From a4737065b388de42d6cd51b7a15c4620fa8088b0 Mon Sep 17 00:00:00 2001
From: msyamkumar <msyamkumar@wisc.edu>
Date: Wed, 23 Nov 2022 08:06:21 -0600
Subject: [PATCH] Lec 32 materials
---
.../demo_lec_31-checkpoint.ipynb | 179 --
.../demo_lec_31_template-checkpoint.ipynb | 109 -
.../lec_32_database1-checkpoint.ipynb | 2842 -----------------
...lec_32_database1_template-checkpoint.ipynb | 542 ----
4 files changed, 3672 deletions(-)
delete mode 100644 f22/meena_lec_notes/lec-32/.ipynb_checkpoints/demo_lec_31-checkpoint.ipynb
delete mode 100644 f22/meena_lec_notes/lec-32/.ipynb_checkpoints/demo_lec_31_template-checkpoint.ipynb
delete mode 100644 f22/meena_lec_notes/lec-32/.ipynb_checkpoints/lec_32_database1-checkpoint.ipynb
delete mode 100644 f22/meena_lec_notes/lec-32/.ipynb_checkpoints/lec_32_database1_template-checkpoint.ipynb
diff --git a/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/demo_lec_31-checkpoint.ipynb b/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/demo_lec_31-checkpoint.ipynb
deleted file mode 100644
index 03961a4..0000000
--- a/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/demo_lec_31-checkpoint.ipynb
+++ /dev/null
@@ -1,179 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>.container { width:100% !important; }</style>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from IPython.core.display import display, HTML\n",
- "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import csv\n",
- "import os\n",
- "import csv"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# copied from https://automatetheboringstuff.com/2e/chapter16/\n",
- "def process_csv(filename):\n",
- " exampleFile = open(filename)\n",
- " exampleReader = csv.reader(exampleFile)\n",
- " exampleData = list(exampleReader)\n",
- " return exampleData"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Example 1: List Visualization\n",
- "### Write a gen_html function\n",
- "- Input: shopping_list and path to shopping.html\n",
- "- Outcome: create shopping.html file\n",
- "\n",
- "### Pseudocode\n",
- "1. Open \"shopping.html\" in write mode.\n",
- "2. Write \\<ul\\> tag into the html file\n",
- "3. Iterate over each item in shopping list.\n",
- "4. Write each item with <\\li\\> tag.\n",
- "5. After you are done iterating, write \\</ul\\> tag.\n",
- "6. Close the file object."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "def gen_html(shopping_list, html_path):\n",
- " f = open(html_path, \"w\")\n",
- " f.write(\"<ul>\\n\")\n",
- " for item in shopping_list:\n",
- " f.write(\"<li>\" + str(item) + \"\\n\")\n",
- " f.write(\"</ul>\\n\")\n",
- " f.close()\n",
- " \n",
- "gen_html([\"apples\", \"oranges\", \"milk\", \"banana\"], \"shopping.html\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Example 2: Dictionary Visualization\n",
- "### Write a csv_to_html function\n",
- "- Input: path to review1.csv and path to reviews.html\n",
- "- Outcome 1: create a html file for each review \n",
- "- Outcome 2: create reviews.html file containing link to a html file for each review\n",
- "\n",
- "### Pseudocode\n",
- "1. Create data_html folder using os.mkdir. Make sure to use try ... except blocks to catch FileExistsError\n",
- "2. Use process_csv function to read csv data and split the header and the data\n",
- "3. For each review, extract review id, review title, review text.\n",
- "4. generate the \\<rid\\>.html for each review inside data_html folder.\n",
- " - Open \\<rid\\>.html in write mode\n",
- " - Add review title using \\<h1\\> tag\n",
- " - Add review text inside\\<p\\> tag\n",
- " - Close \\<rid\\>.html file object\n",
- "5. generate a reviews.html file which has link to each review html page \\<rid\\>.html\n",
- " - Open reviews.html file in write mode\n",
- " - Add each \\<rid\\>.html as hyperlink using \\<a\\> tag.\n",
- " - Close reviews.html file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "def csv_to_html(csv_path, html_path):\n",
- " try:\n",
- " os.mkdir(\"data_html\")\n",
- " except FileExistsError:\n",
- " pass\n",
- " \n",
- " reviews_data = process_csv(csv_path)\n",
- " reviews_header = reviews_data[0]\n",
- " reviews_data = reviews_data[1:]\n",
- " \n",
- " reviews_file = open(html_path, \"w\")\n",
- " reviews_file.write(\"<ul>\\n\")\n",
- " \n",
- " for row in reviews_data:\n",
- " rid = row[reviews_header.index(\"review id\")]\n",
- " title = row[reviews_header.index(\"review title\")]\n",
- " text = row[reviews_header.index(\"review text\")]\n",
- "\n",
- " # STEP 4: generate the <rid>.html for each review inside data folder\n",
- " review_path = os.path.join(\"data_html\", str(rid) + \".html\")\n",
- " html_file = open(review_path, \"w\")\n",
- " html_file.write(\"<h1>{}</h1><p>{}</p>\".format(title, text))\n",
- " html_file.close()\n",
- " \n",
- " # STEP 5: generate a reviews.html file which has link to each review html page <rid>.html\n",
- " reviews_file.write('<li><a href = \"{}\">{}</a>'.format(review_path, str(rid) + \":\" + str(title)) + \"<br>\\n\")\n",
- " \n",
- " reviews_file.write(\"</ul>\\n\")\n",
- " reviews_file.close() \n",
- "\n",
- "csv_to_html(os.path.join(\"data\", \"review1.csv\"), \"reviews.html\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "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": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/demo_lec_31_template-checkpoint.ipynb b/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/demo_lec_31_template-checkpoint.ipynb
deleted file mode 100644
index 9932d75..0000000
--- a/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/demo_lec_31_template-checkpoint.ipynb
+++ /dev/null
@@ -1,109 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from IPython.core.display import display, HTML\n",
- "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import csv\n",
- "import os"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Example 1: List Visualization\n",
- "### Write a gen_html function\n",
- "- Input: shopping_list and path to shopping.html\n",
- "- Outcome: create shopping.html file\n",
- "\n",
- "### Pseudocode\n",
- "1. Open \"shopping.html\" in write mode.\n",
- "2. Write \\<ul\\> tag into the html file\n",
- "3. Iterate over each item in shopping list.\n",
- "4. Write each item with \\<li\\> tag.\n",
- "5. After you are done iterating, write \\</ul\\> tag.\n",
- "6. Close the file object."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Example 2: Dictionary Visualization\n",
- "### Write a csv_to_html function\n",
- "- Input: path to review1.csv and path to reviews.html\n",
- "- Outcome 1: create a html file for each review \n",
- "- Outcome 2: create reviews.html file containing link to a html file for each review\n",
- "\n",
- "### Pseudocode\n",
- "1. Create data_html folder using os.mkdir. Make sure to use try ... except blocks to catch FileExistsError\n",
- "2. Use process_csv function to read csv data and split the header and the data\n",
- "3. For each review, extract review id, review title, review text.\n",
- "4. generate the \\<rid\\>.html for each review inside data_html folder.\n",
- " - Open \\<rid\\>.html in write mode\n",
- " - Add review title using \\<h1\\> tag\n",
- " - Add review text inside\\<p\\> tag\n",
- " - Close \\<rid\\>.html file object\n",
- "5. generate a reviews.html file which has link to each review html page \\<rid\\>.html\n",
- " - Open reviews.html file in write mode\n",
- " - Add each \\<rid\\>.html as hyperlink using \\<a\\> tag.\n",
- " - Close reviews.html file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "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": "3.8.8"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/lec_32_database1-checkpoint.ipynb b/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/lec_32_database1-checkpoint.ipynb
deleted file mode 100644
index d39dc73..0000000
--- a/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/lec_32_database1-checkpoint.ipynb
+++ /dev/null
@@ -1,2842 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "# known import statements\n",
- "from bs4 import BeautifulSoup\n",
- "import os\n",
- "import pandas as pd\n",
- "\n",
- "# let's import sqlite3 module\n",
- "import sqlite3"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Warmup 1: Explore this HTML table of volunteer hours"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<table>\n",
- " <tr> \n",
- " <th>Name</th>\n",
- " <th>Week 1</th>\n",
- " <th>Week 2</th\n",
- " ><th>Week 3</th> \n",
- " </tr>\n",
- " <tr> \n",
- " <td>Therese</td>\n",
- " <td>13</td>\n",
- " <td>4</td>\n",
- " <td>5</td> \n",
- " </tr>\n",
- " <tr> \n",
- " <td>Carl</td>\n",
- " <td>5</td>\n",
- " <td>7</td>\n",
- " <td>8</td> \n",
- " </tr>\n",
- " <tr> \n",
- " <td>Marie</td>\n",
- " <td>2</td>\n",
- " <td>9</td>\n",
- " <td>11</td> \n",
- " </tr>\n",
- "</table>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Warmup 2a: Parse \"hours.html\" using BeautifulSoup\n",
- "\n",
- "#### Step 1: Read contents from \"hours.html\" file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "f = open(\"hours.html\")\n",
- "data = f.read()\n",
- "f.close()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Step 2: Create a BeautifulSoup object instance"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "bs_obj = BeautifulSoup(data, 'html.parser')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Step 3: Parse the table"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "table = bs_obj.find(\"table\") # works only if there is 1 table\n",
- "\n",
- "# Q: what method do you need if the HTML has more than 1 table? \n",
- "# A: find_all method and then extract the appropriate table using indexing"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Step 4: Parse the header\n",
- "- Bonus: Use list comprehension "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['Name', 'Week 1', 'Week 2', 'Week 3']"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "th_elements = table.find_all(\"th\")\n",
- "header = [th.get_text() for th in th_elements]\n",
- "header"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Step 5: Parse the data rows and store data into a list of dict\n",
- "- Remember that you need to skip over the first tr (which contains the header)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'Name': 'Therese', 'Week 1': 13, 'Week 2': 4, 'Week 3': 5},\n",
- " {'Name': 'Carl', 'Week 1': 5, 'Week 2': 7, 'Week 3': 8},\n",
- " {'Name': 'Marie', 'Week 1': 2, 'Week 2': 9, 'Week 3': 11}]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Find all tr elements\n",
- "tr_elements = bs_obj.find_all(\"tr\")\n",
- "\n",
- "# Skip first tr row (header row)\n",
- "tr_elements = tr_elements[1:]\n",
- "\n",
- "# Initialize empty list\n",
- "work_hours = []\n",
- "\n",
- "# Iterate through the tr elements\n",
- "for tr in tr_elements:\n",
- " # Find all \"td\" elements in this row\n",
- " td_elements = tr.find_all(\"td\")\n",
- " \n",
- " # Create row dictionary\n",
- " row_dict = {} # Key: column name (header); Value: cell's value\n",
- " \n",
- " # Iterate over indices of td elements\n",
- " for idx in range(len(td_elements)): # Assumes that td_elements and header have same length\n",
- " # Extract the td text\n",
- " td_val = td_elements[idx].get_text()\n",
- "\n",
- " # Make appropriate type conversions\n",
- " # Use header instead of hardcoing index\n",
- " if header[idx] in [\"Week 1\", \"Week 2\", \"Week 3\"]:\n",
- " td_val = int(td_val)\n",
- " \n",
- " # Insert key-value pairs \n",
- " row_dict[header[idx]] = td_val\n",
- " \n",
- " # Append row dictionary into list\n",
- " work_hours.append(row_dict)\n",
- " \n",
- "work_hours"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Warmup 3: Use appropriate os module to assert that bus.db in this directory"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "assert os.path.exists(\"bus.db\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## April 20: DataBase1\n",
- "\n",
- "### Learning Objectives:\n",
- "\n",
- "- Explain how a database is different from a CSV file or a JSON file\n",
- "- Use SQLite to connect to a database and pandas to query the database\n",
- "- Write basic queries on a database using SELECT, FROM, WHERE, ORDER BY, and LIMIT\n",
- "\n",
- "We will get started with slides."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "sqlite3.Connection"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Get the Bus data from 'bus.db'\n",
- "db_name = \"bus.db\"\n",
- "assert os.path.exists(db_name)\n",
- "# Why do we have to assert that database exists?\n",
- "# If the database file does not exist, connect function creates a brand new one!\n",
- "\n",
- "# open a connection object to our database file\n",
- "conn = sqlite3.connect(db_name)\n",
- "\n",
- "# Important note: we need to close 'conn' when we are done, at the end of the notebook file\n",
- "type(conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Pandas has a .read_sql function `pd.read_sql(query, connection)`\n",
- "- Allows us to process an SQL `query` on a SQL `connection`\n",
- "- stores the result in a Pandas DataFrame\n",
- "- First SQL query to always run on a database:\n",
- "```\n",
- "select * from sqlite_master\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "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>type</th>\n",
- " <th>name</th>\n",
- " <th>tbl_name</th>\n",
- " <th>rootpage</th>\n",
- " <th>sql</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>table</td>\n",
- " <td>boarding</td>\n",
- " <td>boarding</td>\n",
- " <td>2</td>\n",
- " <td>CREATE TABLE \"boarding\" (\\n\"index\" INTEGER,\\n ...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>index</td>\n",
- " <td>ix_boarding_index</td>\n",
- " <td>boarding</td>\n",
- " <td>3</td>\n",
- " <td>CREATE INDEX \"ix_boarding_index\"ON \"boarding\" ...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>table</td>\n",
- " <td>routes</td>\n",
- " <td>routes</td>\n",
- " <td>55</td>\n",
- " <td>CREATE TABLE \"routes\" (\\n\"index\" INTEGER,\\n \"...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>index</td>\n",
- " <td>ix_routes_index</td>\n",
- " <td>routes</td>\n",
- " <td>57</td>\n",
- " <td>CREATE INDEX \"ix_routes_index\"ON \"routes\" (\"in...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " type name tbl_name rootpage \\\n",
- "0 table boarding boarding 2 \n",
- "1 index ix_boarding_index boarding 3 \n",
- "2 table routes routes 55 \n",
- "3 index ix_routes_index routes 57 \n",
- "\n",
- " sql \n",
- "0 CREATE TABLE \"boarding\" (\\n\"index\" INTEGER,\\n ... \n",
- "1 CREATE INDEX \"ix_boarding_index\"ON \"boarding\" ... \n",
- "2 CREATE TABLE \"routes\" (\\n\"index\" INTEGER,\\n \"... \n",
- "3 CREATE INDEX \"ix_routes_index\"ON \"routes\" (\"in... "
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# This SQL query helps us know the table names, we don't use the other info \n",
- "df = pd.read_sql(\"select * from sqlite_master\", conn)\n",
- "df\n",
- "\n",
- "# Key observation: there are two tables: boarding and routes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Databases are more structured than CSV and JSON files:\n",
- "- all data contained inside one or more tables\n",
- "- all tables must be named, all columns must be named \n",
- "- all values in a column must be the same type"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CREATE TABLE \"boarding\" (\n",
- "\"index\" INTEGER,\n",
- " \"StopID\" INTEGER,\n",
- " \"Route\" INTEGER,\n",
- " \"Lat\" REAL,\n",
- " \"Lon\" REAL,\n",
- " \"DailyBoardings\" REAL\n",
- ")\n",
- "CREATE INDEX \"ix_boarding_index\"ON \"boarding\" (\"index\")\n",
- "CREATE TABLE \"routes\" (\n",
- "\"index\" INTEGER,\n",
- " \"OBJECTID\" INTEGER,\n",
- " \"trips_routes_route_id\" INTEGER,\n",
- " \"route_short_name\" INTEGER,\n",
- " \"route_url\" TEXT,\n",
- " \"ShapeSTLength\" REAL\n",
- ")\n",
- "CREATE INDEX \"ix_routes_index\"ON \"routes\" (\"index\")\n"
- ]
- }
- ],
- "source": [
- "# The SQL queries in sql column of the returned DataFrame show\n",
- "# how database was set up (not part of CS220).\n",
- "\n",
- "# Let's focus on the table names and column names\n",
- "for command in df[\"sql\"]:\n",
- " print(command)\n",
- " \n",
- "# Key observation: SQL has its own types (pandas takes care of the type conversions) \n",
- "# and the types are strictly enforced"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Most basic SQL query\n",
- "```\n",
- "SELECT <Column(s)> \n",
- "FROM <Table name>\n",
- "```\n",
- "- `SELECT` and `FROM` are mandatory clauses in a SQL query\n",
- "- Can use * to mean \"all columns\""
- ]
- },
- {
- "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>index</th>\n",
- " <th>OBJECTID</th>\n",
- " <th>trips_routes_route_id</th>\n",
- " <th>route_short_name</th>\n",
- " <th>route_url</th>\n",
- " <th>ShapeSTLength</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>63</td>\n",
- " <td>8052</td>\n",
- " <td>1</td>\n",
- " <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
- " <td>32379.426524</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1</td>\n",
- " <td>64</td>\n",
- " <td>8053</td>\n",
- " <td>2</td>\n",
- " <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
- " <td>96906.965571</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2</td>\n",
- " <td>65</td>\n",
- " <td>8054</td>\n",
- " <td>3</td>\n",
- " <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
- " <td>76436.645644</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>3</td>\n",
- " <td>66</td>\n",
- " <td>8055</td>\n",
- " <td>4</td>\n",
- " <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
- " <td>64774.133485</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>4</td>\n",
- " <td>67</td>\n",
- " <td>8056</td>\n",
- " <td>5</td>\n",
- " <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
- " <td>61216.722662</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>57</th>\n",
- " <td>57</td>\n",
- " <td>120</td>\n",
- " <td>8109</td>\n",
- " <td>78</td>\n",
- " <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
- " <td>95826.277218</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>58</th>\n",
- " <td>58</td>\n",
- " <td>121</td>\n",
- " <td>8110</td>\n",
- " <td>80</td>\n",
- " <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
- " <td>31831.761009</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>59</th>\n",
- " <td>59</td>\n",
- " <td>122</td>\n",
- " <td>8111</td>\n",
- " <td>81</td>\n",
- " <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
- " <td>26536.800591</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>60</th>\n",
- " <td>60</td>\n",
- " <td>123</td>\n",
- " <td>8112</td>\n",
- " <td>82</td>\n",
- " <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
- " <td>23287.980173</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>61</th>\n",
- " <td>61</td>\n",
- " <td>124</td>\n",
- " <td>8113</td>\n",
- " <td>84</td>\n",
- " <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
- " <td>20681.958334</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>62 rows × 6 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " index OBJECTID trips_routes_route_id route_short_name \\\n",
- "0 0 63 8052 1 \n",
- "1 1 64 8053 2 \n",
- "2 2 65 8054 3 \n",
- "3 3 66 8055 4 \n",
- "4 4 67 8056 5 \n",
- ".. ... ... ... ... \n",
- "57 57 120 8109 78 \n",
- "58 58 121 8110 80 \n",
- "59 59 122 8111 81 \n",
- "60 60 123 8112 82 \n",
- "61 61 124 8113 84 \n",
- "\n",
- " route_url ShapeSTLength \n",
- "0 http://www.cityofmadison.com/Metro/schedules/R... 32379.426524 \n",
- "1 http://www.cityofmadison.com/Metro/schedules/R... 96906.965571 \n",
- "2 http://www.cityofmadison.com/Metro/schedules/R... 76436.645644 \n",
- "3 http://www.cityofmadison.com/Metro/schedules/R... 64774.133485 \n",
- "4 http://www.cityofmadison.com/Metro/schedules/R... 61216.722662 \n",
- ".. ... ... \n",
- "57 http://www.cityofmadison.com/Metro/schedules/R... 95826.277218 \n",
- "58 http://www.cityofmadison.com/Metro/schedules/R... 31831.761009 \n",
- "59 http://www.cityofmadison.com/Metro/schedules/R... 26536.800591 \n",
- "60 http://www.cityofmadison.com/Metro/schedules/R... 23287.980173 \n",
- "61 http://www.cityofmadison.com/Metro/schedules/R... 20681.958334 \n",
- "\n",
- "[62 rows x 6 columns]"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# pandas continues to be an awesome tool\n",
- "# pandas allows us to write a SQL query and create a DataFrame\n",
- "pd.read_sql(\"select * from routes\", conn)"
- ]
- },
- {
- "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>index</th>\n",
- " <th>StopID</th>\n",
- " <th>Route</th>\n",
- " <th>Lat</th>\n",
- " <th>Lon</th>\n",
- " <th>DailyBoardings</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>0</td>\n",
- " <td>1163</td>\n",
- " <td>27</td>\n",
- " <td>43.073655</td>\n",
- " <td>-89.385427</td>\n",
- " <td>1.03</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1</td>\n",
- " <td>1163</td>\n",
- " <td>47</td>\n",
- " <td>43.073655</td>\n",
- " <td>-89.385427</td>\n",
- " <td>0.11</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>2</td>\n",
- " <td>1163</td>\n",
- " <td>75</td>\n",
- " <td>43.073655</td>\n",
- " <td>-89.385427</td>\n",
- " <td>0.34</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>3</td>\n",
- " <td>1164</td>\n",
- " <td>6</td>\n",
- " <td>43.106465</td>\n",
- " <td>-89.340021</td>\n",
- " <td>10.59</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>4</td>\n",
- " <td>1167</td>\n",
- " <td>3</td>\n",
- " <td>43.077867</td>\n",
- " <td>-89.369993</td>\n",
- " <td>3.11</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3967</th>\n",
- " <td>3967</td>\n",
- " <td>6533</td>\n",
- " <td>67</td>\n",
- " <td>43.057329</td>\n",
- " <td>-89.510756</td>\n",
- " <td>16.88</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3968</th>\n",
- " <td>3968</td>\n",
- " <td>6539</td>\n",
- " <td>15</td>\n",
- " <td>43.064361</td>\n",
- " <td>-89.517233</td>\n",
- " <td>15.53</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3969</th>\n",
- " <td>3969</td>\n",
- " <td>6541</td>\n",
- " <td>3</td>\n",
- " <td>43.049934</td>\n",
- " <td>-89.478167</td>\n",
- " <td>2.56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3970</th>\n",
- " <td>3970</td>\n",
- " <td>6543</td>\n",
- " <td>70</td>\n",
- " <td>43.093289</td>\n",
- " <td>-89.501726</td>\n",
- " <td>0.11</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3971</th>\n",
- " <td>3971</td>\n",
- " <td>6543</td>\n",
- " <td>71</td>\n",
- " <td>43.093289</td>\n",
- " <td>-89.501726</td>\n",
- " <td>6.73</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>3972 rows × 6 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " index StopID Route Lat Lon DailyBoardings\n",
- "0 0 1163 27 43.073655 -89.385427 1.03\n",
- "1 1 1163 47 43.073655 -89.385427 0.11\n",
- "2 2 1163 75 43.073655 -89.385427 0.34\n",
- "3 3 1164 6 43.106465 -89.340021 10.59\n",
- "4 4 1167 3 43.077867 -89.369993 3.11\n",
- "... ... ... ... ... ... ...\n",
- "3967 3967 6533 67 43.057329 -89.510756 16.88\n",
- "3968 3968 6539 15 43.064361 -89.517233 15.53\n",
- "3969 3969 6541 3 43.049934 -89.478167 2.56\n",
- "3970 3970 6543 70 43.093289 -89.501726 0.11\n",
- "3971 3971 6543 71 43.093289 -89.501726 6.73\n",
- "\n",
- "[3972 rows x 6 columns]"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# TODO: Now write a SQL query for displaying all columns from boarding table\n",
- "pd.read_sql(\"select * from boarding\", conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Optional SQL clauses\n",
- "- WHERE: filters rows based on a column condition\n",
- "- ORDER BY: sorting (`ASC` or `DESC` after the column name specify the ordering)\n",
- "- LIMIT: simplistic filter (similar to slicing / head/tail functions in pandas DataFrames)"
- ]
- },
- {
- "attachments": {
- "Screen%20Shot%202021-11-23%20at%201.43.54%20PM.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What are all the details of route 80 bus stops?"
