAQDAaSgtLVVsbKzzFkbN8Zqw1K1bN/n4+KigoMClvaCgQFFRUU2OiYqKalH/wMBA9enTR3369NGQIUMUHx+v//f//p/LJb+f8vf3l7+/f6P2kJAQwhIAAF7ml6bQeM234SwWixITE7VlyxZnm8Ph0JYtW5SamtrkmNTUVJf+0o93x26u/0/36+6O6wAAoPPwmjNLkjRnzhxNmjRJSUlJSk5O1uLFi1VRUaEpU6ZIkm655RbFxMQoMzNTkjRr1iwNHz5czz33nEaNGqU333xTO3bs0PLlyyVJFRUVeuqpp3TttdcqOjpaRUVFWrJkiY4dO6YbbrjBY8cJAAA6Dq8KS+PHj9eJEyf06KOPKj8/X4MHD9amTZuck7iPHDniMpt96NChWr16tR5++GE9+OCDio+P17p165SQkCBJ8vHx0b59+/T666+rqKhIXbt21cUXX6xPPvlEAwYM8MgxAgCAjsWr1lnqqE6uzWS325mzBACAl2jp57fXzFkCAADwBMISAACAG4QlAAAANwhLAAAAbhCWAAAA3PCqpQMAAHA4DB0rqVJFbb0CLb6KCQ2Q2cxNzNF+CEsAAK9xsLBM7+cU6NCJclXXN8jq66Pe4UHKSIhUnwj39/cCWouwBADwCgcLy7Tis8MqrqhVtM2qLpYAVdbWKyfPrjx7laYMiyMwoV0wZwkA0OE5HIbezylQcUWt4iOCFGz1k4/ZpGCrn+IjglRcUavNewrkcLDOMtoeYQkA0OEdK6nSoRPlirZZG90h3mQyKdpm1cHCch0rqfJQhTibEZYAAB1eRW29qusb1MXS9OyRAIuPauobVFFbf4YrQ2dAWAIAdHiBFl9ZfX1U2UwYqqptkL+vjwKbCVPA6SAsAQA6vJjQAPUOD9Jxe7V+fv93wzB03F6tPhFBigkN8FCFOJsRlgAAHZ7ZbFJGQqTCAi06UFiusuo61TscKquu04HCcoUFWjRyQCTrLaFdEJYAAF6hT0SwpgyLU0J3m0oq63S4qEIllXUaGGNj2QC0Ky7uAgC8Rp+IYPUaEcQK3jijCEsAAK9iNpsUG9bF02WgE+EyHAAAgBuEJQAAADcISwAAAG4QlgAAANwgLAEAALhBWAIAAHCDsAQAAOAGYQkAAMANwhIAAIAbrOANAHByOAxuJQL8DGEJACBJOlhYpvdzCnToRLmq6xtk9fVR7/AgZSREcpNadGqEJQCADhaWacVnh1VcUatom1VdLAGqrK1XTp5defYqTRkWR2BCp8WcJQDo5BwOQ+/nFKi4olbxEUEKtvrJx2xSsNVP8RFBKq6o1eY9BXI4DE+XCngEYQkAOrljJVU6dKJc0TarTCbX+Ukmk0nRNqsOFpbrWEmVhyoEPIuwBACdXEVtvarrG9TF0vTMjACLj2rqG1RRW3+GKwM6BsISAHRygRZfWX19VNlMGKqqbZC/r48CmwlTwNmOsAQAnVxMaIB6hwfpuL1ahuE6L8kwDB23V6tPRJBiQgM8VCHgWYQlAOjkzGaTMhIiFRZo0YHCcpVV16ne4VBZdZ0OFJYrLNCikQMiWW8JnZbXhaUlS5YoLi5OVqtVKSkp+vLLL932X7t2rfr27Sur1aqBAwdq48aNzm11dXX67W9/q4EDByowMFDdu3fXLbfcory8vPY+DADoUPpEBGvKsDgldLeppLJOh4sqVFJZp4ExNpYNQKfnVReg16xZozlz5mjZsmVKSUnR4sWLlZGRof379ysiIqJR/88//1wTJkxQZmamrr76aq1evVqjR4/Wrl27lJCQoMrKSu3atUuPPPKIBg0apB9++EGzZs3Stddeqx07dnjgCAHAc/pEBKvXiCBW8AZ+xmT8/AJ1B5aSkqKLL75YL774oiTJ4XAoNjZWM2bM0Lx58xr1Hz9+vCoqKrRhwwZn25AhQzR48GAtW7asydfYvn27kpOT9d1336lHjx4tqqu0tFQ2m012u10hISGtODIAAHCmtfTz22suw9XW1mrnzp1KT093tpnNZqWnpys7O7vJMdnZ2S79JSkjI6PZ/pJkt9tlMpkUGhrabJ+amhqVlpa6PAAAwNnJa8JSUVGRGhoaFBkZ6dIeGRmp/Pz8Jsfk5+efUv/q6mr99re/1YQJE9wmzMzMTNlsNucjNjb2FI8GAAB4C68JS+2trq5ON954owzD0NKlS932nT9/vux2u/ORm5t7hqoEAABnmtdM8O7WrZt8fHxUUFDg0l5QUKCoqKgmx0RFRbWo/8mg9N1332nr1q2/OO/I399f/v7+rTgKAADgbbzmzJLFYlFiYqK2bNnibHM4HNqyZYtSU1ObHJOamurSX5KysrJc+p8MSgcOHNAHH3ygrl27ts8BAAAAr+Q1Z5Ykac6cOZo0aZKSkpKUnJysxYsXq6KiQlOmTJEk3XLLLYqJiVFmZqYkadasWRo+fLiee+45jRo1Sm+++aZ27Nih5cuXS/oxKI0bN067du3Shg0b1NDQ4JzPFBYWJovF4pkDBQAAHYZXhaXx48frxIkTevTRR5Wfn6/Bgwdr06ZNzkncR44ckdn8n5NlQ4cO1erVq/Xwww/rwQcfVHx8vNatW6eEhARJ0rFjx7R+/XpJ0uDBg11e68MPP9SIESPOyHEBAICOy6vWWeqoWGcJAADvc9atswQAAOAJhCUAAAA3CEsAAABuEJYAAADcICwBAAC4QVgCAABwg7AEAADghlctStmZOByGjpVUqaK2XoEWX8WEBshsNnm6LAAAOh3CUgd0sLBM7+cU6NCJclXXN8jq66Pe4UHKSIhUn4hgT5cHAECnQljqYA4WlmnFZ4dVXFGraJtVXSwBqqytV06eXXn2Kk0ZFkdgAgDgDGLOUgficBh6P6dAxRW1io8IUrDVTz5mk4KtfoqPCFJxRa027ymQw8EdagAAOFMISx3IsZIqHTpRrmibVSaT6/wkk8mkaJtVBwvLdaykykMVAgDQ+RCWOpCK2npV1zeoi6Xpq6MBFh/V1Deoorb+DFcGAEDnRVjqQAItvrL6+qiymTBUVdsgf18fBTYTpgAAQNsjLHUgMaEB6h0epOP2ahmG67wkwzB03F6tPhFBigkN8FCFAAB0PoSlDsRsNikjIVJhgRYdKCxXWXWd6h0OlVXX6UBhucICLRo5IJL1lgAAOIMISx1Mn4hgTRkWp4TuNpVU1ulwUYVKKus0MMbGsgEAAHgAk186oD4Rweo1IogVvAEA6AAISx2U2WxSbFgXT5cBAECnx2U4AAAANwhLAAAAbhCWAAAA3CAsAQAAuEFYAgAAcIOwBAAA4AZLBwDAGeJwGKyfBnghwhIAnAEHC8v0fk6BDp0oV3V9g6y+PuodHqSMhEhW5gc6OMISALSzg4VlWvHZYRVX1CraZlUXS4Aqa+uVk2dXnr2KWxkBHRxzlgCgHTkcht7PKVBxRa3iI4IUbPWTj9mkYKuf4iOCVFxRq817CuRwGJ4uFUAzCEsA0I6OlVTp0IlyRdusMplc5yeZTCZF26w6WFiuYyVVHqoQwC/hMhyAX8TE5NarqK1XdX2DulgCmtweYPFRQWm1Kmrrz3BlAFqKsATALSYmn55Ai6+svj6qrK1XsNWv0faq2gb5+/oo0MI/x0BHxWU4AM06OTE5J8+u0C5+6tUtSKFd/JSTZ9eKzw7rYGGZp0vs8GJCA9Q7PEjH7dUyDNd5SYZh6Li9Wn0ighQT2vSZJwCeR1gC0CQmJrcNs9mkjIRIhQVadKCwXGXVdap3OFRWXacDheUKC7Ro5IBILmsCHRhhCUCTmJjcdvpEBGvKsDgldLeppLJOh4sqVFJZp4ExNpYNALyA14WlJUuWKC4uTlarVSkpKfryyy/d9l+7dq369u0rq9WqgQMHauPGjS7b33nnHY0cOVJdu3aVyWTS7t2727F6wHv8Z2Jy03NpAiw+qqlvYGJyC/WJCNbdI3pr9uXna0ZavGZffr7uGt6boAR4Aa8KS2vWrNGcOXP02GOPadeuXRo0aJAyMjJUWFjYZP/PP/9cEyZM0NSpU/XVV19p9OjRGj16tHJycpx9KioqdMkll2jhwoVn6jAAr/DTiclNYWLyqTObTYoN66K+USGKDevCpTfAS5iMn8847MBSUlJ08cUX68UXX5QkORwOxcbGasaMGZo3b16j/uPHj1dFRYU2bNjgbBsyZIgGDx6sZcuWufQ9fPiwevbsqa+++kqDBw8+pbpKS0tls9lkt9sVEhJy6gcGdEAOh6Glfz+knDy74iOCXC7FGYahA4XlGhhj013De/OhD8ArtfTz22vOLNXW1mrnzp1KT093tpnNZqWnpys7O7vJMdnZ2S79JSkjI6PZ/gD+g4nJAPAjrzl/XlRUpIaGBkVGRrq0R0ZGat++fU2Oyc/Pb7J/fn7+adVSU1Ojmpoa5/PS0tLT2h/QUZ2cmHxynaWC0mr5+/poYIxNIwewzhKAzsFrwlJHkpmZqQULFni6DOCM6BMRrF4jgljBG0Cn5TWX4bp16yYfHx8VFBS4tBcUFCgqKqrJMVFRUafUv6Xmz58vu93ufOTm5p7W/oCOjonJADozrwlLFotFiYmJ2rJli7PN4XBoy5YtSk1NbXJMamqqS39JysrKarZ/S/n7+yskJMTlAQAAzk5edRluzpw5mjRpkpKSkpScnKzFixeroqJCU6ZMkSTdcsstiomJUWZmpiRp1qxZGj58uJ577jmNGjVKb775pnbs2KHly5c791lcXKwjR44oLy9PkrR//35JP56VOt0zUAAAwPt5VVgaP368Tpw4oUcffVT5+fkaPHiwNm3a5JzEfeTIEZnN/zlZNnToUK1evVoPP/ywHnzwQcXHx2vdunVKSEhw9lm/fr0zbEnSTTfdJEl67LHH9Pjjj5+ZAwMAAB2WV62z1FGxzhIAAN6npZ/fXnVmCThVDofBt7gAAKeFsISz1sHCMuf6QNX1DbL6+qh3eJAyElgfCADQcoQlnJUOFpZpxWeHVVxRq2ibVV0sAaqsrVdOnl159iru9A4AaDGvWToAaCmHw9D7OQUqrqhVfESQgq1+8jGbFGz1U3xEkIorarV5T4EcDqbrAQB+GWEJZ51jJVU6dKJc0Tary81fJclkMinaZtXBwnIdK6nyUIUAAG9CWMJZp6K2XtX1Depiafoqc4DFRzX1DaqorT/DlQEAvBFhCWedQIuvrL4+qmwmDFXVNsjf10eBzYQpAAB+irCEs05MaIB6hwfpuL1aP19GzDAMHbdXq09EkGJCAzxUIdqDw2Eot7hS+/JLlVtcyZw0AG2G/7XGWcdsNikjIVJ59iodKPxx7lKAxUdVtQ06bq9WWKBFIwdEst7SWYRlIgC0J8ISzkp9IoI1ZVic8wO0oLRa/r4+Ghhj08gBfICeTVgmAkB7IyzhrNUnIli9RgSxgvdZ7OfLRJz89mOw1U9B/r46UFiuzXsK1KtbED93AK1GWMJZzWw2KTasi6fLQDs5lWUi+D0A0FpM8AbgtVgmAsCZQFgC4LVYJgLAmUBYAuC1WCYCwJlAWALgtU4uExEWaNGBwnKVVdep3uFQWXWdDhSWs0wEgDbBuWm0msNh8E0zeBzLRABob4QltAqLAKIjYZkIAO2JsIRTxiKA6IhYJgJAe2HOEk7JzxcBDLb6ycdsUrDVT/ERQSquqNXmPQXclwsAcNYgLOGUnMoigAAAnA0ISzglLAIIAOhsCEs4JSwCCADobAhLOCUsAggA6GwISzglLAIIAOhsCEs4ZScXAUzoblNJZZ0OF1WopLJOA2NsLBsAADjrMLEErcIigACAzoKwhFZjEUAAQGfAZTgAAAA3CEsAAABuEJYAAADcICwBAAC4QVgCAABwg7AEAADgBmEJAADADa8LS0uWLFFcXJysVqtSUlL05Zdfuu2/du1a9e3bV1arVQMHDtTGjRtdthuGoUcffVTR0dEKCAhQenq6Dhw40J6HAAAAvIhXhaU1a9Zozpw5euyxx7Rr1y4NGjRIGRkZKiwsbLL/559/rgkTJmjq1Kn66quvNHr0aI0ePVo5OTnOPs8884xeeOEFLVu2TNu2bVNgYKAyMjJUXV19pg4LAAB0YCbj57eO78BSUlJ08cUX68UXX5QkORwOxcbGasaMGZo3b16j/uPHj1dFRYU2bNjgbBsyZIgGDx6sZcuWyTAMde/eXXPnztV9990nSbLb7YqMjNRrr72mm266qUV1lZaWymazyW63KyQkpA2OFAAAtLeWfn57zZml2tpa7dy5U+np6c42s9ms9PR0ZWdnNzkmOzvbpb8kZWRkOPt/++23ys/Pd+ljs9mUkpLS7D4BAEDn4jX3hisqKlJDQ4MiIyNd2iMjI7Vv374mx+Tn5zfZPz8/37n9ZFtzfZpSU1Ojmpoa5/PS0tKWHwgAAPAqXnNmqSPJzMyUzWZzPmJjYz1dEgAAaCdeE5a6desmHx8fFRQUuLQXFBQoKiqqyTFRUVFu+5/876nsU5Lmz58vu93ufOTm5p7y8QAAAO/gNWHJYrEoMTFRW7ZscbY5HA5t2bJFqampTY5JTU116S9JWVlZzv49e/ZUVFSUS5/S0lJt27at2X1Kkr+/v0JCQlweAADg7OQ1c5Ykac6cOZo0aZKSkpKUnJysxYsXq6KiQlOmTJEk3XLLLYqJiVFmZqYkadasWRo+fLiee+45jRo1Sm+++aZ27Nih5cuXS5JMJpPuvfdePfnkk4qPj1fPnj31yCOPqHv37ho9erSnDhMAAHQgXhWWxo8frxMnTujRRx9Vfn6+Bg8erE2bNjknaB85ckRm839Olg0dOlSrV6/Www8/rAcffFDx8fFat26dEhISnH0eeOABVVRU6I477lBJSYkuueQSbdq0SVar9YwfHwAA6Hi8ap2ljop1lgAA8D5n3TpLAAAAnkBYAgAAcIOwBAAA4AZhCQAAwA3CEgAAgBuEJQAAADcISwAAAG4QlgAAANwgLAEAALhBWAIAAHCjzcJSSUlJW+0KAACgw2hVWFq4cKHWrFnjfH7jjTeqa9euiomJ0ddff91mxQEAAHhaq8LSsmXLFBsbK0nKyspSVlaW/va3v+nKK6/U/fff36YFAgAAeJJvawbl5+c7w9KGDRt04403auTIkYqLi1NKSkqbFggAAOBJrTqzdM455yg3N1eStGnTJqWnp0uSDMNQQ0ND21UHAADgYa06szRmzBj95je/UXx8vL7//ntdeeWVkqSvvvpKffr0adMCAQAAPKlVYWnRokWKi4tTbm6unnnmGQUFBUmSjh8/rnvuuadNCwQAAPAkk2EYhqeL8HalpaWy2Wyy2+0KCQnxdDkAAKAFWvr53eIzS+vXr2/xi1977bUt7gsAANCRtTgsjR49ukX9TCYTk7wBAMBZo8VhyeFwtGcdAAAAHRL3hgMAAHCjVd+Gk6SKigp99NFHOnLkiGpra122zZw587QLAwAA6AhaFZa++uorXXXVVaqsrFRFRYXCwsJUVFSkLl26KCIigrAEAADOGq26DDd79mxdc801+uGHHxQQEKAvvvhC3333nRITE/WHP/yhrWsEAADwmFaFpd27d2vu3Lkym83y8fFRTU2NYmNj9cwzz+jBBx9s6xoBAAA8plVhyc/PT2bzj0MjIiJ05MgRSZLNZnPeMw4AAOBs0Ko5SxdeeKG2b9+u+Ph4DR8+XI8++qiKior05z//WQkJCW1dIwAAgMe06szS008/rejoaEnSU089pXPOOUd33323Tpw4oeXLl7dpgQAAAJ7EveHaAPeGAwDA+7T085tFKQEAANxo1Zylnj17ymQyNbv93//+d6sLAgAA6EhaFZbuvfdel+d1dXX66quvtGnTJt1///1tURcAAECH0KqwNGvWrCbblyxZoh07dpxWQQAAAB1Jm85ZuvLKK/X222+35S4BAAA8qk3D0ltvvaWwsLC23CUAAIBHtSosXXjhhbroooucjwsvvFDR0dF68MEH2+12J8XFxZo4caJCQkIUGhqqqVOnqry83O2Y6upqTZs2TV27dlVQUJDGjh2rgoIClz4zZ85UYmKi/P39NXjw4HapHQAAeK9WzVkaPXq0y3Oz2azw8HCNGDFCffv2bYu6Gpk4caKOHz+urKws1dXVacqUKbrjjju0evXqZsfMnj1b//d//6e1a9fKZrNp+vTpGjNmjD777DOXfrfeequ2bdumf/zjH+1SOwAA8F5esSjl3r171b9/f23fvl1JSUmSpE2bNumqq67S0aNH1b1790Zj7Ha7wsPDtXr1ao0bN06StG/fPvXr10/Z2dkaMmSIS//HH39c69at0+7du0+5PhalBADA+7T087vFZ5ZKS0tb/OJtHRiys7MVGhrqDEqSlJ6eLrPZrG3btun6669vNGbnzp2qq6tTenq6s61v377q0aNHk2HpVNTU1Kimpsb5/FTeGwAA4F1aHJZCQ0PdLkT5Uw0NDa0uqCn5+fmKiIhwafP19VVYWJjy8/ObHWOxWBQaGurSHhkZ2eyYlsrMzNSCBQtOax8AAMA7tHiC94cffqitW7dq69atevXVVxUREaEHHnhA7777rt5991098MADioyM1KuvvtriF583b55MJpPbx759+1p1YO1p/vz5stvtzkdubq6nSwIAAO2kxWeWhg8f7vzzE088oeeff14TJkxwtl177bUaOHCgli9frkmTJrVon3PnztXkyZPd9unVq5eioqJUWFjo0l5fX6/i4mJFRUU1OS4qKkq1tbUqKSlxObtUUFDQ7JiW8vf3l7+//2ntAwAAeIdWfRsuOztby5Yta9SelJSk2267rcX7CQ8PV3h4+C/2S01NVUlJiXbu3KnExERJ0tatW+VwOJSSktLkmMTERPn5+WnLli0aO3asJGn//v06cuSIUlNTW1wjAADwDIfD0LGSKlXU1ivQ4quY0ACZzS2bEtSWWhWWYmNj9corr+iZZ55xaf/Tn/6k2NjYNinsp/r166crrrhCt99+u5YtW6a6ujpNnz5dN910k/ObcMeOHVNaWppWrlyp5ORk2Ww2TZ06VXPmzFFYWJhCQkI0Y8YMpaamukzuPnjwoMrLy5Wfn6+qqirnt+H69+8vi8XS5scCAAB+2cHCMr2fU6BDJ8pVXd8gq6+PeocHKSMhUn0igs9oLa0KS4sWLdLYsWP1t7/9zXlm58svv9SBAwfa7XYnq1at0vTp05WWliaz2ayxY8fqhRdecG6vq6vT/v37VVlZ6VLnyb41NTXKyMjQSy+95LLf2267TR999JHz+YUXXihJ+vbbbxUXF9cuxwIAAJp3sLBMKz47rOKKWkXbrOpiCVBlbb1y8uzKs1dpyrC4MxqYWr3OUm5urpYuXeqcgN2vXz/ddddd7XJmqaNjnSUAANqGw2Fo6d8PKSfPrviIIJdv4huGoQOF5RoYY9Ndw3uf9iW5Nl9n6ediY2P19NNPt3Y4AABAI8dKqnToRLmibdZGSxaZTCZF26w6WFiuYyVVig3rckZqanFY+sc//qGEhASZzeZfvC3IBRdccNqFAQCAzqeitl7V9Q3qYglocnuAxUcFpdWqqK0/YzW1OCwNHjzYuTjk4MGDZTKZ1NQVPJPJ1OaLUgIAgM4h0OIrq6+PKmvrFWz1a7S9qrZB/r4+CrS0+uLYKWvxK3377bfOr/l/++237VYQAADovGJCA9Q7PEg5eXYF+fs2mrN03F6tgTE2xYQ2feapPbQ4LJ133nlN/hkAAKCtmM0mZSREKs9epQOFP85dCrD4qKq2Qcft1QoLtGjkgMgzut5Si2938lOvv/66/u///s/5/IEHHlBoaKiGDh2q7777rs2KAwAAnU+fiGBNGRanhO42lVTW6XBRhUoq6zQwxnbGlw2QWrl0wK9+9SstXbpUl112mbKzs5WWlqbFixdrw4YN8vX11TvvvNMetXZYLB0AAEDba+8VvNt16YDc3Fz16dNHkrRu3TqNGzdOd9xxh4YNG6YRI0a0qmAAAICfMptNZ2x5ALd1tGZQUFCQvv/+e0nS5s2bdfnll0uSrFarqqqq2q46AAAAD2vVmaXLL79ct912my688EL961//0lVXXSVJ2rNnD7cIAQAAZ5VWnVlasmSJUlNTdeLECb399tvq2rWrJGnnzp2aMGFCmxYIAADgSa2+Nxz+gwneAAB4n5Z+frfqzJIkffLJJ7r55ps1dOhQHTt2TJL05z//WZ9++mlrdwkAANDhtCosvf3228rIyFBAQIB27dqlmpoaSZLdbufmugAA4KzSqrD05JNPatmyZXrllVfk5/ef+7YMGzZMu3btarPiAAAAPK1VYWn//v269NJLG7XbbDaVlJScbk0AAAAdRqvCUlRUlA4ePNio/dNPP1WvXr1OuygAAICOolVh6fbbb9esWbO0bds2mUwm5eXladWqVZo7d67uvvvutq4RAADAY1q1KOW8efPkcDiUlpamyspKXXrppfL399f999+v2267ra1rBAAA8JhWnVkymUx66KGHVFxcrJycHH3xxRc6ceKEbDabevbs2dY1Amc1h8NQbnGl9uWXKre4Ug4HS58BQEdySmeWampq9PjjjysrK8t5Jmn06NFasWKFrr/+evn4+Gj27NntVStw1jlYWKb3cwp06ES5qusbZPX1Ue/wIGUkRKpPRLCnywMA6BTD0qOPPqqXX35Z6enp+vzzz3XDDTdoypQp+uKLL/Tcc8/phhtukI+PT3vVCpxVDhaWacVnh1VcUatom1VdLAGqrK1XTp5defYqTRkWR2ACgA7glMLS2rVrtXLlSl177bXKycnRBRdcoPr6en399dcymUztVSNw1nE4DL2fU6DiilrFRwQ5//4EW/0U5O+rA4Xl2rynQL26Bcls5u8WAHjSKc1ZOnr0qBITEyVJCQkJ8vf31+zZswlKwCk6VlKlQyfKFW2zNvr7YzKZFG2z6mBhuY6VVHmoQgDASacUlhoaGmSxWJzPfX19FRQU1OZFAWe7itp6Vdc3qIul6ZO7ARYf1dQ3qKK2/gxXBgD4uVO6DGcYhiZPnix/f39JUnV1te666y4FBga69HvnnXfarkLgLBRo8ZXV10eVtfUKtvo12l5V2yB/Xx8FNhOmAABnzin9Szxp0iSX5zfffHObFgN0FjGhAeodHqScPLuC/H1dLsUZhqHj9moNjLEpJjTAg1UCAKRTDEsrVqxorzqATsVsNikjIVJ59iodKPxx7lKAxUdVtQ06bq9WWKBFIwdEMrkbADqAVi1KCeD09YkI1pRhcUroblNJZZ0OF1WopLJOA2NsLBsAAB0IEyIAD+oTEaxeI4J0rKRKFbX1CrT4KiY0gDNKANCBEJYADzObTYoN6+LpMgAAzeAyHAAAgBuEJQAAADcISwAAAG4QlgAAANzwmrBUXFysiRMnKiQkRKGhoZo6darKy8vdjqmurta0adPUtWtXBQUFaezYsSooKHBu//rrrzVhwgTFxsYqICBA/fr10//8z/+096EAAAAv4jVhaeLEidqzZ4+ysrK0YcMGffzxx7rjjjvcjpk9e7bee+89rV27Vh999JHy8vI0ZswY5/adO3cqIiJCb7zxhvbs2aOHHnpI8+fP14svvtjehwMAALyEyTAMw9NF/JK9e/eqf//+2r59u5KSkiRJmzZt0lVXXaWjR4+qe/fujcbY7XaFh4dr9erVGjdunCRp37596tevn7KzszVkyJAmX2vatGnau3evtm7d2uL6SktLZbPZZLfbFRIS0oojBAAAZ1pLP7+94sxSdna2QkNDnUFJktLT02U2m7Vt27Ymx+zcuVN1dXVKT093tvXt21c9evRQdnZ2s69lt9sVFhbmtp6amhqVlpa6PAAAwNnJK8JSfn6+IiIiXNp8fX0VFham/Pz8ZsdYLBaFhoa6tEdGRjY75vPPP9eaNWt+8fJeZmambDab8xEbG9vygwEAAF7Fo2Fp3rx5MplMbh/79u07I7Xk5OTouuuu02OPPaaRI0e67Tt//nzZ7XbnIzc394zUCAAAzjyP3u5k7ty5mjx5sts+vXr1UlRUlAoLC13a6+vrVVxcrKioqCbHRUVFqba2ViUlJS5nlwoKChqN+eabb5SWlqY77rhDDz/88C/W7e/vL39//1/sBwAAvJ9Hw1J4eLjCw8N/sV9qaqpKSkq0c+dOJSYmSpK2bt0qh8OhlJSUJsckJibKz89PW7Zs0dixYyVJ+/fv15EjR5Samurst2fPHl122WWaNGmSnnrqqTY4KgAAcDbxim/DSdKVV16pgoICLVu2THV1dZoyZYqSkpK0evVqSdKxY8eUlpamlStXKjk5WZJ09913a+PGjXrttdcUEhKiGTNmSPpxbpL046W3yy67TBkZGXr22Wedr+Xj49OiEHcS34YDAMD7tPTz26Nnlk7FqlWrNH36dKWlpclsNmvs2LF64YUXnNvr6uq0f/9+VVZWOtsWLVrk7FtTU6OMjAy99NJLzu1vvfWWTpw4oTfeeENvvPGGs/28887T4cOHz8hxAQCAjs1rzix1ZJxZAgDA+5xV6ywBAAB4CmEJAADADcISAACAG4QlAAAANwhLAAAAbhCWAAAA3CAsAQAAuEFYAgAAcIOwBAAA4AZhCQAAwA3CEgAAgBuEJQAAADcISwAAAG4QlgAAANwgLAEAALhBWAIAAHCDsAQAAOAGYQkAAMANwhIAAIAbhCUAAAA3CEsAAABuEJYAAADcICwBAAC4QVgCAABwg7AEAADgBmEJAADADcISAACAG4QlAAAANwhLAAAAbhCWAAAA3CAsAQAAuEFYAgAAcIOwBAAA4IavpwsAAOBs43AYOlZSpYraegVafBUTGiCz2eTpstBKhCUAANrQwcIyvZ9ToEMnylVd3yCrr496hwcpIyFSfSKCPV0eWsFrLsMVFxdr4sSJCgkJUWhoqKZOnary8nK3Y6qrqzVt2jR17dpVQUFBGjt2rAoKCpzbv//+e11xxRXq3r27/P39FRsbq+nTp6u0tLS9DwcAcBY6WFimFZ8dVk6eXaFd/NSrW5BCu/gpJ8+uFZ8d1sHCMk+XiFbwmrA0ceJE7dmzR1lZWdqwYYM+/vhj3XHHHW7HzJ49W++9957Wrl2rjz76SHl5eRozZoxzu9ls1nXXXaf169frX//6l1577TV98MEHuuuuu9r7cAAAZxmHw9D7OQUqrqhVfESQgq1+8jGbFGz1U3xEkIorarV5T4EcDsPTpeIUmQzD6PA/tb1796p///7avn27kpKSJEmbNm3SVVddpaNHj6p79+6NxtjtdoWHh2v16tUaN26cJGnfvn3q16+fsrOzNWTIkCZf64UXXtCzzz6r3NzcFtdXWloqm80mu92ukJCQVhwhAMDb5RZXalHWvxTaxU/BVr9G28uq61RSWafZl5+v2LAuHqgQP9fSz2+vOLOUnZ2t0NBQZ1CSpPT0dJnNZm3btq3JMTt37lRdXZ3S09OdbX379lWPHj2UnZ3d5Ji8vDy98847Gj58uNt6ampqVFpa6vIAAHRuFbX1qq5vUBdL09OBAyw+qqlvUEVt/RmuDKfLK8JSfn6+IiIiXNp8fX0VFham/Pz8ZsdYLBaFhoa6tEdGRjYaM2HCBHXp0kUxMTEKCQnRn/70J7f1ZGZmymazOR+xsbGnflAAgLNKoMVXVl8fVTYThqpqG+Tv66PAZsIUOi6PhqV58+bJZDK5fezbt6/d61i0aJF27dqlv/71rzp06JDmzJnjtv/8+fNlt9udj1O5ZAcAODvFhAaod3iQjtur9fMZLoZh6Li9Wn0ighQTGuChCtFaHo23c+fO1eTJk9326dWrl6KiolRYWOjSXl9fr+LiYkVFRTU5LioqSrW1tSopKXE5u1RQUNBoTFRUlKKiotS3b1+FhYXp17/+tR555BFFR0c3uW9/f3/5+/v/8gECADoNs9mkjIRI5dmrdKCwXNE2qwIsPqqqbdBxe7XCAi0aOSCS9Za8kEfDUnh4uMLDw3+xX2pqqkpKSrRz504lJiZKkrZu3SqHw6GUlJQmxyQmJsrPz09btmzR2LFjJUn79+/XkSNHlJqa2uxrORwOST/OSwIA4FT0iQjWlGFxznWWCkqr5e/ro4ExNo0cwDpL3sorvg0nSVdeeaUKCgq0bNky1dXVacqUKUpKStLq1aslSceOHVNaWppWrlyp5ORkSdLdd9+tjRs36rXXXlNISIhmzJghSfr8888lSRs3blRBQYEuvvhiBQUFac+ePbr//vsVFhamTz/9tMW18W04AMBPsYK3d2jp57fXzDJbtWqVpk+frrS0NJnNZo0dO1YvvPCCc3tdXZ3279+vyspKZ9uiRYucfWtqapSRkaGXXnrJuT0gIECvvPKKZs+erZqaGsXGxmrMmDGaN2/eGT02AMDZxWw2sTzAWcRrzix1ZJxZAgDA+5xV6ywBAAB4CmEJAADADcISAACAG4QlAAAANwhLAAAAbhCWAAAA3CAsAQAAuEFYAgAAcIOwBAAA4AZhCQAAwA3CEgAAgBuEJQAAADcISwAAAG4QlgAAANwgLAEAALhBWAIAAHCDsAQAAOAGYQkAAMANwhIAAIAbhCUAAAA3CEsAAABuEJYAAADcICwBAAC4QVgCAABwg7AEAADgBmEJAADADcISAACAG4QlAAAANwhLAAAAbhCWAAAA3CAsAQAAuEFYAgAAcIOwBAAA4AZhCQAAwA3CEgAAgBteE5aKi4s1ceJEhYSEKDQ0VFOnTlV5ebnbMdXV1Zo2bZq6du2qoKAgjR07VgUFBU32/f7773XuuefKZDKppKSkHY4AAAB4I68JSxMnTtSePXuUlZWlDRs26OOPP9Ydd9zhdszs2bP13nvvae3atfroo4+Ul5enMWPGNNl36tSpuuCCC9qjdAAA4MVMhmEYni7il+zdu1f9+/fX9u3blZSUJEnatGmTrrrqKh09elTdu3dvNMZutys8PFyrV6/WuHHjJEn79u1Tv379lJ2drSFDhjj7Ll26VGvWrNGjjz6qtLQ0/fDDDwoNDW1xfaWlpbLZbLLb7QoJCTm9gwUAAGdESz+/veLMUnZ2tkJDQ51BSZLS09NlNpu1bdu2Jsfs3LlTdXV1Sk9Pd7b17dtXPXr0UHZ2trPtm2++0RNPPKGVK1fKbG7Z21FTU6PS0lKXBwAAODt5RVjKz89XRESES5uvr6/CwsKUn5/f7BiLxdLoDFFkZKRzTE1NjSZMmKBnn31WPXr0aHE9mZmZstlszkdsbOypHRAAAPAaHg1L8+bNk8lkcvvYt29fu73+/Pnz1a9fP918882nPM5utzsfubm57VQhAADwNF9PvvjcuXM1efJkt3169eqlqKgoFRYWurTX19eruLhYUVFRTY6LiopSbW2tSkpKXM4uFRQUOMds3bpV//znP/XWW29Jkk5O3+rWrZseeughLViwoMl9+/v7y9/fvyWHCAAAvJxHw1J4eLjCw8N/sV9qaqpKSkq0c+dOJSYmSvox6DgcDqWkpDQ5JjExUX5+ftqyZYvGjh0rSdq/f7+OHDmi1NRUSdLbb7+tqqoq55jt27fr1ltv1SeffKLevXuf7uEBAICzgEfDUkv169dPV1xxhW6//XYtW7ZMdXV1mj59um666SbnN+GOHTumtLQ0rVy5UsnJybLZbJo6darmzJmjsLAwhYSEaMaMGUpNTXV+E+7ngaioqMj5eqfybTgAAHD28oqwJEmrVq3S9OnTlZaWJrPZrLFjx+qFF15wbq+rq9P+/ftVWVnpbFu0aJGzb01NjTIyMvTSSy95onwAAOClvGKdpY6OdZYAAPA+Z9U6SwAAAJ5CWAIAAHCDsAQAAOAGYQkAAMANwhIAAIAbhCUAAAA3CEsAAABuEJYAAADcICwBAAC4QVgCAABwg7AEAADgBmEJAADADcISAACAG4QlAAAANwhLAAAAbhCWAAAA3CAsAQAAuEFYAgAAcMPX0wUAAIAzz+EwdKykShW19Qq0+ComNEBms8nTZXVIhCUAADqZg4Vlej+nQIdOlKu6vkFWXx/1Dg9SRkKk+kQEe7q8DoewBABAJ3KwsEwrPjus4opaRdus6mIJUGVtvXLy7MqzV2nKsDgC088wZwkAgE7C4TD0fk6BiitqFR8RpGCrn3zMJgVb/RQfEaTiilpt3lMgh8PwdKkdCmEJAIBO4lhJlQ6dKFe0zSqTyXV+kslkUrTNqoOF5TpWUuWhCjsmwhIAAJ1ERW29qusb1MXS9CycAIuPauobVFFbf4Yr69gISwAAdBKBFl9ZfX1U2UwYqqptkL+vjwKbCVOdFWEJAIBOIiY0QL3Dg3TcXi3DcJ2XZBiGjtur1SciSDGhAR6qsGMiLAEA0EmYzSZlJEQqLNCiA4XlKquuU73DobLqOh0oLFdYoEUjB0Sy3tLPEJYAAOhE+kQEa8qwOCV0t6mksk6HiypUUlmngTE2lg1oBhclAQDoZPpEBKvXiCBW8G4hwhIAAJ2Q2WxSbFgXT5fhFbgMBwAA4AZhCQAAwA3CEgAAgBuEJQAAADcISwAAAG54TVgqLi7WxIkTFRISotDQUE2dOlXl5eVux1RXV2vatGnq2rWrgoKCNHbsWBUUFLj0MZlMjR5vvvlmex4KAADwIl4TliZOnKg9e/YoKytLGzZs0Mcff6w77rjD7ZjZs2frvffe09q1a/XRRx8pLy9PY8aMadRvxYoVOn78uPMxevTodjoKAADgbUzGz28O0wHt3btX/fv31/bt25WUlCRJ2rRpk6666iodPXpU3bt3bzTGbrcrPDxcq1ev1rhx4yRJ+/btU79+/ZSdna0hQ4ZI+vHM0rvvvntaAam0tFQ2m012u10hISGt3g8AADhzWvr57RVnlrKzsxUaGuoMSpKUnp4us9msbdu2NTlm586dqqurU3p6urOtb9++6tGjh7Kzs136Tps2Td26dVNycrJeffXVRjcXBAAAnZdXrOCdn5+viIgIlzZfX1+FhYUpPz+/2TEWi0WhoaEu7ZGRkS5jnnjiCV122WXq0qWLNm/erHvuuUfl5eWaOXNms/XU1NSopqbG+dxut0v6MaECAADvcPJz+5dOkng0LM2bN08LFy5022fv3r3tWsMjjzzi/POFF16oiooKPfvss27DUmZmphYsWNCoPTY2tl1qBAAA7aesrEw2m63Z7R4NS3PnztXkyZPd9unVq5eioqJUWFjo0l5fX6/i4mJFRUU1OS4qKkq1tbUqKSlxObtUUFDQ7BhJSklJ0e9+9zvV1NTI39+/yT7z58/XnDlznM8dDoeKi4vVtWtXmUxtdxPC0tJSxcbGKjc3l7lQp4H3sW3wPrYN3se2wfvYNjr7+2gYhsrKypqc+/xTHg1L4eHhCg8P/8V+qampKikp0c6dO5WYmChJ2rp1qxwOh1JSUpock5iYKD8/P23ZskVjx46VJO3fv19HjhxRampqs6+1e/dunXPOOc0GJUny9/dvtP3nl/vaUkhISKf8JW5rvI9tg/exbfA+tg3ex7bRmd9Hd2eUTvKKOUv9+vXTFVdcodtvv13Lli1TXV2dpk+frptuusmZBo8dO6a0tDStXLlSycnJstlsmjp1qubMmaOwsDCFhIRoxowZSk1NdX4T7r333lNBQYGGDBkiq9WqrKwsPf3007rvvvs8ebgAAKAD8YqwJEmrVq3S9OnTlZaWJrPZrLFjx+qFF15wbq+rq9P+/ftVWVnpbFu0aJGzb01NjTIyMvTSSy85t/v5+WnJkiWaPXu2DMNQnz599Pzzz+v2228/o8cGAAA6Lq8JS2FhYVq9enWz2+Pi4hrNZrdarVqyZImWLFnS5JgrrrhCV1xxRZvW2Zb8/f312GOPub0kiF/G+9g2eB/bBu9j2+B9bBu8jy3jFYtSAgAAeIpXLEoJAADgKYQlAAAANwhLAAAAbhCWAAAA3CAsdWBLlixRXFycrFarUlJS9OWXX3q6JK+SmZmpiy++WMHBwYqIiNDo0aO1f/9+T5fl1X7/+9/LZDLp3nvv9XQpXunYsWO6+eab1bVrVwUEBGjgwIHasWOHp8vyKg0NDXrkkUfUs2dPBQQEqHfv3vrd737HDdB/wccff6xrrrlG3bt3l8lk0rp161y2G4ahRx99VNHR0QoICFB6eroOHDjgmWI7IMJSB7VmzRrNmTNHjz32mHbt2qVBgwYpIyOj0W1f0LyPPvpI06ZN0xdffKGsrCzV1dVp5MiRqqio8HRpXmn79u16+eWXdcEFF3i6FK/0ww8/aNiwYfLz89Pf/vY3ffPNN3ruued0zjnneLo0r7Jw4UItXbpUL774ovbu3auFCxfqmWee0R//+EdPl9ahVVRUaNCgQc0upfPMM8/ohRde0LJly7Rt2zYFBgYqIyND1dXVZ7jSDspAh5ScnGxMmzbN+byhocHo3r27kZmZ6cGqvFthYaEhyfjoo488XYrXKSsrM+Lj442srCxj+PDhxqxZszxdktf57W9/a1xyySWeLsPrjRo1yrj11ltd2saMGWNMnDjRQxV5H0nGu+++63zucDiMqKgo49lnn3W2lZSUGP7+/sb//u//eqDCjoczSx1QbW2tdu7cqfT0dGeb2WxWenq6srOzPViZd7Pb7ZJ+XOAUp2batGkaNWqUy+8kTs369euVlJSkG264QREREbrwwgv1yiuveLosrzN06FBt2bJF//rXvyRJX3/9tT799FNdeeWVHq7Me3377bfKz893+ftts9mUkpLCZ87/z2tW8O5MioqK1NDQoMjISJf2yMhI7du3z0NVeTeHw6F7771Xw4YNU0JCgqfL8Spvvvmmdu3ape3bt3u6FK/273//W0uXLtWcOXP04IMPavv27Zo5c6YsFosmTZrk6fK8xrx581RaWqq+ffvKx8dHDQ0NeuqppzRx4kRPl+a18vPzJanJz5yT2zo7whI6hWnTpiknJ0effvqpp0vxKrm5uZo1a5aysrJktVo9XY5XczgcSkpK0tNPPy1JuvDCC5WTk6Nly5YRlk7BX/7yF61atUqrV6/WgAEDtHv3bt17773q3r077yPaDZfhOqBu3brJx8dHBQUFLu0FBQWKioryUFXea/r06dqwYYM+/PBDnXvuuZ4ux6vs3LlThYWFuuiii+Tr6ytfX1999NFHeuGFF+Tr66uGhgZPl+g1oqOj1b9/f5e2fv366ciRIx6qyDvdf//9mjdvnm666SYNHDhQ//3f/63Zs2crMzPT06V5rZOfK3zmNI+w1AFZLBYlJiZqy5YtzjaHw6EtW7YoNTXVg5V5F8MwNH36dL377rvaunWrevbs6emSvE5aWpr++c9/avfu3c5HUlKSJk6cqN27d8vHx8fTJXqNYcOGNVq64l//+pfOO+88D1XknSorK2U2u350+fj4yOFweKgi79ezZ09FRUW5fOaUlpZq27ZtfOb8/7gM10HNmTNHkyZNUlJSkpKTk7V48WJVVFRoypQpni7Na0ybNk2rV6/WX//6VwUHBzuvvdtsNgUEBHi4Ou8QHBzcaI5XYGCgunbtytyvUzR79mwNHTpUTz/9tG688UZ9+eWXWr58uZYvX+7p0rzKNddco6eeeko9evTQgAED9NVXX+n555/Xrbfe6unSOrTy8nIdPHjQ+fzbb7/V7t27FRYWph49eujee+/Vk08+qfj4ePXs2VOPPPKIunfvrtGjR3uu6I7E01/HQ/P++Mc/Gj169DAsFouRnJxsfPHFF54uyatIavKxYsUKT5fm1Vg6oPXee+89IyEhwfD39zf69u1rLF++3NMleZ3S0lJj1qxZRo8ePQyr1Wr06tXLeOihh4yamhpPl9ahffjhh03+ezhp0iTDMH5cPuCRRx4xIiMjDX9/fyMtLc3Yv3+/Z4vuQEyGwbKnAAAAzWHOEgAAgBuEJQAAADcISwAAAG4QlgAAANwgLAEAALhBWAIAAHCDsAQAAOAGYQkA3DCZTFq3bp2nywDgQYQlAGetyZMnc7sGAKeNsAQAAOAGYQlApzBixAjNnDlTDzzwgMLCwhQVFaXHH3/cpc+BAwd06aWXymq1qn///srKymq0n9zcXN14440KDQ1VWFiYrrvuOh0+fFiStG/fPnXp0kWrV6929v/LX/6igIAAffPNN+15eADaEWEJQKfx+uuvKzAwUNu2bdMzzzyjJ554whmIHA6HxowZI4vFom3btmnZsmX67W9/6zK+rq5OGRkZCg4O1ieffKLPPvtMQUFBuuKKK1RbW6u+ffvqD3/4g+655x4dOXJER48e1V133aWFCxeqf//+njhkAG2AG+kCOGtNnjxZJSUlWrdunUaMGKGGhgZ98sknzu3Jycm67LLL9Pvf/16bN2/WqFGj9N1336l79+6SpE2bNunKK6/Uu+++q9GjR+uNN97Qk08+qb1798pkMkmSamtrFRoaqnXr1mnkyJGSpKuvvlqlpaWyWCzy8fHRpk2bnP0BeB9fTxcAAGfKBRdc4PI8OjpahYWFkqS9e/cqNjbWGZQkKTU11aX/119/rYMHDyo4ONilvbq6WocOHXI+f/XVV3X++efLbDZrz549BCXAyxGWAHQafn5+Ls9NJpMcDkeLx5eXlysxMVGrVq1qtC08PNz556+//loVFRUym806fvy4oqOjW180AI8jLAGApH79+ik3N9cl3HzxxRcufS666CKtWbNGERERCgkJaXI/xcXFmjx5sh566CEdP35cEydO1K5duxQQENDuxwCgfTDBGwAkpaen6/zzz9ekSZP09ddf65NPPtFDDz3k0mfixInq1q2brrvuOn3yySf69ttv9fe//10zZ87U0aNHJUl33XWXYmNj9fDDD+v5559XQ0OD7rvvPk8cEoA2QlgCAElms1nvvvuuqqqqlJycrNtuu01PPfWUS58uXbro448/Vo8ePTRmzBj169dPU6dOVXV1tUJCQrRy5Upt3LhRf/7zn+Xr66vAwEC98cYbeuWVV/S3v/3NQ0cG4HTxbTgAAAA3OLMEAADgBmEJAADADcISAACAG4QlAAAANwhLAAAAbhCWAAAA3CAsAQAAuEFYAgAAcIOwBAAA4AZhCQAAwA3CEgAAgBuEJQAAADf+P6+1Kfl83UlgAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3509,7 +3948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 285,
+   "execution_count": 747,
    "metadata": {},
    "outputs": [
     {
@@ -3520,7 +3959,7 @@
        },
        "data": [
         {
-         "hovertemplate": "energy_heating=%{x}<br>carbon=%{y}<extra></extra>",
+         "hovertemplate": "total=%{x}<br>carbon=%{y}<extra></extra>",
          "legendgroup": "",
          "marker": {
           "color": "#636efa",
@@ -3532,35 +3971,33 @@
          "showlegend": false,
          "type": "scatter",
          "x": [
-          3.1562576439596217,
-          2.43041402208785,
-          2.8735859313814993,
-          3.1181375060994765,
-          2.240820003968087,
-          2.474151822482052,
-          2.6777606081631156,
-          2.5837882812797175,
-          2.434724285758699,
-          2.43293336970549,
-          2.249065325119517,
-          2.6373422201057712,
-          2.214246034789166
+          -0.03451352403139175,
+          -0.04069572092524283,
+          -0.04687791781909403,
+          -0.05306011471294511,
+          -0.0592423116067963,
+          -0.0642143104647586,
+          -0.07382501162546044,
+          -0.07998346501588552,
+          -0.095047145075093,
+          -0.09483018117653078,
+          -0.04114499124062597,
+          -0.13875258967704102
          ],
          "xaxis": "x",
          "y": [
-          1.9620982675870644,
-          1.83853921534835,
-          1.8442588285912038,
-          1.8411212133551575,
-          1.6758004100720165,
-          1.6995957006611646,
-          1.708231522772542,
-          1.7228929806778095,
-          1.6282695881123612,
-          1.5812493333363307,
-          1.3224884827700845,
-          1.4400051912343974,
-          1.3839804112432956
+          -0.0566985935966168,
+          -0.07272463686555179,
+          -0.07338141484816153,
+          -0.10179789881353377,
+          -0.07816725610783361,
+          -0.08640536146009997,
+          -0.08675599049779897,
+          -0.08116878515343734,
+          -0.08662884554579156,
+          -0.14256658710787662,
+          -0.12342358327643835,
+          -0.153828616355248
          ],
          "yaxis": "y"
         }
@@ -4395,7 +4832,7 @@
           1
          ],
          "title": {
-          "text": "energy_heating"
+          "text": "total"
          }
         },
         "yaxis": {
@@ -4416,12 +4853,12 @@
     }
    ],
    "source": [
-    "px.scatter(df_be, x=\"energy_heating\", y=\"carbon\")"
+    "px.scatter(df_be, x=\"total\", y=\"carbon\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 286,
+   "execution_count": 748,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4430,16 +4867,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 287,
+   "execution_count": 749,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8016784112747927"
+       "0.1960374425066873"
       ]
      },
-     "execution_count": 287,
+     "execution_count": 749,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4447,13 +4884,6 @@
    "source": [
     "r2_score(y_be, preds_be)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 39f4615c5021557bede1e5083d54102dfa184768 Mon Sep 17 00:00:00 2001
From: mehallhm <michaelhmehall@gmail.com>
Date: Thu, 6 Jun 2024 00:01:47 +0200
Subject: [PATCH 19/34] feat: ml model

---
 api/backend/ml_models/__init__.py      |    0
 api/backend/ml_models/model_alpha.py   |   87 +
 api/backend/ml_models/train_helpers.py |   88 +
 eda/eda3.ipynb                         | 3107 +++++++++++++++---------
 eda/eda3_alpha_migration.ipynb         |  311 +++
 5 files changed, 2414 insertions(+), 1179 deletions(-)
 create mode 100644 api/backend/ml_models/__init__.py
 create mode 100644 api/backend/ml_models/model_alpha.py
 create mode 100644 api/backend/ml_models/train_helpers.py
 create mode 100644 eda/eda3_alpha_migration.ipynb

diff --git a/api/backend/ml_models/__init__.py b/api/backend/ml_models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/api/backend/ml_models/model_alpha.py b/api/backend/ml_models/model_alpha.py
new file mode 100644
index 0000000..6ad2201
--- /dev/null
+++ b/api/backend/ml_models/model_alpha.py
@@ -0,0 +1,87 @@
+"""
+"""
+
+import numpy as np
+import pandas as pd
+import pandasdmx as sdmx
+import train_helpers
+from sklearn.metrics import r2_score
+
+def train():
+	"""
+	"""
+	estat = sdmx.Request("ESTAT")
+	resp = estat.data(
+		"ENV_AIR_GGE",
+		key={
+			"unit": "THS_T",
+			"freq": "A",
+			"src_crf": "TOTX4_MEMONIA",
+			"airpol": "GHG"
+		}
+	)
+	emission_df = resp.to_pandas(datetime={'dim': 'TIME_PERIOD'}).droplevel(level=['unit', 'freq', 'src_crf', 'airpol'], axis=1)
+	melted_emissions_df = train_helpers.melt_smdx_dataframe(emission_df)
+
+	resp = estat.data(
+			"NRG_D_HHQ",
+			key={
+				"siec": "TOTAL",
+				"unit": "TJ",
+				"nrg_bal": "FC_OTH_HH_E",
+				"freq": "A",
+			}
+		)
+	household_energy_df = resp.to_pandas(datetime={'dim': 'TIME_PERIOD', 'freq': 'freq'}).droplevel(level=["siec", "unit", "nrg_bal"], axis=1)
+	melted_household_energy_df = train_helpers.melt_smdx_dataframe(household_energy_df)
+
+	merged_df = train_helpers.merge_dataframes([melted_emissions_df, melted_household_energy_df])
+	merged_df = merged_df.drop(merged_df[(merged_df.geo == "EU27_2020") | (merged_df.geo == "EU20")].index)
+	merged_df = merged_df.drop("year", axis=1)
+	standard_df = train_helpers.standardize(merged_df)
+
+	df_dummies = pd.get_dummies(standard_df, dtype=int, columns=["geo"])
+	df_dummies = df_dummies.fillna(0)
+
+	#X = np.pad(df_dummies.iloc[:, 1:].to_numpy(dtype=np.float64), ((0,0), (1,0)), mode="constant", constant_values=1)
+	X = np.pad(standard_df.iloc[:,1:3].to_numpy(dtype=np.float64), ((0,0), (1,0)), mode="constant", constant_values=1)
+	y = np.array(df_dummies["emissions"], dtype=np.float64)
+
+	m = np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, y))
+
+	return m
+
+def test(X: np.array, y: np.array) -> any:
+	"""
+	"""
+	np_remove = lambda a, i: np.concatenate([a[:i,], a[i + 1:,]])
+	lin_reg = lambda X, Y: np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, Y))
+
+	y_pred = []
+	for i in range(len(X)):
+		holdout_X = X[i]
+		
+		loo_X = np_remove(X, i)
+		loo_y = np_remove(Y, i)
+		loo_b = lin_reg(loo_X, loo_y)
+
+		y_hat = np.matmul(holdout_X, loo_b)
+		y_pred.append(y_hat)
+	
+	r2 = r2_score(y, y_pred)
+
+	return r2
+	
+
+def predict(feats:np.array, beta:np.array) -> float:
+	"""
+	Predict the 
+
+	:param feats: The unpadded input features from the user
+	:param beta: The slopes (and intercept) for the trained model
+	:returns: The predicted value
+	"""
+	x = np.pad(feats, ((0, 0), (1, 0)), mode="constant", constant_values=1)
+	y_hat = np.matmul(x, beta)
+
+	return y_hat[0]
\ No newline at end of file
diff --git a/api/backend/ml_models/train_helpers.py b/api/backend/ml_models/train_helpers.py
new file mode 100644
index 0000000..ae60e13
--- /dev/null
+++ b/api/backend/ml_models/train_helpers.py
@@ -0,0 +1,88 @@
+"""
+"""
+
+import pandas as pd
+import numpy as np
+from functools import reduce
+
+def melt_smdx_dataframe(df: pd.DataFrame) -> pd.DataFrame:
+	"""
+	Given an ESTAT smdx dataframe, convert the datetimes to years and melt
+
+	:param df: The raw SDMX parsed dataframe from ESTAT
+	:returns: A melted dataframe with the columns of:
+		`year` - the year of the observation
+		`geo` - the country of the observation
+		`value` - the value of the observation
+	"""
+	df = df.reset_index()
+	df["year"] = df["TIME_PERIOD"].dt.year
+	df = df.drop("TIME_PERIOD", axis=1)
+	return pd.melt(df, id_vars="year")
+
+def merge_dataframes(dataframes: list[pd.DataFrame]) -> pd.DataFrame:
+	"""
+	"""
+	for i, df in enumerate(dataframes):
+		df.columns = ["geo", "year", i]
+
+	merged_df = reduce(lambda l, r: pd.merge(l, r, left_on=["year", "geo"], right_on=["year", "geo"]), dataframes)
+	return merged_df
+
+def fill_holes(df: pd.DataFrame) -> pd.DataFrame:
+	"""
+	"""
+	lin_reg = lambda X, Y: np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, Y))
+
+	dfs = []
+
+	for name, group in df.groupby('geo'):
+		cols = [[name for _ in range(len(group.index))]]
+		for i in range(1, len(group.columns)):
+			d = group.iloc[:, i:i+1].to_numpy()
+
+			missing_mask = np.isnan(d) | (d == 0)
+			present_mask = ~missing_mask
+
+			missing_mask = missing_mask.reshape(1, -1)[0]
+			present_mask = present_mask.reshape(1, -1)[0]
+
+			if not np.any(missing_mask):
+				d = d.reshape(1, -1)[0]
+				cols.append(d)
+				continue
+
+			if not np.any(present_mask):
+				d = d.reshape(1, -1)[0]
+				cols.append(d)
+				continue
+
+			x_present = np.pad(np.arange(len(d))[present_mask].reshape(-1, 1), ((0, 0), (1, 0)), mode="constant", constant_values=1)
+			y_present = d[present_mask]
+
+			w = lin_reg(x_present, y_present)
+
+			x_missing = np.pad(np.arange(len(d))[missing_mask].reshape(-1, 1), ((0, 0), (1, 0)), mode="constant", constant_values=1)
+			y_missing_pred = np.matmul(x_missing, w)
+
+			d[missing_mask] = y_missing_pred
+			d = d.reshape(1, -1)[0]
+
+			cols.append(d)
+			
+		dfs.append(pd.DataFrame(cols).T)	
+		
+	df_unswissed = pd.concat(dfs, axis=0)
+	df_unswissed.columns = df.columns
+	return df_unswissed
+
+def standardize(df: pd.DataFrame) -> pd.DataFrame:
+	"""
+	"""
+	df_standard = pd.DataFrame()
+	for feat in df.columns:
+		if feat == "geo": continue
+		df_standard[f'{feat}'] = ((df[feat] - df[feat].mean()) / df[feat].std())
+	df_standard["geo"] = df["geo"]
+
+	return df_standard
\ No newline at end of file
diff --git a/eda/eda3.ipynb b/eda/eda3.ipynb
index 9a5e343..093270c 100644
--- a/eda/eda3.ipynb
+++ b/eda/eda3.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,14 +40,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-04 23:17:27,003 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n"
+      "2024-06-05 23:12:54,193 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n"
      ]
     },
     {
@@ -120,7 +120,7 @@
        "4  1989  AT    NaN"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -144,15 +144,6 @@
     "emission_melted_df.head()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "px.scatter(emission_melted_df, x=\"TIME_PERIOD\", y=\"value\", color=\"geo\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -162,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,38 +185,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 598,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-05 21:23:32,675 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
-      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
-      "\n",
-      "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
-      "\n",
-      "2024-06-05 21:23:32,827 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
-      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
-      "\n",
-      "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
-      "\n",
-      "2024-06-05 21:23:33,017 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
-      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
-      "\n",
-      "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
-      "\n",
-      "2024-06-05 21:23:33,194 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
-      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
-      "\n",
-      "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
-      "\n",
-      "2024-06-05 21:23:33,390 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
-      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning:\n",
-      "\n",
-      "'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
-      "\n"
+      "2024-06-05 23:12:55,611 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
+      "  return dispatch(args[0].__class__)(*args, **kw)\n",
+      "2024-06-05 23:12:55,729 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
+      "  return dispatch(args[0].__class__)(*args, **kw)\n",
+      "2024-06-05 23:12:55,856 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
+      "  return dispatch(args[0].__class__)(*args, **kw)\n",
+      "2024-06-05 23:12:56,009 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
+      "  return dispatch(args[0].__class__)(*args, **kw)\n",
+      "2024-06-05 23:12:56,214 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
+      "  return dispatch(args[0].__class__)(*args, **kw)\n"
      ]
     }
    ],
@@ -236,14 +217,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 599,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-05 21:23:35,400 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n"
+      "2024-06-05 23:12:57,492 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n"
      ]
     },
     {
@@ -316,7 +297,7 @@
        "4  2015  AL  109.229"
       ]
      },
-     "execution_count": 599,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -349,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 703,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -465,7 +446,7 @@
        "4        7179.133  278096.422  1539.983  "
       ]
      },
-     "execution_count": 703,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -480,16 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 704,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res_carbon_df.drop(index=72, axis=0, inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 705,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -605,7 +577,7 @@
        "4        7179.133  278096.422  1539.983  "
       ]
      },
-     "execution_count": 705,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -616,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 706,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -653,71 +625,71 @@
        "    <tr>\n",
        "      <th>carbon</th>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.953864</td>\n",
-       "      <td>0.860277</td>\n",
-       "      <td>0.953774</td>\n",
-       "      <td>0.958315</td>\n",
-       "      <td>0.955910</td>\n",
-       "      <td>0.996810</td>\n",
+       "      <td>0.953869</td>\n",
+       "      <td>0.860313</td>\n",
+       "      <td>0.953779</td>\n",
+       "      <td>0.958319</td>\n",
+       "      <td>0.955914</td>\n",
+       "      <td>0.996808</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>energy_heating</th>\n",
-       "      <td>0.953864</td>\n",
+       "      <td>0.953869</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.927136</td>\n",
-       "      <td>0.994901</td>\n",
-       "      <td>0.993171</td>\n",
+       "      <td>0.927161</td>\n",
+       "      <td>0.994903</td>\n",
+       "      <td>0.993174</td>\n",
        "      <td>0.999384</td>\n",
-       "      <td>0.957210</td>\n",
+       "      <td>0.957200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>energy_cooling</th>\n",
-       "      <td>0.860277</td>\n",
-       "      <td>0.927136</td>\n",
+       "      <td>0.860313</td>\n",
+       "      <td>0.927161</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.927544</td>\n",
-       "      <td>0.933284</td>\n",
-       "      <td>0.932756</td>\n",
-       "      <td>0.871485</td>\n",
+       "      <td>0.927569</td>\n",
+       "      <td>0.933307</td>\n",
+       "      <td>0.932779</td>\n",
+       "      <td>0.871504</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>energy_water_heating</th>\n",
-       "      <td>0.953774</td>\n",
-       "      <td>0.994901</td>\n",
-       "      <td>0.927544</td>\n",
+       "      <td>0.953779</td>\n",
+       "      <td>0.994903</td>\n",
+       "      <td>0.927569</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.994353</td>\n",
-       "      <td>0.996930</td>\n",
-       "      <td>0.958951</td>\n",
+       "      <td>0.994355</td>\n",
+       "      <td>0.996931</td>\n",
+       "      <td>0.958940</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>energy_cooking</th>\n",
-       "      <td>0.958315</td>\n",
-       "      <td>0.993171</td>\n",
-       "      <td>0.933284</td>\n",
-       "      <td>0.994353</td>\n",
+       "      <td>0.958319</td>\n",
+       "      <td>0.993174</td>\n",
+       "      <td>0.933307</td>\n",
+       "      <td>0.994355</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.995190</td>\n",
-       "      <td>0.960897</td>\n",
+       "      <td>0.995192</td>\n",
+       "      <td>0.960886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>total</th>\n",
-       "      <td>0.955910</td>\n",
+       "      <td>0.955914</td>\n",
        "      <td>0.999384</td>\n",
-       "      <td>0.932756</td>\n",
-       "      <td>0.996930</td>\n",
-       "      <td>0.995190</td>\n",
+       "      <td>0.932779</td>\n",
+       "      <td>0.996931</td>\n",
+       "      <td>0.995192</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.959333</td>\n",
+       "      <td>0.959322</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gas</th>\n",
-       "      <td>0.996810</td>\n",
-       "      <td>0.957210</td>\n",
-       "      <td>0.871485</td>\n",
-       "      <td>0.958951</td>\n",
-       "      <td>0.960897</td>\n",
-       "      <td>0.959333</td>\n",
+       "      <td>0.996808</td>\n",
+       "      <td>0.957200</td>\n",
+       "      <td>0.871504</td>\n",
+       "      <td>0.958940</td>\n",
+       "      <td>0.960886</td>\n",
+       "      <td>0.959322</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -726,25 +698,25 @@
       ],
       "text/plain": [
        "                        carbon  energy_heating  energy_cooling  \\\n",
-       "carbon                1.000000        0.953864        0.860277   \n",
-       "energy_heating        0.953864        1.000000        0.927136   \n",
-       "energy_cooling        0.860277        0.927136        1.000000   \n",
-       "energy_water_heating  0.953774        0.994901        0.927544   \n",
-       "energy_cooking        0.958315        0.993171        0.933284   \n",
-       "total                 0.955910        0.999384        0.932756   \n",
-       "gas                   0.996810        0.957210        0.871485   \n",
+       "carbon                1.000000        0.953869        0.860313   \n",
+       "energy_heating        0.953869        1.000000        0.927161   \n",
+       "energy_cooling        0.860313        0.927161        1.000000   \n",
+       "energy_water_heating  0.953779        0.994903        0.927569   \n",
+       "energy_cooking        0.958319        0.993174        0.933307   \n",
+       "total                 0.955914        0.999384        0.932779   \n",
+       "gas                   0.996808        0.957200        0.871504   \n",
        "\n",
        "                      energy_water_heating  energy_cooking     total       gas  \n",
-       "carbon                            0.953774        0.958315  0.955910  0.996810  \n",
-       "energy_heating                    0.994901        0.993171  0.999384  0.957210  \n",
-       "energy_cooling                    0.927544        0.933284  0.932756  0.871485  \n",
-       "energy_water_heating              1.000000        0.994353  0.996930  0.958951  \n",
-       "energy_cooking                    0.994353        1.000000  0.995190  0.960897  \n",
-       "total                             0.996930        0.995190  1.000000  0.959333  \n",
-       "gas                               0.958951        0.960897  0.959333  1.000000  "
+       "carbon                            0.953779        0.958319  0.955914  0.996808  \n",
+       "energy_heating                    0.994903        0.993174  0.999384  0.957200  \n",
+       "energy_cooling                    0.927569        0.933307  0.932779  0.871504  \n",
+       "energy_water_heating              1.000000        0.994355  0.996931  0.958940  \n",
+       "energy_cooking                    0.994355        1.000000  0.995192  0.960886  \n",
+       "total                             0.996931        0.995192  1.000000  0.959322  \n",
+       "gas                               0.958940        0.960886  0.959322  1.000000  "
       ]
      },
-     "execution_count": 706,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -755,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 707,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -764,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 708,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -780,7 +752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 709,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -890,7 +862,7 @@
        "4       7179.133  278096.422  1539.983  "
       ]
      },
-     "execution_count": 709,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -957,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 710,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -994,57 +966,57 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-0.278063</td>\n",
-       "      <td>-0.151182</td>\n",
-       "      <td>-0.520127</td>\n",
-       "      <td>-0.16618</td>\n",
-       "      <td>-0.443228</td>\n",
-       "      <td>-0.194221</td>\n",
-       "      <td>-0.20869</td>\n",
+       "      <td>-0.277234</td>\n",
+       "      <td>-0.150436</td>\n",
+       "      <td>-0.519111</td>\n",
+       "      <td>-0.165819</td>\n",
+       "      <td>-0.442083</td>\n",
+       "      <td>-0.193489</td>\n",
+       "      <td>-0.208284</td>\n",
        "      <td>AT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-0.292585</td>\n",
-       "      <td>-0.13864</td>\n",
-       "      <td>-0.516839</td>\n",
-       "      <td>-0.159931</td>\n",
-       "      <td>-0.439427</td>\n",
-       "      <td>-0.184739</td>\n",
-       "      <td>-0.222157</td>\n",
+       "      <td>-0.291775</td>\n",
+       "      <td>-0.137877</td>\n",
+       "      <td>-0.515819</td>\n",
+       "      <td>-0.159561</td>\n",
+       "      <td>-0.438278</td>\n",
+       "      <td>-0.183995</td>\n",
+       "      <td>-0.22177</td>\n",
        "      <td>AT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.29092</td>\n",
-       "      <td>-0.111683</td>\n",
-       "      <td>-0.517989</td>\n",
-       "      <td>-0.163681</td>\n",
-       "      <td>-0.430231</td>\n",
-       "      <td>-0.167029</td>\n",
-       "      <td>-0.231146</td>\n",
+       "      <td>-0.290107</td>\n",
+       "      <td>-0.110884</td>\n",
+       "      <td>-0.51697</td>\n",
+       "      <td>-0.163316</td>\n",
+       "      <td>-0.429072</td>\n",
+       "      <td>-0.166262</td>\n",
+       "      <td>-0.230772</td>\n",
        "      <td>AT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.309431</td>\n",
-       "      <td>-0.183871</td>\n",
-       "      <td>-0.519215</td>\n",
-       "      <td>-0.151659</td>\n",
-       "      <td>-0.435901</td>\n",
-       "      <td>-0.217583</td>\n",
-       "      <td>-0.240014</td>\n",
+       "      <td>-0.308642</td>\n",
+       "      <td>-0.183169</td>\n",
+       "      <td>-0.518198</td>\n",
+       "      <td>-0.151277</td>\n",
+       "      <td>-0.434749</td>\n",
+       "      <td>-0.216883</td>\n",
+       "      <td>-0.239653</td>\n",
        "      <td>AT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.297228</td>\n",
-       "      <td>-0.14274</td>\n",
-       "      <td>-0.519914</td>\n",
-       "      <td>-0.156581</td>\n",
-       "      <td>-0.435139</td>\n",
-       "      <td>-0.187847</td>\n",
-       "      <td>-0.235452</td>\n",
+       "      <td>-0.296424</td>\n",
+       "      <td>-0.141983</td>\n",
+       "      <td>-0.518897</td>\n",
+       "      <td>-0.156206</td>\n",
+       "      <td>-0.433986</td>\n",
+       "      <td>-0.187107</td>\n",
+       "      <td>-0.235084</td>\n",
        "      <td>AT</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1053,21 +1025,21 @@
       ],
       "text/plain": [
        "     carbon energy_heating energy_cooling energy_water_heating energy_cooking  \\\n",
-       "0 -0.278063      -0.151182      -0.520127             -0.16618      -0.443228   \n",
-       "1 -0.292585       -0.13864      -0.516839            -0.159931      -0.439427   \n",
-       "2  -0.29092      -0.111683      -0.517989            -0.163681      -0.430231   \n",
-       "3 -0.309431      -0.183871      -0.519215            -0.151659      -0.435901   \n",
-       "4 -0.297228       -0.14274      -0.519914            -0.156581      -0.435139   \n",
+       "0 -0.277234      -0.150436      -0.519111            -0.165819      -0.442083   \n",
+       "1 -0.291775      -0.137877      -0.515819            -0.159561      -0.438278   \n",
+       "2 -0.290107      -0.110884       -0.51697            -0.163316      -0.429072   \n",
+       "3 -0.308642      -0.183169      -0.518198            -0.151277      -0.434749   \n",
+       "4 -0.296424      -0.141983      -0.518897            -0.156206      -0.433986   \n",
        "\n",
        "      total       gas geo  \n",
-       "0 -0.194221  -0.20869  AT  \n",
-       "1 -0.184739 -0.222157  AT  \n",
-       "2 -0.167029 -0.231146  AT  \n",
-       "3 -0.217583 -0.240014  AT  \n",
-       "4 -0.187847 -0.235452  AT  "
+       "0 -0.193489 -0.208284  AT  \n",
+       "1 -0.183995  -0.22177  AT  \n",
+       "2 -0.166262 -0.230772  AT  \n",
+       "3 -0.216883 -0.239653  AT  \n",
+       "4 -0.187107 -0.235084  AT  "
       ]
      },
-     "execution_count": 710,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1091,14 +1063,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 711,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(335, 35)\n"
+      "(336, 35)\n"
      ]
     },
     {
@@ -1148,13 +1120,13 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-0.278063</td>\n",
-       "      <td>-0.151182</td>\n",
-       "      <td>-0.520127</td>\n",
-       "      <td>-0.16618</td>\n",
-       "      <td>-0.443228</td>\n",
-       "      <td>-0.194221</td>\n",
-       "      <td>-0.20869</td>\n",
+       "      <td>-0.277234</td>\n",
+       "      <td>-0.150436</td>\n",
+       "      <td>-0.519111</td>\n",
+       "      <td>-0.165819</td>\n",
+       "      <td>-0.442083</td>\n",
+       "      <td>-0.193489</td>\n",
+       "      <td>-0.208284</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -1172,13 +1144,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-0.292585</td>\n",
-       "      <td>-0.13864</td>\n",
-       "      <td>-0.516839</td>\n",
-       "      <td>-0.159931</td>\n",
-       "      <td>-0.439427</td>\n",
-       "      <td>-0.184739</td>\n",
-       "      <td>-0.222157</td>\n",
+       "      <td>-0.291775</td>\n",
+       "      <td>-0.137877</td>\n",
+       "      <td>-0.515819</td>\n",
+       "      <td>-0.159561</td>\n",
+       "      <td>-0.438278</td>\n",
+       "      <td>-0.183995</td>\n",
+       "      <td>-0.22177</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -1196,13 +1168,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.29092</td>\n",
-       "      <td>-0.111683</td>\n",
-       "      <td>-0.517989</td>\n",
-       "      <td>-0.163681</td>\n",
-       "      <td>-0.430231</td>\n",
-       "      <td>-0.167029</td>\n",
-       "      <td>-0.231146</td>\n",
+       "      <td>-0.290107</td>\n",
+       "      <td>-0.110884</td>\n",
+       "      <td>-0.51697</td>\n",
+       "      <td>-0.163316</td>\n",
+       "      <td>-0.429072</td>\n",
+       "      <td>-0.166262</td>\n",
+       "      <td>-0.230772</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -1220,13 +1192,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.309431</td>\n",
-       "      <td>-0.183871</td>\n",
-       "      <td>-0.519215</td>\n",
-       "      <td>-0.151659</td>\n",
-       "      <td>-0.435901</td>\n",
-       "      <td>-0.217583</td>\n",
-       "      <td>-0.240014</td>\n",
+       "      <td>-0.308642</td>\n",
+       "      <td>-0.183169</td>\n",
+       "      <td>-0.518198</td>\n",
+       "      <td>-0.151277</td>\n",
+       "      <td>-0.434749</td>\n",
+       "      <td>-0.216883</td>\n",
+       "      <td>-0.239653</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -1244,13 +1216,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.297228</td>\n",
-       "      <td>-0.14274</td>\n",
-       "      <td>-0.519914</td>\n",
-       "      <td>-0.156581</td>\n",
-       "      <td>-0.435139</td>\n",
-       "      <td>-0.187847</td>\n",
-       "      <td>-0.235452</td>\n",
+       "      <td>-0.296424</td>\n",
+       "      <td>-0.141983</td>\n",
+       "      <td>-0.518897</td>\n",
+       "      <td>-0.156206</td>\n",
+       "      <td>-0.433986</td>\n",
+       "      <td>-0.187107</td>\n",
+       "      <td>-0.235084</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -1273,18 +1245,18 @@
       ],
       "text/plain": [
        "     carbon energy_heating energy_cooling energy_water_heating energy_cooking  \\\n",
-       "0 -0.278063      -0.151182      -0.520127             -0.16618      -0.443228   \n",
-       "1 -0.292585       -0.13864      -0.516839            -0.159931      -0.439427   \n",
-       "2  -0.29092      -0.111683      -0.517989            -0.163681      -0.430231   \n",
-       "3 -0.309431      -0.183871      -0.519215            -0.151659      -0.435901   \n",
-       "4 -0.297228       -0.14274      -0.519914            -0.156581      -0.435139   \n",
+       "0 -0.277234      -0.150436      -0.519111            -0.165819      -0.442083   \n",
+       "1 -0.291775      -0.137877      -0.515819            -0.159561      -0.438278   \n",
+       "2 -0.290107      -0.110884       -0.51697            -0.163316      -0.429072   \n",
+       "3 -0.308642      -0.183169      -0.518198            -0.151277      -0.434749   \n",
+       "4 -0.296424      -0.141983      -0.518897            -0.156206      -0.433986   \n",
        "\n",
        "      total       gas  geo_AT  geo_BE  geo_BG  ...  geo_LV  geo_MT  geo_NL  \\\n",
-       "0 -0.194221  -0.20869       1       0       0  ...       0       0       0   \n",
-       "1 -0.184739 -0.222157       1       0       0  ...       0       0       0   \n",
-       "2 -0.167029 -0.231146       1       0       0  ...       0       0       0   \n",
-       "3 -0.217583 -0.240014       1       0       0  ...       0       0       0   \n",
-       "4 -0.187847 -0.235452       1       0       0  ...       0       0       0   \n",
+       "0 -0.193489 -0.208284       1       0       0  ...       0       0       0   \n",
+       "1 -0.183995  -0.22177       1       0       0  ...       0       0       0   \n",
+       "2 -0.166262 -0.230772       1       0       0  ...       0       0       0   \n",
+       "3 -0.216883 -0.239653       1       0       0  ...       0       0       0   \n",
+       "4 -0.187107 -0.235084       1       0       0  ...       0       0       0   \n",
        "\n",
        "   geo_NO  geo_PL  geo_PT  geo_RO  geo_SE  geo_SI  geo_SK  \n",
        "0       0       0       0       0       0       0       0  \n",
@@ -1296,7 +1268,7 @@
        "[5 rows x 35 columns]"
       ]
      },
-     "execution_count": 711,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1309,17 +1281,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 712,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/jk/vqmr1l1d1dnflmyh1qdjmhk40000gn/T/ipykernel_66892/3693932479.py:1: FutureWarning:\n",
-      "\n",
-      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
-      "\n"
+      "/var/folders/jk/vqmr1l1d1dnflmyh1qdjmhk40000gn/T/ipykernel_96953/3693932479.py:1: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+      "  df_dummies = df_dummies.fillna(0)\n"
      ]
     }
    ],
@@ -1329,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 713,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1345,21 +1315,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 714,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.        , -0.19422063, -0.2086896 ,  1.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "array([[ 1.        , -0.15506195, -0.21718148,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  1.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ],\n",
-       "       [ 1.        , -0.18473927, -0.22215674,  1.        ,  0.        ,\n",
-       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "       [ 1.        , -0.14198966, -0.25949037,  0.        ,  0.        ,\n",
+       "         0.        ,  0.        ,  1.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
        "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
@@ -1367,7 +1337,7 @@
        "         0.        ]])"
       ]
      },
-     "execution_count": 714,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1375,28 +1345,28 @@
    "source": [
     "X = np.pad(df_dummies.iloc[:, 1:].to_numpy(), ((0,0), (1,0)), mode=\"constant\", constant_values=1)\n",
     "y = np.array(df_dummies[\"carbon\"])\n",
-    "X[0:2,]"
+    "X[56:58,]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 715,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([  56.20252722,  127.95282355,   43.70567339,  -24.29766787,\n",
-       "        -44.87242756,   24.04409072,   48.15089027,   -7.80834782,\n",
-       "       -715.48225259,   -2.65882844,   40.86351043,  -17.92484837,\n",
-       "       -162.67244527,    4.80913169, -438.8064785 ,   38.26739083,\n",
-       "         -0.98892811,   14.71399105, -353.58122047,   45.08681054,\n",
-       "         58.88487034,   33.86351043,   63.92759016,  -87.57534671,\n",
-       "         14.38389138, -198.7054641 ,    9.95865107,  -34.07436775,\n",
-       "        -45.87242756,   28.10817045,   43.28875074])"
+       "array([  4.88508124,  11.90934986,   4.345609  ,  -4.94743023,\n",
+       "        -3.93159802,  -0.56534299,   1.50409325,  -3.03722218,\n",
+       "       -66.98241677,  -3.2859403 ,   2.73019393,  -1.59730943,\n",
+       "       -14.18092016,  -5.02689332, -38.15626913,  -0.97102755,\n",
+       "        -4.95647721,   1.94370399, -34.24900068,   1.07903285,\n",
+       "         0.81998587,   2.20177111,   4.81320064, -10.74424902,\n",
+       "        -2.00751748, -17.99937653,   1.31870399,  -3.93838325,\n",
+       "        -6.4031752 ,   2.67108654,   1.30513352])"
       ]
      },
-     "execution_count": 715,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1409,23 +1379,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 716,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.78907501  1.14997642 -0.72151283  6.11610034  2.12409377]\n"
+      "[2.99456721 2.92556497 2.75515702 3.37808212 3.01583775]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "1173.1"
+       "789.7"
       ]
      },
-     "execution_count": 716,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1439,12 +1409,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 717,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1464,12 +1434,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 757,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1489,12 +1459,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 758,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1514,7 +1484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 719,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -1525,7 +1495,7 @@
        },
        "data": [
         {
-         "hovertemplate": "geo=AT<br>total=%{x}<br>carbon=%{y}<extra></extra>",
+         "hovertemplate": "geo=AT<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "AT",
          "marker": {
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@@ -1533,42 +1503,54 @@
          },
          "mode": "markers",
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          "showlegend": true,
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@@ -1576,42 +1558,54 @@
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          "legendgroup": "BG",
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@@ -1619,42 +1613,54 @@
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@@ -2606,42 +2878,54 @@
          },
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          "legendgroup": "SI",
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@@ -2649,42 +2933,54 @@
          },
          "mode": "markers",
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          "legendgroup": "SK",
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@@ -2692,39 +2988,51 @@
          },
          "mode": "markers",
          "name": "SK",
-         "orientation": "v",
+         "scene": "scene",
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        "layout": {
@@ -2737,6 +3045,33 @@
         "margin": {
          "t": 60
         },
+        "scene": {
+         "domain": {
+          "x": [
+           0,
+           1
+          ],
+          "y": [
+           0,
+           1
+          ]
+         },
+         "xaxis": {
+          "title": {
+           "text": "total"
+          }
+         },
+         "yaxis": {
+          "title": {
+           "text": "carbon"
+          }
+         },
+         "zaxis": {
+          "title": {
+           "text": "gas"
+          }
+         }
+        },
         "template": {
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@@ -3552,26 +3887,6 @@
            "zerolinewidth": 2
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-        },
-        "xaxis": {
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-        "yaxis": {
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-         "title": {
-          "text": "carbon"
-         }
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       }
@@ -3581,12 +3896,12 @@
     }
    ],
    "source": [
-    "px.scatter(df_merged_s, y=\"carbon\", x=\"total\", color=\"geo\")"
+    "px.scatter_3d(df_merged_s, y=\"carbon\", x=\"total\", z=\"gas\", color=\"geo\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 753,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3595,7 +3910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 756,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -3604,13 +3919,13 @@
        "Text(0, 0.5, 'Carbon Emissions')"
       ]
      },
-     "execution_count": 756,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3629,7 +3944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 720,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3638,7 +3953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 721,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3647,7 +3962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 722,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3678,25 +3993,459 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 723,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       " -3.786442961397237,\n",
+       " 4.5548119056673695,\n",
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+       " -3.9326568600678122,\n",
+       " -4.417456916099285,\n",
+       " 1.6377216538610657,\n",
+       " 1.9059494732479116,\n",
+       " -1.759958727115987,\n",
+       " -4.307263410879386,\n",
+       " -1.0934711859049813,\n",
+       " -0.6313772328388394,\n",
+       " 10.997845353574235,\n",
+       " -4.255199668746133,\n",
+       " -0.21662436364265503,\n",
+       " -1.0551874735631062,\n",
+       " 1.4809552783433517,\n",
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+       " -0.8333659491032606,\n",
+       " -2.689002877948957,\n",
+       " 14.845395948046388,\n",
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+       " -0.42825303036703133,\n",
+       " 3.3942733729923873,\n",
+       " 1.2443914699761107,\n",
+       " 8.89757309612195,\n",
+       " -0.7282367524696269,\n",
+       " 32.75349024948851,\n",
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+       " 3.071291838914613,\n",
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+       " 2.884033091345067,\n",
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+       " 3.132010852102951,\n",
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+       " -1.4072241944765764,\n",
+       " -1.6396475241521742,\n",
+       " -3.379027143368356,\n",
+       " -6.585683484614638,\n",
+       " 4.627055058872216,\n",
+       " 2.1994793535967254,\n",
+       " -4.070636259727401,\n",
+       " -2.4597314966092796,\n",
+       " 2.626823683047789,\n",
+       " -2.3678295687752797,\n",
+       " 3.061013786501775,\n",
+       " -43.4353005998456,\n",
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+       " 1.826111521433555,\n",
+       " 2.2746779840383518,\n",
+       " 1.8874782311839438,\n",
+       " 0.35914930449060734,\n",
+       " -17.200935965386712,\n",
+       " 3.0132726402727172,\n",
+       " -2.6874460156759206,\n",
+       " -1.490055592978667,\n",
+       " 17.029116777804564,\n",
+       " 0.055582048991594135,\n",
+       " -0.5162334689451846,\n",
+       " 5.501174779069544,\n",
+       " -0.9063143055849174,\n",
+       " 1.5185213867390992,\n",
+       " -2.927593254465638,\n",
+       " 1.5332591605564048,\n",
+       " -5.461679201829682,\n",
+       " -0.6175956579353006,\n",
+       " -3.4641342656864893,\n",
+       " 0.6998717974537119,\n",
+       " -9.195916927661147,\n",
+       " -2.7439408707523096,\n",
+       " -2.4255772730909264,\n",
+       " 2.7478742059501613,\n",
+       " 0.49705553950229664,\n",
+       " -0.9743572322300764,\n",
+       " -47.91467046657994,\n",
+       " -2.941478739540134,\n",
+       " 0.1512309368396263,\n",
+       " -5.011507258365128,\n",
+       " -0.4665421423547016,\n",
+       " -3.765413153751876,\n",
+       " -0.7432884336557142,\n",
+       " -0.49542319141974245,\n",
+       " 5.161964118370179,\n",
+       " 2.050913875730515,\n",
+       " -0.06385082902354,\n",
+       " -4.603081600819895,\n",
+       " 12.213436357510592,\n",
+       " 15.652070685456877,\n",
+       " -4.5415554528087085]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds = loo_cv_pred(X, y)\n",
+    "preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "        1.34894993,  1.3265508 ,  1.26211542,  1.27560328,  1.33235534,\n",
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+       "       -0.3944802 , -0.41003631, -0.4180442 , -0.4269596 , -0.42841344,\n",
+       "       -0.42703199, -0.43063343, -0.43435417, -0.44214113, -0.47283123,\n",
+       "       -0.46361596, -0.47212465, -0.61336908, -0.61663366, -0.62021221,\n",
+       "       -0.62879618, -0.62781276, -0.62330975, -0.62284816, -0.62364294,\n",
+       "       -0.62632137, -0.63290291, -0.63215571, -0.63434584, -0.48310933,\n",
+       "       -0.49543109, -0.49714015, -0.5075253 , -0.50357197, -0.50126838,\n",
+       "       -0.49540784, -0.4962518 , -0.50817206, -0.52297777, -0.50210547,\n",
+       "       -0.52322675])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "preds = loo_cv_pred(X, y)"
+    "y"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 724,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8419084630885562"
+       "-4476.691935155938"
       ]
      },
-     "execution_count": 724,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3714,7 +4463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 725,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -3751,57 +4500,57 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-0.056699</td>\n",
-       "      <td>0.056867</td>\n",
-       "      <td>-0.568738</td>\n",
-       "      <td>-0.197789</td>\n",
-       "      <td>-0.478802</td>\n",
-       "      <td>-0.034514</td>\n",
-       "      <td>-0.335104</td>\n",
+       "      <td>-0.05559</td>\n",
+       "      <td>0.057893</td>\n",
+       "      <td>-0.567774</td>\n",
+       "      <td>-0.197473</td>\n",
+       "      <td>-0.477695</td>\n",
+       "      <td>-0.03357</td>\n",
+       "      <td>-0.334876</td>\n",
        "      <td>BE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-0.072725</td>\n",
-       "      <td>0.049004</td>\n",
-       "      <td>-0.550216</td>\n",
-       "      <td>-0.195549</td>\n",
-       "      <td>-0.478933</td>\n",
-       "      <td>-0.040696</td>\n",
-       "      <td>-0.344081</td>\n",
+       "      <td>-0.071637</td>\n",
+       "      <td>0.050019</td>\n",
+       "      <td>-0.549232</td>\n",
+       "      <td>-0.19523</td>\n",
+       "      <td>-0.477826</td>\n",
+       "      <td>-0.03976</td>\n",
+       "      <td>-0.343867</td>\n",
        "      <td>BE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.073381</td>\n",
-       "      <td>0.04114</td>\n",
-       "      <td>-0.531693</td>\n",
-       "      <td>-0.193309</td>\n",
-       "      <td>-0.479064</td>\n",
-       "      <td>-0.046878</td>\n",
-       "      <td>-0.344678</td>\n",
+       "      <td>-0.072294</td>\n",
+       "      <td>0.042145</td>\n",
+       "      <td>-0.53069</td>\n",
+       "      <td>-0.192987</td>\n",
+       "      <td>-0.477957</td>\n",
+       "      <td>-0.045951</td>\n",
+       "      <td>-0.344464</td>\n",
        "      <td>BE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.101798</td>\n",
-       "      <td>0.033276</td>\n",
-       "      <td>-0.513171</td>\n",
-       "      <td>-0.19107</td>\n",
-       "      <td>-0.479195</td>\n",
-       "      <td>-0.05306</td>\n",
-       "      <td>-0.319312</td>\n",
+       "      <td>-0.100747</td>\n",
+       "      <td>0.03427</td>\n",
+       "      <td>-0.512148</td>\n",
+       "      <td>-0.190744</td>\n",
+       "      <td>-0.478088</td>\n",
+       "      <td>-0.052141</td>\n",
+       "      <td>-0.319062</td>\n",
        "      <td>BE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.078167</td>\n",
-       "      <td>0.025413</td>\n",
-       "      <td>-0.494649</td>\n",
-       "      <td>-0.18883</td>\n",
-       "      <td>-0.479326</td>\n",
-       "      <td>-0.059242</td>\n",
-       "      <td>-0.296488</td>\n",
+       "      <td>-0.077086</td>\n",
+       "      <td>0.026396</td>\n",
+       "      <td>-0.493606</td>\n",
+       "      <td>-0.188501</td>\n",
+       "      <td>-0.478219</td>\n",
+       "      <td>-0.058331</td>\n",
+       "      <td>-0.296207</td>\n",
        "      <td>BE</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -3810,21 +4559,21 @@
       ],
       "text/plain": [
        "     carbon energy_heating energy_cooling energy_water_heating energy_cooking  \\\n",
-       "0 -0.056699       0.056867      -0.568738            -0.197789      -0.478802   \n",
-       "1 -0.072725       0.049004      -0.550216            -0.195549      -0.478933   \n",
-       "2 -0.073381        0.04114      -0.531693            -0.193309      -0.479064   \n",
-       "3 -0.101798       0.033276      -0.513171             -0.19107      -0.479195   \n",
-       "4 -0.078167       0.025413      -0.494649             -0.18883      -0.479326   \n",
+       "0  -0.05559       0.057893      -0.567774            -0.197473      -0.477695   \n",
+       "1 -0.071637       0.050019      -0.549232             -0.19523      -0.477826   \n",
+       "2 -0.072294       0.042145       -0.53069            -0.192987      -0.477957   \n",
+       "3 -0.100747        0.03427      -0.512148            -0.190744      -0.478088   \n",
+       "4 -0.077086       0.026396      -0.493606            -0.188501      -0.478219   \n",
        "\n",
        "      total       gas geo  \n",
-       "0 -0.034514 -0.335104  BE  \n",
-       "1 -0.040696 -0.344081  BE  \n",
-       "2 -0.046878 -0.344678  BE  \n",
-       "3  -0.05306 -0.319312  BE  \n",
-       "4 -0.059242 -0.296488  BE  "
+       "0  -0.03357 -0.334876  BE  \n",
+       "1  -0.03976 -0.343867  BE  \n",
+       "2 -0.045951 -0.344464  BE  \n",
+       "3 -0.052141 -0.319062  BE  \n",
+       "4 -0.058331 -0.296207  BE  "
       ]
      },
-     "execution_count": 725,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3836,17 +4585,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 742,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.        , -0.03451352],\n",
-       "       [ 1.        , -0.04069572]])"
+       "array([[ 1.        , -0.03356972],\n",
+       "       [ 1.        , -0.03976014]])"
       ]
      },
-     "execution_count": 742,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3859,7 +4608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 743,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3868,15 +4617,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 744,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.015909982113676932 0.004008857952391476 0.007476999077431337\n",
-      " -0.016814565780291363 0.010940996033058412]\n"
+      "[0.015930055989496518 0.00401391599181232 0.007486432925308192\n",
+      " -0.016835780983615864 0.010954800460627176]\n"
      ]
     },
     {
@@ -3885,7 +4634,7 @@
        "0.0"
       ]
      },
-     "execution_count": 744,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3898,12 +4647,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 745,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3923,12 +4672,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 746,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3948,7 +4697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 747,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -3971,33 +4720,33 @@
          "showlegend": false,
          "type": "scatter",
          "x": [
-          -0.03451352403139175,
-          -0.04069572092524283,
-          -0.04687791781909403,
-          -0.05306011471294511,
-          -0.0592423116067963,
-          -0.0642143104647586,
-          -0.07382501162546044,
-          -0.07998346501588552,
-          -0.095047145075093,
-          -0.09483018117653078,
-          -0.04114499124062597,
-          -0.13875258967704102
+          -0.03356971850481728,
+          -0.039760144245446716,
+          -0.045950569986076266,
+          -0.052140995726705705,
+          -0.058331421467335255,
+          -0.06331003833140232,
+          -0.07293353186812225,
+          -0.0791001825014064,
+          -0.09418391315383401,
+          -0.09396666046429532,
+          -0.04021001256452511,
+          -0.1379475321253766
          ],
          "xaxis": "x",
          "y": [
-          -0.0566985935966168,
-          -0.07272463686555179,
-          -0.07338141484816153,
-          -0.10179789881353377,
-          -0.07816725610783361,
-          -0.08640536146009997,
-          -0.08675599049779897,
-          -0.08116878515343734,
-          -0.08662884554579156,
-          -0.14256658710787662,
-          -0.12342358327643835,
-          -0.153828616355248
+          -0.05559045641183317,
+          -0.07163671999278103,
+          -0.07229432664254888,
+          -0.10074666413473658,
+          -0.07708620627375727,
+          -0.08533470577371326,
+          -0.08568577720560927,
+          -0.08009152239598367,
+          -0.08555847183280695,
+          -0.1415667909272685,
+          -0.12239963406542592,
+          -0.15284302965488766
          ],
          "yaxis": "y"
         }
@@ -4858,7 +5607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 748,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4867,16 +5616,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 749,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.1960374425066873"
+       "0.19603744250668642"
       ]
      },
-     "execution_count": 749,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/eda/eda3_alpha_migration.ipynb b/eda/eda3_alpha_migration.ipynb
new file mode 100644
index 0000000..a00d3a6
--- /dev/null
+++ b/eda/eda3_alpha_migration.ipynb
@@ -0,0 +1,311 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/site-packages/pandasdmx/remote.py:11: RuntimeWarning: optional dependency requests_cache is not installed; cache options to Session() have no effect\n",
+      "  warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import plotly.express as px\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandasdmx as sdmx\n",
+    "from functools import reduce"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def melt_smdx_dataframe(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "\t\"\"\"\n",
+    "\tGiven an ESTAT smdx dataframe, convert the datetimes to years and melt\n",
+    "\n",
+    "\t:param df: The raw SDMX parsed dataframe from ESTAT\n",
+    "\t:returns: A melted dataframe with the columns of:\n",
+    "\t\t`year` - the year of the observation\n",
+    "\t\t`geo` - the country of the observation\n",
+    "\t\t`value` - the value of the observation\n",
+    "\t\"\"\"\n",
+    "\tdf = df.reset_index()\n",
+    "\tdf[\"year\"] = df[\"TIME_PERIOD\"].dt.year\n",
+    "\tdf = df.drop(\"TIME_PERIOD\", axis=1)\n",
+    "\treturn pd.melt(df, id_vars=\"year\")\n",
+    "\n",
+    "def merge_dataframes(dataframes: list[pd.DataFrame]) -> pd.DataFrame:\n",
+    "\t\"\"\"\n",
+    "\t\"\"\"\n",
+    "\tfor i, df in enumerate(dataframes):\n",
+    "\t\tdf.columns = [\"geo\", \"year\", i]\n",
+    "\n",
+    "\tmerged_df = reduce(lambda l, r: pd.merge(l, r, left_on=[\"year\", \"geo\"], right_on=[\"year\", \"geo\"]), dataframes)\n",
+    "\treturn merged_df\n",
+    "\n",
+    "def fill_holes(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "\t\"\"\"\n",
+    "\t\"\"\"\n",
+    "\tlin_reg = lambda X, Y: np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, Y))\n",
+    "\n",
+    "\tdfs = []\n",
+    "\n",
+    "\tfor name, group in df.groupby('geo'):\n",
+    "\t\tcols = [[name for _ in range(len(group.index))]]\n",
+    "\t\tfor i in range(1, len(group.columns)):\n",
+    "\t\t\td = group.iloc[:, i:i+1].to_numpy()\n",
+    "\n",
+    "\t\t\tmissing_mask = np.isnan(d) | (d == 0)\n",
+    "\t\t\tpresent_mask = ~missing_mask\n",
+    "\n",
+    "\t\t\tmissing_mask = missing_mask.reshape(1, -1)[0]\n",
+    "\t\t\tpresent_mask = present_mask.reshape(1, -1)[0]\n",
+    "\n",
+    "\t\t\tif not np.any(missing_mask):\n",
+    "\t\t\t\td = d.reshape(1, -1)[0]\n",
+    "\t\t\t\tcols.append(d)\n",
+    "\t\t\t\tcontinue\n",
+    "\n",
+    "\t\t\tif not np.any(present_mask):\n",
+    "\t\t\t\td = d.reshape(1, -1)[0]\n",
+    "\t\t\t\tcols.append(d)\n",
+    "\t\t\t\tcontinue\n",
+    "\n",
+    "\t\t\tx_present = np.pad(np.arange(len(d))[present_mask].reshape(-1, 1), ((0, 0), (1, 0)), mode=\"constant\", constant_values=1)\n",
+    "\t\t\ty_present = d[present_mask]\n",
+    "\n",
+    "\t\t\tw = lin_reg(x_present, y_present)\n",
+    "\n",
+    "\t\t\tx_missing = np.pad(np.arange(len(d))[missing_mask].reshape(-1, 1), ((0, 0), (1, 0)), mode=\"constant\", constant_values=1)\n",
+    "\t\t\ty_missing_pred = np.matmul(x_missing, w)\n",
+    "\n",
+    "\t\t\td[missing_mask] = y_missing_pred\n",
+    "\t\t\td = d.reshape(1, -1)[0]\n",
+    "\n",
+    "\t\t\tcols.append(d)\n",
+    "\t\t\t\n",
+    "\t\tdfs.append(pd.DataFrame(cols).T)\t\n",
+    "\t\t\n",
+    "\tdf_unswissed = pd.concat(dfs, axis=0)\n",
+    "\tdf_unswissed.columns = df.columns\n",
+    "\treturn df_unswissed\n",
+    "\n",
+    "def standardize(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "\t\"\"\"\n",
+    "\t\"\"\"\n",
+    "\tdf_standard = pd.DataFrame()\n",
+    "\tfor feat in df.columns:\n",
+    "\t\tif feat == \"geo\": continue\n",
+    "\t\tdf_standard[f'{feat}'] = ((df[feat] - df[feat].mean()) / df[feat].std())\n",
+    "\tdf_standard[\"geo\"] = df[\"geo\"]\n",
+    "\n",
+    "\treturn df_standard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-06-05 23:46:41,043 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "2024-06-05 23:46:41,240 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
+      "  return dispatch(args[0].__class__)(*args, **kw)\n",
+      "2024-06-05 23:46:41,456 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "/var/folders/jk/vqmr1l1d1dnflmyh1qdjmhk40000gn/T/ipykernel_97540/3566454452.py:46: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+      "  df_dummies = df_dummies.fillna(0)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([3.89988871e-17, 4.61239971e-01, 5.29738712e-01])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "estat = sdmx.Request(\"ESTAT\")\n",
+    "resp = estat.data(\n",
+    "\t\"ENV_AIR_GGE\",\n",
+    "\tkey={\n",
+    "\t\t\"unit\": \"THS_T\",\n",
+    "\t\t\"freq\": \"A\",\n",
+    "\t\t\"src_crf\": \"TOTX4_MEMONIA\",\n",
+    "\t\t\"airpol\": \"GHG\"\n",
+    "\t}\n",
+    ")\n",
+    "emission_df = resp.to_pandas(datetime={'dim': 'TIME_PERIOD'}).droplevel(level=['unit', 'freq', 'src_crf', 'airpol'], axis=1)\n",
+    "melted_emissions_df = melt_smdx_dataframe(emission_df)\n",
+    "\n",
+    "resp = estat.data(\n",
+    "\t\t\"NRG_D_HHQ\",\n",
+    "\t\tkey={\n",
+    "\t\t\t\"siec\": \"TOTAL\",\n",
+    "\t\t\t\"unit\": \"TJ\",\n",
+    "\t\t\t\"nrg_bal\": \"FC_OTH_HH_E\",\n",
+    "\t\t\t\"freq\": \"A\",\n",
+    "\t\t}\n",
+    "\t)\n",
+    "household_energy_df = resp.to_pandas(datetime={'dim': 'TIME_PERIOD', 'freq': 'freq'}).droplevel(level=[\"siec\", \"unit\", \"nrg_bal\"], axis=1)\n",
+    "melted_household_energy_df = melt_smdx_dataframe(household_energy_df)\n",
+    "\n",
+    "resp = estat.data(\n",
+    "\t\"TEN00127\",\n",
+    "\tkey={\n",
+    "\t\t\"unit\": \"KTOE\",\n",
+    "\t\t\"freq\": \"A\",\n",
+    "\t\t\"siec\": \"O4652XR5210B\",\n",
+    "\t\t\"nrg_bal\": \"FC_TRA_ROAD_E\"\n",
+    "\t}\n",
+    ")\n",
+    "gas_df = resp.to_pandas(datetime={'dim': 'TIME_PERIOD'}).droplevel(level=['unit', 'freq', 'siec', \"nrg_bal\"], axis=1)\n",
+    "melted_gas_df = melt_smdx_dataframe(gas_df)\n",
+    "\n",
+    "merged_df = merge_dataframes([melted_emissions_df, melted_household_energy_df, melted_gas_df])\n",
+    "merged_df.columns = [\"year\", \"geo\", \"emissions\", \"energy\", \"gas\"]\n",
+    "merged_df = merged_df.drop(merged_df[(merged_df.geo == \"EU27_2020\") | (merged_df.geo == \"EU20\")].index)\n",
+    "merged_df = merged_df.drop(\"year\", axis=1)\n",
+    "merged_df = fill_holes(merged_df)\n",
+    "standard_df = standardize(merged_df)\n",
+    "\n",
+    "df_dummies = pd.get_dummies(standard_df, dtype=int, columns=[\"geo\"])\n",
+    "df_dummies = df_dummies.fillna(0)\n",
+    "\n",
+    "#X = np.pad(df_dummies.iloc[:, 1:].to_numpy(dtype=np.float64), ((0,0), (1,0)), mode=\"constant\", constant_values=1)\n",
+    "X = np.pad(standard_df.iloc[:,1:3].to_numpy(dtype=np.float64), ((0,0), (1,0)), mode=\"constant\", constant_values=1)\n",
+    "y = np.array(df_dummies[\"emissions\"], dtype=np.float64)\n",
+    "\n",
+    "m = np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, y))\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import r2_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np_remove = lambda a, i: np.concatenate([a[:i,], a[i + 1:,]])\n",
+    "lin_reg = lambda X, Y: np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, Y))\n",
+    "\n",
+    "def loo_cv_pred(X, Y):\n",
+    "\t\"\"\"\n",
+    "\tPredict Y values using leave one out cross validation\n",
+    "\n",
+    "\t:param X: The X features array (including bias column)\n",
+    "\t:param Y: The true Y values\n",
+    "\t:return: An array of the predicted Y-Vals\n",
+    "\t\"\"\"\n",
+    "\ty_pred = []\n",
+    "\tfor i in range(len(X)):\n",
+    "\t\tholdout_X = X[i]\n",
+    "\t\t\n",
+    "\t\tloo_X = np_remove(X, i)\n",
+    "\t\tloo_y = np_remove(Y, i)\n",
+    "\t\tloo_b = lin_reg(loo_X, loo_y)\n",
+    "\n",
+    "\t\ty_hat = np.matmul(holdout_X, loo_b)\n",
+    "\t\ty_pred.append(y_hat)\n",
+    "\t\n",
+    "\treturn y_pred\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9607943812769059"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds = loo_cv_pred(X, y)\n",
+    "r2_score(y, preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.0002890124341005411"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(y - preds).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "wafflers",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 27fd56f8adeda0a290bc4034837176d48e1a0f54 Mon Sep 17 00:00:00 2001
From: mehallhm <michaelhmehall@gmail.com>
Date: Thu, 6 Jun 2024 00:16:02 +0200
Subject: [PATCH 20/34] bug: fixed requirements

---
 api/requirements.txt | 97 ++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 90 insertions(+), 7 deletions(-)

diff --git a/api/requirements.txt b/api/requirements.txt
index 7661e6e..e504611 100644
--- a/api/requirements.txt
+++ b/api/requirements.txt
@@ -1,7 +1,90 @@
-werkzeug==2.3.8
-flask==2.3.3
-flask-restful==0.3.9
-flask-login==0.6.2
-flask-mysql==1.5.2
-cryptography==38.0.1
-python-dotenv==1.0.1
\ No newline at end of file
+altair==5.3.0
+appnope @ file:///home/conda/feedstock_root/build_artifacts/appnope_1707233003401/work
+asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1698341106958/work
+attrs==23.2.0
+blinker==1.8.2
+cachetools==5.3.3
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+comm @ file:///home/conda/feedstock_root/build_artifacts/comm_1710320294760/work
+contourpy==1.2.1
+cycler==0.12.1
+debugpy @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_563_nwtkoc/croot/debugpy_1690905063850/work
+decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
+et-xmlfile==1.1.0
+exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1704921103267/work
+executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1698579936712/work
+fastjsonschema==2.19.1
+Flask==3.0.3
+fonttools==4.52.4
+gitdb==4.0.11
+GitPython==3.1.43
+idna==3.7
+importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1710971335535/work
+ipykernel @ file:///Users/runner/miniforge3/conda-bld/ipykernel_1708996616394/work
+ipython @ file:///home/conda/feedstock_root/build_artifacts/ipython_1715263367085/work
+itsdangerous==2.2.0
+jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1696326070614/work
+Jinja2==3.1.4
+joblib==1.4.2
+jsonschema==4.22.0
+jsonschema-specifications==2023.12.1
+jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1716472197302/work
+jupyter_core @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_782yoyc_98/croot/jupyter_core_1698937318631/work
+kiwisolver==1.4.5
+lxml==5.2.2
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.0
+matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1713250518406/work
+mdurl==0.1.2
+nbformat==5.10.4
+nest_asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1705850609492/work
+numpy==1.26.4
+openpyxl==3.1.3
+packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1710075952259/work
+pandas==2.2.2
+pandaSDMX==1.10.0
+parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1712320355065/work
+pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1706113125309/work
+pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work
+pillow==10.3.0
+platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1715777629804/work
+plotly==5.22.0
+prompt-toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1702399386289/work
+protobuf==4.25.3
+psutil @ file:///Users/cbousseau/work/recipes/ci_py311_2/psutil_1678995687212/work
+ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
+pure-eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1642875951954/work
+pyarrow==16.1.0
+pydantic==1.10.15
+pydeck==0.9.1
+Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1714846767233/work
+pyparsing==3.1.2
+python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1709299778482/work
+pytz==2024.1
+pyzmq @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_43pxpbos3z/croot/pyzmq_1705605108344/work
+referencing==0.35.1
+requests==2.32.2
+rich==13.7.1
+rpds-py==0.18.1
+scikit-learn==1.5.0
+scipy==1.13.1
+seaborn==0.13.2
+six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
+smmap==5.0.1
+stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1669632077133/work
+streamlit==1.35.0
+tenacity==8.3.0
+threadpoolctl==3.5.0
+toml==0.10.2
+toolz==0.12.1
+tornado @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_3a5nrn2jeh/croot/tornado_1696936974091/work
+traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1713535121073/work
+typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1712329955671/work
+tzdata==2024.1
+urllib3==2.2.1
+wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1704731205417/work
+Werkzeug==3.0.3
+zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1695255097490/work

From ae7b5970461a98be88eabacc25bd219f991d62aa Mon Sep 17 00:00:00 2001
From: aahil n <nishad.a@northeastern.edu>
Date: Thu, 6 Jun 2024 00:24:33 +0200
Subject: [PATCH 21/34] updated requirements

ty michael
---
 api/requirements.txt           | 75 +++-------------------------------
 app/src/pages/02_userSurvey.py |  3 +-
 2 files changed, 7 insertions(+), 71 deletions(-)

diff --git a/api/requirements.txt b/api/requirements.txt
index e504611..77ade17 100644
--- a/api/requirements.txt
+++ b/api/requirements.txt
@@ -1,90 +1,27 @@
-altair==5.3.0
-appnope @ file:///home/conda/feedstock_root/build_artifacts/appnope_1707233003401/work
-asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1698341106958/work
-attrs==23.2.0
 blinker==1.8.2
-cachetools==5.3.3
-certifi==2024.2.2
+certifi==2024.6.2
 charset-normalizer==3.3.2
 click==8.1.7
-comm @ file:///home/conda/feedstock_root/build_artifacts/comm_1710320294760/work
-contourpy==1.2.1
-cycler==0.12.1
-debugpy @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_563_nwtkoc/croot/debugpy_1690905063850/work
-decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
-et-xmlfile==1.1.0
-exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1704921103267/work
-executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1698579936712/work
-fastjsonschema==2.19.1
+colorama==0.4.6
 Flask==3.0.3
-fonttools==4.52.4
-gitdb==4.0.11
-GitPython==3.1.43
 idna==3.7
-importlib_metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1710971335535/work
-ipykernel @ file:///Users/runner/miniforge3/conda-bld/ipykernel_1708996616394/work
-ipython @ file:///home/conda/feedstock_root/build_artifacts/ipython_1715263367085/work
 itsdangerous==2.2.0
-jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1696326070614/work
 Jinja2==3.1.4
 joblib==1.4.2
-jsonschema==4.22.0
-jsonschema-specifications==2023.12.1
-jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1716472197302/work
-jupyter_core @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_782yoyc_98/croot/jupyter_core_1698937318631/work
-kiwisolver==1.4.5
 lxml==5.2.2
-markdown-it-py==3.0.0
 MarkupSafe==2.1.5
-matplotlib==3.9.0
-matplotlib-inline @ file:///home/conda/feedstock_root/build_artifacts/matplotlib-inline_1713250518406/work
-mdurl==0.1.2
-nbformat==5.10.4
-nest_asyncio @ file:///home/conda/feedstock_root/build_artifacts/nest-asyncio_1705850609492/work
 numpy==1.26.4
-openpyxl==3.1.3
-packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1710075952259/work
 pandas==2.2.2
 pandaSDMX==1.10.0
-parso @ file:///home/conda/feedstock_root/build_artifacts/parso_1712320355065/work
-pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1706113125309/work
-pickleshare @ file:///home/conda/feedstock_root/build_artifacts/pickleshare_1602536217715/work
-pillow==10.3.0
-platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1715777629804/work
-plotly==5.22.0
-prompt-toolkit @ file:///home/conda/feedstock_root/build_artifacts/prompt-toolkit_1702399386289/work
-protobuf==4.25.3
-psutil @ file:///Users/cbousseau/work/recipes/ci_py311_2/psutil_1678995687212/work
-ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
-pure-eval @ file:///home/conda/feedstock_root/build_artifacts/pure_eval_1642875951954/work
-pyarrow==16.1.0
 pydantic==1.10.15
-pydeck==0.9.1
-Pygments @ file:///home/conda/feedstock_root/build_artifacts/pygments_1714846767233/work
-pyparsing==3.1.2
-python-dateutil @ file:///home/conda/feedstock_root/build_artifacts/python-dateutil_1709299778482/work
+python-dateutil==2.9.0.post0
 pytz==2024.1
-pyzmq @ file:///private/var/folders/k1/30mswbxs7r1g6zwn8y4fyt500000gp/T/abs_43pxpbos3z/croot/pyzmq_1705605108344/work
-referencing==0.35.1
-requests==2.32.2
-rich==13.7.1
-rpds-py==0.18.1
+requests==2.32.3
 scikit-learn==1.5.0
 scipy==1.13.1
-seaborn==0.13.2
-six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work
-smmap==5.0.1
-stack-data @ file:///home/conda/feedstock_root/build_artifacts/stack_data_1669632077133/work
-streamlit==1.35.0
-tenacity==8.3.0
+six==1.16.0
 threadpoolctl==3.5.0
-toml==0.10.2
-toolz==0.12.1
-tornado @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_3a5nrn2jeh/croot/tornado_1696936974091/work
-traitlets @ file:///home/conda/feedstock_root/build_artifacts/traitlets_1713535121073/work
-typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1712329955671/work
+typing_extensions==4.12.1
 tzdata==2024.1
 urllib3==2.2.1
-wcwidth @ file:///home/conda/feedstock_root/build_artifacts/wcwidth_1704731205417/work
 Werkzeug==3.0.3
-zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1695255097490/work
diff --git a/app/src/pages/02_userSurvey.py b/app/src/pages/02_userSurvey.py
index 2ad7688..fd91908 100644
--- a/app/src/pages/02_userSurvey.py
+++ b/app/src/pages/02_userSurvey.py
@@ -84,8 +84,7 @@
 
     elif (fuel_type == "Electric"): 
         st.write("Please include charging data in residential data.")
-    
-    
+        
     if st.button("Submit Car Data"):
         if fuel_type and fuel_used:
             api_url = "http://api:4000/u/UserAddCar"

From f6146aac600ed87ad3f0b40c3882bcb005fefd16 Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Thu, 6 Jun 2024 00:25:32 +0200
Subject: [PATCH 22/34] Added accurate country emission data, changed user
 survey history routes

---
 app/src/pages/03_userHistory.py |  8 +++----
 database/01_carbon-data.sql     | 40 ++++++++++++++++++++++++++-------
 2 files changed, 36 insertions(+), 12 deletions(-)

diff --git a/app/src/pages/03_userHistory.py b/app/src/pages/03_userHistory.py
index 390500f..a761453 100644
--- a/app/src/pages/03_userHistory.py
+++ b/app/src/pages/03_userHistory.py
@@ -9,7 +9,7 @@
 
 data = {} 
 try:
-  data = requests.get('http://api:4000/u/userhistory').json()
+  data = requests.get('http://api:4000/u/UserCars').json()
 except:
   st.write("**Important**: Could not connect to sample api, so using dummy data.")
   data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
@@ -18,7 +18,7 @@
 
 data = {} 
 try:
-  data = requests.get('http://api:4000/u/userhistory').json()
+  data = requests.get('http://api:4000/u/UserResidential').json()
 except:
   st.write("**Important**: Could not connect to sample api, so using dummy data.")
   data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
@@ -27,7 +27,7 @@
 
 data = {} 
 try:
-  data = requests.get('http://api:4000/u/userhistory').json()
+  data = requests.get('http://api:4000/u/UserFlights').json()
 except:
   st.write("**Important**: Could not connect to sample api, so using dummy data.")
   data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
@@ -36,7 +36,7 @@
 
 data = {} 
 try:
-  data = requests.get('http://api:4000/u/userhistory').json()
+  data = requests.get('http://api:4000/u/UserTransport').json()
 except:
   st.write("**Important**: Could not connect to sample api, so using dummy data.")
   data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
diff --git a/database/01_carbon-data.sql b/database/01_carbon-data.sql
index 842c1b5..e6521e5 100644
--- a/database/01_carbon-data.sql
+++ b/database/01_carbon-data.sql
@@ -1,13 +1,37 @@
 USE CarbonConnect;
 
-INSERT INTO Country (id, emissions, name) VALUES (1, '5.8 million metric tons', 'Austria');
-INSERT INTO Country (id, emissions, name) VALUES (2, '15.2 million metric tons', 'Belgium');
-INSERT INTO Country (id, emissions, name) VALUES (3, '20.7 million metric tons', 'Bulgaria');
-INSERT INTO Country (id, emissions, name) VALUES (4, '7.3 million metric tons', 'Croatia');
-INSERT INTO Country (id, emissions, name) VALUES (5, '12.1 million metric tons', 'Cyprus');
-INSERT INTO Country (id, emissions, name) VALUES (6, '18.5 million metric tons', 'Czechia');
-INSERT INTO Country (id, emissions, name) VALUES (7, '9.8 million metric tons', 'Denmark');
-INSERT INTO Country (id, emissions, name) VALUES (8, '14.3 million metric tons', 'Estonia');
+INSERT INTO Country (id, emissions, name) VALUES (1, 108871.68, 'Belgium');
+INSERT INTO Country (id, emissions, name) VALUES (2, 59082.72, 'Bulgaria');
+INSERT INTO Country (id, emissions, name) VALUES (3, 118500.35, 'Czechia');
+INSERT INTO Country (id, emissions, name) VALUES (4, 44243.3, 'Denmark');
+INSERT INTO Country (id, emissions, name) VALUES (5, 777380.09, 'Germany');
+INSERT INTO Country (id, emissions, name) VALUES (6, 14125.06, 'Estonia');
+INSERT INTO Country (id, emissions, name) VALUES (7, 63650.14, 'Ireland');
+INSERT INTO Country (id, emissions, name) VALUES (8, 82242.9, 'Greece');
+INSERT INTO Country (id, emissions, name) VALUES (9, 309286.15, 'Spain');
+INSERT INTO Country (id, emissions, name) VALUES (10, 409732.55, 'France');
+INSERT INTO Country (id, emissions, name) VALUES (11, 26258.1, 'Croatia');
+INSERT INTO Country (id, emissions, name) VALUES (12, 419467.07, 'Italy');
+INSERT INTO Country (id, emissions, name) VALUES (13, 9572.78, 'Cyprus');
+INSERT INTO Country (id, emissions, name) VALUES (14, 10568.91, 'Latvia');
+INSERT INTO Country (id, emissions, name) VALUES (15, 19249.2, 'Lithuania');
+INSERT INTO Country (id, emissions, name) VALUES (16, 10145.27, 'Luxembourg');
+INSERT INTO Country (id, emissions, name) VALUES (17, 60331.72, 'Hungary');
+INSERT INTO Country (id, emissions, name) VALUES (18, 2647.72, 'Malta');
+INSERT INTO Country (id, emissions, name) VALUES (19, 162999.6, 'Netherlands');
+INSERT INTO Country (id, emissions, name) VALUES (20, 74825.78, 'Austria');
+INSERT INTO Country (id, emissions, name) VALUES (21, 383434.04, 'Poland');
+INSERT INTO Country (id, emissions, name) VALUES (22, 60581.36, 'Portugal');
+INSERT INTO Country (id, emissions, name) VALUES (23, 109992.76, 'Romania');
+INSERT INTO Country (id, emissions, name) VALUES (24, 15676.64, 'Slovenia');
+INSERT INTO Country (id, emissions, name) VALUES (25, 37183,78, 'Slovakia');
+INSERT INTO Country (id, emissions, name) VALUES (26, 47341.71, 'Finland');
+INSERT INTO Country (id, emissions, name) VALUES (27, 47074.61, 'Sweden');
+INSERT INTO Country (id, emissions, name) VALUES (28, 5402.55, 'Iceland');
+INSERT INTO Country (id, emissions, name) VALUES (29, 50255.43, 'Norway');
+INSERT INTO Country (id, emissions, name) VALUES (30, 45847.17, 'Switzerland');
+
+
 