- ]
- },
- {
- "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>index</th>\n",
- " <th>StopID</th>\n",
- " <th>Route</th>\n",
- " <th>Lat</th>\n",
- " <th>Lon</th>\n",
- " <th>DailyBoardings</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>732</td>\n",
- " <td>2007</td>\n",
- " <td>80</td>\n",
- " <td>43.076436</td>\n",
- " <td>-89.424388</td>\n",
- " <td>72.82</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>733</td>\n",
- " <td>2014</td>\n",
- " <td>80</td>\n",
- " <td>43.089239</td>\n",
- " <td>-89.433760</td>\n",
- " <td>99.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>735</td>\n",
- " <td>2018</td>\n",
- " <td>80</td>\n",
- " <td>43.086293</td>\n",
- " <td>-89.435043</td>\n",
- " <td>6.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>737</td>\n",
- " <td>2023</td>\n",
- " <td>80</td>\n",
- " <td>43.078800</td>\n",
- " <td>-89.429795</td>\n",
- " <td>100.05</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>738</td>\n",
- " <td>2026</td>\n",
- " <td>80</td>\n",
- " <td>43.086248</td>\n",
- " <td>-89.436661</td>\n",
- " <td>18.45</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>739</td>\n",
- " <td>2027</td>\n",
- " <td>80</td>\n",
- " <td>43.080259</td>\n",
- " <td>-89.428067</td>\n",
- " <td>4.34</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>740</td>\n",
- " <td>2034</td>\n",
- " <td>80</td>\n",
- " <td>43.086445</td>\n",
- " <td>-89.433772</td>\n",
- " <td>120.73</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>741</td>\n",
- " <td>2039</td>\n",
- " <td>80</td>\n",
- " <td>43.089158</td>\n",
- " <td>-89.438057</td>\n",
- " <td>86.27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>742</td>\n",
- " <td>2041</td>\n",
- " <td>80</td>\n",
- " <td>43.084252</td>\n",
- " <td>-89.433487</td>\n",
- " <td>1.56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>743</td>\n",
- " <td>2048</td>\n",
- " <td>80</td>\n",
- " <td>43.084386</td>\n",
- " <td>-89.433784</td>\n",
- " <td>83.38</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>744</td>\n",
- " <td>2050</td>\n",
- " <td>80</td>\n",
- " <td>43.080886</td>\n",
- " <td>-89.428351</td>\n",
- " <td>5.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>745</td>\n",
- " <td>2053</td>\n",
- " <td>80</td>\n",
- " <td>43.077045</td>\n",
- " <td>-89.424906</td>\n",
- " <td>3.78</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>746</td>\n",
- " <td>2054</td>\n",
- " <td>80</td>\n",
- " <td>43.086668</td>\n",
- " <td>-89.441612</td>\n",
- " <td>177.54</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>747</td>\n",
- " <td>2061</td>\n",
- " <td>80</td>\n",
- " <td>43.089784</td>\n",
- " <td>-89.437007</td>\n",
- " <td>57.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>749</td>\n",
- " <td>2071</td>\n",
- " <td>80</td>\n",
- " <td>43.090501</td>\n",
- " <td>-89.435587</td>\n",
- " <td>32.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>750</td>\n",
- " <td>2076</td>\n",
- " <td>80</td>\n",
- " <td>43.079006</td>\n",
- " <td>-89.429203</td>\n",
- " <td>41.69</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>751</td>\n",
- " <td>2082</td>\n",
- " <td>80</td>\n",
- " <td>43.086328</td>\n",
- " <td>-89.438587</td>\n",
- " <td>270.14</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>755</td>\n",
- " <td>2088</td>\n",
- " <td>80</td>\n",
- " <td>43.076759</td>\n",
- " <td>-89.425770</td>\n",
- " <td>4.56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>760</td>\n",
- " <td>2091</td>\n",
- " <td>80</td>\n",
- " <td>43.076973</td>\n",
- " <td>-89.428499</td>\n",
- " <td>248.24</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>790</td>\n",
- " <td>2125</td>\n",
- " <td>80</td>\n",
- " <td>43.077349</td>\n",
- " <td>-89.428844</td>\n",
- " <td>61.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>811</td>\n",
- " <td>2145</td>\n",
- " <td>80</td>\n",
- " <td>43.076319</td>\n",
- " <td>-89.412882</td>\n",
- " <td>321.06</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>865</td>\n",
- " <td>2195</td>\n",
- " <td>80</td>\n",
- " <td>43.076347</td>\n",
- " <td>-89.416104</td>\n",
- " <td>984.51</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>906</td>\n",
- " <td>2240</td>\n",
- " <td>80</td>\n",
- " <td>43.078988</td>\n",
- " <td>-89.426659</td>\n",
- " <td>0.67</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>923</td>\n",
- " <td>2267</td>\n",
- " <td>80</td>\n",
- " <td>43.076382</td>\n",
- " <td>-89.419943</td>\n",
- " <td>455.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>968</td>\n",
- " <td>2349</td>\n",
- " <td>80</td>\n",
- " <td>43.078388</td>\n",
- " <td>-89.430227</td>\n",
- " <td>561.96</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>1087</td>\n",
- " <td>5</td>\n",
- " <td>80</td>\n",
- " <td>43.070947</td>\n",
- " <td>-89.406982</td>\n",
- " <td>317.94</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>1088</td>\n",
- " <td>10</td>\n",
- " <td>80</td>\n",
- " <td>43.075933</td>\n",
- " <td>-89.400154</td>\n",
- " <td>750.61</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>1092</td>\n",
- " <td>39</td>\n",
- " <td>80</td>\n",
- " <td>43.071895</td>\n",
- " <td>-89.397341</td>\n",
- " <td>628.88</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>1095</td>\n",
- " <td>49</td>\n",
- " <td>80</td>\n",
- " <td>43.075529</td>\n",
- " <td>-89.397191</td>\n",
- " <td>690.92</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>1099</td>\n",
- " <td>52</td>\n",
- " <td>80</td>\n",
- " <td>43.076131</td>\n",
- " <td>-89.405660</td>\n",
- " <td>243.91</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>1104</td>\n",
- " <td>60</td>\n",
- " <td>80</td>\n",
- " <td>43.075996</td>\n",
- " <td>-89.403660</td>\n",
- " <td>160.42</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>1106</td>\n",
- " <td>61</td>\n",
- " <td>80</td>\n",
- " <td>43.070893</td>\n",
- " <td>-89.403698</td>\n",
- " <td>154.41</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>32</th>\n",
- " <td>1109</td>\n",
- " <td>73</td>\n",
- " <td>80</td>\n",
- " <td>43.070820</td>\n",
- " <td>-89.398650</td>\n",
- " <td>412.10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>33</th>\n",
- " <td>1110</td>\n",
- " <td>77</td>\n",
- " <td>80</td>\n",
- " <td>43.070857</td>\n",
- " <td>-89.401119</td>\n",
- " <td>143.07</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>34</th>\n",
- " <td>1245</td>\n",
- " <td>184</td>\n",
- " <td>80</td>\n",
- " <td>43.075254</td>\n",
- " <td>-89.410413</td>\n",
- " <td>237.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>1341</td>\n",
- " <td>298</td>\n",
- " <td>80</td>\n",
- " <td>43.075290</td>\n",
- " <td>-89.412894</td>\n",
- " <td>94.05</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>1351</td>\n",
- " <td>336</td>\n",
- " <td>80</td>\n",
- " <td>43.072028</td>\n",
- " <td>-89.409136</td>\n",
- " <td>528.28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>37</th>\n",
- " <td>1412</td>\n",
- " <td>438</td>\n",
- " <td>80</td>\n",
- " <td>43.074985</td>\n",
- " <td>-89.406401</td>\n",
- " <td>451.01</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>38</th>\n",
- " <td>1476</td>\n",
- " <td>488</td>\n",
- " <td>80</td>\n",
- " <td>43.075200</td>\n",
- " <td>-89.407339</td>\n",
- " <td>607.87</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>39</th>\n",
- " <td>1519</td>\n",
- " <td>532</td>\n",
- " <td>80</td>\n",
- " <td>43.075254</td>\n",
- " <td>-89.412549</td>\n",
- " <td>137.52</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>40</th>\n",
- " <td>1574</td>\n",
- " <td>573</td>\n",
- " <td>80</td>\n",
- " <td>43.075030</td>\n",
- " <td>-89.410339</td>\n",
- " <td>159.97</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>41</th>\n",
- " <td>1664</td>\n",
- " <td>706</td>\n",
- " <td>80</td>\n",
- " <td>43.073767</td>\n",
- " <td>-89.406352</td>\n",
- " <td>74.37</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>42</th>\n",
- " <td>1715</td>\n",
- " <td>765</td>\n",
- " <td>80</td>\n",
- " <td>43.073076</td>\n",
- " <td>-89.397291</td>\n",
- " <td>317.61</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>43</th>\n",
- " <td>3002</td>\n",
- " <td>2442</td>\n",
- " <td>80</td>\n",
- " <td>43.076588</td>\n",
- " <td>-89.419301</td>\n",
- " <td>91.27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>44</th>\n",
- " <td>3256</td>\n",
- " <td>2881</td>\n",
- " <td>80</td>\n",
- " <td>43.084225</td>\n",
- " <td>-89.429092</td>\n",
- " <td>12.78</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>3329</td>\n",
- " <td>2978</td>\n",
- " <td>80</td>\n",
- " <td>43.076561</td>\n",
- " <td>-89.416289</td>\n",
- " <td>88.71</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>46</th>\n",
- " <td>3341</td>\n",
- " <td>2996</td>\n",
- " <td>80</td>\n",
- " <td>43.076534</td>\n",
- " <td>-89.413067</td>\n",
- " <td>89.16</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " index StopID Route Lat Lon DailyBoardings\n",
- "0 732 2007 80 43.076436 -89.424388 72.82\n",
- "1 733 2014 80 43.089239 -89.433760 99.50\n",
- "2 735 2018 80 43.086293 -89.435043 6.23\n",
- "3 737 2023 80 43.078800 -89.429795 100.05\n",
- "4 738 2026 80 43.086248 -89.436661 18.45\n",
- "5 739 2027 80 43.080259 -89.428067 4.34\n",
- "6 740 2034 80 43.086445 -89.433772 120.73\n",
- "7 741 2039 80 43.089158 -89.438057 86.27\n",
- "8 742 2041 80 43.084252 -89.433487 1.56\n",
- "9 743 2048 80 43.084386 -89.433784 83.38\n",
- "10 744 2050 80 43.080886 -89.428351 5.00\n",
- "11 745 2053 80 43.077045 -89.424906 3.78\n",
- "12 746 2054 80 43.086668 -89.441612 177.54\n",
- "13 747 2061 80 43.089784 -89.437007 57.81\n",
- "14 749 2071 80 43.090501 -89.435587 32.02\n",
- "15 750 2076 80 43.079006 -89.429203 41.69\n",
- "16 751 2082 80 43.086328 -89.438587 270.14\n",
- "17 755 2088 80 43.076759 -89.425770 4.56\n",
- "18 760 2091 80 43.076973 -89.428499 248.24\n",
- "19 790 2125 80 43.077349 -89.428844 61.81\n",
- "20 811 2145 80 43.076319 -89.412882 321.06\n",
- "21 865 2195 80 43.076347 -89.416104 984.51\n",
- "22 906 2240 80 43.078988 -89.426659 0.67\n",
- "23 923 2267 80 43.076382 -89.419943 455.02\n",
- "24 968 2349 80 43.078388 -89.430227 561.96\n",
- "25 1087 5 80 43.070947 -89.406982 317.94\n",
- "26 1088 10 80 43.075933 -89.400154 750.61\n",
- "27 1092 39 80 43.071895 -89.397341 628.88\n",
- "28 1095 49 80 43.075529 -89.397191 690.92\n",
- "29 1099 52 80 43.076131 -89.405660 243.91\n",
- "30 1104 60 80 43.075996 -89.403660 160.42\n",
- "31 1106 61 80 43.070893 -89.403698 154.41\n",
- "32 1109 73 80 43.070820 -89.398650 412.10\n",
- "33 1110 77 80 43.070857 -89.401119 143.07\n",
- "34 1245 184 80 43.075254 -89.410413 237.79\n",
- "35 1341 298 80 43.075290 -89.412894 94.05\n",
- "36 1351 336 80 43.072028 -89.409136 528.28\n",
- "37 1412 438 80 43.074985 -89.406401 451.01\n",
- "38 1476 488 80 43.075200 -89.407339 607.87\n",
- "39 1519 532 80 43.075254 -89.412549 137.52\n",
- "40 1574 573 80 43.075030 -89.410339 159.97\n",
- "41 1664 706 80 43.073767 -89.406352 74.37\n",
- "42 1715 765 80 43.073076 -89.397291 317.61\n",
- "43 3002 2442 80 43.076588 -89.419301 91.27\n",
- "44 3256 2881 80 43.084225 -89.429092 12.78\n",
- "45 3329 2978 80 43.076561 -89.416289 88.71\n",
- "46 3341 2996 80 43.076534 -89.413067 89.16"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query = \"\"\"\n",
- "select *\n",
- "from boarding\n",
- "where Route = 80\n",
- "\"\"\"\n",
- "pd.read_sql(query, conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Sort the route 80 rows based on ascending order of DailyBoardings column."
- ]
- },
- {
- "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>index</th>\n",
- " <th>StopID</th>\n",
- " <th>Route</th>\n",
- " <th>Lat</th>\n",
- " <th>Lon</th>\n",
- " <th>DailyBoardings</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>906</td>\n",
- " <td>2240</td>\n",
- " <td>80</td>\n",
- " <td>43.078988</td>\n",
- " <td>-89.426659</td>\n",
- " <td>0.67</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>742</td>\n",
- " <td>2041</td>\n",
- " <td>80</td>\n",
- " <td>43.084252</td>\n",
- " <td>-89.433487</td>\n",
- " <td>1.56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>745</td>\n",
- " <td>2053</td>\n",
- " <td>80</td>\n",
- " <td>43.077045</td>\n",
- " <td>-89.424906</td>\n",
- " <td>3.78</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>739</td>\n",
- " <td>2027</td>\n",
- " <td>80</td>\n",
- " <td>43.080259</td>\n",
- " <td>-89.428067</td>\n",
- " <td>4.34</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>755</td>\n",
- " <td>2088</td>\n",
- " <td>80</td>\n",
- " <td>43.076759</td>\n",
- " <td>-89.425770</td>\n",
- " <td>4.56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>744</td>\n",
- " <td>2050</td>\n",
- " <td>80</td>\n",
- " <td>43.080886</td>\n",
- " <td>-89.428351</td>\n",
- " <td>5.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>735</td>\n",
- " <td>2018</td>\n",
- " <td>80</td>\n",
- " <td>43.086293</td>\n",
- " <td>-89.435043</td>\n",
- " <td>6.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>3256</td>\n",
- " <td>2881</td>\n",
- " <td>80</td>\n",
- " <td>43.084225</td>\n",
- " <td>-89.429092</td>\n",
- " <td>12.78</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>738</td>\n",
- " <td>2026</td>\n",
- " <td>80</td>\n",
- " <td>43.086248</td>\n",
- " <td>-89.436661</td>\n",
- " <td>18.45</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>749</td>\n",
- " <td>2071</td>\n",
- " <td>80</td>\n",
- " <td>43.090501</td>\n",
- " <td>-89.435587</td>\n",
- " <td>32.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>750</td>\n",
- " <td>2076</td>\n",
- " <td>80</td>\n",
- " <td>43.079006</td>\n",
- " <td>-89.429203</td>\n",
- " <td>41.69</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>747</td>\n",
- " <td>2061</td>\n",
- " <td>80</td>\n",
- " <td>43.089784</td>\n",
- " <td>-89.437007</td>\n",
- " <td>57.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>790</td>\n",
- " <td>2125</td>\n",
- " <td>80</td>\n",
- " <td>43.077349</td>\n",
- " <td>-89.428844</td>\n",
- " <td>61.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>732</td>\n",
- " <td>2007</td>\n",
- " <td>80</td>\n",
- " <td>43.076436</td>\n",
- " <td>-89.424388</td>\n",
- " <td>72.82</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>1664</td>\n",
- " <td>706</td>\n",
- " <td>80</td>\n",
- " <td>43.073767</td>\n",
- " <td>-89.406352</td>\n",
- " <td>74.37</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>743</td>\n",
- " <td>2048</td>\n",
- " <td>80</td>\n",
- " <td>43.084386</td>\n",
- " <td>-89.433784</td>\n",
- " <td>83.38</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>741</td>\n",
- " <td>2039</td>\n",
- " <td>80</td>\n",
- " <td>43.089158</td>\n",
- " <td>-89.438057</td>\n",
- " <td>86.27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>3329</td>\n",
- " <td>2978</td>\n",
- " <td>80</td>\n",
- " <td>43.076561</td>\n",
- " <td>-89.416289</td>\n",
- " <td>88.71</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>3341</td>\n",
- " <td>2996</td>\n",
- " <td>80</td>\n",
- " <td>43.076534</td>\n",
- " <td>-89.413067</td>\n",
- " <td>89.16</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>3002</td>\n",
- " <td>2442</td>\n",
- " <td>80</td>\n",
- " <td>43.076588</td>\n",
- " <td>-89.419301</td>\n",
- " <td>91.27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>1341</td>\n",
- " <td>298</td>\n",
- " <td>80</td>\n",
- " <td>43.075290</td>\n",
- " <td>-89.412894</td>\n",
- " <td>94.05</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>733</td>\n",
- " <td>2014</td>\n",
- " <td>80</td>\n",
- " <td>43.089239</td>\n",
- " <td>-89.433760</td>\n",
- " <td>99.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>737</td>\n",
- " <td>2023</td>\n",
- " <td>80</td>\n",
- " <td>43.078800</td>\n",
- " <td>-89.429795</td>\n",
- " <td>100.05</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>740</td>\n",
- " <td>2034</td>\n",
- " <td>80</td>\n",
- " <td>43.086445</td>\n",
- " <td>-89.433772</td>\n",
- " <td>120.73</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>1519</td>\n",
- " <td>532</td>\n",
- " <td>80</td>\n",
- " <td>43.075254</td>\n",
- " <td>-89.412549</td>\n",
- " <td>137.52</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>1110</td>\n",
- " <td>77</td>\n",
- " <td>80</td>\n",
- " <td>43.070857</td>\n",
- " <td>-89.401119</td>\n",