 
 INSERT INTO EmissionTags (id, description) VALUES

From 00fcedfb8869437fafa0fffb2b5e19739aca24ff Mon Sep 17 00:00:00 2001
From: Babalola-a <147947002+Babalola-a@users.noreply.github.com>
Date: Thu, 6 Jun 2024 00:34:06 +0200
Subject: [PATCH 23/34] created post and delete requests for NGOs

---
 api/backend/ngo/ngo_routes.py | 270 +++++++++++++++++++++++-----------
 1 file changed, 186 insertions(+), 84 deletions(-)

diff --git a/api/backend/ngo/ngo_routes.py b/api/backend/ngo/ngo_routes.py
index c432732..88cc23a 100644
--- a/api/backend/ngo/ngo_routes.py
+++ b/api/backend/ngo/ngo_routes.py
@@ -1,130 +1,232 @@
+
 from flask import Blueprint, request, jsonify, make_response, current_app
 import json
 from backend.db_connection import db
 
+
 ngo = Blueprint('ngo', __name__)
 current_id = 1
 
+
 # Gets my ngo data
 @ngo.route('/ngomine', methods=['GET'])
 def get_mine():
-    cursor = db.get_db().cursor()
+   cursor = db.get_db().cursor()
 
-    cursor.execute('SELECT * FROM NGO WHERE id = 1')
 
-    column_headers = [x[0] for x in cursor.description]
+   cursor.execute('SELECT * FROM NGO WHERE id = 1')
 
-    json_data = []
 
-    theData = cursor.fetchall()
+   column_headers = [x[0] for x in cursor.description]
 
-    for row in theData:
-        json_data.append(dict(zip(column_headers, row)))
 
-    return jsonify(json_data)
+   json_data = []
+
+
+   theData = cursor.fetchall()
+
+
+   for row in theData:
+       json_data.append(dict(zip(column_headers, row)))
+
+
+   return jsonify(json_data)
+
 
 # updates an ngo to the NGO table given filled out data
 @ngo.route('/NGOupdate', methods=['PUT'])
 def add_new_NGO():
-    current_app.logger.info('ngo_routes.py: POST /NGOadd')
-    
-    recieved_data = request.json
-    current_app.logger.info(recieved_data)
+   current_app.logger.info('ngo_routes.py: POST /NGOadd')
+  
+   recieved_data = request.json
+   current_app.logger.info(recieved_data)
 
-    name = recieved_data['name']
-    website = recieved_data['website']
-    email = recieved_data['email']
 
-    query = 'UPDATE NGO SET website = %s, name = %s, contact = %s WHERE id = 1'
+   name = recieved_data['name']
+   website = recieved_data['website']
+   email = recieved_data['email']
 
-    data = (name, website, email)
-    cursor = db.get_db().cursor()
-    cursor.execute(query, data)
-    db.get_db().commit()
 
-    # new_id = cursor.lastrowid
-    
-    # selected_tags = recieved_data.get('tags', []) 
-    # if selected_tags:
-    #     insert_query = 'INSERT INTO NGOTags (ngo_id, tag_id) VALUES (%s, %s)'
-    #     # for each tag gets the tag_id from the emission tags table, :)
-    #     # also inserts into NGO tags
-    #     for tag_name in selected_tags:
-    #         tag_query = 'SELECT id FROM EmissionTags WHERE description = %s'
-    #         cursor.execute(tag_query, (tag_name,))
-    #         tag_row = cursor.fetchone()
-    #         tag_id = tag_row[0]
-    #         cursor.execute(insert_query, (new_id, tag_id))
-    #         db.get_db().commit()
+   query = 'UPDATE NGO SET website = %s, name = %s, contact = %s WHERE id = 1'
+
+
+   data = (name, website, email)
+   cursor = db.get_db().cursor()
+   cursor.execute(query, data)
+   db.get_db().commit()
+
+
+   # new_id = cursor.lastrowid
+  
+   # selected_tags = recieved_data.get('tags', [])
+   # if selected_tags:
+   #     insert_query = 'INSERT INTO NGOTags (ngo_id, tag_id) VALUES (%s, %s)'
+   #     # for each tag gets the tag_id from the emission tags table, :)
+   #     # also inserts into NGO tags
+   #     for tag_name in selected_tags:
+   #         tag_query = 'SELECT id FROM EmissionTags WHERE description = %s'
+   #         cursor.execute(tag_query, (tag_name,))
+   #         tag_row = cursor.fetchone()
+   #         tag_id = tag_row[0]
+   #         cursor.execute(insert_query, (new_id, tag_id))
+   #         db.get_db().commit()
+
+
+   return 'Success'
 
-    return 'Success'
 
 @ngo.route('/tags', methods=['GET'])
 def get_tags():
-    # get a cursor object from the database
-    cursor = db.get_db().cursor()
-    cursor.execute('''
-        SELECT description 
-        FROM EmissionTags 
-        WHERE EmissionTags.id IN (
-            SELECT tag_id 
-            FROM NGOTags 
-            WHERE NGOTags.ngo_id = 1
-        );
-    ''')
-    
-    # grab the column headers from the returned data
-    column_headers = [x[0] for x in cursor.description]
+   # get a cursor object from the database
+   cursor = db.get_db().cursor()
+   cursor.execute('''
+       SELECT description
+       FROM EmissionTags
+       WHERE EmissionTags.id IN (
+           SELECT tag_id
+           FROM NGOTags
+           WHERE NGOTags.ngo_id = 1
+       );
+   ''')
+  
+   # grab the column headers from the returned data
+   column_headers = [x[0] for x in cursor.description]
 
-    # create an empty dictionary object to use in 
-    # putting column headers together with data
-    json_data = []
 
-    # fetch all the data from the cursor
-    theData = cursor.fetchall()
+   # create an empty dictionary object to use in
+   # putting column headers together with data
+   json_data = []
+
+
+   # fetch all the data from the cursor
+   theData = cursor.fetchall()
+
+
+   # for each of the rows, zip the data elements together with
+   # the column headers.
+   for row in theData:
+       json_data.append(dict(zip(column_headers, row)))
+
+
+   return jsonify(json_data)
 
-    # for each of the rows, zip the data elements together with
-    # the column headers. 
-    for row in theData:
-        json_data.append(dict(zip(column_headers, row)))
 
-    return jsonify(json_data)
 
 
 # get all of the matching Enterprises based on tags
 @ngo.route('/EnterpriseMatch', methods=['GET'])
 def get_matches():
-    # get a cursor object from the database
+   # get a cursor object from the database
+   cursor = db.get_db().cursor()
+
+
+   cursor.execute('''
+   SELECT Enterprises.name, EmissionTags.description
+   FROM Enterprises
+   JOIN EntTags ON Enterprises.id = EntTags.enterprise_id
+   JOIN EmissionTags ON EntTags.tag_id = EmissionTags.id
+   WHERE EmissionTags.id IN (
+       SELECT tag_id
+       FROM EntTags
+       WHERE EntTags.enterprise_id = 1
+   );
+''')
+  
+   # grab the column headers from the returned data
+   column_headers = [x[0] for x in cursor.description]
+
+
+   # create an empty dictionary object to use in
+   # putting column headers together with data
+   json_data = []
+
+
+   # fetch all the data from the cursor
+   theData = cursor.fetchall()
+
+
+   # for each of the rows, zip the data elements together with
+   # the column headers.
+   for row in theData:
+       json_data.append(dict(zip(column_headers, row)))
+
+
+   return jsonify(json_data)
+
+
+@ngo.route('/NGOadd', methods=['POST'])
+def add_tags():
+   current_app.logger.info('POST /ngo/tags route')
+
+
+   info = request.json
+   tag_description = info.get('tag')
+
+
+   cursor = db.get_db().cursor()
+
+
+   select_query = '''
+   SELECT
+       ET.id AS tag_id
+   FROM
+       EmissionTags ET
+   WHERE
+       ET.description = %s
+   '''
+   cursor.execute(select_query, (tag_description,))
+   tag_row = cursor.fetchone()
+
+
+   if tag_row is None:
+       return jsonify({"error": "Tag not found"}), 404
+
+
+   new_tag_id = tag_row[0]
+
+
+   insert_query = 'INSERT INTO NGOTags (ngo_id, tag_id) VALUES (1, %s)'
+   cursor.execute(insert_query, (new_tag_id,))
+   db.get_db().commit()
+
+
+   return jsonify({"message": "Success"}), 200
+
+
+
+@ngo.route('/TagDelete', methods=['DELETE'])
+def delete_tags():
+    current_app.logger.info('DELETE /ngo/tags route')
+
+    info = request.json
+    tag_description = info.get('tag')
+
     cursor = db.get_db().cursor()
 
-    cursor.execute('''
-    SELECT Enterprises.name, EmissionTags.description
-    FROM Enterprises
-    JOIN EntTags ON Enterprises.id = EntTags.enterprise_id
-    JOIN EmissionTags ON EntTags.tag_id = EmissionTags.id
-    WHERE EmissionTags.id IN (
-        SELECT tag_id
-        FROM EntTags
-        WHERE EntTags.enterprise_id = 1
-    );
-''')
-    
-    # grab the column headers from the returned data
-    column_headers = [x[0] for x in cursor.description]
 
-    # create an empty dictionary object to use in 
-    # putting column headers together with data
-    json_data = []
+    select_query = '''
+        SELECT id FROM EmissionTags WHERE description = %s;
+        '''
+    cursor.execute(select_query, (tag_description,))
+    tag_row = cursor.fetchone()
+    current_app.logger.info(select_query)
 
-    # fetch all the data from the cursor
-    theData = cursor.fetchall()
+    if tag_row is None:
+        current_app.logger.info(f'Tag not found: {tag_description}')
+        return jsonify({"error": "Tag not found"}), 404
 
-    # for each of the rows, zip the data elements together with
-    # the column headers. 
-    for row in theData:
-        json_data.append(dict(zip(column_headers, row)))
+    tag_id = tag_row[0]
 
-    return jsonify(json_data)
+    delete_query = '''
+    DELETE FROM NGOTags WHERE ngo_id = 1 AND tag_id = %s;
+    '''
+    cursor.execute(delete_query, (tag_id,))
+    db.get_db().commit()
+
+    current_app.logger.info(f'Successfully deleted tag: {tag_description} for NGO ID: {ngo_id}')
+    return 'Success'
+  
+    
 
 
 # get all of the matching users based on tags

From 6dec69056788acd4cfd5d929b449854298d971e0 Mon Sep 17 00:00:00 2001
From: Babalola-a <147947002+Babalola-a@users.noreply.github.com>
Date: Thu, 6 Jun 2024 00:43:46 +0200
Subject: [PATCH 24/34] Update 21_NGOInfo.py

updating NGO info
---
 app/src/pages/21_NGOInfo.py | 51 +++++++++++++++++++++++++++++++++++++
 1 file changed, 51 insertions(+)

diff --git a/app/src/pages/21_NGOInfo.py b/app/src/pages/21_NGOInfo.py
index bb291ae..8fa6605 100644
--- a/app/src/pages/21_NGOInfo.py
+++ b/app/src/pages/21_NGOInfo.py
@@ -69,7 +69,58 @@
 
 st.dataframe(data)
 
+def add_tags():
+    st.write('### Add New NGO Tags')
+
+    options = ["Transport", "Flights", "Energy", "Heat"]
+
+    selected_tags = st.selectbox("Select your associated tags", options)
+
+    if st.button("Add Tags"):
+        add_tags_api_url = "http://api:4000/n/NGOadd"
+        tag_data = {"tag": selected_tags}
+        try:
+            response = requests.post(add_tags_api_url, json=tag_data)
+            if response.status_code == 200:
+                st.success("Tags successfully added!")
+            else:
+                st.error(f"Failed to add tags. Status code: {response.status_code}")
+        except Exception as e:
+            st.error(f"An error occurred while adding tags: {e}")
+ 
+add_tags()
+st.write("Display my current tags")
+data = {} 
+try:
+  data = requests.get('http://api:4000/n/tags').json()
+except:
+  st.write("**Important**: Could not connect to sample api, so using dummy data.")
+  data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
+st.dataframe(data)
+
+
 if st.button('Proceed to tools'):
     st.switch_page('pages/22_NGOTools.py')
 
 
+def delete_tags():
+    st.write('### Delete NGO Tags')
+
+    options = ["Transport", "Flights", "Energy", "Heat"]
+
+    selected_tag = st.selectbox("Select tags to delete", options)  
+
+    if st.button("Delete Tags"):
+        delete_tags_api_url = "http://api:4000/n/TagDelete"
+        tag_data = {"tag": selected_tag}  
+
+        try:
+            response = requests.delete(delete_tags_api_url, json=tag_data)
+            if response.status_code == 200:
+                st.success("Tags successfully deleted!")
+            
+        except Exception as e:
+            st.error(f"An error occurred while deleting tags: {e}")
+
+delete_tags()
+

From 222cf2638bfc1b7cf4828a35d1bd68dca04a7ef8 Mon Sep 17 00:00:00 2001
From: mehallhm <michaelhmehall@gmail.com>
Date: Thu, 6 Jun 2024 00:47:56 +0200
Subject: [PATCH 25/34] chore: docstrings and cleanup

---
 api/backend/ml_models/model_alpha.py |   71 +-
 eda/clean.ipynb                      |    0
 eda/regression.ipynb                 |   51 -
 eda/scraper.ipynb                    | 1417 --------------------------
 eda/scraper.py                       |  129 ---
 5 files changed, 56 insertions(+), 1612 deletions(-)
 delete mode 100644 eda/clean.ipynb
 delete mode 100644 eda/regression.ipynb
 delete mode 100644 eda/scraper.ipynb
 delete mode 100644 eda/scraper.py

diff --git a/api/backend/ml_models/model_alpha.py b/api/backend/ml_models/model_alpha.py
index 6ad2201..9abcca5 100644
--- a/api/backend/ml_models/model_alpha.py
+++ b/api/backend/ml_models/model_alpha.py
@@ -1,4 +1,6 @@
 """
+The Train, Test, and Predict functions for the CO2 Emission Linear Regression
+ML Model
 """
 
 import numpy as np
@@ -7,8 +9,11 @@
 import train_helpers
 from sklearn.metrics import r2_score
 
-def train():
+def train() -> np.array:
 	"""
+	Calculates the slopes for the CO2 emissions regression model.
+
+	:returns: An array with the slopes in shape (3,)
 	"""
 	estat = sdmx.Request("ESTAT")
 	resp = estat.data(
@@ -20,7 +25,9 @@ def train():
 			"airpol": "GHG"
 		}
 	)
-	emission_df = resp.to_pandas(datetime={'dim': 'TIME_PERIOD'}).droplevel(level=['unit', 'freq', 'src_crf', 'airpol'], axis=1)
+	emission_df = (resp
+				.to_pandas(datetime={'dim': 'TIME_PERIOD'})
+				.droplevel(level=['unit', 'freq', 'src_crf', 'airpol'], axis=1))
 	melted_emissions_df = train_helpers.melt_smdx_dataframe(emission_df)
 
 	resp = estat.data(
@@ -32,37 +39,66 @@ def train():
 				"freq": "A",
 			}
 		)
-	household_energy_df = resp.to_pandas(datetime={'dim': 'TIME_PERIOD', 'freq': 'freq'}).droplevel(level=["siec", "unit", "nrg_bal"], axis=1)
+	household_energy_df = (resp
+				.to_pandas(datetime={'dim': 'TIME_PERIOD', 'freq': 'freq'})
+				.droplevel(level=["siec", "unit", "nrg_bal"], axis=1))
 	melted_household_energy_df = train_helpers.melt_smdx_dataframe(household_energy_df)
 
-	merged_df = train_helpers.merge_dataframes([melted_emissions_df, melted_household_energy_df])
-	merged_df = merged_df.drop(merged_df[(merged_df.geo == "EU27_2020") | (merged_df.geo == "EU20")].index)
+	resp = estat.data(
+		"TEN00127",
+		key={
+			"unit": "KTOE",
+			"freq": "A",
+			"siec": "O4652XR5210B",
+			"nrg_bal": "FC_TRA_ROAD_E"
+		}
+	)
+	gas_df = (resp
+				.to_pandas(datetime={'dim': 'TIME_PERIOD'})
+				.droplevel(level=['unit', 'freq', 'siec', "nrg_bal"], axis=1))
+	melted_gas_df = train_helpers.melt_smdx_dataframe(gas_df)
+
+	merged_df = train_helpers.merge_dataframes([melted_emissions_df,
+											  melted_household_energy_df,
+											  melted_gas_df])
+	merged_df.columns = ["year", "geo", "emissions", "energy", "gas"]
+	merged_df = merged_df.drop(merged_df[(merged_df.geo == "EU27_2020") |
+									   (merged_df.geo == "EU20")].index)
 	merged_df = merged_df.drop("year", axis=1)
 	standard_df = train_helpers.standardize(merged_df)
 
 	df_dummies = pd.get_dummies(standard_df, dtype=int, columns=["geo"])
 	df_dummies = df_dummies.fillna(0)
 
-	#X = np.pad(df_dummies.iloc[:, 1:].to_numpy(dtype=np.float64), ((0,0), (1,0)), mode="constant", constant_values=1)
-	X = np.pad(standard_df.iloc[:,1:3].to_numpy(dtype=np.float64), ((0,0), (1,0)), mode="constant", constant_values=1)
+	#X = np.pad(df_dummies.iloc[:, 1:].to_numpy(dtype=np.float64),
+	# ((0,0), (1,0)), mode="constant", constant_values=1)
+	X = np.pad(standard_df.iloc[:,1:3].to_numpy(dtype=np.float64),
+			 ((0,0), (1,0)), mode="constant", constant_values=1)
 	y = np.array(df_dummies["emissions"], dtype=np.float64)
 
 	m = np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, y))
 
 	return m
 
+
 def test(X: np.array, y: np.array) -> any:
 	"""
+	Tests the CO2 emissions regression model
+
+	:param X: The padded X features
+	:param y: The y features
+	:returns: The R2 value of the model w/ LOO-CV
 	"""
 	np_remove = lambda a, i: np.concatenate([a[:i,], a[i + 1:,]])
-	lin_reg = lambda X, Y: np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, Y))
+	lin_reg = lambda X, Y: np.matmul(np.linalg.inv(np.matmul(X.T, X)),
+								   np.matmul(X.T, Y))
 
 	y_pred = []
 	for i in range(len(X)):
 		holdout_X = X[i]
 		
 		loo_X = np_remove(X, i)
-		loo_y = np_remove(Y, i)
+		loo_y = np_remove(y, i)
 		loo_b = lin_reg(loo_X, loo_y)
 
 		y_hat = np.matmul(holdout_X, loo_b)
@@ -73,15 +109,20 @@ def test(X: np.array, y: np.array) -> any:
 	return r2
 	
 
-def predict(feats:np.array, beta:np.array) -> float:
+def predict(feats:list[float], beta:list[float]) -> float:
 	"""
-	Predict the 
+	Predicts the Greenhouse Gas Emissions for an inividual user in ktonnes.
 
-	:param feats: The unpadded input features from the user
-	:param beta: The slopes (and intercept) for the trained model
-	:returns: The predicted value
+	:param feats: The unpadded input features from the user:
+		- Motor Gassoline in ktoes
+		- Household Energy in TJ
+	:param beta: The slopes (and intercept) for the trained model of shape: (3,)
+	:returns: The predicted greenhouse gass emission in CO2 equiventlents
+		measured in ktonnes
 	"""
-	x = np.pad(feats, ((0, 0), (1, 0)), mode="constant", constant_values=1)
+	x = np.pad(np.array(feats, dtype=np.float64), ((0, 0), (1, 0)),
+			 mode="constant", constant_values=1)
+	beta = np.array(beta, dtype=np.float64)
 	y_hat = np.matmul(x, beta)
 
 	return y_hat[0]
\ No newline at end of file
diff --git a/eda/clean.ipynb b/eda/clean.ipynb
deleted file mode 100644
index e69de29..0000000
diff --git a/eda/regression.ipynb b/eda/regression.ipynb
deleted file mode 100644
index 93a92f3..0000000
--- a/eda/regression.ipynb
+++ /dev/null
@@ -1,51 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def standardize_feats(df: pd.DataFrame) -> pd.DataFrame:\n",
-    "\t\"\"\"\n",
-    "\tStandardize all numerical columns of the inputed dataframe\n",
-    "\t:param df: The dataframe to standardize\n",
-    "\t:returns: A new dataframe with all numerical columns standardized\n",
-    "\t\"\"\"\n",
-    "\tfor feat in df.columns:\n",
-    "\t\tpass"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "wafflers",
-   "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/eda/scraper.ipynb b/eda/scraper.ipynb
deleted file mode 100644
index 1fc534e..0000000
--- a/eda/scraper.ipynb
+++ /dev/null
@@ -1,1417 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "res = requests.get(\"https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data/sdg_13_10?format=JSON&lang=EN\")\n",
-    "raw = res.json()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'version': '2.0',\n",
-       " 'class': 'dataset',\n",
-       " 'label': 'Net greenhouse gas emissions',\n",
-       " 'source': 'ESTAT',\n",
-       " 'updated': '2024-05-21T11:00:00+0200',\n",
-       " 'value': {'2871': 100.0,\n",
-       "  '2872': 104.7,\n",
-       "  '2873': 96.6,\n",
-       "  '2874': 97.2,\n",
-       "  '2875': 97.5,\n",
-       "  '2876': 101.7,\n",
-       "  '2877': 105.8,\n",
-       "  '2878': 105.4,\n",
-       "  '2879': 104.6,\n",
-       "  '2880': 102.6,\n",
-       "  '2881': 103.0,\n",
-       "  '2882': 107.8,\n",
-       "  '2883': 109.8,\n",
-       "  '2884': 116.4,\n",
-       "  '2885': 116.5,\n",
-       "  '2886': 118.3,\n",
-       "  '2887': 115.4,\n",
-       "  '2888': 112.1,\n",
-       "  '2889': 111.3,\n",
-       "  '2890': 102.7,\n",
-       "  '2891': 108.6,\n",
-       "  '2892': 106.0,\n",
-       "  '2893': 102.5,\n",
-       "  '2894': 102.9,\n",
-       "  '2895': 98.4,\n",
-       "  '2896': 101.4,\n",
-       "  '2897': 102.8,\n",
-       "  '2898': 105.6,\n",
-       "  '2899': 101.9,\n",
-       "  '2900': 103.8,\n",
-       "  '2901': 93.9,\n",
-       "  '2902': 98.3,\n",
-       "  '2903': 93.6,\n",
-       "  '2244': 100.0,\n",
-       "  '2245': 101.4,\n",
-       "  '2246': 101.1,\n",
-       "  '2247': 100.3,\n",
-       "  '2248': 103.4,\n",
-       "  '2249': 105.0,\n",
-       "  '2250': 107.8,\n",
-       "  '2251': 102.3,\n",
-       "  '2252': 106.1,\n",
-       "  '2253': 102.3,\n",
-       "  '2254': 103.1,\n",
-       "  '2255': 101.7,\n",
-       "  '2256': 101.3,\n",
-       "  '2257': 101.8,\n",
-       "  '2258': 102.2,\n",
-       "  '2259': 100.0,\n",
-       "  '2260': 98.3,\n",
-       "  '2261': 96.1,\n",
-       "  '2262': 96.3,\n",
-       "  '2263': 87.5,\n",
-       "  '2264': 92.5,\n",
-       "  '2265': 85.7,\n",
-       "  '2266': 83.6,\n",
-       "  '2267': 83.5,\n",
-       "  '2268': 79.8,\n",
-       "  '2269': 82.9,\n",
-       "  '2270': 81.8,\n",
-       "  '2271': 81.8,\n",
-       "  '2272': 82.5,\n",
-       "  '2273': 81.8,\n",
-       "  '2274': 74.5,\n",
-       "  '2275': 77.0,\n",
-       "  '2276': 73.1,\n",
-       "  '2277': 100.0,\n",
-       "  '2278': 81.9,\n",
-       "  '2279': 76.5,\n",
-       "  '2280': 76.1,\n",
-       "  '2281': 71.8,\n",
-       "  '2282': 73.4,\n",
-       "  '2283': 73.5,\n",
-       "  '2284': 70.1,\n",
-       "  '2285': 66.0,\n",
-       "  '2286': 58.8,\n",
-       "  '2287': 58.0,\n",
-       "  '2288': 61.0,\n",
-       "  '2289': 58.4,\n",
-       "  '2290': 63.4,\n",
-       "  '2291': 62.5,\n",
-       "  '2292': 63.1,\n",
-       "  '2293': 63.9,\n",
-       "  '2294': 67.8,\n",
-       "  '2295': 66.5,\n",
-       "  '2296': 57.5,\n",
-       "  '2297': 60.2,\n",
-       "  '2298': 65.6,\n",
-       "  '2299': 60.4,\n",
-       "  '2300': 55.0,\n",
-       "  '2301': 58.0,\n",
-       "  '2302': 61.3,\n",
-       "  '2303': 58.7,\n",
-       "  '2304': 60.6,\n",
-       "  '2305': 56.3,\n",
-       "  '2306': 54.9,\n",
-       "  '2307': 48.6,\n",
-       "  '2308': 54.7,\n",
-       "  '2309': 59.2,\n",
-       "  '3201': 100.0,\n",
-       "  '3202': 103.1,\n",
-       "  '3203': 102.9,\n",
-       "  '3204': 98.6,\n",
-       "  '3205': 97.0,\n",
-       "  '3206': 98.9,\n",
-       "  '3207': 100.2,\n",
-       "  '3208': 98.4,\n",
-       "  '3209': 101.3,\n",
-       "  '3210': 101.5,\n",
-       "  '3211': 100.6,\n",
-       "  '3212': 102.8,\n",
-       "  '3213': 99.5,\n",
-       "  '3214': 100.6,\n",
-       "  '3215': 101.3,\n",
-       "  '3216': 102.5,\n",
-       "  '3217': 102.2,\n",
-       "  '3218': 99.3,\n",
-       "  '3219': 102.3,\n",
-       "  '3220': 99.4,\n",
-       "  '3221': 102.5,\n",
-       "  '3222': 95.9,\n",
-       "  '3223': 98.4,\n",
-       "  '3224': 99.9,\n",
-       "  '3225': 93.2,\n",
-       "  '3226': 92.6,\n",
-       "  '3227': 93.5,\n",
-       "  '3228': 92.1,\n",
-       "  '3229': 90.1,\n",
-       "  '3230': 89.8,\n",
-       "  '3231': 78.9,\n",
-       "  '3232': 81.6,\n",
-       "  '3233': 78.9,\n",
-       "  '2640': 100.0,\n",
-       "  '2641': 110.2,\n",
-       "  '2642': 116.7,\n",
-       "  '2643': 119.3,\n",
-       "  '2644': 123.7,\n",
-       "  '2645': 123.5,\n",
-       "  '2646': 128.9,\n",
-       "  '2647': 130.2,\n",
-       "  '2648': 135.9,\n",
-       "  '2649': 140.6,\n",
-       "  '2650': 145.1,\n",
-       "  '2651': 146.5,\n",
-       "  '2652': 149.7,\n",
-       "  '2653': 157.0,\n",
-       "  '2654': 158.9,\n",
-       "  '2655': 159.9,\n",
-       "  '2656': 164.0,\n",
-       "  '2657': 169.1,\n",
-       "  '2658': 173.1,\n",
-       "  '2659': 168.6,\n",
-       "  '2660': 163.6,\n",
-       "  '2661': 159.3,\n",
-       "  '2662': 150.3,\n",
-       "  '2663': 138.3,\n",
-       "  '2664': 144.2,\n",
-       "  '2665': 144.5,\n",
-       "  '2666': 153.4,\n",
-       "  '2667': 158.0,\n",
-       "  '2668': 157.0,\n",
-       "  '2669': 157.6,\n",
-       "  '2670': 140.5,\n",
-       "  '2671': 147.3,\n",
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-       "  '2310': 100.0,\n",
-       "  '2311': 90.6,\n",
-       "  '2312': 87.7,\n",
-       "  '2313': 84.3,\n",
-       "  '2314': 80.0,\n",
-       "  '2315': 79.5,\n",
-       "  '2316': 81.0,\n",
-       "  '2317': 78.8,\n",
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-       "  '2319': 70.7,\n",
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-       "  '2321': 75.7,\n",
-       "  '2322': 73.8,\n",
-       "  '2323': 75.3,\n",
-       "  '2324': 75.9,\n",
-       "  '2325': 75.0,\n",
-       "  '2326': 75.7,\n",
-       "  '2327': 76.5,\n",
-       "  '2328': 74.0,\n",
-       "  '2329': 69.1,\n",
-       "  '2330': 70.5,\n",
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-       "  '2332': 67.8,\n",
-       "  '2333': 65.1,\n",
-       "  '2334': 64.0,\n",
-       "  '2335': 64.8,\n",
-       "  '2336': 65.6,\n",
-       "  '2337': 66.1,\n",
-       "  '2338': 65.2,\n",
-       "  '2339': 62.3,\n",
-       "  '2340': 56.6,\n",
-       "  '2341': 59.4,\n",
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-       "  '2376': 100.0,\n",
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-       "  '2380': 90.5,\n",
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-       "  '2383': 88.6,\n",
-       "  '2384': 86.7,\n",
-       "  '2385': 84.1,\n",
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-       "  '2388': 83.3,\n",
-       "  '2389': 82.7,\n",
-       "  '2390': 81.0,\n",
-       "  '2391': 80.0,\n",
-       "  '2392': 81.2,\n",
-       "  '2393': 78.1,\n",
-       "  '2394': 78.7,\n",
-       "  '2395': 73.2,\n",
-       "  '2396': 75.4,\n",
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-       "  '3109': 101.5,\n",
-       "  '3110': 102.2,\n",
-       "  '3111': 98.2,\n",
-       "  '3112': 96.5,\n",
-       "  '3113': 97.5,\n",
-       "  '3114': 98.0,\n",
-       "  '3115': 98.4,\n",
-       "  '3116': 97.7,\n",
-       "  '3117': 94.0,\n",
-       "  '3118': 93.6,\n",
-       "  '3119': 92.3,\n",
-       "  '3120': 89.4,\n",
-       "  '3121': 83.0,\n",
-       "  '3122': 91.3,\n",
-       "  '3123': 85.5,\n",
-       "  '3124': 81.4,\n",
-       "  '3125': 79.2,\n",
-       "  '3126': 76.9,\n",
-       "  '3127': 76.5,\n",
-       "  '3128': 76.9,\n",
-       "  '3129': 75.9,\n",
-       "  '3130': 74.9,\n",
-       "  '3131': 72.8,\n",
-       "  '3132': 64.6,\n",
-       "  '3133': 67.1,\n",
-       "  '3134': 64.8,\n",
-       "  '3003': 100.0,\n",
-       "  '3004': 92.7,\n",
-       "  '3005': 92.7,\n",
-       "  '3006': 94.0,\n",
-       "  '3007': 96.2,\n",
-       "  '3008': 100.3,\n",
-       "  '3009': 103.4,\n",
-       "  '3010': 106.3,\n",
-       "  '3011': 104.1,\n",
-       "  '3012': 100.9,\n",
-       "  '3013': 99.8,\n",
-       "  '3014': 106.5,\n",
-       "  '3015': 108.0,\n",
-       "  '3016': 106.2,\n",
-       "  '3017': 108.2,\n",
-       "  '3018': 109.6,\n",
-       "  '3019': 110.6,\n",
-       "  '3020': 111.7,\n",
-       "  '3021': 115.6,\n",
-       "  '3022': 103.9,\n",
-       "  '3023': 105.1,\n",
-       "  '3024': 104.7,\n",
-       "  '3025': 101.3,\n",
-       "  '3026': 97.7,\n",
-       "  '3027': 88.9,\n",
-       "  '3028': 89.9,\n",
-       "  '3029': 94.5,\n",
-       "  '3030': 95.0,\n",
-       "  '3031': 94.2,\n",
-       "  '3032': 91.4,\n",
-       "  '3033': 84.6,\n",
-       "  '3034': 85.4,\n",
-       "  '3035': 83.2,\n",
-       "  '3036': 100.0,\n",
-       "  '3037': 87.4,\n",
-       "  '3038': 80.0,\n",
-       "  '3039': 75.1,\n",
-       "  '3040': 71.7,\n",
-       "  '3041': 72.4,\n",
-       "  '3042': 72.2,\n",
-       "  '3043': 71.9,\n",
-       "  '3044': 70.9,\n",
-       "  '3045': 69.2,\n",
-       "  ...},\n",
-       " 'status': {'3354': 'b',\n",
-       "  '3355': 'b',\n",
-       "  '4311': 'b',\n",
-       "  '3430': 'b',\n",
-       "  '3431': 'b',\n",
-       "  '3486': 'b',\n",
-       "  '3523': 'b',\n",
-       "  '3268': 'b',\n",
-       "  '3275': 'b',\n",
-       "  '3277': 'b',\n",
-       "  '3278': 'b',\n",
-       "  '3285': 'b',\n",
-       "  '3287': 'b',\n",
-       "  '3288': 'b',\n",
-       "  '3289': 'b',\n",
-       "  '3291': 'b',\n",
-       "  '3292': 'b',\n",
-       "  '3294': 'b',\n",
-       "  '3295': 'e',\n",
-       "  '3296': 'bep',\n",
-       "  '3297': 'ep',\n",
-       "  '3298': 'bep',\n",
-       "  '3299': 'bep',\n",
-       "  '3654': 'b',\n",
-       "  '3659': 'p',\n",
-       "  '3660': 'p',\n",
-       "  '3661': 'p',\n",
-       "  '3662': 'p',\n",
-       "  '3663': 'e',\n",
-       "  '3664': 'e',\n",
-       "  '3665': 'e',\n",
-       "  '3666': 'e',\n",
-       "  '3667': 'e',\n",
-       "  '3668': 'e',\n",
-       "  '3669': 'e',\n",
-       "  '3670': 'e',\n",
-       "  '3671': 'e',\n",
-       "  '3672': 'e',\n",
-       "  '3673': 'e',\n",
-       "  '3674': 'b',\n",
-       "  '3883': 'b',\n",
-       "  '3559': 'e',\n",
-       "  '3560': 'e',\n",
-       "  '3725': 'b',\n",
-       "  '3850': 'b',\n",
-       "  '3855': 'b',\n",
-       "  '3895': 'b',\n",
-       "  '4003': 'b',\n",
-       "  '4013': 'b',\n",
-       "  '4021': 'e',\n",
-       "  '4022': 'e',\n",
-       "  '4023': 'ep',\n",
-       "  '4024': 'b',\n",
-       "  '4057': 'bp',\n",
-       "  '4058': 'bp',\n",
-       "  '4088': 'e',\n",
-       "  '4089': 'e',\n",
-       "  '4090': 'e',\n",
-       "  '4091': 'e',\n",
-       "  '4110': 'b',\n",
-       "  '1176': 'b',\n",
-       "  '1177': 'b',\n",
-       "  '2133': 'b',\n",
-       "  '1252': 'b',\n",
-       "  '1253': 'b',\n",
-       "  '1308': 'b',\n",
-       "  '1345': 'b',\n",
-       "  '1090': 'b',\n",
-       "  '1097': 'b',\n",
-       "  '1099': 'b',\n",
-       "  '1100': 'b',\n",
-       "  '1107': 'b',\n",
-       "  '1109': 'b',\n",
-       "  '1110': 'b',\n",
-       "  '1111': 'b',\n",
-       "  '1113': 'b',\n",
-       "  '1114': 'b',\n",
-       "  '1116': 'b',\n",
-       "  '1117': 'e',\n",
-       "  '1118': 'bep',\n",
-       "  '1119': 'ep',\n",
-       "  '1120': 'bep',\n",
-       "  '1121': 'bep',\n",
-       "  '1476': 'b',\n",
-       "  '1481': 'p',\n",
-       "  '1482': 'p',\n",
-       "  '1483': 'p',\n",
-       "  '1484': 'p',\n",
-       "  '1485': 'e',\n",
-       "  '1486': 'e',\n",
-       "  '1487': 'e',\n",
-       "  '1488': 'e',\n",
-       "  '1489': 'e',\n",
-       "  '1490': 'e',\n",
-       "  '1491': 'e',\n",
-       "  '1492': 'e',\n",
-       "  '1493': 'e',\n",
-       "  '1494': 'e',\n",
-       "  '1495': 'e',\n",
-       "  '1496': 'b',\n",
-       "  '1705': 'b',\n",
-       "  '1381': 'e',\n",
-       "  '1382': 'e',\n",
-       "  '1547': 'b',\n",
-       "  '1672': 'b',\n",
-       "  '1677': 'b',\n",
-       "  '1717': 'b',\n",
-       "  '1825': 'b',\n",
-       "  '1835': 'b',\n",
-       "  '1843': 'e',\n",
-       "  '1844': 'e',\n",
-       "  '1845': 'ep',\n",
-       "  '1846': 'b',\n",
-       "  '1879': 'bp',\n",
-       "  '1880': 'bp',\n",
-       "  '1910': 'e',\n",
-       "  '1911': 'e',\n",
-       "  '1912': 'e',\n",
-       "  '1913': 'e',\n",
-       "  '1932': 'b'},\n",
-       " 'id': ['freq', 'airpol', 'src_crf', 'unit', 'geo', 'time'],\n",
-       " 'size': [1, 1, 2, 2, 33, 33],\n",
-       " 'dimension': {'freq': {'label': 'Time frequency',\n",
-       "   'category': {'index': {'A': 0}, 'label': {'A': 'Annual'}}},\n",
-       "  'airpol': {'label': 'Air pollutants and greenhouse gases',\n",
-       "   'category': {'index': {'GHG': 0},\n",
-       "    'label': {'GHG': 'Greenhouse gases (CO2, N2O in CO2 equivalent, CH4 in CO2 equivalent, HFC in CO2 equivalent, PFC in CO2 equivalent, SF6 in CO2 equivalent, NF3 in CO2 equivalent)'}}},\n",
-       "  'src_crf': {'label': 'Source sectors for greenhouse gas emissions (Common reporting format, UNFCCC)',\n",
-       "   'category': {'index': {'TOTXMEMONIA': 0, 'TOTX4_MEMONIA': 1},\n",
-       "    'label': {'TOTXMEMONIA': 'Total (excluding memo items, including international aviation)',\n",
-       "     'TOTX4_MEMONIA': 'Total (excluding LULUCF and memo items, including international aviation)'}}},\n",
-       "  'unit': {'label': 'Unit of measure',\n",
-       "   'category': {'index': {'I90': 0, 'T_HAB': 1},\n",
-       "    'label': {'I90': 'Index, 1990=100', 'T_HAB': 'Tonnes per capita'}}},\n",
-       "  'geo': {'label': 'Geopolitical entity (reporting)',\n",
-       "   'category': {'index': {'EU27_2020': 0,\n",
-       "     'EU28': 1,\n",
-       "     'BE': 2,\n",
-       "     'BG': 3,\n",
-       "     'CZ': 4,\n",
-       "     'DK': 5,\n",
-       "     'DE': 6,\n",
-       "     'EE': 7,\n",
-       "     'IE': 8,\n",
-       "     'EL': 9,\n",
-       "     'ES': 10,\n",
-       "     'FR': 11,\n",
-       "     'HR': 12,\n",
-       "     'IT': 13,\n",
-       "     'CY': 14,\n",
-       "     'LV': 15,\n",
-       "     'LT': 16,\n",
-       "     'LU': 17,\n",
-       "     'HU': 18,\n",
-       "     'MT': 19,\n",
-       "     'NL': 20,\n",
-       "     'AT': 21,\n",
-       "     'PL': 22,\n",
-       "     'PT': 23,\n",
-       "     'RO': 24,\n",
-       "     'SI': 25,\n",
-       "     'SK': 26,\n",
-       "     'FI': 27,\n",
-       "     'SE': 28,\n",
-       "     'IS': 29,\n",
-       "     'NO': 30,\n",
-       "     'CH': 31,\n",
-       "     'UK': 32},\n",
-       "    'label': {'EU27_2020': 'European Union - 27 countries (from 2020)',\n",
-       "     'EU28': 'European Union - 28 countries (2013-2020)',\n",
-       "     'BE': 'Belgium',\n",
-       "     'BG': 'Bulgaria',\n",
-       "     'CZ': 'Czechia',\n",
-       "     'DK': 'Denmark',\n",
-       "     'DE': 'Germany',\n",
-       "     'EE': 'Estonia',\n",
-       "     'IE': 'Ireland',\n",
-       "     'EL': 'Greece',\n",
-       "     'ES': 'Spain',\n",
-       "     'FR': 'France',\n",
-       "     'HR': 'Croatia',\n",
-       "     'IT': 'Italy',\n",
-       "     'CY': 'Cyprus',\n",
-       "     'LV': 'Latvia',\n",
-       "     'LT': 'Lithuania',\n",
-       "     'LU': 'Luxembourg',\n",
-       "     'HU': 'Hungary',\n",
-       "     'MT': 'Malta',\n",
-       "     'NL': 'Netherlands',\n",
-       "     'AT': 'Austria',\n",
-       "     'PL': 'Poland',\n",
-       "     'PT': 'Portugal',\n",
-       "     'RO': 'Romania',\n",
-       "     'SI': 'Slovenia',\n",
-       "     'SK': 'Slovakia',\n",
-       "     'FI': 'Finland',\n",
-       "     'SE': 'Sweden',\n",
-       "     'IS': 'Iceland',\n",
-       "     'NO': 'Norway',\n",
-       "     'CH': 'Switzerland',\n",
-       "     'UK': 'United Kingdom'}}},\n",
-       "  'time': {'label': 'Time',\n",
-       "   'category': {'index': {'1990': 0,\n",
-       "     '1991': 1,\n",
-       "     '1992': 2,\n",
-       "     '1993': 3,\n",
-       "     '1994': 4,\n",
-       "     '1995': 5,\n",
-       "     '1996': 6,\n",
-       "     '1997': 7,\n",
-       "     '1998': 8,\n",
-       "     '1999': 9,\n",
-       "     '2000': 10,\n",
-       "     '2001': 11,\n",
-       "     '2002': 12,\n",
-       "     '2003': 13,\n",
-       "     '2004': 14,\n",
-       "     '2005': 15,\n",
-       "     '2006': 16,\n",
-       "     '2007': 17,\n",
-       "     '2008': 18,\n",
-       "     '2009': 19,\n",
-       "     '2010': 20,\n",
-       "     '2011': 21,\n",
-       "     '2012': 22,\n",
-       "     '2013': 23,\n",
-       "     '2014': 24,\n",
-       "     '2015': 25,\n",
-       "     '2016': 26,\n",
-       "     '2017': 27,\n",
-       "     '2018': 28,\n",
-       "     '2019': 29,\n",
-       "     '2020': 30,\n",
-       "     '2021': 31,\n",
-       "     '2022': 32},\n",
-       "    'label': {'1990': '1990',\n",
-       "     '1991': '1991',\n",
-       "     '1992': '1992',\n",
-       "     '1993': '1993',\n",
-       "     '1994': '1994',\n",
-       "     '1995': '1995',\n",
-       "     '1996': '1996',\n",
-       "     '1997': '1997',\n",
-       "     '1998': '1998',\n",
-       "     '1999': '1999',\n",
-       "     '2000': '2000',\n",
-       "     '2001': '2001',\n",
-       "     '2002': '2002',\n",
-       "     '2003': '2003',\n",
-       "     '2004': '2004',\n",
-       "     '2005': '2005',\n",
-       "     '2006': '2006',\n",
-       "     '2007': '2007',\n",
-       "     '2008': '2008',\n",
-       "     '2009': '2009',\n",
-       "     '2010': '2010',\n",
-       "     '2011': '2011',\n",
-       "     '2012': '2012',\n",
-       "     '2013': '2013',\n",
-       "     '2014': '2014',\n",
-       "     '2015': '2015',\n",
-       "     '2016': '2016',\n",
-       "     '2017': '2017',\n",
-       "     '2018': '2018',\n",
-       "     '2019': '2019',\n",
-       "     '2020': '2020',\n",
-       "     '2021': '2021',\n",
-       "     '2022': '2022'}}}},\n",
-       " 'extension': {'lang': 'EN',\n",
-       "  'description': 'The indicator measures total national emissions (from both ESD and ETS sectors) including international aviation of the so called ‘Kyoto basket’ of greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and the so-called F-gases (hydrofluorocarbons, perfluorocarbons, nitrogen triflouride (NF3) and sulphur hexafluoride (SF6)) from all sectors of the GHG emission inventories (including international aviation and indirect CO2). The indicator is presented in two forms: as net emissions including land use, land use change and forestry (LULUCF) as well as excluding LULUCF. Using each gas’ individual global warming potential (GWP), they are being integrated into a single indicator expressed in units of CO2 equivalents. The GHG emission inventories are submitted annually by the EU Member States to the United Nations Framework Convention on Climate Change (UNFCCC).',\n",
-       "  'id': 'SDG_13_10',\n",
-       "  'agencyId': 'ESTAT',\n",
-       "  'version': '1.0',\n",
-       "  'datastructure': {'id': 'SDG_13_10', 'agencyId': 'ESTAT', 'version': '37.0'},\n",
-       "  'annotation': [{'type': 'CREATED', 'date': '2017-11-06T15:11:59+0100'},\n",
-       "   {'type': 'DISSEMINATION_DOI_XML',\n",
-       "    'title': '<adms:identifier xmlns:adms=\"http://www.w3.org/ns/adms#\" xmlns:skos=\"http://www.w3.org/2004/02/skos/core.html\" xmlns:dct=\"http://purl.org/dc/terms/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\"><adms:Identifier rdf:about=\"https://doi.org/10.2908/SDG_13_10\"><skos:notation rdf:datatype=\"http://purl.org/spar/datacite/doi\">10.2908/SDG_13_10</skos:notation><dct:creator rdf:resource=\"http://publications.europa.eu/resource/authority/corporate-body/ESTAT\"/><dct:issued rdf:datatype=\"http://www.w3.org/2001/XMLSchema#date\">2023-01-23</dct:issued></adms:Identifier></adms:identifier>'},\n",
-       "   {'type': 'DISSEMINATION_OBJECT_TYPE', 'title': 'DATASET'},\n",
-       "   {'type': 'DISSEMINATION_PRESENTATIONS',\n",
-       "    'title': '[{\"names\":{\"de\":{\"locale\":\"de\",\"value\":\"Standardpräsentation\",\"html\":false},\"en\":{\"locale\":\"en\",\"value\":\"Default presentation\",\"html\":false},\"fr\":{\"locale\":\"fr\",\"value\":\"Présentation par défaut\",\"html\":false}},\"descriptions\":{},\"code\":\"default\",\"useAsDefault\":true,\"order\":0,\"defaultView\":\"view.table\",\"jsonFormat\":null,\"preselection\":[{\"code\":\"freq\",\"multipleSelection\":null,\"singleSelection\":\"A\"},{\"code\":\"airpol\",\"multipleSelection\":null,\"singleSelection\":\"GHG\"},{\"code\":\"src_crf\",\"multipleSelection\":null,\"singleSelection\":null},{\"code\":\"unit\",\"multipleSelection\":null,\"singleSelection\":null},{\"code\":\"geo\",\"multipleSelection\":null,\"singleSelection\":null}],\"preselectionTimeFilter\":{\"code\":\"time\",\"multipleSelection\":null,\"singleSelection\":null,\"mode\":\"ALL\",\"from\":null,\"to\":null,\"fixed\":null},\"layoutFormat\":{\"layoutAxes\":{\"y1\":\"time\",\"x1\":\"geo\"},\"fixedMinMax\":false,\"mapProjection\":null,\"mapShowAllGeo\":true,\"mapNumberOfColors\":0,\"rankingMode\":false,\"highlightMode\":false,\"labelOption\":null,\"businessRulesStrategy\":null,\"mapClassificartionMethode\":null}}]'},\n",
-       "   {'type': 'DISSEMINATION_PRESENTATIONS_VERSION', 'title': 'v0_4_X'},\n",
-       "   {'type': 'DISSEMINATION_SOURCE_DATASET', 'title': 'DEMO_GIND,ENV_AIR_GGE'},\n",
-       "   {'type': 'ESMS_HTML',\n",
-       "    'title': 'Explanatory texts (metadata)',\n",
-       "    'href': 'https://ec.europa.eu/eurostat/cache/metadata/en/sdg_13_10_esmsip2.htm'},\n",
-       "   {'type': 'ESMS_SDMX',\n",
-       "    'title': 'Explanatory texts (metadata)',\n",
-       "    'href': 'https://ec.europa.eu/eurostat/api/dissemination/files?file=metadata/sdg_13_10_esmsip2.sdmx.zip'},\n",
-       "   {'type': 'OBS_COUNT', 'title': '4092'},\n",
-       "   {'type': 'OBS_PERIOD_OVERALL_LATEST', 'title': '2022'},\n",
-       "   {'type': 'OBS_PERIOD_OVERALL_OLDEST', 'title': '1990'},\n",
-       "   {'type': 'SOURCE_INSTITUTIONS',\n",
-       "    'text': 'European Environment Agency (EEA)'},\n",
-       "   {'type': 'UPDATE_DATA', 'date': '2024-05-21T11:00:00+0200'},\n",
-       "   {'type': 'UPDATE_STRUCTURE', 'date': '2024-05-21T11:00:00+0200'}],\n",
-       "  'status': {'label': {'b': 'break in time series',\n",
-       "    'e': 'estimated',\n",
-       "    'bep': 'break in time series, estimated, provisional',\n",
-       "    'ep': 'estimated, provisional',\n",
-       "    'p': 'provisional',\n",
-       "    'bp': 'break in time series, provisional'}},\n",
-       "  'positions-with-no-data': {'freq': [],\n",
-       "   'airpol': [],\n",
-       "   'src_crf': [],\n",
-       "   'unit': [],\n",
-       "   'geo': [1, 32],\n",
-       "   'time': []}}}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "raw"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('TOTXMEMONIA', 'Total (excluding memo items, including international aviation)')\n",
-      "('I90', 'Index, 1990=100')\n",
-      "('T_HAB', 'Tonnes per capita')\n",
-      "('TOTX4_MEMONIA', 'Total (excluding LULUCF and memo items, including international aviation)')\n",
-      "('I90', 'Index, 1990=100')\n",
-      "('T_HAB', 'Tonnes per capita')\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['TOTXMEMONIA_I90',\n",
-       " 'TOTXMEMONIA_T_HAB',\n",
-       " 'TOTX4_MEMONIA_I90',\n",
-       " 'TOTX4_MEMONIA_T_HAB']"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "t = []\n",
-    "for j in raw[\"dimension\"][\"src_crf\"][\"category\"][\"label\"].items():\n",
-    "\tfor k in raw[\"dimension\"][\"unit\"][\"category\"][\"label\"].items():\n",
-    "\t\tt.append(j[0] + \"_\" + k[0])\n",
-    "t"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "wafflers",
-   "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": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/eda/scraper.py b/eda/scraper.py
deleted file mode 100644
index c3e8b93..0000000
--- a/eda/scraper.py
+++ /dev/null
@@ -1,129 +0,0 @@
-"""
-Gets and parses data from Eurostat through their `statistics` API
-"""
-
-import requests
-import pandas as pd
-import numpy as np
-
-def parse_df_from_eurostat(values: dict[str: str], tables: list[str], rows: list[str], cols: list[str], label:str="", col_label:str="") -> list[pd.DataFrame]:
-	"""
-	Given a dict of indexed scaler values and the label lists, parse into a list of dataframes
-	Note: Across all tables, the size (row x col) should be the same!
-
-	:param values: A dict with indexed scaler values, ie {'572': 145.5} where the index is the cell that value occupies
-	:param tables: A list of the table labels
-	:param rows: A list of row labels
-	:param cols: A list of column labels 
-	:param label: The optional column label for the row keys
-	:param col_label: The optional label for the column header group
-	:returns: A dataframe with multiindexes for each table
-	"""
-	calc_index = lambda i, j, k: k + (j * len(cols)) + (i * (len(cols) * len(rows)))
-	data_3d = []
-	for i in range(len(tables)):
-		data_2d = []
-		for j in range(len(rows)):
-			builder_row = []
-			for k in range(len(cols)):
-				if str(calc_index(i, j, k)) in values:
-					builder_row.append(values[str(calc_index(i, j, k))])
-				else:
-					builder_row.append(np.NaN)
-			data_2d.append(builder_row)
-		data_3d.append(data_2d)
-	
-
-	dataframes = []
-	for table in data_3d:
-		df = pd.DataFrame(table)
-		dataframes.append(df)
-	
-	df = pd.concat(dataframes, axis=1)
-	cols = [l[0] for l in cols]
-	df.columns = pd.MultiIndex.from_product([tables, cols], names=["table", col_label])
-	if label:
-		df[label] = [i[0] for i in rows]
-
-	return df
-
-
-def get_eurostat_dataframe(dataset:str, tables:any) -> tuple[pd.DataFrame, list[str]]:
-	"""
-	Gets the eurostat dataframe from a given dataset id and tables function.
-
-	:param dataset: The dataset id to scrape and parse
-	:param tables: A function that takes in the raw response and returns
-		a list of tables that the dataset contains
-	:returns: A tuple with the dataframe and an array of the tables
-	"""
-	url = "https://ec.europa.eu/eurostat/api/dissemination/statistics/1.0/data" 
-	params = "format=JSON&lang=EN"
-	res = requests.get(f"{url}/{dataset}?{params}")
-	raw = res.json()
-
-	values = raw["value"]
-	rows = list(raw["dimension"]["geo"]["category"]["label"].items())
-	cols = list(raw["dimension"]["time"]["category"]["label"].items())
-	tables = tables(raw)
-
-	return parse_df_from_eurostat(values, tables, rows, cols, label="country", col_label="time"), tables
-
-
-def scrape_sdg_07_40() -> tuple[pd.DataFrame, list[str]]:
-	"""
-	Scrapes the `sdg_07_40` dataset from eurostat.
-
-	Title: Share of renewable energy in gross final energy consumption
-	  by sector
-	Description: The indicator measures the share of renewable energy
-	  consumption in gross final energy consumption according to the Renewable
-	  Energy Directive. The gross final energy consumption is the energy used
-	  by end-consumers (final energy consumption) plus grid losses and 
-	  self-consumption of power plants.
-	
-	:returns: A tuple with the dataframe and a list of tables
-	"""
-	dataset = "sdg_07_40"
-	tables = lambda raw: [i[0] for i in raw["dimension"]["nrg_bal"]["category"]["label"].items()]
-
-	return get_eurostat_dataframe(dataset, tables)
-
-
-def scrape_sdg_13_10() -> tuple[pd.DataFrame, list[str]]:
-	"""
-	Scrapes the `sdg_13_10` dataset from eurostat.
-
-	Title: Net greenhouse gas emissions
-	Description: The indicator measures total national emissions (from both ESD
-	  and ETS sectors) including international aviation of the so called
-	    ‘Kyoto basket’ of greenhouse gases, including carbon dioxide (CO2), 
-		methane (CH4), nitrous oxide (N2O), and the so-called F-gases 
-		(hydrofluorocarbons, perfluorocarbons, nitrogen triflouride (NF3) and
-		sulphur hexafluoride (SF6)) from all sectors of the GHG emission
-		inventories (including international aviation and indirect CO2).
-		The indicator is presented in two forms: as net emissions including
-		land use, land use change and forestry (LULUCF) as well as excluding
-		LULUCF. Using each gas’ individual global warming potential (GWP),
-		they are being integrated into a single indicator expressed in units
-		of CO2 equivalents. The GHG emission inventories are submitted annually
-		by the EU Member States to the United Nations Framework Convention on
-		Climate Change (UNFCCC).
-	
-	:returns: A tuple with the dataframe and a list of tables
-	"""
-	dataset = "sdg_13_10"
-	def tables(raw:str) -> list[str]:
-		"""
-		Generates tables list from raw response
-	
-		:param raw: The raw response from eurostat
-		:returns: A list of the available tables
-		"""
-		t = []
-		for j in raw["dimension"]["src_crf"]["category"]["label"].items():
-			for k in raw["dimension"]["unit"]["category"]["label"].items():
-				t.append(j[0] + "_" + k[0])
-		return t
-
-	return get_eurostat_dataframe(dataset, tables)
\ No newline at end of file

From 4ff94ae04725756de67f1540af12dbcf36627d2d Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Thu, 6 Jun 2024 02:03:07 +0200
Subject: [PATCH 26/34] Adding mockaroo data for the used website tables

will need to add mockaroo data for the rest of the tables.
---
 api/backend/ngo/ngo_routes.py |   8 +-
 app/src/pages/21_NGOInfo.py   |   2 +-
 database/00_carbon.sql        |   4 +-
 database/01_carbon-data.sql   | 413 ++++++++++++++++++++++++++++++----
 4 files changed, 378 insertions(+), 49 deletions(-)

diff --git a/api/backend/ngo/ngo_routes.py b/api/backend/ngo/ngo_routes.py
index 88cc23a..c9dbcb9 100644
--- a/api/backend/ngo/ngo_routes.py
+++ b/api/backend/ngo/ngo_routes.py
@@ -236,11 +236,9 @@ def get_usermatches():
     cursor = db.get_db().cursor()
 
     cursor.execute('''
-    SELECT DISTINCT User.id
-    FROM User
-    JOIN UserTags ON User.id = UserTags.tag_id
-    JOIN EmissionTags ON UserTags.tag_id = EmissionTags.id
-    WHERE EmissionTags.id IN (
+    SELECT DISTINCT UserTags.user_id
+    FROM UserTags
+    WHERE UserTags.tag_id IN (
         SELECT tag_id
         FROM NGOTags
         WHERE NGOTags.ngo_id = 1
diff --git a/app/src/pages/21_NGOInfo.py b/app/src/pages/21_NGOInfo.py
index 8fa6605..fc0f007 100644
--- a/app/src/pages/21_NGOInfo.py
+++ b/app/src/pages/21_NGOInfo.py
@@ -13,7 +13,7 @@
     st.write("NGO")
     st.image('https://cdn-icons-png.freepik.com/256/3101/3101045.png?ga=GA1.1.1507691374.1717099387', width = 50)
 
-st.write("# To get started fill in the following information")
+st.write("# Edit your info if so desired")
 
 st.write('')
 
diff --git a/database/00_carbon.sql b/database/00_carbon.sql
index 91966c5..bce0479 100644
--- a/database/00_carbon.sql
+++ b/database/00_carbon.sql
@@ -14,7 +14,7 @@ CREATE TABLE IF NOT EXISTS Country (
 DROP TABLE IF EXISTS NGO;
 CREATE TABLE IF NOT EXISTS NGO (
    id INT PRIMARY KEY AUTO_INCREMENT,
-   website VARCHAR(255) UNIQUE NOT NULL,
+   website VARCHAR(255) NOT NULL,
    name VARCHAR(50),
    contact VARCHAR(50)
 );
@@ -37,7 +37,7 @@ CREATE TABLE IF NOT EXISTS Enterprises (
    name VARCHAR(50),
    type VARCHAR(255),
    emission_result INT,
-   misc_emissions VARCHAR(255),
+   misc_emissions INT,
    country_id INT,
    FOREIGN KEY (country_id) REFERENCES Country(id)
        ON UPDATE CASCADE
diff --git a/database/01_carbon-data.sql b/database/01_carbon-data.sql
index e6521e5..c2790e5 100644
--- a/database/01_carbon-data.sql
+++ b/database/01_carbon-data.sql
@@ -24,67 +24,398 @@ INSERT INTO Country (id, emissions, name) VALUES (21, 383434.04, 'Poland');
 INSERT INTO Country (id, emissions, name) VALUES (22, 60581.36, 'Portugal');
 INSERT INTO Country (id, emissions, name) VALUES (23, 109992.76, 'Romania');
 INSERT INTO Country (id, emissions, name) VALUES (24, 15676.64, 'Slovenia');
-INSERT INTO Country (id, emissions, name) VALUES (25, 37183,78, 'Slovakia');
+INSERT INTO Country (id, emissions, name) VALUES (25, 37183.78, 'Slovakia');
 INSERT INTO Country (id, emissions, name) VALUES (26, 47341.71, 'Finland');
 INSERT INTO Country (id, emissions, name) VALUES (27, 47074.61, 'Sweden');
 INSERT INTO Country (id, emissions, name) VALUES (28, 5402.55, 'Iceland');
 INSERT INTO Country (id, emissions, name) VALUES (29, 50255.43, 'Norway');
 INSERT INTO Country (id, emissions, name) VALUES (30, 45847.17, 'Switzerland');
 
-
-
-
 INSERT INTO EmissionTags (id, description) VALUES
 (1, 'Transport'),
 (2, 'Flights'),
 (3, 'Energy'),
 (4, 'Heat');
 
-INSERT INTO Enterprises (id, name, type, emission_result, misc_emissions, country_id) VALUES
-(1, 'Enterprise One', 'Type A', 10, 'Misc A', 1),
-(2, 'Enterprise Two', 'Type B', 20, 'Misc B', 2),
-(3, 'Enterprise Three', 'Type C', 30, 'Misc C', 3),
-(4, 'Enterprise Four', 'Type D', 40, 'Misc D', 3);
+insert into NGO (id, website, name, contact) values (1, 'EcoUnity.com', 'EcoUnity Europe', 'Joe@EcoUnity.org');
+insert into NGO (id, website, name, contact) values (2, 'seesaa.net', 'Grimes, Fahey and Mertz', 'rattlee1@accuweather.com');
+insert into NGO (id, website, name, contact) values (3, 'fema.gov', 'Terry-Witting', 'sdand2@angelfire.com');
+insert into NGO (id, website, name, contact) values (4, 'twitpic.com', 'Kub-Anderson', 'swilson3@pinterest.com');
+insert into NGO (id, website, name, contact) values (5, 'netvibes.com', 'Bosco, Davis and Feil', 'baspinal4@example.com');
+insert into NGO (id, website, name, contact) values (6, 'pbs.org', 'Morar LLC', 'lwagg5@answers.com');
+insert into NGO (id, website, name, contact) values (7, 'bloglovin.com', 'Macejkovic-Ebert', 'rmushett6@pbs.org');
+insert into NGO (id, website, name, contact) values (8, 'fda.gov', 'Cormier, Ratke and Koelpin', 'weakins7@wordpress.com');
+insert into NGO (id, website, name, contact) values (9, 'ustream.tv', 'Becker-Brakus', 'mchislett8@imdb.com');
+insert into NGO (id, website, name, contact) values (10, 'elegantthemes.com', 'Miller-O''Conner', 'pchrippes9@auda.org.au');
+insert into NGO (id, website, name, contact) values (11, 'ezinearticles.com', 'Hirthe-Emmerich', 'kmillionsa@tamu.edu');
+insert into NGO (id, website, name, contact) values (12, 'simplemachines.org', 'Jacobson, Torphy and Strosin', 'sgowanlockb@nature.com');
+insert into NGO (id, website, name, contact) values (13, 'bigcartel.com', 'Waters, Legros and Toy', 'npirdyc@sphinn.com');
+insert into NGO (id, website, name, contact) values (14, 'apple.com', 'Ortiz, Pollich and Jerde', 'tiversd@wp.com');
+insert into NGO (id, website, name, contact) values (15, 'slashdot.org', 'Corwin-Shields', 'bbeechcrafte@google.com.br');
+insert into NGO (id, website, name, contact) values (16, 'networksolutions.com', 'Zieme, Bergstrom and Schultz', 'srowswellf@moonfruit.com');
+insert into NGO (id, website, name, contact) values (17, 'un.org', 'Hand-Schroeder', 'rtimbridgeg@mayoclinic.com');
+insert into NGO (id, website, name, contact) values (18, 'phoca.cz', 'Feest-Armstrong', 'tionnh@slashdot.org');
+insert into NGO (id, website, name, contact) values (19, 'blogspot.com', 'Rice Inc', 'struelocki@mtv.com');
+insert into NGO (id, website, name, contact) values (20, 'businessinsider.com', 'Marks Group', 'brothamj@addtoany.com');
+insert into NGO (id, website, name, contact) values (21, 'hud.gov', 'Barrows Inc', 'elownesk@gizmodo.com');
+insert into NGO (id, website, name, contact) values (22, 'goodreads.com', 'Deckow-Adams', 'sbrowettl@blogspot.com');
+insert into NGO (id, website, name, contact) values (23, 'homestead.com', 'Brekke, Hauck and Buckridge', 'cjeevesm@japanpost.jp');
+insert into NGO (id, website, name, contact) values (24, 'blogtalkradio.com', 'Hackett Inc', 'skerrichn@nih.gov');
+insert into NGO (id, website, name, contact) values (25, 'economist.com', 'Veum and Sons', 'jphilbrooko@blogspot.com');
+insert into NGO (id, website, name, contact) values (26, 'ameblo.jp', 'Gutmann-Lockman', 'gharkessp@bandcamp.com');
+insert into NGO (id, website, name, contact) values (27, 'dropbox.com', 'Kutch-Hahn', 'eaveyardq@umn.edu');
+insert into NGO (id, website, name, contact) values (28, 'dedecms.com', 'Fay-Murphy', 'kyellowleesr@omniture.com');
+insert into NGO (id, website, name, contact) values (29, '1und1.de', 'Kohler, Ankunding and Doyle', 'darmells@drupal.org');
+insert into NGO (id, website, name, contact) values (30, 'webnode.com', 'Lueilwitz-McGlynn', 'akeeleyt@cyberchimps.com');
+insert into NGO (id, website, name, contact) values (31, 'prnewswire.com', 'Baumbach-Kemmer', 'ksucreu@imageshack.us');
+insert into NGO (id, website, name, contact) values (32, 'lycos.com', 'Rodriguez, Kassulke and Moore', 'ldallingv@phpbb.com');
+insert into NGO (id, website, name, contact) values (33, 't-online.de', 'Grimes Group', 'fjanotaw@odnoklassniki.ru');
+insert into NGO (id, website, name, contact) values (34, 'drupal.org', 'Schmitt Group', 'jhimsworthx@icio.us');
+insert into NGO (id, website, name, contact) values (35, 'mlb.com', 'Mohr-Huel', 'tcunliffey@geocities.com');
+insert into NGO (id, website, name, contact) values (36, 'uol.com.br', 'Mante, Simonis and Gusikowski', 'mgoodwinz@pcworld.com');
+insert into NGO (id, website, name, contact) values (37, 'utexas.edu', 'Upton LLC', 'hjoska10@theglobeandmail.com');
+insert into NGO (id, website, name, contact) values (38, 'infoseek.co.jp', 'Steuber-Pfannerstill', 'aalsina11@cpanel.net');
+insert into NGO (id, website, name, contact) values (39, 'imgur.com', 'Lockman, Baumbach and Cassin', 'cnassie12@diigo.com');
+insert into NGO (id, website, name, contact) values (40, 'list-manage.com', 'Kreiger-Bednar', 'rfretwell13@cnet.com');
+insert into NGO (id, website, name, contact) values (41, 'netscape.com', 'Lindgren, McLaughlin and Wisoky', 'xhealings14@pinterest.com');
+insert into NGO (id, website, name, contact) values (42, 'wikia.com', 'Ernser LLC', 'tdurrad15@dyndns.org');
+insert into NGO (id, website, name, contact) values (43, 'wordpress.org', 'Becker, Quigley and Grimes', 'awaulker16@360.cn');
+insert into NGO (id, website, name, contact) values (44, 'opensource.org', 'Hudson, Shields and Buckridge', 'cgussie17@blinklist.com');
+insert into NGO (id, website, name, contact) values (45, 'businessinsider.com', 'Schaden-Stanton', 'gmangon18@google.com.au');
+insert into NGO (id, website, name, contact) values (46, 'unicef.org', 'Wunsch and Sons', 'pbullock19@amazonaws.com');
+insert into NGO (id, website, name, contact) values (47, 'salon.com', 'Glover-Herzog', 'gvenney1a@dell.com');
+insert into NGO (id, website, name, contact) values (48, 'mac.com', 'Smitham-Konopelski', 'kpugsley1b@dailymail.co.uk');
+insert into NGO (id, website, name, contact) values (49, 'washingtonpost.com', 'Dickens-Rohan', 'nbartoloma1c@yellowpages.com');
+insert into NGO (id, website, name, contact) values (50, 'istockphoto.com', 'Nader Group', 'jletts1d@bbb.org');
+
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (1, 'EcoForward Enterprises', 'Lighting', 160, 112, 20);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (2, 'Padberg, McKenzie and Ritchie', 'Real Estate Investment Trusts', 144, 94, 1);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (3, 'Batz-Schneider', 'Savings Institutions', 53, 136, 9);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (4, 'Aufderhar Inc', 'Oil/Gas Transmission', 158, 50, 4);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (5, 'Runte-Labadie', 'Investment Bankers/Brokers/Service', 91, 232, 13);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (6, 'Denesik-O''Connell', 'n/a', 16, 272, 1);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (7, 'Adams-Pagac', 'Computer Software: Programming, Data Processing', 229, 195, 4);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (8, 'Gulgowski, Prohaska and Lesch', 'n/a', 73, 198, 22);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (9, 'Windler, Kuhlman and Spencer', 'Biotechnology: Electromedical & Electrotherapeutic Apparatus', 127, 217, 8);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (10, 'Orn LLC', 'Industrial Machinery/Components', 188, 180, 5);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (11, 'Smitham, Rowe and Price', 'Major Pharmaceuticals', 267, 285, 10);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (12, 'Hoppe-Roberts', 'Major Pharmaceuticals', 101, 294, 13);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (13, 'Considine Group', 'Newspapers/Magazines', 93, 181, 24);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (14, 'Kassulke, Stark and O''Kon', 'Major Banks', 300, 164, 1);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (15, 'Lindgren Group', 'Farming/Seeds/Milling', 60, 270, 2);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (16, 'Macejkovic, Ullrich and Auer', 'Oilfield Services/Equipment', 240, 247, 21);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (17, 'Pfannerstill, Weissnat and Haley', 'Coal Mining', 20, 34, 7);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (18, 'Zieme Group', 'Medical/Nursing Services', 274, 273, 17);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (19, 'Buckridge-Rath', 'n/a', 181, 264, 8);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (20, 'Rempel Inc', 'Major Pharmaceuticals', 287, 202, 9);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (21, 'Goldner-Heaney', 'Specialty Chemicals', 264, 285, 22);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (22, 'Kuhic Group', 'Beverages (Production/Distribution)', 111, 238, 8);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (23, 'Brakus, Gottlieb and Rippin', 'Oil & Gas Production', 96, 16, 20);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (24, 'Pacocha-Prohaska', 'Major Pharmaceuticals', 37, 168, 18);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (25, 'Runolfsdottir-Kovacek', 'EDP Services', 155, 71, 19);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (26, 'Veum, Bartoletti and Crona', 'Industrial Specialties', 295, 14, 14);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (27, 'Parker-Green', 'Business Services', 193, 8, 11);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (28, 'Bernhard-Ratke', 'n/a', 239, 220, 28);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (29, 'Stoltenberg-Harris', 'n/a', 203, 140, 11);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (30, 'Reynolds Group', 'Ophthalmic Goods', 67, 181, 12);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (31, 'Pagac-Hermann', 'Electrical Products', 107, 66, 24);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (32, 'Stroman Group', 'n/a', 88, 261, 16);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (33, 'Armstrong-Wilkinson', 'Other Specialty Stores', 238, 99, 16);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (34, 'Waelchi, Witting and Brown', 'n/a', 14, 191, 5);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (35, 'Jakubowski-Haley', 'Hospital/Nursing Management', 127, 140, 9);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (36, 'Friesen, Kutch and Lehner', 'Semiconductors', 209, 175, 20);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (37, 'Ullrich Group', 'Hotels/Resorts', 18, 74, 18);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (38, 'Kertzmann, McGlynn and Halvorson', 'n/a', 248, 42, 20);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (39, 'Davis, Mueller and Tremblay', 'Finance: Consumer Services', 136, 153, 27);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (40, 'Schulist-Shanahan', 'Metal Fabrications', 186, 230, 10);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (41, 'DuBuque and Sons', 'Telecommunications Equipment', 224, 136, 12);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (42, 'Torphy-Rath', 'Homebuilding', 27, 35, 26);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (43, 'Beatty LLC', 'Industrial Machinery/Components', 241, 167, 4);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (44, 'Doyle, Morar and Lehner', 'Major Banks', 253, 198, 12);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (45, 'Crooks Inc', 'Automotive Aftermarket', 158, 4, 20);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (46, 'Hamill Inc', 'Major Pharmaceuticals', 54, 132, 14);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (47, 'Kshlerin and Sons', 'Air Freight/Delivery Services', 53, 257, 1);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (48, 'Schmitt, Swaniawski and Buckridge', 'n/a', 171, 180, 10);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (49, 'Schinner, Sanford and Stark', 'Metal Fabrications', 180, 261, 9);
+insert into Enterprises (id, name, type, emission_result, misc_emissions, country_id) values (50, 'Bins, Heller and Ondricka', 'Packaged Foods', 199, 150, 16);
 
-INSERT INTO EntTags (enterprise_id, tag_id) VALUES
-(1, 1),
-(1, 2),
-(2, 1),
-(2, 3),
-(3, 2),
-(3, 3),
-(4, 2),
-(4, 1);
 
-INSERT INTO NGO (id, website, name, contact) VALUES
-(1, 'logo1.png', 'NGO One', 'contact1@ngo1.org'),
-(2, 'logo2.png', 'NGO Two', 'contact2@ngo2.org'),
-(3, 'logo3.png', 'NGO Three', 'contact3@ngo3.org');
+insert into User (id, emission_result, country_id) values (1, 10, 17);
+insert into User (id, emission_result, country_id) values (2, 45, 20);
+insert into User (id, emission_result, country_id) values (3, 51, 23);
+insert into User (id, emission_result, country_id) values (4, 20, 24);
+insert into User (id, emission_result, country_id) values (5, 36, 9);
+insert into User (id, emission_result, country_id) values (6, 69, 1);
+insert into User (id, emission_result, country_id) values (7, 20, 5);
+insert into User (id, emission_result, country_id) values (8, 91, 6);
+insert into User (id, emission_result, country_id) values (9, 81, 29);
+insert into User (id, emission_result, country_id) values (10, 84, 26);
+insert into User (id, emission_result, country_id) values (11, 85, 11);
+insert into User (id, emission_result, country_id) values (12, 16, 12);
+insert into User (id, emission_result, country_id) values (13, 90, 24);
+insert into User (id, emission_result, country_id) values (14, 29, 29);
+insert into User (id, emission_result, country_id) values (15, 68, 7);
+insert into User (id, emission_result, country_id) values (16, 30, 5);
+insert into User (id, emission_result, country_id) values (17, 19, 15);
+insert into User (id, emission_result, country_id) values (18, 86, 22);
+insert into User (id, emission_result, country_id) values (19, 80, 16);
+insert into User (id, emission_result, country_id) values (20, 39, 8);
+insert into User (id, emission_result, country_id) values (21, 36, 19);
+insert into User (id, emission_result, country_id) values (22, 16, 9);
+insert into User (id, emission_result, country_id) values (23, 43, 15);
+insert into User (id, emission_result, country_id) values (24, 55, 10);
+insert into User (id, emission_result, country_id) values (25, 6, 11);
+insert into User (id, emission_result, country_id) values (26, 53, 21);
+insert into User (id, emission_result, country_id) values (27, 93, 5);
+insert into User (id, emission_result, country_id) values (28, 68, 11);
+insert into User (id, emission_result, country_id) values (29, 63, 23);
+insert into User (id, emission_result, country_id) values (30, 100, 23);
+insert into User (id, emission_result, country_id) values (31, 81, 19);
+insert into User (id, emission_result, country_id) values (32, 34, 23);
+insert into User (id, emission_result, country_id) values (33, 59, 25);
+insert into User (id, emission_result, country_id) values (34, 56, 22);
+insert into User (id, emission_result, country_id) values (35, 16, 4);
+insert into User (id, emission_result, country_id) values (36, 48, 23);
+insert into User (id, emission_result, country_id) values (37, 51, 9);
+insert into User (id, emission_result, country_id) values (38, 69, 17);
+insert into User (id, emission_result, country_id) values (39, 11, 29);
+insert into User (id, emission_result, country_id) values (40, 58, 19);
+insert into User (id, emission_result, country_id) values (41, 59, 14);
+insert into User (id, emission_result, country_id) values (42, 68, 8);
+insert into User (id, emission_result, country_id) values (43, 51, 1);
+insert into User (id, emission_result, country_id) values (44, 5, 20);
+insert into User (id, emission_result, country_id) values (45, 76, 2);
+insert into User (id, emission_result, country_id) values (46, 8, 30);
+insert into User (id, emission_result, country_id) values (47, 42, 4);
+insert into User (id, emission_result, country_id) values (48, 76, 9);
+insert into User (id, emission_result, country_id) values (49, 88, 27);
+insert into User (id, emission_result, country_id) values (50, 51, 10);
 
-INSERT INTO NGOTags (ngo_id, tag_id) VALUES
-(1, 1),
-(1, 2),
-(2, 1),
-(2, 3),
-(3, 2),
-(3, 3);
+insert into EntTags (enterprise_id, tag_id) values (1, 1);
+insert into EntTags (enterprise_id, tag_id) values (1, 2);
+insert into EntTags (enterprise_id, tag_id) values (2, 3);
+insert into EntTags (enterprise_id, tag_id) values (2, 1);
+insert into EntTags (enterprise_id, tag_id) values (3, 2);
+insert into EntTags (enterprise_id, tag_id) values (3, 3);
+insert into EntTags (enterprise_id, tag_id) values (4, 1);
+insert into EntTags (enterprise_id, tag_id) values (4, 2);
+insert into EntTags (enterprise_id, tag_id) values (14, 4);
+insert into EntTags (enterprise_id, tag_id) values (44, 1);
+insert into EntTags (enterprise_id, tag_id) values (17, 1);
+insert into EntTags (enterprise_id, tag_id) values (16, 2);
+insert into EntTags (enterprise_id, tag_id) values (47, 2);
+insert into EntTags (enterprise_id, tag_id) values (19, 2);
+insert into EntTags (enterprise_id, tag_id) values (35, 2);
+insert into EntTags (enterprise_id, tag_id) values (28, 2);
+insert into EntTags (enterprise_id, tag_id) values (1, 3);
+insert into EntTags (enterprise_id, tag_id) values (14, 2);
+insert into EntTags (enterprise_id, tag_id) values (22, 4);
+insert into EntTags (enterprise_id, tag_id) values (31, 1);
+insert into EntTags (enterprise_id, tag_id) values (29, 3);
+insert into EntTags (enterprise_id, tag_id) values (50, 3);
+insert into EntTags (enterprise_id, tag_id) values (36, 1);
+insert into EntTags (enterprise_id, tag_id) values (31, 2);
+insert into EntTags (enterprise_id, tag_id) values (10, 1);
+insert into EntTags (enterprise_id, tag_id) values (3, 1);
+insert into EntTags (enterprise_id, tag_id) values (37, 1);
+insert into EntTags (enterprise_id, tag_id) values (49, 1);
+insert into EntTags (enterprise_id, tag_id) values (16, 3);
+insert into EntTags (enterprise_id, tag_id) values (32, 3);
+insert into EntTags (enterprise_id, tag_id) values (8, 2);
+insert into EntTags (enterprise_id, tag_id) values (34, 2);
+insert into EntTags (enterprise_id, tag_id) values (14, 3);
+insert into EntTags (enterprise_id, tag_id) values (20, 4);
+insert into EntTags (enterprise_id, tag_id) values (44, 2);
+insert into EntTags (enterprise_id, tag_id) values (41, 2);
+insert into EntTags (enterprise_id, tag_id) values (27, 4);
+insert into EntTags (enterprise_id, tag_id) values (28, 3);
+insert into EntTags (enterprise_id, tag_id) values (39, 3);
+insert into EntTags (enterprise_id, tag_id) values (48, 4);
+insert into EntTags (enterprise_id, tag_id) values (36, 3);
+insert into EntTags (enterprise_id, tag_id) values (33, 1);
+insert into EntTags (enterprise_id, tag_id) values (50, 4);
+insert into EntTags (enterprise_id, tag_id) values (32, 2);
+insert into EntTags (enterprise_id, tag_id) values (23, 3);
+insert into EntTags (enterprise_id, tag_id) values (31, 4);
+insert into EntTags (enterprise_id, tag_id) values (38, 2);
+insert into EntTags (enterprise_id, tag_id) values (1, 4);
+insert into EntTags (enterprise_id, tag_id) values (40, 4);
+insert into EntTags (enterprise_id, tag_id) values (9, 1);
+insert into EntTags (enterprise_id, tag_id) values (29, 4);
+insert into EntTags (enterprise_id, tag_id) values (7, 4);
+insert into EntTags (enterprise_id, tag_id) values (24, 2);
+insert into EntTags (enterprise_id, tag_id) values (45, 3);
+insert into EntTags (enterprise_id, tag_id) values (21, 3);
+insert into EntTags (enterprise_id, tag_id) values (6, 3);
+insert into EntTags (enterprise_id, tag_id) values (5, 1);
+insert into EntTags (enterprise_id, tag_id) values (49, 4);
+insert into EntTags (enterprise_id, tag_id) values (36, 2);
+insert into EntTags (enterprise_id, tag_id) values (10, 2);
+insert into EntTags (enterprise_id, tag_id) values (36, 4);
+insert into EntTags (enterprise_id, tag_id) values (11, 4);
+insert into EntTags (enterprise_id, tag_id) values (13, 3);
+insert into EntTags (enterprise_id, tag_id) values (20, 3);
+insert into EntTags (enterprise_id, tag_id) values (46, 2);
+insert into EntTags (enterprise_id, tag_id) values (2, 4);
+insert into EntTags (enterprise_id, tag_id) values (30, 4);
+insert into EntTags (enterprise_id, tag_id) values (17, 3);
+insert into EntTags (enterprise_id, tag_id) values (26, 1);
+insert into EntTags (enterprise_id, tag_id) values (2, 2);
+insert into EntTags (enterprise_id, tag_id) values (30, 1);
+insert into EntTags (enterprise_id, tag_id) values (16, 4);
+insert into EntTags (enterprise_id, tag_id) values (49, 2);
+insert into EntTags (enterprise_id, tag_id) values (45, 4);
+insert into EntTags (enterprise_id, tag_id) values (23, 1);
+insert into EntTags (enterprise_id, tag_id) values (15, 4);
+
+insert into NGOTags (ngo_id, tag_id) values (32, 3);
+insert into NGOTags (ngo_id, tag_id) values (29, 1);
+insert into NGOTags (ngo_id, tag_id) values (9, 1);
+insert into NGOTags (ngo_id, tag_id) values (34, 3);
+insert into NGOTags (ngo_id, tag_id) values (22, 4);
+insert into NGOTags (ngo_id, tag_id) values (17, 3);
+insert into NGOTags (ngo_id, tag_id) values (6, 3);
+insert into NGOTags (ngo_id, tag_id) values (2, 1);
+insert into NGOTags (ngo_id, tag_id) values (17, 2);
+insert into NGOTags (ngo_id, tag_id) values (41, 4);
+insert into NGOTags (ngo_id, tag_id) values (21, 4);
+insert into NGOTags (ngo_id, tag_id) values (40, 4);
+insert into NGOTags (ngo_id, tag_id) values (30, 3);
+insert into NGOTags (ngo_id, tag_id) values (15, 2);
+insert into NGOTags (ngo_id, tag_id) values (25, 4);
+insert into NGOTags (ngo_id, tag_id) values (44, 1);
+insert into NGOTags (ngo_id, tag_id) values (33, 4);
+insert into NGOTags (ngo_id, tag_id) values (37, 4);
+insert into NGOTags (ngo_id, tag_id) values (22, 2);
+insert into NGOTags (ngo_id, tag_id) values (22, 3);
+insert into NGOTags (ngo_id, tag_id) values (9, 2);
+insert into NGOTags (ngo_id, tag_id) values (46, 1);
+insert into NGOTags (ngo_id, tag_id) values (12, 1);
+insert into NGOTags (ngo_id, tag_id) values (43, 3);
+insert into NGOTags (ngo_id, tag_id) values (27, 4);
+insert into NGOTags (ngo_id, tag_id) values (24, 2);
+insert into NGOTags (ngo_id, tag_id) values (44, 3);
+insert into NGOTags (ngo_id, tag_id) values (11, 4);
+insert into NGOTags (ngo_id, tag_id) values (1, 4);
+insert into NGOTags (ngo_id, tag_id) values (18, 1);
+insert into NGOTags (ngo_id, tag_id) values (39, 2);
+insert into NGOTags (ngo_id, tag_id) values (10, 2);
+insert into NGOTags (ngo_id, tag_id) values (38, 2);
+insert into NGOTags (ngo_id, tag_id) values (31, 3);
+insert into NGOTags (ngo_id, tag_id) values (50, 2);
+insert into NGOTags (ngo_id, tag_id) values (30, 4);
+insert into NGOTags (ngo_id, tag_id) values (37, 2);
+insert into NGOTags (ngo_id, tag_id) values (42, 3);
+insert into NGOTags (ngo_id, tag_id) values (35, 3);
+insert into NGOTags (ngo_id, tag_id) values (8, 3);
+insert into NGOTags (ngo_id, tag_id) values (20, 4);
+insert into NGOTags (ngo_id, tag_id) values (45, 3);
+insert into NGOTags (ngo_id, tag_id) values (23, 3);
+insert into NGOTags (ngo_id, tag_id) values (5, 1);
+insert into NGOTags (ngo_id, tag_id) values (38, 1);
+insert into NGOTags (ngo_id, tag_id) values (15, 4);
+insert into NGOTags (ngo_id, tag_id) values (38, 4);
+insert into NGOTags (ngo_id, tag_id) values (49, 2);
+insert into NGOTags (ngo_id, tag_id) values (36, 1);
+insert into NGOTags (ngo_id, tag_id) values (13, 1);
+insert into NGOTags (ngo_id, tag_id) values (28, 1);
+insert into NGOTags (ngo_id, tag_id) values (43, 4);
+insert into NGOTags (ngo_id, tag_id) values (36, 2);
+insert into NGOTags (ngo_id, tag_id) values (33, 3);
+insert into NGOTags (ngo_id, tag_id) values (14, 2);
+insert into NGOTags (ngo_id, tag_id) values (4, 2);
+insert into NGOTags (ngo_id, tag_id) values (50, 4);
+insert into NGOTags (ngo_id, tag_id) values (16, 4);
+insert into NGOTags (ngo_id, tag_id) values (47, 1);
+insert into NGOTags (ngo_id, tag_id) values (48, 3);
+insert into NGOTags (ngo_id, tag_id) values (1, 3);
+insert into NGOTags (ngo_id, tag_id) values (26, 3);
+insert into NGOTags (ngo_id, tag_id) values (42, 1);
+insert into NGOTags (ngo_id, tag_id) values (19, 1);
+insert into NGOTags (ngo_id, tag_id) values (20, 3);
+insert into NGOTags (ngo_id, tag_id) values (7, 4);
+insert into NGOTags (ngo_id, tag_id) values (18, 4);
+insert into NGOTags (ngo_id, tag_id) values (3, 2);
+insert into NGOTags (ngo_id, tag_id) values (14, 4);
+insert into NGOTags (ngo_id, tag_id) values (47, 4);
+insert into NGOTags (ngo_id, tag_id) values (12, 2);
+insert into NGOTags (ngo_id, tag_id) values (31, 1);
+insert into NGOTags (ngo_id, tag_id) values (7, 2);
+insert into NGOTags (ngo_id, tag_id) values (34, 1);
+insert into NGOTags (ngo_id, tag_id) values (8, 1);
+insert into NGOTags (ngo_id, tag_id) values (50, 1);
+insert into NGOTags (ngo_id, tag_id) values (37, 3);
+insert into NGOTags (ngo_id, tag_id) values (13, 3);
+insert into NGOTags (ngo_id, tag_id) values (35, 4);
+insert into NGOTags (ngo_id, tag_id) values (41, 2);
+insert into NGOTags (ngo_id, tag_id) values (10, 4);
+insert into NGOTags (ngo_id, tag_id) values (25, 1);
+insert into NGOTags (ngo_id, tag_id) values (48, 4);
+insert into NGOTags (ngo_id, tag_id) values (24, 4);
+insert into NGOTags (ngo_id, tag_id) values (2, 4);
+insert into NGOTags (ngo_id, tag_id) values (19, 4);
+insert into NGOTags (ngo_id, tag_id) values (5, 4);
+insert into NGOTags (ngo_id, tag_id) values (28, 4);
+insert into NGOTags (ngo_id, tag_id) values (12, 4);
+insert into NGOTags (ngo_id, tag_id) values (3, 1);
+insert into NGOTags (ngo_id, tag_id) values (6, 4);
+insert into NGOTags (ngo_id, tag_id) values (43, 1);
+insert into NGOTags (ngo_id, tag_id) values (39, 4);
+insert into NGOTags (ngo_id, tag_id) values (23, 4);
+insert into NGOTags (ngo_id, tag_id) values (26, 1);
+insert into NGOTags (ngo_id, tag_id) values (4, 3);
+
+insert into UserTags (user_id, tag_id) values (5, 1);
+insert into UserTags (user_id, tag_id) values (33, 3);
+insert into UserTags (user_id, tag_id) values (1, 2);
+insert into UserTags (user_id, tag_id) values (33, 4);
+insert into UserTags (user_id, tag_id) values (24, 4);
+insert into UserTags (user_id, tag_id) values (38, 1);
+insert into UserTags (user_id, tag_id) values (33, 2);
+insert into UserTags (user_id, tag_id) values (11, 4);
+insert into UserTags (user_id, tag_id) values (44, 1);
+insert into UserTags (user_id, tag_id) values (36, 2);
+insert into UserTags (user_id, tag_id) values (29, 4);
+insert into UserTags (user_id, tag_id) values (16, 2);
+insert into UserTags (user_id, tag_id) values (14, 4);
+insert into UserTags (user_id, tag_id) values (35, 3);
+insert into UserTags (user_id, tag_id) values (40, 4);
+insert into UserTags (user_id, tag_id) values (24, 1);
+insert into UserTags (user_id, tag_id) values (50, 1);
+insert into UserTags (user_id, tag_id) values (19, 4);
+insert into UserTags (user_id, tag_id) values (22, 2);
+insert into UserTags (user_id, tag_id) values (38, 3);
+insert into UserTags (user_id, tag_id) values (2, 1);
+insert into UserTags (user_id, tag_id) values (2, 3);
+insert into UserTags (user_id, tag_id) values (48, 2);
+insert into UserTags (user_id, tag_id) values (17, 3);
+insert into UserTags (user_id, tag_id) values (31, 4);
+insert into UserTags (user_id, tag_id) values (15, 4);
+insert into UserTags (user_id, tag_id) values (29, 2);
+insert into UserTags (user_id, tag_id) values (13, 2);
+insert into UserTags (user_id, tag_id) values (15, 1);
+insert into UserTags (user_id, tag_id) values (44, 3);
+insert into UserTags (user_id, tag_id) values (35, 1);
+insert into UserTags (user_id, tag_id) values (48, 4);
+insert into UserTags (user_id, tag_id) values (34, 4);
+insert into UserTags (user_id, tag_id) values (8, 3);
+insert into UserTags (user_id, tag_id) values (5, 4);
+insert into UserTags (user_id, tag_id) values (23, 4);
+insert into UserTags (user_id, tag_id) values (13, 3);
+insert into UserTags (user_id, tag_id) values (36, 1);
+insert into UserTags (user_id, tag_id) values (28, 2);
+insert into UserTags (user_id, tag_id) values (50, 4);
+insert into UserTags (user_id, tag_id) values (14, 3);
+insert into UserTags (user_id, tag_id) values (38, 4);
+insert into UserTags (user_id, tag_id) values (20, 2);
+insert into UserTags (user_id, tag_id) values (22, 4);
+insert into UserTags (user_id, tag_id) values (49, 2);
+insert into UserTags (user_id, tag_id) values (43, 1);
 
-INSERT INTO User (id, emission_result, country_id) VALUES
-(1, 100, 1),
-(2, 200, 2),
-(3, 300, 3);
 
 INSERT INTO ResData (id, user_id, elec_usage, emission_tags, heating, water_heating, cooking_gas) VALUES
 (200, 1, 1000.0, 'res', 2000.0, 1.0, 11.0)
 
-INSERT INTO UserTags (user_id, tag_id) VALUES
-(1, 1),
-(1, 2),
-(2, 3),
-(2, 4),
-(3, 1),
-(3, 3);
-
 INSERT INTO Cars (id, user_id, fuel_type, emission_tags, fuel_used) VALUES
 (1, 1, 'Gasoline/Hybrid', 'car', 100.0),
 (2, 2, 'Diesel', 'car', 200.0),

From b81ebcff5aa6d813a625b52fc26a0e86ea72c3d4 Mon Sep 17 00:00:00 2001
From: Babalola-a <147947002+Babalola-a@users.noreply.github.com>
Date: Thu, 6 Jun 2024 02:10:55 +0200
Subject: [PATCH 27/34] Adding App Demo and API Routes

---
 .../team_posts/phase_3/enterprisehome.jpg     | Bin 0 -> 191300 bytes
 blog/content/team_posts/phase_3/homepage.jpg  | Bin 0 -> 162894 bytes
 .../team_posts/phase_3/ngohomepage.jpg        | Bin 0 -> 163886 bytes
 blog/content/team_posts/phase_3/ngomatch.jpg  | Bin 0 -> 253949 bytes
 blog/content/team_posts/phase_3/ngosurvey.jpg | Bin 0 -> 198905 bytes
 blog/content/team_posts/phase_3/phase3.md     | 105 ++++++++++++++++++
 .../team_posts/phase_3/userhomepage.jpg       | Bin 0 -> 183012 bytes
 .../content/team_posts/phase_3/usersurvey.jpg | Bin 0 -> 193749 bytes
 8 files changed, 105 insertions(+)
 create mode 100644 blog/content/team_posts/phase_3/enterprisehome.jpg
 create mode 100644 blog/content/team_posts/phase_3/homepage.jpg
 create mode 100644 blog/content/team_posts/phase_3/ngohomepage.jpg
 create mode 100644 blog/content/team_posts/phase_3/ngomatch.jpg
 create mode 100644 blog/content/team_posts/phase_3/ngosurvey.jpg
 create mode 100644 blog/content/team_posts/phase_3/phase3.md
 create mode 100644 blog/content/team_posts/phase_3/userhomepage.jpg
 create mode 100644 blog/content/team_posts/phase_3/usersurvey.jpg

diff --git a/blog/content/team_posts/phase_3/enterprisehome.jpg b/blog/content/team_posts/phase_3/enterprisehome.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7831d03342776733e1232e66e662cc87daba3b71
GIT binary patch
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diff --git a/blog/content/team_posts/phase_3/phase3.md b/blog/content/team_posts/phase_3/phase3.md
new file mode 100644
index 0000000..1fa426a
--- /dev/null
+++ b/blog/content/team_posts/phase_3/phase3.md
@@ -0,0 +1,105 @@
+---
+title: "Project - Phase II"
+date: 2024-05-28
+draft: false
+description: "Implementation"
+tags: ["authors", "config", "docs"]
+slug: "phase_three"
+authors:
+  - "aahil"
+  - "anjola"
+  - "justin"
+  - "michael"
+showAuthorsBadges: false
+---
+
+# API routes and purposes 
+
+### User Routes 
+u/UserCars (GET)
+Gets all car survey data history for the user 
+u/UserAddCar (PUT) 
+Allows the user to add additional car survey data 
+
+u/UserResidential (GET)
+Gets all residential survey data history for the user
+
+u/UserFlights (GET)
+Gets all flight survey data history for the user
+
+u/UserTransport (GET)
+Gets all transport survey data history for the user
+
+u/UserAddRes (PUT)
+Updates this user's residential data based on survey
+
+
+### NGO Routes
+
+n/NGOupdate (PUT)
+Updates the NGO given filled out data in NGO table
+
+n/tags (GET)
+Gets the tag description for all tags for the NGO user
+
+n/EnterpriseMatch (GET)
+Gets the all matching enterprise names and descriptions based on tags
+
+n/ngomine (GET)
+Gets all my NGO data from the NGO table
+
+n/UserMatch (GET)
+Gets all of the matching users based on tags
+
+n/TagDelete
+Allows NGOs to delete tags that they were associated with 
+n/NGOadd
+Allows NGOs to add tags that they want to be associated with 
+
+
+### Enterprise Routes
+
+e/tags (GET)
+Gets all of the enterprise’s tags from database
+
+e/NGOMatch (GET)
+Gets all of the matching NGO's based on tags
+
+e/EntCompare (GET)
+Gets my emissions, my country's, and the average of other companies in same country 
+
+e/EntSupplyChain (GET)
+Gets all the supply chain history for this enterprise
+
+e/EntCosts (GET)
+Gets all the operating cost history for this enterprise
+
+e/EntFlights (GET)
+Gets all the flights history for this enterprise
+
+# Application Protype 
+## Database ER Diagrams
+
+
+| User Homescreen                   | Enterprise Homescreen                        | NGO homescreen  Diagram                   |
+| ----------------------------------- | --------------------------------------------- | --------------------------------- |
+| ![User Homescreen](./userhomepage.jpg) | ![Enterprise Homescreen](./enterprisehomescreen.jpg) | ![NGO Homescreen](./ngohomepage.jpg) |
+These are images of each of the user personas. The homescreen for each user persona allows them to implement the features mentioned in their respective user persona stories. The main features for users and enterprises include taking surveys to gather further information, while the NGO persona does not take a survey. Instead, NGOs select the tags they want to be associated with and are matched with users based on these tags.
+
+| User Homescreen                   | Enterprise Homescreen   
+![NGO Survey](./ngosurvey.jpg) | [NGO Match](./ngomatch.jpg)
+In the NGO user persona, NGOs decide which tags they want to be associated with. Based on these tags, they receive a list, similar to the example above, allowing them to see which users and enterprises they have been matched with.
+
+User Survey 
+![User Survey](./usersurvey.jpg) 
+In the general user persona, users take a survey, and based on this survey data, the app interacts with a machine learning model to estimate future carbon emissions and provide data on their current carbon emissions.
+
+Enterprise Survey 
+![Enterprise Survey](./enterprisehome.jpg) 
+Enterprises, similar to general users, also take a survey. This survey allows them to match with general users and NGOs. 
+
+
+
+
+
+
diff --git a/blog/content/team_posts/phase_3/userhomepage.jpg b/blog/content/team_posts/phase_3/userhomepage.jpg
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literal 0
HcmV?d00001


From 1a3354a1418b8bb590d8d86e8201684480d23823 Mon Sep 17 00:00:00 2001
From: mehallhm <michaelhmehall@gmail.com>
Date: Thu, 6 Jun 2024 02:20:51 +0200
Subject: [PATCH 28/34] bug: fix container building

---
 api/backend/ml_models/model_alpha.py |  99 +++-
 api/requirements.txt                 |   9 +-
 eda/eda3.ipynb                       | 701 +++++++++++++++++++++------
 3 files changed, 661 insertions(+), 148 deletions(-)

diff --git a/api/backend/ml_models/model_alpha.py b/api/backend/ml_models/model_alpha.py
index 9abcca5..35d319b 100644
--- a/api/backend/ml_models/model_alpha.py
+++ b/api/backend/ml_models/model_alpha.py
@@ -6,8 +6,9 @@
 import numpy as np
 import pandas as pd
 import pandasdmx as sdmx
-import train_helpers
 from sklearn.metrics import r2_score
+from functools import reduce
+import pandasdmx as sdmx
 
 def train() -> np.array:
 	"""
@@ -28,7 +29,7 @@ def train() -> np.array:
 	emission_df = (resp
 				.to_pandas(datetime={'dim': 'TIME_PERIOD'})
 				.droplevel(level=['unit', 'freq', 'src_crf', 'airpol'], axis=1))
-	melted_emissions_df = train_helpers.melt_smdx_dataframe(emission_df)
+	melted_emissions_df = melt_smdx_dataframe(emission_df)
 
 	resp = estat.data(
 			"NRG_D_HHQ",
@@ -42,7 +43,7 @@ def train() -> np.array:
 	household_energy_df = (resp
 				.to_pandas(datetime={'dim': 'TIME_PERIOD', 'freq': 'freq'})
 				.droplevel(level=["siec", "unit", "nrg_bal"], axis=1))
-	melted_household_energy_df = train_helpers.melt_smdx_dataframe(household_energy_df)
+	melted_household_energy_df = melt_smdx_dataframe(household_energy_df)
 
 	resp = estat.data(
 		"TEN00127",
@@ -56,16 +57,16 @@ def train() -> np.array:
 	gas_df = (resp
 				.to_pandas(datetime={'dim': 'TIME_PERIOD'})
 				.droplevel(level=['unit', 'freq', 'siec', "nrg_bal"], axis=1))
-	melted_gas_df = train_helpers.melt_smdx_dataframe(gas_df)
+	melted_gas_df = melt_smdx_dataframe(gas_df)
 
-	merged_df = train_helpers.merge_dataframes([melted_emissions_df,
+	merged_df = merge_dataframes([melted_emissions_df,
 											  melted_household_energy_df,
 											  melted_gas_df])
 	merged_df.columns = ["year", "geo", "emissions", "energy", "gas"]
 	merged_df = merged_df.drop(merged_df[(merged_df.geo == "EU27_2020") |
 									   (merged_df.geo == "EU20")].index)
 	merged_df = merged_df.drop("year", axis=1)
-	standard_df = train_helpers.standardize(merged_df)
+	standard_df = standardize(merged_df)
 
 	df_dummies = pd.get_dummies(standard_df, dtype=int, columns=["geo"])
 	df_dummies = df_dummies.fillna(0)
@@ -125,4 +126,88 @@ def predict(feats:list[float], beta:list[float]) -> float:
 	beta = np.array(beta, dtype=np.float64)
 	y_hat = np.matmul(x, beta)
 
-	return y_hat[0]
\ No newline at end of file
+	return y_hat[0]
+
+
+
+def melt_smdx_dataframe(df: pd.DataFrame) -> pd.DataFrame:
+	"""
+	Given an ESTAT smdx dataframe, convert the datetimes to years and melt
+
+	:param df: The raw SDMX parsed dataframe from ESTAT
+	:returns: A melted dataframe with the columns of:
+		`year` - the year of the observation
+		`geo` - the country of the observation
+		`value` - the value of the observation
+	"""
+	df = df.reset_index()
+	df["year"] = df["TIME_PERIOD"].dt.year
+	df = df.drop("TIME_PERIOD", axis=1)
+	return pd.melt(df, id_vars="year")
+
+def merge_dataframes(dataframes: list[pd.DataFrame]) -> pd.DataFrame:
+	"""
+	"""
+	for i, df in enumerate(dataframes):
+		df.columns = ["geo", "year", i]
+
+	merged_df = reduce(lambda l, r: pd.merge(l, r, left_on=["year", "geo"], right_on=["year", "geo"]), dataframes)
+	return merged_df
+
+def fill_holes(df: pd.DataFrame) -> pd.DataFrame:
+	"""
+	"""
+	lin_reg = lambda X, Y: np.matmul(np.linalg.inv(np.matmul(X.T, X)), np.matmul(X.T, Y))
+
+	dfs = []
+
+	for name, group in df.groupby('geo'):
+		cols = [[name for _ in range(len(group.index))]]
+		for i in range(1, len(group.columns)):
+			d = group.iloc[:, i:i+1].to_numpy()
+
+			missing_mask = np.isnan(d) | (d == 0)
+			present_mask = ~missing_mask
+
+			missing_mask = missing_mask.reshape(1, -1)[0]
+			present_mask = present_mask.reshape(1, -1)[0]
+
+			if not np.any(missing_mask):
+				d = d.reshape(1, -1)[0]
+				cols.append(d)
+				continue
+
+			if not np.any(present_mask):
+				d = d.reshape(1, -1)[0]
+				cols.append(d)
+				continue
+
+			x_present = np.pad(np.arange(len(d))[present_mask].reshape(-1, 1), ((0, 0), (1, 0)), mode="constant", constant_values=1)
+			y_present = d[present_mask]
+
+			w = lin_reg(x_present, y_present)
+
+			x_missing = np.pad(np.arange(len(d))[missing_mask].reshape(-1, 1), ((0, 0), (1, 0)), mode="constant", constant_values=1)
+			y_missing_pred = np.matmul(x_missing, w)
+
+			d[missing_mask] = y_missing_pred
+			d = d.reshape(1, -1)[0]
+
+			cols.append(d)
+			
+		dfs.append(pd.DataFrame(cols).T)	
+		
+	df_unswissed = pd.concat(dfs, axis=0)
+	df_unswissed.columns = df.columns
+	return df_unswissed
+
+def standardize(df: pd.DataFrame) -> pd.DataFrame:
+	"""
+	"""
+	df_standard = pd.DataFrame()
+	for feat in df.columns:
+		if feat == "geo": continue
+		df_standard[f'{feat}'] = ((df[feat] - df[feat].mean()) / df[feat].std())
+	df_standard["geo"] = df["geo"]
+
+	return df_standard
\ No newline at end of file
diff --git a/api/requirements.txt b/api/requirements.txt
index 77ade17..1f62ccb 100644
--- a/api/requirements.txt
+++ b/api/requirements.txt
@@ -3,7 +3,6 @@ certifi==2024.6.2
 charset-normalizer==3.3.2
 click==8.1.7
 colorama==0.4.6
-Flask==3.0.3
 idna==3.7
 itsdangerous==2.2.0
 Jinja2==3.1.4
@@ -24,4 +23,10 @@ threadpoolctl==3.5.0
 typing_extensions==4.12.1
 tzdata==2024.1
 urllib3==2.2.1
-Werkzeug==3.0.3
+werkzeug==2.3.8
+flask==2.3.3
+flask-restful==0.3.9
+flask-login==0.6.2
+flask-mysql==1.5.2
+cryptography==38.0.1
+python-dotenv==1.0.1
\ No newline at end of file
diff --git a/eda/eda3.ipynb b/eda/eda3.ipynb
index 093270c..339a966 100644
--- a/eda/eda3.ipynb
+++ b/eda/eda3.ipynb
@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/site-packages/pandasdmx/remote.py:11: RuntimeWarning: optional dependency requests_cache is not installed; cache options to Session() have no effect\n",
+      "  warn(\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -24,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,14 +49,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-05 23:12:54,193 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n"
+      "2024-06-06 01:29:44,221 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n"
      ]
     },
     {
@@ -120,7 +129,7 @@
        "4  1989  AT    NaN"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -153,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,26 +194,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-05 23:12:55,611 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "2024-06-06 01:29:45,509 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
       "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
       "  return dispatch(args[0].__class__)(*args, **kw)\n",
-      "2024-06-05 23:12:55,729 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "2024-06-06 01:29:45,631 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
       "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
       "  return dispatch(args[0].__class__)(*args, **kw)\n",
-      "2024-06-05 23:12:55,856 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "2024-06-06 01:29:45,836 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
       "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
       "  return dispatch(args[0].__class__)(*args, **kw)\n",
-      "2024-06-05 23:12:56,009 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "2024-06-06 01:29:45,955 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
       "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
       "  return dispatch(args[0].__class__)(*args, **kw)\n",
-      "2024-06-05 23:12:56,214 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
+      "2024-06-06 01:29:46,068 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n",
       "/opt/homebrew/Caskroom/miniconda/base/envs/wafflers/lib/python3.11/functools.py:909: FutureWarning: 'A' is deprecated and will be removed in a future version, please use 'Y' instead.\n",
       "  return dispatch(args[0].__class__)(*args, **kw)\n"
      ]
@@ -217,14 +226,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-06-05 23:12:57,492 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n"
+      "2024-06-06 01:29:47,302 pandasdmx.reader.sdmxml - INFO: Use supplied dsd=… argument for non–structure-specific message\n"
      ]
     },
     {
@@ -297,7 +306,7 @@
        "4  2015  AL  109.229"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -330,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -361,7 +370,7 @@
        "      <th>energy_cooling</th>\n",
        "      <th>energy_water_heating</th>\n",
        "      <th>energy_cooking</th>\n",
-       "      <th>total</th>\n",
+       "      <th>household_total</th>\n",
        "      <th>gas</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -438,22 +447,22 @@
        "3  2014  AT  78716.82      178513.717          27.645             42495.391   \n",
        "4  2015  AT  81081.58      193886.979          25.714             42104.965   \n",
        "\n",
-       "   energy_cooking       total       gas  \n",
-       "0        6891.695  274577.058  1632.260  \n",
-       "1        7026.774  279812.556  1585.826  \n",
-       "2        7353.560  289591.863  1554.830  \n",
-       "3        7152.047  261676.431  1524.254  \n",
-       "4        7179.133  278096.422  1539.983  "
+       "   energy_cooking  household_total       gas  \n",
+       "0        6891.695       274577.058  1632.260  \n",
+       "1        7026.774       279812.556  1585.826  \n",
+       "2        7353.560       289591.863  1554.830  \n",
+       "3        7152.047       261676.431  1524.254  \n",
+       "4        7179.133       278096.422  1539.983  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "to_merge_dfs = [emission_melted_df, *energy_dfs, gas_melted_df]\n",
-    "columns = [\"year\", \"geo\", \"carbon\", \"energy_heating\", \"energy_cooling\", \"energy_water_heating\", \"energy_cooking\", \"total\", \"gas\"]\n",
+    "columns = [\"year\", \"geo\", \"carbon\", \"energy_heating\", \"energy_cooling\", \"energy_water_heating\", \"energy_cooking\", \"household_total\", \"gas\"]\n",
     "res_carbon_df = reduce(lambda l, r: pd.merge(l, r, left_on=[\"year\", \"geo\"], right_on=[\"year\", \"geo\"]), to_merge_dfs)\n",
     "res_carbon_df.columns = columns\n",
     "res_carbon_df.head()"
@@ -461,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -492,7 +501,7 @@
        "      <th>energy_cooling</th>\n",
        "      <th>energy_water_heating</th>\n",
        "      <th>energy_cooking</th>\n",
-       "      <th>total</th>\n",
+       "      <th>household_total</th>\n",
        "      <th>gas</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -569,15 +578,15 @@
        "3  2014  AT  78716.82      178513.717          27.645             42495.391   \n",
        "4  2015  AT  81081.58      193886.979          25.714             42104.965   \n",
        "\n",
-       "   energy_cooking       total       gas  \n",
-       "0        6891.695  274577.058  1632.260  \n",
-       "1        7026.774  279812.556  1585.826  \n",
-       "2        7353.560  289591.863  1554.830  \n",
-       "3        7152.047  261676.431  1524.254  \n",
-       "4        7179.133  278096.422  1539.983  "
+       "   energy_cooking  household_total       gas  \n",
+       "0        6891.695       274577.058  1632.260  \n",
+       "1        7026.774       279812.556  1585.826  \n",
+       "2        7353.560       289591.863  1554.830  \n",
+       "3        7152.047       261676.431  1524.254  \n",
+       "4        7179.133       278096.422  1539.983  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -588,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -617,7 +626,7 @@
        "      <th>energy_cooling</th>\n",
        "      <th>energy_water_heating</th>\n",
        "      <th>energy_cooking</th>\n",
-       "      <th>total</th>\n",
+       "      <th>household_total</th>\n",
        "      <th>gas</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -673,7 +682,7 @@
        "      <td>0.960886</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>total</th>\n",
+       "      <th>household_total</th>\n",
        "      <td>0.955914</td>\n",
        "      <td>0.999384</td>\n",
        "      <td>0.932779</td>\n",
@@ -703,20 +712,29 @@
        "energy_cooling        0.860313        0.927161        1.000000   \n",
        "energy_water_heating  0.953779        0.994903        0.927569   \n",
        "energy_cooking        0.958319        0.993174        0.933307   \n",
-       "total                 0.955914        0.999384        0.932779   \n",
+       "household_total       0.955914        0.999384        0.932779   \n",
        "gas                   0.996808        0.957200        0.871504   \n",
        "\n",
-       "                      energy_water_heating  energy_cooking     total       gas  \n",
-       "carbon                            0.953779        0.958319  0.955914  0.996808  \n",
-       "energy_heating                    0.994903        0.993174  0.999384  0.957200  \n",
-       "energy_cooling                    0.927569        0.933307  0.932779  0.871504  \n",
-       "energy_water_heating              1.000000        0.994355  0.996931  0.958940  \n",
-       "energy_cooking                    0.994355        1.000000  0.995192  0.960886  \n",
-       "total                             0.996931        0.995192  1.000000  0.959322  \n",
-       "gas                               0.958940        0.960886  0.959322  1.000000  "
+       "                      energy_water_heating  energy_cooking  household_total  \\\n",
+       "carbon                            0.953779        0.958319         0.955914   \n",
+       "energy_heating                    0.994903        0.993174         0.999384   \n",
+       "energy_cooling                    0.927569        0.933307         0.932779   \n",
+       "energy_water_heating              1.000000        0.994355         0.996931   \n",
+       "energy_cooking                    0.994355        1.000000         0.995192   \n",
+       "household_total                   0.996931        0.995192         1.000000   \n",
+       "gas                               0.958940        0.960886         0.959322   \n",
+       "\n",
+       "                           gas  \n",
+       "carbon                0.996808  \n",
+       "energy_heating        0.957200  \n",
+       "energy_cooling        0.871504  \n",
+       "energy_water_heating  0.958940  \n",
+       "energy_cooking        0.960886  \n",
+       "household_total       0.959322  \n",
+       "gas                   1.000000  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -727,7 +745,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.cm as cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Greenhouse Gas Emissions (ktonne CO2 equivelent)')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = cm.rainbow(np.linspace(0, 1, len(res_carbon_df)))\n",
+    "plt.scatter(x=res_carbon_df[\"energy_cooling\"], y=res_carbon_df[\"carbon\"], color=colors, alpha=0.5)\n",
+    "plt.title(\"Energy Used for Cooling vs. Greenhouse Emissions\")\n",
+    "plt.xlabel(\"Household Energy for Cooling (TJ)\")\n",
+    "plt.ylabel(\"Greenhouse Gas Emissions (ktonne CO2 equivelent)\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -736,7 +797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -752,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -782,7 +843,7 @@
        "      <th>energy_cooling</th>\n",
        "      <th>energy_water_heating</th>\n",
        "      <th>energy_cooking</th>\n",
-       "      <th>total</th>\n",
+       "      <th>household_total</th>\n",
        "      <th>gas</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -854,15 +915,15 @@
        "3  AT  78716.82     178513.717         27.645            42495.391   \n",
        "4  AT  81081.58     193886.979         25.714            42104.965   \n",
        "\n",
-       "  energy_cooking       total       gas  \n",
-       "0       6891.695  274577.058   1632.26  \n",
-       "1       7026.774  279812.556  1585.826  \n",
-       "2        7353.56  289591.863   1554.83  \n",
-       "3       7152.047  261676.431  1524.254  \n",
-       "4       7179.133  278096.422  1539.983  "
+       "  energy_cooking household_total       gas  \n",
+       "0       6891.695      274577.058   1632.26  \n",
+       "1       7026.774      279812.556  1585.826  \n",
+       "2        7353.56      289591.863   1554.83  \n",
+       "3       7152.047      261676.431  1524.254  \n",
+       "4       7179.133      278096.422  1539.983  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -929,7 +990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -958,7 +1019,7 @@
        "      <th>energy_cooling</th>\n",
        "      <th>energy_water_heating</th>\n",
        "      <th>energy_cooking</th>\n",
-       "      <th>total</th>\n",
+       "      <th>household_total</th>\n",
        "      <th>gas</th>\n",
        "      <th>geo</th>\n",
        "    </tr>\n",
@@ -1031,15 +1092,15 @@
        "3 -0.308642      -0.183169      -0.518198            -0.151277      -0.434749   \n",
        "4 -0.296424      -0.141983      -0.518897            -0.156206      -0.433986   \n",
        "\n",
-       "      total       gas geo  \n",
-       "0 -0.193489 -0.208284  AT  \n",
-       "1 -0.183995  -0.22177  AT  \n",
-       "2 -0.166262 -0.230772  AT  \n",
-       "3 -0.216883 -0.239653  AT  \n",
-       "4 -0.187107 -0.235084  AT  "
+       "  household_total       gas geo  \n",
+       "0       -0.193489 -0.208284  AT  \n",
+       "1       -0.183995  -0.22177  AT  \n",
+       "2       -0.166262 -0.230772  AT  \n",
+       "3       -0.216883 -0.239653  AT  \n",
+       "4       -0.187107 -0.235084  AT  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1063,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1099,7 +1160,7 @@
        "      <th>energy_cooling</th>\n",
        "      <th>energy_water_heating</th>\n",
        "      <th>energy_cooking</th>\n",
-       "      <th>total</th>\n",
+       "      <th>household_total</th>\n",
        "      <th>gas</th>\n",
        "      <th>geo_AT</th>\n",
        "      <th>geo_BE</th>\n",
@@ -1251,24 +1312,24 @@
        "3 -0.308642      -0.183169      -0.518198            -0.151277      -0.434749   \n",
        "4 -0.296424      -0.141983      -0.518897            -0.156206      -0.433986   \n",
        "\n",
-       "      total       gas  geo_AT  geo_BE  geo_BG  ...  geo_LV  geo_MT  geo_NL  \\\n",
-       "0 -0.193489 -0.208284       1       0       0  ...       0       0       0   \n",
-       "1 -0.183995  -0.22177       1       0       0  ...       0       0       0   \n",
-       "2 -0.166262 -0.230772       1       0       0  ...       0       0       0   \n",
-       "3 -0.216883 -0.239653       1       0       0  ...       0       0       0   \n",
-       "4 -0.187107 -0.235084       1       0       0  ...       0       0       0   \n",
+       "  household_total       gas  geo_AT  geo_BE  geo_BG  ...  geo_LV  geo_MT  \\\n",
+       "0       -0.193489 -0.208284       1       0       0  ...       0       0   \n",
+       "1       -0.183995  -0.22177       1       0       0  ...       0       0   \n",
+       "2       -0.166262 -0.230772       1       0       0  ...       0       0   \n",
+       "3       -0.216883 -0.239653       1       0       0  ...       0       0   \n",
+       "4       -0.187107 -0.235084       1       0       0  ...       0       0   \n",
        "\n",
-       "   geo_NO  geo_PL  geo_PT  geo_RO  geo_SE  geo_SI  geo_SK  \n",
-       "0       0       0       0       0       0       0       0  \n",
-       "1       0       0       0       0       0       0       0  \n",
-       "2       0       0       0       0       0       0       0  \n",
-       "3       0       0       0       0       0       0       0  \n",
-       "4       0       0       0       0       0       0       0  \n",
+       "   geo_NL  geo_NO  geo_PL  geo_PT  geo_RO  geo_SE  geo_SI  geo_SK  \n",
+       "0       0       0       0       0       0       0       0       0  \n",
+       "1       0       0       0       0       0       0       0       0  \n",
+       "2       0       0       0       0       0       0       0       0  \n",
+       "3       0       0       0       0       0       0       0       0  \n",
+       "4       0       0       0       0       0       0       0       0  \n",
        "\n",
        "[5 rows x 35 columns]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1281,14 +1342,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/jk/vqmr1l1d1dnflmyh1qdjmhk40000gn/T/ipykernel_96953/3693932479.py:1: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
+      "/var/folders/jk/vqmr1l1d1dnflmyh1qdjmhk40000gn/T/ipykernel_97545/3693932479.py:1: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
       "  df_dummies = df_dummies.fillna(0)\n"
      ]
     }
@@ -1299,7 +1360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1315,7 +1376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1337,7 +1398,7 @@
        "         0.        ]])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1350,7 +1411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1366,7 +1427,7 @@
        "        -6.4031752 ,   2.67108654,   1.30513352])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1379,7 +1440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1395,7 +1456,7 @@
        "789.7"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1409,7 +1470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1434,7 +1495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1459,7 +1520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1484,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -1495,7 +1556,7 @@
        },
        "data": [
         {
-         "hovertemplate": "geo=AT<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=AT<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "AT",
          "marker": {
           "color": "#636efa",
@@ -1550,7 +1611,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=BE<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=BE<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "BE",
          "marker": {
           "color": "#EF553B",
@@ -1605,7 +1666,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=BG<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=BG<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "BG",
          "marker": {
           "color": "#00cc96",
@@ -1660,7 +1721,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=CY<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=CY<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "CY",
          "marker": {
           "color": "#ab63fa",
@@ -1715,7 +1776,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=CZ<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=CZ<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "CZ",
          "marker": {
           "color": "#FFA15A",
@@ -1770,7 +1831,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=DE<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=DE<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "DE",
          "marker": {
           "color": "#19d3f3",
@@ -1825,7 +1886,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=DK<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=DK<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "DK",
          "marker": {
           "color": "#FF6692",
@@ -1880,7 +1941,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=EE<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=EE<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "EE",
          "marker": {
           "color": "#B6E880",
@@ -1935,7 +1996,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=EL<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=EL<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "EL",
          "marker": {
           "color": "#FF97FF",
@@ -1990,7 +2051,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=ES<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=ES<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "ES",
          "marker": {
           "color": "#FECB52",
@@ -2045,7 +2106,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=FI<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=FI<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "FI",
          "marker": {
           "color": "#636efa",
@@ -2100,7 +2161,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=FR<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=FR<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "FR",
          "marker": {
           "color": "#EF553B",
@@ -2155,7 +2216,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=HR<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=HR<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "HR",
          "marker": {
           "color": "#00cc96",
@@ -2210,7 +2271,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=HU<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=HU<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "HU",
          "marker": {
           "color": "#ab63fa",
@@ -2265,7 +2326,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=IE<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=IE<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "IE",
          "marker": {
           "color": "#FFA15A",
@@ -2320,7 +2381,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=IT<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=IT<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "IT",
          "marker": {
           "color": "#19d3f3",
@@ -2375,7 +2436,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=LT<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=LT<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "LT",
          "marker": {
           "color": "#FF6692",
@@ -2430,7 +2491,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=LU<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=LU<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "LU",
          "marker": {
           "color": "#B6E880",
@@ -2485,7 +2546,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=LV<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=LV<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "LV",
          "marker": {
           "color": "#FF97FF",
@@ -2540,7 +2601,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=MT<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=MT<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "MT",
          "marker": {
           "color": "#FECB52",
@@ -2595,7 +2656,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=NL<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=NL<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "NL",
          "marker": {
           "color": "#636efa",
@@ -2650,7 +2711,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=NO<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=NO<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "NO",
          "marker": {
           "color": "#EF553B",
@@ -2705,7 +2766,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=PL<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=PL<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "PL",
          "marker": {
           "color": "#00cc96",
@@ -2760,7 +2821,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=PT<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=PT<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "PT",
          "marker": {
           "color": "#ab63fa",
@@ -2815,7 +2876,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=RO<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=RO<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "RO",
          "marker": {
           "color": "#FFA15A",
@@ -2870,7 +2931,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=SE<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=SE<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "SE",
          "marker": {
           "color": "#19d3f3",
@@ -2925,7 +2986,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=SI<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=SI<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "SI",
          "marker": {
           "color": "#FF6692",
@@ -2980,7 +3041,7 @@
          ]
         },
         {
-         "hovertemplate": "geo=SK<br>total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
+         "hovertemplate": "geo=SK<br>household_total=%{x}<br>carbon=%{y}<br>gas=%{z}<extra></extra>",
          "legendgroup": "SK",
          "marker": {
           "color": "#B6E880",
@@ -3058,7 +3119,7 @@
          },
          "xaxis": {
           "title": {
-           "text": "total"
+           "text": "household_total"
           }
          },
          "yaxis": {
@@ -3896,12 +3957,12 @@
     }
    ],
    "source": [
-    "px.scatter_3d(df_merged_s, y=\"carbon\", x=\"total\", z=\"gas\", color=\"geo\")"
+    "px.scatter_3d(df_merged_s, y=\"carbon\", x=\"household_total\", z=\"gas\", color=\"geo\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3910,7 +3971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -3919,7 +3980,7 @@
        "Text(0, 0.5, 'Carbon Emissions')"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3936,7 +3997,7 @@
    ],
    "source": [
     "colors = cm.rainbow(np.linspace(0, 1, len(df_merged_s)))\n",
-    "plt.scatter(x=df_merged_s[\"total\"], y=df_merged_s[\"carbon\"], color=colors, alpha=0.5)\n",
+    "plt.scatter(x=df_merged_s[\"household_total\"], y=df_merged_s[\"carbon\"], color=colors, alpha=0.5)\n",
     "plt.title(\"Transportation Gas Usage vs Carbon\")\n",
     "plt.xlabel(\"Oil & Gas Usage in Transportation (ktoes)\")\n",
     "plt.ylabel(\"Carbon Emissions\")"
@@ -3944,7 +4005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3953,7 +4014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3962,7 +4023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3993,9 +4054,351 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
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+      "(31,)\n",
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+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n",
+      "(31,)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -4337,7 +4740,7 @@
        " -4.5415554528087085]"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4349,7 +4752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -4425,7 +4828,7 @@
        "       -0.52322675])"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4436,7 +4839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -4445,7 +4848,7 @@
        "-4476.691935155938"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4454,6 +4857,26 @@
     "r2_score(y, preds)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-4.195576792520727"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(y - preds).mean()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