- " <td>143.07</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>1106</td>\n",
- " <td>61</td>\n",
- " <td>80</td>\n",
- " <td>43.070893</td>\n",
- " <td>-89.403698</td>\n",
- " <td>154.41</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>1574</td>\n",
- " <td>573</td>\n",
- " <td>80</td>\n",
- " <td>43.075030</td>\n",
- " <td>-89.410339</td>\n",
- " <td>159.97</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>1104</td>\n",
- " <td>60</td>\n",
- " <td>80</td>\n",
- " <td>43.075996</td>\n",
- " <td>-89.403660</td>\n",
- " <td>160.42</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>746</td>\n",
- " <td>2054</td>\n",
- " <td>80</td>\n",
- " <td>43.086668</td>\n",
- " <td>-89.441612</td>\n",
- " <td>177.54</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>1245</td>\n",
- " <td>184</td>\n",
- " <td>80</td>\n",
- " <td>43.075254</td>\n",
- " <td>-89.410413</td>\n",
- " <td>237.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>1099</td>\n",
- " <td>52</td>\n",
- " <td>80</td>\n",
- " <td>43.076131</td>\n",
- " <td>-89.405660</td>\n",
- " <td>243.91</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>32</th>\n",
- " <td>760</td>\n",
- " <td>2091</td>\n",
- " <td>80</td>\n",
- " <td>43.076973</td>\n",
- " <td>-89.428499</td>\n",
- " <td>248.24</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>33</th>\n",
- " <td>751</td>\n",
- " <td>2082</td>\n",
- " <td>80</td>\n",
- " <td>43.086328</td>\n",
- " <td>-89.438587</td>\n",
- " <td>270.14</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>34</th>\n",
- " <td>1715</td>\n",
- " <td>765</td>\n",
- " <td>80</td>\n",
- " <td>43.073076</td>\n",
- " <td>-89.397291</td>\n",
- " <td>317.61</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>1087</td>\n",
- " <td>5</td>\n",
- " <td>80</td>\n",
- " <td>43.070947</td>\n",
- " <td>-89.406982</td>\n",
- " <td>317.94</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>811</td>\n",
- " <td>2145</td>\n",
- " <td>80</td>\n",
- " <td>43.076319</td>\n",
- " <td>-89.412882</td>\n",
- " <td>321.06</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>37</th>\n",
- " <td>1109</td>\n",
- " <td>73</td>\n",
- " <td>80</td>\n",
- " <td>43.070820</td>\n",
- " <td>-89.398650</td>\n",
- " <td>412.10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>38</th>\n",
- " <td>1412</td>\n",
- " <td>438</td>\n",
- " <td>80</td>\n",
- " <td>43.074985</td>\n",
- " <td>-89.406401</td>\n",
- " <td>451.01</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>39</th>\n",
- " <td>923</td>\n",
- " <td>2267</td>\n",
- " <td>80</td>\n",
- " <td>43.076382</td>\n",
- " <td>-89.419943</td>\n",
- " <td>455.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>40</th>\n",
- " <td>1351</td>\n",
- " <td>336</td>\n",
- " <td>80</td>\n",
- " <td>43.072028</td>\n",
- " <td>-89.409136</td>\n",
- " <td>528.28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>41</th>\n",
- " <td>968</td>\n",
- " <td>2349</td>\n",
- " <td>80</td>\n",
- " <td>43.078388</td>\n",
- " <td>-89.430227</td>\n",
- " <td>561.96</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>42</th>\n",
- " <td>1476</td>\n",
- " <td>488</td>\n",
- " <td>80</td>\n",
- " <td>43.075200</td>\n",
- " <td>-89.407339</td>\n",
- " <td>607.87</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>43</th>\n",
- " <td>1092</td>\n",
- " <td>39</td>\n",
- " <td>80</td>\n",
- " <td>43.071895</td>\n",
- " <td>-89.397341</td>\n",
- " <td>628.88</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>44</th>\n",
- " <td>1095</td>\n",
- " <td>49</td>\n",
- " <td>80</td>\n",
- " <td>43.075529</td>\n",
- " <td>-89.397191</td>\n",
- " <td>690.92</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>1088</td>\n",
- " <td>10</td>\n",
- " <td>80</td>\n",
- " <td>43.075933</td>\n",
- " <td>-89.400154</td>\n",
- " <td>750.61</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>46</th>\n",
- " <td>865</td>\n",
- " <td>2195</td>\n",
- " <td>80</td>\n",
- " <td>43.076347</td>\n",
- " <td>-89.416104</td>\n",
- " <td>984.51</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " index StopID Route Lat Lon DailyBoardings\n",
- "0 906 2240 80 43.078988 -89.426659 0.67\n",
- "1 742 2041 80 43.084252 -89.433487 1.56\n",
- "2 745 2053 80 43.077045 -89.424906 3.78\n",
- "3 739 2027 80 43.080259 -89.428067 4.34\n",
- "4 755 2088 80 43.076759 -89.425770 4.56\n",
- "5 744 2050 80 43.080886 -89.428351 5.00\n",
- "6 735 2018 80 43.086293 -89.435043 6.23\n",
- "7 3256 2881 80 43.084225 -89.429092 12.78\n",
- "8 738 2026 80 43.086248 -89.436661 18.45\n",
- "9 749 2071 80 43.090501 -89.435587 32.02\n",
- "10 750 2076 80 43.079006 -89.429203 41.69\n",
- "11 747 2061 80 43.089784 -89.437007 57.81\n",
- "12 790 2125 80 43.077349 -89.428844 61.81\n",
- "13 732 2007 80 43.076436 -89.424388 72.82\n",
- "14 1664 706 80 43.073767 -89.406352 74.37\n",
- "15 743 2048 80 43.084386 -89.433784 83.38\n",
- "16 741 2039 80 43.089158 -89.438057 86.27\n",
- "17 3329 2978 80 43.076561 -89.416289 88.71\n",
- "18 3341 2996 80 43.076534 -89.413067 89.16\n",
- "19 3002 2442 80 43.076588 -89.419301 91.27\n",
- "20 1341 298 80 43.075290 -89.412894 94.05\n",
- "21 733 2014 80 43.089239 -89.433760 99.50\n",
- "22 737 2023 80 43.078800 -89.429795 100.05\n",
- "23 740 2034 80 43.086445 -89.433772 120.73\n",
- "24 1519 532 80 43.075254 -89.412549 137.52\n",
- "25 1110 77 80 43.070857 -89.401119 143.07\n",
- "26 1106 61 80 43.070893 -89.403698 154.41\n",
- "27 1574 573 80 43.075030 -89.410339 159.97\n",
- "28 1104 60 80 43.075996 -89.403660 160.42\n",
- "29 746 2054 80 43.086668 -89.441612 177.54\n",
- "30 1245 184 80 43.075254 -89.410413 237.79\n",
- "31 1099 52 80 43.076131 -89.405660 243.91\n",
- "32 760 2091 80 43.076973 -89.428499 248.24\n",
- "33 751 2082 80 43.086328 -89.438587 270.14\n",
- "34 1715 765 80 43.073076 -89.397291 317.61\n",
- "35 1087 5 80 43.070947 -89.406982 317.94\n",
- "36 811 2145 80 43.076319 -89.412882 321.06\n",
- "37 1109 73 80 43.070820 -89.398650 412.10\n",
- "38 1412 438 80 43.074985 -89.406401 451.01\n",
- "39 923 2267 80 43.076382 -89.419943 455.02\n",
- "40 1351 336 80 43.072028 -89.409136 528.28\n",
- "41 968 2349 80 43.078388 -89.430227 561.96\n",
- "42 1476 488 80 43.075200 -89.407339 607.87\n",
- "43 1092 39 80 43.071895 -89.397341 628.88\n",
- "44 1095 49 80 43.075529 -89.397191 690.92\n",
- "45 1088 10 80 43.075933 -89.400154 750.61\n",
- "46 865 2195 80 43.076347 -89.416104 984.51"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query = \"\"\"\n",
- "select *\n",
- "from boarding\n",
- "where Route = 80\n",
- "order by DailyBoardings ASC\n",
- "\"\"\"\n",
- "pd.read_sql(query, conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Sort the route 80 rows based on descending order of DailyBoardings column."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "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>index</th>\n",
- " <th>StopID</th>\n",
- " <th>Route</th>\n",
- " <th>Lat</th>\n",
- " <th>Lon</th>\n",
- " <th>DailyBoardings</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>865</td>\n",
- " <td>2195</td>\n",
- " <td>80</td>\n",
- " <td>43.076347</td>\n",
- " <td>-89.416104</td>\n",
- " <td>984.51</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>1088</td>\n",
- " <td>10</td>\n",
- " <td>80</td>\n",
- " <td>43.075933</td>\n",
- " <td>-89.400154</td>\n",
- " <td>750.61</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>1095</td>\n",
- " <td>49</td>\n",
- " <td>80</td>\n",
- " <td>43.075529</td>\n",
- " <td>-89.397191</td>\n",
- " <td>690.92</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>1092</td>\n",
- " <td>39</td>\n",
- " <td>80</td>\n",
- " <td>43.071895</td>\n",
- " <td>-89.397341</td>\n",
- " <td>628.88</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>1476</td>\n",
- " <td>488</td>\n",
- " <td>80</td>\n",
- " <td>43.075200</td>\n",
- " <td>-89.407339</td>\n",
- " <td>607.87</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>968</td>\n",
- " <td>2349</td>\n",
- " <td>80</td>\n",
- " <td>43.078388</td>\n",
- " <td>-89.430227</td>\n",
- " <td>561.96</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>1351</td>\n",
- " <td>336</td>\n",
- " <td>80</td>\n",
- " <td>43.072028</td>\n",
- " <td>-89.409136</td>\n",
- " <td>528.28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>923</td>\n",
- " <td>2267</td>\n",
- " <td>80</td>\n",
- " <td>43.076382</td>\n",
- " <td>-89.419943</td>\n",
- " <td>455.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>1412</td>\n",
- " <td>438</td>\n",
- " <td>80</td>\n",
- " <td>43.074985</td>\n",
- " <td>-89.406401</td>\n",
- " <td>451.01</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>1109</td>\n",
- " <td>73</td>\n",
- " <td>80</td>\n",
- " <td>43.070820</td>\n",
- " <td>-89.398650</td>\n",
- " <td>412.10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>811</td>\n",
- " <td>2145</td>\n",
- " <td>80</td>\n",
- " <td>43.076319</td>\n",
- " <td>-89.412882</td>\n",
- " <td>321.06</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>1087</td>\n",
- " <td>5</td>\n",
- " <td>80</td>\n",
- " <td>43.070947</td>\n",
- " <td>-89.406982</td>\n",
- " <td>317.94</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>1715</td>\n",
- " <td>765</td>\n",
- " <td>80</td>\n",
- " <td>43.073076</td>\n",
- " <td>-89.397291</td>\n",
- " <td>317.61</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>751</td>\n",
- " <td>2082</td>\n",
- " <td>80</td>\n",
- " <td>43.086328</td>\n",
- " <td>-89.438587</td>\n",
- " <td>270.14</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>760</td>\n",
- " <td>2091</td>\n",
- " <td>80</td>\n",
- " <td>43.076973</td>\n",
- " <td>-89.428499</td>\n",
- " <td>248.24</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>1099</td>\n",
- " <td>52</td>\n",
- " <td>80</td>\n",
- " <td>43.076131</td>\n",
- " <td>-89.405660</td>\n",
- " <td>243.91</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>1245</td>\n",
- " <td>184</td>\n",
- " <td>80</td>\n",
- " <td>43.075254</td>\n",
- " <td>-89.410413</td>\n",
- " <td>237.79</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>746</td>\n",
- " <td>2054</td>\n",
- " <td>80</td>\n",
- " <td>43.086668</td>\n",
- " <td>-89.441612</td>\n",
- " <td>177.54</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>1104</td>\n",
- " <td>60</td>\n",
- " <td>80</td>\n",
- " <td>43.075996</td>\n",
- " <td>-89.403660</td>\n",
- " <td>160.42</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>1574</td>\n",
- " <td>573</td>\n",
- " <td>80</td>\n",
- " <td>43.075030</td>\n",
- " <td>-89.410339</td>\n",
- " <td>159.97</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>1106</td>\n",
- " <td>61</td>\n",
- " <td>80</td>\n",
- " <td>43.070893</td>\n",
- " <td>-89.403698</td>\n",
- " <td>154.41</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>1110</td>\n",
- " <td>77</td>\n",
- " <td>80</td>\n",
- " <td>43.070857</td>\n",
- " <td>-89.401119</td>\n",
- " <td>143.07</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>1519</td>\n",
- " <td>532</td>\n",
- " <td>80</td>\n",
- " <td>43.075254</td>\n",
- " <td>-89.412549</td>\n",
- " <td>137.52</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>740</td>\n",
- " <td>2034</td>\n",
- " <td>80</td>\n",
- " <td>43.086445</td>\n",
- " <td>-89.433772</td>\n",
- " <td>120.73</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>737</td>\n",
- " <td>2023</td>\n",
- " <td>80</td>\n",
- " <td>43.078800</td>\n",
- " <td>-89.429795</td>\n",
- " <td>100.05</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>733</td>\n",
- " <td>2014</td>\n",
- " <td>80</td>\n",
- " <td>43.089239</td>\n",
- " <td>-89.433760</td>\n",
- " <td>99.50</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>1341</td>\n",
- " <td>298</td>\n",
- " <td>80</td>\n",
- " <td>43.075290</td>\n",
- " <td>-89.412894</td>\n",
- " <td>94.05</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>3002</td>\n",
- " <td>2442</td>\n",
- " <td>80</td>\n",
- " <td>43.076588</td>\n",
- " <td>-89.419301</td>\n",
- " <td>91.27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>3341</td>\n",
- " <td>2996</td>\n",
- " <td>80</td>\n",
- " <td>43.076534</td>\n",
- " <td>-89.413067</td>\n",
- " <td>89.16</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>3329</td>\n",
- " <td>2978</td>\n",
- " <td>80</td>\n",
- " <td>43.076561</td>\n",
- " <td>-89.416289</td>\n",
- " <td>88.71</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>30</th>\n",
- " <td>741</td>\n",
- " <td>2039</td>\n",
- " <td>80</td>\n",
- " <td>43.089158</td>\n",
- " <td>-89.438057</td>\n",
- " <td>86.27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>743</td>\n",
- " <td>2048</td>\n",
- " <td>80</td>\n",
- " <td>43.084386</td>\n",
- " <td>-89.433784</td>\n",
- " <td>83.38</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>32</th>\n",
- " <td>1664</td>\n",
- " <td>706</td>\n",
- " <td>80</td>\n",
- " <td>43.073767</td>\n",
- " <td>-89.406352</td>\n",
- " <td>74.37</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>33</th>\n",
- " <td>732</td>\n",
- " <td>2007</td>\n",
- " <td>80</td>\n",
- " <td>43.076436</td>\n",
- " <td>-89.424388</td>\n",
- " <td>72.82</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>34</th>\n",
- " <td>790</td>\n",
- " <td>2125</td>\n",
- " <td>80</td>\n",
- " <td>43.077349</td>\n",
- " <td>-89.428844</td>\n",
- " <td>61.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>747</td>\n",
- " <td>2061</td>\n",
- " <td>80</td>\n",
- " <td>43.089784</td>\n",
- " <td>-89.437007</td>\n",
- " <td>57.81</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>750</td>\n",
- " <td>2076</td>\n",
- " <td>80</td>\n",
- " <td>43.079006</td>\n",
- " <td>-89.429203</td>\n",
- " <td>41.69</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>37</th>\n",
- " <td>749</td>\n",
- " <td>2071</td>\n",
- " <td>80</td>\n",
- " <td>43.090501</td>\n",
- " <td>-89.435587</td>\n",
- " <td>32.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>38</th>\n",
- " <td>738</td>\n",
- " <td>2026</td>\n",
- " <td>80</td>\n",
- " <td>43.086248</td>\n",
- " <td>-89.436661</td>\n",
- " <td>18.45</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>39</th>\n",
- " <td>3256</td>\n",
- " <td>2881</td>\n",
- " <td>80</td>\n",
- " <td>43.084225</td>\n",
- " <td>-89.429092</td>\n",
- " <td>12.78</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>40</th>\n",
- " <td>735</td>\n",
- " <td>2018</td>\n",
- " <td>80</td>\n",
- " <td>43.086293</td>\n",
- " <td>-89.435043</td>\n",
- " <td>6.23</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>41</th>\n",
- " <td>744</td>\n",
- " <td>2050</td>\n",
- " <td>80</td>\n",
- " <td>43.080886</td>\n",
- " <td>-89.428351</td>\n",
- " <td>5.00</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>42</th>\n",
- " <td>755</td>\n",
- " <td>2088</td>\n",
- " <td>80</td>\n",
- " <td>43.076759</td>\n",
- " <td>-89.425770</td>\n",
- " <td>4.56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>43</th>\n",
- " <td>739</td>\n",
- " <td>2027</td>\n",
- " <td>80</td>\n",
- " <td>43.080259</td>\n",
- " <td>-89.428067</td>\n",
- " <td>4.34</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>44</th>\n",
- " <td>745</td>\n",
- " <td>2053</td>\n",
- " <td>80</td>\n",
- " <td>43.077045</td>\n",
- " <td>-89.424906</td>\n",
- " <td>3.78</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>742</td>\n",
- " <td>2041</td>\n",
- " <td>80</td>\n",
- " <td>43.084252</td>\n",
- " <td>-89.433487</td>\n",
- " <td>1.56</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>46</th>\n",
- " <td>906</td>\n",
- " <td>2240</td>\n",
- " <td>80</td>\n",
- " <td>43.078988</td>\n",
- " <td>-89.426659</td>\n",
- " <td>0.67</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " index StopID Route Lat Lon DailyBoardings\n",
- "0 865 2195 80 43.076347 -89.416104 984.51\n",
- "1 1088 10 80 43.075933 -89.400154 750.61\n",
- "2 1095 49 80 43.075529 -89.397191 690.92\n",
- "3 1092 39 80 43.071895 -89.397341 628.88\n",
- "4 1476 488 80 43.075200 -89.407339 607.87\n",
- "5 968 2349 80 43.078388 -89.430227 561.96\n",
- "6 1351 336 80 43.072028 -89.409136 528.28\n",
- "7 923 2267 80 43.076382 -89.419943 455.02\n",
- "8 1412 438 80 43.074985 -89.406401 451.01\n",
- "9 1109 73 80 43.070820 -89.398650 412.10\n",
- "10 811 2145 80 43.076319 -89.412882 321.06\n",
- "11 1087 5 80 43.070947 -89.406982 317.94\n",
- "12 1715 765 80 43.073076 -89.397291 317.61\n",
- "13 751 2082 80 43.086328 -89.438587 270.14\n",
- "14 760 2091 80 43.076973 -89.428499 248.24\n",
- "15 1099 52 80 43.076131 -89.405660 243.91\n",
- "16 1245 184 80 43.075254 -89.410413 237.79\n",
- "17 746 2054 80 43.086668 -89.441612 177.54\n",
- "18 1104 60 80 43.075996 -89.403660 160.42\n",
- "19 1574 573 80 43.075030 -89.410339 159.97\n",
- "20 1106 61 80 43.070893 -89.403698 154.41\n",
- "21 1110 77 80 43.070857 -89.401119 143.07\n",
- "22 1519 532 80 43.075254 -89.412549 137.52\n",
- "23 740 2034 80 43.086445 -89.433772 120.73\n",
- "24 737 2023 80 43.078800 -89.429795 100.05\n",
- "25 733 2014 80 43.089239 -89.433760 99.50\n",
- "26 1341 298 80 43.075290 -89.412894 94.05\n",
- "27 3002 2442 80 43.076588 -89.419301 91.27\n",
- "28 3341 2996 80 43.076534 -89.413067 89.16\n",
- "29 3329 2978 80 43.076561 -89.416289 88.71\n",
- "30 741 2039 80 43.089158 -89.438057 86.27\n",
- "31 743 2048 80 43.084386 -89.433784 83.38\n",
- "32 1664 706 80 43.073767 -89.406352 74.37\n",
- "33 732 2007 80 43.076436 -89.424388 72.82\n",
- "34 790 2125 80 43.077349 -89.428844 61.81\n",
- "35 747 2061 80 43.089784 -89.437007 57.81\n",
- "36 750 2076 80 43.079006 -89.429203 41.69\n",
- "37 749 2071 80 43.090501 -89.435587 32.02\n",
- "38 738 2026 80 43.086248 -89.436661 18.45\n",
- "39 3256 2881 80 43.084225 -89.429092 12.78\n",
- "40 735 2018 80 43.086293 -89.435043 6.23\n",
- "41 744 2050 80 43.080886 -89.428351 5.00\n",
- "42 755 2088 80 43.076759 -89.425770 4.56\n",
- "43 739 2027 80 43.080259 -89.428067 4.34\n",
- "44 745 2053 80 43.077045 -89.424906 3.78\n",
- "45 742 2041 80 43.084252 -89.433487 1.56\n",
- "46 906 2240 80 43.078988 -89.426659 0.67"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query = \"\"\"\n",
- "select *\n",
- "from boarding\n",
- "where Route = 80\n",
- "order by DailyBoardings DESC\n",
- "\"\"\"\n",
- "pd.read_sql(query, conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Which 10 bus stops have the lowest DailyBoardings and for what bus?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "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>Route</th>\n",
- " <th>DailyBoardings</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>80</td>\n",
- " <td>984.51</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>80</td>\n",
- " <td>750.61</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>80</td>\n",
- " <td>690.92</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>80</td>\n",
- " <td>628.88</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>80</td>\n",
- " <td>607.87</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>80</td>\n",
- " <td>561.96</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>80</td>\n",
- " <td>528.28</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>80</td>\n",
- " <td>455.02</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>80</td>\n",
- " <td>451.01</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>80</td>\n",
- " <td>412.10</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Route DailyBoardings\n",
- "0 80 984.51\n",
- "1 80 750.61\n",
- "2 80 690.92\n",
- "3 80 628.88\n",
- "4 80 607.87\n",
- "5 80 561.96\n",
- "6 80 528.28\n",
- "7 80 455.02\n",
- "8 80 451.01\n",
- "9 80 412.10"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query = \"\"\"\n",
- "SELECT Route, DailyBoardings \n",
- "FROM boarding \n",
- "ORDER BY DailyBoardings DESC\n",
- "LIMIT 10\n",
- "\"\"\"\n",
- "pd.read_sql(query, conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What are the top 3 stops (based on DailyBoardings) of route 3?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "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>StopID</th>\n",
- " <th>DailyBoardings</th>\n",
- " <th>Route</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>581</td>\n",
- " <td>109.95</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>7100</td>\n",
- " <td>109.51</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>670</td>\n",
- " <td>103.17</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " StopID DailyBoardings Route\n",
- "0 581 109.95 3\n",
- "1 7100 109.51 3\n",
- "2 670 103.17 3"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query = \"\"\"\n",
- "SELECT StopID, DailyBoardings, Route \n",
- "FROM boarding \n",
- "WHERE route = 3 \n",
- "ORDER BY DailyBoardings DESC\n",
- "LIMIT 3\n",
- "\"\"\"\n",
- "pd.read_sql(query, conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Go West - which bus should I take to go as far west as possible?\n",
- "- Smallest Longitude"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "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>index</th>\n",
- " <th>StopID</th>\n",
- " <th>Route</th>\n",
- " <th>Lat</th>\n",
- " <th>Lon</th>\n",
- " <th>DailyBoardings</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>3489</td>\n",
- " <td>4400</td>\n",
- " <td>55</td>\n",
- " <td>42.995476</td>\n",
- " <td>-89.564243</td>\n",
- " <td>59.31</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " index StopID Route Lat Lon DailyBoardings\n",
- "0 3489 4400 55 42.995476 -89.564243 59.31"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "qry = \"\"\"\n",
- "SELECT *\n",
- "FROM boarding\n",
- "ORDER BY Lon ASC\n",
- "LIMIT 1\n",
- "\"\"\"\n",
- "pd.read_sql(qry, conn)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: make a tuple out of this lat-long and enter that tuple into Google Maps\n",
- "# TODO: Where is this location?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How many people get on a bus in Madison every day?\n",
- "- we are interested in boarding table to answer this question"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "55987.18"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Answer using pandas\n",
- "qry = \"\"\"\n",
- "SELECT DailyBoardings \n",
- "FROM boarding\n",
- "\"\"\"\n",
- "df = pd.read_sql(qry, conn)\n",
- "bus_riders = df[\"DailyBoardings\"]\n",
- "bus_riders.sum()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "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>SUM(DailyBoardings)</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>55987.18</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " SUM(DailyBoardings)\n",
- "0 55987.18"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Next lecture, we'll learn all about SQL summarization\n",
- "\n",
- "#Using SQL summarization\n",
- "qry = \"\"\"\n",
- "SELECT SUM(DailyBoardings)\n",
- "FROM boarding\n",
- "\"\"\"\n",
- "pd.read_sql(qry, conn)"
- ]
- },
- {
- "attachments": {
- "Screen%20Shot%202021-11-23%20at%201.47.20%20PM.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [],
- "source": [
- "# don't forget to close your connection!\n",
- "conn.close()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/lec_32_database1_template-checkpoint.ipynb b/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/lec_32_database1_template-checkpoint.ipynb
deleted file mode 100644
index b2c0cd1..0000000
--- a/f22/meena_lec_notes/lec-32/.ipynb_checkpoints/lec_32_database1_template-checkpoint.ipynb
+++ /dev/null
@@ -1,542 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# known import statements\n",
- "from bs4 import BeautifulSoup\n",
- "import os\n",
- "import pandas as pd\n",
- "\n",
- "# let's import sqlite3 module\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Warmup 1: Explore this HTML table of volunteer hours"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<table>\n",
- " <tr> \n",
- " <th>Name</th>\n",
- " <th>Week 1</th>\n",
- " <th>Week 2</th\n",
- " ><th>Week 3</th> \n",
- " </tr>\n",
- " <tr> \n",
- " <td>Therese</td>\n",
- " <td>13</td>\n",
- " <td>4</td>\n",
- " <td>5</td> \n",
- " </tr>\n",
- " <tr> \n",
- " <td>Carl</td>\n",
- " <td>5</td>\n",
- " <td>7</td>\n",
- " <td>8</td> \n",
- " </tr>\n",
- " <tr> \n",
- " <td>Marie</td>\n",
- " <td>2</td>\n",
- " <td>9</td>\n",
- " <td>11</td> \n",
- " </tr>\n",
- "</table>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Warmup 2a: Parse \"hours.html\" using BeautifulSoup\n",
- "\n",
- "#### Step 1: Read contents from \"hours.html\" file"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Step 2: Create a BeautifulSoup object instance"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Step 3: Parse the table"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Use find method to find the table\n",
- "# works only if there is 1 table\n",
- "\n",
- "\n",
- "# Q: what method do you need if the HTML has more than 1 table? \n",
- "# A: "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Step 4: Parse the header\n",
- "- Bonus: Use list comprehension "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Step 5: Parse the data rows and store data into a list of dict\n",
- "- Remember that you need to skip over the first tr (which contains the header)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Find all tr elements\n",
- "tr_elements = ???\n",
- "\n",
- "# Skip first tr row (header row)\n",
- "tr_elements = ???\n",
- "\n",
- "# Initialize empty list\n",
- "work_hours = []\n",
- "\n",
- "# Iterate through the tr elements\n",
- "\n",
- " # Find all \"td\" elements in this row\n",
- " \n",
- " \n",
- " # Create row dictionary\n",
- " row_dict = {} # Key: column name (header); Value: cell's value\n",
- " \n",
- " # Iterate over indices of td elements\n",
- " # Assumes that td_elements and header have same length\n",
- " \n",
- " # Extract the td text\n",
- "\n",
- " \n",
- " # Make appropriate type conversions\n",
- " # Use header instead of hardcoing index\n",
- " \n",
- " \n",
- " # Insert key-value pairs \n",
- " \n",
- " \n",
- " # Append row dictionary into list\n",
- " \n",
- " \n",
- "work_hours"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Warmup 3: Use appropriate os module to assert that bus.db in this directory"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## April 20: DataBase1\n",
- "\n",
- "### Learning Objectives:\n",
- "\n",
- "- Explain how a database is different from a CSV file or a JSON file\n",
- "- Use SQLite to connect to a database and pandas to query the database\n",
- "- Write basic queries on a database using SELECT, FROM, WHERE, ORDER BY, and LIMIT\n",
- "\n",
- "We will get started with slides."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Get the Bus data from 'bus.db'\n",
- "db_name = \"bus.db\"\n",
- "assert os.path.exists(db_name)\n",
- "# Why do we have to assert that database exists?\n",
- "# If the database file does not exist, connect function creates a brand new one!\n",
- "\n",
- "# open a connection object to our database file\n",
- "\n",
- "# Important note: we need to close 'conn' when we are done, at the end of the notebook file\n",
- "type(conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Pandas has a .read_sql function `pd.read_sql(query, connection)`\n",
- "- Allows us to process an SQL `query` on a SQL `connection`\n",
- "- stores the result in a Pandas DataFrame\n",
- "- First SQL query to always run on a database:\n",
- "```\n",
- "select * from sqlite_master\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# This SQL query helps us know the table names, we don't use the other info \n",
- "\n",
- "\n",
- "# Key observation: there are two tables: boarding and routes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Databases are more structured than CSV and JSON files:\n",
- "- all data contained inside one or more tables\n",
- "- all tables must be named, all columns must be named \n",
- "- all values in a column must be the same type"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Extract the \"sql\" column from df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# The SQL queries in sql column of the returned DataFrame show\n",
- "# how database was set up (not part of CS220).\n",
- "\n",
- "# Let's focus on the table names and column names\n",
- "\n",
- " \n",
- "# Key observation: SQL has its own types (pandas takes care of the type conversions) \n",
- "# and the types are strictly enforced"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Most basic SQL query\n",
- "```\n",
- "SELECT <Column(s)> \n",
- "FROM <Table name>\n",
- "```\n",
- "- `SELECT` and `FROM` are mandatory clauses in a SQL query\n",
- "- Can use * to mean \"all columns\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# pandas continues to be an awesome tool\n",
- "# pandas allows us to write a SQL query and create a DataFrame\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: Now write a SQL query for displaying all columns from boarding table\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Optional SQL clauses\n",
- "- WHERE: filters rows based on a column condition\n",
- "- ORDER BY: sorting (`ASC` or `DESC` after the column name specify the ordering)\n",
- "- LIMIT: simplistic filter (similar to slicing / head/tail functions in pandas DataFrames)"
- ]
- },
- {
- "attachments": {
- "Screen%20Shot%202021-11-23%20at%201.43.54%20PM.png": {
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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What are all the details of route 80 bus stops?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "query = \"\"\"\n",
- "\n",
- "\"\"\"\n",
- "pd.read_sql(query, conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Sort the route 80 rows based on ascending order of DailyBoardings column."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "query = \"\"\"\n",
- "\n",
- "\"\"\"\n",
- "pd.read_sql(query, conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Sort the route 80 rows based on descending order of DailyBoardings column."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "query = \"\"\"\n",
- "\n",
- "\"\"\"\n",
- "pd.read_sql(query, conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Which 10 bus stops have the lowest DailyBoardings and for what bus?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "query = \"\"\"\n",
- "\n",
- "\"\"\"\n",
- "pd.read_sql(query, conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### What are the top 3 stops (based on DailyBoardings) of route 3?"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "query = \"\"\"\n",
- "\n",
- "\"\"\"\n",
- "pd.read_sql(query, conn)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Go West - which bus should I take to go as far west as possible?\n",
- "- Smallest Longitude"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "qry = \"\"\"\n",
- "\n",
- "\"\"\"\n",
- "pd.read_sql(qry, conn)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# TODO: make a tuple out of this lat-long and enter that tuple into Google Maps\n",
- "# TODO: Where is this location?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How many people get on a bus in Madison every day?\n",
- "- we are interested in boarding table to answer this question"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Answer using pandas\n",
- "qry = \"\"\"\n",
- "\n",
- "\"\"\"\n",
- "df = pd.read_sql(qry, conn)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Next lecture, we'll learn all about SQL summarization\n",
- "\n",
- "#Using SQL summarization\n",
- "qry = \"\"\"\n",
- "\n",
- "\"\"\"\n",
- "pd.read_sql(qry, conn)"
- ]
- },
- {
- "attachments": {
- "Screen%20Shot%202021-11-23%20at%201.47.20%20PM.png": {