From ce4c6df8b2a4bc5ab6ca42f23bae10a3ab46651a Mon Sep 17 00:00:00 2001
From: mehallhm <michaelhmehall@gmail.com>
Date: Thu, 6 Jun 2024 02:32:17 +0200
Subject: [PATCH 29/34] bug: fix predict dimentionality

---
 api/backend/ml_models/model_alpha.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/api/backend/ml_models/model_alpha.py b/api/backend/ml_models/model_alpha.py
index 35d319b..78d4f12 100644
--- a/api/backend/ml_models/model_alpha.py
+++ b/api/backend/ml_models/model_alpha.py
@@ -121,12 +121,11 @@ def predict(feats:list[float], beta:list[float]) -> float:
 	:returns: The predicted greenhouse gass emission in CO2 equiventlents
 		measured in ktonnes
 	"""
-	x = np.pad(np.array(feats, dtype=np.float64), ((0, 0), (1, 0)),
-			 mode="constant", constant_values=1)
+	x = np.concatenate([[1], np.array(feats, dtype=np.float64)])
 	beta = np.array(beta, dtype=np.float64)
 	y_hat = np.matmul(x, beta)
 
-	return y_hat[0]
+	return y_hat
 
 
 

From fbbc092a56536383a5b0b9f200b82081800a64e0 Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Thu, 6 Jun 2024 02:40:25 +0200
Subject: [PATCH 30/34] Team blog revised

---
 .../team_posts/phase_3/enterprisehome.jpg     | Bin 191300 -> 0 bytes
 blog/content/team_posts/phase_3/index.md      | 107 ++++++++++++++++++
 blog/content/team_posts/phase_3/ngotags.jpg   | Bin 0 -> 33263 bytes
 blog/content/team_posts/phase_3/phase3.md     | 105 -----------------
 .../team_posts/phase_3/userhistory.jpg        | Bin 0 -> 33860 bytes
 .../team_posts/phase_3/usersurveyres.jpg      | Bin 0 -> 58994 bytes
 6 files changed, 107 insertions(+), 105 deletions(-)
 delete mode 100644 blog/content/team_posts/phase_3/enterprisehome.jpg
 create mode 100644 blog/content/team_posts/phase_3/ngotags.jpg
 delete mode 100644 blog/content/team_posts/phase_3/phase3.md
 create mode 100644 blog/content/team_posts/phase_3/userhistory.jpg
 create mode 100644 blog/content/team_posts/phase_3/usersurveyres.jpg

diff --git a/blog/content/team_posts/phase_3/enterprisehome.jpg b/blog/content/team_posts/phase_3/enterprisehome.jpg
deleted file mode 100644
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diff --git a/blog/content/team_posts/phase_3/index.md b/blog/content/team_posts/phase_3/index.md
index e69de29..305c6c3 100644
--- a/blog/content/team_posts/phase_3/index.md
+++ b/blog/content/team_posts/phase_3/index.md
@@ -0,0 +1,107 @@
+---
+title: "Project - Phase III"
+date: 2024-05-28
+draft: false
+description: "Implementation"
+tags: ["authors", "config", "docs"]
+slug: "phase_three"
+authors:
+  - "aahil"
+  - "anjola"
+  - "justin"
+  - "michael"
+showAuthorsBadges: false
+---
+
+# API routes and purposes
+
+### User Routes
+
+u/UserCars (GET)
+Gets all car survey data history for the user
+u/UserAddCar (PUT)
+Allows the user to add additional car survey data
+
+u/UserResidential (GET)
+Gets all residential survey data history for the user
+
+u/UserFlights (GET)
+Gets all flight survey data history for the user
+
+u/UserTransport (GET)
+Gets all transport survey data history for the user
+
+u/UserAddRes (PUT)
+Updates this user's residential data based on survey
+
+### NGO Routes
+
+n/NGOupdate (PUT)
+Updates the NGO given filled out data in NGO table
+
+n/tags (GET)
+Gets the tag description for all tags for the NGO user
+
+n/EnterpriseMatch (GET)
+Gets the all matching enterprise names and descriptions based on tags
+
+n/ngomine (GET)
+Gets all my NGO data from the NGO table
+
+n/UserMatch (GET)
+Gets all of the matching users based on tags
+
+n/TagDelete (DELETE)
+Allows NGOs to delete tags that they were associated with
+
+n/NGOadd (POST)
+Allows NGOs to add tags that they want to be associated with
+
+### Enterprise Routes
+
+e/tags (GET)
+Gets all of the enterprise’s tags from database
+
+e/NGOMatch (GET)
+Gets all of the matching NGO's based on tags
+
+e/EntCompare (GET)
+Gets my emissions, my country's, and the average of other companies in same country
+
+e/EntSupplyChain (GET)
+Gets all the supply chain history for this enterprise
+
+e/EntCosts (GET)
+Gets all the operating cost history for this enterprise
+
+e/EntFlights (GET)
+Gets all the flights history for this enterprise
+
+# Application Prototype
+
+## Database ER Diagrams
+
+| User Homescreen                        | Enterprise Homescreen                          | NGO homescreen Diagram               |
+| -------------------------------------- | ---------------------------------------------- | ------------------------------------ |
+| ![User Homescreen](./userhomepage.jpg) | ![Enterprise Homescreen](./enterprisehome.jpg) | ![NGO Homescreen](./ngohomepage.jpg) |
+
+These are images of each of the user personas. The homescreen for each user persona allows them to implement the features mentioned in their respective user persona stories. The main features for users and enterprises include taking surveys to gather further information, while the NGO persona does not take a survey. Instead, NGOs select the tags they want to be associated with and are matched with users based on these tags.
+
+| NGO Survey  
+![NGO Survey](./ngosurvey.jpg)
+![NGO Tags](./ngotags.jpg)
+In the NGO user persona, NGOs decide which tags they want to be associated with.  
+NGO users can add or delete tags if they so desire and they can also update their recorded information.
+
+| Enterprise NGO Match
+![NGO Match](./ngomatch.jpg)
+Enterprises have a feature where they can match with NGOs with the same tags as displayed in this screenshot.
+There is also a comparison to your average company emission within the same country to give enterprises more insight on their emissions.
+
+| User Survey
+![User Survey](./usersurvey.jpg)
+![User Survey](./usersurveyres.jpg)
+In the general user persona, users take a survey, and based on this survey data, the app interacts with a machine learning model to estimate future carbon emissions and provide data on their current carbon emissions.
+
+![User History](./userhistory.jpg)
+Users can also see their survey history and previous emission data.
diff --git a/blog/content/team_posts/phase_3/ngotags.jpg b/blog/content/team_posts/phase_3/ngotags.jpg
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diff --git a/blog/content/team_posts/phase_3/phase3.md b/blog/content/team_posts/phase_3/phase3.md
deleted file mode 100644
index 1fa426a..0000000
--- a/blog/content/team_posts/phase_3/phase3.md
+++ /dev/null
@@ -1,105 +0,0 @@
----
-title: "Project - Phase II"
-date: 2024-05-28
-draft: false
-description: "Implementation"
-tags: ["authors", "config", "docs"]
-slug: "phase_three"
-authors:
-  - "aahil"
-  - "anjola"
-  - "justin"
-  - "michael"
-showAuthorsBadges: false
----
-
-# API routes and purposes 
-
-### User Routes 
-u/UserCars (GET)
-Gets all car survey data history for the user 
-u/UserAddCar (PUT) 
-Allows the user to add additional car survey data 
-
-u/UserResidential (GET)
-Gets all residential survey data history for the user
-
-u/UserFlights (GET)
-Gets all flight survey data history for the user
-
-u/UserTransport (GET)
-Gets all transport survey data history for the user
-
-u/UserAddRes (PUT)
-Updates this user's residential data based on survey
-
-
-### NGO Routes
-
-n/NGOupdate (PUT)
-Updates the NGO given filled out data in NGO table
-
-n/tags (GET)
-Gets the tag description for all tags for the NGO user
-
-n/EnterpriseMatch (GET)
-Gets the all matching enterprise names and descriptions based on tags
-
-n/ngomine (GET)
-Gets all my NGO data from the NGO table
-
-n/UserMatch (GET)
-Gets all of the matching users based on tags
-
-n/TagDelete
-Allows NGOs to delete tags that they were associated with 
-n/NGOadd
-Allows NGOs to add tags that they want to be associated with 
-
-
-### Enterprise Routes
-
-e/tags (GET)
-Gets all of the enterprise’s tags from database
-
-e/NGOMatch (GET)
-Gets all of the matching NGO's based on tags
-
-e/EntCompare (GET)
-Gets my emissions, my country's, and the average of other companies in same country 
-
-e/EntSupplyChain (GET)
-Gets all the supply chain history for this enterprise
-
-e/EntCosts (GET)
-Gets all the operating cost history for this enterprise
-
-e/EntFlights (GET)
-Gets all the flights history for this enterprise
-
-# Application Protype 
-## Database ER Diagrams
-
-
-| User Homescreen                   | Enterprise Homescreen                        | NGO homescreen  Diagram                   |
-| ----------------------------------- | --------------------------------------------- | --------------------------------- |
-| ![User Homescreen](./userhomepage.jpg) | ![Enterprise Homescreen](./enterprisehomescreen.jpg) | ![NGO Homescreen](./ngohomepage.jpg) |
-These are images of each of the user personas. The homescreen for each user persona allows them to implement the features mentioned in their respective user persona stories. The main features for users and enterprises include taking surveys to gather further information, while the NGO persona does not take a survey. Instead, NGOs select the tags they want to be associated with and are matched with users based on these tags.
-
-| User Homescreen                   | Enterprise Homescreen   
-![NGO Survey](./ngosurvey.jpg) | [NGO Match](./ngomatch.jpg)
-In the NGO user persona, NGOs decide which tags they want to be associated with. Based on these tags, they receive a list, similar to the example above, allowing them to see which users and enterprises they have been matched with.
-
-User Survey 
-![User Survey](./usersurvey.jpg) 
-In the general user persona, users take a survey, and based on this survey data, the app interacts with a machine learning model to estimate future carbon emissions and provide data on their current carbon emissions.
-
-Enterprise Survey 
-![Enterprise Survey](./enterprisehome.jpg) 
-Enterprises, similar to general users, also take a survey. This survey allows them to match with general users and NGOs. 
-
-
-
-
-
-
diff --git a/blog/content/team_posts/phase_3/userhistory.jpg b/blog/content/team_posts/phase_3/userhistory.jpg
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From ac4781cc09eb570d25ff7a776e4bc1fba89014b4 Mon Sep 17 00:00:00 2001
From: Juwang110 <Juwang110@gmail.com>
Date: Thu, 6 Jun 2024 02:46:36 +0200
Subject: [PATCH 31/34] Added individual phase 3 reflection

---
 blog/content/justin/phase3.md | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 blog/content/justin/phase3.md

diff --git a/blog/content/justin/phase3.md b/blog/content/justin/phase3.md
new file mode 100644
index 0000000..5eb611b
--- /dev/null
+++ b/blog/content/justin/phase3.md
@@ -0,0 +1,13 @@
+---
+title: "Phase III Reflection"
+date: 2024-06-06
+draft: false
+slug: "3"
+tags: ["reflection"]
+authors:
+  - "justin"
+---
+
+# Phase 3 Dialogue Reflection
+
+Phase III of the project was a whole other beast and was by far the most time-consuming phase. Perhaps a day after phase II ended, as a team we consulted with Dr. Fontenot due to confusion on how to connect personas based on emission tags. We then changed our ER diagram and made tags a new strong entity with 3 many to many relationships between the personas. The SQL DDL was updated accordingly. I did some work by myself throughout the week based on the wireframe from phase II. I made the initial landing page and figured out page navigation for some pages. Later that week I added NGO enterprise matching functionality with two get API routes which matched NGOs with enterprises based on shared tags. This was my first time working with the API and I was pretty confused about all the things I needed to change with the actual blueprint, making the blueprint in rest_entry.py and calling the route through the API URL. However, it was a great learning experience and I eventually figured it out which made writing future routes a lot easier. Next, I added the Enterprise compare tool which was another get request. This feature shows the logged-in enterprise’s emissions, their country, and the average of all enterprise’s emissions within that country. In my free Sunday in Luxembourg, I tried to add an NGO info section page that would have the NGO user fill out the information to be posted into the table (POST request). This feature took me extremely long for no reason. I thought I had the logistics right with collecting and sending the data but I kept getting an error. I had a back-and-forth Slack conversation with Dr. Fontenot about it and decided to leave it till Monday. It turned out my api url was slightly wrong causing the whole thing to not work. I eventually managed to make the successful POST request and that feature was completed. On the day before phase III was due we met as a group and had a productive conversation about where this app was going to go. We discussed with Dr. Fontenot that the user persona’s logging in should already have survey/info data filled in. However, I had already created the NGO info questionnaire. Thus, we decided to make the info questionnaire a PUT request to edit information and the surveys would also be PUT requests if users wanted to retake their survey. We decided to leave the survey off until the due date because, on the Data Science side, we still needed to finalize what questions we were going to ask (if they actually impacted emissions). I instead worked on adding history pages for each persona (displays survey result history). On the day of the due date, we slaved away. We still didn’t know what survey questions to ask due to the data science side not being done. So I changed the NGO POST route to a PUT route instead given the NGO user is already logged in. After continuing to work on setting up pages, we got news from Michael that there wasn’t a strong correlation between many of the initial factors we anticipated and emissions. In fact, only two features he found were strongly correlated meaning we only could have two emissions questions. This was obviously very disappointing, but there was little we could do. Finishing up the night, we implemented a bunch of the features and I helped Anjola create a POST, and DELETE request for changing and adding NGO tags. I also hard-coded country and emission data into our database, as well as mockarooed some of the entities. I assisted Aahil in the integration of the ML model and the finishing touches. Then, edits were made to the blog and we called it a night.

From 8ff1ef31fa1304ee077f013bdf1097fca4c4b337 Mon Sep 17 00:00:00 2001
From: mehallhm <michaelhmehall@gmail.com>
Date: Thu, 6 Jun 2024 03:03:29 +0200
Subject: [PATCH 32/34] personal reflection

---
 blog/content/micha/phase3.md | 19 +++++++++++++++++++
 eda/eda3.ipynb               | 34 +++++++++++++++++-----------------
 2 files changed, 36 insertions(+), 17 deletions(-)
 create mode 100644 blog/content/micha/phase3.md

diff --git a/blog/content/micha/phase3.md b/blog/content/micha/phase3.md
new file mode 100644
index 0000000..183ad33
--- /dev/null
+++ b/blog/content/micha/phase3.md
@@ -0,0 +1,19 @@
+---
+title: "Phase III Reflection"
+date: 2024-06-06
+draft: false
+slug: "3"
+tags: ["reflection"]
+authors:
+    - "michael"
+---
+
+### Project Reflection
+
+This project phase was dominated by implimentation of the machine learning model that was partially discovered in Phase II. However, as I was building it, nearly every part had to go back to the drawing board. Starting off, the scraper / parser combination I was using to get the data from Eurostat was made for a three dimentional table, not some of the more complex five or six dimentions that some of the new data I found was structured as. As a result, I could either roll my own implementation again or use a library; I choose the later and used fthe SDMX standard after our visit to Eurostat. After discovering some of its odities, such as what needed to be capitalized and what did not, it turned out to be much easier to pull data them my hand rolled solution. After that was switched out, other pieces such as merging the data and standardizing could be pulled from the previous codebase with light moditifcations.
+
+After getting a new data pipeline constructed, I also went back to the beginning for what data to use in order to draw the conclusion of carbon emissions. First, choosing which interpretation of "Carbon Emissions" had to be made, and, after a little general topic research, I dived into specifics for areas that make up the global carbon emission footprint. These primarily include Energy, Transportation, Agriculture, and Industry. Since we wanted to focus on residential people for this first model, I found great datasets for household energy and transportation, after bouncing ideas off of some of the professors. After finding the data and cleaning it using linear regression within each block of observations, I constructed the model to find that the original set of features insufficiant. To risk repeat myself from the team post, we settled on total household energy rather than by household sector, and used fuel as a proxy for transportation. However, even when using this smaller dataset, we ran into issues of overfitting, caused by the one-hot encoded country data. After dicussion with one of the professors, it was determined that the country was not strictly necessary for the model as the data was linear enough without it. After that, the model was good enough for now, and was tested using LOO-CV to find a final R2.
+
+### Belgium Reflection
+
+While not in regards to Belgium, the lecture on the GDPR was one of my favorite guest speakers. Not only was the speaker incredibly knoledgeable, he also operated on a shared basis of understanding, allowing him to skip some of the technical aspects that we already knew. The law and legislation that the EU has been pushing and the reasoning _why_ is super interesting and could potentially give Europe that leg up which the legislators belive it will. I have always admired the digital restraits that the EU puts on companies in favor of people and hearing an expert talk about one specifc one was very interesting. He also aroused something in me that enjoys that type of work where it operates in both a technical aspect as well as, in this case, a legal one.
diff --git a/eda/eda3.ipynb b/eda/eda3.ipynb
index 339a966..e9f8948 100644
--- a/eda/eda3.ipynb
+++ b/eda/eda3.ipynb
@@ -1470,12 +1470,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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/73jHO/j4xz9OKpXiBz/4ATU1NSMGZlPpYx/7GHfccQeXXXYZzzzzDEuXLuWee+5hy5Yt3HLLLcRiMaCwxLFq1Sruuusujj32WCorK1m9ejWrV68+4utfeOGFfOlLXyIYDHLFFVcMyd1JJpMsWrSI973vfaxdu5ZoNMpDDz3EU089xTe/+c2Bx33ve9/jxhtv5JFHHjli36pf/epX/OY3v+Gb3/zmwNIMwMknn8xVV13F9773PS677DJOO+20o74vZ511FosWLeI3v/kNoVBoYBt7v/PPP5+f/vSnxONxVq1axZNPPslDDz10xO3z/a688kruuecezjvvPC644AJ27drFz372s2F5Wx/60Ie4++67+Yd/+AceeeQRNmzYgOu6bN++nbvvvpv777+fdevW8ZWvfIXHHnuMt7/97SxZsoT29na+//3vs2jRIs4444yjjkeIcZvhmSIhisbhlp+UKiwpLF++XC1fvlw5jqOUUmrXrl3qwx/+sJo/f77y+Xyqrq5OnX/++eqee+4ZeN6//uu/qvXr16vy8nIVCoXUcccdp772ta8py7IGHjPS9utsNqs+9alPqXnz5qlIJKLe8Y53qKamphGXYx544AG1evVq5ff71cqVK9XPfvazEV/z3nvvVSeeeKIKBoNq6dKl6uabb1Y/+tGPpn35SanCks/ll1+uqqqqlN/vV2vWrFGbN28e9twnnnhCnXrqqcrv9496KWrnzp0DW6sff/zxIffl83l17bXXqrVr16pYLKYikYhau3at+v73vz/kcf3v35GWrpLJpFq0aJE66aSTBj4Tg/X19amFCxeqU045ZcT7R3LttdcqQF1wwQXD7uvp6Rl4z6LRqDr33HPV9u3bh23XHmn5SSmlvvnNb6q6ujoVCATUhg0b1NNPPz3iv41lWermm29WJ5xwggoEAqqiokKdeuqp6sYbb1SJREIppdTDDz+s3vWud6mFCxcqv9+vFi5cqC6++GK1Y8eOUf2eQoyXptQROqUJIYQQQhQJyakRQgghREmQoEYIIYQQJUGCGiGEEEKUBAlqhBBCCFESJKgRQgghREmQoEYIIYQQJWFOFd/zPI+WlhZisdhhe50IIYQQYnZRSpFMJlm4cOGwZreDzamgpqWlZUhVTyGEEEIUj6amJhYtWnTY++dUUNNfXr2pqYmysrIZHo0QQgghRqOvr4/6+vqB8/jhzKmgpn/JqaysTIIaIYQQosgcLXVEEoWFEEIIURIkqBFCCCFESZCgRgghhBAlQYIaIYQQQpQECWqEEEIIURIkqBFCCCFESZCgRgghhBAlQYIaIYQQQpQECWqEEEIIURLmVEVhIUTx8zxFc2+WtOUQ8ZvUlYfQdWlQK2Y/+exOPQlqhBBFo7E9yf1b29jVkSLnuARNg+XVUc5dXUtDzZF7wggx1Y4UtMhnd3pIUCOEKAqN7Uk2b9lLd9piQTxI2B8iYzlsbUnQkshy+YalcnIQM+ZIQQsgn91pIkGNEGLW8zzF/Vvb6E5brKiJDjS1iwV9RAMmO9tTPLCtjWVVUZnOF9PuSAF3c2+GoM+Qz+40kURhIcSs19ybZVdHigXx4LAuvZqmsSAepLE9RXNvdoZGKOaqQwPuWNCHoWvEgj5W1ERp7s3y191dzC+Tz+50kKBGCDHrpS2HnOMS9o88uRzyG+Qdl7TlTPPIxFx3tIC7IuynN2PjemrE58tnd3JJUCOEmPUifpOgaZA5zIE/a7kETIPIYYIeIabK0QLuWNAEDZI5e8T75bM7uSSoEULMenXlIZZXR2lN5FBq6BWvUorWRI6Gmih15aEZGqGYq44WcJu6RjzkpztjyWd3GkhQI4SY9XRd49zVtVRG/OxsT5HM2TieRzJns7M9RWXEz6YTaiXRUky7owXcB/ryvH5ZJXXlYfnsTgOZ7xJCFIWGmhiXb1g6sG22rS9HwDRYUxdn0wlS60PMjP6AuyWRZWd7Ibcm5DfIWi6tiRyVET8Xn74YQD6700BTh4aWJayvr494PE4ikaCsrGymhyOEGAepyipmo8F1avJOIU+moSY6JGiRz+74jfb8LTM1Qoiiousa9ZXhmR6GEEM01MRYtjF6xKBFPrtTT4IaIYQQYhJI0DLzJFFYCCGEECVBghohhBBClISiCmqam5v54Ac/yLx58wiFQqxZs4ann356poclhBBCTIjnKZq6M2w/0EdTdwbvMBWIxZEVTU5NT08PGzZs4E1vehN/+MMfqK6uZufOnVRUVMz00IQQQohxO1KHb9nuPTZFE9TcfPPN1NfXs3nz5oHbjjnmmBkckRBCCDExR+rw3ZLIcvmGpRLYjEHRLD/de++9rFu3jve///3U1NRw8skn84Mf/OCIz8nn8/T19Q35EkIIIWaDo3X47k5bPLCtTZaixqBogprdu3dz2223sWLFCu6//34+8YlP8KlPfYqf/OQnh33OTTfdRDweH/iqr6+fxhELIYQQh3e0Dt8L4kEa21M092ZnaITFp2gqCvv9ftatW8cTTzwxcNunPvUpnnrqKZ588skRn5PP58nn8wPf9/X1UV9fLxWFhRBCzLjtB/r4zsM7WVYVxRihsrDjeeztTPPJs1dw3Py5fc4abUXhopmpWbBgAatWrRpy2/HHH8++ffsO+5xAIEBZWdmQLyGEEGI2OFqH76xVaLcQ8RdN+uuMK5qgZsOGDbzyyitDbtuxYwdLliyZoREJIYQQ43e0Dt+tiRwNNVHqykMzNMLiUzRBzWc+8xn+8pe/8G//9m80NjZy55138r//9//mqquumumhCSGEEGPW3+G7MuJnZ3uKZM7G8TySOZud7SkqI342nVArTS/HoGhyagB+97vfcf3117Nz506OOeYYPvvZz/LRj3501M+XLt1CCCFmm9F0+J7rRnv+LqqgZqIkqBFCCDEbeZ46YofvuW6052/JPhJCCCFmmHT4nhxFk1MjhBBCCHEkEtQIIYQQoiRIUCOEEEKIkiBBjRBCCCFKggQ1QgghhCgJEtQIIYQQoiTIlm4hhBCiSEg9myOToEYIIYQoAoMrD+ccl6BpsLw6yrmrpfJwPwlqhBBCiFmusT3J5i176U5bLIgHCftDZCyHrS0JWhJZLt+wVAIbJKdGCCGEmNU8T3H/1ja60xYraqLEgj4MXSMW9LGiJkp32uKBbW143pzpenRYEtQIIYQQs1hzb5ZdHSkWxINo2tD8GU3TWBAP0tieork3O0MjnD0kqBFCCCFmsbTlkHNcwv6RM0ZCfoO845K2nGke2ewjQY0QQggxi0X8JkHTIHOYoCVruQRMg8hhgp65RIIaIYQQYharKw+xvDpKayKHUkPzZpRStCZyNNREqSsPzdAIZw8JaoQQQohZTNc1zl1dS2XEz872FMmcjeN5JHM2O9tTVIb9rFkUZ0d7kqbuzJxOGNbUoWFfCevr6yMej5NIJCgrK5vp4QghhBCjNrhOTd4pLDmVh32goDdrl3TtmtGev2UBTgghhCgCDTUxlm2MDlQU7kzm+f2LrfRkbKldc5AENUIIIUSR0HWN+sownqd4+KV2ejI2K2qiA1u9Y0Ef0YDJzvYUD2xrY1lVdE61UZCcGiGEEKLISO2akUlQI4QQQhQZqV0zMglqhBBCiCIjtWtGJkGNEEIIUWSkds3IJKgRQgghisxRa9dE/Gw6oXZOJQmDBDVCCCFEUWqoiXH5hqWsXhinN2OztzNNb8ZmTV18Tm7nBtnSLYQQQhStQ2vXRPwmdeWhOTdD00+CGiGEEKKI9deuEbL8JIQQQogSITM1Ylw8T8l0pxBCzGJz8TgtQU2JmY4P8eCmaqXcQE0IIYrVXD1OS1BTQqbjQ9zYnmTzlr10py1poCaEELPQXD5OS05Niej/EL/YnMDUNcqCPkxd48XmBJu37KWxPTnhn+F5ivu3ttGdtlhREyUW9GHoGrGgjxU1UbrTFg9sa8Pz1NFfTAghxKSb68dpmakpAf0f4n3dGWzHZWdbEsvz8Os61TE/acuZlG6tY2mgJpn4Qggx/eb6cVqCmhLQ3Jvl7009NHVn6MvZKFUok+0p6EjniQVM/IZGc+/CCX2IX2ugNnLZ7ZDfoK0vN+caqAkhxGwx14/TsvxUApI5m8a2FF2pPEqBroHrKfKORzbv0taX45lXe9nWkpjQz5EGakIIMbvN9eO0BDUloC9n052xADB1jaztYXsKUwe/qaEBqbzDPc/sn1BuzeEaqCmlSGQsdrQlqYr5WVAWnOivJIQQYhzmeqNLCWpKQMZyCx9eDXK2i+d5oBSuB55XWEfVUPRmJpYgNlIDtY5kji2NnTzwUiGnZ3dHmjse2z0piclCCCHGZq43uizN+ac5Rtc0Qj6DvOuRshw8DwphS+F/NcBvaFRGAhNOEOtvoHb/1jb+3tTDjrYkjqtYUB5kZW0ZQZ8+J7YNCiHEdBpLDbLBx+ldHSna+nIETIM1dXE2nSB1asQst6wqQlUswP6eDK732u39H3cF2J7CdlzyhjbhBLGGmhhLz4zw7/e/Qs52aaiOUhbyDWTaRwMmO9tTk7LjSggh5rrx1CCbq40uJagpAYsqwqxfWkFjW2HJRwf0gwuLhZ1QhdmcV9qSrD9m3qQkiLX25ehM5Tm2NkYs6Bty31zYNiimz1ws9S5Ev4kU0puLjS4lqCkBuq6xtr6Cnz/VhO4Vcms8j4GpGlOHgKnRlbZwlZqURN5Dtw0qpUjmHCzXw2/ohPwGead0tw2K6TFXS70LAcML6fXPhseCPpkRPwwJakpE0G8QDfjQgorerIPjqv6UGhzAsQrrUns7C4m8Ez0pDN42aLseu9rTdGcsHM/D1HUifoOKiL9ktw2KqTeXS70LAVJIbzzkjFMi5kX8hPwGfkPDciFnOXhK4R4MbBSF+jUVYf+knBT6tw3+ZU8XPek8OdsjGjTxGSaW47K/N4vjKbK2zNSIsZMrVCGkkN54yJbuEnFKfQVLKsMcSObRgIqwid/U8ekaAVPD0MBv6tiuR0N1ZML9P3Rd4y2raunL2nSkLCIBE59ReP103qU6GqAs6OOhl9pLtseImDpjuUIVolTN9UJ64yFBTYkwTZ13nLQQDcg7LnkXXAWaBo4LpqGzIB6kJ2OTyruTclII+Q2qYwEWlYfIOx49GYuc7VFTFuTkxRWsqI3KiUeMy2tXqCMfrAs5W65coYqSNtcL6Y2HhHclZG19OcfNj9GWyNGdsbEdD12DkN9kfjxIPOSjN2NhuR7lYd+Epy3TloPf1HndsnlkLHcgSTgWNNE0DcfzZGpUjMvgK9RDd9eBXKGKuaG/kF5LIsvO9sLMZchvkLVcWhO5ki+kNx5yRCghEb/J4soIaxaWcyCZ5YX9CUI+nYjfh4cinXcwNA2/oU/KSaH/xJO1XcpCcuIRk6f/CnVrS4JowByyBNV/hbqmLi5XqKLkNdTEuPQNS7jn6WZ2daTwlEd5yD8nCumNhyw/lZD+E8GBZI6G6ijVsQDdGZumngz7e7Ls686QsV0sx52UaUuZGhVTZa6XeheiX2N7kge3tdORzOEqha5pVMcCnHO8BDQjkaCmhAw+ETzXlKA3Y+O4Ctv18FwPv6HhuB6P7ezE0LUJnxTkxCOmUn+p99UL4/RmbPZ2punN2Kypi8t2bjEn9Jc12NqSoCLiZ/XCOEvmRWjqyfKTJ/dKj70RyLpAiemfqvzqb1/GcjzKQyYZy8NTioBPJ+gzMHSd2liAZVXRSfl5c7XHiJh6oyn1LhWHRSmSsgbjI0FNCQr5TKqifhbEq8jYLs09GRJZG9tTgEYsYPBqd2bSCjbN1R4jYnocqdS7VBwWpUoK742PBDUzbCquMtOWQ971CPkNXj2QJmO5BH0GIU1DKUVv1qYzbfFya9+k/THMxR4jYmZJxWFRyqTw3vhIUDODpuoqM+I3CRg6rxxIksjaeJ6iN2PjHUwy8xkarlI882oP5xwvOS+i+MjUvCh1UtZgfCRReBJ5nqKpO8P2A300dWeOWEm3/yrzxeYEpq5RFvRh6hovNifYvGViCWB15SGqYwGaerKkcjZpy8U0NIKmDigSGRvHUexs65PCeKIoScVhUepkd+n4SIg3SRrbk9y39QAvNifIWA5hv8maujjnrZ4/bNal/ypzX3cG23bZ0ZbE9jx8uk5N1E/aciZ0lanrGqcsqeCup5pwPEU0YOB5Hinbw3K8garDL+zvY1tLYlKXjSRpU0wHmZoXpU4K742PBDWToLE9yS0P7eTlll56sw55x8PQNF5uTvByax+fecuxQwKb5t4sf2/qYX9Pht6MjespCim80JOxiId8BEx9QglglQcbXLqeIud45GwXTyn8hk7Yb+B4irTl8N/P7KehJjopuQeStCmmi0zNi7lAdpeOXdH+xX/961/n+uuv59Of/jS33HLLjI3D8xR3/nUfj+7oIJkbelWYyDm0Jg8QMA2+8f61AxF1Mmezsy1FZyqPqxTKUwP5LpquYTnewOMO9zOPNhsSDZqUhXzYtkvW8VBKETR1TKOw4ui4LiGfTsZ2JyX3QJI2xXSSisNirpDdpWNTlEHNU089xR133MGJJ54400Nhf0+Ge59rHhbQ9LNd+O0Lzbz7pDrOXFkNQF/OpiudJ+e4KFWYoQHwDgY4GtCZytM3QlAz2tmQWMDH4sowuztSpPIOQZ+Baei4SmE5HrquURbysTAemvC2QEnaFNNNpubFXCK7S0ev6BKFU6kUl1xyCT/4wQ+oqKiY6eHw8oEEnemRZ1T62S78+IldA4nD6bxD3vZwPUBBfwqYRuF7V0He9kjnhwZKg6tLlod9LKuKUh72sbVleHJxXXmIk+srKA/58Rs6nqfI2i624xEwCjugKsJ+qqL+CXc7lqRNMROk4rAQ4lBFN1Nz1VVX8fa3v51zzjmHf/3Xfz3iY/P5PPl8fuD7vr6+SR/Po690jOpxz+ztpaknw5J5EbrSFu7BAMeD16KaQVyl6EpbA9+PdTak/0p2R3uSA31Zwn4Dy3HpzRYCGFPT6Pbp/G1PNxUR/4RyDyRpU8wUmZoXQgxWVEHNz3/+c5599lmeeuqpUT3+pptu4sYbb5zSMbX25Eb1uIztsqczzZJ5EV5uSRSCmSPwFLQlXnvt8VSXbKiJcdWblnNdV5qtrX3YzmsJyZiQyjkkcw6Op8ha7uh/6UNI0uZwsgts+sjUvBCiX9GcZZqamvj0pz/Ngw8+SDAYHNVzrr/+ej772c8OfN/X10d9ff2kjkuN8jzVH4c4jsdjO0Y3u/PC/gSep9B1bdyzIU3dWboz9kBA089yFF2OTSxgYBoaD73cRkPN+HJeJGlzKNkFJoQQM6NocmqeeeYZ2tvbOeWUUzBNE9M0efTRR/nOd76DaZq47vCZhkAgQFlZ2ZCvyXbiotGdpCJ+g2VVEZ7Z182BvvzRnwC8ciDJ/p7Mwee/NhsykpFmQ3YcSPKdh3fS3pdncKiiBn315V3aEjl2tiXHnfMi3bpfM1LeUzxk8re9XXzrwR08vrPjiEUZhRBCjF/RzNScffbZvPjii0Nuu/zyyznuuOP4whe+gGEYMzKu1y2v5nt/3HPU5aTXLZvHooowj+7owHGP9uiCzlSeXR0pFs+LjDgbopQimXPIOy7NvVnWL503MBvieYp7nmmiNZEDvCOOb39vjlBL34RyXqSewsh5T12pPC+19tGdtkjlHfZ2pnnbmgUjFmUUk0uWAIWYe4omqInFYqxevXrIbZFIhHnz5g27fTqtXzKP4xbEeKn18G0NyoImnz57JbquEfQZjPZCPWd7dCYLszqHbmEN+XRaenN0pPKk8w4hn8nyqjy7O1M01MQGcnAMHWz36D+wqSuFf4IH/LmetHlo3tOezjR/3d1FxnIKNYg0aE3keHRHB62JnOzQmUKyBCjmCgnehyqaoGa2Mk2d/2/TSr74660HZ0WGivoNrj13JcctLCx9rVtagc/Qsb2jz9YoGDLD0j8bcudf9/HI9naytkskYLJ0XoSF5UFa+3Js3rKXyzcsxfEUrirktDijmBiyXdja2seyCR7w53LS5uC8p65Unr/u6SKVd4gEDAxdw3YKVZzb+rIEfbrU7pkiUghSzBUSvA9X1EHNn/70p5keAgBnH18LwI/+vJtX2pLkHQ9T11heHeHjGxt4y6r5A49dUhnhmOoI21qO3rBS16A6Fhhy27KqKJVhP4vnhakrDxEwDWLB15aj+rd2n3/iAsqDJq434o7xYTQN2keZ63M0c/XKoT/vKZ232dbSRzrvEA2YKAo7zWxX4SpFZ8rCcRUBw5hQ0UMxnBSCFHOFBO8jK+qgZjY5+/hazlpRzbNNPXSlLeZF/JxSX4FpDs3F1nWN/2/TSj7y46eP+pq6BjpDD7zNvVl2d6ZZXh0dtn168NZuBdSUhQrtF+CIOTU6YBoaNWV+mroz4w5GPE/xxK5OHnqpnZbeDHm3P7iL8r5T6zl2fmn/gfXnPf1tbxc96TyGruEpRdpy8Tw10KoiYBoksjYvtfbxcmufBDWTqH8JcH5ZkGTOwXI9/IY+EPiPVPpAiGIjwfvhSVAziUxTZ/0x8476uI3H1nBSXRnPNR+lGKAGW3Z1cOax1QMfzNFu7c7aLuuWVvCHra1Yjkcyf/g6NB4QDZjsac/wp+2d45rGbGxPDiyL9eUc1MFeVn5T46WWPv68s5OL1tfz5uNqS3bmpj/vaVtrgpTlogEZyxkotGgYGiG/ganr2K6G7Xo882oP5xw/N3aGTYe05dCZytPSm6U3W9iFZ+o6lWE/y2silIV8UghSFL3x1C2bK4pmS3cp0XWNj29sOOrjbLdQq2bwVuuxbO0+fkEZK+fHqK8IYx7hX1oDAj6Dlw8kj9p+YSSN7Ul+9PheHt/ZSd710PCwXY+c45LOu1iux+7ONN96YAc3/nYrt/1p11Ffs1g11MT4wPrFVEf9KFVI9lYK/KZBNGDiM3Qcz8NTUFsWpL0vJ+0jJlFnMk9Td4YDiULeUkXYT9Cn057M8VxTL6292TlXCFKUntcubkf+HIf8xoTb3xQrCWpmiDVCXZ2R7OvODOnW3b/E0ZrIodTQbJn+QncNNVHqykMD/Z+qywLMLwsS8RscOh/g02FReRClYH5ZgFjQh6FrxII+VtRE6U5bPLCt7bC1VfqnQZt7M2gUmmWm8h6Op8jbLsm8QyJjEzILJ/O2RJ4Xm0cXLBWrNyyv4u0nLqSuMkTQZxD0GYR9Bqau4biKdN4lHDBZXRfHcr05eeCZCp6neG5fLz5TxzB0/IaOrmkETIPKiJ9M3mFrcx/Lq6NzphCkKE3jqVs2V0hQM0P2dqVH9bh03iE1qLHloYXuEpk8TV0ZXtjfy1/3dFER8g0Uuut/bCRg4niK5dVRTlgYY0E8QFnQZH6Zn7OOrSYUMPGbGvYhgctomlH2T4NWhv0k8y7JnI2i0Gm8f1rUVZBzPDRNI5V3mF8WOGqwVMx0XeO81fNZURMj7DcIHAzo0pZ7cPu9wXHzY7heId9jLh54pkJ/vtnqhWWE/SbdaYu84+IpheV6OF6hvMGJ9XFZ7hNFbSwXt3ONHE1niDZszuQwj9M0osGh/0z9W7u//0gj//PigYEcloCpYzser3ZlBvJgGmpiXLx+MXs7CwnAtuthuwq/qeM3DBo70vRmLMrDPvzG8Bj3aM0o+6dB50X8A/kjQdNAoQaSlJVWaNCpeeAphe2pkl/z7e+71Z2yaGxP4jd1HA+Up/D7dJp7suxsS7G8JjqhvlviNf2fxWVVUSIBH43tKXoyhaKHpq4zPx4kYOrDdhTOtLm6W1CM36F1yxbEg4T8BlnLpTWRm1NV3A8lQc0MOW1pJX5dwzrKTIVP1+hOWcNuf7Urw9Ov9pC2XExDQ6dQ3G1He4obf/sS8NpW8w3Lq3jb6vk8uqODnoyF5ofysB+/qZPKObTaHmbOxXa9gSrF/btGQB1xGrN/GjRnu+gHZ2YUCqVAKQ7O2IB28HtD1/Ab+pzo3H1sbRn/cv4qbn2kkaaeNL2Zwu8a9OnkbZeyUOE9/cmTe+fs9svJNHhKvjLi57SlFcM+y4msM6tmxqTOiBgvqeI+stnz113kHMc76nbuwU5bUsny6ggvt6UO+xifDhURP8839fKG5VUDUbfjeHz/kUY6knkCho5p6GhaIWhwXI+OZI7b/tTIWSuqMU0dXdfYtLqWB19uI3lw+cdv6tiuh+W4lIdMHA+2NvcS8pv0ZAq7RgxNQyk4Y0XVYacx+6dBn9zdSSRQCG4sR2EahbF6qhDUGDqgQVnQRyxokso7c2LN99j5Ma5683K++tuXOZDIE/brhSTheIjl1REqwv45vf1yMo3USiQWNA/bSmSmSZ0RMVFzvYr7SEr7jDJNHn65jR/9eTfb25JYjoff1DmuNsZH3rhsYLbkUKap87nzjuNzv3ienow97H4dWFgRZk1dnF0d6SHLNE/v62Znewrt4LJO3nLwDq6rmvrBGZu2FE/v6+Z1y6oACPlMqmMBTF0jbblkLAtD16mNhzgh6ufF/Ql2tKWpCJvMiwXwY5LI2HhK0Z58rf3CsHEenAbd0ZZkd0eaqqifzpRF3ins8EEDv65hGDqmrnFMdQRgTnXuDvlMqqJ+FsSr8Jn6kLopQMkvxU2XsbYSmUlSZ0RMlrlcxX0kkig8QQ+/3MYX/vsFntzTTVfaJpl36UrbPLmnmy/89ws8/HLbYZ979vG1XHPOCuIhE1Mr/GPoGoR9OsfOj3HmimoWlIeGbc3b2ZYiZ7u4riLveDiuwvUUjqvI2h45yyNruewcNAuUthz8ps7rls3jdcsqOX5BGfUVIaqjfipChdkTXQefaZCxXCzHo64ixFnHVuN66ohJvYX8kQaWV0dxPKiKBSgLFQ7OIV9hJsnQNBbPC1Me8s25zt1pyyHvetSUBamKFt6bwbUl5vL2y8nWPyW/IB7k7/t6BxLyl86LcPLi+EArkZneeTeWOiNCiNGTmZoJcByPf/ufl+gcIefFU9CZsvi3/3l5YBloJG8+rpa/7+sl77jkbBfT0FlYFiIeLpz4kjl72DJN0NQLMzMHZ0LUwSUeTSvksDiqcCWYGDQD1J9v0JrIsrsjzf7eLDm7kKDqM3RcT1EeMlm3pIKAzxgym+A39aPOJBw7P8a/vON4bn1kF12pPPPLgoR8Bi2JLLs60mhAechPIuvMuTXfwbkeh1aBhrm9/XIqjLaVyEzOgoy2iKYEukKMjRxFJ+Cve7rY1Zk54mN2dab5654uNqyoHvH+uvIQK2pibG1JsKaufMhVW//WvEOXaeoqQxjawSTjgwGNd0iPJw1o6knheQpd16grD1Ee9vH7F1vJWC6aVjjZKhSZvEPG9jB1CPoM5kWH7g4Z7QH22NoyPvnmhoHEte6MRTzk5/2nVrC2vpyqWGBOrvmOlOvR73D/xmL8RttKZCaX+yTQFWJqyF/MBNy3rXXUjztcUDOerXnlYT/xsElH0kZRmKk5lKbB3s7skAO38hRZy8X11EDnaFeBruvoWmGr9+6ONJUR/5AT71gOsJK4Npxsv5xexTALIoGuEFNDgpoJ+Nue7kl53Fi35sUCPo6ZF6Ur1YM7UkADoApXrP3ViJt7s7QkcpSFfNiuOlivprANOxY08Zs6WcvlQF+WZC5GWahw9TieA6wkrg0n2y+nTzHMgkigK8TUkKBmAlz3SL2vx/a4scxw1JWHOKY6wtOv9oz4WppWSDjuy9kDQU3ackjbhWTh2jJfIblYKYyDOTMZy2Vfd4ZE1qEnYxEOyAF2ssks1vQollkQCXSFmHwS1EzAcQvKaOw8+u6E4xaUjer1RjvDoesaq+vi/OKZ/YWcGgpBTH/usFKApuF6ivTBarURv0nEV/jndrxCA8vBTEMjHvIR9OlkbZe9nWk5wE4BmcWaesU0CyKBrhCTS4KaCbjq7AZ+9+Lht2z3+8Sbl/NqV5o9nYXtpcuqIiyqCE/owLWwPITPKOzmUIohy1AahXYEg3dg15WHWFMXZ09XmmTOxj8ob0YpRTJrE/IZvHPtQt550kIytisHWFG0imkWRAJdISaPBDUTsLImzsqaCK+0H7455dJ5IX7xt2Yeb+ygO2ODgsqInzeuqOKS1y0Z98E17DfwGwbKc7Ddgd3daBSq97oeOC70pAvbzXVd47w189neluT5pl7a+vLEwz5Qiq5UHlfBqoVRNp0wn8XzIuMakxCzicyCCDH3SPG9CdB1jVs/eCqLK4Ij3r+gzE9VNMg9z+xnb2eGvoxNImuztzPN3U838dXfvTTuImBlQR/RgIHtgkdh+UnTQD/4L2rohSWlF/cnBormNdTEuOacFbzl+ELn7gOJLHs6M6TyLkGfga7Bgy+1saOtj6buDNsPFP6/FDtpi7mhfxbkuPll1FdObHZUCDH7yUzNBDXUxPjR5ev57bNN/G5rG8mcTSzo4/zVNbzam+ehl9rJ2u7Atuv+8CBteTy1p4c7/7KPL56/aswH2660hXuwRo2uXnthj8JsTchnUBHx05rIDdnW3VAT44vnr+LPjR38aMseelIWtWUB5peFMA2dv+zu4oGXDlAdK/SHkgZ7QgghioUENZOgoSbGpzcdz/vWLx2Y5nZdjyt+8hQ52+VgjbxhsrbLw9vbuHTDUpaMYcnH8xTPN/USNHV8hoap69ieQh2MnHQNDKNQZMzQGbEex6OvdNDak0PTYE9XhqaeHEFTJ2059OUcTEPjdcfMI2u70mBPCCGmgecpWS6dIAlqJsmhyX5/eqWdnrSFc4SVGwUc6MuxqyM1pqCmubfQ6uD4BWUksjZKFSoB93fqtl0PDY2KsI+gzxxWj+OJXZ08sr0dTykiARPlQt52aOkpzP7UlgVI5xwyljvQw2k2lJYXYjLIiUPMRo3tyYHE9pzjyiz5OElQM4Uc7+j1afKOoiOZH9Prpi2HrO1SHQtQWxakrS+HpwpNLTUg6NMJmAZpy2VtfcWQehyep3jopXb6cg6GVujAbbse7qA2C829WSIBk45UfqD54mwoLS/ERMmJQ8xGje1JNm/ZS3faYkE8SNgfImM5Mks+DhLUTJFjqiIETYO+/NEDG28Uwc9gnck8r3al2dGWxHY9HNcj7yg0HUxdw/U8MpZLQ210WD2O5t4sO9uT5GwXyynUsDm0zYLlKDQcXmpJAFAdDRzsJD3+0vJydSxmmpw4xGzkeYr7t7bRnbZYURMdKLURC8os+XhIUDNF6ivC1FeEaU8njvrYXe2pUb9uY3uS37/YiuMpHLeQR5O1vUKdGg8sFLoGEb9B6JACewDJnE17Xw5XeXiKEfN9FIW6N+19eRLZTqqjASIBk4qIf1yl5eXqWMw0OXGI2aq5N8uujkKRyMHVr2H2NGAtJrKle4rousbqRfFRPfblA8lRbZvuPzD3ZGzWL61EoWjuzRWaUvJaZWFNK/wxHEjkeGBb25DXTuUdUnkHHY1Bm6aGcT2F3yx8PDQN9vdm6Ujmydpjm6npvzre2pKgPOxjWVWU8rCPrS0JNm/ZO+4t7UKMxVhOHEJMp9casI58wViYJXdntAFrMZGgZgqtrouP6g3OWu6oDqaDD8xKKTqT+YGqwf0LWBoQD5q4nqI1kWXHgb4hrx0NmvhMDcv1OFIc5alCgOS4ilTepToaoCzo46GX2kddt+bQq+NY0Ieha8SCPlbUROlOW8OCLiGmgpw4xGw1uAHrSGZDA9ZiIkHNFDrtmEpiwSN/EDUg747uYNp/YM7ZHk/u7ibvFEIZQyt8AQcDFY2Q3yCZcziQHJoHEwv4qIoEODSNZ6QJ96ztoYDqWICTF1ewojY6pqtZuToWs4WcOMRs1d+AtTWRGyjL0a+/AWtDTXTGG7AWCwlqptDiyginH1M5YsDQT9eguSdPW1/uqK8X8ZsEDJ1XDvQNCVTcg72f+peTCvcpHNdDedqQA3VdeYhjqqKgvdZWAUZehgr5dRpqomxYPo/KiH/MV7NydXxknqekcvM0kROHmK36G7BWRvzsbE+RzNk4nkcyZ7OzPTWrGrAWA7ksmUK6rvHB1y3l8V2dZKyhUyMa4Dc0fEZht9KWnV28saH6iB/cuvIQ1bEgf93Tjd/Qhu1a6pd3FHbaRtegKuYfcqDWdY1Tl1bwPy+24h6crnG915avhoxf0zhhYRn6wd4LY72aHXx1HAv6ht0/l6+OJXl6ehVT524x9xRTA9bZbu6dTaZZTTzA8uoorxzow/UOtjXQCsGMoesETA2/abCr4+jZ7bqusW5pBX94sYW+rH3En+spMLRCMvDuztSQP4oNDVXUVYRo78sVKh4f5jWSOYdE1qYi7Kcva9PYkeKEhXEWlI3c6+pQ/VfHW1sSRAPmkCWo/qvjNXXxOXd1LFuLZ4acOMRsJg1YJ4cENVMs4jepiQXoSgdxPUXedlEU6smE/CZRv4EHeMob1TLM8QvKWDwvwrbm3sPuXOoX8ul4rhq2VbW+IsxZK6p54KUDB/NyDrZX4LUlLA2wHI8nGjtp6s7Q1pfH1DWCPoM7Hts9qhkFuToeTrYWzyw5cYjZ7NDK9GLsJKiZYv2zFS+3JpkfC6BpGq5SGAdna3oyNvGwj/LQ6GrA1JWHWFET46WWPkzdxfPAHeFxhlbIs9ENhtU40HWND7xuMS8fSNDcmxvYCg6FYMYwwKfr5GyPrrSNIsPSqggra2MEfcaYZhTk6ngoqUkx8+TEIUTpkqBmium6xvtOreeve7o50JdjflmQoM/Adj16MjZBn0HYZ7KiNjaqZRhd1zhnVQ2/f7EFd4R1Iw3wGYUlLqUUnseIybgNNTHWL53HX3f3YOiFE6qmgc/QCfuNg0GRTd5WNNREOf2YeQMn4bHOKIx0dbygLEhrX47tB/qm/Gp5NlUzfi15euR/65DfoK1v/JWbhRBiLpOgZho01ET5wOmL+T+P76E1kSXkMwj4DOJhH2GfyeJ54TEtw7xheVWhWnHKGnafBjheIQnZNHR0ncMm4y6qDBM42CfKb2poaBh6IbhxXA/PO7hDStNI5hxiQfNg8DP2GYXBV8eN7UnueGz3tCTJzraEXEmeFkKIqSNHzik2+KRaWxZAeeAqRXnIpK48zIra2JiXYRo7kvTlLDRe24rd/98eoClwPEV10MRzoWHhyFtV1y2tIBrwkco7hHzmkKAq73hYrkIHWnuz9GZsKsN+ltdEqIwExj2jMJ1JsrMxIVeSp4UQYupIUDOFDj2pLiwP0VDtsLszRSRg8t5TF7FhedWYlkI8T3HP082kLY+F8SBtyTyOp4YkDRcSfRXxiJ95scBhZ4GWVEZ4Y8M87n+pjb6cQ8hv4NM1MrZLMlcIViIBnepYEMfzaE/mSOZtTqovx2foY55RmM4kWc9T3Lf1APt7MtSVh1AHKyTPdEKuJE8LIcTUkeJ7U+RwLQLKQj7WLipHKXhx/9GbXR6qP9HUb+osrAixdF6IsF/HGFRMT9cgEjBZt6TiiLMRuq7xj29uYN3SCoI+g5zt0pOxSOacg1vCwVUarb1ZPE9RGfGTtVwa21O09I69WNl0Vhh+Ylcnv3+xlf09Wf62t5snd3fx9N4eutP5Ga9m3J88vXphnN6Mzd7ONL0ZmzV1cdnOLYQQEyAzNVPk0BO4UopkzsFyPfyGzvyywLh2uaQtB1d5BEydZM4hZysChn6wQaXCb+joGqycX8blG45hybzIEV+voSbGv5y/ij+80MqjOzrY1ZnGyzvEQz7iIR89GZverE3OcVkQD+I3dV7tynDaMcExzyhMV5JsY3uSO/+2j660RW1ZgIBZSMwePNNUFvLNaEKubC0WQojJJ0HNFBl8Au9OWzS2p+jJWDiuh2noxEMmAdMY80k14jepCPlp78uzvyeLrkHAZxDyF7aKZ/MOeQ/qKsPUV4wuWGqoifGJjRGSeQdXKbrTFtWxAIauE/abdKfz9GZtWhM55kUDxIImbz9xwZhnFKYjSbZ/hiydd6gI+dA1HV3TCsnQEZ3utMWujjQra6MznpArW4un12zaBSeEmBoS1EyR/hN4S2+Gne1pspZDNOjDFzSxXcWBRB5dg45knuPmj/51+xNNn23qBRi0jFNo/uQq8BkasTGerFv7cnSmLBpqorzY3Ifjga4pdA3Kwz6CPgPH9WiojhIwdY6fXzam1x889qlMku2fIVtWFcFyFO3JHOpggcNCJWedlp5Cn6U3rqiWhNw5YrbtghNCTA3JqZkideUhllVF2NrSR8ZyqIz4CZiFWQO/oWHqhZowLzQlxtTIUNc1Tlpcjg5EAwYBn47jeqStQi2aaNDktGMq6M3aY8oX6Z9ZqokFqQj76U7nae7J0tSTpbk3R3fGoi9nc6AvN+qaOiONfaobt/X/HpGAj+pYgFTOYXdnmj2daV450MfuzhQtiRxNPRm6Unl2d6bG/bNEcehP2N/akqA87GNZVZTysI+tLQk2b9lLY3typocohJgkEtRMkf7gw3Y8XNfDcj08pcg7Lt1pi3DAZHVd2UDPp7GoigWorwyzuDJCPOgjFjSpCPs5tjbGW46vYeX8sjF3v+6fWcra7sFgwKU3a6NpEDR1lIKM5dGdtlg5PzbuwGOqk2QHz5Dt6UwTMHUCPgPb8XA8hespNA2WzgvT2peTk1qJO1zCfizoY0VNlO60xQPb2qRDuhAlQpafplB/8GE5Hr1Zm1TOxlMasaDJsqoI8+NBXu3KjCuvpioaIB4q5KX0Jx/3F8dL5uwx5Yt4nsJTirKQSWN7EsvxiAYMwn6drO2Rczxs16M6FmBRRZhXDiR508qaCQU2U5Uk2z9Ddu8LLbieorYsQEtvDstnEDA1craHaWh4ChqqIzR2pKXXUgmTthRCzC0S1EyhwcFHb8Zid2eaVM4hnXd4+UCSfd0ZKiKj6/k02ODclMH1XmDsuSmDcw06U3l2d6RJZG1qygJUhAOkLZdUziHsNzhtaQV+05iUk8BUJcn2z5D997P7UUqRtlyyjovf1HE8RchvUhn205OxSeVdOamVOGlLIcTcIkHNFOoPPv6yu4uejEXOdomFfPgMDdvx2N+bxfEUWWuklpSHN1kF3EYqDugzNP66p5vOZB7bVfgMncqIn2XVhUrCrlKz/iQweIasrS9H3vYI+nQigUJAE/Dp9GQsLNejPDyzW7tBduVMJWlLIcTcIn/JU0jXNc4+vobfvtBCRzJHTSyAqWvYrkcq71Ad9VMW8vHQy2001Ixt+WOi3a8PV913cWWE/T2FtgiW42HqWmFmqTVJWyLP/HhgSk4Ck3liHzxDtqgyxDN7ewj5jYEdV3nHxdR1/IY+4yc12ZUztaQthRBziwQ1U6ixPcl/P7ufrlQey/Vo6sniM3JEAibzIgGWVUUoD/vGvfwxkdyUwxUHzDsuhqaRthyUKlQRjgRMbNejrS9LU0+Gt6yqndSTwGSf2AefyBqqIyyIh2hP5iAAOcuhO2NTHQsQ8evs6szM2EltNvamKjXSlkKIuUWCminS2J7klod28sL+XlJ5B0MDT0HG8bAcha5pvHzAG3cRvn7jzU05XHFA23HpythYrsLQCo0x87ZL/uDuISi0YpgsU3FiH3wia+xIMz8eoC2Z45UDSRxPYRw8gT3wUjvHzo/NyEltOvtgzXUTndUUQhQPCWqmgOcp7vzrPp7e2006X1jG8Qa103Y9l5ztUhn2j7sI30Qdrjig39DoztiYBwOaA4kcnioMPugzqIkFeLU7MymJtVN5Yh98Ivt7Uw8Zy8FTioBZyK3xm/rkRmdjJLtyppe0pRBibpCgZgrs78nw6I4OutMWlntI/QsFDtCXdVgQV5g6GHqhCN9YO3ZPxKFbn+dF/AAkLK9QywWFUgrt4GMDpo6mQSrnsKMtycutfRM+2U71ib2hJsbSMyP8+/2vkLNdlldF0DQN2yv0yIoGjBnb0i27cqaftKUQovRJ8b0psKsjxf6ezPCAZpCc49GezE+oCN9EHFocsC9XqEDcmsiRzjtk7cLY+2dp+vsnRYM+HFfxzKs9Ey5Y9tqJfeTYOuQ3xlxE8FCF9g95jq2NUR4JEA/7qYoGKAv50HV9xrp1D96VM5KZTmAWQohiJEHNFDjQlyXvHP2EH/YZnFRfzoLy0IRP3uPRv/U5Hiosg/VmbTQg6CvMyrgepPIu+/vbJfRk6clYLCgP0t6Xm3AgMB0n9ukInMajP5m5NZFDqaGflf5dOQ01UdmVI4QQYyBBzRTY25EZ1eMCpk5lJDBjV+URv8m8iB/D0IiHfBwzL8wxVRHmRQMooP9Uq5SHBoWqyHmX2rIglutNOBCYjhP7bJ0RmY4+WEIIMddIUDMFMvboTvbtyTye583YVXldeYjqWJADiRwVYR+RgA+/qWM5HrpWyKPVdbA9cD1FRdhH9GCuh9/QJxwITMeJ/XCBk1KKRMZiR1uS6liABWXBCf0u4zHVfbCEEGKuKZoF+5tuuolf/vKXbN++nVAoxBve8AZuvvlmVq5cOdNDG2ZR+eiSEfuyNk/s7mJRRZhzVo2/l9J46brGuqUV3Le1lWTOBq2wIylvexiahqYrYkEfjqeoKSsUs8s7Hq29OdYtqZyUIGyqt9uOVKcka7vsOJCkNZHD1DWCPoM7Hts9IwXvZFeOGA2pOi3E6BRNUPPoo49y1VVXcdppp+E4Dv/0T//Epk2beOmll4hEIjM9vCHeckIt33xwxxEThQEsj4OzJH4e3NaOrmnTflI9fkEZx86P0ZO2SFsuGcvB9jzi4UJCsHuwrkvA1LFcj1TOxjQ0Tl1SMWkH1ak+sR+6vXtHWxLHVSwoD7KyNkbQZ8xowTvZlSOORKpOCzF6EwpqLMuivb0dz/OG3L548eIJDWok991335Dvf/zjH1NTU8MzzzzDmWeeOek/byKWzovSUBPlpdbkER+nAYsrw0QDJn/b20Vzb4aPnHHMtB6o6spDnFxfwYvNCdaUBejJ2mzdnyAWNHGVorkni65rZCwX09Apj/ipCPs5fkHZpI5jqk/sr23v3k7OdmmojlIW8g1sJZeCd2I2kqrTQozNuIKanTt38pGPfIQnnnhiyO1KKTRNw3XH1qBxPBKJBACVlZVT/rPGStc1PnD6Yv7l19s42h6o3R0pWntzBHw6rYkcIZ/BP7991bSdVAcvz7Ql89TG/ESDJi2JHBqKeMjH0qoIZSEf5SGTtj6LExcVZ6+cwvZui2NrY8OaG0rBOzHbSNVpIcZuXEHNZZddhmma/O53v2PBggXDCqdNNc/zuOaaa9iwYQOrV68+7OPy+Tz5fH7g+76+vukYHgBvWDaP8rBJT+bwScMKSOYdbFfhdwx0Df64vZ2Nx9XwxhXV0zbW/uWZO/+yj0d3dHAgkSXreCgFhm7Rlc4TD/kI+cwZayswGaTgnSgmUnVaiLEbV1Dz3HPP8cwzz3DcccdN9nhG5aqrrmLr1q08/vjjR3zcTTfdxI033jhNo3rNwy+38aM/7yaVO/rJUQM0DbKWg2loWI7Hwy+3TWt14X5tyTwZ2yUcMNF1D8txcZUinXdxPaiKFfdmucHbuw+dqQEpeCdmFwnChRi7cZ2lVq1aRWdn52SPZVSuvvpqfve73/HII4+waNGiIz72+uuvJ5FIDHw1NTVN+fgefrmNm/6wnW2tSQ5JNRpR1vZI5ZyDSbgutuPR0jvxwnZj4XmK+148wM72JCGfTsDUMQ2Nyoiv0KXbb2DqGgtiARxX8cC2tglXE54JUvBOFJPZWmNJiNlsXEHNzTffzOc//3n+9Kc/0dXVRV9f35CvqaCU4uqrr+ZXv/oVf/zjHznmmGOO+pxAIEBZWdmQr6nkOB4/3rKXnrRFKm8zqswiBYauoWsarvLIOS69GWtar76ae7O82JzA9RQBn0HWdjE0DVeBhkbIb6DrGh1pi1jQnJG2ApNBCt6JYiJBuBBjN64Q/5xzzgHg7LPPHnL7VCYKX3XVVdx555385je/IRaLceDAAQDi8Tih0Oz4o362qYe9XWlSeRtnFLM0UFh6UhRmS3yGjqcUnak8YZ8xpWMdLG05pA8WDLQdj3TepTAqDU0DUy/8v+16GLpGxnKKdsp7quviCDFZRqqxFPIbZC2X1kROgnAhRjCuoOaRRx6Z7HEc1W233QbAxo0bh9y+efNmLrvssmkfz0i60hbJbH5UfZ8GHDwe+Uwdnw55p1AXZjoXdyJ+k4jPxHI8kjkHVylMXSuMQ4HlFpKGlSpUFi72KW8peCeKhQThQozNuM5MZ5111mSP46gOnX6djeZF/ORHO0VDYQYk4jcImDqappHKO0QCJgvKQmTtqd8W36+uPMTqhWU8/WoPluMSMHUcrxDFaAenkhSAUiSzNifWlxf9lLcUvBPFQoJwMRnmSlXqcV9u9/b28n/+z//h5ZdfBuCEE07gIx/5CPF4fNIGV2xOqa9A13VgdIFNediHUoq86+F6imjQZE1dGWVB/7TOhOi6xslLKvj5003kHW2gk6XtKpTyUEDQZ5B3FQG/UVJT3nPlD10UNwnCxUTMparU4zpzPv3005x77rmEQiHWr18PwLe+9S2+9rWv8cADD3DKKadM6iCLhWnqLK4I8XJb+qiPDfl0KsM+LNcDNCojfo6fH6U748xI8l9VLMDy6ii9GYuWRI5M3sGhUA8j5DeoCPsJmDpvP3FByfwRzKU/dCHE3DTXqlKPK6j5zGc+wzvf+U5+8IMfYJqFl3AchyuvvJJrrrmGxx57bFIHWUz+1yl1/Osfdhz1ccfNj1EW8lER9lMW9GHocKAvP2PJfxG/SVW0ENicgqInY6NUIXk5YOrYrofjKo6fP7U7yCbb4WZi5tofuhBi7pmLVanHPVMzOKABME2Tz3/+86xbt27SBleMTl9WjcGOI27n1oDz1ywk53js6kjRlc7PePJf//bRrS0JVtREiYf8QCGXqS9rs687wwkL4ywoC0772MbrcDMxb1lVy4Mvza0/dCHE3DMXq1KPK6gpKytj3759wyoKNzU1EYvN7avb8oifE+rKeKH58PV6YiGTtlSOz286jtaDFUFnOp9jpO2jGctha3MfbX05fIZO0Kdzx2O7i2J55kgzMTvakqQth8WV4Tnzhy6GklwqMRfMxarU4wpqLrzwQq644gq+8Y1v8IY3vAGALVu2cO2113LxxRdP6gCLTV15iNctq6KxPUXGHp4wHPXrrKiJ0tGXp7UvN6tOmoO3jz7e2MFLrX1YjkfA1IkEDHrSNn/Z0zXrl2eONuX67L4eOpJ5VtaOPP5S/EMXr5FcKjFXzMXWMOP6Tb7xjW+gaRof/vCHcZzCgd/n8/GJT3yCr3/965M6wGKj6xp+UyPvjrwDKm155GwXy/Vm5UmzoSaGc7zity+0FFojVIYAjZzjcaAvR3mo8JGZzcszR59yDfFqV4b2ZI6F5cODylL8QxcFI83gpfMOf9vbxbbWBBevXzwjfdeEmAqD0wqiAXPI8bC/KvWaunjRl+gYbFxHbb/fz3/+539y0003sWvXLgCWL19OODx7Zh1miuN4/OmVjsP2fVLAy61JYoHZedL0PMUvn22mL2tTFvTRksiTs12UKlQ/7k5b5B2PnW3+Wbs8c7Qp1+poANPQaGxPEfGblIV8A3/spfqHLkaewetO59nVnqYrnSeRtdnbmeFtq+dz3pr5Mmsjit5crEo9obNqOBymvLx84L8FPLOvm31d6SNWBPYU7OnMUBsNDL19Fqzz989yOJ5HV28eT4FpaAd7QSlsV/FqV4aQ35yVM01w5CnX7nSel1r6yFouecvlgb42FsSDHDs/RshnlOwfuhg+g9edzvNcUy9ZyyUaNAn4DNJ5h6de7aa1Lzerl1iFGK25VpV6XEGN4zjceOONfOc73yGVSgEQjUb55Cc/yQ033IDPN3ztbq7Y0ZYiN4qqwqm8w3PNvaw/Zh5QmBa/78UDvNicIG07RHwma+rih71inKoAKG052K5LKufgeoqgz6B/xtLUNHQNspZif0+WoDGufqhT7nBTrt3pPH/f10NHymJRRYhja6LsaEvRmsjRkcpzbG2MUxZXlOQfuhg6g6eUYld7mqzlUhnxo2kanlJkrMLnpzttzeolViHGYi5VpR5XUPPJT36SX/7yl/z7v/87r3/96wF48skn+fKXv0xXV9dAn6a5KOgzRtXSIW97dCTzQCGgueWhnexoS+K6Ho6n8BS80pbk5QN9fOYtxw45yU5lomPEb6I8cDx18ANfaGzZT6nClKbjehxI5lhaHZ3Qz5sKI025Bn0GL7X00ZGyqI4GWLUgTmXET3UsSF/WprEjxbLqCB974zJMc3YGa2JiBs/gKQXdGYto8LWg13Y9TF0nYBosiBtFtQNuNszyitltrlSlHldQc+edd/Lzn/+ct771rQO3nXjiidTX13PxxRfP6aBm3dIKQj4dO3/k2RpHgesqPE9x51/28XxTL1BoTWA5Lq6n8JTi8cZOgqbOf7z/pGkpGldXHqIyEkChMPVCDyhNKzR/8gDHVfhMHb+h05Oxx/1zptqhU67d6RQtvVnmRfysqIlRES7MJmqaRjzs59jaGJ1Ja9btSBOTZ/AMXkXYh+N5+IzCIVApRSrnUFMWJBY0cZUqmh1wsptLiNeM65I0EAiwdOnSYbcfc8wx+P3+iY6pqC2pjLB8FLMXGpCxHZp6MvxlTzeup8jZLnnHxWfqRAImIb+B5Sge3t7Bnxs7hiU6xoI+DF0jFvSxoiY6MGXueeNv/qnrGhuPq8ZvFGacXAV5W5F1FHmn8D1K4XiKrDV9TTfHo6Emxic2Luc9p9RRFvLhKUXe9nj5QB9P7e2hO20NPDbkN8g7blGcxMT49M/gVUYKSe5KQd4p/M11py1CfpPl1YUE4mLZAdd/kbO1JUF52MeyqijlYR9bWxJs3rKXxvbkTA9RiGk1rqDm6quv5qtf/Sr5fH7gtnw+z9e+9jWuvvrqSRtcMdJ1jTceW3PEx2iAoRWWePZ0punN5PEOBgohn4Gpa2ga+AydWNAgZ7vc+1wLTT2ZgURHgL6sTWcqT1+2MGMyuGjcRLzjxIUsmRfG8QrT2v0hkqGBTwfbK3TvfmpP96w/aO7uTHHf1gMksjbRoI9I0CDoM+hI5niuqXcgsCmWk5iYmP4ZvNOWVhIwddr78uQsl5qyICfVl1MZ8Q/sgJuJHmxjMR0XOUIUm1Efwd/73vcO+f6hhx5i0aJFrF27FoDnn38ey7I4++yzJ3eERegNDfP40ZY9pPPusF1Q/avcmqYR8hkAuF7hijEwKCl3MEPXaE1k2dOZJue45GyDl1t76MlYOK6HaehUhP0srQqPa7bh0PX4BWVBVs6P8WpXhpztYuoapnEw78Dx0HWN+WUBejKzO5ly8EH/xLo4tqNoT+aojBhURvx0py12daQoD5XLNu45pKEmxj9ujHJSfTl3/m0f6bzDsqoI4YBJMmcXzQ64uVgCX4ijGXVQE4/Hh3z/v/7X/xryfX19/eSMqASsW1zJsbVRXtjfh3vwKqk/uFGArkHAZ9Dam+XUxRVEAiZ9OZuQ3xjyOkoVEopDfgP/wZ1GluPxzKuF5apo0IcvaGK7io5kju50nvrK8JhmG3YcSPKLp/exraUPy1NUhnwsmRchmXM4eXE5z77ag+0pHFehaRAOmMyL+kHTiAXNWX3QHHzQ13Wd5TURknmb7nQhQTQcMGnry/FCc4JFFeFZfxITk0fXNc5YUc38eHAgH6U9OfM92MZiLpbAF+JoRn3227x5M1BIqGtqaqK6uppQSK5qR2KaOhectpiXWrcNBDX950qlCv9dFjR5bn+Cd5xUx6lLKvifF3Kkcw6hgImuFRKG83ZhpqcuGijMxFSGydkuXWmL2lgAlEJDI2Dq+MI+9vVkqXW8UTedfPjlNv79vu0092ZxPYUG+EydF5sTKOCUxRUsLA9hGhqupzANnVjARAE9GQtD18hYzqw9aB560K+MBDipvpxd7Wm6Mxa265KzPZZVRbnkdYtn/UlMTL5i3uoa9hm4nqK5J0N52E8sOLRirCypirlozJ92pRQNDQ1s27aNFStWTMWYSsKJdXEqw3660vlCXsrBirxBn8H8eBBPKZq6M6TzDh994zJe2N9Lc2+WTN7Bdr1CMKRpBE2DtF2opbGvJ0Nrbw7LcdnblcFv6oT9BrGgieMqykM+/KY+qh08O9r6+Pf7trOnK1OItACUwnI9snkHNI2d7UlMXSPoMwiYr80iWY6Lqeu4nprVB82RivBVRgJULPWTzDn0ZCyytsvlG5ayeF5khkcrZkoxbnXtr2vV1J2lO50nHvIxLxJgeU2ksHtRKmOLOWrMicK6rrNixQq6urqmYjwlI5V3MHSNY+aFaaiOsqgiRH1lmGOqwlRE/AR8BhnLJZV3OHZ+jM+fdxx15SFsr1AXJuQ3qYz4iQUNPE/R2JHmh3/eQ8pyWBAPUn5wS3JvxuZAIkc0aHLqkgoCpn7UmRPPU/zvR3exuyOF7XjYrsL1FAoNn6HjHqxT09KTJeQ3SOYccrZLxnLIWQ7JnENF2Ecy58zqZMoFZUGqogF2tCVJZKyB+kHawaWzvOOxdlE5iypm7wnN8wrB7/YDfTR1ZyTpUwzseNrW2sdx86PMiwbI2R7NvRme3ddDU3eane2posgLEmKyjesS++tf/zrXXnstt912G6tXr57sMZWEaLCwJTttuXieIud4eEqRyDoETRtd1wj7DaLBwj/Bm1bW8PjODizHK5x8NQiYBvOiAY6ZF+apvT1Yrkd50CTk9xEP+bGcQqG+ZM4m7DMImPqoZk6e2NXJYzs6cbyDO7F0gEJFVdtV+EytsPzlKvpyDomMzYFEFl3TUBRmm8rDPuZFA7P2oNlfu2N3Z4p9XRl2d6SLrh2C1B8pPRMtkjdS/6pIwEdje4qedJ6ulMUrKsnb1iyUz4mYk8YV1Hz4wx8mk8mwdu1a/H7/sNya7u7uSRlcMYsFfMyL+Hm5rw/bVYT8BkFDx/YUvVkb09BYVBEiFijMuDT3ZklkHTauLGwHt1wPv6ETC5okcw6uUhgahAM+kjmbyogfv6mD4xH0GRzoy2EaGq9bVnXEmRPPUzz0UvtAKwdNY2AdXtc0vIPVjAvHWYVluwPby12vUJBPo1CE783H1czKg+bgAoWLK8PUxAK8ciBZVO0QprrIoph+kxGkjrTjqTLi57SlFa8tqVou569dwBJZUhVz0LiCmltuuWWSh1F6FpQFMXUdQ9eI+A1yjiLveGiaRnnIJOd4+Ax9IKm3P6l1YSCEcciVm+V6QCHHpq4ixN5Oj/092YMzNR6eKuyK8hk6K+fHjnjl19ybpTWRJezTSeUK6TT9jRA0CoHNa8nNGtUxPycuqsByPSzHw2/q+HSNPV1p/ryzg4aaKPUV4Vkz2zHSlWws6KMqGiiadggj/Q4AsaCPaMBkZ3tqVm+lF8NNVpB6uB1PmqZRFvIRDhjs7UyTtWd3YUwhpsq4gppLL710ssdRclr7cgR8OlXRAI7rURY2CjMhqjD7EQ35hyT1HqmzdGE7d+Hk5dN1bNejO21hux6aBoZWSOYtC/n44/Z2lswLH/YAmbYcdB1qy4J0pS3cg+0aDF0DDRSFqsGmphHw6SyrjhIPF6pEK6Vo6smwrSNNb9qisT1Fa2+OExeVz5qp7sPV7iimdghSf6S0TGaQeqTjBMiOJyEmfKmay+Xo6+sb8iUKwYPf1Dl1SSU1ZaGBkuxKQW08xCmLy4ck9fb3pWlN5IY1xIwGDAxNw3EVrxxI0JHM4/fpVEZ8xEM+dL0QgBw7iiqiEb9JyGeyrDpCWch3cPkJPKVwXTWQZxMOGFRFA9SWFa4Iu9N5HtvZwR+3t7OjLVmoZJxz6MtavNjcO2tKsr92JTvyQb0Y2iGUwu8gXjOWIPVojnScKJZKyEJMpXGF8+l0mi984QvcfffdI+6Ccl2Z+uy/ogr69IH17sF5Mqm8Q972Bq6oRuosHfIbZC2X1kSOFfOjNLalaE9ZeEoRMg3QwHE8ogGToM9gT1eGlbXRI17FD27q94blVTzR2Eky7+ApdXD5CSIBk1MWV2DoGlnbxc7Z/H1fL03dGbJ2odmme/B4+vSrvdRXhliYd2fFksh4rmRnW4fj/t8hnS8ELYM/N8XUl6jYTNXnYDKL5B3tODHbk9+FmGrjOip+/vOf55FHHuG2227jQx/6ELfeeivNzc3ccccdfP3rX5/sMRalwcHDipooZaHXTrCHqyFxaGfptr7cQIXTNYvi/OSJvRiaxr6eDJrjouuFxpeV4cJsTXfaGmi5cLgD5OCDYnfa4sxjq9ndmaK9L0/WdokETDatquXi9Ut48KU2XmxOkMhYdKctMpaDqwp5OBqFXBzbU+ztzBSWsjyPd6xdOKNLIoPf92hgaDGykd732bjDqK48RHnYx+M7O9E0cFUhQbsy7GdZdZiutC31RybZVH4OJnvJ6EjHidmc/C7EdBhXUPPb3/6W//qv/2Ljxo1cfvnlvPGNb6ShoYElS5bwf//v/+WSSy6Z7HEWnfFeUR2uwumO9uRAInBf3sFvFBJgA6aOdjBXJ5136MvZRz1AHnpQrCsPMb8sxPyyAIsqIgT8Gs/v7+WEuhivHOjj1e4MGcvl4IapIQ0uoRDkpHMO21oSbGtJzGhQM5b3fbbuMNrdWSjZn7VddE0jHvYBiubeDE09GdbWl8vV+CSa6s/BWAPt0SjmSshCTKVxBTXd3d0sW7YMgLKysoEt3GeccQaf+MQnJm90RW68V1QjVTjtTOZ5tSs9UAQv4XjkbY95UT8hv4nteuiaRk/G4vRj5h31AHnoQfG5pl7ufmofv/x788FdWhDx+1hRE0bTDuYDHfIaQ9J2NMg7Ho/t6GDTqvkzenAdzfs+W3cY9Y/L9RRnHVvNro50oXGp5xHyGXhAbSzAsqrotI2plE3H52CqloyKsRKyEFNtXEHNsmXL2LNnD4sXL+a4447j7rvvZv369fz2t7+lvLx8kodY3CbjiqqxPcnvX2zF8RSuBwviQQ4k8vRmbXKOy/x4kFTOIWAaLCoffWPG/oPiwy+38f1HdtHWlx1IPnQ96M1YPLPPImgauMob9vzBMY3jgd/QOJDIzYpdOUd732frDqPB44oFfVRG/EPysUDRk7FnxXtcCqbrcyBLRkJMj3EFNZdffjnPP/88Z511Ftdddx3veMc7+N73vodlWXz729+e7DEWvdFeUXme4tXuNE/v7SFnuxxbG+XkRRXcv7WNnozN+qWVPL8/QdZyqI756cva9GZtXu3MUB0LcMaKKj5w+tgaMzqOx+Yte+hM5gYK78Fr7aBcD1LW6BK/FYpUfvY0uDzS+z5bOxwfOq7++iP9HM+jPZmfNe9xsZvOz4EsGQkx9cYV1HzmM58Z+O9zzjmH7du388wzz7BixQrWrFkzaYObSxrbk3z/j438ubGLVN5GKfCbOosrw8SCJscvKCMW9HFSfTmN7Una+/IYukZ5yEfQNLj67BW8+6S6MR8gn23qobE9WZgFUoACXS9s8wYG8miORqdQLyd1sGXDbDdb633M1nGVqul+v2XJSIipNaY6NX/84x9ZtWrVsFo0S5Ys4eyzz+aiiy7iz3/+86QOcC5obE/y1d+9xP0vtZHK2wRNg5Bfx/U8drYleX5/gra+/MDj+6sAg0bAZ2AaGtGgOa4rvq60Rc72cFw1ZHam/2u0DL3QhBNNH5Z7MxvN1nofs3VcpUrebyFKy5iCmltuuYWPfvSjlJWVDbsvHo/z8Y9/nG9961uTNri5wPMUf3ihla3NfQO1YtKWTSrrYLserqvIWi7PvtpFZzLHc029dKbylIV8zI8HCfkMUnmX/3m+dVzF7+ZF/OgaHGzEwOA45tDgJHiYi1WfAT5DozzspyrqL4oS7f3Jm5URPzvbUyRzNo7nkczZM9rheLaOq1TJ+y1EaRlTUPP8889z3nnnHfb+TZs28cwzz0x4UMXEcTz+tqeLP2xt5W97unBGu15zUHNvlqf29pDOO1iOR9b2sFywFVguOBSCi7Y+i7/s7iJrOYVmloZG3nLoSVuUBU160hb3bz1w2ErCh3NKfQW18aFXoQdXoYZTEDJ1/EahSF9hyanQ7LIqFmD1whhV0UDRLI30J2+uXhinN2OztzNNb6ZQA2YmG0bO1nGVKnm/hSgdYzr7tLW14fMNX3ceeDHTpKOjY8KDKhYPv9zGDx9t5KUDqYHdKavmR7nyrAbOPr52VK+Rthw6kjnyjnfEZRsP2NedZXFliIzlcqAvS1+2UAk4kbNpT+ZpTmQ5sb6cN66oHvXvYJo67zxxAS+3Hn2Wx28aWK5CQyNgUug+bhosKA9x2tIKujM2a+qKa6p+tiZvztZxlSp5v4UoDWMKaurq6ti6dSsNDQ0j3v/CCy+wYMGCSRnYbPfwy238f794nt6MPXBb1vZ4cm8vL7Y8w39efOqoApuwz6AvZ48qD8VTis60hZPIYbkKXdcoC5iYhkbe9mjpzfKDP+9mQTw4pqvLN66s5ruPNJKxDj/LpAHlYZOco+jN2ORthabBvFiA+soQ+3uz1JWHinKqfrYmb87WcZUqeb+FKH5jWn5629vexr/8y7+Qy+WG3ZfNZrnhhhs4//zzJ21ws5XjeHz5N1uHBDSDpSzFNT//+6iWohRgjjII0LVCDo6uaYT8BpVhHyG/gc/QCfp0dA2aujPcv/XwDS1Hksm7lAV9xAIGfkPD1AvLShoHl5d0MA0NhU5Z0EdtWZB42EfAp5O1HLa3JulM5gkWwa6nYuF5iqbuDNsP9NHUnRnzsqIQQsxFY5qp+eIXv8gvf/lLjj32WK6++mpWrlwJwPbt27n11ltxXZd//ud/npKBziZP7OmgqXd4YDdYMu/y2V88x3cuPuWIj8vaLvGwH3qO/HpQ2I2UtQrdm8N+Y2BGRCmwXEVZyIcOvNDcO6ZiYdGASVnQh0/X8IB03sVTCssppA/7DB2foXNKfTnRkI90zuGZfT2Uh32sXRSnLOjH0DVaEzk2b9kreQgTNBv7UQkhRDEYU1BTW1vLE088wSc+8Qmuv/76gS2QmqZx7rnncuutt1JbO7pckmL22783j+pxf3ixldx7HIKH2zZEoU5GJGCgM3Tn0Uj6dydZrkNQ6WiqkNdiOR4+Q2NeJEDWdshYh29oOZJY0MfieWH292SwXUU85EPTIGe5tCfzKAqdu8vCPkxd46kDhS39G5bPY140OOh1Zq69QKmYrf2ohBCiGIx5m8qSJUv4/e9/T09PD42NjSilWLFiBRUVFVMxvlmp+SizNP1sD+56dh+XvmHZYR9TVx5iYXkYXethNCsMfkPHb2jkbBfbVbiewtA1dE2nK5XHNDTC/rEVC6srD3FyfQV5x8NxPHqyNq7r4TMNllVH6M04RAImXak8rgc+XWP10oohAQ3MbHuBUjBb+1GJ2c/zlCQ5C8E4KwoDVFRUcNppp03mWIpGXUUY6BnVY5uPsqyk6xrvPGkh9289cNR2BKYOlREfSmkk8zamrhEJmPiNQqG+RNbGNHUWlgfHtANpcMO9rpTFosowhq7heopkzmH1Qj/nrZlPdSzAgUSOu55qYmH5yAHLTLUXKAWztR+VmN1kuVKI14wpUVgUvOvEulE9TgfqKoJHfdwbG6o5cdHwgoaDaUDIb7J0XuRgtWGFpmsYmobjeeRsD9PQCftMdMZ+hdZfq2NNXfxgMGPjeooTF8W5/IylvHFFNcfNL2N5dZSQr1BWfiRSxn/8XutDNPJ7F/Ib5J2xLS2K0ta/XLm1JUF52MeyqijlYR9bWxJs3rJ3XAU5hShmcuYZh9ctn8eCmElr8sgnl1jI5MJTFo/qNWvKQvj0HuzDJNZoFHY/LawIEfIbBH3GkMaGQZ/BoooQy6oi4+7iPJpaHf1l5be2JIgGzCEzCv1l5dfUxYuqVs1sIX2fxFjIcqUQw8nRcRxMU+df33sS//h/nyHvjJwIY+oaH3790iMmCfdr7s2StVzmx0P0Zi2SudeWofqDGe9gUvCiihDpvMupiytI5OzCbiifQUXET1nIh6sUezvT476aP1qtjsFLVTvbC0slIb9B1nJpTeSkrPwESMAoxkKWK4UYTpafxuns42v5/iWnUhcfvrwU8Rt8YuNy/r9NK0f1WmnLQddhYXkQn6ET8WmEfIWEYJ+hoWuFhpFlIZPmnix7O1M8sbuLl1r7aOrJ0pGycJVC07RpuZqXsvJTQ/oQibGQ5UohhpOZmgk4+/hazlpRzRN7Ovif51vJWC6nLK7g4nWLRzVD0y/iNwn5TCpC4HlgmgYakLNdXE/hqcJsjU/XeWJXF35Tx/OgOubH8RTtyRzJvM3aRXG60va0XM1LWfmp0R8w9id+tvXlCJgGa+ribDpBEj/Fa2S5Uojh5NM+Qaapc+aKWs5cMf76PP3LDk/u7iTo0+jLumTtQi8oHdD0Qp2YvqxF3lWsX1pJd8amN2MTDZqUh310JPP8bU8Ppy2tnLareSkrPzUkYBSjIcuVQgwnQc0s0L/s8NjODrpSNvbBgjX9hygNUBRmbDRNI5l3WLsozq6OND0ZC8cr7HwyDZ3z1syXq/kSIAGjOBrJbxNiOAlqZglPKXrSeUChAYPz/nRNw3Y8bE9RGfGTzjv4DJ3TllaQzDlYroeuQVcqT3UsMFO/ghBimslypRBDSVAzC3ie4p6nm0nlXRaWh9jfk0UpBjp391cO1rRCtWBXKSzXQ9M0ykKFtfRkziboM6dk/VyqlQoxe8lypRCvkaBmFujfmqkBqZyDNyig0Q5+KQW6pkjlHOZF/PiN1zauTeX6uVQrFWL2k+VKIQokqJkF0pZDMmeTzNvkLBddKwQxhVyawlKUphWWoXK2i6sUoHA8b0rXz6W5ohBCiGIiQc0sEPYZJHM2KFCahk8H1yvk2eha4b9REA4axIImJ9VXkMg6tCfzU7Z+LtVKhRBCFBsJamaBwmyMjt80yLkKhULXQXkMLEXpwMLyEDWxAJdvWEos6JvS9fOjVSudXxbg+f29PLazg+XVUVnDF0IIMeMkqJkFsrZLVdSP7XqkLBvHVXge9LeBMrVCH6mVtVEMXScW9E35+vlr1UqH5+h0p/PsbEvR1JPhh4/vpiYalDwbIYQQM07aJMwCEb9JVTTA4soghl6oFmzoEDA1Qn6dgE/H8xQtiRwNNdFpKaY1uFrpYN3pPM819dKayBL0GSybJ12BhRBCzA5FF9TceuutLF26lGAwyOmnn87f/va3mR7ShNWVF7prN/XkqAj5qAj7CPlNYgEf5UETU9NwFViOxznHT08xrf5qpa2JHEoV9mIppdjVniaTdzANndqyIOVhH7GgjxU1UbrTFg9sa8PzRm7yKYQQQkylogpq7rrrLj772c9yww038Oyzz7J27VrOPfdc2tvbZ3poE6LrGictLsd2PExdo7osQCxo4ipFX85F0zUWV4apjgUI+Y1pG9OhzRV7MzZtyRyOB2G/yfLq1xKID+0KLIQQQky3ogpqvvWtb/HRj36Uyy+/nFWrVnH77bcTDof50Y9+NNNDm7CqWID6yjDz4yGg0KW7IuynoSbKm1bWsHFlNQFTn9aOu4d2497TlSZruyyIBzmpvpzKiH/I46UrsBDTz/MUTd0Zth/oo6k7IzOlYk4rmkRhy7J45plnuP766wdu03Wdc845hyeffHLE5+TzefL5/MD3fX19Uz7O8erPq4mHfHhK0dKbxTnYFmFhPETacmak4+7gaqW7OlL8v7/uY2F5kLKQf9hjpSuwENNLimMKMVTRnH06OztxXZfa2qHdsGtra9m+ffuIz7npppu48cYbp2N4E9afw/LHV9poT+RI5BxcT2HoGvGgSU08yNnH1c5Ix93+aqV15SG2NfextSVBLOiTrsBCzCApjinEcEW1/DRW119/PYlEYuCrqalppod0WLquURYy2dmWoj2Vx9A1YkEDQ9doTxW2UMeChRh0pqaaR8qzcTyPZM5mZ3tKugLPMFmGmDsOLY4ZC/oOHjMkaV/MbUUzU1NVVYVhGLS1tQ25va2tjfnz54/4nEAgQCBQHF2rHcfjgW1t+AyNWMBPzlFYjkLXNKoifnKOx6+fa6Y3bbGnKzNjU83SFXh2kmWIueVoxTEHJ+1LTygxlxRNUOP3+zn11FN5+OGHefe73w2A53k8/PDDXH311TM7uEnwbFMPe7vS1MaC+AyNnFMovRc0dQI+g650nu2tSUxd44SF8RmdapauwLOH5yme2NXJnX/bRzrvsKwqwsKALEOUuiMVx4RC0n5bX06S9sWcUzRBDcBnP/tZLr30UtatW8f69eu55ZZbSKfTXH755TM9tAnrSltkLRfX88g76mDfJ42Qz6AiUkjCdTyPeRE/saAPmNk+TNIVeOY1tie5b+sBfv9iK11pi4qQD8tRNNREqYz4pUdXCRtcHLP/eDDYSEn7nqfkQkSUvKIKai688EI6Ojr40pe+xIEDBzjppJO47777hiUPF6Oc5ZK1XfIOhAMmhqbjKkXacsjaDnnHxWfohA/ZWSRTzXNTf5Lo/p4MecejtiyArul0JHOk8s7Alnv5bJSm/o0FW1sSRAPmUZP2ZXlSzBVFlyh89dVX8+qrr5LP5/nrX//K6aefPtNDmjDPU+zvzhD0GYUGlp6H43mgIOQzyNsuWcujPFQ4SR1K6sPMLYOTROvKQ2gaBEyDgKlTGfGTtRx2daRQSslno0SNJWm/PwDe2pKgPOxjWZW0NhGlq+iCmlLU3JtlT1eGY2sjeErRlbbpTVv0Zix6Mha2q9A0jSXzQuj68H8yqQ8ztwxOEg2YBqauY7uFHCxN04gGTbrTFsmcI5+NEnZoccy9nWl6MzZr6uIDeVSyS0rMNXKkmwXSlkNnKk9fzqE85COZd7AcD1cpHFcRNHXK/IUpZqWU1IeZ4wYnieoaVIb9tCdz+CM6mqbhM3RS+cKSZc/Bk5x8NkrT0ZL2ZZeUmGskqJkFwj6DzlSeTN6hvjKMUopUvpAYbGoaqbyN32cOTDUviAcJ+Q2ylktrIif1YeaYQ5NEl9dESOZtutMW0aBZWMJUhRPaooqwfDZK3JGS9mWXlJhrJKiZBQoTvxqKwolH13XKQoVlJqUUGdsjYBq879RFvLi/T+rDzHGHJolWRgKcVF/OrvY0Xek8iaxNZSTA+qWVnLt6vnw25rDx7JISopjJJ3kWyNouVVE/msbA1bbPKORJpHIO0aDJvIifmrIgn9hYLdsy57j+JNGWRHZg5q4s5GPl/Ci7OzUaaqJctH4xG5ZXyWdjjhvrLikhip0ENbNAfzPLqqif1kSenoxFKu9g6jo1ZUHmlwUAjYjflPowAjh8ZefTj5knM3diwEgBsCxdi1ImQc0sMPhqat2SclJ5F8v18Bs60YBBY0darqbEMFLZWYyGtDYRc4kENbPA4Kupxo40C+JBysM+spZLY0darqbEYcnMnRgNCYDFXCFBzSwhV1NCiKk0UgAsrRNEqZGgZhaRqykhxHSR1gmiFElQM8vIcoIQYqr1t07oTlssiAcJ+6WzuygNEtQIIcQgpb4kc2jrhP5t3rGgTzq7i6InQY0QQhw0F5ZkpHWCKGXS0FIIIWDOdLN+rXXCyNe00tldFDMJaoQQc95c6mY9uHXCSKR1gihmEtQIIea8sSzJFLv+Yp+tiRxKDQ3S+lsnNNREpdinKEoS1Agh5ry5tCTTX+yzMuJnZ3uKZM7G8TySOZud7Skp9imKmgQ1Qog5b64tyfQX+1y9ME5vxmZvZ5rejM2aurhs5xZFrTT+QoUQYgLmYjdrKfYpSpEENUKIOW+udrOWYp+i1MjykxBCIEsyQpQCmakRQoiDZElGiOImQY0QQgwiSzJCFC9ZfhJCCCFESZCgRgghhBAlQYIaIYQQQpQECWqEEEIIURIkUVgIIYQQE+J5albsGpSgRgghhBDj1tie5P6tbezqSJFzXIKmwfLqKOeurp32+k4S1AghhBBiXBrbk2zespfutMWCeJCwP0TGctjakqAlkZ32wpWSUyOEEEKIMfM8xf1b2+hOW6yoiRIL+jB0jVjQx4qaKN1piwe2teF5atrGJEGNEEIIIcasuTfLro5Cr7TBTWABNE1jQTxIY3uK5t7stI1JghohhBBCjFnacsg5LmH/yJksIb9B3nFJW860jUmCGiGEEEKMWcRvEjQNMocJWrKWS8A0iBwm6JkKEtQIIYQQYszqykMsr47Smsih1NC8GaUUrYkcDTVR6spD0zYmCWqEEEIIMWa6rnHu6loqI352tqdI5mwczyOZs9nZnqIy4mfTCbXTWq9GghohhBBCjEtDTYzLNyxl9cI4vRmbvZ1pejM2a+ri076dG6ROjRBCCCEmoKEmxrKNUakoLIQQQojip+sa9ZXhmR6GLD8JIYQQojRIUCOEEEKIkiBBjRBCCCFKggQ1QgghhCgJEtQIIYQQoiRIUCOEEEKIkiBBjRBCCCFKggQ1QgghhCgJEtQIIYQQoiRIUCOEEEKIkiBBjRBCCCFKgvR+EkIIMSqep2ZF00IhDkeCGiGEEEfV2J7k/q1t7OpIkXNcgqbB8uoo566upaEmNtPDEwKQoEYIIcRRNLYn2bxlL91piwXxIGF/iIzlsLUlQUsiy+UblkpgI2YFyakRQghxWJ6nuH9rG91pixU1UWJBH4auEQv6WFETpTtt8cC2NjxPzfRQhZCgRgghxOE192bZ1ZFiQTyIpg3Nn9E0jQXxII3tKZp7szM0QiFeUxRBzd69e7niiis45phjCIVCLF++nBtuuAHLsmZ6aEIIUdLSlkPOcQn7R85WCPkN8o5L2nKmeWRCDFcUOTXbt2/H8zzuuOMOGhoa2Lp1Kx/96EdJp9N84xvfmOnhCSFEyYr4TYKmQcZyiAV9w+7PWi4B0yBymKBHiOlUFJ/C8847j/POO2/g+2XLlvHKK69w2223SVAjhBBTqK48xPLqKFtbEkQD5pAlKKUUrYkca+ri1JWHZnCUQhQUxfLTSBKJBJWVlTM9DCGEKGm6rnHu6loqI352tqdI5mwczyOZs9nZnqIy4mfTCbVSr0bMCkUxU3OoxsZGvvvd7x51liafz5PP5we+7+vrm+qhCSFEyWmoiXH5hqUDdWra+nIETIM1dXE2nSB1asTsMaNBzXXXXcfNN998xMe8/PLLHHfccQPfNzc3c9555/H+97+fj370o0d87k033cSNN944KWMVQoi5rKEmxrKNUakoLGY1TSk1Y8UFOjo66OrqOuJjli1bht/vB6ClpYWNGzfyute9jh//+Mfo+pFXz0aaqamvryeRSFBWVjbxX0AIIYqctD4QxaCvr494PH7U8/eMztRUV1dTXV09qsc2Nzfzpje9iVNPPZXNmzcfNaABCAQCBAKBiQ5TCCFKkrQ+EKWmKHJqmpub2bhxI0uWLOEb3/gGHR0dA/fNnz9/BkcmhBDFSVofiFJUFEHNgw8+SGNjI42NjSxatGjIfTO4eiaEEEXp0NYH/du0Y0Ef0YDJzvYUD2xrY1lVVJaiRFEpii3dl112GUqpEb+EEEKMjbQ+EKWqKIIaIYQQk0daH4hSJUGNEELMMYNbH4xEWh+IYiVBjRBCzDH9rQ9aE7lhy/j9rQ8aaqLS+kAUHQlqhBBijpHWB6JUSVAjhBBzUH/rg9UL4/RmbPZ2punN2Kypi8t2blG0ZMFUCCHmKGl9IEqNBDVCCDGH6bpGfWV4pochxKSQ5SchhBBClAQJaoQQQghREiSoEUIIIURJkKBGCCGEECVBghohhBBClAQJaoQQQghREiSoEUIIIURJkKBGCCGEECVBghohhBBClAQJaoQQQghREiSoEUIIIURJkKBGCCGEECVBghohhBBClAQJaoQQQghREiSoEUIIIURJkKBGCCGEECVBghohhBBClAQJaoQQQghREiSoEUIIIURJkKBGCCGEECVBghohhBBClAQJaoQQQghREiSoEUIIIURJkKBGCCGEECVBghohhBBClAQJaoQQQghREiSoEUIIIURJkKBGCCGEECVBghohhBBClAQJaoQQQghREiSoEUIIIURJkKBGCCGEECVBghohhBBClAQJaoQQQghREiSoEUIIIURJkKBGCCGEECVBghohhBBClARzpgcghBBibvA8RXNvlrTlEPGb1JWH0HVtpoclSogENUIIIaZcY3uS+7e2sasjRc5xCZoGy6ujnLu6loaa2EwPT5QICWqEEEJMqcb2JJu37KU7bbEgHiTsD5GxHLa2JGhJZLl8w1IJbMSkkJwaIYQQU8bzFPdvbaM7bbGiJkos6MPQNWJBHytqonSnLR7Y1obnqZkeqigBEtQIIYSYMs29WXZ1pFgQD6JpQ/NnNE1jQTxIY3uK5t7sDI1QlBIJaoQQQkyZtOWQc1zC/pGzHUJ+g7zjkracaR6ZKEUS1AghhJgyEb9J0DTIHCZoyVouAdMgcpigR4ixkKBGCCHElKkrD7G8OkprIodSQ/NmlFK0JnI01ESpKw/N0AhFKZGgRgghxJTRdY1zV9dSGfGzsz1FMmfjeB7JnM3O9hSVET+bTqiVejViUkhQI4QQYko11MS4fMNSVi+M05ux2duZpjdjs6YuLtu5xaSSRUwhhBBTrqEmxrKNUakoLKaUBDVCCCGmha5r1FeGZ3oYooQV3fJTPp/npJNOQtM0nnvuuZkejhBCCCFmiaILaj7/+c+zcOHCmR6GEEIIIWaZogpq/vCHP/DAAw/wjW98Y6aHIoQQQohZpmhyatra2vjoRz/Kr3/9a8Lh0a3J5vN58vn8wPd9fX1TNTwhhBBCzLCimKlRSnHZZZfxD//wD6xbt27Uz7vpppuIx+MDX/X19VM4SiGEEELMpBkNaq677jo0TTvi1/bt2/nud79LMpnk+uuvH9PrX3/99SQSiYGvpqamKfpNhBBCCDHTNHVo3epp1NHRQVdX1xEfs2zZMi644AJ++9vfDunw6rouhmFwySWX8JOf/GRUP6+vr494PE4ikaCsrGxCYxdCCCHE9Bjt+XtGg5rR2rdv35B8mJaWFs4991zuueceTj/9dBYtWjSq15GgRgghhCg+oz1/F0Wi8OLFi4d8H41GAVi+fPmoAxohhBBClLaiCGomS/+klOyCEkIIIYpH/3n7aItLRRnULF269Ki/2EiSySSA7IISQgghilAymSQejx/2/qLIqZksnufR0tJCLBYbknQsJkdfXx/19fU0NTVJztI0k/d+5sh7P3PkvZ850/3eK6VIJpMsXLgQXT/8xu2inKkZL13XJQdnGpSVlckBZobIez9z5L2fOfLez5zpfO+PNEPTryiK7wkhhBBCHI0ENUIIIYQoCRLUiEkTCAS44YYbCAQCMz2UOUfe+5kj7/3Mkfd+5szW935OJQoLIYQQonTJTI0QQgghSoIENUIIIYQoCRLUCCGEEKIkSFAjhBBCiJIgQY2YFLfeeitLly4lGAxy+umn87e//W2mhzQnPPbYY7zjHe9g4cKFaJrGr3/965ke0pxw0003cdpppxGLxaipqeHd7343r7zyykwPa8647bbbOPHEEwcKv73+9a/nD3/4w0wPa875+te/jqZpXHPNNTM9lAES1IgJu+uuu/jsZz/LDTfcwLPPPsvatWs599xzaW9vn+mhlbx0Os3atWu59dZbZ3ooc8qjjz7KVVddxV/+8hcefPBBbNtm06ZNpNPpmR7anLBo0SK+/vWv88wzz/D000/z5je/mXe9611s27Ztpoc2Zzz11FPccccdnHjiiTM9lCFkS7eYsNNPP53TTjuN733ve0Chx1Z9fT2f/OQnue6662Z4dHOHpmn86le/4t3vfvdMD2XO6ejooKamhkcffZQzzzxzpoczJ1VWVvIf//EfXHHFFTM9lJKXSqU45ZRT+P73v8+//uu/ctJJJ3HLLbfM9LAAmakRE2RZFs888wznnHPOwG26rnPOOefw5JNPzuDIhJg+iUQCKJxYxfRyXZef//znpNNpXv/618/0cOaEq666ire//e1DjvuzxZxqaCkmX2dnJ67rUltbO+T22tpatm/fPkOjEmL6eJ7HNddcw4YNG1i9evVMD2fOePHFF3n9619PLpcjGo3yq1/9ilWrVs30sErez3/+c5599lmeeuqpmR7KiCSoEUKICbjqqqvYunUrjz/++EwPZU5ZuXIlzz33HIlEgnvuuYdLL72URx99VAKbKdTU1MSnP/1pHnzwQYLB4EwPZ0QS1IgJqaqqwjAM2trahtze1tbG/PnzZ2hUQkyPq6++mt/97nc89thjLFq0aKaHM6f4/X4aGhoAOPXUU3nqqaf4z//8T+64444ZHlnpeuaZZ2hvb+eUU04ZuM11XR577DG+973vkc/nMQxjBkcoOTVigvx+P6eeeioPP/zwwG2e5/Hwww/L+rYoWUoprr76an71q1/xxz/+kWOOOWamhzTneZ5HPp+f6WGUtLPPPpsXX3yR5557buBr3bp1XHLJJTz33HMzHtCAzNSISfDZz36WSy+9lHXr1rF+/XpuueUW0uk0l19++UwPreSlUikaGxsHvt+zZw/PPfcclZWVLF68eAZHVtquuuoq7rzzTn7zm98Qi8U4cOAAAPF4nFAoNMOjK33XX389b33rW1m8eDHJZJI777yTP/3pT9x///0zPbSSFovFhuWNRSIR5s2bN2vyySSoERN24YUX0tHRwZe+9CUOHDjASSedxH333TcseVhMvqeffpo3velNA99/9rOfBeDSSy/lxz/+8QyNqvTddtttAGzcuHHI7Zs3b+ayyy6b/gHNMe3t7Xz4wx+mtbWVeDzOiSeeyP33389b3vKWmR6amGFSp0YIIYQQJUFyaoQQQghREiSoEUIIIURJkKBGCCGEECVBghohhBBClAQJaoQQQghREiSoEUIIIURJkKBGCCGEECVBghohhBBClAQJaoQQ0+qyyy5D07RhX+edd95MD00IUeSkTYIQYtqdd955bN68echtgUBgyn6eZVn4/f4pe30hxOwgMzVCiGkXCASYP3/+kK+KigoANE3jhz/8Ie95z3sIh8OsWLGCe++9d8jzt27dylvf+lai0Si1tbV86EMforOzc+D+jRs3cvXVV3PNNddQVVXFueeeC8C9997LihUrCAaDvOlNb+InP/kJmqbR29tLOp2mrKyMe+65Z8jP+vWvf00kEiGZTE7xuyKEmCgJaoQQs86NN97IBRdcwAsvvMDb3vY2LrnkErq7uwHo7e3lzW9+MyeffDJPP/009913H21tbVxwwQVDXuMnP/kJfr+fLVu2cPvtt7Nnzx7e97738e53v5vnn3+ej3/84/zzP//zwOMjkQgXXXTRsBmkzZs38773vY9YLDb1v7gQYmKUEEJMo0svvVQZhqEikciQr6997WtKKaUA9cUvfnHg8alUSgHqD3/4g1JKqa9+9atq06ZNQ16zqalJAeqVV15RSil11llnqZNPPnnIY77whS+o1atXD7ntn//5nxWgenp6lFJK/fWvf1WGYaiWlhallFJtbW3KNE31pz/9afLeACHElJGcGiHEtHvTm97EbbfdNuS2ysrKgf8+8cQTB/47EolQVlZGe3s7AM8//zyPPPII0Wh02Ovu2rWLY489FoBTTz11yH2vvPIKp5122pDb1q9fP+z7E044gZ/85Cdcd911/OxnP2PJkiWceeaZ4/gthRDTTYIaIcS0i0QiNDQ0HPZ+n8835HtN0/A8D4BUKsU73vEObr755mHPW7BgwZCfMR5XXnklt956K9dddx2bN2/m8ssvR9O0cb2WEGJ6SVAjhCgqp5xyCv/93//N0qVLMc3RH8JWrlzJ73//+yG3PfXUU8Me98EPfpDPf/7zfOc73+Gll17i0ksvnfCYhRDTQxKFhRDTLp/Pc+DAgSFfg3cvHclVV11Fd3c3F198MU899RS7du3i/vvv5/LLL8d13cM+7+Mf/zjbt2/nC1/4Ajt27ODuu+/mxz/+McCQmZiKigre+973cu2117Jp0yYWLVo0od9VCDF9JKgRQky7++67jwULFgz5OuOMM0b13IULF7JlyxZc12XTpk2sWbOGa665hvLycnT98Ie0Y445hnvuuYdf/vKXnHjiidx2220Du58OrZFzxRVXYFkWH/nIR8b/Swohpp2mlFIzPQghhJgJX/va17j99ttpamoacvtPf/pTPvOZz9DS0iJF+4QoIpJTI4SYM77//e9z2mmnMW/ePLZs2cJ//Md/cPXVVw/cn8lkaG1t5etf/zof//jHJaARosjI8pMQYs7YuXMn73rXu1i1ahVf/epX+f/bs0MbgGEYioIeIyNkvyAz40xd1gFKKn3dURPDJ/ucUzPzzu+9tfeutVZ193+LAp94PwEAEVxqAIAIogYAiCBqAIAIogYAiCBqAIAIogYAiCBqAIAIogYAiCBqAIAID1msyjqYszexAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1487,20 +1487,20 @@
    "source": [
     "# x values versus residuals\n",
     "plt.scatter(X[:, 1:2], res, alpha=0.5)\n",
-    "plt.xlabel(\"Energy\")\n",
-    "plt.ylabel(\"Carbon\")\n",
-    "plt.title(\"Residual Plot vs. X Values\")\n",
+    "plt.xlabel(\"Household Energy\")\n",
+    "plt.ylabel(\"Residuals\")\n",
+    "plt.title(\"Residual Plot vs. Energy Values\")\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1512,9 +1512,9 @@
    "source": [
     "# x values versus residuals\n",
     "plt.scatter(X[:, 2:3], res, alpha=0.5)\n",
-    "plt.xlabel(\"Gas\")\n",
-    "plt.ylabel(\"Carbon\")\n",
-    "plt.title(\"Residual Plot vs. X Values\")\n",
+    "plt.xlabel(\"Gasoline Consumption\")\n",
+    "plt.ylabel(\"Residuals\")\n",
+    "plt.title(\"Residual Plot vs. Gasonline\")\n",
     "plt.show()"
    ]
   },
@@ -3971,22 +3971,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0, 0.5, 'Carbon Emissions')"