- "image/png": 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i2aXVqF2uk6sbr+ejb2GgYMhphDB6b/zM5Ippvlm0uZOFiiWb5YrVG+t7NrW2+XZJ9oEOjo76TvLOAi4kV6zrsttH9xGPbs87XvU7z3of23XIJ9mX6kch+/p7BDgF2gfZBFtSLEPMQjXDZMMFIlh200QiIlejflB/Rq/FouOI8UIJGnucEqOTCpNb9k6l0OznS5VJ0z5gk+53MD7j8KHKzLas4ezvucyHFY7Y50UcPZxfV/Do2Lvj64WcRcrFdidDSw6WnjnVfLq3bObMr3PM5RLntSvsLpArY6sOVRfD+1x33dwl4mXFescrUVfzrtU1dF4fufGlCdPMcVPylsZtixa3O4GtMW0pd1PbD3QcvJfReeh+5oPsh7mPDncdfnz4yeHu3Kc5z7J6DvWm96X2730e91fUwO7ByBcxQ0kvDw4fe1U+0vD6wejLN5/GwVvihOA7+UmdKfNpv5mz7z99VJ5N+tT6+de85kLcl8tf3y2yL1l+T/nR8HN6hXvVYS37V+f2/Bsj9JE7kJ9R7ehDGEesOHYBdxOfQXCg4aYZpT1PF06vzoBgaGdMZ7IgMhB7SUeZbVkYWJ6yZrOZsEPszRwRnEKcQ1w53Drcn3hKec14v/GV8ZvxfxY4LqghOCK0V5hfuFXEW2RVtFhMSaxbPEB8VeKYpJRkm5Sj1JR0qoyIzJBs7g6DHd/kquQ9FegU2hQjlQSU+pXTVBRUxlXz1LTVPqmXaphrLGqe17LX+qVdp+Oui9W9oUfWJ+rfNYg05DfsNUozVjKeNikxtYXvHbfNoyykLN5Zlll5WLNaP7cpsHWwI9kN2p908HYUdvzgdNU50cXYlcF12K3SPdrDwJPWc9DrzM5gbwXvlV33ffJ9vfwk/JbInf7HAnwDFYNQQYPBtZSUEKdQ6TB02JvwWxHFu+MjXaM0qLzRqOjZmIHY9rj6+LKEvD2pifFJocn+e3fuc0tx2u+Qap9mf8Ah3emge8bOQwGZoVnR2Sk5mbkFh8uO1OQ1Hr2X31cweuzzCVShRJFX8dGT90tWTsme9is7cebx2dVyhfMBFSUXeqpQ1Vo18bX1dR8vSV4Oqa+9MndNpWH/9e5Gjqaw5s5bfLdTWt62WrW1tMt3XOyUun/1ocGj4ccJ3XxPe3sO9zk9Fx0Agx+H3g1/eA3eiIzvmqidQs8kfgSfKubJX3WX1H46rxZvzP/W976NglEB4MQhADa+5zjUAJB7EQCxPQCwwbmnHR0ATuoAIWAKoMUOAFlo/zk/IDjxJAAS4AGSQA2YwfllGJxTFoF60AUmwBrEDilDjlA0dAJqhl7DOZ80wgWRiqhHjCIZkAbIeOQl5BScpXmhSlGv4EzMB30B/QmjgknFPMPyYMOxbTgSjoJrx3PiY/H9BEVCEWGVhkzzlFadtpqOnS6XHkGfRP+dIZZhiTGRCWLKJrISK0hqpF7mEBYsywVWY9Yptkx2KfZejhhODs5WLn9uGu5rPB68SN5LfJ5wRtAnkCdoK8Qk9Fy4SMRTVEB0SuyieLSEhiQk2SWVL+0JR+e8bP+OFrlK+QKFfYoUJUdlDRU+VUh1TK1F/bhGiKa2Fr3WiHaNToyurh5Or0+/weC6YZPRLeNWk3umXWY95oMWo5bTVgvWK7Y4O1Z7UQc1RysnsnOyS7Frq9uMB8lT3ytyZ7n3gA/BV98vidzs/z1QLSgpuD2EEOocVhG+uNsssixqLlorJid2NF4p4eiehSTX5Af7tFNaUy3TJtKzMrQzQVZfzpXDp/IK8s2OIY/fL8wvDigxPCVdJnhWpFypwqYyqrq09sklUK961abB/UZwU/LNE7ev3elvW+rg7TR7EPPo7ONn3Ws9Mn07nx8ZuDtEGiaPXBqdHeeeUJvUm5Z/T//hxeyRzzvm2hfMvnR+U1gsWVr+Yf/zwvLCqsZayq+7m/vH1vwT4fmXAKrABLiCELAPHAd1oBOMgh8QCZKDbKAI6CjUAL1EAIQEnOWnIa4i3sJ5vBUyHdmGXEFpow6gutHs6EB0IwaP8cY0YhmxYdgnOGlcHm4R74V/QJAlFNEgaaJoxmmdaR/TGdK10mvR34Gz2EeM9oyjcJ66TjxOkiU9ZY6AM89mVl82GrZm9kAOVo6HnHu4pLnGuYt4bHlxvB18+/kNBDACTwULhXyFZYVXRbpFy8SixI0luCS+SD6WOi+dIuMpq7lDQo5dHi+/qjCnOKH0Qvmxym3Vi2ol6oc0qJqeWobakjqMOou6w3qt+vUGVw0bjJqMb5u0mXaaPTbvtXhh+cZq2nrBZsUOZ8/qIOao7mTt7O+y17XE7ab7sMeal+BOC++YXWd9evwgsop/REBN4FSwKCUk5GrocrhpROHumSgt6t7otlhUnFV8UcJUonrSkeTpfcYp1an0aXsOTMP7SW+mRdbDHLPc7iMOeWP5Kcd4j98tDCymP9lc6n+aVPbg7N5ylfNfLlytiq3RqsNcHLh84UryNa/rKo30TRM3r98+cMemjf3ueEdNJ/WB1iNs1+CTmqf7e7z6dJ6LDDANPhpyfjn5KvE18+i1Mafx1YnqSfdphpmuD5mzlp8Z514snP0asqjyHfGjZ7l0NeiX4vb8IwEG0G7uAOJABY4ANxAODoIz4DYYhte/IGQBxUIV0CCCBmEEr/wOJA5pjzyD/IKyQFWh8Wgq+g3GCV7tNtgBHBn3E19IUCdM0pyk1aMdoUui56fvYohnlGScYDpD9CNJkL4zP2QpY01i82TX45DiZOei4UZwr/Is867yAwEsfAPlEZYV0RZ1EAsS3y9xUvIGnHfPyzLuUJBzld+nUKHYo7SiIqHqrlag3q/JrOWuXaEzp6etf9jgjZGicY7JuJmWeaHFFys760u2tHZh9o8dJZ1ynD+4WrjVeuA9KV4PvUV3HfSZ9DMkVwYgA/2D7lFEQzJCZ8KtIuojWaISqGMxRrGX49kT9u35mOQGr1OVlKpUjrQj6aiDyRlfMj2yrmav5zodrjqyfNQx//IxwnHKiQdFUsW5J+dKXU/dKRM9kw/v/f7nuy9oVlZVM9Uk1k5ddLzUUi96Je/qUoP39QeNMk1Hm+dv2d++fIfQGtjW2k7sCLjXeB/1wO5h6aOJxxJPKN2VT8d7OHvt+w72X3/+doAwKPfCYYj68shwzat7IwOvp0YX3qyOQ2+xE5h3mEkwuTz1aXp05un75g/lHzNnIz5Zf5aaw869mW9eyPri8VXi65dvLYtpS0bfMd87f6T81Py5sHxhxWOVsNq4Rv5F9+vauvvG/EcHKipsHh8QrQEA6NH19a+iAGALAFjLX19fKV9fXzsPJxsjANwN2/oPafOsYQTgLNcG6ruy+K//cv4H0+bUkdTdbT4AAAGdaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJYTVAgQ29yZSA1LjQuMCI+CiAgIDxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOmV4aWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vZXhpZi8xLjAvIj4KICAgICAgICAgPGV4aWY6UGl4ZWxYRGltZW5zaW9uPjY1MDwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlBpeGVsWURpbWVuc2lvbj4zODY8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4K1WPHqQAAQABJREFUeAHsXQeYFEUTrSMjIBkkKILkLCBZQEQQAyqYkCBGFNNvAsUABsw5AaISFUVABUHJGRQUSYIgSM45Z/p/r+dmbndvd+/2AtztVd03NzOd+83szJvqruoYAxEVRUARUAQUAUVAEVAEFAFFIACBTAHneqoIKAKKgCKgCCgCioAioAhYBJQo6o2gCCgCioAioAgoAoqAIhAUASWKQWHRQEVAEVAEFAFFQBFQBBQBJYp6DygCioAioAgoAoqAIqAIBEVAiWJQWDRQEVAEFAFFQBFQBBQBRUCJot4DioAioAgoAoqAIqAIKAJBEVCiGBQWDVQEFAFFQBFQBBQBRUARUKKo94AioAgoAoqAIqAIKAKKQFAElCgGhUUDFQFFQBFQBBQBRUARUASUKOo9oAgoAoqAIqAIKAKKgCIQFAElikFh0UBFQBFQBBQBRUARUAQUASWKeg8oAoqAIqAIKAKKgCKgCARFQIliUFg0UBFQBBQBRUARUAQUAUVAiaLeA4qAIqAIKAKKgCKgCCgCQRFQohgUFg1UBBQBRUARUAQUAUVAEVCiqPeAIqAIKAKKgCKgCCgCikBQBJQoBoVFAxUBRUARUAQUAUVAEVAElCjqPaAIKAKKgCKgCCgCioAiEBSBLEFDM0jg6dOnZe7cuWKMkZiYGGnYsKFkzpw5zfX+yJEjMnnyZPnvv/9k69atki9fPilfvrxUrVpVKlSokObaqw1SBBQBRUARUAQUgehAIAYkyURHVyLvxebNm6VkyZJexo0bN/qdexHn8ODTTz+VZ555Rg4dOhS0Fbfccou8/fbbUqpUqaDxGqgIKAKKgCKgCCgCikBSEdCh5zDIvfXWW9KkSRN59dVXw6RK3ahly5aFJIms+fvvv5datWrJli1bUrchWroioAgoAoqAIqAIZDgElCiGuOR//PGH9OjRQ2bNmiUvvPCCHaIOkfSsBHNIvFy5ctKoUSMpVqyYX5179uyRu+66yy9MTxQBRUARUAQUAUVAEUguAkoUQyB48uRJv5jAc7/IVDypWbOmvP7668Jh8lWrVsns2bPt8eDBgyV79uxezRMnThQOnasoAoqAIqAIKAKKgCKQUghkaGOWcCA2aNDAahRHjhwpN910kzRt2jRo8qVLlwq3O+64I2h8cgO7du0arwga3nTu3Fl27dolTz75pBfPdlx44YXeuR4oAoqAIqAIKAKKgCKQHARUoxgGvTfeeENWr15tjUWCJVu0aJE0b95clixZEiw61cOuu+46vzpoFa2iCCgCioAioAgoAopASiGQpoki3cKkVVm4cKFceeWVVquXlDYePXo0Kdn88uzfv9/vvEqVKn7neqIIKAKKgCKgCCgCikByEEhzQ8/0F0grY2rrSITy5MljjThuvPFGueeee6R48eIh+8v5ewsWLJB//vlH1q5dG9Ra+JVXXrEEL2QhsRF79+4VauxcH4tjx46VAgUK2FjW0bJlS9m3b589HzhwoMycOdMrkkPDv/76q227G8hy+vbtK+PGjZO//vrL+kOkUQotltu3by8dOnRwkyZ6T42nK1mzZpX69eu7p7pXBBQBRUARUAQUAUUg+QjQj2JakQ8//JA+HUNuWbJkMceOHYvX3HXr1pk2bdqEzOdb5pAhQ7z8mzZt8ssDY5AE4+bNm2fy5s3rl8+3fPd4586dXllwkm1ALMPm6dKli4EG1csT7ABaSAMCbUaMGGEuv/xyv/JuvvnmYFk0TBFQBBQBRUARUAQUgSQjkGY0igcOHLDGIyBanhQuXFgYfvz4cRt26tQpq+HzEuCA2rnGjRvL2RimpuVxq1atbJt82xDu+MyZM1Yz+eeff3rJaK1cokQJa73s9m3QoEGSP39+ee+997x0gQdfffWVPPTQQ4HBcuutt8rQoUPjhWuAIqAIKAKKgCKgCCgCyUEgzcxRpN9CaAu9vnz22WeyY8cOOXjwoEyZMsUajXiRsQd0WQNNnB9JpHVy//795eeff7bL3jFv0aJFA7Mm6fyCCy4QaBTl77//9rOCvvPOO20Yw7mtWLFCChUqZOv48ssvxZckdu/e3Q5Zr1mzRji87Uv8uAoLtKMRt41W2dmyZYs4n2ZQBBQBRUARUAQUAUUgHAJpRqN4/vnn+7UTQ7f2nHPvaFnMbfHixX6+Az/44AM/i2M6xn755Zf9yuEJy96+fXu88EgD6PS6cuXKNhu1f66QQLrhbpi779Wrl3sotWvXljfffNM7z5kzp7z//vuCoWRhf0+cOCEki1ySLxLhHEcS7XfeeSeSbJpWEVAEFAFFQBFQBBSBsAikGY1i1apV/TR/JFiYd+dH8GrUqCE0FHFlxowZ7qHQMXXv3r2987RwQPKH+YleU5566ik7jM7hZnfj0DSHjl2hIU4ouf322+WXX36xRJJY+Armd1pXPr5heqwIKAKKgCKgCCgCikByEEgzRDFHjhxWmwaDFa8/o0aNshbP1LJhFqYX7h5wHWRX7r//fsmUKc10xzYrkPRR88d+Bm7snyv02xhKaHV99dVXCwnn/PnzpXXr1l5Szt8cPny4d64HioAioAgoAoqAIqAIJBeBNMWs2rVrJ+PHj5d8+fJ5/eIcxYcfftgSJNcdDSPph3DDhg1eurJly3rHaeVg/fr1ETdly5YticrDOYmPPfaYX1ou8aeiCCgCioAioAgoAopASiGQpogiO3XVVVdZYxBq33yFaxlziPb06dM2mFq58847z0vizmn0AtLAget30W0K50mS+IbbfIeq3Xyh9i1atJBcuXJ50ZHk9TLpgSKgCCgCioAioAgoAiEQSHNEke2kccg333xjrZZ9NYWTJk2SwYMH265wrqKvAclvv/0WooupE+w7VzLYsDhrDVx3mQYnuXPnDrv5kt+EWk6XQL4rvFxyySUJZdF4RUARUAQUAUVAEVAEEo1AmiGKnJvn60aGPeASeQyjP0VX6O7GlerVq7uH1iXOypUrvfPUPvB1R7N06dKg1dFA56KLLvLiHn30UTl8+LB3HnhADGgB7cquXbus+5x///3XDfLbv/jii0JjGFcCDVzccN0rAoqAIqAIKAKKgCKQFATSDFGcM2eOXHbZZdKpUyc/X4LU1vmSIfoedOXJJ5/0/AfStQyX3KORh68E5veNS86x7zxKWl/7zkfkMoR000Ot4wMPPOBVQ9+J7OPUqVO9IXRqBUmGma5SpUqexpSZuIQh/UlWrFjRWoDTHRCtnkeOHClc0pDnrtDVDg1dVBQBRUARUAQUAUVAEUgpBOJMjFOqxGSUQ1I3bNgwu1WoUEE4lDp37lxvTWUWfcUVV3g1kFj17NnTc4tDjVyjRo3sSi3VqlWzw7I0jkmsgYhXcCIO6tata7WYTEqyV6ZMGesnkXVxBRdq96jx69Gjh1095vvvv7el0hk3NaVcnaVgwYLWfU6ooWu3GSTKtADnFkpee+0124ZQ8RquCCgCioAioAgoAopApAikGaJIx9q+wmHkwKHkKlWqCN3g+AqJ4p49e+Tjjz+2LnToJmb69Ol2802X0se33XabvPXWW14bSeYWLFgQrxq67OHyejRkmTlzphdPP4qBBJYaSPYxUunatWs8C+hIy9D0ioAioAgoAoqAIqAIBCKQZoaeadE8evRoO3wa6A+R8wHpIocEMG/evH59IMGks2nOXWzQoIFwCNZXihQp4nvqd+zrs5ERvue+x4FxPKe1MYecuXxfYHvLlSsn3bp1YzIr1B6OGTNGuHJMsPZwuT9aeXPlGZJPV0qXLi3jxo0Tug1iGb5CTOrUqWP73a9fPz9H5L7p9FgRUAQUAUVAEVAEFIGkIhCDYc/4nqyTWloK5aO2bePGjXZYlus0kzAFahxDVUXNHv0J0udi+fLlhS5quHcNQoYMGWLnQYbKn5RwDj2zTs6TpPEKrbbDCY1UNm3aZOdXlipVys/FTah87Bfd39B3JJck5NB8IJkNlVfDFQFFQBFQBBQBRUARSAoCaZIoJqUj4fKkNlEMV7fGKQKKgCKgCCgCioAikF4RSDNDz+kVQG23IqAIKAKKgCKgCCgC0YqAEsVovbLaL0VAEVAEFAFFQBFQBJKJgBLFZAKo2RUBRUARiBiB/VgcYMMYkeP7Is6amhnonYE+bXfv3p2a1WjZioAikI4QUKKYji6WNlURUASSh4A5fULkz2dFNk9KXkHJzb20j8gPN8Cr/orgJe3EwgF/PHVWieTXX39tlx1t3LixfPLJJ8HbpaGKgCKQ4RBQopjhLrl2WBHIWAiYM6e9Dsfs/kNk9hsgi7d5YWnyYFFPkTnvimyZeFaad+zYMeESo1wEoE+fPrrK01lBXStRBNIHAmnG4XZqwsWvY3fpP34tqygCikAGQWDFJxJzcLVIXWe5S1O4vsS0/FSkaBp/DtTvL1J8rJgLr5OY5F6qfdBazmwk0mZPyJK4vCgXLuBKUlzEQEURUAQUAReBDEEUW7Zs6fZX94qAIgAEuDTm7NmzrQP4Nm3aWH+jYYHZ/afIyr4YKsX+vEtEyj0IInOlk+XkIZH/vkbcP/Banwvh+L1d0MSJO7JV5K/nRIrg/NBake2/ihRqKlINZCR7Pqx/mUA8SqGr15iVIE6bf3DKvOh21H+Xc8z/J/aLLIOj+p0zRGKwwlOJG8RUelhiQBJl8uMixZDm5AGRfNUlpmwXkb3oQ7Y8IgWqM7cjG8ehbcgvZ2w6U7q9xGSOXS1qIdp/5hTaXVdkzeciWfOLVMawcKE6bu7gexK0DT+JHN0MD/2lRS6+RST3hf5pqTFc8T7at1ukdBeRSzo58ZzDuG+JxBzZJHJ+eSdsx1yRVf2AI+IKNROpjnZlO9+J4/9/BwGjH0WOob4CIIZVnkC5B0VGVsUe8XPuBj4xInVQn08++nQdOHAgS5AdO3bY+8L9oOZypFxxij5iq1evbtedtwnxb9q0aXa5Uq5SNWLECEs0u3TpInnyAFsVRUARiBoEMoQfxai5WtoRRSAFEPjiiy/kvvvu80qiM3uuetSwYUMvzO9g7XciP4OcgUN5Qr7TFr769ywWGVMTJNGLcQ4uvVWkCfJxrt039QIicVoK243In1A8c04GIf17Ko/ipF43kfrQDB7dKfIdVl8KrJ9aw4kPxaXn0QXYWq8SGQjiVaW5SIspYk6flJhfKoMAQuvoK4VxcgMJXnGRwSBX+2IjcShotnChpA7IkwekOZgsew9M6kl/zLIhYeuRIIztMPzdGcPfQ+PnvBzkr9arIr8/IvIbiO7NE0B8W4KIf4tr0N6p281VCKe3HwehRcFTWoEsg3T6Csv6sw/IuG8gju9ZC8J6sRf466+/SuvWrb3zTp06CRcmeP3116V3796WJLqRJIKff/65XQDh9ttvl7FjxwqJoks0O3bsaJcsddPrXhFQBNI/AjpHMf1fQ+2BIhARAv/99588++yzdvWjb775Rk6ePCnffQdSF0SMATucGksSa4PcdF4icnV/aPTedFL/EksSK10uchO0Z81eAQlB1F8jRFYPjisxLw7vXiNyL8gXFU7rsZ08nHD89lkOSSyApF2gobtzuVP+gs9AEnfB4OMxhySWRPwtk0CsQJZq3gIydrPI9cOd8mt3ELl/O4hp/KHXmIXPOCSRxLD15yLXDREhCQb/lJnXOvnd/9cOFvPQcZGqIG7YyZbJboz/fvciYAaSyPGaRk+LtBsH8geSdwLnE9AukltXKjUT6YT0TV9yQuaB2J2A9jNQpiM/CSr71HW3CPFG92NWoc1bQaJJEnMingS5E8h74x4YXm8q0hH9Jqkl/sTg/h1+JBGh0qJFC/nhhx94KJ999pl8+umnMmrUKDsEfemll9pjLiVKMjlo0CA7h9Emxj+uSsVVtObPny9PP/20dO/e3Y3SvSKgCEQJAnyUqSgCikAGQoAkkcODR48elcyZM9u1yg8fPhwUgZiDIHeHEEWi0TiW+OWv5qQ9sMqSFRJD02KaxGTKLHJRG5HM50HDBaL035cil77jpC2F8DxlnOMSDUT+mSdy4F/nnP9DxW+b6aQ5it1ckCXKSWzgrxyalc2xZPBylFekPmOhgbvK2Wcnu4RwqDhnEef4ONiVr6yB5o9yBchWsSuc4wuuFPmqBAjkIqEhTAxDSbbKdnaOS3dxiNm+vxkTXzaMckhdVbS3zltOfMlrgBXaugGn26bF5an5GoaJazjbyl6IQ1RguYc2iRyMzbLyTUwBwLYH5JKyay6I51bnuOo9IJDdnGPfYfVMCMKl8TBwUnj/uRRo3ry8wLjM2PPeGDp0qD0nMaxYsaI9btKkiRQvXtxqG6lpdOXNN9+UWrVqyWWXXeYG6V4RUASiCAE+QlQUAUUgAyGQM2dOeffdd+2a5F9++aVwHXFuQeUYtFeU852d3/+j0FBRCkKzRZLoijt379AsN8R/f15p5/zMcf9w98w3/vQRJxRkVPKDCHKr+YBIPWznFcd8vNhMLgl1y0jsPrZ4yQ+y5gqHm8F15TT6dcxH++fG57zAOQrV/iMbnfgCAcSpcCcn/DCIXzDJXcUJPeU2KjbRGbJkCJ/WLgaXxGJQ4npgENvGPOVsspT4Ry1h/vz5hcufupI7d26pWbOmrF+/3rtfcuTIYUmim0b3ioAiEH0IKFGMvmuqPVIEwiJALdFTTz0lHHaeMGGC8GUfUvJVcqLIffYu9U+Wv7pzvgG7fcvtsXVFs/x1J7zQbc4+Of85fEqBWs/QCKN+X2e7sC00ZMWgiYO2kPJ3rObOOXP+Z8rq7I/Fatx849zjQoWdo7/fdkMwLD7a0aKSLJ4XSwrjYhM+KlDXSbPiCTGnYpns8b3QBA51wqlBDJTDW0TWxWooXY2tm+Z8EMCcOCGXL3dfHAZVe2AOJcbc3fTLn0F9ASSTZfApD65Jo6DESpUqVaynCN4frmzYsME6465UqZLVQjM8UyZ9hbj46F4RiFYE9FcerVdW+6UIhEBg8WLMYYuVwYMHW2OFLVu2WIMWN9zbZ8uLOXlXOadDQAxHg7GNwkYDD8bVhpaM/GMwtGGIixmI2SxLf0UcwqpjKDWZYoo2gYUvCtkBrvgF2NJ4zIn8FnWPbInhWBiT1PjCqYE+B4cg/GeQqs+x37UAJA9EkrICQ8G/NhD5EuGHNzth7v/LvnWO6FtxKOK/w/ZjOyes7qtuqsj2Ze8EeUMWVGXb/BOI+AAMg3MI/yLA5Q5xs9SZDZ0+DcRQ9ymcV74y+BBxta5MLTKsNhx1o41jisAopzTI5XciZXANqHHdhvoG5BIZV8Hpy9z7bRarHQVRjBlXXmQ48v71ohMe5j/nG2bPnl3atm0r7du3l27dukmNGjWsJpFTF1QUAUUg4yCgRDHjXGvtqSJgEaDFc+nSpeXmm2+W8ePHS/Pmza1mcfLkycERuhxEqybIEzigULO4BRs4IsU0AFFr8Jgzh49xJEPkZ+3mwc0MtJGZmAniavd8jxmXQHxMFpCsG9fDdQwKxVCw/AuSy2mGJK+5y2BuIzSLV/dziNJehNN6ORv2MZnRxorwnwiCxafcyt+cNmZiJMTdF2sOI5wfoZlE2B5sIFuWdLV4DyT0OZxAUJSfZKZ6D8K5mMEkK8ha27VwdQMix/mU646jPdhXuxYGM7vhoQYNKlBHBF0jmbR9Yh00eGmGtgQRQ01qHRBQEsIN2NbudHC+GHnoZuimRSIlEX4C2+pVjqXzeQyANPrBIdvE5iDOc13M0KDiagirVasmkyZNkgoVKlhDp759+0q+fPmEq7d06NDB5nXTBi1IAxUBRSBqEFD3OFFzKbUjikDiEeAwJC1Wc+UCqYFwbV+uyhFOrD9DaORMzqLgYVnjJ2Vc9oJiyV382GSHWFc2J/aKyVEYZIvMK0A4n5IEkcTJR5x8+6Gpo2oyjHDdZfpLTChdmCICo2zdmMtpcpUI3mbWeeqwGMy39OvTvAdE5vePc4/jW/CRbQ7OQa4Bh7pjjgMH1BdP4LPSYH6lXz3xEsUPOHTokJw6dcoSxfixGqIIKALRjoASxWi/wto/RUARSFcIGJDVmJEg4lvR7DuXQTNbJV21XxurCCgC0YUAB2VUFAFFQBFQBNIIAjGLX3JIIuY5mryV00irtBmKgCKQURGInUCUUbuv/VYEFAFFIG0hYKq/KDFHMRG0GvbBhtjTVnO1NYqAIhDlCOjQc5RfYO2eIqAIKAKKgCKgCCgCSUVAh56TipzmUwQUAUVAEVAEFAFFIMoR0KHnKL/A2r0oR+A4XK/shZXrHviM2Qv/Lnu5x7kNw/6J57GqCp36qSgCioAioAgoApEjoEPPkWOmORSBVEHAYBm9GBK+3fCR5257cLwL3qZ3cb9dZCdMYbetwTrKPE5EM/6B1WyFKolIqEkUAUVAEVAEFIH4CKhGMT4mGqIIpBwCx7Akxg54cd7JjeQOexK+7SB8O0j64D15I5wlg88F8QyY+HZg0RS5oIZIYSw5VwCrdhTA0nQF4BexQAK+A2NrOA7N5LZt26RIkSLCtaBTSk6fPi2bNm2SAgUKSJ48eVKq2KgtZ+fOndZnYbFixaK2j4nq2IkDcCwOR+FckvDiW+Gc3PHxaU6fkJjNE0TyXBy3dGGiCtREioAikFQElCgmFTnNl2ERsI6nqfEjAbSEDxaq27htcvabsCrHf0uxJRGiishXsppIUaysUQTErxCIX8Gi2ED6fAlgPiwncn7eJFbin23q1KlyzTXXyPfff29XbPGPTfrZf//9J+XLl5c333xTunfvnvSCUinnkiVL7JrGTZs2TaUaIiu2U6dO8vfff8vGjRsjyxhNqU8cxFKKuK+5Ug6lIVaUgaNwqfq0xBxejyUM24hUaS7SYooTr/8VAUUgVRFQopiq8Grh6Q6Bo9BgbMVLGmsf2/1WkL8t0PptRtjGf7GO8b8YHo6wV1SGVCgF8ncJyB9WzCgCbVFRbCSAhUAALRGkBhBEMIv+JCNEN1nJH3jgAVm2bJns379fXdEkC8kUzPzPxw5JxM9DKsGn5MJeztKD5e5LwUq0KEVAEUgsAvpWSixSmi79I3ACC+FuAeHbBOJH8rcJ2gkeb4DGYvU8kcVcKDcC4XBvqXoiJUACL7gQG0kgiF9hkEDuqQ2k1i/KhEu6vfvuu9KrF17g6Vw++ugj2bVrl5LEFLiOf/zxh/z777/Svn375JW2+3cnf6MxIhddj4+qxiJHMU+XSzMeT8zE3ORVr7kVAUXAHwEliv546Fl6RuDIYZC+dSB/a6H9w3499pYE/i0yGwYgJpGd4zK5FauCBJaFFrCUSDGSQAwDFy/uaAQvwD57jkQWlnaTrV69Wn799Ve7zjPJn6/MnTtXZs+ebecstmnTxs4xZPzBgwftEPXWrVulZMmSlmDdeeedwvNx48ZZzRzTV6zI8fM4OXnypIwfP17mz58vdevWtWW4saHqYvz27dtlzJgxcgIkv2XLllKuXDk3myV4jNu8ebPUqlVLrr32Wi8uXD4vEQ727Nlj++SGJTYf+zN69GhZtWqVNGjQQJo1awZlsPM4HTt2rDC+Ro0a8tNPP9m5mW3btvXW0uaczXBYuW1x9+zfggULLAbVq1ePhy3TrVmzRubMmWPr5XmdOnVs/TxetGiRLF++3K7VTOwLFYqbt8q2noER1Q033MCkdhie/apfv75UqVJFOHd1ypQpsnjxYrn88sttuNtPmyH2H69rq1at5Oqrr7ZriJcqVUpatGhhY7mm+O+//27nqpYpU8Ze/6xZsURhMFnxCX6/IIiUlR9ibiLaumsBiCKmeZS53QkP/L9jrsiqfiKHVkI730yk+nMi2c4PTKXnioAikFQEMN9KRRFIFwicOXXKmA1rjZk52Zgh/Yx5pbsxd99gTOPSBhwwcVsBpLuyqjH3tjPmpaeM+fJjY375wZjFfxqzczsMj8+kCyyS28hvv/3W5MiRg9TZZMuWze55jDmKZsCAAd45w/BSNyAh5tixYwakyC+O8StWrDD58uXzC8d8RNtEECm/cKbn1q1bNxsfqi5GHjhwwOTPn99gdRKTKVMmmw+kxebDcLGBwYcNc+M6duxo48Llswl8/oHcGBDeiPOBtPr1C8TY4sOC6tWrZ0CmTO7cuc15551n04F02f6sW7cuJFbM69senr/22mt+14fYdenSxYA4M9ps2LDBVKhQwa8tMBoy77zzjgEJNq1bt/aLO//8882QIUNsXv5jW6tWxe8hVogr63j//fdtCOat2nP2h+EYqneTevuFCxcalst4d2M+yqxZswxIoxfO+GrVqpl//vnHy+93MA5t+QC/UXf7p78xw3H+MTbK/lVO3KTmzvma4cZ86JOe+YYJlss+7sTrf0VAEUg2ArG/vmSXowUoAimDAF+Aa/ASmfyzMZ/jZdWjqzFtLzemDG5VvGQS3JiuTX1j/tfFmHdfMmbkUGN+n2nMpvV4eYBoqhgMtZq8efOaokWLmh9++MFA+2UGDhxoX+Ykis8++6zdYFBhvvnmGxv+6KOPWuR27NhhLrnkEnPZZZcZHsNK1/z5558G2ibz119/GWgpTYkSJSyJYwaXKDZq1MhAc2imTZtmiQkJA7RkYesaNWqUrbtPnz4GFtkGw90Ghh62Hc2bN7dkbPLkyQZaTks8WeaMGTNMuHw2s88/X2IWST5o5szIkSNtu0hQWTdJEYXkK3v27AbaWnMK99wTTzxh49nWcFgxr297WD7LZXlsG7SQHvHDsD+Tm86dO9u6JkyYYLix3v/973827rbbbrP577//fgPtsOnbt68pXbq0Jf68VpRwRJEfBiSIl156qSXBbAPvh0BhOuLOtr7yyiv2vti3b5+9z3iPQYNpieu8efMMPyD44QHNqL3vAssyJw4aMwbEnYRv9yJz5vTJ8ETxS6RjWhLGo7uNmYBnBc+X4wNQRRFQBFIEAWesBL9wFUXgrCKwdTPGyzBUtHqFyL//YJgJVsJ/zUjYUjgbWtkEFsHlMDRcGkPDF2MrVVrkQjBEDAmHWxs35qx2MO1WxmFCGm/cc889cuONN9qG4oXuNRhE0Q6XHj16VDJnzizQ2MnhwxjWhxQuXFg4bAgtpD1mWK5cuezwJMM5xMg0HAr1FQ5Hc4iW8uSTT8pdd91lh7bD1XXFFVfYdnAuJOvv3bu3rRvkS0DK7LD49OnThRuHNykcxu7atWvQfDZBmH+h6guWZeLEiQKybYe9Xbc/xMuVCy+80A7F8pwYv/fee7Jy5Urbbw7lhsPKLWPo0KH2cNCgQd5wc5MmTTADorhAK2jx2L17t7AuDstTQNLtUDPxALETaO+kf//+Ng5k3Q4733LLLTJ8+HCpWbOmDQ/1D6RTrr/+esHHhN2DaAo+EuIlZzq6P6JAs+jdF6yDQ/kvv/yyveaM55A2tKoyYsQIO5xdu3ZtBsdJ1twiWTG3VzZhekchicmUJS4u8OgQ0hyMDVz5Jp4h2PbA1RRl11z8e9ge6j9FQBFIHgJhfoXJK1hzKwIGPvRi1sNQZOVybJgnSDK4GA/weXjAhxO+Jy6rBzIIQli2AuYmYV6aJYRlMGcptI8/JYLhQI2Lg7bHnsR7SccmoR9FGqvwBc8XO+ewcQslTP/zzz/L008/bX0A0nciPmNDJRcMldo4DBFbn42h6sKwsyWTGGoVDMFaQsh6SF5JFjF0LqyLQr+DJJ2cwxcqH8PDSST5OLeR7SLpcwkX2xRM6JuSQvIWCVZ0kcM20b2QKxjOtvVxPiKvya233iqcI0r3PjynOyIM61ufmGwPr5+vcJ4hZe3atV5wuGs7bNgwefjhh4Vk1Z1zeeWVV3p5wx1gWNxGB7ahcePGliiuX79eQt2D4cr14s7EEvNMCMkf20/uyWWLNPGS6YEioAgkDwH+xFQUgWQhYEnBejgN/PVHkfdeFrnnJpGa2SWGk/svgVHDNW1FHn9BpN8If5LYvLLII51EPnpNZPwokRXLRI4dg1sakIwxv4m8O0DkwadEWmGiPVcXCUMSk9WBDJbZNTShViyYkBQ89dRTgmFGwXCmJWS+6WjM4GoYGU4n0e3atRNqrDBf0Ro9+KYPPKaGisJ2hKuL2jLMn7OGHCSB1BZ+8cUXVmtFsklr5Z49e1oSSSL50ksvWTITKl9gOwLPI8lHtzr0wUgtYST+ISPBisYke/futdfAbSvJF0lipUqVrKb3jjvukIYNG9owajRJuqmxvfjiiwXzI602kAYprlDLR2HZFJJYkkuX5GIY2YbzHwkk83711VeCYXNbH0ljMOE9QfE1iqpcGb9viFunPcE/zI+1h+xDsuR8fEDyu5HfMHSdU7+vs1XtATU3jM9UFAFFIEUQUI1iisCYgQo5sF/knyXwJ4ghnmULRf6cJTGz1oQGoCCimjTCm6mWSHm8nCrg5UHyWLBw6Dwak6oIYM6ZtUiltojDhnyh062JK7RwdWXw4MHW2nYL/EpOnz7dWvdecMEFQgfdr776qh1ixrw3m4ZlkcTAuMFa33J4kXVRaJ1M4sEh419++cVqyTgcTS2kK4F1sR0kknfffbdHQKhFpNx3332WEJFs3n777XbKAa11H3nkEXscKp9bV7A98UhsPraN2j5ae2MuoS2Olsm+2r9gdZBI04I7GFbUDvoKsSGGtJjm8DXrI+kijiTOFMwNtASaaXv37m3JIcM5XYAk+vnnn7fDxZivKDAgsaSTVs/Ej0KyRstnzEG1UwN4TV3hSj0k6g8++KBcdNFFVkvMcoMJCSenfRA/kkbeH2wrra8x/9V+QDRr1sxagRMD9inZRJENqdZVZD6G1odhCPsinGfGc2X9TpG6D8FDgaM9DdZeDVMEFIEIEEiRmY5aSHQisH2rMZPGGvNeb2M6worxQrwrMGE96JYN4Tc0NOb5R4z5+gtjFv4Os9X90YlLFPSKxgYgC9bwBI8LXFJnowEFSJA1eqBVNMiLgZsTG//cc8/Zns+cOdNgyNCGYb6cwXCwNbKgdTKNI+69916D4WFrjQsiZdMyjnXQkIGWuNBi2bLC1UUDCbjDsfnYFrhwMRi+9dAHMbLtZLmsD3MMzdKlS61hRbh8XgE4gDsXz+o5ofp888H/osHcTMP+P/TQQwbk2Vo502iEluFly5b1krsGPZinaK3q2f9gWIEA+rWHBRBrDPna9OwnNIXm66+/9sqmgYtr9Q3yaTAE7Vk1Y1jeGpEULFjQYkjDFGLkGgSxEFpG0xCJ7aGVNoaxbVr2j9bjtO52rx2vOT4WvLoDD9544w2LA9NjON7mpxESjWrYNrafdTz22GPWACkwv3c+oZljkHJokxP0HZ4trtXzwbVO3JSrbRwxM7PvNOYLpKERC7fvsW2b7eTV/4qAIpBsBGJYAn7AKhkdgV07MH9wAQxK5ossmC3y/VQ81kOA0gB+BOtgnlI1aAkr1RCpjLmEiVxTOESJGpzGEOBjgXPqaKhC4bAsCIdfKwPD6GPRNexgHDVgrgaKWjQOFVPz5A5TuoUlVBeHX2k847bFzefuWRcNSwLLTSifmz9wn9h8xIcaTvaRQ66cO5nYdbLDYRXYHp6zfA4Pww2RF00NJuf/0TcitarEl1o8GhLBqtmbO8kMsFC3eYljMOGcS2LIPgQKh6NZv6//xcA07jnnjLJvvu1kHNvO9tFoCkTSTZ6y+yPbxGQvKDGZs6ZsuVqaIpDBEVCimBFvgOOYh7TkTwwbYx7g/Jkiv4zBGsUhgGgJIli3CZzY1sFaqxhGLId5RRhaUlEEFIFziwCHeWk97rs+N+eVdujQwVpZP/744+e2gVq7IqAIRAUCShSj4jIm0IltW0AI54j8DlI4cxxWKVkbPANJYb2mIrVgOVijNsa5yqXe13/wFmioIqAIJBIBzo+k1TCtxzkXlJo6zg+l5hX+Gu3KOYksSpMpAoqAIhASASWKIaFJxxFrV8GhHEjhHAwf/wwrx41B+lIDQ1BX3IEhZBia1K4vpnxl+CzLFCShBikCioAioAgoAopARkVAxxCj4crTNc2syTCBhLuT4XAzczhIp25oKNII8wrrwhLw0rrwjJvXL1EqzRryq0NPFAFFQBFQBBQBRSB9IZBmiSL9gdGZLSdnc5K1ig8Ce3ZhCHmKyLTxIiOGBJ9f2OlauKW5yhJDU6UGJnjHn6TuU2LIQ7rIoLGAK3SFQv9s4YSOgmH5KrBStc5/w6VNzTgaSXBzDSpSsy4tWxFQBBQBRUARiEYE0uxYIwki/a3BtURU4U6LwEWL4IMwAoFPDZE/5oq88Ty0ghc7Pghvuh2Oqn1IYsdrRL74CE6rl4pNP+RnkXsfgxFKrSSTRDaRjo3pS83dPvnkkwRbznlSdOYM1x1h0yYFi7AFxkb++OOP1gE0LWw5X4v+2yLFPDH1aBpFQBFQBBQBRSDaEUizGkUu80TXE9RgRZN88MEHAv9v1vUIne6GlIP7RKb8Aovk0RLzuePQ1y/tNbBCbt0OWsOWYqrW9JtfmJLDyFwyjatBcM1WrvjgLpnm15aAE/iQk1q1aslVV0GjGUYSjUWYMgKj4MdObrrpJuuaBf7qrFsQaqbhC1CWLVsmdBatkggE9q8U4VYUFu/Z41yyJCJnqibhfbh69Wq7qkugu56UqHj58uV21Rkuy8f1mF3hb4DrGAe6fXHjda8IKAKKQNQigKE5lbOIAFZUoHdCQ0fE8WTrZmO+/NiY6+vRuaX/djHOn3nAmCnjjDlyOF7W1A7Auqy23VgqLcWqCotFEmuhI2ksEWawnJlXAh1LE3Ms/+aF6UECCMzq5Dgv3j43eMIdcKi+4Eljju0NHp8KoVg5xTrs5rWks+2UlltuucXeJywfywx6zqXpeJphcDeT0lVqeYqAIqAIpHkE0pRGkU5ZuZQUHd5SqFEcMGCAnyNdDmdyqS26gihZsqQd3qRm4Z133pF+/frZ5ay43NX//vc/qz3i+rCffvqpXfaKa7Jyzh01TFxzleud0udYiRIlbH3uPzqYxcoEQu0U10HF6gvW/QTXvy1cGEtExQp9lnH5KwqXQXvhhRfs2qoc+vzjjz+kWrVqtk3UQtDh8ObNm4WObSlr1661Tmwzbd8qmSaPk1LTRknMjNU2zvvXCn4Lb7gDax1fL1KmghcceEDcuH4uVk2wbaamkstjcdk1OuK95557/LKwf0OGDLGuNHCHClbTsEO1XP7s5MmT0qpVK+nSpYtfnsSccF1gLsPGMilcNozXIlBCYeGmw+oTftec4Vwy7f333xesKiH79++3/evYsaPg5e5ms3vOZ2W9vsJlxHgfpZqjX9/KMsrxop4iyzFPtiAMo0r7Lz2XGhDwnuWzgb913qdXXgnDrBQUPlfoj5DrVfP+4drRXPKO95qKIqAIKAIZGoG0RGXxMrBLgHH5K5Ar+xWPB7VfEz/77DNTrFgxG8dlu0Aq7DEuot1j9QC7p3aAQs1AqVKlbBgIoV9a5mE9XILMFQwxmUsuucSm4x5rkhoMo9pzrDRhl9Ry0/bp08dgrVnD5cWqV69uQERtOp5jBQR7/OWXX9rkTZo0seduOwP3L6MtSGBM+5bGfDcIa2vtcqtJcO9q5i677DIDgmywhq637BaX5PIVrOlrl1YLrN/tM/F78cUXfbPY48RoFNlXLp1WpkwZ29dQGpiEsHjppZf86uc15/JjbDPIvQFJ8M47d+7slzbwBB8KBiTeaqJA1AOj9TwUAglpFA+sNmbZ++bMyRTQbu9dbsxP+UO1xIZj2oC9/j169AibLqmRvOd5f8H/oC0Cq54YzJG2x6pRTCqqmk8RUASiAQFqf9KkYD6cfXAHEkU2lkNQfKhDU2jbfu2119rz559/3nB9UxImLGLv9YvrpbrECFo/Ayteu9Zo37597RqkBQoU8IaCYfhg0/bs2dNAU+eVweFMrqnKcvni8BXMp7R5SFyh9bJls90kqe7atAtAWL554mFz9QXOuqufov3DYrdvG9c02wf1S/KQMrRotl3suytTp041mFNlunXr5gZZbBo2bGjb+sADDxiuw8r2QTNq0xKjXr16eel9DxJDFN30MFKxdYQiinwJDx061EBzadPBQMaeM4zr2EIT6hZl16Ul6cbSaAYGKV44liTzCD3bHyjQWlqC6F53GNcEJtFzErTFrxvz28PGLH3XmIMb4jBxieJfvY2Zjo+uSc2NWT0kLn7jOKyxe5cx+1fGhW2fYwzz/VIXw9LdjTnu/5FnVg00ZtoNiK9jzO+PGXNovTF7lxkzAI+hz7CxvDl3x8sHK3rjPg+wEomZNWuWV+emTZvs7+y7774zK1as8MJ5wHuW0w348ffbb78ZTpvwXefYTcznA9dA5r3Ce5H5oB03I0aMsEmCEUWMSBh+GL388st+H5rMwN/V559/bsviOs8qioAioAikZwTSNVGktojC+Up8yMPa1p63b9/eLj5vT/DPJYoYhnWDvD0f9MyLoWZLFnmMVQ5gOIzF5gMELl9sWgxT+cWQKHLRexLDeLJ8iTO3EOUis3kWG+vY0bq+MaOG4WV5MF6WSAOoZcOwqiG5hbW41YRgrVezbt06Q0LlCokW64ZBh8EQsxts9/3797dxZ4MouhW7mtCg8zVjE1Fryza3a9fOYCqA30YSzDhMH3CL9NvzRY6hb5sG0xQMhq/94jP0CYnhR7gnP/DZSNbWjnRgcYmibzyP/3zOiSe55PmmCc75muHGfOhTFuOGwQD/1HEnfjI05cHK+jwgD9McXOvkif1PzR6vs7t16gQyCiHxczX3bhymTBh+JFBGjhxp8/CjiB95TMMRBPfjzSbCv7vvvtsrm2nGjBljP2J4z1ACiSK1m+6ohlsupkF4aTnywN+jG4epLjZO/ykCioAikB4RSLPucfDATrS4c884l5DChe9B9OLlb9q0abywli1b2jBaxC5dutQe02LXLdM3A7Rx9hSaC99ge4wh6Lj5eMePw7/hYJFm5TF5sTrc2vRz0tc6XzK1cuZWnf7qB5G2HURy5Y5XVqQBnE9JVzCcVwWSLLT25ZJeDRo0kEmTJnnFcX4f5dZbb403BzAhC2WvkLN8sGTJElvjqFGjhJbwvhuGpG2cO+8zsGkgAHbuJrSUAs2TnacYmCZDnu+Ge6apT2LNbvS+0dMi7cZh2cb2IidwPuFmkaM742Cp1EykE9I3fckJm9cH6Q7ExbtH05GfdOt6rATUFX43K8GxO9x9xqz6XGTrVJFlE0VyIr4ldOmdFos07gGLavweO26HVTXC6Sr1fhzfv0Mk98U4iRNarHMOLoXXnHOOeT/gw8je5zzmHEPe91z/GB8XcZlxBG2j/PTTTzJw4ED7G4F22i+evx9M0bBhnEfMcsIJ50pyjvPkyZPtHEZ8sNh50/ggtb83zrF+9dVX7Rxh+oNVS/twaGqcIqAIpHUE0pQxS2qDRSMWaBf8quGLgUKDEwwr22NfB9M2IPYfjUMo0BjEhgTsYJgiX8GXYc83/COevl+k84MicGNzBi83mTDFGrf4J0r6GQkynVzPnz9fSGKhAbF7vhhJlm6++WZLntlHSjAS7eKQ9FYkPSeNW0KJ+5J95plnLAkOTEfDHRq/hBMa1JD488WuAgQ2jHJIXVWQuzpvOZCUhB/OXSB5G3C6bVocTDVfEylQw9lW9nKcu+9zPji8RIc2iRyMPVv5pgi3PSCXlF1zQTzxu6BUhVFVpW7OcQF8QLnCz9XM2HIWcUP89pif6jndp7FSnjx5BNMUbBoSQ36kUTD31Rqe0VALoww2jP+6du0q1113nXceeEBfm64TeRqrsb5QQsMxDH3bDzMaj3GDhtImp+Ec62L7oJm3bnbYDl83O6HK1XBFQBFQBNIqAlGhUUwsuBhSEhhzeMlp0cwHOoXWjjDEkKJFiwp9B9Jq2VeoQaCzaQwnSTzN5FG8KDatF7mgeBxJrFtYZNgAkSOHRd7qb0kiy8ud29Eg0urZV6g5o5VlUgRzMuWhhx4Sajy5IsoTTzxhtWd169aV49BuUptGqV27tpBYffXVVzJjxgyvKjq+xjCwd362DhKDBftGIcmDwZDfRsvuNWvWeJpgpqOD9kCiz+sJdb/3IcB0GVqObHS6X8DB1sOicCfn8LBzv3jh7kHuKs7RKYcYucFy5qhzyKdJ/vrOdgk+yOphKwGL/WOxGso85bwsyT3ghxE/2MqXL+8VxfuJ/g8xx9DvY8gdCfASJuOApJBkkR4Z6FOUG4ah7e+H9yfbRG8J9HhADT+dvbteHJJRrWZVBBQBReCcIRD60/ksN4kvcg4BucOI/FKn8GFLckNp3ry5/VqnGwsKCQ8MUewx/3388ccC60V7TjcvPKcrDVeoueKLhNoFzIuzD3TWy3NYCttkmPwu119/vSWDdLHDBz6J1nvvvWdd6zz33HMCC2H7Ivqo+1Oyf8JPsnr5f3bUrTdKiKmOl+FljaTtY0/YvG7d7p5kjUKXNbDYtQ6hp02bJnT0yxfanDlz3KSJ3sNnoCWGJJ8sk1o4aj0wgd+SQ1h927JIgjkk9vTTT9sXGIkWXcfA8MW67gmskC9jurshlu7LjoSNrkooLI/DbhTMibTDe8TTjZ83b55HxPlipcNuTgtwJTFYcCidw43U1lBzBCt0ufDCC+21o4aYDpjvuOMO4fAyhUPoJPUcPuQwNacUYK6ajbv//vvtPsP/K1AXEAzGKj5PiKnwoMRkySFyfC80gY6WzmoQ98VqBF2wDm8RWfe3c5a/msjGH90YrBuOez4nTskXy90Hlzm1nLhD67DuOEgp01OWPyOm0iOo7zzn3P1Pgom8vHeCTflwk/nu6dqKKwDRJZM7VEyn2Pz98L7mB50rMDJzD5O9p9NtrlbE0QUOfbsfO/yNYD6w/UjhKkYw2LLusl5//XXhM4W/ORVFQBFQBNIlAng4pwk5dOiQ5xIHQJpg28MPP2xeeeUVLw4vFUMjDAyx2jC6paGFIx7O9hw+16zRhmvMAuJoQNAMVnux7lXo2obGLO7kdxcIzOvzs5hlW0CKDHzxOUYuSxaawzc2MfkyBW8n20Xr51CCeUt+fWXZnHAPjWKoLGHDabVNy2cMn3nYcNI+XQQFs/KkNScIorUkxlCbodU4CKDN62vMgnldhpbcwa4Fwzih3xUQ0JDpmJZt47UJlMRggY8HQ6fZrosctz14IZu33nrLWpm75cKHpXVZxGvgpiMW8LHpJtH9iUPG0N0lDUf6YvsxuzEfx56P5gqQsJ53jVm+R/i4GnHxE6908As0ZpnbNc5YBWWYnwo7xjK/PWSsU+4vYsunwczP5Y0Zgv2c+5yyvo6NG1vWmG9wvPCFeNeIVvy8nrSKp0ADbw3I+JunkdqDDz7o/aboFYHiGrMEGp/ZyIB/zM/y3WcBLfJDGbPwnmVautuiNTb8q1qXUPzNY7Uhg49RaxyHj1SbjoZyKoqAIqAIpFcE8FSOfnGJovuSYY+DWTUHIkGCgiHoOHct69bARPJG6D0Am7v1xuoUO7cHZk3UOd1w0JVGYtoSrkCWQZc0dOeDoVjrJsTXtU+ovL71sgy+/HyJYqh8qRGeGCz4Eofm1VovB3Ob5NsuXjtam9ISPtDK1Tddhj2mZfHY0nGWz5/gnp5yrTFHdzuQ/P2hMf0Q5loqk+DNbG/MiVgr/QCiaO+l2Xca4xJC5iPJ3DbbKW83XBuN9CmPZf/1khO3Dt4CYCFt6+qP/covnXCf/y5RpFW/K/xd16hRw/MZSp+qvr9xlyi6/hDdfMH2mLph73+XKF599dUeUXTdPWFKh5cVcw/tByd/M/yYonsdGMMZTOmwvkQZThJ7ww036P3noaYHioAikB4RiGGj8VCLauEwLCe6c3iSw5QRy0FYeb77kshL78VlffExkUdgmFKoSFxYOj7iEC6HrDlnkxPwVTIGAub0SYk5ul1MrhLBh32P7xM5dRgq4eL+8fMw/3A+5t7ePAHzEFv6g3Vkm5jsBSUmc1b/cJyZU8ck5vhuWPuXiBcnR7aKyXmBfz3xU8ULwWiEnTfoGmvFS5CKAZwPSwObQAMYTtXgNAsayqgoAoqAIpCeEYh6oohhVjs/iPOXOJ+Rc4o4X45zmxKylrUX9kdYgrYFuXTp9IO3izwHq84SjoV0er74bDsJtDv5n3MxabHJeYdvvPFGeu+atj+VEIBzRIkZCRJIY+Y7l8FlQKyRSyrVp8UqAoqAIqAInDsE0owxS2pBQDJIMuSrOCVRJCEKK5sxCf/RDiKjZznJ6BPxg69FatQJmy29RXKdZ9ctkNt2rlutogiEQiBmMbTrJInwEmXyVpaYUAk1XBFQBBQBRSDdIxD1GsUkXaERg0Ruuysu61BMnOpwX8RDYnEF6JEiED0I2OHq3zH0XA0eBvKUip6OaU8UAUVAEVAE4iGgRNEXkgP7RR6/E353fnJCO7QWeX+gSOGivqn0WBFQBBQBRUARUAQUgQyBQNQPPSf6Kv6DuVbXwt/bf7E5vhuEte5AGlUUAUVAEVAEFAFFQBHIoAgoUeSFnzQWa9A6DrcF6zHLSKzKUrpcBr0ltNuKgCKgCCgCioAioAg4CMQtX5BRERnaL44k3ttOZM42JYkZ9V7QfisCioAioAgoAoqAHwIZmyh++pZI5wcdQN56QWQAlnrLwbXIVBQBRUARUAQUAUVAEVAEMq4xS//3RR54wrkDBn0Kf3DOmsV6SygCioAioAgoAoqAIqAIOAhkTKI4Gv4Q23V0EBg2QKTDvXo/KAKKgCKgCCgCioAioAgEIJDxiOJf80Vq1XNg6Icl+bo+HgCJnioCioAioAgoAoqAIqAIEIGMRRT37REpW1AES81K9/tF3sRatSqKgCKgCCgCioAioAgoAkERyFjGLE/e7ZDE5hVF+mBeoooioAgoAoqAIqAIKAKKQEgEMo5GcfLPIldd7wCxHl61LyodEhSNUAQUAUVAEVAEFAFFQBEQyRgaxVOnRB6MJYl931GSqHe+IqAIKAKKgCKgCCgCiUAgYxDFkUNEVgONstjufSwRsGgSRUARUAQUAUVAEVAEFIGMQRT73ONc6ddBGLPoqoV62ysCioAioAgoAoqAIpAYBKJ/juKCOSJ1G4tkAxyHTohkzZoYXDSNIqAIKAKKgCKgCCgCGR6B6Nco/jDcucjPYhUWJYkZ/oZXABQBRUARUAQUAUUg8QhEv0axXIwzP3H+bJHLGiUeGU2pCCgCioAioAgoAopABkcguoninl0iBQvbS2xg+RyTOXMGv9zafUVAEVAEFAFFQBFQBBKPQHQPPa9Y5iDRrLySxMTfE5pSEVAEFAFFQBFQBBQBi0B0E8XN65zLXKGGXm5FQBFQBBQBRUARUAQUgQgRiG6ieBKOtimZYem8ab3Ijm3Ouf5XBBQBRUARUAQUAUVAEUgQgeico7h6hcj7r4p89o0/AL/NEKnXxD9MzxQBRUARUAQUAUVAEVAEgiIQlUTRnDkjMVdWEpm+Kq7TBUTMLoTHwApaRRFQBBQBRUARUAQUAUUgQQSicug5JhO6NeBH/84/9pSSRH9E9EwRUAQUAUVAEVAEFIGwCEQlUbQ9LguN4idvxHX+5k5xx3qkCCgCioAioAgoAoqAIpAgAlE59Oz22pw+7QxB7/1XZLFxg3WvCCgCioAioAgoAoqAIpAIBLIkIk26TWIdbH/xk8jk8em2D9pwRUARUAQUAUVAEVAEzhUCUa1RdEE1xuj8RBcM3SsCioAioAgoAoqAIpBIBKJ3jqIPAGrp7ANGhIfbtm2Tv//+29uOHDmSYAkbN26UVq1ayWeffZZg2tRMwA+EM7CAV0lfCPz888/SsmVLmThxoqxZs8beS++9916CnTiFZTo3bdokS5culR07doRM/+6778o111wj+/fvD5kmKfd9yMJSMOK///6zv8WVK1d6pbpt5e8uErntttukY8eONstLL70kV199tbAsFUVAEVAEfB6yknYAAEAASURBVBHIEETRt8N6HBkCFSpUkKpVq3rbJ598kmABCxcutC/5cePGhU178OBBWbRoUdg0SYn88ccfpV27dlKkSBHJlSuXNGvWLFXqSUrboinPkiVLwpKtUH0loeHHRyghGZo0aZKsXbtW9u7da++lv/76K1RyG96zZ0/Jnz+/XHjhhVK9enUpWrSohLr/hg8fLr/88ousWuXjPiug9KTc9wFFpPjp4sWL5ZJLLrG/xYoVK8rOnTttHZUrV7ZhV111VUR1EuMpU6bYPAsWLJAJEybIoUOHgpaxdetWYRoVRUARyHgIKFHMeNc8oh5TuzNs2DChFoZyGgZCCUnZsmWlVq1aktCL64MPPpDatWvL8ePHEyoy0fGzZ8+Wm266SaZNm2a1Uo0bN5aZM2dKixYtVFuSaBQTTsj74LLLLpO33nor4cQ+Kf79919LakhKQknhwoVtFPe+x6HST58+XV5//XUpWbKkPPfcc/LKK6/Igw8+aEljsDy8Jxo1aiSlS5cOFm3DknLfhywshSL4wcaPoGuvvdaW6GrLGUay6J4ntrpg+PLjKpiQpNavX1/GjBkTLFrDFAFFIIoRSLPGLCfNSQt71hgsv5eAnDFnBIOMkjkmcd3hkOQJOS7ZY3IkUDKcdEeQNsHC0mGCyy+/3LZ6w4YN8uSTTyaqB1WqVJE///wzwbRHjx61LzdqjS644IIE0ycmQbVq1eTbb7+VG264QXLkcK7v/fffLwMGDLAapnvuuScxxWiaBBAgUTxx4oTs3r07gZT+0Z07d7YfBh06dPCP8DlzyWGhQoWsVphRbphPMu+wX79+9piawpo1a3rhoQ74gZKQJOW+T6jM5MZnzpzZ3tfUrvpqS5s0aWJ/P5EOPRPTbNmy2WaRIGbPnl3OP//8oM3kR1+pUqUsAee0APe3FTSxBioCikBUIZA4ZnWWukzC9/3hITLk8FAZf2Ieaj0qLbI2lupZq8isEwtkQMH+UiNbHdua0+aUjDw8TIYf/lZ+OglSYvZJy6wN5drzWsu9uR+S8zLlsem2nNooj+3uJgflsNTMWk3yIrzf4eGy4cx/KLuJvJ7/DamTvUHEaW2GCP5xmHX9+vVhv/r5sE4KYeLcrB9++EGoWaFGjQ/8SpUqyZYtW+T222+XQHJ07NgxGTJkiHCImES4Xr16dqj2kUcekZMnT9o5YV26dImgd05SaokGDx5sy2QI50DdeOON8cohwdi8ebPs2bPHxnGI0XfI6+KLL5YsWfxvTWo03n//fTtkybll7B/nV91yyy1+5efNm9fW6xtYp04dSxSTO1eVmk/OlZsxY4YdFi1WrJjVWu7bt09Iarp37+5VS4w/+ugjez04lFq8eHG59NJL5amnnvIjPaNHj5aRI0dKJjiJZxzndc6bN0/y5ctnr5vvdYgkrduQxOLG9IntH4c83WFPXovVq1e71VmiEUorRdx+++03e304NBxKeP1z5sxpiQn3xI5a6kDh0Ok///wjy5Yts1GzZs0SbhQO0XIeoiv8jTz66KN2KJthJDr8eAi8z9z0kez5m+G1njx5slBjSgJGbSu1m+H6mZg6VqxYIbzu3F900UXSunXrsNk4x3DEiBHCaQEFCxYUak+vv/76oHmIqUsUiVcwjN2MJKl8PjzxxBPyxRdfyMMPP+xG6V4RUASiHQEQhTQjT+zqamQDlIPYam+uYWpuruadM2z0oeG2rafPnDL37egQG5fF3L3jNtNtZxdTeGNJG3b5lnpm/+m9Nu3WU5tNm60t/copsfEiIxsL2bAWWxt7/Y8krZcpkQfQpNCRY9gNQ0uJLM0/2bPPPmvLxcvJ/O9//zNt2rQxIEU27M477/RLjBeZwfyreO3Ai8KGgfCYF1980S8PT0Bybfxrr70WL84N+PLLL025cuVMmTJlbNrHH3/cjfLbQwMSr35fbDCx3i89yJPBC93mgbbSXHnlld45cQ0nc+fONRiWM1mzZjUgp+GSho2DcYTBvDDbBpAXWyYIudcPkEAvP7SvxsWT+7Zt2xoMxdu0mEdnMBTupQX5NSATXjm8bhgS9c7/+OOPJKVlpkhwS2z/pk6d6rXN95q5xyBgBsTJa7PvAbS8Ni+IsW9wko+hPQ7ZFpBNv3JB3A0+iAzIkAEJt/lAcv3SBDtJ6L7Hx47hPcn+Yz6sATk1GAK35/hoMfgYC1ZsosKgKTXuPUZceW9wg2bPlg9S6JXTvHlzwzT40LRxvEfdawKiaNj/5Ao0lrbMEiVKGHzsJbc4za8IKALpBAFqf9KE/HLkB0vcMm8sYmYcnei1afaxKQjPY+NcovjdwUH2vMKm8mbliWVe2h2ntpqbtrW2cb32PO2F88AloH33v2OOnz5mDp7eb27Y1sqG/3tiRZLT+mUMcwKth/nqq6/CbtC4hCkhdBRfSNA2wL/4aS8RX+gYRjLdunXzwhjfsGFD+7B/4IEHDF80sGI233zzjU3LF0uvXr289L4HCb0wfdNCe2rrCEUUMSneDB061MAy2qaDgYw9Z9jXX39toAn1ioPRg4HWw/DFB8MXL5zExiVjbH+g8EVGgui+LGFBG5gkonOSPZYFLaZxCQaxg8bWhpO0uALjGRsGAwsDTZYbbDAkbqA5tNfqwIEDXnj//v1tehJRaFdtOIYWbViPHj28dDxIbNpIcUts/w4fPmwwxGv4UUA8SFB43dwNWmW/9ronvB7QStnryDJSQjB1wZCUu22HdtGeMyxcHZhCYdvuXsdwbUnovuf9QBzuvvtuw/ZQMFfQ8AOA4fwoS4pgeNl+DPEjgr9llokpGua+++6z5bLsQKLIMBjyGGiRbZUkdtBA2vT8mEwJ4QcR64HGMiWK0zIUAUUgHSCQZohij92PWtI2+GDfeLBNODzGUEtI0kh5YGcnm/bHw9/GS0viSFJYaXNFvziGXbKprDl65ogX/sWBj4OWE0lar7AEDjCsZ19ifPGE2nzJQwLF+UVTo0FNA4kJX+Kw6DR80axbt86QULlCosWHPLUOgVofl4CcDaLotsfVhMKi0g2Kt+/Tp49tM6yYDYbV/TaSYPYH893i5WMAyQyGvm0aanncF2jQxGECMSxuSQ6JKa+jrxBHzNkybB+FfWGb+ELlyz1Q7r33XhtPIuiKiz0/JFxhXl4nGPu4QXaf2LSR4BZJ/9zGEAf2s2vXrm5Q2D012UxP8p7SQpLGstmPxEhKEUVee37A8LcH4xe/e5Mffa5Wj32PVGCMY/v08ssv+2Xlx56rnQ4kimwH3Ob4pedHG8kmNdkpIS7W33//fUoUp2UoAopAOkDAfyIYnrbnSladxDJ7kOrZasVrQsvzrhdurvwdm7Yy5hwGSrkslRGURVacXi3HzFHJEZPTS1IXcx19z4tnKWHj8Dr30rgHkaR184Tb44UqgwYNCpfEuvXgnLJIhfOj7rjjDsGwsF9WzqF75513bBwjXJckt956a7y5WQlZKPsVfBZPONeKMmrUKLsFq9qd6xgYh5eacIPGUWg8wTlpH3/8cWCyBM85P4zGG7T6dOd0uZk4x83Xgpc+/Ci0+saL203m7aHRtXO8WKbvHDom8DXEYF4MMQoNfoJJQmkjwS2S/gVrS2LC6A+RwvmG0SKcm+len+uuuy5kt0LdnyEzIMKddxlo9MO5rE2bNrXziwPzY0hYypcv7xecO3duqVu3rowdO1bo4obPhOSImz+ca6HklK95FQFFIO0hkGaIYuksFwlMkWXjqfVSM1vdeEiBdHsv3vJZSsusU7/JplMbpFxWEsM42XZ6M05OyfkxRSQr/tKKQPsltMgNJ4EP+XBpfeP48qXF4/z58+2kd2gm7X7gwIGWKN18882W4NBAggJtlW92e0zjknMlNG4JJa5xzzPPPCPt27ePl4yGO5iPFi/cN4AGNSReNDYIJXQkTjc6dNcTaGHLFzCFxkEJCQ0OKKGsgd0y6PMvNSUS3CLpX2Cbw10737Q07qHQiCRaxMWY1xLDw9YgKbBvNGbhFkyWL19ujWv48RD4UZEnTx6bJTCcgSR8wYT3HK9H4McM09MYxf39B8ub2DDM9bVJMbSf2CyaThFQBNI5AmmGKNbNXl/kSH95dd9rUidbAymWpaSF9qg5Il8e+Fi+PPy1vJmvj9Us1steT748Nlxe299HamSvIwUyFbJpj5tj8vr+Xva4Y45rEu0u52xcQ1pBcksNYbkkURiWtP7h3DqoQaQFLVergIGJJUEkVhjiFJJHaiYotMjGMLCb7aztqe2grF271lp0uhVTG8aXHZ0Ku5iR5NFXnq9g6M86Zmb7adlMoaaD1p7cXKFPPH5ouCTODXf3tLgmPrTk5QuV7kd8ST21KDDSsVblbAd9MrpCbRGdPfMFTVyZjsSAdcIQxWsX07N8Oix3tUJuGamxjwS3SPrntpXXh6SB185XeC3Y7wYNGviRQuJC2b59u2/ydH1M4kVLYWoW+aHl+jd0O0UtKh1aux9qbjj39D+J+ac2iBr+7777zjfa3jfjx4+X559/XjD/0yOh/Bikdj2YULv5wgsvyJtvvulF0wKa14PacAyFe+FJPXA/dPh7UVEEFIGMgUCaIYptc3WUmw+PkpEnfpaK2+rKAznbSraYbPL90XGy8gxWUIjJJyWzlLJXpVOerjLyyGiZeHKmNNzaSO7KdRu8KJ6RScemy7STc6BNLCY98r9o0+45s0s+2v+GPZ5zcqm8tu856QnCOfPYJJBSh3gMPjRIcmbKIXWzN0502qtzxnf7cq5uGb4gOKzKlzb91FHTQTchdEVCDRmHMCkkMK+++qo8/fTTAoML62KGBIvakGAaRWop6e6GhIy+DikkSq52iOVRU0ohufrpp58sIXPjSVIx59HGU5NEP4y+2g62jUL3PWw35lNaR9nUtFDLMmfOHEuA6TIG1suWOMJwwTpSphsgvoRJPDjsDiMYWxaH0EnI6EaErkE4hEf3MxT6UwwmHC523b1wiJlDyb5EkXnoEuSKK66w5bK9jKfbG2ptiQ2Mg2zRJIFMS5ckJOKwQrdpSdbpWgfzyqzbFMx3tOl5jUgIKLyGvDZ0BE3Szzx068J8dEsSSVp+OESCW2L7Zxsa+4/Xj1pY4k88eI3o6JzaJi6/5zudgf0lCec96js64FtepMecUsCPIddnJ6z1PXJKh9ru0D7r4/QMdwiYbqQonKrBDycKjHK8D6dI7nuWy3roponLVnIZPMxBti6UeL1YN39jgaMF7ooorJtYBQqvN+8H9pHL9fH3yvbznL9HCu8LulRifndJPxJQ/jZIDElgOeTM31zfvn0Dq0jSufthEM6VTpIK1kyKgCKQdhHAgyzNCC2R++ztCQMTuHaA8Qm3+ltqm957uptVJ5b7tXPvqd023Dct09+3s6NZezJu8viGk2vhCgfuMGLLo8udU2dOms/3f+CFMe6dfS+bSNL6NeYcnxQoUMDQ8vm8884zuNPsRhcgeHkZWr8GCrQMBi8vO9kew6wGmhADAmjz+RqzgPhZIw63zMA9NFFe0SCgXt2B6XjOttEaNVCw4ovnroTpQD4NLbJpweoKXpDW2tN1keOWT3dCeDEaTth3BatUWDc2GLLz2kMs6GoklNBAAC9ia5RA9z7B2sm8NKYBIfLKpbECtIvWDU2g4Qp8/PlZXbt9w8vfz8gFQ+peeXThAyJg4+GL0AvnNaKFbiRp2d5IcIu0f0wPImtdztCSm/3jnq6PPv/8cz8LfKal0CKX6X7//XcnIJn/XQt+937w3fPed4VGLrwHfOMDj+EX0E1uIrnvmYmGSa4Fvlsu73caWfGeCSbwjWgNTOjSBivJBEtioB03IHxeu3l/0B1Uly5dbBityEHMrcsf1gsNvbW2dvvK3wA0y8ly0ePbMN6DdNdDN0DwHeobpceKgCIQxQjEsG94yKQp4aosm06vk2z4KxGrRQzVwJNwvL3l9HphnpJZLoaxSvTMgQrV58BwatW4pjGHlqjN4DAoh/qowQknvPTuHCiWQU0kNYC9e/cOly1V4kDOrHaHzprdNgVWRE0KtSTcc15iqFUkmI9aPmo5OX+MQ9iJGXbj3M5wZbrt4VwwtpfLprkaKTcucM92uA63XUOAwDSpfR4JbmxLJP1jemoQqSnlXMdwcxC5FGSnTp3sNIdAwyuWk96FWmn+/ngPE4uEfn/8nfI3mNA9xHKp8aeT+YTSEkOWyfuTTuD5XEgpoTaTxjV0vE1NqooioAhkDATSJFHMGNCnrV6ea6KYttDQ1qQGAhzWJ7nmSjbuh0Fq1KNlpg4CXLqPQ+acB+xOnUidmrRURUARSEsIZEpLjdG2nH0EuE4s5zC6xiCc90QLYxVFIKURoIaNc+ioIeO6zCrpBwHO9eWSiTSuUZKYfq6btlQRSAkEVKOYEihqGYqAIqAIKAKKgCKgCEQhAqpRjMKLql1SBBQBRUARUAQUAUUgJRBQopgSKGoZioAioAgoAoqAIqAIRCECShSj8KJqlxQBRUARUAQUAUVAEUgJBJQopgSKWoYioAgoAoqAIqAIKAJRiIASxSi8qNolRUARUAQUAUVAEVAEUgIBJYopgaKWoQikQQSwxAs8j+9Ogy3TJikCioAioAikFwSUKKaXK6XtVAQiRCBmxCCRzz+IMJcmVwQUAUVAEVAE4hBQP4pxWOiRIhA9COzZJVKwsAhXcNt3UiRLlujp29nqycQxIi3bnK3atB5FQBFQBNIkAqpRTJOXRRulCCQTgZeedAo4jN20X5JZWAbMvvA3kVY3iGzfmgE7r11WBBQBRSAOASWKcVjokSIQHQgsmC3y0ZC4vnz1cdyxHiUOgZdjifanbyQuvaZSBBQBRSBKEdCh5yi9sNqtDIrASQwzX5ZbZPEJfwC2bha5oLh/mJ4FR2D+LJF6TeLidm4XKVQk7lyPFAFFQBHIQAioRjEDXWztagZAgFbOj7zj39HmFUWm/+ofpmehEej9qH9cv3f9z/VMEVAEFIEMhIBqFDPQxdauZiAEuncVeftzkU/fEun2dAbqeDK7OnuayOXN4xdC46D8BeOHa4gioAgoAlGOgGoUo/wCa/cyKAIL5zodr1A5gwKQxG5fXEbkD2DXrLxTQLumIrOmiJw+ncQCNZsioAgoAukbASWK6fv6aesVgfgIHD8mMmWZE167Qfx4DQmNQMlSIsTspjudNBWqiTSGhlHnKIbGTGMUAUUgqhFQohjVl1c7lyERmDvD6XadAiL5sKlEjkDFqk6eRbGa2chL0ByKgCKgCEQFAkoUo+IyaicUAR8Exo9yTto96BOohxEhULOuk3z8QpETARbkERWkiRUBRUARSN8IqDFL+r5+2npFwB8Bkprs2Z2wxX+KVK/lH69niUegWowIR/A5R5HDzyqKgCKgCGRABFSjmAEvunY5ihGY8rPTueoiptqlUdzRs9C127o7lYz+5ixUplUoAoqAIpA2EVCimDavi7ZKEUgaAp/0cfI9+I7ExEAjppJ0BNp1dPK+/6XI4UNJL0dzKgKKgCKQjhFQopiOL542XRHwQ2DRAhHOqaN0uMfZ6/+kI1AJFs/NY90LffNF0svRnIqAIqAIpGMElCim44unTVcE/BB45SnntDfWKc6Tzy9KT5KIwNOxGtr7Hxc5cjiJhWg2RUARUATSLwJKFNPvtdOWKwJxCMyYJDJ6pnP+UOzcurhYPUoiAqbVDSINSjq5P3gliaVoNkVAEVAE0i8CavWcfq+dtlwRcBCgg+2aOUX+wemH0IA92lORSUkEuFLLZY2cEpdgaF+NhFISXS1LEVAE0jgCqlFM4xdIm6cIJIjA270ckshV57o+kWByTRAhAnUaijx9v5PpdrgbUsOWCAHU5IqAIpCeEVCNYnq+etp2RWDOdPj4u8LBYf4saL4aKyapgcDRIyK1cjmEvPP1Ygb+KDGZ9Ds7NaDWMhUBRSBtIaBPurR1PbQ1ikDiEdi6OY4kvvaMksTEIxd5ypzniYxd7uQbMlZiXnhMjDGRl6M5FAFFQBFIZwgoUUxnF0ybqwhYBGiB266+A0YrONZ+Wg0tUv3OKFtJZPpEp5rXPpGYV3soWUx10LUCRUARONcIKFE811dA61cEIkWAy/R1