+       "Text(0, 0.5, 'Greenhouse Emissions (ktonnes)')"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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FHWJet24dI0eO7PT63Pa1s+XNffbs2R0++AsLC7n//vuJx+M7/N0PGDCAo48+mieffLJ92xNPPIHL5erQXZPuNUzn97rFtq//FStWoLVm1KhRnc51yZIlVFdXp31Nuvu63Xa/Lftu2a+mpoZIJMLo0aM77Td27Fhs2+6ynmZHDj/8cE4//XRuvPFGBgwYwMknn8wDDzzQofZjxYoVNDU1UVRU1OmcW1pa2q/N1znssMM45phjOvwceOCBnfbb9jrk5OQAMGTIkE7bbdve4Wvw615bXq+XW2+9lZdeeoni4mIOO+wwbrvttq8dMp3p96504+iLpEZlN+lqlEBjYyOHH3442dnZ3HTTTYwcORKfz8f8+fP52c9+1ulblGmaXR5bt/WHg/OiXrZsGf/61794+eWX+cc//sGf//xnbrjhhi6LELfWVaHcjrZvq6cjXH784x/zwAMPcMUVV3DggQeSk5ODUoqzzz57h9+qM6k717QrY8aM4fPPPyeZTHb4pjZp0qTtPqa753vYYYexatUqnn/+ef7zn/9w//3384c//IF77rmHCy+8cLvH9/l8gJMQdSUSibTvA1BUVMSCBQt45ZVXeOmll3jppZd44IEH+O53v9tewPeb3/yG66+/ngsuuIBf/epX5OfnYxgGV1xxRVq/oy2Puf3225kyZUqX+3TVorG1s88+mzlz5rBgwQKmTJnCk08+ydFHH91h9Ee61zCd3+sW277+bdtGKcVLL73U5etsy3mmc026+7pN9/Xdle29D2xb+K+U4umnn+bDDz/khRde4JVXXuGCCy7gjjvu4MMPPyQUCmHbNkVFRTz66KNdHrOwsLDH8e3I9q5DOtenO6+tK664ghNPPJHnnnuOV155heuvv55bbrmF119/nalTp+78CXUz9t0Rx+4giUovevPNN6mrq+OZZ57pMCfDmjVrduq4wWCQs846i7POOotEIsFpp53GzTffzHXXXdfhg2rFihUdvgWuXLkS27bbi6yGDRuGbdusWLGiQ/FqVVUVjY2NDBs2rFvxbO8N7umnn2b27Nnccccd7dtisRiNjY3denxXhg0bxsKFC7Ftu8M3ky3daN2N+euccMIJfPjhhzz77LOceeaZ3XpMd88XID8/nzlz5jBnzhxaWlo47LDD+OUvf7nDD9kt57Zs2bL2ZuCtLV++nAkTJnTY5vF4OPHEEznxxBOxbZsf/ehH3HvvvVx//fWUl5fz9NNPc+SRR/L//t//6/C4xsbGDonBsGHDWLx4MVrrDr+vlStXdnjclm6I7OxsjjnmmO2ey46ccsopXHzxxe3dP8uXL+e6667rtF861zCd3+v2jBw5Eq01ZWVl7LPPPjvcD3bumqSrsLCQQCDAsmXLOt23dOlSDMNob3XY8o29sbGxQ7Hqtt/0t/jGN77BN77xDW6++WYee+wxzjvvPB5//HEuvPBCRo4cyauvvsrBBx+8Rw7h785ra+TIkVx99dVcffXVrFixgilTpnDHHXfwyCOPdHnMXfXe1dM4+iLp+ulFWzLirTPgRCLBn//857SPWVdX1+G2x+Nh3LhxaK079Rf/6U9/6nD7zjvvBGgfQXTccccBMHfu3A77/f73vwfg+OOP71ZMwWCwyw9j0zQ7fXO58847O31D2zIfRVfH2NZxxx1HZWVlhxqGVCrFnXfeSSgU4vDDD+9WzF/nhz/8IcXFxVx55ZUsX7680/1dfSPr7vlu+zsMhUKUl5d3Gja5rWnTplFUVNTeXbC15557jk2bNrX/brt6HsMw2lsOtjy+q5ifeuopNm3a1GHbzJkz2bRpE//85z/bt8ViMe67775OMY4cOZLf/e53XXax1NTU7PAcwakRmDlzJk8++SSPP/44Ho+n02SC6V7DdH6v23PaaadhmiY33nhjp8dprdtjzMQ1SZdpmhx77LE8//zzHYasVlVV8dhjj3HIIYeQnZ0NfJVQvf322+37bZl6YGsNDQ2dzndLS9GW63/mmWdiWRa/+tWvOsWUSqW69bfeW77utRWJRDpN8zBy5EiysrJ2+PrL9HtXunH0RdKi0osOOugg8vLymD17NpdddhlKKf72t7+l1Sy7xbHHHktJSQkHH3wwxcXFLFmyhLvuuovjjz+erKysDvuuWbOGk046iW9+85t88MEHPPLII5x77rlMnjwZgMmTJzN79mz+8pe/tHdTffTRRzz00EOccsopHHnkkd2Kadq0abz66qv8/ve/p7S0lLKyMg444ABOOOEE/va3v5GTk8O4ceP44IMPePXVV9uHTW4xZcoUTNPk1ltvpampCa/Xy1FHHUVRUVGn57rooou49957Of/88/n0008ZPnw4Tz/9NO+99x5z587tdA3SlZ+fz7PPPsuJJ57I5MmTOfvss9l///1xu91s2LCBp556CujYj9zd8x03bhxHHHEE06ZNIz8/n08++YSnn376a9dt8Xg8/O53v2P27Nnsv//+nHXWWRQUFPDZZ5/x17/+lUmTJrUPEQdn6Hl9fT1HHXUUgwcPZt26ddx5551MmTKlvQXthBNO4KabbmLOnDkcdNBBfPHFFzz66KMdCi/BGWp61113cc4553D55ZczcOBAHn300fYWvC2tLIZhcP/99zNr1izGjx/PnDlzGDRoEJs2beKNN94gOzubF1544Wuv/1lnncW3v/1t/vznPzNz5sxOw1HTvYbp/F63Z+TIkfz617/muuuuY+3atZxyyilkZWWxZs0ann32WS666CJ+8pOfZOyapOvXv/418+bN45BDDuFHP/oRLpeLe++9l3g83mGepmOPPZahQ4fyve99j2uuuQbTNPnrX/9KYWEh69evb9/voYce4s9//jOnnnoqI0eOpLm5mfvuu4/s7Oz2Lz+HH344F198MbfccgsLFizg2GOPxe12s2LFCp566in++Mc/csYZZ3xt7E8//XSXXYUzZsxonwIi077utbV8+XKOPvpozjzzTMaNG4fL5eLZZ5+lqqqKs88+e7vHzfR7V7px9Em7dYxRP7C94cnbGzL83nvv6W984xva7/fr0tJS/dOf/lS/8sornYbjbu8Ys2fP7jCc7t5779WHHXaYLigo0F6vV48cOVJfc801uqmpqX2fLUP7Fi9erM844wydlZWl8/Ly9KWXXqqj0WiH4yeTSX3jjTfqsrIy7Xa79ZAhQ/R1113XYei01s4Qvu0N2Vu6dKk+7LDDtN/v10D7UL+GhgY9Z84cPWDAAB0KhfTMmTP10qVLOw0H1Frr++67T48YMUKbptnh2mw7xE9rrauqqtqP6/F49MSJE/UDDzzQYZ8tQ/y6GroH6F/84hddnsu2Kioq9DXXXKPHjRun/X6/9nq9esSIEfq73/2ufvvttzvs293z/fWvf62nT5+uc3Nztd/v12PGjNE333xzh+GVO/LSSy/pI488UmdnZ2u3263Lysr0VVddpRsaGjrs9/TTT+tjjz1WFxUVaY/Ho4cOHaovvvhiXVFR0b5PLBbTV199tR44cKD2+/364IMP1h988EGX13316tX6+OOP136/XxcWFuqrr75a/+Mf/9CA/vDDDzvs+9lnn+nTTjut/XU6bNgwfeaZZ+rXXnutW+cYDofbX09bD+fM1DXsye91y99TTU1Nl8f6xz/+oQ855BAdDAZ1MBjUY8aM0ZdccoletmxZj6/J9p5r2yHCWjuv40suuaRTPF39fc2fP1/PnDlTh0IhHQgE9JFHHqnff//9To/99NNP9QEHHND+evn973/f6bnnz5+vzznnHD106FDt9Xp1UVGRPuGEE/Qnn3zS6Xh/+ctf9LRp07Tf79dZWVl64sSJ+qc//anevHlzl9dy2+uwvZ8t7w9bhic/9dRTXV6vbYdQd3V9e/r3WVtbqy+55BI9ZswYHQwGdU5Ojj7ggAP0k08+2eG5dvV7V3fj2BMorXfi67vYI/3yl7/kxhtvpKamZpdPPy36t7lz53LllVeyceNGBg0a1NvhCCH2QFKjIoTIiG1HG8ViMe69915GjRolSYoQIm1SoyKEyIjTTjuNoUOHMmXKFJqamnjkkUdYunTpdoegCiFEd0iiIoTIiJkzZ3L//ffz6KOPYlkW48aN4/HHH+8wY6wQQvSU1KgIIYQQos+SGhUhhBBC9FmSqAghhBCiz9qja1Rs22bz5s1kZWX1aJp1IYQQQvQerTXNzc2UlpZ2WohxW3t0orJ58+ZOq18KIYQQYs+wYcMGBg8evMN99uhEZcuUwhs2bGhfj0IIIYQQfVs4HGbIkCHdWhpgj05UtnT3ZGdnS6IihBBC7GG6U7YhxbRCCCGE6LMkURFCCCFEnyWJihBCCCH6LElUhBBCCNFnSaIihBBCiD5LEhUhhBBC9FmSqAghhBCiz5JERQghhBB9liQqQgghhOiz9uiZaYUQQoi93cYmm/mbLVbU2SgFYwYY7FtqUpLVP9oaJFERQggh+qiPN1o882WShhiEPKCB5bUWH2ywOWuiiwnFZm+HuMtJoiKEEEL0QZvCNs8sTpKwYfQA1b4ujtaatY2apxalKM0yyA98/Xo5e7L+0W4khBBC7GE+q7BoiMLgbNVh8T6lFMNyFVUtmoVVVi9GuHtIoiKEEEL0QavrNAF31ysMG0rhNmFtg+6FyHYvSVSEEEKIPsgwnJqU7dEazL271weQGhUhhBCiTxpbaLCoysa2bZoTipoWTTSpcZuQ71ekbCgv2PvbGyRREUIIIfqgyQNN3lln8d46m1jSImEpTAUpWxO1FCPyFOX5e3+Tyt6figkhhBB7oAEBxYRCg8aopiWhUEqjAcNQDPCDCbyzdu8vppUWFSGEEKIPStmaDY2asQMUPreiMaYxFOT6FEVBRTgGn260ObxMUxDce1tWJFERQggh+qCaVk1FWFOaZRDyKobmdLw/P6BZWQcbmmwKgpmd+C2R0qyo1YRjGp8LRhU6MfQGSVSEEEKIXSye0tS0alBQFFR4ujNcR4OtwdjOrls26wyPUF5UYfPvJSk2hzW2rQHFgKDiiHKDQ0aYGF0Ml96VJFERQgghdpGEpXl7rcX7G1JsaHJqTIbkKI4uc3PgEANze1kIkB9wEoT6iE3A03m/prZp9UuyMpc4rKixefyzJJEEDMkBj8vAsjXVLZrnFlkoBYeO2L2pgxTTCiGEELtAytY8tSjJffOTvL/RYnWTzapGm9fXWtz2XownFyXRO2gO8boU3xhq0ppUtMQ77pewNJUtmvHFRsYSFa0176y2aIrB8HzwuJzjmoZiYLbCY8Jbq2wiid07yZy0qAghhBC7wJIam38uT1ET1bgMyPE63ThxC+pj8NDCJBNKTCYWbb++5KDhBhXNBh+ut6lo0XhdmqaYJmkrJhYrThzn6nLm2nTURWBFrU1xqOvZcItCsK5Bs6ZeM75k93X/SIuKEEIIsQu8v95ic7NTjJrnU7hNhWkoAm5FcQDqopp/Lk3u8BgeU/GtSS7m7GcyIARrmjSbI5okmpoYvLU2RUuGWjjiKU3KAs92mjDcpkJrZ7/dqc8kKr/97W9RSnHFFVf0dihCCCHETltaZ2EBQXfn+1ymwmvCohpNImWTsPR2u4FchqI6oqmNaYbmKQ4eZjKpxEAp+PcKi78tSBJN7nzykONTBD2K5njX90cSzqy4Of5+WEz78ccfc++99zJp0qTeDkUIIYTICJehsG22O0rG0lDVavGLdxIAlGYZHFBqsm+JgWurItuqFpvXVltkeZ35U7YoDkGuDxZW2XxWYXPQ0J0bohzyKqYMVsxbZpPnd1pQttBas6kJ9ilSDM/bvYlKr7eotLS0cN5553HfffeRl5fX2+EIIYQQGTGm0EADSatza0dTXNOYhBZLkbQ0ClhaZ/PgwiRPLUmSsr96zOIam6Y4FAY6P4fX5RS5frQpMzPUHj7CxT6FBqtqNZXNmpa4prZVs7wGBoQUx4117XCk0q7Q64nKJZdcwvHHH88xxxzT26EIIYQQGTOr3EVxECpbNNGkM9+J1tAc12xqcWaZ3bfYaUkZEFCMzFUUBeDtDRafVtrtx2lJOHOmbK9oNuhR1Ec1dgYmVMkLKGbv7+a4cSY+l6Ip5sR86AiDC6a7GNELiyD2atfP448/zvz58/n444+7tX88Hice/6rzLBwO76rQhBBCiJ1SmmVw4VQP932WpDFuYySc1oG4DVrByFwYmtPxYzjbq6iJwn83WUwfaKCUwu/akuToLpOVaFIzOFtlbCK2XL/i+HFujhqlaU2Az0WvzUoLvZiobNiwgcsvv5x58+bh8/m69ZhbbrmFG2+8cRdHJoQQQmTGCaNd5AcU/16ZYl2Tja0haUNWUrN/qYmvi0/hHC9UtNjELPC7YGyhQZYX6qNQsE33T9LSRFOw36DMTqEP4Hcr/F0UAu9uSu9otpld6LnnnuPUU0/FNL+6uJZloZTCMAzi8XiH+6DrFpUhQ4bQ1NREdnb2botdCCGE6IlYSrO+SZOyNUtqbV5Zk2J0vuqyhaSy1cZtKH55qLdtSLDmhWUpXl5pEfJAYUBhGtAYg6oWzdhCg+/t6yarF1s9eiocDpOTk9Otz+9ea1E5+uij+eKLLzpsmzNnDmPGjOFnP/tZpyQFwOv14vV6d1eIQgghREb4XIp9CpxEIuBRvLMxRXMCsrf5SNNa0xiDGWVm+6gbpRTH7eMi4Fa8t95ibaPG1s5jDxtmctw+rj0qSempXktUsrKymDBhQodtwWCQgoKCTtuFEEKIvcWwbMXkIpP320bqZHmcZCRla9aHocCvOGCbrhyXoThmpIsDh5isb3IKZwsDiqJQr4+J2eX6xDwqQgghRH+hlOJbY53ijwXVFptaQKFRCkpDBmeMcTE0u+sEJOhRjC3ce1tPutJrNSqZ0JM+LiGEECITkpZmZaNNOOEUu47KM/C7ep482FqzrkmzssEmZTstKRMKDQLuvT8R2SNqVIQQQog9zZe1Fi+sckbwpLQz6+zAoGJmmYtvtA0n7i5DKcpyFWW5e3/3zc6QREUIIYTohuX1Ng8uStKcgEFZCr9LkbA0Fa02jy5OoPDwjdLMDxPu7ySNE0IIIb6G1prX1qdojGtG5tLe1eMxFcPa6knmrU0R72K6fLFzJFERQgghvkZ1RLO83qYk2PXcJwNDik0tNqsa7S4eLXaGJCpCCCHE14hZkLA03u307HhNhWVDLLV74+oPJFERQgghvka2RxFwK1qSXd/fmtR4XZCzF0+81lskURFCCCG+Rp5PMaXIoKpVY9kd61C01mxs1ozIMSnLkUQl02TUjxBCCNENxwxzsaZJs7TepigAQbfT1VMV0RQFFCeWuzK2grH4irSoCCGEEN1QHDS4aLKbbw430Rpqopq4BQcPMrl4sodRefKRuitIi4oQQgjRTUUBg7PGepg1QhNOaAJuRb5PWlF2JUlUhBBCiB7K9iqypXB2t5B2KiGEEEL0WZKoCCGEEKLPkkRFCCGEEH2WJCpCCCGE6LMkURFCCCFEnyWJihBCCCH6LElUhBBCCNFnSaIihBBC7CStNUlLY2v99TuLHpEJ34QQQog0JSzN/CqbDyssKltsvKZiWonBAQNNioPSFpAJkqgIIYQQaUhYmr8vSfLeJgvTUGR7oDlp8/xKm48rbeZMcDMiV5KVnSVXUAghhEjDhxUW722yGBhSjMxVFAYUpSGD0fmKylabp5YlSVrSFbSzJFERQgghesiyNR9ssnCbkOXpuOaPoRRDsxVrwpplDXYvRbj3kERFCCGE6KGWJFRHNLnbWZjQ71JYtqYmIi0qO0sSFSGEEKKHXAaYCrbXs6O1Rmsw5VN2p8klFEIIIXoo6FaMHWBQF9XoLoYkN8adLqFyKabdaXIFhRBCiDQcMshFtkexNqxJ2U6yorWmKa6pbNXsV2IwMNh115DoPhmeLIQQQqRhVJ7BeePcPLM8xcoGG9BoIOiGwwabnDrKjVKSqOwsSVSEEEKINO1bbDIqz2BRrU19VOM2YZ88g2HZSpKUDJFERQghhNgJWR7FgaVmb4ex15IaFSGEEEL0WZKoCCGEEKLPkkRFCCGEEH2WJCpCCCGE6LMkURFCCCFEn9XjUT9Llizh8ccf55133mHdunVEIhEKCwuZOnUqM2fO5PTTT8fr9e6KWIUQQgjRzyjd1dy/XZg/fz4//elPeffddzn44IOZPn06paWl+P1+6uvrWbRoEe+88w7hcJif/vSnXHHFFbs8YQmHw+Tk5NDU1ER2dvYufS4hhBC7h601yxttFtRY1EQ12R7F5AEm4/INPOb25yaJpjRf1ltURjSmgrJsg1E5BqYh85n0NT35/O52olJWVsY111zDueeeS25u7nb3++CDD/jjH//IpEmT+PnPf96jwHtKEhUhhNi7pGzN0yuTvL3JImZpfKYiYWsUin2LDL47xkPQ3TnxWNlk8eiyJOtb7PaFAr0mjM83+fY+bvJ9UunQl/Tk87vbXT/Lly/H7XZ/7X4HHnggBx54IMlksruHFkIIIQB4Z7PFvPUWJQFFrver5CKS1Py30iLHk+Tc0Z4Oj6mK2DywJEFVRFOWpdpbXVqTmk+qLWwNl0z04JaWlT1St1PMr0tSGhsbe7S/EEIIsbW4pXlnU4qAC3K9HZOKgFtR5Fd8Um1RE7U73PdJdYqNLTblOapD11DQrRiZrfiy3mJJQ8fHiD1HWm1ht956K0888UT77TPPPJOCggIGDRrE559/nrHghBBC9B/VEU1VRFPg67rlo8CnaIzDhuaOFQuf1dpkuRVGF2vr+F2KhA0rG61dErPY9dJKVO655x6GDBkCwLx585g3bx4vvfQSs2bN4pprrslogEIIIcQWXaUwcQtcO/g0M9EkpUFlj5XWooSVlZXticq//vUvzjzzTI499liGDx/OAQcckNEAhRBC9A9FAUVRQFEb1V0WzNbHNDkeGBzqeF9ZtuKdzVAa7HxMS2tsFMUBKabdU6X1m8vLy2PDhg0AvPzyyxxzzDEAaK2xLGleE0II0XNeU3FoqYtIChrjHbt3IilNVVSzb5FJ0TZJx/5FLrwm1MU6PkZrzfpmTbHfGd4s9kxptaicdtppnHvuuYwaNYq6ujpmzZoFwGeffUZ5eXlGAxRCCNF/HDrIpCpi8/Zmi4qIjd9UxC2NUorpxSanjOg8UGNCvsE3h7p4aX2Kurgm1wOWhoY45HkVZ43ykOeVET97qrQSlT/84Q8MHz6cDRs2cNtttxEKhQCoqKjgRz/6UUYDFEII0X+4DcWZo9xMLjQ7Tfg2vsDA28WEb0opTipzMyzL5IOqFGvDNi4Djh1i8o0SFyOypdtnT9btCd/6IpnwTQghxLaStsYAmZG2D+vJ53faaebf/vY3DjnkEEpLS1m3bh0Ac+fO5fnnn0/3kEIIIUQnsZTmi1qLDytSfFlnkbB2/P3abShJUvYiaSUqd999N1dddRWzZs2isbGxvYA2NzeXuXPnZjI+IYQQ/ZTWmv9WpPjtx3Hu/CzB/V8k+L/PEtz2cZwFNTJwo79IK1G58847ue+++/if//kfTPOrSur99tuPL774ImPBCSGE6L8+rbJ5ZEmS2qhmeLZiTL7JkJBiU4vm4S8TfFknyUp/kFaismbNGqZOndppu9frpbW1daeDEkII0b8lLc0r65JYGoZnf7Vqss+lGJGjaEnCq+tS2HtumaXoprQSlbKyMhYsWNBp+8svv8zYsWN3NiYhhBD93JqwzcZmzcBg16N8BgYVqxptNrVIorK3S2t48lVXXcUll1xCLBZDa81HH33E3//+d2655Rbuv//+TMcohBCin4mlnNE7vi6GIwN4TUjYTqGt2LullahceOGF+P1+/vd//5dIJMK5555LaWkpf/zjHzn77LMzHaMQQoh+Jter8LsUzUnI9nS+vzlJl6ssi71PWokKwHnnncd5551HJBKhpaWFoqKiTMYlhBCiHxuSpdgnz2BBjUUoz+iwMrKlNZWtNocNclEoa/js9dJOVLYIBAIEAoFMxCKEEEIATh3KiSPcVEU0S+ttigJOC0skqamOaoZnG3yzbKc/wsQeIK1UtKqqiu985zuUlpbicrkwTbPDjxBCCLGzhucYXDzJwxFDXKRsqI1qbODYYS4unuRhYFBaU/qDtNLR888/n/Xr13P99dczcOBAlEqvj/Duu+/m7rvvZu3atQCMHz+eG264oX2RQyGEEP3bkCyD747z0BjXtCY1WW5FttSl9CtpJSrvvvsu77zzDlOmTNmpJx88eDC//e1vGTVqFFprHnroIU4++WQ+++wzxo8fv1PHFkIIsffI9SopnO2n0kpUhgwZQibWMjzxxBM73L755pu5++67+fDDDyVREUIIIUR6NSpz587l2muvbe+yyQTLsnj88cdpbW3lwAMPzNhxhRBCCLHnSqtF5ayzziISiTBy5EgCgQBut7vD/fX19d0+1hdffMGBBx5ILBYjFArx7LPPMm7cuC73jcfjxOPx9tvhcDid8IUQQgixh0grUcnkCsmjR49mwYIFNDU18fTTTzN79mzeeuutLpOVW265hRtvvDFjzy2EEEKIvk3pTBSbZNAxxxzDyJEjuffeezvd11WLypAhQ2hqaiI7O3t3himEEEKINIXDYXJycrr1+Z32bDm2bbNy5Uqqq6uxbbvDfYcddli6h8W27Q7JyNa8Xi9erzftYwshhBBiz5JWovLhhx9y7rnnsm7duk6jf5RSWJbVreNcd911zJo1i6FDh9Lc3Mxjjz3Gm2++ySuvvJJOWEIIIYTYy6SVqPzgBz9gv/3248UXX9ypCd+qq6v57ne/S0VFBTk5OUyaNIlXXnmFGTNmpHU8IYQQQuxd0qpRCQaDfP7555SXl++KmLqtJ31cQgghhOgbevL5ndY8KgcccAArV65MKzghhBBCiO5Kq+vnxz/+MVdffTWVlZVMnDix0zwqkyZNykhwQgghhOjf0ur6MYzODTFKKbTWPSqm3VnS9SOEEELseXb58OQ1a9akFZgQQgghRE+klagMGzYs03EIIYQQQnSS9oRvq1atYu7cuSxZsgSAcePGcfnllzNy5MiMBSeEEEKI/i2tUT+vvPIK48aN46OPPmLSpElMmjSJ//73v4wfP5558+ZlOkYhhBBC9FNpFdNOnTqVmTNn8tvf/rbD9muvvZb//Oc/zJ8/P2MB7ogU0wohhBB7nl0+j8qSJUv43ve+12n7BRdcwOLFi9M5pBBCCCFEJ2klKoWFhSxYsKDT9gULFlBUVLSzMQkhhBBCAGkW037/+9/noosuYvXq1Rx00EEAvPfee9x6661cddVVGQ1QCCGEEP1XWjUqWmvmzp3LHXfcwebNmwEoLS3lmmuu4bLLLkt7kcKekhoVIYQQYs/Tk8/vtBKVrTU3NwOQlZW1M4dJiyQqQgghxJ5nlxfTHnXUUTQ2NgJOgrIlSQmHwxx11FHpHFIIIYQQopO0EpU333yTRCLRaXssFuOdd97Z6aCEEEIIIaCHxbQLFy5s///ixYuprKxsv21ZFi+//DKDBg3KXHRCCCGE6Nd6lKhMmTIFpRRKqS67ePx+P3feeWfGghNCCCFE/9ajRGXNmjVorRkxYgQfffQRhYWF7fd5PB6KioowTTPjQQohhBCif+pRorJl1eSGhgZycnK63GflypWUl5fvfGRCCCGE6PfSKqY94YQTiMfjnbYvW7aMI444YmdjEkIIIYQA0kxUQqEQp556KqlUqn3bkiVLOOKIIzj99NMzFpwQQggh+re0EpVnnnmGpqYmzjvvPLTWLFq0iCOOOIJzzjmHP/7xj5mOUQghhBD9VFqJit/v58UXX2TZsmWceeaZHH300Xz3u9/l97//fabjE0IIIUQ/1u1i2nA43OG2YRg88cQTzJgxg9NPP53rr7++fR+Zzl4IIYQQmdDttX4Mw+hyscEtD1dKobVGKYVlWZmNcjtkrR8hhBBiz9OTz+9ut6i88cYbOx2YEEIIIURPdDtROfzww3dlHEIIIYQQnXS7mHb9+vU9OvCmTZt6HIwQQgghxNa6najsv//+XHzxxXz88cfb3aepqYn77ruPCRMm8I9//CMjAQohhBCi/+p218/ixYu5+eabmTFjBj6fj2nTplFaWorP56OhoYHFixfz5Zdfsu+++3Lbbbdx3HHH7cq4hRBCCNEPdHvUzxbRaJQXX3yRd999l3Xr1hGNRhkwYABTp05l5syZTJgwYVfF2omM+hFCCCH2PD35/O5xotKXSKIihBBC7Hl68vmd1sy0QgghhBC7gyQqQgghhOizJFERQgghRJ8liYoQQggh+ixJVIQQQgjRZ6WVqDz00EO8+OKL7bd/+tOfkpuby0EHHcS6desyFpwQQggh+re0EpXf/OY3+P1+AD744AP+9Kc/cdtttzFgwACuvPLKjAYohBBCiP6r2zPTbm3Dhg2Ul5cD8Nxzz3H66adz0UUXcfDBB3PEEUdkMj4hhBBC9GNptaiEQiHq6uoA+M9//sOMGTMA8Pl8RKPRzEUnhBBCiH4trRaVGTNmcOGFFzJ16lSWL1/evq7Pl19+yfDhwzMZnxBCCCH6sbRaVP70pz9x4IEHUlNTwz/+8Q8KCgoA+PTTTznnnHMyGqAQQggh+i9Z60cIIYQQu1VPPr/T6voBaGxs5KOPPqK6uhrbttu3K6X4zne+k+5hhRBCCCHapZWovPDCC5x33nm0tLSQnZ2NUqr9PklUhBBCCJEpadWoXH311VxwwQW0tLTQ2NhIQ0ND+099fX2mYxRCCCFEP5VWorJp0yYuu+wyAoFApuMRQgghhGiXVqIyc+ZMPvnkk0zHIoQQQgjRQVo1KscffzzXXHMNixcvZuLEibjd7g73n3TSSRkJTgghhBD9W1rDkw1j+w0xSiksy9qpoLpLhicLIYQQe55dPjx56+HIQgghhBC7Slo1KkIIIYQQu0Paicpbb73FiSeeSHl5OeXl5Zx00km88847mYxNCCGEEP1cWonKI488wjHHHEMgEOCyyy7jsssuw+/3c/TRR/PYY49lOkYhhBBC9FNpFdOOHTuWiy66iCuvvLLD9t///vfcd999LFmyJGMB7ogU0wohhBB7np58fqfVorJ69WpOPPHETttPOukk1qxZk84hhRBCCCE6SStRGTJkCK+99lqn7a+++ipDhgzZ6aCEEEIIISDN4clXX301l112GQsWLOCggw4C4L333uPBBx/kj3/8Y7ePc8stt/DMM8+wdOlS/H4/Bx10ELfeeiujR49OJywhhBBC7GXSSlR++MMfUlJSwh133MGTTz4JOHUrTzzxBCeffHK3j/PWW29xySWXsP/++5NKpfj5z3/Osccey+LFiwkGg+mEJoToZ2xtU0+YKHEUijyyCCp/b4clhMiQtIppd5WamhqKiop46623OOyww752fymmFaJ/a9ItLNVraaQZGxsNePFQSiGj1BBcyuztEIUQXdjlM9PuKk1NTQDk5+d3eX88Hicej7ffDofDuyUuIUTf06qjLNQraCFKFgHcuNBoYsRZwyZsbTOOMpRSvR2qEGIndDtRyc/PZ/ny5QwYMIC8vLwd/vHX19f3OBDbtrniiis4+OCDmTBhQpf73HLLLdx44409PrYQYu+zWdcQppUCclA470cKhR8fCkUFNQyhmGykG1mIPVm3E5U//OEPZGVltf8/099SLrnkEhYtWsS777673X2uu+46rrrqqvbb4XBYRhkJ0Q/Z2qaCWrx42pOUrXnx0EqMepokURFiD9ftRGX27Nnt/z///PMzGsSll17Kv/71L95++20GDx683f28Xi9erzejzy2E2PNoNBY25nZmWFBt6YuFLKAqxJ4urXlU5s+fzxdffNF++/nnn+eUU07h5z//OYlEotvH0Vpz6aWX8uyzz/L6669TVlaWTjhCiH7GwCCAjwTJLu+32xIUH57dGZYQYhdIK1G5+OKLWb58OeDMUnvWWWcRCAR46qmn+OlPf9rt41xyySU88sgjPPbYY2RlZVFZWUllZSXRaDSdsITYY0W0xSKrhXeTDbyfamStFSVip/gi1cIziWoeiVfwUqKW1VYUu4uBepbWrLWiLEy1sDjVSrNO9cJZfKVVR9mgK1mjN1Gha0jorhOKdCmlGKSKsNEk6XiuGk0zEYL4KSQvo88rhNj90hqenJOTw/z58xk5ciS33norr7/+Oq+88grvvfceZ599Nhs2bOjek2+nzuWBBx7oVveSDE8We4M1VoTXUvXU2gk0sOUPMqpt3Cg8ysCD4dxWin3NbI5052G2/f1stGK8kWxgsx1v/8jOVi6mmiEOdOe077c7WNpmld7ARqqIk4C2LpggfkapoZSoARl8LotFehWbqMGNiRcPNpoocTy4GKdGMDCDzyeEyJxdPjxZa41tO02rr776KieccALgTK1fW1vbo+MI0Z/V2AleStbRSopByteWVGi+SLWwSccZrLyUGX6MtmSjRaf42GpigOFmiiuLajvBC4la6nWKYuXBpwxsrWnQKd5ONWIDh3lyd9v5rNEbWc1GfHjJbxuNY2PTTIQv9SpcuBigMhOPqUzGM5Jcstika4gSQ6EoZQCDVTEFKicjzyOE6F1pJSr77bcfv/71rznmmGN46623uPvuuwFYs2YNxcXFGQ1QiL3Zl1YLjTrJMOVrb2Fs0RYtWOThJkyKMClycQMQUi7C2mJhqoWJZojPUy3Utj1+SzJjKEWBcqNs+MxqZrIdIsfY9VMmxXSCjVTjxUMAX/t2A4NsgjQQZoOudIYTZ6iVx6VMhjGQwRSTIImBwoNb5k4RYi+SVo3K3LlzmT9/Ppdeein/8z//Q3l5OQBPP/10+9o/Qogd01qzwo4QwuzwwRrRNimt8SsDCydx2VqOclGnE9TaCZZZreTgak9StparXDTrFOvs2K4+FQAa26ax92+VpGyhUATw0UCYGPEuHr1zTGXgV168yiNJihB7mbS+Zk2aNKnDqJ8tbr/9dkxTpqwWojs0ThFsV0nG1my67iJNoklpjVd1/X3DUAqlFYndNER3y0gbo4t5TZztBjbJ7Z6PEEJ0Ja0WlQ0bNrBx48b22x999BFXXHEFDz/8MG63O2PBCbE3M5RioOGldZsWk5AycStFTFsowE/H5L9Rpyg2vBQqD1mGi4juOhFJaY0CQuyeLw8+fJgYnUbhbBEniRcPXuQ9QgjRfWklKueeey5vvPEGAJWVlcyYMYOPPvqI//mf/+Gmm27KaIBC7M3GmSFMpQhvNZw4oAzycdOIhQeDXOU0fGqtabCTaGCyGcJrGEwwQ0SwSGyTrGitqdIJCg03ZebuWUk4lxB5ZBGmBb1Nq0kKizgJSinEpfrUEmNCiD4urURl0aJFTJ8+HYAnn3ySCRMm8P777/Poo4/y4IMPZjI+IfZqIw0/081sWrBYb8eo10lq7SSmoSg03GQpN5vtBBvtOGt1nASaQ1y5jDYCbLSjKGWRYxis1hGq7DgRbRHWKdbpOF4Uh7vztts1lGmGMthHDSdEkDqaaCFClDhhWmiimWIKGKJKdkssQoi9R1pfbZLJZPtU9q+++ionnXQSAGPGjKGioiJz0QmxlzOU4hBXHgMNH4utFirsOC7DYLKRxWgjSAsWi1MtVOg4fmVQbgYYoXz806pivR0loW0sp9qFWixS2otPmUw0g0x1ZTHU7FzYuivlqBD7MoaNuooq6rCwCeKnVBVRSiFuaU0RQvRQWu8a48eP55577uH4449n3rx5/OpXvwJg8+bNFBQUZDRAIfZ2hlKMMgOMMgMkbJtVdoRldisvJqtp0Ali2JhK4dKKzcko/ySBG4ORRgC/YaK1JmraVOo4pcrkOHcx+ar3hugGlZ/RajjleggWNi5MjN3UqiOE2PuklajceuutnHrqqdx+++3Mnj2byZMnA/DPf/6zvUtICNEzSW3zQrKKRXYLBoqUbVGBs3bWANzs4wrSQIJ6O0kQkzg2/rahzQFMBuOjigR1OkGB0ftr3JjKxNxNhbxCiL1XWonKEUccQW1tLeFwmLy8r9bSuOiiiwgEAhkLToj+otFO8kSigk+sJtxaYShFBAsfilzldupO7Cig8WNiAxV2nBzT3T4Y2KMM0LDWjrKPGerFsxFCiMxJu8PYNM0OSQrA8OHDdzYeIfqdsE7xXKKKz6wwHq3IVm4ipIhjYaHwaJOAMmm0U3gNhQm4lUGzThHTNv6tulVMDfFdOG9KSqeopp4qXUeCJAF8FKsCBpAn3TtCiF2i24nKvvvuy2uvvUZeXh5Tp07dYf/3/PnzMxKcEP3BwlSYtXYUj1b4lIGhwNAKFwqFIkySYrxEsQlog5QCLwob3WHyNK01STS5uIi3LWj4dZPJ9URcJ/hSr6RG16MwcGHQSJgKaiiliLGMwFTS1SOEyKxuJyonn3xy+0ifU045ZVfFI0S/ktQ2S+xWQpg0KtWedphtqw6bODPQxrQFaHKUi4i2aCWFV5lOd0+bSp2gRadYaIdZkmwmS7kZb2Qx2ghlZIjySr2eKl1HLlm4tkpIEjrJRioJ4qdMDd7p5xFCiK0pvQcvYdyTZaKF6ItadIoH45vwaFin4zTrJFnKhdaaWpLEsbDRhHARUCYTjRCVOs4qHaFAuSk3gmigWsep0nFCymSw8uNRioi2SKAZbYQ41lWIN43WDq01cRI06Ra+0Mvw4MGvvJ32a9ateJWHA9QkmdBNCPG1evL5vdPvKC0tLdh2xz5xSRqE6B4PBl5lENcWxcpDs04R1RY+DHJwUYcmrlPElE0JXprbOnummDnkKxeNOoXWGgtNgfKwjwpiGk53T7ZyE9c2S+0WSiwv+5m5RIiSwsKLB18XCcfWwrqZ9bqCOt1AK1EaCJNHNi5t4FYdp8H34SWiY0RUjGykkFcIkTlpJSpr1qzh0ksv5c033yQW+2plVq01Siksy9rBo4UQW3iUwRgjyLt2A4PxMlT52KRjNOGs84MGHwYlhpc85SakTPY3c5joysaPQRSLTXaMf6eqyFduzG1qUrzKIIDBl3YVptpEM83Y2salXOSTjyKfjbZFBIsc5aLcCDFI+QjTzCK9jIiOEcCPFy8KCNNMggRFekCnZAVo67ASQojMSStR+fa3v43Wmr/+9a8UFxfLsupC7ISJZhar7Sgb7RgFhptcHaJGJ2nUSUKGydGuARziyiOlND4M3FvVmwRxkcTGAnxbde1oNJokoAkQIUYFVdpDLkEMTGI6wad6Dc16I3FdggsvCWy+sJqZZGYRUpVEiZNHDkopXNrEgwdwuoIaCVPIV5M7RokRUn4C7N6ZcIUQe7+0EpXPP/+cTz/9lNGjR2c6HiH6nVzDzYnuQt5PNbLGjhDFxqMMxplZTHNlM94I7fDLgIlCo7G1RimwCWPRgE0UjcYigoFBFvm4caHRVGmbJu0mQIIcI4IHp7u2RaeYb1UxzFVPKVntz+tSLrJ0kAaaMDGcLiSdwkbTSpQESUYxTEb9CCEyLq1EZf/992fDhg2SqAiRIfmGh+PdhTToJGGdwqUMipWnQ+vJ9pQaPkLKRZgUQZpIUY0zfsgDJNHKmXK/ms0YmFhAEzZ+5cXAhUUYzQAULkLKRUTbNNpxhhgd50nKIxsLizCtJImzjgqSOKs+B/GzWVfj1m5KGCCtrEKIjEkrUbn//vv5wQ9+wKZNm5gwYQJud8e+6kmTJmUkOCH6E6UU+cpDPj2b/j5buZlgZPFfqwpTVePCQOFFAzGdwFAKU9m0EMGPDxsTW8fxqCTgw8aFxkK1vR2ElJsomlaSZG0Vi6EMCnU+LkyqqUOjySWLbEK4cNFChEX2CizDYjCySrIQIjPSSlRqampYtWoVc+bMad+mlJJiWiF6yQFmHmGq2WiniOJDtbV0+BS4lIWJiW7rIjJwgbLRaCCKQRC11Zo8Cj9ae4kS6ZCobNFKBDduhjGoQ1ePhyyadSur9UaKKMDTRbGtEEL0VFqJygUXXMDUqVP5+9//LsW0QvQBbmVQrjwow48mgIXGg4GBQVV7gmJiYePXCgPQgMJqm//2q7eCsLbJUgPwECasmwkSwFQmKZ2imVaSpMghu8t6lCB+GnQzdaqRgRTuvgsghNhrpZWorFu3jn/+85+Ul5dnOh4hRJrcyo0HRd5WI2/qtdP6kcIipVO4MEjQipskFhbO3LdfzfnYolMksJlsDqbESLBWb6SZVqe1FEWoLQkK0vXio0bbwogJkrv4bIUQ/UVaicpRRx3F559/LomKEH1InsrBtF0kSOBRX3XZmJi4tIsWmklhY2PhQgEuUlpj0UKLriVKEDcG+5o5TDJyMJWikAIaCZNSKTy4ydJB/stCkjoFXXQL2dqZ/NG983NJCiEEkGaicuKJJ3LllVfyxRdfMHHixE7FtCeddFJGghNCdF8O2RSrQjZTiaVtfHjx4QU0SZK4ceHFi6ut3FZhEFYRNG4C1DFM5TPayGOkEWxfzNClTAaw1egfBSV2IStYR0D7Oq2Y3EqUgPJRQO7uO3EhxF4trbV+DGP7QyZ3ZzGtrPUjREcpnWK5tYoqqkmRQmHQQitRHcGFSZAgABqbOAlcuFBkU08zcYoxyKNMBRlvZlOinC6kqI5Sp+uI6xgu5cJHgNV6M020EMSHBzc2Nq3E0EozRpUxRA3szcsghOjjdvlaP9uu7SOE6H0tuoVN9kaadB0GKUxsslSIfSjjCxYTIUqMr5a8cOOhBS+NJHBj4yGFBXxuN7FWt3KMUYRbNbLeWkecGLRNj29ikq1y8asCGggTIY6BIksFGKpKpYhWCJFR0pEs9jzahmQcDBe4ZAgsQItuZom1mIiO4MNPiCxSpIjoZqqUJpccfHgxlQvQuHHTpKGRKEFcKAzcuHApN9m4qCDOu/YahlKPiUkOee2j+xI6Qb2uZbAawihjEjHiGBhkE5SZaYUQGdejROW4447j73//Ozk5OQD89re/5Qc/+AG5ubkA1NXVceihh7J48eKMByr6IW1D7TqoXAotdWC6wHBDpAniLU6iUjwKhk6G7KLejrbXaK1ZZ60jols7JBQuXHi1lybdiNn2p57dNi2+rTWrCLelKDYaA9U2kkcpxQDbTTMbaVEpStWADs/nUR5sHaDGrqbUGESByt1hfE06SUSn8CiDfDwynYEQokd6lKi88sorxOPx9tu/+c1vOPPMM9sTlVQqxbJlyzIaoOhHtIbmaoiHAQVVq2DjQrBTYHigbh1EGsEXguJ9wLZg9X+hegVMOh4GDOvtM+gVEVpp0o0ECHZKApRSBHSAKDG8ykuYJkI6CwtFEhs3NpoEJjkYbfUrAG5lYeooWgfpakFkL16aaKRZhwmorocq1+o4n1oNrNOtJLSNSxkMUn6mGrkMNrp+jBBCbKtHicq2dbdp1OEK0bWWGlj9LjRsgFQCWhuhqRbyhsCA4dBY6WwPDoBkKzTXwMCxEMyDho2w5HU48Nv9sisooZOkSOHfztwmLtxAlGHGUKp0Nc26GQuNIoKNgYcc3AzskOQ4f9sak64L55VSoGmb3bazOh3nZauSGjtOnnKTrdwksVltt1ClYxxLCUMlWRFCdIPUqIjeF2mAL/8NLdVOIhL0QmMNaAtaKp3ko6kWDBPcHucb/pbuH18WZBdDUyXUroXcEoi1gMsDwXzoB90MLmViYGBhYXSRWGzZnqtyGKgG0qAbiBIlbjWxQtuEVC5qm2HGzYCJF4/qegRfUicxMbfbmjLfaqTajjFEBdqHOrsx8GOymRgf2fUMUn7MfvD7EULsnB4lKkqpLpuWhegxrSFSD7WrYcN8qFsDhSPB7YdUHJIx8Oc4yUrTZkgkneQDwHRDIgLxiJOouDxOce3i1yDR9ljTBQVDYeT+TovMHkRrTRKNgm6tnhwkRJbKokmHySGn07GiqpUccgkSdBYWbKs58apCqlKVVJKgUDsrNWutaSJFi7IZTykmVSR1AvdWE8jZ2qaVFgrUALLoPKywWSdZq1vJU572JGULpRQDtIdKO0qlEWOQ8qdxhYQQ/UmPu37OP/98vF4vALFYjB/84AcEg07f9tb1K0J0ojXEm8FKOgWyGz+DcBU0bnTui7VA7iDILsFpNtHg9kG0ESzbKZ7d2pbPwHgU6jdCJOwkJVkFkEpCxTJo2ARTjoeSUbv1VNNha80q3coSu5lqO44ChhoBxhlZDDK2/4FuKIPBxlBaraWEdRNBgpjKhaVTtNKKS7sZbA7pNDlbsfIxwyziXbuOGh3H1k5HTgiT6SqfaUY267RBpa4Aori1CwsbixTZKocR5sguv6hEsUhom5DqehVorzJJoYkii5cKIb5ejxKV2bNnd7j97W9/u9M+3/3ud3cuIrF3ql0DGz93kpLWeghXOEvMaAVbEtxkA0TDzmgfX8gpnHV5QZng80Os1UlcrITTquINOY+rXuXUrxSPch4HzuO8QSdRWfYOFA53HtNH2VrzoVXPJ3YjGk0QFzbwhd3EKruFI1yFjDGytvv4AqOAfRjNensdrboFW9sYGIRUiKHGMPKNgi4fN8QIcIbysVFHaSaFC8Ug5SenbeXjcj2KPJ1PjV1NRLXix02hUcgAVYhXebs8phcTt1IksPF00RWV0DamVni3U/8ihBBb61Gi8sADD+yqOMTebPOXsOw1p0vHlw3RJmhtckbt+ELg8YKtQRmQjELVSigd7+wXjzitLTmFzv6RRsCG3FKnm6i51klGggXg8XV8XqWcYctNlVC3AYpG9MbZd8t6HeVTu5EgJtnqq4QqR7uoIcG7qTpK3b4O921rgDGAPJVHWIdJkcSNm2yV06klZVtuZVCmgl3e53QVFVJodH8St2xcDFEBltrNBDE7tbrUkaDQ8DJQ+bZzBCGE+Ip8pRG7VrwFVr3rJBt5Q5zum0Srk0R4/E5LiHI5tSiGAZ4AJKJOUlMwzElcUklAfdWCYnqcx1QsharlzjHiLbBugTOEOZX46vldHrBtp1upD1tut5BCd0pElFIMwEMTSVbbka89jqlM8ow8Co0ico28r01SdgWlFFPNPHKVm006RlRbaK2Ja4sKHcWFYpqRh6sXYhNC7Hlk1I/YtWpXOzUmOaXOpG0tNU4tiW2DywfYTlePy+skJ1tmNm2pg4JBMHAk5AwDT9Dp9sktdVpfqlfA2k/BnwvJpJMAacuZIC4ahoFjnK4eK+UkRW6v8zy25ezbx4rAq3UcP13P6moohaEVDTrR5f19UYnyMdMs4SO7ns06Sq22caEoMnzsa+QxcjstOEIIsS1JVMSuk4w7RbP1m6BmtZM0GG6nmHbLwpUut5M8ZBdBvNUptkU7k7zlD4PBU2HAyI7HTSVg1QdOt1HuICfxqN8MwVwn4WltcLp78odASy14A1C7Eb58w2mdCeXCkIkwaNxXI4kyxNY2jTRSa9cSJ44XLwOMAeSSu8PWDTeK1HbmJAFnvhJ3VzOv9WGlhp+TVCnVxIlqCw8GxcorLSlCiB6RREXsGpEGWPQSbFjgzDaLcuZBMS0nmbAiTuKiLafoVbnAm+0kLe4QjJnhzDbb1Yda7TpnwrfcgU7LSN5AZ4K4aJNzLJcHGiuc1plE1Elklr/rdB253FC3EarXQvUamHp8xpIVS1ustFdSqSvRaExMLCwqrUqKVTHlRvl218IpN0JssGqwte40pDeuLUyMHY786asMpSjB1+XstkII0R2SqIjMsy1Y/B+oXw+BPGekj+kF03RaU8D5v207LStaQ6KlLUkJOMOTh03rOkkBpx4F/dUoHl8WlI5xJnyLhJ0uJNuCYv9XycqA4U4NDDiz2SaisOELyBsE5dMzctob7A1s1psJEcK9Va1JUifZrDfjtb0MN4d3+dhRRpDFdpjNOkax9rbPnxLVFtXEKVdBhsicI0KIfijtNti//e1vHHzwwZSWlrJu3ToA5s6dy/PPP5+x4MQeqn491G+AnBInYXB5Qaec/29JLtx+JxFRhlM7YrjBm+PMMjvqMMgZ+NXx4q3OPCkNm52uG9PtJDe2/dU+gWynO2foJCgqg4GjYdxRTmKUU/JVkrKFx+/EsH7hV8nTTkjqJFVU4cXbIUkBcCs3XrxUUUViO3Um2crNsWYRpcpHNXE26AgbdIQmkow2QhzjKpIuEyFEv5RWi8rdd9/NDTfcwBVXXMHNN9+M1VZvkJuby9y5czn55JMzGqTYwzRXOTUmrrYCVk8Q0E7CkYrjFNBqKBgMqZTTJVQwwunKGTQBikY5XTqJCKz6L2z60plDRSlnMreBY5xhzpF6CG21sq9S4A9Bax2MmOZ0LVmpzsOWt/BlOVPxx1qd+pad0EorMR0jRKjrp8JHs24mQgQPXXc1FRs+TlelrNdR6nUChaJEOcN4t+0OEkKI/iKtROXOO+/kvvvu45RTTuG3v/1t+/b99tuPn/zkJxkLTuyhtq4J9QahWYMvx6kRScWdH8MFg/d1uoWGTYPyQ9paWdo+kJNxWPBvqFjiTKWfU+S0oLQ2wNK3nFaSpirQNc6aPobpTJ3fVAWhAhg6FRqr2uKxu+5GslNYymBpIsV6uxWAwW4Xo70ePGkmBmonizFcymCECjICGRUjhBCQZqKyZs0apk6d2mm71+ultbV1p4MSe7jQACdxSCWcJKJps1NX4g05rStWytkn1uhsGzTRmT9la5XLoHJ528RubTOgmjgJSmuDUzxbth9Ur3RWTwYn+RkwDMYc4YwiMt1tM9yGu2wxibTU81HeCJ6JWFg486yYSjHC7eas3CwGurv/5xEkiE/5iOkYgS5WMY4Rw6d8Xd4nhBBi+9JKVMrKyliwYAHDhg3rsP3ll19m7NixGQlM7MEKhjk1Jo2bnOHDBWXOfCqxpq9qS+yU8/9t61G22LTYqStxdzFNeyDXGfnjy4KDZ0PDBqd2xZ8NeYPQbYvrGcE8p25l+fvOsXxZTouNbZForGaVdvFe0WiGuN14DaclJG5rViQSPNoY5gcFuYS2rW3ZDrdyU0wxa1iDW7s7FdPGiVNGGZ7trH8jhBCia2klKldddRWXXHIJsVgMrTUfffQRf//737nlllu4//77Mx2j2NOYbhg7A758yWntMN2QVQjNdWDFIW8wDNsPBo5z/t+VSJNT8NoVpZzhrvFWZ46UktEANFkWn0XjzI/GaLFtBpgm+w3bn6mpBO6NXzrPrxRoqPNmMW/IgfiLR+LaqpvHayhGetysTiT5MhbngED3R9oMMYYQt+PO8GT91fBkA4NSVcoQY0i3jyWEEMKRVqJy4YUX4vf7+d///V8ikQjnnnsupaWl/PGPf+Tss8/OdIxiT5RdDPue4cwgW7PK6e4p+wYU7+PMUvt1NSD+LKhv6vo+rZ2frRKZmlSKRxrDrEokCRgKn1KsSiRZltB8Oewgzhk+FV/tOmeEjz+L1zxFrNcuRnQRh0spTBRL44keJSqmMhlljKKQwh5P+CaEEKJrac+jct5553HeeecRiURoaWmhqKgok3GJvYE3BEOmOj89VTq2bTbbZOdVj2Nhp6albZFBrTUvNLeyKpFkpMfd3kJSCERtm09jcQZnZTFj1DfaD9FS14gZ3/6U9KaChL39mWK3x1AG+eSTb+b3+LFCCCE6S+srXjQaJRJxFkgLBAJEo1Hmzp3Lf/7zn4wGJ/qxgWOgcKSz6nGkyRm5Y6WcGWlbG2HoFKfVBtiUSrEsHmegy+zQjWNrTau2CdtJ/h5u4qWWMBWpJFprhrhdRLVG687JiNaamNYM7kExrRBCiF0jrUTl5JNP5uGHHwagsbGR6dOnc8cdd3DyySdz9913ZzRA0U95/DD1BCjb30lQ6jY40+K7/c5EbmMOb+8+WpNIUmNZJLUm2ZZ4JLTNomSMhckoDVhsspL8s6WZexrreSfaygSvmyylqEimOj11Rcoi1zCZ7N/O/CtCCCF2m7S+Ms6fP58//OEPADz99NOUlJTw2Wef8Y9//IMbbriBH/7whxkNUvRTviyYPAtGHeispqwMZ3hyW21Kq20zr7WV11pbWZNMUGMlCRgmpYZJRNlUWUmylYmtIG7YjHC7qbMs/l9jA8VuN1HDpiJpsSmWYrDpwlCKJssmaBicmB2kVFpUhBCi16X1ThyJRMjKygLgP//5D6eddhqGYfCNb3yjfTp9ITImkOv8bCVm2zweDvN5LEaWYZBvGNhAUmuWJBPYyqbANHArRYNlkWeaJDRssFLUWCkSaMZ5vNgKalMWFbZmmOnlkECA/QJeyr0yjFgIIfqCtLp+ysvLee6559iwYQOvvPIKxx57LADV1dVkZ2dnNEAhurIoHmdRLMYwl4tBLhdDPG6S2sm8XUrTom1sDS22jQIGuUyWJ+NEbZsBbUlNyDCZ4PWyn99HodvghJwAZ+dlSZIihBB9SFqJyg033MBPfvIThg8fzgEHHMCBBx4IOK0rXc1YK0SmLYjFMJXCaxi02jaFpovBbhdJrWm1bOK2zfpUgrBtUew2MRQ02zbZhoHRNrnbljLakGFgKpgfj2F3UVwrhBCi96TV9XPGGWdwyCGHUFFRweTJk9u3H3300Zx66qkZC06I7Wm0LKK25tNojEbLIqY1BuBWOImIBqUUHqWoSqWotlKk0JhK0WLZZJsmvq1GCGUbJtVWirjW+GUBQCGE6DPSrhYsKSmhpKSkw7bp06fvdEBCdEeL1ixPJDCBmNYkbJskmiQaA41bQcBQFJgGWikqUkniWhPEwgYGuVwd5pyz0BgoTElShBCiT0krUTnyyCNRO3hDf/3117t1nLfffpvbb7+dTz/9lIqKCp599llOOeWUdEIS/UizZVOXskhpTVxrLMBnGChtYaOwNKQAtzJotG1MBT5l0GQnabAVY7xeSraaRE5rTYNlc6g/gBuoSiVZk0yQBHINk1FuD75urvkjhBAis9JKVKZMmdLhdjKZZMGCBSxatIjZs2d3+zitra1MnjyZCy64gNNOOy2dUEQ/tCwRR2vIUorNtk1AKdCaJAAaBbhR+GxFmc9Ds7ZJ2DZR2yRgGAw0XSg0oEhpzeZUilzDYJLXxwutzcyPR9qLcBWKgS4Xxwez2cfTxQKJQgghdqm0EpUtc6hs65e//CUtLS3dPs6sWbOYNWtWOiGIfixs2xgoPMogV2mSChJaY+OsVehXCjeKpFJ4MJjqdRKMJfE42aYibNtUWRYGTkFtscvFCcEsFifivB1tocBwUexyoZQiqTWbrCRPtTTynaw8hrplRJAQQuxOGZ3R6tvf/jbTp0/nd7/7XSYP2y4ejxOPx9tvh8PhXfI8om/zKQMbZ/r7PNOFW0ESTaOVIorGRGHiJC1W29geSzt1KycHsylxu1idTJLSmnzTZIzHS6tt81RLIwWGizzTbH8ut1IMM92sSiX4JBaRREUIIXazjHa8f/DBB/h8u27a8VtuuYWcnJz2nyFDhuyy5xJ91yiPm1zTBKVIaI1LKfzKIM90YWhn+vygYaBwkhqtNeuTSUpcbsb5fAx1ezgiEOSYYIh9fX4ChsHqVIJm2ya3i1oUpRR5hsmShDMPixBCiN0nrRaVbetJtNZUVFTwySefcP3112cksK5cd911XHXVVe23w+GwJCv9UKHLxcEBP+uSSSqSKfzKaflQGrZUn7TaNlmGQQKbFUmLItPFqVlZhLZTFJtqmz9le0XibqWIaU0KmWdFCCF2p7QSlZycnA63DcNg9OjR3HTTTe2z1O4KXq8Xr1cKGgXMCAVRWvO3pmY2pVKYgM9QFJouPG0Tug32uBjocjHW42WKz0eha/sv9zzDxGxrofF0kayEbZti08SvZPSPEELsTmklKg888ECm4xCiR1xK8c3sLPYL+Pl3cytfxGMkNGQrxSifl4MCfso93a8nKfd4GGS62GQlGW66O7SsRG2bmLaZ5svC1bY9bFt8kYixKBElqjUlhotJXj+j3V6Zi0UIITJop4ppP/30U5YsWQLA+PHjezx9fktLCytXrmy/vWbNGhYsWEB+fj5Dhw7dmdBEPzHA5eK7eTkkdDYtto0LyDKMHc7z0xWPMjg+mM0TLU2sTCXINUzcKJq1RULDvl4/03zOqs1VbaOA1qcS+JSBW0FlKsnCZIxveAMcF8huT2iEEELsHKV1zxc3qa6u5uyzz+bNN98kNzcXgMbGRo488kgef/xxCgsLu3WcN998kyOPPLLT9tmzZ/Pggw9+7ePD4TA5OTk0NTXJYogiIzanknwci7A4HiOF0yW0n8/PVJ8frzKwtObB5nqWJuOMcLk7tJ402xY1lsXpoVz29wZ67ySEEKKP68nnd1qJyllnncXq1at5+OGHGTt2LACLFy9m9uzZlJeX8/e//z29yHtIEhWxq8S1TUqDT3WcVn91Ms5fm+sZYDiTx21rQyrBIJeH72cVSBeQEEJsR08+v9Pq+nn55Zd59dVX25MUgHHjxvGnP/1plxbTCrEraK3ZbKXYkEpgAYWmixEuD16jc6JRY6VIaN1lkgKQY5jUpFK0aJscZXa5jxBCiO5LK1GxbRu3291pu9vtxpZ5JsQepMW2+HckzOJEjEhb46ILGOb2cEIgh0Gujq9zQykUTnLTVR2MDSiV4QmKhBCiH0vr/fSoo47i8ssvZ/Pmze3bNm3axJVXXsnRRx+dseCE2JUsrXm+NcxH8Qghw2Cky02520OJy8XqZIKnWhppsFIdHjPEdBNQirDuOiGvtyyGuTyEZBizEEJkRFrvpnfddRfhcJjhw4czcuRIRo4cSVlZGeFwmDvvvDPTMQqxS6xNJViSjDLYdJNtmO0tJD5lUOZys8lKsDAR7fCYYtPFOI+PaitFZKvWQ601VVYSj1Ls7w30eNSREEKIrqXV9TNkyBDmz5/Pq6++ytKlSwEYO3YsxxxzTEaDE2JXWpNKENd0WW9iKEVAGXyRiHG4P6t9u1KK4wI5JDV8mYiSsJy1hSw0uYbJ8YEsRrtlUkIhhMiUtOdRUUoxY8YMZsyYkcl4hNhtEraNsYMp8d1KEe9iUFzQMDgzlMvaVIDVyTixLYsbun0MMDO6zqcQQvR7ab+rvvbaa7z22mtUV1d3KqD961//utOBCbGr5ZkuNGBrjdFFV02rbTPC03XriEspyt1eyqX1RAghdqm0EpUbb7yRm266if3224+BAwdKf7zYpbTWVFgp1qcS2ECh4aLM7dnp2V/Hun28abiotFKUbjO6p9m2UAome3fdauBCCCG+XlqJyj333MODDz7Id77znUzHI0QHrbbNi5EwXyZitNo2CoVLwTCXmxODnYcP90SuaTIzkMULrU2sSibIMwxMpWi0LWwNB/qCjHFLoiKEEL0prUQlkUhw0EEHZToWITqwtOafrU18Eo9SYjorISuliGmbVckkT7Y0cn5WHnk7UReyrzdAtmHyaSzCqlSclNYMd3nY1xtgsscvs8sKIUQvS+sd/sILL+Sxxx7j+uuvz3Q8QrRbl0qwKBFjkOkmuNXIHJ8yGOFysyqV4PN4jCMCoZ16nnK3l5EuD1GtsdEElNFlzYoQQojdr9uJylVXXdX+f9u2+ctf/sKrr77KpEmTOs1S+/vf/z5zEYp+a3UyQUzrDknKFoZSBJXBwkR0pxMVcEaxBSQ5EUKIPqfbicpnn33W4faUKVMAWLRoUYftUlgrMiWpNeYOXk6utuHD25vOXgghxJ6v24nKG2+8sSvjEKKTXNPE1tsfPtyibYa7fZKkCCHEXkwWJBF91li3j3zDpGKb9XbAGT5sAFM8/t0fmBBCiN0mrWLa1tZWfvvb3253wrfVq1dnJDjRv+WaJrOC2Tzf2sSqVII85QwfbrItUsCB3gBjtzMhmxBCiL1D2qN+3nrrLb7zne/IhG9il5rq9RMyDD6JRViVTJDSmqEuD/t6/Uz1+nd60jchhBB9W1qJyksvvcSLL77IwQcfnOl4hOhklNtLuctDZKvhwzK/iRBC9A9pJSp5eXnk5+dnOhYhtkspRVCSEyGE6HfSKqb91a9+xQ033EAkEsl0PEIIIYQQ7dJqUbnjjjtYtWoVxcXFDB8+vNOEb/Pnz89IcEIIIYTo39JKVE455ZQMhyGEEEII0ZnSWuveDiJd4XCYnJwcmpqayM7O7u1whBBCCNENPfn8TnvCt8bGRu6//36uu+466uvrAafLZ9OmTekeUgghhBCig7S6fhYuXMgxxxxDTk4Oa9eu5fvf/z75+fk888wzrF+/nocffjjTcQohhBCiH0qrReWqq67i/PPPZ8WKFfh8vvbtxx13HG+//XbGghNCCCFE/5ZWovLxxx9z8cUXd9o+aNAgKisrdzooIYQQQghIM1Hxer2Ew+FO25cvX05hYeFOByWEEEIIAWkmKieddBI33XQTyWQScGYNXb9+PT/72c84/fTTMxqgEEIIIfqvtBKVO+64g5aWFoqKiohGoxx++OGUl5eTlZXFzTffnOkYhRBCCNFPpTXqJycnh3nz5vHuu++ycOFCWlpa2HfffTnmmGMyHZ/op+xkKzrRBEpheAtQpqe3QxJCCNEL0kpUtjjkkEPYb7/98Hq9KFkwTmSATkVJ1H1OqmUNOhVFKYVyZ+PKGY07byxKmb0dohBCiN0orUTFtm1uvvlm7rnnHqqqqli+fDkjRozg+uuvZ/jw4Xzve9/LdJxiL2MnmrGalmO1VkG8AZ2KAAo72Yw2DAgUY/iLQNvoZJhkzX/RVgzPgGmSFAshRD+SVo3Kr3/9ax588EFuu+02PJ6vmuQnTJjA/fffn7HgxN5H2ymSFe8T+/JeEiufxlr3ElbF+1i1C7BqPofGlaiGlajqz6FxNSrRguHJA3cWqcal2PH63j4FIYQQu1FaicrDDz/MX/7yF8477zxM86um+MmTJ7N06dKMBSf2LlprktUfk9z0BjreCFYKtAZlojBQ2nJuaxsVD6PDG6B+OYTXo8wA2ophRWSJBiGE6E/S6vrZtGkT5eXlnbbbtt0+ZFmIbdmxWlKVH2DHm5zuGysGygBlgp0CNEprwETZNkaiFSwbkk63EG43OhXr5bMQQgixO6XVojJu3DjeeeedTtuffvpppk6dutNBib2PTraSXPcyunUz2Em0lQDbAm2D/ipJUVpj2BYKnNYVgGQcVb8SFQtjuHw7eBYhhBB7m7RaVG644QZmz57Npk2bsG2bZ555hmXLlvHwww/zr3/9K9Mxij2c0+XzEXakEgwTlIGyLVA4P9oGrVEolNZoaCuYtVF2AmVrtJXEbKpADfP37skIIYTYrdJqUTn55JN54YUXePXVVwkGg9xwww0sWbKEF154gRkzZmQ6RrGH07E6rJYNKF8uKJfTUmK4APVVq4nWX/0fjbItlK3Btp2aFTRGKg7rXkdHqnvpTIQQQuxuPW5RSaVS/OY3v+GCCy5g3rx5uyImsZex43VgxcFXiIrWglWH1hpltNWmaFDt9SkKA1DaRivjq6HIrhDK9EKsEXvzBxgjTnAeL4QQYq/W4xYVl8vFbbfdRiqV2hXxiL1RW0OJMlwYocHgDoK20IaJ8xJ0unu0oq1lBTBcKHcIDDeYfpQ3B5QCXy66tQpaK3rtdIQQQuw+aXX9HH300bz11luZjkXspQxfPhgesGIoXz5m3hiM4ECU6Ue7vGjTDaYXbfqcBEWZToKCjVIuJ0kBMNwoX77T2pLovHq3EEKIvU9axbSzZs3i2muv5YsvvmDatGkEg8EO95900kkZCU7sHZRvAGZwEFbzajCKUZ5sjLwsZ/RPPIy2Y3gGHYXy5mOveBrqloPpAlcATK/TPWQlUMGBaMODQjvDmoUQQuz1lNbtFYzdZhjb/5BQSmFZ1k4F1V3hcJicnByamprIzs7eLc8p0mDF0fUrSdV8jBVvBE8Q7fKAlQDDgyt/PK7CfVHKwG6twl76OLplM5geUMqpTfEPQAVK0MkWlJ3CKD/ZKc4VQgixx+nJ53faa/0I8XV0ohWqF0DtErDiuNCYqSR2shE7ayAqbwJm9nBUoKS9aNYIFsM+p2Ov/KdTgOvNRXmz0cqFTkVQiSYYMEmSFCGE6Cd2avVkIbqirQS6egFUfIxqWutUyXqCEChCZQ/DFW+CRBz8xRAc2OnxRtZgVNlM7Ir/QrwJInEUGm16oWA8xsDpu/2chBBC9I4eJSrRaJTXXnuNE044AYDrrruOeDzefr9pmvzqV7/C55PZQ/srbafQG9+G2qUQqXVqTNxBp5uneSNYCXTOMFRrFVR/DjnDu6w3UbkjMYID0c3rIdEKhgsjVOp0AcnqyUII0W/0KFF56KGHePHFF9sTlbvuuovx48fj9zuzhS5dupTS0lKuvPLKzEcq9gzNG6FhJbgDKG07BbHKAJfP+belwvnX9EFrtZPMBIu6PJRyB1D5Y3bzCQghhOhLejR04tFHH+Wiiy7qsO2xxx7jjTfe4I033uD222/nySefzGiAYs+im9Z8NfOstr9qLUlFIdYEyWZoWgPhtdC0FhpWbDUjrRBCCNFRjxKVlStXMnHixPbbPp+vwwig6dOns3jx4sxFJ/Y8iRZnDhTD4yQrdhJSMYg1Ov/H9dX9WkPV59C4qrejFkII0Uf1qOunsbGxQ01KTU1Nh/tt2+5wv+iHPCGn+8flQ3uzIVKLspNOUmK2DUlWpvNvsNhZpHBLrYohtd1CCCE66lGLyuDBg1m0aNF271+4cCGDBw/e6aDEnkvllDlT3VtxCA10kpNkxLnTTjndQXbKGQUUGgi+fKdOJVKz4wMLIYTol3qUqBx33HHccMMNxGKxTvdFo1FuvPFGjj/++IwFJ/ZAWYMhbx+Iti1EmFXqjPzRttOK4vJB9hDIHQkuv9MNZFtt3UJCCCFERz2ambaqqoopU6bg8Xi49NJL2WeffQBYtmwZd911F6lUis8++4zi4uJdFvDWZGbavql9HpX6FRBrQIU3ODUpwUIIljgzzW6RjEAqAvucDP4BvRazEEKI3WeXzUxbXFzM+++/zw9/+EOuvfZatuQ4SilmzJjBn//8592WpIi+S5ke1MDp6MKJEGuATf+F8HpU1pCOc6ZoDbF6p3XFV9B7AQshhOiz0lrrB6C+vp6VK1cCUF5eTn5+fkYD647+2qKSsjSrNmtWV2jiSSjIhjFDDQpz+uhEaLEGWDPPmTfFm+10BVkJSDSBvwCGHwOBwt6OUgghxG7Sk8/vtBOVvqC/JSopS7N8k+aVTyw21YLL0LgMRcqGnCAcva/J/vuovjlza6wRahZB42qnHsVwQU4ZFE4A/+5PcoUQQvSeXb4oYab96U9/4vbbb6eyspLJkydz5513Mn26rOeyhdaaz9fYvL3I4uNlNg0timw/FOYqyoog6IXqRnjpI4vcoMk+g/tgouLLhSGHQMk0sGJtU+sHejsqIYQQfVyvJypPPPEEV111Fffccw8HHHAAc+fOZebMmSxbtoyioq6nVt8rWSmoWAMbl0G0BUJ5MHQMzdlD+NMrSf79hSbWNro3163BgMp6aI3BpGGK4jzFmkrN/BU2owb10VYVALff+RFCCCG6ode7fg444AD2339/7rrrLsCZNG7IkCH8+Mc/5tprr93hY/eGrh+tIdUcw/zwGYxlHzhJijLB5aLFW8CfKg/gcesoUuqrnNJQ4DWgPASppGJ4kWKfUoOGFo1lw2Wnugh4+2iiIoQQot/bY7p+EokEn376Kdddd137NsMwOOaYY/jggw867R+PxzvMfBsOh3dLnLuCbcHa+bDyQyhY8zzlyedwezWegIHLDSmtqFjXwsGEWeXN4wP3/l89VkPMhtUtMDKgqW5SlBVpDAUp7bS6CCGEEHuDHk34lmm1tbVYltVpSHNxcTGVlZWd9r/lllvIyclp/xkyZMjuCjWjbAv+/Xt45Gr45OEqSupfxIolqWvIo6o6n4bWfDa0ZKGsJAPtKg5PvodLJUHR/qM1RG1oTjlFtkkLmloVJfkKv/frIhBCCCH2DL2aqPTUddddR1NTU/vPhg0bejuktLz0B3jrQWiqhvLcjwi66mmIDCAWM4m2QN1mTX3EoEHlYGIxyl5DoVWLZqteOuW0rNSEoblWsWGtJpnU7FtuYBh9q9tnDx5YJoQQopf1atfPgAEDME2TqqqqDturqqooKSnptL/X68Xr3bObCypXwvt/d2png3kwILQRZSfJM9cTIIyLJBpFpNVHnW8AKZdJtg5jeWxShkbZGtNWKK1AQRKwkpqNawwmDFQMy+3tM3TUJC0+ak7wSUuSqK0Z7DE5IMvD1JAbV18t9BVCCNHn9GqLisfjYdq0abz22mvt22zb5rXXXuPAAw/sxch2nfn/hFgr+EKgrRRFrsUE7VryrAo8yRbMRBxXIkZWvImhzWsoiVaQxKTGzEcbYLsg5dbYhtNK4bGhzGUwvcAkq9Jg/qu7LgmI2ZpPWxI8UdvKozWtvNkUozHVuSBmXSzF/21u4fHaKNVJi7it+aw1yT2Vrfy9JkLSlhYWIYQQ3dPrw5OvuuoqZs+ezX777cf06dOZO3cura2tzJkzp7dD2yUqljuLC6M1E7P+yTDP5xiJJFiarVMMZWsM2yIQayHl0cTxtN+nFVgujRGHoZtcZMddNLghEIRVC2HfozS5hZlNWCoTFg/VtLI8msLWoJTGRjGwMcbZAwJMDjrxpbTm77URNsQtxvpdmG2tJyVAOGXzelOC4V4Xh+bs2S1jQgghdo9eT1TOOussampquOGGG6isrGTKlCm8/PLLe+2aQS4PmB6wEnH2L3gen92MsrVTHQtf/YuzhpJOWuTFGyi2aqhydZxXxp3UFGqDUC4kE9BUDy1h2LAS6qo12ob8IsgrYqfmVUnYmodrWlkcSVLuc+Ftq4GxtGZt3OJvNRHyXQZDvC6WRVOsjFoM936VpGyR7TKoSdm815zgoGxPp/uFEEKIbfV6ogJw6aWXcumll/Z2GLvFsCmw7H0oC35MiWslpk45yYnWoHFG9Wz519YoBQXxOgZZVe2JitLgToHbMsixFEqBx+sconoTvPgwBHOc24EgDB8L3zhWE+rBWkC1SYuKpI0JNFk2y6IpRm6VpACYSjHCa7I4avFRS4IhXhdVCZukBr/Z9XPluwwqEhYtlibHJYmKEEKIHesTiUp/MvUE+PSfMKxlASZJlKZDKwrA1n1AGvCmYqCdBEVpMGxwpSAYV/gDzs5aQ2MdJFOQSkDpcKeLqTUMiz+C1ib45nkar3/7yUFd0mJ93OKd5hiroimabaeIKWzZhFM25T6z02OUUuSYioWtSU4vcCajA43WustWHEtrTLbsJ4QQQuyYJCq7WU4RnPKzJC23V5HChae9y4cOCQo4t5UGrRUNOsdJahS44+CLKwbGDcy232BLE0QjEMgCrx+MtjLpUI5ze/0KWL0Yxk5ztltaU5uycXqdNK+F43zSHGdhJEmjZVPoMhgbcFNoGnzcarEpabMkkmJ8wNUpATEUWG2nMdJnEjIVjZYmr4sWk5qUzUFZXkKSqQghhOgGSVR6wcjJceKHu2j5uBj/lhqVHWhRIfzJJIYXVBICrYqgqchvNggnAA3xOHgD4PODb5u1/tweMF2wahGM3lfzbkuc15qiVCYtIpbNqphF1NZ4UEQsTYHbIKZhYWuSSUE3Q70uNiUs1sZTDPKanRKQppRmao7zUhrsMZkW8vBGUxyXgizTyZhsrdmQsAgZBodme/ruWkRCCCH6FElUeoPHizc/iDGxjNQXVbitpu3umlImi/1j0F4wTQilFNkegwOyTUoDBqkEmG5oaoCaCnB7IbeLtRw9Xmhqtbm1opmXw1ESttM9U2fZxG0wAbWlSceCQpeJrWF5NMX0kIcCl0FF0qY6YZHn+mpUe1XCImAqpoecUT9KKc4c4Cdpw/zWBOvjFgZgoyh2G5xW4GdswJ3BiymEEGJvJolKb3C5YfRU3DWbsA85BvvtFzESsU67JQ0PKwPjqfcNptVbzDBTMSxlMmWYSXKTASbkFTv1KbVVTm1K0WgIdrG+U2tM89moZj5tjOBREDIUaxMW8a16nlJoTKVIaU1tyqLEZdJia8KWZnLQQ31TjDXxFD5TYaIIW5qgqTg5z8cY/1cvpZBp8P2SACtjXpZGkyRtyHcbTAm6OyQ5QgghxNeRRKW3jNsf1i7F2LgSxk6FdcshFkGnklhaUWsWszQwkQAxNuVMY7qZR0GNSfkYxYxzYN1SWPop1LUtiTRiHPhDkJXbudQl2goVBXEWFUeJagutDKpTFkmtnRn/2rphlAYLSKKdtYTaRiIltKbEbTDC52J8wN2ezEzPcrF/yMM+vq7qVhT7+F3s45eXmBBCiPTJp0hvCeXAN8+FT9+AZQucCVCaG1AeHypvIKohQEmzZo3rG6SSR1MWczFsPzhwFvhDijH7wT5TNS1tC0gHs51Dffw6xCKQU+DkH82NEI7ZLD2xmTozhQZitk2ibeizhrZRRwoDJ1GxAIUmamv8ysCtFHUpmyK3yflFQYZ65WUjhBBi95BPnN6UnQ9Hng7TjoLGWqjeCHVVmC1hCry5NDVMILJsFP5NLswqaFgLb3+oyBsGQw6C0v0U2XlfHW6/ozRZefDlR1Bf5RSw+go05vQIDblJArYirttaXJTT56M02Kr9Ji7AxllDKG5rct2KqG3TrBXH5/kY4uk8RFkIIYTYVSRR6Quy85yfoaMAaK6G9+6GpS9DzWpIJNr2U+B1QWkZbPwvlB0FUy9wimkBEmiscUkKyy3qG1NUpVI0eyy+TCSxkxq3gqR2prnf0nqi2pIUW2sUiixDEbM1cQ22UmSZioEeF4dnezks2yujdYQQQuxWkqj0IeFqWP8pfPgQrP8Iok1Or0x7aqAhnoR1K5wEw54HOSM0/sNSfBqN82k0TnXKYn0iSZOtCSnFQNuk1XJWXo5a4DMUCcs5nEHb/CfKOZ5LQ8rWWEChy+DSohCHZvsY4u04I60QQgixu0ii0huaaqCxrQo2pxidXcjCFxQfPwlNFVC3FmIt0DauF0M7/90y24qNs55P44Q4H9REaaiIsyaZxFQQUoqY1hSbBhGt2Zx0hgeXmCbrbYu41mSbirqUJgXtU/Z7FOQZBkkUuYbiF4Ny+Wauf3dfGSGEEKIDSVR2p1grfP4qrP0CWhpBaVK+ApatGMtr/zmG5pYs4hGnGFYrnCTCANsGUlslKxoqxsf5fN9mXF4bO2HhNxQeBRtTFm4UBaYiz1DUpmySaOJaM9htUpmyCGCAS9Fk2aTQeJRimOnCMhT5psm5BQFm5vh68UIJIYQQDklUdpdUEt59Cr580xnhE4+gtcZKuSmJfslRo5ZQGR2DsiPUeIMs95SxPjocd4WPwAovoFCptkN5bTYd3gopyIuaVOskOaaBDbiwSGqbJsumwGWSbRrEtSaqnYQkxzQo97gIGib1KYvKpMUgl4shHjcjvS72C3kZ5e083FgIIYToDZKoZEAyoYlFndlfvT7nAz6lLWpoopkoMZLkrl1KyWf/xmyqx0ilnGYSbePRkO9uJKdkEyXGEtbmDiHLbzPM/JgvPaOY5z+Mpgo/hY/nE1jtAw3hkUmihRYDKl24y51pZU2c1hYDhQk02zZ5GJiA31AUmia1SYtmrWmybCytyDINZmQHOC03iEdqUIQQQvRBkqjshNaw5stPNcsWQJQYRm6M4vFRcse0UJFVSStx4qSwgUM+/zcDG2pQKWcuE9U2mRoAGgwrRb65gcgA2DB4MKA5qPFTfMNjfHzoJOxDK7D+30DMl4tIBm1QEPApCgcpqpQi0VZn4jecGWNt7RTKptpaUsb5PFSYKRosm0ODfkrcLsb7PYzwuDCk9UQIIUQfJYlKmlqaNE//VbN0VRzv/hsITanGM7CZ1f4k+FLOQoPOtK8oS1OyfhOG5fTdKNvuuFqydtbZcSVTFFfXkgz42FRaQjzoZp/GNSwfM5haXzbRSzbgGduK6+WBmApKD9AMLFFsjjozzeabBjmmQdi222aX1bTamiFuk5jW2ArOycvi+Jxgz05Wa2f55SXzYdMaMEwYOd6ZUbewNINXVQghhOhIEpU0JJM2v79Js2xzjOEXLcQ/uh4VSmKZGsNltY+kUVqR3dhM6fI1ZNc2opIW2Fu1pGxJVJQCw9nfE0uQ29hI3YB8on4/ueEwxfEmrBwPFU0+kkc0QoOPceODMMpCKRflXjetbQsM+pXC19ZCsjllETAMUIq41szICjAjO9D5hMINUFsJ2oZIK6xdCvU1EMyCUZOcwt+P34B4DELZTrfV+y/D4k9gxrdg5Lhdf9GFEEL0S5Ko9JDWmj/8WvPZUpvyHy0hZ2ItdkqBVtiWxvBocsLNDKmpYdiaTQxYuQl3axR3LIGytkpSOh7U+UeBYdn4I3HcyRSW12jPZdxuiwEFLURjWbi/28j0UC4fp+KsT6QodplM8XpYnkiyORZjRFMd5W6D/KwsxjfUUNTaxNBwHTnadop641FIJiFc70xhG485CyU21TkLA+XkQ+kwqN4ECz9wEpmyMTBs1FcxDyiBivXwxrNQMrjrlRCFEEKInSSJSg+9+i+b9z+C0PgG8ibVkmhx4w6mcMUs9mlYx8T1qyhqbMQVT6I2N6ESFiTahuts25KyNVuDodqHH9uGIhSJ0hIM0JwTAjS+gE1oWJJwq4tEC5wzMMQbzVE2JlO4Wpv51rsvMnHxxxRFwvjRuFJJcLsgGoF4xHl+bTvjn20bthTQ+nzgD4K2nISjNeyMTBoxFlY2Q2P9/2/vzuOjKu/Fj3/OrJlM9g0SyCJEIGxq2KGKCojIK4JtESn9CUjF60JBRIWrshQXbnu1erUo9CopFQq9haC1LxAEEUSWsATDliYQIJiEJSGQdZKZeX5/nGR0SAiJgpPI9/16zcvMc+Y8853nBOebZztQUgRR7Tw3METToE17OH0Mcg7CLQOvZ7MLIYS4QUmi0gxnv1Esex9cJgjpVoRmcmO/4GBg3gG65J/A31GJAf1mf5RVQ3XtMJDTDUYNnFeo+LtzVUwGHDYrCrA6qjl6cwdqzGY0BQYMaJZqLFV+HLuo8UiiH7fZrBQcPkDY//4X/ieyMZpMaJpBTzZqh32wWCAsAs6fgRoHGI1gNEFNNQQE6slLaYleHhwOZrPemxIZDeVl+hBQ8TmoqP25jtGo13++8Ho2uxBCiBuYJCrNsGOLm1IHYAFTYDUdT+dx1/F0Qh2lGJT6Nt9QQGUNmqb0m+togGYAze0Z5vFSN7FWA7fRQLndhr3KQU7HeHI6xutTXpSGwWkGczVBpy5x874NkJ6N9dhRErIPwqWLYLWCwQRuhz6U43bpwzoGg35cQ++50ZS+nhkFLhdYzFBVCSaT3pMSFaMPAV04r59rNIGzun6iUhe7yXydWlwIIcSNThKVJiovVRzYB+7amwdHlhczOGcPISfz0bLPwrlysBjhpnC0DqGeIRyUG9Any3qGWtyXzVXRQBkMKLORkzfFcqhbZ/Jj2nAuIhy3pg8HVdcYsOGgx8Z0On58kMQzJ6GyRE8wXC69bpMZqqr0HXBtNjDWbmvrckF5KVgttRN93fo+/JpBn7NiterBKg2qHeBy6j0lNQ4Ii4SLxbV3L7xszKraoScyMQnXr+GFEELc0CRRaaJqB1RU4FnR0+PcUUJ27sa4NRtKq2uXI2tgNkK7ILT7k8Bu0ctU7XyQup4VI/pwUC1l1HBaLZzpnkDaL0ZSabZQ1z+jALfSqHCY6b7nAF3/tZuQcxfxM6InJhYXOBx6QlLtqH0PDRzVemKCVrsH/3fXQ9fGgUvv4TEa9YTD7QRlrk2k3GDxg/BoOHUMqiv1eSyeBqmC/FOQ0Fl/CCGEENeBJCpNZLMDptq7DDsdDPq/97Dsy0Q5vx3yQUPfZS33AqTug7E9IcyvNrlRehJT49YTAZMBt9GIUuCyWym6qS2nu3Wk2vjtJXEpqHYbOV9iw1TmInZ7Nn6lbsLMCjNGvU6DUU803G79UVOjJx2q9nldD4rZTO2GLTqzRZ+jYjF9O3zjVrW3Za4BkwVCI/VzQyL0YaGiM3C+QD/faNJXAt0zRq9LCCGEuA4kUWkiP5tGdBzknIIB3/yd0EOZ4FLeC3gUeiJi0uCSAz4/hjb+Fn30x+FEuRWaUdOfK/3hDPGnuGM0F+MiORYVhUMz4HIbqHYbcbqNlFaauVjqT/vs83QsqSbG5MRkssGlEj0hwQTUDsEoVZu8GMBZO3NX0/SyoBB93onSant3tG+Tl8pyvffEYNCHqC6ch4hoKCrU7wvQfwj87F745oReZjBCdBzE3SxJihBCiOtKEpVm6NgZTuw9xwO5r+t7olyJU6EZ3HD6ElysRnUIhVIHVDlRLoUyG6m2+VEVFkhZVCjFgUEcCW7HelsSpaV+VNYYuVhq4VKRlfKLfgQ6TTygmYizGmt7RGonuCqlr+ipdnw7F0W59d4RV+2QkMUM/nZ93onbpc9bUeh7qfjZ9Ncrt94rE9kWEjrp81JCI/WJszd1gfYd9eMhET9SSwshhBA6SVSaIambgQrjDtpX5lz1tUopcLrQzpRDcjyOcDMuk5EaTaNGM+A0miix+nPcP5JdER3ICG5PZY2FikoT2SeDKC+14q4wYjdqpMRYGRZrhD3B+lLginLwD9BX6Gga2Pz1JKTu/kHVDj35sAdA23bQLlYvM5n0IR3l1CfcOKshNEJPTjp0gbiOENVe70URQgghWgBJVJohPh4qDUcxuK+0Icp3uBRuzYCzy71Yfz6X7VWFZFaep7jyIvnKTY3SKDP6UW7yw2Uy4FZQVWnicE4opZcsWBREWg38IsbMC11tRPrZodttkJcL7jK9h8MeqK/mMRj1IRizRR/OsfpBYhL8vycgsZu+wZufzXsyrNvd8EoeIYQQogWRRKUZDEYI7+JAmQxQffXXVwYFsO9nfRloMDDQP5oSk4VLfsEUVlTgMLgwGGpX4rigstzMqdNBlJda8DPC8DATo2OsPBRnxVK3rPmu+/RN277cqM8jqUs0qir0/4aEQZeecPswGDxCT05A73Gp92EM16xdhBBCiOtFEpVGKKXYW17A15Vf4dbKKcVMZJyT+wP9CCiqbnAnfM+5Bo3c/rewv1ci7VUFHQwB3G+JoKvRTlhNKWuLyilWLhwOA2WlVi5c8MPhNhBk1ngszsrTiTYirZclE0EhMG4KdEuGTf+Ekzn6XJTgUEi6BbreCt2T9edCCCHET4AkKldQ6rjIOscarFQQZQVQtAX8uxlwdAjDXlKG5nI3eK7S4EzHdqx6bSp+mos8VU4HArBoBrqa7HQNtXO3oYYlJxx8XlSDo0YRZILkYBPPJvoxKLyRlTT+dug/WH+43frcFINRekiEEEL8JEmi0gCHs4oN1X/Hj2r93j2ApaiMNp8d5uZ3P8N6oljfHr9287fvUhqUR4fyyYLJFMZFEo2b6gZumdwt2MybPU2cq1ZUOd2EWowEmps5X8RgkARFCCHET5okKg3IdO7CpKoxuF1Ya2oI232MuGU7CNudi8GpwN+iZyQVNbU7zOqJiLKYqIgN4WyfTiRlZpGb3ANXbAAhNHwvHE3TiLJqcPkQjxBCCCEASVTqUUpR4Mim7YVighwO/HKLCP9wJ7ZD+XqSAvrE1QAL2Ez6HZJdCowaKtCMMy4MS7WT4KKL3HQ4m4uxXelgCPDthxJCCCFaKUlULuNyVRB3/jQ2pxOUwv/AacxnSjFWNLDMx2gAW21viAYaGpaySqw2PzTlJqawmF6GNoRrsi+JEEII8X1IonKZ4qIt2KocUFOD5nBiPX4eQ7VLvw9OYxRobjDUuACwOKGHtS2xRlmBI4QQQnxfkqh8x/mze6jJ+4wQl8IAqKoaLNVONFW7OVoDk2IvpwwGzBYbCdYwLF37yYZqQgghxA8gszhrXSzJxnniQ6y1SQqAZjWh2gSimYy4/a6e0ykjWG3+hFoCsCT11Pc2EUIIIcT3JolKrYrj/4vp8g4TTaOma1vcwX5gt6AaaS2lgbKaMUS1hZ8NhV9O0O+1I4QQQojvTYZ+gJqacow1jgaPOW+OxDG4I34b/w1VTgyXqtAa2jvFFkTJqCnEPpwCnbrrNwAUQgghxA8i36aAs+rslbfDN2hUD+yAq10I5j2n4N/nMX1TgqnMgTIaqIoJ5v/aTWab/SkWzfX/dhWQEEIIIX4wSVQAg0kfoqm9RWADL9Bw3RRO9U3hnAkNpNJiRhlNVFYb+cuakXxzqDOv/kbDJkmKEEIIcU1JogJY/CJwa2C8yqKeGoOBYrOdktIQtu5M5vDBTpgd/vzXM5DcQ5pSCCGEuNbk2xV9K3tjcHdUycErDgEpoNDwOHu2x5NfCO2D4dHfaiR1bHh7fCGEEEL8cJKo1ArvNJmzma9iqDxXL1lRgLHNHfROuJnevXwRnRBCCHFjkkSllqZptOn5ApfO7qYyLw3NVQVoaH7hBCROwuYf4+sQhRBCiBuOJCqXCYrqS1BUX1+HIYQQQghkwzchhBBCtGCSqAghhBCixZJERQghhBAtliQqQgghhGixJFERQgghRIsliYoQQgghWixJVIQQQgjRYkmiIoQQQogWSxIVIYQQQrRYkqgIIYQQosVq1VvoK6UAuHTpko8jEUIIIURT1X1v132PN6ZVJyqlpaUAxMbG+jgSIYQQQjRXaWkpwcHBjb5GU01JZ1oot9tNfn4+gYGBaJrm63BajUuXLhEbG0teXh5BQUG+DqdVk7a8dqQtrx1py2tH2vLa+W5bBgYGUlpaSkxMDAZD47NQWnWPisFgoH379r4Oo9UKCgqSf3jXiLTltSNtee1IW1470pbXTl1bXq0npY5MphVCCCFEiyWJihBCCCFaLElUbkBWq5W5c+ditVp9HUqrJ2157UhbXjvSlteOtOW1833bslVPphVCCCHET5v0qAghhBCixZJERQghhBAtliQqQgghhGixJFERQgghRIslicoN5k9/+hMJCQn4+fnRr18/du/e7euQWqWtW7eSkpJCTEwMmqaxdu1aX4fUKr322mv06dOHwMBAoqKiGD16NFlZWb4Oq1V699136dmzp2czrQEDBrBu3Tpfh/WTsHDhQjRNY/r06b4OpdWZN28emqZ5Pbp06dKsOiRRuYGsWrWKGTNmMHfuXPbt28ctt9zC8OHDOXv2rK9Da3XKy8u55ZZb+NOf/uTrUFq1L774gieffJKdO3eyceNGampquOeeeygvL/d1aK1O+/btWbhwIXv37mXPnj3cfffdjBo1ikOHDvk6tFYtPT2dxYsX07NnT1+H0mp169aNgoICz+PLL79s1vmyPPkG0q9fP/r06cM777wD6PdKio2NZerUqcyaNcvH0bVemqaRlpbG6NGjfR1Kq3fu3DmioqL44osvuOOOO3wdTqsXFhbGH/7wByZPnuzrUFqlsrIykpOTWbRoES+//DK33norb775pq/DalXmzZvH2rVrycjI+N51SI/KDaK6upq9e/cydOhQT5nBYGDo0KHs2LHDh5EJ8a2LFy8C+hes+P5cLhcrV66kvLycAQMG+DqcVuvJJ59k5MiRXv/fFM2XnZ1NTEwMHTp0YPz48Zw6dapZ57fqmxKKpjt//jwul4s2bdp4lbdp04ajR4/6KCohvuV2u5k+fTqDBg2ie/fuvg6nVcrMzGTAgAFUVVUREBBAWloaXbt29XVYrdLKlSvZt28f6enpvg6lVevXrx+pqal07tyZgoIC5s+fz+23387BgwcJDAxsUh2SqAghWoQnn3ySgwcPNnv8Wnyrc+fOZGRkcPHiRf7xj38wYcIEvvjiC0lWmikvL49p06axceNG/Pz8fB1OqzZixAjPzz179qRfv37Ex8fz97//vclDkpKo3CAiIiIwGo2cOXPGq/zMmTO0bdvWR1EJoXvqqaf45JNP2Lp1K+3bt/d1OK2WxWIhMTERgF69epGens5bb73F4sWLfRxZ67J3717Onj1LcnKyp8zlcrF161beeecdHA4HRqPRhxG2XiEhIXTq1ImcnJwmnyNzVG4QFouFXr16sWnTJk+Z2+1m06ZNMoYtfEYpxVNPPUVaWhqbN2/mpptu8nVIPylutxuHw+HrMFqdIUOGkJmZSUZGhufRu3dvxo8fT0ZGhiQpP0BZWRnHjh0jOjq6yedIj8oNZMaMGUyYMIHevXvTt29f3nzzTcrLy5k0aZKvQ2t1ysrKvP4iyM3NJSMjg7CwMOLi4nwYWevy5JNPsmLFCj766CMCAwMpLCwEIDg4GJvN5uPoWpfZs2czYsQI4uLiKC0tZcWKFWzZsoVPP/3U16G1OoGBgfXmSdntdsLDw2X+VDPNnDmTlJQU4uPjyc/PZ+7cuRiNRsaNG9fkOiRRuYGMHTuWc+fOMWfOHAoLC7n11ltZv359vQm24ur27NnDXXfd5Xk+Y8YMACZMmEBqaqqPomp93n33XQDuvPNOr/KlS5cyceLEHz+gVuzs2bM8/PDDFBQUEBwcTM+ePfn0008ZNmyYr0MTN7DTp08zbtw4ioqKiIyM5Gc/+xk7d+4kMjKyyXXIPipCCCGEaLFkjooQQgghWixJVIQQQgjRYkmiIoQQQogWSxIVIYQQQrRYkqgIIYQQosWSREUIIYQQLZYkKkIIIYRosSRRES1OQkICb775pue5pmmsXbvWZ/GI+k6cOIGmaWRkZPg6lBvWxIkTGT169I/yXu+//z733HOPT967Ofr378/q1at9HYa4xiRRET+avLw8HnnkEWJiYrBYLMTHxzNt2jSKioq8Xpeens6UKVOaVXd6ejqDBg3CbrcTFRXFL3/5S5xOZ5POvXTpEi+99BLdunXDZrMRHh5Onz59+P3vf8+FCxeaFUdz3HnnnUyfPr1eeWpqKiEhIdftfa+F2NhYCgoKvvd24qmpqWia1ujjxIkT1zZoH9uyZQuaplFSUtKs866UFL711ls/yi7IVVVVvPTSS8ydO7fJ58ybN49bb731+gV1BS+++CKzZs3C7Xb/6O8trh9JVMSP4vjx4/Tu3Zvs7Gz+9re/kZOTw3vvvee5KWJxcbHntZGRkfj7+zer/rFjxxIYGMiePXv4/PPPvba3b0xxcTH9+/dn6dKlzJw5k127drFv3z5eeeUV9u/fz4oVK5oVx43CaDTStm1bTKbvdxeOsWPHUlBQ4HkMGDCARx991KssNjbW8/rq6uprFbpP1NTUXPM6g4ODf5SE9h//+AdBQUEMGjTour/XDzVixAhKS0tZt26dr0MR15IS4kdw7733qvbt26uKigqv8oKCAuXv76/+4z/+w1MWHx+v/vjHP3qeAyotLa3R+jt27KgWL17c7Lgee+wxZbfb1TfffNPgcbfb7fl52bJlqlevXiogIEC1adNGjRs3Tp05c8ZzvLi4WP3qV79SERERys/PTyUmJqoPPvjgiu89ePBgNW3atHrlS5cuVcHBwZ7nn3/+uerTp4/y9/dXwcHBauDAgerEiRNKKaVycnLU/fffr6KiopTdble9e/dWGzdu9KovPz9f3XfffcrPz08lJCSo5cuX12vjCxcuqMmTJ6uIiAgVGBio7rrrLpWRkXHF2HNzcxWg9u/f74kRUJ999pnq1auXstlsasCAAero0aNXrKOxtpgwYYIaNWqUevnll1V0dLRKSEhQSl39GjQljoyMDHXnnXeqgIAAFRgYqJKTk1V6erpX26elpanExERltVrVPffco06dOuUV76JFi1SHDh2U2WxWnTp1UsuWLfM6DqhFixaplJQU5e/vryZMmKAAr8eECROUUkqtW7dODRo0SAUHB6uwsDA1cuRIlZOT41XXdx+DBw/2aqM6VVVVaurUqSoyMlJZrVY1aNAgtXv37ma1TUNGjhypZs6c6VV2+Xvv3r1bRUREqIULF6qlS5fWi3np0qVKKaVOnjyp7r//fmW321VgYKAaM2aMKiws9Kp77dq16rbbblNWq1XddNNNat68eaqmpkYppf97nDt3roqNjVUWi0VFR0erqVOnep0/adIk9etf/7rRzyRaF+lREdddcXExn376KU888US9O+K2bduW8ePHs2rVKtQPuO3UqFGjePnll5s1XOB2u1m1ahW//vWviYmJafA1mqZ5fq6pqWHBggUcOHCAtWvXcuLECa8b57300kscPnyYdevWceTIEd59910iIiK+70cCwOl0Mnr0aAYPHszXX3/Njh07mDJliieusrIy7rvvPjZt2sT+/fu59957SUlJ4dSpU546Hn74YfLz89myZQurV69myZIlnD171ut9xowZw9mzZ1m3bh179+4lOTmZIUOGePV0NcULL7zA66+/zp49ezCZTDzyyCPf+7Nv2rSJrKwsNm7cyCeffAJc/Ro0JY7x48fTvn170tPT2bt3L7NmzcJsNnuOV1RU8Morr7Bs2TK2b99OSUkJDz30kOd4Wloa06ZN45lnnuHgwYM89thjTJo0ic8//9wrhnnz5vHAAw+QmZnJ/PnzPXMnsrKyKCgo4K233gKgvLycGTNmsGfPHjZt2oTBYOCBBx7wDF/s3r0bgM8++4yCggLWrFnTYHs999xzrF69mr/85S/s27ePxMREhg8fXu8aNvcaffnll/Tu3fuKxzdv3sywYcN45ZVXeP755xk7dizPPPMM3bp18/SOjR07FrfbzahRoyguLuaLL75g48aNHD9+nLFjx3rq2rZtGw8//DDTpk3j8OHDLF68mNTUVF555RUAVq9ezR//+EcWL15MdnY2a9eupUePHl7x9O3bl23btjX6mUQr4+tMSfz07dy5s9FekTfeeEMBnr+Mm9ujkpqaqsLCwtRrr72m4uLi1KFDhzzH/vu//1t169atwfMKCwsVoN544w2v8uTkZGW325XdblcPPfTQFd83PT1dAaq0tFQppVRKSoqaNGnSFV9/uab0qBQVFSlAbdmypcn1duvWTb399ttKKaWOHDmiAE+PgVJKZWdnK8DTxtu2bVNBQUGqqqrKq57Geqka61Gp869//UsBqrKy8qoxN9Sj0qZNG+VwOBo97/Jr0JQ4AgMDVWpqaoP11fUG7Ny501NW14a7du1SSik1cOBA9eijj3qdN2bMGHXfffd5ngNq+vTpXq+pi+3ChQuNfqZz584pQGVmZiql6rd1ne/2apSVlSmz2ayWL1/uOV5dXa1iYmLU73//+ya3zeUuXLigALV169YG33vNmjUqICBArVy50uv43Llz1S233OJVtmHDBmU0Gr16pw4dOqQAT8/PkCFD1Kuvvup13l//+lcVHR2tlFLq9ddfV506dVLV1dUNxquUUh999JEyGAzK5XJd8TWidZEeFfGjUdfhRt1ut5tZs2axYMECZs2axZw5c7jjjjvYuXMnAJmZmdx+++3NqjMtLY2MjAyGDx9OZWWlp3zv3r2kpKQQFxdHYGAggwcPBvD0Xjz++OOsXLmSW2+9leeee46vvvrqB3++sLAwJk6cyPDhw0lJSeGtt96ioKDAc7ysrIyZM2eSlJRESEgIAQEBHDlyxBNTVlYWJpOJ5ORkzzmJiYmEhoZ6nh84cICysjLCw8MJCAjwPHJzczl27Fiz4u3Zs6fn5+joaIB6vTdN1aNHDywWi1fZ1a5BU+KYMWMGv/nNbxg6dCgLFy6s9xlNJhN9+vTxPO/SpQshISEcOXIEgCNHjtSbrzFo0CDP8TqN9UJ8V3Z2NuPGjaNDhw4EBQWRkJDQ4GdqzLFjx6ipqfGKy2w207dv33pxNeca1f3++/n51Tu2a9cuxowZw1//+levXpErOXLkCLGxsV5zj7p27erVtgcOHOB3v/ud1+9h3dyliooKxowZQ2VlJR06dODRRx8lLS2t3qR5m82G2+3G4XBcNSbROkiiIq67xMRENE2r9z/MOkeOHCE0NJTIyMhm13327FkKCwu57bbbAJg8eTIvvvgiQ4cOZeXKlaxevZpJkyY1eG5kZCQhISFkZWV5lcfFxZGYmEhgYKCnrLy8nOHDhxMUFMTy5ctJT08nLS0N+Hai54gRIzh58iRPP/00+fn5DBkyhJkzZ14x9qCgIC5evFivvKSkhODgYM/zpUuXsmPHDgYOHMiqVavo1KmTJxGbOXMmaWlpvPrqq2zbto2MjAx69OjRrMmnZWVlREdHk5GR4fXIysri2WefbXI9gNcQSt3w1PddgWG3272eN+UaNCWOefPmcejQIUaOHMnmzZvp2rWrp55r6fL4ryQlJYXi4mL+/Oc/s2vXLnbt2gVcvwnEzblG4eHhaJrW4Oq3jh070qVLFz744INrNlm4rKyM+fPne/0eZmZmkp2djZ+fH7GxsWRlZbFo0SJsNhtPPPEEd9xxh9f7FxcXY7fb6w0zi9ZLEhVx3YWHhzNs2DAWLVrk1UMBUFhYyPLlyxk7dqzXfJCmCg0NxWazsXXrVk/Z9OnTef755xk3bhx33303ffv2bfBcg8HAgw8+yIcffkh+fn6j73P06FGKiopYuHAht99+O126dGnwr9DIyEgmTJjAhx9+yJtvvsmSJUuuWGfnzp3Zt29fvfJ9+/bRqVMnr7LbbruN2bNn89VXX9G9e3fPaqTt27czceJEHnjgAXr06EHbtm295ul07twZp9PJ/v37PWU5OTleXzzJyckUFhZiMplITEz0evzQOTbXUlOvQVN06tSJp59+mg0bNvDzn/+cpUuXeo45nU727NnjeZ6VlUVJSQlJSUkAJCUlsX37dq/6tm/fTteuXRt9z7reIZfL5SkrKioiKyuLF198kSFDhpCUlFQvKWjovMt17NgRi8XiFVdNTQ3p6elXjetqMXft2pXDhw/XOxYREcHmzZvJycnhwQcf9EoWLBZLvXiTkpLIy8sjLy/PU3b48GFKSko8MSYnJ5OVlVXv9zAxMRGDQf+6stlspKSk8D//8z9s2bKFHTt2kJmZ6anz4MGDnj9cxE+DJCriR/HOO+/gcDgYPnw4W7duJS8vj/Xr1zNs2DDatWvnmSzXXFarlWnTpjF//nzefvttsrOzPT0Ldrudbdu21esx+a5XX32Vdu3a0bdvXz744AO+/vprjh07RlpaGjt27MBoNAJ6L4vFYuHtt9/m+PHjfPzxxyxYsMCrrjlz5vDRRx+Rk5PDoUOH+OSTTzxfbg15/PHH+fe//81vf/tbvv76a7KysnjjjTf429/+xjPPPANAbm4us2fPZseOHZw8eZINGzaQnZ3tqffmm29mzZo1ZGRkcODAAX71q195/XXcpUsXhg4dypQpU9i9ezf79+9nypQp2Gw2T2I4dOhQBgwYwOjRo9mwYQMnTpzgq6++4oUXXvD6wva1plyDq6msrOSpp55iy5YtnDx5ku3bt5Oenu51ncxmM1OnTmXXrl3s3buXiRMn0r9/f0/C++yzz5Kamsq7775LdnY2b7zxBmvWrGm09wwgPj4eTdP45JNPOHfuHGVlZYSGhhIeHs6SJUvIyclh8+bNzJgxw+u8qKgobDYb69ev58yZMw32wtntdh5//HGeffZZ1q9fz+HDh3n00UepqKhg8uTJzWqjyw0fPpwvv/yywWNRUVFs3ryZo0ePMm7cOM8wTEJCArm5uWRkZHD+/HkcDgdDhw6lR48ejB8/nn379rF7924efvhhBg8e7BkmmzNnDsuWLWP+/PkcOnSII0eOsHLlSl588UVA33/n/fff5+DBgxw/fpwPP/wQm81GfHy8J6Zt27Z5bU4nfgJ8PUlG3DhOnDjhmSRpNptVbGysmjp1qjp//rzX65o7mdblcqn33ntPde/eXfn5+an27duradOmqXPnzqnhw4erjh07qnPnzl3x/JKSEjV79mzVpUsXZbValc1mUz179lQvvfSSKioq8rxuxYoVKiEhQVmtVjVgwAD18ccfe01yXLBggUpKSlI2m02FhYWpUaNGqePHjzfaJrt371bDhg1TkZGRKjg4WPXr18/rsxYWFqrRo0er6OhoZbFYVHx8vJozZ45nomBubq666667lM1mU7Gxseqdd96pNzE1Pz9fjRgxQlmtVhUfH69WrFihoqKi1Hvvved5zaVLl9TUqVNVTEyM59qMHz++3rLcOleaTPvdiaL79+9XgMrNzW20DZS68vLky13tGlwtDofDoR566CHP8taYmBj11FNPeSaT1k1kXr16terQoYOyWq1q6NCh6uTJk15xNGV5ckO/s7/73e9U27ZtlaZpnuXJGzduVElJScpqtaqePXuqLVu21Dv/z3/+s4qNjVUGg+GKy5MrKyvV1KlTVURERKPLk5t7jQ4dOqRsNpsqKSnxlF3+3vn5+apTp07qwQcfVE6nU1VVValf/OIXKiQkpNnLk9evX68GDhyobDabCgoKUn379lVLlixRSimVlpam+vXrp4KCgpTdblf9+/f3mhx8+vRpZTabVV5e3hU/j2h9NKWuwwxHIUSLdfr0aWJjY/nss88YMmSIr8NpUVJTU5k+fXqzd4/9qRszZgzJycnMnj3b16E06vnnn+fChQuNDrmK1uf7bSsphGg1Nm/eTFlZGT169KCgoIDnnnuOhIQE7rjjDl+HJlqJP/zhD/zzn//0dRhXFRUVVW/oTLR+kqgI8RNXU1PDf/7nf3L8+HECAwMZOHAgy5cv91r9IURjEhISmDp1qq/DuKq6uV3ip0WGfoQQQgjRYsmqHyGEEEK0WJKoCCGEEKLFkkRFCCGEEC2WJCpCCCGEaLEkURFCCCFEiyWJihBCCCFaLElUhBBCCNFiSaIihBBCiBZLEhUhhBBCtFj/H33t11yKCBCoAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3997,10 +3997,10 @@
    ],
    "source": [
     "colors = cm.rainbow(np.linspace(0, 1, len(df_merged_s)))\n",
-    "plt.scatter(x=df_merged_s[\"household_total\"], y=df_merged_s[\"carbon\"], color=colors, alpha=0.5)\n",
-    "plt.title(\"Transportation Gas Usage vs Carbon\")\n",
+    "plt.scatter(x=df_merged_s[\"gas\"], y=df_merged_s[\"carbon\"], color=colors, alpha=0.5)\n",
+    "plt.title(\"Transportation Gas Usage vs Greenhouse Emissions\")\n",
     "plt.xlabel(\"Oil & Gas Usage in Transportation (ktoes)\")\n",
-    "plt.ylabel(\"Carbon Emissions\")"
+    "plt.ylabel(\"Greenhouse Emissions (ktonnes)\")"
    ]
   },
   {