ai0yb5PIBcg87FeRLFkiLUXTJwWBpleJ/DTCyfni2xLzzANiTp9OSkmaRxFQBBSBdIGAzlFMF5dJG6kIxCJAktjlepHhsZqt1StELqmo8JxtBMaPErn2ZqfW264UGTBSfVee7Wug9SkCisBZQUCJ4lmBWStRBFIAAQ43d75WZNQMp7DFf4hUr50CBWsRSUJg7nSRRrGGRGVQwtjFIpWxyLaKIqAIKAJRhIAOPUfRxdSuRDEC27eKtMKSch5J/FNJ4rm+3A2bify3SqRGNuzRmCo1RD59S4eiz/V10foVAUUgRRFQopiicGphikAqIPDXfAwvFxeZvdaZk8jh5urw56dy7hEoXQ5zRfeKPH6X05aHe0jMFRVEli85923TFigCioAikAIIKFFMARC1CEUgNRAwZ86IfPY2/PfVg5Nn1NC8ssiSbTonMTXATk6ZdJ3z3lciE34SgXJRZq1xtIvdu4rs3pmckjWvIqAIKALnHAEliuf8EmgDFIEgCGxaLzFt4P7GXbcZ1rXyy18ihYsGSaxBaQKBlm1EdkG72D12FZe3PxcpVETk/ZdEDh5IE03URigCioAiECkCaswSKWKaXhFITQROnYIF7Qci3Z6Oq2X8aJHWN8Wd61HaR2DFUpFnQBjH/BbX1rdfFLnnUZH8BePC9EgRUAQUgTSOgBLFNH6BtHkZCIEZk0Qeu05k8Qmn0x2vcYY0VYuYfm+C2VNFej0kMvWfuD5w3eh7HxMpj6kEKoqAIqAIpHEElCim8QukzcsACCxaIPLy4yI/zHE6ewF2gzHfjUOZKukeAS71F8M1ud/u6a9hbF0TGsYn4Y+xnUiOnOm+n9oBRUARiE4ElChG53XVXqUHBBbMFnmnl8gIaJ1c+bCPyP0gjUocXESia88h6c/fE/lgkH+/Huogchsspxs0Peer7GzatEly5MghhQoV8m9jMs+2bdsmmTJlkiJFMG/zHMv27dtl9erVUrFiRSlYMG4qwIYNG+T888+XfPnyneMWavWKQNpBQI1ZztK1OHr0qPz222/y77//pnqNe/bskY0bN6Z4PaexVNn69evl4MGDKV52hinw5EmRH78TuRLrBte9PI4k9npCZOd2kUehdVKSGL23QyX4wnx/oMgBGL0M+hTEsLjT10+/FmnSQiRrVsfVztTxIsePnRMcKlWqJN26dUvxuq+++mq59tprU7zcSAv8+uuv5cILL5TGjRvL22+/LSNGjJATWPGIz7VSpUrJyy+/HGmRml4RiGoElCgm5/LuhXZg7r0JlrB27VqpXLmyNGjQQG66KfWNEp566im56KKLEmxXpAn+++8/ufjii6Vv376RZj0r6ZcsWSIzZsw4K3VFXMk6uEx57RmR7PCfctPtcXPWGEaC2Ptdx0I24oI1Q7pEIA80VneCjM3dLLLyb5E+PUTKxPaE2sYrQaj4wXBHK4dQ8v5RSTYCx44dk0cffdRqEfv06SM8v+2222TqVB+tfrJr0QIUgehCIEt0decs9mYfHu4jq4vkZZ1fhK24d+/esm7dOnnggQfkiiuuCJtWI5OOAPFdtmyZ7N+/X2JiYpJeUErlpA+9n78XGdZPZDI+KlyhDcNTH4vc3BnrA5/vhuo+oyJAo5aeb4h59nWJoaPusbhnRuFj7I89zpre7rreJQBQ204ijbG2NIaoTclS5/w+f+utt+Tee++VAgUKpIurt2bNGuGIS48ePaRnz56ye/duqVmzpjRr1kxOUtuvoggoAvEQSD2ieHwffIrNh8PZhYKnGVxCVBNTrIXEZIZGBWJOn5SYnXNE9i4TOX1UJF8V+IhrAI1L/rhGrh8tcib2x1vsKgzX/COyA+4msuBLu2RrkdwXO2kTmy62ZHPqmMTsXgBNDsrKeYFI0WYo68LYWGdn27d3sZPm5CGRXCVFirdEesyv2Y92jKoqcgRpc2Bb+52TKX919KOSc+zzf8GCBVKmTJk0qYn7448/7HB4+/btfVqcPg8/+ugj2bVr17l9eW7eIDJxDD4iBouMx1rMvvLArSKdHhSDl3yaILK+bdPjc46AvSe4DCC3Z14V2bJJZMrP+MjAMPSQsSJQPsrHQ50NhzF8lN5+vUj9JnDKXlekai08p3JH3I8jR47IqFGjZNWqVfY5dfz4ca8MzlccN26c/fhq06aNndPnRr766qvywgsv2HuZRLFJkyZ2xGH06NG2LI6gkIBlyeL/mlm6dKmMGTNGihYtKrfeequdE8gyw9XFNk2ZMkUWL14sl19+udSvX98rl795lrd582apVatWyOFtlj9wIIb9ITt27JDZs2fbNnBKzYEDByRnTrxXAuQMnN6PHTtWOFpRokQJadu2rTd/keEklzVq1JCffvpJ8uTJY+PdOY+h+sMhbg5316tXTzhXct68ebY/LVq0sH0YOXKk1XhS05mVUxFiJbH9dNPrXhFIMQRgkZfysvwjYz4SYz4I2Ba/7tS1bbYxQwPimLYftuWfxrWnr0+ar32OmfYzbNvnOGkTm46pVw4I3rYlb8bVu3uRMYMD6mOdI7HtX2XMF0HiGP9nz7gyYo+GDBlicuXKZapWrWoGDRrkxR8+fNhguMMwHg8sgzkyXhwPvvrqKzNz5kyzdetWg3k0Bg8iv/jAk8mTJxs8uM27775rmjZtanCDeEnmzJlj3nzzTYOHpMEXtBf++++/G0zaNrfffrv54osvzKRJk2xcqPR4kdhyWQ9eHqZXr1527xWIg1B5mWbRokXmjTfeMHgQmr179/pmM3/99Zd55513bPuhFfSLC5fPN+GECRMMHsBeUGLzeRmScnD0iDHTJxrT+0ljamQzAMh/u6GhMd/huu/fl5TSNY8iYBE4c+oUfkALjPn0LWNube5/j/nec1Vx/93bzpiP8ayd+qsxWzdjgZ8zIVHEnGNTtmxZ+7sGKTEgq/b4lltuMRgFsc8HPkvcrXv37ras119/3Qtz4/h8a9mypV84jEUMhndtHhAqvzjmq1Chgn0mhauLma+55hqbF6TT7jF6YMvks6JYsWI2DIYydt+xY0cbF/jvl19+sfFuezt16mSfezwHWTMgizb+8ccft1lPAXO3P27ZmHpjtmzZYuNB9Azbkzt3bnPeeefZvFWqVLHlhOuP+xwFUTbZs2e3ZbANXbt2NTAgMrwOPC9durQ5dOiQrSuSfgb2W88VgeQiEMcmkluSm3/dqPgEkSSKG4ni0V3GDIg9d8MD91umOqX5EsDANDwfVzWydGuG+7eN5NOX0P73nTEnDhrTP0T7SBQnNPEvw7ddi151UfD2fIi4DyY+FCizZs0ymDTthTO+WrVq5p9//vHy8cHEhyMfTG7+UGSRD0Y3TbZs2bxjFjZgwADvnGn4ECKZW7hwoYF1n18c6wuVnmW5Dzi3LnePie+MDpuXJJXp3Qc9NBA2D/998sknJnPmzPYlxRcV+0AySQmXzybw+deqVStTsmRJGxJJPp8iEj7cv8d5Ab/2jDEtawR/YXdoBXI40Jg9uNdVFIFUQODM6dP46P3bmGH48H3sTmPq4PeE31fIrXlFY9aujtcSzJm2v0tMj7GEDZo2A82aIVH8888/DbR39iMOFsIGGjVLylgIpncYEir+pqEhNMwHrZ+pW7eu/e3Cwtm4zyU+7ygkitC2mcGDBxsY9ZnOnTvb/BgGDlsXiSafG5deeqklndB+mm+++caW2bx5cxvHD2Vo6gyfRWwT5irbeN9/0P6ZH374wcZ/9tlnltDxA5npgxFFzMW2cSTHJGz8OGbahx56yBZLoshn+q+//mpIKp944gkbz7aEw859jvKDft++fQaaUFO4cGGbd9iwYVZp4JaVlH769lmPFYGUQCATbvyUlb/axZXHoZGrPsBcrF9Emr4kUgJDt6sxJHc4Lolc3R/xEzFk4hO25Dmfk9hDemq4bTqc1NaMi9uNYetACZdu5dtxqevcKXLLIZEWH8WFbcAQ8qoBIhgJ9+TS25BuEnzafSpSGfPKWs7Ahr0rxXDwGH7j3GrEbzfnJubPn19o8cehEc6JufnmmwUaRYEGzQ474EEkIIl2GAYPHLdkGT9+vNx1110CzaLg4RR0SGX48OGCh4vgy9cOj3AIxddghgYozz77rLWCxkPHDpV899131riGQyeUV155xQ7FMD5Ueq9ROGjUqJHMnTtXpk2bJtCUCh66wiHscHnduthPpnWtKokJjW84ZESsWAY0HHbCOYeEQuXzbU+w46Tm8yuLy679PjN2pRQMzVeKwZzUAlhz+Wo7p0wmYmoCpW5hOFV+QmQ67mNMjpdhv4rc2kVX4LDg6L/UQCAmEx7dnNvY4V7H1c6C3XhuYS7Mwt8xTN3PWUbwSkyPcYUOv/PCgMZH8AIRkBprAfziiy/aeYYgLIKPNpuK1s8c7uUcPg57Mg6EycbRhQw+gu0xh1oZhw88mThxorRr1074HONQLIUeH1yBlkxAEO1v/MMPP7RDqyCSEq4ukDG5/vrrBaMOds+hXk6VYR3MS3c706dPF4yaCIfRKXw+BQrIpuTNayeV273bvsB07rlr4MJ6MBJin9XMw6FiV2g9jQ9Ui9mNN95og1euXBm2P25efJjbdhQvXlxq165th/A7dOhgMQHJtsnoZSLSfrrl614RSCkE/CePpESpO30KqXwLyNVjTkAJvFwpy99x9vzP6YgV7nfOL2om8v/2zgTepqr948+VREpRJElkyGsqoqJISUh5Nb+vNAnNqaRB+iOleXhL8TbSLGlCUXmTVCqaKNJAgyEqVIZM6//8nnPXvvuce865507HPff+ls9x9t5r2Gt/97n7/M6znmetBTMi22ty/hAjB/T/jtNEah4p0linD1l0WuTw30FuzkaycsvUX9KnOSpY8Qqnn9UnMiuERMWrO+JpySqnD85ax4RLpryNhygevHjYYRvCDn4pmIJh4MCB1g78bSCS4LcCHxw8NJDq168veIAjwS8nXtLhYjs8fPhwUaukbYfnAINIxMMND2v0A/OYQaSiP94BHQ99POiREpW3zOz/4KsE/yMkXAPELPx9ktVVC4XAdwk+S2pBNHGK+njoIvIQfVBrJg7ZF9BXX31lU/EkqmcFk/xX0HpRTT6mPwguHxJ1yHaOqKdisYcGFHRUkXiESDX8OmEige1MAFHSLdVXEa/sBDGYtXKF/kD/OtcPF/jX4VmgVrm4PrPw2Zs8ebIMGjTIxAp+uKG9ZAmBIuecc04gMFEWQidewnMKfooIPsvrXPgxfMkll4gObwc+gW3atLG2Mecj+oakw9D2HGrRQv3FC5nwoxsJz03fPvoQfr6GT+Hnh4RYzet6wvWwXbNmzahD+K5Awj1Ce2BYXNcZdWLukEAcAkVvUawQOsum30M72ZuVsucNw+5GFWJuWyRjy6/ZBfRtp5zNYAsBLEgVIr9SIztx/k9WLrsJqwU9WL9B9Gvv7jqPWehXt3YtC4E2yVLk+ZSsRFQeJnRFgjgMJ8zphYRfkD61a9fObyZ816ELE3/41R8v4YGlfov2IHr00UcFztl4JUr5La8+RtaUdwZPdC71gxScH47c+OUNKyISviSQIFzxMMarU6dO9rCHI3eielYpyX8FrRfVZMtDI9bCS8/UwPb7ItYaWAzf/V5k+L0iXdWCQJEYhYw7JYuABcjstbcOA3TM1TFYABFkB2scBElsWrVqlVkHMYKwYMGCXD9W8feJ5K2M2MbMAwj8gFUNIyXJEoL8EPABa2Kyc+F5hWAW9ds2Cyh+7EKw4cclnj+wdiKCeeTIkfbCj2ZYHQubdBjdmkDQiW8b77169cqz6WTXk2flmALFfZ0xp+MuCeQiUPRCcV8VWz59NV3kdbWOfXCBTjKcJTL3Wv3J18Xn2hBv1uv6Bz3zXyLfzc85vo8O4xVHqq3DyD7hyhvfoEPi03RIcZhIlVYawawitvYJvkRkCHqCCtPZF2skaweRF/UakHZRi5JPK3XjnVN0+TXNe+MofzThO+ZTRIJlMZyee+4528VD0yc8EPNKWFkAD1IMEcVL+AUOUYZhZQ32sF+lvhyGYpDCD/pk5X298Lv6/Ngu+pGsLobc+/TpY184EMAQlJjKBg9hJAxjhR/Gl156qYnbRPWsUpL/ClovqklMgPyh3uD7nhA579KIpUYFLRMJlBYCsBbix1rnzp1t6HbMmDGBaIQ4xETU+BGnfs2ifs2Wh5EPJFgDkSDShgwZYu4xGBGBANUgPEH0LhIEIaalQcIPYQ2CM9cS/CDEM0798ey5kOhcWNEFoyvXX3+9zdAAq6Z/Nvbr188ilvH8wXMO14PtCRMm2PkK8x9+bGI0BsPA2MYoEDghGjmvlBe7vOrH5hfndcaei/skEEsgohRijxZmv/U9OswxJSKy0M6iT/Q/vDTtuVxczY6S1aCplvkycuwbCMSQSKymu/+4IpJX1P8fdKOed7zIn9rwJn1NOTv6DLAwtntIpK6KwyUopGkVXg/apkRccsTt00WyKuohNS5Z+mxi5L1hyCqanRX71q1bN2ndurVN04CHSceOHW1qBWxj6oWwUIytG2//wgsvFEwLg7nM+vfvbw9vDNv6hAe3T+pEbg9+jdozKwKGZ2BxgMCDaIRPjgbZ+OISWx7TQyBhKgqIU/gHaSShNGrUSDAcjYe0T7F1MY2GBprYgxZ1zdKhhSGcIRzx5YMvEfjtLFmyRCCcscQWHsrx6vnzJHqH1bIg9RK1x+MkUBoJ4JkB4YVnCMSeHybGcCvcXfC8wgT7GpxhoyB4TsEdBq4d+NuETyL8geFPDTEDMQcXFPgUwlcaQ6pY/QSiEEPFTz75ZLDyScOGDe0HI9xY0IdE5/roo4+srkZaWzm45uCHJhJEJkYp8LzBMfQb0/RgqplkyQvN8Lt/Jvl3PJvwQxjXBNGL0Q74WA4bNsya9nX9efx+Xuy8/7Q/D+pjO3bfH8d7Qa8TdZlIoNAE9A+06NMf3zn3qkafYrobHxWM6Wzm32Pn2rZ1s86jcp5G7IXyH9XtqYc7t255Tn8eCuWv0qkhkJbPyGkTdZBSLYeya7937rWDoqe4GaXtTNTX4kik7bYtOlXNe3005FaP+f7/R7cna+SgT9+Mc25CKB+R0p/c4HOj3tX/z6lwCY4hIlAfshYxpzfQplcYMGCARe35Qvqwcerr43eTviPCTgWn01+/cCAKXqikQtGmWVD/Fqdzljl9gFq+PvysTUxXow9zizjW4WunTuAJy6Pf+pAOptBABLU+3J0GoFhbyc6lv8aD/mE6C10VwergPx06Mj7qaG59Q6S4CmCnfpUuWb2ggewNDRgKop7zUy+2He6TAAnkEMC0MT7hb1UFk9+1d+Qj4tknTP3lyyASWX3sfJZN1aM+2hY1HRwMbSQ7F54HOqQbKh29ib4hsrk4Eq4Dkc/5TcmuJ79t+fLFeZ3+HHwngTCBLOwUWm0ma2Ddj5pbXtzOe0f9YgqqbPpDvZ3Xi+xcMziUto11S0XK6VBipT0Tn3Ljb9q/P8VV0v7vEGfYcd0ybUMdM5O1kaB1/HrXP3qzAoZ/TSYoXuDDuMVwiNb5HK0NDMt6Z2kcwC9l/Cr3Ttp5lccQEfoN520/fO07l6wuLIkI5IGVId71oh/wuQz3De3mVc+fO/a9oPVi2+E+CZAACZAACZRVAsUvFMsqWV43CZAACZAACZAACWQ4gXIZ3n92nwRIgARIgARIgARIoJgIUCgWE1g2SwIkQAIkQAIkQAKZToBCMdPvIPtPAiRAAiRAAiRAAsVEgEKxmMCyWRIgARIgARIgARLIdAIUipl+B9l/EiABEiABEiABEigmAhSKxQSWzZIACZBAaSOAabRWrsRyVEzpJIClDjElGRMJbA8CGSMUsXYmloViykwCWC0GS3nFW1O2sFeEuRmxxBjmhwwnfmbCNLhNAgUngHlYsYydTopv8762bNnS1nMueIusmR8CWLHroosuyk+VjCiLdcHfeeedjOhrWe5kxgjFM888U/wi7WX5hmXiteuKMNK0aVO7f1iWryjT008/Lfvuu68cccQRtlQYlgLEhOBI/MwUJWm2VZYJYNm+8ePH6/JTkfUZPvvsM8HfNRMJFIbABRdcYMs9+s9VYdpi3eIjkDFCsfgQsOXiJDBlyhSZMGGCHH744baerF8vuijOuXHjRrnssstsJRddElCwj/VnsWY1EwmQQNERwJrOsQnWIKwbz5ScwO233y6///578kKlKHfOnDny7LPPpnRFWGMcP+7jrdSVUgMslBYC5dNyljJ+El0j1BasHzp0aNGQ2KA+Qqtmi6z5SqTC7iJVm4ur0Vb/2LJ1/wb1ZVkxPXKuCrvp8oJ7676a97HUYK0uIlXqRfUDv+ay1i4UWTlLZPM6kZrtxVVrmdPe6vl6ri8jdaodJIKlDJepGPt7lUj1tlq+Q1R74Z2PPvrIdvFAaNWqVTir0NvfffedPYCvueYaGTx4sA0963rVouteF7ptNkACJJBDQNd1z9kJbVWqVCm0l/rm22+/LUuXLrUfdhAKEFK6tr3oevW2ZOekSZMEQhQ/LE866aRgedGJEyfaMp8QFnvttZc0b95c6tSpE5y4oO3qWtUyffp00fXqpX379nLYYYcFy5PCN/DVV1+1/uIZ1r17dzsfhuMhiDAMv3r1avnggw/kwAMPlOOPPz4QPjfddJPccMMNtl+tWjXp0KGDNGzYMOk1onG0jWtdtGiR7L///oL+JUpw53nxxRetbNu2be35F15aFdc0depUadCggXTq1ClgGdse/CDxw37t2rXSo0cPady4cWyRYD8RLzzvu3TpIl27drVr2G+//eSYY46Rxx9/3M6Pa3/qqaekUaNGdg7cd/i9IsE9CQxRH/dixYoVxrpZs2bBebGB5z5cjbwbU+vWrY17VCHuFC0BFQlFmnTYzz333HPuxhtvdG+++WbUIu1ffvmle+SRR9xPP/3kxo4d62699VY3b9684PzJ6uqHx9WuXTsoiw0sPq/WI/fEE0+4WbNmOdSPTTrU6caNG2fnxbl1yCQoon8Y7qWXXnI6pOIWLFgQHMeGPhjcyy+/HBzTD7S1MX/+fDum1iunf1Ru5MiR7t133426zqCSbmBReB0WdfXr17f6jz76qNM1noMin376qdPhU2sr2YL3QYXPbnLuQR0Bujfm9azu//pppNiSl3Lnh8ujDZ/WLnJubExbKPtqbbdt87pIqf8dn9PeQ5p3f0z5j67wrUW9z5w50x111FEYq3KjRo1yP/zwQ5Cf7Lofe+wxh7rqk+ruuOMO98orrwT1/AY+QwMHDrS2zz33XLsH+lA1xuqzaMViPzO6lrTdU3w2cR/04e6bc6nez6ACN0igDBH4z3/+Y39r+Fv2rxNOOKHABNTy73beeWeHv13fXu/eve3ZeOyxx9qxcuXK2XvdunWdWi7tXAcccEBQHvVQ5vzzz3f420YqaLvHHXectasCy951SNTaw/N+7733tmO+P+gn0o8//mjHVQC6HXbYwe200062f9VVV1n+LbfcYvv++vCO7z08/5NdI56TKuqsrgp0p6LYtk899VRrN/Y/35Y/jwo8e56hnAo0q+uvC32Nl5YsWeJ23333qP5effXV8YrasXi8PvnkE1elSpWoNlAOCeywjXvp+4nnevgZfeedd1rennvuaZ8NlANTfBcggXfs/dcfFg71mIqXgH7jF21K9qHN64OQrG74A4UeQ5zpr5XgQ4cPlf66dAsXLrQLyutDBYFXoUKFqPr6izYQm4ceeqjTXzIBHDwwcI577rnHjsX7QwkKZ29AfOgvvKhzoA0IXAjPbt26ReXhjwyiN2H6fnyOYAsLP789TiLiLi+hiPI/TnJu/UrnHg6Jvsd1G0LQtzetQ6QrYaHo88LvEI5//5Gr23369Im6PojvVK470UMlfILXX389qm31RzSRCL76q9SKhj8zeT2cU7mf4fNzmwTKEoFt27a5u+66y2lQhVMLnuvfv79bs2ZNgRFA0OFvVa1NTq1QbtCgQU4tiG706NF2HCJFR2LsBzTKXXzxxXYuCAU85/GjUIfDTXwgH31DKki7eE5DSKll0ASWWvLcM888Y+0dffTRlvfWW285tXw5DSix/mkARiAU0R+1fjkNprPvjKpVq1pdtcy5K664wsrDIKLR4k4tcXle44knnmh1hg0bZm2inlpuXSKhqL777oUXXrA+QMSCB74fkdQaa/swmOiQsBsyZIgdj/1v7ty5Ti2pDj/gUVYtuSaQY8thPxEvHAcXnH/EiBF2vf4z4kX28OHDTfjhfuKZHH5Ge32g7kSWB+MP2sJnDemss84y4Tht2jSHF0Tk5Zdfbnn8r3gJ6Dd80aZkH9q8PgjJ6oY/UDoU4HTYweGXB9qEMMCDBb++WrRoYda9ZB8q/FHhAwgxiIcCLINetOnwsAFJJhQT/aHEI4k/clgT27RpY3843mroH2j4I8AfBB6Q9erVs2vAH2vc9GJIxD2h28tnOPfZiBxhB/H23bPOhYXiaD32y3vOrZzt3H912wu8Wec6962KUr8/VvMg9tbpL/f/ZJfT8viCcGGheJ/mLXrcuXm359RFG96aGeo4HvRnn322sdZhJrsvqVx3oodKqGlrC9Zg3McHH3zQLLewGGM/nlBM9gWUn/sZ7gO3SYAECkbAPwcgUMIJYgh/w1deeaWJGggbWI10yNeKQSjqUGNQBcIL+Tqka8cK2q4XZ507dzahhMZ0aNOex7Vq1Qr6AmMC+gdrobcoXnfddUF/LrnkEsvHcx9Jh51t31tEcSzZNeJ5i+vRAL3IsxcVNO2yyy4JhaIXYxghu/DCC+18ENFIOjRu+xiNC4+QWWboPx3qDowks2fPdurCY/0IFYnajMcLBSCIwQcW6HDCMx3fg7Ep/L3u9UH4+w/f8+pKZNV0yN8srb4NHZIP8vwxvhcPgSL3UYTTM6ZQgP+JfuD1MyOyYcMGe/f/6XCDqKneAhzgZwI/DKRU6vpymBJFhxBFhx+tLnxK1HxujrHwycBUKYiGVSul5cPXBZF6SE8++aS96zBA4IcB3xF9IIha9ER/yVl+ov/0l4xFaqlQsXcVIaJ/BHGLV69eXeDfo9ZLwTaS9z+Bf81///tfO4ZgDxW+og8R83uBr12uFJ79paFOlVDzSHF7dZCsd28Q2Zpdeu0CEfUvDFJF3arRLrJbt73Igncj26se1z/nLUExWaObo6vk7GNLb1vWWvWDDKcWJ4s0PEeV4TbJmnF1znk3rQ6Xsu3KlSuLDi/ZNq4d0cjwu0nlusETkZZI8BmKTfDBwecMCe/+sxZbzu/7ABf9FSvq8mCHUQc+Mfm5n749vpMACRSOQMWKFXP5LaurjjWK7wcdTrZtFV8J/erwXMVzE/5sPhWkXfjN4Tz4ToCfoQ6Liv64Fzwv0J7viw5DiwpDUYOEP13Ue40aNWw/9jsvXCjZNcLvTkecRC2ZgZ9juG68bfj5qYA1vz7/vYF+I2FKI3zfqPCWnj172velCrJczcDXdPLkyaKWXbtmXK9Kjlzl/IF4vOD/mCy1a5f9PZSsUEweeKL/SIiyV8ODHHnkkebj+f3335fKKYNiEJSI3SIXisk+tPGuOPxBSLWu/pKzpiAOwwlTpMAxWn08kn6o1L9NdHjAHGp9ff3FJvgjg5Os/qqzw/7dlwm/F+QPxdfHQw1/yLH994Jo8eLFvmj0O/zGNdbE0sal9pa1Rf+Iwn/PCG5JlNZni0TL18CXijWjS9ZHkMsO0ceyYvbL72L5FjijsTEQk6mm/Fx3QR4qyfqR7OGMeoW5n8nOyzwSIIH4BNTKlCsDU6DpsKLoiI6cfLL+KM1OCLSIl9QvTvC8VH/JIDu/7eI5j+AM9Y0WHbo1QQXRqH7rohZMi+xGsBy+I5Ag5tRiGHxPBCeOs+GDgBDQ6FOya4TwRfDKjBkz7Dy+vq8b7x1TzGDasa+//lrUQis67BwUg8FEXYBER8zklFNOsaBKiMrYABHMOQvemFJszJgxct5551mATNBQaCMZL/yARwpfr68a7774vFTee/XqZYYVfEfju1rdDUwAp1KXZQpHIPdfauHaE3xoEa2GD60OB+ertVTrNmnSxNqNDcHXIBo7jslJ8aGC2MCHCr/u8KHy1kfM6YcoNTyQfIL4RFnUxQcaAha/WPwvMx2e9EXtAeEfLOq7YuXxYEmU8MeDX4k+qUOvWdpgkUQ7PvnrQf/iptqn5hxe8IrIe+eIvLa3SETXRvJqdc4pgy2cdt5tIh9eJhLR15H8GudFIqAje5H/qx0lcsSrIgerlXOvHtruOnG7NgiXKNR2fq67sA+V2I7i4YyELyD1Tw1e+JyEH3yp3M/YtrlPAiRQNARgAYOlH4IN2xg10uFgi472Z8DIEaxiiCZW32I7DItZspSsXfyAxQjG9ddfL998841Z0vzzp1+/foIfmYgA1iAVs7hhG1N+pZIwYoYEoanD6KK+eXZdya4RVj1EHuO6b7vtNhNuPsI33jkxggaBiQUp1K3KimBxA0QHw4r473//W9Sn255ziaahgSjGiA9GV/A9CAGOc8LwEpuS8cL3Js4ByyymBUL0s49qjm0nv/uYmPv99983cagBLhSJ+QVYmPJFPaINnwJEXcH3xPunwYEVDrLxfBDgCAzfRKRkdcO+DPoBNj8VvW6nVjl37bXXmpM19uG8iwTnY+zDSRrBI+EEp2k4wuqQgtMHiPl1+IgvtSxZUfg8oj58PhBgouH8to9gFvjbwWFZ//id/voy/xEVr+FTRG3DYVsfPObgq7/mnD5knE6bYO3BaRj+OAimQOQc/C7Rfty0RiOzH9HnmPcrjH2f3j1SLeyjGFsG+6P09edPbtvWzc69pr4/8cpkH9u2ZWO0j+Kss3O6FvZ5XPa/nOOhLe8z4yPSU7lusMJnJ6+EiHfcI0SNIyXzUUTEvT6c7Z7DjwmfSdwX/TGR7/uZV7+YTwIkkJyAiheLbI1XCs9u+IjjeYi/b/huY+YKJPgowhddhVHw/MQsGz4VpF0VghaF7KOLDz74YKcWPd+kU1ck6wP6gj5hJgf44mHmBRxTgRmURRAHjsF/EQn+g2qps+8bRHkPGDDAjie7RhTA9wq+UxBkg/bwwndVvKRTjzl183HwpUSQSM2aNe07Cb7vmOEBzz3UR/S2zjcbrwnzh4SfPhiAfd++fe1awVt/SEfVyYsXZjNBH9CWWv7Mfxzc8N0Xm1RIBrOZeH2gwjcoFtYHiCXAdwOuBd/fOgSdPPgzaIUbhSWgiqFoU7IPbV4fhGR1wx8o9Fh/1ViEGz4w+ODA2Rd/hIhMQ8rrQ4WQe/VFCaYeQNi+Fxyoj+hcRIHhw462vehFH/P6Q0H9cMK58PBBP/HHjL5hOgfw2GOPPew4Hgh4AEHQJE1/fKsXp9HYPloZgSfP6OvjQTl/0LFCMTydDsqu+ig4xbatW5x7/3znntbjXjCiTY2gdh9erm3qtBPhYJZEQnFFZAqDoOHsDTy4cN1eKKZy3YkeKrFte6EIh20kTKuDc3344Ye2H/uZSfRwzu/9tMb5HwmQQLESwLMcAXHh5INZEMTiAwPD+alsx2sX9XTkKWmbCKKEkaIgCc8Y9Dk2JepLbLm89mEMwbMVCW0iOMUnHEcwTazg8/nhd/TTJ1yvb9MfC78n46UjcVHTj4XrFWQbkfEQiTAM3XvvvRZchOAYPO/DwS8FaZt18iaQhSIKu0gTnE/hAKw31nwV9ItfUp2YNb91MTSMCVFh4ldRZ9cBszv8/zDciCEH5GNYF6Z4/VCZf4O/YPhSoA21KPpDUe/wm0SwBK4wDP0XAAA25UlEQVQhNmE4GvXhTJ1Kgr+ICsNcReHvgvNj+CBfaZ36KcInccfK0dV+eFnk5RMjxzTew529VbLWq4/PjlW1fCTAKLpC9t4mdSTfvEZcpX0kq1zu641bpxAHC3zdhTgnquKe4bOCYJtwyu/9DNflNgmQQPETwLAvhm3xjGcqOwQwlI0gWAz5w9cSSacwkjPOOEPuvvtu0WmIyg6M7XClEc/TIj6xj3RFs94BONVT5Lcu/P/UzB3VvFrlzB8DPon+QwXfQ3yo3tYVAXxkWCr9w2z6iRLEMF6ppngiEXXh11GgVHmflKpZ4EnlnNULElaqoFHP+orI7YSliiyjwNddyB4k+kzm934WshusTgIkQAIkkAIBdT0TRJzr8LXo1Dv2Qx9+lPgOwUwhTMVLoFiEYvF2Oe/W+aHKmxFLkAAJkECmEUDwSr5HXjLtItnfXAQgErHEHwJaYOzRIXHRiblF3bXijtLlaoAHCkWgWIaeC9WjIqqsTsRRHyp1fC07H6rV80Q+uSpCcud6Im3HFBFVNkMCJEACJEACJFCWCJRaoViWbiKvlQRIgARIgARIgASKg0CRz6NYHJ1kmyRAAiRAAiRAAiRAAuknQKGYfuY8IwmQAAlkJAGdesVWJdmenccqIphcGgmzZGAlLkwWXdoTlq3FZNiYPSOvhIUcwCXZUoJ5tVEU+VhoApOYF2XC4hhwLUtnwspAmD2lrCYKxbJ653ndJEACJJAiAQgyTDWGqcIwFVnLli1t9a0UqxeqGEQBVgjxYhDLzPmVljDtmc6BayuJFOokJazyq6++akLPd0vn+JV9991XsEztqFGj/OGE71jbHlx0zt6EZdKRgVVhMKURIpSLIuGHyn777Wcr9hRFe6m2gVlTLrroolSLF1k5rHKHAJ7tnSgUt/cd4PlJgARIoIQT+L//+z8ZP368zsIfmXb3s88+k9NOOy0tvX788cdtCT+In7KQdGUu+ec//ym6epRdLuZ3RYQvplfTlVVsWbxM4dCpUyebqg5LBDLlnwCWNcY65v7vLv8tFE0NCsWi4chWSIAESKDUEnjjjTdyXRusHbriR67jRX3grLPOEojFjh07FnXTxdLenDlzbIGHgjauy7raZNJYexoJC0Vg4QddHczWjMZ69ZmSevToYYtVYMo6n7AGNK6HKW8CuhKcWdP9YiKJamARCf/DIlGZwhwvlfMoFgYI65IACZAACUQT0PWVow9k76W64la4MkQnfL4gALFgAhJ87+ADBkuaTy+88IKtmqXLn9q8ebq8XL4WOPDt4H3q1Kk2/InVwmDlatOmjcDX7c0335Rjjz3WBC8slji/rrFsVWE1nT59uk3uDKHTtGlTO67L+MmLL74oixYtkrZt25qA9dehS80JyuryoeY/iWFSXVM+aXuWGfoPK4XBtxC+iLpErYlkZGMlK12/2YafQ8WDzW+//dauE/UgHGIT3Ad0eVNjv//++9vwfex9TXTNaEvXYLb2GzRoYAyxmhjmNvzggw/smsFKl9aV7t27S7Nmzez04KRrcQerl910001yww03GFMsZtGhQwdp2LBhbFfl/ffft2vFhNoQm4kWvsCcirDAnn766SaoIEAxKTdW79ElC2XSpEmCHzQQ3yeddFKwAhs+fxiWX7t2rbWP4fFwAquJEyfaPQYr+Hwi4bOra3ybNR3nQMICH7NnzxZd8lcaNWpkx8L/JeqHLilr7gXherAcws0A58NiIbgeDLf7BD9VuCXADQOfW7BDPuYXhd9u7dq1jS1+VMRbTc63k+937RgTCZAACZAACSQkoKth2Lq6+gUTvOuQWMLyyTKwxj3aUb9DK4Z1gevUqeOw3r1+gdsxHap0akVx6o/oHnnkESuvgsTyVIg5/UK07dg8Oxj6D22rcAn67PuPdY9ViNpxHd6zdYSRp+LH1klWP0CHNefRB7x0km8rj6b1CzqqPRUZToeHnfrhuSpVqkTl6Re49SZZe6Hu2ibWW0ZfdFk69/rrr0e1Bx7xkooxpytLWVn01V+nLnlnxd99912nojU4jvzmzZu7hQsXBs0l66NadK0u7hHqqnCzev5e6jK2TldVszwVt27mzJlBu37jlltuiTo/2tGl+Xx28P7www9HlVMx6/SHhOWH2eCACkQ7ry7vF9Tp3bu3w3339wlrRONc6rNpa14vWbLE7jOO+dfVV18dnF+DgJyKYcvDuXH/UU5XgHEPPPCAbd96661BeRW79tlFu7EpWT/U8mx9V99T6xfqDh482NrXH1HWVPizjmuvWrWq9cdfEz4f+mPF6vhrwTvW/i7KhLFvJhIgARIgARJISADC6q677nLq1G+irn///k6DTBKWT5axevVqp8toOg3MsGIvvfRS8EWnvpB27OKLL7Zj+DKNFYPhL8/YvNjz3n///daOWvicWvucWqrc5ZdfbsW8UMSXr1qeHMTQgw8+aGIVokutjg6iYfHixU6tjK5WrVomQDSQxkSjWs8cRAm+mCHEIBY18MD2R4wY4dQCaIwgfpO1F9vnsBhS66XzfNA35MUmtcQ6DTJyGmRkZVEH14J+QSgiH3kQcxB2ENwQRhBBLVq0cCifVx/VGmftqdXS4Z4MGTLEuuGFovpQGhu1eFo5fD5ik1rvTPyiX/PmzTM+ajmLLeauu+46e6nlzul6ztYe2kcKs8E+hCLaU6ut3d9BgwY5tSC60aNH23Fcp1pXnVoPbR+fq7lz5zq14rlPP/3U4XrU2uh05Rc0Z+nEE0+0ssOGDXNqnbV+quXchCLuMcqDp1odnVporSw+B/FSsn6gvFpc7UeKWs3dQw89ZG21a9fOPkvID3/W1cJp+eqn6vDZw9+jWjOtf/Xr17fPKz5zOisAqhZp4tCzfsqYSIAESIAEEhNQq4pceeWV9kpcKrUcDFlieBDRu4iGVSuNqDXK1u1Va5JceumlMm7cONEvTMGwM4ZDC5q8b6WKLKlXr541g+HicDr//PPl+OOPDw5hyBsBJGodFPQHCevDY5gVU86gTUR/Y8jTDz9iqBjDxH6IFHWrV69udTG8naw9DG0mShjSxrmQ8O7PFy6P4W4MoZ533nnSs2dPy0Jkuk/oL4Ysb7zxRhk4cKAdPuyww0QtYDZciyFlFcNJ+4j1lDGMDD9R3DcVwr55e1eLng11Hn744RYVj2H52AQm4IiEwBzPJ7acCkW7TjDF8CncBTDNTrJ02223SatWrcylAOV8/9SiJ2r9s6pgh2HyO+64w1wKMOyOoXj0A36gSKquBEPCiDBHABc+90h+GBf3WMWnDBgwQB599FFRy6mVwbF4yQdgxesHyqsoFfRdBa6ouLaIbv1hYJ+l2PawXCGuYejQocZDhazgGpDwjqUtEzGNbSu/+wxmyS8xlicBEiABEigUAUTx4ktYLTz2pd2rVy+55pprzM8KPmnwscOXcWETBBIEFr74EyUI0nCC6EKCKMCawnjBrxECBl/I8BvDFzymTJk/f76VhRBIlPJqL1G9VI/7OQUhquMl+GIiQRyGE6baQYL4zauPmBoJwgj+cBCjV12VvURsuMHsbfgVwsevoAl+r2otk5o1a9o54eOHV6Kk1loTieF8tTzaLgSev4eXXHKJ+TKi/WnTptn9U0ugXTsEIhL8TyFKITq9SLSM0H/9+vWzvkGoQTzDP1CH8UMlcjaT9cOXgn8lrgEJnzPwi5fU8m1+mzjXyJEjTbSrdT5e0SI/RotikSNlgyRAAiRAAskIwAkfX7B+nj8IRhyDGIPVB075CD4obEJwASxu6stl04zEa89bi3yejyqG9QtfyD4hWECHb836iMCRr7/+WnQYU3TY2RcJgnPCwSR5tRdULuCGD8SA5RCCOzb54BzMOdm5c+cgG0EmSBC8EFNIia4ZATJ9+vSRbt26WZAFhByswvlN3gIW5hPbxtixY02ITp482QJj8gqYgsUxNmGeTYhBsD/55JODbASxYMJ2HMN8nGPGjDFLLAQfEqxysPDOmDHDRKPvb9CAbqA/sAB66+y1114bzo7aTtYPFISghvBGcAqCpR577DETqfh7iE24BwgS+vjjjy0gSH0+RV0vrC+wPOdldY1tLz/7FIr5ocWyJEACJEACRUJAfQVNKMKid9BBB1mbGMK85557zNKIL7/CJnzhqp+bYOgUw8sQTRB2ECOJEsqgT5jkG9Y2CFoM00JYQSBiqBbWHQhHDFMj4csbohTWIFii0D76j6FHzD+ZrD31fUzUlZSOY/JzRFY/9dRTZgVF/8OroUDctW7d2qKnMZ9hRx0+fuWVVwTbEOMQirDYJesjrF4Q7xCaKJvI2pZXh/2QuAZtyAEHHCCwiMH1IJzA1ye4IEBEYRomiLdEVlNf3r/DAnrvvfcKLIbqpxncdwgzCCy0CYsxou3h/gBLIu435gaFCIQ7Aq4V7GCRRn444bMEoYihdm+ZDef77WT9QAQ7xDbOj+FwWCrRFizpGtyV64cN7i8+VxDsXmh7SySsr/isIaocw+iIPEfUdJGlIvV4ZGMkQAIkQAIkkCIBtZA4nQ4kKI3ggsqVK1sAhj+oVhZz4kfgABICU3zUc2yerxN+RzCIWomsDRU4Ti2XFqTig1kQORqbEACilh4LEtEvWwu+ufDCC536zTmd2876iOAWFaJOv6QtH4EcSIiIxTGcSwWwBWAkay/23Dq8a31Vn1DLUgFg+2oRjC0a7COwSIWGBVqgv/6Fa0RC8AMCP1QcWZ5aDp0KEodz+ZSsj+rf6NQ/zuoi8AMBFUg+mEXFnW/GoqkR8BMvoZ9qzbN+IEoafYhNaEv9SS0ASIWbBargenReSesvtj0bXfnFIodj28C++ho6tSha9DrqoE0Vnk6FrlMBaPcH+X379rUyKlwtD3XV0mgBTCr2A5Yq+pBlCedHmzpVjT+U8D1RP8AQbehUNkFddROwoCn8DSAoJ/xZR6AUPruog+AoncrJAmpQGVHmCIhBHj6XCN4pypSFxrRxJhIgARIgARJIKwH4+8FiEx7+xXyBify0CtM5+PLByucDKlJpC0OyqIfgi3CCZQrWHAx7wrqD/oeHSFEP/nwI3AmnRO2FyxTnNnwpVRBawEkiq2CiPsKSCJ9PWK8S1U2172ADix6GeuMlyBIwVsFk2Rh2jb0H8erFO4b7g/76tnwZ9MEHB6F9WInjDWP78v4dn1mdakjq1q1rczSmyiJRP3y7qb7DCgtusdeD+oXhlOz8FIrJ6DCPBEiABEiABEiABLIJIKIaEdFPPPGE+TmWBTAUimXhLvMaSYAESIAESIAECkUAVk5YE2HNg79qUfjRFqpDaapceG/hNHWUpyEBEiABEiABEiCB7UUAywEiGAiBImVFJII1LYrb6xPH85IACZAACZAACZBACSeQewKiEt5hdo8ESIAESIAESIAESCA9BCgU08OZZyEBEiABEiABEiCBjCNAoZhxt4wdJgESIIHSQ+DTTz8VneevWC7o7rvvtmXbMGE2Jo3GiiL5TZiUGXWx0sewYcMEkyhjChmmHAJYTQfTxjCVTgIUiqXzvvKqSIAESKDEE4AIO/LII3OtfFFUHddJim3FDcw9h5U3dGLifDcNkYm6WLJv+vTpttIKhWI0Rqw+g1VeyCWaS2nZo1AsLXeS10ECJEACGUTgjz/+kDPOOEOuuOIK2XfffYul534SbD/xtV/yLD8n83XwjheiXcMThOenrdJadujQofLll1/K8OHDS+sllunrolAs07efF08CJEAC24cAhoFh6bv66quLrQNYx3jPPfcUXb7NVhRp3Lhxvs+FNpCwljO2GzRoQKEYQxGrmlxyySVy++23F5sbQcwpuZtGAjuoz8WwNJ6PpyIBEiABEijjBNatW2dz0R1zzDHB6haTJk0yqxSWJxs7dqzMnz/flknTNYGjaH322Wei6x/LTz/9ZMuuxeaHCyNP1yaWI444wg4fffTRsmDBAsGQdPPmzYMl2zAEDl/JsJDUdZJtibb27dvLqlWrRNcVlmrVqpno1LWMw6exbUzGjNU6kL766it55plnZO3atSYww8u8of+6vrXMnj1bqlSpkmu5wqlTp9pQN/qEZe722WcfaxNL6OnawjJhwgT57rvvrB/e2vn333/LG2+8IegzzqXr/QbXpmsni65xbMvv4TjqbN682crqOti2XadOnaC8rjdsedWrV5eFCxfKk08+acssTp482ZiHGeHYu+++ayzB5oEHHhAwP+qoo6zP/K+UECjKhaPZFgmQAAmQAAnkRUDFhdOvUDdmzJig6KGHHup0WNfpWsxOxYblN23a1OkQtZXRYAnXrVs3O466eKnQcirOgjZS2bjzzjutrvodWnFd/9ipULJz//zzz3ZMxaRTweXOPPPMVJq0MiqwrF0VTE6Hpp2KPNu/6qqrgjZGjRpleWgbLxXFTsWd5aMf3bt3tzr++vCuAtEh79hjj7U8XY/Y3nWtYbds2TKre9xxx9kx8EOdCy64wI4//vjjUcfRNyTflj+Pij+3ceNGy0N/fBv+XDp071QwO+wvWrTIyqlQdzvuuKNr27at7eM/FaJOxXiwz43SQUBKx2XwKkiABEiABDKFwP33329iRINDgi5DKEJcqUXNhNGVV15pZd566y0ro5HHtt+/f383a9YsN3r0aKdDyiZW1BoYtJPXhg53mxhVK6MVVauatQtxpGv42rGLL77Yjs2ZMyev5oJ8LxTVUuk0itv99ttvrlmzZk6HZa0MRKha81ybNm3cDz/84BYvXuyaNGli4gpC0DPp2rWr++ijj9z777/vLr/8cquLa0X/dJje/fXXX27KlCm2j35C4EEgtmzZ0rYnTpzo1Jpp9TTAxMrpcnMO1zJkyBA7rhZRE6joZ+/eva0MxDuSF4rot1p5HcTmgw8+6NSKa+XOO+88KwcBjD698sorto//wNSL0eAgNzKeAIVixt9CXgAJkAAJZBaBPn36mMiYN29e0HEIRfX/C/Y1QtnK6HCm06FqE0MQYeGkw7BWBgIqP0n96aze3LlznQ5/mwUTFjodpnY6zGxCsl27dvlp0nmheN111wX1/HlWrlzpfF87depkgg2iDYINYkuHkt0JJ5xg299//31Q32+ceuqplgfxjHp47brrrq5Vq1ZW5MQTT7T8zp07O4hCn5599lk7Xrt2bffyyy/7w27NmjW2DfF64YUXWhkdurZjXihee+21QXm/ASEIK6K6BZg1F0IXFk+fdGk7awtCmKn0EGAwi/6VMpEACZAACaSPgFrb7GQqOhKetEaNGpYH3z/Ms6hWNznssMOiysN/EEmtc1HH89q57LLLzJdPLXI25U2vXr3kmmuukeXLl0uPHj1ErXYyYMCAvJrJM99fw4YNG8xfERXgd4hpZPBS0SgqLAUcfvnlF/MFjBcBjghxJERb+7oIHsH8jkhPPfWUnHvuuaLWVznwwAPtmnAccz4++uij8ueff0rPnj1FrYA4bHMeqri04Bz4giKBbzipUA7v2rZaXM2nEb6e6BMCkeAT6ZP3meScip5I6XinUCwd95FXQQIkQAIZQ6BFixbWVwRlpJLU2mdBEgi+QOCGT2oxs031ZfSHUnpv2LChqF+fBZSo3UcgGNUfUTCNDiaPVguczQuYUmMpFlKLqZVUH0wZOXJk8Lr00kstIhtR1Qh+ef3113O16INn0Ea4LgQuglzA5LHHHjOhqH6EFoGMRiDI1XprATwQfog0hzBUH0YL1MHckImizuNNAaQWSxPraiG1KY1w/nBC0A/qhQNewvnczkwCFIqZed/YaxIgARLIWALqT2d918CIlK4B4mfw4MHy66+/Sv369WXgwIGigR9mIcP0N/369UupnXAh9f+zXQiogw46SCpXrmxWORyEcMR8iUWZdJhWcC5M3g3L6I033mgiDtcDgYVz4jp1mNkiwmG9Q/SwDuOaZVCHmkX9CW0bdSHaYFGEtRVtXH/99fLNN9/AnSyIYIYVEdHaEJ8QlN76h0hoRJfDgqpDzXaZH3/8sUVT53XN4I6EexBrEdahbIvy9pbFvNpifoYQKD2j6LwSEiABEiCBTCCgq52YL9tFF10UdBfRs2EfRUTX6teo02X4rIwOuTpELO+xxx52HAEcKqScTvQctJHfDQSb6FQ1QTX496lgdCpIg2OpbiAKGP1VwRZUGTFihB2D/yIS2lXx5nbbbTc7jghv+Ajq0LTlI7Bm//33tzxERavl0wJfkImgHvhxIqIa50Egj057Y1HhiGJGeRw/+OCD3YwZM6w9FZTmy4jj8L+8+eab7fh9991n14koZRWormbNmuaXiSAh76Oo4tLKhv/TaXXsvLgHCKoJJ/h2og86iXr4MLdLAYEsXIN+iJhIgARIgARIIG0EOnbsKDq9iw2LxhvmTNYRDH1imBhWscIk+NKpaIuaQBtte9/CwrSdrC78DDWgRFRwxS2GPFg0MUwdm+A/CcsgLKDhpNHP5lsJC2s4wZII/0cVg4FFEfnw/YTlD1ZMtIl74FeyCdcPb2P+R1g1Mf0yVmMJJ7gBYCgayyR639FwPrczlwCFYubeO/acBEiABDKWACbNRjAHfOsQiMFUsgnApoRAGY3KtuHwsMhFHvwnMUm3Tt1Tsi+Evcs3Afoo5hsZK5AACZAACRSWACJn4ScIfzusFMJUsgm89tprotMZSd++fXNZQp977jkTjxD9TKWPQNF665Y+PrwiEiABEiCBYiKAtYExBPrFF1+I+tYV01nYbFEQQOCRzpNoQSyx7eH+YVh6r732is3ifikgwKHnUnATeQkkQAIkQAIkQAIkUBwEOPRcHFTZJgmQAAmQAAmQAAmUAgIUiqXgJvISSIAESIAESIAESKA4CFAoFgdVtkkCJEACJEACJEACpYAAhWIpuIm8BBIgARIgARIgARIoDgIUisVBlW2SAAmQAAmQAAmQQCkgQKFYCm5iUV0C1hvF2p9lOWEVA6yt+scff5RYDFhNQpcLK7H9Y8dIgARIgARKDwEKxdJzLwt9JWeeeaYccsghhW4nvw1guarnn39eNm3alN+qRV5+6dKlUrduXbn33nuLvO2iavCqq66SOnXqFFVzbIcESIAESIAEEhKgUEyIhhnpIvD444/L6aefLljSi4kESIAESIAESKDkEKBQLDn3osz25KyzzhKIxY4dOwYMsGIDhlgLmrDI/fDhw/OsPmfOHMFi9kwkQAIkQAIkQAK5CXAJv9xMSv2Rr776SqZPn25CrHnz5tK9e3fZaaed4l43hmI//vhjGxZu0aKFNG7cOKrc559/LlOnTpUGDRpIp06dZPfdd7d8+PpNmjTJlubaZ5995KSTTgryohrQHQjCrVu3ml9gxYoV5aabbpIbbrhBsrKypFq1atKhQwdp2LBhbDV5//33ZdasWVKjRg3p0aOHlUWhP//8U4477jjzt6xdu7a1c/bZZ8sOO+wQ1cZHH30kXbp0ka5du9oyYvvtt58ccMABQZk333xTPvzwQ8F1H3/88VKuXM7vqs8++8wYoo9oo2nTpkG9eBuJyuNefPDBB9YG7smKFSvsfjRr1iyqGeTNnj1bKlWqJN9//31UHndIgARIgARIoNgIOKYyRWD06NGufPnyTj9QwWv8+PHGQAWPU2EV8Bg5cqSrUKFCUA51zjnnHKe+hFZGrYCW59tTUWfHt2zZ4o499ljLU3Fl7+r355YtWxa0Hd545JFHrIwKJnfLLbfYdrh/Y8eODRe37Ycffjiq3I477ujee+89t3HjRte2bduoPLS1bt26qDY++eQTV6VKlahyKi7djz/+aMf23HNPp+I5KHPllVcG9UeNGuVUdDoVifYCoxdeeCHIj91IVv7OO+8MzrfzzjvbNs47c+bMoJnevXsH/Qzfj6AAN0iABEiABEigmAhIMbXLZrcHgc2bnfvmK+emqGh5YnSuHmg0r1OLnYlBHW513333nYNQUUuVlQ0LRQgfCKxDDz3UTZw40U2ZMsV169bNjg0dOtTKq5XQ9r/99lunQ7huyJAhdhxiFHWvvvpqp0PAVhf7F198seXH/hcWimvXrnVXXHGF1Z83b55buXKl+/vvv2OruOuuu85eGv3rnnnmGSt/2WWXWTnUqV+/vmvTpo3V12juXPUhKN955x2rN2LECCunQTWBUGzZsqX75ZdfnFonnVr33G677WZt/Pzzz8YQbYPn4sWLXZMmTVytWrUcBHJsyqu8F4roO+qrhdT61L9/f2vKXxuE9xdffGFC+MQTT7QysefiPgmQAAmQAAkUNQEOPauCyci0fp3I/E9EPp+rrzkiH78h8tGqqEtxvfpJVmi4VYWRqEASFSXyr3/9y8oOHDgwqo7fefLJJ21TrXnBcDOGgFUQyRNPPCHDhg2TU089VV588UXzLVSrmajgsjo+KEWFj9x66612bNddd7UhVt9+one18skuu+xi2XvssYdUr149blEVioI2N2zYYEPKGBZWq6GVRR21MIpa3xLWx1A7hrWRcE5/Hj8tTs+ePW1IG/kYmr/tttsE0wdhmBgMUUetmsi2/mIIGdPq7L///nbM/5dXeV/u3HPPtes4/PDDZa+99pJFixZZFoa/keBvCTcBJD+8bzv8jwRIgARIgASKkQCFYjHCLaqmnfr7ZS1aIDLnPRWDs0TeURH3RYLWoX06thdp1kqy/t4osnPloKBa2mw71v8tKBDawDx9VatWlUaNGgVHIeAOOugg0SFegQ8ixOb69etFh2UFwgqiUy1kwRyE8AmE7yHSJZdcUqQCB756d911l9x4441y2GGHWX/Qp+JIlStHGEKUqsXTTgGh6a8Nvpl4QZzGpvyWR334XIIrEqYOgggGdyYSIAESIAESSDcBCsV0E0/hfGo2lqyF80VmTVdROFWynp4Wv1ZrVYXtjhdp0VqF4YEiTVqI7BoJJolXwQeEIMpXh5HjFQmOIThD/fhk2rRpQVn13zOR+I9//MPEy2+//SZ9+vSx/FNOOcWEm/ow2lyMqKfD1nLyyScHbeowbLCdbMMLLkQuJ0qwdGI+wcmTJ5vFD8IxnNRvMrAwho+Ht1EGKdl5wuWxjWtCgmhWH07bxn+YqFz9GoN9v5Hf8r6ef0fw0EsvvWSBM7BsMpEACZAACZBAOglQKKaTdrJzrdGpYN6eKvLGK5I15vncJSvooV49VKl0EGndVoVhS5GK0eIod6XoI+rnZhZCDCsvXLjQIoMxZApr2Pnnnx9VeNCgQTYJNqKVYS2EdRECE1Y7DPsi4Tiiijt37mzHEQGMBEsjJqzWIAyZMGGCqA+fvPvuu2YlgzUyr4ShV6TBgwdbFPLq1avlgQceiKqGaGufxo0bZ1HZGiwjM2bMsKHwmjVr2ryMiKBWX0wTkxCz4QTLHfoM0QnRiCHze+65J1wk1zaupV27dsYGQ82Irl6yZIk899xzor6aNjQfrpRX+XDZeNsXXnih3HfffdK3b19Rv0UblsY9YyIBEiABEiCBtBAoaqdHtpcPAsuXOvfIfc51O0htiBpXFH5l6f4Fpzn39CPOLflOR5+35aPhxEUReKLCLoh8Vt8899hjj1kFnSYmKuoZkbcHHnigRfbqh9Ehcvnpp58OGtdhX6d+ghZYsffee7ubb745yHvrrbcsEAbRwahbr149p4IuyA9v4Pwoo1PR2GEElagl0qKOEQk8YMCAcHHbVqFobSI457TTTnPHHHOMtXH99ddbPvp+8MEH2zEEmiAYJ15SH0qnotKuUYd33YIFC6wOAlx8wjb6h4hopF9//dWpSLYAFxxX66JTQed0aNpXiXpPVt4Hs+B6fFJfRKcr5PhdN3fuXNexY8eANc6JFxMJkAAJkAAJFDeBLJxAv3SY0kUAlsNJajF84kGRt+ZFn7Wb+qF1/7fI0d3ENW5m1q7oAtwjARIgARIgARIggfQR4NBzGlhbMMp7b4uMvV/ksVeiz3hGFx3DPUPkGPU/2z0ShYsCkUHc6KLcIwESIAESIAESIIF0EqBQLE7af/4h8vw4ybr1MpFvQyfqcZjIGeoT2PVEnZtlt1AGN0mABEiABEiABEig5BCgUCyOe/GrTkPz8H80GiMnKlagBwffoAKxr8g+dYrjrGyTBEiABEiABEiABIqUAIViUeKE/+Go20RuuD2n1aObiFw+TKSbWg+zp2PJyeQWCZAACZAACZAACZRcAhSKRXFvNm9W/0OdvqX/FTmtnaTT2Fxzk8ghOvk1EwmQAAmQAAmQAAlkIAEKxcLetA9nivQ5UsRPbYfI5RtVNLZuV9iWWZ8ESIAESIAESIAEtiuBctv17Jl8cqy1PKi/yGHZIhFL/E59WdyUTygSM/m+su8kQAIkQAIkQAIBAVoUAxT52Jj3qU5p00rk++w6t1wvcsUQkZ0qclqbfGBkURIgARIgARIggZJNgEIxv/fn+bEip58bqdVC355W0dhMh5uZSIAESIAESIAESKCUEeDQc4o3FJNmy4irc0TiZWeJfLieIjFFfixGAiRAAiRAAiSQeQRoUUzhnrmtWyVrwNkiDzwdKT1Ol98768IUarIICZAACZAACZAACWQuAQrFPO6dLb936Zkio5+NlJz+uq7F3DWPWswmARIgARIgARIggcwnQKGYxz3MGn5ljkj8YEYkyjmPOswmARIgARIgARIggdJAIMtpKg0XUizX8MyjkSX30Pjb00Q6Hlssp2GjJEACJEACJEACJFASCVAoJrorX30h0vTASO5TD+cIxkTleZwESIAESIAESIAEShkBCsV4N3TTJl16b1eRz/UdQSz3jo1XisdIgARIgARIgARIoFQT4PQ48W7vQ3dHRGJdzbx5VLwSPEYCJEACJEACJEACpZ4ALYqxt/i3VSJ71ogc/d9UkaO6xJbgPgmQAAmQAAmQAAmUCQK0KMbe5gdujxw5qQNFYiwb7pMACZAACZAACZQpArQohm/3+nUilXeJHPnkQ5GWh4RzuU0CJEACJEACJEACZYoALYrh2/3q+Mhe+/oUiWEu3CYBEiABEiABEiiTBCgUw7f9+ccie/2uCh/lNgmQAAmQAAmQAAmUSQIceva3fcN6kZ0rR/ZW/ZIT0OLz+U4CJEACJEACJEACZYwALYr+hs/5ILLVthZFomfCdxIgARIgARIggTJNgELR3/7PP45stT/eH+E7CZAACZAACZAACZRpAhSK/vYvmBfZapK9bJ8/zncSIAESIAESIAESKKMEypfR68657Heni3yzQOTBZyLHnrhf5K8/RXr1Fam6R045bpEACZAACZAACZBAGSPAYJb/vSbSqXv0bW9bW9x7P0pWVlb0ce6RAAmQAAmQAAmQQBkiUOaHnt1R3UQ6Noq+5UNHUSRGE+EeCZAACZAACZBAGSRAiyJuOoafOxwTuf2tqoibs4ZCsQz+MfCSSYAESIAESIAEogmUeYsicLgjjhbp0jJCZtgjFInRnxHukQAJkAAJkAAJlFECtCj6G//hTJE+R4qbt1Wyym1//bxlyxb55ptvZNu2bdbDypUrS926dX1v+U4CJEACJEACJEACxU5g+yuiYr/EFE9waAeRifNLhEhEj++//35p0qSJNGvWzF7YzrT0008/yZdffpm02//73//kr7/+SlqGmSRAAiRAAiRAAtuHAIVimHvjpuG97brdu3dvmTBhgjz55JNy+OGHy9atW7drf/J7clhDIXKnTZuWtOrgwYOlS5cusmnTpqTlmEkCJEACJEACJJB+AiVWKG52mwWvVNI2t022ui2pFLUyzjn5221MqXx+yqbUYIqFqlevLqeccopAMDZqFBOVnWIb27PYWWedJX///becccYZSbtx/vnny/vvvy///e9/k5ZjJgmQAAmQAAmQQPoJlCihCME3/q+x0v2XTlLh5930VUE6r2gvA3+7QA5ZfrB8vmlOQAjCEGV7/tJVdli6l5T/uZJ0WXGk3PfHrbJ+m06YnZ2WbflJTv3lBOn6y9Fy7e8D5JY1Q6TusgZSUct31vJz/s5e41nL56esbz+/7x9//LFARLVs2VIOPPBAE1IPP/ywnHTSSfLjjz/mt7mgPNo977zzpF27dtKqVStr96WXXgrywxtff/213HHHHdK9e3dp3LixdOvWzeq2b9/exF247ObNm+Wuu+6yMg0aNJC2bdvKZZddJr/88ku4WNT2O++8I7Nnz5aePXvKXnvtFZUXu9OrVy+BKL711ltznTu2LPdJgARIgARIgATSTEAtZiUmXfnr+U5+FHsdvPRAd9DS5sE+jr/417PW163btrh+K8/Izivv+qw83V206hxX/afadqz9skPd2q2rrezyLUtdj+XHRrWzz091nPy0px07ZvkRwfXnp2xQKR8b9957r9thhx2c3mK37777ujp1tB+67V+vvPJK3NbOPfdcV6FChbh5ODhixAhXrlw5t9NOO7lOnTo5FYCuatWq1u6pp57qNCAmqLt06VK3xx57uN13392ptdJddNFFbr/99gv6sHjx4qDs77//7po2bWp5GkzjjjvuOFe7tjLWPu+2227uk08+CcqGN/75z39amRdeeCF8OOF23759rfwDDzyQsAwzSIAESIAESIAE0k9A0n/K+Gd8ff1LJtx2+KmGe2fDG0GhWRun6/FdLc8LxfF/jrX9A35u5L7eND8ou3LLcnfiim6WN/T3QcFxbHgBOnrtne7vrRvdn1vXun+u6GLHv9m0oMBloyom2YGoKl++vKtWrZp78803g5IzZ850u+yyiwmliRMnBsfDG8mE4ttvv+10BRkTewsW5FzHqlWr3JFHHmntQqD6dM8999ixRx55xB9y69atc//617/s+MqVK4PjEJIQhX369HEbNmyw4xCdvo0DDjggKOs31NfQxHClSpWsXX882TsEMs6jVtVkxZhHAiRAAiRAAiSQZgIlZuh5xoa3VSuIPLb7cOlQsbNt47/Ddzpapu3xtLQvf6jUKF/Djr+9USfI1nRb1Rul0Y45ASjVd6gpt1a7w/Ke3zDJ3sP/1S/XQM7Z9SKpUG4n2aVcFTlh5+Mt+8vNn4eL2XZ+yuaqHOfA+PHjBVPeYMj3mGOyJ/fWchjuRdBKjRo1ZO+9945TM/mh5557DmJfVAzaMLIvveeee1ogDJYhfPrpp/1hUYugbY8ZM0Yeeughefnll2Xu3Lly9913y6effmrDwCiAvqpwtTklMSyOMu+99575E2JoW4WgYAj722+/DdrGxg8//GCBN/Xq1ZOdd945Ki/RDobhkdAeEwmQAAmQAAmQQMkhUL6kdGXR5m+sKy0qtMrVpWN3PkHw8unL7LJNdmzuDwXvDctjGpnysmDrt7LRbZCKWZWCvENUVIb3a5Xfx/J0YDYo4zfyU9bXSfY+f/58y+7QQafhiUldu3ZN6vMXUzxqd968ebYP38TYpMPbgpdaGoMsHRaW0047TXRYWBBI4pMOXQv6AeG56667mgBUK6JlH398RFD7suF3HZ4O78p3331n+7Vq1Yo6nmwHfowQtBCdmDcSfWEiARIgARIgARLY/gRKzDdyvfJ1jMZPW36ISwVWM58ala9nmz9vyR38sWLrUs3bIlWyqsmO+i9daf369TJ16lTRId+4p9xnn4goXbZsWdz8gh5UP0erunz58lxNwCqI/qi/YpCHaWhuv/12Qflnn31WRo8eLZiipnnz5vLaa6+JDitb2Zo1a9o76sLS+Pnnn+d6rVixQg455JCgbWxs3BiJJq9YsWLU8WQ7OiRv4hB9Q/AMEwmQAAmQAAmQQMkgUGKE4iE7HWZEblozUpZv+Tmgs8Gtl1Frb5NWyw+UN9ZHhpMP3elQyx+59mb5fduvQVlMeXPL2qG237vicbJDVnoMppgwGqumIHoYw8feyhd0TDfUX9B2hw8fnksMTZo0STp27CiIXM5vOuKII4J2w2IaB2+55RaBVfCoo44KmsWQM0QhLIHqlygXXHCB3HzzzSYeUcgPJWuwiyDKefXq1aIBMNKiRYuoF1aKmT59eq75Dxs2bGjnShYVHXQmewNlMU8kxLQG5MRmc58ESIAESIAESGA7EUiPkkrh4k6q3FtOWTdRXtg0WRqvOEQuqHSSVMiqIBM2TJGvty0Sydpdapffz1o6c9fz5YX1L8obm2dKu+WHy7mVT5dt+u/NjTPk7c3vqTVxb7mm6v9ZWQjJ+9beatvvbZ4nI9dcL4N3v1lmbnxTblpzix0fp9PsVCpXUQ7Z6YiUy3at1NPq4j8IQ29JhODBJNMQY+H073//W8aOHSsayCKtW7cW7Gsks0yZMkWwOgmGeyHOfIK1b+HChbY7Z84cE1JDh0ZEMA6efvrptnILho8xKTemwsGw9jnnnCOw0L3++usCv0g/9YxvF8Lxzz//NEvgoEGDpE2bNtZ3rASDdPLJJ/uict9994lGOotGTtuk2Biahg+in/4GwhTXEp7nsX79+qKR3aLR0+Y7iSHlvBLKIkGYMpEACZAACZAACZQgAvplX2ISIpFvXj1YI5ErBVHKhy072A37/Wq3aNNXUf1cveU3Ox4ui8jmfqt6u8WbvwnK/rh5sU6Fs3vQHqbc2bJts3to7b3BMdS7c82NLj9lgxPohopDpxZBiz5Wi5rT+RDD2cE2oosHDhxo09joR8Dhtf/++7t+/fq5RYsWBeWwoQLO8n258DuinHXIOCi/du1ap3MbRrWL6XIwTY2KsKAcNlT8Wbtq+QzaR3saUOJuu+22qLLYUSHrVPwFZdEPDVJxOpG2mzVrVq7yOKDWRyv/4Ycfxs2PPXjddddZ+SFDhsRmcZ8ESIAESIAESGA7EsjCufXLv0QlrMjy89YlUkH/7ZNtRUzUwc068fayrT/YKi61y9fVYJXUfeMStVnQ43/88YdUqVIlz+rwHcQQLyKHdQ7DPMunWgDWTASTYEUUDAHH8xPEuTGUjPNiWBnDvvBDzGtibFhMYU1EdDaGiGE1TJSeeuopOfPMM0UFoIwcOTJRseA4LJJoe8mSJQWK/A4a4gYJkAAJkAAJkECREiiRQrFIr5CNpZ0ABGuTJk1kzZo1ttpMMr9DLN+HtazPPvtsG5pPe2d5QhIgARIgARIggYQESkwwS8IeMiPjCMDaiMhqWCHha5ksIcoa1kws4cdEAiRAAiRAAiRQsghQKJas+1FqeoP5GseNGyeIjk6WmjVrZsE/fjqeZGWZRwIkQAIkQAIkkF4CHHpOL2+ejQRIgARIgARIgAQyhgAtihlzq9hREiABEiABEiABEkgvAQrF9PLm2UiABEiABEiABEggYwhQKGbMrWJHSYAESIAESIAESCC9BCgU08ubZyMBEiABEiABEiCBjCFAoZgxt4odJQESIAESIAESIIH0EqBQTC9vno0ESIAESIAESIAEMoYAhWLG3Cp2lARIgARIgARIgATSS4BCMb28eTYSIAESIAESIAESyBgCFIoZc6vYURIgARIgARIgARJILwEKxfTy5tlIgARIgARIgARIIGMIUChmzK1iR0mABEiABEiABEggvQQoFNPLm2cjARIgARIgARIggYwhQKGYMbeKHSUBEiABEiABEiCB9BKgUEwvb56NBEiABEiABEiABDKGAIVixtwqdpQESIAESIAESIAE0kuAQjG9vHk2EiABEiABEiABEsgYAhSKGXOr2FESIAESIAESIAESSC8BCsX08ubZSIAESIAESIAESCBjCFAoZsytYkdJgARIgARIgARIIL0EKBTTy5tnIwESIAESIAESIIGMIUChmDG3ih0lARIgARIgARIggfQSoFBML2+ejQRIgARIgARIgAQyhgCFYsbcKnaUBEiABEiABEiABNJLgEIxvbx5NhIgARIgARIgARLIGAIUihlzq9hREiABEiABEiABEkgvAQrF9PLm2UiABEiABEiABEggYwhQKGbMrWJHSYAESIAESIAESCC9BCgU08ubZyMBEiABEiABEiCBjCFAoZgxt4odJQESIAESIAESIIH0EqBQTC9vno0ESIAESIAESIAEMoYAhWLG3Cp2lARIgARIgARIgATSS4BCMb28eTYSIAESIAESIAESyBgCFIoZc6vYURIgARIgARIgARJILwEKxfTy5tlIgARIgARIgARIIGMIUChmzK1iR0mABEiABEiABEggvQQoFNPLm2cjARIgARIgARIggYwhQKGYMbeKHSUBEiABEiABEiCB9BKgUEwvb56NBEiABEiABEiABDKGAIVixtwqdpQESIAESIAESIAE0kuAQjG9vHk2EiABEiABEiABEsgYAhSKGXOr2FESIAESIAESIAESSC8BCsX08ubZSIAESIAESIAESCBjCFAoZsytYkdJgARIgARIgARIIL0EKBTTy5tnIwESIAESIAESIIGMIUChmDG3ih0lARIgARIgARIggfQSoFBML2+ejQRIgARIgARIgAQyhgCFYsbcKnaUBEiABEiABEiABNJLgEIxvbx5NhIgARIgARIgARLIGAIUihlzq9hREiABEiABEiABEkgvAQrF9PLm2UiABEiABEiABEggYwhQKGbMrWJHSYAESIAESIAESCC9BCgU08ubZyMBEiABEiABEiCBjCFAoZgxt4odJQESIAESIAESIIH0EqBQTC9vno0ESIAESIAESIAEMoYAhWLG3Cp2lARIgARIgARIgATSS4BCMb28eTYSIAESIAESIAESyBgCFIoZc6vYURIgARIgARIgARJILwEKxfTy5tlIgARIgARIgARIIGMIUChmzK1iR0mABEiABEiABEggvQQoFNPLm2cjARIgARIgARIggYwh8P8MEaLLtrnMxgAAAABJRU5ErkJggg=="
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# don't forget to close your connection!\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
--
GitLab