From 55ef44ae412cd8d41b97f615737f0a664cebbb47 Mon Sep 17 00:00:00 2001
From: aahil n <nishad.a@northeastern.edu>
Date: Thu, 6 Jun 2024 03:43:49 +0200
Subject: [PATCH 33/34] connected! ml model and database

---
 api/backend/user/user_routes.py | 59 +++++++++++++++++++++++++++++++++
 app/src/pages/02_userSurvey.py  | 29 +++++++++++++++-
 database/00_carbon.sql          | 15 ++++++++-
 database/01_carbon-data.sql     |  6 ++--
 4 files changed, 105 insertions(+), 4 deletions(-)

diff --git a/api/backend/user/user_routes.py b/api/backend/user/user_routes.py
index 695b9ea..17bffec 100644
--- a/api/backend/user/user_routes.py
+++ b/api/backend/user/user_routes.py
@@ -1,9 +1,68 @@
 from flask import Blueprint, request, jsonify, make_response, current_app
 import json
 from backend.db_connection import db
+from backend.ml_models.model_alpha import predict, train
 
 user = Blueprint('user', __name__)
 
+@user.route('/UserPrediction/', methods=['GET'])
+def predict_value():
+    cursor = db.get_db().cursor()
+    
+    select_heating_query = '''
+        SELECT heating FROM ResData;
+    '''
+    cursor.execute(select_heating_query)
+    heating = cursor.fetchone()[0]
+    current_app.logger.info("THIS IS HEATING", heating)
+    
+    # select_water_query = '''
+    #     SELECT water_heating FROM ResData WHERE user_id = 1;
+    # '''
+    # cursor.execute(select_water_query)
+    # water_heating = cursor.fetchone()[0]
+    # current_app.logger.info(select_water_query)
+    
+    # select_cooking_query = '''
+    #     SELECT cooking_gas FROM ResData WHERE user_id = 1;
+    # '''
+    # cursor.execute(select_cooking_query)
+    # cooking_gas = cursor.fetchone()[0]
+    # current_app.logger.info(select_cooking_query)
+    
+    select_car_query = '''
+        SELECT fuel_used FROM Cars WHERE user_id = 1;
+    '''
+    cursor.execute(select_car_query)
+    fuel_used = cursor.fetchone()[0]
+    current_app.logger.info(select_car_query)
+    
+    select_beta_query = '''
+        SELECT user_values FROM Beta_User ORDER BY id DESC LIMIT 1;
+        '''
+    
+    cursor.execute(select_beta_query)
+    betaValue = cursor.fetchone()[0].split(', ')
+    current_app.logger.info(select_beta_query)
+    
+    if betaValue is None:
+        current_app.logger.info(f'Beta Value not Found: {betaValue}')
+        return jsonify({"error": "BV not found"}), 404
+    
+    feats = [heating, fuel_used]
+    returnVal = predict(feats, betaValue)
+    return_dict = {'result': returnVal}
+
+    the_response = make_response(jsonify(return_dict))
+    the_response.status_code = 200
+    the_response.mimetype = 'application/json'
+    
+    betavals = train()
+    current_app.logger.info("betavals: ", betavals)
+    return the_response
+
+
+
 # Get all the cars history for this user
 @user.route('/UserCars', methods=['GET'])
 def get_cars():
diff --git a/app/src/pages/02_userSurvey.py b/app/src/pages/02_userSurvey.py
index fd91908..9261102 100644
--- a/app/src/pages/02_userSurvey.py
+++ b/app/src/pages/02_userSurvey.py
@@ -115,4 +115,31 @@
 #   st.write("**Important**: Could not connect to sample api, so using dummy data.")
 #   data = {"a":{"b": "123", "c": "hello"}, "z": {"b": "456", "c": "goodbye"}}
 
-# st.dataframe(data)
\ No newline at end of file
+# st.dataframe(data)
+
+
+if st.button("View Prediction"): 
+    api_url = "http://api:4000/u/UserPrediction/"
+    try: 
+        response = requests.get(api_url)
+        responseJSON = response.json()
+        finalCarbon = responseJSON['result']
+        st.write("Estimated Carbon Footprint (tonnes of CO2): ", finalCarbon)
+        
+        # HEY PROFS: THIS IS THE ML PREDICTION
+        if response.status_code == 201 or response.status_code == 200:
+            st.success("Successfully Predicted!")
+
+            
+        else:
+            try:
+                error_message = response.json().get('error', 'No error message provided')
+            except json.JSONDecodeError:
+                error_message = response.text  # Raw response if not JSON
+            
+            st.error(f"Failed to insert data. Status code: {response.status_code}, Error: {error_message}")
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+        
+        
+        
diff --git a/database/00_carbon.sql b/database/00_carbon.sql
index bce0479..8251504 100644
--- a/database/00_carbon.sql
+++ b/database/00_carbon.sql
@@ -1,6 +1,19 @@
 DROP DATABASE IF EXISTS CarbonConnect;
 CREATE DATABASE IF NOT EXISTS CarbonConnect;
-USE CarbonConnect;
+Use CarbonConnect;
+
+
+DROP TABLE IF EXISTS Beta_User;
+CREATE TABLE IF NOT EXISTS Beta_User (
+    id INT PRIMARY KEY AUTO_INCREMENT,
+    user_values VARCHAR(100)
+);
+
+DROP TABLE IF EXISTS Beta_Enterprise;
+CREATE TABLE IF NOT EXISTS Beta_Enterprise (
+    id INT PRIMARY KEY AUTO_INCREMENT,
+    enterprise_values VARCHAR(100)
+);
 
 
 DROP TABLE IF EXISTS Country;
diff --git a/database/01_carbon-data.sql b/database/01_carbon-data.sql
index c2790e5..2e6ac69 100644
--- a/database/01_carbon-data.sql
+++ b/database/01_carbon-data.sql
@@ -414,9 +414,11 @@ insert into UserTags (user_id, tag_id) values (43, 1);
 
 
 INSERT INTO ResData (id, user_id, elec_usage, emission_tags, heating, water_heating, cooking_gas) VALUES
-(200, 1, 1000.0, 'res', 2000.0, 1.0, 11.0)
+(200, 1, 1000.0, 'res', 2000.0, 1.0, 11.0);
 
 INSERT INTO Cars (id, user_id, fuel_type, emission_tags, fuel_used) VALUES
 (1, 1, 'Gasoline/Hybrid', 'car', 100.0),
 (2, 2, 'Diesel', 'car', 200.0),
-(3, 3, 'Electric', 'car', 304.0);
\ No newline at end of file
+(3, 3, 'Electric', 'car', 304.0);
+
+INSERT INTO Beta_User (id, user_values) VALUES (1, '0.001, 0.3, 0.2');
\ No newline at end of file

From 13f1386586e771854c9299370d8283a6c9cc78b5 Mon Sep 17 00:00:00 2001
From: Babalola-a <147947002+Babalola-a@users.noreply.github.com>
Date: Thu, 6 Jun 2024 03:51:24 +0200
Subject: [PATCH 34/34] Updating team blog and also personal blog

---
 blog/content/anjola/phase3.md                 |   0
 .../team_posts/phase_3/EmissionsVsHouse.jpg   | Bin 0 -> 74921 bytes
 .../team_posts/phase_3/GasVsGreenHouse.jpg    | Bin 0 -> 87097 bytes
 .../phase_3/Image 6-6-24 at 3.46 AM 2.jpg     | Bin 0 -> 28919 bytes
 .../team_posts/phase_3/ResiVSConsum.jpg       | Bin 0 -> 49649 bytes
 blog/content/team_posts/phase_3/ResiVsGas.jpg | Bin 0 -> 79202 bytes
 .../team_posts/phase_3/ResidualPlot.jpg       | Bin 0 -> 78717 bytes
 .../team_posts/phase_3/carbonfootprint.jpg    | Bin 0 -> 28919 bytes
 .../team_posts/phase_3/coolinggraph.jpg       | Bin 0 -> 69826 bytes
 .../team_posts/phase_3/enterprisehome.jpg     | Bin 0 -> 205705 bytes
 blog/content/team_posts/phase_3/index.md      |  38 +++++++++++++++++-
 blog/content/team_posts/phase_3/table.jpg     | Bin 0 -> 168397 bytes
 12 files changed, 37 insertions(+), 1 deletion(-)
 create mode 100644 blog/content/anjola/phase3.md
 create mode 100644 blog/content/team_posts/phase_3/EmissionsVsHouse.jpg
 create mode 100644 blog/content/team_posts/phase_3/GasVsGreenHouse.jpg
 create mode 100644 blog/content/team_posts/phase_3/Image 6-6-24 at 3.46 AM 2.jpg
 create mode 100644 blog/content/team_posts/phase_3/ResiVSConsum.jpg
 create mode 100644 blog/content/team_posts/phase_3/ResiVsGas.jpg
 create mode 100644 blog/content/team_posts/phase_3/ResidualPlot.jpg
 create mode 100644 blog/content/team_posts/phase_3/carbonfootprint.jpg
 create mode 100644 blog/content/team_posts/phase_3/coolinggraph.jpg
 create mode 100644 blog/content/team_posts/phase_3/enterprisehome.jpg
 create mode 100644 blog/content/team_posts/phase_3/table.jpg

diff --git a/blog/content/anjola/phase3.md b/blog/content/anjola/phase3.md
new file mode 100644
index 0000000..e69de29
diff --git a/blog/content/team_posts/phase_3/EmissionsVsHouse.jpg b/blog/content/team_posts/phase_3/EmissionsVsHouse.jpg
new file mode 100644
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diff --git a/blog/content/team_posts/phase_3/index.md b/blog/content/team_posts/phase_3/index.md
index 305c6c3..5bedf93 100644
--- a/blog/content/team_posts/phase_3/index.md
+++ b/blog/content/team_posts/phase_3/index.md
@@ -79,7 +79,7 @@ Gets all the flights history for this enterprise
 
 # Application Prototype
 
-## Database ER Diagrams
+
 
 | User Homescreen                        | Enterprise Homescreen                          | NGO homescreen Diagram               |
 | -------------------------------------- | ---------------------------------------------- | ------------------------------------ |
@@ -105,3 +105,39 @@ In the general user persona, users take a survey, and based on this survey data,
 
 ![User History](./userhistory.jpg)
 Users can also see their survey history and previous emission data.
+
+# Machine Learning
+The ML model developed is a standard linear regression model used to predict the greenhouse gas emissions of an individual (measured in ktonnes) based on two features: household energy consumption (in TJ) and Motor Gasoline Consumption (in KTOES).
+
+Originally, household energy consumption was divided into 4 separate categories: heating, cooling, water heating, and cooking. The correlation matrix can be seen below:
+![Table](./table.jpg)
+
+
+As  observed, all of these features have a strong correlation with carbon, the variable name for what is trying to be predicted. Energy used for cooling (energy_cooling) has the weakest correlation, and, upon being graphed in Figure 2, fails the test of linearity.
+
+![CoolingGraph](./coolinggraph.jpg)
+
+Figure 2: A plot of energy used in household cooling against greenhouse gas emissions. Colors represent different countries.
+
+The was then fitted with the remaining subset features (Energy for heating, water heating, and cooking), reaching an R2 of 0.86 in Leave One Out Cross Validation (LOO-CV). However, the group decided to instead only use the total household value instead of the sub-categories since, for an end user, the total would show up on a power bill rather than each of these categories. Additionally, this is when the Gasoline dataset was added (column name gas) to help incorporate transportation, which is a large share of greenhouse gas emissions. In addition, the countries have been one-hot encoded throughout this process.
+
+When this model was fit, an R2 of -4476.7 immediately threw red flags. Upon further inspection of the residuals, which summed to -4.196 rather than the expected 0, it was found that the one-hot encoded countries were overfitting the model. With only 12 observations per country, the 28 features (26 for the 27 countries + 2 features) was too much. After fixing this change, the final model produced an R2 of 0.96. The graphs of the residuals can be seen below.
+
+![ResidualPlot](./ResidualPlot.jpg)
+
+![Residual vs Gasoline](./ResiVsGas.jpg)
+
+![Residual vs Consumption](./ResiVSConsum.jpg)
+
+The residuals generally appear like random noise, and are likewise usually positive. In Figure 5, some autocorrelation can be seen, as some pattern exists. Potentially adding the countries could resolve this, if not for the overfitting issue. In addition, Figure 3 and 4 have some vertical striping, which certainly is not random, but the issue seems minor.
+
+Finally, the scatterplots for the two features can be seen below.
+![Emissions VS House](./EmissionsVsHouse.jpg)
+
+![Gas Vs Greenhosue](./GasVsGreenHouse.jpg)
+
+The scatterplots demonstrate that the R2 does make sense and fits given the data, which is extremely linear. As such, all the linear regression conditions are met.
+
+![Carbon Footprint](./carbonfootprint.jpg)
+
+This machine learning model predicts the amount of CO2 emissions in tons based on heating and gas usage. This is a rough prediction prone to refinement. 
\ No newline at end of file
diff --git a/blog/content/team_posts/phase_3/table.jpg b/blog/content/team_posts/phase_3/table.jpg
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